diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..441062e481ec29f9c71e288a6fc0a213ccbdfb21 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +imgs/vocoder/gan/MSSBCQTD.png filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..8841279a61eec9b77f376027261a2829359b17a0 --- /dev/null +++ b/.gitignore @@ -0,0 +1,61 @@ +# Mac OS files +.DS_Store + +# IDEs +.idea +.vs +.vscode +.cache + +# GitHub files +.github + +# Byte-compiled / optimized / DLL / cached files +__pycache__/ +*.py[cod] +*$py.class +*.pyc +.temp +*.c +*.so +*.o + +# Developing mode +_*.sh +_*.json +*.lst +yard* +*.out +evaluation/evalset_selection +mfa +egs/svc/*wavmark +egs/svc/custom +egs/svc/*/dev* +egs/svc/dev_exp_config.json +bins/svc/demo* +bins/svc/preprocess_custom.py +data +ckpts + +# Data and ckpt +*.pkl +*.pt +*.npy +*.npz +*.tar.gz +*.ckpt +*.wav +*.flac +pretrained/wenet/*conformer_exp + +# Runtime data dirs +processed_data +data +model_ckpt +logs +*.ipynb +*.lst +source_audio +result +conversion_results +get_available_gpu.py \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..68c66d23fb4c573b2fe039374d2f0a561a36f892 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Amphion + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..46ad57df87388dc63f84125364789ef0cf8bc9de --- /dev/null +++ b/app.py @@ -0,0 +1,84 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import subprocess + +command_to_run = "cd ./modules/monotonic_align;mkdir -p monotonic_align;python setup.py build_ext --inplace;cd /home/user/app" + +try: + result = subprocess.check_output(command_to_run, shell=True, text=True) + print("Command output:") + print(result) +except subprocess.CalledProcessError as e: + print(f"Command failed with return code {e.returncode}") + +import gradio as gr +import os +import inference + +SUPPORTED_SPEAKERS = { + "92":"hifitts_92", + "6097":"hifitts_6097", + "6670":"hifitts_6670", + "6671":"hifitts_6671", + "8051":"hifitts_8051", + "9017":"hifitts_9017", + "9136":"hifitts_9136", + "11614":"hifitts_11614", + "11697":"hifitts_11697", + "12787":"hifitts_12787" +} + + +def tts_inference( + input_text, + target_speaker +): + + args_list = ["--config", "./egs/tts/vits_hifitts/exp_config.json"] + args_list += ["--checkpoint_path", "./latest-checkpoint"] + args_list += ["--speaker_name", target_speaker] + args_list += ["--text", input_text] + args_list += ["--mode","single"] + args_list += ["--output_dir", "result"] + args_list += ["--log_level", "debug"] + + os.environ["WORK_DIR"] = "./" + inference.main(args_list) + + ### Display ### + result_file = os.path.join( + "result/{}.wav".format(target_speaker) + ) + return result_file + + +demo_inputs = [ + gr.Textbox( + label="Input text", + type="text", + lines=1, + max_lines=20 + ), + gr.Radio( + choices=list(SUPPORTED_SPEAKERS.keys()), + label="Target Speaker", + value="92" + ) +] + +demo_output = gr.Audio(label="") + + + +demo = gr.Interface( + fn=tts_inference, + inputs=demo_inputs, + outputs=demo_output, + title="Amphion HifiTTS Text-to-Speech Demo", +) + +if __name__ == "__main__": + demo.launch(share=True) \ No newline at end of file diff --git a/bins/calc_metrics.py b/bins/calc_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..04863c4f845114b59a8d4ff3565859691493790f --- /dev/null +++ b/bins/calc_metrics.py @@ -0,0 +1,140 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import numpy as np +import json +import argparse + +from glob import glob +from tqdm import tqdm +from collections import defaultdict + +from evaluation.metrics.energy.energy_rmse import extract_energy_rmse +from evaluation.metrics.energy.energy_pearson_coefficients import ( + extract_energy_pearson_coeffcients, +) +from evaluation.metrics.f0.f0_pearson_coefficients import extract_fpc +from evaluation.metrics.f0.f0_periodicity_rmse import extract_f0_periodicity_rmse +from evaluation.metrics.f0.f0_rmse import extract_f0rmse +from evaluation.metrics.f0.v_uv_f1 import extract_f1_v_uv +from evaluation.metrics.intelligibility.character_error_rate import extract_cer +from evaluation.metrics.intelligibility.word_error_rate import extract_wer +from evaluation.metrics.similarity.speaker_similarity import extract_speaker_similarity +from evaluation.metrics.spectrogram.frechet_distance import extract_fad +from evaluation.metrics.spectrogram.mel_cepstral_distortion import extract_mcd +from evaluation.metrics.spectrogram.multi_resolution_stft_distance import extract_mstft +from evaluation.metrics.spectrogram.pesq import extract_pesq +from evaluation.metrics.spectrogram.scale_invariant_signal_to_distortion_ratio import ( + extract_si_sdr, +) +from evaluation.metrics.spectrogram.scale_invariant_signal_to_noise_ratio import ( + extract_si_snr, +) +from evaluation.metrics.spectrogram.short_time_objective_intelligibility import ( + extract_stoi, +) + +METRIC_FUNC = { + "energy_rmse": extract_energy_rmse, + "energy_pc": extract_energy_pearson_coeffcients, + "fpc": extract_fpc, + "f0_periodicity_rmse": extract_f0_periodicity_rmse, + "f0rmse": extract_f0rmse, + "v_uv_f1": extract_f1_v_uv, + "cer": extract_cer, + "wer": extract_wer, + "speaker_similarity": extract_speaker_similarity, + "fad": extract_fad, + "mcd": extract_mcd, + "mstft": extract_mstft, + "pesq": extract_pesq, + "si_sdr": extract_si_sdr, + "si_snr": extract_si_snr, + "stoi": extract_stoi, +} + + +def calc_metric(ref_dir, deg_dir, dump_dir, metrics, fs=None): + result = defaultdict() + + for metric in tqdm(metrics): + if metric in ["fad", "speaker_similarity"]: + result[metric] = str(METRIC_FUNC[metric](ref_dir, deg_dir)) + continue + + audios_ref = [] + audios_deg = [] + + files = glob(ref_dir + "/*.wav") + + for file in files: + audios_ref.append(file) + uid = file.split("/")[-1].split(".wav")[0] + file_gt = deg_dir + "/{}.wav".format(uid) + audios_deg.append(file_gt) + + if metric in ["v_uv_f1"]: + tp_total = 0 + fp_total = 0 + fn_total = 0 + + for i in tqdm(range(len(audios_ref))): + audio_ref = audios_ref[i] + audio_deg = audios_deg[i] + tp, fp, fn = METRIC_FUNC[metric](audio_ref, audio_deg, fs) + tp_total += tp + fp_total += fp + fn_total += fn + + result[metric] = str(tp_total / (tp_total + (fp_total + fn_total) / 2)) + else: + scores = [] + + for i in tqdm(range(len(audios_ref))): + audio_ref = audios_ref[i] + audio_deg = audios_deg[i] + + score = METRIC_FUNC[metric]( + audio_ref=audio_ref, audio_deg=audio_deg, fs=fs + ) + if not np.isnan(score): + scores.append(score) + + scores = np.array(scores) + result["{}_mean".format(metric)] = str(np.mean(scores)) + result["{}_std".format(metric)] = str(np.std(scores)) + + data = json.dumps(result, indent=4) + + with open(os.path.join(dump_dir, "result.json"), "w", newline="\n") as f: + f.write(data) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--ref_dir", + type=str, + help="Path to the target audio folder.", + ) + parser.add_argument( + "--deg_dir", + type=str, + help="Path to the reference audio folder.", + ) + parser.add_argument( + "--dump_dir", + type=str, + help="Path to dump the results.", + ) + parser.add_argument( + "--metrics", + nargs="+", + help="Metrics used to evaluate.", + ) + args = parser.parse_args() + + calc_metric(args.ref_dir, args.deg_dir, args.dump_dir, args.metrics) diff --git a/bins/svc/inference.py b/bins/svc/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..c3416718be2d0091ee66772933e6734fdcf6c9ab --- /dev/null +++ b/bins/svc/inference.py @@ -0,0 +1,262 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import glob +from tqdm import tqdm +import json +import torch +import time + +from models.svc.diffusion.diffusion_inference import DiffusionInference +from models.svc.comosvc.comosvc_inference import ComoSVCInference +from models.svc.transformer.transformer_inference import TransformerInference +from utils.util import load_config +from utils.audio_slicer import split_audio, merge_segments_encodec +from processors import acoustic_extractor, content_extractor + + +def build_inference(args, cfg, infer_type="from_dataset"): + supported_inference = { + "DiffWaveNetSVC": DiffusionInference, + "DiffComoSVC": ComoSVCInference, + "TransformerSVC": TransformerInference, + } + + inference_class = supported_inference[cfg.model_type] + return inference_class(args, cfg, infer_type) + + +def prepare_for_audio_file(args, cfg, num_workers=1): + preprocess_path = cfg.preprocess.processed_dir + audio_name = cfg.inference.source_audio_name + temp_audio_dir = os.path.join(preprocess_path, audio_name) + + ### eval file + t = time.time() + eval_file = prepare_source_eval_file(cfg, temp_audio_dir, audio_name) + args.source = eval_file + with open(eval_file, "r") as f: + metadata = json.load(f) + print("Prepare for meta eval data: {:.1f}s".format(time.time() - t)) + + ### acoustic features + t = time.time() + acoustic_extractor.extract_utt_acoustic_features_serial( + metadata, temp_audio_dir, cfg + ) + acoustic_extractor.cal_mel_min_max( + dataset=audio_name, output_path=preprocess_path, cfg=cfg, metadata=metadata + ) + acoustic_extractor.cal_pitch_statistics_svc( + dataset=audio_name, output_path=preprocess_path, cfg=cfg, metadata=metadata + ) + print("Prepare for acoustic features: {:.1f}s".format(time.time() - t)) + + ### content features + t = time.time() + content_extractor.extract_utt_content_features_dataloader( + cfg, metadata, num_workers + ) + print("Prepare for content features: {:.1f}s".format(time.time() - t)) + return args, cfg, temp_audio_dir + + +def merge_for_audio_segments(audio_files, args, cfg): + audio_name = cfg.inference.source_audio_name + target_singer_name = args.target_singer + + merge_segments_encodec( + wav_files=audio_files, + fs=cfg.preprocess.sample_rate, + output_path=os.path.join( + args.output_dir, "{}_{}.wav".format(audio_name, target_singer_name) + ), + overlap_duration=cfg.inference.segments_overlap_duration, + ) + + for tmp_file in audio_files: + os.remove(tmp_file) + + +def prepare_source_eval_file(cfg, temp_audio_dir, audio_name): + """ + Prepare the eval file (json) for an audio + """ + + audio_chunks_results = split_audio( + wav_file=cfg.inference.source_audio_path, + target_sr=cfg.preprocess.sample_rate, + output_dir=os.path.join(temp_audio_dir, "wavs"), + max_duration_of_segment=cfg.inference.segments_max_duration, + overlap_duration=cfg.inference.segments_overlap_duration, + ) + + metadata = [] + for i, res in enumerate(audio_chunks_results): + res["index"] = i + res["Dataset"] = audio_name + res["Singer"] = audio_name + res["Uid"] = "{}_{}".format(audio_name, res["Uid"]) + metadata.append(res) + + eval_file = os.path.join(temp_audio_dir, "eval.json") + with open(eval_file, "w") as f: + json.dump(metadata, f, indent=4, ensure_ascii=False, sort_keys=True) + + return eval_file + + +def cuda_relevant(deterministic=False): + torch.cuda.empty_cache() + # TF32 on Ampere and above + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.enabled = True + torch.backends.cudnn.allow_tf32 = True + # Deterministic + torch.backends.cudnn.deterministic = deterministic + torch.backends.cudnn.benchmark = not deterministic + torch.use_deterministic_algorithms(deterministic) + + +def infer(args, cfg, infer_type): + # Build inference + t = time.time() + trainer = build_inference(args, cfg, infer_type) + print("Model Init: {:.1f}s".format(time.time() - t)) + + # Run inference + t = time.time() + output_audio_files = trainer.inference() + print("Model inference: {:.1f}s".format(time.time() - t)) + return output_audio_files + + +def build_parser(): + r"""Build argument parser for inference.py. + Anything else should be put in an extra config YAML file. + """ + + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", + type=str, + required=True, + help="JSON/YAML file for configurations.", + ) + parser.add_argument( + "--acoustics_dir", + type=str, + help="Acoustics model checkpoint directory. If a directory is given, " + "search for the latest checkpoint dir in the directory. If a specific " + "checkpoint dir is given, directly load the checkpoint.", + ) + parser.add_argument( + "--vocoder_dir", + type=str, + required=True, + help="Vocoder checkpoint directory. Searching behavior is the same as " + "the acoustics one.", + ) + parser.add_argument( + "--target_singer", + type=str, + required=True, + help="convert to a specific singer (e.g. --target_singers singer_id).", + ) + parser.add_argument( + "--trans_key", + default=0, + help="0: no pitch shift; autoshift: pitch shift; int: key shift.", + ) + parser.add_argument( + "--source", + type=str, + default="source_audio", + help="Source audio file or directory. If a JSON file is given, " + "inference from dataset is applied. If a directory is given, " + "inference from all wav/flac/mp3 audio files in the directory is applied. " + "Default: inference from all wav/flac/mp3 audio files in ./source_audio", + ) + parser.add_argument( + "--output_dir", + type=str, + default="conversion_results", + help="Output directory. Default: ./conversion_results", + ) + parser.add_argument( + "--log_level", + type=str, + default="warning", + help="Logging level. Default: warning", + ) + parser.add_argument( + "--keep_cache", + action="store_true", + default=True, + help="Keep cache files. Only applicable to inference from files.", + ) + parser.add_argument( + "--diffusion_inference_steps", + type=int, + default=50, + help="Number of inference steps. Only applicable to diffusion inference.", + ) + return parser + + +def main(): + ### Parse arguments and config + args = build_parser().parse_args() + cfg = load_config(args.config) + + # CUDA settings + cuda_relevant() + + if os.path.isdir(args.source): + ### Infer from file + + # Get all the source audio files (.wav, .flac, .mp3) + source_audio_dir = args.source + audio_list = [] + for suffix in ["wav", "flac", "mp3"]: + audio_list += glob.glob( + os.path.join(source_audio_dir, "**/*.{}".format(suffix)), recursive=True + ) + print("There are {} source audios: ".format(len(audio_list))) + + # Infer for every file as dataset + output_root_path = args.output_dir + for audio_path in tqdm(audio_list): + audio_name = audio_path.split("/")[-1].split(".")[0] + args.output_dir = os.path.join(output_root_path, audio_name) + print("\n{}\nConversion for {}...\n".format("*" * 10, audio_name)) + + cfg.inference.source_audio_path = audio_path + cfg.inference.source_audio_name = audio_name + cfg.inference.segments_max_duration = 10.0 + cfg.inference.segments_overlap_duration = 1.0 + + # Prepare metadata and features + args, cfg, cache_dir = prepare_for_audio_file(args, cfg) + + # Infer from file + output_audio_files = infer(args, cfg, infer_type="from_file") + + # Merge the split segments + merge_for_audio_segments(output_audio_files, args, cfg) + + # Keep or remove caches + if not args.keep_cache: + os.removedirs(cache_dir) + + else: + ### Infer from dataset + infer(args, cfg, infer_type="from_dataset") + + +if __name__ == "__main__": + main() diff --git a/bins/svc/preprocess.py b/bins/svc/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..78d68d6d2097a3f21925a03ddee37cea0f19b5cc --- /dev/null +++ b/bins/svc/preprocess.py @@ -0,0 +1,182 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import faulthandler + +faulthandler.enable() + +import os +import argparse +import json +from multiprocessing import cpu_count + + +from utils.util import load_config +from preprocessors.processor import preprocess_dataset +from preprocessors.metadata import cal_metadata +from processors import acoustic_extractor, content_extractor, data_augment + + +def extract_acoustic_features(dataset, output_path, cfg, n_workers=1): + """Extract acoustic features of utterances in the dataset + + Args: + dataset (str): name of dataset, e.g. opencpop + output_path (str): directory that stores train, test and feature files of datasets + cfg (dict): dictionary that stores configurations + n_workers (int, optional): num of processes to extract features in parallel. Defaults to 1. + """ + types = ["train", "test"] if "eval" not in dataset else ["test"] + metadata = [] + dataset_output = os.path.join(output_path, dataset) + + for dataset_type in types: + dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type)) + with open(dataset_file, "r") as f: + metadata.extend(json.load(f)) + + # acoustic_extractor.extract_utt_acoustic_features_parallel( + # metadata, dataset_output, cfg, n_workers=n_workers + # ) + acoustic_extractor.extract_utt_acoustic_features_serial( + metadata, dataset_output, cfg + ) + + +def extract_content_features(dataset, output_path, cfg, num_workers=1): + """Extract content features of utterances in the dataset + + Args: + dataset (str): name of dataset, e.g. opencpop + output_path (str): directory that stores train, test and feature files of datasets + cfg (dict): dictionary that stores configurations + """ + types = ["train", "test"] if "eval" not in dataset else ["test"] + metadata = [] + for dataset_type in types: + dataset_output = os.path.join(output_path, dataset) + dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type)) + with open(dataset_file, "r") as f: + metadata.extend(json.load(f)) + + content_extractor.extract_utt_content_features_dataloader( + cfg, metadata, num_workers + ) + + +def preprocess(cfg, args): + """Proprocess raw data of single or multiple datasets (in cfg.dataset) + + Args: + cfg (dict): dictionary that stores configurations + args (ArgumentParser): specify the configuration file and num_workers + """ + # Specify the output root path to save the processed data + output_path = cfg.preprocess.processed_dir + os.makedirs(output_path, exist_ok=True) + + ## Split train and test sets + for dataset in cfg.dataset: + print("Preprocess {}...".format(dataset)) + preprocess_dataset( + dataset, + cfg.dataset_path[dataset], + output_path, + cfg.preprocess, + is_custom_dataset=cfg.use_custom_dataset, + ) + + # Data augmentation: create new wav files with pitch shift, formant shift, equalizer, time stretch + try: + assert isinstance( + cfg.preprocess.data_augment, list + ), "Please provide a list of datasets need to be augmented." + if len(cfg.preprocess.data_augment) > 0: + new_datasets_list = [] + for dataset in cfg.preprocess.data_augment: + new_datasets = data_augment.augment_dataset(cfg, dataset) + new_datasets_list.extend(new_datasets) + cfg.dataset.extend(new_datasets_list) + print("Augmentation datasets: ", cfg.dataset) + except: + print("No Data Augmentation.") + + # Dump metadata of datasets (singers, train/test durations, etc.) + cal_metadata(cfg) + + ## Prepare the acoustic features + for dataset in cfg.dataset: + # Skip augmented datasets which do not need to extract acoustic features + # We will copy acoustic features from the original dataset later + if ( + "pitch_shift" in dataset + or "formant_shift" in dataset + or "equalizer" in dataset in dataset + ): + continue + print( + "Extracting acoustic features for {} using {} workers ...".format( + dataset, args.num_workers + ) + ) + extract_acoustic_features(dataset, output_path, cfg, args.num_workers) + # Calculate the statistics of acoustic features + if cfg.preprocess.mel_min_max_norm: + acoustic_extractor.cal_mel_min_max(dataset, output_path, cfg) + + if cfg.preprocess.extract_pitch: + acoustic_extractor.cal_pitch_statistics_svc(dataset, output_path, cfg) + + # Copy acoustic features for augmented datasets by creating soft-links + for dataset in cfg.dataset: + if "pitch_shift" in dataset: + src_dataset = dataset.replace("_pitch_shift", "") + src_dataset_dir = os.path.join(output_path, src_dataset) + elif "formant_shift" in dataset: + src_dataset = dataset.replace("_formant_shift", "") + src_dataset_dir = os.path.join(output_path, src_dataset) + elif "equalizer" in dataset: + src_dataset = dataset.replace("_equalizer", "") + src_dataset_dir = os.path.join(output_path, src_dataset) + else: + continue + dataset_dir = os.path.join(output_path, dataset) + metadata = [] + for split in ["train", "test"] if not "eval" in dataset else ["test"]: + metadata_file_path = os.path.join(src_dataset_dir, "{}.json".format(split)) + with open(metadata_file_path, "r") as f: + metadata.extend(json.load(f)) + print("Copying acoustic features for {}...".format(dataset)) + acoustic_extractor.copy_acoustic_features( + metadata, dataset_dir, src_dataset_dir, cfg + ) + if cfg.preprocess.mel_min_max_norm: + acoustic_extractor.cal_mel_min_max(dataset, output_path, cfg) + + if cfg.preprocess.extract_pitch: + acoustic_extractor.cal_pitch_statistics(dataset, output_path, cfg) + + # Prepare the content features + for dataset in cfg.dataset: + print("Extracting content features for {}...".format(dataset)) + extract_content_features(dataset, output_path, cfg, args.num_workers) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", default="config.json", help="json files for configurations." + ) + parser.add_argument("--num_workers", type=int, default=int(cpu_count())) + parser.add_argument("--prepare_alignment", type=bool, default=False) + + args = parser.parse_args() + cfg = load_config(args.config) + + preprocess(cfg, args) + + +if __name__ == "__main__": + main() diff --git a/bins/svc/train.py b/bins/svc/train.py new file mode 100644 index 0000000000000000000000000000000000000000..8e55cdc1dd1f15784a433513f50148e096956cba --- /dev/null +++ b/bins/svc/train.py @@ -0,0 +1,107 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse + +import torch + +from models.svc.diffusion.diffusion_trainer import DiffusionTrainer +from models.svc.comosvc.comosvc_trainer import ComoSVCTrainer +from models.svc.transformer.transformer_trainer import TransformerTrainer +from utils.util import load_config + + +def build_trainer(args, cfg): + supported_trainer = { + "DiffWaveNetSVC": DiffusionTrainer, + "DiffComoSVC": ComoSVCTrainer, + "TransformerSVC": TransformerTrainer, + } + + trainer_class = supported_trainer[cfg.model_type] + trainer = trainer_class(args, cfg) + return trainer + + +def cuda_relevant(deterministic=False): + torch.cuda.empty_cache() + # TF32 on Ampere and above + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.enabled = True + torch.backends.cudnn.allow_tf32 = True + # Deterministic + torch.backends.cudnn.deterministic = deterministic + torch.backends.cudnn.benchmark = not deterministic + torch.use_deterministic_algorithms(deterministic) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", + default="config.json", + help="json files for configurations.", + required=True, + ) + parser.add_argument( + "--exp_name", + type=str, + default="exp_name", + help="A specific name to note the experiment", + required=True, + ) + parser.add_argument( + "--resume", + action="store_true", + help="If specified, to resume from the existing checkpoint.", + ) + parser.add_argument( + "--resume_from_ckpt_path", + type=str, + default="", + help="The specific checkpoint path that you want to resume from.", + ) + parser.add_argument( + "--resume_type", + type=str, + default="", + help="`resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights", + ) + + parser.add_argument( + "--log_level", default="warning", help="logging level (debug, info, warning)" + ) + args = parser.parse_args() + cfg = load_config(args.config) + + # Data Augmentation + if ( + type(cfg.preprocess.data_augment) == list + and len(cfg.preprocess.data_augment) > 0 + ): + new_datasets_list = [] + for dataset in cfg.preprocess.data_augment: + new_datasets = [ + f"{dataset}_pitch_shift" if cfg.preprocess.use_pitch_shift else None, + f"{dataset}_formant_shift" + if cfg.preprocess.use_formant_shift + else None, + f"{dataset}_equalizer" if cfg.preprocess.use_equalizer else None, + f"{dataset}_time_stretch" if cfg.preprocess.use_time_stretch else None, + ] + new_datasets_list.extend(filter(None, new_datasets)) + cfg.dataset.extend(new_datasets_list) + + # CUDA settings + cuda_relevant() + + # Build trainer + trainer = build_trainer(args, cfg) + + trainer.train_loop() + + +if __name__ == "__main__": + main() diff --git a/bins/tta/inference.py b/bins/tta/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..85179049c27d2f7c7d6d258afc333bec203dd207 --- /dev/null +++ b/bins/tta/inference.py @@ -0,0 +1,94 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +from argparse import ArgumentParser +import os + +from models.tta.ldm.audioldm_inference import AudioLDMInference +from utils.util import save_config, load_model_config, load_config +import numpy as np +import torch + + +def build_inference(args, cfg): + supported_inference = { + "AudioLDM": AudioLDMInference, + } + + inference_class = supported_inference[cfg.model_type] + inference = inference_class(args, cfg) + return inference + + +def build_parser(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--config", + type=str, + required=True, + help="JSON/YAML file for configurations.", + ) + parser.add_argument( + "--text", + help="Text to be synthesized", + type=str, + default="Text to be synthesized.", + ) + parser.add_argument( + "--checkpoint_path", + type=str, + ) + parser.add_argument( + "--vocoder_path", type=str, help="Checkpoint path of the vocoder" + ) + parser.add_argument( + "--vocoder_config_path", type=str, help="Config path of the vocoder" + ) + parser.add_argument( + "--output_dir", + type=str, + default=None, + help="Output dir for saving generated results", + ) + parser.add_argument( + "--num_steps", + type=int, + default=200, + help="The total number of denosing steps", + ) + parser.add_argument( + "--guidance_scale", + type=float, + default=4.0, + help="The scale of classifer free guidance", + ) + parser.add_argument("--local_rank", default=-1, type=int) + return parser + + +def main(): + # Parse arguments + args = build_parser().parse_args() + # args, infer_type = formulate_parser(args) + + # Parse config + cfg = load_config(args.config) + if torch.cuda.is_available(): + args.local_rank = torch.device("cuda") + else: + args.local_rank = torch.device("cpu") + print("args: ", args) + + # Build inference + inferencer = build_inference(args, cfg) + + # Run inference + inferencer.inference() + + +if __name__ == "__main__": + main() diff --git a/bins/tta/preprocess.py b/bins/tta/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..288d4769dabb042567b0583a4ecfb87eecf0c292 --- /dev/null +++ b/bins/tta/preprocess.py @@ -0,0 +1,194 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import faulthandler + +faulthandler.enable() + +import os +import argparse +import json +import pyworld as pw +from multiprocessing import cpu_count + + +from utils.util import load_config +from preprocessors.processor import preprocess_dataset, prepare_align +from preprocessors.metadata import cal_metadata +from processors import acoustic_extractor, content_extractor, data_augment + + +def extract_acoustic_features(dataset, output_path, cfg, n_workers=1): + """Extract acoustic features of utterances in the dataset + + Args: + dataset (str): name of dataset, e.g. opencpop + output_path (str): directory that stores train, test and feature files of datasets + cfg (dict): dictionary that stores configurations + n_workers (int, optional): num of processes to extract features in parallel. Defaults to 1. + """ + types = ["train", "test"] if "eval" not in dataset else ["test"] + metadata = [] + for dataset_type in types: + dataset_output = os.path.join(output_path, dataset) + dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type)) + with open(dataset_file, "r") as f: + metadata.extend(json.load(f)) + + # acoustic_extractor.extract_utt_acoustic_features_parallel( + # metadata, dataset_output, cfg, n_workers=n_workers + # ) + acoustic_extractor.extract_utt_acoustic_features_serial( + metadata, dataset_output, cfg + ) + + +def extract_content_features(dataset, output_path, cfg, num_workers=1): + """Extract content features of utterances in the dataset + + Args: + dataset (str): name of dataset, e.g. opencpop + output_path (str): directory that stores train, test and feature files of datasets + cfg (dict): dictionary that stores configurations + """ + types = ["train", "test"] if "eval" not in dataset else ["test"] + metadata = [] + for dataset_type in types: + dataset_output = os.path.join(output_path, dataset) + dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type)) + with open(dataset_file, "r") as f: + metadata.extend(json.load(f)) + + content_extractor.extract_utt_content_features_dataloader( + cfg, metadata, num_workers + ) + + +def preprocess(cfg, args): + """Proprocess raw data of single or multiple datasets (in cfg.dataset) + + Args: + cfg (dict): dictionary that stores configurations + args (ArgumentParser): specify the configuration file and num_workers + """ + # Specify the output root path to save the processed data + output_path = cfg.preprocess.processed_dir + os.makedirs(output_path, exist_ok=True) + + ## Split train and test sets + for dataset in cfg.dataset: + print("Preprocess {}...".format(dataset)) + + if args.prepare_alignment: + ## Prepare alignment with MFA + print("Prepare alignment {}...".format(dataset)) + prepare_align( + dataset, cfg.dataset_path[dataset], cfg.preprocess, output_path + ) + preprocess_dataset( + dataset, + cfg.dataset_path[dataset], + output_path, + cfg.preprocess, + is_custom_dataset=cfg.use_custom_dataset, + ) + + # Data augmentation: create new wav files with pitch shift, formant shift, equalizer, time stretch + try: + assert isinstance( + cfg.preprocess.data_augment, list + ), "Please provide a list of datasets need to be augmented." + if len(cfg.preprocess.data_augment) > 0: + new_datasets_list = [] + for dataset in cfg.preprocess.data_augment: + new_datasets = data_augment.augment_dataset(cfg, dataset) + new_datasets_list.extend(new_datasets) + cfg.dataset.extend(new_datasets_list) + print("Augmentation datasets: ", cfg.dataset) + except: + print("No Data Augmentation.") + + # Dump metadata of datasets (singers, train/test durations, etc.) + cal_metadata(cfg) + + ## Prepare the acoustic features + for dataset in cfg.dataset: + # Skip augmented datasets which do not need to extract acoustic features + # We will copy acoustic features from the original dataset later + if ( + "pitch_shift" in dataset + or "formant_shift" in dataset + or "equalizer" in dataset in dataset + ): + continue + print( + "Extracting acoustic features for {} using {} workers ...".format( + dataset, args.num_workers + ) + ) + extract_acoustic_features(dataset, output_path, cfg, args.num_workers) + # Calculate the statistics of acoustic features + if cfg.preprocess.mel_min_max_norm: + acoustic_extractor.cal_mel_min_max(dataset, output_path, cfg) + + if cfg.preprocess.extract_pitch: + acoustic_extractor.cal_pitch_statistics(dataset, output_path, cfg) + if cfg.preprocess.extract_energy: + acoustic_extractor.cal_energy_statistics(dataset, output_path, cfg) + + if cfg.preprocess.align_mel_duration: + acoustic_extractor.align_duration_mel(dataset, output_path, cfg) + + # Copy acoustic features for augmented datasets by creating soft-links + for dataset in cfg.dataset: + if "pitch_shift" in dataset: + src_dataset = dataset.replace("_pitch_shift", "") + src_dataset_dir = os.path.join(output_path, src_dataset) + elif "formant_shift" in dataset: + src_dataset = dataset.replace("_formant_shift", "") + src_dataset_dir = os.path.join(output_path, src_dataset) + elif "equalizer" in dataset: + src_dataset = dataset.replace("_equalizer", "") + src_dataset_dir = os.path.join(output_path, src_dataset) + else: + continue + dataset_dir = os.path.join(output_path, dataset) + metadata = [] + for split in ["train", "test"] if not "eval" in dataset else ["test"]: + metadata_file_path = os.path.join(src_dataset_dir, "{}.json".format(split)) + with open(metadata_file_path, "r") as f: + metadata.extend(json.load(f)) + print("Copying acoustic features for {}...".format(dataset)) + acoustic_extractor.copy_acoustic_features( + metadata, dataset_dir, src_dataset_dir, cfg + ) + if cfg.preprocess.mel_min_max_norm: + acoustic_extractor.cal_mel_min_max(dataset, output_path, cfg) + + if cfg.preprocess.extract_pitch: + acoustic_extractor.cal_pitch_statistics(dataset, output_path, cfg) + + # Prepare the content features + for dataset in cfg.dataset: + print("Extracting content features for {}...".format(dataset)) + extract_content_features(dataset, output_path, cfg, args.num_workers) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", default="config.json", help="json files for configurations." + ) + parser.add_argument("--num_workers", type=int, default=int(cpu_count())) + parser.add_argument("--prepare_alignment", type=bool, default=False) + + args = parser.parse_args() + cfg = load_config(args.config) + + preprocess(cfg, args) + + +if __name__ == "__main__": + main() diff --git a/bins/tta/train_tta.py b/bins/tta/train_tta.py new file mode 100644 index 0000000000000000000000000000000000000000..42d8210b37b8a91db873e23e4fc40263acf0f06b --- /dev/null +++ b/bins/tta/train_tta.py @@ -0,0 +1,77 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import torch + +from models.tta.autoencoder.autoencoder_trainer import AutoencoderKLTrainer +from models.tta.ldm.audioldm_trainer import AudioLDMTrainer +from utils.util import load_config + + +def build_trainer(args, cfg): + supported_trainer = { + "AutoencoderKL": AutoencoderKLTrainer, + "AudioLDM": AudioLDMTrainer, + } + + trainer_class = supported_trainer[cfg.model_type] + trainer = trainer_class(args, cfg) + return trainer + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", + default="config.json", + help="json files for configurations.", + required=True, + ) + parser.add_argument( + "--num_workers", type=int, default=6, help="Number of dataloader workers." + ) + parser.add_argument( + "--exp_name", + type=str, + default="exp_name", + help="A specific name to note the experiment", + required=True, + ) + parser.add_argument( + "--resume", + type=str, + default=None, + # action="store_true", + help="The model name to restore", + ) + parser.add_argument( + "--log_level", default="info", help="logging level (info, debug, warning)" + ) + parser.add_argument("--stdout_interval", default=5, type=int) + parser.add_argument("--local_rank", default=-1, type=int) + args = parser.parse_args() + cfg = load_config(args.config) + cfg.exp_name = args.exp_name + + # Model saving dir + args.log_dir = os.path.join(cfg.log_dir, args.exp_name) + os.makedirs(args.log_dir, exist_ok=True) + + if not cfg.train.ddp: + args.local_rank = torch.device("cuda") + + # Build trainer + trainer = build_trainer(args, cfg) + + # Restore models + if args.resume: + trainer.restore() + trainer.train() + + +if __name__ == "__main__": + main() diff --git a/bins/tts/inference.py b/bins/tts/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..08bed3cf8261d5e19d68f026de4398e85ad553de --- /dev/null +++ b/bins/tts/inference.py @@ -0,0 +1,162 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +from argparse import ArgumentParser +import os + +from models.tts.fastspeech2.fs2_inference import FastSpeech2Inference +from models.tts.vits.vits_inference import VitsInference +from models.tts.valle.valle_inference import VALLEInference +from utils.util import load_config +import torch + + +def build_inference(args, cfg): + supported_inference = { + "FastSpeech2": FastSpeech2Inference, + "VITS": VitsInference, + "VALLE": VALLEInference, + } + + inference_class = supported_inference[cfg.model_type] + inference = inference_class(args, cfg) + return inference + + +def cuda_relevant(deterministic=False): + torch.cuda.empty_cache() + # TF32 on Ampere and above + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.enabled = True + torch.backends.cudnn.allow_tf32 = True + # Deterministic + torch.backends.cudnn.deterministic = deterministic + torch.backends.cudnn.benchmark = not deterministic + torch.use_deterministic_algorithms(deterministic) + + +def build_parser(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--config", + type=str, + required=True, + help="JSON/YAML file for configurations.", + ) + parser.add_argument( + "--dataset", + type=str, + help="convert from the source data", + default=None, + ) + parser.add_argument( + "--testing_set", + type=str, + help="train, test, golden_test", + default="test", + ) + parser.add_argument( + "--test_list_file", + type=str, + help="convert from the test list file", + default=None, + ) + parser.add_argument( + "--speaker_name", + type=str, + default=None, + help="speaker name for multi-speaker synthesis, for single-sentence mode only", + ) + parser.add_argument( + "--text", + help="Text to be synthesized.", + type=str, + default="", + ) + parser.add_argument( + "--vocoder_dir", + type=str, + default=None, + help="Vocoder checkpoint directory. Searching behavior is the same as " + "the acoustics one.", + ) + parser.add_argument( + "--acoustics_dir", + type=str, + default=None, + help="Acoustic model checkpoint directory. If a directory is given, " + "search for the latest checkpoint dir in the directory. If a specific " + "checkpoint dir is given, directly load the checkpoint.", + ) + parser.add_argument( + "--checkpoint_path", + type=str, + default=None, + help="Acoustic model checkpoint directory. If a directory is given, " + "search for the latest checkpoint dir in the directory. If a specific " + "checkpoint dir is given, directly load the checkpoint.", + ) + parser.add_argument( + "--mode", + type=str, + choices=["batch", "single"], + required=True, + help="Synthesize a whole dataset or a single sentence", + ) + parser.add_argument( + "--log_level", + type=str, + default="warning", + help="Logging level. Default: warning", + ) + parser.add_argument( + "--pitch_control", + type=float, + default=1.0, + help="control the pitch of the whole utterance, larger value for higher pitch", + ) + parser.add_argument( + "--energy_control", + type=float, + default=1.0, + help="control the energy of the whole utterance, larger value for larger volume", + ) + parser.add_argument( + "--duration_control", + type=float, + default=1.0, + help="control the speed of the whole utterance, larger value for slower speaking rate", + ) + parser.add_argument( + "--output_dir", + type=str, + default=None, + help="Output dir for saving generated results", + ) + return parser + +def main(): + # Parse arguments + parser = build_parser() + VALLEInference.add_arguments(parser) + args = parser.parse_args() + + # Parse config + cfg = load_config(args.config) + + # CUDA settings + cuda_relevant() + + # Build inference + inferencer = build_inference(args, cfg) + + # Run inference + inferencer.inference() + + +if __name__ == "__main__": + main() diff --git a/bins/tts/preprocess.py b/bins/tts/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..5ddc0e989d4d046702cad4807a3167e99d4c996b --- /dev/null +++ b/bins/tts/preprocess.py @@ -0,0 +1,253 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import faulthandler +faulthandler.enable() + +import os +import argparse +import json +import pyworld as pw +from multiprocessing import cpu_count + + +from utils.util import load_config +from preprocessors.processor import preprocess_dataset, prepare_align +from preprocessors.metadata import cal_metadata +from processors import acoustic_extractor, content_extractor, data_augment, phone_extractor + + +def extract_acoustic_features(dataset, output_path, cfg, num_workers=1): + """Extract acoustic features of utterances in the dataset + + Args: + dataset (str): name of dataset, e.g. opencpop + output_path (str): directory that stores train, test and feature files of datasets + cfg (dict): dictionary that stores configurations + n_workers (int, optional): num of processes to extract features in parallel. Defaults to 1. + """ + # types = ["train", "test"] if "eval" not in dataset else ["test"] + types = list() + types.append((cfg.preprocess.train_file).split('.')[0]) + types.append((cfg.preprocess.valid_file).split('.')[0]) + if 'test' not in types: + types.append('test') + if "eval" in dataset: + types = ["test"] + print('types: ', types) + metadata = [] + for dataset_type in types: + dataset_output = os.path.join(output_path, dataset) + dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type)) + with open(dataset_file, "r") as f: + metadata.extend(json.load(f)) + + if num_workers > 1: + acoustic_extractor.extract_utt_acoustic_features_parallel( + metadata, dataset_output, cfg, num_workers=num_workers + ) + else: + acoustic_extractor.extract_utt_acoustic_features_serial( + metadata, dataset_output, cfg + ) + +def extract_content_features(dataset, output_path, cfg, num_workers=1): + """Extract content features of utterances in the dataset + + Args: + dataset (str): name of dataset, e.g. opencpop + output_path (str): directory that stores train, test and feature files of datasets + cfg (dict): dictionary that stores configurations + """ + # types = ["train", "test"] if "eval" not in dataset else ["test"] + + types = list() + types.append((cfg.preprocess.train_file).split('.')[0]) + types.append((cfg.preprocess.valid_file).split('.')[0]) + if 'test' not in types: + types.append('test') + if "eval" in dataset: + types = ["test"] + + metadata = [] + for dataset_type in types: + dataset_output = os.path.join(output_path, dataset) + # dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type)) + dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type)) + with open(dataset_file, "r") as f: + metadata.extend(json.load(f)) + + content_extractor.extract_utt_content_features_dataloader( + cfg, metadata, num_workers + ) + +def extract_phonme_sequences(dataset, output_path, cfg): + """Extract phoneme features of utterances in the dataset + + Args: + dataset (str): name of dataset, e.g. opencpop + output_path (str): directory that stores train, test and feature files of datasets + cfg (dict): dictionary that stores configurations + + """ + # types = ["train", "test"] if "eval" not in dataset else ["test"] + + types = list() + types.append((cfg.preprocess.train_file).split('.')[0]) + types.append((cfg.preprocess.valid_file).split('.')[0]) + if 'test' not in types: + types.append('test') + if "eval" in dataset: + types = ["test"] + + metadata = [] + for dataset_type in types: + dataset_output = os.path.join(output_path, dataset) + dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type)) + with open(dataset_file, "r") as f: + metadata.extend(json.load(f)) + phone_extractor.extract_utt_phone_sequence( + cfg, metadata + ) + + +def preprocess(cfg, args): + """Proprocess raw data of single or multiple datasets (in cfg.dataset) + + Args: + cfg (dict): dictionary that stores configurations + args (ArgumentParser): specify the configuration file and num_workers + """ + # Specify the output root path to save the processed data + output_path = cfg.preprocess.processed_dir + os.makedirs(output_path, exist_ok=True) + + ''' + + ## Split train and test sets + for dataset in cfg.dataset: + print("Preprocess {}...".format(dataset)) + + if args.prepare_alignment: + ## Prepare alignment with MFA + print("Prepare alignment {}...".format(dataset)) + prepare_align( + dataset, cfg.dataset_path[dataset], cfg.preprocess, output_path + ) + + preprocess_dataset( + dataset, + cfg.dataset_path[dataset], + output_path, + cfg.preprocess, + is_custom_dataset=cfg.use_custom_dataset, + ) + + # Data augmentation: create new wav files with pitch shift, formant shift, equalizer, time stretch + try: + assert isinstance( + cfg.preprocess.data_augment, list + ), "Please provide a list of datasets need to be augmented." + if len(cfg.preprocess.data_augment) > 0: + new_datasets_list = [] + for dataset in cfg.preprocess.data_augment: + new_datasets = data_augment.augment_dataset(cfg, dataset) + new_datasets_list.extend(new_datasets) + cfg.dataset.extend(new_datasets_list) + print("Augmentation datasets: ", cfg.dataset) + except: + print("No Data Augmentation.") + + # Dump metadata of datasets (singers, train/test durations, etc.) + cal_metadata(cfg) + ''' + ## Prepare the acoustic features + for dataset in cfg.dataset: + # Skip augmented datasets which do not need to extract acoustic features + # We will copy acoustic features from the original dataset later + if ( + "pitch_shift" in dataset + or "formant_shift" in dataset + or "equalizer" in dataset in dataset + ): + continue + print( + "Extracting acoustic features for {} using {} workers ...".format( + dataset, args.num_workers + ) + ) + extract_acoustic_features(dataset, output_path, cfg, args.num_workers) + # Calculate the statistics of acoustic features + if cfg.preprocess.mel_min_max_norm: + acoustic_extractor.cal_mel_min_max(dataset, output_path, cfg) + + if cfg.preprocess.extract_pitch: + acoustic_extractor.cal_pitch_statistics(dataset, output_path, cfg) + + if cfg.preprocess.extract_energy: + acoustic_extractor.cal_energy_statistics(dataset, output_path, cfg) + + if cfg.preprocess.pitch_norm: + acoustic_extractor.normalize(dataset, cfg.preprocess.pitch_dir, cfg) + + if cfg.preprocess.energy_norm: + acoustic_extractor.normalize(dataset, cfg.preprocess.energy_dir, cfg) + + # Copy acoustic features for augmented datasets by creating soft-links + for dataset in cfg.dataset: + if "pitch_shift" in dataset: + src_dataset = dataset.replace("_pitch_shift", "") + src_dataset_dir = os.path.join(output_path, src_dataset) + elif "formant_shift" in dataset: + src_dataset = dataset.replace("_formant_shift", "") + src_dataset_dir = os.path.join(output_path, src_dataset) + elif "equalizer" in dataset: + src_dataset = dataset.replace("_equalizer", "") + src_dataset_dir = os.path.join(output_path, src_dataset) + else: + continue + dataset_dir = os.path.join(output_path, dataset) + metadata = [] + for split in ["train", "test"] if not "eval" in dataset else ["test"]: + metadata_file_path = os.path.join(src_dataset_dir, "{}.json".format(split)) + with open(metadata_file_path, "r") as f: + metadata.extend(json.load(f)) + print("Copying acoustic features for {}...".format(dataset)) + acoustic_extractor.copy_acoustic_features( + metadata, dataset_dir, src_dataset_dir, cfg + ) + if cfg.preprocess.mel_min_max_norm: + acoustic_extractor.cal_mel_min_max(dataset, output_path, cfg) + + if cfg.preprocess.extract_pitch: + acoustic_extractor.cal_pitch_statistics(dataset, output_path, cfg) + + # Prepare the content features + for dataset in cfg.dataset: + print("Extracting content features for {}...".format(dataset)) + extract_content_features(dataset, output_path, cfg, args.num_workers) + + # Prepare the phenome squences + if cfg.preprocess.extract_phone: + for dataset in cfg.dataset: + print("Extracting phoneme sequence for {}...".format(dataset)) + extract_phonme_sequences(dataset, output_path, cfg) + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", default="config.json", help="json files for configurations." + ) + parser.add_argument("--num_workers", type=int, default=int(cpu_count())) + parser.add_argument("--prepare_alignment", type=bool, default=False) + + args = parser.parse_args() + cfg = load_config(args.config) + + preprocess(cfg, args) + + +if __name__ == "__main__": + main() diff --git a/bins/tts/train.py b/bins/tts/train.py new file mode 100644 index 0000000000000000000000000000000000000000..d50901a724bb3a4994eb1a36a5138e70914383d3 --- /dev/null +++ b/bins/tts/train.py @@ -0,0 +1,107 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse + +import torch + +from models.tts.fastspeech2.fs2_trainer import FastSpeech2Trainer +from models.tts.vits.vits_trainer import VITSTrainer +from models.tts.valle.valle_trainer import VALLETrainer +from utils.util import load_config + + +def build_trainer(args, cfg): + supported_trainer = { + "FastSpeech2": FastSpeech2Trainer, + "VITS": VITSTrainer, + "VALLE": VALLETrainer, + } + + trainer_class = supported_trainer[cfg.model_type] + trainer = trainer_class(args, cfg) + return trainer + + +def cuda_relevant(deterministic=False): + torch.cuda.empty_cache() + # TF32 on Ampere and above + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.enabled = True + torch.backends.cudnn.allow_tf32 = True + # Deterministic + torch.backends.cudnn.deterministic = deterministic + torch.backends.cudnn.benchmark = not deterministic + torch.use_deterministic_algorithms(deterministic) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", + default="config.json", + help="json files for configurations.", + required=True, + ) + parser.add_argument( + "--exp_name", + type=str, + default="exp_name", + help="A specific name to note the experiment", + required=True, + ) + parser.add_argument( + "--resume", action="store_true", help="The model name to restore" + ) + parser.add_argument( + "--log_level", default="warning", help="logging level (debug, info, warning)" + ) + parser.add_argument( + "--resume_type", + type=str, + default="resume", + help="Resume training or finetuning.", + ) + parser.add_argument( + "--checkpoint_path", + type=str, + default=None, + help="Checkpoint for resume training or finetuning.", + ) + + VALLETrainer.add_arguments(parser) + args = parser.parse_args() + cfg = load_config(args.config) + + # Data Augmentation + if ( + type(cfg.preprocess.data_augment) == list + and len(cfg.preprocess.data_augment) > 0 + ): + new_datasets_list = [] + for dataset in cfg.preprocess.data_augment: + new_datasets = [ + f"{dataset}_pitch_shift" if cfg.preprocess.use_pitch_shift else None, + f"{dataset}_formant_shift" + if cfg.preprocess.use_formant_shift + else None, + f"{dataset}_equalizer" if cfg.preprocess.use_equalizer else None, + f"{dataset}_time_stretch" if cfg.preprocess.use_time_stretch else None, + ] + new_datasets_list.extend(filter(None, new_datasets)) + cfg.dataset.extend(new_datasets_list) + + # # CUDA settings + cuda_relevant() + + # Build trainer + trainer = build_trainer(args, cfg) + torch.set_num_threads(1) + torch.set_num_interop_threads(1) + trainer.train_loop() + + +if __name__ == "__main__": + main() diff --git a/bins/vocoder/inference.py b/bins/vocoder/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..38502a9a8813377652fda1f8d27cdde47fa95689 --- /dev/null +++ b/bins/vocoder/inference.py @@ -0,0 +1,114 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os + +import torch + +from models.vocoders.vocoder_inference import VocoderInference +from utils.util import load_config + + +def build_inference(args, cfg, infer_type="infer_from_dataset"): + supported_inference = { + "GANVocoder": VocoderInference, + } + + inference_class = supported_inference[cfg.model_type] + return inference_class(args, cfg, infer_type) + + +def cuda_relevant(deterministic=False): + torch.cuda.empty_cache() + # TF32 on Ampere and above + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.enabled = True + torch.backends.cudnn.allow_tf32 = True + # Deterministic + torch.backends.cudnn.deterministic = deterministic + torch.backends.cudnn.benchmark = not deterministic + torch.use_deterministic_algorithms(deterministic) + + +def build_parser(): + r"""Build argument parser for inference.py. + Anything else should be put in an extra config YAML file. + """ + + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", + type=str, + required=True, + help="JSON/YAML file for configurations.", + ) + parser.add_argument( + "--infer_mode", + type=str, + required=None, + ) + parser.add_argument( + "--infer_datasets", + nargs="+", + default=None, + ) + parser.add_argument( + "--feature_folder", + type=str, + default=None, + ) + parser.add_argument( + "--audio_folder", + type=str, + default=None, + ) + parser.add_argument( + "--vocoder_dir", + type=str, + required=True, + help="Vocoder checkpoint directory. Searching behavior is the same as " + "the acoustics one.", + ) + parser.add_argument( + "--output_dir", + type=str, + default="result", + help="Output directory. Default: ./result", + ) + parser.add_argument( + "--log_level", + type=str, + default="warning", + help="Logging level. Default: warning", + ) + parser.add_argument( + "--keep_cache", + action="store_true", + default=False, + help="Keep cache files. Only applicable to inference from files.", + ) + return parser + + +def main(): + # Parse arguments + args = build_parser().parse_args() + + # Parse config + cfg = load_config(args.config) + + # CUDA settings + cuda_relevant() + + # Build inference + trainer = build_inference(args, cfg, args.infer_mode) + + # Run inference + trainer.inference() + + +if __name__ == "__main__": + main() diff --git a/bins/vocoder/preprocess.py b/bins/vocoder/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..6ec0a255e96ae39cd9c55f7df39d0236f910db84 --- /dev/null +++ b/bins/vocoder/preprocess.py @@ -0,0 +1,150 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import faulthandler + +faulthandler.enable() + +import os +import argparse +import json +import pyworld as pw +from multiprocessing import cpu_count + + +from utils.util import load_config +from preprocessors.processor import preprocess_dataset, prepare_align +from preprocessors.metadata import cal_metadata +from processors import acoustic_extractor, content_extractor, data_augment + + +def extract_acoustic_features(dataset, output_path, cfg, n_workers=1): + """Extract acoustic features of utterances in the dataset + + Args: + dataset (str): name of dataset, e.g. opencpop + output_path (str): directory that stores train, test and feature files of datasets + cfg (dict): dictionary that stores configurations + n_workers (int, optional): num of processes to extract features in parallel. Defaults to 1. + """ + types = ["train", "test"] if "eval" not in dataset else ["test"] + metadata = [] + for dataset_type in types: + dataset_output = os.path.join(output_path, dataset) + dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type)) + with open(dataset_file, "r") as f: + metadata.extend(json.load(f)) + + acoustic_extractor.extract_utt_acoustic_features_serial( + metadata, dataset_output, cfg + ) + + +def preprocess(cfg, args): + """Proprocess raw data of single or multiple datasets (in cfg.dataset) + + Args: + cfg (dict): dictionary that stores configurations + args (ArgumentParser): specify the configuration file and num_workers + """ + # Specify the output root path to save the processed data + output_path = cfg.preprocess.processed_dir + os.makedirs(output_path, exist_ok=True) + + ## Split train and test sets + for dataset in cfg.dataset: + print("Preprocess {}...".format(dataset)) + + preprocess_dataset( + dataset, + cfg.dataset_path[dataset], + output_path, + cfg.preprocess, + is_custom_dataset=cfg.use_custom_dataset, + ) + + # Data augmentation: create new wav files with pitch shift, formant shift, equalizer, time stretch + try: + assert isinstance( + cfg.preprocess.data_augment, list + ), "Please provide a list of datasets need to be augmented." + if len(cfg.preprocess.data_augment) > 0: + new_datasets_list = [] + for dataset in cfg.preprocess.data_augment: + new_datasets = data_augment.augment_dataset(cfg, dataset) + new_datasets_list.extend(new_datasets) + cfg.dataset.extend(new_datasets_list) + print("Augmentation datasets: ", cfg.dataset) + except: + print("No Data Augmentation.") + + # Dump metadata of datasets (singers, train/test durations, etc.) + cal_metadata(cfg) + + ## Prepare the acoustic features + for dataset in cfg.dataset: + # Skip augmented datasets which do not need to extract acoustic features + # We will copy acoustic features from the original dataset later + if ( + "pitch_shift" in dataset + or "formant_shift" in dataset + or "equalizer" in dataset in dataset + ): + continue + print( + "Extracting acoustic features for {} using {} workers ...".format( + dataset, args.num_workers + ) + ) + extract_acoustic_features(dataset, output_path, cfg, args.num_workers) + # Calculate the statistics of acoustic features + if cfg.preprocess.mel_min_max_norm: + acoustic_extractor.cal_mel_min_max(dataset, output_path, cfg) + + # Copy acoustic features for augmented datasets by creating soft-links + for dataset in cfg.dataset: + if "pitch_shift" in dataset: + src_dataset = dataset.replace("_pitch_shift", "") + src_dataset_dir = os.path.join(output_path, src_dataset) + elif "formant_shift" in dataset: + src_dataset = dataset.replace("_formant_shift", "") + src_dataset_dir = os.path.join(output_path, src_dataset) + elif "equalizer" in dataset: + src_dataset = dataset.replace("_equalizer", "") + src_dataset_dir = os.path.join(output_path, src_dataset) + else: + continue + dataset_dir = os.path.join(output_path, dataset) + metadata = [] + for split in ["train", "test"] if not "eval" in dataset else ["test"]: + metadata_file_path = os.path.join(src_dataset_dir, "{}.json".format(split)) + with open(metadata_file_path, "r") as f: + metadata.extend(json.load(f)) + print("Copying acoustic features for {}...".format(dataset)) + acoustic_extractor.copy_acoustic_features( + metadata, dataset_dir, src_dataset_dir, cfg + ) + if cfg.preprocess.mel_min_max_norm: + acoustic_extractor.cal_mel_min_max(dataset, output_path, cfg) + + if cfg.preprocess.extract_pitch: + acoustic_extractor.cal_pitch_statistics(dataset, output_path, cfg) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", default="config.json", help="json files for configurations." + ) + parser.add_argument("--num_workers", type=int, default=int(cpu_count())) + + args = parser.parse_args() + cfg = load_config(args.config) + + preprocess(cfg, args) + + +if __name__ == "__main__": + main() diff --git a/bins/vocoder/train.py b/bins/vocoder/train.py new file mode 100644 index 0000000000000000000000000000000000000000..65284f42c8b7709e435dd49702c919a667b087c3 --- /dev/null +++ b/bins/vocoder/train.py @@ -0,0 +1,90 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse + +import torch + +from models.vocoders.gan.gan_vocoder_trainer import GANVocoderTrainer +from utils.util import load_config + + +def build_trainer(args, cfg): + supported_trainer = { + "GANVocoder": GANVocoderTrainer, + } + + trainer_class = supported_trainer[cfg.model_type] + trainer = trainer_class(args, cfg) + return trainer + + +def cuda_relevant(deterministic=False): + torch.cuda.empty_cache() + # TF32 on Ampere and above + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.enabled = True + torch.backends.cudnn.allow_tf32 = True + # Deterministic + torch.backends.cudnn.deterministic = deterministic + torch.backends.cudnn.benchmark = not deterministic + torch.use_deterministic_algorithms(deterministic) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", + default="config.json", + help="json files for configurations.", + required=True, + ) + parser.add_argument( + "--exp_name", + type=str, + default="exp_name", + help="A specific name to note the experiment", + required=True, + ) + parser.add_argument( + "--resume_type", + type=str, + help="resume for continue to train, finetune for finetuning", + ) + parser.add_argument( + "--checkpoint", + type=str, + help="checkpoint to resume", + ) + parser.add_argument( + "--log_level", default="warning", help="logging level (debug, info, warning)" + ) + args = parser.parse_args() + cfg = load_config(args.config) + + # Data Augmentation + if cfg.preprocess.data_augment: + new_datasets_list = [] + for dataset in cfg.preprocess.data_augment: + new_datasets = [ + # f"{dataset}_pitch_shift", + # f"{dataset}_formant_shift", + f"{dataset}_equalizer", + f"{dataset}_time_stretch", + ] + new_datasets_list.extend(new_datasets) + cfg.dataset.extend(new_datasets_list) + + # CUDA settings + cuda_relevant() + + # Build trainer + trainer = build_trainer(args, cfg) + + trainer.train_loop() + + +if __name__ == "__main__": + main() diff --git a/config/audioldm.json b/config/audioldm.json new file mode 100644 index 0000000000000000000000000000000000000000..c0cab48cda2f00ac6c1687da5ab3b6d6a263e317 --- /dev/null +++ b/config/audioldm.json @@ -0,0 +1,92 @@ +{ + "base_config": "config/base.json", + "model_type": "AudioLDM", + "task_type": "tta", + "dataset": [ + "AudioCaps" + ], + "preprocess": { + // feature used for model training + "use_spkid": false, + "use_uv": false, + "use_frame_pitch": false, + "use_phone_pitch": false, + "use_frame_energy": false, + "use_phone_energy": false, + "use_mel": false, + "use_audio": false, + "use_label": false, + "use_one_hot": false, + "cond_mask_prob": 0.1 + }, + // model + "model": { + "audioldm": { + "image_size": 32, + "in_channels": 4, + "out_channels": 4, + "model_channels": 256, + "attention_resolutions": [ + 4, + 2, + 1 + ], + "num_res_blocks": 2, + "channel_mult": [ + 1, + 2, + 4 + ], + "num_heads": 8, + "use_spatial_transformer": true, + "transformer_depth": 1, + "context_dim": 768, + "use_checkpoint": true, + "legacy": false + }, + "autoencoderkl": { + "ch": 128, + "ch_mult": [ + 1, + 1, + 2, + 2, + 4 + ], + "num_res_blocks": 2, + "in_channels": 1, + "z_channels": 4, + "out_ch": 1, + "double_z": true + }, + "noise_scheduler": { + "num_train_timesteps": 1000, + "beta_start": 0.00085, + "beta_end": 0.012, + "beta_schedule": "scaled_linear", + "clip_sample": false, + "steps_offset": 1, + "set_alpha_to_one": false, + "skip_prk_steps": true, + "prediction_type": "epsilon" + } + }, + // train + "train": { + "lronPlateau": { + "factor": 0.9, + "patience": 100, + "min_lr": 4.0e-5, + "verbose": true + }, + "adam": { + "lr": 5.0e-5, + "betas": [ + 0.9, + 0.999 + ], + "weight_decay": 1.0e-2, + "eps": 1.0e-8 + } + } +} \ No newline at end of file diff --git a/config/autoencoderkl.json b/config/autoencoderkl.json new file mode 100644 index 0000000000000000000000000000000000000000..72e677770da689af33d4c7bd6f3506927fb24380 --- /dev/null +++ b/config/autoencoderkl.json @@ -0,0 +1,69 @@ +{ + "base_config": "config/base.json", + "model_type": "AutoencoderKL", + "task_type": "tta", + "dataset": [ + "AudioCaps" + ], + "preprocess": { + // feature used for model training + "use_spkid": false, + "use_uv": false, + "use_frame_pitch": false, + "use_phone_pitch": false, + "use_frame_energy": false, + "use_phone_energy": false, + "use_mel": false, + "use_audio": false, + "use_label": false, + "use_one_hot": false + }, + // model + "model": { + "autoencoderkl": { + "ch": 128, + "ch_mult": [ + 1, + 1, + 2, + 2, + 4 + ], + "num_res_blocks": 2, + "in_channels": 1, + "z_channels": 4, + "out_ch": 1, + "double_z": true + }, + "loss": { + "kl_weight": 1e-8, + "disc_weight": 0.5, + "disc_factor": 1.0, + "logvar_init": 0.0, + "min_adapt_d_weight": 0.0, + "max_adapt_d_weight": 10.0, + "disc_start": 50001, + "disc_in_channels": 1, + "disc_num_layers": 3, + "use_actnorm": false + } + }, + // train + "train": { + "lronPlateau": { + "factor": 0.9, + "patience": 100, + "min_lr": 4.0e-5, + "verbose": true + }, + "adam": { + "lr": 4.0e-4, + "betas": [ + 0.9, + 0.999 + ], + "weight_decay": 1.0e-2, + "eps": 1.0e-8 + } + } +} \ No newline at end of file diff --git a/config/base.json b/config/base.json new file mode 100644 index 0000000000000000000000000000000000000000..5fa71dfcdde7058eae634007cdf4f034184e753b --- /dev/null +++ b/config/base.json @@ -0,0 +1,220 @@ +{ + "supported_model_type": [ + "GANVocoder", + "Fastspeech2", + "DiffSVC", + "Transformer", + "EDM", + "CD" + ], + "task_type": "", + "dataset": [], + "use_custom_dataset": false, + "preprocess": { + "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon" + // trim audio silence + "data_augment": false, + "trim_silence": false, + "num_silent_frames": 8, + "trim_fft_size": 512, // fft size used in trimming + "trim_hop_size": 128, // hop size used in trimming + "trim_top_db": 30, // top db used in trimming sensitive to each dataset + // acoustic features + "extract_mel": false, + "mel_extract_mode": "", + "extract_linear_spec": false, + "extract_mcep": false, + "extract_pitch": false, + "extract_acoustic_token": false, + "pitch_remove_outlier": false, + "extract_uv": false, + "pitch_norm": false, + "extract_audio": false, + "extract_label": false, + "pitch_extractor": "parselmouth", // pyin, dio, pyworld, pyreaper, parselmouth, CWT (Continuous Wavelet Transform) + "extract_energy": false, + "energy_remove_outlier": false, + "energy_norm": false, + "energy_extract_mode": "from_mel", + "extract_duration": false, + "extract_amplitude_phase": false, + "mel_min_max_norm": false, + // lingusitic features + "extract_phone": false, + "lexicon_path": "./text/lexicon/librispeech-lexicon.txt", + // content features + "extract_whisper_feature": false, + "extract_contentvec_feature": false, + "extract_mert_feature": false, + "extract_wenet_feature": false, + // Settings for data preprocessing + "n_mel": 80, + "win_size": 480, + "hop_size": 120, + "sample_rate": 24000, + "n_fft": 1024, + "fmin": 0, + "fmax": 12000, + "min_level_db": -115, + "ref_level_db": 20, + "bits": 8, + // Directory names of processed data or extracted features + "processed_dir": "processed_data", + "trimmed_wav_dir": "trimmed_wavs", // directory name of silence trimed wav + "raw_data": "raw_data", + "phone_dir": "phones", + "wav_dir": "wavs", // directory name of processed wav (such as downsampled waveform) + "audio_dir": "audios", + "log_amplitude_dir": "log_amplitudes", + "phase_dir": "phases", + "real_dir": "reals", + "imaginary_dir": "imaginarys", + "label_dir": "labels", + "linear_dir": "linears", + "mel_dir": "mels", // directory name of extraced mel features + "mcep_dir": "mcep", // directory name of extraced mcep features + "dur_dir": "durs", + "symbols_dict": "symbols.dict", + "lab_dir": "labs", // directory name of extraced label features + "wenet_dir": "wenet", // directory name of extraced wenet features + "contentvec_dir": "contentvec", // directory name of extraced wenet features + "pitch_dir": "pitches", // directory name of extraced pitch features + "energy_dir": "energys", // directory name of extracted energy features + "phone_pitch_dir": "phone_pitches", // directory name of extraced pitch features + "phone_energy_dir": "phone_energys", // directory name of extracted energy features + "uv_dir": "uvs", // directory name of extracted unvoiced features + "duration_dir": "duration", // ground-truth duration file + "phone_seq_file": "phone_seq_file", // phoneme sequence file + "file_lst": "file.lst", + "train_file": "train.json", // training set, the json file contains detailed information about the dataset, including dataset name, utterance id, duration of the utterance + "valid_file": "valid.json", // validattion set + "spk2id": "spk2id.json", // used for multi-speaker dataset + "utt2spk": "utt2spk", // used for multi-speaker dataset + "emo2id": "emo2id.json", // used for multi-emotion dataset + "utt2emo": "utt2emo", // used for multi-emotion dataset + // Features used for model training + "use_text": false, + "use_phone": false, + "use_phn_seq": false, + "use_lab": false, + "use_linear": false, + "use_mel": false, + "use_min_max_norm_mel": false, + "use_wav": false, + "use_phone_pitch": false, + "use_log_scale_pitch": false, + "use_phone_energy": false, + "use_phone_duration": false, + "use_log_scale_energy": false, + "use_wenet": false, + "use_dur": false, + "use_spkid": false, // True: use speaker id for multi-speaker dataset + "use_emoid": false, // True: use emotion id for multi-emotion dataset + "use_frame_pitch": false, + "use_uv": false, + "use_frame_energy": false, + "use_frame_duration": false, + "use_audio": false, + "use_label": false, + "use_one_hot": false, + "use_amplitude_phase": false, + "data_augment": false, + "align_mel_duration": false + }, + "train": { + "ddp": true, + "random_seed": 970227, + "batch_size": 16, + "max_steps": 1000000, + // Trackers + "tracker": [ + "tensorboard" + // "wandb", + // "cometml", + // "mlflow", + ], + "max_epoch": -1, + // -1 means no limit + "save_checkpoint_stride": [ + 5, + 20 + ], + // unit is epoch + "keep_last": [ + 3, + -1 + ], + // -1 means infinite, if one number will broadcast + "run_eval": [ + false, + true + ], + // if one number will broadcast + // Fix the random seed + "random_seed": 10086, + // Optimizer + "optimizer": "AdamW", + "adamw": { + "lr": 4.0e-4 + // nn model lr + }, + // LR Scheduler + "scheduler": "ReduceLROnPlateau", + "reducelronplateau": { + "factor": 0.8, + "patience": 10, + // unit is epoch + "min_lr": 1.0e-4 + }, + // Batchsampler + "sampler": { + "holistic_shuffle": true, + "drop_last": true + }, + // Dataloader + "dataloader": { + "num_worker": 32, + "pin_memory": true + }, + "gradient_accumulation_step": 1, + "total_training_steps": 50000, + "save_summary_steps": 500, + "save_checkpoints_steps": 10000, + "valid_interval": 10000, + "keep_checkpoint_max": 5, + "multi_speaker_training": false, // True: train multi-speaker model; False: training single-speaker model; + "max_epoch": -1, + // -1 means no limit + "save_checkpoint_stride": [ + 5, + 20 + ], + // unit is epoch + "keep_last": [ + 3, + -1 + ], + // -1 means infinite, if one number will broadcast + "run_eval": [ + false, + true + ], + // Batchsampler + "sampler": { + "holistic_shuffle": true, + "drop_last": true + }, + // Dataloader + "dataloader": { + "num_worker": 32, + "pin_memory": true + }, + // Trackers + "tracker": [ + "tensorboard" + // "wandb", + // "cometml", + // "mlflow", + ], + }, +} diff --git a/config/comosvc.json b/config/comosvc.json new file mode 100644 index 0000000000000000000000000000000000000000..19599e940be8f13e40fe6ac59ff82e720452d10e --- /dev/null +++ b/config/comosvc.json @@ -0,0 +1,216 @@ +{ + "base_config": "config/base.json", + "model_type": "DiffComoSVC", + "task_type": "svc", + "use_custom_dataset": false, + "preprocess": { + // data augmentations + "use_pitch_shift": false, + "use_formant_shift": false, + "use_time_stretch": false, + "use_equalizer": false, + // acoustic features + "extract_mel": true, + "mel_min_max_norm": true, + "extract_pitch": true, + "pitch_extractor": "parselmouth", + "extract_uv": true, + "extract_energy": true, + // content features + "extract_whisper_feature": false, + "whisper_sample_rate": 16000, + "extract_contentvec_feature": false, + "contentvec_sample_rate": 16000, + "extract_wenet_feature": false, + "wenet_sample_rate": 16000, + "extract_mert_feature": false, + "mert_sample_rate": 16000, + // Default config for whisper + "whisper_frameshift": 0.01, + "whisper_downsample_rate": 2, + // Default config for content vector + "contentvec_frameshift": 0.02, + // Default config for mert + "mert_model": "m-a-p/MERT-v1-330M", + "mert_feature_layer": -1, + "mert_hop_size": 320, + // 24k + "mert_frameshit": 0.01333, + // 10ms + "wenet_frameshift": 0.01, + // wenetspeech is 4, gigaspeech is 6 + "wenet_downsample_rate": 4, + // Default config + "n_mel": 100, + "win_size": 1024, + // todo + "hop_size": 256, + "sample_rate": 24000, + "n_fft": 1024, + // todo + "fmin": 0, + "fmax": 12000, + // todo + "f0_min": 50, + // ~C2 + "f0_max": 1100, + //1100, // ~C6(1100), ~G5(800) + "pitch_bin": 256, + "pitch_max": 1100.0, + "pitch_min": 50.0, + "is_label": true, + "is_mu_law": true, + "bits": 8, + "mel_min_max_stats_dir": "mel_min_max_stats", + "whisper_dir": "whisper", + "contentvec_dir": "contentvec", + "wenet_dir": "wenet", + "mert_dir": "mert", + // Extract content features using dataloader + "pin_memory": true, + "num_workers": 8, + "content_feature_batch_size": 16, + // Features used for model training + "use_mel": true, + "use_min_max_norm_mel": true, + "use_frame_pitch": true, + "use_uv": true, + "use_frame_energy": true, + "use_log_scale_pitch": false, + "use_log_scale_energy": false, + "use_spkid": true, + // Meta file + "train_file": "train.json", + "valid_file": "test.json", + "spk2id": "singers.json", + "utt2spk": "utt2singer" + }, + "model": { + "teacher_model_path": "[Your Teacher Model Path].bin", + "condition_encoder": { + "merge_mode": "add", + "input_melody_dim": 1, + "use_log_f0": true, + "n_bins_melody": 256, + //# Quantization (0 for not quantization) + "output_melody_dim": 384, + "input_loudness_dim": 1, + "use_log_loudness": true, + "n_bins_loudness": 256, + "output_loudness_dim": 384, + "use_whisper": false, + "use_contentvec": false, + "use_wenet": false, + "use_mert": false, + "whisper_dim": 1024, + "contentvec_dim": 256, + "mert_dim": 256, + "wenet_dim": 512, + "content_encoder_dim": 384, + "output_singer_dim": 384, + "singer_table_size": 512, + "output_content_dim": 384, + "use_spkid": true + }, + "comosvc": { + "distill": false, + // conformer encoder + "input_dim": 384, + "output_dim": 100, + "n_heads": 2, + "n_layers": 6, + "filter_channels": 512, + "dropout": 0.1, + // karras diffusion + "P_mean": -1.2, + "P_std": 1.2, + "sigma_data": 0.5, + "sigma_min": 0.002, + "sigma_max": 80, + "rho": 7, + "n_timesteps": 40, + }, + "diffusion": { + // Diffusion steps encoder + "step_encoder": { + "dim_raw_embedding": 128, + "dim_hidden_layer": 512, + "activation": "SiLU", + "num_layer": 2, + "max_period": 10000 + }, + // Diffusion decoder + "model_type": "bidilconv", + // bidilconv, unet2d, TODO: unet1d + "bidilconv": { + "base_channel": 384, + "n_res_block": 20, + "conv_kernel_size": 3, + "dilation_cycle_length": 4, + // specially, 1 means no dilation + "conditioner_size": 100 + } + }, + }, + "train": { + // Basic settings + "fast_steps": 0, + "batch_size": 32, + "gradient_accumulation_step": 1, + "max_epoch": -1, + // -1 means no limit + "save_checkpoint_stride": [ + 10, + 100 + ], + // unit is epoch + "keep_last": [ + 3, + -1 + ], + // -1 means infinite, if one number will broadcast + "run_eval": [ + false, + true + ], + // if one number will broadcast + // Fix the random seed + "random_seed": 10086, + // Batchsampler + "sampler": { + "holistic_shuffle": true, + "drop_last": true + }, + // Dataloader + "dataloader": { + "num_worker": 32, + "pin_memory": true + }, + // Trackers + "tracker": [ + "tensorboard" + // "wandb", + // "cometml", + // "mlflow", + ], + // Optimizer + "optimizer": "AdamW", + "adamw": { + "lr": 4.0e-4 + // nn model lr + }, + // LR Scheduler + "scheduler": "ReduceLROnPlateau", + "reducelronplateau": { + "factor": 0.8, + "patience": 10, + // unit is epoch + "min_lr": 1.0e-4 + } + }, + "inference": { + "comosvc": { + "inference_steps": 40 + } + } +} \ No newline at end of file diff --git a/config/diffusion.json b/config/diffusion.json new file mode 100644 index 0000000000000000000000000000000000000000..7ef72f39d3dd578afc35d086a3d19fc892bbefda --- /dev/null +++ b/config/diffusion.json @@ -0,0 +1,227 @@ +{ + // FIXME: THESE ARE LEGACY + "base_config": "config/base.json", + "model_type": "diffusion", + "task_type": "svc", + "use_custom_dataset": false, + "preprocess": { + // data augmentations + "use_pitch_shift": false, + "use_formant_shift": false, + "use_time_stretch": false, + "use_equalizer": false, + // acoustic features + "extract_mel": true, + "mel_min_max_norm": true, + "extract_pitch": true, + "pitch_extractor": "parselmouth", + "extract_uv": true, + "extract_energy": true, + // content features + "extract_whisper_feature": false, + "whisper_sample_rate": 16000, + "extract_contentvec_feature": false, + "contentvec_sample_rate": 16000, + "extract_wenet_feature": false, + "wenet_sample_rate": 16000, + "extract_mert_feature": false, + "mert_sample_rate": 16000, + // Default config for whisper + "whisper_frameshift": 0.01, + "whisper_downsample_rate": 2, + // Default config for content vector + "contentvec_frameshift": 0.02, + // Default config for mert + "mert_model": "m-a-p/MERT-v1-330M", + "mert_feature_layer": -1, + "mert_hop_size": 320, + // 24k + "mert_frameshit": 0.01333, + // 10ms + "wenet_frameshift": 0.01, + // wenetspeech is 4, gigaspeech is 6 + "wenet_downsample_rate": 4, + // Default config + "n_mel": 100, + "win_size": 1024, + // todo + "hop_size": 256, + "sample_rate": 24000, + "n_fft": 1024, + // todo + "fmin": 0, + "fmax": 12000, + // todo + "f0_min": 50, + // ~C2 + "f0_max": 1100, + //1100, // ~C6(1100), ~G5(800) + "pitch_bin": 256, + "pitch_max": 1100.0, + "pitch_min": 50.0, + "is_label": true, + "is_mu_law": true, + "bits": 8, + "mel_min_max_stats_dir": "mel_min_max_stats", + "whisper_dir": "whisper", + "contentvec_dir": "contentvec", + "wenet_dir": "wenet", + "mert_dir": "mert", + // Extract content features using dataloader + "pin_memory": true, + "num_workers": 8, + "content_feature_batch_size": 16, + // Features used for model training + "use_mel": true, + "use_min_max_norm_mel": true, + "use_frame_pitch": true, + "use_uv": true, + "use_frame_energy": true, + "use_log_scale_pitch": false, + "use_log_scale_energy": false, + "use_spkid": true, + // Meta file + "train_file": "train.json", + "valid_file": "test.json", + "spk2id": "singers.json", + "utt2spk": "utt2singer" + }, + "model": { + "condition_encoder": { + "merge_mode": "add", + "input_melody_dim": 1, + "use_log_f0": true, + "n_bins_melody": 256, + //# Quantization (0 for not quantization) + "output_melody_dim": 384, + "input_loudness_dim": 1, + "use_log_loudness": true, + "n_bins_loudness": 256, + "output_loudness_dim": 384, + "use_whisper": false, + "use_contentvec": false, + "use_wenet": false, + "use_mert": false, + "whisper_dim": 1024, + "contentvec_dim": 256, + "mert_dim": 256, + "wenet_dim": 512, + "content_encoder_dim": 384, + "output_singer_dim": 384, + "singer_table_size": 512, + "output_content_dim": 384, + "use_spkid": true + }, + // FIXME: FOLLOWING ARE NEW!! + "diffusion": { + "scheduler": "ddpm", + "scheduler_settings": { + "num_train_timesteps": 1000, + "beta_start": 1.0e-4, + "beta_end": 0.02, + "beta_schedule": "linear" + }, + // Diffusion steps encoder + "step_encoder": { + "dim_raw_embedding": 128, + "dim_hidden_layer": 512, + "activation": "SiLU", + "num_layer": 2, + "max_period": 10000 + }, + // Diffusion decoder + "model_type": "bidilconv", + // bidilconv, unet2d, TODO: unet1d + "bidilconv": { + "base_channel": 384, + "n_res_block": 20, + "conv_kernel_size": 3, + "dilation_cycle_length": 4, + // specially, 1 means no dilation + "conditioner_size": 384 + }, + "unet2d": { + "in_channels": 1, + "out_channels": 1, + "down_block_types": [ + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "DownBlock2D" + ], + "mid_block_type": "UNetMidBlock2DCrossAttn", + "up_block_types": [ + "UpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D" + ], + "only_cross_attention": false + } + } + }, + // FIXME: FOLLOWING ARE NEW!! + "train": { + // Basic settings + "batch_size": 64, + "gradient_accumulation_step": 1, + "max_epoch": -1, + // -1 means no limit + "save_checkpoint_stride": [ + 5, + 20 + ], + // unit is epoch + "keep_last": [ + 3, + -1 + ], + // -1 means infinite, if one number will broadcast + "run_eval": [ + false, + true + ], + // if one number will broadcast + // Fix the random seed + "random_seed": 10086, + // Batchsampler + "sampler": { + "holistic_shuffle": true, + "drop_last": true + }, + // Dataloader + "dataloader": { + "num_worker": 32, + "pin_memory": true + }, + // Trackers + "tracker": [ + "tensorboard" + // "wandb", + // "cometml", + // "mlflow", + ], + // Optimizer + "optimizer": "AdamW", + "adamw": { + "lr": 4.0e-4 + // nn model lr + }, + // LR Scheduler + "scheduler": "ReduceLROnPlateau", + "reducelronplateau": { + "factor": 0.8, + "patience": 10, + // unit is epoch + "min_lr": 1.0e-4 + } + }, + "inference": { + "diffusion": { + "scheduler": "pndm", + "scheduler_settings": { + "num_inference_timesteps": 1000 + } + } + } +} \ No newline at end of file diff --git a/config/fs2.json b/config/fs2.json new file mode 100644 index 0000000000000000000000000000000000000000..d6f04b593c6f6d8ef67e416b9a3d614d3f963b4f --- /dev/null +++ b/config/fs2.json @@ -0,0 +1,117 @@ +{ + "base_config": "config/tts.json", + "model_type": "FastSpeech2", + "task_type": "tts", + "dataset": ["LJSpeech"], + "preprocess": { + // acoustic features + "extract_audio": true, + "extract_mel": true, + "mel_extract_mode": "taco", + "mel_min_max_norm": false, + "extract_pitch": true, + "extract_uv": false, + "pitch_extractor": "dio", + "extract_energy": true, + "energy_extract_mode": "from_tacotron_stft", + "extract_duration": true, + "use_phone": true, + "pitch_norm": true, + "energy_norm": true, + "pitch_remove_outlier": true, + "energy_remove_outlier": true, + + // Default config + "n_mel": 80, + "win_size": 1024, // todo + "hop_size": 256, + "sample_rate": 22050, + "n_fft": 1024, // todo + "fmin": 0, + "fmax": 8000, // todo + "raw_data": "raw_data", + "text_cleaners": ["english_cleaners"], + "f0_min": 71, // ~C2 + "f0_max": 800, //1100, // ~C6(1100), ~G5(800) + "pitch_bin": 256, + "pitch_max": 1100.0, + "pitch_min": 50.0, + "is_label": true, + "is_mu_law": true, + "bits": 8, + + "mel_min_max_stats_dir": "mel_min_max_stats", + "whisper_dir": "whisper", + "content_vector_dir": "content_vector", + "wenet_dir": "wenet", + "mert_dir": "mert", + "spk2id":"spk2id.json", + "utt2spk":"utt2spk", + + // Features used for model training + "use_mel": true, + "use_min_max_norm_mel": false, + "use_frame_pitch": false, + "use_frame_energy": false, + "use_phone_pitch": true, + "use_phone_energy": true, + "use_log_scale_pitch": false, + "use_log_scale_energy": false, + "use_spkid": false, + "align_mel_duration": true, + "text_cleaners": ["english_cleaners"] + }, + "model": { + // Settings for transformer + "transformer": { + "encoder_layer": 4, + "encoder_head": 2, + "encoder_hidden": 256, + "decoder_layer": 6, + "decoder_head": 2, + "decoder_hidden": 256, + "conv_filter_size": 1024, + "conv_kernel_size": [9, 1], + "encoder_dropout": 0.2, + "decoder_dropout": 0.2 + }, + + // Settings for variance_predictor + "variance_predictor":{ + "filter_size": 256, + "kernel_size": 3, + "dropout": 0.5 + }, + "variance_embedding":{ + "pitch_quantization": "linear", // support 'linear' or 'log', 'log' is allowed only if the pitch values are not normalized during preprocessing + "energy_quantization": "linear", // support 'linear' or 'log', 'log' is allowed only if the energy values are not normalized during preprocessing + "n_bins": 256 + }, + "max_seq_len": 1000 + }, + "train":{ + "batch_size": 16, + "sort_sample": true, + "drop_last": true, + "group_size": 4, + "grad_clip_thresh": 1.0, + "dataloader": { + "num_worker": 8, + "pin_memory": true + }, + "lr_scheduler":{ + "num_warmup": 4000 + }, + // LR Scheduler + "scheduler": "NoamLR", + // Optimizer + "optimizer": "Adam", + "adam": { + "lr": 0.0625, + "betas": [0.9, 0.98], + "eps": 0.000000001, + "weight_decay": 0.0 + }, + } + +} diff --git a/config/transformer.json b/config/transformer.json new file mode 100644 index 0000000000000000000000000000000000000000..258bfddb36a21adb91b9e1921a57f738a789a835 --- /dev/null +++ b/config/transformer.json @@ -0,0 +1,180 @@ +{ + "base_config": "config/base.json", + "model_type": "Transformer", + "task_type": "svc", + "use_custom_dataset": false, + "preprocess": { + // data augmentations + "use_pitch_shift": false, + "use_formant_shift": false, + "use_time_stretch": false, + "use_equalizer": false, + // acoustic features + "extract_mel": true, + "mel_min_max_norm": true, + "extract_pitch": true, + "pitch_extractor": "parselmouth", + "extract_uv": true, + "extract_energy": true, + // content features + "extract_whisper_feature": false, + "whisper_sample_rate": 16000, + "extract_contentvec_feature": false, + "contentvec_sample_rate": 16000, + "extract_wenet_feature": false, + "wenet_sample_rate": 16000, + "extract_mert_feature": false, + "mert_sample_rate": 16000, + // Default config for whisper + "whisper_frameshift": 0.01, + "whisper_downsample_rate": 2, + // Default config for content vector + "contentvec_frameshift": 0.02, + // Default config for mert + "mert_model": "m-a-p/MERT-v1-330M", + "mert_feature_layer": -1, + "mert_hop_size": 320, + // 24k + "mert_frameshit": 0.01333, + // 10ms + "wenet_frameshift": 0.01, + // wenetspeech is 4, gigaspeech is 6 + "wenet_downsample_rate": 4, + // Default config + "n_mel": 100, + "win_size": 1024, + // todo + "hop_size": 256, + "sample_rate": 24000, + "n_fft": 1024, + // todo + "fmin": 0, + "fmax": 12000, + // todo + "f0_min": 50, + // ~C2 + "f0_max": 1100, + //1100, // ~C6(1100), ~G5(800) + "pitch_bin": 256, + "pitch_max": 1100.0, + "pitch_min": 50.0, + "is_label": true, + "is_mu_law": true, + "bits": 8, + "mel_min_max_stats_dir": "mel_min_max_stats", + "whisper_dir": "whisper", + "contentvec_dir": "contentvec", + "wenet_dir": "wenet", + "mert_dir": "mert", + // Extract content features using dataloader + "pin_memory": true, + "num_workers": 8, + "content_feature_batch_size": 16, + // Features used for model training + "use_mel": true, + "use_min_max_norm_mel": true, + "use_frame_pitch": true, + "use_uv": true, + "use_frame_energy": true, + "use_log_scale_pitch": false, + "use_log_scale_energy": false, + "use_spkid": true, + // Meta file + "train_file": "train.json", + "valid_file": "test.json", + "spk2id": "singers.json", + "utt2spk": "utt2singer" + }, + "model": { + "condition_encoder": { + "merge_mode": "add", + "input_melody_dim": 1, + "use_log_f0": true, + "n_bins_melody": 256, + //# Quantization (0 for not quantization) + "output_melody_dim": 384, + "input_loudness_dim": 1, + "use_log_loudness": true, + "n_bins_loudness": 256, + "output_loudness_dim": 384, + "use_whisper": false, + "use_contentvec": true, + "use_wenet": false, + "use_mert": false, + "whisper_dim": 1024, + "contentvec_dim": 256, + "mert_dim": 256, + "wenet_dim": 512, + "content_encoder_dim": 384, + "output_singer_dim": 384, + "singer_table_size": 512, + "output_content_dim": 384, + "use_spkid": true + }, + "transformer": { + "type": "conformer", + // 'conformer' or 'transformer' + "input_dim": 384, + "output_dim": 100, + "n_heads": 2, + "n_layers": 6, + "filter_channels": 512, + "dropout": 0.1, + } + }, + "train": { + // Basic settings + "batch_size": 64, + "gradient_accumulation_step": 1, + "max_epoch": -1, + // -1 means no limit + "save_checkpoint_stride": [ + 10, + 100 + ], + // unit is epoch + "keep_last": [ + 3, + -1 + ], + // -1 means infinite, if one number will broadcast + "run_eval": [ + false, + true + ], + // if one number will broadcast + // Fix the random seed + "random_seed": 10086, + // Batchsampler + "sampler": { + "holistic_shuffle": true, + "drop_last": true + }, + // Dataloader + "dataloader": { + "num_worker": 32, + "pin_memory": true + }, + // Trackers + "tracker": [ + "tensorboard" + // "wandb", + // "cometml", + // "mlflow", + ], + // Optimizer + "optimizer": "AdamW", + "adamw": { + "lr": 4.0e-4 + // nn model lr + }, + // LR Scheduler + "scheduler": "ReduceLROnPlateau", + "reducelronplateau": { + "factor": 0.8, + "patience": 10, + // unit is epoch + "min_lr": 1.0e-4 + } + } +} \ No newline at end of file diff --git a/config/tts.json b/config/tts.json new file mode 100644 index 0000000000000000000000000000000000000000..241cea376a4857f34cead5a6bae30b6f82af032a --- /dev/null +++ b/config/tts.json @@ -0,0 +1,26 @@ +{ + "base_config": "config/base.json", + "supported_model_type": [ + "Fastspeech2", + "VITS", + "VALLE", + ], + "task_type": "tts", + "preprocess": { + "language": "en-us", + // linguistic features + "extract_phone": true, + "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)" + "lexicon_path": "./text/lexicon/librispeech-lexicon.txt", + // Directory names of processed data or extracted features + "phone_dir": "phones", + "use_phone": true, + // "spk2id": "spk2id.json", // used for multi-speaker dataset + // "utt2spk": "utt2spk", // used for multi-speaker dataset + "add_blank": true + }, + "model": { + "text_token_num": 512, + } + +} diff --git a/config/valle.json b/config/valle.json new file mode 100644 index 0000000000000000000000000000000000000000..4b62d85dd64b577d8ca4ff31d5edf5ce47303a03 --- /dev/null +++ b/config/valle.json @@ -0,0 +1,52 @@ +{ + "base_config": "config/tts.json", + "model_type": "VALLE", + "task_type": "tts", + "dataset": [ + "libritts" + ], + "preprocess": { + "extract_phone": true, + "phone_extractor": "espeak", // phoneme extractor: espeak, pypinyin, pypinyin_initials_finals or lexicon + "extract_acoustic_token": true, + "acoustic_token_extractor": "Encodec", // acoustic token extractor: encodec, dac(todo) + "acoustic_token_dir": "acoutic_tokens", + "use_text": false, + "use_phone": true, + "use_acoustic_token": true, + "symbols_dict": "symbols.dict", + "min_duration": 0.5, // the duration lowerbound to filter the audio with duration < min_duration + "max_duration": 14, // the duration uperbound to filter the audio with duration > max_duration. + "sampling_rate": 24000, + }, + "model": { + "text_token_num": 512, + "audio_token_num": 1024, + "decoder_dim": 1024, // embedding dimension of the decoder model + "nhead": 16, // number of attention heads in the decoder layers + "num_decoder_layers": 12, // number of decoder layers + "norm_first": true, // pre or post Normalization. + "add_prenet": false, // whether add PreNet after Inputs + "prefix_mode": 0, // mode for how to prefix VALL-E NAR Decoder, 0: no prefix, 1: 0 to random, 2: random to random, 4: chunk of pre or post utterance + "share_embedding": true, // share the parameters of the output projection layer with the parameters of the acoustic embedding + "nar_scale_factor": 1, // model scale factor which will be assigned different meanings in different models + "prepend_bos": false, // whether prepend to the acoustic tokens -> AR Decoder inputs + "num_quantizers": 8, // numbert of the audio quantization layers + // "scaling_xformers": false, // Apply Reworked Conformer scaling on Transformers + }, + "train": { + "ddp": false, + "train_stage": 1, // 0: train all modules, For VALL_E, support 1: AR Decoder 2: NAR Decoder(s) + "max_epoch": 20, + "optimizer": "ScaledAdam", + "scheduler": "Eden", + "warmup_steps": 200, // number of steps that affects how rapidly the learning rate decreases + "base_lr": 0.05, // base learning rate." + "valid_interval": 1000, + "log_epoch_step": 1000, + "save_checkpoint_stride": [ + 1, + 1 + ] + } +} \ No newline at end of file diff --git a/config/vits.json b/config/vits.json new file mode 100644 index 0000000000000000000000000000000000000000..5fc5494154bea4e2eb5cac9131bba91d746cf320 --- /dev/null +++ b/config/vits.json @@ -0,0 +1,101 @@ +{ + "base_config": "config/tts.json", + "model_type": "VITS", + "task_type": "tts", + "preprocess": { + "extract_phone": true, + "extract_mel": true, + "n_mel": 80, + "fmin": 0, + "fmax": null, + "extract_linear_spec": true, + "extract_audio": true, + "use_linear": true, + "use_mel": true, + "use_audio": true, + "use_text": false, + "use_phone": true, + "lexicon_path": "./text/lexicon/librispeech-lexicon.txt", + "n_fft": 1024, + "win_size": 1024, + "hop_size": 256, + "segment_size": 8192, + "text_cleaners": [ + "english_cleaners" + ] + }, + "model": { + "text_token_num": 512, + "inter_channels": 192, + "hidden_channels": 192, + "filter_channels": 768, + "n_heads": 2, + "n_layers": 6, + "kernel_size": 3, + "p_dropout": 0.1, + "resblock": "1", + "resblock_kernel_sizes": [ + 3, + 7, + 11 + ], + "resblock_dilation_sizes": [ + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ] + ], + "upsample_rates": [ + 8, + 8, + 2, + 2 + ], + "upsample_initial_channel": 512, + "upsample_kernel_sizes": [ + 16, + 16, + 4, + 4 + ], + "n_layers_q": 3, + "use_spectral_norm": false, + "n_speakers": 10, // number of speakers, while be automatically set if n_speakers is 0 and multi_speaker_training is true + "gin_channels": 256, + "use_sdp": true + }, + "train": { + "fp16_run": true, + "learning_rate": 2e-4, + "betas": [ + 0.8, + 0.99 + ], + "eps": 1e-9, + "batch_size": 16, + "lr_decay": 0.999875, + // "segment_size": 8192, + "init_lr_ratio": 1, + "warmup_epochs": 0, + "c_mel": 45, + "c_kl": 1.0, + "AdamW": { + "betas": [ + 0.8, + 0.99 + ], + "eps": 1e-9, + } + } +} \ No newline at end of file diff --git a/config/vocoder.json b/config/vocoder.json new file mode 100644 index 0000000000000000000000000000000000000000..1ce29c67f4c1dc6406634b170ca73ef731d4b000 --- /dev/null +++ b/config/vocoder.json @@ -0,0 +1,84 @@ +{ + "base_config": "config/base.json", + "dataset": [ + "LJSpeech", + "LibriTTS", + "opencpop", + "m4singer", + "svcc", + "svcceval", + "pjs", + "opensinger", + "popbutfy", + "nus48e", + "popcs", + "kising", + "csd", + "opera", + "vctk", + "lijian", + "cdmusiceval" + ], + "task_type": "vocoder", + "preprocess": { + // acoustic features + "extract_mel": true, + "extract_pitch": false, + "extract_uv": false, + "extract_audio": true, + "extract_label": false, + "extract_one_hot": false, + "extract_amplitude_phase": false, + "pitch_extractor": "parselmouth", + // Settings for data preprocessing + "n_mel": 100, + "win_size": 1024, + "hop_size": 256, + "sample_rate": 24000, + "n_fft": 1024, + "fmin": 0, + "fmax": 12000, + "f0_min": 50, + "f0_max": 1100, + "pitch_bin": 256, + "pitch_max": 1100.0, + "pitch_min": 50.0, + "is_mu_law": false, + "bits": 8, + "cut_mel_frame": 32, + // Directory names of processed data or extracted features + "spk2id": "singers.json", + // Features used for model training + "use_mel": true, + "use_frame_pitch": false, + "use_uv": false, + "use_audio": true, + "use_label": false, + "use_one_hot": false, + "train_file": "train.json", + "valid_file": "test.json" + }, + "train": { + "random_seed": 114514, + "batch_size": 64, + "gradient_accumulation_step": 1, + "max_epoch": 1000000, + "save_checkpoint_stride": [ + 20 + ], + "run_eval": [ + true + ], + "sampler": { + "holistic_shuffle": true, + "drop_last": true + }, + "dataloader": { + "num_worker": 4, + "pin_memory": true + }, + "tracker": [ + "tensorboard" + ], + } +} \ No newline at end of file diff --git a/egs/datasets/README.md b/egs/datasets/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c19195d257a7868aa5961e6c7de06cd8921278fd --- /dev/null +++ b/egs/datasets/README.md @@ -0,0 +1,381 @@ +# Datasets Format + +Amphion support the following academic datasets (sort alphabetically): + +- [Datasets Format](#datasets-format) + - [AudioCaps](#audiocaps) + - [CSD](#csd) + - [KiSing](#kising) + - [LibriTTS](#libritts) + - [LJSpeech](#ljspeech) + - [M4Singer](#m4singer) + - [NUS-48E](#nus-48e) + - [Opencpop](#opencpop) + - [OpenSinger](#opensinger) + - [Opera](#opera) + - [PopBuTFy](#popbutfy) + - [PopCS](#popcs) + - [PJS](#pjs) + - [SVCC](#svcc) + - [VCTK](#vctk) + +The downloading link and the file structure tree of each dataset is displayed as follows. + +## AudioCaps + +AudioCaps is a dataset of around 44K audio-caption pairs, where each audio clip corresponds to a caption with rich semantic information. You can download the dataset [here](https://github.com/cdjkim/audiocaps). The file structure tree is like: + +```plaintext +[AudioCaps dataset path] +┣ AudioCpas +┃   ┣ wav +┃ ┃ ┣ ---1_cCGK4M_0_10000.wav +┃ ┃ ┣ ---lTs1dxhU_30000_40000.wav +┃ ┃ ┣ ... +``` + +## CSD + +The official CSD dataset can be download [here](https://zenodo.org/records/4785016). The file structure tree is like: + +```plaintext +[CSD dataset path] + ┣ english + ┣ korean + ┣ utterances + ┃ ┣ en001a + ┃ ┃ ┣ {UtterenceID}.wav + ┃ ┣ en001b + ┃ ┣ en002a + ┃ ┣ en002b + ┃ ┣ ... + ┣ README +``` + +## KiSing + +The official KiSing dataset can be download [here](http://shijt.site/index.php/2021/05/16/kising-the-first-open-source-mandarin-singing-voice-synthesis-corpus/). The file structure tree is like: + +```plaintext +[KiSing dataset path] + ┣ clean + ┃ ┣ 421 + ┃ ┣ 422 + ┃ ┣ ... +``` + +## LibriTTS + +The official LibriTTS dataset can be download [here](https://www.openslr.org/60/). The file structure tree is like: + +```plaintext +[LibriTTS dataset path] + ┣ BOOKS.txt + ┣ CHAPTERS.txt + ┣ eval_sentences10.tsv + ┣ LICENSE.txt + ┣ NOTE.txt + ┣ reader_book.tsv + ┣ README_librispeech.txt + ┣ README_libritts.txt + ┣ speakers.tsv + ┣ SPEAKERS.txt + ┣ dev-clean (Subset) + ┃ ┣ 1272{Speaker_ID} + ┃ ┃ ┣ 128104 {Chapter_ID} + ┃ ┃ ┃ ┣ 1272_128104_000001_000000.normalized.txt + ┃ ┃ ┃ ┣ 1272_128104_000001_000000.original.txt + ┃ ┃ ┃ ┣ 1272_128104_000001_000000.wav + ┃ ┃ ┃ ┣ ... + ┃ ┃ ┃ ┣ 1272_128104.book.tsv + ┃ ┃ ┃ ┣ 1272_128104.trans.tsv + ┃ ┃ ┣ ... + ┃ ┣ ... + ┣ dev-other (Subset) + ┃ ┣ 116 (Speaker) + ┃ ┃ ┣ 288045 {Chapter_ID} + ┃ ┃ ┃ ┣ 116_288045_000003_000000.normalized.txt + ┃ ┃ ┃ ┣ 116_288045_000003_000000.original.txt + ┃ ┃ ┃ ┣ 116_288045_000003_000000.wav + ┃ ┃ ┃ ┣ ... + ┃ ┃ ┃ ┣ 116_288045.book.tsv + ┃ ┃ ┃ ┣ 116_288045.trans.tsv + ┃ ┃ ┣ ... + ┃ ┣ ... + ┃ ┣ ... + ┣ test-clean (Subset) + ┃ ┣ {Speaker_ID} + ┃ ┃ ┣ {Chapter_ID} + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.normalized.txt + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.original.txt + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.wav + ┃ ┃ ┃ ┣ ... + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.book.tsv + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.trans.tsv + ┃ ┃ ┣ ... + ┃ ┣ ... + ┣ test-other + ┃ ┣ {Speaker_ID} + ┃ ┃ ┣ {Chapter_ID} + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.normalized.txt + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.original.txt + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.wav + ┃ ┃ ┃ ┣ ... + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.book.tsv + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.trans.tsv + ┃ ┃ ┣ ... + ┃ ┣ ... + ┣ train-clean-100 + ┃ ┣ {Speaker_ID} + ┃ ┃ ┣ {Chapter_ID} + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.normalized.txt + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.original.txt + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.wav + ┃ ┃ ┃ ┣ ... + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.book.tsv + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.trans.tsv + ┃ ┃ ┣ ... + ┃ ┣ ... + ┣ train-clean-360 + ┃ ┣ {Speaker_ID} + ┃ ┃ ┣ {Chapter_ID} + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.normalized.txt + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.original.txt + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.wav + ┃ ┃ ┃ ┣ ... + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.book.tsv + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.trans.tsv + ┃ ┃ ┣ ... + ┃ ┣ ... + ┣ train-other-500 + ┃ ┣ {Speaker_ID} + ┃ ┃ ┣ {Chapter_ID} + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.normalized.txt + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.original.txt + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.wav + ┃ ┃ ┃ ┣ ... + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.book.tsv + ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.trans.tsv + ┃ ┃ ┣ ... + ┃ ┣ ... +``` + + +## LJSpeech + +The official LibriTTS dataset can be download [here](https://keithito.com/LJ-Speech-Dataset/). The file structure tree is like: + +```plaintext +[LJSpeech dataset path] + ┣ metadata.csv + ┣ wavs + ┃ ┣ LJ001-0001.wav + ┃ ┣ LJ001-0002.wav + ┃ ┣ ... + ┣ README +``` + +## M4Singer + +The official M4Singer dataset can be downloaded [here](https://drive.google.com/file/d/1xC37E59EWRRFFLdG3aJkVqwtLDgtFNqW/view). The file structure tree is like: + +```plaintext +[M4Singer dataset path] + ┣ {Singer_1}#{Song_1} + ┃ ┣ 0000.mid + ┃ ┣ 0000.TextGrid + ┃ ┣ 0000.wav + ┃ ┣ ... + ┣ {Singer_1}#{Song_2} + ┣ ... + ┣ {Singer_2}#{Song_1} + ┣ {Singer_2}#{Song_2} + ┣ ... + ┗ meta.json +``` + +## NUS-48E + +The official NUS-48E dataset can be download [here](https://drive.google.com/drive/folders/12pP9uUl0HTVANU3IPLnumTJiRjPtVUMx). The file structure tree is like: + +```plaintext +[NUS-48E dataset path] + ┣ {SpeakerID} + ┃ ┣ read + ┃ ┃ ┣ {SongID}.txt + ┃ ┃ ┣ {SongID}.wav + ┃ ┃ ┣ ... + ┃ ┣ sing + ┃ ┃ ┣ {SongID}.txt + ┃ ┃ ┣ {SongID}.wav + ┃ ┃ ┣ ... + ┣ ... + ┣ README.txt + +``` + +## Opencpop + +The official Opera dataset can be downloaded [here](https://wenet.org.cn/opencpop/). The file structure tree is like: + +```plaintext +[Opencpop dataset path] + ┣ midis + ┃ ┣ 2001.midi + ┃ ┣ 2002.midi + ┃ ┣ 2003.midi + ┃ ┣ ... + ┣ segments + ┃ ┣ wavs + ┃ ┃ ┣ 2001000001.wav + ┃ ┃ ┣ 2001000002.wav + ┃ ┃ ┣ 2001000003.wav + ┃ ┃ ┣ ... + ┃ ┣ test.txt + ┃ ┣ train.txt + ┃ ┗ transcriptions.txt + ┣ textgrids + ┃ ┣ 2001.TextGrid + ┃ ┣ 2002.TextGrid + ┃ ┣ 2003.TextGrid + ┃ ┣ ... + ┣ wavs + ┃ ┣ 2001.wav + ┃ ┣ 2002.wav + ┃ ┣ 2003.wav + ┃ ┣ ... + ┣ TERMS_OF_ACCESS + ┗ readme.md +``` + +## OpenSinger + +The official OpenSinger dataset can be downloaded [here](https://drive.google.com/file/d/1EofoZxvalgMjZqzUEuEdleHIZ6SHtNuK/view). The file structure tree is like: + +```plaintext +[OpenSinger dataset path] + ┣ ManRaw + ┃ ┣ {Singer_1}_{Song_1} + ┃ ┃ ┣ {Singer_1}_{Song_1}_0.lab + ┃ ┃ ┣ {Singer_1}_{Song_1}_0.txt + ┃ ┃ ┣ {Singer_1}_{Song_1}_0.wav + ┃ ┃ ┣ ... + ┃ ┣ {Singer_1}_{Song_2} + ┃ ┣ ... + ┣ WomanRaw + ┣ LICENSE + ┗ README.md +``` + +## Opera + +The official Opera dataset can be downloaded [here](http://isophonics.net/SingingVoiceDataset). The file structure tree is like: + +```plaintext +[Opera dataset path] + ┣ monophonic + ┃ ┣ chinese + ┃ ┃ ┣ {Gender}_{SingerID} + ┃ ┃ ┃ ┣ {Emotion}_{SongID}.wav + ┃ ┃ ┃ ┣ ... + ┃ ┃ ┣ ... + ┃ ┣ western + ┣ polyphonic + ┃ ┣ chinese + ┃ ┣ western + ┣ CrossculturalDataSet.xlsx +``` + +## PopBuTFy + +The official PopBuTFy dataset can be downloaded [here](https://github.com/MoonInTheRiver/NeuralSVB). The file structure tree is like: + +```plaintext +[PopBuTFy dataset path] + ┣ data + ┃ ┣ {SingerID}#singing#{SongName}_Amateur + ┃ ┃ ┣ {SingerID}#singing#{SongName}_Amateur_{UtteranceID}.mp3 + ┃ ┃ ┣ ... + ┃ ┣ {SingerID}#singing#{SongName}_Professional + ┃ ┃ ┣ {SingerID}#singing#{SongName}_Professional_{UtteranceID}.mp3 + ┃ ┃ ┣ ... + ┣ text_labels + ┗ TERMS_OF_ACCESS +``` + +## PopCS + +The official PopCS dataset can be downloaded [here](https://github.com/MoonInTheRiver/DiffSinger/blob/master/resources/apply_form.md). The file structure tree is like: + +```plaintext +[PopCS dataset path] + ┣ popcs + ┃ ┣ popcs-{SongName} + ┃ ┃ ┣ {UtteranceID}_ph.txt + ┃ ┃ ┣ {UtteranceID}_wf0.wav + ┃ ┃ ┣ {UtteranceID}.TextGrid + ┃ ┃ ┣ {UtteranceID}.txt + ┃ ┃ ┣ ... + ┃ ┣ ... + ┗ TERMS_OF_ACCESS +``` + +## PJS + +The official PJS dataset can be downloaded [here](https://sites.google.com/site/shinnosuketakamichi/research-topics/pjs_corpus). The file structure tree is like: + +```plaintext +[PJS dataset path] + ┣ PJS_corpus_ver1.1 + ┃ ┣ background_noise + ┃ ┣ pjs{SongID} + ┃ ┃ ┣ pjs{SongID}_song.wav + ┃ ┃ ┣ pjs{SongID}_speech.wav + ┃ ┃ ┣ pjs{SongID}.lab + ┃ ┃ ┣ pjs{SongID}.mid + ┃ ┃ ┣ pjs{SongID}.musicxml + ┃ ┃ ┣ pjs{SongID}.txt + ┃ ┣ ... +``` + +## SVCC + +The official SVCC dataset can be downloaded [here](https://github.com/lesterphillip/SVCC23_FastSVC/tree/main/egs/generate_dataset). The file structure tree is like: + +```plaintext +[SVCC dataset path] + ┣ Data + ┃ ┣ CDF1 + ┃ ┃ ┣ 10001.wav + ┃ ┃ ┣ 10002.wav + ┃ ┃ ┣ ... + ┃ ┣ CDM1 + ┃ ┣ IDF1 + ┃ ┣ IDM1 + ┗ README.md +``` + +## VCTK + +The official VCTK dataset can be downloaded [here](https://datashare.ed.ac.uk/handle/10283/3443). The file structure tree is like: + +```plaintext +[VCTK dataset path] + ┣ txt + ┃ ┣ {Speaker_1} + ┃ ┃ ┣ {Speaker_1}_001.txt + ┃ ┃ ┣ {Speaker_1}_002.txt + ┃ ┃ ┣ ... + ┃ ┣ {Speaker_2} + ┃ ┣ ... + ┣ wav48_silence_trimmed + ┃ ┣ {Speaker_1} + ┃ ┃ ┣ {Speaker_1}_001_mic1.flac + ┃ ┃ ┣ {Speaker_1}_001_mic2.flac + ┃ ┃ ┣ {Speaker_1}_002_mic1.flac + ┃ ┃ ┣ ... + ┃ ┣ {Speaker_2} + ┃ ┣ ... + ┣ speaker-info.txt + ┗ update.txt +``` diff --git a/egs/metrics/README.md b/egs/metrics/README.md new file mode 100644 index 0000000000000000000000000000000000000000..fa582f3116cd50df8cbb3bf93a84c05dc4b029ca --- /dev/null +++ b/egs/metrics/README.md @@ -0,0 +1,93 @@ +# Amphion Evaluation Recipe + +## Supported Evaluation Metrics + +Until now, Amphion Evaluation has supported the following objective metrics: + +- **F0 Modeling**: + - F0 Pearson Coefficients (FPC) + - F0 Periodicity Root Mean Square Error (PeriodicityRMSE) + - F0 Root Mean Square Error (F0RMSE) + - Voiced/Unvoiced F1 Score (V/UV F1) +- **Energy Modeling**: + - Energy Root Mean Square Error (EnergyRMSE) + - Energy Pearson Coefficients (EnergyPC) +- **Intelligibility**: + - Character Error Rate (CER) based on [Whipser](https://github.com/openai/whisper) + - Word Error Rate (WER) based on [Whipser](https://github.com/openai/whisper) +- **Spectrogram Distortion**: + - Frechet Audio Distance (FAD) + - Mel Cepstral Distortion (MCD) + - Multi-Resolution STFT Distance (MSTFT) + - Perceptual Evaluation of Speech Quality (PESQ) + - Short Time Objective Intelligibility (STOI) + - Scale Invariant Signal to Distortion Ratio (SISDR) + - Scale Invariant Signal to Noise Ratio (SISNR) +- **Speaker Similarity**: + - Cosine similarity based on [Rawnet3](https://github.com/Jungjee/RawNet) + - Cosine similarity based on [WeSpeaker](https://github.com/wenet-e2e/wespeaker) (👨‍💻 developing) + +We provide a recipe to demonstrate how to objectively evaluate your generated audios. There are three steps in total: + +1. Pretrained Models Preparation +2. Audio Data Preparation +3. Evaluation + +## 1. Pretrained Models Preparation + +If you want to calculate `RawNet3` based speaker similarity, you need to download the pretrained model first, as illustrated [here](../../pretrained/README.md). + +## 2. Aduio Data Preparation + +Prepare reference audios and generated audios in two folders, the `ref_dir` contains the reference audio and the `gen_dir` contains the generated audio. Here is an example. + +```plaintext + ┣ {ref_dir} + ┃ ┣ sample1.wav + ┃ ┣ sample2.wav + ┣ {gen_dir} + ┃ ┣ sample1.wav + ┃ ┣ sample2.wav +``` + +You have to make sure that the pairwise **reference audio and generated audio are named the same**, as illustrated above (sample1 to sample1, sample2 to sample2). + +## 3. Evaluation + +Run the `run.sh` with specified refenrece folder, generated folder, dump folder and metrics. + +```bash +cd Amphion +sh egs/metrics/run.sh \ + --reference_folder [Your path to the reference audios] \ + --generated_folder [Your path to the generated audios] \ + --dump_folder [Your path to dump the objective results] \ + --metrics [The metrics you need] \ +``` + +As for the metrics, an example is provided below: + +```bash +--metrics "mcd pesq fad" +``` + +All currently available metrics keywords are listed below: + +| Keys | Description | +| --------------------- | ------------------------------------------ | +| `fpc` | F0 Pearson Coefficients | +| `f0_periodicity_rmse` | F0 Periodicity Root Mean Square Error | +| `f0rmse` | F0 Root Mean Square Error | +| `v_uv_f1` | Voiced/Unvoiced F1 Score | +| `energy_rmse` | Energy Root Mean Square Error | +| `energy_pc` | Energy Pearson Coefficients | +| `cer` | Character Error Rate | +| `wer` | Word Error Rate | +| `speaker_similarity` | Cos Similarity based on RawNet3 | +| `fad` | Frechet Audio Distance | +| `mcd` | Mel Cepstral Distortion | +| `mstft` | Multi-Resolution STFT Distance | +| `pesq` | Perceptual Evaluation of Speech Quality | +| `si_sdr` | Scale Invariant Signal to Distortion Ratio | +| `si_snr` | Scale Invariant Signal to Noise Ratio | +| `stoi` | Short Time Objective Intelligibility | diff --git a/egs/metrics/run.sh b/egs/metrics/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..6e3c3d528bc2fb093aa6d1ec11a5fdbe6ac51d43 --- /dev/null +++ b/egs/metrics/run.sh @@ -0,0 +1,40 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $exp_dir)) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Parse the Given Parameters from the Commond ########### +options=$(getopt -o c:n:s --long gpu:,reference_folder:,generated_folder:,dump_folder:,metrics: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Reference Audio Folder + --reference_folder) shift; ref_dir=$1 ; shift ;; + # Generated Audio Folder + --generated_folder) shift; deg_dir=$1 ; shift ;; + # Result Dumping Folder + --dump_folder) shift; dump_dir=$1 ; shift ;; + # Metrics to Compute + --metrics) shift; metrics=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + +######## Calculate Objective Metrics ########### +CUDA_VISIBLE_DEVICES=$gpu python "$work_dir"/bins/calc_metrics.py \ + --ref_dir $ref_dir + --deg_dir $deg_dir + --dump_dir $dump_dir + --metrics $metrics + --fs \ No newline at end of file diff --git a/egs/svc/DiffComoSVC/README.md b/egs/svc/DiffComoSVC/README.md new file mode 100644 index 0000000000000000000000000000000000000000..aecf0d33fdb31f566339a1a29926457541efe8a3 --- /dev/null +++ b/egs/svc/DiffComoSVC/README.md @@ -0,0 +1,234 @@ +# Accelerating Diffusion-based Singing Voice Conversion through Consistency Distillation +
+
+ +
+
+ +This is an implement of [Consistency Models](https://arxiv.org/abs/2303.01469) for accelerating diffusion-based singing voice conversion. The overall architecture follows "[Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion](https://arxiv.org/abs/2310.11160)" (NeurIPS 2023 Workshop on Machine Learning for Audio), only a slightly modification is applied on acoustic model. Specifically, + +* The acoustic model is a conformer which generates a coarse spectrogram and a diffusion decoder based on Bidirectional Non-Causal Dilated CNN which polish the former spectrogram for better. This is similar to [CoMoSpeech: One-Step Speech and Singing Voice Synthesis via Consistency Model](https://comospeech.github.io/) +* To accelerate diffusion model, we apply consistency distillation from [Consistency Models](https://arxiv.org/abs/2303.01469). For teacher model, the diffusion schedule of the diffusion decoder follows [karras diffusion](https://arxiv.org/abs/2206.00364). For distilling teacher model, the condition encoder and the conformer part of acoustic model are frozen while the diffusion decoder model is updated via exponential moving average. See Figure above for details. + +There are five stages in total: + +1. Data preparation +2. Features extraction +3. Teacher Model Training +4. Consistency Distillation +5. Inference/conversion + +## 1. Data Preparation + +### Dataset Download + +By default, we utilize the five datasets for training: M4Singer, Opencpop, OpenSinger, SVCC, and VCTK. How to download them is detailed [here](../../datasets/README.md). + +### Configuration + +Specify the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json + "dataset": [ + "m4singer", + "opencpop", + "opensinger", + "svcc", + "vctk" + ], + "dataset_path": { + // TODO: Fill in your dataset path + "m4singer": "[M4Singer dataset path]", + "opencpop": "[Opencpop dataset path]", + "opensinger": "[OpenSinger dataset path]", + "svcc": "[SVCC dataset path]", + "vctk": "[VCTK dataset path]" + }, +``` + +## 2. Features Extraction + +### Content-based Pretrained Models Download + +By default, we utilize the Whisper and ContentVec to extract content features. How to download them is detailed [here](../../../pretrained/README.md). + +### Configuration + +Specify the dataset path and the output path for saving the processed data and the training model in `exp_config.json`: + +```json + // TODO: Fill in the output log path + "log_dir": "[Your path to save logs and checkpoints]", + "preprocess": { + // TODO: Fill in the output data path + "processed_dir": "[Your path to save processed data]", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`). + +```bash +cd Amphion +sh egs/svc/DiffComoSVC/run.sh --stage 1 +``` + +Note: The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. + +## 3. Teacher Model Training + +### Configuration + +Set the `distill` in `config/comosvc.json` to `false` for teacher model training, you can also specify the detailed configuration for conformer encoder and diffusion process here: + +```JSON +"comosvc":{ + "distill": false, + // conformer encoder + "input_dim": 384, + "output_dim": 100, + "n_heads": 2, + "n_layers": 6, + "filter_channels":512, + // karras diffusion + "P_mean": -1.2, + "P_std": 1.2, + "sigma_data": 0.5, + "sigma_min": 0.002, + "sigma_max": 80, + "rho": 7, + "n_timesteps": 40, + }, +``` + +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines. + +```json +"train": { + "batch_size": 32, + ... + "adamw": { + "lr": 2.0e-4 + }, + ... + } +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `[Your path to save logs and checkpoints]/[YourExptName]`. + +```bash +cd Amphion +sh egs/svc/DiffComoSVC/run.sh --stage 2 --name [YourExptName] +``` + +Note: The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can specify it when running `run.sh` such as: + +```bash +cd Amphion +sh egs/svc/DiffComoSVC/run.sh --stage 2 --name [YourExptName] --gpu "0,1,2,3" +``` + +## 4. Consistency Distillation + +### Configuration + +Set the `distill` in `config/comosvc.json` to `true` for teacher model training, and specify the `teacher_model_path` for consistency distillation. You can also specify the detailed configuration for conformer encoder and diffusion process here: + +```JSON +"model": { + "teacher_model_path":"[Your_teacher_model_checkpoint].bin", + ... + "comosvc":{ + "distill": true, + // conformer encoder + "input_dim": 384, + "output_dim": 100, + "n_heads": 2, + "n_layers": 6, + "filter_channels":512, + // karras diffusion + "P_mean": -1.2, + "P_std": 1.2, + "sigma_data": 0.5, + "sigma_min": 0.002, + "sigma_max": 80, + "rho": 7, + "n_timesteps": 40, + }, +``` + +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines. + +```json +"train": { + "batch_size": 32, + ... + "adamw": { + "lr": 2.0e-4 + }, + ... + } +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `[Your path to save logs and checkpoints]/[YourExptName]`. + +```bash +cd Amphion +sh egs/svc/DiffComoSVC/run.sh --stage 2 --name [YourExptName] +``` + +Note: The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can specify it when running `run.sh` such as: + +```bash +cd Amphion +sh egs/svc/DiffComoSVC/run.sh --stage 2 --name [YourExptName] --gpu "0,1,2,3" +``` + +## 5. Inference/Conversion + +### Pretrained Vocoder Download + +We fine-tune the official BigVGAN pretrained model with over 120 hours singing voice data. The benifits of fine-tuning has been investigated in our paper (see this [demo page](https://www.zhangxueyao.com/data/MultipleContentsSVC/vocoder.html)). The final pretrained singing voice vocoder is released [here](../../../pretrained/README.md#amphion-singing-bigvgan) (called `Amphion Singing BigVGAN`). + +### Run + +For inference/conversion, you need to specify the following configurations when running `run.sh`: + +| Parameters | Description | Example | +| --------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | +| `--infer_expt_dir` | The experimental directory which contains `checkpoint` | `[Your path to save logs and checkpoints]/[YourExptName]` | +| `--infer_output_dir` | The output directory to save inferred audios. | `[Your path to save logs and checkpoints]/[YourExptName]/result` | +| `--infer_source_file` or `--infer_source_audio_dir` | The inference source (can be a json file or a dir). | The `infer_source_file` could be `[Your path to save processed data]/[YourDataset]/test.json`, and the `infer_source_audio_dir` is a folder which includes several audio files (*.wav, *.mp3 or *.flac). | +| `--infer_target_speaker` | The target speaker you want to convert into. You can refer to `[Your path to save logs and checkpoints]/[YourExptName]/singers.json` to choose a trained speaker. | For opencpop dataset, the speaker name would be `opencpop_female1`. | +| `--infer_key_shift` | How many semitones you want to transpose. | `"autoshfit"` (by default), `3`, `-3`, etc. | + +For example, if you want to make `opencpop_female1` sing the songs in the `[Your Audios Folder]`, just run: + +```bash +cd Amphion +sh egs/svc/DiffComoSVC/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir [Your path to save logs and checkpoints]/[YourExptName] \ + --infer_output_dir [Your path to save logs and checkpoints]/[YourExptName]/result \ + --infer_source_audio_dir [Your Audios Folder] \ + --infer_target_speaker "opencpop_female1" \ + --infer_key_shift "autoshift" +``` +Specially, you can configurate the inference steps for teacher model by setting `inference` at `exp_config`(student model is always one-step sampling): +```json + "inference": { + "comosvc": { + "inference_steps": 40 + } + } +``` + +# Reference +https://github.com/zhenye234/CoMoSpeech + +https://github.com/openai/consistency_models \ No newline at end of file diff --git a/egs/svc/DiffComoSVC/exp_config.json b/egs/svc/DiffComoSVC/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..37f977f21a09ea64eb218388b38fe1c1f7350a04 --- /dev/null +++ b/egs/svc/DiffComoSVC/exp_config.json @@ -0,0 +1,143 @@ +{ + "base_config": "config/comosvc.json", + "model_type": "DiffComoSVC", + "dataset": [ + "m4singer", + "opencpop", + "opensinger", + "svcc", + "vctk" + ], + "dataset_path": { + // TODO: Fill in your dataset path + "m4singer": "[M4Singer dataset path]", + "opencpop": "[Opencpop dataset path]", + "opensinger": "[OpenSinger dataset path]", + "svcc": "[SVCC dataset path]", + "vctk": "[VCTK dataset path]" + }, + // TODO: Fill in the output log path + "log_dir": "[Your path to save logs and checkpoints]", + "preprocess": { + // TODO: Fill in the output data path + "processed_dir": "[Your path to save processed data]", + // Config for features extraction + "extract_mel": true, + "extract_pitch": true, + "extract_energy": true, + "extract_whisper_feature": true, + "extract_contentvec_feature": true, + "extract_wenet_feature": false, + "whisper_batch_size": 30, // decrease it if your GPU is out of memory + "contentvec_batch_size": 1, + // Fill in the content-based pretrained model's path + "contentvec_file": "pretrained/contentvec/checkpoint_best_legacy_500.pt", + "wenet_model_path": "pretrained/wenet/20220506_u2pp_conformer_exp/final.pt", + "wenet_config": "pretrained/wenet/20220506_u2pp_conformer_exp/train.yaml", + "whisper_model": "medium", + "whisper_model_path": "pretrained/whisper/medium.pt", + // Config for features usage + "use_mel": true, + "use_min_max_norm_mel": true, + "use_frame_pitch": true, + "use_frame_energy": true, + "use_spkid": true, + "use_whisper": true, + "use_contentvec": true, + "use_wenet": false, + "n_mel": 100, + "sample_rate": 24000 + }, + "model": { + "teacher_model_path":"[Your_teacher_model_checkpoint].bin", + "condition_encoder": { + // Config for features usage + "use_whisper": true, + "use_contentvec": true, + "use_wenet": false, + "whisper_dim": 1024, + "contentvec_dim": 256, + "wenet_dim": 512, + "use_singer_encoder": false, + "pitch_min": 50, + "pitch_max": 1100 + }, + "comosvc":{ + "distill": false, + // conformer encoder + "input_dim": 384, + "output_dim": 100, + "n_heads": 2, + "n_layers": 6, + "filter_channels":512, + "dropout":0.1, + // karras diffusion + "P_mean": -1.2, + "P_std": 1.2, + "sigma_data": 0.5, + "sigma_min": 0.002, + "sigma_max": 80, + "rho": 7, + "n_timesteps": 40, + }, + "diffusion": { + // Diffusion steps encoder + "step_encoder": { + "dim_raw_embedding": 128, + "dim_hidden_layer": 512, + "activation": "SiLU", + "num_layer": 2, + "max_period": 10000 + }, + // Diffusion decoder + "model_type": "bidilconv", + // bidilconv, unet2d, TODO: unet1d + "bidilconv": { + "base_channel": 384, + "n_res_block": 20, + "conv_kernel_size": 3, + "dilation_cycle_length": 4, + // specially, 1 means no dilation + "conditioner_size": 100 + } + } + }, + "train": { + "batch_size": 64, + "gradient_accumulation_step": 1, + "max_epoch": -1, // -1 means no limit + "save_checkpoint_stride": [ + 50, + 50 + ], + "keep_last": [ + 5, + -1 + ], + "run_eval": [ + false, + true + ], + "adamw": { + "lr": 4.0e-4 + }, + "reducelronplateau": { + "factor": 0.8, + "patience": 10, + "min_lr": 1.0e-4 + }, + "dataloader": { + "num_worker": 8, + "pin_memory": true + }, + "sampler": { + "holistic_shuffle": false, + "drop_last": true + } + }, + "inference": { + "comosvc": { + "inference_steps": 40 + } + } +} \ No newline at end of file diff --git a/egs/svc/DiffComoSVC/run.sh b/egs/svc/DiffComoSVC/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..8dc870fdef8b1464000021def5627f91d1676bbe --- /dev/null +++ b/egs/svc/DiffComoSVC/run.sh @@ -0,0 +1,150 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Parse the Given Parameters from the Commond ########### +options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,resume_from_ckpt_path:,resume_type:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Training] Resume configuration + --resume) shift; resume=$1 ; shift ;; + # [Only for Training] The specific checkpoint path that you want to resume from. + --resume_from_ckpt_path) shift; resume_from_ckpt_path=$1 ; shift ;; + # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. + --resume_type) shift; resume_type=$1 ; shift ;; + + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + # [Only for Inference] The inference source (can be a json file or a dir). For example, the source_file can be "[Your path to save processed data]/[YourDataset]/test.json", and the source_audio_dir can be "$work_dir/source_audio" which includes several audio files (*.wav, *.mp3 or *.flac). + --infer_source_file) shift; infer_source_file=$1 ; shift ;; + --infer_source_audio_dir) shift; infer_source_audio_dir=$1 ; shift ;; + # [Only for Inference] Specify the target speaker you want to convert into. You can refer to "[Your path to save logs and checkpoints]/[Your Expt Name]/singers.json". In this singer look-up table, you can see the usable speaker names (all the keys of the dictionary). For example, for opencpop dataset, the speaker name would be "opencpop_female1". + --infer_target_speaker) shift; infer_target_speaker=$1 ; shift ;; + # [Only for Inference] For advanced users, you can modify the trans_key parameters into an integer (which means the semitones you want to transpose). Its default value is "autoshift". + --infer_key_shift) shift; infer_key_shift=$1 ; shift ;; + # [Only for Inference] The vocoder dir. Its default value is Amphion/pretrained/bigvgan. See Amphion/pretrained/README.md to download the pretrained BigVGAN vocoders. + --infer_vocoder_dir) shift; infer_vocoder_dir=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/svc/preprocess.py \ + --config $exp_config \ + --num_workers 4 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + if [ "$resume" = true ]; then + echo "Automatically resume from the experimental dir..." + CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/svc/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume + else + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/svc/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume_from_ckpt_path "$resume_from_ckpt_path" \ + --resume_type "$resume_type" + fi +fi + +######## Inference/Conversion ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$expt_dir/result" + fi + + if [ -z "$infer_source_file" ] && [ -z "$infer_source_audio_dir" ]; then + echo "[Error] Please specify the source file/dir. The inference source (can be a json file or a dir). For example, the source_file can be "[Your path to save processed data]/[YourDataset]/test.json", and the source_audio_dir should include several audio files (*.wav, *.mp3 or *.flac)." + exit 1 + fi + + if [ -z "$infer_source_file" ]; then + infer_source=$infer_source_audio_dir + fi + + if [ -z "$infer_source_audio_dir" ]; then + infer_source=$infer_source_file + fi + + if [ -z "$infer_target_speaker" ]; then + echo "[Error] Please specify the target speaker. You can refer to "[Your path to save logs and checkpoints]/[Your Expt Name]/singers.json". In this singer look-up table, you can see the usable speaker names (all the keys of the dictionary). For example, for opencpop dataset, the speaker name would be "opencpop_female1"" + exit 1 + fi + + if [ -z "$infer_key_shift" ]; then + infer_key_shift="autoshift" + fi + + if [ -z "$infer_vocoder_dir" ]; then + infer_vocoder_dir="$work_dir"/pretrained/bigvgan + echo "[Warning] You don't specify the infer_vocoder_dir. It is set $infer_vocoder_dir by default. Make sure that you have followed Amphoion/pretrained/README.md to download the pretrained BigVGAN vocoder checkpoint." + fi + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/svc/inference.py \ + --config $exp_config \ + --acoustics_dir $infer_expt_dir \ + --vocoder_dir $infer_vocoder_dir \ + --target_singer $infer_target_speaker \ + --trans_key $infer_key_shift \ + --source $infer_source \ + --output_dir $infer_output_dir \ + --log_level debug +fi \ No newline at end of file diff --git a/egs/svc/MultipleContentsSVC/README.md b/egs/svc/MultipleContentsSVC/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ac999e6253076f79ca59ed05fac168e5679feaea --- /dev/null +++ b/egs/svc/MultipleContentsSVC/README.md @@ -0,0 +1,153 @@ +# Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion + +[![arXiv](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2310.11160) +[![demo](https://img.shields.io/badge/SVC-Demo-red)](https://www.zhangxueyao.com/data/MultipleContentsSVC/index.html) + +
+
+ +
+
+ +This is the official implementation of the paper "[Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion](https://arxiv.org/abs/2310.11160)" (NeurIPS 2023 Workshop on Machine Learning for Audio). Specially, + +- The muptile content features are from [Whipser](https://github.com/wenet-e2e/wenet) and [ContentVec](https://github.com/auspicious3000/contentvec). +- The acoustic model is based on Bidirectional Non-Causal Dilated CNN (called `DiffWaveNetSVC` in Amphion), which is similar to [WaveNet](https://arxiv.org/pdf/1609.03499.pdf), [DiffWave](https://openreview.net/forum?id=a-xFK8Ymz5J), and [DiffSVC](https://ieeexplore.ieee.org/document/9688219). +- The vocoder is [BigVGAN](https://github.com/NVIDIA/BigVGAN) architecture and we fine-tuned it in over 120 hours singing voice data. + +There are four stages in total: + +1. Data preparation +2. Features extraction +3. Training +4. Inference/conversion + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## 1. Data Preparation + +### Dataset Download + +By default, we utilize the five datasets for training: M4Singer, Opencpop, OpenSinger, SVCC, and VCTK. How to download them is detailed [here](../../datasets/README.md). + +### Configuration + +Specify the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json + "dataset": [ + "m4singer", + "opencpop", + "opensinger", + "svcc", + "vctk" + ], + "dataset_path": { + // TODO: Fill in your dataset path + "m4singer": "[M4Singer dataset path]", + "opencpop": "[Opencpop dataset path]", + "opensinger": "[OpenSinger dataset path]", + "svcc": "[SVCC dataset path]", + "vctk": "[VCTK dataset path]" + }, +``` + +## 2. Features Extraction + +### Content-based Pretrained Models Download + +By default, we utilize the Whisper and ContentVec to extract content features. How to download them is detailed [here](../../../pretrained/README.md). + +### Configuration + +Specify the dataset path and the output path for saving the processed data and the training model in `exp_config.json`: + +```json + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/svc" + "log_dir": "ckpts/svc", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`). + +```bash +sh egs/svc/MultipleContentsSVC/run.sh --stage 1 +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. + +## 3. Training + +### Configuration + +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines. + +```json +"train": { + "batch_size": 32, + ... + "adamw": { + "lr": 2.0e-4 + }, + ... + } +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/svc/[YourExptName]`. + +```bash +sh egs/svc/MultipleContentsSVC/run.sh --stage 2 --name [YourExptName] +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. + +## 4. Inference/Conversion + +### Pretrained Vocoder Download + +We fine-tune the official BigVGAN pretrained model with over 120 hours singing voice data. The benifits of fine-tuning has been investigated in our paper (see this [demo page](https://www.zhangxueyao.com/data/MultipleContentsSVC/vocoder.html)). The final pretrained singing voice vocoder is released [here](../../../pretrained/README.md#amphion-singing-bigvgan) (called `Amphion Singing BigVGAN`). + +### Run + +For inference/conversion, you need to specify the following configurations when running `run.sh`: + +| Parameters | Description | Example | +| --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `--infer_expt_dir` | The experimental directory which contains `checkpoint` | `Amphion/ckpts/svc/[YourExptName]` | +| `--infer_output_dir` | The output directory to save inferred audios. | `Amphion/ckpts/svc/[YourExptName]/result` | +| `--infer_source_file` or `--infer_source_audio_dir` | The inference source (can be a json file or a dir). | The `infer_source_file` could be `Amphion/data/[YourDataset]/test.json`, and the `infer_source_audio_dir` is a folder which includes several audio files (*.wav, *.mp3 or *.flac). | +| `--infer_target_speaker` | The target speaker you want to convert into. You can refer to `Amphion/ckpts/svc/[YourExptName]/singers.json` to choose a trained speaker. | For opencpop dataset, the speaker name would be `opencpop_female1`. | +| `--infer_key_shift` | How many semitones you want to transpose. | `"autoshfit"` (by default), `3`, `-3`, etc. | + +For example, if you want to make `opencpop_female1` sing the songs in the `[Your Audios Folder]`, just run: + +```bash +sh egs/svc/MultipleContentsSVC/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/svc/[YourExptName] \ + --infer_output_dir Amphion/ckpts/svc/[YourExptName]/result \ + --infer_source_audio_dir [Your Audios Folder] \ + --infer_target_speaker "opencpop_female1" \ + --infer_key_shift "autoshift" +``` + +## Citations + +```bibtex +@article{zhang2023leveraging, + title={Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion}, + author={Zhang, Xueyao and Gu, Yicheng and Chen, Haopeng and Fang, Zihao and Zou, Lexiao and Xue, Liumeng and Wu, Zhizheng}, + journal={Machine Learning for Audio Worshop, NeurIPS 2023}, + year={2023} +} +``` diff --git a/egs/svc/MultipleContentsSVC/exp_config.json b/egs/svc/MultipleContentsSVC/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..7047855abd18c25760fcdd46ec63da5c4b7ad8ba --- /dev/null +++ b/egs/svc/MultipleContentsSVC/exp_config.json @@ -0,0 +1,126 @@ +{ + "base_config": "config/diffusion.json", + "model_type": "DiffWaveNetSVC", + "dataset": [ + "m4singer", + "opencpop", + "opensinger", + "svcc", + "vctk" + ], + "dataset_path": { + // TODO: Fill in your dataset path + "m4singer": "[M4Singer dataset path]", + "opencpop": "[Opencpop dataset path]", + "opensinger": "[OpenSinger dataset path]", + "svcc": "[SVCC dataset path]", + "vctk": "[VCTK dataset path]" + }, + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/svc" + "log_dir": "ckpts/svc", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + // Config for features extraction + "extract_mel": true, + "extract_pitch": true, + "extract_energy": true, + "extract_whisper_feature": true, + "extract_contentvec_feature": true, + "extract_wenet_feature": false, + "whisper_batch_size": 30, // decrease it if your GPU is out of memory + "contentvec_batch_size": 1, + // Fill in the content-based pretrained model's path + "contentvec_file": "pretrained/contentvec/checkpoint_best_legacy_500.pt", + "wenet_model_path": "pretrained/wenet/20220506_u2pp_conformer_exp/final.pt", + "wenet_config": "pretrained/wenet/20220506_u2pp_conformer_exp/train.yaml", + "whisper_model": "medium", + "whisper_model_path": "pretrained/whisper/medium.pt", + // Config for features usage + "use_mel": true, + "use_min_max_norm_mel": true, + "use_frame_pitch": true, + "use_frame_energy": true, + "use_spkid": true, + "use_whisper": true, + "use_contentvec": true, + "use_wenet": false, + "n_mel": 100, + "sample_rate": 24000 + }, + "model": { + "condition_encoder": { + // Config for features usage + "use_whisper": true, + "use_contentvec": true, + "use_wenet": false, + "whisper_dim": 1024, + "contentvec_dim": 256, + "wenet_dim": 512, + "use_singer_encoder": false, + "pitch_min": 50, + "pitch_max": 1100 + }, + "diffusion": { + "scheduler": "ddpm", + "scheduler_settings": { + "num_train_timesteps": 1000, + "beta_start": 1.0e-4, + "beta_end": 0.02, + "beta_schedule": "linear" + }, + // Diffusion steps encoder + "step_encoder": { + "dim_raw_embedding": 128, + "dim_hidden_layer": 512, + "activation": "SiLU", + "num_layer": 2, + "max_period": 10000 + }, + // Diffusion decoder + "model_type": "bidilconv", + // bidilconv, unet2d, TODO: unet1d + "bidilconv": { + "base_channel": 512, + "n_res_block": 40, + "conv_kernel_size": 3, + "dilation_cycle_length": 4, + // specially, 1 means no dilation + "conditioner_size": 384 + } + } + }, + "train": { + "batch_size": 32, + "gradient_accumulation_step": 1, + "max_epoch": -1, // -1 means no limit + "save_checkpoint_stride": [ + 3, + 50 + ], + "keep_last": [ + 3, + 2 + ], + "run_eval": [ + true, + true + ], + "adamw": { + "lr": 2.0e-4 + }, + "reducelronplateau": { + "factor": 0.8, + "patience": 30, + "min_lr": 1.0e-4 + }, + "dataloader": { + "num_worker": 8, + "pin_memory": true + }, + "sampler": { + "holistic_shuffle": false, + "drop_last": true + } + } +} \ No newline at end of file diff --git a/egs/svc/MultipleContentsSVC/run.sh b/egs/svc/MultipleContentsSVC/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..8dc870fdef8b1464000021def5627f91d1676bbe --- /dev/null +++ b/egs/svc/MultipleContentsSVC/run.sh @@ -0,0 +1,150 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Parse the Given Parameters from the Commond ########### +options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,resume_from_ckpt_path:,resume_type:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Training] Resume configuration + --resume) shift; resume=$1 ; shift ;; + # [Only for Training] The specific checkpoint path that you want to resume from. + --resume_from_ckpt_path) shift; resume_from_ckpt_path=$1 ; shift ;; + # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. + --resume_type) shift; resume_type=$1 ; shift ;; + + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + # [Only for Inference] The inference source (can be a json file or a dir). For example, the source_file can be "[Your path to save processed data]/[YourDataset]/test.json", and the source_audio_dir can be "$work_dir/source_audio" which includes several audio files (*.wav, *.mp3 or *.flac). + --infer_source_file) shift; infer_source_file=$1 ; shift ;; + --infer_source_audio_dir) shift; infer_source_audio_dir=$1 ; shift ;; + # [Only for Inference] Specify the target speaker you want to convert into. You can refer to "[Your path to save logs and checkpoints]/[Your Expt Name]/singers.json". In this singer look-up table, you can see the usable speaker names (all the keys of the dictionary). For example, for opencpop dataset, the speaker name would be "opencpop_female1". + --infer_target_speaker) shift; infer_target_speaker=$1 ; shift ;; + # [Only for Inference] For advanced users, you can modify the trans_key parameters into an integer (which means the semitones you want to transpose). Its default value is "autoshift". + --infer_key_shift) shift; infer_key_shift=$1 ; shift ;; + # [Only for Inference] The vocoder dir. Its default value is Amphion/pretrained/bigvgan. See Amphion/pretrained/README.md to download the pretrained BigVGAN vocoders. + --infer_vocoder_dir) shift; infer_vocoder_dir=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/svc/preprocess.py \ + --config $exp_config \ + --num_workers 4 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + if [ "$resume" = true ]; then + echo "Automatically resume from the experimental dir..." + CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/svc/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume + else + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/svc/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume_from_ckpt_path "$resume_from_ckpt_path" \ + --resume_type "$resume_type" + fi +fi + +######## Inference/Conversion ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$expt_dir/result" + fi + + if [ -z "$infer_source_file" ] && [ -z "$infer_source_audio_dir" ]; then + echo "[Error] Please specify the source file/dir. The inference source (can be a json file or a dir). For example, the source_file can be "[Your path to save processed data]/[YourDataset]/test.json", and the source_audio_dir should include several audio files (*.wav, *.mp3 or *.flac)." + exit 1 + fi + + if [ -z "$infer_source_file" ]; then + infer_source=$infer_source_audio_dir + fi + + if [ -z "$infer_source_audio_dir" ]; then + infer_source=$infer_source_file + fi + + if [ -z "$infer_target_speaker" ]; then + echo "[Error] Please specify the target speaker. You can refer to "[Your path to save logs and checkpoints]/[Your Expt Name]/singers.json". In this singer look-up table, you can see the usable speaker names (all the keys of the dictionary). For example, for opencpop dataset, the speaker name would be "opencpop_female1"" + exit 1 + fi + + if [ -z "$infer_key_shift" ]; then + infer_key_shift="autoshift" + fi + + if [ -z "$infer_vocoder_dir" ]; then + infer_vocoder_dir="$work_dir"/pretrained/bigvgan + echo "[Warning] You don't specify the infer_vocoder_dir. It is set $infer_vocoder_dir by default. Make sure that you have followed Amphoion/pretrained/README.md to download the pretrained BigVGAN vocoder checkpoint." + fi + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/svc/inference.py \ + --config $exp_config \ + --acoustics_dir $infer_expt_dir \ + --vocoder_dir $infer_vocoder_dir \ + --target_singer $infer_target_speaker \ + --trans_key $infer_key_shift \ + --source $infer_source \ + --output_dir $infer_output_dir \ + --log_level debug +fi \ No newline at end of file diff --git a/egs/svc/README.md b/egs/svc/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5961eaab3782ff96ddbb65a246527ab768498fa5 --- /dev/null +++ b/egs/svc/README.md @@ -0,0 +1,34 @@ +# Amphion Singing Voice Conversion (SVC) Recipe + +## Quick Start + +We provide a **[beginner recipe](MultipleContentsSVC)** to demonstrate how to train a cutting edge SVC model. Specifically, it is also an official implementation of the paper "[Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion](https://arxiv.org/abs/2310.11160)" (NeurIPS 2023 Workshop on Machine Learning for Audio). Some demos can be seen [here](https://www.zhangxueyao.com/data/MultipleContentsSVC/index.html). + +## Supported Model Architectures + +The main idea of SVC is to first disentangle the speaker-agnostic representations from the source audio, and then inject the desired speaker information to synthesize the target, which usually utilizes an acoustic decoder and a subsequent waveform synthesizer (vocoder): + +
+
+ +
+
+ +Until now, Amphion SVC has supported the following features and models: + +- **Speaker-agnostic Representations**: + - Content Features: Sourcing from [WeNet](https://github.com/wenet-e2e/wenet), [Whisper](https://github.com/openai/whisper), and [ContentVec](https://github.com/auspicious3000/contentvec). + - Prosody Features: F0 and energy. +- **Speaker Embeddings**: + - Speaker Look-Up Table. + - Reference Encoder (👨‍💻 developing): It can be used for zero-shot SVC. +- **Acoustic Decoders**: + - Diffusion-based models: + - **[DiffWaveNetSVC](MultipleContentsSVC)**: The encoder is based on Bidirectional Non-Causal Dilated CNN, which is similar to [WaveNet](https://arxiv.org/pdf/1609.03499.pdf), [DiffWave](https://openreview.net/forum?id=a-xFK8Ymz5J), and [DiffSVC](https://ieeexplore.ieee.org/document/9688219). + - **[DiffComoSVC](DiffComoSVC)** (👨‍💻 developing): The diffusion framework is based on [Consistency Model](https://proceedings.mlr.press/v202/song23a.html). It can significantly accelerate the inference process of the diffusion model. + - Transformer-based models: + - **[TransformerSVC](TransformerSVC)**: Encoder-only and Non-autoregressive Transformer Architecture. + - VAE- and Flow-based models: + - **[VitsSVC]()** (👨‍💻 developing): It is designed as a [VITS](https://arxiv.org/abs/2106.06103)-like model whose textual input is replaced by the content features, which is similar to [so-vits-svc](https://github.com/svc-develop-team/so-vits-svc). +- **Waveform Synthesizers (Vocoders)**: + - The supported vocoders can be seen in [Amphion Vocoder Recipe](../vocoder/README.md). diff --git a/egs/svc/TransformerSVC/README.md b/egs/svc/TransformerSVC/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1797e32f310994ce4c0a1ff3e7789b397b358907 --- /dev/null +++ b/egs/svc/TransformerSVC/README.md @@ -0,0 +1,164 @@ +# Transformer for Singing Voice Conversion + +This is an implementation of **vanilla transformer encoder**/**conformer** as acoustic model for singing voice conversion. + +There are four stages in total: + +1. Data preparation +2. Features extraction +3. Training +4. Inference/conversion + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## 1. Data Preparation + +### Dataset Download + +By default, we utilize the five datasets for training: M4Singer, Opencpop, OpenSinger, SVCC, and VCTK. How to download them is detailed [here](../../datasets/README.md). + +### Configuration + +Specify the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json + "dataset": [ + "m4singer", + "opencpop", + "opensinger", + "svcc", + "vctk" + ], + "dataset_path": { + // TODO: Fill in your dataset path + "m4singer": "[M4Singer dataset path]", + "opencpop": "[Opencpop dataset path]", + "opensinger": "[OpenSinger dataset path]", + "svcc": "[SVCC dataset path]", + "vctk": "[VCTK dataset path]" + }, +``` + +## 2. Features Extraction + +### Content-based Pretrained Models Download + +By default, we utilize the Whisper and ContentVec to extract content features. How to download them is detailed [here](../../../pretrained/README.md). + +### Configuration + +Specify the dataset path and the output path for saving the processed data and the training model in `exp_config.json`: + +```json + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/svc" + "log_dir": "ckpts/svc", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`). + +```bash +sh egs/svc/TransformerSVC/run.sh --stage 1 +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. + +## 3. Training + +### Configuration +Specify the detailed configuration for transformer block in `exp_config.json`. For key `type`, `conformer` and `transformer` are supported: +```json +"model": { + ... + "transformer":{ + // 'conformer' or 'transformer' + "type": "conformer", + "input_dim": 384, + "output_dim": 100, + "n_heads": 2, + "n_layers": 6, + "filter_channels":512, + "dropout":0.1, + } + } +``` +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines. + +```json +"train": { + "batch_size": 32, + ... + "adamw": { + "lr": 2.0e-4 + }, + ... + } +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/svc/[YourExptName]`. + +```bash +sh egs/svc/TransformerSVC/run.sh --stage 2 --name [YourExptName] +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. + +## 4. Inference/Conversion + +### Pretrained Vocoder Download + +We fine-tune the official BigVGAN pretrained model with over 120 hours singing voice data. The benifits of fine-tuning has been investigated in our paper (see this [demo page](https://www.zhangxueyao.com/data/MultipleContentsSVC/vocoder.html)). The final pretrained singing voice vocoder is released [here](../../../pretrained/README.md#amphion-singing-bigvgan) (called `Amphion Singing BigVGAN`). + +### Run + +For inference/conversion, you need to specify the following configurations when running `run.sh`: + +| Parameters | Description | Example | +| --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `--infer_expt_dir` | The experimental directory which contains `checkpoint` | `Amphion/ckpts/svc/[YourExptName]` | +| `--infer_output_dir` | The output directory to save inferred audios. | `Amphion/ckpts/svc/[YourExptName]/result` | +| `--infer_source_file` or `--infer_source_audio_dir` | The inference source (can be a json file or a dir). | The `infer_source_file` could be `Amphion/data/[YourDataset]/test.json`, and the `infer_source_audio_dir` is a folder which includes several audio files (*.wav, *.mp3 or *.flac). | +| `--infer_target_speaker` | The target speaker you want to convert into. You can refer to `Amphion/ckpts/svc/[YourExptName]/singers.json` to choose a trained speaker. | For opencpop dataset, the speaker name would be `opencpop_female1`. | +| `--infer_key_shift` | How many semitones you want to transpose. | `"autoshfit"` (by default), `3`, `-3`, etc. | + +For example, if you want to make `opencpop_female1` sing the songs in the `[Your Audios Folder]`, just run: + +```bash +cd Amphion +sh egs/svc/TransformerSVC/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/svc/[YourExptName] \ + --infer_output_dir Amphion/ckpts/svc/[YourExptName]/result \ + --infer_source_audio_dir [Your Audios Folder] \ + --infer_target_speaker "opencpop_female1" \ + --infer_key_shift "autoshift" +``` + +## Citations + +```bibtex +@inproceedings{transformer, + author = {Ashish Vaswani and + Noam Shazeer and + Niki Parmar and + Jakob Uszkoreit and + Llion Jones and + Aidan N. Gomez and + Lukasz Kaiser and + Illia Polosukhin}, + title = {Attention is All you Need}, + booktitle = {{NIPS}}, + pages = {5998--6008}, + year = {2017} +} +``` \ No newline at end of file diff --git a/egs/svc/TransformerSVC/exp_config.json b/egs/svc/TransformerSVC/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..4ad85fbe47dd4945e579200a7e5d82a03a02457b --- /dev/null +++ b/egs/svc/TransformerSVC/exp_config.json @@ -0,0 +1,108 @@ +{ + "base_config": "config/transformer.json", + "model_type": "TransformerSVC", + "dataset": [ + "m4singer", + "opencpop", + "opensinger", + "svcc", + "vctk" + ], + "dataset_path": { + // TODO: Fill in your dataset path + "m4singer": "[M4Singer dataset path]", + "opencpop": "[Opencpop dataset path]", + "opensinger": "[OpenSinger dataset path]", + "svcc": "[SVCC dataset path]", + "vctk": "[VCTK dataset path]" + }, + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/svc" + "log_dir": "ckpts/svc", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + // Config for features extraction + "extract_mel": true, + "extract_pitch": true, + "extract_energy": true, + "extract_whisper_feature": true, + "extract_contentvec_feature": true, + "extract_wenet_feature": false, + "whisper_batch_size": 30, // decrease it if your GPU is out of memory + "contentvec_batch_size": 1, + // Fill in the content-based pretrained model's path + "contentvec_file": "pretrained/contentvec/checkpoint_best_legacy_500.pt", + "wenet_model_path": "pretrained/wenet/20220506_u2pp_conformer_exp/final.pt", + "wenet_config": "pretrained/wenet/20220506_u2pp_conformer_exp/train.yaml", + "whisper_model": "medium", + "whisper_model_path": "pretrained/whisper/medium.pt", + // Config for features usage + "use_mel": true, + "use_min_max_norm_mel": true, + "use_frame_pitch": true, + "use_frame_energy": true, + "use_spkid": true, + "use_whisper": true, + "use_contentvec": true, + "use_wenet": false, + "n_mel": 100, + "sample_rate": 24000 + }, + "model": { + "condition_encoder": { + // Config for features usage + "use_whisper": true, + "use_contentvec": true, + "use_wenet": false, + "whisper_dim": 1024, + "contentvec_dim": 256, + "wenet_dim": 512, + "use_singer_encoder": false, + "pitch_min": 50, + "pitch_max": 1100 + }, + "transformer": { + // 'conformer' or 'transformer' + "type": "conformer", + "input_dim": 384, + "output_dim": 100, + "n_heads": 2, + "n_layers": 6, + "filter_channels": 512, + "dropout": 0.1, + } + }, + "train": { + "batch_size": 64, + "gradient_accumulation_step": 1, + "max_epoch": -1, // -1 means no limit + "save_checkpoint_stride": [ + 50, + 50 + ], + "keep_last": [ + 5, + -1 + ], + "run_eval": [ + false, + true + ], + "adamw": { + "lr": 4.0e-4 + }, + "reducelronplateau": { + "factor": 0.8, + "patience": 10, + "min_lr": 1.0e-4 + }, + "dataloader": { + "num_worker": 8, + "pin_memory": true + }, + "sampler": { + "holistic_shuffle": false, + "drop_last": true + } + } +} \ No newline at end of file diff --git a/egs/svc/TransformerSVC/run.sh b/egs/svc/TransformerSVC/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..8dc870fdef8b1464000021def5627f91d1676bbe --- /dev/null +++ b/egs/svc/TransformerSVC/run.sh @@ -0,0 +1,150 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Parse the Given Parameters from the Commond ########### +options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,resume_from_ckpt_path:,resume_type:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Training] Resume configuration + --resume) shift; resume=$1 ; shift ;; + # [Only for Training] The specific checkpoint path that you want to resume from. + --resume_from_ckpt_path) shift; resume_from_ckpt_path=$1 ; shift ;; + # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. + --resume_type) shift; resume_type=$1 ; shift ;; + + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + # [Only for Inference] The inference source (can be a json file or a dir). For example, the source_file can be "[Your path to save processed data]/[YourDataset]/test.json", and the source_audio_dir can be "$work_dir/source_audio" which includes several audio files (*.wav, *.mp3 or *.flac). + --infer_source_file) shift; infer_source_file=$1 ; shift ;; + --infer_source_audio_dir) shift; infer_source_audio_dir=$1 ; shift ;; + # [Only for Inference] Specify the target speaker you want to convert into. You can refer to "[Your path to save logs and checkpoints]/[Your Expt Name]/singers.json". In this singer look-up table, you can see the usable speaker names (all the keys of the dictionary). For example, for opencpop dataset, the speaker name would be "opencpop_female1". + --infer_target_speaker) shift; infer_target_speaker=$1 ; shift ;; + # [Only for Inference] For advanced users, you can modify the trans_key parameters into an integer (which means the semitones you want to transpose). Its default value is "autoshift". + --infer_key_shift) shift; infer_key_shift=$1 ; shift ;; + # [Only for Inference] The vocoder dir. Its default value is Amphion/pretrained/bigvgan. See Amphion/pretrained/README.md to download the pretrained BigVGAN vocoders. + --infer_vocoder_dir) shift; infer_vocoder_dir=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/svc/preprocess.py \ + --config $exp_config \ + --num_workers 4 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + if [ "$resume" = true ]; then + echo "Automatically resume from the experimental dir..." + CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/svc/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume + else + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/svc/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume_from_ckpt_path "$resume_from_ckpt_path" \ + --resume_type "$resume_type" + fi +fi + +######## Inference/Conversion ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$expt_dir/result" + fi + + if [ -z "$infer_source_file" ] && [ -z "$infer_source_audio_dir" ]; then + echo "[Error] Please specify the source file/dir. The inference source (can be a json file or a dir). For example, the source_file can be "[Your path to save processed data]/[YourDataset]/test.json", and the source_audio_dir should include several audio files (*.wav, *.mp3 or *.flac)." + exit 1 + fi + + if [ -z "$infer_source_file" ]; then + infer_source=$infer_source_audio_dir + fi + + if [ -z "$infer_source_audio_dir" ]; then + infer_source=$infer_source_file + fi + + if [ -z "$infer_target_speaker" ]; then + echo "[Error] Please specify the target speaker. You can refer to "[Your path to save logs and checkpoints]/[Your Expt Name]/singers.json". In this singer look-up table, you can see the usable speaker names (all the keys of the dictionary). For example, for opencpop dataset, the speaker name would be "opencpop_female1"" + exit 1 + fi + + if [ -z "$infer_key_shift" ]; then + infer_key_shift="autoshift" + fi + + if [ -z "$infer_vocoder_dir" ]; then + infer_vocoder_dir="$work_dir"/pretrained/bigvgan + echo "[Warning] You don't specify the infer_vocoder_dir. It is set $infer_vocoder_dir by default. Make sure that you have followed Amphoion/pretrained/README.md to download the pretrained BigVGAN vocoder checkpoint." + fi + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/svc/inference.py \ + --config $exp_config \ + --acoustics_dir $infer_expt_dir \ + --vocoder_dir $infer_vocoder_dir \ + --target_singer $infer_target_speaker \ + --trans_key $infer_key_shift \ + --source $infer_source \ + --output_dir $infer_output_dir \ + --log_level debug +fi \ No newline at end of file diff --git a/egs/svc/_template/run.sh b/egs/svc/_template/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..8dc870fdef8b1464000021def5627f91d1676bbe --- /dev/null +++ b/egs/svc/_template/run.sh @@ -0,0 +1,150 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Parse the Given Parameters from the Commond ########### +options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,resume_from_ckpt_path:,resume_type:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Training] Resume configuration + --resume) shift; resume=$1 ; shift ;; + # [Only for Training] The specific checkpoint path that you want to resume from. + --resume_from_ckpt_path) shift; resume_from_ckpt_path=$1 ; shift ;; + # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. + --resume_type) shift; resume_type=$1 ; shift ;; + + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + # [Only for Inference] The inference source (can be a json file or a dir). For example, the source_file can be "[Your path to save processed data]/[YourDataset]/test.json", and the source_audio_dir can be "$work_dir/source_audio" which includes several audio files (*.wav, *.mp3 or *.flac). + --infer_source_file) shift; infer_source_file=$1 ; shift ;; + --infer_source_audio_dir) shift; infer_source_audio_dir=$1 ; shift ;; + # [Only for Inference] Specify the target speaker you want to convert into. You can refer to "[Your path to save logs and checkpoints]/[Your Expt Name]/singers.json". In this singer look-up table, you can see the usable speaker names (all the keys of the dictionary). For example, for opencpop dataset, the speaker name would be "opencpop_female1". + --infer_target_speaker) shift; infer_target_speaker=$1 ; shift ;; + # [Only for Inference] For advanced users, you can modify the trans_key parameters into an integer (which means the semitones you want to transpose). Its default value is "autoshift". + --infer_key_shift) shift; infer_key_shift=$1 ; shift ;; + # [Only for Inference] The vocoder dir. Its default value is Amphion/pretrained/bigvgan. See Amphion/pretrained/README.md to download the pretrained BigVGAN vocoders. + --infer_vocoder_dir) shift; infer_vocoder_dir=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/svc/preprocess.py \ + --config $exp_config \ + --num_workers 4 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + if [ "$resume" = true ]; then + echo "Automatically resume from the experimental dir..." + CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/svc/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume + else + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/svc/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume_from_ckpt_path "$resume_from_ckpt_path" \ + --resume_type "$resume_type" + fi +fi + +######## Inference/Conversion ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$expt_dir/result" + fi + + if [ -z "$infer_source_file" ] && [ -z "$infer_source_audio_dir" ]; then + echo "[Error] Please specify the source file/dir. The inference source (can be a json file or a dir). For example, the source_file can be "[Your path to save processed data]/[YourDataset]/test.json", and the source_audio_dir should include several audio files (*.wav, *.mp3 or *.flac)." + exit 1 + fi + + if [ -z "$infer_source_file" ]; then + infer_source=$infer_source_audio_dir + fi + + if [ -z "$infer_source_audio_dir" ]; then + infer_source=$infer_source_file + fi + + if [ -z "$infer_target_speaker" ]; then + echo "[Error] Please specify the target speaker. You can refer to "[Your path to save logs and checkpoints]/[Your Expt Name]/singers.json". In this singer look-up table, you can see the usable speaker names (all the keys of the dictionary). For example, for opencpop dataset, the speaker name would be "opencpop_female1"" + exit 1 + fi + + if [ -z "$infer_key_shift" ]; then + infer_key_shift="autoshift" + fi + + if [ -z "$infer_vocoder_dir" ]; then + infer_vocoder_dir="$work_dir"/pretrained/bigvgan + echo "[Warning] You don't specify the infer_vocoder_dir. It is set $infer_vocoder_dir by default. Make sure that you have followed Amphoion/pretrained/README.md to download the pretrained BigVGAN vocoder checkpoint." + fi + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/svc/inference.py \ + --config $exp_config \ + --acoustics_dir $infer_expt_dir \ + --vocoder_dir $infer_vocoder_dir \ + --target_singer $infer_target_speaker \ + --trans_key $infer_key_shift \ + --source $infer_source \ + --output_dir $infer_output_dir \ + --log_level debug +fi \ No newline at end of file diff --git a/egs/tta/README.md b/egs/tta/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1d944688768697da9e0cee60445410d6ce115694 --- /dev/null +++ b/egs/tta/README.md @@ -0,0 +1,19 @@ +# Amphion Text-to-Audio (TTA) Recipe + +## Quick Start + +We provide a **[beginner recipe](RECIPE.md)** to demonstrate how to train a cutting edge TTA model. Specifically, it is designed as a latent diffusion model like [AudioLDM](https://arxiv.org/abs/2301.12503), [Make-an-Audio](https://arxiv.org/abs/2301.12661), and [AUDIT](https://arxiv.org/abs/2304.00830). + +## Supported Model Architectures + +Until now, Amphion has supported a latent diffusion based text-to-audio model: + +
+
+ +
+
+ +Similar to [AUDIT](https://arxiv.org/abs/2304.00830), we implement it in two-stage training: +1. Training the VAE which is called `AutoencoderKL` in Amphion. +2. Training the conditional latent diffusion model which is called `AudioLDM` in Amphion. \ No newline at end of file diff --git a/egs/tta/RECIPE.md b/egs/tta/RECIPE.md new file mode 100644 index 0000000000000000000000000000000000000000..6682921fd88eb20f9c54f4defdfd856b6154c1c2 --- /dev/null +++ b/egs/tta/RECIPE.md @@ -0,0 +1,156 @@ +# Text-to-Audio with Latent Diffusion Model + +This is the quicktour for training a text-to-audio model with the popular and powerful generative model: [Latent Diffusion Model](https://arxiv.org/abs/2112.10752). Specially, this recipe is also the official implementation of the text-to-audio generation part of our NeurIPS 2023 paper "[AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models](https://arxiv.org/abs/2304.00830)". You can check the last part of [AUDIT demos](https://audit-demo.github.io/) to see same text-to-audio examples. + +
+
+ +
+
+ +We train this latent diffusion model in two stages: +1. In the first stage, we aims to obtain a high-quality VAE (called `AutoencoderKL` in Amphion), in order that we can project +the input mel-spectrograms to an efficient, low-dimensional latent space. Specially, we train the VAE with GAN loss to improve the reconstruction quality. +1. In the second stage, we aims to obtain a text-controllable diffusion model (called `AudioLDM` in Amphion). We use U-Net architecture diffusion model, and use T5 encoder as text encoder. + +There are four stages in total for training the text-to-audio model: + +1. Data preparation and processing +2. Train the VAE model +3. Train the latent diffusion model +4. Inference + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## Overview + +```sh +# Train the VAE model +sh egs/tta/autoencoderkl/run_train.sh + +# Train the latent diffusion model +sh egs/tta/audioldm/run_train.sh + +# Inference +sh egs/tta/audioldm/run_inference.sh +``` + +## 1. Data preparation and processing + +### Dataset Download + +We take [AudioCaps](https://audiocaps.github.io/) as an example, AudioCaps is a dataset of around 44K audio-caption pairs, where each audio clip corresponds to a caption with rich semantic information. You can download the dataset [here](https://github.com/cdjkim/audiocaps). + + + + +### Data Processing + +- Download AudioCaps dataset to `[Your path to save tta dataset]` and modify `preprocess.processed_dir` in `egs/tta/.../exp_config.json`. + +```json +{ + "dataset": [ + "AudioCaps" + ], + "preprocess": { + // Specify the output root path to save the processed data + "processed_dir": "[Your path to save tta dataset]", + ... + } +} +``` + +The folder structure of your downloaded data should be similar to: + +```plaintext +.../[Your path to save tta dataset] +┣ AudioCpas +┃   ┣ wav +┃ ┃ ┣ ---1_cCGK4M_0_10000.wav +┃ ┃ ┣ ---lTs1dxhU_30000_40000.wav +┃ ┃ ┣ ... +``` + +- Then you may process the data to mel-specgram and save it as `.npy` format. If you use the data we provide, we have processed all the wav data. + +- Generate a json file to save the metadata, the json file is like: + +```json +[ + { + "Dataset": "AudioCaps", + "Uid": "---1_cCGK4M_0_10000", + "Caption": "Idling car, train blows horn and passes" + }, + { + "Dataset": "AudioCaps", + "Uid": "---lTs1dxhU_30000_40000", + "Caption": "A racing vehicle engine is heard passing by" + }, + ... +] +``` +- Finally, the folder structure is like: + +```plaintext +.../[Your path to save tta dataset] +┣ AudioCpas +┃   ┣ wav +┃ ┃ ┣ ---1_cCGK4M_0_10000.wav +┃ ┃ ┣ ---lTs1dxhU_30000_40000.wav +┃ ┃ ┣ ... +┃   ┣ mel +┃ ┃ ┣ ---1_cCGK4M_0_10000.npy +┃ ┃ ┣ ---lTs1dxhU_30000_40000.npy +┃ ┃ ┣ ... +┃   ┣ train.json +┃   ┣ valid.json +┃   ┣ ... +``` + +## 2. Training the VAE Model + +The first stage model is a VAE model trained with GAN loss (called `AutoencoderKL` in Amphion), run the follow commands: + +```sh +sh egs/tta/autoencoderkl/run_train.sh +``` + +## 3. Training the Latent Diffusion Model + +The second stage model is a condition diffusion model with a T5 text encoder (called `AudioLDM` in Amphion), run the following commands: + +```sh +sh egs/tta/audioldm/run_train.sh +``` + +## 4. Inference + +Now you can generate audio with your pre-trained latent diffusion model, run the following commands and modify the `text` argument. + +```sh +sh egs/tta/audioldm/run_inference.sh \ +--text "A man is whistling" +``` + +## Citations + +```bibtex +@article{wang2023audit, + title={AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models}, + author={Wang, Yuancheng and Ju, Zeqian and Tan, Xu and He, Lei and Wu, Zhizheng and Bian, Jiang and Zhao, Sheng}, + journal={NeurIPS 2023}, + year={2023} +} + +@article{liu2023audioldm, + title={{AudioLDM}: Text-to-Audio Generation with Latent Diffusion Models}, + author={Liu, Haohe and Chen, Zehua and Yuan, Yi and Mei, Xinhao and Liu, Xubo and Mandic, Danilo and Wang, Wenwu and Plumbley, Mark D}, + journal={Proceedings of the International Conference on Machine Learning}, + year={2023} +} +``` \ No newline at end of file diff --git a/egs/tta/audioldm/exp_config.json b/egs/tta/audioldm/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..5b57a771593094401fd38747c66951afe6d6d1f3 --- /dev/null +++ b/egs/tta/audioldm/exp_config.json @@ -0,0 +1,90 @@ +{ + "base_config": "egs/tta/audioldm/exp_config_base.json", + "dataset": [ + "AudioCaps" + ], + "preprocess": { + // Specify the output root path to save the processed data + "processed_dir": "data", + // For example: "/home/TTADataset/processed_data" + + // feature + "use_spkid": false, + "use_uv": false, + "use_frame_pitch": false, + "use_phone_pitch": false, + "use_frame_energy": false, + "use_phone_energy": false, + "use_mel": false, + "use_audio": false, + "use_label": false, + "use_one_hot": false, + // feature for text to audio + "use_caption": true, + "use_melspec": true, + "use_wav": false, + // feature dir + "melspec_dir": "mel", + "wav_dir": "wav" + }, + // Specify the output root path to save model ckpts and logs + "log_dir": "ckpts/tta", + // For example: "/home/TTADataset/processed_data/logs" + + // model + "model": { + "audioldm": { + "image_size": 32, + "in_channels": 4, + "out_channels": 4, + "model_channels": 256, + "attention_resolutions": [4, 2, 1], + "num_res_blocks": 2, + "channel_mult": [1, 2, 4], + "num_heads": 8, + "use_spatial_transformer": true, + "transformer_depth": 1, + "context_dim": 768, + "use_checkpoint": true, + "legacy": false + }, + "autoencoderkl": { + "ch": 128, + "ch_mult": [1,1,2,2,4], + "num_res_blocks": 2, + "in_channels": 1, + "z_channels": 4, + "out_ch": 1, + "double_z": true + }, + "noise_scheduler": { + "num_train_timesteps": 1000, + "beta_start": 0.00085, + "beta_end": 0.012, + "beta_schedule": "scaled_linear", + "clip_sample": false, + "steps_offset": 1, + "set_alpha_to_one": false, + "skip_prk_steps": true, + "prediction_type": "epsilon" + }, + "autoencoder_path": "ckpts/tta/autoencoder_kl_debug/checkpoints/step-0445000_loss-0.3306.pt" + }, + + // train + "train": { + "adam": { + "lr": 5.0e-5 + }, + "ddp": false, + "random_seed": 12345, + "batch_size": 12, + "epochs": 50000, + "max_steps": 1000000, + "total_training_steps": 800000, + "save_summary_steps": 1000, + "save_checkpoints_steps": 5000, + "valid_interval": 5000, + "keep_checkpoint_max": 100 + } + } \ No newline at end of file diff --git a/egs/tta/audioldm/exp_config_base.json b/egs/tta/audioldm/exp_config_base.json new file mode 100644 index 0000000000000000000000000000000000000000..8201c070715112119d11750f68f76c83d80a9aa4 --- /dev/null +++ b/egs/tta/audioldm/exp_config_base.json @@ -0,0 +1,11 @@ +{ + "base_config": "config/audioldm.json", + "model_type": "AudioLDM", + "dataset": [ + "AudioCaps" + ], + "preprocess": { + "train_file": "train.json", + "valid_file": "vaild.json" + } +} \ No newline at end of file diff --git a/egs/tta/audioldm/exp_config_latent_4_10_78.json b/egs/tta/audioldm/exp_config_latent_4_10_78.json new file mode 100644 index 0000000000000000000000000000000000000000..09ae7c38cf332e74ecb4bc485d44a5f8985d43c5 --- /dev/null +++ b/egs/tta/audioldm/exp_config_latent_4_10_78.json @@ -0,0 +1,88 @@ +{ + "base_config": "egs/tta/audioldm/exp_config_base.json", + "dataset": [ + "AudioCaps" + ], + "preprocess": { + // Specify the output root path to save the processed data + "processed_dir": "data", + + // feature + "use_spkid": false, + "use_uv": false, + "use_frame_pitch": false, + "use_phone_pitch": false, + "use_frame_energy": false, + "use_phone_energy": false, + "use_mel": false, + "use_audio": false, + "use_label": false, + "use_one_hot": false, + // feature for text to audio + "use_caption": true, + "use_melspec": true, + "use_wav": false, + // feature dir + "melspec_dir": "mel", + "wav_dir": "wav" + }, + // Specify the output root path to save model ckpts and logs + "log_dir": "ckpts/tta", + + // model + "model": { + "audioldm": { + "image_size": 32, + "in_channels": 4, + "out_channels": 4, + "model_channels": 256, + "attention_resolutions": [4, 2, 1], + "num_res_blocks": 2, + "channel_mult": [1, 2, 4], + "num_heads": 8, + "use_spatial_transformer": true, + "transformer_depth": 1, + "context_dim": 768, + "use_checkpoint": true, + "legacy": false + }, + "autoencoderkl": { + "ch": 128, + "ch_mult": [1,2,2,4], + "num_res_blocks": 2, + "in_channels": 1, + "z_channels": 4, + "out_ch": 1, + "double_z": true + }, + "noise_scheduler": { + "num_train_timesteps": 1000, + "beta_start": 0.00085, + "beta_end": 0.012, + "beta_schedule": "scaled_linear", + "clip_sample": false, + "steps_offset": 1, + "set_alpha_to_one": false, + "skip_prk_steps": true, + "prediction_type": "epsilon" + }, + "autoencoder_path": "ckpts/tta/autoencoder_kl_debug_latent_size_4_10_78/checkpoints/step-0390000_loss-0.2876.pt" + }, + + // train + "train": { + "adam": { + "lr": 2.0e-5 + }, + "ddp": false, + "random_seed": 12345, + "batch_size": 12, + "epochs": 50000, + "max_steps": 1000000, + "total_training_steps": 800000, + "save_summary_steps": 1000, + "save_checkpoints_steps": 5000, + "valid_interval": 5000, + "keep_checkpoint_max": 100 + } + } \ No newline at end of file diff --git a/egs/tta/audioldm/run_inference.sh b/egs/tta/audioldm/run_inference.sh new file mode 100644 index 0000000000000000000000000000000000000000..181244efece8e164eb44fb04f051dbb80721c248 --- /dev/null +++ b/egs/tta/audioldm/run_inference.sh @@ -0,0 +1,52 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Set Experiment Configuration ########### +exp_config="$exp_dir/exp_config.json" +exp_name="audioldm_debug_latent_size_4_5_39" +checkpoint_path="$work_dir/ckpts/tta/audioldm_debug_latent_size_4_5_39/checkpoints/step-0570000_loss-0.2521.pt" +output_dir="$work_dir/temp" +vocoder_config_path="$work_dir/ckpts/tta/hifigan_checkpoints/config.json" +vocoder_path="$work_dir/ckpts/tta/hifigan_checkpoints/g_01250000" +num_steps=200 +guidance_scale=4.0 + +export CUDA_VISIBLE_DEVICES="0" + +######## Parse Command Line Arguments ########### +while [[ $# -gt 0 ]] +do +key="$1" + +case $key in + --text) + text="$2" + shift # past argument + shift # past value + ;; + *) # unknown option + shift # past argument + ;; +esac +done + +######## Run inference ########### +python "${work_dir}"/bins/tta/inference.py \ + --config=$exp_config \ + --checkpoint_path=$checkpoint_path \ + --text="$text" \ + --vocoder_path=$vocoder_path \ + --vocoder_config_path=$vocoder_config_path \ + --num_steps=$num_steps \ + --guidance_scale=$guidance_scale \ + --output_dir=$output_dir diff --git a/egs/tta/audioldm/run_inference_latent_4_10_78.sh b/egs/tta/audioldm/run_inference_latent_4_10_78.sh new file mode 100644 index 0000000000000000000000000000000000000000..3c247e9e6d2919e14e395deb83397439bb22efc5 --- /dev/null +++ b/egs/tta/audioldm/run_inference_latent_4_10_78.sh @@ -0,0 +1,52 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Set Experiment Configuration ########### +exp_config="$exp_dir/exp_config_v2.json" +exp_name="audioldm_debug_latent_size_4_10_78" +checkpoint_path="$work_dir/ckpts/tta/audioldm_debug_latent_size_4_10_78/checkpoints/step-0325000_loss-0.1936.pt" +output_dir="$work_dir/temp" +vocoder_config_path="$work_dir/ckpts/tta/hifigan_checkpoints/config.json" +vocoder_path="$work_dir/ckpts/tta/hifigan_checkpoints/g_01250000" +num_steps=200 +guidance_scale=4.0 + +export CUDA_VISIBLE_DEVICES="0" + +######## Parse Command Line Arguments ########### +while [[ $# -gt 0 ]] +do +key="$1" + +case $key in + --text) + text="$2" + shift # past argument + shift # past value + ;; + *) # unknown option + shift # past argument + ;; +esac +done + +######## Run inference ########### +python "${work_dir}"/bins/tta/inference.py \ + --config=$exp_config \ + --checkpoint_path=$checkpoint_path \ + --text="A man is whistling" \ + --vocoder_path=$vocoder_path \ + --vocoder_config_path=$vocoder_config_path \ + --num_steps=$num_steps \ + --guidance_scale=$guidance_scale \ + --output_dir=$output_dir \ \ No newline at end of file diff --git a/egs/tta/audioldm/run_train.sh b/egs/tta/audioldm/run_train.sh new file mode 100644 index 0000000000000000000000000000000000000000..b1060db6cf02ccd46bdc8ecac304e26233cea354 --- /dev/null +++ b/egs/tta/audioldm/run_train.sh @@ -0,0 +1,26 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Set Experiment Configuration ########### +exp_config="$exp_dir/exp_config.json" +exp_name="audioldm_debug_latent_size_4_5_39" + +num_workers=8 +export CUDA_VISIBLE_DEVICES="0" + +######## Train Model ########### +python "${work_dir}"/bins/tta/train_tta.py \ + --config=$exp_config \ + --num_workers=$num_workers \ + --exp_name=$exp_name \ + --stdout_interval=25 \ \ No newline at end of file diff --git a/egs/tta/audioldm/run_train_latent_4_10_78.sh b/egs/tta/audioldm/run_train_latent_4_10_78.sh new file mode 100644 index 0000000000000000000000000000000000000000..f61c0de52983ba6d4976b115fdc22c65696cbc8d --- /dev/null +++ b/egs/tta/audioldm/run_train_latent_4_10_78.sh @@ -0,0 +1,26 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Set Experiment Configuration ########### +exp_config="$exp_dir/exp_config_latent_4_10_78.json" +exp_name="audioldm_debug_latent_size_4_10_78" + +num_workers=8 +export CUDA_VISIBLE_DEVICES="0" + +######## Train Model ########### +python "${work_dir}"/bins/tta/train_tta.py \ + --config=$exp_config \ + --num_workers=$num_workers \ + --exp_name=$exp_name \ + --stdout_interval=25 \ \ No newline at end of file diff --git a/egs/tta/autoencoderkl/exp_config.json b/egs/tta/autoencoderkl/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..e0d401051fea5920ade4ad7d765e816fdbec5e9d --- /dev/null +++ b/egs/tta/autoencoderkl/exp_config.json @@ -0,0 +1,49 @@ +{ + "base_config": "egs/tta/autoencoderkl/exp_config_base.json", + "dataset": [ + "AudioCaps" + ], + "preprocess": { + // Specify the output root path to save the processed data + "processed_dir": "data", + + // feature + "use_spk": false, + "use_spkid": false, + "use_uv": false, + "use_frame_pitch": false, + "use_phone_pitch": false, + "use_frame_energy": false, + "use_phone_energy": false, + "use_mel": false, + "use_audio": false, + "use_label": false, + "use_one_hot": false, + // feature for text to audio + "use_caption": true, + "use_melspec": true, + "use_wav": false, + // feature dir + "melspec_dir": "mel", + "wav_dir": "wav" + }, + // Specify the output root path to save model ckpts and logs + "log_dir": "ckpts/tta", + + // train + "train": { + "adam": { + "lr": 4.0e-5 + }, + "ddp": false, + "random_seed": 12345, + "batch_size": 12, + "epochs": 50000, + "max_steps": 1000000, + "total_training_steps": 800000, + "save_summary_steps": 1000, + "save_checkpoints_steps": 5000, + "valid_interval": 5000, + "keep_checkpoint_max": 100 + } + } \ No newline at end of file diff --git a/egs/tta/autoencoderkl/exp_config_base.json b/egs/tta/autoencoderkl/exp_config_base.json new file mode 100644 index 0000000000000000000000000000000000000000..d25badd4e99bb435e5eaf0cfc2ceeb96d75775e7 --- /dev/null +++ b/egs/tta/autoencoderkl/exp_config_base.json @@ -0,0 +1,11 @@ +{ + "base_config": "config/autoencoderkl.json", + "model_type": "AutoencoderKL", + "dataset": [ + "AudioCaps" + ], + "preprocess": { + "train_file": "train.json", + "valid_file": "vaild.json" + } +} \ No newline at end of file diff --git a/egs/tta/autoencoderkl/exp_config_latent_4_10_78.json b/egs/tta/autoencoderkl/exp_config_latent_4_10_78.json new file mode 100644 index 0000000000000000000000000000000000000000..911018a28c8e2d6a471a6505261e8050151e4d60 --- /dev/null +++ b/egs/tta/autoencoderkl/exp_config_latent_4_10_78.json @@ -0,0 +1,59 @@ +{ + "base_config": "egs/tta/autoencoderkl/exp_config_base.json", + "dataset": [ + "AudioCaps" + ], + "preprocess": { + // Specify the output root path to save the processed data + "processed_dir": "data", + + // feature + "use_spkid": false, + "use_uv": false, + "use_frame_pitch": false, + "use_phone_pitch": false, + "use_frame_energy": false, + "use_phone_energy": false, + "use_mel": false, + "use_audio": false, + "use_label": false, + "use_one_hot": false, + // feature for text to audio + "use_caption": true, + "use_melspec": true, + "use_wav": false, + // feature dir + "melspec_dir": "mel", + "wav_dir": "wav" + }, + // Specify the output root path to save model ckpts and logs + "log_dir": "ckpts/tta", + + "model": { + "autoencoderkl": { + "ch": 128, + "ch_mult": [1,2,2,4], + "num_res_blocks": 2, + "in_channels": 1, + "z_channels": 4, + "out_ch": 1, + "double_z": true + } + }, + // train + "train": { + "adam": { + "lr": 4.0e-5 + }, + "ddp": false, + "random_seed": 12345, + "batch_size": 12, + "epochs": 50000, + "max_steps": 1000000, + "total_training_steps": 800000, + "save_summary_steps": 1000, + "save_checkpoints_steps": 5000, + "valid_interval": 5000, + "keep_checkpoint_max": 100 + } + } \ No newline at end of file diff --git a/egs/tta/autoencoderkl/run_train.sh b/egs/tta/autoencoderkl/run_train.sh new file mode 100644 index 0000000000000000000000000000000000000000..00a25693166dcee2d7b96dc6aa957ce96f8ef872 --- /dev/null +++ b/egs/tta/autoencoderkl/run_train.sh @@ -0,0 +1,26 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Set Experiment Configuration ########### +exp_config="$exp_dir/exp_config.json" +exp_name="autoencoder_kl_debug" + +num_workers=8 +export CUDA_VISIBLE_DEVICES="0" + +######## Train Model ########### +python "${work_dir}"/bins/tta/train_tta.py \ + --config=$exp_config \ + --num_workers=$num_workers \ + --exp_name=$exp_name \ + --stdout_interval=25 \ \ No newline at end of file diff --git a/egs/tta/autoencoderkl/run_train_latent_4_10_78.sh b/egs/tta/autoencoderkl/run_train_latent_4_10_78.sh new file mode 100644 index 0000000000000000000000000000000000000000..041627d9c43b56ee4f1657733062f18eb313e8b0 --- /dev/null +++ b/egs/tta/autoencoderkl/run_train_latent_4_10_78.sh @@ -0,0 +1,26 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Set Experiment Configuration ########### +exp_config="$exp_dir/exp_config_latent_4_10_78.json" +exp_name="autoencoder_kl_debug_latent_size_4_10_78" + +num_workers=8 +export CUDA_VISIBLE_DEVICES="0" + +######## Train Model ########### +python "${work_dir}"/bins/tta/train_tta.py \ + --config=$exp_config \ + --num_workers=$num_workers \ + --exp_name=$exp_name \ + --stdout_interval=25 \ \ No newline at end of file diff --git a/egs/tts/FastSpeech2/README.md b/egs/tts/FastSpeech2/README.md new file mode 100644 index 0000000000000000000000000000000000000000..90108cf2a6c84d37dbb0537862e068376ea10c66 --- /dev/null +++ b/egs/tts/FastSpeech2/README.md @@ -0,0 +1,132 @@ + +# FastSpeech2 Recipe + +In this recipe, we will show how to train [FastSpeech2](https://openreview.net/forum?id=piLPYqxtWuA) using Amphion's infrastructure. FastSpeech2 is a non-autoregressive TTS architecture that utilizes feed-forward Transformer blocks. + +There are four stages in total: + +1. Data preparation +2. Features extraction +3. Training +4. Inference + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## 1. Data Preparation + +### Dataset Download +You can use the commonly used TTS dataset to train TTS model, e.g., LJSpeech, VCTK, LibriTTS, etc. We strongly recommend you use LJSpeech to train TTS model for the first time. How to download dataset is detailed [here](../../datasets/README.md). + +### Configuration + +After downloading the dataset, you can set the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json + "dataset": [ + "LJSpeech", + ], + "dataset_path": { + // TODO: Fill in your dataset path + "LJSpeech": "[LJSpeech dataset path]", + }, +``` + +## 2. Features Extraction + +### Configuration + +Specify the `processed_dir` and the `log_dir` and for saving the processed data and the checkpoints in `exp_config.json`: + +```json + // TODO: Fill in the output log path + "log_dir": "ckpts/tts", + "preprocess": { + // TODO: Fill in the output data path + "processed_dir": "data", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`): + +```bash +sh egs/tts/FastSpeech2/run.sh --stage 1 +``` + +## 3. Training + +### Configuration + +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines. + +``` +"train": { + "batch_size": 16, + } +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `ckpts/tts/[YourExptName]`. + +```bash +sh egs/tts/FastSpeech2/run.sh --stage 2 --name [YourExptName] +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. + + +## 4. Inference + +### Configuration + +For inference, you need to specify the following configurations when running `run.sh`: + + +| Parameters | Description | Example | +| --------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `--infer_expt_dir` | The experimental directory which contains `checkpoint` | `ckpts/tts/[YourExptName]` | +| `--infer_output_dir` | The output directory to save inferred audios. | `ckpts/tts/[YourExptName]/result` | +| `--infer_mode` | The inference mode, e.g., "`single`", "`batch`". | "`single`" to generate a clip of speech, "`batch`" to generate a batch of speech at a time. | +| `--infer_dataset` | The dataset used for inference. | For LJSpeech dataset, the inference dataset would be `LJSpeech`. | +| `--infer_testing_set` | The subset of the inference dataset used for inference, e.g., train, test, golden_test | For LJSpeech dataset, the testing set would be  "`test`" split from LJSpeech at the feature extraction, or "`golden_test`" cherry-picked from test set as template testing set. | +| `--infer_text` | The text to be synthesized. | "`This is a clip of generated speech with the given text from a TTS model.`" | + +### Run +For example, if you want to generate speech of all testing set split from LJSpeech, just run: + +```bash +sh egs/tts/FastSpeech2/run.sh --stage 3 \ + --infer_expt_dir ckpts/tts/[YourExptName] \ + --infer_output_dir ckpts/tts/[YourExptName]/result \ + --infer_mode "batch" \ + --infer_dataset "LJSpeech" \ + --infer_testing_set "test" +``` + +Or, if you want to generate a single clip of speech from a given text, just run: + +```bash +sh egs/tts/FastSpeech2/run.sh --stage 3 \ + --infer_expt_dir ckpts/tts/[YourExptName] \ + --infer_output_dir ckpts/tts/[YourExptName]/result \ + --infer_mode "single" \ + --infer_text "This is a clip of generated speech with the given text from a TTS model." +``` + +We will release a pre-trained FastSpeech2 model trained on LJSpeech. So you can download the pre-trained model and generate speech following the above inference instruction. + + +```bibtex +@inproceedings{ren2020fastspeech, + title={FastSpeech 2: Fast and High-Quality End-to-End Text to Speech}, + author={Ren, Yi and Hu, Chenxu and Tan, Xu and Qin, Tao and Zhao, Sheng and Zhao, Zhou and Liu, Tie-Yan}, + booktitle={International Conference on Learning Representations}, + year={2020} +} +``` diff --git a/egs/tts/FastSpeech2/exp_config.json b/egs/tts/FastSpeech2/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..6ba9022226ea987417913fb587e2a60ddb68bcb6 --- /dev/null +++ b/egs/tts/FastSpeech2/exp_config.json @@ -0,0 +1,21 @@ +{ + "base_config": "config/fs2.json", + "model_type": "FastSpeech2", + "dataset": [ + "LJSpeech" + ], + "dataset_path": { + // TODO: Fill in your dataset path + "LJSpeech": "[LJSpeech dataset path]" + }, + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "ckpts/tts", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + "sample_rate": 22050, + }, + "train": { + "batch_size": 16, + } +} diff --git a/egs/tts/FastSpeech2/prepare_mfa.sh b/egs/tts/FastSpeech2/prepare_mfa.sh new file mode 100644 index 0000000000000000000000000000000000000000..e8ce825b6a359f63e77e3ae001a48f14f3c69146 --- /dev/null +++ b/egs/tts/FastSpeech2/prepare_mfa.sh @@ -0,0 +1,14 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +#!/bin/bash +mkdir mfa +cd mfa +wget https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner/releases/download/v1.1.0-beta.2/montreal-forced-aligner_linux.tar.gz +tar -zxvf montreal-forced-aligner_linux.tar.gz +cd mfa +mkdir lexicon +cd lexicon +wget http://www.openslr.org/resources/11/librispeech-lexicon.txt \ No newline at end of file diff --git a/egs/tts/FastSpeech2/run.sh b/egs/tts/FastSpeech2/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..ad8da71e49783d5f47c3becb7d284506236678c2 --- /dev/null +++ b/egs/tts/FastSpeech2/run.sh @@ -0,0 +1,150 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +cd $work_dir/modules/monotonic_align +mkdir -p monotonic_align +python setup.py build_ext --inplace +cd $work_dir + +mfa_dir=$work_dir/mfa +echo $mfa_dir + +######## Parse the Given Parameters from the Commond ########### +# options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir:,name:,stage: -- "$@") +options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_mode:,infer_dataset:,infer_testing_set:,infer_text:,name:,stage: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + # [Only for Inference] The inference mode. It can be "batch" to generate speech by batch, or "single" to generage a single clip of speech. + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inference dataset. It is only used when the inference model is "batch". + --infer_dataset) shift; infer_dataset=$1 ; shift ;; + # [Only for Inference] The inference testing set. It is only used when the inference model is "batch". It can be "test" set split from the dataset, or "golden_test" carefully selected from the testing set. + --infer_testing_set) shift; infer_testing_set=$1 ; shift ;; + # [Only for Inference] The text to be synthesized from. It is only used when the inference model is "single". + --infer_text) shift; infer_text=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + if [ ! -d "$mfa_dir" ]; then + bash ${exp_dir}/prepare_mfa.sh + fi + CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/tts/preprocess.py \ + --config=$exp_config \ + --num_workers=4 \ + --prepare_alignment=true +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/tts/train.py \ + --config $exp_config \ + --exp_name $exp_name \ + --log_level debug +fi + +######## Inference ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$expt_dir/result" + fi + + if [ -z "$infer_mode" ]; then + echo "[Error] Please specify the inference mode, e.g., "batch", "single"" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_dataset" ]; then + echo "[Error] Please specify the dataset used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_testing_set" ]; then + echo "[Error] Please specify the testing set used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "single" ] && [ -z "$infer_text" ]; then + echo "[Error] Please specify the text to be synthesized when the inference mode is single" + exit 1 + fi + + if [ "$infer_mode" = "single" ]; then + echo 'Text: ' ${infer_text} + infer_dataset=None + infer_testing_set=None + elif [ "$infer_mode" = "batch" ]; then + infer_text='' + fi + + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \ + --config $exp_config \ + --acoustics_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --mode $infer_mode \ + --dataset $infer_dataset \ + --testing_set $infer_testing_set \ + --text "$infer_text" \ + --log_level debug \ + --vocoder_dir /mntnfs/lee_data1/chenxi/processed_data/ljspeech/model_ckpt/hifigan/checkpoints + + + +fi \ No newline at end of file diff --git a/egs/tts/README.md b/egs/tts/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2f288fa935e0ccefcd8ca37c00baa3763a5bedbe --- /dev/null +++ b/egs/tts/README.md @@ -0,0 +1,17 @@ + +# Amphion Text-to-Speech (TTS) Recipe + +## Quick Start + +We provide a **[beginner recipe](VALLE/)** to demonstrate how to train a cutting edge TTS model. Specifically, it is Amphion's re-implementation for [Vall-E](https://arxiv.org/abs/2301.02111), which is a zero-shot TTS architecture that uses a neural codec language model with discrete codes. + +## Supported Model Architectures + +Until now, Amphion TTS supports the following models or architectures, +- **[FastSpeech2](FastSpeech2)**: A non-autoregressive TTS architecture that utilizes feed-forward Transformer blocks. +- **[VITS](VITS)**: An end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning +- **[Vall-E](VALLE)**: A zero-shot TTS architecture that uses a neural codec language model with discrete codes. +- **[NaturalSpeech2](NaturalSpeech2)** (👨‍💻 developing): An architecture for TTS that utilizes a latent diffusion model to generate natural-sounding voices. + +## Amphion TTS Demo +Here are some [TTS samples](https://openhlt.github.io/Amphion_TTS_Demo/) from Amphion (👨‍💻 developing). diff --git a/egs/tts/VALLE/README.md b/egs/tts/VALLE/README.md new file mode 100644 index 0000000000000000000000000000000000000000..dd228b43d5e805a44f4d6afb833c108f8c2806a7 --- /dev/null +++ b/egs/tts/VALLE/README.md @@ -0,0 +1,139 @@ +# VALL-E Recipe + +In this recipe, we will show how to train [VALL-E](https://arxiv.org/abs/2301.02111) using Amphion's infrastructure. VALL-E is a zero-shot TTS architecture that uses a neural codec language model with discrete codes. + +There are four stages in total: + +1. Data preparation +2. Features extraction +3. Training +4. Inference + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## 1. Data Preparation + +### Dataset Download +You can use the commonly used TTS dataset to train VALL-E model, e.g., LibriTTS, etc. We strongly recommend you use LibriTTS to train VALL-E model for the first time. How to download dataset is detailed [here](../../datasets/README.md). + +### Configuration + +After downloading the dataset, you can set the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json + "dataset": [ + "libritts", + ], + "dataset_path": { + // TODO: Fill in your dataset path + "libritts": "[LibriTTS dataset path]", + }, +``` + +## 2. Features Extraction + +### Configuration + +Specify the `processed_dir` and the `log_dir` and for saving the processed data and the checkpoints in `exp_config.json`: + +```json + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "ckpts/tts", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`): + +```bash +sh egs/tts/VALLE/run.sh --stage 1 +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. + + +## 3. Training + +### Configuration + +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines. + +``` +"train": { + "batch_size": 4, + } +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/tts/[YourExptName]`. + +Specifically, VALL-E need to train a autoregressive (AR) model and then a non-autoregressive (NAR) model. So, you can set `--model_train_stage 1` to train AR model, and set `--model_train_stage 2` to train NAR model, where `--ar_model_ckpt_dir` should be set as the ckeckpoint path to the trained AR model. + + +Train a AR moel, just run: + +```bash +sh egs/tts/VALLE/run.sh --stage 2 --model_train_stage 1 --name [YourExptName] +``` + +Train a NAR model, just run: +```bash +sh egs/tts/VALLE/run.sh --stage 2 --model_train_stage 2 --ar_model_ckpt_dir [ARModelPath] --name [YourExptName] +``` + + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. + + +## 4. Inference + +### Configuration + +For inference, you need to specify the following configurations when running `run.sh`: + + + +| Parameters | Description | Example | +| --------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `--infer_expt_dir` | The experimental directory of NAR model which contains `checkpoint` | `Amphion/ckpts/tts/[YourExptName]` | +| `--infer_output_dir` | The output directory to save inferred audios. | `Amphion/ckpts/tts/[YourExptName]/result` | +| `--infer_mode` | The inference mode, e.g., "`single`", "`batch`". | "`single`" to generate a clip of speech, "`batch`" to generate a batch of speech at a time. | +| `--infer_text` | The text to be synthesized. | "`This is a clip of generated speech with the given text from a TTS model.`" | +| `--infer_text_prompt` | The text prompt for inference. | The text prompt should be aligned with the audio prompt. | +| `--infer_audio_prompt` | The audio prompt for inference. | The audio prompt should be aligned with text prompt.| +| `--test_list_file` | The test list file used for batch inference. | The format of test list file is `text\|text_prompt\|audio_prompt`.| + + +### Run +For example, if you want to generate a single clip of speech, just run: + +```bash +sh egs/tts/VALLE/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \ + --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \ + --infer_mode "single" \ + --infer_text "This is a clip of generated speech with the given text from a TTS model." \ + --infer_text_prompt "But even the unsuccessful dramatist has his moments." \ + --infer_audio_prompt egs/tts/VALLE/prompt_examples/7176_92135_000004_000000.wav +``` + + +We will release a pre-trained VALL-E. So you can download the pre-trained model and generate speech following the above inference instruction. + +```bibtex +@article{wang2023neural, + title={Neural codec language models are zero-shot text to speech synthesizers}, + author={Wang, Chengyi and Chen, Sanyuan and Wu, Yu and Zhang, Ziqiang and Zhou, Long and Liu, Shujie and Chen, Zhuo and Liu, Yanqing and Wang, Huaming and Li, Jinyu and others}, + journal={arXiv preprint arXiv:2301.02111}, + year={2023} +} +``` \ No newline at end of file diff --git a/egs/tts/VALLE/exp_config.json b/egs/tts/VALLE/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..c8730265ea07a1617972d78b9d426a5aba355371 --- /dev/null +++ b/egs/tts/VALLE/exp_config.json @@ -0,0 +1,29 @@ +{ + "base_config": "config/valle.json", + "model_type": "VALLE", + "dataset": [ + "libritts" + ], + "dataset_path": { + "libritts": "[LibriTTS dataset path]" + }, + "preprocess": { + "extract_phone": true, + "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)" + "extract_acoustic_token": true, + "use_phone": true, + "use_acoustic_token": true, + "processed_dir": "Amphion/data/", + "sampling_rate": "24000", // "Audio sampling rate." + "valid_file": "test.json", + }, + "model": { + "prefix_mode": 1, // "The mode for how to prefix VALL-E NAR Decoder, 0: no prefix, 1: 0 to random, 2: random to random, 4: chunk of pre or post utterance.", + }, + "log_dir": "Amphion/ckpts/tts/valle", + "train": { + "batch_size": 4, + "train_stage": 1, // 0: train all modules, For VALL_E, support 1: AR Decoder 2: NAR Decoder(s) + "max_epoch": 20, // "Number of epochs to train." + } +} \ No newline at end of file diff --git a/egs/tts/VALLE/prompt_examples/260_123440_000010_000004.normalized.txt b/egs/tts/VALLE/prompt_examples/260_123440_000010_000004.normalized.txt new file mode 100644 index 0000000000000000000000000000000000000000..ed9d2b859d3c7bbb7bcc2476323d1a219ee8d53f --- /dev/null +++ b/egs/tts/VALLE/prompt_examples/260_123440_000010_000004.normalized.txt @@ -0,0 +1 @@ +I almost think I can remember feeling a little different. \ No newline at end of file diff --git a/egs/tts/VALLE/prompt_examples/5142_33396_000002_000004.normalized.txt b/egs/tts/VALLE/prompt_examples/5142_33396_000002_000004.normalized.txt new file mode 100644 index 0000000000000000000000000000000000000000..4dab0ebd446cb814acccb2b97fee31a0d5b7444d --- /dev/null +++ b/egs/tts/VALLE/prompt_examples/5142_33396_000002_000004.normalized.txt @@ -0,0 +1 @@ +Ten sons sat at meat with him, and I was the youngest. \ No newline at end of file diff --git a/egs/tts/VALLE/prompt_examples/6829_68771_000027_000000.normalized.txt b/egs/tts/VALLE/prompt_examples/6829_68771_000027_000000.normalized.txt new file mode 100644 index 0000000000000000000000000000000000000000..04db64ff2711c35a78cbb210d11fce06865d831c --- /dev/null +++ b/egs/tts/VALLE/prompt_examples/6829_68771_000027_000000.normalized.txt @@ -0,0 +1 @@ +The girl entered, and gave an involuntary cry of surprise. \ No newline at end of file diff --git a/egs/tts/VALLE/prompt_examples/7176_92135_000004_000000.normalized.txt b/egs/tts/VALLE/prompt_examples/7176_92135_000004_000000.normalized.txt new file mode 100644 index 0000000000000000000000000000000000000000..6400078bd5d5fd4043a0019fc1e8b276b408cd39 --- /dev/null +++ b/egs/tts/VALLE/prompt_examples/7176_92135_000004_000000.normalized.txt @@ -0,0 +1 @@ +But even the unsuccessful dramatist has his moments. \ No newline at end of file diff --git a/egs/tts/VALLE/run.sh b/egs/tts/VALLE/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..104f9f32619145952fac5218a3a24134837c4ad9 --- /dev/null +++ b/egs/tts/VALLE/run.sh @@ -0,0 +1,158 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +cd $work_dir/modules/monotonic_align +mkdir -p monotonic_align +python setup.py build_ext --inplace +cd $work_dir + +######## Parse the Given Parameters from the Commond ########### +options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,ar_model_ckpt_dir:,infer_output_dir:,infer_mode:,infer_test_list_file:,infer_text:,infer_text_prompt:,infer_audio_prompt:,model_train_stage:,name:,stage: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Training] Model training stage. + --model_train_stage) shift; model_train_stage=$1 ; shift ;; + # [Only for Training] The stage1 ckpt dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --ar_model_ckpt_dir) shift; ar_model_ckpt_dir=$1 ; shift ;; + + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + + # [Only for Inference] The inference mode. It can be "batch" to generate speech by batch, or "single" to generage a single clip of speech. + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inference test list file. It is only used when the inference model is "batch". + --infer_test_list_file) shift; infer_test_list_file=$1 ; shift ;; + # [Only for Inference] The text to be synthesized from. It is only used when the inference model is "single". + --infer_text) shift; infer_text=$1 ; shift ;; + # [Only for Inference] The inference text prompt. It is only used when the inference model is "single". + --infer_text_prompt) shift; infer_text_prompt=$1 ; shift ;; + # [Only for Inference] The inference audio prompt. It is only used when the inference model is "single". + --infer_audio_prompt) shift; infer_audio_prompt=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/tts/preprocess.py \ + --config=$exp_config \ + --num_workers=4 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + + if [ "$model_train_stage" = "2" ] && [ -z "$ar_model_ckpt_dir" ]; then + echo "[Error] Please specify the ckeckpoint path to the trained model in stage1." + exit 1 + fi + + if [ "$model_train_stage" = "1" ]; then + ar_model_ckpt_dir=None + fi + + echo "Exprimental Name: $exp_name" + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch --main_process_port 29510 \ + "${work_dir}"/bins/tts/train.py \ + --config $exp_config \ + --exp_name $exp_name \ + --log_level debug \ + --train_stage $model_train_stage \ + --checkpoint_path $ar_model_ckpt_dir +fi + + +######## Inference ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$expt_dir/result" + fi + + if [ -z "$infer_mode" ]; then + echo "[Error] Please specify the inference mode, e.g., "batch", "single"" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_test_list_file" ]; then + echo "[Error] Please specify the test list file used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "single" ] && [ -z "$infer_text" ]; then + echo "[Error] Please specify the text to be synthesized when the inference mode is single" + exit 1 + fi + + if [ "$infer_mode" = "single" ]; then + echo 'Text: ' ${infer_text} + infer_test_list_file=None + elif [ "$infer_mode" = "batch" ]; then + infer_text="" + infer_text_prompt="" + infer_audio_prompt="" + fi + + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \ + --config $exp_config \ + --log_level debug \ + --acoustics_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --mode $infer_mode \ + --text "$infer_text" \ + --text_prompt "$infer_text_prompt" \ + --audio_prompt $infer_audio_prompt\ + --test_list_file $infer_test_list_file \ + +fi diff --git a/egs/tts/VITS/README.md b/egs/tts/VITS/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f7b97ff7d37c14a1b0218565d7f2639d4f001f05 --- /dev/null +++ b/egs/tts/VITS/README.md @@ -0,0 +1,135 @@ + +# VITS Recipe + +In this recipe, we will show how to train [VITS](https://arxiv.org/abs/2106.06103) using Amphion's infrastructure. VITS is an end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning. + +There are four stages in total: + +1. Data preparation +2. Features extraction +3. Training +4. Inference + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## 1. Data Preparation + +### Dataset Download +You can use the commonly used TTS dataset to train TTS model, e.g., LJSpeech, VCTK, LibriTTS, etc. We strongly recommend you use LJSpeech to train TTS model for the first time. How to download dataset is detailed [here](../../datasets/README.md). + +### Configuration + +After downloading the dataset, you can set the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json + "dataset": [ + "LJSpeech", + ], + "dataset_path": { + // TODO: Fill in your dataset path + "LJSpeech": "[LJSpeech dataset path]", + }, +``` + +## 2. Features Extraction + +### Configuration + +Specify the `processed_dir` and the `log_dir` and for saving the processed data and the checkpoints in `exp_config.json`: + +```json + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "ckpts/tts", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`): + +```bash +sh egs/tts/VITS/run.sh --stage 1 +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. + +## 3. Training + +### Configuration + +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines. + +``` +"train": { + "batch_size": 16, + } +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/tts/[YourExptName]`. + +```bash +sh egs/tts/VITS/run.sh --stage 2 --name [YourExptName] +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. + + +## 4. Inference + +### Configuration + +For inference, you need to specify the following configurations when running `run.sh`: + + +| Parameters | Description | Example | +| --------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `--infer_expt_dir` | The experimental directory which contains `checkpoint` | `Amphion/ckpts/tts/[YourExptName]` | +| `--infer_output_dir` | The output directory to save inferred audios. | `Amphion/ckpts/tts/[YourExptName]/result` | +| `--infer_mode` | The inference mode, e.g., "`single`", "`batch`". | "`single`" to generate a clip of speech, "`batch`" to generate a batch of speech at a time. | +| `--infer_dataset` | The dataset used for inference. | For LJSpeech dataset, the inference dataset would be `LJSpeech`. | +| `--infer_testing_set` | The subset of the inference dataset used for inference, e.g., train, test, golden_test | For LJSpeech dataset, the testing set would be  "`test`" split from LJSpeech at the feature extraction, or "`golden_test`" cherry-picked from test set as template testing set. | +| `--infer_text` | The text to be synthesized. | "`This is a clip of generated speech with the given text from a TTS model.`" | + +### Run +For example, if you want to generate speech of all testing set split from LJSpeech, just run: + +```bash +sh egs/tts/VITS/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \ + --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \ + --infer_mode "batch" \ + --infer_dataset "LJSpeech" \ + --infer_testing_set "test" +``` + +Or, if you want to generate a single clip of speech from a given text, just run: + +```bash +sh egs/tts/VITS/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \ + --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \ + --infer_mode "single" \ + --infer_text "This is a clip of generated speech with the given text from a TTS model." +``` + +We will release a pre-trained VITS model trained on LJSpeech. So you can download the pre-trained model and generate speech following the above inference instruction. + + +```bibtex +@inproceedings{kim2021conditional, + title={Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech}, + author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee}, + booktitle={International Conference on Machine Learning}, + pages={5530--5540}, + year={2021}, +} +``` \ No newline at end of file diff --git a/egs/tts/VITS/exp_config.json b/egs/tts/VITS/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..b210a2656186844fe1d0fc88e515fdc6cc207dcb --- /dev/null +++ b/egs/tts/VITS/exp_config.json @@ -0,0 +1,27 @@ +{ + "base_config": "config/vits.json", + "model_type": "VITS", + "dataset": [ + "LJSpeech" + ], + "dataset_path": { + // TODO: Fill in your dataset path + "LJSpeech": "[LJSpeech dataset path]" + }, + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "ckpts/tts", + "preprocess": { + "use_phone": true, + // linguistic features + "extract_phone": true, + "phone_extractor": "lexicon", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)" + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + + "sample_rate": 22050, + "valid_file": "test.json", // validattion set + }, + "train": { + "batch_size": 16, + } +} \ No newline at end of file diff --git a/egs/tts/VITS/run.sh b/egs/tts/VITS/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..8f330d32bca818b92f1f2c71ed78a6c43b703aea --- /dev/null +++ b/egs/tts/VITS/run.sh @@ -0,0 +1,142 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +cd $work_dir/modules/monotonic_align +mkdir -p monotonic_align +python setup.py build_ext --inplace +cd $work_dir + +######## Parse the Given Parameters from the Commond ########### +# options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir:,name:,stage: -- "$@") +options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_mode:,infer_dataset:,infer_testing_set:,infer_text:,name:,stage: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + # [Only for Inference] The inference mode. It can be "batch" to generate speech by batch, or "single" to generage a single clip of speech. + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inference dataset. It is only used when the inference model is "batch". + --infer_dataset) shift; infer_dataset=$1 ; shift ;; + # [Only for Inference] The inference testing set. It is only used when the inference model is "batch". It can be "test" set split from the dataset, or "golden_test" carefully selected from the testing set. + --infer_testing_set) shift; infer_testing_set=$1 ; shift ;; + # [Only for Inference] The text to be synthesized from. It is only used when the inference model is "single". + --infer_text) shift; infer_text=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/tts/preprocess.py \ + --config=$exp_config \ + --num_workers=4 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/tts/train.py \ + --config $exp_config \ + --exp_name $exp_name \ + --log_level debug +fi + +######## Inference ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$expt_dir/result" + fi + + if [ -z "$infer_mode" ]; then + echo "[Error] Please specify the inference mode, e.g., "batch", "single"" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_dataset" ]; then + echo "[Error] Please specify the dataset used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_testing_set" ]; then + echo "[Error] Please specify the testing set used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "single" ] && [ -z "$infer_text" ]; then + echo "[Error] Please specify the text to be synthesized when the inference mode is single" + exit 1 + fi + + if [ "$infer_mode" = "single" ]; then + echo 'Text: ' ${infer_text} + infer_dataset=None + infer_testing_set=None + elif [ "$infer_mode" = "batch" ]; then + infer_text='' + fi + + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \ + --config $exp_config \ + --acoustics_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --mode $infer_mode \ + --dataset $infer_dataset \ + --testing_set $infer_testing_set \ + --text "$infer_text" \ + --log_level debug + + + +fi diff --git a/egs/tts/vits_hifitts/README.md b/egs/tts/vits_hifitts/README.md new file mode 100644 index 0000000000000000000000000000000000000000..bbaedfef27aeceb233c4f27700019cea11624cd5 --- /dev/null +++ b/egs/tts/vits_hifitts/README.md @@ -0,0 +1,135 @@ + +# vits_hifitts Recipe + +In this recipe, we will show how to train [vits_hifitts](https://arxiv.org/abs/2106.06103) using Amphion's infrastructure. vits_hifitts is an end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning. + +There are four stages in total: + +1. Data preparation +2. Features extraction +3. Training +4. Inference + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## 1. Data Preparation + +### Dataset Download +You can use the commonly used TTS dataset to train TTS model, e.g., LJSpeech, VCTK, LibriTTS, etc. We strongly recommend you use LJSpeech to train TTS model for the first time. How to download dataset is detailed [here](../../datasets/README.md). + +### Configuration + +After downloading the dataset, you can set the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json + "dataset": [ + "LJSpeech", + ], + "dataset_path": { + // TODO: Fill in your dataset path + "LJSpeech": "[LJSpeech dataset path]", + }, +``` + +## 2. Features Extraction + +### Configuration + +Specify the `processed_dir` and the `log_dir` and for saving the processed data and the checkpoints in `exp_config.json`: + +```json + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "ckpts/tts", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`): + +```bash +sh egs/tts/vits_hifitts/run.sh --stage 1 +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. + +## 3. Training + +### Configuration + +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines. + +``` +"train": { + "batch_size": 16, + } +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/tts/[YourExptName]`. + +```bash +sh egs/tts/vits_hifitts/run.sh --stage 2 --name [YourExptName] +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. + + +## 4. Inference + +### Configuration + +For inference, you need to specify the following configurations when running `run.sh`: + + +| Parameters | Description | Example | +| --------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `--infer_expt_dir` | The experimental directory which contains `checkpoint` | `Amphion/ckpts/tts/[YourExptName]` | +| `--infer_output_dir` | The output directory to save inferred audios. | `Amphion/ckpts/tts/[YourExptName]/result` | +| `--infer_mode` | The inference mode, e.g., "`single`", "`batch`". | "`single`" to generate a clip of speech, "`batch`" to generate a batch of speech at a time. | +| `--infer_dataset` | The dataset used for inference. | For LJSpeech dataset, the inference dataset would be `LJSpeech`. | +| `--infer_testing_set` | The subset of the inference dataset used for inference, e.g., train, test, golden_test | For LJSpeech dataset, the testing set would be  "`test`" split from LJSpeech at the feature extraction, or "`golden_test`" cherry-picked from test set as template testing set. | +| `--infer_text` | The text to be synthesized. | "`This is a clip of generated speech with the given text from a TTS model.`" | + +### Run +For example, if you want to generate speech of all testing set split from LJSpeech, just run: + +```bash +sh egs/tts/vits_hifitts/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \ + --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \ + --infer_mode "batch" \ + --infer_dataset "LJSpeech" \ + --infer_testing_set "test" +``` + +Or, if you want to generate a single clip of speech from a given text, just run: + +```bash +sh egs/tts/vits_hifitts/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \ + --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \ + --infer_mode "single" \ + --infer_text "This is a clip of generated speech with the given text from a TTS model." +``` + +We will release a pre-trained vits_hifitts model trained on LJSpeech. So you can download the pre-trained model and generate speech following the above inference instruction. + + +```bibtex +@inproceedings{kim2021conditional, + title={Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech}, + author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee}, + booktitle={International Conference on Machine Learning}, + pages={5530--5540}, + year={2021}, +} +``` \ No newline at end of file diff --git a/egs/tts/vits_hifitts/exp_config.json b/egs/tts/vits_hifitts/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..c32318816b3f9c27bedf54b647f3ec943019d05f --- /dev/null +++ b/egs/tts/vits_hifitts/exp_config.json @@ -0,0 +1,31 @@ +{ + "base_config": "config/vits.json", + "model_type": "VITS", + "dataset": [ + "hifitts" + ], + "dataset_path": { + // TODO: Fill in your dataset path + "hifitts": "/mnt/workspace/xueliumeng/data/hifitts/hi_fi_tts_v0" + }, + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "/mnt/workspace/tzeying/data/vits_on_libritts_hifitts/logs", + "preprocess": { + "extract_audio": true, + "use_phone": true, + // linguistic features + "extract_phone": true, + "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)" + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "/mnt/workspace/xueliumeng/data/vits_on_libritts_hifitts/processed_data", + "sample_rate": 24000, + "train_file": "train_all.json", + "valid_file": "valid_100.json", // validattion set + "use_spkid": true, // True: use speaker id for multi-speaker dataset + }, + "train": { + "batch_size": 16, + "multi_speaker_training": true, // True: train multi-speaker model; False: training single-speaker model; + //"n_speakers": 2500, // number of speakers, while be automatically set if n_speakers is 0 and multi_speaker_training is true + } +} \ No newline at end of file diff --git a/egs/tts/vits_hifitts/run.sh b/egs/tts/vits_hifitts/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..7daef8f682317f2dcbad55fef9b23ba63fd21ed2 --- /dev/null +++ b/egs/tts/vits_hifitts/run.sh @@ -0,0 +1,148 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +cd $work_dir/modules/monotonic_align +mkdir -p monotonic_align +python setup.py build_ext --inplace +cd $work_dir + +######## Parse the Given Parameters from the Commond ########### +# options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir:,name:,stage: -- "$@") +options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_mode:,infer_dataset:,infer_testing_set:,infer_text:,infer_speaker_name:,name:,stage: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + # [Only for Inference] The inference mode. It can be "batch" to generate speech by batch, or "single" to generage a single clip of speech. + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inference dataset. It is only used when the inference model is "batch". + --infer_dataset) shift; infer_dataset=$1 ; shift ;; + # [Only for Inference] The inference testing set. It is only used when the inference model is "batch". It can be "test" set split from the dataset, or "golden_test" carefully selected from the testing set. + --infer_testing_set) shift; infer_testing_set=$1 ; shift ;; + # [Only for Inference] The text to be synthesized from. It is only used when the inference model is "single". + --infer_text) shift; infer_text=$1 ; shift ;; + # [Only for Inference] The speaker voice to be delivered in the synthesized speech. It is only used when the inference model is "single". + --infer_speaker_name) shift; infer_speaker_name=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu /home/pai/envs/amphion/bin/python "${work_dir}"/bins/tts/preprocess.py \ + --config=$exp_config \ + --num_workers=4 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/tts/train.py \ + --config $exp_config \ + --exp_name $exp_name \ + --resume \ + --log_level debug \ + --checkpoint_path "/mnt/workspace/tzeying/data/vits_on_libritts_hifitts/logs/HifiTTS_all/checkpoint/epoch-0030_step-0312356_loss-38.448391" +fi + +######## Inference ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$expt_dir/result" + fi + + if [ -z "$infer_mode" ]; then + echo "[Error] Please specify the inference mode, e.g., "batch", "single"" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_dataset" ]; then + echo "[Error] Please specify the dataset used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_testing_set" ]; then + echo "[Error] Please specify the testing set used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "single" ] && [ -z "$infer_text" ]; then + echo "[Error] Please specify the text to be synthesized when the inference mode is single" + exit 1 + fi + + if [ "$infer_mode" = "single" ]; then + echo 'Text: ' ${infer_text} + infer_dataset=None + infer_testing_set=None + elif [ "$infer_mode" = "batch" ]; then + infer_text='' + infer_speaker_name=None + fi + + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \ + --config $exp_config \ + --checkpoint_path $infer_expt_dir \ + --output_dir $infer_output_dir \ + --mode $infer_mode \ + --dataset $infer_dataset \ + --testing_set $infer_testing_set \ + --text "$infer_text" \ + --speaker_name $infer_speaker_name \ + --log_level debug + + + +fi diff --git a/egs/tts/vits_hifitts_1w/README.md b/egs/tts/vits_hifitts_1w/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3f54419f6cdf400606efa48d7424faaf74c1664a --- /dev/null +++ b/egs/tts/vits_hifitts_1w/README.md @@ -0,0 +1,135 @@ + +# vits_hifitts_1w Recipe + +In this recipe, we will show how to train [vits_hifitts_1w](https://arxiv.org/abs/2106.06103) using Amphion's infrastructure. vits_hifitts_1w is an end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning. + +There are four stages in total: + +1. Data preparation +2. Features extraction +3. Training +4. Inference + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## 1. Data Preparation + +### Dataset Download +You can use the commonly used TTS dataset to train TTS model, e.g., LJSpeech, VCTK, LibriTTS, etc. We strongly recommend you use LJSpeech to train TTS model for the first time. How to download dataset is detailed [here](../../datasets/README.md). + +### Configuration + +After downloading the dataset, you can set the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json + "dataset": [ + "LJSpeech", + ], + "dataset_path": { + // TODO: Fill in your dataset path + "LJSpeech": "[LJSpeech dataset path]", + }, +``` + +## 2. Features Extraction + +### Configuration + +Specify the `processed_dir` and the `log_dir` and for saving the processed data and the checkpoints in `exp_config.json`: + +```json + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "ckpts/tts", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`): + +```bash +sh egs/tts/vits_hifitts_1w/run.sh --stage 1 +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. + +## 3. Training + +### Configuration + +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines. + +``` +"train": { + "batch_size": 16, + } +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/tts/[YourExptName]`. + +```bash +sh egs/tts/vits_hifitts_1w/run.sh --stage 2 --name [YourExptName] +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. + + +## 4. Inference + +### Configuration + +For inference, you need to specify the following configurations when running `run.sh`: + + +| Parameters | Description | Example | +| --------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `--infer_expt_dir` | The experimental directory which contains `checkpoint` | `Amphion/ckpts/tts/[YourExptName]` | +| `--infer_output_dir` | The output directory to save inferred audios. | `Amphion/ckpts/tts/[YourExptName]/result` | +| `--infer_mode` | The inference mode, e.g., "`single`", "`batch`". | "`single`" to generate a clip of speech, "`batch`" to generate a batch of speech at a time. | +| `--infer_dataset` | The dataset used for inference. | For LJSpeech dataset, the inference dataset would be `LJSpeech`. | +| `--infer_testing_set` | The subset of the inference dataset used for inference, e.g., train, test, golden_test | For LJSpeech dataset, the testing set would be  "`test`" split from LJSpeech at the feature extraction, or "`golden_test`" cherry-picked from test set as template testing set. | +| `--infer_text` | The text to be synthesized. | "`This is a clip of generated speech with the given text from a TTS model.`" | + +### Run +For example, if you want to generate speech of all testing set split from LJSpeech, just run: + +```bash +sh egs/tts/vits_hifitts_1w/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \ + --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \ + --infer_mode "batch" \ + --infer_dataset "LJSpeech" \ + --infer_testing_set "test" +``` + +Or, if you want to generate a single clip of speech from a given text, just run: + +```bash +sh egs/tts/vits_hifitts_1w/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \ + --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \ + --infer_mode "single" \ + --infer_text "This is a clip of generated speech with the given text from a TTS model." +``` + +We will release a pre-trained vits_hifitts_1w model trained on LJSpeech. So you can download the pre-trained model and generate speech following the above inference instruction. + + +```bibtex +@inproceedings{kim2021conditional, + title={Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech}, + author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee}, + booktitle={International Conference on Machine Learning}, + pages={5530--5540}, + year={2021}, +} +``` \ No newline at end of file diff --git a/egs/tts/vits_hifitts_1w/exp_config.json b/egs/tts/vits_hifitts_1w/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..e32cc92c5e69e53950dff4f7d9a2c1a130b29752 --- /dev/null +++ b/egs/tts/vits_hifitts_1w/exp_config.json @@ -0,0 +1,31 @@ +{ + "base_config": "config/vits.json", + "model_type": "VITS", + "dataset": [ + "hifitts" + ], + "dataset_path": { + // TODO: Fill in your dataset path + "hifitts": "/mnt/workspace/xueliumeng/data/hifitts/hi_fi_tts_v0" + }, + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "/mnt/workspace/xueliumeng/data/vits_on_libritts_hifitts/logs", + "preprocess": { + "extract_audio": true, + "use_phone": true, + // linguistic features + "extract_phone": true, + "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)" + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "/mnt/workspace/xueliumeng/data/vits_on_libritts_hifitts/processed_data", + "sample_rate": 24000, + "train_file": "train_1w.json", + "valid_file": "valid.json", // validattion set + "use_spkid": true, // True: use speaker id for multi-speaker dataset + }, + "train": { + "batch_size": 16, + "multi_speaker_training": true, // True: train multi-speaker model; False: training single-speaker model; + "n_speakers": 2500, // number of speakers, while be automatically set if n_speakers is 0 and multi_speaker_training is true + } +} \ No newline at end of file diff --git a/egs/tts/vits_hifitts_1w/run.sh b/egs/tts/vits_hifitts_1w/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..5bb4bf76d2e530a0000eeb6c6f87ec8c60d90079 --- /dev/null +++ b/egs/tts/vits_hifitts_1w/run.sh @@ -0,0 +1,146 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +cd $work_dir/modules/monotonic_align +mkdir -p monotonic_align +python setup.py build_ext --inplace +cd $work_dir + +######## Parse the Given Parameters from the Commond ########### +# options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir:,name:,stage: -- "$@") +options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_mode:,infer_dataset:,infer_testing_set:,infer_text:,infer_speaker_name:,name:,stage: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + # [Only for Inference] The inference mode. It can be "batch" to generate speech by batch, or "single" to generage a single clip of speech. + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inference dataset. It is only used when the inference model is "batch". + --infer_dataset) shift; infer_dataset=$1 ; shift ;; + # [Only for Inference] The inference testing set. It is only used when the inference model is "batch". It can be "test" set split from the dataset, or "golden_test" carefully selected from the testing set. + --infer_testing_set) shift; infer_testing_set=$1 ; shift ;; + # [Only for Inference] The text to be synthesized from. It is only used when the inference model is "single". + --infer_text) shift; infer_text=$1 ; shift ;; + # [Only for Inference] The speaker voice to be delivered in the synthesized speech. It is only used when the inference model is "single". + --infer_speaker_name) shift; infer_speaker_name=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu /home/pai/envs/amphion/bin/python "${work_dir}"/bins/tts/preprocess.py \ + --config=$exp_config \ + --num_workers=4 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/tts/train.py \ + --config $exp_config \ + --exp_name $exp_name \ + --log_level debug +fi + +######## Inference ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$expt_dir/result" + fi + + if [ -z "$infer_mode" ]; then + echo "[Error] Please specify the inference mode, e.g., "batch", "single"" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_dataset" ]; then + echo "[Error] Please specify the dataset used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_testing_set" ]; then + echo "[Error] Please specify the testing set used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "single" ] && [ -z "$infer_text" ]; then + echo "[Error] Please specify the text to be synthesized when the inference mode is single" + exit 1 + fi + + if [ "$infer_mode" = "single" ]; then + echo 'Text: ' ${infer_text} + infer_dataset=None + infer_testing_set=None + elif [ "$infer_mode" = "batch" ]; then + infer_text='' + infer_speaker_name=None + fi + + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \ + --config $exp_config \ + --acoustics_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --mode $infer_mode \ + --dataset $infer_dataset \ + --testing_set $infer_testing_set \ + --text "$infer_text" \ + --speaker_name $infer_speaker_name \ + --log_level debug + + + +fi diff --git a/egs/tts/vits_hifitts_5k/.ipynb_checkpoints/README-checkpoint.md b/egs/tts/vits_hifitts_5k/.ipynb_checkpoints/README-checkpoint.md new file mode 100644 index 0000000000000000000000000000000000000000..3f54419f6cdf400606efa48d7424faaf74c1664a --- /dev/null +++ b/egs/tts/vits_hifitts_5k/.ipynb_checkpoints/README-checkpoint.md @@ -0,0 +1,135 @@ + +# vits_hifitts_1w Recipe + +In this recipe, we will show how to train [vits_hifitts_1w](https://arxiv.org/abs/2106.06103) using Amphion's infrastructure. vits_hifitts_1w is an end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning. + +There are four stages in total: + +1. Data preparation +2. Features extraction +3. Training +4. Inference + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## 1. Data Preparation + +### Dataset Download +You can use the commonly used TTS dataset to train TTS model, e.g., LJSpeech, VCTK, LibriTTS, etc. We strongly recommend you use LJSpeech to train TTS model for the first time. How to download dataset is detailed [here](../../datasets/README.md). + +### Configuration + +After downloading the dataset, you can set the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json + "dataset": [ + "LJSpeech", + ], + "dataset_path": { + // TODO: Fill in your dataset path + "LJSpeech": "[LJSpeech dataset path]", + }, +``` + +## 2. Features Extraction + +### Configuration + +Specify the `processed_dir` and the `log_dir` and for saving the processed data and the checkpoints in `exp_config.json`: + +```json + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "ckpts/tts", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`): + +```bash +sh egs/tts/vits_hifitts_1w/run.sh --stage 1 +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. + +## 3. Training + +### Configuration + +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines. + +``` +"train": { + "batch_size": 16, + } +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/tts/[YourExptName]`. + +```bash +sh egs/tts/vits_hifitts_1w/run.sh --stage 2 --name [YourExptName] +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. + + +## 4. Inference + +### Configuration + +For inference, you need to specify the following configurations when running `run.sh`: + + +| Parameters | Description | Example | +| --------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `--infer_expt_dir` | The experimental directory which contains `checkpoint` | `Amphion/ckpts/tts/[YourExptName]` | +| `--infer_output_dir` | The output directory to save inferred audios. | `Amphion/ckpts/tts/[YourExptName]/result` | +| `--infer_mode` | The inference mode, e.g., "`single`", "`batch`". | "`single`" to generate a clip of speech, "`batch`" to generate a batch of speech at a time. | +| `--infer_dataset` | The dataset used for inference. | For LJSpeech dataset, the inference dataset would be `LJSpeech`. | +| `--infer_testing_set` | The subset of the inference dataset used for inference, e.g., train, test, golden_test | For LJSpeech dataset, the testing set would be  "`test`" split from LJSpeech at the feature extraction, or "`golden_test`" cherry-picked from test set as template testing set. | +| `--infer_text` | The text to be synthesized. | "`This is a clip of generated speech with the given text from a TTS model.`" | + +### Run +For example, if you want to generate speech of all testing set split from LJSpeech, just run: + +```bash +sh egs/tts/vits_hifitts_1w/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \ + --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \ + --infer_mode "batch" \ + --infer_dataset "LJSpeech" \ + --infer_testing_set "test" +``` + +Or, if you want to generate a single clip of speech from a given text, just run: + +```bash +sh egs/tts/vits_hifitts_1w/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \ + --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \ + --infer_mode "single" \ + --infer_text "This is a clip of generated speech with the given text from a TTS model." +``` + +We will release a pre-trained vits_hifitts_1w model trained on LJSpeech. So you can download the pre-trained model and generate speech following the above inference instruction. + + +```bibtex +@inproceedings{kim2021conditional, + title={Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech}, + author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee}, + booktitle={International Conference on Machine Learning}, + pages={5530--5540}, + year={2021}, +} +``` \ No newline at end of file diff --git a/egs/tts/vits_hifitts_5k/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/egs/tts/vits_hifitts_5k/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..363fcab7ed6e9634e198cf5555ceb88932c9a245 --- /dev/null +++ b/egs/tts/vits_hifitts_5k/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/egs/tts/vits_hifitts_5k/.ipynb_checkpoints/run-checkpoint.sh b/egs/tts/vits_hifitts_5k/.ipynb_checkpoints/run-checkpoint.sh new file mode 100644 index 0000000000000000000000000000000000000000..5bb4bf76d2e530a0000eeb6c6f87ec8c60d90079 --- /dev/null +++ b/egs/tts/vits_hifitts_5k/.ipynb_checkpoints/run-checkpoint.sh @@ -0,0 +1,146 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +cd $work_dir/modules/monotonic_align +mkdir -p monotonic_align +python setup.py build_ext --inplace +cd $work_dir + +######## Parse the Given Parameters from the Commond ########### +# options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir:,name:,stage: -- "$@") +options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_mode:,infer_dataset:,infer_testing_set:,infer_text:,infer_speaker_name:,name:,stage: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + # [Only for Inference] The inference mode. It can be "batch" to generate speech by batch, or "single" to generage a single clip of speech. + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inference dataset. It is only used when the inference model is "batch". + --infer_dataset) shift; infer_dataset=$1 ; shift ;; + # [Only for Inference] The inference testing set. It is only used when the inference model is "batch". It can be "test" set split from the dataset, or "golden_test" carefully selected from the testing set. + --infer_testing_set) shift; infer_testing_set=$1 ; shift ;; + # [Only for Inference] The text to be synthesized from. It is only used when the inference model is "single". + --infer_text) shift; infer_text=$1 ; shift ;; + # [Only for Inference] The speaker voice to be delivered in the synthesized speech. It is only used when the inference model is "single". + --infer_speaker_name) shift; infer_speaker_name=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu /home/pai/envs/amphion/bin/python "${work_dir}"/bins/tts/preprocess.py \ + --config=$exp_config \ + --num_workers=4 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/tts/train.py \ + --config $exp_config \ + --exp_name $exp_name \ + --log_level debug +fi + +######## Inference ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$expt_dir/result" + fi + + if [ -z "$infer_mode" ]; then + echo "[Error] Please specify the inference mode, e.g., "batch", "single"" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_dataset" ]; then + echo "[Error] Please specify the dataset used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_testing_set" ]; then + echo "[Error] Please specify the testing set used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "single" ] && [ -z "$infer_text" ]; then + echo "[Error] Please specify the text to be synthesized when the inference mode is single" + exit 1 + fi + + if [ "$infer_mode" = "single" ]; then + echo 'Text: ' ${infer_text} + infer_dataset=None + infer_testing_set=None + elif [ "$infer_mode" = "batch" ]; then + infer_text='' + infer_speaker_name=None + fi + + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \ + --config $exp_config \ + --acoustics_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --mode $infer_mode \ + --dataset $infer_dataset \ + --testing_set $infer_testing_set \ + --text "$infer_text" \ + --speaker_name $infer_speaker_name \ + --log_level debug + + + +fi diff --git a/egs/tts/vits_hifitts_5k/README.md b/egs/tts/vits_hifitts_5k/README.md new file mode 100644 index 0000000000000000000000000000000000000000..40be829478b0e81a7213d383a189bc159f7d8d7d --- /dev/null +++ b/egs/tts/vits_hifitts_5k/README.md @@ -0,0 +1,137 @@ + +# vits_hifitts_1w Recipe + +In this recipe, we will show how to train [vits_hifitts_1w](https://arxiv.org/abs/2106.06103) using Amphion's infrastructure. vits_hifitts_1w is an end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning. + +There are four stages in total: + +1. Data preparation +2. Features extraction +3. Training +4. Inference + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## 1. Data Preparation + +### Dataset Download +You can use the commonly used TTS dataset to train TTS model, e.g., LJSpeech, VCTK, LibriTTS, etc. We strongly recommend you use LJSpeech to train TTS model for the first time. How to download dataset is detailed [here](../../datasets/README.md). + +### Configuration + +After downloading the dataset, you can set the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json + "dataset": [ + "LJSpeech", + ], + "dataset_path": { + // TODO: Fill in your dataset path + "LJSpeech": "[LJSpeech dataset path]", + }, +``` + +## 2. Features Extraction + +### Configuration + +Specify the `processed_dir` and the `log_dir` and for saving the processed data and the checkpoints in `exp_config.json`: + +```json + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "ckpts/tts", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`): + +```bash +sh egs/tts/vits_hifitts_5k/run.sh --stage 1 +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. + +## 3. Training + +### Configuration + +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines. + +``` +"train": { + "batch_size": 16, + } +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/tts/[YourExptName]`. + +```bash +sh egs/tts/vits_hifitts_5k/run.sh --stage 2 --name [YourExptName] +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. + + +## 4. Inference + +### Configuration + +For inference, you need to specify the following configurations when running `run.sh`: + + +| Parameters | Description | Example | +| --------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `--infer_expt_dir` | The experimental directory which contains `checkpoint` | `Amphion/ckpts/tts/[YourExptName]` | +| `--infer_output_dir` | The output directory to save inferred audios. | `Amphion/ckpts/tts/[YourExptName]/result` | +| `--infer_mode` | The inference mode, e.g., "`single`", "`batch`". | "`single`" to generate a clip of speech, "`batch`" to generate a batch of speech at a time. | +| `--infer_dataset` | The dataset used for inference. | For LJSpeech dataset, the inference dataset would be `LJSpeech`. | +| `--infer_testing_set` | The subset of the inference dataset used for inference, e.g., train, test, golden_test | For LJSpeech dataset, the testing set would be  "`test`" split from LJSpeech at the feature extraction, or "`golden_test`" cherry-picked from test set as template testing set. | +| `--infer_text` | The text to be synthesized. | "`This is a clip of generated speech with the given text from a TTS model.`" | + +### Run +For example, if you want to generate speech of all testing set split from LJSpeech, just run: + +```bash +sh egs/tts/vits_hifitts_1w/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \ + --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \ + --infer_mode "batch" \ + --infer_dataset "LJSpeech" \ + --infer_testing_set "test" +``` + +Or, if you want to generate a single clip of speech from a given text, just run: + +```bash +sh egs/tts/vits_hifitts_1w/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \ + --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \ + --infer_mode "single" \ + --infer_text "This is a clip of generated speech with the given text from a TTS model." +``` + +sh egs/tts/vits_hifitts_5k/run.sh --stage 3 --gpu "0" --infer_expt_dir /mnt/workspace/tzeying/data/vits_on_libritts_hifitts/logs/HifiTTS_5k --infer_output_dir  /mnt/workspace/tzeying/inf_hifitts_5k_80k/resultspk_92 --infer_mode "single" --infer_text "This is a clip of generated speech with the given text from a TTS model."  --infer_speaker_name "hifitts_92" + +We will release a pre-trained vits_hifitts_1w model trained on LJSpeech. So you can download the pre-trained model and generate speech following the above inference instruction. + + +```bibtex +@inproceedings{kim2021conditional, + title={Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech}, + author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee}, + booktitle={International Conference on Machine Learning}, + pages={5530--5540}, + year={2021}, +} +``` \ No newline at end of file diff --git a/egs/tts/vits_hifitts_5k/Untitled.ipynb b/egs/tts/vits_hifitts_5k/Untitled.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..363fcab7ed6e9634e198cf5555ceb88932c9a245 --- /dev/null +++ b/egs/tts/vits_hifitts_5k/Untitled.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/egs/tts/vits_hifitts_5k/exp_config.json b/egs/tts/vits_hifitts_5k/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..c38705571d68fce0add4a58ba989428092007f98 --- /dev/null +++ b/egs/tts/vits_hifitts_5k/exp_config.json @@ -0,0 +1,31 @@ +{ + "base_config": "config/vits.json", + "model_type": "VITS", + "dataset": [ + "hifitts" + ], + "dataset_path": { + // TODO: Fill in your dataset path + "hifitts": "/mnt/workspace/xueliumeng/data/hifitts/hi_fi_tts_v0" + }, + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "/mnt/workspace/tzeying/data/vits_on_libritts_hifitts/logs", + "preprocess": { + "extract_audio": true, + "use_phone": true, + // linguistic features + "extract_phone": true, + "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)" + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "/mnt/workspace/tzeying/data/vits_on_libritts_hifitts/processed_data", + "sample_rate": 24000, + "train_file": "train_5k.json", + "valid_file": "valid.json", // validattion set + "use_spkid": true, // True: use speaker id for multi-speaker dataset + }, + "train": { + "batch_size": 16, + "multi_speaker_training": true, // True: train multi-speaker model; False: training single-speaker model; + //"n_speakers": 2500, // number of speakers, while be automatically set if n_speakers is 0 and multi_speaker_training is true + } +} \ No newline at end of file diff --git a/egs/tts/vits_hifitts_5k/run.sh b/egs/tts/vits_hifitts_5k/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..f592aa7015b27aedf5e2720ac4158379ea63f62b --- /dev/null +++ b/egs/tts/vits_hifitts_5k/run.sh @@ -0,0 +1,157 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +cd $work_dir/modules/monotonic_align +mkdir -p monotonic_align +python setup.py build_ext --inplace +cd $work_dir + +######## Parse the Given Parameters from the Commond ########### +# options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir:,name:,stage: -- "$@") +options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_mode:,infer_dataset:,infer_testing_set:,infer_text:,infer_speaker_name:,name:,stage: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + # [Only for Inference] The inference mode. It can be "batch" to generate speech by batch, or "single" to generage a single clip of speech. + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inference dataset. It is only used when the inference model is "batch". + --infer_dataset) shift; infer_dataset=$1 ; shift ;; + # [Only for Inference] The inference testing set. It is only used when the inference model is "batch". It can be "test" set split from the dataset, or "golden_test" carefully selected from the testing set. + --infer_testing_set) shift; infer_testing_set=$1 ; shift ;; + # [Only for Inference] The text to be synthesized from. It is only used when the inference model is "single". + --infer_text) shift; infer_text=$1 ; shift ;; + # [Only for Inference] The speaker voice to be delivered in the synthesized speech. It is only used when the inference model is "single". + --infer_speaker_name) shift; infer_speaker_name=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu /home/pai/envs/amphion/bin/python "${work_dir}"/bins/tts/preprocess.py \ + --config=$exp_config \ + --num_workers=4 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/tts/train.py \ + --config $exp_config \ + --exp_name $exp_name \ + --log_level debug +fi + +######## Inference ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$expt_dir/result" + fi + + if [ -z "$infer_mode" ]; then + echo "[Error] Please specify the inference mode, e.g., "batch", "single"" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_dataset" ]; then + echo "[Error] Please specify the dataset used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_testing_set" ]; then + echo "[Error] Please specify the testing set used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "single" ] && [ -z "$infer_text" ]; then + echo "[Error] Please specify the text to be synthesized when the inference mode is single" + exit 1 + fi + + if [ "$infer_mode" = "single" ]; then + echo 'Text: ' ${infer_text} + infer_dataset=None + infer_testing_set=None + elif [ "$infer_mode" = "batch" ]; then + infer_text='' + infer_speaker_name=None + fi + + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \ + --config $exp_config \ + --acoustics_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --mode $infer_mode \ + --dataset $infer_dataset \ + --testing_set $infer_testing_set \ + --text "$infer_text" \ + --speaker_name $infer_speaker_name \ + --log_level debug + + # CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \ + # --config $exp_config \ + # --acoustics_dir $infer_expt_dir \ + # --output_dir $infer_output_dir \ + # --mode $infer_mode \ + # --dataset $infer_dataset \ + # --test_list_file $infer_testing_set \ + # --text "$infer_text" \ + # --speaker_name $infer_speaker_name \ + # --log_level debug + + + +fi diff --git a/egs/tts/vits_hifitts_libritts/README.md b/egs/tts/vits_hifitts_libritts/README.md new file mode 100644 index 0000000000000000000000000000000000000000..7366bea12164f3510e11c2910711a430b8f693af --- /dev/null +++ b/egs/tts/vits_hifitts_libritts/README.md @@ -0,0 +1,135 @@ + +# vits_hifitts_libritts Recipe + +In this recipe, we will show how to train [vits_hifitts_libritts](https://arxiv.org/abs/2106.06103) using Amphion's infrastructure. vits_hifitts_libritts is an end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning. + +There are four stages in total: + +1. Data preparation +2. Features extraction +3. Training +4. Inference + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## 1. Data Preparation + +### Dataset Download +You can use the commonly used TTS dataset to train TTS model, e.g., LJSpeech, VCTK, LibriTTS, etc. We strongly recommend you use LJSpeech to train TTS model for the first time. How to download dataset is detailed [here](../../datasets/README.md). + +### Configuration + +After downloading the dataset, you can set the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json + "dataset": [ + "LJSpeech", + ], + "dataset_path": { + // TODO: Fill in your dataset path + "LJSpeech": "[LJSpeech dataset path]", + }, +``` + +## 2. Features Extraction + +### Configuration + +Specify the `processed_dir` and the `log_dir` and for saving the processed data and the checkpoints in `exp_config.json`: + +```json + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "ckpts/tts", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`): + +```bash +sh egs/tts/vits_hifitts_libritts/run.sh --stage 1 +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. + +## 3. Training + +### Configuration + +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines. + +``` +"train": { + "batch_size": 16, + } +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/tts/[YourExptName]`. + +```bash +sh egs/tts/vits_hifitts_libritts/run.sh --stage 2 --name [YourExptName] +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. + + +## 4. Inference + +### Configuration + +For inference, you need to specify the following configurations when running `run.sh`: + + +| Parameters | Description | Example | +| --------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `--infer_expt_dir` | The experimental directory which contains `checkpoint` | `Amphion/ckpts/tts/[YourExptName]` | +| `--infer_output_dir` | The output directory to save inferred audios. | `Amphion/ckpts/tts/[YourExptName]/result` | +| `--infer_mode` | The inference mode, e.g., "`single`", "`batch`". | "`single`" to generate a clip of speech, "`batch`" to generate a batch of speech at a time. | +| `--infer_dataset` | The dataset used for inference. | For LJSpeech dataset, the inference dataset would be `LJSpeech`. | +| `--infer_testing_set` | The subset of the inference dataset used for inference, e.g., train, test, golden_test | For LJSpeech dataset, the testing set would be  "`test`" split from LJSpeech at the feature extraction, or "`golden_test`" cherry-picked from test set as template testing set. | +| `--infer_text` | The text to be synthesized. | "`This is a clip of generated speech with the given text from a TTS model.`" | + +### Run +For example, if you want to generate speech of all testing set split from LJSpeech, just run: + +```bash +sh egs/tts/vits_hifitts_libritts/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \ + --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \ + --infer_mode "batch" \ + --infer_dataset "LJSpeech" \ + --infer_testing_set "test" +``` + +Or, if you want to generate a single clip of speech from a given text, just run: + +```bash +sh egs/tts/vits_hifitts_libritts/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \ + --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \ + --infer_mode "single" \ + --infer_text "This is a clip of generated speech with the given text from a TTS model." +``` + +We will release a pre-trained vits_hifitts_libritts model trained on LJSpeech. So you can download the pre-trained model and generate speech following the above inference instruction. + + +```bibtex +@inproceedings{kim2021conditional, + title={Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech}, + author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee}, + booktitle={International Conference on Machine Learning}, + pages={5530--5540}, + year={2021}, +} +``` \ No newline at end of file diff --git a/egs/tts/vits_hifitts_libritts/exp_config.json b/egs/tts/vits_hifitts_libritts/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..33260c4170ebe39e32e37f606151c61b8fadc221 --- /dev/null +++ b/egs/tts/vits_hifitts_libritts/exp_config.json @@ -0,0 +1,33 @@ +{ + "base_config": "config/vits.json", + "model_type": "VITS", + "dataset": [ + "hifitts", + "libritts" + ], + "dataset_path": { + // TODO: Fill in your dataset path + "hifitts": "/mnt/workspace/xueliumeng/data/hifitts/hi_fi_tts_v0", + "libritts": "/mnt/workspace/xueliumeng/data/libritts/raw/LibriTTS" + }, + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "/mnt/workspace/xueliumeng/data/vits_on_libritts_hifitts/logs", + "preprocess": { + "extract_audio": true, + "use_phone": true, + // linguistic features + "extract_phone": true, + "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)" + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "/mnt/workspace/xueliumeng/data/vits_on_libritts_hifitts/processed_data", + "sample_rate": 24000, + "train_file": "train_all.json", + "valid_file": "valid.json", // validattion set + "use_spkid": true, // True: use speaker id for multi-speaker dataset + }, + "train": { + "batch_size": 16, + "multi_speaker_training": true, // True: train multi-speaker model; False: training single-speaker model; + "n_speakers": 2500, // number of speakers, while be automatically set if n_speakers is 0 and multi_speaker_training is true + } +} \ No newline at end of file diff --git a/egs/tts/vits_hifitts_libritts/run.sh b/egs/tts/vits_hifitts_libritts/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..5bb4bf76d2e530a0000eeb6c6f87ec8c60d90079 --- /dev/null +++ b/egs/tts/vits_hifitts_libritts/run.sh @@ -0,0 +1,146 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +cd $work_dir/modules/monotonic_align +mkdir -p monotonic_align +python setup.py build_ext --inplace +cd $work_dir + +######## Parse the Given Parameters from the Commond ########### +# options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir:,name:,stage: -- "$@") +options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_mode:,infer_dataset:,infer_testing_set:,infer_text:,infer_speaker_name:,name:,stage: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + # [Only for Inference] The inference mode. It can be "batch" to generate speech by batch, or "single" to generage a single clip of speech. + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inference dataset. It is only used when the inference model is "batch". + --infer_dataset) shift; infer_dataset=$1 ; shift ;; + # [Only for Inference] The inference testing set. It is only used when the inference model is "batch". It can be "test" set split from the dataset, or "golden_test" carefully selected from the testing set. + --infer_testing_set) shift; infer_testing_set=$1 ; shift ;; + # [Only for Inference] The text to be synthesized from. It is only used when the inference model is "single". + --infer_text) shift; infer_text=$1 ; shift ;; + # [Only for Inference] The speaker voice to be delivered in the synthesized speech. It is only used when the inference model is "single". + --infer_speaker_name) shift; infer_speaker_name=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu /home/pai/envs/amphion/bin/python "${work_dir}"/bins/tts/preprocess.py \ + --config=$exp_config \ + --num_workers=4 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/tts/train.py \ + --config $exp_config \ + --exp_name $exp_name \ + --log_level debug +fi + +######## Inference ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$expt_dir/result" + fi + + if [ -z "$infer_mode" ]; then + echo "[Error] Please specify the inference mode, e.g., "batch", "single"" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_dataset" ]; then + echo "[Error] Please specify the dataset used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_testing_set" ]; then + echo "[Error] Please specify the testing set used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "single" ] && [ -z "$infer_text" ]; then + echo "[Error] Please specify the text to be synthesized when the inference mode is single" + exit 1 + fi + + if [ "$infer_mode" = "single" ]; then + echo 'Text: ' ${infer_text} + infer_dataset=None + infer_testing_set=None + elif [ "$infer_mode" = "batch" ]; then + infer_text='' + infer_speaker_name=None + fi + + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \ + --config $exp_config \ + --acoustics_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --mode $infer_mode \ + --dataset $infer_dataset \ + --testing_set $infer_testing_set \ + --text "$infer_text" \ + --speaker_name $infer_speaker_name \ + --log_level debug + + + +fi diff --git a/egs/tts/vits_libritts/README.md b/egs/tts/vits_libritts/README.md new file mode 100644 index 0000000000000000000000000000000000000000..80800e51afb057665c441aa3ff2cc1f476209b78 --- /dev/null +++ b/egs/tts/vits_libritts/README.md @@ -0,0 +1,135 @@ + +# vits_libritts Recipe + +In this recipe, we will show how to train [vits_libritts](https://arxiv.org/abs/2106.06103) using Amphion's infrastructure. vits_libritts is an end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning. + +There are four stages in total: + +1. Data preparation +2. Features extraction +3. Training +4. Inference + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## 1. Data Preparation + +### Dataset Download +You can use the commonly used TTS dataset to train TTS model, e.g., LJSpeech, VCTK, LibriTTS, etc. We strongly recommend you use LJSpeech to train TTS model for the first time. How to download dataset is detailed [here](../../datasets/README.md). + +### Configuration + +After downloading the dataset, you can set the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json + "dataset": [ + "LJSpeech", + ], + "dataset_path": { + // TODO: Fill in your dataset path + "LJSpeech": "[LJSpeech dataset path]", + }, +``` + +## 2. Features Extraction + +### Configuration + +Specify the `processed_dir` and the `log_dir` and for saving the processed data and the checkpoints in `exp_config.json`: + +```json + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "ckpts/tts", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`): + +```bash +sh egs/tts/vits_libritts/run.sh --stage 1 +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. + +## 3. Training + +### Configuration + +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines. + +``` +"train": { + "batch_size": 16, + } +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/tts/[YourExptName]`. + +```bash +sh egs/tts/vits_libritts/run.sh --stage 2 --name [YourExptName] +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. + + +## 4. Inference + +### Configuration + +For inference, you need to specify the following configurations when running `run.sh`: + + +| Parameters | Description | Example | +| --------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `--infer_expt_dir` | The experimental directory which contains `checkpoint` | `Amphion/ckpts/tts/[YourExptName]` | +| `--infer_output_dir` | The output directory to save inferred audios. | `Amphion/ckpts/tts/[YourExptName]/result` | +| `--infer_mode` | The inference mode, e.g., "`single`", "`batch`". | "`single`" to generate a clip of speech, "`batch`" to generate a batch of speech at a time. | +| `--infer_dataset` | The dataset used for inference. | For LJSpeech dataset, the inference dataset would be `LJSpeech`. | +| `--infer_testing_set` | The subset of the inference dataset used for inference, e.g., train, test, golden_test | For LJSpeech dataset, the testing set would be  "`test`" split from LJSpeech at the feature extraction, or "`golden_test`" cherry-picked from test set as template testing set. | +| `--infer_text` | The text to be synthesized. | "`This is a clip of generated speech with the given text from a TTS model.`" | + +### Run +For example, if you want to generate speech of all testing set split from LJSpeech, just run: + +```bash +sh egs/tts/vits_libritts/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \ + --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \ + --infer_mode "batch" \ + --infer_dataset "LJSpeech" \ + --infer_testing_set "test" +``` + +Or, if you want to generate a single clip of speech from a given text, just run: + +```bash +sh egs/tts/vits_libritts/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \ + --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \ + --infer_mode "single" \ + --infer_text "This is a clip of generated speech with the given text from a TTS model." +``` + +We will release a pre-trained vits_libritts model trained on LJSpeech. So you can download the pre-trained model and generate speech following the above inference instruction. + + +```bibtex +@inproceedings{kim2021conditional, + title={Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech}, + author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee}, + booktitle={International Conference on Machine Learning}, + pages={5530--5540}, + year={2021}, +} +``` \ No newline at end of file diff --git a/egs/tts/vits_libritts/exp_config.json b/egs/tts/vits_libritts/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..202a225d76affee2cca81a9482c04fd359150870 --- /dev/null +++ b/egs/tts/vits_libritts/exp_config.json @@ -0,0 +1,31 @@ +{ + "base_config": "config/vits.json", + "model_type": "VITS", + "dataset": [ + "libritts" + ], + "dataset_path": { + // TODO: Fill in your dataset path + "libritts": "/mnt/workspace/xueliumeng/data/libritts/raw/LibriTTS" + }, + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "/mnt/workspace/tzeying/data/vits_on_libritts_hifitts/logs", + "preprocess": { + "extract_audio": true, + "use_phone": true, + // linguistic features + "extract_phone": true, + "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)" + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "/mnt/workspace/xueliumeng/data/vits_on_libritts_hifitts/processed_data/", + "sample_rate": 24000, + "train_file": "train_all.json", + "valid_file": "valid.json", // validattion set + "use_spkid": true, // True: use speaker id for multi-speaker dataset + }, + "train": { + "batch_size": 16, + "multi_speaker_training": true, // True: train multi-speaker model; False: training single-speaker model; + //"n_speakers": 2500, // number of speakers, while be automatically set if n_speakers is 0 and multi_speaker_training is true + } +} \ No newline at end of file diff --git a/egs/tts/vits_libritts/run.sh b/egs/tts/vits_libritts/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..6ccde390d237491171d88cb4a8f99b1b06cf0b0f --- /dev/null +++ b/egs/tts/vits_libritts/run.sh @@ -0,0 +1,146 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +cd $work_dir/modules/monotonic_align +mkdir -p monotonic_align +python setup.py build_ext --inplace +cd $work_dir + +######## Parse the Given Parameters from the Commond ########### +# options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir:,name:,stage: -- "$@") +options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_mode:,infer_dataset:,infer_testing_set:,infer_text:,infer_speaker_name:,name:,stage: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + # [Only for Inference] The inference mode. It can be "batch" to generate speech by batch, or "single" to generage a single clip of speech. + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inference dataset. It is only used when the inference model is "batch". + --infer_dataset) shift; infer_dataset=$1 ; shift ;; + # [Only for Inference] The inference testing set. It is only used when the inference model is "batch". It can be "test" set split from the dataset, or "golden_test" carefully selected from the testing set. + --infer_testing_set) shift; infer_testing_set=$1 ; shift ;; + # [Only for Inference] The text to be synthesized from. It is only used when the inference model is "single". + --infer_text) shift; infer_text=$1 ; shift ;; + # [Only for Inference] The speaker voice to be delivered in the synthesized speech. It is only used when the inference model is "single". + --infer_speaker_name) shift; infer_speaker_name=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu /home/pai/envs/amphion/bin/python "${work_dir}"/bins/tts/preprocess.py \ + --config=$exp_config \ + --num_workers=50 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/tts/train.py \ + --config $exp_config \ + --exp_name $exp_name \ + --log_level debug +fi + +######## Inference ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$expt_dir/result" + fi + + if [ -z "$infer_mode" ]; then + echo "[Error] Please specify the inference mode, e.g., "batch", "single"" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_dataset" ]; then + echo "[Error] Please specify the dataset used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_testing_set" ]; then + echo "[Error] Please specify the testing set used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "single" ] && [ -z "$infer_text" ]; then + echo "[Error] Please specify the text to be synthesized when the inference mode is single" + exit 1 + fi + + if [ "$infer_mode" = "single" ]; then + echo 'Text: ' ${infer_text} + infer_dataset=None + infer_testing_set=None + elif [ "$infer_mode" = "batch" ]; then + infer_text='' + infer_speaker_name=None + fi + + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \ + --config $exp_config \ + --acoustics_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --mode $infer_mode \ + --dataset $infer_dataset \ + --testing_set $infer_testing_set \ + --text "$infer_text" \ + --speaker_name $infer_speaker_name \ + --log_level debug + + + +fi diff --git a/egs/tts/vits_libritts_1w/README.md b/egs/tts/vits_libritts_1w/README.md new file mode 100644 index 0000000000000000000000000000000000000000..28316e6f2ea7d1a4d99bf1b24e0597f8a465396c --- /dev/null +++ b/egs/tts/vits_libritts_1w/README.md @@ -0,0 +1,138 @@ + +# vits_libritts_1w Recipe + +In this recipe, we will show how to train [vits_libritts_1w](https://arxiv.org/abs/2106.06103) using Amphion's infrastructure. vits_libritts_1w is an end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning. + +There are four stages in total: + +1. Data preparation +2. Features extraction +3. Training +4. Inference + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## 1. Data Preparation + +### Dataset Download +You can use the commonly used TTS dataset to train TTS model, e.g., LJSpeech, VCTK, LibriTTS, etc. We strongly recommend you use LJSpeech to train TTS model for the first time. How to download dataset is detailed [here](../../datasets/README.md). + +### Configuration + +After downloading the dataset, you can set the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json + "dataset": [ + "LJSpeech", + ], + "dataset_path": { + // TODO: Fill in your dataset path + "LJSpeech": "[LJSpeech dataset path]", + }, +``` + +## 2. Features Extraction + +### Configuration + +Specify the `processed_dir` and the `log_dir` and for saving the processed data and the checkpoints in `exp_config.json`: + +```json + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "ckpts/tts", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`): + +```bash +sh egs/tts/vits_libritts_1w/run.sh --stage 1 +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. + +## 3. Training + +### Configuration + +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines. + +``` +"train": { + "batch_size": 16, + } +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/tts/[YourExptName]`. + +```bash +sh egs/tts/vits_libritts_1w/run.sh --stage 2 --name [YourExptName] +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. + + +## 4. Inference + +### Configuration + +For inference, you need to specify the following configurations when running `run.sh`: + + +| Parameters | Description | Example | +| --------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `--infer_expt_dir` | The experimental directory which contains `checkpoint` | `Amphion/ckpts/tts/[YourExptName]` | +| `--infer_output_dir` | The output directory to save inferred audios. | `Amphion/ckpts/tts/[YourExptName]/result` | +| `--infer_mode` | The inference mode, e.g., "`single`", "`batch`". | "`single`" to generate a clip of speech, "`batch`" to generate a batch of speech at a time. | +| `--infer_dataset` | The dataset used for inference. | For LJSpeech dataset, the inference dataset would be `LJSpeech`. | +| `--infer_testing_set` | The subset of the inference dataset used for inference, e.g., train, test, golden_test | For LJSpeech dataset, the testing set would be  "`test`" split from LJSpeech at the feature extraction, or "`golden_test`" cherry-picked from test set as template testing set. | +| `--infer_text` | The text to be synthesized. | "`This is a clip of generated speech with the given text from a TTS model.`" | + +### Run +For example, if you want to generate speech of all testing set split from LJSpeech, just run: + +```bash +sh egs/tts/vits_libritts_1w/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w \ + --infer_output_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w/result \ + --infer_mode "batch" \ + --infer_dataset "LJSpeech" \ + --infer_testing_set "test" +``` + +Or, if you want to generate a single clip of speech from a given text, just run: + +```bash +sh egs/tts/vits_libritts_1w/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w \ + --infer_output_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w/result \ + --infer_mode "single" \ + --infer_text "This is a clip of generated speech with the given text from a TTS model." + + +sh egs/tts/vits_libritts_1w/run.sh --stage 3 --gpu "0" --infer_expt_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w --infer_output_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w/result --infer_mode "single" --infer_text "This is a clip of generated speech with the given text from a TTS model." +``` + +We will release a pre-trained vits_libritts_1w model trained on LJSpeech. So you can download the pre-trained model and generate speech following the above inference instruction. + + +```bibtex +@inproceedings{kim2021conditional, + title={Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech}, + author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee}, + booktitle={International Conference on Machine Learning}, + pages={5530--5540}, + year={2021}, +} +``` \ No newline at end of file diff --git a/egs/tts/vits_libritts_1w/exp_config.json b/egs/tts/vits_libritts_1w/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..769f48149ed20f365f33117b8f489b87dcd34b06 --- /dev/null +++ b/egs/tts/vits_libritts_1w/exp_config.json @@ -0,0 +1,31 @@ +{ + "base_config": "config/vits.json", + "model_type": "VITS", + "dataset": [ + "libritts" + ], + "dataset_path": { + // TODO: Fill in your dataset path + "libritts": "/mnt/workspace/xueliumeng/data/libritts/raw/LibriTTS" + }, + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "/mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs", + "preprocess": { + "extract_audio": true, + "use_phone": true, + // linguistic features + "extract_phone": true, + "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)" + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "/mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/processed_data", + "sample_rate": 24000, + "train_file": "train_seen_spk_5k_cleaned.json", + "valid_file": "valid_100.json", // validattion set + "use_spkid": true, // True: use speaker id for multi-speaker dataset + }, + "train": { + "batch_size": 16, + "multi_speaker_training": true, // True: train multi-speaker model; False: training single-speaker model; + // "n_speakers": 2500, // number of speakers, while be automatically set if n_speakers is 0 and multi_speaker_training is true + } +} \ No newline at end of file diff --git a/egs/tts/vits_libritts_1w/run.sh b/egs/tts/vits_libritts_1w/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..5bb4bf76d2e530a0000eeb6c6f87ec8c60d90079 --- /dev/null +++ b/egs/tts/vits_libritts_1w/run.sh @@ -0,0 +1,146 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +cd $work_dir/modules/monotonic_align +mkdir -p monotonic_align +python setup.py build_ext --inplace +cd $work_dir + +######## Parse the Given Parameters from the Commond ########### +# options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir:,name:,stage: -- "$@") +options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_mode:,infer_dataset:,infer_testing_set:,infer_text:,infer_speaker_name:,name:,stage: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + # [Only for Inference] The inference mode. It can be "batch" to generate speech by batch, or "single" to generage a single clip of speech. + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inference dataset. It is only used when the inference model is "batch". + --infer_dataset) shift; infer_dataset=$1 ; shift ;; + # [Only for Inference] The inference testing set. It is only used when the inference model is "batch". It can be "test" set split from the dataset, or "golden_test" carefully selected from the testing set. + --infer_testing_set) shift; infer_testing_set=$1 ; shift ;; + # [Only for Inference] The text to be synthesized from. It is only used when the inference model is "single". + --infer_text) shift; infer_text=$1 ; shift ;; + # [Only for Inference] The speaker voice to be delivered in the synthesized speech. It is only used when the inference model is "single". + --infer_speaker_name) shift; infer_speaker_name=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu /home/pai/envs/amphion/bin/python "${work_dir}"/bins/tts/preprocess.py \ + --config=$exp_config \ + --num_workers=4 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/tts/train.py \ + --config $exp_config \ + --exp_name $exp_name \ + --log_level debug +fi + +######## Inference ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$expt_dir/result" + fi + + if [ -z "$infer_mode" ]; then + echo "[Error] Please specify the inference mode, e.g., "batch", "single"" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_dataset" ]; then + echo "[Error] Please specify the dataset used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_testing_set" ]; then + echo "[Error] Please specify the testing set used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "single" ] && [ -z "$infer_text" ]; then + echo "[Error] Please specify the text to be synthesized when the inference mode is single" + exit 1 + fi + + if [ "$infer_mode" = "single" ]; then + echo 'Text: ' ${infer_text} + infer_dataset=None + infer_testing_set=None + elif [ "$infer_mode" = "batch" ]; then + infer_text='' + infer_speaker_name=None + fi + + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \ + --config $exp_config \ + --acoustics_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --mode $infer_mode \ + --dataset $infer_dataset \ + --testing_set $infer_testing_set \ + --text "$infer_text" \ + --speaker_name $infer_speaker_name \ + --log_level debug + + + +fi diff --git a/egs/tts/vits_libritts_5k/README.md b/egs/tts/vits_libritts_5k/README.md new file mode 100644 index 0000000000000000000000000000000000000000..28316e6f2ea7d1a4d99bf1b24e0597f8a465396c --- /dev/null +++ b/egs/tts/vits_libritts_5k/README.md @@ -0,0 +1,138 @@ + +# vits_libritts_1w Recipe + +In this recipe, we will show how to train [vits_libritts_1w](https://arxiv.org/abs/2106.06103) using Amphion's infrastructure. vits_libritts_1w is an end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning. + +There are four stages in total: + +1. Data preparation +2. Features extraction +3. Training +4. Inference + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## 1. Data Preparation + +### Dataset Download +You can use the commonly used TTS dataset to train TTS model, e.g., LJSpeech, VCTK, LibriTTS, etc. We strongly recommend you use LJSpeech to train TTS model for the first time. How to download dataset is detailed [here](../../datasets/README.md). + +### Configuration + +After downloading the dataset, you can set the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json + "dataset": [ + "LJSpeech", + ], + "dataset_path": { + // TODO: Fill in your dataset path + "LJSpeech": "[LJSpeech dataset path]", + }, +``` + +## 2. Features Extraction + +### Configuration + +Specify the `processed_dir` and the `log_dir` and for saving the processed data and the checkpoints in `exp_config.json`: + +```json + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "ckpts/tts", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`): + +```bash +sh egs/tts/vits_libritts_1w/run.sh --stage 1 +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. + +## 3. Training + +### Configuration + +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines. + +``` +"train": { + "batch_size": 16, + } +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/tts/[YourExptName]`. + +```bash +sh egs/tts/vits_libritts_1w/run.sh --stage 2 --name [YourExptName] +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. + + +## 4. Inference + +### Configuration + +For inference, you need to specify the following configurations when running `run.sh`: + + +| Parameters | Description | Example | +| --------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `--infer_expt_dir` | The experimental directory which contains `checkpoint` | `Amphion/ckpts/tts/[YourExptName]` | +| `--infer_output_dir` | The output directory to save inferred audios. | `Amphion/ckpts/tts/[YourExptName]/result` | +| `--infer_mode` | The inference mode, e.g., "`single`", "`batch`". | "`single`" to generate a clip of speech, "`batch`" to generate a batch of speech at a time. | +| `--infer_dataset` | The dataset used for inference. | For LJSpeech dataset, the inference dataset would be `LJSpeech`. | +| `--infer_testing_set` | The subset of the inference dataset used for inference, e.g., train, test, golden_test | For LJSpeech dataset, the testing set would be  "`test`" split from LJSpeech at the feature extraction, or "`golden_test`" cherry-picked from test set as template testing set. | +| `--infer_text` | The text to be synthesized. | "`This is a clip of generated speech with the given text from a TTS model.`" | + +### Run +For example, if you want to generate speech of all testing set split from LJSpeech, just run: + +```bash +sh egs/tts/vits_libritts_1w/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w \ + --infer_output_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w/result \ + --infer_mode "batch" \ + --infer_dataset "LJSpeech" \ + --infer_testing_set "test" +``` + +Or, if you want to generate a single clip of speech from a given text, just run: + +```bash +sh egs/tts/vits_libritts_1w/run.sh --stage 3 --gpu "0" \ + --infer_expt_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w \ + --infer_output_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w/result \ + --infer_mode "single" \ + --infer_text "This is a clip of generated speech with the given text from a TTS model." + + +sh egs/tts/vits_libritts_1w/run.sh --stage 3 --gpu "0" --infer_expt_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w --infer_output_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w/result --infer_mode "single" --infer_text "This is a clip of generated speech with the given text from a TTS model." +``` + +We will release a pre-trained vits_libritts_1w model trained on LJSpeech. So you can download the pre-trained model and generate speech following the above inference instruction. + + +```bibtex +@inproceedings{kim2021conditional, + title={Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech}, + author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee}, + booktitle={International Conference on Machine Learning}, + pages={5530--5540}, + year={2021}, +} +``` \ No newline at end of file diff --git a/egs/tts/vits_libritts_5k/exp_config.json b/egs/tts/vits_libritts_5k/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..d99d65a454eda5b7a4a7320f7a89f325fd203265 --- /dev/null +++ b/egs/tts/vits_libritts_5k/exp_config.json @@ -0,0 +1,31 @@ +{ + "base_config": "config/vits.json", + "model_type": "VITS", + "dataset": [ + "libritts" + ], + "dataset_path": { + // TODO: Fill in your dataset path + "libritts": "/mnt/workspace/xueliumeng/data/libritts/raw/LibriTTS" + }, + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts" + "log_dir": "/mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs", + "preprocess": { + "extract_audio": true, + "use_phone": true, + // linguistic features + "extract_phone": true, + "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)" + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "/mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/processed_data", + "sample_rate": 24000, + "train_file": "train_seen_spk_100.json", + "valid_file": "valid.json", // validattion set + "use_spkid": true, // True: use speaker id for multi-speaker dataset + }, + "train": { + "batch_size": 16, + "multi_speaker_training": true, // True: train multi-speaker model; False: training single-speaker model; + // "n_speakers": 2500, // number of speakers, while be automatically set if n_speakers is 0 and multi_speaker_training is true + } +} \ No newline at end of file diff --git a/egs/tts/vits_libritts_5k/run.sh b/egs/tts/vits_libritts_5k/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..5bb4bf76d2e530a0000eeb6c6f87ec8c60d90079 --- /dev/null +++ b/egs/tts/vits_libritts_5k/run.sh @@ -0,0 +1,146 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +cd $work_dir/modules/monotonic_align +mkdir -p monotonic_align +python setup.py build_ext --inplace +cd $work_dir + +######## Parse the Given Parameters from the Commond ########### +# options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir:,name:,stage: -- "$@") +options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_mode:,infer_dataset:,infer_testing_set:,infer_text:,infer_speaker_name:,name:,stage: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + # [Only for Inference] The inference mode. It can be "batch" to generate speech by batch, or "single" to generage a single clip of speech. + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inference dataset. It is only used when the inference model is "batch". + --infer_dataset) shift; infer_dataset=$1 ; shift ;; + # [Only for Inference] The inference testing set. It is only used when the inference model is "batch". It can be "test" set split from the dataset, or "golden_test" carefully selected from the testing set. + --infer_testing_set) shift; infer_testing_set=$1 ; shift ;; + # [Only for Inference] The text to be synthesized from. It is only used when the inference model is "single". + --infer_text) shift; infer_text=$1 ; shift ;; + # [Only for Inference] The speaker voice to be delivered in the synthesized speech. It is only used when the inference model is "single". + --infer_speaker_name) shift; infer_speaker_name=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu /home/pai/envs/amphion/bin/python "${work_dir}"/bins/tts/preprocess.py \ + --config=$exp_config \ + --num_workers=4 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/tts/train.py \ + --config $exp_config \ + --exp_name $exp_name \ + --log_level debug +fi + +######## Inference ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$expt_dir/result" + fi + + if [ -z "$infer_mode" ]; then + echo "[Error] Please specify the inference mode, e.g., "batch", "single"" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_dataset" ]; then + echo "[Error] Please specify the dataset used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "batch" ] && [ -z "$infer_testing_set" ]; then + echo "[Error] Please specify the testing set used in inference when the inference mode is batch" + exit 1 + fi + + if [ "$infer_mode" = "single" ] && [ -z "$infer_text" ]; then + echo "[Error] Please specify the text to be synthesized when the inference mode is single" + exit 1 + fi + + if [ "$infer_mode" = "single" ]; then + echo 'Text: ' ${infer_text} + infer_dataset=None + infer_testing_set=None + elif [ "$infer_mode" = "batch" ]; then + infer_text='' + infer_speaker_name=None + fi + + + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \ + --config $exp_config \ + --acoustics_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --mode $infer_mode \ + --dataset $infer_dataset \ + --testing_set $infer_testing_set \ + --text "$infer_text" \ + --speaker_name $infer_speaker_name \ + --log_level debug + + + +fi diff --git a/egs/vocoder/README.md b/egs/vocoder/README.md new file mode 100644 index 0000000000000000000000000000000000000000..213253570b19b5c1d3746ee17911c7eb464ecc73 --- /dev/null +++ b/egs/vocoder/README.md @@ -0,0 +1,23 @@ +# Amphion Vocoder Recipe + +## Quick Start + +We provide a [**beginner recipe**](gan/tfr_enhanced_hifigan/README.md) to demonstrate how to train a high quality HiFi-GAN speech vocoder. Specially, it is also an official implementation of our paper "[Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fidelity Vocoder](https://arxiv.org/abs/2311.14957)". Some demos can be seen [here](https://vocodexelysium.github.io/MS-SB-CQTD/). + +## Supported Models + +Neural vocoder generates audible waveforms from acoustic representations, which is one of the key parts for current audio generation systems. Until now, Amphion has supported various widely-used vocoders according to different vocoder types, including: + +- **GAN-based vocoders**, which we have provided [**a unified recipe**](gan/README.md) : + - [MelGAN](https://arxiv.org/abs/1910.06711) + - [HiFi-GAN](https://arxiv.org/abs/2010.05646) + - [NSF-HiFiGAN](https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts) + - [BigVGAN](https://arxiv.org/abs/2206.04658) + - [APNet](https://arxiv.org/abs/2305.07952) +- **Flow-based vocoders** (👨‍💻 developing): + - [WaveGlow](https://arxiv.org/abs/1811.00002) +- **Diffusion-based vocoders** (👨‍💻 developing): + - [Diffwave](https://arxiv.org/abs/2009.09761) +- **Auto-regressive based vocoders** (👨‍💻 developing): + - [WaveNet](https://arxiv.org/abs/1609.03499) + - [WaveRNN](https://arxiv.org/abs/1802.08435v1) \ No newline at end of file diff --git a/egs/vocoder/diffusion/README.md b/egs/vocoder/diffusion/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/egs/vocoder/diffusion/exp_config_base.json b/egs/vocoder/diffusion/exp_config_base.json new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/egs/vocoder/gan/README.md b/egs/vocoder/gan/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f8b0b352729c1f5a7935a588563d93ae30d1ef52 --- /dev/null +++ b/egs/vocoder/gan/README.md @@ -0,0 +1,224 @@ +# Amphion GAN-based Vocoder Recipe + +## Supported Model Architectures + +GAN-based Vocoder consists of a generator and multiple discriminators, as illustrated below: + +
+
+ +
+
+ +Until now, Amphion GAN-based Vocoder has supported the following generators and discriminators. + +- **Generators** + - [MelGAN](https://arxiv.org/abs/1910.06711) + - [HiFi-GAN](https://arxiv.org/abs/2010.05646) + - [NSF-HiFiGAN](https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts) + - [BigVGAN](https://arxiv.org/abs/2206.04658) + - [APNet](https://arxiv.org/abs/2305.07952) +- **Discriminators** + - [Multi-Scale Discriminator](https://arxiv.org/abs/2010.05646) + - [Multi-Period Discriminator](https://arxiv.org/abs/2010.05646) + - [Multi-Resolution Discriminator](https://arxiv.org/abs/2011.09631) + - [Multi-Scale Short-Time Fourier Transform Discriminator](https://arxiv.org/abs/2210.13438) + - [**Multi-Scale Constant-Q Transfrom Discriminator (ours)**](https://arxiv.org/abs/2311.14957) + +You can use any vocoder architecture with any dataset you want. There are four steps in total: + +1. Data preparation +2. Feature extraction +3. Training +4. Inference + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## 1. Data Preparation + +You can train the vocoder with any datasets. Amphion's supported open-source datasets are detailed [here](../../../datasets/README.md). + +### Configuration + +Specify the dataset path in `exp_config_base.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json +"dataset": [ + "csd", + "kising", + "m4singer", + "nus48e", + "opencpop", + "opensinger", + "opera", + "pjs", + "popbutfy", + "popcs", + "ljspeech", + "vctk", + "libritts", +], +"dataset_path": { + // TODO: Fill in your dataset path + "csd": "[dataset path]", + "kising": "[dataset path]", + "m4singer": "[dataset path]", + "nus48e": "[dataset path]", + "opencpop": "[dataset path]", + "opensinger": "[dataset path]", + "opera": "[dataset path]", + "pjs": "[dataset path]", + "popbutfy": "[dataset path]", + "popcs": "[dataset path]", + "ljspeech": "[dataset path]", + "vctk": "[dataset path]", + "libritts": "[dataset path]", +}, +``` + +### 2. Feature Extraction + +The needed features are speficied in the individual vocoder direction so it doesn't require any modification. + +### Configuration + +Specify the dataset path and the output path for saving the processed data and the training model in `exp_config_base.json`: + +```json + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/vocoder" + "log_dir": "ckpts/vocoder", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`). + +```bash +sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 1 +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. + +## 3. Training + +### Configuration + +We provide the default hyparameters in the `exp_config_base.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines. + +```json +"train": { + "batch_size": 16, + "max_epoch": 1000000, + "save_checkpoint_stride": [20], + "adamw": { + "lr": 2.0e-4, + "adam_b1": 0.8, + "adam_b2": 0.99 + }, + "exponential_lr": { + "lr_decay": 0.999 + }, +} +``` + +You can also choose any amount of prefered discriminators for training in the `exp_config_base.json`. + +```json +"discriminators": [ + "msd", + "mpd", + "msstftd", + "mssbcqtd", +], +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/vocoder/[YourExptName]`. + +```bash +sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 2 --name [YourExptName] +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. + + +## 4. Inference + +### Run + +Run the `run.sh` as the training stage (set `--stage 3`), we provide three different inference modes, including `infer_from_dataset`, `infer_from_feature`, `and infer_from_audio`. + +```bash +sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 3 \ + --infer_mode [Your chosen inference mode] \ + --infer_datasets [Datasets you want to inference, needed when infer_from_dataset] \ + --infer_feature_dir [Your path to your predicted acoustic features, needed when infer_from_feature] \ + --infer_audio_dir [Your path to your audio files, needed when infer_form_audio] \ + --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ + --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ +``` + +#### a. Inference from Dataset + +Run the `run.sh` with specified datasets, here is an example. + +```bash +sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 3 \ + --infer_mode infer_from_dataset \ + --infer_datasets "libritts vctk ljspeech" \ + --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ + --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ +``` + +#### b. Inference from Features + +If you want to inference from your generated acoustic features, you should first prepare your acoustic features into the following structure: + +```plaintext + ┣ {infer_feature_dir} + ┃ ┣ mels + ┃ ┃ ┣ sample1.npy + ┃ ┃ ┣ sample2.npy + ┃ ┣ f0s (required if you use NSF-HiFiGAN) + ┃ ┃ ┣ sample1.npy + ┃ ┃ ┣ sample2.npy +``` + +Then run the `run.sh` with specificed folder direction, here is an example. + +```bash +sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 3 \ + --infer_mode infer_from_feature \ + --infer_feature_dir [Your path to your predicted acoustic features] \ + --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ + --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ +``` + +#### c. Inference from Audios + +If you want to inference from audios for quick analysis synthesis, you should first prepare your audios into the following structure: + +```plaintext + ┣ audios + ┃ ┣ sample1.wav + ┃ ┣ sample2.wav +``` + +Then run the `run.sh` with specificed folder direction, here is an example. + +```bash +sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 3 \ + --infer_mode infer_from_audio \ + --infer_audio_dir [Your path to your audio files] \ + --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ + --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ +``` diff --git a/egs/vocoder/gan/_template/run.sh b/egs/vocoder/gan/_template/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..f50e7fab6f761ba69a5c45131479813b02f4d050 --- /dev/null +++ b/egs/vocoder/gan/_template/run.sh @@ -0,0 +1,143 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Parse the Given Parameters from the Commond ########### +options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Training] Resume configuration + --resume) shift; resume=$1 ; shift ;; + # [Only for Training] The specific checkpoint path that you want to resume from. + --checkpoint) shift; cehckpoint=$1 ; shift ;; + # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. + --resume_type) shift; resume_type=$1 ; shift ;; + + # [Only for Inference] The inference mode + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inferenced datasets + --infer_datasets) shift; infer_datasets=$1 ; shift ;; + # [Only for Inference] The feature dir for inference + --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; + # [Only for Inference] The audio dir for inference + --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ + --config $exp_config \ + --num_workers 8 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + if [ "$resume" = true ]; then + echo "Automatically resume from the experimental dir..." + CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume + else + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --checkpoint "$checkpoint" \ + --resume_type "$resume_type" + fi +fi + +######## Inference/Conversion ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$infer_expt_dir/result" + fi + + if [ $infer_mode = "infer_from_dataset" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --infer_datasets $infer_datasets \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_feature" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --feature_folder $infer_feature_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_audio" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --audio_folder $infer_audio_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + +fi \ No newline at end of file diff --git a/egs/vocoder/gan/apnet/exp_config.json b/egs/vocoder/gan/apnet/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..520d3da3ea36201b8fa71e0fb1fd0d1ef9bbfdc0 --- /dev/null +++ b/egs/vocoder/gan/apnet/exp_config.json @@ -0,0 +1,45 @@ +{ + "base_config": "egs/vocoder/gan/exp_config_base.json", + "preprocess": { + // acoustic features + "extract_mel": true, + "extract_audio": true, + "extract_amplitude_phase": true, + + // Features used for model training + "use_mel": true, + "use_audio": true, + "use_amplitude_phase": true + }, + "model": { + "generator": "apnet", + "apnet": { + "ASP_channel": 512, + "ASP_resblock_kernel_sizes": [3,7,11], + "ASP_resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], + "ASP_input_conv_kernel_size": 7, + "ASP_output_conv_kernel_size": 7, + + "PSP_channel": 512, + "PSP_resblock_kernel_sizes": [3,7,11], + "PSP_resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], + "PSP_input_conv_kernel_size": 7, + "PSP_output_R_conv_kernel_size": 7, + "PSP_output_I_conv_kernel_size": 7, + } + }, + "train": { + "criterions": [ + "feature", + "discriminator", + "generator", + "mel", + "phase", + "amplitude", + "consistency" + ] + }, + "inference": { + "batch_size": 1, + } +} diff --git a/egs/vocoder/gan/apnet/run.sh b/egs/vocoder/gan/apnet/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..f50e7fab6f761ba69a5c45131479813b02f4d050 --- /dev/null +++ b/egs/vocoder/gan/apnet/run.sh @@ -0,0 +1,143 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Parse the Given Parameters from the Commond ########### +options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Training] Resume configuration + --resume) shift; resume=$1 ; shift ;; + # [Only for Training] The specific checkpoint path that you want to resume from. + --checkpoint) shift; cehckpoint=$1 ; shift ;; + # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. + --resume_type) shift; resume_type=$1 ; shift ;; + + # [Only for Inference] The inference mode + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inferenced datasets + --infer_datasets) shift; infer_datasets=$1 ; shift ;; + # [Only for Inference] The feature dir for inference + --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; + # [Only for Inference] The audio dir for inference + --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ + --config $exp_config \ + --num_workers 8 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + if [ "$resume" = true ]; then + echo "Automatically resume from the experimental dir..." + CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume + else + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --checkpoint "$checkpoint" \ + --resume_type "$resume_type" + fi +fi + +######## Inference/Conversion ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$infer_expt_dir/result" + fi + + if [ $infer_mode = "infer_from_dataset" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --infer_datasets $infer_datasets \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_feature" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --feature_folder $infer_feature_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_audio" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --audio_folder $infer_audio_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + +fi \ No newline at end of file diff --git a/egs/vocoder/gan/bigvgan/exp_config.json b/egs/vocoder/gan/bigvgan/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..cbed8a4bcd082f8a4cfa368a372f35c7e1a94973 --- /dev/null +++ b/egs/vocoder/gan/bigvgan/exp_config.json @@ -0,0 +1,66 @@ +{ + "base_config": "egs/vocoder/gan/exp_config_base.json", + "preprocess": { + // acoustic features + "extract_mel": true, + "extract_audio": true, + + // Features used for model training + "use_mel": true, + "use_audio": true + }, + "model": { + "generator": "bigvgan", + "bigvgan": { + "resblock": "1", + "activation": "snakebeta", + "snake_logscale": true, + "upsample_rates": [ + 8, + 8, + 2, + 2, + ], + "upsample_kernel_sizes": [ + 16, + 16, + 4, + 4 + ], + "upsample_initial_channel": 512, + "resblock_kernel_sizes": [ + 3, + 7, + 11 + ], + "resblock_dilation_sizes": [ + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ] + ] + } + }, + "train": { + "criterions": [ + "feature", + "discriminator", + "generator", + "mel", + ] + }, + "inference": { + "batch_size": 1, + } +} diff --git a/egs/vocoder/gan/bigvgan/run.sh b/egs/vocoder/gan/bigvgan/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..f50e7fab6f761ba69a5c45131479813b02f4d050 --- /dev/null +++ b/egs/vocoder/gan/bigvgan/run.sh @@ -0,0 +1,143 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Parse the Given Parameters from the Commond ########### +options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Training] Resume configuration + --resume) shift; resume=$1 ; shift ;; + # [Only for Training] The specific checkpoint path that you want to resume from. + --checkpoint) shift; cehckpoint=$1 ; shift ;; + # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. + --resume_type) shift; resume_type=$1 ; shift ;; + + # [Only for Inference] The inference mode + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inferenced datasets + --infer_datasets) shift; infer_datasets=$1 ; shift ;; + # [Only for Inference] The feature dir for inference + --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; + # [Only for Inference] The audio dir for inference + --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ + --config $exp_config \ + --num_workers 8 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + if [ "$resume" = true ]; then + echo "Automatically resume from the experimental dir..." + CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume + else + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --checkpoint "$checkpoint" \ + --resume_type "$resume_type" + fi +fi + +######## Inference/Conversion ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$infer_expt_dir/result" + fi + + if [ $infer_mode = "infer_from_dataset" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --infer_datasets $infer_datasets \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_feature" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --feature_folder $infer_feature_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_audio" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --audio_folder $infer_audio_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + +fi \ No newline at end of file diff --git a/egs/vocoder/gan/bigvgan_large/exp_config.json b/egs/vocoder/gan/bigvgan_large/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..a15dc3573839cd05085ac4540b651b98f94a1bf4 --- /dev/null +++ b/egs/vocoder/gan/bigvgan_large/exp_config.json @@ -0,0 +1,70 @@ +{ + "base_config": "egs/vocoder/gan/exp_config_base.json", + "preprocess": { + // acoustic features + "extract_mel": true, + "extract_audio": true, + + // Features used for model training + "use_mel": true, + "use_audio": true + }, + "model": { + "generator": "bigvgan", + "bigvgan": { + "resblock": "1", + "activation": "snakebeta", + "snake_logscale": true, + "upsample_rates": [ + 4, + 4, + 2, + 2, + 2, + 2 + ], + "upsample_kernel_sizes": [ + 8, + 8, + 4, + 4, + 4, + 4 + ], + "upsample_initial_channel": 1536, + "resblock_kernel_sizes": [ + 3, + 7, + 11 + ], + "resblock_dilation_sizes": [ + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ] + ] + }, + }, + "train": { + "criterions": [ + "feature", + "discriminator", + "generator", + "mel", + ] + }, + "inference": { + "batch_size": 1, + } +} diff --git a/egs/vocoder/gan/bigvgan_large/run.sh b/egs/vocoder/gan/bigvgan_large/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..f50e7fab6f761ba69a5c45131479813b02f4d050 --- /dev/null +++ b/egs/vocoder/gan/bigvgan_large/run.sh @@ -0,0 +1,143 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Parse the Given Parameters from the Commond ########### +options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Training] Resume configuration + --resume) shift; resume=$1 ; shift ;; + # [Only for Training] The specific checkpoint path that you want to resume from. + --checkpoint) shift; cehckpoint=$1 ; shift ;; + # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. + --resume_type) shift; resume_type=$1 ; shift ;; + + # [Only for Inference] The inference mode + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inferenced datasets + --infer_datasets) shift; infer_datasets=$1 ; shift ;; + # [Only for Inference] The feature dir for inference + --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; + # [Only for Inference] The audio dir for inference + --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ + --config $exp_config \ + --num_workers 8 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + if [ "$resume" = true ]; then + echo "Automatically resume from the experimental dir..." + CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume + else + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --checkpoint "$checkpoint" \ + --resume_type "$resume_type" + fi +fi + +######## Inference/Conversion ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$infer_expt_dir/result" + fi + + if [ $infer_mode = "infer_from_dataset" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --infer_datasets $infer_datasets \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_feature" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --feature_folder $infer_feature_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_audio" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --audio_folder $infer_audio_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + +fi \ No newline at end of file diff --git a/egs/vocoder/gan/exp_config_base.json b/egs/vocoder/gan/exp_config_base.json new file mode 100644 index 0000000000000000000000000000000000000000..84dbf7bcc0f5044c1e1f1d67b59cf26a338dff3a --- /dev/null +++ b/egs/vocoder/gan/exp_config_base.json @@ -0,0 +1,111 @@ +{ + "base_config": "config/vocoder.json", + "model_type": "GANVocoder", + // TODO: Choose your needed datasets + "dataset": [ + "csd", + "kising", + "m4singer", + "nus48e", + "opencpop", + "opensinger", + "opera", + "pjs", + "popbutfy", + "popcs", + "ljspeech", + "vctk", + "libritts", + ], + "dataset_path": { + // TODO: Fill in your dataset path + "csd": "[dataset path]", + "kising": "[dataset path]", + "m4singer": "[dataset path]", + "nus48e": "[dataset path]", + "opencpop": "[dataset path]", + "opensinger": "[dataset path]", + "opera": "[dataset path]", + "pjs": "[dataset path]", + "popbutfy": "[dataset path]", + "popcs": "[dataset path]", + "ljspeech": "[dataset path]", + "vctk": "[dataset path]", + "libritts": "[dataset path]", + }, + // TODO: Fill in the output log path + "log_dir": "ckpts/vocoder", + "preprocess": { + // Acoustic features + "extract_mel": true, + "extract_audio": true, + "extract_pitch": false, + "extract_uv": false, + "pitch_extractor": "parselmouth", + + // Features used for model training + "use_mel": true, + "use_frame_pitch": false, + "use_uv": false, + "use_audio": true, + + // TODO: Fill in the output data path + "processed_dir": "data/", + "n_mel": 100, + "sample_rate": 24000 + }, + "model": { + // TODO: Choose your needed discriminators + "discriminators": [ + "msd", + "mpd", + "msstftd", + "mssbcqtd", + ], + "mpd": { + "mpd_reshapes": [ + 2, + 3, + 5, + 7, + 11 + ], + "use_spectral_norm": false, + "discriminator_channel_mult_factor": 1 + }, + "mrd": { + "resolutions": [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]], + "use_spectral_norm": false, + "discriminator_channel_mult_factor": 1, + "mrd_override": false + }, + "msstftd": { + "filters": 32 + }, + "mssbcqtd": { + hop_lengths: [512, 256, 256], + filters: 32, + max_filters: 1024, + filters_scale: 1, + dilations: [1, 2, 4], + in_channels: 1, + out_channels: 1, + n_octaves: [9, 9, 9], + bins_per_octaves: [24, 36, 48] + }, + }, + "train": { + // TODO: Choose a suitable batch size, training epoch, and save stride + "batch_size": 32, + "max_epoch": 1000000, + "save_checkpoint_stride": [20], + "adamw": { + "lr": 2.0e-4, + "adam_b1": 0.8, + "adam_b2": 0.99 + }, + "exponential_lr": { + "lr_decay": 0.999 + }, + } +} \ No newline at end of file diff --git a/egs/vocoder/gan/hifigan/exp_config.json b/egs/vocoder/gan/hifigan/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..b06712eac472a8452d7cfbfcf8bdf9bf3b232514 --- /dev/null +++ b/egs/vocoder/gan/hifigan/exp_config.json @@ -0,0 +1,59 @@ +{ + "base_config": "egs/vocoder/gan/exp_config_base.json", + "preprocess": { + // acoustic features + "extract_mel": true, + "extract_audio": true, + + // Features used for model training + "use_mel": true, + "use_audio": true + }, + "model": { + "generator": "hifigan", + "hifigan": { + "resblock": "2", + "upsample_rates": [ + 8, + 8, + 4 + ], + "upsample_kernel_sizes": [ + 16, + 16, + 8 + ], + "upsample_initial_channel": 256, + "resblock_kernel_sizes": [ + 3, + 5, + 7 + ], + "resblock_dilation_sizes": [ + [ + 1, + 2 + ], + [ + 2, + 6 + ], + [ + 3, + 12 + ] + ] + } + }, + "train": { + "criterions": [ + "feature", + "discriminator", + "generator", + "mel", + ] + }, + "inference": { + "batch_size": 1, + } +} diff --git a/egs/vocoder/gan/hifigan/run.sh b/egs/vocoder/gan/hifigan/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..f50e7fab6f761ba69a5c45131479813b02f4d050 --- /dev/null +++ b/egs/vocoder/gan/hifigan/run.sh @@ -0,0 +1,143 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Parse the Given Parameters from the Commond ########### +options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Training] Resume configuration + --resume) shift; resume=$1 ; shift ;; + # [Only for Training] The specific checkpoint path that you want to resume from. + --checkpoint) shift; cehckpoint=$1 ; shift ;; + # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. + --resume_type) shift; resume_type=$1 ; shift ;; + + # [Only for Inference] The inference mode + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inferenced datasets + --infer_datasets) shift; infer_datasets=$1 ; shift ;; + # [Only for Inference] The feature dir for inference + --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; + # [Only for Inference] The audio dir for inference + --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ + --config $exp_config \ + --num_workers 8 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + if [ "$resume" = true ]; then + echo "Automatically resume from the experimental dir..." + CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume + else + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --checkpoint "$checkpoint" \ + --resume_type "$resume_type" + fi +fi + +######## Inference/Conversion ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$infer_expt_dir/result" + fi + + if [ $infer_mode = "infer_from_dataset" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --infer_datasets $infer_datasets \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_feature" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --feature_folder $infer_feature_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_audio" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --audio_folder $infer_audio_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + +fi \ No newline at end of file diff --git a/egs/vocoder/gan/melgan/exp_config.json b/egs/vocoder/gan/melgan/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..9470600bc0d15afd07107f816995e8c67ad52d9a --- /dev/null +++ b/egs/vocoder/gan/melgan/exp_config.json @@ -0,0 +1,34 @@ +{ + "base_config": "egs/vocoder/gan/exp_config_base.json", + "preprocess": { + // acoustic features + "extract_mel": true, + "extract_audio": true, + + // Features used for model training + "use_mel": true, + "use_audio": true + }, + "model": { + "generator": "melgan", + "melgan": { + "ratios": [8, 8, 2, 2], + "ngf": 32, + "n_residual_layers": 3, + "num_D": 3, + "ndf": 16, + "n_layers": 4, + "downsampling_factor": 4 + }, + }, + "train": { + "criterions": [ + "feature", + "discriminator", + "generator", + ] + }, + "inference": { + "batch_size": 1, + } +} diff --git a/egs/vocoder/gan/melgan/run.sh b/egs/vocoder/gan/melgan/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..f50e7fab6f761ba69a5c45131479813b02f4d050 --- /dev/null +++ b/egs/vocoder/gan/melgan/run.sh @@ -0,0 +1,143 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Parse the Given Parameters from the Commond ########### +options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Training] Resume configuration + --resume) shift; resume=$1 ; shift ;; + # [Only for Training] The specific checkpoint path that you want to resume from. + --checkpoint) shift; cehckpoint=$1 ; shift ;; + # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. + --resume_type) shift; resume_type=$1 ; shift ;; + + # [Only for Inference] The inference mode + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inferenced datasets + --infer_datasets) shift; infer_datasets=$1 ; shift ;; + # [Only for Inference] The feature dir for inference + --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; + # [Only for Inference] The audio dir for inference + --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ + --config $exp_config \ + --num_workers 8 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + if [ "$resume" = true ]; then + echo "Automatically resume from the experimental dir..." + CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume + else + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --checkpoint "$checkpoint" \ + --resume_type "$resume_type" + fi +fi + +######## Inference/Conversion ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$infer_expt_dir/result" + fi + + if [ $infer_mode = "infer_from_dataset" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --infer_datasets $infer_datasets \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_feature" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --feature_folder $infer_feature_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_audio" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --audio_folder $infer_audio_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + +fi \ No newline at end of file diff --git a/egs/vocoder/gan/nsfhifigan/exp_config.json b/egs/vocoder/gan/nsfhifigan/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..ead9dc8909b6a5283dd12b721cfb4519cc3953ca --- /dev/null +++ b/egs/vocoder/gan/nsfhifigan/exp_config.json @@ -0,0 +1,83 @@ +{ + "base_config": "egs/vocoder/gan/exp_config_base.json", + "preprocess": { + // acoustic features + "extract_mel": true, + "extract_audio": true, + "extract_pitch": true, + + // Features used for model training + "use_mel": true, + "use_audio": true, + "use_frame_pitch": true + }, + "model": { + "generator": "nsfhifigan", + "nsfhifigan": { + "resblock": "1", + "harmonic_num": 8, + "upsample_rates": [ + 8, + 4, + 2, + 2, + 2 + ], + "upsample_kernel_sizes": [ + 16, + 8, + 4, + 4, + 4 + ], + "upsample_initial_channel": 768, + "resblock_kernel_sizes": [ + 3, + 7, + 11 + ], + "resblock_dilation_sizes": [ + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ] + ] + }, + "mpd": { + "mpd_reshapes": [ + 2, + 3, + 5, + 7, + 11, + 17, + 23, + 37 + ], + "use_spectral_norm": false, + "discriminator_channel_multi": 1 + } + }, + "train": { + "criterions": [ + "feature", + "discriminator", + "generator", + "mel", + ] + }, + "inference": { + "batch_size": 1, + } +} diff --git a/egs/vocoder/gan/nsfhifigan/run.sh b/egs/vocoder/gan/nsfhifigan/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..f50e7fab6f761ba69a5c45131479813b02f4d050 --- /dev/null +++ b/egs/vocoder/gan/nsfhifigan/run.sh @@ -0,0 +1,143 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Parse the Given Parameters from the Commond ########### +options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Training] Resume configuration + --resume) shift; resume=$1 ; shift ;; + # [Only for Training] The specific checkpoint path that you want to resume from. + --checkpoint) shift; cehckpoint=$1 ; shift ;; + # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. + --resume_type) shift; resume_type=$1 ; shift ;; + + # [Only for Inference] The inference mode + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inferenced datasets + --infer_datasets) shift; infer_datasets=$1 ; shift ;; + # [Only for Inference] The feature dir for inference + --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; + # [Only for Inference] The audio dir for inference + --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ + --config $exp_config \ + --num_workers 8 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + if [ "$resume" = true ]; then + echo "Automatically resume from the experimental dir..." + CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume + else + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --checkpoint "$checkpoint" \ + --resume_type "$resume_type" + fi +fi + +######## Inference/Conversion ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$infer_expt_dir/result" + fi + + if [ $infer_mode = "infer_from_dataset" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --infer_datasets $infer_datasets \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_feature" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --feature_folder $infer_feature_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_audio" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --audio_folder $infer_audio_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + +fi \ No newline at end of file diff --git a/egs/vocoder/gan/tfr_enhanced_hifigan/README.md b/egs/vocoder/gan/tfr_enhanced_hifigan/README.md new file mode 100644 index 0000000000000000000000000000000000000000..71350bab8aa19eaa41369e37faa9c5767241c8ac --- /dev/null +++ b/egs/vocoder/gan/tfr_enhanced_hifigan/README.md @@ -0,0 +1,185 @@ +# Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fedility Vocoder + +[![arXiv](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2311.14957) +[![demo](https://img.shields.io/badge/Vocoder-Demo-red)](https://vocodexelysium.github.io/MS-SB-CQTD/) + +
+
+ +
+
+ +This is the official implementation of the paper "[Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fidelity Vocoder](https://arxiv.org/abs/2311.14957)". In this recipe, we will illustrate how to train a high quality HiFi-GAN on LibriTTS, VCTK and LJSpeech via utilizing multiple Time-Frequency-Representation-based Discriminators. + +There are four stages in total: + +1. Data preparation +2. Feature extraction +3. Training +4. Inference + +> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: +> ```bash +> cd Amphion +> ``` + +## 1. Data Preparation + +### Dataset Download + +By default, we utilize the three datasets for training: LibriTTS, VCTK and LJSpeech. How to download them is detailed in [here](../../../datasets/README.md). + +### Configuration + +Specify the dataset path in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. + +```json +"dataset": [ + "ljspeech", + "vctk", + "libritts", +], +"dataset_path": { + // TODO: Fill in your dataset path + "ljspeech": "[LJSpeech dataset path]", + "vctk": "[VCTK dataset path]", + "libritts": "[LibriTTS dataset path]", +}, +``` + +## 2. Features Extraction + +For HiFiGAN, only the Mel-Spectrogram and the Output Audio are needed for training. + +### Configuration + +Specify the dataset path and the output path for saving the processed data and the training model in `exp_config.json`: + +```json + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/vocoder" + "log_dir": "ckpts/vocoder", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + ... + }, +``` + +### Run + +Run the `run.sh` as the preproces stage (set `--stage 1`). + +```bash +sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 1 +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. + +## 3. Training + +### Configuration + +We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines. + +```json +"train": { + "batch_size": 32, + ... +} +``` + +### Run + +Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/vocoder/[YourExptName]`. + +```bash +sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 2 --name [YourExptName] +``` + +> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. + +## 4. Inference + +### Pretrained Vocoder Download + +We trained a HiFiGAN checkpoint with around 685 hours Speech data. The final pretrained checkpoint is released [here](../../../../pretrained/hifigan/README.md). + +### Run + +Run the `run.sh` as the training stage (set `--stage 3`), we provide three different inference modes, including `infer_from_dataset`, `infer_from_feature`, `and infer_from audio`. + +```bash +sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 3 \ + --infer_mode [Your chosen inference mode] \ + --infer_datasets [Datasets you want to inference, needed when infer_from_dataset] \ + --infer_feature_dir [Your path to your predicted acoustic features, needed when infer_from_feature] \ + --infer_audio_dir [Your path to your audio files, needed when infer_form_audio] \ + --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ + --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ +``` + +#### a. Inference from Dataset + +Run the `run.sh` with specified datasets, here is an example. + +```bash +sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 3 \ + --infer_mode infer_from_dataset \ + --infer_datasets "libritts vctk ljspeech" \ + --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ + --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ +``` + +#### b. Inference from Features + +If you want to inference from your generated acoustic features, you should first prepare your acoustic features into the following structure: + +```plaintext + ┣ {infer_feature_dir} + ┃ ┣ mels + ┃ ┃ ┣ sample1.npy + ┃ ┃ ┣ sample2.npy +``` + +Then run the `run.sh` with specificed folder direction, here is an example. + +```bash +sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 3 \ + --infer_mode infer_from_feature \ + --infer_feature_dir [Your path to your predicted acoustic features] \ + --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ + --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ +``` + +#### c. Inference from Audios + +If you want to inference from audios for quick analysis synthesis, you should first prepare your audios into the following structure: + +```plaintext + ┣ audios + ┃ ┣ sample1.wav + ┃ ┣ sample2.wav +``` + +Then run the `run.sh` with specificed folder direction, here is an example. + +```bash +sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 3 \ + --infer_mode infer_from_audio \ + --infer_audio_dir [Your path to your audio files] \ + --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ + --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ +``` + +## Citations + +```bibtex +@misc{gu2023cqt, + title={Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fidelity Vocoder}, + author={Yicheng Gu and Xueyao Zhang and Liumeng Xue and Zhizheng Wu}, + year={2023}, + eprint={2311.14957}, + archivePrefix={arXiv}, + primaryClass={cs.SD} +} +``` \ No newline at end of file diff --git a/egs/vocoder/gan/tfr_enhanced_hifigan/exp_config.json b/egs/vocoder/gan/tfr_enhanced_hifigan/exp_config.json new file mode 100644 index 0000000000000000000000000000000000000000..aada80f98f6e43ccea9c6096c8fba71a46f32b67 --- /dev/null +++ b/egs/vocoder/gan/tfr_enhanced_hifigan/exp_config.json @@ -0,0 +1,118 @@ +{ + "base_config": "egs/vocoder/gan/exp_config_base.json", + "model_type": "GANVocoder", + "dataset": [ + "ljspeech", + "vctk", + "libritts", + ], + "dataset_path": { + // TODO: Fill in your dataset path + "ljspeech": "[dataset path]", + "vctk": "[dataset path]", + "libritts": "[dataset path]", + }, + // TODO: Fill in the output log path. The default value is "Amphion/ckpts/vocoder" + "log_dir": "ckpts/vocoder", + "preprocess": { + // TODO: Fill in the output data path. The default value is "Amphion/data" + "processed_dir": "data", + // acoustic features + "extract_mel": true, + "extract_audio": true, + "extract_pitch": false, + "extract_uv": false, + "extract_amplitude_phase": false, + "pitch_extractor": "parselmouth", + // Features used for model training + "use_mel": true, + "use_frame_pitch": false, + "use_uv": false, + "use_audio": true, + "n_mel": 100, + "sample_rate": 24000 + }, + "model": { + "generator": "hifigan", + "discriminators": [ + "msd", + "mpd", + "mssbcqtd", + "msstftd", + ], + "hifigan": { + "resblock": "1", + "upsample_rates": [ + 8, + 4, + 2, + 2, + 2 + ], + "upsample_kernel_sizes": [ + 16, + 8, + 4, + 4, + 4 + ], + "upsample_initial_channel": 768, + "resblock_kernel_sizes": [ + 3, + 5, + 7 + ], + "resblock_dilation_sizes": [ + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ] + ] + }, + "mpd": { + "mpd_reshapes": [ + 2, + 3, + 5, + 7, + 11, + 17, + 23, + 37 + ], + "use_spectral_norm": false, + "discriminator_channel_multi": 1 + } + }, + "train": { + "batch_size": 16, + "adamw": { + "lr": 2.0e-4, + "adam_b1": 0.8, + "adam_b2": 0.99 + }, + "exponential_lr": { + "lr_decay": 0.999 + }, + "criterions": [ + "feature", + "discriminator", + "generator", + "mel", + ] + }, + "inference": { + "batch_size": 1, + } +} \ No newline at end of file diff --git a/egs/vocoder/gan/tfr_enhanced_hifigan/run.sh b/egs/vocoder/gan/tfr_enhanced_hifigan/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..857d81ff1098a66fcc0b47c876dd5692d1a1e777 --- /dev/null +++ b/egs/vocoder/gan/tfr_enhanced_hifigan/run.sh @@ -0,0 +1,145 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +######## Parse the Given Parameters from the Commond ########### +options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") +eval set -- "$options" + +while true; do + case $1 in + # Experimental Configuration File + -c | --config) shift; exp_config=$1 ; shift ;; + # Experimental Name + -n | --name) shift; exp_name=$1 ; shift ;; + # Running Stage + -s | --stage) shift; running_stage=$1 ; shift ;; + # Visible GPU machines. The default value is "0". + --gpu) shift; gpu=$1 ; shift ;; + + # [Only for Training] Resume configuration + --resume) shift; resume=$1 ; shift ;; + # [Only for Training] The specific checkpoint path that you want to resume from. + --checkpoint) shift; cehckpoint=$1 ; shift ;; + # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. + --resume_type) shift; resume_type=$1 ; shift ;; + + # [Only for Inference] The inference mode + --infer_mode) shift; infer_mode=$1 ; shift ;; + # [Only for Inference] The inferenced datasets + --infer_datasets) shift; infer_datasets=$1 ; shift ;; + # [Only for Inference] The feature dir for inference + --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; + # [Only for Inference] The audio dir for inference + --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; + # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" + --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; + # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" + --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; + + --) shift ; break ;; + *) echo "Invalid option: $1" exit 1 ;; + esac +done + + +### Value check ### +if [ -z "$running_stage" ]; then + echo "[Error] Please specify the running stage" + exit 1 +fi + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config.json +fi +echo "Exprimental Configuration File: $exp_config" + +if [ -z "$gpu" ]; then + gpu="0" +fi + +######## Features Extraction ########### +if [ $running_stage -eq 1 ]; then + CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ + --config $exp_config \ + --num_workers 8 +fi + +######## Training ########### +if [ $running_stage -eq 2 ]; then + if [ -z "$exp_name" ]; then + echo "[Error] Please specify the experiments name" + exit 1 + fi + echo "Exprimental Name: $exp_name" + + if [ "$resume" = true ]; then + echo "Automatically resume from the experimental dir..." + CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --resume + else + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ + --config "$exp_config" \ + --exp_name "$exp_name" \ + --log_level info \ + --checkpoint "$checkpoint" \ + --resume_type "$resume_type" + fi +fi + +######## Inference/Conversion ########### +if [ $running_stage -eq 3 ]; then + if [ -z "$infer_expt_dir" ]; then + echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" + exit 1 + fi + + if [ -z "$infer_output_dir" ]; then + infer_output_dir="$infer_expt_dir/result" + fi + + echo $infer_datasets + + if [ $infer_mode = "infer_from_dataset" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --infer_datasets $infer_datasets \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_feature" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --feature_folder $infer_feature_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + + if [ $infer_mode = "infer_from_audio" ]; then + CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ + --config $exp_config \ + --infer_mode $infer_mode \ + --audio_folder $infer_audio_dir \ + --vocoder_dir $infer_expt_dir \ + --output_dir $infer_output_dir \ + --log_level debug + fi + +fi \ No newline at end of file diff --git a/env.sh b/env.sh new file mode 100644 index 0000000000000000000000000000000000000000..546f3d6c9058f1a62fc2edc26bfa84f04281e97e --- /dev/null +++ b/env.sh @@ -0,0 +1,27 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# Install ffmpeg in Linux +conda install -c conda-forge ffmpeg + +# Pip packages +pip install setuptools ruamel.yaml tqdm colorama easydict tabulate loguru json5 Cython unidecode inflect argparse g2p_en tgt librosa matplotlib typeguard einops omegaconf hydra-core humanfriendly pandas + +pip install tensorboard tensorboardX torch==2.0.1 torchaudio==2.0.2 torchvision==0.15.2 transformers diffusers accelerate praat-parselmouth audiomentations pedalboard ffmpeg-python==0.2.0 pyworld diffsptk nnAudio unidecode inflect ptwt + +pip install torchmetrics pymcd openai-whisper frechet_audio_distance asteroid + +pip install https://github.com/vBaiCai/python-pesq/archive/master.zip + +pip install fairseq + +pip install git+https://github.com/lhotse-speech/lhotse + +pip install -U encodec + +pip install phonemizer==3.2.1 pypinyin==0.48.0 + +# Uninstall nvidia-cublas-cu11 if there exist some bugs about CUDA version +# pip uninstall nvidia-cublas-cu11 diff --git a/evaluation/__init__.py b/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evaluation/features/__init__.py b/evaluation/features/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evaluation/features/long_term_average_spectrum.py b/evaluation/features/long_term_average_spectrum.py new file mode 100644 index 0000000000000000000000000000000000000000..b6724eecf0748a25e1b9afeae853064e2f26fa1e --- /dev/null +++ b/evaluation/features/long_term_average_spectrum.py @@ -0,0 +1,19 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import librosa +from scipy import signal + + +def extract_ltas(audio, fs=None, n_fft=1024, hop_length=256): + """Extract Long-Term Average Spectrum for a given audio.""" + if fs != None: + y, _ = librosa.load(audio, sr=fs) + else: + y, fs = librosa.load(audio) + frequency, density = signal.welch( + x=y, fs=fs, window="hann", nperseg=hop_length, nfft=n_fft + ) + return frequency, density diff --git a/evaluation/features/signal_to_noise_ratio.py b/evaluation/features/signal_to_noise_ratio.py new file mode 100644 index 0000000000000000000000000000000000000000..42abf0e1e721a680029fc57c743dfe06c3629fe9 --- /dev/null +++ b/evaluation/features/signal_to_noise_ratio.py @@ -0,0 +1,133 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import scipy.signal as sig +import copy +import librosa + + +def bandpower(ps, mode="time"): + """ + estimate bandpower, see https://de.mathworks.com/help/signal/ref/bandpower.html + """ + if mode == "time": + x = ps + l2norm = np.linalg.norm(x) ** 2.0 / len(x) + return l2norm + elif mode == "psd": + return sum(ps) + + +def getIndizesAroundPeak(arr, peakIndex, searchWidth=1000): + peakBins = [] + magMax = arr[peakIndex] + curVal = magMax + for i in range(searchWidth): + newBin = peakIndex + i + if newBin >= len(arr): + break + newVal = arr[newBin] + if newVal > curVal: + break + else: + peakBins.append(int(newBin)) + curVal = newVal + curVal = magMax + for i in range(searchWidth): + newBin = peakIndex - i + if newBin < 0: + break + newVal = arr[newBin] + if newVal > curVal: + break + else: + peakBins.append(int(newBin)) + curVal = newVal + return np.array(list(set(peakBins))) + + +def freqToBin(fAxis, Freq): + return np.argmin(abs(fAxis - Freq)) + + +def getPeakInArea(psd, faxis, estimation, searchWidthHz=10): + """ + returns bin and frequency of the maximum in an area + """ + binLow = freqToBin(faxis, estimation - searchWidthHz) + binHi = freqToBin(faxis, estimation + searchWidthHz) + peakbin = binLow + np.argmax(psd[binLow : binHi + 1]) + return peakbin, faxis[peakbin] + + +def getHarmonics(fund, sr, nHarmonics=6, aliased=False): + harmonicMultipliers = np.arange(2, nHarmonics + 2) + harmonicFs = fund * harmonicMultipliers + if not aliased: + harmonicFs[harmonicFs > sr / 2] = -1 + harmonicFs = np.delete(harmonicFs, harmonicFs == -1) + else: + nyqZone = np.floor(harmonicFs / (sr / 2)) + oddEvenNyq = nyqZone % 2 + harmonicFs = np.mod(harmonicFs, sr / 2) + harmonicFs[oddEvenNyq == 1] = (sr / 2) - harmonicFs[oddEvenNyq == 1] + return harmonicFs + + +def extract_snr(audio, sr=None): + """Extract Signal-to-Noise Ratio for a given audio.""" + if sr != None: + audio, _ = librosa.load(audio, sr=sr) + else: + audio, sr = librosa.load(audio, sr=sr) + faxis, ps = sig.periodogram( + audio, fs=sr, window=("kaiser", 38) + ) # get periodogram, parametrized like in matlab + fundBin = np.argmax( + ps + ) # estimate fundamental at maximum amplitude, get the bin number + fundIndizes = getIndizesAroundPeak( + ps, fundBin + ) # get bin numbers around fundamental peak + fundFrequency = faxis[fundBin] # frequency of fundamental + + nHarmonics = 18 + harmonicFs = getHarmonics( + fundFrequency, sr, nHarmonics=nHarmonics, aliased=True + ) # get harmonic frequencies + + harmonicBorders = np.zeros([2, nHarmonics], dtype=np.int16).T + fullHarmonicBins = np.array([], dtype=np.int16) + fullHarmonicBinList = [] + harmPeakFreqs = [] + harmPeaks = [] + for i, harmonic in enumerate(harmonicFs): + searcharea = 0.1 * fundFrequency + estimation = harmonic + + binNum, freq = getPeakInArea(ps, faxis, estimation, searcharea) + harmPeakFreqs.append(freq) + harmPeaks.append(ps[binNum]) + allBins = getIndizesAroundPeak(ps, binNum, searchWidth=1000) + fullHarmonicBins = np.append(fullHarmonicBins, allBins) + fullHarmonicBinList.append(allBins) + harmonicBorders[i, :] = [allBins[0], allBins[-1]] + + fundIndizes.sort() + pFund = bandpower(ps[fundIndizes[0] : fundIndizes[-1]]) # get power of fundamental + + noisePrepared = copy.copy(ps) + noisePrepared[fundIndizes] = 0 + noisePrepared[fullHarmonicBins] = 0 + noiseMean = np.median(noisePrepared[noisePrepared != 0]) + noisePrepared[fundIndizes] = noiseMean + noisePrepared[fullHarmonicBins] = noiseMean + + noisePower = bandpower(noisePrepared) + + r = 10 * np.log10(pFund / noisePower) + + return r, 10 * np.log10(noisePower) diff --git a/evaluation/features/singing_power_ratio.py b/evaluation/features/singing_power_ratio.py new file mode 100644 index 0000000000000000000000000000000000000000..18ad86b6110ea42dab808fe69be8dd4b84cd841b --- /dev/null +++ b/evaluation/features/singing_power_ratio.py @@ -0,0 +1,108 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import librosa + +from utils.util import JsonHParams +from utils.f0 import get_f0_features_using_parselmouth, get_pitch_sub_median +from utils.mel import extract_mel_features + + +def extract_spr( + audio, + fs=None, + hop_length=256, + win_length=1024, + n_fft=1024, + n_mels=128, + f0_min=37, + f0_max=1000, + pitch_bin=256, + pitch_max=1100.0, + pitch_min=50.0, +): + """Compute Singing Power Ratio (SPR) from a given audio. + audio: path to the audio. + fs: sampling rate. + hop_length: hop length. + win_length: window length. + n_mels: number of mel filters. + f0_min: lower limit for f0. + f0_max: upper limit for f0. + pitch_bin: number of bins for f0 quantization. + pitch_max: upper limit for f0 quantization. + pitch_min: lower limit for f0 quantization. + """ + # Load audio + if fs != None: + audio, _ = librosa.load(audio, sr=fs) + else: + audio, fs = librosa.load(audio) + audio = torch.from_numpy(audio) + + # Initialize config + cfg = JsonHParams() + cfg.sample_rate = fs + cfg.hop_size = hop_length + cfg.win_size = win_length + cfg.n_fft = n_fft + cfg.n_mel = n_mels + cfg.f0_min = f0_min + cfg.f0_max = f0_max + cfg.pitch_bin = pitch_bin + cfg.pitch_max = pitch_max + cfg.pitch_min = pitch_min + + # Extract mel spectrograms + + cfg.fmin = 2000 + cfg.fmax = 4000 + + mel1 = extract_mel_features( + y=audio.unsqueeze(0), + cfg=cfg, + ).squeeze(0) + + cfg.fmin = 0 + cfg.fmax = 2000 + + mel2 = extract_mel_features( + y=audio.unsqueeze(0), + cfg=cfg, + ).squeeze(0) + + f0 = get_f0_features_using_parselmouth( + audio, + cfg, + )[0] + + # Mel length alignment + length = min(len(f0), mel1.shape[-1]) + f0 = f0[:length] + mel1 = mel1[:, :length] + mel2 = mel2[:, :length] + + # Compute SPR + res = [] + + for i in range(mel1.shape[-1]): + if f0[i] <= 1: + continue + + chunk1 = mel1[:, i] + chunk2 = mel2[:, i] + + max1 = max(chunk1.numpy()) + max2 = max(chunk2.numpy()) + + tmp_res = max2 - max1 + + res.append(tmp_res) + + if len(res) == 0: + return False + else: + return sum(res) / len(res) diff --git a/evaluation/metrics/__init__.py b/evaluation/metrics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evaluation/metrics/energy/__init__.py b/evaluation/metrics/energy/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evaluation/metrics/energy/energy_pearson_coefficients.py b/evaluation/metrics/energy/energy_pearson_coefficients.py new file mode 100644 index 0000000000000000000000000000000000000000..7c97c8cb348edec6a6952d6df7f18f13fc0b01ac --- /dev/null +++ b/evaluation/metrics/energy/energy_pearson_coefficients.py @@ -0,0 +1,91 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import librosa +import torch + +import numpy as np +from numpy import linalg as LA + +from torchmetrics import PearsonCorrCoef + + +def extract_energy_pearson_coeffcients( + audio_ref, + audio_deg, + fs=None, + n_fft=1024, + hop_length=256, + win_length=1024, + method="dtw", + db_scale=True, +): + """Compute Energy Pearson Coefficients between the predicted and the ground truth audio. + audio_ref: path to the ground truth audio. + audio_deg: path to the predicted audio. + fs: sampling rate. + n_fft: fft size. + hop_length: hop length. + win_length: window length. + method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio. + "cut" will cut both audios into a same length according to the one with the shorter length. + db_scale: the ground truth and predicted audio will be converted to db_scale if "True". + """ + # Initialize method + pearson = PearsonCorrCoef() + + # Load audio + if fs != None: + audio_ref, _ = librosa.load(audio_ref, sr=fs) + audio_deg, _ = librosa.load(audio_deg, sr=fs) + else: + audio_ref, fs = librosa.load(audio_ref) + audio_deg, fs = librosa.load(audio_deg) + + # STFT + spec_ref = librosa.stft( + y=audio_ref, n_fft=n_fft, hop_length=hop_length, win_length=win_length + ) + spec_deg = librosa.stft( + y=audio_deg, n_fft=n_fft, hop_length=hop_length, win_length=win_length + ) + + # Get magnitudes + mag_ref = np.abs(spec_ref).T + mag_deg = np.abs(spec_deg).T + + # Convert spectrogram to energy + energy_ref = LA.norm(mag_ref, axis=1) + energy_deg = LA.norm(mag_deg, axis=1) + + # Convert to db_scale + if db_scale: + energy_ref = 20 * np.log10(energy_ref) + energy_deg = 20 * np.log10(energy_deg) + + # Audio length alignment + if method == "cut": + length = min(len(energy_ref), len(energy_deg)) + energy_ref = energy_ref[:length] + energy_deg = energy_deg[:length] + elif method == "dtw": + _, wp = librosa.sequence.dtw(energy_ref, energy_deg, backtrack=True) + energy_gt_new = [] + energy_pred_new = [] + for i in range(wp.shape[0]): + gt_index = wp[i][0] + pred_index = wp[i][1] + energy_gt_new.append(energy_ref[gt_index]) + energy_pred_new.append(energy_deg[pred_index]) + energy_ref = np.array(energy_gt_new) + energy_deg = np.array(energy_pred_new) + assert len(energy_ref) == len(energy_deg) + + # Convert to tensor + energy_ref = torch.from_numpy(energy_ref) + energy_deg = torch.from_numpy(energy_deg) + + return pearson(energy_ref, energy_deg).numpy().tolist() diff --git a/evaluation/metrics/energy/energy_rmse.py b/evaluation/metrics/energy/energy_rmse.py new file mode 100644 index 0000000000000000000000000000000000000000..557ae879c9b16ca30257abf290d5a14f7e8728d1 --- /dev/null +++ b/evaluation/metrics/energy/energy_rmse.py @@ -0,0 +1,86 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import librosa +import torch + +import numpy as np +from numpy import linalg as LA + + +def extract_energy_rmse( + audio_ref, + audio_deg, + fs=None, + n_fft=1024, + hop_length=256, + win_length=1024, + method="dtw", + db_scale=True, +): + """Compute Energy Root Mean Square Error (RMSE) between the predicted and the ground truth audio. + audio_ref: path to the ground truth audio. + audio_deg: path to the predicted audio. + fs: sampling rate. + n_fft: fft size. + hop_length: hop length. + win_length: window length. + method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio. + "cut" will cut both audios into a same length according to the one with the shorter length. + db_scale: the ground truth and predicted audio will be converted to db_scale if "True". + """ + # Load audio + if fs != None: + audio_ref, _ = librosa.load(audio_ref, sr=fs) + audio_deg, _ = librosa.load(audio_deg, sr=fs) + else: + audio_ref, fs = librosa.load(audio_ref) + audio_deg, fs = librosa.load(audio_deg) + + # STFT + spec_ref = librosa.stft( + y=audio_ref, n_fft=n_fft, hop_length=hop_length, win_length=win_length + ) + spec_deg = librosa.stft( + y=audio_deg, n_fft=n_fft, hop_length=hop_length, win_length=win_length + ) + + # Get magnitudes + mag_ref = np.abs(spec_ref).T + mag_deg = np.abs(spec_deg).T + + # Convert spectrogram to energy + energy_ref = LA.norm(mag_ref, axis=1) + energy_deg = LA.norm(mag_deg, axis=1) + + # Convert to db_scale + if db_scale: + energy_ref = 20 * np.log10(energy_ref) + energy_deg = 20 * np.log10(energy_deg) + + # Audio length alignment + if method == "cut": + length = min(len(energy_ref), len(energy_deg)) + energy_ref = energy_ref[:length] + energy_deg = energy_deg[:length] + elif method == "dtw": + _, wp = librosa.sequence.dtw(energy_ref, energy_deg, backtrack=True) + energy_gt_new = [] + energy_pred_new = [] + for i in range(wp.shape[0]): + gt_index = wp[i][0] + pred_index = wp[i][1] + energy_gt_new.append(energy_ref[gt_index]) + energy_pred_new.append(energy_deg[pred_index]) + energy_ref = np.array(energy_gt_new) + energy_deg = np.array(energy_pred_new) + assert len(energy_ref) == len(energy_deg) + + # Compute RMSE + energy_mse = np.square(np.subtract(energy_ref, energy_deg)).mean() + energy_rmse = math.sqrt(energy_mse) + + return energy_rmse diff --git a/evaluation/metrics/f0/__init__.py b/evaluation/metrics/f0/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evaluation/metrics/f0/f0_pearson_coefficients.py b/evaluation/metrics/f0/f0_pearson_coefficients.py new file mode 100644 index 0000000000000000000000000000000000000000..8aad28359e99add54603bd7f1bf1fb0eae1e725e --- /dev/null +++ b/evaluation/metrics/f0/f0_pearson_coefficients.py @@ -0,0 +1,111 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import librosa + +import numpy as np + +from torchmetrics import PearsonCorrCoef + +from utils.util import JsonHParams +from utils.f0 import get_f0_features_using_parselmouth, get_pitch_sub_median + + +def extract_fpc( + audio_ref, + audio_deg, + fs=None, + hop_length=256, + f0_min=50, + f0_max=1100, + pitch_bin=256, + pitch_min=50, + pitch_max=1100, + need_mean=True, + method="dtw", +): + """Compute F0 Pearson Distance (FPC) between the predicted and the ground truth audio. + audio_ref: path to the ground truth audio. + audio_deg: path to the predicted audio. + fs: sampling rate. + hop_length: hop length. + f0_min: lower limit for f0. + f0_max: upper limit for f0. + pitch_bin: number of bins for f0 quantization. + pitch_max: upper limit for f0 quantization. + pitch_min: lower limit for f0 quantization. + need_mean: subtract the mean value from f0 if "True". + method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio. + "cut" will cut both audios into a same length according to the one with the shorter length. + """ + # Initialize method + pearson = PearsonCorrCoef() + + # Load audio + if fs != None: + audio_ref, _ = librosa.load(audio_ref, sr=fs) + audio_deg, _ = librosa.load(audio_deg, sr=fs) + else: + audio_ref, fs = librosa.load(audio_ref) + audio_deg, fs = librosa.load(audio_deg) + + # Initialize config + cfg = JsonHParams() + cfg.sample_rate = fs + cfg.hop_size = hop_length + cfg.f0_min = f0_min + cfg.f0_max = f0_max + cfg.pitch_bin = pitch_bin + cfg.pitch_max = pitch_max + cfg.pitch_min = pitch_min + + # Compute f0 + f0_ref = get_f0_features_using_parselmouth( + audio_ref, + cfg, + )[0] + + f0_deg = get_f0_features_using_parselmouth( + audio_deg, + cfg, + )[0] + + # Subtract mean value from f0 + if need_mean: + f0_ref = torch.from_numpy(f0_ref) + f0_deg = torch.from_numpy(f0_deg) + + f0_ref = get_pitch_sub_median(f0_ref).numpy() + f0_deg = get_pitch_sub_median(f0_deg).numpy() + + # Avoid silence + min_length = min(len(f0_ref), len(f0_deg)) + if min_length <= 1: + return 1 + + # F0 length alignment + if method == "cut": + length = min(len(f0_ref), len(f0_deg)) + f0_ref = f0_ref[:length] + f0_deg = f0_deg[:length] + elif method == "dtw": + _, wp = librosa.sequence.dtw(f0_ref, f0_deg, backtrack=True) + f0_gt_new = [] + f0_pred_new = [] + for i in range(wp.shape[0]): + gt_index = wp[i][0] + pred_index = wp[i][1] + f0_gt_new.append(f0_ref[gt_index]) + f0_pred_new.append(f0_deg[pred_index]) + f0_ref = np.array(f0_gt_new) + f0_deg = np.array(f0_pred_new) + assert len(f0_ref) == len(f0_deg) + + # Convert to tensor + f0_ref = torch.from_numpy(f0_ref) + f0_deg = torch.from_numpy(f0_deg) + + return pearson(f0_ref, f0_deg).numpy().tolist() diff --git a/evaluation/metrics/f0/f0_periodicity_rmse.py b/evaluation/metrics/f0/f0_periodicity_rmse.py new file mode 100644 index 0000000000000000000000000000000000000000..f25e68d316d44a44c88f0fc53fb2ec3fbe713e97 --- /dev/null +++ b/evaluation/metrics/f0/f0_periodicity_rmse.py @@ -0,0 +1,112 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torchcrepe +import math +import librosa +import torch + +import numpy as np + + +def extract_f0_periodicity_rmse( + audio_ref, + audio_deg, + fs=None, + hop_length=256, + method="dtw", +): + """Compute f0 periodicity Root Mean Square Error (RMSE) between the predicted and the ground truth audio. + audio_ref: path to the ground truth audio. + audio_deg: path to the predicted audio. + fs: sampling rate. + hop_length: hop length. + method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio. + "cut" will cut both audios into a same length according to the one with the shorter length. + """ + # Load audio + if fs != None: + audio_ref, _ = librosa.load(audio_ref, sr=fs) + audio_deg, _ = librosa.load(audio_deg, sr=fs) + else: + audio_ref, fs = librosa.load(audio_ref) + audio_deg, fs = librosa.load(audio_deg) + + # Convert to torch + audio_ref = torch.from_numpy(audio_ref).unsqueeze(0) + audio_deg = torch.from_numpy(audio_deg).unsqueeze(0) + + # Get periodicity + pitch_ref, periodicity_ref = torchcrepe.predict( + audio_ref, + sample_rate=fs, + hop_length=hop_length, + fmin=0, + fmax=1500, + model="full", + return_periodicity=True, + device="cuda:0", + ) + pitch_deg, periodicity_deg = torchcrepe.predict( + audio_deg, + sample_rate=fs, + hop_length=hop_length, + fmin=0, + fmax=1500, + model="full", + return_periodicity=True, + device="cuda:0", + ) + + # Cut silence + periodicity_ref = ( + torchcrepe.threshold.Silence()( + periodicity_ref, + audio_ref, + fs, + hop_length=hop_length, + ) + .squeeze(0) + .numpy() + ) + periodicity_deg = ( + torchcrepe.threshold.Silence()( + periodicity_deg, + audio_deg, + fs, + hop_length=hop_length, + ) + .squeeze(0) + .numpy() + ) + + # Avoid silence audio + min_length = min(len(periodicity_ref), len(periodicity_deg)) + if min_length <= 1: + return 0 + + # Periodicity length alignment + if method == "cut": + length = min(len(periodicity_ref), len(periodicity_deg)) + periodicity_ref = periodicity_ref[:length] + periodicity_deg = periodicity_deg[:length] + elif method == "dtw": + _, wp = librosa.sequence.dtw(periodicity_ref, periodicity_deg, backtrack=True) + periodicity_ref_new = [] + periodicity_deg_new = [] + for i in range(wp.shape[0]): + ref_index = wp[i][0] + deg_index = wp[i][1] + periodicity_ref_new.append(periodicity_ref[ref_index]) + periodicity_deg_new.append(periodicity_deg[deg_index]) + periodicity_ref = np.array(periodicity_ref_new) + periodicity_deg = np.array(periodicity_deg_new) + assert len(periodicity_ref) == len(periodicity_deg) + + # Compute RMSE + periodicity_mse = np.square(np.subtract(periodicity_ref, periodicity_deg)).mean() + periodicity_rmse = math.sqrt(periodicity_mse) + + return periodicity_rmse diff --git a/evaluation/metrics/f0/f0_rmse.py b/evaluation/metrics/f0/f0_rmse.py new file mode 100644 index 0000000000000000000000000000000000000000..af072ef2d5fa9b887b9b39ab2da922dcb3cf3453 --- /dev/null +++ b/evaluation/metrics/f0/f0_rmse.py @@ -0,0 +1,110 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import librosa +import torch + +import numpy as np + +from utils.util import JsonHParams +from utils.f0 import get_f0_features_using_parselmouth, get_pitch_sub_median + + +ZERO = 1e-8 + + +def extract_f0rmse( + audio_ref, + audio_deg, + fs=None, + hop_length=256, + f0_min=37, + f0_max=1000, + pitch_bin=256, + pitch_max=1100.0, + pitch_min=50.0, + need_mean=True, + method="dtw", +): + """Compute F0 Root Mean Square Error (RMSE) between the predicted and the ground truth audio. + audio_ref: path to the ground truth audio. + audio_deg: path to the predicted audio. + fs: sampling rate. + hop_length: hop length. + f0_min: lower limit for f0. + f0_max: upper limit for f0. + pitch_bin: number of bins for f0 quantization. + pitch_max: upper limit for f0 quantization. + pitch_min: lower limit for f0 quantization. + need_mean: subtract the mean value from f0 if "True". + method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio. + "cut" will cut both audios into a same length according to the one with the shorter length. + """ + # Load audio + if fs != None: + audio_ref, _ = librosa.load(audio_ref, sr=fs) + audio_deg, _ = librosa.load(audio_deg, sr=fs) + else: + audio_ref, fs = librosa.load(audio_ref) + audio_deg, fs = librosa.load(audio_deg) + + # Initialize config for f0 extraction + cfg = JsonHParams() + cfg.sample_rate = fs + cfg.hop_size = hop_length + cfg.f0_min = f0_min + cfg.f0_max = f0_max + cfg.pitch_bin = pitch_bin + cfg.pitch_max = pitch_max + cfg.pitch_min = pitch_min + + # Extract f0 + f0_ref = get_f0_features_using_parselmouth( + audio_ref, + cfg, + )[0] + + f0_deg = get_f0_features_using_parselmouth( + audio_deg, + cfg, + )[0] + + # Subtract mean value from f0 + if need_mean: + f0_ref = torch.from_numpy(f0_ref) + f0_deg = torch.from_numpy(f0_deg) + + f0_ref = get_pitch_sub_median(f0_ref).numpy() + f0_deg = get_pitch_sub_median(f0_deg).numpy() + + # Avoid silence + min_length = min(len(f0_ref), len(f0_deg)) + if min_length <= 1: + return 0 + + # F0 length alignment + if method == "cut": + length = min(len(f0_ref), len(f0_deg)) + f0_ref = f0_ref[:length] + f0_deg = f0_deg[:length] + elif method == "dtw": + _, wp = librosa.sequence.dtw(f0_ref, f0_deg, backtrack=True) + f0_gt_new = [] + f0_pred_new = [] + for i in range(wp.shape[0]): + gt_index = wp[i][0] + pred_index = wp[i][1] + f0_gt_new.append(f0_ref[gt_index]) + f0_pred_new.append(f0_deg[pred_index]) + f0_ref = np.array(f0_gt_new) + f0_deg = np.array(f0_pred_new) + assert len(f0_ref) == len(f0_deg) + + # Compute RMSE + f0_mse = np.square(np.subtract(f0_ref, f0_deg)).mean() + f0_rmse = math.sqrt(f0_mse) + + return f0_rmse diff --git a/evaluation/metrics/f0/v_uv_f1.py b/evaluation/metrics/f0/v_uv_f1.py new file mode 100644 index 0000000000000000000000000000000000000000..24e73156bd531789256564959bf37f66e6400f29 --- /dev/null +++ b/evaluation/metrics/f0/v_uv_f1.py @@ -0,0 +1,110 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import librosa +import torch + +import numpy as np + +from utils.util import JsonHParams +from utils.f0 import get_f0_features_using_parselmouth + + +ZERO = 1e-8 + + +def extract_f1_v_uv( + audio_ref, + audio_deg, + fs=None, + hop_length=256, + f0_min=37, + f0_max=1000, + pitch_bin=256, + pitch_max=1100.0, + pitch_min=50.0, + method="dtw", +): + """Compute F1 socre of voiced/unvoiced accuracy between the predicted and the ground truth audio. + audio_ref: path to the ground truth audio. + audio_deg: path to the predicted audio. + fs: sampling rate. + hop_length: hop length. + f0_min: lower limit for f0. + f0_max: upper limit for f0. + pitch_bin: number of bins for f0 quantization. + pitch_max: upper limit for f0 quantization. + pitch_min: lower limit for f0 quantization. + need_mean: subtract the mean value from f0 if "True". + method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio. + "cut" will cut both audios into a same length according to the one with the shorter length. + """ + # Load audio + if fs != None: + audio_ref, _ = librosa.load(audio_ref, sr=fs) + audio_deg, _ = librosa.load(audio_deg, sr=fs) + else: + audio_ref, fs = librosa.load(audio_ref) + audio_deg, fs = librosa.load(audio_deg) + + # Initialize config + cfg = JsonHParams() + cfg.sample_rate = fs + cfg.hop_size = hop_length + cfg.f0_min = f0_min + cfg.f0_max = f0_max + cfg.pitch_bin = pitch_bin + cfg.pitch_max = pitch_max + cfg.pitch_min = pitch_min + + # Compute f0 + f0_ref = get_f0_features_using_parselmouth( + audio_ref, + cfg, + )[0] + + f0_deg = get_f0_features_using_parselmouth( + audio_deg, + cfg, + )[0] + + # Avoid silence + min_length = min(len(f0_ref), len(f0_deg)) + if min_length <= 1: + return 0, 0, 0 + + # F0 length alignment + if method == "cut": + length = min(len(f0_ref), len(f0_deg)) + f0_ref = f0_ref[:length] + f0_deg = f0_deg[:length] + elif method == "dtw": + _, wp = librosa.sequence.dtw(f0_ref, f0_deg, backtrack=True) + f0_gt_new = [] + f0_pred_new = [] + for i in range(wp.shape[0]): + gt_index = wp[i][0] + pred_index = wp[i][1] + f0_gt_new.append(f0_ref[gt_index]) + f0_pred_new.append(f0_deg[pred_index]) + f0_ref = np.array(f0_gt_new) + f0_deg = np.array(f0_pred_new) + assert len(f0_ref) == len(f0_deg) + + # Get voiced/unvoiced parts + ref_voiced = torch.Tensor([f0_ref != 0]).bool() + deg_voiced = torch.Tensor([f0_deg != 0]).bool() + + # Compute TP, FP, FN + true_postives = (ref_voiced & deg_voiced).sum() + false_postives = (~ref_voiced & deg_voiced).sum() + false_negatives = (ref_voiced & ~deg_voiced).sum() + + return ( + true_postives.numpy().tolist(), + false_postives.numpy().tolist(), + false_negatives.numpy().tolist(), + ) diff --git a/evaluation/metrics/intelligibility/__init__.py b/evaluation/metrics/intelligibility/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evaluation/metrics/intelligibility/character_error_rate.py b/evaluation/metrics/intelligibility/character_error_rate.py new file mode 100644 index 0000000000000000000000000000000000000000..9440d0897d27351a4a03ca761dcec5b621c7fa81 --- /dev/null +++ b/evaluation/metrics/intelligibility/character_error_rate.py @@ -0,0 +1,81 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import whisper + +from torchmetrics import CharErrorRate + + +def extract_cer( + content_gt=None, + audio_ref=None, + audio_deg=None, + fs=None, + language="chinese", + remove_space=True, + remove_punctuation=True, + mode="gt_audio", +): + """Compute Character Error Rate (CER) between the predicted and the ground truth audio. + content_gt: the ground truth content. + audio_ref: path to the ground truth audio. + audio_deg: path to the predicted audio. + mode: "gt_content" computes the CER between the predicted content obtained from the whisper model and the ground truth content. + both content_gt and audio_deg are needed. + "gt_audio" computes the CER between the extracted ground truth and predicted contents obtained from the whisper model. + both audio_ref and audio_deg are needed. + """ + # Get ground truth content + if mode == "gt_content": + assert content_gt != None + if language == "chinese": + prompt = "以下是普通话的句子" + model = whisper.load_model("large").cuda() + result_deg = model.transcribe( + audio_deg, language="zh", verbose=True, initial_prompt=prompt + ) + elif language == "english": + model = whisper.load_model("large").cuda() + result_deg = model.transcribe(audio_deg, language="en", verbose=True) + elif mode == "gt_audio": + assert audio_ref != None + if language == "chinese": + prompt = "以下是普通话的句子" + model = whisper.load_model("large").cuda() + result_ref = model.transcribe( + audio_ref, language="zh", verbose=True, initial_prompt=prompt + ) + result_deg = model.transcribe( + audio_deg, language="zh", verbose=True, initial_prompt=prompt + ) + elif language == "english": + model = whisper.load_model("large").cuda() + result_ref = model.transcribe(audio_deg, language="en", verbose=True) + result_deg = model.transcribe(audio_deg, language="en", verbose=True) + content_gt = result_ref["text"] + if remove_space: + content_gt = content_gt.replace(" ", "") + if remove_punctuation: + content_gt = content_gt.replace(".", "") + content_gt = content_gt.replace("'", "") + content_gt = content_gt.replace("-", "") + content_gt = content_gt.replace(",", "") + content_gt = content_gt.replace("!", "") + content_gt = content_gt.lower() + + # Get predicted truth content + content_pred = result_deg["text"] + if remove_space: + content_pred = content_pred.replace(" ", "") + if remove_punctuation: + content_pred = content_pred.replace(".", "") + content_pred = content_pred.replace("'", "") + content_pred = content_pred.replace("-", "") + content_pred = content_pred.replace(",", "") + content_pred = content_pred.replace("!", "") + content_pred = content_pred.lower() + cer = CharErrorRate() + + return cer(content_pred, content_gt).numpy().tolist() diff --git a/evaluation/metrics/intelligibility/word_error_rate.py b/evaluation/metrics/intelligibility/word_error_rate.py new file mode 100644 index 0000000000000000000000000000000000000000..337efb9b6a1fc3bef48fdb7ac47e168272c8f9ec --- /dev/null +++ b/evaluation/metrics/intelligibility/word_error_rate.py @@ -0,0 +1,81 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import whisper + +from torchmetrics import WordErrorRate + + +def extract_wer( + content_gt=None, + audio_ref=None, + audio_deg=None, + fs=None, + language="chinese", + remove_space=True, + remove_punctuation=True, + mode="gt_audio", +): + """Compute Word Error Rate (WER) between the predicted and the ground truth audio. + content_gt: the ground truth content. + audio_ref: path to the ground truth audio. + audio_deg: path to the predicted audio. + mode: "gt_content" computes the WER between the predicted content obtained from the whisper model and the ground truth content. + both content_gt and audio_deg are needed. + "gt_audio" computes the WER between the extracted ground truth and predicted contents obtained from the whisper model. + both audio_ref and audio_deg are needed. + """ + # Get ground truth content + if mode == "gt_content": + assert content_gt != None + if language == "chinese": + prompt = "以下是普通话的句子" + model = whisper.load_model("large").cuda() + result_deg = model.transcribe( + audio_deg, language="zh", verbose=True, initial_prompt=prompt + ) + elif language == "english": + model = whisper.load_model("large").cuda() + result_deg = model.transcribe(audio_deg, language="en", verbose=True) + elif mode == "gt_audio": + assert audio_ref != None + if language == "chinese": + prompt = "以下是普通话的句子" + model = whisper.load_model("large").cuda() + result_ref = model.transcribe( + audio_ref, language="zh", verbose=True, initial_prompt=prompt + ) + result_deg = model.transcribe( + audio_deg, language="zh", verbose=True, initial_prompt=prompt + ) + elif language == "english": + model = whisper.load_model("large").cuda() + result_ref = model.transcribe(audio_deg, language="en", verbose=True) + result_deg = model.transcribe(audio_deg, language="en", verbose=True) + content_gt = result_ref["text"] + if remove_space: + content_gt = content_gt.replace(" ", "") + if remove_punctuation: + content_gt = content_gt.replace(".", "") + content_gt = content_gt.replace("'", "") + content_gt = content_gt.replace("-", "") + content_gt = content_gt.replace(",", "") + content_gt = content_gt.replace("!", "") + content_gt = content_gt.lower() + + # Get predicted content + content_pred = result_deg["text"] + if remove_space: + content_pred = content_pred.replace(" ", "") + if remove_punctuation: + content_pred = content_pred.replace(".", "") + content_pred = content_pred.replace("'", "") + content_pred = content_pred.replace("-", "") + content_pred = content_pred.replace(",", "") + content_pred = content_pred.replace("!", "") + content_pred = content_pred.lower() + wer = WordErrorRate() + + return wer(content_pred, content_gt).numpy().tolist() diff --git a/evaluation/metrics/similarity/__init__.py b/evaluation/metrics/similarity/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evaluation/metrics/similarity/models/RawNetBasicBlock.py b/evaluation/metrics/similarity/models/RawNetBasicBlock.py new file mode 100644 index 0000000000000000000000000000000000000000..1bf2bf08e28d29fd31e51b55394220ea7f1a591b --- /dev/null +++ b/evaluation/metrics/similarity/models/RawNetBasicBlock.py @@ -0,0 +1,146 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class PreEmphasis(torch.nn.Module): + def __init__(self, coef: float = 0.97) -> None: + super().__init__() + self.coef = coef + # make kernel + # In pytorch, the convolution operation uses cross-correlation. So, filter is flipped. + self.register_buffer( + "flipped_filter", + torch.FloatTensor([-self.coef, 1.0]).unsqueeze(0).unsqueeze(0), + ) + + def forward(self, input: torch.tensor) -> torch.tensor: + assert ( + len(input.size()) == 2 + ), "The number of dimensions of input tensor must be 2!" + # reflect padding to match lengths of in/out + input = input.unsqueeze(1) + input = F.pad(input, (1, 0), "reflect") + return F.conv1d(input, self.flipped_filter) + + +class AFMS(nn.Module): + """ + Alpha-Feature map scaling, added to the output of each residual block[1,2]. + + Reference: + [1] RawNet2 : https://www.isca-speech.org/archive/Interspeech_2020/pdfs/1011.pdf + [2] AMFS : https://www.koreascience.or.kr/article/JAKO202029757857763.page + """ + + def __init__(self, nb_dim: int) -> None: + super().__init__() + self.alpha = nn.Parameter(torch.ones((nb_dim, 1))) + self.fc = nn.Linear(nb_dim, nb_dim) + self.sig = nn.Sigmoid() + + def forward(self, x): + y = F.adaptive_avg_pool1d(x, 1).view(x.size(0), -1) + y = self.sig(self.fc(y)).view(x.size(0), x.size(1), -1) + + x = x + self.alpha + x = x * y + return x + + +class Bottle2neck(nn.Module): + def __init__( + self, + inplanes, + planes, + kernel_size=None, + dilation=None, + scale=4, + pool=False, + ): + super().__init__() + + width = int(math.floor(planes / scale)) + + self.conv1 = nn.Conv1d(inplanes, width * scale, kernel_size=1) + self.bn1 = nn.BatchNorm1d(width * scale) + + self.nums = scale - 1 + + convs = [] + bns = [] + + num_pad = math.floor(kernel_size / 2) * dilation + + for i in range(self.nums): + convs.append( + nn.Conv1d( + width, + width, + kernel_size=kernel_size, + dilation=dilation, + padding=num_pad, + ) + ) + bns.append(nn.BatchNorm1d(width)) + + self.convs = nn.ModuleList(convs) + self.bns = nn.ModuleList(bns) + + self.conv3 = nn.Conv1d(width * scale, planes, kernel_size=1) + self.bn3 = nn.BatchNorm1d(planes) + + self.relu = nn.ReLU() + + self.width = width + + self.mp = nn.MaxPool1d(pool) if pool else False + self.afms = AFMS(planes) + + if inplanes != planes: # if change in number of filters + self.residual = nn.Sequential( + nn.Conv1d(inplanes, planes, kernel_size=1, stride=1, bias=False) + ) + else: + self.residual = nn.Identity() + + def forward(self, x): + residual = self.residual(x) + + out = self.conv1(x) + out = self.relu(out) + out = self.bn1(out) + + spx = torch.split(out, self.width, 1) + for i in range(self.nums): + if i == 0: + sp = spx[i] + else: + sp = sp + spx[i] + sp = self.convs[i](sp) + sp = self.relu(sp) + sp = self.bns[i](sp) + if i == 0: + out = sp + else: + out = torch.cat((out, sp), 1) + + out = torch.cat((out, spx[self.nums]), 1) + + out = self.conv3(out) + out = self.relu(out) + out = self.bn3(out) + + out += residual + if self.mp: + out = self.mp(out) + out = self.afms(out) + + return out diff --git a/evaluation/metrics/similarity/models/RawNetModel.py b/evaluation/metrics/similarity/models/RawNetModel.py new file mode 100644 index 0000000000000000000000000000000000000000..3e687cf77557b89e2f4b938d202e69f0affb1eb9 --- /dev/null +++ b/evaluation/metrics/similarity/models/RawNetModel.py @@ -0,0 +1,142 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# -*- encoding: utf-8 -*- + +import torch +import torch.nn as nn +from asteroid_filterbanks import Encoder, ParamSincFB + +from .RawNetBasicBlock import Bottle2neck, PreEmphasis + + +class RawNet3(nn.Module): + def __init__(self, block, model_scale, context, summed, C=1024, **kwargs): + super().__init__() + + nOut = kwargs["nOut"] + + self.context = context + self.encoder_type = kwargs["encoder_type"] + self.log_sinc = kwargs["log_sinc"] + self.norm_sinc = kwargs["norm_sinc"] + self.out_bn = kwargs["out_bn"] + self.summed = summed + + self.preprocess = nn.Sequential( + PreEmphasis(), nn.InstanceNorm1d(1, eps=1e-4, affine=True) + ) + self.conv1 = Encoder( + ParamSincFB( + C // 4, + 251, + stride=kwargs["sinc_stride"], + ) + ) + self.relu = nn.ReLU() + self.bn1 = nn.BatchNorm1d(C // 4) + + self.layer1 = block( + C // 4, C, kernel_size=3, dilation=2, scale=model_scale, pool=5 + ) + self.layer2 = block(C, C, kernel_size=3, dilation=3, scale=model_scale, pool=3) + self.layer3 = block(C, C, kernel_size=3, dilation=4, scale=model_scale) + self.layer4 = nn.Conv1d(3 * C, 1536, kernel_size=1) + + if self.context: + attn_input = 1536 * 3 + else: + attn_input = 1536 + print("self.encoder_type", self.encoder_type) + if self.encoder_type == "ECA": + attn_output = 1536 + elif self.encoder_type == "ASP": + attn_output = 1 + else: + raise ValueError("Undefined encoder") + + self.attention = nn.Sequential( + nn.Conv1d(attn_input, 128, kernel_size=1), + nn.ReLU(), + nn.BatchNorm1d(128), + nn.Conv1d(128, attn_output, kernel_size=1), + nn.Softmax(dim=2), + ) + + self.bn5 = nn.BatchNorm1d(3072) + + self.fc6 = nn.Linear(3072, nOut) + self.bn6 = nn.BatchNorm1d(nOut) + + self.mp3 = nn.MaxPool1d(3) + + def forward(self, x): + """ + :param x: input mini-batch (bs, samp) + """ + + with torch.cuda.amp.autocast(enabled=False): + x = self.preprocess(x) + x = torch.abs(self.conv1(x)) + if self.log_sinc: + x = torch.log(x + 1e-6) + if self.norm_sinc == "mean": + x = x - torch.mean(x, dim=-1, keepdim=True) + elif self.norm_sinc == "mean_std": + m = torch.mean(x, dim=-1, keepdim=True) + s = torch.std(x, dim=-1, keepdim=True) + s[s < 0.001] = 0.001 + x = (x - m) / s + + if self.summed: + x1 = self.layer1(x) + x2 = self.layer2(x1) + x3 = self.layer3(self.mp3(x1) + x2) + else: + x1 = self.layer1(x) + x2 = self.layer2(x1) + x3 = self.layer3(x2) + + x = self.layer4(torch.cat((self.mp3(x1), x2, x3), dim=1)) + x = self.relu(x) + + t = x.size()[-1] + + if self.context: + global_x = torch.cat( + ( + x, + torch.mean(x, dim=2, keepdim=True).repeat(1, 1, t), + torch.sqrt( + torch.var(x, dim=2, keepdim=True).clamp(min=1e-4, max=1e4) + ).repeat(1, 1, t), + ), + dim=1, + ) + else: + global_x = x + + w = self.attention(global_x) + + mu = torch.sum(x * w, dim=2) + sg = torch.sqrt( + (torch.sum((x**2) * w, dim=2) - mu**2).clamp(min=1e-4, max=1e4) + ) + + x = torch.cat((mu, sg), 1) + + x = self.bn5(x) + + x = self.fc6(x) + + if self.out_bn: + x = self.bn6(x) + + return x + + +def MainModel(**kwargs): + model = RawNet3(Bottle2neck, model_scale=8, context=True, summed=True, **kwargs) + return model diff --git a/evaluation/metrics/similarity/models/__init__.py b/evaluation/metrics/similarity/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evaluation/metrics/similarity/speaker_similarity.py b/evaluation/metrics/similarity/speaker_similarity.py new file mode 100644 index 0000000000000000000000000000000000000000..82e3968da2ba945707382f4f8627b7902edf8a96 --- /dev/null +++ b/evaluation/metrics/similarity/speaker_similarity.py @@ -0,0 +1,119 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os + +import numpy as np +import soundfile as sf +import torch +import torch.nn.functional as F +from tqdm import tqdm +import librosa + +from .models.RawNetModel import RawNet3 +from .models.RawNetBasicBlock import Bottle2neck + + +def extract_speaker_embd( + model, fn: str, n_samples: int, n_segments: int = 10, gpu: bool = False +) -> np.ndarray: + audio, sample_rate = sf.read(fn) + if len(audio.shape) > 1: + raise ValueError( + f"RawNet3 supports mono input only. Input data has a shape of {audio.shape}." + ) + + if sample_rate != 16000: + # resample to 16000kHz + audio = librosa.resample(audio, orig_sr=sample_rate, target_sr=16000) + # print("resample to 16000kHz!") + if len(audio) < n_samples: # RawNet3 was trained using utterances of 3 seconds + shortage = n_samples - len(audio) + 1 + audio = np.pad(audio, (0, shortage), "wrap") + + audios = [] + startframe = np.linspace(0, len(audio) - n_samples, num=n_segments) + for asf in startframe: + audios.append(audio[int(asf) : int(asf) + n_samples]) + + audios = torch.from_numpy(np.stack(audios, axis=0).astype(np.float32)) + if gpu: + audios = audios.to("cuda") + with torch.no_grad(): + output = model(audios) + + return output + + +def extract_speaker_similarity(target_path, reference_path): + model = RawNet3( + Bottle2neck, + model_scale=8, + context=True, + summed=True, + encoder_type="ECA", + nOut=256, + out_bn=False, + sinc_stride=10, + log_sinc=True, + norm_sinc="mean", + grad_mult=1, + ) + + gpu = False + model.load_state_dict( + torch.load( + "pretrained/rawnet3/model.pt", + map_location=lambda storage, loc: storage, + )["model"] + ) + model.eval() + print("RawNet3 initialised & weights loaded!") + + if torch.cuda.is_available(): + print("Cuda available, conducting inference on GPU") + model = model.to("cuda") + gpu = True + # for target_path, reference_path in zip(target_paths, ref_paths): + # print(f"Extracting embeddings for target singers...") + + target_embeddings = [] + for file in tqdm(os.listdir(target_path)): + output = extract_speaker_embd( + model, + fn=os.path.join(target_path, file), + n_samples=48000, + n_segments=10, + gpu=gpu, + ).mean(0) + target_embeddings.append(output.detach().cpu().numpy()) + target_embeddings = np.array(target_embeddings) + target_embedding = np.mean(target_embeddings, axis=0) + + # print(f"Extracting embeddings for reference singer...") + + reference_embeddings = [] + for file in tqdm(os.listdir(reference_path)): + output = extract_speaker_embd( + model, + fn=os.path.join(reference_path, file), + n_samples=48000, + n_segments=10, + gpu=gpu, + ).mean(0) + reference_embeddings.append(output.detach().cpu().numpy()) + reference_embeddings = np.array(reference_embeddings) + + # print("Calculating cosine similarity...") + + cos_sim = F.cosine_similarity( + torch.from_numpy(np.mean(target_embeddings, axis=0)).unsqueeze(0), + torch.from_numpy(np.mean(reference_embeddings, axis=0)).unsqueeze(0), + dim=1, + ) + + # print(f"Mean cosine similarity: {cos_sim.item()}") + + return cos_sim.item() diff --git a/evaluation/metrics/spectrogram/__init__.py b/evaluation/metrics/spectrogram/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evaluation/metrics/spectrogram/frechet_distance.py b/evaluation/metrics/spectrogram/frechet_distance.py new file mode 100644 index 0000000000000000000000000000000000000000..81ee41329ef82e42a56bca7ccffb19755ad1d938 --- /dev/null +++ b/evaluation/metrics/spectrogram/frechet_distance.py @@ -0,0 +1,31 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from frechet_audio_distance import FrechetAudioDistance + + +def extract_fad( + audio_dir1, + audio_dir2, + mode="vggish", + use_pca=False, + use_activation=False, + verbose=False, +): + """Extract Frechet Audio Distance for two given audio folders. + audio_dir1: path to the ground truth audio folder. + audio_dir2: path to the predicted audio folder. + mode: "vggish", "pann", "clap" for different models. + """ + frechet = FrechetAudioDistance( + model_name=mode, + use_pca=use_pca, + use_activation=use_activation, + verbose=verbose, + ) + + fad_score = frechet.score(audio_dir1, audio_dir2) + + return fad_score diff --git a/evaluation/metrics/spectrogram/mel_cepstral_distortion.py b/evaluation/metrics/spectrogram/mel_cepstral_distortion.py new file mode 100644 index 0000000000000000000000000000000000000000..35487c4bceac42617f3fbf6266e306da33778e29 --- /dev/null +++ b/evaluation/metrics/spectrogram/mel_cepstral_distortion.py @@ -0,0 +1,21 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from pymcd.mcd import Calculate_MCD + + +def extract_mcd(audio_ref, audio_deg, fs=None, mode="dtw_sl"): + """Extract Mel-Cepstral Distance for a two given audio. + Args: + audio_ref: The given reference audio. It is an audio path. + audio_deg: The given synthesized audio. It is an audio path. + mode: "plain", "dtw" and "dtw_sl". + """ + mcd_toolbox = Calculate_MCD(MCD_mode=mode) + if fs != None: + mcd_toolbox.SAMPLING_RATE = fs + mcd_value = mcd_toolbox.calculate_mcd(audio_ref, audio_deg) + + return mcd_value diff --git a/evaluation/metrics/spectrogram/multi_resolution_stft_distance.py b/evaluation/metrics/spectrogram/multi_resolution_stft_distance.py new file mode 100644 index 0000000000000000000000000000000000000000..4dc5c35aa9c9efb91bdabe09e0cf3d279fb5a7b6 --- /dev/null +++ b/evaluation/metrics/spectrogram/multi_resolution_stft_distance.py @@ -0,0 +1,225 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import librosa +import torch + +import numpy as np + + +def extract_mstft( + audio_ref, + audio_deg, + fs=None, + mid_freq=None, + high_freq=None, + method="cut", + version="pwg", +): + """Compute Multi-Scale STFT Distance (mstft) between the predicted and the ground truth audio. + audio_ref: path to the ground truth audio. + audio_deg: path to the predicted audio. + fs: sampling rate. + med_freq: division frequency for mid frequency parts. + high_freq: division frequency for high frequency parts. + method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio. + "cut" will cut both audios into a same length according to the one with the shorter length. + version: "pwg" will use the computational version provided by ParallelWaveGAN. + "encodec" will use the computational version provided by Encodec. + """ + # Load audio + if fs != None: + audio_ref, _ = librosa.load(audio_ref, sr=fs) + audio_deg, _ = librosa.load(audio_deg, sr=fs) + else: + audio_ref, fs = librosa.load(audio_ref) + audio_deg, fs = librosa.load(audio_deg) + + # Automatically choose mid_freq and high_freq if they are not given + if mid_freq == None: + mid_freq = fs // 6 + if high_freq == None: + high_freq = fs // 3 + + # Audio length alignment + if len(audio_ref) != len(audio_deg): + if method == "cut": + length = min(len(audio_ref), len(audio_deg)) + audio_ref = audio_ref[:length] + audio_deg = audio_deg[:length] + elif method == "dtw": + _, wp = librosa.sequence.dtw(audio_ref, audio_deg, backtrack=True) + audio_ref_new = [] + audio_deg_new = [] + for i in range(wp.shape[0]): + ref_index = wp[i][0] + deg_index = wp[i][1] + audio_ref_new.append(audio_ref[ref_index]) + audio_deg_new.append(audio_deg[deg_index]) + audio_ref = np.array(audio_ref_new) + audio_deg = np.array(audio_deg_new) + assert len(audio_ref) == len(audio_deg) + + # Define loss function + l1Loss = torch.nn.L1Loss(reduction="mean") + l2Loss = torch.nn.MSELoss(reduction="mean") + + # Compute distance + if version == "encodec": + n_fft = 1024 + + mstft = 0 + mstft_low = 0 + mstft_mid = 0 + mstft_high = 0 + + freq_resolution = fs / n_fft + mid_freq_index = 1 + int(np.floor(mid_freq / freq_resolution)) + high_freq_index = 1 + int(np.floor(high_freq / freq_resolution)) + + for i in range(5, 11): + hop_length = 2**i // 4 + win_length = 2**i + + spec_ref = librosa.stft( + y=audio_ref, n_fft=n_fft, hop_length=hop_length, win_length=win_length + ) + spec_deg = librosa.stft( + y=audio_deg, n_fft=n_fft, hop_length=hop_length, win_length=win_length + ) + + mag_ref = np.abs(spec_ref) + mag_deg = np.abs(spec_deg) + + mag_ref = torch.from_numpy(mag_ref) + mag_deg = torch.from_numpy(mag_deg) + mstft += l1Loss(mag_ref, mag_deg) + l2Loss(mag_ref, mag_deg) + + mag_ref_low = mag_ref[:mid_freq_index, :] + mag_deg_low = mag_deg[:mid_freq_index, :] + mstft_low += l1Loss(mag_ref_low, mag_deg_low) + l2Loss( + mag_ref_low, mag_deg_low + ) + + mag_ref_mid = mag_ref[mid_freq_index:high_freq_index, :] + mag_deg_mid = mag_deg[mid_freq_index:high_freq_index, :] + mstft_mid += l1Loss(mag_ref_mid, mag_deg_mid) + l2Loss( + mag_ref_mid, mag_deg_mid + ) + + mag_ref_high = mag_ref[high_freq_index:, :] + mag_deg_high = mag_deg[high_freq_index:, :] + mstft_high += l1Loss(mag_ref_high, mag_deg_high) + l2Loss( + mag_ref_high, mag_deg_high + ) + + mstft /= 6 + mstft_low /= 6 + mstft_mid /= 6 + mstft_high /= 6 + + return mstft + elif version == "pwg": + fft_sizes = [1024, 2048, 512] + hop_sizes = [120, 240, 50] + win_sizes = [600, 1200, 240] + + audio_ref = torch.from_numpy(audio_ref) + audio_deg = torch.from_numpy(audio_deg) + + mstft_sc = 0 + mstft_sc_low = 0 + mstft_sc_mid = 0 + mstft_sc_high = 0 + + mstft_mag = 0 + mstft_mag_low = 0 + mstft_mag_mid = 0 + mstft_mag_high = 0 + + for n_fft, hop_length, win_length in zip(fft_sizes, hop_sizes, win_sizes): + spec_ref = torch.stft( + audio_ref, n_fft, hop_length, win_length, return_complex=False + ) + spec_deg = torch.stft( + audio_deg, n_fft, hop_length, win_length, return_complex=False + ) + + real_ref = spec_ref[..., 0] + imag_ref = spec_ref[..., 1] + real_deg = spec_deg[..., 0] + imag_deg = spec_deg[..., 1] + + mag_ref = torch.sqrt( + torch.clamp(real_ref**2 + imag_ref**2, min=1e-7) + ).transpose(1, 0) + mag_deg = torch.sqrt( + torch.clamp(real_deg**2 + imag_deg**2, min=1e-7) + ).transpose(1, 0) + sc_loss = torch.norm(mag_ref - mag_deg, p="fro") / torch.norm( + mag_ref, p="fro" + ) + mag_loss = l1Loss(torch.log(mag_ref), torch.log(mag_deg)) + + mstft_sc += sc_loss + mstft_mag += mag_loss + + freq_resolution = fs / n_fft + mid_freq_index = 1 + int(np.floor(mid_freq / freq_resolution)) + high_freq_index = 1 + int(np.floor(high_freq / freq_resolution)) + + mag_ref_low = mag_ref[:, :mid_freq_index] + mag_deg_low = mag_deg[:, :mid_freq_index] + sc_loss_low = torch.norm(mag_ref_low - mag_deg_low, p="fro") / torch.norm( + mag_ref_low, p="fro" + ) + mag_loss_low = l1Loss(torch.log(mag_ref_low), torch.log(mag_deg_low)) + + mstft_sc_low += sc_loss_low + mstft_mag_low += mag_loss_low + + mag_ref_mid = mag_ref[:, mid_freq_index:high_freq_index] + mag_deg_mid = mag_deg[:, mid_freq_index:high_freq_index] + sc_loss_mid = torch.norm(mag_ref_mid - mag_deg_mid, p="fro") / torch.norm( + mag_ref_mid, p="fro" + ) + mag_loss_mid = l1Loss(torch.log(mag_ref_mid), torch.log(mag_deg_mid)) + + mstft_sc_mid += sc_loss_mid + mstft_mag_mid += mag_loss_mid + + mag_ref_high = mag_ref[:, high_freq_index:] + mag_deg_high = mag_deg[:, high_freq_index:] + sc_loss_high = torch.norm( + mag_ref_high - mag_deg_high, p="fro" + ) / torch.norm(mag_ref_high, p="fro") + mag_loss_high = l1Loss(torch.log(mag_ref_high), torch.log(mag_deg_high)) + + mstft_sc_high += sc_loss_high + mstft_mag_high += mag_loss_high + + # Normalize distances + mstft_sc /= len(fft_sizes) + mstft_sc_low /= len(fft_sizes) + mstft_sc_mid /= len(fft_sizes) + mstft_sc_high /= len(fft_sizes) + + mstft_mag /= len(fft_sizes) + mstft_mag_low /= len(fft_sizes) + mstft_mag_mid /= len(fft_sizes) + mstft_mag_high /= len(fft_sizes) + + # return ( + # mstft_sc.numpy().tolist(), + # mstft_sc_low.numpy().tolist(), + # mstft_sc_mid.numpy().tolist(), + # mstft_sc_high.numpy().tolist(), + # mstft_mag.numpy().tolist(), + # mstft_mag_low.numpy().tolist(), + # mstft_mag_mid.numpy().tolist(), + # mstft_mag_high.numpy().tolist(), + # ) + + return mstft_sc.numpy().tolist() + mstft_mag.numpy().tolist() diff --git a/evaluation/metrics/spectrogram/pesq.py b/evaluation/metrics/spectrogram/pesq.py new file mode 100644 index 0000000000000000000000000000000000000000..919cb23a4e8f88bb94138cbd4134640cc0a510c9 --- /dev/null +++ b/evaluation/metrics/spectrogram/pesq.py @@ -0,0 +1,56 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import librosa + +import numpy as np + +from pypesq import pesq + + +def extract_pesq(audio_ref, audio_deg, fs=None, method="cut"): + """Extract PESQ for a two given audio. + audio1: the given reference audio. It is a numpy array. + audio2: the given synthesized audio. It is a numpy array. + fs: sampling rate. + method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio. + "cut" will cut both audios into a same length according to the one with the shorter length. + """ + # Load audio + if fs != None: + audio_ref, _ = librosa.load(audio_ref, sr=fs) + audio_deg, _ = librosa.load(audio_deg, sr=fs) + else: + audio_ref, fs = librosa.load(audio_ref) + audio_deg, fs = librosa.load(audio_deg) + + # Resample + if fs != 16000: + audio_ref = librosa.resample(audio_ref, orig_sr=fs, target_sr=16000) + audio_deg = librosa.resample(audio_deg, orig_sr=fs, target_sr=16000) + fs = 16000 + + # Audio length alignment + if len(audio_ref) != len(audio_deg): + if method == "cut": + length = min(len(audio_ref), len(audio_deg)) + audio_ref = audio_ref[:length] + audio_deg = audio_deg[:length] + elif method == "dtw": + _, wp = librosa.sequence.dtw(audio_ref, audio_deg, backtrack=True) + audio_ref_new = [] + audio_deg_new = [] + for i in range(wp.shape[0]): + ref_index = wp[i][0] + deg_index = wp[i][1] + audio_ref_new.append(audio_ref[ref_index]) + audio_deg_new.append(audio_deg[deg_index]) + audio_ref = np.array(audio_ref_new) + audio_deg = np.array(audio_deg_new) + assert len(audio_ref) == len(audio_deg) + + # Compute pesq + score = pesq(audio_ref, audio_deg, fs) + return score diff --git a/evaluation/metrics/spectrogram/scale_invariant_signal_to_distortion_ratio.py b/evaluation/metrics/spectrogram/scale_invariant_signal_to_distortion_ratio.py new file mode 100644 index 0000000000000000000000000000000000000000..a7f8254eabaccd66fab45f86cd1dc08ac571eb57 --- /dev/null +++ b/evaluation/metrics/spectrogram/scale_invariant_signal_to_distortion_ratio.py @@ -0,0 +1,45 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import librosa + +import numpy as np + +from torchmetrics import ScaleInvariantSignalDistortionRatio + + +def extract_si_sdr(audio_ref, audio_deg, fs=None, method="cut"): + si_sdr = ScaleInvariantSignalDistortionRatio() + + if fs != None: + audio_ref, _ = librosa.load(audio_ref, sr=fs) + audio_deg, _ = librosa.load(audio_deg, sr=fs) + else: + audio_ref, fs = librosa.load(audio_ref) + audio_deg, fs = librosa.load(audio_deg) + + if len(audio_ref) != len(audio_deg): + if method == "cut": + length = min(len(audio_ref), len(audio_deg)) + audio_ref = audio_ref[:length] + audio_deg = audio_deg[:length] + elif method == "dtw": + _, wp = librosa.sequence.dtw(audio_ref, audio_deg, backtrack=True) + audio_ref_new = [] + audio_deg_new = [] + for i in range(wp.shape[0]): + ref_index = wp[i][0] + deg_index = wp[i][1] + audio_ref_new.append(audio_ref[ref_index]) + audio_deg_new.append(audio_deg[deg_index]) + audio_ref = np.array(audio_ref_new) + audio_deg = np.array(audio_deg_new) + assert len(audio_ref) == len(audio_deg) + + audio_ref = torch.from_numpy(audio_ref) + audio_deg = torch.from_numpy(audio_deg) + + return si_sdr(audio_deg, audio_ref) diff --git a/evaluation/metrics/spectrogram/scale_invariant_signal_to_noise_ratio.py b/evaluation/metrics/spectrogram/scale_invariant_signal_to_noise_ratio.py new file mode 100644 index 0000000000000000000000000000000000000000..2cd1d66a48bd1d68d95d6cfdbbe73e6046896cfd --- /dev/null +++ b/evaluation/metrics/spectrogram/scale_invariant_signal_to_noise_ratio.py @@ -0,0 +1,45 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import librosa + +import numpy as np + +from torchmetrics import ScaleInvariantSignalNoiseRatio + + +def extract_si_snr(audio_ref, audio_deg, fs=None, method="cut"): + si_snr = ScaleInvariantSignalNoiseRatio() + + if fs != None: + audio_ref, _ = librosa.load(audio_ref, sr=fs) + audio_deg, _ = librosa.load(audio_deg, sr=fs) + else: + audio_ref, fs = librosa.load(audio_ref) + audio_deg, fs = librosa.load(audio_deg) + + if len(audio_ref) != len(audio_deg): + if method == "cut": + length = min(len(audio_ref), len(audio_deg)) + audio_ref = audio_ref[:length] + audio_deg = audio_deg[:length] + elif method == "dtw": + _, wp = librosa.sequence.dtw(audio_ref, audio_deg, backtrack=True) + audio_ref_new = [] + audio_deg_new = [] + for i in range(wp.shape[0]): + ref_index = wp[i][0] + deg_index = wp[i][1] + audio_ref_new.append(audio_ref[ref_index]) + audio_deg_new.append(audio_deg[deg_index]) + audio_ref = np.array(audio_ref_new) + audio_deg = np.array(audio_deg_new) + assert len(audio_ref) == len(audio_deg) + + audio_ref = torch.from_numpy(audio_ref) + audio_deg = torch.from_numpy(audio_deg) + + return si_snr(audio_deg, audio_ref) diff --git a/evaluation/metrics/spectrogram/short_time_objective_intelligibility.py b/evaluation/metrics/spectrogram/short_time_objective_intelligibility.py new file mode 100644 index 0000000000000000000000000000000000000000..ab447a444e2303a28e5af7a89b872f242e92c227 --- /dev/null +++ b/evaluation/metrics/spectrogram/short_time_objective_intelligibility.py @@ -0,0 +1,56 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import librosa + +import numpy as np + +from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility + + +def extract_stoi(audio_ref, audio_deg, fs=None, extended=False, method="cut"): + """Compute Short-Time Objective Intelligibility between the predicted and the ground truth audio. + audio_ref: path to the ground truth audio. + audio_deg: path to the predicted audio. + fs: sampling rate. + method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio. + "cut" will cut both audios into a same length according to the one with the shorter length. + """ + # Load audio + if fs != None: + audio_ref, _ = librosa.load(audio_ref, sr=fs) + audio_deg, _ = librosa.load(audio_deg, sr=fs) + else: + audio_ref, fs = librosa.load(audio_ref) + audio_deg, fs = librosa.load(audio_deg) + + # Initialize method + stoi = ShortTimeObjectiveIntelligibility(fs, extended) + + # Audio length alignment + if len(audio_ref) != len(audio_deg): + if method == "cut": + length = min(len(audio_ref), len(audio_deg)) + audio_ref = audio_ref[:length] + audio_deg = audio_deg[:length] + elif method == "dtw": + _, wp = librosa.sequence.dtw(audio_ref, audio_deg, backtrack=True) + audio_ref_new = [] + audio_deg_new = [] + for i in range(wp.shape[0]): + ref_index = wp[i][0] + deg_index = wp[i][1] + audio_ref_new.append(audio_ref[ref_index]) + audio_deg_new.append(audio_deg[deg_index]) + audio_ref = np.array(audio_ref_new) + audio_deg = np.array(audio_deg_new) + assert len(audio_ref) == len(audio_deg) + + # Convert to tensor + audio_ref = torch.from_numpy(audio_ref) + audio_deg = torch.from_numpy(audio_deg) + + return stoi(audio_deg, audio_ref).numpy().tolist() diff --git a/imgs/svc/DiffComoSVC.png b/imgs/svc/DiffComoSVC.png new file mode 100644 index 0000000000000000000000000000000000000000..40eddeec8384e0d5416febd556376f5ed46c8f4a Binary files /dev/null and b/imgs/svc/DiffComoSVC.png differ diff --git a/imgs/svc/MultipleContentsSVC.png b/imgs/svc/MultipleContentsSVC.png new file mode 100644 index 0000000000000000000000000000000000000000..6d5cc04f639f5d8e3b4c8cecad0c350c96bc092f Binary files /dev/null and b/imgs/svc/MultipleContentsSVC.png differ diff --git a/imgs/svc/pipeline.png b/imgs/svc/pipeline.png new file mode 100644 index 0000000000000000000000000000000000000000..eb5aafa2ed457f611c13dc45f40f7b005c3598e8 Binary files /dev/null and b/imgs/svc/pipeline.png differ diff --git a/imgs/tta/DiffusionTTA.png b/imgs/tta/DiffusionTTA.png new file mode 100644 index 0000000000000000000000000000000000000000..a55bd92323315398a7d91b410f4f3225b54179f1 Binary files /dev/null and b/imgs/tta/DiffusionTTA.png differ diff --git a/imgs/vocoder/gan/MSSBCQTD.png b/imgs/vocoder/gan/MSSBCQTD.png new file mode 100644 index 0000000000000000000000000000000000000000..417e20fbaa8997ac37141d5068029ad02a97cbac --- /dev/null +++ b/imgs/vocoder/gan/MSSBCQTD.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:98706f1abc595abf10513cd05a1ba2177c4ee991f6786a9c3ca81513eea95c5a +size 1773360 diff --git a/imgs/vocoder/gan/pipeline.png b/imgs/vocoder/gan/pipeline.png new file mode 100644 index 0000000000000000000000000000000000000000..274828515ad906905d6c267d636995277a2b4caf Binary files /dev/null and b/imgs/vocoder/gan/pipeline.png differ diff --git a/inference.py b/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..a3025629d23af83c8b4a804a80115907044c7c83 --- /dev/null +++ b/inference.py @@ -0,0 +1,158 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +from argparse import ArgumentParser +import os + +from models.tts.fastspeech2.fs2_inference import FastSpeech2Inference +from models.tts.vits.vits_inference import VitsInference +from models.tts.valle.valle_inference import VALLEInference +from utils.util import load_config +import torch + + +def build_inference(args, cfg): + supported_inference = { + "FastSpeech2": FastSpeech2Inference, + "VITS": VitsInference, + "VALLE": VALLEInference, + } + + inference_class = supported_inference[cfg.model_type] + inference = inference_class(args, cfg) + return inference + + +def cuda_relevant(deterministic=False): + torch.cuda.empty_cache() + # TF32 on Ampere and above + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.enabled = True + torch.backends.cudnn.allow_tf32 = True + # Deterministic + torch.backends.cudnn.deterministic = deterministic + torch.backends.cudnn.benchmark = not deterministic + torch.use_deterministic_algorithms(deterministic) + + +def build_parser(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--config", + type=str, + required=True, + help="JSON/YAML file for configurations.", + ) + parser.add_argument( + "--dataset", + type=str, + help="convert from the source data", + default=None, + ) + parser.add_argument( + "--testing_set", + type=str, + help="train, test, golden_test", + default="test", + ) + parser.add_argument( + "--test_list_file", + type=str, + help="convert from the test list file", + default=None, + ) + parser.add_argument( + "--speaker_name", + type=str, + default=None, + help="speaker name for multi-speaker synthesis, for single-sentence mode only", + ) + parser.add_argument( + "--text", + help="Text to be synthesized.", + type=str, + default="", + ) + parser.add_argument( + "--vocoder_dir", + type=str, + default=None, + help="Vocoder checkpoint directory. Searching behavior is the same as " + "the acoustics one.", + ) + parser.add_argument( + "--acoustics_dir", + type=str, + default=None, + help="Acoustic model checkpoint directory. If a directory is given, " + "search for the latest checkpoint dir in the directory. If a specific " + "checkpoint dir is given, directly load the checkpoint.", + ) + parser.add_argument( + "--checkpoint_path", + type=str, + default=None, + help="Acoustic model checkpoint directory. If a directory is given, " + "search for the latest checkpoint dir in the directory. If a specific " + "checkpoint dir is given, directly load the checkpoint.", + ) + parser.add_argument( + "--mode", + type=str, + choices=["batch", "single"], + required=True, + help="Synthesize a whole dataset or a single sentence", + ) + parser.add_argument( + "--log_level", + type=str, + default="warning", + help="Logging level. Default: warning", + ) + parser.add_argument( + "--pitch_control", + type=float, + default=1.0, + help="control the pitch of the whole utterance, larger value for higher pitch", + ) + parser.add_argument( + "--energy_control", + type=float, + default=1.0, + help="control the energy of the whole utterance, larger value for larger volume", + ) + parser.add_argument( + "--duration_control", + type=float, + default=1.0, + help="control the speed of the whole utterance, larger value for slower speaking rate", + ) + parser.add_argument( + "--output_dir", + type=str, + default=None, + help="Output dir for saving generated results", + ) + return parser + +def main(args_list): + # Parse arguments and config + args = parser.parse_args(args_list) + cfg = load_config(args.config) + + # CUDA settings + cuda_relevant() + + # Build inference + inferencer = build_inference(args, cfg) + + # Run inference + inferencer.inference() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/inference.sh b/inference.sh new file mode 100644 index 0000000000000000000000000000000000000000..308096ce989d0b1daeff08f1cfe845acbcc19b96 --- /dev/null +++ b/inference.sh @@ -0,0 +1,45 @@ +#!/bin/bash + +Check if a text file is provided as an argument +if [ "$#" -ne 1 ]; then + echo "Usage: $0 " + exit 1 +fi + +text_file="$1" + +# Check if the provided text file exists +if [ ! -f "$text_file" ]; then + echo "Error: Text file '$text_file' not found." + exit 1 +fi +# Define the list of speaker IDs +speakers="92 6097 6670 6671 8051 9017 9136 11614 11697 12787" +counter=1 + +# Read lines from text_file.txt +while IFS= read -r line; do + # Skip empty lines + [ -z "$line" ] && continue + + label="text$counter" + counter=$((counter+1)) + + # Iterate through each fixed speaker ID + for speaker_id in $speakers; do + output_dir="/mnt/workspace/tzeying/inference/inf_librihifi_320k/trial1/${label}/${speaker_id}" + speaker_name="hifitts_${speaker_id}" + + sh /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/Amphion/egs/tts/vits_hifitts/run.sh \ + --stage 3 \ + --gpu "4" \ + --infer_expt_dir /mnt/workspace/xueliumeng/data/vits_on_libritts_hifitts/logs/Libri_HifiTTS_All \ + --infer_output_dir "$output_dir" \ + --infer_mode "single" \ + --infer_text "$line" \ + --infer_speaker_name "$speaker_name" + + echo "Processing complete for speaker ID: $speaker_id" + done + echo "Processing complete for line: $counter" +done < "$text_file" diff --git a/linear_size_0_fid.lst b/linear_size_0_fid.lst new file mode 100644 index 0000000000000000000000000000000000000000..c8023d95fd52db4b7c03ced5e324cf7357eddd27 --- /dev/null +++ b/linear_size_0_fid.lst @@ -0,0 +1,2 @@ +train-other-500#4021#180238#4021_180238_000101_000001.npy +train-other-500#5009#2500#5009_2500_000047_000000.npy diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/__pycache__/__init__.cpython-39.pyc b/models/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c29fbc92726eca0efabe230b819f7cfd6e0532d8 Binary files /dev/null and b/models/__pycache__/__init__.cpython-39.pyc differ diff --git a/models/base/__init__.py b/models/base/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fe0221047a62e0b9b3ddd112c79a700c48834fd1 --- /dev/null +++ b/models/base/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .new_trainer import BaseTrainer +from .new_inference import BaseInference diff --git a/models/base/__pycache__/__init__.cpython-39.pyc b/models/base/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e6b3ee9115ebc2fc130589cddf86d0a18f9609b5 Binary files /dev/null and b/models/base/__pycache__/__init__.cpython-39.pyc differ diff --git a/models/base/__pycache__/base_dataset.cpython-39.pyc b/models/base/__pycache__/base_dataset.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b5a629e58b2a1e9aa19b790c71a32d1ccf27f1fb Binary files /dev/null and b/models/base/__pycache__/base_dataset.cpython-39.pyc differ diff --git a/models/base/__pycache__/base_sampler.cpython-39.pyc b/models/base/__pycache__/base_sampler.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..77274b9a1cbe245c2e9ffc0f0dfc5770b59a846d Binary files /dev/null and b/models/base/__pycache__/base_sampler.cpython-39.pyc differ diff --git a/models/base/__pycache__/new_inference.cpython-39.pyc b/models/base/__pycache__/new_inference.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..743a15f326cf9c8590885786db724c60436a7c0c Binary files /dev/null and b/models/base/__pycache__/new_inference.cpython-39.pyc differ diff --git a/models/base/__pycache__/new_trainer.cpython-39.pyc b/models/base/__pycache__/new_trainer.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ca472dee822c751e919c84e526d181543bbf52f Binary files /dev/null and b/models/base/__pycache__/new_trainer.cpython-39.pyc differ diff --git a/models/base/base_dataset.py b/models/base/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e7d9848bb08f29669a51f3dde200d31bafe1d8da --- /dev/null +++ b/models/base/base_dataset.py @@ -0,0 +1,350 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import numpy as np +import torch.utils.data +from torch.nn.utils.rnn import pad_sequence +from utils.data_utils import * +from processors.acoustic_extractor import cal_normalized_mel +from text import text_to_sequence +from text.text_token_collation import phoneIDCollation + + +class BaseDataset(torch.utils.data.Dataset): + def __init__(self, cfg, dataset, is_valid=False): + """ + Args: + cfg: config + dataset: dataset name + is_valid: whether to use train or valid dataset + """ + + assert isinstance(dataset, str) + + # self.data_root = processed_data_dir + self.cfg = cfg + + processed_data_dir = os.path.join(cfg.preprocess.processed_dir, dataset) + meta_file = cfg.preprocess.valid_file if is_valid else cfg.preprocess.train_file + self.metafile_path = os.path.join(processed_data_dir, meta_file) + self.metadata = self.get_metadata() + + + + ''' + load spk2id and utt2spk from json file + spk2id: {spk1: 0, spk2: 1, ...} + utt2spk: {dataset_uid: spk1, ...} + ''' + if cfg.preprocess.use_spkid: + spk2id_path = os.path.join(processed_data_dir, cfg.preprocess.spk2id) + with open(spk2id_path, "r") as f: + self.spk2id = json.load(f) + + utt2spk_path = os.path.join(processed_data_dir, cfg.preprocess.utt2spk) + self.utt2spk = dict() + with open(utt2spk_path, "r") as f: + for line in f.readlines(): + utt, spk = line.strip().split('\t') + self.utt2spk[utt] = spk + + + if cfg.preprocess.use_uv: + self.utt2uv_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + self.utt2uv_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.uv_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_frame_pitch: + self.utt2frame_pitch_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2frame_pitch_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.pitch_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_frame_energy: + self.utt2frame_energy_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2frame_energy_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.energy_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_mel: + self.utt2mel_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2mel_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.mel_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_linear: + self.utt2linear_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2linear_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.linear_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_audio: + self.utt2audio_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2audio_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.audio_dir, + uid + ".npy", + ) + elif cfg.preprocess.use_label: + self.utt2label_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2label_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.label_dir, + uid + ".npy", + ) + elif cfg.preprocess.use_one_hot: + self.utt2one_hot_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2one_hot_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.one_hot_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_text or cfg.preprocess.use_phone: + self.utt2seq = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + if cfg.preprocess.use_text: + text = utt_info["Text"] + sequence = text_to_sequence(text, cfg.preprocess.text_cleaners) + elif cfg.preprocess.use_phone: + # load phoneme squence from phone file + phone_path = os.path.join(processed_data_dir, + cfg.preprocess.phone_dir, + uid+'.phone' + ) + with open(phone_path, 'r') as fin: + phones = fin.readlines() + assert len(phones) == 1 + phones = phones[0].strip() + phones_seq = phones.split(' ') + + phon_id_collator = phoneIDCollation(cfg, dataset=dataset) + sequence = phon_id_collator.get_phone_id_sequence(cfg, phones_seq) + + self.utt2seq[utt] = sequence + + + def get_metadata(self): + with open(self.metafile_path, "r", encoding="utf-8") as f: + metadata = json.load(f) + + return metadata + + def get_dataset_name(self): + return self.metadata[0]["Dataset"] + + def __getitem__(self, index): + utt_info = self.metadata[index] + + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + single_feature = dict() + + if self.cfg.preprocess.use_spkid: + single_feature["spk_id"] = np.array( + [self.spk2id[self.utt2spk[utt]]], dtype=np.int32 + ) + + if self.cfg.preprocess.use_mel: + mel = np.load(self.utt2mel_path[utt]) + assert mel.shape[0] == self.cfg.preprocess.n_mel # [n_mels, T] + if self.cfg.preprocess.use_min_max_norm_mel: + # do mel norm + mel = cal_normalized_mel(mel, utt_info["Dataset"], self.cfg.preprocess) + + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = mel.shape[1] + single_feature["mel"] = mel.T # [T, n_mels] + + if self.cfg.preprocess.use_linear: + linear = np.load(self.utt2linear_path[utt]) + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = linear.shape[1] + single_feature["linear"] = linear.T # [T, n_linear] + + if self.cfg.preprocess.use_frame_pitch: + frame_pitch_path = self.utt2frame_pitch_path[utt] + frame_pitch = np.load(frame_pitch_path) + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = len(frame_pitch) + aligned_frame_pitch = align_length( + frame_pitch, single_feature["target_len"] + ) + single_feature["frame_pitch"] = aligned_frame_pitch + + if self.cfg.preprocess.use_uv: + frame_uv_path = self.utt2uv_path[utt] + frame_uv = np.load(frame_uv_path) + aligned_frame_uv = align_length(frame_uv, single_feature["target_len"]) + aligned_frame_uv = [ + 0 if frame_uv else 1 for frame_uv in aligned_frame_uv + ] + aligned_frame_uv = np.array(aligned_frame_uv) + single_feature["frame_uv"] = aligned_frame_uv + + if self.cfg.preprocess.use_frame_energy: + frame_energy_path = self.utt2frame_energy_path[utt] + frame_energy = np.load(frame_energy_path) + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = len(frame_energy) + aligned_frame_energy = align_length( + frame_energy, single_feature["target_len"] + ) + single_feature["frame_energy"] = aligned_frame_energy + + if self.cfg.preprocess.use_audio: + audio = np.load(self.utt2audio_path[utt]) + single_feature["audio"] = audio + single_feature["audio_len"] = audio.shape[0] + + if self.cfg.preprocess.use_phone or self.cfg.preprocess.use_text: + single_feature["phone_seq"] = np.array(self.utt2seq[utt]) + single_feature["phone_len"] = len(self.utt2seq[utt]) + + return single_feature + + def __len__(self): + return len(self.metadata) + + +class BaseCollator(object): + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, cfg): + self.cfg = cfg + + def __call__(self, batch): + packed_batch_features = dict() + + # mel: [b, T, n_mels] + # frame_pitch, frame_energy: [1, T] + # target_len: [1] + # spk_id: [b, 1] + # mask: [b, T, 1] + + for key in batch[0].keys(): + if key == "target_len": + packed_batch_features["target_len"] = torch.LongTensor( + [b["target_len"] for b in batch] + ) + masks = [ + torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "phone_len": + packed_batch_features["phone_len"] = torch.LongTensor( + [b["phone_len"] for b in batch] + ) + masks = [ + torch.ones((b["phone_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["phn_mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "audio_len": + packed_batch_features["audio_len"] = torch.LongTensor( + [b["audio_len"] for b in batch] + ) + masks = [ + torch.ones((b["audio_len"], 1), dtype=torch.long) for b in batch + ] + else: + values = [torch.from_numpy(b[key]) for b in batch] + packed_batch_features[key] = pad_sequence( + values, batch_first=True, padding_value=0 + ) + return packed_batch_features + + +class BaseTestDataset(torch.utils.data.Dataset): + def __init__(self, cfg, args): + raise NotImplementedError + + + def get_metadata(self): + raise NotImplementedError + + def __getitem__(self, index): + raise NotImplementedError + + def __len__(self): + return len(self.metadata) + + +class BaseTestCollator(object): + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, cfg): + raise NotImplementedError + + def __call__(self, batch): + raise NotImplementedError diff --git a/models/base/base_inference.py b/models/base/base_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..2713f19a0d61f06bca1f01de5ccd8a3b4d2cc02f --- /dev/null +++ b/models/base/base_inference.py @@ -0,0 +1,220 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import re +import time +from pathlib import Path + +import torch +from torch.utils.data import DataLoader +from tqdm import tqdm + +from models.vocoders.vocoder_inference import synthesis +from torch.utils.data import DataLoader +from utils.util import set_all_random_seed +from utils.util import load_config + + +def parse_vocoder(vocoder_dir): + r"""Parse vocoder config""" + vocoder_dir = os.path.abspath(vocoder_dir) + ckpt_list = [ckpt for ckpt in Path(vocoder_dir).glob("*.pt")] + ckpt_list.sort(key=lambda x: int(x.stem), reverse=True) + ckpt_path = str(ckpt_list[0]) + vocoder_cfg = load_config(os.path.join(vocoder_dir, "args.json"), lowercase=True) + vocoder_cfg.model.bigvgan = vocoder_cfg.vocoder + return vocoder_cfg, ckpt_path + + +class BaseInference(object): + def __init__(self, cfg, args): + self.cfg = cfg + self.args = args + self.model_type = cfg.model_type + self.avg_rtf = list() + set_all_random_seed(10086) + os.makedirs(args.output_dir, exist_ok=True) + + if torch.cuda.is_available(): + self.device = torch.device("cuda") + else: + self.device = torch.device("cpu") + torch.set_num_threads(10) # inference on 1 core cpu. + + # Load acoustic model + self.model = self.create_model().to(self.device) + state_dict = self.load_state_dict() + self.load_model(state_dict) + self.model.eval() + + # Load vocoder model if necessary + if self.args.checkpoint_dir_vocoder is not None: + self.get_vocoder_info() + + def create_model(self): + raise NotImplementedError + + def load_state_dict(self): + self.checkpoint_file = self.args.checkpoint_file + if self.checkpoint_file is None: + assert self.args.checkpoint_dir is not None + checkpoint_path = os.path.join(self.args.checkpoint_dir, "checkpoint") + checkpoint_filename = open(checkpoint_path).readlines()[-1].strip() + self.checkpoint_file = os.path.join( + self.args.checkpoint_dir, checkpoint_filename + ) + + self.checkpoint_dir = os.path.split(self.checkpoint_file)[0] + + print("Restore acoustic model from {}".format(self.checkpoint_file)) + raw_state_dict = torch.load(self.checkpoint_file, map_location=self.device) + self.am_restore_step = re.findall(r"step-(.+?)_loss", self.checkpoint_file)[0] + + return raw_state_dict + + def load_model(self, model): + raise NotImplementedError + + def get_vocoder_info(self): + self.checkpoint_dir_vocoder = self.args.checkpoint_dir_vocoder + self.vocoder_cfg = os.path.join( + os.path.dirname(self.checkpoint_dir_vocoder), "args.json" + ) + self.cfg.vocoder = load_config(self.vocoder_cfg, lowercase=True) + self.vocoder_tag = self.checkpoint_dir_vocoder.split("/")[-2].split(":")[-1] + self.vocoder_steps = self.checkpoint_dir_vocoder.split("/")[-1].split(".")[0] + + def build_test_utt_data(self): + raise NotImplementedError + + def build_testdata_loader(self, args, target_speaker=None): + datasets, collate = self.build_test_dataset() + self.test_dataset = datasets(self.cfg, args, target_speaker) + self.test_collate = collate(self.cfg) + self.test_batch_size = min( + self.cfg.train.batch_size, len(self.test_dataset.metadata) + ) + test_loader = DataLoader( + self.test_dataset, + collate_fn=self.test_collate, + num_workers=self.args.num_workers, + batch_size=self.test_batch_size, + shuffle=False, + ) + return test_loader + + def inference_each_batch(self, batch_data): + raise NotImplementedError + + def inference_for_batches(self, args, target_speaker=None): + ###### Construct test_batch ###### + loader = self.build_testdata_loader(args, target_speaker) + + n_batch = len(loader) + now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time())) + print( + "Model eval time: {}, batch_size = {}, n_batch = {}".format( + now, self.test_batch_size, n_batch + ) + ) + self.model.eval() + + ###### Inference for each batch ###### + pred_res = [] + with torch.no_grad(): + for i, batch_data in enumerate(loader if n_batch == 1 else tqdm(loader)): + # Put the data to device + for k, v in batch_data.items(): + batch_data[k] = batch_data[k].to(self.device) + + y_pred, stats = self.inference_each_batch(batch_data) + + pred_res += y_pred + + return pred_res + + def inference(self, feature): + raise NotImplementedError + + def synthesis_by_vocoder(self, pred): + audios_pred = synthesis( + self.vocoder_cfg, + self.checkpoint_dir_vocoder, + len(pred), + pred, + ) + return audios_pred + + def __call__(self, utt): + feature = self.build_test_utt_data(utt) + start_time = time.time() + with torch.no_grad(): + outputs = self.inference(feature)[0] + time_used = time.time() - start_time + rtf = time_used / ( + outputs.shape[1] + * self.cfg.preprocess.hop_size + / self.cfg.preprocess.sample_rate + ) + print("Time used: {:.3f}, RTF: {:.4f}".format(time_used, rtf)) + self.avg_rtf.append(rtf) + audios = outputs.cpu().squeeze().numpy().reshape(-1, 1) + return audios + + +def base_parser(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", default="config.json", help="json files for configurations." + ) + parser.add_argument("--use_ddp_inference", default=False) + parser.add_argument("--n_workers", default=1, type=int) + parser.add_argument("--local_rank", default=-1, type=int) + parser.add_argument( + "--batch_size", default=1, type=int, help="Batch size for inference" + ) + parser.add_argument( + "--num_workers", + default=1, + type=int, + help="Worker number for inference dataloader", + ) + parser.add_argument( + "--checkpoint_dir", + type=str, + default=None, + help="Checkpoint dir including model file and configuration", + ) + parser.add_argument( + "--checkpoint_file", help="checkpoint file", type=str, default=None + ) + parser.add_argument( + "--test_list", help="test utterance list for testing", type=str, default=None + ) + parser.add_argument( + "--checkpoint_dir_vocoder", + help="Vocoder's checkpoint dir including model file and configuration", + type=str, + default=None, + ) + parser.add_argument( + "--output_dir", + type=str, + default=None, + help="Output dir for saving generated results", + ) + return parser + + +if __name__ == "__main__": + parser = base_parser() + args = parser.parse_args() + cfg = load_config(args.config) + + # Build inference + inference = BaseInference(cfg, args) + inference() diff --git a/models/base/base_sampler.py b/models/base/base_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..149d1437eb1d3a00ca8c9895b150b39b2a3635fa --- /dev/null +++ b/models/base/base_sampler.py @@ -0,0 +1,136 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import random + +from torch.utils.data import ConcatDataset, Dataset +from torch.utils.data.sampler import ( + BatchSampler, + RandomSampler, + Sampler, + SequentialSampler, +) + + +class ScheduledSampler(Sampler): + """A sampler that samples data from a given concat-dataset. + + Args: + concat_dataset (ConcatDataset): a concatenated dataset consisting of all datasets + batch_size (int): batch size + holistic_shuffle (bool): whether to shuffle the whole dataset or not + logger (logging.Logger): logger to print warning message + + Usage: + For cfg.train.batch_size = 3, cfg.train.holistic_shuffle = False, cfg.train.drop_last = True: + >>> list(ScheduledSampler(ConcatDataset([0, 1, 2], [3, 4, 5], [6, 7, 8]]))) + [3, 4, 5, 0, 1, 2, 6, 7, 8] + """ + + def __init__( + self, + concat_dataset, + batch_size, + holistic_shuffle, + logger=None, + loader_type="train", + ): + if not isinstance(concat_dataset, ConcatDataset): + raise ValueError( + "concat_dataset must be an instance of ConcatDataset, but got {}".format( + type(concat_dataset) + ) + ) + if not isinstance(batch_size, int): + raise ValueError( + "batch_size must be an integer, but got {}".format(type(batch_size)) + ) + if not isinstance(holistic_shuffle, bool): + raise ValueError( + "holistic_shuffle must be a boolean, but got {}".format( + type(holistic_shuffle) + ) + ) + + self.concat_dataset = concat_dataset + self.batch_size = batch_size + self.holistic_shuffle = holistic_shuffle + + affected_dataset_name = [] + affected_dataset_len = [] + for dataset in concat_dataset.datasets: + dataset_len = len(dataset) + dataset_name = dataset.get_dataset_name() + if dataset_len < batch_size: + affected_dataset_name.append(dataset_name) + affected_dataset_len.append(dataset_len) + + self.type = loader_type + for dataset_name, dataset_len in zip( + affected_dataset_name, affected_dataset_len + ): + if not loader_type == "valid": + logger.warning( + "The {} dataset {} has a length of {}, which is smaller than the batch size {}. This may cause unexpected behavior.".format( + loader_type, dataset_name, dataset_len, batch_size + ) + ) + + def __len__(self): + # the number of batches with drop last + num_of_batches = sum( + [ + math.floor(len(dataset) / self.batch_size) + for dataset in self.concat_dataset.datasets + ] + ) + # if samples are not enough for one batch, we don't drop last + if self.type == "valid" and num_of_batches < 1: + return len(self.concat_dataset) + return num_of_batches * self.batch_size + + def __iter__(self): + iters = [] + for dataset in self.concat_dataset.datasets: + iters.append( + SequentialSampler(dataset).__iter__() + if not self.holistic_shuffle + else RandomSampler(dataset).__iter__() + ) + # e.g. [0, 200, 400] + init_indices = [0] + self.concat_dataset.cumulative_sizes[:-1] + output_batches = [] + for dataset_idx in range(len(self.concat_dataset.datasets)): + cur_batch = [] + for idx in iters[dataset_idx]: + cur_batch.append(idx + init_indices[dataset_idx]) + if len(cur_batch) == self.batch_size: + output_batches.append(cur_batch) + cur_batch = [] + # if loader_type is valid, we don't need to drop last + if self.type == "valid" and len(cur_batch) > 0: + output_batches.append(cur_batch) + + # force drop last in training + random.shuffle(output_batches) + output_indices = [item for sublist in output_batches for item in sublist] + return iter(output_indices) + + +def build_samplers(concat_dataset: Dataset, cfg, logger, loader_type): + sampler = ScheduledSampler( + concat_dataset, + cfg.train.batch_size, + cfg.train.sampler.holistic_shuffle, + logger, + loader_type, + ) + batch_sampler = BatchSampler( + sampler, + cfg.train.batch_size, + cfg.train.sampler.drop_last if not loader_type == "valid" else False, + ) + return sampler, batch_sampler diff --git a/models/base/base_trainer.py b/models/base/base_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..8782216dc13ce5d9de05ae790faeb82cf7cfd501 --- /dev/null +++ b/models/base/base_trainer.py @@ -0,0 +1,348 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import collections +import json +import os +import sys +import time + +import torch +import torch.distributed as dist +from torch.nn.parallel import DistributedDataParallel +from torch.utils.data import ConcatDataset, DataLoader +from torch.utils.tensorboard import SummaryWriter + +from models.base.base_sampler import BatchSampler +from utils.util import ( + Logger, + remove_older_ckpt, + save_config, + set_all_random_seed, + ValueWindow, +) + + +class BaseTrainer(object): + def __init__(self, args, cfg): + self.args = args + self.log_dir = args.log_dir + self.cfg = cfg + + self.checkpoint_dir = os.path.join(args.log_dir, "checkpoints") + os.makedirs(self.checkpoint_dir, exist_ok=True) + if not cfg.train.ddp or args.local_rank == 0: + self.sw = SummaryWriter(os.path.join(args.log_dir, "events")) + self.logger = self.build_logger() + self.time_window = ValueWindow(50) + + self.step = 0 + self.epoch = -1 + self.max_epochs = self.cfg.train.epochs + self.max_steps = self.cfg.train.max_steps + + # set random seed & init distributed training + set_all_random_seed(self.cfg.train.random_seed) + if cfg.train.ddp: + dist.init_process_group(backend="nccl") + + if cfg.model_type not in ["AutoencoderKL", "AudioLDM"]: + self.singers = self.build_singers_lut() + + # setup data_loader + self.data_loader = self.build_data_loader() + + # setup model & enable distributed training + self.model = self.build_model() + print(self.model) + + if isinstance(self.model, dict): + for key, value in self.model.items(): + value.cuda(self.args.local_rank) + if key == "PQMF": + continue + if cfg.train.ddp: + self.model[key] = DistributedDataParallel( + value, device_ids=[self.args.local_rank] + ) + else: + self.model.cuda(self.args.local_rank) + if cfg.train.ddp: + self.model = DistributedDataParallel( + self.model, device_ids=[self.args.local_rank] + ) + + # create criterion + self.criterion = self.build_criterion() + if isinstance(self.criterion, dict): + for key, value in self.criterion.items(): + self.criterion[key].cuda(args.local_rank) + else: + self.criterion.cuda(self.args.local_rank) + + # optimizer + self.optimizer = self.build_optimizer() + self.scheduler = self.build_scheduler() + + # save config file + self.config_save_path = os.path.join(self.checkpoint_dir, "args.json") + + def build_logger(self): + log_file = os.path.join(self.checkpoint_dir, "train.log") + logger = Logger(log_file, level=self.args.log_level).logger + + return logger + + def build_dataset(self): + raise NotImplementedError + + def build_data_loader(self): + Dataset, Collator = self.build_dataset() + # build dataset instance for each dataset and combine them by ConcatDataset + datasets_list = [] + for dataset in self.cfg.dataset: + subdataset = Dataset(self.cfg, dataset, is_valid=False) + datasets_list.append(subdataset) + train_dataset = ConcatDataset(datasets_list) + + train_collate = Collator(self.cfg) + # TODO: multi-GPU training + if self.cfg.train.ddp: + raise NotImplementedError("DDP is not supported yet.") + + # sampler will provide indices to batch_sampler, which will perform batching and yield batch indices + batch_sampler = BatchSampler( + cfg=self.cfg, concat_dataset=train_dataset, dataset_list=datasets_list + ) + + # use batch_sampler argument instead of (sampler, shuffle, drop_last, batch_size) + train_loader = DataLoader( + train_dataset, + collate_fn=train_collate, + num_workers=self.args.num_workers, + batch_sampler=batch_sampler, + pin_memory=False, + ) + if not self.cfg.train.ddp or self.args.local_rank == 0: + datasets_list = [] + for dataset in self.cfg.dataset: + subdataset = Dataset(self.cfg, dataset, is_valid=True) + datasets_list.append(subdataset) + valid_dataset = ConcatDataset(datasets_list) + valid_collate = Collator(self.cfg) + batch_sampler = BatchSampler( + cfg=self.cfg, concat_dataset=valid_dataset, dataset_list=datasets_list + ) + valid_loader = DataLoader( + valid_dataset, + collate_fn=valid_collate, + num_workers=1, + batch_sampler=batch_sampler, + ) + else: + raise NotImplementedError("DDP is not supported yet.") + # valid_loader = None + data_loader = {"train": train_loader, "valid": valid_loader} + return data_loader + + def build_singers_lut(self): + # combine singers + if not os.path.exists(os.path.join(self.log_dir, self.cfg.preprocess.spk2id)): + singers = collections.OrderedDict() + else: + with open( + os.path.join(self.log_dir, self.cfg.preprocess.spk2id), "r" + ) as singer_file: + singers = json.load(singer_file) + singer_count = len(singers) + for dataset in self.cfg.dataset: + singer_lut_path = os.path.join( + self.cfg.preprocess.processed_dir, dataset, self.cfg.preprocess.spk2id + ) + with open(singer_lut_path, "r") as singer_lut_path: + singer_lut = json.load(singer_lut_path) + for singer in singer_lut.keys(): + if singer not in singers: + singers[singer] = singer_count + singer_count += 1 + with open( + os.path.join(self.log_dir, self.cfg.preprocess.spk2id), "w" + ) as singer_file: + json.dump(singers, singer_file, indent=4, ensure_ascii=False) + print( + "singers have been dumped to {}".format( + os.path.join(self.log_dir, self.cfg.preprocess.spk2id) + ) + ) + return singers + + def build_model(self): + raise NotImplementedError() + + def build_optimizer(self): + raise NotImplementedError + + def build_scheduler(self): + raise NotImplementedError() + + def build_criterion(self): + raise NotImplementedError + + def get_state_dict(self): + raise NotImplementedError + + def save_config_file(self): + save_config(self.config_save_path, self.cfg) + + # TODO, save without module. + def save_checkpoint(self, state_dict, saved_model_path): + torch.save(state_dict, saved_model_path) + + def load_checkpoint(self): + checkpoint_path = os.path.join(self.checkpoint_dir, "checkpoint") + assert os.path.exists(checkpoint_path) + checkpoint_filename = open(checkpoint_path).readlines()[-1].strip() + model_path = os.path.join(self.checkpoint_dir, checkpoint_filename) + assert os.path.exists(model_path) + if not self.cfg.train.ddp or self.args.local_rank == 0: + self.logger.info(f"Re(store) from {model_path}") + checkpoint = torch.load(model_path, map_location="cpu") + return checkpoint + + def load_model(self, checkpoint): + raise NotImplementedError + + def restore(self): + checkpoint = self.load_checkpoint() + self.load_model(checkpoint) + + def train_step(self, data): + raise NotImplementedError( + f"Need to implement function {sys._getframe().f_code.co_name} in " + f"your sub-class of {self.__class__.__name__}. " + ) + + @torch.no_grad() + def eval_step(self): + raise NotImplementedError( + f"Need to implement function {sys._getframe().f_code.co_name} in " + f"your sub-class of {self.__class__.__name__}. " + ) + + def write_summary(self, losses, stats): + raise NotImplementedError( + f"Need to implement function {sys._getframe().f_code.co_name} in " + f"your sub-class of {self.__class__.__name__}. " + ) + + def write_valid_summary(self, losses, stats): + raise NotImplementedError( + f"Need to implement function {sys._getframe().f_code.co_name} in " + f"your sub-class of {self.__class__.__name__}. " + ) + + def echo_log(self, losses, mode="Training"): + message = [ + "{} - Epoch {} Step {}: [{:.3f} s/step]".format( + mode, self.epoch + 1, self.step, self.time_window.average + ) + ] + + for key in sorted(losses.keys()): + if isinstance(losses[key], dict): + for k, v in losses[key].items(): + message.append( + str(k).split("/")[-1] + "=" + str(round(float(v), 5)) + ) + else: + message.append( + str(key).split("/")[-1] + "=" + str(round(float(losses[key]), 5)) + ) + self.logger.info(", ".join(message)) + + def eval_epoch(self): + self.logger.info("Validation...") + valid_losses = {} + for i, batch_data in enumerate(self.data_loader["valid"]): + for k, v in batch_data.items(): + if isinstance(v, torch.Tensor): + batch_data[k] = v.cuda() + valid_loss, valid_stats, total_valid_loss = self.eval_step(batch_data, i) + for key in valid_loss: + if key not in valid_losses: + valid_losses[key] = 0 + valid_losses[key] += valid_loss[key] + + # Add mel and audio to the Tensorboard + # Average loss + for key in valid_losses: + valid_losses[key] /= i + 1 + self.echo_log(valid_losses, "Valid") + return valid_losses, valid_stats + + def train_epoch(self): + for i, batch_data in enumerate(self.data_loader["train"]): + start_time = time.time() + # Put the data to cuda device + for k, v in batch_data.items(): + if isinstance(v, torch.Tensor): + batch_data[k] = v.cuda(self.args.local_rank) + + # Training step + train_losses, train_stats, total_loss = self.train_step(batch_data) + self.time_window.append(time.time() - start_time) + + if self.args.local_rank == 0 or not self.cfg.train.ddp: + if self.step % self.args.stdout_interval == 0: + self.echo_log(train_losses, "Training") + + if self.step % self.cfg.train.save_summary_steps == 0: + self.logger.info(f"Save summary as step {self.step}") + self.write_summary(train_losses, train_stats) + + if ( + self.step % self.cfg.train.save_checkpoints_steps == 0 + and self.step != 0 + ): + saved_model_name = "step-{:07d}_loss-{:.4f}.pt".format( + self.step, total_loss + ) + saved_model_path = os.path.join( + self.checkpoint_dir, saved_model_name + ) + saved_state_dict = self.get_state_dict() + self.save_checkpoint(saved_state_dict, saved_model_path) + self.save_config_file() + # keep max n models + remove_older_ckpt( + saved_model_name, + self.checkpoint_dir, + max_to_keep=self.cfg.train.keep_checkpoint_max, + ) + + if self.step != 0 and self.step % self.cfg.train.valid_interval == 0: + if isinstance(self.model, dict): + for key in self.model.keys(): + self.model[key].eval() + else: + self.model.eval() + # Evaluate one epoch and get average loss + valid_losses, valid_stats = self.eval_epoch() + if isinstance(self.model, dict): + for key in self.model.keys(): + self.model[key].train() + else: + self.model.train() + # Write validation losses to summary. + self.write_valid_summary(valid_losses, valid_stats) + self.step += 1 + + def train(self): + for epoch in range(max(0, self.epoch), self.max_epochs): + self.train_epoch() + self.epoch += 1 + if self.step > self.max_steps: + self.logger.info("Training finished!") + break diff --git a/models/base/new_dataset.py b/models/base/new_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..2201bb4132ab86d1110092d7ab9e509296367a22 --- /dev/null +++ b/models/base/new_dataset.py @@ -0,0 +1,50 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import os +from abc import abstractmethod +from pathlib import Path + +import json5 +import torch +import yaml + + +# TODO: for training and validating +class BaseDataset(torch.utils.data.Dataset): + r"""Base dataset for training and validating.""" + + def __init__(self, args, cfg, is_valid=False): + pass + + +class BaseTestDataset(torch.utils.data.Dataset): + r"""Test dataset for inference.""" + + def __init__(self, args=None, cfg=None, infer_type="from_dataset"): + assert infer_type in ["from_dataset", "from_file"] + + self.args = args + self.cfg = cfg + self.infer_type = infer_type + + @abstractmethod + def __getitem__(self, index): + pass + + def __len__(self): + return len(self.metadata) + + def get_metadata(self): + path = Path(self.args.source) + if path.suffix == ".json" or path.suffix == ".jsonc": + metadata = json5.load(open(self.args.source, "r")) + elif path.suffix == ".yaml" or path.suffix == ".yml": + metadata = yaml.full_load(open(self.args.source, "r")) + else: + raise ValueError(f"Unsupported file type: {path.suffix}") + + return metadata diff --git a/models/base/new_inference.py b/models/base/new_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..4813fca4aba192fb8737dd74f37f6d430e1909a4 --- /dev/null +++ b/models/base/new_inference.py @@ -0,0 +1,249 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import random +import re +import time +from abc import abstractmethod +from pathlib import Path + +import accelerate +import json5 +import numpy as np +import torch +from accelerate.logging import get_logger +from torch.utils.data import DataLoader + +from models.vocoders.vocoder_inference import synthesis +from utils.io import save_audio +from utils.util import load_config +from utils.audio_slicer import is_silence + +EPS = 1.0e-12 + + +class BaseInference(object): + def __init__(self, args=None, cfg=None, infer_type="from_dataset"): + super().__init__() + + start = time.monotonic_ns() + self.args = args + self.cfg = cfg + + assert infer_type in ["from_dataset", "from_file"] + self.infer_type = infer_type + + # init with accelerate + self.accelerator = accelerate.Accelerator() + self.accelerator.wait_for_everyone() + + # Use accelerate logger for distributed inference + with self.accelerator.main_process_first(): + self.logger = get_logger("inference", log_level=args.log_level) + + # Log some info + self.logger.info("=" * 56) + self.logger.info("||\t\t" + "New inference process started." + "\t\t||") + self.logger.info("=" * 56) + self.logger.info("\n") + self.logger.debug(f"Using {args.log_level.upper()} logging level.") + + self.acoustics_dir = args.acoustics_dir + self.logger.debug(f"Acoustic dir: {args.acoustics_dir}") + self.vocoder_dir = args.vocoder_dir + self.logger.debug(f"Vocoder dir: {args.vocoder_dir}") + # should be in svc inferencer + # self.target_singer = args.target_singer + # self.logger.info(f"Target singers: {args.target_singer}") + # self.trans_key = args.trans_key + # self.logger.info(f"Trans key: {args.trans_key}") + + os.makedirs(args.output_dir, exist_ok=True) + + # set random seed + with self.accelerator.main_process_first(): + start = time.monotonic_ns() + self._set_random_seed(self.cfg.train.random_seed) + end = time.monotonic_ns() + self.logger.debug( + f"Setting random seed done in {(end - start) / 1e6:.2f}ms" + ) + self.logger.debug(f"Random seed: {self.cfg.train.random_seed}") + + # setup data_loader + with self.accelerator.main_process_first(): + self.logger.info("Building dataset...") + start = time.monotonic_ns() + self.test_dataloader = self._build_dataloader() + end = time.monotonic_ns() + self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms") + + # setup model + with self.accelerator.main_process_first(): + self.logger.info("Building model...") + start = time.monotonic_ns() + self.model = self._build_model() + end = time.monotonic_ns() + # self.logger.debug(self.model) + self.logger.info(f"Building model done in {(end - start) / 1e6:.3f}ms") + + # init with accelerate + self.logger.info("Initializing accelerate...") + start = time.monotonic_ns() + self.accelerator = accelerate.Accelerator() + self.model = self.accelerator.prepare(self.model) + end = time.monotonic_ns() + self.accelerator.wait_for_everyone() + self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.3f}ms") + + with self.accelerator.main_process_first(): + self.logger.info("Loading checkpoint...") + start = time.monotonic_ns() + # TODO: Also, suppose only use latest one yet + self.__load_model(os.path.join(args.acoustics_dir, "checkpoint")) + end = time.monotonic_ns() + self.logger.info(f"Loading checkpoint done in {(end - start) / 1e6:.3f}ms") + + self.model.eval() + self.accelerator.wait_for_everyone() + + ### Abstract methods ### + @abstractmethod + def _build_test_dataset(self): + pass + + @abstractmethod + def _build_model(self): + pass + + @abstractmethod + @torch.inference_mode() + def _inference_each_batch(self, batch_data): + pass + + ### Abstract methods end ### + + @torch.inference_mode() + def inference(self): + for i, batch in enumerate(self.test_dataloader): + y_pred = self._inference_each_batch(batch).cpu() + mel_min, mel_max = self.test_dataset.target_mel_extrema + y_pred = (y_pred + 1.0) / 2.0 * (mel_max - mel_min + EPS) + mel_min + y_ls = y_pred.chunk(self.test_batch_size) + tgt_ls = batch["target_len"].cpu().chunk(self.test_batch_size) + j = 0 + for it, l in zip(y_ls, tgt_ls): + l = l.item() + it = it.squeeze(0)[:l] + uid = self.test_dataset.metadata[i * self.test_batch_size + j]["Uid"] + torch.save(it, os.path.join(self.args.output_dir, f"{uid}.pt")) + j += 1 + + vocoder_cfg, vocoder_ckpt = self._parse_vocoder(self.args.vocoder_dir) + + res = synthesis( + cfg=vocoder_cfg, + vocoder_weight_file=vocoder_ckpt, + n_samples=None, + pred=[ + torch.load( + os.path.join(self.args.output_dir, "{}.pt".format(i["Uid"])) + ).numpy(force=True) + for i in self.test_dataset.metadata + ], + ) + + output_audio_files = [] + for it, wav in zip(self.test_dataset.metadata, res): + uid = it["Uid"] + file = os.path.join(self.args.output_dir, f"{uid}.wav") + output_audio_files.append(file) + + wav = wav.numpy(force=True) + save_audio( + file, + wav, + self.cfg.preprocess.sample_rate, + add_silence=False, + turn_up=not is_silence(wav, self.cfg.preprocess.sample_rate), + ) + os.remove(os.path.join(self.args.output_dir, f"{uid}.pt")) + + return sorted(output_audio_files) + + # TODO: LEGACY CODE + def _build_dataloader(self): + datasets, collate = self._build_test_dataset() + self.test_dataset = datasets(self.args, self.cfg, self.infer_type) + self.test_collate = collate(self.cfg) + self.test_batch_size = min( + self.cfg.train.batch_size, len(self.test_dataset.metadata) + ) + test_dataloader = DataLoader( + self.test_dataset, + collate_fn=self.test_collate, + num_workers=1, + batch_size=self.test_batch_size, + shuffle=False, + ) + return test_dataloader + + def __load_model(self, checkpoint_dir: str = None, checkpoint_path: str = None): + r"""Load model from checkpoint. If checkpoint_path is None, it will + load the latest checkpoint in checkpoint_dir. If checkpoint_path is not + None, it will load the checkpoint specified by checkpoint_path. **Only use this + method after** ``accelerator.prepare()``. + """ + if checkpoint_path is None: + ls = [] + for i in Path(checkpoint_dir).iterdir(): + if re.match(r"epoch-\d+_step-\d+_loss-[\d.]+", str(i.stem)): + ls.append(i) + ls.sort( + key=lambda x: int(x.stem.split("_")[-3].split("-")[-1]), reverse=True + ) + checkpoint_path = ls[0] + else: + checkpoint_path = Path(checkpoint_path) + self.accelerator.load_state(str(checkpoint_path)) + # set epoch and step + self.epoch = int(checkpoint_path.stem.split("_")[-3].split("-")[-1]) + self.step = int(checkpoint_path.stem.split("_")[-2].split("-")[-1]) + return str(checkpoint_path) + + @staticmethod + def _set_random_seed(seed): + r"""Set random seed for all possible random modules.""" + random.seed(seed) + np.random.seed(seed) + torch.random.manual_seed(seed) + + @staticmethod + def _parse_vocoder(vocoder_dir): + r"""Parse vocoder config""" + vocoder_dir = os.path.abspath(vocoder_dir) + ckpt_list = [ckpt for ckpt in Path(vocoder_dir).glob("*.pt")] + ckpt_list.sort(key=lambda x: int(x.stem), reverse=True) + ckpt_path = str(ckpt_list[0]) + vocoder_cfg = load_config( + os.path.join(vocoder_dir, "args.json"), lowercase=True + ) + return vocoder_cfg, ckpt_path + + @staticmethod + def __count_parameters(model): + return sum(p.numel() for p in model.parameters()) + + def __dump_cfg(self, path): + os.makedirs(os.path.dirname(path), exist_ok=True) + json5.dump( + self.cfg, + open(path, "w"), + indent=4, + sort_keys=True, + ensure_ascii=False, + quote_keys=True, + ) diff --git a/models/base/new_trainer.py b/models/base/new_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..5d013d2bc2f2e47e5c7646cac5c63cc88c04486b --- /dev/null +++ b/models/base/new_trainer.py @@ -0,0 +1,722 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import os +import random +import shutil +import time +from abc import abstractmethod +from pathlib import Path + +import accelerate +import json5 +import numpy as np +import torch +from accelerate.logging import get_logger +from accelerate.utils import ProjectConfiguration +from torch.utils.data import ConcatDataset, DataLoader +from tqdm import tqdm + +from models.base.base_sampler import build_samplers +from optimizer.optimizers import NoamLR + + +class BaseTrainer(object): + r"""The base trainer for all tasks. Any trainer should inherit from this class.""" + + def __init__(self, args=None, cfg=None): + super().__init__() + + self.args = args + self.cfg = cfg + + cfg.exp_name = args.exp_name + + # init with accelerate + self._init_accelerator() + self.accelerator.wait_for_everyone() + + # Use accelerate logger for distributed training + with self.accelerator.main_process_first(): + self.logger = get_logger(args.exp_name, log_level=args.log_level) + + # Log some info + self.logger.info("=" * 56) + self.logger.info("||\t\t" + "New training process started." + "\t\t||") + self.logger.info("=" * 56) + self.logger.info("\n") + self.logger.debug(f"Using {args.log_level.upper()} logging level.") + self.logger.info(f"Experiment name: {args.exp_name}") + self.logger.info(f"Experiment directory: {self.exp_dir}") + self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") + if self.accelerator.is_main_process: + os.makedirs(self.checkpoint_dir, exist_ok=True) + self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") + + # init counts + self.batch_count: int = 0 + self.step: int = 0 + self.epoch: int = 0 + self.max_epoch = ( + self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf") + ) + self.logger.info( + "Max epoch: {}".format( + self.max_epoch if self.max_epoch < float("inf") else "Unlimited" + ) + ) + + # Check values + if self.accelerator.is_main_process: + self.__check_basic_configs() + # Set runtime configs + self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride + self.checkpoints_path = [ + [] for _ in range(len(self.save_checkpoint_stride)) + ] + self.keep_last = [ + i if i > 0 else float("inf") for i in self.cfg.train.keep_last + ] + self.run_eval = self.cfg.train.run_eval + + # set random seed + with self.accelerator.main_process_first(): + start = time.monotonic_ns() + self._set_random_seed(self.cfg.train.random_seed) + end = time.monotonic_ns() + self.logger.debug( + f"Setting random seed done in {(end - start) / 1e6:.2f}ms" + ) + self.logger.debug(f"Random seed: {self.cfg.train.random_seed}") + + # setup data_loader + with self.accelerator.main_process_first(): + self.logger.info("Building dataset...") + start = time.monotonic_ns() + self.train_dataloader, self.valid_dataloader = self._build_dataloader() + end = time.monotonic_ns() + self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms") + + # setup model + with self.accelerator.main_process_first(): + self.logger.info("Building model...") + start = time.monotonic_ns() + self.model = self._build_model() + end = time.monotonic_ns() + self.logger.debug(self.model) + self.logger.info(f"Building model done in {(end - start) / 1e6:.2f}ms") + self.logger.info( + f"Model parameters: {self.__count_parameters(self.model)/1e6:.2f}M" + ) + # optimizer & scheduler + with self.accelerator.main_process_first(): + self.logger.info("Building optimizer and scheduler...") + start = time.monotonic_ns() + self.optimizer = self.__build_optimizer() + self.scheduler = self.__build_scheduler() + end = time.monotonic_ns() + self.logger.info( + f"Building optimizer and scheduler done in {(end - start) / 1e6:.2f}ms" + ) + + # accelerate prepare + self.logger.info("Initializing accelerate...") + start = time.monotonic_ns() + ( + self.train_dataloader, + self.valid_dataloader, + self.model, + self.optimizer, + self.scheduler, + ) = self.accelerator.prepare( + self.train_dataloader, + self.valid_dataloader, + self.model, + self.optimizer, + self.scheduler, + ) + end = time.monotonic_ns() + self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.2f}ms") + + # create criterion + with self.accelerator.main_process_first(): + self.logger.info("Building criterion...") + start = time.monotonic_ns() + self.criterion = self._build_criterion() + end = time.monotonic_ns() + self.logger.info(f"Building criterion done in {(end - start) / 1e6:.2f}ms") + + # Resume or Finetune + with self.accelerator.main_process_first(): + if args.resume: + ## Automatically resume according to the current exprimental name + self.logger.info("Resuming from {}...".format(self.checkpoint_dir)) + start = time.monotonic_ns() + ckpt_path = self.__load_model( + checkpoint_dir=self.checkpoint_dir, resume_type=args.resume_type + ) + end = time.monotonic_ns() + self.logger.info( + f"Resuming from checkpoint done in {(end - start) / 1e6:.2f}ms" + ) + self.checkpoints_path = json.load( + open(os.path.join(ckpt_path, "ckpts.json"), "r") + ) + elif args.resume_from_ckpt_path and args.resume_from_ckpt_path != "": + ## Resume from the given checkpoint path + if not os.path.exists(args.resume_from_ckpt_path): + raise ValueError( + "[Error] The resumed checkpoint path {} don't exist.".format( + args.resume_from_ckpt_path + ) + ) + + self.logger.info( + "Resuming from {}...".format(args.resume_from_ckpt_path) + ) + start = time.monotonic_ns() + ckpt_path = self.__load_model( + checkpoint_path=args.resume_from_ckpt_path, + resume_type=args.resume_type, + ) + end = time.monotonic_ns() + self.logger.info( + f"Resuming from checkpoint done in {(end - start) / 1e6:.2f}ms" + ) + + # save config file path + self.config_save_path = os.path.join(self.exp_dir, "args.json") + + ### Following are abstract methods that should be implemented in child classes ### + @abstractmethod + def _build_dataset(self): + r"""Build dataset for model training/validating/evaluating.""" + pass + + @staticmethod + @abstractmethod + def _build_criterion(): + r"""Build criterion function for model loss calculation.""" + pass + + @abstractmethod + def _build_model(self): + r"""Build model for training/validating/evaluating.""" + pass + + @abstractmethod + def _forward_step(self, batch): + r"""One forward step of the neural network. This abstract method is trying to + unify ``_train_step`` and ``_valid_step`` and avoid redundant implementation. + However, for special case that using different forward step pattern for + training and validating, you could just override this method with ``pass`` and + implement ``_train_step`` and ``_valid_step`` separately. + """ + pass + + @abstractmethod + def _save_auxiliary_states(self): + r"""To save some auxiliary states when saving model's ckpt""" + pass + + ### Abstract methods end ### + + ### THIS IS MAIN ENTRY ### + def train_loop(self): + r"""Training loop. The public entry of training process.""" + # Wait everyone to prepare before we move on + self.accelerator.wait_for_everyone() + # dump config file + if self.accelerator.is_main_process: + self.__dump_cfg(self.config_save_path) + self.model.train() + self.optimizer.zero_grad() + # Wait to ensure good to go + self.accelerator.wait_for_everyone() + while self.epoch < self.max_epoch: + self.logger.info("\n") + self.logger.info("-" * 32) + self.logger.info("Epoch {}: ".format(self.epoch)) + + ### TODO: change the return values of _train_epoch() to a loss dict, or (total_loss, loss_dict) + ### It's inconvenient for the model with multiple losses + # Do training & validating epoch + train_loss = self._train_epoch() + self.logger.info(" |- Train/Loss: {:.6f}".format(train_loss)) + valid_loss = self._valid_epoch() + self.logger.info(" |- Valid/Loss: {:.6f}".format(valid_loss)) + self.accelerator.log( + {"Epoch/Train Loss": train_loss, "Epoch/Valid Loss": valid_loss}, + step=self.epoch, + ) + + self.accelerator.wait_for_everyone() + # TODO: what is scheduler? + self.scheduler.step(valid_loss) # FIXME: use epoch track correct? + + # Check if hit save_checkpoint_stride and run_eval + run_eval = False + if self.accelerator.is_main_process: + save_checkpoint = False + hit_dix = [] + for i, num in enumerate(self.save_checkpoint_stride): + if self.epoch % num == 0: + save_checkpoint = True + hit_dix.append(i) + run_eval |= self.run_eval[i] + + self.accelerator.wait_for_everyone() + if self.accelerator.is_main_process and save_checkpoint: + path = os.path.join( + self.checkpoint_dir, + "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( + self.epoch, self.step, train_loss + ), + ) + self.tmp_checkpoint_save_path = path + self.accelerator.save_state(path) + print(f"save checkpoint in {path}") + json.dump( + self.checkpoints_path, + open(os.path.join(path, "ckpts.json"), "w"), + ensure_ascii=False, + indent=4, + ) + self._save_auxiliary_states() + + # Remove old checkpoints + to_remove = [] + for idx in hit_dix: + self.checkpoints_path[idx].append(path) + while len(self.checkpoints_path[idx]) > self.keep_last[idx]: + to_remove.append((idx, self.checkpoints_path[idx].pop(0))) + + # Search conflicts + total = set() + for i in self.checkpoints_path: + total |= set(i) + do_remove = set() + for idx, path in to_remove[::-1]: + if path in total: + self.checkpoints_path[idx].insert(0, path) + else: + do_remove.add(path) + + # Remove old checkpoints + for path in do_remove: + shutil.rmtree(path, ignore_errors=True) + self.logger.debug(f"Remove old checkpoint: {path}") + + self.accelerator.wait_for_everyone() + if run_eval: + # TODO: run evaluation + pass + + # Update info for each epoch + self.epoch += 1 + + # Finish training and save final checkpoint + self.accelerator.wait_for_everyone() + if self.accelerator.is_main_process: + self.accelerator.save_state( + os.path.join( + self.checkpoint_dir, + "final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( + self.epoch, self.step, valid_loss + ), + ) + ) + self._save_auxiliary_states() + + self.accelerator.end_training() + + ### Following are methods that can be used directly in child classes ### + def _train_epoch(self): + r"""Training epoch. Should return average loss of a batch (sample) over + one epoch. See ``train_loop`` for usage. + """ + self.model.train() + epoch_sum_loss: float = 0.0 + epoch_step: int = 0 + for batch in tqdm( + self.train_dataloader, + desc=f"Training Epoch {self.epoch}", + unit="batch", + colour="GREEN", + leave=False, + dynamic_ncols=True, + smoothing=0.04, + disable=not self.accelerator.is_main_process, + ): + # Do training step and BP + with self.accelerator.accumulate(self.model): + loss = self._train_step(batch) + self.accelerator.backward(loss) + self.optimizer.step() + self.optimizer.zero_grad() + self.batch_count += 1 + + # Update info for each step + # TODO: step means BP counts or batch counts? + if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: + epoch_sum_loss += loss + self.accelerator.log( + { + "Step/Train Loss": loss, + "Step/Learning Rate": self.optimizer.param_groups[0]["lr"], + }, + step=self.step, + ) + self.step += 1 + epoch_step += 1 + + self.accelerator.wait_for_everyone() + return ( + epoch_sum_loss + / len(self.train_dataloader) + * self.cfg.train.gradient_accumulation_step + ) + + @torch.inference_mode() + def _valid_epoch(self): + r"""Testing epoch. Should return average loss of a batch (sample) over + one epoch. See ``train_loop`` for usage. + """ + self.model.eval() + epoch_sum_loss = 0.0 + for batch in tqdm( + self.valid_dataloader, + desc=f"Validating Epoch {self.epoch}", + unit="batch", + colour="GREEN", + leave=False, + dynamic_ncols=True, + smoothing=0.04, + disable=not self.accelerator.is_main_process, + ): + batch_loss = self._valid_step(batch) + epoch_sum_loss += batch_loss.item() + + self.accelerator.wait_for_everyone() + return epoch_sum_loss / len(self.valid_dataloader) + + def _train_step(self, batch): + r"""Training forward step. Should return average loss of a sample over + one batch. Provoke ``_forward_step`` is recommended except for special case. + See ``_train_epoch`` for usage. + """ + return self._forward_step(batch) + + @torch.inference_mode() + def _valid_step(self, batch): + r"""Testing forward step. Should return average loss of a sample over + one batch. Provoke ``_forward_step`` is recommended except for special case. + See ``_test_epoch`` for usage. + """ + return self._forward_step(batch) + + def __load_model( + self, + checkpoint_dir: str = None, + checkpoint_path: str = None, + resume_type: str = "", + ): + r"""Load model from checkpoint. If checkpoint_path is None, it will + load the latest checkpoint in checkpoint_dir. If checkpoint_path is not + None, it will load the checkpoint specified by checkpoint_path. **Only use this + method after** ``accelerator.prepare()``. + """ + if checkpoint_path is None: + ls = [str(i) for i in Path(checkpoint_dir).glob("*")] + ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True) + checkpoint_path = ls[0] + self.logger.info("Resume from {}...".format(checkpoint_path)) + + if resume_type in ["resume", ""]: + # Load all the things, including model weights, optimizer, scheduler, and random states. + self.accelerator.load_state(input_dir=checkpoint_path) + + # set epoch and step + self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + 1 + self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + 1 + + elif resume_type == "finetune": + # Load only the model weights + accelerate.load_checkpoint_and_dispatch( + self.accelerator.unwrap_model(self.model), + os.path.join(checkpoint_path, "pytorch_model.bin"), + ) + self.logger.info("Load model weights for finetune...") + + else: + raise ValueError("Resume_type must be `resume` or `finetune`.") + + return checkpoint_path + + # TODO: LEGACY CODE + def _build_dataloader(self): + Dataset, Collator = self._build_dataset() + + # build dataset instance for each dataset and combine them by ConcatDataset + datasets_list = [] + for dataset in self.cfg.dataset: + subdataset = Dataset(self.cfg, dataset, is_valid=False) + datasets_list.append(subdataset) + train_dataset = ConcatDataset(datasets_list) + train_collate = Collator(self.cfg) + _, batch_sampler = build_samplers(train_dataset, self.cfg, self.logger, "train") + self.logger.debug(f"train batch_sampler: {list(batch_sampler)}") + self.logger.debug(f"length: {train_dataset.cumulative_sizes}") + # TODO: use config instead of (sampler, shuffle, drop_last, batch_size) + train_loader = DataLoader( + train_dataset, + collate_fn=train_collate, + batch_sampler=batch_sampler, + num_workers=self.cfg.train.dataloader.num_worker, + pin_memory=self.cfg.train.dataloader.pin_memory, + ) + + # Build valid dataloader + datasets_list = [] + for dataset in self.cfg.dataset: + subdataset = Dataset(self.cfg, dataset, is_valid=True) + datasets_list.append(subdataset) + valid_dataset = ConcatDataset(datasets_list) + valid_collate = Collator(self.cfg) + _, batch_sampler = build_samplers(valid_dataset, self.cfg, self.logger, "valid") + self.logger.debug(f"valid batch_sampler: {list(batch_sampler)}") + self.logger.debug(f"length: {valid_dataset.cumulative_sizes}") + valid_loader = DataLoader( + valid_dataset, + collate_fn=valid_collate, + batch_sampler=batch_sampler, + num_workers=self.cfg.train.dataloader.num_worker, + pin_memory=self.cfg.train.dataloader.pin_memory, + ) + return train_loader, valid_loader + + @staticmethod + def _set_random_seed(seed): + r"""Set random seed for all possible random modules.""" + random.seed(seed) + np.random.seed(seed) + torch.random.manual_seed(seed) + + def _check_nan(self, loss, y_pred, y_gt): + if torch.any(torch.isnan(loss)): + self.logger.fatal("Fatal Error: Training is down since loss has Nan!") + self.logger.error("loss = {:.6f}".format(loss.item()), in_order=True) + if torch.any(torch.isnan(y_pred)): + self.logger.error( + f"y_pred has Nan: {torch.any(torch.isnan(y_pred))}", in_order=True + ) + else: + self.logger.debug( + f"y_pred has Nan: {torch.any(torch.isnan(y_pred))}", in_order=True + ) + if torch.any(torch.isnan(y_gt)): + self.logger.error( + f"y_gt has Nan: {torch.any(torch.isnan(y_gt))}", in_order=True + ) + else: + self.logger.debug( + f"y_gt has nan: {torch.any(torch.isnan(y_gt))}", in_order=True + ) + if torch.any(torch.isnan(y_pred)): + self.logger.error(f"y_pred: {y_pred}", in_order=True) + else: + self.logger.debug(f"y_pred: {y_pred}", in_order=True) + if torch.any(torch.isnan(y_gt)): + self.logger.error(f"y_gt: {y_gt}", in_order=True) + else: + self.logger.debug(f"y_gt: {y_gt}", in_order=True) + + # TODO: still OK to save tracking? + self.accelerator.end_training() + raise RuntimeError("Loss has Nan! See log for more info.") + + ### Protected methods end ### + + ## Following are private methods ## + ## !!! These are inconvenient for GAN-based model training. It'd be better to move these to svc_trainer.py if needed. + def __build_optimizer(self): + r"""Build optimizer for model.""" + # Make case-insensitive matching + if self.cfg.train.optimizer.lower() == "adadelta": + optimizer = torch.optim.Adadelta( + self.model.parameters(), **self.cfg.train.adadelta + ) + self.logger.info("Using Adadelta optimizer.") + elif self.cfg.train.optimizer.lower() == "adagrad": + optimizer = torch.optim.Adagrad( + self.model.parameters(), **self.cfg.train.adagrad + ) + self.logger.info("Using Adagrad optimizer.") + elif self.cfg.train.optimizer.lower() == "adam": + optimizer = torch.optim.Adam(self.model.parameters(), **self.cfg.train.adam) + self.logger.info("Using Adam optimizer.") + elif self.cfg.train.optimizer.lower() == "adamw": + optimizer = torch.optim.AdamW( + self.model.parameters(), **self.cfg.train.adamw + ) + elif self.cfg.train.optimizer.lower() == "sparseadam": + optimizer = torch.optim.SparseAdam( + self.model.parameters(), **self.cfg.train.sparseadam + ) + elif self.cfg.train.optimizer.lower() == "adamax": + optimizer = torch.optim.Adamax( + self.model.parameters(), **self.cfg.train.adamax + ) + elif self.cfg.train.optimizer.lower() == "asgd": + optimizer = torch.optim.ASGD(self.model.parameters(), **self.cfg.train.asgd) + elif self.cfg.train.optimizer.lower() == "lbfgs": + optimizer = torch.optim.LBFGS( + self.model.parameters(), **self.cfg.train.lbfgs + ) + elif self.cfg.train.optimizer.lower() == "nadam": + optimizer = torch.optim.NAdam( + self.model.parameters(), **self.cfg.train.nadam + ) + elif self.cfg.train.optimizer.lower() == "radam": + optimizer = torch.optim.RAdam( + self.model.parameters(), **self.cfg.train.radam + ) + elif self.cfg.train.optimizer.lower() == "rmsprop": + optimizer = torch.optim.RMSprop( + self.model.parameters(), **self.cfg.train.rmsprop + ) + elif self.cfg.train.optimizer.lower() == "rprop": + optimizer = torch.optim.Rprop( + self.model.parameters(), **self.cfg.train.rprop + ) + elif self.cfg.train.optimizer.lower() == "sgd": + optimizer = torch.optim.SGD(self.model.parameters(), **self.cfg.train.sgd) + else: + raise NotImplementedError( + f"Optimizer {self.cfg.train.optimizer} not supported yet!" + ) + return optimizer + + def __build_scheduler(self): + r"""Build scheduler for optimizer.""" + # Make case-insensitive matching + if self.cfg.train.scheduler.lower() == "lambdalr": + scheduler = torch.optim.lr_scheduler.LambdaLR( + self.optimizer, **self.cfg.train.lambdalr + ) + elif self.cfg.train.scheduler.lower() == "multiplicativelr": + scheduler = torch.optim.lr_scheduler.MultiplicativeLR( + self.optimizer, **self.cfg.train.multiplicativelr + ) + elif self.cfg.train.scheduler.lower() == "steplr": + scheduler = torch.optim.lr_scheduler.StepLR( + self.optimizer, **self.cfg.train.steplr + ) + elif self.cfg.train.scheduler.lower() == "multisteplr": + scheduler = torch.optim.lr_scheduler.MultiStepLR( + self.optimizer, **self.cfg.train.multisteplr + ) + elif self.cfg.train.scheduler.lower() == "constantlr": + scheduler = torch.optim.lr_scheduler.ConstantLR( + self.optimizer, **self.cfg.train.constantlr + ) + elif self.cfg.train.scheduler.lower() == "linearlr": + scheduler = torch.optim.lr_scheduler.LinearLR( + self.optimizer, **self.cfg.train.linearlr + ) + elif self.cfg.train.scheduler.lower() == "exponentiallr": + scheduler = torch.optim.lr_scheduler.ExponentialLR( + self.optimizer, **self.cfg.train.exponentiallr + ) + elif self.cfg.train.scheduler.lower() == "polynomiallr": + scheduler = torch.optim.lr_scheduler.PolynomialLR( + self.optimizer, **self.cfg.train.polynomiallr + ) + elif self.cfg.train.scheduler.lower() == "cosineannealinglr": + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + self.optimizer, **self.cfg.train.cosineannealinglr + ) + elif self.cfg.train.scheduler.lower() == "sequentiallr": + scheduler = torch.optim.lr_scheduler.SequentialLR( + self.optimizer, **self.cfg.train.sequentiallr + ) + elif self.cfg.train.scheduler.lower() == "reducelronplateau": + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + self.optimizer, **self.cfg.train.reducelronplateau + ) + elif self.cfg.train.scheduler.lower() == "cycliclr": + scheduler = torch.optim.lr_scheduler.CyclicLR( + self.optimizer, **self.cfg.train.cycliclr + ) + elif self.cfg.train.scheduler.lower() == "onecyclelr": + scheduler = torch.optim.lr_scheduler.OneCycleLR( + self.optimizer, **self.cfg.train.onecyclelr + ) + elif self.cfg.train.scheduler.lower() == "cosineannearingwarmrestarts": + scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( + self.optimizer, **self.cfg.train.cosineannearingwarmrestarts + ) + elif self.cfg.train.scheduler.lower() == "noamlr": + scheduler = NoamLR(self.optimizer, **self.cfg.train.lr_scheduler) + else: + raise NotImplementedError( + f"Scheduler {self.cfg.train.scheduler} not supported yet!" + ) + return scheduler + + def _init_accelerator(self): + self.exp_dir = os.path.join( + os.path.abspath(self.cfg.log_dir), self.args.exp_name + ) + project_config = ProjectConfiguration( + project_dir=self.exp_dir, + logging_dir=os.path.join(self.exp_dir, "log"), + ) + self.accelerator = accelerate.Accelerator( + gradient_accumulation_steps=self.cfg.train.gradient_accumulation_step, + log_with=self.cfg.train.tracker, + project_config=project_config, + ) + if self.accelerator.is_main_process: + os.makedirs(project_config.project_dir, exist_ok=True) + os.makedirs(project_config.logging_dir, exist_ok=True) + with self.accelerator.main_process_first(): + self.accelerator.init_trackers(self.args.exp_name) + + def __check_basic_configs(self): + if self.cfg.train.gradient_accumulation_step <= 0: + self.logger.fatal("Invalid gradient_accumulation_step value!") + self.logger.error( + f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." + ) + self.accelerator.end_training() + raise ValueError( + f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." + ) + # TODO: check other values + + @staticmethod + def __count_parameters(model): + model_param = 0.0 + if isinstance(model, dict): + for key, value in model.items(): + model_param += sum(p.numel() for p in model[key].parameters()) + else: + model_param = sum(p.numel() for p in model.parameters()) + return model_param + + def __dump_cfg(self, path): + os.makedirs(os.path.dirname(path), exist_ok=True) + json5.dump( + self.cfg, + open(path, "w"), + indent=4, + sort_keys=True, + ensure_ascii=False, + quote_keys=True, + ) + + ### Private methods end ### diff --git a/models/svc/__init__.py b/models/svc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/svc/base/__init__.py b/models/svc/base/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..38c2b1686db550b3b9892b8bc6e594cd847aafd1 --- /dev/null +++ b/models/svc/base/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .svc_inference import SVCInference +from .svc_trainer import SVCTrainer diff --git a/models/svc/base/svc_dataset.py b/models/svc/base/svc_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..cad356bd97b92b9917db6c973b31a42552f5fa76 --- /dev/null +++ b/models/svc/base/svc_dataset.py @@ -0,0 +1,425 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import random +import torch +from torch.nn.utils.rnn import pad_sequence +import json +import os +import numpy as np +from utils.data_utils import * +from processors.acoustic_extractor import cal_normalized_mel, load_mel_extrema +from processors.content_extractor import ( + ContentvecExtractor, + WhisperExtractor, + WenetExtractor, +) +from models.base.base_dataset import ( + BaseCollator, + BaseDataset, +) +from models.base.new_dataset import BaseTestDataset + +EPS = 1.0e-12 + + +class SVCDataset(BaseDataset): + def __init__(self, cfg, dataset, is_valid=False): + BaseDataset.__init__(self, cfg, dataset, is_valid=is_valid) + + cfg = self.cfg + + if cfg.model.condition_encoder.use_whisper: + self.whisper_aligner = WhisperExtractor(self.cfg) + self.utt2whisper_path = load_content_feature_path( + self.metadata, cfg.preprocess.processed_dir, cfg.preprocess.whisper_dir + ) + + if cfg.model.condition_encoder.use_contentvec: + self.contentvec_aligner = ContentvecExtractor(self.cfg) + self.utt2contentVec_path = load_content_feature_path( + self.metadata, + cfg.preprocess.processed_dir, + cfg.preprocess.contentvec_dir, + ) + + if cfg.model.condition_encoder.use_mert: + self.utt2mert_path = load_content_feature_path( + self.metadata, cfg.preprocess.processed_dir, cfg.preprocess.mert_dir + ) + if cfg.model.condition_encoder.use_wenet: + self.wenet_aligner = WenetExtractor(self.cfg) + self.utt2wenet_path = load_content_feature_path( + self.metadata, cfg.preprocess.processed_dir, cfg.preprocess.wenet_dir + ) + + def __getitem__(self, index): + single_feature = BaseDataset.__getitem__(self, index) + + utt_info = self.metadata[index] + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + if self.cfg.model.condition_encoder.use_whisper: + assert "target_len" in single_feature.keys() + aligned_whisper_feat = self.whisper_aligner.offline_align( + np.load(self.utt2whisper_path[utt]), single_feature["target_len"] + ) + single_feature["whisper_feat"] = aligned_whisper_feat + + if self.cfg.model.condition_encoder.use_contentvec: + assert "target_len" in single_feature.keys() + aligned_contentvec = self.contentvec_aligner.offline_align( + np.load(self.utt2contentVec_path[utt]), single_feature["target_len"] + ) + single_feature["contentvec_feat"] = aligned_contentvec + + if self.cfg.model.condition_encoder.use_mert: + assert "target_len" in single_feature.keys() + aligned_mert_feat = align_content_feature_length( + np.load(self.utt2mert_path[utt]), + single_feature["target_len"], + source_hop=self.cfg.preprocess.mert_hop_size, + ) + single_feature["mert_feat"] = aligned_mert_feat + + if self.cfg.model.condition_encoder.use_wenet: + assert "target_len" in single_feature.keys() + aligned_wenet_feat = self.wenet_aligner.offline_align( + np.load(self.utt2wenet_path[utt]), single_feature["target_len"] + ) + single_feature["wenet_feat"] = aligned_wenet_feat + + # print(single_feature.keys()) + # for k, v in single_feature.items(): + # if type(v) in [torch.Tensor, np.ndarray]: + # print(k, v.shape) + # else: + # print(k, v) + # exit() + + return self.clip_if_too_long(single_feature) + + def __len__(self): + return len(self.metadata) + + def random_select(self, feature_seq_len, max_seq_len, ending_ts=2812): + """ + ending_ts: to avoid invalid whisper features for over 30s audios + 2812 = 30 * 24000 // 256 + """ + ts = max(feature_seq_len - max_seq_len, 0) + ts = min(ts, ending_ts - max_seq_len) + + start = random.randint(0, ts) + end = start + max_seq_len + return start, end + + def clip_if_too_long(self, sample, max_seq_len=512): + """ + sample : + { + 'spk_id': (1,), + 'target_len': int + 'mel': (seq_len, dim), + 'frame_pitch': (seq_len,) + 'frame_energy': (seq_len,) + 'content_vector_feat': (seq_len, dim) + } + """ + if sample["target_len"] <= max_seq_len: + return sample + + start, end = self.random_select(sample["target_len"], max_seq_len) + sample["target_len"] = end - start + + for k in sample.keys(): + if k not in ["spk_id", "target_len"]: + sample[k] = sample[k][start:end] + + return sample + + +class SVCCollator(BaseCollator): + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, cfg): + BaseCollator.__init__(self, cfg) + + def __call__(self, batch): + parsed_batch_features = BaseCollator.__call__(self, batch) + return parsed_batch_features + + +class SVCTestDataset(BaseTestDataset): + def __init__(self, args, cfg, infer_type): + BaseTestDataset.__init__(self, args, cfg, infer_type) + self.metadata = self.get_metadata() + + target_singer = args.target_singer + self.cfg = cfg + self.trans_key = args.trans_key + assert type(target_singer) == str + + self.target_singer = target_singer.split("_")[-1] + self.target_dataset = target_singer.replace( + "_{}".format(self.target_singer), "" + ) + + self.target_mel_extrema = load_mel_extrema(cfg.preprocess, self.target_dataset) + self.target_mel_extrema = torch.as_tensor( + self.target_mel_extrema[0] + ), torch.as_tensor(self.target_mel_extrema[1]) + + ######### Load source acoustic features ######### + if cfg.preprocess.use_spkid: + spk2id_path = os.path.join(args.acoustics_dir, cfg.preprocess.spk2id) + # utt2sp_path = os.path.join(self.data_root, cfg.preprocess.utt2spk) + + with open(spk2id_path, "r") as f: + self.spk2id = json.load(f) + # print("self.spk2id", self.spk2id) + + if cfg.preprocess.use_uv: + self.utt2uv_path = { + f'{utt_info["Dataset"]}_{utt_info["Uid"]}': os.path.join( + cfg.preprocess.processed_dir, + utt_info["Dataset"], + cfg.preprocess.uv_dir, + utt_info["Uid"] + ".npy", + ) + for utt_info in self.metadata + } + + if cfg.preprocess.use_frame_pitch: + self.utt2frame_pitch_path = { + f'{utt_info["Dataset"]}_{utt_info["Uid"]}': os.path.join( + cfg.preprocess.processed_dir, + utt_info["Dataset"], + cfg.preprocess.pitch_dir, + utt_info["Uid"] + ".npy", + ) + for utt_info in self.metadata + } + + # Target F0 median + target_f0_statistics_path = os.path.join( + cfg.preprocess.processed_dir, + self.target_dataset, + cfg.preprocess.pitch_dir, + "statistics.json", + ) + self.target_pitch_median = json.load(open(target_f0_statistics_path, "r"))[ + f"{self.target_dataset}_{self.target_singer}" + ]["voiced_positions"]["median"] + + # Source F0 median (if infer from file) + if infer_type == "from_file": + source_audio_name = cfg.inference.source_audio_name + source_f0_statistics_path = os.path.join( + cfg.preprocess.processed_dir, + source_audio_name, + cfg.preprocess.pitch_dir, + "statistics.json", + ) + self.source_pitch_median = json.load( + open(source_f0_statistics_path, "r") + )[f"{source_audio_name}_{source_audio_name}"]["voiced_positions"][ + "median" + ] + else: + self.source_pitch_median = None + + if cfg.preprocess.use_frame_energy: + self.utt2frame_energy_path = { + f'{utt_info["Dataset"]}_{utt_info["Uid"]}': os.path.join( + cfg.preprocess.processed_dir, + utt_info["Dataset"], + cfg.preprocess.energy_dir, + utt_info["Uid"] + ".npy", + ) + for utt_info in self.metadata + } + + if cfg.preprocess.use_mel: + self.utt2mel_path = { + f'{utt_info["Dataset"]}_{utt_info["Uid"]}': os.path.join( + cfg.preprocess.processed_dir, + utt_info["Dataset"], + cfg.preprocess.mel_dir, + utt_info["Uid"] + ".npy", + ) + for utt_info in self.metadata + } + + ######### Load source content features' path ######### + if cfg.model.condition_encoder.use_whisper: + self.whisper_aligner = WhisperExtractor(cfg) + self.utt2whisper_path = load_content_feature_path( + self.metadata, cfg.preprocess.processed_dir, cfg.preprocess.whisper_dir + ) + + if cfg.model.condition_encoder.use_contentvec: + self.contentvec_aligner = ContentvecExtractor(cfg) + self.utt2contentVec_path = load_content_feature_path( + self.metadata, + cfg.preprocess.processed_dir, + cfg.preprocess.contentvec_dir, + ) + + if cfg.model.condition_encoder.use_mert: + self.utt2mert_path = load_content_feature_path( + self.metadata, cfg.preprocess.processed_dir, cfg.preprocess.mert_dir + ) + if cfg.model.condition_encoder.use_wenet: + self.wenet_aligner = WenetExtractor(cfg) + self.utt2wenet_path = load_content_feature_path( + self.metadata, cfg.preprocess.processed_dir, cfg.preprocess.wenet_dir + ) + + def __getitem__(self, index): + single_feature = {} + + utt_info = self.metadata[index] + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + source_dataset = self.metadata[index]["Dataset"] + + if self.cfg.preprocess.use_spkid: + single_feature["spk_id"] = np.array( + [self.spk2id[f"{self.target_dataset}_{self.target_singer}"]], + dtype=np.int32, + ) + + ######### Get Acoustic Features Item ######### + if self.cfg.preprocess.use_mel: + mel = np.load(self.utt2mel_path[utt]) + assert mel.shape[0] == self.cfg.preprocess.n_mel # [n_mels, T] + if self.cfg.preprocess.use_min_max_norm_mel: + # mel norm + mel = cal_normalized_mel(mel, source_dataset, self.cfg.preprocess) + + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = mel.shape[1] + single_feature["mel"] = mel.T # [T, n_mels] + + if self.cfg.preprocess.use_frame_pitch: + frame_pitch_path = self.utt2frame_pitch_path[utt] + frame_pitch = np.load(frame_pitch_path) + + if self.trans_key: + try: + self.trans_key = int(self.trans_key) + except: + pass + if type(self.trans_key) == int: + frame_pitch = transpose_key(frame_pitch, self.trans_key) + elif self.trans_key: + assert self.target_singer + + frame_pitch = pitch_shift_to_target( + frame_pitch, self.target_pitch_median, self.source_pitch_median + ) + + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = len(frame_pitch) + aligned_frame_pitch = align_length( + frame_pitch, single_feature["target_len"] + ) + single_feature["frame_pitch"] = aligned_frame_pitch + + if self.cfg.preprocess.use_uv: + frame_uv_path = self.utt2uv_path[utt] + frame_uv = np.load(frame_uv_path) + aligned_frame_uv = align_length(frame_uv, single_feature["target_len"]) + aligned_frame_uv = [ + 0 if frame_uv else 1 for frame_uv in aligned_frame_uv + ] + aligned_frame_uv = np.array(aligned_frame_uv) + single_feature["frame_uv"] = aligned_frame_uv + + if self.cfg.preprocess.use_frame_energy: + frame_energy_path = self.utt2frame_energy_path[utt] + frame_energy = np.load(frame_energy_path) + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = len(frame_energy) + aligned_frame_energy = align_length( + frame_energy, single_feature["target_len"] + ) + single_feature["frame_energy"] = aligned_frame_energy + + ######### Get Content Features Item ######### + if self.cfg.model.condition_encoder.use_whisper: + assert "target_len" in single_feature.keys() + aligned_whisper_feat = self.whisper_aligner.offline_align( + np.load(self.utt2whisper_path[utt]), single_feature["target_len"] + ) + single_feature["whisper_feat"] = aligned_whisper_feat + + if self.cfg.model.condition_encoder.use_contentvec: + assert "target_len" in single_feature.keys() + aligned_contentvec = self.contentvec_aligner.offline_align( + np.load(self.utt2contentVec_path[utt]), single_feature["target_len"] + ) + single_feature["contentvec_feat"] = aligned_contentvec + + if self.cfg.model.condition_encoder.use_mert: + assert "target_len" in single_feature.keys() + aligned_mert_feat = align_content_feature_length( + np.load(self.utt2mert_path[utt]), + single_feature["target_len"], + source_hop=self.cfg.preprocess.mert_hop_size, + ) + single_feature["mert_feat"] = aligned_mert_feat + + if self.cfg.model.condition_encoder.use_wenet: + assert "target_len" in single_feature.keys() + aligned_wenet_feat = self.wenet_aligner.offline_align( + np.load(self.utt2wenet_path[utt]), single_feature["target_len"] + ) + single_feature["wenet_feat"] = aligned_wenet_feat + + return single_feature + + def __len__(self): + return len(self.metadata) + + +class SVCTestCollator: + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, cfg): + self.cfg = cfg + + def __call__(self, batch): + packed_batch_features = dict() + + # mel: [b, T, n_mels] + # frame_pitch, frame_energy: [1, T] + # target_len: [1] + # spk_id: [b, 1] + # mask: [b, T, 1] + + for key in batch[0].keys(): + if key == "target_len": + packed_batch_features["target_len"] = torch.LongTensor( + [b["target_len"] for b in batch] + ) + masks = [ + torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + else: + values = [torch.from_numpy(b[key]) for b in batch] + packed_batch_features[key] = pad_sequence( + values, batch_first=True, padding_value=0 + ) + + return packed_batch_features diff --git a/models/svc/base/svc_inference.py b/models/svc/base/svc_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..52f88d5d915e1616292c03927b4f51557351f58b --- /dev/null +++ b/models/svc/base/svc_inference.py @@ -0,0 +1,15 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from models.base.new_inference import BaseInference +from models.svc.base.svc_dataset import SVCTestCollator, SVCTestDataset + + +class SVCInference(BaseInference): + def __init__(self, args=None, cfg=None, infer_type="from_dataset"): + BaseInference.__init__(self, args, cfg, infer_type) + + def _build_test_dataset(self): + return SVCTestDataset, SVCTestCollator diff --git a/models/svc/base/svc_trainer.py b/models/svc/base/svc_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..a2a093a86712bb7ccfa786a6c18dd1683ffc013c --- /dev/null +++ b/models/svc/base/svc_trainer.py @@ -0,0 +1,111 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import os + +import torch +import torch.nn as nn + +from models.base.new_trainer import BaseTrainer +from models.svc.base.svc_dataset import SVCCollator, SVCDataset + + +class SVCTrainer(BaseTrainer): + r"""The base trainer for all SVC models. It inherits from BaseTrainer and implements + ``build_criterion``, ``_build_dataset`` and ``_build_singer_lut`` methods. You can inherit from this + class, and implement ``_build_model``, ``_forward_step``. + """ + + def __init__(self, args=None, cfg=None): + self.args = args + self.cfg = cfg + + self._init_accelerator() + + # Only for SVC tasks + with self.accelerator.main_process_first(): + self.singers = self._build_singer_lut() + + # Super init + BaseTrainer.__init__(self, args, cfg) + + # Only for SVC tasks + self.task_type = "SVC" + self.logger.info("Task type: {}".format(self.task_type)) + + ### Following are methods only for SVC tasks ### + # TODO: LEGACY CODE, NEED TO BE REFACTORED + def _build_dataset(self): + return SVCDataset, SVCCollator + + @staticmethod + def _build_criterion(): + criterion = nn.MSELoss(reduction="none") + return criterion + + @staticmethod + def _compute_loss(criterion, y_pred, y_gt, loss_mask): + """ + Args: + criterion: MSELoss(reduction='none') + y_pred, y_gt: (bs, seq_len, D) + loss_mask: (bs, seq_len, 1) + Returns: + loss: Tensor of shape [] + """ + + # (bs, seq_len, D) + loss = criterion(y_pred, y_gt) + # expand loss_mask to (bs, seq_len, D) + loss_mask = loss_mask.repeat(1, 1, loss.shape[-1]) + + loss = torch.sum(loss * loss_mask) / torch.sum(loss_mask) + return loss + + def _save_auxiliary_states(self): + """ + To save the singer's look-up table in the checkpoint saving path + """ + with open( + os.path.join(self.tmp_checkpoint_save_path, self.cfg.preprocess.spk2id), "w" + ) as f: + json.dump(self.singers, f, indent=4, ensure_ascii=False) + + def _build_singer_lut(self): + resumed_singer_path = None + if self.args.resume_from_ckpt_path and self.args.resume_from_ckpt_path != "": + resumed_singer_path = os.path.join( + self.args.resume_from_ckpt_path, self.cfg.preprocess.spk2id + ) + if os.path.exists(os.path.join(self.exp_dir, self.cfg.preprocess.spk2id)): + resumed_singer_path = os.path.join(self.exp_dir, self.cfg.preprocess.spk2id) + + if resumed_singer_path: + with open(resumed_singer_path, "r") as f: + singers = json.load(f) + else: + singers = dict() + + for dataset in self.cfg.dataset: + singer_lut_path = os.path.join( + self.cfg.preprocess.processed_dir, dataset, self.cfg.preprocess.spk2id + ) + with open(singer_lut_path, "r") as singer_lut_path: + singer_lut = json.load(singer_lut_path) + for singer in singer_lut.keys(): + if singer not in singers: + singers[singer] = len(singers) + + with open( + os.path.join(self.exp_dir, self.cfg.preprocess.spk2id), "w" + ) as singer_file: + json.dump(singers, singer_file, indent=4, ensure_ascii=False) + print( + "singers have been dumped to {}".format( + os.path.join(self.exp_dir, self.cfg.preprocess.spk2id) + ) + ) + return singers diff --git a/models/svc/comosvc/__init__.py b/models/svc/comosvc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..19f1cb162e95d8a992002beaa0c0d8bada9cddd5 --- /dev/null +++ b/models/svc/comosvc/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/models/svc/comosvc/comosvc.py b/models/svc/comosvc/comosvc.py new file mode 100644 index 0000000000000000000000000000000000000000..6cecd7a3f40f3a78f0df06ef2340159d321d6117 --- /dev/null +++ b/models/svc/comosvc/comosvc.py @@ -0,0 +1,377 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Adapted from https://github.com/zhenye234/CoMoSpeech""" + +import torch +import torch.nn as nn +import copy +import numpy as np +import math +from tqdm.auto import tqdm + +from utils.ssim import SSIM + +from models.svc.transformer.conformer import Conformer, BaseModule +from models.svc.diffusion.diffusion_wrapper import DiffusionWrapper +from models.svc.comosvc.utils import slice_segments, rand_ids_segments + + +class Consistency(nn.Module): + def __init__(self, cfg, distill=False): + super().__init__() + self.cfg = cfg + # self.denoise_fn = GradLogPEstimator2d(96) + self.denoise_fn = DiffusionWrapper(self.cfg) + self.cfg = cfg.model.comosvc + self.teacher = not distill + self.P_mean = self.cfg.P_mean + self.P_std = self.cfg.P_std + self.sigma_data = self.cfg.sigma_data + self.sigma_min = self.cfg.sigma_min + self.sigma_max = self.cfg.sigma_max + self.rho = self.cfg.rho + self.N = self.cfg.n_timesteps + self.ssim_loss = SSIM() + + # Time step discretization + step_indices = torch.arange(self.N) + # karras boundaries formula + t_steps = ( + self.sigma_min ** (1 / self.rho) + + step_indices + / (self.N - 1) + * (self.sigma_max ** (1 / self.rho) - self.sigma_min ** (1 / self.rho)) + ) ** self.rho + self.t_steps = torch.cat( + [torch.zeros_like(t_steps[:1]), self.round_sigma(t_steps)] + ) + + def init_consistency_training(self): + self.denoise_fn_ema = copy.deepcopy(self.denoise_fn) + self.denoise_fn_pretrained = copy.deepcopy(self.denoise_fn) + + def EDMPrecond(self, x, sigma, cond, denoise_fn, mask, spk=None): + """ + karras diffusion reverse process + + Args: + x: noisy mel-spectrogram [B x n_mel x L] + sigma: noise level [B x 1 x 1] + cond: output of conformer encoder [B x n_mel x L] + denoise_fn: denoiser neural network e.g. DilatedCNN + mask: mask of padded frames [B x n_mel x L] + + Returns: + denoised mel-spectrogram [B x n_mel x L] + """ + sigma = sigma.reshape(-1, 1, 1) + + c_skip = self.sigma_data**2 / (sigma**2 + self.sigma_data**2) + c_out = sigma * self.sigma_data / (sigma**2 + self.sigma_data**2).sqrt() + c_in = 1 / (self.sigma_data**2 + sigma**2).sqrt() + c_noise = sigma.log() / 4 + + x_in = c_in * x + x_in = x_in.transpose(1, 2) + x = x.transpose(1, 2) + cond = cond.transpose(1, 2) + F_x = denoise_fn(x_in, c_noise.squeeze(), cond) + # F_x = denoise_fn((c_in * x), mask, cond, c_noise.flatten()) + D_x = c_skip * x + c_out * (F_x) + D_x = D_x.transpose(1, 2) + return D_x + + def EDMLoss(self, x_start, cond, mask): + """ + compute loss for EDM model + + Args: + x_start: ground truth mel-spectrogram [B x n_mel x L] + cond: output of conformer encoder [B x n_mel x L] + mask: mask of padded frames [B x n_mel x L] + """ + rnd_normal = torch.randn([x_start.shape[0], 1, 1], device=x_start.device) + sigma = (rnd_normal * self.P_std + self.P_mean).exp() + weight = (sigma**2 + self.sigma_data**2) / (sigma * self.sigma_data) ** 2 + + # follow Grad-TTS, start from Gaussian noise with mean cond and std I + noise = (torch.randn_like(x_start) + cond) * sigma + D_yn = self.EDMPrecond(x_start + noise, sigma, cond, self.denoise_fn, mask) + loss = weight * ((D_yn - x_start) ** 2) + loss = torch.sum(loss * mask) / torch.sum(mask) + return loss + + def round_sigma(self, sigma): + return torch.as_tensor(sigma) + + def edm_sampler( + self, + latents, + cond, + nonpadding, + num_steps=50, + sigma_min=0.002, + sigma_max=80, + rho=7, + S_churn=0, + S_min=0, + S_max=float("inf"), + S_noise=1, + # S_churn=40 ,S_min=0.05,S_max=50,S_noise=1.003,# S_churn=0, S_min=0, S_max=float('inf'), S_noise=1, + # S_churn=30 ,S_min=0.01,S_max=30,S_noise=1.007, + # S_churn=30 ,S_min=0.01,S_max=1,S_noise=1.007, + # S_churn=80 ,S_min=0.05,S_max=50,S_noise=1.003, + ): + """ + karras diffusion sampler + + Args: + latents: noisy mel-spectrogram [B x n_mel x L] + cond: output of conformer encoder [B x n_mel x L] + nonpadding: mask of padded frames [B x n_mel x L] + num_steps: number of steps for diffusion inference + + Returns: + denoised mel-spectrogram [B x n_mel x L] + """ + # Time step discretization. + step_indices = torch.arange(num_steps, device=latents.device) + + num_steps = num_steps + 1 + t_steps = ( + sigma_max ** (1 / rho) + + step_indices + / (num_steps - 1) + * (sigma_min ** (1 / rho) - sigma_max ** (1 / rho)) + ) ** rho + t_steps = torch.cat([self.round_sigma(t_steps), torch.zeros_like(t_steps[:1])]) + + # Main sampling loop. + x_next = latents * t_steps[0] + # wrap in tqdm for progress bar + bar = tqdm(enumerate(zip(t_steps[:-1], t_steps[1:]))) + for i, (t_cur, t_next) in bar: + x_cur = x_next + # Increase noise temporarily. + gamma = ( + min(S_churn / num_steps, np.sqrt(2) - 1) + if S_min <= t_cur <= S_max + else 0 + ) + t_hat = self.round_sigma(t_cur + gamma * t_cur) + t = torch.zeros((x_cur.shape[0], 1, 1), device=x_cur.device) + t[:, 0, 0] = t_hat + t_hat = t + x_hat = x_cur + ( + t_hat**2 - t_cur**2 + ).sqrt() * S_noise * torch.randn_like(x_cur) + # Euler step. + denoised = self.EDMPrecond(x_hat, t_hat, cond, self.denoise_fn, nonpadding) + d_cur = (x_hat - denoised) / t_hat + x_next = x_hat + (t_next - t_hat) * d_cur + + return x_next + + def CTLoss_D(self, y, cond, mask): + """ + compute loss for consistency distillation + + Args: + y: ground truth mel-spectrogram [B x n_mel x L] + cond: output of conformer encoder [B x n_mel x L] + mask: mask of padded frames [B x n_mel x L] + """ + with torch.no_grad(): + mu = 0.95 + for p, ema_p in zip( + self.denoise_fn.parameters(), self.denoise_fn_ema.parameters() + ): + ema_p.mul_(mu).add_(p, alpha=1 - mu) + + n = torch.randint(1, self.N, (y.shape[0],)) + z = torch.randn_like(y) + cond + + tn_1 = self.t_steps[n + 1].reshape(-1, 1, 1).to(y.device) + f_theta = self.EDMPrecond(y + tn_1 * z, tn_1, cond, self.denoise_fn, mask) + + with torch.no_grad(): + tn = self.t_steps[n].reshape(-1, 1, 1).to(y.device) + + # euler step + x_hat = y + tn_1 * z + denoised = self.EDMPrecond( + x_hat, tn_1, cond, self.denoise_fn_pretrained, mask + ) + d_cur = (x_hat - denoised) / tn_1 + y_tn = x_hat + (tn - tn_1) * d_cur + + f_theta_ema = self.EDMPrecond(y_tn, tn, cond, self.denoise_fn_ema, mask) + + # loss = (f_theta - f_theta_ema.detach()) ** 2 + # loss = torch.sum(loss * mask) / torch.sum(mask) + loss = self.ssim_loss(f_theta, f_theta_ema.detach()) + loss = torch.sum(loss * mask) / torch.sum(mask) + + return loss + + def get_t_steps(self, N): + N = N + 1 + step_indices = torch.arange(N) # , device=latents.device) + t_steps = ( + self.sigma_min ** (1 / self.rho) + + step_indices + / (N - 1) + * (self.sigma_max ** (1 / self.rho) - self.sigma_min ** (1 / self.rho)) + ) ** self.rho + + return t_steps.flip(0) + + def CT_sampler(self, latents, cond, nonpadding, t_steps=1): + """ + consistency distillation sampler + + Args: + latents: noisy mel-spectrogram [B x n_mel x L] + cond: output of conformer encoder [B x n_mel x L] + nonpadding: mask of padded frames [B x n_mel x L] + t_steps: number of steps for diffusion inference + + Returns: + denoised mel-spectrogram [B x n_mel x L] + """ + # one-step + if t_steps == 1: + t_steps = [80] + # multi-step + else: + t_steps = self.get_t_steps(t_steps) + + t_steps = torch.as_tensor(t_steps).to(latents.device) + latents = latents * t_steps[0] + _t = torch.zeros((latents.shape[0], 1, 1), device=latents.device) + _t[:, 0, 0] = t_steps + x = self.EDMPrecond(latents, _t, cond, self.denoise_fn_ema, nonpadding) + + for t in t_steps[1:-1]: + z = torch.randn_like(x) + cond + x_tn = x + (t**2 - self.sigma_min**2).sqrt() * z + _t = torch.zeros((x.shape[0], 1, 1), device=x.device) + _t[:, 0, 0] = t + t = _t + print(t) + x = self.EDMPrecond(x_tn, t, cond, self.denoise_fn_ema, nonpadding) + return x + + def forward(self, x, nonpadding, cond, t_steps=1, infer=False): + """ + calculate loss or sample mel-spectrogram + + Args: + x: + training: ground truth mel-spectrogram [B x n_mel x L] + inference: output of encoder [B x n_mel x L] + """ + if self.teacher: # teacher model -- karras diffusion + if not infer: + loss = self.EDMLoss(x, cond, nonpadding) + return loss + else: + shape = (cond.shape[0], self.cfg.n_mel, cond.shape[2]) + x = torch.randn(shape, device=x.device) + cond + x = self.edm_sampler(x, cond, nonpadding, t_steps) + + return x + else: # Consistency distillation + if not infer: + loss = self.CTLoss_D(x, cond, nonpadding) + return loss + + else: + shape = (cond.shape[0], self.cfg.n_mel, cond.shape[2]) + x = torch.randn(shape, device=x.device) + cond + x = self.CT_sampler(x, cond, nonpadding, t_steps=1) + + return x + + +class ComoSVC(BaseModule): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.cfg.model.comosvc.n_mel = self.cfg.preprocess.n_mel + self.distill = self.cfg.model.comosvc.distill + self.encoder = Conformer(self.cfg.model.comosvc) + self.decoder = Consistency(self.cfg, distill=self.distill) + self.ssim_loss = SSIM() + + @torch.no_grad() + def forward(self, x_mask, x, n_timesteps, temperature=1.0): + """ + Generates mel-spectrogram from pitch, content vector, energy. Returns: + 1. encoder outputs (from conformer) + 2. decoder outputs (from diffusion-based decoder) + + Args: + x_mask : mask of padded frames in mel-spectrogram. [B x L x n_mel] + x : output of encoder framework. [B x L x d_condition] + n_timesteps : number of steps to use for reverse diffusion in decoder. + temperature : controls variance of terminal distribution. + """ + + # Get encoder_outputs `mu_x` + mu_x = self.encoder(x, x_mask) + encoder_outputs = mu_x + + mu_x = mu_x.transpose(1, 2) + x_mask = x_mask.transpose(1, 2) + + # Generate sample by performing reverse dynamics + decoder_outputs = self.decoder( + mu_x, x_mask, mu_x, t_steps=n_timesteps, infer=True + ) + decoder_outputs = decoder_outputs.transpose(1, 2) + return encoder_outputs, decoder_outputs + + def compute_loss(self, x_mask, x, mel, out_size=None, skip_diff=False): + """ + Computes 2 losses: + 1. prior loss: loss between mel-spectrogram and encoder outputs. + 2. diffusion loss: loss between gaussian noise and its reconstruction by diffusion-based decoder. + + Args: + x_mask : mask of padded frames in mel-spectrogram. [B x L x n_mel] + x : output of encoder framework. [B x L x d_condition] + mel : ground truth mel-spectrogram. [B x L x n_mel] + """ + + mu_x = self.encoder(x, x_mask) + # prior loss + prior_loss = torch.sum( + 0.5 * ((mel - mu_x) ** 2 + math.log(2 * math.pi)) * x_mask + ) + prior_loss = prior_loss / (torch.sum(x_mask) * self.cfg.model.comosvc.n_mel) + # ssim loss + ssim_loss = self.ssim_loss(mu_x, mel) + ssim_loss = torch.sum(ssim_loss * x_mask) / torch.sum(x_mask) + + x_mask = x_mask.transpose(1, 2) + mu_x = mu_x.transpose(1, 2) + mel = mel.transpose(1, 2) + if not self.distill and skip_diff: + diff_loss = prior_loss.clone() + diff_loss.fill_(0) + + # Cut a small segment of mel-spectrogram in order to increase batch size + else: + if self.distill: + mu_y = mu_x.detach() + else: + mu_y = mu_x + mask_y = x_mask + + diff_loss = self.decoder(mel, mask_y, mu_y, infer=False) + + return ssim_loss, prior_loss, diff_loss diff --git a/models/svc/comosvc/comosvc_inference.py b/models/svc/comosvc/comosvc_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..2783ec7e468c367c7d2f5f8988ed1f7e272d4cb7 --- /dev/null +++ b/models/svc/comosvc/comosvc_inference.py @@ -0,0 +1,39 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from models.svc.base import SVCInference +from modules.encoder.condition_encoder import ConditionEncoder +from models.svc.comosvc.comosvc import ComoSVC + + +class ComoSVCInference(SVCInference): + def __init__(self, args, cfg, infer_type="from_dataset"): + SVCInference.__init__(self, args, cfg, infer_type) + + def _build_model(self): + # TODO: sort out the config + self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min + self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max + self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder) + self.acoustic_mapper = ComoSVC(self.cfg) + if self.cfg.model.comosvc.distill: + self.acoustic_mapper.decoder.init_consistency_training() + model = torch.nn.ModuleList([self.condition_encoder, self.acoustic_mapper]) + return model + + def _inference_each_batch(self, batch_data): + device = self.accelerator.device + for k, v in batch_data.items(): + batch_data[k] = v.to(device) + + cond = self.condition_encoder(batch_data) + mask = batch_data["mask"] + encoder_pred, decoder_pred = self.acoustic_mapper( + mask, cond, self.cfg.inference.comosvc.inference_steps + ) + + return decoder_pred diff --git a/models/svc/comosvc/comosvc_trainer.py b/models/svc/comosvc/comosvc_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..5ba49fd4539b8ae351137a85595ff9cfba1f4677 --- /dev/null +++ b/models/svc/comosvc/comosvc_trainer.py @@ -0,0 +1,295 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import os +import json5 +from collections import OrderedDict +from tqdm import tqdm +import json +import shutil + +from models.svc.base import SVCTrainer +from modules.encoder.condition_encoder import ConditionEncoder +from models.svc.comosvc.comosvc import ComoSVC + + +class ComoSVCTrainer(SVCTrainer): + r"""The base trainer for all diffusion models. It inherits from SVCTrainer and + implements ``_build_model`` and ``_forward_step`` methods. + """ + + def __init__(self, args=None, cfg=None): + SVCTrainer.__init__(self, args, cfg) + self.distill = cfg.model.comosvc.distill + self.skip_diff = True + if self.distill: # and args.resume is None: + self.teacher_model_path = cfg.model.teacher_model_path + self.teacher_state_dict = self._load_teacher_state_dict() + self._load_teacher_model(self.teacher_state_dict) + self.acoustic_mapper.decoder.init_consistency_training() + + ### Following are methods only for comoSVC models ### + def _load_teacher_state_dict(self): + self.checkpoint_file = self.teacher_model_path + print("Load teacher acoustic model from {}".format(self.checkpoint_file)) + raw_state_dict = torch.load(self.checkpoint_file) # , map_location=self.device) + return raw_state_dict + + def _load_teacher_model(self, state_dict): + raw_dict = state_dict + clean_dict = OrderedDict() + for k, v in raw_dict.items(): + if k.startswith("module."): + clean_dict[k[7:]] = v + else: + clean_dict[k] = v + self.model.load_state_dict(clean_dict) + + def _build_model(self): + r"""Build the model for training. This function is called in ``__init__`` function.""" + + # TODO: sort out the config + self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min + self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max + self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder) + self.acoustic_mapper = ComoSVC(self.cfg) + model = torch.nn.ModuleList([self.condition_encoder, self.acoustic_mapper]) + return model + + def _forward_step(self, batch): + r"""Forward step for training and inference. This function is called + in ``_train_step`` & ``_test_step`` function. + """ + loss = {} + mask = batch["mask"] + mel_input = batch["mel"] + cond = self.condition_encoder(batch) + if self.distill: + cond = cond.detach() + self.skip_diff = True if self.step < self.cfg.train.fast_steps else False + ssim_loss, prior_loss, diff_loss = self.acoustic_mapper.compute_loss( + mask, cond, mel_input, skip_diff=self.skip_diff + ) + if self.distill: + loss["distil_loss"] = diff_loss + else: + loss["ssim_loss_encoder"] = ssim_loss + loss["prior_loss_encoder"] = prior_loss + loss["diffusion_loss_decoder"] = diff_loss + + return loss + + def _train_epoch(self): + r"""Training epoch. Should return average loss of a batch (sample) over + one epoch. See ``train_loop`` for usage. + """ + self.model.train() + epoch_sum_loss: float = 0.0 + epoch_step: int = 0 + for batch in tqdm( + self.train_dataloader, + desc=f"Training Epoch {self.epoch}", + unit="batch", + colour="GREEN", + leave=False, + dynamic_ncols=True, + smoothing=0.04, + disable=not self.accelerator.is_main_process, + ): + # Do training step and BP + with self.accelerator.accumulate(self.model): + loss = self._train_step(batch) + total_loss = 0 + for k, v in loss.items(): + total_loss += v + self.accelerator.backward(total_loss) + enc_grad_norm = torch.nn.utils.clip_grad_norm_( + self.acoustic_mapper.encoder.parameters(), max_norm=1 + ) + dec_grad_norm = torch.nn.utils.clip_grad_norm_( + self.acoustic_mapper.decoder.parameters(), max_norm=1 + ) + self.optimizer.step() + self.optimizer.zero_grad() + self.batch_count += 1 + + # Update info for each step + # TODO: step means BP counts or batch counts? + if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: + epoch_sum_loss += total_loss + log_info = {} + for k, v in loss.items(): + key = "Step/Train Loss/{}".format(k) + log_info[key] = v + log_info["Step/Learning Rate"]: self.optimizer.param_groups[0]["lr"] + self.accelerator.log( + log_info, + step=self.step, + ) + self.step += 1 + epoch_step += 1 + + self.accelerator.wait_for_everyone() + return ( + epoch_sum_loss + / len(self.train_dataloader) + * self.cfg.train.gradient_accumulation_step, + loss, + ) + + def train_loop(self): + r"""Training loop. The public entry of training process.""" + # Wait everyone to prepare before we move on + self.accelerator.wait_for_everyone() + # dump config file + if self.accelerator.is_main_process: + self.__dump_cfg(self.config_save_path) + self.model.train() + self.optimizer.zero_grad() + # Wait to ensure good to go + self.accelerator.wait_for_everyone() + while self.epoch < self.max_epoch: + self.logger.info("\n") + self.logger.info("-" * 32) + self.logger.info("Epoch {}: ".format(self.epoch)) + + ### TODO: change the return values of _train_epoch() to a loss dict, or (total_loss, loss_dict) + ### It's inconvenient for the model with multiple losses + # Do training & validating epoch + train_loss, loss = self._train_epoch() + self.logger.info(" |- Train/Loss: {:.6f}".format(train_loss)) + for k, v in loss.items(): + self.logger.info(" |- Train/Loss/{}: {:.6f}".format(k, v)) + valid_loss = self._valid_epoch() + self.logger.info(" |- Valid/Loss: {:.6f}".format(valid_loss)) + self.accelerator.log( + {"Epoch/Train Loss": train_loss, "Epoch/Valid Loss": valid_loss}, + step=self.epoch, + ) + + self.accelerator.wait_for_everyone() + # TODO: what is scheduler? + self.scheduler.step(valid_loss) # FIXME: use epoch track correct? + + # Check if hit save_checkpoint_stride and run_eval + run_eval = False + if self.accelerator.is_main_process: + save_checkpoint = False + hit_dix = [] + for i, num in enumerate(self.save_checkpoint_stride): + if self.epoch % num == 0: + save_checkpoint = True + hit_dix.append(i) + run_eval |= self.run_eval[i] + + self.accelerator.wait_for_everyone() + if ( + self.accelerator.is_main_process + and save_checkpoint + and (self.distill or not self.skip_diff) + ): + path = os.path.join( + self.checkpoint_dir, + "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( + self.epoch, self.step, train_loss + ), + ) + self.accelerator.save_state(path) + json.dump( + self.checkpoints_path, + open(os.path.join(path, "ckpts.json"), "w"), + ensure_ascii=False, + indent=4, + ) + + # Remove old checkpoints + to_remove = [] + for idx in hit_dix: + self.checkpoints_path[idx].append(path) + while len(self.checkpoints_path[idx]) > self.keep_last[idx]: + to_remove.append((idx, self.checkpoints_path[idx].pop(0))) + + # Search conflicts + total = set() + for i in self.checkpoints_path: + total |= set(i) + do_remove = set() + for idx, path in to_remove[::-1]: + if path in total: + self.checkpoints_path[idx].insert(0, path) + else: + do_remove.add(path) + + # Remove old checkpoints + for path in do_remove: + shutil.rmtree(path, ignore_errors=True) + self.logger.debug(f"Remove old checkpoint: {path}") + + self.accelerator.wait_for_everyone() + if run_eval: + # TODO: run evaluation + pass + + # Update info for each epoch + self.epoch += 1 + + # Finish training and save final checkpoint + self.accelerator.wait_for_everyone() + if self.accelerator.is_main_process: + self.accelerator.save_state( + os.path.join( + self.checkpoint_dir, + "final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( + self.epoch, self.step, valid_loss + ), + ) + ) + self.accelerator.end_training() + + @torch.inference_mode() + def _valid_epoch(self): + r"""Testing epoch. Should return average loss of a batch (sample) over + one epoch. See ``train_loop`` for usage. + """ + self.model.eval() + epoch_sum_loss = 0.0 + for batch in tqdm( + self.valid_dataloader, + desc=f"Validating Epoch {self.epoch}", + unit="batch", + colour="GREEN", + leave=False, + dynamic_ncols=True, + smoothing=0.04, + disable=not self.accelerator.is_main_process, + ): + batch_loss = self._valid_step(batch) + for k, v in batch_loss.items(): + epoch_sum_loss += v + + self.accelerator.wait_for_everyone() + return epoch_sum_loss / len(self.valid_dataloader) + + @staticmethod + def __count_parameters(model): + model_param = 0.0 + if isinstance(model, dict): + for key, value in model.items(): + model_param += sum(p.numel() for p in model[key].parameters()) + else: + model_param = sum(p.numel() for p in model.parameters()) + return model_param + + def __dump_cfg(self, path): + os.makedirs(os.path.dirname(path), exist_ok=True) + json5.dump( + self.cfg, + open(path, "w"), + indent=4, + sort_keys=True, + ensure_ascii=False, + quote_keys=True, + ) diff --git a/models/svc/comosvc/utils.py b/models/svc/comosvc/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3f576f9a237d0a22ddfdb160122b906da9bcf889 --- /dev/null +++ b/models/svc/comosvc/utils.py @@ -0,0 +1,31 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + + +def slice_segments(x, ids_str, segment_size=200): + ret = torch.zeros_like(x[:, :, :segment_size]) + for i in range(x.size(0)): + idx_str = ids_str[i] + idx_end = idx_str + segment_size + ret[i] = x[i, :, idx_str:idx_end] + return ret + + +def rand_ids_segments(lengths, segment_size=200): + b = lengths.shape[0] + ids_str_max = lengths - segment_size + ids_str = (torch.rand([b]).to(device=lengths.device) * ids_str_max).to( + dtype=torch.long + ) + return ids_str + + +def fix_len_compatibility(length, num_downsamplings_in_unet=2): + while True: + if length % (2**num_downsamplings_in_unet) == 0: + return length + length += 1 diff --git a/models/svc/diffusion/__init__.py b/models/svc/diffusion/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/svc/diffusion/diffusion_inference.py b/models/svc/diffusion/diffusion_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..69b7c899180fb080576c161d2184fba111c69b2a --- /dev/null +++ b/models/svc/diffusion/diffusion_inference.py @@ -0,0 +1,63 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from diffusers import DDIMScheduler, DDPMScheduler, PNDMScheduler + +from models.svc.base import SVCInference +from models.svc.diffusion.diffusion_inference_pipeline import DiffusionInferencePipeline +from models.svc.diffusion.diffusion_wrapper import DiffusionWrapper +from modules.encoder.condition_encoder import ConditionEncoder + + +class DiffusionInference(SVCInference): + def __init__(self, args=None, cfg=None, infer_type="from_dataset"): + SVCInference.__init__(self, args, cfg, infer_type) + + settings = { + **cfg.model.diffusion.scheduler_settings, + **cfg.inference.diffusion.scheduler_settings, + } + settings.pop("num_inference_timesteps") + + if cfg.inference.diffusion.scheduler.lower() == "ddpm": + self.scheduler = DDPMScheduler(**settings) + self.logger.info("Using DDPM scheduler.") + elif cfg.inference.diffusion.scheduler.lower() == "ddim": + self.scheduler = DDIMScheduler(**settings) + self.logger.info("Using DDIM scheduler.") + elif cfg.inference.diffusion.scheduler.lower() == "pndm": + self.scheduler = PNDMScheduler(**settings) + self.logger.info("Using PNDM scheduler.") + else: + raise NotImplementedError( + "Unsupported scheduler type: {}".format( + cfg.inference.diffusion.scheduler.lower() + ) + ) + + self.pipeline = DiffusionInferencePipeline( + self.model[1], + self.scheduler, + cfg.inference.diffusion.scheduler_settings.num_inference_timesteps, + ) + + def _build_model(self): + self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min + self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max + self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder) + self.acoustic_mapper = DiffusionWrapper(self.cfg) + model = torch.nn.ModuleList([self.condition_encoder, self.acoustic_mapper]) + return model + + def _inference_each_batch(self, batch_data): + device = self.accelerator.device + for k, v in batch_data.items(): + batch_data[k] = v.to(device) + + conditioner = self.model[0](batch_data) + noise = torch.randn_like(batch_data["mel"], device=device) + y_pred = self.pipeline(noise, conditioner) + return y_pred diff --git a/models/svc/diffusion/diffusion_inference_pipeline.py b/models/svc/diffusion/diffusion_inference_pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..e2461aada99179ac17a2aaffebdb24864af1f5ee --- /dev/null +++ b/models/svc/diffusion/diffusion_inference_pipeline.py @@ -0,0 +1,47 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from diffusers import DiffusionPipeline + + +class DiffusionInferencePipeline(DiffusionPipeline): + def __init__(self, network, scheduler, num_inference_timesteps=1000): + super().__init__() + + self.register_modules(network=network, scheduler=scheduler) + self.num_inference_timesteps = num_inference_timesteps + + @torch.inference_mode() + def __call__( + self, + initial_noise: torch.Tensor, + conditioner: torch.Tensor = None, + ): + r""" + Args: + initial_noise: The initial noise to be denoised. + conditioner:The conditioner. + n_inference_steps: The number of denoising steps. More denoising steps + usually lead to a higher quality at the expense of slower inference. + """ + + mel = initial_noise + batch_size = mel.size(0) + self.scheduler.set_timesteps(self.num_inference_timesteps) + + for t in self.progress_bar(self.scheduler.timesteps): + timestep = torch.full((batch_size,), t, device=mel.device, dtype=torch.long) + + # 1. predict noise model_output + model_output = self.network(mel, timestep, conditioner) + + # 2. denoise, compute previous step: x_t -> x_t-1 + mel = self.scheduler.step(model_output, t, mel).prev_sample + + # 3. clamp + mel = mel.clamp(-1.0, 1.0) + + return mel diff --git a/models/svc/diffusion/diffusion_trainer.py b/models/svc/diffusion/diffusion_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6f5aeb56a825f84c57bb1d2ba9a5ff5a32d5f486 --- /dev/null +++ b/models/svc/diffusion/diffusion_trainer.py @@ -0,0 +1,88 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from diffusers import DDPMScheduler + +from models.svc.base import SVCTrainer +from modules.encoder.condition_encoder import ConditionEncoder +from .diffusion_wrapper import DiffusionWrapper + + +class DiffusionTrainer(SVCTrainer): + r"""The base trainer for all diffusion models. It inherits from SVCTrainer and + implements ``_build_model`` and ``_forward_step`` methods. + """ + + def __init__(self, args=None, cfg=None): + SVCTrainer.__init__(self, args, cfg) + + # Only for SVC tasks using diffusion + self.noise_scheduler = DDPMScheduler( + **self.cfg.model.diffusion.scheduler_settings, + ) + self.diffusion_timesteps = ( + self.cfg.model.diffusion.scheduler_settings.num_train_timesteps + ) + + ### Following are methods only for diffusion models ### + def _build_model(self): + r"""Build the model for training. This function is called in ``__init__`` function.""" + + # TODO: sort out the config + self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min + self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max + self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder) + self.acoustic_mapper = DiffusionWrapper(self.cfg) + model = torch.nn.ModuleList([self.condition_encoder, self.acoustic_mapper]) + + num_of_params_encoder = self.count_parameters(self.condition_encoder) + num_of_params_am = self.count_parameters(self.acoustic_mapper) + num_of_params = num_of_params_encoder + num_of_params_am + log = "Diffusion Model's Parameters: #Encoder is {:.2f}M, #Diffusion is {:.2f}M. The total is {:.2f}M".format( + num_of_params_encoder / 1e6, num_of_params_am / 1e6, num_of_params / 1e6 + ) + self.logger.info(log) + + return model + + def count_parameters(self, model): + model_param = 0.0 + if isinstance(model, dict): + for key, value in model.items(): + model_param += sum(p.numel() for p in model[key].parameters()) + else: + model_param = sum(p.numel() for p in model.parameters()) + return model_param + + def _forward_step(self, batch): + r"""Forward step for training and inference. This function is called + in ``_train_step`` & ``_test_step`` function. + """ + + device = self.accelerator.device + + mel_input = batch["mel"] + noise = torch.randn_like(mel_input, device=device, dtype=torch.float32) + batch_size = mel_input.size(0) + timesteps = torch.randint( + 0, + self.diffusion_timesteps, + (batch_size,), + device=device, + dtype=torch.long, + ) + + noisy_mel = self.noise_scheduler.add_noise(mel_input, noise, timesteps) + conditioner = self.condition_encoder(batch) + + y_pred = self.acoustic_mapper(noisy_mel, timesteps, conditioner) + + # TODO: Predict noise or gt should be configurable + loss = self._compute_loss(self.criterion, y_pred, noise, batch["mask"]) + self._check_nan(loss, y_pred, noise) + + # FIXME: Clarify that we should not divide it with batch size here + return loss diff --git a/models/svc/diffusion/diffusion_wrapper.py b/models/svc/diffusion/diffusion_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..ef66c2b6b85ceb8fe7a2cf9b53c62edc6b3ef6bc --- /dev/null +++ b/models/svc/diffusion/diffusion_wrapper.py @@ -0,0 +1,73 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn + +from modules.diffusion import BiDilConv +from modules.encoder.position_encoder import PositionEncoder + + +class DiffusionWrapper(nn.Module): + def __init__(self, cfg): + super().__init__() + + self.cfg = cfg + self.diff_cfg = cfg.model.diffusion + + self.diff_encoder = PositionEncoder( + d_raw_emb=self.diff_cfg.step_encoder.dim_raw_embedding, + d_out=self.diff_cfg.bidilconv.base_channel, + d_mlp=self.diff_cfg.step_encoder.dim_hidden_layer, + activation_function=self.diff_cfg.step_encoder.activation, + n_layer=self.diff_cfg.step_encoder.num_layer, + max_period=self.diff_cfg.step_encoder.max_period, + ) + + # FIXME: Only support BiDilConv now for debug + if self.diff_cfg.model_type.lower() == "bidilconv": + self.neural_network = BiDilConv( + input_channel=self.cfg.preprocess.n_mel, **self.diff_cfg.bidilconv + ) + else: + raise ValueError( + f"Unsupported diffusion model type: {self.diff_cfg.model_type}" + ) + + def forward(self, x, t, c): + """ + Args: + x: [N, T, mel_band] of mel spectrogram + t: Diffusion time step with shape of [N] + c: [N, T, conditioner_size] of conditioner + + Returns: + [N, T, mel_band] of mel spectrogram + """ + + assert ( + x.size()[:-1] == c.size()[:-1] + ), "x mismatch with c, got \n x: {} \n c: {}".format(x.size(), c.size()) + assert x.size(0) == t.size( + 0 + ), "x mismatch with t, got \n x: {} \n t: {}".format(x.size(), t.size()) + assert t.dim() == 1, "t must be 1D tensor, got {}".format(t.dim()) + + N, T, mel_band = x.size() + + x = x.transpose(1, 2).contiguous() # [N, mel_band, T] + c = c.transpose(1, 2).contiguous() # [N, conditioner_size, T] + t = self.diff_encoder(t).contiguous() # [N, base_channel] + + h = self.neural_network(x, t, c) + h = h.transpose(1, 2).contiguous() # [N, T, mel_band] + + assert h.size() == ( + N, + T, + mel_band, + ), "h mismatch with input x, got \n h: {} \n x: {}".format( + h.size(), (N, T, mel_band) + ) + return h diff --git a/models/svc/transformer/__init__.py b/models/svc/transformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/svc/transformer/conformer.py b/models/svc/transformer/conformer.py new file mode 100644 index 0000000000000000000000000000000000000000..5e48019cfc17d5f3825ce989f4852cec55fe1daa --- /dev/null +++ b/models/svc/transformer/conformer.py @@ -0,0 +1,405 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import torch +import numpy as np +import torch.nn as nn +from utils.util import convert_pad_shape + + +class BaseModule(torch.nn.Module): + def __init__(self): + super(BaseModule, self).__init__() + + @property + def nparams(self): + """ + Returns number of trainable parameters of the module. + """ + num_params = 0 + for name, param in self.named_parameters(): + if param.requires_grad: + num_params += np.prod(param.detach().cpu().numpy().shape) + return num_params + + def relocate_input(self, x: list): + """ + Relocates provided tensors to the same device set for the module. + """ + device = next(self.parameters()).device + for i in range(len(x)): + if isinstance(x[i], torch.Tensor) and x[i].device != device: + x[i] = x[i].to(device) + return x + + +class LayerNorm(BaseModule): + def __init__(self, channels, eps=1e-4): + super(LayerNorm, self).__init__() + self.channels = channels + self.eps = eps + + self.gamma = torch.nn.Parameter(torch.ones(channels)) + self.beta = torch.nn.Parameter(torch.zeros(channels)) + + def forward(self, x): + n_dims = len(x.shape) + mean = torch.mean(x, 1, keepdim=True) + variance = torch.mean((x - mean) ** 2, 1, keepdim=True) + + x = (x - mean) * torch.rsqrt(variance + self.eps) + + shape = [1, -1] + [1] * (n_dims - 2) + x = x * self.gamma.view(*shape) + self.beta.view(*shape) + return x + + +class ConvReluNorm(BaseModule): + def __init__( + self, + in_channels, + hidden_channels, + out_channels, + kernel_size, + n_layers, + p_dropout, + eps=1e-5, + ): + super(ConvReluNorm, self).__init__() + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.p_dropout = p_dropout + self.eps = eps + + self.conv_layers = torch.nn.ModuleList() + self.conv_layers.append( + torch.nn.Conv1d( + in_channels, hidden_channels, kernel_size, padding=kernel_size // 2 + ) + ) + self.relu_drop = torch.nn.Sequential( + torch.nn.ReLU(), torch.nn.Dropout(p_dropout) + ) + for _ in range(n_layers - 1): + self.conv_layers.append( + torch.nn.Conv1d( + hidden_channels, + hidden_channels, + kernel_size, + padding=kernel_size // 2, + ) + ) + self.proj = torch.nn.Conv1d(hidden_channels, out_channels, 1) + self.proj.weight.data.zero_() + self.proj.bias.data.zero_() + + def forward(self, x, x_mask): + for i in range(self.n_layers): + x = self.conv_layers[i](x * x_mask) + x = self.instance_norm(x, x_mask) + x = self.relu_drop(x) + x = self.proj(x) + return x * x_mask + + def instance_norm(self, x, mask, return_mean_std=False): + mean, std = self.calc_mean_std(x, mask) + x = (x - mean) / std + if return_mean_std: + return x, mean, std + else: + return x + + def calc_mean_std(self, x, mask=None): + x = x * mask + B, C = x.shape[:2] + mn = x.view(B, C, -1).mean(-1) + sd = (x.view(B, C, -1).var(-1) + self.eps).sqrt() + mn = mn.view(B, C, *((len(x.shape) - 2) * [1])) + sd = sd.view(B, C, *((len(x.shape) - 2) * [1])) + return mn, sd + + +class MultiHeadAttention(BaseModule): + def __init__( + self, + channels, + out_channels, + n_heads, + window_size=None, + heads_share=True, + p_dropout=0.0, + proximal_bias=False, + proximal_init=False, + ): + super(MultiHeadAttention, self).__init__() + assert channels % n_heads == 0 + + self.channels = channels + self.out_channels = out_channels + self.n_heads = n_heads + self.window_size = window_size + self.heads_share = heads_share + self.proximal_bias = proximal_bias + self.p_dropout = p_dropout + self.attn = None + + self.k_channels = channels // n_heads + self.conv_q = torch.nn.Conv1d(channels, channels, 1) + self.conv_k = torch.nn.Conv1d(channels, channels, 1) + self.conv_v = torch.nn.Conv1d(channels, channels, 1) + if window_size is not None: + n_heads_rel = 1 if heads_share else n_heads + rel_stddev = self.k_channels**-0.5 + self.emb_rel_k = torch.nn.Parameter( + torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) + * rel_stddev + ) + self.emb_rel_v = torch.nn.Parameter( + torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) + * rel_stddev + ) + self.conv_o = torch.nn.Conv1d(channels, out_channels, 1) + self.drop = torch.nn.Dropout(p_dropout) + + torch.nn.init.xavier_uniform_(self.conv_q.weight) + torch.nn.init.xavier_uniform_(self.conv_k.weight) + if proximal_init: + self.conv_k.weight.data.copy_(self.conv_q.weight.data) + self.conv_k.bias.data.copy_(self.conv_q.bias.data) + torch.nn.init.xavier_uniform_(self.conv_v.weight) + + def forward(self, x, c, attn_mask=None): + q = self.conv_q(x) + k = self.conv_k(c) + v = self.conv_v(c) + + x, self.attn = self.attention(q, k, v, mask=attn_mask) + + x = self.conv_o(x) + return x + + def attention(self, query, key, value, mask=None): + b, d, t_s, t_t = (*key.size(), query.size(2)) + query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) + key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) + value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) + + scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels) + if self.window_size is not None: + assert ( + t_s == t_t + ), "Relative attention is only available for self-attention." + key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) + rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings) + rel_logits = self._relative_position_to_absolute_position(rel_logits) + scores_local = rel_logits / math.sqrt(self.k_channels) + scores = scores + scores_local + if self.proximal_bias: + assert t_s == t_t, "Proximal bias is only available for self-attention." + scores = scores + self._attention_bias_proximal(t_s).to( + device=scores.device, dtype=scores.dtype + ) + if mask is not None: + scores = scores.masked_fill(mask == 0, -1e4) + p_attn = torch.nn.functional.softmax(scores, dim=-1) + p_attn = self.drop(p_attn) + output = torch.matmul(p_attn, value) + if self.window_size is not None: + relative_weights = self._absolute_position_to_relative_position(p_attn) + value_relative_embeddings = self._get_relative_embeddings( + self.emb_rel_v, t_s + ) + output = output + self._matmul_with_relative_values( + relative_weights, value_relative_embeddings + ) + output = output.transpose(2, 3).contiguous().view(b, d, t_t) + return output, p_attn + + def _matmul_with_relative_values(self, x, y): + ret = torch.matmul(x, y.unsqueeze(0)) + return ret + + def _matmul_with_relative_keys(self, x, y): + ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) + return ret + + def _get_relative_embeddings(self, relative_embeddings, length): + pad_length = max(length - (self.window_size + 1), 0) + slice_start_position = max((self.window_size + 1) - length, 0) + slice_end_position = slice_start_position + 2 * length - 1 + if pad_length > 0: + padded_relative_embeddings = torch.nn.functional.pad( + relative_embeddings, + convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]), + ) + else: + padded_relative_embeddings = relative_embeddings + used_relative_embeddings = padded_relative_embeddings[ + :, slice_start_position:slice_end_position + ] + return used_relative_embeddings + + def _relative_position_to_absolute_position(self, x): + batch, heads, length, _ = x.size() + x = torch.nn.functional.pad( + x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]) + ) + x_flat = x.view([batch, heads, length * 2 * length]) + x_flat = torch.nn.functional.pad( + x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]) + ) + x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[ + :, :, :length, length - 1 : + ] + return x_final + + def _absolute_position_to_relative_position(self, x): + batch, heads, length, _ = x.size() + x = torch.nn.functional.pad( + x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]) + ) + x_flat = x.view([batch, heads, length**2 + length * (length - 1)]) + x_flat = torch.nn.functional.pad( + x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]) + ) + x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] + return x_final + + def _attention_bias_proximal(self, length): + r = torch.arange(length, dtype=torch.float32) + diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) + return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) + + +class FFN(BaseModule): + def __init__( + self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0.0 + ): + super(FFN, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + + self.conv_1 = torch.nn.Conv1d( + in_channels, filter_channels, kernel_size, padding=kernel_size // 2 + ) + self.conv_2 = torch.nn.Conv1d( + filter_channels, out_channels, kernel_size, padding=kernel_size // 2 + ) + self.drop = torch.nn.Dropout(p_dropout) + + def forward(self, x, x_mask): + x = self.conv_1(x * x_mask) + x = torch.relu(x) + x = self.drop(x) + x = self.conv_2(x * x_mask) + return x * x_mask + + +class Encoder(BaseModule): + def __init__( + self, + hidden_channels, + filter_channels, + n_heads=2, + n_layers=6, + kernel_size=3, + p_dropout=0.1, + window_size=4, + **kwargs + ): + super(Encoder, self).__init__() + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.window_size = window_size + + self.drop = torch.nn.Dropout(p_dropout) + self.attn_layers = torch.nn.ModuleList() + self.norm_layers_1 = torch.nn.ModuleList() + self.ffn_layers = torch.nn.ModuleList() + self.norm_layers_2 = torch.nn.ModuleList() + for _ in range(self.n_layers): + self.attn_layers.append( + MultiHeadAttention( + hidden_channels, + hidden_channels, + n_heads, + window_size=window_size, + p_dropout=p_dropout, + ) + ) + self.norm_layers_1.append(LayerNorm(hidden_channels)) + self.ffn_layers.append( + FFN( + hidden_channels, + hidden_channels, + filter_channels, + kernel_size, + p_dropout=p_dropout, + ) + ) + self.norm_layers_2.append(LayerNorm(hidden_channels)) + + def forward(self, x, x_mask): + attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) + for i in range(self.n_layers): + x = x * x_mask + y = self.attn_layers[i](x, x, attn_mask) + y = self.drop(y) + x = self.norm_layers_1[i](x + y) + y = self.ffn_layers[i](x, x_mask) + y = self.drop(y) + x = self.norm_layers_2[i](x + y) + x = x * x_mask + return x + + +class Conformer(BaseModule): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.n_heads = self.cfg.n_heads + self.n_layers = self.cfg.n_layers + self.hidden_channels = self.cfg.input_dim + self.filter_channels = self.cfg.filter_channels + self.output_dim = self.cfg.output_dim + self.dropout = self.cfg.dropout + + self.conformer_encoder = Encoder( + self.hidden_channels, + self.filter_channels, + n_heads=self.n_heads, + n_layers=self.n_layers, + kernel_size=3, + p_dropout=self.dropout, + window_size=4, + ) + self.projection = nn.Conv1d(self.hidden_channels, self.output_dim, 1) + + def forward(self, x, x_mask): + """ + Args: + x: (N, seq_len, input_dim) + Returns: + output: (N, seq_len, output_dim) + """ + # (N, seq_len, d_model) + x = x.transpose(1, 2) + x_mask = x_mask.transpose(1, 2) + output = self.conformer_encoder(x, x_mask) + # (N, seq_len, output_dim) + output = self.projection(output) + output = output.transpose(1, 2) + return output diff --git a/models/svc/transformer/transformer.py b/models/svc/transformer/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..fd3cdb6c2d0fc93534d005b9f67a3058c9185c60 --- /dev/null +++ b/models/svc/transformer/transformer.py @@ -0,0 +1,82 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import torch +import torch.nn as nn +from torch.nn import TransformerEncoder, TransformerEncoderLayer + + +class Transformer(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + + dropout = self.cfg.dropout + nhead = self.cfg.n_heads + nlayers = self.cfg.n_layers + input_dim = self.cfg.input_dim + output_dim = self.cfg.output_dim + + d_model = input_dim + self.pos_encoder = PositionalEncoding(d_model, dropout) + encoder_layers = TransformerEncoderLayer( + d_model, nhead, dropout=dropout, batch_first=True + ) + self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) + + self.output_mlp = nn.Linear(d_model, output_dim) + + def forward(self, x, mask=None): + """ + Args: + x: (N, seq_len, input_dim) + Returns: + output: (N, seq_len, output_dim) + """ + # (N, seq_len, d_model) + src = self.pos_encoder(x) + # model_stats["pos_embedding"] = x + # (N, seq_len, d_model) + output = self.transformer_encoder(src) + # (N, seq_len, output_dim) + output = self.output_mlp(output) + return output + + +class PositionalEncoding(nn.Module): + def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + + position = torch.arange(max_len).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model) + ) + + # Assume that x is (seq_len, N, d) + # pe = torch.zeros(max_len, 1, d_model) + # pe[:, 0, 0::2] = torch.sin(position * div_term) + # pe[:, 0, 1::2] = torch.cos(position * div_term) + + # Assume that x in (N, seq_len, d) + pe = torch.zeros(1, max_len, d_model) + pe[0, :, 0::2] = torch.sin(position * div_term) + pe[0, :, 1::2] = torch.cos(position * div_term) + + self.register_buffer("pe", pe) + + def forward(self, x): + """ + Args: + x: Tensor, shape [N, seq_len, d] + """ + # Old: Assume that x is (seq_len, N, d), and self.pe is (max_len, 1, d_model) + # x = x + self.pe[: x.size(0)] + + # Now: self.pe is (1, max_len, d) + x = x + self.pe[:, : x.size(1), :] + + return self.dropout(x) diff --git a/models/svc/transformer/transformer_inference.py b/models/svc/transformer/transformer_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..f6299c532aec6cb9283ee87ee9f0142f0b5c981b --- /dev/null +++ b/models/svc/transformer/transformer_inference.py @@ -0,0 +1,45 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import time +import numpy as np +import torch +from tqdm import tqdm +import torch.nn as nn +from collections import OrderedDict + +from models.svc.base import SVCInference +from modules.encoder.condition_encoder import ConditionEncoder +from models.svc.transformer.transformer import Transformer +from models.svc.transformer.conformer import Conformer + + +class TransformerInference(SVCInference): + def __init__(self, args=None, cfg=None, infer_type="from_dataset"): + SVCInference.__init__(self, args, cfg, infer_type) + + def _build_model(self): + self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min + self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max + self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder) + if self.cfg.model.transformer.type == "transformer": + self.acoustic_mapper = Transformer(self.cfg.model.transformer) + elif self.cfg.model.transformer.type == "conformer": + self.acoustic_mapper = Conformer(self.cfg.model.transformer) + else: + raise NotImplementedError + model = torch.nn.ModuleList([self.condition_encoder, self.acoustic_mapper]) + return model + + def _inference_each_batch(self, batch_data): + device = self.accelerator.device + for k, v in batch_data.items(): + batch_data[k] = v.to(device) + + condition = self.condition_encoder(batch_data) + y_pred = self.acoustic_mapper(condition, batch_data["mask"]) + + return y_pred diff --git a/models/svc/transformer/transformer_trainer.py b/models/svc/transformer/transformer_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..3633078475d26e708280bc354f091bb9ab01ae45 --- /dev/null +++ b/models/svc/transformer/transformer_trainer.py @@ -0,0 +1,52 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from models.svc.base import SVCTrainer +from modules.encoder.condition_encoder import ConditionEncoder +from models.svc.transformer.transformer import Transformer +from models.svc.transformer.conformer import Conformer +from utils.ssim import SSIM + + +class TransformerTrainer(SVCTrainer): + def __init__(self, args, cfg): + SVCTrainer.__init__(self, args, cfg) + self.ssim_loss = SSIM() + + def _build_model(self): + self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min + self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max + self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder) + if self.cfg.model.transformer.type == "transformer": + self.acoustic_mapper = Transformer(self.cfg.model.transformer) + elif self.cfg.model.transformer.type == "conformer": + self.acoustic_mapper = Conformer(self.cfg.model.transformer) + else: + raise NotImplementedError + model = torch.nn.ModuleList([self.condition_encoder, self.acoustic_mapper]) + return model + + def _forward_step(self, batch): + total_loss = 0 + device = self.accelerator.device + mel = batch["mel"] + mask = batch["mask"] + + condition = self.condition_encoder(batch) + mel_pred = self.acoustic_mapper(condition, mask) + + l1_loss = torch.sum(torch.abs(mel_pred - mel) * batch["mask"]) / torch.sum( + batch["mask"] + ) + self._check_nan(l1_loss, mel_pred, mel) + total_loss += l1_loss + ssim_loss = self.ssim_loss(mel_pred, mel) + ssim_loss = torch.sum(ssim_loss * batch["mask"]) / torch.sum(batch["mask"]) + self._check_nan(ssim_loss, mel_pred, mel) + total_loss += ssim_loss + + return total_loss diff --git a/models/tta/autoencoder/__init__.py b/models/tta/autoencoder/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/tta/autoencoder/autoencoder.py b/models/tta/autoencoder/autoencoder.py new file mode 100644 index 0000000000000000000000000000000000000000..d97bfdc959d41bb8edd38993c052b7ad07d8cd92 --- /dev/null +++ b/models/tta/autoencoder/autoencoder.py @@ -0,0 +1,405 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from modules.distributions.distributions import DiagonalGaussianDistribution + + +def nonlinearity(x): + # swish + return x * torch.sigmoid(x) + + +def Normalize(in_channels): + return torch.nn.GroupNorm( + num_groups=32, num_channels=in_channels, eps=1e-6, affine=True + ) + + +class Upsample2d(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Upsample1d(Upsample2d): + def __init__(self, in_channels, with_conv): + super().__init__(in_channels, with_conv) + if self.with_conv: + self.conv = torch.nn.Conv1d( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + + +class Downsample2d(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=2, padding=0 + ) + self.pad = (0, 1, 0, 1) + else: + self.avg_pool = nn.AvgPool2d(kernel_size=2, stride=2) + + def forward(self, x): + if self.with_conv: # bp: check self.avgpool and self.pad + x = torch.nn.functional.pad(x, self.pad, mode="constant", value=0) + x = self.conv(x) + else: + x = self.avg_pool(x) + return x + + +class Downsample1d(Downsample2d): + def __init__(self, in_channels, with_conv): + super().__init__(in_channels, with_conv) + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + # TODO: can we replace it just with conv2d with padding 1? + self.conv = torch.nn.Conv1d( + in_channels, in_channels, kernel_size=3, stride=2, padding=0 + ) + self.pad = (1, 1) + else: + self.avg_pool = nn.AvgPool1d(kernel_size=2, stride=2) + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + else: + self.nin_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x + h + + +class ResnetBlock1d(ResnetBlock): + def __init__( + self, + *, + in_channels, + out_channels=None, + conv_shortcut=False, + dropout, + temb_channels=512 + ): + super().__init__( + in_channels=in_channels, + out_channels=out_channels, + conv_shortcut=conv_shortcut, + dropout=dropout, + ) + + self.conv1 = torch.nn.Conv1d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + self.conv2 = torch.nn.Conv1d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv1d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + else: + self.nin_shortcut = torch.nn.Conv1d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 + ) + + +class Encoder2d(nn.Module): + def __init__( + self, + *, + ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks, + dropout=0.0, + resamp_with_conv=True, + in_channels, + z_channels, + double_z=True, + **ignore_kwargs + ): + super().__init__() + self.ch = ch + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.in_channels = in_channels + + # downsampling + self.conv_in = torch.nn.Conv2d( + in_channels, self.ch, kernel_size=3, stride=1, padding=1 + ) + + in_ch_mult = (1,) + tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResnetBlock( + in_channels=block_in, out_channels=block_out, dropout=dropout + ) + ) + block_in = block_out + down = nn.Module() + down.block = block + if i_level != self.num_resolutions - 1: + down.downsample = Downsample2d(block_in, resamp_with_conv) + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, out_channels=block_in, dropout=dropout + ) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, out_channels=block_in, dropout=dropout + ) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, + 2 * z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1, + ) + + def forward(self, x): + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1]) + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h) + h = self.mid.block_2(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +# TODO: Encoder1d +class Encoder1d(Encoder2d): + ... + + +class Decoder2d(nn.Module): + def __init__( + self, + *, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks, + dropout=0.0, + resamp_with_conv=True, + in_channels, + z_channels, + give_pre_end=False, + **ignorekwargs + ): + super().__init__() + self.ch = ch + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.in_channels = in_channels + self.give_pre_end = give_pre_end + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,) + tuple(ch_mult) + block_in = ch * ch_mult[self.num_resolutions - 1] + # self.z_shape = (1,z_channels,curr_res,curr_res) + # print("Working with z of shape {} = {} dimensions.".format( + # self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d( + z_channels, block_in, kernel_size=3, stride=1, padding=1 + ) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, out_channels=block_in, dropout=dropout + ) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, out_channels=block_in, dropout=dropout + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + ResnetBlock( + in_channels=block_in, out_channels=block_out, dropout=dropout + ) + ) + block_in = block_out + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample2d(block_in, resamp_with_conv) + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, z): + self.last_z_shape = z.shape + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h) + h = self.mid.block_2(h) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +# TODO: decoder1d +class Decoder1d(Decoder2d): + ... + + +class AutoencoderKL(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.encoder = Encoder2d( + ch=cfg.ch, + ch_mult=cfg.ch_mult, + num_res_blocks=cfg.num_res_blocks, + in_channels=cfg.in_channels, + z_channels=cfg.z_channels, + double_z=cfg.double_z, + ) + self.decoder = Decoder2d( + ch=cfg.ch, + ch_mult=cfg.ch_mult, + num_res_blocks=cfg.num_res_blocks, + out_ch=cfg.out_ch, + z_channels=cfg.z_channels, + in_channels=None, + ) + assert self.cfg.double_z + + self.quant_conv = torch.nn.Conv2d(2 * cfg.z_channels, 2 * cfg.z_channels, 1) + self.post_quant_conv = torch.nn.Conv2d(cfg.z_channels, cfg.z_channels, 1) + self.embed_dim = cfg.z_channels + + def encode(self, x): + h = self.encoder(x) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def decode(self, z): + z = self.post_quant_conv(z) + dec = self.decoder(z) + return dec + + def forward(self, input, sample_posterior=True): + posterior = self.encode(input) + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + dec = self.decode(z) + return dec, posterior + + def get_last_layer(self): + return self.decoder.conv_out.weight diff --git a/models/tta/autoencoder/autoencoder_dataset.py b/models/tta/autoencoder/autoencoder_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..75828eea1933d45c84a2395e9599c5ae5d7f597e --- /dev/null +++ b/models/tta/autoencoder/autoencoder_dataset.py @@ -0,0 +1,114 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import random +import torch +from torch.nn.utils.rnn import pad_sequence +from utils.data_utils import * +from models.base.base_dataset import ( + BaseCollator, + BaseDataset, + BaseTestDataset, + BaseTestCollator, +) +import librosa + + +class AutoencoderKLDataset(BaseDataset): + def __init__(self, cfg, dataset, is_valid=False): + BaseDataset.__init__(self, cfg, dataset, is_valid=is_valid) + + cfg = self.cfg + + # utt2melspec + if cfg.preprocess.use_melspec: + self.utt2melspec_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2melspec_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.melspec_dir, + uid + ".npy", + ) + + # utt2wav + if cfg.preprocess.use_wav: + self.utt2wav_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2wav_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.wav_dir, + uid + ".wav", + ) + + def __getitem__(self, index): + # melspec: (n_mels, T) + # wav: (T,) + + single_feature = BaseDataset.__getitem__(self, index) + + utt_info = self.metadata[index] + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + if self.cfg.preprocess.use_melspec: + single_feature["melspec"] = np.load(self.utt2melspec_path[utt]) + + if self.cfg.preprocess.use_wav: + wav, sr = librosa.load( + self.utt2wav_path[utt], sr=16000 + ) # hard coding for 16KHz... + single_feature["wav"] = wav + + return single_feature + + def __len__(self): + return len(self.metadata) + + def __len__(self): + return len(self.metadata) + + +class AutoencoderKLCollator(BaseCollator): + def __init__(self, cfg): + BaseCollator.__init__(self, cfg) + + def __call__(self, batch): + # mel: (B, n_mels, T) + # wav (option): (B, T) + + packed_batch_features = dict() + + for key in batch[0].keys(): + if key == "melspec": + packed_batch_features["melspec"] = torch.from_numpy( + np.array([b["melspec"][:, :624] for b in batch]) + ) + + if key == "wav": + values = [torch.from_numpy(b[key]) for b in batch] + packed_batch_features[key] = pad_sequence( + values, batch_first=True, padding_value=0 + ) + + return packed_batch_features + + +class AutoencoderKLTestDataset(BaseTestDataset): + ... + + +class AutoencoderKLTestCollator(BaseTestCollator): + ... diff --git a/models/tta/autoencoder/autoencoder_loss.py b/models/tta/autoencoder/autoencoder_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..5916aa36ebdfba6f2608514767f3e3761b57269f --- /dev/null +++ b/models/tta/autoencoder/autoencoder_loss.py @@ -0,0 +1,305 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import functools +import torch.nn.functional as F + + +def hinge_d_loss(logits_real, logits_fake): + loss_real = torch.mean(F.relu(1.0 - logits_real)) + loss_fake = torch.mean(F.relu(1.0 + logits_fake)) + d_loss = 0.5 * (loss_real + loss_fake) + return d_loss + + +def vanilla_d_loss(logits_real, logits_fake): + d_loss = 0.5 * ( + torch.mean(F.softplus(-logits_real)) + torch.mean(F.softplus(logits_fake)) + ) + return d_loss + + +def adopt_weight(weight, global_step, threshold=0, value=0.0): + if global_step < threshold: + weight = value + return weight + + +class ActNorm(nn.Module): + def __init__( + self, num_features, logdet=False, affine=True, allow_reverse_init=False + ): + assert affine + super().__init__() + self.logdet = logdet + self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1)) + self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1)) + self.allow_reverse_init = allow_reverse_init + + self.register_buffer("initialized", torch.tensor(0, dtype=torch.uint8)) + + def initialize(self, input): + with torch.no_grad(): + flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1) + mean = ( + flatten.mean(1) + .unsqueeze(1) + .unsqueeze(2) + .unsqueeze(3) + .permute(1, 0, 2, 3) + ) + std = ( + flatten.std(1) + .unsqueeze(1) + .unsqueeze(2) + .unsqueeze(3) + .permute(1, 0, 2, 3) + ) + + self.loc.data.copy_(-mean) + self.scale.data.copy_(1 / (std + 1e-6)) + + def forward(self, input, reverse=False): + if reverse: + return self.reverse(input) + if len(input.shape) == 2: + input = input[:, :, None, None] + squeeze = True + else: + squeeze = False + + _, _, height, width = input.shape + + if self.training and self.initialized.item() == 0: + self.initialize(input) + self.initialized.fill_(1) + + h = self.scale * (input + self.loc) + + if squeeze: + h = h.squeeze(-1).squeeze(-1) + + if self.logdet: + log_abs = torch.log(torch.abs(self.scale)) + logdet = height * width * torch.sum(log_abs) + logdet = logdet * torch.ones(input.shape[0]).to(input) + return h, logdet + + return h + + def reverse(self, output): + if self.training and self.initialized.item() == 0: + if not self.allow_reverse_init: + raise RuntimeError( + "Initializing ActNorm in reverse direction is " + "disabled by default. Use allow_reverse_init=True to enable." + ) + else: + self.initialize(output) + self.initialized.fill_(1) + + if len(output.shape) == 2: + output = output[:, :, None, None] + squeeze = True + else: + squeeze = False + + h = output / self.scale - self.loc + + if squeeze: + h = h.squeeze(-1).squeeze(-1) + return h + + +def weights_init(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + nn.init.normal_(m.weight.data, 0.0, 0.02) + elif classname.find("BatchNorm") != -1: + nn.init.normal_(m.weight.data, 1.0, 0.02) + nn.init.constant_(m.bias.data, 0) + + +class NLayerDiscriminator(nn.Module): + """Defines a PatchGAN discriminator as in Pix2Pix + --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py + """ + + def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False): + """Construct a PatchGAN discriminator + Parameters: + input_nc (int) -- the number of channels in input images + ndf (int) -- the number of filters in the last conv layer + n_layers (int) -- the number of conv layers in the discriminator + norm_layer -- normalization layer + """ + super(NLayerDiscriminator, self).__init__() + if not use_actnorm: + norm_layer = nn.BatchNorm2d + else: + norm_layer = ActNorm + if ( + type(norm_layer) == functools.partial + ): # no need to use bias as BatchNorm2d has affine parameters + use_bias = norm_layer.func != nn.BatchNorm2d + else: + use_bias = norm_layer != nn.BatchNorm2d + + kw = 4 + padw = 1 + sequence = [ + nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), + nn.LeakyReLU(0.2, True), + ] + nf_mult = 1 + nf_mult_prev = 1 + for n in range(1, n_layers): # gradually increase the number of filters + nf_mult_prev = nf_mult + nf_mult = min(2**n, 8) + sequence += [ + nn.Conv2d( + ndf * nf_mult_prev, + ndf * nf_mult, + kernel_size=kw, + stride=2, + padding=padw, + bias=use_bias, + ), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True), + ] + + nf_mult_prev = nf_mult + nf_mult = min(2**n_layers, 8) + sequence += [ + nn.Conv2d( + ndf * nf_mult_prev, + ndf * nf_mult, + kernel_size=kw, + stride=1, + padding=padw, + bias=use_bias, + ), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True), + ] + + sequence += [ + nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw) + ] # output 1 channel prediction map + self.main = nn.Sequential(*sequence) + + def forward(self, input): + """Standard forward.""" + return self.main(input) + + +class AutoencoderLossWithDiscriminator(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.kl_weight = cfg.kl_weight + self.logvar = nn.Parameter(torch.ones(size=()) * cfg.logvar_init) + + self.discriminator = NLayerDiscriminator( + input_nc=cfg.disc_in_channels, + n_layers=cfg.disc_num_layers, + use_actnorm=cfg.use_actnorm, + ).apply(weights_init) + + self.discriminator_iter_start = cfg.disc_start + self.discriminator_weight = cfg.disc_weight + self.disc_factor = cfg.disc_factor + self.disc_loss = hinge_d_loss + + def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer): + nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] + g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] + + d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) + d_weight = torch.clamp( + d_weight, self.cfg.min_adapt_d_weight, self.cfg.max_adapt_d_weight + ).detach() + d_weight = d_weight * self.discriminator_weight + return d_weight + + def forward( + self, + inputs, + reconstructions, + posteriors, + optimizer_idx, + global_step, + last_layer, + split="train", + weights=None, + ): + rec_loss = torch.abs( + inputs.contiguous() - reconstructions.contiguous() + ) # l1 loss + nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar + weighted_nll_loss = nll_loss + if weights is not None: + weighted_nll_loss = weights * nll_loss + # weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0] + weighted_nll_loss = torch.mean(weighted_nll_loss) + # nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] + nll_loss = torch.mean(nll_loss) + kl_loss = posteriors.kl() + kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] + # ? kl_loss = torch.mean(kl_loss) + + # now the GAN part + if optimizer_idx == 0: + logits_fake = self.discriminator(reconstructions.contiguous()) + g_loss = -torch.mean(logits_fake) + + if self.disc_factor > 0.0: + try: + d_weight = self.calculate_adaptive_weight( + nll_loss, g_loss, last_layer=last_layer + ) + except RuntimeError: + assert not self.training + d_weight = torch.tensor(0.0) + else: + d_weight = torch.tensor(0.0) + + disc_factor = adopt_weight( + self.disc_factor, global_step, threshold=self.discriminator_iter_start + ) + + total_loss = ( + weighted_nll_loss + + self.kl_weight * kl_loss + + d_weight * disc_factor * g_loss + ) + + return { + "loss": total_loss, + "kl_loss": kl_loss, + "rec_loss": rec_loss.mean(), + "nll_loss": nll_loss, + "g_loss": g_loss, + "d_weight": d_weight, + "disc_factor": torch.tensor(disc_factor), + } + + if optimizer_idx == 1: + logits_real = self.discriminator(inputs.contiguous().detach()) + logits_fake = self.discriminator(reconstructions.contiguous().detach()) + + disc_factor = adopt_weight( + self.disc_factor, global_step, threshold=self.discriminator_iter_start + ) + d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) + + return { + "d_loss": d_loss, + "logits_real": logits_real.mean(), + "logits_fake": logits_fake.mean(), + } diff --git a/models/tta/autoencoder/autoencoder_trainer.py b/models/tta/autoencoder/autoencoder_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..1faf02fa26c9bdc69faf2344fc2f722336d68a71 --- /dev/null +++ b/models/tta/autoencoder/autoencoder_trainer.py @@ -0,0 +1,187 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from models.base.base_trainer import BaseTrainer +from models.tta.autoencoder.autoencoder_dataset import ( + AutoencoderKLDataset, + AutoencoderKLCollator, +) +from models.tta.autoencoder.autoencoder import AutoencoderKL +from models.tta.autoencoder.autoencoder_loss import AutoencoderLossWithDiscriminator +from torch.optim import Adam, AdamW +from torch.optim.lr_scheduler import ReduceLROnPlateau +from torch.nn import MSELoss, L1Loss +import torch.nn.functional as F +from torch.utils.data import ConcatDataset, DataLoader + + +class AutoencoderKLTrainer(BaseTrainer): + def __init__(self, args, cfg): + BaseTrainer.__init__(self, args, cfg) + self.cfg = cfg + self.save_config_file() + + def build_dataset(self): + return AutoencoderKLDataset, AutoencoderKLCollator + + def build_optimizer(self): + opt_ae = torch.optim.AdamW(self.model.parameters(), **self.cfg.train.adam) + opt_disc = torch.optim.AdamW( + self.criterion.discriminator.parameters(), **self.cfg.train.adam + ) + optimizer = {"opt_ae": opt_ae, "opt_disc": opt_disc} + return optimizer + + def build_data_loader(self): + Dataset, Collator = self.build_dataset() + # build dataset instance for each dataset and combine them by ConcatDataset + datasets_list = [] + for dataset in self.cfg.dataset: + subdataset = Dataset(self.cfg, dataset, is_valid=False) + datasets_list.append(subdataset) + train_dataset = ConcatDataset(datasets_list) + + train_collate = Collator(self.cfg) + + # use batch_sampler argument instead of (sampler, shuffle, drop_last, batch_size) + train_loader = DataLoader( + train_dataset, + collate_fn=train_collate, + num_workers=self.args.num_workers, + batch_size=self.cfg.train.batch_size, + pin_memory=False, + ) + if not self.cfg.train.ddp or self.args.local_rank == 0: + datasets_list = [] + for dataset in self.cfg.dataset: + subdataset = Dataset(self.cfg, dataset, is_valid=True) + datasets_list.append(subdataset) + valid_dataset = ConcatDataset(datasets_list) + valid_collate = Collator(self.cfg) + + valid_loader = DataLoader( + valid_dataset, + collate_fn=valid_collate, + num_workers=1, + batch_size=self.cfg.train.batch_size, + ) + else: + raise NotImplementedError("DDP is not supported yet.") + # valid_loader = None + data_loader = {"train": train_loader, "valid": valid_loader} + return data_loader + + # TODO: check it... + def build_scheduler(self): + return None + # return ReduceLROnPlateau(self.optimizer["opt_ae"], **self.cfg.train.lronPlateau) + + def write_summary(self, losses, stats): + for key, value in losses.items(): + self.sw.add_scalar(key, value, self.step) + + def write_valid_summary(self, losses, stats): + for key, value in losses.items(): + self.sw.add_scalar(key, value, self.step) + + def build_criterion(self): + return AutoencoderLossWithDiscriminator(self.cfg.model.loss) + + def get_state_dict(self): + if self.scheduler != None: + state_dict = { + "model": self.model.state_dict(), + "optimizer_ae": self.optimizer["opt_ae"].state_dict(), + "optimizer_disc": self.optimizer["opt_disc"].state_dict(), + "scheduler": self.scheduler.state_dict(), + "step": self.step, + "epoch": self.epoch, + "batch_size": self.cfg.train.batch_size, + } + else: + state_dict = { + "model": self.model.state_dict(), + "optimizer_ae": self.optimizer["opt_ae"].state_dict(), + "optimizer_disc": self.optimizer["opt_disc"].state_dict(), + "step": self.step, + "epoch": self.epoch, + "batch_size": self.cfg.train.batch_size, + } + return state_dict + + def load_model(self, checkpoint): + self.step = checkpoint["step"] + self.epoch = checkpoint["epoch"] + + self.model.load_state_dict(checkpoint["model"]) + self.optimizer["opt_ae"].load_state_dict(checkpoint["optimizer_ae"]) + self.optimizer["opt_disc"].load_state_dict(checkpoint["optimizer_disc"]) + if self.scheduler != None: + self.scheduler.load_state_dict(checkpoint["scheduler"]) + + def build_model(self): + self.model = AutoencoderKL(self.cfg.model.autoencoderkl) + return self.model + + # TODO: train step + def train_step(self, data): + global_step = self.step + optimizer_idx = global_step % 2 + + train_losses = {} + total_loss = 0 + train_states = {} + + inputs = data["melspec"].unsqueeze(1) # (B, 80, T) -> (B, 1, 80, T) + reconstructions, posterior = self.model(inputs) + # train_stats.update(stat) + + train_losses = self.criterion( + inputs=inputs, + reconstructions=reconstructions, + posteriors=posterior, + optimizer_idx=optimizer_idx, + global_step=global_step, + last_layer=self.model.get_last_layer(), + split="train", + ) + + if optimizer_idx == 0: + total_loss = train_losses["loss"] + self.optimizer["opt_ae"].zero_grad() + total_loss.backward() + self.optimizer["opt_ae"].step() + + else: + total_loss = train_losses["d_loss"] + self.optimizer["opt_disc"].zero_grad() + total_loss.backward() + self.optimizer["opt_disc"].step() + + for item in train_losses: + train_losses[item] = train_losses[item].item() + + return train_losses, train_states, total_loss.item() + + # TODO: eval step + @torch.no_grad() + def eval_step(self, data, index): + valid_loss = {} + total_valid_loss = 0 + valid_stats = {} + + inputs = data["melspec"].unsqueeze(1) # (B, 80, T) -> (B, 1, 80, T) + reconstructions, posterior = self.model(inputs) + + loss = F.l1_loss(inputs, reconstructions) + valid_loss["loss"] = loss + + total_valid_loss += loss + + for item in valid_loss: + valid_loss[item] = valid_loss[item].item() + + return valid_loss, valid_stats, total_valid_loss.item() diff --git a/models/tta/ldm/__init__.py b/models/tta/ldm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/tta/ldm/attention.py b/models/tta/ldm/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..8e241fc8be6456a1c3bc3b8d8efe648ea4e42740 --- /dev/null +++ b/models/tta/ldm/attention.py @@ -0,0 +1,329 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from inspect import isfunction +import math +import torch +import torch.nn.functional as F +from torch import nn, einsum +from einops import rearrange, repeat + + +class CheckpointFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, run_function, length, *args): + ctx.run_function = run_function + ctx.input_tensors = list(args[:length]) + ctx.input_params = list(args[length:]) + + with torch.no_grad(): + output_tensors = ctx.run_function(*ctx.input_tensors) + return output_tensors + + @staticmethod + def backward(ctx, *output_grads): + ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] + with torch.enable_grad(): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + input_grads = torch.autograd.grad( + output_tensors, + ctx.input_tensors + ctx.input_params, + output_grads, + allow_unused=True, + ) + del ctx.input_tensors + del ctx.input_params + del output_tensors + return (None, None) + input_grads + + +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + args = tuple(inputs) + tuple(params) + return CheckpointFunction.apply(func, len(inputs), *args) + else: + return func(*inputs) + + +def exists(val): + return val is not None + + +def uniq(arr): + return {el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = ( + nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU()) + if not glu + else GEGLU(dim, inner_dim) + ) + + self.net = nn.Sequential( + project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def Normalize(in_channels): + return torch.nn.GroupNorm( + num_groups=32, num_channels=in_channels, eps=1e-6, affine=True + ) + + +class LinearAttention(nn.Module): + def __init__(self, dim, heads=4, dim_head=32): + super().__init__() + self.heads = heads + hidden_dim = dim_head * heads + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False) + self.to_out = nn.Conv2d(hidden_dim, dim, 1) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x) + q, k, v = rearrange( + qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3 + ) + k = k.softmax(dim=-1) + context = torch.einsum("bhdn,bhen->bhde", k, v) + out = torch.einsum("bhde,bhdn->bhen", context, q) + out = rearrange( + out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w + ) + return self.to_out(out) + + +class SpatialSelfAttention(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = rearrange(q, "b c h w -> b (h w) c") + k = rearrange(k, "b c h w -> b c (h w)") + w_ = torch.einsum("bij,bjk->bik", q, k) + + w_ = w_ * (int(c) ** (-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = rearrange(v, "b c h w -> b c (h w)") + w_ = rearrange(w_, "b i j -> b j i") + h_ = torch.einsum("bij,bjk->bik", v, w_) + h_ = rearrange(h_, "b c (h w) -> b c h w", h=h) + h_ = self.proj_out(h_) + + return x + h_ + + +class CrossAttention(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head**-0.5 + self.heads = heads + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) + ) + + def forward(self, x, context=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + v = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v)) + + sim = einsum("b i d, b j d -> b i j", q, k) * self.scale + + if exists(mask): + mask = rearrange(mask, "b ... -> b (...)") + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, "b j -> (b h) () j", h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + attn = sim.softmax(dim=-1) + + out = einsum("b i j, b j d -> b i d", attn, v) + out = rearrange(out, "(b h) n d -> b n (h d)", h=h) + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + def __init__( + self, + dim, + n_heads, + d_head, + dropout=0.0, + context_dim=None, + gated_ff=True, + checkpoint=True, + ): + super().__init__() + self.attn1 = CrossAttention( + query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout + ) # is a self-attention + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = CrossAttention( + query_dim=dim, + context_dim=context_dim, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + ) # is self-attn if context is none + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + + def forward(self, x, context=None): + return checkpoint( + self._forward, (x, context), self.parameters(), self.checkpoint + ) + + def _forward(self, x, context=None): + x = self.attn1(self.norm1(x)) + x + x = self.attn2(self.norm2(x), context=context) + x + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + """ + + def __init__( + self, in_channels, n_heads, d_head, depth=1, dropout=0.0, context_dim=None + ): + super().__init__() + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = Normalize(in_channels) + + self.proj_in = nn.Conv2d( + in_channels, inner_dim, kernel_size=1, stride=1, padding=0 + ) + + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim + ) + for d in range(depth) + ] + ) + + self.proj_out = zero_module( + nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + ) + + def forward(self, x, context=None): + # note: if no context is given, cross-attention defaults to self-attention + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + x = self.proj_in(x) + x = rearrange(x, "b c h w -> b (h w) c") + for block in self.transformer_blocks: + x = block(x, context=context) + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) + x = self.proj_out(x) + return x + x_in diff --git a/models/tta/ldm/audioldm.py b/models/tta/ldm/audioldm.py new file mode 100644 index 0000000000000000000000000000000000000000..bf43d07ee6a793bde6acbd330b8606c50da26df9 --- /dev/null +++ b/models/tta/ldm/audioldm.py @@ -0,0 +1,926 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from abc import abstractmethod +from functools import partial +import math +from typing import Iterable + +import os +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from einops import repeat + +from models.tta.ldm.attention import SpatialTransformer + +# from attention import SpatialTransformer + + +class CheckpointFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, run_function, length, *args): + ctx.run_function = run_function + ctx.input_tensors = list(args[:length]) + ctx.input_params = list(args[length:]) + + with torch.no_grad(): + output_tensors = ctx.run_function(*ctx.input_tensors) + return output_tensors + + @staticmethod + def backward(ctx, *output_grads): + ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] + with torch.enable_grad(): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + input_grads = torch.autograd.grad( + output_tensors, + ctx.input_tensors + ctx.input_params, + output_grads, + allow_unused=True, + ) + del ctx.input_tensors + del ctx.input_params + del output_tensors + return (None, None) + input_grads + + +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + args = tuple(inputs) + tuple(params) + return CheckpointFunction.apply(func, len(inputs), *args) + else: + return func(*inputs) + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + else: + embedding = repeat(timesteps, "b -> b d", d=dim) + return embedding + + +class GroupNorm32(nn.GroupNorm): + def forward(self, x): + return super().forward(x.float()).type(x.dtype) + + +def normalization(channels): + """ + Make a standard normalization layer. + :param channels: number of input channels. + :return: an nn.Module for normalization. + """ + return GroupNorm32(32, channels) + + +def count_flops_attn(model, _x, y): + """ + A counter for the `thop` package to count the operations in an + attention operation. + Meant to be used like: + macs, params = thop.profile( + model, + inputs=(inputs, timestamps), + custom_ops={QKVAttention: QKVAttention.count_flops}, + ) + """ + b, c, *spatial = y[0].shape + num_spatial = int(np.prod(spatial)) + # We perform two matmuls with the same number of ops. + # The first computes the weight matrix, the second computes + # the combination of the value vectors. + matmul_ops = 2 * b * (num_spatial**2) * c + model.total_ops += torch.DoubleTensor([matmul_ops]) + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class QKVAttention(nn.Module): + """ + A module which performs QKV attention and splits in a different order. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.chunk(3, dim=1) # [N x (H * C) x T] + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = torch.einsum( + "bct,bcs->bts", + (q * scale).view(bs * self.n_heads, ch, length), + (k * scale).view(bs * self.n_heads, ch, length), + ) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + a = torch.einsum( + "bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length) + ) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class QKVAttentionLegacy(nn.Module): + """ + A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = torch.einsum( + "bct,bcs->bts", q * scale, k * scale + ) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + a = torch.einsum("bts,bcs->bct", weight, v) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class AttentionPool2d(nn.Module): + """ + Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py + """ + + def __init__( + self, + spacial_dim: int, + embed_dim: int, + num_heads_channels: int, + output_dim: int = None, + ): + super().__init__() + self.positional_embedding = nn.Parameter( + torch.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5 + ) + self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) + self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) + self.num_heads = embed_dim // num_heads_channels + self.attention = QKVAttention(self.num_heads) + + def forward(self, x): + b, c, *_spatial = x.shape + x = x.reshape(b, c, -1) # NC(HW) + x = torch.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) + x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1) + x = self.qkv_proj(x) + x = self.attention(x) + x = self.c_proj(x) + return x[:, :, 0] + + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb, context=None): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb) + elif isinstance(layer, SpatialTransformer): + x = layer(x, context) + else: + x = layer(x) + return x + + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = conv_nd( + dims, self.channels, self.out_channels, 3, padding=padding + ) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate( + x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" + ) + else: + x = F.interpolate(x, scale_factor=2, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + + +class TransposedUpsample(nn.Module): + "Learned 2x upsampling without padding" + + def __init__(self, channels, out_channels=None, ks=5): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + + self.up = nn.ConvTranspose2d( + self.channels, self.out_channels, kernel_size=ks, stride=2 + ) + + def forward(self, x): + return self.up(x) + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, + self.channels, + self.out_channels, + 3, + stride=stride, + padding=padding, + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param use_checkpoint: if True, use gradient checkpointing on this module. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + up=False, + down=False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, 3, padding=1), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), + nn.Linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + ), + ) + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd( + dims, channels, self.out_channels, 3, padding=1 + ) + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + return checkpoint( + self._forward, (x, emb), self.parameters(), self.use_checkpoint + ) + + def _forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = torch.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class AttentionBlock(nn.Module): + """ + An attention block that allows spatial positions to attend to each other. + Originally ported from here, but adapted to the N-d case. + https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. + """ + + def __init__( + self, + channels, + num_heads=1, + num_head_channels=-1, + use_checkpoint=False, + use_new_attention_order=False, + ): + super().__init__() + self.channels = channels + if num_head_channels == -1: + self.num_heads = num_heads + else: + assert ( + channels % num_head_channels == 0 + ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" + self.num_heads = channels // num_head_channels + self.use_checkpoint = use_checkpoint + self.norm = normalization(channels) + self.qkv = conv_nd(1, channels, channels * 3, 1) + if use_new_attention_order: + # split qkv before split heads + self.attention = QKVAttention(self.num_heads) + else: + # split heads before split qkv + self.attention = QKVAttentionLegacy(self.num_heads) + + self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) + + def forward(self, x): + return checkpoint( + self._forward, (x,), self.parameters(), True + ) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!! + # return pt_checkpoint(self._forward, x) # pytorch + + def _forward(self, x): + b, c, *spatial = x.shape + x = x.reshape(b, c, -1) + qkv = self.qkv(self.norm(x)) + h = self.attention(qkv) + h = self.proj_out(h) + return (x + h).reshape(b, c, *spatial) + + +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding. + :param in_channels: channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + use_fp16=False, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + ): + super().__init__() + if use_spatial_transformer: + assert ( + context_dim is not None + ), "Fool!! You forgot to include the dimension of your cross-attention conditioning..." + + if context_dim is not None: + assert ( + use_spatial_transformer + ), "Fool!! You forgot to use the spatial transformer for your cross-attention conditioning..." + from omegaconf.listconfig import ListConfig + + if type(context_dim) == ListConfig: + context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert ( + num_head_channels != -1 + ), "Either num_heads or num_head_channels has to be set" + + if num_head_channels == -1: + assert ( + num_heads != -1 + ), "Either num_heads or num_head_channels has to be set" + + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.num_res_blocks = num_res_blocks + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = torch.float16 if use_fp16 else torch.float32 + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + nn.Linear(model_channels, time_embed_dim), + nn.SiLU(), + nn.Linear(time_embed_dim, time_embed_dim), + ) + + if self.num_classes is not None: + self.label_emb = nn.Embedding(num_classes, time_embed_dim) + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + conv_nd(dims, in_channels, model_channels, 3, padding=1) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for _ in range(num_res_blocks): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + # num_heads = 1 + dim_head = ( + ch // num_heads + if use_spatial_transformer + else num_head_channels + ) + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) + if not use_spatial_transformer + else SpatialTransformer( + ch, + num_heads, + dim_head, + depth=transformer_depth, + context_dim=context_dim, + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + # num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) + if not use_spatial_transformer + else SpatialTransformer( + ch, + num_heads, + dim_head, + depth=transformer_depth, + context_dim=context_dim, + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + ) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(num_res_blocks + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + time_embed_dim, + dropout, + out_channels=model_channels * mult, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = model_channels * mult + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + # num_heads = 1 + dim_head = ( + ch // num_heads + if use_spatial_transformer + else num_head_channels + ) + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads_upsample, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) + if not use_spatial_transformer + else SpatialTransformer( + ch, + num_heads, + dim_head, + depth=transformer_depth, + context_dim=context_dim, + ) + ) + if level and i == num_res_blocks: + out_ch = ch + layers.append( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), + ) + if self.predict_codebook_ids: + self.id_predictor = nn.Sequential( + normalization(ch), + conv_nd(dims, model_channels, n_embed, 1), + # nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits + ) + + def forward(self, x, timesteps=None, context=None, y=None, **kwargs): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :param context: conditioning plugged in via crossattn + :param y: an [N] Tensor of labels, if class-conditional. + :return: an [N x C x ...] Tensor of outputs. + """ + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) + emb = self.time_embed(t_emb) + + if self.num_classes is not None: + assert y.shape == (x.shape[0],) + emb = emb + self.label_emb(y) + + h = x.type(self.dtype) + for module in self.input_blocks: + h = module(h, emb, context) + hs.append(h) + h = self.middle_block(h, emb, context) + for module in self.output_blocks: + # print(h.shape, hs[-1].shape) + if h.shape != hs[-1].shape: + if h.shape[-1] > hs[-1].shape[-1]: + h = h[:, :, :, : hs[-1].shape[-1]] + if h.shape[-2] > hs[-1].shape[-2]: + h = h[:, :, : hs[-1].shape[-2], :] + h = torch.cat([h, hs.pop()], dim=1) + h = module(h, emb, context) + # print(h.shape) + h = h.type(x.dtype) + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) + + +class AudioLDM(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.unet = UNetModel( + image_size=cfg.image_size, + in_channels=cfg.in_channels, + out_channels=cfg.out_channels, + model_channels=cfg.model_channels, + attention_resolutions=cfg.attention_resolutions, + num_res_blocks=cfg.num_res_blocks, + channel_mult=cfg.channel_mult, + num_heads=cfg.num_heads, + use_spatial_transformer=cfg.use_spatial_transformer, + transformer_depth=cfg.transformer_depth, + context_dim=cfg.context_dim, + use_checkpoint=cfg.use_checkpoint, + legacy=cfg.legacy, + ) + + def forward(self, x, timesteps=None, context=None, y=None): + x = self.unet(x=x, timesteps=timesteps, context=context, y=y) + return x diff --git a/models/tta/ldm/audioldm_dataset.py b/models/tta/ldm/audioldm_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..344eeb51a164118d16b1651963e364b02135d7de --- /dev/null +++ b/models/tta/ldm/audioldm_dataset.py @@ -0,0 +1,153 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import random +import torch +from torch.nn.utils.rnn import pad_sequence +from utils.data_utils import * + + +from models.base.base_dataset import ( + BaseCollator, + BaseDataset, + BaseTestDataset, + BaseTestCollator, +) +import librosa + +from transformers import AutoTokenizer + + +class AudioLDMDataset(BaseDataset): + def __init__(self, cfg, dataset, is_valid=False): + BaseDataset.__init__(self, cfg, dataset, is_valid=is_valid) + + self.cfg = cfg + + # utt2melspec + if cfg.preprocess.use_melspec: + self.utt2melspec_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2melspec_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.melspec_dir, + uid + ".npy", + ) + + # utt2wav + if cfg.preprocess.use_wav: + self.utt2wav_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2wav_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.wav_dir, + uid + ".wav", + ) + + # utt2caption + if cfg.preprocess.use_caption: + self.utt2caption = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2caption[utt] = utt_info["Caption"] + + def __getitem__(self, index): + # melspec: (n_mels, T) + # wav: (T,) + + single_feature = BaseDataset.__getitem__(self, index) + + utt_info = self.metadata[index] + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + if self.cfg.preprocess.use_melspec: + single_feature["melspec"] = np.load(self.utt2melspec_path[utt]) + + if self.cfg.preprocess.use_wav: + wav, sr = librosa.load( + self.utt2wav_path[utt], sr=16000 + ) # hard coding for 16KHz... + single_feature["wav"] = wav + + if self.cfg.preprocess.use_caption: + cond_mask = np.random.choice( + [1, 0], + p=[ + self.cfg.preprocess.cond_mask_prob, + 1 - self.cfg.preprocess.cond_mask_prob, + ], + ) # (0.1, 0.9) + if cond_mask: + single_feature["caption"] = "" + else: + single_feature["caption"] = self.utt2caption[utt] + + return single_feature + + def __len__(self): + return len(self.metadata) + + +class AudioLDMCollator(BaseCollator): + def __init__(self, cfg): + BaseCollator.__init__(self, cfg) + + self.tokenizer = AutoTokenizer.from_pretrained("t5-base", model_max_length=512) + + def __call__(self, batch): + # mel: (B, n_mels, T) + # wav (option): (B, T) + # text_input_ids: (B, L) + # text_attention_mask: (B, L) + + packed_batch_features = dict() + + for key in batch[0].keys(): + if key == "melspec": + packed_batch_features["melspec"] = torch.from_numpy( + np.array([b["melspec"][:, :624] for b in batch]) + ) + + if key == "wav": + values = [torch.from_numpy(b[key]) for b in batch] + packed_batch_features[key] = pad_sequence( + values, batch_first=True, padding_value=0 + ) + + if key == "caption": + captions = [b[key] for b in batch] + text_input = self.tokenizer( + captions, return_tensors="pt", truncation=True, padding="longest" + ) + text_input_ids = text_input["input_ids"] + text_attention_mask = text_input["attention_mask"] + + packed_batch_features["text_input_ids"] = text_input_ids + packed_batch_features["text_attention_mask"] = text_attention_mask + + return packed_batch_features + + +class AudioLDMTestDataset(BaseTestDataset): + ... + + +class AudioLDMTestCollator(BaseTestCollator): + ... diff --git a/models/tta/ldm/audioldm_inference.py b/models/tta/ldm/audioldm_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..a37b40639aeef4d4a8ade1324171e7be11009d8d --- /dev/null +++ b/models/tta/ldm/audioldm_inference.py @@ -0,0 +1,193 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import time +import numpy as np +import torch +from tqdm import tqdm +import torch.nn as nn +from collections import OrderedDict +import json + +from models.tta.autoencoder.autoencoder import AutoencoderKL +from models.tta.ldm.inference_utils.vocoder import Generator +from models.tta.ldm.audioldm import AudioLDM +from transformers import T5EncoderModel, AutoTokenizer +from diffusers import PNDMScheduler + +import matplotlib.pyplot as plt +from scipy.io.wavfile import write + + +class AttrDict(dict): + def __init__(self, *args, **kwargs): + super(AttrDict, self).__init__(*args, **kwargs) + self.__dict__ = self + + +class AudioLDMInference: + def __init__(self, args, cfg): + self.cfg = cfg + self.args = args + + self.build_autoencoderkl() + self.build_textencoder() + + self.model = self.build_model() + self.load_state_dict() + + self.build_vocoder() + + self.out_path = self.args.output_dir + self.out_mel_path = os.path.join(self.out_path, "mel") + self.out_wav_path = os.path.join(self.out_path, "wav") + os.makedirs(self.out_mel_path, exist_ok=True) + os.makedirs(self.out_wav_path, exist_ok=True) + + def build_autoencoderkl(self): + self.autoencoderkl = AutoencoderKL(self.cfg.model.autoencoderkl) + self.autoencoder_path = self.cfg.model.autoencoder_path + checkpoint = torch.load(self.autoencoder_path, map_location="cpu") + self.autoencoderkl.load_state_dict(checkpoint["model"]) + self.autoencoderkl.cuda(self.args.local_rank) + self.autoencoderkl.requires_grad_(requires_grad=False) + self.autoencoderkl.eval() + + def build_textencoder(self): + self.tokenizer = AutoTokenizer.from_pretrained("t5-base", model_max_length=512) + self.text_encoder = T5EncoderModel.from_pretrained("t5-base") + self.text_encoder.cuda(self.args.local_rank) + self.text_encoder.requires_grad_(requires_grad=False) + self.text_encoder.eval() + + def build_vocoder(self): + config_file = os.path.join(self.args.vocoder_config_path) + with open(config_file) as f: + data = f.read() + json_config = json.loads(data) + h = AttrDict(json_config) + self.vocoder = Generator(h).to(self.args.local_rank) + checkpoint_dict = torch.load( + self.args.vocoder_path, map_location=self.args.local_rank + ) + self.vocoder.load_state_dict(checkpoint_dict["generator"]) + + def build_model(self): + self.model = AudioLDM(self.cfg.model.audioldm) + return self.model + + def load_state_dict(self): + self.checkpoint_path = self.args.checkpoint_path + checkpoint = torch.load(self.checkpoint_path, map_location="cpu") + self.model.load_state_dict(checkpoint["model"]) + self.model.cuda(self.args.local_rank) + + def get_text_embedding(self): + text = self.args.text + + prompt = [text] + + text_input = self.tokenizer( + prompt, + max_length=self.tokenizer.model_max_length, + truncation=True, + padding="do_not_pad", + return_tensors="pt", + ) + text_embeddings = self.text_encoder( + text_input.input_ids.to(self.args.local_rank) + )[0] + + max_length = text_input.input_ids.shape[-1] + uncond_input = self.tokenizer( + [""] * 1, padding="max_length", max_length=max_length, return_tensors="pt" + ) + uncond_embeddings = self.text_encoder( + uncond_input.input_ids.to(self.args.local_rank) + )[0] + text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) + + return text_embeddings + + def inference(self): + text_embeddings = self.get_text_embedding() + print(text_embeddings.shape) + + num_steps = self.args.num_steps + guidance_scale = self.args.guidance_scale + + noise_scheduler = PNDMScheduler( + num_train_timesteps=1000, + beta_start=0.00085, + beta_end=0.012, + beta_schedule="scaled_linear", + skip_prk_steps=True, + set_alpha_to_one=False, + steps_offset=1, + prediction_type="epsilon", + ) + + noise_scheduler.set_timesteps(num_steps) + + latents = torch.randn( + ( + 1, + self.cfg.model.autoencoderkl.z_channels, + 80 // (2 ** (len(self.cfg.model.autoencoderkl.ch_mult) - 1)), + 624 // (2 ** (len(self.cfg.model.autoencoderkl.ch_mult) - 1)), + ) + ).to(self.args.local_rank) + + self.model.eval() + for t in tqdm(noise_scheduler.timesteps): + t = t.to(self.args.local_rank) + + # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes. + latent_model_input = torch.cat([latents] * 2) + + latent_model_input = noise_scheduler.scale_model_input( + latent_model_input, timestep=t + ) + # print(latent_model_input.shape) + + # predict the noise residual + with torch.no_grad(): + noise_pred = self.model( + latent_model_input, torch.cat([t.unsqueeze(0)] * 2), text_embeddings + ) + + # perform guidance + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + # compute the previous noisy sample x_t -> x_t-1 + latents = noise_scheduler.step(noise_pred, t, latents).prev_sample + # print(latents.shape) + + latents_out = latents + print(latents_out.shape) + + with torch.no_grad(): + mel_out = self.autoencoderkl.decode(latents_out) + print(mel_out.shape) + + melspec = mel_out[0, 0].cpu().detach().numpy() + plt.imsave(os.path.join(self.out_mel_path, self.args.text + ".png"), melspec) + + self.vocoder.eval() + self.vocoder.remove_weight_norm() + with torch.no_grad(): + melspec = np.expand_dims(melspec, 0) + melspec = torch.FloatTensor(melspec).to(self.args.local_rank) + + y = self.vocoder(melspec) + audio = y.squeeze() + audio = audio * 32768.0 + audio = audio.cpu().numpy().astype("int16") + + write(os.path.join(self.out_wav_path, self.args.text + ".wav"), 16000, audio) diff --git a/models/tta/ldm/audioldm_trainer.py b/models/tta/ldm/audioldm_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..bd4a241867b139f0ce314b9c78d053cf711a83df --- /dev/null +++ b/models/tta/ldm/audioldm_trainer.py @@ -0,0 +1,251 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from models.base.base_trainer import BaseTrainer +from diffusers import DDPMScheduler +from models.tta.ldm.audioldm_dataset import AudioLDMDataset, AudioLDMCollator +from models.tta.autoencoder.autoencoder import AutoencoderKL +from models.tta.ldm.audioldm import AudioLDM, UNetModel +import torch +import torch.nn as nn +from torch.nn import MSELoss, L1Loss +import torch.nn.functional as F +from torch.utils.data import ConcatDataset, DataLoader + +from transformers import T5EncoderModel +from diffusers import DDPMScheduler + + +class AudioLDMTrainer(BaseTrainer): + def __init__(self, args, cfg): + BaseTrainer.__init__(self, args, cfg) + self.cfg = cfg + + self.build_autoencoderkl() + self.build_textencoder() + self.nosie_scheduler = self.build_noise_scheduler() + + self.save_config_file() + + def build_autoencoderkl(self): + self.autoencoderkl = AutoencoderKL(self.cfg.model.autoencoderkl) + self.autoencoder_path = self.cfg.model.autoencoder_path + checkpoint = torch.load(self.autoencoder_path, map_location="cpu") + self.autoencoderkl.load_state_dict(checkpoint["model"]) + self.autoencoderkl.cuda(self.args.local_rank) + self.autoencoderkl.requires_grad_(requires_grad=False) + self.autoencoderkl.eval() + + def build_textencoder(self): + self.text_encoder = T5EncoderModel.from_pretrained("t5-base") + self.text_encoder.cuda(self.args.local_rank) + self.text_encoder.requires_grad_(requires_grad=False) + self.text_encoder.eval() + + def build_noise_scheduler(self): + nosie_scheduler = DDPMScheduler( + num_train_timesteps=self.cfg.model.noise_scheduler.num_train_timesteps, + beta_start=self.cfg.model.noise_scheduler.beta_start, + beta_end=self.cfg.model.noise_scheduler.beta_end, + beta_schedule=self.cfg.model.noise_scheduler.beta_schedule, + clip_sample=self.cfg.model.noise_scheduler.clip_sample, + # steps_offset=self.cfg.model.noise_scheduler.steps_offset, + # set_alpha_to_one=self.cfg.model.noise_scheduler.set_alpha_to_one, + # skip_prk_steps=self.cfg.model.noise_scheduler.skip_prk_steps, + prediction_type=self.cfg.model.noise_scheduler.prediction_type, + ) + return nosie_scheduler + + def build_dataset(self): + return AudioLDMDataset, AudioLDMCollator + + def build_data_loader(self): + Dataset, Collator = self.build_dataset() + # build dataset instance for each dataset and combine them by ConcatDataset + datasets_list = [] + for dataset in self.cfg.dataset: + subdataset = Dataset(self.cfg, dataset, is_valid=False) + datasets_list.append(subdataset) + train_dataset = ConcatDataset(datasets_list) + + train_collate = Collator(self.cfg) + + # use batch_sampler argument instead of (sampler, shuffle, drop_last, batch_size) + train_loader = DataLoader( + train_dataset, + collate_fn=train_collate, + num_workers=self.args.num_workers, + batch_size=self.cfg.train.batch_size, + pin_memory=False, + ) + if not self.cfg.train.ddp or self.args.local_rank == 0: + datasets_list = [] + for dataset in self.cfg.dataset: + subdataset = Dataset(self.cfg, dataset, is_valid=True) + datasets_list.append(subdataset) + valid_dataset = ConcatDataset(datasets_list) + valid_collate = Collator(self.cfg) + + valid_loader = DataLoader( + valid_dataset, + collate_fn=valid_collate, + num_workers=1, + batch_size=self.cfg.train.batch_size, + ) + else: + raise NotImplementedError("DDP is not supported yet.") + # valid_loader = None + data_loader = {"train": train_loader, "valid": valid_loader} + return data_loader + + def build_optimizer(self): + optimizer = torch.optim.AdamW(self.model.parameters(), **self.cfg.train.adam) + return optimizer + + # TODO: check it... + def build_scheduler(self): + return None + # return ReduceLROnPlateau(self.optimizer["opt_ae"], **self.cfg.train.lronPlateau) + + def write_summary(self, losses, stats): + for key, value in losses.items(): + self.sw.add_scalar(key, value, self.step) + + def write_valid_summary(self, losses, stats): + for key, value in losses.items(): + self.sw.add_scalar(key, value, self.step) + + def build_criterion(self): + criterion = nn.MSELoss(reduction="mean") + return criterion + + def get_state_dict(self): + if self.scheduler != None: + state_dict = { + "model": self.model.state_dict(), + "optimizer": self.optimizer.state_dict(), + "scheduler": self.scheduler.state_dict(), + "step": self.step, + "epoch": self.epoch, + "batch_size": self.cfg.train.batch_size, + } + else: + state_dict = { + "model": self.model.state_dict(), + "optimizer": self.optimizer.state_dict(), + "step": self.step, + "epoch": self.epoch, + "batch_size": self.cfg.train.batch_size, + } + return state_dict + + def load_model(self, checkpoint): + self.step = checkpoint["step"] + self.epoch = checkpoint["epoch"] + + self.model.load_state_dict(checkpoint["model"]) + self.optimizer.load_state_dict(checkpoint["optimizer"]) + if self.scheduler != None: + self.scheduler.load_state_dict(checkpoint["scheduler"]) + + def build_model(self): + self.model = AudioLDM(self.cfg.model.audioldm) + return self.model + + @torch.no_grad() + def mel_to_latent(self, melspec): + posterior = self.autoencoderkl.encode(melspec) + latent = posterior.sample() # (B, 4, 5, 78) + return latent + + @torch.no_grad() + def get_text_embedding(self, text_input_ids, text_attention_mask): + text_embedding = self.text_encoder( + input_ids=text_input_ids, attention_mask=text_attention_mask + ).last_hidden_state + return text_embedding # (B, T, 768) + + def train_step(self, data): + train_losses = {} + total_loss = 0 + train_stats = {} + + melspec = data["melspec"].unsqueeze(1) # (B, 80, T) -> (B, 1, 80, T) + latents = self.mel_to_latent(melspec) + + text_embedding = self.get_text_embedding( + data["text_input_ids"], data["text_attention_mask"] + ) + + noise = torch.randn_like(latents).float() + + bsz = latents.shape[0] + timesteps = torch.randint( + 0, + self.cfg.model.noise_scheduler.num_train_timesteps, + (bsz,), + device=latents.device, + ) + timesteps = timesteps.long() + + with torch.no_grad(): + noisy_latents = self.nosie_scheduler.add_noise(latents, noise, timesteps) + + model_pred = self.model( + noisy_latents, timesteps=timesteps, context=text_embedding + ) + + loss = self.criterion(model_pred, noise) + + train_losses["loss"] = loss + total_loss += loss + + self.optimizer.zero_grad() + total_loss.backward() + self.optimizer.step() + + for item in train_losses: + train_losses[item] = train_losses[item].item() + + return train_losses, train_stats, total_loss.item() + + # TODO: eval step + @torch.no_grad() + def eval_step(self, data, index): + valid_loss = {} + total_valid_loss = 0 + valid_stats = {} + + melspec = data["melspec"].unsqueeze(1) # (B, 80, T) -> (B, 1, 80, T) + latents = self.mel_to_latent(melspec) + + text_embedding = self.get_text_embedding( + data["text_input_ids"], data["text_attention_mask"] + ) + + noise = torch.randn_like(latents).float() + + bsz = latents.shape[0] + timesteps = torch.randint( + 0, + self.cfg.model.noise_scheduler.num_train_timesteps, + (bsz,), + device=latents.device, + ) + timesteps = timesteps.long() + + noisy_latents = self.nosie_scheduler.add_noise(latents, noise, timesteps) + + model_pred = self.model(noisy_latents, timesteps, text_embedding) + + loss = self.criterion(model_pred, noise) + valid_loss["loss"] = loss + + total_valid_loss += loss + + for item in valid_loss: + valid_loss[item] = valid_loss[item].item() + + return valid_loss, valid_stats, total_valid_loss.item() diff --git a/models/tta/ldm/inference_utils/utils.py b/models/tta/ldm/inference_utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fd61c0c262be55a9b98f12c1ce1043eeddfcc739 --- /dev/null +++ b/models/tta/ldm/inference_utils/utils.py @@ -0,0 +1,62 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import glob +import os +import matplotlib +import torch +from torch.nn.utils import weight_norm + +matplotlib.use("Agg") +import matplotlib.pylab as plt + + +def plot_spectrogram(spectrogram): + fig, ax = plt.subplots(figsize=(10, 2)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") + plt.colorbar(im, ax=ax) + + fig.canvas.draw() + plt.close() + + return fig + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def apply_weight_norm(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + weight_norm(m) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print("Loading '{}'".format(filepath)) + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def save_checkpoint(filepath, obj): + print("Saving checkpoint to {}".format(filepath)) + torch.save(obj, filepath) + print("Complete.") + + +def scan_checkpoint(cp_dir, prefix): + pattern = os.path.join(cp_dir, prefix + "????????") + cp_list = glob.glob(pattern) + if len(cp_list) == 0: + return None + return sorted(cp_list)[-1] diff --git a/models/tta/ldm/inference_utils/vocoder.py b/models/tta/ldm/inference_utils/vocoder.py new file mode 100644 index 0000000000000000000000000000000000000000..19e17c1e2b3e20154305180705ccbf8b5e49c346 --- /dev/null +++ b/models/tta/ldm/inference_utils/vocoder.py @@ -0,0 +1,408 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm +from models.tta.ldm.inference_utils.utils import get_padding, init_weights + +LRELU_SLOPE = 0.1 + + +class ResBlock1(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.h = h + self.convs1 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.h = h + self.convs = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + ] + ) + self.convs.apply(init_weights) + + def forward(self, x): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class Generator(torch.nn.Module): + def __init__(self, h): + super(Generator, self).__init__() + self.h = h + self.num_kernels = len(h.resblock_kernel_sizes) + self.num_upsamples = len(h.upsample_rates) + self.conv_pre = weight_norm( + Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3) + ) + resblock = ResBlock1 if h.resblock == "1" else ResBlock2 + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): + self.ups.append( + weight_norm( + ConvTranspose1d( + h.upsample_initial_channel // (2**i), + h.upsample_initial_channel // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = h.upsample_initial_channel // (2 ** (i + 1)) + for j, (k, d) in enumerate( + zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes) + ): + self.resblocks.append(resblock(h, ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + + def forward(self, x): + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print("Removing weight norm...") + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super(DiscriminatorP, self).__init__() + self.period = period + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList( + [ + norm_f( + Conv2d( + 1, + 32, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f( + Conv2d( + 32, + 128, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f( + Conv2d( + 128, + 512, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f( + Conv2d( + 512, + 1024, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), + ] + ) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiPeriodDiscriminator(torch.nn.Module): + def __init__(self): + super(MultiPeriodDiscriminator, self).__init__() + self.discriminators = nn.ModuleList( + [ + DiscriminatorP(2), + DiscriminatorP(3), + DiscriminatorP(5), + DiscriminatorP(7), + DiscriminatorP(11), + ] + ) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(DiscriminatorS, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList( + [ + norm_f(Conv1d(1, 128, 15, 1, padding=7)), + norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), + norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), + norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ] + ) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiScaleDiscriminator(torch.nn.Module): + def __init__(self): + super(MultiScaleDiscriminator, self).__init__() + self.discriminators = nn.ModuleList( + [ + DiscriminatorS(use_spectral_norm=True), + DiscriminatorS(), + DiscriminatorS(), + ] + ) + self.meanpools = nn.ModuleList( + [AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)] + ) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + if i != 0: + y = self.meanpools[i - 1](y) + y_hat = self.meanpools[i - 1](y_hat) + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +def feature_loss(fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss += torch.mean(torch.abs(rl - gl)) + + return loss * 2 + + +def discriminator_loss(disc_real_outputs, disc_generated_outputs): + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean((1 - dr) ** 2) + g_loss = torch.mean(dg**2) + loss += r_loss + g_loss + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + return loss, r_losses, g_losses + + +def generator_loss(disc_outputs): + loss = 0 + gen_losses = [] + for dg in disc_outputs: + l = torch.mean((1 - dg) ** 2) + gen_losses.append(l) + loss += l + + return loss, gen_losses diff --git a/models/tts/base/__init__.py b/models/tts/base/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..268a381ec731d684418beab0d60cad84b22c4533 --- /dev/null +++ b/models/tts/base/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# from .tts_inferece import TTSInference +from .tts_trainer import TTSTrainer diff --git a/models/tts/base/__pycache__/__init__.cpython-39.pyc b/models/tts/base/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..021e019f09917265c70ced15c3a216264f6da1ae Binary files /dev/null and b/models/tts/base/__pycache__/__init__.cpython-39.pyc differ diff --git a/models/tts/base/__pycache__/tts_dataset.cpython-39.pyc b/models/tts/base/__pycache__/tts_dataset.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a31af270e10a74f4e1c904f20203d1cb3ebc9059 Binary files /dev/null and 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Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import os +import torchaudio +import numpy as np +import torch +from utils.data_utils import * +from torch.nn.utils.rnn import pad_sequence +from text import text_to_sequence +from text.text_token_collation import phoneIDCollation +from processors.acoustic_extractor import cal_normalized_mel + +from models.base.base_dataset import ( + BaseDataset, + BaseCollator, + BaseTestDataset, + BaseTestCollator +) + +from processors.content_extractor import ( + ContentvecExtractor, + WenetExtractor, + WhisperExtractor, +) + + +class TTSDataset(BaseDataset): + def __init__(self, cfg, dataset, is_valid=False): + """ + Args: + cfg: config + dataset: dataset name + is_valid: whether to use train or valid dataset + """ + + assert isinstance(dataset, str) + + self.cfg = cfg + + processed_data_dir = os.path.join(cfg.preprocess.processed_dir, dataset) + meta_file = cfg.preprocess.valid_file if is_valid else cfg.preprocess.train_file + self.metafile_path = os.path.join(processed_data_dir, meta_file) + self.metadata = self.get_metadata() + + + + ''' + load spk2id and utt2spk from json file + spk2id: {spk1: 0, spk2: 1, ...} + utt2spk: {dataset_uid: spk1, ...} + ''' + if cfg.preprocess.use_spkid: + spk2id_path = os.path.join(processed_data_dir, cfg.preprocess.spk2id) + with open(spk2id_path, "r") as f: + self.spk2id = json.load(f) + + utt2spk_path = os.path.join(processed_data_dir, cfg.preprocess.utt2spk) + self.utt2spk = dict() + with open(utt2spk_path, "r") as f: + for line in f.readlines(): + utt, spk = line.strip().split('\t') + self.utt2spk[utt] = spk + + + if cfg.preprocess.use_uv: + self.utt2uv_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + self.utt2uv_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.uv_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_frame_pitch: + self.utt2frame_pitch_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2frame_pitch_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.pitch_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_frame_energy: + self.utt2frame_energy_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2frame_energy_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.energy_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_mel: + self.utt2mel_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2mel_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.mel_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_linear: + self.utt2linear_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2linear_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.linear_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_audio: + self.utt2audio_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + if cfg.preprocess.extract_audio: + self.utt2audio_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.audio_dir, + uid + ".wav", + ) + else: + self.utt2audio_path[utt] = utt_info["Path"] + + # self.utt2audio_path[utt] = os.path.join( + # cfg.preprocess.processed_dir, + # dataset, + # cfg.preprocess.audio_dir, + # uid + ".numpy", + # ) + + elif cfg.preprocess.use_label: + self.utt2label_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2label_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.label_dir, + uid + ".npy", + ) + elif cfg.preprocess.use_one_hot: + self.utt2one_hot_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2one_hot_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.one_hot_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_text or cfg.preprocess.use_phone: + self.utt2seq = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + if cfg.preprocess.use_text: + text = utt_info["Text"] + sequence = text_to_sequence(text, cfg.preprocess.text_cleaners) + elif cfg.preprocess.use_phone: + # load phoneme squence from phone file + phone_path = os.path.join(processed_data_dir, + cfg.preprocess.phone_dir, + uid+'.phone' + ) + with open(phone_path, 'r') as fin: + phones = fin.readlines() + assert len(phones) == 1 + phones = phones[0].strip() + phones_seq = phones.split(' ') + + phon_id_collator = phoneIDCollation(cfg, dataset=dataset) + sequence = phon_id_collator.get_phone_id_sequence(cfg, phones_seq) + + # print('cfg.preprocess.add_blank: ', cfg.preprocess.add_blank) + if cfg.preprocess.add_blank: + sequence = intersperse(sequence, 0) + + + self.utt2seq[utt] = sequence + + def __getitem__(self, index): + utt_info = self.metadata[index] + + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + single_feature = dict() + + if self.cfg.preprocess.use_spkid: + single_feature["spk_id"] = np.array( + [self.spk2id[self.utt2spk[utt]]], dtype=np.int32 + ) + + if self.cfg.preprocess.use_mel: + mel = np.load(self.utt2mel_path[utt]) + assert mel.shape[0] == self.cfg.preprocess.n_mel # [n_mels, T] + if self.cfg.preprocess.use_min_max_norm_mel: + # do mel norm + mel = cal_normalized_mel(mel, utt_info["Dataset"], self.cfg.preprocess) + + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = mel.shape[1] + single_feature["mel"] = mel.T # [T, n_mels] + + if self.cfg.preprocess.use_linear: + linear = np.load(self.utt2linear_path[utt]) + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = linear.shape[1] + single_feature["linear"] = linear.T # [T, n_linear] + + if self.cfg.preprocess.use_frame_pitch: + frame_pitch_path = self.utt2frame_pitch_path[utt] + frame_pitch = np.load(frame_pitch_path) + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = len(frame_pitch) + aligned_frame_pitch = align_length( + frame_pitch, single_feature["target_len"] + ) + single_feature["frame_pitch"] = aligned_frame_pitch + + if self.cfg.preprocess.use_uv: + frame_uv_path = self.utt2uv_path[utt] + frame_uv = np.load(frame_uv_path) + aligned_frame_uv = align_length(frame_uv, single_feature["target_len"]) + aligned_frame_uv = [ + 0 if frame_uv else 1 for frame_uv in aligned_frame_uv + ] + aligned_frame_uv = np.array(aligned_frame_uv) + single_feature["frame_uv"] = aligned_frame_uv + + if self.cfg.preprocess.use_frame_energy: + frame_energy_path = self.utt2frame_energy_path[utt] + frame_energy = np.load(frame_energy_path) + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = len(frame_energy) + aligned_frame_energy = align_length( + frame_energy, single_feature["target_len"] + ) + single_feature["frame_energy"] = aligned_frame_energy + + if self.cfg.preprocess.use_audio: + audio, sr = torchaudio.load(self.utt2audio_path[utt]) + audio = audio.cpu().numpy().squeeze() + single_feature["audio"] = audio + single_feature["audio_len"] = audio.shape[0] + + + if self.cfg.preprocess.use_phone or self.cfg.preprocess.use_text: + single_feature["phone_seq"] = np.array(self.utt2seq[utt]) + single_feature["phone_len"] = len(self.utt2seq[utt]) + + return single_feature + + def __len__(self): + + return super().__len__() + + def get_metadata(self): + return super().get_metadata() + +class TTSCollator(BaseCollator): + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, cfg): + super().__init__(cfg) + + def __call__(self, batch): + parsed_batch_features = super().__call__(batch) + return parsed_batch_features + + +class TTSTestDataset(BaseTestDataset): + def __init__(self, args, cfg): + self.cfg = cfg + + # inference from test list file + if args.test_list_file is not None: + # construst metadata + self.metadata = [] + + with open(args.test_list_file, "r") as fin: + for idx, line in enumerate(fin.readlines()): + utt_info = {} + + utt_info["Dataset"] = "test" + utt_info["Text"] = line.strip() + utt_info["Uid"] = str(idx) + self.metadata.append(utt_info) + + else: + assert args.testing_set + self.metafile_path = os.path.join( + cfg.preprocess.processed_dir, + args.dataset, + "{}.json".format(args.testing_set), + ) + self.metadata = self.get_metadata() + + + def __getitem__(self, index): + single_feature = {} + + return single_feature + + def __len__(self): + return len(self.metadata) + + + +class TTSTestCollator(BaseTestCollator): + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, cfg): + self.cfg = cfg + + def __call__(self, batch): + packed_batch_features = dict() + + # mel: [b, T, n_mels] + # frame_pitch, frame_energy: [1, T] + # target_len: [1] + # spk_id: [b, 1] + # mask: [b, T, 1] + + for key in batch[0].keys(): + if key == "target_len": + packed_batch_features["target_len"] = torch.LongTensor( + [b["target_len"] for b in batch] + ) + masks = [ + torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "phone_len": + packed_batch_features["phone_len"] = torch.LongTensor( + [b["phone_len"] for b in batch] + ) + masks = [ + torch.ones((b["phone_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["phn_mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "audio_len": + packed_batch_features["audio_len"] = torch.LongTensor( + [b["audio_len"] for b in batch] + ) + masks = [ + torch.ones((b["audio_len"], 1), dtype=torch.long) for b in batch + ] + else: + values = [torch.from_numpy(b[key]) for b in batch] + packed_batch_features[key] = pad_sequence( + values, batch_first=True, padding_value=0 + ) + return packed_batch_features diff --git a/models/tts/base/tts_inferece.py b/models/tts/base/tts_inferece.py new file mode 100644 index 0000000000000000000000000000000000000000..78b424a03b49c4a5497a7065c35d24954d2a99fd --- /dev/null +++ b/models/tts/base/tts_inferece.py @@ -0,0 +1,270 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import torch +import time +import accelerate +import random +import numpy as np +from tqdm import tqdm +from accelerate.logging import get_logger +from torch.utils.data import DataLoader + + +from abc import abstractmethod +from pathlib import Path +from utils.io import save_audio +from utils.util import load_config +from models.vocoders.vocoder_inference import synthesis + + +class TTSInference(object): + def __init__(self, args=None, cfg=None): + super().__init__() + + start = time.monotonic_ns() + self.args = args + self.cfg = cfg + self.infer_type = args.mode + + # get exp_dir + if self.args.acoustics_dir is not None: + self.exp_dir = self.args.acoustics_dir + elif self.args.checkpoint_path is not None: + self.exp_dir = os.path.dirname( + os.path.dirname(self.args.checkpoint_path)) + + # Init accelerator + self.accelerator = accelerate.Accelerator() + self.accelerator.wait_for_everyone() + self.device = self.accelerator.device + + + # Get logger + with self.accelerator.main_process_first(): + self.logger = get_logger("inference", log_level=args.log_level) + + # Log some info + self.logger.info("=" * 56) + self.logger.info("||\t\t" + "New inference process started." + "\t\t||") + self.logger.info("=" * 56) + self.logger.info("\n") + + self.acoustic_model_dir = args.acoustics_dir + self.logger.debug(f"Acoustic model dir: {args.acoustics_dir}") + + if args.vocoder_dir is not None: + self.vocoder_dir = args.vocoder_dir + self.logger.debug(f"Vocoder dir: {args.vocoder_dir}") + + os.makedirs(args.output_dir, exist_ok=True) + + # Set random seed + with self.accelerator.main_process_first(): + start = time.monotonic_ns() + self._set_random_seed(self.cfg.train.random_seed) + end = time.monotonic_ns() + self.logger.debug( + f"Setting random seed done in {(end - start) / 1e6:.2f}ms" + ) + self.logger.debug(f"Random seed: {self.cfg.train.random_seed}") + + # Setup data loader + if self.infer_type == "batch": + with self.accelerator.main_process_first(): + self.logger.info("Building dataset...") + start = time.monotonic_ns() + self.test_dataloader = self._build_test_dataloader() + end = time.monotonic_ns() + self.logger.info( + f"Building dataset done in {(end - start) / 1e6:.2f}ms" + ) + + # Build model + with self.accelerator.main_process_first(): + self.logger.info("Building model...") + start = time.monotonic_ns() + self.model = self._build_model() + end = time.monotonic_ns() + self.logger.info(f"Building model done in {(end - start) / 1e6:.3f}ms") + + # Init with accelerate + self.logger.info("Initializing accelerate...") + start = time.monotonic_ns() + self.accelerator = accelerate.Accelerator() + self.model = self.accelerator.prepare(self.model) + if self.infer_type == "batch": + self.test_dataloader = self.accelerator.prepare(self.test_dataloader) + end = time.monotonic_ns() + self.accelerator.wait_for_everyone() + self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.3f}ms") + + with self.accelerator.main_process_first(): + self.logger.info("Loading checkpoint...") + start = time.monotonic_ns() + if args.acoustics_dir is not None: + self._load_model( + checkpoint_dir=os.path.join(args.acoustics_dir, "checkpoint") + ) + elif args.checkpoint_path is not None: + self._load_model(checkpoint_path=args.checkpoint_path) + else: + print("Either checkpoint dir or checkpoint path should be provided.") + + end = time.monotonic_ns() + self.logger.info(f"Loading checkpoint done in {(end - start) / 1e6:.3f}ms") + + self.model.eval() + self.accelerator.wait_for_everyone() + + def _build_test_dataset(self): + pass + + def _build_model(self): + pass + + # TODO: LEGACY CODE + def _build_test_dataloader(self): + datasets, collate = self._build_test_dataset() + self.test_dataset = datasets(self.args, self.cfg) + self.test_collate = collate(self.cfg) + self.test_batch_size = min( + self.cfg.train.batch_size, len(self.test_dataset.metadata) + ) + test_dataloader = DataLoader( + self.test_dataset, + collate_fn=self.test_collate, + num_workers=1, + batch_size=self.test_batch_size, + shuffle=False, + ) + return test_dataloader + + def _load_model( + self, + checkpoint_dir: str = None, + checkpoint_path: str = None, + old_mode: bool = False, + ): + r"""Load model from checkpoint. If checkpoint_path is None, it will + load the latest checkpoint in checkpoint_dir. If checkpoint_path is not + None, it will load the checkpoint specified by checkpoint_path. **Only use this + method after** ``accelerator.prepare()``. + """ + + if checkpoint_path is None: + assert checkpoint_dir is not None + # Load the latest accelerator state dicts + ls = [ + str(i) for i in Path(checkpoint_dir).glob("*") if not "audio" in str(i) + ] + ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True) + checkpoint_path = ls[0] + + self.accelerator.load_state(str(checkpoint_path)) + return str(checkpoint_path) + + def inference(self): + if self.infer_type == "single": + out_dir = os.path.join(self.args.output_dir, "single") + os.makedirs(out_dir, exist_ok=True) + + pred_audio = self.inference_for_single_utterance() + save_path = os.path.join(out_dir, "test_pred.wav") + save_audio(save_path, pred_audio, self.cfg.preprocess.sample_rate) + + elif self.infer_type == "batch": + out_dir = os.path.join(self.args.output_dir, "batch") + os.makedirs(out_dir, exist_ok=True) + + pred_audio_list = self.inference_for_batches() + for it, wav in zip(self.test_dataset.metadata, pred_audio_list): + uid = it["Uid"] + save_audio( + os.path.join(out_dir, f"{uid}.wav"), + wav.numpy(), + self.cfg.preprocess.sample_rate, + add_silence=True, + turn_up=True, + ) + tmp_file = os.path.join(out_dir, f"{uid}.pt") + if os.path.exists(tmp_file): + os.remove(tmp_file) + print('Saved to: ', out_dir) + + @torch.inference_mode() + def inference_for_batches(self): + y_pred = [] + for i, batch in tqdm(enumerate(self.test_dataloader)): + y_pred, mel_lens, _ = self._inference_each_batch(batch) + y_ls = y_pred.chunk(self.test_batch_size) + tgt_ls = mel_lens.chunk(self.test_batch_size) + j = 0 + for it, l in zip(y_ls, tgt_ls): + l = l.item() + it = it.squeeze(0)[:l].detach().cpu() + + uid = self.test_dataset.metadata[i * self.test_batch_size + j]["Uid"] + torch.save(it, os.path.join(self.args.output_dir, f"{uid}.pt")) + j += 1 + + vocoder_cfg, vocoder_ckpt = self._parse_vocoder(self.args.vocoder_dir) + res = synthesis( + cfg=vocoder_cfg, + vocoder_weight_file=vocoder_ckpt, + n_samples=None, + pred=[ + torch.load( + os.path.join(self.args.output_dir, "{}.pt".format(item["Uid"])) + ).numpy() + for item in self.test_dataset.metadata + ], + ) + for it, wav in zip(self.test_dataset.metadata, res): + uid = it["Uid"] + save_audio( + os.path.join(self.args.output_dir, f"{uid}.wav"), + wav.numpy(), + 22050, + add_silence=True, + turn_up=True, + ) + + @abstractmethod + @torch.inference_mode() + def _inference_each_batch(self, batch_data): + pass + + def inference_for_single_utterance(self, text): + pass + + def synthesis_by_vocoder(self, pred): + audios_pred = synthesis( + self.vocoder_cfg, + self.checkpoint_dir_vocoder, + len(pred), + pred, + ) + + return audios_pred + + @staticmethod + def _parse_vocoder(vocoder_dir): + r"""Parse vocoder config""" + vocoder_dir = os.path.abspath(vocoder_dir) + ckpt_list = [ckpt for ckpt in Path(vocoder_dir).glob("*.pt")] + ckpt_list.sort(key=lambda x: int(x.stem), reverse=True) + ckpt_path = str(ckpt_list[0]) + vocoder_cfg = load_config( + os.path.join(vocoder_dir, "args.json"), lowercase=True + ) + return vocoder_cfg, ckpt_path + + def _set_random_seed(self, seed): + """Set random seed for all possible random modules.""" + random.seed(seed) + np.random.seed(seed) + torch.random.manual_seed(seed) diff --git a/models/tts/base/tts_trainer.py b/models/tts/base/tts_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..f0172b040d9eef1720f5bb1bf76517a28872f6da --- /dev/null +++ b/models/tts/base/tts_trainer.py @@ -0,0 +1,700 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import os +import shutil +import torch +import time +from pathlib import Path +import torch +from tqdm import tqdm +import re +import logging +import json5 +import accelerate +from accelerate.logging import get_logger +from accelerate.utils import ProjectConfiguration +from torch.utils.data import ConcatDataset, DataLoader +from accelerate import DistributedDataParallelKwargs +from schedulers.scheduler import Eden +from models.base.base_sampler import build_samplers +from models.base.new_trainer import BaseTrainer + +class TTSTrainer(BaseTrainer): + r"""The base trainer for all TTS models. It inherits from BaseTrainer and implements + ``build_criterion``, ``_build_dataset`` and ``_build_singer_lut`` methods. You can inherit from this + class, and implement ``_build_model``, ``_forward_step``. + """ + + def __init__(self, args=None, cfg=None): + self.args = args + self.cfg = cfg + + cfg.exp_name = args.exp_name + + # init with accelerate + self._init_accelerator() + self.accelerator.wait_for_everyone() + + with self.accelerator.main_process_first(): + self.logger = get_logger(args.exp_name, log_level="INFO") + + # Log some info + self.logger.info("=" * 56) + self.logger.info("||\t\t" + "New training process started." + "\t\t||") + self.logger.info("=" * 56) + self.logger.info("\n") + self.logger.debug(f"Using {args.log_level.upper()} logging level.") + self.logger.info(f"Experiment name: {args.exp_name}") + self.logger.info(f"Experiment directory: {self.exp_dir}") + self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") + if self.accelerator.is_main_process: + os.makedirs(self.checkpoint_dir, exist_ok=True) + self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") + + # init counts + self.batch_count: int = 0 + self.step: int = 0 + self.epoch: int = 0 + self.max_epoch = ( + self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf") + ) + self.logger.info( + "Max epoch: {}".format( + self.max_epoch if self.max_epoch < float("inf") else "Unlimited" + ) + ) + + # Check values + if self.accelerator.is_main_process: + self.__check_basic_configs() + # Set runtime configs + self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride + self.checkpoints_path = [ + [] for _ in range(len(self.save_checkpoint_stride)) + ] + self.keep_last = [ + i if i > 0 else float("inf") for i in self.cfg.train.keep_last + ] + self.run_eval = self.cfg.train.run_eval + + # set random seed + with self.accelerator.main_process_first(): + start = time.monotonic_ns() + self._set_random_seed(self.cfg.train.random_seed) + end = time.monotonic_ns() + self.logger.debug( + f"Setting random seed done in {(end - start) / 1e6:.2f}ms" + ) + self.logger.debug(f"Random seed: {self.cfg.train.random_seed}") + + # setup data_loader + with self.accelerator.main_process_first(): + self.logger.info("Building dataset...") + start = time.monotonic_ns() + self.train_dataloader, self.valid_dataloader = self._build_dataloader() + end = time.monotonic_ns() + self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms") + + # save phone table to exp dir. Should be done before building model due to loading phone table in model + if cfg.preprocess.use_phone and cfg.preprocess.phone_extractor != 'lexicon': + self._save_phone_symbols_file_to_exp_path() + + + # setup model + with self.accelerator.main_process_first(): + self.logger.info("Building model...") + start = time.monotonic_ns() + self.model = self._build_model() + end = time.monotonic_ns() + self.logger.debug(self.model) + self.logger.info(f"Building model done in {(end - start) / 1e6:.2f}ms") + self.logger.info( + f"Model parameters: {self.__count_parameters(self.model)/1e6:.2f}M" + ) + + # optimizer & scheduler + with self.accelerator.main_process_first(): + self.logger.info("Building optimizer and scheduler...") + start = time.monotonic_ns() + self.optimizer = self._build_optimizer() + self.scheduler = self._build_scheduler() + end = time.monotonic_ns() + self.logger.info( + f"Building optimizer and scheduler done in {(end - start) / 1e6:.2f}ms" + ) + + # create criterion + with self.accelerator.main_process_first(): + self.logger.info("Building criterion...") + start = time.monotonic_ns() + self.criterion = self._build_criterion() + end = time.monotonic_ns() + self.logger.info(f"Building criterion done in {(end - start) / 1e6:.2f}ms") + + # Resume or Finetune + with self.accelerator.main_process_first(): + self._check_resume() + + # accelerate prepare + self.logger.info("Initializing accelerate...") + start = time.monotonic_ns() + self._accelerator_prepare() + end = time.monotonic_ns() + self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.2f}ms") + + # save config file path + self.config_save_path = os.path.join(self.exp_dir, "args.json") + self.device = self.accelerator.device + + if cfg.preprocess.use_spkid and cfg.train.multi_speaker_training: + self.speakers = self._build_speaker_lut() + self.utt2spk_dict = self._build_utt2spk_dict() + + + # Only for TTS tasks + self.task_type = "TTS" + self.logger.info("Task type: {}".format(self.task_type)) + + def _check_resume(self): + # if args.resume: + if self.args.resume or (self.cfg.model_type == 'VALLE' and self.args.train_stage == 2): + if (self.cfg.model_type == 'VALLE' and self.args.train_stage == 2): + self.args.resume_type = 'finetune' + + self.logger.info("Resuming from checkpoint...") + start = time.monotonic_ns() + self.ckpt_path = self._load_model(self.checkpoint_dir, self.args.checkpoint_path, self.args.resume_type) + end = time.monotonic_ns() + self.logger.info( + f"Resuming from checkpoint done in {(end - start) / 1e6:.2f}ms" + ) + self.checkpoints_path = json.load( + open(os.path.join(self.ckpt_path, "ckpts.json"), "r") + ) + + self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") + if self.accelerator.is_main_process: + os.makedirs(self.checkpoint_dir, exist_ok=True) + self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") + + def _init_accelerator(self): + self.exp_dir = os.path.join( + os.path.abspath(self.cfg.log_dir), self.args.exp_name + ) + project_config = ProjectConfiguration( + project_dir=self.exp_dir, + logging_dir=os.path.join(self.exp_dir, "log"), + ) + kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) + self.accelerator = accelerate.Accelerator( + gradient_accumulation_steps=self.cfg.train.gradient_accumulation_step, + log_with=self.cfg.train.tracker, + project_config=project_config, + kwargs_handlers=[kwargs] + ) + if self.accelerator.is_main_process: + os.makedirs(project_config.project_dir, exist_ok=True) + os.makedirs(project_config.logging_dir, exist_ok=True) + with self.accelerator.main_process_first(): + self.accelerator.init_trackers(self.args.exp_name) + + + def _accelerator_prepare(self): + ( + self.train_dataloader, + self.valid_dataloader, + ) = self.accelerator.prepare( + self.train_dataloader, + self.valid_dataloader, + ) + + if isinstance(self.model, dict): + for key in self.model.keys(): + self.model[key] = self.accelerator.prepare(self.model[key]) + else: + self.model = self.accelerator.prepare(self.model) + + if isinstance(self.optimizer, dict): + for key in self.optimizer.keys(): + self.optimizer[key] = self.accelerator.prepare(self.optimizer[key]) + else: + self.optimizer = self.accelerator.prepare(self.optimizer) + + if isinstance(self.scheduler, dict): + for key in self.scheduler.keys(): + self.scheduler[key] = self.accelerator.prepare(self.scheduler[key]) + else: + self.scheduler = self.accelerator.prepare(self.scheduler) + + ### Following are methods only for TTS tasks ### + def _build_dataset(self): + pass + + def _build_criterion(self): + pass + + def _build_model(self): + pass + + def _build_dataloader(self): + """Build dataloader which merges a series of datasets.""" + # Build dataset instance for each dataset and combine them by ConcatDataset + Dataset, Collator = self._build_dataset() + + # Build train set + datasets_list = [] + for dataset in self.cfg.dataset: + subdataset = Dataset(self.cfg, dataset, is_valid=False) + datasets_list.append(subdataset) + train_dataset = ConcatDataset(datasets_list) + train_collate = Collator(self.cfg) + _, batch_sampler = build_samplers(train_dataset, self.cfg, self.logger, "train") + train_loader = DataLoader( + train_dataset, + collate_fn=train_collate, + batch_sampler=batch_sampler, + num_workers=self.cfg.train.dataloader.num_worker, + pin_memory=self.cfg.train.dataloader.pin_memory, + ) + + # Build test set + datasets_list = [] + for dataset in self.cfg.dataset: + subdataset = Dataset(self.cfg, dataset, is_valid=True) + datasets_list.append(subdataset) + valid_dataset = ConcatDataset(datasets_list) + valid_collate = Collator(self.cfg) + _, batch_sampler = build_samplers(valid_dataset, self.cfg, self.logger, "valid") + valid_loader = DataLoader( + valid_dataset, + collate_fn=valid_collate, + batch_sampler=batch_sampler, + num_workers=self.cfg.train.dataloader.num_worker, + pin_memory=self.cfg.train.dataloader.pin_memory, + ) + return train_loader, valid_loader + + def _build_optimizer(self): + pass + + def _build_scheduler(self): + pass + + def _load_model(self, checkpoint_dir, checkpoint_path=None, resume_type="resume"): + """Load model from checkpoint. If a folder is given, it will + load the latest checkpoint in checkpoint_dir. If a path is given + it will load the checkpoint specified by checkpoint_path. + **Only use this method after** ``accelerator.prepare()``. + """ + if checkpoint_path is None: + ls = [str(i) for i in Path(checkpoint_dir).glob("*")] + ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True) + checkpoint_path = ls[0] + self.logger.info("Load model from {}".format(checkpoint_path)) + print("Load model from {}".format(checkpoint_path)) + if resume_type == "resume": + self.accelerator.load_state(checkpoint_path) + self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + 1 + self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + 1 + elif resume_type == "finetune": + self.model.load_state_dict( + torch.load( + os.path.join(checkpoint_path, + "pytorch_model.bin" + ) + ) + ) + self.model.cuda(self.accelerator.device) + self.logger.info("Load model weights for finetune SUCCESS!") + else: + raise ValueError("Unsupported resume type: {}".format(resume_type)) + + return checkpoint_path + + ### THIS IS MAIN ENTRY ### + def train_loop(self): + r"""Training loop. The public entry of training process.""" + # Wait everyone to prepare before we move on + self.accelerator.wait_for_everyone() + # dump config file + if self.accelerator.is_main_process: + self.__dump_cfg(self.config_save_path) + + # self.optimizer.zero_grad() + # Wait to ensure good to go + + self.accelerator.wait_for_everyone() + while self.epoch < self.max_epoch: + self.logger.info("\n") + self.logger.info("-" * 32) + self.logger.info("Epoch {}: ".format(self.epoch)) + + # Do training & validating epoch + train_total_loss, train_losses = self._train_epoch() + if isinstance(train_losses, dict): + for key, loss in train_losses.items(): + self.logger.info(" |- Train/{} Loss: {:.6f}".format(key, loss)) + self.accelerator.log( + {"Epoch/Train {} Loss".format(key): loss}, + step=self.epoch, + ) + + valid_total_loss, valid_losses = self._valid_epoch() + if isinstance(valid_losses, dict): + for key, loss in valid_losses.items(): + self.logger.info(" |- Valid/{} Loss: {:.6f}".format(key, loss)) + self.accelerator.log( + {"Epoch/Train {} Loss".format(key): loss}, + step=self.epoch, + ) + + self.logger.info(" |- Train/Loss: {:.6f}".format(train_total_loss)) + self.logger.info(" |- Valid/Loss: {:.6f}".format(valid_total_loss)) + self.accelerator.log( + { + "Epoch/Train Loss": train_total_loss, + "Epoch/Valid Loss": valid_total_loss, + }, + step=self.epoch, + ) + + self.accelerator.wait_for_everyone() + + # Check if hit save_checkpoint_stride and run_eval + run_eval = False + if self.accelerator.is_main_process: + save_checkpoint = False + hit_dix = [] + for i, num in enumerate(self.save_checkpoint_stride): + if self.epoch % num == 0: + save_checkpoint = True + hit_dix.append(i) + run_eval |= self.run_eval[i] + + self.accelerator.wait_for_everyone() + if self.accelerator.is_main_process and save_checkpoint: + path = os.path.join( + self.checkpoint_dir, + "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( + self.epoch, self.step, train_total_loss + ), + ) + self.accelerator.save_state(path) + + json.dump( + self.checkpoints_path, + open(os.path.join(path, "ckpts.json"), "w"), + ensure_ascii=False, + indent=4, + ) + + # Remove old checkpoints + to_remove = [] + for idx in hit_dix: + self.checkpoints_path[idx].append(path) + while len(self.checkpoints_path[idx]) > self.keep_last[idx]: + to_remove.append((idx, self.checkpoints_path[idx].pop(0))) + + # Search conflicts + total = set() + for i in self.checkpoints_path: + total |= set(i) + do_remove = set() + for idx, path in to_remove[::-1]: + if path in total: + self.checkpoints_path[idx].insert(0, path) + else: + do_remove.add(path) + + # Remove old checkpoints + for path in do_remove: + shutil.rmtree(path, ignore_errors=True) + self.logger.debug(f"Remove old checkpoint: {path}") + + self.accelerator.wait_for_everyone() + if run_eval: + # TODO: run evaluation + pass + + # Update info for each epoch + self.epoch += 1 + + # Finish training and save final checkpoint + self.accelerator.wait_for_everyone() + if self.accelerator.is_main_process: + path = os.path.join(self.checkpoint_dir, + "final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( + self.epoch, self.step, valid_total_loss + )) + self.accelerator.save_state( + os.path.join( + self.checkpoint_dir, + "final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( + self.epoch, self.step, valid_total_loss + ), + ) + ) + + json.dump( + self.checkpoints_path, + open(os.path.join(path, "ckpts.json"), "w"), + ensure_ascii=False, + indent=4, + ) + + self.accelerator.end_training() + + ### Following are methods that can be used directly in child classes ### + def _train_epoch(self): + r"""Training epoch. Should return average loss of a batch (sample) over + one epoch. See ``train_loop`` for usage. + """ + if isinstance(self.model, dict): + for key in self.model.keys(): + self.model[key].train() + else: + self.model.train() + + epoch_sum_loss: float = 0.0 + epoch_losses: dict = {} + epoch_step: int = 0 + for batch in tqdm( + self.train_dataloader, + desc=f"Training Epoch {self.epoch}", + unit="batch", + colour="GREEN", + leave=False, + dynamic_ncols=True, + smoothing=0.04, + disable=not self.accelerator.is_main_process, + ): + + # Do training step and BP + with self.accelerator.accumulate(self.model): + total_loss, train_losses, _ = self._train_step(batch) + self.batch_count += 1 + + # Update info for each step + # TODO: step means BP counts or batch counts? + if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: + if isinstance(self.scheduler, dict): + for key in self.scheduler.keys(): + self.scheduler[key].step() + else: + if isinstance(self.scheduler, Eden): + self.scheduler.step_batch(self.step) + else: + self.scheduler.step() + + epoch_sum_loss += total_loss + + if isinstance(train_losses, dict): + for key, value in train_losses.items(): + epoch_losses[key] += value + + if isinstance(train_losses, dict): + for key, loss in train_losses.items(): + self.accelerator.log( + {"Epoch/Train {} Loss".format(key): loss}, + step=self.step, + ) + + self.step += 1 + epoch_step += 1 + + self.accelerator.wait_for_everyone() + + epoch_sum_loss = ( + epoch_sum_loss + / len(self.train_dataloader) + * self.cfg.train.gradient_accumulation_step + ) + + for key in epoch_losses.keys(): + epoch_losses[key] = ( + epoch_losses[key] + / len(self.train_dataloader) + * self.cfg.train.gradient_accumulation_step + ) + + return epoch_sum_loss, epoch_losses + + @torch.inference_mode() + def _valid_epoch(self): + r"""Testing epoch. Should return average loss of a batch (sample) over + one epoch. See ``train_loop`` for usage. + """ + if isinstance(self.model, dict): + for key in self.model.keys(): + self.model[key].eval() + else: + self.model.eval() + + epoch_sum_loss = 0.0 + epoch_losses = dict() + for batch in tqdm( + self.valid_dataloader, + desc=f"Validating Epoch {self.epoch}", + unit="batch", + colour="GREEN", + leave=False, + dynamic_ncols=True, + smoothing=0.04, + disable=not self.accelerator.is_main_process, + ): + total_loss, valid_losses, valid_stats = self._valid_step(batch) + epoch_sum_loss += total_loss + if isinstance(valid_losses, dict): + for key, value in valid_losses.items(): + if key not in epoch_losses.keys(): + epoch_losses[key] = value + else: + epoch_losses[key] += value + + epoch_sum_loss = epoch_sum_loss / len(self.valid_dataloader) + for key in epoch_losses.keys(): + epoch_losses[key] = epoch_losses[key] / len(self.valid_dataloader) + + self.accelerator.wait_for_everyone() + + return epoch_sum_loss, epoch_losses + + def _train_step(self): + pass + + def _valid_step(self, batch): + pass + + def _inference(self): + pass + + + def _is_valid_pattern(self, directory_name): + directory_name = str(directory_name) + pattern = r"^epoch-\d{4}_step-\d{7}_loss-\d{1}\.\d{6}" + return re.match(pattern, directory_name) is not None + + def _check_basic_configs(self): + if self.cfg.train.gradient_accumulation_step <= 0: + self.logger.fatal("Invalid gradient_accumulation_step value!") + self.logger.error( + f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." + ) + self.accelerator.end_training() + raise ValueError( + f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." + ) + + def __dump_cfg(self, path): + os.makedirs(os.path.dirname(path), exist_ok=True) + json5.dump( + self.cfg, + open(path, "w"), + indent=4, + sort_keys=True, + ensure_ascii=False, + quote_keys=True, + ) + + def __check_basic_configs(self): + if self.cfg.train.gradient_accumulation_step <= 0: + self.logger.fatal("Invalid gradient_accumulation_step value!") + self.logger.error( + f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." + ) + self.accelerator.end_training() + raise ValueError( + f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." + ) + # TODO: check other values + + + @staticmethod + def __count_parameters(model): + model_param = 0.0 + if isinstance(model, dict): + for key, value in model.items(): + model_param += sum(p.numel() for p in model[key].parameters()) + else: + model_param = sum(p.numel() for p in model.parameters()) + return model_param + + def _build_speaker_lut(self): + # combine speakers + if not os.path.exists(os.path.join(self.exp_dir, self.cfg.preprocess.spk2id)): + speakers = {} + else: + with open( + os.path.join(self.exp_dir, self.cfg.preprocess.spk2id), "r" + ) as speaker_file: + speakers = json.load(speaker_file) + for dataset in self.cfg.dataset: + speaker_lut_path = os.path.join( + self.cfg.preprocess.processed_dir, dataset, self.cfg.preprocess.spk2id + ) + with open(speaker_lut_path, "r") as speaker_lut_path: + singer_lut = json.load(speaker_lut_path) + for singer in singer_lut.keys(): + if singer not in speakers: + speakers[singer] = len(speakers) + with open( + os.path.join(self.exp_dir, self.cfg.preprocess.spk2id), "w" + ) as speaker_file: + json.dump(speakers, speaker_file, indent=4, ensure_ascii=False) + print( + "speakers have been dumped to {}".format( + os.path.join(self.exp_dir, self.cfg.preprocess.spk2id) + ) + ) + return speakers + + def _build_utt2spk_dict(self): + # combine speakers + utt2spk = {} + if not os.path.exists(os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk)): + utt2spk = {} + else: + with open( + os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk), "r" + ) as utt2spk_file: + for line in utt2spk_file.readlines(): + utt, spk = line.strip().split('\t') + utt2spk[utt] = spk + for dataset in self.cfg.dataset: + utt2spk_dict_path = os.path.join( + self.cfg.preprocess.processed_dir, dataset, self.cfg.preprocess.utt2spk + ) + with open(utt2spk_dict_path, "r") as utt2spk_dict: + for line in utt2spk_dict.readlines(): + utt, spk = line.strip().split('\t') + if utt not in utt2spk.keys(): + utt2spk[utt] = spk + with open( + os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk), "w" + ) as utt2spk_file: + for utt, spk in utt2spk.items(): + utt2spk_file.write(utt+'\t'+spk+'\n') + print( + "utterance and speaker mapper have been dumped to {}".format( + os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk) + ) + ) + return utt2spk + + + + def _save_phone_symbols_file_to_exp_path(self): + phone_symbols_file = os.path.join(self.cfg.preprocess.processed_dir, + self.cfg.dataset[0], + self.cfg.preprocess.symbols_dict) + phone_symbols_file_to_exp_path = os.path.join(self.exp_dir, self.cfg.preprocess.symbols_dict) + shutil.copy(phone_symbols_file, phone_symbols_file_to_exp_path) + print( + "phone symbols been dumped to {}".format( + os.path.join(self.exp_dir, self.cfg.preprocess.symbols_dict) + ) + ) \ No newline at end of file diff --git a/models/tts/fastspeech2/__init__.py b/models/tts/fastspeech2/__init__.py new file mode 100644 index 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b/models/tts/fastspeech2/fs2.py @@ -0,0 +1,549 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This code is modified from https://github.com/ming024/FastSpeech2/blob/master/model/fastspeech2.py +import torch +import torch.nn as nn +import numpy as np +import torch.nn.functional as F + +from modules.transformer.Models import Encoder, Decoder +from modules.transformer.Layers import PostNet +from collections import OrderedDict + +import os +import json + + +def get_mask_from_lengths(lengths, max_len=None): + device = lengths.device + batch_size = lengths.shape[0] + if max_len is None: + max_len = torch.max(lengths).item() + + ids = torch.arange(0, max_len).unsqueeze(0).expand(batch_size, -1).to(device) + mask = ids >= lengths.unsqueeze(1).expand(-1, max_len) + + return mask + + +def pad(input_ele, mel_max_length=None): + if mel_max_length: + max_len = mel_max_length + else: + max_len = max([input_ele[i].size(0) for i in range(len(input_ele))]) + + out_list = list() + for i, batch in enumerate(input_ele): + if len(batch.shape) == 1: + one_batch_padded = F.pad( + batch, (0, max_len - batch.size(0)), "constant", 0.0 + ) + elif len(batch.shape) == 2: + one_batch_padded = F.pad( + batch, (0, 0, 0, max_len - batch.size(0)), "constant", 0.0 + ) + out_list.append(one_batch_padded) + out_padded = torch.stack(out_list) + return out_padded + + +class VarianceAdaptor(nn.Module): + """Variance Adaptor""" + + def __init__(self, cfg): + super(VarianceAdaptor, self).__init__() + self.duration_predictor = VariancePredictor(cfg) + self.length_regulator = LengthRegulator() + self.pitch_predictor = VariancePredictor(cfg) + self.energy_predictor = VariancePredictor(cfg) + + # assign the pitch/energy feature level + if cfg.preprocess.use_frame_pitch: + self.pitch_feature_level = "frame_level" + self.pitch_dir = cfg.preprocess.pitch_dir + else: + self.pitch_feature_level = "phoneme_level" + self.pitch_dir = cfg.preprocess.phone_pitch_dir + + if cfg.preprocess.use_frame_energy: + self.energy_feature_level = "frame_level" + self.energy_dir = cfg.preprocess.energy_dir + else: + self.energy_feature_level = "phoneme_level" + self.energy_dir = cfg.preprocess.phone_energy_dir + + assert self.pitch_feature_level in ["phoneme_level", "frame_level"] + assert self.energy_feature_level in ["phoneme_level", "frame_level"] + + pitch_quantization = cfg.model.variance_embedding.pitch_quantization + energy_quantization = cfg.model.variance_embedding.energy_quantization + n_bins = cfg.model.variance_embedding.n_bins + assert pitch_quantization in ["linear", "log"] + assert energy_quantization in ["linear", "log"] + + with open( + os.path.join( + cfg.preprocess.processed_dir, + cfg.dataset[0], + self.energy_dir, + "statistics.json", + ) + ) as f: + stats = json.load(f) + stats = stats[cfg.dataset[0] + "_" + cfg.dataset[0]] + mean, std = ( + stats["voiced_positions"]["mean"], + stats["voiced_positions"]["std"], + ) + energy_min = (stats["total_positions"]["min"] - mean) / std + energy_max = (stats["total_positions"]["max"] - mean) / std + + with open( + os.path.join( + cfg.preprocess.processed_dir, + cfg.dataset[0], + self.pitch_dir, + "statistics.json", + ) + ) as f: + stats = json.load(f) + stats = stats[cfg.dataset[0] + "_" + cfg.dataset[0]] + mean, std = ( + stats["voiced_positions"]["mean"], + stats["voiced_positions"]["std"], + ) + pitch_min = (stats["total_positions"]["min"] - mean) / std + pitch_max = (stats["total_positions"]["max"] - mean) / std + + if pitch_quantization == "log": + self.pitch_bins = nn.Parameter( + torch.exp( + torch.linspace(np.log(pitch_min), np.log(pitch_max), n_bins - 1) + ), + requires_grad=False, + ) + else: + self.pitch_bins = nn.Parameter( + torch.linspace(pitch_min, pitch_max, n_bins - 1), + requires_grad=False, + ) + if energy_quantization == "log": + self.energy_bins = nn.Parameter( + torch.exp( + torch.linspace(np.log(energy_min), np.log(energy_max), n_bins - 1) + ), + requires_grad=False, + ) + else: + self.energy_bins = nn.Parameter( + torch.linspace(energy_min, energy_max, n_bins - 1), + requires_grad=False, + ) + + self.pitch_embedding = nn.Embedding( + n_bins, cfg.model.transformer.encoder_hidden + ) + self.energy_embedding = nn.Embedding( + n_bins, cfg.model.transformer.encoder_hidden + ) + + def get_pitch_embedding(self, x, target, mask, control): + prediction = self.pitch_predictor(x, mask) + if target is not None: + embedding = self.pitch_embedding(torch.bucketize(target, self.pitch_bins)) + else: + prediction = prediction * control + embedding = self.pitch_embedding( + torch.bucketize(prediction, self.pitch_bins) + ) + return prediction, embedding + + def get_energy_embedding(self, x, target, mask, control): + prediction = self.energy_predictor(x, mask) + if target is not None: + embedding = self.energy_embedding(torch.bucketize(target, self.energy_bins)) + else: + prediction = prediction * control + embedding = self.energy_embedding( + torch.bucketize(prediction, self.energy_bins) + ) + return prediction, embedding + + def forward( + self, + x, + src_mask, + mel_mask=None, + max_len=None, + pitch_target=None, + energy_target=None, + duration_target=None, + p_control=1.0, + e_control=1.0, + d_control=1.0, + ): + log_duration_prediction = self.duration_predictor(x, src_mask) + if self.pitch_feature_level == "phoneme_level": + pitch_prediction, pitch_embedding = self.get_pitch_embedding( + x, pitch_target, src_mask, p_control + ) + x = x + pitch_embedding + if self.energy_feature_level == "phoneme_level": + energy_prediction, energy_embedding = self.get_energy_embedding( + x, energy_target, src_mask, p_control + ) + x = x + energy_embedding + + if duration_target is not None: + x, mel_len = self.length_regulator(x, duration_target, max_len) + duration_rounded = duration_target + else: + duration_rounded = torch.clamp( + (torch.round(torch.exp(log_duration_prediction) - 1) * d_control), + min=0, + ) + x, mel_len = self.length_regulator(x, duration_rounded, max_len) + mel_mask = get_mask_from_lengths(mel_len) + + if self.pitch_feature_level == "frame_level": + pitch_prediction, pitch_embedding = self.get_pitch_embedding( + x, pitch_target, mel_mask, p_control + ) + x = x + pitch_embedding + if self.energy_feature_level == "frame_level": + energy_prediction, energy_embedding = self.get_energy_embedding( + x, energy_target, mel_mask, p_control + ) + x = x + energy_embedding + + return ( + x, + pitch_prediction, + energy_prediction, + log_duration_prediction, + duration_rounded, + mel_len, + mel_mask, + ) + + +class LengthRegulator(nn.Module): + """Length Regulator""" + + def __init__(self): + super(LengthRegulator, self).__init__() + + def LR(self, x, duration, max_len): + device = x.device + output = list() + mel_len = list() + for batch, expand_target in zip(x, duration): + expanded = self.expand(batch, expand_target) + output.append(expanded) + mel_len.append(expanded.shape[0]) + + if max_len is not None: + output = pad(output, max_len) + else: + output = pad(output) + + return output, torch.LongTensor(mel_len).to(device) + + def expand(self, batch, predicted): + out = list() + + for i, vec in enumerate(batch): + expand_size = predicted[i].item() + out.append(vec.expand(max(int(expand_size), 0), -1)) + out = torch.cat(out, 0) + + return out + + def forward(self, x, duration, max_len): + output, mel_len = self.LR(x, duration, max_len) + return output, mel_len + + +class VariancePredictor(nn.Module): + """Duration, Pitch and Energy Predictor""" + + def __init__(self, cfg): + super(VariancePredictor, self).__init__() + + self.input_size = cfg.model.transformer.encoder_hidden + self.filter_size = cfg.model.variance_predictor.filter_size + self.kernel = cfg.model.variance_predictor.kernel_size + self.conv_output_size = cfg.model.variance_predictor.filter_size + self.dropout = cfg.model.variance_predictor.dropout + + self.conv_layer = nn.Sequential( + OrderedDict( + [ + ( + "conv1d_1", + Conv( + self.input_size, + self.filter_size, + kernel_size=self.kernel, + padding=(self.kernel - 1) // 2, + ), + ), + ("relu_1", nn.ReLU()), + ("layer_norm_1", nn.LayerNorm(self.filter_size)), + ("dropout_1", nn.Dropout(self.dropout)), + ( + "conv1d_2", + Conv( + self.filter_size, + self.filter_size, + kernel_size=self.kernel, + padding=1, + ), + ), + ("relu_2", nn.ReLU()), + ("layer_norm_2", nn.LayerNorm(self.filter_size)), + ("dropout_2", nn.Dropout(self.dropout)), + ] + ) + ) + + self.linear_layer = nn.Linear(self.conv_output_size, 1) + + def forward(self, encoder_output, mask): + out = self.conv_layer(encoder_output) + out = self.linear_layer(out) + out = out.squeeze(-1) + + if mask is not None: + out = out.masked_fill(mask, 0.0) + + return out + + +class Conv(nn.Module): + """ + Convolution Module + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0, + dilation=1, + bias=True, + w_init="linear", + ): + """ + :param in_channels: dimension of input + :param out_channels: dimension of output + :param kernel_size: size of kernel + :param stride: size of stride + :param padding: size of padding + :param dilation: dilation rate + :param bias: boolean. if True, bias is included. + :param w_init: str. weight inits with xavier initialization. + """ + super(Conv, self).__init__() + + self.conv = nn.Conv1d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=bias, + ) + + def forward(self, x): + x = x.contiguous().transpose(1, 2) + x = self.conv(x) + x = x.contiguous().transpose(1, 2) + + return x + + +class FastSpeech2(nn.Module): + def __init__(self, cfg) -> None: + super(FastSpeech2, self).__init__() + self.cfg = cfg + self.encoder = Encoder(cfg.model) + self.variance_adaptor = VarianceAdaptor(cfg) + self.decoder = Decoder(cfg.model) + self.mel_linear = nn.Linear( + cfg.model.transformer.decoder_hidden, + cfg.preprocess.n_mel, + ) + self.postnet = PostNet(n_mel_channels=cfg.preprocess.n_mel) + + self.speaker_emb = None + if cfg.train.multi_speaker_training: + with open( + os.path.join( + cfg.preprocess.processed_dir, cfg.dataset[0], "spk2id.json" + ), + "r", + ) as f: + n_speaker = len(json.load(f)) + self.speaker_emb = nn.Embedding( + n_speaker, + cfg.model.transformer.encoder_hidden, + ) + + def forward(self, data, p_control=1.0, e_control=1.0, d_control=1.0): + speakers = data["spk_id"] + texts = data["texts"] + src_lens = data["text_len"] + max_src_len = max(src_lens) + mels = data["mel"] if "mel" in data else None + mel_lens = data["target_len"] if "target_len" in data else None + max_mel_len = max(mel_lens) if "target_len" in data else None + p_targets = data["pitch"] if "pitch" in data else None + e_targets = data["energy"] if "energy" in data else None + d_targets = data["durations"] if "durations" in data else None + src_masks = data["text_mask"].squeeze(-1) > 0 + src_masks = ~src_masks + mel_masks = ( + get_mask_from_lengths(mel_lens, max_mel_len) + if mel_lens is not None + else None + ) + output = self.encoder(texts, src_masks) + + if self.speaker_emb is not None: + output = output + self.speaker_emb(speakers).unsqueeze(1).expand( + -1, max_src_len, -1 + ) + + ( + output, + p_predictions, + e_predictions, + log_d_predictions, + d_rounded, + mel_lens, + mel_masks, + ) = self.variance_adaptor( + output, + src_masks, + mel_masks, + max_mel_len, + p_targets, + e_targets, + d_targets, + p_control, + e_control, + d_control, + ) + + output, mel_masks = self.decoder(output, mel_masks) + output = self.mel_linear(output) + + postnet_output = self.postnet(output) + output + + return { + "output": output, + "postnet_output": postnet_output, + "p_predictions": p_predictions, + "e_predictions": e_predictions, + "log_d_predictions": log_d_predictions, + "d_rounded": d_rounded, + "src_masks": src_masks, + "mel_masks": mel_masks, + "src_lens": src_lens, + "mel_lens": mel_lens, + } + + +class FastSpeech2Loss(nn.Module): + """FastSpeech2 Loss""" + + def __init__(self, cfg): + super(FastSpeech2Loss, self).__init__() + if cfg.preprocess.use_frame_pitch: + self.pitch_feature_level = "frame_level" + else: + self.pitch_feature_level = "phoneme_level" + + if cfg.preprocess.use_frame_energy: + self.energy_feature_level = "frame_level" + else: + self.energy_feature_level = "phoneme_level" + + self.mse_loss = nn.MSELoss() + self.mae_loss = nn.L1Loss() + + def forward(self, data, predictions): + mel_targets = data["mel"] + pitch_targets = data["pitch"].float() + energy_targets = data["energy"].float() + duration_targets = data["durations"] + + mel_predictions = predictions["output"] + postnet_mel_predictions = predictions["postnet_output"] + pitch_predictions = predictions["p_predictions"] + energy_predictions = predictions["e_predictions"] + log_duration_predictions = predictions["log_d_predictions"] + src_masks = predictions["src_masks"] + mel_masks = predictions["mel_masks"] + + src_masks = ~src_masks + mel_masks = ~mel_masks + + log_duration_targets = torch.log(duration_targets.float() + 1) + mel_targets = mel_targets[:, : mel_masks.shape[1], :] + mel_masks = mel_masks[:, : mel_masks.shape[1]] + + log_duration_targets.requires_grad = False + pitch_targets.requires_grad = False + energy_targets.requires_grad = False + mel_targets.requires_grad = False + + if self.pitch_feature_level == "phoneme_level": + pitch_predictions = pitch_predictions.masked_select(src_masks) + pitch_targets = pitch_targets.masked_select(src_masks) + elif self.pitch_feature_level == "frame_level": + pitch_predictions = pitch_predictions.masked_select(mel_masks) + pitch_targets = pitch_targets.masked_select(mel_masks) + + if self.energy_feature_level == "phoneme_level": + energy_predictions = energy_predictions.masked_select(src_masks) + energy_targets = energy_targets.masked_select(src_masks) + if self.energy_feature_level == "frame_level": + energy_predictions = energy_predictions.masked_select(mel_masks) + energy_targets = energy_targets.masked_select(mel_masks) + + log_duration_predictions = log_duration_predictions.masked_select(src_masks) + log_duration_targets = log_duration_targets.masked_select(src_masks) + + mel_predictions = mel_predictions.masked_select(mel_masks.unsqueeze(-1)) + postnet_mel_predictions = postnet_mel_predictions.masked_select( + mel_masks.unsqueeze(-1) + ) + mel_targets = mel_targets.masked_select(mel_masks.unsqueeze(-1)) + + mel_loss = self.mae_loss(mel_predictions, mel_targets) + postnet_mel_loss = self.mae_loss(postnet_mel_predictions, mel_targets) + + pitch_loss = self.mse_loss(pitch_predictions, pitch_targets) + energy_loss = self.mse_loss(energy_predictions, energy_targets) + duration_loss = self.mse_loss(log_duration_predictions, log_duration_targets) + + total_loss = ( + mel_loss + postnet_mel_loss + duration_loss + pitch_loss + energy_loss + ) + + return { + "loss": total_loss, + "mel_loss": mel_loss, + "postnet_mel_loss": postnet_mel_loss, + "pitch_loss": pitch_loss, + "energy_loss": energy_loss, + "duration_loss": duration_loss, + } diff --git a/models/tts/fastspeech2/fs2_dataset.py b/models/tts/fastspeech2/fs2_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..9d019aee76766d19ed6b72b2e3345b36d84ed04d --- /dev/null +++ b/models/tts/fastspeech2/fs2_dataset.py @@ -0,0 +1,424 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import random +import torch +from torch.nn.utils.rnn import pad_sequence +from utils.data_utils import * +from models.base.base_dataset import ( + BaseCollator, + BaseDataset, + BaseTestDataset, + BaseTestCollator, +) +from text import text_to_sequence + + +class FS2Dataset(BaseDataset): + def __init__(self, cfg, dataset, is_valid=False): + BaseDataset.__init__(self, cfg, dataset, is_valid=is_valid) + self.batch_size = cfg.train.batch_size + cfg = cfg.preprocess + # utt2duration + self.utt2duration_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2duration_path[utt] = os.path.join( + cfg.processed_dir, + dataset, + cfg.duration_dir, + uid + ".npy", + ) + self.utt2dur = self.read_duration() + + if cfg.use_frame_energy: + self.frame_utt2energy, self.energy_statistic = load_energy( + self.metadata, + cfg.processed_dir, + cfg.energy_dir, + use_log_scale=cfg.use_log_scale_energy, + utt2spk=self.preprocess.utt2spk if cfg.use_spkid else None, + return_norm=True, + ) + elif cfg.use_phone_energy: + self.phone_utt2energy, self.energy_statistic = load_energy( + self.metadata, + cfg.processed_dir, + cfg.phone_energy_dir, + use_log_scale=cfg.use_log_scale_energy, + utt2spk=self.utt2spk if cfg.use_spkid else None, + return_norm=True, + ) + + if cfg.use_frame_pitch: + self.frame_utt2pitch, self.pitch_statistic = load_energy( + self.metadata, + cfg.processed_dir, + cfg.pitch_dir, + use_log_scale=cfg.energy_extract_mode, + utt2spk=self.utt2spk if cfg.use_spkid else None, + return_norm=True, + ) + + elif cfg.use_phone_pitch: + self.phone_utt2pitch, self.pitch_statistic = load_energy( + self.metadata, + cfg.processed_dir, + cfg.phone_pitch_dir, + use_log_scale=cfg.use_log_scale_pitch, + utt2spk=self.utt2spk if cfg.use_spkid else None, + return_norm=True, + ) + + # utt2lab + self.utt2lab_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2lab_path[utt] = os.path.join( + cfg.processed_dir, + dataset, + cfg.lab_dir, + uid + ".txt", + ) + + self.speaker_map = {} + if os.path.exists(os.path.join(cfg.processed_dir, "spk2id.json")): + with open( + os.path.exists(os.path.join(cfg.processed_dir, "spk2id.json")) + ) as f: + self.speaker_map = json.load(f) + + self.metadata = self.check_metadata() + + def __getitem__(self, index): + single_feature = BaseDataset.__getitem__(self, index) + + utt_info = self.metadata[index] + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + duration = self.utt2dur[utt] + + # text + f = open(self.utt2lab_path[utt], "r") + phones = f.readlines()[0].strip() + f.close() + # todo: add cleaner(chenxi) + phones_ids = np.array(text_to_sequence(phones, ["english_cleaners"])) + text_len = len(phones_ids) + + if self.cfg.preprocess.use_frame_pitch: + pitch = self.frame_utt2pitch[utt] + elif self.cfg.preprocess.use_phone_pitch: + pitch = self.phone_utt2pitch[utt] + + if self.cfg.preprocess.use_frame_energy: + energy = self.frame_utt2energy[utt] + elif self.cfg.preprocess.use_phone_energy: + energy = self.phone_utt2energy[utt] + + # speaker + if len(self.speaker_map) > 0: + speaker_id = self.speaker_map[utt_info["Singer"]] + else: + speaker_id = 0 + + single_feature.update( + { + "durations": duration, + "texts": phones_ids, + "spk_id": speaker_id, + "text_len": text_len, + "pitch": pitch, + "energy": energy, + "uid": uid, + } + ) + return self.clip_if_too_long(single_feature) + + def read_duration(self): + # read duration + utt2dur = {} + for index in range(len(self.metadata)): + utt_info = self.metadata[index] + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + if not os.path.exists(self.utt2mel_path[utt]) or not os.path.exists( + self.utt2duration_path[utt] + ): + continue + + mel = np.load(self.utt2mel_path[utt]).transpose(1, 0) + duration = np.load(self.utt2duration_path[utt]) + assert mel.shape[0] == sum( + duration + ), f"{utt}: mismatch length between mel {mel.shape[0]} and sum(duration) {sum(duration)}" + utt2dur[utt] = duration + return utt2dur + + def __len__(self): + return len(self.metadata) + + def random_select(self, feature_seq_len, max_seq_len, ending_ts=2812): + """ + ending_ts: to avoid invalid whisper features for over 30s audios + 2812 = 30 * 24000 // 256 + """ + ts = max(feature_seq_len - max_seq_len, 0) + ts = min(ts, ending_ts - max_seq_len) + + start = random.randint(0, ts) + end = start + max_seq_len + return start, end + + def clip_if_too_long(self, sample, max_seq_len=1000): + """ + sample : + { + 'spk_id': (1,), + 'target_len': int + 'mel': (seq_len, dim), + 'frame_pitch': (seq_len,) + 'frame_energy': (seq_len,) + 'content_vector_feat': (seq_len, dim) + } + """ + if sample["target_len"] <= max_seq_len: + return sample + + start, end = self.random_select(sample["target_len"], max_seq_len) + sample["target_len"] = end - start + + for k in sample.keys(): + if k not in ["spk_id", "target_len"]: + sample[k] = sample[k][start:end] + + return sample + + def check_metadata(self): + new_metadata = [] + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + if not os.path.exists(self.utt2duration_path[utt]) or not os.path.exists( + self.utt2mel_path[utt] + ): + continue + else: + new_metadata.append(utt_info) + return new_metadata + + +class FS2Collator(BaseCollator): + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, cfg): + BaseCollator.__init__(self, cfg) + self.sort = cfg.train.sort_sample + self.batch_size = cfg.train.batch_size + self.drop_last = cfg.train.drop_last + + def __call__(self, batch): + # mel: [b, T, n_mels] + # frame_pitch, frame_energy: [1, T] + # target_len: [1] + # spk_id: [b, 1] + # mask: [b, T, 1] + packed_batch_features = dict() + + for key in batch[0].keys(): + if key == "target_len": + packed_batch_features["target_len"] = torch.LongTensor( + [b["target_len"] for b in batch] + ) + masks = [ + torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "text_len": + packed_batch_features["text_len"] = torch.LongTensor( + [b["text_len"] for b in batch] + ) + masks = [ + torch.ones((b["text_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["text_mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "spk_id": + packed_batch_features["spk_id"] = torch.LongTensor( + [b["spk_id"] for b in batch] + ) + elif key == "uid": + packed_batch_features[key] = [b["uid"] for b in batch] + else: + values = [torch.from_numpy(b[key]) for b in batch] + packed_batch_features[key] = pad_sequence( + values, batch_first=True, padding_value=0 + ) + return packed_batch_features + + +class FS2TestDataset(BaseTestDataset): + def __init__(self, args, cfg, infer_type=None): + datasets = cfg.dataset + cfg = cfg.preprocess + is_bigdata = False + + assert len(datasets) >= 1 + if len(datasets) > 1: + datasets.sort() + bigdata_version = "_".join(datasets) + processed_data_dir = os.path.join(cfg.processed_dir, bigdata_version) + is_bigdata = True + else: + processed_data_dir = os.path.join(cfg.processed_dir, args.dataset) + + if args.test_list_file: + self.metafile_path = args.test_list_file + self.metadata = self.get_metadata() + else: + assert args.testing_set + source_metafile_path = os.path.join( + cfg.processed_dir, + args.dataset, + "{}.json".format(args.testing_set), + ) + with open(source_metafile_path, "r") as f: + self.metadata = json.load(f) + + self.cfg = cfg + self.datasets = datasets + self.data_root = processed_data_dir + self.is_bigdata = is_bigdata + self.source_dataset = args.dataset + + ######### Load source acoustic features ######### + if cfg.use_spkid: + spk2id_path = os.path.join(self.data_root, cfg.spk2id) + utt2sp_path = os.path.join(self.data_root, cfg.utt2spk) + self.spk2id, self.utt2spk = get_spk_map(spk2id_path, utt2sp_path, datasets) + + # utt2lab + self.utt2lab_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + self.utt2lab_path[utt] = os.path.join( + cfg.processed_dir, + dataset, + cfg.lab_dir, + uid + ".txt", + ) + + self.speaker_map = {} + if os.path.exists(os.path.join(cfg.processed_dir, "spk2id.json")): + with open( + os.path.exists(os.path.join(cfg.processed_dir, "spk2id.json")) + ) as f: + self.speaker_map = json.load(f) + + def __getitem__(self, index): + single_feature = {} + + utt_info = self.metadata[index] + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + # text + f = open(self.utt2lab_path[utt], "r") + phones = f.readlines()[0].strip() + f.close() + + phones_ids = np.array(text_to_sequence(phones, self.cfg.text_cleaners)) + text_len = len(phones_ids) + + # speaker + if len(self.speaker_map) > 0: + speaker_id = self.speaker_map[utt_info["Singer"]] + else: + speaker_id = 0 + + single_feature.update( + { + "texts": phones_ids, + "spk_id": speaker_id, + "text_len": text_len, + } + ) + + return single_feature + + def __len__(self): + return len(self.metadata) + + def get_metadata(self): + with open(self.metafile_path, "r", encoding="utf-8") as f: + metadata = json.load(f) + + return metadata + + +class FS2TestCollator(BaseTestCollator): + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, cfg): + self.cfg = cfg + + def __call__(self, batch): + packed_batch_features = dict() + + # mel: [b, T, n_mels] + # frame_pitch, frame_energy: [1, T] + # target_len: [1] + # spk_id: [b, 1] + # mask: [b, T, 1] + + for key in batch[0].keys(): + if key == "target_len": + packed_batch_features["target_len"] = torch.LongTensor( + [b["target_len"] for b in batch] + ) + masks = [ + torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "text_len": + packed_batch_features["text_len"] = torch.LongTensor( + [b["text_len"] for b in batch] + ) + masks = [ + torch.ones((b["text_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["text_mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "spk_id": + packed_batch_features["spk_id"] = torch.LongTensor( + [b["spk_id"] for b in batch] + ) + else: + values = [torch.from_numpy(b[key]) for b in batch] + packed_batch_features[key] = pad_sequence( + values, batch_first=True, padding_value=0 + ) + + return packed_batch_features diff --git a/models/tts/fastspeech2/fs2_inference.py b/models/tts/fastspeech2/fs2_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..d864c9f367e6214f19b326177940a1d3f06f4a97 --- /dev/null +++ b/models/tts/fastspeech2/fs2_inference.py @@ -0,0 +1,119 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import torch +from tqdm import tqdm +from collections import OrderedDict + +from models.tts.base.tts_inferece import TTSInference +from models.tts.fastspeech2.fs2_dataset import FS2TestDataset, FS2TestCollator +from utils.util import load_config +from utils.io import save_audio +from models.tts.fastspeech2.fs2 import FastSpeech2 +from models.vocoders.vocoder_inference import synthesis +from pathlib import Path + + +class FastSpeech2Inference(TTSInference): + def __init__(self, args, cfg): + TTSInference.__init__(self, args, cfg) + self.args = args + self.cfg = cfg + self.infer_type = args.mode + + def _build_model(self): + self.model = FastSpeech2(self.cfg) + return self.model + + def load_model(self, state_dict): + raw_dict = state_dict["model"] + clean_dict = OrderedDict() + for k, v in raw_dict.items(): + if k.startswith("module."): + clean_dict[k[7:]] = v + else: + clean_dict[k] = v + + self.model.load_state_dict(clean_dict) + + def _build_test_dataset(self): + return FS2TestDataset, FS2TestCollator + + @staticmethod + def _parse_vocoder(vocoder_dir): + r"""Parse vocoder config""" + vocoder_dir = os.path.abspath(vocoder_dir) + ckpt_list = [ckpt for ckpt in Path(vocoder_dir).glob("*.pt")] + # last step (different from the base *int(x.stem)*) + ckpt_list.sort( + key=lambda x: int(x.stem.split("_")[-2].split("-")[-1]), reverse=True + ) + ckpt_path = str(ckpt_list[0]) + vocoder_cfg = load_config( + os.path.join(vocoder_dir, "args.json"), lowercase=True + ) + return vocoder_cfg, ckpt_path + + @torch.inference_mode() + def inference(self): + y_pred = [] + for i, batch in tqdm(enumerate(self.test_dataloader)): + y_pred, mel_lens, _ = self._inference_each_batch(batch) + y_ls = y_pred.chunk(self.test_batch_size) + tgt_ls = mel_lens.chunk(self.test_batch_size) + j = 0 + for it, l in zip(y_ls, tgt_ls): + l = l.item() + it = it.squeeze(0)[:l].detach().cpu() + + uid = self.test_dataset.metadata[i * self.test_batch_size + j]["Uid"] + torch.save(it, os.path.join(self.args.output_dir, f"{uid}.pt")) + j += 1 + + vocoder_cfg, vocoder_ckpt = self._parse_vocoder(self.args.vocoder_dir) + res = synthesis( + cfg=vocoder_cfg, + vocoder_weight_file=vocoder_ckpt, + n_samples=None, + pred=[ + torch.load( + os.path.join(self.args.output_dir, "{}.pt".format(item["Uid"])) + ).numpy() + for item in self.test_dataset.metadata + ], + ) + for it, wav in zip(self.test_dataset.metadata, res): + uid = it["Uid"] + save_audio( + os.path.join(self.args.output_dir, f"{uid}.wav"), + wav.numpy(), + self.cfg.preprocess.sample_rate, + add_silence=True, + turn_up=True, + ) + os.remove(os.path.join(self.args.output_dir, f"{uid}.pt")) + + def _inference_each_batch(self, batch_data): + device = self.accelerator.device + control_values = ( + self.args.pitch_control, + self.args.energy_control, + self.args.duration_control, + ) + for k, v in batch_data.items(): + batch_data[k] = v.to(device) + + pitch_control, energy_control, duration_control = control_values + + output = self.model( + batch_data, + p_control=pitch_control, + e_control=energy_control, + d_control=duration_control, + ) + pred_res = output["postnet_output"] + mel_lens = output["mel_lens"].cpu() + return pred_res, mel_lens, 0 diff --git a/models/tts/fastspeech2/fs2_trainer.py b/models/tts/fastspeech2/fs2_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..f0bc1201b192e30339c8f6e877467cc6b1f7a938 --- /dev/null +++ b/models/tts/fastspeech2/fs2_trainer.py @@ -0,0 +1,155 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +from tqdm import tqdm +from models.tts.base import TTSTrainer +from models.tts.fastspeech2.fs2 import FastSpeech2, FastSpeech2Loss +from models.tts.fastspeech2.fs2_dataset import FS2Dataset, FS2Collator +from optimizer.optimizers import NoamLR + + +class FastSpeech2Trainer(TTSTrainer): + def __init__(self, args, cfg): + TTSTrainer.__init__(self, args, cfg) + self.cfg = cfg + + def _build_dataset(self): + return FS2Dataset, FS2Collator + + def __build_scheduler(self): + return NoamLR(self.optimizer, **self.cfg.train.lr_scheduler) + + def _write_summary(self, losses, stats): + for key, value in losses.items(): + self.sw.add_scalar("train/" + key, value, self.step) + lr = self.optimizer.state_dict()["param_groups"][0]["lr"] + self.sw.add_scalar("learning_rate", lr, self.step) + + def _write_valid_summary(self, losses, stats): + for key, value in losses.items(): + self.sw.add_scalar("val/" + key, value, self.step) + + def _build_criterion(self): + return FastSpeech2Loss(self.cfg) + + def get_state_dict(self): + state_dict = { + "model": self.model.state_dict(), + "optimizer": self.optimizer.state_dict(), + "scheduler": self.scheduler.state_dict(), + "step": self.step, + "epoch": self.epoch, + "batch_size": self.cfg.train.batch_size, + } + return state_dict + + def _build_optimizer(self): + optimizer = torch.optim.Adam(self.model.parameters(), **self.cfg.train.adam) + return optimizer + + def _build_scheduler(self): + scheduler = NoamLR(self.optimizer, **self.cfg.train.lr_scheduler) + return scheduler + + def _build_model(self): + self.model = FastSpeech2(self.cfg) + return self.model + + def _train_epoch(self): + r"""Training epoch. Should return average loss of a batch (sample) over + one epoch. See ``train_loop`` for usage. + """ + self.model.train() + epoch_sum_loss: float = 0.0 + epoch_step: int = 0 + epoch_losses: dict = {} + for batch in tqdm( + self.train_dataloader, + desc=f"Training Epoch {self.epoch}", + unit="batch", + colour="GREEN", + leave=False, + dynamic_ncols=True, + smoothing=0.04, + disable=not self.accelerator.is_main_process, + ): + # Do training step and BP + with self.accelerator.accumulate(self.model): + loss, train_losses = self._train_step(batch) + self.accelerator.backward(loss) + grad_clip_thresh = self.cfg.train.grad_clip_thresh + nn.utils.clip_grad_norm_(self.model.parameters(), grad_clip_thresh) + self.optimizer.step() + self.scheduler.step() + self.optimizer.zero_grad() + self.batch_count += 1 + + # Update info for each step + if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: + epoch_sum_loss += loss + for key, value in train_losses.items(): + if key not in epoch_losses.keys(): + epoch_losses[key] = value + else: + epoch_losses[key] += value + + self.accelerator.log( + { + "Step/Train Loss": loss, + "Step/Learning Rate": self.optimizer.param_groups[0]["lr"], + }, + step=self.step, + ) + self.step += 1 + epoch_step += 1 + + self.accelerator.wait_for_everyone() + + epoch_sum_loss = ( + epoch_sum_loss + / len(self.train_dataloader) + * self.cfg.train.gradient_accumulation_step + ) + + for key in epoch_losses.keys(): + epoch_losses[key] = ( + epoch_losses[key] + / len(self.train_dataloader) + * self.cfg.train.gradient_accumulation_step + ) + return epoch_sum_loss, epoch_losses + + def _train_step(self, data): + train_losses = {} + total_loss = 0 + train_stats = {} + + preds = self.model(data) + + train_losses = self.criterion(data, preds) + + total_loss = train_losses["loss"] + for key, value in train_losses.items(): + train_losses[key] = value.item() + + return total_loss, train_losses + + @torch.no_grad() + def _valid_step(self, data): + valid_loss = {} + total_valid_loss = 0 + valid_stats = {} + + preds = self.model(data) + + valid_losses = self.criterion(data, preds) + + total_valid_loss = valid_losses["loss"] + for key, value in valid_losses.items(): + valid_losses[key] = value.item() + + return total_valid_loss, valid_losses, valid_stats diff --git a/models/tts/valle/__init__.py b/models/tts/valle/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/tts/valle/__pycache__/__init__.cpython-39.pyc b/models/tts/valle/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..afe47a1026b693468ec1dcf96a2d02f24790d552 Binary files /dev/null and b/models/tts/valle/__pycache__/__init__.cpython-39.pyc differ diff --git a/models/tts/valle/__pycache__/valle.cpython-39.pyc b/models/tts/valle/__pycache__/valle.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2537bc66f8a882524b64f9e81c0618f28c4708f0 Binary files /dev/null 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/dev/null and b/models/tts/valle/__pycache__/valle_trainer.cpython-39.pyc differ diff --git a/models/tts/valle/valle.py b/models/tts/valle/valle.py new file mode 100644 index 0000000000000000000000000000000000000000..184ae84861c1f61d7e8121a5358f36731b037c44 --- /dev/null +++ b/models/tts/valle/valle.py @@ -0,0 +1,822 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This code is modified from https://github.com/lifeiteng/vall-e/blob/main/valle/models/valle.py + +import random +from typing import Dict, Iterator, List, Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchmetrics.classification import MulticlassAccuracy +from utils.util import make_pad_mask +from utils.topk_sampling import topk_sampling +from modules.general import Transpose +from modules.encoder import TokenEmbedding +from modules.general import PromptedFeatures +from modules.transformer import SinePositionalEmbedding +from modules.norms import AdaptiveLayerNorm, LayerNorm +from modules.transformer.transformer import ( + TransformerEncoder, + TransformerEncoderLayer +) + +class VALLE(nn.Module): + + def __init__( + self, + cfg, + decoder_cls=TransformerEncoder, + decoder_layer_cls=TransformerEncoderLayer + ): + super().__init__() + decoder_dim = cfg.decoder_dim + nhead = cfg.nhead + nar_scale_factor = cfg.nar_scale_factor + num_quantizers = cfg.num_quantizers + num_decoder_layers = cfg.num_decoder_layers + nar_decoder_dim = int(decoder_dim * nar_scale_factor) + + self.ar_text_embedding = TokenEmbedding(decoder_dim, cfg.text_token_num) + self.nar_text_embedding = TokenEmbedding(nar_decoder_dim, cfg.text_token_num) + + self.ar_audio_prepend_bos = cfg.prepend_bos + self.ar_audio_embedding = TokenEmbedding( + decoder_dim, cfg.audio_token_num + 1 + int(cfg.prepend_bos) + ) + self.audio_token_num = cfg.audio_token_num + + # PreNet of AR + if cfg.add_prenet: + self.ar_text_prenet = nn.Sequential( + Transpose(), + nn.Conv1d(decoder_dim, decoder_dim, kernel_size=5, padding="same"), + nn.BatchNorm1d(decoder_dim), + nn.ReLU(), + nn.Dropout(0.5), + nn.Conv1d(decoder_dim, decoder_dim, kernel_size=5, padding="same"), + nn.BatchNorm1d(decoder_dim), + nn.ReLU(), + nn.Dropout(0.5), + nn.Conv1d(decoder_dim, decoder_dim, kernel_size=5, padding="same"), + nn.BatchNorm1d(decoder_dim), + nn.ReLU(), + nn.Dropout(0.5), + Transpose(), + nn.Linear(decoder_dim, decoder_dim), + ) + + self.ar_audio_prenet = nn.Sequential( + nn.Linear(decoder_dim, 256), + nn.ReLU(), + nn.Dropout(0.25), + nn.Linear(256, 256), + nn.ReLU(), + nn.Dropout(0.25), + nn.Linear(256, decoder_dim), + ) + else: + self.ar_text_prenet = nn.Identity() + self.ar_audio_prenet = nn.Identity() + + self.ar_text_position = SinePositionalEmbedding( + decoder_dim, + dropout=0.1, + scale=False, + alpha=True, + ) + self.ar_audio_position = SinePositionalEmbedding( + decoder_dim, + dropout=0.1, + scale=False, + alpha=True, + ) + + self.ar_decoder = decoder_cls( + decoder_layer_cls( + decoder_dim, + nhead, + dim_feedforward=decoder_dim * 4, # *4? + dropout=0.1, + batch_first=True, + norm_first=cfg.norm_first, + ), + num_layers=num_decoder_layers, + norm=LayerNorm(decoder_dim) if cfg.norm_first else None, + ) + self.ar_predict_layer = nn.Linear( + decoder_dim, cfg.audio_token_num + 1, bias=False + ) + + self.ar_accuracy_metric = MulticlassAccuracy( + cfg.audio_token_num + 1, + top_k=10, + average="micro", + multidim_average="global", + ignore_index=cfg.audio_token_num, + ) + + self.rng = random.Random(0) + self.num_heads = nhead + self.prefix_mode = cfg.prefix_mode + self.num_quantizers = num_quantizers + + assert num_quantizers >= 1 + if num_quantizers > 1: + self.nar_audio_embeddings = nn.ModuleList( + [TokenEmbedding(nar_decoder_dim, cfg.audio_token_num + 1)] # Why the first layer is audio_token_num + 1? + + [ + TokenEmbedding(nar_decoder_dim, cfg.audio_token_num) + for i in range(num_quantizers - 1) + ] + ) + + if cfg.add_prenet: + self.nar_text_prenet = nn.Sequential( + Transpose(), + nn.Conv1d( + nar_decoder_dim, nar_decoder_dim, kernel_size=5, padding="same" + ), + nn.BatchNorm1d(nar_decoder_dim), + nn.ReLU(), + nn.Dropout(0.5), + nn.Conv1d( + nar_decoder_dim, nar_decoder_dim, kernel_size=5, padding="same" + ), + nn.BatchNorm1d(nar_decoder_dim), + nn.ReLU(), + nn.Dropout(0.5), + nn.Conv1d( + nar_decoder_dim, nar_decoder_dim, kernel_size=5, padding="same" + ), + nn.BatchNorm1d(nar_decoder_dim), + nn.ReLU(), + nn.Dropout(0.5), + Transpose(), + nn.Linear(nar_decoder_dim, nar_decoder_dim), + ) + self.nar_audio_prenet = nn.Sequential( + nn.Linear(nar_decoder_dim, 256), + nn.ReLU(), + nn.Dropout(0.25), + nn.Linear(256, 256), + nn.ReLU(), + nn.Dropout(0.25), + nn.Linear(256, nar_decoder_dim), + ) + else: + self.nar_text_prenet = nn.Identity() + self.nar_audio_prenet = nn.Identity() + + self.nar_text_position = SinePositionalEmbedding( + nar_decoder_dim, + dropout=0.0, + scale=False, + alpha=False, + ) + self.nar_audio_position = SinePositionalEmbedding( + nar_decoder_dim, + dropout=0.1, + scale=False, + alpha=False, + ) + + self.nar_decoder = decoder_cls( + decoder_layer_cls( + nar_decoder_dim, + int(nhead * nar_scale_factor), + dim_feedforward=nar_decoder_dim * 4, + dropout=0.1, + batch_first=True, + norm_first=cfg.norm_first, + adaptive_layer_norm=True, + ), + num_layers=int(num_decoder_layers * nar_scale_factor), + norm=AdaptiveLayerNorm( + nar_decoder_dim, norm=nn.LayerNorm(nar_decoder_dim) + ) + if cfg.norm_first + else None, + ) + self.nar_predict_layers = nn.ModuleList( + [ + nn.Linear(nar_decoder_dim, cfg.audio_token_num, bias=False) + for i in range(num_quantizers - 1) + ] + ) + self.nar_stage_embeddings = nn.ModuleList( + [ + TokenEmbedding(nar_decoder_dim, 1) + for i in range(num_quantizers - 1) + ] + ) + + if cfg.share_embedding: + for j in range(0, num_quantizers - 2): + self.nar_predict_layers[ + j + ].weight = self.nar_audio_embeddings[j + 2].weight + + self.nar_accuracy_metric = MulticlassAccuracy( + cfg.audio_token_num + 1, + top_k=10, + average="micro", + multidim_average="global", + ignore_index=cfg.audio_token_num, + ) + + + def forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + y: Union[torch.Tensor, PromptedFeatures], + y_lens: Union[torch.Tensor, PromptedFeatures], + reduction: str = "sum", + train_stage: int = 0, + **kwargs, + ) -> Tuple[torch.Tensor, Union[torch.Tensor, None]]: + + """ + Args: + x: + A 2-D tensor of shape (N, S). + x_lens: + A 1-D tensor of shape (N,). It contains the number of tokens in `x` + before padding. + y: + A 3-D tensor of shape (N, T, 8). + y_lens: + A 1-D tensor of shape (N,). It contains the number of tokens in `x` + before padding. + train_stage: + 0: AR & NAR modules, 1: AR modules, 2: NAR modules + Returns: + Return the predicted audio code matrix, cross-entropy loss and Top-10 accuracy. + """ + assert x.ndim == 2, x.shape + assert x_lens.ndim == 1, x_lens.shape + + y_prompts_codes = None + if isinstance(y, PromptedFeatures): + y_prompts_codes, y = y.data + prompts_len, y_lens = y_lens.data + assert prompts_len.min() == prompts_len.max() + assert self.prefix_mode == 4 + y_prompts_codes = y_prompts_codes.type(torch.int64) + + assert y.ndim == 3, y.shape + assert y_lens.ndim == 1, y_lens.shape + + x_mask = make_pad_mask(x_lens).to(x.device) + y_mask = make_pad_mask(y_lens).to(y.device) + y_mask_int = y_mask.type(torch.int64) + + text = x + codes = y.type(torch.int64) * (1 - y_mask_int.unsqueeze(dim=-1)) + + y, targets = self.pad_y_eos( + codes[..., 0], y_mask_int, eos_id=self.audio_token_num + ) + self.y_mask_int = y_mask_int + + metrics = {} + total_loss = 0.0 + + xy_padding_mask = torch.concat([x_mask, y_mask], dim=1) + if self.ar_audio_prepend_bos: + ar_xy_padding_mask = torch.concat( + [x_mask, F.pad(y_mask, (1, 0), value=False)], dim=1 + ) + else: + ar_xy_padding_mask = xy_padding_mask + self.xy_padding_mask = xy_padding_mask + self.ar_xy_padding_mask = ar_xy_padding_mask + + # AR Decoder + if train_stage in [0, 1]: + ar_loss, ar_metrics = self._forward_ar_decoder( + text, x_lens.max(), y, y_lens.max(), targets, x_mask, y_mask, reduction + ) + total_loss += ar_loss + metrics["AR_Top100Acc"] = ar_metrics + + + # NAR Decoder + if self.ar_audio_prepend_bos: + y = y[:, 1:] + + if self.num_quantizers > 1 and train_stage in [0, 2]: + nar_loss, nar_metrics = self._forward_nar_decoder( + text, x_lens, y, y_lens, codes, y_prompts_codes, x_mask, y_mask, reduction + ) + total_loss += nar_loss + metrics["NAR_Top100Acc"] = nar_metrics + + if train_stage == 0: + total_loss = total_loss / 2.0 + + return total_loss, metrics + + + + def _forward_ar_decoder( + self, x, x_len, y, y_lens, targets, x_mask, y_mask, reduction + ): + x = self.ar_text_embedding(x) + x = self.ar_text_prenet(x) + x = self.ar_text_position(x) + + y_len = y_lens.max() + int(self.ar_audio_prepend_bos) + + x_attn_mask = F.pad( + torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device), + (0, y_len), + value=True, + ) + y_attn_mask = F.pad( + torch.triu( + torch.ones(y_len, y_len, dtype=torch.bool, device=x.device), + diagonal=1, + ), + (x_len, 0), + value=False, + ) + xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0) + + bsz, src_len = x.shape[0], x_len + y_len + _xy_padding_mask = ( + self.ar_xy_padding_mask.view(bsz, 1, 1, src_len) + .expand(-1, self.num_heads, -1, -1) + .reshape(bsz * self.num_heads, 1, src_len) + ) + xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask) + + new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype) + new_attn_mask.masked_fill_(xy_attn_mask, float("-inf")) + xy_attn_mask = new_attn_mask + + y_emb = self.ar_audio_embedding(y) + y_emb = self.ar_audio_prenet(y_emb) + y_pos = self.ar_audio_position(y_emb) + + xy_pos = torch.concat([x, y_pos], dim=1) + + xy_dec, _ = self.ar_decoder( + (xy_pos, None), + mask=xy_attn_mask, + ) + logits = self.ar_predict_layer(xy_dec[:, x_len:]).permute(0, 2, 1) + ar_loss = F.cross_entropy(logits, targets, reduction=reduction) + + ar_metrics = self.ar_accuracy_metric( + logits.detach(), targets + ).item() * y_lens.sum().type(torch.float32) + + return ar_loss, ar_metrics + + def _forward_nar_decoder( + self, x, x_lens, y, y_lens, codes, y_prompts_codes, x_mask, y_mask, reduction + ): + + num_nar_layers = self.num_quantizers - 1 + nar_stage = self.rng.choices( + [_k for _k in range(1, self.num_quantizers)], + weights=[1.0 / num_nar_layers] * num_nar_layers, + k=1, + )[0] + + x = self.nar_text_embedding(x) + x = self.nar_text_prenet(x) + x = self.nar_text_position(x) + + y_emb, prefix_len = self._prepare_prompts( + y, y_lens, codes, nar_stage, y_prompts_codes + ) + + + y_len = y_lens.max() + targets = codes[..., nar_stage] + self.audio_token_num * self.y_mask_int + if self.prefix_mode in [2, 4]: + xy_padding_mask = torch.concat( + [ + x_mask, + F.pad(y_mask, (y_emb.shape[1] - y_len, 0), value=False), + ], + dim=1, + ) + elif self.prefix_mode == 1: + targets = targets[:, prefix_len:] + + y_pos = self.nar_audio_prenet(y_emb) + y_pos = self.nar_audio_position(y_pos) + xy_pos = torch.concat([x, y_pos], dim=1) + xy_dec, _ = self.nar_decoder( + (xy_pos, self.nar_stage_embeddings[nar_stage - 1].weight), + src_key_padding_mask=self.xy_padding_mask, + ) + xy_dec = xy_dec[:, x_lens.max() + prefix_len :] + if self.prefix_mode == 4: + prefix_len = 0 + logits = self.nar_predict_layers[nar_stage - 1](xy_dec).permute( + 0, 2, 1 + ) + + total_length = (y_lens).sum().type(torch.float32) + nar_loss = ( + F.cross_entropy( + logits, + targets, + ignore_index=self.audio_token_num, + reduction=reduction, + ) + * (total_length / (total_length - prefix_len * x.shape[0])) + ) + nar_metrics = ( + self.nar_accuracy_metric( + F.pad( + logits.detach(), + (0, 0, 0, 1, 0, 0), + value=logits.min().cpu().item(), + ), + targets, + ).item() + * total_length + ) + return nar_loss, nar_metrics + + def inference( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + y: torch.Tensor, + enroll_x_lens: torch.Tensor, + top_k: int = -100, + temperature: float = 1.0, + ) -> torch.Tensor: + """ + Args: + x: + A 2-D tensor of shape (1, S). + x_lens: + A 1-D tensor of shape (1,). It contains the number of tokens in `x` + before padding. + y: + A 3-D tensor of shape (1, T, 8). + top_k: (`optional`) int + The number of highest probability tokens to keep for top-k-filtering. Default to -100. + temperature: (`optional`) float + The value used to module the next token probabilities. Must be strictly positive. Default to 1.0. + Returns: + Return the predicted audio code matrix. + """ + assert x.ndim == 2, x.shape + assert x_lens.ndim == 1, x_lens.shape + assert y.ndim == 3, y.shape + assert y.shape[0] == 1, y.shape + + assert torch.all(x_lens > 0) + + text = x + x = self.ar_text_embedding(text) + x = self.ar_text_prenet(x) + x = self.ar_text_position(x) + + text_len = x_lens.max() + prompts = y + prefix_len = y.shape[1] + + # AR Decoder + y = prompts[..., 0] + if self.ar_audio_prepend_bos: + y = F.pad(y, (1, 0), value=self.audio_token_num + 1) + + x_len = x_lens.max() + x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool) + + while True: + y_emb = self.ar_audio_embedding(y) + y_emb = self.ar_audio_prenet(y_emb) + y_pos = self.ar_audio_position(y_emb) + xy_pos = torch.concat([x, y_pos], dim=1) + + y_len = y.shape[1] + x_attn_mask_pad = F.pad( + x_attn_mask, + (0, y_len), + value=True, + ) + y_attn_mask = F.pad( + torch.triu( + torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1 + ), + (x_len, 0), + value=False, + ) + xy_attn_mask = torch.concat( + [x_attn_mask_pad, y_attn_mask], dim=0 + ).to(y.device) + + xy_dec, _ = self.ar_decoder( + (xy_pos, None), + mask=xy_attn_mask, + ) + logits = self.ar_predict_layer(xy_dec[:, -1]) + samples = topk_sampling( + logits, top_k=top_k, top_p=1.0, temperature=temperature + ) + + if ( + torch.argmax(logits, dim=-1)[0] == self.audio_token_num + or samples[0, 0] == self.audio_token_num + or (y.shape[1] - prompts.shape[1]) > x_lens.max() * 16 + ): + if prompts.shape[1] == y.shape[1]: + raise SyntaxError( + "well trained model shouldn't reach here." + ) + + break + + y = torch.concat([y, samples], dim=1) + + codes = [y[:, prefix_len + int(self.ar_audio_prepend_bos) :]] + if self.num_quantizers == 1: + return torch.stack(codes, dim=-1) + + # Non-AR Decoders + y_emb = self.nar_audio_embeddings[0]( + y[:, int(self.ar_audio_prepend_bos) :] + ) + + if self.prefix_mode in [2, 4]: + enrolled_len = enroll_x_lens.max().item() + # SOS + Synthesis Text + EOS + text = torch.concat( + [ + text[:, :1], + text[:, enrolled_len - 1 :], + ], + dim=1, + ) + text_len = text_len - (enrolled_len - 2) + assert text.shape[0] == 1 + + x = self.nar_text_embedding(text) + x = self.nar_text_prenet(x) + x = self.nar_text_position(x) + + if self.prefix_mode == 0: + for i, (predict_layer, embedding_layer) in enumerate( + zip( + self.nar_predict_layers, + self.nar_audio_embeddings[1:], + ) + ): + y_pos = self.nar_audio_prenet(y_emb) + y_pos = self.nar_audio_position(y_pos) + xy_pos = torch.concat([x, y_pos], dim=1) + + xy_dec, _ = self.nar_decoder( + (xy_pos, self.nar_stage_embeddings[i].weight) + ) + logits = predict_layer(xy_dec[:, text_len + prefix_len :]) + + samples = torch.argmax(logits, dim=-1) + codes.append(samples) + + if i < self.num_quantizers - 2: + y_emb[:, :prefix_len] += embedding_layer( + prompts[..., i + 1] + ) + y_emb[:, prefix_len:] += embedding_layer(samples) + else: + for j in range(1, self.num_quantizers): + y_emb[:, :prefix_len] += self.nar_audio_embeddings[j]( + prompts[..., j] + ) + + for i, (predict_layer, embedding_layer) in enumerate( + zip( + self.nar_predict_layers, + self.nar_audio_embeddings[1:], + ) + ): + y_pos = self.nar_audio_prenet(y_emb) + y_pos = self.nar_audio_position(y_pos) + xy_pos = torch.concat([x, y_pos], dim=1) + + xy_dec, _ = self.nar_decoder( + (xy_pos, self.nar_stage_embeddings[i].weight) + ) + logits = predict_layer(xy_dec[:, text_len + prefix_len :]) + + samples = torch.argmax(logits, dim=-1) + codes.append(samples) + + if i < self.num_quantizers - 2: + y_emb[:, prefix_len:] += embedding_layer(samples) + + assert len(codes) == self.num_quantizers + return torch.stack(codes, dim=-1) + + def continual( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + y: torch.Tensor, + ) -> torch.Tensor: + """ + Args: + x: + A 2-D tensor of shape (1, S). + x_lens: + A 1-D tensor of shape (1,). It contains the number of tokens in `x` + before padding. + y: + A 3-D tensor of shape (1, T, 8). + Returns: + Return the predicted audio code matrix. + """ + assert x.ndim == 2, x.shape + assert x_lens.ndim == 1, x_lens.shape + assert y.ndim == 3, y.shape + assert y.shape[0] == 1, y.shape + + assert torch.all(x_lens > 0) + assert self.num_quantizers == 8 + + text = x + x = self.ar_text_embedding(text) + x = self.ar_text_prenet(x) + x = self.ar_text_position(x) + + text_len = x_lens.max() + + prefix_len = min(int(y.shape[1] * 0.5), 3 * 75) + + # AR Decoder + prompts = y[:, :prefix_len] + + codes = [y[:, prefix_len:, 0]] + # Non-AR Decoders + x = self.nar_text_embedding(text) + x = self.nar_text_prenet(x) + x = self.nar_text_position(x) + + y_emb = self.nar_audio_embeddings[0](y[..., 0]) + + if self.prefix_mode == 0: + for i, (predict_layer, embedding_layer) in enumerate( + zip( + self.nar_predict_layers, + self.nar_audio_embeddings[1:], + ) + ): + y_pos = self.nar_audio_position(y_emb) + y_pos = self.nar_audio_prenet(y_pos) + xy_pos = torch.concat([x, y_pos], dim=1) + + xy_dec, _ = self.nar_decoder( + (xy_pos, self.nar_stage_embeddings[i].weight) + ) + logits = predict_layer(xy_dec[:, text_len + prefix_len :]) + + samples = torch.argmax(logits, dim=-1) + codes.append(samples) + + if i < 6: + y_emb[:, :prefix_len] += embedding_layer( + prompts[..., i + 1] + ) + y_emb[:, prefix_len:] += embedding_layer(samples) + else: + for j in range(1, 8): + y_emb[:, :prefix_len] += self.nar_audio_embeddings[j]( + prompts[..., j] + ) + + for i, (predict_layer, embedding_layer) in enumerate( + zip( + self.nar_predict_layers, + self.nar_audio_embeddings[1:], + ) + ): + y_pos = self.nar_audio_prenet(y_emb) + y_pos = self.nar_audio_position(y_pos) + xy_pos = torch.concat([x, y_pos], dim=1) + + xy_dec, _ = self.nar_decoder( + (xy_pos, self.nar_stage_embeddings[i].weight) + ) + logits = predict_layer(xy_dec[:, text_len + prefix_len :]) + + samples = torch.argmax(logits, dim=-1) + codes.append(samples) + + if i < 6: + y_emb[:, prefix_len:] += embedding_layer(samples) + + assert len(codes) == 8 + return torch.stack(codes, dim=-1) + + def stage_parameters(self, stage: int = 1) -> Iterator[nn.Parameter]: + assert stage > 0 + if stage == 1: + for name, param in self.named_parameters(): + if name.startswith("ar_"): + yield param + + if stage == 2: + for name, param in self.named_parameters(): + if name.startswith("nar_"): + yield param + + def stage_named_parameters( + self, stage: int = 1 + ) -> Iterator[Tuple[str, nn.Parameter]]: + assert stage > 0 + if stage == 1: + for pair in self.named_parameters(): + if pair[0].startswith("ar_"): + yield pair + + if stage == 2: + for pair in self.named_parameters(): + if pair[0].startswith("nar_"): + yield pair + + def pad_y_eos(self, y, y_mask_int, eos_id): + targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad( + y_mask_int, (0, 1), value=1 + ) + if self.ar_audio_prepend_bos: + return ( + F.pad(targets[:, :-1], (1, 0), value=self.audio_token_num + 1), + targets, + ) + + return targets[:, :-1], targets[:, 1:] + + def _prepare_prompts(self, y, y_lens, codes, nar_stage, y_prompts_codes): + # 5.1 For the NAR acoustic prompt tokens, we select a random segment waveform of 3 seconds + # from the same utterance. + # We implement this differently. + if self.prefix_mode == 0: + # no prefix + prefix_len = 0 + y_emb = self.nar_audio_embeddings[0](y) + for j in range(1, nar_stage): + # Formula (4) (5) + y_emb = y_emb + self.nar_audio_embeddings[j](codes[..., j]) + elif self.prefix_mode == 1: + # prefix at begining + int_low = (0.25 * y_lens.min()).type(torch.int64).item() + prefix_len = torch.randint(int_low, int_low * 2, size=()).item() + prefix_len = min(prefix_len, 225) # 24000/320 * 3s = 225 frames + + y_prompts = self.nar_audio_embeddings[0](y[:, :prefix_len]) + y_emb = self.nar_audio_embeddings[0](y[:, prefix_len:]) + for j in range(1, self.num_quantizers): + y_prompts += self.nar_audio_embeddings[j]( + codes[:, :prefix_len, j] + ) + if j < nar_stage: + y_emb += self.nar_audio_embeddings[j]( + codes[:, prefix_len:, j] + ) + y_emb = torch.concat([y_prompts, y_emb], axis=1) + elif self.prefix_mode in [2, 4]: + if self.prefix_mode == 2: + # random prefix + prefix_len = min(225, int(0.25 * y_lens.min().item())) + + y_prompts_codes = [] + for b in range(codes.shape[0]): + start = self.rng.randint(0, y_lens[b].item() - prefix_len) + y_prompts_codes.append( + torch.clone(codes[b, start : start + prefix_len]) + ) + codes[ + b, start : start + prefix_len, nar_stage + ] = NUM_AUDIO_TOKENS + y_prompts_codes = torch.stack(y_prompts_codes, dim=0) + else: + prefix_len = y_prompts_codes.shape[1] + + y_prompts = self.nar_audio_embeddings[0](y_prompts_codes[..., 0]) + y_emb = self.nar_audio_embeddings[0](y) + for j in range(1, self.num_quantizers): + y_prompts += self.nar_audio_embeddings[j]( + y_prompts_codes[..., j] + ) + if j < nar_stage: + y_emb += self.nar_audio_embeddings[j](codes[..., j]) + y_emb = torch.concat([y_prompts, y_emb], axis=1) + else: + raise ValueError + + return y_emb, prefix_len diff --git a/models/tts/valle/valle_dataset.py b/models/tts/valle/valle_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..0c6ad456e3a821080164b1ad42b3e8682d039b46 --- /dev/null +++ b/models/tts/valle/valle_dataset.py @@ -0,0 +1,197 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch.nn.utils.rnn import pad_sequence +from utils.data_utils import * +from models.tts.base.tts_dataset import ( + TTSDataset, + TTSCollator, + TTSTestDataset, + TTSTestCollator, +) + +from utils.tokenizer import tokenize_audio + +class VALLEDataset(TTSDataset): + def __init__(self, cfg, dataset, is_valid=False): + super().__init__(cfg, dataset, is_valid=is_valid) + + """ + Args: + cfg: config + dataset: dataset name + is_valid: whether to use train or valid dataset + """ + + assert isinstance(dataset, str) + + assert cfg.preprocess.use_acoustic_token == True + if cfg.preprocess.use_acoustic_token: + self.utt2acousticToken_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2acousticToken_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.acoustic_token_dir, # code + uid + ".npy", + ) + + + def __len__(self): + return super().__len__() + + def get_metadata(self): + metadata_filter = [] + with open(self.metafile_path, "r", encoding="utf-8") as f: + metadata = json.load(f) + for utt_info in metadata: + duration = utt_info['Duration'] + if duration >= self.cfg.preprocess.max_duration or duration <= self.cfg.preprocess.min_duration: + continue + metadata_filter.append(utt_info) + + return metadata_filter + + def get_dur(self, idx): + utt_info = self.metadata[idx] + return utt_info['Duration'] + + + def __getitem__(self, index): + single_feature = super().__getitem__(index) + + utt_info = self.metadata[index] + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + # acoustic token + if self.cfg.preprocess.use_acoustic_token: + acoustic_token = np.load(self.utt2acousticToken_path[utt]) + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = acoustic_token.shape[0] + single_feature["acoustic_token"] = acoustic_token # [T, 8] + + + return single_feature + +class VALLECollator(TTSCollator): + def __init__(self, cfg): + super().__init__(cfg) + + def __call__(self, batch): + parsed_batch_features = super().__call__(batch) + return parsed_batch_features + +class VALLETestDataset(TTSTestDataset): + def __init__(self,args, cfg): + super().__init__(args, cfg) + + # prepare data + assert cfg.preprocess.use_acoustic_token == True + if cfg.preprocess.use_acoustic_token: + self.utt2acousticToken = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + # extract acoustic token + audio_file = utt_info["Audio_pormpt_path"] + encoded_frames = tokenize_audio(self.audio_tokenizer, audio_file) + audio_prompt_token = encoded_frames[0][0].transpose(2, 1).squeeze(0).cpu().numpy() + self.utt2acousticToken[utt] = audio_prompt_token + + def __getitem__(self, index): + utt_info = self.metadata[index] + + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + single_feature = dict() + + # acoustic token + if self.cfg.preprocess.use_acoustic_token: + acoustic_token = self.utt2acousticToken[utt] + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = acoustic_token.shape[0] + single_feature["acoustic_token"] = acoustic_token # [T, 8] + + # phone sequence todo + if self.cfg.preprocess.use_phone: + single_feature["phone_seq"] = np.array(self.utt2seq[utt]) + single_feature["phone_len"] = len(self.utt2seq[utt]) + single_feature["pmt_phone_seq"] = np.array(self.utt2pmtseq[utt]) + single_feature["pmt_phone_len"] = len(self.utt2pmtseq[utt]) + + return single_feature + + def get_metadata(self): + with open(self.metafile_path, "r", encoding="utf-8") as f: + metadata = json.load(f) + return metadata + + def __len__(self): + return len(self.metadata) + +class VALLETestCollator(TTSTestCollator): + + def __init__(self, cfg): + self.cfg = cfg + + def __call__(self, batch): + packed_batch_features = dict() + + for key in batch[0].keys(): + if key == "target_len": + packed_batch_features["target_len"] = torch.LongTensor( + [b["target_len"] for b in batch] + ) + masks = [ + torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "phone_len": + packed_batch_features["phone_len"] = torch.LongTensor( + [b["phone_len"] for b in batch] + ) + masks = [ + torch.ones((b["phone_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["phn_mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "pmt_phone_len": + packed_batch_features["pmt_phone_len"] = torch.LongTensor( + [b["pmt_phone_len"] for b in batch] + ) + masks = [ + torch.ones((b["pmt_phone_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["pmt_phone_len_mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "audio_len": + packed_batch_features["audio_len"] = torch.LongTensor( + [b["audio_len"] for b in batch] + ) + masks = [ + torch.ones((b["audio_len"], 1), dtype=torch.long) for b in batch + ] + else: + values = [torch.from_numpy(b[key]) for b in batch] + packed_batch_features[key] = pad_sequence( + values, batch_first=True, padding_value=0 + ) + + return packed_batch_features diff --git a/models/tts/valle/valle_inference.py b/models/tts/valle/valle_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..5240a95fde92cdd6237471b917683de8a5029533 --- /dev/null +++ b/models/tts/valle/valle_inference.py @@ -0,0 +1,233 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import numpy as np +import torch +import torchaudio +import argparse + + +from text.g2p_module import G2PModule +from utils.tokenizer import AudioTokenizer, tokenize_audio +from models.tts.valle.valle import VALLE +from models.tts.base.tts_inferece import TTSInference +from models.tts.valle.valle_dataset import VALLETestDataset, VALLETestCollator +from processors.phone_extractor import phoneExtractor +from text.text_token_collation import phoneIDCollation + +class VALLEInference(TTSInference): + def __init__(self, args=None, cfg=None): + TTSInference.__init__(self, args, cfg) + + self.g2p_module = G2PModule(backend=self.cfg.preprocess.phone_extractor) + text_token_path = os.path.join( + cfg.preprocess.processed_dir, + cfg.dataset[0], + cfg.preprocess.symbols_dict + ) + self.audio_tokenizer = AudioTokenizer() + + + def _build_model(self): + model = VALLE(self.cfg.model) + return model + + def _build_test_dataset(self): + return VALLETestDataset, VALLETestCollator + + + def inference_one_clip(self, text, text_prompt, audio_file, save_name="pred"): + # get phone symbol file + phone_symbol_file = os.path.join(self.exp_dir, self.cfg.preprocess.symbols_dict) + assert os.path.exists(phone_symbol_file) + # convert text to phone sequence + phone_extractor = phoneExtractor(self.cfg) + # convert phone sequence to phone id sequence + phon_id_collator = phoneIDCollation(self.cfg, symbols_dict_file=phone_symbol_file) + + text=f"{text_prompt} {text}".strip() + phone_seq = phone_extractor.extract_phone(text) # phone_seq: list + phone_id_seq = phon_id_collator.get_phone_id_sequence(self.cfg, phone_seq) + phone_id_seq_len = torch.IntTensor([len(phone_id_seq)]).to(self.device) + + # convert phone sequence to phone id sequence + phone_id_seq = np.array([phone_id_seq]) + phone_id_seq = torch.from_numpy(phone_id_seq).to(self.device) + + # extract acoustic token + encoded_frames = tokenize_audio(self.audio_tokenizer, audio_file) + audio_prompt_token = encoded_frames[0][0].transpose(2, 1).to(self.device) + + # copysyn + if self.args.copysyn: + samples = self.audio_tokenizer.decode(encoded_frames) + audio_copysyn = samples[0].cpu().detach() + + out_path = os.path.join(self.args.output_dir, self.infer_type, f"{save_name}_copysyn.wav") + torchaudio.save(out_path, + audio_copysyn, + self.cfg.preprocess.sampling_rate + ) + + + if self.args.continual: + encoded_frames = self.model.continual( + phone_id_seq, + phone_id_seq_len, + audio_prompt_token, + ) + else: + enroll_x_lens = None + if text_prompt: + # prompt_phone_seq = tokenize_text(self.g2p_module, text=f"{text_prompt}".strip()) + # _, enroll_x_lens = self.text_tokenizer.get_token_id_seq(prompt_phone_seq) + + text = f"{text_prompt}".strip() + prompt_phone_seq = phone_extractor.extract_phone(text) # phone_seq: list + prompt_phone_id_seq = phon_id_collator.get_phone_id_sequence(self.cfg, prompt_phone_seq) + prompt_phone_id_seq_len = torch.IntTensor([len(prompt_phone_id_seq)]).to(self.device) + + encoded_frames = self.model.inference( + phone_id_seq, + phone_id_seq_len, + audio_prompt_token, + enroll_x_lens=prompt_phone_id_seq_len, + top_k=self.args.top_k, + temperature=self.args.temperature, + ) + + samples = self.audio_tokenizer.decode( + [(encoded_frames.transpose(2, 1), None)] + ) + + audio = samples[0].squeeze(0).cpu().detach() + + return audio + + + def inference_for_single_utterance(self): + text = self.args.text + text_prompt = self.args.text_prompt + audio_file = self.args.audio_prompt + + if not self.args.continual: + assert text != "" + else: + text = "" + assert text_prompt != "" + assert audio_file != "" + + audio = self.inference_one_clip(text, text_prompt, audio_file) + + return audio + + + def inference_for_batches(self): + test_list_file = self.args.test_list_file + assert test_list_file is not None + + pred_res = [] + with open(test_list_file, "r") as fin: + for idx, line in enumerate(fin.readlines()): + fields = line.strip().split("|") + if self.args.continual: + assert len(fields) == 2 + text_prompt, audio_prompt_path = fields + text = "" + else: + assert len(fields) == 3 + text_prompt, audio_prompt_path, text = fields + + audio = self.inference_one_clip(text, text_prompt, audio_prompt_path, str(idx)) + pred_res.append(audio) + + return pred_res + + ''' + TODO: batch inference + ###### Construct test_batch ###### + n_batch = len(self.test_dataloader) + now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time())) + print( + "Model eval time: {}, batch_size = {}, n_batch = {}".format( + now, self.test_batch_size, n_batch + ) + ) + + ###### Inference for each batch ###### + pred_res = [] + with torch.no_grad(): + for i, batch_data in enumerate( + self.test_dataloader if n_batch == 1 else tqdm(self.test_dataloader) + ): + if self.args.continual: + encoded_frames = self.model.continual( + batch_data["phone_seq"], + batch_data["phone_len"], + batch_data["acoustic_token"], + ) + else: + encoded_frames = self.model.inference( + batch_data["phone_seq"], + batch_data["phone_len"], + batch_data["acoustic_token"], + enroll_x_lens=batch_data["pmt_phone_len"], + top_k=self.args.top_k, + temperature=self.args.temperature + ) + + samples = self.audio_tokenizer.decode( + [(encoded_frames.transpose(2, 1), None)] + ) + + + for idx in range(samples.size(0)): + audio = samples[idx].cpu() + pred_res.append(audio) + + return pred_res + ''' + + def add_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--text_prompt", + type=str, + default="", + help="Text prompt that should be aligned with --audio_prompt.", + ) + + parser.add_argument( + "--audio_prompt", + type=str, + default="", + help="Audio prompt that should be aligned with --text_prompt.", + ) + parser.add_argument( + "--top-k", + type=int, + default=-100, + help="Whether AR Decoder do top_k(if > 0) sampling.", + ) + + parser.add_argument( + "--temperature", + type=float, + default=1.0, + help="The temperature of AR Decoder top_k sampling.", + ) + + parser.add_argument( + "--continual", + action="store_true", + help="Inference for continual task.", + ) + + parser.add_argument( + "--copysyn", + action="store_true", + help="Copysyn: generate audio by decoder of the original audio tokenizer.", + ) + diff --git a/models/tts/valle/valle_trainer.py b/models/tts/valle/valle_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..567a6d00cbabf5b59ece3ee20425f60da38b6dae --- /dev/null +++ b/models/tts/valle/valle_trainer.py @@ -0,0 +1,266 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +from tqdm import tqdm +import torch +from torch.nn.parallel import DistributedDataParallel +from optimizer.optimizers import Eve, ScaledAdam +from schedulers.scheduler import NoamScheduler, Eden +from models.tts.valle.valle_dataset import VALLEDataset, VALLECollator +from models.tts.base import TTSTrainer +from models.tts.valle.valle import VALLE + +class VALLETrainer(TTSTrainer): + def __init__(self, args, cfg): + TTSTrainer.__init__(self, args, cfg) + + def _build_model(self): + + model = VALLE(self.cfg.model) + + return model + + def _build_dataset(self): + return VALLEDataset, VALLECollator + + def _build_optimizer(self): + if self.args.train_stage: + if isinstance(self.model, DistributedDataParallel): + model = self.model.module + else: + model = self.model + model_parameters = model.stage_parameters(self.args.train_stage) + else: + model_parameters = self.model.parameters() + + + if self.cfg.train.optimizer == "ScaledAdam": + parameters_names = [] + if self.args.train_stage != 0: + parameters_names.append( + [ + name_param_pair[0] + for name_param_pair in model.stage_named_parameters( + self.args.train_stage + ) + ] + ) + else: + parameters_names.append( + [ + name_param_pair[0] + for name_param_pair in model.named_parameters() + ] + ) + + optimizer = ScaledAdam( + model_parameters, + lr=self.cfg.train.base_lr, + betas=(0.9, 0.95), + clipping_scale=2.0, + parameters_names=parameters_names, + show_dominant_parameters=False, + clipping_update_period=1000, + ) + elif self.cfg.train.optimizer == "Eve": + optimizer = Eve( + model_parameters, + lr=self.cfg.train.base_lr, + betas=(0.9, 0.98), + target_rms=0.1, + ) + elif self.cfg.train.optimizer == "AdamW": + optimizer = torch.optim.AdamW( + model_parameters, + lr=self.cfg.train.base_lr, + betas=(0.9, 0.95), + weight_decay=1e-2, + eps=1e-8, + ) + elif self.cfg.train.optimizer == "Adam": + optimizer = torch.optim.Adam( + model_parameters, + lr=self.cfg.train.base_lr, + betas=(0.9, 0.95), + eps=1e-8, + ) + else: + raise NotImplementedError() + + return optimizer + + def _build_scheduler(self): + if self.cfg.train.scheduler.lower() == "eden": + scheduler = Eden(self.optimizer, 5000, 4, warmup_batches=self.cfg.train.warmup_steps) + elif self.cfg.train.scheduler.lower() == "noam": + scheduler = NoamScheduler( + self.cfg.train.base_lr, + self.optimizer, + self.cfg.model.decoder_dim, + warmup_steps=self.cfg.train.warmup_steps, + ) + elif self.cfg.train.scheduler.lower() == "cosine": + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + self.cfg.train.warmup_steps, + self.optimizer, + eta_min=self.cfg.train.base_lr, + ) + else: + raise NotImplementedError(f"{self.cfg.train.scheduler}") + + return scheduler + + def _train_epoch(self): + r"""Training epoch. Should return average loss of a batch (sample) over + one epoch. See ``train_loop`` for usage. + """ + if isinstance(self.model, dict): + for key in self.model.keys(): + self.model[key].train() + else: + self.model.train() + + epoch_sum_loss: float = 0.0 + epoch_losses: dict = {} + epoch_step: int = 0 + for batch in tqdm( + self.train_dataloader, + desc=f"Training Epoch {self.epoch}", + unit="batch", + colour="GREEN", + leave=False, + dynamic_ncols=True, + smoothing=0.04, + disable=not self.accelerator.is_main_process, + ): + + # Do training step and BP + with self.accelerator.accumulate(self.model): + total_loss, train_losses = self._train_step(batch) + self.accelerator.backward(total_loss) + self.optimizer.step() + self.optimizer.zero_grad() + self.batch_count += 1 + + if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: + if self.cfg.train.optimizer not in ["ScaledAdam", "Eve"]: + torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0) + + for k in range(self.cfg.train.gradient_accumulation_step): + if isinstance(self.scheduler, Eden): + self.scheduler.step_batch(self.step) + else: + self.scheduler.step() + + epoch_sum_loss += total_loss.detach().cpu().item() + + if isinstance(train_losses, dict): + for key, value in train_losses.items(): + if key not in epoch_losses.keys(): + epoch_losses[key] = value + else: + epoch_losses[key] += value + + if isinstance(train_losses, dict): + for key, loss in train_losses.items(): + self.accelerator.log( + {"Step/Train {}".format(key): "{:.6f}".format(loss)}, + step=self.step, + ) + else: + self.accelerator.log( + {"Step/Train Loss": loss}, + step=self.step, + ) + + self.accelerator.log( + {"Step/lr": self.scheduler.get_last_lr()[0]}, + step=self.step, + ) + + # print loss every log_epoch_step steps + # if epoch_step % self.cfg.train.log_epoch_step == 0: + # for key, loss in train_losses.items(): + # self.logger.info("Step/Train {}: {:.6f}".format(key, loss)) + # print("Step/Train {}: {:.6f}".format(key, loss)) + + + self.step += 1 + epoch_step += 1 + + + self.accelerator.wait_for_everyone() + + epoch_sum_loss = ( + epoch_sum_loss + / len(self.train_dataloader) + * self.cfg.train.gradient_accumulation_step + ) + + for key in epoch_losses.keys(): + epoch_losses[key] = ( + epoch_losses[key] + / len(self.train_dataloader) + * self.cfg.train.gradient_accumulation_step + ) + + return epoch_sum_loss, epoch_losses + + + def _train_step(self, batch, is_training=True): + text_tokens = batch["phone_seq"].to(self.device) + text_tokens_lens = batch["phone_len"].to(self.device) + assert text_tokens.ndim == 2 + + audio_features = batch["acoustic_token"].to(self.device) + audio_features_lens = batch["target_len"].to(self.device) + assert audio_features.ndim == 3 + + + with torch.set_grad_enabled(is_training): + loss, losses = self.model( + x=text_tokens, + x_lens=text_tokens_lens, + y=audio_features, + y_lens=audio_features_lens, + train_stage=self.args.train_stage + ) + + assert loss.requires_grad == is_training + + loss_dict = {} + frames_sum = (audio_features_lens).sum() + + avg_loss = loss / frames_sum + + loss_dict['loss'] = avg_loss.detach().cpu().item() + for l in losses: + loss_dict[l] = losses[l].detach().cpu().item() / frames_sum.item() + + return avg_loss, loss_dict + + def _valid_step(self, batch): + valid_losses = {} + total_loss = 0 + valid_stats = {} + + total_loss, valid_losses = self._train_step( + batch=batch, + is_training=False, + ) + assert total_loss.requires_grad is False + + total_loss = total_loss.detach().cpu().item() + + return total_loss, valid_losses, valid_stats + + def add_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--train_stage", + type=int, + default="1", + help="0: train all modules, 1: AR Decoder, 2: NAR Decoder", + ) diff --git a/models/tts/vits/__init__.py b/models/tts/vits/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git 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file in the root directory of this source tree. + +# This code is modified from https://github.com/jaywalnut310/vits/blob/main/models.py +import math +import torch +from torch import nn +from torch.nn import functional as F + +from utils.util import * +from modules.flow.modules import * +from modules.base.base_module import * +from modules import monotonic_align +from modules.transformer.attentions import Encoder +from modules.duration_predictor.standard_duration_predictor import DurationPredictor +from modules.duration_predictor.stochastic_duration_predictor import ( + StochasticDurationPredictor, +) +from models.vocoders.gan.generator.hifigan import HiFiGAN_vits as Generator + + +class TextEncoder(nn.Module): + def __init__( + self, + n_vocab, + out_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + ): + super().__init__() + self.n_vocab = n_vocab + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + + self.emb = nn.Embedding(n_vocab, hidden_channels) + nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) + + self.encoder = Encoder( + hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout + ) + self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) + + def forward(self, x, x_lengths): + x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] + x = torch.transpose(x, 1, -1) # [b, h, t] + x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) + + x = self.encoder(x * x_mask, x_mask) + stats = self.proj(x) * x_mask + + m, logs = torch.split(stats, self.out_channels, dim=1) + return x, m, logs, x_mask + + +class ResidualCouplingBlock(nn.Module): + def __init__( + self, + channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + n_flows=4, + gin_channels=0, + ): + super().__init__() + self.channels = channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.n_flows = n_flows + self.gin_channels = gin_channels + + self.flows = nn.ModuleList() + for i in range(n_flows): + self.flows.append( + ResidualCouplingLayer( + channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + gin_channels=gin_channels, + mean_only=True, + ) + ) + self.flows.append(Flip()) + + def forward(self, x, x_mask, g=None, reverse=False): + if not reverse: + for flow in self.flows: + x, _ = flow(x, x_mask, g=g, reverse=reverse) + else: + for flow in reversed(self.flows): + x = flow(x, x_mask, g=g, reverse=reverse) + return x + + +class PosteriorEncoder(nn.Module): + def __init__( + self, + in_channels, + out_channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + gin_channels=0, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.gin_channels = gin_channels + + self.pre = nn.Conv1d(in_channels, hidden_channels, 1) + self.enc = WN( + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + gin_channels=gin_channels, + ) + self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) + + def forward(self, x, x_lengths, g=None): + x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) + x = self.pre(x) * x_mask + x = self.enc(x, x_mask, g=g) + stats = self.proj(x) * x_mask + m, logs = torch.split(stats, self.out_channels, dim=1) + z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask + return z, m, logs, x_mask + + +class SynthesizerTrn(nn.Module): + """ + Synthesizer for Training + """ + + def __init__( + self, + n_vocab, + spec_channels, + segment_size, + inter_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + resblock, + resblock_kernel_sizes, + resblock_dilation_sizes, + upsample_rates, + upsample_initial_channel, + upsample_kernel_sizes, + n_speakers=0, + gin_channels=0, + use_sdp=True, + **kwargs, + ): + super().__init__() + self.n_vocab = n_vocab + self.spec_channels = spec_channels + self.inter_channels = inter_channels + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.resblock = resblock + self.resblock_kernel_sizes = resblock_kernel_sizes + self.resblock_dilation_sizes = resblock_dilation_sizes + self.upsample_rates = upsample_rates + self.upsample_initial_channel = upsample_initial_channel + self.upsample_kernel_sizes = upsample_kernel_sizes + self.segment_size = segment_size + self.n_speakers = n_speakers + self.gin_channels = gin_channels + + self.use_sdp = use_sdp + + self.enc_p = TextEncoder( + n_vocab, + inter_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + ) + self.dec = Generator( + inter_channels, + resblock, + resblock_kernel_sizes, + resblock_dilation_sizes, + upsample_rates, + upsample_initial_channel, + upsample_kernel_sizes, + gin_channels=gin_channels, + ) + self.enc_q = PosteriorEncoder( + spec_channels, + inter_channels, + hidden_channels, + 5, + 1, + 16, + gin_channels=gin_channels, + ) + self.flow = ResidualCouplingBlock( + inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels + ) + + if use_sdp: + self.dp = StochasticDurationPredictor( + hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels + ) + else: + self.dp = DurationPredictor( + hidden_channels, 256, 3, 0.5, gin_channels=gin_channels + ) + + if n_speakers >= 1: + self.emb_g = nn.Embedding(n_speakers, gin_channels) + + + def forward(self, data): + x = data["phone_seq"] + x_lengths = data["phone_len"] + y = data["linear"] + y_lengths = data["target_len"] + + x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) + + # print('self.n_speakers: ', self.n_speakers) + if self.n_speakers > 0: + g = self.emb_g(data["spk_id"].squeeze(-1)).unsqueeze(-1) # [b, h, 1] + # print('g.shape: ', g.shape) + else: + g = None + # print('g is None') + + z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) + z_p = self.flow(z, y_mask, g=g) + + with torch.no_grad(): + # negative cross-entropy + s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t] + neg_cent1 = torch.sum( + -0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True + ) # [b, 1, t_s] + neg_cent2 = torch.matmul( + -0.5 * (z_p**2).transpose(1, 2), s_p_sq_r + ) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] + neg_cent3 = torch.matmul( + z_p.transpose(1, 2), (m_p * s_p_sq_r) + ) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] + neg_cent4 = torch.sum( + -0.5 * (m_p**2) * s_p_sq_r, [1], keepdim=True + ) # [b, 1, t_s] + neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4 + + attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) + attn = ( + monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)) + .unsqueeze(1) + .detach() + ) + + w = attn.sum(2) + if self.use_sdp: + l_length = self.dp(x, x_mask, w, g=g) + l_length = l_length / torch.sum(x_mask) + else: + logw_ = torch.log(w + 1e-6) * x_mask + logw = self.dp(x, x_mask, g=g) + l_length = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(x_mask) + + # expand prior + m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) + logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) + + z_slice, ids_slice = rand_slice_segments(z, y_lengths, self.segment_size) + o = self.dec(z_slice, g=g) + outputs = { + "y_hat": o, + "l_length": l_length, + "attn": attn, + "ids_slice": ids_slice, + "x_mask": x_mask, + "z_mask": y_mask, + "z": z, + "z_p": z_p, + "m_p": m_p, + "logs_p": logs_p, + "m_q": m_q, + "logs_q": logs_q, + } + return outputs + + def infer( + self, + x, + x_lengths, + sid=None, + noise_scale=1, + length_scale=1, + noise_scale_w=1.0, + max_len=None, + ): + x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) + if self.n_speakers > 0: + sid = sid.squeeze(-1) + g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] + else: + g = None + + print('g.shape: ', g.shape) + + if self.use_sdp: + logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) + else: + logw = self.dp(x, x_mask, g=g) + w = torch.exp(logw) * x_mask * length_scale + w_ceil = torch.ceil(w) + y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() + y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(x_mask.dtype) + attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) + attn = generate_path(w_ceil, attn_mask) + + m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose( + 1, 2 + ) # [b, t', t], [b, t, d] -> [b, d, t'] + logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose( + 1, 2 + ) # [b, t', t], [b, t, d] -> [b, d, t'] + + z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale + z = self.flow(z_p, y_mask, g=g, reverse=True) + o = self.dec((z * y_mask)[:, :, :max_len], g=g) + + outputs = { + "y_hat": o, + "attn": attn, + "mask": y_mask, + "z": z, + "z_p": z_p, + "m_p": m_p, + "logs_p": logs_p, + } + + return outputs + + def voice_conversion(self, y, y_lengths, sid_src, sid_tgt): + assert self.n_speakers > 0, "n_speakers have to be larger than 0." + g_src = self.emb_g(sid_src).unsqueeze(-1) + g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) + z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src) + z_p = self.flow(z, y_mask, g=g_src) + z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True) + o_hat = self.dec(z_hat * y_mask, g=g_tgt) + return o_hat, y_mask, (z, z_p, z_hat) diff --git a/models/tts/vits/vits_dataset.py b/models/tts/vits/vits_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..17ea4501492c3137459f105f0b71b7290eab266c --- /dev/null +++ b/models/tts/vits/vits_dataset.py @@ -0,0 +1,136 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import numpy as np +from text import text_to_sequence +from text.text_token_collation import phoneIDCollation +from models.tts.base.tts_dataset import ( + TTSDataset, + TTSCollator, + TTSTestDataset, + TTSTestCollator +) + + +class VITSDataset(TTSDataset): + def __init__(self, cfg, dataset, is_valid): + super().__init__(cfg, dataset, is_valid=is_valid) + + def __getitem__(self, index): + single_feature = super().__getitem__(index) + return single_feature + + def __len__(self): + return super().__len__() + + def get_metadata(self): + metadata_filter = [] + with open(self.metafile_path, "r", encoding="utf-8") as f: + metadata = json.load(f) + for utt_info in metadata: + duration = utt_info['Duration'] + frame_len = duration * self.cfg.preprocess.sample_rate // self.cfg.preprocess.hop_size + if frame_len < self.cfg.preprocess.segment_size // self.cfg.preprocess.hop_size: + continue + + metadata_filter.append(utt_info) + + return metadata_filter + + +class VITSCollator(TTSCollator): + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, cfg): + super().__init__(cfg) + + def __call__(self, batch): + parsed_batch_features = super().__call__(batch) + return parsed_batch_features + + +class VITSTestDataset(TTSTestDataset): + def __init__(self, args, cfg): + super().__init__(args, cfg) + + if cfg.preprocess.use_spkid: + processed_data_dir = os.path.join(cfg.preprocess.processed_dir, args.dataset) + spk2id_path = os.path.join(processed_data_dir, cfg.preprocess.spk2id) + with open(spk2id_path, "r") as f: + self.spk2id = json.load(f) + + utt2spk_path = os.path.join(processed_data_dir, cfg.preprocess.utt2spk) + self.utt2spk = dict() + with open(utt2spk_path, "r") as f: + for line in f.readlines(): + utt, spk = line.strip().split('\t') + self.utt2spk[utt] = spk + + if cfg.preprocess.use_text or cfg.preprocess.use_phone: + self.utt2seq = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + if cfg.preprocess.use_text: + text = utt_info["Text"] + sequence = text_to_sequence(text, cfg.preprocess.text_cleaners) + elif cfg.preprocess.use_phone: + # load phoneme squence from phone file + phone_path = os.path.join(processed_data_dir, + cfg.preprocess.phone_dir, + uid+'.phone' + ) + with open(phone_path, 'r') as fin: + phones = fin.readlines() + assert len(phones) == 1 + phones = phones[0].strip() + phones_seq = phones.split(' ') + + phon_id_collator = phoneIDCollation(cfg, dataset=dataset) + sequence = phon_id_collator.get_phone_id_sequence(cfg, phones_seq) + + self.utt2seq[utt] = sequence + + def __getitem__(self, index): + utt_info = self.metadata[index] + + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + single_feature = dict() + + if self.cfg.preprocess.use_spkid: + single_feature["spk_id"] = np.array( + [self.spk2id[self.utt2spk[utt]]], dtype=np.int32 + ) + + if self.cfg.preprocess.use_phone or self.cfg.preprocess.use_text: + single_feature["phone_seq"] = np.array(self.utt2seq[utt]) + single_feature["phone_len"] = len(self.utt2seq[utt]) + + return single_feature + + def get_metadata(self): + with open(self.metafile_path, "r", encoding="utf-8") as f: + metadata = json.load(f) + return metadata + + def __len__(self): + return len(self.metadata) + + +class VITSTestCollator(TTSTestCollator): + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, cfg): + self.cfg = cfg + + def __call__(self, batch): + return super().__call__(batch) \ No newline at end of file diff --git a/models/tts/vits/vits_inference.py b/models/tts/vits/vits_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..0889b9025a0c13c648a519482ce1323fd7022e47 --- /dev/null +++ b/models/tts/vits/vits_inference.py @@ -0,0 +1,154 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import time +import numpy as np +from tqdm import tqdm +import torch +import json +from models.tts.base.tts_inferece import TTSInference +from models.tts.vits.vits_dataset import VITSTestDataset, VITSTestCollator +from models.tts.vits.vits import SynthesizerTrn +from processors.phone_extractor import phoneExtractor +from text.text_token_collation import phoneIDCollation +from utils.data_utils import * + + +class VitsInference(TTSInference): + def __init__(self, args=None, cfg=None): + TTSInference.__init__(self, args, cfg) + + + def _build_model(self): + net_g = SynthesizerTrn( + # symbols_len, + self.cfg.model.text_token_num, + self.cfg.preprocess.n_fft // 2 + 1, + self.cfg.preprocess.segment_size // self.cfg.preprocess.hop_size, + **self.cfg.model, + ) + + return net_g + + def _build_test_dataset(sefl): + return VITSTestDataset, VITSTestCollator + + def build_save_dir(self, dataset, speaker): + save_dir = os.path.join( + self.args.output_dir, + "tts_am_step-{}_{}".format(self.am_restore_step, self.args.mode), + ) + if dataset is not None: + save_dir = os.path.join(save_dir, "data_{}".format(dataset)) + if speaker != -1: + save_dir = os.path.join( + save_dir, + "spk_{}".format(speaker), + ) + os.makedirs(save_dir, exist_ok=True) + print("Saving to ", save_dir) + return save_dir + + def inference_for_batches( + self, noise_scale=0.667, noise_scale_w=0.8, length_scale=1 + ): + ###### Construct test_batch ###### + n_batch = len(self.test_dataloader) + now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time())) + print( + "Model eval time: {}, batch_size = {}, n_batch = {}".format( + now, self.test_batch_size, n_batch + ) + ) + self.model.eval() + + ###### Inference for each batch ###### + pred_res = [] + with torch.no_grad(): + for i, batch_data in enumerate( + self.test_dataloader if n_batch == 1 else tqdm(self.test_dataloader) + ): + + spk_id = None + if self.cfg.preprocess.use_spkid and self.cfg.train.multi_speaker_training: + spk_id = batch_data["spk_id"] + + outputs = self.model.infer( + batch_data["phone_seq"], + batch_data["phone_len"], + spk_id, + noise_scale=noise_scale, + noise_scale_w=noise_scale_w, + length_scale=length_scale, + ) + + audios = outputs["y_hat"] + masks = outputs["mask"] + + for idx in range(audios.size(0)): + audio = audios[idx, 0, :].data.cpu().float() + mask = masks[idx, :, :] + audio_length = ( + mask.sum([0, 1]).long() * self.cfg.preprocess.hop_size + ) + audio_length = audio_length.cpu().numpy() + audio = audio[:audio_length] + pred_res.append(audio) + + return pred_res + + def inference_for_single_utterance( + self, noise_scale=0.667, noise_scale_w=0.8, length_scale=1 + ): + + text = self.args.text + + + # get phone symbol file + phone_symbol_file = os.path.join(self.exp_dir, self.cfg.preprocess.symbols_dict) + assert os.path.exists(phone_symbol_file) + # convert text to phone sequence + phone_extractor = phoneExtractor(self.cfg) + phone_seq = phone_extractor.extract_phone(text) # phone_seq: list + # convert phone sequence to phone id sequence + phon_id_collator = phoneIDCollation(self.cfg, symbols_dict_file=phone_symbol_file) + phone_id_seq = phon_id_collator.get_phone_id_sequence(self.cfg, phone_seq) + + if self.cfg.preprocess.add_blank: + phone_id_seq = intersperse(phone_id_seq, 0) + + # convert phone sequence to phone id sequence + phone_id_seq = np.array(phone_id_seq) + phone_id_seq = torch.from_numpy(phone_id_seq) + + # get speaker id if multi-speaker training and use speaker id + if self.cfg.preprocess.use_spkid and self.cfg.train.multi_speaker_training: + spk2id_file = os.path.join(self.exp_dir, self.cfg.preprocess.spk2id) + with open(spk2id_file, 'r') as f: + spk2id = json.load(f) + speaker_id = spk2id[self.args.speaker_name] + print("speaker name:",self.args.speaker_name) + print("speaker id:",speaker_id) + speaker_id = torch.from_numpy( + np.array([speaker_id], dtype=np.int32) + ).unsqueeze(0) + + with torch.no_grad(): + x_tst = phone_id_seq.to(self.device).unsqueeze(0) + x_tst_lengths = torch.LongTensor([phone_id_seq.size(0)]).to(self.device) + speaker_id = speaker_id.to(self.device) + outputs = self.model.infer( + x_tst, + x_tst_lengths, + sid=speaker_id, + noise_scale=noise_scale, + noise_scale_w=noise_scale_w, + length_scale=length_scale, + ) + + audio = outputs["y_hat"][0, 0].data.cpu().float().numpy() + + return audio diff --git a/models/tts/vits/vits_trainer.py b/models/tts/vits/vits_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..8ab0a0302a03e5d7ab17d2dabaf488ce21da824b --- /dev/null +++ b/models/tts/vits/vits_trainer.py @@ -0,0 +1,441 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +from torch.optim.lr_scheduler import ExponentialLR + +from tqdm import tqdm + +from utils.util import * +from utils.mel import mel_spectrogram_torch +from models.tts.base import TTSTrainer +from models.tts.vits.vits import SynthesizerTrn +from models.tts.vits.vits_dataset import VITSDataset, VITSCollator +from models.vocoders.gan.discriminator.mpd import ( + MultiPeriodDiscriminator_vits as MultiPeriodDiscriminator, +) + + +class VITSTrainer(TTSTrainer): + def __init__(self, args, cfg): + TTSTrainer.__init__(self, args, cfg) + + if cfg.preprocess.use_spkid and cfg.train.multi_speaker_training: + if cfg.model.n_speakers == 0: + cfg.model.n_speaker = len(self.speakers) + + + def _build_model(self): + + net_g = SynthesizerTrn( + self.cfg.model.text_token_num, + self.cfg.preprocess.n_fft // 2 + 1, + self.cfg.preprocess.segment_size // self.cfg.preprocess.hop_size, + **self.cfg.model, + ) + net_d = MultiPeriodDiscriminator(self.cfg.model.use_spectral_norm) + model = {"generator": net_g, "discriminator": net_d} + + return model + + def _build_dataset(self): + return VITSDataset, VITSCollator + + def _build_optimizer(self): + optimizer_g = torch.optim.AdamW( + self.model["generator"].parameters(), + self.cfg.train.learning_rate, + betas=self.cfg.train.AdamW.betas, + eps=self.cfg.train.AdamW.eps, + ) + optimizer_d = torch.optim.AdamW( + self.model["discriminator"].parameters(), + self.cfg.train.learning_rate, + betas=self.cfg.train.AdamW.betas, + eps=self.cfg.train.AdamW.eps, + ) + optimizer = {"optimizer_g": optimizer_g, "optimizer_d": optimizer_d} + + return optimizer + + def _build_scheduler(self): + scheduler_g = ExponentialLR( + self.optimizer["optimizer_g"], + gamma=self.cfg.train.lr_decay, + last_epoch=self.epoch - 1, + ) + scheduler_d = ExponentialLR( + self.optimizer["optimizer_d"], + gamma=self.cfg.train.lr_decay, + last_epoch=self.epoch - 1, + ) + + scheduler = {"scheduler_g": scheduler_g, "scheduler_d": scheduler_d} + return scheduler + + def _build_criterion(self): + class GeneratorLoss(nn.Module): + def __init__(self, cfg): + super(GeneratorLoss, self).__init__() + self.cfg = cfg + self.l1_loss = nn.L1Loss() + + def generator_loss(self, disc_outputs): + loss = 0 + gen_losses = [] + for dg in disc_outputs: + dg = dg.float() + l = torch.mean((1 - dg) ** 2) + gen_losses.append(l) + loss += l + + return loss, gen_losses + + def feature_loss(self, fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + rl = rl.float().detach() + gl = gl.float() + loss += torch.mean(torch.abs(rl - gl)) + + return loss * 2 + + def kl_loss(self, z_p, logs_q, m_p, logs_p, z_mask): + """ + z_p, logs_q: [b, h, t_t] + m_p, logs_p: [b, h, t_t] + """ + z_p = z_p.float() + logs_q = logs_q.float() + m_p = m_p.float() + logs_p = logs_p.float() + z_mask = z_mask.float() + + kl = logs_p - logs_q - 0.5 + kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p) + kl = torch.sum(kl * z_mask) + l = kl / torch.sum(z_mask) + return l + + def forward( + self, + outputs_g, + outputs_d, + y_mel, + y_hat_mel, + ): + loss_g = {} + + # duration loss + loss_dur = torch.sum(outputs_g["l_length"].float()) + loss_g["loss_dur"] = loss_dur + + # mel loss + loss_mel = self.l1_loss(y_mel, y_hat_mel) * self.cfg.train.c_mel + loss_g["loss_mel"] = loss_mel + + # kl loss + loss_kl = ( + self.kl_loss( + outputs_g["z_p"], + outputs_g["logs_q"], + outputs_g["m_p"], + outputs_g["logs_p"], + outputs_g["z_mask"], + ) + * self.cfg.train.c_kl + ) + loss_g["loss_kl"] = loss_kl + + # feature loss + loss_fm = self.feature_loss(outputs_d["fmap_rs"], outputs_d["fmap_gs"]) + loss_g["loss_fm"] = loss_fm + + # gan loss + loss_gen, losses_gen = self.generator_loss(outputs_d["y_d_hat_g"]) + loss_g["loss_gen"] = loss_gen + loss_g["loss_gen_all"] = ( + loss_dur + loss_mel + loss_kl + loss_fm + loss_gen + ) + + return loss_g + + class DiscriminatorLoss(nn.Module): + def __init__(self, cfg): + super(DiscriminatorLoss, self).__init__() + self.cfg = cfg + self.l1Loss = torch.nn.L1Loss(reduction="mean") + + def __call__(self, disc_real_outputs, disc_generated_outputs): + loss_d = {} + + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + dr = dr.float() + dg = dg.float() + r_loss = torch.mean((1 - dr) ** 2) + g_loss = torch.mean(dg**2) + loss += r_loss + g_loss + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + loss_d["loss_disc_all"] = loss + + return loss_d + + criterion = { + "generator": GeneratorLoss(self.cfg), + "discriminator": DiscriminatorLoss(self.cfg), + } + return criterion + + def write_summary( + self, + losses, + stats, + images={}, + audios={}, + audio_sampling_rate=24000, + tag="train", + ): + for key, value in losses.items(): + self.sw.add_scalar(tag + "/" + key, value, self.step) + self.sw.add_scalar( + "learning_rate", + self.optimizer["optimizer_g"].param_groups[0]["lr"], + self.step, + ) + + if len(images) != 0: + for key, value in images.items(): + self.sw.add_image(key, value, self.global_step, batchformats="HWC") + if len(audios) != 0: + for key, value in audios.items(): + self.sw.add_audio(key, value, self.global_step, audio_sampling_rate) + + def write_valid_summary( + self, losses, stats, images={}, audios={}, audio_sampling_rate=24000, tag="val" + ): + for key, value in losses.items(): + self.sw.add_scalar(tag + "/" + key, value, self.step) + + if len(images) != 0: + for key, value in images.items(): + self.sw.add_image(key, value, self.global_step, batchformats="HWC") + if len(audios) != 0: + for key, value in audios.items(): + self.sw.add_audio(key, value, self.global_step, audio_sampling_rate) + + def get_state_dict(self): + state_dict = { + "generator": self.model["generator"].state_dict(), + "discriminator": self.model["discriminator"].state_dict(), + "optimizer_g": self.optimizer["optimizer_g"].state_dict(), + "optimizer_d": self.optimizer["optimizer_d"].state_dict(), + "scheduler_g": self.scheduler["scheduler_g"].state_dict(), + "scheduler_d": self.scheduler["scheduler_d"].state_dict(), + "step": self.step, + "epoch": self.epoch, + "batch_size": self.cfg.train.batch_size, + } + return state_dict + + def load_model(self, checkpoint): + self.step = checkpoint["step"] + self.epoch = checkpoint["epoch"] + self.model["generator"].load_state_dict(checkpoint["generator"]) + self.model["discriminator"].load_state_dict(checkpoint["discriminator"]) + self.optimizer["optimizer_g"].load_state_dict(checkpoint["optimizer_g"]) + self.optimizer["optimizer_d"].load_state_dict(checkpoint["optimizer_d"]) + self.scheduler["scheduler_g"].load_state_dict(checkpoint["scheduler_g"]) + self.scheduler["scheduler_d"].load_state_dict(checkpoint["scheduler_d"]) + + @torch.inference_mode() + def _valid_step(self, batch): + r"""Testing forward step. Should return average loss of a sample over + one batch. Provoke ``_forward_step`` is recommended except for special case. + See ``_test_epoch`` for usage. + """ + + valid_losses = {} + total_loss = 0 + valid_stats = {} + + batch["linear"] = batch["linear"].transpose(2, 1) # [b, d, t] + batch["mel"] = batch["mel"].transpose(2, 1) # [b, d, t] + batch["audio"] = batch["audio"].unsqueeze(1) # [b, d, t] + + # Discriminator + # Generator output + outputs_g = self.model["generator"](batch) + + y_mel = slice_segments( + batch["mel"], + outputs_g["ids_slice"], + self.cfg.preprocess.segment_size // self.cfg.preprocess.hop_size, + ) + y_hat_mel = mel_spectrogram_torch( + outputs_g["y_hat"].squeeze(1), self.cfg.preprocess + ) + y = slice_segments( + batch["audio"], + outputs_g["ids_slice"] * self.cfg.preprocess.hop_size, + self.cfg.preprocess.segment_size, + ) + + # Discriminator output + outputs_d = self.model["discriminator"](y, outputs_g["y_hat"].detach()) + ## Discriminator loss + loss_d = self.criterion["discriminator"]( + outputs_d["y_d_hat_r"], outputs_d["y_d_hat_g"] + ) + valid_losses.update(loss_d) + + ## Generator + outputs_d = self.model["discriminator"](y, outputs_g["y_hat"]) + loss_g = self.criterion["generator"](outputs_g, outputs_d, y_mel, y_hat_mel) + valid_losses.update(loss_g) + + for item in valid_losses: + valid_losses[item] = valid_losses[item].item() + + total_loss = loss_g["loss_gen_all"] + loss_d["loss_disc_all"] + + return ( + total_loss.item(), + valid_losses, + valid_stats, + ) + + def _train_step(self, batch): + r"""Forward step for training and inference. This function is called + in ``_train_step`` & ``_test_step`` function. + """ + + train_losses = {} + total_loss = 0 + training_stats = {} + + batch["linear"] = batch["linear"].transpose(2, 1) # [b, d, t] + batch["mel"] = batch["mel"].transpose(2, 1) # [b, d, t] + batch["audio"] = batch["audio"].unsqueeze(1) # [b, d, t] + + # Train Discriminator + # Generator output + outputs_g = self.model["generator"](batch) + + y_mel = slice_segments( + batch["mel"], + outputs_g["ids_slice"], + self.cfg.preprocess.segment_size // self.cfg.preprocess.hop_size, + ) + y_hat_mel = mel_spectrogram_torch( + outputs_g["y_hat"].squeeze(1), self.cfg.preprocess + ) + y = slice_segments( + batch["audio"], + outputs_g["ids_slice"] * self.cfg.preprocess.hop_size, + self.cfg.preprocess.segment_size, + ) + + # Discriminator output + outputs_d = self.model["discriminator"](y, outputs_g["y_hat"].detach()) + ## Discriminator loss + loss_d = self.criterion["discriminator"]( + outputs_d["y_d_hat_r"], outputs_d["y_d_hat_g"] + ) + train_losses.update(loss_d) + + # BP and Grad Updated + self.optimizer["optimizer_d"].zero_grad() + self.accelerator.backward(loss_d["loss_disc_all"]) + self.optimizer["optimizer_d"].step() + + ## Train Generator + outputs_d = self.model["discriminator"](y, outputs_g["y_hat"]) + loss_g = self.criterion["generator"](outputs_g, outputs_d, y_mel, y_hat_mel) + train_losses.update(loss_g) + + # BP and Grad Updated + self.optimizer["optimizer_g"].zero_grad() + self.accelerator.backward(loss_g["loss_gen_all"]) + self.optimizer["optimizer_g"].step() + + for item in train_losses: + train_losses[item] = train_losses[item].item() + + total_loss = loss_g["loss_gen_all"] + loss_d["loss_disc_all"] + + return ( + total_loss.item(), + train_losses, + training_stats, + ) + + def _train_epoch(self): + r"""Training epoch. Should return average loss of a batch (sample) over + one epoch. See ``train_loop`` for usage. + """ + epoch_sum_loss: float = 0.0 + epoch_losses: dict = {} + epoch_step: int = 0 + for batch in tqdm( + self.train_dataloader, + desc=f"Training Epoch {self.epoch}", + unit="batch", + colour="GREEN", + leave=False, + dynamic_ncols=True, + smoothing=0.04, + disable=not self.accelerator.is_main_process, + ): + with self.accelerator.accumulate(self.model): + total_loss, train_losses, training_stats = self._train_step(batch) + self.batch_count += 1 + + if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: + epoch_sum_loss += total_loss + for key, value in train_losses.items(): + if key not in epoch_losses.keys(): + epoch_losses[key] = value + else: + epoch_losses[key] += value + + self.accelerator.log( + { + "Step/Generator Loss": train_losses["loss_gen_all"], + "Step/Discriminator Loss": train_losses["loss_disc_all"], + "Step/Generator Learning Rate": self.optimizer[ + "optimizer_d" + ].param_groups[0]["lr"], + "Step/Discriminator Learning Rate": self.optimizer[ + "optimizer_g" + ].param_groups[0]["lr"], + }, + step=self.step, + ) + self.step += 1 + epoch_step += 1 + + self.accelerator.wait_for_everyone() + + epoch_sum_loss = ( + epoch_sum_loss + / len(self.train_dataloader) + * self.cfg.train.gradient_accumulation_step + ) + + for key in epoch_losses.keys(): + epoch_losses[key] = ( + epoch_losses[key] + / len(self.train_dataloader) + * self.cfg.train.gradient_accumulation_step + ) + + return epoch_sum_loss, epoch_losses diff --git a/models/vocoders/__pycache__/vocoder_dataset.cpython-39.pyc b/models/vocoders/__pycache__/vocoder_dataset.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e2d0d0ba92d065f43f6b5fd2e95447e145e47451 Binary files /dev/null and b/models/vocoders/__pycache__/vocoder_dataset.cpython-39.pyc differ diff --git a/models/vocoders/__pycache__/vocoder_inference.cpython-39.pyc b/models/vocoders/__pycache__/vocoder_inference.cpython-39.pyc new file mode 100644 index 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the root directory of this source tree. + +from torch import nn +from torch.nn import functional as F + + +class Conv1d(nn.Conv1d): + """Extended nn.Conv1d for incremental dilated convolutions""" + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.clear_buffer() + self._linearized_weight = None + self.register_backward_hook(self._clear_linearized_weight) + + def incremental_forward(self, input): + # input (B, T, C) + # run forward pre hooks + for hook in self._forward_pre_hooks.values(): + hook(self, input) + + # reshape weight + weight = self._get_linearized_weight() + kw = self.kernel_size[0] + dilation = self.dilation[0] + + bsz = input.size(0) + if kw > 1: + input = input.data + if self.input_buffer is None: + self.input_buffer = input.new( + bsz, kw + (kw - 1) * (dilation - 1), input.size(2) + ) + self.input_buffer.zero_() + else: + # shift buffer + self.input_buffer[:, :-1, :] = self.input_buffer[:, 1:, :].clone() + # append next input + self.input_buffer[:, -1, :] = input[:, -1, :] + input = self.input_buffer + if dilation > 1: + input = input[:, 0::dilation, :].contiguous() + output = F.linear(input.view(bsz, -1), weight, self.bias) + return output.view(bsz, 1, -1) + + def clear_buffer(self): + self.input_buffer = None + + def _get_linearized_weight(self): + if self._linearized_weight is None: + kw = self.kernel_size[0] + # nn.Conv1d + if self.weight.size() == (self.out_channels, self.in_channels, kw): + weight = self.weight.transpose(1, 2).contiguous() + else: + # fairseq.modules.conv_tbc.ConvTBC + weight = self.weight.transpose(2, 1).transpose(1, 0).contiguous() + assert weight.size() == (self.out_channels, kw, self.in_channels) + self._linearized_weight = weight.view(self.out_channels, -1) + return self._linearized_weight + + def _clear_linearized_weight(self, *args): + self._linearized_weight = None diff --git a/models/vocoders/autoregressive/wavenet/modules.py b/models/vocoders/autoregressive/wavenet/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..13d51e52a50af3bc1f7fe9627aeae8d2b1b28b7d --- /dev/null +++ b/models/vocoders/autoregressive/wavenet/modules.py @@ -0,0 +1,152 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import math + +from torch import nn +from torch.nn import functional as F + +from .conv import Conv1d as conv_Conv1d + + +def Conv1d(in_channels, out_channels, kernel_size, dropout=0, **kwargs): + m = conv_Conv1d(in_channels, out_channels, kernel_size, **kwargs) + nn.init.kaiming_normal_(m.weight, nonlinearity="relu") + if m.bias is not None: + nn.init.constant_(m.bias, 0) + return nn.utils.weight_norm(m) + + +def Conv1d1x1(in_channels, out_channels, bias=True): + return Conv1d( + in_channels, out_channels, kernel_size=1, padding=0, dilation=1, bias=bias + ) + + +def _conv1x1_forward(conv, x, is_incremental): + if is_incremental: + x = conv.incremental_forward(x) + else: + x = conv(x) + return x + + +class ResidualConv1dGLU(nn.Module): + """Residual dilated conv1d + Gated linear unit + + Args: + residual_channels (int): Residual input / output channels + gate_channels (int): Gated activation channels. + kernel_size (int): Kernel size of convolution layers. + skip_out_channels (int): Skip connection channels. If None, set to same + as ``residual_channels``. + cin_channels (int): Local conditioning channels. If negative value is + set, local conditioning is disabled. + dropout (float): Dropout probability. + padding (int): Padding for convolution layers. If None, proper padding + is computed depends on dilation and kernel_size. + dilation (int): Dilation factor. + """ + + def __init__( + self, + residual_channels, + gate_channels, + kernel_size, + skip_out_channels=None, + cin_channels=-1, + dropout=1 - 0.95, + padding=None, + dilation=1, + causal=True, + bias=True, + *args, + **kwargs, + ): + super(ResidualConv1dGLU, self).__init__() + self.dropout = dropout + + if skip_out_channels is None: + skip_out_channels = residual_channels + if padding is None: + # no future time stamps available + if causal: + padding = (kernel_size - 1) * dilation + else: + padding = (kernel_size - 1) // 2 * dilation + self.causal = causal + + self.conv = Conv1d( + residual_channels, + gate_channels, + kernel_size, + padding=padding, + dilation=dilation, + bias=bias, + *args, + **kwargs, + ) + + # mel conditioning + self.conv1x1c = Conv1d1x1(cin_channels, gate_channels, bias=False) + + gate_out_channels = gate_channels // 2 + self.conv1x1_out = Conv1d1x1(gate_out_channels, residual_channels, bias=bias) + self.conv1x1_skip = Conv1d1x1(gate_out_channels, skip_out_channels, bias=bias) + + def forward(self, x, c=None): + return self._forward(x, c, False) + + def incremental_forward(self, x, c=None): + return self._forward(x, c, True) + + def clear_buffer(self): + for c in [ + self.conv, + self.conv1x1_out, + self.conv1x1_skip, + self.conv1x1c, + ]: + if c is not None: + c.clear_buffer() + + def _forward(self, x, c, is_incremental): + """Forward + + Args: + x (Tensor): B x C x T + c (Tensor): B x C x T, Mel conditioning features + Returns: + Tensor: output + """ + residual = x + x = F.dropout(x, p=self.dropout, training=self.training) + if is_incremental: + splitdim = -1 + x = self.conv.incremental_forward(x) + else: + splitdim = 1 + x = self.conv(x) + # remove future time steps + x = x[:, :, : residual.size(-1)] if self.causal else x + + a, b = x.split(x.size(splitdim) // 2, dim=splitdim) + + assert self.conv1x1c is not None + c = _conv1x1_forward(self.conv1x1c, c, is_incremental) + ca, cb = c.split(c.size(splitdim) // 2, dim=splitdim) + a, b = a + ca, b + cb + + x = torch.tanh(a) * torch.sigmoid(b) + + # For skip connection + s = _conv1x1_forward(self.conv1x1_skip, x, is_incremental) + + # For residual connection + x = _conv1x1_forward(self.conv1x1_out, x, is_incremental) + + x = (x + residual) * math.sqrt(0.5) + return x, s diff --git a/models/vocoders/autoregressive/wavenet/upsample.py b/models/vocoders/autoregressive/wavenet/upsample.py new file mode 100644 index 0000000000000000000000000000000000000000..b664302cd56545f1709a4f1874ebadd8e9375a9c --- /dev/null +++ b/models/vocoders/autoregressive/wavenet/upsample.py @@ -0,0 +1,109 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +import numpy as np + +from torch import nn +from torch.nn import functional as F + + +class Stretch2d(nn.Module): + def __init__(self, x_scale, y_scale, mode="nearest"): + super(Stretch2d, self).__init__() + self.x_scale = x_scale + self.y_scale = y_scale + self.mode = mode + + def forward(self, x): + return F.interpolate( + x, scale_factor=(self.y_scale, self.x_scale), mode=self.mode + ) + + +def _get_activation(upsample_activation): + nonlinear = getattr(nn, upsample_activation) + return nonlinear + + +class UpsampleNetwork(nn.Module): + def __init__( + self, + upsample_scales, + upsample_activation="none", + upsample_activation_params={}, + mode="nearest", + freq_axis_kernel_size=1, + cin_pad=0, + cin_channels=128, + ): + super(UpsampleNetwork, self).__init__() + self.up_layers = nn.ModuleList() + total_scale = np.prod(upsample_scales) + self.indent = cin_pad * total_scale + for scale in upsample_scales: + freq_axis_padding = (freq_axis_kernel_size - 1) // 2 + k_size = (freq_axis_kernel_size, scale * 2 + 1) + padding = (freq_axis_padding, scale) + stretch = Stretch2d(scale, 1, mode) + conv = nn.Conv2d(1, 1, kernel_size=k_size, padding=padding, bias=False) + conv.weight.data.fill_(1.0 / np.prod(k_size)) + conv = nn.utils.weight_norm(conv) + self.up_layers.append(stretch) + self.up_layers.append(conv) + if upsample_activation != "none": + nonlinear = _get_activation(upsample_activation) + self.up_layers.append(nonlinear(**upsample_activation_params)) + + def forward(self, c): + """ + Args: + c : B x C x T + """ + + # B x 1 x C x T + c = c.unsqueeze(1) + for f in self.up_layers: + c = f(c) + # B x C x T + c = c.squeeze(1) + + if self.indent > 0: + c = c[:, :, self.indent : -self.indent] + return c + + +class ConvInUpsampleNetwork(nn.Module): + def __init__( + self, + upsample_scales, + upsample_activation="none", + upsample_activation_params={}, + mode="nearest", + freq_axis_kernel_size=1, + cin_pad=0, + cin_channels=128, + ): + super(ConvInUpsampleNetwork, self).__init__() + # To capture wide-context information in conditional features + # meaningless if cin_pad == 0 + ks = 2 * cin_pad + 1 + self.conv_in = nn.Conv1d( + cin_channels, cin_channels, kernel_size=ks, padding=cin_pad, bias=False + ) + self.upsample = UpsampleNetwork( + upsample_scales, + upsample_activation, + upsample_activation_params, + mode, + freq_axis_kernel_size, + cin_pad=cin_pad, + cin_channels=cin_channels, + ) + + def forward(self, c): + c_up = self.upsample(self.conv_in(c)) + return c_up diff --git a/models/vocoders/autoregressive/wavenet/wavenet.py b/models/vocoders/autoregressive/wavenet/wavenet.py new file mode 100644 index 0000000000000000000000000000000000000000..d63f22c2600fd0f83e5bdf339ebb121b3d2f35e6 --- /dev/null +++ b/models/vocoders/autoregressive/wavenet/wavenet.py @@ -0,0 +1,170 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +from torch import nn +from torch.nn import functional as F + +from .modules import Conv1d1x1, ResidualConv1dGLU +from .upsample import ConvInUpsampleNetwork + + +def receptive_field_size( + total_layers, num_cycles, kernel_size, dilation=lambda x: 2**x +): + """Compute receptive field size + + Args: + total_layers (int): total layers + num_cycles (int): cycles + kernel_size (int): kernel size + dilation (lambda): lambda to compute dilation factor. ``lambda x : 1`` + to disable dilated convolution. + + Returns: + int: receptive field size in sample + + """ + assert total_layers % num_cycles == 0 + + layers_per_cycle = total_layers // num_cycles + dilations = [dilation(i % layers_per_cycle) for i in range(total_layers)] + return (kernel_size - 1) * sum(dilations) + 1 + + +class WaveNet(nn.Module): + """The WaveNet model that supports local and global conditioning. + + Args: + out_channels (int): Output channels. If input_type is mu-law quantized + one-hot vecror. this must equal to the quantize channels. Other wise + num_mixtures x 3 (pi, mu, log_scale). + layers (int): Number of total layers + stacks (int): Number of dilation cycles + residual_channels (int): Residual input / output channels + gate_channels (int): Gated activation channels. + skip_out_channels (int): Skip connection channels. + kernel_size (int): Kernel size of convolution layers. + dropout (float): Dropout probability. + input_dim (int): Number of mel-spec dimension. + upsample_scales (list): List of upsample scale. + ``np.prod(upsample_scales)`` must equal to hop size. Used only if + upsample_conditional_features is enabled. + freq_axis_kernel_size (int): Freq-axis kernel_size for transposed + convolution layers for upsampling. If you only care about time-axis + upsampling, set this to 1. + scalar_input (Bool): If True, scalar input ([-1, 1]) is expected, otherwise + quantized one-hot vector is expected.. + """ + + def __init__(self, cfg): + super(WaveNet, self).__init__() + self.cfg = cfg + self.scalar_input = self.cfg.VOCODER.SCALAR_INPUT + self.out_channels = self.cfg.VOCODER.OUT_CHANNELS + self.cin_channels = self.cfg.VOCODER.INPUT_DIM + self.residual_channels = self.cfg.VOCODER.RESIDUAL_CHANNELS + self.layers = self.cfg.VOCODER.LAYERS + self.stacks = self.cfg.VOCODER.STACKS + self.gate_channels = self.cfg.VOCODER.GATE_CHANNELS + self.kernel_size = self.cfg.VOCODER.KERNEL_SIZE + self.skip_out_channels = self.cfg.VOCODER.SKIP_OUT_CHANNELS + self.dropout = self.cfg.VOCODER.DROPOUT + self.upsample_scales = self.cfg.VOCODER.UPSAMPLE_SCALES + self.mel_frame_pad = self.cfg.VOCODER.MEL_FRAME_PAD + + assert self.layers % self.stacks == 0 + + layers_per_stack = self.layers // self.stacks + if self.scalar_input: + self.first_conv = Conv1d1x1(1, self.residual_channels) + else: + self.first_conv = Conv1d1x1(self.out_channels, self.residual_channels) + + self.conv_layers = nn.ModuleList() + for layer in range(self.layers): + dilation = 2 ** (layer % layers_per_stack) + conv = ResidualConv1dGLU( + self.residual_channels, + self.gate_channels, + kernel_size=self.kernel_size, + skip_out_channels=self.skip_out_channels, + bias=True, + dilation=dilation, + dropout=self.dropout, + cin_channels=self.cin_channels, + ) + self.conv_layers.append(conv) + + self.last_conv_layers = nn.ModuleList( + [ + nn.ReLU(inplace=True), + Conv1d1x1(self.skip_out_channels, self.skip_out_channels), + nn.ReLU(inplace=True), + Conv1d1x1(self.skip_out_channels, self.out_channels), + ] + ) + + self.upsample_net = ConvInUpsampleNetwork( + upsample_scales=self.upsample_scales, + cin_pad=self.mel_frame_pad, + cin_channels=self.cin_channels, + ) + + self.receptive_field = receptive_field_size( + self.layers, self.stacks, self.kernel_size + ) + + def forward(self, x, mel, softmax=False): + """Forward step + + Args: + x (Tensor): One-hot encoded audio signal, shape (B x C x T) + mel (Tensor): Local conditioning features, + shape (B x cin_channels x T) + softmax (bool): Whether applies softmax or not. + + Returns: + Tensor: output, shape B x out_channels x T + """ + B, _, T = x.size() + + mel = self.upsample_net(mel) + assert mel.shape[-1] == x.shape[-1] + + x = self.first_conv(x) + skips = 0 + for f in self.conv_layers: + x, h = f(x, mel) + skips += h + skips *= math.sqrt(1.0 / len(self.conv_layers)) + + x = skips + for f in self.last_conv_layers: + x = f(x) + + x = F.softmax(x, dim=1) if softmax else x + + return x + + def clear_buffer(self): + self.first_conv.clear_buffer() + for f in self.conv_layers: + f.clear_buffer() + for f in self.last_conv_layers: + try: + f.clear_buffer() + except AttributeError: + pass + + def make_generation_fast_(self): + def remove_weight_norm(m): + try: + nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + + self.apply(remove_weight_norm) diff --git a/models/vocoders/autoregressive/wavernn/__pycache__/wavernn.cpython-39.pyc b/models/vocoders/autoregressive/wavernn/__pycache__/wavernn.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4f96800a3c1bf944107c7681639e59123277bb89 Binary files /dev/null and b/models/vocoders/autoregressive/wavernn/__pycache__/wavernn.cpython-39.pyc differ diff --git a/models/vocoders/autoregressive/wavernn/wavernn.py b/models/vocoders/autoregressive/wavernn/wavernn.py new file mode 100644 index 0000000000000000000000000000000000000000..c7475fa8fe8b4575bf714e615349582ff98bbc27 --- /dev/null +++ b/models/vocoders/autoregressive/wavernn/wavernn.py @@ -0,0 +1,188 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import numpy as np + + +class ResBlock(nn.Module): + def __init__(self, dims): + super().__init__() + self.conv1 = nn.Conv1d(dims, dims, kernel_size=1, bias=False) + self.conv2 = nn.Conv1d(dims, dims, kernel_size=1, bias=False) + self.batch_norm1 = nn.BatchNorm1d(dims) + self.batch_norm2 = nn.BatchNorm1d(dims) + + def forward(self, x): + residual = x + x = self.conv1(x) + x = self.batch_norm1(x) + x = F.relu(x) + x = self.conv2(x) + x = self.batch_norm2(x) + x = x + residual + return x + + +class MelResNet(nn.Module): + def __init__(self, res_blocks, in_dims, compute_dims, res_out_dims, pad): + super().__init__() + kernel_size = pad * 2 + 1 + self.conv_in = nn.Conv1d( + in_dims, compute_dims, kernel_size=kernel_size, bias=False + ) + self.batch_norm = nn.BatchNorm1d(compute_dims) + self.layers = nn.ModuleList() + for i in range(res_blocks): + self.layers.append(ResBlock(compute_dims)) + self.conv_out = nn.Conv1d(compute_dims, res_out_dims, kernel_size=1) + + def forward(self, x): + x = self.conv_in(x) + x = self.batch_norm(x) + x = F.relu(x) + for f in self.layers: + x = f(x) + x = self.conv_out(x) + return x + + +class Stretch2d(nn.Module): + def __init__(self, x_scale, y_scale): + super().__init__() + self.x_scale = x_scale + self.y_scale = y_scale + + def forward(self, x): + b, c, h, w = x.size() + x = x.unsqueeze(-1).unsqueeze(3) + x = x.repeat(1, 1, 1, self.y_scale, 1, self.x_scale) + return x.view(b, c, h * self.y_scale, w * self.x_scale) + + +class UpsampleNetwork(nn.Module): + def __init__( + self, feat_dims, upsample_scales, compute_dims, res_blocks, res_out_dims, pad + ): + super().__init__() + total_scale = np.cumproduct(upsample_scales)[-1] + self.indent = pad * total_scale + self.resnet = MelResNet(res_blocks, feat_dims, compute_dims, res_out_dims, pad) + self.resnet_stretch = Stretch2d(total_scale, 1) + self.up_layers = nn.ModuleList() + for scale in upsample_scales: + kernel_size = (1, scale * 2 + 1) + padding = (0, scale) + stretch = Stretch2d(scale, 1) + conv = nn.Conv2d(1, 1, kernel_size=kernel_size, padding=padding, bias=False) + conv.weight.data.fill_(1.0 / kernel_size[1]) + self.up_layers.append(stretch) + self.up_layers.append(conv) + + def forward(self, m): + aux = self.resnet(m).unsqueeze(1) + aux = self.resnet_stretch(aux) + aux = aux.squeeze(1) + m = m.unsqueeze(1) + for f in self.up_layers: + m = f(m) + m = m.squeeze(1)[:, :, self.indent : -self.indent] + return m.transpose(1, 2), aux.transpose(1, 2) + + +class WaveRNN(nn.Module): + def __init__(self, cfg): + super().__init__() + + self.cfg = cfg + self.pad = self.cfg.VOCODER.MEL_FRAME_PAD + + if self.cfg.VOCODER.MODE == "mu_law_quantize": + self.n_classes = 2**self.cfg.VOCODER.BITS + elif self.cfg.VOCODER.MODE == "mu_law" or self.cfg.VOCODER: + self.n_classes = 30 + + self._to_flatten = [] + + self.rnn_dims = self.cfg.VOCODER.RNN_DIMS + self.aux_dims = self.cfg.VOCODER.RES_OUT_DIMS // 4 + self.hop_length = self.cfg.VOCODER.HOP_LENGTH + self.fc_dims = self.cfg.VOCODER.FC_DIMS + self.upsample_factors = self.cfg.VOCODER.UPSAMPLE_FACTORS + self.feat_dims = self.cfg.VOCODER.INPUT_DIM + self.compute_dims = self.cfg.VOCODER.COMPUTE_DIMS + self.res_out_dims = self.cfg.VOCODER.RES_OUT_DIMS + self.res_blocks = self.cfg.VOCODER.RES_BLOCKS + + self.upsample = UpsampleNetwork( + self.feat_dims, + self.upsample_factors, + self.compute_dims, + self.res_blocks, + self.res_out_dims, + self.pad, + ) + self.I = nn.Linear(self.feat_dims + self.aux_dims + 1, self.rnn_dims) + + self.rnn1 = nn.GRU(self.rnn_dims, self.rnn_dims, batch_first=True) + self.rnn2 = nn.GRU( + self.rnn_dims + self.aux_dims, self.rnn_dims, batch_first=True + ) + self._to_flatten += [self.rnn1, self.rnn2] + + self.fc1 = nn.Linear(self.rnn_dims + self.aux_dims, self.fc_dims) + self.fc2 = nn.Linear(self.fc_dims + self.aux_dims, self.fc_dims) + self.fc3 = nn.Linear(self.fc_dims, self.n_classes) + + self.num_params() + + self._flatten_parameters() + + def forward(self, x, mels): + device = next(self.parameters()).device + + self._flatten_parameters() + + batch_size = x.size(0) + h1 = torch.zeros(1, batch_size, self.rnn_dims, device=device) + h2 = torch.zeros(1, batch_size, self.rnn_dims, device=device) + mels, aux = self.upsample(mels) + + aux_idx = [self.aux_dims * i for i in range(5)] + a1 = aux[:, :, aux_idx[0] : aux_idx[1]] + a2 = aux[:, :, aux_idx[1] : aux_idx[2]] + a3 = aux[:, :, aux_idx[2] : aux_idx[3]] + a4 = aux[:, :, aux_idx[3] : aux_idx[4]] + + x = torch.cat([x.unsqueeze(-1), mels, a1], dim=2) + x = self.I(x) + res = x + x, _ = self.rnn1(x, h1) + + x = x + res + res = x + x = torch.cat([x, a2], dim=2) + x, _ = self.rnn2(x, h2) + + x = x + res + x = torch.cat([x, a3], dim=2) + x = F.relu(self.fc1(x)) + + x = torch.cat([x, a4], dim=2) + x = F.relu(self.fc2(x)) + return self.fc3(x) + + def num_params(self, print_out=True): + parameters = filter(lambda p: p.requires_grad, self.parameters()) + parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000 + if print_out: + print("Trainable Parameters: %.3fM" % parameters) + return parameters + + def _flatten_parameters(self): + [m.flatten_parameters() for m in self._to_flatten] diff --git a/models/vocoders/diffusion/diffusion_vocoder_dataset.py b/models/vocoders/diffusion/diffusion_vocoder_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/vocoders/diffusion/diffusion_vocoder_inference.py b/models/vocoders/diffusion/diffusion_vocoder_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/vocoders/diffusion/diffusion_vocoder_trainer.py b/models/vocoders/diffusion/diffusion_vocoder_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/vocoders/diffusion/diffwave/__pycache__/diffwave.cpython-39.pyc b/models/vocoders/diffusion/diffwave/__pycache__/diffwave.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..472d36bd06ef8dfcc630bab519f7ee69fa343dba Binary files /dev/null and b/models/vocoders/diffusion/diffwave/__pycache__/diffwave.cpython-39.pyc differ diff --git a/models/vocoders/diffusion/diffwave/diffwave.py b/models/vocoders/diffusion/diffwave/diffwave.py new file mode 100644 index 0000000000000000000000000000000000000000..c9379b0b622c6da8a754f2cc87fd7723eacfa995 --- /dev/null +++ b/models/vocoders/diffusion/diffwave/diffwave.py @@ -0,0 +1,173 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from math import sqrt + + +Linear = nn.Linear +ConvTranspose2d = nn.ConvTranspose2d + + +def Conv1d(*args, **kwargs): + layer = nn.Conv1d(*args, **kwargs) + nn.init.kaiming_normal_(layer.weight) + return layer + + +@torch.jit.script +def silu(x): + return x * torch.sigmoid(x) + + +class DiffusionEmbedding(nn.Module): + def __init__(self, max_steps): + super().__init__() + self.register_buffer( + "embedding", self._build_embedding(max_steps), persistent=False + ) + self.projection1 = Linear(128, 512) + self.projection2 = Linear(512, 512) + + def forward(self, diffusion_step): + if diffusion_step.dtype in [torch.int32, torch.int64]: + x = self.embedding[diffusion_step] + else: + x = self._lerp_embedding(diffusion_step) + x = self.projection1(x) + x = silu(x) + x = self.projection2(x) + x = silu(x) + return x + + def _lerp_embedding(self, t): + low_idx = torch.floor(t).long() + high_idx = torch.ceil(t).long() + low = self.embedding[low_idx] + high = self.embedding[high_idx] + return low + (high - low) * (t - low_idx) + + def _build_embedding(self, max_steps): + steps = torch.arange(max_steps).unsqueeze(1) # [T,1] + dims = torch.arange(64).unsqueeze(0) # [1,64] + table = steps * 10.0 ** (dims * 4.0 / 63.0) # [T,64] + table = torch.cat([torch.sin(table), torch.cos(table)], dim=1) + return table + + +class SpectrogramUpsampler(nn.Module): + def __init__(self, upsample_factors): + super().__init__() + self.conv1 = ConvTranspose2d( + 1, + 1, + [3, upsample_factors[0] * 2], + stride=[1, upsample_factors[0]], + padding=[1, upsample_factors[0] // 2], + ) + self.conv2 = ConvTranspose2d( + 1, + 1, + [3, upsample_factors[1] * 2], + stride=[1, upsample_factors[1]], + padding=[1, upsample_factors[1] // 2], + ) + + def forward(self, x): + x = torch.unsqueeze(x, 1) + x = self.conv1(x) + x = F.leaky_relu(x, 0.4) + x = self.conv2(x) + x = F.leaky_relu(x, 0.4) + x = torch.squeeze(x, 1) + return x + + +class ResidualBlock(nn.Module): + def __init__(self, n_mels, residual_channels, dilation): + super().__init__() + self.dilated_conv = Conv1d( + residual_channels, + 2 * residual_channels, + 3, + padding=dilation, + dilation=dilation, + ) + self.diffusion_projection = Linear(512, residual_channels) + + self.conditioner_projection = Conv1d(n_mels, 2 * residual_channels, 1) + + self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1) + + def forward(self, x, diffusion_step, conditioner): + diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1) + y = x + diffusion_step + + conditioner = self.conditioner_projection(conditioner) + y = self.dilated_conv(y) + conditioner + + gate, filter = torch.chunk(y, 2, dim=1) + y = torch.sigmoid(gate) * torch.tanh(filter) + + y = self.output_projection(y) + residual, skip = torch.chunk(y, 2, dim=1) + return (x + residual) / sqrt(2.0), skip + + +class DiffWave(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.cfg.VOCODER.NOISE_SCHEDULE = np.linspace( + self.cfg.VOCODER.NOISE_SCHEDULE_FACTORS[0], + self.cfg.VOCODER.NOISE_SCHEDULE_FACTORS[1], + self.cfg.VOCODER.NOISE_SCHEDULE_FACTORS[2], + ).tolist() + self.input_projection = Conv1d(1, self.cfg.VOCODER.RESIDUAL_CHANNELS, 1) + self.diffusion_embedding = DiffusionEmbedding( + len(self.cfg.VOCODER.NOISE_SCHEDULE) + ) + self.spectrogram_upsampler = SpectrogramUpsampler( + self.cfg.VOCODER.UPSAMPLE_FACTORS + ) + + self.residual_layers = nn.ModuleList( + [ + ResidualBlock( + self.cfg.VOCODER.INPUT_DIM, + self.cfg.VOCODER.RESIDUAL_CHANNELS, + 2 ** (i % self.cfg.VOCODER.DILATION_CYCLE_LENGTH), + ) + for i in range(self.cfg.VOCODER.RESIDUAL_LAYERS) + ] + ) + self.skip_projection = Conv1d( + self.cfg.VOCODER.RESIDUAL_CHANNELS, self.cfg.VOCODER.RESIDUAL_CHANNELS, 1 + ) + self.output_projection = Conv1d(self.cfg.VOCODER.RESIDUAL_CHANNELS, 1, 1) + nn.init.zeros_(self.output_projection.weight) + + def forward(self, audio, diffusion_step, spectrogram): + x = audio.unsqueeze(1) + x = self.input_projection(x) + x = F.relu(x) + + diffusion_step = self.diffusion_embedding(diffusion_step) + spectrogram = self.spectrogram_upsampler(spectrogram) + + skip = None + for layer in self.residual_layers: + x, skip_connection = layer(x, diffusion_step, spectrogram) + skip = skip_connection if skip is None else skip_connection + skip + + x = skip / sqrt(len(self.residual_layers)) + x = self.skip_projection(x) + x = F.relu(x) + x = self.output_projection(x) + return x diff --git a/models/vocoders/dsp/world/world.py b/models/vocoders/dsp/world/world.py new file mode 100644 index 0000000000000000000000000000000000000000..59f28e8e896f883fe6ce243dfb7f254e78fd09c6 --- /dev/null +++ b/models/vocoders/dsp/world/world.py @@ -0,0 +1,183 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# 1. Extract WORLD features including F0, AP, SP +# 2. Transform between SP and MCEP +import torchaudio +import pyworld as pw +import numpy as np +import torch +import diffsptk +import os +from tqdm import tqdm +import pickle +import json +import re +import torchaudio + +from cuhkszsvc.configs.config_parse import get_wav_path, get_wav_file_path +from utils.io import has_existed + + +def get_mcep_params(fs): + """Hyperparameters of transformation between SP and MCEP + + Reference: + https://github.com/CSTR-Edinburgh/merlin/blob/master/misc/scripts/vocoder/world_v2/copy_synthesis.sh + + """ + if fs in [44100, 48000]: + fft_size = 2048 + alpha = 0.77 + if fs in [16000]: + fft_size = 1024 + alpha = 0.58 + return fft_size, alpha + + +def extract_world_features(wave_file, fs, frameshift): + # waveform: (1, seq) + waveform, sample_rate = torchaudio.load(wave_file) + if sample_rate != fs: + waveform = torchaudio.functional.resample( + waveform, orig_freq=sample_rate, new_freq=fs + ) + # x: (seq,) + x = np.array(torch.clamp(waveform[0], -1.0, 1.0), dtype=np.double) + + _f0, t = pw.dio(x, fs, frame_period=frameshift) # raw pitch extractor + f0 = pw.stonemask(x, _f0, t, fs) # pitch refinement + sp = pw.cheaptrick(x, f0, t, fs) # extract smoothed spectrogram + ap = pw.d4c(x, f0, t, fs) # extract aperiodicity + + return f0, sp, ap, fs + + +def sp2mcep(x, mcsize, fs): + fft_size, alpha = get_mcep_params(fs) + x = torch.as_tensor(x, dtype=torch.float) + + tmp = diffsptk.ScalarOperation("SquareRoot")(x) + tmp = diffsptk.ScalarOperation("Multiplication", 32768.0)(tmp) + mgc = diffsptk.MelCepstralAnalysis( + cep_order=mcsize - 1, fft_length=fft_size, alpha=alpha, n_iter=1 + )(tmp) + return mgc.numpy() + + +def mcep2sp(x, mcsize, fs): + fft_size, alpha = get_mcep_params(fs) + x = torch.as_tensor(x, dtype=torch.float) + + tmp = diffsptk.MelGeneralizedCepstrumToSpectrum( + alpha=alpha, + cep_order=mcsize - 1, + fft_length=fft_size, + )(x) + tmp = diffsptk.ScalarOperation("Division", 32768.0)(tmp) + sp = diffsptk.ScalarOperation("Power", 2)(tmp) + return sp.double().numpy() + + +def extract_mcep_features_of_dataset( + output_path, dataset_path, dataset, mcsize, fs, frameshift, splits=None +): + output_dir = os.path.join(output_path, dataset, "mcep/{}".format(fs)) + + if not splits: + splits = ["train", "test"] if dataset != "m4singer" else ["test"] + + for dataset_type in splits: + print("-" * 20) + print("Dataset: {}, {}".format(dataset, dataset_type)) + + output_file = os.path.join(output_dir, "{}.pkl".format(dataset_type)) + if has_existed(output_file): + continue + + # Extract SP features + print("\nExtracting SP featuers...") + sp_features = get_world_features_of_dataset( + output_path, dataset_path, dataset, dataset_type, fs, frameshift + ) + + # SP to MCEP + print("\nTransform SP to MCEP...") + mcep_features = [sp2mcep(sp, mcsize=mcsize, fs=fs) for sp in tqdm(sp_features)] + + # Save + os.makedirs(output_dir, exist_ok=True) + with open(output_file, "wb") as f: + pickle.dump(mcep_features, f) + + +def get_world_features_of_dataset( + output_path, + dataset_path, + dataset, + dataset_type, + fs, + frameshift, + save_sp_feature=False, +): + data_dir = os.path.join(output_path, dataset) + wave_dir = get_wav_path(dataset_path, dataset) + + # Dataset + dataset_file = os.path.join(data_dir, "{}.json".format(dataset_type)) + if not os.path.exists(dataset_file): + print("File {} has not existed.".format(dataset_file)) + return None + + with open(dataset_file, "r") as f: + datasets = json.load(f) + + # Save dir + f0_dir = os.path.join(output_path, dataset, "f0") + os.makedirs(f0_dir, exist_ok=True) + + # Extract + f0_features = [] + sp_features = [] + for utt in tqdm(datasets): + wave_file = get_wav_file_path(dataset, wave_dir, utt) + f0, sp, _, _ = extract_world_features(wave_file, fs, frameshift) + + sp_features.append(sp) + f0_features.append(f0) + + # Save sp + if save_sp_feature: + sp_dir = os.path.join(output_path, dataset, "sp") + os.makedirs(sp_dir, exist_ok=True) + with open(os.path.join(sp_dir, "{}.pkl".format(dataset_type)), "wb") as f: + pickle.dump(sp_features, f) + + # F0 statistics + f0_statistics_file = os.path.join(f0_dir, "{}_f0.pkl".format(dataset_type)) + f0_statistics(f0_features, f0_statistics_file) + + return sp_features + + +def f0_statistics(f0_features, path): + print("\nF0 statistics...") + + total_f0 = [] + for f0 in tqdm(f0_features): + total_f0 += [f for f in f0 if f != 0] + + mean = sum(total_f0) / len(total_f0) + print("Min = {}, Max = {}, Mean = {}".format(min(total_f0), max(total_f0), mean)) + + with open(path, "wb") as f: + pickle.dump([mean, total_f0], f) + + +def world_synthesis(f0, sp, ap, fs, frameshift): + y = pw.synthesize( + f0, sp, ap, fs, frame_period=frameshift + ) # synthesize an utterance using the parameters + return y diff --git a/models/vocoders/flow/flow_vocoder_dataset.py b/models/vocoders/flow/flow_vocoder_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/vocoders/flow/flow_vocoder_inference.py b/models/vocoders/flow/flow_vocoder_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/vocoders/flow/flow_vocoder_trainer.py b/models/vocoders/flow/flow_vocoder_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/vocoders/flow/waveglow/__pycache__/waveglow.cpython-39.pyc b/models/vocoders/flow/waveglow/__pycache__/waveglow.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3afb6276a6e3580e6aa724d8262c2ca209bd67d0 Binary files /dev/null and b/models/vocoders/flow/waveglow/__pycache__/waveglow.cpython-39.pyc differ diff --git a/models/vocoders/flow/waveglow/waveglow.py b/models/vocoders/flow/waveglow/waveglow.py new file mode 100644 index 0000000000000000000000000000000000000000..13e2a1bf8f5e3c3d47a031ceec87e4ff111cd5fe --- /dev/null +++ b/models/vocoders/flow/waveglow/waveglow.py @@ -0,0 +1,249 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch.autograd import Variable +import torch.nn.functional as F + + +@torch.jit.script +def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): + n_channels_int = n_channels[0] + in_act = input_a + input_b + t_act = torch.tanh(in_act[:, :n_channels_int, :]) + s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) + acts = t_act * s_act + return acts + + +class Invertible1x1Conv(torch.nn.Module): + """ + The layer outputs both the convolution, and the log determinant + of its weight matrix. If reverse=True it does convolution with + inverse + """ + + def __init__(self, c): + super(Invertible1x1Conv, self).__init__() + self.conv = torch.nn.Conv1d( + c, c, kernel_size=1, stride=1, padding=0, bias=False + ) + + # Sample a random orthonormal matrix to initialize weights + W = torch.linalg.qr(torch.FloatTensor(c, c).normal_())[0] + + # Ensure determinant is 1.0 not -1.0 + if torch.det(W) < 0: + W[:, 0] = -1 * W[:, 0] + W = W.view(c, c, 1) + self.conv.weight.data = W + + def forward(self, z, reverse=False): + # shape + batch_size, group_size, n_of_groups = z.size() + + W = self.conv.weight.squeeze() + + if reverse: + if not hasattr(self, "W_inverse"): + # Reverse computation + W_inverse = W.float().inverse() + W_inverse = Variable(W_inverse[..., None]) + if z.type() == "torch.cuda.HalfTensor": + W_inverse = W_inverse.half() + self.W_inverse = W_inverse + z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0) + return z + else: + # Forward computation + log_det_W = batch_size * n_of_groups * torch.logdet(W) + z = self.conv(z) + return z, log_det_W + + +class WN(torch.nn.Module): + """ + This is the WaveNet like layer for the affine coupling. The primary difference + from WaveNet is the convolutions need not be causal. There is also no dilation + size reset. The dilation only doubles on each layer + """ + + def __init__( + self, n_in_channels, n_mel_channels, n_layers, n_channels, kernel_size + ): + super(WN, self).__init__() + assert kernel_size % 2 == 1 + assert n_channels % 2 == 0 + self.n_layers = n_layers + self.n_channels = n_channels + self.in_layers = torch.nn.ModuleList() + self.res_skip_layers = torch.nn.ModuleList() + + start = torch.nn.Conv1d(n_in_channels, n_channels, 1) + start = torch.nn.utils.weight_norm(start, name="weight") + self.start = start + + # Initializing last layer to 0 makes the affine coupling layers + # do nothing at first. This helps with training stability + end = torch.nn.Conv1d(n_channels, 2 * n_in_channels, 1) + end.weight.data.zero_() + end.bias.data.zero_() + self.end = end + + cond_layer = torch.nn.Conv1d(n_mel_channels, 2 * n_channels * n_layers, 1) + self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight") + + for i in range(n_layers): + dilation = 2**i + padding = int((kernel_size * dilation - dilation) / 2) + in_layer = torch.nn.Conv1d( + n_channels, + 2 * n_channels, + kernel_size, + dilation=dilation, + padding=padding, + ) + in_layer = torch.nn.utils.weight_norm(in_layer, name="weight") + self.in_layers.append(in_layer) + + # last one is not necessary + if i < n_layers - 1: + res_skip_channels = 2 * n_channels + else: + res_skip_channels = n_channels + res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1) + res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight") + self.res_skip_layers.append(res_skip_layer) + + def forward(self, forward_input): + audio, spect = forward_input + audio = self.start(audio) + output = torch.zeros_like(audio) + n_channels_tensor = torch.IntTensor([self.n_channels]) + + spect = self.cond_layer(spect) + + for i in range(self.n_layers): + spect_offset = i * 2 * self.n_channels + acts = fused_add_tanh_sigmoid_multiply( + self.in_layers[i](audio), + spect[:, spect_offset : spect_offset + 2 * self.n_channels, :], + n_channels_tensor, + ) + + res_skip_acts = self.res_skip_layers[i](acts) + if i < self.n_layers - 1: + audio = audio + res_skip_acts[:, : self.n_channels, :] + output = output + res_skip_acts[:, self.n_channels :, :] + else: + output = output + res_skip_acts + + return self.end(output) + + +class WaveGlow(torch.nn.Module): + def __init__(self, cfg): + super(WaveGlow, self).__init__() + + self.cfg = cfg + + self.upsample = torch.nn.ConvTranspose1d( + self.cfg.VOCODER.INPUT_DIM, + self.cfg.VOCODER.INPUT_DIM, + 1024, + stride=256, + ) + assert self.cfg.VOCODER.N_GROUP % 2 == 0 + self.n_flows = self.cfg.VOCODER.N_FLOWS + self.n_group = self.cfg.VOCODER.N_GROUP + self.n_early_every = self.cfg.VOCODER.N_EARLY_EVERY + self.n_early_size = self.cfg.VOCODER.N_EARLY_SIZE + self.WN = torch.nn.ModuleList() + self.convinv = torch.nn.ModuleList() + + n_half = int(self.cfg.VOCODER.N_GROUP / 2) + + # Set up layers with the right sizes based on how many dimensions + # have been output already + n_remaining_channels = self.cfg.VOCODER.N_GROUP + for k in range(self.cfg.VOCODER.N_FLOWS): + if k % self.n_early_every == 0 and k > 0: + n_half = n_half - int(self.n_early_size / 2) + n_remaining_channels = n_remaining_channels - self.n_early_size + self.convinv.append(Invertible1x1Conv(n_remaining_channels)) + self.WN.append( + WN( + n_half, + self.cfg.VOCODER.INPUT_DIM * self.cfg.VOCODER.N_GROUP, + self.cfg.VOCODER.N_LAYERS, + self.cfg.VOCODER.N_CHANNELS, + self.cfg.VOCODER.KERNEL_SIZE, + ) + ) + self.n_remaining_channels = n_remaining_channels # Useful during inference + + def forward(self, forward_input): + """ + forward_input[0] = mel_spectrogram: batch x n_mel_channels x frames + forward_input[1] = audio: batch x time + """ + spect, audio = forward_input + + # Upsample spectrogram to size of audio + spect = self.upsample(spect) + assert spect.size(2) >= audio.size(1) + if spect.size(2) > audio.size(1): + spect = spect[:, :, : audio.size(1)] + + spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3) + spect = ( + spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1) + ) + + audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1) + output_audio = [] + log_s_list = [] + log_det_W_list = [] + + for k in range(self.n_flows): + if k % self.n_early_every == 0 and k > 0: + output_audio.append(audio[:, : self.n_early_size, :]) + audio = audio[:, self.n_early_size :, :] + + audio, log_det_W = self.convinv[k](audio) + log_det_W_list.append(log_det_W) + + n_half = int(audio.size(1) / 2) + audio_0 = audio[:, :n_half, :] + audio_1 = audio[:, n_half:, :] + + output = self.WN[k]((audio_0, spect)) + log_s = output[:, n_half:, :] + b = output[:, :n_half, :] + audio_1 = torch.exp(log_s) * audio_1 + b + log_s_list.append(log_s) + + audio = torch.cat([audio_0, audio_1], 1) + + output_audio.append(audio) + return torch.cat(output_audio, 1), log_s_list, log_det_W_list + + @staticmethod + def remove_weightnorm(model): + waveglow = model + for WN in waveglow.WN: + WN.start = torch.nn.utils.remove_weight_norm(WN.start) + WN.in_layers = remove(WN.in_layers) + WN.cond_layer = torch.nn.utils.remove_weight_norm(WN.cond_layer) + WN.res_skip_layers = remove(WN.res_skip_layers) + return waveglow + + +def remove(conv_list): + new_conv_list = torch.nn.ModuleList() + for old_conv in conv_list: + old_conv = torch.nn.utils.remove_weight_norm(old_conv) + new_conv_list.append(old_conv) + return new_conv_list diff --git a/models/vocoders/gan/__pycache__/gan_vocoder_inference.cpython-39.pyc b/models/vocoders/gan/__pycache__/gan_vocoder_inference.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b7f5b8304a3a4243c04b1a9fdd7bc0bac3341183 Binary files /dev/null and b/models/vocoders/gan/__pycache__/gan_vocoder_inference.cpython-39.pyc differ diff --git a/models/vocoders/gan/discriminator/__pycache__/mpd.cpython-39.pyc b/models/vocoders/gan/discriminator/__pycache__/mpd.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9362b0fcfb64d74a4f3abef11f25b7c716e778e9 Binary files /dev/null and b/models/vocoders/gan/discriminator/__pycache__/mpd.cpython-39.pyc differ diff --git a/models/vocoders/gan/discriminator/mpd.py b/models/vocoders/gan/discriminator/mpd.py new file mode 100644 index 0000000000000000000000000000000000000000..f28711d18847a106a998cab90871fe6303a4fd08 --- /dev/null +++ b/models/vocoders/gan/discriminator/mpd.py @@ -0,0 +1,269 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch.nn import Conv2d, Conv1d +from torch.nn.utils import weight_norm, spectral_norm +from torch import nn +from modules.vocoder_blocks import * + +LRELU_SLOPE = 0.1 + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, cfg, period, kernel_size=5, stride=3, use_spectral_norm=False): + super(DiscriminatorP, self).__init__() + self.period = period + self.d_mult = cfg.model.mpd.discriminator_channel_mult_factor + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList( + [ + norm_f( + Conv2d( + 1, + int(32 * self.d_mult), + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f( + Conv2d( + int(32 * self.d_mult), + int(128 * self.d_mult), + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f( + Conv2d( + int(128 * self.d_mult), + int(512 * self.d_mult), + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f( + Conv2d( + int(512 * self.d_mult), + int(1024 * self.d_mult), + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f( + Conv2d( + int(1024 * self.d_mult), + int(1024 * self.d_mult), + (kernel_size, 1), + (stride, 1), + padding=(2, 0), + ) + ), + ] + ) + self.conv_post = norm_f( + Conv2d(int(1024 * self.d_mult), 1, (3, 1), 1, padding=(1, 0)) + ) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiPeriodDiscriminator(torch.nn.Module): + def __init__(self, cfg): + super(MultiPeriodDiscriminator, self).__init__() + self.mpd_reshapes = cfg.model.mpd.mpd_reshapes + print("mpd_reshapes: {}".format(self.mpd_reshapes)) + discriminators = [ + DiscriminatorP(cfg, rs, use_spectral_norm=cfg.model.mpd.use_spectral_norm) + for rs in self.mpd_reshapes + ] + self.discriminators = nn.ModuleList(discriminators) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +# TODO: merge with DiscriminatorP (lmxue, yicheng) +class DiscriminatorP_vits(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super(DiscriminatorP_vits, self).__init__() + self.period = period + self.use_spectral_norm = use_spectral_norm + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList( + [ + norm_f( + Conv2d( + 1, + 32, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(kernel_size, 1), 0), + ) + ), + norm_f( + Conv2d( + 32, + 128, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(kernel_size, 1), 0), + ) + ), + norm_f( + Conv2d( + 128, + 512, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(kernel_size, 1), 0), + ) + ), + norm_f( + Conv2d( + 512, + 1024, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(kernel_size, 1), 0), + ) + ), + norm_f( + Conv2d( + 1024, + 1024, + (kernel_size, 1), + 1, + padding=(get_padding(kernel_size, 1), 0), + ) + ), + ] + ) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(DiscriminatorS, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList( + [ + norm_f(Conv1d(1, 16, 15, 1, padding=7)), + norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), + norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ] + ) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +# TODO: merge with MultiPeriodDiscriminator (lmxue, yicheng) +class MultiPeriodDiscriminator_vits(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(MultiPeriodDiscriminator_vits, self).__init__() + periods = [2, 3, 5, 7, 11] + + discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] + discs = discs + [ + DiscriminatorP_vits(i, use_spectral_norm=use_spectral_norm) for i in periods + ] + self.discriminators = nn.ModuleList(discs) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + y_d_gs.append(y_d_g) + fmap_rs.append(fmap_r) + fmap_gs.append(fmap_g) + + outputs = { + "y_d_hat_r": y_d_rs, + "y_d_hat_g": y_d_gs, + "fmap_rs": fmap_rs, + "fmap_gs": fmap_gs, + } + + return outputs diff --git a/models/vocoders/gan/discriminator/mrd.py b/models/vocoders/gan/discriminator/mrd.py new file mode 100644 index 0000000000000000000000000000000000000000..38ee80bfbf82b6aa63c80dbc2c6ffed8cb50a924 --- /dev/null +++ b/models/vocoders/gan/discriminator/mrd.py @@ -0,0 +1,160 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm +from torch import nn + +LRELU_SLOPE = 0.1 + + +# This code is a refined MRD adopted from BigVGAN under the MIT License +# https://github.com/NVIDIA/BigVGAN + + +class DiscriminatorR(nn.Module): + def __init__(self, cfg, resolution): + super().__init__() + + self.resolution = resolution + assert ( + len(self.resolution) == 3 + ), "MRD layer requires list with len=3, got {}".format(self.resolution) + self.lrelu_slope = LRELU_SLOPE + + norm_f = ( + weight_norm if cfg.model.mrd.use_spectral_norm == False else spectral_norm + ) + if cfg.model.mrd.mrd_override: + print( + "INFO: overriding MRD use_spectral_norm as {}".format( + cfg.model.mrd.mrd_use_spectral_norm + ) + ) + norm_f = ( + weight_norm + if cfg.model.mrd.mrd_use_spectral_norm == False + else spectral_norm + ) + self.d_mult = cfg.model.mrd.discriminator_channel_mult_factor + if cfg.model.mrd.mrd_override: + print( + "INFO: overriding mrd channel multiplier as {}".format( + cfg.model.mrd.mrd_channel_mult + ) + ) + self.d_mult = cfg.model.mrd.mrd_channel_mult + + self.convs = nn.ModuleList( + [ + norm_f(nn.Conv2d(1, int(32 * self.d_mult), (3, 9), padding=(1, 4))), + norm_f( + nn.Conv2d( + int(32 * self.d_mult), + int(32 * self.d_mult), + (3, 9), + stride=(1, 2), + padding=(1, 4), + ) + ), + norm_f( + nn.Conv2d( + int(32 * self.d_mult), + int(32 * self.d_mult), + (3, 9), + stride=(1, 2), + padding=(1, 4), + ) + ), + norm_f( + nn.Conv2d( + int(32 * self.d_mult), + int(32 * self.d_mult), + (3, 9), + stride=(1, 2), + padding=(1, 4), + ) + ), + norm_f( + nn.Conv2d( + int(32 * self.d_mult), + int(32 * self.d_mult), + (3, 3), + padding=(1, 1), + ) + ), + ] + ) + self.conv_post = norm_f( + nn.Conv2d(int(32 * self.d_mult), 1, (3, 3), padding=(1, 1)) + ) + + def forward(self, x): + fmap = [] + + x = self.spectrogram(x) + x = x.unsqueeze(1) + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, self.lrelu_slope) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + def spectrogram(self, x): + n_fft, hop_length, win_length = self.resolution + x = F.pad( + x, + (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)), + mode="reflect", + ) + x = x.squeeze(1) + x = torch.stft( + x, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + center=False, + return_complex=True, + ) + x = torch.view_as_real(x) # [B, F, TT, 2] + mag = torch.norm(x, p=2, dim=-1) # [B, F, TT] + + return mag + + +class MultiResolutionDiscriminator(nn.Module): + def __init__(self, cfg, debug=False): + super().__init__() + self.resolutions = cfg.model.mrd.resolutions + assert ( + len(self.resolutions) == 3 + ), "MRD requires list of list with len=3, each element having a list with len=3. got {}".format( + self.resolutions + ) + self.discriminators = nn.ModuleList( + [DiscriminatorR(cfg, resolution) for resolution in self.resolutions] + ) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(x=y) + y_d_g, fmap_g = d(x=y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs diff --git a/models/vocoders/gan/discriminator/msd.py b/models/vocoders/gan/discriminator/msd.py new file mode 100644 index 0000000000000000000000000000000000000000..4c1556aea581878dcbe10f7a3bdebc33a4972e2c --- /dev/null +++ b/models/vocoders/gan/discriminator/msd.py @@ -0,0 +1,88 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch.nn import Conv1d, AvgPool1d +from torch.nn.utils import weight_norm, spectral_norm +from torch import nn +from modules.vocoder_blocks import * + + +LRELU_SLOPE = 0.1 + + +class DiscriminatorS(nn.Module): + def __init__(self, use_spectral_norm=False): + super(DiscriminatorS, self).__init__() + + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + + self.convs = nn.ModuleList( + [ + norm_f(Conv1d(1, 128, 15, 1, padding=7)), + norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), + norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), + norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ] + ) + + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiScaleDiscriminator(nn.Module): + def __init__(self, cfg): + super(MultiScaleDiscriminator, self).__init__() + + self.cfg = cfg + + self.discriminators = nn.ModuleList( + [ + DiscriminatorS(use_spectral_norm=True), + DiscriminatorS(), + DiscriminatorS(), + ] + ) + + self.meanpools = nn.ModuleList( + [AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)] + ) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + + for i, d in enumerate(self.discriminators): + if i != 0: + y = self.meanpools[i - 1](y) + y_hat = self.meanpools[i - 1](y_hat) + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs diff --git a/models/vocoders/gan/discriminator/mssbcqtd.py b/models/vocoders/gan/discriminator/mssbcqtd.py new file mode 100644 index 0000000000000000000000000000000000000000..213de5441754944a360707e99a3734ad035d9077 --- /dev/null +++ b/models/vocoders/gan/discriminator/mssbcqtd.py @@ -0,0 +1,182 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch import nn +from modules.vocoder_blocks import * + +from einops import rearrange +import torchaudio.transforms as T + +from nnAudio import features + +LRELU_SLOPE = 0.1 + + +class DiscriminatorCQT(nn.Module): + def __init__(self, cfg, hop_length, n_octaves, bins_per_octave): + super(DiscriminatorCQT, self).__init__() + self.cfg = cfg + + self.filters = cfg.model.mssbcqtd.filters + self.max_filters = cfg.model.mssbcqtd.max_filters + self.filters_scale = cfg.model.mssbcqtd.filters_scale + self.kernel_size = (3, 9) + self.dilations = cfg.model.mssbcqtd.dilations + self.stride = (1, 2) + + self.in_channels = cfg.model.mssbcqtd.in_channels + self.out_channels = cfg.model.mssbcqtd.out_channels + self.fs = cfg.preprocess.sample_rate + self.hop_length = hop_length + self.n_octaves = n_octaves + self.bins_per_octave = bins_per_octave + + self.cqt_transform = features.cqt.CQT2010v2( + sr=self.fs * 2, + hop_length=self.hop_length, + n_bins=self.bins_per_octave * self.n_octaves, + bins_per_octave=self.bins_per_octave, + output_format="Complex", + pad_mode="constant", + ) + + self.conv_pres = nn.ModuleList() + for i in range(self.n_octaves): + self.conv_pres.append( + NormConv2d( + self.in_channels * 2, + self.in_channels * 2, + kernel_size=self.kernel_size, + padding=get_2d_padding(self.kernel_size), + ) + ) + + self.convs = nn.ModuleList() + + self.convs.append( + NormConv2d( + self.in_channels * 2, + self.filters, + kernel_size=self.kernel_size, + padding=get_2d_padding(self.kernel_size), + ) + ) + + in_chs = min(self.filters_scale * self.filters, self.max_filters) + for i, dilation in enumerate(self.dilations): + out_chs = min( + (self.filters_scale ** (i + 1)) * self.filters, self.max_filters + ) + self.convs.append( + NormConv2d( + in_chs, + out_chs, + kernel_size=self.kernel_size, + stride=self.stride, + dilation=(dilation, 1), + padding=get_2d_padding(self.kernel_size, (dilation, 1)), + norm="weight_norm", + ) + ) + in_chs = out_chs + out_chs = min( + (self.filters_scale ** (len(self.dilations) + 1)) * self.filters, + self.max_filters, + ) + self.convs.append( + NormConv2d( + in_chs, + out_chs, + kernel_size=(self.kernel_size[0], self.kernel_size[0]), + padding=get_2d_padding((self.kernel_size[0], self.kernel_size[0])), + norm="weight_norm", + ) + ) + + self.conv_post = NormConv2d( + out_chs, + self.out_channels, + kernel_size=(self.kernel_size[0], self.kernel_size[0]), + padding=get_2d_padding((self.kernel_size[0], self.kernel_size[0])), + norm="weight_norm", + ) + + self.activation = torch.nn.LeakyReLU(negative_slope=LRELU_SLOPE) + self.resample = T.Resample(orig_freq=self.fs, new_freq=self.fs * 2) + + def forward(self, x): + fmap = [] + + x = self.resample(x) + + z = self.cqt_transform(x) + + z_amplitude = z[:, :, :, 0].unsqueeze(1) + z_phase = z[:, :, :, 1].unsqueeze(1) + + z = torch.cat([z_amplitude, z_phase], dim=1) + z = rearrange(z, "b c w t -> b c t w") + + latent_z = [] + for i in range(self.n_octaves): + latent_z.append( + self.conv_pres[i]( + z[ + :, + :, + :, + i * self.bins_per_octave : (i + 1) * self.bins_per_octave, + ] + ) + ) + latent_z = torch.cat(latent_z, dim=-1) + + for i, l in enumerate(self.convs): + latent_z = l(latent_z) + + latent_z = self.activation(latent_z) + fmap.append(latent_z) + + latent_z = self.conv_post(latent_z) + + return latent_z, fmap + + +class MultiScaleSubbandCQTDiscriminator(nn.Module): + def __init__(self, cfg): + super(MultiScaleSubbandCQTDiscriminator, self).__init__() + + self.cfg = cfg + + self.discriminators = nn.ModuleList( + [ + DiscriminatorCQT( + cfg, + hop_length=cfg.model.mssbcqtd.hop_lengths[i], + n_octaves=cfg.model.mssbcqtd.n_octaves[i], + bins_per_octave=cfg.model.mssbcqtd.bins_per_octaves[i], + ) + for i in range(len(cfg.model.mssbcqtd.hop_lengths)) + ] + ) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + + for disc in self.discriminators: + y_d_r, fmap_r = disc(y) + y_d_g, fmap_g = disc(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs diff --git a/models/vocoders/gan/discriminator/msstftd.py b/models/vocoders/gan/discriminator/msstftd.py new file mode 100644 index 0000000000000000000000000000000000000000..83dedb78848d2d73ac667e7a191f05de1ed7bf21 --- /dev/null +++ b/models/vocoders/gan/discriminator/msstftd.py @@ -0,0 +1,226 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This code is adopted from META's Encodec under MIT License +# https://github.com/facebookresearch/encodec + +"""MS-STFT discriminator, provided here for reference.""" + +import typing as tp + +import torchaudio +import torch +from torch import nn +from einops import rearrange + +from modules.vocoder_blocks import * + + +FeatureMapType = tp.List[torch.Tensor] +LogitsType = torch.Tensor +DiscriminatorOutput = tp.Tuple[tp.List[LogitsType], tp.List[FeatureMapType]] + + +def get_2d_padding( + kernel_size: tp.Tuple[int, int], dilation: tp.Tuple[int, int] = (1, 1) +): + return ( + ((kernel_size[0] - 1) * dilation[0]) // 2, + ((kernel_size[1] - 1) * dilation[1]) // 2, + ) + + +class DiscriminatorSTFT(nn.Module): + """STFT sub-discriminator. + Args: + filters (int): Number of filters in convolutions + in_channels (int): Number of input channels. Default: 1 + out_channels (int): Number of output channels. Default: 1 + n_fft (int): Size of FFT for each scale. Default: 1024 + hop_length (int): Length of hop between STFT windows for each scale. Default: 256 + kernel_size (tuple of int): Inner Conv2d kernel sizes. Default: ``(3, 9)`` + stride (tuple of int): Inner Conv2d strides. Default: ``(1, 2)`` + dilations (list of int): Inner Conv2d dilation on the time dimension. Default: ``[1, 2, 4]`` + win_length (int): Window size for each scale. Default: 1024 + normalized (bool): Whether to normalize by magnitude after stft. Default: True + norm (str): Normalization method. Default: `'weight_norm'` + activation (str): Activation function. Default: `'LeakyReLU'` + activation_params (dict): Parameters to provide to the activation function. + growth (int): Growth factor for the filters. Default: 1 + """ + + def __init__( + self, + filters: int, + in_channels: int = 1, + out_channels: int = 1, + n_fft: int = 1024, + hop_length: int = 256, + win_length: int = 1024, + max_filters: int = 1024, + filters_scale: int = 1, + kernel_size: tp.Tuple[int, int] = (3, 9), + dilations: tp.List = [1, 2, 4], + stride: tp.Tuple[int, int] = (1, 2), + normalized: bool = True, + norm: str = "weight_norm", + activation: str = "LeakyReLU", + activation_params: dict = {"negative_slope": 0.2}, + ): + super().__init__() + assert len(kernel_size) == 2 + assert len(stride) == 2 + self.filters = filters + self.in_channels = in_channels + self.out_channels = out_channels + self.n_fft = n_fft + self.hop_length = hop_length + self.win_length = win_length + self.normalized = normalized + self.activation = getattr(torch.nn, activation)(**activation_params) + self.spec_transform = torchaudio.transforms.Spectrogram( + n_fft=self.n_fft, + hop_length=self.hop_length, + win_length=self.win_length, + window_fn=torch.hann_window, + normalized=self.normalized, + center=False, + pad_mode=None, + power=None, + ) + spec_channels = 2 * self.in_channels + self.convs = nn.ModuleList() + self.convs.append( + NormConv2d( + spec_channels, + self.filters, + kernel_size=kernel_size, + padding=get_2d_padding(kernel_size), + ) + ) + in_chs = min(filters_scale * self.filters, max_filters) + for i, dilation in enumerate(dilations): + out_chs = min((filters_scale ** (i + 1)) * self.filters, max_filters) + self.convs.append( + NormConv2d( + in_chs, + out_chs, + kernel_size=kernel_size, + stride=stride, + dilation=(dilation, 1), + padding=get_2d_padding(kernel_size, (dilation, 1)), + norm=norm, + ) + ) + in_chs = out_chs + out_chs = min( + (filters_scale ** (len(dilations) + 1)) * self.filters, max_filters + ) + self.convs.append( + NormConv2d( + in_chs, + out_chs, + kernel_size=(kernel_size[0], kernel_size[0]), + padding=get_2d_padding((kernel_size[0], kernel_size[0])), + norm=norm, + ) + ) + self.conv_post = NormConv2d( + out_chs, + self.out_channels, + kernel_size=(kernel_size[0], kernel_size[0]), + padding=get_2d_padding((kernel_size[0], kernel_size[0])), + norm=norm, + ) + + def forward(self, x: torch.Tensor): + """Discriminator STFT Module is the sub module of MultiScaleSTFTDiscriminator. + + Args: + x (torch.Tensor): input tensor of shape [B, 1, Time] + + Returns: + z: z is the output of the last convolutional layer of shape + fmap: fmap is the list of feature maps of every convolutional layer of shape + """ + fmap = [] + z = self.spec_transform(x) # [B, 2, Freq, Frames, 2] + z = torch.cat([z.real, z.imag], dim=1) + z = rearrange(z, "b c w t -> b c t w") + for i, layer in enumerate(self.convs): + z = layer(z) + + z = self.activation(z) + fmap.append(z) + z = self.conv_post(z) + return z, fmap + + +class MultiScaleSTFTDiscriminator(nn.Module): + """Multi-Scale STFT (MS-STFT) discriminator. + Args: + filters (int): Number of filters in convolutions + in_channels (int): Number of input channels. Default: 1 + out_channels (int): Number of output channels. Default: 1 + n_ffts (Sequence[int]): Size of FFT for each scale + hop_lengths (Sequence[int]): Length of hop between STFT windows for each scale + win_lengths (Sequence[int]): Window size for each scale + **kwargs: additional args for STFTDiscriminator + """ + + def __init__( + self, + cfg, + in_channels: int = 1, + out_channels: int = 1, + n_ffts: tp.List[int] = [1024, 2048, 512], + hop_lengths: tp.List[int] = [256, 512, 256], + win_lengths: tp.List[int] = [1024, 2048, 512], + **kwargs, + ): + self.cfg = cfg + super().__init__() + assert len(n_ffts) == len(hop_lengths) == len(win_lengths) + self.discriminators = nn.ModuleList( + [ + DiscriminatorSTFT( + filters=self.cfg.model.msstftd.filters, + in_channels=in_channels, + out_channels=out_channels, + n_fft=n_ffts[i], + win_length=win_lengths[i], + hop_length=hop_lengths[i], + **kwargs, + ) + for i in range(len(n_ffts)) + ] + ) + self.num_discriminators = len(self.discriminators) + + def forward(self, y, y_hat) -> DiscriminatorOutput: + """Multi-Scale STFT (MS-STFT) discriminator. + + Args: + x (torch.Tensor): input waveform + + Returns: + logits: list of every discriminator's output + fmaps: list of every discriminator's feature maps, + each feature maps is a list of Discriminator STFT's every layer + """ + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + + for disc in self.discriminators: + y_d_r, fmap_r = disc(y) + y_d_g, fmap_g = disc(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs diff --git a/models/vocoders/gan/gan_vocoder_dataset.py b/models/vocoders/gan/gan_vocoder_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..5bf87c371647a44fb5bcae33701eda65616e5fd7 --- /dev/null +++ b/models/vocoders/gan/gan_vocoder_dataset.py @@ -0,0 +1,205 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import random + +import numpy as np + +from torch.nn import functional as F + +from torch.nn.utils.rnn import pad_sequence +from utils.data_utils import * +from models.vocoders.vocoder_dataset import VocoderDataset + + +class GANVocoderDataset(VocoderDataset): + def __init__(self, cfg, dataset, is_valid=False): + """ + Args: + cfg: config + dataset: dataset name + is_valid: whether to use train or valid dataset + """ + super().__init__(cfg, dataset, is_valid) + + eval_index = random.randint(0, len(self.metadata) - 1) + eval_utt_info = self.metadata[eval_index] + eval_utt = "{}_{}".format(eval_utt_info["Dataset"], eval_utt_info["Uid"]) + self.eval_audio = np.load(self.utt2audio_path[eval_utt]) + if cfg.preprocess.use_mel: + self.eval_mel = np.load(self.utt2mel_path[eval_utt]) + if cfg.preprocess.use_frame_pitch: + self.eval_pitch = np.load(self.utt2frame_pitch_path[eval_utt]) + + def __getitem__(self, index): + utt_info = self.metadata[index] + + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + single_feature = dict() + + if self.cfg.preprocess.use_mel: + mel = np.load(self.utt2mel_path[utt]) + assert mel.shape[0] == self.cfg.preprocess.n_mel + + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = mel.shape[1] + + if single_feature["target_len"] <= self.cfg.preprocess.cut_mel_frame: + mel = np.pad( + mel, + ((0, 0), (0, self.cfg.preprocess.cut_mel_frame - mel.shape[-1])), + mode="constant", + ) + else: + if "start" not in single_feature.keys(): + start = random.randint( + 0, mel.shape[-1] - self.cfg.preprocess.cut_mel_frame + ) + end = start + self.cfg.preprocess.cut_mel_frame + single_feature["start"] = start + single_feature["end"] = end + mel = mel[:, single_feature["start"] : single_feature["end"]] + single_feature["mel"] = mel + + if self.cfg.preprocess.use_frame_pitch: + frame_pitch = np.load(self.utt2frame_pitch_path[utt]) + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = len(frame_pitch) + aligned_frame_pitch = align_length( + frame_pitch, single_feature["target_len"] + ) + + if single_feature["target_len"] <= self.cfg.preprocess.cut_mel_frame: + aligned_frame_pitch = np.pad( + aligned_frame_pitch, + ( + ( + 0, + self.cfg.preprocess.cut_mel_frame + * self.cfg.preprocess.hop_size + - audio.shape[-1], + ) + ), + mode="constant", + ) + else: + if "start" not in single_feature.keys(): + start = random.randint( + 0, + aligned_frame_pitch.shape[-1] + - self.cfg.preprocess.cut_mel_frame, + ) + end = start + self.cfg.preprocess.cut_mel_frame + single_feature["start"] = start + single_feature["end"] = end + aligned_frame_pitch = aligned_frame_pitch[ + single_feature["start"] : single_feature["end"] + ] + single_feature["frame_pitch"] = aligned_frame_pitch + + if self.cfg.preprocess.use_audio: + audio = np.load(self.utt2audio_path[utt]) + + assert "target_len" in single_feature.keys() + + if ( + audio.shape[-1] + <= self.cfg.preprocess.cut_mel_frame * self.cfg.preprocess.hop_size + ): + audio = np.pad( + audio, + ( + ( + 0, + self.cfg.preprocess.cut_mel_frame + * self.cfg.preprocess.hop_size + - audio.shape[-1], + ) + ), + mode="constant", + ) + else: + if "start" not in single_feature.keys(): + audio = audio[ + 0 : self.cfg.preprocess.cut_mel_frame + * self.cfg.preprocess.hop_size + ] + else: + audio = audio[ + single_feature["start"] + * self.cfg.preprocess.hop_size : single_feature["end"] + * self.cfg.preprocess.hop_size, + ] + single_feature["audio"] = audio + + if self.cfg.preprocess.use_amplitude_phase: + logamp = np.load(self.utt2logamp_path[utt]) + pha = np.load(self.utt2pha_path[utt]) + rea = np.load(self.utt2rea_path[utt]) + imag = np.load(self.utt2imag_path[utt]) + + assert "target_len" in single_feature.keys() + + if single_feature["target_len"] <= self.cfg.preprocess.cut_mel_frame: + logamp = np.pad( + logamp, + ((0, 0), (0, self.cfg.preprocess.cut_mel_frame - mel.shape[-1])), + mode="constant", + ) + pha = np.pad( + pha, + ((0, 0), (0, self.cfg.preprocess.cut_mel_frame - mel.shape[-1])), + mode="constant", + ) + rea = np.pad( + rea, + ((0, 0), (0, self.cfg.preprocess.cut_mel_frame - mel.shape[-1])), + mode="constant", + ) + imag = np.pad( + imag, + ((0, 0), (0, self.cfg.preprocess.cut_mel_frame - mel.shape[-1])), + mode="constant", + ) + else: + logamp = logamp[:, single_feature["start"] : single_feature["end"]] + pha = pha[:, single_feature["start"] : single_feature["end"]] + rea = rea[:, single_feature["start"] : single_feature["end"]] + imag = imag[:, single_feature["start"] : single_feature["end"]] + single_feature["logamp"] = logamp + single_feature["pha"] = pha + single_feature["rea"] = rea + single_feature["imag"] = imag + + return single_feature + + +class GANVocoderCollator(object): + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, cfg): + self.cfg = cfg + + def __call__(self, batch): + packed_batch_features = dict() + + # mel: [b, n_mels, frame] + # frame_pitch: [b, frame] + # audios: [b, frame * hop_size] + + for key in batch[0].keys(): + if key in ["target_len", "start", "end"]: + continue + else: + values = [torch.from_numpy(b[key]) for b in batch] + packed_batch_features[key] = pad_sequence( + values, batch_first=True, padding_value=0 + ) + + return packed_batch_features diff --git a/models/vocoders/gan/gan_vocoder_inference.py b/models/vocoders/gan/gan_vocoder_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..cb69631662dedf4fc73a29f675f0a4bc361b03ec --- /dev/null +++ b/models/vocoders/gan/gan_vocoder_inference.py @@ -0,0 +1,110 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from utils.util import pad_mels_to_tensors, pad_f0_to_tensors + + +def vocoder_inference(cfg, model, mels, f0s=None, device=None, fast_inference=False): + """Inference the vocoder + Args: + mels: A tensor of mel-specs with the shape (batch_size, num_mels, frames) + Returns: + audios: A tensor of audios with the shape (batch_size, seq_len) + """ + model.eval() + + with torch.no_grad(): + mels = mels.to(device) + if f0s != None: + f0s = f0s.to(device) + + if f0s == None and not cfg.preprocess.extract_amplitude_phase: + output = model.forward(mels) + elif cfg.preprocess.extract_amplitude_phase: + ( + _, + _, + _, + _, + output, + ) = model.forward(mels) + else: + output = model.forward(mels, f0s) + + return output.squeeze(1).detach().cpu() + + +def synthesis_audios(cfg, model, mels, f0s=None, batch_size=None, fast_inference=False): + """Inference the vocoder + Args: + mels: A list of mel-specs + Returns: + audios: A list of audios + """ + # Get the device + device = next(model.parameters()).device + + audios = [] + + # Pad the given list into tensors + mel_batches, mel_frames = pad_mels_to_tensors(mels, batch_size) + if f0s != None: + f0_batches = pad_f0_to_tensors(f0s, batch_size) + + if f0s == None: + for mel_batch, mel_frame in zip(mel_batches, mel_frames): + for i in range(mel_batch.shape[0]): + mel = mel_batch[i] + frame = mel_frame[i] + audio = vocoder_inference( + cfg, + model, + mel.unsqueeze(0), + device=device, + fast_inference=fast_inference, + ).squeeze(0) + + # # Apply fade_out to make the sound more natural + # fade_out = torch.linspace( + # 1, 0, steps=20 * model.cfg.preprocess.hop_size + # ).cpu() + + # calculate the audio length + audio_length = frame * model.cfg.preprocess.hop_size + audio = audio[:audio_length] + + # audio[-20 * model.cfg.preprocess.hop_size :] *= fade_out + + audios.append(audio) + else: + for mel_batch, f0_batch, mel_frame in zip(mel_batches, f0_batches, mel_frames): + for i in range(mel_batch.shape[0]): + mel = mel_batch[i] + f0 = f0_batch[i] + frame = mel_frame[i] + audio = vocoder_inference( + cfg, + model, + mel.unsqueeze(0), + f0s=f0.unsqueeze(0), + device=device, + fast_inference=fast_inference, + ).squeeze(0) + + # # Apply fade_out to make the sound more natural + # fade_out = torch.linspace( + # 1, 0, steps=20 * model.cfg.preprocess.hop_size + # ).cpu() + + # calculate the audio length + audio_length = frame * model.cfg.preprocess.hop_length + audio = audio[:audio_length] + + # audio[-20 * model.cfg.preprocess.hop_size :] *= fade_out + + audios.append(audio) + return audios diff --git a/models/vocoders/gan/gan_vocoder_trainer.py b/models/vocoders/gan/gan_vocoder_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..2fb9c8f03a7de14d0162bfd671d33b76890293a5 --- /dev/null +++ b/models/vocoders/gan/gan_vocoder_trainer.py @@ -0,0 +1,1112 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import sys +import time +import torch +import json +import itertools +import accelerate +import torch.distributed as dist +import torch.nn.functional as F +from tqdm import tqdm +from torch.nn.parallel import DistributedDataParallel +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler +from torch.utils.tensorboard import SummaryWriter + +from torch.optim import AdamW +from torch.optim.lr_scheduler import ExponentialLR + +from librosa.filters import mel as librosa_mel_fn + +from accelerate.logging import get_logger +from pathlib import Path + +from utils.io import save_audio +from utils.data_utils import * +from utils.util import ( + Logger, + ValueWindow, + remove_older_ckpt, + set_all_random_seed, + save_config, +) +from utils.mel import extract_mel_features +from models.vocoders.vocoder_trainer import VocoderTrainer +from models.vocoders.gan.gan_vocoder_dataset import ( + GANVocoderDataset, + GANVocoderCollator, +) + +from models.vocoders.gan.generator.bigvgan import BigVGAN +from models.vocoders.gan.generator.hifigan import HiFiGAN +from models.vocoders.gan.generator.melgan import MelGAN +from models.vocoders.gan.generator.nsfhifigan import NSFHiFiGAN +from models.vocoders.gan.generator.apnet import APNet + +from models.vocoders.gan.discriminator.mpd import MultiPeriodDiscriminator +from models.vocoders.gan.discriminator.mrd import MultiResolutionDiscriminator +from models.vocoders.gan.discriminator.mssbcqtd import MultiScaleSubbandCQTDiscriminator +from models.vocoders.gan.discriminator.msd import MultiScaleDiscriminator +from models.vocoders.gan.discriminator.msstftd import MultiScaleSTFTDiscriminator + +from models.vocoders.gan.gan_vocoder_inference import vocoder_inference + +supported_generators = { + "bigvgan": BigVGAN, + "hifigan": HiFiGAN, + "melgan": MelGAN, + "nsfhifigan": NSFHiFiGAN, + "apnet": APNet, +} + +supported_discriminators = { + "mpd": MultiPeriodDiscriminator, + "msd": MultiScaleDiscriminator, + "mrd": MultiResolutionDiscriminator, + "msstftd": MultiScaleSTFTDiscriminator, + "mssbcqtd": MultiScaleSubbandCQTDiscriminator, +} + + +class GANVocoderTrainer(VocoderTrainer): + def __init__(self, args, cfg): + super().__init__() + + self.args = args + self.cfg = cfg + + cfg.exp_name = args.exp_name + + # Init accelerator + self._init_accelerator() + self.accelerator.wait_for_everyone() + + # Init logger + with self.accelerator.main_process_first(): + self.logger = get_logger(args.exp_name, log_level=args.log_level) + + self.logger.info("=" * 56) + self.logger.info("||\t\t" + "New training process started." + "\t\t||") + self.logger.info("=" * 56) + self.logger.info("\n") + self.logger.debug(f"Using {args.log_level.upper()} logging level.") + self.logger.info(f"Experiment name: {args.exp_name}") + self.logger.info(f"Experiment directory: {self.exp_dir}") + self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") + if self.accelerator.is_main_process: + os.makedirs(self.checkpoint_dir, exist_ok=True) + self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") + + # Init training status + self.batch_count: int = 0 + self.step: int = 0 + self.epoch: int = 0 + + self.max_epoch = ( + self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf") + ) + self.logger.info( + "Max epoch: {}".format( + self.max_epoch if self.max_epoch < float("inf") else "Unlimited" + ) + ) + + # Check potential erorrs + if self.accelerator.is_main_process: + self._check_basic_configs() + self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride + self.checkpoints_path = [ + [] for _ in range(len(self.save_checkpoint_stride)) + ] + self.run_eval = self.cfg.train.run_eval + + # Set random seed + with self.accelerator.main_process_first(): + start = time.monotonic_ns() + self._set_random_seed(self.cfg.train.random_seed) + end = time.monotonic_ns() + self.logger.debug( + f"Setting random seed done in {(end - start) / 1e6:.2f}ms" + ) + self.logger.debug(f"Random seed: {self.cfg.train.random_seed}") + + # Build dataloader + with self.accelerator.main_process_first(): + self.logger.info("Building dataset...") + start = time.monotonic_ns() + self.train_dataloader, self.valid_dataloader = self._build_dataloader() + end = time.monotonic_ns() + self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms") + + # Build model + with self.accelerator.main_process_first(): + self.logger.info("Building model...") + start = time.monotonic_ns() + self.generator, self.discriminators = self._build_model() + end = time.monotonic_ns() + self.logger.debug(self.generator) + for _, discriminator in self.discriminators.items(): + self.logger.debug(discriminator) + self.logger.info(f"Building model done in {(end - start) / 1e6:.2f}ms") + self.logger.info(f"Model parameters: {self._count_parameters()/1e6:.2f}M") + + # Build optimizers and schedulers + with self.accelerator.main_process_first(): + self.logger.info("Building optimizer and scheduler...") + start = time.monotonic_ns() + ( + self.generator_optimizer, + self.discriminator_optimizer, + ) = self._build_optimizer() + ( + self.generator_scheduler, + self.discriminator_scheduler, + ) = self._build_scheduler() + end = time.monotonic_ns() + self.logger.info( + f"Building optimizer and scheduler done in {(end - start) / 1e6:.2f}ms" + ) + + # Accelerator preparing + self.logger.info("Initializing accelerate...") + start = time.monotonic_ns() + ( + self.train_dataloader, + self.valid_dataloader, + self.generator, + self.generator_optimizer, + self.discriminator_optimizer, + self.generator_scheduler, + self.discriminator_scheduler, + ) = self.accelerator.prepare( + self.train_dataloader, + self.valid_dataloader, + self.generator, + self.generator_optimizer, + self.discriminator_optimizer, + self.generator_scheduler, + self.discriminator_scheduler, + ) + for key, discriminator in self.discriminators.items(): + self.discriminators[key] = self.accelerator.prepare_model(discriminator) + end = time.monotonic_ns() + self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.2f}ms") + + # Build criterions + with self.accelerator.main_process_first(): + self.logger.info("Building criterion...") + start = time.monotonic_ns() + self.criterions = self._build_criterion() + end = time.monotonic_ns() + self.logger.info(f"Building criterion done in {(end - start) / 1e6:.2f}ms") + + # Resume checkpoints + with self.accelerator.main_process_first(): + if args.resume_type: + self.logger.info("Resuming from checkpoint...") + start = time.monotonic_ns() + ckpt_path = Path(args.checkpoint) + if self._is_valid_pattern(ckpt_path.parts[-1]): + ckpt_path = self._load_model( + None, args.checkpoint, args.resume_type + ) + else: + ckpt_path = self._load_model( + args.checkpoint, resume_type=args.resume_type + ) + end = time.monotonic_ns() + self.logger.info( + f"Resuming from checkpoint done in {(end - start) / 1e6:.2f}ms" + ) + self.checkpoints_path = json.load( + open(os.path.join(ckpt_path, "ckpts.json"), "r") + ) + + self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") + if self.accelerator.is_main_process: + os.makedirs(self.checkpoint_dir, exist_ok=True) + self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") + + # Save config + self.config_save_path = os.path.join(self.exp_dir, "args.json") + + def _build_dataset(self): + return GANVocoderDataset, GANVocoderCollator + + def _build_criterion(self): + class feature_criterion(torch.nn.Module): + def __init__(self, cfg): + super(feature_criterion, self).__init__() + self.cfg = cfg + self.l1Loss = torch.nn.L1Loss(reduction="mean") + self.l2Loss = torch.nn.MSELoss(reduction="mean") + self.relu = torch.nn.ReLU() + + def __call__(self, fmap_r, fmap_g): + loss = 0 + + if self.cfg.model.generator in [ + "hifigan", + "nsfhifigan", + "bigvgan", + "apnet", + ]: + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss += torch.mean(torch.abs(rl - gl)) + + loss = loss * 2 + elif self.cfg.model.generator in ["melgan"]: + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss += self.l1Loss(rl, gl) + + loss = loss * 10 + elif self.cfg.model.generator in ["codec"]: + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss = loss + self.l1Loss(rl, gl) / torch.mean( + torch.abs(rl) + ) + + KL_scale = len(fmap_r) * len(fmap_r[0]) + + loss = 3 * loss / KL_scale + else: + raise NotImplementedError + + return loss + + class discriminator_criterion(torch.nn.Module): + def __init__(self, cfg): + super(discriminator_criterion, self).__init__() + self.cfg = cfg + self.l1Loss = torch.nn.L1Loss(reduction="mean") + self.l2Loss = torch.nn.MSELoss(reduction="mean") + self.relu = torch.nn.ReLU() + + def __call__(self, disc_real_outputs, disc_generated_outputs): + loss = 0 + r_losses = [] + g_losses = [] + + if self.cfg.model.generator in [ + "hifigan", + "nsfhifigan", + "bigvgan", + "apnet", + ]: + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean((1 - dr) ** 2) + g_loss = torch.mean(dg**2) + loss += r_loss + g_loss + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + elif self.cfg.model.generator in ["melgan"]: + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean(self.relu(1 - dr)) + g_loss = torch.mean(self.relu(1 + dg)) + loss = loss + r_loss + g_loss + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + elif self.cfg.model.generator in ["codec"]: + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean(self.relu(1 - dr)) + g_loss = torch.mean(self.relu(1 + dg)) + loss = loss + r_loss + g_loss + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + loss = loss / len(disc_real_outputs) + else: + raise NotImplementedError + + return loss, r_losses, g_losses + + class generator_criterion(torch.nn.Module): + def __init__(self, cfg): + super(generator_criterion, self).__init__() + self.cfg = cfg + self.l1Loss = torch.nn.L1Loss(reduction="mean") + self.l2Loss = torch.nn.MSELoss(reduction="mean") + self.relu = torch.nn.ReLU() + + def __call__(self, disc_outputs): + loss = 0 + gen_losses = [] + + if self.cfg.model.generator in [ + "hifigan", + "nsfhifigan", + "bigvgan", + "apnet", + ]: + for dg in disc_outputs: + l = torch.mean((1 - dg) ** 2) + gen_losses.append(l) + loss += l + elif self.cfg.model.generator in ["melgan"]: + for dg in disc_outputs: + l = -torch.mean(dg) + gen_losses.append(l) + loss += l + elif self.cfg.model.generator in ["codec"]: + for dg in disc_outputs: + l = torch.mean(self.relu(1 - dg)) / len(disc_outputs) + gen_losses.append(l) + loss += l + else: + raise NotImplementedError + + return loss, gen_losses + + class mel_criterion(torch.nn.Module): + def __init__(self, cfg): + super(mel_criterion, self).__init__() + self.cfg = cfg + self.l1Loss = torch.nn.L1Loss(reduction="mean") + self.l2Loss = torch.nn.MSELoss(reduction="mean") + self.relu = torch.nn.ReLU() + + def __call__(self, y_gt, y_pred): + loss = 0 + + if self.cfg.model.generator in [ + "hifigan", + "nsfhifigan", + "bigvgan", + "melgan", + "codec", + "apnet", + ]: + y_gt_mel = extract_mel_features(y_gt, self.cfg.preprocess) + y_pred_mel = extract_mel_features( + y_pred.squeeze(1), self.cfg.preprocess + ) + + loss = self.l1Loss(y_gt_mel, y_pred_mel) * 45 + else: + raise NotImplementedError + + return loss + + class wav_criterion(torch.nn.Module): + def __init__(self, cfg): + super(wav_criterion, self).__init__() + self.cfg = cfg + self.l1Loss = torch.nn.L1Loss(reduction="mean") + self.l2Loss = torch.nn.MSELoss(reduction="mean") + self.relu = torch.nn.ReLU() + + def __call__(self, y_gt, y_pred): + loss = 0 + + if self.cfg.model.generator in [ + "hifigan", + "nsfhifigan", + "bigvgan", + "apnet", + ]: + loss = self.l2Loss(y_gt, y_pred.squeeze(1)) * 100 + elif self.cfg.model.generator in ["melgan"]: + loss = self.l1Loss(y_gt, y_pred.squeeze(1)) / 10 + elif self.cfg.model.generator in ["codec"]: + loss = self.l1Loss(y_gt, y_pred.squeeze(1)) + self.l2Loss( + y_gt, y_pred.squeeze(1) + ) + loss /= 10 + else: + raise NotImplementedError + + return loss + + class phase_criterion(torch.nn.Module): + def __init__(self, cfg): + super(phase_criterion, self).__init__() + self.cfg = cfg + self.l1Loss = torch.nn.L1Loss(reduction="mean") + self.l2Loss = torch.nn.MSELoss(reduction="mean") + self.relu = torch.nn.ReLU() + + def __call__(self, phase_gt, phase_pred): + n_fft = self.cfg.preprocess.n_fft + frames = phase_gt.size()[-1] + + GD_matrix = ( + torch.triu(torch.ones(n_fft // 2 + 1, n_fft // 2 + 1), diagonal=1) + - torch.triu(torch.ones(n_fft // 2 + 1, n_fft // 2 + 1), diagonal=2) + - torch.eye(n_fft // 2 + 1) + ) + GD_matrix = GD_matrix.to(phase_pred.device) + + GD_r = torch.matmul(phase_gt.permute(0, 2, 1), GD_matrix) + GD_g = torch.matmul(phase_pred.permute(0, 2, 1), GD_matrix) + + PTD_matrix = ( + torch.triu(torch.ones(frames, frames), diagonal=1) + - torch.triu(torch.ones(frames, frames), diagonal=2) + - torch.eye(frames) + ) + PTD_matrix = PTD_matrix.to(phase_pred.device) + + PTD_r = torch.matmul(phase_gt, PTD_matrix) + PTD_g = torch.matmul(phase_pred, PTD_matrix) + + IP_loss = torch.mean(-torch.cos(phase_gt - phase_pred)) + GD_loss = torch.mean(-torch.cos(GD_r - GD_g)) + PTD_loss = torch.mean(-torch.cos(PTD_r - PTD_g)) + + return 100 * (IP_loss + GD_loss + PTD_loss) + + class amplitude_criterion(torch.nn.Module): + def __init__(self, cfg): + super(amplitude_criterion, self).__init__() + self.cfg = cfg + self.l1Loss = torch.nn.L1Loss(reduction="mean") + self.l2Loss = torch.nn.MSELoss(reduction="mean") + self.relu = torch.nn.ReLU() + + def __call__(self, log_amplitude_gt, log_amplitude_pred): + amplitude_loss = self.l2Loss(log_amplitude_gt, log_amplitude_pred) + + return 45 * amplitude_loss + + class consistency_criterion(torch.nn.Module): + def __init__(self, cfg): + super(consistency_criterion, self).__init__() + self.cfg = cfg + self.l1Loss = torch.nn.L1Loss(reduction="mean") + self.l2Loss = torch.nn.MSELoss(reduction="mean") + self.relu = torch.nn.ReLU() + + def __call__( + self, + rea_gt, + rea_pred, + rea_pred_final, + imag_gt, + imag_pred, + imag_pred_final, + ): + C_loss = torch.mean( + torch.mean( + (rea_pred - rea_pred_final) ** 2 + + (imag_pred - imag_pred_final) ** 2, + (1, 2), + ) + ) + + L_R = self.l1Loss(rea_gt, rea_pred) + L_I = self.l1Loss(imag_gt, imag_pred) + + return 20 * (C_loss + 2.25 * (L_R + L_I)) + + criterions = dict() + for key in self.cfg.train.criterions: + if key == "feature": + criterions["feature"] = feature_criterion(self.cfg) + elif key == "discriminator": + criterions["discriminator"] = discriminator_criterion(self.cfg) + elif key == "generator": + criterions["generator"] = generator_criterion(self.cfg) + elif key == "mel": + criterions["mel"] = mel_criterion(self.cfg) + elif key == "wav": + criterions["wav"] = wav_criterion(self.cfg) + elif key == "phase": + criterions["phase"] = phase_criterion(self.cfg) + elif key == "amplitude": + criterions["amplitude"] = amplitude_criterion(self.cfg) + elif key == "consistency": + criterions["consistency"] = consistency_criterion(self.cfg) + else: + raise NotImplementedError + + return criterions + + def _build_model(self): + generator = supported_generators[self.cfg.model.generator](self.cfg) + discriminators = dict() + for key in self.cfg.model.discriminators: + discriminators[key] = supported_discriminators[key](self.cfg) + + return generator, discriminators + + def _build_optimizer(self): + optimizer_params_generator = [dict(params=self.generator.parameters())] + generator_optimizer = AdamW( + optimizer_params_generator, + lr=self.cfg.train.adamw.lr, + betas=(self.cfg.train.adamw.adam_b1, self.cfg.train.adamw.adam_b2), + ) + + optimizer_params_discriminator = [] + for discriminator in self.discriminators.keys(): + optimizer_params_discriminator.append( + dict(params=self.discriminators[discriminator].parameters()) + ) + discriminator_optimizer = AdamW( + optimizer_params_discriminator, + lr=self.cfg.train.adamw.lr, + betas=(self.cfg.train.adamw.adam_b1, self.cfg.train.adamw.adam_b2), + ) + + return generator_optimizer, discriminator_optimizer + + def _build_scheduler(self): + discriminator_scheduler = ExponentialLR( + self.discriminator_optimizer, + gamma=self.cfg.train.exponential_lr.lr_decay, + last_epoch=self.epoch - 1, + ) + + generator_scheduler = ExponentialLR( + self.generator_optimizer, + gamma=self.cfg.train.exponential_lr.lr_decay, + last_epoch=self.epoch - 1, + ) + + return generator_scheduler, discriminator_scheduler + + def train_loop(self): + """Training process""" + self.accelerator.wait_for_everyone() + + # Dump config + if self.accelerator.is_main_process: + self._dump_cfg(self.config_save_path) + self.generator.train() + for key in self.discriminators.keys(): + self.discriminators[key].train() + self.generator_optimizer.zero_grad() + self.discriminator_optimizer.zero_grad() + + # Sync and start training + self.accelerator.wait_for_everyone() + while self.epoch < self.max_epoch: + self.logger.info("\n") + self.logger.info("-" * 32) + self.logger.info("Epoch {}: ".format(self.epoch)) + + # Train and Validate + train_total_loss, train_losses = self._train_epoch() + for key, loss in train_losses.items(): + self.logger.info(" |- Train/{} Loss: {:.6f}".format(key, loss)) + self.accelerator.log( + {"Epoch/Train {} Loss".format(key): loss}, + step=self.epoch, + ) + valid_total_loss, valid_losses = self._valid_epoch() + for key, loss in valid_losses.items(): + self.logger.info(" |- Valid/{} Loss: {:.6f}".format(key, loss)) + self.accelerator.log( + {"Epoch/Valid {} Loss".format(key): loss}, + step=self.epoch, + ) + self.accelerator.log( + { + "Epoch/Train Total Loss": train_total_loss, + "Epoch/Valid Total Loss": valid_total_loss, + }, + step=self.epoch, + ) + + # Update scheduler + self.accelerator.wait_for_everyone() + self.generator_scheduler.step() + self.discriminator_scheduler.step() + + # Check save checkpoint interval + run_eval = False + if self.accelerator.is_main_process: + save_checkpoint = False + for i, num in enumerate(self.save_checkpoint_stride): + if self.epoch % num == 0: + save_checkpoint = True + run_eval |= self.run_eval[i] + + # Save checkpoints + self.accelerator.wait_for_everyone() + if self.accelerator.is_main_process and save_checkpoint: + path = os.path.join( + self.checkpoint_dir, + "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( + self.epoch, self.step, valid_total_loss + ), + ) + self.accelerator.save_state(path) + json.dump( + self.checkpoints_path, + open(os.path.join(path, "ckpts.json"), "w"), + ensure_ascii=False, + indent=4, + ) + + # Save eval audios + self.accelerator.wait_for_everyone() + if self.accelerator.is_main_process and run_eval: + for i in range(len(self.valid_dataloader.dataset.eval_audios)): + if self.cfg.preprocess.use_frame_pitch: + eval_audio = self._inference( + self.valid_dataloader.dataset.eval_mels[i], + eval_pitch=self.valid_dataloader.dataset.eval_pitchs[i], + use_pitch=True, + ) + else: + eval_audio = self._inference( + self.valid_dataloader.dataset.eval_mels[i] + ) + path = os.path.join( + self.checkpoint_dir, + "epoch-{:04d}_step-{:07d}_loss-{:.6f}_eval_audio_{}.wav".format( + self.epoch, + self.step, + valid_total_loss, + self.valid_dataloader.dataset.eval_dataset_names[i], + ), + ) + path_gt = os.path.join( + self.checkpoint_dir, + "epoch-{:04d}_step-{:07d}_loss-{:.6f}_eval_audio_{}_gt.wav".format( + self.epoch, + self.step, + valid_total_loss, + self.valid_dataloader.dataset.eval_dataset_names[i], + ), + ) + save_audio(path, eval_audio, self.cfg.preprocess.sample_rate) + save_audio( + path_gt, + self.valid_dataloader.dataset.eval_audios[i], + self.cfg.preprocess.sample_rate, + ) + + self.accelerator.wait_for_everyone() + + self.epoch += 1 + + # Finish training + self.accelerator.wait_for_everyone() + path = os.path.join( + self.checkpoint_dir, + "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( + self.epoch, self.step, valid_total_loss + ), + ) + self.accelerator.save_state(path) + + def _train_epoch(self): + """Training epoch. Should return average loss of a batch (sample) over + one epoch. See ``train_loop`` for usage. + """ + self.generator.train() + for key, _ in self.discriminators.items(): + self.discriminators[key].train() + + epoch_losses: dict = {} + epoch_total_loss: int = 0 + + for batch in tqdm( + self.train_dataloader, + desc=f"Training Epoch {self.epoch}", + unit="batch", + colour="GREEN", + leave=False, + dynamic_ncols=True, + smoothing=0.04, + disable=not self.accelerator.is_main_process, + ): + # Get losses + total_loss, losses = self._train_step(batch) + self.batch_count += 1 + + # Log info + if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: + self.accelerator.log( + { + "Step/Generator Learning Rate": self.generator_optimizer.param_groups[ + 0 + ][ + "lr" + ], + "Step/Discriminator Learning Rate": self.discriminator_optimizer.param_groups[ + 0 + ][ + "lr" + ], + }, + step=self.step, + ) + for key, _ in losses.items(): + self.accelerator.log( + { + "Step/Train {} Loss".format(key): losses[key], + }, + step=self.step, + ) + + if not epoch_losses: + epoch_losses = losses + else: + for key, value in losses.items(): + epoch_losses[key] += value + epoch_total_loss += total_loss + self.step += 1 + + # Get and log total losses + self.accelerator.wait_for_everyone() + epoch_total_loss = ( + epoch_total_loss + / len(self.train_dataloader) + * self.cfg.train.gradient_accumulation_step + ) + for key in epoch_losses.keys(): + epoch_losses[key] = ( + epoch_losses[key] + / len(self.train_dataloader) + * self.cfg.train.gradient_accumulation_step + ) + return epoch_total_loss, epoch_losses + + def _train_step(self, data): + """Training forward step. Should return average loss of a sample over + one batch. Provoke ``_forward_step`` is recommended except for special case. + See ``_train_epoch`` for usage. + """ + # Init losses + train_losses = {} + total_loss = 0 + + generator_losses = {} + generator_total_loss = 0 + discriminator_losses = {} + discriminator_total_loss = 0 + + # Use input feature to get predictions + mel_input = data["mel"] + audio_gt = data["audio"] + + if self.cfg.preprocess.extract_amplitude_phase: + logamp_gt = data["logamp"] + pha_gt = data["pha"] + rea_gt = data["rea"] + imag_gt = data["imag"] + + if self.cfg.preprocess.use_frame_pitch: + pitch_input = data["frame_pitch"] + + if self.cfg.preprocess.use_frame_pitch: + pitch_input = pitch_input.float() + audio_pred = self.generator.forward(mel_input, pitch_input) + elif self.cfg.preprocess.extract_amplitude_phase: + ( + logamp_pred, + pha_pred, + rea_pred, + imag_pred, + audio_pred, + ) = self.generator.forward(mel_input) + from utils.mel import amplitude_phase_spectrum + + _, _, rea_pred_final, imag_pred_final = amplitude_phase_spectrum( + audio_pred.squeeze(1), self.cfg.preprocess + ) + else: + audio_pred = self.generator.forward(mel_input) + + # Calculate and BP Discriminator losses + self.discriminator_optimizer.zero_grad() + for key, _ in self.discriminators.items(): + y_r, y_g, _, _ = self.discriminators[key].forward( + audio_gt.unsqueeze(1), audio_pred.detach() + ) + ( + discriminator_losses["{}_discriminator".format(key)], + _, + _, + ) = self.criterions["discriminator"](y_r, y_g) + discriminator_total_loss += discriminator_losses[ + "{}_discriminator".format(key) + ] + + self.accelerator.backward(discriminator_total_loss) + self.discriminator_optimizer.step() + + # Calculate and BP Generator losses + self.generator_optimizer.zero_grad() + for key, _ in self.discriminators.items(): + y_r, y_g, f_r, f_g = self.discriminators[key].forward( + audio_gt.unsqueeze(1), audio_pred + ) + generator_losses["{}_feature".format(key)] = self.criterions["feature"]( + f_r, f_g + ) + generator_losses["{}_generator".format(key)], _ = self.criterions[ + "generator" + ](y_g) + generator_total_loss += generator_losses["{}_feature".format(key)] + generator_total_loss += generator_losses["{}_generator".format(key)] + + if "mel" in self.criterions.keys(): + generator_losses["mel"] = self.criterions["mel"](audio_gt, audio_pred) + generator_total_loss += generator_losses["mel"] + + if "wav" in self.criterions.keys(): + generator_losses["wav"] = self.criterions["wav"](audio_gt, audio_pred) + generator_total_loss += generator_losses["wav"] + + if "amplitude" in self.criterions.keys(): + generator_losses["amplitude"] = self.criterions["amplitude"]( + logamp_gt, logamp_pred + ) + generator_total_loss += generator_losses["amplitude"] + + if "phase" in self.criterions.keys(): + generator_losses["phase"] = self.criterions["phase"](pha_gt, pha_pred) + generator_total_loss += generator_losses["phase"] + + if "consistency" in self.criterions.keys(): + generator_losses["consistency"] = self.criterions["consistency"]( + rea_gt, rea_pred, rea_pred_final, imag_gt, imag_pred, imag_pred_final + ) + generator_total_loss += generator_losses["consistency"] + + self.accelerator.backward(generator_total_loss) + self.generator_optimizer.step() + + # Get the total losses + total_loss = discriminator_total_loss + generator_total_loss + train_losses.update(discriminator_losses) + train_losses.update(generator_losses) + + for key, _ in train_losses.items(): + train_losses[key] = train_losses[key].item() + + return total_loss.item(), train_losses + + def _valid_epoch(self): + """Testing epoch. Should return average loss of a batch (sample) over + one epoch. See ``train_loop`` for usage. + """ + self.generator.eval() + for key, _ in self.discriminators.items(): + self.discriminators[key].eval() + + epoch_losses: dict = {} + epoch_total_loss: int = 0 + + for batch in tqdm( + self.valid_dataloader, + desc=f"Validating Epoch {self.epoch}", + unit="batch", + colour="GREEN", + leave=False, + dynamic_ncols=True, + smoothing=0.04, + disable=not self.accelerator.is_main_process, + ): + # Get losses + total_loss, losses = self._valid_step(batch) + + # Log info + for key, _ in losses.items(): + self.accelerator.log( + { + "Step/Valid {} Loss".format(key): losses[key], + }, + step=self.step, + ) + + if not epoch_losses: + epoch_losses = losses + else: + for key, value in losses.items(): + epoch_losses[key] += value + epoch_total_loss += total_loss + + # Get and log total losses + self.accelerator.wait_for_everyone() + epoch_total_loss = epoch_total_loss / len(self.valid_dataloader) + for key in epoch_losses.keys(): + epoch_losses[key] = epoch_losses[key] / len(self.valid_dataloader) + return epoch_total_loss, epoch_losses + + def _valid_step(self, data): + """Testing forward step. Should return average loss of a sample over + one batch. Provoke ``_forward_step`` is recommended except for special case. + See ``_test_epoch`` for usage. + """ + # Init losses + valid_losses = {} + total_loss = 0 + + generator_losses = {} + generator_total_loss = 0 + discriminator_losses = {} + discriminator_total_loss = 0 + + # Use feature inputs to get the predicted audio + mel_input = data["mel"] + audio_gt = data["audio"] + + if self.cfg.preprocess.extract_amplitude_phase: + logamp_gt = data["logamp"] + pha_gt = data["pha"] + rea_gt = data["rea"] + imag_gt = data["imag"] + + if self.cfg.preprocess.use_frame_pitch: + pitch_input = data["frame_pitch"] + + if self.cfg.preprocess.use_frame_pitch: + pitch_input = pitch_input.float() + audio_pred = self.generator.forward(mel_input, pitch_input) + elif self.cfg.preprocess.extract_amplitude_phase: + ( + logamp_pred, + pha_pred, + rea_pred, + imag_pred, + audio_pred, + ) = self.generator.forward(mel_input) + from utils.mel import amplitude_phase_spectrum + + _, _, rea_pred_final, imag_pred_final = amplitude_phase_spectrum( + audio_pred.squeeze(1), self.cfg.preprocess + ) + else: + audio_pred = self.generator.forward(mel_input) + + # Get Discriminator losses + for key, _ in self.discriminators.items(): + y_r, y_g, _, _ = self.discriminators[key].forward( + audio_gt.unsqueeze(1), audio_pred + ) + ( + discriminator_losses["{}_discriminator".format(key)], + _, + _, + ) = self.criterions["discriminator"](y_r, y_g) + discriminator_total_loss += discriminator_losses[ + "{}_discriminator".format(key) + ] + + for key, _ in self.discriminators.items(): + y_r, y_g, f_r, f_g = self.discriminators[key].forward( + audio_gt.unsqueeze(1), audio_pred + ) + generator_losses["{}_feature".format(key)] = self.criterions["feature"]( + f_r, f_g + ) + generator_losses["{}_generator".format(key)], _ = self.criterions[ + "generator" + ](y_g) + generator_total_loss += generator_losses["{}_feature".format(key)] + generator_total_loss += generator_losses["{}_generator".format(key)] + + if "mel" in self.criterions.keys(): + generator_losses["mel"] = self.criterions["mel"](audio_gt, audio_pred) + generator_total_loss += generator_losses["mel"] + if "mel" in self.criterions.keys(): + generator_losses["mel"] = self.criterions["mel"](audio_gt, audio_pred) + generator_total_loss += generator_losses["mel"] + + if "wav" in self.criterions.keys(): + generator_losses["wav"] = self.criterions["wav"](audio_gt, audio_pred) + generator_total_loss += generator_losses["wav"] + if "wav" in self.criterions.keys(): + generator_losses["wav"] = self.criterions["wav"](audio_gt, audio_pred) + generator_total_loss += generator_losses["wav"] + + if "amplitude" in self.criterions.keys(): + generator_losses["amplitude"] = self.criterions["amplitude"]( + logamp_gt, logamp_pred + ) + generator_total_loss += generator_losses["amplitude"] + + if "phase" in self.criterions.keys(): + generator_losses["phase"] = self.criterions["phase"](pha_gt, pha_pred) + generator_total_loss += generator_losses["phase"] + + if "consistency" in self.criterions.keys(): + generator_losses["consistency"] = self.criterions["consistency"]( + rea_gt, + rea_pred, + rea_pred_final, + imag_gt, + imag_pred, + imag_pred_final, + ) + generator_total_loss += generator_losses["consistency"] + + total_loss = discriminator_total_loss + generator_total_loss + valid_losses.update(discriminator_losses) + valid_losses.update(generator_losses) + + for item in valid_losses: + valid_losses[item] = valid_losses[item].item() + for item in valid_losses: + valid_losses[item] = valid_losses[item].item() + + return total_loss.item(), valid_losses + return total_loss.item(), valid_losses + + def _inference(self, eval_mel, eval_pitch=None, use_pitch=False): + """Inference during training for test audios.""" + if use_pitch: + eval_pitch = align_length(eval_pitch, eval_mel.shape[1]) + eval_audio = vocoder_inference( + self.cfg, + self.generator, + torch.from_numpy(eval_mel).unsqueeze(0), + f0s=torch.from_numpy(eval_pitch).unsqueeze(0).float(), + device=next(self.generator.parameters()).device, + ).squeeze(0) + else: + eval_audio = vocoder_inference( + self.cfg, + self.generator, + torch.from_numpy(eval_mel).unsqueeze(0), + device=next(self.generator.parameters()).device, + ).squeeze(0) + return eval_audio + + def _load_model(self, checkpoint_dir, checkpoint_path=None, resume_type="resume"): + """Load model from checkpoint. If checkpoint_path is None, it will + load the latest checkpoint in checkpoint_dir. If checkpoint_path is not + None, it will load the checkpoint specified by checkpoint_path. **Only use this + method after** ``accelerator.prepare()``. + """ + if checkpoint_path is None: + ls = [str(i) for i in Path(checkpoint_dir).glob("*")] + ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True) + checkpoint_path = ls[0] + if resume_type == "resume": + self.accelerator.load_state(checkpoint_path) + self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + 1 + self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + 1 + elif resume_type == "finetune": + accelerate.load_checkpoint_and_dispatch( + self.accelerator.unwrap_model(self.generator), + os.path.join(checkpoint_path, "pytorch_model.bin"), + ) + for key, _ in self.discriminators.items(): + accelerate.load_checkpoint_and_dispatch( + self.accelerator.unwrap_model(self.discriminators[key]), + os.path.join(checkpoint_path, "pytorch_model.bin"), + ) + self.logger.info("Load model weights for finetune SUCCESS!") + else: + raise ValueError("Unsupported resume type: {}".format(resume_type)) + return checkpoint_path + + def _count_parameters(self): + result = sum(p.numel() for p in self.generator.parameters()) + for _, discriminator in self.discriminators.items(): + result += sum(p.numel() for p in discriminator.parameters()) + return result diff --git a/models/vocoders/gan/generator/__pycache__/apnet.cpython-39.pyc b/models/vocoders/gan/generator/__pycache__/apnet.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fb19a5a8b8feab1fb366b8d446915bb18e4b7a16 Binary files /dev/null and b/models/vocoders/gan/generator/__pycache__/apnet.cpython-39.pyc differ diff --git a/models/vocoders/gan/generator/__pycache__/bigvgan.cpython-39.pyc b/models/vocoders/gan/generator/__pycache__/bigvgan.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b618f9673c9a351fdba0a33cca905261eb55f264 Binary files /dev/null and b/models/vocoders/gan/generator/__pycache__/bigvgan.cpython-39.pyc differ diff --git a/models/vocoders/gan/generator/__pycache__/hifigan.cpython-39.pyc b/models/vocoders/gan/generator/__pycache__/hifigan.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..09aeff02403b0685ea9edc7457c52a3b41422c70 Binary files /dev/null and b/models/vocoders/gan/generator/__pycache__/hifigan.cpython-39.pyc differ diff --git a/models/vocoders/gan/generator/__pycache__/melgan.cpython-39.pyc b/models/vocoders/gan/generator/__pycache__/melgan.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..187d4c36eff588f1ef82b2733e264668faaae99d Binary files /dev/null and b/models/vocoders/gan/generator/__pycache__/melgan.cpython-39.pyc differ diff --git a/models/vocoders/gan/generator/__pycache__/nsfhifigan.cpython-39.pyc b/models/vocoders/gan/generator/__pycache__/nsfhifigan.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5fe6dd1f9581291196b147b660ff7fb04e069aa3 Binary files /dev/null and b/models/vocoders/gan/generator/__pycache__/nsfhifigan.cpython-39.pyc differ diff --git a/models/vocoders/gan/generator/apnet.py b/models/vocoders/gan/generator/apnet.py new file mode 100644 index 0000000000000000000000000000000000000000..f39dd6b01f6be5a6bdd2e04ca822a4a9c9b4c9b4 --- /dev/null +++ b/models/vocoders/gan/generator/apnet.py @@ -0,0 +1,395 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +import torch.nn as nn +from modules.vocoder_blocks import * + +LRELU_SLOPE = 0.1 + + +class ISTFT(nn.Module): + """ + Custom implementation of ISTFT since torch.istft doesn't allow custom padding (other than `center=True`) with + windowing. This is because the NOLA (Nonzero Overlap Add) check fails at the edges. + See issue: https://github.com/pytorch/pytorch/issues/62323 + Specifically, in the context of neural vocoding we are interested in "same" padding analogous to CNNs. + The NOLA constraint is met as we trim padded samples anyway. + + Args: + n_fft (int): Size of Fourier transform. + hop_length (int): The distance between neighboring sliding window frames. + win_length (int): The size of window frame and STFT filter. + padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same". + """ + + def __init__( + self, + n_fft: int, + hop_length: int, + win_length: int, + padding: str = "same", + ): + super().__init__() + if padding not in ["center", "same"]: + raise ValueError("Padding must be 'center' or 'same'.") + self.padding = padding + self.n_fft = n_fft + self.hop_length = hop_length + self.win_length = win_length + + def forward(self, spec: torch.Tensor, window) -> torch.Tensor: + """ + Compute the Inverse Short Time Fourier Transform (ISTFT) of a complex spectrogram. + + Args: + spec (Tensor): Input complex spectrogram of shape (B, N, T), where B is the batch size, + N is the number of frequency bins, and T is the number of time frames. + + Returns: + Tensor: Reconstructed time-domain signal of shape (B, L), where L is the length of the output signal. + """ + if self.padding == "center": + # Fallback to pytorch native implementation + return torch.istft( + spec, + self.n_fft, + self.hop_length, + self.win_length, + window, + center=True, + ) + elif self.padding == "same": + pad = (self.win_length - self.hop_length) // 2 + else: + raise ValueError("Padding must be 'center' or 'same'.") + + assert spec.dim() == 3, "Expected a 3D tensor as input" + B, N, T = spec.shape + + # Inverse FFT + ifft = torch.fft.irfft(spec, self.n_fft, dim=1, norm="backward") + ifft = ifft * window[None, :, None] + + # Overlap and Add + output_size = (T - 1) * self.hop_length + self.win_length + y = torch.nn.functional.fold( + ifft, + output_size=(1, output_size), + kernel_size=(1, self.win_length), + stride=(1, self.hop_length), + )[:, 0, 0, pad:-pad] + + # Window envelope + window_sq = window.square().expand(1, T, -1).transpose(1, 2) + window_envelope = torch.nn.functional.fold( + window_sq, + output_size=(1, output_size), + kernel_size=(1, self.win_length), + stride=(1, self.hop_length), + ).squeeze()[pad:-pad] + + # Normalize + assert (window_envelope > 1e-11).all() + y = y / window_envelope + + return y + +# The ASP and PSP Module are adopted from APNet under the MIT License +# https://github.com/YangAi520/APNet/blob/main/models.py + +class ASPResBlock(torch.nn.Module): + def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ASPResBlock, self).__init__() + self.cfg = cfg + self.convs1 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + +class PSPResBlock(torch.nn.Module): + def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3, 5)): + super(PSPResBlock, self).__init__() + self.cfg = cfg + self.convs1 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + +class APNet(torch.nn.Module): + def __init__(self, cfg): + super(APNet, self).__init__() + self.cfg = cfg + self.ASP_num_kernels = len(cfg.model.apnet.ASP_resblock_kernel_sizes) + self.PSP_num_kernels = len(cfg.model.apnet.PSP_resblock_kernel_sizes) + + self.ASP_input_conv = weight_norm( + Conv1d( + cfg.preprocess.n_mel, + cfg.model.apnet.ASP_channel, + cfg.model.apnet.ASP_input_conv_kernel_size, + 1, + padding=get_padding(cfg.model.apnet.ASP_input_conv_kernel_size, 1), + ) + ) + self.PSP_input_conv = weight_norm( + Conv1d( + cfg.preprocess.n_mel, + cfg.model.apnet.PSP_channel, + cfg.model.apnet.PSP_input_conv_kernel_size, + 1, + padding=get_padding(cfg.model.apnet.PSP_input_conv_kernel_size, 1), + ) + ) + + self.ASP_ResNet = nn.ModuleList() + for j, (k, d) in enumerate( + zip( + cfg.model.apnet.ASP_resblock_kernel_sizes, + cfg.model.apnet.ASP_resblock_dilation_sizes, + ) + ): + self.ASP_ResNet.append(ASPResBlock(cfg, cfg.model.apnet.ASP_channel, k, d)) + + self.PSP_ResNet = nn.ModuleList() + for j, (k, d) in enumerate( + zip( + cfg.model.apnet.PSP_resblock_kernel_sizes, + cfg.model.apnet.PSP_resblock_dilation_sizes, + ) + ): + self.PSP_ResNet.append(PSPResBlock(cfg, cfg.model.apnet.PSP_channel, k, d)) + + self.ASP_output_conv = weight_norm( + Conv1d( + cfg.model.apnet.ASP_channel, + cfg.preprocess.n_fft // 2 + 1, + cfg.model.apnet.ASP_output_conv_kernel_size, + 1, + padding=get_padding(cfg.model.apnet.ASP_output_conv_kernel_size, 1), + ) + ) + self.PSP_output_R_conv = weight_norm( + Conv1d( + cfg.model.apnet.PSP_channel, + cfg.preprocess.n_fft // 2 + 1, + cfg.model.apnet.PSP_output_R_conv_kernel_size, + 1, + padding=get_padding(cfg.model.apnet.PSP_output_R_conv_kernel_size, 1), + ) + ) + self.PSP_output_I_conv = weight_norm( + Conv1d( + cfg.model.apnet.PSP_channel, + cfg.preprocess.n_fft // 2 + 1, + cfg.model.apnet.PSP_output_I_conv_kernel_size, + 1, + padding=get_padding(cfg.model.apnet.PSP_output_I_conv_kernel_size, 1), + ) + ) + + self.iSTFT = ISTFT( + self.cfg.preprocess.n_fft, + hop_length=self.cfg.preprocess.hop_size, + win_length=self.cfg.preprocess.win_size, + ) + + self.ASP_output_conv.apply(init_weights) + self.PSP_output_R_conv.apply(init_weights) + self.PSP_output_I_conv.apply(init_weights) + + def forward(self, mel): + logamp = self.ASP_input_conv(mel) + logamps = None + for j in range(self.ASP_num_kernels): + if logamps is None: + logamps = self.ASP_ResNet[j](logamp) + else: + logamps += self.ASP_ResNet[j](logamp) + logamp = logamps / self.ASP_num_kernels + logamp = F.leaky_relu(logamp) + logamp = self.ASP_output_conv(logamp) + + pha = self.PSP_input_conv(mel) + phas = None + for j in range(self.PSP_num_kernels): + if phas is None: + phas = self.PSP_ResNet[j](pha) + else: + phas += self.PSP_ResNet[j](pha) + pha = phas / self.PSP_num_kernels + pha = F.leaky_relu(pha) + R = self.PSP_output_R_conv(pha) + I = self.PSP_output_I_conv(pha) + + pha = torch.atan2(I, R) + + rea = torch.exp(logamp) * torch.cos(pha) + imag = torch.exp(logamp) * torch.sin(pha) + + spec = torch.cat((rea.unsqueeze(-1), imag.unsqueeze(-1)), -1) + + spec = torch.view_as_complex(spec) + + audio = self.iSTFT.forward( + spec, torch.hann_window(self.cfg.preprocess.win_size).to(mel.device) + ) + + return logamp, pha, rea, imag, audio.unsqueeze(1) diff --git a/models/vocoders/gan/generator/bigvgan.py b/models/vocoders/gan/generator/bigvgan.py new file mode 100644 index 0000000000000000000000000000000000000000..205f8d697b9a49aa4ac3f4c46015fef861d4fe1b --- /dev/null +++ b/models/vocoders/gan/generator/bigvgan.py @@ -0,0 +1,344 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +import torch.nn as nn + +from torch.nn import Conv1d, ConvTranspose1d +from torch.nn.utils import weight_norm, remove_weight_norm + +from modules.vocoder_blocks import * +from modules.activation_functions import * +from modules.anti_aliasing import * + +LRELU_SLOPE = 0.1 + +# The AMPBlock Module is adopted from BigVGAN under the MIT License +# https://github.com/NVIDIA/BigVGAN + +class AMPBlock1(torch.nn.Module): + def __init__( + self, cfg, channels, kernel_size=3, dilation=(1, 3, 5), activation=None + ): + super(AMPBlock1, self).__init__() + self.cfg = cfg + + self.convs1 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + self.convs2.apply(init_weights) + + self.num_layers = len(self.convs1) + len( + self.convs2 + ) # total number of conv layers + + if ( + activation == "snake" + ): # periodic nonlinearity with snake function and anti-aliasing + self.activations = nn.ModuleList( + [ + Activation1d( + activation=Snake( + channels, alpha_logscale=cfg.model.bigvgan.snake_logscale + ) + ) + for _ in range(self.num_layers) + ] + ) + elif ( + activation == "snakebeta" + ): # periodic nonlinearity with snakebeta function and anti-aliasing + self.activations = nn.ModuleList( + [ + Activation1d( + activation=SnakeBeta( + channels, alpha_logscale=cfg.model.bigvgan.snake_logscale + ) + ) + for _ in range(self.num_layers) + ] + ) + else: + raise NotImplementedError( + "activation incorrectly specified. check the config file and look for 'activation'." + ) + + def forward(self, x): + acts1, acts2 = self.activations[::2], self.activations[1::2] + for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2): + xt = a1(x) + xt = c1(xt) + xt = a2(xt) + xt = c2(xt) + x = xt + x + + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class AMPBlock2(torch.nn.Module): + def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3), activation=None): + super(AMPBlock2, self).__init__() + self.cfg = cfg + + self.convs = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + ] + ) + self.convs.apply(init_weights) + + self.num_layers = len(self.convs) # total number of conv layers + + if ( + activation == "snake" + ): # periodic nonlinearity with snake function and anti-aliasing + self.activations = nn.ModuleList( + [ + Activation1d( + activation=Snake( + channels, alpha_logscale=cfg.model.bigvgan.snake_logscale + ) + ) + for _ in range(self.num_layers) + ] + ) + elif ( + activation == "snakebeta" + ): # periodic nonlinearity with snakebeta function and anti-aliasing + self.activations = nn.ModuleList( + [ + Activation1d( + activation=SnakeBeta( + channels, alpha_logscale=cfg.model.bigvgan.snake_logscale + ) + ) + for _ in range(self.num_layers) + ] + ) + else: + raise NotImplementedError( + "activation incorrectly specified. check the config file and look for 'activation'." + ) + + def forward(self, x): + for c, a in zip(self.convs, self.activations): + xt = a(x) + xt = c(xt) + x = xt + x + + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class BigVGAN(torch.nn.Module): + def __init__(self, cfg): + super(BigVGAN, self).__init__() + self.cfg = cfg + + self.num_kernels = len(cfg.model.bigvgan.resblock_kernel_sizes) + self.num_upsamples = len(cfg.model.bigvgan.upsample_rates) + + # Conv pre to boost channels + self.conv_pre = weight_norm( + Conv1d( + cfg.preprocess.n_mel, + cfg.model.bigvgan.upsample_initial_channel, + 7, + 1, + padding=3, + ) + ) + + resblock = AMPBlock1 if cfg.model.bigvgan.resblock == "1" else AMPBlock2 + + # Upsamplers + self.ups = nn.ModuleList() + for i, (u, k) in enumerate( + zip( + cfg.model.bigvgan.upsample_rates, + cfg.model.bigvgan.upsample_kernel_sizes, + ) + ): + self.ups.append( + nn.ModuleList( + [ + weight_norm( + ConvTranspose1d( + cfg.model.bigvgan.upsample_initial_channel // (2**i), + cfg.model.bigvgan.upsample_initial_channel + // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ] + ) + ) + + # Res Blocks with AMP and Anti-aliasing + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = cfg.model.bigvgan.upsample_initial_channel // (2 ** (i + 1)) + for j, (k, d) in enumerate( + zip( + cfg.model.bigvgan.resblock_kernel_sizes, + cfg.model.bigvgan.resblock_dilation_sizes, + ) + ): + self.resblocks.append( + resblock(cfg, ch, k, d, activation=cfg.model.bigvgan.activation) + ) + + # Conv post for result + if ( + cfg.model.bigvgan.activation == "snake" + ): + activation_post = Snake(ch, alpha_logscale=cfg.model.bigvgan.snake_logscale) + self.activation_post = Activation1d(activation=activation_post) + elif ( + cfg.model.bigvgan.activation == "snakebeta" + ): + activation_post = SnakeBeta( + ch, alpha_logscale=cfg.model.bigvgan.snake_logscale + ) + self.activation_post = Activation1d(activation=activation_post) + else: + raise NotImplementedError( + "activation incorrectly specified. check the config file and look for 'activation'." + ) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + + # Weight Norm + for i in range(len(self.ups)): + self.ups[i].apply(init_weights) + self.conv_post.apply(init_weights) + + def forward(self, x): + x = self.conv_pre(x) + + for i in range(self.num_upsamples): + for i_up in range(len(self.ups[i])): + x = self.ups[i][i_up](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + + x = self.activation_post(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print("Removing weight norm...") + for l in self.ups: + for l_i in l: + remove_weight_norm(l_i) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) diff --git a/models/vocoders/gan/generator/hifigan.py b/models/vocoders/gan/generator/hifigan.py new file mode 100644 index 0000000000000000000000000000000000000000..9f5f32498f5eb6441db787b0ae204a1eeff36aa3 --- /dev/null +++ b/models/vocoders/gan/generator/hifigan.py @@ -0,0 +1,449 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch.nn import Conv1d, ConvTranspose1d +from torch.nn.utils import weight_norm, remove_weight_norm +from modules.vocoder_blocks import * + + +LRELU_SLOPE = 0.1 + + +class ResBlock1(torch.nn.Module): + def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.cfg = cfg + self.convs1 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.cfg = cfg + self.convs = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + ] + ) + self.convs.apply(init_weights) + + def forward(self, x): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class HiFiGAN(torch.nn.Module): + def __init__(self, cfg): + super(HiFiGAN, self).__init__() + self.cfg = cfg + self.num_kernels = len(self.cfg.model.hifigan.resblock_kernel_sizes) + self.num_upsamples = len(self.cfg.model.hifigan.upsample_rates) + self.conv_pre = weight_norm( + Conv1d( + cfg.preprocess.n_mel, + self.cfg.model.hifigan.upsample_initial_channel, + 7, + 1, + padding=3, + ) + ) + resblock = ResBlock1 if self.cfg.model.hifigan.resblock == "1" else ResBlock2 + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate( + zip( + self.cfg.model.hifigan.upsample_rates, + self.cfg.model.hifigan.upsample_kernel_sizes, + ) + ): + self.ups.append( + weight_norm( + ConvTranspose1d( + self.cfg.model.hifigan.upsample_initial_channel // (2**i), + self.cfg.model.hifigan.upsample_initial_channel + // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = self.cfg.model.hifigan.upsample_initial_channel // (2 ** (i + 1)) + for j, (k, d) in enumerate( + zip( + self.cfg.model.hifigan.resblock_kernel_sizes, + self.cfg.model.hifigan.resblock_dilation_sizes, + ) + ): + self.resblocks.append(resblock(self.cfg, ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + + def forward(self, x): + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print("Removing weight norm...") + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +# todo: merge with ResBlock1 (lmxue, yicheng) +class ResBlock1_vits(torch.nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1_vits, self).__init__() + self.convs1 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + self.convs2.apply(init_weights) + + def forward(self, x, x_mask=None): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + if x_mask is not None: + xt = xt * x_mask + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + if x_mask is not None: + xt = xt * x_mask + xt = c2(xt) + x = xt + x + if x_mask is not None: + x = x * x_mask + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +# todo: merge with ResBlock2 (lmxue, yicheng) +class ResBlock2_vits(torch.nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2_vits, self).__init__() + self.convs = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + ] + ) + self.convs.apply(init_weights) + + def forward(self, x, x_mask=None): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + if x_mask is not None: + xt = xt * x_mask + xt = c(xt) + x = xt + x + if x_mask is not None: + x = x * x_mask + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +# todo: merge with HiFiGAN (lmxue, yicheng) +class HiFiGAN_vits(torch.nn.Module): + def __init__( + self, + initial_channel, + resblock, + resblock_kernel_sizes, + resblock_dilation_sizes, + upsample_rates, + upsample_initial_channel, + upsample_kernel_sizes, + gin_channels=0, + ): + super(HiFiGAN_vits, self).__init__() + self.num_kernels = len(resblock_kernel_sizes) + self.num_upsamples = len(upsample_rates) + self.conv_pre = Conv1d( + initial_channel, upsample_initial_channel, 7, 1, padding=3 + ) + resblock = ResBlock1_vits if resblock == "1" else ResBlock2_vits + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): + self.ups.append( + weight_norm( + ConvTranspose1d( + upsample_initial_channel // (2**i), + upsample_initial_channel // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = upsample_initial_channel // (2 ** (i + 1)) + for j, (k, d) in enumerate( + zip(resblock_kernel_sizes, resblock_dilation_sizes) + ): + self.resblocks.append(resblock(ch, k, d)) + + self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) + self.ups.apply(init_weights) + + if gin_channels != 0: + self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) + + def forward(self, x, g=None): + x = self.conv_pre(x) + if g is not None: + x = x + self.cond(g) + + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() diff --git a/models/vocoders/gan/generator/melgan.py b/models/vocoders/gan/generator/melgan.py new file mode 100644 index 0000000000000000000000000000000000000000..ca90d684ca1f5a0a813db4192540adac0cee2558 --- /dev/null +++ b/models/vocoders/gan/generator/melgan.py @@ -0,0 +1,103 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + +from torch.nn.utils import weight_norm + +# This code is adopted from MelGAN under the MIT License +# https://github.com/descriptinc/melgan-neurips + +def weights_init(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(0.0, 0.02) + elif classname.find("BatchNorm2d") != -1: + m.weight.data.normal_(1.0, 0.02) + m.bias.data.fill_(0) + + +def WNConv1d(*args, **kwargs): + return weight_norm(nn.Conv1d(*args, **kwargs)) + + +def WNConvTranspose1d(*args, **kwargs): + return weight_norm(nn.ConvTranspose1d(*args, **kwargs)) + + +class ResnetBlock(nn.Module): + def __init__(self, dim, dilation=1): + super().__init__() + self.block = nn.Sequential( + nn.LeakyReLU(0.2), + nn.ReflectionPad1d(dilation), + WNConv1d(dim, dim, kernel_size=3, dilation=dilation), + nn.LeakyReLU(0.2), + WNConv1d(dim, dim, kernel_size=1), + ) + self.shortcut = WNConv1d(dim, dim, kernel_size=1) + + def forward(self, x): + return self.shortcut(x) + self.block(x) + + +class MelGAN(nn.Module): + def __init__(self, cfg): + super().__init__() + + self.cfg = cfg + + self.hop_length = np.prod(self.cfg.model.melgan.ratios) + mult = int(2 ** len(self.cfg.model.melgan.ratios)) + + model = [ + nn.ReflectionPad1d(3), + WNConv1d( + self.cfg.preprocess.n_mel, + mult * self.cfg.model.melgan.ngf, + kernel_size=7, + padding=0, + ), + ] + + # Upsample to raw audio scale + for i, r in enumerate(self.cfg.model.melgan.ratios): + model += [ + nn.LeakyReLU(0.2), + WNConvTranspose1d( + mult * self.cfg.model.melgan.ngf, + mult * self.cfg.model.melgan.ngf // 2, + kernel_size=r * 2, + stride=r, + padding=r // 2 + r % 2, + output_padding=r % 2, + ), + ] + + for j in range(self.cfg.model.melgan.n_residual_layers): + model += [ + ResnetBlock(mult * self.cfg.model.melgan.ngf // 2, dilation=3**j) + ] + + mult //= 2 + + model += [ + nn.LeakyReLU(0.2), + nn.ReflectionPad1d(3), + WNConv1d(self.cfg.model.melgan.ngf, 1, kernel_size=7, padding=0), + nn.Tanh(), + ] + + self.model = nn.Sequential(*model) + self.apply(weights_init) + + def forward(self, x): + return self.model(x) diff --git a/models/vocoders/gan/generator/nsfhifigan.py b/models/vocoders/gan/generator/nsfhifigan.py new file mode 100644 index 0000000000000000000000000000000000000000..8deb4ce7a9348f4a03bfef55fd21c634b3f25a78 --- /dev/null +++ b/models/vocoders/gan/generator/nsfhifigan.py @@ -0,0 +1,281 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +import numpy as np +import torch.nn as nn +import torch.nn.functional as F + +from torch.nn import Conv1d, ConvTranspose1d +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm + +from modules.neural_source_filter import * +from modules.vocoder_blocks import * + + +LRELU_SLOPE = 0.1 + + +class ResBlock1(nn.Module): + def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.cfg = cfg + + self.convs1 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(nn.Module): + def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock1, self).__init__() + self.cfg = cfg + + self.convs = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + ] + ) + self.convs.apply(init_weights) + + def forward(self, x): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + +# This NSF Module is adopted from Xin Wang's NSF under the MIT License +# https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts + +class SourceModuleHnNSF(nn.Module): + def __init__( + self, fs, harmonic_num=0, amp=0.1, noise_std=0.003, voiced_threshold=0 + ): + super(SourceModuleHnNSF, self).__init__() + + self.amp = amp + self.noise_std = noise_std + self.l_sin_gen = SineGen(fs, harmonic_num, amp, noise_std, voiced_threshold) + + self.l_linear = nn.Linear(harmonic_num + 1, 1) + self.l_tanh = nn.Tanh() + + def forward(self, x, upp): + sine_wavs, uv, _ = self.l_sin_gen(x, upp) + sine_merge = self.l_tanh(self.l_linear(sine_wavs)) + return sine_merge + + +class NSFHiFiGAN(nn.Module): + def __init__(self, cfg): + super(NSFHiFiGAN, self).__init__() + + self.cfg = cfg + self.num_kernels = len(self.cfg.model.nsfhifigan.resblock_kernel_sizes) + self.num_upsamples = len(self.cfg.model.nsfhifigan.upsample_rates) + self.m_source = SourceModuleHnNSF( + fs=self.cfg.preprocess.sample_rate, + harmonic_num=self.cfg.model.nsfhifigan.harmonic_num, + ) + self.noise_convs = nn.ModuleList() + self.conv_pre = weight_norm( + Conv1d( + self.cfg.preprocess.n_mel, + self.cfg.model.nsfhifigan.upsample_initial_channel, + 7, + 1, + padding=3, + ) + ) + + resblock = ResBlock1 if self.cfg.model.nsfhifigan.resblock == "1" else ResBlock2 + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate( + zip( + self.cfg.model.nsfhifigan.upsample_rates, + self.cfg.model.nsfhifigan.upsample_kernel_sizes, + ) + ): + c_cur = self.cfg.model.nsfhifigan.upsample_initial_channel // (2 ** (i + 1)) + self.ups.append( + weight_norm( + ConvTranspose1d( + self.cfg.model.nsfhifigan.upsample_initial_channel // (2**i), + self.cfg.model.nsfhifigan.upsample_initial_channel + // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + if i + 1 < len(self.cfg.model.nsfhifigan.upsample_rates): + stride_f0 = int( + np.prod(self.cfg.model.nsfhifigan.upsample_rates[i + 1 :]) + ) + self.noise_convs.append( + Conv1d( + 1, + c_cur, + kernel_size=stride_f0 * 2, + stride=stride_f0, + padding=stride_f0 // 2, + ) + ) + else: + self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) + + self.resblocks = nn.ModuleList() + ch = self.cfg.model.nsfhifigan.upsample_initial_channel + for i in range(len(self.ups)): + ch //= 2 + for j, (k, d) in enumerate( + zip( + self.cfg.model.nsfhifigan.resblock_kernel_sizes, + self.cfg.model.nsfhifigan.resblock_dilation_sizes, + ) + ): + self.resblocks.append(resblock(cfg, ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + self.upp = int(np.prod(self.cfg.model.nsfhifigan.upsample_rates)) + + def forward(self, x, f0): + har_source = self.m_source(f0, self.upp).transpose(1, 2) + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + x_source = self.noise_convs[i](har_source) + + length = min(x.shape[-1], x_source.shape[-1]) + x = x[:, :, :length] + x_source = x[:, :, :length] + + x = x + x_source + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x diff --git a/models/vocoders/gan/generator/sifigan.py b/models/vocoders/gan/generator/sifigan.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/vocoders/vocoder_dataset.py b/models/vocoders/vocoder_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..7df17b97ba7a4f770f01971324126eca4a2db272 --- /dev/null +++ b/models/vocoders/vocoder_dataset.py @@ -0,0 +1,264 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Iterable +import torch +import numpy as np +import torch.utils.data +from torch.nn.utils.rnn import pad_sequence +from utils.data_utils import * +from torch.utils.data import ConcatDataset, Dataset + + +class VocoderDataset(torch.utils.data.Dataset): + def __init__(self, cfg, dataset, is_valid=False): + """ + Args: + cfg: config + dataset: dataset name + is_valid: whether to use train or valid dataset + """ + assert isinstance(dataset, str) + + processed_data_dir = os.path.join(cfg.preprocess.processed_dir, dataset) + + meta_file = cfg.preprocess.valid_file if is_valid else cfg.preprocess.train_file + self.metafile_path = os.path.join(processed_data_dir, meta_file) + self.metadata = self.get_metadata() + + self.data_root = processed_data_dir + self.cfg = cfg + + if cfg.preprocess.use_audio: + self.utt2audio_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2audio_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.audio_dir, + uid + ".npy", + ) + elif cfg.preprocess.use_label: + self.utt2label_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2label_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.label_dir, + uid + ".npy", + ) + elif cfg.preprocess.use_one_hot: + self.utt2one_hot_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2one_hot_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.one_hot_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_mel: + self.utt2mel_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2mel_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.mel_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_frame_pitch: + self.utt2frame_pitch_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2frame_pitch_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.pitch_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_uv: + self.utt2uv_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + self.utt2uv_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.uv_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_amplitude_phase: + self.utt2logamp_path = {} + self.utt2pha_path = {} + self.utt2rea_path = {} + self.utt2imag_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + self.utt2logamp_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.log_amplitude_dir, + uid + ".npy", + ) + self.utt2pha_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.phase_dir, + uid + ".npy", + ) + self.utt2rea_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.real_dir, + uid + ".npy", + ) + self.utt2imag_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.imaginary_dir, + uid + ".npy", + ) + + def __getitem__(self, index): + utt_info = self.metadata[index] + + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + single_feature = dict() + + if self.cfg.preprocess.use_mel: + mel = np.load(self.utt2mel_path[utt]) + assert mel.shape[0] == self.cfg.preprocess.n_mel # [n_mels, T] + + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = mel.shape[1] + + single_feature["mel"] = mel + + if self.cfg.preprocess.use_frame_pitch: + frame_pitch = np.load(self.utt2frame_pitch_path[utt]) + + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = len(frame_pitch) + + aligned_frame_pitch = align_length( + frame_pitch, single_feature["target_len"] + ) + + single_feature["frame_pitch"] = aligned_frame_pitch + + if self.cfg.preprocess.use_audio: + audio = np.load(self.utt2audio_path[utt]) + + single_feature["audio"] = audio + + return single_feature + + def get_metadata(self): + with open(self.metafile_path, "r", encoding="utf-8") as f: + metadata = json.load(f) + + return metadata + + def get_dataset_name(self): + return self.metadata[0]["Dataset"] + + def __len__(self): + return len(self.metadata) + + +class VocoderConcatDataset(ConcatDataset): + def __init__(self, datasets: Iterable[Dataset], full_audio_inference=False): + """Concatenate a series of datasets with their random inference audio merged.""" + super().__init__(datasets) + + self.cfg = self.datasets[0].cfg + + self.metadata = [] + + # Merge metadata + for dataset in self.datasets: + self.metadata += dataset.metadata + + # Merge random inference features + if full_audio_inference: + self.eval_audios = [] + self.eval_dataset_names = [] + if self.cfg.preprocess.use_mel: + self.eval_mels = [] + if self.cfg.preprocess.use_frame_pitch: + self.eval_pitchs = [] + for dataset in self.datasets: + self.eval_audios.append(dataset.eval_audio) + self.eval_dataset_names.append(dataset.get_dataset_name()) + if self.cfg.preprocess.use_mel: + self.eval_mels.append(dataset.eval_mel) + if self.cfg.preprocess.use_frame_pitch: + self.eval_pitchs.append(dataset.eval_pitch) + + +class VocoderCollator(object): + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, cfg): + self.cfg = cfg + + def __call__(self, batch): + packed_batch_features = dict() + + # mel: [b, n_mels, frame] + # frame_pitch: [b, frame] + # audios: [b, frame * hop_size] + + for key in batch[0].keys(): + if key == "target_len": + packed_batch_features["target_len"] = torch.LongTensor( + [b["target_len"] for b in batch] + ) + masks = [ + torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "mel": + values = [torch.from_numpy(b[key]).T for b in batch] + packed_batch_features[key] = pad_sequence( + values, batch_first=True, padding_value=0 + ) + else: + values = [torch.from_numpy(b[key]) for b in batch] + packed_batch_features[key] = pad_sequence( + values, batch_first=True, padding_value=0 + ) + + return packed_batch_features diff --git a/models/vocoders/vocoder_inference.py b/models/vocoders/vocoder_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..dfd09ee6aa44c544c51a62c0e014cca4260cc6a8 --- /dev/null +++ b/models/vocoders/vocoder_inference.py @@ -0,0 +1,488 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import torch +import json +import json5 +import time +import accelerate +import random +import numpy as np +import shutil + +from pathlib import Path +from tqdm import tqdm +from glob import glob +from accelerate.logging import get_logger +from torch.utils.data import DataLoader + +from models.vocoders.vocoder_dataset import ( + VocoderDataset, + VocoderCollator, + VocoderConcatDataset, +) + +from models.vocoders.gan.generator import bigvgan, hifigan, melgan, nsfhifigan, apnet +from models.vocoders.flow.waveglow import waveglow +from models.vocoders.diffusion.diffwave import diffwave +from models.vocoders.autoregressive.wavenet import wavenet +from models.vocoders.autoregressive.wavernn import wavernn +from models.vocoders.gan import gan_vocoder_inference +from utils.io import save_audio + +_vocoders = { + "diffwave": diffwave.DiffWave, + "wavernn": wavernn.WaveRNN, + "wavenet": wavenet.WaveNet, + "waveglow": waveglow.WaveGlow, + "nsfhifigan": nsfhifigan.NSFHiFiGAN, + "bigvgan": bigvgan.BigVGAN, + "hifigan": hifigan.HiFiGAN, + "melgan": melgan.MelGAN, + "apnet": apnet.APNet, +} + +_vocoder_infer_funcs = { + # "world": world_inference.synthesis_audios, + # "wavernn": wavernn_inference.synthesis_audios, + # "wavenet": wavenet_inference.synthesis_audios, + # "diffwave": diffwave_inference.synthesis_audios, + "nsfhifigan": gan_vocoder_inference.synthesis_audios, + "bigvgan": gan_vocoder_inference.synthesis_audios, + "melgan": gan_vocoder_inference.synthesis_audios, + "hifigan": gan_vocoder_inference.synthesis_audios, + "apnet": gan_vocoder_inference.synthesis_audios, +} + + +class VocoderInference(object): + def __init__(self, args=None, cfg=None, infer_type="from_dataset"): + super().__init__() + + start = time.monotonic_ns() + self.args = args + self.cfg = cfg + self.infer_type = infer_type + + # Init accelerator + self.accelerator = accelerate.Accelerator() + self.accelerator.wait_for_everyone() + + # Get logger + with self.accelerator.main_process_first(): + self.logger = get_logger("inference", log_level=args.log_level) + + # Log some info + self.logger.info("=" * 56) + self.logger.info("||\t\t" + "New inference process started." + "\t\t||") + self.logger.info("=" * 56) + self.logger.info("\n") + + self.vocoder_dir = args.vocoder_dir + self.logger.debug(f"Vocoder dir: {args.vocoder_dir}") + + os.makedirs(args.output_dir, exist_ok=True) + if os.path.exists(os.path.join(args.output_dir, "pred")): + shutil.rmtree(os.path.join(args.output_dir, "pred")) + if os.path.exists(os.path.join(args.output_dir, "gt")): + shutil.rmtree(os.path.join(args.output_dir, "gt")) + os.makedirs(os.path.join(args.output_dir, "pred"), exist_ok=True) + os.makedirs(os.path.join(args.output_dir, "gt"), exist_ok=True) + + # Set random seed + with self.accelerator.main_process_first(): + start = time.monotonic_ns() + self._set_random_seed(self.cfg.train.random_seed) + end = time.monotonic_ns() + self.logger.debug( + f"Setting random seed done in {(end - start) / 1e6:.2f}ms" + ) + self.logger.debug(f"Random seed: {self.cfg.train.random_seed}") + + # Setup inference mode + if self.infer_type == "infer_from_dataset": + self.cfg.dataset = self.args.infer_datasets + elif self.infer_type == "infer_from_feature": + self._build_tmp_dataset_from_feature() + self.cfg.dataset = ["tmp"] + elif self.infer_type == "infer_from_audio": + self._build_tmp_dataset_from_audio() + self.cfg.dataset = ["tmp"] + + # Setup data loader + with self.accelerator.main_process_first(): + self.logger.info("Building dataset...") + start = time.monotonic_ns() + self.test_dataloader = self._build_dataloader() + end = time.monotonic_ns() + self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms") + + # Build model + with self.accelerator.main_process_first(): + self.logger.info("Building model...") + start = time.monotonic_ns() + self.model = self._build_model() + end = time.monotonic_ns() + self.logger.info(f"Building model done in {(end - start) / 1e6:.3f}ms") + + # Init with accelerate + self.logger.info("Initializing accelerate...") + start = time.monotonic_ns() + self.accelerator = accelerate.Accelerator() + (self.model, self.test_dataloader) = self.accelerator.prepare( + self.model, self.test_dataloader + ) + end = time.monotonic_ns() + self.accelerator.wait_for_everyone() + self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.3f}ms") + + with self.accelerator.main_process_first(): + self.logger.info("Loading checkpoint...") + start = time.monotonic_ns() + if os.path.isdir(args.vocoder_dir): + if os.path.isdir(os.path.join(args.vocoder_dir, "checkpoint")): + self._load_model(os.path.join(args.vocoder_dir, "checkpoint")) + else: + self._load_model(os.path.join(args.vocoder_dir)) + else: + self._load_model(os.path.join(args.vocoder_dir)) + end = time.monotonic_ns() + self.logger.info(f"Loading checkpoint done in {(end - start) / 1e6:.3f}ms") + + self.model.eval() + self.accelerator.wait_for_everyone() + + def _build_tmp_dataset_from_feature(self): + if os.path.exists(os.path.join(self.cfg.preprocess.processed_dir, "tmp")): + shutil.rmtree(os.path.join(self.cfg.preprocess.processed_dir, "tmp")) + + utts = [] + mels = glob(os.path.join(self.args.feature_folder, "mels", "*.npy")) + for i, mel in enumerate(mels): + uid = mel.split("/")[-1].split(".")[0] + utt = {"Dataset": "tmp", "Uid": uid, "index": i} + utts.append(utt) + + os.makedirs(os.path.join(self.cfg.preprocess.processed_dir, "tmp")) + with open( + os.path.join(self.cfg.preprocess.processed_dir, "tmp", "test.json"), "w" + ) as f: + json.dump(utts, f) + + meta_info = {"dataset": "tmp", "test": {"size": len(utts)}} + + with open( + os.path.join(self.cfg.preprocess.processed_dir, "tmp", "meta_info.json"), + "w", + ) as f: + json.dump(meta_info, f) + + features = glob(os.path.join(self.args.feature_folder, "*")) + for feature in features: + feature_name = feature.split("/")[-1] + if os.path.isfile(feature): + continue + shutil.copytree( + os.path.join(self.args.feature_folder, feature_name), + os.path.join(self.cfg.preprocess.processed_dir, "tmp", feature_name), + ) + + def _build_tmp_dataset_from_audio(self): + if os.path.exists(os.path.join(self.cfg.preprocess.processed_dir, "tmp")): + shutil.rmtree(os.path.join(self.cfg.preprocess.processed_dir, "tmp")) + + utts = [] + audios = glob(os.path.join(self.args.audio_folder, "*")) + for i, audio in enumerate(audios): + uid = audio.split("/")[-1].split(".")[0] + utt = {"Dataset": "tmp", "Uid": uid, "index": i, "Path": audio} + utts.append(utt) + + os.makedirs(os.path.join(self.cfg.preprocess.processed_dir, "tmp")) + with open( + os.path.join(self.cfg.preprocess.processed_dir, "tmp", "test.json"), "w" + ) as f: + json.dump(utts, f) + + meta_info = {"dataset": "tmp", "test": {"size": len(utts)}} + + with open( + os.path.join(self.cfg.preprocess.processed_dir, "tmp", "meta_info.json"), + "w", + ) as f: + json.dump(meta_info, f) + + from processors import acoustic_extractor + + acoustic_extractor.extract_utt_acoustic_features_serial( + utts, os.path.join(self.cfg.preprocess.processed_dir, "tmp"), self.cfg + ) + + def _build_test_dataset(self): + return VocoderDataset, VocoderCollator + + def _build_model(self): + model = _vocoders[self.cfg.model.generator](self.cfg) + return model + + def _build_dataloader(self): + """Build dataloader which merges a series of datasets.""" + Dataset, Collator = self._build_test_dataset() + + datasets_list = [] + for dataset in self.cfg.dataset: + subdataset = Dataset(self.cfg, dataset, is_valid=True) + datasets_list.append(subdataset) + test_dataset = VocoderConcatDataset(datasets_list, full_audio_inference=False) + test_collate = Collator(self.cfg) + test_batch_size = min(self.cfg.inference.batch_size, len(test_dataset)) + test_dataloader = DataLoader( + test_dataset, + collate_fn=test_collate, + num_workers=1, + batch_size=test_batch_size, + shuffle=False, + ) + self.test_batch_size = test_batch_size + self.test_dataset = test_dataset + return test_dataloader + + def _load_model(self, checkpoint_dir, from_multi_gpu=False): + """Load model from checkpoint. If a folder is given, it will + load the latest checkpoint in checkpoint_dir. If a path is given + it will load the checkpoint specified by checkpoint_path. + **Only use this method after** ``accelerator.prepare()``. + """ + if os.path.isdir(checkpoint_dir): + if "epoch" in checkpoint_dir and "step" in checkpoint_dir: + checkpoint_path = checkpoint_dir + else: + # Load the latest accelerator state dicts + ls = [ + str(i) + for i in Path(checkpoint_dir).glob("*") + if not "audio" in str(i) + ] + ls.sort( + key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True + ) + checkpoint_path = ls[0] + accelerate.load_checkpoint_and_dispatch( + self.accelerator.unwrap_model(self.model), + os.path.join(checkpoint_path, "pytorch_model.bin"), + ) + return str(checkpoint_path) + else: + # Load old .pt checkpoints + if self.cfg.model.generator in [ + "bigvgan", + "hifigan", + "melgan", + "nsfhifigan", + ]: + ckpt = torch.load( + checkpoint_dir, + map_location=torch.device("cuda") + if torch.cuda.is_available() + else torch.device("cpu"), + ) + if from_multi_gpu: + pretrained_generator_dict = ckpt["generator_state_dict"] + generator_dict = self.model.state_dict() + + new_generator_dict = { + k.split("module.")[-1]: v + for k, v in pretrained_generator_dict.items() + if ( + k.split("module.")[-1] in generator_dict + and v.shape == generator_dict[k.split("module.")[-1]].shape + ) + } + + generator_dict.update(new_generator_dict) + + self.model.load_state_dict(generator_dict) + else: + self.model.load_state_dict(ckpt["generator_state_dict"]) + else: + self.model.load_state_dict(torch.load(checkpoint_dir)["state_dict"]) + return str(checkpoint_dir) + + def inference(self): + """Inference via batches""" + for i, batch in tqdm(enumerate(self.test_dataloader)): + if self.cfg.preprocess.use_frame_pitch: + audio_pred = self.model.forward( + batch["mel"].transpose(-1, -2), batch["frame_pitch"].float() + ).cpu() + elif self.cfg.preprocess.extract_amplitude_phase: + audio_pred = self.model.forward(batch["mel"].transpose(-1, -2))[-1] + else: + audio_pred = ( + self.model.forward(batch["mel"].transpose(-1, -2)).detach().cpu() + ) + audio_ls = audio_pred.chunk(self.test_batch_size) + audio_gt_ls = batch["audio"].cpu().chunk(self.test_batch_size) + length_ls = batch["target_len"].cpu().chunk(self.test_batch_size) + j = 0 + for it, it_gt, l in zip(audio_ls, audio_gt_ls, length_ls): + l = l.item() + it = it.squeeze(0).squeeze(0)[: l * self.cfg.preprocess.hop_size] + it_gt = it_gt.squeeze(0)[: l * self.cfg.preprocess.hop_size] + uid = self.test_dataset.metadata[i * self.test_batch_size + j]["Uid"] + save_audio( + os.path.join(self.args.output_dir, "pred", "{}.wav").format(uid), + it, + self.cfg.preprocess.sample_rate, + ) + save_audio( + os.path.join(self.args.output_dir, "gt", "{}.wav").format(uid), + it_gt, + self.cfg.preprocess.sample_rate, + ) + j += 1 + + if os.path.exists(os.path.join(self.cfg.preprocess.processed_dir, "tmp")): + shutil.rmtree(os.path.join(self.cfg.preprocess.processed_dir, "tmp")) + + def _set_random_seed(self, seed): + """Set random seed for all possible random modules.""" + random.seed(seed) + np.random.seed(seed) + torch.random.manual_seed(seed) + + def _count_parameters(self, model): + return sum(p.numel() for p in model.parameters()) + + def _dump_cfg(self, path): + os.makedirs(os.path.dirname(path), exist_ok=True) + json5.dump( + self.cfg, + open(path, "w"), + indent=4, + sort_keys=True, + ensure_ascii=False, + quote_keys=True, + ) + + +def load_nnvocoder( + cfg, + vocoder_name, + weights_file, + from_multi_gpu=False, +): + """Load the specified vocoder. + cfg: the vocoder config filer. + weights_file: a folder or a .pt path. + from_multi_gpu: automatically remove the "module" string in state dicts if "True". + """ + print("Loading Vocoder from Weights file: {}".format(weights_file)) + + # Build model + model = _vocoders[vocoder_name](cfg) + if not os.path.isdir(weights_file): + # Load from .pt file + if vocoder_name in ["bigvgan", "hifigan", "melgan", "nsfhifigan"]: + ckpt = torch.load( + weights_file, + map_location=torch.device("cuda") + if torch.cuda.is_available() + else torch.device("cpu"), + ) + if from_multi_gpu: + pretrained_generator_dict = ckpt["generator_state_dict"] + generator_dict = model.state_dict() + + new_generator_dict = { + k.split("module.")[-1]: v + for k, v in pretrained_generator_dict.items() + if ( + k.split("module.")[-1] in generator_dict + and v.shape == generator_dict[k.split("module.")[-1]].shape + ) + } + + generator_dict.update(new_generator_dict) + + model.load_state_dict(generator_dict) + else: + model.load_state_dict(ckpt["generator_state_dict"]) + else: + model.load_state_dict(torch.load(weights_file)["state_dict"]) + else: + # Load from accelerator state dict + weights_file = os.path.join(weights_file, "checkpoint") + ls = [str(i) for i in Path(weights_file).glob("*") if not "audio" in str(i)] + ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True) + checkpoint_path = ls[0] + accelerator = accelerate.Accelerator() + model = accelerator.prepare(model) + accelerator.load_state(checkpoint_path) + + if torch.cuda.is_available(): + model = model.cuda() + + model = model.eval() + return model + + +def tensorize(data, device, n_samples): + """ + data: a list of numpy array + """ + assert type(data) == list + if n_samples: + data = data[:n_samples] + data = [torch.as_tensor(x, device=device) for x in data] + return data + + +def synthesis( + cfg, + vocoder_weight_file, + n_samples, + pred, + f0s=None, + batch_size=64, + fast_inference=False, +): + """Synthesis audios from a given vocoder and series of given features. + cfg: vocoder config. + vocoder_weight_file: a folder of accelerator state dict or a path to the .pt file. + pred: a list of numpy arrays. [(seq_len1, acoustic_features_dim), (seq_len2, acoustic_features_dim), ...] + """ + + vocoder_name = cfg.model.generator + + print("Synthesis audios using {} vocoder...".format(vocoder_name)) + + ###### TODO: World Vocoder Refactor ###### + # if vocoder_name == "world": + # world_inference.synthesis_audios( + # cfg, dataset_name, split, n_samples, pred, save_dir, tag + # ) + # return + + # ====== Loading neural vocoder model ====== + vocoder = load_nnvocoder( + cfg, vocoder_name, weights_file=vocoder_weight_file, from_multi_gpu=True + ) + device = next(vocoder.parameters()).device + + # ====== Inference for predicted acoustic features ====== + # pred: (frame_len, n_mels) -> (n_mels, frame_len) + mels_pred = tensorize([p.T for p in pred], device, n_samples) + print("For predicted mels, #sample = {}...".format(len(mels_pred))) + audios_pred = _vocoder_infer_funcs[vocoder_name]( + cfg, + vocoder, + mels_pred, + f0s=f0s, + batch_size=batch_size, + fast_inference=fast_inference, + ) + return audios_pred diff --git a/models/vocoders/vocoder_sampler.py b/models/vocoders/vocoder_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..9d29f88a291dcf7386cadaeae0d990c8e76ebf98 --- /dev/null +++ b/models/vocoders/vocoder_sampler.py @@ -0,0 +1,126 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import random + +from torch.utils.data import ConcatDataset, Dataset +from torch.utils.data.sampler import ( + BatchSampler, + RandomSampler, + Sampler, + SequentialSampler, +) + + +class ScheduledSampler(Sampler): + """A sampler that samples data from a given concat-dataset. + + Args: + concat_dataset (ConcatDataset): a concatenated dataset consisting of all datasets + batch_size (int): batch size + holistic_shuffle (bool): whether to shuffle the whole dataset or not + logger (logging.Logger): logger to print warning message + + Usage: + For cfg.train.batch_size = 3, cfg.train.holistic_shuffle = False, cfg.train.drop_last = True: + >>> list(ScheduledSampler(ConcatDataset([0, 1, 2], [3, 4, 5], [6, 7, 8]]))) + [3, 4, 5, 0, 1, 2, 6, 7, 8] + """ + + def __init__( + self, concat_dataset, batch_size, holistic_shuffle, logger=None, type="train" + ): + if not isinstance(concat_dataset, ConcatDataset): + raise ValueError( + "concat_dataset must be an instance of ConcatDataset, but got {}".format( + type(concat_dataset) + ) + ) + if not isinstance(batch_size, int): + raise ValueError( + "batch_size must be an integer, but got {}".format(type(batch_size)) + ) + if not isinstance(holistic_shuffle, bool): + raise ValueError( + "holistic_shuffle must be a boolean, but got {}".format( + type(holistic_shuffle) + ) + ) + + self.concat_dataset = concat_dataset + self.batch_size = batch_size + self.holistic_shuffle = holistic_shuffle + + affected_dataset_name = [] + affected_dataset_len = [] + for dataset in concat_dataset.datasets: + dataset_len = len(dataset) + dataset_name = dataset.get_dataset_name() + if dataset_len < batch_size: + affected_dataset_name.append(dataset_name) + affected_dataset_len.append(dataset_len) + + self.type = type + for dataset_name, dataset_len in zip( + affected_dataset_name, affected_dataset_len + ): + if not type == "valid": + logger.warning( + "The {} dataset {} has a length of {}, which is smaller than the batch size {}. This may cause unexpected behavior.".format( + type, dataset_name, dataset_len, batch_size + ) + ) + + def __len__(self): + # the number of batches with drop last + num_of_batches = sum( + [ + math.floor(len(dataset) / self.batch_size) + for dataset in self.concat_dataset.datasets + ] + ) + return num_of_batches * self.batch_size + + def __iter__(self): + iters = [] + for dataset in self.concat_dataset.datasets: + iters.append( + SequentialSampler(dataset).__iter__() + if self.holistic_shuffle + else RandomSampler(dataset).__iter__() + ) + init_indices = [0] + self.concat_dataset.cumulative_sizes[:-1] + output_batches = [] + for dataset_idx in range(len(self.concat_dataset.datasets)): + cur_batch = [] + for idx in iters[dataset_idx]: + cur_batch.append(idx + init_indices[dataset_idx]) + if len(cur_batch) == self.batch_size: + output_batches.append(cur_batch) + cur_batch = [] + if self.type == "valid" and len(cur_batch) > 0: + output_batches.append(cur_batch) + cur_batch = [] + # force drop last in training + random.shuffle(output_batches) + output_indices = [item for sublist in output_batches for item in sublist] + return iter(output_indices) + + +def build_samplers(concat_dataset: Dataset, cfg, logger, type): + sampler = ScheduledSampler( + concat_dataset, + cfg.train.batch_size, + cfg.train.sampler.holistic_shuffle, + logger, + type, + ) + batch_sampler = BatchSampler( + sampler, + cfg.train.batch_size, + cfg.train.sampler.drop_last if not type == "valid" else False, + ) + return sampler, batch_sampler diff --git a/models/vocoders/vocoder_trainer.py b/models/vocoders/vocoder_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..5821e735a64f07fcf9c782712670e24ce6a91c04 --- /dev/null +++ b/models/vocoders/vocoder_trainer.py @@ -0,0 +1,180 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import random +from pathlib import Path +import re + +import accelerate +import json5 +import numpy as np +import torch +from accelerate.utils import ProjectConfiguration +from torch.utils.data import DataLoader +from tqdm import tqdm + +from models.vocoders.vocoder_dataset import VocoderConcatDataset +from models.vocoders.vocoder_sampler import build_samplers + + +class VocoderTrainer: + def __init__(self): + super().__init__() + + def _init_accelerator(self): + """Initialize the accelerator components.""" + self.exp_dir = os.path.join( + os.path.abspath(self.cfg.log_dir), self.args.exp_name + ) + project_config = ProjectConfiguration( + project_dir=self.exp_dir, logging_dir=os.path.join(self.exp_dir, "log") + ) + self.accelerator = accelerate.Accelerator( + gradient_accumulation_steps=self.cfg.train.gradient_accumulation_step, + log_with=self.cfg.train.tracker, + project_config=project_config, + ) + if self.accelerator.is_main_process: + os.makedirs(project_config.project_dir, exist_ok=True) + os.makedirs(project_config.logging_dir, exist_ok=True) + with self.accelerator.main_process_first(): + self.accelerator.init_trackers(self.args.exp_name) + + def _build_dataset(self): + pass + + def _build_criterion(self): + pass + + def _build_model(self): + pass + + def _build_dataloader(self): + """Build dataloader which merges a series of datasets.""" + # Build dataset instance for each dataset and combine them by ConcatDataset + Dataset, Collator = self._build_dataset() + + # Build train set + datasets_list = [] + for dataset in self.cfg.dataset: + subdataset = Dataset(self.cfg, dataset, is_valid=False) + datasets_list.append(subdataset) + train_dataset = VocoderConcatDataset(datasets_list, full_audio_inference=True) + train_collate = Collator(self.cfg) + _, batch_sampler = build_samplers(train_dataset, self.cfg, self.logger, "train") + train_loader = DataLoader( + train_dataset, + collate_fn=train_collate, + batch_sampler=batch_sampler, + num_workers=self.cfg.train.dataloader.num_worker, + pin_memory=self.cfg.train.dataloader.pin_memory, + ) + + # Build test set + datasets_list = [] + for dataset in self.cfg.dataset: + subdataset = Dataset(self.cfg, dataset, is_valid=True) + datasets_list.append(subdataset) + valid_dataset = VocoderConcatDataset(datasets_list, full_audio_inference=True) + valid_collate = Collator(self.cfg) + _, batch_sampler = build_samplers(valid_dataset, self.cfg, self.logger, "train") + valid_loader = DataLoader( + valid_dataset, + collate_fn=valid_collate, + batch_sampler=batch_sampler, + num_workers=self.cfg.train.dataloader.num_worker, + pin_memory=self.cfg.train.dataloader.pin_memory, + ) + return train_loader, valid_loader + + def _build_optimizer(self): + pass + + def _build_scheduler(self): + pass + + def _load_model(self, checkpoint_dir, checkpoint_path=None, resume_type="resume"): + """Load model from checkpoint. If a folder is given, it will + load the latest checkpoint in checkpoint_dir. If a path is given + it will load the checkpoint specified by checkpoint_path. + **Only use this method after** ``accelerator.prepare()``. + """ + if checkpoint_path is None: + ls = [str(i) for i in Path(checkpoint_dir).glob("*")] + ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True) + checkpoint_path = ls[0] + if resume_type == "resume": + self.accelerator.load_state(checkpoint_path) + elif resume_type == "finetune": + accelerate.load_checkpoint_and_dispatch( + self.accelerator.unwrap_model(self.model), + os.path.join(checkpoint_path, "pytorch_model.bin"), + ) + self.logger.info("Load model weights for finetune SUCCESS!") + else: + raise ValueError("Unsupported resume type: {}".format(resume_type)) + self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + 1 + self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + 1 + return checkpoint_path + + def train_loop(self): + pass + + def _train_epoch(self): + pass + + def _valid_epoch(self): + pass + + def _train_step(self): + pass + + def _valid_step(self): + pass + + def _inference(self): + pass + + def _set_random_seed(self, seed): + """Set random seed for all possible random modules.""" + random.seed(seed) + np.random.seed(seed) + torch.random.manual_seed(seed) + + def _check_nan(self, loss): + if torch.any(torch.isnan(loss)): + self.logger.fatal("Fatal Error: NaN!") + self.logger.error("loss = {:.6f}".format(loss.item()), in_order=True) + + def _check_basic_configs(self): + if self.cfg.train.gradient_accumulation_step <= 0: + self.logger.fatal("Invalid gradient_accumulation_step value!") + self.logger.error( + f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." + ) + self.accelerator.end_training() + raise ValueError( + f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." + ) + + def _count_parameters(self): + pass + + def _dump_cfg(self, path): + os.makedirs(os.path.dirname(path), exist_ok=True) + json5.dump( + self.cfg, + open(path, "w"), + indent=4, + sort_keys=True, + ensure_ascii=False, + quote_keys=True, + ) + + def _is_valid_pattern(self, directory_name): + directory_name = str(directory_name) + pattern = r"^epoch-\d{4}_step-\d{7}_loss-\d{1}\.\d{6}" + return re.match(pattern, directory_name) is not None diff --git a/modules/__init__.py b/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/__pycache__/__init__.cpython-39.pyc b/modules/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5b2c57ba9b08fa6feac679192bd2523b02b97a05 Binary files /dev/null and b/modules/__pycache__/__init__.cpython-39.pyc differ diff --git a/modules/activation_functions/__init__.py b/modules/activation_functions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d35dfbc27a825ea8ed286074d40971764b890468 --- /dev/null +++ b/modules/activation_functions/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .gated_activation_unit import GaU +from .snake import Snake, SnakeBeta diff --git a/modules/activation_functions/__pycache__/__init__.cpython-39.pyc b/modules/activation_functions/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f5b0cf0ca40b3f6f94cd5563b79038c3dd17750a Binary files /dev/null and b/modules/activation_functions/__pycache__/__init__.cpython-39.pyc differ diff --git a/modules/activation_functions/__pycache__/gated_activation_unit.cpython-39.pyc b/modules/activation_functions/__pycache__/gated_activation_unit.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0936d7f1d40a15944ecaf232c50fe424b6884594 Binary files /dev/null and b/modules/activation_functions/__pycache__/gated_activation_unit.cpython-39.pyc differ diff --git a/modules/activation_functions/__pycache__/snake.cpython-39.pyc b/modules/activation_functions/__pycache__/snake.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d1d5377f069adce18ec3b8e0f127ec643414cc25 Binary files /dev/null and b/modules/activation_functions/__pycache__/snake.cpython-39.pyc differ diff --git a/modules/activation_functions/gated_activation_unit.py b/modules/activation_functions/gated_activation_unit.py new file mode 100644 index 0000000000000000000000000000000000000000..e55956981fa99299059dd1c02236011fbadac049 --- /dev/null +++ b/modules/activation_functions/gated_activation_unit.py @@ -0,0 +1,61 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + +from modules.general.utils import Conv1d + + +class GaU(nn.Module): + r"""Gated Activation Unit (GaU) proposed in `Gated Activation Units for Neural + Networks `_. + + Args: + channels: number of input channels. + kernel_size: kernel size of the convolution. + dilation: dilation rate of the convolution. + d_context: dimension of context tensor, None if don't use context. + """ + + def __init__( + self, + channels: int, + kernel_size: int = 3, + dilation: int = 1, + d_context: int = None, + ): + super().__init__() + + self.context = d_context + + self.conv = Conv1d( + channels, + channels * 2, + kernel_size, + dilation=dilation, + padding=dilation * (kernel_size - 1) // 2, + ) + + if self.context: + self.context_proj = Conv1d(d_context, channels * 2, 1) + + def forward(self, x: torch.Tensor, context: torch.Tensor = None): + r"""Calculate forward propagation. + + Args: + x: input tensor with shape [B, C, T]. + context: context tensor with shape [B, ``d_context``, T], default to None. + """ + + h = self.conv(x) + + if self.context: + h = h + self.context_proj(context) + + h1, h2 = h.chunk(2, 1) + h = torch.tanh(h1) * torch.sigmoid(h2) + + return h diff --git a/modules/activation_functions/snake.py b/modules/activation_functions/snake.py new file mode 100644 index 0000000000000000000000000000000000000000..e5398819f2e7c836e02189321cbf048bedcd9e91 --- /dev/null +++ b/modules/activation_functions/snake.py @@ -0,0 +1,122 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import nn, pow, sin +from torch.nn import Parameter + + +class Snake(nn.Module): + r"""Implementation of a sine-based periodic activation function. + Alpha is initialized to 1 by default, higher values means higher frequency. + It will be trained along with the rest of your model. + + Args: + in_features: shape of the input + alpha: trainable parameter + + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + + References: + This activation function is from this paper by Liu Ziyin, Tilman Hartwig, + Masahito Ueda: https://arxiv.org/abs/2006.08195 + + Examples: + >>> a1 = Snake(256) + >>> x = torch.randn(256) + >>> x = a1(x) + """ + + def __init__( + self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False + ): + super(Snake, self).__init__() + self.in_features = in_features + + # initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: # log scale alphas initialized to zeros + self.alpha = Parameter(torch.zeros(in_features) * alpha) + else: # linear scale alphas initialized to ones + self.alpha = Parameter(torch.ones(in_features) * alpha) + + self.alpha.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + r"""Forward pass of the function. Applies the function to the input elementwise. + Snake ∶= x + 1/a * sin^2 (ax) + """ + + alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] + if self.alpha_logscale: + alpha = torch.exp(alpha) + x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2) + + return x + + +class SnakeBeta(nn.Module): + r"""A modified Snake function which uses separate parameters for the magnitude + of the periodic components. Alpha is initialized to 1 by default, + higher values means higher frequency. Beta is initialized to 1 by default, + higher values means higher magnitude. Both will be trained along with the + rest of your model. + + Args: + in_features: shape of the input + alpha: trainable parameter that controls frequency + beta: trainable parameter that controls magnitude + + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + + References: + This activation function is a modified version based on this paper by Liu Ziyin, + Tilman Hartwig, Masahito Ueda: https://arxiv.org/abs/2006.08195 + + Examples: + >>> a1 = SnakeBeta(256) + >>> x = torch.randn(256) + >>> x = a1(x) + """ + + def __init__( + self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False + ): + super(SnakeBeta, self).__init__() + self.in_features = in_features + + # initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: # log scale alphas initialized to zeros + self.alpha = Parameter(torch.zeros(in_features) * alpha) + self.beta = Parameter(torch.zeros(in_features) * alpha) + else: # linear scale alphas initialized to ones + self.alpha = Parameter(torch.ones(in_features) * alpha) + self.beta = Parameter(torch.ones(in_features) * alpha) + + self.alpha.requires_grad = alpha_trainable + self.beta.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + r"""Forward pass of the function. Applies the function to the input elementwise. + SnakeBeta ∶= x + 1/b * sin^2 (xa) + """ + + alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] + beta = self.beta.unsqueeze(0).unsqueeze(-1) + if self.alpha_logscale: + alpha = torch.exp(alpha) + beta = torch.exp(beta) + x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2) + + return x diff --git a/modules/anti_aliasing/__init__.py b/modules/anti_aliasing/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..193756bfa04218d10f65fbed8665a8a0abad1cd4 --- /dev/null +++ b/modules/anti_aliasing/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .act import * +from .filter import * +from .resample import * diff --git a/modules/anti_aliasing/__pycache__/__init__.cpython-39.pyc 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b/modules/anti_aliasing/__pycache__/resample.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3cf27e236f5692a7786d1d7172d6729407c0a34b Binary files /dev/null and b/modules/anti_aliasing/__pycache__/resample.cpython-39.pyc differ diff --git a/modules/anti_aliasing/act.py b/modules/anti_aliasing/act.py new file mode 100644 index 0000000000000000000000000000000000000000..3404fa138650c7a16bf918b6cb51b5912b656e8c --- /dev/null +++ b/modules/anti_aliasing/act.py @@ -0,0 +1,35 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn + +from .resample import * + +# This code is adopted from BigVGAN under the MIT License +# https://github.com/NVIDIA/BigVGAN + +class Activation1d(nn.Module): + def __init__( + self, + activation, + up_ratio: int = 2, + down_ratio: int = 2, + up_kernel_size: int = 12, + down_kernel_size: int = 12, + ): + super().__init__() + self.up_ratio = up_ratio + self.down_ratio = down_ratio + self.act = activation + self.upsample = UpSample1d(up_ratio, up_kernel_size) + self.downsample = DownSample1d(down_ratio, down_kernel_size) + + # x: [B,C,T] + def forward(self, x): + x = self.upsample(x) + x = self.act(x) + x = self.downsample(x) + + return x diff --git a/modules/anti_aliasing/filter.py b/modules/anti_aliasing/filter.py new file mode 100644 index 0000000000000000000000000000000000000000..b91688866bd073c7ce268004791ec1263a6d76d7 --- /dev/null +++ b/modules/anti_aliasing/filter.py @@ -0,0 +1,99 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +import math + +if "sinc" in dir(torch): + sinc = torch.sinc +else: + # This code is adopted from adefossez's julius.core.sinc under the MIT License + # https://adefossez.github.io/julius/julius/core.html + def sinc(x: torch.Tensor): + """ + Implementation of sinc, i.e. sin(pi * x) / (pi * x) + __Warning__: Different to julius.sinc, the input is multiplied by `pi`! + """ + return torch.where( + x == 0, + torch.tensor(1.0, device=x.device, dtype=x.dtype), + torch.sin(math.pi * x) / math.pi / x, + ) + + +# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License +# https://adefossez.github.io/julius/julius/lowpass.html +def kaiser_sinc_filter1d( + cutoff, half_width, kernel_size +): # return filter [1,1,kernel_size] + even = kernel_size % 2 == 0 + half_size = kernel_size // 2 + + # For kaiser window + delta_f = 4 * half_width + A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95 + if A > 50.0: + beta = 0.1102 * (A - 8.7) + elif A >= 21.0: + beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0) + else: + beta = 0.0 + window = torch.kaiser_window(kernel_size, beta=beta, periodic=False) + + # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio + if even: + time = torch.arange(-half_size, half_size) + 0.5 + else: + time = torch.arange(kernel_size) - half_size + if cutoff == 0: + filter_ = torch.zeros_like(time) + else: + filter_ = 2 * cutoff * window * sinc(2 * cutoff * time) + # Normalize filter to have sum = 1, otherwise we will have a small leakage + # of the constant component in the input signal. + filter_ /= filter_.sum() + filter = filter_.view(1, 1, kernel_size) + + return filter + + +class LowPassFilter1d(nn.Module): + def __init__( + self, + cutoff=0.5, + half_width=0.6, + stride: int = 1, + padding: bool = True, + padding_mode: str = "replicate", + kernel_size: int = 12, + ): + # kernel_size should be even number for stylegan3 setup, + # in this implementation, odd number is also possible. + super().__init__() + if cutoff < -0.0: + raise ValueError("Minimum cutoff must be larger than zero.") + if cutoff > 0.5: + raise ValueError("A cutoff above 0.5 does not make sense.") + self.kernel_size = kernel_size + self.even = kernel_size % 2 == 0 + self.pad_left = kernel_size // 2 - int(self.even) + self.pad_right = kernel_size // 2 + self.stride = stride + self.padding = padding + self.padding_mode = padding_mode + filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size) + self.register_buffer("filter", filter) + + # input [B, C, T] + def forward(self, x): + _, C, _ = x.shape + + if self.padding: + x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode) + out = F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C) + + return out diff --git a/modules/anti_aliasing/resample.py b/modules/anti_aliasing/resample.py new file mode 100644 index 0000000000000000000000000000000000000000..735cb86e2fcf12f86317d972844e04ae3bacf14e --- /dev/null +++ b/modules/anti_aliasing/resample.py @@ -0,0 +1,64 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +#################### Anti-aliasing #################### + +import torch.nn as nn +from torch.nn import functional as F + +from .filter import * + +# This code is adopted from BigVGAN under the MIT License +# https://github.com/NVIDIA/BigVGAN + +class UpSample1d(nn.Module): + def __init__(self, ratio=2, kernel_size=None): + super().__init__() + self.ratio = ratio + self.kernel_size = ( + int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size + ) + self.stride = ratio + self.pad = self.kernel_size // ratio - 1 + self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2 + self.pad_right = ( + self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2 + ) + filter = kaiser_sinc_filter1d( + cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size + ) + self.register_buffer("filter", filter) + + # x: [B, C, T] + def forward(self, x): + _, C, _ = x.shape + + x = F.pad(x, (self.pad, self.pad), mode="replicate") + x = self.ratio * F.conv_transpose1d( + x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C + ) + x = x[..., self.pad_left : -self.pad_right] + + return x + + +class DownSample1d(nn.Module): + def __init__(self, ratio=2, kernel_size=None): + super().__init__() + self.ratio = ratio + self.kernel_size = ( + int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size + ) + self.lowpass = LowPassFilter1d( + cutoff=0.5 / ratio, + half_width=0.6 / ratio, + stride=ratio, + kernel_size=self.kernel_size, + ) + + def forward(self, x): + xx = self.lowpass(x) + + return xx diff --git a/modules/base/__pycache__/base_module.cpython-39.pyc b/modules/base/__pycache__/base_module.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a96fb5360b12aa9ae82f387760b19b9e39641e40 Binary files /dev/null and b/modules/base/__pycache__/base_module.cpython-39.pyc differ diff --git a/modules/base/base_module.py b/modules/base/base_module.py new file mode 100644 index 0000000000000000000000000000000000000000..c3c81206f4f130b706fffd45e93a054ce1231ddd --- /dev/null +++ b/modules/base/base_module.py @@ -0,0 +1,75 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import nn +from torch.nn import functional as F + + +class LayerNorm(nn.Module): + def __init__(self, channels, eps=1e-5): + super().__init__() + self.channels = channels + self.eps = eps + + self.gamma = nn.Parameter(torch.ones(channels)) + self.beta = nn.Parameter(torch.zeros(channels)) + + def forward(self, x): + x = x.transpose(1, -1) + x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) + return x.transpose(1, -1) + + +class ConvReluNorm(nn.Module): + def __init__( + self, + in_channels, + hidden_channels, + out_channels, + kernel_size, + n_layers, + p_dropout, + ): + super().__init__() + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.p_dropout = p_dropout + assert n_layers > 1, "Number of layers should be larger than 0." + + self.conv_layers = nn.ModuleList() + self.norm_layers = nn.ModuleList() + self.conv_layers.append( + nn.Conv1d( + in_channels, hidden_channels, kernel_size, padding=kernel_size // 2 + ) + ) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout)) + for _ in range(n_layers - 1): + self.conv_layers.append( + nn.Conv1d( + hidden_channels, + hidden_channels, + kernel_size, + padding=kernel_size // 2, + ) + ) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.proj = nn.Conv1d(hidden_channels, out_channels, 1) + self.proj.weight.data.zero_() + self.proj.bias.data.zero_() + + def forward(self, x, x_mask): + x_org = x + for i in range(self.n_layers): + x = self.conv_layers[i](x * x_mask) + x = self.norm_layers[i](x) + x = self.relu_drop(x) + x = x_org + self.proj(x) + return x * x_mask diff --git a/modules/diffusion/__init__.py b/modules/diffusion/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bf1e4f9e71ef3b0ef2d677a9c0db8e03b022eaea --- /dev/null +++ b/modules/diffusion/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .bidilconv.bidilated_conv import BiDilConv +from .unet.unet import UNet diff --git a/modules/diffusion/bidilconv/bidilated_conv.py b/modules/diffusion/bidilconv/bidilated_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..ac75522a3da8a40ae1b76656dd2a78097d6d94f3 --- /dev/null +++ b/modules/diffusion/bidilconv/bidilated_conv.py @@ -0,0 +1,102 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch.nn as nn + +from modules.general.utils import Conv1d, zero_module +from .residual_block import ResidualBlock + + +class BiDilConv(nn.Module): + r"""Dilated CNN architecture with residual connections, default diffusion decoder. + + Args: + input_channel: The number of input channels. + base_channel: The number of base channels. + n_res_block: The number of residual blocks. + conv_kernel_size: The kernel size of convolutional layers. + dilation_cycle_length: The cycle length of dilation. + conditioner_size: The size of conditioner. + """ + + def __init__( + self, + input_channel, + base_channel, + n_res_block, + conv_kernel_size, + dilation_cycle_length, + conditioner_size, + output_channel: int = -1, + ): + super().__init__() + + self.input_channel = input_channel + self.base_channel = base_channel + self.n_res_block = n_res_block + self.conv_kernel_size = conv_kernel_size + self.dilation_cycle_length = dilation_cycle_length + self.conditioner_size = conditioner_size + self.output_channel = output_channel if output_channel > 0 else input_channel + + self.input = nn.Sequential( + Conv1d( + input_channel, + base_channel, + 1, + ), + nn.ReLU(), + ) + + self.residual_blocks = nn.ModuleList( + [ + ResidualBlock( + channels=base_channel, + kernel_size=conv_kernel_size, + dilation=2 ** (i % dilation_cycle_length), + d_context=conditioner_size, + ) + for i in range(n_res_block) + ] + ) + + self.out_proj = nn.Sequential( + Conv1d( + base_channel, + base_channel, + 1, + ), + nn.ReLU(), + zero_module( + Conv1d( + base_channel, + self.output_channel, + 1, + ), + ), + ) + + def forward(self, x, y, context=None): + """ + Args: + x: Noisy mel-spectrogram [B x ``n_mel`` x L] + y: FILM embeddings with the shape of (B, ``base_channel``) + context: Context with the shape of [B x ``d_context`` x L], default to None. + """ + + h = self.input(x) + + skip = None + for i in range(self.n_res_block): + h, skip_connection = self.residual_blocks[i](h, y, context) + skip = skip_connection if skip is None else skip_connection + skip + + out = skip / math.sqrt(self.n_res_block) + + out = self.out_proj(out) + + return out diff --git a/modules/diffusion/bidilconv/residual_block.py b/modules/diffusion/bidilconv/residual_block.py new file mode 100644 index 0000000000000000000000000000000000000000..df75d4fcb307af58fafe7258ba6e4a7a8cedd2f6 --- /dev/null +++ b/modules/diffusion/bidilconv/residual_block.py @@ -0,0 +1,73 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn as nn + +from modules.activation_functions import GaU +from modules.general.utils import Conv1d + + +class ResidualBlock(nn.Module): + r"""Residual block with dilated convolution, main portion of ``BiDilConv``. + + Args: + channels: The number of channels of input and output. + kernel_size: The kernel size of dilated convolution. + dilation: The dilation rate of dilated convolution. + d_context: The dimension of content encoder output, None if don't use context. + """ + + def __init__( + self, + channels: int = 256, + kernel_size: int = 3, + dilation: int = 1, + d_context: int = None, + ): + super().__init__() + + self.context = d_context + + self.gau = GaU( + channels, + kernel_size, + dilation, + d_context, + ) + + self.out_proj = Conv1d( + channels, + channels * 2, + 1, + ) + + def forward( + self, + x: torch.Tensor, + y_emb: torch.Tensor, + context: torch.Tensor = None, + ): + """ + Args: + x: Latent representation inherited from previous residual block + with the shape of [B x C x T]. + y_emb: Embeddings with the shape of [B x C], which will be FILM on the x. + context: Context with the shape of [B x ``d_context`` x T], default to None. + """ + + h = x + y_emb[..., None] + + if self.context: + h = self.gau(h, context) + else: + h = self.gau(h) + + h = self.out_proj(h) + res, skip = h.chunk(2, 1) + + return (res + x) / math.sqrt(2.0), skip diff --git a/modules/diffusion/karras/karras_diffusion.py b/modules/diffusion/karras/karras_diffusion.py new file mode 100644 index 0000000000000000000000000000000000000000..f590c25a04a3db9cc16308de4e43f31d77e13f11 --- /dev/null +++ b/modules/diffusion/karras/karras_diffusion.py @@ -0,0 +1,979 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Based on: https://github.com/crowsonkb/k-diffusion +""" +import random + +import numpy as np +import torch as th +import torch.nn as nn +import torch.nn.functional as F + +# from piq import LPIPS +from utils.ssim import SSIM + +from modules.diffusion.karras.random_utils import get_generator + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def append_dims(x, target_dims): + """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" + dims_to_append = target_dims - x.ndim + if dims_to_append < 0: + raise ValueError( + f"input has {x.ndim} dims but target_dims is {target_dims}, which is less" + ) + return x[(...,) + (None,) * dims_to_append] + + +def append_zero(x): + return th.cat([x, x.new_zeros([1])]) + + +def get_weightings(weight_schedule, snrs, sigma_data): + if weight_schedule == "snr": + weightings = snrs + elif weight_schedule == "snr+1": + weightings = snrs + 1 + elif weight_schedule == "karras": + weightings = snrs + 1.0 / sigma_data**2 + elif weight_schedule == "truncated-snr": + weightings = th.clamp(snrs, min=1.0) + elif weight_schedule == "uniform": + weightings = th.ones_like(snrs) + else: + raise NotImplementedError() + return weightings + + +class KarrasDenoiser: + def __init__( + self, + sigma_data: float = 0.5, + sigma_max=80.0, + sigma_min=0.002, + rho=7.0, + weight_schedule="karras", + distillation=False, + loss_norm="l2", + ): + self.sigma_data = sigma_data + self.sigma_max = sigma_max + self.sigma_min = sigma_min + self.weight_schedule = weight_schedule + self.distillation = distillation + self.loss_norm = loss_norm + # if loss_norm == "lpips": + # self.lpips_loss = LPIPS(replace_pooling=True, reduction="none") + if loss_norm == "ssim": + self.ssim_loss = SSIM() + self.rho = rho + self.num_timesteps = 40 + + def get_snr(self, sigmas): + return sigmas**-2 + + def get_sigmas(self, sigmas): + return sigmas + + def get_scalings(self, sigma): + c_skip = self.sigma_data**2 / (sigma**2 + self.sigma_data**2) + c_out = sigma * self.sigma_data / (sigma**2 + self.sigma_data**2) ** 0.5 + c_in = 1 / (sigma**2 + self.sigma_data**2) ** 0.5 + return c_skip, c_out, c_in + + def get_scalings_for_boundary_condition(self, sigma): + c_skip = self.sigma_data**2 / ( + (sigma - self.sigma_min) ** 2 + self.sigma_data**2 + ) + c_out = ( + (sigma - self.sigma_min) + * self.sigma_data + / (sigma**2 + self.sigma_data**2) ** 0.5 + ) + c_in = 1 / (sigma**2 + self.sigma_data**2) ** 0.5 + return c_skip, c_out, c_in + + def training_losses(self, model, x_start, sigmas, condition=None, noise=None): + if noise is None: + noise = th.randn_like(x_start) + + terms = {} + + dims = x_start.ndim + x_t = x_start + noise * append_dims(sigmas, dims) + model_output, denoised = self.denoise(model, x_t, sigmas, condition) + + snrs = self.get_snr(sigmas) + weights = append_dims( + get_weightings(self.weight_schedule, snrs, self.sigma_data), dims + ) + # terms["xs_mse"] = mean_flat((denoised - x_start) ** 2) + terms["mse"] = mean_flat(weights * (denoised - x_start) ** 2) + # terms["mae"] = mean_flat(weights * th.abs(denoised - x_start)) + # terms["mse"] = nn.MSELoss(reduction="none")(denoised, x_start) + + # if "vb" in terms: + # terms["loss"] = terms["mse"] + terms["vb"] + # else: + terms["loss"] = terms["mse"] + + return terms + + def consistency_losses( + self, + model, + x_start, + num_scales, + # model_kwargs=None, + condition=None, + target_model=None, + teacher_model=None, + teacher_diffusion=None, + noise=None, + ): + if noise is None: + noise = th.randn_like(x_start) + + dims = x_start.ndim + + def denoise_fn(x, t): + return self.denoise(model, x, t, condition)[1] + + if target_model: + + @th.no_grad() + def target_denoise_fn(x, t): + return self.denoise(target_model, x, t, condition)[1] + + else: + raise NotImplementedError("Must have a target model") + + if teacher_model: + + @th.no_grad() + def teacher_denoise_fn(x, t): + return teacher_diffusion.denoise(teacher_model, x, t, condition)[1] + + @th.no_grad() + def heun_solver(samples, t, next_t, x0): + x = samples + if teacher_model is None: + denoiser = x0 + else: + denoiser = teacher_denoise_fn(x, t) + + d = (x - denoiser) / append_dims(t, dims) + samples = x + d * append_dims(next_t - t, dims) + if teacher_model is None: + denoiser = x0 + else: + denoiser = teacher_denoise_fn(samples, next_t) + + next_d = (samples - denoiser) / append_dims(next_t, dims) + samples = x + (d + next_d) * append_dims((next_t - t) / 2, dims) + + return samples + + @th.no_grad() + def euler_solver(samples, t, next_t, x0): + x = samples + if teacher_model is None: + denoiser = x0 + else: + denoiser = teacher_denoise_fn(x, t) + d = (x - denoiser) / append_dims(t, dims) + samples = x + d * append_dims(next_t - t, dims) + + return samples + + indices = th.randint( + 0, num_scales - 1, (x_start.shape[0],), device=x_start.device + ) + + t = self.sigma_max ** (1 / self.rho) + indices / (num_scales - 1) * ( + self.sigma_min ** (1 / self.rho) - self.sigma_max ** (1 / self.rho) + ) + t = t**self.rho + + t2 = self.sigma_max ** (1 / self.rho) + (indices + 1) / (num_scales - 1) * ( + self.sigma_min ** (1 / self.rho) - self.sigma_max ** (1 / self.rho) + ) + t2 = t2**self.rho + + x_t = x_start + noise * append_dims(t, dims) + + dropout_state = th.get_rng_state() + distiller = denoise_fn(x_t, t) + + if teacher_model is None: + x_t2 = euler_solver(x_t, t, t2, x_start).detach() + else: + x_t2 = heun_solver(x_t, t, t2, x_start).detach() + + th.set_rng_state(dropout_state) + distiller_target = target_denoise_fn(x_t2, t2) + distiller_target = distiller_target.detach() + + snrs = self.get_snr(t) + weights = get_weightings(self.weight_schedule, snrs, self.sigma_data) + if self.loss_norm == "l1": + diffs = th.abs(distiller - distiller_target) + loss = mean_flat(diffs) * weights + elif self.loss_norm == "l2": + # diffs = (distiller - distiller_target) ** 2 + loss = F.mse_loss(distiller, distiller_target) + # loss = mean_flat(diffs) * weights + elif self.loss_norm == "ssim": + loss = self.ssim_loss(distiller, distiller_target) * weights + # elif self.loss_norm == "l2-32": + # distiller = F.interpolate(distiller, size=32, mode="bilinear") + # distiller_target = F.interpolate( + # distiller_target, + # size=32, + # mode="bilinear", + # ) + # diffs = (distiller - distiller_target) ** 2 + # loss = mean_flat(diffs) * weights + # elif self.loss_norm == "lpips": + # if x_start.shape[-1] < 256: + # distiller = F.interpolate(distiller, size=224, mode="bilinear") + # distiller_target = F.interpolate( + # distiller_target, size=224, mode="bilinear" + # ) + + # loss = ( + # self.lpips_loss( + # (distiller + 1) / 2.0, + # (distiller_target + 1) / 2.0, + # ) + # * weights + # ) + else: + raise ValueError(f"Unknown loss norm {self.loss_norm}") + + terms = {} + terms["loss"] = loss + + return terms + + # def progdist_losses( + # self, + # model, + # x_start, + # num_scales, + # model_kwargs=None, + # teacher_model=None, + # teacher_diffusion=None, + # noise=None, + # ): + # if model_kwargs is None: + # model_kwargs = {} + # if noise is None: + # noise = th.randn_like(x_start) + + # dims = x_start.ndim + + # def denoise_fn(x, t): + # return self.denoise(model, x, t, **model_kwargs)[1] + + # @th.no_grad() + # def teacher_denoise_fn(x, t): + # return teacher_diffusion.denoise(teacher_model, x, t, **model_kwargs)[1] + + # @th.no_grad() + # def euler_solver(samples, t, next_t): + # x = samples + # denoiser = teacher_denoise_fn(x, t) + # d = (x - denoiser) / append_dims(t, dims) + # samples = x + d * append_dims(next_t - t, dims) + + # return samples + + # @th.no_grad() + # def euler_to_denoiser(x_t, t, x_next_t, next_t): + # denoiser = x_t - append_dims(t, dims) * (x_next_t - x_t) / append_dims( + # next_t - t, dims + # ) + # return denoiser + + # indices = th.randint(0, num_scales, (x_start.shape[0],), device=x_start.device) + + # t = self.sigma_max ** (1 / self.rho) + indices / num_scales * ( + # self.sigma_min ** (1 / self.rho) - self.sigma_max ** (1 / self.rho) + # ) + # t = t**self.rho + + # t2 = self.sigma_max ** (1 / self.rho) + (indices + 0.5) / num_scales * ( + # self.sigma_min ** (1 / self.rho) - self.sigma_max ** (1 / self.rho) + # ) + # t2 = t2**self.rho + + # t3 = self.sigma_max ** (1 / self.rho) + (indices + 1) / num_scales * ( + # self.sigma_min ** (1 / self.rho) - self.sigma_max ** (1 / self.rho) + # ) + # t3 = t3**self.rho + + # x_t = x_start + noise * append_dims(t, dims) + + # denoised_x = denoise_fn(x_t, t) + + # x_t2 = euler_solver(x_t, t, t2).detach() + # x_t3 = euler_solver(x_t2, t2, t3).detach() + + # target_x = euler_to_denoiser(x_t, t, x_t3, t3).detach() + + # snrs = self.get_snr(t) + # weights = get_weightings(self.weight_schedule, snrs, self.sigma_data) + # if self.loss_norm == "l1": + # diffs = th.abs(denoised_x - target_x) + # loss = mean_flat(diffs) * weights + # elif self.loss_norm == "l2": + # diffs = (denoised_x - target_x) ** 2 + # loss = mean_flat(diffs) * weights + # elif self.loss_norm == "lpips": + # if x_start.shape[-1] < 256: + # denoised_x = F.interpolate(denoised_x, size=224, mode="bilinear") + # target_x = F.interpolate(target_x, size=224, mode="bilinear") + # loss = ( + # self.lpips_loss( + # (denoised_x + 1) / 2.0, + # (target_x + 1) / 2.0, + # ) + # * weights + # ) + # else: + # raise ValueError(f"Unknown loss norm {self.loss_norm}") + + # terms = {} + # terms["loss"] = loss + + # return terms + + def denoise(self, model, x_t, sigmas, condition): + if not self.distillation: + c_skip, c_out, c_in = [ + append_dims(x, x_t.ndim) for x in self.get_scalings(sigmas) + ] + else: + c_skip, c_out, c_in = [ + append_dims(x, x_t.ndim) + for x in self.get_scalings_for_boundary_condition(sigmas) + ] + rescaled_t = 1000 * 0.25 * th.log(sigmas + 1e-44) + # rescaled_t = rescaled_t[:, None] + model_output = model(c_in * x_t, rescaled_t, condition) + denoised = c_out * model_output + c_skip * x_t + return model_output, denoised + + +def karras_sample( + diffusion, + model, + shape, + steps, + clip_denoised=True, + progress=True, + callback=None, + # model_kwargs=None, + condition=None, + device=None, + sigma_min=0.002, + sigma_max=80, # higher for highres? + rho=7.0, + sampler="heun", + s_churn=0.0, + s_tmin=0.0, + s_tmax=float("inf"), + s_noise=1.0, + generator=None, + ts=None, +): + if generator is None: + generator = get_generator("dummy") + + if sampler == "progdist": + sigmas = get_sigmas_karras(steps + 1, sigma_min, sigma_max, rho, device=device) + else: + sigmas = get_sigmas_karras(steps, sigma_min, sigma_max, rho, device=device) + th.manual_seed(42) + x_T = generator.randn(*shape, device=device) * sigma_max + sigmas = sigmas.unsqueeze(-1) + sample_fn = { + "heun": sample_heun, + "dpm": sample_dpm, + "ancestral": sample_euler_ancestral, + "onestep": sample_onestep, + "progdist": sample_progdist, + "euler": sample_euler, + "multistep": stochastic_iterative_sampler, + }[sampler] + + if sampler in ["heun", "dpm"]: + sampler_args = dict( + s_churn=s_churn, s_tmin=s_tmin, s_tmax=s_tmax, s_noise=s_noise + ) + elif sampler == "multistep": + sampler_args = dict( + ts=ts, t_min=sigma_min, t_max=sigma_max, rho=diffusion.rho, steps=steps + ) + else: + sampler_args = {} + + def denoiser(x_t, sigma): + _, denoised = diffusion.denoise(model, x_t, sigma, condition) + if clip_denoised: + denoised = denoised.clamp(-1, 1) + return denoised + + x_0 = sample_fn( + denoiser, + x_T, + sigmas, + generator, + progress=progress, + callback=callback, + **sampler_args, + ) + return x_0.clamp(-1, 1) + + +def get_sigmas_karras(n, sigma_min, sigma_max, rho=7.0, device="cpu"): + """Constructs the noise schedule of Karras et al. (2022).""" + ramp = th.linspace(0, 1, n) + min_inv_rho = sigma_min ** (1 / rho) + max_inv_rho = sigma_max ** (1 / rho) + sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho + return append_zero(sigmas).to(device) + + +def to_d(x, sigma, denoised): + """Converts a denoiser output to a Karras ODE derivative.""" + return (x - denoised) / append_dims(sigma, x.ndim) + + +def get_ancestral_step(sigma_from, sigma_to): + """Calculates the noise level (sigma_down) to step down to and the amount + of noise to add (sigma_up) when doing an ancestral sampling step.""" + sigma_up = ( + sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2 + ) ** 0.5 + sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5 + return sigma_down, sigma_up + + +@th.no_grad() +def sample_euler_ancestral(model, x, sigmas, generator, progress=False, callback=None): + """Ancestral sampling with Euler method steps.""" + s_in = x.new_ones([x.shape[0]]) + indices = range(len(sigmas) - 1) + if progress: + from tqdm.auto import tqdm + + indices = tqdm(indices) + + for i in indices: + denoised = model(x, sigmas[i] * s_in) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1]) + if callback is not None: + callback( + { + "x": x, + "i": i, + "sigma": sigmas[i], + "sigma_hat": sigmas[i], + "denoised": denoised, + } + ) + d = to_d(x, sigmas[i], denoised) + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + x = x + generator.randn_like(x) * sigma_up + return x + + +@th.no_grad() +def sample_midpoint_ancestral(model, x, ts, generator, progress=False, callback=None): + """Ancestral sampling with midpoint method steps.""" + s_in = x.new_ones([x.shape[0]]) + step_size = 1 / len(ts) + if progress: + from tqdm.auto import tqdm + + ts = tqdm(ts) + + for tn in ts: + dn = model(x, tn * s_in) + dn_2 = model(x + (step_size / 2) * dn, (tn + step_size / 2) * s_in) + x = x + step_size * dn_2 + if callback is not None: + callback({"x": x, "tn": tn, "dn": dn, "dn_2": dn_2}) + return x + + +@th.no_grad() +def sample_heun( + denoiser, + x, + sigmas, + generator, + progress=False, + callback=None, + s_churn=0.0, + s_tmin=0.0, + s_tmax=float("inf"), + s_noise=1.0, +): + """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" + s_in = x.new_ones([x.shape[0]]) + indices = range(len(sigmas) - 1) + if progress: + from tqdm.auto import tqdm + + indices = tqdm(indices) + + for i in indices: + gamma = ( + min(s_churn / (len(sigmas) - 1), 2**0.5 - 1) + if s_tmin <= sigmas[i] <= s_tmax + else 0.0 + ) + eps = generator.randn_like(x) * s_noise + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5 + denoised = denoiser(x, sigma_hat * s_in) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback( + { + "x": x, + "i": i, + "sigma": sigmas[i], + "sigma_hat": sigma_hat, + "denoised": denoised, + } + ) + dt = sigmas[i + 1] - sigma_hat + if sigmas[i + 1] == 0: + # Euler method + x = x + d * dt + else: + # Heun's method + x_2 = x + d * dt + denoised_2 = denoiser(x_2, sigmas[i + 1] * s_in) + d_2 = to_d(x_2, sigmas[i + 1], denoised_2) + d_prime = (d + d_2) / 2 + x = x + d_prime * dt + return x + + +@th.no_grad() +def sample_euler( + denoiser, + x, + sigmas, + generator, + progress=False, + callback=None, +): + """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" + s_in = x.new_ones([x.shape[0]]) + indices = range(len(sigmas) - 1) + if progress: + from tqdm.auto import tqdm + + indices = tqdm(indices) + + for i in indices: + sigma = sigmas[i] + denoised = denoiser(x, sigma * s_in) + d = to_d(x, sigma, denoised) + if callback is not None: + callback( + { + "x": x, + "i": i, + "sigma": sigmas[i], + "denoised": denoised, + } + ) + dt = sigmas[i + 1] - sigma + x = x + d * dt + return x + + +@th.no_grad() +def sample_dpm( + denoiser, + x, + sigmas, + generator, + progress=False, + callback=None, + s_churn=0.0, + s_tmin=0.0, + s_tmax=float("inf"), + s_noise=1.0, +): + """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).""" + s_in = x.new_ones([x.shape[0]]) + indices = range(len(sigmas) - 1) + if progress: + from tqdm.auto import tqdm + + indices = tqdm(indices) + + for i in indices: + gamma = ( + min(s_churn / (len(sigmas) - 1), 2**0.5 - 1) + if s_tmin <= sigmas[i] <= s_tmax + else 0.0 + ) + eps = generator.randn_like(x) * s_noise + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5 + denoised = denoiser(x, sigma_hat * s_in) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback( + { + "x": x, + "i": i, + "sigma": sigmas[i], + "sigma_hat": sigma_hat, + "denoised": denoised, + } + ) + # Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule + sigma_mid = ((sigma_hat ** (1 / 3) + sigmas[i + 1] ** (1 / 3)) / 2) ** 3 + dt_1 = sigma_mid - sigma_hat + dt_2 = sigmas[i + 1] - sigma_hat + x_2 = x + d * dt_1 + denoised_2 = denoiser(x_2, sigma_mid * s_in) + d_2 = to_d(x_2, sigma_mid, denoised_2) + x = x + d_2 * dt_2 + return x + + +@th.no_grad() +def sample_onestep( + distiller, + x, + sigmas, + generator=None, + progress=False, + callback=None, +): + """Single-step generation from a distilled model.""" + s_in = x.new_ones([x.shape[0]]) + return distiller(x, sigmas[0] * s_in) + + +@th.no_grad() +def stochastic_iterative_sampler( + distiller, + x, + sigmas, + generator, + ts, + progress=False, + callback=None, + t_min=0.002, + t_max=80.0, + rho=7.0, + steps=40, +): + t_max_rho = t_max ** (1 / rho) + t_min_rho = t_min ** (1 / rho) + s_in = x.new_ones([x.shape[0]]) + + for i in range(len(ts) - 1): + t = (t_max_rho + ts[i] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho + x0 = distiller(x, t * s_in) + next_t = (t_max_rho + ts[i + 1] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho + next_t = np.clip(next_t, t_min, t_max) + x = x0 + generator.randn_like(x) * np.sqrt(next_t**2 - t_min**2) + + return x + + +@th.no_grad() +def sample_progdist( + denoiser, + x, + sigmas, + generator=None, + progress=False, + callback=None, +): + s_in = x.new_ones([x.shape[0]]) + sigmas = sigmas[:-1] # skip the zero sigma + + indices = range(len(sigmas) - 1) + if progress: + from tqdm.auto import tqdm + + indices = tqdm(indices) + + for i in indices: + sigma = sigmas[i] + denoised = denoiser(x, sigma * s_in) + d = to_d(x, sigma, denoised) + if callback is not None: + callback( + { + "x": x, + "i": i, + "sigma": sigma, + "denoised": denoised, + } + ) + dt = sigmas[i + 1] - sigma + x = x + d * dt + + return x + + +# @th.no_grad() +# def iterative_colorization( +# distiller, +# images, +# x, +# ts, +# t_min=0.002, +# t_max=80.0, +# rho=7.0, +# steps=40, +# generator=None, +# ): +# def obtain_orthogonal_matrix(): +# vector = np.asarray([0.2989, 0.5870, 0.1140]) +# vector = vector / np.linalg.norm(vector) +# matrix = np.eye(3) +# matrix[:, 0] = vector +# matrix = np.linalg.qr(matrix)[0] +# if np.sum(matrix[:, 0]) < 0: +# matrix = -matrix +# return matrix + +# Q = th.from_numpy(obtain_orthogonal_matrix()).to(dist_util.dev()).to(th.float32) +# mask = th.zeros(*x.shape[1:], device=dist_util.dev()) +# mask[0, ...] = 1.0 + +# def replacement(x0, x1): +# x0 = th.einsum("bchw,cd->bdhw", x0, Q) +# x1 = th.einsum("bchw,cd->bdhw", x1, Q) + +# x_mix = x0 * mask + x1 * (1.0 - mask) +# x_mix = th.einsum("bdhw,cd->bchw", x_mix, Q) +# return x_mix + +# t_max_rho = t_max ** (1 / rho) +# t_min_rho = t_min ** (1 / rho) +# s_in = x.new_ones([x.shape[0]]) +# images = replacement(images, th.zeros_like(images)) + +# for i in range(len(ts) - 1): +# t = (t_max_rho + ts[i] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho +# x0 = distiller(x, t * s_in) +# x0 = th.clamp(x0, -1.0, 1.0) +# x0 = replacement(images, x0) +# next_t = (t_max_rho + ts[i + 1] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho +# next_t = np.clip(next_t, t_min, t_max) +# x = x0 + generator.randn_like(x) * np.sqrt(next_t**2 - t_min**2) + +# return x, images + + +# @th.no_grad() +# def iterative_inpainting( +# distiller, +# images, +# x, +# ts, +# t_min=0.002, +# t_max=80.0, +# rho=7.0, +# steps=40, +# generator=None, +# ): +# from PIL import Image, ImageDraw, ImageFont + +# image_size = x.shape[-1] + +# # create a blank image with a white background +# img = Image.new("RGB", (image_size, image_size), color="white") + +# # get a drawing context for the image +# draw = ImageDraw.Draw(img) + +# # load a font +# font = ImageFont.truetype("arial.ttf", 250) + +# # draw the letter "C" in black +# draw.text((50, 0), "S", font=font, fill=(0, 0, 0)) + +# # convert the image to a numpy array +# img_np = np.array(img) +# img_np = img_np.transpose(2, 0, 1) +# img_th = th.from_numpy(img_np).to(dist_util.dev()) + +# mask = th.zeros(*x.shape, device=dist_util.dev()) +# mask = mask.reshape(-1, 7, 3, image_size, image_size) + +# mask[::2, :, img_th > 0.5] = 1.0 +# mask[1::2, :, img_th < 0.5] = 1.0 +# mask = mask.reshape(-1, 3, image_size, image_size) + +# def replacement(x0, x1): +# x_mix = x0 * mask + x1 * (1 - mask) +# return x_mix + +# t_max_rho = t_max ** (1 / rho) +# t_min_rho = t_min ** (1 / rho) +# s_in = x.new_ones([x.shape[0]]) +# images = replacement(images, -th.ones_like(images)) + +# for i in range(len(ts) - 1): +# t = (t_max_rho + ts[i] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho +# x0 = distiller(x, t * s_in) +# x0 = th.clamp(x0, -1.0, 1.0) +# x0 = replacement(images, x0) +# next_t = (t_max_rho + ts[i + 1] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho +# next_t = np.clip(next_t, t_min, t_max) +# x = x0 + generator.randn_like(x) * np.sqrt(next_t**2 - t_min**2) + +# return x, images + + +# @th.no_grad() +# def iterative_superres( +# distiller, +# images, +# x, +# ts, +# t_min=0.002, +# t_max=80.0, +# rho=7.0, +# steps=40, +# generator=None, +# ): +# patch_size = 8 + +# def obtain_orthogonal_matrix(): +# vector = np.asarray([1] * patch_size**2) +# vector = vector / np.linalg.norm(vector) +# matrix = np.eye(patch_size**2) +# matrix[:, 0] = vector +# matrix = np.linalg.qr(matrix)[0] +# if np.sum(matrix[:, 0]) < 0: +# matrix = -matrix +# return matrix + +# Q = th.from_numpy(obtain_orthogonal_matrix()).to(dist_util.dev()).to(th.float32) + +# image_size = x.shape[-1] + +# def replacement(x0, x1): +# x0_flatten = ( +# x0.reshape(-1, 3, image_size, image_size) +# .reshape( +# -1, +# 3, +# image_size // patch_size, +# patch_size, +# image_size // patch_size, +# patch_size, +# ) +# .permute(0, 1, 2, 4, 3, 5) +# .reshape(-1, 3, image_size**2 // patch_size**2, patch_size**2) +# ) +# x1_flatten = ( +# x1.reshape(-1, 3, image_size, image_size) +# .reshape( +# -1, +# 3, +# image_size // patch_size, +# patch_size, +# image_size // patch_size, +# patch_size, +# ) +# .permute(0, 1, 2, 4, 3, 5) +# .reshape(-1, 3, image_size**2 // patch_size**2, patch_size**2) +# ) +# x0 = th.einsum("bcnd,de->bcne", x0_flatten, Q) +# x1 = th.einsum("bcnd,de->bcne", x1_flatten, Q) +# x_mix = x0.new_zeros(x0.shape) +# x_mix[..., 0] = x0[..., 0] +# x_mix[..., 1:] = x1[..., 1:] +# x_mix = th.einsum("bcne,de->bcnd", x_mix, Q) +# x_mix = ( +# x_mix.reshape( +# -1, +# 3, +# image_size // patch_size, +# image_size // patch_size, +# patch_size, +# patch_size, +# ) +# .permute(0, 1, 2, 4, 3, 5) +# .reshape(-1, 3, image_size, image_size) +# ) +# return x_mix + +# def average_image_patches(x): +# x_flatten = ( +# x.reshape(-1, 3, image_size, image_size) +# .reshape( +# -1, +# 3, +# image_size // patch_size, +# patch_size, +# image_size // patch_size, +# patch_size, +# ) +# .permute(0, 1, 2, 4, 3, 5) +# .reshape(-1, 3, image_size**2 // patch_size**2, patch_size**2) +# ) +# x_flatten[..., :] = x_flatten.mean(dim=-1, keepdim=True) +# return ( +# x_flatten.reshape( +# -1, +# 3, +# image_size // patch_size, +# image_size // patch_size, +# patch_size, +# patch_size, +# ) +# .permute(0, 1, 2, 4, 3, 5) +# .reshape(-1, 3, image_size, image_size) +# ) + +# t_max_rho = t_max ** (1 / rho) +# t_min_rho = t_min ** (1 / rho) +# s_in = x.new_ones([x.shape[0]]) +# images = average_image_patches(images) + +# for i in range(len(ts) - 1): +# t = (t_max_rho + ts[i] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho +# x0 = distiller(x, t * s_in) +# x0 = th.clamp(x0, -1.0, 1.0) +# x0 = replacement(images, x0) +# next_t = (t_max_rho + ts[i + 1] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho +# next_t = np.clip(next_t, t_min, t_max) +# x = x0 + generator.randn_like(x) * np.sqrt(next_t**2 - t_min**2) + +# return x, images diff --git a/modules/diffusion/karras/random_utils.py b/modules/diffusion/karras/random_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7ebf4265934edad153adaf83777731afa313d000 --- /dev/null +++ b/modules/diffusion/karras/random_utils.py @@ -0,0 +1,177 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch as th + + +def get_generator(generator, num_samples=0, seed=0): + if generator == "dummy": + return DummyGenerator() + elif generator == "determ": + return DeterministicGenerator(num_samples, seed) + elif generator == "determ-indiv": + return DeterministicIndividualGenerator(num_samples, seed) + else: + raise NotImplementedError + + +class DummyGenerator: + def randn(self, *args, **kwargs): + return th.randn(*args, **kwargs) + + def randint(self, *args, **kwargs): + return th.randint(*args, **kwargs) + + def randn_like(self, *args, **kwargs): + return th.randn_like(*args, **kwargs) + + +class DeterministicGenerator: + """ + RNG to deterministically sample num_samples samples that does not depend on batch_size or mpi_machines + Uses a single rng and samples num_samples sized randomness and subsamples the current indices + """ + + def __init__(self, num_samples, seed=0): + print("Warning: Distributed not initialised, using single rank") + self.rank = 0 + self.world_size = 1 + self.num_samples = num_samples + self.done_samples = 0 + self.seed = seed + self.rng_cpu = th.Generator() + if th.cuda.is_available(): + self.rng_cuda = th.Generator(dist_util.dev()) + self.set_seed(seed) + + def get_global_size_and_indices(self, size): + global_size = (self.num_samples, *size[1:]) + indices = th.arange( + self.done_samples + self.rank, + self.done_samples + self.world_size * int(size[0]), + self.world_size, + ) + indices = th.clamp(indices, 0, self.num_samples - 1) + assert ( + len(indices) == size[0] + ), f"rank={self.rank}, ws={self.world_size}, l={len(indices)}, bs={size[0]}" + return global_size, indices + + def get_generator(self, device): + return self.rng_cpu if th.device(device).type == "cpu" else self.rng_cuda + + def randn(self, *size, dtype=th.float, device="cpu"): + global_size, indices = self.get_global_size_and_indices(size) + generator = self.get_generator(device) + return th.randn(*global_size, generator=generator, dtype=dtype, device=device)[ + indices + ] + + def randint(self, low, high, size, dtype=th.long, device="cpu"): + global_size, indices = self.get_global_size_and_indices(size) + generator = self.get_generator(device) + return th.randint( + low, high, generator=generator, size=global_size, dtype=dtype, device=device + )[indices] + + def randn_like(self, tensor): + size, dtype, device = tensor.size(), tensor.dtype, tensor.device + return self.randn(*size, dtype=dtype, device=device) + + def set_done_samples(self, done_samples): + self.done_samples = done_samples + self.set_seed(self.seed) + + def get_seed(self): + return self.seed + + def set_seed(self, seed): + self.rng_cpu.manual_seed(seed) + if th.cuda.is_available(): + self.rng_cuda.manual_seed(seed) + + +class DeterministicIndividualGenerator: + """ + RNG to deterministically sample num_samples samples that does not depend on batch_size or mpi_machines + Uses a separate rng for each sample to reduce memoery usage + """ + + def __init__(self, num_samples, seed=0): + print("Warning: Distributed not initialised, using single rank") + self.rank = 0 + self.world_size = 1 + self.num_samples = num_samples + self.done_samples = 0 + self.seed = seed + self.rng_cpu = [th.Generator() for _ in range(num_samples)] + if th.cuda.is_available(): + self.rng_cuda = [th.Generator(dist_util.dev()) for _ in range(num_samples)] + self.set_seed(seed) + + def get_size_and_indices(self, size): + indices = th.arange( + self.done_samples + self.rank, + self.done_samples + self.world_size * int(size[0]), + self.world_size, + ) + indices = th.clamp(indices, 0, self.num_samples - 1) + assert ( + len(indices) == size[0] + ), f"rank={self.rank}, ws={self.world_size}, l={len(indices)}, bs={size[0]}" + return (1, *size[1:]), indices + + def get_generator(self, device): + return self.rng_cpu if th.device(device).type == "cpu" else self.rng_cuda + + def randn(self, *size, dtype=th.float, device="cpu"): + size, indices = self.get_size_and_indices(size) + generator = self.get_generator(device) + return th.cat( + [ + th.randn(*size, generator=generator[i], dtype=dtype, device=device) + for i in indices + ], + dim=0, + ) + + def randint(self, low, high, size, dtype=th.long, device="cpu"): + size, indices = self.get_size_and_indices(size) + generator = self.get_generator(device) + return th.cat( + [ + th.randint( + low, + high, + generator=generator[i], + size=size, + dtype=dtype, + device=device, + ) + for i in indices + ], + dim=0, + ) + + def randn_like(self, tensor): + size, dtype, device = tensor.size(), tensor.dtype, tensor.device + return self.randn(*size, dtype=dtype, device=device) + + def set_done_samples(self, done_samples): + self.done_samples = done_samples + + def get_seed(self): + return self.seed + + def set_seed(self, seed): + [ + rng_cpu.manual_seed(i + self.num_samples * seed) + for i, rng_cpu in enumerate(self.rng_cpu) + ] + if th.cuda.is_available(): + [ + rng_cuda.manual_seed(i + self.num_samples * seed) + for i, rng_cuda in enumerate(self.rng_cuda) + ] diff --git a/modules/diffusion/karras/sample.py b/modules/diffusion/karras/sample.py new file mode 100644 index 0000000000000000000000000000000000000000..6d7472a37342c8e8eb06ccd6e292a28b80e8dfef --- /dev/null +++ b/modules/diffusion/karras/sample.py @@ -0,0 +1,185 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from abc import ABC, abstractmethod + +import numpy as np +import torch as th +from scipy.stats import norm +import torch.distributed as dist + + +def create_named_schedule_sampler(name, diffusion): + """ + Create a ScheduleSampler from a library of pre-defined samplers. + + :param name: the name of the sampler. + :param diffusion: the diffusion object to sample for. + """ + if name == "uniform": + return UniformSampler(diffusion) + elif name == "loss-second-moment": + return LossSecondMomentResampler(diffusion) + elif name == "lognormal": + return LogNormalSampler() + else: + raise NotImplementedError(f"unknown schedule sampler: {name}") + + +class ScheduleSampler(ABC): + """ + A distribution over timesteps in the diffusion process, intended to reduce + variance of the objective. + + By default, samplers perform unbiased importance sampling, in which the + objective's mean is unchanged. + However, subclasses may override sample() to change how the resampled + terms are reweighted, allowing for actual changes in the objective. + """ + + @abstractmethod + def weights(self): + """ + Get a numpy array of weights, one per diffusion step. + + The weights needn't be normalized, but must be positive. + """ + + def sample(self, batch_size, device): + """ + Importance-sample timesteps for a batch. + + :param batch_size: the number of timesteps. + :param device: the torch device to save to. + :return: a tuple (timesteps, weights): + - timesteps: a tensor of timestep indices. + - weights: a tensor of weights to scale the resulting losses. + """ + w = self.weights() + p = w / np.sum(w) + indices_np = np.random.choice(len(p), size=(batch_size,), p=p) + indices = th.from_numpy(indices_np).long().to(device) + weights_np = 1 / (len(p) * p[indices_np]) + weights = th.from_numpy(weights_np).float().to(device) + return indices, weights + + +class UniformSampler(ScheduleSampler): + def __init__(self, diffusion): + self.diffusion = diffusion + self._weights = np.ones([diffusion.num_timesteps]) + + def weights(self): + return self._weights + + +class LossAwareSampler(ScheduleSampler): + def update_with_local_losses(self, local_ts, local_losses): + """ + Update the reweighting using losses from a model. + + Call this method from each rank with a batch of timesteps and the + corresponding losses for each of those timesteps. + This method will perform synchronization to make sure all of the ranks + maintain the exact same reweighting. + + :param local_ts: an integer Tensor of timesteps. + :param local_losses: a 1D Tensor of losses. + """ + batch_sizes = [ + th.tensor([0], dtype=th.int32, device=local_ts.device) + for _ in range(dist.get_world_size()) + ] + dist.all_gather( + batch_sizes, + th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device), + ) + + # Pad all_gather batches to be the maximum batch size. + batch_sizes = [x.item() for x in batch_sizes] + max_bs = max(batch_sizes) + + timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes] + loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes] + dist.all_gather(timestep_batches, local_ts) + dist.all_gather(loss_batches, local_losses) + timesteps = [ + x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs] + ] + losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]] + self.update_with_all_losses(timesteps, losses) + + @abstractmethod + def update_with_all_losses(self, ts, losses): + """ + Update the reweighting using losses from a model. + + Sub-classes should override this method to update the reweighting + using losses from the model. + + This method directly updates the reweighting without synchronizing + between workers. It is called by update_with_local_losses from all + ranks with identical arguments. Thus, it should have deterministic + behavior to maintain state across workers. + + :param ts: a list of int timesteps. + :param losses: a list of float losses, one per timestep. + """ + + +class LossSecondMomentResampler(LossAwareSampler): + def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001): + self.diffusion = diffusion + self.history_per_term = history_per_term + self.uniform_prob = uniform_prob + self._loss_history = np.zeros( + [diffusion.num_timesteps, history_per_term], dtype=np.float64 + ) + self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int) + + def weights(self): + if not self._warmed_up(): + return np.ones([self.diffusion.num_timesteps], dtype=np.float64) + weights = np.sqrt(np.mean(self._loss_history**2, axis=-1)) + weights /= np.sum(weights) + weights *= 1 - self.uniform_prob + weights += self.uniform_prob / len(weights) + return weights + + def update_with_all_losses(self, ts, losses): + for t, loss in zip(ts, losses): + if self._loss_counts[t] == self.history_per_term: + # Shift out the oldest loss term. + self._loss_history[t, :-1] = self._loss_history[t, 1:] + self._loss_history[t, -1] = loss + else: + self._loss_history[t, self._loss_counts[t]] = loss + self._loss_counts[t] += 1 + + def _warmed_up(self): + return (self._loss_counts == self.history_per_term).all() + + +class LogNormalSampler: + def __init__(self, p_mean=-1.2, p_std=1.2, even=False): + self.p_mean = p_mean + self.p_std = p_std + self.even = even + if self.even: + self.inv_cdf = lambda x: norm.ppf(x, loc=p_mean, scale=p_std) + self.rank, self.size = dist.get_rank(), dist.get_world_size() + + def sample(self, bs, device): + if self.even: + # buckets = [1/G] + start_i, end_i = self.rank * bs, (self.rank + 1) * bs + global_batch_size = self.size * bs + locs = (th.arange(start_i, end_i) + th.rand(bs)) / global_batch_size + log_sigmas = th.tensor(self.inv_cdf(locs), dtype=th.float32, device=device) + else: + log_sigmas = self.p_mean + self.p_std * th.randn(bs, device=device) + sigmas = th.exp(log_sigmas) + weights = th.ones_like(sigmas) + return sigmas, weights diff --git a/modules/diffusion/unet/attention.py b/modules/diffusion/unet/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..f5a210cb392e3e1beb9f8c2884978f6b0b71ced4 --- /dev/null +++ b/modules/diffusion/unet/attention.py @@ -0,0 +1,241 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from modules.general.utils import Conv1d, normalization, zero_module +from .basic import UNetBlock + + +class AttentionBlock(UNetBlock): + r"""A spatial transformer encoder block that allows spatial positions to attend + to each other. Reference from `latent diffusion repo + `_. + + Args: + channels: Number of channels in the input. + num_head_channels: Number of channels per attention head. + num_heads: Number of attention heads. Overrides ``num_head_channels`` if set. + encoder_channels: Number of channels in the encoder output for cross-attention. + If ``None``, then self-attention is performed. + use_self_attention: Whether to use self-attention before cross-attention, only applicable if encoder_channels is set. + dims: Number of spatial dimensions, i.e. 1 for temporal signals, 2 for images. + h_dim: The dimension of the height, would be applied if ``dims`` is 2. + encoder_hdim: The dimension of the height of the encoder output, would be applied if ``dims`` is 2. + p_dropout: Dropout probability. + """ + + def __init__( + self, + channels: int, + num_head_channels: int = 32, + num_heads: int = -1, + encoder_channels: int = None, + use_self_attention: bool = False, + dims: int = 1, + h_dim: int = 100, + encoder_hdim: int = 384, + p_dropout: float = 0.0, + ): + super().__init__() + + self.channels = channels + self.p_dropout = p_dropout + self.dims = dims + + if dims == 1: + self.channels = channels + elif dims == 2: + # We consider the channel as product of channel and height, i.e. C x H + # This is because we want to apply attention on the audio signal, which is 1D + self.channels = channels * h_dim + else: + raise ValueError(f"invalid number of dimensions: {dims}") + + if num_head_channels == -1: + assert ( + self.channels % num_heads == 0 + ), f"q,k,v channels {self.channels} is not divisible by num_heads {num_heads}" + self.num_heads = num_heads + self.num_head_channels = self.channels // num_heads + else: + assert ( + self.channels % num_head_channels == 0 + ), f"q,k,v channels {self.channels} is not divisible by num_head_channels {num_head_channels}" + self.num_heads = self.channels // num_head_channels + self.num_head_channels = num_head_channels + + if encoder_channels is not None: + self.use_self_attention = use_self_attention + + if dims == 1: + self.encoder_channels = encoder_channels + elif dims == 2: + self.encoder_channels = encoder_channels * encoder_hdim + else: + raise ValueError(f"invalid number of dimensions: {dims}") + + if use_self_attention: + self.self_attention = BasicAttentionBlock( + self.channels, + self.num_head_channels, + self.num_heads, + p_dropout=self.p_dropout, + ) + self.cross_attention = BasicAttentionBlock( + self.channels, + self.num_head_channels, + self.num_heads, + self.encoder_channels, + p_dropout=self.p_dropout, + ) + else: + self.encoder_channels = None + self.self_attention = BasicAttentionBlock( + self.channels, + self.num_head_channels, + self.num_heads, + p_dropout=self.p_dropout, + ) + + def forward(self, x: torch.Tensor, encoder_output: torch.Tensor = None): + r""" + Args: + x: input tensor with shape [B x ``channels`` x ...] + encoder_output: feature tensor with shape [B x ``encoder_channels`` x ...], if ``None``, then self-attention is performed. + + Returns: + output tensor with shape [B x ``channels`` x ...] + """ + shape = x.size() + x = x.reshape(shape[0], self.channels, -1).contiguous() + + if self.encoder_channels is None: + assert ( + encoder_output is None + ), "encoder_output must be None for self-attention." + h = self.self_attention(x) + + else: + assert ( + encoder_output is not None + ), "encoder_output must be given for cross-attention." + encoder_output = encoder_output.reshape( + shape[0], self.encoder_channels, -1 + ).contiguous() + + if self.use_self_attention: + x = self.self_attention(x) + h = self.cross_attention(x, encoder_output) + + return h.reshape(*shape).contiguous() + + +class BasicAttentionBlock(nn.Module): + def __init__( + self, + channels: int, + num_head_channels: int = 32, + num_heads: int = -1, + context_channels: int = None, + p_dropout: float = 0.0, + ): + super().__init__() + + self.channels = channels + self.p_dropout = p_dropout + self.context_channels = context_channels + + if num_head_channels == -1: + assert ( + self.channels % num_heads == 0 + ), f"q,k,v channels {self.channels} is not divisible by num_heads {num_heads}" + self.num_heads = num_heads + self.num_head_channels = self.channels // num_heads + else: + assert ( + self.channels % num_head_channels == 0 + ), f"q,k,v channels {self.channels} is not divisible by num_head_channels {num_head_channels}" + self.num_heads = self.channels // num_head_channels + self.num_head_channels = num_head_channels + + if context_channels is not None: + self.to_q = nn.Sequential( + normalization(self.channels), + Conv1d(self.channels, self.channels, 1), + ) + self.to_kv = Conv1d(context_channels, 2 * self.channels, 1) + else: + self.to_qkv = nn.Sequential( + normalization(self.channels), + Conv1d(self.channels, 3 * self.channels, 1), + ) + + self.linear = Conv1d(self.channels, self.channels) + + self.proj_out = nn.Sequential( + normalization(self.channels), + Conv1d(self.channels, self.channels, 1), + nn.GELU(), + nn.Dropout(p=self.p_dropout), + zero_module(Conv1d(self.channels, self.channels, 1)), + ) + + def forward(self, q: torch.Tensor, kv: torch.Tensor = None): + r""" + Args: + q: input tensor with shape [B, ``channels``, L] + kv: feature tensor with shape [B, ``context_channels``, T], if ``None``, then self-attention is performed. + + Returns: + output tensor with shape [B, ``channels``, L] + """ + N, C, L = q.size() + + if self.context_channels is not None: + assert kv is not None, "kv must be given for cross-attention." + + q = ( + self.to_q(q) + .reshape(self.num_heads, self.num_head_channels, -1) + .transpose(-1, -2) + .contiguous() + ) + kv = ( + self.to_kv(kv) + .reshape(2, self.num_heads, self.num_head_channels, -1) + .transpose(-1, -2) + .chunk(2) + ) + k, v = ( + kv[0].squeeze(0).contiguous(), + kv[1].squeeze(0).contiguous(), + ) + + else: + qkv = ( + self.to_qkv(q) + .reshape(3, self.num_heads, self.num_head_channels, -1) + .transpose(-1, -2) + .chunk(3) + ) + q, k, v = ( + qkv[0].squeeze(0).contiguous(), + qkv[1].squeeze(0).contiguous(), + qkv[2].squeeze(0).contiguous(), + ) + + h = F.scaled_dot_product_attention(q, k, v, dropout_p=self.p_dropout).transpose( + -1, -2 + ) + h = h.reshape(N, -1, L).contiguous() + h = self.linear(h) + + x = q + h + h = self.proj_out(x) + + return x + h diff --git a/modules/diffusion/unet/basic.py b/modules/diffusion/unet/basic.py new file mode 100644 index 0000000000000000000000000000000000000000..9cd4bc55ab58b3ebb7f9b2c0ec8dd37ad6d43721 --- /dev/null +++ b/modules/diffusion/unet/basic.py @@ -0,0 +1,15 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn +from abc import abstractmethod + + +class UNetBlock(nn.Module): + r"""Any module where forward() takes timestep embeddings as a second argument.""" + + @abstractmethod + def forward(self, x, emb): + r"""Apply the module to `x` given `emb` timestep embeddings.""" diff --git a/modules/diffusion/unet/resblock.py b/modules/diffusion/unet/resblock.py new file mode 100644 index 0000000000000000000000000000000000000000..144c867785bbf76fa877ea6e24dfbdcffb78008e --- /dev/null +++ b/modules/diffusion/unet/resblock.py @@ -0,0 +1,178 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +from .basic import UNetBlock +from modules.general.utils import ( + append_dims, + ConvNd, + normalization, + zero_module, +) + + +class ResBlock(UNetBlock): + r"""A residual block that can optionally change the number of channels. + + Args: + channels: the number of input channels. + emb_channels: the number of timestep embedding channels. + dropout: the rate of dropout. + out_channels: if specified, the number of out channels. + use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + dims: determines if the signal is 1D, 2D, or 3D. + up: if True, use this block for upsampling. + down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout: float = 0.0, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + up=False, + down=False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_scale_shift_norm = use_scale_shift_norm + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + ConvNd(dims, channels, self.out_channels, 3, padding=1), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), + ConvNd( + dims, + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + 1, + ), + ) + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + ConvNd(dims, self.out_channels, self.out_channels, 3, padding=1) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = ConvNd( + dims, channels, self.out_channels, 3, padding=1 + ) + else: + self.skip_connection = ConvNd(dims, channels, self.out_channels, 1) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + + x: an [N x C x ...] Tensor of features. + emb: an [N x emb_channels x ...] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb) + emb_out = append_dims(emb_out, h.dim()) + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = torch.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class Upsample(nn.Module): + r"""An upsampling layer with an optional convolution. + + Args: + channels: channels in the inputs and outputs. + dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + out_channels: if specified, the number of out channels. + """ + + def __init__(self, channels, dims=2, out_channels=None): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.dims = dims + self.conv = ConvNd(dims, self.channels, self.out_channels, 3, padding=1) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate( + x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" + ) + else: + x = F.interpolate(x, scale_factor=2, mode="nearest") + x = self.conv(x) + return x + + +class Downsample(nn.Module): + r"""A downsampling layer with an optional convolution. + + Args: + channels: channels in the inputs and outputs. + dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + out_channels: if specified, the number of output channels. + """ + + def __init__(self, channels, dims=2, out_channels=None): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + self.op = ConvNd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=1 + ) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) diff --git a/modules/diffusion/unet/unet.py b/modules/diffusion/unet/unet.py new file mode 100644 index 0000000000000000000000000000000000000000..2d39f5d1e07f64012ba08268a0a4a5e71ad01e88 --- /dev/null +++ b/modules/diffusion/unet/unet.py @@ -0,0 +1,310 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + +from modules.encoder.position_encoder import PositionEncoder +from modules.general.utils import append_dims, ConvNd, normalization, zero_module +from .attention import AttentionBlock +from .resblock import Downsample, ResBlock, Upsample + + +class UNet(nn.Module): + r"""The full UNet model with attention and timestep embedding. + + Args: + dims: determines if the signal is 1D (temporal), 2D(spatial). + in_channels: channels in the input Tensor. + model_channels: base channel count for the model. + out_channels: channels in the output Tensor. + num_res_blocks: number of residual blocks per downsample. + channel_mult: channel multiplier for each level of the UNet. + num_attn_blocks: number of attention blocks at place. + attention_resolutions: a collection of downsample rates at which attention will + take place. May be a set, list, or tuple. For example, if this contains 4, + then at 4x downsampling, attention will be used. + num_heads: the number of attention heads in each attention layer. + num_head_channels: if specified, ignore num_heads and instead use a fixed + channel width per attention head. + d_context: if specified, use for cross-attention channel project. + p_dropout: the dropout probability. + use_self_attention: Apply self attention before cross attention. + num_classes: if specified (as an int), then this model will be class-conditional + with ``num_classes`` classes. + use_extra_film: if specified, use an extra FiLM-like conditioning mechanism. + d_emb: if specified, use for FiLM-like conditioning. + use_scale_shift_norm: use a FiLM-like conditioning mechanism. + resblock_updown: use residual blocks for up/downsampling. + """ + + def __init__( + self, + dims: int = 1, + in_channels: int = 100, + model_channels: int = 128, + out_channels: int = 100, + h_dim: int = 128, + num_res_blocks: int = 1, + channel_mult: tuple = (1, 2, 4), + num_attn_blocks: int = 1, + attention_resolutions: tuple = (1, 2, 4), + num_heads: int = 1, + num_head_channels: int = -1, + d_context: int = None, + context_hdim: int = 128, + p_dropout: float = 0.0, + num_classes: int = -1, + use_extra_film: str = None, + d_emb: int = None, + use_scale_shift_norm: bool = True, + resblock_updown: bool = False, + ): + super().__init__() + + self.dims = dims + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.num_res_blocks = num_res_blocks + self.channel_mult = channel_mult + self.num_attn_blocks = num_attn_blocks + self.attention_resolutions = attention_resolutions + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.d_context = d_context + self.p_dropout = p_dropout + self.num_classes = num_classes + self.use_extra_film = use_extra_film + self.d_emb = d_emb + self.use_scale_shift_norm = use_scale_shift_norm + self.resblock_updown = resblock_updown + + time_embed_dim = model_channels * 4 + self.pos_enc = PositionEncoder(model_channels, time_embed_dim) + + assert ( + num_classes == -1 or use_extra_film is None + ), "You cannot set both num_classes and use_extra_film." + + if self.num_classes > 0: + # TODO: if used for singer, norm should be 1, correct? + self.label_emb = nn.Embedding(num_classes, time_embed_dim, max_norm=1.0) + elif use_extra_film is not None: + assert ( + d_emb is not None + ), "d_emb must be specified if use_extra_film is not None" + assert use_extra_film in [ + "add", + "concat", + ], f"use_extra_film only supported by add or concat. Your input is {use_extra_film}" + self.use_extra_film = use_extra_film + self.film_emb = ConvNd(dims, d_emb, time_embed_dim, 1) + if use_extra_film == "concat": + time_embed_dim *= 2 + + # Input blocks + ch = input_ch = int(channel_mult[0] * model_channels) + self.input_blocks = nn.ModuleList( + [UNetSequential(ConvNd(dims, in_channels, ch, 3, padding=1))] + ) + self._feature_size = ch + input_block_chans = [ch] + ds = 1 + for level, mult in enumerate(channel_mult): + for _ in range(num_res_blocks): + layers = [ + ResBlock( + ch, + time_embed_dim, + p_dropout, + out_channels=int(mult * model_channels), + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = int(mult * model_channels) + if ds in attention_resolutions: + for _ in range(num_attn_blocks): + layers.append( + AttentionBlock( + ch, + num_heads=num_heads, + num_head_channels=num_head_channels, + encoder_channels=d_context, + dims=dims, + h_dim=h_dim // (level + 1), + encoder_hdim=context_hdim, + p_dropout=p_dropout, + ) + ) + self.input_blocks.append(UNetSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + UNetSequential( + ResBlock( + ch, + time_embed_dim, + p_dropout, + out_channels=out_ch, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + ) + if resblock_updown + else Downsample(ch, dims=dims, out_channels=out_ch) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + # Middle blocks + self.middle_block = UNetSequential( + ResBlock( + ch, + time_embed_dim, + p_dropout, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + ), + AttentionBlock( + ch, + num_heads=num_heads, + num_head_channels=num_head_channels, + encoder_channels=d_context, + dims=dims, + h_dim=h_dim // (level + 1), + encoder_hdim=context_hdim, + p_dropout=p_dropout, + ), + ResBlock( + ch, + time_embed_dim, + p_dropout, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + ), + ) + self._feature_size += ch + + # Output blocks + self.output_blocks = nn.ModuleList([]) + for level, mult in tuple(enumerate(channel_mult))[::-1]: + for i in range(num_res_blocks + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + time_embed_dim, + p_dropout, + out_channels=int(model_channels * mult), + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = int(model_channels * mult) + if ds in attention_resolutions: + for _ in range(num_attn_blocks): + layers.append( + AttentionBlock( + ch, + num_heads=num_heads, + num_head_channels=num_head_channels, + encoder_channels=d_context, + dims=dims, + h_dim=h_dim // (level + 1), + encoder_hdim=context_hdim, + p_dropout=p_dropout, + ) + ) + if level and i == num_res_blocks: + out_ch = ch + layers.append( + ResBlock( + ch, + time_embed_dim, + p_dropout, + out_channels=out_ch, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + ) + if resblock_updown + else Upsample(ch, dims=dims, out_channels=out_ch) + ) + ds //= 2 + self.output_blocks.append(UNetSequential(*layers)) + self._feature_size += ch + + # Final proj out + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(ConvNd(dims, input_ch, out_channels, 3, padding=1)), + ) + + def forward(self, x, timesteps=None, context=None, y=None, **kwargs): + r"""Apply the model to an input batch. + + Args: + x: an [N x C x ...] Tensor of inputs. + timesteps: a 1-D batch of timesteps, i.e. [N]. + context: conditioning Tensor with shape of [N x ``d_context`` x ...] plugged + in via cross attention. + y: an [N] Tensor of labels, if **class-conditional**. + an [N x ``d_emb`` x ...] Tensor if **film-embed conditional**. + + Returns: + an [N x C x ...] Tensor of outputs. + """ + assert (y is None) or ( + (y is not None) + and ((self.num_classes > 0) or (self.use_extra_film is not None)) + ), f"y must be specified if num_classes or use_extra_film is not None. \nGot num_classes: {self.num_classes}\t\nuse_extra_film: {self.use_extra_film}\t\n" + + hs = [] + emb = self.pos_enc(timesteps) + emb = append_dims(emb, x.dim()) + + if self.num_classes > 0: + assert y.size() == (x.size(0),) + emb = emb + self.label_emb(y) + elif self.use_extra_film is not None: + assert y.size() == (x.size(0), self.d_emb, *x.size()[2:]) + y = self.film_emb(y) + if self.use_extra_film == "add": + emb = emb + y + elif self.use_extra_film == "concat": + emb = torch.cat([emb, y], dim=1) + + h = x + for module in self.input_blocks: + h = module(h, emb, context) + hs.append(h) + h = self.middle_block(h, emb, context) + for module in self.output_blocks: + h = torch.cat([h, hs.pop()], dim=1) + h = module(h, emb, context) + + return self.out(h) + + +class UNetSequential(nn.Sequential): + r"""A sequential module that passes embeddings to the children that support it.""" + + def forward(self, x, emb=None, context=None): + for layer in self: + if isinstance(layer, ResBlock): + x = layer(x, emb) + elif isinstance(layer, AttentionBlock): + x = layer(x, context) + else: + x = layer(x) + return x diff --git a/modules/distributions/__init__.py b/modules/distributions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/distributions/distributions.py b/modules/distributions/distributions.py new file mode 100644 index 0000000000000000000000000000000000000000..0287d76c7ff2d0c1436c8e8b24ccd43513d38714 --- /dev/null +++ b/modules/distributions/distributions.py @@ -0,0 +1,107 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import numpy as np + + +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to( + device=self.parameters.device + ) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape).to( + device=self.parameters.device + ) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.0]) + else: + if other is None: + return 0.5 * torch.sum( + torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, + dim=[1, 2, 3], + ) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var + - 1.0 + - self.logvar + + other.logvar, + dim=[1, 2, 3], + ) + + def nll(self, sample, dims=[1, 2, 3]): + if self.deterministic: + return torch.Tensor([0.0]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims, + ) + + def mode(self): + return self.mean + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [ + x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + torch.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) diff --git a/modules/duration_predictor/__init__.py b/modules/duration_predictor/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/duration_predictor/__pycache__/__init__.cpython-39.pyc b/modules/duration_predictor/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..72831a5b5d49bfbfcf1b8769565cd80fad3c1bbc Binary files /dev/null and b/modules/duration_predictor/__pycache__/__init__.cpython-39.pyc differ diff --git a/modules/duration_predictor/__pycache__/standard_duration_predictor.cpython-39.pyc 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b/modules/duration_predictor/standard_duration_predictor.py @@ -0,0 +1,53 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This code is modified from https://github.com/jaywalnut310/vits/blob/main/models.py + +import torch +from torch import nn +from modules.base.base_module import LayerNorm + + +class DurationPredictor(nn.Module): + def __init__( + self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0 + ): + super().__init__() + + self.in_channels = in_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.gin_channels = gin_channels + + self.drop = nn.Dropout(p_dropout) + self.conv_1 = nn.Conv1d( + in_channels, filter_channels, kernel_size, padding=kernel_size // 2 + ) + self.norm_1 = LayerNorm(filter_channels) + self.conv_2 = nn.Conv1d( + filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 + ) + self.norm_2 = LayerNorm(filter_channels) + self.proj = nn.Conv1d(filter_channels, 1, 1) + + if gin_channels != 0: + self.cond = nn.Conv1d(gin_channels, in_channels, 1) + + def forward(self, x, x_mask, g=None): + x = torch.detach(x) + if g is not None: + g = torch.detach(g) + x = x + self.cond(g) + x = self.conv_1(x * x_mask) + x = torch.relu(x) + x = self.norm_1(x) + x = self.drop(x) + x = self.conv_2(x * x_mask) + x = torch.relu(x) + x = self.norm_2(x) + x = self.drop(x) + x = self.proj(x * x_mask) + return x * x_mask diff --git a/modules/duration_predictor/stochastic_duration_predictor.py b/modules/duration_predictor/stochastic_duration_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..60bbb95547bb89a24bb8852c57a462a2d2326a32 --- /dev/null +++ b/modules/duration_predictor/stochastic_duration_predictor.py @@ -0,0 +1,120 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This code is modified from https://github.com/jaywalnut310/vits/blob/main/models.pyimport torch + +from torch import nn +from torch.nn import functional as F +import math +from modules.flow.modules import * + + +class StochasticDurationPredictor(nn.Module): + def __init__( + self, + in_channels, + filter_channels, + kernel_size, + p_dropout, + n_flows=4, + gin_channels=0, + ): + super().__init__() + filter_channels = in_channels + self.in_channels = in_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.n_flows = n_flows + self.gin_channels = gin_channels + + self.log_flow = Log() + self.flows = nn.ModuleList() + self.flows.append(ElementwiseAffine(2)) + for i in range(n_flows): + self.flows.append(ConvFlow(2, filter_channels, kernel_size, n_layers=3)) + self.flows.append(Flip()) + + self.post_pre = nn.Conv1d(1, filter_channels, 1) + self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) + self.post_convs = DDSConv( + filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout + ) + self.post_flows = nn.ModuleList() + self.post_flows.append(ElementwiseAffine(2)) + for i in range(4): + self.post_flows.append( + ConvFlow(2, filter_channels, kernel_size, n_layers=3) + ) + self.post_flows.append(Flip()) + + self.pre = nn.Conv1d(in_channels, filter_channels, 1) + self.proj = nn.Conv1d(filter_channels, filter_channels, 1) + self.convs = DDSConv( + filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout + ) + if gin_channels != 0: + self.cond = nn.Conv1d(gin_channels, filter_channels, 1) + + def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): + x = torch.detach(x) + x = self.pre(x) + if g is not None: + g = torch.detach(g) + x = x + self.cond(g) + x = self.convs(x, x_mask) + x = self.proj(x) * x_mask + + if not reverse: + flows = self.flows + assert w is not None + + logdet_tot_q = 0 + h_w = self.post_pre(w) + h_w = self.post_convs(h_w, x_mask) + h_w = self.post_proj(h_w) * x_mask + e_q = ( + torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) + * x_mask + ) + z_q = e_q + for flow in self.post_flows: + z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) + logdet_tot_q += logdet_q + z_u, z1 = torch.split(z_q, [1, 1], 1) + u = torch.sigmoid(z_u) * x_mask + z0 = (w - u) * x_mask + logdet_tot_q += torch.sum( + (F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2] + ) + logq = ( + torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2]) + - logdet_tot_q + ) + + logdet_tot = 0 + z0, logdet = self.log_flow(z0, x_mask) + logdet_tot += logdet + z = torch.cat([z0, z1], 1) + for flow in flows: + z, logdet = flow(z, x_mask, g=x, reverse=reverse) + logdet_tot = logdet_tot + logdet + nll = ( + torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2]) + - logdet_tot + ) + return nll + logq + else: + flows = list(reversed(self.flows)) + flows = flows[:-2] + [flows[-1]] + z = ( + torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) + * noise_scale + ) + for flow in flows: + z = flow(z, x_mask, g=x, reverse=reverse) + z0, z1 = torch.split(z, [1, 1], 1) + logw = z0 + return logw diff --git a/modules/encoder/__init__.py b/modules/encoder/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..da72ddbeaac062191c7e8e04b62824a46d5ae9aa --- /dev/null +++ b/modules/encoder/__init__.py @@ -0,0 +1 @@ +from .token_encoder import TokenEmbedding \ No newline at end of file diff --git a/modules/encoder/__pycache__/__init__.cpython-39.pyc b/modules/encoder/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..17bfb65014dbee7ba74ad93e84f5e2bfbf2f64dd Binary files /dev/null and b/modules/encoder/__pycache__/__init__.cpython-39.pyc differ diff --git a/modules/encoder/__pycache__/token_encoder.cpython-39.pyc b/modules/encoder/__pycache__/token_encoder.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d03d5b10caf6cd3c67df30b4c190e992abd94cad Binary files /dev/null and b/modules/encoder/__pycache__/token_encoder.cpython-39.pyc differ diff --git a/modules/encoder/condition_encoder.py b/modules/encoder/condition_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..88ee892efcdfb7538eb1dca8ffc84cd452b073bc --- /dev/null +++ b/modules/encoder/condition_encoder.py @@ -0,0 +1,251 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +import torch.nn as nn +from torchaudio.models import Conformer +from models.svc.transformer.transformer import PositionalEncoding + +from utils.f0 import f0_to_coarse + + +class ContentEncoder(nn.Module): + def __init__(self, cfg, input_dim, output_dim): + super().__init__() + self.cfg = cfg + + assert input_dim != 0 + self.nn = nn.Linear(input_dim, output_dim) + + # Introduce conformer or not + if ( + "use_conformer_for_content_features" in cfg + and cfg.use_conformer_for_content_features + ): + self.pos_encoder = PositionalEncoding(input_dim) + self.conformer = Conformer( + input_dim=input_dim, + num_heads=2, + ffn_dim=256, + num_layers=6, + depthwise_conv_kernel_size=3, + ) + else: + self.conformer = None + + def forward(self, x, length=None): + # x: (N, seq_len, input_dim) -> (N, seq_len, output_dim) + if self.conformer: + x = self.pos_encoder(x) + x, _ = self.conformer(x, length) + return self.nn(x) + + +class MelodyEncoder(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + + self.input_dim = self.cfg.input_melody_dim + self.output_dim = self.cfg.output_melody_dim + self.n_bins = self.cfg.n_bins_melody + self.pitch_min = self.cfg.pitch_min + self.pitch_max = self.cfg.pitch_max + + if self.input_dim != 0: + if self.n_bins == 0: + # Not use quantization + self.nn = nn.Linear(self.input_dim, self.output_dim) + else: + self.f0_min = cfg.f0_min + self.f0_max = cfg.f0_max + + self.nn = nn.Embedding( + num_embeddings=self.n_bins, + embedding_dim=self.output_dim, + padding_idx=None, + ) + self.uv_embedding = nn.Embedding(2, self.output_dim) + # self.conformer = Conformer( + # input_dim=self.output_dim, + # num_heads=4, + # ffn_dim=128, + # num_layers=4, + # depthwise_conv_kernel_size=3, + # ) + + def forward(self, x, uv=None, length=None): + # x: (N, frame_len) + # print(x.shape) + if self.n_bins == 0: + x = x.unsqueeze(-1) + else: + x = f0_to_coarse(x, self.n_bins, self.f0_min, self.f0_max) + x = self.nn(x) + if uv is not None: + uv = self.uv_embedding(uv) + x = x + uv + # x, _ = self.conformer(x, length) + return x + + +class LoudnessEncoder(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + + self.input_dim = self.cfg.input_loudness_dim + self.output_dim = self.cfg.output_loudness_dim + self.n_bins = self.cfg.n_bins_loudness + + if self.input_dim != 0: + if self.n_bins == 0: + # Not use quantization + self.nn = nn.Linear(self.input_dim, self.output_dim) + else: + # TODO: set trivially now + self.loudness_min = 1e-30 + self.loudness_max = 1.5 + + if cfg.use_log_loudness: + self.energy_bins = nn.Parameter( + torch.exp( + torch.linspace( + np.log(self.loudness_min), + np.log(self.loudness_max), + self.n_bins - 1, + ) + ), + requires_grad=False, + ) + + self.nn = nn.Embedding( + num_embeddings=self.n_bins, + embedding_dim=self.output_dim, + padding_idx=None, + ) + + def forward(self, x): + # x: (N, frame_len) + if self.n_bins == 0: + x = x.unsqueeze(-1) + else: + x = torch.bucketize(x, self.energy_bins) + return self.nn(x) + + +class SingerEncoder(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + + self.input_dim = 1 + self.output_dim = self.cfg.output_singer_dim + + self.nn = nn.Embedding( + num_embeddings=cfg.singer_table_size, + embedding_dim=self.output_dim, + padding_idx=None, + ) + + def forward(self, x): + # x: (N, 1) -> (N, 1, output_dim) + return self.nn(x) + + +class ConditionEncoder(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + + self.merge_mode = cfg.merge_mode + + if cfg.use_whisper: + self.whisper_encoder = ContentEncoder( + self.cfg, self.cfg.whisper_dim, self.cfg.content_encoder_dim + ) + + if cfg.use_contentvec: + self.contentvec_encoder = ContentEncoder( + self.cfg, self.cfg.contentvec_dim, self.cfg.content_encoder_dim + ) + + if cfg.use_mert: + self.mert_encoder = ContentEncoder( + self.cfg, self.cfg.mert_dim, self.cfg.content_encoder_dim + ) + + if cfg.use_wenet: + self.wenet_encoder = ContentEncoder( + self.cfg, self.cfg.wenet_dim, self.cfg.content_encoder_dim + ) + + self.melody_encoder = MelodyEncoder(self.cfg) + self.loudness_encoder = LoudnessEncoder(self.cfg) + if cfg.use_spkid: + self.singer_encoder = SingerEncoder(self.cfg) + + def forward(self, x): + outputs = [] + + if "frame_pitch" in x.keys(): + if "frame_uv" not in x.keys(): + x["frame_uv"] = None + pitch_enc_out = self.melody_encoder( + x["frame_pitch"], uv=x["frame_uv"], length=x["target_len"] + ) + outputs.append(pitch_enc_out) + + if "frame_energy" in x.keys(): + loudness_enc_out = self.loudness_encoder(x["frame_energy"]) + outputs.append(loudness_enc_out) + + if "whisper_feat" in x.keys(): + # whisper_feat: [b, T, 1024] + whiser_enc_out = self.whisper_encoder( + x["whisper_feat"], length=x["target_len"] + ) + outputs.append(whiser_enc_out) + seq_len = whiser_enc_out.shape[1] + + if "contentvec_feat" in x.keys(): + contentvec_enc_out = self.contentvec_encoder( + x["contentvec_feat"], length=x["target_len"] + ) + outputs.append(contentvec_enc_out) + seq_len = contentvec_enc_out.shape[1] + + if "mert_feat" in x.keys(): + mert_enc_out = self.mert_encoder(x["mert_feat"], length=x["target_len"]) + outputs.append(mert_enc_out) + seq_len = mert_enc_out.shape[1] + + if "wenet_feat" in x.keys(): + wenet_enc_out = self.wenet_encoder(x["wenet_feat"], length=x["target_len"]) + outputs.append(wenet_enc_out) + seq_len = wenet_enc_out.shape[1] + + if "spk_id" in x.keys(): + speaker_enc_out = self.singer_encoder(x["spk_id"]) # [b, 1, 384] + assert ( + "whisper_feat" in x.keys() + or "contentvec_feat" in x.keys() + or "mert_feat" in x.keys() + or "wenet_feat" in x.keys() + ) + singer_info = speaker_enc_out.expand(-1, seq_len, -1) + outputs.append(singer_info) + + encoder_output = None + if self.merge_mode == "concat": + encoder_output = torch.cat(outputs, dim=-1) + if self.merge_mode == "add": + # (#modules, N, seq_len, output_dim) + outputs = torch.cat([out[None, :, :, :] for out in outputs], dim=0) + # (N, seq_len, output_dim) + encoder_output = torch.sum(outputs, dim=0) + + return encoder_output diff --git a/modules/encoder/conv_encoder.py b/modules/encoder/conv_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..cfbd2e290a678e1e5ec1332f73d654c7f13e9fc5 --- /dev/null +++ b/modules/encoder/conv_encoder.py @@ -0,0 +1,103 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.utils import spectral_norm +from modules.generic.conv import Conv1d + + +class ConvEncoder(nn.Module): + def __init__(self, in_channels, z_channels, spk_channels, num_dilation_layer=10): + super(ConvEncoder, self).__init__() + + self.in_channels = in_channels + self.z_channels = z_channels + self.spk_channels = spk_channels + + self.pre_process = Conv1d(in_channels, 512, kernel_size=3) + + self.dilated_conv_layers = nn.ModuleList() + for i in range(num_dilation_layer): + dilation = 2**i + self.dilated_conv_layers.append( + DilatedConvBlock(512, 512, z_channels, spk_channels, dilation) + ) + + def forward(self, inputs, z, s): + inputs = inputs.transpose(1, 2) + outputs = self.pre_process(inputs) + print(inputs.shape) + for layer in self.dilated_conv_layers: + outputs = layer(outputs, z, s) + + encoder_outputs = outputs.transpose(1, 2) + return encoder_outputs + + +class DilatedConvBlock(nn.Module): + """A stack of dilated convolutions interspersed + with batch normalisation and ReLU activations""" + + def __init__(self, in_channels, out_channels, z_channels, s_channels, dilation): + super(DilatedConvBlock, self).__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + self.z_channels = z_channels + self.s_channels = s_channels + + self.conv1d = Conv1d( + in_channels, out_channels, kernel_size=3, dilation=dilation + ) + self.batch_layer = BatchNorm1dLayer(out_channels, s_channels, z_channels) + + def forward(self, inputs, z, s): + outputs = self.conv1d(inputs) + outputs = self.batch_layer(outputs, z, s) + return F.relu(outputs) + + +class BatchNorm1dLayer(nn.Module): + """The latents z and speaker embedding s modulate the scale and + shift parameters of the batch normalisation layers""" + + def __init__(self, num_features, s_channels=128, z_channels=128): + super().__init__() + + self.num_features = num_features + self.s_channels = s_channels + self.z_channels = z_channels + self.batch_nrom = nn.BatchNorm1d(num_features, affine=False) + + self.scale_layer = spectral_norm(nn.Linear(z_channels, num_features)) + self.scale_layer.weight.data.normal_(1, 0.02) # Initialise scale at N(1, 0.02) + self.scale_layer.bias.data.zero_() # Initialise bias at 0 + + self.shift_layer = spectral_norm(nn.Linear(s_channels, num_features)) + self.shift_layer.weight.data.normal_(1, 0.02) # Initialise scale at N(1, 0.02) + self.shift_layer.bias.data.zero_() # Initialise bias at 0 + + def forward(self, inputs, z, s): + outputs = self.batch_nrom(inputs) + scale = self.scale_layer(z) + scale = scale.view(-1, self.num_features, 1) + + shift = self.shift_layer(s) + shift = shift.view(-1, self.num_features, 1) + + outputs = scale * outputs + shift + + return outputs + + +if __name__ == "__main__": + model = ConvEncoder(256, 64, 64) + encoder_inputs = torch.randn(2, 256, 10) + z = torch.randn(2, 64) + speaker = torch.randn(1, 64) + outputs, duration = model(encoder_inputs, z, speaker) + print(outputs.shape, duration.shape) diff --git a/modules/encoder/position_encoder.py b/modules/encoder/position_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..0f67c688a2b55390c75142ba57d159aca4a31c91 --- /dev/null +++ b/modules/encoder/position_encoder.py @@ -0,0 +1,85 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn as nn + +from modules.general.utils import Linear + + +class PositionEncoder(nn.Module): + r"""Encoder of positional embedding, generates PE and then + feed into 2 full-connected layers with ``SiLU``. + + Args: + d_raw_emb: The dimension of raw embedding vectors. + d_out: The dimension of output embedding vectors, default to ``d_raw_emb``. + d_mlp: The dimension of hidden layer in MLP, default to ``d_raw_emb`` * 4. + activation_function: The activation function used in MLP, default to ``SiLU``. + n_layer: The number of layers in MLP, default to 2. + max_period: controls the minimum frequency of the embeddings. + """ + + def __init__( + self, + d_raw_emb: int = 128, + d_out: int = None, + d_mlp: int = None, + activation_function: str = "SiLU", + n_layer: int = 2, + max_period: int = 10000, + ): + super().__init__() + + self.d_raw_emb = d_raw_emb + self.d_out = d_raw_emb if d_out is None else d_out + self.d_mlp = d_raw_emb * 4 if d_mlp is None else d_mlp + self.n_layer = n_layer + self.max_period = max_period + + if activation_function.lower() == "silu": + self.activation_function = "SiLU" + elif activation_function.lower() == "relu": + self.activation_function = "ReLU" + elif activation_function.lower() == "gelu": + self.activation_function = "GELU" + else: + raise ValueError("activation_function must be one of SiLU, ReLU, GELU") + self.activation_function = activation_function + + tmp = [Linear(self.d_raw_emb, self.d_mlp), getattr(nn, activation_function)()] + for _ in range(self.n_layer - 1): + tmp.append(Linear(self.d_mlp, self.d_mlp)) + tmp.append(getattr(nn, activation_function)()) + tmp.append(Linear(self.d_mlp, self.d_out)) + + self.out = nn.Sequential(*tmp) + + def forward(self, steps: torch.Tensor) -> torch.Tensor: + r"""Create and return sinusoidal timestep embeddings directly. + + Args: + steps: a 1D Tensor of N indices, one per batch element. + These may be fractional. + + Returns: + an [N x ``d_out``] Tensor of positional embeddings. + """ + + half = self.d_raw_emb // 2 + freqs = torch.exp( + -math.log(self.max_period) + / half + * torch.arange(half, dtype=torch.float32, device=steps.device) + ) + args = steps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if self.d_raw_emb % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + return self.out(embedding) diff --git a/modules/encoder/token_encoder.py b/modules/encoder/token_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..9f556013000c7bc060a4934885d36cd4c5b72201 --- /dev/null +++ b/modules/encoder/token_encoder.py @@ -0,0 +1,24 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This code is modified from https://github.com/lifeiteng/vall-e + +import torch +import torch.nn as nn + +class TokenEmbedding(nn.Module): + def __init__(self, dim_model: int, vocab_size: int, dropout: float = 0.0): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + self.word_embeddings = nn.Embedding(vocab_size, dim_model) + + @property + def weight(self) -> torch.Tensor: + return self.word_embeddings.weight + + def forward(self, x: torch.Tensor): + x = self.word_embeddings(x) + x = self.dropout(x) + return x diff --git a/modules/flow/__pycache__/modules.cpython-39.pyc b/modules/flow/__pycache__/modules.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f2bffa69a35178e416b48f555c0900bd46f849f5 Binary files /dev/null and b/modules/flow/__pycache__/modules.cpython-39.pyc differ diff --git a/modules/flow/modules.py b/modules/flow/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..f4d897a4ae151a90d4a1fb886108903511ff14b7 --- /dev/null +++ b/modules/flow/modules.py @@ -0,0 +1,457 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This code is modified from https://github.com/jaywalnut310/vits/ + +import math +import torch +from torch import nn +from torch.nn import functional as F + +from torch.nn import Conv1d +from torch.nn.utils import weight_norm, remove_weight_norm + +from utils.util import * +from modules.transformer.transforms import ( + piecewise_rational_quadratic_transform, +) +from modules.base.base_module import LayerNorm + +LRELU_SLOPE = 0.1 + + +class DDSConv(nn.Module): + """ + Dialted and Depth-Separable Convolution + """ + + def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0): + super().__init__() + self.channels = channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.p_dropout = p_dropout + + self.drop = nn.Dropout(p_dropout) + self.convs_sep = nn.ModuleList() + self.convs_1x1 = nn.ModuleList() + self.norms_1 = nn.ModuleList() + self.norms_2 = nn.ModuleList() + for i in range(n_layers): + dilation = kernel_size**i + padding = (kernel_size * dilation - dilation) // 2 + self.convs_sep.append( + nn.Conv1d( + channels, + channels, + kernel_size, + groups=channels, + dilation=dilation, + padding=padding, + ) + ) + self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) + self.norms_1.append(LayerNorm(channels)) + self.norms_2.append(LayerNorm(channels)) + + def forward(self, x, x_mask, g=None): + if g is not None: + x = x + g + for i in range(self.n_layers): + y = self.convs_sep[i](x * x_mask) + y = self.norms_1[i](y) + y = F.gelu(y) + y = self.convs_1x1[i](y) + y = self.norms_2[i](y) + y = F.gelu(y) + y = self.drop(y) + x = x + y + return x * x_mask + + +class WN(torch.nn.Module): + def __init__( + self, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + gin_channels=0, + p_dropout=0, + ): + super(WN, self).__init__() + assert kernel_size % 2 == 1 + self.hidden_channels = hidden_channels + self.kernel_size = (kernel_size,) + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.gin_channels = gin_channels + self.p_dropout = p_dropout + + self.in_layers = torch.nn.ModuleList() + self.res_skip_layers = torch.nn.ModuleList() + self.drop = nn.Dropout(p_dropout) + + if gin_channels != 0: + cond_layer = torch.nn.Conv1d( + gin_channels, 2 * hidden_channels * n_layers, 1 + ) + self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight") + + for i in range(n_layers): + dilation = dilation_rate**i + padding = int((kernel_size * dilation - dilation) / 2) + in_layer = torch.nn.Conv1d( + hidden_channels, + 2 * hidden_channels, + kernel_size, + dilation=dilation, + padding=padding, + ) + in_layer = torch.nn.utils.weight_norm(in_layer, name="weight") + self.in_layers.append(in_layer) + + # last one is not necessary + if i < n_layers - 1: + res_skip_channels = 2 * hidden_channels + else: + res_skip_channels = hidden_channels + + res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) + res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight") + self.res_skip_layers.append(res_skip_layer) + + def forward(self, x, x_mask, g=None, **kwargs): + output = torch.zeros_like(x) + n_channels_tensor = torch.IntTensor([self.hidden_channels]) + + if g is not None: + g = self.cond_layer(g) + + for i in range(self.n_layers): + x_in = self.in_layers[i](x) + if g is not None: + cond_offset = i * 2 * self.hidden_channels + g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :] + else: + g_l = torch.zeros_like(x_in) + + acts = fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor) + acts = self.drop(acts) + + res_skip_acts = self.res_skip_layers[i](acts) + if i < self.n_layers - 1: + res_acts = res_skip_acts[:, : self.hidden_channels, :] + x = (x + res_acts) * x_mask + output = output + res_skip_acts[:, self.hidden_channels :, :] + else: + output = output + res_skip_acts + return output * x_mask + + def remove_weight_norm(self): + if self.gin_channels != 0: + torch.nn.utils.remove_weight_norm(self.cond_layer) + for l in self.in_layers: + torch.nn.utils.remove_weight_norm(l) + for l in self.res_skip_layers: + torch.nn.utils.remove_weight_norm(l) + + +class ResBlock1(torch.nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.convs1 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + self.convs2.apply(init_weights) + + def forward(self, x, x_mask=None): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + if x_mask is not None: + xt = xt * x_mask + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + if x_mask is not None: + xt = xt * x_mask + xt = c2(xt) + x = xt + x + if x_mask is not None: + x = x * x_mask + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.convs = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + ] + ) + self.convs.apply(init_weights) + + def forward(self, x, x_mask=None): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + if x_mask is not None: + xt = xt * x_mask + xt = c(xt) + x = xt + x + if x_mask is not None: + x = x * x_mask + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class Log(nn.Module): + def forward(self, x, x_mask, reverse=False, **kwargs): + if not reverse: + y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask + logdet = torch.sum(-y, [1, 2]) + return y, logdet + else: + x = torch.exp(x) * x_mask + return x + + +class Flip(nn.Module): + def forward(self, x, *args, reverse=False, **kwargs): + x = torch.flip(x, [1]) + if not reverse: + logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) + return x, logdet + else: + return x + + +class ElementwiseAffine(nn.Module): + def __init__(self, channels): + super().__init__() + self.channels = channels + self.m = nn.Parameter(torch.zeros(channels, 1)) + self.logs = nn.Parameter(torch.zeros(channels, 1)) + + def forward(self, x, x_mask, reverse=False, **kwargs): + if not reverse: + y = self.m + torch.exp(self.logs) * x + y = y * x_mask + logdet = torch.sum(self.logs * x_mask, [1, 2]) + return y, logdet + else: + x = (x - self.m) * torch.exp(-self.logs) * x_mask + return x + + +class ResidualCouplingLayer(nn.Module): + def __init__( + self, + channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + p_dropout=0, + gin_channels=0, + mean_only=False, + ): + assert channels % 2 == 0, "channels should be divisible by 2" + super().__init__() + self.channels = channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.half_channels = channels // 2 + self.mean_only = mean_only + + self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) + self.enc = WN( + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + p_dropout=p_dropout, + gin_channels=gin_channels, + ) + self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) + self.post.weight.data.zero_() + self.post.bias.data.zero_() + + def forward(self, x, x_mask, g=None, reverse=False): + x0, x1 = torch.split(x, [self.half_channels] * 2, 1) + h = self.pre(x0) * x_mask + h = self.enc(h, x_mask, g=g) + stats = self.post(h) * x_mask + if not self.mean_only: + m, logs = torch.split(stats, [self.half_channels] * 2, 1) + else: + m = stats + logs = torch.zeros_like(m) + + if not reverse: + x1 = m + x1 * torch.exp(logs) * x_mask + x = torch.cat([x0, x1], 1) + logdet = torch.sum(logs, [1, 2]) + return x, logdet + else: + x1 = (x1 - m) * torch.exp(-logs) * x_mask + x = torch.cat([x0, x1], 1) + return x + + +class ConvFlow(nn.Module): + def __init__( + self, + in_channels, + filter_channels, + kernel_size, + n_layers, + num_bins=10, + tail_bound=5.0, + ): + super().__init__() + self.in_channels = in_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.num_bins = num_bins + self.tail_bound = tail_bound + self.half_channels = in_channels // 2 + + self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) + self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0) + self.proj = nn.Conv1d( + filter_channels, self.half_channels * (num_bins * 3 - 1), 1 + ) + self.proj.weight.data.zero_() + self.proj.bias.data.zero_() + + def forward(self, x, x_mask, g=None, reverse=False): + x0, x1 = torch.split(x, [self.half_channels] * 2, 1) + h = self.pre(x0) + h = self.convs(h, x_mask, g=g) + h = self.proj(h) * x_mask + + b, c, t = x0.shape + h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] + + unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels) + unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt( + self.filter_channels + ) + unnormalized_derivatives = h[..., 2 * self.num_bins :] + + x1, logabsdet = piecewise_rational_quadratic_transform( + x1, + unnormalized_widths, + unnormalized_heights, + unnormalized_derivatives, + inverse=reverse, + tails="linear", + tail_bound=self.tail_bound, + ) + + x = torch.cat([x0, x1], 1) * x_mask + logdet = torch.sum(logabsdet * x_mask, [1, 2]) + if not reverse: + return x, logdet + else: + return x diff --git a/modules/general/__init__.py b/modules/general/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7904fc1893708d17477220b6b6296127b232b8c1 --- /dev/null +++ b/modules/general/__init__.py @@ -0,0 +1,3 @@ +from .input_strategies import PromptedFeatures, PromptedPrecomputedFeatures +from .scaling import 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b/modules/general/__pycache__/scaling.cpython-39.pyc differ diff --git a/modules/general/__pycache__/utils.cpython-39.pyc b/modules/general/__pycache__/utils.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2dae15c295dec2696dbdde0fdff385698bfac6c6 Binary files /dev/null and b/modules/general/__pycache__/utils.cpython-39.pyc differ diff --git a/modules/general/input_strategies.py b/modules/general/input_strategies.py new file mode 100644 index 0000000000000000000000000000000000000000..51d57879ff3b56996d0488f7b1d019096e9ae350 --- /dev/null +++ b/modules/general/input_strategies.py @@ -0,0 +1,129 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +# This code is modified from +# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/data/input_strategies.py +import random +from collections import defaultdict +from concurrent.futures import ThreadPoolExecutor +from typing import Tuple, Type + +from lhotse import CutSet +from lhotse.dataset.collation import collate_features +from lhotse.dataset.input_strategies import ( + ExecutorType, + PrecomputedFeatures, + _get_executor, +) +from lhotse.utils import fastcopy + + +class PromptedFeatures: + def __init__(self, prompts, features): + self.prompts = prompts + self.features = features + + def to(self, device): + return PromptedFeatures( + self.prompts.to(device), self.features.to(device) + ) + + def sum(self): + return self.features.sum() + + @property + def ndim(self): + return self.features.ndim + + @property + def data(self): + return (self.prompts, self.features) + + +class PromptedPrecomputedFeatures(PrecomputedFeatures): + + def __init__( + self, + dataset: str, + cuts: CutSet, + num_workers: int = 0, + executor_type: Type[ExecutorType] = ThreadPoolExecutor, + ) -> None: + super().__init__(num_workers, executor_type) + self.utt2neighbors = self._create_utt2neighbors(dataset, cuts) + + def __call__( + self, cuts: CutSet + ) -> Tuple[PromptedFeatures, PromptedFeatures]: + features, features_lens = self._collate_features(cuts) + prompts, prompts_lens = self._collate_prompts(cuts) + return PromptedFeatures(prompts, features), PromptedFeatures(prompts_lens, features_lens) + + def _create_utt2neighbors(self, dataset, cuts): + utt2neighbors = defaultdict(lambda: []) + utt2cut = {cut.id: cut for cut in cuts} + if dataset.lower() == "libritts": + self._process_libritts_dataset(utt2neighbors, utt2cut, cuts) + elif dataset.lower() == "ljspeech": + self._process_ljspeech_dataset(utt2neighbors, utt2cut, cuts) + else: + raise ValueError("Unsupported dataset") + return utt2neighbors + + def _process_libritts_dataset(self, utt2neighbors, utt2cut, cuts): + speaker2utts = defaultdict(lambda: []) + for cut in cuts: + speaker = cut.supervisions[0].speaker + speaker2utts[speaker].append(cut.id) + + for spk, uttids in speaker2utts.items(): + sorted_uttids = sorted(uttids) + if len(sorted_uttids) == 1: + utt2neighbors[sorted_uttids[0]].append(utt2cut[sorted_uttids[0]]) + continue + + utt2prevutt = dict(zip(sorted_uttids, [sorted_uttids[1]] + sorted_uttids[:-1])) + utt2postutt = dict(zip(sorted_uttids[:-1], sorted_uttids[1:])) + for utt in sorted_uttids: + if utt in utt2prevutt: + utt2neighbors[utt].append(utt2cut[utt2prevutt[utt]]) + if utt in utt2postutt: + utt2neighbors[utt].append(utt2cut[utt2postutt[utt]]) + + def _process_ljspeech_dataset(self, utt2neighbors, utt2cut, cuts): + uttids = [cut.id for cut in cuts] + if len(uttids) == 1: + utt2neighbors[uttids[0]].append(utt2cut[uttids[0]]) + return + + utt2prevutt = dict(zip(uttids, [uttids[1]] + uttids[:-1])) + utt2postutt = dict(zip(uttids[:-1], uttids[1:])) + for utt in uttids: + prevutt, postutt = utt2prevutt.get(utt), utt2postutt.get(utt) + if prevutt and utt[:5] == prevutt[:5]: + utt2neighbors[utt].append(utt2cut[prevutt]) + if postutt and utt[:5] == postutt[:5]: + utt2neighbors[utt].append(utt2cut[postutt]) + + def _collate_features(self, cuts): + return collate_features( + cuts, executor=_get_executor(self.num_workers, executor_type=self._executor_type) + ) + + def _collate_prompts(self, cuts): + prompts_cuts = [] + for k, cut in enumerate(cuts): + prompts_cut = random.choice(self.utt2neighbors[cut.id]) + prompts_cuts.append(fastcopy(prompts_cut, id=f"{cut.id}-{str(k)}")) + + mini_duration = min([cut.duration for cut in prompts_cuts] + [3.0]) + prompts_cuts = CutSet( + cuts={k: cut for k, cut in enumerate(prompts_cuts)} + ).truncate(max_duration=mini_duration, offset_type="random", preserve_id=False) + + return collate_features( + prompts_cuts, executor=_get_executor(self.num_workers, executor_type=self._executor_type) + ) diff --git a/modules/general/scaling.py b/modules/general/scaling.py new file mode 100644 index 0000000000000000000000000000000000000000..de49a28f816f4cd21a037d6121f12959af54c4bd --- /dev/null +++ b/modules/general/scaling.py @@ -0,0 +1,1386 @@ +# This module is modified from https://github.com/Plachtaa/VALL-E-X/blob/3faaf8ccadb154d63b38070caf518ce9309ea0f4/modules/scaling.py + + +import logging +import random +import math +from typing import Optional, Tuple, Union + +import torch +import torch.nn as nn +from torch import Tensor + +class Transpose(nn.Identity): + """(N, T, D) -> (N, D, T)""" + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input.transpose(1, 2) + + +class ActivationBalancerFunction(torch.autograd.Function): + @staticmethod + def forward( + ctx, + x: Tensor, + scale_factor: Tensor, + sign_factor: Optional[Tensor], + channel_dim: int, + ) -> Tensor: + if channel_dim < 0: + channel_dim += x.ndim + ctx.channel_dim = channel_dim + xgt0 = x > 0 + if sign_factor is None: + ctx.save_for_backward(xgt0, scale_factor) + else: + ctx.save_for_backward(xgt0, scale_factor, sign_factor) + return x + + @staticmethod + def backward(ctx, x_grad: Tensor) -> Tuple[Tensor, None, None, None]: + if len(ctx.saved_tensors) == 3: + xgt0, scale_factor, sign_factor = ctx.saved_tensors + for _ in range(ctx.channel_dim, x_grad.ndim - 1): + scale_factor = scale_factor.unsqueeze(-1) + sign_factor = sign_factor.unsqueeze(-1) + factor = sign_factor + scale_factor * (xgt0.to(x_grad.dtype) - 0.5) + else: + xgt0, scale_factor = ctx.saved_tensors + for _ in range(ctx.channel_dim, x_grad.ndim - 1): + scale_factor = scale_factor.unsqueeze(-1) + factor = scale_factor * (xgt0.to(x_grad.dtype) - 0.5) + neg_delta_grad = x_grad.abs() * factor + return ( + x_grad - neg_delta_grad, + None, + None, + None, + ) + + +def _compute_scale_factor( + x: Tensor, + channel_dim: int, + min_abs: float, + max_abs: float, + gain_factor: float, + max_factor: float, +) -> Tensor: + if channel_dim < 0: + channel_dim += x.ndim + sum_dims = [d for d in range(x.ndim) if d != channel_dim] + x_abs_mean = torch.mean(x.abs(), dim=sum_dims).to(torch.float32) + + if min_abs == 0.0: + below_threshold = 0.0 + else: + # below_threshold is 0 if x_abs_mean > min_abs, can be at most max_factor if + # x_abs)_mean , min_abs. + below_threshold = ( + (min_abs - x_abs_mean) * (gain_factor / min_abs) + ).clamp(min=0, max=max_factor) + + above_threshold = ((x_abs_mean - max_abs) * (gain_factor / max_abs)).clamp( + min=0, max=max_factor + ) + + return below_threshold - above_threshold + + +def _compute_sign_factor( + x: Tensor, + channel_dim: int, + min_positive: float, + max_positive: float, + gain_factor: float, + max_factor: float, +) -> Tensor: + if channel_dim < 0: + channel_dim += x.ndim + sum_dims = [d for d in range(x.ndim) if d != channel_dim] + proportion_positive = torch.mean((x > 0).to(torch.float32), dim=sum_dims) + if min_positive == 0.0: + factor1 = 0.0 + else: + # 0 if proportion_positive >= min_positive, else can be + # as large as max_factor. + factor1 = ( + (min_positive - proportion_positive) * (gain_factor / min_positive) + ).clamp_(min=0, max=max_factor) + + if max_positive == 1.0: + factor2 = 0.0 + else: + # 0 if self.proportion_positive <= max_positive, else can be + # as large as -max_factor. + factor2 = ( + (proportion_positive - max_positive) + * (gain_factor / (1.0 - max_positive)) + ).clamp_(min=0, max=max_factor) + sign_factor = factor1 - factor2 + # require min_positive != 0 or max_positive != 1: + assert not isinstance(sign_factor, float) + return sign_factor + + +class ActivationScaleBalancerFunction(torch.autograd.Function): + """ + This object is used in class ActivationBalancer when the user specified + min_positive=0, max_positive=1, so there are no constraints on the signs + of the activations and only the absolute value has a constraint. + """ + + @staticmethod + def forward( + ctx, + x: Tensor, + sign_factor: Tensor, + scale_factor: Tensor, + channel_dim: int, + ) -> Tensor: + if channel_dim < 0: + channel_dim += x.ndim + ctx.channel_dim = channel_dim + xgt0 = x > 0 + ctx.save_for_backward(xgt0, sign_factor, scale_factor) + return x + + @staticmethod + def backward(ctx, x_grad: Tensor) -> Tuple[Tensor, None, None, None]: + xgt0, sign_factor, scale_factor = ctx.saved_tensors + for _ in range(ctx.channel_dim, x_grad.ndim - 1): + sign_factor = sign_factor.unsqueeze(-1) + scale_factor = scale_factor.unsqueeze(-1) + + factor = sign_factor + scale_factor * (xgt0.to(x_grad.dtype) - 0.5) + neg_delta_grad = x_grad.abs() * factor + return ( + x_grad - neg_delta_grad, + None, + None, + None, + ) + + +class RandomClampFunction(torch.autograd.Function): + @staticmethod + def forward( + ctx, + x: Tensor, + min: Optional[float], + max: Optional[float], + prob: float, + reflect: float, + ) -> Tensor: + x_clamped = torch.clamp(x, min=min, max=max) + mask = torch.rand_like(x) < prob + ans = torch.where(mask, x_clamped, x) + if x.requires_grad: + ctx.save_for_backward(ans == x) + ctx.reflect = reflect + if reflect != 0.0: + ans = ans * (1.0 + reflect) - (x * reflect) + return ans + + @staticmethod + def backward( + ctx, ans_grad: Tensor + ) -> Tuple[Tensor, None, None, None, None]: + (is_same,) = ctx.saved_tensors + x_grad = ans_grad * is_same.to(ans_grad.dtype) + reflect = ctx.reflect + if reflect != 0.0: + x_grad = x_grad * (1.0 + reflect) - (ans_grad * reflect) + return x_grad, None, None, None, None + + +def random_clamp( + x: Tensor, + min: Optional[float] = None, + max: Optional[float] = None, + prob: float = 0.5, + reflect: float = 0.0, +): + return RandomClampFunction.apply(x, min, max, prob, reflect) + + +def random_cast_to_half(x: Tensor, min_abs: float = 5.0e-06) -> Tensor: + """ + A randomized way of casting a floating point value to half precision. + """ + if x.dtype == torch.float16: + return x + x_abs = x.abs() + is_too_small = x_abs < min_abs + # for elements where is_too_small is true, random_val will contain +-min_abs with + # probability (x.abs() / min_abs), and 0.0 otherwise. [so this preserves expectations, + # for those elements]. + random_val = min_abs * x.sign() * (torch.rand_like(x) * min_abs < x_abs) + return torch.where(is_too_small, random_val, x).to(torch.float16) + + +class RandomGradFunction(torch.autograd.Function): + """ + Does nothing in forward pass; in backward pass, gets rid of very small grads using + randomized approach that preserves expectations (intended to reduce roundoff). + """ + + @staticmethod + def forward(ctx, x: Tensor, min_abs: float) -> Tensor: + ctx.min_abs = min_abs + return x + + @staticmethod + def backward(ctx, ans_grad: Tensor) -> Tuple[Tensor, None]: + if ans_grad.dtype == torch.float16: + return ( + random_cast_to_half( + ans_grad.to(torch.float32), min_abs=ctx.min_abs + ), + None, + ) + else: + return ans_grad, None + + +class RandomGrad(torch.nn.Module): + """ + Gets rid of very small gradients using an expectation-preserving method, intended to increase + accuracy of training when using amp (automatic mixed precision) + """ + + def __init__(self, min_abs: float = 5.0e-06): + super(RandomGrad, self).__init__() + self.min_abs = min_abs + + def forward(self, x: Tensor): + if ( + torch.jit.is_scripting() + or not self.training + or torch.jit.is_tracing() + ): + return x + else: + return RandomGradFunction.apply(x, self.min_abs) + + +class SoftmaxFunction(torch.autograd.Function): + """ + Tries to handle half-precision derivatives in a randomized way that should + be more accurate for training than the default behavior. + """ + + @staticmethod + def forward(ctx, x: Tensor, dim: int): + ans = x.softmax(dim=dim) + # if x dtype is float16, x.softmax() returns a float32 because + # (presumably) that op does not support float16, and autocast + # is enabled. + if torch.is_autocast_enabled(): + ans = ans.to(torch.float16) + ctx.save_for_backward(ans) + ctx.x_dtype = x.dtype + ctx.dim = dim + return ans + + @staticmethod + def backward(ctx, ans_grad: Tensor): + (ans,) = ctx.saved_tensors + with torch.cuda.amp.autocast(enabled=False): + ans_grad = ans_grad.to(torch.float32) + ans = ans.to(torch.float32) + x_grad = ans_grad * ans + x_grad = x_grad - ans * x_grad.sum(dim=ctx.dim, keepdim=True) + return x_grad, None + + +def softmax(x: Tensor, dim: int): + if torch.jit.is_scripting() or torch.jit.is_tracing(): + return x.softmax(dim) + + return SoftmaxFunction.apply(x, dim) + + +class MaxEigLimiterFunction(torch.autograd.Function): + @staticmethod + def forward( + ctx, + x: Tensor, + coeffs: Tensor, + direction: Tensor, + channel_dim: int, + grad_scale: float, + ) -> Tensor: + ctx.channel_dim = channel_dim + ctx.grad_scale = grad_scale + ctx.save_for_backward(x.detach(), coeffs.detach(), direction.detach()) + return x + + @staticmethod + def backward(ctx, x_grad, *args): + with torch.enable_grad(): + (x_orig, coeffs, new_direction) = ctx.saved_tensors + x_orig.requires_grad = True + num_channels = x_orig.shape[ctx.channel_dim] + x = x_orig.transpose(ctx.channel_dim, -1).reshape(-1, num_channels) + new_direction.requires_grad = False + x = x - x.mean(dim=0) + x_var = (x ** 2).mean() + x_residual = x - coeffs * new_direction + x_residual_var = (x_residual ** 2).mean() + # `variance_proportion` is the proportion of the variance accounted for + # by the top eigen-direction. This is to be minimized. + variance_proportion = (x_var - x_residual_var) / (x_var + 1.0e-20) + variance_proportion.backward() + x_orig_grad = x_orig.grad + x_extra_grad = ( + x_orig.grad + * ctx.grad_scale + * x_grad.norm() + / (x_orig_grad.norm() + 1.0e-20) + ) + return x_grad + x_extra_grad.detach(), None, None, None, None + + +class BasicNorm(torch.nn.Module): + """ + This is intended to be a simpler, and hopefully cheaper, replacement for + LayerNorm. The observation this is based on, is that Transformer-type + networks, especially with pre-norm, sometimes seem to set one of the + feature dimensions to a large constant value (e.g. 50), which "defeats" + the LayerNorm because the output magnitude is then not strongly dependent + on the other (useful) features. Presumably the weight and bias of the + LayerNorm are required to allow it to do this. + + So the idea is to introduce this large constant value as an explicit + parameter, that takes the role of the "eps" in LayerNorm, so the network + doesn't have to do this trick. We make the "eps" learnable. + + Args: + num_channels: the number of channels, e.g. 512. + channel_dim: the axis/dimension corresponding to the channel, + interprted as an offset from the input's ndim if negative. + shis is NOT the num_channels; it should typically be one of + {-2, -1, 0, 1, 2, 3}. + eps: the initial "epsilon" that we add as ballast in: + scale = ((input_vec**2).mean() + epsilon)**-0.5 + Note: our epsilon is actually large, but we keep the name + to indicate the connection with conventional LayerNorm. + learn_eps: if true, we learn epsilon; if false, we keep it + at the initial value. + eps_min: float + eps_max: float + """ + + def __init__( + self, + num_channels: int, + channel_dim: int = -1, # CAUTION: see documentation. + eps: float = 0.25, + learn_eps: bool = True, + eps_min: float = -3.0, + eps_max: float = 3.0, + ) -> None: + super(BasicNorm, self).__init__() + self.num_channels = num_channels + self.channel_dim = channel_dim + if learn_eps: + self.eps = nn.Parameter(torch.tensor(eps).log().detach()) + else: + self.register_buffer("eps", torch.tensor(eps).log().detach()) + self.eps_min = eps_min + self.eps_max = eps_max + + def forward(self, x: Tensor) -> Tensor: + assert x.shape[self.channel_dim] == self.num_channels + eps = self.eps + if self.training and random.random() < 0.25: + # with probability 0.25, in training mode, clamp eps between the min + # and max; this will encourage it to learn parameters within the + # allowed range by making parameters that are outside the allowed + # range noisy. + + # gradients to allow the parameter to get back into the allowed + # region if it happens to exit it. + eps = eps.clamp(min=self.eps_min, max=self.eps_max) + scales = ( + torch.mean(x ** 2, dim=self.channel_dim, keepdim=True) + eps.exp() + ) ** -0.5 + return x * scales + + +def ScaledLinear(*args, initial_scale: float = 1.0, **kwargs) -> nn.Linear: + """ + Behaves like a constructor of a modified version of nn.Linear + that gives an easy way to set the default initial parameter scale. + + Args: + Accepts the standard args and kwargs that nn.Linear accepts + e.g. in_features, out_features, bias=False. + + initial_scale: you can override this if you want to increase + or decrease the initial magnitude of the module's output + (affects the initialization of weight_scale and bias_scale). + Another option, if you want to do something like this, is + to re-initialize the parameters. + """ + ans = nn.Linear(*args, **kwargs) + with torch.no_grad(): + ans.weight[:] *= initial_scale + if ans.bias is not None: + torch.nn.init.uniform_( + ans.bias, -0.1 * initial_scale, 0.1 * initial_scale + ) + return ans + + +def ScaledConv1d( + *args, + initial_scale: float = 1.0, + kernel_size: int = 3, + padding: str = "same", + **kwargs, +) -> nn.Conv1d: + """ + Behaves like a constructor of a modified version of nn.Conv1d + that gives an easy way to set the default initial parameter scale. + + Args: + Accepts the standard args and kwargs that nn.Linear accepts + e.g. in_features, out_features, bias=False. + + initial_scale: you can override this if you want to increase + or decrease the initial magnitude of the module's output + (affects the initialization of weight_scale and bias_scale). + Another option, if you want to do something like this, is + to re-initialize the parameters. + """ + ans = nn.Conv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs) + with torch.no_grad(): + ans.weight[:] *= initial_scale + if ans.bias is not None: + torch.nn.init.uniform_( + ans.bias, -0.1 * initial_scale, 0.1 * initial_scale + ) + return ans + + +def TransposeScaledConv1d( + *args, + initial_scale: float = 1.0, + kernel_size: int = 3, + padding: str = "same", + **kwargs, +) -> nn.Sequential: + """ + Transpose -> ScaledConv1d + """ + return nn.Sequential( + Transpose(), + ScaledConv1d( + *args, + initial_scale=initial_scale, + kernel_size=kernel_size, + padding=padding, + **kwargs, + ), + ) + + +def ScaledConv1dTranspose( + *args, + initial_scale: float = 1.0, + kernel_size: int = 3, + padding: str = "same", + **kwargs, +) -> nn.Sequential: + """ + Transpose -> ScaledConv1d + """ + return nn.Sequential( + ScaledConv1d( + *args, + initial_scale=initial_scale, + kernel_size=kernel_size, + padding=padding, + **kwargs, + ), + Transpose(), + ) + + +def TransposeConv1d( + *args, kernel_size: int = 3, padding: str = "same", **kwargs +) -> nn.Sequential: + """ + Transpose -> Conv1d + """ + return nn.Sequential( + Transpose(), + nn.Conv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs), + ) + + +def Conv1dTranspose( + *args, kernel_size: int = 3, padding: str = "same", **kwargs +) -> nn.Sequential: + """ + ScaledConv1d -> Transpose + """ + return nn.Sequential( + nn.Conv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs), + Transpose(), + ) + + +class SRLinear(nn.Linear): + """https://arxiv.org/abs/2303.06296 + Stabilizing Transformer Training by Preventing Attention Entropy Collapse + """ + + def __init__(self, in_features, out_features, bias=True, **kwargs): + super().__init__(in_features, out_features, bias=bias, **kwargs) + self.register_buffer( + "u", nn.functional.normalize(torch.randn(in_features), dim=0) + ) + with torch.no_grad(): + sigma = self.get_sigma() + self.register_buffer("spectral_norm", sigma) + self.sigma = nn.Parameter(torch.ones(1)) + + def get_sigma(self): + with torch.no_grad(): + u = self.u + v = self.weight.mv(u) + v = nn.functional.normalize(v, dim=0) + u = self.weight.T.mv(v) + u = nn.functional.normalize(u, dim=0) + self.u.data.copy_(u) + return torch.einsum("c,cd,d->", v, self.weight, u) + + def get_weight(self): + sigma = self.get_sigma() + if self.training: + self.spectral_norm.data.copy_(sigma) + weight = (self.sigma / sigma) * self.weight + return weight + + def forward(self, x): + return nn.functional.linear(x, self.get_weight(), self.bias) + + +class SRConv1d(SRLinear): + def __init__( + self, + in_features, + out_features, + kernel_size, + stride: int = 1, + padding: str = "same", + bias: bool = True, + **kwargs, + ): + in_features = in_features * kernel_size + super().__init__(in_features, out_features, bias=bias, **kwargs) + nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + + def forward(self, x): + in_features = self.in_features // self.kernel_size + weight = self.get_weight().view( + self.out_features, in_features, self.kernel_size + ) + return nn.functional.conv1d( + x, weight, bias=self.bias, stride=self.stride, padding=self.padding + ) + + +def TransposeSRConv1d( + *args, kernel_size: int = 3, padding: str = "same", **kwargs +) -> nn.Sequential: + """ + Transpose -> SRConv1d + """ + return nn.Sequential( + Transpose(), + SRConv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs), + ) + + +def SRConv1dTranspose( + *args, kernel_size: int = 3, padding: str = "same", **kwargs +) -> nn.Sequential: + """ + SRConv1d -> Transpose + """ + return nn.Sequential( + SRConv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs), + Transpose(), + ) + + +class ActivationBalancer(torch.nn.Module): + """ + Modifies the backpropped derivatives of a function to try to encourage, for + each channel, that it is positive at least a proportion `threshold` of the + time. It does this by multiplying negative derivative values by up to + (1+max_factor), and positive derivative values by up to (1-max_factor), + interpolated from 1 at the threshold to those extremal values when none + of the inputs are positive. + + Args: + num_channels: the number of channels + channel_dim: the dimension/axis corresponding to the channel, e.g. + -1, 0, 1, 2; will be interpreted as an offset from x.ndim if negative. + min_positive: the minimum, per channel, of the proportion of the time + that (x > 0), below which we start to modify the derivatives. + max_positive: the maximum, per channel, of the proportion of the time + that (x > 0), above which we start to modify the derivatives. + max_factor: the maximum factor by which we modify the derivatives for + either the sign constraint or the magnitude constraint; + e.g. with max_factor=0.02, the the derivatives would be multiplied by + values in the range [0.98..1.02]. + sign_gain_factor: determines the 'gain' with which we increase the + change in gradient once the constraints on min_positive and max_positive + are violated. + scale_gain_factor: determines the 'gain' with which we increase the + change in gradient once the constraints on min_abs and max_abs + are violated. + min_abs: the minimum average-absolute-value difference from the mean + value per channel, which we allow, before we start to modify + the derivatives to prevent this. + max_abs: the maximum average-absolute-value difference from the mean + value per channel, which we allow, before we start to modify + the derivatives to prevent this. + min_prob: determines the minimum probability with which we modify the + gradients for the {min,max}_positive and {min,max}_abs constraints, + on each forward(). This is done randomly to prevent all layers + from doing it at the same time. Early in training we may use + higher probabilities than this; it will decay to this value. + """ + + def __init__( + self, + num_channels: int, + channel_dim: int, + min_positive: float = 0.05, + max_positive: float = 0.95, + max_factor: float = 0.04, + sign_gain_factor: float = 0.01, + scale_gain_factor: float = 0.02, + min_abs: float = 0.2, + max_abs: float = 100.0, + min_prob: float = 0.1, + ): + super(ActivationBalancer, self).__init__() + self.num_channels = num_channels + self.channel_dim = channel_dim + self.min_positive = min_positive + self.max_positive = max_positive + self.max_factor = max_factor + self.min_abs = min_abs + self.max_abs = max_abs + self.min_prob = min_prob + self.sign_gain_factor = sign_gain_factor + self.scale_gain_factor = scale_gain_factor + + # count measures how many times the forward() function has been called. + # We occasionally sync this to a tensor called `count`, that exists to + # make sure it is synced to disk when we load and save the model. + self.cpu_count = 0 + self.register_buffer("count", torch.tensor(0, dtype=torch.int64)) + + def forward(self, x: Tensor) -> Tensor: + if ( + torch.jit.is_scripting() + or not x.requires_grad + or torch.jit.is_tracing() + ): + return _no_op(x) + + count = self.cpu_count + self.cpu_count += 1 + + if random.random() < 0.01: + # Occasionally sync self.cpu_count with self.count. + # count affects the decay of 'prob'. don't do this on every iter, + # because syncing with the GPU is slow. + self.cpu_count = max(self.cpu_count, self.count.item()) + self.count.fill_(self.cpu_count) + + # the prob of doing some work exponentially decreases from 0.5 till it hits + # a floor at min_prob (==0.1, by default) + prob = max(self.min_prob, 0.5 ** (1 + (count / 4000.0))) + + if random.random() < prob: + sign_gain_factor = 0.5 + if self.min_positive != 0.0 or self.max_positive != 1.0: + sign_factor = _compute_sign_factor( + x, + self.channel_dim, + self.min_positive, + self.max_positive, + gain_factor=self.sign_gain_factor / prob, + max_factor=self.max_factor, + ) + else: + sign_factor = None + + scale_factor = _compute_scale_factor( + x.detach(), + self.channel_dim, + min_abs=self.min_abs, + max_abs=self.max_abs, + gain_factor=self.scale_gain_factor / prob, + max_factor=self.max_factor, + ) + return ActivationBalancerFunction.apply( + x, + scale_factor, + sign_factor, + self.channel_dim, + ) + else: + return _no_op(x) + + +def penalize_abs_values_gt(x: Tensor, limit: float, penalty: float) -> Tensor: + """ + Returns x unmodified, but in backprop will put a penalty for the excess of + the absolute values of elements of x over the limit "limit". E.g. if + limit == 10.0, then if x has any values over 10 it will get a penalty. + + Caution: the value of this penalty will be affected by grad scaling used + in automatic mixed precision training. For this reasons we use this, + it shouldn't really matter, or may even be helpful; we just use this + to disallow really implausible values of scores to be given to softmax. + """ + x_sign = x.sign() + over_limit = (x.abs() - limit) > 0 + # The following is a memory efficient way to penalize the absolute values of + # x that's over the limit. (The memory efficiency comes when you think + # about which items torch needs to cache for the autograd, and which ones it + # can throw away). The numerical value of aux_loss as computed here will + # actually be larger than it should be, by limit * over_limit.sum(), but it + # has the same derivative as the real aux_loss which is penalty * (x.abs() - + # limit).relu(). + aux_loss = penalty * ((x_sign * over_limit).to(torch.int8) * x) + # note: we don't do sum() here on aux)_loss, but it's as if we had done + # sum() due to how with_loss() works. + x = with_loss(x, aux_loss) + # you must use x for something, or this will be ineffective. + return x + + +def _diag(x: Tensor): # like .diag(), but works for tensors with 3 dims. + if x.ndim == 2: + return x.diag() + else: + (batch, dim, dim) = x.shape + x = x.reshape(batch, dim * dim) + x = x[:, :: dim + 1] + assert x.shape == (batch, dim) + return x + + +def _whitening_metric(x: Tensor, num_groups: int): + """ + Computes the "whitening metric", a value which will be 1.0 if all the eigenvalues of + of the centered feature covariance are the same within each group's covariance matrix + and also between groups. + Args: + x: a Tensor of shape (*, num_channels) + num_groups: the number of groups of channels, a number >=1 that divides num_channels + Returns: + Returns a scalar Tensor that will be 1.0 if the data is "perfectly white" and + greater than 1.0 otherwise. + """ + assert x.dtype != torch.float16 + x = x.reshape(-1, x.shape[-1]) + (num_frames, num_channels) = x.shape + assert num_channels % num_groups == 0 + channels_per_group = num_channels // num_groups + x = x.reshape(num_frames, num_groups, channels_per_group).transpose(0, 1) + # x now has shape (num_groups, num_frames, channels_per_group) + # subtract the mean so we use the centered, not uncentered, covariance. + # My experience has been that when we "mess with the gradients" like this, + # it's better not do anything that tries to move the mean around, because + # that can easily cause instability. + x = x - x.mean(dim=1, keepdim=True) + # x_covar: (num_groups, channels_per_group, channels_per_group) + x_covar = torch.matmul(x.transpose(1, 2), x) + x_covar_mean_diag = _diag(x_covar).mean() + # the following expression is what we'd get if we took the matrix product + # of each covariance and measured the mean of its trace, i.e. + # the same as _diag(torch.matmul(x_covar, x_covar)).mean(). + x_covarsq_mean_diag = (x_covar ** 2).sum() / ( + num_groups * channels_per_group + ) + # this metric will be >= 1.0; the larger it is, the less 'white' the data was. + metric = x_covarsq_mean_diag / (x_covar_mean_diag ** 2 + 1.0e-20) + return metric + + +class WhiteningPenaltyFunction(torch.autograd.Function): + @staticmethod + def forward( + ctx, + x: Tensor, + num_groups: int, + whitening_limit: float, + grad_scale: float, + ) -> Tensor: + ctx.save_for_backward(x) + ctx.num_groups = num_groups + ctx.whitening_limit = whitening_limit + ctx.grad_scale = grad_scale + return x + + @staticmethod + def backward(ctx, x_grad: Tensor): + (x_orig,) = ctx.saved_tensors + with torch.enable_grad(): + with torch.cuda.amp.autocast(enabled=False): + x_detached = x_orig.to(torch.float32).detach() + x_detached.requires_grad = True + + metric = _whitening_metric(x_detached, ctx.num_groups) + + if random.random() < 0.005 or __name__ == "__main__": + logging.info( + f"Whitening: num_groups={ctx.num_groups}, num_channels={x_orig.shape[-1]}, " + f"metric={metric.item():.2f} vs. limit={ctx.whitening_limit}" + ) + + (metric - ctx.whitening_limit).relu().backward() + penalty_grad = x_detached.grad + scale = ctx.grad_scale * ( + x_grad.to(torch.float32).norm() + / (penalty_grad.norm() + 1.0e-20) + ) + penalty_grad = penalty_grad * scale + return x_grad + penalty_grad.to(x_grad.dtype), None, None, None + + +class Whiten(nn.Module): + def __init__( + self, + num_groups: int, + whitening_limit: float, + prob: Union[float, Tuple[float, float]], + grad_scale: float, + ): + """ + Args: + num_groups: the number of groups to divide the channel dim into before + whitening. We will attempt to make the feature covariance + within each group, after mean subtraction, as "white" as possible, + while having the same trace across all groups. + whitening_limit: a value greater than 1.0, that dictates how much + freedom we have to violate the constraints. 1.0 would mean perfectly + white, with exactly the same trace across groups; larger values + give more freedom. E.g. 2.0. + prob: the probability with which we apply the gradient modification + (also affects the grad scale). May be supplied as a float, + or as a pair (min_prob, max_prob) + + grad_scale: determines the scale on the gradient term from this object, + relative to the rest of the gradient on the attention weights. + E.g. 0.02 (you may want to use smaller values than this if prob is large) + """ + super(Whiten, self).__init__() + assert num_groups >= 1 + assert whitening_limit >= 1 + assert grad_scale >= 0 + self.num_groups = num_groups + self.whitening_limit = whitening_limit + if isinstance(prob, float): + assert 0 < prob <= 1 + self.prob = prob + else: + (self.min_prob, self.max_prob) = prob + assert 0 < self.min_prob < self.max_prob <= 1 + self.prob = self.max_prob + + self.grad_scale = grad_scale + + def forward(self, x: Tensor) -> Tensor: + """ + In the forward pass, this function just returns the input unmodified. + In the backward pass, it will modify the gradients to ensure that the + distribution in each group has close to (lambda times I) as the covariance + after mean subtraction, with the same lambda across groups. + For whitening_limit > 1, there will be more freedom to violate this + constraint. + + Args: + x: the input of shape (*, num_channels) + + Returns: + x, unmodified. You should make sure + you use the returned value, or the graph will be freed + and nothing will happen in backprop. + """ + if ( + not x.requires_grad + or random.random() > self.prob + or self.grad_scale == 0 + ): + return _no_op(x) + else: + if hasattr(self, "min_prob") and random.random() < 0.25: + # occasionally switch between min_prob and max_prob, based on whether + # we are above or below the threshold. + if ( + _whitening_metric(x.to(torch.float32), self.num_groups) + > self.whitening_limit + ): + # there would be a change to the grad. + self.prob = self.max_prob + else: + self.prob = self.min_prob + + return WhiteningPenaltyFunction.apply( + x, self.num_groups, self.whitening_limit, self.grad_scale + ) + + +class WithLoss(torch.autograd.Function): + @staticmethod + def forward(ctx, x: Tensor, y: Tensor): + ctx.y_shape = y.shape + return x + + @staticmethod + def backward(ctx, ans_grad: Tensor): + return ans_grad, torch.ones( + ctx.y_shape, dtype=ans_grad.dtype, device=ans_grad.device + ) + + +def with_loss(x, y): + if torch.jit.is_scripting() or torch.jit.is_tracing(): + return x + # returns x but adds y.sum() to the loss function. + return WithLoss.apply(x, y) + + +def _no_op(x: Tensor) -> Tensor: + if torch.jit.is_scripting() or torch.jit.is_tracing(): + return x + else: + # a no-op function that will have a node in the autograd graph, + # to avoid certain bugs relating to backward hooks + return x.chunk(1, dim=-1)[0] + + +class Identity(torch.nn.Module): + def __init__(self): + super(Identity, self).__init__() + + def forward(self, x): + return _no_op(x) + + +class MaxEig(torch.nn.Module): + """ + Modifies the backpropped derivatives of a function to try to discourage + that any given direction in activation space accounts for more than + a specified proportion of the covariance (e.g. 0.2). + + + Args: + num_channels: the number of channels + channel_dim: the dimension/axis corresponding to the channel, e.g. + -1, 0, 1, 2; will be interpreted as an offset from x.ndim if negative. + max_var_per_eig: the maximum proportion of the variance of the + features/channels, after mean subtraction, that can come from + any given eigenvalue. + min_prob: the minimum probability with which we apply this during any invocation + of forward(), assuming last time we applied the constraint it was + not active; supplied for speed. + scale: determines the scale with which we modify the gradients, relative + to the existing / unmodified gradients + """ + + def __init__( + self, + num_channels: int, + channel_dim: int, + max_var_per_eig: float = 0.2, + min_prob: float = 0.01, + scale: float = 0.01, + ): + super(MaxEig, self).__init__() + self.num_channels = num_channels + self.channel_dim = channel_dim + self.scale = scale + assert max_var_per_eig == 0.0 or max_var_per_eig > 1.0 / num_channels + self.max_var_per_eig = max_var_per_eig + + # we figure out the dominant direction using the power method: starting with + # a random vector, keep multiplying by the covariance and renormalizing. + with torch.no_grad(): + # arbitrary.. would use randn() but want to leave the rest of the model's + # random parameters unchanged for comparison + direction = torch.arange(num_channels).to(torch.float) + direction = direction / direction.norm() + self.register_buffer("max_eig_direction", direction) + + self.min_prob = min_prob + # cur_prob is the current probability we'll use to apply the ActivationBalancer. + # We'll regress this towards prob, each tiem we try to apply it and it is not + # active. + self.cur_prob = 1.0 + + def forward(self, x: Tensor) -> Tensor: + if ( + torch.jit.is_scripting() + or self.max_var_per_eig <= 0 + or random.random() > self.cur_prob + or torch.jit.is_tracing() + ): + return _no_op(x) + + with torch.cuda.amp.autocast(enabled=False): + eps = 1.0e-20 + orig_x = x + x = x.to(torch.float32) + with torch.no_grad(): + x = x.transpose(self.channel_dim, -1).reshape( + -1, self.num_channels + ) + x = x - x.mean(dim=0) + new_direction, coeffs = self._find_direction_coeffs( + x, self.max_eig_direction + ) + x_var = (x ** 2).mean() + x_residual = x - coeffs * new_direction + x_residual_var = (x_residual ** 2).mean() + + # `variance_proportion` is the proportion of the variance accounted for + # by the top eigen-direction. + variance_proportion = (x_var - x_residual_var) / ( + x_var + 1.0e-20 + ) + + # ensure new direction is nonzero even if x == 0, by including `direction`. + self._set_direction( + 0.1 * self.max_eig_direction + new_direction + ) + + if random.random() < 0.01 or __name__ == "__main__": + logging.info( + f"variance_proportion = {variance_proportion.item()}, shape={tuple(orig_x.shape)}, cur_prob={self.cur_prob}" + ) + + if variance_proportion >= self.max_var_per_eig: + # The constraint is active. Note, we should quite rarely + # reach here, only near the beginning of training if we are + # starting to diverge, should this constraint be active. + cur_prob = self.cur_prob + self.cur_prob = ( + 1.0 # next time, do the update with probability 1.0. + ) + return MaxEigLimiterFunction.apply( + orig_x, coeffs, new_direction, self.channel_dim, self.scale + ) + else: + # let self.cur_prob exponentially approach self.min_prob, as + # long as the constraint is inactive. + self.cur_prob = 0.75 * self.cur_prob + 0.25 * self.min_prob + return orig_x + + def _set_direction(self, direction: Tensor): + """ + Sets self.max_eig_direction to a normalized version of `direction` + """ + direction = direction.detach() + direction = direction / direction.norm() + direction_sum = direction.sum().item() + if direction_sum - direction_sum == 0: # no inf/nan + self.max_eig_direction[:] = direction + else: + logging.info( + f"Warning: sum of direction in MaxEig is {direction_sum}, " + "num_channels={self.num_channels}, channel_dim={self.channel_dim}" + ) + + def _find_direction_coeffs( + self, x: Tensor, prev_direction: Tensor + ) -> Tuple[Tensor, Tensor, Tensor]: + """ + Figure out (an approximation to) the proportion of the variance of a set of + feature vectors that can be attributed to the top eigen-direction. + Args: + x: a Tensor of shape (num_frames, num_channels), with num_frames > 1. + prev_direction: a Tensor of shape (num_channels,), that is our previous estimate + of the top eigen-direction, or a random direction if this is the first + iteration. Does not have to be normalized, but should be nonzero. + + Returns: (cur_direction, coeffs), where: + cur_direction: a Tensor of shape (num_channels,) that is the current + estimate of the top eigen-direction. + coeffs: a Tensor of shape (num_frames, 1) that minimizes, or + approximately minimizes, (x - coeffs * cur_direction).norm() + """ + (num_frames, num_channels) = x.shape + assert num_channels > 1 and num_frames > 1 + assert prev_direction.shape == (num_channels,) + # `coeffs` are the coefficients of `prev_direction` in x. + # actually represent the coeffs up to a constant positive factor. + coeffs = (x * prev_direction).sum(dim=1, keepdim=True) + 1.0e-10 + cur_direction = (x * coeffs).sum(dim=0) / ( + (coeffs ** 2).sum() + 1.0e-20 + ) + return cur_direction, coeffs + + +class DoubleSwishFunction(torch.autograd.Function): + """ + double_swish(x) = x * torch.sigmoid(x-1) + This is a definition, originally motivated by its close numerical + similarity to swish(swish(x)), where swish(x) = x * sigmoid(x). + + Memory-efficient derivative computation: + double_swish(x) = x * s, where s(x) = torch.sigmoid(x-1) + double_swish'(x) = d/dx double_swish(x) = x * s'(x) + x' * s(x) = x * s'(x) + s(x). + Now, s'(x) = s(x) * (1-s(x)). + double_swish'(x) = x * s'(x) + s(x). + = x * s(x) * (1-s(x)) + s(x). + = double_swish(x) * (1-s(x)) + s(x) + ... so we just need to remember s(x) but not x itself. + """ + + @staticmethod + def forward(ctx, x: Tensor) -> Tensor: + requires_grad = x.requires_grad + x_dtype = x.dtype + if x.dtype == torch.float16: + x = x.to(torch.float32) + + s = torch.sigmoid(x - 1.0) + y = x * s + + if requires_grad: + deriv = y * (1 - s) + s + # notes on derivative of x * sigmoid(x - 1): + # https://www.wolframalpha.com/input?i=d%2Fdx+%28x+*+sigmoid%28x-1%29%29 + # min \simeq -0.043638. Take floor as -0.043637 so it's a lower bund + # max \simeq 1.1990. Take ceil to be 1.2 so it's an upper bound. + # the combination of "+ torch.rand_like(deriv)" and casting to torch.uint8 (which + # floors), should be expectation-preserving. + floor = -0.043637 + ceil = 1.2 + d_scaled = (deriv - floor) * ( + 255.0 / (ceil - floor) + ) + torch.rand_like(deriv) + if __name__ == "__main__": + # for self-testing only. + assert d_scaled.min() >= 0.0 + assert d_scaled.max() < 256.0 + d_int = d_scaled.to(torch.uint8) + ctx.save_for_backward(d_int) + if x.dtype == torch.float16 or torch.is_autocast_enabled(): + y = y.to(torch.float16) + return y + + @staticmethod + def backward(ctx, y_grad: Tensor) -> Tensor: + (d,) = ctx.saved_tensors + # the same constants as used in forward pass. + floor = -0.043637 + ceil = 1.2 + d = d * ((ceil - floor) / 255.0) + floor + return y_grad * d + + +class DoubleSwish(torch.nn.Module): + def forward(self, x: Tensor) -> Tensor: + """Return double-swish activation function which is an approximation to Swish(Swish(x)), + that we approximate closely with x * sigmoid(x-1). + """ + if torch.jit.is_scripting() or torch.jit.is_tracing(): + return x * torch.sigmoid(x - 1.0) + return DoubleSwishFunction.apply(x) + + +def BalancedDoubleSwish( + d_model, channel_dim=-1, max_abs=10.0, min_prob=0.25 +) -> nn.Sequential: + """ + ActivationBalancer -> DoubleSwish + """ + balancer = ActivationBalancer( + d_model, channel_dim=channel_dim, max_abs=max_abs, min_prob=min_prob + ) + return nn.Sequential( + balancer, + DoubleSwish(), + ) + + +def _test_max_eig(): + for proportion in [0.1, 0.5, 10.0]: + logging.info(f"proportion = {proportion}") + x = torch.randn(100, 128) + direction = torch.randn(128) + coeffs = torch.randn(100, 1) + x += proportion * direction * coeffs + + x.requires_grad = True + + num_channels = 128 + m = MaxEig( + num_channels, 1, 0.5, scale=0.1 # channel_dim # max_var_per_eig + ) # grad_scale + + for _ in range(4): + y = m(x) + + y_grad = torch.randn_like(x) + y.backward(gradient=y_grad) + + if proportion < 0.2: + assert torch.allclose(x.grad, y_grad, atol=1.0e-02) + elif proportion > 1.0: + assert not torch.allclose(x.grad, y_grad) + + +def _test_whiten(): + for proportion in [0.1, 0.5, 10.0]: + logging.info(f"_test_whiten(): proportion = {proportion}") + x = torch.randn(100, 128) + direction = torch.randn(128) + coeffs = torch.randn(100, 1) + x += proportion * direction * coeffs + + x.requires_grad = True + + num_channels = 128 + m = Whiten( + 1, 5.0, prob=1.0, grad_scale=0.1 # num_groups # whitening_limit, + ) # grad_scale + + for _ in range(4): + y = m(x) + + y_grad = torch.randn_like(x) + y.backward(gradient=y_grad) + + if proportion < 0.2: + assert torch.allclose(x.grad, y_grad) + elif proportion > 1.0: + assert not torch.allclose(x.grad, y_grad) + + +def _test_activation_balancer_sign(): + probs = torch.arange(0, 1, 0.01) + N = 1000 + x = 1.0 * ( + (2.0 * (torch.rand(probs.numel(), N) < probs.unsqueeze(-1))) - 1.0 + ) + x = x.detach() + x.requires_grad = True + m = ActivationBalancer( + probs.numel(), + channel_dim=0, + min_positive=0.05, + max_positive=0.95, + max_factor=0.2, + min_abs=0.0, + ) + + y_grad = torch.sign(torch.randn(probs.numel(), N)) + + y = m(x) + y.backward(gradient=y_grad) + print("_test_activation_balancer_sign: x = ", x) + print("_test_activation_balancer_sign: y grad = ", y_grad) + print("_test_activation_balancer_sign: x grad = ", x.grad) + + +def _test_activation_balancer_magnitude(): + magnitudes = torch.arange(0, 1, 0.01) + N = 1000 + x = torch.sign(torch.randn(magnitudes.numel(), N)) * magnitudes.unsqueeze( + -1 + ) + x = x.detach() + x.requires_grad = True + m = ActivationBalancer( + magnitudes.numel(), + channel_dim=0, + min_positive=0.0, + max_positive=1.0, + max_factor=0.2, + min_abs=0.2, + max_abs=0.8, + min_prob=1.0, + ) + + y_grad = torch.sign(torch.randn(magnitudes.numel(), N)) + + y = m(x) + y.backward(gradient=y_grad) + print("_test_activation_balancer_magnitude: x = ", x) + print("_test_activation_balancer_magnitude: y grad = ", y_grad) + print("_test_activation_balancer_magnitude: x grad = ", x.grad) + + +def _test_basic_norm(): + num_channels = 128 + m = BasicNorm(num_channels=num_channels, channel_dim=1) + + x = torch.randn(500, num_channels) + + y = m(x) + + assert y.shape == x.shape + x_rms = (x ** 2).mean().sqrt() + y_rms = (y ** 2).mean().sqrt() + print("x rms = ", x_rms) + print("y rms = ", y_rms) + assert y_rms < x_rms + assert y_rms > 0.5 * x_rms + + +def _test_double_swish_deriv(): + x = torch.randn(10, 12, dtype=torch.double) * 3.0 + x.requires_grad = True + m = DoubleSwish() + + tol = (1.2 - (-0.043637)) / 255.0 + torch.autograd.gradcheck(m, x, atol=tol) + + # for self-test. + x = torch.randn(1000, 1000, dtype=torch.double) * 3.0 + x.requires_grad = True + y = m(x) + + +def _test_softmax(): + a = torch.randn(2, 10, dtype=torch.float64) + b = a.clone() + a.requires_grad = True + b.requires_grad = True + a.softmax(dim=1)[:, 0].sum().backward() + print("a grad = ", a.grad) + softmax(b, dim=1)[:, 0].sum().backward() + print("b grad = ", b.grad) + assert torch.allclose(a.grad, b.grad) + + +if __name__ == "__main__": + logging.getLogger().setLevel(logging.INFO) + torch.set_num_threads(1) + torch.set_num_interop_threads(1) + _test_softmax() + _test_whiten() + _test_max_eig() + _test_activation_balancer_sign() + _test_activation_balancer_magnitude() + _test_basic_norm() + _test_double_swish_deriv() diff --git a/modules/general/utils.py b/modules/general/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..38c94dfbb9859661322f3d2e8204b423fffae736 --- /dev/null +++ b/modules/general/utils.py @@ -0,0 +1,100 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + + +def normalization(channels: int, groups: int = 32): + r"""Make a standard normalization layer, i.e. GroupNorm. + + Args: + channels: number of input channels. + groups: number of groups for group normalization. + + Returns: + a ``nn.Module`` for normalization. + """ + assert groups > 0, f"invalid number of groups: {groups}" + return nn.GroupNorm(groups, channels) + + +def Linear(*args, **kwargs): + r"""Wrapper of ``nn.Linear`` with kaiming_normal_ initialization.""" + layer = nn.Linear(*args, **kwargs) + nn.init.kaiming_normal_(layer.weight) + return layer + + +def Conv1d(*args, **kwargs): + r"""Wrapper of ``nn.Conv1d`` with kaiming_normal_ initialization.""" + layer = nn.Conv1d(*args, **kwargs) + nn.init.kaiming_normal_(layer.weight) + return layer + + +def Conv2d(*args, **kwargs): + r"""Wrapper of ``nn.Conv2d`` with kaiming_normal_ initialization.""" + layer = nn.Conv2d(*args, **kwargs) + nn.init.kaiming_normal_(layer.weight) + return layer + + +def ConvNd(dims: int = 1, *args, **kwargs): + r"""Wrapper of N-dimension convolution with kaiming_normal_ initialization. + + Args: + dims: number of dimensions of the convolution. + """ + if dims == 1: + return Conv1d(*args, **kwargs) + elif dims == 2: + return Conv2d(*args, **kwargs) + else: + raise ValueError(f"invalid number of dimensions: {dims}") + + +def zero_module(module: nn.Module): + r"""Zero out the parameters of a module and return it.""" + nn.init.zeros_(module.weight) + nn.init.zeros_(module.bias) + return module + + +def scale_module(module: nn.Module, scale): + r"""Scale the parameters of a module and return it.""" + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def mean_flat(tensor: torch.Tensor): + r"""Take the mean over all non-batch dimensions.""" + return tensor.mean(dim=tuple(range(1, tensor.dim()))) + + +def append_dims(x, target_dims): + r"""Appends dimensions to the end of a tensor until + it has target_dims dimensions. + """ + dims_to_append = target_dims - x.dim() + if dims_to_append < 0: + raise ValueError( + f"input has {x.dim()} dims but target_dims is {target_dims}, which is less" + ) + return x[(...,) + (None,) * dims_to_append] + + +def append_zero(x, count=1): + r"""Appends ``count`` zeros to the end of a tensor along the last dimension.""" + assert count > 0, f"invalid count: {count}" + return torch.cat([x, x.new_zeros((*x.size()[:-1], count))], dim=-1) + + +class Transpose(nn.Identity): + """(N, T, D) -> (N, D, T)""" + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input.transpose(1, 2) diff --git a/modules/monotonic_align/__init__.py b/modules/monotonic_align/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..784d9081ca1aa1355b893dbd91d159d6f911cb4b --- /dev/null +++ b/modules/monotonic_align/__init__.py @@ -0,0 +1,21 @@ +# This code from https://github.com/jaywalnut310/vits/ + +import numpy as np +import torch +from .monotonic_align.core import maximum_path_c + + +def maximum_path(neg_cent, mask): + """Cython optimized version. + neg_cent: [b, t_t, t_s] + mask: [b, t_t, t_s] + """ + device = neg_cent.device + dtype = neg_cent.dtype + neg_cent = neg_cent.data.cpu().numpy().astype(np.float32) + path = np.zeros(neg_cent.shape, dtype=np.int32) + + t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32) + t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32) + maximum_path_c(path, neg_cent, t_t_max, t_s_max) + return torch.from_numpy(path).to(device=device, dtype=dtype) diff --git a/modules/monotonic_align/__pycache__/__init__.cpython-39.pyc b/modules/monotonic_align/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0950ce1b20ae2db0daff1e26e5d7e65de7012937 Binary files /dev/null and b/modules/monotonic_align/__pycache__/__init__.cpython-39.pyc differ diff --git a/modules/monotonic_align/build/lib.linux-x86_64-cpython-39/monotonic_align/core.cpython-39-x86_64-linux-gnu.so b/modules/monotonic_align/build/lib.linux-x86_64-cpython-39/monotonic_align/core.cpython-39-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..9904fb869463865478f1c8b5180d853a4ef5804b Binary files /dev/null and b/modules/monotonic_align/build/lib.linux-x86_64-cpython-39/monotonic_align/core.cpython-39-x86_64-linux-gnu.so differ diff --git a/modules/monotonic_align/build/temp.linux-x86_64-cpython-39/core.o b/modules/monotonic_align/build/temp.linux-x86_64-cpython-39/core.o new file mode 100644 index 0000000000000000000000000000000000000000..b5502d6df146527eba2f0145bab65af420463ef5 Binary files /dev/null and b/modules/monotonic_align/build/temp.linux-x86_64-cpython-39/core.o differ diff --git a/modules/monotonic_align/core.c b/modules/monotonic_align/core.c new file mode 100644 index 0000000000000000000000000000000000000000..3e5e3b27e0bfa29b00bf91ed6c3bec1c9933b19d --- /dev/null +++ b/modules/monotonic_align/core.c @@ -0,0 +1,27505 @@ +/* Generated by Cython 3.0.6 */ + +/* BEGIN: Cython Metadata +{ + "distutils": { + "include_dirs": [ + "/home/pai/envs/amphion/lib/python3.9/site-packages/numpy/core/include" + ], + "name": "monotonic_align.core", + "sources": [ + "core.pyx" + ] + }, + "module_name": "monotonic_align.core" +} +END: Cython Metadata */ + +#ifndef PY_SSIZE_T_CLEAN +#define PY_SSIZE_T_CLEAN +#endif /* PY_SSIZE_T_CLEAN */ +#if defined(CYTHON_LIMITED_API) && 0 + #ifndef Py_LIMITED_API + #if CYTHON_LIMITED_API+0 > 0x03030000 + #define Py_LIMITED_API CYTHON_LIMITED_API + #else + #define Py_LIMITED_API 0x03030000 + #endif + #endif +#endif + +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02070000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) + #error Cython requires Python 2.7+ or Python 3.3+. +#else +#if defined(CYTHON_LIMITED_API) && CYTHON_LIMITED_API +#define __PYX_EXTRA_ABI_MODULE_NAME "limited" +#else +#define __PYX_EXTRA_ABI_MODULE_NAME "" +#endif +#define CYTHON_ABI "3_0_6" __PYX_EXTRA_ABI_MODULE_NAME +#define __PYX_ABI_MODULE_NAME "_cython_" CYTHON_ABI +#define __PYX_TYPE_MODULE_PREFIX __PYX_ABI_MODULE_NAME "." +#define CYTHON_HEX_VERSION 0x030006F0 +#define CYTHON_FUTURE_DIVISION 1 +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(_WIN32) && !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#define __PYX_COMMA , +#ifndef HAVE_LONG_LONG + #define HAVE_LONG_LONG +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#define __PYX_LIMITED_VERSION_HEX PY_VERSION_HEX +#if defined(GRAALVM_PYTHON) + /* For very preliminary testing purposes. Most variables are set the same as PyPy. + The existence of this section does not imply that anything works or is even tested */ + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 1 + #define CYTHON_COMPILING_IN_NOGIL 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 0 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #undef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3) + #endif + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #undef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif +#elif defined(PYPY_VERSION) + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #ifndef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 0 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #undef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3) + #endif + #if PY_VERSION_HEX < 0x03090000 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT) + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #undef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1 && PYPY_VERSION_NUM >= 0x07030C00) + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif +#elif defined(CYTHON_LIMITED_API) + #ifdef Py_LIMITED_API + #undef __PYX_LIMITED_VERSION_HEX + #define __PYX_LIMITED_VERSION_HEX Py_LIMITED_API + #endif + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 1 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 0 + #undef CYTHON_CLINE_IN_TRACEBACK + #define CYTHON_CLINE_IN_TRACEBACK 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 1 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #endif + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #undef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS 1 + #endif + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 1 + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #endif + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif +#elif defined(Py_GIL_DISABLED) || defined(Py_NOGIL) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #ifndef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 1 + #endif + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #ifndef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 0 + #endif + #ifndef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #ifndef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL (PY_MAJOR_VERSION < 3 || PY_VERSION_HEX >= 0x03060000 && PY_VERSION_HEX < 0x030C00A6) + #endif + #ifndef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL (PY_VERSION_HEX >= 0x030700A1) + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS 1 + #endif + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT) + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #ifndef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #endif + #if PY_VERSION_HEX < 0x030400a1 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #elif !defined(CYTHON_USE_TP_FINALIZE) + #define CYTHON_USE_TP_FINALIZE 1 + #endif + #if PY_VERSION_HEX < 0x030600B1 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #elif !defined(CYTHON_USE_DICT_VERSIONS) + #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX < 0x030C00A5) + #endif + #if PY_VERSION_HEX < 0x030700A3 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #elif !defined(CYTHON_USE_EXC_INFO_STACK) + #define CYTHON_USE_EXC_INFO_STACK 1 + #endif + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 1 + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if !defined(CYTHON_VECTORCALL) +#define CYTHON_VECTORCALL (CYTHON_FAST_PYCCALL && PY_VERSION_HEX >= 0x030800B1) +#endif +#define CYTHON_BACKPORT_VECTORCALL (CYTHON_METH_FASTCALL && PY_VERSION_HEX < 0x030800B1) +#if CYTHON_USE_PYLONG_INTERNALS + #if PY_MAJOR_VERSION < 3 + #include "longintrepr.h" + #endif + #undef SHIFT + #undef BASE + #undef MASK + #ifdef SIZEOF_VOID_P + enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; + #endif +#endif +#ifndef __has_attribute + #define __has_attribute(x) 0 +#endif +#ifndef __has_cpp_attribute + #define __has_cpp_attribute(x) 0 +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED + #if defined(__cplusplus) + /* for clang __has_cpp_attribute(maybe_unused) is true even before C++17 + * but leads to warnings with -pedantic, since it is a C++17 feature */ + #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) + #if __has_cpp_attribute(maybe_unused) + #define CYTHON_UNUSED [[maybe_unused]] + #endif + #endif + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR + #define CYTHON_MAYBE_UNUSED_VAR(x) CYTHON_UNUSED_VAR(x) +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_USE_CPP_STD_MOVE + #if defined(__cplusplus) && (\ + __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1600)) + #define CYTHON_USE_CPP_STD_MOVE 1 + #else + #define CYTHON_USE_CPP_STD_MOVE 0 + #endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) +#ifdef _MSC_VER + #ifndef _MSC_STDINT_H_ + #if _MSC_VER < 1300 + typedef unsigned char uint8_t; + typedef unsigned short uint16_t; + typedef unsigned int uint32_t; + #else + typedef unsigned __int8 uint8_t; + typedef unsigned __int16 uint16_t; + typedef unsigned __int32 uint32_t; + #endif + #endif + #if _MSC_VER < 1300 + #ifdef _WIN64 + typedef unsigned long long __pyx_uintptr_t; + #else + typedef unsigned int __pyx_uintptr_t; + #endif + #else + #ifdef _WIN64 + typedef unsigned __int64 __pyx_uintptr_t; + #else + typedef unsigned __int32 __pyx_uintptr_t; + #endif + #endif +#else + #include + typedef uintptr_t __pyx_uintptr_t; +#endif +#ifndef CYTHON_FALLTHROUGH + #if defined(__cplusplus) + /* for clang __has_cpp_attribute(fallthrough) is true even before C++17 + * but leads to warnings with -pedantic, since it is a C++17 feature */ + #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) + #if __has_cpp_attribute(fallthrough) + #define CYTHON_FALLTHROUGH [[fallthrough]] + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_cpp_attribute(clang::fallthrough) + #define CYTHON_FALLTHROUGH [[clang::fallthrough]] + #elif __has_cpp_attribute(gnu::fallthrough) + #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] + #endif + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_attribute(fallthrough) + #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) + #else + #define CYTHON_FALLTHROUGH + #endif + #endif + #if defined(__clang__) && defined(__apple_build_version__) + #if __apple_build_version__ < 7000000 + #undef CYTHON_FALLTHROUGH + #define CYTHON_FALLTHROUGH + #endif + #endif +#endif +#ifdef __cplusplus + template + struct __PYX_IS_UNSIGNED_IMPL {static const bool value = T(0) < T(-1);}; + #define __PYX_IS_UNSIGNED(type) (__PYX_IS_UNSIGNED_IMPL::value) +#else + #define __PYX_IS_UNSIGNED(type) (((type)-1) > 0) +#endif +#if CYTHON_COMPILING_IN_PYPY == 1 + #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x030A0000) +#else + #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000) +#endif +#define __PYX_REINTERPRET_FUNCION(func_pointer, other_pointer) ((func_pointer)(void(*)(void))(other_pointer)) + +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #elif defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif + +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_DefaultClassType PyClass_Type + #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_DefaultClassType PyType_Type +#if CYTHON_COMPILING_IN_LIMITED_API + static CYTHON_INLINE PyObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f, + PyObject *code, PyObject *c, PyObject* n, PyObject *v, + PyObject *fv, PyObject *cell, PyObject* fn, + PyObject *name, int fline, PyObject *lnos) { + PyObject *exception_table = NULL; + PyObject *types_module=NULL, *code_type=NULL, *result=NULL; + #if __PYX_LIMITED_VERSION_HEX < 0x030B0000 + PyObject *version_info; // borrowed + PyObject *py_minor_version = NULL; + #endif + long minor_version = 0; + PyObject *type, *value, *traceback; + PyErr_Fetch(&type, &value, &traceback); + #if __PYX_LIMITED_VERSION_HEX >= 0x030B0000 + minor_version = 11; // we don't yet need to distinguish between versions > 11 + #else + if (!(version_info = PySys_GetObject("version_info"))) goto end; + if (!(py_minor_version = PySequence_GetItem(version_info, 1))) goto end; + minor_version = PyLong_AsLong(py_minor_version); + Py_DECREF(py_minor_version); + if (minor_version == -1 && PyErr_Occurred()) goto end; + #endif + if (!(types_module = PyImport_ImportModule("types"))) goto end; + if (!(code_type = PyObject_GetAttrString(types_module, "CodeType"))) goto end; + if (minor_version <= 7) { + (void)p; + result = PyObject_CallFunction(code_type, "iiiiiOOOOOOiOO", a, k, l, s, f, code, + c, n, v, fn, name, fline, lnos, fv, cell); + } else if (minor_version <= 10) { + result = PyObject_CallFunction(code_type, "iiiiiiOOOOOOiOO", a,p, k, l, s, f, code, + c, n, v, fn, name, fline, lnos, fv, cell); + } else { + if (!(exception_table = PyBytes_FromStringAndSize(NULL, 0))) goto end; + result = PyObject_CallFunction(code_type, "iiiiiiOOOOOOOiOO", a,p, k, l, s, f, code, + c, n, v, fn, name, name, fline, lnos, exception_table, fv, cell); + } + end: + Py_XDECREF(code_type); + Py_XDECREF(exception_table); + Py_XDECREF(types_module); + if (type) { + PyErr_Restore(type, value, traceback); + } + return result; + } + #ifndef CO_OPTIMIZED + #define CO_OPTIMIZED 0x0001 + #endif + #ifndef CO_NEWLOCALS + #define CO_NEWLOCALS 0x0002 + #endif + #ifndef CO_VARARGS + #define CO_VARARGS 0x0004 + #endif + #ifndef CO_VARKEYWORDS + #define CO_VARKEYWORDS 0x0008 + #endif + #ifndef CO_ASYNC_GENERATOR + #define CO_ASYNC_GENERATOR 0x0200 + #endif + #ifndef CO_GENERATOR + #define CO_GENERATOR 0x0020 + #endif + #ifndef CO_COROUTINE + #define CO_COROUTINE 0x0080 + #endif +#elif PY_VERSION_HEX >= 0x030B0000 + static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f, + PyObject *code, PyObject *c, PyObject* n, PyObject *v, + PyObject *fv, PyObject *cell, PyObject* fn, + PyObject *name, int fline, PyObject *lnos) { + PyCodeObject *result; + PyObject *empty_bytes = PyBytes_FromStringAndSize("", 0); // we don't have access to __pyx_empty_bytes here + if (!empty_bytes) return NULL; + result = + #if PY_VERSION_HEX >= 0x030C0000 + PyUnstable_Code_NewWithPosOnlyArgs + #else + PyCode_NewWithPosOnlyArgs + #endif + (a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, name, fline, lnos, empty_bytes); + Py_DECREF(empty_bytes); + return result; + } +#elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#else + #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#endif +#endif +#if PY_VERSION_HEX >= 0x030900A4 || defined(Py_IS_TYPE) + #define __Pyx_IS_TYPE(ob, type) Py_IS_TYPE(ob, type) +#else + #define __Pyx_IS_TYPE(ob, type) (((const PyObject*)ob)->ob_type == (type)) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_Is) + #define __Pyx_Py_Is(x, y) Py_Is(x, y) +#else + #define __Pyx_Py_Is(x, y) ((x) == (y)) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsNone) + #define __Pyx_Py_IsNone(ob) Py_IsNone(ob) +#else + #define __Pyx_Py_IsNone(ob) __Pyx_Py_Is((ob), Py_None) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsTrue) + #define __Pyx_Py_IsTrue(ob) Py_IsTrue(ob) +#else + #define __Pyx_Py_IsTrue(ob) __Pyx_Py_Is((ob), Py_True) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsFalse) + #define __Pyx_Py_IsFalse(ob) Py_IsFalse(ob) +#else + #define __Pyx_Py_IsFalse(ob) __Pyx_Py_Is((ob), Py_False) +#endif +#define __Pyx_NoneAsNull(obj) (__Pyx_Py_IsNone(obj) ? NULL : (obj)) +#if PY_VERSION_HEX >= 0x030900F0 && !CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyObject_GC_IsFinalized(o) PyObject_GC_IsFinalized(o) +#else + #define __Pyx_PyObject_GC_IsFinalized(o) _PyGC_FINALIZED(o) +#endif +#ifndef CO_COROUTINE + #define CO_COROUTINE 0x80 +#endif +#ifndef CO_ASYNC_GENERATOR + #define CO_ASYNC_GENERATOR 0x200 +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef Py_TPFLAGS_SEQUENCE + #define Py_TPFLAGS_SEQUENCE 0 +#endif +#ifndef Py_TPFLAGS_MAPPING + #define Py_TPFLAGS_MAPPING 0 +#endif +#ifndef METH_STACKLESS + #define METH_STACKLESS 0 +#endif +#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) + #ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + #endif + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); + typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast + #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords +#endif +#if CYTHON_METH_FASTCALL + #define __Pyx_METH_FASTCALL METH_FASTCALL + #define __Pyx_PyCFunction_FastCall __Pyx_PyCFunctionFast + #define __Pyx_PyCFunction_FastCallWithKeywords __Pyx_PyCFunctionFastWithKeywords +#else + #define __Pyx_METH_FASTCALL METH_VARARGS + #define __Pyx_PyCFunction_FastCall PyCFunction + #define __Pyx_PyCFunction_FastCallWithKeywords PyCFunctionWithKeywords +#endif +#if CYTHON_VECTORCALL + #define __pyx_vectorcallfunc vectorcallfunc + #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET PY_VECTORCALL_ARGUMENTS_OFFSET + #define __Pyx_PyVectorcall_NARGS(n) PyVectorcall_NARGS((size_t)(n)) +#elif CYTHON_BACKPORT_VECTORCALL + typedef PyObject *(*__pyx_vectorcallfunc)(PyObject *callable, PyObject *const *args, + size_t nargsf, PyObject *kwnames); + #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET ((size_t)1 << (8 * sizeof(size_t) - 1)) + #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(((size_t)(n)) & ~__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET)) +#else + #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET 0 + #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(n)) +#endif +#if PY_MAJOR_VERSION >= 0x030900B1 +#define __Pyx_PyCFunction_CheckExact(func) PyCFunction_CheckExact(func) +#else +#define __Pyx_PyCFunction_CheckExact(func) PyCFunction_Check(func) +#endif +#define __Pyx_CyOrPyCFunction_Check(func) PyCFunction_Check(func) +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_CyOrPyCFunction_GET_FUNCTION(func) (((PyCFunctionObject*)(func))->m_ml->ml_meth) +#elif !CYTHON_COMPILING_IN_LIMITED_API +#define __Pyx_CyOrPyCFunction_GET_FUNCTION(func) PyCFunction_GET_FUNCTION(func) +#endif +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_CyOrPyCFunction_GET_FLAGS(func) (((PyCFunctionObject*)(func))->m_ml->ml_flags) +static CYTHON_INLINE PyObject* __Pyx_CyOrPyCFunction_GET_SELF(PyObject *func) { + return (__Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_STATIC) ? NULL : ((PyCFunctionObject*)func)->m_self; +} +#endif +static CYTHON_INLINE int __Pyx__IsSameCFunction(PyObject *func, void *cfunc) { +#if CYTHON_COMPILING_IN_LIMITED_API + return PyCFunction_Check(func) && PyCFunction_GetFunction(func) == (PyCFunction) cfunc; +#else + return PyCFunction_Check(func) && PyCFunction_GET_FUNCTION(func) == (PyCFunction) cfunc; +#endif +} +#define __Pyx_IsSameCFunction(func, cfunc) __Pyx__IsSameCFunction(func, cfunc) +#if __PYX_LIMITED_VERSION_HEX < 0x030900B1 + #define __Pyx_PyType_FromModuleAndSpec(m, s, b) ((void)m, PyType_FromSpecWithBases(s, b)) + typedef PyObject *(*__Pyx_PyCMethod)(PyObject *, PyTypeObject *, PyObject *const *, size_t, PyObject *); +#else + #define __Pyx_PyType_FromModuleAndSpec(m, s, b) PyType_FromModuleAndSpec(m, s, b) + #define __Pyx_PyCMethod PyCMethod +#endif +#ifndef METH_METHOD + #define METH_METHOD 0x200 +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_PyThreadState_Current PyThreadState_Get() +#elif !CYTHON_FAST_THREAD_STATE + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#elif PY_VERSION_HEX >= 0x030d00A1 + #define __Pyx_PyThreadState_Current PyThreadState_GetUnchecked() +#elif PY_VERSION_HEX >= 0x03060000 + #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() +#elif PY_VERSION_HEX >= 0x03000000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#else + #define __Pyx_PyThreadState_Current _PyThreadState_Current +#endif +#if CYTHON_COMPILING_IN_LIMITED_API +static CYTHON_INLINE void *__Pyx_PyModule_GetState(PyObject *op) +{ + void *result; + result = PyModule_GetState(op); + if (!result) + Py_FatalError("Couldn't find the module state"); + return result; +} +#endif +#define __Pyx_PyObject_GetSlot(obj, name, func_ctype) __Pyx_PyType_GetSlot(Py_TYPE(obj), name, func_ctype) +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_PyType_GetSlot(type, name, func_ctype) ((func_ctype) PyType_GetSlot((type), Py_##name)) +#else + #define __Pyx_PyType_GetSlot(type, name, func_ctype) ((type)->name) +#endif +#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) +#include "pythread.h" +#define Py_tss_NEEDS_INIT 0 +typedef int Py_tss_t; +static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { + *key = PyThread_create_key(); + return 0; +} +static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { + Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); + *key = Py_tss_NEEDS_INIT; + return key; +} +static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { + PyObject_Free(key); +} +static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { + return *key != Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { + PyThread_delete_key(*key); + *key = Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { + return PyThread_set_key_value(*key, value); +} +static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { + return PyThread_get_key_value(*key); +} +#endif +#if PY_MAJOR_VERSION < 3 + #if CYTHON_COMPILING_IN_PYPY + #if PYPY_VERSION_NUM < 0x07030600 + #if defined(__cplusplus) && __cplusplus >= 201402L + [[deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")]] + #elif defined(__GNUC__) || defined(__clang__) + __attribute__ ((__deprecated__("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6"))) + #elif defined(_MSC_VER) + __declspec(deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")) + #endif + static CYTHON_INLINE int PyGILState_Check(void) { + return 0; + } + #else // PYPY_VERSION_NUM < 0x07030600 + #endif // PYPY_VERSION_NUM < 0x07030600 + #else + static CYTHON_INLINE int PyGILState_Check(void) { + PyThreadState * tstate = _PyThreadState_Current; + return tstate && (tstate == PyGILState_GetThisThreadState()); + } + #endif +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030d0000 || defined(_PyDict_NewPresized) +#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) +#else +#define __Pyx_PyDict_NewPresized(n) PyDict_New() +#endif +#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX > 0x030600B4 && PY_VERSION_HEX < 0x030d0000 && CYTHON_USE_UNICODE_INTERNALS +#define __Pyx_PyDict_GetItemStrWithError(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) +static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStr(PyObject *dict, PyObject *name) { + PyObject *res = __Pyx_PyDict_GetItemStrWithError(dict, name); + if (res == NULL) PyErr_Clear(); + return res; +} +#elif PY_MAJOR_VERSION >= 3 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07020000) +#define __Pyx_PyDict_GetItemStrWithError PyDict_GetItemWithError +#define __Pyx_PyDict_GetItemStr PyDict_GetItem +#else +static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStrWithError(PyObject *dict, PyObject *name) { +#if CYTHON_COMPILING_IN_PYPY + return PyDict_GetItem(dict, name); +#else + PyDictEntry *ep; + PyDictObject *mp = (PyDictObject*) dict; + long hash = ((PyStringObject *) name)->ob_shash; + assert(hash != -1); + ep = (mp->ma_lookup)(mp, name, hash); + if (ep == NULL) { + return NULL; + } + return ep->me_value; +#endif +} +#define __Pyx_PyDict_GetItemStr PyDict_GetItem +#endif +#if CYTHON_USE_TYPE_SLOTS + #define __Pyx_PyType_GetFlags(tp) (((PyTypeObject *)tp)->tp_flags) + #define __Pyx_PyType_HasFeature(type, feature) ((__Pyx_PyType_GetFlags(type) & (feature)) != 0) + #define __Pyx_PyObject_GetIterNextFunc(obj) (Py_TYPE(obj)->tp_iternext) +#else + #define __Pyx_PyType_GetFlags(tp) (PyType_GetFlags((PyTypeObject *)tp)) + #define __Pyx_PyType_HasFeature(type, feature) PyType_HasFeature(type, feature) + #define __Pyx_PyObject_GetIterNextFunc(obj) PyIter_Next +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_SetItemOnTypeDict(tp, k, v) PyObject_GenericSetAttr((PyObject*)tp, k, v) +#else + #define __Pyx_SetItemOnTypeDict(tp, k, v) PyDict_SetItem(tp->tp_dict, k, v) +#endif +#if CYTHON_USE_TYPE_SPECS && PY_VERSION_HEX >= 0x03080000 +#define __Pyx_PyHeapTypeObject_GC_Del(obj) {\ + PyTypeObject *type = Py_TYPE((PyObject*)obj);\ + assert(__Pyx_PyType_HasFeature(type, Py_TPFLAGS_HEAPTYPE));\ + PyObject_GC_Del(obj);\ + Py_DECREF(type);\ +} +#else +#define __Pyx_PyHeapTypeObject_GC_Del(obj) PyObject_GC_Del(obj) +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GetLength(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_ReadChar(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((void)u, 1114111U) + #define __Pyx_PyUnicode_KIND(u) ((void)u, (0)) + #define __Pyx_PyUnicode_DATA(u) ((void*)u) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)k, PyUnicode_ReadChar((PyObject*)(d), i)) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GetLength(u)) +#elif PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #if PY_VERSION_HEX >= 0x030C0000 + #define __Pyx_PyUnicode_READY(op) (0) + #else + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #endif + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) ((int)PyUnicode_KIND(u)) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, (Py_UCS4) ch) + #if PY_VERSION_HEX >= 0x030C0000 + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u)) + #else + #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000 + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length)) + #else + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) + #endif + #endif +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535U : 1114111U) + #define __Pyx_PyUnicode_KIND(u) ((int)sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = (Py_UNICODE) ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #if !defined(PyUnicode_DecodeUnicodeEscape) + #define PyUnicode_DecodeUnicodeEscape(s, size, errors) PyUnicode_Decode(s, size, "unicode_escape", errors) + #endif + #if !defined(PyUnicode_Contains) || (PY_MAJOR_VERSION == 2 && PYPY_VERSION_NUM < 0x07030500) + #undef PyUnicode_Contains + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) + #endif + #if !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) + #endif + #if !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) + #endif +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#ifndef PyObject_Unicode + #define PyObject_Unicode PyObject_Str +#endif +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#if CYTHON_COMPILING_IN_CPYTHON + #define __Pyx_PySequence_ListKeepNew(obj)\ + (likely(PyList_CheckExact(obj) && Py_REFCNT(obj) == 1) ? __Pyx_NewRef(obj) : PySequence_List(obj)) +#else + #define __Pyx_PySequence_ListKeepNew(obj) PySequence_List(obj) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) __Pyx_IS_TYPE(obj, &PySet_Type) +#endif +#if PY_VERSION_HEX >= 0x030900A4 + #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size) +#else + #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size) +#endif +#if CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_PySequence_ITEM(o, i) PySequence_ITEM(o, i) + #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) + #define __Pyx_PyTuple_SET_ITEM(o, i, v) (PyTuple_SET_ITEM(o, i, v), (0)) + #define __Pyx_PyList_SET_ITEM(o, i, v) (PyList_SET_ITEM(o, i, v), (0)) + #define __Pyx_PyTuple_GET_SIZE(o) PyTuple_GET_SIZE(o) + #define __Pyx_PyList_GET_SIZE(o) PyList_GET_SIZE(o) + #define __Pyx_PySet_GET_SIZE(o) PySet_GET_SIZE(o) + #define __Pyx_PyBytes_GET_SIZE(o) PyBytes_GET_SIZE(o) + #define __Pyx_PyByteArray_GET_SIZE(o) PyByteArray_GET_SIZE(o) +#else + #define __Pyx_PySequence_ITEM(o, i) PySequence_GetItem(o, i) + #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) + #define __Pyx_PyTuple_SET_ITEM(o, i, v) PyTuple_SetItem(o, i, v) + #define __Pyx_PyList_SET_ITEM(o, i, v) PyList_SetItem(o, i, v) + #define __Pyx_PyTuple_GET_SIZE(o) PyTuple_Size(o) + #define __Pyx_PyList_GET_SIZE(o) PyList_Size(o) + #define __Pyx_PySet_GET_SIZE(o) PySet_Size(o) + #define __Pyx_PyBytes_GET_SIZE(o) PyBytes_Size(o) + #define __Pyx_PyByteArray_GET_SIZE(o) PyByteArray_Size(o) +#endif +#if PY_VERSION_HEX >= 0x030d00A1 + #define __Pyx_PyImport_AddModuleRef(name) PyImport_AddModuleRef(name) +#else + static CYTHON_INLINE PyObject *__Pyx_PyImport_AddModuleRef(const char *name) { + PyObject *module = PyImport_AddModule(name); + Py_XINCREF(module); + return module; + } +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define __Pyx_Py3Int_Check(op) PyLong_Check(op) + #define __Pyx_Py3Int_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#else + #define __Pyx_Py3Int_Check(op) (PyLong_Check(op) || PyInt_Check(op)) + #define __Pyx_Py3Int_CheckExact(op) (PyLong_CheckExact(op) || PyInt_CheckExact(op)) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsHash_t +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsSsize_t +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef __Pyx_PyAsyncMethodsStruct + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; +#endif + +#if defined(_WIN32) || defined(WIN32) || defined(MS_WINDOWS) + #if !defined(_USE_MATH_DEFINES) + #define _USE_MATH_DEFINES + #endif +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + +#define __PYX_MARK_ERR_POS(f_index, lineno) \ + { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; } +#define __PYX_ERR(f_index, lineno, Ln_error) \ + { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; } + +#ifdef CYTHON_EXTERN_C + #undef __PYX_EXTERN_C + #define __PYX_EXTERN_C CYTHON_EXTERN_C +#elif defined(__PYX_EXTERN_C) + #ifdef _MSC_VER + #pragma message ("Please do not define the '__PYX_EXTERN_C' macro externally. Use 'CYTHON_EXTERN_C' instead.") + #else + #warning Please do not define the '__PYX_EXTERN_C' macro externally. Use 'CYTHON_EXTERN_C' instead. + #endif +#else + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__monotonic_align__core +#define __PYX_HAVE_API__monotonic_align__core +/* Early includes */ +#include "pythread.h" +#include +#include +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { + return (size_t) i < (size_t) limit; +} +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) + #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s); +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +static CYTHON_INLINE PyObject* __Pyx_PyByteArray_FromString(const char*); +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyObject_AsWritableString(s) ((char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableSString(s) ((signed char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +#if CYTHON_COMPILING_IN_LIMITED_API +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const wchar_t *u) +{ + const wchar_t *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#else +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) +{ + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#endif +#define __Pyx_PyUnicode_FromOrdinal(o) PyUnicode_FromOrdinal((int)o) +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +#define __Pyx_PySequence_Tuple(obj)\ + (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #if PY_VERSION_HEX >= 0x030C00A7 + #ifndef _PyLong_SIGN_MASK + #define _PyLong_SIGN_MASK 3 + #endif + #ifndef _PyLong_NON_SIZE_BITS + #define _PyLong_NON_SIZE_BITS 3 + #endif + #define __Pyx_PyLong_Sign(x) (((PyLongObject*)x)->long_value.lv_tag & _PyLong_SIGN_MASK) + #define __Pyx_PyLong_IsNeg(x) ((__Pyx_PyLong_Sign(x) & 2) != 0) + #define __Pyx_PyLong_IsNonNeg(x) (!__Pyx_PyLong_IsNeg(x)) + #define __Pyx_PyLong_IsZero(x) (__Pyx_PyLong_Sign(x) & 1) + #define __Pyx_PyLong_IsPos(x) (__Pyx_PyLong_Sign(x) == 0) + #define __Pyx_PyLong_CompactValueUnsigned(x) (__Pyx_PyLong_Digits(x)[0]) + #define __Pyx_PyLong_DigitCount(x) ((Py_ssize_t) (((PyLongObject*)x)->long_value.lv_tag >> _PyLong_NON_SIZE_BITS)) + #define __Pyx_PyLong_SignedDigitCount(x)\ + ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * __Pyx_PyLong_DigitCount(x)) + #if defined(PyUnstable_Long_IsCompact) && defined(PyUnstable_Long_CompactValue) + #define __Pyx_PyLong_IsCompact(x) PyUnstable_Long_IsCompact((PyLongObject*) x) + #define __Pyx_PyLong_CompactValue(x) PyUnstable_Long_CompactValue((PyLongObject*) x) + #else + #define __Pyx_PyLong_IsCompact(x) (((PyLongObject*)x)->long_value.lv_tag < (2 << _PyLong_NON_SIZE_BITS)) + #define __Pyx_PyLong_CompactValue(x) ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * (Py_ssize_t) __Pyx_PyLong_Digits(x)[0]) + #endif + typedef Py_ssize_t __Pyx_compact_pylong; + typedef size_t __Pyx_compact_upylong; + #else // Py < 3.12 + #define __Pyx_PyLong_IsNeg(x) (Py_SIZE(x) < 0) + #define __Pyx_PyLong_IsNonNeg(x) (Py_SIZE(x) >= 0) + #define __Pyx_PyLong_IsZero(x) (Py_SIZE(x) == 0) + #define __Pyx_PyLong_IsPos(x) (Py_SIZE(x) > 0) + #define __Pyx_PyLong_CompactValueUnsigned(x) ((Py_SIZE(x) == 0) ? 0 : __Pyx_PyLong_Digits(x)[0]) + #define __Pyx_PyLong_DigitCount(x) __Pyx_sst_abs(Py_SIZE(x)) + #define __Pyx_PyLong_SignedDigitCount(x) Py_SIZE(x) + #define __Pyx_PyLong_IsCompact(x) (Py_SIZE(x) == 0 || Py_SIZE(x) == 1 || Py_SIZE(x) == -1) + #define __Pyx_PyLong_CompactValue(x)\ + ((Py_SIZE(x) == 0) ? (sdigit) 0 : ((Py_SIZE(x) < 0) ? -(sdigit)__Pyx_PyLong_Digits(x)[0] : (sdigit)__Pyx_PyLong_Digits(x)[0])) + typedef sdigit __Pyx_compact_pylong; + typedef digit __Pyx_compact_upylong; + #endif + #if PY_VERSION_HEX >= 0x030C00A5 + #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->long_value.ob_digit) + #else + #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->ob_digit) + #endif +#endif +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +#include +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = (char) c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#include +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ +static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } + +#if !CYTHON_USE_MODULE_STATE +static PyObject *__pyx_m = NULL; +#endif +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm = __FILE__; +static const char *__pyx_filename; + +/* #### Code section: filename_table ### */ + +static const char *__pyx_f[] = { + "core.pyx", + "", +}; +/* #### Code section: utility_code_proto_before_types ### */ +/* ForceInitThreads.proto */ +#ifndef __PYX_FORCE_INIT_THREADS + #define __PYX_FORCE_INIT_THREADS 0 +#endif + +/* NoFastGil.proto */ +#define __Pyx_PyGILState_Ensure PyGILState_Ensure +#define __Pyx_PyGILState_Release PyGILState_Release +#define __Pyx_FastGIL_Remember() +#define __Pyx_FastGIL_Forget() +#define __Pyx_FastGilFuncInit() + +/* BufferFormatStructs.proto */ +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + +/* Atomics.proto */ +#include +#ifndef CYTHON_ATOMICS + #define CYTHON_ATOMICS 1 +#endif +#define __PYX_CYTHON_ATOMICS_ENABLED() CYTHON_ATOMICS +#define __pyx_atomic_int_type int +#define __pyx_nonatomic_int_type int +#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\ + (__STDC_VERSION__ >= 201112L) &&\ + !defined(__STDC_NO_ATOMICS__)) + #include +#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\ + (__cplusplus >= 201103L) ||\ + (defined(_MSC_VER) && _MSC_VER >= 1700))) + #include +#endif +#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\ + (__STDC_VERSION__ >= 201112L) &&\ + !defined(__STDC_NO_ATOMICS__) &&\ + ATOMIC_INT_LOCK_FREE == 2) + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type atomic_int + #define __pyx_atomic_incr_aligned(value) atomic_fetch_add_explicit(value, 1, memory_order_relaxed) + #define __pyx_atomic_decr_aligned(value) atomic_fetch_sub_explicit(value, 1, memory_order_acq_rel) + #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER) + #pragma message ("Using standard C atomics") + #elif defined(__PYX_DEBUG_ATOMICS) + #warning "Using standard C atomics" + #endif +#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\ + (__cplusplus >= 201103L) ||\ +\ + (defined(_MSC_VER) && _MSC_VER >= 1700)) &&\ + ATOMIC_INT_LOCK_FREE == 2) + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type std::atomic_int + #define __pyx_atomic_incr_aligned(value) std::atomic_fetch_add_explicit(value, 1, std::memory_order_relaxed) + #define __pyx_atomic_decr_aligned(value) std::atomic_fetch_sub_explicit(value, 1, std::memory_order_acq_rel) + #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER) + #pragma message ("Using standard C++ atomics") + #elif defined(__PYX_DEBUG_ATOMICS) + #warning "Using standard C++ atomics" + #endif +#elif CYTHON_ATOMICS && (__GNUC__ >= 5 || (__GNUC__ == 4 &&\ + (__GNUC_MINOR__ > 1 ||\ + (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL__ >= 2)))) + #define __pyx_atomic_incr_aligned(value) __sync_fetch_and_add(value, 1) + #define __pyx_atomic_decr_aligned(value) __sync_fetch_and_sub(value, 1) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using GNU atomics" + #endif +#elif CYTHON_ATOMICS && defined(_MSC_VER) + #include + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type long + #undef __pyx_nonatomic_int_type + #define __pyx_nonatomic_int_type long + #pragma intrinsic (_InterlockedExchangeAdd) + #define __pyx_atomic_incr_aligned(value) _InterlockedExchangeAdd(value, 1) + #define __pyx_atomic_decr_aligned(value) _InterlockedExchangeAdd(value, -1) + #ifdef __PYX_DEBUG_ATOMICS + #pragma message ("Using MSVC atomics") + #endif +#else + #undef CYTHON_ATOMICS + #define CYTHON_ATOMICS 0 + #ifdef __PYX_DEBUG_ATOMICS + #warning "Not using atomics" + #endif +#endif +#if CYTHON_ATOMICS + #define __pyx_add_acquisition_count(memview)\ + __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview)) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview)) +#else + #define __pyx_add_acquisition_count(memview)\ + __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) +#endif + +/* MemviewSliceStruct.proto */ +struct __pyx_memoryview_obj; +typedef struct { + struct __pyx_memoryview_obj *memview; + char *data; + Py_ssize_t shape[8]; + Py_ssize_t strides[8]; + Py_ssize_t suboffsets[8]; +} __Pyx_memviewslice; +#define __Pyx_MemoryView_Len(m) (m.shape[0]) + +/* #### Code section: numeric_typedefs ### */ +/* #### Code section: complex_type_declarations ### */ +/* #### Code section: type_declarations ### */ + +/*--- Type declarations ---*/ +struct __pyx_array_obj; +struct __pyx_MemviewEnum_obj; +struct __pyx_memoryview_obj; +struct __pyx_memoryviewslice_obj; +struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each; + +/* "monotonic_align/core.pyx":7 + * @cython.boundscheck(False) + * @cython.wraparound(False) + * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil: # <<<<<<<<<<<<<< + * cdef int x + * cdef int y + */ +struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each { + int __pyx_n; + float max_neg_val; +}; + +/* "View.MemoryView":114 + * @cython.collection_type("sequence") + * @cname("__pyx_array") + * cdef class array: # <<<<<<<<<<<<<< + * + * cdef: + */ +struct __pyx_array_obj { + PyObject_HEAD + struct __pyx_vtabstruct_array *__pyx_vtab; + char *data; + Py_ssize_t len; + char *format; + int ndim; + Py_ssize_t *_shape; + Py_ssize_t *_strides; + Py_ssize_t itemsize; + PyObject *mode; + PyObject *_format; + void (*callback_free_data)(void *); + int free_data; + int dtype_is_object; +}; + + +/* "View.MemoryView":302 + * + * @cname('__pyx_MemviewEnum') + * cdef class Enum(object): # <<<<<<<<<<<<<< + * cdef object name + * def __init__(self, name): + */ +struct __pyx_MemviewEnum_obj { + PyObject_HEAD + PyObject *name; +}; + + +/* "View.MemoryView":337 + * + * @cname('__pyx_memoryview') + * cdef class memoryview: # <<<<<<<<<<<<<< + * + * cdef object obj + */ +struct __pyx_memoryview_obj { + PyObject_HEAD + struct __pyx_vtabstruct_memoryview *__pyx_vtab; + PyObject *obj; + PyObject *_size; + PyObject *_array_interface; + PyThread_type_lock lock; + __pyx_atomic_int_type acquisition_count; + Py_buffer view; + int flags; + int dtype_is_object; + __Pyx_TypeInfo *typeinfo; +}; + + +/* "View.MemoryView":952 + * @cython.collection_type("sequence") + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< + * "Internal class for passing memoryview slices to Python" + * + */ +struct __pyx_memoryviewslice_obj { + struct __pyx_memoryview_obj __pyx_base; + __Pyx_memviewslice from_slice; + PyObject *from_object; + PyObject *(*to_object_func)(char *); + int (*to_dtype_func)(char *, PyObject *); +}; + + + +/* "View.MemoryView":114 + * @cython.collection_type("sequence") + * @cname("__pyx_array") + * cdef class array: # <<<<<<<<<<<<<< + * + * cdef: + */ + +struct __pyx_vtabstruct_array { + PyObject *(*get_memview)(struct __pyx_array_obj *); +}; +static struct __pyx_vtabstruct_array *__pyx_vtabptr_array; + + +/* "View.MemoryView":337 + * + * @cname('__pyx_memoryview') + * cdef class memoryview: # <<<<<<<<<<<<<< + * + * cdef object obj + */ + +struct __pyx_vtabstruct_memoryview { + char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *); + PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *); + PyObject *(*_get_base)(struct __pyx_memoryview_obj *); +}; +static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview; + + +/* "View.MemoryView":952 + * @cython.collection_type("sequence") + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< + * "Internal class for passing memoryview slices to Python" + * + */ + +struct __pyx_vtabstruct__memoryviewslice { + struct __pyx_vtabstruct_memoryview __pyx_base; +}; +static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice; +/* #### Code section: utility_code_proto ### */ + +/* --- Runtime support code (head) --- */ +/* Refnanny.proto */ +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, Py_ssize_t); + void (*DECREF)(void*, PyObject*, Py_ssize_t); + void (*GOTREF)(void*, PyObject*, Py_ssize_t); + void (*GIVEREF)(void*, PyObject*, Py_ssize_t); + void* (*SetupContext)(const char*, Py_ssize_t, const char*); + void (*FinishContext)(void**); + } __Pyx_RefNannyAPIStruct; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); + #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; +#ifdef WITH_THREAD + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + if (acquire_gil) {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), (__LINE__), (__FILE__));\ + PyGILState_Release(__pyx_gilstate_save);\ + } else {\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), (__LINE__), (__FILE__));\ + } + #define __Pyx_RefNannyFinishContextNogil() {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __Pyx_RefNannyFinishContext();\ + PyGILState_Release(__pyx_gilstate_save);\ + } +#else + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), (__LINE__), (__FILE__)) + #define __Pyx_RefNannyFinishContextNogil() __Pyx_RefNannyFinishContext() +#endif + #define __Pyx_RefNannyFinishContextNogil() {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __Pyx_RefNannyFinishContext();\ + PyGILState_Release(__pyx_gilstate_save);\ + } + #define __Pyx_RefNannyFinishContext()\ + __Pyx_RefNanny->FinishContext(&__pyx_refnanny) + #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_XINCREF(r) do { if((r) == NULL); else {__Pyx_INCREF(r); }} while(0) + #define __Pyx_XDECREF(r) do { if((r) == NULL); else {__Pyx_DECREF(r); }} while(0) + #define __Pyx_XGOTREF(r) do { if((r) == NULL); else {__Pyx_GOTREF(r); }} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) == NULL); else {__Pyx_GIVEREF(r);}} while(0) +#else + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) + #define __Pyx_RefNannyFinishContextNogil() + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) +#endif +#define __Pyx_Py_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; Py_XDECREF(tmp);\ + } while (0) +#define __Pyx_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_XDECREF(tmp);\ + } while (0) +#define __Pyx_DECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_DECREF(tmp);\ + } while (0) +#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) +#define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyErrExceptionMatches.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); +#else +#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) +#endif + +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; +#if PY_VERSION_HEX >= 0x030C00A6 +#define __Pyx_PyErr_Occurred() (__pyx_tstate->current_exception != NULL) +#define __Pyx_PyErr_CurrentExceptionType() (__pyx_tstate->current_exception ? (PyObject*) Py_TYPE(__pyx_tstate->current_exception) : (PyObject*) NULL) +#else +#define __Pyx_PyErr_Occurred() (__pyx_tstate->curexc_type != NULL) +#define __Pyx_PyErr_CurrentExceptionType() (__pyx_tstate->curexc_type) +#endif +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#define __Pyx_PyErr_Occurred() (PyErr_Occurred() != NULL) +#define __Pyx_PyErr_CurrentExceptionType() PyErr_Occurred() +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A6 +#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) +#else +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#endif +#else +#define __Pyx_PyErr_Clear() PyErr_Clear() +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* PyObjectGetAttrStrNoError.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name); + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* TupleAndListFromArray.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyList_FromArray(PyObject *const *src, Py_ssize_t n); +static CYTHON_INLINE PyObject* __Pyx_PyTuple_FromArray(PyObject *const *src, Py_ssize_t n); +#endif + +/* IncludeStringH.proto */ +#include + +/* BytesEquals.proto */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); + +/* UnicodeEquals.proto */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); + +/* fastcall.proto */ +#if CYTHON_AVOID_BORROWED_REFS + #define __Pyx_Arg_VARARGS(args, i) PySequence_GetItem(args, i) +#elif CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_Arg_VARARGS(args, i) PyTuple_GET_ITEM(args, i) +#else + #define __Pyx_Arg_VARARGS(args, i) PyTuple_GetItem(args, i) +#endif +#if CYTHON_AVOID_BORROWED_REFS + #define __Pyx_Arg_NewRef_VARARGS(arg) __Pyx_NewRef(arg) + #define __Pyx_Arg_XDECREF_VARARGS(arg) Py_XDECREF(arg) +#else + #define __Pyx_Arg_NewRef_VARARGS(arg) arg // no-op + #define __Pyx_Arg_XDECREF_VARARGS(arg) // no-op - arg is borrowed +#endif +#define __Pyx_NumKwargs_VARARGS(kwds) PyDict_Size(kwds) +#define __Pyx_KwValues_VARARGS(args, nargs) NULL +#define __Pyx_GetKwValue_VARARGS(kw, kwvalues, s) __Pyx_PyDict_GetItemStrWithError(kw, s) +#define __Pyx_KwargsAsDict_VARARGS(kw, kwvalues) PyDict_Copy(kw) +#if CYTHON_METH_FASTCALL + #define __Pyx_Arg_FASTCALL(args, i) args[i] + #define __Pyx_NumKwargs_FASTCALL(kwds) PyTuple_GET_SIZE(kwds) + #define __Pyx_KwValues_FASTCALL(args, nargs) ((args) + (nargs)) + static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s); +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030d0000 + CYTHON_UNUSED static PyObject *__Pyx_KwargsAsDict_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues); + #else + #define __Pyx_KwargsAsDict_FASTCALL(kw, kwvalues) _PyStack_AsDict(kwvalues, kw) + #endif + #define __Pyx_Arg_NewRef_FASTCALL(arg) arg // no-op, __Pyx_Arg_FASTCALL is direct and this needs + #define __Pyx_Arg_XDECREF_FASTCALL(arg) // no-op - arg was returned from array +#else + #define __Pyx_Arg_FASTCALL __Pyx_Arg_VARARGS + #define __Pyx_NumKwargs_FASTCALL __Pyx_NumKwargs_VARARGS + #define __Pyx_KwValues_FASTCALL __Pyx_KwValues_VARARGS + #define __Pyx_GetKwValue_FASTCALL __Pyx_GetKwValue_VARARGS + #define __Pyx_KwargsAsDict_FASTCALL __Pyx_KwargsAsDict_VARARGS + #define __Pyx_Arg_NewRef_FASTCALL(arg) __Pyx_Arg_NewRef_VARARGS(arg) + #define __Pyx_Arg_XDECREF_FASTCALL(arg) __Pyx_Arg_XDECREF_VARARGS(arg) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS +#define __Pyx_ArgsSlice_VARARGS(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_VARARGS(args, start), stop - start) +#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_FASTCALL(args, start), stop - start) +#else +#define __Pyx_ArgsSlice_VARARGS(args, start, stop) PyTuple_GetSlice(args, start, stop) +#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) PyTuple_GetSlice(args, start, stop) +#endif + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject *const *kwvalues, + PyObject **argnames[], + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, + const char* function_name); + +/* ArgTypeTest.proto */ +#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ + ((likely(__Pyx_IS_TYPE(obj, type) | (none_allowed && (obj == Py_None)))) ? 1 :\ + __Pyx__ArgTypeTest(obj, type, name, exact)) +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#if !CYTHON_VECTORCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); +#endif +#define __Pyx_BUILD_ASSERT_EXPR(cond)\ + (sizeof(char [1 - 2*!(cond)]) - 1) +#ifndef Py_MEMBER_SIZE +#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) +#endif +#if !CYTHON_VECTORCALL +#if PY_VERSION_HEX >= 0x03080000 + #include "frameobject.h" +#if PY_VERSION_HEX >= 0x030b00a6 && !CYTHON_COMPILING_IN_LIMITED_API + #ifndef Py_BUILD_CORE + #define Py_BUILD_CORE 1 + #endif + #include "internal/pycore_frame.h" +#endif + #define __Pxy_PyFrame_Initialize_Offsets() + #define __Pyx_PyFrame_GetLocalsplus(frame) ((frame)->f_localsplus) +#else + static size_t __pyx_pyframe_localsplus_offset = 0; + #include "frameobject.h" + #define __Pxy_PyFrame_Initialize_Offsets()\ + ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ + (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) + #define __Pyx_PyFrame_GetLocalsplus(frame)\ + (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) +#endif +#endif +#endif + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* PyObjectCallMethO.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +/* PyObjectFastCall.proto */ +#define __Pyx_PyObject_FastCall(func, args, nargs) __Pyx_PyObject_FastCallDict(func, args, (size_t)(nargs), NULL) +static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs); + +/* RaiseUnexpectedTypeError.proto */ +static int __Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj); + +/* GCCDiagnostics.proto */ +#if !defined(__INTEL_COMPILER) && defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) +#define __Pyx_HAS_GCC_DIAGNOSTIC +#endif + +/* BuildPyUnicode.proto */ +static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, + int prepend_sign, char padding_char); + +/* CIntToPyUnicode.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char); + +/* CIntToPyUnicode.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char); + +/* JoinPyUnicode.proto */ +static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, + Py_UCS4 max_char); + +/* StrEquals.proto */ +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals +#else +#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals +#endif + +/* PyObjectFormatSimple.proto */ +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + PyObject_Format(s, f)) +#elif PY_MAJOR_VERSION < 3 + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + likely(PyString_CheckExact(s)) ? PyUnicode_FromEncodedObject(s, NULL, "strict") :\ + PyObject_Format(s, f)) +#elif CYTHON_USE_TYPE_SLOTS + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + likely(PyLong_CheckExact(s)) ? PyLong_Type.tp_repr(s) :\ + likely(PyFloat_CheckExact(s)) ? PyFloat_Type.tp_repr(s) :\ + PyObject_Format(s, f)) +#else + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + PyObject_Format(s, f)) +#endif + +CYTHON_UNUSED static int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ +/* GetAttr.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); + +/* GetItemInt.proto */ +#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ + __Pyx_GetItemInt_Generic(o, to_py_func(i)))) +#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, + int is_list, int wraparound, int boundscheck); + +/* PyObjectCallOneArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +/* ObjectGetItem.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key); +#else +#define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) +#endif + +/* KeywordStringCheck.proto */ +static int __Pyx_CheckKeywordStrings(PyObject *kw, const char* function_name, int kw_allowed); + +/* DivInt[Py_ssize_t].proto */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); + +/* UnaryNegOverflows.proto */ +#define __Pyx_UNARY_NEG_WOULD_OVERFLOW(x)\ + (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) + +/* GetAttr3.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); + +/* PyDictVersioning.proto */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +#define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) +#define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ + (version_var) = __PYX_GET_DICT_VERSION(dict);\ + (cache_var) = (value); +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ + (VAR) = __pyx_dict_cached_value;\ + } else {\ + (VAR) = __pyx_dict_cached_value = (LOOKUP);\ + __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ + }\ +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); +#else +#define __PYX_GET_DICT_VERSION(dict) (0) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); +#endif + +/* GetModuleGlobalName.proto */ +#if CYTHON_USE_DICT_VERSIONS +#define __Pyx_GetModuleGlobalName(var, name) do {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ + (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ + __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} while(0) +#define __Pyx_GetModuleGlobalNameUncached(var, name) do {\ + PY_UINT64_T __pyx_dict_version;\ + PyObject *__pyx_dict_cached_value;\ + (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} while(0) +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); +#else +#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) +#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); +#endif + +/* AssertionsEnabled.proto */ +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define __Pyx_init_assertions_enabled() (0) + #define __pyx_assertions_enabled() (1) +#elif CYTHON_COMPILING_IN_LIMITED_API || (CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030C0000) + static int __pyx_assertions_enabled_flag; + #define __pyx_assertions_enabled() (__pyx_assertions_enabled_flag) + static int __Pyx_init_assertions_enabled(void) { + PyObject *builtins, *debug, *debug_str; + int flag; + builtins = PyEval_GetBuiltins(); + if (!builtins) goto bad; + debug_str = PyUnicode_FromStringAndSize("__debug__", 9); + if (!debug_str) goto bad; + debug = PyObject_GetItem(builtins, debug_str); + Py_DECREF(debug_str); + if (!debug) goto bad; + flag = PyObject_IsTrue(debug); + Py_DECREF(debug); + if (flag == -1) goto bad; + __pyx_assertions_enabled_flag = flag; + return 0; + bad: + __pyx_assertions_enabled_flag = 1; + return -1; + } +#else + #define __Pyx_init_assertions_enabled() (0) + #define __pyx_assertions_enabled() (!Py_OptimizeFlag) +#endif + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +/* GetTopmostException.proto */ +#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE +static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); +#endif + +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* SwapException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* ImportDottedModule.proto */ +static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple); +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple); +#endif + +/* FastTypeChecks.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) +#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2) +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); +#else +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_TypeCheck2(obj, type1, type2) (PyObject_TypeCheck(obj, (PyTypeObject *)type1) || PyObject_TypeCheck(obj, (PyTypeObject *)type2)) +#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) +#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) +#endif +#define __Pyx_PyErr_ExceptionMatches2(err1, err2) __Pyx_PyErr_GivenExceptionMatches2(__Pyx_PyErr_CurrentExceptionType(), err1, err2) +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) + +CYTHON_UNUSED static int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +/* ListCompAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len)) { + Py_INCREF(x); + #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030d0000 + L->ob_item[len] = x; + #else + PyList_SET_ITEM(list, len, x); + #endif + __Pyx_SET_SIZE(list, len + 1); + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) +#endif + +/* PySequenceMultiply.proto */ +#define __Pyx_PySequence_Multiply_Left(mul, seq) __Pyx_PySequence_Multiply(seq, mul) +static CYTHON_INLINE PyObject* __Pyx_PySequence_Multiply(PyObject *seq, Py_ssize_t mul); + +/* SetItemInt.proto */ +#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\ + __Pyx_SetItemInt_Generic(o, to_py_func(i), v))) +static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v); +static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, + int is_list, int wraparound, int boundscheck); + +/* RaiseUnboundLocalError.proto */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); + +/* DivInt[long].proto */ +static CYTHON_INLINE long __Pyx_div_long(long, long); + +/* PySequenceContains.proto */ +static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) { + int result = PySequence_Contains(seq, item); + return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); +} + +/* ImportFrom.proto */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); + +/* HasAttr.proto */ +#if __PYX_LIMITED_VERSION_HEX >= 0x030d00A1 +#define __Pyx_HasAttr(o, n) PyObject_HasAttrWithError(o, n) +#else +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); +#endif + +/* ErrOccurredWithGIL.proto */ +static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void); + +/* PyObject_GenericGetAttrNoDict.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr +#endif + +/* PyObject_GenericGetAttr.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr +#endif + +/* IncludeStructmemberH.proto */ +#include + +/* FixUpExtensionType.proto */ +#if CYTHON_USE_TYPE_SPECS +static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type); +#endif + +/* PyObjectCallNoArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); + +/* PyObjectGetMethod.proto */ +static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method); + +/* PyObjectCallMethod0.proto */ +static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name); + +/* ValidateBasesTuple.proto */ +#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS +static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases); +#endif + +/* PyType_Ready.proto */ +CYTHON_UNUSED static int __Pyx_PyType_Ready(PyTypeObject *t); + +/* SetVTable.proto */ +static int __Pyx_SetVtable(PyTypeObject* typeptr , void* vtable); + +/* GetVTable.proto */ +static void* __Pyx_GetVtable(PyTypeObject *type); + +/* MergeVTables.proto */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_MergeVtables(PyTypeObject *type); +#endif + +/* SetupReduce.proto */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_setup_reduce(PyObject* type_obj); +#endif + +/* FetchSharedCythonModule.proto */ +static PyObject *__Pyx_FetchSharedCythonABIModule(void); + +/* FetchCommonType.proto */ +#if !CYTHON_USE_TYPE_SPECS +static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type); +#else +static PyTypeObject* __Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases); +#endif + +/* PyMethodNew.proto */ +#if CYTHON_COMPILING_IN_LIMITED_API +static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) { + PyObject *typesModule=NULL, *methodType=NULL, *result=NULL; + CYTHON_UNUSED_VAR(typ); + if (!self) + return __Pyx_NewRef(func); + typesModule = PyImport_ImportModule("types"); + if (!typesModule) return NULL; + methodType = PyObject_GetAttrString(typesModule, "MethodType"); + Py_DECREF(typesModule); + if (!methodType) return NULL; + result = PyObject_CallFunctionObjArgs(methodType, func, self, NULL); + Py_DECREF(methodType); + return result; +} +#elif PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) { + CYTHON_UNUSED_VAR(typ); + if (!self) + return __Pyx_NewRef(func); + return PyMethod_New(func, self); +} +#else + #define __Pyx_PyMethod_New PyMethod_New +#endif + +/* PyVectorcallFastCallDict.proto */ +#if CYTHON_METH_FASTCALL +static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw); +#endif + +/* CythonFunctionShared.proto */ +#define __Pyx_CyFunction_USED +#define __Pyx_CYFUNCTION_STATICMETHOD 0x01 +#define __Pyx_CYFUNCTION_CLASSMETHOD 0x02 +#define __Pyx_CYFUNCTION_CCLASS 0x04 +#define __Pyx_CYFUNCTION_COROUTINE 0x08 +#define __Pyx_CyFunction_GetClosure(f)\ + (((__pyx_CyFunctionObject *) (f))->func_closure) +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_CyFunction_GetClassObj(f)\ + (((__pyx_CyFunctionObject *) (f))->func_classobj) +#else + #define __Pyx_CyFunction_GetClassObj(f)\ + ((PyObject*) ((PyCMethodObject *) (f))->mm_class) +#endif +#define __Pyx_CyFunction_SetClassObj(f, classobj)\ + __Pyx__CyFunction_SetClassObj((__pyx_CyFunctionObject *) (f), (classobj)) +#define __Pyx_CyFunction_Defaults(type, f)\ + ((type *)(((__pyx_CyFunctionObject *) (f))->defaults)) +#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\ + ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g) +typedef struct { +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject_HEAD + PyObject *func; +#elif PY_VERSION_HEX < 0x030900B1 + PyCFunctionObject func; +#else + PyCMethodObject func; +#endif +#if CYTHON_BACKPORT_VECTORCALL + __pyx_vectorcallfunc func_vectorcall; +#endif +#if PY_VERSION_HEX < 0x030500A0 || CYTHON_COMPILING_IN_LIMITED_API + PyObject *func_weakreflist; +#endif + PyObject *func_dict; + PyObject *func_name; + PyObject *func_qualname; + PyObject *func_doc; + PyObject *func_globals; + PyObject *func_code; + PyObject *func_closure; +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + PyObject *func_classobj; +#endif + void *defaults; + int defaults_pyobjects; + size_t defaults_size; // used by FusedFunction for copying defaults + int flags; + PyObject *defaults_tuple; + PyObject *defaults_kwdict; + PyObject *(*defaults_getter)(PyObject *); + PyObject *func_annotations; + PyObject *func_is_coroutine; +} __pyx_CyFunctionObject; +#undef __Pyx_CyOrPyCFunction_Check +#define __Pyx_CyFunction_Check(obj) __Pyx_TypeCheck(obj, __pyx_CyFunctionType) +#define __Pyx_CyOrPyCFunction_Check(obj) __Pyx_TypeCheck2(obj, __pyx_CyFunctionType, &PyCFunction_Type) +#define __Pyx_CyFunction_CheckExact(obj) __Pyx_IS_TYPE(obj, __pyx_CyFunctionType) +static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void *cfunc); +#undef __Pyx_IsSameCFunction +#define __Pyx_IsSameCFunction(func, cfunc) __Pyx__IsSameCyOrCFunction(func, cfunc) +static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml, + int flags, PyObject* qualname, + PyObject *closure, + PyObject *module, PyObject *globals, + PyObject* code); +static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj); +static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m, + size_t size, + int pyobjects); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m, + PyObject *tuple); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m, + PyObject *dict); +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m, + PyObject *dict); +static int __pyx_CyFunction_init(PyObject *module); +#if CYTHON_METH_FASTCALL +static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +#if CYTHON_BACKPORT_VECTORCALL +#define __Pyx_CyFunction_func_vectorcall(f) (((__pyx_CyFunctionObject*)f)->func_vectorcall) +#else +#define __Pyx_CyFunction_func_vectorcall(f) (((PyCFunctionObject*)f)->vectorcall) +#endif +#endif + +/* CythonFunction.proto */ +static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, + int flags, PyObject* qualname, + PyObject *closure, + PyObject *module, PyObject *globals, + PyObject* code); + +/* CLineInTraceback.proto */ +#ifdef CYTHON_CLINE_IN_TRACEBACK +#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) +#else +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); +#endif + +/* CodeObjectCache.proto */ +#if !CYTHON_COMPILING_IN_LIMITED_API +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); +#endif + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +/* BufferStructDeclare.proto */ +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +/* MemviewSliceIsContig.proto */ +static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); + +/* OverlappingSlices.proto */ +static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize); + +/* IsLittleEndian.proto */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); + +/* BufferFormatCheck.proto */ +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type); + +/* TypeInfoCompare.proto */ +static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); + +/* MemviewSliceValidateAndInit.proto */ +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag); + +/* MemviewSliceCopyTemplate.proto */ +static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object); + +/* MemviewSliceInit.proto */ +#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d +#define __Pyx_MEMVIEW_DIRECT 1 +#define __Pyx_MEMVIEW_PTR 2 +#define __Pyx_MEMVIEW_FULL 4 +#define __Pyx_MEMVIEW_CONTIG 8 +#define __Pyx_MEMVIEW_STRIDED 16 +#define __Pyx_MEMVIEW_FOLLOW 32 +#define __Pyx_IS_C_CONTIG 1 +#define __Pyx_IS_F_CONTIG 2 +static int __Pyx_init_memviewslice( + struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference); +static CYTHON_INLINE int __pyx_add_acquisition_count_locked( + __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock); +static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( + __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock); +#define __pyx_get_slice_count_pointer(memview) (&memview->acquisition_count) +#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) +#define __PYX_XCLEAR_MEMVIEW(slice, have_gil) __Pyx_XCLEAR_MEMVIEW(slice, have_gil, __LINE__) +static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); +static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *, int, int); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); + +/* FormatTypeName.proto */ +#if CYTHON_COMPILING_IN_LIMITED_API +typedef PyObject *__Pyx_TypeName; +#define __Pyx_FMT_TYPENAME "%U" +static __Pyx_TypeName __Pyx_PyType_GetName(PyTypeObject* tp); +#define __Pyx_DECREF_TypeName(obj) Py_XDECREF(obj) +#else +typedef const char *__Pyx_TypeName; +#define __Pyx_FMT_TYPENAME "%.200s" +#define __Pyx_PyType_GetName(tp) ((tp)->tp_name) +#define __Pyx_DECREF_TypeName(obj) +#endif + +/* CheckBinaryVersion.proto */ +static unsigned long __Pyx_get_runtime_version(void); +static int __Pyx_check_binary_version(unsigned long ct_version, unsigned long rt_version, int allow_newer); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + +/* #### Code section: module_declarations ### */ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ +static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ +static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview__get_base(struct __pyx_memoryview_obj *__pyx_v_self); /* proto*/ +static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryviewslice__get_base(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto*/ + +/* Module declarations from "cython.view" */ + +/* Module declarations from "cython.dataclasses" */ + +/* Module declarations from "cython" */ + +/* Module declarations from "monotonic_align.core" */ +static PyObject *__pyx_collections_abc_Sequence = 0; +static PyObject *generic = 0; +static PyObject *strided = 0; +static PyObject *indirect = 0; +static PyObject *contiguous = 0; +static PyObject *indirect_contiguous = 0; +static int __pyx_memoryview_thread_locks_used; +static PyThread_type_lock __pyx_memoryview_thread_locks[8]; +static void __pyx_f_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice, __Pyx_memviewslice, int, int, struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each *__pyx_optional_args); /*proto*/ +static void __pyx_f_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/ +static int __pyx_array_allocate_buffer(struct __pyx_array_obj *); /*proto*/ +static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ +static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ +static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ +static PyObject *_unellipsify(PyObject *, int); /*proto*/ +static int assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ +static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ +static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ +static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ +static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ +static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ +static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ +static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ +static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ +static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memoryview_err_dim(PyObject *, PyObject *, int); /*proto*/ +static int __pyx_memoryview_err(PyObject *, PyObject *); /*proto*/ +static int __pyx_memoryview_err_no_memory(void); /*proto*/ +static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ +static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ +static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ +/* #### Code section: typeinfo ### */ +static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, __PYX_IS_UNSIGNED(int) ? 'U' : 'I', __PYX_IS_UNSIGNED(int), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 }; +/* #### Code section: before_global_var ### */ +#define __Pyx_MODULE_NAME "monotonic_align.core" +extern int __pyx_module_is_main_monotonic_align__core; +int __pyx_module_is_main_monotonic_align__core = 0; + +/* Implementation of "monotonic_align.core" */ +/* #### Code section: global_var ### */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin___import__; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_MemoryError; +static PyObject *__pyx_builtin_enumerate; +static PyObject *__pyx_builtin_TypeError; +static PyObject *__pyx_builtin_AssertionError; +static PyObject *__pyx_builtin_Ellipsis; +static PyObject *__pyx_builtin_id; +static PyObject *__pyx_builtin_IndexError; +/* #### Code section: string_decls ### */ +static const char __pyx_k_[] = ": "; +static const char __pyx_k_O[] = "O"; +static const char __pyx_k_c[] = "c"; +static const char __pyx_k__2[] = "."; +static const char __pyx_k__3[] = "*"; +static const char __pyx_k__6[] = "'"; +static const char __pyx_k__7[] = ")"; +static const char __pyx_k_gc[] = "gc"; +static const char __pyx_k_id[] = "id"; +static const char __pyx_k__23[] = "?"; +static const char __pyx_k_abc[] = "abc"; +static const char __pyx_k_and[] = " and "; +static const char __pyx_k_got[] = " (got "; +static const char __pyx_k_new[] = "__new__"; +static const char __pyx_k_obj[] = "obj"; +static const char __pyx_k_sys[] = "sys"; +static const char __pyx_k_base[] = "base"; +static const char __pyx_k_dict[] = "__dict__"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_mode[] = "mode"; +static const char __pyx_k_name[] = "name"; +static const char __pyx_k_ndim[] = "ndim"; +static const char __pyx_k_pack[] = "pack"; +static const char __pyx_k_size[] = "size"; +static const char __pyx_k_spec[] = "__spec__"; +static const char __pyx_k_step[] = "step"; +static const char __pyx_k_stop[] = "stop"; +static const char __pyx_k_t_xs[] = "t_xs"; +static const char __pyx_k_t_ys[] = "t_ys"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_ASCII[] = "ASCII"; +static const char __pyx_k_class[] = "__class__"; +static const char __pyx_k_count[] = "count"; +static const char __pyx_k_error[] = "error"; +static const char __pyx_k_flags[] = "flags"; +static const char __pyx_k_index[] = "index"; +static const char __pyx_k_paths[] = "paths"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_shape[] = "shape"; +static const char __pyx_k_start[] = "start"; +static const char __pyx_k_enable[] = "enable"; +static const char __pyx_k_encode[] = "encode"; +static const char __pyx_k_format[] = "format"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_name_2[] = "__name__"; +static const char __pyx_k_pickle[] = "pickle"; +static const char __pyx_k_reduce[] = "__reduce__"; +static const char __pyx_k_struct[] = "struct"; +static const char __pyx_k_unpack[] = "unpack"; +static const char __pyx_k_update[] = "update"; +static const char __pyx_k_values[] = "values"; +static const char __pyx_k_disable[] = "disable"; +static const char __pyx_k_fortran[] = "fortran"; +static const char __pyx_k_memview[] = "memview"; +static const char __pyx_k_Ellipsis[] = "Ellipsis"; +static const char __pyx_k_Sequence[] = "Sequence"; +static const char __pyx_k_core_pyx[] = "core.pyx"; +static const char __pyx_k_getstate[] = "__getstate__"; +static const char __pyx_k_itemsize[] = "itemsize"; +static const char __pyx_k_pyx_type[] = "__pyx_type"; +static const char __pyx_k_register[] = "register"; +static const char __pyx_k_setstate[] = "__setstate__"; +static const char __pyx_k_TypeError[] = "TypeError"; +static const char __pyx_k_enumerate[] = "enumerate"; +static const char __pyx_k_isenabled[] = "isenabled"; +static const char __pyx_k_pyx_state[] = "__pyx_state"; +static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; +static const char __pyx_k_IndexError[] = "IndexError"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_pyx_result[] = "__pyx_result"; +static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; +static const char __pyx_k_MemoryError[] = "MemoryError"; +static const char __pyx_k_PickleError[] = "PickleError"; +static const char __pyx_k_collections[] = "collections"; +static const char __pyx_k_initializing[] = "_initializing"; +static const char __pyx_k_is_coroutine[] = "_is_coroutine"; +static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; +static const char __pyx_k_stringsource[] = ""; +static const char __pyx_k_version_info[] = "version_info"; +static const char __pyx_k_class_getitem[] = "__class_getitem__"; +static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; +static const char __pyx_k_AssertionError[] = "AssertionError"; +static const char __pyx_k_maximum_path_c[] = "maximum_path_c"; +static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; +static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; +static const char __pyx_k_collections_abc[] = "collections.abc"; +static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; +static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; +static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; +static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; +static const char __pyx_k_asyncio_coroutines[] = "asyncio.coroutines"; +static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; +static const char __pyx_k_strided_and_direct[] = ""; +static const char __pyx_k_monotonic_align_core[] = "monotonic_align.core"; +static const char __pyx_k_strided_and_indirect[] = ""; +static const char __pyx_k_Invalid_shape_in_axis[] = "Invalid shape in axis "; +static const char __pyx_k_contiguous_and_direct[] = ""; +static const char __pyx_k_Cannot_index_with_type[] = "Cannot index with type '"; +static const char __pyx_k_MemoryView_of_r_object[] = ""; +static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; +static const char __pyx_k_contiguous_and_indirect[] = ""; +static const char __pyx_k_Dimension_d_is_not_direct[] = "Dimension %d is not direct"; +static const char __pyx_k_Index_out_of_bounds_axis_d[] = "Index out of bounds (axis %d)"; +static const char __pyx_k_Step_may_not_be_zero_axis_d[] = "Step may not be zero (axis %d)"; +static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; +static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; +static const char __pyx_k_strided_and_direct_or_indirect[] = ""; +static const char __pyx_k_All_dimensions_preceding_dimensi[] = "All dimensions preceding dimension %d must be indexed and not sliced"; +static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; +static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; +static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; +static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; +static const char __pyx_k_Cannot_transpose_memoryview_with[] = "Cannot transpose memoryview with indirect dimensions"; +static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; +static const char __pyx_k_Incompatible_checksums_0x_x_vs_0[] = "Incompatible checksums (0x%x vs (0x82a3537, 0x6ae9995, 0xb068931) = (name))"; +static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; +static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got "; +static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis "; +static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; +static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension "; +static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; +static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; +/* #### Code section: decls ### */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ +static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ +static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ +static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */ +static PyObject *__pyx_pf_15monotonic_align_4core_maximum_path_c(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_paths, __Pyx_memviewslice __pyx_v_values, __Pyx_memviewslice __pyx_v_t_ys, __Pyx_memviewslice __pyx_v_t_xs); /* proto */ +static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +/* #### Code section: late_includes ### */ +/* #### Code section: module_state ### */ +typedef struct { + PyObject *__pyx_d; + PyObject *__pyx_b; + PyObject *__pyx_cython_runtime; + PyObject *__pyx_empty_tuple; + PyObject *__pyx_empty_bytes; + PyObject *__pyx_empty_unicode; + #ifdef __Pyx_CyFunction_USED + PyTypeObject *__pyx_CyFunctionType; + #endif + #ifdef __Pyx_FusedFunction_USED + PyTypeObject *__pyx_FusedFunctionType; + #endif + #ifdef __Pyx_Generator_USED + PyTypeObject *__pyx_GeneratorType; + #endif + #ifdef __Pyx_IterableCoroutine_USED + PyTypeObject *__pyx_IterableCoroutineType; + #endif + #ifdef __Pyx_Coroutine_USED + PyTypeObject *__pyx_CoroutineAwaitType; + #endif + #ifdef __Pyx_Coroutine_USED + PyTypeObject *__pyx_CoroutineType; + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + PyObject *__pyx_type___pyx_array; + PyObject *__pyx_type___pyx_MemviewEnum; + PyObject *__pyx_type___pyx_memoryview; + PyObject *__pyx_type___pyx_memoryviewslice; + #endif + PyTypeObject *__pyx_array_type; + PyTypeObject *__pyx_MemviewEnum_type; + PyTypeObject *__pyx_memoryview_type; + PyTypeObject *__pyx_memoryviewslice_type; + PyObject *__pyx_kp_u_; + PyObject *__pyx_n_s_ASCII; + PyObject *__pyx_kp_s_All_dimensions_preceding_dimensi; + PyObject *__pyx_n_s_AssertionError; + PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; + PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; + PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor; + PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; + PyObject *__pyx_kp_u_Cannot_index_with_type; + PyObject *__pyx_kp_s_Cannot_transpose_memoryview_with; + PyObject *__pyx_kp_s_Dimension_d_is_not_direct; + PyObject *__pyx_n_s_Ellipsis; + PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; + PyObject *__pyx_kp_s_Incompatible_checksums_0x_x_vs_0; + PyObject *__pyx_n_s_IndexError; + PyObject *__pyx_kp_s_Index_out_of_bounds_axis_d; + PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; + PyObject *__pyx_kp_u_Invalid_mode_expected_c_or_fortr; + PyObject *__pyx_kp_u_Invalid_shape_in_axis; + PyObject *__pyx_n_s_MemoryError; + PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; + PyObject *__pyx_kp_s_MemoryView_of_r_object; + PyObject *__pyx_n_b_O; + PyObject *__pyx_kp_u_Out_of_bounds_on_buffer_access_a; + PyObject *__pyx_n_s_PickleError; + PyObject *__pyx_n_s_Sequence; + PyObject *__pyx_kp_s_Step_may_not_be_zero_axis_d; + PyObject *__pyx_n_s_TypeError; + PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; + PyObject *__pyx_n_s_ValueError; + PyObject *__pyx_n_s_View_MemoryView; + PyObject *__pyx_kp_u__2; + PyObject *__pyx_n_s__23; + PyObject *__pyx_n_s__3; + PyObject *__pyx_kp_u__6; + PyObject *__pyx_kp_u__7; + PyObject *__pyx_n_s_abc; + PyObject *__pyx_n_s_allocate_buffer; + PyObject *__pyx_kp_u_and; + PyObject *__pyx_n_s_asyncio_coroutines; + PyObject *__pyx_n_s_base; + PyObject *__pyx_n_s_c; + PyObject *__pyx_n_u_c; + PyObject *__pyx_n_s_class; + PyObject *__pyx_n_s_class_getitem; + PyObject *__pyx_n_s_cline_in_traceback; + PyObject *__pyx_n_s_collections; + PyObject *__pyx_kp_s_collections_abc; + PyObject *__pyx_kp_s_contiguous_and_direct; + PyObject *__pyx_kp_s_contiguous_and_indirect; + PyObject *__pyx_kp_s_core_pyx; + PyObject *__pyx_n_s_count; + PyObject *__pyx_n_s_dict; + PyObject *__pyx_kp_u_disable; + PyObject *__pyx_n_s_dtype_is_object; + PyObject *__pyx_kp_u_enable; + PyObject *__pyx_n_s_encode; + PyObject *__pyx_n_s_enumerate; + PyObject *__pyx_n_s_error; + PyObject *__pyx_n_s_flags; + PyObject *__pyx_n_s_format; + PyObject *__pyx_n_s_fortran; + PyObject *__pyx_n_u_fortran; + PyObject *__pyx_kp_u_gc; + PyObject *__pyx_n_s_getstate; + PyObject *__pyx_kp_u_got; + PyObject *__pyx_kp_u_got_differing_extents_in_dimensi; + PyObject *__pyx_n_s_id; + PyObject *__pyx_n_s_import; + PyObject *__pyx_n_s_index; + PyObject *__pyx_n_s_initializing; + PyObject *__pyx_n_s_is_coroutine; + PyObject *__pyx_kp_u_isenabled; + PyObject *__pyx_n_s_itemsize; + PyObject *__pyx_kp_s_itemsize_0_for_cython_array; + PyObject *__pyx_n_s_main; + PyObject *__pyx_n_s_maximum_path_c; + PyObject *__pyx_n_s_memview; + PyObject *__pyx_n_s_mode; + PyObject *__pyx_n_s_monotonic_align_core; + PyObject *__pyx_n_s_name; + PyObject *__pyx_n_s_name_2; + PyObject *__pyx_n_s_ndim; + PyObject *__pyx_n_s_new; + PyObject *__pyx_kp_s_no_default___reduce___due_to_non; + PyObject *__pyx_n_s_obj; + PyObject *__pyx_n_s_pack; + PyObject *__pyx_n_s_paths; + PyObject *__pyx_n_s_pickle; + PyObject *__pyx_n_s_pyx_PickleError; + PyObject *__pyx_n_s_pyx_checksum; + PyObject *__pyx_n_s_pyx_result; + PyObject *__pyx_n_s_pyx_state; + PyObject *__pyx_n_s_pyx_type; + PyObject *__pyx_n_s_pyx_unpickle_Enum; + PyObject *__pyx_n_s_pyx_vtable; + PyObject *__pyx_n_s_range; + PyObject *__pyx_n_s_reduce; + PyObject *__pyx_n_s_reduce_cython; + PyObject *__pyx_n_s_reduce_ex; + PyObject *__pyx_n_s_register; + PyObject *__pyx_n_s_setstate; + PyObject *__pyx_n_s_setstate_cython; + PyObject *__pyx_n_s_shape; + PyObject *__pyx_n_s_size; + PyObject *__pyx_n_s_spec; + PyObject *__pyx_n_s_start; + PyObject *__pyx_n_s_step; + PyObject *__pyx_n_s_stop; + PyObject *__pyx_kp_s_strided_and_direct; + PyObject *__pyx_kp_s_strided_and_direct_or_indirect; + PyObject *__pyx_kp_s_strided_and_indirect; + PyObject *__pyx_kp_s_stringsource; + PyObject *__pyx_n_s_struct; + PyObject *__pyx_n_s_sys; + PyObject *__pyx_n_s_t_xs; + PyObject *__pyx_n_s_t_ys; + PyObject *__pyx_n_s_test; + PyObject *__pyx_kp_s_unable_to_allocate_array_data; + PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; + PyObject *__pyx_n_s_unpack; + PyObject *__pyx_n_s_update; + PyObject *__pyx_n_s_values; + PyObject *__pyx_n_s_version_info; + PyObject *__pyx_int_0; + PyObject *__pyx_int_1; + PyObject *__pyx_int_3; + PyObject *__pyx_int_112105877; + PyObject *__pyx_int_136983863; + PyObject *__pyx_int_184977713; + PyObject *__pyx_int_neg_1; + float __pyx_k__9; + PyObject *__pyx_slice__5; + PyObject *__pyx_tuple__4; + PyObject *__pyx_tuple__8; + PyObject *__pyx_tuple__10; + PyObject *__pyx_tuple__11; + PyObject *__pyx_tuple__12; + PyObject *__pyx_tuple__13; + PyObject *__pyx_tuple__14; + PyObject *__pyx_tuple__15; + PyObject *__pyx_tuple__16; + PyObject *__pyx_tuple__17; + PyObject *__pyx_tuple__18; + PyObject *__pyx_tuple__19; + PyObject *__pyx_tuple__21; + PyObject *__pyx_codeobj__20; + PyObject *__pyx_codeobj__22; +} __pyx_mstate; + +#if CYTHON_USE_MODULE_STATE +#ifdef __cplusplus +namespace { + extern struct PyModuleDef __pyx_moduledef; +} /* anonymous namespace */ +#else +static struct PyModuleDef __pyx_moduledef; +#endif + +#define __pyx_mstate(o) ((__pyx_mstate *)__Pyx_PyModule_GetState(o)) + +#define __pyx_mstate_global (__pyx_mstate(PyState_FindModule(&__pyx_moduledef))) + +#define __pyx_m (PyState_FindModule(&__pyx_moduledef)) +#else +static __pyx_mstate __pyx_mstate_global_static = +#ifdef __cplusplus + {}; +#else + {0}; +#endif +static __pyx_mstate *__pyx_mstate_global = &__pyx_mstate_global_static; +#endif +/* #### Code section: module_state_clear ### */ +#if CYTHON_USE_MODULE_STATE +static int __pyx_m_clear(PyObject *m) { + __pyx_mstate *clear_module_state = __pyx_mstate(m); + if (!clear_module_state) return 0; + Py_CLEAR(clear_module_state->__pyx_d); + Py_CLEAR(clear_module_state->__pyx_b); + Py_CLEAR(clear_module_state->__pyx_cython_runtime); + Py_CLEAR(clear_module_state->__pyx_empty_tuple); + Py_CLEAR(clear_module_state->__pyx_empty_bytes); + Py_CLEAR(clear_module_state->__pyx_empty_unicode); + #ifdef __Pyx_CyFunction_USED + Py_CLEAR(clear_module_state->__pyx_CyFunctionType); + #endif + #ifdef __Pyx_FusedFunction_USED + Py_CLEAR(clear_module_state->__pyx_FusedFunctionType); + #endif + Py_CLEAR(clear_module_state->__pyx_array_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_array); + Py_CLEAR(clear_module_state->__pyx_MemviewEnum_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_MemviewEnum); + Py_CLEAR(clear_module_state->__pyx_memoryview_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_memoryview); + Py_CLEAR(clear_module_state->__pyx_memoryviewslice_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_memoryviewslice); + Py_CLEAR(clear_module_state->__pyx_kp_u_); + Py_CLEAR(clear_module_state->__pyx_n_s_ASCII); + Py_CLEAR(clear_module_state->__pyx_kp_s_All_dimensions_preceding_dimensi); + Py_CLEAR(clear_module_state->__pyx_n_s_AssertionError); + Py_CLEAR(clear_module_state->__pyx_kp_s_Buffer_view_does_not_expose_stri); + Py_CLEAR(clear_module_state->__pyx_kp_s_Can_only_create_a_buffer_that_is); + Py_CLEAR(clear_module_state->__pyx_kp_s_Cannot_assign_to_read_only_memor); + Py_CLEAR(clear_module_state->__pyx_kp_s_Cannot_create_writable_memory_vi); + Py_CLEAR(clear_module_state->__pyx_kp_u_Cannot_index_with_type); + Py_CLEAR(clear_module_state->__pyx_kp_s_Cannot_transpose_memoryview_with); + Py_CLEAR(clear_module_state->__pyx_kp_s_Dimension_d_is_not_direct); + Py_CLEAR(clear_module_state->__pyx_n_s_Ellipsis); + Py_CLEAR(clear_module_state->__pyx_kp_s_Empty_shape_tuple_for_cython_arr); + Py_CLEAR(clear_module_state->__pyx_kp_s_Incompatible_checksums_0x_x_vs_0); + Py_CLEAR(clear_module_state->__pyx_n_s_IndexError); + Py_CLEAR(clear_module_state->__pyx_kp_s_Index_out_of_bounds_axis_d); + Py_CLEAR(clear_module_state->__pyx_kp_s_Indirect_dimensions_not_supporte); + Py_CLEAR(clear_module_state->__pyx_kp_u_Invalid_mode_expected_c_or_fortr); + Py_CLEAR(clear_module_state->__pyx_kp_u_Invalid_shape_in_axis); + Py_CLEAR(clear_module_state->__pyx_n_s_MemoryError); + Py_CLEAR(clear_module_state->__pyx_kp_s_MemoryView_of_r_at_0x_x); + Py_CLEAR(clear_module_state->__pyx_kp_s_MemoryView_of_r_object); + Py_CLEAR(clear_module_state->__pyx_n_b_O); + Py_CLEAR(clear_module_state->__pyx_kp_u_Out_of_bounds_on_buffer_access_a); + Py_CLEAR(clear_module_state->__pyx_n_s_PickleError); + Py_CLEAR(clear_module_state->__pyx_n_s_Sequence); + Py_CLEAR(clear_module_state->__pyx_kp_s_Step_may_not_be_zero_axis_d); + Py_CLEAR(clear_module_state->__pyx_n_s_TypeError); + Py_CLEAR(clear_module_state->__pyx_kp_s_Unable_to_convert_item_to_object); + Py_CLEAR(clear_module_state->__pyx_n_s_ValueError); + Py_CLEAR(clear_module_state->__pyx_n_s_View_MemoryView); + Py_CLEAR(clear_module_state->__pyx_kp_u__2); + Py_CLEAR(clear_module_state->__pyx_n_s__23); + Py_CLEAR(clear_module_state->__pyx_n_s__3); + Py_CLEAR(clear_module_state->__pyx_kp_u__6); + Py_CLEAR(clear_module_state->__pyx_kp_u__7); + Py_CLEAR(clear_module_state->__pyx_n_s_abc); + Py_CLEAR(clear_module_state->__pyx_n_s_allocate_buffer); + Py_CLEAR(clear_module_state->__pyx_kp_u_and); + Py_CLEAR(clear_module_state->__pyx_n_s_asyncio_coroutines); + Py_CLEAR(clear_module_state->__pyx_n_s_base); + Py_CLEAR(clear_module_state->__pyx_n_s_c); + Py_CLEAR(clear_module_state->__pyx_n_u_c); + Py_CLEAR(clear_module_state->__pyx_n_s_class); + Py_CLEAR(clear_module_state->__pyx_n_s_class_getitem); + Py_CLEAR(clear_module_state->__pyx_n_s_cline_in_traceback); + Py_CLEAR(clear_module_state->__pyx_n_s_collections); + Py_CLEAR(clear_module_state->__pyx_kp_s_collections_abc); + Py_CLEAR(clear_module_state->__pyx_kp_s_contiguous_and_direct); + Py_CLEAR(clear_module_state->__pyx_kp_s_contiguous_and_indirect); + Py_CLEAR(clear_module_state->__pyx_kp_s_core_pyx); + Py_CLEAR(clear_module_state->__pyx_n_s_count); + Py_CLEAR(clear_module_state->__pyx_n_s_dict); + Py_CLEAR(clear_module_state->__pyx_kp_u_disable); + Py_CLEAR(clear_module_state->__pyx_n_s_dtype_is_object); + Py_CLEAR(clear_module_state->__pyx_kp_u_enable); + Py_CLEAR(clear_module_state->__pyx_n_s_encode); + Py_CLEAR(clear_module_state->__pyx_n_s_enumerate); + Py_CLEAR(clear_module_state->__pyx_n_s_error); + Py_CLEAR(clear_module_state->__pyx_n_s_flags); + Py_CLEAR(clear_module_state->__pyx_n_s_format); + Py_CLEAR(clear_module_state->__pyx_n_s_fortran); + Py_CLEAR(clear_module_state->__pyx_n_u_fortran); + Py_CLEAR(clear_module_state->__pyx_kp_u_gc); + Py_CLEAR(clear_module_state->__pyx_n_s_getstate); + Py_CLEAR(clear_module_state->__pyx_kp_u_got); + Py_CLEAR(clear_module_state->__pyx_kp_u_got_differing_extents_in_dimensi); + Py_CLEAR(clear_module_state->__pyx_n_s_id); + Py_CLEAR(clear_module_state->__pyx_n_s_import); + Py_CLEAR(clear_module_state->__pyx_n_s_index); + Py_CLEAR(clear_module_state->__pyx_n_s_initializing); + Py_CLEAR(clear_module_state->__pyx_n_s_is_coroutine); + Py_CLEAR(clear_module_state->__pyx_kp_u_isenabled); + Py_CLEAR(clear_module_state->__pyx_n_s_itemsize); + Py_CLEAR(clear_module_state->__pyx_kp_s_itemsize_0_for_cython_array); + Py_CLEAR(clear_module_state->__pyx_n_s_main); + Py_CLEAR(clear_module_state->__pyx_n_s_maximum_path_c); + Py_CLEAR(clear_module_state->__pyx_n_s_memview); + Py_CLEAR(clear_module_state->__pyx_n_s_mode); + Py_CLEAR(clear_module_state->__pyx_n_s_monotonic_align_core); + Py_CLEAR(clear_module_state->__pyx_n_s_name); + Py_CLEAR(clear_module_state->__pyx_n_s_name_2); + Py_CLEAR(clear_module_state->__pyx_n_s_ndim); + Py_CLEAR(clear_module_state->__pyx_n_s_new); + Py_CLEAR(clear_module_state->__pyx_kp_s_no_default___reduce___due_to_non); + Py_CLEAR(clear_module_state->__pyx_n_s_obj); + Py_CLEAR(clear_module_state->__pyx_n_s_pack); + Py_CLEAR(clear_module_state->__pyx_n_s_paths); + Py_CLEAR(clear_module_state->__pyx_n_s_pickle); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_PickleError); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_checksum); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_result); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_state); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_type); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_unpickle_Enum); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_vtable); + Py_CLEAR(clear_module_state->__pyx_n_s_range); + Py_CLEAR(clear_module_state->__pyx_n_s_reduce); + Py_CLEAR(clear_module_state->__pyx_n_s_reduce_cython); + Py_CLEAR(clear_module_state->__pyx_n_s_reduce_ex); + Py_CLEAR(clear_module_state->__pyx_n_s_register); + Py_CLEAR(clear_module_state->__pyx_n_s_setstate); + Py_CLEAR(clear_module_state->__pyx_n_s_setstate_cython); + Py_CLEAR(clear_module_state->__pyx_n_s_shape); + Py_CLEAR(clear_module_state->__pyx_n_s_size); + Py_CLEAR(clear_module_state->__pyx_n_s_spec); + Py_CLEAR(clear_module_state->__pyx_n_s_start); + Py_CLEAR(clear_module_state->__pyx_n_s_step); + Py_CLEAR(clear_module_state->__pyx_n_s_stop); + Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_direct); + Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_direct_or_indirect); + Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_indirect); + Py_CLEAR(clear_module_state->__pyx_kp_s_stringsource); + Py_CLEAR(clear_module_state->__pyx_n_s_struct); + Py_CLEAR(clear_module_state->__pyx_n_s_sys); + Py_CLEAR(clear_module_state->__pyx_n_s_t_xs); + Py_CLEAR(clear_module_state->__pyx_n_s_t_ys); + Py_CLEAR(clear_module_state->__pyx_n_s_test); + Py_CLEAR(clear_module_state->__pyx_kp_s_unable_to_allocate_array_data); + Py_CLEAR(clear_module_state->__pyx_kp_s_unable_to_allocate_shape_and_str); + Py_CLEAR(clear_module_state->__pyx_n_s_unpack); + Py_CLEAR(clear_module_state->__pyx_n_s_update); + Py_CLEAR(clear_module_state->__pyx_n_s_values); + Py_CLEAR(clear_module_state->__pyx_n_s_version_info); + Py_CLEAR(clear_module_state->__pyx_int_0); + Py_CLEAR(clear_module_state->__pyx_int_1); + Py_CLEAR(clear_module_state->__pyx_int_3); + Py_CLEAR(clear_module_state->__pyx_int_112105877); + Py_CLEAR(clear_module_state->__pyx_int_136983863); + Py_CLEAR(clear_module_state->__pyx_int_184977713); + Py_CLEAR(clear_module_state->__pyx_int_neg_1); + Py_CLEAR(clear_module_state->__pyx_slice__5); + Py_CLEAR(clear_module_state->__pyx_tuple__4); + Py_CLEAR(clear_module_state->__pyx_tuple__8); + Py_CLEAR(clear_module_state->__pyx_tuple__10); + Py_CLEAR(clear_module_state->__pyx_tuple__11); + Py_CLEAR(clear_module_state->__pyx_tuple__12); + Py_CLEAR(clear_module_state->__pyx_tuple__13); + Py_CLEAR(clear_module_state->__pyx_tuple__14); + Py_CLEAR(clear_module_state->__pyx_tuple__15); + Py_CLEAR(clear_module_state->__pyx_tuple__16); + Py_CLEAR(clear_module_state->__pyx_tuple__17); + Py_CLEAR(clear_module_state->__pyx_tuple__18); + Py_CLEAR(clear_module_state->__pyx_tuple__19); + Py_CLEAR(clear_module_state->__pyx_tuple__21); + Py_CLEAR(clear_module_state->__pyx_codeobj__20); + Py_CLEAR(clear_module_state->__pyx_codeobj__22); + return 0; +} +#endif +/* #### Code section: module_state_traverse ### */ +#if CYTHON_USE_MODULE_STATE +static int __pyx_m_traverse(PyObject *m, visitproc visit, void *arg) { + __pyx_mstate *traverse_module_state = __pyx_mstate(m); + if (!traverse_module_state) return 0; + Py_VISIT(traverse_module_state->__pyx_d); + Py_VISIT(traverse_module_state->__pyx_b); + Py_VISIT(traverse_module_state->__pyx_cython_runtime); + Py_VISIT(traverse_module_state->__pyx_empty_tuple); + Py_VISIT(traverse_module_state->__pyx_empty_bytes); + Py_VISIT(traverse_module_state->__pyx_empty_unicode); + #ifdef __Pyx_CyFunction_USED + Py_VISIT(traverse_module_state->__pyx_CyFunctionType); + #endif + #ifdef __Pyx_FusedFunction_USED + Py_VISIT(traverse_module_state->__pyx_FusedFunctionType); + #endif + Py_VISIT(traverse_module_state->__pyx_array_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_array); + Py_VISIT(traverse_module_state->__pyx_MemviewEnum_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_MemviewEnum); + Py_VISIT(traverse_module_state->__pyx_memoryview_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_memoryview); + Py_VISIT(traverse_module_state->__pyx_memoryviewslice_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_memoryviewslice); + Py_VISIT(traverse_module_state->__pyx_kp_u_); + Py_VISIT(traverse_module_state->__pyx_n_s_ASCII); + Py_VISIT(traverse_module_state->__pyx_kp_s_All_dimensions_preceding_dimensi); + Py_VISIT(traverse_module_state->__pyx_n_s_AssertionError); + Py_VISIT(traverse_module_state->__pyx_kp_s_Buffer_view_does_not_expose_stri); + Py_VISIT(traverse_module_state->__pyx_kp_s_Can_only_create_a_buffer_that_is); + Py_VISIT(traverse_module_state->__pyx_kp_s_Cannot_assign_to_read_only_memor); + Py_VISIT(traverse_module_state->__pyx_kp_s_Cannot_create_writable_memory_vi); + Py_VISIT(traverse_module_state->__pyx_kp_u_Cannot_index_with_type); + Py_VISIT(traverse_module_state->__pyx_kp_s_Cannot_transpose_memoryview_with); + Py_VISIT(traverse_module_state->__pyx_kp_s_Dimension_d_is_not_direct); + Py_VISIT(traverse_module_state->__pyx_n_s_Ellipsis); + Py_VISIT(traverse_module_state->__pyx_kp_s_Empty_shape_tuple_for_cython_arr); + Py_VISIT(traverse_module_state->__pyx_kp_s_Incompatible_checksums_0x_x_vs_0); + Py_VISIT(traverse_module_state->__pyx_n_s_IndexError); + Py_VISIT(traverse_module_state->__pyx_kp_s_Index_out_of_bounds_axis_d); + Py_VISIT(traverse_module_state->__pyx_kp_s_Indirect_dimensions_not_supporte); + Py_VISIT(traverse_module_state->__pyx_kp_u_Invalid_mode_expected_c_or_fortr); + Py_VISIT(traverse_module_state->__pyx_kp_u_Invalid_shape_in_axis); + Py_VISIT(traverse_module_state->__pyx_n_s_MemoryError); + Py_VISIT(traverse_module_state->__pyx_kp_s_MemoryView_of_r_at_0x_x); + Py_VISIT(traverse_module_state->__pyx_kp_s_MemoryView_of_r_object); + Py_VISIT(traverse_module_state->__pyx_n_b_O); + Py_VISIT(traverse_module_state->__pyx_kp_u_Out_of_bounds_on_buffer_access_a); + Py_VISIT(traverse_module_state->__pyx_n_s_PickleError); + Py_VISIT(traverse_module_state->__pyx_n_s_Sequence); + Py_VISIT(traverse_module_state->__pyx_kp_s_Step_may_not_be_zero_axis_d); + Py_VISIT(traverse_module_state->__pyx_n_s_TypeError); + Py_VISIT(traverse_module_state->__pyx_kp_s_Unable_to_convert_item_to_object); + Py_VISIT(traverse_module_state->__pyx_n_s_ValueError); + Py_VISIT(traverse_module_state->__pyx_n_s_View_MemoryView); + Py_VISIT(traverse_module_state->__pyx_kp_u__2); + Py_VISIT(traverse_module_state->__pyx_n_s__23); + Py_VISIT(traverse_module_state->__pyx_n_s__3); + Py_VISIT(traverse_module_state->__pyx_kp_u__6); + Py_VISIT(traverse_module_state->__pyx_kp_u__7); + Py_VISIT(traverse_module_state->__pyx_n_s_abc); + Py_VISIT(traverse_module_state->__pyx_n_s_allocate_buffer); + Py_VISIT(traverse_module_state->__pyx_kp_u_and); + Py_VISIT(traverse_module_state->__pyx_n_s_asyncio_coroutines); + Py_VISIT(traverse_module_state->__pyx_n_s_base); + Py_VISIT(traverse_module_state->__pyx_n_s_c); + Py_VISIT(traverse_module_state->__pyx_n_u_c); + Py_VISIT(traverse_module_state->__pyx_n_s_class); + Py_VISIT(traverse_module_state->__pyx_n_s_class_getitem); + Py_VISIT(traverse_module_state->__pyx_n_s_cline_in_traceback); + Py_VISIT(traverse_module_state->__pyx_n_s_collections); + Py_VISIT(traverse_module_state->__pyx_kp_s_collections_abc); + Py_VISIT(traverse_module_state->__pyx_kp_s_contiguous_and_direct); + Py_VISIT(traverse_module_state->__pyx_kp_s_contiguous_and_indirect); + Py_VISIT(traverse_module_state->__pyx_kp_s_core_pyx); + Py_VISIT(traverse_module_state->__pyx_n_s_count); + Py_VISIT(traverse_module_state->__pyx_n_s_dict); + Py_VISIT(traverse_module_state->__pyx_kp_u_disable); + Py_VISIT(traverse_module_state->__pyx_n_s_dtype_is_object); + Py_VISIT(traverse_module_state->__pyx_kp_u_enable); + Py_VISIT(traverse_module_state->__pyx_n_s_encode); + Py_VISIT(traverse_module_state->__pyx_n_s_enumerate); + Py_VISIT(traverse_module_state->__pyx_n_s_error); + Py_VISIT(traverse_module_state->__pyx_n_s_flags); + Py_VISIT(traverse_module_state->__pyx_n_s_format); + Py_VISIT(traverse_module_state->__pyx_n_s_fortran); + Py_VISIT(traverse_module_state->__pyx_n_u_fortran); + Py_VISIT(traverse_module_state->__pyx_kp_u_gc); + Py_VISIT(traverse_module_state->__pyx_n_s_getstate); + Py_VISIT(traverse_module_state->__pyx_kp_u_got); + Py_VISIT(traverse_module_state->__pyx_kp_u_got_differing_extents_in_dimensi); + Py_VISIT(traverse_module_state->__pyx_n_s_id); + Py_VISIT(traverse_module_state->__pyx_n_s_import); + Py_VISIT(traverse_module_state->__pyx_n_s_index); + Py_VISIT(traverse_module_state->__pyx_n_s_initializing); + Py_VISIT(traverse_module_state->__pyx_n_s_is_coroutine); + Py_VISIT(traverse_module_state->__pyx_kp_u_isenabled); + Py_VISIT(traverse_module_state->__pyx_n_s_itemsize); + Py_VISIT(traverse_module_state->__pyx_kp_s_itemsize_0_for_cython_array); + Py_VISIT(traverse_module_state->__pyx_n_s_main); + Py_VISIT(traverse_module_state->__pyx_n_s_maximum_path_c); + Py_VISIT(traverse_module_state->__pyx_n_s_memview); + Py_VISIT(traverse_module_state->__pyx_n_s_mode); + Py_VISIT(traverse_module_state->__pyx_n_s_monotonic_align_core); + Py_VISIT(traverse_module_state->__pyx_n_s_name); + Py_VISIT(traverse_module_state->__pyx_n_s_name_2); + Py_VISIT(traverse_module_state->__pyx_n_s_ndim); + Py_VISIT(traverse_module_state->__pyx_n_s_new); + Py_VISIT(traverse_module_state->__pyx_kp_s_no_default___reduce___due_to_non); + Py_VISIT(traverse_module_state->__pyx_n_s_obj); + Py_VISIT(traverse_module_state->__pyx_n_s_pack); + Py_VISIT(traverse_module_state->__pyx_n_s_paths); + Py_VISIT(traverse_module_state->__pyx_n_s_pickle); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_PickleError); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_checksum); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_result); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_state); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_type); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_unpickle_Enum); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_vtable); + Py_VISIT(traverse_module_state->__pyx_n_s_range); + Py_VISIT(traverse_module_state->__pyx_n_s_reduce); + Py_VISIT(traverse_module_state->__pyx_n_s_reduce_cython); + Py_VISIT(traverse_module_state->__pyx_n_s_reduce_ex); + Py_VISIT(traverse_module_state->__pyx_n_s_register); + Py_VISIT(traverse_module_state->__pyx_n_s_setstate); + Py_VISIT(traverse_module_state->__pyx_n_s_setstate_cython); + Py_VISIT(traverse_module_state->__pyx_n_s_shape); + Py_VISIT(traverse_module_state->__pyx_n_s_size); + Py_VISIT(traverse_module_state->__pyx_n_s_spec); + Py_VISIT(traverse_module_state->__pyx_n_s_start); + Py_VISIT(traverse_module_state->__pyx_n_s_step); + Py_VISIT(traverse_module_state->__pyx_n_s_stop); + Py_VISIT(traverse_module_state->__pyx_kp_s_strided_and_direct); + Py_VISIT(traverse_module_state->__pyx_kp_s_strided_and_direct_or_indirect); + Py_VISIT(traverse_module_state->__pyx_kp_s_strided_and_indirect); + Py_VISIT(traverse_module_state->__pyx_kp_s_stringsource); + Py_VISIT(traverse_module_state->__pyx_n_s_struct); + Py_VISIT(traverse_module_state->__pyx_n_s_sys); + Py_VISIT(traverse_module_state->__pyx_n_s_t_xs); + Py_VISIT(traverse_module_state->__pyx_n_s_t_ys); + Py_VISIT(traverse_module_state->__pyx_n_s_test); + Py_VISIT(traverse_module_state->__pyx_kp_s_unable_to_allocate_array_data); + Py_VISIT(traverse_module_state->__pyx_kp_s_unable_to_allocate_shape_and_str); + Py_VISIT(traverse_module_state->__pyx_n_s_unpack); + Py_VISIT(traverse_module_state->__pyx_n_s_update); + Py_VISIT(traverse_module_state->__pyx_n_s_values); + Py_VISIT(traverse_module_state->__pyx_n_s_version_info); + Py_VISIT(traverse_module_state->__pyx_int_0); + Py_VISIT(traverse_module_state->__pyx_int_1); + Py_VISIT(traverse_module_state->__pyx_int_3); + Py_VISIT(traverse_module_state->__pyx_int_112105877); + Py_VISIT(traverse_module_state->__pyx_int_136983863); + Py_VISIT(traverse_module_state->__pyx_int_184977713); + Py_VISIT(traverse_module_state->__pyx_int_neg_1); + Py_VISIT(traverse_module_state->__pyx_slice__5); + Py_VISIT(traverse_module_state->__pyx_tuple__4); + Py_VISIT(traverse_module_state->__pyx_tuple__8); + Py_VISIT(traverse_module_state->__pyx_tuple__10); + Py_VISIT(traverse_module_state->__pyx_tuple__11); + Py_VISIT(traverse_module_state->__pyx_tuple__12); + Py_VISIT(traverse_module_state->__pyx_tuple__13); + Py_VISIT(traverse_module_state->__pyx_tuple__14); + Py_VISIT(traverse_module_state->__pyx_tuple__15); + Py_VISIT(traverse_module_state->__pyx_tuple__16); + Py_VISIT(traverse_module_state->__pyx_tuple__17); + Py_VISIT(traverse_module_state->__pyx_tuple__18); + Py_VISIT(traverse_module_state->__pyx_tuple__19); + Py_VISIT(traverse_module_state->__pyx_tuple__21); + Py_VISIT(traverse_module_state->__pyx_codeobj__20); + Py_VISIT(traverse_module_state->__pyx_codeobj__22); + return 0; +} +#endif +/* #### Code section: module_state_defines ### */ +#define __pyx_d __pyx_mstate_global->__pyx_d +#define __pyx_b __pyx_mstate_global->__pyx_b +#define __pyx_cython_runtime __pyx_mstate_global->__pyx_cython_runtime +#define __pyx_empty_tuple __pyx_mstate_global->__pyx_empty_tuple +#define __pyx_empty_bytes __pyx_mstate_global->__pyx_empty_bytes +#define __pyx_empty_unicode __pyx_mstate_global->__pyx_empty_unicode +#ifdef __Pyx_CyFunction_USED +#define __pyx_CyFunctionType __pyx_mstate_global->__pyx_CyFunctionType +#endif +#ifdef __Pyx_FusedFunction_USED +#define __pyx_FusedFunctionType __pyx_mstate_global->__pyx_FusedFunctionType +#endif +#ifdef __Pyx_Generator_USED +#define __pyx_GeneratorType __pyx_mstate_global->__pyx_GeneratorType +#endif +#ifdef __Pyx_IterableCoroutine_USED +#define __pyx_IterableCoroutineType __pyx_mstate_global->__pyx_IterableCoroutineType +#endif +#ifdef __Pyx_Coroutine_USED +#define __pyx_CoroutineAwaitType __pyx_mstate_global->__pyx_CoroutineAwaitType +#endif +#ifdef __Pyx_Coroutine_USED +#define __pyx_CoroutineType __pyx_mstate_global->__pyx_CoroutineType +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#define __pyx_type___pyx_array __pyx_mstate_global->__pyx_type___pyx_array +#define __pyx_type___pyx_MemviewEnum __pyx_mstate_global->__pyx_type___pyx_MemviewEnum +#define __pyx_type___pyx_memoryview __pyx_mstate_global->__pyx_type___pyx_memoryview +#define __pyx_type___pyx_memoryviewslice __pyx_mstate_global->__pyx_type___pyx_memoryviewslice +#endif +#define __pyx_array_type __pyx_mstate_global->__pyx_array_type +#define __pyx_MemviewEnum_type __pyx_mstate_global->__pyx_MemviewEnum_type +#define __pyx_memoryview_type __pyx_mstate_global->__pyx_memoryview_type +#define __pyx_memoryviewslice_type __pyx_mstate_global->__pyx_memoryviewslice_type +#define __pyx_kp_u_ __pyx_mstate_global->__pyx_kp_u_ +#define __pyx_n_s_ASCII __pyx_mstate_global->__pyx_n_s_ASCII +#define __pyx_kp_s_All_dimensions_preceding_dimensi __pyx_mstate_global->__pyx_kp_s_All_dimensions_preceding_dimensi +#define __pyx_n_s_AssertionError __pyx_mstate_global->__pyx_n_s_AssertionError +#define __pyx_kp_s_Buffer_view_does_not_expose_stri __pyx_mstate_global->__pyx_kp_s_Buffer_view_does_not_expose_stri +#define __pyx_kp_s_Can_only_create_a_buffer_that_is __pyx_mstate_global->__pyx_kp_s_Can_only_create_a_buffer_that_is +#define __pyx_kp_s_Cannot_assign_to_read_only_memor __pyx_mstate_global->__pyx_kp_s_Cannot_assign_to_read_only_memor +#define __pyx_kp_s_Cannot_create_writable_memory_vi __pyx_mstate_global->__pyx_kp_s_Cannot_create_writable_memory_vi +#define __pyx_kp_u_Cannot_index_with_type __pyx_mstate_global->__pyx_kp_u_Cannot_index_with_type +#define __pyx_kp_s_Cannot_transpose_memoryview_with __pyx_mstate_global->__pyx_kp_s_Cannot_transpose_memoryview_with +#define __pyx_kp_s_Dimension_d_is_not_direct __pyx_mstate_global->__pyx_kp_s_Dimension_d_is_not_direct +#define __pyx_n_s_Ellipsis __pyx_mstate_global->__pyx_n_s_Ellipsis +#define __pyx_kp_s_Empty_shape_tuple_for_cython_arr __pyx_mstate_global->__pyx_kp_s_Empty_shape_tuple_for_cython_arr +#define __pyx_kp_s_Incompatible_checksums_0x_x_vs_0 __pyx_mstate_global->__pyx_kp_s_Incompatible_checksums_0x_x_vs_0 +#define __pyx_n_s_IndexError __pyx_mstate_global->__pyx_n_s_IndexError +#define __pyx_kp_s_Index_out_of_bounds_axis_d __pyx_mstate_global->__pyx_kp_s_Index_out_of_bounds_axis_d +#define __pyx_kp_s_Indirect_dimensions_not_supporte __pyx_mstate_global->__pyx_kp_s_Indirect_dimensions_not_supporte +#define __pyx_kp_u_Invalid_mode_expected_c_or_fortr __pyx_mstate_global->__pyx_kp_u_Invalid_mode_expected_c_or_fortr +#define __pyx_kp_u_Invalid_shape_in_axis __pyx_mstate_global->__pyx_kp_u_Invalid_shape_in_axis +#define __pyx_n_s_MemoryError __pyx_mstate_global->__pyx_n_s_MemoryError +#define __pyx_kp_s_MemoryView_of_r_at_0x_x __pyx_mstate_global->__pyx_kp_s_MemoryView_of_r_at_0x_x +#define __pyx_kp_s_MemoryView_of_r_object __pyx_mstate_global->__pyx_kp_s_MemoryView_of_r_object +#define __pyx_n_b_O __pyx_mstate_global->__pyx_n_b_O +#define __pyx_kp_u_Out_of_bounds_on_buffer_access_a __pyx_mstate_global->__pyx_kp_u_Out_of_bounds_on_buffer_access_a +#define __pyx_n_s_PickleError __pyx_mstate_global->__pyx_n_s_PickleError +#define __pyx_n_s_Sequence __pyx_mstate_global->__pyx_n_s_Sequence +#define __pyx_kp_s_Step_may_not_be_zero_axis_d __pyx_mstate_global->__pyx_kp_s_Step_may_not_be_zero_axis_d +#define __pyx_n_s_TypeError __pyx_mstate_global->__pyx_n_s_TypeError +#define __pyx_kp_s_Unable_to_convert_item_to_object __pyx_mstate_global->__pyx_kp_s_Unable_to_convert_item_to_object +#define __pyx_n_s_ValueError __pyx_mstate_global->__pyx_n_s_ValueError +#define __pyx_n_s_View_MemoryView __pyx_mstate_global->__pyx_n_s_View_MemoryView +#define __pyx_kp_u__2 __pyx_mstate_global->__pyx_kp_u__2 +#define __pyx_n_s__23 __pyx_mstate_global->__pyx_n_s__23 +#define __pyx_n_s__3 __pyx_mstate_global->__pyx_n_s__3 +#define __pyx_kp_u__6 __pyx_mstate_global->__pyx_kp_u__6 +#define __pyx_kp_u__7 __pyx_mstate_global->__pyx_kp_u__7 +#define __pyx_n_s_abc __pyx_mstate_global->__pyx_n_s_abc +#define __pyx_n_s_allocate_buffer __pyx_mstate_global->__pyx_n_s_allocate_buffer +#define __pyx_kp_u_and __pyx_mstate_global->__pyx_kp_u_and +#define __pyx_n_s_asyncio_coroutines __pyx_mstate_global->__pyx_n_s_asyncio_coroutines +#define __pyx_n_s_base __pyx_mstate_global->__pyx_n_s_base +#define __pyx_n_s_c __pyx_mstate_global->__pyx_n_s_c +#define __pyx_n_u_c __pyx_mstate_global->__pyx_n_u_c +#define __pyx_n_s_class __pyx_mstate_global->__pyx_n_s_class +#define __pyx_n_s_class_getitem __pyx_mstate_global->__pyx_n_s_class_getitem +#define __pyx_n_s_cline_in_traceback __pyx_mstate_global->__pyx_n_s_cline_in_traceback +#define __pyx_n_s_collections __pyx_mstate_global->__pyx_n_s_collections +#define __pyx_kp_s_collections_abc __pyx_mstate_global->__pyx_kp_s_collections_abc +#define __pyx_kp_s_contiguous_and_direct __pyx_mstate_global->__pyx_kp_s_contiguous_and_direct +#define __pyx_kp_s_contiguous_and_indirect __pyx_mstate_global->__pyx_kp_s_contiguous_and_indirect +#define __pyx_kp_s_core_pyx __pyx_mstate_global->__pyx_kp_s_core_pyx +#define __pyx_n_s_count __pyx_mstate_global->__pyx_n_s_count +#define __pyx_n_s_dict __pyx_mstate_global->__pyx_n_s_dict +#define __pyx_kp_u_disable __pyx_mstate_global->__pyx_kp_u_disable +#define __pyx_n_s_dtype_is_object __pyx_mstate_global->__pyx_n_s_dtype_is_object +#define __pyx_kp_u_enable __pyx_mstate_global->__pyx_kp_u_enable +#define __pyx_n_s_encode __pyx_mstate_global->__pyx_n_s_encode +#define __pyx_n_s_enumerate __pyx_mstate_global->__pyx_n_s_enumerate +#define __pyx_n_s_error __pyx_mstate_global->__pyx_n_s_error +#define __pyx_n_s_flags __pyx_mstate_global->__pyx_n_s_flags +#define __pyx_n_s_format __pyx_mstate_global->__pyx_n_s_format +#define __pyx_n_s_fortran __pyx_mstate_global->__pyx_n_s_fortran +#define __pyx_n_u_fortran __pyx_mstate_global->__pyx_n_u_fortran +#define __pyx_kp_u_gc __pyx_mstate_global->__pyx_kp_u_gc +#define __pyx_n_s_getstate __pyx_mstate_global->__pyx_n_s_getstate +#define __pyx_kp_u_got __pyx_mstate_global->__pyx_kp_u_got +#define __pyx_kp_u_got_differing_extents_in_dimensi __pyx_mstate_global->__pyx_kp_u_got_differing_extents_in_dimensi +#define __pyx_n_s_id __pyx_mstate_global->__pyx_n_s_id +#define __pyx_n_s_import __pyx_mstate_global->__pyx_n_s_import +#define __pyx_n_s_index __pyx_mstate_global->__pyx_n_s_index +#define __pyx_n_s_initializing __pyx_mstate_global->__pyx_n_s_initializing +#define __pyx_n_s_is_coroutine __pyx_mstate_global->__pyx_n_s_is_coroutine +#define __pyx_kp_u_isenabled __pyx_mstate_global->__pyx_kp_u_isenabled +#define __pyx_n_s_itemsize __pyx_mstate_global->__pyx_n_s_itemsize +#define __pyx_kp_s_itemsize_0_for_cython_array __pyx_mstate_global->__pyx_kp_s_itemsize_0_for_cython_array +#define __pyx_n_s_main __pyx_mstate_global->__pyx_n_s_main +#define __pyx_n_s_maximum_path_c __pyx_mstate_global->__pyx_n_s_maximum_path_c +#define __pyx_n_s_memview __pyx_mstate_global->__pyx_n_s_memview +#define __pyx_n_s_mode __pyx_mstate_global->__pyx_n_s_mode +#define __pyx_n_s_monotonic_align_core __pyx_mstate_global->__pyx_n_s_monotonic_align_core +#define __pyx_n_s_name __pyx_mstate_global->__pyx_n_s_name +#define __pyx_n_s_name_2 __pyx_mstate_global->__pyx_n_s_name_2 +#define __pyx_n_s_ndim __pyx_mstate_global->__pyx_n_s_ndim +#define __pyx_n_s_new __pyx_mstate_global->__pyx_n_s_new +#define __pyx_kp_s_no_default___reduce___due_to_non __pyx_mstate_global->__pyx_kp_s_no_default___reduce___due_to_non +#define __pyx_n_s_obj __pyx_mstate_global->__pyx_n_s_obj +#define __pyx_n_s_pack __pyx_mstate_global->__pyx_n_s_pack +#define __pyx_n_s_paths __pyx_mstate_global->__pyx_n_s_paths +#define __pyx_n_s_pickle __pyx_mstate_global->__pyx_n_s_pickle +#define __pyx_n_s_pyx_PickleError __pyx_mstate_global->__pyx_n_s_pyx_PickleError +#define __pyx_n_s_pyx_checksum __pyx_mstate_global->__pyx_n_s_pyx_checksum +#define __pyx_n_s_pyx_result __pyx_mstate_global->__pyx_n_s_pyx_result +#define __pyx_n_s_pyx_state __pyx_mstate_global->__pyx_n_s_pyx_state +#define __pyx_n_s_pyx_type __pyx_mstate_global->__pyx_n_s_pyx_type +#define __pyx_n_s_pyx_unpickle_Enum __pyx_mstate_global->__pyx_n_s_pyx_unpickle_Enum +#define __pyx_n_s_pyx_vtable __pyx_mstate_global->__pyx_n_s_pyx_vtable +#define __pyx_n_s_range __pyx_mstate_global->__pyx_n_s_range +#define __pyx_n_s_reduce __pyx_mstate_global->__pyx_n_s_reduce +#define __pyx_n_s_reduce_cython __pyx_mstate_global->__pyx_n_s_reduce_cython +#define __pyx_n_s_reduce_ex __pyx_mstate_global->__pyx_n_s_reduce_ex +#define __pyx_n_s_register __pyx_mstate_global->__pyx_n_s_register +#define __pyx_n_s_setstate __pyx_mstate_global->__pyx_n_s_setstate +#define __pyx_n_s_setstate_cython __pyx_mstate_global->__pyx_n_s_setstate_cython +#define __pyx_n_s_shape __pyx_mstate_global->__pyx_n_s_shape +#define __pyx_n_s_size __pyx_mstate_global->__pyx_n_s_size +#define __pyx_n_s_spec __pyx_mstate_global->__pyx_n_s_spec +#define __pyx_n_s_start __pyx_mstate_global->__pyx_n_s_start +#define __pyx_n_s_step __pyx_mstate_global->__pyx_n_s_step +#define __pyx_n_s_stop __pyx_mstate_global->__pyx_n_s_stop +#define __pyx_kp_s_strided_and_direct __pyx_mstate_global->__pyx_kp_s_strided_and_direct +#define __pyx_kp_s_strided_and_direct_or_indirect __pyx_mstate_global->__pyx_kp_s_strided_and_direct_or_indirect +#define __pyx_kp_s_strided_and_indirect __pyx_mstate_global->__pyx_kp_s_strided_and_indirect +#define __pyx_kp_s_stringsource __pyx_mstate_global->__pyx_kp_s_stringsource +#define __pyx_n_s_struct __pyx_mstate_global->__pyx_n_s_struct +#define __pyx_n_s_sys __pyx_mstate_global->__pyx_n_s_sys +#define __pyx_n_s_t_xs __pyx_mstate_global->__pyx_n_s_t_xs +#define __pyx_n_s_t_ys __pyx_mstate_global->__pyx_n_s_t_ys +#define __pyx_n_s_test __pyx_mstate_global->__pyx_n_s_test +#define __pyx_kp_s_unable_to_allocate_array_data __pyx_mstate_global->__pyx_kp_s_unable_to_allocate_array_data +#define __pyx_kp_s_unable_to_allocate_shape_and_str __pyx_mstate_global->__pyx_kp_s_unable_to_allocate_shape_and_str +#define __pyx_n_s_unpack __pyx_mstate_global->__pyx_n_s_unpack +#define __pyx_n_s_update __pyx_mstate_global->__pyx_n_s_update +#define __pyx_n_s_values __pyx_mstate_global->__pyx_n_s_values +#define __pyx_n_s_version_info __pyx_mstate_global->__pyx_n_s_version_info +#define __pyx_int_0 __pyx_mstate_global->__pyx_int_0 +#define __pyx_int_1 __pyx_mstate_global->__pyx_int_1 +#define __pyx_int_3 __pyx_mstate_global->__pyx_int_3 +#define __pyx_int_112105877 __pyx_mstate_global->__pyx_int_112105877 +#define __pyx_int_136983863 __pyx_mstate_global->__pyx_int_136983863 +#define __pyx_int_184977713 __pyx_mstate_global->__pyx_int_184977713 +#define __pyx_int_neg_1 __pyx_mstate_global->__pyx_int_neg_1 +#define __pyx_k__9 __pyx_mstate_global->__pyx_k__9 +#define __pyx_slice__5 __pyx_mstate_global->__pyx_slice__5 +#define __pyx_tuple__4 __pyx_mstate_global->__pyx_tuple__4 +#define __pyx_tuple__8 __pyx_mstate_global->__pyx_tuple__8 +#define __pyx_tuple__10 __pyx_mstate_global->__pyx_tuple__10 +#define __pyx_tuple__11 __pyx_mstate_global->__pyx_tuple__11 +#define __pyx_tuple__12 __pyx_mstate_global->__pyx_tuple__12 +#define __pyx_tuple__13 __pyx_mstate_global->__pyx_tuple__13 +#define __pyx_tuple__14 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__Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +/* "View.MemoryView":793 + * + * @cname('__pyx_memoryview_slice_memviewslice') + * cdef int slice_memviewslice( # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, + */ + +static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) { + Py_ssize_t __pyx_v_new_shape; + int __pyx_v_negative_step; + int __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + #ifdef WITH_THREAD + PyGILState_STATE __pyx_gilstate_save; + #endif + + /* "View.MemoryView":813 + * cdef bint negative_step + * + * if not is_slice: # <<<<<<<<<<<<<< + * + * if start < 0: + */ + __pyx_t_1 = (!__pyx_v_is_slice); + if (__pyx_t_1) { + + /* "View.MemoryView":815 + * if not is_slice: + * + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if not 0 <= start < shape: + */ + __pyx_t_1 = (__pyx_v_start < 0); + if (__pyx_t_1) { + + /* "View.MemoryView":816 + * + * if start < 0: + * start += shape # <<<<<<<<<<<<<< + * if not 0 <= start < shape: + * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) + */ + __pyx_v_start = (__pyx_v_start + __pyx_v_shape); + + /* "View.MemoryView":815 + * if not is_slice: + * + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if not 0 <= start < shape: + */ + } + + /* "View.MemoryView":817 + * if start < 0: + * start += shape + * if not 0 <= start < shape: # <<<<<<<<<<<<<< + * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) + * else: + */ + __pyx_t_1 = (0 <= __pyx_v_start); + if (__pyx_t_1) { + __pyx_t_1 = (__pyx_v_start < __pyx_v_shape); + } + __pyx_t_2 = (!__pyx_t_1); + if (__pyx_t_2) { + + /* "View.MemoryView":818 + * start += shape + * if not 0 <= start < shape: + * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< + * else: + * + */ + __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_IndexError, __pyx_kp_s_Index_out_of_bounds_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 818, __pyx_L1_error) + + /* "View.MemoryView":817 + * if start < 0: + * start += shape + * if not 0 <= start < shape: # <<<<<<<<<<<<<< + * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) + * else: + */ + } + + /* "View.MemoryView":813 + * cdef bint negative_step + * + * if not is_slice: # <<<<<<<<<<<<<< + * + * if start < 0: + */ + goto __pyx_L3; + } + + /* "View.MemoryView":821 + * else: + * + * if have_step: # <<<<<<<<<<<<<< + * negative_step = step < 0 + * if step == 0: + */ + /*else*/ { + __pyx_t_2 = (__pyx_v_have_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":822 + * + * if have_step: + * negative_step = step < 0 # <<<<<<<<<<<<<< + * if step == 0: + * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) + */ + __pyx_v_negative_step = (__pyx_v_step < 0); + + /* "View.MemoryView":823 + * if have_step: + * negative_step = step < 0 + * if step == 0: # <<<<<<<<<<<<<< + * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) + * else: + */ + __pyx_t_2 = (__pyx_v_step == 0); + if (__pyx_t_2) { + + /* "View.MemoryView":824 + * negative_step = step < 0 + * if step == 0: + * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< + * else: + * negative_step = False + */ + __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_ValueError, __pyx_kp_s_Step_may_not_be_zero_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 824, __pyx_L1_error) + + /* "View.MemoryView":823 + * if have_step: + * negative_step = step < 0 + * if step == 0: # <<<<<<<<<<<<<< + * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) + * else: + */ + } + + /* "View.MemoryView":821 + * else: + * + * if have_step: # <<<<<<<<<<<<<< + * negative_step = step < 0 + * if step == 0: + */ + goto __pyx_L6; + } + + /* "View.MemoryView":826 + * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) + * else: + * negative_step = False # <<<<<<<<<<<<<< + * step = 1 + * + */ + /*else*/ { + __pyx_v_negative_step = 0; + + /* "View.MemoryView":827 + * else: + * negative_step = False + * step = 1 # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_step = 1; + } + __pyx_L6:; + + /* "View.MemoryView":830 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape + */ + __pyx_t_2 = (__pyx_v_have_start != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":831 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: + */ + __pyx_t_2 = (__pyx_v_start < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":832 + * if have_start: + * if start < 0: + * start += shape # <<<<<<<<<<<<<< + * if start < 0: + * start = 0 + */ + __pyx_v_start = (__pyx_v_start + __pyx_v_shape); + + /* "View.MemoryView":833 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: + */ + __pyx_t_2 = (__pyx_v_start < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":834 + * start += shape + * if start < 0: + * start = 0 # <<<<<<<<<<<<<< + * elif start >= shape: + * if negative_step: + */ + __pyx_v_start = 0; + + /* "View.MemoryView":833 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: + */ + } + + /* "View.MemoryView":831 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: + */ + goto __pyx_L9; + } + + /* "View.MemoryView":835 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 + */ + __pyx_t_2 = (__pyx_v_start >= __pyx_v_shape); + if (__pyx_t_2) { + + /* "View.MemoryView":836 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + if (__pyx_v_negative_step) { + + /* "View.MemoryView":837 + * elif start >= shape: + * if negative_step: + * start = shape - 1 # <<<<<<<<<<<<<< + * else: + * start = shape + */ + __pyx_v_start = (__pyx_v_shape - 1); + + /* "View.MemoryView":836 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + goto __pyx_L11; + } + + /* "View.MemoryView":839 + * start = shape - 1 + * else: + * start = shape # <<<<<<<<<<<<<< + * else: + * if negative_step: + */ + /*else*/ { + __pyx_v_start = __pyx_v_shape; + } + __pyx_L11:; + + /* "View.MemoryView":835 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 + */ + } + __pyx_L9:; + + /* "View.MemoryView":830 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape + */ + goto __pyx_L8; + } + + /* "View.MemoryView":841 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + /*else*/ { + if (__pyx_v_negative_step) { + + /* "View.MemoryView":842 + * else: + * if negative_step: + * start = shape - 1 # <<<<<<<<<<<<<< + * else: + * start = 0 + */ + __pyx_v_start = (__pyx_v_shape - 1); + + /* "View.MemoryView":841 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + goto __pyx_L12; + } + + /* "View.MemoryView":844 + * start = shape - 1 + * else: + * start = 0 # <<<<<<<<<<<<<< + * + * if have_stop: + */ + /*else*/ { + __pyx_v_start = 0; + } + __pyx_L12:; + } + __pyx_L8:; + + /* "View.MemoryView":846 + * start = 0 + * + * if have_stop: # <<<<<<<<<<<<<< + * if stop < 0: + * stop += shape + */ + __pyx_t_2 = (__pyx_v_have_stop != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":847 + * + * if have_stop: + * if stop < 0: # <<<<<<<<<<<<<< + * stop += shape + * if stop < 0: + */ + __pyx_t_2 = (__pyx_v_stop < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":848 + * if have_stop: + * if stop < 0: + * stop += shape # <<<<<<<<<<<<<< + * if stop < 0: + * stop = 0 + */ + __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); + + /* "View.MemoryView":849 + * if stop < 0: + * stop += shape + * if stop < 0: # <<<<<<<<<<<<<< + * stop = 0 + * elif stop > shape: + */ + __pyx_t_2 = (__pyx_v_stop < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":850 + * stop += shape + * if stop < 0: + * stop = 0 # <<<<<<<<<<<<<< + * elif stop > shape: + * stop = shape + */ + __pyx_v_stop = 0; + + /* "View.MemoryView":849 + * if stop < 0: + * stop += shape + * if stop < 0: # <<<<<<<<<<<<<< + * stop = 0 + * elif stop > shape: + */ + } + + /* "View.MemoryView":847 + * + * if have_stop: + * if stop < 0: # <<<<<<<<<<<<<< + * stop += shape + * if stop < 0: + */ + goto __pyx_L14; + } + + /* "View.MemoryView":851 + * if stop < 0: + * stop = 0 + * elif stop > shape: # <<<<<<<<<<<<<< + * stop = shape + * else: + */ + __pyx_t_2 = (__pyx_v_stop > __pyx_v_shape); + if (__pyx_t_2) { + + /* "View.MemoryView":852 + * stop = 0 + * elif stop > shape: + * stop = shape # <<<<<<<<<<<<<< + * else: + * if negative_step: + */ + __pyx_v_stop = __pyx_v_shape; + + /* "View.MemoryView":851 + * if stop < 0: + * stop = 0 + * elif stop > shape: # <<<<<<<<<<<<<< + * stop = shape + * else: + */ + } + __pyx_L14:; + + /* "View.MemoryView":846 + * start = 0 + * + * if have_stop: # <<<<<<<<<<<<<< + * if stop < 0: + * stop += shape + */ + goto __pyx_L13; + } + + /* "View.MemoryView":854 + * stop = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * stop = -1 + * else: + */ + /*else*/ { + if (__pyx_v_negative_step) { + + /* "View.MemoryView":855 + * else: + * if negative_step: + * stop = -1 # <<<<<<<<<<<<<< + * else: + * stop = shape + */ + __pyx_v_stop = -1L; + + /* "View.MemoryView":854 + * stop = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * stop = -1 + * else: + */ + goto __pyx_L16; + } + + /* "View.MemoryView":857 + * stop = -1 + * else: + * stop = shape # <<<<<<<<<<<<<< + * + * + */ + /*else*/ { + __pyx_v_stop = __pyx_v_shape; + } + __pyx_L16:; + } + __pyx_L13:; + + /* "View.MemoryView":861 + * + * with cython.cdivision(True): + * new_shape = (stop - start) // step # <<<<<<<<<<<<<< + * + * if (stop - start) - step * new_shape: + */ + __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); + + /* "View.MemoryView":863 + * new_shape = (stop - start) // step + * + * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< + * new_shape += 1 + * + */ + __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":864 + * + * if (stop - start) - step * new_shape: + * new_shape += 1 # <<<<<<<<<<<<<< + * + * if new_shape < 0: + */ + __pyx_v_new_shape = (__pyx_v_new_shape + 1); + + /* "View.MemoryView":863 + * new_shape = (stop - start) // step + * + * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< + * new_shape += 1 + * + */ + } + + /* "View.MemoryView":866 + * new_shape += 1 + * + * if new_shape < 0: # <<<<<<<<<<<<<< + * new_shape = 0 + * + */ + __pyx_t_2 = (__pyx_v_new_shape < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":867 + * + * if new_shape < 0: + * new_shape = 0 # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_new_shape = 0; + + /* "View.MemoryView":866 + * new_shape += 1 + * + * if new_shape < 0: # <<<<<<<<<<<<<< + * new_shape = 0 + * + */ + } + + /* "View.MemoryView":870 + * + * + * dst.strides[new_ndim] = stride * step # <<<<<<<<<<<<<< + * dst.shape[new_ndim] = new_shape + * dst.suboffsets[new_ndim] = suboffset + */ + (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step); + + /* "View.MemoryView":871 + * + * dst.strides[new_ndim] = stride * step + * dst.shape[new_ndim] = new_shape # <<<<<<<<<<<<<< + * dst.suboffsets[new_ndim] = suboffset + * + */ + (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape; + + /* "View.MemoryView":872 + * dst.strides[new_ndim] = stride * step + * dst.shape[new_ndim] = new_shape + * dst.suboffsets[new_ndim] = suboffset # <<<<<<<<<<<<<< + * + * + */ + (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset; + } + __pyx_L3:; + + /* "View.MemoryView":875 + * + * + * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< + * dst.data += start * stride + * else: + */ + __pyx_t_2 = ((__pyx_v_suboffset_dim[0]) < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":876 + * + * if suboffset_dim[0] < 0: + * dst.data += start * stride # <<<<<<<<<<<<<< + * else: + * dst.suboffsets[suboffset_dim[0]] += start * stride + */ + __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride)); + + /* "View.MemoryView":875 + * + * + * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< + * dst.data += start * stride + * else: + */ + goto __pyx_L19; + } + + /* "View.MemoryView":878 + * dst.data += start * stride + * else: + * dst.suboffsets[suboffset_dim[0]] += start * stride # <<<<<<<<<<<<<< + * + * if suboffset >= 0: + */ + /*else*/ { + __pyx_t_3 = (__pyx_v_suboffset_dim[0]); + (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride)); + } + __pyx_L19:; + + /* "View.MemoryView":880 + * dst.suboffsets[suboffset_dim[0]] += start * stride + * + * if suboffset >= 0: # <<<<<<<<<<<<<< + * if not is_slice: + * if new_ndim == 0: + */ + __pyx_t_2 = (__pyx_v_suboffset >= 0); + if (__pyx_t_2) { + + /* "View.MemoryView":881 + * + * if suboffset >= 0: + * if not is_slice: # <<<<<<<<<<<<<< + * if new_ndim == 0: + * dst.data = ( dst.data)[0] + suboffset + */ + __pyx_t_2 = (!__pyx_v_is_slice); + if (__pyx_t_2) { + + /* "View.MemoryView":882 + * if suboffset >= 0: + * if not is_slice: + * if new_ndim == 0: # <<<<<<<<<<<<<< + * dst.data = ( dst.data)[0] + suboffset + * else: + */ + __pyx_t_2 = (__pyx_v_new_ndim == 0); + if (__pyx_t_2) { + + /* "View.MemoryView":883 + * if not is_slice: + * if new_ndim == 0: + * dst.data = ( dst.data)[0] + suboffset # <<<<<<<<<<<<<< + * else: + * _err_dim(PyExc_IndexError, "All dimensions preceding dimension %d " + */ + __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset); + + /* "View.MemoryView":882 + * if suboffset >= 0: + * if not is_slice: + * if new_ndim == 0: # <<<<<<<<<<<<<< + * dst.data = ( dst.data)[0] + suboffset + * else: + */ + goto __pyx_L22; + } + + /* "View.MemoryView":885 + * dst.data = ( dst.data)[0] + suboffset + * else: + * _err_dim(PyExc_IndexError, "All dimensions preceding dimension %d " # <<<<<<<<<<<<<< + * "must be indexed and not sliced", dim) + * else: + */ + /*else*/ { + + /* "View.MemoryView":886 + * else: + * _err_dim(PyExc_IndexError, "All dimensions preceding dimension %d " + * "must be indexed and not sliced", dim) # <<<<<<<<<<<<<< + * else: + * suboffset_dim[0] = new_ndim + */ + __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_IndexError, __pyx_kp_s_All_dimensions_preceding_dimensi, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 885, __pyx_L1_error) + } + __pyx_L22:; + + /* "View.MemoryView":881 + * + * if suboffset >= 0: + * if not is_slice: # <<<<<<<<<<<<<< + * if new_ndim == 0: + * dst.data = ( dst.data)[0] + suboffset + */ + goto __pyx_L21; + } + + /* "View.MemoryView":888 + * "must be indexed and not sliced", dim) + * else: + * suboffset_dim[0] = new_ndim # <<<<<<<<<<<<<< + * + * return 0 + */ + /*else*/ { + (__pyx_v_suboffset_dim[0]) = __pyx_v_new_ndim; + } + __pyx_L21:; + + /* "View.MemoryView":880 + * dst.suboffsets[suboffset_dim[0]] += start * stride + * + * if suboffset >= 0: # <<<<<<<<<<<<<< + * if not is_slice: + * if new_ndim == 0: + */ + } + + /* "View.MemoryView":890 + * suboffset_dim[0] = new_ndim + * + * return 0 # <<<<<<<<<<<<<< + * + * + */ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":793 + * + * @cname('__pyx_memoryview_slice_memviewslice') + * cdef int slice_memviewslice( # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, + */ + + /* function exit code */ + __pyx_L1_error:; + #ifdef WITH_THREAD + __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("View.MemoryView.slice_memviewslice", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = -1; + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":896 + * + * @cname('__pyx_pybuffer_index') + * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, # <<<<<<<<<<<<<< + * Py_ssize_t dim) except NULL: + * cdef Py_ssize_t shape, stride, suboffset = -1 + */ + +static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, Py_ssize_t __pyx_v_index, Py_ssize_t __pyx_v_dim) { + Py_ssize_t __pyx_v_shape; + Py_ssize_t __pyx_v_stride; + Py_ssize_t __pyx_v_suboffset; + Py_ssize_t __pyx_v_itemsize; 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(__pyx_t_1) { + + /* "View.MemoryView":1095 + * + * if isinstance(memview, _memoryviewslice): + * to_object_func = (<_memoryviewslice> memview).to_object_func # <<<<<<<<<<<<<< + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func + * else: + */ + __pyx_t_2 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_object_func; + __pyx_v_to_object_func = __pyx_t_2; + + /* "View.MemoryView":1096 + * if isinstance(memview, _memoryviewslice): + * to_object_func = (<_memoryviewslice> memview).to_object_func + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func # <<<<<<<<<<<<<< + * else: + * to_object_func = NULL + */ + __pyx_t_3 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_dtype_func; + __pyx_v_to_dtype_func = __pyx_t_3; + + /* "View.MemoryView":1094 + * cdef int (*to_dtype_func)(char *, object) except 0 + * + * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< + * to_object_func = (<_memoryviewslice> memview).to_object_func + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1098 + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func + * else: + * to_object_func = NULL # <<<<<<<<<<<<<< + * to_dtype_func = NULL + * + */ + /*else*/ { + __pyx_v_to_object_func = NULL; + + /* "View.MemoryView":1099 + * else: + * to_object_func = NULL + * to_dtype_func = NULL # <<<<<<<<<<<<<< + * + * return memoryview_fromslice(memviewslice[0], memview.view.ndim, + */ + __pyx_v_to_dtype_func = NULL; + } + __pyx_L3:; + + /* "View.MemoryView":1101 + * to_dtype_func = NULL + * + * return memoryview_fromslice(memviewslice[0], memview.view.ndim, # <<<<<<<<<<<<<< + * to_object_func, to_dtype_func, + * memview.dtype_is_object) + */ + __Pyx_XDECREF(__pyx_r); + + /* "View.MemoryView":1103 + * return memoryview_fromslice(memviewslice[0], memview.view.ndim, + * to_object_func, to_dtype_func, + * memview.dtype_is_object) # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_4 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 1101, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_4); + __pyx_r = __pyx_t_4; + __pyx_t_4 = 0; + goto __pyx_L0; + + /* "View.MemoryView":1087 + * + * @cname('__pyx_memoryview_copy_object_from_slice') + * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< + * """ + * Create a new memoryview object from a given memoryview object and slice. + */ + + /* function exit code */ + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_4); + __Pyx_AddTraceback("View.MemoryView.memoryview_copy_from_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = 0; + __pyx_L0:; + __Pyx_XGIVEREF(__pyx_r); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +/* "View.MemoryView":1109 + * + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) noexcept nogil: # <<<<<<<<<<<<<< + * return -arg if arg < 0 else arg + * + */ + +static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { + Py_ssize_t __pyx_r; + Py_ssize_t __pyx_t_1; + int __pyx_t_2; + + /* "View.MemoryView":1110 + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) noexcept nogil: + * return -arg if arg < 0 else arg # <<<<<<<<<<<<<< + * + * @cname('__pyx_get_best_slice_order') + */ + __pyx_t_2 = (__pyx_v_arg < 0); + if (__pyx_t_2) { + __pyx_t_1 = (-__pyx_v_arg); + } else { + __pyx_t_1 = __pyx_v_arg; + } + __pyx_r = __pyx_t_1; + goto __pyx_L0; + + /* "View.MemoryView":1109 + * + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) noexcept nogil: # <<<<<<<<<<<<<< + * return -arg if arg < 0 else arg + * + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1113 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) noexcept nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + +static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) { + int __pyx_v_i; + Py_ssize_t __pyx_v_c_stride; + Py_ssize_t __pyx_v_f_stride; + char __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1118 + * """ + * cdef int i + * cdef Py_ssize_t c_stride = 0 # <<<<<<<<<<<<<< + * cdef Py_ssize_t f_stride = 0 + * + */ + __pyx_v_c_stride = 0; + + /* "View.MemoryView":1119 + * cdef int i + * cdef Py_ssize_t c_stride = 0 + * cdef Py_ssize_t f_stride = 0 # <<<<<<<<<<<<<< + * + * for i in range(ndim - 1, -1, -1): + */ + __pyx_v_f_stride = 0; + + /* "View.MemoryView":1121 + * cdef Py_ssize_t f_stride = 0 + * + * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] + */ + for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { + __pyx_v_i = __pyx_t_1; + + /* "View.MemoryView":1122 + * + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * c_stride = mslice.strides[i] + * break + */ + __pyx_t_2 = ((__pyx_v_mslice->shape[__pyx_v_i]) > 1); + if (__pyx_t_2) { + + /* "View.MemoryView":1123 + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * + */ + __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1124 + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * for i in range(ndim): + */ + goto __pyx_L4_break; + + /* "View.MemoryView":1122 + * + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * c_stride = mslice.strides[i] + * break + */ + } + } + __pyx_L4_break:; + + /* "View.MemoryView":1126 + * break + * + * for i in range(ndim): # <<<<<<<<<<<<<< + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] + */ + __pyx_t_1 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_1; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_i = __pyx_t_4; + + /* "View.MemoryView":1127 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break + */ + __pyx_t_2 = ((__pyx_v_mslice->shape[__pyx_v_i]) > 1); + if (__pyx_t_2) { + + /* "View.MemoryView":1128 + * for i in range(ndim): + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * + */ + __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1129 + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + */ + goto __pyx_L7_break; + + /* "View.MemoryView":1127 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break + */ + } + } + __pyx_L7_break:; + + /* "View.MemoryView":1131 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + __pyx_t_2 = (abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)); + if (__pyx_t_2) { + + /* "View.MemoryView":1132 + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + * return 'C' # <<<<<<<<<<<<<< + * else: + * return 'F' + */ + __pyx_r = 'C'; + goto __pyx_L0; + + /* "View.MemoryView":1131 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + } + + /* "View.MemoryView":1134 + * return 'C' + * else: + * return 'F' # <<<<<<<<<<<<<< + * + * @cython.cdivision(True) + */ + /*else*/ { + __pyx_r = 'F'; + goto __pyx_L0; + } + + /* "View.MemoryView":1113 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) noexcept nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1137 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + +static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + CYTHON_UNUSED Py_ssize_t __pyx_v_i; + CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; + Py_ssize_t __pyx_v_dst_extent; + Py_ssize_t __pyx_v_src_stride; + Py_ssize_t __pyx_v_dst_stride; + int __pyx_t_1; + int __pyx_t_2; + Py_ssize_t __pyx_t_3; + Py_ssize_t __pyx_t_4; + Py_ssize_t __pyx_t_5; + + /* "View.MemoryView":1144 + * + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + */ + __pyx_v_src_extent = (__pyx_v_src_shape[0]); + + /* "View.MemoryView":1145 + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] + */ + __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); + + /* "View.MemoryView":1146 + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + */ + __pyx_v_src_stride = (__pyx_v_src_strides[0]); + + /* "View.MemoryView":1147 + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< + * + * if ndim == 1: + */ + __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); + + /* "View.MemoryView":1149 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + __pyx_t_1 = (__pyx_v_ndim == 1); + if (__pyx_t_1) { + + /* "View.MemoryView":1150 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + __pyx_t_2 = (__pyx_v_src_stride > 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + __pyx_t_2 = (__pyx_v_dst_stride > 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + + /* "View.MemoryView":1151 + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + */ + __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); + if (__pyx_t_2) { + __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); + } + __pyx_t_1 = __pyx_t_2; + __pyx_L5_bool_binop_done:; + + /* "View.MemoryView":1150 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + if (__pyx_t_1) { + + /* "View.MemoryView":1152 + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); + + /* "View.MemoryView":1150 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + goto __pyx_L4; + } + + /* "View.MemoryView":1154 + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + */ + /*else*/ { + __pyx_t_3 = __pyx_v_dst_extent; + __pyx_t_4 = __pyx_t_3; + for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { + __pyx_v_i = __pyx_t_5; + + /* "View.MemoryView":1155 + * else: + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< + * src_data += src_stride + * dst_data += dst_stride + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); + + /* "View.MemoryView":1156 + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * else: + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1157 + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L4:; + + /* "View.MemoryView":1149 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1159 + * dst_data += dst_stride + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * _copy_strided_to_strided(src_data, src_strides + 1, + * dst_data, dst_strides + 1, + */ + /*else*/ { + __pyx_t_3 = __pyx_v_dst_extent; + __pyx_t_4 = __pyx_t_3; + for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { + __pyx_v_i = __pyx_t_5; + + /* "View.MemoryView":1160 + * else: + * for i in range(dst_extent): + * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< + * dst_data, dst_strides + 1, + * src_shape + 1, dst_shape + 1, + */ + _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); + + /* "View.MemoryView":1164 + * src_shape + 1, dst_shape + 1, + * ndim - 1, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1165 + * ndim - 1, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L3:; + + /* "View.MemoryView":1137 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + + /* function exit code */ +} + +/* "View.MemoryView":1167 + * dst_data += dst_stride + * + * 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"View.MemoryView":1312 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + __pyx_t_2 = __pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim); + if (__pyx_t_2) { + + /* "View.MemoryView":1313 + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< + * + * if direct_copy: + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); + + /* "View.MemoryView":1312 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + } + __pyx_L12:; + + /* "View.MemoryView":1315 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + */ + if (__pyx_v_direct_copy) { + + /* "View.MemoryView":1317 + * if direct_copy: + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) # <<<<<<<<<<<<<< + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1318 + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * free(tmpdata) + */ + (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); + + /* "View.MemoryView":1319 + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) # <<<<<<<<<<<<<< + * free(tmpdata) + * return 0 + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1320 + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * + */ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1321 + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * if order == 'F' == get_best_order(&dst, ndim): + */ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1315 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + */ + } + + /* "View.MemoryView":1307 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * + */ + } + + /* "View.MemoryView":1323 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_2 = (__pyx_v_order == 'F'); + if (__pyx_t_2) { + __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); + } + if (__pyx_t_2) { + + /* "View.MemoryView":1326 + * + * + * transpose_memslice(&src) # <<<<<<<<<<<<<< + * transpose_memslice(&dst) + * + */ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1326, __pyx_L1_error) + + /* "View.MemoryView":1327 + * + * transpose_memslice(&src) + * transpose_memslice(&dst) # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + */ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1327, __pyx_L1_error) + + /* "View.MemoryView":1323 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * + */ + } + + /* "View.MemoryView":1329 + * transpose_memslice(&dst) + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) # <<<<<<<<<<<<<< + * copy_strided_to_strided(&src, &dst, ndim, itemsize) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1330 + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * + */ + copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); + + /* "View.MemoryView":1331 + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * copy_strided_to_strided(&src, &dst, ndim, itemsize) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) # <<<<<<<<<<<<<< + * + * free(tmpdata) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1333 + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * + */ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1334 + * + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_broadcast_leading') + */ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1265 + * + * @cname('__pyx_memoryview_copy_contents') + * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice dst, + * int src_ndim, int dst_ndim, + */ + + /* function exit code */ + __pyx_L1_error:; + #ifdef WITH_THREAD + __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = -1; + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1337 + * + * @cname('__pyx_memoryview_broadcast_leading') + * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< + * int ndim, + * int ndim_other) noexcept nogil: + */ + +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) { + int __pyx_v_i; + int __pyx_v_offset; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + + /* "View.MemoryView":1341 + * int ndim_other) noexcept nogil: + * cdef int i + * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<< + * + * for i in range(ndim - 1, -1, -1): + */ + __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim); + + /* "View.MemoryView":1343 + * cdef int offset = ndim_other - ndim + * + * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] + */ + for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { + __pyx_v_i = __pyx_t_1; + + /* "View.MemoryView":1344 + * + * for i in range(ndim - 1, -1, -1): + * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<< + * mslice.strides[i + offset] = mslice.strides[i] + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + */ + (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]); + + /* "View.MemoryView":1345 + * for i in range(ndim - 1, -1, -1): + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<< + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + * + */ + (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1346 + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] # <<<<<<<<<<<<<< + * + * for i in range(offset): + */ + (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]); + } + + /* "View.MemoryView":1348 + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + * + * for i in range(offset): # <<<<<<<<<<<<<< + * mslice.shape[i] = 1 + * mslice.strides[i] = mslice.strides[0] + */ + __pyx_t_1 = __pyx_v_offset; + __pyx_t_2 = __pyx_t_1; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; + + /* "View.MemoryView":1349 + * + * for i in range(offset): + * mslice.shape[i] = 1 # <<<<<<<<<<<<<< + * mslice.strides[i] = mslice.strides[0] + * mslice.suboffsets[i] = -1 + */ + (__pyx_v_mslice->shape[__pyx_v_i]) = 1; + + /* "View.MemoryView":1350 + * for i in range(offset): + * mslice.shape[i] = 1 + * mslice.strides[i] = mslice.strides[0] # <<<<<<<<<<<<<< + * mslice.suboffsets[i] = -1 + * + */ + (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]); + + /* "View.MemoryView":1351 + * mslice.shape[i] = 1 + * mslice.strides[i] = mslice.strides[0] + * mslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< + * + * + */ + (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L; + } + + /* "View.MemoryView":1337 + * + * @cname('__pyx_memoryview_broadcast_leading') + * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< + * int ndim, + * int ndim_other) noexcept nogil: + */ + + /* function exit code */ +} + +/* "View.MemoryView":1359 + * + * @cname('__pyx_memoryview_refcount_copying') + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: # <<<<<<<<<<<<<< + * + * if dtype_is_object: + */ + +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) { + + /* "View.MemoryView":1361 + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: + * + * if dtype_is_object: # <<<<<<<<<<<<<< + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc) + * + */ + if (__pyx_v_dtype_is_object) { + + /* "View.MemoryView":1362 + * + * if dtype_is_object: + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc) # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') + */ + __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc); + + /* "View.MemoryView":1361 + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: + * + * if dtype_is_object: # <<<<<<<<<<<<<< + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc) + * + */ + } + + /* "View.MemoryView":1359 + * + * @cname('__pyx_memoryview_refcount_copying') + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: # <<<<<<<<<<<<<< + * + * if dtype_is_object: + */ + + /* function exit code */ +} + +/* "View.MemoryView":1365 + * + * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') + * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< + * Py_ssize_t *strides, int ndim, + * bint inc) noexcept with gil: + */ + +static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) { + #ifdef WITH_THREAD + PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + + /* "View.MemoryView":1368 + * Py_ssize_t *strides, int ndim, + * bint inc) noexcept with gil: + * refcount_objects_in_slice(data, shape, strides, ndim, inc) # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_refcount_objects_in_slice') + */ + __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, __pyx_v_shape, __pyx_v_strides, __pyx_v_ndim, __pyx_v_inc); + + /* "View.MemoryView":1365 + * + * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') + * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< + * Py_ssize_t *strides, int ndim, + * bint inc) noexcept with gil: + */ + + /* function exit code */ + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif +} + +/* "View.MemoryView":1371 + * + * @cname('__pyx_memoryview_refcount_objects_in_slice') + * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< + * Py_ssize_t *strides, int ndim, bint inc) noexcept: + * cdef Py_ssize_t i + */ + +static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) { + CYTHON_UNUSED Py_ssize_t __pyx_v_i; + Py_ssize_t __pyx_v_stride; + Py_ssize_t __pyx_t_1; + Py_ssize_t __pyx_t_2; + Py_ssize_t __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1374 + * Py_ssize_t *strides, int ndim, bint inc) noexcept: + * cdef Py_ssize_t i + * cdef Py_ssize_t stride = strides[0] # <<<<<<<<<<<<<< + * + * for i in range(shape[0]): + */ + __pyx_v_stride = (__pyx_v_strides[0]); + + /* "View.MemoryView":1376 + * cdef Py_ssize_t stride = strides[0] + * + * for i in range(shape[0]): # <<<<<<<<<<<<<< + * if ndim == 1: + * if inc: + */ + __pyx_t_1 = (__pyx_v_shape[0]); + __pyx_t_2 = __pyx_t_1; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; + + /* "View.MemoryView":1377 + * + * for i in range(shape[0]): + * if ndim == 1: # <<<<<<<<<<<<<< + * if inc: + * Py_INCREF(( data)[0]) + */ + __pyx_t_4 = (__pyx_v_ndim == 1); + if (__pyx_t_4) { + + /* "View.MemoryView":1378 + * for i in range(shape[0]): + * if ndim == 1: + * if inc: # <<<<<<<<<<<<<< + * Py_INCREF(( data)[0]) + * else: + */ + if (__pyx_v_inc) { + + /* "View.MemoryView":1379 + * if ndim == 1: + * if inc: + * Py_INCREF(( data)[0]) # <<<<<<<<<<<<<< + * else: + * Py_DECREF(( data)[0]) + */ + Py_INCREF((((PyObject **)__pyx_v_data)[0])); + + /* "View.MemoryView":1378 + * for i in range(shape[0]): + * if ndim == 1: + * if inc: # <<<<<<<<<<<<<< + * Py_INCREF(( data)[0]) + * else: + */ + goto __pyx_L6; + } + + /* "View.MemoryView":1381 + * Py_INCREF(( data)[0]) + * else: + * Py_DECREF(( data)[0]) # <<<<<<<<<<<<<< + * else: + * refcount_objects_in_slice(data, shape + 1, strides + 1, ndim - 1, inc) + */ + /*else*/ { + Py_DECREF((((PyObject **)__pyx_v_data)[0])); + } + __pyx_L6:; + + /* "View.MemoryView":1377 + * + * for i in range(shape[0]): + * if ndim == 1: # <<<<<<<<<<<<<< + * if inc: + * Py_INCREF(( data)[0]) + */ + goto __pyx_L5; + } + + /* "View.MemoryView":1383 + * Py_DECREF(( data)[0]) + * else: + * refcount_objects_in_slice(data, shape + 1, strides + 1, ndim - 1, inc) # <<<<<<<<<<<<<< + * + * data += stride + */ + /*else*/ { + __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_inc); + } + __pyx_L5:; + + /* "View.MemoryView":1385 + * refcount_objects_in_slice(data, shape + 1, strides + 1, ndim - 1, inc) + * + * data += stride # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_data = (__pyx_v_data + __pyx_v_stride); + } + + /* "View.MemoryView":1371 + * + * @cname('__pyx_memoryview_refcount_objects_in_slice') + * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< + * Py_ssize_t *strides, int ndim, bint inc) noexcept: + * cdef Py_ssize_t i + */ + + /* function exit code */ +} + +/* "View.MemoryView":1391 + * + * @cname('__pyx_memoryview_slice_assign_scalar') + * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim, # <<<<<<<<<<<<<< + * size_t itemsize, void *item, + * bint dtype_is_object) noexcept nogil: + */ + +static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item, int __pyx_v_dtype_is_object) { + + /* "View.MemoryView":1394 + * size_t itemsize, void *item, + * bint dtype_is_object) noexcept nogil: + * refcount_copying(dst, dtype_is_object, ndim, inc=False) # <<<<<<<<<<<<<< + * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, itemsize, item) + * refcount_copying(dst, dtype_is_object, ndim, inc=True) + */ + __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1395 + * bint dtype_is_object) noexcept nogil: + * refcount_copying(dst, dtype_is_object, ndim, inc=False) + * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, itemsize, item) # <<<<<<<<<<<<<< + * refcount_copying(dst, dtype_is_object, ndim, inc=True) + * + */ + __pyx_memoryview__slice_assign_scalar(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_itemsize, __pyx_v_item); + + /* "View.MemoryView":1396 + * refcount_copying(dst, dtype_is_object, ndim, inc=False) + * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, itemsize, item) + * refcount_copying(dst, dtype_is_object, ndim, inc=True) # <<<<<<<<<<<<<< + * + * + */ + __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1391 + * + * @cname('__pyx_memoryview_slice_assign_scalar') + * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim, # <<<<<<<<<<<<<< + * size_t itemsize, void *item, + * bint dtype_is_object) noexcept nogil: + */ + + /* function exit code */ +} + +/* "View.MemoryView":1400 + * + * @cname('__pyx_memoryview__slice_assign_scalar') + * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< + * Py_ssize_t *strides, int ndim, + * size_t itemsize, void *item) noexcept nogil: + */ + +static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item) { + CYTHON_UNUSED Py_ssize_t __pyx_v_i; + Py_ssize_t __pyx_v_stride; + Py_ssize_t __pyx_v_extent; + int __pyx_t_1; + Py_ssize_t __pyx_t_2; + Py_ssize_t __pyx_t_3; + Py_ssize_t __pyx_t_4; + + /* "View.MemoryView":1404 + * size_t itemsize, void *item) noexcept nogil: + * cdef Py_ssize_t i + * cdef Py_ssize_t stride = strides[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t extent = shape[0] + * + */ + __pyx_v_stride = (__pyx_v_strides[0]); + + /* "View.MemoryView":1405 + * cdef Py_ssize_t i + * cdef Py_ssize_t stride = strides[0] + * cdef Py_ssize_t extent = shape[0] # <<<<<<<<<<<<<< + * + * if ndim == 1: + */ + __pyx_v_extent = (__pyx_v_shape[0]); + + /* "View.MemoryView":1407 + * cdef Py_ssize_t extent = shape[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * for i in range(extent): + * memcpy(data, item, itemsize) + */ + __pyx_t_1 = (__pyx_v_ndim == 1); + if (__pyx_t_1) { + + /* "View.MemoryView":1408 + * + * if ndim == 1: + * for i in range(extent): # <<<<<<<<<<<<<< + * memcpy(data, item, itemsize) + * data += stride + */ + __pyx_t_2 = __pyx_v_extent; + __pyx_t_3 = __pyx_t_2; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_i = __pyx_t_4; + + /* 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goto bad; + return o; + bad: + Py_DECREF(o); o = 0; + return NULL; +} + +static void __pyx_tp_dealloc_array(PyObject *o) { + struct __pyx_array_obj *p = (struct __pyx_array_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && (!PyType_IS_GC(Py_TYPE(o)) || !__Pyx_PyObject_GC_IsFinalized(o))) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_array) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); + __pyx_array___dealloc__(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->mode); + Py_CLEAR(p->_format); + #if CYTHON_USE_TYPE_SLOTS || CYTHON_COMPILING_IN_PYPY + (*Py_TYPE(o)->tp_free)(o); + #else + { + freefunc tp_free = (freefunc)PyType_GetSlot(Py_TYPE(o), Py_tp_free); + if (tp_free) tp_free(o); + } + #endif +} +static PyObject *__pyx_sq_item_array(PyObject *o, Py_ssize_t i) { + PyObject *r; + PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; + r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); + Py_DECREF(x); + return r; +} + +static int __pyx_mp_ass_subscript_array(PyObject *o, PyObject *i, PyObject *v) { + if (v) { + return __pyx_array___setitem__(o, i, v); + } + else { + __Pyx_TypeName o_type_name; + o_type_name = __Pyx_PyType_GetName(Py_TYPE(o)); + PyErr_Format(PyExc_NotImplementedError, + "Subscript deletion not supported by " __Pyx_FMT_TYPENAME, o_type_name); + __Pyx_DECREF_TypeName(o_type_name); + return -1; + } +} + +static PyObject *__pyx_tp_getattro_array(PyObject *o, PyObject *n) { + PyObject *v = __Pyx_PyObject_GenericGetAttr(o, n); + if (!v && PyErr_ExceptionMatches(PyExc_AttributeError)) { + PyErr_Clear(); + v = __pyx_array___getattr__(o, n); + } + return v; +} + +static PyObject *__pyx_getprop___pyx_array_memview(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(o); +} + +static PyMethodDef __pyx_methods_array[] = { + {"__getattr__", (PyCFunction)__pyx_array___getattr__, METH_O|METH_COEXIST, 0}, + {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_array_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_array_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_array[] = { + {(char *)"memview", __pyx_getprop___pyx_array_memview, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +#if !CYTHON_COMPILING_IN_LIMITED_API + +static PyBufferProcs __pyx_tp_as_buffer_array = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_array_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; +#endif +static PyType_Slot __pyx_type___pyx_array_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc_array}, + {Py_sq_length, (void *)__pyx_array___len__}, + {Py_sq_item, (void *)__pyx_sq_item_array}, + {Py_mp_length, (void *)__pyx_array___len__}, + {Py_mp_subscript, (void *)__pyx_array___getitem__}, + {Py_mp_ass_subscript, (void *)__pyx_mp_ass_subscript_array}, + {Py_tp_getattro, (void *)__pyx_tp_getattro_array}, + #if defined(Py_bf_getbuffer) + {Py_bf_getbuffer, (void *)__pyx_array_getbuffer}, + #endif + {Py_tp_methods, (void *)__pyx_methods_array}, + {Py_tp_getset, (void *)__pyx_getsets_array}, + {Py_tp_new, (void *)__pyx_tp_new_array}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_array_spec = { + "monotonic_align.core.array", + sizeof(struct __pyx_array_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_SEQUENCE, + __pyx_type___pyx_array_slots, +}; +#else + +static PySequenceMethods __pyx_tp_as_sequence_array = { + __pyx_array___len__, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + __pyx_sq_item_array, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + 0, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyMappingMethods __pyx_tp_as_mapping_array = { + __pyx_array___len__, /*mp_length*/ + __pyx_array___getitem__, /*mp_subscript*/ + __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_array = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_array_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; + +static PyTypeObject __pyx_type___pyx_array = { + PyVarObject_HEAD_INIT(0, 0) + "monotonic_align.core.""array", /*tp_name*/ + sizeof(struct __pyx_array_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_array, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + 0, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_array, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_array, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + __pyx_tp_getattro_array, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_array, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_SEQUENCE, /*tp_flags*/ + 0, /*tp_doc*/ + 0, /*tp_traverse*/ + 0, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_array, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_array, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_array, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif + +static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { + struct __pyx_MemviewEnum_obj *p; + PyObject *o; + #if CYTHON_COMPILING_IN_LIMITED_API + allocfunc alloc_func = (allocfunc)PyType_GetSlot(t, Py_tp_alloc); + o = alloc_func(t, 0); + #else + if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + #endif + p = ((struct __pyx_MemviewEnum_obj *)o); + p->name = Py_None; Py_INCREF(Py_None); + return o; +} + +static void __pyx_tp_dealloc_Enum(PyObject *o) { + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_Enum) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + PyObject_GC_UnTrack(o); + Py_CLEAR(p->name); + #if CYTHON_USE_TYPE_SLOTS || CYTHON_COMPILING_IN_PYPY + (*Py_TYPE(o)->tp_free)(o); + #else + { + freefunc tp_free = (freefunc)PyType_GetSlot(Py_TYPE(o), Py_tp_free); + if (tp_free) tp_free(o); + } + #endif +} + +static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + if (p->name) { + e = (*v)(p->name, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_Enum(PyObject *o) { + PyObject* tmp; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + tmp = ((PyObject*)p->name); + p->name = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + return 0; +} + +static PyObject *__pyx_specialmethod___pyx_MemviewEnum___repr__(PyObject *self, CYTHON_UNUSED PyObject *arg) { + return __pyx_MemviewEnum___repr__(self); +} + +static PyMethodDef __pyx_methods_Enum[] = { + {"__repr__", (PyCFunction)__pyx_specialmethod___pyx_MemviewEnum___repr__, METH_NOARGS|METH_COEXIST, 0}, + {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +static PyType_Slot __pyx_type___pyx_MemviewEnum_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc_Enum}, + {Py_tp_repr, (void *)__pyx_MemviewEnum___repr__}, + {Py_tp_traverse, (void *)__pyx_tp_traverse_Enum}, + {Py_tp_clear, (void *)__pyx_tp_clear_Enum}, + {Py_tp_methods, (void *)__pyx_methods_Enum}, + {Py_tp_init, (void *)__pyx_MemviewEnum___init__}, + {Py_tp_new, (void *)__pyx_tp_new_Enum}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_MemviewEnum_spec = { + "monotonic_align.core.Enum", + sizeof(struct __pyx_MemviewEnum_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, + __pyx_type___pyx_MemviewEnum_slots, +}; +#else + +static PyTypeObject __pyx_type___pyx_MemviewEnum = { + PyVarObject_HEAD_INIT(0, 0) + "monotonic_align.core.""Enum", /*tp_name*/ + sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_Enum, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_MemviewEnum___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_Enum, /*tp_traverse*/ + __pyx_tp_clear_Enum, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_Enum, /*tp_methods*/ + 0, /*tp_members*/ + 0, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + __pyx_MemviewEnum___init__, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_Enum, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif +static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; + +static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryview_obj *p; + PyObject *o; + #if CYTHON_COMPILING_IN_LIMITED_API + allocfunc alloc_func = (allocfunc)PyType_GetSlot(t, Py_tp_alloc); + o = alloc_func(t, 0); + #else + if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + #endif + p = ((struct __pyx_memoryview_obj *)o); + p->__pyx_vtab = __pyx_vtabptr_memoryview; + p->obj = Py_None; Py_INCREF(Py_None); + p->_size = Py_None; Py_INCREF(Py_None); + p->_array_interface = Py_None; Py_INCREF(Py_None); + p->view.obj = NULL; + if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; + return o; + bad: + Py_DECREF(o); o = 0; + return NULL; +} + +static void __pyx_tp_dealloc_memoryview(PyObject *o) { + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_memoryview) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); + __pyx_memoryview___dealloc__(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->obj); + Py_CLEAR(p->_size); + Py_CLEAR(p->_array_interface); + #if CYTHON_USE_TYPE_SLOTS || CYTHON_COMPILING_IN_PYPY + (*Py_TYPE(o)->tp_free)(o); + #else + { + freefunc tp_free = (freefunc)PyType_GetSlot(Py_TYPE(o), Py_tp_free); + if (tp_free) tp_free(o); + } + #endif +} + +static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + if (p->obj) { + e = (*v)(p->obj, a); if (e) return e; + } + if (p->_size) { + e = (*v)(p->_size, a); if (e) return e; + } + if (p->_array_interface) { + e = (*v)(p->_array_interface, a); if (e) return e; + } + if (p->view.obj) { + e = (*v)(p->view.obj, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_memoryview(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + tmp = ((PyObject*)p->obj); + p->obj = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_size); + p->_size = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_array_interface); + p->_array_interface = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + Py_CLEAR(p->view.obj); + return 0; +} +static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { + PyObject *r; + PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; + r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); + Py_DECREF(x); + return r; +} + +static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { + if (v) { + return __pyx_memoryview___setitem__(o, i, v); + } + else { + __Pyx_TypeName o_type_name; + o_type_name = __Pyx_PyType_GetName(Py_TYPE(o)); + PyErr_Format(PyExc_NotImplementedError, + "Subscript deletion not supported by " __Pyx_FMT_TYPENAME, o_type_name); + __Pyx_DECREF_TypeName(o_type_name); + return -1; + } +} + +static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); +} + +static PyObject *__pyx_specialmethod___pyx_memoryview___repr__(PyObject *self, CYTHON_UNUSED PyObject *arg) { + return __pyx_memoryview___repr__(self); +} + +static PyMethodDef __pyx_methods_memoryview[] = { + {"__repr__", (PyCFunction)__pyx_specialmethod___pyx_memoryview___repr__, METH_NOARGS|METH_COEXIST, 0}, + {"is_c_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_c_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"is_f_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_f_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"copy", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"copy_fortran", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy_fortran, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_memoryview[] = { + {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, + {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, + {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, + {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, + {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, + {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, + {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, + {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, + {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +#if !CYTHON_COMPILING_IN_LIMITED_API + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; +#endif +static PyType_Slot __pyx_type___pyx_memoryview_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc_memoryview}, + {Py_tp_repr, (void *)__pyx_memoryview___repr__}, + {Py_sq_length, (void *)__pyx_memoryview___len__}, + {Py_sq_item, (void *)__pyx_sq_item_memoryview}, + {Py_mp_length, (void *)__pyx_memoryview___len__}, + {Py_mp_subscript, (void *)__pyx_memoryview___getitem__}, + {Py_mp_ass_subscript, (void *)__pyx_mp_ass_subscript_memoryview}, + {Py_tp_str, (void *)__pyx_memoryview___str__}, + #if defined(Py_bf_getbuffer) + {Py_bf_getbuffer, (void *)__pyx_memoryview_getbuffer}, + #endif + {Py_tp_traverse, (void *)__pyx_tp_traverse_memoryview}, + {Py_tp_clear, (void *)__pyx_tp_clear_memoryview}, + {Py_tp_methods, (void *)__pyx_methods_memoryview}, + {Py_tp_getset, (void *)__pyx_getsets_memoryview}, + {Py_tp_new, (void *)__pyx_tp_new_memoryview}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_memoryview_spec = { + "monotonic_align.core.memoryview", + sizeof(struct __pyx_memoryview_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, + __pyx_type___pyx_memoryview_slots, +}; +#else + +static PySequenceMethods __pyx_tp_as_sequence_memoryview = { + __pyx_memoryview___len__, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + __pyx_sq_item_memoryview, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + 0, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyMappingMethods __pyx_tp_as_mapping_memoryview = { + __pyx_memoryview___len__, /*mp_length*/ + __pyx_memoryview___getitem__, /*mp_subscript*/ + __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; + +static PyTypeObject __pyx_type___pyx_memoryview = { + PyVarObject_HEAD_INIT(0, 0) + "monotonic_align.core.""memoryview", /*tp_name*/ + sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_memoryview___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + __pyx_memoryview___str__, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_memoryview, /*tp_traverse*/ + __pyx_tp_clear_memoryview, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_memoryview, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_memoryview, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_memoryview, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif +static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; + +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryviewslice_obj *p; + PyObject *o = __pyx_tp_new_memoryview(t, a, k); + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryviewslice_obj *)o); + p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; + p->from_object = Py_None; Py_INCREF(Py_None); + p->from_slice.memview = NULL; + return o; +} + +static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc__memoryviewslice) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); + __pyx_memoryviewslice___dealloc__(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->from_object); + PyObject_GC_Track(o); + __pyx_tp_dealloc_memoryview(o); +} + +static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; + if (p->from_object) { + e = (*v)(p->from_object, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear__memoryviewslice(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + __pyx_tp_clear_memoryview(o); + tmp = ((PyObject*)p->from_object); + p->from_object = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + __PYX_XCLEAR_MEMVIEW(&p->from_slice, 1); + return 0; +} + +static PyMethodDef __pyx_methods__memoryviewslice[] = { + {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +static PyType_Slot __pyx_type___pyx_memoryviewslice_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc__memoryviewslice}, + {Py_tp_doc, (void *)PyDoc_STR("Internal class for passing memoryview slices to Python")}, + {Py_tp_traverse, (void *)__pyx_tp_traverse__memoryviewslice}, + {Py_tp_clear, (void *)__pyx_tp_clear__memoryviewslice}, + {Py_tp_methods, (void *)__pyx_methods__memoryviewslice}, + {Py_tp_new, (void *)__pyx_tp_new__memoryviewslice}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_memoryviewslice_spec = { + "monotonic_align.core._memoryviewslice", + sizeof(struct __pyx_memoryviewslice_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC|Py_TPFLAGS_SEQUENCE, + __pyx_type___pyx_memoryviewslice_slots, +}; +#else + +static PyTypeObject __pyx_type___pyx_memoryviewslice = { + PyVarObject_HEAD_INIT(0, 0) + "monotonic_align.core.""_memoryviewslice", /*tp_name*/ + sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + #if CYTHON_COMPILING_IN_PYPY || 0 + __pyx_memoryview___repr__, /*tp_repr*/ + #else + 0, /*tp_repr*/ + #endif + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + #if CYTHON_COMPILING_IN_PYPY || 0 + __pyx_memoryview___str__, /*tp_str*/ + #else + 0, /*tp_str*/ + #endif + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC|Py_TPFLAGS_SEQUENCE, /*tp_flags*/ + PyDoc_STR("Internal class for passing memoryview slices to Python"), /*tp_doc*/ + __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/ + __pyx_tp_clear__memoryviewslice, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods__memoryviewslice, /*tp_methods*/ + 0, /*tp_members*/ + 0, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new__memoryviewslice, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif + +static PyMethodDef __pyx_methods[] = { + {0, 0, 0, 0} +}; +#ifndef CYTHON_SMALL_CODE +#if defined(__clang__) + #define CYTHON_SMALL_CODE +#elif defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 3)) + #define CYTHON_SMALL_CODE __attribute__((cold)) +#else + #define CYTHON_SMALL_CODE +#endif +#endif +/* #### Code section: pystring_table ### */ + +static int __Pyx_CreateStringTabAndInitStrings(void) { + __Pyx_StringTabEntry __pyx_string_tab[] = { + {&__pyx_kp_u_, __pyx_k_, sizeof(__pyx_k_), 0, 1, 0, 0}, + {&__pyx_n_s_ASCII, __pyx_k_ASCII, sizeof(__pyx_k_ASCII), 0, 0, 1, 1}, + {&__pyx_kp_s_All_dimensions_preceding_dimensi, __pyx_k_All_dimensions_preceding_dimensi, sizeof(__pyx_k_All_dimensions_preceding_dimensi), 0, 0, 1, 0}, + {&__pyx_n_s_AssertionError, __pyx_k_AssertionError, sizeof(__pyx_k_AssertionError), 0, 0, 1, 1}, + {&__pyx_kp_s_Buffer_view_does_not_expose_stri, __pyx_k_Buffer_view_does_not_expose_stri, sizeof(__pyx_k_Buffer_view_does_not_expose_stri), 0, 0, 1, 0}, + {&__pyx_kp_s_Can_only_create_a_buffer_that_is, __pyx_k_Can_only_create_a_buffer_that_is, sizeof(__pyx_k_Can_only_create_a_buffer_that_is), 0, 0, 1, 0}, + {&__pyx_kp_s_Cannot_assign_to_read_only_memor, __pyx_k_Cannot_assign_to_read_only_memor, sizeof(__pyx_k_Cannot_assign_to_read_only_memor), 0, 0, 1, 0}, + {&__pyx_kp_s_Cannot_create_writable_memory_vi, __pyx_k_Cannot_create_writable_memory_vi, sizeof(__pyx_k_Cannot_create_writable_memory_vi), 0, 0, 1, 0}, + {&__pyx_kp_u_Cannot_index_with_type, __pyx_k_Cannot_index_with_type, sizeof(__pyx_k_Cannot_index_with_type), 0, 1, 0, 0}, + {&__pyx_kp_s_Cannot_transpose_memoryview_with, __pyx_k_Cannot_transpose_memoryview_with, sizeof(__pyx_k_Cannot_transpose_memoryview_with), 0, 0, 1, 0}, + {&__pyx_kp_s_Dimension_d_is_not_direct, __pyx_k_Dimension_d_is_not_direct, sizeof(__pyx_k_Dimension_d_is_not_direct), 0, 0, 1, 0}, + {&__pyx_n_s_Ellipsis, __pyx_k_Ellipsis, sizeof(__pyx_k_Ellipsis), 0, 0, 1, 1}, + {&__pyx_kp_s_Empty_shape_tuple_for_cython_arr, __pyx_k_Empty_shape_tuple_for_cython_arr, sizeof(__pyx_k_Empty_shape_tuple_for_cython_arr), 0, 0, 1, 0}, + {&__pyx_kp_s_Incompatible_checksums_0x_x_vs_0, __pyx_k_Incompatible_checksums_0x_x_vs_0, sizeof(__pyx_k_Incompatible_checksums_0x_x_vs_0), 0, 0, 1, 0}, + {&__pyx_n_s_IndexError, __pyx_k_IndexError, 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+ if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#endif + +/* PyObjectGetAttrStrNoError */ +#if __PYX_LIMITED_VERSION_HEX < 0x030d00A1 +static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + __Pyx_PyErr_Clear(); +} +#endif +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) { + PyObject *result; +#if __PYX_LIMITED_VERSION_HEX >= 0x030d00A1 + (void) PyObject_GetOptionalAttr(obj, attr_name, &result); + return result; +#else +#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1 + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) { + return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1); + } +#endif + result = __Pyx_PyObject_GetAttrStr(obj, attr_name); + if (unlikely(!result)) { + __Pyx_PyObject_GetAttrStr_ClearAttributeError(); + } + return result; +#endif +} + +/* GetBuiltinName */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name) { + PyObject* result = __Pyx_PyObject_GetAttrStrNoError(__pyx_b, name); + if (unlikely(!result) && !PyErr_Occurred()) { + PyErr_Format(PyExc_NameError, +#if PY_MAJOR_VERSION >= 3 + "name '%U' is not defined", name); +#else + "name '%.200s' is not defined", PyString_AS_STRING(name)); +#endif + } + return result; +} + +/* TupleAndListFromArray */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE void __Pyx_copy_object_array(PyObject *const *CYTHON_RESTRICT src, PyObject** CYTHON_RESTRICT dest, Py_ssize_t length) { + PyObject *v; + Py_ssize_t i; + for (i = 0; i < length; i++) { + v = dest[i] = src[i]; + Py_INCREF(v); + } +} +static CYTHON_INLINE PyObject * +__Pyx_PyTuple_FromArray(PyObject *const *src, Py_ssize_t n) +{ + PyObject *res; + if (n <= 0) { + Py_INCREF(__pyx_empty_tuple); + return __pyx_empty_tuple; + } + res = PyTuple_New(n); + if (unlikely(res == NULL)) return NULL; + __Pyx_copy_object_array(src, ((PyTupleObject*)res)->ob_item, n); + return res; +} +static CYTHON_INLINE PyObject * +__Pyx_PyList_FromArray(PyObject *const *src, Py_ssize_t n) +{ + PyObject *res; + if (n <= 0) { + return PyList_New(0); + } + res = PyList_New(n); + if (unlikely(res == NULL)) return NULL; + __Pyx_copy_object_array(src, ((PyListObject*)res)->ob_item, n); + return res; +} +#endif + +/* BytesEquals */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API + return PyObject_RichCompareBool(s1, s2, equals); +#else + if (s1 == s2) { + return (equals == Py_EQ); + } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { + const char *ps1, *ps2; + Py_ssize_t length = PyBytes_GET_SIZE(s1); + if (length != PyBytes_GET_SIZE(s2)) + return (equals == Py_NE); + ps1 = PyBytes_AS_STRING(s1); + ps2 = PyBytes_AS_STRING(s2); + if (ps1[0] != ps2[0]) { + return (equals == Py_NE); + } else if (length == 1) { + return (equals == Py_EQ); + } else { + int result; +#if CYTHON_USE_UNICODE_INTERNALS && (PY_VERSION_HEX < 0x030B0000) + Py_hash_t hash1, hash2; + hash1 = ((PyBytesObject*)s1)->ob_shash; + hash2 = ((PyBytesObject*)s2)->ob_shash; + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + return (equals == Py_NE); + } +#endif + result = memcmp(ps1, ps2, (size_t)length); + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { + return (equals == Py_NE); + } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { + return (equals == Py_NE); + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +#endif +} + +/* UnicodeEquals */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API + return PyObject_RichCompareBool(s1, s2, equals); +#else +#if PY_MAJOR_VERSION < 3 + PyObject* owned_ref = NULL; +#endif + int s1_is_unicode, s2_is_unicode; + if (s1 == s2) { + goto return_eq; + } + s1_is_unicode = PyUnicode_CheckExact(s1); + s2_is_unicode = PyUnicode_CheckExact(s2); +#if PY_MAJOR_VERSION < 3 + if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { + owned_ref = PyUnicode_FromObject(s2); + if (unlikely(!owned_ref)) + return -1; + s2 = owned_ref; + s2_is_unicode = 1; + } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { + owned_ref = PyUnicode_FromObject(s1); + if (unlikely(!owned_ref)) + return -1; + s1 = owned_ref; + s1_is_unicode = 1; + } else if (((!s2_is_unicode) & (!s1_is_unicode))) { + return __Pyx_PyBytes_Equals(s1, s2, equals); + } +#endif + if (s1_is_unicode & s2_is_unicode) { + Py_ssize_t length; + int kind; + void *data1, *data2; + if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) + return -1; + length = __Pyx_PyUnicode_GET_LENGTH(s1); + if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { + goto return_ne; + } +#if CYTHON_USE_UNICODE_INTERNALS + { + Py_hash_t hash1, hash2; + #if CYTHON_PEP393_ENABLED + hash1 = ((PyASCIIObject*)s1)->hash; + hash2 = ((PyASCIIObject*)s2)->hash; + #else + hash1 = ((PyUnicodeObject*)s1)->hash; + hash2 = ((PyUnicodeObject*)s2)->hash; + #endif + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + goto return_ne; + } + } +#endif + kind = __Pyx_PyUnicode_KIND(s1); + if (kind != __Pyx_PyUnicode_KIND(s2)) { + goto return_ne; + } + data1 = __Pyx_PyUnicode_DATA(s1); + data2 = __Pyx_PyUnicode_DATA(s2); + if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { + goto return_ne; + } else if (length == 1) { + goto return_eq; + } else { + int result = memcmp(data1, data2, (size_t)(length * kind)); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & s2_is_unicode) { + goto return_ne; + } else if ((s2 == Py_None) & s1_is_unicode) { + goto return_ne; + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +return_eq: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ); +return_ne: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_NE); +#endif +} + +/* fastcall */ +#if CYTHON_METH_FASTCALL +static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s) +{ + Py_ssize_t i, n = PyTuple_GET_SIZE(kwnames); + for (i = 0; i < n; i++) + { + if (s == PyTuple_GET_ITEM(kwnames, i)) return kwvalues[i]; + } + for (i = 0; i < n; i++) + { + int eq = __Pyx_PyUnicode_Equals(s, PyTuple_GET_ITEM(kwnames, i), Py_EQ); + if (unlikely(eq != 0)) { + if (unlikely(eq < 0)) return NULL; // error + return kwvalues[i]; + } + } + return NULL; // not found (no exception set) +} +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030d0000 +CYTHON_UNUSED static PyObject *__Pyx_KwargsAsDict_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues) { + Py_ssize_t i, nkwargs = PyTuple_GET_SIZE(kwnames); + PyObject *dict; + dict = PyDict_New(); + if (unlikely(!dict)) + return NULL; + for (i=0; i= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +/* ParseKeywords */ +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject *const *kwvalues, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + int kwds_is_tuple = CYTHON_METH_FASTCALL && likely(PyTuple_Check(kwds)); + while (1) { + Py_XDECREF(key); key = NULL; + Py_XDECREF(value); value = NULL; + if (kwds_is_tuple) { + Py_ssize_t size; +#if CYTHON_ASSUME_SAFE_MACROS + size = PyTuple_GET_SIZE(kwds); +#else + size = PyTuple_Size(kwds); + if (size < 0) goto bad; +#endif + if (pos >= size) break; +#if CYTHON_AVOID_BORROWED_REFS + key = __Pyx_PySequence_ITEM(kwds, pos); + if (!key) goto bad; +#elif CYTHON_ASSUME_SAFE_MACROS + key = PyTuple_GET_ITEM(kwds, pos); +#else + key = PyTuple_GetItem(kwds, pos); + if (!key) goto bad; +#endif + value = kwvalues[pos]; + pos++; + } + else + { + if (!PyDict_Next(kwds, &pos, &key, &value)) break; +#if CYTHON_AVOID_BORROWED_REFS + Py_INCREF(key); +#endif + } + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; +#if CYTHON_AVOID_BORROWED_REFS + Py_INCREF(value); // transfer ownership of value to values + Py_DECREF(key); +#endif + key = NULL; + value = NULL; + continue; + } +#if !CYTHON_AVOID_BORROWED_REFS + Py_INCREF(key); +#endif + Py_INCREF(value); + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; +#if CYTHON_AVOID_BORROWED_REFS + value = NULL; // ownership transferred to values +#endif + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = ( + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key) + ); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; +#if CYTHON_AVOID_BORROWED_REFS + value = NULL; // ownership transferred to values +#endif + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + Py_XDECREF(key); + Py_XDECREF(value); + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + #if PY_MAJOR_VERSION < 3 + PyErr_Format(PyExc_TypeError, + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + PyErr_Format(PyExc_TypeError, + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + Py_XDECREF(key); + Py_XDECREF(value); + return -1; +} + +/* ArgTypeTest */ +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) +{ + __Pyx_TypeName type_name; + __Pyx_TypeName obj_type_name; + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + else if (exact) { + #if PY_MAJOR_VERSION == 2 + if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(__Pyx_TypeCheck(obj, type))) return 1; + } + type_name = __Pyx_PyType_GetName(type); + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected " __Pyx_FMT_TYPENAME + ", got " __Pyx_FMT_TYPENAME ")", name, type_name, obj_type_name); + __Pyx_DECREF_TypeName(type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return 0; +} + +/* RaiseException */ +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + __Pyx_PyThreadState_declare + CYTHON_UNUSED_VAR(cause); + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_PyThreadState_assign + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } + if (cause) { + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { + #if PY_VERSION_HEX >= 0x030C00A6 + PyException_SetTraceback(value, tb); + #elif CYTHON_FAST_THREAD_STATE + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#else + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +/* PyFunctionFastCall */ +#if CYTHON_FAST_PYCALL && !CYTHON_VECTORCALL +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = __Pyx_PyFrame_GetLocalsplus(f); + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + #if PY_MAJOR_VERSION < 3 + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) { + return NULL; + } + #else + if (unlikely(Py_EnterRecursiveCall(" while calling a Python object"))) { + return NULL; + } + #endif + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif + +/* PyObjectCall */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = Py_TYPE(func)->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + #if PY_MAJOR_VERSION < 3 + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + #else + if (unlikely(Py_EnterRecursiveCall(" while calling a Python object"))) + return NULL; + #endif + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallMethO */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = __Pyx_CyOrPyCFunction_GET_FUNCTION(func); + self = __Pyx_CyOrPyCFunction_GET_SELF(func); + #if PY_MAJOR_VERSION < 3 + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + #else + if (unlikely(Py_EnterRecursiveCall(" while calling a Python object"))) + return NULL; + #endif + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectFastCall */ +#if PY_VERSION_HEX < 0x03090000 || CYTHON_COMPILING_IN_LIMITED_API +static PyObject* __Pyx_PyObject_FastCall_fallback(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs) { + PyObject *argstuple; + PyObject *result = 0; + size_t i; + argstuple = PyTuple_New((Py_ssize_t)nargs); + if (unlikely(!argstuple)) return NULL; + for (i = 0; i < nargs; i++) { + Py_INCREF(args[i]); + if (__Pyx_PyTuple_SET_ITEM(argstuple, (Py_ssize_t)i, args[i]) < 0) goto bad; + } + result = __Pyx_PyObject_Call(func, argstuple, kwargs); + bad: + Py_DECREF(argstuple); + return result; +} +#endif +static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t _nargs, PyObject *kwargs) { + Py_ssize_t nargs = __Pyx_PyVectorcall_NARGS(_nargs); +#if CYTHON_COMPILING_IN_CPYTHON + if (nargs == 0 && kwargs == NULL) { + if (__Pyx_CyOrPyCFunction_Check(func) && likely( __Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_NOARGS)) + return __Pyx_PyObject_CallMethO(func, NULL); + } + else if (nargs == 1 && kwargs == NULL) { + if (__Pyx_CyOrPyCFunction_Check(func) && likely( __Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_O)) + return __Pyx_PyObject_CallMethO(func, args[0]); + } +#endif + #if PY_VERSION_HEX < 0x030800B1 + #if CYTHON_FAST_PYCCALL + if (PyCFunction_Check(func)) { + if (kwargs) { + return _PyCFunction_FastCallDict(func, args, nargs, kwargs); + } else { + return _PyCFunction_FastCallKeywords(func, args, nargs, NULL); + } + } + #if PY_VERSION_HEX >= 0x030700A1 + if (!kwargs && __Pyx_IS_TYPE(func, &PyMethodDescr_Type)) { + return _PyMethodDescr_FastCallKeywords(func, args, nargs, NULL); + } + #endif + #endif + #if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs); + } + #endif + #endif + if (kwargs == NULL) { + #if CYTHON_VECTORCALL + #if PY_VERSION_HEX < 0x03090000 + vectorcallfunc f = _PyVectorcall_Function(func); + #else + vectorcallfunc f = PyVectorcall_Function(func); + #endif + if (f) { + return f(func, args, (size_t)nargs, NULL); + } + #elif defined(__Pyx_CyFunction_USED) && CYTHON_BACKPORT_VECTORCALL + if (__Pyx_CyFunction_CheckExact(func)) { + __pyx_vectorcallfunc f = __Pyx_CyFunction_func_vectorcall(func); + if (f) return f(func, args, (size_t)nargs, NULL); + } + #endif + } + if (nargs == 0) { + return __Pyx_PyObject_Call(func, __pyx_empty_tuple, kwargs); + } + #if PY_VERSION_HEX >= 0x03090000 && !CYTHON_COMPILING_IN_LIMITED_API + return PyObject_VectorcallDict(func, args, (size_t)nargs, kwargs); + #else + return __Pyx_PyObject_FastCall_fallback(func, args, (size_t)nargs, kwargs); + #endif +} + +/* RaiseUnexpectedTypeError */ +static int +__Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj) +{ + __Pyx_TypeName obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, "Expected %s, got " __Pyx_FMT_TYPENAME, + expected, obj_type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return 0; +} + +/* CIntToDigits */ +static const char DIGIT_PAIRS_10[2*10*10+1] = { + "00010203040506070809" + "10111213141516171819" + "20212223242526272829" + "30313233343536373839" + "40414243444546474849" + "50515253545556575859" + "60616263646566676869" + "70717273747576777879" + "80818283848586878889" + "90919293949596979899" +}; +static const char DIGIT_PAIRS_8[2*8*8+1] = { + "0001020304050607" + "1011121314151617" + "2021222324252627" + "3031323334353637" + "4041424344454647" + "5051525354555657" + "6061626364656667" + "7071727374757677" +}; +static const char DIGITS_HEX[2*16+1] = { + "0123456789abcdef" + "0123456789ABCDEF" +}; + +/* BuildPyUnicode */ +static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, + int prepend_sign, char padding_char) { + PyObject *uval; + Py_ssize_t uoffset = ulength - clength; +#if CYTHON_USE_UNICODE_INTERNALS + Py_ssize_t i; +#if CYTHON_PEP393_ENABLED + void *udata; + uval = PyUnicode_New(ulength, 127); + if (unlikely(!uval)) return NULL; + udata = PyUnicode_DATA(uval); +#else + Py_UNICODE *udata; + uval = PyUnicode_FromUnicode(NULL, ulength); + if (unlikely(!uval)) return NULL; + udata = PyUnicode_AS_UNICODE(uval); +#endif + if (uoffset > 0) { + i = 0; + if (prepend_sign) { + __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, 0, '-'); + i++; + } + for (; i < uoffset; i++) { + __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, i, padding_char); + } + } + for (i=0; i < clength; i++) { + __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, uoffset+i, chars[i]); + } +#else + { + PyObject *sign = NULL, *padding = NULL; + uval = NULL; + if (uoffset > 0) { + prepend_sign = !!prepend_sign; + if (uoffset > prepend_sign) { + padding = PyUnicode_FromOrdinal(padding_char); + if (likely(padding) && uoffset > prepend_sign + 1) { + PyObject *tmp; + PyObject *repeat = PyInt_FromSsize_t(uoffset - prepend_sign); + if (unlikely(!repeat)) goto done_or_error; + tmp = PyNumber_Multiply(padding, repeat); + Py_DECREF(repeat); + Py_DECREF(padding); + padding = tmp; + } + if (unlikely(!padding)) goto done_or_error; + } + if (prepend_sign) { + sign = PyUnicode_FromOrdinal('-'); + if (unlikely(!sign)) goto done_or_error; + } + } + uval = PyUnicode_DecodeASCII(chars, clength, NULL); + if (likely(uval) && padding) { + PyObject *tmp = PyNumber_Add(padding, uval); + Py_DECREF(uval); + uval = tmp; + } + if (likely(uval) && sign) { + PyObject *tmp = PyNumber_Add(sign, uval); + Py_DECREF(uval); + uval = tmp; + } +done_or_error: + Py_XDECREF(padding); + Py_XDECREF(sign); + } +#endif + return uval; +} + +/* CIntToPyUnicode */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char) { + char digits[sizeof(int)*3+2]; + char *dpos, *end = digits + sizeof(int)*3+2; + const char *hex_digits = DIGITS_HEX; + Py_ssize_t length, ulength; + int prepend_sign, last_one_off; + int remaining; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (format_char == 'X') { + hex_digits += 16; + format_char = 'x'; + } + remaining = value; + last_one_off = 0; + dpos = end; + do { + int digit_pos; + switch (format_char) { + case 'o': + digit_pos = abs((int)(remaining % (8*8))); + remaining = (int) (remaining / (8*8)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2); + last_one_off = (digit_pos < 8); + break; + case 'd': + digit_pos = abs((int)(remaining % (10*10))); + remaining = (int) (remaining / (10*10)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2); + last_one_off = (digit_pos < 10); + break; + case 'x': + *(--dpos) = hex_digits[abs((int)(remaining % 16))]; + remaining = (int) (remaining / 16); + break; + default: + assert(0); + break; + } + } while (unlikely(remaining != 0)); + assert(!last_one_off || *dpos == '0'); + dpos += last_one_off; + length = end - dpos; + ulength = length; + prepend_sign = 0; + if (!is_unsigned && value <= neg_one) { + if (padding_char == ' ' || width <= length + 1) { + *(--dpos) = '-'; + ++length; + } else { + prepend_sign = 1; + } + ++ulength; + } + if (width > ulength) { + ulength = width; + } + if (ulength == 1) { + return PyUnicode_FromOrdinal(*dpos); + } + return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); +} + +/* CIntToPyUnicode */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char) { + char digits[sizeof(Py_ssize_t)*3+2]; + char *dpos, *end = digits + sizeof(Py_ssize_t)*3+2; + const char *hex_digits = DIGITS_HEX; + Py_ssize_t length, ulength; + int prepend_sign, last_one_off; + Py_ssize_t remaining; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const Py_ssize_t neg_one = (Py_ssize_t) -1, const_zero = (Py_ssize_t) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (format_char == 'X') { + hex_digits += 16; + format_char = 'x'; + } + remaining = value; + last_one_off = 0; + dpos = end; + do { + int digit_pos; + switch (format_char) { + case 'o': + digit_pos = abs((int)(remaining % (8*8))); + remaining = (Py_ssize_t) (remaining / (8*8)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2); + last_one_off = (digit_pos < 8); + break; + case 'd': + digit_pos = abs((int)(remaining % (10*10))); + remaining = (Py_ssize_t) (remaining / (10*10)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2); + last_one_off = (digit_pos < 10); + break; + case 'x': + *(--dpos) = hex_digits[abs((int)(remaining % 16))]; + remaining = (Py_ssize_t) (remaining / 16); + break; + default: + assert(0); + break; + } + } while (unlikely(remaining != 0)); + assert(!last_one_off || *dpos == '0'); + dpos += last_one_off; + length = end - dpos; + ulength = length; + prepend_sign = 0; + if (!is_unsigned && value <= neg_one) { + if (padding_char == ' ' || width <= length + 1) { + *(--dpos) = '-'; + ++length; + } else { + prepend_sign = 1; + } + ++ulength; + } + if (width > ulength) { + ulength = width; + } + if (ulength == 1) { + return PyUnicode_FromOrdinal(*dpos); + } + return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); +} + +/* JoinPyUnicode */ +static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, + Py_UCS4 max_char) { +#if CYTHON_USE_UNICODE_INTERNALS && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + PyObject *result_uval; + int result_ukind, kind_shift; + Py_ssize_t i, char_pos; + void *result_udata; + CYTHON_MAYBE_UNUSED_VAR(max_char); +#if CYTHON_PEP393_ENABLED + result_uval = PyUnicode_New(result_ulength, max_char); + if (unlikely(!result_uval)) return NULL; + result_ukind = (max_char <= 255) ? PyUnicode_1BYTE_KIND : (max_char <= 65535) ? PyUnicode_2BYTE_KIND : PyUnicode_4BYTE_KIND; + kind_shift = (result_ukind == PyUnicode_4BYTE_KIND) ? 2 : result_ukind - 1; + result_udata = PyUnicode_DATA(result_uval); +#else + result_uval = PyUnicode_FromUnicode(NULL, result_ulength); + if (unlikely(!result_uval)) return NULL; + result_ukind = sizeof(Py_UNICODE); + kind_shift = (result_ukind == 4) ? 2 : result_ukind - 1; + result_udata = PyUnicode_AS_UNICODE(result_uval); +#endif + assert(kind_shift == 2 || kind_shift == 1 || kind_shift == 0); + char_pos = 0; + for (i=0; i < value_count; i++) { + int ukind; + Py_ssize_t ulength; + void *udata; + PyObject *uval = PyTuple_GET_ITEM(value_tuple, i); + if (unlikely(__Pyx_PyUnicode_READY(uval))) + goto bad; + ulength = __Pyx_PyUnicode_GET_LENGTH(uval); + if (unlikely(!ulength)) + continue; + if (unlikely((PY_SSIZE_T_MAX >> kind_shift) - ulength < char_pos)) + goto overflow; + ukind = __Pyx_PyUnicode_KIND(uval); + udata = __Pyx_PyUnicode_DATA(uval); + if (!CYTHON_PEP393_ENABLED || ukind == result_ukind) { + memcpy((char *)result_udata + (char_pos << kind_shift), udata, (size_t) (ulength << kind_shift)); + } else { + #if PY_VERSION_HEX >= 0x030d0000 + if (unlikely(PyUnicode_CopyCharacters(result_uval, char_pos, uval, 0, ulength) < 0)) goto bad; + #elif CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030300F0 || defined(_PyUnicode_FastCopyCharacters) + _PyUnicode_FastCopyCharacters(result_uval, char_pos, uval, 0, ulength); + #else + Py_ssize_t j; + for (j=0; j < ulength; j++) { + Py_UCS4 uchar = __Pyx_PyUnicode_READ(ukind, udata, j); + __Pyx_PyUnicode_WRITE(result_ukind, result_udata, char_pos+j, uchar); + } + #endif + } + char_pos += ulength; + } + return result_uval; +overflow: + PyErr_SetString(PyExc_OverflowError, "join() result is too long for a Python string"); +bad: + Py_DECREF(result_uval); + return NULL; +#else + CYTHON_UNUSED_VAR(max_char); + CYTHON_UNUSED_VAR(result_ulength); + CYTHON_UNUSED_VAR(value_count); + return PyUnicode_Join(__pyx_empty_unicode, value_tuple); +#endif +} + +/* GetAttr */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { +#if CYTHON_USE_TYPE_SLOTS +#if PY_MAJOR_VERSION >= 3 + if (likely(PyUnicode_Check(n))) +#else + if (likely(PyString_Check(n))) +#endif + return __Pyx_PyObject_GetAttrStr(o, n); +#endif + return PyObject_GetAttr(o, n); +} + +/* GetItemInt */ +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { + PyObject *r; + if (unlikely(!j)) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyList_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyTuple_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); + if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { + PyObject *r = PyList_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } + else if (PyTuple_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } else { + PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; + PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; + if (mm && mm->mp_subscript) { + PyObject *r, *key = PyInt_FromSsize_t(i); + if (unlikely(!key)) return NULL; + r = mm->mp_subscript(o, key); + Py_DECREF(key); + return r; + } + if (likely(sm && sm->sq_item)) { + if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { + Py_ssize_t l = sm->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return NULL; + PyErr_Clear(); + } + } + return sm->sq_item(o, i); + } + } +#else + if (is_list || !PyMapping_Check(o)) { + return PySequence_GetItem(o, i); + } +#endif + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +} + +/* PyObjectCallOneArg */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *args[2] = {NULL, arg}; + return __Pyx_PyObject_FastCall(func, args+1, 1 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); +} + +/* ObjectGetItem */ +#if CYTHON_USE_TYPE_SLOTS +static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject *index) { + PyObject *runerr = NULL; + Py_ssize_t key_value; + key_value = __Pyx_PyIndex_AsSsize_t(index); + if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { + return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); + } + if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { + __Pyx_TypeName index_type_name = __Pyx_PyType_GetName(Py_TYPE(index)); + PyErr_Clear(); + PyErr_Format(PyExc_IndexError, + "cannot fit '" __Pyx_FMT_TYPENAME "' into an index-sized integer", index_type_name); + __Pyx_DECREF_TypeName(index_type_name); + } + return NULL; +} +static PyObject *__Pyx_PyObject_GetItem_Slow(PyObject *obj, PyObject *key) { + __Pyx_TypeName obj_type_name; + if (likely(PyType_Check(obj))) { + PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(obj, __pyx_n_s_class_getitem); + if (!meth) { + PyErr_Clear(); + } else { + PyObject *result = __Pyx_PyObject_CallOneArg(meth, key); + Py_DECREF(meth); + return result; + } + } + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, + "'" __Pyx_FMT_TYPENAME "' object is not subscriptable", obj_type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return NULL; +} +static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key) { + PyTypeObject *tp = Py_TYPE(obj); + PyMappingMethods *mm = tp->tp_as_mapping; + PySequenceMethods *sm = tp->tp_as_sequence; + if (likely(mm && mm->mp_subscript)) { + return mm->mp_subscript(obj, key); + } + if (likely(sm && sm->sq_item)) { + return __Pyx_PyObject_GetIndex(obj, key); + } + return __Pyx_PyObject_GetItem_Slow(obj, key); +} +#endif + +/* KeywordStringCheck */ +static int __Pyx_CheckKeywordStrings( + PyObject *kw, + const char* function_name, + int kw_allowed) +{ + PyObject* key = 0; + Py_ssize_t pos = 0; +#if CYTHON_COMPILING_IN_PYPY + if (!kw_allowed && PyDict_Next(kw, &pos, &key, 0)) + goto invalid_keyword; + return 1; +#else + if (CYTHON_METH_FASTCALL && likely(PyTuple_Check(kw))) { + Py_ssize_t kwsize; +#if CYTHON_ASSUME_SAFE_MACROS + kwsize = PyTuple_GET_SIZE(kw); +#else + kwsize = PyTuple_Size(kw); + if (kwsize < 0) return 0; +#endif + if (unlikely(kwsize == 0)) + return 1; + if (!kw_allowed) { +#if CYTHON_ASSUME_SAFE_MACROS + key = PyTuple_GET_ITEM(kw, 0); +#else + key = PyTuple_GetItem(kw, pos); + if (!key) return 0; +#endif + goto invalid_keyword; + } +#if PY_VERSION_HEX < 0x03090000 + for (pos = 0; pos < kwsize; pos++) { +#if CYTHON_ASSUME_SAFE_MACROS + key = PyTuple_GET_ITEM(kw, pos); +#else + key = PyTuple_GetItem(kw, pos); + if (!key) return 0; +#endif + if (unlikely(!PyUnicode_Check(key))) + goto invalid_keyword_type; + } +#endif + return 1; + } + while (PyDict_Next(kw, &pos, &key, 0)) { + #if PY_MAJOR_VERSION < 3 + if (unlikely(!PyString_Check(key))) + #endif + if (unlikely(!PyUnicode_Check(key))) + goto invalid_keyword_type; + } + if (!kw_allowed && unlikely(key)) + goto invalid_keyword; + return 1; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + return 0; +#endif +invalid_keyword: + #if PY_MAJOR_VERSION < 3 + PyErr_Format(PyExc_TypeError, + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + PyErr_Format(PyExc_TypeError, + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif + return 0; +} + +/* DivInt[Py_ssize_t] */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { + Py_ssize_t q = a / b; + Py_ssize_t r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* GetAttr3 */ +#if __PYX_LIMITED_VERSION_HEX < 0x030d00A1 +static PyObject *__Pyx_GetAttr3Default(PyObject *d) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + return NULL; + __Pyx_PyErr_Clear(); + Py_INCREF(d); + return d; +} +#endif +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { + PyObject *r; +#if __PYX_LIMITED_VERSION_HEX >= 0x030d00A1 + int res = PyObject_GetOptionalAttr(o, n, &r); + return (res != 0) ? r : __Pyx_NewRef(d); +#else + #if CYTHON_USE_TYPE_SLOTS + if (likely(PyString_Check(n))) { + r = __Pyx_PyObject_GetAttrStrNoError(o, n); + if (unlikely(!r) && likely(!PyErr_Occurred())) { + r = __Pyx_NewRef(d); + } + return r; + } + #endif + r = PyObject_GetAttr(o, n); + return (likely(r)) ? r : __Pyx_GetAttr3Default(d); +#endif +} + +/* PyDictVersioning */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { + PyObject **dictptr = NULL; + Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; + if (offset) { +#if CYTHON_COMPILING_IN_CPYTHON + dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); +#else + dictptr = _PyObject_GetDictPtr(obj); +#endif + } + return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; +} +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) + return 0; + return obj_dict_version == __Pyx_get_object_dict_version(obj); +} +#endif + +/* GetModuleGlobalName */ +#if CYTHON_USE_DICT_VERSIONS +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) +#else +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) +#endif +{ + PyObject *result; +#if !CYTHON_AVOID_BORROWED_REFS +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && PY_VERSION_HEX < 0x030d0000 + result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } else if (unlikely(PyErr_Occurred())) { + return NULL; + } +#elif CYTHON_COMPILING_IN_LIMITED_API + if (unlikely(!__pyx_m)) { + return NULL; + } + result = PyObject_GetAttr(__pyx_m, name); + if (likely(result)) { + return result; + } +#else + result = PyDict_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } +#endif +#else + result = PyObject_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } + PyErr_Clear(); +#endif + return __Pyx_GetBuiltinName(name); +} + +/* RaiseTooManyValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* RaiseNoneIterError */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* ExtTypeTest */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + __Pyx_TypeName obj_type_name; + __Pyx_TypeName type_name; + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(__Pyx_TypeCheck(obj, type))) + return 1; + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + type_name = __Pyx_PyType_GetName(type); + PyErr_Format(PyExc_TypeError, + "Cannot convert " __Pyx_FMT_TYPENAME " to " __Pyx_FMT_TYPENAME, + obj_type_name, type_name); + __Pyx_DECREF_TypeName(obj_type_name); + __Pyx_DECREF_TypeName(type_name); + return 0; +} + +/* GetTopmostException */ +#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE +static _PyErr_StackItem * +__Pyx_PyErr_GetTopmostException(PyThreadState *tstate) +{ + _PyErr_StackItem *exc_info = tstate->exc_info; + while ((exc_info->exc_value == NULL || exc_info->exc_value == Py_None) && + exc_info->previous_item != NULL) + { + exc_info = exc_info->previous_item; + } + return exc_info; +} +#endif + +/* SaveResetException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 + _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); + PyObject *exc_value = exc_info->exc_value; + if (exc_value == NULL || exc_value == Py_None) { + *value = NULL; + *type = NULL; + *tb = NULL; + } else { + *value = exc_value; + Py_INCREF(*value); + *type = (PyObject*) Py_TYPE(exc_value); + Py_INCREF(*type); + *tb = PyException_GetTraceback(exc_value); + } + #elif CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); + *type = exc_info->exc_type; + *value = exc_info->exc_value; + *tb = exc_info->exc_traceback; + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); + #else + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); + #endif +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 + _PyErr_StackItem *exc_info = tstate->exc_info; + PyObject *tmp_value = exc_info->exc_value; + exc_info->exc_value = value; + Py_XDECREF(tmp_value); + Py_XDECREF(type); + Py_XDECREF(tb); + #else + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = type; + exc_info->exc_value = value; + exc_info->exc_traceback = tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); + #endif +} +#endif + +/* GetException */ +#if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) +#endif +{ + PyObject *local_type = NULL, *local_value, *local_tb = NULL; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if PY_VERSION_HEX >= 0x030C00A6 + local_value = tstate->current_exception; + tstate->current_exception = 0; + if (likely(local_value)) { + local_type = (PyObject*) Py_TYPE(local_value); + Py_INCREF(local_type); + local_tb = PyException_GetTraceback(local_value); + } + #else + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; + #endif +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE && PY_VERSION_HEX >= 0x030C00A6 + if (unlikely(tstate->current_exception)) +#elif CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + #if PY_MAJOR_VERSION >= 3 + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } + #endif + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + #if CYTHON_USE_EXC_INFO_STACK + { + _PyErr_StackItem *exc_info = tstate->exc_info; + #if PY_VERSION_HEX >= 0x030B00a4 + tmp_value = exc_info->exc_value; + exc_info->exc_value = local_value; + tmp_type = NULL; + tmp_tb = NULL; + Py_XDECREF(local_type); + Py_XDECREF(local_tb); + #else + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = local_type; + exc_info->exc_value = local_value; + exc_info->exc_traceback = local_tb; + #endif + } + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +} + +/* SwapException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_value = exc_info->exc_value; + exc_info->exc_value = *value; + if (tmp_value == NULL || tmp_value == Py_None) { + Py_XDECREF(tmp_value); + tmp_value = NULL; + tmp_type = NULL; + tmp_tb = NULL; + } else { + tmp_type = (PyObject*) Py_TYPE(tmp_value); + Py_INCREF(tmp_type); + #if CYTHON_COMPILING_IN_CPYTHON + tmp_tb = ((PyBaseExceptionObject*) tmp_value)->traceback; + Py_XINCREF(tmp_tb); + #else + tmp_tb = PyException_GetTraceback(tmp_value); + #endif + } + #elif CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = *type; + exc_info->exc_value = *value; + exc_info->exc_traceback = *tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = *type; + tstate->exc_value = *value; + tstate->exc_traceback = *tb; + #endif + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); + PyErr_SetExcInfo(*type, *value, *tb); + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#endif + +/* Import */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *module = 0; + PyObject *empty_dict = 0; + PyObject *empty_list = 0; + #if PY_MAJOR_VERSION < 3 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (unlikely(!py_import)) + goto bad; + if (!from_list) { + empty_list = PyList_New(0); + if (unlikely(!empty_list)) + goto bad; + from_list = empty_list; + } + #endif + empty_dict = PyDict_New(); + if (unlikely(!empty_dict)) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.') != NULL) { + module = PyImport_ImportModuleLevelObject( + name, __pyx_d, empty_dict, from_list, 1); + if (unlikely(!module)) { + if (unlikely(!PyErr_ExceptionMatches(PyExc_ImportError))) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_MAJOR_VERSION < 3 + PyObject *py_level = PyInt_FromLong(level); + if (unlikely(!py_level)) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, __pyx_d, empty_dict, from_list, py_level, (PyObject *)NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, __pyx_d, empty_dict, from_list, level); + #endif + } + } +bad: + Py_XDECREF(empty_dict); + Py_XDECREF(empty_list); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_import); + #endif + return module; +} + +/* ImportDottedModule */ +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx__ImportDottedModule_Error(PyObject *name, PyObject *parts_tuple, Py_ssize_t count) { + PyObject *partial_name = NULL, *slice = NULL, *sep = NULL; + if (unlikely(PyErr_Occurred())) { + PyErr_Clear(); + } + if (likely(PyTuple_GET_SIZE(parts_tuple) == count)) { + partial_name = name; + } else { + slice = PySequence_GetSlice(parts_tuple, 0, count); + if (unlikely(!slice)) + goto bad; + sep = PyUnicode_FromStringAndSize(".", 1); + if (unlikely(!sep)) + goto bad; + partial_name = PyUnicode_Join(sep, slice); + } + PyErr_Format( +#if PY_MAJOR_VERSION < 3 + PyExc_ImportError, + "No module named '%s'", PyString_AS_STRING(partial_name)); +#else +#if PY_VERSION_HEX >= 0x030600B1 + PyExc_ModuleNotFoundError, +#else + PyExc_ImportError, +#endif + "No module named '%U'", partial_name); +#endif +bad: + Py_XDECREF(sep); + Py_XDECREF(slice); + Py_XDECREF(partial_name); + return NULL; +} +#endif +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx__ImportDottedModule_Lookup(PyObject *name) { + PyObject *imported_module; +#if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400) + PyObject *modules = PyImport_GetModuleDict(); + if (unlikely(!modules)) + return NULL; + imported_module = __Pyx_PyDict_GetItemStr(modules, name); + Py_XINCREF(imported_module); +#else + imported_module = PyImport_GetModule(name); +#endif + return imported_module; +} +#endif +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple) { + Py_ssize_t i, nparts; + nparts = PyTuple_GET_SIZE(parts_tuple); + for (i=1; i < nparts && module; i++) { + PyObject *part, *submodule; +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + part = PyTuple_GET_ITEM(parts_tuple, i); +#else + part = PySequence_ITEM(parts_tuple, i); +#endif + submodule = __Pyx_PyObject_GetAttrStrNoError(module, part); +#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_DECREF(part); +#endif + Py_DECREF(module); + module = submodule; + } + if (unlikely(!module)) { + return __Pyx__ImportDottedModule_Error(name, parts_tuple, i); + } + return module; +} +#endif +static PyObject *__Pyx__ImportDottedModule(PyObject *name, PyObject *parts_tuple) { +#if PY_MAJOR_VERSION < 3 + PyObject *module, *from_list, *star = __pyx_n_s__3; + CYTHON_UNUSED_VAR(parts_tuple); + from_list = PyList_New(1); + if (unlikely(!from_list)) + return NULL; + Py_INCREF(star); + PyList_SET_ITEM(from_list, 0, star); + module = __Pyx_Import(name, from_list, 0); + Py_DECREF(from_list); + return module; +#else + PyObject *imported_module; + PyObject *module = __Pyx_Import(name, NULL, 0); + if (!parts_tuple || unlikely(!module)) + return module; + imported_module = __Pyx__ImportDottedModule_Lookup(name); + if (likely(imported_module)) { + Py_DECREF(module); + return imported_module; + } + PyErr_Clear(); + return __Pyx_ImportDottedModule_WalkParts(module, name, parts_tuple); +#endif +} +static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple) { +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030400B1 + PyObject *module = __Pyx__ImportDottedModule_Lookup(name); + if (likely(module)) { + PyObject *spec = __Pyx_PyObject_GetAttrStrNoError(module, __pyx_n_s_spec); + if (likely(spec)) { + PyObject *unsafe = __Pyx_PyObject_GetAttrStrNoError(spec, __pyx_n_s_initializing); + if (likely(!unsafe || !__Pyx_PyObject_IsTrue(unsafe))) { + Py_DECREF(spec); + spec = NULL; + } + Py_XDECREF(unsafe); + } + if (likely(!spec)) { + PyErr_Clear(); + return module; + } + Py_DECREF(spec); + Py_DECREF(module); + } else if (PyErr_Occurred()) { + PyErr_Clear(); + } +#endif + return __Pyx__ImportDottedModule(name, parts_tuple); +} + +/* FastTypeChecks */ +#if CYTHON_COMPILING_IN_CPYTHON +static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { + while (a) { + a = __Pyx_PyType_GetSlot(a, tp_base, PyTypeObject*); + if (a == b) + return 1; + } + return b == &PyBaseObject_Type; +} +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (a == b) return 1; + mro = a->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(a, b); +} +static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (cls == a || cls == b) return 1; + mro = cls->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + PyObject *base = PyTuple_GET_ITEM(mro, i); + if (base == (PyObject *)a || base == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(cls, a) || __Pyx_InBases(cls, b); +} +#if PY_MAJOR_VERSION == 2 +static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { + PyObject *exception, *value, *tb; + int res; + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&exception, &value, &tb); + res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + if (!res) { + res = PyObject_IsSubclass(err, exc_type2); + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + } + __Pyx_ErrRestore(exception, value, tb); + return res; +} +#else +static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { + if (exc_type1) { + return __Pyx_IsAnySubtype2((PyTypeObject*)err, (PyTypeObject*)exc_type1, (PyTypeObject*)exc_type2); + } else { + return __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); + } +} +#endif +static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + assert(PyExceptionClass_Check(exc_type)); + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; itp_as_sequence && type->tp_as_sequence->sq_repeat)) { + return type->tp_as_sequence->sq_repeat(seq, mul); + } else +#endif + { + return __Pyx_PySequence_Multiply_Generic(seq, mul); + } +} + +/* SetItemInt */ +static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) { + int r; + if (unlikely(!j)) return -1; + r = PyObject_SetItem(o, j, v); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list, + CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o)); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o)))) { + PyObject* old = PyList_GET_ITEM(o, n); + Py_INCREF(v); + PyList_SET_ITEM(o, n, v); + Py_DECREF(old); + return 1; + } + } else { + PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; + PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; + if (mm && mm->mp_ass_subscript) { + int r; + PyObject *key = PyInt_FromSsize_t(i); + if (unlikely(!key)) return -1; + r = mm->mp_ass_subscript(o, key, v); + Py_DECREF(key); + return r; + } + if (likely(sm && sm->sq_ass_item)) { + if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { + Py_ssize_t l = sm->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return -1; + PyErr_Clear(); + } + } + return sm->sq_ass_item(o, i, v); + } + } +#else + if (is_list || !PyMapping_Check(o)) + { + return PySequence_SetItem(o, i, v); + } +#endif + return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v); +} + +/* RaiseUnboundLocalError */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { + PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); +} + +/* DivInt[long] */ +static CYTHON_INLINE long __Pyx_div_long(long a, long b) { + long q = a / b; + long r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* ImportFrom */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { + PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); + if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { + const char* module_name_str = 0; + PyObject* module_name = 0; + PyObject* module_dot = 0; + PyObject* full_name = 0; + PyErr_Clear(); + module_name_str = PyModule_GetName(module); + if (unlikely(!module_name_str)) { goto modbad; } + module_name = PyUnicode_FromString(module_name_str); + if (unlikely(!module_name)) { goto modbad; } + module_dot = PyUnicode_Concat(module_name, __pyx_kp_u__2); + if (unlikely(!module_dot)) { goto modbad; } + full_name = PyUnicode_Concat(module_dot, name); + if (unlikely(!full_name)) { goto modbad; } + #if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400) + { + PyObject *modules = PyImport_GetModuleDict(); + if (unlikely(!modules)) + goto modbad; + value = PyObject_GetItem(modules, full_name); + } + #else + value = PyImport_GetModule(full_name); + #endif + modbad: + Py_XDECREF(full_name); + Py_XDECREF(module_dot); + Py_XDECREF(module_name); + } + if (unlikely(!value)) { + PyErr_Format(PyExc_ImportError, + #if PY_MAJOR_VERSION < 3 + "cannot import name %.230s", PyString_AS_STRING(name)); + #else + "cannot import name %S", name); + #endif + } + return value; +} + +/* HasAttr */ +#if __PYX_LIMITED_VERSION_HEX < 0x030d00A1 +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { + PyObject *r; + if (unlikely(!__Pyx_PyBaseString_Check(n))) { + PyErr_SetString(PyExc_TypeError, + "hasattr(): attribute name must be string"); + return -1; + } + r = __Pyx_GetAttr(o, n); + if (!r) { + PyErr_Clear(); + return 0; + } else { + Py_DECREF(r); + return 1; + } +} +#endif + +/* ErrOccurredWithGIL */ +static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void) { + int err; + #ifdef WITH_THREAD + PyGILState_STATE _save = PyGILState_Ensure(); + #endif + err = !!PyErr_Occurred(); + #ifdef WITH_THREAD + PyGILState_Release(_save); + #endif + return err; +} + +/* PyObject_GenericGetAttrNoDict */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { + __Pyx_TypeName type_name = __Pyx_PyType_GetName(tp); + PyErr_Format(PyExc_AttributeError, +#if PY_MAJOR_VERSION >= 3 + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'", + type_name, attr_name); +#else + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'", + type_name, PyString_AS_STRING(attr_name)); +#endif + __Pyx_DECREF_TypeName(type_name); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { + PyObject *descr; + PyTypeObject *tp = Py_TYPE(obj); + if (unlikely(!PyString_Check(attr_name))) { + return PyObject_GenericGetAttr(obj, attr_name); + } + assert(!tp->tp_dictoffset); + descr = _PyType_Lookup(tp, attr_name); + if (unlikely(!descr)) { + return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); + } + Py_INCREF(descr); + #if PY_MAJOR_VERSION < 3 + if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) + #endif + { + descrgetfunc f = Py_TYPE(descr)->tp_descr_get; + if (unlikely(f)) { + PyObject *res = f(descr, obj, (PyObject *)tp); + Py_DECREF(descr); + return res; + } + } + return descr; +} +#endif + +/* PyObject_GenericGetAttr */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { + if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { + return PyObject_GenericGetAttr(obj, attr_name); + } + return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); +} +#endif + +/* FixUpExtensionType */ +#if CYTHON_USE_TYPE_SPECS +static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type) { +#if PY_VERSION_HEX > 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + CYTHON_UNUSED_VAR(spec); + CYTHON_UNUSED_VAR(type); +#else + const PyType_Slot *slot = spec->slots; + while (slot && slot->slot && slot->slot != Py_tp_members) + slot++; + if (slot && slot->slot == Py_tp_members) { + int changed = 0; +#if !(PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON) + const +#endif + PyMemberDef *memb = (PyMemberDef*) slot->pfunc; + while (memb && memb->name) { + if (memb->name[0] == '_' && memb->name[1] == '_') { +#if PY_VERSION_HEX < 0x030900b1 + if (strcmp(memb->name, "__weaklistoffset__") == 0) { + assert(memb->type == T_PYSSIZET); + assert(memb->flags == READONLY); + type->tp_weaklistoffset = memb->offset; + changed = 1; + } + else if (strcmp(memb->name, "__dictoffset__") == 0) { + assert(memb->type == T_PYSSIZET); + assert(memb->flags == READONLY); + type->tp_dictoffset = memb->offset; + changed = 1; + } +#if CYTHON_METH_FASTCALL + else if (strcmp(memb->name, "__vectorcalloffset__") == 0) { + assert(memb->type == T_PYSSIZET); + assert(memb->flags == READONLY); +#if PY_VERSION_HEX >= 0x030800b4 + type->tp_vectorcall_offset = memb->offset; +#else + type->tp_print = (printfunc) memb->offset; +#endif + changed = 1; + } +#endif +#else + if ((0)); +#endif +#if PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON + else if (strcmp(memb->name, "__module__") == 0) { + PyObject *descr; + assert(memb->type == T_OBJECT); + assert(memb->flags == 0 || memb->flags == READONLY); + descr = PyDescr_NewMember(type, memb); + if (unlikely(!descr)) + return -1; + if (unlikely(PyDict_SetItem(type->tp_dict, PyDescr_NAME(descr), descr) < 0)) { + Py_DECREF(descr); + return -1; + } + Py_DECREF(descr); + changed = 1; + } +#endif + } + memb++; + } + if (changed) + PyType_Modified(type); + } +#endif + return 0; +} +#endif + +/* PyObjectCallNoArg */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { + PyObject *arg[2] = {NULL, NULL}; + return __Pyx_PyObject_FastCall(func, arg + 1, 0 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); +} + +/* PyObjectGetMethod */ +static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) { + PyObject *attr; +#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP + __Pyx_TypeName type_name; + PyTypeObject *tp = Py_TYPE(obj); + PyObject *descr; + descrgetfunc f = NULL; + PyObject **dictptr, *dict; + int meth_found = 0; + assert (*method == NULL); + if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) { + attr = __Pyx_PyObject_GetAttrStr(obj, name); + goto try_unpack; + } + if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) { + return 0; + } + descr = _PyType_Lookup(tp, name); + if (likely(descr != NULL)) { + Py_INCREF(descr); +#if defined(Py_TPFLAGS_METHOD_DESCRIPTOR) && Py_TPFLAGS_METHOD_DESCRIPTOR + if (__Pyx_PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_METHOD_DESCRIPTOR)) +#elif PY_MAJOR_VERSION >= 3 + #ifdef __Pyx_CyFunction_USED + if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr))) + #else + if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type))) + #endif +#else + #ifdef __Pyx_CyFunction_USED + if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr))) + #else + if (likely(PyFunction_Check(descr))) + #endif +#endif + { + meth_found = 1; + } else { + f = Py_TYPE(descr)->tp_descr_get; + if (f != NULL && PyDescr_IsData(descr)) { + attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); + Py_DECREF(descr); + goto try_unpack; + } + } + } + dictptr = _PyObject_GetDictPtr(obj); + if (dictptr != NULL && (dict = *dictptr) != NULL) { + Py_INCREF(dict); + attr = __Pyx_PyDict_GetItemStr(dict, name); + if (attr != NULL) { + Py_INCREF(attr); + Py_DECREF(dict); + Py_XDECREF(descr); + goto try_unpack; + } + Py_DECREF(dict); + } + if (meth_found) { + *method = descr; + return 1; + } + if (f != NULL) { + attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); + Py_DECREF(descr); + goto try_unpack; + } + if (likely(descr != NULL)) { + *method = descr; + return 0; + } + type_name = __Pyx_PyType_GetName(tp); + PyErr_Format(PyExc_AttributeError, +#if PY_MAJOR_VERSION >= 3 + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'", + type_name, name); +#else + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'", + type_name, PyString_AS_STRING(name)); +#endif + __Pyx_DECREF_TypeName(type_name); + return 0; +#else + attr = __Pyx_PyObject_GetAttrStr(obj, name); + goto try_unpack; +#endif +try_unpack: +#if CYTHON_UNPACK_METHODS + if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) { + PyObject *function = PyMethod_GET_FUNCTION(attr); + Py_INCREF(function); + Py_DECREF(attr); + *method = function; + return 1; + } +#endif + *method = attr; + return 0; +} + +/* PyObjectCallMethod0 */ +static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) { + PyObject *method = NULL, *result = NULL; + int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method); + if (likely(is_method)) { + result = __Pyx_PyObject_CallOneArg(method, obj); + Py_DECREF(method); + return result; + } + if (unlikely(!method)) goto bad; + result = __Pyx_PyObject_CallNoArg(method); + Py_DECREF(method); +bad: + return result; +} + +/* ValidateBasesTuple */ +#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS +static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases) { + Py_ssize_t i, n; +#if CYTHON_ASSUME_SAFE_MACROS + n = PyTuple_GET_SIZE(bases); +#else + n = PyTuple_Size(bases); + if (n < 0) return -1; +#endif + for (i = 1; i < n; i++) + { +#if CYTHON_AVOID_BORROWED_REFS + PyObject *b0 = PySequence_GetItem(bases, i); + if (!b0) return -1; +#elif CYTHON_ASSUME_SAFE_MACROS + PyObject *b0 = PyTuple_GET_ITEM(bases, i); +#else + PyObject *b0 = PyTuple_GetItem(bases, i); + if (!b0) return -1; +#endif + PyTypeObject *b; +#if PY_MAJOR_VERSION < 3 + if (PyClass_Check(b0)) + { + PyErr_Format(PyExc_TypeError, "base class '%.200s' is an old-style class", + PyString_AS_STRING(((PyClassObject*)b0)->cl_name)); +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + return -1; + } +#endif + b = (PyTypeObject*) b0; + if (!__Pyx_PyType_HasFeature(b, Py_TPFLAGS_HEAPTYPE)) + { + __Pyx_TypeName b_name = __Pyx_PyType_GetName(b); + PyErr_Format(PyExc_TypeError, + "base class '" __Pyx_FMT_TYPENAME "' is not a heap type", b_name); + __Pyx_DECREF_TypeName(b_name); +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + return -1; + } + if (dictoffset == 0) + { + Py_ssize_t b_dictoffset = 0; +#if CYTHON_USE_TYPE_SLOTS || CYTHON_COMPILING_IN_PYPY + b_dictoffset = b->tp_dictoffset; +#else + PyObject *py_b_dictoffset = PyObject_GetAttrString((PyObject*)b, "__dictoffset__"); + if (!py_b_dictoffset) goto dictoffset_return; + b_dictoffset = PyLong_AsSsize_t(py_b_dictoffset); + Py_DECREF(py_b_dictoffset); + if (b_dictoffset == -1 && PyErr_Occurred()) goto dictoffset_return; +#endif + if (b_dictoffset) { + { + __Pyx_TypeName b_name = __Pyx_PyType_GetName(b); + PyErr_Format(PyExc_TypeError, + "extension type '%.200s' has no __dict__ slot, " + "but base type '" __Pyx_FMT_TYPENAME "' has: " + "either add 'cdef dict __dict__' to the extension type " + "or add '__slots__ = [...]' to the base type", + type_name, b_name); + __Pyx_DECREF_TypeName(b_name); + } +#if !(CYTHON_USE_TYPE_SLOTS || CYTHON_COMPILING_IN_PYPY) + dictoffset_return: +#endif +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + return -1; + } + } +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + } + return 0; +} +#endif + +/* PyType_Ready */ +static int __Pyx_PyType_Ready(PyTypeObject *t) { +#if CYTHON_USE_TYPE_SPECS || !(CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API) || defined(PYSTON_MAJOR_VERSION) + (void)__Pyx_PyObject_CallMethod0; +#if CYTHON_USE_TYPE_SPECS + (void)__Pyx_validate_bases_tuple; +#endif + return PyType_Ready(t); +#else + int r; + PyObject *bases = __Pyx_PyType_GetSlot(t, tp_bases, PyObject*); + if (bases && unlikely(__Pyx_validate_bases_tuple(t->tp_name, t->tp_dictoffset, bases) == -1)) + return -1; +#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION) + { + int gc_was_enabled; + #if PY_VERSION_HEX >= 0x030A00b1 + gc_was_enabled = PyGC_Disable(); + (void)__Pyx_PyObject_CallMethod0; + #else + PyObject *ret, *py_status; + PyObject *gc = NULL; + #if PY_VERSION_HEX >= 0x030700a1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM+0 >= 0x07030400) + gc = PyImport_GetModule(__pyx_kp_u_gc); + #endif + if (unlikely(!gc)) gc = PyImport_Import(__pyx_kp_u_gc); + if (unlikely(!gc)) return -1; + py_status = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_isenabled); + if (unlikely(!py_status)) { + Py_DECREF(gc); + return -1; + } + gc_was_enabled = __Pyx_PyObject_IsTrue(py_status); + Py_DECREF(py_status); + if (gc_was_enabled > 0) { + ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_disable); + if (unlikely(!ret)) { + Py_DECREF(gc); + return -1; + } + Py_DECREF(ret); + } else if (unlikely(gc_was_enabled == -1)) { + Py_DECREF(gc); + return -1; + } + #endif + t->tp_flags |= Py_TPFLAGS_HEAPTYPE; +#if PY_VERSION_HEX >= 0x030A0000 + t->tp_flags |= Py_TPFLAGS_IMMUTABLETYPE; +#endif +#else + (void)__Pyx_PyObject_CallMethod0; +#endif + r = PyType_Ready(t); +#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION) + t->tp_flags &= ~Py_TPFLAGS_HEAPTYPE; + #if PY_VERSION_HEX >= 0x030A00b1 + if (gc_was_enabled) + PyGC_Enable(); + #else + if (gc_was_enabled) { + PyObject *tp, *v, *tb; + PyErr_Fetch(&tp, &v, &tb); + ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_enable); + if (likely(ret || r == -1)) { + Py_XDECREF(ret); + PyErr_Restore(tp, v, tb); + } else { + Py_XDECREF(tp); + Py_XDECREF(v); + Py_XDECREF(tb); + r = -1; + } + } + Py_DECREF(gc); + #endif + } +#endif + return r; +#endif +} + +/* SetVTable */ +static int __Pyx_SetVtable(PyTypeObject *type, void *vtable) { + PyObject *ob = PyCapsule_New(vtable, 0, 0); + if (unlikely(!ob)) + goto bad; +#if CYTHON_COMPILING_IN_LIMITED_API + if (unlikely(PyObject_SetAttr((PyObject *) type, __pyx_n_s_pyx_vtable, ob) < 0)) +#else + if (unlikely(PyDict_SetItem(type->tp_dict, __pyx_n_s_pyx_vtable, ob) < 0)) +#endif + goto bad; + Py_DECREF(ob); + return 0; +bad: + Py_XDECREF(ob); + return -1; +} + +/* GetVTable */ +static void* __Pyx_GetVtable(PyTypeObject *type) { + void* ptr; +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *ob = PyObject_GetAttr((PyObject *)type, __pyx_n_s_pyx_vtable); +#else + PyObject *ob = PyObject_GetItem(type->tp_dict, __pyx_n_s_pyx_vtable); +#endif + if (!ob) + goto bad; + ptr = PyCapsule_GetPointer(ob, 0); + if (!ptr && !PyErr_Occurred()) + PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type"); + Py_DECREF(ob); + return ptr; +bad: + Py_XDECREF(ob); + return NULL; +} + +/* MergeVTables */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_MergeVtables(PyTypeObject *type) { + int i; + void** base_vtables; + __Pyx_TypeName tp_base_name; + __Pyx_TypeName base_name; + void* unknown = (void*)-1; + PyObject* bases = type->tp_bases; + int base_depth = 0; + { + PyTypeObject* base = type->tp_base; + while (base) { + base_depth += 1; + base = base->tp_base; + } + } + base_vtables = (void**) malloc(sizeof(void*) * (size_t)(base_depth + 1)); + base_vtables[0] = unknown; + for (i = 1; i < PyTuple_GET_SIZE(bases); i++) { + void* base_vtable = __Pyx_GetVtable(((PyTypeObject*)PyTuple_GET_ITEM(bases, i))); + if (base_vtable != NULL) { + int j; + PyTypeObject* base = type->tp_base; + for (j = 0; j < base_depth; j++) { + if (base_vtables[j] == unknown) { + base_vtables[j] = __Pyx_GetVtable(base); + base_vtables[j + 1] = unknown; + } + if (base_vtables[j] == base_vtable) { + break; + } else if (base_vtables[j] == NULL) { + goto bad; + } + base = base->tp_base; + } + } + } + PyErr_Clear(); + free(base_vtables); + return 0; +bad: + tp_base_name = __Pyx_PyType_GetName(type->tp_base); + base_name = __Pyx_PyType_GetName((PyTypeObject*)PyTuple_GET_ITEM(bases, i)); + PyErr_Format(PyExc_TypeError, + "multiple bases have vtable conflict: '" __Pyx_FMT_TYPENAME "' and '" __Pyx_FMT_TYPENAME "'", tp_base_name, base_name); + __Pyx_DECREF_TypeName(tp_base_name); + __Pyx_DECREF_TypeName(base_name); + free(base_vtables); + return -1; +} +#endif + +/* SetupReduce */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { + int ret; + PyObject *name_attr; + name_attr = __Pyx_PyObject_GetAttrStrNoError(meth, __pyx_n_s_name_2); + if (likely(name_attr)) { + ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); + } else { + ret = -1; + } + if (unlikely(ret < 0)) { + PyErr_Clear(); + ret = 0; + } + Py_XDECREF(name_attr); + return ret; +} +static int __Pyx_setup_reduce(PyObject* type_obj) { + int ret = 0; + PyObject *object_reduce = NULL; + PyObject *object_getstate = NULL; + PyObject *object_reduce_ex = NULL; + PyObject *reduce = NULL; + PyObject *reduce_ex = NULL; + PyObject *reduce_cython = NULL; + PyObject *setstate = NULL; + PyObject *setstate_cython = NULL; + PyObject *getstate = NULL; +#if CYTHON_USE_PYTYPE_LOOKUP + getstate = _PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate); +#else + getstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_getstate); + if (!getstate && PyErr_Occurred()) { + goto __PYX_BAD; + } +#endif + if (getstate) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_getstate = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_getstate); +#else + object_getstate = __Pyx_PyObject_GetAttrStrNoError((PyObject*)&PyBaseObject_Type, __pyx_n_s_getstate); + if (!object_getstate && PyErr_Occurred()) { + goto __PYX_BAD; + } +#endif + if (object_getstate != getstate) { + goto __PYX_GOOD; + } + } +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; +#else + object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; +#endif + reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; + if (reduce_ex == object_reduce_ex) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; +#else + object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; +#endif + reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; + if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { + reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython); + if (likely(reduce_cython)) { + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (reduce == object_reduce || PyErr_Occurred()) { + goto __PYX_BAD; + } + setstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate); + if (!setstate) PyErr_Clear(); + if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { + setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython); + if (likely(setstate_cython)) { + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (!setstate || PyErr_Occurred()) { + goto __PYX_BAD; + } + } + PyType_Modified((PyTypeObject*)type_obj); + } + } + goto __PYX_GOOD; +__PYX_BAD: + if (!PyErr_Occurred()) { + __Pyx_TypeName type_obj_name = + __Pyx_PyType_GetName((PyTypeObject*)type_obj); + PyErr_Format(PyExc_RuntimeError, + "Unable to initialize pickling for " __Pyx_FMT_TYPENAME, type_obj_name); + __Pyx_DECREF_TypeName(type_obj_name); + } + ret = -1; +__PYX_GOOD: +#if !CYTHON_USE_PYTYPE_LOOKUP + Py_XDECREF(object_reduce); + Py_XDECREF(object_reduce_ex); + Py_XDECREF(object_getstate); + Py_XDECREF(getstate); +#endif + Py_XDECREF(reduce); + Py_XDECREF(reduce_ex); + Py_XDECREF(reduce_cython); + Py_XDECREF(setstate); + Py_XDECREF(setstate_cython); + return ret; +} +#endif + +/* FetchSharedCythonModule */ +static PyObject *__Pyx_FetchSharedCythonABIModule(void) { + return __Pyx_PyImport_AddModuleRef((char*) __PYX_ABI_MODULE_NAME); +} + +/* FetchCommonType */ +static int __Pyx_VerifyCachedType(PyObject *cached_type, + const char *name, + Py_ssize_t basicsize, + Py_ssize_t expected_basicsize) { + if (!PyType_Check(cached_type)) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s is not a type object", name); + return -1; + } + if (basicsize != expected_basicsize) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s has the wrong size, try recompiling", + name); + return -1; + } + return 0; +} +#if !CYTHON_USE_TYPE_SPECS +static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { + PyObject* abi_module; + const char* object_name; + PyTypeObject *cached_type = NULL; + abi_module = __Pyx_FetchSharedCythonABIModule(); + if (!abi_module) return NULL; + object_name = strrchr(type->tp_name, '.'); + object_name = object_name ? object_name+1 : type->tp_name; + cached_type = (PyTypeObject*) PyObject_GetAttrString(abi_module, object_name); + if (cached_type) { + if (__Pyx_VerifyCachedType( + (PyObject *)cached_type, + object_name, + cached_type->tp_basicsize, + type->tp_basicsize) < 0) { + goto bad; + } + goto done; + } + if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; + PyErr_Clear(); + if (PyType_Ready(type) < 0) goto bad; + if (PyObject_SetAttrString(abi_module, object_name, (PyObject *)type) < 0) + goto bad; + Py_INCREF(type); + cached_type = type; +done: + Py_DECREF(abi_module); + return cached_type; +bad: + Py_XDECREF(cached_type); + cached_type = NULL; + goto done; +} +#else +static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases) { + PyObject *abi_module, *cached_type = NULL; + const char* object_name = strrchr(spec->name, '.'); + object_name = object_name ? object_name+1 : spec->name; + abi_module = __Pyx_FetchSharedCythonABIModule(); + if (!abi_module) return NULL; + cached_type = PyObject_GetAttrString(abi_module, object_name); + if (cached_type) { + Py_ssize_t basicsize; +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *py_basicsize; + py_basicsize = PyObject_GetAttrString(cached_type, "__basicsize__"); + if (unlikely(!py_basicsize)) goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (unlikely(basicsize == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; +#else + basicsize = likely(PyType_Check(cached_type)) ? ((PyTypeObject*) cached_type)->tp_basicsize : -1; +#endif + if (__Pyx_VerifyCachedType( + cached_type, + object_name, + basicsize, + spec->basicsize) < 0) { + goto bad; + } + goto done; + } + if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; + PyErr_Clear(); + CYTHON_UNUSED_VAR(module); + cached_type = __Pyx_PyType_FromModuleAndSpec(abi_module, spec, bases); + if (unlikely(!cached_type)) goto bad; + if (unlikely(__Pyx_fix_up_extension_type_from_spec(spec, (PyTypeObject *) cached_type) < 0)) goto bad; + if (PyObject_SetAttrString(abi_module, object_name, cached_type) < 0) goto bad; +done: + Py_DECREF(abi_module); + assert(cached_type == NULL || PyType_Check(cached_type)); + return (PyTypeObject *) cached_type; +bad: + Py_XDECREF(cached_type); + cached_type = NULL; + goto done; +} +#endif + +/* PyVectorcallFastCallDict */ +#if CYTHON_METH_FASTCALL +static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw) +{ + PyObject *res = NULL; + PyObject *kwnames; + PyObject **newargs; + PyObject **kwvalues; + Py_ssize_t i, pos; + size_t j; + PyObject *key, *value; + unsigned long keys_are_strings; + Py_ssize_t nkw = PyDict_GET_SIZE(kw); + newargs = (PyObject **)PyMem_Malloc((nargs + (size_t)nkw) * sizeof(args[0])); + if (unlikely(newargs == NULL)) { + PyErr_NoMemory(); + return NULL; + } + for (j = 0; j < nargs; j++) newargs[j] = args[j]; + kwnames = PyTuple_New(nkw); + if (unlikely(kwnames == NULL)) { + PyMem_Free(newargs); + return NULL; + } + kwvalues = newargs + nargs; + pos = i = 0; + keys_are_strings = Py_TPFLAGS_UNICODE_SUBCLASS; + while (PyDict_Next(kw, &pos, &key, &value)) { + keys_are_strings &= Py_TYPE(key)->tp_flags; + Py_INCREF(key); + Py_INCREF(value); + PyTuple_SET_ITEM(kwnames, i, key); + kwvalues[i] = value; + i++; + } + if (unlikely(!keys_are_strings)) { + PyErr_SetString(PyExc_TypeError, "keywords must be strings"); + goto cleanup; + } + res = vc(func, newargs, nargs, kwnames); +cleanup: + Py_DECREF(kwnames); + for (i = 0; i < nkw; i++) + Py_DECREF(kwvalues[i]); + PyMem_Free(newargs); + return res; +} +static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw) +{ + if (likely(kw == NULL) || PyDict_GET_SIZE(kw) == 0) { + return vc(func, args, nargs, NULL); + } + return __Pyx_PyVectorcall_FastCallDict_kw(func, vc, args, nargs, kw); +} +#endif + +/* CythonFunctionShared */ +#if CYTHON_COMPILING_IN_LIMITED_API +static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void *cfunc) { + if (__Pyx_CyFunction_Check(func)) { + return PyCFunction_GetFunction(((__pyx_CyFunctionObject*)func)->func) == (PyCFunction) cfunc; + } else if (PyCFunction_Check(func)) { + return PyCFunction_GetFunction(func) == (PyCFunction) cfunc; + } + return 0; +} +#else +static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void *cfunc) { + return __Pyx_CyOrPyCFunction_Check(func) && __Pyx_CyOrPyCFunction_GET_FUNCTION(func) == (PyCFunction) cfunc; +} +#endif +static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) { +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + __Pyx_Py_XDECREF_SET( + __Pyx_CyFunction_GetClassObj(f), + ((classobj) ? __Pyx_NewRef(classobj) : NULL)); +#else + __Pyx_Py_XDECREF_SET( + ((PyCMethodObject *) (f))->mm_class, + (PyTypeObject*)((classobj) ? __Pyx_NewRef(classobj) : NULL)); +#endif +} +static PyObject * +__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure) +{ + CYTHON_UNUSED_VAR(closure); + if (unlikely(op->func_doc == NULL)) { +#if CYTHON_COMPILING_IN_LIMITED_API + op->func_doc = PyObject_GetAttrString(op->func, "__doc__"); + if (unlikely(!op->func_doc)) return NULL; +#else + if (((PyCFunctionObject*)op)->m_ml->ml_doc) { +#if PY_MAJOR_VERSION >= 3 + op->func_doc = PyUnicode_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc); +#else + op->func_doc = PyString_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc); +#endif + if (unlikely(op->func_doc == NULL)) + return NULL; + } else { + Py_INCREF(Py_None); + return Py_None; + } +#endif + } + Py_INCREF(op->func_doc); + return op->func_doc; +} +static int +__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (value == NULL) { + value = Py_None; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_doc, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (unlikely(op->func_name == NULL)) { +#if CYTHON_COMPILING_IN_LIMITED_API + op->func_name = PyObject_GetAttrString(op->func, "__name__"); +#elif PY_MAJOR_VERSION >= 3 + op->func_name = PyUnicode_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name); +#else + op->func_name = PyString_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name); +#endif + if (unlikely(op->func_name == NULL)) + return NULL; + } + Py_INCREF(op->func_name); + return op->func_name; +} +static int +__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) +#else + if (unlikely(value == NULL || !PyString_Check(value))) +#endif + { + PyErr_SetString(PyExc_TypeError, + "__name__ must be set to a string object"); + return -1; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_name, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + Py_INCREF(op->func_qualname); + return op->func_qualname; +} +static int +__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) +#else + if (unlikely(value == NULL || !PyString_Check(value))) +#endif + { + PyErr_SetString(PyExc_TypeError, + "__qualname__ must be set to a string object"); + return -1; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_qualname, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (unlikely(op->func_dict == NULL)) { + op->func_dict = PyDict_New(); + if (unlikely(op->func_dict == NULL)) + return NULL; + } + Py_INCREF(op->func_dict); + return op->func_dict; +} +static int +__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (unlikely(value == NULL)) { + PyErr_SetString(PyExc_TypeError, + "function's dictionary may not be deleted"); + return -1; + } + if (unlikely(!PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "setting function's dictionary to a non-dict"); + return -1; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_dict, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + Py_INCREF(op->func_globals); + return op->func_globals; +} +static PyObject * +__Pyx_CyFunction_get_closure(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(op); + CYTHON_UNUSED_VAR(context); + Py_INCREF(Py_None); + return Py_None; +} +static PyObject * +__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, void *context) +{ + PyObject* result = (op->func_code) ? op->func_code : Py_None; + CYTHON_UNUSED_VAR(context); + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) { + int result = 0; + PyObject *res = op->defaults_getter((PyObject *) op); + if (unlikely(!res)) + return -1; + #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + op->defaults_tuple = PyTuple_GET_ITEM(res, 0); + Py_INCREF(op->defaults_tuple); + op->defaults_kwdict = PyTuple_GET_ITEM(res, 1); + Py_INCREF(op->defaults_kwdict); + #else + op->defaults_tuple = __Pyx_PySequence_ITEM(res, 0); + if (unlikely(!op->defaults_tuple)) result = -1; + else { + op->defaults_kwdict = __Pyx_PySequence_ITEM(res, 1); + if (unlikely(!op->defaults_kwdict)) result = -1; + } + #endif + Py_DECREF(res); + return result; +} +static int +__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (!value) { + value = Py_None; + } else if (unlikely(value != Py_None && !PyTuple_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__defaults__ must be set to a tuple object"); + return -1; + } + PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__defaults__ will not " + "currently affect the values used in function calls", 1); + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->defaults_tuple, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, void *context) { + PyObject* result = op->defaults_tuple; + CYTHON_UNUSED_VAR(context); + if (unlikely(!result)) { + if (op->defaults_getter) { + if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL; + result = op->defaults_tuple; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (!value) { + value = Py_None; + } else if (unlikely(value != Py_None && !PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__kwdefaults__ must be set to a dict object"); + return -1; + } + PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__kwdefaults__ will not " + "currently affect the values used in function calls", 1); + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->defaults_kwdict, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, void *context) { + PyObject* result = op->defaults_kwdict; + CYTHON_UNUSED_VAR(context); + if (unlikely(!result)) { + if (op->defaults_getter) { + if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL; + result = op->defaults_kwdict; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (!value || value == Py_None) { + value = NULL; + } else if (unlikely(!PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__annotations__ must be set to a dict object"); + return -1; + } + Py_XINCREF(value); + __Pyx_Py_XDECREF_SET(op->func_annotations, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, void *context) { + PyObject* result = op->func_annotations; + CYTHON_UNUSED_VAR(context); + if (unlikely(!result)) { + result = PyDict_New(); + if (unlikely(!result)) return NULL; + op->func_annotations = result; + } + Py_INCREF(result); + return result; +} +static PyObject * +__Pyx_CyFunction_get_is_coroutine(__pyx_CyFunctionObject *op, void *context) { + int is_coroutine; + CYTHON_UNUSED_VAR(context); + if (op->func_is_coroutine) { + return __Pyx_NewRef(op->func_is_coroutine); + } + is_coroutine = op->flags & __Pyx_CYFUNCTION_COROUTINE; +#if PY_VERSION_HEX >= 0x03050000 + if (is_coroutine) { + PyObject *module, *fromlist, *marker = __pyx_n_s_is_coroutine; + fromlist = PyList_New(1); + if (unlikely(!fromlist)) return NULL; + Py_INCREF(marker); +#if CYTHON_ASSUME_SAFE_MACROS + PyList_SET_ITEM(fromlist, 0, marker); +#else + if (unlikely(PyList_SetItem(fromlist, 0, marker) < 0)) { + Py_DECREF(marker); + Py_DECREF(fromlist); + return NULL; + } +#endif + module = PyImport_ImportModuleLevelObject(__pyx_n_s_asyncio_coroutines, NULL, NULL, fromlist, 0); + Py_DECREF(fromlist); + if (unlikely(!module)) goto ignore; + op->func_is_coroutine = __Pyx_PyObject_GetAttrStr(module, marker); + Py_DECREF(module); + if (likely(op->func_is_coroutine)) { + return __Pyx_NewRef(op->func_is_coroutine); + } +ignore: + PyErr_Clear(); + } +#endif + op->func_is_coroutine = __Pyx_PyBool_FromLong(is_coroutine); + return __Pyx_NewRef(op->func_is_coroutine); +} +#if CYTHON_COMPILING_IN_LIMITED_API +static PyObject * +__Pyx_CyFunction_get_module(__pyx_CyFunctionObject *op, void *context) { + CYTHON_UNUSED_VAR(context); + return PyObject_GetAttrString(op->func, "__module__"); +} +static int +__Pyx_CyFunction_set_module(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + return PyObject_SetAttrString(op->func, "__module__", value); +} +#endif +static PyGetSetDef __pyx_CyFunction_getsets[] = { + {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {(char *) "__doc__", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0}, + {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0}, + {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0}, + {(char *) "_is_coroutine", (getter)__Pyx_CyFunction_get_is_coroutine, 0, 0, 0}, +#if CYTHON_COMPILING_IN_LIMITED_API + {"__module__", (getter)__Pyx_CyFunction_get_module, (setter)__Pyx_CyFunction_set_module, 0, 0}, +#endif + {0, 0, 0, 0, 0} +}; +static PyMemberDef __pyx_CyFunction_members[] = { +#if !CYTHON_COMPILING_IN_LIMITED_API + {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), 0, 0}, +#endif +#if CYTHON_USE_TYPE_SPECS + {(char *) "__dictoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_dict), READONLY, 0}, +#if CYTHON_METH_FASTCALL +#if CYTHON_BACKPORT_VECTORCALL + {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_vectorcall), READONLY, 0}, +#else +#if !CYTHON_COMPILING_IN_LIMITED_API + {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(PyCFunctionObject, vectorcall), READONLY, 0}, +#endif +#endif +#endif +#if PY_VERSION_HEX < 0x030500A0 || CYTHON_COMPILING_IN_LIMITED_API + {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_weakreflist), READONLY, 0}, +#else + {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(PyCFunctionObject, m_weakreflist), READONLY, 0}, +#endif +#endif + {0, 0, 0, 0, 0} +}; +static PyObject * +__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, PyObject *args) +{ + CYTHON_UNUSED_VAR(args); +#if PY_MAJOR_VERSION >= 3 + Py_INCREF(m->func_qualname); + return m->func_qualname; +#else + return PyString_FromString(((PyCFunctionObject*)m)->m_ml->ml_name); +#endif +} +static PyMethodDef __pyx_CyFunction_methods[] = { + {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0}, + {0, 0, 0, 0} +}; +#if PY_VERSION_HEX < 0x030500A0 || CYTHON_COMPILING_IN_LIMITED_API +#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist) +#else +#define __Pyx_CyFunction_weakreflist(cyfunc) (((PyCFunctionObject*)cyfunc)->m_weakreflist) +#endif +static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname, + PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { +#if !CYTHON_COMPILING_IN_LIMITED_API + PyCFunctionObject *cf = (PyCFunctionObject*) op; +#endif + if (unlikely(op == NULL)) + return NULL; +#if CYTHON_COMPILING_IN_LIMITED_API + op->func = PyCFunction_NewEx(ml, (PyObject*)op, module); + if (unlikely(!op->func)) return NULL; +#endif + op->flags = flags; + __Pyx_CyFunction_weakreflist(op) = NULL; +#if !CYTHON_COMPILING_IN_LIMITED_API + cf->m_ml = ml; + cf->m_self = (PyObject *) op; +#endif + Py_XINCREF(closure); + op->func_closure = closure; +#if !CYTHON_COMPILING_IN_LIMITED_API + Py_XINCREF(module); + cf->m_module = module; +#endif + op->func_dict = NULL; + op->func_name = NULL; + Py_INCREF(qualname); + op->func_qualname = qualname; + op->func_doc = NULL; +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + op->func_classobj = NULL; +#else + ((PyCMethodObject*)op)->mm_class = NULL; +#endif + op->func_globals = globals; + Py_INCREF(op->func_globals); + Py_XINCREF(code); + op->func_code = code; + op->defaults_pyobjects = 0; + op->defaults_size = 0; + op->defaults = NULL; + op->defaults_tuple = NULL; + op->defaults_kwdict = NULL; + op->defaults_getter = NULL; + op->func_annotations = NULL; + op->func_is_coroutine = NULL; +#if CYTHON_METH_FASTCALL + switch (ml->ml_flags & (METH_VARARGS | METH_FASTCALL | METH_NOARGS | METH_O | METH_KEYWORDS | METH_METHOD)) { + case METH_NOARGS: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_NOARGS; + break; + case METH_O: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_O; + break; + case METH_METHOD | METH_FASTCALL | METH_KEYWORDS: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD; + break; + case METH_FASTCALL | METH_KEYWORDS: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS; + break; + case METH_VARARGS | METH_KEYWORDS: + __Pyx_CyFunction_func_vectorcall(op) = NULL; + break; + default: + PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction"); + Py_DECREF(op); + return NULL; + } +#endif + return (PyObject *) op; +} +static int +__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m) +{ + Py_CLEAR(m->func_closure); +#if CYTHON_COMPILING_IN_LIMITED_API + Py_CLEAR(m->func); +#else + Py_CLEAR(((PyCFunctionObject*)m)->m_module); +#endif + Py_CLEAR(m->func_dict); + Py_CLEAR(m->func_name); + Py_CLEAR(m->func_qualname); + Py_CLEAR(m->func_doc); + Py_CLEAR(m->func_globals); + Py_CLEAR(m->func_code); +#if !CYTHON_COMPILING_IN_LIMITED_API +#if PY_VERSION_HEX < 0x030900B1 + Py_CLEAR(__Pyx_CyFunction_GetClassObj(m)); +#else + { + PyObject *cls = (PyObject*) ((PyCMethodObject *) (m))->mm_class; + ((PyCMethodObject *) (m))->mm_class = NULL; + Py_XDECREF(cls); + } +#endif +#endif + Py_CLEAR(m->defaults_tuple); + Py_CLEAR(m->defaults_kwdict); + Py_CLEAR(m->func_annotations); + Py_CLEAR(m->func_is_coroutine); + if (m->defaults) { + PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); + int i; + for (i = 0; i < m->defaults_pyobjects; i++) + Py_XDECREF(pydefaults[i]); + PyObject_Free(m->defaults); + m->defaults = NULL; + } + return 0; +} +static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m) +{ + if (__Pyx_CyFunction_weakreflist(m) != NULL) + PyObject_ClearWeakRefs((PyObject *) m); + __Pyx_CyFunction_clear(m); + __Pyx_PyHeapTypeObject_GC_Del(m); +} +static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m) +{ + PyObject_GC_UnTrack(m); + __Pyx__CyFunction_dealloc(m); +} +static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg) +{ + Py_VISIT(m->func_closure); +#if CYTHON_COMPILING_IN_LIMITED_API + Py_VISIT(m->func); +#else + Py_VISIT(((PyCFunctionObject*)m)->m_module); +#endif + Py_VISIT(m->func_dict); + Py_VISIT(m->func_name); + Py_VISIT(m->func_qualname); + Py_VISIT(m->func_doc); + Py_VISIT(m->func_globals); + Py_VISIT(m->func_code); +#if !CYTHON_COMPILING_IN_LIMITED_API + Py_VISIT(__Pyx_CyFunction_GetClassObj(m)); +#endif + Py_VISIT(m->defaults_tuple); + Py_VISIT(m->defaults_kwdict); + Py_VISIT(m->func_is_coroutine); + if (m->defaults) { + PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); + int i; + for (i = 0; i < m->defaults_pyobjects; i++) + Py_VISIT(pydefaults[i]); + } + return 0; +} +static PyObject* +__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) +{ +#if PY_MAJOR_VERSION >= 3 + return PyUnicode_FromFormat("", + op->func_qualname, (void *)op); +#else + return PyString_FromFormat("", + PyString_AsString(op->func_qualname), (void *)op); +#endif +} +static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) { +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *f = ((__pyx_CyFunctionObject*)func)->func; + PyObject *py_name = NULL; + PyCFunction meth; + int flags; + meth = PyCFunction_GetFunction(f); + if (unlikely(!meth)) return NULL; + flags = PyCFunction_GetFlags(f); + if (unlikely(flags < 0)) return NULL; +#else + PyCFunctionObject* f = (PyCFunctionObject*)func; + PyCFunction meth = f->m_ml->ml_meth; + int flags = f->m_ml->ml_flags; +#endif + Py_ssize_t size; + switch (flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) { + case METH_VARARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) + return (*meth)(self, arg); + break; + case METH_VARARGS | METH_KEYWORDS: + return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw); + case METH_NOARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { +#if CYTHON_ASSUME_SAFE_MACROS + size = PyTuple_GET_SIZE(arg); +#else + size = PyTuple_Size(arg); + if (unlikely(size < 0)) return NULL; +#endif + if (likely(size == 0)) + return (*meth)(self, NULL); +#if CYTHON_COMPILING_IN_LIMITED_API + py_name = __Pyx_CyFunction_get_name((__pyx_CyFunctionObject*)func, NULL); + if (!py_name) return NULL; + PyErr_Format(PyExc_TypeError, + "%.200S() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", + py_name, size); + Py_DECREF(py_name); +#else + PyErr_Format(PyExc_TypeError, + "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", + f->m_ml->ml_name, size); +#endif + return NULL; + } + break; + case METH_O: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { +#if CYTHON_ASSUME_SAFE_MACROS + size = PyTuple_GET_SIZE(arg); +#else + size = PyTuple_Size(arg); + if (unlikely(size < 0)) return NULL; +#endif + if (likely(size == 1)) { + PyObject *result, *arg0; + #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + arg0 = PyTuple_GET_ITEM(arg, 0); + #else + arg0 = __Pyx_PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL; + #endif + result = (*meth)(self, arg0); + #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_DECREF(arg0); + #endif + return result; + } +#if CYTHON_COMPILING_IN_LIMITED_API + py_name = __Pyx_CyFunction_get_name((__pyx_CyFunctionObject*)func, NULL); + if (!py_name) return NULL; + PyErr_Format(PyExc_TypeError, + "%.200S() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", + py_name, size); + Py_DECREF(py_name); +#else + PyErr_Format(PyExc_TypeError, + "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", + f->m_ml->ml_name, size); +#endif + return NULL; + } + break; + default: + PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction"); + return NULL; + } +#if CYTHON_COMPILING_IN_LIMITED_API + py_name = __Pyx_CyFunction_get_name((__pyx_CyFunctionObject*)func, NULL); + if (!py_name) return NULL; + PyErr_Format(PyExc_TypeError, "%.200S() takes no keyword arguments", + py_name); + Py_DECREF(py_name); +#else + PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments", + f->m_ml->ml_name); +#endif + return NULL; +} +static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *self, *result; +#if CYTHON_COMPILING_IN_LIMITED_API + self = PyCFunction_GetSelf(((__pyx_CyFunctionObject*)func)->func); + if (unlikely(!self) && PyErr_Occurred()) return NULL; +#else + self = ((PyCFunctionObject*)func)->m_self; +#endif + result = __Pyx_CyFunction_CallMethod(func, self, arg, kw); + return result; +} +static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) { + PyObject *result; + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func; +#if CYTHON_METH_FASTCALL + __pyx_vectorcallfunc vc = __Pyx_CyFunction_func_vectorcall(cyfunc); + if (vc) { +#if CYTHON_ASSUME_SAFE_MACROS + return __Pyx_PyVectorcall_FastCallDict(func, vc, &PyTuple_GET_ITEM(args, 0), (size_t)PyTuple_GET_SIZE(args), kw); +#else + (void) &__Pyx_PyVectorcall_FastCallDict; + return PyVectorcall_Call(func, args, kw); +#endif + } +#endif + if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { + Py_ssize_t argc; + PyObject *new_args; + PyObject *self; +#if CYTHON_ASSUME_SAFE_MACROS + argc = PyTuple_GET_SIZE(args); +#else + argc = PyTuple_Size(args); + if (unlikely(!argc) < 0) return NULL; +#endif + new_args = PyTuple_GetSlice(args, 1, argc); + if (unlikely(!new_args)) + return NULL; + self = PyTuple_GetItem(args, 0); + if (unlikely(!self)) { + Py_DECREF(new_args); +#if PY_MAJOR_VERSION > 2 + PyErr_Format(PyExc_TypeError, + "unbound method %.200S() needs an argument", + cyfunc->func_qualname); +#else + PyErr_SetString(PyExc_TypeError, + "unbound method needs an argument"); +#endif + return NULL; + } + result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw); + Py_DECREF(new_args); + } else { + result = __Pyx_CyFunction_Call(func, args, kw); + } + return result; +} +#if CYTHON_METH_FASTCALL +static CYTHON_INLINE int __Pyx_CyFunction_Vectorcall_CheckArgs(__pyx_CyFunctionObject *cyfunc, Py_ssize_t nargs, PyObject *kwnames) +{ + int ret = 0; + if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { + if (unlikely(nargs < 1)) { + PyErr_Format(PyExc_TypeError, "%.200s() needs an argument", + ((PyCFunctionObject*)cyfunc)->m_ml->ml_name); + return -1; + } + ret = 1; + } + if (unlikely(kwnames) && unlikely(PyTuple_GET_SIZE(kwnames))) { + PyErr_Format(PyExc_TypeError, + "%.200s() takes no keyword arguments", ((PyCFunctionObject*)cyfunc)->m_ml->ml_name); + return -1; + } + return ret; +} +static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + if (unlikely(nargs != 0)) { + PyErr_Format(PyExc_TypeError, + "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", + def->ml_name, nargs); + return NULL; + } + return def->ml_meth(self, NULL); +} +static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + if (unlikely(nargs != 1)) { + PyErr_Format(PyExc_TypeError, + "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", + def->ml_name, nargs); + return NULL; + } + return def->ml_meth(self, args[0]); +} +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + return ((_PyCFunctionFastWithKeywords)(void(*)(void))def->ml_meth)(self, args, nargs, kwnames); +} +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; + PyTypeObject *cls = (PyTypeObject *) __Pyx_CyFunction_GetClassObj(cyfunc); +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + return ((__Pyx_PyCMethod)(void(*)(void))def->ml_meth)(self, cls, args, (size_t)nargs, kwnames); +} +#endif +#if CYTHON_USE_TYPE_SPECS +static PyType_Slot __pyx_CyFunctionType_slots[] = { + {Py_tp_dealloc, (void *)__Pyx_CyFunction_dealloc}, + {Py_tp_repr, (void *)__Pyx_CyFunction_repr}, + {Py_tp_call, (void *)__Pyx_CyFunction_CallAsMethod}, + {Py_tp_traverse, (void *)__Pyx_CyFunction_traverse}, + {Py_tp_clear, (void *)__Pyx_CyFunction_clear}, + {Py_tp_methods, (void *)__pyx_CyFunction_methods}, + {Py_tp_members, (void *)__pyx_CyFunction_members}, + {Py_tp_getset, (void *)__pyx_CyFunction_getsets}, + {Py_tp_descr_get, (void *)__Pyx_PyMethod_New}, + {0, 0}, +}; +static PyType_Spec __pyx_CyFunctionType_spec = { + __PYX_TYPE_MODULE_PREFIX "cython_function_or_method", + sizeof(__pyx_CyFunctionObject), + 0, +#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR + Py_TPFLAGS_METHOD_DESCRIPTOR | +#endif +#if (defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL) + _Py_TPFLAGS_HAVE_VECTORCALL | +#endif + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE, + __pyx_CyFunctionType_slots +}; +#else +static PyTypeObject __pyx_CyFunctionType_type = { + PyVarObject_HEAD_INIT(0, 0) + __PYX_TYPE_MODULE_PREFIX "cython_function_or_method", + sizeof(__pyx_CyFunctionObject), + 0, + (destructor) __Pyx_CyFunction_dealloc, +#if !CYTHON_METH_FASTCALL + 0, +#elif CYTHON_BACKPORT_VECTORCALL + (printfunc)offsetof(__pyx_CyFunctionObject, func_vectorcall), +#else + offsetof(PyCFunctionObject, vectorcall), +#endif + 0, + 0, +#if PY_MAJOR_VERSION < 3 + 0, +#else + 0, +#endif + (reprfunc) __Pyx_CyFunction_repr, + 0, + 0, + 0, + 0, + __Pyx_CyFunction_CallAsMethod, + 0, + 0, + 0, + 0, +#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR + Py_TPFLAGS_METHOD_DESCRIPTOR | +#endif +#if defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL + _Py_TPFLAGS_HAVE_VECTORCALL | +#endif + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE, + 0, + (traverseproc) __Pyx_CyFunction_traverse, + (inquiry) __Pyx_CyFunction_clear, + 0, +#if PY_VERSION_HEX < 0x030500A0 + offsetof(__pyx_CyFunctionObject, func_weakreflist), +#else + offsetof(PyCFunctionObject, m_weakreflist), +#endif + 0, + 0, + __pyx_CyFunction_methods, + __pyx_CyFunction_members, + __pyx_CyFunction_getsets, + 0, + 0, + __Pyx_PyMethod_New, + 0, + offsetof(__pyx_CyFunctionObject, func_dict), + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, +#if PY_VERSION_HEX >= 0x030400a1 + 0, +#endif +#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, +#endif +#if __PYX_NEED_TP_PRINT_SLOT + 0, +#endif +#if PY_VERSION_HEX >= 0x030C0000 + 0, +#endif +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, +#endif +}; +#endif +static int __pyx_CyFunction_init(PyObject *module) { +#if CYTHON_USE_TYPE_SPECS + __pyx_CyFunctionType = __Pyx_FetchCommonTypeFromSpec(module, &__pyx_CyFunctionType_spec, NULL); +#else + CYTHON_UNUSED_VAR(module); + __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type); +#endif + if (unlikely(__pyx_CyFunctionType == NULL)) { + return -1; + } + return 0; +} +static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults = PyObject_Malloc(size); + if (unlikely(!m->defaults)) + return PyErr_NoMemory(); + memset(m->defaults, 0, size); + m->defaults_pyobjects = pyobjects; + m->defaults_size = size; + return m->defaults; +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_tuple = tuple; + Py_INCREF(tuple); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_kwdict = dict; + Py_INCREF(dict); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->func_annotations = dict; + Py_INCREF(dict); +} + +/* CythonFunction */ +static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname, + PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { + PyObject *op = __Pyx_CyFunction_Init( + PyObject_GC_New(__pyx_CyFunctionObject, __pyx_CyFunctionType), + ml, flags, qualname, closure, module, globals, code + ); + if (likely(op)) { + PyObject_GC_Track(op); + } + return op; +} + +/* CLineInTraceback */ +#ifndef CYTHON_CLINE_IN_TRACEBACK +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) { + PyObject *use_cline; + PyObject *ptype, *pvalue, *ptraceback; +#if CYTHON_COMPILING_IN_CPYTHON + PyObject **cython_runtime_dict; +#endif + CYTHON_MAYBE_UNUSED_VAR(tstate); + if (unlikely(!__pyx_cython_runtime)) { + return c_line; + } + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); +#if CYTHON_COMPILING_IN_CPYTHON + cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); + if (likely(cython_runtime_dict)) { + __PYX_PY_DICT_LOOKUP_IF_MODIFIED( + use_cline, *cython_runtime_dict, + __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) + } else +#endif + { + PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStrNoError(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); + if (use_cline_obj) { + use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; + Py_DECREF(use_cline_obj); + } else { + PyErr_Clear(); + use_cline = NULL; + } + } + if (!use_cline) { + c_line = 0; + (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); + } + else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { + c_line = 0; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + return c_line; +} +#endif + +/* CodeObjectCache */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} +#endif + +/* AddTraceback */ +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" +#if PY_VERSION_HEX >= 0x030b00a6 && !CYTHON_COMPILING_IN_LIMITED_API + #ifndef Py_BUILD_CORE + #define Py_BUILD_CORE 1 + #endif + #include "internal/pycore_frame.h" +#endif +#if CYTHON_COMPILING_IN_LIMITED_API +static PyObject *__Pyx_PyCode_Replace_For_AddTraceback(PyObject *code, PyObject *scratch_dict, + PyObject *firstlineno, PyObject *name) { + PyObject *replace = NULL; + if (unlikely(PyDict_SetItemString(scratch_dict, "co_firstlineno", firstlineno))) return NULL; + if (unlikely(PyDict_SetItemString(scratch_dict, "co_name", name))) return NULL; + replace = PyObject_GetAttrString(code, "replace"); + if (likely(replace)) { + PyObject *result; + result = PyObject_Call(replace, __pyx_empty_tuple, scratch_dict); + Py_DECREF(replace); + return result; + } + PyErr_Clear(); + #if __PYX_LIMITED_VERSION_HEX < 0x030780000 + { + PyObject *compiled = NULL, *result = NULL; + if (unlikely(PyDict_SetItemString(scratch_dict, "code", code))) return NULL; + if (unlikely(PyDict_SetItemString(scratch_dict, "type", (PyObject*)(&PyType_Type)))) return NULL; + compiled = Py_CompileString( + "out = type(code)(\n" + " code.co_argcount, code.co_kwonlyargcount, code.co_nlocals, code.co_stacksize,\n" + " code.co_flags, code.co_code, code.co_consts, code.co_names,\n" + " code.co_varnames, code.co_filename, co_name, co_firstlineno,\n" + " code.co_lnotab)\n", "", Py_file_input); + if (!compiled) return NULL; + result = PyEval_EvalCode(compiled, scratch_dict, scratch_dict); + Py_DECREF(compiled); + if (!result) PyErr_Print(); + Py_DECREF(result); + result = PyDict_GetItemString(scratch_dict, "out"); + if (result) Py_INCREF(result); + return result; + } + #else + return NULL; + #endif +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyObject *code_object = NULL, *py_py_line = NULL, *py_funcname = NULL, *dict = NULL; + PyObject *replace = NULL, *getframe = NULL, *frame = NULL; + PyObject *exc_type, *exc_value, *exc_traceback; + int success = 0; + if (c_line) { + (void) __pyx_cfilenm; + (void) __Pyx_CLineForTraceback(__Pyx_PyThreadState_Current, c_line); + } + PyErr_Fetch(&exc_type, &exc_value, &exc_traceback); + code_object = Py_CompileString("_getframe()", filename, Py_eval_input); + if (unlikely(!code_object)) goto bad; + py_py_line = PyLong_FromLong(py_line); + if (unlikely(!py_py_line)) goto bad; + py_funcname = PyUnicode_FromString(funcname); + if (unlikely(!py_funcname)) goto bad; + dict = PyDict_New(); + if (unlikely(!dict)) goto bad; + { + PyObject *old_code_object = code_object; + code_object = __Pyx_PyCode_Replace_For_AddTraceback(code_object, dict, py_py_line, py_funcname); + Py_DECREF(old_code_object); + } + if (unlikely(!code_object)) goto bad; + getframe = PySys_GetObject("_getframe"); + if (unlikely(!getframe)) goto bad; + if (unlikely(PyDict_SetItemString(dict, "_getframe", getframe))) goto bad; + frame = PyEval_EvalCode(code_object, dict, dict); + if (unlikely(!frame) || frame == Py_None) goto bad; + success = 1; + bad: + PyErr_Restore(exc_type, exc_value, exc_traceback); + Py_XDECREF(code_object); + Py_XDECREF(py_py_line); + Py_XDECREF(py_funcname); + Py_XDECREF(dict); + Py_XDECREF(replace); + if (success) { + PyTraceBack_Here( + (struct _frame*)frame); + } + Py_XDECREF(frame); +} +#else +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = NULL; + PyObject *py_funcname = NULL; + #if PY_MAJOR_VERSION < 3 + PyObject *py_srcfile = NULL; + py_srcfile = PyString_FromString(filename); + if (!py_srcfile) goto bad; + #endif + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + if (!py_funcname) goto bad; + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + if (!py_funcname) goto bad; + funcname = PyUnicode_AsUTF8(py_funcname); + if (!funcname) goto bad; + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + if (!py_funcname) goto bad; + #endif + } + #if PY_MAJOR_VERSION < 3 + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + #else + py_code = PyCode_NewEmpty(filename, funcname, py_line); + #endif + Py_XDECREF(py_funcname); // XDECREF since it's only set on Py3 if cline + return py_code; +bad: + Py_XDECREF(py_funcname); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_srcfile); + #endif + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject *ptype, *pvalue, *ptraceback; + if (c_line) { + c_line = __Pyx_CLineForTraceback(tstate, c_line); + } + py_code = __pyx_find_code_object(c_line ? -c_line : py_line); + if (!py_code) { + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) { + /* If the code object creation fails, then we should clear the + fetched exception references and propagate the new exception */ + Py_XDECREF(ptype); + Py_XDECREF(pvalue); + Py_XDECREF(ptraceback); + goto bad; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); + } + py_frame = PyFrame_New( + tstate, /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} +#endif + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + __Pyx_TypeName obj_type_name; + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, + "'" __Pyx_FMT_TYPENAME "' does not have the buffer interface", + obj_type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if ((0)) {} + view->obj = NULL; + Py_DECREF(obj); +} +#endif + + +/* MemviewSliceIsContig */ +static int +__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) +{ + int i, index, step, start; + Py_ssize_t itemsize = mvs.memview->view.itemsize; + if (order == 'F') { + step = 1; + start = 0; + } else { + step = -1; + start = ndim - 1; + } + for (i = 0; i < ndim; i++) { + index = start + step * i; + if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) + return 0; + itemsize *= mvs.shape[index]; + } + return 1; +} + +/* OverlappingSlices */ +static void +__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, + void **out_start, void **out_end, + int ndim, size_t itemsize) +{ + char *start, *end; + int i; + start = end = slice->data; + for (i = 0; i < ndim; i++) { + Py_ssize_t stride = slice->strides[i]; + Py_ssize_t extent = slice->shape[i]; + if (extent == 0) { + *out_start = *out_end = start; + return; + } else { + if (stride > 0) + end += stride * (extent - 1); + else + start += stride * (extent - 1); + } + } + *out_start = start; + *out_end = end + itemsize; +} +static int +__pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize) +{ + void *start1, *end1, *start2, *end2; + __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); + __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); + return (start1 < end2) && (start2 < end1); +} + +/* IsLittleEndian */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) +{ + union { + uint32_t u32; + uint8_t u8[4]; + } S; + S.u32 = 0x01020304; + return S.u8[0] == 4; +} + +/* BufferFormatCheck */ +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t <= '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case '?': return "'bool'"; + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparsable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, int is_complex) { + CYTHON_UNUSED_VAR(is_complex); + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, int is_complex) { + CYTHON_UNUSED_VAR(is_complex); + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static int +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number, ndim; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return -1; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return -1; + ndim = ctx->head->field->type->ndim; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return -1; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + return -1; + } + if (*ts != ',' && *ts != ')') { + PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + return -1; + } + if (*ts == ',') ts++; + i++; + } + if (i != ndim) { + PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + return -1; + } + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return -1; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return 0; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + CYTHON_FALLTHROUGH; + case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && + (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + CYTHON_FALLTHROUGH; + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (__pyx_buffmt_parse_array(ctx, &ts) < 0) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} + +/* TypeInfoCompare */ + static int +__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) +{ + int i; + if (!a || !b) + return 0; + if (a == b) + return 1; + if (a->size != b->size || a->typegroup != b->typegroup || + a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { + if (a->typegroup == 'H' || b->typegroup == 'H') { + return a->size == b->size; + } else { + return 0; + } + } + if (a->ndim) { + for (i = 0; i < a->ndim; i++) + if (a->arraysize[i] != b->arraysize[i]) + return 0; + } + if (a->typegroup == 'S') { + if (a->flags != b->flags) + return 0; + if (a->fields || b->fields) { + if (!(a->fields && b->fields)) + return 0; + for (i = 0; a->fields[i].type && b->fields[i].type; i++) { + __Pyx_StructField *field_a = a->fields + i; + __Pyx_StructField *field_b = b->fields + i; + if (field_a->offset != field_b->offset || + !__pyx_typeinfo_cmp(field_a->type, field_b->type)) + return 0; + } + return !a->fields[i].type && !b->fields[i].type; + } + } + return 1; +} + +/* MemviewSliceValidateAndInit */ + static int +__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) +{ + if (buf->shape[dim] <= 1) + return 1; + if (buf->strides) { + if (spec & __Pyx_MEMVIEW_CONTIG) { + if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { + if (unlikely(buf->strides[dim] != sizeof(void *))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly contiguous " + "in dimension %d.", dim); + goto fail; + } + } else if (unlikely(buf->strides[dim] != buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_FOLLOW) { + Py_ssize_t stride = buf->strides[dim]; + if (stride < 0) + stride = -stride; + if (unlikely(stride < buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + } else { + if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not contiguous in " + "dimension %d", dim); + goto fail; + } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not indirect in " + "dimension %d", dim); + goto fail; + } else if (unlikely(buf->suboffsets)) { + PyErr_SetString(PyExc_ValueError, + "Buffer exposes suboffsets but no strides"); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_check_suboffsets(Py_buffer *buf, int dim, int ndim, int spec) +{ + CYTHON_UNUSED_VAR(ndim); + if (spec & __Pyx_MEMVIEW_DIRECT) { + if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) { + PyErr_Format(PyExc_ValueError, + "Buffer not compatible with direct access " + "in dimension %d.", dim); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_PTR) { + if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly accessible " + "in dimension %d.", dim); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) +{ + int i; + if (c_or_f_flag & __Pyx_IS_F_CONTIG) { + Py_ssize_t stride = 1; + for (i = 0; i < ndim; i++) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not fortran contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { + Py_ssize_t stride = 1; + for (i = ndim - 1; i >- 1; i--) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not C contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } + return 1; +fail: + return 0; +} +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj) +{ + struct __pyx_memoryview_obj *memview, *new_memview; + __Pyx_RefNannyDeclarations + Py_buffer *buf; + int i, spec = 0, retval = -1; + __Pyx_BufFmt_Context ctx; + int from_memoryview = __pyx_memoryview_check(original_obj); + __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); + if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) + original_obj)->typeinfo)) { + memview = (struct __pyx_memoryview_obj *) original_obj; + new_memview = NULL; + } else { + memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + original_obj, buf_flags, 0, dtype); + new_memview = memview; + if (unlikely(!memview)) + goto fail; + } + buf = &memview->view; + if (unlikely(buf->ndim != ndim)) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + ndim, buf->ndim); + goto fail; + } + if (new_memview) { + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail; + } + if (unlikely((unsigned) buf->itemsize != dtype->size)) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " + "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", + buf->itemsize, + (buf->itemsize > 1) ? "s" : "", + dtype->name, + dtype->size, + (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->len > 0) { + for (i = 0; i < ndim; i++) { + spec = axes_specs[i]; + if (unlikely(!__pyx_check_strides(buf, i, ndim, spec))) + goto fail; + if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec))) + goto fail; + } + if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))) + goto fail; + } + if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, + new_memview != NULL) == -1)) { + goto fail; + } + retval = 0; + goto no_fail; +fail: + Py_XDECREF(new_memview); + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, + (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3, + &__Pyx_TypeInfo_int, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, + (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3, + &__Pyx_TypeInfo_float, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, + (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, + &__Pyx_TypeInfo_int, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* MemviewSliceCopyTemplate */ + static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object) +{ + __Pyx_RefNannyDeclarations + int i; + __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; + struct __pyx_memoryview_obj *from_memview = from_mvs->memview; + Py_buffer *buf = &from_memview->view; + PyObject *shape_tuple = NULL; + PyObject *temp_int = NULL; + struct __pyx_array_obj *array_obj = NULL; + struct __pyx_memoryview_obj *memview_obj = NULL; + __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); + for (i = 0; i < ndim; i++) { + if (unlikely(from_mvs->suboffsets[i] >= 0)) { + PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " + "indirect dimensions (axis %d)", i); + goto fail; + } + } + shape_tuple = PyTuple_New(ndim); + if (unlikely(!shape_tuple)) { + goto fail; + } + __Pyx_GOTREF(shape_tuple); + for(i = 0; i < ndim; i++) { + temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); + if(unlikely(!temp_int)) { + goto fail; + } else { + PyTuple_SET_ITEM(shape_tuple, i, temp_int); + temp_int = NULL; + } + } + array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); + if (unlikely(!array_obj)) { + goto fail; + } + __Pyx_GOTREF(array_obj); + memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + (PyObject *) array_obj, contig_flag, + dtype_is_object, + from_mvs->memview->typeinfo); + if (unlikely(!memview_obj)) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) + goto fail; + if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, + dtype_is_object) < 0)) + goto fail; + goto no_fail; +fail: + __Pyx_XDECREF(new_mvs.memview); + new_mvs.memview = NULL; + new_mvs.data = NULL; +no_fail: + __Pyx_XDECREF(shape_tuple); + __Pyx_XDECREF(temp_int); + __Pyx_XDECREF(array_obj); + __Pyx_RefNannyFinishContext(); + return new_mvs; +} + +/* MemviewSliceInit */ + static int +__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference) +{ + __Pyx_RefNannyDeclarations + int i, retval=-1; + Py_buffer *buf = &memview->view; + __Pyx_RefNannySetupContext("init_memviewslice", 0); + if (unlikely(memviewslice->memview || memviewslice->data)) { + PyErr_SetString(PyExc_ValueError, + "memviewslice is already initialized!"); + goto fail; + } + if (buf->strides) { + for (i = 0; i < ndim; i++) { + memviewslice->strides[i] = buf->strides[i]; + } + } else { + Py_ssize_t stride = buf->itemsize; + for (i = ndim - 1; i >= 0; i--) { + memviewslice->strides[i] = stride; + stride *= buf->shape[i]; + } + } + for (i = 0; i < ndim; i++) { + memviewslice->shape[i] = buf->shape[i]; + if (buf->suboffsets) { + memviewslice->suboffsets[i] = buf->suboffsets[i]; + } else { + memviewslice->suboffsets[i] = -1; + } + } + memviewslice->memview = memview; + memviewslice->data = (char *)buf->buf; + if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { + Py_INCREF(memview); + } + retval = 0; + goto no_fail; +fail: + memviewslice->memview = 0; + memviewslice->data = 0; + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} +#ifndef Py_NO_RETURN +#define Py_NO_RETURN +#endif +static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { + va_list vargs; + char msg[200]; +#if PY_VERSION_HEX >= 0x030A0000 || defined(HAVE_STDARG_PROTOTYPES) + va_start(vargs, fmt); +#else + va_start(vargs); +#endif + vsnprintf(msg, 200, fmt, vargs); + va_end(vargs); + Py_FatalError(msg); +} +static CYTHON_INLINE int +__pyx_add_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)++; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE int +__pyx_sub_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)--; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE void +__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) +{ + __pyx_nonatomic_int_type old_acquisition_count; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) { + return; + } + old_acquisition_count = __pyx_add_acquisition_count(memview); + if (unlikely(old_acquisition_count <= 0)) { + if (likely(old_acquisition_count == 0)) { + if (have_gil) { + Py_INCREF((PyObject *) memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_INCREF((PyObject *) memview); + PyGILState_Release(_gilstate); + } + } else { + __pyx_fatalerror("Acquisition count is %d (line %d)", + old_acquisition_count+1, lineno); + } + } +} +static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *memslice, + int have_gil, int lineno) { + __pyx_nonatomic_int_type old_acquisition_count; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) { + memslice->memview = NULL; + return; + } + old_acquisition_count = __pyx_sub_acquisition_count(memview); + memslice->data = NULL; + if (likely(old_acquisition_count > 1)) { + memslice->memview = NULL; + } else if (likely(old_acquisition_count == 1)) { + if (have_gil) { + Py_CLEAR(memslice->memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_CLEAR(memslice->memview); + PyGILState_Release(_gilstate); + } + } else { + __pyx_fatalerror("Acquisition count is %d (line %d)", + old_acquisition_count-1, lineno); + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; +#if !CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030d0000 + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); +#else + PyObject *from_bytes, *result = NULL; + PyObject *py_bytes = NULL, *arg_tuple = NULL, *kwds = NULL, *order_str = NULL; + from_bytes = PyObject_GetAttrString((PyObject*)&PyLong_Type, "from_bytes"); + if (!from_bytes) return NULL; + py_bytes = PyBytes_FromStringAndSize((char*)bytes, sizeof(int)); + if (!py_bytes) goto limited_bad; + order_str = PyUnicode_FromString(little ? "little" : "big"); + if (!order_str) goto limited_bad; + arg_tuple = PyTuple_Pack(2, py_bytes, order_str); + if (!arg_tuple) goto limited_bad; + if (!is_unsigned) { + kwds = PyDict_New(); + if (!kwds) goto limited_bad; + if (PyDict_SetItemString(kwds, "signed", __Pyx_NewRef(Py_True))) goto limited_bad; + } + result = PyObject_Call(from_bytes, arg_tuple, kwds); + limited_bad: + Py_XDECREF(kwds); + Py_XDECREF(arg_tuple); + Py_XDECREF(order_str); + Py_XDECREF(py_bytes); + Py_XDECREF(from_bytes); + return result; +#endif + } +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if ((sizeof(int) < sizeof(long))) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + if (unlikely(__Pyx_PyLong_IsNeg(x))) { + goto raise_neg_overflow; + } else if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_DigitCount(x)) { + case 2: + if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) >= 2 * PyLong_SHIFT)) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) >= 3 * PyLong_SHIFT)) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) >= 4 * PyLong_SHIFT)) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if ((sizeof(int) <= sizeof(unsigned long))) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(int) <= sizeof(unsigned PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_SignedDigitCount(x)) { + case -2: + if ((8 * sizeof(int) - 1 > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } + } +#endif + if ((sizeof(int) <= sizeof(long))) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(int) <= sizeof(PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); +#if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } +#endif + if (likely(v)) { + int ret = -1; +#if PY_VERSION_HEX < 0x030d0000 && !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); +#else + PyObject *stepval = NULL, *mask = NULL, *shift = NULL; + int bits, remaining_bits, is_negative = 0; + long idigit; + int chunk_size = (sizeof(long) < 8) ? 30 : 62; + if (unlikely(!PyLong_CheckExact(v))) { + PyObject *tmp = v; + v = PyNumber_Long(v); + assert(PyLong_CheckExact(v)); + Py_DECREF(tmp); + if (unlikely(!v)) return (int) -1; + } +#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(x) == 0) + return (int) 0; + is_negative = Py_SIZE(x) < 0; +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + is_negative = result == 1; + } +#endif + if (is_unsigned && unlikely(is_negative)) { + goto raise_neg_overflow; + } else if (is_negative) { + stepval = PyNumber_Invert(v); + if (unlikely(!stepval)) + return (int) -1; + } else { + stepval = __Pyx_NewRef(v); + } + val = (int) 0; + mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; + shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; + for (bits = 0; bits < (int) sizeof(int) * 8 - chunk_size; bits += chunk_size) { + PyObject *tmp, *digit; + digit = PyNumber_And(stepval, mask); + if (unlikely(!digit)) goto done; + idigit = PyLong_AsLong(digit); + Py_DECREF(digit); + if (unlikely(idigit < 0)) goto done; + tmp = PyNumber_Rshift(stepval, shift); + if (unlikely(!tmp)) goto done; + Py_DECREF(stepval); stepval = tmp; + val |= ((int) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(stepval) == 0) + goto unpacking_done; + #endif + } + idigit = PyLong_AsLong(stepval); + if (unlikely(idigit < 0)) goto done; + remaining_bits = ((int) sizeof(int) * 8) - bits - (is_unsigned ? 0 : 1); + if (unlikely(idigit >= (1L << remaining_bits))) + goto raise_overflow; + val |= ((int) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + unpacking_done: + #endif + if (!is_unsigned) { + if (unlikely(val & (((int) 1) << (sizeof(int) * 8 - 1)))) + goto raise_overflow; + if (is_negative) + val = ~val; + } + ret = 0; + done: + Py_XDECREF(shift); + Py_XDECREF(mask); + Py_XDECREF(stepval); +#endif + Py_DECREF(v); + if (likely(!ret)) + return val; + } + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const long neg_one = (long) -1, const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; +#if !CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030d0000 + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); +#else + PyObject *from_bytes, *result = NULL; + PyObject *py_bytes = NULL, *arg_tuple = NULL, *kwds = NULL, *order_str = NULL; + from_bytes = PyObject_GetAttrString((PyObject*)&PyLong_Type, "from_bytes"); + if (!from_bytes) return NULL; + py_bytes = PyBytes_FromStringAndSize((char*)bytes, sizeof(long)); + if (!py_bytes) goto limited_bad; + order_str = PyUnicode_FromString(little ? "little" : "big"); + if (!order_str) goto limited_bad; + arg_tuple = PyTuple_Pack(2, py_bytes, order_str); + if (!arg_tuple) goto limited_bad; + if (!is_unsigned) { + kwds = PyDict_New(); + if (!kwds) goto limited_bad; + if (PyDict_SetItemString(kwds, "signed", __Pyx_NewRef(Py_True))) goto limited_bad; + } + result = PyObject_Call(from_bytes, arg_tuple, kwds); + limited_bad: + Py_XDECREF(kwds); + Py_XDECREF(arg_tuple); + Py_XDECREF(order_str); + Py_XDECREF(py_bytes); + Py_XDECREF(from_bytes); + return result; +#endif + } +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const long neg_one = (long) -1, const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if ((sizeof(long) < sizeof(long))) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + if (unlikely(__Pyx_PyLong_IsNeg(x))) { + goto raise_neg_overflow; + } else if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_DigitCount(x)) { + case 2: + if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) >= 2 * PyLong_SHIFT)) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) >= 3 * PyLong_SHIFT)) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) >= 4 * PyLong_SHIFT)) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if ((sizeof(long) <= sizeof(unsigned long))) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(long) <= sizeof(unsigned PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_SignedDigitCount(x)) { + case -2: + if ((8 * sizeof(long) - 1 > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } + } +#endif + if ((sizeof(long) <= sizeof(long))) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(long) <= sizeof(PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); +#if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } +#endif + if (likely(v)) { + int ret = -1; +#if PY_VERSION_HEX < 0x030d0000 && !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); +#else + PyObject *stepval = NULL, *mask = NULL, *shift = NULL; + int bits, remaining_bits, is_negative = 0; + long idigit; + int chunk_size = (sizeof(long) < 8) ? 30 : 62; + if (unlikely(!PyLong_CheckExact(v))) { + PyObject *tmp = v; + v = PyNumber_Long(v); + assert(PyLong_CheckExact(v)); + Py_DECREF(tmp); + if (unlikely(!v)) return (long) -1; + } +#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(x) == 0) + return (long) 0; + is_negative = Py_SIZE(x) < 0; +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + is_negative = result == 1; + } +#endif + if (is_unsigned && unlikely(is_negative)) { + goto raise_neg_overflow; + } else if (is_negative) { + stepval = PyNumber_Invert(v); + if (unlikely(!stepval)) + return (long) -1; + } else { + stepval = __Pyx_NewRef(v); + } + val = (long) 0; + mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; + shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; + for (bits = 0; bits < (int) sizeof(long) * 8 - chunk_size; bits += chunk_size) { + PyObject *tmp, *digit; + digit = PyNumber_And(stepval, mask); + if (unlikely(!digit)) goto done; + idigit = PyLong_AsLong(digit); + Py_DECREF(digit); + if (unlikely(idigit < 0)) goto done; + tmp = PyNumber_Rshift(stepval, shift); + if (unlikely(!tmp)) goto done; + Py_DECREF(stepval); stepval = tmp; + val |= ((long) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(stepval) == 0) + goto unpacking_done; + #endif + } + idigit = PyLong_AsLong(stepval); + if (unlikely(idigit < 0)) goto done; + remaining_bits = ((int) sizeof(long) * 8) - bits - (is_unsigned ? 0 : 1); + if (unlikely(idigit >= (1L << remaining_bits))) + goto raise_overflow; + val |= ((long) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + unpacking_done: + #endif + if (!is_unsigned) { + if (unlikely(val & (((long) 1) << (sizeof(long) * 8 - 1)))) + goto raise_overflow; + if (is_negative) + val = ~val; + } + ret = 0; + done: + Py_XDECREF(shift); + Py_XDECREF(mask); + Py_XDECREF(stepval); +#endif + Py_DECREF(v); + if (likely(!ret)) + return val; + } + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const char neg_one = (char) -1, const_zero = (char) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if ((sizeof(char) < sizeof(long))) { + __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (char) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + if (unlikely(__Pyx_PyLong_IsNeg(x))) { + goto raise_neg_overflow; + } else if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_DigitCount(x)) { + case 2: + if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) >= 2 * PyLong_SHIFT)) { + return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 3: + if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) >= 3 * PyLong_SHIFT)) { + return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 4: + if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) >= 4 * PyLong_SHIFT)) { + return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + } + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (char) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if ((sizeof(char) <= sizeof(unsigned long))) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(char) <= sizeof(unsigned PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_SignedDigitCount(x)) { + case -2: + if ((8 * sizeof(char) - 1 > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { + return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 2: + if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { + return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -3: + if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { + return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 3: + if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { + return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -4: + if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) { + return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 4: + if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) { + return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + } + } +#endif + if ((sizeof(char) <= sizeof(long))) { + __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(char) <= sizeof(PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { + char val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); +#if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } +#endif + if (likely(v)) { + int ret = -1; +#if PY_VERSION_HEX < 0x030d0000 && !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); +#else + PyObject *stepval = NULL, *mask = NULL, *shift = NULL; + int bits, remaining_bits, is_negative = 0; + long idigit; + int chunk_size = (sizeof(long) < 8) ? 30 : 62; + if (unlikely(!PyLong_CheckExact(v))) { + PyObject *tmp = v; + v = PyNumber_Long(v); + assert(PyLong_CheckExact(v)); + Py_DECREF(tmp); + if (unlikely(!v)) return (char) -1; + } +#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(x) == 0) + return (char) 0; + is_negative = Py_SIZE(x) < 0; +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (char) -1; + is_negative = result == 1; + } +#endif + if (is_unsigned && unlikely(is_negative)) { + goto raise_neg_overflow; + } else if (is_negative) { + stepval = PyNumber_Invert(v); + if (unlikely(!stepval)) + return (char) -1; + } else { + stepval = __Pyx_NewRef(v); + } + val = (char) 0; + mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; + shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; + for (bits = 0; bits < (int) sizeof(char) * 8 - chunk_size; bits += chunk_size) { + PyObject *tmp, *digit; + digit = PyNumber_And(stepval, mask); + if (unlikely(!digit)) goto done; + idigit = PyLong_AsLong(digit); + Py_DECREF(digit); + if (unlikely(idigit < 0)) goto done; + tmp = PyNumber_Rshift(stepval, shift); + if (unlikely(!tmp)) goto done; + Py_DECREF(stepval); stepval = tmp; + val |= ((char) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(stepval) == 0) + goto unpacking_done; + #endif + } + idigit = PyLong_AsLong(stepval); + if (unlikely(idigit < 0)) goto done; + remaining_bits = ((int) sizeof(char) * 8) - bits - (is_unsigned ? 0 : 1); + if (unlikely(idigit >= (1L << remaining_bits))) + goto raise_overflow; + val |= ((char) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + unpacking_done: + #endif + if (!is_unsigned) { + if (unlikely(val & (((char) 1) << (sizeof(char) * 8 - 1)))) + goto raise_overflow; + if (is_negative) + val = ~val; + } + ret = 0; + done: + Py_XDECREF(shift); + Py_XDECREF(mask); + Py_XDECREF(stepval); +#endif + Py_DECREF(v); + if (likely(!ret)) + return val; + } + return (char) -1; + } + } else { + char val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (char) -1; + val = __Pyx_PyInt_As_char(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to char"); + return (char) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to char"); + return (char) -1; +} + +/* FormatTypeName */ + #if CYTHON_COMPILING_IN_LIMITED_API +static __Pyx_TypeName +__Pyx_PyType_GetName(PyTypeObject* tp) +{ + PyObject *name = __Pyx_PyObject_GetAttrStr((PyObject *)tp, + __pyx_n_s_name_2); + if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) { + PyErr_Clear(); + Py_XDECREF(name); + name = __Pyx_NewRef(__pyx_n_s__23); + } + return name; +} +#endif + +/* CheckBinaryVersion */ + static unsigned long __Pyx_get_runtime_version(void) { +#if __PYX_LIMITED_VERSION_HEX >= 0x030B00A4 + return Py_Version & ~0xFFUL; +#else + const char* rt_version = Py_GetVersion(); + unsigned long version = 0; + unsigned long factor = 0x01000000UL; + unsigned int digit = 0; + int i = 0; + while (factor) { + while ('0' <= rt_version[i] && rt_version[i] <= '9') { + digit = digit * 10 + (unsigned int) (rt_version[i] - '0'); + ++i; + } + version += factor * digit; + if (rt_version[i] != '.') + break; + digit = 0; + factor >>= 8; + ++i; + } + return version; +#endif +} +static int __Pyx_check_binary_version(unsigned long ct_version, unsigned long rt_version, int allow_newer) { + const unsigned long MAJOR_MINOR = 0xFFFF0000UL; + if ((rt_version & MAJOR_MINOR) == (ct_version & MAJOR_MINOR)) + return 0; + if (likely(allow_newer && (rt_version & MAJOR_MINOR) > (ct_version & MAJOR_MINOR))) + return 1; + { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compile time Python version %d.%d " + "of module '%.100s' " + "%s " + "runtime version %d.%d", + (int) (ct_version >> 24), (int) ((ct_version >> 16) & 0xFF), + __Pyx_MODULE_NAME, + (allow_newer) ? "was newer than" : "does not match", + (int) (rt_version >> 24), (int) ((rt_version >> 16) & 0xFF) + ); + return PyErr_WarnEx(NULL, message, 1); + } +} + +/* InitStrings */ + #if PY_MAJOR_VERSION >= 3 +static int __Pyx_InitString(__Pyx_StringTabEntry t, PyObject **str) { + if (t.is_unicode | t.is_str) { + if (t.intern) { + *str = PyUnicode_InternFromString(t.s); + } else if (t.encoding) { + *str = PyUnicode_Decode(t.s, t.n - 1, t.encoding, NULL); + } else { + *str = PyUnicode_FromStringAndSize(t.s, t.n - 1); + } + } else { + *str = PyBytes_FromStringAndSize(t.s, t.n - 1); + } + if (!*str) + return -1; + if (PyObject_Hash(*str) == -1) + return -1; + return 0; +} +#endif +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION >= 3 + __Pyx_InitString(*t, t->p); + #else + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + if (!*t->p) + return -1; + if (PyObject_Hash(*t->p) == -1) + return -1; + #endif + ++t; + } + return 0; +} + +#include +static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s) { + size_t len = strlen(s); + if (unlikely(len > (size_t) PY_SSIZE_T_MAX)) { + PyErr_SetString(PyExc_OverflowError, "byte string is too long"); + return -1; + } + return (Py_ssize_t) len; +} +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + Py_ssize_t len = __Pyx_ssize_strlen(c_str); + if (unlikely(len < 0)) return NULL; + return __Pyx_PyUnicode_FromStringAndSize(c_str, len); +} +static CYTHON_INLINE PyObject* __Pyx_PyByteArray_FromString(const char* c_str) { + Py_ssize_t len = __Pyx_ssize_strlen(c_str); + if (unlikely(len < 0)) return NULL; + return PyByteArray_FromStringAndSize(c_str, len); +} +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#if !CYTHON_PEP393_ENABLED +static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +} +#else +static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (likely(PyUnicode_IS_ASCII(o))) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +} +#endif +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { + return __Pyx_PyUnicode_AsStringAndSize(o, length); + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY && !CYTHON_COMPILING_IN_LIMITED_API) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { + int retval; + if (unlikely(!x)) return -1; + retval = __Pyx_PyObject_IsTrue(x); + Py_DECREF(x); + return retval; +} +static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { + __Pyx_TypeName result_type_name = __Pyx_PyType_GetName(Py_TYPE(result)); +#if PY_MAJOR_VERSION >= 3 + if (PyLong_Check(result)) { + if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, + "__int__ returned non-int (type " __Pyx_FMT_TYPENAME "). " + "The ability to return an instance of a strict subclass of int is deprecated, " + "and may be removed in a future version of Python.", + result_type_name)) { + __Pyx_DECREF_TypeName(result_type_name); + Py_DECREF(result); + return NULL; + } + __Pyx_DECREF_TypeName(result_type_name); + return result; + } +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type " __Pyx_FMT_TYPENAME ")", + type_name, type_name, result_type_name); + __Pyx_DECREF_TypeName(result_type_name); + Py_DECREF(result); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x) || PyLong_Check(x))) +#else + if (likely(PyLong_Check(x))) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = m->nb_int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = m->nb_long(x); + } + #else + if (likely(m && m->nb_int)) { + name = "int"; + res = m->nb_int(x); + } + #endif +#else + if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { + res = PyNumber_Int(x); + } +#endif + if (likely(res)) { +#if PY_MAJOR_VERSION < 3 + if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { +#else + if (unlikely(!PyLong_CheckExact(res))) { +#endif + return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(b); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(__Pyx_PyLong_IsCompact(b))) { + return __Pyx_PyLong_CompactValue(b); + } else { + const digit* digits = __Pyx_PyLong_Digits(b); + const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(b); + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) { + if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) { + return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o); +#if PY_MAJOR_VERSION < 3 + } else if (likely(PyInt_CheckExact(o))) { + return PyInt_AS_LONG(o); +#endif + } else { + Py_ssize_t ival; + PyObject *x; + x = PyNumber_Index(o); + if (!x) return -1; + ival = PyInt_AsLong(x); + Py_DECREF(x); + return ival; + } +} +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { + return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +/* #### Code section: utility_code_pragmas_end ### */ +#ifdef _MSC_VER +#pragma warning( pop ) +#endif + + + +/* #### Code section: end ### */ +#endif /* Py_PYTHON_H */ diff --git a/modules/monotonic_align/core.pyx b/modules/monotonic_align/core.pyx new file mode 100644 index 0000000000000000000000000000000000000000..bfaabd4d21c2299cdd978f0cc0caefa20ad186e5 --- /dev/null +++ b/modules/monotonic_align/core.pyx @@ -0,0 +1,42 @@ +cimport cython +from cython.parallel import prange + + +@cython.boundscheck(False) +@cython.wraparound(False) +cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil: + cdef int x + cdef int y + cdef float v_prev + cdef float v_cur + cdef float tmp + cdef int index = t_x - 1 + + for y in range(t_y): + for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): + if x == y: + v_cur = max_neg_val + else: + v_cur = value[y-1, x] + if x == 0: + if y == 0: + v_prev = 0. + else: + v_prev = max_neg_val + else: + v_prev = value[y-1, x-1] + value[y, x] += max(v_prev, v_cur) + + for y in range(t_y - 1, -1, -1): + path[y, index] = 1 + if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]): + index = index - 1 + + +@cython.boundscheck(False) +@cython.wraparound(False) +cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil: + cdef int b = paths.shape[0] + cdef int i + for i in prange(b, nogil=True): + maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i]) diff --git a/modules/monotonic_align/monotonic_align/core.cpython-39-x86_64-linux-gnu.so b/modules/monotonic_align/monotonic_align/core.cpython-39-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..9904fb869463865478f1c8b5180d853a4ef5804b Binary files /dev/null and b/modules/monotonic_align/monotonic_align/core.cpython-39-x86_64-linux-gnu.so differ diff --git a/modules/monotonic_align/setup.py b/modules/monotonic_align/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..773fafd96fb0f1bac88037187542c84eccb26ef5 --- /dev/null +++ b/modules/monotonic_align/setup.py @@ -0,0 +1,22 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This code is modified from https://github.com/jaywalnut310/vits/ + +from distutils.core import setup, Extension +from Cython.Build import cythonize +import numpy + +extension = Extension( + name="monotonic_align.core", + sources=["core.pyx"], + include_dirs=[numpy.get_include()], + # Define additional arguments if needed +) + +setup( + name="monotonic_align", + ext_modules=cythonize([extension]), +) diff --git a/modules/neural_source_filter/__init__.py b/modules/neural_source_filter/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..aa2749078427c312fa046f354ded858bae82f666 --- /dev/null +++ b/modules/neural_source_filter/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .sine_excitation import * diff --git a/modules/neural_source_filter/__pycache__/__init__.cpython-39.pyc b/modules/neural_source_filter/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e00e2c461011f7676ddb91b37e6c24e3a1539f6d Binary files /dev/null and b/modules/neural_source_filter/__pycache__/__init__.cpython-39.pyc differ diff --git a/modules/neural_source_filter/__pycache__/sine_excitation.cpython-39.pyc b/modules/neural_source_filter/__pycache__/sine_excitation.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6ce7e88bd9e2c7bf62a454c92556be06b8d66961 Binary files /dev/null and b/modules/neural_source_filter/__pycache__/sine_excitation.cpython-39.pyc differ diff --git a/modules/neural_source_filter/sine_excitation.py b/modules/neural_source_filter/sine_excitation.py new file mode 100644 index 0000000000000000000000000000000000000000..08b72b2fc4f5e3065e32b2891bdd7f510c727e70 --- /dev/null +++ b/modules/neural_source_filter/sine_excitation.py @@ -0,0 +1,82 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +#################### NSF #################### + +import torch + +import numpy as np +import torch.nn as nn +import torch.nn.functional as F + +# This code is adopted from Xin Wang's NSF under the MIT License +# https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts + + +class SineGen(nn.Module): + def __init__( + self, fs, harmonic_num=0, amp=0.1, noise_std=0.003, voiced_threshold=0 + ): + super(SineGen, self).__init__() + self.amp = amp + self.noise_std = noise_std + self.harmonic_num = harmonic_num + self.dim = harmonic_num + 1 + self.fs = fs + self.voice_threshold = voiced_threshold + + def _f0toUnvoiced(self, f0): + uv = torch.ones_like(f0) + uv = uv * (f0 > self.voice_threshold) + return uv + + @torch.no_grad() + def forward(self, f0, upp): + f0 = f0.unsqueeze(-1) + fn = torch.multiply( + f0, torch.arange(1, self.dim + 1, device=f0.device).reshape(1, 1, -1) + ) + rad_values = (fn / self.fs) % 1 + rand_ini = torch.rand(fn.shape[0], fn.shape[2], device=fn.device) + rand_ini[:, 0] = 0 + rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini + is_half = rad_values.dtype is not torch.float32 + tmp_over_one = torch.cumsum(rad_values.double(), 1) + if is_half: + tmp_over_one = tmp_over_one.half() + else: + tmp_over_one = tmp_over_one.float() + tmp_over_one *= upp + tmp_over_one = F.interpolate( + tmp_over_one.transpose(2, 1), + scale_factor=upp, + mode="linear", + align_corners=True, + ).transpose(2, 1) + rad_values = F.interpolate( + rad_values.transpose(2, 1), scale_factor=upp, mode="nearest" + ).transpose(2, 1) + tmp_over_one %= 1 + tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0 + cumsum_shift = torch.zeros_like(rad_values) + cumsum_shift[:, 1:, :] = tmp_over_one_idx * (-1.0) + rad_values = rad_values.double() + cumsum_shift = cumsum_shift.double() + sine_waves = torch.sin( + torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi + ) + if is_half: + sine_waves = sine_waves.half() + else: + sine_waves = sine_waves.float() + sine_waves = sine_waves * self.amp + uv = self._f0toUnvoiced(f0) + uv = F.interpolate( + uv.transpose(2, 1), scale_factor=upp, mode="nearest" + ).transpose(2, 1) + noise_amp = uv * self.noise_std + (1 - uv) * self.amp / 3 + noise = noise_amp * torch.randn_like(sine_waves) + sine_waves = sine_waves * uv + noise + return sine_waves, uv, noise diff --git a/modules/norms/__init__.py b/modules/norms/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d8c2d90b9abd8375822d40e0b9714e6eab581f5a --- /dev/null +++ b/modules/norms/__init__.py @@ -0,0 +1 @@ +from .norm import AdaptiveLayerNorm, LayerNorm, BalancedBasicNorm, IdentityNorm \ No newline at end of file diff --git a/modules/norms/__pycache__/__init__.cpython-39.pyc b/modules/norms/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ef039207551a01f482d16ec4427c762592af6f18 Binary files /dev/null and b/modules/norms/__pycache__/__init__.cpython-39.pyc differ diff --git a/modules/norms/__pycache__/norm.cpython-39.pyc b/modules/norms/__pycache__/norm.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..40cfb9e04cf97599ee7d76cc00e5cc8970d355ca Binary files /dev/null and b/modules/norms/__pycache__/norm.cpython-39.pyc differ diff --git a/modules/norms/norm.py b/modules/norms/norm.py new file mode 100644 index 0000000000000000000000000000000000000000..4f74ecd7a43c3cd9419a60c0acd1f439975c98b4 --- /dev/null +++ b/modules/norms/norm.py @@ -0,0 +1,171 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import copy +import numbers +from typing import Any, List, Tuple, Union + +import torch +from torch import Tensor, nn +from torch.nn import functional as F + +from modules.general.scaling import ActivationBalancer +from modules.general.scaling import BasicNorm as _BasicNorm + + +_shape_t = Union[int, List[int], torch.Size] + + +class LayerNorm(nn.Module): + __constants__ = ["normalized_shape", "eps", "elementwise_affine"] + normalized_shape: Tuple[int, ...] + eps: float + elementwise_affine: bool + + def __init__( + self, + normalized_shape: _shape_t, + eps: float = 1e-5, + elementwise_affine: bool = True, + device=None, + dtype=None, + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + super(LayerNorm, self).__init__() + if isinstance(normalized_shape, numbers.Integral): + normalized_shape = (normalized_shape,) + self.normalized_shape = tuple(normalized_shape) + self.eps = eps + self.elementwise_affine = elementwise_affine + if self.elementwise_affine: + self.weight = nn.Parameter( + torch.empty(self.normalized_shape, **factory_kwargs) + ) + self.bias = nn.Parameter( + torch.empty(self.normalized_shape, **factory_kwargs) + ) + else: + self.register_parameter("weight", None) + self.register_parameter("bias", None) + + self.reset_parameters() + + def reset_parameters(self) -> None: + if self.elementwise_affine: + nn.init.ones_(self.weight) + nn.init.zeros_(self.bias) + + def forward(self, input: Tensor, embedding: Any = None) -> Tensor: + if isinstance(input, tuple): + input, embedding = input + output = F.layer_norm(input, self.normalized_shape, self.weight, self.bias, self.eps) + return output, embedding + + assert embedding is None + return F.layer_norm(input, self.normalized_shape, self.weight, self.bias, self.eps) + + + def extra_repr(self) -> str: + return ( + "{normalized_shape}, eps={eps}, " + "elementwise_affine={elementwise_affine}".format(**self.__dict__) + ) + + +class AdaptiveLayerNorm(nn.Module): + r"""Adaptive Layer Normalization""" + + def __init__(self, d_model, norm) -> None: + super(AdaptiveLayerNorm, self).__init__() + self.project_layer = nn.Linear(d_model, 2 * d_model) + self.norm = norm + self.d_model = d_model + self.eps = self.norm.eps + + def forward(self, input: Tensor, embedding: Tensor = None) -> Tensor: + if isinstance(input, tuple): + input, embedding = input + weight, bias = torch.split( + self.project_layer(embedding), + split_size_or_sections=self.d_model, + dim=-1, + ) + return (weight * self.norm(input) + bias, embedding) + + weight, bias = torch.split( + self.project_layer(embedding), + split_size_or_sections=self.d_model, + dim=-1, + ) + return weight * self.norm(input) + bias + + +class BasicNorm(_BasicNorm): + def __init__( + self, + d_model: int, + eps: float = 1e-5, + device=None, + dtype=None, + ): + super(BasicNorm, self).__init__(d_model, eps=eps) + + def forward(self, input: Tensor, embedding: Any = None) -> Tensor: + if isinstance(input, tuple): + input, embedding = input + return ( + super(BasicNorm, self).forward(input), + embedding, + ) + + assert embedding is None + return super(BasicNorm, self).forward(input) + + +class BalancedBasicNorm(nn.Module): + def __init__( + self, + d_model: int, + eps: float = 1e-5, + device=None, + dtype=None, + ): + super(BalancedBasicNorm, self).__init__() + self.balancer = ActivationBalancer( + d_model, + channel_dim=-1, + min_positive=0.45, + max_positive=0.55, + max_abs=6.0, + ) + self.norm = BasicNorm(d_model, eps, device=device, dtype=dtype) + + def forward(self, input: Tensor, embedding: Any = None) -> Tensor: + if isinstance(input, tuple): + input, embedding = input + return self.norm((self.balancer(input), embedding)) + + assert embedding is None + return self.norm(self.balancer(input)) + + +class IdentityNorm(nn.Module): + def __init__( + self, + d_model: int, + eps: float = 1e-5, + device=None, + dtype=None, + ) -> None: + super(IdentityNorm, self).__init__() + + def forward(self, input: Tensor, embedding: Any = None) -> Tensor: + if isinstance(input, tuple): + return input + + assert embedding is None + return input + diff --git a/modules/transformer/Constants.py b/modules/transformer/Constants.py new file mode 100644 index 0000000000000000000000000000000000000000..5bd586f3185114b7ab22f9a2479e93088d74ced0 --- /dev/null +++ b/modules/transformer/Constants.py @@ -0,0 +1,14 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +PAD = 0 +UNK = 1 +BOS = 2 +EOS = 3 + +PAD_WORD = "" +UNK_WORD = "" +BOS_WORD = "" +EOS_WORD = "" diff --git a/modules/transformer/Layers.py b/modules/transformer/Layers.py new file mode 100644 index 0000000000000000000000000000000000000000..915b86a7f434456f24643e28fd93813bd6e13d52 --- /dev/null +++ b/modules/transformer/Layers.py @@ -0,0 +1,137 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +from torch.nn import functional as F +from .SubLayers import MultiHeadAttention, PositionwiseFeedForward + + +class FFTBlock(torch.nn.Module): + """FFT Block""" + + def __init__(self, d_model, n_head, d_k, d_v, d_inner, kernel_size, dropout=0.1): + super(FFTBlock, self).__init__() + self.slf_attn = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout) + self.pos_ffn = PositionwiseFeedForward( + d_model, d_inner, kernel_size, dropout=dropout + ) + + def forward(self, enc_input, mask=None, slf_attn_mask=None): + enc_output, enc_slf_attn = self.slf_attn( + enc_input, enc_input, enc_input, mask=slf_attn_mask + ) + enc_output = enc_output.masked_fill(mask.unsqueeze(-1), 0) + + enc_output = self.pos_ffn(enc_output) + enc_output = enc_output.masked_fill(mask.unsqueeze(-1), 0) + + return enc_output, enc_slf_attn + + +class ConvNorm(torch.nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=None, + dilation=1, + bias=True, + w_init_gain="linear", + ): + super(ConvNorm, self).__init__() + + if padding is None: + assert kernel_size % 2 == 1 + padding = int(dilation * (kernel_size - 1) / 2) + + self.conv = torch.nn.Conv1d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=bias, + ) + + def forward(self, signal): + conv_signal = self.conv(signal) + + return conv_signal + + +class PostNet(nn.Module): + """ + PostNet: Five 1-d convolution with 512 channels and kernel size 5 + """ + + def __init__( + self, + n_mel_channels=80, + postnet_embedding_dim=512, + postnet_kernel_size=5, + postnet_n_convolutions=5, + ): + super(PostNet, self).__init__() + self.convolutions = nn.ModuleList() + + self.convolutions.append( + nn.Sequential( + ConvNorm( + n_mel_channels, + postnet_embedding_dim, + kernel_size=postnet_kernel_size, + stride=1, + padding=int((postnet_kernel_size - 1) / 2), + dilation=1, + w_init_gain="tanh", + ), + nn.BatchNorm1d(postnet_embedding_dim), + ) + ) + + for i in range(1, postnet_n_convolutions - 1): + self.convolutions.append( + nn.Sequential( + ConvNorm( + postnet_embedding_dim, + postnet_embedding_dim, + kernel_size=postnet_kernel_size, + stride=1, + padding=int((postnet_kernel_size - 1) / 2), + dilation=1, + w_init_gain="tanh", + ), + nn.BatchNorm1d(postnet_embedding_dim), + ) + ) + + self.convolutions.append( + nn.Sequential( + ConvNorm( + postnet_embedding_dim, + n_mel_channels, + kernel_size=postnet_kernel_size, + stride=1, + padding=int((postnet_kernel_size - 1) / 2), + dilation=1, + w_init_gain="linear", + ), + nn.BatchNorm1d(n_mel_channels), + ) + ) + + def forward(self, x): + x = x.contiguous().transpose(1, 2) + + for i in range(len(self.convolutions) - 1): + x = F.dropout(torch.tanh(self.convolutions[i](x)), 0.5, self.training) + x = F.dropout(self.convolutions[-1](x), 0.5, self.training) + + x = x.contiguous().transpose(1, 2) + return x diff --git a/modules/transformer/Models.py b/modules/transformer/Models.py new file mode 100644 index 0000000000000000000000000000000000000000..0f2bf3796da5324dbdacd50297563d135e08af6c --- /dev/null +++ b/modules/transformer/Models.py @@ -0,0 +1,181 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import numpy as np + +from .Layers import FFTBlock +from text.symbols import symbols + +PAD = 0 +UNK = 1 +BOS = 2 +EOS = 3 + +PAD_WORD = "" +UNK_WORD = "" +BOS_WORD = "" +EOS_WORD = "" + + +def get_sinusoid_encoding_table(n_position, d_hid, padding_idx=None): + """Sinusoid position encoding table""" + + def cal_angle(position, hid_idx): + return position / np.power(10000, 2 * (hid_idx // 2) / d_hid) + + def get_posi_angle_vec(position): + return [cal_angle(position, hid_j) for hid_j in range(d_hid)] + + sinusoid_table = np.array( + [get_posi_angle_vec(pos_i) for pos_i in range(n_position)] + ) + + sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i + sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 + + if padding_idx is not None: + # zero vector for padding dimension + sinusoid_table[padding_idx] = 0.0 + + return torch.FloatTensor(sinusoid_table) + + +class Encoder(nn.Module): + """Encoder""" + + def __init__(self, config): + super(Encoder, self).__init__() + + n_position = config["max_seq_len"] + 1 + n_src_vocab = len(symbols) + 1 + d_word_vec = config["transformer"]["encoder_hidden"] + n_layers = config["transformer"]["encoder_layer"] + n_head = config["transformer"]["encoder_head"] + d_k = d_v = ( + config["transformer"]["encoder_hidden"] + // config["transformer"]["encoder_head"] + ) + d_model = config["transformer"]["encoder_hidden"] + d_inner = config["transformer"]["conv_filter_size"] + kernel_size = config["transformer"]["conv_kernel_size"] + dropout = config["transformer"]["encoder_dropout"] + + self.max_seq_len = config["max_seq_len"] + self.d_model = d_model + + self.src_word_emb = nn.Embedding(n_src_vocab, d_word_vec, padding_idx=PAD) + self.position_enc = nn.Parameter( + get_sinusoid_encoding_table(n_position, d_word_vec).unsqueeze(0), + requires_grad=False, + ) + + self.layer_stack = nn.ModuleList( + [ + FFTBlock( + d_model, n_head, d_k, d_v, d_inner, kernel_size, dropout=dropout + ) + for _ in range(n_layers) + ] + ) + + def forward(self, src_seq, mask, return_attns=False): + enc_slf_attn_list = [] + batch_size, max_len = src_seq.shape[0], src_seq.shape[1] + + # -- Prepare masks + slf_attn_mask = mask.unsqueeze(1).expand(-1, max_len, -1) + + # -- Forward + if not self.training and src_seq.shape[1] > self.max_seq_len: + enc_output = self.src_word_emb(src_seq) + get_sinusoid_encoding_table( + src_seq.shape[1], self.d_model + )[: src_seq.shape[1], :].unsqueeze(0).expand(batch_size, -1, -1).to( + src_seq.device + ) + else: + enc_output = self.src_word_emb(src_seq) + self.position_enc[ + :, :max_len, : + ].expand(batch_size, -1, -1) + + for enc_layer in self.layer_stack: + enc_output, enc_slf_attn = enc_layer( + enc_output, mask=mask, slf_attn_mask=slf_attn_mask + ) + if return_attns: + enc_slf_attn_list += [enc_slf_attn] + + return enc_output + + +class Decoder(nn.Module): + """Decoder""" + + def __init__(self, config): + super(Decoder, self).__init__() + + n_position = config["max_seq_len"] + 1 + d_word_vec = config["transformer"]["decoder_hidden"] + n_layers = config["transformer"]["decoder_layer"] + n_head = config["transformer"]["decoder_head"] + d_k = d_v = ( + config["transformer"]["decoder_hidden"] + // config["transformer"]["decoder_head"] + ) + d_model = config["transformer"]["decoder_hidden"] + d_inner = config["transformer"]["conv_filter_size"] + kernel_size = config["transformer"]["conv_kernel_size"] + dropout = config["transformer"]["decoder_dropout"] + + self.max_seq_len = config["max_seq_len"] + self.d_model = d_model + + self.position_enc = nn.Parameter( + get_sinusoid_encoding_table(n_position, d_word_vec).unsqueeze(0), + requires_grad=False, + ) + + self.layer_stack = nn.ModuleList( + [ + FFTBlock( + d_model, n_head, d_k, d_v, d_inner, kernel_size, dropout=dropout + ) + for _ in range(n_layers) + ] + ) + + def forward(self, enc_seq, mask, return_attns=False): + dec_slf_attn_list = [] + batch_size, max_len = enc_seq.shape[0], enc_seq.shape[1] + + # -- Forward + if not self.training and enc_seq.shape[1] > self.max_seq_len: + # -- Prepare masks + slf_attn_mask = mask.unsqueeze(1).expand(-1, max_len, -1) + dec_output = enc_seq + get_sinusoid_encoding_table( + enc_seq.shape[1], self.d_model + )[: enc_seq.shape[1], :].unsqueeze(0).expand(batch_size, -1, -1).to( + enc_seq.device + ) + else: + max_len = min(max_len, self.max_seq_len) + + # -- Prepare masks + slf_attn_mask = mask.unsqueeze(1).expand(-1, max_len, -1) + dec_output = enc_seq[:, :max_len, :] + self.position_enc[ + :, :max_len, : + ].expand(batch_size, -1, -1) + mask = mask[:, :max_len] + slf_attn_mask = slf_attn_mask[:, :, :max_len] + + for dec_layer in self.layer_stack: + dec_output, dec_slf_attn = dec_layer( + dec_output, mask=mask, slf_attn_mask=slf_attn_mask + ) + if return_attns: + dec_slf_attn_list += [dec_slf_attn] + + return dec_output, mask diff --git a/modules/transformer/Modules.py b/modules/transformer/Modules.py new file mode 100644 index 0000000000000000000000000000000000000000..339aedd0cd3ec603724204e98aced5372cd0a334 --- /dev/null +++ b/modules/transformer/Modules.py @@ -0,0 +1,29 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import numpy as np + + +class ScaledDotProductAttention(nn.Module): + """Scaled Dot-Product Attention""" + + def __init__(self, temperature): + super().__init__() + self.temperature = temperature + self.softmax = nn.Softmax(dim=2) + + def forward(self, q, k, v, mask=None): + attn = torch.bmm(q, k.transpose(1, 2)) + attn = attn / self.temperature + + if mask is not None: + attn = attn.masked_fill(mask, -np.inf) + + attn = self.softmax(attn) + output = torch.bmm(attn, v) + + return output, attn diff --git a/modules/transformer/SubLayers.py b/modules/transformer/SubLayers.py new file mode 100644 index 0000000000000000000000000000000000000000..c1d1b88dfee5eaaec4cc9bfd2017d88847b18b60 --- /dev/null +++ b/modules/transformer/SubLayers.py @@ -0,0 +1,97 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + +from .Modules import ScaledDotProductAttention + + +class MultiHeadAttention(nn.Module): + """Multi-Head Attention module""" + + def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1): + super().__init__() + + self.n_head = n_head + self.d_k = d_k + self.d_v = d_v + + self.w_qs = nn.Linear(d_model, n_head * d_k) + self.w_ks = nn.Linear(d_model, n_head * d_k) + self.w_vs = nn.Linear(d_model, n_head * d_v) + + self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5)) + self.layer_norm = nn.LayerNorm(d_model) + + self.fc = nn.Linear(n_head * d_v, d_model) + + self.dropout = nn.Dropout(dropout) + + def forward(self, q, k, v, mask=None): + d_k, d_v, n_head = self.d_k, self.d_v, self.n_head + + sz_b, len_q, _ = q.size() + sz_b, len_k, _ = k.size() + sz_b, len_v, _ = v.size() + + residual = q + + q = self.w_qs(q).view(sz_b, len_q, n_head, d_k) + k = self.w_ks(k).view(sz_b, len_k, n_head, d_k) + v = self.w_vs(v).view(sz_b, len_v, n_head, d_v) + q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k) # (n*b) x lq x dk + k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_k, d_k) # (n*b) x lk x dk + v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v) # (n*b) x lv x dv + + mask = mask.repeat(n_head, 1, 1) # (n*b) x .. x .. + output, attn = self.attention(q, k, v, mask=mask) + + output = output.view(n_head, sz_b, len_q, d_v) + output = ( + output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_q, -1) + ) # b x lq x (n*dv) + + output = self.dropout(self.fc(output)) + output = self.layer_norm(output + residual) + + return output, attn + + +class PositionwiseFeedForward(nn.Module): + """A two-feed-forward-layer module""" + + def __init__(self, d_in, d_hid, kernel_size, dropout=0.1): + super().__init__() + + # Use Conv1D + # position-wise + self.w_1 = nn.Conv1d( + d_in, + d_hid, + kernel_size=kernel_size[0], + padding=(kernel_size[0] - 1) // 2, + ) + # position-wise + self.w_2 = nn.Conv1d( + d_hid, + d_in, + kernel_size=kernel_size[1], + padding=(kernel_size[1] - 1) // 2, + ) + + self.layer_norm = nn.LayerNorm(d_in) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + residual = x + output = x.transpose(1, 2) + output = self.w_2(F.relu(self.w_1(output))) + output = output.transpose(1, 2) + output = self.dropout(output) + output = self.layer_norm(output + residual) + + return output diff --git a/modules/transformer/__init__.py b/modules/transformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fa7f8736235e79a02b4fab47c4b53dd4f4311469 --- /dev/null +++ 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directory of this source tree. + +import math +import torch +from torch import nn +from torch.nn import functional as F + +from utils.util import * +from modules.base.base_module import * +from modules.base.base_module import LayerNorm + + +class Encoder(nn.Module): + def __init__( + self, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size=1, + p_dropout=0.0, + window_size=4, + **kwargs, + ): + super().__init__() + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.window_size = window_size + + self.drop = nn.Dropout(p_dropout) + self.attn_layers = nn.ModuleList() + self.norm_layers_1 = nn.ModuleList() + self.ffn_layers = nn.ModuleList() + self.norm_layers_2 = nn.ModuleList() + for i in range(self.n_layers): + self.attn_layers.append( + MultiHeadAttention( + hidden_channels, + hidden_channels, + n_heads, + p_dropout=p_dropout, + window_size=window_size, + ) + ) + self.norm_layers_1.append(LayerNorm(hidden_channels)) + self.ffn_layers.append( + FFN( + hidden_channels, + hidden_channels, + filter_channels, + kernel_size, + p_dropout=p_dropout, + ) + ) + self.norm_layers_2.append(LayerNorm(hidden_channels)) + + def forward(self, x, x_mask): + attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) + x = x * x_mask + for i in range(self.n_layers): + y = self.attn_layers[i](x, x, attn_mask) + y = self.drop(y) + x = self.norm_layers_1[i](x + y) + + y = self.ffn_layers[i](x, x_mask) + y = self.drop(y) + x = self.norm_layers_2[i](x + y) + x = x * x_mask + return x + + +class Decoder(nn.Module): + def __init__( + self, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size=1, + p_dropout=0.0, + proximal_bias=False, + proximal_init=True, + **kwargs, + ): + super().__init__() + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.proximal_bias = proximal_bias + self.proximal_init = proximal_init + + self.drop = nn.Dropout(p_dropout) + self.self_attn_layers = nn.ModuleList() + self.norm_layers_0 = nn.ModuleList() + self.encdec_attn_layers = nn.ModuleList() + self.norm_layers_1 = nn.ModuleList() + self.ffn_layers = nn.ModuleList() + self.norm_layers_2 = nn.ModuleList() + for i in range(self.n_layers): + self.self_attn_layers.append( + MultiHeadAttention( + hidden_channels, + hidden_channels, + n_heads, + p_dropout=p_dropout, + proximal_bias=proximal_bias, + proximal_init=proximal_init, + ) + ) + self.norm_layers_0.append(LayerNorm(hidden_channels)) + self.encdec_attn_layers.append( + MultiHeadAttention( + hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout + ) + ) + self.norm_layers_1.append(LayerNorm(hidden_channels)) + self.ffn_layers.append( + FFN( + hidden_channels, + hidden_channels, + filter_channels, + kernel_size, + p_dropout=p_dropout, + causal=True, + ) + ) + self.norm_layers_2.append(LayerNorm(hidden_channels)) + + def forward(self, x, x_mask, h, h_mask): + """ + x: decoder input + h: encoder output + """ + self_attn_mask = subsequent_mask(x_mask.size(2)).to( + device=x.device, dtype=x.dtype + ) + encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1) + x = x * x_mask + for i in range(self.n_layers): + y = self.self_attn_layers[i](x, x, self_attn_mask) + y = self.drop(y) + x = self.norm_layers_0[i](x + y) + + y = self.encdec_attn_layers[i](x, h, encdec_attn_mask) + y = self.drop(y) + x = self.norm_layers_1[i](x + y) + + y = self.ffn_layers[i](x, x_mask) + y = self.drop(y) + x = self.norm_layers_2[i](x + y) + x = x * x_mask + return x + + +class MultiHeadAttention(nn.Module): + def __init__( + self, + channels, + out_channels, + n_heads, + p_dropout=0.0, + window_size=None, + heads_share=True, + block_length=None, + proximal_bias=False, + proximal_init=False, + ): + super().__init__() + assert channels % n_heads == 0 + + self.channels = channels + self.out_channels = out_channels + self.n_heads = n_heads + self.p_dropout = p_dropout + self.window_size = window_size + self.heads_share = heads_share + self.block_length = block_length + self.proximal_bias = proximal_bias + self.proximal_init = proximal_init + self.attn = None + + self.k_channels = channels // n_heads + self.conv_q = nn.Conv1d(channels, channels, 1) + self.conv_k = nn.Conv1d(channels, channels, 1) + self.conv_v = nn.Conv1d(channels, channels, 1) + self.conv_o = nn.Conv1d(channels, out_channels, 1) + self.drop = nn.Dropout(p_dropout) + + if window_size is not None: + n_heads_rel = 1 if heads_share else n_heads + rel_stddev = self.k_channels**-0.5 + self.emb_rel_k = nn.Parameter( + torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) + * rel_stddev + ) + self.emb_rel_v = nn.Parameter( + torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) + * rel_stddev + ) + + nn.init.xavier_uniform_(self.conv_q.weight) + nn.init.xavier_uniform_(self.conv_k.weight) + nn.init.xavier_uniform_(self.conv_v.weight) + if proximal_init: + with torch.no_grad(): + self.conv_k.weight.copy_(self.conv_q.weight) + self.conv_k.bias.copy_(self.conv_q.bias) + + def forward(self, x, c, attn_mask=None): + q = self.conv_q(x) + k = self.conv_k(c) + v = self.conv_v(c) + + x, self.attn = self.attention(q, k, v, mask=attn_mask) + + x = self.conv_o(x) + return x + + def attention(self, query, key, value, mask=None): + # reshape [b, d, t] -> [b, n_h, t, d_k] + b, d, t_s, t_t = (*key.size(), query.size(2)) + query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) + key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) + value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) + + scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) + if self.window_size is not None: + assert ( + t_s == t_t + ), "Relative attention is only available for self-attention." + key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) + rel_logits = self._matmul_with_relative_keys( + query / math.sqrt(self.k_channels), key_relative_embeddings + ) + scores_local = self._relative_position_to_absolute_position(rel_logits) + scores = scores + scores_local + if self.proximal_bias: + assert t_s == t_t, "Proximal bias is only available for self-attention." + scores = scores + self._attention_bias_proximal(t_s).to( + device=scores.device, dtype=scores.dtype + ) + if mask is not None: + scores = scores.masked_fill(mask == 0, -1e4) + if self.block_length is not None: + assert ( + t_s == t_t + ), "Local attention is only available for self-attention." + block_mask = ( + torch.ones_like(scores) + .triu(-self.block_length) + .tril(self.block_length) + ) + scores = scores.masked_fill(block_mask == 0, -1e4) + p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] + p_attn = self.drop(p_attn) + output = torch.matmul(p_attn, value) + if self.window_size is not None: + relative_weights = self._absolute_position_to_relative_position(p_attn) + value_relative_embeddings = self._get_relative_embeddings( + self.emb_rel_v, t_s + ) + output = output + self._matmul_with_relative_values( + relative_weights, value_relative_embeddings + ) + output = ( + output.transpose(2, 3).contiguous().view(b, d, t_t) + ) # [b, n_h, t_t, d_k] -> [b, d, t_t] + return output, p_attn + + def _matmul_with_relative_values(self, x, y): + """ + x: [b, h, l, m] + y: [h or 1, m, d] + ret: [b, h, l, d] + """ + ret = torch.matmul(x, y.unsqueeze(0)) + return ret + + def _matmul_with_relative_keys(self, x, y): + """ + x: [b, h, l, d] + y: [h or 1, m, d] + ret: [b, h, l, m] + """ + ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) + return ret + + def _get_relative_embeddings(self, relative_embeddings, length): + max_relative_position = 2 * self.window_size + 1 + # Pad first before slice to avoid using cond ops. + pad_length = max(length - (self.window_size + 1), 0) + slice_start_position = max((self.window_size + 1) - length, 0) + slice_end_position = slice_start_position + 2 * length - 1 + if pad_length > 0: + padded_relative_embeddings = F.pad( + relative_embeddings, + convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]), + ) + else: + padded_relative_embeddings = relative_embeddings + used_relative_embeddings = padded_relative_embeddings[ + :, slice_start_position:slice_end_position + ] + return used_relative_embeddings + + def _relative_position_to_absolute_position(self, x): + """ + x: [b, h, l, 2*l-1] + ret: [b, h, l, l] + """ + batch, heads, length, _ = x.size() + # Concat columns of pad to shift from relative to absolute indexing. + x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) + + # Concat extra elements so to add up to shape (len+1, 2*len-1). + x_flat = x.view([batch, heads, length * 2 * length]) + x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])) + + # Reshape and slice out the padded elements. + x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[ + :, :, :length, length - 1 : + ] + return x_final + + def _absolute_position_to_relative_position(self, x): + """ + x: [b, h, l, l] + ret: [b, h, l, 2*l-1] + """ + batch, heads, length, _ = x.size() + # padd along column + x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])) + x_flat = x.view([batch, heads, length**2 + length * (length - 1)]) + # add 0's in the beginning that will skew the elements after reshape + x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]])) + x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] + return x_final + + def _attention_bias_proximal(self, length): + """Bias for self-attention to encourage attention to close positions. + Args: + length: an integer scalar. + Returns: + a Tensor with shape [1, 1, length, length] + """ + r = torch.arange(length, dtype=torch.float32) + diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) + return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) + + +class FFN(nn.Module): + def __init__( + self, + in_channels, + out_channels, + filter_channels, + kernel_size, + p_dropout=0.0, + activation=None, + causal=False, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.activation = activation + self.causal = causal + + if causal: + self.padding = self._causal_padding + else: + self.padding = self._same_padding + + self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size) + self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size) + self.drop = nn.Dropout(p_dropout) + + def forward(self, x, x_mask): + x = self.conv_1(self.padding(x * x_mask)) + if self.activation == "gelu": + x = x * torch.sigmoid(1.702 * x) + else: + x = torch.relu(x) + x = self.drop(x) + x = self.conv_2(self.padding(x * x_mask)) + return x * x_mask + + def _causal_padding(self, x): + if self.kernel_size == 1: + return x + pad_l = self.kernel_size - 1 + pad_r = 0 + padding = [[0, 0], [0, 0], [pad_l, pad_r]] + x = F.pad(x, convert_pad_shape(padding)) + return x + + def _same_padding(self, x): + if self.kernel_size == 1: + return x + pad_l = (self.kernel_size - 1) // 2 + pad_r = self.kernel_size // 2 + padding = [[0, 0], [0, 0], [pad_l, pad_r]] + x = F.pad(x, convert_pad_shape(padding)) + return x diff --git a/modules/transformer/mh_attention.py b/modules/transformer/mh_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..32654d9e2cf064e6a0afb0cdea321ca46a42d306 --- /dev/null +++ b/modules/transformer/mh_attention.py @@ -0,0 +1,430 @@ +from typing import Optional, Tuple + +import torch +from torch import Tensor +from torch.nn import Linear, Module +from torch.nn import functional as F +from torch.nn.init import constant_, xavier_normal_, xavier_uniform_ +from torch.nn.modules.linear import NonDynamicallyQuantizableLinear +from torch.nn.parameter import Parameter + + +class MultiheadAttention(Module): + r"""Allows the model to jointly attend to information + from different representation subspaces as described in the paper: + `Attention Is All You Need `_. + + Multi-Head Attention is defined as: + + .. math:: + \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O + + where :math:`head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)`. + + ``forward()`` will use a special optimized implementation if all of the following + conditions are met: + + - self attention is being computed (i.e., ``query``, ``key``, and ``value`` are the same tensor. This + restriction will be loosened in the future.) + - Either autograd is disabled (using ``torch.inference_mode`` or ``torch.no_grad``) or no tensor argument ``requires_grad`` + - training is disabled (using ``.eval()``) + - dropout is 0 + - ``add_bias_kv`` is ``False`` + - ``add_zero_attn`` is ``False`` + - ``batch_first`` is ``True`` and the input is batched + - ``kdim`` and ``vdim`` are equal to ``embed_dim`` + - at most one of ``key_padding_mask`` or ``attn_mask`` is passed + - if a `NestedTensor `_ is passed, neither ``key_padding_mask`` + nor ``attn_mask`` is passed + + If the optimized implementation is in use, a + `NestedTensor `_ can be passed for + ``query``/``key``/``value`` to represent padding more efficiently than using a + padding mask. In this case, a `NestedTensor `_ + will be returned, and an additional speedup proportional to the fraction of the input + that is padding can be expected. + + Args: + embed_dim: Total dimension of the model. + num_heads: Number of parallel attention heads. Note that ``embed_dim`` will be split + across ``num_heads`` (i.e. each head will have dimension ``embed_dim // num_heads``). + dropout: Dropout probability on ``attn_output_weights``. Default: ``0.0`` (no dropout). + bias: If specified, adds bias to input / output projection layers. Default: ``True``. + add_bias_kv: If specified, adds bias to the key and value sequences at dim=0. Default: ``False``. + add_zero_attn: If specified, adds a new batch of zeros to the key and value sequences at dim=1. + Default: ``False``. + kdim: Total number of features for keys. Default: ``None`` (uses ``kdim=embed_dim``). + vdim: Total number of features for values. Default: ``None`` (uses ``vdim=embed_dim``). + batch_first: If ``True``, then the input and output tensors are provided + as (batch, seq, feature). Default: ``False`` (seq, batch, feature). + + Examples:: + + >>> # xdoctest: +SKIP + >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads) + >>> attn_output, attn_output_weights = multihead_attn(query, key, value) + + """ + __constants__ = ["batch_first"] + bias_k: Optional[torch.Tensor] + bias_v: Optional[torch.Tensor] + + def __init__( + self, + embed_dim, + num_heads, + dropout=0.0, + bias=True, + add_bias_kv=False, + add_zero_attn=False, + kdim=None, + vdim=None, + batch_first=False, + linear1_cls=Linear, + linear2_cls=Linear, + device=None, + dtype=None, + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + self.kdim = kdim if kdim is not None else embed_dim + self.vdim = vdim if vdim is not None else embed_dim + self._qkv_same_embed_dim = ( + self.kdim == embed_dim and self.vdim == embed_dim + ) + + self.num_heads = num_heads + self.dropout = dropout + self.batch_first = batch_first + self.head_dim = embed_dim // num_heads + assert ( + self.head_dim * num_heads == self.embed_dim + ), "embed_dim must be divisible by num_heads" + + if add_bias_kv: + self.bias_k = Parameter( + torch.empty((1, 1, embed_dim), **factory_kwargs) + ) + self.bias_v = Parameter( + torch.empty((1, 1, embed_dim), **factory_kwargs) + ) + else: + self.bias_k = self.bias_v = None + + if linear1_cls == Linear: + if not self._qkv_same_embed_dim: + self.q_proj_weight = Parameter( + torch.empty((embed_dim, embed_dim), **factory_kwargs) + ) + self.k_proj_weight = Parameter( + torch.empty((embed_dim, self.kdim), **factory_kwargs) + ) + self.v_proj_weight = Parameter( + torch.empty((embed_dim, self.vdim), **factory_kwargs) + ) + self.register_parameter("in_proj_weight", None) + else: + self.in_proj_weight = Parameter( + torch.empty((3 * embed_dim, embed_dim), **factory_kwargs) + ) + self.register_parameter("q_proj_weight", None) + self.register_parameter("k_proj_weight", None) + self.register_parameter("v_proj_weight", None) + + if bias: + self.in_proj_bias = Parameter( + torch.empty(3 * embed_dim, **factory_kwargs) + ) + else: + self.register_parameter("in_proj_bias", None) + self.out_proj = NonDynamicallyQuantizableLinear( + embed_dim, embed_dim, bias=bias, **factory_kwargs + ) + + self._reset_parameters() + else: + if not self._qkv_same_embed_dim: + raise NotImplementedError + else: + self.in_proj_linear = linear1_cls( + embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs + ) + self.in_proj_weight = self.in_proj_linear.weight + + self.register_parameter("q_proj_weight", None) + self.register_parameter("k_proj_weight", None) + self.register_parameter("v_proj_weight", None) + + if bias: + self.in_proj_bias = self.in_proj_linear.bias + else: + self.register_parameter("in_proj_bias", None) + + self.out_proj = linear2_cls( + embed_dim, embed_dim, bias=bias, **factory_kwargs + ) + + if self.bias_k is not None: + xavier_normal_(self.bias_k) + if self.bias_v is not None: + xavier_normal_(self.bias_v) + + self.add_zero_attn = add_zero_attn + + def _reset_parameters(self): + if self._qkv_same_embed_dim: + xavier_uniform_(self.in_proj_weight) + else: + xavier_uniform_(self.q_proj_weight) + xavier_uniform_(self.k_proj_weight) + xavier_uniform_(self.v_proj_weight) + + if self.in_proj_bias is not None: + constant_(self.in_proj_bias, 0.0) + constant_(self.out_proj.bias, 0.0) + + if self.bias_k is not None: + xavier_normal_(self.bias_k) + if self.bias_v is not None: + xavier_normal_(self.bias_v) + + def __setstate__(self, state): + if "_qkv_same_embed_dim" not in state: + state["_qkv_same_embed_dim"] = True + + super(MultiheadAttention, self).__setstate__(state) + + def forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + key_padding_mask: Optional[Tensor] = None, + need_weights: bool = True, + attn_mask: Optional[Tensor] = None, + average_attn_weights: bool = True, + ) -> Tuple[Tensor, Optional[Tensor]]: + r""" + Args: + query: Query embeddings of shape :math:`(L, E_q)` for unbatched input, :math:`(L, N, E_q)` when ``batch_first=False`` + or :math:`(N, L, E_q)` when ``batch_first=True``, where :math:`L` is the target sequence length, + :math:`N` is the batch size, and :math:`E_q` is the query embedding dimension ``embed_dim``. + Queries are compared against key-value pairs to produce the output. + See "Attention Is All You Need" for more details. + key: Key embeddings of shape :math:`(S, E_k)` for unbatched input, :math:`(S, N, E_k)` when ``batch_first=False`` + or :math:`(N, S, E_k)` when ``batch_first=True``, where :math:`S` is the source sequence length, + :math:`N` is the batch size, and :math:`E_k` is the key embedding dimension ``kdim``. + See "Attention Is All You Need" for more details. + value: Value embeddings of shape :math:`(S, E_v)` for unbatched input, :math:`(S, N, E_v)` when + ``batch_first=False`` or :math:`(N, S, E_v)` when ``batch_first=True``, where :math:`S` is the source + sequence length, :math:`N` is the batch size, and :math:`E_v` is the value embedding dimension ``vdim``. + See "Attention Is All You Need" for more details. + key_padding_mask: If specified, a mask of shape :math:`(N, S)` indicating which elements within ``key`` + to ignore for the purpose of attention (i.e. treat as "padding"). For unbatched `query`, shape should be :math:`(S)`. + Binary and byte masks are supported. + For a binary mask, a ``True`` value indicates that the corresponding ``key`` value will be ignored for + the purpose of attention. For a float mask, it will be directly added to the corresponding ``key`` value. + need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``. + Default: ``True``. + attn_mask: If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape + :math:`(L, S)` or :math:`(N\cdot\text{num\_heads}, L, S)`, where :math:`N` is the batch size, + :math:`L` is the target sequence length, and :math:`S` is the source sequence length. A 2D mask will be + broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch. + Binary, byte, and float masks are supported. For a binary mask, a ``True`` value indicates that the + corresponding position is not allowed to attend. For a byte mask, a non-zero value indicates that the + corresponding position is not allowed to attend. For a float mask, the mask values will be added to + the attention weight. + average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across + heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an + effect when ``need_weights=True``. Default: ``True`` (i.e. average weights across heads) + + Outputs: + - **attn_output** - Attention outputs of shape :math:`(L, E)` when input is unbatched, + :math:`(L, N, E)` when ``batch_first=False`` or :math:`(N, L, E)` when ``batch_first=True``, + where :math:`L` is the target sequence length, :math:`N` is the batch size, and :math:`E` is the + embedding dimension ``embed_dim``. + - **attn_output_weights** - Only returned when ``need_weights=True``. If ``average_attn_weights=True``, + returns attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or + :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and + :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per + head of shape :math:`(\text{num\_heads}, L, S)` when input is unbatched or :math:`(N, \text{num\_heads}, L, S)`. + + .. note:: + `batch_first` argument is ignored for unbatched inputs. + """ + is_batched = query.dim() == 3 + if key_padding_mask is not None: + _kpm_dtype = key_padding_mask.dtype + if _kpm_dtype != torch.bool and not torch.is_floating_point( + key_padding_mask + ): + raise AssertionError( + "only bool and floating types of key_padding_mask are supported" + ) + why_not_fast_path = "" + if not is_batched: + why_not_fast_path = f"input not batched; expected query.dim() of 3 but got {query.dim()}" + elif query is not key or key is not value: + # When lifting this restriction, don't forget to either + # enforce that the dtypes all match or test cases where + # they don't! + why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)" + elif ( + self.in_proj_bias is not None + and query.dtype != self.in_proj_bias.dtype + ): + why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match" + elif ( + self.in_proj_weight is not None + and query.dtype != self.in_proj_weight.dtype + ): + # this case will fail anyway, but at least they'll get a useful error message. + why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match" + elif self.training: + why_not_fast_path = "training is enabled" + elif not self.batch_first: + why_not_fast_path = "batch_first was not True" + elif self.bias_k is not None: + why_not_fast_path = "self.bias_k was not None" + elif self.bias_v is not None: + why_not_fast_path = "self.bias_v was not None" + elif self.dropout: + why_not_fast_path = f"dropout was {self.dropout}, required zero" + elif self.add_zero_attn: + why_not_fast_path = "add_zero_attn was enabled" + elif not self._qkv_same_embed_dim: + why_not_fast_path = "_qkv_same_embed_dim was not True" + elif attn_mask is not None: + why_not_fast_path = "attn_mask was not None" + elif query.is_nested and key_padding_mask is not None: + why_not_fast_path = ( + "key_padding_mask is not supported with NestedTensor input" + ) + elif self.num_heads % 2 == 1: + why_not_fast_path = "num_heads is odd" + elif torch.is_autocast_enabled(): + why_not_fast_path = "autocast is enabled" + + if not why_not_fast_path: + tensor_args = ( + query, + key, + value, + self.in_proj_weight, + self.in_proj_bias, + self.out_proj.weight, + self.out_proj.bias, + ) + # We have to use list comprehensions below because TorchScript does not support + # generator expressions. + if torch.overrides.has_torch_function(tensor_args): + why_not_fast_path = "some Tensor argument has_torch_function" + elif not all( + [ + (x is None or x.is_cuda or "cpu" in str(x.device)) + for x in tensor_args + ] + ): + why_not_fast_path = ( + "some Tensor argument is neither CUDA nor CPU" + ) + elif torch.is_grad_enabled() and any( + [x is not None and x.requires_grad for x in tensor_args] + ): + why_not_fast_path = ( + "grad is enabled and at least one of query or the " + "input/output projection weights or biases requires_grad" + ) + if not why_not_fast_path: + return torch._native_multi_head_attention( + query, + key, + value, + self.embed_dim, + self.num_heads, + self.in_proj_weight, + self.in_proj_bias, + self.out_proj.weight, + self.out_proj.bias, + key_padding_mask + if key_padding_mask is not None + else attn_mask, + need_weights, + average_attn_weights, + 1 + if key_padding_mask is not None + else 0 + if attn_mask is not None + else None, + ) + + any_nested = query.is_nested or key.is_nested or value.is_nested + assert not any_nested, ( + "MultiheadAttention does not support NestedTensor outside of its fast path. " + + f"The fast path was not hit because {why_not_fast_path}" + ) + + if self.batch_first and is_batched: + # make sure that the transpose op does not affect the "is" property + if key is value: + if query is key: + query = key = value = query.transpose(1, 0) + else: + query, key = [x.transpose(1, 0) for x in (query, key)] + value = key + else: + query, key, value = [ + x.transpose(1, 0) for x in (query, key, value) + ] + + if not self._qkv_same_embed_dim: + attn_output, attn_output_weights = F.multi_head_attention_forward( + query, + key, + value, + self.embed_dim, + self.num_heads, + self.in_proj_weight, + self.in_proj_bias, + self.bias_k, + self.bias_v, + self.add_zero_attn, + self.dropout, + self.out_proj.weight, + self.out_proj.bias, + training=self.training, + key_padding_mask=key_padding_mask, + need_weights=need_weights, + attn_mask=attn_mask, + use_separate_proj_weight=True, + q_proj_weight=self.q_proj_weight, + k_proj_weight=self.k_proj_weight, + v_proj_weight=self.v_proj_weight, + average_attn_weights=average_attn_weights, + ) + else: + attn_output, attn_output_weights = F.multi_head_attention_forward( + query, + key, + value, + self.embed_dim, + self.num_heads, + self.in_proj_weight, + self.in_proj_bias, + self.bias_k, + self.bias_v, + self.add_zero_attn, + self.dropout, + self.out_proj.weight, + self.out_proj.bias, + training=self.training, + key_padding_mask=key_padding_mask, + need_weights=need_weights, + attn_mask=attn_mask, + average_attn_weights=average_attn_weights, + ) + if self.batch_first and is_batched: + return attn_output.transpose(1, 0), attn_output_weights + else: + return attn_output, attn_output_weights diff --git a/modules/transformer/position_embedding.py b/modules/transformer/position_embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..3271acd60969ab05cc3cf06245a27aebec77f88b --- /dev/null +++ b/modules/transformer/position_embedding.py @@ -0,0 +1,109 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import math + +class SinePositionalEmbedding(nn.Module): + def __init__( + self, + dim_model: int, + dropout: float = 0.0, + scale: bool = False, + alpha: bool = False, + ): + super().__init__() + self.dim_model = dim_model + self.x_scale = math.sqrt(dim_model) if scale else 1.0 + self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha) + self.dropout = torch.nn.Dropout(p=dropout) + + self.reverse = False + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, 4000)) + + def extend_pe(self, x): + """Reset the positional encodings.""" + if self.pe is not None: + if self.pe.size(1) >= x.size(1): + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + pe = torch.zeros(x.size(1), self.dim_model) + if self.reverse: + position = torch.arange( + x.size(1) - 1, -1, -1.0, dtype=torch.float32 + ).unsqueeze(1) + else: + position = torch.arange( + 0, x.size(1), dtype=torch.float32 + ).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, self.dim_model, 2, dtype=torch.float32) + * -(math.log(10000.0) / self.dim_model) + ) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + pe = pe.unsqueeze(0) + self.pe = pe.to(device=x.device, dtype=x.dtype).detach() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + self.extend_pe(x) + output = x.unsqueeze(-1) if x.ndim == 2 else x + output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)] + return self.dropout(output) + + +# import torch +# import torch.nn as nn +# import math + +# class SinePositionalEmbedding(nn.Module): +# def __init__( +# self, +# dim_model: int, +# dropout: float = 0.0, +# scale: bool = False, +# alpha: bool = False, +# ): +# super().__init__() +# self.dim_model = dim_model +# self.x_scale = math.sqrt(dim_model) if scale else 1.0 +# self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha) +# self.dropout = torch.nn.Dropout(p=dropout) + +# self.reverse = False +# self.pe = None +# self.extend_pe(torch.zeros(1, 4000)) + +# def extend_pe(self, x): +# """Reset the positional encodings.""" +# if self._pe_needs_extension(x): +# self.pe = self._generate_positional_encodings(x) + +# def _pe_needs_extension(self, x): +# return self.pe is None or self.pe.size(1) < x.size(1) or self.pe.dtype != x.dtype or self.pe.device != x.device + +# def _generate_positional_encodings(self, x): +# pe = torch.zeros(x.size(1), self.dim_model) +# position = self._get_position_tensor(x) +# div_term = self._get_div_term() +# pe[:, 0::2] = torch.sin(position * div_term) +# pe[:, 1::2] = torch.cos(position * div_term) +# return pe.unsqueeze(0).to(device=x.device, dtype=x.dtype).detach() + +# def _get_position_tensor(self, x): +# position = torch.arange(x.size(1), dtype=torch.float32).unsqueeze(1) +# return position.flip(0) if self.reverse else position + +# def _get_div_term(self): +# return torch.exp(torch.arange(0, self.dim_model, 2, dtype=torch.float32) * -(math.log(10000.0) / self.dim_model)) + +# def forward(self, x: torch.Tensor) -> torch.Tensor: +# self.extend_pe(x) +# output = x.unsqueeze(-1) if x.ndim == 2 else x +# output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)] +# return self.dropout(output) diff --git a/modules/transformer/transformer.py b/modules/transformer/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..b0d39365c09d3dcbd47710c6bd493829e29aeb23 --- /dev/null +++ b/modules/transformer/transformer.py @@ -0,0 +1,400 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import copy +from functools import partial +from typing import Any, Callable, List, Optional, Union + +import torch +from torch import Tensor, nn +from torch.nn import functional as F + +from modules.norms import AdaptiveLayerNorm, LayerNorm, BalancedBasicNorm, IdentityNorm +from modules.transformer import MultiheadAttention +from modules.general.scaling import BalancedDoubleSwish + + +class TransformerEncoderLayer(nn.Module): + __constants__ = ["batch_first", "norm_first"] + + def __init__( + self, + d_model: int, + nhead: int, + dim_feedforward: int = 2048, + dropout: float = 0.1, + activation: Union[str, Callable[[Tensor], Tensor]] = F.relu, + batch_first: bool = False, + norm_first: bool = False, + device=None, + dtype=None, + linear1_self_attention_cls: nn.Module = nn.Linear, + linear2_self_attention_cls: nn.Module = nn.Linear, + linear1_feedforward_cls: nn.Module = nn.Linear, + linear2_feedforward_cls: nn.Module = nn.Linear, + layer_norm_cls: nn.Module = LayerNorm, + layer_norm_eps: float = 1e-5, + adaptive_layer_norm=False, + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + super(TransformerEncoderLayer, self).__init__() + self.self_attn = MultiheadAttention( + d_model, + nhead, + dropout=dropout, + batch_first=batch_first, + linear1_cls=linear1_self_attention_cls, + linear2_cls=linear2_self_attention_cls, + **factory_kwargs, + ) + + # Implementation of Feedforward model + self.linear1 = linear1_feedforward_cls( + d_model, dim_feedforward, **factory_kwargs + ) + self.dropout = nn.Dropout(dropout) + self.linear2 = linear2_feedforward_cls( + dim_feedforward, d_model, **factory_kwargs + ) + + self.norm_first = norm_first + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + if isinstance(activation, str): + activation = _get_activation_fn(activation) + elif isinstance(activation, partial): + activation = activation(d_model) + elif activation == BalancedDoubleSwish: + activation = BalancedDoubleSwish(d_model) + + self.activation = activation + + norm1 = layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs) + if layer_norm_cls == IdentityNorm: + norm2 = BalancedBasicNorm( + d_model, eps=layer_norm_eps, **factory_kwargs + ) + else: + norm2 = layer_norm_cls( + d_model, eps=layer_norm_eps, **factory_kwargs + ) + + if adaptive_layer_norm: + self.norm1 = AdaptiveLayerNorm(d_model, norm1) + self.norm2 = AdaptiveLayerNorm(d_model, norm2) + else: + self.norm1 = norm1 + self.norm2 = norm2 + + def __setstate__(self, state): + super(TransformerEncoderLayer, self).__setstate__(state) + if not hasattr(self, "activation"): + self.activation = F.relu + + def forward( + self, + src: Tensor, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + ) -> Tensor: + r"""Pass the input through the encoder layer. + + Args: + src: the sequence to the encoder layer (required). + src_mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + + Shape: + see the docs in Transformer class. + """ + x, stage_embedding = src, None + is_src_tuple = False + if isinstance(src, tuple): + x, stage_embedding = src + is_src_tuple = True + + if src_key_padding_mask is not None: + _skpm_dtype = src_key_padding_mask.dtype + if _skpm_dtype != torch.bool and not torch.is_floating_point( + src_key_padding_mask + ): + raise AssertionError( + "only bool and floating types of key_padding_mask are supported" + ) + + if self.norm_first: + x = x + self._sa_block( + self.norm1(x, stage_embedding), + src_mask, + src_key_padding_mask, + ) + x = x + self._ff_block(self.norm2(x, stage_embedding)) + else: + x = self.norm1( + x + self._sa_block(x, src_mask, src_key_padding_mask), + stage_embedding, + ) + x = self.norm2(x + self._ff_block(x), stage_embedding) + + if is_src_tuple: + return (x, stage_embedding) + return x + + def _sa_block( + self, + x: Tensor, + attn_mask: Optional[Tensor], + key_padding_mask: Optional[Tensor], + ) -> Tensor: + x = self.self_attn( + x, + x, + x, + attn_mask=attn_mask, + key_padding_mask=key_padding_mask, + need_weights=False, + )[0] + return self.dropout1(x) + + def _ff_block(self, x: Tensor) -> Tensor: + x = self.linear2(self.dropout(self.activation(self.linear1(x)))) + return self.dropout2(x) + +class TransformerEncoder(nn.Module): + """TransformerEncoder is a stack of N encoder layers.""" + + def __init__(self, encoder_layer, num_layers, norm=None): + super(TransformerEncoder, self).__init__() + self.layers = _get_clones(encoder_layer, num_layers) + self.num_layers = num_layers + self.norm = norm + + def forward( + self, + src: Tensor, + mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + return_layer_states: bool = False, + ) -> Tensor: + + # Pass the input through the encoder layers + output = src + layer_states = [] if return_layer_states else None + + for mod in self.layers: + output = self._apply_module(mod, output, mask, src_key_padding_mask, layer_states) + + if self.norm is not None: + output = self.norm(output) + + return (layer_states, output) if return_layer_states else output + + def _apply_module(self, module, output, mask, key_padding_mask, layer_states): + # Apply a single transformer module + output = module(output, src_mask=mask, src_key_padding_mask=key_padding_mask) + if layer_states is not None: + layer_states.append(output) + return output + +class TransformerDecoderLayer(nn.Module): + __constants__ = ["batch_first", "norm_first"] + + def __init__( + self, + d_model: int, + nhead: int, + dim_feedforward: int = 2048, + dropout: float = 0.1, + activation: Union[str, Callable[[Tensor], Tensor]] = F.relu, + linear1_self_attention_cls: nn.Module = nn.Linear, + linear2_self_attention_cls: nn.Module = nn.Linear, + linear1_feedforward_cls: nn.Module = nn.Linear, + linear2_feedforward_cls: nn.Module = nn.Linear, + batch_first: bool = False, + norm_first: bool = False, + device=None, + dtype=None, + layer_norm_cls: nn.Module = LayerNorm, + layer_norm_eps: float = 1e-5, + adaptive_layer_norm=False, + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + super(TransformerDecoderLayer, self).__init__() + self.self_attn = MultiheadAttention( + d_model, + nhead, + dropout=dropout, + batch_first=batch_first, + linear1_cls=linear1_self_attention_cls, + linear2_cls=linear2_self_attention_cls, + **factory_kwargs, + ) + self.multihead_attn = MultiheadAttention( + d_model, + nhead, + dropout=dropout, + batch_first=batch_first, + linear1_cls=linear1_self_attention_cls, + linear2_cls=linear2_self_attention_cls, + **factory_kwargs, + ) + self.linear1 = linear1_feedforward_cls( + d_model, dim_feedforward, **factory_kwargs + ) + self.dropout = nn.Dropout(dropout) + self.linear2 = linear2_feedforward_cls( + dim_feedforward, d_model, **factory_kwargs + ) + + self.norm_first = norm_first + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.dropout3 = nn.Dropout(dropout) + + self.activation = self._get_activation_fn(activation) + self.norm1, self.norm2, self.norm3 = self._init_norm_layers( + d_model, layer_norm_cls, layer_norm_eps, adaptive_layer_norm, factory_kwargs + ) + + def forward( + self, + tgt: Tensor, + memory: Tensor, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + ) -> Tensor: + r"""Pass the inputs (and mask) through the decoder layer. + + Args: + tgt: the sequence to the decoder layer (required). + memory: the sequence from the last layer of the encoder (required). + tgt_mask: the mask for the tgt sequence (optional). + memory_mask: the mask for the memory sequence (optional). + tgt_key_padding_mask: the mask for the tgt keys per batch (optional). + memory_key_padding_mask: the mask for the memory keys per batch (optional). + + Shape: + see the docs in Transformer class. + """ + tgt_is_tuple = False + if isinstance(tgt, tuple): + x, stage_embedding = tgt + tgt_is_tuple = True + else: + x, stage_embedding = tgt, None + + if self.norm_first: + x = x + self._sa_block( + self.norm1(x, stage_embedding), tgt_mask, tgt_key_padding_mask + ) + x = x + self._mha_block( + self.norm2(x, stage_embedding), + memory, + memory_mask, + memory_key_padding_mask, + ) + x = x + self._ff_block(self.norm3(x, stage_embedding)) + else: + x = self.norm1( + x + self._sa_block(x, tgt_mask, tgt_key_padding_mask), + stage_embedding, + ) + x = self.norm2( + x + + self._mha_block( + x, memory, memory_mask, memory_key_padding_mask + ), + stage_embedding, + ) + x = self.norm3(x + self._ff_block(x), stage_embedding) + + if tgt_is_tuple: + return (x, stage_embedding) + return x + + def _sa_block( + self, + x: Tensor, + attn_mask: Optional[Tensor], + key_padding_mask: Optional[Tensor], + ) -> Tensor: + x = self.self_attn( + x, + x, + x, + attn_mask=attn_mask, + key_padding_mask=key_padding_mask, + need_weights=False, + )[0] + return self.dropout1(x) + + def _mha_block( + self, + x: Tensor, + mem: Tensor, + attn_mask: Optional[Tensor], + key_padding_mask: Optional[Tensor], + ) -> Tensor: + x = self.multihead_attn( + x, + mem, + mem, + attn_mask=attn_mask, + key_padding_mask=key_padding_mask, + need_weights=False, + )[0] + return self.dropout2(x) + + def _ff_block(self, x: Tensor) -> Tensor: + x = self.linear2(self.dropout(self.activation(self.linear1(x)))) + return self.dropout3(x) + + def _get_activation_fn(self, activation): + if isinstance(activation, str): + return _get_activation_fn(activation) + elif callable(activation): + return activation + else: + raise ValueError("Unsupported activation type") + + def _init_norm_layers(self, d_model, layer_norm_cls, layer_norm_eps, adaptive_layer_norm, factory_kwargs): + if adaptive_layer_norm: + return ( + AdaptiveLayerNorm(d_model, layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs)), + AdaptiveLayerNorm(d_model, layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs)), + AdaptiveLayerNorm(d_model, layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs)) + ) + else: + return ( + layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs), + layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs), + layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs) if layer_norm_cls != IdentityNorm + else BalancedBasicNorm(d_model, eps=layer_norm_eps, **factory_kwargs) + ) + + +def _get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +def _get_activation_fn(activation: str) -> Callable[[Tensor], Tensor]: + if activation == "relu": + return F.relu + elif activation == "gelu": + return F.gelu + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation) + ) + + +class Transpose(nn.Identity): + """(N, T, D) -> (N, D, T)""" + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input.transpose(1, 2) diff --git a/modules/transformer/transforms.py b/modules/transformer/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..d28b2e037e80f81ab75665c1f397c930bd0eca20 --- /dev/null +++ b/modules/transformer/transforms.py @@ -0,0 +1,214 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch.nn import functional as F + +import numpy as np + + +DEFAULT_MIN_BIN_WIDTH = 1e-3 +DEFAULT_MIN_BIN_HEIGHT = 1e-3 +DEFAULT_MIN_DERIVATIVE = 1e-3 + + +def piecewise_rational_quadratic_transform( + inputs, + unnormalized_widths, + unnormalized_heights, + unnormalized_derivatives, + inverse=False, + tails=None, + tail_bound=1.0, + min_bin_width=DEFAULT_MIN_BIN_WIDTH, + min_bin_height=DEFAULT_MIN_BIN_HEIGHT, + min_derivative=DEFAULT_MIN_DERIVATIVE, +): + if tails is None: + spline_fn = rational_quadratic_spline + spline_kwargs = {} + else: + spline_fn = unconstrained_rational_quadratic_spline + spline_kwargs = {"tails": tails, "tail_bound": tail_bound} + + outputs, logabsdet = spline_fn( + inputs=inputs, + unnormalized_widths=unnormalized_widths, + unnormalized_heights=unnormalized_heights, + unnormalized_derivatives=unnormalized_derivatives, + inverse=inverse, + min_bin_width=min_bin_width, + min_bin_height=min_bin_height, + min_derivative=min_derivative, + **spline_kwargs, + ) + return outputs, logabsdet + + +def searchsorted(bin_locations, inputs, eps=1e-6): + bin_locations[..., -1] += eps + return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1 + + +def unconstrained_rational_quadratic_spline( + inputs, + unnormalized_widths, + unnormalized_heights, + unnormalized_derivatives, + inverse=False, + tails="linear", + tail_bound=1.0, + min_bin_width=DEFAULT_MIN_BIN_WIDTH, + min_bin_height=DEFAULT_MIN_BIN_HEIGHT, + min_derivative=DEFAULT_MIN_DERIVATIVE, +): + inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) + outside_interval_mask = ~inside_interval_mask + + outputs = torch.zeros_like(inputs) + logabsdet = torch.zeros_like(inputs) + + if tails == "linear": + unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1)) + constant = np.log(np.exp(1 - min_derivative) - 1) + unnormalized_derivatives[..., 0] = constant + unnormalized_derivatives[..., -1] = constant + + outputs[outside_interval_mask] = inputs[outside_interval_mask] + logabsdet[outside_interval_mask] = 0 + else: + raise RuntimeError("{} tails are not implemented.".format(tails)) + + ( + outputs[inside_interval_mask], + logabsdet[inside_interval_mask], + ) = rational_quadratic_spline( + inputs=inputs[inside_interval_mask], + unnormalized_widths=unnormalized_widths[inside_interval_mask, :], + unnormalized_heights=unnormalized_heights[inside_interval_mask, :], + unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :], + inverse=inverse, + left=-tail_bound, + right=tail_bound, + bottom=-tail_bound, + top=tail_bound, + min_bin_width=min_bin_width, + min_bin_height=min_bin_height, + min_derivative=min_derivative, + ) + + return outputs, logabsdet + + +def rational_quadratic_spline( + inputs, + unnormalized_widths, + unnormalized_heights, + unnormalized_derivatives, + inverse=False, + left=0.0, + right=1.0, + bottom=0.0, + top=1.0, + min_bin_width=DEFAULT_MIN_BIN_WIDTH, + min_bin_height=DEFAULT_MIN_BIN_HEIGHT, + min_derivative=DEFAULT_MIN_DERIVATIVE, +): + if torch.min(inputs) < left or torch.max(inputs) > right: + raise ValueError("Input to a transform is not within its domain") + + num_bins = unnormalized_widths.shape[-1] + + if min_bin_width * num_bins > 1.0: + raise ValueError("Minimal bin width too large for the number of bins") + if min_bin_height * num_bins > 1.0: + raise ValueError("Minimal bin height too large for the number of bins") + + widths = F.softmax(unnormalized_widths, dim=-1) + widths = min_bin_width + (1 - min_bin_width * num_bins) * widths + cumwidths = torch.cumsum(widths, dim=-1) + cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0) + cumwidths = (right - left) * cumwidths + left + cumwidths[..., 0] = left + cumwidths[..., -1] = right + widths = cumwidths[..., 1:] - cumwidths[..., :-1] + + derivatives = min_derivative + F.softplus(unnormalized_derivatives) + + heights = F.softmax(unnormalized_heights, dim=-1) + heights = min_bin_height + (1 - min_bin_height * num_bins) * heights + cumheights = torch.cumsum(heights, dim=-1) + cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0) + cumheights = (top - bottom) * cumheights + bottom + cumheights[..., 0] = bottom + cumheights[..., -1] = top + heights = cumheights[..., 1:] - cumheights[..., :-1] + + if inverse: + bin_idx = searchsorted(cumheights, inputs)[..., None] + else: + bin_idx = searchsorted(cumwidths, inputs)[..., None] + + input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0] + input_bin_widths = widths.gather(-1, bin_idx)[..., 0] + + input_cumheights = cumheights.gather(-1, bin_idx)[..., 0] + delta = heights / widths + input_delta = delta.gather(-1, bin_idx)[..., 0] + + input_derivatives = derivatives.gather(-1, bin_idx)[..., 0] + input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0] + + input_heights = heights.gather(-1, bin_idx)[..., 0] + + if inverse: + a = (inputs - input_cumheights) * ( + input_derivatives + input_derivatives_plus_one - 2 * input_delta + ) + input_heights * (input_delta - input_derivatives) + b = input_heights * input_derivatives - (inputs - input_cumheights) * ( + input_derivatives + input_derivatives_plus_one - 2 * input_delta + ) + c = -input_delta * (inputs - input_cumheights) + + discriminant = b.pow(2) - 4 * a * c + assert (discriminant >= 0).all() + + root = (2 * c) / (-b - torch.sqrt(discriminant)) + outputs = root * input_bin_widths + input_cumwidths + + theta_one_minus_theta = root * (1 - root) + denominator = input_delta + ( + (input_derivatives + input_derivatives_plus_one - 2 * input_delta) + * theta_one_minus_theta + ) + derivative_numerator = input_delta.pow(2) * ( + input_derivatives_plus_one * root.pow(2) + + 2 * input_delta * theta_one_minus_theta + + input_derivatives * (1 - root).pow(2) + ) + logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) + + return outputs, -logabsdet + else: + theta = (inputs - input_cumwidths) / input_bin_widths + theta_one_minus_theta = theta * (1 - theta) + + numerator = input_heights * ( + input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta + ) + denominator = input_delta + ( + (input_derivatives + input_derivatives_plus_one - 2 * input_delta) + * theta_one_minus_theta + ) + outputs = input_cumheights + numerator / denominator + + derivative_numerator = input_delta.pow(2) * ( + input_derivatives_plus_one * theta.pow(2) + + 2 * input_delta * theta_one_minus_theta + + input_derivatives * (1 - theta).pow(2) + ) + logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) + + return outputs, logabsdet diff --git a/modules/vocoder_blocks/__init__.py b/modules/vocoder_blocks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b678da73bf7b15cf9c42a1f4f36379f62fc43896 --- /dev/null +++ b/modules/vocoder_blocks/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .gan_utils import * +from .norm2d import * diff --git a/modules/vocoder_blocks/__pycache__/__init__.cpython-39.pyc b/modules/vocoder_blocks/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..681c3c512d282b2719ea55d467310987f9d66dfa Binary files /dev/null and b/modules/vocoder_blocks/__pycache__/__init__.cpython-39.pyc differ diff --git a/modules/vocoder_blocks/__pycache__/gan_utils.cpython-39.pyc b/modules/vocoder_blocks/__pycache__/gan_utils.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..697d4e1adb5bcb291987a9ab834f8e1c8e327fab Binary files /dev/null and b/modules/vocoder_blocks/__pycache__/gan_utils.cpython-39.pyc differ diff --git a/modules/vocoder_blocks/__pycache__/norm2d.cpython-39.pyc b/modules/vocoder_blocks/__pycache__/norm2d.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0d054b609ffa98528fa935542eeaa8332a32ce4b Binary files /dev/null and b/modules/vocoder_blocks/__pycache__/norm2d.cpython-39.pyc differ diff --git a/modules/vocoder_blocks/gan_utils.py b/modules/vocoder_blocks/gan_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0e6fc38cb3c047ea856f8d63b0f3a755644e98ee --- /dev/null +++ b/modules/vocoder_blocks/gan_utils.py @@ -0,0 +1,28 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +#################### GAN utils #################### + + +import typing as tp + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + + +def get_2d_padding( + kernel_size: tp.Tuple[int, int], dilation: tp.Tuple[int, int] = (1, 1) +): + return ( + ((kernel_size[0] - 1) * dilation[0]) // 2, + ((kernel_size[1] - 1) * dilation[1]) // 2, + ) + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) diff --git a/modules/vocoder_blocks/norm2d.py b/modules/vocoder_blocks/norm2d.py new file mode 100644 index 0000000000000000000000000000000000000000..a907f907faae2ce9cf9e695b4a8b103ab39773ce --- /dev/null +++ b/modules/vocoder_blocks/norm2d.py @@ -0,0 +1,92 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +#################### Norm2D for Discriminators #################### + +import torch +import torch.nn as nn +import einops +from torch.nn.utils import spectral_norm, weight_norm + +CONV_NORMALIZATIONS = frozenset( + [ + "none", + "weight_norm", + "spectral_norm", + "time_layer_norm", + "layer_norm", + "time_group_norm", + ] +) + + +class ConvLayerNorm(nn.LayerNorm): + """ + Convolution-friendly LayerNorm that moves channels to last dimensions + before running the normalization and moves them back to original position right after. + """ + + def __init__(self, normalized_shape, **kwargs): + super().__init__(normalized_shape, **kwargs) + + def forward(self, x): + x = einops.rearrange(x, "b ... t -> b t ...") + x = super().forward(x) + x = einops.rearrange(x, "b t ... -> b ... t") + return + + +def apply_parametrization_norm(module: nn.Module, norm: str = "none") -> nn.Module: + assert norm in CONV_NORMALIZATIONS + if norm == "weight_norm": + return weight_norm(module) + elif norm == "spectral_norm": + return spectral_norm(module) + else: + # We already check was in CONV_NORMALIZATION, so any other choice + # doesn't need reparametrization. + return module + + +def get_norm_module( + module: nn.Module, causal: bool = False, norm: str = "none", **norm_kwargs +) -> nn.Module: + """Return the proper normalization module. If causal is True, this will ensure the returned + module is causal, or return an error if the normalization doesn't support causal evaluation. + """ + assert norm in CONV_NORMALIZATIONS + if norm == "layer_norm": + assert isinstance(module, nn.modules.conv._ConvNd) + return ConvLayerNorm(module.out_channels, **norm_kwargs) + elif norm == "time_group_norm": + if causal: + raise ValueError("GroupNorm doesn't support causal evaluation.") + assert isinstance(module, nn.modules.conv._ConvNd) + return nn.GroupNorm(1, module.out_channels, **norm_kwargs) + else: + return nn.Identity() + + +class NormConv2d(nn.Module): + """Wrapper around Conv2d and normalization applied to this conv + to provide a uniform interface across normalization approaches. + """ + + def __init__( + self, + *args, + norm: str = "none", + norm_kwargs={}, + **kwargs, + ): + super().__init__() + self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm) + self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs) + self.norm_type = norm + + def forward(self, x): + x = self.conv(x) + x = self.norm(x) + return x diff --git a/modules/wenet_extractor/README.md b/modules/wenet_extractor/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b8f0abc4d5d9fda42edba488d01bd40222a78304 --- /dev/null +++ b/modules/wenet_extractor/README.md @@ -0,0 +1,23 @@ +## Acknowledgement + +This module borrows some codes from [WeNet](https://github.com/wenet-e2e/wenet). + +## Citations + +```bibtex +@inproceedings{yao2021wenet, + title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, + author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, + booktitle={Proc. Interspeech}, + year={2021}, + address={Brno, Czech Republic }, + organization={IEEE} +} + +@article{zhang2022wenet, + title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, + author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, + journal={arXiv preprint arXiv:2203.15455}, + year={2022} +} +``` \ No newline at end of file diff --git a/modules/wenet_extractor/__init__.py b/modules/wenet_extractor/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/wenet_extractor/__pycache__/__init__.cpython-39.pyc b/modules/wenet_extractor/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c83612d6acad96c10b3f8aca499f322563971d08 Binary files /dev/null and b/modules/wenet_extractor/__pycache__/__init__.cpython-39.pyc differ diff --git a/modules/wenet_extractor/bin/alignment.py b/modules/wenet_extractor/bin/alignment.py new file mode 100644 index 0000000000000000000000000000000000000000..0f3fcb4ea01b29efdc87aff79778a21d335d699e --- /dev/null +++ b/modules/wenet_extractor/bin/alignment.py @@ -0,0 +1,245 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +from __future__ import print_function + +import argparse +import copy +import logging +import os +import sys + +import torch +import yaml +from torch.utils.data import DataLoader +from textgrid import TextGrid, IntervalTier + +from wenet.dataset.dataset import Dataset +from wenet.utils.checkpoint import load_checkpoint +from wenet.utils.file_utils import read_symbol_table, read_non_lang_symbols +from wenet.utils.ctc_util import forced_align +from wenet.utils.common import get_subsample +from wenet.utils.init_model import init_model + + +def generator_textgrid(maxtime, lines, output): + # Download Praat: https://www.fon.hum.uva.nl/praat/ + interval = maxtime / (len(lines) + 1) + margin = 0.0001 + + tg = TextGrid(maxTime=maxtime) + linetier = IntervalTier(name="line", maxTime=maxtime) + + i = 0 + for l in lines: + s, e, w = l.split() + linetier.add(minTime=float(s) + margin, maxTime=float(e), mark=w) + + tg.append(linetier) + print("successfully generator {}".format(output)) + tg.write(output) + + +def get_frames_timestamp(alignment): + # convert alignment to a praat format, which is a doing phonetics + # by computer and helps analyzing alignment + timestamp = [] + # get frames level duration for each token + start = 0 + end = 0 + while end < len(alignment): + while end < len(alignment) and alignment[end] == 0: + end += 1 + if end == len(alignment): + timestamp[-1] += alignment[start:] + break + end += 1 + while end < len(alignment) and alignment[end - 1] == alignment[end]: + end += 1 + timestamp.append(alignment[start:end]) + start = end + return timestamp + + +def get_labformat(timestamp, subsample): + begin = 0 + duration = 0 + labformat = [] + for idx, t in enumerate(timestamp): + # 25ms frame_length,10ms hop_length, 1/subsample + subsample = get_subsample(configs) + # time duration + duration = len(t) * 0.01 * subsample + if idx < len(timestamp) - 1: + print("{:.2f} {:.2f} {}".format(begin, begin + duration, char_dict[t[-1]])) + labformat.append( + "{:.2f} {:.2f} {}\n".format(begin, begin + duration, char_dict[t[-1]]) + ) + else: + non_blank = 0 + for i in t: + if i != 0: + token = i + break + print("{:.2f} {:.2f} {}".format(begin, begin + duration, char_dict[token])) + labformat.append( + "{:.2f} {:.2f} {}\n".format(begin, begin + duration, char_dict[token]) + ) + begin = begin + duration + return labformat + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="use ctc to generate alignment") + parser.add_argument("--config", required=True, help="config file") + parser.add_argument("--input_file", required=True, help="format data file") + parser.add_argument( + "--data_type", + default="raw", + choices=["raw", "shard"], + help="train and cv data type", + ) + parser.add_argument( + "--gpu", type=int, default=-1, help="gpu id for this rank, -1 for cpu" + ) + parser.add_argument("--checkpoint", required=True, help="checkpoint model") + parser.add_argument("--dict", required=True, help="dict file") + parser.add_argument( + "--non_lang_syms", help="non-linguistic symbol file. One symbol per line." + ) + parser.add_argument("--result_file", required=True, help="alignment result file") + parser.add_argument("--batch_size", type=int, default=1, help="batch size") + parser.add_argument( + "--gen_praat", action="store_true", help="convert alignment to a praat format" + ) + parser.add_argument( + "--bpe_model", default=None, type=str, help="bpe model for english part" + ) + + args = parser.parse_args() + print(args) + logging.basicConfig( + level=logging.DEBUG, format="%(asctime)s %(levelname)s %(message)s" + ) + os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) + + if args.batch_size > 1: + logging.fatal("alignment mode must be running with batch_size == 1") + sys.exit(1) + + with open(args.config, "r") as fin: + configs = yaml.load(fin, Loader=yaml.FullLoader) + + # Load dict + char_dict = {} + with open(args.dict, "r") as fin: + for line in fin: + arr = line.strip().split() + assert len(arr) == 2 + char_dict[int(arr[1])] = arr[0] + eos = len(char_dict) - 1 + + symbol_table = read_symbol_table(args.dict) + + # Init dataset and data loader + ali_conf = copy.deepcopy(configs["dataset_conf"]) + + ali_conf["filter_conf"]["max_length"] = 102400 + ali_conf["filter_conf"]["min_length"] = 0 + ali_conf["filter_conf"]["token_max_length"] = 102400 + ali_conf["filter_conf"]["token_min_length"] = 0 + ali_conf["filter_conf"]["max_output_input_ratio"] = 102400 + ali_conf["filter_conf"]["min_output_input_ratio"] = 0 + ali_conf["speed_perturb"] = False + ali_conf["spec_aug"] = False + ali_conf["shuffle"] = False + ali_conf["sort"] = False + ali_conf["fbank_conf"]["dither"] = 0.0 + ali_conf["batch_conf"]["batch_type"] = "static" + ali_conf["batch_conf"]["batch_size"] = args.batch_size + non_lang_syms = read_non_lang_symbols(args.non_lang_syms) + + ali_dataset = Dataset( + args.data_type, + args.input_file, + symbol_table, + ali_conf, + args.bpe_model, + non_lang_syms, + partition=False, + ) + + ali_data_loader = DataLoader(ali_dataset, batch_size=None, num_workers=0) + + # Init asr model from configs + model = init_model(configs) + + load_checkpoint(model, args.checkpoint) + use_cuda = args.gpu >= 0 and torch.cuda.is_available() + device = torch.device("cuda" if use_cuda else "cpu") + model = model.to(device) + + model.eval() + with torch.no_grad(), open(args.result_file, "w", encoding="utf-8") as fout: + for batch_idx, batch in enumerate(ali_data_loader): + print("#" * 80) + key, feat, target, feats_length, target_length = batch + print(key) + + feat = feat.to(device) + target = target.to(device) + feats_length = feats_length.to(device) + target_length = target_length.to(device) + # Let's assume B = batch_size and N = beam_size + # 1. Encoder + encoder_out, encoder_mask = model._forward_encoder( + feat, feats_length + ) # (B, maxlen, encoder_dim) + maxlen = encoder_out.size(1) + ctc_probs = model.ctc.log_softmax(encoder_out) # (1, maxlen, vocab_size) + # print(ctc_probs.size(1)) + ctc_probs = ctc_probs.squeeze(0) + target = target.squeeze(0) + alignment = forced_align(ctc_probs, target) + print(alignment) + fout.write("{} {}\n".format(key[0], alignment)) + + if args.gen_praat: + timestamp = get_frames_timestamp(alignment) + print(timestamp) + subsample = get_subsample(configs) + labformat = get_labformat(timestamp, subsample) + + lab_path = os.path.join( + os.path.dirname(args.result_file), key[0] + ".lab" + ) + with open(lab_path, "w", encoding="utf-8") as f: + f.writelines(labformat) + + textgrid_path = os.path.join( + os.path.dirname(args.result_file), key[0] + ".TextGrid" + ) + generator_textgrid( + maxtime=(len(alignment) + 1) * 0.01 * subsample, + lines=labformat, + output=textgrid_path, + ) diff --git a/modules/wenet_extractor/bin/average_model.py b/modules/wenet_extractor/bin/average_model.py new file mode 100644 index 0000000000000000000000000000000000000000..25998b01136b077f93d8f70abf7e4a1fccf981a1 --- /dev/null +++ b/modules/wenet_extractor/bin/average_model.py @@ -0,0 +1,104 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +import os +import argparse +import glob + +import yaml +import numpy as np +import torch + + +def get_args(): + parser = argparse.ArgumentParser(description="average model") + parser.add_argument("--dst_model", required=True, help="averaged model") + parser.add_argument("--src_path", required=True, help="src model path for average") + parser.add_argument("--val_best", action="store_true", help="averaged model") + parser.add_argument("--num", default=5, type=int, help="nums for averaged model") + parser.add_argument( + "--min_epoch", default=0, type=int, help="min epoch used for averaging model" + ) + parser.add_argument( + "--max_epoch", + default=65536, + type=int, + help="max epoch used for averaging model", + ) + + args = parser.parse_args() + print(args) + return args + + +def main(): + args = get_args() + checkpoints = [] + val_scores = [] + if args.val_best: + yamls = glob.glob("{}/[!train]*.yaml".format(args.src_path)) + for y in yamls: + with open(y, "r") as f: + dic_yaml = yaml.load(f, Loader=yaml.FullLoader) + loss = dic_yaml["cv_loss"] + epoch = dic_yaml["epoch"] + if epoch >= args.min_epoch and epoch <= args.max_epoch: + val_scores += [[epoch, loss]] + val_scores = np.array(val_scores) + sort_idx = np.argsort(val_scores[:, -1]) + sorted_val_scores = val_scores[sort_idx][::1] + print("best val scores = " + str(sorted_val_scores[: args.num, 1])) + print( + "selected epochs = " + + str(sorted_val_scores[: args.num, 0].astype(np.int64)) + ) + path_list = [ + args.src_path + "/{}.pt".format(int(epoch)) + for epoch in sorted_val_scores[: args.num, 0] + ] + else: + path_list = glob.glob("{}/[0-9]*.pt".format(args.src_path)) + path_list = sorted(path_list, key=os.path.getmtime) + path_list = path_list[-args.num :] + print(path_list) + avg = None + num = args.num + assert num == len(path_list) + for path in path_list: + print("Processing {}".format(path)) + states = torch.load(path, map_location=torch.device("cpu")) + if avg is None: + avg = states + else: + for k in avg.keys(): + avg[k] += states[k] + # average + for k in avg.keys(): + if avg[k] is not None: + # pytorch 1.6 use true_divide instead of /= + avg[k] = torch.true_divide(avg[k], num) + print("Saving to {}".format(args.dst_model)) + torch.save(avg, args.dst_model) + + +if __name__ == "__main__": + main() diff --git a/modules/wenet_extractor/bin/export_jit.py b/modules/wenet_extractor/bin/export_jit.py new file mode 100644 index 0000000000000000000000000000000000000000..1e2b7558c87a3991548bf5d9cf26543776599894 --- /dev/null +++ b/modules/wenet_extractor/bin/export_jit.py @@ -0,0 +1,80 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +from __future__ import print_function + +import argparse +import os + +import torch +import yaml + +from wenet.utils.checkpoint import load_checkpoint +from wenet.utils.init_model import init_model + + +def get_args(): + parser = argparse.ArgumentParser(description="export your script model") + parser.add_argument("--config", required=True, help="config file") + parser.add_argument("--checkpoint", required=True, help="checkpoint model") + parser.add_argument("--output_file", default=None, help="output file") + parser.add_argument( + "--output_quant_file", default=None, help="output quantized model file" + ) + args = parser.parse_args() + return args + + +def main(): + args = get_args() + # No need gpu for model export + os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + + with open(args.config, "r") as fin: + configs = yaml.load(fin, Loader=yaml.FullLoader) + model = init_model(configs) + print(model) + + load_checkpoint(model, args.checkpoint) + # Export jit torch script model + + if args.output_file: + script_model = torch.jit.script(model) + script_model.save(args.output_file) + print("Export model successfully, see {}".format(args.output_file)) + + # Export quantized jit torch script model + if args.output_quant_file: + quantized_model = torch.quantization.quantize_dynamic( + model, {torch.nn.Linear}, dtype=torch.qint8 + ) + print(quantized_model) + script_quant_model = torch.jit.script(quantized_model) + script_quant_model.save(args.output_quant_file) + print( + "Export quantized model successfully, " + "see {}".format(args.output_quant_file) + ) + + +if __name__ == "__main__": + main() diff --git a/modules/wenet_extractor/bin/export_onnx_bpu.py b/modules/wenet_extractor/bin/export_onnx_bpu.py new file mode 100644 index 0000000000000000000000000000000000000000..ed21b39d3a38f4f2b967bc9ae8a6b148e0a9fde7 --- /dev/null +++ b/modules/wenet_extractor/bin/export_onnx_bpu.py @@ -0,0 +1,1122 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + + +from __future__ import print_function + +import os +import sys +import copy +import math +import yaml +import logging +from typing import Tuple + +import torch +import numpy as np + +from wenet.transformer.embedding import NoPositionalEncoding +from wenet.utils.checkpoint import load_checkpoint +from wenet.utils.init_model import init_model +from wenet.bin.export_onnx_cpu import get_args, to_numpy, print_input_output_info + + +try: + import onnx + import onnxruntime +except ImportError: + print("Please install onnx and onnxruntime!") + sys.exit(1) + + +logger = logging.getLogger(__file__) +logger.setLevel(logging.INFO) + + +class BPULayerNorm(torch.nn.Module): + """Refactor torch.nn.LayerNorm to meet 4-D dataflow.""" + + def __init__(self, module, chunk_size=8, run_on_bpu=False): + super().__init__() + original = copy.deepcopy(module) + self.hidden = module.weight.size(0) + self.chunk_size = chunk_size + self.run_on_bpu = run_on_bpu + + if self.run_on_bpu: + self.weight = torch.nn.Parameter( + module.weight.reshape(1, self.hidden, 1, 1).repeat(1, 1, 1, chunk_size) + ) + self.bias = torch.nn.Parameter( + module.bias.reshape(1, self.hidden, 1, 1).repeat(1, 1, 1, chunk_size) + ) + self.negtive = torch.nn.Parameter( + torch.ones((1, self.hidden, 1, chunk_size)) * -1.0 + ) + self.eps = torch.nn.Parameter( + torch.zeros((1, self.hidden, 1, chunk_size)) + module.eps + ) + self.mean_conv_1 = torch.nn.Conv2d(self.hidden, 1, 1, bias=False) + self.mean_conv_1.weight = torch.nn.Parameter( + torch.ones(self.hidden, self.hidden, 1, 1) / (1.0 * self.hidden) + ) + self.mean_conv_2 = torch.nn.Conv2d(self.hidden, 1, 1, bias=False) + self.mean_conv_2.weight = torch.nn.Parameter( + torch.ones(self.hidden, self.hidden, 1, 1) / (1.0 * self.hidden) + ) + else: + self.norm = module + + self.check_equal(original) + + def check_equal(self, module): + random_data = torch.randn(1, self.chunk_size, self.hidden) + orig_out = module(random_data) + new_out = self.forward(random_data.transpose(1, 2).unsqueeze(2)) + np.testing.assert_allclose( + to_numpy(orig_out), + to_numpy(new_out.squeeze(2).transpose(1, 2)), + rtol=1e-02, + atol=1e-03, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if self.run_on_bpu: + u = self.mean_conv_1(x) # (1, h, 1, c) + numerator = x + u * self.negtive # (1, h, 1, c) + s = torch.pow(numerator, 2) # (1, h, 1, c) + s = self.mean_conv_2(s) # (1, h, 1, c) + denominator = torch.sqrt(s + self.eps) # (1, h, 1, c) + x = torch.div(numerator, denominator) # (1, h, 1, c) + x = x * self.weight + self.bias + else: + x = x.squeeze(2).transpose(1, 2).contiguous() + x = self.norm(x) + x = x.transpose(1, 2).contiguous().unsqueeze(2) + return x + + +class BPUIdentity(torch.nn.Module): + """Refactor torch.nn.Identity(). + For inserting BPU node whose input == output. + """ + + def __init__(self, channels): + super().__init__() + self.channels = channels + self.identity_conv = torch.nn.Conv2d( + channels, channels, 1, groups=channels, bias=False + ) + torch.nn.init.dirac_(self.identity_conv.weight.data, groups=channels) + + self.check_equal() + + def check_equal(self): + random_data = torch.randn(1, self.channels, 1, 10) + result = self.forward(random_data) + np.testing.assert_allclose( + to_numpy(random_data), to_numpy(result), rtol=1e-02, atol=1e-03 + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Identity with 4-D dataflow, input == output. + Args: + x (torch.Tensor): (batch, in_channel, 1, time) + + Returns: + (torch.Tensor): (batch, in_channel, 1, time). + """ + return self.identity_conv(x) + + +class BPULinear(torch.nn.Module): + """Refactor torch.nn.Linear or pointwise_conv""" + + def __init__(self, module, is_pointwise_conv=False): + super().__init__() + # Unchanged submodules and attributes + original = copy.deepcopy(module) + self.idim = module.weight.size(1) + self.odim = module.weight.size(0) + self.is_pointwise_conv = is_pointwise_conv + + # Modify weight & bias + self.linear = torch.nn.Conv2d(self.idim, self.odim, 1, 1) + if is_pointwise_conv: + # (odim, idim, kernel=1) -> (odim, idim, 1, 1) + self.linear.weight = torch.nn.Parameter(module.weight.unsqueeze(-1)) + else: + # (odim, idim) -> (odim, idim, 1, 1) + self.linear.weight = torch.nn.Parameter( + module.weight.unsqueeze(2).unsqueeze(3) + ) + self.linear.bias = module.bias + + self.check_equal(original) + + def check_equal(self, module): + random_data = torch.randn(1, 8, self.idim) + if self.is_pointwise_conv: + random_data = random_data.transpose(1, 2) + original_result = module(random_data) + if self.is_pointwise_conv: + random_data = random_data.transpose(1, 2) + original_result = original_result.transpose(1, 2) + random_data = random_data.transpose(1, 2).unsqueeze(2) + new_result = self.forward(random_data) + np.testing.assert_allclose( + to_numpy(original_result), + to_numpy(new_result.squeeze(2).transpose(1, 2)), + rtol=1e-02, + atol=1e-03, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Linear with 4-D dataflow. + Args: + x (torch.Tensor): (batch, in_channel, 1, time) + Returns: + (torch.Tensor): (batch, out_channel, 1, time). + """ + return self.linear(x) + + +class BPUGlobalCMVN(torch.nn.Module): + """Refactor wenet/transformer/cmvn.py::GlobalCMVN""" + + def __init__(self, module): + super().__init__() + # Unchanged submodules and attributes + self.norm_var = module.norm_var + + # NOTE(xcsong): Expand to 4-D tensor, (mel_dim) -> (1, 1, mel_dim, 1) + self.mean = module.mean.unsqueeze(-1).unsqueeze(0).unsqueeze(0) + self.istd = module.istd.unsqueeze(-1).unsqueeze(0).unsqueeze(0) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """CMVN with 4-D dataflow. + Args: + x (torch.Tensor): (batch, 1, mel_dim, time) + Returns: + (torch.Tensor): normalized feature with same shape. + """ + x = x - self.mean + if self.norm_var: + x = x * self.istd + return x + + +class BPUConv2dSubsampling8(torch.nn.Module): + """Refactor wenet/transformer/subsampling.py::Conv2dSubsampling8 + + NOTE(xcsong): Only support pos_enc_class == NoPositionalEncoding + """ + + def __init__(self, module): + super().__init__() + # Unchanged submodules and attributes + original = copy.deepcopy(module) + self.right_context = module.right_context + self.subsampling_rate = module.subsampling_rate + assert isinstance(module.pos_enc, NoPositionalEncoding) + + # 1. Modify self.conv + # NOTE(xcsong): We change input shape from (1, 1, frames, mel_dim) + # to (1, 1, mel_dim, frames) for more efficient computation. + self.conv = module.conv + for idx in [0, 2, 4]: + self.conv[idx].weight = torch.nn.Parameter( + module.conv[idx].weight.transpose(2, 3) + ) + + # 2. Modify self.linear + # NOTE(xcsong): Split final projection to meet the requirment of + # maximum kernel_size (7 for XJ3) + self.linear = torch.nn.ModuleList() + odim = module.linear.weight.size(0) # 512, in this case + freq = module.linear.weight.size(1) // odim # 4608 // 512 == 9 + self.odim, self.freq = odim, freq + weight = module.linear.weight.reshape( + odim, odim, freq, 1 + ) # (odim, odim * freq) -> (odim, odim, freq, 1) + self.split_size = [] + num_split = (freq - 1) // 7 + 1 # XJ3 requires kernel_size <= 7 + slice_begin = 0 + for idx in range(num_split): + kernel_size = min(freq, (idx + 1) * 7) - idx * 7 + conv_ele = torch.nn.Conv2d(odim, odim, (kernel_size, 1), (kernel_size, 1)) + conv_ele.weight = torch.nn.Parameter( + weight[:, :, slice_begin : slice_begin + kernel_size, :] + ) + conv_ele.bias = torch.nn.Parameter(torch.zeros_like(conv_ele.bias)) + self.linear.append(conv_ele) + self.split_size.append(kernel_size) + slice_begin += kernel_size + self.linear[0].bias = torch.nn.Parameter(module.linear.bias) + + self.check_equal(original) + + def check_equal(self, module): + random_data = torch.randn(1, 67, 80) + mask = torch.zeros(1, 1, 67) + original_result, _, _ = module(random_data, mask) # (1, 8, 512) + random_data = random_data.transpose(1, 2).unsqueeze(0) # (1, 1, 80, 67) + new_result = self.forward(random_data) # (1, 512, 1, 8) + np.testing.assert_allclose( + to_numpy(original_result), + to_numpy(new_result.squeeze(2).transpose(1, 2)), + rtol=1e-02, + atol=1e-03, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Subsample x with 4-D dataflow. + Args: + x (torch.Tensor): Input tensor (#batch, 1, mel_dim, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, odim, 1, time'), + where time' = time // 8. + """ + x = self.conv(x) # (1, odim, freq, time') + x_out = torch.zeros(x.size(0), self.odim, 1, x.size(3)) + x = torch.split(x, self.split_size, dim=2) + for idx, (x_part, layer) in enumerate(zip(x, self.linear)): + x_out += layer(x_part) + return x_out + + +class BPUMultiHeadedAttention(torch.nn.Module): + """Refactor wenet/transformer/attention.py::MultiHeadedAttention + + NOTE(xcsong): Only support attention_class == MultiHeadedAttention, + we do not consider RelPositionMultiHeadedAttention currently. + """ + + def __init__(self, module, chunk_size, left_chunks): + super().__init__() + # Unchanged submodules and attributes + original = copy.deepcopy(module) + self.d_k = module.d_k + self.h = module.h + n_feat = self.d_k * self.h + self.chunk_size = chunk_size + self.left_chunks = left_chunks + self.time = chunk_size * (left_chunks + 1) + self.activation = torch.nn.Softmax(dim=-1) + + # 1. Modify self.linear_x + self.linear_q = BPULinear(module.linear_q) + self.linear_k = BPULinear(module.linear_k) + self.linear_v = BPULinear(module.linear_v) + self.linear_out = BPULinear(module.linear_out) + # 2. denom + self.register_buffer( + "denom", torch.full((1, self.h, 1, 1), 1.0 / math.sqrt(self.d_k)) + ) + + self.check_equal(original) + + def check_equal(self, module): + random_data = torch.randn(1, self.chunk_size, self.d_k * self.h) + mask = torch.ones((1, self.h, self.chunk_size, self.time), dtype=torch.bool) + cache = torch.zeros(1, self.h, self.chunk_size * self.left_chunks, self.d_k * 2) + original_out, original_cache = module( + random_data, + random_data, + random_data, + mask[:, 0, :, :], + torch.empty(0), + cache, + ) + random_data = random_data.transpose(1, 2).unsqueeze(2) + cache = cache.reshape( + 1, self.h, self.d_k * 2, self.chunk_size * self.left_chunks + ) + new_out, new_cache = self.forward( + random_data, random_data, random_data, mask, cache + ) + np.testing.assert_allclose( + to_numpy(original_out), + to_numpy(new_out.squeeze(2).transpose(1, 2)), + rtol=1e-02, + atol=1e-03, + ) + np.testing.assert_allclose( + to_numpy(original_cache), + to_numpy(new_cache.transpose(2, 3)), + rtol=1e-02, + atol=1e-03, + ) + + def forward( + self, + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + mask: torch.Tensor, + cache: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute scaled dot product attention. + + Args: + q (torch.Tensor): Query tensor (#batch, size, 1, chunk_size). + k (torch.Tensor): Key tensor (#batch, size, 1, chunk_size). + v (torch.Tensor): Value tensor (#batch, size, 1, chunk_size). + mask (torch.Tensor): Mask tensor, + (#batch, head, chunk_size, cache_t + chunk_size). + cache (torch.Tensor): Cache tensor + (1, head, d_k * 2, cache_t), + where `cache_t == chunk_size * left_chunks`. + + + Returns: + torch.Tensor: Output tensor (#batch, size, 1, chunk_size). + torch.Tensor: Cache tensor + (1, head, d_k * 2, cache_t + chunk_size) + where `cache_t == chunk_size * left_chunks` + """ + # 1. Forward QKV + q = self.linear_q(q) # (1, d, 1, c) d == size, c == chunk_size + k = self.linear_k(k) # (1, d, 1, c) + v = self.linear_v(v) # (1, d, 1, c) + q = q.view(1, self.h, self.d_k, self.chunk_size) + k = k.view(1, self.h, self.d_k, self.chunk_size) + v = v.view(1, self.h, self.d_k, self.chunk_size) + q = q.transpose(2, 3) # (batch, head, time1, d_k) + k_cache, v_cache = torch.split(cache, cache.size(2) // 2, dim=2) + k = torch.cat((k_cache, k), dim=3) + v = torch.cat((v_cache, v), dim=3) + new_cache = torch.cat((k, v), dim=2) + # 2. (Q^T)K + scores = torch.matmul(q, k) * self.denom # (#b, n_head, time1, time2) + # 3. Forward attention + mask = mask.eq(0) + scores = scores.masked_fill(mask, -float("inf")) + attn = self.activation(scores).masked_fill(mask, 0.0) + attn = attn.transpose(2, 3) + x = torch.matmul(v, attn) + x = x.view(1, self.d_k * self.h, 1, self.chunk_size) + x_out = self.linear_out(x) + return x_out, new_cache + + +class BPUConvolution(torch.nn.Module): + """Refactor wenet/transformer/convolution.py::ConvolutionModule + + NOTE(xcsong): Only suport use_layer_norm == False + """ + + def __init__(self, module): + super().__init__() + # Unchanged submodules and attributes + original = copy.deepcopy(module) + self.lorder = module.lorder + self.use_layer_norm = False + self.activation = module.activation + channels = module.pointwise_conv1.weight.size(1) + self.channels = channels + kernel_size = module.depthwise_conv.weight.size(2) + assert module.use_layer_norm is False + + # 1. Modify self.pointwise_conv1 + self.pointwise_conv1 = BPULinear(module.pointwise_conv1, True) + + # 2. Modify self.depthwise_conv + self.depthwise_conv = torch.nn.Conv2d( + channels, channels, (1, kernel_size), stride=1, groups=channels + ) + self.depthwise_conv.weight = torch.nn.Parameter( + module.depthwise_conv.weight.unsqueeze(-2) + ) + self.depthwise_conv.bias = torch.nn.Parameter(module.depthwise_conv.bias) + + # 3. Modify self.norm, Only support batchnorm2d + self.norm = torch.nn.BatchNorm2d(channels) + self.norm.training = False + self.norm.num_features = module.norm.num_features + self.norm.eps = module.norm.eps + self.norm.momentum = module.norm.momentum + self.norm.weight = torch.nn.Parameter(module.norm.weight) + self.norm.bias = torch.nn.Parameter(module.norm.bias) + self.norm.running_mean = module.norm.running_mean + self.norm.running_var = module.norm.running_var + + # 4. Modify self.pointwise_conv2 + self.pointwise_conv2 = BPULinear(module.pointwise_conv2, True) + + # 5. Identity conv, for running `concat` on BPU + self.identity = BPUIdentity(channels) + + self.check_equal(original) + + def check_equal(self, module): + random_data = torch.randn(1, 8, self.channels) + cache = torch.zeros((1, self.channels, self.lorder)) + original_out, original_cache = module(random_data, cache=cache) + random_data = random_data.transpose(1, 2).unsqueeze(2) + cache = cache.unsqueeze(2) + new_out, new_cache = self.forward(random_data, cache) + np.testing.assert_allclose( + to_numpy(original_out), + to_numpy(new_out.squeeze(2).transpose(1, 2)), + rtol=1e-02, + atol=1e-03, + ) + np.testing.assert_allclose( + to_numpy(original_cache), + to_numpy(new_cache.squeeze(2)), + rtol=1e-02, + atol=1e-03, + ) + + def forward( + self, x: torch.Tensor, cache: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute convolution module. + Args: + x (torch.Tensor): Input tensor (#batch, channels, 1, chunk_size). + cache (torch.Tensor): left context cache, it is only + used in causal convolution (#batch, channels, 1, cache_t). + Returns: + torch.Tensor: Output tensor (#batch, channels, 1, chunk_size). + torch.Tensor: Cache tensor (#batch, channels, 1, cache_t). + """ + # Concat cache + x = torch.cat((self.identity(cache), self.identity(x)), dim=3) + new_cache = x[:, :, :, -self.lorder :] + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channel, 1, dim) + x = torch.nn.functional.glu(x, dim=1) # (b, channel, 1, dim) + + # Depthwise Conv + x = self.depthwise_conv(x) + x = self.activation(self.norm(x)) + x = self.pointwise_conv2(x) + return x, new_cache + + +class BPUFFN(torch.nn.Module): + """Refactor wenet/transformer/positionwise_feed_forward.py::PositionwiseFeedForward""" + + def __init__(self, module): + super().__init__() + # Unchanged submodules and attributes + original = copy.deepcopy(module) + self.activation = module.activation + + # 1. Modify self.w_x + self.w_1 = BPULinear(module.w_1) + self.w_2 = BPULinear(module.w_2) + + self.check_equal(original) + + def check_equal(self, module): + random_data = torch.randn(1, 8, self.w_1.idim) + original_out = module(random_data) + random_data = random_data.transpose(1, 2).unsqueeze(2) + new_out = self.forward(random_data) + np.testing.assert_allclose( + to_numpy(original_out), + to_numpy(new_out.squeeze(2).transpose(1, 2)), + rtol=1e-02, + atol=1e-03, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward function. + + Args: + xs: input tensor (B, D, 1, L) + Returns: + output tensor, (B, D, 1, L) + """ + return self.w_2(self.activation(self.w_1(x))) + + +class BPUConformerEncoderLayer(torch.nn.Module): + """Refactor wenet/transformer/encoder_layer.py::ConformerEncoderLayer""" + + def __init__(self, module, chunk_size, left_chunks, ln_run_on_bpu=False): + super().__init__() + # Unchanged submodules and attributes + original = copy.deepcopy(module) + self.size = module.size + assert module.normalize_before is True + assert module.concat_after is False + + # 1. Modify submodules + self.feed_forward_macaron = BPUFFN(module.feed_forward_macaron) + self.self_attn = BPUMultiHeadedAttention( + module.self_attn, chunk_size, left_chunks + ) + self.conv_module = BPUConvolution(module.conv_module) + self.feed_forward = BPUFFN(module.feed_forward) + + # 2. Modify norms + self.norm_ff = BPULayerNorm(module.norm_ff, chunk_size, ln_run_on_bpu) + self.norm_mha = BPULayerNorm(module.norm_mha, chunk_size, ln_run_on_bpu) + self.norm_ff_macron = BPULayerNorm( + module.norm_ff_macaron, chunk_size, ln_run_on_bpu + ) + self.norm_conv = BPULayerNorm(module.norm_conv, chunk_size, ln_run_on_bpu) + self.norm_final = BPULayerNorm(module.norm_final, chunk_size, ln_run_on_bpu) + + # 3. 4-D ff_scale + self.register_buffer( + "ff_scale", torch.full((1, self.size, 1, 1), module.ff_scale) + ) + + self.check_equal(original) + + def check_equal(self, module): + time1 = self.self_attn.chunk_size + time2 = self.self_attn.time + h, d_k = self.self_attn.h, self.self_attn.d_k + random_x = torch.randn(1, time1, self.size) + att_mask = torch.ones(1, h, time1, time2) + att_cache = torch.zeros(1, h, time2 - time1, d_k * 2) + cnn_cache = torch.zeros(1, self.size, self.conv_module.lorder) + original_x, _, original_att_cache, original_cnn_cache = module( + random_x, + att_mask[:, 0, :, :], + torch.empty(0), + att_cache=att_cache, + cnn_cache=cnn_cache, + ) + random_x = random_x.transpose(1, 2).unsqueeze(2) + att_cache = att_cache.reshape(1, h, d_k * 2, time2 - time1) + cnn_cache = cnn_cache.unsqueeze(2) + new_x, new_att_cache, new_cnn_cache = self.forward( + random_x, att_mask, att_cache, cnn_cache + ) + np.testing.assert_allclose( + to_numpy(original_att_cache), + to_numpy(new_att_cache.transpose(2, 3)), + rtol=1e-02, + atol=1e-03, + ) + np.testing.assert_allclose( + to_numpy(original_x), + to_numpy(new_x.squeeze(2).transpose(1, 2)), + rtol=1e-02, + atol=1e-03, + ) + np.testing.assert_allclose( + to_numpy(original_cnn_cache), + to_numpy(new_cnn_cache.squeeze(2)), + rtol=1e-02, + atol=1e-03, + ) + + def forward( + self, + x: torch.Tensor, + att_mask: torch.Tensor, + att_cache: torch.Tensor, + cnn_cache: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute encoded features. + + Args: + x (torch.Tensor): (#batch, size, 1, chunk_size) + att_mask (torch.Tensor): Mask tensor for the input + (#batch, head, chunk_size, cache_t1 + chunk_size), + att_cache (torch.Tensor): Cache tensor of the KEY & VALUE + (#batch=1, head, d_k * 2, cache_t1), head * d_k == size. + cnn_cache (torch.Tensor): Convolution cache in conformer layer + (#batch=1, size, 1, cache_t2) + Returns: + torch.Tensor: Output tensor (#batch, size, 1, chunk_size). + torch.Tensor: att_cache tensor, + (1, head, d_k * 2, cache_t1 + chunk_size). + torch.Tensor: cnn_cahce tensor (#batch, size, 1, cache_t2). + """ + # 1. ffn_macaron + residual = x + x = self.norm_ff_macron(x) + x = residual + self.ff_scale * self.feed_forward_macaron(x) + + # 2. attention + residual = x + x = self.norm_mha(x) + x_att, new_att_cache = self.self_attn(x, x, x, att_mask, att_cache) + x = residual + x_att + + # 3. convolution + residual = x + x = self.norm_conv(x) + x, new_cnn_cache = self.conv_module(x, cnn_cache) + x = residual + x + + # 4. ffn + residual = x + x = self.norm_ff(x) + x = residual + self.ff_scale * self.feed_forward(x) + + # 5. final post-norm + x = self.norm_final(x) + + return x, new_att_cache, new_cnn_cache + + +class BPUConformerEncoder(torch.nn.Module): + """Refactor wenet/transformer/encoder.py::ConformerEncoder""" + + def __init__(self, module, chunk_size, left_chunks, ln_run_on_bpu=False): + super().__init__() + # Unchanged submodules and attributes + original = copy.deepcopy(module) + output_size = module.output_size() + self._output_size = module.output_size() + self.after_norm = module.after_norm + self.chunk_size = chunk_size + self.left_chunks = left_chunks + self.head = module.encoders[0].self_attn.h + self.layers = len(module.encoders) + + # 1. Modify submodules + self.global_cmvn = BPUGlobalCMVN(module.global_cmvn) + self.embed = BPUConv2dSubsampling8(module.embed) + self.encoders = torch.nn.ModuleList() + for layer in module.encoders: + self.encoders.append( + BPUConformerEncoderLayer(layer, chunk_size, left_chunks, ln_run_on_bpu) + ) + + # 2. Auxiliary conv + self.identity_cnncache = BPUIdentity(output_size) + + self.check_equal(original) + + def check_equal(self, module): + time1 = self.encoders[0].self_attn.chunk_size + time2 = self.encoders[0].self_attn.time + layers = self.layers + h, d_k = self.head, self.encoders[0].self_attn.d_k + decoding_window = ( + (self.chunk_size - 1) * module.embed.subsampling_rate + + module.embed.right_context + + 1 + ) + lorder = self.encoders[0].conv_module.lorder + random_x = torch.randn(1, decoding_window, 80) + att_mask = torch.ones(1, h, time1, time2) + att_cache = torch.zeros(layers, h, time2 - time1, d_k * 2) + cnn_cache = torch.zeros(layers, 1, self._output_size, lorder) + orig_x, orig_att_cache, orig_cnn_cache = module.forward_chunk( + random_x, + 0, + time2 - time1, + att_mask=att_mask[:, 0, :, :], + att_cache=att_cache, + cnn_cache=cnn_cache, + ) + random_x = random_x.unsqueeze(0) + att_cache = att_cache.reshape(1, h * layers, d_k * 2, time2 - time1) + cnn_cache = cnn_cache.reshape(1, self._output_size, layers, lorder) + new_x, new_att_cache, new_cnn_cache = self.forward( + random_x, att_cache, cnn_cache, att_mask + ) + caches = torch.split(new_att_cache, h, dim=1) + caches = [c.transpose(2, 3) for c in caches] + np.testing.assert_allclose( + to_numpy(orig_att_cache), + to_numpy(torch.cat(caches, dim=0)), + rtol=1e-02, + atol=1e-03, + ) + np.testing.assert_allclose( + to_numpy(orig_x), + to_numpy(new_x.squeeze(2).transpose(1, 2)), + rtol=1e-02, + atol=1e-03, + ) + np.testing.assert_allclose( + to_numpy(orig_cnn_cache), + to_numpy(new_cnn_cache.transpose(0, 2).transpose(1, 2)), + rtol=1e-02, + atol=1e-03, + ) + + def forward( + self, + xs: torch.Tensor, + att_cache: torch.Tensor, + cnn_cache: torch.Tensor, + att_mask: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ Forward just one chunk + + Args: + xs (torch.Tensor): chunk input, with shape (b=1, 1, time, mel-dim), + where `time == (chunk_size - 1) * subsample_rate + \ + subsample.right_context + 1` + att_cache (torch.Tensor): cache tensor for KEY & VALUE in + transformer/conformer attention, with shape + (1, head * elayers, d_k * 2, cache_t1), where + `head * d_k == hidden-dim` and + `cache_t1 == chunk_size * left_chunks`. + cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, + (1, hidden-dim, elayers, cache_t2), where + `cache_t2 == cnn.lorder - 1` + att_mask (torch.Tensor): Mask tensor for the input + (#batch, head, chunk_size, cache_t1 + chunk_size), + + Returns: + torch.Tensor: output of current input xs, + with shape (b=1, hidden-dim, 1, chunk_size). + torch.Tensor: new attention cache required for next chunk, with + same shape as the original att_cache. + torch.Tensor: new conformer cnn cache required for next chunk, with + same shape as the original cnn_cache. + """ + # xs: (B, 1, time, mel_dim) -> (B, 1, mel_dim, time) + xs = xs.transpose(2, 3) + xs = self.global_cmvn(xs) + # xs: (B, 1, mel_dim, time) -> (B, hidden_dim, 1, chunk_size) + xs = self.embed(xs) + + att_cache = torch.split(att_cache, self.head, dim=1) + cnn_cache = self.identity_cnncache(cnn_cache) + cnn_cache = torch.split(cnn_cache, 1, dim=2) + r_att_cache = [] + r_cnn_cache = [] + for i, layer in enumerate(self.encoders): + xs, new_att_cache, new_cnn_cache = layer( + xs, att_mask, att_cache=att_cache[i], cnn_cache=cnn_cache[i] + ) + r_att_cache.append(new_att_cache[:, :, :, self.chunk_size :]) + r_cnn_cache.append(new_cnn_cache) + r_att_cache = torch.cat(r_att_cache, dim=1) + r_cnn_cache = self.identity_cnncache(torch.cat(r_cnn_cache, dim=2)) + + xs = xs.squeeze(2).transpose(1, 2).contiguous() + xs = self.after_norm(xs) + # NOTE(xcsong): 4D in, 4D out to meet the requirment of CTC input. + xs = xs.transpose(1, 2).contiguous().unsqueeze(2) # (B, C, 1, T) + + return (xs, r_att_cache, r_cnn_cache) + + +class BPUCTC(torch.nn.Module): + """Refactor wenet/transformer/ctc.py::CTC""" + + def __init__(self, module): + super().__init__() + # Unchanged submodules and attributes + original = copy.deepcopy(module) + self.idim = module.ctc_lo.weight.size(1) + num_class = module.ctc_lo.weight.size(0) + + # 1. Modify self.ctc_lo, Split final projection to meet the + # requirment of maximum in/out channels (2048 for XJ3) + self.ctc_lo = torch.nn.ModuleList() + self.split_size = [] + num_split = (num_class - 1) // 2048 + 1 + for idx in range(num_split): + out_channel = min(num_class, (idx + 1) * 2048) - idx * 2048 + conv_ele = torch.nn.Conv2d(self.idim, out_channel, 1, 1) + self.ctc_lo.append(conv_ele) + self.split_size.append(out_channel) + orig_weight = torch.split(module.ctc_lo.weight, self.split_size, dim=0) + orig_bias = torch.split(module.ctc_lo.bias, self.split_size, dim=0) + for i, (w, b) in enumerate(zip(orig_weight, orig_bias)): + w = w.unsqueeze(2).unsqueeze(3) + self.ctc_lo[i].weight = torch.nn.Parameter(w) + self.ctc_lo[i].bias = torch.nn.Parameter(b) + + self.check_equal(original) + + def check_equal(self, module): + random_data = torch.randn(1, 100, self.idim) + original_result = module.ctc_lo(random_data) + random_data = random_data.transpose(1, 2).unsqueeze(2) + new_result = self.forward(random_data) + np.testing.assert_allclose( + to_numpy(original_result), + to_numpy(new_result.squeeze(2).transpose(1, 2)), + rtol=1e-02, + atol=1e-03, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """frame activations, without softmax. + + Args: + Tensor x: 4d tensor (B, hidden_dim, 1, chunk_size) + Returns: + torch.Tensor: (B, num_class, 1, chunk_size) + """ + out = [] + for i, layer in enumerate(self.ctc_lo): + out.append(layer(x)) + out = torch.cat(out, dim=1) + return out + + +def export_encoder(asr_model, args): + logger.info("Stage-1: export encoder") + decode_window, mel_dim = args.decoding_window, args.feature_size + encoder = BPUConformerEncoder( + asr_model.encoder, + args.chunk_size, + args.num_decoding_left_chunks, + args.ln_run_on_bpu, + ) + encoder.eval() + encoder_outpath = os.path.join(args.output_dir, "encoder.onnx") + + logger.info("Stage-1.1: prepare inputs for encoder") + chunk = torch.randn((1, 1, decode_window, mel_dim)) + required_cache_size = encoder.chunk_size * encoder.left_chunks + kv_time = required_cache_size + encoder.chunk_size + hidden, layers = encoder._output_size, len(encoder.encoders) + head = encoder.encoders[0].self_attn.h + d_k = hidden // head + lorder = encoder.encoders[0].conv_module.lorder + att_cache = torch.zeros(1, layers * head, d_k * 2, required_cache_size) + att_mask = torch.ones((1, head, encoder.chunk_size, kv_time)) + att_mask[:, :, :, :required_cache_size] = 0 + cnn_cache = torch.zeros((1, hidden, layers, lorder)) + inputs = (chunk, att_cache, cnn_cache, att_mask) + logger.info( + "chunk.size(): {} att_cache.size(): {} " + "cnn_cache.size(): {} att_mask.size(): {}".format( + list(chunk.size()), + list(att_cache.size()), + list(cnn_cache.size()), + list(att_mask.size()), + ) + ) + + logger.info("Stage-1.2: torch.onnx.export") + # NOTE(xcsong): Below attributes will be used in + # onnx2horizonbin.py::generate_config() + attributes = {} + attributes["input_name"] = "chunk;att_cache;cnn_cache;att_mask" + attributes["output_name"] = "output;r_att_cache;r_cnn_cache" + attributes["input_type"] = "featuremap;featuremap;featuremap;featuremap" + attributes["norm_type"] = "no_preprocess;no_preprocess;no_preprocess;no_preprocess" + attributes["input_layout_train"] = "NCHW;NCHW;NCHW;NCHW" + attributes["input_layout_rt"] = "NCHW;NCHW;NCHW;NCHW" + attributes[ + "input_shape" + ] = "{}x{}x{}x{};{}x{}x{}x{};{}x{}x{}x{};{}x{}x{}x{}".format( + chunk.size(0), + chunk.size(1), + chunk.size(2), + chunk.size(3), + att_cache.size(0), + att_cache.size(1), + att_cache.size(2), + att_cache.size(3), + cnn_cache.size(0), + cnn_cache.size(1), + cnn_cache.size(2), + cnn_cache.size(3), + att_mask.size(0), + att_mask.size(1), + att_mask.size(2), + att_mask.size(3), + ) + torch.onnx.export( # NOTE(xcsong): only support opset==11 + encoder, + inputs, + encoder_outpath, + opset_version=11, + export_params=True, + do_constant_folding=True, + input_names=attributes["input_name"].split(";"), + output_names=attributes["output_name"].split(";"), + dynamic_axes=None, + verbose=False, + ) + onnx_encoder = onnx.load(encoder_outpath) + for k in vars(args): + meta = onnx_encoder.metadata_props.add() + meta.key, meta.value = str(k), str(getattr(args, k)) + for k in attributes: + meta = onnx_encoder.metadata_props.add() + meta.key, meta.value = str(k), str(attributes[k]) + onnx.checker.check_model(onnx_encoder) + onnx.helper.printable_graph(onnx_encoder.graph) + onnx.save(onnx_encoder, encoder_outpath) + print_input_output_info(onnx_encoder, "onnx_encoder") + logger.info("Export onnx_encoder, done! see {}".format(encoder_outpath)) + + logger.info("Stage-1.3: check onnx_encoder and torch_encoder") + torch_output = [] + torch_chunk, torch_att_mask = copy.deepcopy(chunk), copy.deepcopy(att_mask) + torch_att_cache = copy.deepcopy(att_cache) + torch_cnn_cache = copy.deepcopy(cnn_cache) + for i in range(10): + logger.info( + "torch chunk-{}: {}, att_cache: {}, cnn_cache: {}" + ", att_mask: {}".format( + i, + list(torch_chunk.size()), + list(torch_att_cache.size()), + list(torch_cnn_cache.size()), + list(torch_att_mask.size()), + ) + ) + torch_att_mask[:, :, :, -(encoder.chunk_size * (i + 1)) :] = 1 + out, torch_att_cache, torch_cnn_cache = encoder( + torch_chunk, torch_att_cache, torch_cnn_cache, torch_att_mask + ) + torch_output.append(out) + torch_output = torch.cat(torch_output, dim=-1) + + onnx_output = [] + onnx_chunk, onnx_att_mask = to_numpy(chunk), to_numpy(att_mask) + onnx_att_cache = to_numpy(att_cache) + onnx_cnn_cache = to_numpy(cnn_cache) + ort_session = onnxruntime.InferenceSession(encoder_outpath) + input_names = [node.name for node in onnx_encoder.graph.input] + for i in range(10): + logger.info( + "onnx chunk-{}: {}, att_cache: {}, cnn_cache: {}," + " att_mask: {}".format( + i, + onnx_chunk.shape, + onnx_att_cache.shape, + onnx_cnn_cache.shape, + onnx_att_mask.shape, + ) + ) + onnx_att_mask[:, :, :, -(encoder.chunk_size * (i + 1)) :] = 1 + ort_inputs = { + "chunk": onnx_chunk, + "att_cache": onnx_att_cache, + "cnn_cache": onnx_cnn_cache, + "att_mask": onnx_att_mask, + } + ort_outs = ort_session.run(None, ort_inputs) + onnx_att_cache, onnx_cnn_cache = ort_outs[1], ort_outs[2] + onnx_output.append(ort_outs[0]) + onnx_output = np.concatenate(onnx_output, axis=-1) + + np.testing.assert_allclose( + to_numpy(torch_output), onnx_output, rtol=1e-03, atol=1e-04 + ) + meta = ort_session.get_modelmeta() + logger.info("custom_metadata_map={}".format(meta.custom_metadata_map)) + logger.info("Check onnx_encoder, pass!") + return encoder, ort_session + + +def export_ctc(asr_model, args): + logger.info("Stage-2: export ctc") + ctc = BPUCTC(asr_model.ctc).eval() + ctc_outpath = os.path.join(args.output_dir, "ctc.onnx") + + logger.info("Stage-2.1: prepare inputs for ctc") + hidden = torch.randn((1, args.output_size, 1, args.chunk_size)) + + logger.info("Stage-2.2: torch.onnx.export") + # NOTE(xcsong): Below attributes will be used in + # onnx2horizonbin.py::generate_config() + attributes = {} + attributes["input_name"], attributes["input_type"] = "hidden", "featuremap" + attributes["norm_type"] = "no_preprocess" + attributes["input_layout_train"] = "NCHW" + attributes["input_layout_rt"] = "NCHW" + attributes["input_shape"] = "{}x{}x{}x{}".format( + hidden.size(0), + hidden.size(1), + hidden.size(2), + hidden.size(3), + ) + torch.onnx.export( + ctc, + hidden, + ctc_outpath, + opset_version=11, + export_params=True, + do_constant_folding=True, + input_names=["hidden"], + output_names=["probs"], + dynamic_axes=None, + verbose=False, + ) + onnx_ctc = onnx.load(ctc_outpath) + for k in vars(args): + meta = onnx_ctc.metadata_props.add() + meta.key, meta.value = str(k), str(getattr(args, k)) + for k in attributes: + meta = onnx_ctc.metadata_props.add() + meta.key, meta.value = str(k), str(attributes[k]) + onnx.checker.check_model(onnx_ctc) + onnx.helper.printable_graph(onnx_ctc.graph) + onnx.save(onnx_ctc, ctc_outpath) + print_input_output_info(onnx_ctc, "onnx_ctc") + logger.info("Export onnx_ctc, done! see {}".format(ctc_outpath)) + + logger.info("Stage-2.3: check onnx_ctc and torch_ctc") + torch_output = ctc(hidden) + ort_session = onnxruntime.InferenceSession(ctc_outpath) + onnx_output = ort_session.run(None, {"hidden": to_numpy(hidden)}) + + np.testing.assert_allclose( + to_numpy(torch_output), onnx_output[0], rtol=1e-03, atol=1e-04 + ) + meta = ort_session.get_modelmeta() + logger.info("custom_metadata_map={}".format(meta.custom_metadata_map)) + logger.info("Check onnx_ctc, pass!") + return ctc, ort_session + + +def export_decoder(asr_model, args): + logger.info("Currently, Decoder is not supported.") + + +if __name__ == "__main__": + torch.manual_seed(777) + args = get_args() + args.ln_run_on_bpu = False + # NOTE(xcsong): XJ3 BPU only support static shapes + assert args.chunk_size > 0 + assert args.num_decoding_left_chunks > 0 + os.system("mkdir -p " + args.output_dir) + os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + + with open(args.config, "r") as fin: + configs = yaml.load(fin, Loader=yaml.FullLoader) + + model = init_model(configs) + load_checkpoint(model, args.checkpoint) + model.eval() + print(model) + + args.feature_size = configs["input_dim"] + args.output_size = model.encoder.output_size() + args.decoding_window = ( + (args.chunk_size - 1) * model.encoder.embed.subsampling_rate + + model.encoder.embed.right_context + + 1 + ) + + export_encoder(model, args) + export_ctc(model, args) + export_decoder(model, args) diff --git a/modules/wenet_extractor/bin/export_onnx_cpu.py b/modules/wenet_extractor/bin/export_onnx_cpu.py new file mode 100644 index 0000000000000000000000000000000000000000..9797b79e2ea6db1e6691c0656aaabc2308df4d22 --- /dev/null +++ b/modules/wenet_extractor/bin/export_onnx_cpu.py @@ -0,0 +1,500 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +from __future__ import print_function + +import argparse +import os +import copy +import sys + +import torch +import yaml +import numpy as np + +from wenet.utils.checkpoint import load_checkpoint +from wenet.utils.init_model import init_model + +try: + import onnx + import onnxruntime + from onnxruntime.quantization import quantize_dynamic, QuantType +except ImportError: + print("Please install onnx and onnxruntime!") + sys.exit(1) + + +def get_args(): + parser = argparse.ArgumentParser(description="export your script model") + parser.add_argument("--config", required=True, help="config file") + parser.add_argument("--checkpoint", required=True, help="checkpoint model") + parser.add_argument("--output_dir", required=True, help="output directory") + parser.add_argument( + "--chunk_size", required=True, type=int, help="decoding chunk size" + ) + parser.add_argument( + "--num_decoding_left_chunks", required=True, type=int, help="cache chunks" + ) + parser.add_argument( + "--reverse_weight", + default=0.5, + type=float, + help="reverse_weight in attention_rescoing", + ) + args = parser.parse_args() + return args + + +def to_numpy(tensor): + if tensor.requires_grad: + return tensor.detach().cpu().numpy() + else: + return tensor.cpu().numpy() + + +def print_input_output_info(onnx_model, name, prefix="\t\t"): + input_names = [node.name for node in onnx_model.graph.input] + input_shapes = [ + [d.dim_value for d in node.type.tensor_type.shape.dim] + for node in onnx_model.graph.input + ] + output_names = [node.name for node in onnx_model.graph.output] + output_shapes = [ + [d.dim_value for d in node.type.tensor_type.shape.dim] + for node in onnx_model.graph.output + ] + print("{}{} inputs : {}".format(prefix, name, input_names)) + print("{}{} input shapes : {}".format(prefix, name, input_shapes)) + print("{}{} outputs: {}".format(prefix, name, output_names)) + print("{}{} output shapes : {}".format(prefix, name, output_shapes)) + + +def export_encoder(asr_model, args): + print("Stage-1: export encoder") + encoder = asr_model.encoder + encoder.forward = encoder.forward_chunk + encoder_outpath = os.path.join(args["output_dir"], "encoder.onnx") + + print("\tStage-1.1: prepare inputs for encoder") + chunk = torch.randn((args["batch"], args["decoding_window"], args["feature_size"])) + offset = 0 + # NOTE(xcsong): The uncertainty of `next_cache_start` only appears + # in the first few chunks, this is caused by dynamic att_cache shape, i,e + # (0, 0, 0, 0) for 1st chunk and (elayers, head, ?, d_k*2) for subsequent + # chunks. One way to ease the ONNX export is to keep `next_cache_start` + # as a fixed value. To do this, for the **first** chunk, if + # left_chunks > 0, we feed real cache & real mask to the model, otherwise + # fake cache & fake mask. In this way, we get: + # 1. 16/-1 mode: next_cache_start == 0 for all chunks + # 2. 16/4 mode: next_cache_start == chunk_size for all chunks + # 3. 16/0 mode: next_cache_start == chunk_size for all chunks + # 4. -1/-1 mode: next_cache_start == 0 for all chunks + # NO MORE DYNAMIC CHANGES!! + # + # NOTE(Mddct): We retain the current design for the convenience of supporting some + # inference frameworks without dynamic shapes. If you're interested in all-in-one + # model that supports different chunks please see: + # https://github.com/wenet-e2e/wenet/pull/1174 + + if args["left_chunks"] > 0: # 16/4 + required_cache_size = args["chunk_size"] * args["left_chunks"] + offset = required_cache_size + # Real cache + att_cache = torch.zeros( + ( + args["num_blocks"], + args["head"], + required_cache_size, + args["output_size"] // args["head"] * 2, + ) + ) + # Real mask + att_mask = torch.ones( + (args["batch"], 1, required_cache_size + args["chunk_size"]), + dtype=torch.bool, + ) + att_mask[:, :, :required_cache_size] = 0 + elif args["left_chunks"] <= 0: # 16/-1, -1/-1, 16/0 + required_cache_size = -1 if args["left_chunks"] < 0 else 0 + # Fake cache + att_cache = torch.zeros( + ( + args["num_blocks"], + args["head"], + 0, + args["output_size"] // args["head"] * 2, + ) + ) + # Fake mask + att_mask = torch.ones((0, 0, 0), dtype=torch.bool) + cnn_cache = torch.zeros( + ( + args["num_blocks"], + args["batch"], + args["output_size"], + args["cnn_module_kernel"] - 1, + ) + ) + inputs = (chunk, offset, required_cache_size, att_cache, cnn_cache, att_mask) + print( + "\t\tchunk.size(): {}\n".format(chunk.size()), + "\t\toffset: {}\n".format(offset), + "\t\trequired_cache: {}\n".format(required_cache_size), + "\t\tatt_cache.size(): {}\n".format(att_cache.size()), + "\t\tcnn_cache.size(): {}\n".format(cnn_cache.size()), + "\t\tatt_mask.size(): {}\n".format(att_mask.size()), + ) + + print("\tStage-1.2: torch.onnx.export") + dynamic_axes = { + "chunk": {1: "T"}, + "att_cache": {2: "T_CACHE"}, + "att_mask": {2: "T_ADD_T_CACHE"}, + "output": {1: "T"}, + "r_att_cache": {2: "T_CACHE"}, + } + # NOTE(xcsong): We keep dynamic axes even if in 16/4 mode, this is + # to avoid padding the last chunk (which usually contains less + # frames than required). For users who want static axes, just pop + # out specific axis. + # if args['chunk_size'] > 0: # 16/4, 16/-1, 16/0 + # dynamic_axes.pop('chunk') + # dynamic_axes.pop('output') + # if args['left_chunks'] >= 0: # 16/4, 16/0 + # # NOTE(xsong): since we feed real cache & real mask into the + # # model when left_chunks > 0, the shape of cache will never + # # be changed. + # dynamic_axes.pop('att_cache') + # dynamic_axes.pop('r_att_cache') + torch.onnx.export( + encoder, + inputs, + encoder_outpath, + opset_version=13, + export_params=True, + do_constant_folding=True, + input_names=[ + "chunk", + "offset", + "required_cache_size", + "att_cache", + "cnn_cache", + "att_mask", + ], + output_names=["output", "r_att_cache", "r_cnn_cache"], + dynamic_axes=dynamic_axes, + verbose=False, + ) + onnx_encoder = onnx.load(encoder_outpath) + for k, v in args.items(): + meta = onnx_encoder.metadata_props.add() + meta.key, meta.value = str(k), str(v) + onnx.checker.check_model(onnx_encoder) + onnx.helper.printable_graph(onnx_encoder.graph) + # NOTE(xcsong): to add those metadatas we need to reopen + # the file and resave it. + onnx.save(onnx_encoder, encoder_outpath) + print_input_output_info(onnx_encoder, "onnx_encoder") + # Dynamic quantization + model_fp32 = encoder_outpath + model_quant = os.path.join(args["output_dir"], "encoder.quant.onnx") + quantize_dynamic(model_fp32, model_quant, weight_type=QuantType.QUInt8) + print("\t\tExport onnx_encoder, done! see {}".format(encoder_outpath)) + + print("\tStage-1.3: check onnx_encoder and torch_encoder") + torch_output = [] + torch_chunk = copy.deepcopy(chunk) + torch_offset = copy.deepcopy(offset) + torch_required_cache_size = copy.deepcopy(required_cache_size) + torch_att_cache = copy.deepcopy(att_cache) + torch_cnn_cache = copy.deepcopy(cnn_cache) + torch_att_mask = copy.deepcopy(att_mask) + for i in range(10): + print( + "\t\ttorch chunk-{}: {}, offset: {}, att_cache: {}," + " cnn_cache: {}, att_mask: {}".format( + i, + list(torch_chunk.size()), + torch_offset, + list(torch_att_cache.size()), + list(torch_cnn_cache.size()), + list(torch_att_mask.size()), + ) + ) + # NOTE(xsong): att_mask of the first few batches need changes if + # we use 16/4 mode. + if args["left_chunks"] > 0: # 16/4 + torch_att_mask[:, :, -(args["chunk_size"] * (i + 1)) :] = 1 + out, torch_att_cache, torch_cnn_cache = encoder( + torch_chunk, + torch_offset, + torch_required_cache_size, + torch_att_cache, + torch_cnn_cache, + torch_att_mask, + ) + torch_output.append(out) + torch_offset += out.size(1) + torch_output = torch.cat(torch_output, dim=1) + + onnx_output = [] + onnx_chunk = to_numpy(chunk) + onnx_offset = np.array((offset)).astype(np.int64) + onnx_required_cache_size = np.array((required_cache_size)).astype(np.int64) + onnx_att_cache = to_numpy(att_cache) + onnx_cnn_cache = to_numpy(cnn_cache) + onnx_att_mask = to_numpy(att_mask) + ort_session = onnxruntime.InferenceSession(encoder_outpath) + input_names = [node.name for node in onnx_encoder.graph.input] + for i in range(10): + print( + "\t\tonnx chunk-{}: {}, offset: {}, att_cache: {}," + " cnn_cache: {}, att_mask: {}".format( + i, + onnx_chunk.shape, + onnx_offset, + onnx_att_cache.shape, + onnx_cnn_cache.shape, + onnx_att_mask.shape, + ) + ) + # NOTE(xsong): att_mask of the first few batches need changes if + # we use 16/4 mode. + if args["left_chunks"] > 0: # 16/4 + onnx_att_mask[:, :, -(args["chunk_size"] * (i + 1)) :] = 1 + ort_inputs = { + "chunk": onnx_chunk, + "offset": onnx_offset, + "required_cache_size": onnx_required_cache_size, + "att_cache": onnx_att_cache, + "cnn_cache": onnx_cnn_cache, + "att_mask": onnx_att_mask, + } + # NOTE(xcsong): If we use 16/-1, -1/-1 or 16/0 mode, `next_cache_start` + # will be hardcoded to 0 or chunk_size by ONNX, thus + # required_cache_size and att_mask are no more needed and they will + # be removed by ONNX automatically. + for k in list(ort_inputs): + if k not in input_names: + ort_inputs.pop(k) + ort_outs = ort_session.run(None, ort_inputs) + onnx_att_cache, onnx_cnn_cache = ort_outs[1], ort_outs[2] + onnx_output.append(ort_outs[0]) + onnx_offset += ort_outs[0].shape[1] + onnx_output = np.concatenate(onnx_output, axis=1) + + np.testing.assert_allclose( + to_numpy(torch_output), onnx_output, rtol=1e-03, atol=1e-05 + ) + meta = ort_session.get_modelmeta() + print("\t\tcustom_metadata_map={}".format(meta.custom_metadata_map)) + print("\t\tCheck onnx_encoder, pass!") + + +def export_ctc(asr_model, args): + print("Stage-2: export ctc") + ctc = asr_model.ctc + ctc.forward = ctc.log_softmax + ctc_outpath = os.path.join(args["output_dir"], "ctc.onnx") + + print("\tStage-2.1: prepare inputs for ctc") + hidden = torch.randn( + ( + args["batch"], + args["chunk_size"] if args["chunk_size"] > 0 else 16, + args["output_size"], + ) + ) + + print("\tStage-2.2: torch.onnx.export") + dynamic_axes = {"hidden": {1: "T"}, "probs": {1: "T"}} + torch.onnx.export( + ctc, + hidden, + ctc_outpath, + opset_version=13, + export_params=True, + do_constant_folding=True, + input_names=["hidden"], + output_names=["probs"], + dynamic_axes=dynamic_axes, + verbose=False, + ) + onnx_ctc = onnx.load(ctc_outpath) + for k, v in args.items(): + meta = onnx_ctc.metadata_props.add() + meta.key, meta.value = str(k), str(v) + onnx.checker.check_model(onnx_ctc) + onnx.helper.printable_graph(onnx_ctc.graph) + onnx.save(onnx_ctc, ctc_outpath) + print_input_output_info(onnx_ctc, "onnx_ctc") + # Dynamic quantization + model_fp32 = ctc_outpath + model_quant = os.path.join(args["output_dir"], "ctc.quant.onnx") + quantize_dynamic(model_fp32, model_quant, weight_type=QuantType.QUInt8) + print("\t\tExport onnx_ctc, done! see {}".format(ctc_outpath)) + + print("\tStage-2.3: check onnx_ctc and torch_ctc") + torch_output = ctc(hidden) + ort_session = onnxruntime.InferenceSession(ctc_outpath) + onnx_output = ort_session.run(None, {"hidden": to_numpy(hidden)}) + + np.testing.assert_allclose( + to_numpy(torch_output), onnx_output[0], rtol=1e-03, atol=1e-05 + ) + print("\t\tCheck onnx_ctc, pass!") + + +def export_decoder(asr_model, args): + print("Stage-3: export decoder") + decoder = asr_model + # NOTE(lzhin): parameters of encoder will be automatically removed + # since they are not used during rescoring. + decoder.forward = decoder.forward_attention_decoder + decoder_outpath = os.path.join(args["output_dir"], "decoder.onnx") + + print("\tStage-3.1: prepare inputs for decoder") + # hardcode time->200 nbest->10 len->20, they are dynamic axes. + encoder_out = torch.randn((1, 200, args["output_size"])) + hyps = torch.randint(low=0, high=args["vocab_size"], size=[10, 20]) + hyps[:, 0] = args["vocab_size"] - 1 # + hyps_lens = torch.randint(low=15, high=21, size=[10]) + + print("\tStage-3.2: torch.onnx.export") + dynamic_axes = { + "hyps": {0: "NBEST", 1: "L"}, + "hyps_lens": {0: "NBEST"}, + "encoder_out": {1: "T"}, + "score": {0: "NBEST", 1: "L"}, + "r_score": {0: "NBEST", 1: "L"}, + } + inputs = (hyps, hyps_lens, encoder_out, args["reverse_weight"]) + torch.onnx.export( + decoder, + inputs, + decoder_outpath, + opset_version=13, + export_params=True, + do_constant_folding=True, + input_names=["hyps", "hyps_lens", "encoder_out", "reverse_weight"], + output_names=["score", "r_score"], + dynamic_axes=dynamic_axes, + verbose=False, + ) + onnx_decoder = onnx.load(decoder_outpath) + for k, v in args.items(): + meta = onnx_decoder.metadata_props.add() + meta.key, meta.value = str(k), str(v) + onnx.checker.check_model(onnx_decoder) + onnx.helper.printable_graph(onnx_decoder.graph) + onnx.save(onnx_decoder, decoder_outpath) + print_input_output_info(onnx_decoder, "onnx_decoder") + model_fp32 = decoder_outpath + model_quant = os.path.join(args["output_dir"], "decoder.quant.onnx") + quantize_dynamic(model_fp32, model_quant, weight_type=QuantType.QUInt8) + print("\t\tExport onnx_decoder, done! see {}".format(decoder_outpath)) + + print("\tStage-3.3: check onnx_decoder and torch_decoder") + torch_score, torch_r_score = decoder( + hyps, hyps_lens, encoder_out, args["reverse_weight"] + ) + ort_session = onnxruntime.InferenceSession(decoder_outpath) + input_names = [node.name for node in onnx_decoder.graph.input] + ort_inputs = { + "hyps": to_numpy(hyps), + "hyps_lens": to_numpy(hyps_lens), + "encoder_out": to_numpy(encoder_out), + "reverse_weight": np.array((args["reverse_weight"])), + } + for k in list(ort_inputs): + if k not in input_names: + ort_inputs.pop(k) + onnx_output = ort_session.run(None, ort_inputs) + + np.testing.assert_allclose( + to_numpy(torch_score), onnx_output[0], rtol=1e-03, atol=1e-05 + ) + if args["is_bidirectional_decoder"] and args["reverse_weight"] > 0.0: + np.testing.assert_allclose( + to_numpy(torch_r_score), onnx_output[1], rtol=1e-03, atol=1e-05 + ) + print("\t\tCheck onnx_decoder, pass!") + + +def main(): + torch.manual_seed(777) + args = get_args() + output_dir = args.output_dir + os.system("mkdir -p " + output_dir) + os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + + with open(args.config, "r") as fin: + configs = yaml.load(fin, Loader=yaml.FullLoader) + + model = init_model(configs) + load_checkpoint(model, args.checkpoint) + model.eval() + print(model) + + arguments = {} + arguments["output_dir"] = output_dir + arguments["batch"] = 1 + arguments["chunk_size"] = args.chunk_size + arguments["left_chunks"] = args.num_decoding_left_chunks + arguments["reverse_weight"] = args.reverse_weight + arguments["output_size"] = configs["encoder_conf"]["output_size"] + arguments["num_blocks"] = configs["encoder_conf"]["num_blocks"] + arguments["cnn_module_kernel"] = configs["encoder_conf"].get("cnn_module_kernel", 1) + arguments["head"] = configs["encoder_conf"]["attention_heads"] + arguments["feature_size"] = configs["input_dim"] + arguments["vocab_size"] = configs["output_dim"] + # NOTE(xcsong): if chunk_size == -1, hardcode to 67 + arguments["decoding_window"] = ( + (args.chunk_size - 1) * model.encoder.embed.subsampling_rate + + model.encoder.embed.right_context + + 1 + if args.chunk_size > 0 + else 67 + ) + arguments["encoder"] = configs["encoder"] + arguments["decoder"] = configs["decoder"] + arguments["subsampling_rate"] = model.subsampling_rate() + arguments["right_context"] = model.right_context() + arguments["sos_symbol"] = model.sos_symbol() + arguments["eos_symbol"] = model.eos_symbol() + arguments["is_bidirectional_decoder"] = 1 if model.is_bidirectional_decoder() else 0 + + # NOTE(xcsong): Please note that -1/-1 means non-streaming model! It is + # not a [16/4 16/-1 16/0] all-in-one model and it should not be used in + # streaming mode (i.e., setting chunk_size=16 in `decoder_main`). If you + # want to use 16/-1 or any other streaming mode in `decoder_main`, + # please export onnx in the same config. + if arguments["left_chunks"] > 0: + assert arguments["chunk_size"] > 0 # -1/4 not supported + + export_encoder(model, arguments) + export_ctc(model, arguments) + export_decoder(model, arguments) + + +if __name__ == "__main__": + main() diff --git a/modules/wenet_extractor/bin/export_onnx_gpu.py b/modules/wenet_extractor/bin/export_onnx_gpu.py new file mode 100644 index 0000000000000000000000000000000000000000..0b3d9be24633680cbeac1e2261e68bb7cabdffcb --- /dev/null +++ b/modules/wenet_extractor/bin/export_onnx_gpu.py @@ -0,0 +1,1121 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +from __future__ import print_function + +import argparse +import os +import sys + +import torch +import yaml +import logging + +import torch.nn.functional as F +from wenet.utils.checkpoint import load_checkpoint +from wenet.transformer.ctc import CTC +from wenet.transformer.decoder import TransformerDecoder +from wenet.transformer.encoder import BaseEncoder +from wenet.utils.init_model import init_model +from wenet.utils.mask import make_pad_mask + +try: + import onnxruntime +except ImportError: + print("Please install onnxruntime-gpu!") + sys.exit(1) + +logger = logging.getLogger(__file__) +logger.setLevel(logging.INFO) + + +class Encoder(torch.nn.Module): + def __init__(self, encoder: BaseEncoder, ctc: CTC, beam_size: int = 10): + super().__init__() + self.encoder = encoder + self.ctc = ctc + self.beam_size = beam_size + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + ): + """Encoder + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + Returns: + encoder_out: B x T x F + encoder_out_lens: B + ctc_log_probs: B x T x V + beam_log_probs: B x T x beam_size + beam_log_probs_idx: B x T x beam_size + """ + encoder_out, encoder_mask = self.encoder(speech, speech_lengths, -1, -1) + encoder_out_lens = encoder_mask.squeeze(1).sum(1) + ctc_log_probs = self.ctc.log_softmax(encoder_out) + encoder_out_lens = encoder_out_lens.int() + beam_log_probs, beam_log_probs_idx = torch.topk( + ctc_log_probs, self.beam_size, dim=2 + ) + return ( + encoder_out, + encoder_out_lens, + ctc_log_probs, + beam_log_probs, + beam_log_probs_idx, + ) + + +class StreamingEncoder(torch.nn.Module): + def __init__(self, model, required_cache_size, beam_size, transformer=False): + super().__init__() + self.ctc = model.ctc + self.subsampling_rate = model.encoder.embed.subsampling_rate + self.embed = model.encoder.embed + self.global_cmvn = model.encoder.global_cmvn + self.required_cache_size = required_cache_size + self.beam_size = beam_size + self.encoder = model.encoder + self.transformer = transformer + + def forward(self, chunk_xs, chunk_lens, offset, att_cache, cnn_cache, cache_mask): + """Streaming Encoder + Args: + xs (torch.Tensor): chunk input, with shape (b, time, mel-dim), + where `time == (chunk_size - 1) * subsample_rate + \ + subsample.right_context + 1` + offset (torch.Tensor): offset with shape (b, 1) + 1 is retained for triton deployment + required_cache_size (int): cache size required for next chunk + compuation + > 0: actual cache size + <= 0: not allowed in streaming gpu encoder ` + att_cache (torch.Tensor): cache tensor for KEY & VALUE in + transformer/conformer attention, with shape + (b, elayers, head, cache_t1, d_k * 2), where + `head * d_k == hidden-dim` and + `cache_t1 == chunk_size * num_decoding_left_chunks`. + cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, + (b, elayers, b, hidden-dim, cache_t2), where + `cache_t2 == cnn.lorder - 1` + cache_mask: (torch.Tensor): cache mask with shape (b, required_cache_size) + in a batch of request, each request may have different + history cache. Cache mask is used to indidate the effective + cache for each request + Returns: + torch.Tensor: log probabilities of ctc output and cutoff by beam size + with shape (b, chunk_size, beam) + torch.Tensor: index of top beam size probabilities for each timestep + with shape (b, chunk_size, beam) + torch.Tensor: output of current input xs, + with shape (b, chunk_size, hidden-dim). + torch.Tensor: new attention cache required for next chunk, with + same shape (b, elayers, head, cache_t1, d_k * 2) + as the original att_cache + torch.Tensor: new conformer cnn cache required for next chunk, with + same shape as the original cnn_cache. + torch.Tensor: new cache mask, with same shape as the original + cache mask + """ + offset = offset.squeeze(1) + T = chunk_xs.size(1) + chunk_mask = ~make_pad_mask(chunk_lens, T).unsqueeze(1) + # B X 1 X T + chunk_mask = chunk_mask.to(chunk_xs.dtype) + # transpose batch & num_layers dim + att_cache = torch.transpose(att_cache, 0, 1) + cnn_cache = torch.transpose(cnn_cache, 0, 1) + + # rewrite encoder.forward_chunk + # <---------forward_chunk START---------> + xs = self.global_cmvn(chunk_xs) + # chunk mask is important for batch inferencing since + # different sequence in a batch has different length + xs, pos_emb, chunk_mask = self.embed(xs, chunk_mask, offset) + cache_size = att_cache.size(3) # required cache size + masks = torch.cat((cache_mask, chunk_mask), dim=2) + index = offset - cache_size + + pos_emb = self.embed.position_encoding(index, cache_size + xs.size(1)) + pos_emb = pos_emb.to(dtype=xs.dtype) + + next_cache_start = -self.required_cache_size + r_cache_mask = masks[:, :, next_cache_start:] + + r_att_cache = [] + r_cnn_cache = [] + for i, layer in enumerate(self.encoder.encoders): + xs, _, new_att_cache, new_cnn_cache = layer( + xs, masks, pos_emb, att_cache=att_cache[i], cnn_cache=cnn_cache[i] + ) + # shape(new_att_cache) is (B, head, attention_key_size, d_k * 2), + # shape(new_cnn_cache) is (B, hidden-dim, cache_t2) + r_att_cache.append(new_att_cache[:, :, next_cache_start:, :].unsqueeze(1)) + if not self.transformer: + r_cnn_cache.append(new_cnn_cache.unsqueeze(1)) + if self.encoder.normalize_before: + chunk_out = self.encoder.after_norm(xs) + else: + chunk_out = xs + + r_att_cache = torch.cat(r_att_cache, dim=1) # concat on layers idx + if not self.transformer: + r_cnn_cache = torch.cat(r_cnn_cache, dim=1) # concat on layers + + # <---------forward_chunk END---------> + + log_ctc_probs = self.ctc.log_softmax(chunk_out) + log_probs, log_probs_idx = torch.topk(log_ctc_probs, self.beam_size, dim=2) + log_probs = log_probs.to(chunk_xs.dtype) + + r_offset = offset + chunk_out.shape[1] + # the below ops not supported in Tensorrt + # chunk_out_lens = torch.div(chunk_lens, subsampling_rate, + # rounding_mode='floor') + chunk_out_lens = chunk_lens // self.subsampling_rate + r_offset = r_offset.unsqueeze(1) + + return ( + log_probs, + log_probs_idx, + chunk_out, + chunk_out_lens, + r_offset, + r_att_cache, + r_cnn_cache, + r_cache_mask, + ) + + +class StreamingSqueezeformerEncoder(torch.nn.Module): + def __init__(self, model, required_cache_size, beam_size): + super().__init__() + self.ctc = model.ctc + self.subsampling_rate = model.encoder.embed.subsampling_rate + self.embed = model.encoder.embed + self.global_cmvn = model.encoder.global_cmvn + self.required_cache_size = required_cache_size + self.beam_size = beam_size + self.encoder = model.encoder + self.reduce_idx = model.encoder.reduce_idx + self.recover_idx = model.encoder.recover_idx + if self.reduce_idx is None: + self.time_reduce = None + else: + if self.recover_idx is None: + self.time_reduce = "normal" # no recovery at the end + else: + self.time_reduce = "recover" # recovery at the end + assert len(self.reduce_idx) == len(self.recover_idx) + + def calculate_downsampling_factor(self, i: int) -> int: + if self.reduce_idx is None: + return 1 + else: + reduce_exp, recover_exp = 0, 0 + for exp, rd_idx in enumerate(self.reduce_idx): + if i >= rd_idx: + reduce_exp = exp + 1 + if self.recover_idx is not None: + for exp, rc_idx in enumerate(self.recover_idx): + if i >= rc_idx: + recover_exp = exp + 1 + return int(2 ** (reduce_exp - recover_exp)) + + def forward(self, chunk_xs, chunk_lens, offset, att_cache, cnn_cache, cache_mask): + """Streaming Encoder + Args: + xs (torch.Tensor): chunk input, with shape (b, time, mel-dim), + where `time == (chunk_size - 1) * subsample_rate + \ + subsample.right_context + 1` + offset (torch.Tensor): offset with shape (b, 1) + 1 is retained for triton deployment + required_cache_size (int): cache size required for next chunk + compuation + > 0: actual cache size + <= 0: not allowed in streaming gpu encoder ` + att_cache (torch.Tensor): cache tensor for KEY & VALUE in + transformer/conformer attention, with shape + (b, elayers, head, cache_t1, d_k * 2), where + `head * d_k == hidden-dim` and + `cache_t1 == chunk_size * num_decoding_left_chunks`. + cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, + (b, elayers, b, hidden-dim, cache_t2), where + `cache_t2 == cnn.lorder - 1` + cache_mask: (torch.Tensor): cache mask with shape (b, required_cache_size) + in a batch of request, each request may have different + history cache. Cache mask is used to indidate the effective + cache for each request + Returns: + torch.Tensor: log probabilities of ctc output and cutoff by beam size + with shape (b, chunk_size, beam) + torch.Tensor: index of top beam size probabilities for each timestep + with shape (b, chunk_size, beam) + torch.Tensor: output of current input xs, + with shape (b, chunk_size, hidden-dim). + torch.Tensor: new attention cache required for next chunk, with + same shape (b, elayers, head, cache_t1, d_k * 2) + as the original att_cache + torch.Tensor: new conformer cnn cache required for next chunk, with + same shape as the original cnn_cache. + torch.Tensor: new cache mask, with same shape as the original + cache mask + """ + offset = offset.squeeze(1) + T = chunk_xs.size(1) + chunk_mask = ~make_pad_mask(chunk_lens, T).unsqueeze(1) + # B X 1 X T + chunk_mask = chunk_mask.to(chunk_xs.dtype) + # transpose batch & num_layers dim + att_cache = torch.transpose(att_cache, 0, 1) + cnn_cache = torch.transpose(cnn_cache, 0, 1) + + # rewrite encoder.forward_chunk + # <---------forward_chunk START---------> + xs = self.global_cmvn(chunk_xs) + # chunk mask is important for batch inferencing since + # different sequence in a batch has different length + xs, pos_emb, chunk_mask = self.embed(xs, chunk_mask, offset) + elayers, cache_size = att_cache.size(0), att_cache.size(3) + att_mask = torch.cat((cache_mask, chunk_mask), dim=2) + index = offset - cache_size + + pos_emb = self.embed.position_encoding(index, cache_size + xs.size(1)) + pos_emb = pos_emb.to(dtype=xs.dtype) + + next_cache_start = -self.required_cache_size + r_cache_mask = att_mask[:, :, next_cache_start:] + + r_att_cache = [] + r_cnn_cache = [] + mask_pad = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool) + mask_pad = mask_pad.unsqueeze(1) + max_att_len: int = 0 + recover_activations: List[ + Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor] + ] = [] + index = 0 + xs_lens = torch.tensor([xs.size(1)], device=xs.device, dtype=torch.int) + xs = self.encoder.preln(xs) + for i, layer in enumerate(self.encoder.encoders): + if self.reduce_idx is not None: + if self.time_reduce is not None and i in self.reduce_idx: + recover_activations.append((xs, att_mask, pos_emb, mask_pad)) + xs, xs_lens, att_mask, mask_pad = self.encoder.time_reduction_layer( + xs, xs_lens, att_mask, mask_pad + ) + pos_emb = pos_emb[:, ::2, :] + if self.encoder.pos_enc_layer_type == "rel_pos_repaired": + pos_emb = pos_emb[:, : xs.size(1) * 2 - 1, :] + index += 1 + + if self.recover_idx is not None: + if self.time_reduce == "recover" and i in self.recover_idx: + index -= 1 + ( + recover_tensor, + recover_att_mask, + recover_pos_emb, + recover_mask_pad, + ) = recover_activations[index] + # recover output length for ctc decode + xs = xs.unsqueeze(2).repeat(1, 1, 2, 1).flatten(1, 2) + xs = self.encoder.time_recover_layer(xs) + recoverd_t = recover_tensor.size(1) + xs = recover_tensor + xs[:, :recoverd_t, :].contiguous() + att_mask = recover_att_mask + pos_emb = recover_pos_emb + mask_pad = recover_mask_pad + + factor = self.calculate_downsampling_factor(i) + + xs, _, new_att_cache, new_cnn_cache = layer( + xs, + att_mask, + pos_emb, + att_cache=att_cache[i][:, :, ::factor, :][ + :, :, : pos_emb.size(1) - xs.size(1), : + ] + if elayers > 0 + else att_cache[:, :, ::factor, :], + cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache, + ) + cached_att = new_att_cache[:, :, next_cache_start // factor :, :] + cached_cnn = new_cnn_cache.unsqueeze(1) + cached_att = ( + cached_att.unsqueeze(3).repeat(1, 1, 1, factor, 1).flatten(2, 3) + ) + if i == 0: + # record length for the first block as max length + max_att_len = cached_att.size(2) + r_att_cache.append(cached_att[:, :, :max_att_len, :].unsqueeze(1)) + r_cnn_cache.append(cached_cnn) + + chunk_out = xs + r_att_cache = torch.cat(r_att_cache, dim=1) # concat on layers idx + r_cnn_cache = torch.cat(r_cnn_cache, dim=1) # concat on layers + + # <---------forward_chunk END---------> + + log_ctc_probs = self.ctc.log_softmax(chunk_out) + log_probs, log_probs_idx = torch.topk(log_ctc_probs, self.beam_size, dim=2) + log_probs = log_probs.to(chunk_xs.dtype) + + r_offset = offset + chunk_out.shape[1] + # the below ops not supported in Tensorrt + # chunk_out_lens = torch.div(chunk_lens, subsampling_rate, + # rounding_mode='floor') + chunk_out_lens = chunk_lens // self.subsampling_rate + r_offset = r_offset.unsqueeze(1) + + return ( + log_probs, + log_probs_idx, + chunk_out, + chunk_out_lens, + r_offset, + r_att_cache, + r_cnn_cache, + r_cache_mask, + ) + + +class StreamingEfficientConformerEncoder(torch.nn.Module): + def __init__(self, model, required_cache_size, beam_size): + super().__init__() + self.ctc = model.ctc + self.subsampling_rate = model.encoder.embed.subsampling_rate + self.embed = model.encoder.embed + self.global_cmvn = model.encoder.global_cmvn + self.required_cache_size = required_cache_size + self.beam_size = beam_size + self.encoder = model.encoder + + # Efficient Conformer + self.stride_layer_idx = model.encoder.stride_layer_idx + self.stride = model.encoder.stride + self.num_blocks = model.encoder.num_blocks + self.cnn_module_kernel = model.encoder.cnn_module_kernel + + def calculate_downsampling_factor(self, i: int) -> int: + factor = 1 + for idx, stride_idx in enumerate(self.stride_layer_idx): + if i > stride_idx: + factor *= self.stride[idx] + return factor + + def forward(self, chunk_xs, chunk_lens, offset, att_cache, cnn_cache, cache_mask): + """Streaming Encoder + Args: + chunk_xs (torch.Tensor): chunk input, with shape (b, time, mel-dim), + where `time == (chunk_size - 1) * subsample_rate + \ + subsample.right_context + 1` + chunk_lens (torch.Tensor): + offset (torch.Tensor): offset with shape (b, 1) + 1 is retained for triton deployment + att_cache (torch.Tensor): cache tensor for KEY & VALUE in + transformer/conformer attention, with shape + (b, elayers, head, cache_t1, d_k * 2), where + `head * d_k == hidden-dim` and + `cache_t1 == chunk_size * num_decoding_left_chunks`. + cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, + (b, elayers, hidden-dim, cache_t2), where + `cache_t2 == cnn.lorder - 1` + cache_mask: (torch.Tensor): cache mask with shape (b, required_cache_size) + in a batch of request, each request may have different + history cache. Cache mask is used to indidate the effective + cache for each request + Returns: + torch.Tensor: log probabilities of ctc output and cutoff by beam size + with shape (b, chunk_size, beam) + torch.Tensor: index of top beam size probabilities for each timestep + with shape (b, chunk_size, beam) + torch.Tensor: output of current input xs, + with shape (b, chunk_size, hidden-dim). + torch.Tensor: new attention cache required for next chunk, with + same shape (b, elayers, head, cache_t1, d_k * 2) + as the original att_cache + torch.Tensor: new conformer cnn cache required for next chunk, with + same shape as the original cnn_cache. + torch.Tensor: new cache mask, with same shape as the original + cache mask + """ + offset = offset.squeeze(1) # (b, ) + offset *= self.calculate_downsampling_factor(self.num_blocks + 1) + + T = chunk_xs.size(1) + chunk_mask = ~make_pad_mask(chunk_lens, T).unsqueeze(1) # (b, 1, T) + # B X 1 X T + chunk_mask = chunk_mask.to(chunk_xs.dtype) + # transpose batch & num_layers dim + # Shape(att_cache): (elayers, b, head, cache_t1, d_k * 2) + # Shape(cnn_cache): (elayers, b, outsize, cnn_kernel) + att_cache = torch.transpose(att_cache, 0, 1) + cnn_cache = torch.transpose(cnn_cache, 0, 1) + + # rewrite encoder.forward_chunk + # <---------forward_chunk START---------> + xs = self.global_cmvn(chunk_xs) + # chunk mask is important for batch inferencing since + # different sequence in a batch has different length + xs, pos_emb, chunk_mask = self.embed(xs, chunk_mask, offset) + cache_size = att_cache.size(3) # required cache size + masks = torch.cat((cache_mask, chunk_mask), dim=2) + att_mask = torch.cat((cache_mask, chunk_mask), dim=2) + index = offset - cache_size + + pos_emb = self.embed.position_encoding(index, cache_size + xs.size(1)) + pos_emb = pos_emb.to(dtype=xs.dtype) + + next_cache_start = -self.required_cache_size + r_cache_mask = masks[:, :, next_cache_start:] + + r_att_cache = [] + r_cnn_cache = [] + mask_pad = chunk_mask.to(torch.bool) + max_att_len, max_cnn_len = 0, 0 # for repeat_interleave of new_att_cache + for i, layer in enumerate(self.encoder.encoders): + factor = self.calculate_downsampling_factor(i) + # NOTE(xcsong): Before layer.forward + # shape(att_cache[i:i + 1]) is (b, head, cache_t1, d_k * 2), + # shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2) + # shape(new_att_cache) = [ batch, head, time2, outdim//head * 2 ] + att_cache_trunc = 0 + if xs.size(1) + att_cache.size(3) / factor > pos_emb.size(1): + # The time step is not divisible by the downsampling multiple + # We propose to double the chunk_size. + att_cache_trunc = ( + xs.size(1) + att_cache.size(3) // factor - pos_emb.size(1) + 1 + ) + xs, _, new_att_cache, new_cnn_cache = layer( + xs, + att_mask, + pos_emb, + mask_pad=mask_pad, + att_cache=att_cache[i][:, :, ::factor, :][:, :, att_cache_trunc:, :], + cnn_cache=cnn_cache[i, :, :, :] if cnn_cache.size(0) > 0 else cnn_cache, + ) + + if i in self.stride_layer_idx: + # compute time dimension for next block + efficient_index = self.stride_layer_idx.index(i) + att_mask = att_mask[ + :, :: self.stride[efficient_index], :: self.stride[efficient_index] + ] + mask_pad = mask_pad[ + :, :: self.stride[efficient_index], :: self.stride[efficient_index] + ] + pos_emb = pos_emb[:, :: self.stride[efficient_index], :] + + # shape(new_att_cache) = [batch, head, time2, outdim] + new_att_cache = new_att_cache[:, :, next_cache_start // factor :, :] + # shape(new_cnn_cache) = [batch, 1, outdim, cache_t2] + new_cnn_cache = new_cnn_cache.unsqueeze(1) # shape(1):layerID + + # use repeat_interleave to new_att_cache + # new_att_cache = new_att_cache.repeat_interleave(repeats=factor, dim=2) + new_att_cache = ( + new_att_cache.unsqueeze(3).repeat(1, 1, 1, factor, 1).flatten(2, 3) + ) + # padding new_cnn_cache to cnn.lorder for casual convolution + new_cnn_cache = F.pad( + new_cnn_cache, (self.cnn_module_kernel - 1 - new_cnn_cache.size(3), 0) + ) + + if i == 0: + # record length for the first block as max length + max_att_len = new_att_cache.size(2) + max_cnn_len = new_cnn_cache.size(3) + + # update real shape of att_cache and cnn_cache + r_att_cache.append(new_att_cache[:, :, -max_att_len:, :].unsqueeze(1)) + r_cnn_cache.append(new_cnn_cache[:, :, :, -max_cnn_len:]) + + if self.encoder.normalize_before: + chunk_out = self.encoder.after_norm(xs) + else: + chunk_out = xs + + # shape of r_att_cache: (b, elayers, head, time2, outdim) + r_att_cache = torch.cat(r_att_cache, dim=1) # concat on layers idx + # shape of r_cnn_cache: (b, elayers, outdim, cache_t2) + r_cnn_cache = torch.cat(r_cnn_cache, dim=1) # concat on layers + + # <---------forward_chunk END---------> + + log_ctc_probs = self.ctc.log_softmax(chunk_out) + log_probs, log_probs_idx = torch.topk(log_ctc_probs, self.beam_size, dim=2) + log_probs = log_probs.to(chunk_xs.dtype) + + r_offset = offset + chunk_out.shape[1] + # the below ops not supported in Tensorrt + # chunk_out_lens = torch.div(chunk_lens, subsampling_rate, + # rounding_mode='floor') + chunk_out_lens = ( + chunk_lens + // self.subsampling_rate + // self.calculate_downsampling_factor(self.num_blocks + 1) + ) + chunk_out_lens += 1 + r_offset = r_offset.unsqueeze(1) + + return ( + log_probs, + log_probs_idx, + chunk_out, + chunk_out_lens, + r_offset, + r_att_cache, + r_cnn_cache, + r_cache_mask, + ) + + +class Decoder(torch.nn.Module): + def __init__( + self, + decoder: TransformerDecoder, + ctc_weight: float = 0.5, + reverse_weight: float = 0.0, + beam_size: int = 10, + decoder_fastertransformer: bool = False, + ): + super().__init__() + self.decoder = decoder + self.ctc_weight = ctc_weight + self.reverse_weight = reverse_weight + self.beam_size = beam_size + self.decoder_fastertransformer = decoder_fastertransformer + + def forward( + self, + encoder_out: torch.Tensor, + encoder_lens: torch.Tensor, + hyps_pad_sos_eos: torch.Tensor, + hyps_lens_sos: torch.Tensor, + r_hyps_pad_sos_eos: torch.Tensor, + ctc_score: torch.Tensor, + ): + """Encoder + Args: + encoder_out: B x T x F + encoder_lens: B + hyps_pad_sos_eos: B x beam x (T2+1), + hyps with sos & eos and padded by ignore id + hyps_lens_sos: B x beam, length for each hyp with sos + r_hyps_pad_sos_eos: B x beam x (T2+1), + reversed hyps with sos & eos and padded by ignore id + ctc_score: B x beam, ctc score for each hyp + Returns: + decoder_out: B x beam x T2 x V + r_decoder_out: B x beam x T2 x V + best_index: B + """ + B, T, F = encoder_out.shape + bz = self.beam_size + B2 = B * bz + encoder_out = encoder_out.repeat(1, bz, 1).view(B2, T, F) + encoder_mask = ~make_pad_mask(encoder_lens, T).unsqueeze(1) + encoder_mask = encoder_mask.repeat(1, bz, 1).view(B2, 1, T) + T2 = hyps_pad_sos_eos.shape[2] - 1 + hyps_pad = hyps_pad_sos_eos.view(B2, T2 + 1) + hyps_lens = hyps_lens_sos.view( + B2, + ) + hyps_pad_sos = hyps_pad[:, :-1].contiguous() + hyps_pad_eos = hyps_pad[:, 1:].contiguous() + + r_hyps_pad = r_hyps_pad_sos_eos.view(B2, T2 + 1) + r_hyps_pad_sos = r_hyps_pad[:, :-1].contiguous() + r_hyps_pad_eos = r_hyps_pad[:, 1:].contiguous() + + decoder_out, r_decoder_out, _ = self.decoder( + encoder_out, + encoder_mask, + hyps_pad_sos, + hyps_lens, + r_hyps_pad_sos, + self.reverse_weight, + ) + decoder_out = torch.nn.functional.log_softmax(decoder_out, dim=-1) + V = decoder_out.shape[-1] + decoder_out = decoder_out.view(B2, T2, V) + mask = ~make_pad_mask(hyps_lens, T2) # B2 x T2 + # mask index, remove ignore id + index = torch.unsqueeze(hyps_pad_eos * mask, 2) + score = decoder_out.gather(2, index).squeeze(2) # B2 X T2 + # mask padded part + score = score * mask + decoder_out = decoder_out.view(B, bz, T2, V) + if self.reverse_weight > 0: + r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1) + r_decoder_out = r_decoder_out.view(B2, T2, V) + index = torch.unsqueeze(r_hyps_pad_eos * mask, 2) + r_score = r_decoder_out.gather(2, index).squeeze(2) + r_score = r_score * mask + score = score * (1 - self.reverse_weight) + self.reverse_weight * r_score + r_decoder_out = r_decoder_out.view(B, bz, T2, V) + score = torch.sum(score, axis=1) # B2 + score = torch.reshape(score, (B, bz)) + self.ctc_weight * ctc_score + best_index = torch.argmax(score, dim=1) + if self.decoder_fastertransformer: + return decoder_out, best_index + else: + return best_index + + +def to_numpy(tensors): + out = [] + if type(tensors) == torch.tensor: + tensors = [tensors] + for tensor in tensors: + if tensor.requires_grad: + tensor = tensor.detach().cpu().numpy() + else: + tensor = tensor.cpu().numpy() + out.append(tensor) + return out + + +def test(xlist, blist, rtol=1e-3, atol=1e-5, tolerate_small_mismatch=True): + for a, b in zip(xlist, blist): + try: + torch.testing.assert_allclose(a, b, rtol=rtol, atol=atol) + except AssertionError as error: + if tolerate_small_mismatch: + print(error) + else: + raise + + +def export_offline_encoder(model, configs, args, logger, encoder_onnx_path): + bz = 32 + seq_len = 100 + beam_size = args.beam_size + feature_size = configs["input_dim"] + + speech = torch.randn(bz, seq_len, feature_size, dtype=torch.float32) + speech_lens = torch.randint(low=10, high=seq_len, size=(bz,), dtype=torch.int32) + encoder = Encoder(model.encoder, model.ctc, beam_size) + encoder.eval() + + torch.onnx.export( + encoder, + (speech, speech_lens), + encoder_onnx_path, + export_params=True, + opset_version=13, + do_constant_folding=True, + input_names=["speech", "speech_lengths"], + output_names=[ + "encoder_out", + "encoder_out_lens", + "ctc_log_probs", + "beam_log_probs", + "beam_log_probs_idx", + ], + dynamic_axes={ + "speech": {0: "B", 1: "T"}, + "speech_lengths": {0: "B"}, + "encoder_out": {0: "B", 1: "T_OUT"}, + "encoder_out_lens": {0: "B"}, + "ctc_log_probs": {0: "B", 1: "T_OUT"}, + "beam_log_probs": {0: "B", 1: "T_OUT"}, + "beam_log_probs_idx": {0: "B", 1: "T_OUT"}, + }, + verbose=False, + ) + + with torch.no_grad(): + o0, o1, o2, o3, o4 = encoder(speech, speech_lens) + + providers = ["CUDAExecutionProvider"] + ort_session = onnxruntime.InferenceSession(encoder_onnx_path, providers=providers) + ort_inputs = {"speech": to_numpy(speech), "speech_lengths": to_numpy(speech_lens)} + ort_outs = ort_session.run(None, ort_inputs) + + # check encoder output + test(to_numpy([o0, o1, o2, o3, o4]), ort_outs) + logger.info("export offline onnx encoder succeed!") + onnx_config = { + "beam_size": args.beam_size, + "reverse_weight": args.reverse_weight, + "ctc_weight": args.ctc_weight, + "fp16": args.fp16, + } + return onnx_config + + +def export_online_encoder(model, configs, args, logger, encoder_onnx_path): + decoding_chunk_size = args.decoding_chunk_size + subsampling = model.encoder.embed.subsampling_rate + context = model.encoder.embed.right_context + 1 + decoding_window = (decoding_chunk_size - 1) * subsampling + context + batch_size = 32 + audio_len = decoding_window + feature_size = configs["input_dim"] + output_size = configs["encoder_conf"]["output_size"] + num_layers = configs["encoder_conf"]["num_blocks"] + # in transformer the cnn module will not be available + transformer = False + cnn_module_kernel = configs["encoder_conf"].get("cnn_module_kernel", 1) - 1 + if not cnn_module_kernel: + transformer = True + num_decoding_left_chunks = args.num_decoding_left_chunks + required_cache_size = decoding_chunk_size * num_decoding_left_chunks + if configs["encoder"] == "squeezeformer": + encoder = StreamingSqueezeformerEncoder( + model, required_cache_size, args.beam_size + ) + elif configs["encoder"] == "efficientConformer": + encoder = StreamingEfficientConformerEncoder( + model, required_cache_size, args.beam_size + ) + else: + encoder = StreamingEncoder( + model, required_cache_size, args.beam_size, transformer + ) + encoder.eval() + + # begin to export encoder + chunk_xs = torch.randn(batch_size, audio_len, feature_size, dtype=torch.float32) + chunk_lens = torch.ones(batch_size, dtype=torch.int32) * audio_len + + offset = torch.arange(0, batch_size).unsqueeze(1) + # (elayers, b, head, cache_t1, d_k * 2) + head = configs["encoder_conf"]["attention_heads"] + d_k = configs["encoder_conf"]["output_size"] // head + att_cache = torch.randn( + batch_size, num_layers, head, required_cache_size, d_k * 2, dtype=torch.float32 + ) + cnn_cache = torch.randn( + batch_size, num_layers, output_size, cnn_module_kernel, dtype=torch.float32 + ) + + cache_mask = torch.ones(batch_size, 1, required_cache_size, dtype=torch.float32) + input_names = [ + "chunk_xs", + "chunk_lens", + "offset", + "att_cache", + "cnn_cache", + "cache_mask", + ] + output_names = [ + "log_probs", + "log_probs_idx", + "chunk_out", + "chunk_out_lens", + "r_offset", + "r_att_cache", + "r_cnn_cache", + "r_cache_mask", + ] + input_tensors = (chunk_xs, chunk_lens, offset, att_cache, cnn_cache, cache_mask) + if transformer: + output_names.pop(6) + + all_names = input_names + output_names + dynamic_axes = {} + for name in all_names: + # only the first dimension is dynamic + # all other dimension is fixed + dynamic_axes[name] = {0: "B"} + + torch.onnx.export( + encoder, + input_tensors, + encoder_onnx_path, + export_params=True, + opset_version=14, + do_constant_folding=True, + input_names=input_names, + output_names=output_names, + dynamic_axes=dynamic_axes, + verbose=False, + ) + + with torch.no_grad(): + torch_outs = encoder( + chunk_xs, chunk_lens, offset, att_cache, cnn_cache, cache_mask + ) + if transformer: + torch_outs = list(torch_outs).pop(6) + ort_session = onnxruntime.InferenceSession( + encoder_onnx_path, providers=["CUDAExecutionProvider"] + ) + ort_inputs = {} + + input_tensors = to_numpy(input_tensors) + for idx, name in enumerate(input_names): + ort_inputs[name] = input_tensors[idx] + if transformer: + del ort_inputs["cnn_cache"] + ort_outs = ort_session.run(None, ort_inputs) + test(to_numpy(torch_outs), ort_outs, rtol=1e-03, atol=1e-05) + logger.info("export to onnx streaming encoder succeed!") + onnx_config = { + "subsampling_rate": subsampling, + "context": context, + "decoding_chunk_size": decoding_chunk_size, + "num_decoding_left_chunks": num_decoding_left_chunks, + "beam_size": args.beam_size, + "fp16": args.fp16, + "feat_size": feature_size, + "decoding_window": decoding_window, + "cnn_module_kernel_cache": cnn_module_kernel, + } + return onnx_config + + +def export_rescoring_decoder( + model, configs, args, logger, decoder_onnx_path, decoder_fastertransformer +): + bz, seq_len = 32, 100 + beam_size = args.beam_size + decoder = Decoder( + model.decoder, + model.ctc_weight, + model.reverse_weight, + beam_size, + decoder_fastertransformer, + ) + decoder.eval() + + hyps_pad_sos_eos = torch.randint(low=3, high=1000, size=(bz, beam_size, seq_len)) + hyps_lens_sos = torch.randint( + low=3, high=seq_len, size=(bz, beam_size), dtype=torch.int32 + ) + r_hyps_pad_sos_eos = torch.randint(low=3, high=1000, size=(bz, beam_size, seq_len)) + + output_size = configs["encoder_conf"]["output_size"] + encoder_out = torch.randn(bz, seq_len, output_size, dtype=torch.float32) + encoder_out_lens = torch.randint(low=3, high=seq_len, size=(bz,), dtype=torch.int32) + ctc_score = torch.randn(bz, beam_size, dtype=torch.float32) + + input_names = [ + "encoder_out", + "encoder_out_lens", + "hyps_pad_sos_eos", + "hyps_lens_sos", + "r_hyps_pad_sos_eos", + "ctc_score", + ] + output_names = ["best_index"] + if decoder_fastertransformer: + output_names.insert(0, "decoder_out") + + torch.onnx.export( + decoder, + ( + encoder_out, + encoder_out_lens, + hyps_pad_sos_eos, + hyps_lens_sos, + r_hyps_pad_sos_eos, + ctc_score, + ), + decoder_onnx_path, + export_params=True, + opset_version=13, + do_constant_folding=True, + input_names=input_names, + output_names=output_names, + dynamic_axes={ + "encoder_out": {0: "B", 1: "T"}, + "encoder_out_lens": {0: "B"}, + "hyps_pad_sos_eos": {0: "B", 2: "T2"}, + "hyps_lens_sos": {0: "B"}, + "r_hyps_pad_sos_eos": {0: "B", 2: "T2"}, + "ctc_score": {0: "B"}, + "best_index": {0: "B"}, + }, + verbose=False, + ) + with torch.no_grad(): + o0 = decoder( + encoder_out, + encoder_out_lens, + hyps_pad_sos_eos, + hyps_lens_sos, + r_hyps_pad_sos_eos, + ctc_score, + ) + providers = ["CUDAExecutionProvider"] + ort_session = onnxruntime.InferenceSession(decoder_onnx_path, providers=providers) + + input_tensors = [ + encoder_out, + encoder_out_lens, + hyps_pad_sos_eos, + hyps_lens_sos, + r_hyps_pad_sos_eos, + ctc_score, + ] + ort_inputs = {} + input_tensors = to_numpy(input_tensors) + for idx, name in enumerate(input_names): + ort_inputs[name] = input_tensors[idx] + + # if model.reverse weight == 0, + # the r_hyps_pad will be removed + # from the onnx decoder since it doen't play any role + if model.reverse_weight == 0: + del ort_inputs["r_hyps_pad_sos_eos"] + ort_outs = ort_session.run(None, ort_inputs) + + # check decoder output + if decoder_fastertransformer: + test(to_numpy(o0), ort_outs, rtol=1e-03, atol=1e-05) + else: + test(to_numpy([o0]), ort_outs, rtol=1e-03, atol=1e-05) + logger.info("export to onnx decoder succeed!") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="export x86_gpu model") + parser.add_argument("--config", required=True, help="config file") + parser.add_argument("--checkpoint", required=True, help="checkpoint model") + parser.add_argument( + "--cmvn_file", + required=False, + default="", + type=str, + help="global_cmvn file, default path is in config file", + ) + parser.add_argument( + "--reverse_weight", + default=-1.0, + type=float, + required=False, + help="reverse weight for bitransformer," + "default value is in config file", + ) + parser.add_argument( + "--ctc_weight", + default=-1.0, + type=float, + required=False, + help="ctc weight, default value is in config file", + ) + parser.add_argument( + "--beam_size", + default=10, + type=int, + required=False, + help="beam size would be ctc output size", + ) + parser.add_argument( + "--output_onnx_dir", + default="onnx_model", + help="output onnx encoder and decoder directory", + ) + parser.add_argument( + "--fp16", + action="store_true", + help="whether to export fp16 model, default false", + ) + # arguments for streaming encoder + parser.add_argument( + "--streaming", + action="store_true", + help="whether to export streaming encoder, default false", + ) + parser.add_argument( + "--decoding_chunk_size", + default=16, + type=int, + required=False, + help="the decoding chunk size, <=0 is not supported", + ) + parser.add_argument( + "--num_decoding_left_chunks", + default=5, + type=int, + required=False, + help="number of left chunks, <= 0 is not supported", + ) + parser.add_argument( + "--decoder_fastertransformer", + action="store_true", + help="return decoder_out and best_index for ft", + ) + args = parser.parse_args() + + torch.manual_seed(0) + torch.set_printoptions(precision=10) + + with open(args.config, "r") as fin: + configs = yaml.load(fin, Loader=yaml.FullLoader) + if args.cmvn_file and os.path.exists(args.cmvn_file): + configs["cmvn_file"] = args.cmvn_file + if args.reverse_weight != -1.0 and "reverse_weight" in configs["model_conf"]: + configs["model_conf"]["reverse_weight"] = args.reverse_weight + print("Update reverse weight to", args.reverse_weight) + if args.ctc_weight != -1: + print("Update ctc weight to ", args.ctc_weight) + configs["model_conf"]["ctc_weight"] = args.ctc_weight + configs["encoder_conf"]["use_dynamic_chunk"] = False + + model = init_model(configs) + load_checkpoint(model, args.checkpoint) + model.eval() + + if not os.path.exists(args.output_onnx_dir): + os.mkdir(args.output_onnx_dir) + encoder_onnx_path = os.path.join(args.output_onnx_dir, "encoder.onnx") + export_enc_func = None + if args.streaming: + assert args.decoding_chunk_size > 0 + assert args.num_decoding_left_chunks > 0 + export_enc_func = export_online_encoder + else: + export_enc_func = export_offline_encoder + + onnx_config = export_enc_func(model, configs, args, logger, encoder_onnx_path) + + decoder_onnx_path = os.path.join(args.output_onnx_dir, "decoder.onnx") + export_rescoring_decoder( + model, configs, args, logger, decoder_onnx_path, args.decoder_fastertransformer + ) + + if args.fp16: + try: + import onnxmltools + from onnxmltools.utils.float16_converter import convert_float_to_float16 + except ImportError: + print("Please install onnxmltools!") + sys.exit(1) + encoder_onnx_model = onnxmltools.utils.load_model(encoder_onnx_path) + encoder_onnx_model = convert_float_to_float16(encoder_onnx_model) + encoder_onnx_path = os.path.join(args.output_onnx_dir, "encoder_fp16.onnx") + onnxmltools.utils.save_model(encoder_onnx_model, encoder_onnx_path) + decoder_onnx_model = onnxmltools.utils.load_model(decoder_onnx_path) + decoder_onnx_model = convert_float_to_float16(decoder_onnx_model) + decoder_onnx_path = os.path.join(args.output_onnx_dir, "decoder_fp16.onnx") + onnxmltools.utils.save_model(decoder_onnx_model, decoder_onnx_path) + # dump configurations + + config_dir = os.path.join(args.output_onnx_dir, "config.yaml") + with open(config_dir, "w") as out: + yaml.dump(onnx_config, out) diff --git a/modules/wenet_extractor/bin/recognize.py b/modules/wenet_extractor/bin/recognize.py new file mode 100644 index 0000000000000000000000000000000000000000..b78463b78e49790faaf72811ec76d44453eb52ff --- /dev/null +++ b/modules/wenet_extractor/bin/recognize.py @@ -0,0 +1,435 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +from __future__ import print_function + +import argparse +import copy +import logging +import os +import sys + +import torch +import yaml +from torch.utils.data import DataLoader + +from wenet.dataset.dataset import Dataset +from wenet.paraformer.search.beam_search import build_beam_search +from wenet.utils.checkpoint import load_checkpoint +from wenet.utils.file_utils import read_symbol_table, read_non_lang_symbols +from wenet.utils.config import override_config +from wenet.utils.init_model import init_model + + +def get_args(): + parser = argparse.ArgumentParser(description="recognize with your model") + parser.add_argument("--config", required=True, help="config file") + parser.add_argument("--test_data", required=True, help="test data file") + parser.add_argument( + "--data_type", + default="raw", + choices=["raw", "shard"], + help="train and cv data type", + ) + parser.add_argument( + "--gpu", type=int, default=-1, help="gpu id for this rank, -1 for cpu" + ) + parser.add_argument("--checkpoint", required=True, help="checkpoint model") + parser.add_argument("--dict", required=True, help="dict file") + parser.add_argument( + "--non_lang_syms", help="non-linguistic symbol file. One symbol per line." + ) + parser.add_argument( + "--beam_size", type=int, default=10, help="beam size for search" + ) + parser.add_argument("--penalty", type=float, default=0.0, help="length penalty") + parser.add_argument("--result_file", required=True, help="asr result file") + parser.add_argument("--batch_size", type=int, default=16, help="asr result file") + parser.add_argument( + "--mode", + choices=[ + "attention", + "ctc_greedy_search", + "ctc_prefix_beam_search", + "attention_rescoring", + "rnnt_greedy_search", + "rnnt_beam_search", + "rnnt_beam_attn_rescoring", + "ctc_beam_td_attn_rescoring", + "hlg_onebest", + "hlg_rescore", + "paraformer_greedy_search", + "paraformer_beam_search", + ], + default="attention", + help="decoding mode", + ) + + parser.add_argument( + "--search_ctc_weight", + type=float, + default=1.0, + help="ctc weight for nbest generation", + ) + parser.add_argument( + "--search_transducer_weight", + type=float, + default=0.0, + help="transducer weight for nbest generation", + ) + parser.add_argument( + "--ctc_weight", + type=float, + default=0.0, + help="ctc weight for rescoring weight in \ + attention rescoring decode mode \ + ctc weight for rescoring weight in \ + transducer attention rescore decode mode", + ) + + parser.add_argument( + "--transducer_weight", + type=float, + default=0.0, + help="transducer weight for rescoring weight in " + "transducer attention rescore mode", + ) + parser.add_argument( + "--attn_weight", + type=float, + default=0.0, + help="attention weight for rescoring weight in " + "transducer attention rescore mode", + ) + parser.add_argument( + "--decoding_chunk_size", + type=int, + default=-1, + help="""decoding chunk size, + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + 0: used for training, it's prohibited here""", + ) + parser.add_argument( + "--num_decoding_left_chunks", + type=int, + default=-1, + help="number of left chunks for decoding", + ) + parser.add_argument( + "--simulate_streaming", action="store_true", help="simulate streaming inference" + ) + parser.add_argument( + "--reverse_weight", + type=float, + default=0.0, + help="""right to left weight for attention rescoring + decode mode""", + ) + parser.add_argument( + "--bpe_model", default=None, type=str, help="bpe model for english part" + ) + parser.add_argument( + "--override_config", action="append", default=[], help="override yaml config" + ) + parser.add_argument( + "--connect_symbol", + default="", + type=str, + help="used to connect the output characters", + ) + + parser.add_argument( + "--word", default="", type=str, help="word file, only used for hlg decode" + ) + parser.add_argument( + "--hlg", default="", type=str, help="hlg file, only used for hlg decode" + ) + parser.add_argument( + "--lm_scale", + type=float, + default=0.0, + help="lm scale for hlg attention rescore decode", + ) + parser.add_argument( + "--decoder_scale", + type=float, + default=0.0, + help="lm scale for hlg attention rescore decode", + ) + parser.add_argument( + "--r_decoder_scale", + type=float, + default=0.0, + help="lm scale for hlg attention rescore decode", + ) + + args = parser.parse_args() + print(args) + return args + + +def main(): + args = get_args() + logging.basicConfig( + level=logging.DEBUG, format="%(asctime)s %(levelname)s %(message)s" + ) + os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) + + if ( + args.mode + in [ + "ctc_prefix_beam_search", + "attention_rescoring", + "paraformer_beam_search", + ] + and args.batch_size > 1 + ): + logging.fatal( + "decoding mode {} must be running with batch_size == 1".format(args.mode) + ) + sys.exit(1) + + with open(args.config, "r") as fin: + configs = yaml.load(fin, Loader=yaml.FullLoader) + if len(args.override_config) > 0: + configs = override_config(configs, args.override_config) + + symbol_table = read_symbol_table(args.dict) + test_conf = copy.deepcopy(configs["dataset_conf"]) + + test_conf["filter_conf"]["max_length"] = 102400 + test_conf["filter_conf"]["min_length"] = 0 + test_conf["filter_conf"]["token_max_length"] = 102400 + test_conf["filter_conf"]["token_min_length"] = 0 + test_conf["filter_conf"]["max_output_input_ratio"] = 102400 + test_conf["filter_conf"]["min_output_input_ratio"] = 0 + test_conf["speed_perturb"] = False + test_conf["spec_aug"] = False + test_conf["spec_sub"] = False + test_conf["spec_trim"] = False + test_conf["shuffle"] = False + test_conf["sort"] = False + if "fbank_conf" in test_conf: + test_conf["fbank_conf"]["dither"] = 0.0 + elif "mfcc_conf" in test_conf: + test_conf["mfcc_conf"]["dither"] = 0.0 + test_conf["batch_conf"]["batch_type"] = "static" + test_conf["batch_conf"]["batch_size"] = args.batch_size + non_lang_syms = read_non_lang_symbols(args.non_lang_syms) + + test_dataset = Dataset( + args.data_type, + args.test_data, + symbol_table, + test_conf, + args.bpe_model, + non_lang_syms, + partition=False, + ) + + test_data_loader = DataLoader(test_dataset, batch_size=None, num_workers=0) + + # Init asr model from configs + model = init_model(configs) + + # Load dict + char_dict = {v: k for k, v in symbol_table.items()} + eos = len(char_dict) - 1 + + load_checkpoint(model, args.checkpoint) + use_cuda = args.gpu >= 0 and torch.cuda.is_available() + device = torch.device("cuda" if use_cuda else "cpu") + model = model.to(device) + + model.eval() + + # Build BeamSearchCIF object + if args.mode == "paraformer_beam_search": + paraformer_beam_search = build_beam_search(model, args, device) + else: + paraformer_beam_search = None + + with torch.no_grad(), open(args.result_file, "w") as fout: + for batch_idx, batch in enumerate(test_data_loader): + keys, feats, target, feats_lengths, target_lengths = batch + feats = feats.to(device) + target = target.to(device) + feats_lengths = feats_lengths.to(device) + target_lengths = target_lengths.to(device) + if args.mode == "attention": + hyps, _ = model.recognize( + feats, + feats_lengths, + beam_size=args.beam_size, + decoding_chunk_size=args.decoding_chunk_size, + num_decoding_left_chunks=args.num_decoding_left_chunks, + simulate_streaming=args.simulate_streaming, + ) + hyps = [hyp.tolist() for hyp in hyps] + elif args.mode == "ctc_greedy_search": + hyps, _ = model.ctc_greedy_search( + feats, + feats_lengths, + decoding_chunk_size=args.decoding_chunk_size, + num_decoding_left_chunks=args.num_decoding_left_chunks, + simulate_streaming=args.simulate_streaming, + ) + elif args.mode == "rnnt_greedy_search": + assert feats.size(0) == 1 + assert "predictor" in configs + hyps = model.greedy_search( + feats, + feats_lengths, + decoding_chunk_size=args.decoding_chunk_size, + num_decoding_left_chunks=args.num_decoding_left_chunks, + simulate_streaming=args.simulate_streaming, + ) + elif args.mode == "rnnt_beam_search": + assert feats.size(0) == 1 + assert "predictor" in configs + hyps = model.beam_search( + feats, + feats_lengths, + decoding_chunk_size=args.decoding_chunk_size, + beam_size=args.beam_size, + num_decoding_left_chunks=args.num_decoding_left_chunks, + simulate_streaming=args.simulate_streaming, + ctc_weight=args.search_ctc_weight, + transducer_weight=args.search_transducer_weight, + ) + elif args.mode == "rnnt_beam_attn_rescoring": + assert feats.size(0) == 1 + assert "predictor" in configs + hyps = model.transducer_attention_rescoring( + feats, + feats_lengths, + decoding_chunk_size=args.decoding_chunk_size, + beam_size=args.beam_size, + num_decoding_left_chunks=args.num_decoding_left_chunks, + simulate_streaming=args.simulate_streaming, + ctc_weight=args.ctc_weight, + transducer_weight=args.transducer_weight, + attn_weight=args.attn_weight, + reverse_weight=args.reverse_weight, + search_ctc_weight=args.search_ctc_weight, + search_transducer_weight=args.search_transducer_weight, + ) + elif args.mode == "ctc_beam_td_attn_rescoring": + assert feats.size(0) == 1 + assert "predictor" in configs + hyps = model.transducer_attention_rescoring( + feats, + feats_lengths, + decoding_chunk_size=args.decoding_chunk_size, + beam_size=args.beam_size, + num_decoding_left_chunks=args.num_decoding_left_chunks, + simulate_streaming=args.simulate_streaming, + ctc_weight=args.ctc_weight, + transducer_weight=args.transducer_weight, + attn_weight=args.attn_weight, + reverse_weight=args.reverse_weight, + search_ctc_weight=args.search_ctc_weight, + search_transducer_weight=args.search_transducer_weight, + beam_search_type="ctc", + ) + # ctc_prefix_beam_search and attention_rescoring only return one + # result in List[int], change it to List[List[int]] for compatible + # with other batch decoding mode + elif args.mode == "ctc_prefix_beam_search": + assert feats.size(0) == 1 + hyp, _ = model.ctc_prefix_beam_search( + feats, + feats_lengths, + args.beam_size, + decoding_chunk_size=args.decoding_chunk_size, + num_decoding_left_chunks=args.num_decoding_left_chunks, + simulate_streaming=args.simulate_streaming, + ) + hyps = [hyp] + elif args.mode == "attention_rescoring": + assert feats.size(0) == 1 + hyp, _ = model.attention_rescoring( + feats, + feats_lengths, + args.beam_size, + decoding_chunk_size=args.decoding_chunk_size, + num_decoding_left_chunks=args.num_decoding_left_chunks, + ctc_weight=args.ctc_weight, + simulate_streaming=args.simulate_streaming, + reverse_weight=args.reverse_weight, + ) + hyps = [hyp] + elif args.mode == "hlg_onebest": + hyps = model.hlg_onebest( + feats, + feats_lengths, + decoding_chunk_size=args.decoding_chunk_size, + num_decoding_left_chunks=args.num_decoding_left_chunks, + simulate_streaming=args.simulate_streaming, + hlg=args.hlg, + word=args.word, + symbol_table=symbol_table, + ) + elif args.mode == "hlg_rescore": + hyps = model.hlg_rescore( + feats, + feats_lengths, + decoding_chunk_size=args.decoding_chunk_size, + num_decoding_left_chunks=args.num_decoding_left_chunks, + simulate_streaming=args.simulate_streaming, + lm_scale=args.lm_scale, + decoder_scale=args.decoder_scale, + r_decoder_scale=args.r_decoder_scale, + hlg=args.hlg, + word=args.word, + symbol_table=symbol_table, + ) + elif args.mode == "paraformer_beam_search": + hyps = model.paraformer_beam_search( + feats, + feats_lengths, + beam_search=paraformer_beam_search, + decoding_chunk_size=args.decoding_chunk_size, + num_decoding_left_chunks=args.num_decoding_left_chunks, + simulate_streaming=args.simulate_streaming, + ) + elif args.mode == "paraformer_greedy_search": + hyps = model.paraformer_greedy_search( + feats, + feats_lengths, + decoding_chunk_size=args.decoding_chunk_size, + num_decoding_left_chunks=args.num_decoding_left_chunks, + simulate_streaming=args.simulate_streaming, + ) + for i, key in enumerate(keys): + content = [] + for w in hyps[i]: + if w == eos: + break + content.append(char_dict[w]) + logging.info("{} {}".format(key, args.connect_symbol.join(content))) + fout.write("{} {}\n".format(key, args.connect_symbol.join(content))) + + +if __name__ == "__main__": + main() diff --git a/modules/wenet_extractor/bin/recognize_onnx_gpu.py b/modules/wenet_extractor/bin/recognize_onnx_gpu.py new file mode 100644 index 0000000000000000000000000000000000000000..2b42788b1947ce4f58a2e8ef51d10ec0e7bca2d7 --- /dev/null +++ b/modules/wenet_extractor/bin/recognize_onnx_gpu.py @@ -0,0 +1,285 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +from __future__ import print_function + +import argparse +import copy +import logging +import os +import sys + +import torch +import yaml +from torch.utils.data import DataLoader + +from wenet.dataset.dataset import Dataset +from wenet.utils.common import IGNORE_ID +from wenet.utils.file_utils import read_symbol_table +from wenet.utils.config import override_config + +import onnxruntime as rt +import multiprocessing +import numpy as np + +try: + from swig_decoders import ( + map_batch, + ctc_beam_search_decoder_batch, + TrieVector, + PathTrie, + ) +except ImportError: + print( + "Please install ctc decoders first by refering to\n" + + "https://github.com/Slyne/ctc_decoder.git" + ) + sys.exit(1) + + +def get_args(): + parser = argparse.ArgumentParser(description="recognize with your model") + parser.add_argument("--config", required=True, help="config file") + parser.add_argument("--test_data", required=True, help="test data file") + parser.add_argument( + "--data_type", + default="raw", + choices=["raw", "shard"], + help="train and cv data type", + ) + parser.add_argument( + "--gpu", type=int, default=-1, help="gpu id for this rank, -1 for cpu" + ) + parser.add_argument("--dict", required=True, help="dict file") + parser.add_argument("--encoder_onnx", required=True, help="encoder onnx file") + parser.add_argument("--decoder_onnx", required=True, help="decoder onnx file") + parser.add_argument("--result_file", required=True, help="asr result file") + parser.add_argument("--batch_size", type=int, default=32, help="asr result file") + parser.add_argument( + "--mode", + choices=["ctc_greedy_search", "ctc_prefix_beam_search", "attention_rescoring"], + default="attention_rescoring", + help="decoding mode", + ) + parser.add_argument( + "--bpe_model", default=None, type=str, help="bpe model for english part" + ) + parser.add_argument( + "--override_config", action="append", default=[], help="override yaml config" + ) + parser.add_argument( + "--fp16", + action="store_true", + help="whether to export fp16 model, default false", + ) + args = parser.parse_args() + print(args) + return args + + +def main(): + args = get_args() + logging.basicConfig( + level=logging.DEBUG, format="%(asctime)s %(levelname)s %(message)s" + ) + os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) + + with open(args.config, "r") as fin: + configs = yaml.load(fin, Loader=yaml.FullLoader) + if len(args.override_config) > 0: + configs = override_config(configs, args.override_config) + + reverse_weight = configs["model_conf"].get("reverse_weight", 0.0) + symbol_table = read_symbol_table(args.dict) + test_conf = copy.deepcopy(configs["dataset_conf"]) + test_conf["filter_conf"]["max_length"] = 102400 + test_conf["filter_conf"]["min_length"] = 0 + test_conf["filter_conf"]["token_max_length"] = 102400 + test_conf["filter_conf"]["token_min_length"] = 0 + test_conf["filter_conf"]["max_output_input_ratio"] = 102400 + test_conf["filter_conf"]["min_output_input_ratio"] = 0 + test_conf["speed_perturb"] = False + test_conf["spec_aug"] = False + test_conf["spec_sub"] = False + test_conf["spec_trim"] = False + test_conf["shuffle"] = False + test_conf["sort"] = False + test_conf["fbank_conf"]["dither"] = 0.0 + test_conf["batch_conf"]["batch_type"] = "static" + test_conf["batch_conf"]["batch_size"] = args.batch_size + + test_dataset = Dataset( + args.data_type, + args.test_data, + symbol_table, + test_conf, + args.bpe_model, + partition=False, + ) + + test_data_loader = DataLoader(test_dataset, batch_size=None, num_workers=0) + + # Init asr model from configs + use_cuda = args.gpu >= 0 and torch.cuda.is_available() + if use_cuda: + EP_list = ["CUDAExecutionProvider", "CPUExecutionProvider"] + else: + EP_list = ["CPUExecutionProvider"] + + encoder_ort_session = rt.InferenceSession(args.encoder_onnx, providers=EP_list) + decoder_ort_session = None + if args.mode == "attention_rescoring": + decoder_ort_session = rt.InferenceSession(args.decoder_onnx, providers=EP_list) + + # Load dict + vocabulary = [] + char_dict = {} + with open(args.dict, "r") as fin: + for line in fin: + arr = line.strip().split() + assert len(arr) == 2 + char_dict[int(arr[1])] = arr[0] + vocabulary.append(arr[0]) + eos = sos = len(char_dict) - 1 + with torch.no_grad(), open(args.result_file, "w") as fout: + for _, batch in enumerate(test_data_loader): + keys, feats, _, feats_lengths, _ = batch + feats, feats_lengths = feats.numpy(), feats_lengths.numpy() + if args.fp16: + feats = feats.astype(np.float16) + ort_inputs = { + encoder_ort_session.get_inputs()[0].name: feats, + encoder_ort_session.get_inputs()[1].name: feats_lengths, + } + ort_outs = encoder_ort_session.run(None, ort_inputs) + ( + encoder_out, + encoder_out_lens, + ctc_log_probs, + beam_log_probs, + beam_log_probs_idx, + ) = ort_outs + beam_size = beam_log_probs.shape[-1] + batch_size = beam_log_probs.shape[0] + num_processes = min(multiprocessing.cpu_count(), batch_size) + if args.mode == "ctc_greedy_search": + if beam_size != 1: + log_probs_idx = beam_log_probs_idx[:, :, 0] + batch_sents = [] + for idx, seq in enumerate(log_probs_idx): + batch_sents.append(seq[0 : encoder_out_lens[idx]].tolist()) + hyps = map_batch(batch_sents, vocabulary, num_processes, True, 0) + elif args.mode in ("ctc_prefix_beam_search", "attention_rescoring"): + batch_log_probs_seq_list = beam_log_probs.tolist() + batch_log_probs_idx_list = beam_log_probs_idx.tolist() + batch_len_list = encoder_out_lens.tolist() + batch_log_probs_seq = [] + batch_log_probs_ids = [] + batch_start = [] # only effective in streaming deployment + batch_root = TrieVector() + root_dict = {} + for i in range(len(batch_len_list)): + num_sent = batch_len_list[i] + batch_log_probs_seq.append(batch_log_probs_seq_list[i][0:num_sent]) + batch_log_probs_ids.append(batch_log_probs_idx_list[i][0:num_sent]) + root_dict[i] = PathTrie() + batch_root.append(root_dict[i]) + batch_start.append(True) + score_hyps = ctc_beam_search_decoder_batch( + batch_log_probs_seq, + batch_log_probs_ids, + batch_root, + batch_start, + beam_size, + num_processes, + 0, + -2, + 0.99999, + ) + if args.mode == "ctc_prefix_beam_search": + hyps = [] + for cand_hyps in score_hyps: + hyps.append(cand_hyps[0][1]) + hyps = map_batch(hyps, vocabulary, num_processes, False, 0) + if args.mode == "attention_rescoring": + ctc_score, all_hyps = [], [] + max_len = 0 + for hyps in score_hyps: + cur_len = len(hyps) + if len(hyps) < beam_size: + hyps += (beam_size - cur_len) * [(-float("INF"), (0,))] + cur_ctc_score = [] + for hyp in hyps: + cur_ctc_score.append(hyp[0]) + all_hyps.append(list(hyp[1])) + if len(hyp[1]) > max_len: + max_len = len(hyp[1]) + ctc_score.append(cur_ctc_score) + if args.fp16: + ctc_score = np.array(ctc_score, dtype=np.float16) + else: + ctc_score = np.array(ctc_score, dtype=np.float32) + hyps_pad_sos_eos = ( + np.ones((batch_size, beam_size, max_len + 2), dtype=np.int64) + * IGNORE_ID + ) + r_hyps_pad_sos_eos = ( + np.ones((batch_size, beam_size, max_len + 2), dtype=np.int64) + * IGNORE_ID + ) + hyps_lens_sos = np.ones((batch_size, beam_size), dtype=np.int32) + k = 0 + for i in range(batch_size): + for j in range(beam_size): + cand = all_hyps[k] + l = len(cand) + 2 + hyps_pad_sos_eos[i][j][0:l] = [sos] + cand + [eos] + r_hyps_pad_sos_eos[i][j][0:l] = [sos] + cand[::-1] + [eos] + hyps_lens_sos[i][j] = len(cand) + 1 + k += 1 + decoder_ort_inputs = { + decoder_ort_session.get_inputs()[0].name: encoder_out, + decoder_ort_session.get_inputs()[1].name: encoder_out_lens, + decoder_ort_session.get_inputs()[2].name: hyps_pad_sos_eos, + decoder_ort_session.get_inputs()[3].name: hyps_lens_sos, + decoder_ort_session.get_inputs()[-1].name: ctc_score, + } + if reverse_weight > 0: + r_hyps_pad_sos_eos_name = decoder_ort_session.get_inputs()[4].name + decoder_ort_inputs[r_hyps_pad_sos_eos_name] = r_hyps_pad_sos_eos + best_index = decoder_ort_session.run(None, decoder_ort_inputs)[0] + best_sents = [] + k = 0 + for idx in best_index: + cur_best_sent = all_hyps[k : k + beam_size][idx] + best_sents.append(cur_best_sent) + k += beam_size + hyps = map_batch(best_sents, vocabulary, num_processes) + + for i, key in enumerate(keys): + content = hyps[i] + logging.info("{} {}".format(key, content)) + fout.write("{} {}\n".format(key, content)) + + +if __name__ == "__main__": + main() diff --git a/modules/wenet_extractor/bin/train.py b/modules/wenet_extractor/bin/train.py new file mode 100644 index 0000000000000000000000000000000000000000..eba3d91304331806e99840a044a8537e9127a08c --- /dev/null +++ b/modules/wenet_extractor/bin/train.py @@ -0,0 +1,358 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +from __future__ import print_function + +import argparse +import copy +import logging +import os + +import torch +import torch.distributed as dist +import torch.optim as optim +import yaml +from tensorboardX import SummaryWriter +from torch.utils.data import DataLoader + +from wenet.dataset.dataset import Dataset +from wenet.utils.checkpoint import ( + load_checkpoint, + save_checkpoint, + load_trained_modules, +) +from wenet.utils.executor import Executor +from wenet.utils.file_utils import read_symbol_table, read_non_lang_symbols +from wenet.utils.scheduler import WarmupLR, NoamHoldAnnealing +from wenet.utils.config import override_config +from wenet.utils.init_model import init_model + + +def get_args(): + parser = argparse.ArgumentParser(description="training your network") + parser.add_argument("--config", required=True, help="config file") + parser.add_argument( + "--data_type", + default="raw", + choices=["raw", "shard"], + help="train and cv data type", + ) + parser.add_argument("--train_data", required=True, help="train data file") + parser.add_argument("--cv_data", required=True, help="cv data file") + parser.add_argument( + "--gpu", type=int, default=-1, help="gpu id for this local rank, -1 for cpu" + ) + parser.add_argument("--model_dir", required=True, help="save model dir") + parser.add_argument("--checkpoint", help="checkpoint model") + parser.add_argument( + "--tensorboard_dir", default="tensorboard", help="tensorboard log dir" + ) + parser.add_argument( + "--ddp.rank", + dest="rank", + default=0, + type=int, + help="global rank for distributed training", + ) + parser.add_argument( + "--ddp.world_size", + dest="world_size", + default=-1, + type=int, + help="""number of total processes/gpus for + distributed training""", + ) + parser.add_argument( + "--ddp.dist_backend", + dest="dist_backend", + default="nccl", + choices=["nccl", "gloo"], + help="distributed backend", + ) + parser.add_argument( + "--ddp.init_method", dest="init_method", default=None, help="ddp init method" + ) + parser.add_argument( + "--num_workers", + default=0, + type=int, + help="num of subprocess workers for reading", + ) + parser.add_argument( + "--pin_memory", + action="store_true", + default=False, + help="Use pinned memory buffers used for reading", + ) + parser.add_argument( + "--use_amp", + action="store_true", + default=False, + help="Use automatic mixed precision training", + ) + parser.add_argument( + "--fp16_grad_sync", + action="store_true", + default=False, + help="Use fp16 gradient sync for ddp", + ) + parser.add_argument("--cmvn", default=None, help="global cmvn file") + parser.add_argument( + "--symbol_table", required=True, help="model unit symbol table for training" + ) + parser.add_argument( + "--non_lang_syms", help="non-linguistic symbol file. One symbol per line." + ) + parser.add_argument("--prefetch", default=100, type=int, help="prefetch number") + parser.add_argument( + "--bpe_model", default=None, type=str, help="bpe model for english part" + ) + parser.add_argument( + "--override_config", action="append", default=[], help="override yaml config" + ) + parser.add_argument( + "--enc_init", + default=None, + type=str, + help="Pre-trained model to initialize encoder", + ) + parser.add_argument( + "--enc_init_mods", + default="encoder.", + type=lambda s: [str(mod) for mod in s.split(",") if s != ""], + help="List of encoder modules \ + to initialize ,separated by a comma", + ) + parser.add_argument("--lfmmi_dir", default="", required=False, help="LF-MMI dir") + + args = parser.parse_args() + return args + + +def main(): + args = get_args() + logging.basicConfig( + level=logging.DEBUG, format="%(asctime)s %(levelname)s %(message)s" + ) + os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) + + # Set random seed + torch.manual_seed(777) + with open(args.config, "r") as fin: + configs = yaml.load(fin, Loader=yaml.FullLoader) + if len(args.override_config) > 0: + configs = override_config(configs, args.override_config) + + distributed = args.world_size > 1 + if distributed: + logging.info("training on multiple gpus, this gpu {}".format(args.gpu)) + dist.init_process_group( + args.dist_backend, + init_method=args.init_method, + world_size=args.world_size, + rank=args.rank, + ) + + symbol_table = read_symbol_table(args.symbol_table) + + train_conf = configs["dataset_conf"] + cv_conf = copy.deepcopy(train_conf) + cv_conf["speed_perturb"] = False + cv_conf["spec_aug"] = False + cv_conf["spec_sub"] = False + cv_conf["spec_trim"] = False + cv_conf["shuffle"] = False + non_lang_syms = read_non_lang_symbols(args.non_lang_syms) + + train_dataset = Dataset( + args.data_type, + args.train_data, + symbol_table, + train_conf, + args.bpe_model, + non_lang_syms, + True, + ) + cv_dataset = Dataset( + args.data_type, + args.cv_data, + symbol_table, + cv_conf, + args.bpe_model, + non_lang_syms, + partition=False, + ) + + train_data_loader = DataLoader( + train_dataset, + batch_size=None, + pin_memory=args.pin_memory, + num_workers=args.num_workers, + prefetch_factor=args.prefetch, + ) + cv_data_loader = DataLoader( + cv_dataset, + batch_size=None, + pin_memory=args.pin_memory, + num_workers=args.num_workers, + prefetch_factor=args.prefetch, + ) + + if "fbank_conf" in configs["dataset_conf"]: + input_dim = configs["dataset_conf"]["fbank_conf"]["num_mel_bins"] + else: + input_dim = configs["dataset_conf"]["mfcc_conf"]["num_mel_bins"] + vocab_size = len(symbol_table) + + # Save configs to model_dir/train.yaml for inference and export + configs["input_dim"] = input_dim + configs["output_dim"] = vocab_size + configs["cmvn_file"] = args.cmvn + configs["is_json_cmvn"] = True + configs["lfmmi_dir"] = args.lfmmi_dir + + if args.rank == 0: + saved_config_path = os.path.join(args.model_dir, "train.yaml") + with open(saved_config_path, "w") as fout: + data = yaml.dump(configs) + fout.write(data) + + # Init asr model from configs + model = init_model(configs) + print(model) + num_params = sum(p.numel() for p in model.parameters()) + print("the number of model params: {:,d}".format(num_params)) + + # !!!IMPORTANT!!! + # Try to export the model by script, if fails, we should refine + # the code to satisfy the script export requirements + if args.rank == 0: + script_model = torch.jit.script(model) + script_model.save(os.path.join(args.model_dir, "init.zip")) + executor = Executor() + # If specify checkpoint, load some info from checkpoint + if args.checkpoint is not None: + infos = load_checkpoint(model, args.checkpoint) + elif args.enc_init is not None: + logging.info("load pretrained encoders: {}".format(args.enc_init)) + infos = load_trained_modules(model, args) + else: + infos = {} + start_epoch = infos.get("epoch", -1) + 1 + cv_loss = infos.get("cv_loss", 0.0) + step = infos.get("step", -1) + + num_epochs = configs.get("max_epoch", 100) + model_dir = args.model_dir + writer = None + if args.rank == 0: + os.makedirs(model_dir, exist_ok=True) + exp_id = os.path.basename(model_dir) + writer = SummaryWriter(os.path.join(args.tensorboard_dir, exp_id)) + + if distributed: + assert torch.cuda.is_available() + # cuda model is required for nn.parallel.DistributedDataParallel + model.cuda() + model = torch.nn.parallel.DistributedDataParallel( + model, find_unused_parameters=True + ) + device = torch.device("cuda") + if args.fp16_grad_sync: + from torch.distributed.algorithms.ddp_comm_hooks import ( + default as comm_hooks, + ) + + model.register_comm_hook(state=None, hook=comm_hooks.fp16_compress_hook) + else: + use_cuda = args.gpu >= 0 and torch.cuda.is_available() + device = torch.device("cuda" if use_cuda else "cpu") + model = model.to(device) + + if configs["optim"] == "adam": + optimizer = optim.Adam(model.parameters(), **configs["optim_conf"]) + elif configs["optim"] == "adamw": + optimizer = optim.AdamW(model.parameters(), **configs["optim_conf"]) + else: + raise ValueError("unknown optimizer: " + configs["optim"]) + if configs["scheduler"] == "warmuplr": + scheduler = WarmupLR(optimizer, **configs["scheduler_conf"]) + elif configs["scheduler"] == "NoamHoldAnnealing": + scheduler = NoamHoldAnnealing(optimizer, **configs["scheduler_conf"]) + else: + raise ValueError("unknown scheduler: " + configs["scheduler"]) + + final_epoch = None + configs["rank"] = args.rank + configs["is_distributed"] = distributed + configs["use_amp"] = args.use_amp + if start_epoch == 0 and args.rank == 0: + save_model_path = os.path.join(model_dir, "init.pt") + save_checkpoint(model, save_model_path) + + # Start training loop + executor.step = step + scheduler.set_step(step) + # used for pytorch amp mixed precision training + scaler = None + if args.use_amp: + scaler = torch.cuda.amp.GradScaler() + + for epoch in range(start_epoch, num_epochs): + train_dataset.set_epoch(epoch) + configs["epoch"] = epoch + lr = optimizer.param_groups[0]["lr"] + logging.info("Epoch {} TRAIN info lr {}".format(epoch, lr)) + executor.train( + model, + optimizer, + scheduler, + train_data_loader, + device, + writer, + configs, + scaler, + ) + total_loss, num_seen_utts = executor.cv(model, cv_data_loader, device, configs) + cv_loss = total_loss / num_seen_utts + + logging.info("Epoch {} CV info cv_loss {}".format(epoch, cv_loss)) + if args.rank == 0: + save_model_path = os.path.join(model_dir, "{}.pt".format(epoch)) + save_checkpoint( + model, + save_model_path, + {"epoch": epoch, "lr": lr, "cv_loss": cv_loss, "step": executor.step}, + ) + writer.add_scalar("epoch/cv_loss", cv_loss, epoch) + writer.add_scalar("epoch/lr", lr, epoch) + final_epoch = epoch + + if final_epoch is not None and args.rank == 0: + final_model_path = os.path.join(model_dir, "final.pt") + os.remove(final_model_path) if os.path.exists(final_model_path) else None + os.symlink("{}.pt".format(final_epoch), final_model_path) + writer.close() + + +if __name__ == "__main__": + main() diff --git a/modules/wenet_extractor/cif/__pycache__/predictor.cpython-39.pyc b/modules/wenet_extractor/cif/__pycache__/predictor.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ef13cd32518d9e54dfffd674d0d9c28094af3473 Binary files /dev/null and b/modules/wenet_extractor/cif/__pycache__/predictor.cpython-39.pyc differ diff --git a/modules/wenet_extractor/cif/predictor.py b/modules/wenet_extractor/cif/predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..91428c4c418da3026e6865f334e4b4cdafd02900 --- /dev/null +++ b/modules/wenet_extractor/cif/predictor.py @@ -0,0 +1,274 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +from typing import Optional + +import torch +from torch import nn +from modules.wenet_extractor.utils.mask import make_pad_mask + + +class Predictor(nn.Module): + def __init__( + self, + idim, + l_order, + r_order, + threshold=1.0, + dropout=0.1, + smooth_factor=1.0, + noise_threshold=0, + tail_threshold=0.45, + ): + super().__init__() + + self.pad = nn.ConstantPad1d((l_order, r_order), 0.0) + self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim) + self.cif_output = nn.Linear(idim, 1) + self.dropout = torch.nn.Dropout(p=dropout) + self.threshold = threshold + self.smooth_factor = smooth_factor + self.noise_threshold = noise_threshold + self.tail_threshold = tail_threshold + + def forward( + self, + hidden, + target_label: Optional[torch.Tensor] = None, + mask: torch.Tensor = torch.tensor(0), + ignore_id: int = -1, + mask_chunk_predictor: Optional[torch.Tensor] = None, + target_label_length: Optional[torch.Tensor] = None, + ): + h = hidden + context = h.transpose(1, 2) + queries = self.pad(context) + memory = self.cif_conv1d(queries) + output = memory + context + output = self.dropout(output) + output = output.transpose(1, 2) + output = torch.relu(output) + output = self.cif_output(output) + alphas = torch.sigmoid(output) + alphas = torch.nn.functional.relu( + alphas * self.smooth_factor - self.noise_threshold + ) + if mask is not None: + mask = mask.transpose(-1, -2).float() + alphas = alphas * mask + if mask_chunk_predictor is not None: + alphas = alphas * mask_chunk_predictor + alphas = alphas.squeeze(-1) + mask = mask.squeeze(-1) + if target_label_length is not None: + target_length = target_label_length + elif target_label is not None: + target_length = (target_label != ignore_id).float().sum(-1) + else: + target_length = None + token_num = alphas.sum(-1) + if target_length is not None: + alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1)) + elif self.tail_threshold > 0.0: + hidden, alphas, token_num = self.tail_process_fn( + hidden, alphas, token_num, mask=mask + ) + + acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold) + + if target_length is None and self.tail_threshold > 0.0: + token_num_int = torch.max(token_num).type(torch.int32).item() + acoustic_embeds = acoustic_embeds[:, :token_num_int, :] + + return acoustic_embeds, token_num, alphas, cif_peak + + def tail_process_fn( + self, + hidden, + alphas, + token_num: Optional[torch.Tensor] = None, + mask: Optional[torch.Tensor] = None, + ): + b, t, d = hidden.size() + tail_threshold = self.tail_threshold + if mask is not None: + zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device) + ones_t = torch.ones_like(zeros_t) + mask_1 = torch.cat([mask, zeros_t], dim=1) + mask_2 = torch.cat([ones_t, mask], dim=1) + mask = mask_2 - mask_1 + tail_threshold = mask * tail_threshold + alphas = torch.cat([alphas, zeros_t], dim=1) + alphas = torch.add(alphas, tail_threshold) + else: + tail_threshold_tensor = torch.tensor( + [tail_threshold], dtype=alphas.dtype + ).to(alphas.device) + tail_threshold_tensor = torch.reshape(tail_threshold_tensor, (1, 1)) + alphas = torch.cat([alphas, tail_threshold_tensor], dim=1) + zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device) + hidden = torch.cat([hidden, zeros], dim=1) + token_num = alphas.sum(dim=-1) + token_num_floor = torch.floor(token_num) + + return hidden, alphas, token_num_floor + + def gen_frame_alignments( + self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None + ): + batch_size, maximum_length = alphas.size() + int_type = torch.int32 + + is_training = self.training + if is_training: + token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type) + else: + token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type) + + max_token_num = torch.max(token_num).item() + + alphas_cumsum = torch.cumsum(alphas, dim=1) + alphas_cumsum = torch.floor(alphas_cumsum).type(int_type) + alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1) + + index = torch.ones([batch_size, max_token_num], dtype=int_type) + index = torch.cumsum(index, dim=1) + index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device) + + index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type) + index_div_bool_zeros = index_div.eq(0) + index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1 + index_div_bool_zeros_count = torch.clamp( + index_div_bool_zeros_count, 0, encoder_sequence_length.max() + ) + token_num_mask = (~make_pad_mask(token_num, max_len=max_token_num)).to( + token_num.device + ) + index_div_bool_zeros_count *= token_num_mask + + index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat( + 1, 1, maximum_length + ) + ones = torch.ones_like(index_div_bool_zeros_count_tile) + zeros = torch.zeros_like(index_div_bool_zeros_count_tile) + ones = torch.cumsum(ones, dim=2) + cond = index_div_bool_zeros_count_tile == ones + index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones) + + index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type( + torch.bool + ) + index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type( + int_type + ) + index_div_bool_zeros_count_tile_out = torch.sum( + index_div_bool_zeros_count_tile, dim=1 + ) + index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type( + int_type + ) + predictor_mask = ( + ( + ~make_pad_mask( + encoder_sequence_length, max_len=encoder_sequence_length.max() + ) + ) + .type(int_type) + .to(encoder_sequence_length.device) + ) + index_div_bool_zeros_count_tile_out = ( + index_div_bool_zeros_count_tile_out * predictor_mask + ) + + predictor_alignments = index_div_bool_zeros_count_tile_out + predictor_alignments_length = predictor_alignments.sum(-1).type( + encoder_sequence_length.dtype + ) + return predictor_alignments.detach(), predictor_alignments_length.detach() + + +class MAELoss(nn.Module): + def __init__(self, normalize_length=False): + super(MAELoss, self).__init__() + self.normalize_length = normalize_length + self.criterion = torch.nn.L1Loss(reduction="sum") + + def forward(self, token_length, pre_token_length): + loss_token_normalizer = token_length.size(0) + if self.normalize_length: + loss_token_normalizer = token_length.sum().type(torch.float32) + loss = self.criterion(token_length, pre_token_length) + loss = loss / loss_token_normalizer + return loss + + +def cif(hidden: torch.Tensor, alphas: torch.Tensor, threshold: float): + batch_size, len_time, hidden_size = hidden.size() + + # loop varss + integrate = torch.zeros([batch_size], device=hidden.device) + frame = torch.zeros([batch_size, hidden_size], device=hidden.device) + # intermediate vars along time + list_fires = [] + list_frames = [] + + for t in range(len_time): + alpha = alphas[:, t] + distribution_completion = ( + torch.ones([batch_size], device=hidden.device) - integrate + ) + + integrate += alpha + list_fires.append(integrate) + + fire_place = integrate >= threshold + integrate = torch.where( + fire_place, + integrate - torch.ones([batch_size], device=hidden.device), + integrate, + ) + cur = torch.where(fire_place, distribution_completion, alpha) + remainds = alpha - cur + + frame += cur[:, None] * hidden[:, t, :] + list_frames.append(frame) + frame = torch.where( + fire_place[:, None].repeat(1, hidden_size), + remainds[:, None] * hidden[:, t, :], + frame, + ) + + fires = torch.stack(list_fires, 1) + frames = torch.stack(list_frames, 1) + list_ls = [] + len_labels = torch.round(alphas.sum(-1)).int() + max_label_len = len_labels.max() + for b in range(batch_size): + fire = fires[b, :] + l = torch.index_select( + frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze() + ) + pad_l = torch.zeros( + [int(max_label_len - l.size(0)), hidden_size], device=hidden.device + ) + list_ls.append(torch.cat([l, pad_l], 0)) + return torch.stack(list_ls, 0), fires diff --git a/modules/wenet_extractor/dataset/__init__.py b/modules/wenet_extractor/dataset/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/wenet_extractor/dataset/dataset.py b/modules/wenet_extractor/dataset/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..c4ef135bf083be9a18be62d2981196b26b81b394 --- /dev/null +++ b/modules/wenet_extractor/dataset/dataset.py @@ -0,0 +1,211 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +import random + +import torch +import torch.distributed as dist +from torch.utils.data import IterableDataset + +import wenet.dataset.processor as processor +from wenet.utils.file_utils import read_lists + + +class Processor(IterableDataset): + def __init__(self, source, f, *args, **kw): + assert callable(f) + self.source = source + self.f = f + self.args = args + self.kw = kw + + def set_epoch(self, epoch): + self.source.set_epoch(epoch) + + def __iter__(self): + """Return an iterator over the source dataset processed by the + given processor. + """ + assert self.source is not None + assert callable(self.f) + return self.f(iter(self.source), *self.args, **self.kw) + + def apply(self, f): + assert callable(f) + return Processor(self, f, *self.args, **self.kw) + + +class DistributedSampler: + def __init__(self, shuffle=True, partition=True): + self.epoch = -1 + self.update() + self.shuffle = shuffle + self.partition = partition + + def update(self): + assert dist.is_available() + if dist.is_initialized(): + self.rank = dist.get_rank() + self.world_size = dist.get_world_size() + else: + self.rank = 0 + self.world_size = 1 + worker_info = torch.utils.data.get_worker_info() + if worker_info is None: + self.worker_id = 0 + self.num_workers = 1 + else: + self.worker_id = worker_info.id + self.num_workers = worker_info.num_workers + return dict( + rank=self.rank, + world_size=self.world_size, + worker_id=self.worker_id, + num_workers=self.num_workers, + ) + + def set_epoch(self, epoch): + self.epoch = epoch + + def sample(self, data): + """Sample data according to rank/world_size/num_workers + + Args: + data(List): input data list + + Returns: + List: data list after sample + """ + data = list(range(len(data))) + # TODO(Binbin Zhang): fix this + # We can not handle uneven data for CV on DDP, so we don't + # sample data by rank, that means every GPU gets the same + # and all the CV data + if self.partition: + if self.shuffle: + random.Random(self.epoch).shuffle(data) + data = data[self.rank :: self.world_size] + data = data[self.worker_id :: self.num_workers] + return data + + +class DataList(IterableDataset): + def __init__(self, lists, shuffle=True, partition=True): + self.lists = lists + self.sampler = DistributedSampler(shuffle, partition) + + def set_epoch(self, epoch): + self.sampler.set_epoch(epoch) + + def __iter__(self): + sampler_info = self.sampler.update() + indexes = self.sampler.sample(self.lists) + for index in indexes: + # yield dict(src=src) + data = dict(src=self.lists[index]) + data.update(sampler_info) + yield data + + +def Dataset( + data_type, + data_list_file, + symbol_table, + conf, + bpe_model=None, + non_lang_syms=None, + partition=True, +): + """Construct dataset from arguments + + We have two shuffle stage in the Dataset. The first is global + shuffle at shards tar/raw file level. The second is global shuffle + at training samples level. + + Args: + data_type(str): raw/shard + bpe_model(str): model for english bpe part + partition(bool): whether to do data partition in terms of rank + """ + assert data_type in ["raw", "shard"] + lists = read_lists(data_list_file) + shuffle = conf.get("shuffle", True) + dataset = DataList(lists, shuffle=shuffle, partition=partition) + if data_type == "shard": + dataset = Processor(dataset, processor.url_opener) + dataset = Processor(dataset, processor.tar_file_and_group) + else: + dataset = Processor(dataset, processor.parse_raw) + + dataset = Processor( + dataset, + processor.tokenize, + symbol_table, + bpe_model, + non_lang_syms, + conf.get("split_with_space", False), + ) + filter_conf = conf.get("filter_conf", {}) + dataset = Processor(dataset, processor.filter, **filter_conf) + + resample_conf = conf.get("resample_conf", {}) + dataset = Processor(dataset, processor.resample, **resample_conf) + + speed_perturb = conf.get("speed_perturb", False) + if speed_perturb: + dataset = Processor(dataset, processor.speed_perturb) + + feats_type = conf.get("feats_type", "fbank") + assert feats_type in ["fbank", "mfcc"] + if feats_type == "fbank": + fbank_conf = conf.get("fbank_conf", {}) + dataset = Processor(dataset, processor.compute_fbank, **fbank_conf) + elif feats_type == "mfcc": + mfcc_conf = conf.get("mfcc_conf", {}) + dataset = Processor(dataset, processor.compute_mfcc, **mfcc_conf) + + spec_aug = conf.get("spec_aug", True) + spec_sub = conf.get("spec_sub", False) + spec_trim = conf.get("spec_trim", False) + if spec_aug: + spec_aug_conf = conf.get("spec_aug_conf", {}) + dataset = Processor(dataset, processor.spec_aug, **spec_aug_conf) + if spec_sub: + spec_sub_conf = conf.get("spec_sub_conf", {}) + dataset = Processor(dataset, processor.spec_sub, **spec_sub_conf) + if spec_trim: + spec_trim_conf = conf.get("spec_trim_conf", {}) + dataset = Processor(dataset, processor.spec_trim, **spec_trim_conf) + + if shuffle: + shuffle_conf = conf.get("shuffle_conf", {}) + dataset = Processor(dataset, processor.shuffle, **shuffle_conf) + + sort = conf.get("sort", True) + if sort: + sort_conf = conf.get("sort_conf", {}) + dataset = Processor(dataset, processor.sort, **sort_conf) + + batch_conf = conf.get("batch_conf", {}) + dataset = Processor(dataset, processor.batch, **batch_conf) + dataset = Processor(dataset, processor.padding) + return dataset diff --git a/modules/wenet_extractor/dataset/kaldi_io.py b/modules/wenet_extractor/dataset/kaldi_io.py new file mode 100644 index 0000000000000000000000000000000000000000..1bddd67e69411270b7d9d799fb35a2f30c6fd744 --- /dev/null +++ b/modules/wenet_extractor/dataset/kaldi_io.py @@ -0,0 +1,852 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +import numpy as np +import sys, os, re, gzip, struct + +################################################# +# Adding kaldi tools to shell path, + +# Select kaldi, +if not "KALDI_ROOT" in os.environ: + # Default! To change run python with 'export KALDI_ROOT=/some_dir python' + os.environ["KALDI_ROOT"] = "/mnt/matylda5/iveselyk/Tools/kaldi-trunk" + +# Add kaldi tools to path, +os.environ["PATH"] = ( + os.popen( + "echo $KALDI_ROOT/src/bin:$KALDI_ROOT/tools/openfst/bin:$KALDI_ROOT/src/fstbin/:$KALDI_ROOT/src/gmmbin/:$KALDI_ROOT/src/featbin/:$KALDI_ROOT/src/lm/:$KALDI_ROOT/src/sgmmbin/:$KALDI_ROOT/src/sgmm2bin/:$KALDI_ROOT/src/fgmmbin/:$KALDI_ROOT/src/latbin/:$KALDI_ROOT/src/nnetbin:$KALDI_ROOT/src/nnet2bin:$KALDI_ROOT/src/nnet3bin:$KALDI_ROOT/src/online2bin/:$KALDI_ROOT/src/ivectorbin/:$KALDI_ROOT/src/lmbin/" + ) + .readline() + .strip() + + ":" + + os.environ["PATH"] +) + + +################################################# +# Define all custom exceptions, +class UnsupportedDataType(Exception): + pass + + +class UnknownVectorHeader(Exception): + pass + + +class UnknownMatrixHeader(Exception): + pass + + +class BadSampleSize(Exception): + pass + + +class BadInputFormat(Exception): + pass + + +class SubprocessFailed(Exception): + pass + + +################################################# +# Data-type independent helper functions, + + +def open_or_fd(file, mode="rb"): + """fd = open_or_fd(file) + Open file, gzipped file, pipe, or forward the file-descriptor. + Eventually seeks in the 'file' argument contains ':offset' suffix. + """ + offset = None + try: + # strip 'ark:' prefix from r{x,w}filename (optional), + if re.search("^(ark|scp)(,scp|,b|,t|,n?f|,n?p|,b?o|,n?s|,n?cs)*:", file): + (prefix, file) = file.split(":", 1) + # separate offset from filename (optional), + if re.search(":[0-9]+$", file): + (file, offset) = file.rsplit(":", 1) + # input pipe? + if file[-1] == "|": + fd = popen(file[:-1], "rb") # custom, + # output pipe? + elif file[0] == "|": + fd = popen(file[1:], "wb") # custom, + # is it gzipped? + elif file.split(".")[-1] == "gz": + fd = gzip.open(file, mode) + # a normal file... + else: + fd = open(file, mode) + except TypeError: + # 'file' is opened file descriptor, + fd = file + # Eventually seek to offset, + if offset != None: + fd.seek(int(offset)) + return fd + + +# based on '/usr/local/lib/python3.4/os.py' +def popen(cmd, mode="rb"): + if not isinstance(cmd, str): + raise TypeError("invalid cmd type (%s, expected string)" % type(cmd)) + + import subprocess, io, threading + + # cleanup function for subprocesses, + def cleanup(proc, cmd): + ret = proc.wait() + if ret > 0: + raise SubprocessFailed("cmd %s returned %d !" % (cmd, ret)) + return + + # text-mode, + if mode == "r": + proc = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE) + threading.Thread(target=cleanup, args=(proc, cmd)).start() # clean-up thread, + return io.TextIOWrapper(proc.stdout) + elif mode == "w": + proc = subprocess.Popen(cmd, shell=True, stdin=subprocess.PIPE) + threading.Thread(target=cleanup, args=(proc, cmd)).start() # clean-up thread, + return io.TextIOWrapper(proc.stdin) + # binary, + elif mode == "rb": + proc = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE) + threading.Thread(target=cleanup, args=(proc, cmd)).start() # clean-up thread, + return proc.stdout + elif mode == "wb": + proc = subprocess.Popen(cmd, shell=True, stdin=subprocess.PIPE) + threading.Thread(target=cleanup, args=(proc, cmd)).start() # clean-up thread, + return proc.stdin + # sanity, + else: + raise ValueError("invalid mode %s" % mode) + + +def read_key(fd): + """[key] = read_key(fd) + Read the utterance-key from the opened ark/stream descriptor 'fd'. + """ + key = "" + while 1: + char = fd.read(1).decode("latin1") + if char == "": + break + if char == " ": + break + key += char + key = key.strip() + if key == "": + return None # end of file, + assert re.match("^\S+$", key) != None # check format (no whitespace!) + return key + + +################################################# +# Integer vectors (alignments, ...), + + +def read_ali_ark(file_or_fd): + """Alias to 'read_vec_int_ark()'""" + return read_vec_int_ark(file_or_fd) + + +def read_vec_int_ark(file_or_fd): + """generator(key,vec) = read_vec_int_ark(file_or_fd) + Create generator of (key,vector) tuples, which reads from the ark file/stream. + file_or_fd : ark, gzipped ark, pipe or opened file descriptor. + + Read ark to a 'dictionary': + d = { u:d for u,d in kaldi_io.read_vec_int_ark(file) } + """ + fd = open_or_fd(file_or_fd) + try: + key = read_key(fd) + while key: + ali = read_vec_int(fd) + yield key, ali + key = read_key(fd) + finally: + if fd is not file_or_fd: + fd.close() + + +def read_vec_int_scp(file_or_fd): + """generator(key,vec) = read_vec_int_scp(file_or_fd) + Returns generator of (key,vector) tuples, read according to kaldi scp. + file_or_fd : scp, gzipped scp, pipe or opened file descriptor. + + Iterate the scp: + for key,vec in kaldi_io.read_vec_int_scp(file): + ... + + Read scp to a 'dictionary': + d = { key:vec for key,mat in kaldi_io.read_vec_int_scp(file) } + """ + fd = open_or_fd(file_or_fd) + try: + for line in fd: + (key, rxfile) = line.decode().split(" ") + vec = read_vec_int(rxfile) + yield key, vec + finally: + if fd is not file_or_fd: + fd.close() + + +def read_vec_int(file_or_fd): + """[int-vec] = read_vec_int(file_or_fd) + Read kaldi integer vector, ascii or binary input, + """ + fd = open_or_fd(file_or_fd) + binary = fd.read(2).decode() + if binary == "\0B": # binary flag + assert fd.read(1).decode() == "\4" + # int-size + vec_size = np.frombuffer(fd.read(4), dtype="int32", count=1)[0] # vector dim + # Elements from int32 vector are sored in tuples: (sizeof(int32), value), + vec = np.frombuffer( + fd.read(vec_size * 5), + dtype=[("size", "int8"), ("value", "int32")], + count=vec_size, + ) + assert vec[0]["size"] == 4 # int32 size, + ans = vec[:]["value"] # values are in 2nd column, + else: # ascii, + arr = (binary + fd.readline().decode()).strip().split() + try: + arr.remove("[") + arr.remove("]") # optionally + except ValueError: + pass + ans = np.array(arr, dtype=int) + if fd is not file_or_fd: + fd.close() # cleanup + return ans + + +# Writing, +def write_vec_int(file_or_fd, v, key=""): + """write_vec_int(f, v, key='') + Write a binary kaldi integer vector to filename or stream. + Arguments: + file_or_fd : filename or opened file descriptor for writing, + v : the vector to be stored, + key (optional) : used for writing ark-file, the utterance-id gets written before the vector. + + Example of writing single vector: + kaldi_io.write_vec_int(filename, vec) + + Example of writing arkfile: + with open(ark_file,'w') as f: + for key,vec in dict.iteritems(): + kaldi_io.write_vec_flt(f, vec, key=key) + """ + fd = open_or_fd(file_or_fd, mode="wb") + if sys.version_info[0] == 3: + assert fd.mode == "wb" + try: + if key != "": + fd.write( + (key + " ").encode("latin1") + ) # ark-files have keys (utterance-id), + fd.write("\0B".encode()) # we write binary! + # dim, + fd.write("\4".encode()) # int32 type, + fd.write(struct.pack(np.dtype("int32").char, v.shape[0])) + # data, + for i in range(len(v)): + fd.write("\4".encode()) # int32 type, + fd.write(struct.pack(np.dtype("int32").char, v[i])) # binary, + finally: + if fd is not file_or_fd: + fd.close() + + +################################################# +# Float vectors (confidences, ivectors, ...), + + +# Reading, +def read_vec_flt_scp(file_or_fd): + """generator(key,mat) = read_vec_flt_scp(file_or_fd) + Returns generator of (key,vector) tuples, read according to kaldi scp. + file_or_fd : scp, gzipped scp, pipe or opened file descriptor. + + Iterate the scp: + for key,vec in kaldi_io.read_vec_flt_scp(file): + ... + + Read scp to a 'dictionary': + d = { key:mat for key,mat in kaldi_io.read_mat_scp(file) } + """ + fd = open_or_fd(file_or_fd) + try: + for line in fd: + (key, rxfile) = line.decode().split(" ") + vec = read_vec_flt(rxfile) + yield key, vec + finally: + if fd is not file_or_fd: + fd.close() + + +def read_vec_flt_ark(file_or_fd): + """generator(key,vec) = read_vec_flt_ark(file_or_fd) + Create generator of (key,vector) tuples, reading from an ark file/stream. + file_or_fd : ark, gzipped ark, pipe or opened file descriptor. + + Read ark to a 'dictionary': + d = { u:d for u,d in kaldi_io.read_vec_flt_ark(file) } + """ + fd = open_or_fd(file_or_fd) + try: + key = read_key(fd) + while key: + ali = read_vec_flt(fd) + yield key, ali + key = read_key(fd) + finally: + if fd is not file_or_fd: + fd.close() + + +def read_vec_flt(file_or_fd): + """[flt-vec] = read_vec_flt(file_or_fd) + Read kaldi float vector, ascii or binary input, + """ + fd = open_or_fd(file_or_fd) + binary = fd.read(2).decode() + if binary == "\0B": # binary flag + # Data type, + header = fd.read(3).decode() + if header == "FV ": + sample_size = 4 # floats + elif header == "DV ": + sample_size = 8 # doubles + else: + raise UnknownVectorHeader("The header contained '%s'" % header) + assert sample_size > 0 + # Dimension, + assert fd.read(1).decode() == "\4" + # int-size + vec_size = np.frombuffer(fd.read(4), dtype="int32", count=1)[0] # vector dim + # Read whole vector, + buf = fd.read(vec_size * sample_size) + if sample_size == 4: + ans = np.frombuffer(buf, dtype="float32") + elif sample_size == 8: + ans = np.frombuffer(buf, dtype="float64") + else: + raise BadSampleSize + return ans + else: # ascii, + arr = (binary + fd.readline().decode()).strip().split() + try: + arr.remove("[") + arr.remove("]") # optionally + except ValueError: + pass + ans = np.array(arr, dtype=float) + if fd is not file_or_fd: + fd.close() # cleanup + return ans + + +# Writing, +def write_vec_flt(file_or_fd, v, key=""): + """write_vec_flt(f, v, key='') + Write a binary kaldi vector to filename or stream. Supports 32bit and 64bit floats. + Arguments: + file_or_fd : filename or opened file descriptor for writing, + v : the vector to be stored, + key (optional) : used for writing ark-file, the utterance-id gets written before the vector. + + Example of writing single vector: + kaldi_io.write_vec_flt(filename, vec) + + Example of writing arkfile: + with open(ark_file,'w') as f: + for key,vec in dict.iteritems(): + kaldi_io.write_vec_flt(f, vec, key=key) + """ + fd = open_or_fd(file_or_fd, mode="wb") + if sys.version_info[0] == 3: + assert fd.mode == "wb" + try: + if key != "": + fd.write( + (key + " ").encode("latin1") + ) # ark-files have keys (utterance-id), + fd.write("\0B".encode()) # we write binary! + # Data-type, + if v.dtype == "float32": + fd.write("FV ".encode()) + elif v.dtype == "float64": + fd.write("DV ".encode()) + else: + raise UnsupportedDataType( + "'%s', please use 'float32' or 'float64'" % v.dtype + ) + # Dim, + fd.write("\04".encode()) + fd.write(struct.pack(np.dtype("uint32").char, v.shape[0])) # dim + # Data, + fd.write(v.tobytes()) + finally: + if fd is not file_or_fd: + fd.close() + + +################################################# +# Float matrices (features, transformations, ...), + + +# Reading, +def read_mat_scp(file_or_fd): + """generator(key,mat) = read_mat_scp(file_or_fd) + Returns generator of (key,matrix) tuples, read according to kaldi scp. + file_or_fd : scp, gzipped scp, pipe or opened file descriptor. + + Iterate the scp: + for key,mat in kaldi_io.read_mat_scp(file): + ... + + Read scp to a 'dictionary': + d = { key:mat for key,mat in kaldi_io.read_mat_scp(file) } + """ + fd = open_or_fd(file_or_fd) + try: + for line in fd: + (key, rxfile) = line.decode().split(" ") + mat = read_mat(rxfile) + yield key, mat + finally: + if fd is not file_or_fd: + fd.close() + + +def read_mat_ark(file_or_fd): + """generator(key,mat) = read_mat_ark(file_or_fd) + Returns generator of (key,matrix) tuples, read from ark file/stream. + file_or_fd : scp, gzipped scp, pipe or opened file descriptor. + + Iterate the ark: + for key,mat in kaldi_io.read_mat_ark(file): + ... + + Read ark to a 'dictionary': + d = { key:mat for key,mat in kaldi_io.read_mat_ark(file) } + """ + fd = open_or_fd(file_or_fd) + try: + key = read_key(fd) + while key: + mat = read_mat(fd) + yield key, mat + key = read_key(fd) + finally: + if fd is not file_or_fd: + fd.close() + + +def read_mat(file_or_fd): + """[mat] = read_mat(file_or_fd) + Reads single kaldi matrix, supports ascii and binary. + file_or_fd : file, gzipped file, pipe or opened file descriptor. + """ + fd = open_or_fd(file_or_fd) + try: + binary = fd.read(2).decode() + if binary == "\0B": + mat = _read_mat_binary(fd) + else: + assert binary == " [" + mat = _read_mat_ascii(fd) + finally: + if fd is not file_or_fd: + fd.close() + return mat + + +def _read_mat_binary(fd): + # Data type + header = fd.read(3).decode() + # 'CM', 'CM2', 'CM3' are possible values, + if header.startswith("CM"): + return _read_compressed_mat(fd, header) + elif header == "FM ": + sample_size = 4 # floats + elif header == "DM ": + sample_size = 8 # doubles + else: + raise UnknownMatrixHeader("The header contained '%s'" % header) + assert sample_size > 0 + # Dimensions + s1, rows, s2, cols = np.frombuffer( + fd.read(10), dtype="int8,int32,int8,int32", count=1 + )[0] + # Read whole matrix + buf = fd.read(rows * cols * sample_size) + if sample_size == 4: + vec = np.frombuffer(buf, dtype="float32") + elif sample_size == 8: + vec = np.frombuffer(buf, dtype="float64") + else: + raise BadSampleSize + mat = np.reshape(vec, (rows, cols)) + return mat + + +def _read_mat_ascii(fd): + rows = [] + while 1: + line = fd.readline().decode() + if len(line) == 0: + raise BadInputFormat # eof, should not happen! + if len(line.strip()) == 0: + continue # skip empty line + arr = line.strip().split() + if arr[-1] != "]": + rows.append(np.array(arr, dtype="float32")) # not last line + else: + rows.append(np.array(arr[:-1], dtype="float32")) # last line + mat = np.vstack(rows) + return mat + + +def _read_compressed_mat(fd, format): + """Read a compressed matrix, + see: https://github.com/kaldi-asr/kaldi/blob/master/src/matrix/compressed-matrix.h + methods: CompressedMatrix::Read(...), CompressedMatrix::CopyToMat(...), + """ + assert format == "CM " # The formats CM2, CM3 are not supported... + + # Format of header 'struct', + global_header = np.dtype( + [ + ("minvalue", "float32"), + ("range", "float32"), + ("num_rows", "int32"), + ("num_cols", "int32"), + ] + ) # member '.format' is not written, + per_col_header = np.dtype( + [ + ("percentile_0", "uint16"), + ("percentile_25", "uint16"), + ("percentile_75", "uint16"), + ("percentile_100", "uint16"), + ] + ) + + # Mapping for percentiles in col-headers, + def uint16_to_float(value, min, range): + return np.float32(min + range * 1.52590218966964e-05 * value) + + # Mapping for matrix elements, + def uint8_to_float_v2(vec, p0, p25, p75, p100): + # Split the vector by masks, + mask_0_64 = vec <= 64 + mask_193_255 = vec > 192 + mask_65_192 = ~(mask_0_64 | mask_193_255) + # Sanity check (useful but slow...), + # assert(len(vec) == np.sum(np.hstack([mask_0_64,mask_65_192,mask_193_255]))) + # assert(len(vec) == np.sum(np.any([mask_0_64,mask_65_192,mask_193_255], axis=0))) + # Build the float vector, + ans = np.empty(len(vec), dtype="float32") + ans[mask_0_64] = p0 + (p25 - p0) / 64.0 * vec[mask_0_64] + ans[mask_65_192] = p25 + (p75 - p25) / 128.0 * (vec[mask_65_192] - 64) + ans[mask_193_255] = p75 + (p100 - p75) / 63.0 * (vec[mask_193_255] - 192) + return ans + + # Read global header, + globmin, globrange, rows, cols = np.frombuffer( + fd.read(16), dtype=global_header, count=1 + )[0] + + # The data is structed as [Colheader, ... , Colheader, Data, Data , .... ] + # { cols }{ size } + col_headers = np.frombuffer(fd.read(cols * 8), dtype=per_col_header, count=cols) + data = np.reshape( + np.frombuffer(fd.read(cols * rows), dtype="uint8", count=cols * rows), + newshape=(cols, rows), + ) # stored as col-major, + + mat = np.empty((cols, rows), dtype="float32") + for i, col_header in enumerate(col_headers): + col_header_flt = [ + uint16_to_float(percentile, globmin, globrange) for percentile in col_header + ] + mat[i] = uint8_to_float_v2(data[i], *col_header_flt) + + return mat.T # transpose! col-major -> row-major, + + +def write_ark_scp(key, mat, ark_fout, scp_out): + mat_offset = write_mat(ark_fout, mat, key) + scp_line = "{}\t{}:{}".format(key, ark_fout.name, mat_offset) + scp_out.write(scp_line) + scp_out.write("\n") + + +# Writing, +def write_mat(file_or_fd, m, key=""): + """write_mat(f, m, key='') + Write a binary kaldi matrix to filename or stream. Supports 32bit and 64bit floats. + Arguments: + file_or_fd : filename of opened file descriptor for writing, + m : the matrix to be stored, + key (optional) : used for writing ark-file, the utterance-id gets written before the matrix. + + Example of writing single matrix: + kaldi_io.write_mat(filename, mat) + + Example of writing arkfile: + with open(ark_file,'w') as f: + for key,mat in dict.iteritems(): + kaldi_io.write_mat(f, mat, key=key) + """ + mat_offset = 0 + fd = open_or_fd(file_or_fd, mode="wb") + if sys.version_info[0] == 3: + assert fd.mode == "wb" + try: + if key != "": + fd.write( + (key + " ").encode("latin1") + ) # ark-files have keys (utterance-id), + mat_offset = fd.tell() + fd.write("\0B".encode()) # we write binary! + # Data-type, + if m.dtype == "float32": + fd.write("FM ".encode()) + elif m.dtype == "float64": + fd.write("DM ".encode()) + else: + raise UnsupportedDataType( + "'%s', please use 'float32' or 'float64'" % m.dtype + ) + # Dims, + fd.write("\04".encode()) + fd.write(struct.pack(np.dtype("uint32").char, m.shape[0])) # rows + fd.write("\04".encode()) + fd.write(struct.pack(np.dtype("uint32").char, m.shape[1])) # cols + # Data, + fd.write(m.tobytes()) + finally: + if fd is not file_or_fd: + fd.close() + return mat_offset + + +################################################# +# 'Posterior' kaldi type (posteriors, confusion network, nnet1 training targets, ...) +# Corresponds to: vector > > +# - outer vector: time axis +# - inner vector: records at the time +# - tuple: int = index, float = value +# + + +def read_cnet_ark(file_or_fd): + """Alias of function 'read_post_ark()', 'cnet' = confusion network""" + return read_post_ark(file_or_fd) + + +def read_post_ark(file_or_fd): + """generator(key,vec>) = read_post_ark(file) + Returns generator of (key,posterior) tuples, read from ark file. + file_or_fd : ark, gzipped ark, pipe or opened file descriptor. + + Iterate the ark: + for key,post in kaldi_io.read_post_ark(file): + ... + + Read ark to a 'dictionary': + d = { key:post for key,post in kaldi_io.read_post_ark(file) } + """ + fd = open_or_fd(file_or_fd) + try: + key = read_key(fd) + while key: + post = read_post(fd) + yield key, post + key = read_key(fd) + finally: + if fd is not file_or_fd: + fd.close() + + +def read_post(file_or_fd): + """[post] = read_post(file_or_fd) + Reads single kaldi 'Posterior' in binary format. + + The 'Posterior' is C++ type 'vector > >', + the outer-vector is usually time axis, inner-vector are the records + at given time, and the tuple is composed of an 'index' (integer) + and a 'float-value'. The 'float-value' can represent a probability + or any other numeric value. + + Returns vector of vectors of tuples. + """ + fd = open_or_fd(file_or_fd) + ans = [] + binary = fd.read(2).decode() + assert binary == "\0B" + # binary flag + assert fd.read(1).decode() == "\4" + # int-size + outer_vec_size = np.frombuffer(fd.read(4), dtype="int32", count=1)[ + 0 + ] # number of frames (or bins) + + # Loop over 'outer-vector', + for i in range(outer_vec_size): + assert fd.read(1).decode() == "\4" + # int-size + inner_vec_size = np.frombuffer(fd.read(4), dtype="int32", count=1)[ + 0 + ] # number of records for frame (or bin) + data = np.frombuffer( + fd.read(inner_vec_size * 10), + dtype=[ + ("size_idx", "int8"), + ("idx", "int32"), + ("size_post", "int8"), + ("post", "float32"), + ], + count=inner_vec_size, + ) + assert data[0]["size_idx"] == 4 + assert data[0]["size_post"] == 4 + ans.append(data[["idx", "post"]].tolist()) + + if fd is not file_or_fd: + fd.close() + return ans + + +################################################# +# Kaldi Confusion Network bin begin/end times, +# (kaldi stores CNs time info separately from the Posterior). +# + + +def read_cntime_ark(file_or_fd): + """generator(key,vec>) = read_cntime_ark(file_or_fd) + Returns generator of (key,cntime) tuples, read from ark file. + file_or_fd : file, gzipped file, pipe or opened file descriptor. + + Iterate the ark: + for key,time in kaldi_io.read_cntime_ark(file): + ... + + Read ark to a 'dictionary': + d = { key:time for key,time in kaldi_io.read_post_ark(file) } + """ + fd = open_or_fd(file_or_fd) + try: + key = read_key(fd) + while key: + cntime = read_cntime(fd) + yield key, cntime + key = read_key(fd) + finally: + if fd is not file_or_fd: + fd.close() + + +def read_cntime(file_or_fd): + """[cntime] = read_cntime(file_or_fd) + Reads single kaldi 'Confusion Network time info', in binary format: + C++ type: vector >. + (begin/end times of bins at the confusion network). + + Binary layout is ' ...' + + file_or_fd : file, gzipped file, pipe or opened file descriptor. + + Returns vector of tuples. + """ + fd = open_or_fd(file_or_fd) + binary = fd.read(2).decode() + assert binary == "\0B" + # assuming it's binary + + assert fd.read(1).decode() == "\4" + # int-size + vec_size = np.frombuffer(fd.read(4), dtype="int32", count=1)[ + 0 + ] # number of frames (or bins) + + data = np.frombuffer( + fd.read(vec_size * 10), + dtype=[ + ("size_beg", "int8"), + ("t_beg", "float32"), + ("size_end", "int8"), + ("t_end", "float32"), + ], + count=vec_size, + ) + assert data[0]["size_beg"] == 4 + assert data[0]["size_end"] == 4 + ans = data[["t_beg", "t_end"]].tolist() # Return vector of tuples (t_beg,t_end), + + if fd is not file_or_fd: + fd.close() + return ans + + +################################################# +# Segments related, +# + + +# Segments as 'Bool vectors' can be handy, +# - for 'superposing' the segmentations, +# - for frame-selection in Speaker-ID experiments, +def read_segments_as_bool_vec(segments_file): + """[ bool_vec ] = read_segments_as_bool_vec(segments_file) + using kaldi 'segments' file for 1 wav, format : ' ' + - t-beg, t-end is in seconds, + - assumed 100 frames/second, + """ + segs = np.loadtxt(segments_file, dtype="object,object,f,f", ndmin=1) + # Sanity checks, + assert len(segs) > 0 # empty segmentation is an error, + assert ( + len(np.unique([rec[1] for rec in segs])) == 1 + ) # segments with only 1 wav-file, + # Convert time to frame-indexes, + start = np.rint([100 * rec[2] for rec in segs]).astype(int) + end = np.rint([100 * rec[3] for rec in segs]).astype(int) + # Taken from 'read_lab_to_bool_vec', htk.py, + frms = np.repeat( + np.r_[np.tile([False, True], len(end)), False], + np.r_[np.c_[start - np.r_[0, end[:-1]], end - start].flat, 0], + ) + assert np.sum(end - start) == np.sum(frms) + return frms diff --git a/modules/wenet_extractor/dataset/processor.py b/modules/wenet_extractor/dataset/processor.py new file mode 100644 index 0000000000000000000000000000000000000000..df66f60affce9b5f22dcec0b283ed2c3599031c0 --- /dev/null +++ b/modules/wenet_extractor/dataset/processor.py @@ -0,0 +1,656 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +import logging +import json +import random +import re +import tarfile +from subprocess import PIPE, Popen +from urllib.parse import urlparse + +import torch +import torchaudio +import torchaudio.compliance.kaldi as kaldi +from torch.nn.utils.rnn import pad_sequence + +AUDIO_FORMAT_SETS = set(["flac", "mp3", "m4a", "ogg", "opus", "wav", "wma"]) + + +def url_opener(data): + """Give url or local file, return file descriptor + Inplace operation. + + Args: + data(Iterable[str]): url or local file list + + Returns: + Iterable[{src, stream}] + """ + for sample in data: + assert "src" in sample + # TODO(Binbin Zhang): support HTTP + url = sample["src"] + try: + pr = urlparse(url) + # local file + if pr.scheme == "" or pr.scheme == "file": + stream = open(url, "rb") + # network file, such as HTTP(HDFS/OSS/S3)/HTTPS/SCP + else: + cmd = f"wget -q -O - {url}" + process = Popen(cmd, shell=True, stdout=PIPE) + sample.update(process=process) + stream = process.stdout + sample.update(stream=stream) + yield sample + except Exception as ex: + logging.warning("Failed to open {}".format(url)) + + +def tar_file_and_group(data): + """Expand a stream of open tar files into a stream of tar file contents. + And groups the file with same prefix + + Args: + data: Iterable[{src, stream}] + + Returns: + Iterable[{key, wav, txt, sample_rate}] + """ + for sample in data: + assert "stream" in sample + stream = tarfile.open(fileobj=sample["stream"], mode="r|*") + prev_prefix = None + example = {} + valid = True + for tarinfo in stream: + name = tarinfo.name + pos = name.rfind(".") + assert pos > 0 + prefix, postfix = name[:pos], name[pos + 1 :] + if prev_prefix is not None and prefix != prev_prefix: + example["key"] = prev_prefix + if valid: + yield example + example = {} + valid = True + with stream.extractfile(tarinfo) as file_obj: + try: + if postfix == "txt": + example["txt"] = file_obj.read().decode("utf8").strip() + elif postfix in AUDIO_FORMAT_SETS: + waveform, sample_rate = torchaudio.load(file_obj) + example["wav"] = waveform + example["sample_rate"] = sample_rate + else: + example[postfix] = file_obj.read() + except Exception as ex: + valid = False + logging.warning("error to parse {}".format(name)) + prev_prefix = prefix + if prev_prefix is not None: + example["key"] = prev_prefix + yield example + stream.close() + if "process" in sample: + sample["process"].communicate() + sample["stream"].close() + + +def parse_raw(data): + """Parse key/wav/txt from json line + + Args: + data: Iterable[str], str is a json line has key/wav/txt + + Returns: + Iterable[{key, wav, txt, sample_rate}] + """ + for sample in data: + assert "src" in sample + json_line = sample["src"] + obj = json.loads(json_line) + assert "key" in obj + assert "wav" in obj + assert "txt" in obj + key = obj["key"] + wav_file = obj["wav"] + txt = obj["txt"] + try: + if "start" in obj: + assert "end" in obj + sample_rate = torchaudio.backend.sox_io_backend.info( + wav_file + ).sample_rate + start_frame = int(obj["start"] * sample_rate) + end_frame = int(obj["end"] * sample_rate) + waveform, _ = torchaudio.backend.sox_io_backend.load( + filepath=wav_file, + num_frames=end_frame - start_frame, + frame_offset=start_frame, + ) + else: + waveform, sample_rate = torchaudio.load(wav_file) + example = dict(key=key, txt=txt, wav=waveform, sample_rate=sample_rate) + yield example + except Exception as ex: + logging.warning("Failed to read {}".format(wav_file)) + + +def filter( + data, + max_length=10240, + min_length=10, + token_max_length=200, + token_min_length=1, + min_output_input_ratio=0.0005, + max_output_input_ratio=1, +): + """Filter sample according to feature and label length + Inplace operation. + + Args:: + data: Iterable[{key, wav, label, sample_rate}] + max_length: drop utterance which is greater than max_length(10ms) + min_length: drop utterance which is less than min_length(10ms) + token_max_length: drop utterance which is greater than + token_max_length, especially when use char unit for + english modeling + token_min_length: drop utterance which is + less than token_max_length + min_output_input_ratio: minimal ration of + token_length / feats_length(10ms) + max_output_input_ratio: maximum ration of + token_length / feats_length(10ms) + + Returns: + Iterable[{key, wav, label, sample_rate}] + """ + for sample in data: + assert "sample_rate" in sample + assert "wav" in sample + assert "label" in sample + # sample['wav'] is torch.Tensor, we have 100 frames every second + num_frames = sample["wav"].size(1) / sample["sample_rate"] * 100 + if num_frames < min_length: + continue + if num_frames > max_length: + continue + if len(sample["label"]) < token_min_length: + continue + if len(sample["label"]) > token_max_length: + continue + if num_frames != 0: + if len(sample["label"]) / num_frames < min_output_input_ratio: + continue + if len(sample["label"]) / num_frames > max_output_input_ratio: + continue + yield sample + + +def resample(data, resample_rate=16000): + """Resample data. + Inplace operation. + + Args: + data: Iterable[{key, wav, label, sample_rate}] + resample_rate: target resample rate + + Returns: + Iterable[{key, wav, label, sample_rate}] + """ + print("resample...") + for sample in data: + assert "sample_rate" in sample + assert "wav" in sample + sample_rate = sample["sample_rate"] + print("sample_rate: ", sample_rate) + print("resample_rate: ", resample_rate) + waveform = sample["wav"] + if sample_rate != resample_rate: + sample["sample_rate"] = resample_rate + sample["wav"] = torchaudio.transforms.Resample( + orig_freq=sample_rate, new_freq=resample_rate + )(waveform) + yield sample + + +def speed_perturb(data, speeds=None): + """Apply speed perturb to the data. + Inplace operation. + + Args: + data: Iterable[{key, wav, label, sample_rate}] + speeds(List[float]): optional speed + + Returns: + Iterable[{key, wav, label, sample_rate}] + """ + if speeds is None: + speeds = [0.9, 1.0, 1.1] + for sample in data: + assert "sample_rate" in sample + assert "wav" in sample + sample_rate = sample["sample_rate"] + waveform = sample["wav"] + speed = random.choice(speeds) + if speed != 1.0: + wav, _ = torchaudio.sox_effects.apply_effects_tensor( + waveform, + sample_rate, + [["speed", str(speed)], ["rate", str(sample_rate)]], + ) + sample["wav"] = wav + + yield sample + + +def compute_fbank(data, num_mel_bins=23, frame_length=25, frame_shift=10, dither=0.0): + """Extract fbank + + Args: + data: Iterable[{key, wav, label, sample_rate}] + + Returns: + Iterable[{key, feat, label}] + """ + for sample in data: + assert "sample_rate" in sample + assert "wav" in sample + assert "key" in sample + assert "label" in sample + sample_rate = sample["sample_rate"] + waveform = sample["wav"] + waveform = waveform * (1 << 15) + # Only keep key, feat, label + mat = kaldi.fbank( + waveform, + num_mel_bins=num_mel_bins, + frame_length=frame_length, + frame_shift=frame_shift, + dither=dither, + energy_floor=0.0, + sample_frequency=sample_rate, + ) + yield dict(key=sample["key"], label=sample["label"], feat=mat) + + +def compute_mfcc( + data, + num_mel_bins=23, + frame_length=25, + frame_shift=10, + dither=0.0, + num_ceps=40, + high_freq=0.0, + low_freq=20.0, +): + """Extract mfcc + + Args: + data: Iterable[{key, wav, label, sample_rate}] + + Returns: + Iterable[{key, feat, label}] + """ + for sample in data: + assert "sample_rate" in sample + assert "wav" in sample + assert "key" in sample + assert "label" in sample + sample_rate = sample["sample_rate"] + waveform = sample["wav"] + waveform = waveform * (1 << 15) + # Only keep key, feat, label + mat = kaldi.mfcc( + waveform, + num_mel_bins=num_mel_bins, + frame_length=frame_length, + frame_shift=frame_shift, + dither=dither, + num_ceps=num_ceps, + high_freq=high_freq, + low_freq=low_freq, + sample_frequency=sample_rate, + ) + yield dict(key=sample["key"], label=sample["label"], feat=mat) + + +def __tokenize_by_bpe_model(sp, txt): + tokens = [] + # CJK(China Japan Korea) unicode range is [U+4E00, U+9FFF], ref: + # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) + pattern = re.compile(r"([\u4e00-\u9fff])") + # Example: + # txt = "你好 ITS'S OKAY 的" + # chars = ["你", "好", " ITS'S OKAY ", "的"] + chars = pattern.split(txt.upper()) + mix_chars = [w for w in chars if len(w.strip()) > 0] + for ch_or_w in mix_chars: + # ch_or_w is a single CJK charater(i.e., "你"), do nothing. + if pattern.fullmatch(ch_or_w) is not None: + tokens.append(ch_or_w) + # ch_or_w contains non-CJK charaters(i.e., " IT'S OKAY "), + # encode ch_or_w using bpe_model. + else: + for p in sp.encode_as_pieces(ch_or_w): + tokens.append(p) + + return tokens + + +def tokenize( + data, symbol_table, bpe_model=None, non_lang_syms=None, split_with_space=False +): + """Decode text to chars or BPE + Inplace operation + + Args: + data: Iterable[{key, wav, txt, sample_rate}] + + Returns: + Iterable[{key, wav, txt, tokens, label, sample_rate}] + """ + if non_lang_syms is not None: + non_lang_syms_pattern = re.compile(r"(\[[^\[\]]+\]|<[^<>]+>|{[^{}]+})") + else: + non_lang_syms = {} + non_lang_syms_pattern = None + + if bpe_model is not None: + import sentencepiece as spm + + sp = spm.SentencePieceProcessor() + sp.load(bpe_model) + else: + sp = None + + for sample in data: + assert "txt" in sample + txt = sample["txt"].strip() + if non_lang_syms_pattern is not None: + parts = non_lang_syms_pattern.split(txt.upper()) + parts = [w for w in parts if len(w.strip()) > 0] + else: + parts = [txt] + + label = [] + tokens = [] + for part in parts: + if part in non_lang_syms: + tokens.append(part) + else: + if bpe_model is not None: + tokens.extend(__tokenize_by_bpe_model(sp, part)) + else: + if split_with_space: + part = part.split(" ") + for ch in part: + if ch == " ": + ch = "▁" + tokens.append(ch) + + for ch in tokens: + if ch in symbol_table: + label.append(symbol_table[ch]) + elif "" in symbol_table: + label.append(symbol_table[""]) + + sample["tokens"] = tokens + sample["label"] = label + yield sample + + +def spec_aug(data, num_t_mask=2, num_f_mask=2, max_t=50, max_f=10, max_w=80): + """Do spec augmentation + Inplace operation + + Args: + data: Iterable[{key, feat, label}] + num_t_mask: number of time mask to apply + num_f_mask: number of freq mask to apply + max_t: max width of time mask + max_f: max width of freq mask + max_w: max width of time warp + + Returns + Iterable[{key, feat, label}] + """ + for sample in data: + assert "feat" in sample + x = sample["feat"] + assert isinstance(x, torch.Tensor) + y = x.clone().detach() + max_frames = y.size(0) + max_freq = y.size(1) + # time mask + for i in range(num_t_mask): + start = random.randint(0, max_frames - 1) + length = random.randint(1, max_t) + end = min(max_frames, start + length) + y[start:end, :] = 0 + # freq mask + for i in range(num_f_mask): + start = random.randint(0, max_freq - 1) + length = random.randint(1, max_f) + end = min(max_freq, start + length) + y[:, start:end] = 0 + sample["feat"] = y + yield sample + + +def spec_sub(data, max_t=20, num_t_sub=3): + """Do spec substitute + Inplace operation + ref: U2++, section 3.2.3 [https://arxiv.org/abs/2106.05642] + + Args: + data: Iterable[{key, feat, label}] + max_t: max width of time substitute + num_t_sub: number of time substitute to apply + + Returns + Iterable[{key, feat, label}] + """ + for sample in data: + assert "feat" in sample + x = sample["feat"] + assert isinstance(x, torch.Tensor) + y = x.clone().detach() + max_frames = y.size(0) + for i in range(num_t_sub): + start = random.randint(0, max_frames - 1) + length = random.randint(1, max_t) + end = min(max_frames, start + length) + # only substitute the earlier time chosen randomly for current time + pos = random.randint(0, start) + y[start:end, :] = x[start - pos : end - pos, :] + sample["feat"] = y + yield sample + + +def spec_trim(data, max_t=20): + """Trim tailing frames. Inplace operation. + ref: TrimTail [https://arxiv.org/abs/2211.00522] + + Args: + data: Iterable[{key, feat, label}] + max_t: max width of length trimming + + Returns + Iterable[{key, feat, label}] + """ + for sample in data: + assert "feat" in sample + x = sample["feat"] + assert isinstance(x, torch.Tensor) + max_frames = x.size(0) + length = random.randint(1, max_t) + if length < max_frames / 2: + y = x.clone().detach()[: max_frames - length] + sample["feat"] = y + yield sample + + +def shuffle(data, shuffle_size=10000): + """Local shuffle the data + + Args: + data: Iterable[{key, feat, label}] + shuffle_size: buffer size for shuffle + + Returns: + Iterable[{key, feat, label}] + """ + buf = [] + for sample in data: + buf.append(sample) + if len(buf) >= shuffle_size: + random.shuffle(buf) + for x in buf: + yield x + buf = [] + # The sample left over + random.shuffle(buf) + for x in buf: + yield x + + +def sort(data, sort_size=500): + """Sort the data by feature length. + Sort is used after shuffle and before batch, so we can group + utts with similar lengths into a batch, and `sort_size` should + be less than `shuffle_size` + + Args: + data: Iterable[{key, feat, label}] + sort_size: buffer size for sort + + Returns: + Iterable[{key, feat, label}] + """ + + buf = [] + for sample in data: + buf.append(sample) + if len(buf) >= sort_size: + buf.sort(key=lambda x: x["feat"].size(0)) + for x in buf: + yield x + buf = [] + # The sample left over + buf.sort(key=lambda x: x["feat"].size(0)) + for x in buf: + yield x + + +def static_batch(data, batch_size=16): + """Static batch the data by `batch_size` + + Args: + data: Iterable[{key, feat, label}] + batch_size: batch size + + Returns: + Iterable[List[{key, feat, label}]] + """ + buf = [] + for sample in data: + buf.append(sample) + if len(buf) >= batch_size: + yield buf + buf = [] + if len(buf) > 0: + yield buf + + +def dynamic_batch(data, max_frames_in_batch=12000): + """Dynamic batch the data until the total frames in batch + reach `max_frames_in_batch` + + Args: + data: Iterable[{key, feat, label}] + max_frames_in_batch: max_frames in one batch + + Returns: + Iterable[List[{key, feat, label}]] + """ + buf = [] + longest_frames = 0 + for sample in data: + assert "feat" in sample + assert isinstance(sample["feat"], torch.Tensor) + new_sample_frames = sample["feat"].size(0) + longest_frames = max(longest_frames, new_sample_frames) + frames_after_padding = longest_frames * (len(buf) + 1) + if frames_after_padding > max_frames_in_batch: + yield buf + buf = [sample] + longest_frames = new_sample_frames + else: + buf.append(sample) + if len(buf) > 0: + yield buf + + +def batch(data, batch_type="static", batch_size=16, max_frames_in_batch=12000): + """Wrapper for static/dynamic batch""" + if batch_type == "static": + return static_batch(data, batch_size) + elif batch_type == "dynamic": + return dynamic_batch(data, max_frames_in_batch) + else: + logging.fatal("Unsupported batch type {}".format(batch_type)) + + +def padding(data): + """Padding the data into training data + + Args: + data: Iterable[List[{key, feat, label}]] + + Returns: + Iterable[Tuple(keys, feats, labels, feats lengths, label lengths)] + """ + for sample in data: + assert isinstance(sample, list) + feats_length = torch.tensor( + [x["feat"].size(0) for x in sample], dtype=torch.int32 + ) + order = torch.argsort(feats_length, descending=True) + feats_lengths = torch.tensor( + [sample[i]["feat"].size(0) for i in order], dtype=torch.int32 + ) + sorted_feats = [sample[i]["feat"] for i in order] + sorted_keys = [sample[i]["key"] for i in order] + sorted_labels = [ + torch.tensor(sample[i]["label"], dtype=torch.int64) for i in order + ] + label_lengths = torch.tensor( + [x.size(0) for x in sorted_labels], dtype=torch.int32 + ) + + padded_feats = pad_sequence(sorted_feats, batch_first=True, padding_value=0) + padding_labels = pad_sequence(sorted_labels, batch_first=True, padding_value=-1) + + yield (sorted_keys, padded_feats, padding_labels, feats_lengths, label_lengths) diff --git a/modules/wenet_extractor/dataset/wav_distortion.py b/modules/wenet_extractor/dataset/wav_distortion.py new file mode 100644 index 0000000000000000000000000000000000000000..ee2dd8cedae66b25410e8fdd322e0a70675a2040 --- /dev/null +++ b/modules/wenet_extractor/dataset/wav_distortion.py @@ -0,0 +1,345 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +import sys +import random +import math + +import torchaudio +import torch + +torchaudio.set_audio_backend("sox_io") + + +def db2amp(db): + return pow(10, db / 20) + + +def amp2db(amp): + return 20 * math.log10(amp) + + +def make_poly_distortion(conf): + """Generate a db-domain ploynomial distortion function + + f(x) = a * x^m * (1-x)^n + x + + Args: + conf: a dict {'a': #int, 'm': #int, 'n': #int} + + Returns: + The ploynomial function, which could be applied on + a float amplitude value + """ + a = conf["a"] + m = conf["m"] + n = conf["n"] + + def poly_distortion(x): + abs_x = abs(x) + if abs_x < 0.000001: + x = x + else: + db_norm = amp2db(abs_x) / 100 + 1 + if db_norm < 0: + db_norm = 0 + db_norm = a * pow(db_norm, m) * pow((1 - db_norm), n) + db_norm + if db_norm > 1: + db_norm = 1 + db = (db_norm - 1) * 100 + amp = db2amp(db) + if amp >= 0.9997: + amp = 0.9997 + if x > 0: + x = amp + else: + x = -amp + return x + + return poly_distortion + + +def make_quad_distortion(): + return make_poly_distortion({"a": 1, "m": 1, "n": 1}) + + +# the amplitude are set to max for all non-zero point +def make_max_distortion(conf): + """Generate a max distortion function + + Args: + conf: a dict {'max_db': float } + 'max_db': the maxium value. + + Returns: + The max function, which could be applied on + a float amplitude value + """ + max_db = conf["max_db"] + if max_db: + max_amp = db2amp(max_db) # < 0.997 + else: + max_amp = 0.997 + + def max_distortion(x): + if x > 0: + x = max_amp + elif x < 0: + x = -max_amp + else: + x = 0.0 + return x + + return max_distortion + + +def make_amp_mask(db_mask=None): + """Get a amplitude domain mask from db domain mask + + Args: + db_mask: Optional. A list of tuple. if None, using default value. + + Returns: + A list of tuple. The amplitude domain mask + """ + if db_mask is None: + db_mask = [(-110, -95), (-90, -80), (-65, -60), (-50, -30), (-15, 0)] + amp_mask = [(db2amp(db[0]), db2amp(db[1])) for db in db_mask] + return amp_mask + + +default_mask = make_amp_mask() + + +def generate_amp_mask(mask_num): + """Generate amplitude domain mask randomly in [-100db, 0db] + + Args: + mask_num: the slot number of the mask + + Returns: + A list of tuple. each tuple defines a slot. + e.g. [(-100, -80), (-65, -60), (-50, -30), (-15, 0)] + for #mask_num = 4 + """ + a = [0] * 2 * mask_num + a[0] = 0 + m = [] + for i in range(1, 2 * mask_num): + a[i] = a[i - 1] + random.uniform(0.5, 1) + max_val = a[2 * mask_num - 1] + for i in range(0, mask_num): + l = ((a[2 * i] - max_val) / max_val) * 100 + r = ((a[2 * i + 1] - max_val) / max_val) * 100 + m.append((l, r)) + return make_amp_mask(m) + + +def make_fence_distortion(conf): + """Generate a fence distortion function + + In this fence-like shape function, the values in mask slots are + set to maxium, while the values not in mask slots are set to 0. + Use seperated masks for Positive and negetive amplitude. + + Args: + conf: a dict {'mask_number': int,'max_db': float } + 'mask_number': the slot number in mask. + 'max_db': the maxium value. + + Returns: + The fence function, which could be applied on + a float amplitude value + """ + mask_number = conf["mask_number"] + max_db = conf["max_db"] + max_amp = db2amp(max_db) # 0.997 + if mask_number <= 0: + positive_mask = default_mask + negative_mask = make_amp_mask([(-50, 0)]) + else: + positive_mask = generate_amp_mask(mask_number) + negative_mask = generate_amp_mask(mask_number) + + def fence_distortion(x): + is_in_mask = False + if x > 0: + for mask in positive_mask: + if x >= mask[0] and x <= mask[1]: + is_in_mask = True + return max_amp + if not is_in_mask: + return 0.0 + elif x < 0: + abs_x = abs(x) + for mask in negative_mask: + if abs_x >= mask[0] and abs_x <= mask[1]: + is_in_mask = True + return max_amp + if not is_in_mask: + return 0.0 + return x + + return fence_distortion + + +# +def make_jag_distortion(conf): + """Generate a jag distortion function + + In this jag-like shape function, the values in mask slots are + not changed, while the values not in mask slots are set to 0. + Use seperated masks for Positive and negetive amplitude. + + Args: + conf: a dict {'mask_number': #int} + 'mask_number': the slot number in mask. + + Returns: + The jag function,which could be applied on + a float amplitude value + """ + mask_number = conf["mask_number"] + if mask_number <= 0: + positive_mask = default_mask + negative_mask = make_amp_mask([(-50, 0)]) + else: + positive_mask = generate_amp_mask(mask_number) + negative_mask = generate_amp_mask(mask_number) + + def jag_distortion(x): + is_in_mask = False + if x > 0: + for mask in positive_mask: + if x >= mask[0] and x <= mask[1]: + is_in_mask = True + return x + if not is_in_mask: + return 0.0 + elif x < 0: + abs_x = abs(x) + for mask in negative_mask: + if abs_x >= mask[0] and abs_x <= mask[1]: + is_in_mask = True + return x + if not is_in_mask: + return 0.0 + return x + + return jag_distortion + + +# gaining 20db means amp = amp * 10 +# gaining -20db means amp = amp / 10 +def make_gain_db(conf): + """Generate a db domain gain function + + Args: + conf: a dict {'db': #float} + 'db': the gaining value + + Returns: + The db gain function, which could be applied on + a float amplitude value + """ + db = conf["db"] + + def gain_db(x): + return min(0.997, x * pow(10, db / 20)) + + return gain_db + + +def distort(x, func, rate=0.8): + """Distort a waveform in sample point level + + Args: + x: the origin wavefrom + func: the distort function + rate: sample point-level distort probability + + Returns: + the distorted waveform + """ + for i in range(0, x.shape[1]): + a = random.uniform(0, 1) + if a < rate: + x[0][i] = func(float(x[0][i])) + return x + + +def distort_chain(x, funcs, rate=0.8): + for i in range(0, x.shape[1]): + a = random.uniform(0, 1) + if a < rate: + for func in funcs: + x[0][i] = func(float(x[0][i])) + return x + + +# x is numpy +def distort_wav_conf(x, distort_type, distort_conf, rate=0.1): + if distort_type == "gain_db": + gain_db = make_gain_db(distort_conf) + x = distort(x, gain_db) + elif distort_type == "max_distortion": + max_distortion = make_max_distortion(distort_conf) + x = distort(x, max_distortion, rate=rate) + elif distort_type == "fence_distortion": + fence_distortion = make_fence_distortion(distort_conf) + x = distort(x, fence_distortion, rate=rate) + elif distort_type == "jag_distortion": + jag_distortion = make_jag_distortion(distort_conf) + x = distort(x, jag_distortion, rate=rate) + elif distort_type == "poly_distortion": + poly_distortion = make_poly_distortion(distort_conf) + x = distort(x, poly_distortion, rate=rate) + elif distort_type == "quad_distortion": + quad_distortion = make_quad_distortion() + x = distort(x, quad_distortion, rate=rate) + elif distort_type == "none_distortion": + pass + else: + print("unsupport type") + return x + + +def distort_wav_conf_and_save(distort_type, distort_conf, rate, wav_in, wav_out): + x, sr = torchaudio.load(wav_in) + x = x.detach().numpy() + out = distort_wav_conf(x, distort_type, distort_conf, rate) + torchaudio.save(wav_out, torch.from_numpy(out), sr) + + +if __name__ == "__main__": + distort_type = sys.argv[1] + wav_in = sys.argv[2] + wav_out = sys.argv[3] + conf = None + rate = 0.1 + if distort_type == "new_jag_distortion": + conf = {"mask_number": 4} + elif distort_type == "new_fence_distortion": + conf = {"mask_number": 1, "max_db": -30} + elif distort_type == "poly_distortion": + conf = {"a": 4, "m": 2, "n": 2} + distort_wav_conf_and_save(distort_type, conf, rate, wav_in, wav_out) diff --git a/modules/wenet_extractor/efficient_conformer/__init__.py b/modules/wenet_extractor/efficient_conformer/__init__.py new file mode 100644 index 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[WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""Multi-Head Attention layer definition.""" + +import math +from typing import Tuple, Optional + +import torch +from torch import nn +import torch.nn.functional as F +from modules.wenet_extractor.transformer.attention import MultiHeadedAttention + + +class GroupedRelPositionMultiHeadedAttention(MultiHeadedAttention): + """Multi-Head Attention layer with relative position encoding. + Paper: + https://arxiv.org/abs/1901.02860 + https://arxiv.org/abs/2109.01163 + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + """ + + def __init__(self, n_head, n_feat, dropout_rate, group_size=3): + """Construct an RelPositionMultiHeadedAttention object.""" + super().__init__(n_head, n_feat, dropout_rate) + # linear transformation for positional encoding + self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) + self.group_size = group_size + self.d_k = n_feat // n_head # for GroupedAttention + self.n_feat = n_feat + # these two learnable bias are used in matrix c and matrix d + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k * self.group_size)) + self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k * self.group_size)) + torch.nn.init.xavier_uniform_(self.pos_bias_u) + torch.nn.init.xavier_uniform_(self.pos_bias_v) + + def rel_shift(self, x, zero_triu: bool = False): + """Compute relative positinal encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, size). + zero_triu (bool): If true, return the lower triangular part of + the matrix. + Returns: + torch.Tensor: Output tensor. + """ + + zero_pad = torch.zeros( + (x.size()[0], x.size()[1], x.size()[2], 1), device=x.device, dtype=x.dtype + ) + x_padded = torch.cat([zero_pad, x], dim=-1) + + x_padded = x_padded.view(x.size()[0], x.size()[1], x.size(3) + 1, x.size(2)) + x = x_padded[:, :, 1:].view_as(x) + + if zero_triu: + ones = torch.ones((x.size(2), x.size(3))) + x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :] + + return x + + def pad4group(self, Q, K, V, P, mask, group_size: int = 3): + """ + q: (#batch, time1, size) -> (#batch, head, time1, size/head) + k,v: (#batch, time2, size) -> (#batch, head, time2, size/head) + p: (#batch, time2, size) + """ + # Compute Overflows + overflow_Q = Q.size(2) % group_size + overflow_KV = K.size(2) % group_size + + # if-else for ONNX export + # 0 // 0.00000000000000001 = 0 + # 1 // 1.00000000000000001 = 1 + padding_Q = (group_size - overflow_Q) * int( + overflow_Q // (overflow_Q + 0.00000000000000001) + ) + padding_KV = (group_size - overflow_KV) * int( + overflow_KV // (overflow_KV + 0.00000000000000001) + ) + + batch_size, _, seq_len_KV, _ = K.size() + + # Input Padding (B, T, D) -> (B, T + P, D) + Q = F.pad(Q, (0, 0, 0, padding_Q), value=0.0) + K = F.pad(K, (0, 0, 0, padding_KV), value=0.0) + V = F.pad(V, (0, 0, 0, padding_KV), value=0.0) + + if mask is not None and mask.size(2) > 0: # time2 > 0: + mask = mask[:, ::group_size, ::group_size] + + Q = ( + Q.transpose(1, 2) + .contiguous() + .view(batch_size, -1, self.h, self.d_k * group_size) + .transpose(1, 2) + ) + K = ( + K.transpose(1, 2) + .contiguous() + .view(batch_size, -1, self.h, self.d_k * group_size) + .transpose(1, 2) + ) + V = ( + V.transpose(1, 2) + .contiguous() + .view(batch_size, -1, self.h, self.d_k * group_size) + .transpose(1, 2) + ) + + # process pos_emb + P_batch_size = P.size(0) + overflow_P = P.size(1) % group_size + padding_P = group_size - overflow_P if overflow_P else 0 + P = F.pad(P, (0, 0, 0, padding_P), value=0.0) + P = P.view(P_batch_size, -1, self.h, self.d_k * group_size).transpose(1, 2) + + return Q, K, V, P, mask, padding_Q + + def forward_attention( + self, + value: torch.Tensor, + scores: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + padding_q: Optional[int] = None, + ) -> torch.Tensor: + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value, size + (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score, size + (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask, size (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + padding_q : for GroupedAttention in efficent conformer + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + # NOTE(xcsong): When will `if mask.size(2) > 0` be True? + # 1. onnx(16/4) [WHY? Because we feed real cache & real mask for the + # 1st chunk to ease the onnx export.] + # 2. pytorch training + if mask.size(2) > 0: # time2 > 0 + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + # For last chunk, time2 might be larger than scores.size(-1) + mask = mask[:, :, :, : scores.size(-1)] # (batch, 1, *, time2) + scores = scores.masked_fill(mask, -float("inf")) + attn = torch.softmax(scores, dim=-1).masked_fill( + mask, 0.0 + ) # (batch, head, time1, time2) + # NOTE(xcsong): When will `if mask.size(2) > 0` be False? + # 1. onnx(16/-1, -1/-1, 16/0) + # 2. jit (16/-1, -1/-1, 16/0, 16/4) + else: + attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + + # n_feat!=h*d_k may be happened in GroupAttention + x = ( + x.transpose(1, 2).contiguous().view(n_batch, -1, self.n_feat) + ) # (batch, time1, d_model) + if padding_q is not None: + # for GroupedAttention in efficent conformer + x = x[:, : x.size(1) - padding_q] + + return self.linear_out(x) # (batch, time1, d_model) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute 'Scaled Dot Product Attention' with rel. positional encoding. + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + pos_emb (torch.Tensor): Positional embedding tensor + (#batch, time2, size). + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + """ + q = self.linear_q(query) + k = self.linear_k(key) # (#batch, time2, size) + v = self.linear_v(value) + p = self.linear_pos(pos_emb) # (#batch, time2, size) + + batch_size, seq_len_KV, _ = k.size() # seq_len_KV = time2 + + # (#batch, time2, size) -> (#batch, head, time2, size/head) + q = q.view(batch_size, -1, self.h, self.d_k).transpose(1, 2) + k = k.view(batch_size, -1, self.h, self.d_k).transpose(1, 2) + v = v.view(batch_size, -1, self.h, self.d_k).transpose(1, 2) + if cache.size(0) > 0: + # use attention cache + key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + new_cache = torch.cat((k, v), dim=-1) + + # May be k and p does not match. eg. time2=18+18/2=27 > mask=36/2=18 + if mask is not None and mask.size(2) > 0: + time2 = mask.size(2) + k = k[:, :, -time2:, :] + v = v[:, :, -time2:, :] + + # q k v p: (batch, head, time1, d_k) + q, k, v, p, mask, padding_q = self.pad4group(q, k, v, p, mask, self.group_size) + + # q_with_bias_u & q_with_bias_v = (batch, head, time1, d_k) + q = q.transpose(1, 2) # (batch, time1, head, d_k) + q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) + q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) + + # compute attention score + # first compute matrix a and matrix c + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + # (batch, head, time1, time2) + matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) + + # compute matrix b and matrix d + # (batch, head, time1, time2) + matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + # Remove rel_shift since it is useless in speech recognition, + # and it requires special attention for streaming. + # matrix_bd = self.rel_shift(matrix_bd) + + scores = (matrix_ac + matrix_bd) / math.sqrt( + self.d_k * self.group_size + ) # (batch, head, time1, time2) + + return self.forward_attention(v, scores, mask, padding_q), new_cache diff --git a/modules/wenet_extractor/efficient_conformer/convolution.py b/modules/wenet_extractor/efficient_conformer/convolution.py new file mode 100644 index 0000000000000000000000000000000000000000..88ca82bffbd6ed5fd48a64d71f61bc281594efa2 --- /dev/null +++ b/modules/wenet_extractor/efficient_conformer/convolution.py @@ -0,0 +1,163 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""ConvolutionModule definition.""" +from typing import Tuple + +import torch +from torch import nn + + +class ConvolutionModule(nn.Module): + """ConvolutionModule in Conformer model.""" + + def __init__( + self, + channels: int, + kernel_size: int = 15, + activation: nn.Module = nn.ReLU(), + norm: str = "batch_norm", + causal: bool = False, + bias: bool = True, + stride: int = 1, + ): + """Construct an ConvolutionModule object. + Args: + channels (int): The number of channels of conv layers. + kernel_size (int): Kernel size of conv layers. + causal (int): Whether use causal convolution or not + stride (int): Stride Convolution, for efficient Conformer + """ + super().__init__() + + self.pointwise_conv1 = nn.Conv1d( + channels, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + # self.lorder is used to distinguish if it's a causal convolution, + # if self.lorder > 0: it's a causal convolution, the input will be + # padded with self.lorder frames on the left in forward. + # else: it's a symmetrical convolution + if causal: + padding = 0 + self.lorder = kernel_size - 1 + else: + # kernel_size should be an odd number for none causal convolution + assert (kernel_size - 1) % 2 == 0 + padding = (kernel_size - 1) // 2 + self.lorder = 0 + + self.depthwise_conv = nn.Conv1d( + channels, + channels, + kernel_size, + stride=stride, # for depthwise_conv in StrideConv + padding=padding, + groups=channels, + bias=bias, + ) + + assert norm in ["batch_norm", "layer_norm"] + if norm == "batch_norm": + self.use_layer_norm = False + self.norm = nn.BatchNorm1d(channels) + else: + self.use_layer_norm = True + self.norm = nn.LayerNorm(channels) + + self.pointwise_conv2 = nn.Conv1d( + channels, + channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + self.activation = activation + self.stride = stride + + def forward( + self, + x: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + cache: torch.Tensor = torch.zeros((0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute convolution module. + Args: + x (torch.Tensor): Input tensor (#batch, time, channels). + mask_pad (torch.Tensor): used for batch padding (#batch, 1, time), + (0, 0, 0) means fake mask. + cache (torch.Tensor): left context cache, it is only + used in causal convolution (#batch, channels, cache_t), + (0, 0, 0) meas fake cache. + Returns: + torch.Tensor: Output tensor (#batch, time, channels). + """ + # exchange the temporal dimension and the feature dimension + x = x.transpose(1, 2) # (#batch, channels, time) + + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + x.masked_fill_(~mask_pad, 0.0) + + if self.lorder > 0: + if cache.size(2) == 0: # cache_t == 0 + x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0) + else: + # When export ONNX，the first cache is not None but all-zero, + # cause shape error in residual block, + # eg. cache14 + x9 = 23, 23-7+1=17 != 9 + cache = cache[:, :, -self.lorder :] + assert cache.size(0) == x.size(0) # equal batch + assert cache.size(1) == x.size(1) # equal channel + x = torch.cat((cache, x), dim=2) + assert x.size(2) > self.lorder + new_cache = x[:, :, -self.lorder :] + else: + # It's better we just return None if no cache is requried, + # However, for JIT export, here we just fake one tensor instead of + # None. + new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channel, dim) + x = nn.functional.glu(x, dim=1) # (batch, channel, dim) + + # 1D Depthwise Conv + x = self.depthwise_conv(x) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.activation(self.norm(x)) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.pointwise_conv2(x) + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + if mask_pad.size(2) != x.size(2): + mask_pad = mask_pad[:, :, :: self.stride] + x.masked_fill_(~mask_pad, 0.0) + + return x.transpose(1, 2), new_cache diff --git a/modules/wenet_extractor/efficient_conformer/encoder.py b/modules/wenet_extractor/efficient_conformer/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..e23c50763f4fddf610371e116e478fcf27f65ea8 --- /dev/null +++ b/modules/wenet_extractor/efficient_conformer/encoder.py @@ -0,0 +1,627 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""Encoder definition.""" +from typing import Tuple, Optional, List, Union + +import torch +import logging +import torch.nn.functional as F + +from modules.wenet_extractor.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, +) +from modules.wenet_extractor.transformer.embedding import PositionalEncoding +from modules.wenet_extractor.transformer.embedding import RelPositionalEncoding +from modules.wenet_extractor.transformer.embedding import NoPositionalEncoding +from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling4 +from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling6 +from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling8 +from modules.wenet_extractor.transformer.subsampling import LinearNoSubsampling +from modules.wenet_extractor.transformer.attention import MultiHeadedAttention +from modules.wenet_extractor.transformer.attention import ( + RelPositionMultiHeadedAttention, +) +from modules.wenet_extractor.transformer.encoder_layer import ConformerEncoderLayer + +from modules.wenet_extractor.efficient_conformer.subsampling import Conv2dSubsampling2 +from modules.wenet_extractor.efficient_conformer.convolution import ConvolutionModule +from modules.wenet_extractor.efficient_conformer.attention import ( + GroupedRelPositionMultiHeadedAttention, +) +from modules.wenet_extractor.efficient_conformer.encoder_layer import ( + StrideConformerEncoderLayer, +) + +from modules.wenet_extractor.utils.common import get_activation +from modules.wenet_extractor.utils.mask import make_pad_mask +from modules.wenet_extractor.utils.mask import add_optional_chunk_mask + + +class EfficientConformerEncoder(torch.nn.Module): + """Conformer encoder module.""" + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = "conv2d", + pos_enc_layer_type: str = "rel_pos", + normalize_before: bool = True, + static_chunk_size: int = 0, + use_dynamic_chunk: bool = False, + global_cmvn: torch.nn.Module = None, + use_dynamic_left_chunk: bool = False, + macaron_style: bool = True, + activation_type: str = "swish", + use_cnn_module: bool = True, + cnn_module_kernel: int = 15, + causal: bool = False, + cnn_module_norm: str = "batch_norm", + stride_layer_idx: Optional[Union[int, List[int]]] = 3, + stride: Optional[Union[int, List[int]]] = 2, + group_layer_idx: Optional[Union[int, List[int], tuple]] = (0, 1, 2, 3), + group_size: int = 3, + stride_kernel: bool = True, + **kwargs, + ): + """Construct Efficient Conformer Encoder + + Args: + input_size to use_dynamic_chunk, see in BaseEncoder + macaron_style (bool): Whether to use macaron style for + positionwise layer. + activation_type (str): Encoder activation function type. + use_cnn_module (bool): Whether to use convolution module. + cnn_module_kernel (int): Kernel size of convolution module. + causal (bool): whether to use causal convolution or not. + stride_layer_idx (list): layer id with StrideConv, start from 0 + stride (list): stride size of each StrideConv in efficient conformer + group_layer_idx (list): layer id with GroupedAttention, start from 0 + group_size (int): group size of every GroupedAttention layer + stride_kernel (bool): default True. True: recompute cnn kernels with stride. + """ + super().__init__() + self._output_size = output_size + + if pos_enc_layer_type == "abs_pos": + pos_enc_class = PositionalEncoding + elif pos_enc_layer_type == "rel_pos": + pos_enc_class = RelPositionalEncoding + elif pos_enc_layer_type == "no_pos": + pos_enc_class = NoPositionalEncoding + else: + raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type) + + if input_layer == "linear": + subsampling_class = LinearNoSubsampling + elif input_layer == "conv2d2": + subsampling_class = Conv2dSubsampling2 + elif input_layer == "conv2d": + subsampling_class = Conv2dSubsampling4 + elif input_layer == "conv2d6": + subsampling_class = Conv2dSubsampling6 + elif input_layer == "conv2d8": + subsampling_class = Conv2dSubsampling8 + else: + raise ValueError("unknown input_layer: " + input_layer) + + logging.info( + f"input_layer = {input_layer}, " f"subsampling_class = {subsampling_class}" + ) + + self.global_cmvn = global_cmvn + self.embed = subsampling_class( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate), + ) + self.input_layer = input_layer + self.normalize_before = normalize_before + self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5) + self.static_chunk_size = static_chunk_size + self.use_dynamic_chunk = use_dynamic_chunk + self.use_dynamic_left_chunk = use_dynamic_left_chunk + + activation = get_activation(activation_type) + self.num_blocks = num_blocks + self.attention_heads = attention_heads + self.cnn_module_kernel = cnn_module_kernel + self.global_chunk_size = 0 + self.chunk_feature_map = 0 + + # efficient conformer configs + self.stride_layer_idx = ( + [stride_layer_idx] if type(stride_layer_idx) == int else stride_layer_idx + ) + self.stride = [stride] if type(stride) == int else stride + self.group_layer_idx = ( + [group_layer_idx] if type(group_layer_idx) == int else group_layer_idx + ) + self.grouped_size = group_size # group size of every GroupedAttention layer + + assert len(self.stride) == len(self.stride_layer_idx) + self.cnn_module_kernels = [cnn_module_kernel] # kernel size of each StridedConv + for i in self.stride: + if stride_kernel: + self.cnn_module_kernels.append(self.cnn_module_kernels[-1] // i) + else: + self.cnn_module_kernels.append(self.cnn_module_kernels[-1]) + + logging.info( + f"stride_layer_idx= {self.stride_layer_idx}, " + f"stride = {self.stride}, " + f"cnn_module_kernel = {self.cnn_module_kernels}, " + f"group_layer_idx = {self.group_layer_idx}, " + f"grouped_size = {self.grouped_size}" + ) + + # feed-forward module definition + positionwise_layer = PositionwiseFeedForward + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + activation, + ) + # convolution module definition + convolution_layer = ConvolutionModule + + # encoder definition + index = 0 + layers = [] + for i in range(num_blocks): + # self-attention module definition + if i in self.group_layer_idx: + encoder_selfattn_layer = GroupedRelPositionMultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + self.grouped_size, + ) + else: + if pos_enc_layer_type == "no_pos": + encoder_selfattn_layer = MultiHeadedAttention + else: + encoder_selfattn_layer = RelPositionMultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + ) + + # conformer module definition + if i in self.stride_layer_idx: + # conformer block with downsampling + convolution_layer_args_stride = ( + output_size, + self.cnn_module_kernels[index], + activation, + cnn_module_norm, + causal, + True, + self.stride[index], + ) + layers.append( + StrideConformerEncoderLayer( + output_size, + encoder_selfattn_layer(*encoder_selfattn_layer_args), + positionwise_layer(*positionwise_layer_args), + positionwise_layer(*positionwise_layer_args) + if macaron_style + else None, + convolution_layer(*convolution_layer_args_stride) + if use_cnn_module + else None, + torch.nn.AvgPool1d( + kernel_size=self.stride[index], + stride=self.stride[index], + padding=0, + ceil_mode=True, + count_include_pad=False, + ), # pointwise_conv_layer + dropout_rate, + normalize_before, + ) + ) + index = index + 1 + else: + # conformer block + convolution_layer_args_normal = ( + output_size, + self.cnn_module_kernels[index], + activation, + cnn_module_norm, + causal, + ) + layers.append( + ConformerEncoderLayer( + output_size, + encoder_selfattn_layer(*encoder_selfattn_layer_args), + positionwise_layer(*positionwise_layer_args), + positionwise_layer(*positionwise_layer_args) + if macaron_style + else None, + convolution_layer(*convolution_layer_args_normal) + if use_cnn_module + else None, + dropout_rate, + normalize_before, + ) + ) + + self.encoders = torch.nn.ModuleList(layers) + + def set_global_chunk_size(self, chunk_size): + """Used in ONNX export.""" + logging.info(f"set global chunk size: {chunk_size}, default is 0.") + self.global_chunk_size = chunk_size + if self.embed.subsampling_rate == 2: + self.chunk_feature_map = 2 * self.global_chunk_size + 1 + elif self.embed.subsampling_rate == 6: + self.chunk_feature_map = 6 * self.global_chunk_size + 5 + elif self.embed.subsampling_rate == 8: + self.chunk_feature_map = 8 * self.global_chunk_size + 7 + else: + self.chunk_feature_map = 4 * self.global_chunk_size + 3 + + def output_size(self) -> int: + return self._output_size + + def calculate_downsampling_factor(self, i: int) -> int: + factor = 1 + for idx, stride_idx in enumerate(self.stride_layer_idx): + if i > stride_idx: + factor *= self.stride[idx] + return factor + + def forward( + self, + xs: torch.Tensor, + xs_lens: torch.Tensor, + decoding_chunk_size: int = 0, + num_decoding_left_chunks: int = -1, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Embed positions in tensor. + Args: + xs: padded input tensor (B, T, D) + xs_lens: input length (B) + decoding_chunk_size: decoding chunk size for dynamic chunk + 0: default for training, use random dynamic chunk. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + num_decoding_left_chunks: number of left chunks, this is for decoding, + the chunk size is decoding_chunk_size. + >=0: use num_decoding_left_chunks + <0: use all left chunks + Returns: + encoder output tensor xs, and subsampled masks + xs: padded output tensor (B, T' ~= T/subsample_rate, D) + masks: torch.Tensor batch padding mask after subsample + (B, 1, T' ~= T/subsample_rate) + """ + T = xs.size(1) + masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) + if self.global_cmvn is not None: + xs = self.global_cmvn(xs) + xs, pos_emb, masks = self.embed(xs, masks) + mask_pad = masks # (B, 1, T/subsample_rate) + chunk_masks = add_optional_chunk_mask( + xs, + masks, + self.use_dynamic_chunk, + self.use_dynamic_left_chunk, + decoding_chunk_size, + self.static_chunk_size, + num_decoding_left_chunks, + ) + index = 0 # traverse stride + for i, layer in enumerate(self.encoders): + # layer return : x, mask, new_att_cache, new_cnn_cache + xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) + if i in self.stride_layer_idx: + masks = masks[:, :, :: self.stride[index]] + chunk_masks = chunk_masks[ + :, :: self.stride[index], :: self.stride[index] + ] + mask_pad = masks + pos_emb = pos_emb[:, :: self.stride[index], :] + index = index + 1 + + if self.normalize_before: + xs = self.after_norm(xs) + # Here we assume the mask is not changed in encoder layers, so just + # return the masks before encoder layers, and the masks will be used + # for cross attention with decoder later + return xs, masks + + def forward_chunk( + self, + xs: torch.Tensor, + offset: int, + required_cache_size: int, + att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), + cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), + att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Forward just one chunk + + Args: + xs (torch.Tensor): chunk input + offset (int): current offset in encoder output time stamp + required_cache_size (int): cache size required for next chunk + compuation + >=0: actual cache size + <0: means all history cache is required + att_cache (torch.Tensor): cache tensor for KEY & VALUE in + transformer/conformer attention, with shape + (elayers, head, cache_t1, d_k * 2), where + `head * d_k == hidden-dim` and + `cache_t1 == chunk_size * num_decoding_left_chunks`. + cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, + (elayers, b=1, hidden-dim, cache_t2), where + `cache_t2 == cnn.lorder - 1` + att_mask : mask matrix of self attention + + Returns: + torch.Tensor: output of current input xs + torch.Tensor: subsampling cache required for next chunk computation + List[torch.Tensor]: encoder layers output cache required for next + chunk computation + List[torch.Tensor]: conformer cnn cache + + """ + assert xs.size(0) == 1 + + # using downsampling factor to recover offset + offset *= self.calculate_downsampling_factor(self.num_blocks + 1) + + chunk_masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool) + chunk_masks = chunk_masks.unsqueeze(1) # (1, 1, xs-time) + + real_len = 0 + if self.global_chunk_size > 0: + # for ONNX decode simulation， padding xs to chunk_size + real_len = xs.size(1) + pad_len = self.chunk_feature_map - real_len + xs = F.pad(xs, (0, 0, 0, pad_len), value=0.0) + chunk_masks = F.pad(chunk_masks, (0, pad_len), value=0.0) + + if self.global_cmvn is not None: + xs = self.global_cmvn(xs) + + # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim) + xs, pos_emb, chunk_masks = self.embed(xs, chunk_masks, offset) + elayers, cache_t1 = att_cache.size(0), att_cache.size(2) + chunk_size = xs.size(1) + attention_key_size = cache_t1 + chunk_size + # NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim) + # shape(pos_emb) = (b=1, chunk_size, emb_size=output_size=hidden-dim) + + if required_cache_size < 0: + next_cache_start = 0 + elif required_cache_size == 0: + next_cache_start = attention_key_size + else: + next_cache_start = max(attention_key_size - required_cache_size, 0) + + r_att_cache = [] + r_cnn_cache = [] + mask_pad = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool) + mask_pad = mask_pad.unsqueeze(1) # batchPad (b=1, 1, time=chunk_size) + + if self.global_chunk_size > 0: + # for ONNX decode simulation + pos_emb = self.embed.position_encoding( + offset=max(offset - cache_t1, 0), size=cache_t1 + self.global_chunk_size + ) + att_mask[:, :, -self.global_chunk_size :] = chunk_masks + mask_pad = chunk_masks.to(torch.bool) + else: + pos_emb = self.embed.position_encoding( + offset=offset - cache_t1, size=attention_key_size + ) + + max_att_len, max_cnn_len = 0, 0 # for repeat_interleave of new_att_cache + for i, layer in enumerate(self.encoders): + factor = self.calculate_downsampling_factor(i) + # NOTE(xcsong): Before layer.forward + # shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2), + # shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2) + # shape(new_att_cache) = [ batch, head, time2, outdim//head * 2 ] + att_cache_trunc = 0 + if xs.size(1) + att_cache.size(2) / factor > pos_emb.size(1): + # The time step is not divisible by the downsampling multiple + att_cache_trunc = ( + xs.size(1) + att_cache.size(2) // factor - pos_emb.size(1) + 1 + ) + xs, _, new_att_cache, new_cnn_cache = layer( + xs, + att_mask, + pos_emb, + mask_pad=mask_pad, + att_cache=att_cache[i : i + 1, :, ::factor, :][ + :, :, att_cache_trunc:, : + ], + cnn_cache=cnn_cache[i, :, :, :] if cnn_cache.size(0) > 0 else cnn_cache, + ) + + if i in self.stride_layer_idx: + # compute time dimension for next block + efficient_index = self.stride_layer_idx.index(i) + att_mask = att_mask[ + :, :: self.stride[efficient_index], :: self.stride[efficient_index] + ] + mask_pad = mask_pad[ + :, :: self.stride[efficient_index], :: self.stride[efficient_index] + ] + pos_emb = pos_emb[:, :: self.stride[efficient_index], :] + + # shape(new_att_cache) = [batch, head, time2, outdim] + new_att_cache = new_att_cache[:, :, next_cache_start // factor :, :] + # shape(new_cnn_cache) = [1, batch, outdim, cache_t2] + new_cnn_cache = new_cnn_cache.unsqueeze(0) + + # use repeat_interleave to new_att_cache + new_att_cache = new_att_cache.repeat_interleave(repeats=factor, dim=2) + # padding new_cnn_cache to cnn.lorder for casual convolution + new_cnn_cache = F.pad( + new_cnn_cache, (self.cnn_module_kernel - 1 - new_cnn_cache.size(3), 0) + ) + + if i == 0: + # record length for the first block as max length + max_att_len = new_att_cache.size(2) + max_cnn_len = new_cnn_cache.size(3) + + # update real shape of att_cache and cnn_cache + r_att_cache.append(new_att_cache[:, :, -max_att_len:, :]) + r_cnn_cache.append(new_cnn_cache[:, :, :, -max_cnn_len:]) + + if self.normalize_before: + xs = self.after_norm(xs) + + # NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2), + # ? may be larger than cache_t1, it depends on required_cache_size + r_att_cache = torch.cat(r_att_cache, dim=0) + # NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2) + r_cnn_cache = torch.cat(r_cnn_cache, dim=0) + + if self.global_chunk_size > 0 and real_len: + chunk_real_len = ( + real_len + // self.embed.subsampling_rate + // self.calculate_downsampling_factor(self.num_blocks + 1) + ) + # Keeping 1 more timestep can mitigate information leakage + # from the encoder caused by the padding + xs = xs[:, : chunk_real_len + 1, :] + + return xs, r_att_cache, r_cnn_cache + + def forward_chunk_by_chunk( + self, + xs: torch.Tensor, + decoding_chunk_size: int, + num_decoding_left_chunks: int = -1, + use_onnx=False, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Forward input chunk by chunk with chunk_size like a streaming + fashion + + Here we should pay special attention to computation cache in the + streaming style forward chunk by chunk. Three things should be taken + into account for computation in the current network: + 1. transformer/conformer encoder layers output cache + 2. convolution in conformer + 3. convolution in subsampling + + However, we don't implement subsampling cache for: + 1. We can control subsampling module to output the right result by + overlapping input instead of cache left context, even though it + wastes some computation, but subsampling only takes a very + small fraction of computation in the whole model. + 2. Typically, there are several covolution layers with subsampling + in subsampling module, it is tricky and complicated to do cache + with different convolution layers with different subsampling + rate. + 3. Currently, nn.Sequential is used to stack all the convolution + layers in subsampling, we need to rewrite it to make it work + with cache, which is not prefered. + Args: + xs (torch.Tensor): (1, max_len, dim) + decoding_chunk_size (int): decoding chunk size + num_decoding_left_chunks (int): + use_onnx (bool): True for simulating ONNX model inference. + """ + assert decoding_chunk_size > 0 + # The model is trained by static or dynamic chunk + assert self.static_chunk_size > 0 or self.use_dynamic_chunk + subsampling = self.embed.subsampling_rate + context = self.embed.right_context + 1 # Add current frame + stride = subsampling * decoding_chunk_size + decoding_window = (decoding_chunk_size - 1) * subsampling + context + num_frames = xs.size(1) + + outputs = [] + offset = 0 + required_cache_size = decoding_chunk_size * num_decoding_left_chunks + if use_onnx: + logging.info("Simulating for ONNX runtime ...") + att_cache: torch.Tensor = torch.zeros( + ( + self.num_blocks, + self.attention_heads, + required_cache_size, + self.output_size() // self.attention_heads * 2, + ), + device=xs.device, + ) + cnn_cache: torch.Tensor = torch.zeros( + (self.num_blocks, 1, self.output_size(), self.cnn_module_kernel - 1), + device=xs.device, + ) + self.set_global_chunk_size(chunk_size=decoding_chunk_size) + else: + logging.info("Simulating for JIT runtime ...") + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) + + # Feed forward overlap input step by step + for cur in range(0, num_frames - context + 1, stride): + end = min(cur + decoding_window, num_frames) + logging.info( + f"-->> frame chunk msg: cur={cur}, " + f"end={end}, num_frames={end-cur}, " + f"decoding_window={decoding_window}" + ) + if use_onnx: + att_mask: torch.Tensor = torch.ones( + (1, 1, required_cache_size + decoding_chunk_size), + dtype=torch.bool, + device=xs.device, + ) + if cur == 0: + att_mask[:, :, :required_cache_size] = 0 + else: + att_mask: torch.Tensor = torch.ones( + (0, 0, 0), dtype=torch.bool, device=xs.device + ) + + chunk_xs = xs[:, cur:end, :] + (y, att_cache, cnn_cache) = self.forward_chunk( + chunk_xs, offset, required_cache_size, att_cache, cnn_cache, att_mask + ) + outputs.append(y) + offset += y.size(1) + + ys = torch.cat(outputs, 1) + masks = torch.ones(1, 1, ys.size(1), device=ys.device, dtype=torch.bool) + return ys, masks diff --git a/modules/wenet_extractor/efficient_conformer/encoder_layer.py b/modules/wenet_extractor/efficient_conformer/encoder_layer.py new file mode 100644 index 0000000000000000000000000000000000000000..9156c0edf9f79df6425accd542369e139591ed42 --- /dev/null +++ b/modules/wenet_extractor/efficient_conformer/encoder_layer.py @@ -0,0 +1,172 @@ +## This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""Encoder self-attention layer definition.""" + +from typing import Optional, Tuple +import torch +from torch import nn + + +class StrideConformerEncoderLayer(nn.Module): + """Encoder layer module. + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` + instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward` instance can be used as the argument. + feed_forward_macaron (torch.nn.Module): Additional feed-forward module + instance. + `PositionwiseFeedForward` instance can be used as the argument. + conv_module (torch.nn.Module): Convolution module instance. + `ConvlutionModule` instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): + True: use layer_norm before each sub-block. + False: use layer_norm after each sub-block. + """ + + def __init__( + self, + size: int, + self_attn: torch.nn.Module, + feed_forward: Optional[nn.Module] = None, + feed_forward_macaron: Optional[nn.Module] = None, + conv_module: Optional[nn.Module] = None, + pointwise_conv_layer: Optional[nn.Module] = None, + dropout_rate: float = 0.1, + normalize_before: bool = True, + ): + """Construct an EncoderLayer object.""" + super().__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.feed_forward_macaron = feed_forward_macaron + self.conv_module = conv_module + self.pointwise_conv_layer = pointwise_conv_layer + self.norm_ff = nn.LayerNorm(size, eps=1e-5) # for the FNN module + self.norm_mha = nn.LayerNorm(size, eps=1e-5) # for the MHA module + if feed_forward_macaron is not None: + self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-5) + self.ff_scale = 0.5 + else: + self.ff_scale = 1.0 + if self.conv_module is not None: + self.norm_conv = nn.LayerNorm(size, eps=1e-5) # for the CNN module + self.norm_final = nn.LayerNorm( + size, eps=1e-5 + ) # for the final output of the block + self.dropout = nn.Dropout(dropout_rate) + self.size = size + self.normalize_before = normalize_before + self.concat_linear = nn.Linear(size + size, size) + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute encoded features. + + Args: + x (torch.Tensor): (#batch, time, size) + mask (torch.Tensor): Mask tensor for the input (#batch, time，time), + (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): positional encoding, must not be None + for ConformerEncoderLayer. + mask_pad (torch.Tensor): batch padding mask used for conv module. + (#batch, 1，time), (0, 0, 0) means fake mask. + att_cache (torch.Tensor): Cache tensor of the KEY & VALUE + (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. + cnn_cache (torch.Tensor): Convolution cache in conformer layer + (#batch=1, size, cache_t2) + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time, time). + torch.Tensor: att_cache tensor, + (#batch=1, head, cache_t1 + time, d_k * 2). + torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2). + """ + + # whether to use macaron style + if self.feed_forward_macaron is not None: + residual = x + if self.normalize_before: + x = self.norm_ff_macaron(x) + x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x)) + if not self.normalize_before: + x = self.norm_ff_macaron(x) + + # multi-headed self-attention module + residual = x + if self.normalize_before: + x = self.norm_mha(x) + + x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache) + + x = residual + self.dropout(x_att) + if not self.normalize_before: + x = self.norm_mha(x) + + # convolution module + # Fake new cnn cache here, and then change it in conv_module + new_cnn_cache = torch.tensor([0.0], dtype=x.dtype, device=x.device) + if self.conv_module is not None: + residual = x + if self.normalize_before: + x = self.norm_conv(x) + x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache) + + # add pointwise_conv for efficient conformer + # pointwise_conv_layer does not change shape + if self.pointwise_conv_layer is not None: + residual = residual.transpose(1, 2) + residual = self.pointwise_conv_layer(residual) + residual = residual.transpose(1, 2) + assert residual.size(0) == x.size(0) + assert residual.size(1) == x.size(1) + assert residual.size(2) == x.size(2) + + x = residual + self.dropout(x) + + if not self.normalize_before: + x = self.norm_conv(x) + + # feed forward module + residual = x + if self.normalize_before: + x = self.norm_ff(x) + + x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm_ff(x) + + if self.conv_module is not None: + x = self.norm_final(x) + + return x, mask, new_att_cache, new_cnn_cache diff --git a/modules/wenet_extractor/efficient_conformer/subsampling.py b/modules/wenet_extractor/efficient_conformer/subsampling.py new file mode 100644 index 0000000000000000000000000000000000000000..6017084c2accb40c86be16d798e587b4f32c26d4 --- /dev/null +++ b/modules/wenet_extractor/efficient_conformer/subsampling.py @@ -0,0 +1,81 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + + +"""Subsampling layer definition.""" + +from typing import Tuple, Union + +import torch +from modules.wenet_extractor.transformer.subsampling import BaseSubsampling + + +class Conv2dSubsampling2(BaseSubsampling): + """Convolutional 2D subsampling (to 1/4 length). + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__( + self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module + ): + """Construct an Conv2dSubsampling4 object.""" + super().__init__() + self.conv = torch.nn.Sequential(torch.nn.Conv2d(1, odim, 3, 2), torch.nn.ReLU()) + self.out = torch.nn.Sequential(torch.nn.Linear(odim * ((idim - 1) // 2), odim)) + self.pos_enc = pos_enc_class + # The right context for every conv layer is computed by: + # (kernel_size - 1) * frame_rate_of_this_layer + self.subsampling_rate = 2 + # 2 = (3 - 1) * 1 + self.right_context = 2 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Subsample x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 2. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 2. + torch.Tensor: positional encoding + + """ + x = x.unsqueeze(1) # (b, c=1, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask[:, :, :-2:2] diff --git a/modules/wenet_extractor/paraformer/__pycache__/paraformer.cpython-39.pyc b/modules/wenet_extractor/paraformer/__pycache__/paraformer.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..69629c67efc127566b1bed9f8c6f6f0eb919ca82 Binary files /dev/null and b/modules/wenet_extractor/paraformer/__pycache__/paraformer.cpython-39.pyc differ diff --git a/modules/wenet_extractor/paraformer/__pycache__/utils.cpython-39.pyc b/modules/wenet_extractor/paraformer/__pycache__/utils.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f6a59dbebae400e8d0ef9bf902897988c7786a26 Binary files /dev/null and b/modules/wenet_extractor/paraformer/__pycache__/utils.cpython-39.pyc differ diff --git a/modules/wenet_extractor/paraformer/paraformer.py b/modules/wenet_extractor/paraformer/paraformer.py new file mode 100644 index 0000000000000000000000000000000000000000..8f144baa2a5df2292fa4616952338c8f937bfcba --- /dev/null +++ b/modules/wenet_extractor/paraformer/paraformer.py @@ -0,0 +1,366 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +from typing import Dict, Optional, Tuple + +import torch + +from modules.wenet_extractor.cif.predictor import MAELoss +from modules.wenet_extractor.paraformer.search.beam_search import Hypothesis +from modules.wenet_extractor.transformer.asr_model import ASRModel +from modules.wenet_extractor.transformer.ctc import CTC +from modules.wenet_extractor.transformer.decoder import TransformerDecoder +from modules.wenet_extractor.transformer.encoder import TransformerEncoder +from modules.wenet_extractor.utils.common import IGNORE_ID, add_sos_eos, th_accuracy +from modules.wenet_extractor.utils.mask import make_pad_mask + + +class Paraformer(ASRModel): + """Paraformer: Fast and Accurate Parallel Transformer for + Non-autoregressive End-to-End Speech Recognition + see https://arxiv.org/pdf/2206.08317.pdf + """ + + def __init__( + self, + vocab_size: int, + encoder: TransformerEncoder, + decoder: TransformerDecoder, + ctc: CTC, + predictor, + ctc_weight: float = 0.5, + predictor_weight: float = 1.0, + predictor_bias: int = 0, + ignore_id: int = IGNORE_ID, + reverse_weight: float = 0.0, + lsm_weight: float = 0.0, + length_normalized_loss: bool = False, + ): + assert 0.0 <= ctc_weight <= 1.0, ctc_weight + assert 0.0 <= predictor_weight <= 1.0, predictor_weight + + super().__init__( + vocab_size, + encoder, + decoder, + ctc, + ctc_weight, + ignore_id, + reverse_weight, + lsm_weight, + length_normalized_loss, + ) + self.predictor = predictor + self.predictor_weight = predictor_weight + self.predictor_bias = predictor_bias + self.criterion_pre = MAELoss(normalize_length=length_normalized_loss) + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + ) -> Dict[str, Optional[torch.Tensor]]: + """Frontend + Encoder + Decoder + Calc loss + + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + """ + assert text_lengths.dim() == 1, text_lengths.shape + # Check that batch_size is unified + assert ( + speech.shape[0] + == speech_lengths.shape[0] + == text.shape[0] + == text_lengths.shape[0] + ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape) + # 1. Encoder + encoder_out, encoder_mask = self.encoder(speech, speech_lengths) + encoder_out_lens = encoder_mask.squeeze(1).sum(1) + + # 2a. Attention-decoder branch + if self.ctc_weight != 1.0: + loss_att, acc_att, loss_pre = self._calc_att_loss( + encoder_out, encoder_mask, text, text_lengths + ) + else: + # loss_att = None + # loss_pre = None + loss_att: torch.Tensor = torch.tensor(0) + loss_pre: torch.Tensor = torch.tensor(0) + + # 2b. CTC branch + if self.ctc_weight != 0.0: + loss_ctc = self.ctc(encoder_out, encoder_out_lens, text, text_lengths) + else: + loss_ctc = None + + if loss_ctc is None: + loss = loss_att + self.predictor_weight * loss_pre + # elif loss_att is None: + elif loss_att == torch.tensor(0): + loss = loss_ctc + else: + loss = ( + self.ctc_weight * loss_ctc + + (1 - self.ctc_weight) * loss_att + + self.predictor_weight * loss_pre + ) + return { + "loss": loss, + "loss_att": loss_att, + "loss_ctc": loss_ctc, + "loss_pre": loss_pre, + } + + def _calc_att_loss( + self, + encoder_out: torch.Tensor, + encoder_mask: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ) -> Tuple[torch.Tensor, float, torch.Tensor]: + if self.predictor_bias == 1: + _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_pad_lens = ys_pad_lens + self.predictor_bias + pre_acoustic_embeds, pre_token_length, _, pre_peak_index = self.predictor( + encoder_out, ys_pad, encoder_mask, ignore_id=self.ignore_id + ) + # 1. Forward decoder + decoder_out, _, _ = self.decoder( + encoder_out, encoder_mask, pre_acoustic_embeds, ys_pad_lens + ) + + # 2. Compute attention loss + loss_att = self.criterion_att(decoder_out, ys_pad) + acc_att = th_accuracy( + decoder_out.view(-1, self.vocab_size), + ys_pad, + ignore_label=self.ignore_id, + ) + loss_pre: torch.Tensor = self.criterion_pre( + ys_pad_lens.type_as(pre_token_length), pre_token_length + ) + + return loss_att, acc_att, loss_pre + + def calc_predictor(self, encoder_out, encoder_mask): + encoder_mask = ( + ~make_pad_mask(encoder_mask, max_len=encoder_out.size(1))[:, None, :] + ).to(encoder_out.device) + pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor( + encoder_out, None, encoder_mask, ignore_id=self.ignore_id + ) + return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index + + def cal_decoder_with_predictor( + self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens + ): + decoder_out, _, _ = self.decoder( + encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens + ) + decoder_out = torch.log_softmax(decoder_out, dim=-1) + return decoder_out, ys_pad_lens + + def recognize(self): + raise NotImplementedError + + def paraformer_greedy_search( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + decoding_chunk_size: int = -1, + num_decoding_left_chunks: int = -1, + simulate_streaming: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Apply beam search on attention decoder + + Args: + speech (torch.Tensor): (batch, max_len, feat_dim) + speech_length (torch.Tensor): (batch, ) + decoding_chunk_size (int): decoding chunk for dynamic chunk + trained model. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + 0: used for training, it's prohibited here + simulate_streaming (bool): whether do encoder forward in a + streaming fashion + + Returns: + torch.Tensor: decoding result, (batch, max_result_len) + """ + assert speech.shape[0] == speech_lengths.shape[0] + assert decoding_chunk_size != 0 + device = speech.device + batch_size = speech.shape[0] + + # Let's assume B = batch_size and N = beam_size + # 1. Encoder + encoder_out, encoder_mask = self._forward_encoder( + speech, + speech_lengths, + decoding_chunk_size, + num_decoding_left_chunks, + simulate_streaming, + ) # (B, maxlen, encoder_dim) + encoder_out_lens = encoder_mask.squeeze(1).sum(1) + # 2. Predictor + predictor_outs = self.calc_predictor(encoder_out, encoder_mask) + pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = ( + predictor_outs[0], + predictor_outs[1], + predictor_outs[2], + predictor_outs[3], + ) + pre_token_length = pre_token_length.round().long() + if torch.max(pre_token_length) < 1: + return torch.tensor([]), torch.tensor([]) + # 2. Decoder forward + decoder_outs = self.cal_decoder_with_predictor( + encoder_out, encoder_out_lens, pre_acoustic_embeds, pre_token_length + ) + decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1] + hyps = [] + b, n, d = decoder_out.size() + for i in range(b): + x = encoder_out[i, : encoder_out_lens[i], :] + am_scores = decoder_out[i, : pre_token_length[i], :] + yseq = am_scores.argmax(dim=-1) + score = am_scores.max(dim=-1)[0] + score = torch.sum(score, dim=-1) + # pad with mask tokens to ensure compatibility with sos/eos tokens + yseq = torch.tensor( + [self.sos] + yseq.tolist() + [self.eos], device=yseq.device + ) + nbest_hyps = [Hypothesis(yseq=yseq, score=score)] + + for hyp in nbest_hyps: + assert isinstance(hyp, (Hypothesis)), type(hyp) + + # remove sos/eos and get hyps + last_pos = -1 + if isinstance(hyp.yseq, list): + token_int = hyp.yseq[1:last_pos] + else: + token_int = hyp.yseq[1:last_pos].tolist() + + # remove blank symbol id and unk id, which is assumed to be 0 + # and 1 + token_int = list(filter(lambda x: x != 0 and x != 1, token_int)) + hyps.append(token_int) + return hyps + + def paraformer_beam_search( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + beam_search: torch.nn.Module = None, + decoding_chunk_size: int = -1, + num_decoding_left_chunks: int = -1, + simulate_streaming: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Apply beam search on attention decoder + + Args: + speech (torch.Tensor): (batch, max_len, feat_dim) + speech_lengths (torch.Tensor): (batch, ) + beam_search (torch.nn.Moudle): beam search module + decoding_chunk_size (int): decoding chunk for dynamic chunk + trained model. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + 0: used for training, it's prohibited here + simulate_streaming (bool): whether do encoder forward in a + streaming fashion + + Returns: + torch.Tensor: decoding result, (batch, max_result_len) + """ + assert speech.shape[0] == speech_lengths.shape[0] + assert decoding_chunk_size != 0 + device = speech.device + batch_size = speech.shape[0] + + # Let's assume B = batch_size and N = beam_size + # 1. Encoder + encoder_out, encoder_mask = self._forward_encoder( + speech, + speech_lengths, + decoding_chunk_size, + num_decoding_left_chunks, + simulate_streaming, + ) # (B, maxlen, encoder_dim) + encoder_out_lens = encoder_mask.squeeze(1).sum(1) + # 2. Predictor + predictor_outs = self.calc_predictor(encoder_out, encoder_mask) + pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = ( + predictor_outs[0], + predictor_outs[1], + predictor_outs[2], + predictor_outs[3], + ) + pre_token_length = pre_token_length.round().long() + if torch.max(pre_token_length) < 1: + return torch.tensor([]), torch.tensor([]) + # 2. Decoder forward + decoder_outs = self.cal_decoder_with_predictor( + encoder_out, encoder_out_lens, pre_acoustic_embeds, pre_token_length + ) + decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1] + hyps = [] + b, n, d = decoder_out.size() + for i in range(b): + x = encoder_out[i, : encoder_out_lens[i], :] + am_scores = decoder_out[i, : pre_token_length[i], :] + if beam_search is not None: + nbest_hyps = beam_search(x=x, am_scores=am_scores) + nbest_hyps = nbest_hyps[:1] + else: + yseq = am_scores.argmax(dim=-1) + score = am_scores.max(dim=-1)[0] + score = torch.sum(score, dim=-1) + # pad with mask tokens to ensure compatibility with sos/eos + # tokens + yseq = torch.tensor( + [self.sos] + yseq.tolist() + [self.eos], device=yseq.device + ) + nbest_hyps = [Hypothesis(yseq=yseq, score=score)] + + for hyp in nbest_hyps: + assert isinstance(hyp, (Hypothesis)), type(hyp) + + # remove sos/eos and get hyps + last_pos = -1 + if isinstance(hyp.yseq, list): + token_int = hyp.yseq[1:last_pos] + else: + token_int = hyp.yseq[1:last_pos].tolist() + + # remove blank symbol id and unk id, which is assumed to be 0 + # and 1 + token_int = list(filter(lambda x: x != 0 and x != 1, token_int)) + hyps.append(token_int) + return hyps diff --git a/modules/wenet_extractor/paraformer/search/__pycache__/beam_search.cpython-39.pyc b/modules/wenet_extractor/paraformer/search/__pycache__/beam_search.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..289699d4af5bf04e72935d843be6e9d31a8e3fa4 Binary files /dev/null and b/modules/wenet_extractor/paraformer/search/__pycache__/beam_search.cpython-39.pyc differ diff --git a/modules/wenet_extractor/paraformer/search/__pycache__/ctc.cpython-39.pyc b/modules/wenet_extractor/paraformer/search/__pycache__/ctc.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8c3070c91afce6aa6a7c9d0fe6e4d3ad7085e0ee Binary files /dev/null and b/modules/wenet_extractor/paraformer/search/__pycache__/ctc.cpython-39.pyc differ diff --git a/modules/wenet_extractor/paraformer/search/__pycache__/ctc_prefix_score.cpython-39.pyc b/modules/wenet_extractor/paraformer/search/__pycache__/ctc_prefix_score.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eeac14bc199cad848d368b65d1c474fbc97fb271 Binary files /dev/null and b/modules/wenet_extractor/paraformer/search/__pycache__/ctc_prefix_score.cpython-39.pyc differ diff --git a/modules/wenet_extractor/paraformer/search/__pycache__/scorer_interface.cpython-39.pyc b/modules/wenet_extractor/paraformer/search/__pycache__/scorer_interface.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..87bfc30bfcdb31eba90a80aa835d84b576b4a102 Binary files /dev/null and b/modules/wenet_extractor/paraformer/search/__pycache__/scorer_interface.cpython-39.pyc differ diff --git a/modules/wenet_extractor/paraformer/search/beam_search.py b/modules/wenet_extractor/paraformer/search/beam_search.py new file mode 100644 index 0000000000000000000000000000000000000000..5389ce4ac7f0026ab1dcf042c62b9c790bd61085 --- /dev/null +++ b/modules/wenet_extractor/paraformer/search/beam_search.py @@ -0,0 +1,479 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +from itertools import chain +from typing import Any +from typing import Dict +from typing import List +from typing import Tuple +from typing import Union +from typing import NamedTuple + +import torch + +from modules.wenet_extractor.paraformer.utils import end_detect +from modules.wenet_extractor.paraformer.search.ctc import CTCPrefixScorer +from modules.wenet_extractor.paraformer.search.scorer_interface import ( + ScorerInterface, + PartialScorerInterface, +) + + +class Hypothesis(NamedTuple): + """Hypothesis data type.""" + + yseq: torch.Tensor + score: Union[float, torch.Tensor] = 0 + scores: Dict[str, Union[float, torch.Tensor]] = dict() + states: Dict[str, Any] = dict() + + def asdict(self) -> dict: + """Convert data to JSON-friendly dict.""" + return self._replace( + yseq=self.yseq.tolist(), + score=float(self.score), + scores={k: float(v) for k, v in self.scores.items()}, + )._asdict() + + +class BeamSearchCIF(torch.nn.Module): + """Beam search implementation.""" + + def __init__( + self, + scorers: Dict[str, ScorerInterface], + weights: Dict[str, float], + beam_size: int, + vocab_size: int, + sos: int, + eos: int, + pre_beam_ratio: float = 1.5, + pre_beam_score_key: str = None, + ): + """Initialize beam search. + + Args: + scorers (dict[str, ScorerInterface]): Dict of decoder modules + e.g., Decoder, CTCPrefixScorer, LM + The scorer will be ignored if it is `None` + weights (dict[str, float]): Dict of weights for each scorers + The scorer will be ignored if its weight is 0 + beam_size (int): The number of hypotheses kept during search + vocab_size (int): The number of vocabulary + sos (int): Start of sequence id + eos (int): End of sequence id + pre_beam_score_key (str): key of scores to perform pre-beam search + pre_beam_ratio (float): beam size in the pre-beam search + will be `int(pre_beam_ratio * beam_size)` + + """ + super().__init__() + # set scorers + self.weights = weights + self.scorers = dict() + self.full_scorers = dict() + self.part_scorers = dict() + # this module dict is required for recursive cast + # `self.to(device, dtype)` in `recog.py` + self.nn_dict = torch.nn.ModuleDict() + for k, v in scorers.items(): + w = weights.get(k, 0) + if w == 0 or v is None: + continue + assert isinstance( + v, ScorerInterface + ), f"{k} ({type(v)}) does not implement ScorerInterface" + self.scorers[k] = v + if isinstance(v, PartialScorerInterface): + self.part_scorers[k] = v + else: + self.full_scorers[k] = v + if isinstance(v, torch.nn.Module): + self.nn_dict[k] = v + + # set configurations + self.sos = sos + self.eos = eos + self.pre_beam_size = int(pre_beam_ratio * beam_size) + self.beam_size = beam_size + self.n_vocab = vocab_size + if ( + pre_beam_score_key is not None + and pre_beam_score_key != "full" + and pre_beam_score_key not in self.full_scorers + ): + raise KeyError( + f"{pre_beam_score_key} is not found in " f"{self.full_scorers}" + ) + self.pre_beam_score_key = pre_beam_score_key + self.do_pre_beam = ( + self.pre_beam_score_key is not None + and self.pre_beam_size < self.n_vocab + and len(self.part_scorers) > 0 + ) + + def init_hyp(self, x: torch.Tensor) -> List[Hypothesis]: + """Get an initial hypothesis data. + + Args: + x (torch.Tensor): The encoder output feature + + Returns: + Hypothesis: The initial hypothesis. + + """ + init_states = dict() + init_scores = dict() + for k, d in self.scorers.items(): + init_states[k] = d.init_state(x) + init_scores[k] = 0.0 + return [ + Hypothesis( + score=0.0, + scores=init_scores, + states=init_states, + yseq=torch.tensor([self.sos], device=x.device), + ) + ] + + @staticmethod + def append_token(xs: torch.Tensor, x: int) -> torch.Tensor: + """Append new token to prefix tokens. + + Args: + xs (torch.Tensor): The prefix token + x (int): The new token to append + + Returns: + torch.Tensor: New tensor contains: xs + [x] with xs.dtype and + xs.device + + """ + x = torch.tensor([x], dtype=xs.dtype, device=xs.device) + return torch.cat((xs, x)) + + def score_full( + self, hyp: Hypothesis, x: torch.Tensor + ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: + """Score new hypothesis by `self.full_scorers`. + + Args: + hyp (Hypothesis): Hypothesis with prefix tokens to score + x (torch.Tensor): Corresponding input feature + + Returns: + Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of + score dict of `hyp` that has string keys of `self.full_scorers` + and tensor score values of shape: `(self.n_vocab,)`, + and state dict that has string keys + and state values of `self.full_scorers` + + """ + scores = dict() + states = dict() + for k, d in self.full_scorers.items(): + scores[k], states[k] = d.score(hyp.yseq, hyp.states[k], x) + return scores, states + + def score_partial( + self, hyp: Hypothesis, ids: torch.Tensor, x: torch.Tensor + ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: + """Score new hypothesis by `self.part_scorers`. + + Args: + hyp (Hypothesis): Hypothesis with prefix tokens to score + ids (torch.Tensor): 1D tensor of new partial tokens to score + x (torch.Tensor): Corresponding input feature + + Returns: + Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of + score dict of `hyp` that has string keys of `self.part_scorers` + and tensor score values of shape: `(len(ids),)`, + and state dict that has string keys + and state values of `self.part_scorers` + + """ + scores = dict() + states = dict() + for k, d in self.part_scorers.items(): + scores[k], states[k] = d.score_partial(hyp.yseq, ids, hyp.states[k], x) + return scores, states + + def beam( + self, weighted_scores: torch.Tensor, ids: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute topk full token ids and partial token ids. + + Args: + weighted_scores (torch.Tensor): The weighted sum scores for each + tokens. + Its shape is `(self.n_vocab,)`. + ids (torch.Tensor): The partial token ids to compute topk + + Returns: + Tuple[torch.Tensor, torch.Tensor]: + The topk full token ids and partial token ids. + Their shapes are `(self.beam_size,)` + + """ + # no pre beam performed + if weighted_scores.size(0) == ids.size(0): + top_ids = weighted_scores.topk(self.beam_size)[1] + return top_ids, top_ids + + # mask pruned in pre-beam not to select in topk + tmp = weighted_scores[ids] + weighted_scores[:] = -float("inf") + weighted_scores[ids] = tmp + top_ids = weighted_scores.topk(self.beam_size)[1] + local_ids = weighted_scores[ids].topk(self.beam_size)[1] + return top_ids, local_ids + + @staticmethod + def merge_scores( + prev_scores: Dict[str, float], + next_full_scores: Dict[str, torch.Tensor], + full_idx: int, + next_part_scores: Dict[str, torch.Tensor], + part_idx: int, + ) -> Dict[str, torch.Tensor]: + """Merge scores for new hypothesis. + + Args: + prev_scores (Dict[str, float]): + The previous hypothesis scores by `self.scorers` + next_full_scores (Dict[str, torch.Tensor]): scores by + `self.full_scorers` + full_idx (int): The next token id for `next_full_scores` + next_part_scores (Dict[str, torch.Tensor]): + scores of partial tokens by `self.part_scorers` + part_idx (int): The new token id for `next_part_scores` + + Returns: + Dict[str, torch.Tensor]: The new score dict. + Its keys are names of `self.full_scorers` and + `self.part_scorers`. + Its values are scalar tensors by the scorers. + + """ + new_scores = dict() + for k, v in next_full_scores.items(): + new_scores[k] = prev_scores[k] + v[full_idx] + for k, v in next_part_scores.items(): + new_scores[k] = prev_scores[k] + v[part_idx] + return new_scores + + def merge_states(self, states: Any, part_states: Any, part_idx: int) -> Any: + """Merge states for new hypothesis. + + Args: + states: states of `self.full_scorers` + part_states: states of `self.part_scorers` + part_idx (int): The new token id for `part_scores` + + Returns: + Dict[str, torch.Tensor]: The new score dict. + Its keys are names of `self.full_scorers` and + `self.part_scorers`. + Its values are states of the scorers. + + """ + new_states = dict() + for k, v in states.items(): + new_states[k] = v + for k, d in self.part_scorers.items(): + new_states[k] = d.select_state(part_states[k], part_idx) + return new_states + + def search( + self, running_hyps: List[Hypothesis], x: torch.Tensor, am_score: torch.Tensor + ) -> List[Hypothesis]: + """Search new tokens for running hypotheses and encoded speech x. + + Args: + running_hyps (List[Hypothesis]): Running hypotheses on beam + x (torch.Tensor): Encoded speech feature (T, D) + + Returns: + List[Hypotheses]: Best sorted hypotheses + + """ + best_hyps = [] + part_ids = torch.arange(self.n_vocab, device=x.device) # no pre-beam + for hyp in running_hyps: + # scoring + weighted_scores = torch.zeros(self.n_vocab, dtype=x.dtype, device=x.device) + weighted_scores += am_score + scores, states = self.score_full(hyp, x) + for k in self.full_scorers: + weighted_scores += self.weights[k] * scores[k] + # partial scoring + if self.do_pre_beam: + pre_beam_scores = ( + weighted_scores + if self.pre_beam_score_key == "full" + else scores[self.pre_beam_score_key] + ) + part_ids = torch.topk(pre_beam_scores, self.pre_beam_size)[1] + part_scores, part_states = self.score_partial(hyp, part_ids, x) + for k in self.part_scorers: + weighted_scores[part_ids] += self.weights[k] * part_scores[k] + # add previous hyp score + weighted_scores += hyp.score + + # update hyps + for j, part_j in zip(*self.beam(weighted_scores, part_ids)): + # will be (2 x beam at most) + best_hyps.append( + Hypothesis( + score=weighted_scores[j], + yseq=self.append_token(hyp.yseq, j), + scores=self.merge_scores( + hyp.scores, scores, j, part_scores, part_j + ), + states=self.merge_states(states, part_states, part_j), + ) + ) + + # sort and prune 2 x beam -> beam + best_hyps = sorted(best_hyps, key=lambda x: x.score, reverse=True)[ + : min(len(best_hyps), self.beam_size) + ] + return best_hyps + + def forward( + self, + x: torch.Tensor, + am_scores: torch.Tensor, + maxlenratio: float = 0.0, + minlenratio: float = 0.0, + ) -> List[Hypothesis]: + """Perform beam search. + + Args: + x (torch.Tensor): Encoded speech feature (T, D) + maxlenratio (float): Input length ratio to obtain max output length. + If maxlenratio=0.0 (default), it uses a end-detect function + to automatically find maximum hypothesis lengths + If maxlenratio<0.0, its absolute value is interpreted + as a constant max output length. + minlenratio (float): Input length ratio to obtain min output length. + + Returns: + list[Hypothesis]: N-best decoding results + + """ + # set length bounds + maxlen = am_scores.shape[0] + + # main loop of prefix search + running_hyps = self.init_hyp(x) + ended_hyps = [] + for i in range(maxlen): + best = self.search(running_hyps, x, am_scores[i]) + # post process of one iteration + running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps) + # end detection + if maxlenratio == 0.0 and end_detect([h.asdict() for h in ended_hyps], i): + break + + nbest_hyps = sorted(ended_hyps, key=lambda x: x.score, reverse=True) + # check the number of hypotheses reaching to eos + if len(nbest_hyps) == 0: + return ( + [] + if minlenratio < 0.1 + else self.forward(x, maxlenratio, max(0.0, minlenratio - 0.1)) + ) + + best = nbest_hyps[0] + return nbest_hyps + + def post_process( + self, + i: int, + maxlen: int, + maxlenratio: float, + running_hyps: List[Hypothesis], + ended_hyps: List[Hypothesis], + ) -> List[Hypothesis]: + """Perform post-processing of beam search iterations. + + Args: + i (int): The length of hypothesis tokens. + maxlen (int): The maximum length of tokens in beam search. + maxlenratio (int): The maximum length ratio in beam search. + running_hyps (List[Hypothesis]): The running hypotheses in beam + search. + ended_hyps (List[Hypothesis]): The ended hypotheses in beam search. + + Returns: + List[Hypothesis]: The new running hypotheses. + + """ + + # add eos in the final loop to avoid that there are no ended hyps + if i == maxlen - 1: + # logging.info("adding in the last position in the loop") + running_hyps = [ + h._replace(yseq=self.append_token(h.yseq, self.eos)) + for h in running_hyps + ] + + # add ended hypotheses to a final list, and removed them from current + # hypotheses + # (this will be a problem, number of hyps < beam) + remained_hyps = [] + for hyp in running_hyps: + if hyp.yseq[-1] == self.eos: + # e.g., Word LM needs to add final score + for k, d in chain(self.full_scorers.items(), self.part_scorers.items()): + s = d.final_score(hyp.states[k]) + hyp.scores[k] += s + hyp = hyp._replace(score=hyp.score + self.weights[k] * s) + ended_hyps.append(hyp) + else: + remained_hyps.append(hyp) + return remained_hyps + + +def build_beam_search(model, args, device): + scorers = {} + if model.ctc is not None: + ctc = CTCPrefixScorer(ctc=model.ctc, eos=model.eos) + scorers.update(ctc=ctc) + weights = dict( + decoder=1.0 - args.ctc_weight, + ctc=args.ctc_weight, + length_bonus=args.penalty, + ) + beam_search = BeamSearchCIF( + beam_size=args.beam_size, + weights=weights, + scorers=scorers, + sos=model.sos, + eos=model.eos, + vocab_size=model.vocab_size, + pre_beam_score_key=None if args.ctc_weight == 1.0 else "full", + ) + beam_search.to(device=device, dtype=torch.float32).eval() + return beam_search diff --git a/modules/wenet_extractor/paraformer/search/ctc.py b/modules/wenet_extractor/paraformer/search/ctc.py new file mode 100644 index 0000000000000000000000000000000000000000..a40c337ed3ce69051292096a9648b6675f3b8049 --- /dev/null +++ b/modules/wenet_extractor/paraformer/search/ctc.py @@ -0,0 +1,181 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""ScorerInterface implementation for CTC.""" +import numpy as np +import torch + +from modules.wenet_extractor.paraformer.search.ctc_prefix_score import CTCPrefixScore +from modules.wenet_extractor.paraformer.search.ctc_prefix_score import CTCPrefixScoreTH +from modules.wenet_extractor.paraformer.search.scorer_interface import ( + BatchPartialScorerInterface, +) + + +class CTCPrefixScorer(BatchPartialScorerInterface): + """Decoder interface wrapper for CTCPrefixScore.""" + + def __init__(self, ctc: torch.nn.Module, eos: int): + """Initialize class. + + Args: + ctc (torch.nn.Module): The CTC implementation. + For example, :class:`espnet.nets.pytorch_backend.ctc.CTC` + eos (int): The end-of-sequence id. + + """ + self.ctc = ctc + self.eos = eos + self.impl = None + + def init_state(self, x: torch.Tensor): + """Get an initial state for decoding. + + Args: + x (torch.Tensor): The encoded feature tensor + + Returns: initial state + + """ + logp = self.ctc.log_softmax(x.unsqueeze(0)).detach().squeeze(0).cpu().numpy() + # TODO(karita): use CTCPrefixScoreTH + self.impl = CTCPrefixScore(logp, 0, self.eos, np) + return 0, self.impl.initial_state() + + def select_state(self, state, i, new_id=None): + """Select state with relative ids in the main beam search. + + Args: + state: Decoder state for prefix tokens + i (int): Index to select a state in the main beam search + new_id (int): New label id to select a state if necessary + + Returns: + state: pruned state + + """ + if type(state) == tuple: + if len(state) == 2: # for CTCPrefixScore + sc, st = state + return sc[i], st[i] + else: # for CTCPrefixScoreTH (need new_id > 0) + r, log_psi, f_min, f_max, scoring_idmap = state + s = log_psi[i, new_id].expand(log_psi.size(1)) + if scoring_idmap is not None: + return r[:, :, i, scoring_idmap[i, new_id]], s, f_min, f_max + else: + return r[:, :, i, new_id], s, f_min, f_max + return None if state is None else state[i] + + def score_partial(self, y, ids, state, x): + """Score new token. + + Args: + y (torch.Tensor): 1D prefix token + next_tokens (torch.Tensor): torch.int64 next token to score + state: decoder state for prefix tokens + x (torch.Tensor): 2D encoder feature that generates ys + + Returns: + tuple[torch.Tensor, Any]: + Tuple of a score tensor for y that has a shape + `(len(next_tokens),)` and next state for ys + + """ + prev_score, state = state + presub_score, new_st = self.impl(y.cpu(), ids.cpu(), state) + tscore = torch.as_tensor( + presub_score - prev_score, device=x.device, dtype=x.dtype + ) + return tscore, (presub_score, new_st) + + def batch_init_state(self, x: torch.Tensor): + """Get an initial state for decoding. + + Args: + x (torch.Tensor): The encoded feature tensor + + Returns: initial state + + """ + logp = self.ctc.log_softmax(x.unsqueeze(0)) # assuming batch_size = 1 + xlen = torch.tensor([logp.size(1)]) + self.impl = CTCPrefixScoreTH(logp, xlen, 0, self.eos) + return None + + def batch_score_partial(self, y, ids, state, x): + """Score new token. + + Args: + y (torch.Tensor): 1D prefix token + ids (torch.Tensor): torch.int64 next token to score + state: decoder state for prefix tokens + x (torch.Tensor): 2D encoder feature that generates ys + + Returns: + tuple[torch.Tensor, Any]: + Tuple of a score tensor for y that has a shape + `(len(next_tokens),)` and next state for ys + + """ + batch_state = ( + ( + torch.stack([s[0] for s in state], dim=2), + torch.stack([s[1] for s in state]), + state[0][2], + state[0][3], + ) + if state[0] is not None + else None + ) + return self.impl(y, batch_state, ids) + + def extend_prob(self, x: torch.Tensor): + """Extend probs for decoding. + + This extension is for streaming decoding + as in Eq (14) in https://arxiv.org/abs/2006.14941 + + Args: + x (torch.Tensor): The encoded feature tensor + + """ + logp = self.ctc.log_softmax(x.unsqueeze(0)) + self.impl.extend_prob(logp) + + def extend_state(self, state): + """Extend state for decoding. + + This extension is for streaming decoding + as in Eq (14) in https://arxiv.org/abs/2006.14941 + + Args: + state: The states of hyps + + Returns: exteded state + + """ + new_state = [] + for s in state: + new_state.append(self.impl.extend_state(s)) + + return new_state diff --git a/modules/wenet_extractor/paraformer/search/ctc_prefix_score.py b/modules/wenet_extractor/paraformer/search/ctc_prefix_score.py new file mode 100644 index 0000000000000000000000000000000000000000..b4a4eb50b7556f6a463aef5428dba3ebc4f49875 --- /dev/null +++ b/modules/wenet_extractor/paraformer/search/ctc_prefix_score.py @@ -0,0 +1,377 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +import torch +import numpy as np + +import six + + +class CTCPrefixScore(object): + """Compute CTC label sequence scores + + which is based on Algorithm 2 in WATANABE et al. + "HYBRID CTC/ATTENTION ARCHITECTURE FOR END-TO-END SPEECH RECOGNITION," + but extended to efficiently compute the probablities of multiple labels + simultaneously + """ + + def __init__(self, x, blank, eos, xp): + self.xp = xp + self.logzero = -10000000000.0 + self.blank = blank + self.eos = eos + self.input_length = len(x) + self.x = x + + def initial_state(self): + """Obtain an initial CTC state + + :return: CTC state + """ + # initial CTC state is made of a frame x 2 tensor that corresponds to + # r_t^n() and r_t^b(), where 0 and 1 of axis=1 represent + # superscripts n and b (non-blank and blank), respectively. + r = self.xp.full((self.input_length, 2), self.logzero, dtype=np.float32) + r[0, 1] = self.x[0, self.blank] + for i in six.moves.range(1, self.input_length): + r[i, 1] = r[i - 1, 1] + self.x[i, self.blank] + return r + + def __call__(self, y, cs, r_prev): + """Compute CTC prefix scores for next labels + + :param y : prefix label sequence + :param cs : array of next labels + :param r_prev: previous CTC state + :return ctc_scores, ctc_states + """ + # initialize CTC states + output_length = len(y) - 1 # ignore sos + # new CTC states are prepared as a frame x (n or b) x n_labels tensor + # that corresponds to r_t^n(h) and r_t^b(h). + r = self.xp.ndarray((self.input_length, 2, len(cs)), dtype=np.float32) + xs = self.x[:, cs] + if output_length == 0: + r[0, 0] = xs[0] + r[0, 1] = self.logzero + else: + r[output_length - 1] = self.logzero + + # prepare forward probabilities for the last label + r_sum = self.xp.logaddexp( + r_prev[:, 0], r_prev[:, 1] + ) # log(r_t^n(g) + r_t^b(g)) + last = y[-1] + if output_length > 0 and last in cs: + log_phi = self.xp.ndarray((self.input_length, len(cs)), dtype=np.float32) + for i in six.moves.range(len(cs)): + log_phi[:, i] = r_sum if cs[i] != last else r_prev[:, 1] + else: + log_phi = r_sum + + # compute forward probabilities log(r_t^n(h)), log(r_t^b(h)), + # and log prefix probabilities log(psi) + start = max(output_length, 1) + log_psi = r[start - 1, 0] + for t in six.moves.range(start, self.input_length): + r[t, 0] = self.xp.logaddexp(r[t - 1, 0], log_phi[t - 1]) + xs[t] + r[t, 1] = ( + self.xp.logaddexp(r[t - 1, 0], r[t - 1, 1]) + self.x[t, self.blank] + ) + log_psi = self.xp.logaddexp(log_psi, log_phi[t - 1] + xs[t]) + + # get P(...eos|X) that ends with the prefix itself + eos_pos = self.xp.where(cs == self.eos)[0] + if len(eos_pos) > 0: + log_psi[eos_pos] = r_sum[-1] # log(r_T^n(g) + r_T^b(g)) + + # exclude blank probs + blank_pos = self.xp.where(cs == self.blank)[0] + if len(blank_pos) > 0: + log_psi[blank_pos] = self.logzero + + # return the log prefix probability and CTC states, where the label axis + # of the CTC states is moved to the first axis to slice it easily + return log_psi, self.xp.rollaxis(r, 2) + + +class CTCPrefixScoreTH(object): + """Batch processing of CTCPrefixScore + + which is based on Algorithm 2 in WATANABE et al. + "HYBRID CTC/ATTENTION ARCHITECTURE FOR END-TO-END SPEECH RECOGNITION," + but extended to efficiently compute the label probablities for multiple + hypotheses simultaneously + See also Seki et al. "Vectorized Beam Search for CTC-Attention-Based + Speech Recognition," In INTERSPEECH (pp. 3825-3829), 2019. + """ + + def __init__(self, x, xlens, blank, eos, margin=0): + """Construct CTC prefix scorer + + :param torch.Tensor x: input label posterior sequences (B, T, O) + :param torch.Tensor xlens: input lengths (B,) + :param int blank: blank label id + :param int eos: end-of-sequence id + :param int margin: margin parameter for windowing (0 means no windowing) + """ + # In the comment lines, + # we assume T: input_length, B: batch size, W: beam width, O: output dim + self.logzero = -10000000000.0 + self.blank = blank + self.eos = eos + self.batch = x.size(0) + self.input_length = x.size(1) + self.odim = x.size(2) + self.dtype = x.dtype + self.device = ( + torch.device("cuda:%d" % x.get_device()) + if x.is_cuda + else torch.device("cpu") + ) + # Pad the rest of posteriors in the batch + # TODO(takaaki-hori): need a better way without for-loops + for i, l in enumerate(xlens): + if l < self.input_length: + x[i, l:, :] = self.logzero + x[i, l:, blank] = 0 + # Reshape input x + xn = x.transpose(0, 1) # (B, T, O) -> (T, B, O) + xb = xn[:, :, self.blank].unsqueeze(2).expand(-1, -1, self.odim) + self.x = torch.stack([xn, xb]) # (2, T, B, O) + self.end_frames = torch.as_tensor(xlens) - 1 + + # Setup CTC windowing + self.margin = margin + if margin > 0: + self.frame_ids = torch.arange( + self.input_length, dtype=self.dtype, device=self.device + ) + # Base indices for index conversion + self.idx_bh = None + self.idx_b = torch.arange(self.batch, device=self.device) + self.idx_bo = (self.idx_b * self.odim).unsqueeze(1) + + def __call__(self, y, state, scoring_ids=None, att_w=None): + """Compute CTC prefix scores for next labels + + :param list y: prefix label sequences + :param tuple state: previous CTC state + :param torch.Tensor pre_scores: scores for pre-selection of hypotheses + (BW, O) + :param torch.Tensor att_w: attention weights to decide CTC window + :return new_state, ctc_local_scores (BW, O) + """ + output_length = len(y[0]) - 1 # ignore sos + last_ids = [yi[-1] for yi in y] # last output label ids + n_bh = len(last_ids) # batch * hyps + n_hyps = n_bh // self.batch # assuming each utterance has the same + self.scoring_num = scoring_ids.size(-1) if scoring_ids is not None else 0 + # prepare state info + if state is None: + r_prev = torch.full( + (self.input_length, 2, self.batch, n_hyps), + self.logzero, + dtype=self.dtype, + device=self.device, + ) + r_prev[:, 1] = torch.cumsum(self.x[0, :, :, self.blank], 0).unsqueeze(2) + r_prev = r_prev.view(-1, 2, n_bh) + s_prev = 0.0 + f_min_prev = 0 + f_max_prev = 1 + else: + r_prev, s_prev, f_min_prev, f_max_prev = state + + # select input dimensions for scoring + if self.scoring_num > 0: + scoring_idmap = torch.full( + (n_bh, self.odim), -1, dtype=torch.long, device=self.device + ) + snum = self.scoring_num + if self.idx_bh is None or n_bh > len(self.idx_bh): + self.idx_bh = torch.arange(n_bh, device=self.device).view(-1, 1) + scoring_idmap[self.idx_bh[:n_bh], scoring_ids] = torch.arange( + snum, device=self.device + ) + scoring_idx = ( + scoring_ids + self.idx_bo.repeat(1, n_hyps).view(-1, 1) + ).view(-1) + x_ = torch.index_select( + self.x.view(2, -1, self.batch * self.odim), 2, scoring_idx + ).view(2, -1, n_bh, snum) + else: + scoring_ids = None + scoring_idmap = None + snum = self.odim + x_ = self.x.unsqueeze(3).repeat(1, 1, 1, n_hyps, 1).view(2, -1, n_bh, snum) + + # new CTC forward probs are prepared as a (T x 2 x BW x S) tensor + # that corresponds to r_t^n(h) and r_t^b(h) in a batch. + r = torch.full( + (self.input_length, 2, n_bh, snum), + self.logzero, + dtype=self.dtype, + device=self.device, + ) + if output_length == 0: + r[0, 0] = x_[0, 0] + + r_sum = torch.logsumexp(r_prev, 1) + log_phi = r_sum.unsqueeze(2).repeat(1, 1, snum) + if scoring_ids is not None: + for idx in range(n_bh): + pos = scoring_idmap[idx, last_ids[idx]] + if pos >= 0: + log_phi[:, idx, pos] = r_prev[:, 1, idx] + else: + for idx in range(n_bh): + log_phi[:, idx, last_ids[idx]] = r_prev[:, 1, idx] + + # decide start and end frames based on attention weights + if att_w is not None and self.margin > 0: + f_arg = torch.matmul(att_w, self.frame_ids) + f_min = max(int(f_arg.min().cpu()), f_min_prev) + f_max = max(int(f_arg.max().cpu()), f_max_prev) + start = min(f_max_prev, max(f_min - self.margin, output_length, 1)) + end = min(f_max + self.margin, self.input_length) + else: + f_min = f_max = 0 + start = max(output_length, 1) + end = self.input_length + + # compute forward probabilities log(r_t^n(h)) and log(r_t^b(h)) + for t in range(start, end): + rp = r[t - 1] + rr = torch.stack([rp[0], log_phi[t - 1], rp[0], rp[1]]).view( + 2, 2, n_bh, snum + ) + r[t] = torch.logsumexp(rr, 1) + x_[:, t] + + # compute log prefix probabilities log(psi) + log_phi_x = torch.cat((log_phi[0].unsqueeze(0), log_phi[:-1]), dim=0) + x_[0] + if scoring_ids is not None: + log_psi = torch.full( + (n_bh, self.odim), self.logzero, dtype=self.dtype, device=self.device + ) + log_psi_ = torch.logsumexp( + torch.cat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), dim=0), + dim=0, + ) + for si in range(n_bh): + log_psi[si, scoring_ids[si]] = log_psi_[si] + else: + log_psi = torch.logsumexp( + torch.cat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), dim=0), + dim=0, + ) + + for si in range(n_bh): + log_psi[si, self.eos] = r_sum[self.end_frames[si // n_hyps], si] + + # exclude blank probs + log_psi[:, self.blank] = self.logzero + + return (log_psi - s_prev), (r, log_psi, f_min, f_max, scoring_idmap) + + def index_select_state(self, state, best_ids): + """Select CTC states according to best ids + + :param state : CTC state + :param best_ids : index numbers selected by beam pruning (B, W) + :return selected_state + """ + r, s, f_min, f_max, scoring_idmap = state + # convert ids to BHO space + n_bh = len(s) + n_hyps = n_bh // self.batch + vidx = (best_ids + (self.idx_b * (n_hyps * self.odim)).view(-1, 1)).view(-1) + # select hypothesis scores + s_new = torch.index_select(s.view(-1), 0, vidx) + s_new = s_new.view(-1, 1).repeat(1, self.odim).view(n_bh, self.odim) + # convert ids to BHS space (S: scoring_num) + if scoring_idmap is not None: + snum = self.scoring_num + hyp_idx = (best_ids // self.odim + (self.idx_b * n_hyps).view(-1, 1)).view( + -1 + ) + label_ids = torch.fmod(best_ids, self.odim).view(-1) + score_idx = scoring_idmap[hyp_idx, label_ids] + score_idx[score_idx == -1] = 0 + vidx = score_idx + hyp_idx * snum + else: + snum = self.odim + # select forward probabilities + r_new = torch.index_select(r.view(-1, 2, n_bh * snum), 2, vidx).view( + -1, 2, n_bh + ) + return r_new, s_new, f_min, f_max + + def extend_prob(self, x): + """Extend CTC prob. + + :param torch.Tensor x: input label posterior sequences (B, T, O) + """ + + if self.x.shape[1] < x.shape[1]: # self.x (2,T,B,O); x (B,T,O) + # Pad the rest of posteriors in the batch + # TODO(takaaki-hori): need a better way without for-loops + xlens = [x.size(1)] + for i, l in enumerate(xlens): + if l < self.input_length: + x[i, l:, :] = self.logzero + x[i, l:, self.blank] = 0 + tmp_x = self.x + xn = x.transpose(0, 1) # (B, T, O) -> (T, B, O) + xb = xn[:, :, self.blank].unsqueeze(2).expand(-1, -1, self.odim) + self.x = torch.stack([xn, xb]) # (2, T, B, O) + self.x[:, : tmp_x.shape[1], :, :] = tmp_x + self.input_length = x.size(1) + self.end_frames = torch.as_tensor(xlens) - 1 + + def extend_state(self, state): + """Compute CTC prefix state. + + + :param state : CTC state + :return ctc_state + """ + + if state is None: + # nothing to do + return state + else: + r_prev, s_prev, f_min_prev, f_max_prev = state + + r_prev_new = torch.full( + (self.input_length, 2), + self.logzero, + dtype=self.dtype, + device=self.device, + ) + start = max(r_prev.shape[0], 1) + r_prev_new[0:start] = r_prev + for t in six.moves.range(start, self.input_length): + r_prev_new[t, 1] = r_prev_new[t - 1, 1] + self.x[0, t, :, self.blank] + + return r_prev_new, s_prev, f_min_prev, f_max_prev diff --git a/modules/wenet_extractor/paraformer/search/scorer_interface.py b/modules/wenet_extractor/paraformer/search/scorer_interface.py new file mode 100644 index 0000000000000000000000000000000000000000..ed021726f3a93dddd9b00eab23bc6fbe7583a04b --- /dev/null +++ b/modules/wenet_extractor/paraformer/search/scorer_interface.py @@ -0,0 +1,208 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""Scorer interface module.""" +from abc import ABC +from typing import Any +from typing import List +from typing import Tuple + +import torch + + +class ScorerInterface: + """Scorer interface for beam search. + + The scorer performs scoring of the all tokens in vocabulary. + + Examples: + * Search heuristics + * :class:`espnet.nets.scorers.length_bonus.LengthBonus` + * Decoder networks of the sequence-to-sequence models + * :class:`espnet.nets.pytorch_backend.nets.transformer.decoder + .Decoder` + * :class:`espnet.nets.pytorch_backend.nets.rnn.decoders.Decoder` + * Neural language models + * :class:`espnet.nets.pytorch_backend.lm.transformer.TransformerLM` + * :class:`espnet.nets.pytorch_backend.lm.default.DefaultRNNLM` + * :class:`espnet.nets.pytorch_backend.lm.seq_rnn.SequentialRNNLM` + + """ + + def init_state(self, x: torch.Tensor) -> Any: + """Get an initial state for decoding (optional). + + Args: + x (torch.Tensor): The encoded feature tensor + + Returns: initial state + + """ + return None + + def select_state(self, state: Any, i: int, new_id: int = None) -> Any: + """Select state with relative ids in the main beam search. + + Args: + state: Decoder state for prefix tokens + i (int): Index to select a state in the main beam search + new_id (int): New label index to select a state if necessary + + Returns: + state: pruned state + + """ + return None if state is None else state[i] + + def score( + self, y: torch.Tensor, state: Any, x: torch.Tensor + ) -> Tuple[torch.Tensor, Any]: + """Score new token (required). + + Args: + y (torch.Tensor): 1D torch.int64 prefix tokens. + state: Scorer state for prefix tokens + x (torch.Tensor): The encoder feature that generates ys. + + Returns: + tuple[torch.Tensor, Any]: Tuple of + scores for next token that has a shape of `(n_vocab)` + and next state for ys + + """ + raise NotImplementedError + + def final_score(self, state: Any) -> float: + """Score eos (optional). + + Args: + state: Scorer state for prefix tokens + + Returns: + float: final score + + """ + return 0.0 + + +class BatchScorerInterface(ScorerInterface, ABC): + """Batch scorer interface.""" + + def batch_init_state(self, x: torch.Tensor) -> Any: + """Get an initial state for decoding (optional). + + Args: + x (torch.Tensor): The encoded feature tensor + + Returns: initial state + + """ + return self.init_state(x) + + def batch_score( + self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor + ) -> Tuple[torch.Tensor, List[Any]]: + """Score new token batch (required). + + Args: + ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). + states (List[Any]): Scorer states for prefix tokens. + xs (torch.Tensor): + The encoder feature that generates ys (n_batch, xlen, n_feat). + + Returns: + tuple[torch.Tensor, List[Any]]: Tuple of + batchfied scores for next token with shape of `(n_batch, + n_vocab)` + and next state list for ys. + + """ + scores = list() + outstates = list() + for i, (y, state, x) in enumerate(zip(ys, states, xs)): + score, outstate = self.score(y, state, x) + outstates.append(outstate) + scores.append(score) + scores = torch.cat(scores, 0).view(ys.shape[0], -1) + return scores, outstates + + +class PartialScorerInterface(ScorerInterface, ABC): + """Partial scorer interface for beam search. + + The partial scorer performs scoring when non-partial scorer finished scoring + and receives pre-pruned next tokens to score because it is too heavy to + score all the tokens. + + Examples: + * Prefix search for connectionist-temporal-classification models + * :class:`espnet.nets.scorers.ctc.CTCPrefixScorer` + + """ + + def score_partial( + self, y: torch.Tensor, next_tokens: torch.Tensor, state: Any, x: torch.Tensor + ) -> Tuple[torch.Tensor, Any]: + """Score new token (required). + + Args: + y (torch.Tensor): 1D prefix token + next_tokens (torch.Tensor): torch.int64 next token to score + state: decoder state for prefix tokens + x (torch.Tensor): The encoder feature that generates ys + + Returns: + tuple[torch.Tensor, Any]: + Tuple of a score tensor for y that has a shape + `(len(next_tokens),)` and next state for ys + + """ + raise NotImplementedError + + +class BatchPartialScorerInterface(BatchScorerInterface, PartialScorerInterface, ABC): + """Batch partial scorer interface for beam search.""" + + def batch_score_partial( + self, + ys: torch.Tensor, + next_tokens: torch.Tensor, + states: List[Any], + xs: torch.Tensor, + ) -> Tuple[torch.Tensor, Any]: + """Score new token (required). + + Args: + ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). + next_tokens (torch.Tensor): torch.int64 tokens to score (n_batch, + n_token). + states (List[Any]): Scorer states for prefix tokens. + xs (torch.Tensor): + The encoder feature that generates ys (n_batch, xlen, n_feat). + + Returns: + tuple[torch.Tensor, Any]: + Tuple of a score tensor for ys that has a shape `(n_batch, + n_vocab)` + and next states for ys + """ + raise NotImplementedError diff --git a/modules/wenet_extractor/paraformer/utils.py b/modules/wenet_extractor/paraformer/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..83d644f7643f3f0c213a54ab4b634e89e51a1c40 --- /dev/null +++ b/modules/wenet_extractor/paraformer/utils.py @@ -0,0 +1,76 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +from typing import Optional + +import six +import torch +import numpy as np + + +def sequence_mask( + lengths, + maxlen: Optional[int] = None, + dtype: torch.dtype = torch.float32, + device: Optional[torch.device] = None, +) -> torch.Tensor: + if maxlen is None: + maxlen = lengths.max() + row_vector = torch.arange(0, maxlen, 1).to(lengths.device) + matrix = torch.unsqueeze(lengths, dim=-1) + mask = row_vector < matrix + mask = mask.detach() + + return mask.type(dtype).to(device) if device is not None else mask.type(dtype) + + +def end_detect(ended_hyps, i, M=3, d_end=np.log(1 * np.exp(-10))): + """End detection. + + described in Eq. (50) of S. Watanabe et al + "Hybrid CTC/Attention Architecture for End-to-End Speech Recognition" + + :param ended_hyps: + :param i: + :param M: + :param d_end: + :return: + """ + if len(ended_hyps) == 0: + return False + count = 0 + best_hyp = sorted(ended_hyps, key=lambda x: x["score"], reverse=True)[0] + for m in six.moves.range(M): + # get ended_hyps with their length is i - m + hyp_length = i - m + hyps_same_length = [x for x in ended_hyps if len(x["yseq"]) == hyp_length] + if len(hyps_same_length) > 0: + best_hyp_same_length = sorted( + hyps_same_length, key=lambda x: x["score"], reverse=True + )[0] + if best_hyp_same_length["score"] - best_hyp["score"] < d_end: + count += 1 + + if count == M: + return True + else: + return False diff --git a/modules/wenet_extractor/squeezeformer/__init__.py b/modules/wenet_extractor/squeezeformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/wenet_extractor/squeezeformer/__pycache__/__init__.cpython-39.pyc b/modules/wenet_extractor/squeezeformer/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e391565f684d0c50df6295aa4ca23580f58c80fc Binary files /dev/null and b/modules/wenet_extractor/squeezeformer/__pycache__/__init__.cpython-39.pyc differ diff --git a/modules/wenet_extractor/squeezeformer/__pycache__/attention.cpython-39.pyc b/modules/wenet_extractor/squeezeformer/__pycache__/attention.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3897c13525bc1c57a958308be21de7d4e49ab1bb Binary files /dev/null and b/modules/wenet_extractor/squeezeformer/__pycache__/attention.cpython-39.pyc differ diff --git a/modules/wenet_extractor/squeezeformer/__pycache__/conv2d.cpython-39.pyc b/modules/wenet_extractor/squeezeformer/__pycache__/conv2d.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..749521e285fda9806d35ce51f50bc325f795389c Binary files /dev/null and b/modules/wenet_extractor/squeezeformer/__pycache__/conv2d.cpython-39.pyc differ diff --git a/modules/wenet_extractor/squeezeformer/__pycache__/convolution.cpython-39.pyc b/modules/wenet_extractor/squeezeformer/__pycache__/convolution.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8bea051db158db66c0a413ca25b716d53d279997 Binary files /dev/null and b/modules/wenet_extractor/squeezeformer/__pycache__/convolution.cpython-39.pyc differ diff --git a/modules/wenet_extractor/squeezeformer/__pycache__/encoder.cpython-39.pyc b/modules/wenet_extractor/squeezeformer/__pycache__/encoder.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..84c2d6b5bf2c8c7ebd8ad04de0b782898035ee57 Binary files /dev/null and b/modules/wenet_extractor/squeezeformer/__pycache__/encoder.cpython-39.pyc differ diff --git a/modules/wenet_extractor/squeezeformer/__pycache__/encoder_layer.cpython-39.pyc b/modules/wenet_extractor/squeezeformer/__pycache__/encoder_layer.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..09625fa216b91b1fd3b99abd88b54e7e90fa18e4 Binary files /dev/null and b/modules/wenet_extractor/squeezeformer/__pycache__/encoder_layer.cpython-39.pyc differ diff --git a/modules/wenet_extractor/squeezeformer/__pycache__/positionwise_feed_forward.cpython-39.pyc b/modules/wenet_extractor/squeezeformer/__pycache__/positionwise_feed_forward.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4f0dac25f9f125a57d05accef01d8304bc50ff39 Binary files /dev/null and b/modules/wenet_extractor/squeezeformer/__pycache__/positionwise_feed_forward.cpython-39.pyc differ diff --git a/modules/wenet_extractor/squeezeformer/__pycache__/subsampling.cpython-39.pyc b/modules/wenet_extractor/squeezeformer/__pycache__/subsampling.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ed11a9b5934be907a27672a8c58babf3b46fb41a Binary files /dev/null and b/modules/wenet_extractor/squeezeformer/__pycache__/subsampling.cpython-39.pyc differ diff --git a/modules/wenet_extractor/squeezeformer/attention.py b/modules/wenet_extractor/squeezeformer/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..830da3dccc0257afc36747c7f572455746ef652e --- /dev/null +++ b/modules/wenet_extractor/squeezeformer/attention.py @@ -0,0 +1,239 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""Multi-Head Attention layer definition.""" + +import math +import torch +import torch.nn as nn +from modules.wenet_extractor.transformer.attention import MultiHeadedAttention +from typing import Tuple + + +class RelPositionMultiHeadedAttention(MultiHeadedAttention): + """Multi-Head Attention layer with relative position encoding. + Paper: https://arxiv.org/abs/1901.02860 + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + """ + + def __init__( + self, + n_head, + n_feat, + dropout_rate, + do_rel_shift=False, + adaptive_scale=False, + init_weights=False, + ): + """Construct an RelPositionMultiHeadedAttention object.""" + super().__init__(n_head, n_feat, dropout_rate) + # linear transformation for positional encoding + self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) + # these two learnable bias are used in matrix c and matrix d + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + self.do_rel_shift = do_rel_shift + self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k)) + self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k)) + torch.nn.init.xavier_uniform_(self.pos_bias_u) + torch.nn.init.xavier_uniform_(self.pos_bias_v) + self.adaptive_scale = adaptive_scale + self.ada_scale = nn.Parameter( + torch.ones([1, 1, n_feat]), requires_grad=adaptive_scale + ) + self.ada_bias = nn.Parameter( + torch.zeros([1, 1, n_feat]), requires_grad=adaptive_scale + ) + if init_weights: + self.init_weights() + + def init_weights(self): + input_max = (self.h * self.d_k) ** -0.5 + torch.nn.init.uniform_(self.linear_q.weight, -input_max, input_max) + torch.nn.init.uniform_(self.linear_q.bias, -input_max, input_max) + torch.nn.init.uniform_(self.linear_k.weight, -input_max, input_max) + torch.nn.init.uniform_(self.linear_k.bias, -input_max, input_max) + torch.nn.init.uniform_(self.linear_v.weight, -input_max, input_max) + torch.nn.init.uniform_(self.linear_v.bias, -input_max, input_max) + torch.nn.init.uniform_(self.linear_pos.weight, -input_max, input_max) + torch.nn.init.uniform_(self.linear_out.weight, -input_max, input_max) + torch.nn.init.uniform_(self.linear_out.bias, -input_max, input_max) + + def rel_shift(self, x, zero_triu: bool = False): + """Compute relative positinal encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, size). + zero_triu (bool): If true, return the lower triangular part of + the matrix. + Returns: + torch.Tensor: Output tensor. + """ + + zero_pad = torch.zeros( + (x.size()[0], x.size()[1], x.size()[2], 1), device=x.device, dtype=x.dtype + ) + x_padded = torch.cat([zero_pad, x], dim=-1) + + x_padded = x_padded.view(x.size()[0], x.size()[1], x.size(3) + 1, x.size(2)) + x = x_padded[:, :, 1:].view_as(x) + + if zero_triu: + ones = torch.ones((x.size(2), x.size(3))) + x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :] + + return x + + def forward_attention( + self, + value: torch.Tensor, + scores: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + ) -> torch.Tensor: + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value, size + (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score, size + (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask, size (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + # NOTE(xcsong): When will `if mask.size(2) > 0` be True? + # 1. onnx(16/4) [WHY? Because we feed real cache & real mask for the + # 1st chunk to ease the onnx export.] + # 2. pytorch training + if mask.size(2) > 0: # time2 > 0 + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + # For last chunk, time2 might be larger than scores.size(-1) + mask = mask[:, :, :, : scores.size(-1)] # (batch, 1, *, time2) + scores = scores.masked_fill(mask, -float("inf")) + # (batch, head, time1, time2) + attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0) + # NOTE(xcsong): When will `if mask.size(2) > 0` be False? + # 1. onnx(16/-1, -1/-1, 16/0) + # 2. jit (16/-1, -1/-1, 16/0, 16/4) + else: + attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = ( + x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute 'Scaled Dot Product Attention' with rel. positional encoding. + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): Positional embedding tensor + (#batch, time2, size). + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + """ + if self.adaptive_scale: + query = self.ada_scale * query + self.ada_bias + key = self.ada_scale * key + self.ada_bias + value = self.ada_scale * value + self.ada_bias + q, k, v = self.forward_qkv(query, key, value) + q = q.transpose(1, 2) # (batch, time1, head, d_k) + + # NOTE(xcsong): + # when export onnx model, for 1st chunk, we feed + # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode) + # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode). + # In all modes, `if cache.size(0) > 0` will alwayse be `True` + # and we will always do splitting and + # concatnation(this will simplify onnx export). Note that + # it's OK to concat & split zero-shaped tensors(see code below). + # when export jit model, for 1st chunk, we always feed + # cache(0, 0, 0, 0) since jit supports dynamic if-branch. + # >>> a = torch.ones((1, 2, 0, 4)) + # >>> b = torch.ones((1, 2, 3, 4)) + # >>> c = torch.cat((a, b), dim=2) + # >>> torch.equal(b, c) # True + # >>> d = torch.split(a, 2, dim=-1) + # >>> torch.equal(d[0], d[1]) # True + if cache.size(0) > 0: + key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's + # non-trivial to calculate `next_cache_start` here. + new_cache = torch.cat((k, v), dim=-1) + + n_batch_pos = pos_emb.size(0) + p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) + p = p.transpose(1, 2) # (batch, head, time1, d_k) + + # (batch, head, time1, d_k) + q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) + # (batch, head, time1, d_k) + q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) + + # compute attention score + # first compute matrix a and matrix c + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + # (batch, head, time1, time2) + matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) + + # compute matrix b and matrix d + # (batch, head, time1, time2) + matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + # Remove rel_shift since it is useless in speech recognition, + # and it requires special attention for streaming. + if self.do_rel_shift: + matrix_bd = self.rel_shift(matrix_bd) + + scores = (matrix_ac + matrix_bd) / math.sqrt( + self.d_k + ) # (batch, head, time1, time2) + + return self.forward_attention(v, scores, mask), new_cache diff --git a/modules/wenet_extractor/squeezeformer/conv2d.py b/modules/wenet_extractor/squeezeformer/conv2d.py new file mode 100644 index 0000000000000000000000000000000000000000..01d2981e4b6853013ce1a629496e03b04517d0c0 --- /dev/null +++ b/modules/wenet_extractor/squeezeformer/conv2d.py @@ -0,0 +1,93 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""Conv2d Module with Valid Padding""" + +import torch.nn.functional as F +from torch.nn.modules.conv import _ConvNd, _size_2_t, Union, _pair, Tensor, Optional + + +class Conv2dValid(_ConvNd): + """ + Conv2d operator for VALID mode padding. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: _size_2_t, + stride: _size_2_t = 1, + padding: Union[str, _size_2_t] = 0, + dilation: _size_2_t = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = "zeros", # TODO: refine this type + device=None, + dtype=None, + valid_trigx: bool = False, + valid_trigy: bool = False, + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + kernel_size_ = _pair(kernel_size) + stride_ = _pair(stride) + padding_ = padding if isinstance(padding, str) else _pair(padding) + dilation_ = _pair(dilation) + super(Conv2dValid, self).__init__( + in_channels, + out_channels, + kernel_size_, + stride_, + padding_, + dilation_, + False, + _pair(0), + groups, + bias, + padding_mode, + **factory_kwargs, + ) + self.valid_trigx = valid_trigx + self.valid_trigy = valid_trigy + + def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]): + validx, validy = 0, 0 + if self.valid_trigx: + validx = ( + input.size(-2) * (self.stride[-2] - 1) - 1 + self.kernel_size[-2] + ) // 2 + if self.valid_trigy: + validy = ( + input.size(-1) * (self.stride[-1] - 1) - 1 + self.kernel_size[-1] + ) // 2 + return F.conv2d( + input, + weight, + bias, + self.stride, + (validx, validy), + self.dilation, + self.groups, + ) + + def forward(self, input: Tensor) -> Tensor: + return self._conv_forward(input, self.weight, self.bias) diff --git a/modules/wenet_extractor/squeezeformer/convolution.py b/modules/wenet_extractor/squeezeformer/convolution.py new file mode 100644 index 0000000000000000000000000000000000000000..f0ec43dadcd22aff53f79c459888344def57ef12 --- /dev/null +++ b/modules/wenet_extractor/squeezeformer/convolution.py @@ -0,0 +1,182 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""ConvolutionModule definition.""" + +from typing import Tuple + +import torch +from torch import nn + + +class ConvolutionModule(nn.Module): + """ConvolutionModule in Conformer model.""" + + def __init__( + self, + channels: int, + kernel_size: int = 15, + activation: nn.Module = nn.ReLU(), + norm: str = "batch_norm", + causal: bool = False, + bias: bool = True, + adaptive_scale: bool = False, + init_weights: bool = False, + ): + """Construct an ConvolutionModule object. + Args: + channels (int): The number of channels of conv layers. + kernel_size (int): Kernel size of conv layers. + causal (int): Whether use causal convolution or not + """ + super().__init__() + self.bias = bias + self.channels = channels + self.kernel_size = kernel_size + self.adaptive_scale = adaptive_scale + self.ada_scale = torch.nn.Parameter( + torch.ones([1, 1, channels]), requires_grad=adaptive_scale + ) + self.ada_bias = torch.nn.Parameter( + torch.zeros([1, 1, channels]), requires_grad=adaptive_scale + ) + + self.pointwise_conv1 = nn.Conv1d( + channels, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + # self.lorder is used to distinguish if it's a causal convolution, + # if self.lorder > 0: it's a causal convolution, the input will be + # padded with self.lorder frames on the left in forward. + # else: it's a symmetrical convolution + if causal: + padding = 0 + self.lorder = kernel_size - 1 + else: + # kernel_size should be an odd number for none causal convolution + assert (kernel_size - 1) % 2 == 0 + padding = (kernel_size - 1) // 2 + self.lorder = 0 + self.depthwise_conv = nn.Conv1d( + channels, + channels, + kernel_size, + stride=1, + padding=padding, + groups=channels, + bias=bias, + ) + + assert norm in ["batch_norm", "layer_norm"] + if norm == "batch_norm": + self.use_layer_norm = False + self.norm = nn.BatchNorm1d(channels) + else: + self.use_layer_norm = True + self.norm = nn.LayerNorm(channels) + + self.pointwise_conv2 = nn.Conv1d( + channels, + channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + self.activation = activation + if init_weights: + self.init_weights() + + def init_weights(self): + pw_max = self.channels**-0.5 + dw_max = self.kernel_size**-0.5 + torch.nn.init.uniform_(self.pointwise_conv1.weight.data, -pw_max, pw_max) + if self.bias: + torch.nn.init.uniform_(self.pointwise_conv1.bias.data, -pw_max, pw_max) + torch.nn.init.uniform_(self.depthwise_conv.weight.data, -dw_max, dw_max) + if self.bias: + torch.nn.init.uniform_(self.depthwise_conv.bias.data, -dw_max, dw_max) + torch.nn.init.uniform_(self.pointwise_conv2.weight.data, -pw_max, pw_max) + if self.bias: + torch.nn.init.uniform_(self.pointwise_conv2.bias.data, -pw_max, pw_max) + + def forward( + self, + x: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + cache: torch.Tensor = torch.zeros((0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute convolution module. + Args: + x (torch.Tensor): Input tensor (#batch, time, channels). + mask_pad (torch.Tensor): used for batch padding (#batch, 1, time), + (0, 0, 0) means fake mask. + cache (torch.Tensor): left context cache, it is only + used in causal convolution (#batch, channels, cache_t), + (0, 0, 0) meas fake cache. + Returns: + torch.Tensor: Output tensor (#batch, time, channels). + """ + if self.adaptive_scale: + x = self.ada_scale * x + self.ada_bias + # exchange the temporal dimension and the feature dimension + x = x.transpose(1, 2) # (#batch, channels, time) + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + x.masked_fill_(~mask_pad, 0.0) + + if self.lorder > 0: + if cache.size(2) == 0: # cache_t == 0 + x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0) + else: + assert cache.size(0) == x.size(0) # equal batch + assert cache.size(1) == x.size(1) # equal channel + x = torch.cat((cache, x), dim=2) + assert x.size(2) > self.lorder + new_cache = x[:, :, -self.lorder :] + else: + # It's better we just return None if no cache is required, + # However, for JIT export, here we just fake one tensor instead of + # None. + new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channel, dim) + x = nn.functional.glu(x, dim=1) # (batch, channel, dim) + + # 1D Depthwise Conv + x = self.depthwise_conv(x) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.activation(self.norm(x)) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.pointwise_conv2(x) + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + x.masked_fill_(~mask_pad, 0.0) + + return x.transpose(1, 2), new_cache diff --git a/modules/wenet_extractor/squeezeformer/encoder.py b/modules/wenet_extractor/squeezeformer/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..4d4fbe9f615c8d9889a6c2ea821afe392bfab5db --- /dev/null +++ b/modules/wenet_extractor/squeezeformer/encoder.py @@ -0,0 +1,514 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +import torch +import torch.nn as nn +from typing import Tuple, Union, Optional, List +from modules.wenet_extractor.squeezeformer.subsampling import ( + DepthwiseConv2dSubsampling4, + TimeReductionLayer1D, + TimeReductionLayer2D, + TimeReductionLayerStream, +) +from modules.wenet_extractor.squeezeformer.encoder_layer import ( + SqueezeformerEncoderLayer, +) +from modules.wenet_extractor.transformer.embedding import RelPositionalEncoding +from modules.wenet_extractor.transformer.attention import MultiHeadedAttention +from modules.wenet_extractor.squeezeformer.attention import ( + RelPositionMultiHeadedAttention, +) +from modules.wenet_extractor.squeezeformer.positionwise_feed_forward import ( + PositionwiseFeedForward, +) +from modules.wenet_extractor.squeezeformer.convolution import ConvolutionModule +from modules.wenet_extractor.utils.mask import make_pad_mask, add_optional_chunk_mask +from modules.wenet_extractor.utils.common import get_activation + + +class SqueezeformerEncoder(nn.Module): + def __init__( + self, + input_size: int = 80, + encoder_dim: int = 256, + output_size: int = 256, + attention_heads: int = 4, + num_blocks: int = 12, + reduce_idx: Optional[Union[int, List[int]]] = 5, + recover_idx: Optional[Union[int, List[int]]] = 11, + feed_forward_expansion_factor: int = 4, + dw_stride: bool = False, + input_dropout_rate: float = 0.1, + pos_enc_layer_type: str = "rel_pos", + time_reduction_layer_type: str = "conv1d", + do_rel_shift: bool = True, + feed_forward_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.1, + cnn_module_kernel: int = 31, + cnn_norm_type: str = "batch_norm", + dropout: float = 0.1, + causal: bool = False, + adaptive_scale: bool = True, + activation_type: str = "swish", + init_weights: bool = True, + global_cmvn: torch.nn.Module = None, + normalize_before: bool = False, + use_dynamic_chunk: bool = False, + concat_after: bool = False, + static_chunk_size: int = 0, + use_dynamic_left_chunk: bool = False, + ): + """Construct SqueezeformerEncoder + + Args: + input_size to use_dynamic_chunk, see in Transformer BaseEncoder. + encoder_dim (int): The hidden dimension of encoder layer. + output_size (int): The output dimension of final projection layer. + attention_heads (int): Num of attention head in attention module. + num_blocks (int): Num of encoder layers. + reduce_idx Optional[Union[int, List[int]]]: + reduce layer index, from 40ms to 80ms per frame. + recover_idx Optional[Union[int, List[int]]]: + recover layer index, from 80ms to 40ms per frame. + feed_forward_expansion_factor (int): Enlarge coefficient of FFN. + dw_stride (bool): Whether do depthwise convolution + on subsampling module. + input_dropout_rate (float): Dropout rate of input projection layer. + pos_enc_layer_type (str): Self attention type. + time_reduction_layer_type (str): Conv1d or Conv2d reduction layer. + do_rel_shift (bool): Whether to do relative shift + operation on rel-attention module. + cnn_module_kernel (int): Kernel size of CNN module. + activation_type (str): Encoder activation function type. + use_cnn_module (bool): Whether to use convolution module. + cnn_module_kernel (int): Kernel size of convolution module. + adaptive_scale (bool): Whether to use adaptive scale. + init_weights (bool): Whether to initialize weights. + causal (bool): whether to use causal convolution or not. + """ + super(SqueezeformerEncoder, self).__init__() + self.global_cmvn = global_cmvn + self.reduce_idx: Optional[Union[int, List[int]]] = ( + [reduce_idx] if type(reduce_idx) == int else reduce_idx + ) + self.recover_idx: Optional[Union[int, List[int]]] = ( + [recover_idx] if type(recover_idx) == int else recover_idx + ) + self.check_ascending_list() + if reduce_idx is None: + self.time_reduce = None + else: + if recover_idx is None: + self.time_reduce = "normal" # no recovery at the end + else: + self.time_reduce = "recover" # recovery at the end + assert len(self.reduce_idx) == len(self.recover_idx) + self.reduce_stride = 2 + self._output_size = output_size + self.normalize_before = normalize_before + self.static_chunk_size = static_chunk_size + self.use_dynamic_chunk = use_dynamic_chunk + self.use_dynamic_left_chunk = use_dynamic_left_chunk + self.pos_enc_layer_type = pos_enc_layer_type + activation = get_activation(activation_type) + + # self-attention module definition + if pos_enc_layer_type != "rel_pos": + encoder_selfattn_layer = MultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + ) + else: + encoder_selfattn_layer = RelPositionMultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + encoder_dim, + attention_dropout_rate, + do_rel_shift, + adaptive_scale, + init_weights, + ) + + # feed-forward module definition + positionwise_layer = PositionwiseFeedForward + positionwise_layer_args = ( + encoder_dim, + encoder_dim * feed_forward_expansion_factor, + feed_forward_dropout_rate, + activation, + adaptive_scale, + init_weights, + ) + + # convolution module definition + convolution_layer = ConvolutionModule + convolution_layer_args = ( + encoder_dim, + cnn_module_kernel, + activation, + cnn_norm_type, + causal, + True, + adaptive_scale, + init_weights, + ) + + self.embed = DepthwiseConv2dSubsampling4( + 1, + encoder_dim, + RelPositionalEncoding(encoder_dim, dropout_rate=0.1), + dw_stride, + input_size, + input_dropout_rate, + init_weights, + ) + + self.preln = nn.LayerNorm(encoder_dim) + self.encoders = torch.nn.ModuleList( + [ + SqueezeformerEncoderLayer( + encoder_dim, + encoder_selfattn_layer(*encoder_selfattn_layer_args), + positionwise_layer(*positionwise_layer_args), + convolution_layer(*convolution_layer_args), + positionwise_layer(*positionwise_layer_args), + normalize_before, + dropout, + concat_after, + ) + for _ in range(num_blocks) + ] + ) + if time_reduction_layer_type == "conv1d": + time_reduction_layer = TimeReductionLayer1D + time_reduction_layer_args = { + "channel": encoder_dim, + "out_dim": encoder_dim, + } + elif time_reduction_layer_type == "stream": + time_reduction_layer = TimeReductionLayerStream + time_reduction_layer_args = { + "channel": encoder_dim, + "out_dim": encoder_dim, + } + else: + time_reduction_layer = TimeReductionLayer2D + time_reduction_layer_args = {"encoder_dim": encoder_dim} + + self.time_reduction_layer = time_reduction_layer(**time_reduction_layer_args) + self.time_recover_layer = nn.Linear(encoder_dim, encoder_dim) + self.final_proj = None + if output_size != encoder_dim: + self.final_proj = nn.Linear(encoder_dim, output_size) + + def output_size(self) -> int: + return self._output_size + + def forward( + self, + xs: torch.Tensor, + xs_lens: torch.Tensor, + decoding_chunk_size: int = 0, + num_decoding_left_chunks: int = -1, + ) -> Tuple[torch.Tensor, torch.Tensor]: + T = xs.size(1) + masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) + if self.global_cmvn is not None: + xs = self.global_cmvn(xs) + xs, pos_emb, masks = self.embed(xs, masks) + mask_pad = masks # (B, 1, T/subsample_rate) + chunk_masks = add_optional_chunk_mask( + xs, + masks, + self.use_dynamic_chunk, + self.use_dynamic_left_chunk, + decoding_chunk_size, + self.static_chunk_size, + num_decoding_left_chunks, + ) + xs_lens = mask_pad.squeeze(1).sum(1) + xs = self.preln(xs) + recover_activations: List[ + Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor] + ] = [] + index = 0 + for i, layer in enumerate(self.encoders): + if self.reduce_idx is not None: + if self.time_reduce is not None and i in self.reduce_idx: + recover_activations.append((xs, chunk_masks, pos_emb, mask_pad)) + xs, xs_lens, chunk_masks, mask_pad = self.time_reduction_layer( + xs, xs_lens, chunk_masks, mask_pad + ) + pos_emb = pos_emb[:, ::2, :] + index += 1 + + if self.recover_idx is not None: + if self.time_reduce == "recover" and i in self.recover_idx: + index -= 1 + ( + recover_tensor, + recover_chunk_masks, + recover_pos_emb, + recover_mask_pad, + ) = recover_activations[index] + # recover output length for ctc decode + xs = xs.unsqueeze(2).repeat(1, 1, 2, 1).flatten(1, 2) + xs = self.time_recover_layer(xs) + recoverd_t = recover_tensor.size(1) + xs = recover_tensor + xs[:, :recoverd_t, :].contiguous() + chunk_masks = recover_chunk_masks + pos_emb = recover_pos_emb + mask_pad = recover_mask_pad + xs = xs.masked_fill(~mask_pad[:, 0, :].unsqueeze(-1), 0.0) + + xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) + + if self.final_proj is not None: + xs = self.final_proj(xs) + return xs, masks + + def check_ascending_list(self): + if self.reduce_idx is not None: + assert self.reduce_idx == sorted( + self.reduce_idx + ), "reduce_idx should be int or ascending list" + if self.recover_idx is not None: + assert self.recover_idx == sorted( + self.recover_idx + ), "recover_idx should be int or ascending list" + + def calculate_downsampling_factor(self, i: int) -> int: + if self.reduce_idx is None: + return 1 + else: + reduce_exp, recover_exp = 0, 0 + for exp, rd_idx in enumerate(self.reduce_idx): + if i >= rd_idx: + reduce_exp = exp + 1 + if self.recover_idx is not None: + for exp, rc_idx in enumerate(self.recover_idx): + if i >= rc_idx: + recover_exp = exp + 1 + return int(2 ** (reduce_exp - recover_exp)) + + def forward_chunk( + self, + xs: torch.Tensor, + offset: int, + required_cache_size: int, + att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), + cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), + att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ Forward just one chunk + + Args: + xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim), + where `time == (chunk_size - 1) * subsample_rate + \ + subsample.right_context + 1` + offset (int): current offset in encoder output time stamp + required_cache_size (int): cache size required for next chunk + compuation + >=0: actual cache size + <0: means all history cache is required + att_cache (torch.Tensor): cache tensor for KEY & VALUE in + transformer/conformer attention, with shape + (elayers, head, cache_t1, d_k * 2), where + `head * d_k == hidden-dim` and + `cache_t1 == chunk_size * num_decoding_left_chunks`. + cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, + (elayers, b=1, hidden-dim, cache_t2), where + `cache_t2 == cnn.lorder - 1` + + Returns: + torch.Tensor: output of current input xs, + with shape (b=1, chunk_size, hidden-dim). + torch.Tensor: new attention cache required for next chunk, with + dynamic shape (elayers, head, ?, d_k * 2) + depending on required_cache_size. + torch.Tensor: new conformer cnn cache required for next chunk, with + same shape as the original cnn_cache. + + """ + assert xs.size(0) == 1 + # tmp_masks is just for interface compatibility + tmp_masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool) + tmp_masks = tmp_masks.unsqueeze(1) + if self.global_cmvn is not None: + xs = self.global_cmvn(xs) + # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim) + xs, pos_emb, _ = self.embed(xs, tmp_masks, offset) + # NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim) + elayers, cache_t1 = att_cache.size(0), att_cache.size(2) + chunk_size = xs.size(1) + attention_key_size = cache_t1 + chunk_size + pos_emb = self.embed.position_encoding( + offset=offset - cache_t1, size=attention_key_size + ) + if required_cache_size < 0: + next_cache_start = 0 + elif required_cache_size == 0: + next_cache_start = attention_key_size + else: + next_cache_start = max(attention_key_size - required_cache_size, 0) + + r_att_cache = [] + r_cnn_cache = [] + + mask_pad = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool) + mask_pad = mask_pad.unsqueeze(1) + max_att_len: int = 0 + recover_activations: List[ + Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor] + ] = [] + index = 0 + xs_lens = torch.tensor([xs.size(1)], device=xs.device, dtype=torch.int) + xs = self.preln(xs) + for i, layer in enumerate(self.encoders): + # NOTE(xcsong): Before layer.forward + # shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2), + # shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2) + if self.reduce_idx is not None: + if self.time_reduce is not None and i in self.reduce_idx: + recover_activations.append((xs, att_mask, pos_emb, mask_pad)) + xs, xs_lens, att_mask, mask_pad = self.time_reduction_layer( + xs, xs_lens, att_mask, mask_pad + ) + pos_emb = pos_emb[:, ::2, :] + index += 1 + + if self.recover_idx is not None: + if self.time_reduce == "recover" and i in self.recover_idx: + index -= 1 + ( + recover_tensor, + recover_att_mask, + recover_pos_emb, + recover_mask_pad, + ) = recover_activations[index] + # recover output length for ctc decode + xs = xs.unsqueeze(2).repeat(1, 1, 2, 1).flatten(1, 2) + xs = self.time_recover_layer(xs) + recoverd_t = recover_tensor.size(1) + xs = recover_tensor + xs[:, :recoverd_t, :].contiguous() + att_mask = recover_att_mask + pos_emb = recover_pos_emb + mask_pad = recover_mask_pad + if att_mask.size(1) != 0: + xs = xs.masked_fill(~att_mask[:, 0, :].unsqueeze(-1), 0.0) + + factor = self.calculate_downsampling_factor(i) + + xs, _, new_att_cache, new_cnn_cache = layer( + xs, + att_mask, + pos_emb, + att_cache=att_cache[i : i + 1][:, :, ::factor, :][ + :, :, : pos_emb.size(1) - xs.size(1), : + ] + if elayers > 0 + else att_cache[:, :, ::factor, :], + cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache, + ) + # NOTE(xcsong): After layer.forward + # shape(new_att_cache) is (1, head, attention_key_size, d_k * 2), + # shape(new_cnn_cache) is (b=1, hidden-dim, cache_t2) + cached_att = new_att_cache[:, :, next_cache_start // factor :, :] + cached_cnn = new_cnn_cache.unsqueeze(0) + cached_att = ( + cached_att.unsqueeze(3).repeat(1, 1, 1, factor, 1).flatten(2, 3) + ) + if i == 0: + # record length for the first block as max length + max_att_len = cached_att.size(2) + r_att_cache.append(cached_att[:, :, :max_att_len, :]) + r_cnn_cache.append(cached_cnn) + # NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2), + # ? may be larger than cache_t1, it depends on required_cache_size + r_att_cache = torch.cat(r_att_cache, dim=0) + # NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2) + r_cnn_cache = torch.cat(r_cnn_cache, dim=0) + + if self.final_proj is not None: + xs = self.final_proj(xs) + return (xs, r_att_cache, r_cnn_cache) + + def forward_chunk_by_chunk( + self, + xs: torch.Tensor, + decoding_chunk_size: int, + num_decoding_left_chunks: int = -1, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Forward input chunk by chunk with chunk_size like a streaming + fashion + + Here we should pay special attention to computation cache in the + streaming style forward chunk by chunk. Three things should be taken + into account for computation in the current network: + 1. transformer/conformer encoder layers output cache + 2. convolution in conformer + 3. convolution in subsampling + + However, we don't implement subsampling cache for: + 1. We can control subsampling module to output the right result by + overlapping input instead of cache left context, even though it + wastes some computation, but subsampling only takes a very + small fraction of computation in the whole model. + 2. Typically, there are several covolution layers with subsampling + in subsampling module, it is tricky and complicated to do cache + with different convolution layers with different subsampling + rate. + 3. Currently, nn.Sequential is used to stack all the convolution + layers in subsampling, we need to rewrite it to make it work + with cache, which is not prefered. + Args: + xs (torch.Tensor): (1, max_len, dim) + chunk_size (int): decoding chunk size + """ + assert decoding_chunk_size > 0 + # The model is trained by static or dynamic chunk + assert self.static_chunk_size > 0 or self.use_dynamic_chunk + subsampling = self.embed.subsampling_rate + context = self.embed.right_context + 1 # Add current frame + stride = subsampling * decoding_chunk_size + decoding_window = (decoding_chunk_size - 1) * subsampling + context + num_frames = xs.size(1) + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) + outputs = [] + offset = 0 + required_cache_size = decoding_chunk_size * num_decoding_left_chunks + + # Feed forward overlap input step by step + for cur in range(0, num_frames - context + 1, stride): + end = min(cur + decoding_window, num_frames) + chunk_xs = xs[:, cur:end, :] + (y, att_cache, cnn_cache) = self.forward_chunk( + chunk_xs, offset, required_cache_size, att_cache, cnn_cache + ) + outputs.append(y) + offset += y.size(1) + ys = torch.cat(outputs, 1) + masks = torch.ones((1, 1, ys.size(1)), device=ys.device, dtype=torch.bool) + return ys, masks diff --git a/modules/wenet_extractor/squeezeformer/encoder_layer.py b/modules/wenet_extractor/squeezeformer/encoder_layer.py new file mode 100644 index 0000000000000000000000000000000000000000..74e7c0fdeee6f7156468b14416d268702cf4d40a --- /dev/null +++ b/modules/wenet_extractor/squeezeformer/encoder_layer.py @@ -0,0 +1,129 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""SqueezeformerEncoderLayer definition.""" + +import torch +import torch.nn as nn +from typing import Optional, Tuple + + +class SqueezeformerEncoderLayer(nn.Module): + """Encoder layer module. + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` + instance can be used as the argument. + feed_forward1 (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward` instance can be used as the argument. + conv_module (torch.nn.Module): Convolution module instance. + `ConvlutionModule` instance can be used as the argument. + feed_forward2 (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward` instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): + True: use layer_norm before each sub-block. + False: use layer_norm after each sub-block. + """ + + def __init__( + self, + size: int, + self_attn: torch.nn.Module, + feed_forward1: Optional[nn.Module] = None, + conv_module: Optional[nn.Module] = None, + feed_forward2: Optional[nn.Module] = None, + normalize_before: bool = False, + dropout_rate: float = 0.1, + concat_after: bool = False, + ): + super(SqueezeformerEncoderLayer, self).__init__() + self.size = size + self.self_attn = self_attn + self.layer_norm1 = nn.LayerNorm(size) + self.ffn1 = feed_forward1 + self.layer_norm2 = nn.LayerNorm(size) + self.conv_module = conv_module + self.layer_norm3 = nn.LayerNorm(size) + self.ffn2 = feed_forward2 + self.layer_norm4 = nn.LayerNorm(size) + self.normalize_before = normalize_before + self.dropout = nn.Dropout(dropout_rate) + self.concat_after = concat_after + if concat_after: + self.concat_linear = nn.Linear(size + size, size) + else: + self.concat_linear = nn.Identity() + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + # self attention module + residual = x + if self.normalize_before: + x = self.layer_norm1(x) + x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache) + if self.concat_after: + x_concat = torch.cat((x, x_att), dim=-1) + x = residual + self.concat_linear(x_concat) + else: + x = residual + self.dropout(x_att) + if not self.normalize_before: + x = self.layer_norm1(x) + + # ffn module + residual = x + if self.normalize_before: + x = self.layer_norm2(x) + x = self.ffn1(x) + x = residual + self.dropout(x) + if not self.normalize_before: + x = self.layer_norm2(x) + + # conv module + new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + residual = x + if self.normalize_before: + x = self.layer_norm3(x) + x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache) + x = residual + self.dropout(x) + if not self.normalize_before: + x = self.layer_norm3(x) + + # ffn module + residual = x + if self.normalize_before: + x = self.layer_norm4(x) + x = self.ffn2(x) + # we do not use dropout here since it is inside feed forward function + x = residual + self.dropout(x) + if not self.normalize_before: + x = self.layer_norm4(x) + + return x, mask, new_att_cache, new_cnn_cache diff --git a/modules/wenet_extractor/squeezeformer/positionwise_feed_forward.py b/modules/wenet_extractor/squeezeformer/positionwise_feed_forward.py new file mode 100644 index 0000000000000000000000000000000000000000..bfd4aabf9a7e246243d9792616bbe995c2a8a6ba --- /dev/null +++ b/modules/wenet_extractor/squeezeformer/positionwise_feed_forward.py @@ -0,0 +1,88 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""Positionwise feed forward layer definition.""" + +import torch + + +class PositionwiseFeedForward(torch.nn.Module): + """Positionwise feed forward layer. + + FeedForward are appied on each position of the sequence. + The output dim is same with the input dim. + + Args: + idim (int): Input dimenstion. + hidden_units (int): The number of hidden units. + dropout_rate (float): Dropout rate. + activation (torch.nn.Module): Activation function + """ + + def __init__( + self, + idim: int, + hidden_units: int, + dropout_rate: float, + activation: torch.nn.Module = torch.nn.ReLU(), + adaptive_scale: bool = False, + init_weights: bool = False, + ): + """Construct a PositionwiseFeedForward object.""" + super(PositionwiseFeedForward, self).__init__() + self.idim = idim + self.hidden_units = hidden_units + self.w_1 = torch.nn.Linear(idim, hidden_units) + self.activation = activation + self.dropout = torch.nn.Dropout(dropout_rate) + self.w_2 = torch.nn.Linear(hidden_units, idim) + self.ada_scale = None + self.ada_bias = None + self.adaptive_scale = adaptive_scale + self.ada_scale = torch.nn.Parameter( + torch.ones([1, 1, idim]), requires_grad=adaptive_scale + ) + self.ada_bias = torch.nn.Parameter( + torch.zeros([1, 1, idim]), requires_grad=adaptive_scale + ) + if init_weights: + self.init_weights() + + def init_weights(self): + ffn1_max = self.idim**-0.5 + ffn2_max = self.hidden_units**-0.5 + torch.nn.init.uniform_(self.w_1.weight.data, -ffn1_max, ffn1_max) + torch.nn.init.uniform_(self.w_1.bias.data, -ffn1_max, ffn1_max) + torch.nn.init.uniform_(self.w_2.weight.data, -ffn2_max, ffn2_max) + torch.nn.init.uniform_(self.w_2.bias.data, -ffn2_max, ffn2_max) + + def forward(self, xs: torch.Tensor) -> torch.Tensor: + """Forward function. + + Args: + xs: input tensor (B, L, D) + Returns: + output tensor, (B, L, D) + """ + if self.adaptive_scale: + xs = self.ada_scale * xs + self.ada_bias + return self.w_2(self.dropout(self.activation(self.w_1(xs)))) diff --git a/modules/wenet_extractor/squeezeformer/subsampling.py b/modules/wenet_extractor/squeezeformer/subsampling.py new file mode 100644 index 0000000000000000000000000000000000000000..329dd6c51f1719d73307d636dd625cf98a34cd34 --- /dev/null +++ b/modules/wenet_extractor/squeezeformer/subsampling.py @@ -0,0 +1,321 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""DepthwiseConv2dSubsampling4 and TimeReductionLayer definition.""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +from modules.wenet_extractor.transformer.subsampling import BaseSubsampling +from typing import Tuple +from modules.wenet_extractor.squeezeformer.conv2d import Conv2dValid + + +class DepthwiseConv2dSubsampling4(BaseSubsampling): + """Depthwise Convolutional 2D subsampling (to 1/4 length). + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + pos_enc_class (nn.Module): position encoding class. + dw_stride (int): Whether do depthwise convolution. + input_size (int): filter bank dimension. + + """ + + def __init__( + self, + idim: int, + odim: int, + pos_enc_class: torch.nn.Module, + dw_stride: bool = False, + input_size: int = 80, + input_dropout_rate: float = 0.1, + init_weights: bool = True, + ): + super(DepthwiseConv2dSubsampling4, self).__init__() + self.idim = idim + self.odim = odim + self.pw_conv = nn.Conv2d( + in_channels=idim, out_channels=odim, kernel_size=3, stride=2 + ) + self.act1 = nn.ReLU() + self.dw_conv = nn.Conv2d( + in_channels=odim, + out_channels=odim, + kernel_size=3, + stride=2, + groups=odim if dw_stride else 1, + ) + self.act2 = nn.ReLU() + self.pos_enc = pos_enc_class + self.input_proj = nn.Sequential( + nn.Linear(odim * (((input_size - 1) // 2 - 1) // 2), odim), + nn.Dropout(p=input_dropout_rate), + ) + if init_weights: + linear_max = (odim * input_size / 4) ** -0.5 + torch.nn.init.uniform_( + self.input_proj.state_dict()["0.weight"], -linear_max, linear_max + ) + torch.nn.init.uniform_( + self.input_proj.state_dict()["0.bias"], -linear_max, linear_max + ) + self.subsampling_rate = 4 + # 6 = (3 - 1) * 1 + (3 - 1) * 2 + self.right_context = 6 + + def forward( + self, x: torch.Tensor, x_mask: torch.Tensor, offset: int = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + x = x.unsqueeze(1) # (b, c=1, t, f) + x = self.pw_conv(x) + x = self.act1(x) + x = self.dw_conv(x) + x = self.act2(x) + b, c, t, f = x.size() + x = x.permute(0, 2, 1, 3) + x = x.contiguous().view(b, t, c * f) + x, pos_emb = self.pos_enc(x, offset) + x = self.input_proj(x) + return x, pos_emb, x_mask[:, :, :-2:2][:, :, :-2:2] + + +class TimeReductionLayer1D(nn.Module): + """ + Modified NeMo, + Squeezeformer Time Reduction procedure. + Downsamples the audio by `stride` in the time dimension. + Args: + channel (int): input dimension of + MultiheadAttentionMechanism and PositionwiseFeedForward + out_dim (int): Output dimension of the module. + kernel_size (int): Conv kernel size for + depthwise convolution in convolution module + stride (int): Downsampling factor in time dimension. + """ + + def __init__( + self, channel: int, out_dim: int, kernel_size: int = 5, stride: int = 2 + ): + super(TimeReductionLayer1D, self).__init__() + + self.channel = channel + self.out_dim = out_dim + self.kernel_size = kernel_size + self.stride = stride + self.padding = max(0, self.kernel_size - self.stride) + + self.dw_conv = nn.Conv1d( + in_channels=channel, + out_channels=channel, + kernel_size=kernel_size, + stride=stride, + padding=self.padding, + groups=channel, + ) + + self.pw_conv = nn.Conv1d( + in_channels=channel, + out_channels=out_dim, + kernel_size=1, + stride=1, + padding=0, + groups=1, + ) + + self.init_weights() + + def init_weights(self): + dw_max = self.kernel_size**-0.5 + pw_max = self.channel**-0.5 + torch.nn.init.uniform_(self.dw_conv.weight, -dw_max, dw_max) + torch.nn.init.uniform_(self.dw_conv.bias, -dw_max, dw_max) + torch.nn.init.uniform_(self.pw_conv.weight, -pw_max, pw_max) + torch.nn.init.uniform_(self.pw_conv.bias, -pw_max, pw_max) + + def forward( + self, + xs, + xs_lens: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + ): + xs = xs.transpose(1, 2) # [B, C, T] + xs = xs.masked_fill(mask_pad.eq(0), 0.0) + + xs = self.dw_conv(xs) + xs = self.pw_conv(xs) + + xs = xs.transpose(1, 2) # [B, T, C] + + B, T, D = xs.size() + mask = mask[:, :: self.stride, :: self.stride] + mask_pad = mask_pad[:, :, :: self.stride] + L = mask_pad.size(-1) + # For JIT exporting, we remove F.pad operator. + if L - T < 0: + xs = xs[:, : L - T, :].contiguous() + else: + dummy_pad = torch.zeros(B, L - T, D, device=xs.device) + xs = torch.cat([xs, dummy_pad], dim=1) + + xs_lens = torch.div(xs_lens + 1, 2, rounding_mode="trunc") + return xs, xs_lens, mask, mask_pad + + +class TimeReductionLayer2D(nn.Module): + def __init__(self, kernel_size: int = 5, stride: int = 2, encoder_dim: int = 256): + super(TimeReductionLayer2D, self).__init__() + self.encoder_dim = encoder_dim + self.kernel_size = kernel_size + self.dw_conv = Conv2dValid( + in_channels=encoder_dim, + out_channels=encoder_dim, + kernel_size=(kernel_size, 1), + stride=stride, + valid_trigy=True, + ) + self.pw_conv = Conv2dValid( + in_channels=encoder_dim, + out_channels=encoder_dim, + kernel_size=1, + stride=1, + valid_trigx=False, + valid_trigy=False, + ) + + self.kernel_size = kernel_size + self.stride = stride + self.init_weights() + + def init_weights(self): + dw_max = self.kernel_size**-0.5 + pw_max = self.encoder_dim**-0.5 + torch.nn.init.uniform_(self.dw_conv.weight, -dw_max, dw_max) + torch.nn.init.uniform_(self.dw_conv.bias, -dw_max, dw_max) + torch.nn.init.uniform_(self.pw_conv.weight, -pw_max, pw_max) + torch.nn.init.uniform_(self.pw_conv.bias, -pw_max, pw_max) + + def forward( + self, + xs: torch.Tensor, + xs_lens: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + xs = xs.masked_fill(mask_pad.transpose(1, 2).eq(0), 0.0) + xs = xs.unsqueeze(2) + padding1 = self.kernel_size - self.stride + xs = F.pad(xs, (0, 0, 0, 0, 0, padding1, 0, 0), mode="constant", value=0.0) + xs = self.dw_conv(xs.permute(0, 3, 1, 2)) + xs = self.pw_conv(xs).permute(0, 3, 2, 1).squeeze(1).contiguous() + tmp_length = xs.size(1) + xs_lens = torch.div(xs_lens + 1, 2, rounding_mode="trunc") + padding2 = max(0, (xs_lens.max() - tmp_length).data.item()) + batch_size, hidden = xs.size(0), xs.size(-1) + dummy_pad = torch.zeros(batch_size, padding2, hidden, device=xs.device) + xs = torch.cat([xs, dummy_pad], dim=1) + mask = mask[:, ::2, ::2] + mask_pad = mask_pad[:, :, ::2] + return xs, xs_lens, mask, mask_pad + + +class TimeReductionLayerStream(nn.Module): + """ + Squeezeformer Time Reduction procedure. + Downsamples the audio by `stride` in the time dimension. + Args: + channel (int): input dimension of + MultiheadAttentionMechanism and PositionwiseFeedForward + out_dim (int): Output dimension of the module. + kernel_size (int): Conv kernel size for + depthwise convolution in convolution module + stride (int): Downsampling factor in time dimension. + """ + + def __init__( + self, channel: int, out_dim: int, kernel_size: int = 1, stride: int = 2 + ): + super(TimeReductionLayerStream, self).__init__() + + self.channel = channel + self.out_dim = out_dim + self.kernel_size = kernel_size + self.stride = stride + + self.dw_conv = nn.Conv1d( + in_channels=channel, + out_channels=channel, + kernel_size=kernel_size, + stride=stride, + padding=0, + groups=channel, + ) + + self.pw_conv = nn.Conv1d( + in_channels=channel, + out_channels=out_dim, + kernel_size=1, + stride=1, + padding=0, + groups=1, + ) + + self.init_weights() + + def init_weights(self): + dw_max = self.kernel_size**-0.5 + pw_max = self.channel**-0.5 + torch.nn.init.uniform_(self.dw_conv.weight, -dw_max, dw_max) + torch.nn.init.uniform_(self.dw_conv.bias, -dw_max, dw_max) + torch.nn.init.uniform_(self.pw_conv.weight, -pw_max, pw_max) + torch.nn.init.uniform_(self.pw_conv.bias, -pw_max, pw_max) + + def forward( + self, + xs, + xs_lens: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + ): + xs = xs.transpose(1, 2) # [B, C, T] + xs = xs.masked_fill(mask_pad.eq(0), 0.0) + + xs = self.dw_conv(xs) + xs = self.pw_conv(xs) + + xs = xs.transpose(1, 2) # [B, T, C] + + B, T, D = xs.size() + mask = mask[:, :: self.stride, :: self.stride] + mask_pad = mask_pad[:, :, :: self.stride] + L = mask_pad.size(-1) + # For JIT exporting, we remove F.pad operator. + if L - T < 0: + xs = xs[:, : L - T, :].contiguous() + else: + dummy_pad = torch.zeros(B, L - T, D, device=xs.device) + xs = torch.cat([xs, dummy_pad], dim=1) + + xs_lens = torch.div(xs_lens + 1, 2, rounding_mode="trunc") + return xs, xs_lens, mask, mask_pad diff --git a/modules/wenet_extractor/ssl/bestrq/bestqr_model.py b/modules/wenet_extractor/ssl/bestrq/bestqr_model.py new file mode 100644 index 0000000000000000000000000000000000000000..04d065ee457f3c968932171c0b9cb53f21d43254 --- /dev/null +++ b/modules/wenet_extractor/ssl/bestrq/bestqr_model.py @@ -0,0 +1,156 @@ +from typing import Optional, Tuple +import torch + +from wenet.ssl.bestrq.mask import compute_mask_indices +from wenet.utils.mask import make_pad_mask + + +class BestRQModel(torch.nn.Module): + def __init__( + self, + encoder: torch.nn.Module, + input_dim: int = 256, + embedding_dim: int = 256, + num_embeddings: int = 8192, + num_codebooks: int = 1, + dropout_rate: float = 0.1, + mask_prob: float = 0.01, + mask_length: int = 10, + min_masks: int = 2, + layer_norm_epsilon=1e-5, + ) -> None: + super().__init__() + + assert mask_prob > 0.0 + + self.mask_prob = mask_prob + # NOTE: should filter audio less than mask_length + self.mask_length = mask_length + self.min_masks = min_masks + + self.input_dropout = torch.nn.Dropout(dropout_rate) + + # [embedding_dim, num_embeddings] + random_embedding_weight = torch.empty( + num_codebooks, embedding_dim, num_embeddings, requires_grad=False + ) + self.embeddings = torch.nn.init.normal_(random_embedding_weight) + + random_projection_weight = torch.empty( + input_dim, embedding_dim, requires_grad=False + ) + self.projection = torch.nn.init.xavier_normal_(random_projection_weight) + + mask_emb_weight = torch.Tensor(input_dim) + mask_emb_weight.requires_grad = True + self.mask_emb = torch.nn.init.normal_(mask_emb_weight, mean=0, std=0.1) + + self.input_layer_norm = torch.nn.LayerNorm(input_dim, layer_norm_epsilon) + self.encoder = encoder + self.encoder_top_n_out = torch.nn.parameter.Parameter( + torch.Tensor(num_codebooks, self.encoder.output_size(), num_embeddings) + ) + + def forward( + self, + xs: torch.Tensor, + xs_lens: torch.Tensor, + text: Optional[torch.Tensor] = None, + text_length: Optional[torch.Tensor] = None, + ): + # should support nonstreamming and streamming + # TODO(Mddct): streamming future + # eg: full attenton and chunk or dynamic chunk training + # 1 forward subsampling + xs, pos_emb, masks = self._forward_subsampling(xs, xs_lens) + unmasked_xs = xs + # 2 mask features + # 2.0 apply mask + masked_xs, masked_masks = self._apply_mask(xs) + # 2.1 get nearest embedding + target_ids = self._nearest_embedding_idx(unmasked_xs) + # 3 forward xxx-formaer block + out, out_mask = self._forward_encoder_blocks(masked_xs, masks, pos_emb, masks) + # 4 get logits + out = out.unsqueeze(1) # [B, 1, T', dim] + top_n_out = self.encoder_top_n_out.unsqueeze( + 0 + ) # [num_codebooks, dim, num_embeddings] + out = torch.matmul(out, top_n_out) # [B, num_codebooks, T', num_embeddings] + + # 5 compute loss + loss = self._compute_loss(out, target_ids, out_mask.squeeze(1) * masked_masks) + return {"loss": loss} + + def _compute_loss( + self, input: torch.Tensor, target: torch.Tensor, mask: torch.Tensor + ): + input = input.transpose(1, 3) # [B, num_embeddings, T' num_codebooks] + entropy = torch.nn.functional.cross_entropy( + input, target, reduction="none" + ) # [B, T', num_codebooks] + # stop gradient for non mask area + loss = entropy * mask.unsqueeze(2) + return loss.sum() / (mask.sum() * loss.size(2)) + + def _forward_encoder_blocks( + self, + xs: torch.Tensor, + xs_masks: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor, + ): + masks = xs_masks + for layer in self.encoder.encoders: + xs, masks, _, _ = layer(xs, xs_masks, pos_emb, mask_pad) + if self.encoder.normalize_before: + xs = self.encoder.after_norm(xs) + # Here we assume the mask is not changed in encoder layers, so just + # return the masks before encoder layers, and the masks will be used + # for cross attention with decoder later + return xs, masks + + def _nearest_embedding_idx(self, xs: torch.Tensor) -> torch.Tensor: + xs = self.input_layer_norm(xs) + xs = self.input_dropout(xs) + xs = torch.matmul(xs, self.projection.to(xs.device)) + + B, T, C = xs.size() + flattened_input = xs.view(-1, C) + embeddings = self.embeddings.to( + xs.device + ) # [num_codebooks, embedding_dim, num_embeddings] + # [num_codebooks, B*T, num_embeddings] + distance = ( + torch.sum(flattened_input**2, dim=1, keepdim=True).unsqueeze(0) + + torch.sum(embeddings**2, dim=1, keepdim=True) + - 2 * torch.matmul(flattened_input.unsqueeze(0), embeddings) + ) + + out = torch.argmin(distance, dim=-1) # [num_codebooks, B*T] + out = out.transpose(0, 1) # [B*T, num_codebooks] + return out.reshape(B, T, -1) # [B, T, num_codebooks] + + def _apply_mask(self, xs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + masks = compute_mask_indices( + xs.size()[:-1], + self.mask_prob, + self.mask_length, + self.min_masks, + device=xs.device, + ) + masks_expand = masks.unsqueeze(-1) # [B, T, 1] + + mask_emb = self.mask_emb.to(xs.device).view(1, 1, -1) + xs = torch.where(masks_expand, mask_emb, xs) + return xs, masks + + def _forward_subsampling( + self, xs: torch.Tensor, xs_lens: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + T = xs.size(1) + masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) + if self.encoder.global_cmvn is not None: + xs = self.encoder.global_cmvn(xs) + xs, pos_emb, masks = self.encoder.embed(xs, masks) + return xs, pos_emb, masks diff --git a/modules/wenet_extractor/ssl/bestrq/mask.py b/modules/wenet_extractor/ssl/bestrq/mask.py new file mode 100644 index 0000000000000000000000000000000000000000..20bcdd35efa65a6685cba676cb1aef1ae076caa5 --- /dev/null +++ b/modules/wenet_extractor/ssl/bestrq/mask.py @@ -0,0 +1,55 @@ +import torch + + +def _sampler( + pdf: torch.Tensor, num_samples: int, device=torch.device("cpu") +) -> torch.Tensor: + size = pdf.size() + z = -torch.log(torch.rand(size, device=device)) + _, indices = torch.topk(pdf + z, num_samples) + return indices + + +def compute_mask_indices( + size: torch.Size, + mask_prob: float, + mask_length: int, + min_masks: int = 0, + device=torch.device("cpu"), +) -> torch.Tensor: + assert len(size) == 2 + batch_size, seq_length = size + + # compute number of masked span in batch + num_masked_spans = ( + mask_prob * float(seq_length) / float(mask_length) + torch.rand(1)[0] + ) + num_masked_spans = int(num_masked_spans) + num_masked_spans = max(num_masked_spans, min_masks) + + # num_masked <= seq_length + if num_masked_spans * mask_length > seq_length: + num_masked_spans = seq_length // mask_length + + pdf = torch.ones(batch_size, seq_length - (mask_length - 1), device=device) + mask_idxs = _sampler(pdf, num_masked_spans, device=device) + + mask_idxs = ( + mask_idxs.unsqueeze(-1) + .repeat(1, 1, mask_length) + .view(batch_size, num_masked_spans * mask_length) + ) # [B,num_masked_spans*mask_length] + + offset = ( + torch.arange(mask_length, device=device) + .view(1, 1, -1) + .repeat(1, num_masked_spans, 1) + ) # [1,num_masked_spans,mask_length] + offset = offset.view(1, num_masked_spans * mask_length) + + mask_idxs = mask_idxs + offset # [B,num_masked_spans, mask_length] + + ones = torch.ones(batch_size, seq_length, dtype=torch.bool, device=mask_idxs.device) + # masks to fill + full_mask = torch.zeros_like(ones, dtype=torch.bool, device=mask_idxs.device) + return torch.scatter(full_mask, dim=1, index=mask_idxs, src=ones) diff --git a/modules/wenet_extractor/transducer/__init__.py b/modules/wenet_extractor/transducer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/wenet_extractor/transducer/__pycache__/__init__.cpython-39.pyc b/modules/wenet_extractor/transducer/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e7105b9a91439a3a11c6f18025b39b4f9de24bde Binary files /dev/null and b/modules/wenet_extractor/transducer/__pycache__/__init__.cpython-39.pyc differ diff --git a/modules/wenet_extractor/transducer/__pycache__/joint.cpython-39.pyc b/modules/wenet_extractor/transducer/__pycache__/joint.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4c9d3859cae92894fdf484325afb9358a637e653 Binary files /dev/null and b/modules/wenet_extractor/transducer/__pycache__/joint.cpython-39.pyc differ diff --git a/modules/wenet_extractor/transducer/__pycache__/predictor.cpython-39.pyc b/modules/wenet_extractor/transducer/__pycache__/predictor.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4d17d38dba3787db01418a97c83a8b4a093de49a Binary files /dev/null and b/modules/wenet_extractor/transducer/__pycache__/predictor.cpython-39.pyc differ diff --git a/modules/wenet_extractor/transducer/__pycache__/transducer.cpython-39.pyc b/modules/wenet_extractor/transducer/__pycache__/transducer.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d27f84a0932e252e70554619980320f9b41908c7 Binary files /dev/null and b/modules/wenet_extractor/transducer/__pycache__/transducer.cpython-39.pyc differ diff --git a/modules/wenet_extractor/transducer/joint.py b/modules/wenet_extractor/transducer/joint.py new file mode 100644 index 0000000000000000000000000000000000000000..8592136c9f33902942fb01ee42af82761b7ad26b --- /dev/null +++ b/modules/wenet_extractor/transducer/joint.py @@ -0,0 +1,72 @@ +from typing import Optional + +import torch +from torch import nn +from modules.wenet_extractor.utils.common import get_activation + + +class TransducerJoint(torch.nn.Module): + def __init__( + self, + voca_size: int, + enc_output_size: int, + pred_output_size: int, + join_dim: int, + prejoin_linear: bool = True, + postjoin_linear: bool = False, + joint_mode: str = "add", + activation: str = "tanh", + ): + # TODO(Mddct): concat in future + assert joint_mode in ["add"] + super().__init__() + + self.activatoin = get_activation(activation) + self.prejoin_linear = prejoin_linear + self.postjoin_linear = postjoin_linear + self.joint_mode = joint_mode + + if not self.prejoin_linear and not self.postjoin_linear: + assert enc_output_size == pred_output_size == join_dim + # torchscript compatibility + self.enc_ffn: Optional[nn.Linear] = None + self.pred_ffn: Optional[nn.Linear] = None + if self.prejoin_linear: + self.enc_ffn = nn.Linear(enc_output_size, join_dim) + self.pred_ffn = nn.Linear(pred_output_size, join_dim) + # torchscript compatibility + self.post_ffn: Optional[nn.Linear] = None + if self.postjoin_linear: + self.post_ffn = nn.Linear(join_dim, join_dim) + + self.ffn_out = nn.Linear(join_dim, voca_size) + + def forward(self, enc_out: torch.Tensor, pred_out: torch.Tensor): + """ + Args: + enc_out (torch.Tensor): [B, T, E] + pred_out (torch.Tensor): [B, T, P] + Return: + [B,T,U,V] + """ + if ( + self.prejoin_linear + and self.enc_ffn is not None + and self.pred_ffn is not None + ): + enc_out = self.enc_ffn(enc_out) # [B,T,E] -> [B,T,V] + pred_out = self.pred_ffn(pred_out) + + enc_out = enc_out.unsqueeze(2) # [B,T,V] -> [B,T,1,V] + pred_out = pred_out.unsqueeze(1) # [B,U,V] -> [B,1 U, V] + + # TODO(Mddct): concat joint + _ = self.joint_mode + out = enc_out + pred_out # [B,T,U,V] + + if self.postjoin_linear and self.post_ffn is not None: + out = self.post_ffn(out) + + out = self.activatoin(out) + out = self.ffn_out(out) + return out diff --git a/modules/wenet_extractor/transducer/predictor.py b/modules/wenet_extractor/transducer/predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..9c1ffdc7b30c23147ba80ae928eb3bd18f602932 --- /dev/null +++ b/modules/wenet_extractor/transducer/predictor.py @@ -0,0 +1,477 @@ +from typing import List, Optional, Tuple + +import torch +from torch import nn +from modules.wenet_extractor.utils.common import get_activation, get_rnn + + +def ApplyPadding(input, padding, pad_value) -> torch.Tensor: + """ + Args: + input: [bs, max_time_step, dim] + padding: [bs, max_time_step] + """ + return padding * pad_value + input * (1 - padding) + + +class PredictorBase(torch.nn.Module): + # NOTE(Mddct): We can use ABC abstract here, but + # keep this class simple enough for now + def __init__(self) -> None: + super().__init__() + + def init_state( + self, batch_size: int, device: torch.device, method: str = "zero" + ) -> List[torch.Tensor]: + _, _, _ = batch_size, method, device + raise NotImplementedError("this is a base precictor") + + def batch_to_cache(self, cache: List[torch.Tensor]) -> List[List[torch.Tensor]]: + _ = cache + raise NotImplementedError("this is a base precictor") + + def cache_to_batch(self, cache: List[List[torch.Tensor]]) -> List[torch.Tensor]: + _ = cache + raise NotImplementedError("this is a base precictor") + + def forward( + self, + input: torch.Tensor, + cache: Optional[List[torch.Tensor]] = None, + ): + ( + _, + _, + ) = ( + input, + cache, + ) + raise NotImplementedError("this is a base precictor") + + def forward_step( + self, input: torch.Tensor, padding: torch.Tensor, cache: List[torch.Tensor] + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + ( + _, + _, + _, + ) = ( + input, + padding, + cache, + ) + raise NotImplementedError("this is a base precictor") + + +class RNNPredictor(PredictorBase): + def __init__( + self, + voca_size: int, + embed_size: int, + output_size: int, + embed_dropout: float, + hidden_size: int, + num_layers: int, + bias: bool = True, + rnn_type: str = "lstm", + dropout: float = 0.1, + ) -> None: + super().__init__() + self.n_layers = num_layers + self.hidden_size = hidden_size + # disable rnn base out projection + self.embed = nn.Embedding(voca_size, embed_size) + self.dropout = nn.Dropout(embed_dropout) + # NOTE(Mddct): rnn base from torch not support layer norm + # will add layer norm and prune value in cell and layer + # ref: https://github.com/Mddct/neural-lm/blob/main/models/gru_cell.py + self.rnn = get_rnn(rnn_type=rnn_type)( + input_size=embed_size, + hidden_size=hidden_size, + num_layers=num_layers, + bias=bias, + batch_first=True, + dropout=dropout, + ) + self.projection = nn.Linear(hidden_size, output_size) + + def forward( + self, + input: torch.Tensor, + cache: Optional[List[torch.Tensor]] = None, + ) -> torch.Tensor: + """ + Args: + input (torch.Tensor): [batch, max_time). + padding (torch.Tensor): [batch, max_time] + cache : rnn predictor cache[0] == state_m + cache[1] == state_c + Returns: + output: [batch, max_time, output_size] + """ + + # NOTE(Mddct): we don't use pack input format + embed = self.embed(input) # [batch, max_time, emb_size] + embed = self.dropout(embed) + states: Optional[Tuple[torch.Tensor, torch.Tensor]] = None + if cache is None: + state = self.init_state(batch_size=input.size(0), device=input.device) + states = (state[0], state[1]) + else: + assert len(cache) == 2 + states = (cache[0], cache[1]) + out, (m, c) = self.rnn(embed, states) + out = self.projection(out) + + # NOTE(Mddct): Although we don't use staate in transducer + # training forward, we need make it right for padding value + # so we create forward_step for infering, forward for training + _, _ = m, c + return out + + def batch_to_cache(self, cache: List[torch.Tensor]) -> List[List[torch.Tensor]]: + """ + Args: + cache: [state_m, state_c] + state_ms: [1*n_layers, bs, ...] + state_cs: [1*n_layers, bs, ...] + Returns: + new_cache: [[state_m_1, state_c_1], [state_m_2, state_c_2]...] + """ + assert len(cache) == 2 + state_ms = cache[0] + state_cs = cache[1] + + assert state_ms.size(1) == state_cs.size(1) + + new_cache: List[List[torch.Tensor]] = [] + for state_m, state_c in zip( + torch.split(state_ms, 1, dim=1), torch.split(state_cs, 1, dim=1) + ): + new_cache.append([state_m, state_c]) + return new_cache + + def cache_to_batch(self, cache: List[List[torch.Tensor]]) -> List[torch.Tensor]: + """ + Args: + cache : [[state_m_1, state_c_1], [state_m_1, state_c_1]...] + + Returns: + new_caceh: [state_ms, state_cs], + state_ms: [1*n_layers, bs, ...] + state_cs: [1*n_layers, bs, ...] + """ + state_ms = torch.cat([states[0] for states in cache], dim=1) + state_cs = torch.cat([states[1] for states in cache], dim=1) + return [state_ms, state_cs] + + def init_state( + self, + batch_size: int, + device: torch.device, + method: str = "zero", + ) -> List[torch.Tensor]: + assert batch_size > 0 + # TODO(Mddct): xavier init method + _ = method + return [ + torch.zeros(1 * self.n_layers, batch_size, self.hidden_size, device=device), + torch.zeros(1 * self.n_layers, batch_size, self.hidden_size, device=device), + ] + + def forward_step( + self, input: torch.Tensor, padding: torch.Tensor, cache: List[torch.Tensor] + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """ + Args: + input (torch.Tensor): [batch_size, time_step=1] + padding (torch.Tensor): [batch_size,1], 1 is padding value + cache : rnn predictor cache[0] == state_m + cache[1] == state_c + """ + assert len(cache) == 2 + state_m, state_c = cache[0], cache[1] + embed = self.embed(input) # [batch, 1, emb_size] + embed = self.dropout(embed) + out, (m, c) = self.rnn(embed, (state_m, state_c)) + + out = self.projection(out) + m = ApplyPadding(m, padding.unsqueeze(0), state_m) + c = ApplyPadding(c, padding.unsqueeze(0), state_c) + + return (out, [m, c]) + + +class EmbeddingPredictor(PredictorBase): + """Embedding predictor + + Described in: + https://arxiv.org/pdf/2109.07513.pdf + + embed-> proj -> layer norm -> swish + """ + + def __init__( + self, + voca_size: int, + embed_size: int, + embed_dropout: float, + n_head: int, + history_size: int = 2, + activation: str = "swish", + bias: bool = False, + layer_norm_epsilon: float = 1e-5, + ) -> None: + super().__init__() + # multi head + self.num_heads = n_head + self.embed_size = embed_size + self.context_size = history_size + 1 + self.pos_embed = torch.nn.Linear( + embed_size * self.context_size, self.num_heads, bias=bias + ) + self.embed = nn.Embedding(voca_size, self.embed_size) + self.embed_dropout = nn.Dropout(p=embed_dropout) + self.ffn = nn.Linear(self.embed_size, self.embed_size) + self.norm = nn.LayerNorm(self.embed_size, eps=layer_norm_epsilon) + self.activatoin = get_activation(activation) + + def init_state( + self, batch_size: int, device: torch.device, method: str = "zero" + ) -> List[torch.Tensor]: + assert batch_size > 0 + _ = method + return [ + torch.zeros( + batch_size, self.context_size - 1, self.embed_size, device=device + ), + ] + + def batch_to_cache(self, cache: List[torch.Tensor]) -> List[List[torch.Tensor]]: + """ + Args: + cache : [history] + history: [bs, ...] + Returns: + new_ache : [[history_1], [history_2], [history_3]...] + """ + assert len(cache) == 1 + cache_0 = cache[0] + history: List[List[torch.Tensor]] = [] + for h in torch.split(cache_0, 1, dim=0): + history.append([h]) + return history + + def cache_to_batch(self, cache: List[List[torch.Tensor]]) -> List[torch.Tensor]: + """ + Args: + cache : [[history_1], [history_2], [history3]...] + + Returns: + new_caceh: [history], + history: [bs, ...] + """ + history = torch.cat([h[0] for h in cache], dim=0) + return [history] + + def forward(self, input: torch.Tensor, cache: Optional[List[torch.Tensor]] = None): + """forward for training""" + input = self.embed(input) # [bs, seq_len, embed] + input = self.embed_dropout(input) + if cache is None: + zeros = self.init_state(input.size(0), device=input.device)[0] + else: + assert len(cache) == 1 + zeros = cache[0] + + input = torch.cat( + (zeros, input), dim=1 + ) # [bs, context_size-1 + seq_len, embed] + + input = input.unfold(1, self.context_size, 1).permute( + 0, 1, 3, 2 + ) # [bs, seq_len, context_size, embed] + # multi head pos: [n_head, embed, context_size] + multi_head_pos = self.pos_embed.weight.view( + self.num_heads, self.embed_size, self.context_size + ) + + # broadcast dot attenton + input_expand = input.unsqueeze(2) # [bs, seq_len, 1, context_size, embed] + multi_head_pos = multi_head_pos.permute( + 0, 2, 1 + ) # [num_heads, context_size, embed] + + # [bs, seq_len, num_heads, context_size, embed] + weight = input_expand * multi_head_pos + weight = weight.sum(dim=-1, keepdim=False).unsqueeze( + 3 + ) # [bs, seq_len, num_heads, 1, context_size] + output = weight.matmul(input_expand).squeeze( + dim=3 + ) # [bs, seq_len, num_heads, embed] + output = output.sum(dim=2) # [bs, seq_len, embed] + output = output / (self.num_heads * self.context_size) + + output = self.ffn(output) + output = self.norm(output) + output = self.activatoin(output) + return output + + def forward_step( + self, + input: torch.Tensor, + padding: torch.Tensor, + cache: List[torch.Tensor], + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """forward step for inference + Args: + input (torch.Tensor): [batch_size, time_step=1] + padding (torch.Tensor): [batch_size,1], 1 is padding value + cache: for embedding predictor, cache[0] == history + """ + assert input.size(1) == 1 + assert len(cache) == 1 + history = cache[0] + assert history.size(1) == self.context_size - 1 + input = self.embed(input) # [bs, 1, embed] + input = self.embed_dropout(input) + context_input = torch.cat((history, input), dim=1) + input_expand = context_input.unsqueeze(1).unsqueeze( + 2 + ) # [bs, 1, 1, context_size, embed] + + # multi head pos: [n_head, embed, context_size] + multi_head_pos = self.pos_embed.weight.view( + self.num_heads, self.embed_size, self.context_size + ) + + multi_head_pos = multi_head_pos.permute( + 0, 2, 1 + ) # [num_heads, context_size, embed] + # [bs, 1, num_heads, context_size, embed] + weight = input_expand * multi_head_pos + weight = weight.sum(dim=-1, keepdim=False).unsqueeze( + 3 + ) # [bs, 1, num_heads, 1, context_size] + output = weight.matmul(input_expand).squeeze(dim=3) # [bs, 1, num_heads, embed] + output = output.sum(dim=2) # [bs, 1, embed] + output = output / (self.num_heads * self.context_size) + + output = self.ffn(output) + output = self.norm(output) + output = self.activatoin(output) + new_cache = context_input[:, 1:, :] + # TODO(Mddct): we need padding new_cache in future + # new_cache = ApplyPadding(history, padding, new_cache) + return (output, [new_cache]) + + +class ConvPredictor(PredictorBase): + def __init__( + self, + voca_size: int, + embed_size: int, + embed_dropout: float, + history_size: int = 2, + activation: str = "relu", + bias: bool = False, + layer_norm_epsilon: float = 1e-5, + ) -> None: + super().__init__() + + assert history_size >= 0 + self.embed_size = embed_size + self.context_size = history_size + 1 + self.embed = nn.Embedding(voca_size, self.embed_size) + self.embed_dropout = nn.Dropout(p=embed_dropout) + self.conv = nn.Conv1d( + in_channels=embed_size, + out_channels=embed_size, + kernel_size=self.context_size, + padding=0, + groups=embed_size, + bias=bias, + ) + self.norm = nn.LayerNorm(embed_size, eps=layer_norm_epsilon) + self.activatoin = get_activation(activation) + + def init_state( + self, batch_size: int, device: torch.device, method: str = "zero" + ) -> List[torch.Tensor]: + assert batch_size > 0 + assert method == "zero" + return [ + torch.zeros( + batch_size, self.context_size - 1, self.embed_size, device=device + ) + ] + + def cache_to_batch(self, cache: List[List[torch.Tensor]]) -> List[torch.Tensor]: + """ + Args: + cache : [[history_1], [history_2], [history3]...] + + Returns: + new_caceh: [history], + history: [bs, ...] + """ + history = torch.cat([h[0] for h in cache], dim=0) + return [history] + + def batch_to_cache(self, cache: List[torch.Tensor]) -> List[List[torch.Tensor]]: + """ + Args: + cache : [history] + history: [bs, ...] + Returns: + new_ache : [[history_1], [history_2], [history_3]...] + """ + assert len(cache) == 1 + cache_0 = cache[0] + history: List[List[torch.Tensor]] = [] + for h in torch.split(cache_0, 1, dim=0): + history.append([h]) + return history + + def forward(self, input: torch.Tensor, cache: Optional[List[torch.Tensor]] = None): + """forward for training""" + input = self.embed(input) # [bs, seq_len, embed] + input = self.embed_dropout(input) + if cache is None: + zeros = self.init_state(input.size(0), device=input.device)[0] + else: + assert len(cache) == 1 + zeros = cache[0] + + input = torch.cat( + (zeros, input), dim=1 + ) # [bs, context_size-1 + seq_len, embed] + input = input.permute(0, 2, 1) + out = self.conv(input).permute(0, 2, 1) + out = self.activatoin(self.norm(out)) + return out + + def forward_step( + self, input: torch.Tensor, padding: torch.Tensor, cache: List[torch.Tensor] + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """forward step for inference + Args: + input (torch.Tensor): [batch_size, time_step=1] + padding (torch.Tensor): [batch_size,1], 1 is padding value + cache: for embedding predictor, cache[0] == history + """ + assert input.size(1) == 1 + assert len(cache) == 1 + history = cache[0] + assert history.size(1) == self.context_size - 1 + input = self.embed(input) # [bs, 1, embed] + input = self.embed_dropout(input) + context_input = torch.cat((history, input), dim=1) + input = context_input.permute(0, 2, 1) + out = self.conv(input).permute(0, 2, 1) + out = self.activatoin(self.norm(out)) + + new_cache = context_input[:, 1:, :] + # TODO(Mddct): apply padding in future + return (out, [new_cache]) diff --git a/modules/wenet_extractor/transducer/search/__pycache__/greedy_search.cpython-39.pyc b/modules/wenet_extractor/transducer/search/__pycache__/greedy_search.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4352da60ebc06bc6f465be39912eedb5b533350b Binary files /dev/null and b/modules/wenet_extractor/transducer/search/__pycache__/greedy_search.cpython-39.pyc differ diff --git a/modules/wenet_extractor/transducer/search/__pycache__/prefix_beam_search.cpython-39.pyc b/modules/wenet_extractor/transducer/search/__pycache__/prefix_beam_search.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e6faf6e17d4a932d58bf56a0407d58330361cbd9 Binary files /dev/null and b/modules/wenet_extractor/transducer/search/__pycache__/prefix_beam_search.cpython-39.pyc differ diff --git a/modules/wenet_extractor/transducer/search/greedy_search.py b/modules/wenet_extractor/transducer/search/greedy_search.py new file mode 100644 index 0000000000000000000000000000000000000000..df4cfea28902aac0005b0b72e930c790a2d79de4 --- /dev/null +++ b/modules/wenet_extractor/transducer/search/greedy_search.py @@ -0,0 +1,52 @@ +from typing import List + +import torch + + +def basic_greedy_search( + model: torch.nn.Module, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + n_steps: int = 64, +) -> List[List[int]]: + # fake padding + padding = torch.zeros(1, 1).to(encoder_out.device) + # sos + pred_input_step = torch.tensor([model.blank]).reshape(1, 1) + cache = model.predictor.init_state(1, method="zero", device=encoder_out.device) + new_cache: List[torch.Tensor] = [] + t = 0 + hyps = [] + prev_out_nblk = True + pred_out_step = None + per_frame_max_noblk = n_steps + per_frame_noblk = 0 + while t < encoder_out_lens: + encoder_out_step = encoder_out[:, t : t + 1, :] # [1, 1, E] + if prev_out_nblk: + step_outs = model.predictor.forward_step( + pred_input_step, padding, cache + ) # [1, 1, P] + pred_out_step, new_cache = step_outs[0], step_outs[1] + + joint_out_step = model.joint(encoder_out_step, pred_out_step) # [1,1,v] + joint_out_probs = joint_out_step.log_softmax(dim=-1) + + joint_out_max = joint_out_probs.argmax(dim=-1).squeeze() # [] + if joint_out_max != model.blank: + hyps.append(joint_out_max.item()) + prev_out_nblk = True + per_frame_noblk = per_frame_noblk + 1 + pred_input_step = joint_out_max.reshape(1, 1) + # state_m, state_c = clstate_out_m, state_out_c + cache = new_cache + + if joint_out_max == model.blank or per_frame_noblk >= per_frame_max_noblk: + if joint_out_max == model.blank: + prev_out_nblk = False + # TODO(Mddct): make t in chunk for streamming + # or t should't be too lang to predict none blank + t = t + 1 + per_frame_noblk = 0 + + return [hyps] diff --git a/modules/wenet_extractor/transducer/search/prefix_beam_search.py b/modules/wenet_extractor/transducer/search/prefix_beam_search.py new file mode 100644 index 0000000000000000000000000000000000000000..b4b4f4ea46f24f75f43f7cef27d9a12b57a7452d --- /dev/null +++ b/modules/wenet_extractor/transducer/search/prefix_beam_search.py @@ -0,0 +1,149 @@ +from typing import List, Tuple + +import torch +from modules.wenet_extractor.utils.common import log_add + + +class Sequence: + __slots__ = {"hyp", "score", "cache"} + + def __init__( + self, + hyp: List[torch.Tensor], + score, + cache: List[torch.Tensor], + ): + self.hyp = hyp + self.score = score + self.cache = cache + + +class PrefixBeamSearch: + def __init__(self, encoder, predictor, joint, ctc, blank): + self.encoder = encoder + self.predictor = predictor + self.joint = joint + self.ctc = ctc + self.blank = blank + + def forward_decoder_one_step( + self, encoder_x: torch.Tensor, pre_t: torch.Tensor, cache: List[torch.Tensor] + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + padding = torch.zeros(pre_t.size(0), 1, device=encoder_x.device) + pre_t, new_cache = self.predictor.forward_step( + pre_t.unsqueeze(-1), padding, cache + ) + x = self.joint(encoder_x, pre_t) # [beam, 1, 1, vocab] + x = x.log_softmax(dim=-1) + return x, new_cache + + def prefix_beam_search( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + decoding_chunk_size: int = -1, + beam_size: int = 5, + num_decoding_left_chunks: int = -1, + simulate_streaming: bool = False, + ctc_weight: float = 0.3, + transducer_weight: float = 0.7, + ): + """prefix beam search + also see wenet.transducer.transducer.beam_search + """ + assert speech.shape[0] == speech_lengths.shape[0] + assert decoding_chunk_size != 0 + device = speech.device + batch_size = speech.shape[0] + assert batch_size == 1 + + # 1. Encoder + encoder_out, _ = self.encoder( + speech, speech_lengths, decoding_chunk_size, num_decoding_left_chunks + ) # (B, maxlen, encoder_dim) + maxlen = encoder_out.size(1) + + ctc_probs = self.ctc.log_softmax(encoder_out).squeeze(0) + beam_init: List[Sequence] = [] + + # 2. init beam using Sequence to save beam unit + cache = self.predictor.init_state(1, method="zero", device=device) + beam_init.append(Sequence(hyp=[self.blank], score=0.0, cache=cache)) + # 3. start decoding (notice: we use breathwise first searching) + # !!!! In this decoding method: one frame do not output multi units. !!!! + # !!!! Experiments show that this strategy has little impact !!!! + for i in range(maxlen): + # 3.1 building input + # decoder taking the last token to predict the next token + input_hyp = [s.hyp[-1] for s in beam_init] + input_hyp_tensor = torch.tensor(input_hyp, dtype=torch.int, device=device) + # building statement from beam + cache_batch = self.predictor.cache_to_batch([s.cache for s in beam_init]) + # build score tensor to do torch.add() function + scores = torch.tensor([s.score for s in beam_init]).to(device) + + # 3.2 forward decoder + logp, new_cache = self.forward_decoder_one_step( + encoder_out[:, i, :].unsqueeze(1), + input_hyp_tensor, + cache_batch, + ) # logp: (N, 1, 1, vocab_size) + logp = logp.squeeze(1).squeeze(1) # logp: (N, vocab_size) + new_cache = self.predictor.batch_to_cache(new_cache) + + # 3.3 shallow fusion for transducer score + # and ctc score where we can also add the LM score + logp = torch.log( + torch.add( + transducer_weight * torch.exp(logp), + ctc_weight * torch.exp(ctc_probs[i].unsqueeze(0)), + ) + ) + + # 3.4 first beam prune + top_k_logp, top_k_index = logp.topk(beam_size) # (N, N) + scores = torch.add(scores.unsqueeze(1), top_k_logp) + + # 3.5 generate new beam (N*N) + beam_A = [] + for j in range(len(beam_init)): + # update seq + base_seq = beam_init[j] + for t in range(beam_size): + # blank: only update the score + if top_k_index[j, t] == self.blank: + new_seq = Sequence( + hyp=base_seq.hyp.copy(), + score=scores[j, t].item(), + cache=base_seq.cache, + ) + + beam_A.append(new_seq) + # other unit: update hyp score statement and last + else: + hyp_new = base_seq.hyp.copy() + hyp_new.append(top_k_index[j, t].item()) + new_seq = Sequence( + hyp=hyp_new, score=scores[j, t].item(), cache=new_cache[j] + ) + beam_A.append(new_seq) + + # 3.6 prefix fusion + fusion_A = [beam_A[0]] + for j in range(1, len(beam_A)): + s1 = beam_A[j] + if_do_append = True + for t in range(len(fusion_A)): + # notice: A_ can not fusion with A + if s1.hyp == fusion_A[t].hyp: + fusion_A[t].score = log_add([fusion_A[t].score, s1.score]) + if_do_append = False + break + if if_do_append: + fusion_A.append(s1) + + # 4. second pruned + fusion_A.sort(key=lambda x: x.score, reverse=True) + beam_init = fusion_A[:beam_size] + + return beam_init, encoder_out diff --git a/modules/wenet_extractor/transducer/transducer.py b/modules/wenet_extractor/transducer/transducer.py new file mode 100644 index 0000000000000000000000000000000000000000..3618eeb2e355955a606d59e3a91f6253872843e0 --- /dev/null +++ b/modules/wenet_extractor/transducer/transducer.py @@ -0,0 +1,486 @@ +from typing import Dict, List, Optional, Tuple, Union + +import torch +import torchaudio +from torch import nn +from torch.nn.utils.rnn import pad_sequence + +from modules.wenet_extractor.transducer.predictor import PredictorBase +from modules.wenet_extractor.transducer.search.greedy_search import basic_greedy_search +from modules.wenet_extractor.transducer.search.prefix_beam_search import ( + PrefixBeamSearch, +) +from modules.wenet_extractor.transformer.asr_model import ASRModel +from modules.wenet_extractor.transformer.ctc import CTC +from modules.wenet_extractor.transformer.decoder import ( + BiTransformerDecoder, + TransformerDecoder, +) +from modules.wenet_extractor.transformer.label_smoothing_loss import LabelSmoothingLoss +from modules.wenet_extractor.utils.common import ( + IGNORE_ID, + add_blank, + add_sos_eos, + reverse_pad_list, +) + + +class Transducer(ASRModel): + """Transducer-ctc-attention hybrid Encoder-Predictor-Decoder model""" + + def __init__( + self, + vocab_size: int, + blank: int, + encoder: nn.Module, + predictor: PredictorBase, + joint: nn.Module, + attention_decoder: Optional[ + Union[TransformerDecoder, BiTransformerDecoder] + ] = None, + ctc: Optional[CTC] = None, + ctc_weight: float = 0, + ignore_id: int = IGNORE_ID, + reverse_weight: float = 0.0, + lsm_weight: float = 0.0, + length_normalized_loss: bool = False, + transducer_weight: float = 1.0, + attention_weight: float = 0.0, + ) -> None: + assert attention_weight + ctc_weight + transducer_weight == 1.0 + super().__init__( + vocab_size, + encoder, + attention_decoder, + ctc, + ctc_weight, + ignore_id, + reverse_weight, + lsm_weight, + length_normalized_loss, + ) + + self.blank = blank + self.transducer_weight = transducer_weight + self.attention_decoder_weight = 1 - self.transducer_weight - self.ctc_weight + + self.predictor = predictor + self.joint = joint + self.bs = None + + # Note(Mddct): decoder also means predictor in transducer, + # but here decoder is attention decoder + del self.criterion_att + if attention_decoder is not None: + self.criterion_att = LabelSmoothingLoss( + size=vocab_size, + padding_idx=ignore_id, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + ) -> Dict[str, Optional[torch.Tensor]]: + """Frontend + Encoder + predictor + joint + loss + + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + """ + assert text_lengths.dim() == 1, text_lengths.shape + # Check that batch_size is unified + assert ( + speech.shape[0] + == speech_lengths.shape[0] + == text.shape[0] + == text_lengths.shape[0] + ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape) + + # Encoder + encoder_out, encoder_mask = self.encoder(speech, speech_lengths) + encoder_out_lens = encoder_mask.squeeze(1).sum(1) + # predictor + ys_in_pad = add_blank(text, self.blank, self.ignore_id) + predictor_out = self.predictor(ys_in_pad) + # joint + joint_out = self.joint(encoder_out, predictor_out) + # NOTE(Mddct): some loss implementation require pad valid is zero + # torch.int32 rnnt_loss required + rnnt_text = text.to(torch.int64) + rnnt_text = torch.where(rnnt_text == self.ignore_id, 0, rnnt_text).to( + torch.int32 + ) + rnnt_text_lengths = text_lengths.to(torch.int32) + encoder_out_lens = encoder_out_lens.to(torch.int32) + loss = torchaudio.functional.rnnt_loss( + joint_out, + rnnt_text, + encoder_out_lens, + rnnt_text_lengths, + blank=self.blank, + reduction="mean", + ) + loss_rnnt = loss + + loss = self.transducer_weight * loss + # optional attention decoder + loss_att: Optional[torch.Tensor] = None + if self.attention_decoder_weight != 0.0 and self.decoder is not None: + loss_att, _ = self._calc_att_loss( + encoder_out, encoder_mask, text, text_lengths + ) + + # optional ctc + loss_ctc: Optional[torch.Tensor] = None + if self.ctc_weight != 0.0 and self.ctc is not None: + loss_ctc = self.ctc(encoder_out, encoder_out_lens, text, text_lengths) + else: + loss_ctc = None + + if loss_ctc is not None: + loss = loss + self.ctc_weight * loss_ctc.sum() + if loss_att is not None: + loss = loss + self.attention_decoder_weight * loss_att.sum() + # NOTE: 'loss' must be in dict + return { + "loss": loss, + "loss_att": loss_att, + "loss_ctc": loss_ctc, + "loss_rnnt": loss_rnnt, + } + + def init_bs(self): + if self.bs is None: + self.bs = PrefixBeamSearch( + self.encoder, self.predictor, self.joint, self.ctc, self.blank + ) + + def _cal_transducer_score( + self, + encoder_out: torch.Tensor, + encoder_mask: torch.Tensor, + hyps_lens: torch.Tensor, + hyps_pad: torch.Tensor, + ): + # ignore id -> blank, add blank at head + hyps_pad_blank = add_blank(hyps_pad, self.blank, self.ignore_id) + xs_in_lens = encoder_mask.squeeze(1).sum(1).int() + + # 1. Forward predictor + predictor_out = self.predictor(hyps_pad_blank) + # 2. Forward joint + joint_out = self.joint(encoder_out, predictor_out) + rnnt_text = hyps_pad.to(torch.int64) + rnnt_text = torch.where(rnnt_text == self.ignore_id, 0, rnnt_text).to( + torch.int32 + ) + # 3. Compute transducer loss + loss_td = torchaudio.functional.rnnt_loss( + joint_out, + rnnt_text, + xs_in_lens, + hyps_lens.int(), + blank=self.blank, + reduction="none", + ) + return loss_td * -1 + + def _cal_attn_score( + self, + encoder_out: torch.Tensor, + encoder_mask: torch.Tensor, + hyps_pad: torch.Tensor, + hyps_lens: torch.Tensor, + ): + # (beam_size, max_hyps_len) + ori_hyps_pad = hyps_pad + + # td_score = loss_td * -1 + hyps_pad, _ = add_sos_eos(hyps_pad, self.sos, self.eos, self.ignore_id) + hyps_lens = hyps_lens + 1 # Add at begining + # used for right to left decoder + r_hyps_pad = reverse_pad_list(ori_hyps_pad, hyps_lens, self.ignore_id) + r_hyps_pad, _ = add_sos_eos(r_hyps_pad, self.sos, self.eos, self.ignore_id) + decoder_out, r_decoder_out, _ = self.decoder( + encoder_out, + encoder_mask, + hyps_pad, + hyps_lens, + r_hyps_pad, + self.reverse_weight, + ) # (beam_size, max_hyps_len, vocab_size) + decoder_out = torch.nn.functional.log_softmax(decoder_out, dim=-1) + decoder_out = decoder_out.cpu().numpy() + # r_decoder_out will be 0.0, if reverse_weight is 0.0 or decoder is a + # conventional transformer decoder. + r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1) + r_decoder_out = r_decoder_out.cpu().numpy() + return decoder_out, r_decoder_out + + def beam_search( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + decoding_chunk_size: int = -1, + beam_size: int = 5, + num_decoding_left_chunks: int = -1, + simulate_streaming: bool = False, + ctc_weight: float = 0.3, + transducer_weight: float = 0.7, + ): + """beam search + + Args: + speech (torch.Tensor): (batch=1, max_len, feat_dim) + speech_length (torch.Tensor): (batch, ) + beam_size (int): beam size for beam search + decoding_chunk_size (int): decoding chunk for dynamic chunk + trained model. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + 0: used for training, it's prohibited here + simulate_streaming (bool): whether do encoder forward in a + streaming fashion + ctc_weight (float): ctc probability weight in transducer + prefix beam search. + final_prob = ctc_weight * ctc_prob + transducer_weight * transducer_prob + transducer_weight (float): transducer probability weight in + prefix beam search + Returns: + List[List[int]]: best path result + + """ + self.init_bs() + beam, _ = self.bs.prefix_beam_search( + speech, + speech_lengths, + decoding_chunk_size, + beam_size, + num_decoding_left_chunks, + simulate_streaming, + ctc_weight, + transducer_weight, + ) + return beam[0].hyp[1:], beam[0].score + + def transducer_attention_rescoring( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + beam_size: int, + decoding_chunk_size: int = -1, + num_decoding_left_chunks: int = -1, + simulate_streaming: bool = False, + reverse_weight: float = 0.0, + ctc_weight: float = 0.0, + attn_weight: float = 0.0, + transducer_weight: float = 0.0, + search_ctc_weight: float = 1.0, + search_transducer_weight: float = 0.0, + beam_search_type: str = "transducer", + ) -> List[List[int]]: + """beam search + + Args: + speech (torch.Tensor): (batch=1, max_len, feat_dim) + speech_length (torch.Tensor): (batch, ) + beam_size (int): beam size for beam search + decoding_chunk_size (int): decoding chunk for dynamic chunk + trained model. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + 0: used for training, it's prohibited here + simulate_streaming (bool): whether do encoder forward in a + streaming fashion + ctc_weight (float): ctc probability weight using in rescoring. + rescore_prob = ctc_weight * ctc_prob + + transducer_weight * (transducer_loss * -1) + + attn_weight * attn_prob + attn_weight (float): attn probability weight using in rescoring. + transducer_weight (float): transducer probability weight using in + rescoring + search_ctc_weight (float): ctc weight using + in rnnt beam search (seeing in self.beam_search) + search_transducer_weight (float): transducer weight using + in rnnt beam search (seeing in self.beam_search) + Returns: + List[List[int]]: best path result + + """ + + assert speech.shape[0] == speech_lengths.shape[0] + assert decoding_chunk_size != 0 + if reverse_weight > 0.0: + # decoder should be a bitransformer decoder if reverse_weight > 0.0 + assert hasattr(self.decoder, "right_decoder") + device = speech.device + batch_size = speech.shape[0] + # For attention rescoring we only support batch_size=1 + assert batch_size == 1 + # encoder_out: (1, maxlen, encoder_dim), len(hyps) = beam_size + self.init_bs() + if beam_search_type == "transducer": + beam, encoder_out = self.bs.prefix_beam_search( + speech, + speech_lengths, + decoding_chunk_size=decoding_chunk_size, + beam_size=beam_size, + num_decoding_left_chunks=num_decoding_left_chunks, + ctc_weight=search_ctc_weight, + transducer_weight=search_transducer_weight, + ) + beam_score = [s.score for s in beam] + hyps = [s.hyp[1:] for s in beam] + + elif beam_search_type == "ctc": + hyps, encoder_out = self._ctc_prefix_beam_search( + speech, + speech_lengths, + beam_size=beam_size, + decoding_chunk_size=decoding_chunk_size, + num_decoding_left_chunks=num_decoding_left_chunks, + simulate_streaming=simulate_streaming, + ) + beam_score = [hyp[1] for hyp in hyps] + hyps = [hyp[0] for hyp in hyps] + assert len(hyps) == beam_size + + # build hyps and encoder output + hyps_pad = pad_sequence( + [torch.tensor(hyp, device=device, dtype=torch.long) for hyp in hyps], + True, + self.ignore_id, + ) # (beam_size, max_hyps_len) + hyps_lens = torch.tensor( + [len(hyp) for hyp in hyps], device=device, dtype=torch.long + ) # (beam_size,) + + encoder_out = encoder_out.repeat(beam_size, 1, 1) + encoder_mask = torch.ones( + beam_size, 1, encoder_out.size(1), dtype=torch.bool, device=device + ) + + # 2.1 calculate transducer score + td_score = self._cal_transducer_score( + encoder_out, + encoder_mask, + hyps_lens, + hyps_pad, + ) + # 2.2 calculate attention score + decoder_out, r_decoder_out = self._cal_attn_score( + encoder_out, + encoder_mask, + hyps_pad, + hyps_lens, + ) + + # Only use decoder score for rescoring + best_score = -float("inf") + best_index = 0 + for i, hyp in enumerate(hyps): + score = 0.0 + for j, w in enumerate(hyp): + score += decoder_out[i][j][w] + score += decoder_out[i][len(hyp)][self.eos] + td_s = td_score[i] + # add right to left decoder score + if reverse_weight > 0: + r_score = 0.0 + for j, w in enumerate(hyp): + r_score += r_decoder_out[i][len(hyp) - j - 1][w] + r_score += r_decoder_out[i][len(hyp)][self.eos] + score = score * (1 - reverse_weight) + r_score * reverse_weight + # add ctc score + score = ( + score * attn_weight + + beam_score[i] * ctc_weight + + td_s * transducer_weight + ) + if score > best_score: + best_score = score + best_index = i + + return hyps[best_index], best_score + + def greedy_search( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + decoding_chunk_size: int = -1, + num_decoding_left_chunks: int = -1, + simulate_streaming: bool = False, + n_steps: int = 64, + ) -> List[List[int]]: + """greedy search + + Args: + speech (torch.Tensor): (batch=1, max_len, feat_dim) + speech_length (torch.Tensor): (batch, ) + beam_size (int): beam size for beam search + decoding_chunk_size (int): decoding chunk for dynamic chunk + trained model. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + 0: used for training, it's prohibited here + simulate_streaming (bool): whether do encoder forward in a + streaming fashion + Returns: + List[List[int]]: best path result + """ + # TODO(Mddct): batch decode + assert speech.size(0) == 1 + assert speech.shape[0] == speech_lengths.shape[0] + assert decoding_chunk_size != 0 + # TODO(Mddct): forward chunk by chunk + _ = simulate_streaming + # Let's assume B = batch_size + encoder_out, encoder_mask = self.encoder( + speech, + speech_lengths, + decoding_chunk_size, + num_decoding_left_chunks, + ) + encoder_out_lens = encoder_mask.squeeze(1).sum() + hyps = basic_greedy_search(self, encoder_out, encoder_out_lens, n_steps=n_steps) + + return hyps + + @torch.jit.export + def forward_encoder_chunk( + self, + xs: torch.Tensor, + offset: int, + required_cache_size: int, + att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), + cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + return self.encoder.forward_chunk( + xs, offset, required_cache_size, att_cache, cnn_cache + ) + + @torch.jit.export + def forward_predictor_step( + self, xs: torch.Tensor, cache: List[torch.Tensor] + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + assert len(cache) == 2 + # fake padding + padding = torch.zeros(1, 1) + return self.predictor.forward_step(xs, padding, cache) + + @torch.jit.export + def forward_joint_step( + self, enc_out: torch.Tensor, pred_out: torch.Tensor + ) -> torch.Tensor: + return self.joint(enc_out, pred_out) + + @torch.jit.export + def 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b/modules/wenet_extractor/transformer/asr_model.py new file mode 100644 index 0000000000000000000000000000000000000000..4ad7869679ee15fdcf6a759a7c39762d7f166ff2 --- /dev/null +++ b/modules/wenet_extractor/transformer/asr_model.py @@ -0,0 +1,1056 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +from collections import defaultdict +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn.functional as F +from torch.nn.utils.rnn import pad_sequence + +from modules.wenet_extractor.transformer.ctc import CTC +from modules.wenet_extractor.transformer.decoder import TransformerDecoder +from modules.wenet_extractor.transformer.encoder import TransformerEncoder +from modules.wenet_extractor.transformer.label_smoothing_loss import LabelSmoothingLoss +from modules.wenet_extractor.utils.common import ( + IGNORE_ID, + add_sos_eos, + log_add, + remove_duplicates_and_blank, + th_accuracy, + reverse_pad_list, +) +from modules.wenet_extractor.utils.mask import ( + make_pad_mask, + mask_finished_preds, + mask_finished_scores, + subsequent_mask, +) + + +class ASRModel(torch.nn.Module): + """CTC-attention hybrid Encoder-Decoder model""" + + def __init__( + self, + vocab_size: int, + encoder: TransformerEncoder, + decoder: TransformerDecoder, + ctc: CTC, + ctc_weight: float = 0.5, + ignore_id: int = IGNORE_ID, + reverse_weight: float = 0.0, + lsm_weight: float = 0.0, + length_normalized_loss: bool = False, + lfmmi_dir: str = "", + ): + assert 0.0 <= ctc_weight <= 1.0, ctc_weight + + super().__init__() + # note that eos is the same as sos (equivalent ID) + self.sos = vocab_size - 1 + self.eos = vocab_size - 1 + self.vocab_size = vocab_size + self.ignore_id = ignore_id + self.ctc_weight = ctc_weight + self.reverse_weight = reverse_weight + + self.encoder = encoder + self.decoder = decoder + self.ctc = ctc + self.criterion_att = LabelSmoothingLoss( + size=vocab_size, + padding_idx=ignore_id, + smoothing=lsm_weight, + normalize_length=length_normalized_loss, + ) + self.lfmmi_dir = lfmmi_dir + if self.lfmmi_dir != "": + self.load_lfmmi_resource() + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + ) -> Dict[str, Optional[torch.Tensor]]: + """Frontend + Encoder + Decoder + Calc loss + + Args: + speech: (Batch, Length, ...) + speech_lengths: (Batch, ) + text: (Batch, Length) + text_lengths: (Batch,) + """ + + assert text_lengths.dim() == 1, text_lengths.shape + # Check that batch_size is unified + assert ( + speech.shape[0] + == speech_lengths.shape[0] + == text.shape[0] + == text_lengths.shape[0] + ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape) + # 1. Encoder + encoder_out, encoder_mask = self.encoder(speech, speech_lengths) + encoder_out_lens = encoder_mask.squeeze(1).sum(1) + + # 2a. Attention-decoder branch + if self.ctc_weight != 1.0: + loss_att, acc_att = self._calc_att_loss( + encoder_out, encoder_mask, text, text_lengths + ) + else: + loss_att = None + + # 2b. CTC branch or LF-MMI loss + if self.ctc_weight != 0.0: + if self.lfmmi_dir != "": + loss_ctc = self._calc_lfmmi_loss(encoder_out, encoder_mask, text) + else: + loss_ctc = self.ctc(encoder_out, encoder_out_lens, text, text_lengths) + else: + loss_ctc = None + + if loss_ctc is None: + loss = loss_att + elif loss_att is None: + loss = loss_ctc + else: + loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + return {"loss": loss, "loss_att": loss_att, "loss_ctc": loss_ctc} + + def _calc_att_loss( + self, + encoder_out: torch.Tensor, + encoder_mask: torch.Tensor, + ys_pad: torch.Tensor, + ys_pad_lens: torch.Tensor, + ) -> Tuple[torch.Tensor, float]: + ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) + ys_in_lens = ys_pad_lens + 1 + + # reverse the seq, used for right to left decoder + r_ys_pad = reverse_pad_list(ys_pad, ys_pad_lens, float(self.ignore_id)) + r_ys_in_pad, r_ys_out_pad = add_sos_eos( + r_ys_pad, self.sos, self.eos, self.ignore_id + ) + # 1. Forward decoder + decoder_out, r_decoder_out, _ = self.decoder( + encoder_out, + encoder_mask, + ys_in_pad, + ys_in_lens, + r_ys_in_pad, + self.reverse_weight, + ) + # 2. Compute attention loss + loss_att = self.criterion_att(decoder_out, ys_out_pad) + r_loss_att = torch.tensor(0.0) + if self.reverse_weight > 0.0: + r_loss_att = self.criterion_att(r_decoder_out, r_ys_out_pad) + loss_att = ( + loss_att * (1 - self.reverse_weight) + r_loss_att * self.reverse_weight + ) + acc_att = th_accuracy( + decoder_out.view(-1, self.vocab_size), + ys_out_pad, + ignore_label=self.ignore_id, + ) + return loss_att, acc_att + + def _forward_encoder( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + decoding_chunk_size: int = -1, + num_decoding_left_chunks: int = -1, + simulate_streaming: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor]: + # Let's assume B = batch_size + # 1. Encoder + if simulate_streaming and decoding_chunk_size > 0: + encoder_out, encoder_mask = self.encoder.forward_chunk_by_chunk( + speech, + decoding_chunk_size=decoding_chunk_size, + num_decoding_left_chunks=num_decoding_left_chunks, + ) # (B, maxlen, encoder_dim) + else: + encoder_out, encoder_mask = self.encoder( + speech, + speech_lengths, + decoding_chunk_size=decoding_chunk_size, + num_decoding_left_chunks=num_decoding_left_chunks, + ) # (B, maxlen, encoder_dim) + return encoder_out, encoder_mask + + def encoder_extractor( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + decoding_chunk_size: int = -1, + num_decoding_left_chunks: int = -1, + simulate_streaming: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor]: + # assert speech.shape[0] == speech_lengths[0] + assert decoding_chunk_size != 0 + batch_size = speech.shape[0] + + encoder_out, encoder_mask = self._forward_encoder( + speech, + speech_lengths, + decoding_chunk_size, + num_decoding_left_chunks, + simulate_streaming, + ) # (B, maxlen, encoder_dim) + + return encoder_out + + def recognize( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + beam_size: int = 10, + decoding_chunk_size: int = -1, + num_decoding_left_chunks: int = -1, + simulate_streaming: bool = False, + ) -> torch.Tensor: + """Apply beam search on attention decoder + + Args: + speech (torch.Tensor): (batch, max_len, feat_dim) + speech_length (torch.Tensor): (batch, ) + beam_size (int): beam size for beam search + decoding_chunk_size (int): decoding chunk for dynamic chunk + trained model. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + 0: used for training, it's prohibited here + simulate_streaming (bool): whether do encoder forward in a + streaming fashion + + Returns: + torch.Tensor: decoding result, (batch, max_result_len) + """ + assert speech.shape[0] == speech_lengths.shape[0] + assert decoding_chunk_size != 0 + device = speech.device + batch_size = speech.shape[0] + + # Let's assume B = batch_size and N = beam_size + # 1. Encoder + encoder_out, encoder_mask = self._forward_encoder( + speech, + speech_lengths, + decoding_chunk_size, + num_decoding_left_chunks, + simulate_streaming, + ) # (B, maxlen, encoder_dim) + maxlen = encoder_out.size(1) + encoder_dim = encoder_out.size(2) + running_size = batch_size * beam_size + encoder_out = ( + encoder_out.unsqueeze(1) + .repeat(1, beam_size, 1, 1) + .view(running_size, maxlen, encoder_dim) + ) # (B*N, maxlen, encoder_dim) + encoder_mask = ( + encoder_mask.unsqueeze(1) + .repeat(1, beam_size, 1, 1) + .view(running_size, 1, maxlen) + ) # (B*N, 1, max_len) + + hyps = torch.ones([running_size, 1], dtype=torch.long, device=device).fill_( + self.sos + ) # (B*N, 1) + scores = torch.tensor( + [0.0] + [-float("inf")] * (beam_size - 1), dtype=torch.float + ) + scores = ( + scores.to(device).repeat([batch_size]).unsqueeze(1).to(device) + ) # (B*N, 1) + end_flag = torch.zeros_like(scores, dtype=torch.bool, device=device) + cache: Optional[List[torch.Tensor]] = None + # 2. Decoder forward step by step + for i in range(1, maxlen + 1): + # Stop if all batch and all beam produce eos + if end_flag.sum() == running_size: + break + # 2.1 Forward decoder step + hyps_mask = ( + subsequent_mask(i).unsqueeze(0).repeat(running_size, 1, 1).to(device) + ) # (B*N, i, i) + # logp: (B*N, vocab) + logp, cache = self.decoder.forward_one_step( + encoder_out, encoder_mask, hyps, hyps_mask, cache + ) + # 2.2 First beam prune: select topk best prob at current time + top_k_logp, top_k_index = logp.topk(beam_size) # (B*N, N) + top_k_logp = mask_finished_scores(top_k_logp, end_flag) + top_k_index = mask_finished_preds(top_k_index, end_flag, self.eos) + # 2.3 Second beam prune: select topk score with history + scores = scores + top_k_logp # (B*N, N), broadcast add + scores = scores.view(batch_size, beam_size * beam_size) # (B, N*N) + scores, offset_k_index = scores.topk(k=beam_size) # (B, N) + # Update cache to be consistent with new topk scores / hyps + cache_index = (offset_k_index // beam_size).view(-1) # (B*N) + base_cache_index = ( + torch.arange(batch_size, device=device) + .view(-1, 1) + .repeat([1, beam_size]) + * beam_size + ).view( + -1 + ) # (B*N) + cache_index = base_cache_index + cache_index + cache = [torch.index_select(c, dim=0, index=cache_index) for c in cache] + scores = scores.view(-1, 1) # (B*N, 1) + # 2.4. Compute base index in top_k_index, + # regard top_k_index as (B*N*N),regard offset_k_index as (B*N), + # then find offset_k_index in top_k_index + base_k_index = ( + torch.arange(batch_size, device=device) + .view(-1, 1) + .repeat([1, beam_size]) + ) # (B, N) + base_k_index = base_k_index * beam_size * beam_size + best_k_index = base_k_index.view(-1) + offset_k_index.view(-1) # (B*N) + + # 2.5 Update best hyps + best_k_pred = torch.index_select( + top_k_index.view(-1), dim=-1, index=best_k_index + ) # (B*N) + best_hyps_index = best_k_index // beam_size + last_best_k_hyps = torch.index_select( + hyps, dim=0, index=best_hyps_index + ) # (B*N, i) + hyps = torch.cat( + (last_best_k_hyps, best_k_pred.view(-1, 1)), dim=1 + ) # (B*N, i+1) + + # 2.6 Update end flag + end_flag = torch.eq(hyps[:, -1], self.eos).view(-1, 1) + + # 3. Select best of best + scores = scores.view(batch_size, beam_size) + # TODO: length normalization + best_scores, best_index = scores.max(dim=-1) + best_hyps_index = ( + best_index + + torch.arange(batch_size, dtype=torch.long, device=device) * beam_size + ) + best_hyps = torch.index_select(hyps, dim=0, index=best_hyps_index) + best_hyps = best_hyps[:, 1:] + return best_hyps, best_scores + + def ctc_greedy_search( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + decoding_chunk_size: int = -1, + num_decoding_left_chunks: int = -1, + simulate_streaming: bool = False, + ) -> List[List[int]]: + """Apply CTC greedy search + + Args: + speech (torch.Tensor): (batch, max_len, feat_dim) + speech_length (torch.Tensor): (batch, ) + beam_size (int): beam size for beam search + decoding_chunk_size (int): decoding chunk for dynamic chunk + trained model. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + 0: used for training, it's prohibited here + simulate_streaming (bool): whether do encoder forward in a + streaming fashion + Returns: + List[List[int]]: best path result + """ + assert speech.shape[0] == speech_lengths.shape[0] + assert decoding_chunk_size != 0 + batch_size = speech.shape[0] + # Let's assume B = batch_size + encoder_out, encoder_mask = self._forward_encoder( + speech, + speech_lengths, + decoding_chunk_size, + num_decoding_left_chunks, + simulate_streaming, + ) # (B, maxlen, encoder_dim) + maxlen = encoder_out.size(1) + encoder_out_lens = encoder_mask.squeeze(1).sum(1) + ctc_probs = self.ctc.log_softmax(encoder_out) # (B, maxlen, vocab_size) + topk_prob, topk_index = ctc_probs.topk(1, dim=2) # (B, maxlen, 1) + topk_index = topk_index.view(batch_size, maxlen) # (B, maxlen) + mask = make_pad_mask(encoder_out_lens, maxlen) # (B, maxlen) + topk_index = topk_index.masked_fill_(mask, self.eos) # (B, maxlen) + hyps = [hyp.tolist() for hyp in topk_index] + scores = topk_prob.max(1) + hyps = [remove_duplicates_and_blank(hyp) for hyp in hyps] + return hyps, scores + + def _ctc_prefix_beam_search( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + beam_size: int, + decoding_chunk_size: int = -1, + num_decoding_left_chunks: int = -1, + simulate_streaming: bool = False, + ) -> Tuple[List[List[int]], torch.Tensor]: + """CTC prefix beam search inner implementation + + Args: + speech (torch.Tensor): (batch, max_len, feat_dim) + speech_length (torch.Tensor): (batch, ) + beam_size (int): beam size for beam search + decoding_chunk_size (int): decoding chunk for dynamic chunk + trained model. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + 0: used for training, it's prohibited here + simulate_streaming (bool): whether do encoder forward in a + streaming fashion + + Returns: + List[List[int]]: nbest results + torch.Tensor: encoder output, (1, max_len, encoder_dim), + it will be used for rescoring in attention rescoring mode + """ + assert speech.shape[0] == speech_lengths.shape[0] + assert decoding_chunk_size != 0 + batch_size = speech.shape[0] + # For CTC prefix beam search, we only support batch_size=1 + assert batch_size == 1 + # Let's assume B = batch_size and N = beam_size + # 1. Encoder forward and get CTC score + encoder_out, encoder_mask = self._forward_encoder( + speech, + speech_lengths, + decoding_chunk_size, + num_decoding_left_chunks, + simulate_streaming, + ) # (B, maxlen, encoder_dim) + maxlen = encoder_out.size(1) + ctc_probs = self.ctc.log_softmax(encoder_out) # (1, maxlen, vocab_size) + ctc_probs = ctc_probs.squeeze(0) + # cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score)) + cur_hyps = [(tuple(), (0.0, -float("inf")))] + # 2. CTC beam search step by step + for t in range(0, maxlen): + logp = ctc_probs[t] # (vocab_size,) + # key: prefix, value (pb, pnb), default value(-inf, -inf) + next_hyps = defaultdict(lambda: (-float("inf"), -float("inf"))) + # 2.1 First beam prune: select topk best + top_k_logp, top_k_index = logp.topk(beam_size) # (beam_size,) + for s in top_k_index: + s = s.item() + ps = logp[s].item() + for prefix, (pb, pnb) in cur_hyps: + last = prefix[-1] if len(prefix) > 0 else None + if s == 0: # blank + n_pb, n_pnb = next_hyps[prefix] + n_pb = log_add([n_pb, pb + ps, pnb + ps]) + next_hyps[prefix] = (n_pb, n_pnb) + elif s == last: + # Update *ss -> *s; + n_pb, n_pnb = next_hyps[prefix] + n_pnb = log_add([n_pnb, pnb + ps]) + next_hyps[prefix] = (n_pb, n_pnb) + # Update *s-s -> *ss, - is for blank + n_prefix = prefix + (s,) + n_pb, n_pnb = next_hyps[n_prefix] + n_pnb = log_add([n_pnb, pb + ps]) + next_hyps[n_prefix] = (n_pb, n_pnb) + else: + n_prefix = prefix + (s,) + n_pb, n_pnb = next_hyps[n_prefix] + n_pnb = log_add([n_pnb, pb + ps, pnb + ps]) + next_hyps[n_prefix] = (n_pb, n_pnb) + + # 2.2 Second beam prune + next_hyps = sorted( + next_hyps.items(), key=lambda x: log_add(list(x[1])), reverse=True + ) + cur_hyps = next_hyps[:beam_size] + hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in cur_hyps] + return hyps, encoder_out + + def ctc_prefix_beam_search( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + beam_size: int, + decoding_chunk_size: int = -1, + num_decoding_left_chunks: int = -1, + simulate_streaming: bool = False, + ) -> List[int]: + """Apply CTC prefix beam search + + Args: + speech (torch.Tensor): (batch, max_len, feat_dim) + speech_length (torch.Tensor): (batch, ) + beam_size (int): beam size for beam search + decoding_chunk_size (int): decoding chunk for dynamic chunk + trained model. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + 0: used for training, it's prohibited here + simulate_streaming (bool): whether do encoder forward in a + streaming fashion + + Returns: + List[int]: CTC prefix beam search nbest results + """ + hyps, _ = self._ctc_prefix_beam_search( + speech, + speech_lengths, + beam_size, + decoding_chunk_size, + num_decoding_left_chunks, + simulate_streaming, + ) + return hyps[0] + + def attention_rescoring( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + beam_size: int, + decoding_chunk_size: int = -1, + num_decoding_left_chunks: int = -1, + ctc_weight: float = 0.0, + simulate_streaming: bool = False, + reverse_weight: float = 0.0, + ) -> List[int]: + """Apply attention rescoring decoding, CTC prefix beam search + is applied first to get nbest, then we resoring the nbest on + attention decoder with corresponding encoder out + + Args: + speech (torch.Tensor): (batch, max_len, feat_dim) + speech_length (torch.Tensor): (batch, ) + beam_size (int): beam size for beam search + decoding_chunk_size (int): decoding chunk for dynamic chunk + trained model. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + 0: used for training, it's prohibited here + simulate_streaming (bool): whether do encoder forward in a + streaming fashion + reverse_weight (float): right to left decoder weight + ctc_weight (float): ctc score weight + + Returns: + List[int]: Attention rescoring result + """ + assert speech.shape[0] == speech_lengths.shape[0] + assert decoding_chunk_size != 0 + if reverse_weight > 0.0: + # decoder should be a bitransformer decoder if reverse_weight > 0.0 + assert hasattr(self.decoder, "right_decoder") + device = speech.device + batch_size = speech.shape[0] + # For attention rescoring we only support batch_size=1 + assert batch_size == 1 + # encoder_out: (1, maxlen, encoder_dim), len(hyps) = beam_size + hyps, encoder_out = self._ctc_prefix_beam_search( + speech, + speech_lengths, + beam_size, + decoding_chunk_size, + num_decoding_left_chunks, + simulate_streaming, + ) + + assert len(hyps) == beam_size + hyps_pad = pad_sequence( + [torch.tensor(hyp[0], device=device, dtype=torch.long) for hyp in hyps], + True, + self.ignore_id, + ) # (beam_size, max_hyps_len) + ori_hyps_pad = hyps_pad + hyps_lens = torch.tensor( + [len(hyp[0]) for hyp in hyps], device=device, dtype=torch.long + ) # (beam_size,) + hyps_pad, _ = add_sos_eos(hyps_pad, self.sos, self.eos, self.ignore_id) + hyps_lens = hyps_lens + 1 # Add at begining + encoder_out = encoder_out.repeat(beam_size, 1, 1) + encoder_mask = torch.ones( + beam_size, 1, encoder_out.size(1), dtype=torch.bool, device=device + ) + # used for right to left decoder + r_hyps_pad = reverse_pad_list(ori_hyps_pad, hyps_lens, self.ignore_id) + r_hyps_pad, _ = add_sos_eos(r_hyps_pad, self.sos, self.eos, self.ignore_id) + decoder_out, r_decoder_out, _ = self.decoder( + encoder_out, encoder_mask, hyps_pad, hyps_lens, r_hyps_pad, reverse_weight + ) # (beam_size, max_hyps_len, vocab_size) + decoder_out = torch.nn.functional.log_softmax(decoder_out, dim=-1) + decoder_out = decoder_out.cpu().numpy() + # r_decoder_out will be 0.0, if reverse_weight is 0.0 or decoder is a + # conventional transformer decoder. + r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1) + r_decoder_out = r_decoder_out.cpu().numpy() + # Only use decoder score for rescoring + best_score = -float("inf") + best_index = 0 + for i, hyp in enumerate(hyps): + score = 0.0 + for j, w in enumerate(hyp[0]): + score += decoder_out[i][j][w] + score += decoder_out[i][len(hyp[0])][self.eos] + # add right to left decoder score + if reverse_weight > 0: + r_score = 0.0 + for j, w in enumerate(hyp[0]): + r_score += r_decoder_out[i][len(hyp[0]) - j - 1][w] + r_score += r_decoder_out[i][len(hyp[0])][self.eos] + score = score * (1 - reverse_weight) + r_score * reverse_weight + # add ctc score + score += hyp[1] * ctc_weight + if score > best_score: + best_score = score + best_index = i + return hyps[best_index][0], best_score + + @torch.jit.unused + def load_lfmmi_resource(self): + with open("{}/tokens.txt".format(self.lfmmi_dir), "r") as fin: + for line in fin: + arr = line.strip().split() + if arr[0] == "": + self.sos_eos_id = int(arr[1]) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.graph_compiler = MmiTrainingGraphCompiler( + self.lfmmi_dir, + device=device, + oov="", + sos_id=self.sos_eos_id, + eos_id=self.sos_eos_id, + ) + self.lfmmi = LFMMILoss( + graph_compiler=self.graph_compiler, + den_scale=1, + use_pruned_intersect=False, + ) + self.word_table = {} + with open("{}/words.txt".format(self.lfmmi_dir), "r") as fin: + for line in fin: + arr = line.strip().split() + assert len(arr) == 2 + self.word_table[int(arr[1])] = arr[0] + + @torch.jit.unused + def _calc_lfmmi_loss(self, encoder_out, encoder_mask, text): + ctc_probs = self.ctc.log_softmax(encoder_out) + supervision_segments = torch.stack( + ( + torch.arange(len(encoder_mask)), + torch.zeros(len(encoder_mask)), + encoder_mask.squeeze(dim=1).sum(dim=1).to("cpu"), + ), + 1, + ).to(torch.int32) + dense_fsa_vec = k2.DenseFsaVec( + ctc_probs, + supervision_segments, + allow_truncate=3, + ) + text = [ + " ".join([self.word_table[j.item()] for j in i if j != -1]) for i in text + ] + loss = self.lfmmi(dense_fsa_vec=dense_fsa_vec, texts=text) / len(text) + return loss + + def load_hlg_resource_if_necessary(self, hlg, word): + if not hasattr(self, "hlg"): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.hlg = k2.Fsa.from_dict(torch.load(hlg, map_location=device)) + if not hasattr(self.hlg, "lm_scores"): + self.hlg.lm_scores = self.hlg.scores.clone() + if not hasattr(self, "word_table"): + self.word_table = {} + with open(word, "r") as fin: + for line in fin: + arr = line.strip().split() + assert len(arr) == 2 + self.word_table[int(arr[1])] = arr[0] + + @torch.no_grad() + def hlg_onebest( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + decoding_chunk_size: int = -1, + num_decoding_left_chunks: int = -1, + simulate_streaming: bool = False, + hlg: str = "", + word: str = "", + symbol_table: Dict[str, int] = None, + ) -> List[int]: + self.load_hlg_resource_if_necessary(hlg, word) + encoder_out, encoder_mask = self._forward_encoder( + speech, + speech_lengths, + decoding_chunk_size, + num_decoding_left_chunks, + simulate_streaming, + ) # (B, maxlen, encoder_dim) + ctc_probs = self.ctc.log_softmax(encoder_out) # (1, maxlen, vocab_size) + supervision_segments = torch.stack( + ( + torch.arange(len(encoder_mask)), + torch.zeros(len(encoder_mask)), + encoder_mask.squeeze(dim=1).sum(dim=1).cpu(), + ), + 1, + ).to(torch.int32) + lattice = get_lattice( + nnet_output=ctc_probs, + decoding_graph=self.hlg, + supervision_segments=supervision_segments, + search_beam=20, + output_beam=7, + min_active_states=30, + max_active_states=10000, + subsampling_factor=4, + ) + best_path = one_best_decoding(lattice=lattice, use_double_scores=True) + hyps = get_texts(best_path) + hyps = [[symbol_table[k] for j in i for k in self.word_table[j]] for i in hyps] + return hyps + + @torch.no_grad() + def hlg_rescore( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + decoding_chunk_size: int = -1, + num_decoding_left_chunks: int = -1, + simulate_streaming: bool = False, + lm_scale: float = 0, + decoder_scale: float = 0, + r_decoder_scale: float = 0, + hlg: str = "", + word: str = "", + symbol_table: Dict[str, int] = None, + ) -> List[int]: + self.load_hlg_resource_if_necessary(hlg, word) + device = speech.device + encoder_out, encoder_mask = self._forward_encoder( + speech, + speech_lengths, + decoding_chunk_size, + num_decoding_left_chunks, + simulate_streaming, + ) # (B, maxlen, encoder_dim) + ctc_probs = self.ctc.log_softmax(encoder_out) # (1, maxlen, vocab_size) + supervision_segments = torch.stack( + ( + torch.arange(len(encoder_mask)), + torch.zeros(len(encoder_mask)), + encoder_mask.squeeze(dim=1).sum(dim=1).cpu(), + ), + 1, + ).to(torch.int32) + lattice = get_lattice( + nnet_output=ctc_probs, + decoding_graph=self.hlg, + supervision_segments=supervision_segments, + search_beam=20, + output_beam=7, + min_active_states=30, + max_active_states=10000, + subsampling_factor=4, + ) + nbest = Nbest.from_lattice( + lattice=lattice, + num_paths=100, + use_double_scores=True, + nbest_scale=0.5, + ) + nbest = nbest.intersect(lattice) + assert hasattr(nbest.fsa, "lm_scores") + assert hasattr(nbest.fsa, "tokens") + assert isinstance(nbest.fsa.tokens, torch.Tensor) + + tokens_shape = nbest.fsa.arcs.shape().remove_axis(1) + tokens = k2.RaggedTensor(tokens_shape, nbest.fsa.tokens) + tokens = tokens.remove_values_leq(0) + hyps = tokens.tolist() + + # cal attention_score + hyps_pad = pad_sequence( + [torch.tensor(hyp, device=device, dtype=torch.long) for hyp in hyps], + True, + self.ignore_id, + ) # (beam_size, max_hyps_len) + ori_hyps_pad = hyps_pad + hyps_lens = torch.tensor( + [len(hyp) for hyp in hyps], device=device, dtype=torch.long + ) # (beam_size,) + hyps_pad, _ = add_sos_eos(hyps_pad, self.sos, self.eos, self.ignore_id) + hyps_lens = hyps_lens + 1 # Add at begining + encoder_out_repeat = [] + tot_scores = nbest.tot_scores() + repeats = [tot_scores[i].shape[0] for i in range(tot_scores.dim0)] + for i in range(len(encoder_out)): + encoder_out_repeat.append(encoder_out[i : i + 1].repeat(repeats[i], 1, 1)) + encoder_out = torch.concat(encoder_out_repeat, dim=0) + encoder_mask = torch.ones( + encoder_out.size(0), 1, encoder_out.size(1), dtype=torch.bool, device=device + ) + # used for right to left decoder + r_hyps_pad = reverse_pad_list(ori_hyps_pad, hyps_lens, self.ignore_id) + r_hyps_pad, _ = add_sos_eos(r_hyps_pad, self.sos, self.eos, self.ignore_id) + reverse_weight = 0.5 + decoder_out, r_decoder_out, _ = self.decoder( + encoder_out, encoder_mask, hyps_pad, hyps_lens, r_hyps_pad, reverse_weight + ) # (beam_size, max_hyps_len, vocab_size) + decoder_out = torch.nn.functional.log_softmax(decoder_out, dim=-1) + decoder_out = decoder_out + # r_decoder_out will be 0.0, if reverse_weight is 0.0 or decoder is a + # conventional transformer decoder. + r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1) + r_decoder_out = r_decoder_out + + decoder_scores = torch.tensor( + [ + sum([decoder_out[i, j, hyps[i][j]] for j in range(len(hyps[i]))]) + for i in range(len(hyps)) + ], + device=device, + ) + r_decoder_scores = [] + for i in range(len(hyps)): + score = 0 + for j in range(len(hyps[i])): + score += r_decoder_out[i, len(hyps[i]) - j - 1, hyps[i][j]] + score += r_decoder_out[i, len(hyps[i]), self.eos] + r_decoder_scores.append(score) + r_decoder_scores = torch.tensor(r_decoder_scores, device=device) + + am_scores = nbest.compute_am_scores() + ngram_lm_scores = nbest.compute_lm_scores() + tot_scores = ( + am_scores.values + + lm_scale * ngram_lm_scores.values + + decoder_scale * decoder_scores + + r_decoder_scale * r_decoder_scores + ) + ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores) + max_indexes = ragged_tot_scores.argmax() + best_path = k2.index_fsa(nbest.fsa, max_indexes) + hyps = get_texts(best_path) + hyps = [[symbol_table[k] for j in i for k in self.word_table[j]] for i in hyps] + return hyps + + @torch.jit.export + def subsampling_rate(self) -> int: + """Export interface for c++ call, return subsampling_rate of the + model + """ + return self.encoder.embed.subsampling_rate + + @torch.jit.export + def right_context(self) -> int: + """Export interface for c++ call, return right_context of the model""" + return self.encoder.embed.right_context + + @torch.jit.export + def sos_symbol(self) -> int: + """Export interface for c++ call, return sos symbol id of the model""" + return self.sos + + @torch.jit.export + def eos_symbol(self) -> int: + """Export interface for c++ call, return eos symbol id of the model""" + return self.eos + + @torch.jit.export + def forward_encoder_chunk( + self, + xs: torch.Tensor, + offset: int, + required_cache_size: int, + att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), + cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ Export interface for c++ call, give input chunk xs, and return + output from time 0 to current chunk. + + Args: + xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim), + where `time == (chunk_size - 1) * subsample_rate + \ + subsample.right_context + 1` + offset (int): current offset in encoder output time stamp + required_cache_size (int): cache size required for next chunk + compuation + >=0: actual cache size + <0: means all history cache is required + att_cache (torch.Tensor): cache tensor for KEY & VALUE in + transformer/conformer attention, with shape + (elayers, head, cache_t1, d_k * 2), where + `head * d_k == hidden-dim` and + `cache_t1 == chunk_size * num_decoding_left_chunks`. + cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, + (elayers, b=1, hidden-dim, cache_t2), where + `cache_t2 == cnn.lorder - 1` + + Returns: + torch.Tensor: output of current input xs, + with shape (b=1, chunk_size, hidden-dim). + torch.Tensor: new attention cache required for next chunk, with + dynamic shape (elayers, head, ?, d_k * 2) + depending on required_cache_size. + torch.Tensor: new conformer cnn cache required for next chunk, with + same shape as the original cnn_cache. + + """ + return self.encoder.forward_chunk( + xs, offset, required_cache_size, att_cache, cnn_cache + ) + + @torch.jit.export + def ctc_activation(self, xs: torch.Tensor) -> torch.Tensor: + """Export interface for c++ call, apply linear transform and log + softmax before ctc + Args: + xs (torch.Tensor): encoder output + + Returns: + torch.Tensor: activation before ctc + + """ + return self.ctc.log_softmax(xs) + + @torch.jit.export + def is_bidirectional_decoder(self) -> bool: + """ + Returns: + torch.Tensor: decoder output + """ + if hasattr(self.decoder, "right_decoder"): + return True + else: + return False + + @torch.jit.export + def forward_attention_decoder( + self, + hyps: torch.Tensor, + hyps_lens: torch.Tensor, + encoder_out: torch.Tensor, + reverse_weight: float = 0, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Export interface for c++ call, forward decoder with multiple + hypothesis from ctc prefix beam search and one encoder output + Args: + hyps (torch.Tensor): hyps from ctc prefix beam search, already + pad sos at the begining + hyps_lens (torch.Tensor): length of each hyp in hyps + encoder_out (torch.Tensor): corresponding encoder output + r_hyps (torch.Tensor): hyps from ctc prefix beam search, already + pad eos at the begining which is used fo right to left decoder + reverse_weight: used for verfing whether used right to left decoder, + > 0 will use. + + Returns: + torch.Tensor: decoder output + """ + assert encoder_out.size(0) == 1 + num_hyps = hyps.size(0) + assert hyps_lens.size(0) == num_hyps + encoder_out = encoder_out.repeat(num_hyps, 1, 1) + encoder_mask = torch.ones( + num_hyps, + 1, + encoder_out.size(1), + dtype=torch.bool, + device=encoder_out.device, + ) + + # input for right to left decoder + # this hyps_lens has count token, we need minus it. + r_hyps_lens = hyps_lens - 1 + # this hyps has included token, so it should be + # convert the original hyps. + r_hyps = hyps[:, 1:] + # >>> r_hyps + # >>> tensor([[ 1, 2, 3], + # >>> [ 9, 8, 4], + # >>> [ 2, -1, -1]]) + # >>> r_hyps_lens + # >>> tensor([3, 3, 1]) + + # NOTE(Mddct): `pad_sequence` is not supported by ONNX, it is used + # in `reverse_pad_list` thus we have to refine the below code. + # Issue: https://github.com/wenet-e2e/wenet/issues/1113 + # Equal to: + # >>> r_hyps = reverse_pad_list(r_hyps, r_hyps_lens, float(self.ignore_id)) + # >>> r_hyps, _ = add_sos_eos(r_hyps, self.sos, self.eos, self.ignore_id) + max_len = torch.max(r_hyps_lens) + index_range = torch.arange(0, max_len, 1).to(encoder_out.device) + seq_len_expand = r_hyps_lens.unsqueeze(1) + seq_mask = seq_len_expand > index_range # (beam, max_len) + # >>> seq_mask + # >>> tensor([[ True, True, True], + # >>> [ True, True, True], + # >>> [ True, False, False]]) + index = (seq_len_expand - 1) - index_range # (beam, max_len) + # >>> index + # >>> tensor([[ 2, 1, 0], + # >>> [ 2, 1, 0], + # >>> [ 0, -1, -2]]) + index = index * seq_mask + # >>> index + # >>> tensor([[2, 1, 0], + # >>> [2, 1, 0], + # >>> [0, 0, 0]]) + r_hyps = torch.gather(r_hyps, 1, index) + # >>> r_hyps + # >>> tensor([[3, 2, 1], + # >>> [4, 8, 9], + # >>> [2, 2, 2]]) + r_hyps = torch.where(seq_mask, r_hyps, self.eos) + # >>> r_hyps + # >>> tensor([[3, 2, 1], + # >>> [4, 8, 9], + # >>> [2, eos, eos]]) + r_hyps = torch.cat([hyps[:, 0:1], r_hyps], dim=1) + # >>> r_hyps + # >>> tensor([[sos, 3, 2, 1], + # >>> [sos, 4, 8, 9], + # >>> [sos, 2, eos, eos]]) + + decoder_out, r_decoder_out, _ = self.decoder( + encoder_out, encoder_mask, hyps, hyps_lens, r_hyps, reverse_weight + ) # (num_hyps, max_hyps_len, vocab_size) + decoder_out = torch.nn.functional.log_softmax(decoder_out, dim=-1) + + # right to left decoder may be not used during decoding process, + # which depends on reverse_weight param. + # r_dccoder_out will be 0.0, if reverse_weight is 0.0 + r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1) + return decoder_out, r_decoder_out diff --git a/modules/wenet_extractor/transformer/attention.py b/modules/wenet_extractor/transformer/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..a13c0ce0aa408166a990fba119ba2e167f2ee868 --- /dev/null +++ b/modules/wenet_extractor/transformer/attention.py @@ -0,0 +1,326 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""Multi-Head Attention layer definition.""" + +import math +from typing import Tuple + +import torch +from torch import nn + + +class MultiHeadedAttention(nn.Module): + """Multi-Head Attention layer. + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, n_head: int, n_feat: int, dropout_rate: float): + """Construct an MultiHeadedAttention object.""" + super().__init__() + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.h = n_head + self.linear_q = nn.Linear(n_feat, n_feat) + self.linear_k = nn.Linear(n_feat, n_feat) + self.linear_v = nn.Linear(n_feat, n_feat) + self.linear_out = nn.Linear(n_feat, n_feat) + self.dropout = nn.Dropout(p=dropout_rate) + + def forward_qkv( + self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Transform query, key and value. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + + Returns: + torch.Tensor: Transformed query tensor, size + (#batch, n_head, time1, d_k). + torch.Tensor: Transformed key tensor, size + (#batch, n_head, time2, d_k). + torch.Tensor: Transformed value tensor, size + (#batch, n_head, time2, d_k). + + """ + n_batch = query.size(0) + q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) + k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) + v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) + q = q.transpose(1, 2) # (batch, head, time1, d_k) + k = k.transpose(1, 2) # (batch, head, time2, d_k) + v = v.transpose(1, 2) # (batch, head, time2, d_k) + + return q, k, v + + def forward_attention( + self, + value: torch.Tensor, + scores: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + ) -> torch.Tensor: + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value, size + (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score, size + (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask, size (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + # NOTE(xcsong): When will `if mask.size(2) > 0` be True? + # 1. onnx(16/4) [WHY? Because we feed real cache & real mask for the + # 1st chunk to ease the onnx export.] + # 2. pytorch training + if mask.size(2) > 0: # time2 > 0 + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + # For last chunk, time2 might be larger than scores.size(-1) + mask = mask[:, :, :, : scores.size(-1)] # (batch, 1, *, time2) + scores = scores.masked_fill(mask, -float("inf")) + attn = torch.softmax(scores, dim=-1).masked_fill( + mask, 0.0 + ) # (batch, head, time1, time2) + # NOTE(xcsong): When will `if mask.size(2) > 0` be False? + # 1. onnx(16/-1, -1/-1, 16/0) + # 2. jit (16/-1, -1/-1, 16/0, 16/4) + else: + attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = ( + x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + 1.When applying cross attention between decoder and encoder, + the batch padding mask for input is in (#batch, 1, T) shape. + 2.When applying self attention of encoder, + the mask is in (#batch, T, T) shape. + 3.When applying self attention of decoder, + the mask is in (#batch, L, L) shape. + 4.If the different position in decoder see different block + of the encoder, such as Mocha, the passed in mask could be + in (#batch, L, T) shape. But there is no such case in current + Wenet. + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + + """ + q, k, v = self.forward_qkv(query, key, value) + + # NOTE(xcsong): + # when export onnx model, for 1st chunk, we feed + # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode) + # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode). + # In all modes, `if cache.size(0) > 0` will alwayse be `True` + # and we will always do splitting and + # concatnation(this will simplify onnx export). Note that + # it's OK to concat & split zero-shaped tensors(see code below). + # when export jit model, for 1st chunk, we always feed + # cache(0, 0, 0, 0) since jit supports dynamic if-branch. + # >>> a = torch.ones((1, 2, 0, 4)) + # >>> b = torch.ones((1, 2, 3, 4)) + # >>> c = torch.cat((a, b), dim=2) + # >>> torch.equal(b, c) # True + # >>> d = torch.split(a, 2, dim=-1) + # >>> torch.equal(d[0], d[1]) # True + if cache.size(0) > 0: + key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's + # non-trivial to calculate `next_cache_start` here. + new_cache = torch.cat((k, v), dim=-1) + + scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) + return self.forward_attention(v, scores, mask), new_cache + + +class RelPositionMultiHeadedAttention(MultiHeadedAttention): + """Multi-Head Attention layer with relative position encoding. + Paper: https://arxiv.org/abs/1901.02860 + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + """ + + def __init__(self, n_head, n_feat, dropout_rate): + """Construct an RelPositionMultiHeadedAttention object.""" + super().__init__(n_head, n_feat, dropout_rate) + # linear transformation for positional encoding + self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) + # these two learnable bias are used in matrix c and matrix d + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k)) + self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k)) + torch.nn.init.xavier_uniform_(self.pos_bias_u) + torch.nn.init.xavier_uniform_(self.pos_bias_v) + + def rel_shift(self, x, zero_triu: bool = False): + """Compute relative positinal encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, size). + zero_triu (bool): If true, return the lower triangular part of + the matrix. + Returns: + torch.Tensor: Output tensor. + """ + + zero_pad = torch.zeros( + (x.size()[0], x.size()[1], x.size()[2], 1), device=x.device, dtype=x.dtype + ) + x_padded = torch.cat([zero_pad, x], dim=-1) + + x_padded = x_padded.view(x.size()[0], x.size()[1], x.size(3) + 1, x.size(2)) + x = x_padded[:, :, 1:].view_as(x) + + if zero_triu: + ones = torch.ones((x.size(2), x.size(3))) + x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :] + + return x + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute 'Scaled Dot Product Attention' with rel. positional encoding. + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): Positional embedding tensor + (#batch, time2, size). + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + """ + q, k, v = self.forward_qkv(query, key, value) + q = q.transpose(1, 2) # (batch, time1, head, d_k) + + # NOTE(xcsong): + # when export onnx model, for 1st chunk, we feed + # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode) + # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode). + # In all modes, `if cache.size(0) > 0` will alwayse be `True` + # and we will always do splitting and + # concatnation(this will simplify onnx export). Note that + # it's OK to concat & split zero-shaped tensors(see code below). + # when export jit model, for 1st chunk, we always feed + # cache(0, 0, 0, 0) since jit supports dynamic if-branch. + # >>> a = torch.ones((1, 2, 0, 4)) + # >>> b = torch.ones((1, 2, 3, 4)) + # >>> c = torch.cat((a, b), dim=2) + # >>> torch.equal(b, c) # True + # >>> d = torch.split(a, 2, dim=-1) + # >>> torch.equal(d[0], d[1]) # True + if cache.size(0) > 0: + key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's + # non-trivial to calculate `next_cache_start` here. + new_cache = torch.cat((k, v), dim=-1) + + n_batch_pos = pos_emb.size(0) + p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) + p = p.transpose(1, 2) # (batch, head, time1, d_k) + + # (batch, head, time1, d_k) + q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) + # (batch, head, time1, d_k) + q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) + + # compute attention score + # first compute matrix a and matrix c + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + # (batch, head, time1, time2) + matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) + + # compute matrix b and matrix d + # (batch, head, time1, time2) + matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + # Remove rel_shift since it is useless in speech recognition, + # and it requires special attention for streaming. + # matrix_bd = self.rel_shift(matrix_bd) + + scores = (matrix_ac + matrix_bd) / math.sqrt( + self.d_k + ) # (batch, head, time1, time2) + + return self.forward_attention(v, scores, mask), new_cache diff --git a/modules/wenet_extractor/transformer/cmvn.py b/modules/wenet_extractor/transformer/cmvn.py new file mode 100644 index 0000000000000000000000000000000000000000..d97f8924e7bf0db25d475be79256086682aa9f6a --- /dev/null +++ b/modules/wenet_extractor/transformer/cmvn.py @@ -0,0 +1,51 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +import torch + + +class GlobalCMVN(torch.nn.Module): + def __init__(self, mean: torch.Tensor, istd: torch.Tensor, norm_var: bool = True): + """ + Args: + mean (torch.Tensor): mean stats + istd (torch.Tensor): inverse std, std which is 1.0 / std + """ + super().__init__() + assert mean.shape == istd.shape + self.norm_var = norm_var + # The buffer can be accessed from this module using self.mean + self.register_buffer("mean", mean) + self.register_buffer("istd", istd) + + def forward(self, x: torch.Tensor): + """ + Args: + x (torch.Tensor): (batch, max_len, feat_dim) + + Returns: + (torch.Tensor): normalized feature + """ + x = x - self.mean + if self.norm_var: + x = x * self.istd + return x diff --git a/modules/wenet_extractor/transformer/convolution.py b/modules/wenet_extractor/transformer/convolution.py new file mode 100644 index 0000000000000000000000000000000000000000..be1148386f8aab6c1b01244f4a84c7e90aba7952 --- /dev/null +++ b/modules/wenet_extractor/transformer/convolution.py @@ -0,0 +1,154 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""ConvolutionModule definition.""" + +from typing import Tuple + +import torch +from torch import nn + + +class ConvolutionModule(nn.Module): + """ConvolutionModule in Conformer model.""" + + def __init__( + self, + channels: int, + kernel_size: int = 15, + activation: nn.Module = nn.ReLU(), + norm: str = "batch_norm", + causal: bool = False, + bias: bool = True, + ): + """Construct an ConvolutionModule object. + Args: + channels (int): The number of channels of conv layers. + kernel_size (int): Kernel size of conv layers. + causal (int): Whether use causal convolution or not + """ + super().__init__() + + self.pointwise_conv1 = nn.Conv1d( + channels, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + # self.lorder is used to distinguish if it's a causal convolution, + # if self.lorder > 0: it's a causal convolution, the input will be + # padded with self.lorder frames on the left in forward. + # else: it's a symmetrical convolution + if causal: + padding = 0 + self.lorder = kernel_size - 1 + else: + # kernel_size should be an odd number for none causal convolution + assert (kernel_size - 1) % 2 == 0 + padding = (kernel_size - 1) // 2 + self.lorder = 0 + self.depthwise_conv = nn.Conv1d( + channels, + channels, + kernel_size, + stride=1, + padding=padding, + groups=channels, + bias=bias, + ) + + assert norm in ["batch_norm", "layer_norm"] + if norm == "batch_norm": + self.use_layer_norm = False + self.norm = nn.BatchNorm1d(channels) + else: + self.use_layer_norm = True + self.norm = nn.LayerNorm(channels) + + self.pointwise_conv2 = nn.Conv1d( + channels, + channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + self.activation = activation + + def forward( + self, + x: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + cache: torch.Tensor = torch.zeros((0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute convolution module. + Args: + x (torch.Tensor): Input tensor (#batch, time, channels). + mask_pad (torch.Tensor): used for batch padding (#batch, 1, time), + (0, 0, 0) means fake mask. + cache (torch.Tensor): left context cache, it is only + used in causal convolution (#batch, channels, cache_t), + (0, 0, 0) meas fake cache. + Returns: + torch.Tensor: Output tensor (#batch, time, channels). + """ + # exchange the temporal dimension and the feature dimension + x = x.transpose(1, 2) # (#batch, channels, time) + + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + x.masked_fill_(~mask_pad, 0.0) + + if self.lorder > 0: + if cache.size(2) == 0: # cache_t == 0 + x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0) + else: + assert cache.size(0) == x.size(0) # equal batch + assert cache.size(1) == x.size(1) # equal channel + x = torch.cat((cache, x), dim=2) + assert x.size(2) > self.lorder + new_cache = x[:, :, -self.lorder :] + else: + # It's better we just return None if no cache is required, + # However, for JIT export, here we just fake one tensor instead of + # None. + new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channel, dim) + x = nn.functional.glu(x, dim=1) # (batch, channel, dim) + + # 1D Depthwise Conv + x = self.depthwise_conv(x) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.activation(self.norm(x)) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.pointwise_conv2(x) + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + x.masked_fill_(~mask_pad, 0.0) + + return x.transpose(1, 2), new_cache diff --git a/modules/wenet_extractor/transformer/ctc.py b/modules/wenet_extractor/transformer/ctc.py new file mode 100644 index 0000000000000000000000000000000000000000..3cbf13a83137642a61b2c46033907ca22d9d6498 --- /dev/null +++ b/modules/wenet_extractor/transformer/ctc.py @@ -0,0 +1,95 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +import torch +import torch.nn.functional as F + + +class CTC(torch.nn.Module): + """CTC module""" + + def __init__( + self, + odim: int, + encoder_output_size: int, + dropout_rate: float = 0.0, + reduce: bool = True, + ): + """Construct CTC module + Args: + odim: dimension of outputs + encoder_output_size: number of encoder projection units + dropout_rate: dropout rate (0.0 ~ 1.0) + reduce: reduce the CTC loss into a scalar + """ + super().__init__() + eprojs = encoder_output_size + self.dropout_rate = dropout_rate + self.ctc_lo = torch.nn.Linear(eprojs, odim) + + reduction_type = "sum" if reduce else "none" + self.ctc_loss = torch.nn.CTCLoss(reduction=reduction_type) + + def forward( + self, + hs_pad: torch.Tensor, + hlens: torch.Tensor, + ys_pad: torch.Tensor, + ys_lens: torch.Tensor, + ) -> torch.Tensor: + """Calculate CTC loss. + + Args: + hs_pad: batch of padded hidden state sequences (B, Tmax, D) + hlens: batch of lengths of hidden state sequences (B) + ys_pad: batch of padded character id sequence tensor (B, Lmax) + ys_lens: batch of lengths of character sequence (B) + """ + # hs_pad: (B, L, NProj) -> ys_hat: (B, L, Nvocab) + ys_hat = self.ctc_lo(F.dropout(hs_pad, p=self.dropout_rate)) + # ys_hat: (B, L, D) -> (L, B, D) + ys_hat = ys_hat.transpose(0, 1) + ys_hat = ys_hat.log_softmax(2) + loss = self.ctc_loss(ys_hat, ys_pad, hlens, ys_lens) + # Batch-size average + loss = loss / ys_hat.size(1) + return loss + + def log_softmax(self, hs_pad: torch.Tensor) -> torch.Tensor: + """log_softmax of frame activations + + Args: + Tensor hs_pad: 3d tensor (B, Tmax, eprojs) + Returns: + torch.Tensor: log softmax applied 3d tensor (B, Tmax, odim) + """ + return F.log_softmax(self.ctc_lo(hs_pad), dim=2) + + def argmax(self, hs_pad: torch.Tensor) -> torch.Tensor: + """argmax of frame activations + + Args: + torch.Tensor hs_pad: 3d tensor (B, Tmax, eprojs) + Returns: + torch.Tensor: argmax applied 2d tensor (B, Tmax) + """ + return torch.argmax(self.ctc_lo(hs_pad), dim=2) diff --git a/modules/wenet_extractor/transformer/decoder.py b/modules/wenet_extractor/transformer/decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..8312769ad80d5ff06132bc6c5f440ad63b2d53d4 --- /dev/null +++ b/modules/wenet_extractor/transformer/decoder.py @@ -0,0 +1,323 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# +"""Decoder definition.""" +from typing import Tuple, List, Optional + +import torch + +from modules.wenet_extractor.transformer.attention import MultiHeadedAttention +from modules.wenet_extractor.transformer.decoder_layer import DecoderLayer +from modules.wenet_extractor.transformer.embedding import PositionalEncoding +from modules.wenet_extractor.transformer.embedding import NoPositionalEncoding +from modules.wenet_extractor.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, +) +from modules.wenet_extractor.utils.mask import subsequent_mask, make_pad_mask + + +class TransformerDecoder(torch.nn.Module): + """Base class of Transfomer decoder module. + Args: + vocab_size: output dim + encoder_output_size: dimension of attention + attention_heads: the number of heads of multi head attention + linear_units: the hidden units number of position-wise feedforward + num_blocks: the number of decoder blocks + dropout_rate: dropout rate + self_attention_dropout_rate: dropout rate for attention + input_layer: input layer type + use_output_layer: whether to use output layer + pos_enc_class: PositionalEncoding or ScaledPositionalEncoding + normalize_before: + True: use layer_norm before each sub-block of a layer. + False: use layer_norm after each sub-block of a layer. + src_attention: if false, encoder-decoder cross attention is not + applied, such as CIF model + """ + + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + normalize_before: bool = True, + src_attention: bool = True, + ): + super().__init__() + attention_dim = encoder_output_size + + if input_layer == "embed": + self.embed = torch.nn.Sequential( + torch.nn.Embedding(vocab_size, attention_dim), + PositionalEncoding(attention_dim, positional_dropout_rate), + ) + elif input_layer == "none": + self.embed = NoPositionalEncoding(attention_dim, positional_dropout_rate) + else: + raise ValueError(f"only 'embed' is supported: {input_layer}") + + self.normalize_before = normalize_before + self.after_norm = torch.nn.LayerNorm(attention_dim, eps=1e-5) + self.use_output_layer = use_output_layer + self.output_layer = torch.nn.Linear(attention_dim, vocab_size) + self.num_blocks = num_blocks + self.decoders = torch.nn.ModuleList( + [ + DecoderLayer( + attention_dim, + MultiHeadedAttention( + attention_heads, attention_dim, self_attention_dropout_rate + ), + MultiHeadedAttention( + attention_heads, attention_dim, src_attention_dropout_rate + ) + if src_attention + else None, + PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), + dropout_rate, + normalize_before, + ) + for _ in range(self.num_blocks) + ] + ) + + def forward( + self, + memory: torch.Tensor, + memory_mask: torch.Tensor, + ys_in_pad: torch.Tensor, + ys_in_lens: torch.Tensor, + r_ys_in_pad: torch.Tensor = torch.empty(0), + reverse_weight: float = 0.0, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Forward decoder. + Args: + memory: encoded memory, float32 (batch, maxlen_in, feat) + memory_mask: encoder memory mask, (batch, 1, maxlen_in) + ys_in_pad: padded input token ids, int64 (batch, maxlen_out) + ys_in_lens: input lengths of this batch (batch) + r_ys_in_pad: not used in transformer decoder, in order to unify api + with bidirectional decoder + reverse_weight: not used in transformer decoder, in order to unify + api with bidirectional decode + Returns: + (tuple): tuple containing: + x: decoded token score before softmax (batch, maxlen_out, + vocab_size) if use_output_layer is True, + torch.tensor(0.0), in order to unify api with bidirectional decoder + olens: (batch, ) + """ + tgt = ys_in_pad + maxlen = tgt.size(1) + # tgt_mask: (B, 1, L) + tgt_mask = ~make_pad_mask(ys_in_lens, maxlen).unsqueeze(1) + tgt_mask = tgt_mask.to(tgt.device) + # m: (1, L, L) + m = subsequent_mask(tgt_mask.size(-1), device=tgt_mask.device).unsqueeze(0) + # tgt_mask: (B, L, L) + tgt_mask = tgt_mask & m + x, _ = self.embed(tgt) + for layer in self.decoders: + x, tgt_mask, memory, memory_mask = layer(x, tgt_mask, memory, memory_mask) + if self.normalize_before: + x = self.after_norm(x) + if self.use_output_layer: + x = self.output_layer(x) + olens = tgt_mask.sum(1) + return x, torch.tensor(0.0), olens + + def forward_one_step( + self, + memory: torch.Tensor, + memory_mask: torch.Tensor, + tgt: torch.Tensor, + tgt_mask: torch.Tensor, + cache: Optional[List[torch.Tensor]] = None, + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """Forward one step. + This is only used for decoding. + Args: + memory: encoded memory, float32 (batch, maxlen_in, feat) + memory_mask: encoded memory mask, (batch, 1, maxlen_in) + tgt: input token ids, int64 (batch, maxlen_out) + tgt_mask: input token mask, (batch, maxlen_out) + dtype=torch.uint8 in PyTorch 1.2- + dtype=torch.bool in PyTorch 1.2+ (include 1.2) + cache: cached output list of (batch, max_time_out-1, size) + Returns: + y, cache: NN output value and cache per `self.decoders`. + y.shape` is (batch, maxlen_out, token) + """ + x, _ = self.embed(tgt) + new_cache = [] + for i, decoder in enumerate(self.decoders): + if cache is None: + c = None + else: + c = cache[i] + x, tgt_mask, memory, memory_mask = decoder( + x, tgt_mask, memory, memory_mask, cache=c + ) + new_cache.append(x) + if self.normalize_before: + y = self.after_norm(x[:, -1]) + else: + y = x[:, -1] + if self.use_output_layer: + y = torch.log_softmax(self.output_layer(y), dim=-1) + return y, new_cache + + +class BiTransformerDecoder(torch.nn.Module): + """Base class of Transfomer decoder module. + Args: + vocab_size: output dim + encoder_output_size: dimension of attention + attention_heads: the number of heads of multi head attention + linear_units: the hidden units number of position-wise feedforward + num_blocks: the number of decoder blocks + r_num_blocks: the number of right to left decoder blocks + dropout_rate: dropout rate + self_attention_dropout_rate: dropout rate for attention + input_layer: input layer type + use_output_layer: whether to use output layer + pos_enc_class: PositionalEncoding or ScaledPositionalEncoding + normalize_before: + True: use layer_norm before each sub-block of a layer. + False: use layer_norm after each sub-block of a layer. + """ + + def __init__( + self, + vocab_size: int, + encoder_output_size: int, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + r_num_blocks: int = 0, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + self_attention_dropout_rate: float = 0.0, + src_attention_dropout_rate: float = 0.0, + input_layer: str = "embed", + use_output_layer: bool = True, + normalize_before: bool = True, + ): + super().__init__() + self.left_decoder = TransformerDecoder( + vocab_size, + encoder_output_size, + attention_heads, + linear_units, + num_blocks, + dropout_rate, + positional_dropout_rate, + self_attention_dropout_rate, + src_attention_dropout_rate, + input_layer, + use_output_layer, + normalize_before, + ) + + self.right_decoder = TransformerDecoder( + vocab_size, + encoder_output_size, + attention_heads, + linear_units, + r_num_blocks, + dropout_rate, + positional_dropout_rate, + self_attention_dropout_rate, + src_attention_dropout_rate, + input_layer, + use_output_layer, + normalize_before, + ) + + def forward( + self, + memory: torch.Tensor, + memory_mask: torch.Tensor, + ys_in_pad: torch.Tensor, + ys_in_lens: torch.Tensor, + r_ys_in_pad: torch.Tensor, + reverse_weight: float = 0.0, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Forward decoder. + Args: + memory: encoded memory, float32 (batch, maxlen_in, feat) + memory_mask: encoder memory mask, (batch, 1, maxlen_in) + ys_in_pad: padded input token ids, int64 (batch, maxlen_out) + ys_in_lens: input lengths of this batch (batch) + r_ys_in_pad: padded input token ids, int64 (batch, maxlen_out), + used for right to left decoder + reverse_weight: used for right to left decoder + Returns: + (tuple): tuple containing: + x: decoded token score before softmax (batch, maxlen_out, + vocab_size) if use_output_layer is True, + r_x: x: decoded token score (right to left decoder) + before softmax (batch, maxlen_out, vocab_size) + if use_output_layer is True, + olens: (batch, ) + """ + l_x, _, olens = self.left_decoder(memory, memory_mask, ys_in_pad, ys_in_lens) + r_x = torch.tensor(0.0) + if reverse_weight > 0.0: + r_x, _, olens = self.right_decoder( + memory, memory_mask, r_ys_in_pad, ys_in_lens + ) + return l_x, r_x, olens + + def forward_one_step( + self, + memory: torch.Tensor, + memory_mask: torch.Tensor, + tgt: torch.Tensor, + tgt_mask: torch.Tensor, + cache: Optional[List[torch.Tensor]] = None, + ) -> Tuple[torch.Tensor, List[torch.Tensor]]: + """Forward one step. + This is only used for decoding. + Args: + memory: encoded memory, float32 (batch, maxlen_in, feat) + memory_mask: encoded memory mask, (batch, 1, maxlen_in) + tgt: input token ids, int64 (batch, maxlen_out) + tgt_mask: input token mask, (batch, maxlen_out) + dtype=torch.uint8 in PyTorch 1.2- + dtype=torch.bool in PyTorch 1.2+ (include 1.2) + cache: cached output list of (batch, max_time_out-1, size) + Returns: + y, cache: NN output value and cache per `self.decoders`. + y.shape` is (batch, maxlen_out, token) + """ + return self.left_decoder.forward_one_step( + memory, memory_mask, tgt, tgt_mask, cache + ) diff --git a/modules/wenet_extractor/transformer/decoder_layer.py b/modules/wenet_extractor/transformer/decoder_layer.py new file mode 100644 index 0000000000000000000000000000000000000000..fc4171c47d4cccddade323f04ee1730b98feff76 --- /dev/null +++ b/modules/wenet_extractor/transformer/decoder_layer.py @@ -0,0 +1,140 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""Decoder self-attention layer definition.""" +from typing import Optional, Tuple + +import torch +from torch import nn + + +class DecoderLayer(nn.Module): + """Single decoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + src_attn (torch.nn.Module): Inter-attention module instance. + `MultiHeadedAttention` instance can be used as the argument. + If `None` is passed, Inter-attention is not used, such as + CIF, GPT, and other decoder only model. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward` instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): + True: use layer_norm before each sub-block. + False: to use layer_norm after each sub-block. + """ + + def __init__( + self, + size: int, + self_attn: nn.Module, + src_attn: Optional[nn.Module], + feed_forward: nn.Module, + dropout_rate: float, + normalize_before: bool = True, + ): + """Construct an DecoderLayer object.""" + super().__init__() + self.size = size + self.self_attn = self_attn + self.src_attn = src_attn + self.feed_forward = feed_forward + self.norm1 = nn.LayerNorm(size, eps=1e-5) + self.norm2 = nn.LayerNorm(size, eps=1e-5) + self.norm3 = nn.LayerNorm(size, eps=1e-5) + self.dropout = nn.Dropout(dropout_rate) + self.normalize_before = normalize_before + + def forward( + self, + tgt: torch.Tensor, + tgt_mask: torch.Tensor, + memory: torch.Tensor, + memory_mask: torch.Tensor, + cache: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute decoded features. + + Args: + tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). + tgt_mask (torch.Tensor): Mask for input tensor + (#batch, maxlen_out). + memory (torch.Tensor): Encoded memory + (#batch, maxlen_in, size). + memory_mask (torch.Tensor): Encoded memory mask + (#batch, maxlen_in). + cache (torch.Tensor): cached tensors. + (#batch, maxlen_out - 1, size). + + Returns: + torch.Tensor: Output tensor (#batch, maxlen_out, size). + torch.Tensor: Mask for output tensor (#batch, maxlen_out). + torch.Tensor: Encoded memory (#batch, maxlen_in, size). + torch.Tensor: Encoded memory mask (#batch, maxlen_in). + + """ + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + + if cache is None: + tgt_q = tgt + tgt_q_mask = tgt_mask + else: + # compute only the last frame query keeping dim: max_time_out -> 1 + assert cache.shape == ( + tgt.shape[0], + tgt.shape[1] - 1, + self.size, + ), "{cache.shape} == {(tgt.shape[0], tgt.shape[1] - 1, self.size)}" + tgt_q = tgt[:, -1:, :] + residual = residual[:, -1:, :] + tgt_q_mask = tgt_mask[:, -1:, :] + + x = residual + self.dropout(self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)[0]) + if not self.normalize_before: + x = self.norm1(x) + + if self.src_attn is not None: + residual = x + if self.normalize_before: + x = self.norm2(x) + x = residual + self.dropout( + self.src_attn(x, memory, memory, memory_mask)[0] + ) + if not self.normalize_before: + x = self.norm2(x) + + residual = x + if self.normalize_before: + x = self.norm3(x) + x = residual + self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm3(x) + + if cache is not None: + x = torch.cat([cache, x], dim=1) + + return x, tgt_mask, memory, memory_mask diff --git a/modules/wenet_extractor/transformer/embedding.py b/modules/wenet_extractor/transformer/embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..55ee113f8d267875f147a55326883c207cc0c646 --- /dev/null +++ b/modules/wenet_extractor/transformer/embedding.py @@ -0,0 +1,174 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""Positonal Encoding Module.""" + +import math +from typing import Tuple, Union + +import torch +import torch.nn.functional as F + + +class PositionalEncoding(torch.nn.Module): + """Positional encoding. + + :param int d_model: embedding dim + :param float dropout_rate: dropout rate + :param int max_len: maximum input length + + PE(pos, 2i) = sin(pos/(10000^(2i/dmodel))) + PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel))) + """ + + def __init__( + self, + d_model: int, + dropout_rate: float, + max_len: int = 5000, + reverse: bool = False, + ): + """Construct an PositionalEncoding object.""" + super().__init__() + self.d_model = d_model + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.max_len = max_len + + self.pe = torch.zeros(self.max_len, self.d_model) + position = torch.arange(0, self.max_len, dtype=torch.float32).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, self.d_model, 2, dtype=torch.float32) + * -(math.log(10000.0) / self.d_model) + ) + self.pe[:, 0::2] = torch.sin(position * div_term) + self.pe[:, 1::2] = torch.cos(position * div_term) + self.pe = self.pe.unsqueeze(0) + + def forward( + self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Add positional encoding. + + Args: + x (torch.Tensor): Input. Its shape is (batch, time, ...) + offset (int, torch.tensor): position offset + + Returns: + torch.Tensor: Encoded tensor. Its shape is (batch, time, ...) + torch.Tensor: for compatibility to RelPositionalEncoding + """ + + self.pe = self.pe.to(x.device) + pos_emb = self.position_encoding(offset, x.size(1), False) + x = x * self.xscale + pos_emb + return self.dropout(x), self.dropout(pos_emb) + + def position_encoding( + self, offset: Union[int, torch.Tensor], size: int, apply_dropout: bool = True + ) -> torch.Tensor: + """For getting encoding in a streaming fashion + + Attention!!!!! + we apply dropout only once at the whole utterance level in a none + streaming way, but will call this function several times with + increasing input size in a streaming scenario, so the dropout will + be applied several times. + + Args: + offset (int or torch.tensor): start offset + size (int): required size of position encoding + + Returns: + torch.Tensor: Corresponding encoding + """ + # How to subscript a Union type: + # https://github.com/pytorch/pytorch/issues/69434 + if isinstance(offset, int): + assert offset + size < self.max_len + pos_emb = self.pe[:, offset : offset + size] + elif isinstance(offset, torch.Tensor) and offset.dim() == 0: # scalar + assert offset + size < self.max_len + pos_emb = self.pe[:, offset : offset + size] + else: # for batched streaming decoding on GPU + assert torch.max(offset) + size < self.max_len + index = offset.unsqueeze(1) + torch.arange(0, size).to( + offset.device + ) # B X T + flag = index > 0 + # remove negative offset + index = index * flag + pos_emb = F.embedding(index, self.pe[0]) # B X T X d_model + + if apply_dropout: + pos_emb = self.dropout(pos_emb) + return pos_emb + + +class RelPositionalEncoding(PositionalEncoding): + """Relative positional encoding module. + See : Appendix B in https://arxiv.org/abs/1901.02860 + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + """ + + def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000): + """Initialize class.""" + super().__init__(d_model, dropout_rate, max_len, reverse=True) + + def forward( + self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute positional encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + torch.Tensor: Positional embedding tensor (1, time, `*`). + """ + self.pe = self.pe.to(x.device) + x = x * self.xscale + pos_emb = self.position_encoding(offset, x.size(1), False) + return self.dropout(x), self.dropout(pos_emb) + + +class NoPositionalEncoding(torch.nn.Module): + """No position encoding""" + + def __init__(self, d_model: int, dropout_rate: float): + super().__init__() + self.d_model = d_model + self.dropout = torch.nn.Dropout(p=dropout_rate) + + def forward( + self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Just return zero vector for interface compatibility""" + pos_emb = torch.zeros(1, x.size(1), self.d_model).to(x.device) + return self.dropout(x), pos_emb + + def position_encoding( + self, offset: Union[int, torch.Tensor], size: int + ) -> torch.Tensor: + return torch.zeros(1, size, self.d_model) diff --git a/modules/wenet_extractor/transformer/encoder.py b/modules/wenet_extractor/transformer/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..c0563262afec7d74e6a1d26a46c3f03376f290fd --- /dev/null +++ b/modules/wenet_extractor/transformer/encoder.py @@ -0,0 +1,503 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""Encoder definition.""" +from typing import Tuple + +import torch + +from modules.wenet_extractor.transformer.attention import MultiHeadedAttention +from modules.wenet_extractor.transformer.attention import ( + RelPositionMultiHeadedAttention, +) +from modules.wenet_extractor.transformer.convolution import ConvolutionModule +from modules.wenet_extractor.transformer.embedding import PositionalEncoding +from modules.wenet_extractor.transformer.embedding import RelPositionalEncoding +from modules.wenet_extractor.transformer.embedding import NoPositionalEncoding +from modules.wenet_extractor.transformer.encoder_layer import TransformerEncoderLayer +from modules.wenet_extractor.transformer.encoder_layer import ConformerEncoderLayer +from modules.wenet_extractor.transformer.positionwise_feed_forward import ( + PositionwiseFeedForward, +) +from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling4 +from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling6 +from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling8 +from modules.wenet_extractor.transformer.subsampling import LinearNoSubsampling +from modules.wenet_extractor.utils.common import get_activation +from modules.wenet_extractor.utils.mask import make_pad_mask +from modules.wenet_extractor.utils.mask import add_optional_chunk_mask + + +class BaseEncoder(torch.nn.Module): + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = "conv2d", + pos_enc_layer_type: str = "abs_pos", + normalize_before: bool = True, + static_chunk_size: int = 0, + use_dynamic_chunk: bool = False, + global_cmvn: torch.nn.Module = None, + use_dynamic_left_chunk: bool = False, + ): + """ + Args: + input_size (int): input dim + output_size (int): dimension of attention + attention_heads (int): the number of heads of multi head attention + linear_units (int): the hidden units number of position-wise feed + forward + num_blocks (int): the number of decoder blocks + dropout_rate (float): dropout rate + attention_dropout_rate (float): dropout rate in attention + positional_dropout_rate (float): dropout rate after adding + positional encoding + input_layer (str): input layer type. + optional [linear, conv2d, conv2d6, conv2d8] + pos_enc_layer_type (str): Encoder positional encoding layer type. + opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos] + normalize_before (bool): + True: use layer_norm before each sub-block of a layer. + False: use layer_norm after each sub-block of a layer. + static_chunk_size (int): chunk size for static chunk training and + decoding + use_dynamic_chunk (bool): whether use dynamic chunk size for + training or not, You can only use fixed chunk(chunk_size > 0) + or dyanmic chunk size(use_dynamic_chunk = True) + global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module + use_dynamic_left_chunk (bool): whether use dynamic left chunk in + dynamic chunk training + """ + super().__init__() + self._output_size = output_size + + if pos_enc_layer_type == "abs_pos": + pos_enc_class = PositionalEncoding + elif pos_enc_layer_type == "rel_pos": + pos_enc_class = RelPositionalEncoding + elif pos_enc_layer_type == "no_pos": + pos_enc_class = NoPositionalEncoding + else: + raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type) + + if input_layer == "linear": + subsampling_class = LinearNoSubsampling + elif input_layer == "conv2d": + subsampling_class = Conv2dSubsampling4 + elif input_layer == "conv2d6": + subsampling_class = Conv2dSubsampling6 + elif input_layer == "conv2d8": + subsampling_class = Conv2dSubsampling8 + else: + raise ValueError("unknown input_layer: " + input_layer) + + self.global_cmvn = global_cmvn + self.embed = subsampling_class( + input_size, + output_size, + dropout_rate, + pos_enc_class(output_size, positional_dropout_rate), + ) + + self.normalize_before = normalize_before + self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5) + self.static_chunk_size = static_chunk_size + self.use_dynamic_chunk = use_dynamic_chunk + self.use_dynamic_left_chunk = use_dynamic_left_chunk + + def output_size(self) -> int: + return self._output_size + + def forward( + self, + xs: torch.Tensor, + xs_lens: torch.Tensor, + decoding_chunk_size: int = 0, + num_decoding_left_chunks: int = -1, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Embed positions in tensor. + + Args: + xs: padded input tensor (B, T, D) + xs_lens: input length (B) + decoding_chunk_size: decoding chunk size for dynamic chunk + 0: default for training, use random dynamic chunk. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + num_decoding_left_chunks: number of left chunks, this is for decoding, + the chunk size is decoding_chunk_size. + >=0: use num_decoding_left_chunks + <0: use all left chunks + Returns: + encoder output tensor xs, and subsampled masks + xs: padded output tensor (B, T' ~= T/subsample_rate, D) + masks: torch.Tensor batch padding mask after subsample + (B, 1, T' ~= T/subsample_rate) + """ + T = xs.size(1) + masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) + if self.global_cmvn is not None: + xs = self.global_cmvn(xs) + xs, pos_emb, masks = self.embed(xs, masks) + mask_pad = masks # (B, 1, T/subsample_rate) + chunk_masks = add_optional_chunk_mask( + xs, + masks, + self.use_dynamic_chunk, + self.use_dynamic_left_chunk, + decoding_chunk_size, + self.static_chunk_size, + num_decoding_left_chunks, + ) + for layer in self.encoders: + xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) + if self.normalize_before: + xs = self.after_norm(xs) + # Here we assume the mask is not changed in encoder layers, so just + # return the masks before encoder layers, and the masks will be used + # for cross attention with decoder later + return xs, masks + + def forward_chunk( + self, + xs: torch.Tensor, + offset: int, + required_cache_size: int, + att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), + cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), + att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ Forward just one chunk + + Args: + xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim), + where `time == (chunk_size - 1) * subsample_rate + \ + subsample.right_context + 1` + offset (int): current offset in encoder output time stamp + required_cache_size (int): cache size required for next chunk + compuation + >=0: actual cache size + <0: means all history cache is required + att_cache (torch.Tensor): cache tensor for KEY & VALUE in + transformer/conformer attention, with shape + (elayers, head, cache_t1, d_k * 2), where + `head * d_k == hidden-dim` and + `cache_t1 == chunk_size * num_decoding_left_chunks`. + cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, + (elayers, b=1, hidden-dim, cache_t2), where + `cache_t2 == cnn.lorder - 1` + + Returns: + torch.Tensor: output of current input xs, + with shape (b=1, chunk_size, hidden-dim). + torch.Tensor: new attention cache required for next chunk, with + dynamic shape (elayers, head, ?, d_k * 2) + depending on required_cache_size. + torch.Tensor: new conformer cnn cache required for next chunk, with + same shape as the original cnn_cache. + + """ + assert xs.size(0) == 1 + # tmp_masks is just for interface compatibility + tmp_masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool) + tmp_masks = tmp_masks.unsqueeze(1) + if self.global_cmvn is not None: + xs = self.global_cmvn(xs) + # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim) + xs, pos_emb, _ = self.embed(xs, tmp_masks, offset) + # NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim) + elayers, cache_t1 = att_cache.size(0), att_cache.size(2) + chunk_size = xs.size(1) + attention_key_size = cache_t1 + chunk_size + pos_emb = self.embed.position_encoding( + offset=offset - cache_t1, size=attention_key_size + ) + if required_cache_size < 0: + next_cache_start = 0 + elif required_cache_size == 0: + next_cache_start = attention_key_size + else: + next_cache_start = max(attention_key_size - required_cache_size, 0) + r_att_cache = [] + r_cnn_cache = [] + for i, layer in enumerate(self.encoders): + # NOTE(xcsong): Before layer.forward + # shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2), + # shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2) + xs, _, new_att_cache, new_cnn_cache = layer( + xs, + att_mask, + pos_emb, + att_cache=att_cache[i : i + 1] if elayers > 0 else att_cache, + cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache, + ) + # NOTE(xcsong): After layer.forward + # shape(new_att_cache) is (1, head, attention_key_size, d_k * 2), + # shape(new_cnn_cache) is (b=1, hidden-dim, cache_t2) + r_att_cache.append(new_att_cache[:, :, next_cache_start:, :]) + r_cnn_cache.append(new_cnn_cache.unsqueeze(0)) + if self.normalize_before: + xs = self.after_norm(xs) + + # NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2), + # ? may be larger than cache_t1, it depends on required_cache_size + r_att_cache = torch.cat(r_att_cache, dim=0) + # NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2) + r_cnn_cache = torch.cat(r_cnn_cache, dim=0) + + return (xs, r_att_cache, r_cnn_cache) + + def forward_chunk_by_chunk( + self, + xs: torch.Tensor, + decoding_chunk_size: int, + num_decoding_left_chunks: int = -1, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Forward input chunk by chunk with chunk_size like a streaming + fashion + + Here we should pay special attention to computation cache in the + streaming style forward chunk by chunk. Three things should be taken + into account for computation in the current network: + 1. transformer/conformer encoder layers output cache + 2. convolution in conformer + 3. convolution in subsampling + + However, we don't implement subsampling cache for: + 1. We can control subsampling module to output the right result by + overlapping input instead of cache left context, even though it + wastes some computation, but subsampling only takes a very + small fraction of computation in the whole model. + 2. Typically, there are several covolution layers with subsampling + in subsampling module, it is tricky and complicated to do cache + with different convolution layers with different subsampling + rate. + 3. Currently, nn.Sequential is used to stack all the convolution + layers in subsampling, we need to rewrite it to make it work + with cache, which is not prefered. + Args: + xs (torch.Tensor): (1, max_len, dim) + chunk_size (int): decoding chunk size + """ + assert decoding_chunk_size > 0 + # The model is trained by static or dynamic chunk + assert self.static_chunk_size > 0 or self.use_dynamic_chunk + subsampling = self.embed.subsampling_rate + context = self.embed.right_context + 1 # Add current frame + stride = subsampling * decoding_chunk_size + decoding_window = (decoding_chunk_size - 1) * subsampling + context + num_frames = xs.size(1) + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) + outputs = [] + offset = 0 + required_cache_size = decoding_chunk_size * num_decoding_left_chunks + + # Feed forward overlap input step by step + for cur in range(0, num_frames - context + 1, stride): + end = min(cur + decoding_window, num_frames) + chunk_xs = xs[:, cur:end, :] + (y, att_cache, cnn_cache) = self.forward_chunk( + chunk_xs, offset, required_cache_size, att_cache, cnn_cache + ) + outputs.append(y) + offset += y.size(1) + ys = torch.cat(outputs, 1) + masks = torch.ones((1, 1, ys.size(1)), device=ys.device, dtype=torch.bool) + return ys, masks + + +class TransformerEncoder(BaseEncoder): + """Transformer encoder module.""" + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = "conv2d", + pos_enc_layer_type: str = "abs_pos", + normalize_before: bool = True, + static_chunk_size: int = 0, + use_dynamic_chunk: bool = False, + global_cmvn: torch.nn.Module = None, + use_dynamic_left_chunk: bool = False, + ): + """Construct TransformerEncoder + + See Encoder for the meaning of each parameter. + """ + super().__init__( + input_size, + output_size, + attention_heads, + linear_units, + num_blocks, + dropout_rate, + positional_dropout_rate, + attention_dropout_rate, + input_layer, + pos_enc_layer_type, + normalize_before, + static_chunk_size, + use_dynamic_chunk, + global_cmvn, + use_dynamic_left_chunk, + ) + self.encoders = torch.nn.ModuleList( + [ + TransformerEncoderLayer( + output_size, + MultiHeadedAttention( + attention_heads, output_size, attention_dropout_rate + ), + PositionwiseFeedForward(output_size, linear_units, dropout_rate), + dropout_rate, + normalize_before, + ) + for _ in range(num_blocks) + ] + ) + + +class ConformerEncoder(BaseEncoder): + """Conformer encoder module.""" + + def __init__( + self, + input_size: int, + output_size: int = 256, + attention_heads: int = 4, + linear_units: int = 2048, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = "conv2d", + pos_enc_layer_type: str = "rel_pos", + normalize_before: bool = True, + static_chunk_size: int = 0, + use_dynamic_chunk: bool = False, + global_cmvn: torch.nn.Module = None, + use_dynamic_left_chunk: bool = False, + positionwise_conv_kernel_size: int = 1, + macaron_style: bool = True, + selfattention_layer_type: str = "rel_selfattn", + activation_type: str = "swish", + use_cnn_module: bool = True, + cnn_module_kernel: int = 15, + causal: bool = False, + cnn_module_norm: str = "batch_norm", + ): + """Construct ConformerEncoder + + Args: + input_size to use_dynamic_chunk, see in BaseEncoder + positionwise_conv_kernel_size (int): Kernel size of positionwise + conv1d layer. + macaron_style (bool): Whether to use macaron style for + positionwise layer. + selfattention_layer_type (str): Encoder attention layer type, + the parameter has no effect now, it's just for configure + compatibility. + activation_type (str): Encoder activation function type. + use_cnn_module (bool): Whether to use convolution module. + cnn_module_kernel (int): Kernel size of convolution module. + causal (bool): whether to use causal convolution or not. + """ + super().__init__( + input_size, + output_size, + attention_heads, + linear_units, + num_blocks, + dropout_rate, + positional_dropout_rate, + attention_dropout_rate, + input_layer, + pos_enc_layer_type, + normalize_before, + static_chunk_size, + use_dynamic_chunk, + global_cmvn, + use_dynamic_left_chunk, + ) + activation = get_activation(activation_type) + + # self-attention module definition + if pos_enc_layer_type != "rel_pos": + encoder_selfattn_layer = MultiHeadedAttention + else: + encoder_selfattn_layer = RelPositionMultiHeadedAttention + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + ) + # feed-forward module definition + positionwise_layer = PositionwiseFeedForward + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + activation, + ) + # convolution module definition + convolution_layer = ConvolutionModule + convolution_layer_args = ( + output_size, + cnn_module_kernel, + activation, + cnn_module_norm, + causal, + ) + + self.encoders = torch.nn.ModuleList( + [ + ConformerEncoderLayer( + output_size, + encoder_selfattn_layer(*encoder_selfattn_layer_args), + positionwise_layer(*positionwise_layer_args), + positionwise_layer(*positionwise_layer_args) + if macaron_style + else None, + convolution_layer(*convolution_layer_args) + if use_cnn_module + else None, + dropout_rate, + normalize_before, + ) + for _ in range(num_blocks) + ] + ) diff --git a/modules/wenet_extractor/transformer/encoder_layer.py b/modules/wenet_extractor/transformer/encoder_layer.py new file mode 100644 index 0000000000000000000000000000000000000000..6b2ebb2f4fc9cdb5f6cd6c2401597638aa27fa4c --- /dev/null +++ b/modules/wenet_extractor/transformer/encoder_layer.py @@ -0,0 +1,242 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""Encoder self-attention layer definition.""" + +from typing import Optional, Tuple + +import torch +from torch import nn + + +class TransformerEncoderLayer(nn.Module): + """Encoder layer module. + + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` + instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward`, instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): + True: use layer_norm before each sub-block. + False: to use layer_norm after each sub-block. + """ + + def __init__( + self, + size: int, + self_attn: torch.nn.Module, + feed_forward: torch.nn.Module, + dropout_rate: float, + normalize_before: bool = True, + ): + """Construct an EncoderLayer object.""" + super().__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.norm1 = nn.LayerNorm(size, eps=1e-5) + self.norm2 = nn.LayerNorm(size, eps=1e-5) + self.dropout = nn.Dropout(dropout_rate) + self.size = size + self.normalize_before = normalize_before + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute encoded features. + + Args: + x (torch.Tensor): (#batch, time, size) + mask (torch.Tensor): Mask tensor for the input (#batch, time，time), + (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): just for interface compatibility + to ConformerEncoderLayer + mask_pad (torch.Tensor): does not used in transformer layer, + just for unified api with conformer. + att_cache (torch.Tensor): Cache tensor of the KEY & VALUE + (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. + cnn_cache (torch.Tensor): Convolution cache in conformer layer + (#batch=1, size, cache_t2), not used here, it's for interface + compatibility to ConformerEncoderLayer. + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time, time). + torch.Tensor: att_cache tensor, + (#batch=1, head, cache_t1 + time, d_k * 2). + torch.Tensor: cnn_cahce tensor (#batch=1, size, cache_t2). + + """ + residual = x + if self.normalize_before: + x = self.norm1(x) + x_att, new_att_cache = self.self_attn(x, x, x, mask, cache=att_cache) + x = residual + self.dropout(x_att) + if not self.normalize_before: + x = self.norm1(x) + + residual = x + if self.normalize_before: + x = self.norm2(x) + x = residual + self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm2(x) + + fake_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + return x, mask, new_att_cache, fake_cnn_cache + + +class ConformerEncoderLayer(nn.Module): + """Encoder layer module. + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` + instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward` instance can be used as the argument. + feed_forward_macaron (torch.nn.Module): Additional feed-forward module + instance. + `PositionwiseFeedForward` instance can be used as the argument. + conv_module (torch.nn.Module): Convolution module instance. + `ConvlutionModule` instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): + True: use layer_norm before each sub-block. + False: use layer_norm after each sub-block. + """ + + def __init__( + self, + size: int, + self_attn: torch.nn.Module, + feed_forward: Optional[nn.Module] = None, + feed_forward_macaron: Optional[nn.Module] = None, + conv_module: Optional[nn.Module] = None, + dropout_rate: float = 0.1, + normalize_before: bool = True, + ): + """Construct an EncoderLayer object.""" + super().__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.feed_forward_macaron = feed_forward_macaron + self.conv_module = conv_module + self.norm_ff = nn.LayerNorm(size, eps=1e-5) # for the FNN module + self.norm_mha = nn.LayerNorm(size, eps=1e-5) # for the MHA module + if feed_forward_macaron is not None: + self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-5) + self.ff_scale = 0.5 + else: + self.ff_scale = 1.0 + if self.conv_module is not None: + self.norm_conv = nn.LayerNorm(size, eps=1e-5) # for the CNN module + self.norm_final = nn.LayerNorm( + size, eps=1e-5 + ) # for the final output of the block + self.dropout = nn.Dropout(dropout_rate) + self.size = size + self.normalize_before = normalize_before + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute encoded features. + + Args: + x (torch.Tensor): (#batch, time, size) + mask (torch.Tensor): Mask tensor for the input (#batch, time，time), + (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): positional encoding, must not be None + for ConformerEncoderLayer. + mask_pad (torch.Tensor): batch padding mask used for conv module. + (#batch, 1，time), (0, 0, 0) means fake mask. + att_cache (torch.Tensor): Cache tensor of the KEY & VALUE + (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. + cnn_cache (torch.Tensor): Convolution cache in conformer layer + (#batch=1, size, cache_t2) + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time, time). + torch.Tensor: att_cache tensor, + (#batch=1, head, cache_t1 + time, d_k * 2). + torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2). + """ + + # whether to use macaron style + if self.feed_forward_macaron is not None: + residual = x + if self.normalize_before: + x = self.norm_ff_macaron(x) + x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x)) + if not self.normalize_before: + x = self.norm_ff_macaron(x) + + # multi-headed self-attention module + residual = x + if self.normalize_before: + x = self.norm_mha(x) + x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache) + x = residual + self.dropout(x_att) + if not self.normalize_before: + x = self.norm_mha(x) + + # convolution module + # Fake new cnn cache here, and then change it in conv_module + new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + if self.conv_module is not None: + residual = x + if self.normalize_before: + x = self.norm_conv(x) + x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache) + x = residual + self.dropout(x) + + if not self.normalize_before: + x = self.norm_conv(x) + + # feed forward module + residual = x + if self.normalize_before: + x = self.norm_ff(x) + + x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm_ff(x) + + if self.conv_module is not None: + x = self.norm_final(x) + + return x, mask, new_att_cache, new_cnn_cache diff --git a/modules/wenet_extractor/transformer/label_smoothing_loss.py b/modules/wenet_extractor/transformer/label_smoothing_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..5c2b1cae5923fef66665adf9f84604af03c1ea9a --- /dev/null +++ b/modules/wenet_extractor/transformer/label_smoothing_loss.py @@ -0,0 +1,106 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""Label smoothing module.""" + +import torch +from torch import nn + + +class LabelSmoothingLoss(nn.Module): + """Label-smoothing loss. + + In a standard CE loss, the label's data distribution is: + [0,1,2] -> + [ + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 0.0, 1.0], + ] + + In the smoothing version CE Loss,some probabilities + are taken from the true label prob (1.0) and are divided + among other labels. + + e.g. + smoothing=0.1 + [0,1,2] -> + [ + [0.9, 0.05, 0.05], + [0.05, 0.9, 0.05], + [0.05, 0.05, 0.9], + ] + + Args: + size (int): the number of class + padding_idx (int): padding class id which will be ignored for loss + smoothing (float): smoothing rate (0.0 means the conventional CE) + normalize_length (bool): + normalize loss by sequence length if True + normalize loss by batch size if False + """ + + def __init__( + self, + size: int, + padding_idx: int, + smoothing: float, + normalize_length: bool = False, + ): + """Construct an LabelSmoothingLoss object.""" + super(LabelSmoothingLoss, self).__init__() + self.criterion = nn.KLDivLoss(reduction="none") + self.padding_idx = padding_idx + self.confidence = 1.0 - smoothing + self.smoothing = smoothing + self.size = size + self.normalize_length = normalize_length + + def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + """Compute loss between x and target. + + The model outputs and data labels tensors are flatten to + (batch*seqlen, class) shape and a mask is applied to the + padding part which should not be calculated for loss. + + Args: + x (torch.Tensor): prediction (batch, seqlen, class) + target (torch.Tensor): + target signal masked with self.padding_id (batch, seqlen) + Returns: + loss (torch.Tensor) : The KL loss, scalar float value + """ + assert x.size(2) == self.size + batch_size = x.size(0) + x = x.view(-1, self.size) + target = target.view(-1) + # use zeros_like instead of torch.no_grad() for true_dist, + # since no_grad() can not be exported by JIT + true_dist = torch.zeros_like(x) + true_dist.fill_(self.smoothing / (self.size - 1)) + ignore = target == self.padding_idx # (B,) + total = len(target) - ignore.sum().item() + target = target.masked_fill(ignore, 0) # avoid -1 index + true_dist.scatter_(1, target.unsqueeze(1), self.confidence) + kl = self.criterion(torch.log_softmax(x, dim=1), true_dist) + denom = total if self.normalize_length else batch_size + return kl.masked_fill(ignore.unsqueeze(1), 0).sum() / denom diff --git a/modules/wenet_extractor/transformer/positionwise_feed_forward.py b/modules/wenet_extractor/transformer/positionwise_feed_forward.py new file mode 100644 index 0000000000000000000000000000000000000000..eaaefdc61d392d44e503458deb490690b3183a03 --- /dev/null +++ b/modules/wenet_extractor/transformer/positionwise_feed_forward.py @@ -0,0 +1,63 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""Positionwise feed forward layer definition.""" + +import torch + + +class PositionwiseFeedForward(torch.nn.Module): + """Positionwise feed forward layer. + + FeedForward are appied on each position of the sequence. + The output dim is same with the input dim. + + Args: + idim (int): Input dimenstion. + hidden_units (int): The number of hidden units. + dropout_rate (float): Dropout rate. + activation (torch.nn.Module): Activation function + """ + + def __init__( + self, + idim: int, + hidden_units: int, + dropout_rate: float, + activation: torch.nn.Module = torch.nn.ReLU(), + ): + """Construct a PositionwiseFeedForward object.""" + super(PositionwiseFeedForward, self).__init__() + self.w_1 = torch.nn.Linear(idim, hidden_units) + self.activation = activation + self.dropout = torch.nn.Dropout(dropout_rate) + self.w_2 = torch.nn.Linear(hidden_units, idim) + + def forward(self, xs: torch.Tensor) -> torch.Tensor: + """Forward function. + + Args: + xs: input tensor (B, L, D) + Returns: + output tensor, (B, L, D) + """ + return self.w_2(self.dropout(self.activation(self.w_1(xs)))) diff --git a/modules/wenet_extractor/transformer/subsampling.py b/modules/wenet_extractor/transformer/subsampling.py new file mode 100644 index 0000000000000000000000000000000000000000..ffbb5dae0dd013c916cc4a4266cdc838ba48a84f --- /dev/null +++ b/modules/wenet_extractor/transformer/subsampling.py @@ -0,0 +1,257 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + + +"""Subsampling layer definition.""" + +from typing import Tuple, Union + +import torch + + +class BaseSubsampling(torch.nn.Module): + def __init__(self): + super().__init__() + self.right_context = 0 + self.subsampling_rate = 1 + + def position_encoding( + self, offset: Union[int, torch.Tensor], size: int + ) -> torch.Tensor: + return self.pos_enc.position_encoding(offset, size) + + +class LinearNoSubsampling(BaseSubsampling): + """Linear transform the input without subsampling + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__( + self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module + ): + """Construct an linear object.""" + super().__init__() + self.out = torch.nn.Sequential( + torch.nn.Linear(idim, odim), + torch.nn.LayerNorm(odim, eps=1e-5), + torch.nn.Dropout(dropout_rate), + ) + self.pos_enc = pos_enc_class + self.right_context = 0 + self.subsampling_rate = 1 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Input x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: linear input tensor (#batch, time', odim), + where time' = time . + torch.Tensor: linear input mask (#batch, 1, time'), + where time' = time . + + """ + x = self.out(x) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask + + +class Conv2dSubsampling4(BaseSubsampling): + """Convolutional 2D subsampling (to 1/4 length). + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__( + self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module + ): + """Construct an Conv2dSubsampling4 object.""" + super().__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 3, 2), + torch.nn.ReLU(), + ) + self.out = torch.nn.Sequential( + torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim) + ) + self.pos_enc = pos_enc_class + # The right context for every conv layer is computed by: + # (kernel_size - 1) * frame_rate_of_this_layer + self.subsampling_rate = 4 + # 6 = (3 - 1) * 1 + (3 - 1) * 2 + self.right_context = 6 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Subsample x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 4. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 4. + torch.Tensor: positional encoding + + """ + x = x.unsqueeze(1) # (b, c=1, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2] + + +class Conv2dSubsampling6(BaseSubsampling): + """Convolutional 2D subsampling (to 1/6 length). + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + pos_enc (torch.nn.Module): Custom position encoding layer. + """ + + def __init__( + self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module + ): + """Construct an Conv2dSubsampling6 object.""" + super().__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 5, 3), + torch.nn.ReLU(), + ) + self.linear = torch.nn.Linear(odim * (((idim - 1) // 2 - 2) // 3), odim) + self.pos_enc = pos_enc_class + # 10 = (3 - 1) * 1 + (5 - 1) * 2 + self.subsampling_rate = 6 + self.right_context = 10 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Subsample x. + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 6. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 6. + torch.Tensor: positional encoding + """ + x = x.unsqueeze(1) # (b, c, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f)) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask[:, :, 2::2][:, :, 4::3] + + +class Conv2dSubsampling8(BaseSubsampling): + """Convolutional 2D subsampling (to 1/8 length). + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__( + self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module + ): + """Construct an Conv2dSubsampling8 object.""" + super().__init__() + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 3, 2), + torch.nn.ReLU(), + torch.nn.Conv2d(odim, odim, 3, 2), + torch.nn.ReLU(), + ) + self.linear = torch.nn.Linear( + odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2), odim + ) + self.pos_enc = pos_enc_class + self.subsampling_rate = 8 + # 14 = (3 - 1) * 1 + (3 - 1) * 2 + (3 - 1) * 4 + self.right_context = 14 + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + offset: Union[int, torch.Tensor] = 0, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Subsample x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: Subsampled tensor (#batch, time', odim), + where time' = time // 8. + torch.Tensor: Subsampled mask (#batch, 1, time'), + where time' = time // 8. + torch.Tensor: positional encoding + """ + x = x.unsqueeze(1) # (b, c, t, f) + x = self.conv(x) + b, c, t, f = x.size() + x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f)) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2][:, :, 2::2] diff --git a/modules/wenet_extractor/transformer/swish.py b/modules/wenet_extractor/transformer/swish.py new file mode 100644 index 0000000000000000000000000000000000000000..8728089430d0c20fa1575d12e3b6573417b4448c --- /dev/null +++ b/modules/wenet_extractor/transformer/swish.py @@ -0,0 +1,33 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +"""Swish() activation function for Conformer.""" + +import torch + + +class Swish(torch.nn.Module): + """Construct an Swish object.""" + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Return Swish activation function.""" + return x * torch.sigmoid(x) diff --git a/modules/wenet_extractor/utils/__init__.py b/modules/wenet_extractor/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/wenet_extractor/utils/__pycache__/__init__.cpython-39.pyc b/modules/wenet_extractor/utils/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..543fe0b79cc02f308530efbeb6d725312bafa25b Binary files /dev/null and b/modules/wenet_extractor/utils/__pycache__/__init__.cpython-39.pyc differ diff --git a/modules/wenet_extractor/utils/__pycache__/checkpoint.cpython-39.pyc b/modules/wenet_extractor/utils/__pycache__/checkpoint.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..802bc817ed48901692b1b0b21a375bfc57b5e2d0 Binary files /dev/null and b/modules/wenet_extractor/utils/__pycache__/checkpoint.cpython-39.pyc differ diff --git a/modules/wenet_extractor/utils/__pycache__/cmvn.cpython-39.pyc b/modules/wenet_extractor/utils/__pycache__/cmvn.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7047e1cff21a05558706157049f2fc0bd973a6d7 Binary files /dev/null and b/modules/wenet_extractor/utils/__pycache__/cmvn.cpython-39.pyc differ diff --git a/modules/wenet_extractor/utils/__pycache__/common.cpython-39.pyc b/modules/wenet_extractor/utils/__pycache__/common.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..78562791f20e9f2a315e1754b9342f88c47d20f4 Binary files /dev/null and b/modules/wenet_extractor/utils/__pycache__/common.cpython-39.pyc differ diff --git a/modules/wenet_extractor/utils/__pycache__/init_model.cpython-39.pyc b/modules/wenet_extractor/utils/__pycache__/init_model.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6af5a31864b89eab0ba3179a35ab56599caf69c8 Binary files /dev/null and b/modules/wenet_extractor/utils/__pycache__/init_model.cpython-39.pyc differ diff --git a/modules/wenet_extractor/utils/__pycache__/mask.cpython-39.pyc b/modules/wenet_extractor/utils/__pycache__/mask.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..412048ab9afd503702e3e8eda78f9da472b477b6 Binary files /dev/null and b/modules/wenet_extractor/utils/__pycache__/mask.cpython-39.pyc differ diff --git a/modules/wenet_extractor/utils/checkpoint.py b/modules/wenet_extractor/utils/checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..f17784b1e1b41284191b0caff7570dd2066f7e91 --- /dev/null +++ b/modules/wenet_extractor/utils/checkpoint.py @@ -0,0 +1,113 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +import logging +import os +import re + +import yaml +import torch +from collections import OrderedDict + +import datetime + + +def load_checkpoint(model: torch.nn.Module, path: str) -> dict: + if torch.cuda.is_available(): + logging.info("Checkpoint: loading from checkpoint %s for GPU" % path) + checkpoint = torch.load(path) + else: + logging.info("Checkpoint: loading from checkpoint %s for CPU" % path) + checkpoint = torch.load(path, map_location="cpu") + model.load_state_dict(checkpoint, strict=False) + info_path = re.sub(".pt$", ".yaml", path) + configs = {} + if os.path.exists(info_path): + with open(info_path, "r") as fin: + configs = yaml.load(fin, Loader=yaml.FullLoader) + return configs + + +def save_checkpoint(model: torch.nn.Module, path: str, infos=None): + """ + Args: + infos (dict or None): any info you want to save. + """ + logging.info("Checkpoint: save to checkpoint %s" % path) + if isinstance(model, torch.nn.DataParallel): + state_dict = model.module.state_dict() + elif isinstance(model, torch.nn.parallel.DistributedDataParallel): + state_dict = model.module.state_dict() + else: + state_dict = model.state_dict() + torch.save(state_dict, path) + info_path = re.sub(".pt$", ".yaml", path) + if infos is None: + infos = {} + infos["save_time"] = datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S") + with open(info_path, "w") as fout: + data = yaml.dump(infos) + fout.write(data) + + +def filter_modules(model_state_dict, modules): + new_mods = [] + incorrect_mods = [] + mods_model = model_state_dict.keys() + for mod in modules: + if any(key.startswith(mod) for key in mods_model): + new_mods += [mod] + else: + incorrect_mods += [mod] + if incorrect_mods: + logging.warning( + "module(s) %s don't match or (partially match) " + "available modules in model.", + incorrect_mods, + ) + logging.warning("for information, the existing modules in model are:") + logging.warning("%s", mods_model) + + return new_mods + + +def load_trained_modules(model: torch.nn.Module, args: None): + # Load encoder modules with pre-trained model(s). + enc_model_path = args.enc_init + enc_modules = args.enc_init_mods + main_state_dict = model.state_dict() + logging.warning("model(s) found for pre-initialization") + if os.path.isfile(enc_model_path): + logging.info("Checkpoint: loading from checkpoint %s for CPU" % enc_model_path) + model_state_dict = torch.load(enc_model_path, map_location="cpu") + modules = filter_modules(model_state_dict, enc_modules) + partial_state_dict = OrderedDict() + for key, value in model_state_dict.items(): + if any(key.startswith(m) for m in modules): + partial_state_dict[key] = value + main_state_dict.update(partial_state_dict) + else: + logging.warning("model was not found : %s", enc_model_path) + + model.load_state_dict(main_state_dict) + configs = {} + return configs diff --git a/modules/wenet_extractor/utils/cmvn.py b/modules/wenet_extractor/utils/cmvn.py new file mode 100644 index 0000000000000000000000000000000000000000..483adc1f709786ae83f570faf10ee1a1ded85e52 --- /dev/null +++ b/modules/wenet_extractor/utils/cmvn.py @@ -0,0 +1,103 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +import json +import math + +import numpy as np + + +def _load_json_cmvn(json_cmvn_file): + """Load the json format cmvn stats file and calculate cmvn + + Args: + json_cmvn_file: cmvn stats file in json format + + Returns: + a numpy array of [means, vars] + """ + with open(json_cmvn_file) as f: + cmvn_stats = json.load(f) + + means = cmvn_stats["mean_stat"] + variance = cmvn_stats["var_stat"] + count = cmvn_stats["frame_num"] + for i in range(len(means)): + means[i] /= count + variance[i] = variance[i] / count - means[i] * means[i] + if variance[i] < 1.0e-20: + variance[i] = 1.0e-20 + variance[i] = 1.0 / math.sqrt(variance[i]) + cmvn = np.array([means, variance]) + return cmvn + + +def _load_kaldi_cmvn(kaldi_cmvn_file): + """Load the kaldi format cmvn stats file and calculate cmvn + + Args: + kaldi_cmvn_file: kaldi text style global cmvn file, which + is generated by: + compute-cmvn-stats --binary=false scp:feats.scp global_cmvn + + Returns: + a numpy array of [means, vars] + """ + means = [] + variance = [] + with open(kaldi_cmvn_file, "r") as fid: + # kaldi binary file start with '\0B' + if fid.read(2) == "\0B": + logging.error( + "kaldi cmvn binary file is not supported, please " + "recompute it by: compute-cmvn-stats --binary=false " + " scp:feats.scp global_cmvn" + ) + sys.exit(1) + fid.seek(0) + arr = fid.read().split() + assert arr[0] == "[" + assert arr[-2] == "0" + assert arr[-1] == "]" + feat_dim = int((len(arr) - 2 - 2) / 2) + for i in range(1, feat_dim + 1): + means.append(float(arr[i])) + count = float(arr[feat_dim + 1]) + for i in range(feat_dim + 2, 2 * feat_dim + 2): + variance.append(float(arr[i])) + + for i in range(len(means)): + means[i] /= count + variance[i] = variance[i] / count - means[i] * means[i] + if variance[i] < 1.0e-20: + variance[i] = 1.0e-20 + variance[i] = 1.0 / math.sqrt(variance[i]) + cmvn = np.array([means, variance]) + return cmvn + + +def load_cmvn(cmvn_file, is_json): + if is_json: + cmvn = _load_json_cmvn(cmvn_file) + else: + cmvn = _load_kaldi_cmvn(cmvn_file) + return cmvn[0], cmvn[1] diff --git a/modules/wenet_extractor/utils/common.py b/modules/wenet_extractor/utils/common.py new file mode 100644 index 0000000000000000000000000000000000000000..8900ce4cd279e87829807c14a14e2ca38841b89d --- /dev/null +++ b/modules/wenet_extractor/utils/common.py @@ -0,0 +1,266 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# +"""Unility functions for Transformer.""" + +import math +from typing import List, Tuple + +import torch +from torch.nn.utils.rnn import pad_sequence + +IGNORE_ID = -1 + + +def pad_list(xs: List[torch.Tensor], pad_value: int): + """Perform padding for the list of tensors. + + Args: + xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)]. + pad_value (float): Value for padding. + + Returns: + Tensor: Padded tensor (B, Tmax, `*`). + + Examples: + >>> x = [torch.ones(4), torch.ones(2), torch.ones(1)] + >>> x + [tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])] + >>> pad_list(x, 0) + tensor([[1., 1., 1., 1.], + [1., 1., 0., 0.], + [1., 0., 0., 0.]]) + + """ + n_batch = len(xs) + max_len = max([x.size(0) for x in xs]) + pad = torch.zeros(n_batch, max_len, dtype=xs[0].dtype, device=xs[0].device) + pad = pad.fill_(pad_value) + for i in range(n_batch): + pad[i, : xs[i].size(0)] = xs[i] + + return pad + + +def add_blank(ys_pad: torch.Tensor, blank: int, ignore_id: int) -> torch.Tensor: + """Prepad blank for transducer predictor + + Args: + ys_pad (torch.Tensor): batch of padded target sequences (B, Lmax) + blank (int): index of + + Returns: + ys_in (torch.Tensor) : (B, Lmax + 1) + + Examples: + >>> blank = 0 + >>> ignore_id = -1 + >>> ys_pad + tensor([[ 1, 2, 3, 4, 5], + [ 4, 5, 6, -1, -1], + [ 7, 8, 9, -1, -1]], dtype=torch.int32) + >>> ys_in = add_blank(ys_pad, 0, -1) + >>> ys_in + tensor([[0, 1, 2, 3, 4, 5], + [0, 4, 5, 6, 0, 0], + [0, 7, 8, 9, 0, 0]]) + """ + bs = ys_pad.size(0) + _blank = torch.tensor( + [blank], dtype=torch.long, requires_grad=False, device=ys_pad.device + ) + _blank = _blank.repeat(bs).unsqueeze(1) # [bs,1] + out = torch.cat([_blank, ys_pad], dim=1) # [bs, Lmax+1] + return torch.where(out == ignore_id, blank, out) + + +def add_sos_eos( + ys_pad: torch.Tensor, sos: int, eos: int, ignore_id: int +) -> Tuple[torch.Tensor, torch.Tensor]: + """Add and labels. + + Args: + ys_pad (torch.Tensor): batch of padded target sequences (B, Lmax) + sos (int): index of + eos (int): index of + ignore_id (int): index of padding + + Returns: + ys_in (torch.Tensor) : (B, Lmax + 1) + ys_out (torch.Tensor) : (B, Lmax + 1) + + Examples: + >>> sos_id = 10 + >>> eos_id = 11 + >>> ignore_id = -1 + >>> ys_pad + tensor([[ 1, 2, 3, 4, 5], + [ 4, 5, 6, -1, -1], + [ 7, 8, 9, -1, -1]], dtype=torch.int32) + >>> ys_in,ys_out=add_sos_eos(ys_pad, sos_id , eos_id, ignore_id) + >>> ys_in + tensor([[10, 1, 2, 3, 4, 5], + [10, 4, 5, 6, 11, 11], + [10, 7, 8, 9, 11, 11]]) + >>> ys_out + tensor([[ 1, 2, 3, 4, 5, 11], + [ 4, 5, 6, 11, -1, -1], + [ 7, 8, 9, 11, -1, -1]]) + """ + _sos = torch.tensor( + [sos], dtype=torch.long, requires_grad=False, device=ys_pad.device + ) + _eos = torch.tensor( + [eos], dtype=torch.long, requires_grad=False, device=ys_pad.device + ) + ys = [y[y != ignore_id] for y in ys_pad] # parse padded ys + ys_in = [torch.cat([_sos, y], dim=0) for y in ys] + ys_out = [torch.cat([y, _eos], dim=0) for y in ys] + return pad_list(ys_in, eos), pad_list(ys_out, ignore_id) + + +def reverse_pad_list( + ys_pad: torch.Tensor, ys_lens: torch.Tensor, pad_value: float = -1.0 +) -> torch.Tensor: + """Reverse padding for the list of tensors. + + Args: + ys_pad (tensor): The padded tensor (B, Tokenmax). + ys_lens (tensor): The lens of token seqs (B) + pad_value (int): Value for padding. + + Returns: + Tensor: Padded tensor (B, Tokenmax). + + Examples: + >>> x + tensor([[1, 2, 3, 4], [5, 6, 7, 0], [8, 9, 0, 0]]) + >>> pad_list(x, 0) + tensor([[4, 3, 2, 1], + [7, 6, 5, 0], + [9, 8, 0, 0]]) + + """ + r_ys_pad = pad_sequence( + [(torch.flip(y.int()[:i], [0])) for y, i in zip(ys_pad, ys_lens)], + True, + pad_value, + ) + return r_ys_pad + + +def th_accuracy( + pad_outputs: torch.Tensor, pad_targets: torch.Tensor, ignore_label: int +) -> float: + """Calculate accuracy. + + Args: + pad_outputs (Tensor): Prediction tensors (B * Lmax, D). + pad_targets (LongTensor): Target label tensors (B, Lmax). + ignore_label (int): Ignore label id. + + Returns: + float: Accuracy value (0.0 - 1.0). + + """ + pad_pred = pad_outputs.view( + pad_targets.size(0), pad_targets.size(1), pad_outputs.size(1) + ).argmax(2) + mask = pad_targets != ignore_label + numerator = torch.sum( + pad_pred.masked_select(mask) == pad_targets.masked_select(mask) + ) + denominator = torch.sum(mask) + return float(numerator) / float(denominator) + + +def get_rnn(rnn_type: str) -> torch.nn.Module: + assert rnn_type in ["rnn", "lstm", "gru"] + if rnn_type == "rnn": + return torch.nn.RNN + elif rnn_type == "lstm": + return torch.nn.LSTM + else: + return torch.nn.GRU + + +def get_activation(act): + """Return activation function.""" + # Lazy load to avoid unused import + from modules.wenet_extractor.transformer.swish import Swish + + activation_funcs = { + "hardtanh": torch.nn.Hardtanh, + "tanh": torch.nn.Tanh, + "relu": torch.nn.ReLU, + "selu": torch.nn.SELU, + "swish": getattr(torch.nn, "SiLU", Swish), + "gelu": torch.nn.GELU, + } + + return activation_funcs[act]() + + +def get_subsample(config): + input_layer = config["encoder_conf"]["input_layer"] + assert input_layer in ["conv2d", "conv2d6", "conv2d8"] + if input_layer == "conv2d": + return 4 + elif input_layer == "conv2d6": + return 6 + elif input_layer == "conv2d8": + return 8 + + +def remove_duplicates_and_blank(hyp: List[int]) -> List[int]: + new_hyp: List[int] = [] + cur = 0 + while cur < len(hyp): + if hyp[cur] != 0: + new_hyp.append(hyp[cur]) + prev = cur + while cur < len(hyp) and hyp[cur] == hyp[prev]: + cur += 1 + return new_hyp + + +def replace_duplicates_with_blank(hyp: List[int]) -> List[int]: + new_hyp: List[int] = [] + cur = 0 + while cur < len(hyp): + new_hyp.append(hyp[cur]) + prev = cur + cur += 1 + while cur < len(hyp) and hyp[cur] == hyp[prev] and hyp[cur] != 0: + new_hyp.append(0) + cur += 1 + return new_hyp + + +def log_add(args: List[int]) -> float: + """ + Stable log add + """ + if all(a == -float("inf") for a in args): + return -float("inf") + a_max = max(args) + lsp = math.log(sum(math.exp(a - a_max) for a in args)) + return a_max + lsp diff --git a/modules/wenet_extractor/utils/config.py b/modules/wenet_extractor/utils/config.py new file mode 100644 index 0000000000000000000000000000000000000000..5f7ab928eec9d18d1af46655f9f324a7ac1eb9dc --- /dev/null +++ b/modules/wenet_extractor/utils/config.py @@ -0,0 +1,48 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + + +import copy + + +def override_config(configs, override_list): + new_configs = copy.deepcopy(configs) + for item in override_list: + arr = item.split() + if len(arr) != 2: + print(f"the overrive {item} format not correct, skip it") + continue + keys = arr[0].split(".") + s_configs = new_configs + for i, key in enumerate(keys): + if key not in s_configs: + print(f"the overrive {item} format not correct, skip it") + if i == len(keys) - 1: + param_type = type(s_configs[key]) + if param_type != bool: + s_configs[key] = param_type(arr[1]) + else: + s_configs[key] = arr[1] in ["true", "True"] + print(f"override {arr[0]} with {arr[1]}") + else: + s_configs = s_configs[key] + return new_configs diff --git a/modules/wenet_extractor/utils/ctc_util.py b/modules/wenet_extractor/utils/ctc_util.py new file mode 100644 index 0000000000000000000000000000000000000000..03154248bb087d930a9cc579e73d782756cd6ea0 --- /dev/null +++ b/modules/wenet_extractor/utils/ctc_util.py @@ -0,0 +1,96 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +import numpy as np +import torch + + +def insert_blank(label, blank_id=0): + """Insert blank token between every two label token.""" + label = np.expand_dims(label, 1) + blanks = np.zeros((label.shape[0], 1), dtype=np.int64) + blank_id + label = np.concatenate([blanks, label], axis=1) + label = label.reshape(-1) + label = np.append(label, label[0]) + return label + + +def forced_align(ctc_probs: torch.Tensor, y: torch.Tensor, blank_id=0) -> list: + """ctc forced alignment. + + Args: + torch.Tensor ctc_probs: hidden state sequence, 2d tensor (T, D) + torch.Tensor y: id sequence tensor 1d tensor (L) + int blank_id: blank symbol index + Returns: + torch.Tensor: alignment result + """ + y_insert_blank = insert_blank(y, blank_id) + + log_alpha = torch.zeros((ctc_probs.size(0), len(y_insert_blank))) + log_alpha = log_alpha - float("inf") # log of zero + state_path = ( + torch.zeros((ctc_probs.size(0), len(y_insert_blank)), dtype=torch.int16) - 1 + ) # state path + + # init start state + log_alpha[0, 0] = ctc_probs[0][y_insert_blank[0]] + log_alpha[0, 1] = ctc_probs[0][y_insert_blank[1]] + + for t in range(1, ctc_probs.size(0)): + for s in range(len(y_insert_blank)): + if ( + y_insert_blank[s] == blank_id + or s < 2 + or y_insert_blank[s] == y_insert_blank[s - 2] + ): + candidates = torch.tensor( + [log_alpha[t - 1, s], log_alpha[t - 1, s - 1]] + ) + prev_state = [s, s - 1] + else: + candidates = torch.tensor( + [ + log_alpha[t - 1, s], + log_alpha[t - 1, s - 1], + log_alpha[t - 1, s - 2], + ] + ) + prev_state = [s, s - 1, s - 2] + log_alpha[t, s] = torch.max(candidates) + ctc_probs[t][y_insert_blank[s]] + state_path[t, s] = prev_state[torch.argmax(candidates)] + + state_seq = -1 * torch.ones((ctc_probs.size(0), 1), dtype=torch.int16) + + candidates = torch.tensor( + [log_alpha[-1, len(y_insert_blank) - 1], log_alpha[-1, len(y_insert_blank) - 2]] + ) + final_state = [len(y_insert_blank) - 1, len(y_insert_blank) - 2] + state_seq[-1] = final_state[torch.argmax(candidates)] + for t in range(ctc_probs.size(0) - 2, -1, -1): + state_seq[t] = state_path[t + 1, state_seq[t + 1, 0]] + + output_alignment = [] + for t in range(0, ctc_probs.size(0)): + output_alignment.append(y_insert_blank[state_seq[t, 0]]) + + return output_alignment diff --git a/modules/wenet_extractor/utils/executor.py b/modules/wenet_extractor/utils/executor.py new file mode 100644 index 0000000000000000000000000000000000000000..8d55549fec4f4d350e56093f3995ac93a31ff029 --- /dev/null +++ b/modules/wenet_extractor/utils/executor.py @@ -0,0 +1,163 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +import logging +from contextlib import nullcontext + +# if your python version < 3.7 use the below one +# from contextlib import suppress as nullcontext +import torch +from torch.nn.utils import clip_grad_norm_ + + +class Executor: + def __init__(self): + self.step = 0 + + def train( + self, model, optimizer, scheduler, data_loader, device, writer, args, scaler + ): + """Train one epoch""" + model.train() + clip = args.get("grad_clip", 50.0) + log_interval = args.get("log_interval", 10) + rank = args.get("rank", 0) + epoch = args.get("epoch", 0) + accum_grad = args.get("accum_grad", 1) + is_distributed = args.get("is_distributed", True) + use_amp = args.get("use_amp", False) + logging.info( + "using accumulate grad, new batch size is {} times" + " larger than before".format(accum_grad) + ) + if use_amp: + assert scaler is not None + # A context manager to be used in conjunction with an instance of + # torch.nn.parallel.DistributedDataParallel to be able to train + # with uneven inputs across participating processes. + if isinstance(model, torch.nn.parallel.DistributedDataParallel): + model_context = model.join + else: + model_context = nullcontext + num_seen_utts = 0 + with model_context(): + for batch_idx, batch in enumerate(data_loader): + key, feats, target, feats_lengths, target_lengths = batch + feats = feats.to(device) + target = target.to(device) + feats_lengths = feats_lengths.to(device) + target_lengths = target_lengths.to(device) + num_utts = target_lengths.size(0) + if num_utts == 0: + continue + context = None + # Disable gradient synchronizations across DDP processes. + # Within this context, gradients will be accumulated on module + # variables, which will later be synchronized. + if is_distributed and batch_idx % accum_grad != 0: + context = model.no_sync + # Used for single gpu training and DDP gradient synchronization + # processes. + else: + context = nullcontext + with context(): + # autocast context + # The more details about amp can be found in + # https://pytorch.org/docs/stable/notes/amp_examples.html + with torch.cuda.amp.autocast(scaler is not None): + loss_dict = model(feats, feats_lengths, target, target_lengths) + loss = loss_dict["loss"] / accum_grad + if use_amp: + scaler.scale(loss).backward() + else: + loss.backward() + + num_seen_utts += num_utts + if batch_idx % accum_grad == 0: + if rank == 0 and writer is not None: + writer.add_scalar("train_loss", loss, self.step) + # Use mixed precision training + if use_amp: + scaler.unscale_(optimizer) + grad_norm = clip_grad_norm_(model.parameters(), clip) + # Must invoke scaler.update() if unscale_() is used in + # the iteration to avoid the following error: + # RuntimeError: unscale_() has already been called + # on this optimizer since the last update(). + # We don't check grad here since that if the gradient + # has inf/nan values, scaler.step will skip + # optimizer.step(). + scaler.step(optimizer) + scaler.update() + else: + grad_norm = clip_grad_norm_(model.parameters(), clip) + if torch.isfinite(grad_norm): + optimizer.step() + optimizer.zero_grad() + scheduler.step() + self.step += 1 + if batch_idx % log_interval == 0: + lr = optimizer.param_groups[0]["lr"] + log_str = "TRAIN Batch {}/{} loss {:.6f} ".format( + epoch, batch_idx, loss.item() * accum_grad + ) + for name, value in loss_dict.items(): + if name != "loss" and value is not None: + log_str += "{} {:.6f} ".format(name, value.item()) + log_str += "lr {:.8f} rank {}".format(lr, rank) + logging.debug(log_str) + + def cv(self, model, data_loader, device, args): + """Cross validation on""" + model.eval() + rank = args.get("rank", 0) + epoch = args.get("epoch", 0) + log_interval = args.get("log_interval", 10) + # in order to avoid division by 0 + num_seen_utts = 1 + total_loss = 0.0 + with torch.no_grad(): + for batch_idx, batch in enumerate(data_loader): + key, feats, target, feats_lengths, target_lengths = batch + feats = feats.to(device) + target = target.to(device) + feats_lengths = feats_lengths.to(device) + target_lengths = target_lengths.to(device) + num_utts = target_lengths.size(0) + if num_utts == 0: + continue + loss_dict = model(feats, feats_lengths, target, target_lengths) + loss = loss_dict["loss"] + if torch.isfinite(loss): + num_seen_utts += num_utts + total_loss += loss.item() * num_utts + if batch_idx % log_interval == 0: + log_str = "CV Batch {}/{} loss {:.6f} ".format( + epoch, batch_idx, loss.item() + ) + for name, value in loss_dict.items(): + if name != "loss" and value is not None: + log_str += "{} {:.6f} ".format(name, value.item()) + log_str += "history loss {:.6f}".format(total_loss / num_seen_utts) + log_str += " rank {}".format(rank) + logging.debug(log_str) + return total_loss, num_seen_utts diff --git a/modules/wenet_extractor/utils/file_utils.py b/modules/wenet_extractor/utils/file_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..443759bd4da9d7f85d48f0534205f9e5cd7048df --- /dev/null +++ b/modules/wenet_extractor/utils/file_utils.py @@ -0,0 +1,77 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +import re + + +def read_lists(list_file): + lists = [] + with open(list_file, "r", encoding="utf8") as fin: + for line in fin: + lists.append(line.strip()) + return lists + + +def read_non_lang_symbols(non_lang_sym_path): + """read non-linguistic symbol from file. + + The file format is like below: + + {NOISE}\n + {BRK}\n + ... + + + Args: + non_lang_sym_path: non-linguistic symbol file path, None means no any + syms. + + """ + if non_lang_sym_path is None: + return None + else: + syms = read_lists(non_lang_sym_path) + non_lang_syms_pattern = re.compile(r"(\[[^\[\]]+\]|<[^<>]+>|{[^{}]+})") + for sym in syms: + if non_lang_syms_pattern.fullmatch(sym) is None: + + class BadSymbolFormat(Exception): + pass + + raise BadSymbolFormat( + "Non-linguistic symbols should be " + "formatted in {xxx}//[xxx], consider" + " modify '%s' to meet the requirment. " + "More details can be found in discussions here : " + "https://github.com/wenet-e2e/wenet/pull/819" % (sym) + ) + return syms + + +def read_symbol_table(symbol_table_file): + symbol_table = {} + with open(symbol_table_file, "r", encoding="utf8") as fin: + for line in fin: + arr = line.strip().split() + assert len(arr) == 2 + symbol_table[arr[0]] = int(arr[1]) + return symbol_table diff --git a/modules/wenet_extractor/utils/init_model.py b/modules/wenet_extractor/utils/init_model.py new file mode 100644 index 0000000000000000000000000000000000000000..82a391efec400163935c387e1134f179ea48e2cb --- /dev/null +++ b/modules/wenet_extractor/utils/init_model.py @@ -0,0 +1,152 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +import torch +from modules.wenet_extractor.transducer.joint import TransducerJoint +from modules.wenet_extractor.transducer.predictor import ( + ConvPredictor, + EmbeddingPredictor, + RNNPredictor, +) +from modules.wenet_extractor.transducer.transducer import Transducer +from modules.wenet_extractor.transformer.asr_model import ASRModel +from modules.wenet_extractor.transformer.cmvn import GlobalCMVN +from modules.wenet_extractor.transformer.ctc import CTC +from modules.wenet_extractor.transformer.decoder import ( + BiTransformerDecoder, + TransformerDecoder, +) +from modules.wenet_extractor.transformer.encoder import ( + ConformerEncoder, + TransformerEncoder, +) +from modules.wenet_extractor.squeezeformer.encoder import SqueezeformerEncoder +from modules.wenet_extractor.efficient_conformer.encoder import ( + EfficientConformerEncoder, +) +from modules.wenet_extractor.paraformer.paraformer import Paraformer +from modules.wenet_extractor.cif.predictor import Predictor +from modules.wenet_extractor.utils.cmvn import load_cmvn + + +def init_model(configs): + if configs["cmvn_file"] is not None: + mean, istd = load_cmvn(configs["cmvn_file"], configs["is_json_cmvn"]) + global_cmvn = GlobalCMVN( + torch.from_numpy(mean).float(), torch.from_numpy(istd).float() + ) + else: + global_cmvn = None + + input_dim = configs["input_dim"] + vocab_size = configs["output_dim"] + + encoder_type = configs.get("encoder", "conformer") + decoder_type = configs.get("decoder", "bitransformer") + + if encoder_type == "conformer": + encoder = ConformerEncoder( + input_dim, global_cmvn=global_cmvn, **configs["encoder_conf"] + ) + elif encoder_type == "squeezeformer": + encoder = SqueezeformerEncoder( + input_dim, global_cmvn=global_cmvn, **configs["encoder_conf"] + ) + elif encoder_type == "efficientConformer": + encoder = EfficientConformerEncoder( + input_dim, + global_cmvn=global_cmvn, + **configs["encoder_conf"], + **configs["encoder_conf"]["efficient_conf"] + if "efficient_conf" in configs["encoder_conf"] + else {}, + ) + else: + encoder = TransformerEncoder( + input_dim, global_cmvn=global_cmvn, **configs["encoder_conf"] + ) + if decoder_type == "transformer": + decoder = TransformerDecoder( + vocab_size, encoder.output_size(), **configs["decoder_conf"] + ) + else: + assert 0.0 < configs["model_conf"]["reverse_weight"] < 1.0 + assert configs["decoder_conf"]["r_num_blocks"] > 0 + decoder = BiTransformerDecoder( + vocab_size, encoder.output_size(), **configs["decoder_conf"] + ) + ctc = CTC(vocab_size, encoder.output_size()) + + # Init joint CTC/Attention or Transducer model + if "predictor" in configs: + predictor_type = configs.get("predictor", "rnn") + if predictor_type == "rnn": + predictor = RNNPredictor(vocab_size, **configs["predictor_conf"]) + elif predictor_type == "embedding": + predictor = EmbeddingPredictor(vocab_size, **configs["predictor_conf"]) + configs["predictor_conf"]["output_size"] = configs["predictor_conf"][ + "embed_size" + ] + elif predictor_type == "conv": + predictor = ConvPredictor(vocab_size, **configs["predictor_conf"]) + configs["predictor_conf"]["output_size"] = configs["predictor_conf"][ + "embed_size" + ] + else: + raise NotImplementedError("only rnn, embedding and conv type support now") + configs["joint_conf"]["enc_output_size"] = configs["encoder_conf"][ + "output_size" + ] + configs["joint_conf"]["pred_output_size"] = configs["predictor_conf"][ + "output_size" + ] + joint = TransducerJoint(vocab_size, **configs["joint_conf"]) + model = Transducer( + vocab_size=vocab_size, + blank=0, + predictor=predictor, + encoder=encoder, + attention_decoder=decoder, + joint=joint, + ctc=ctc, + **configs["model_conf"], + ) + elif "paraformer" in configs: + predictor = Predictor(**configs["cif_predictor_conf"]) + model = Paraformer( + vocab_size=vocab_size, + encoder=encoder, + decoder=decoder, + ctc=ctc, + predictor=predictor, + **configs["model_conf"], + ) + else: + model = ASRModel( + vocab_size=vocab_size, + encoder=encoder, + decoder=decoder, + ctc=ctc, + lfmmi_dir=configs.get("lfmmi_dir", ""), + **configs["model_conf"], + ) + return model diff --git a/modules/wenet_extractor/utils/mask.py b/modules/wenet_extractor/utils/mask.py new file mode 100644 index 0000000000000000000000000000000000000000..97b6149781893da3fb37e225ef47338a79ef601f --- /dev/null +++ b/modules/wenet_extractor/utils/mask.py @@ -0,0 +1,304 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + + +import torch + +''' +def subsequent_mask( + size: int, + device: torch.device = torch.device("cpu"), +) -> torch.Tensor: + """Create mask for subsequent steps (size, size). + + This mask is used only in decoder which works in an auto-regressive mode. + This means the current step could only do attention with its left steps. + + In encoder, fully attention is used when streaming is not necessary and + the sequence is not long. In this case, no attention mask is needed. + + When streaming is need, chunk-based attention is used in encoder. See + subsequent_chunk_mask for the chunk-based attention mask. + + Args: + size (int): size of mask + str device (str): "cpu" or "cuda" or torch.Tensor.device + dtype (torch.device): result dtype + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_mask(3) + [[1, 0, 0], + [1, 1, 0], + [1, 1, 1]] + """ + ret = torch.ones(size, size, device=device, dtype=torch.bool) + return torch.tril(ret) +''' + + +def subsequent_mask( + size: int, + device: torch.device = torch.device("cpu"), +) -> torch.Tensor: + """Create mask for subsequent steps (size, size). + + This mask is used only in decoder which works in an auto-regressive mode. + This means the current step could only do attention with its left steps. + + In encoder, fully attention is used when streaming is not necessary and + the sequence is not long. In this case, no attention mask is needed. + + When streaming is need, chunk-based attention is used in encoder. See + subsequent_chunk_mask for the chunk-based attention mask. + + Args: + size (int): size of mask + str device (str): "cpu" or "cuda" or torch.Tensor.device + dtype (torch.device): result dtype + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_mask(3) + [[1, 0, 0], + [1, 1, 0], + [1, 1, 1]] + """ + arange = torch.arange(size, device=device) + mask = arange.expand(size, size) + arange = arange.unsqueeze(-1) + mask = mask <= arange + return mask + + +def subsequent_chunk_mask( + size: int, + chunk_size: int, + num_left_chunks: int = -1, + device: torch.device = torch.device("cpu"), +) -> torch.Tensor: + """Create mask for subsequent steps (size, size) with chunk size, + this is for streaming encoder + + Args: + size (int): size of mask + chunk_size (int): size of chunk + num_left_chunks (int): number of left chunks + <0: use full chunk + >=0: use num_left_chunks + device (torch.device): "cpu" or "cuda" or torch.Tensor.device + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_chunk_mask(4, 2) + [[1, 1, 0, 0], + [1, 1, 0, 0], + [1, 1, 1, 1], + [1, 1, 1, 1]] + """ + ret = torch.zeros(size, size, device=device, dtype=torch.bool) + for i in range(size): + if num_left_chunks < 0: + start = 0 + else: + start = max((i // chunk_size - num_left_chunks) * chunk_size, 0) + ending = min((i // chunk_size + 1) * chunk_size, size) + ret[i, start:ending] = True + return ret + + +def add_optional_chunk_mask( + xs: torch.Tensor, + masks: torch.Tensor, + use_dynamic_chunk: bool, + use_dynamic_left_chunk: bool, + decoding_chunk_size: int, + static_chunk_size: int, + num_decoding_left_chunks: int, +): + """Apply optional mask for encoder. + + Args: + xs (torch.Tensor): padded input, (B, L, D), L for max length + mask (torch.Tensor): mask for xs, (B, 1, L) + use_dynamic_chunk (bool): whether to use dynamic chunk or not + use_dynamic_left_chunk (bool): whether to use dynamic left chunk for + training. + decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's + 0: default for training, use random dynamic chunk. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + static_chunk_size (int): chunk size for static chunk training/decoding + if it's greater than 0, if use_dynamic_chunk is true, + this parameter will be ignored + num_decoding_left_chunks: number of left chunks, this is for decoding, + the chunk size is decoding_chunk_size. + >=0: use num_decoding_left_chunks + <0: use all left chunks + + Returns: + torch.Tensor: chunk mask of the input xs. + """ + # Whether to use chunk mask or not + if use_dynamic_chunk: + max_len = xs.size(1) + if decoding_chunk_size < 0: + chunk_size = max_len + num_left_chunks = -1 + elif decoding_chunk_size > 0: + chunk_size = decoding_chunk_size + num_left_chunks = num_decoding_left_chunks + else: + # chunk size is either [1, 25] or full context(max_len). + # Since we use 4 times subsampling and allow up to 1s(100 frames) + # delay, the maximum frame is 100 / 4 = 25. + chunk_size = torch.randint(1, max_len, (1,)).item() + num_left_chunks = -1 + if chunk_size > max_len // 2: + chunk_size = max_len + else: + chunk_size = chunk_size % 25 + 1 + if use_dynamic_left_chunk: + max_left_chunks = (max_len - 1) // chunk_size + num_left_chunks = torch.randint(0, max_left_chunks, (1,)).item() + chunk_masks = subsequent_chunk_mask( + xs.size(1), chunk_size, num_left_chunks, xs.device + ) # (L, L) + chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) + chunk_masks = masks & chunk_masks # (B, L, L) + elif static_chunk_size > 0: + num_left_chunks = num_decoding_left_chunks + chunk_masks = subsequent_chunk_mask( + xs.size(1), static_chunk_size, num_left_chunks, xs.device + ) # (L, L) + chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) + chunk_masks = masks & chunk_masks # (B, L, L) + else: + chunk_masks = masks + return chunk_masks + + +def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor: + """Make mask tensor containing indices of padded part. + + See description of make_non_pad_mask. + + Args: + lengths (torch.Tensor): Batch of lengths (B,). + Returns: + torch.Tensor: Mask tensor containing indices of padded part. + + Examples: + >>> lengths = [5, 3, 2] + >>> make_pad_mask(lengths) + masks = [[0, 0, 0, 0 ,0], + [0, 0, 0, 1, 1], + [0, 0, 1, 1, 1]] + """ + batch_size = lengths.size(0) + max_len = max_len if max_len > 0 else lengths.max().item() + seq_range = torch.arange(0, max_len, dtype=torch.int64, device=lengths.device) + seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len) + seq_length_expand = lengths.unsqueeze(-1) + mask = seq_range_expand >= seq_length_expand + return mask + + +def make_non_pad_mask(lengths: torch.Tensor) -> torch.Tensor: + """Make mask tensor containing indices of non-padded part. + + The sequences in a batch may have different lengths. To enable + batch computing, padding is need to make all sequence in same + size. To avoid the padding part pass value to context dependent + block such as attention or convolution , this padding part is + masked. + + This pad_mask is used in both encoder and decoder. + + 1 for non-padded part and 0 for padded part. + + Args: + lengths (torch.Tensor): Batch of lengths (B,). + Returns: + torch.Tensor: mask tensor containing indices of padded part. + + Examples: + >>> lengths = [5, 3, 2] + >>> make_non_pad_mask(lengths) + masks = [[1, 1, 1, 1 ,1], + [1, 1, 1, 0, 0], + [1, 1, 0, 0, 0]] + """ + return ~make_pad_mask(lengths) + + +def mask_finished_scores(score: torch.Tensor, flag: torch.Tensor) -> torch.Tensor: + """ + If a sequence is finished, we only allow one alive branch. This function + aims to give one branch a zero score and the rest -inf score. + + Args: + score (torch.Tensor): A real value array with shape + (batch_size * beam_size, beam_size). + flag (torch.Tensor): A bool array with shape + (batch_size * beam_size, 1). + + Returns: + torch.Tensor: (batch_size * beam_size, beam_size). + """ + beam_size = score.size(-1) + zero_mask = torch.zeros_like(flag, dtype=torch.bool) + if beam_size > 1: + unfinished = torch.cat((zero_mask, flag.repeat([1, beam_size - 1])), dim=1) + finished = torch.cat((flag, zero_mask.repeat([1, beam_size - 1])), dim=1) + else: + unfinished = zero_mask + finished = flag + score.masked_fill_(unfinished, -float("inf")) + score.masked_fill_(finished, 0) + return score + + +def mask_finished_preds( + pred: torch.Tensor, flag: torch.Tensor, eos: int +) -> torch.Tensor: + """ + If a sequence is finished, all of its branch should be + + Args: + pred (torch.Tensor): A int array with shape + (batch_size * beam_size, beam_size). + flag (torch.Tensor): A bool array with shape + (batch_size * beam_size, 1). + + Returns: + torch.Tensor: (batch_size * beam_size). + """ + beam_size = pred.size(-1) + finished = flag.repeat([1, beam_size]) + return pred.masked_fill_(finished, eos) diff --git a/modules/wenet_extractor/utils/scheduler.py b/modules/wenet_extractor/utils/scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..a5885dbb1faca52272f8825df2da7aa7533d1540 --- /dev/null +++ b/modules/wenet_extractor/utils/scheduler.py @@ -0,0 +1,738 @@ +# This module is from [WeNet](https://github.com/wenet-e2e/wenet). + +# ## Citations + +# ```bibtex +# @inproceedings{yao2021wenet, +# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, +# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, +# booktitle={Proc. Interspeech}, +# year={2021}, +# address={Brno, Czech Republic }, +# organization={IEEE} +# } + +# @article{zhang2022wenet, +# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, +# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, +# journal={arXiv preprint arXiv:2203.15455}, +# year={2022} +# } +# + +from typing import Union + +import math +import warnings +import torch +from torch.optim.lr_scheduler import _LRScheduler + + +class WarmupLR(_LRScheduler): + """The WarmupLR scheduler + + This scheduler is almost same as NoamLR Scheduler except for following + difference: + + NoamLR: + lr = optimizer.lr * model_size ** -0.5 + * min(step ** -0.5, step * warmup_step ** -1.5) + WarmupLR: + lr = optimizer.lr * warmup_step ** 0.5 + * min(step ** -0.5, step * warmup_step ** -1.5) + + Note that the maximum lr equals to optimizer.lr in this scheduler. + + """ + + def __init__( + self, + optimizer: torch.optim.Optimizer, + warmup_steps: Union[int, float] = 25000, + last_epoch: int = -1, + ): + self.warmup_steps = warmup_steps + + # __init__() must be invoked before setting field + # because step() is also invoked in __init__() + super().__init__(optimizer, last_epoch) + + def __repr__(self): + return f"{self.__class__.__name__}(warmup_steps={self.warmup_steps})" + + def get_lr(self): + step_num = self.last_epoch + 1 + if self.warmup_steps == 0: + return [lr * step_num**-0.5 for lr in self.base_lrs] + else: + return [ + lr + * self.warmup_steps**0.5 + * min(step_num**-0.5, step_num * self.warmup_steps**-1.5) + for lr in self.base_lrs + ] + + def set_step(self, step: int): + self.last_epoch = step + + +class WarmupPolicy(_LRScheduler): + """Adds warmup kwargs and warmup logic to lr policy. + All arguments should be passed as kwargs for clarity, + Args: + warmup_steps: Number of training steps in warmup stage + warmup_ratio: Ratio of warmup steps to total steps + max_steps: Total number of steps while training or `None` for + infinite training + """ + + def __init__( + self, + optimizer, + *, + warmup_steps=None, + warmup_ratio=None, + max_steps=None, + min_lr=0.0, + last_epoch=-1, + ): + assert not ( + warmup_steps is not None and warmup_ratio is not None + ), "Either use particular number of step or ratio" + assert ( + warmup_ratio is None or max_steps is not None + ), "If there is a ratio, there should be a total steps" + + # It is necessary to assign all attributes *before* __init__, + # as class is wrapped by an inner class. + self.max_steps = max_steps + if warmup_steps is not None: + self.warmup_steps = warmup_steps + elif warmup_ratio is not None: + self.warmup_steps = int(warmup_ratio * max_steps) + else: + self.warmup_steps = 0 + + self.min_lr = min_lr + super().__init__(optimizer, last_epoch) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn( + "To get the last learning rate computed " + "by the scheduler, please use `get_last_lr()`.", + UserWarning, + stacklevel=2, + ) + + step = self.last_epoch + + if step <= self.warmup_steps and self.warmup_steps > 0: + return self._get_warmup_lr(step) + + if step > self.max_steps: + return [self.min_lr for _ in self.base_lrs] + + return self._get_lr(step) + + def _get_warmup_lr(self, step): + lr_val = (step + 1) / (self.warmup_steps + 1) + return [initial_lr * lr_val for initial_lr in self.base_lrs] + + def _get_lr(self, step): + """Simple const lr policy""" + return self.base_lrs + + +class SquareRootConstantPolicy(_LRScheduler): + """Adds warmup kwargs and warmup logic to lr policy. + All arguments should be passed as kwargs for clarity, + Args: + warmup_steps: Number of training steps in warmup stage + warmup_ratio: Ratio of warmup steps to total steps + max_steps: Total number of steps while training or `None` for + infinite training + """ + + def __init__( + self, + optimizer, + *, + constant_steps=None, + constant_ratio=None, + max_steps=None, + min_lr=0.0, + last_epoch=-1, + ): + assert not ( + constant_steps is not None and constant_ratio is not None + ), "Either use particular number of step or ratio" + assert ( + constant_ratio is None or max_steps is not None + ), "If there is a ratio, there should be a total steps" + + # It is necessary to assign all attributes *before* __init__, + # as class is wrapped by an inner class. + self.max_steps = max_steps + if constant_steps is not None: + self.constant_steps = constant_steps + elif constant_ratio is not None: + self.constant_steps = int(constant_ratio * max_steps) + else: + self.constant_steps = 0 + + self.constant_lr = 1 / (constant_steps**0.5) + self.min_lr = min_lr + super().__init__(optimizer, last_epoch) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn( + "To get the last learning rate computed " + "by the scheduler, please use `get_last_lr()`.", + UserWarning, + stacklevel=2, + ) + + step = self.last_epoch + + if step <= self.constant_steps: + return [self.constant_lr for _ in self.base_lrs] + + if step > self.max_steps: + return [self.min_lr for _ in self.base_lrs] + + return self._get_lr(step) + + def _get_lr(self, step): + """Simple const lr policy""" + return self.base_lrs + + +class WarmupHoldPolicy(WarmupPolicy): + """Variant of WarmupPolicy which maintains high + learning rate for a defined number of steps. + All arguments should be passed as kwargs for clarity, + Args: + warmup_steps: Number of training steps in warmup stage + warmup_ratio: Ratio of warmup steps to total steps + hold_steps: Number of training steps to + hold the learning rate after warm up + hold_ratio: Ratio of hold steps to total steps + max_steps: Total number of steps while training or `None` for + infinite training + """ + + def __init__( + self, + optimizer, + *, + warmup_steps=None, + warmup_ratio=None, + hold_steps=None, + hold_ratio=None, + max_steps=None, + min_lr=0.0, + last_epoch=-1, + ): + assert not ( + hold_steps is not None and hold_ratio is not None + ), "Either use particular number of step or ratio" + assert ( + hold_ratio is None or max_steps is not None + ), "If there is a ratio, there should be a total steps" + + self.min_lr = min_lr + self._last_warmup_lr = 0.0 + + # Necessary to duplicate as class attributes are hidden in inner class + self.max_steps = max_steps + if warmup_steps is not None: + self.warmup_steps = warmup_steps + elif warmup_ratio is not None: + self.warmup_steps = int(warmup_ratio * max_steps) + else: + self.warmup_steps = 0 + + if hold_steps is not None: + self.hold_steps = hold_steps + self.warmup_steps + elif hold_ratio is not None: + self.hold_steps = int(hold_ratio * max_steps) + self.warmup_steps + else: + self.hold_steps = 0 + + super().__init__( + optimizer, + warmup_steps=warmup_steps, + warmup_ratio=warmup_ratio, + max_steps=max_steps, + last_epoch=last_epoch, + min_lr=min_lr, + ) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn( + "To get the last learning rate computed by the scheduler," + " " + "please use `get_last_lr()`.", + UserWarning, + stacklevel=2, + ) + + step = self.last_epoch + + # Warmup phase + if step <= self.warmup_steps and self.warmup_steps > 0: + return self._get_warmup_lr(step) + + # Hold phase + if (step >= self.warmup_steps) and (step < self.hold_steps): + return self.base_lrs + + if step > self.max_steps: + return [self.min_lr for _ in self.base_lrs] + + return self._get_lr(step) + + +class WarmupAnnealHoldPolicy(_LRScheduler): + """Adds warmup kwargs and warmup logic to lr policy. + All arguments should be passed as kwargs for clarity, + Args: + warmup_steps: Number of training steps in warmup stage + warmup_ratio: Ratio of warmup steps to total steps + max_steps: Total number of steps while training or `None` for + infinite training + min_lr: Minimum lr to hold the learning rate after decay at. + constant_steps: Number of steps to keep lr constant at. + constant_ratio: Ratio of steps to keep lr constant. + """ + + def __init__( + self, + optimizer, + *, + warmup_steps=None, + warmup_ratio=None, + constant_steps=None, + constant_ratio=None, + max_steps=None, + min_lr=0.0, + last_epoch=-1, + ): + assert not ( + warmup_steps is not None and warmup_ratio is not None + ), "Either use particular number of step or ratio" + assert not ( + constant_steps is not None and constant_ratio is not None + ), "Either use constant_steps or constant_ratio" + assert ( + warmup_ratio is None or max_steps is not None + ), "If there is a ratio, there should be a total steps" + + # It is necessary to assign all attributes *before* __init__, + # as class is wrapped by an inner class. + self.max_steps = max_steps + + if warmup_steps is not None: + self.warmup_steps = warmup_steps + elif warmup_ratio is not None: + self.warmup_steps = int(warmup_ratio * max_steps) + else: + self.warmup_steps = 0 + + if constant_steps is not None: + self.constant_steps = constant_steps + elif constant_ratio is not None: + self.constant_steps = int(constant_ratio * max_steps) + else: + self.constant_steps = 0 + + self.decay_steps = max_steps - (self.constant_steps + self.warmup_steps) + + self.min_lr = min_lr + super().__init__(optimizer, last_epoch) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn( + "To get the last learning rate computed " + "by the scheduler, please use `get_last_lr()`.", + UserWarning, + stacklevel=2, + ) + + step = self.last_epoch + + # Warmup steps + if self.warmup_steps > 0 and step <= self.warmup_steps: + return self._get_warmup_lr(step) + + # Constant steps after warmup and decay + if ( + self.constant_steps > 0 + and (self.warmup_steps + self.decay_steps) < step <= self.max_steps + ): + return self._get_constant_lr(step) + + # Min lr after max steps of updates + if step > self.max_steps: + return [self.min_lr for _ in self.base_lrs] + + return self._get_lr(step) + + def _get_warmup_lr(self, step): + lr_val = (step + 1) / (self.warmup_steps + 1) + return [initial_lr * lr_val for initial_lr in self.base_lrs] + + def _get_constant_lr(self, step): + return [self.min_lr for _ in self.base_lrs] + + def _get_lr(self, step): + """Simple const lr policy""" + return self.base_lrs + + +def _squareroot_annealing(initial_lr, step, max_steps, min_lr): + mult = ((max_steps - step) / max_steps) ** 0.5 + out_lr = initial_lr * mult + out_lr = max(out_lr, min_lr) + return out_lr + + +def _square_annealing(initial_lr, step, max_steps, min_lr): + mult = ((max_steps - step) / max_steps) ** 2 + out_lr = initial_lr * mult + out_lr = max(out_lr, min_lr) + return out_lr + + +def _cosine_annealing(initial_lr, step, max_steps, min_lr): + mult = 0.5 * (1 + math.cos(math.pi * step / max_steps)) + out_lr = (initial_lr - min_lr) * mult + min_lr + return out_lr + + +def _linear_warmup_with_cosine_annealing( + max_lr, warmup_steps, step, decay_steps, min_lr +): + assert max_lr > min_lr + # Use linear warmup for the initial part. + if warmup_steps > 0 and step <= warmup_steps: + return max_lr * float(step) / float(warmup_steps) + + # For any steps larger than `decay_steps`, use `min_lr`. + if step > warmup_steps + decay_steps: + return min_lr + + # If we are done with the warmup period, use the decay style. + num_steps_ = step - warmup_steps + decay_steps_ = decay_steps + decay_ratio = float(num_steps_) / float(decay_steps_) + assert decay_ratio >= 0.0 + assert decay_ratio <= 1.0 + delta_lr = max_lr - min_lr + + coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0) + + return min_lr + coeff * delta_lr + + +def _poly_decay(initial_lr, step, decay_steps, power, min_lr, cycle): + if cycle: + multiplier = 1.0 if step == 0 else math.ceil(step / decay_steps) + decay_steps *= multiplier + else: + step = min(step, decay_steps) + p = step / decay_steps + lr = (initial_lr - min_lr) * math.pow(1.0 - p, power) + lr += min_lr + return lr + + +def _noam_hold_annealing( + initial_lr, step, warmup_steps, hold_steps, decay_rate, min_lr +): + # hold_steps = total number of steps + # to hold the LR, not the warmup + hold steps. + T_warmup_decay = max(1, warmup_steps**decay_rate) + T_hold_decay = max(1, (step - hold_steps) ** decay_rate) + lr = (initial_lr * T_warmup_decay) / T_hold_decay + lr = max(lr, min_lr) + return lr + + +class SquareAnnealing(WarmupPolicy): + def __init__(self, optimizer, *, max_steps, min_lr=1e-5, last_epoch=-1, **kwargs): + super().__init__( + optimizer=optimizer, + max_steps=max_steps, + last_epoch=last_epoch, + min_lr=min_lr, + **kwargs, + ) + + def _get_lr(self, step): + new_lrs = [ + _square_annealing( + initial_lr=initial_lr, + step=step - self.warmup_steps, + max_steps=self.max_steps - self.warmup_steps, + min_lr=self.min_lr, + ) + for initial_lr in self.base_lrs + ] + return new_lrs + + +class SquareRootAnnealing(WarmupPolicy): + def __init__(self, optimizer, *, max_steps, min_lr=0, last_epoch=-1, **kwargs): + super().__init__( + optimizer=optimizer, + max_steps=max_steps, + last_epoch=last_epoch, + min_lr=min_lr, + **kwargs, + ) + + def _get_lr(self, step): + new_lrs = [ + _squareroot_annealing( + initial_lr=initial_lr, + step=step, + max_steps=self.max_steps, + min_lr=self.min_lr, + ) + for initial_lr in self.base_lrs + ] + return new_lrs + + +class CosineAnnealing(WarmupAnnealHoldPolicy): + def __init__(self, optimizer, *, max_steps, min_lr=0, last_epoch=-1, **kwargs): + super().__init__( + optimizer=optimizer, + max_steps=max_steps, + last_epoch=last_epoch, + min_lr=min_lr, + **kwargs, + ) + + def _get_lr(self, step): + for initial_lr in self.base_lrs: + if initial_lr < self.min_lr: + raise ValueError( + f"{self} received an initial learning rate " + f"that was lower than the minimum learning rate." + ) + + if self.constant_steps is None or self.constant_steps == 0: + new_lrs = [ + _cosine_annealing( + initial_lr=initial_lr, + step=step - self.warmup_steps, + max_steps=self.max_steps - self.warmup_steps, + min_lr=self.min_lr, + ) + for initial_lr in self.base_lrs + ] + else: + new_lrs = self._get_linear_warmup_with_cosine_annealing_lr(step) + return new_lrs + + def _get_warmup_lr(self, step): + if self.constant_steps is None or self.constant_steps == 0: + return super()._get_warmup_lr(step) + else: + # Use linear warmup for the initial part. + return self._get_linear_warmup_with_cosine_annealing_lr(step) + + def _get_constant_lr(self, step): + # Only called when `constant_steps` > 0. + return self._get_linear_warmup_with_cosine_annealing_lr(step) + + def _get_linear_warmup_with_cosine_annealing_lr(self, step): + # Cosine Schedule for Megatron LM, + # slightly different warmup schedule + constant LR at the end. + new_lrs = [ + _linear_warmup_with_cosine_annealing( + max_lr=self.base_lrs[0], + warmup_steps=self.warmup_steps, + step=step, + decay_steps=self.decay_steps, + min_lr=self.min_lr, + ) + for _ in self.base_lrs + ] + return new_lrs + + +class NoamAnnealing(_LRScheduler): + def __init__( + self, + optimizer, + *, + d_model, + warmup_steps=None, + warmup_ratio=None, + max_steps=None, + min_lr=0.0, + last_epoch=-1, + ): + self._normalize = d_model ** (-0.5) + assert not ( + warmup_steps is not None and warmup_ratio is not None + ), "Either use particular number of step or ratio" + assert ( + warmup_ratio is None or max_steps is not None + ), "If there is a ratio, there should be a total steps" + + # It is necessary to assign all attributes *before* __init__, + # as class is wrapped by an inner class. + self.max_steps = max_steps + if warmup_steps is not None: + self.warmup_steps = warmup_steps + elif warmup_ratio is not None: + self.warmup_steps = int(warmup_ratio * max_steps) + else: + self.warmup_steps = 0 + + self.min_lr = min_lr + super().__init__(optimizer, last_epoch) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn( + "To get the last learning rate computed " + "by the scheduler, please use `get_last_lr()`.", + UserWarning, + stacklevel=2, + ) + + step = max(1, self.last_epoch) + + for initial_lr in self.base_lrs: + if initial_lr < self.min_lr: + raise ValueError( + f"{self} received an initial learning rate " + f"that was lower than the minimum learning rate." + ) + + new_lrs = [ + self._noam_annealing(initial_lr=initial_lr, step=step) + for initial_lr in self.base_lrs + ] + return new_lrs + + def _noam_annealing(self, initial_lr, step): + if self.warmup_steps > 0: + mult = self._normalize * min( + step ** (-0.5), step * (self.warmup_steps ** (-1.5)) + ) + else: + mult = self._normalize * step ** (-0.5) + + out_lr = initial_lr * mult + if step > self.warmup_steps: + out_lr = max(out_lr, self.min_lr) + return out_lr + + +class NoamHoldAnnealing(WarmupHoldPolicy): + def __init__( + self, + optimizer, + *, + max_steps, + decay_rate=0.5, + min_lr=0.0, + last_epoch=-1, + **kwargs, + ): + """ + From Nemo: + Implementation of the Noam Hold Annealing policy + from the SqueezeFormer paper. + + Unlike NoamAnnealing, the peak learning rate + can be explicitly set for this scheduler. + The schedule first performs linear warmup, + then holds the peak LR, then decays with some schedule for + the remainder of the steps. + Therefore the min-lr is still dependent + on the hyper parameters selected. + + It's schedule is determined by three factors- + + Warmup Steps: Initial stage, where linear warmup + occurs uptil the peak LR is reached. Unlike NoamAnnealing, + the peak LR is explicitly stated here instead of a scaling factor. + + Hold Steps: Intermediate stage, where the peak LR + is maintained for some number of steps. In this region, + the high peak LR allows the model to converge faster + if training is stable. However the high LR + may also cause instability during training. + Should usually be a significant fraction of training + steps (around 30-40% of the entire training steps). + + Decay Steps: Final stage, where the LR rapidly decays + with some scaling rate (set by decay rate). + To attain Noam decay, use 0.5, + for Squeezeformer recommended decay, use 1.0. + The fast decay after prolonged high LR during + hold phase allows for rapid convergence. + + References: + - [Squeezeformer: + An Efficient Transformer for Automatic Speech Recognition] + (https://arxiv.org/abs/2206.00888) + + Args: + optimizer: Pytorch compatible Optimizer object. + warmup_steps: Number of training steps in warmup stage + warmup_ratio: Ratio of warmup steps to total steps + hold_steps: Number of training steps to + hold the learning rate after warm up + hold_ratio: Ratio of hold steps to total steps + max_steps: Total number of steps while training or `None` for + infinite training + decay_rate: Float value describing the polynomial decay + after the hold period. Default value + of 0.5 corresponds to Noam decay. + min_lr: Minimum learning rate. + """ + self.decay_rate = decay_rate + super().__init__( + optimizer=optimizer, + max_steps=max_steps, + last_epoch=last_epoch, + min_lr=min_lr, + **kwargs, + ) + + def _get_lr(self, step): + if self.warmup_steps is None or self.warmup_steps == 0: + raise ValueError("Noam scheduler cannot be used without warmup steps") + + if self.hold_steps > 0: + hold_steps = self.hold_steps - self.warmup_steps + else: + hold_steps = 0 + + new_lrs = [ + _noam_hold_annealing( + initial_lr, + step=step, + warmup_steps=self.warmup_steps, + hold_steps=hold_steps, + decay_rate=self.decay_rate, + min_lr=self.min_lr, + ) + for initial_lr in self.base_lrs + ] + return new_lrs + + def set_step(self, step: int): + self.last_epoch = step diff --git a/modules/whisper_extractor/README.md b/modules/whisper_extractor/README.md new file mode 100644 index 0000000000000000000000000000000000000000..bfcac7e44a45f52864deb42330d7572c1d6a3962 --- /dev/null +++ b/modules/whisper_extractor/README.md @@ -0,0 +1,23 @@ +## Acknowledgement + +This module borrows some codes from [Whisper](https://github.com/openai/whisper.git). + +## Citations + +```bibtex +@inproceedings{openai-whisper, + author = {Alec Radford and + Jong Wook Kim and + Tao Xu and + Greg Brockman and + Christine McLeavey and + Ilya Sutskever}, + title = {Robust Speech Recognition via Large-Scale Weak Supervision}, + booktitle = {{ICML}}, + series = {Proceedings of Machine Learning Research}, + volume = {202}, + pages = {28492--28518}, + publisher = {{PMLR}}, + year = {2023} +} +``` \ No newline at end of file diff --git a/modules/whisper_extractor/__init__.py b/modules/whisper_extractor/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6b40d4c9101908f2bf4848bdfd438b1ad345aa35 --- /dev/null +++ b/modules/whisper_extractor/__init__.py @@ -0,0 +1,169 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This module is modified from [Whisper](https://github.com/openai/whisper.git). + +# ## Citations + +# ```bibtex +# @inproceedings{openai-whisper, +# author = {Alec Radford and +# Jong Wook Kim and +# Tao Xu and +# Greg Brockman and +# Christine McLeavey and +# Ilya Sutskever}, +# title = {Robust Speech Recognition via Large-Scale Weak Supervision}, +# booktitle = {{ICML}}, +# series = {Proceedings of Machine Learning Research}, +# volume = {202}, +# pages = {28492--28518}, +# publisher = {{PMLR}}, +# year = {2023} +# } +# ``` +# + +import hashlib +import io +import os +import urllib +import warnings +from typing import List, Optional, Union + +import torch +from tqdm import tqdm + +from .audio import load_audio, log_mel_spectrogram, pad_or_trim +from .decoding import DecodingOptions, DecodingResult, decode, detect_language +from .model import Whisper, ModelDimensions +from .transcribe import transcribe +from .version import __version__ + + +_MODELS = { + "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt", + "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt", + "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt", + "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt", + "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt", + "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt", + "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt", + "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt", + "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt", + "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", + "large": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", +} + + +def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]: + os.makedirs(root, exist_ok=True) + + expected_sha256 = url.split("/")[-2] + download_target = os.path.join(root, os.path.basename(url)) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + with open(download_target, "rb") as f: + model_bytes = f.read() + if hashlib.sha256(model_bytes).hexdigest() == expected_sha256: + return model_bytes if in_memory else download_target + else: + warnings.warn( + f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" + ) + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm( + total=int(source.info().get("Content-Length")), + ncols=80, + unit="iB", + unit_scale=True, + unit_divisor=1024, + ) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + model_bytes = open(download_target, "rb").read() + if hashlib.sha256(model_bytes).hexdigest() != expected_sha256: + raise RuntimeError( + "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model." + ) + + return model_bytes if in_memory else download_target + + +def available_models() -> List[str]: + """Returns the names of available models""" + return list(_MODELS.keys()) + + +def load_model( + name: str, + device: Optional[Union[str, torch.device]] = None, + download_root: str = None, + in_memory: bool = False, + checkpoint_file=None, +) -> Whisper: + """ + Load a Whisper ASR model + + Parameters + ---------- + name : str + one of the official model names listed by `whisper.available_models()`, or + path to a model checkpoint containing the model dimensions and the model state_dict. + device : Union[str, torch.device] + the PyTorch device to put the model into + download_root: str + path to download the model files; by default, it uses "~/.cache/whisper" + in_memory: bool + whether to preload the model weights into host memory + + Returns + ------- + model : Whisper + The Whisper ASR model instance + """ + + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + if download_root is None: + download_root = os.getenv( + "XDG_CACHE_HOME", os.path.join(os.path.expanduser("~"), ".cache", "whisper") + ) + + if not os.path.exists(checkpoint_file): + if name in _MODELS: + checkpoint_file = _download(_MODELS[name], download_root, in_memory) + elif os.path.isfile(name): + checkpoint_file = open(name, "rb").read() if in_memory else name + else: + raise RuntimeError( + f"Model {name} not found; available models = {available_models()}" + ) + else: + checkpoint_file = ( + open(checkpoint_file, "rb").read() if in_memory else checkpoint_file + ) + + with ( + io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb") + ) as fp: + checkpoint = torch.load(fp, map_location=device) + del checkpoint_file + + dims = ModelDimensions(**checkpoint["dims"]) + model = Whisper(dims) + model.load_state_dict(checkpoint["model_state_dict"]) + + return model.to(device) diff --git a/modules/whisper_extractor/__main__.py b/modules/whisper_extractor/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..a47432861819924eb1816fda046dc3dafbe0beb7 --- /dev/null +++ b/modules/whisper_extractor/__main__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .transcribe import cli + + +cli() diff --git a/modules/whisper_extractor/__pycache__/__init__.cpython-39.pyc b/modules/whisper_extractor/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b03bb906abe6883b70bc5005b6617f53ae8a53ea Binary files /dev/null and b/modules/whisper_extractor/__pycache__/__init__.cpython-39.pyc differ diff --git a/modules/whisper_extractor/__pycache__/audio.cpython-39.pyc b/modules/whisper_extractor/__pycache__/audio.cpython-39.pyc new file mode 100644 index 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a/modules/whisper_extractor/__pycache__/version.cpython-39.pyc b/modules/whisper_extractor/__pycache__/version.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eabd02d33afd771c7a6f056a1a4509ddced4c472 Binary files /dev/null and b/modules/whisper_extractor/__pycache__/version.cpython-39.pyc differ diff --git a/modules/whisper_extractor/assets/gpt2/merges.txt b/modules/whisper_extractor/assets/gpt2/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..6636bda4a1fd7a63653dffb22683b8162c8de956 --- /dev/null +++ b/modules/whisper_extractor/assets/gpt2/merges.txt @@ -0,0 +1,50001 @@ +#version: 0.2 - Trained by `huggingface/tokenizers` +Ġ t +Ġ a +h e +i n +r e +o n +Ġt he +e r +Ġ s +a t +Ġ w +Ġ o +e n +Ġ c +i t +i s +a n +o r +e s +Ġ b +e d +Ġ f +in g +Ġ p +o u +Ġa n +a l +a r +Ġt o +Ġ m +Ġo f +Ġ in +Ġ d +Ġ h +Ġan d +i c +a s +l e +Ġt h +i on +o m +l l +en t +Ġ n +Ġ l +s t +Ġ re +v e +Ġ e +r o +l y +Ġb e +Ġ g +Ġ T +c t +Ġ S +i d +o t +Ġ I +u t +e t +Ġ A +Ġ is +Ġ on +i m +a m +o w +a y +a d +s e +Ġth at +Ġ C +i g +Ġf or +a c +Ġ y +v er +u r +Ġ u +l d +Ġs t +Ġ M +' s +Ġ he +Ġ it +at ion +it h +i r +c e +Ġy ou +i l +Ġ B +Ġw h +o l +Ġ P +Ġw ith +Ġ 1 +t er +c h +Ġa s +Ġw e +Ġ ( +n d +i ll +Ġ D +i f +Ġ 2 +a g +er s +k e +Ġ " +Ġ H +e m +Ġc on +Ġ W +Ġ R +he r +Ġw as +Ġ r +o d +Ġ F +u l +at e +Ġa t +r i +p p +o re +ĠT he +Ġs e +u s +Ġp ro +Ġh a +u m +Ġa re +Ġd e +a in +an d +Ġo r +ig h +es t +is t +a b +r om +Ġ N +t h +Ġc om +Ġ G +u n +o p +0 0 +Ġ L +Ġn ot +es s +Ġe x +Ġ v +re s +Ġ E +e w +it y +an t +Ġb y +e l +o s +or t +o c +q u +Ġf rom +Ġha ve +Ġs u +i ve +ou ld +Ġs h +Ġth is +n t +r a +p e +igh t +ar t +m ent +Ġa l +u st +en d +- - +al l +Ġ O +ac k +Ġc h +Ġ le +i es +re d +ar d +â Ģ +ou t +Ġ J +Ġa b +e ar +i v +al ly +ou r +o st +g h +p t +Ġp l +as t +Ġc an +a k +om e +u d +T he +Ġh is +Ġd o +Ġg o +Ġh as +g e +' t +Ġ U +r ou +Ġs a +Ġ j +Ġb ut +Ġw or +Ġa ll +e ct +Ġ k +am e +Ġw ill +o k +Ġw he +Ġthe y +id e +0 1 +f f +ic h +p l +t her +Ġt r +. . +Ġin t +i e +u re +ag e +Ġn e +i al +a p +in e +ic e +Ġm e +Ġo ut +an s +on e +on g +ion s +Ġwh o +Ġ K +Ġu p +Ġthe ir +Ġa d +Ġ 3 +Ġu s +at ed +ou s +Ġm ore +u e +o g +ĠS t +in d +i ke +Ġs o +im e +p er +. 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"auer": 18120, "ĠSaudi": 18121, "yeong": 18122, "Ġcurry": 18123, "ĠForest": 18124, "Ñĭл": 18125, "Ġfifteen": 18126, "Ġbolts": 18127, "Ġ{\\": 18128, "¬´": 18129, "Ġsettlement": 18130, "Ġlange": 18131, "Ġbam": 18132, "Get": 18133, "íķĻ": 18134, "Ġswap": 18135, "ĠKhan": 18136, "Ġcommence": 18137, "Ġquarantine": 18138, "Ġscored": 18139, "çĸ": 18140, "Ġ1950": 18141, "Ġthicker": 18142, "Ġsûr": 18143, "åı£": 18144, "ĠLarry": 18145, "Ġallez": 18146, "ìĭľëĬĶ": 18147, "Ġgü": 18148, "Ġspectacular": 18149, "//": 18150, "both": 18151, "Ġstats": 18152, "妳": 18153, "ĠNancy": 18154, "Ġbunu": 18155, "Ġcrust": 18156, "Ġactivated": 18157, "Ġê·¸ëŀ": 18158, "outhe": 18159, "Ġports": 18160, "Ġneural": 18161, "Ġjaw": 18162, "Ġobservations": 18163, "Ġvoit": 18164, "aban": 18165, "ải": 18166, "¦¬ë¥¼": 18167, "omes": 18168, "à¯ĭ": 18169, "qui": 18170, "Ġkindness": 18171, "Ðij": 18172, "Ġ41": 18173, "Ġmoderate": 18174, "Ġangels": 18175, "ĠTamb": 18176, "èt": 18177, "Ġchlor": 18178, "ĠBilly": 18179, "ì²ĺë": 18180, "acon": 18181, "Ġselecting": 18182, "ĠDelta": 18183, "Ġnull": 18184, "denly": 18185, "Ġciud": 18186, "Ġtendency": 18187, "Ġbreakdown": 18188, "Ġmint": 18189, "ÑĦоÑĢм": 18190, "orph": 18191, "Ġdawn": 18192, "spr": 18193, "ĠWILL": 18194, "ächlich": 18195, "Ġpuppy": 18196, "700": 18197, "Ġத": 18198, "Ġfails": 18199, "ĠConc": 18200, "Ġrelatives": 18201, "Ġinviting": 18202, "Ġautonom": 18203, "Ġcomposed": 18204, "Ġunity": 18205, "Ġdecis": 18206, "Ġaccessories": 18207, "ĠCass": 18208, "Ġbist": 18209, "ĠTip": 18210, "째": 18211, "Ġpunt": 18212, "Ġráp": 18213, "éĢ²": 18214, "ANK": 18215, "ãģļ": 18216, "exist": 18217, "Ġcompatible": 18218, "Ġner": 18219, "ĠемÑĥ": 18220, "Ġaplic": 18221, "Ġbapt": 18222, "Ġfailing": 18223, "ĠTamam": 18224, "Ġoscill": 18225, "Ġletzten": 18226, "Ġrepeatedly": 18227, "Ġjungle": 18228, "ĠPush": 18229, "hai": 18230, "Ġη": 18231, "Ġdeadly": 18232, "Ñıж": 18233, "wiÄħ": 18234, "ĠCommon": 18235, "ĠÎķ": 18236, "Ġskate": 18237, "TC": 18238, "ĠMini": 18239, "Ġhobby": 18240, "ần": 18241, "Ġroutes": 18242, "Ġamigos": 18243, "Ġconjun": 18244, "Ġpartnerships": 18245, "Ġnovo": 18246, "Ġaver": 18247, "Ġpouvez": 18248, "bridge": 18249, "Ġpreoc": 18250, "him": 18251, "Ġturb": 18252, "Ġsob": 18253, "ĠSnap": 18254, "Ġì°¸": 18255, "minute": 18256, "Ġtraject": 18257, "ujÄĻ": 18258, "Ġeager": 18259, "Ġregulatory": 18260, "Ġbanking": 18261, "bling": 18262, "ÑĪÑĮ": 18263, "aż": 18264, "Ġbizarre": 18265, "itated": 18266, "dire": 18267, "Ġthreatened": 18268, "Ġshining": 18269, "Ġnesse": 18270, "Ġcorps": 18271, "ĠÑģÑĥ": 18272, "Ġteles": 18273, "Ġtemp": 18274, "tem": 18275, "Ġкан": 18276, "Ġfever": 18277, "New": 18278, "Ġheavier": 18279, "ĠSah": 18280, "bud": 18281, "Ġoutros": 18282, "Ġì°¾": 18283, "Ġëªħ": 18284, "arring": 18285, "Ġê´ľì°®": 18286, "ĠNap": 18287, "Ġsemin": 18288, "ĠThan": 18289, "ifs": 18290, "Ġdesen": 18291, "ĠÑĤакое": 18292, "Ġloses": 18293, "ĠBalt": 18294, "kon": 18295, "ĠнапÑĢ": 18296, "Ġvois": 18297, "ĠMoscow": 18298, "Ġchairs": 18299, "his": 18300, "Ġrefugees": 18301, "kg": 18302, "Ġkole": 18303, "į¨": 18304, "аÑģибо": 18305, "¦½": 18306, "ĠUniverse": 18307, "ĠDirect": 18308, "Ġcheating": 18309, "ĠCin": 18310, "Ġpatri": 18311, "Ġadvise": 18312, "ĠNether": 18313, "Ġprimeiro": 18314, "Ġmentioning": 18315, "nut": 18316, "56": 18317, "arı": 18318, "Ġpetite": 18319, "bled": 18320, "Ġpensar": 18321, "icio": 18322, "IND": 18323, "Ġveteran": 18324, "Ġladder": 18325, "Ġconsequence": 18326, "ожал": 18327, "ĠBurn": 18328, "Ġrug": 18329, "ĠMade": 18330, "Ġgit": 18331, "\"...": 18332, "Ġcompetitors": 18333, "Ġprzed": 18334, "Ġapparent": 18335, "ĠArgentina": 18336, "ĠWorking": 18337, "Ġcollaborate": 18338, "woman": 18339, "Ġretain": 18340, "Ġleurs": 18341, "Ġdashboard": 18342, "×Ļ×ĵ": 18343, "ĠEarly": 18344, "BM": 18345, "ĠеÑij": 18346, "олог": 18347, "Ġsatisfying": 18348, "Ġoftentimes": 18349, "Ġmapping": 18350, "ünkü": 18351, "arth": 18352, "fold": 18353, "Ġlaunching": 18354, "Ġaura": 18355, "Ġprecision": 18356, "works": 18357, "God": 18358, "Ġstrap": 18359, "ĠImper": 18360, "Ġrivers": 18361, "Ġ|": 18362, "Ġcuer": 18363, "regon": 18364, "Ġarrival": 18365, "каÑħ": 18366, "ĠMiami": 18367, "анÑĭ": 18368, "Ġsurvivors": 18369, "ĠSenior": 18370, "David": 18371, "Ġestado": 18372, "Ġsectors": 18373, "Ġpopping": 18374, "Ġchim": 18375, "ayı": 18376, "Ġkunnen": 18377, "Ġgallery": 18378, "Ġsunlight": 18379, "esehen": 18380, "Ġyelling": 18381, "ĠMein": 18382, "ĠPhoenix": 18383, "Ġmano": 18384, "Ġhistoria": 18385, "Ġoccurring": 18386, "欸": 18387, "ì¸": 18388, "ади": 18389, "å¾ħ": 18390, "Ġinstitutional": 18391, "ĠTut": 18392, "ç²": 18393, "Ġslaves": 18394, "ãģ©ãģĨ": 18395, "Ġforgiveness": 18396, "Ġtwin": 18397, "ĠHyun": 18398, "нÑĮ": 18399, "ĠKomm": 18400, "andra": 18401, "shot": 18402, "ssä": 18403, "ĠÑĨе": 18404, "atta": 18405, "Ġexpense": 18406, "ĠGPU": 18407, "ĠPast": 18408, "ribly": 18409, "ĠëŃIJìķ¼": 18410, "Ġгода": 18411, "Ġrespir": 18412, "æĿ±": 18413, "ĠQueens": 18414, "hops": 18415, "Ġsérie": 18416, "Ġpref": 18417, "Ġcomed": 18418, "Ġplut": 18419, "ĠOverall": 18420, "ĠãģĿ": 18421, "Ġcush": 18422, "Ġringing": 18423, "Ġincorrect": 18424, "ĠÑģÑĤÑĢ": 18425, "Ġgeometry": 18426, "Ġadvertis": 18427, "ĠШ": 18428, "Ġreviewed": 18429, "ãģĤãģĤ": 18430, "Ġdozens": 18431, "Ġdetermination": 18432, "ĠPhill": 18433, "Ġcontributed": 18434, "ĠCit": 18435, "Ġpassengers": 18436, "Ġcôté": 18437, "Ġrever": 18438, "Ġtechnological": 18439, "Ġallen": 18440, "Ġraining": 18441, "avi": 18442, "Ġsalty": 18443, "Ġtyping": 18444, "ĠÑĤе": 18445, "Ġtilt": 18446, "Ġì¹ĺ": 18447, "ĠоÑĢ": 18448, "ĠпÑĢÑıм": 18449, "Ġrou": 18450, "Ġarena": 18451, "arat": 18452, "åĪ«": 18453, "HHHH": 18454, "Ġmanufacturers": 18455, "ĠEdward": 18456, "Ġtuck": 18457, "Ġblows": 18458, "ingo": 18459, "ĠMarc": 18460, "ìķĦìĦľ": 18461, "Mich": 18462, "ĠClean": 18463, "è´": 18464, "esto": 18465, "ĠPack": 18466, "Ġshaft": 18467, "BRUNO": 18468, "Ġaven": 18469, "uur": 18470, "ÑģколÑĮко": 18471, "ê´Ģ": 18472, "Ġautomated": 18473, "Ġventure": 18474, "Ġsurveillance": 18475, "ĠGrow": 18476, "ĠEmer": 18477, "ĠдоÑĢ": 18478, "Ġinvestor": 18479, "ĠYok": 18480, "Ġlatter": 18481, "ĠNI": 18482, "Ġfunctioning": 18483, "ĠHamilton": 18484, "Ġ51": 18485, "Ġmurdered": 18486, "Ġanchor": 18487, "Ġcuc": 18488, "ĠSCP": 18489, "ĠMadam": 18490, "Ġconstraints": 18491, "Ġbarn": 18492, "anken": 18493, "Ġë§İìĿĢ": 18494, "ĠMotor": 18495, "ĠDoing": 18496, "Ġamen": 18497, "etts": 18498, "Ġinstructor": 18499, "egt": 18500, "ako": 18501, "Ġposture": 18502, "ivia": 18503, "ĠPolish": 18504, "Ġдва": 18505, "Ġcolorful": 18506, "Ġelbow": 18507, "Ġparle": 18508, "Ġpasser": 18509, "Ġcondem": 18510, "ortal": 18511, "Ġfertil": 18512, "اد": 18513, "ĠColomb": 18514, "Ġalignment": 18515, "Ġastronaut": 18516, "ĠMut": 18517, "Ġsalmon": 18518, "Ġstructured": 18519, "ŀר": 18520, "Ġclicks": 18521, "Ġmiej": 18522, "æĶ¿": 18523, "ãģĦãĤĦ": 18524, "ĠRound": 18525, "Ġrainbow": 18526, "ĠVA": 18527, "ãģĶãģĸ": 18528, "ì§Ī": 18529, "otz": 18530, ",": 21732, "Ġchords": 21733, "ĠSanders": 21734, "Ġë¶Ħë": 21735, "Ben": 21736, "Ġdarüber": 21737, "ilians": 21738, "Ġordering": 21739, "ĠManh": 21740, "Ġkilogram": 21741, "ĠkarÅŁ": 21742, "Ġgrasp": 21743, "Ġghosts": 21744, "alen": 21745, "ĠJedi": 21746, "Ġбли": 21747, "Ġdownloaded": 21748, "Ġconducting": 21749, "ĠHak": 21750, "Ġresearcher": 21751, "ilan": 21752, "good": 21753, "ĠHannah": 21754, "ĠdÃ¼ÅŁÃ¼n": 21755, "ĠMessiah": 21756, "uity": 21757, "iona": 21758, "Ġprobable": 21759, "ĠYE": 21760, "Ġindependently": 21761, "Ġbuffer": 21762, "burn": 21763, "ourd": 21764, "ĠMcK": 21765, "Ġlingu": 21766, "ujemy": 21767, "еÑĢÑĤ": 21768, "Ġintuitive": 21769, "Ġcracks": 21770, "appropri": 21771, "nty": 21772, "Ġgeen": 21773, "Ġlend": 21774, "Ġcertification": 21775, "IDS": 21776, "unter": 21777, "pees": 21778, "Ġtrump": 21779, "Ġbankrupt": 21780, "Ġfeas": 21781, "èĹ": 21782, "Ġduż": 21783, "æ¸ħ": 21784, "Ġviruses": 21785, "Ġ58": 21786, "god": 21787, "Ġжел": 21788, "Ġstalk": 21789, "Ind": 21790, "achi": 21791, "ĠCF": 21792, "ĠCond": 21793, "Ġsanct": 21794, "Ġconten": 21795, "Ġfreed": 21796, "ĠRT": 21797, "Ġmentors": 21798, "족": 21799, "Ġportable": 21800, "ĠPaulo": 21801, "rane": 21802, "HAHA": 21803, "ĠSection": 21804, "çĨ": 21805, "hyun": 21806, "ĠÎŃÏĩ": 21807, "ĠPub": 21808, "ĠIndepend": 21809, "Ġcompounds": 21810, "ĠÑģÑĭ": 21811, "Ġmessaging": 21812, "Ġdedication": 21813, "Ġnoticing": 21814, "Ġdevoted": 21815, "ÑİÑĤÑģÑı": 21816, "Ġsnakes": 21817, "Ġbattlefield": 21818, "pers": 21819, "Ġdela": 21820, "92": 21821, "Ġhai": 21822, "illä": 21823, "érer": 21824, "every": 21825, "Ġresponsive": 21826, "×Ļ×ķ": 21827, "opf": 21828, "éī": 21829, "Ĭ¸": 21830, "Because": 21831, "Ġtourism": 21832, "Ġê·¸ê²Į": 21833, "×ķצ": 21834, "Ġcans": 21835, "stüt": 21836, "Ġdonne": 21837, "ĠDios": 21838, "ĠUber": 21839, "actory": 21840, "Ġoriented": 21841, "ĠHerm": 21842, "Ġpatron": 21843, "urf": 21844, "bei": 21845, "Ġprograma": 21846, "ĠOhh": 21847, "gener": 21848, "Ġfist": 21849, "ĠWendy": 21850, "Ġanda": 21851, "Ġguessed": 21852, "Ġfreak": 21853, "ä¸Ńåľĭ": 21854, "ĠKings": 21855, "chool": 21856, "Ġoffline": 21857, "ĠIndiana": 21858, "ĠAlliance": 21859, "Ġ53": 21860, "Ġparticul": 21861, "ĠFocus": 21862, "Ġinhabit": 21863, "Ġê°ĻìĿĢëį°": 21864, "ĠMcG": 21865, "owski": 21866, "ĠìĿ´ê±´": 21867, "ĠpaÅĦst": 21868, "они": 21869, "itta": 21870, "Ġconfirmation": 21871, "ĠBrooklyn": 21872, "Ġnoodle": 21873, "fund": 21874, "itud": 21875, "Ġgrandparents": 21876, "Ġbarbecue": 21877, "ειÏĤ": 21878, "Ġá": 21879, "Ġballot": 21880, "ĠVeter": 21881, "Ġpipes": 21882, "igious": 21883, "ĠGraph": 21884, "ested": 21885, "Ġë¸Įë": 21886, "ĠKE": 21887, "ãģ¡ãĤĩãģ£ãģ¨": 21888, "Ġeins": 21889, "Ġhatred": 21890, "ãģijãģ©": 21891, "Ġdang": 21892, "eeee": 21893, "Ġarchae": 21894, "ĠJesse": 21895, "Ġdetected": 21896, "Ġseni": 21897, "burgh": 21898, "Ġdisplacement": 21899, "Ġdop": 21900, "Ġconditioning": 21901, "ĠнеÑģколÑĮко": 21902, "Ġdisturbing": 21903, "PH": 21904, "Ġthinner": 21905, "Ġwounded": 21906, "ĠCuando": 21907, "Ġcushion": 21908, "Ġwhites": 21909, "Ġpreferences": 21910, "Ġì¤Ģë¹Ħ": 21911, "Ġkaż": 21912, "ĠGate": 21913, "ĠPath": 21914, "dles": 21915, "à¸Ħร": 21916, "imore": 21917, "Ġë³´ìŬ": 21918, "Ġdisciplines": 21919, "á»ı": 21920, "Ġmesma": 21921, "ĠìĥĪë": 21922, "Ġìĭ¬": 21923, "Ġging": 21924, "Ġumbrella": 21925, "IGHT": 21926, "Ġpension": 21927, "Ġcombining": 21928, "SS": 21929, "Ġrectangle": 21930, "á»ĩt": 21931, "Ġproxim": 21932, "ĠCow": 21933, "¸Į": 21934, "Ġintentional": 21935, "æķĻ": 21936, "Ġdecid": 21937, "ĠÑģкаж": 21938, "ĠUma": 21939, "iasm": 21940, "buz": 21941, "Ġdebris": 21942, "Ġcass": 21943, "ĠProp": 21944, "iska": 21945, "ëł¥": 21946, "esterol": 21947, "ussian": 21948, "ìĿ´ëŀij": 21949, "Ġunlimited": 21950, "Ġadmire": 21951, "Ġtightly": 21952, "Ġgenome": 21953, "ĠJunior": 21954, "venir": 21955, "gus": 21956, "ĠcÄĥ": 21957, "ĠVlad": 21958, "ĠíĤ": 21959, "Ġrelativ": 21960, "inci": 21961, "Ġaunque": 21962, "ĠBoys": 21963, "ÑĨион": 21964, "ĠSwiss": 21965, "Ġphysicians": 21966, "Ġíıī": 21967, 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"ĠTiger": 22025, "ĠONE": 22026, "Ġneurons": 22027, "owanie": 22028, "ÄĹ": 22029, "ĠShar": 22030, "ĠLandes": 22031, "Ġconferences": 22032, "該": 22033, "Ġcredential": 22034, "Ġlime": 22035, "inee": 22036, "xit": 22037, "pay": 22038, "Ġincons": 22039, "Ġ>>:": 22040, "èªį": 22041, "Ġíŀĺë": 22042, "Ġlesser": 22043, "Ġspill": 22044, "Ġpremise": 22045, "Ġ365": 22046, "ĠHost": 22047, "Ġtomar": 22048, "×IJ׾": 22049, "ë²Ī": 22050, "ĠWhats": 22051, "Ġlightweight": 22052, "ĠMap": 22053, "fia": 22054, "ellschaft": 22055, "Ġvendors": 22056, "uesto": 22057, "ĠMister": 22058, "ĠÐŁÑĢи": 22059, "åı³": 22060, "hma": 22061, "Ġintentionally": 22062, "ĠTang": 22063, "éĹ®": 22064, "Ġidentification": 22065, "Ġetcetera": 22066, "ĠNee": 22067, "ĠÑĤÑĢи": 22068, "ê·¸": 22069, "Ġcryptocur": 22070, "Ġinhale": 22071, "Ġaddict": 22072, "åIJĦä½į": 22073, "Ġmau": 22074, "ĠÑĤакаÑı": 22075, "Ġë²Ħ": 22076, "Ġcomprar": 22077, "iedzieÄĩ": 22078, "ĠоÑĤно": 22079, "Ġbeginner": 22080, "ĠмÑĥж": 22081, "Ġobsc": 22082, 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"ĠAH": 25888, "Ġpew": 25889, "Ġdictionary": 25890, "Take": 25891, "shirts": 25892, "ĠHugh": 25893, "ĠعÙĦÙĬ": 25894, "ĠPic": 25895, "Ġenrolled": 25896, "Ġjednak": 25897, "Ġofferings": 25898, "Ġcoraz": 25899, "Life": 25900, "Ġ!!!": 25901, "Ġcler": 25902, "ĠVideos": 25903, "ĠRodrig": 25904, "ĠIdent": 25905, "ĠPos": 25906, "ĠStage": 25907, "ĠRace": 25908, "Ġenact": 25909, "ãģĦãģ¾ãģĹãģŁ": 25910, "ĠGy": 25911, "ĠHispan": 25912, "Ġdefence": 25913, "ĠCampbell": 25914, "matic": 25915, "Ġrelev": 25916, "Ġpeach": 25917, "Ħ¸ìļĶ": 25918, "Ġparadise": 25919, "Ġceremon": 25920, "Ġannoyed": 25921, "æĮĩ": 25922, "lax": 25923, "Ġexploit": 25924, "Ġclause": 25925, "eker": 25926, "ĠBloom": 25927, "nant": 25928, "ateurs": 25929, "Ġheights": 25930, "Even": 25931, "Ñģон": 25932, "Ġoutrage": 25933, "ĠVietnamese": 25934, "ãģ¯ãģ¯": 25935, "TR": 25936, "Ġeer": 25937, "Ġcannon": 25938, "ĠComb": 25939, "IJë§Į": 25940, "è»Ĭ": 25941, "Ġê²ĥëıĦ": 25942, "Ġaccomplishments": 25943, "ĠAnalytics": 25944, "Ġshaping": 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"emas": 26062, "Ġãģ§ãģĻ": 26063, "ĠmajÄħ": 26064, "Ġwebinars": 26065, "Ġcannabis": 26066, "Ġdamals": 26067, "ĠNorthwest": 26068, "Ġpada": 26069, "Ġcrowds": 26070, "Ġfutures": 26071, "Ġän": 26072, "Ġcivilians": 26073, "ĠSachen": 26074, "æį": 26075, "Ġtraces": 26076, "Ġë¨¹ê³ł": 26077, "QU": 26078, "é¡ĺãģĦ": 26079, "ĠIF": 26080, "anın": 26081, "ìĤ´": 26082, "Ġbiblical": 26083, "ĠVed": 26084, "Ġstoring": 26085, "ÑĢавлÑı": 26086, "æĩī該": 26087, "Ġnast": 26088, "Ġdö": 26089, "ÑĢоп": 26090, "elia": 26091, "Ġsideways": 26092, "ĠUnderstand": 26093, "ĠQur": 26094, "Ġperpend": 26095, "ĠMillionen": 26096, "Ġwatermelon": 26097, "ĠDivine": 26098, "ultur": 26099, "abord": 26100, "Ġsuccesses": 26101, "Ġhombre": 26102, "Ġcarp": 26103, "Ġsuscept": 26104, "ungkin": 26105, "Ġkij": 26106, "ulus": 26107, "اج": 26108, "Ġnotch": 26109, "Ġpolynomial": 26110, "å¹²": 26111, "å©": 26112, "Ġúnico": 26113, "Ġtelescope": 26114, "Ġpolitique": 26115, "kiem": 26116, "ĠÎŃνα": 26117, "Ġaggregate": 26118, "ĠGeoff": 26119, "Ġtril": 26120, "ĠGRA": 26121, "Ġsubscriber": 26122, "imet": 26123, "ĠдоллаÑĢ": 26124, "oping": 26125, "Ġtherapeut": 26126, "ĠCancer": 26127, "Ġparade": 26128, "Ġirrig": 26129, "âĻªâĻª": 26130, "Ġclearer": 26131, "Ġbog": 26132, "ĠMaur": 26133, "าà¸ĩ": 26134, "ĠShanghai": 26135, "achte": 26136, "ĠKol": 26137, "elujah": 26138, "Ġhav": 26139, "ĠCrime": 26140, "sek": 26141, "Ġë¡ľ": 26142, "ienna": 26143, "ĠGor": 26144, "èĽ": 26145, "ĠпоÑĤÑĢ": 26146, "ĠкажеÑĤÑģÑı": 26147, "ĠLift": 26148, "ĠSort": 26149, "ĠPsal": 26150, "Ġping": 26151, "ĵĿ": 26152, "phis": 26153, "ĠFUCK": 26154, "ĠSyn": 26155, "Ġbamboo": 26156, "¬ìĺģ": 26157, "cuts": 26158, "Ġmmm": 26159, "Ġfunktioniert": 26160, "Ġ_": 26161, "ÃŃcio": 26162, "Stop": 26163, "Ġimaginary": 26164, "Ġnotamment": 26165, "ĠInitiative": 26166, "ãĥ¥": 26167, "ĠKurt": 26168, "Ġloosen": 26169, "Ġbuscar": 26170, "çģ«": 26171, "Ġzelf": 26172, "Ġprops": 26173, "åĽī": 26174, "Ġmoeten": 26175, "Ġmilli": 26176, "Ġhalls": 26177, "ĠMatch": 26178, "Ġbrackets": 26179, "ĠCou": 26180, "æ¦Ĥ": 26181, "ĠÐľÐ°ÑĢ": 26182, "ISA": 26183, "Ġcigarette": 26184, "Ġcompetitions": 26185, "ĠMIN": 26186, "Ġbehö": 26187, "voor": 26188, "Ġust": 26189, "ĠZi": 26190, "ĠOcc": 26191, "ulates": 26192, "Ġballoons": 26193, "Ġpronto": 26194, "ĠMiy": 26195, "ĠFile": 26196, "ĠклаÑģÑģ": 26197, "нÑĥл": 26198, "Ġcereal": 26199, "Ġincrement": 26200, "Ġrefined": 26201, "åı¦å¤ĸ": 26202, "prising": 26203, "ĠRF": 26204, "Ġrespectful": 26205, "Ġloot": 26206, "asket": 26207, "Ġdeixa": 26208, "ingle": 26209, "Ġfunciona": 26210, "ĠRevel": 26211, "Ġsober": 26212, "Ġperforms": 26213, "ĠGentle": 26214, "ãĤ¨": 26215, "Ġrecipient": 26216, "ĠHause": 26217, "Ġëĥ": 26218, "From": 26219, "Ġministers": 26220, "Ġparadox": 26221, "å°±æĺ¯èªª": 26222, "Ġtasting": 26223, "Ġ×Ķ×Ĺ": 26224, "Ġreuse": 26225, "ĠLane": 26226, "ĠÑģовеÑĢÑĪ": 26227, "Ġremembers": 26228, "Ġfeminist": 26229, "Ġcommitments": 26230, "Ġprojected": 26231, "Ġgaz": 26232, "iyoruz": 26233, "Ġobligations": 26234, "Ro": 26235, "zar": 26236, "Ġchw": 26237, "ĠJAM": 26238, "ĠbÄĻdÄħ": 26239, "aspberry": 26240, "ĠмеÑģÑĤо": 26241, "ë²ķ": 26242, "Ġregulated": 26243, "Ġwicht": 26244, "ĠTrevor": 26245, "Ġsecondly": 26246, "ĠIhre": 26247, "elsh": 26248, "Ġreporters": 26249, "ÑĤоÑĢа": 26250, "oyo": 26251, "GI": 26252, "Ġinterconnect": 26253, "éIJĺ": 26254, "OSH": 26255, "æŃ²": 26256, "Ġbrass": 26257, "Ġignoring": 26258, "ä»ĬæĹ¥": 26259, "infect": 26260, "Ġprojekt": 26261, "oret": 26262, "ÏĦαν": 26263, "ĠÑĤип": 26264, "Ġmutta": 26265, "Ġunboxing": 26266, "Ħ°": 26267, "å¡Ĭ": 26268, "Ġadvised": 26269, "ĠDenver": 26270, "Ġseverely": 26271, "ĠMhm": 26272, "Ġflipped": 26273, "Ġpien": 26274, "Ġkommun": 26275, "ĠFRE": 26276, "Ġà®ĩà®°": 26277, "ainted": 26278, "Ġknives": 26279, "Ġhabl": 26280, "Ġgeworden": 26281, "arettes": 26282, "CS": 26283, "ĠмаленÑĮ": 26284, "Ġgalax": 26285, "Ġninete": 26286, "ê±°ëĤĺ": 26287, "Ġsis": 26288, "Ġadvisory": 26289, "Ġdrilling": 26290, "ĠWouldn": 26291, "ünf": 26292, "gestellt": 26293, "ĠHelen": 26294, "Ġ×ŀ×IJ": 26295, "apolis": 26296, "Ġrzeczy": 26297, "Ġterra": 26298, "Ġhep": 26299, "Ġalgún": 26300, "ikk": 26301, "Ġastronom": 26302, "ĠStarbucks": 26303, "kÄħ": 26304, "Ġpatrol": 26305, "Ġì½Ķ": 26306, "Ġgon": 26307, "ĠãĢIJ": 26308, "Ġsonst": 26309, "Ġencounters": 26310, "Ġretrou": 26311, "Ġsharks": 26312, "Ġdor": 26313, "ĠRever": 26314, "Ġevapor": 26315, "Ġreservoir": 26316, "Ġalleged": 26317, "uler": 26318, "Ġverm": 26319, "Ġcommerce": 26320, "Ġfitted": 26321, "gem": 26322, "Ġtactical": 26323, "Ġlith": 26324, "éīĦå¡Ķ": 26325, "had": 26326, "è®Ĭ": 26327, "Ġcarbohyd": 26328, "Ġlengths": 26329, "ιο": 26330, "Ġdemographic": 26331, "Rob": 26332, "ĠSkin": 26333, "ccoli": 26334, "Ġsimplified": 26335, "Ġreadily": 26336, "ĠCum": 26337, "adesh": 26338, "ĠDÃ¥": 26339, "usst": 26340, "igne": 26341, "eton": 26342, "Ġmenor": 26343, "qi": 26344, "OOM": 26345, "à¸Ńà¸Ļ": 26346, "Ġpsychiat": 26347, "Ġeighty": 26348, "Ġмилли": 26349, "ĠTob": 26350, "edo": 26351, "網": 26352, "ĠÄijến": 26353, "Ġcircuits": 26354, "ĠLAUGH": 26355, "icism": 26356, "emor": 26357, "Ġregener": 26358, "egree": 26359, "Ġbureauc": 26360, "ĠAlber": 26361, "ä¹ĭå¾Į": 26362, "ĠWor": 26363, "夫": 26364, "Ġresin": 26365, "ĠbyÅĤy": 26366, "ĠIG": 26367, "à¯į,": 26368, "Ġ78": 26369, "Ġweeds": 26370, "ĠMyth": 26371, "93": 26372, "æ¿": 26373, "ĠëĤĺìĻĶ": 26374, "év": 26375, "á½": 26376, "ören": 26377, "çar": 26378, "ĠPAUL": 26379, "Ġdisadvant": 26380, "Ġpositioning": 26381, "Ġcocktail": 26382, "Ġagrees": 26383, "nn": 26384, "ĠSally": 26385, "Ms": 26386, "Ġinherent": 26387, "Ġmonetary": 26388, "Ġnatur": 26389, "ĠNh": 26390, "ĠImport": 26391, "Ġleben": 26392, "Ġwi": 26393, "ussy": 26394, "Ġobes": 26395, "Ġwandering": 26396, "Ġìĭłë": 26397, "Äħda": 26398, "etchup": 26399, "Ġdisposal": 26400, "ĠJA": 26401, "ĠCer": 26402, "zilla": 26403, "Ġvirgin": 26404, "ĠSlide": 26405, "andel": 26406, "Ġrighteousness": 26407, "ĠΣ": 26408, "Ġideia": 26409, "ä½łå¥½": 26410, "иÑĢоваÑĤÑĮ": 26411, "ר×IJ": 26412, "Comment": 26413, 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26471, "ĠпÑĢиÑĩ": 26472, "ĠHeavy": 26473, "Ġswords": 26474, "Ġmanipulation": 26475, "Ġfunk": 26476, "ĠvÃ¥r": 26477, "ĠTaliban": 26478, "Ġë°¥": 26479, "Ġacne": 26480, "ürü": 26481, "Ġdeswegen": 26482, "ĠDust": 26483, "Ġsilic": 26484, "Ġhooks": 26485, "Ġblij": 26486, "Ġpetits": 26487, "Ġfilme": 26488, "ĠBereich": 26489, "ĠSaid": 26490, "Ġimposed": 26491, "Ġdiary": 26492, "ĠгоÑĢ": 26493, "ĠGates": 26494, "Ġalta": 26495, "å¸Į": 26496, "Ġchcia": 26497, "pleasant": 26498, "Ġë°Ŀ": 26499, "Ġmożemy": 26500, "ĠAustria": 26501, "Ġbroker": 26502, "Ġsucked": 26503, "èĢĥ": 26504, "Ġcompartment": 26505, "Ġclone": 26506, "Ġ×Ķ×¢": 26507, "ĠDanke": 26508, "Ġnochmal": 26509, "езд": 26510, "Ġadrenal": 26511, "Ġkleinen": 26512, "ãģ¾ãģĹãĤĩãģĨ": 26513, "Ġsubsequently": 26514, "Ġdecentral": 26515, "Ġgenetics": 26516, "Ġê´ij": 26517, "Ġmonitors": 26518, "ĠApplic": 26519, "ĠReporter": 26520, "wert": 26521, "Ġwiem": 26522, "ĠMovement": 26523, "Ġinterviewing": 26524, "Ġhairs": 26525, "Ġpuò": 26526, "ĠChelsea": 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"ĠCrim": 30691, "ût": 30692, "78": 30693, "Ġsincerely": 30694, "raz": 30695, "Ġë³µ": 30696, "Ġarriv": 30697, "Ġconception": 30698, "ĠPersian": 30699, "Ġsjäl": 30700, "Ġstarring": 30701, "ĠìķĦ무": 30702, "ĠForever": 30703, "еÑģÑĤÑĮ": 30704, "Ġveil": 30705, "Ġsubtit": 30706, "odka": 30707, "ĠоÑĤноÑĪ": 30708, "Ġcooks": 30709, "енÑı": 30710, "Kay": 30711, "Ġniños": 30712, "ĠPhone": 30713, "Ġstitching": 30714, "Ġfingerprint": 30715, "é¢ĺ": 30716, "λά": 30717, "Ġdedicate": 30718, "ĠLob": 30719, "Ġblacks": 30720, "ĠBle": 30721, "bout": 30722, "ĠÄijang": 30723, "Ġeks": 30724, "Ġsquash": 30725, "ĠKü": 30726, "odi": 30727, "ĠnÆ°á»Ľc": 30728, "Ġvoyage": 30729, "Ġplayful": 30730, "ĠØ¥ÙĦÙī": 30731, "anic": 30732, "Ġcondemn": 30733, "ĠBöyle": 30734, "ĠPolize": 30735, "ãĤ¿ãĥ¼": 30736, "Ġayuda": 30737, "Ġpam": 30738, "à¹Ħà¸Ľ": 30739, "ĠKathy": 30740, "един": 30741, "нова": 30742, "Ġbrig": 30743, "eger": 30744, "Ġeagle": 30745, "Ġvisions": 30746, "ĠíķŃìĥģ": 30747, "Ġshitty": 30748, "Ġhott": 30749, "ĠBritt": 30750, "utors": 30751, "ENTE": 30752, "æĽ²": 30753, "Ġphon": 30754, "ĠBing": 30755, "ĠподдеÑĢж": 30756, "spring": 30757, "æĸ¯": 30758, "etten": 30759, "Ġpilgr": 30760, "Ġediyor": 30761, "енÑĤÑĭ": 30762, "aggio": 30763, "Ġjul": 30764, "Ġcomprend": 30765, "teil": 30766, "Ġز": 30767, "Ġperformers": 30768, "Ġinfamous": 30769, "ĠMK": 30770, "çª": 30771, "æ³ģ": 30772, "otle": 30773, "eff": 30774, "ĠHash": 30775, "Ġcoward": 30776, "ĠBRA": 30777, "ĠDD": 30778, "Ġcomida": 30779, "Ġplata": 30780, "Ġflap": 30781, "ĠMehr": 30782, "ribution": 30783, "ĠYemen": 30784, "Ġmysteries": 30785, "ĠÄ°yi": 30786, "Ġstell": 30787, "Ġeyeliner": 30788, "Ġdeles": 30789, "Ġnailed": 30790, "Ġillnesses": 30791, "Ġstacks": 30792, "Ġtrabajar": 30793, "flower": 30794, "ciu": 30795, "Ġcrude": 30796, "Ġsubstantially": 30797, "Ġhomem": 30798, "Ġnephew": 30799, "Ġstamps": 30800, "Ġcarbs": 30801, "ÑĮÑĤе": 30802, "mooth": 30803, "Ġtunnels": 30804, "acie": 30805, "æ³¢": 30806, "ĠSeñ": 30807, "ĠHera": 30808, "ĠìķĦëĭĪìĹIJìļĶ": 30809, "ĠWyoming": 30810, "ĠHDMI": 30811, "ĠLis": 30812, "ución": 30813, "Ġsteer": 30814, "оÑİ": 30815, "иÑĤа": 30816, "NT": 30817, "Ġìĸ¼êµ´": 30818, "Ġpalms": 30819, "Ġneon": 30820, "ованиÑı": 30821, "Ġfiltering": 30822, "Ġjouer": 30823, "ĠHö": 30824, "ĠнеÑģ": 30825, "ê²łìĸ´ìļĶ": 30826, "Ġ81": 30827, "Ġstoryline": 30828, "Ġprzep": 30829, "Ġthanking": 30830, "ĠBoeing": 30831, "Ġsoftly": 30832, "jem": 30833, "алÑĮнÑĭÑħ": 30834, "Ġflashlight": 30835, "ĠпÑĥ": 30836, "ĠWOMAN": 30837, "ắc": 30838, "ÃŃch": 30839, "Ġluxurious": 30840, "Ġwün": 30841, "Ġimpactful": 30842, "Ġconson": 30843, "reu": 30844, "irring": 30845, "ifter": 30846, "Ġconstituents": 30847, "èIJ½": 30848, "Ġ94": 30849, "ĠTou": 30850, "gom": 30851, "ĠìĥĿê°ģìĿĦ": 30852, "Ġstereotypes": 30853, "Ġmożli": 30854, "åĪĨ享": 30855, "Ĥ¨": 30856, "Ġpencils": 30857, "ĠÑģлож": 30858, "Ġihrem": 30859, "ĠBesch": 30860, "ĠKoh": 30861, "ĠEntscheid": 30862, "Ġlek": 30863, "Ġförs": 30864, "Ġtotalmente": 30865, "Ġlively": 30866, "Ġentropy": 30867, "Ġdiscern": 30868, "ĠÐĹна": 30869, "Ġdov": 30870, "Ġmythology": 30871, "è¨ĺå¾Ĺ": 30872, "apanese": 30873, "Ġapproximate": 30874, "аÑĤив": 30875, "ifiable": 30876, "ĠSeo": 30877, "åĢĴ": 30878, "´ìĭ¬íŀĪ": 30879, "Ġìĺ·": 30880, "Ġtemporal": 30881, "ĠiT": 30882, "Ġestat": 30883, "ким": 30884, "Ġsprink": 30885, "Ġgrund": 30886, "Ġinfantry": 30887, "Ġschaffen": 30888, "ç´Ħ": 30889, "Ġank": 30890, "riages": 30891, "ĠYeon": 30892, "ĠMoroc": 30893, "Ġinvasive": 30894, "ģĶ": 30895, "Ġparenting": 30896, "ĠRis": 30897, "ibile": 30898, "Ġmods": 30899, "å½¢": 30900, "ĠпÑĢовеÑĢ": 30901, "ĠThing": 30902, "ĠWherever": 30903, "Ġacknowledging": 30904, "Ġpawn": 30905, "ummer": 30906, "orb": 30907, "69": 30908, "Ġretrouve": 30909, "Ġrelies": 30910, "ĠHighway": 30911, "Ġawe": 30912, "ãģ§ãģĻãģĭ": 30913, "itaire": 30914, "Ġapplicant": 30915, "Ġaisle": 30916, "worm": 30917, "Ġpayload": 30918, "Ġcarre": 30919, "ĠBach": 30920, "æł¼": 30921, "Ġì¹ľêµ¬ë": 30922, "ние": 30923, "ĠitÃŃs": 30924, "onnaise": 30925, "sol": 30926, "èı¯": 30927, "algia": 30928, "Ġrocking": 30929, "Ġbesten": 30930, "rites": 30931, "^^": 30932, "иной": 30933, "Ġbaixo": 30934, "Ġ기ìĸµ": 30935, "оÑĤÑĢи": 30936, "sim": 30937, "Ġincarn": 30938, "ëĭ¤ìĿĮ": 30939, "Ġlick": 30940, "sided": 30941, "Ġ71": 30942, "forder": 30943, "Ġresonance": 30944, "Ġtegen": 30945, "Ġmetaph": 30946, "owser": 30947, "Ġ×IJ׳×Ĺ׳×ķ": 30948, "?ãĢį": 30949, "Ġspielen": 30950, "Ġvolley": 30951, "ĶìĿ´íģ¬ìĹħ": 30952, "looked": 30953, "Ġsentenced": 30954, "Ġmultiplying": 30955, "Ġideals": 30956, "Ġwahrscheinlich": 30957, "Ġdeposits": 30958, "bilir": 30959, "Ġeffet": 30960, "illon": 30961, "Īë§Į": 30962, "Ġtestimon": 30963, "Ġzawsze": 30964, "ĠпÑĢоÑĨеÑģÑģ": 30965, "ĠLav": 30966, "ä¸įéĮ¯": 30967, "Ġtravailler": 30968, "Ġlaisse": 30969, "ĠMountains": 30970, "ĠÑĢоб": 30971, "Ġexamined": 30972, "itus": 30973, "Was": 30974, "лÑĭ": 30975, "Ġattributed": 30976, "ĠìĬ¹": 30977, "ĠBaron": 30978, "Ġgep": 30979, "Ġattent": 30980, "ĠCollection": 30981, "Ġtheat": 30982, "ĠCai": 30983, "Ġwells": 30984, "Ġhumano": 30985, "çĹħ": 30986, "ĠHast": 30987, "ĠÑħоÑĤÑı": 30988, "czas": 30989, "Ġpermits": 30990, "Ġlegg": 30991, "Ġepo": 30992, "ĠFen": 30993, "Ġthi": 30994, "ĠFoi": 30995, "Ġélect": 30996, "Ġ83": 30997, "Ġoverth": 30998, "Ġè¬Ŀè¬Ŀ": 30999, "Ġtenant": 31000, "è²·": 31001, "Next": 31002, "Ġpraised": 31003, "security": 31004, "ĠImpact": 31005, "为ä»Ģä¹Ī": 31006, "Ġvouch": 31007, "Ġnegó": 31008, "Ġunve": 31009, "Ġcriticize": 31010, "ĠKenya": 31011, "Ġtactic": 31012, "Ġlogr": 31013, "Ġpois": 31014, "Ġpapa": 31015, "speaks": 31016, "ðŁij": 31017, "ispers": 31018, "Ġsurplus": 31019, "Ġcolder": 31020, "åįĹ": 31021, "åIJ¬": 31022, "plets": 31023, "ĠVienna": 31024, "ĠLead": 31025, "Ġaerial": 31026, "ĠTah": 31027, "енÑĤов": 31028, "ĠGreeks": 31029, "Cam": 31030, "Ġmáxim": 31031, "Ġkuin": 31032, "chio": 31033, "Ġdemonstrates": 31034, "anos": 31035, "ĠCert": 31036, "ĠÑįн": 31037, "Ġblogs": 31038, "ĠìĦľìļ¸": 31039, "Ġbeams": 31040, "иков": 31041, "Ġprompted": 31042, "Ġfrightening": 31043, "ĠPorsche": 31044, "ãģĪãģ¦": 31045, "larını": 31046, "Ġchilling": 31047, "isphere": 31048, "Ġflashing": 31049, "ĠKard": 31050, "bread": 31051, "Ġexh": 31052, "Ġtycker": 31053, "Ġecological": 31054, "ĠMae": 31055, "Ġ×ŀ×IJ×ķ×ĵ": 31056, "ĠëĤĺëıĦ": 31057, "лон": 31058, "yss": 31059, "Ġpergunt": 31060, "Ġprix": 31061, "izzard": 31062, "Ġcancers": 31063, "Ġ91": 31064, "susp": 31065, "ĠItem": 31066, "ÅŁa": 31067, "Ġpest": 31068, "ĠtakÄħ": 31069, "Ġlymph": 31070, "ĠPatri": 31071, "fill": 31072, "Ġreconna": 31073, "Ġoptimism": 31074, "Ġmimic": 31075, "Ġì²ľ": 31076, "ĠMadame": 31077, "ocy": 31078, "lining": 31079, "åijĬ訴": 31080, "erme": 31081, "Ġfolders": 31082, "ĠczÅĤ": 31083, "uchar": 31084, "Ġcurso": 31085, "Ġbreach": 31086, "ниÑĤÑĮ": 31087, "ĠpamiÄĻ": 31088, "Ġelig": 31089, "Ġautop": 31090, "Flow": 31091, "Ġprogrammed": 31092, "ĠProcess": 31093, "Ġfigur": 31094, "ĠSF": 31095, "ĠEles": 31096, "Ġprogrammes": 31097, "Ġdizzy": 31098, "ìĭľê°Ħ": 31099, "Ġлибо": 31100, "Ġsniff": 31101, "ĠSebastian": 31102, "ĠHye": 31103, "Ġ4000": 31104, "Ġpermite": 31105, "æ¢Ŀ": 31106, "ĠзаÑī": 31107, "Ġguit": 31108, "ĠDais": 31109, "Ġaccordance": 31110, "Ġmodular": 31111, "ogeneous": 31112, "æĭį": 31113, "Ġpouquinho": 31114, "Ġartillery": 31115, "Ġlubric": 31116, "Ġvolcan": 31117, "ĠNH": 31118, "ðŁ¤": 31119, "Ġdean": 31120, "Rh": 31121, "Ġministre": 31122, "åĿIJ": 31123, "ĠInv": 31124, "ĠBulgar": 31125, "ĠDaten": 31126, "èİ": 31127, "Im": 31128, "Ġoriginated": 31129, "ĠNixon": 31130, "integr": 31131, "Ġlacks": 31132, "ĠNacht": 31133, "ìĸ´ëĤĺ": 31134, "camera": 31135, "Ġradish": 31136, "kiye": 31137, "Ġanges": 31138, "Ġpréf": 31139, "juk": 31140, "ĠBee": 31141, "ĠBU": 31142, "ĠвоÑģп": 31143, "ĠBT": 31144, "êmes": 31145, "ĠStück": 31146, "ĠInk": 31147, "æĪĸèĢħ": 31148, "ĠSergeant": 31149, "ĠMultip": 31150, "Ġhiçbir": 31151, "ĠСам": 31152, "ĠDé": 31153, "olph": 31154, "ìĸ¸": 31155, "Ġimpat": 31156, "ĠìķĬê³ł": 31157, "ĠÑĤакого": 31158, "ĠнавеÑĢное": 31159, "Ġunpredictable": 31160, "Ġmend": 31161, "ĠìĹĨìĸ´ìļĶ": 31162, "ĠjakieÅĽ": 31163, "Ġanni": 31164, "Ġdonné": 31165, "ĠKirsty": 31166, "Ġrectangular": 31167, "Ġempezar": 31168, "ĠExchange": 31169, "ê°Ķ": 31170, "Ġéconom": 31171, "ãģĵãĤĵ": 31172, "elin": 31173, "reibt": 31174, "Ġ×Ķפ": 31175, "Ġcemetery": 31176, "Ġespañol": 31177, "olin": 31178, "лÑİд": 31179, "Ġgrâce": 31180, "allen": 31181, "ĠPhilos": 31182, "ĠErst": 31183, "ĠìĥĪ": 31184, "ĠVid": 31185, "Give": 31186, "OH": 31187, "μο": 31188, "ĠPare": 31189, "Ġmetabolism": 31190, "Ġmaple": 31191, "Ġaxle": 31192, "ĠDy": 31193, "Ġkomme": 31194, "Ïİν": 31195, "Ġgreatness": 31196, "Ġverified": 31197, "Ġspé": 31198, "ĠFahrenheit": 31199, "ĠBren": 31200, "ĠConfeder": 31201, "Ġhistoire": 31202, "Ġeliminating": 31203, "ĠAdding": 31204, "ĠAbi": 31205, "æĿİ": 31206, "Ġhospitality": 31207, "tim": 31208, "Ġbonito": 31209, "Ġpartes": 31210, "ĠдÑĢÑĥгиÑħ": 31211, "ĠShay": 31212, "ĠSed": 31213, "Ġregrets": 31214, "Ñıми": 31215, "Ġtenants": 31216, "éĢŁ": 31217, "ĠPTS": 31218, "Ġdevi": 31219, "ĠLate": 31220, "uez": 31221, "Ġsöyl": 31222, "ãĤ»": 31223, "Ġìŀ¬ë°Į": 31224, "Ġtoggle": 31225, "Ġmasking": 31226, "алÑĮного": 31227, "Ġpersön": 31228, "Ġamerican": 31229, "fik": 31230, "ĠRGB": 31231, "enson": 31232, "ĠKA": 31233, "wwww": 31234, "ĠÑĢег": 31235, "metics": 31236, "Ġeducator": 31237, "ãĤ·ãĥ«ãĤ¯": 31238, "park": 31239, "елÑĮзÑı": 31240, "arus": 31241, "ÑĢеÑĤ": 31242, "Ġfeito": 31243, "Ġchoir": 31244, "Ġlargo": 31245, "Ġeens": 31246, "Ġwatts": 31247, "ĠSingle": 31248, "Ġsusceptible": 31249, "icer": 31250, "ĠвклÑİÑĩ": 31251, "Ġpus": 31252, "íĻĺ": 31253, "Eng": 31254, "Ġfantas": 31255, "Ġspecification": 31256, "Ġconfronted": 31257, "ĠColumbus": 31258, "ивеÑĤ": 31259, "arım": 31260, "Ġcaffeine": 31261, "munition": 31262, "Ġmigrants": 31263, "lide": 31264, "itations": 31265, "ĠGeme": 31266, "ẫ": 31267, "Ġplanner": 31268, "Ġstimulate": 31269, "Ġaproxim": 31270, "ceu": 31271, "ĠNom": 31272, "Ġvog": 31273, "ĠÑĢаÑģÑĤ": 31274, "Ġenseñ": 31275, "Ġsellers": 31276, "Ġguten": 31277, "zd": 31278, "Cal": 31279, "Ġdescript": 31280, "Ġreconciliation": 31281, "zinho": 31282, "á¹ĩa": 31283, "ãģĺãĤĥãģĤ": 31284, "acyj": 31285, "ĠCOL": 31286, "saw": 31287, "ĠíĻķìĿ¸": 31288, "Ġvarit": 31289, "Ġpartnering": 31290, "Ġdetention": 31291, "Ġbombing": 31292, "clapping": 31293, "iencies": 31294, "ondu": 31295, "AME": 31296, "Ġê°ĻìĬµëĭĪëĭ¤": 31297, "cÃŃa": 31298, "ĠпоÑģÑĤо": 31299, "ĠASMR": 31300, "Ġhomepage": 31301, "Ġsiè": 31302, "antha": 31303, "ĠPoll": 31304, "Ġigen": 31305, "cych": 31306, "Ġê°ijìŀIJ기": 31307, "Ġconsiderably": 31308, "ä»ĸçļĦ": 31309, "ĠArist": 31310, "Ġwithstand": 31311, "Ġqualitative": 31312, "ĠKraft": 31313, "ĠÑįлекÑĤ": 31314, "ĠBead": 31315, "екÑĤив": 31316, "Ġcrushing": 31317, "ì³IJ": 31318, "Ġnavy": 31319, "ÙĪÚº": 31320, "sho": 31321, "Ġoak": 31322, "ippers": 31323, "Ġsoils": 31324, "Ġpigment": 31325, "Ġevitar": 31326, "ãĥĩ": 31327, "Ġfuse": 31328, "ĠDale": 31329, ":\"": 31330, "Ġcomplètement": 31331, "Ġkel": 31332, "à¹Ĩ": 31333, "Ġquatre": 31334, "ĠUM": 31335, "Ġë§IJë": 31336, "æł¹": 31337, "ÃŃr": 31338, "Ġleisure": 31339, "ĠHousing": 31340, "Ġfolds": 31341, "estion": 31342, "ARS": 31343, "Ġmash": 31344, "urpose": 31345, "Ġaccumulated": 31346, "ĠStuff": 31347, "èªŀ": 31348, "Ġtapes": 31349, "ĠÑģилÑĮно": 31350, "ĠLOVE": 31351, "Ġ1982": 31352, "Ġscars": 31353, "Ġcapitalist": 31354, "ĠNed": 31355, "Ġsoften": 31356, "Ġnotably": 31357, "Ġforcément": 31358, "ĠRaum": 31359, "ĠнеобÑħод": 31360, "Ġtrademark": 31361, "Ġfertig": 31362, "Ġ?!": 31363, "æĹł": 31364, "Ġreinforced": 31365, "Ġrecharge": 31366, "ĠPutting": 31367, "Ġvillains": 31368, "Ġhandic": 31369, "Ġadvertisement": 31370, "تÙĬ": 31371, "ĠÑģÑĥм": 31372, "ĠRiley": 31373, "×ķ×ij×": 31374, "京": 31375, "Os": 31376, "از": 31377, "Boy": 31378, "Ġsquish": 31379, "ocket": 31380, "Ġtestify": 31381, "æ¼Ķ": 31382, "Ġ׾×ŀ×": 31383, "ĠмаÑģÑģ": 31384, "manuel": 31385, "ĠArkansas": 31386, "iffe": 31387, "Ġanalysts": 31388, "ĠDeaf": 31389, "Ġjó": 31390, "Ġgroceries": 31391, "ĠWheel": 31392, "ĠÑĢиÑģ": 31393, "Ġcòn": 31394, "ĠCob": 31395, "Ġprisons": 31396, "ève": 31397, "ĠCabinet": 31398, "Ġposed": 31399, "Ġguerre": 31400, "ĠLloyd": 31401, "Ġclerk": 31402, "Ġcrises": 31403, "ĠSho": 31404, "ĠOre": 31405, "ĠFootball": 31406, "ĠAdvis": 31407, "ĠZheng": 31408, "èį": 31409, "ĠAMY": 31410, "Ġunfor": 31411, "Ġmonaster": 31412, "Ġcompile": 31413, "Ġimmortal": 31414, "atable": 31415, "Ġparano": 31416, "Ġtiver": 31417, "ĠSteph": 31418, "ĠFuÃŁ": 31419, "Ġdiscontin": 31420, "Ġripe": 31421, "Ġhacking": 31422, "Ġsiendo": 31423, "Ġseguro": 31424, "altres": 31425, "Ġanderes": 31426, "Ġ리ë": 31427, "Ġexports": 31428, "æŃ¥": 31429, "Ġtabii": 31430, "Ġ기ëĭ¤ë": 31431, "Ġbothering": 31432, "Ġpickle": 31433, "ĠBRIAN": 31434, "Ġaltar": 31435, "ĠпÑĢиб": 31436, "Ġtransferring": 31437, "ĠVors": 31438, "ĠÙĩÙĪ": 31439, "ĠZa": 31440, "ĠFrances": 31441, "Ġbrowse": 31442, "emit": 31443, "Ġchewing": 31444, "ĠFreddy": 31445, "Ġeditors": 31446, "älle": 31447, "ĠíĮĢ": 31448, "ĠSque": 31449, "ĠCultural": 31450, "awk": 31451, "ĠSache": 31452, "ĠCarbon": 31453, "ắt": 31454, "FL": 31455, "ĠNGO": 31456, "peÅĤ": 31457, "ĠSou": 31458, "Ġhvor": 31459, "unintelligible": 31460, "Ġë²ķ": 31461, "Ġ°": 31462, "iin": 31463, "Ġ×¢×Ŀ": 31464, "Ġderrière": 31465, "Ġczym": 31466, "ĠApost": 31467, "Ġregarder": 31468, "Ġagrade": 31469, "ĠCandy": 31470, "Ġmare": 31471, "Ġintroduces": 31472, "birds": 31473, "Ġuniquely": 31474, "Ġmuk": 31475, "Ġcooker": 31476, "Ġcrews": 31477, "Ġjeito": 31478, "ERT": 31479, "¶Ħë": 31480, "nisse": 31481, "Ġef": 31482, "Ġcarte": 31483, "ĠYak": 31484, "ĠPAT": 31485, "ино": 31486, "bokki": 31487, "Ġmates": 31488, "Ġdistint": 31489, "Ġì½Ķë¡ľëĤĺ": 31490, "Ġyıl": 31491, "Ġκάν": 31492, "Ġconfigurations": 31493, "enga": 31494, "recht": 31495, "Happy": 31496, "ãĤĦãģ£ãģ¦": 31497, "invest": 31498, "Ġreconstruct": 31499, "ĠÑįÑĤомÑĥ": 31500, "Ġmosque": 31501, "raum": 31502, "Ġvoyez": 31503, "ĠNBC": 31504, "ĠìŀIJìĭł": 31505, "Ġsturdy": 31506, "Ġкап": 31507, "Ġansch": 31508, "alid": 31509, "Ġmasih": 31510, "ĠREP": 31511, "Ġì½Ķë": 31512, "Ġdeduct": 31513, "Ġsalir": 31514, "wurf": 31515, "ilot": 31516, "ĠMutter": 31517, "olds": 31518, "ĠFEMA": 31519, "ĠBib": 31520, "Ġneighboring": 31521, "Ġbliss": 31522, "Ġíĺ¼": 31523, "лиÑģÑĮ": 31524, "ĠÑĤÑĢеб": 31525, "Ġå°±æĺ¯": 31526, "Ġgrenade": 31527, "Ġegal": 31528, "Ġfinely": 31529, "Ġpetals": 31530, "Ġkeer": 31531, "Ġchyba": 31532, "Ġskipping": 31533, "Ġthirteen": 31534, "Ġgravy": 31535, "ĠSAT": 31536, "61": 31537, "Ġног": 31538, "Ġmins": 31539, "ITE": 31540, "Ġsozial": 31541, "íķĺë©´ìĦľ": 31542, "ruktur": 31543, "Ġвозмож": 31544, "ĠопÑıÑĤÑĮ": 31545, "Ġarth": 31546, "ĠCuban": 31547, "Ġtreasures": 31548, "Ġfertilizer": 31549, "Ġawakening": 31550, "Ġë°±ìĭł": 31551, "Ġrall": 31552, "Ġdepict": 31553, "ĠPablo": 31554, "Ġnineteen": 31555, "Ġwatt": 31556, "Ġentirety": 31557, "KS": 31558, "ĠWoods": 31559, "Sch": 31560, "ĠÚ©ÙĪ": 31561, "ĠDry": 31562, "ãģŀ": 31563, "uve": 31564, "Ġreconstruction": 31565, "Ġanatomy": 31566, "Ī를": 31567, "Ġbaba": 31568, "Ġlistener": 31569, "Ġsharpen": 31570, "ĠPeru": 31571, "ĠвÑĭз": 31572, "Ġrecreation": 31573, "Ġinitiate": 31574, "Ġcalor": 31575, "ĠNaj": 31576, "gee": 31577, "ĠFeels": 31578, "ĠSnapchat": 31579, "ĠTet": 31580, "ĠNest": 31581, "ĠDaf": 31582, "ĠFinish": 31583, "ĠÑĤаким": 31584, "úc": 31585, "izens": 31586, "Ġspins": 31587, "Ġembry": 31588, "Ġpassages": 31589, "Ġcient": 31590, "Ġjustification": 31591, "ä»ĸ說": 31592, "Ġolmaz": 31593, "Ġflooded": 31594, "Ġemoji": 31595, "Ġembracing": 31596, "Ġdiscard": 31597, "ĠBasic": 31598, "agog": 31599, "ĠìľĦíķ´": 31600, "Ġasylum": 31601, "erin": 31602, "Ġfim": 31603, "Ġninja": 31604, "Ġautomate": 31605, "Ġallergic": 31606, "ÿÿÿÿ": 31607, "amam": 31608, "ĠмаÑĢ": 31609, "ĠOi": 31610, "äus": 31611, "Ġinduct": 31612, "ĠBEN": 31613, "ĠzÅĤ": 31614, "Ġkażdy": 31615, "ĠAMP": 31616, "nÄĽ": 31617, "Sure": 31618, "Ġquil": 31619, "Ġespec": 31620, "rok": 31621, "BSCRI": 31622, "Ġliebe": 31623, "pus": 31624, "achsen": 31625, "Ġcricket": 31626, "ëĬIJ": 31627, "ĠFrame": 31628, "ekkür": 31629, "arb": 31630, "ĠpÅĻ": 31631, "иÑģÑģ": 31632, "Ġzeggen": 31633, "Ġdoubles": 31634, "ĠDre": 31635, "test": 31636, "insp": 31637, "boys": 31638, "Ġmão": 31639, "ĠVerse": 31640, "Ġmuscular": 31641, "ĠMALE": 31642, "Ġdulu": 31643, "Ġoccasional": 31644, "Lo": 31645, "conomic": 31646, "Ġvak": 31647, "Ġremedy": 31648, "å¤ł": 31649, "ĠâĻªâĻªâĻª": 31650, "vem": 31651, "Ġönem": 31652, "ĠkarÅŁÄ±": 31653, "ĠSharp": 31654, "hur": 31655, "Ġë°©ë²ķ": 31656, "Ġgrandson": 31657, "Ġaktiv": 31658, "ĠThrones": 31659, "ĠìķĪìĹIJ": 31660, "Ġtots": 31661, "Ġsubd": 31662, "ĠPaula": 31663, "Ġgraves": 31664, "ĠBrent": 31665, "ĠникÑĤо": 31666, "Ġsöz": 31667, "Ġcrec": 31668, "ĠVladimir": 31669, "çĸ«": 31670, "Ġпой": 31671, "Ġ\"-": 31672, "Ġpsy": 31673, "atri": 31674, "idan": 31675, "Ġaún": 31676, "Ġstandardized": 31677, "ì¹ĺë": 31678, "ĠкÑĢов": 31679, "ĠZhu": 31680, "something": 31681, "Ġ750": 31682, "Ġmujeres": 31683, "Ġait": 31684, "éĹ´": 31685, "agu": 31686, "Ġcorrected": 31687, "ikka": 31688, "eled": 31689, "ĠCareer": 31690, "owym": 31691, "Ġroommate": 31692, "Ġdescendants": 31693, "ĠNapoleon": 31694, "ĠÐĶо": 31695, "íĸĪìĸ´ìļĶ": 31696, "Ġbunun": 31697, "ĠMicha": 31698, "ç·ļ": 31699, "Ġdescob": 31700, "PI": 31701, "Ġpalabra": 31702, "Ġtracked": 31703, "Ġdependence": 31704, "ĠBarack": 31705, "åģĩ": 31706, "Ġfertility": 31707, "ĠSouthwest": 31708, "Ġincomplete": 31709, "Ġcomunic": 31710, "Ġcompris": 31711, "ĠRestaur": 31712, "Ġacron": 31713, "κα": 31714, "Ġapprentices": 31715, "Ġmusst": 31716, "ĠAbr": 31717, "Ġpentru": 31718, "ĠConsort": 31719, "ĠAvec": 31720, "Ġdumplings": 31721, "LR": 31722, "Ġwszystkie": 31723, "Ġswamp": 31724, "нев": 31725, "uggle": 31726, "Ġwatercolor": 31727, "Ġproton": 31728, "ĠEspaña": 31729, "ocking": 31730, "овал": 31731, "Ġtakim": 31732, "Very": 31733, "Ġdementia": 31734, "ĠÅŁeyi": 31735, "Jac": 31736, "ĠMacBook": 31737, "ĠLiv": 31738, "fficients": 31739, "ĠHunt": 31740, "Ġoverlay": 31741, "æĦŁè¦º": 31742, "ĠSkype": 31743, "punkt": 31744, "Ġconfined": 31745, "ĠAdrian": 31746, "رÙĥ": 31747, "ĠJeep": 31748, "Ġenquanto": 31749, "Ġanest": 31750, "оÑĤвеÑĤ": 31751, "ĠменÑĮ": 31752, "Ġirrigation": 31753, "á»ijn": 31754, "Ġeighteen": 31755, "ĠPon": 31756, "Ġrescued": 31757, "Ġ1983": 31758, "rü": 31759, "jae": 31760, "ĠJeong": 31761, "Ġamazingly": 31762, "ĠFDP": 31763, "Ġbackstage": 31764, "cue": 31765, "ĠÏĥÏĦην": 31766, "ĠاÙĦص": 31767, "Ġlivestock": 31768, "ĠWarner": 31769, "Ġmajors": 31770, "ãĥģãĥ£": 31771, "Ġcooperative": 31772, "ĠBrady": 31773, "rained": 31774, "rieb": 31775, "Ġ×ij×ŀ×": 31776, "ĠдоволÑĮно": 31777, "ĠFE": 31778, "Ġleaked": 31779, "ĠMercury": 31780, "Ġpersuade": 31781, "Ġtransformer": 31782, "ĠNorweg": 31783, "ĠìŬ룬": 31784, "ĠzrobiÄĩ": 31785, "Ġcardiovascular": 31786, "ĠCrash": 31787, "Ġgossip": 31788, "аÑģÑĤÑĮ": 31789, "Ġ쪽": 31790, "Ġswept": 31791, "ĠHorn": 31792, "ĠAté": 31793, "Ġbukan": 31794, "ĠKaw": 31795, "KY": 31796, "ĠStories": 31797, "Gary": 31798, "Ġgardening": 31799, "ĠQuickly": 31800, "ĠFalcon": 31801, "Ġovat": 31802, "cı": 31803, "ĠComplet": 31804, "ĠDate": 31805, "ĠпÑĢим": 31806, "Ġläuft": 31807, "ĠAudrey": 31808, "ĠWent": 31809, "ĠpelÃŃcul": 31810, "Ġcarriage": 31811, "Ġunacceptable": 31812, "nymi": 31813, "ĠÑģлÑĭÑĪ": 31814, "Ġterre": 31815, "uellement": 31816, "EEEE": 31817, "Ġpharmac": 31818, "hões": 31819, "Ġzich": 31820, "Ġmigrate": 31821, "ĠFry": 31822, "ñana": 31823, "ĠMuito": 31824, "EOVER": 31825, "Ġfortress": 31826, "ĠCompan": 31827, "ĠJSON": 31828, "ordnung": 31829, "Ġwarto": 31830, "Ġungef": 31831, "ìħĶìĦľ": 31832, "ĠÑĢок": 31833, "Ġpaddle": 31834, "Jared": 31835, "Ġsubmitting": 31836, "Ġlatch": 31837, "Ġfug": 31838, "ĠкоÑģ": 31839, "ĠEf": 31840, "Ġlaunches": 31841, "Ġft": 31842, "otechn": 31843, "Ġtravelled": 31844, "اÙģ": 31845, "éģķ": 31846, "Ġproch": 31847, "Ġdedim": 31848, "83": 31849, "Ġrebound": 31850, "ĠLU": 31851, "path": 31852, "ĠÑģпÑĢав": 31853, "Ġöl": 31854, "ĠíĤ¤": 31855, "Ġprivat": 31856, "Ġtractor": 31857, "ĠAttention": 31858, "Ser": 31859, "Ġcoses": 31860, "ária": 31861, "pal": 31862, "ĠìĿĢ": 31863, "Ġsuccessor": 31864, "Ġconnectors": 31865, "ĠÑĥÑģÑĤанов": 31866, "Ġgenocide": 31867, "Ġsufficiently": 31868, "ĠAixò": 31869, "Ġstabilize": 31870, "Ġcongest": 31871, "Ġcarving": 31872, "Ġzost": 31873, "ĠбÑĭÑģÑĤÑĢо": 31874, "Ġshortest": 31875, "Ġlivel": 31876, "Ġ89": 31877, "éģĬ": 31878, "Ġerk": 31879, "Ġportraits": 31880, "à¥Ģ": 31881, "èĺ": 31882, "boat": 31883, "llah": 31884, "ANC": 31885, "Ġempirical": 31886, "ĠEcho": 31887, "ĠNederland": 31888, "è¿Ļä¹Ī": 31889, "Net": 31890, "Ġcuidado": 31891, "ĠRoma": 31892, "Ġcalf": 31893, "Ġgiants": 31894, "ĠExplorer": 31895, "ĠCollect": 31896, "alition": 31897, "ĠDestiny": 31898, "Ġausge": 31899, "ĠEdu": 31900, "ĠClo": 31901, "Ġearrings": 31902, "ĠTrack": 31903, "ĠROS": 31904, "ĠBelle": 31905, "çĻ¾": 31906, "Ġpueda": 31907, "Ġdaytime": 31908, "Ġsupplier": 31909, "ĠSV": 31910, "ĠExhale": 31911, "Ġgalera": 31912, "course": 31913, "Ġcentimeter": 31914, "ĠBast": 31915, "mud": 31916, "Ġsangat": 31917, "ĠPhysical": 31918, "Ġprivately": 31919, "Ġtrata": 31920, "lynn": 31921, "illi": 31922, "Ġë©ĶìĿ´íģ¬ìĹħ": 31923, "Ġcrystall": 31924, "Ġpods": 31925, "ản": 31926, "inator": 31927, "ĠRecords": 31928, "å®ĺ": 31929, "ÄŁimiz": 31930, "issement": 31931, "hare": 31932, "hadow": 31933, "ĠDK": 31934, "ĠìķĮê³ł": 31935, "Ġwyn": 31936, "Ġrequesting": 31937, "ĠDonna": 31938, "ĠìĹ´ìĭ¬íŀĪ": 31939, "inea": 31940, "Ġexert": 31941, "ĠDuncan": 31942, "ĠвеÑĩ": 31943, "ĠHah": 31944, "à¤Ĥ": 31945, "ĠLif": 31946, "ĠFinding": 31947, "ĠNov": 31948, "Ġзнак": 31949, "ĠоÑĦ": 31950, "ĠQuè": 31951, "Ġquarterback": 31952, "ĠÑĦак": 31953, "Ġbipartisan": 31954, "ÄŁin": 31955, "Ġnécess": 31956, "Ġreferendum": 31957, "Ġcompiler": 31958, "Ġprobabil": 31959, "еди": 31960, "Ġtrader": 31961, "æĺĵ": 31962, "ĠRum": 31963, "geme": 31964, "Ġdio": 31965, "ĠbÄĻdziemy": 31966, "ĠÏĢά": 31967, "꾸": 31968, "×ķ×ĺ": 31969, "Ġà¤ķ": 31970, "Ġблаг": 31971, "Ġscalp": 31972, "ĠPause": 31973, "Ġcaption": 31974, "Ġendanger": 31975, "Ġenlar": 31976, "Ġrotten": 31977, "ãĥĥãĥĪ": 31978, "Ġwah": 31979, "èĤī": 31980, "Ġdzi": 31981, "ĠInstall": 31982, "Ay": 31983, "Ġcrear": 31984, "енÑĤа": 31985, "Ġweighing": 31986, "Ġbutterflies": 31987, "ĠGast": 31988, "äºķ": 31989, "horn": 31990, "warz": 31991, "ICEOVER": 31992, "ĠнайÑĤи": 31993, "Ġcoefficients": 31994, "ç°¡åĸ®": 31995, "ĠSpencer": 31996, "ĠHigher": 31997, "Ġcowork": 31998, "å¨ĺ": 31999, "ĠкоÑĤоÑĢое": 32000, "Ġmonit": 32001, "Ġdysfunction": 32002, "ĠÑģÑĤанов": 32003, "Ġtournaments": 32004, "Ġoyster": 32005, "BN": 32006, "Ġtrud": 32007, "slow": 32008, "ĠPenny": 32009, "ĠOdys": 32010, "ær": 32011, "Ġfou": 32012, "Ġenjoyment": 32013, "аÑĤÑĭ": 32014, "ĠwyglÄħda": 32015, "алÑĮнаÑı": 32016, "ĠProtect": 32017, "Ġmoy": 32018, "Ġclaw": 32019, "Ġsuspicion": 32020, "Ġsacrificed": 32021, "Ġgosto": 32022, "Big": 32023, "Ġaggressively": 32024, "Ġvorne": 32025, "ãĥł": 32026, "Ġblamed": 32027, "ĠSehr": 32028, "פר": 32029, "cito": 32030, "Ġseals": 32031, "Ġmujer": 32032, "ĠWeird": 32033, "Ġforens": 32034, "Ġcontributes": 32035, "estra": 32036, "Ġpog": 32037, "LOL": 32038, "Ġhacerlo": 32039, "оÑĤÑĮ": 32040, "fiction": 32041, "79": 32042, "λο": 32043, "大æ¦Ĥ": 32044, "声": 32045, "ĠÑĤоб": 32046, "ĠGS": 32047, "ĠClara": 32048, "itez": 32049, "Ġadvocating": 32050, "ĠíĶĦë": 32051, "sung": 32052, "Ġvertices": 32053, "Ġnavigating": 32054, "Ġeuropé": 32055, "çļĨ": 32056, "Ġslowed": 32057, "Ġforeground": 32058, "ĠIndustrial": 32059, "Ġadore": 32060, "ìĭŃ": 32061, "Ġcréer": 32062, "æŀĹ": 32063, "chnitt": 32064, "Ġunaware": 32065, "Ġcurly": 32066, "entar": 32067, "Ġler": 32068, "Ġprohibited": 32069, "ĠHeroes": 32070, "ĠReed": 32071, "uca": 32072, "Ġsmok": 32073, "Ġkunna": 32074, "zeitig": 32075, "immen": 32076, "ĠLun": 32077, "ĠабÑģолÑİÑĤ": 32078, "Ġdegli": 32079, "Ġvillagers": 32080, "Ġpreset": 32081, "zept": 32082, "uds": 32083, "Ġemit": 32084, "ä½łè¦ģ": 32085, "Ġëī": 32086, "ëĬĶì§Ģ": 32087, "нако": 32088, "Ġosób": 32089, "Ġ1969": 32090, "ĠÐIJÑĢ": 32091, "Ġmanchmal": 32092, "ĠBrock": 32093, "Ġmantra": 32094, "ĠWIL": 32095, "bach": 32096, "inä": 32097, "elas": 32098, "keln": 32099, "Ġdisciple": 32100, "Ġqualc": 32101, "Ġdehyd": 32102, "ìĿ´ëĿ¼ëĬĶ": 32103, "Af": 32104, "ìĦ±ìĿ´": 32105, "Ryan": 32106, "Ġpuppet": 32107, "ĠдÑĢÑĥгие": 32108, "Ġrud": 32109, "Ġpending": 32110, "Plus": 32111, "ĠìķĬìĿĦ": 32112, "Ġbá»ĭ": 32113, "ĠSega": 32114, "çe": 32115, "Ġprogrammer": 32116, "bli": 32117, "Ġunl": 32118, "Ġenslaved": 32119, "Ġsociété": 32120, "Äģh": 32121, "Ġinheritance": 32122, "ĠBangl": 32123, "ermaid": 32124, "Ġpractitioner": 32125, "ĠStalin": 32126, "ĠUser": 32127, "cible": 32128, "Ġcardiac": 32129, "ĠKoreans": 32130, "Ġdumped": 32131, "Ġ×Ķ×Ļ×Ķ": 32132, "áis": 32133, "Ġhydraulic": 32134, "oubtedly": 32135, "ĠPit": 32136, "Ġpicnic": 32137, "Ġbehöver": 32138, "ĠÑģмог": 32139, "Ġbraking": 32140, "é»ij": 32141, "utar": 32142, "ĠìĦ¸ë": 32143, "ubl": 32144, "Ġüz": 32145, "Ġmajesty": 32146, "Ġbers": 32147, "utable": 32148, "Ġhotter": 32149, "çħ§": 32150, "ÛĮÙĨ": 32151, "Ġbiases": 32152, "Ġsubjected": 32153, "Ġnaughty": 32154, "Ġcircus": 32155, "ãģĹãģĭ": 32156, "ĠImmedi": 32157, "ĠStefan": 32158, "ĠTriple": 32159, "enk": 32160, "Ġwit": 32161, "Ġrecycle": 32162, "emie": 32163, "dated": 32164, "Ġunload": 32165, "Ġpopula": 32166, "chin": 32167, "Ġyields": 32168, "Ġenglish": 32169, "ĠBonnie": 32170, "Ġspiders": 32171, "Ãģ": 32172, "Ġerosion": 32173, "éĥ¨åĪĨ": 32174, "ĠNICK": 32175, "иÑıÑħ": 32176, "Ġimpart": 32177, "Ġкни": 32178, "Ġresolutions": 32179, "Ġlithium": 32180, "Ġconvergence": 32181, "ĠTara": 32182, "Ġдве": 32183, "ths": 32184, "ĠCindy": 32185, "æĪijè¦ģ": 32186, "幫": 32187, "ĠDIE": 32188, "Ġassurance": 32189, "ĠопиÑģ": 32190, "Ġbuckets": 32191, "Ġcues": 32192, "ĠQuiet": 32193, "Ġsimilarity": 32194, "Ġfoundational": 32195, "ĠMinist": 32196, "滿": 32197, "Ġpian": 32198, "Ġcentr": 32199, "Ġnumb": 32200, "Ġmonks": 32201, "ujourd": 32202, "enzie": 32203, "Ġskateboard": 32204, "Ġdlatego": 32205, "ĠÑģоÑĤ": 32206, "ĠAE": 32207, "Ġmasterpiece": 32208, "ĠSolomon": 32209, "ĠReddit": 32210, "Ġriot": 32211, "abl": 32212, "ĠJazz": 32213, "Ġelectromagnetic": 32214, "Ġinsecure": 32215, "ĠCompet": 32216, "geries": 32217, "обод": 32218, "ł×ķ": 32219, "ðŁĴ": 32220, "Ġsenators": 32221, "ĠBrisbane": 32222, "ĠAlb": 32223, "uttering": 32224, "ĠAllow": 32225, "zero": 32226, "Ġpai": 32227, "ĠÐIJлекÑģ": 32228, "ĠDisplay": 32229, "ĠBlade": 32230, "ĠApps": 32231, "Ġpä": 32232, "ĠдеÑģÑı": 32233, "Ġquella": 32234, "ĠGao": 32235, "еннÑĭÑħ": 32236, "Ġspoilers": 32237, "Ġgallons": 32238, "ĠÙĦÙĬ": 32239, "ĠZion": 32240, "æľīä¸Ģ": 32241, "onie": 32242, "ragt": 32243, "ĠChand": 32244, "Ġë³ij": 32245, "Ġblunt": 32246, "Ġusu": 32247, "ĠKad": 32248, "rakt": 32249, "Ġcinematic": 32250, "Ġammunition": 32251, "rene": 32252, "Ġfourteen": 32253, "ĠCarn": 32254, "crit": 32255, "Ġtenure": 32256, "vu": 32257, "Ġprincipalmente": 32258, "Ġalleen": 32259, "éĢĻä¸Ģ": 32260, 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34115, "Ġappend": 34116, "Ġforbid": 34117, "ĠHamburg": 34118, "аков": 34119, "Ġê¸Ī": 34120, "ilda": 34121, "Ġpreparations": 34122, "ĠmogÄħ": 34123, "Ġcamino": 34124, "Eric": 34125, "ĠBlind": 34126, "èĪĩ": 34127, "å¹´çļĦ": 34128, "ĠDiscovery": 34129, "ì¸ł": 34130, "çĪ¶": 34131, "Ġinterpreter": 34132, "Ġbred": 34133, "ĠPsalm": 34134, "Ġdefended": 34135, "ìī¬": 34136, "ĠErfahr": 34137, "ĠPeach": 34138, "Ġmoons": 34139, "ĠOst": 34140, "Ġspécial": 34141, "Ġarriver": 34142, "ĠWis": 34143, "uci": 34144, "Ġrobotics": 34145, "IVE": 34146, "Ġsiege": 34147, "arla": 34148, "Ġseparates": 34149, "ĠTC": 34150, "íı°": 34151, "quisite": 34152, "Ġparentheses": 34153, "ике": 34154, "ç«Ļ": 34155, "Ġtrous": 34156, "建": 34157, "ĠÑģилÑĮ": 34158, "Ġbeers": 34159, "ĠплаÑĤ": 34160, "ãģĻãģĶãģĦ": 34161, "Ġsola": 34162, "Ġdès": 34163, "mingham": 34164, "ikte": 34165, "Ġoops": 34166, "Ġtwitch": 34167, "å°ĩ": 34168, "ÏĪ": 34169, "ĠShouldn": 34170, "uvre": 34171, "Ġleer": 34172, "criptions": 34173, "Ġeyeshadow": 34174, "ĠGuo": 34175, "ĠPowell": 34176, "Ġsupuesto": 34177, "Ġana": 34178, "rals": 34179, "ĠMontreal": 34180, "Ġsurfing": 34181, "ĠÐŁÐµÑĢв": 34182, "×ŀ×ķ": 34183, "Ġmilliseconds": 34184, "Ġsuburbs": 34185, "Ġplaneta": 34186, "ÑĥÑĪка": 34187, "hrlich": 34188, "ĠHY": 34189, "ĠسÛĴ": 34190, "ĠMM": 34191, "ĠEff": 34192, "åı¯æĦĽ": 34193, "ĠHS": 34194, "anson": 34195, "Ġì§ģìłij": 34196, "Ġsuo": 34197, "Ġdeploying": 34198, "Ġkunt": 34199, "tering": 34200, "Ġerect": 34201, "ìŀ¥ìĿ´": 34202, "ĠìĿĮìĭĿ": 34203, "Ġspecimen": 34204, "!...": 34205, "æĪij說": 34206, "Ġligne": 34207, "Ġkonst": 34208, "adequ": 34209, "Ġìĥģíĥľ": 34210, "Ġaccessed": 34211, "ĠPole": 34212, "kill": 34213, "Ġë²Ħë": 34214, "Ġauthenticity": 34215, "Ġappelle": 34216, "ulle": 34217, "Ġrevision": 34218, "Ġgoats": 34219, "гли": 34220, "Ġpau": 34221, "ĠRanger": 34222, "ĠImag": 34223, "author": 34224, "Ġeve": 34225, "ĠMessenger": 34226, "Ġnay": 34227, "Ġwholes": 34228, "ätte": 34229, "Ġonwards": 34230, "ĠDepois": 34231, "ĠíijľíĺĦ": 34232, "ĠSARS": 34233, "Ġwszystkich": 34234, "Ġdestru": 34235, "umbing": 34236, "Ġcompatibility": 34237, "Ġmisinformation": 34238, "odore": 34239, "ĠFavor": 34240, "eko": 34241, "ıĮ": 34242, "waukee": 34243, "ĠTeaching": 34244, "ĠKO": 34245, "Ġbetting": 34246, "Ġquests": 34247, "Ġvivre": 34248, "ĠмÑĥзÑĭ": 34249, "Ġsaga": 34250, "Ġswell": 34251, "Ġgehe": 34252, "æĢİ麼樣": 34253, "ĠоÑĢганиз": 34254, "Ġgide": 34255, "ĠGross": 34256, "Ġdalej": 34257, "Ġclaws": 34258, "á»Ļc": 34259, "Ġprejudice": 34260, "Ġinsign": 34261, "ihood": 34262, "Ġpled": 34263, "Ġdónde": 34264, "ĠPolitical": 34265, "Ġpremises": 34266, "undert": 34267, "عت": 34268, "onnen": 34269, "Ġespaço": 34270, "Ġfé": 34271, "ĠHarrison": 34272, "ĠCensus": 34273, "Ġcardio": 34274, "Ġdiy": 34275, "Ġmilieu": 34276, "Ġjournée": 34277, "ĠRelease": 34278, "NIE": 34279, "ĠMuk": 34280, "idée": 34281, "á»įi": 34282, "Ġiçinde": 34283, "ŀĻ": 34284, "Ġresonate": 34285, "Ġmoles": 34286, "ĠFlying": 34287, "ĠGloria": 34288, "ĠPastor": 34289, "ĠArena": 34290, "好ä¸į好": 34291, "NON": 34292, "олов": 34293, "ĠallÃŃ": 34294, "omat": 34295, "ìĸ´ëıĦ": 34296, "ĠcaracterÃŃst": 34297, "Ġdeclining": 34298, "ÑĸÑı": 34299, "anco": 34300, "ĠInform": 34301, "Ġbargain": 34302, "Ġbushes": 34303, "ĠNaturally": 34304, "Ġrechts": 34305, "ĠTensor": 34306, "ĠPatricia": 34307, "Ġprincipio": 34308, "ĠMumbai": 34309, "Ġwomb": 34310, "Ġnostra": 34311, "Ġdilemma": 34312, "Ġirgendwann": 34313, "Ġ1964": 34314, "ĠenergÃŃa": 34315, "ĠнаÑĢ": 34316, "Ġsegregation": 34317, "ĠAthlet": 34318, "Ġ»,": 34319, "Ġyeni": 34320, "ĠSeit": 34321, "Ġvenom": 34322, "Ġdakika": 34323, "ĠëıĮë": 34324, "ĠÃīl": 34325, "Ġfus": 34326, "ĠMog": 34327, "¦½ëĭĪëĭ¤": 34328, "Ġremar": 34329, "ĠTeddy": 34330, "Ġbreasts": 34331, "icans": 34332, "æĶ¶çľĭ": 34333, "kap": 34334, "ĠhÆ¡n": 34335, "ĠJP": 34336, "ãĥ³ãĤ¿": 34337, "Ġresurrect": 34338, "ĠìĿ¸ë": 34339, "herical": 34340, "Ġfotograf": 34341, "ĠJosé": 34342, "Ġlivelihood": 34343, "Ġbibli": 34344, "teri": 34345, "Ġvorstellen": 34346, "ĠAAA": 34347, "Ġassessing": 34348, "YA": 34349, "Ġsplend": 34350, "Ġexcav": 34351, "Ġbaptism": 34352, "yll": 34353, "wow": 34354, "Mac": 34355, "Ġplastics": 34356, "teokbokki": 34357, "Ġintéressant": 34358, "Ġcommanded": 34359, "Ġfamously": 34360, "ĠÐĺли": 34361, "ĠManuel": 34362, "Ġsouthwest": 34363, "Ġdeformation": 34364, "ÃŃculo": 34365, "ĠнаÑħодиÑĤÑģÑı": 34366, "ĠPatter": 34367, "degree": 34368, "ĠczÄĻsto": 34369, "\"-": 34370, "Ġìħĭ": 34371, "Ġmanger": 34372, "ĠTrustee": 34373, "Ģ리": 34374, "Ġpuntos": 34375, "ivable": 34376, "Ġvolatile": 34377, "ĠëĬIJ": 34378, "Ġinstability": 34379, "Ġciel": 34380, "ciÄħ": 34381, "Ġpurity": 34382, "ноÑģÑĤ": 34383, "Sil": 34384, "edar": 34385, "åĻ¨": 34386, "NOUNCER": 34387, "Ġspelled": 34388, "GER": 34389, "Ġsanctuary": 34390, "Ġaccelerating": 34391, "Ġscout": 34392, "ĠпÑĢев": 34393, "fahren": 34394, "ãģĵãģ¡ãĤī": 34395, "ĠëĤĺìĺ¨": 34396, "ĠpoczÄħt": 34397, "ĠMeu": 34398, "kaar": 34399, "³´ê³ł": 34400, "akra": 34401, "Down": 34402, "ĠÃĦr": 34403, 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34690, "Ġнез": 34691, "ĠкаÑĢÑĤ": 34692, "ĠAntwort": 34693, "ĠPfizer": 34694, "ĠFranco": 34695, "ĠwÅĤ": 34696, "Ġfrig": 34697, "esper": 34698, "Ġkale": 34699, "Ġfilmmaker": 34700, "Ġkurt": 34701, "Ġinvalid": 34702, "å±Ģ": 34703, "arella": 34704, "Äĥng": 34705, "ramento": 34706, "Ġnutritional": 34707, "Ġdictators": 34708, "Ġafin": 34709, "Ġfuzzy": 34710, "ĠGina": 34711, "ót": 34712, "ĠExtremadura": 34713, "Ġdemonstrations": 34714, "ĠMontgomery": 34715, "íķ´ìĦ¤": 34716, "ĠGandhi": 34717, "ãĥĿ": 34718, "ç½®": 34719, "Ġreunion": 34720, "ĠjakiÅĽ": 34721, "ĠZug": 34722, "OUGH": 34723, "lifting": 34724, "Ġà²": 34725, "á¹Ľá¹£": 34726, "eb": 34727, "ĠWOW": 34728, "ĠShiva": 34729, "ometry": 34730, "Ġwildly": 34731, "Ġtended": 34732, "Ġmegap": 34733, "ì²ĺ": 34734, "Ġnause": 34735, "Ġgerek": 34736, "ãĥĭ": 34737, "ĠMarcel": 34738, "Ġneste": 34739, "خر": 34740, "Ġfeh": 34741, "åĨħ": 34742, "suspenseful": 34743, "ĠWrestle": 34744, "ĠPalestinians": 34745, "ĠGORD": 34746, "iyet": 34747, "ĠÑĢади": 34748, 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34806, "ĠAntrag": 34807, "ĠAMD": 34808, "ĠCrypt": 34809, "Ġë©Ķ": 34810, "ĠFelipe": 34811, "ĠCOB": 34812, "elu": 34813, "Ġselfies": 34814, "ĠSanti": 34815, "chutz": 34816, "ĠУкÑĢаÑĹ": 34817, "gesamt": 34818, "Ġflock": 34819, "jaz": 34820, "plain": 34821, "Ġwrinkles": 34822, "Ġreais": 34823, "Ġpaljon": 34824, "Ġempowerment": 34825, "Ġattendees": 34826, "ppa": 34827, "Ġneden": 34828, "онÑĭ": 34829, "Ġtimeframe": 34830, "ĠCherry": 34831, "Ġidée": 34832, "Ġgag": 34833, "Ġdonkey": 34834, "Ġông": 34835, "ĠHare": 34836, "éļĽ": 34837, "ĠKara": 34838, "Ġacompan": 34839, "places": 34840, "imientos": 34841, "ĠHamm": 34842, "би": 34843, "uben": 34844, "iliyor": 34845, "Ġthirst": 34846, "Ġkry": 34847, "ĠGeorgetown": 34848, "׳×Ķ": 34849, "Ġorch": 34850, "Ġheartbeat": 34851, "Ġtransformations": 34852, "estones": 34853, "ĠKH": 34854, "Ġcartoons": 34855, "Ġanci": 34856, "Ġworthless": 34857, "Ġtailored": 34858, "pu": 34859, "Americans": 34860, "Ġpiles": 34861, "ĠMonkey": 34862, "Ġbasin": 34863, "ĠTemper": 34864, "ĠPaint": 34865, "Ġpunching": 34866, "Ġbaik": 34867, "ĠOakland": 34868, "vre": 34869, "ÅŁallah": 34870, "ydd": 34871, "Ġcasually": 34872, "odu": 34873, "Ġcoded": 34874, "ĠNorwegian": 34875, "ĠVince": 34876, "Ġpremature": 34877, "ĠPromise": 34878, "екÑģÑĤ": 34879, "Ġdevastated": 34880, "ĠPremium": 34881, "ĠParam": 34882, "ĠÃĸyle": 34883, "umuz": 34884, "PO": 34885, "rators": 34886, "Ġlamps": 34887, "Ġterritorial": 34888, "Ġbackbone": 34889, "listed": 34890, "DY": 34891, "ĠاÙĦر": 34892, "Ġpursued": 34893, "ĠCommons": 34894, "Ġ곡": 34895, "locks": 34896, "edor": 34897, "Ġconceived": 34898, "gere": 34899, "Ġdisappearing": 34900, "ĠSull": 34901, "ĠìĹ°ë": 34902, "Ġhoffe": 34903, "Ġdetox": 34904, "íĶĮ": 34905, "Ġretir": 34906, "ĠëģĿëĤ": 34907, "Ġpergunta": 34908, "ĠBOY": 34909, "ç²¾": 34910, "Ġpenn": 34911, "æĿ¥äºĨ": 34912, "hés": 34913, "hon": 34914, "Ġcatastrophic": 34915, "Ġaust": 34916, "Ġtorso": 34917, "Ġìĸ´ëĬIJ": 34918, "ĠìĤ¬ëŀĮëĵ¤ìĿ´": 34919, "Ġmarvelous": 34920, 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35094, "ĠTransportation": 35095, "ĠMaker": 35096, "Ġjohn": 35097, "Ġrichest": 35098, "Ġtraumat": 35099, "Ġliegen": 35100, "´ë¥¼": 35101, "è¿ĻéĩĮ": 35102, "Ġunrest": 35103, "ĠStraw": 35104, "æĭľæĭľ": 35105, "Ġcoma": 35106, "ĠKristen": 35107, "ĠÐļонеÑĩно": 35108, "ĠBryce": 35109, "ĠÑıкÑĸ": 35110, "Ġpearls": 35111, "ĠпонимаÑİ": 35112, "Ġadditions": 35113, "Ġasympt": 35114, "ĠменÑĮÑĪе": 35115, "Ġscans": 35116, "Child": 35117, "ĠHide": 35118, "кÑĥÑİ": 35119, "etas": 35120, "Ġdank": 35121, "Ġpleas": 35122, "Ġessays": 35123, "Ġjets": 35124, "åħĴ": 35125, "Ġвед": 35126, "Ġpositives": 35127, "hof": 35128, "-)": 35129, "zzo": 35130, "Ġstarters": 35131, "Ġsmiled": 35132, "Ġ1944": 35133, "quiera": 35134, "Ġrok": 35135, "Ġpuesto": 35136, "Nico": 35137, "Ġsimulations": 35138, "Ġà¶": 35139, "Ġintrigued": 35140, "ĠOverwatch": 35141, "åĸĤ": 35142, "sigh": 35143, "bai": 35144, "Ġë§IJê³ł": 35145, "idé": 35146, "Ġcrabs": 35147, "áºŃp": 35148, "ĠIraqi": 35149, "ìĿ´ë¥¼": 35150, "ÑĤÑı": 35151, "ĠSophia": 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36303, "ARIN": 36304, "Ġprogressing": 36305, "ĠGir": 36306, "Ġpenetrate": 36307, "ĠStorage": 36308, "eight": 36309, "ĠÑĤÑĢÑĥ": 36310, "ĠdonÃŃt": 36311, "Ġsizin": 36312, "Ġoutdated": 36313, "ĠнаÑĪи": 36314, "Ġaffir": 36315, "Ġspoons": 36316, "Ġoni": 36317, "Ġflank": 36318, "ĠGol": 36319, "hã": 36320, "Ġpéri": 36321, "Ġhonorable": 36322, "ĠBreathe": 36323, "scenes": 36324, "Ġobviamente": 36325, "икÑģ": 36326, "Ġש×ŀ×": 36327, "Ġsmoothie": 36328, "ŀĪë": 36329, "Ġdime": 36330, "ĠíĸĪìĸ´ìļĶ": 36331, "Ġappel": 36332, "ĠCatholics": 36333, "Ġsingles": 36334, "Ġlaten": 36335, "Ġçünkü": 36336, "ĠVader": 36337, "æıĽ": 36338, "Ġvardı": 36339, "ĠIstanbul": 36340, "gré": 36341, "ĠElsa": 36342, "ël": 36343, "Ġinvece": 36344, "Ġcrane": 36345, "Ġobe": 36346, "ĠShark": 36347, "Ġsmack": 36348, "Ġrestoring": 36349, ".\\": 36350, "Ġë¹łë": 36351, "Ġfaded": 36352, "umbers": 36353, "Singing": 36354, "Ġdepressing": 36355, "thest": 36356, "ĠWahr": 36357, "Ġmultitude": 36358, "ÑĢавÑģÑĤвÑĥйÑĤе": 36359, "rijk": 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"presented": 36589, "Ġprowad": 36590, "ĠTerr": 36591, "ĠPrinceton": 36592, "Ġmorgen": 36593, "Ġattracting": 36594, "ĠSigma": 36595, "igner": 36596, "ĠRechts": 36597, "ĠPeki": 36598, "Ġmethy": 36599, "Ġhamm": 36600, "Ġdireito": 36601, "Ġdelegation": 36602, "иваÑİÑĤ": 36603, "Ġgin": 36604, "Young": 36605, "Ġdependencies": 36606, "ĠBradley": 36607, "buds": 36608, "Ġfis": 36609, "Ġpytanie": 36610, "Ġinterconnected": 36611, "Ġembaixo": 36612, "ĠSas": 36613, "Ġruh": 36614, "ĠSicht": 36615, "Sur": 36616, "Ġsuperb": 36617, "ĠSabbath": 36618, "ĠDanger": 36619, "kol": 36620, "Ġhou": 36621, "supp": 36622, "ĠNacional": 36623, "Ġsuccession": 36624, "Ġvá": 36625, "ĠMaÃŁnahmen": 36626, "ĠJessie": 36627, "ĠIdaho": 36628, "forest": 36629, "ħĺ": 36630, "Ġ×ŀ×ĵ": 36631, "ĠØ£ÙĬ": 36632, "Ġsweetheart": 36633, "Ġneatly": 36634, "ĠEvangel": 36635, "곡": 36636, "ĠSuite": 36637, "ública": 36638, "ĠÑĥли": 36639, "ĠAnnouncer": 36640, "ligh": 36641, "Ġsensations": 36642, "Ġshelters": 36643, "Ġhart": 36644, 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"Ver": 36929, "Ġnorthwest": 36930, "ĠMaintenant": 36931, "ĠLakes": 36932, "аÑıв": 36933, "inté": 36934, "ì°½": 36935, "Ġгаз": 36936, "Ġgiorn": 36937, "Ġdigitally": 36938, "ĠCircuit": 36939, "ì¼Ģ": 36940, "ãĤĬãģ¾ãģĹãģŁ": 36941, "Ġcheerful": 36942, "ĠPeterson": 36943, "ĠDanish": 36944, "ativos": 36945, "Ġliken": 36946, "Ġharbor": 36947, "алиÑģÑĤ": 36948, "xe": 36949, "Ġcurls": 36950, "ĠRhod": 36951, "End": 36952, "ĠET": 36953, "Ġacquaint": 36954, "ĠKelvin": 36955, "Ġtrif": 36956, "ĠAway": 36957, "ìŀIJëĬĶ": 36958, "vs": 36959, "Ġpágina": 36960, "Ġinlet": 36961, "ĠSantos": 36962, "Ġìļ°ìĻĢ": 36963, "Ġyapıyorsun": 36964, "theme": 36965, "Ġsouff": 36966, "Ġinjected": 36967, "Ġpóźniej": 36968, "iverso": 36969, "amped": 36970, "Ġdaher": 36971, "Ġdagger": 36972, "ĠлÑİбим": 36973, "Ġtummy": 36974, "Ġenlightened": 36975, "cents": 36976, "ĠDah": 36977, "Ġcuest": 36978, "ä¾Ĩ說": 36979, "ILY": 36980, "Ġ×ijר": 36981, "Ġbanging": 36982, "ĠEmil": 36983, "ĠCler": 36984, "ĠBorder": 36985, "ижÑĥ": 36986, "Ġpresenters": 36987, "ĠSTUD": 36988, "coins": 36989, "ĠíĻį": 36990, "Ġperks": 36991, "Ġparap": 36992, "Ġcertaines": 36993, "ĠLore": 36994, "öst": 36995, "ĠMARTIN": 36996, "Ġbios": 36997, "Ġwhereby": 36998, "verts": 36999, "ĠMiranda": 37000, "Ġstip": 37001, "澤": 37002, "andez": 37003, "׼׾": 37004, "ujin": 37005, "Ġê¾": 37006, "Ġallergies": 37007, "plate": 37008, "Ġyapıl": 37009, "Ġundertake": 37010, "ĠëĤĺê°Ģ": 37011, "Part": 37012, "Ġkızım": 37013, "hguru": 37014, "ãģĤãģ¨": 37015, "ĠJohns": 37016, "Ġeyelashes": 37017, "Ġdrained": 37018, "ĠstÃ¥r": 37019, "ãģĤãĤĬãģ¾ãģĻ": 37020, "ĠJade": 37021, "Ġcalend": 37022, "film": 37023, "Ġmesa": 37024, "Ġludzie": 37025, "Ġattracts": 37026, "Ġjuices": 37027, "Ġкил": 37028, "Ġnieuwe": 37029, "Ġmencion": 37030, "Ġignition": 37031, "Ġbladder": 37032, "andaag": 37033, "ĠExtension": 37034, "íĤ¨": 37035, "feed": 37036, "ĠÙĪÙĩ": 37037, "Ġspun": 37038, "Ġtät": 37039, "оÑĢоÑĤ": 37040, "tyard": 37041, "ronics": 37042, "ĠHuge": 37043, "Ñĥжд": 37044, "string": 37045, "Ġunjust": 37046, "Ġprawn": 37047, "Ġfrosting": 37048, "Ġdisappearance": 37049, "iosa": 37050, "Ġcardi": 37051, "ĠPriest": 37052, "ĠcientÃŃfic": 37053, "åĵªè£¡": 37054, "ĠÐĴаÑģ": 37055, "Ġë¶Ģíĥģ": 37056, "Ġthieves": 37057, "Ġphysique": 37058, "ĠEugene": 37059, "Ġблиз": 37060, "Ġmonopoly": 37061, "Ġbiography": 37062, "ĠhoÅŁ": 37063, "Ġtö": 37064, "mac": 37065, "Ġshocks": 37066, "ìĦ¸ë": 37067, "hit": 37068, "Ġsnug": 37069, "Ġincl": 37070, "Ġdedic": 37071, "Ġultras": 37072, "ĠизвеÑģÑĤ": 37073, "Ġutilization": 37074, "ĠÑģовеÑĢÑĪенно": 37075, "Ġservi": 37076, "stag": 37077, "180": 37078, "Ġsewer": 37079, "ĠChoice": 37080, "Ġdischarged": 37081, "ĠJD": 37082, "олеÑĤ": 37083, "ĠкваÑĢÑĤи": 37084, "Ġtelescop": 37085, "ĠJeÅĽli": 37086, "ĠNana": 37087, "cale": 37088, "ĠÑĤон": 37089, "mmm": 37090, "äºĨåIJ§": 37091, "Ġgehabt": 37092, "ëĤł": 37093, "æĬķ": 37094, "à¸Ļà¸Ļ": 37095, "Ġether": 37096, "Ġzen": 37097, "Ġresearched": 37098, "ĠCzyli": 37099, "å®Įåħ¨": 37100, "workers": 37101, "Ġ경찰": 37102, "Ġsheriff": 37103, "allo": 37104, "Ġtipos": 37105, "Ġprosecution": 37106, "Ġfrogs": 37107, "Ġfalt": 37108, "jd": 37109, "ĠíĮĶ": 37110, "Ġfiltered": 37111, "ĠOft": 37112, "Ġìį": 37113, "Ġdisfr": 37114, "ĠMustang": 37115, "Ġwoah": 37116, "ĠREALLY": 37117, "Ġмогли": 37118, "Ġentrada": 37119, "ĠигÑĢа": 37120, "Ġmixes": 37121, "ĠавÑĤомоб": 37122, "ÐĻ": 37123, "Ġshin": 37124, "Ġparanormal": 37125, "Ġsomeplace": 37126, "Ġdishon": 37127, "etaan": 37128, "Ġfuerte": 37129, "Ù¹": 37130, "Ġdoom": 37131, "ìĪľ": 37132, "Ġexistential": 37133, "Ġbuld": 37134, "ĠSDK": 37135, "ĠпÑĢавда": 37136, "Ġturnover": 37137, "ĠìĹ¬ê¸°ìĹIJ": 37138, "Ġह": 37139, "Ġmodeled": 37140, "Ġbugün": 37141, "Ġexperimentation": 37142, "Ġmornings": 37143, "Ġmedo": 37144, "Stevie": 37145, "Ġplayable": 37146, "Ġairlines": 37147, "gments": 37148, "Ġ기ë¶Ħ": 37149, "ĠTomb": 37150, "ĠMVP": 37151, "AUDIENCE": 37152, "Ġcheckout": 37153, "Ġpasst": 37154, "Ġbeispiel": 37155, "ĠLinks": 37156, "heavy": 37157, "Ġquestionable": 37158, "Ġìĵ°ë": 37159, "Ġsill": 37160, "Ġmanipulated": 37161, "ĠLoren": 37162, "Ġìľ¼": 37163, "Ġverge": 37164, "ák": 37165, "IES": 37166, "Ġsabot": 37167, "ĠCustomer": 37168, "ależy": 37169, "Ġnominee": 37170, "ĠGad": 37171, "Ġnouvelles": 37172, "ĠSPE": 37173, "istling": 37174, "Ġoval": 37175, "обÑĢаж": 37176, "ifty": 37177, "éĩİ": 37178, "Ġbezel": 37179, "yet": 37180, "Ġfreight": 37181, "ĠHanım": 37182, "rÃŃa": 37183, "Ġzoning": 37184, "Ġindem": 37185, "ĠBü": 37186, "Ġfeminism": 37187, "Ġvoix": 37188, "Ġoficial": 37189, "Ġdiyorum": 37190, "»IJ": 37191, "Ġarose": 37192, "Ġparar": 37193, "ìĿ¸ì§Ģ": 37194, "ĠMartine": 37195, "ĠLect": 37196, "Ġrester": 37197, "Ġdrowning": 37198, "uya": 37199, "cida": 37200, "ĠAriel": 37201, "Ġ02": 37202, "Ġ×Ķ×Ķ": 37203, "ç´ł": 37204, "ĠWert": 37205, "ТÑĭ": 37206, "Ġwidow": 37207, "Ġparchment": 37208, "Ġcottage": 37209, "ĠXL": 37210, "ĠSlack": 37211, "ĠNES": 37212, "Ġrobe": 37213, "Ġgimm": 37214, "Ġcaminho": 37215, "ĠHarper": 37216, "Ġcitrus": 37217, "Ġfirefighters": 37218, "Ġdopamine": 37219, "elets": 37220, "Ġdemocrat": 37221, "ìłľë¡ľ": 37222, "Ġplayback": 37223, "oj": 37224, "ĠпÑĢок": 37225, "ĠSullivan": 37226, "semble": 37227, "ĠWorth": 37228, "ĠMustafa": 37229, "าร": 37230, "Ġmets": 37231, "éĸĢ": 37232, "лоÑģÑĮ": 37233, "Ġinertia": 37234, "Ġuniforms": 37235, "足": 37236, "ério": 37237, "×ķר×Ķ": 37238, "ént": 37239, "Ġà®Ĵ": 37240, "ĠÑģамÑĭÑħ": 37241, "Ġvoulais": 37242, "ĠZimmer": 37243, "ê²łë": 37244, "ĠноÑģ": 37245, "encias": 37246, "Ġrelación": 37247, "Ġ걸ë": 37248, "Ġfaction": 37249, "Ġgosp": 37250, "полож": 37251, "nap": 37252, "hak": 37253, "Ġproceedings": 37254, "ĠìĨĶ": 37255, "ìķĦëĭĪ": 37256, "ĠìŀIJ기": 37257, "Ġwerd": 37258, "Ġsof": 37259, "Ġschlim": 37260, "Ġflavored": 37261, "Ġquadratic": 37262, "ĠBoot": 37263, "Ġpublicity": 37264, "ĠCaro": 37265, "Ġ?\"": 37266, "ниÑĨа": 37267, "mania": 37268, "ĠSUR": 37269, "ĠBUR": 37270, "lance": 37271, "ética": 37272, "Ġzobaczy": 37273, "Ġtrio": 37274, "sama": 37275, "ĠtaÅŁ": 37276, "Ġasymm": 37277, "resser": 37278, "Ġتع": 37279, "ĠпеÑģ": 37280, "Ġbeginnings": 37281, "ladım": 37282, "ĠбÑĭÑģÑĤÑĢ": 37283, "Ġmoo": 37284, "ĠGeneva": 37285, "Ġåľ¨": 37286, "erus": 37287, "borah": 37288, "Ġrefusing": 37289, "bull": 37290, "ĠWaiting": 37291, "ĠIndividual": 37292, "Ġanonym": 37293, "imens": 37294, "Ġmedidas": 37295, "Ġfragrant": 37296, "Ġdirectement": 37297, "ĠìķĦë§Ī": 37298, "uria": 37299, "Ġspherical": 37300, "Ġabge": 37301, "ĠVictorian": 37302, "Ġspectacle": 37303, "ĠRodriguez": 37304, "Ġocup": 37305, "ĠNär": 37306, "marks": 37307, "ngulo": 37308, "ĠLuci": 37309, "Ġshouted": 37310, "Ġregulators": 37311, "ÄŁini": 37312, "Ġdisent": 37313, "ĠÑĢÑĭн": 37314, "ëĤ¨": 37315, "ĠìĤ´ë": 37316, "Ġproblèmes": 37317, "ĠFinger": 37318, "assemble": 37319, "Ġpear": 37320, "Ġdroite": 37321, "ĠEverywhere": 37322, "tam": 37323, "оÑĤив": 37324, "вой": 37325, "ordinate": 37326, "ĠLak": 37327, "ĠmỼi": 37328, "ĠTelevision": 37329, "Ġexponentially": 37330, "avas": 37331, "Ġblev": 37332, "ĠMT": 37333, "俺": 37334, "Connell": 37335, "ĠêµŃ민": 37336, "ĠÑģвоим": 37337, "Ġacha": 37338, "ĠDynasty": 37339, "Jin": 37340, "Ġtore": 37341, "Ġflor": 37342, "Ġмногие": 37343, "æ²Ĵäºĭ": 37344, "owan": 37345, "bah": 37346, "Ġì£Ħ": 37347, "ĠCela": 37348, "Ġìµľê·¼": 37349, "Ġpermettre": 37350, "Ġabras": 37351, "Ġverstehen": 37352, "Ġescort": 37353, "ĠThem": 37354, "ärke": 37355, "porter": 37356, "Ġkahkaha": 37357, "Ġhect": 37358, "Ġdau": 37359, "wah": 37360, "olve": 37361, "ĠAges": 37362, "schaft": 37363, "ĠStell": 37364, "nelle": 37365, "ĠEnsuite": 37366, "ĠÐĴÑģем": 37367, "Ġcréd": 37368, "ĠPP": 37369, "lords": 37370, "grunting": 37371, "Ġcontraction": 37372, "Got": 37373, "Ġacquiring": 37374, "Ġsopr": 37375, "Ġpoisonous": 37376, "RNA": 37377, "Ġanar": 37378, "ĠHof": 37379, "')": 37380, "Ġremarkably": 37381, "Ġinternacional": 37382, "ücke": 37383, "inqu": 37384, "Ġduy": 37385, "Ġbeasts": 37386, "ĠLAN": 37387, "Ġprecedent": 37388, "ĠRPM": 37389, "åij¨": 37390, "Ġselon": 37391, "Ġmorte": 37392, "Ġcomeçou": 37393, "Ñıла": 37394, "Ġinterpreting": 37395, "ĠBurke": 37396, "ÑĤÑĢа": 37397, "ĠìĿ´ëŁ¬": 37398, "Ġpessim": 37399, "ĠNok": 37400, "íĮĿ": 37401, "Female": 37402, "Ġìĭ¤í": 37403, "ĻĢ": 37404, "Ġstimulation": 37405, "Ġslick": 37406, "Ġê°ĢëĬĶ": 37407, "Ġказ": 37408, "ĠHBO": 37409, "Ġpapier": 37410, "Ġkönnten": 37411, "Ñĥбли": 37412, "ĠConstant": 37413, "SPEAKING": 37414, "ĠktórÄħ": 37415, "Ġcosmetics": 37416, "ĠTrend": 37417, "Ġrobbery": 37418, "Ġtitt": 37419, "Ġgjort": 37420, "Ġdietary": 37421, "łĮ": 37422, "ĠKirby": 37423, "ĠпÑĢимеÑĢно": 37424, "Ġqualification": 37425, "Ġìķī": 37426, "Ġcabinets": 37427, "Ġhttp": 37428, "ĠErica": 37429, "義": 37430, "Ġdisadvantages": 37431, "Ġchattering": 37432, "yz": 37433, "feit": 37434, "Ġguild": 37435, "ĠETF": 37436, "ĠDragons": 37437, "ĠHERE": 37438, "venth": 37439, "ÙĦاÙħ": 37440, "Ġmarché": 37441, "Dam": 37442, "Ġphoton": 37443, "Ġestable": 37444, "Mag": 37445, "Ġolhar": 37446, "Ġcoupling": 37447, "ĠHilfe": 37448, "ĠWizard": 37449, "Ġмало": 37450, "help": 37451, "ĠlÃŃnea": 37452, "Ġì«": 37453, "Ġstandalone": 37454, "Ġmorale": 37455, "Ġzweite": 37456, "ãĤĪãĤįãģĹãģı": 37457, "ährt": 37458, "Ġdotted": 37459, "Ġdripping": 37460, "ĠFlag": 37461, "éĿĴ": 37462, "rocket": 37463, "rategy": 37464, "irim": 37465, "Ġíķĺë©´ìĦľ": 37466, "Ġsogenan": 37467, "ĠUno": 37468, "ĠSchutz": 37469, "Ġestilo": 37470, "ĠSubs": 37471, "ĠDaisy": 37472, "ÐĿеÑĤ": 37473, "'...": 37474, "Ġplatinum": 37475, "Ġbirl": 37476, "ĠSovi": 37477, "Ġviolate": 37478, "ÑĥеÑĤÑģÑı": 37479, "rill": 37480, "Ġtraz": 37481, "Ġsnip": 37482, "Ġcumpl": 37483, "à¸Ńà¸ģ": 37484, "Ġcuk": 37485, "éħĴ": 37486, "ĠParlament": 37487, "Ġhypert": 37488, "Ġpulp": 37489, "Ġtongues": 37490, "atto": 37491, "Ġbusca": 37492, "ihn": 37493, "ERO": 37494, "ĠÙĬع": 37495, "Ġvarias": 37496, "ĠMarian": 37497, "Ġbounded": 37498, "Ġpitching": 37499, "Ġdeficiency": 37500, "ĠBlessed": 37501, "ĠExerc": 37502, "uchs": 37503, "ĠnhÆ°ng": 37504, "æľ¬å½ĵ": 37505, "Ġraped": 37506, "hales": 37507, "Ġmala": 37508, "pic": 37509, "Ġ401": 37510, "ÅĽniej": 37511, "arina": 37512, "ëĵ¤ìĿĦ": 37513, "otti": 37514, "Ġдолго": 37515, "Ġtracker": 37516, "ĠShelby": 37517, "Ġvanished": 37518, "Ġbakery": 37519, "Kapı": 37520, "Jesus": 37521, "ĠKR": 37522, "JO": 37523, "ħ¸": 37524, "Ġdiscs": 37525, "ìĦ¯": 37526, "ì§Ģë": 37527, "×Ļצ": 37528, "emary": 37529, "Kendra": 37530, "Ġyük": 37531, "ückt": 37532, "Ġvaz": 37533, "Ġkup": 37534, "aktu": 37535, "ĠÑģпаÑģибо": 37536, "Ġaik": 37537, "Ġnursery": 37538, "Ġendangered": 37539, "êmement": 37540, "ematics": 37541, "Ġresponders": 37542, "ĠRepresentatives": 37543, "Ġsculptures": 37544, "igkeiten": 37545, "Ġdepl": 37546, "Ġinterpretations": 37547, "Ġdeadlines": 37548, "Ġ1942": 37549, "ÃĹ": 37550, "Ġsugars": 37551, "emu": 37552, "lively": 37553, "Ġrecreational": 37554, "Ġdistort": 37555, "Ġunderscore": 37556, "Ġunquote": 37557, "Ġsafest": 37558, "Ġswollen": 37559, "Ġanalyses": 37560, "Ġcommencé": 37561, "妹": 37562, "andin": 37563, "ĠХоÑĢоÑĪо": 37564, "Ġdiarr": 37565, "ãģ¾ãģģ": 37566, "ziest": 37567, "Ġtoothbrush": 37568, "éł»éģĵ": 37569, "uations": 37570, "Ġcade": 37571, "Ġbacklash": 37572, "hind": 37573, "Ġrisque": 37574, "zess": 37575, "ĠìĿ´ìķ¼ê¸°": 37576, "Ġesperar": 37577, "Ġtranslations": 37578, "ioned": 37579, "groans": 37580, "ĠпÑĥÑĤ": 37581, "Ġgenetically": 37582, "éĢł": 37583, "Ġhappiest": 37584, "Ġwerk": 37585, "atoon": 37586, "Ġmusi": 37587, "Ġfunção": 37588, "ĠìŀħëĭĪëĭ¤": 37589, "ĠÑĢай": 37590, "Ġbevor": 37591, "BLANK": 37592, "Ġrepentance": 37593, "Put": 37594, "Ġpotrzeb": 37595, "Ġsala": 37596, "Ġcampa": 37597, "WER": 37598, "ĠdecÃŃa": 37599, "Ġsécurité": 37600, "ĠAppreciate": 37601, "Ñĩи": 37602, "ĠRandom": 37603, "ë³Ħ": 37604, "kah": 37605, "Ġmöj": 37606, "Ġsäger": 37607, "Ġ×Ļ׼×ķ׾": 37608, "Ġ190": 37609, "xtures": 37610, "Eu": 37611, "Ġgä": 37612, "Ġ×ijת": 37613, "ĠCroat": 37614, "apo": 37615, "PLE": 37616, "Ġpersistence": 37617, "åĬ©": 37618, "Ġblends": 37619, "Ġtreffen": 37620, "ĠSantiago": 37621, "ydia": 37622, "aldo": 37623, "ĠTensorFlow": 37624, "ĠDual": 37625, "ãĥľ": 37626, "Ġchiff": 37627, "ìĹ´": 37628, "Ġcontracted": 37629, "Ġsegreg": 37630, "ĠFairy": 37631, "Ġwisely": 37632, "Ġvulnerabilities": 37633, "Ġhandheld": 37634, "Ġgadgets": 37635, "ĠboÅŁ": 37636, "ĠPopular": 37637, "Ġcurvature": 37638, "문": 37639, "ĠMARY": 37640, "ìĿ´ìĬ": 37641, "Ġformulation": 37642, "Ġcelery": 37643, "Ġblurry": 37644, "ĠTS": 37645, "alez": 37646, "Ġws": 37647, "Ġprogramm": 37648, "ĠStack": 37649, "ĠJIM": 37650, "овали": 37651, "ıll": 37652, "Ġpère": 37653, "ĠKanye": 37654, "ĠDelaware": 37655, "Ġãģł": 37656, "Ġdaunting": 37657, "ĠбеÑģ": 37658, "ĠStupid": 37659, "big": 37660, "fficial": 37661, "Ġprecipitation": 37662, "Ġplung": 37663, "ục": 37664, "burse": 37665, "Ġdarle": 37666, "Ġcripp": 37667, "Ġpioneer": 37668, "Ġdisput": 37669, "Ġsean": 37670, "ãģĵãĤĵãģª": 37671, "Ġresistor": 37672, "Ġallein": 37673, "ipples": 37674, "arel": 37675, "Ġendors": 37676, "zust": 37677, "ĠÑĢебÑıÑĤа": 37678, "eded": 37679, "Ġì¹´ë©Ķë": 37680, "Ġlleva": 37681, "Ġkennt": 37682, "Ġбал": 37683, "ĠDocument": 37684, "ĠKnights": 37685, "Ġbuckle": 37686, "Ġìī¬": 37687, "Ġalk": 37688, "ĠEveryday": 37689, "atters": 37690, "Ġtoilets": 37691, "Ġjugar": 37692, "ĠìŀĪì§Ģ": 37693, "Ġgenauso": 37694, "ĠLandesregierung": 37695, "ãģ£ãģ±": 37696, "ije": 37697, "Ġtrailers": 37698, "ĠTigers": 37699, "Ġgitti": 37700, "Ġforgiving": 37701, "Ġconcurrent": 37702, "ĠVu": 37703, "ĠíĬ¹íŀĪ": 37704, "ĠBROWN": 37705, "ounded": 37706, "\";": 37707, "Ġtremb": 37708, "Ġtiet": 37709, "ĠÑĢежим": 37710, "Ġnutshell": 37711, "елиÑĩ": 37712, "Ġlosers": 37713, "ricting": 37714, "Ġredeem": 37715, "defined": 37716, "Nice": 37717, "Ġbroadband": 37718, "KO": 37719, "Ġteasing": 37720, "Ġpartisan": 37721, "ıma": 37722, "Ġìŀ¬ë¯¸": 37723, "ĠJourney": 37724, "Ġslopes": 37725, "uning": 37726, "grunts": 37727, "Ġtäll": 37728, "Ġuncovered": 37729, "ĠmyÅĽlÄĻ": 37730, "ĠEsther": 37731, "äºİ": 37732, "ĠHealthy": 37733, "Ġë°ij": 37734, "rée": 37735, "Ġpolarization": 37736, "Ġflav": 37737, "Ġcambiar": 37738, "Ġyr": 37739, "ĠRanch": 37740, "Ġsplits": 37741, "Ġtrouvé": 37742, "åľĭ家": 37743, "Ġrecorder": 37744, "Ġdépart": 37745, "ÙĪب": 37746, "ĠKry": 37747, "Ġinteressant": 37748, "Ġederim": 37749, "ÅĽwiad": 37750, "ilateral": 37751, "wright": 37752, "Ġpourra": 37753, "êter": 37754, "Ġcamel": 37755, "áŀ": 37756, "Ġrapidement": 37757, "Ġmej": 37758, "Ġstiffness": 37759, "ADAS": 37760, "Ġdiffers": 37761, "Ġalot": 37762, "ĠSig": 37763, "ÑıÑĤелÑĮ": 37764, "Ġabstraction": 37765, "åľĺ": 37766, "Ġkeiner": 37767, "grupp": 37768, "ĠSherlock": 37769, "íĺĶ": 37770, "Ġcite": 37771, "Ġoverflow": 37772, "Ġtại": 37773, "úcar": 37774, "bula": 37775, "Ġconjunto": 37776, "ĠCI": 37777, "Ġmoderator": 37778, "Ġindirectly": 37779, "Ġalleine": 37780, "âĤ": 37781, "ÑĪиб": 37782, "Ġбаб": 37783, "Ġdanach": 37784, "Ġ1939": 37785, "Ġpromet": 37786, "Ġdestinations": 37787, "ĠIllust": 37788, "ικÏĮ": 37789, "Ġsabes": 37790, "Ġheh": 37791, "ĠGesetzent": 37792, "ĠMiz": 37793, "енко": 37794, "ĠMys": 37795, "Ь": 37796, "ĠJudaism": 37797, "Ġmustache": 37798, "Ġstimmt": 37799, "ĠGaza": 37800, "Ġvolte": 37801, "Ġnuo": 37802, "Ġmón": 37803, "ĠComput": 37804, "ูà¹Ī": 37805, "ĠRadi": 37806, "Ġexceptionally": 37807, "Ġassumes": 37808, "éĸĭå¿ĥ": 37809, "ãģĪãģ°": 37810, "inform": 37811, "Ġshrine": 37812, "æĵĬ": 37813, "Ġimplication": 37814, "ĠFitz": 37815, "æ²ĴéĹľä¿Ĥ": 37816, "!.": 37817, "Ġlt": 37818, "Ġalloy": 37819, "Ġethic": 37820, "Ġmonastery": 37821, "ìĭľì£ł": 37822, "icação": 37823, "Ġcoordinating": 37824, "ĠMoto": 37825, "Ġoverlook": 37826, "Ġchois": 37827, "Ġantibiotic": 37828, "ĠMinne": 37829, "ĠBJ": 37830, "ĠApa": 37831, "orian": 37832, "Ġspilled": 37833, "Jam": 37834, "Ġhusbands": 37835, "Ġcreations": 37836, "Ġañ": 37837, "üssel": 37838, "ĠìĿ´ìļ©": 37839, "Ġanalyse": 37840, "rose": 37841, "Ġpunched": 37842, "Ġpresque": 37843, "Ġastronomy": 37844, "Ġschwierig": 37845, "ĠEbola": 37846, "Ġcis": 37847, "Ġacet": 37848, "ĠFX": 37849, "endre": 37850, "ĠìĿĮìķħ": 37851, "Ġwebpage": 37852, "Ġfreaked": 37853, "Ġlatte": 37854, "Ġì¿ł": 37855, "Ġ머ë": 37856, "Never": 37857, "Gra": 37858, "íĻĶ를": 37859, "eyed": 37860, "Ġë°ľëĿ¼": 37861, "Ġespera": 37862, "Ġaparece": 37863, "ração": 37864, "Ġdisruptive": 37865, "ĠJoint": 37866, "urous": 37867, "reas": 37868, "ĠquerÃŃa": 37869, "Ġdistributions": 37870, "Ġexponent": 37871, "ì¹ĺ를": 37872, "Ġdl": 37873, "zhou": 37874, "ĠHearing": 37875, "å·®ä¸įå¤ļ": 37876, "ĠCraw": 37877, "Ġfloats": 37878, "ounced": 37879, "Lab": 37880, "World": 37881, "Ġburdens": 37882, "Ġauthoritarian": 37883, "ĠBolt": 37884, "ĠоднÑĥ": 37885, "Ġpigeon": 37886, "Ġdistractions": 37887, "ĠHerausforder": 37888, "Ġzest": 37889, "esc": 37890, "Ġshakes": 37891, "atas": 37892, "ĠÙħØ´": 37893, "holes": 37894, "Ġthinkers": 37895, "alta": 37896, "Ġarche": 37897, "ĠSuk": 37898, "anha": 37899, "Ġtempting": 37900, "Ġyoutuber": 37901, "Ġvì": 37902, "ĠdziaÅĤa": 37903, "ĠVatican": 37904, "Park": 37905, "Ġsupers": 37906, "ĠNikki": 37907, "ëĬIJë": 37908, "orang": 37909, "ramient": 37910, "鬼": 37911, "Ġê°ĸê³ł": 37912, "Ġdesserts": 37913, "Ġavere": 37914, "ĠGregory": 37915, "Ġëĵ¤ìĸ´ìĺ": 37916, "Ġcosting": 37917, "ĠClinic": 37918, "Ġrebels": 37919, "ĠMob": 37920, "Ġbunlar": 37921, "ĠYours": 37922, "ertime": 37923, "Ġretali": 37924, "mara": 37925, "atus": 37926, "alles": 37927, "ĠдÑĢ": 37928, "ĠдиÑģ": 37929, "Ġdiscounts": 37930, "ĠGUY": 37931, "Ġкакое": 37932, "ĠExperiment": 37933, "rement": 37934, "ĠXiang": 37935, "Ġbate": 37936, "WE": 37937, "Ġspecialize": 37938, "Ġdeity": 37939, "ĠLoki": 37940, "mag": 37941, "ĠNit": 37942, "West": 37943, "Ġmaternal": 37944, "Ġquis": 37945, "åŁºæľ¬": 37946, "broken": 37947, "Ġlasers": 37948, "Ġhakk": 37949, "ĠAngels": 37950, "Ġmastery": 37951, "antis": 37952, "Tiffany": 37953, "eee": 37954, "çij": 37955, "orem": 37956, "Ġinacc": 37957, "Ġjurisdictions": 37958, "ĠKardash": 37959, "æľº": 37960, "Il": 37961, "ĠSinn": 37962, "åĭķçĶ»": 37963, "Ġathletics": 37964, "cÄĻ": 37965, "Ġloosely": 37966, "Ġdieta": 37967, "Ag": 37968, "Ġ??": 37969, "ĠëĮĢíijľ": 37970, "Ġsuperv": 37971, "Ġnutrit": 37972, "Ġdrifting": 37973, "ĠìĦłìĥĿëĭĺ": 37974, "ĠпонÑıл": 37975, "ĠVictory": 37976, "ÙĦØ©": 37977, "×ķ׳×Ķ": 37978, "ĠпиÑĪ": 37979, "Ġshaved": 37980, "Ġmesure": 37981, "onden": 37982, "Ùĥر": 37983, "Ġexile": 37984, "ĠDesde": 37985, "ĠPinterest": 37986, "Ġattachments": 37987, "Ġhombres": 37988, "Ġfines": 37989, "ĠìĦ¸ìĥģ": 37990, "Ġsleeps": 37991, "ĠTaco": 37992, "ĠIRA": 37993, "rios": 37994, "Ġoll": 37995, "etes": 37996, "Ġunut": 37997, "fashioned": 37998, "Ġtreball": 37999, "ĠNearly": 38000, "ĠÑĢеалÑĮно": 38001, "Ġchil": 38002, "éĢ±": 38003, "ÄŁa": 38004, "ĠMEL": 38005, "roscop": 38006, "ĠCG": 38007, "Ġvenge": 38008, "Ġdishwasher": 38009, "algic": 38010, "Ġmodifier": 38011, "Ġembassy": 38012, "timer": 38013, "emics": 38014, "Ġintricate": 38015, "Ġevet": 38016, "ĠëĮĢë°ķ": 38017, "Ġisot": 38018, "ĠнаÑĥÑĩ": 38019, "ĠQuiz": 38020, "reso": 38021, "δÏİ": 38022, "Ġyelled": 38023, "Ġfeder": 38024, "ELLER": 38025, "Ġexceeded": 38026, "onas": 38027, "icano": 38028, "ĠживоÑĤ": 38029, "ĠMao": 38030, "ĠKazuto": 38031, "Ġãħĭãħĭãħĭãħĭ": 38032, "Ġfrontline": 38033, "ĠHungarian": 38034, "Ġüberall": 38035, "awat": 38036, "Ġgrips": 38037, "ições": 38038, "arnya": 38039, "ĠÍ¡": 38040, "Ġseid": 38041, "Ġanak": 38042, "Ġacabou": 38043, "íķij": 38044, "Ġnotorious": 38045, "ĠGodzilla": 38046, "Ġovercoming": 38047, "ĠPend": 38048, "Ġolabilir": 38049, "ülme": 38050, "Ġerhalten": 38051, "ãĤīãģĦ": 38052, "ê·¹": 38053, "ĠMeter": 38054, "Ġstaan": 38055, "Ol": 38056, "Ġchats": 38057, "ĠBuenos": 38058, "ÃŃve": 38059, "aluable": 38060, "Ġstrategically": 38061, "Ġcomprised": 38062, "ĠпеÑĢÑģонаж": 38063, "Ġwann": 38064, "ĠCen": 38065, "ниÑĤе": 38066, "Łģ": 38067, "ĠÑĤобой": 38068, "iad": 38069, "ĠkardeÅŁim": 38070, "ĠCongressman": 38071, "reaming": 38072, "homme": 38073, "Ġcommunaut": 38074, "Ġalcoholic": 38075, "Ġpickled": 38076, "Ġacord": 38077, "position": 38078, "egól": 38079, "Ġtroubling": 38080, "ĠMarcheg": 38081, "Ġzumindest": 38082, "Ġseamlessly": 38083, "Ġolun": 38084, "ĠTVs": 38085, "ĠпÑĢакÑĤиÑĩеÑģки": 38086, "Ġbackend": 38087, "ãģĵãĤĵãģ«ãģ¡ãģ¯": 38088, "idable": 38089, "Ġgadget": 38090, "Ġfaço": 38091, "ĠMarchegiani": 38092, "Ġë°¤": 38093, "Ġaccidental": 38094, "ĠLP": 38095, "Ġeldest": 38096, "ĠAdmiral": 38097, "ĠnÄĥm": 38098, "lever": 38099, "Ġpastel": 38100, "Ġfondo": 38101, "Connie": 38102, "Ġtercer": 38103, "Ġpact": 38104, "ĠMonte": 38105, "Ġmeats": 38106, "ĠSMS": 38107, "ĠAustralians": 38108, "ç¼": 38109, "Rhett": 38110, "Ġexactement": 38111, "Ġë¹¼": 38112, "ĠMOD": 38113, "ç¡": 38114, "ĠRapt": 38115, "ĠNoch": 38116, "Ġabort": 38117, "ĠNaval": 38118, "ĠFuji": 38119, "INTER": 38120, "ĠновÑĭй": 38121, "Ġmiejsce": 38122, "ĠICU": 38123, "ĠGraduate": 38124, "ĠGlen": 38125, "ardi": 38126, "ĠÈĺ": 38127, "Ġsolder": 38128, "Ġprofessions": 38129, "Ġorthog": 38130, "omn": 38131, "introdu": 38132, "ĠDenise": 38133, "ìŀIJ를": 38134, "Ġcorrespondence": 38135, "AMA": 38136, "Ġinflict": 38137, "Ġfand": 38138, "ĠGü": 38139, "ĠÑĩеÑĤ": 38140, "Ġtraced": 38141, "Ġpatents": 38142, "Ġambush": 38143, "Ġlotta": 38144, "ffer": 38145, "ĠWagner": 38146, "Ġimperson": 38147, "Ġextrêmement": 38148, "ÙĤت": 38149, "conduct": 38150, "Att": 38151, "ĠMueller": 38152, "ĠAlicia": 38153, "Ġcyc": 38154, "Ġhacker": 38155, "Ġtys": 38156, "Ġhail": 38157, "ĠзаÑıв": 38158, "Ġpasso": 38159, "Ġì¶Ķê°Ģ": 38160, "ĠÎĪ": 38161, "Ġpackaged": 38162, "ĠCynthia": 38163, "heet": 38164, "ä¸ŃåĽ½": 38165, "ĠNissan": 38166, "ĠQuesto": 38167, "é¨": 38168, "did": 38169, "Ġμια": 38170, "ĠEllis": 38171, "ĠAnalysis": 38172, "cemos": 38173, "Ġaseg": 38174, "ĠMyster": 38175, "ĠCao": 38176, "Ġtuv": 38177, "ĠIndustry": 38178, "ì£¼ê³ł": 38179, "otal": 38180, "Ġpequeño": 38181, "bras": 38182, "Ġcomprehend": 38183, "ĠSimpson": 38184, "ÑģÑĤвие": 38185, "ocracy": 38186, "иÑĩеÑģки": 38187, "ĠMush": 38188, "ĠLaurie": 38189, "Ġtriangular": 38190, "ĠPresents": 38191, "ĠKunden": 38192, "ç´¹": 38193, "æѦ": 38194, "ĠIss": 38195, "ĠDeck": 38196, "á»ĥn": 38197, "ĠDarkness": 38198, "Ġinflammatory": 38199, "eremiah": 38200, "Ġwarmed": 38201, "veyard": 38202, "ĠMemory": 38203, "etty": 38204, "Ġtaxpayers": 38205, "à¸ĵ": 38206, "Ø¡": 38207, "Ġpractise": 38208, "ëĭ¬ë": 38209, "Ġdrilled": 38210, "mÃ¼ÅŁ": 38211, "logo": 38212, "ĠFach": 38213, "¤ë¡ľ": 38214, "Ġübrigens": 38215, "Ġkonnten": 38216, "Ġnormalmente": 38217, "Ġargues": 38218, "ilingual": 38219, "°ë¥¼": 38220, "egal": 38221, "Ġtravaill": 38222, "ovy": 38223, "аÑĤо": 38224, "Ġruth": 38225, "ĠLights": 38226, "Ġconsisted": 38227, "×ijר×Ļ×Ŀ": 38228, "Ġstereotype": 38229, "Ġpayer": 38230, "ĠRee": 38231, "ĠAirbnb": 38232, "Ġdrowned": 38233, "ĠZoe": 38234, "Ġcanopy": 38235, "Ġbarr": 38236, "ĠноÑĩ": 38237, "Ġpagan": 38238, "Ġjars": 38239, "Ġrê": 38240, "erver": 38241, "æĪ¿": 38242, "ieben": 38243, "Ġespect": 38244, "ĠFi": 38245, "Ġunwilling": 38246, "Ġtechnician": 38247, "ặt": 38248, "member": 38249, "ĠCanal": 38250, "سÙħ": 38251, "Ġlieber": 38252, "Ġinference": 38253, "Ġhonoring": 38254, "åijµ": 38255, "ĠCampaign": 38256, "Ġlineage": 38257, "ĠStress": 38258, "Ġvictories": 38259, "Ġdeja": 38260, "×£": 38261, "êtes": 38262, "blick": 38263, "Ġменее": 38264, "oths": 38265, "ĠCouple": 38266, "Jason": 38267, "ĠNicolas": 38268, "екÑģ": 38269, "lib": 38270, "Ġherramient": 38271, "Ġ×IJ×ķ×ŀר": 38272, "Ġвидим": 38273, "millimeter": 38274, "Ġsilhouette": 38275, "Ġdriveway": 38276, "Ġcherish": 38277, "ãħłãħł": 38278, "Ġransom": 38279, "Ġinterdisciplinary": 38280, "ĠPortal": 38281, "Ġtrag": 38282, "thood": 38283, "Ġtedious": 38284, "Ġglossy": 38285, "Ġprépar": 38286, "ĠCay": 38287, "ĠTook": 38288, "ĠBottom": 38289, "Ġzig": 38290, "å«": 38291, "åį±": 38292, "represented": 38293, "à¹Ģลย": 38294, "Ġdesarrollo": 38295, "ìĦľë": 38296, "Ġviscos": 38297, "Ġmilligram": 38298, "ĠGund": 38299, "Ġferment": 38300, "drum": 38301, "Ġdrawers": 38302, "Laugh": 38303, "Ġpelos": 38304, "Ġpavement": 38305, "Ġmemoir": 38306, "avait": 38307, "Ġ2050": 38308, "¤ë¥¼": 38309, "Ġrazón": 38310, "Ġflourish": 38311, "Ġstern": 38312, "ä¸Ī": 38313, "ĠChung": 38314, "Ġserpent": 38315, "ĠGentlemen": 38316, "羣çļĦå¾Ī": 38317, "kook": 38318, "Ġlut": 38319, "importe": 38320, "parent": 38321, "Ġwsz": 38322, "Ġscree": 38323, "ĠMitarbeiter": 38324, "å·´": 38325, "mut": 38326, "Ġìĸĺ기를": 38327, "Ġsemble": 38328, "ĠOW": 38329, "Ġinvestigator": 38330, "ĠCheryl": 38331, "ĠGerald": 38332, "Ġprere": 38333, "Ġcompares": 38334, "nyt": 38335, "Ġdiferença": 38336, "?-": 38337, "Ġquá": 38338, "ר×Ļ": 38339, "Sen": 38340, "Ġheps": 38341, "Ġgratuit": 38342, "Ġconsort": 38343, "ĠSTOP": 38344, "ĠProtestant": 38345, "Ġelectrode": 38346, "âĹ": 38347, "Ġsecurely": 38348, "иÑĩеÑģкой": 38349, "Ġtää": 38350, "Ġregisters": 38351, "ĠHeavenly": 38352, "ogly": 38353, "issä": 38354, "ĠPhysics": 38355, "ĠMerkel": 38356, "Ġrév": 38357, "éĻ¢": 38358, "Ġerased": 38359, "ĠSacramento": 38360, "Ġcoffin": 38361, "Ġexacer": 38362, "Ġlanz": 38363, "Ġpoets": 38364, "ulif": 38365, "Ġì¹ĺë": 38366, "ĠNerd": 38367, "ĠNCT": 38368, "ĠHour": 38369, "nehmer": 38370, "ŀĺëıĦ": 38371, "ĠPrinci": 38372, "Sw": 38373, "mies": 38374, "armed": 38375, "ĠBeatles": 38376, "Ġpropagation": 38377, "Ġexchanged": 38378, "Ġcumulative": 38379, "Ġì§ijìĹIJ": 38380, "Ġdefeating": 38381, "æĬ±": 38382, "bels": 38383, "Ġwes": 38384, "ĠOdyssey": 38385, "ä½łæĥ³": 38386, "avior": 38387, "ĠìľĦìĹIJ": 38388, "Ġbrit": 38389, "Ġhijo": 38390, "DAY": 38391, "ĠاÙĦتÙĬ": 38392, "ĠСеÑĢг": 38393, "Ñĥка": 38394, "edsiÄĻ": 38395, "Ġimpos": 38396, "Ġellas": 38397, "Ġfirearms": 38398, "ĠNR": 38399, "Ġ×ij×IJ": 38400, "ĠÐŁÐ¾ÐºÐ°": 38401, "awi": 38402, "ĠìĦ±ê³µ": 38403, "Ġpupils": 38404, "ĠTack": 38405, "Ġfrase": 38406, "ĠShip": 38407, "Ġstad": 38408, "举": 38409, "ĠGreater": 38410, "unun": 38411, "immung": 38412, "grown": 38413, "ĠNXT": 38414, "ĠAmericas": 38415, "fox": 38416, "Ġmanten": 38417, "éłIJåĤĻ": 38418, "ĠÑģок": 38419, "Ġrikt": 38420, "lectric": 38421, "deep": 38422, "ĠзнаеÑĪÑĮ": 38423, "Ġbenut": 38424, "ĠInfrast": 38425, "ĠEmir": 38426, "ĠоÑĤпÑĢав": 38427, "ĠKimchi": 38428, "ĠFinnish": 38429, "´ìłģ": 38430, "inaire": 38431, "Ġoike": 38432, "æ¸ħæ¥ļ": 38433, "Ġhostage": 38434, "ĠButton": 38435, "ÙĤÙĬ": 38436, "eking": 38437, "ĠKazakh": 38438, "Ġcomforting": 38439, "Ġsog": 38440, "Ġgreeted": 38441, "guitar": 38442, "payer": 38443, "Ġrelational": 38444, "Ġconstruir": 38445, "çī¹åĪ¥": 38446, "opian": 38447, "ĠVolume": 38448, "ieth": 38449, "ÑģÑĤвом": 38450, "urrection": 38451, "liÅĽmy": 38452, "Ġhemisphere": 38453, "ĠBean": 38454, "IGN": 38455, "Ġkötü": 38456, "ĠFallout": 38457, "Ġbrace": 38458, "ç¹¼çºĮ": 38459, "ÏĢά": 38460, "ĠHAS": 38461, "Ġgé": 38462, "Ġcharacterize": 38463, "ặc": 38464, "ĠMilky": 38465, "Ġtumors": 38466, "Ġnuit": 38467, "ĠGaz": 38468, "ĠìŀĪëĭ¤ëĬĶ": 38469, "ĠгаÑĢ": 38470, "essment": 38471, "ĠAbe": 38472, "Ġë½ij": 38473, "ĠEinsatz": 38474, "JIN": 38475, "jä": 38476, "Cry": 38477, "ĠPromised": 38478, "ĠÑģеÑĢд": 38479, "okus": 38480, "Ġscalable": 38481, "ĠпоÑģмоÑĤÑĢеÑĤÑĮ": 38482, "ücklich": 38483, "Ġrealism": 38484, "Ġmayo": 38485, "Ġjuvenile": 38486, "Ġheadlights": 38487, "ĠgörÃ¼ÅŁ": 38488, "ĠReform": 38489, "Ġhalves": 38490, "czne": 38491, "Ġbreakup": 38492, "żej": 38493, "Ġrätt": 38494, "Day": 38495, "ĠìĿ¼ë³¸": 38496, "Ġmuerte": 38497, "Ġtunes": 38498, "ĠSmile": 38499, "record": 38500, "Ġrecherche": 38501, "atisfied": 38502, "Ġpozi": 38503, "Ġcelebrations": 38504, "isexual": 38505, "ĠROB": 38506, "thirds": 38507, "ĠFortune": 38508, "ĠÑĤой": 38509, "Ġbranded": 38510, "loo": 38511, "Ġdud": 38512, "Ġrandomized": 38513, "Ġcombin": 38514, "ä¸ĢäºĽ": 38515, "ieran": 38516, "czenia": 38517, "įãĥ«": 38518, "Ġcurator": 38519, "Ġartery": 38520, "ĠÑĥÑĪ": 38521, "ĠÑĩиÑĤ": 38522, "Ġsubsidies": 38523, "Ġblossom": 38524, "ĠTwilight": 38525, "Ġhyvä": 38526, "ĠPompe": 38527, "ĠCisco": 38528, "ĠÐŁÑĢо": 38529, "Ġbiri": 38530, "Ġgern": 38531, "Ġrebuilt": 38532, "Ġwcze": 38533, "Ġbenefici": 38534, "Ġdrummer": 38535, "Ġsolids": 38536, "Ġdiyorsun": 38537, "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸãģĦãģ¾ãģĹãģŁ": 38538, "lated": 38539, "Ġmuddy": 38540, "Ġholog": 38541, "Ġclaps": 38542, "ĠRings": 38543, "ĠOkey": 38544, "ĠBrave": 38545, "Ġvaluation": 38546, "Ġmigrant": 38547, "Ġintermitt": 38548, "Ġeigene": 38549, "iliary": 38550, "ãĥ¼ãĥĪ": 38551, "markt": 38552, "kr": 38553, "ĠRib": 38554, "á»Ļi": 38555, "Ġaccusations": 38556, "Ġarab": 38557, "wash": 38558, "ĠBardzo": 38559, "Ġugh": 38560, "esters": 38561, "ophren": 38562, "Ġalimentos": 38563, "ĠUz": 38564, "ÖĤ": 38565, "Ġ650": 38566, "ĠпÑĢиеÑħ": 38567, "FI": 38568, "Ġsampai": 38569, "Ġparlé": 38570, "hesion": 38571, "Ġsır": 38572, "Ġapparatus": 38573, "Ġcorrelated": 38574, "ĠPrincipal": 38575, "Ġcorr": 38576, "ĠOfficial": 38577, "иÑĩеÑģкие": 38578, "Ġterminals": 38579, "Should": 38580, "Ġvacun": 38581, "Ġstellt": 38582, "Ġmooi": 38583, "etzung": 38584, "ĠкÑĢа": 38585, "Ġdai": 38586, "Ġпож": 38587, "Team": 38588, "ĠPPE": 38589, "ĠÐŀÑģ": 38590, "ĠLeah": 38591, "ĠIvy": 38592, "yst": 38593, "Ġuhhh": 38594, "Ġnighttime": 38595, "Ġtrendy": 38596, "Ġsecurities": 38597, "Ġcontinents": 38598, "Ġfirsthand": 38599, "ĠVeron": 38600, "ĠëĤ®": 38601, "Ġbrowsing": 38602, "ĠCada": 38603, "tro": 38604, "Ġtramp": 38605, "reib": 38606, "Ġerstmal": 38607, "irler": 38608, "Ġpsic": 38609, "Ġgetir": 38610, "ĠNP": 38611, "Ġdzieci": 38612, "обÑĢаз": 38613, "Ġmagician": 38614, "Ġscrutiny": 38615, "Ġslab": 38616, "ĠOT": 38617, "isty": 38618, "iries": 38619, "orest": 38620, "Ġtasked": 38621, "Ġmorally": 38622, "ìķ¼ì§Ģ": 38623, "ustered": 38624, "Ġfools": 38625, "Ġirrespons": 38626, "Ġeinf": 38627, "Ġviá»ĩc": 38628, "Ġscor": 38629, "Ġpillows": 38630, "ĠGegen": 38631, "Ġtutte": 38632, "Ġquarterly": 38633, "Ġdidnt": 38634, "ĠGym": 38635, "ĠEther": 38636, "ĠØ«": 38637, "лиÑĪком": 38638, "Ġsignaling": 38639, "ĠNode": 38640, "ĠDoncs": 38641, "Ġyah": 38642, "ĠKanal": 38643, "Ġfading": 38644, "etin": 38645, "Ġinfluencers": 38646, "Ġmedals": 38647, "Ġengineered": 38648, "Ġfermented": 38649, "ê²łì§Ģë§Į": 38650, "ĠBeethoven": 38651, "×ŀש": 38652, "inental": 38653, "ĠìķĮ볤": 38654, "ütfen": 38655, "alnya": 38656, "Ġovere": 38657, "Ġdenkt": 38658, "акÑĤеÑĢ": 38659, "Ġâĺ": 38660, "Ġnecesit": 38661, "Ġgenerators": 38662, "grass": 38663, "ĠподÑĥм": 38664, "lieÃŁen": 38665, "Bar": 38666, "ľëıĻ": 38667, "ĠдеÑĤей": 38668, "Ġsucking": 38669, "Ġstencil": 38670, "Ġprimo": 38671, "ĠBreath": 38672, "strom": 38673, "Ġimmensely": 38674, "Ġappreh": 38675, "ìłķìĿ´": 38676, "Pop": 38677, "Ġjong": 38678, "ĠGiul": 38679, "ĠADHD": 38680, "Ġhören": 38681, "Ġelo": 38682, "ivent": 38683, "Ġrus": 38684, "Ġoutrageous": 38685, "Ġmastered": 38686, "Ġ커": 38687, "ÙĪÙģ": 38688, "ipes": 38689, "ĠRudy": 38690, "Jacob": 38691, "Ġbullish": 38692, "Ġtapped": 38693, "Ġfaud": 38694, "izophren": 38695, "ĠÑģоÑħ": 38696, "ĠDarling": 38697, "Ġ1963": 38698, "ĠPrevention": 38699, "²Ķ": 38700, "Ġabdominal": 38701, "stones": 38702, "Ġavaient": 38703, "á»ķi": 38704, "make": 38705, "Ġsare": 38706, "ĠInstant": 38707, "кам": 38708, "Ġkeeper": 38709, "Ġblankets": 38710, "ãģ§ãģĹãĤĩãģĨ": 38711, "Ġsweats": 38712, "ĠMinneapolis": 38713, "åħ¨éĥ¨": 38714, "Ġgenommen": 38715, "Ġfasten": 38716, "ĠBrussels": 38717, "åij¼": 38718, "Ġcafeter": 38719, "Ġabsorbing": 38720, "Ġhago": 38721, "ĠElmo": 38722, "Ġgusto": 38723, "ĠYap": 38724, "Música": 38725, "Ġtert": 38726, "Ġbanda": 38727, "Ġmily": 38728, "Ġthereafter": 38729, "ĠStockholm": 38730, "ĠCarson": 38731, "Ġcalibration": 38732, "avaÅŁ": 38733, "ansa": 38734, "ikke": 38735, "Ġforesee": 38736, "Ġqualche": 38737, "Ġdeste": 38738, "æ¤": 38739, "ünüz": 38740, "Ġforge": 38741, "Dis": 38742, "esten": 38743, "Ġδια": 38744, "Ġencaps": 38745, "ĠGespr": 38746, "Ġchercher": 38747, "ickets": 38748, "ÑĤоÑĢÑĭ": 38749, "Cr": 38750, "ĠТакже": 38751, "Ġrabbits": 38752, "ĠDot": 38753, "heiten": 38754, "Ġcausal": 38755, "ĠFoster": 38756, "ajÄħc": 38757, "Ġbereit": 38758, "Ġayudar": 38759, "é«Ļ": 38760, "ãģ³": 38761, "song": 38762, "comb": 38763, "Ġfringe": 38764, "Ġcybersecurity": 38765, "Ġ뾨": 38766, "Ġkier": 38767, "Ġbeschäft": 38768, "ĠконÑĨе": 38769, "Ġfacilit": 38770, "ĠNamen": 38771, "Ġbilateral": 38772, "tx": 38773, "ĠWissenschaft": 38774, "Ġnuances": 38775, "Ġripping": 38776, "Ġfy": 38777, "ĠSicherheit": 38778, "ĠGhana": 38779, "olon": 38780, "Ġtopped": 38781, "ĠMorocco": 38782, "Ġradial": 38783, "ĠLEE": 38784, "ĠAndreas": 38785, "edd": 38786, "ĠìĹ´ë": 38787, "ĠAirlines": 38788, "ãģĵãĤį": 38789, "Ġvalores": 38790, "ê·ľ": 38791, "Hy": 38792, "ĠзадаÑĩ": 38793, "ĠKendall": 38794, "ĠÑħаÑĢ": 38795, "ĠVamp": 38796, "Ġpython": 38797, "Ġmanageable": 38798, "ĠGente": 38799, "oise": 38800, "iciary": 38801, "Ġimposs": 38802, "ĠBunny": 38803, "iesta": 38804, "Andrew": 38805, "Ġsert": 38806, "ĠCec": 38807, "zzarella": 38808, "Ġautomobile": 38809, "ĠTiere": 38810, "allows": 38811, "åĨĨ": 38812, "Ġë°Ģ": 38813, "ĠScorp": 38814, "ĠJelly": 38815, "agara": 38816, "ĠStretch": 38817, "Ġredef": 38818, "Ġexacerb": 38819, "ĠSHA": 38820, "éf": 38821, "orsa": 38822, "Ġflawed": 38823, "ĠNoel": 38824, "?!?": 38825, "Ġprocent": 38826, "Ġmenstru": 38827, "ĠпÑĢоÑĩ": 38828, "Ġinfants": 38829, "ðŁİµ": 38830, "pause": 38831, "ĠRacing": 38832, "Ġ1948": 38833, "Ġsuperintendent": 38834, "idores": 38835, "idy": 38836, "brahim": 38837, "Ġunlucky": 38838, "Ġperk": 38839, "anci": 38840, "Ġë§ĮëĤĺ": 38841, "ĠÐľÐ¾Ñģкв": 38842, "Ġfinans": 38843, "Ġdiferencia": 38844, "łĪìĿ´": 38845, "éħį": 38846, "ORY": 38847, "ĠTac": 38848, "ÛĮا": 38849, "Ġdesem": 38850, "Ġважно": 38851, "ĠJU": 38852, "ĠìŀĪìŀĸìķĦìļĶ": 38853, "ĠÎĿ": 38854, "Ġinformations": 38855, "ĠHEL": 38856, "hst": 38857, "ĠпоговоÑĢ": 38858, "Ġvoiture": 38859, "Ġreus": 38860, "ändig": 38861, "ĠпоÑħож": 38862, "jing": 38863, "Ġdru": 38864, "altra": 38865, "Ġproduits": 38866, "Ġkite": 38867, "Ġeyeball": 38868, "ĠBelt": 38869, "ĠRestaurant": 38870, "Ġgamb": 38871, "Ġporridge": 38872, "itters": 38873, "Ġconverts": 38874, "Ġyardım": 38875, "Ġmáximo": 38876, "wirtschaft": 38877, "ĠíķĺëĤĺë": 38878, "Ġì¤Ģ": 38879, "Ġiceberg": 38880, "Ġvorbei": 38881, "Ġ256": 38882, "ocratic": 38883, "Ġreckless": 38884, "onner": 38885, "Ġmús": 38886, "Ġlogically": 38887, "ĠPrison": 38888, "ĠNetz": 38889, "Ġvacant": 38890, "Ġnimmt": 38891, "ĠHARR": 38892, "Ġзов": 38893, "ĠDee": 38894, "ringe": 38895, "niest": 38896, "ĠRules": 38897, "ìĬ¤ëŁ½": 38898, "cussions": 38899, "Ġfloral": 38900, "Ġconstrained": 38901, "Ġdifferentiation": 38902, "ĠQuebec": 38903, "ĠÛģÛĮÚº": 38904, "Ġpública": 38905, "itel": 38906, "Ġaccommodations": 38907, "ĠGrü": 38908, "íľ": 38909, "Ġpickles": 38910, "иÑĩеÑģкиÑħ": 38911, "Ġcommissions": 38912, "ĠBaek": 38913, "ĠçocuÄŁ": 38914, "ĠMedium": 38915, "Ġperiodically": 38916, "Ġwonderfully": 38917, "Ġstaffing": 38918, "ìĽIJë": 38919, "rire": 38920, "fle": 38921, "ĠMcL": 38922, "ĠÑĤеп": 38923, "ĠпеÑĢек": 38924, "нолог": 38925, "Ġíģ¬ê²Į": 38926, "çĻ¼çı¾": 38927, "Ġprosperous": 38928, "ĠSpiritual": 38929, "ĠChick": 38930, "DIA": 38931, "ĠÐŁÑĢивеÑĤ": 38932, "ĠperÃŃ": 38933, "ÑĮÑİÑĤ": 38934, "Ġconsultants": 38935, "ĠEarl": 38936, "ä»Ĭå¹´": 38937, "Ġruining": 38938, "оÑĢе": 38939, "Ġpenser": 38940, "Ġtakiej": 38941, "Ġstrengthened": 38942, "ĠLiquid": 38943, "онеÑĨ": 38944, "аваÑĤÑĮ": 38945, "Ġcamer": 38946, "Ġdisagreement": 38947, "Ġbathing": 38948, "ĠYosh": 38949, "aal": 38950, "prechen": 38951, "RISADAS": 38952, "Ġsuperstar": 38953, "æģŃ": 38954, "лÑıÑĤÑĮ": 38955, "Ġnib": 38956, "ĠTherm": 38957, "ĠDANIEL": 38958, "Ġpaw": 38959, "Ġliquids": 38960, "Ġcapacit": 38961, "arken": 38962, "Ġvagina": 38963, "Ġmashed": 38964, "Ġemerges": 38965, "yscy": 38966, "Ġunrelated": 38967, "ĠGuild": 38968, "Ġinverted": 38969, "itives": 38970, "Tra": 38971, "Ġbegr": 38972, "Ġalte": 38973, "ì§ķ": 38974, "ãĤģãģ¦": 38975, "ĠÑĢазÑĢабоÑĤ": 38976, "finder": 38977, "Ġдалее": 38978, "ĠблагодаÑĢ": 38979, "walker": 38980, "Ġcrater": 38981, "assadors": 38982, "rences": 38983, "inski": 38984, "ĠKIM": 38985, "ĠElliot": 38986, "2017": 38987, "ĠSr": 38988, "inka": 38989, "anov": 38990, "Ġìŀĺ못": 38991, "Ġproprietary": 38992, "displaystyle": 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39050, "akat": 39051, "Ġtrench": 39052, "Ġproceeded": 39053, "ĠCoin": 39054, "Ġderecho": 39055, "ĠRede": 39056, "æ¯Ľ": 39057, "аннÑĭй": 39058, "Ġincarcerated": 39059, "ĠRichmond": 39060, "Rock": 39061, "ĠPav": 39062, "ĠKarma": 39063, "uges": 39064, "Ġconteú": 39065, "ë¹Ħ": 39066, "Ġê·¸ë§Į": 39067, "ĠGone": 39068, "ĠwspóÅĤ": 39069, "ĠRahmen": 39070, "unken": 39071, "Ġì¤ijìļĶíķľ": 39072, "Ġib": 39073, "Ġattaching": 39074, "Hay": 39075, "Ġsuka": 39076, "ìį¹": 39077, "Ġpivotal": 39078, "ĠRespect": 39079, "ÃŃda": 39080, "IB": 39081, "ĠVerantwort": 39082, "wiet": 39083, "Ġforensic": 39084, "ÑĢиÑģÑĤ": 39085, "ĠпÑĢинÑĨипе": 39086, "Ġmarkings": 39087, "Ġkettle": 39088, "ĠOpera": 39089, "ĠDoctors": 39090, "Ġshredded": 39091, "Ġrecuer": 39092, "Ġvigil": 39093, "ĠFail": 39094, "Ġentrev": 39095, "ĠдÑĥÑĪ": 39096, "Ġoutbreaks": 39097, "èµ°åIJ§": 39098, "ĠÏĢο": 39099, "Ġrogue": 39100, "angled": 39101, "Ġyearly": 39102, "ĠCreed": 39103, "Ġwam": 39104, "Ġlotus": 39105, "ê³¼ë": 39106, "ãĢģãĢģ": 39107, 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39225, "Ġacompa": 39226, "Ġidé": 39227, "Ġboiler": 39228, "ĠThemen": 39229, "Ġavenue": 39230, "ĠMud": 39231, "ĠновÑĭе": 39232, "Ġwitnessing": 39233, "Ġlance": 39234, "ĠCHAN": 39235, "ĠBever": 39236, "تÙħ": 39237, "Ġchemotherapy": 39238, "King": 39239, "ĠbÄĻdÄĻ": 39240, "Ġatual": 39241, "Ġtive": 39242, "Ġtalkin": 39243, "Ġquedar": 39244, "ieÃŁ": 39245, "edel": 39246, "Ġìĸ´ìłľ": 39247, "Ġjogar": 39248, "Ġör": 39249, "Ġundertaking": 39250, "ĠStrength": 39251, "Ġmilhões": 39252, "ĠWine": 39253, "ĠMolt": 39254, "讲": 39255, "ãģijãĤĮ": 39256, "Ġundermine": 39257, "ĠArchives": 39258, "vana": 39259, "mercial": 39260, "MC": 39261, "Ġcaste": 39262, "пÑĢ": 39263, "Ġlegislators": 39264, "ulators": 39265, "ênio": 39266, "Ġëį°ë": 39267, "ĠÑħоÑĤиÑĤе": 39268, "Ġнек": 39269, "Ġsurn": 39270, "Ġconsci": 39271, "ĠPOW": 39272, "Ġculinary": 39273, "ĠKAT": 39274, "ĠFolks": 39275, "Ñĭваем": 39276, "Ġвок": 39277, "ãģijãĤĭ": 39278, "service": 39279, "pts": 39280, "Ġпобед": 39281, "æĺ¯åķĬ": 39282, "Ġtents": 39283, "Ġnord": 39284, "STE": 39285, "Ġrepublican": 39286, "Ġwyk": 39287, "Ġminions": 39288, "èĻķ": 39289, "Ġmemang": 39290, "jest": 39291, "Ġcomparative": 39292, "Ġtyle": 39293, "carbon": 39294, "bedingt": 39295, "ksen": 39296, "Ġnegativity": 39297, "Ġsjälv": 39298, "Ġdú": 39299, "æīĢæľī": 39300, "Ġrecalled": 39301, "cra": 39302, "ĠTada": 39303, "ĠÑĢÑĥки": 39304, "ĠопÑĢедел": 39305, "Ġprocrast": 39306, "Ġjogos": 39307, "ĠOo": 39308, "ĠHearts": 39309, "Ġéch": 39310, "ĠksiÄħż": 39311, "Ġcoarse": 39312, "ĠTube": 39313, "ĠGreens": 39314, "Ġén": 39315, "Ġdumbbell": 39316, "ĠÑĤи": 39317, "Ġquerer": 39318, "اØŃ": 39319, "Ïĥει": 39320, "ĠпÑĢавилÑĮно": 39321, "Ġпап": 39322, "Ġcompra": 39323, "Ġtér": 39324, "ĠAntes": 39325, "Ġoptimum": 39326, "Ġbiscuit": 39327, "κι": 39328, "aczego": 39329, "Ġìĭľê°ĦìĿ´": 39330, "ĠMarines": 39331, "vero": 39332, "Ġvaccinations": 39333, "Ġpetty": 39334, "riters": 39335, "Ġал": 39336, "country": 39337, "Ġcounters": 39338, "Ġattendant": 39339, "ĠHui": 39340, "ãģ¨ãģĦãģĨãģĵãģ¨ãģ§": 39341, "cka": 39342, "ÑģÑĤвеннÑĭй": 39343, "guy": 39344, "Ġtricked": 39345, "ĠRED": 39346, "Ġthrilling": 39347, "ÏĢοι": 39348, "Ġpiggy": 39349, "Ġanunci": 39350, "ORTER": 39351, "ĠValue": 39352, "Ġrond": 39353, "ĠADA": 39354, "Ġposer": 39355, "hores": 39356, "ĠRoland": 39357, "ĵ¯": 39358, "Ġnoir": 39359, "Ġש×IJ×": 39360, "ë°ľ": 39361, "iemand": 39362, "ĠпоÑĤеÑĢ": 39363, "ê³³": 39364, "Ġê±±": 39365, "Ġformatting": 39366, "ĠLed": 39367, "è§Ģçľ¾": 39368, "Ġkillers": 39369, "ĠÄijấy": 39370, "Ġhaar": 39371, "again": 39372, "!>[": 45687, "minster": 45688, "Ġвли": 45689, "Ġidentifier": 45690, "ĠLambda": 45691, "Ġtros": 45692, "Ġflawless": 45693, "Ġdetrimental": 45694, "Ġbunları": 45695, "War": 45696, "Ġregião": 45697, "羣çļĦæĺ¯": 45698, "ĠBike": 45699, "cessors": 45700, "Ġcùng": 45701, "ĠRN": 45702, "Ġê½ĥ": 45703, "Ġküçük": 45704, "ĠBeginning": 45705, "íĺ¸ë": 45706, "Ġgewe": 45707, "Ġdenote": 45708, "ĠAlberto": 45709, "Ġprobiot": 45710, "Ġode": 45711, "Ġmolar": 45712, 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45768, "éĻ³": 45769, "Ġretiring": 45770, "Ġvampires": 45771, "Ġmerde": 45772, "ĠChanging": 45773, "Ġtame": 45774, "Ġspokesperson": 45775, "Ġcay": 45776, "Ġflirting": 45777, "ĠGrö": 45778, "Ġwär": 45779, "Ġwyb": 45780, "Ġcoeur": 45781, "ạnh": 45782, "ĠìĻĢìĦľ": 45783, "Ġconnais": 45784, "ĠHundreds": 45785, "ĠBea": 45786, "ĠαÏĢ": 45787, "pruch": 45788, "Ġsociedade": 45789, "ĠWhilst": 45790, "ĠKait": 45791, "espace": 45792, "Ġchia": 45793, "ĠErm": 45794, "Ġë°Ķê¿": 45795, "Ġfences": 45796, "ĠMortal": 45797, "ê²ģ": 45798, "ĠгÑĢаÑĦ": 45799, "ĠHomeland": 45800, "ĠJUN": 45801, "isst": 45802, "Ġparlar": 45803, "Ġsporty": 45804, "éo": 45805, "Ġdeepen": 45806, "ĠBehavior": 45807, "éĢı": 45808, "åĵĪåĵĪåĵĪ": 45809, "Ġerrand": 45810, "Ġrotary": 45811, "ĠWellington": 45812, "Wind": 45813, "Ġmesela": 45814, "ảng": 45815, "iende": 45816, "Ġexcell": 45817, "ĠGenius": 45818, "ĠEduardo": 45819, "æľī人": 45820, "ĠÅŁunu": 45821, "ĠÄ°stanbul": 45822, "Ġproduto": 45823, "Ġãħİãħİ": 45824, "OFF": 45825, "Ġwollt": 45826, "çĪĨ": 45827, "Ġëī´ìĬ¤": 45828, "Ġlass": 45829, "Ġhertz": 45830, "Ġaromatic": 45831, "Ġзвон": 45832, "Ġautoc": 45833, "ĠLust": 45834, "Ġ112": 45835, "ĠÎĹ": 45836, "Ġreviewers": 45837, "Ġreceptive": 45838, "å°įäºĨ": 45839, "ând": 45840, "oglo": 45841, "ĠìķĦëĭĻ": 45842, "Ġngo": 45843, "ÑĸÑĤи": 45844, "Ã¥t": 45845, "cono": 45846, "Ġtekrar": 45847, "Ġì£¼ê³ł": 45848, "ĠgelmiÅŁ": 45849, "Ġbedtime": 45850, "ĠArgh": 45851, "ADA": 45852, "ĠгоÑĢода": 45853, "ĠÄĩ": 45854, "Ġalliances": 45855, "giggling": 45856, "Ġyerde": 45857, "Ġspies": 45858, "Ġgutes": 45859, "çi": 45860, "Ġalltid": 45861, "ĠLah": 45862, "ŀIJë": 45863, "ĠdokÅĤad": 45864, "ÙĪÙĬ": 45865, "Ġtoxicity": 45866, "Ġcancellation": 45867, "Ġ1958": 45868, "dro": 45869, "ĠìŀijìĿĢ": 45870, "ĠMotorola": 45871, "Ġmultin": 45872, "Ġenthusiasts": 45873, "ĠMighty": 45874, "ĠCoconut": 45875, ":ãĢĮ": 45876, "ĠPictures": 45877, "Ġsangre": 45878, "Ġblinking": 45879, "olesome": 45880, "ĠìĬ¤íĥĢìĿ¼": 45881, "FP": 45882, "Ġbooming": 45883, "ĠдеÑģÑıÑĤ": 45884, "Ġratchet": 45885, "Ġtimelines": 45886, "leness": 45887, "Ġcages": 45888, "ĠGoodnight": 45889, "ometimes": 45890, "Ġcunning": 45891, "ĠRisk": 45892, "uled": 45893, "dade": 45894, "Ġprata": 45895, "ĠgustarÃŃa": 45896, "amus": 45897, "ĠJinping": 45898, "Ġestrut": 45899, "Ġdescobrir": 45900, "ĠMÄģ": 45901, "ĠAllan": 45902, "ĠåĪĨ": 45903, "Ġ׾ק": 45904, "Ġpreserv": 45905, "ĠStrawberry": 45906, "Äı": 45907, "Lu": 45908, "Ġkro": 45909, "ĠReports": 45910, "ìħĶìķ¼": 45911, "Ġvalt": 45912, "Ġpouvait": 45913, "Ġappar": 45914, "ĠBone": 45915, "Ġpreferably": 45916, "ĠRepública": 45917, "å°±åĪ°": 45918, "Ġherzlich": 45919, "Ġchimney": 45920, "Ġçev": 45921, "Ġvisas": 45922, "Ġverr": 45923, "Ġcultivation": 45924, "ĠArmenia": 45925, "ĠвдÑĢÑĥг": 45926, "Ġcockro": 45927, "retched": 45928, "artz": 45929, "ĠлÑİдÑıм": 45930, "ĠpolÃŃticas": 45931, "ĠPanz": 45932, "ĠAKA": 45933, "ĠëĪĮ룬": 45934, "Ġerro": 45935, "Ġcamper": 45936, "Ġ102": 45937, "स": 45938, "done": 45939, "Ġhoard": 45940, "ĠÐŁÐ¾ÑĤом": 45941, "jeong": 45942, "Ġdesta": 45943, "pak": 45944, "Ġinim": 45945, "Ġgrowers": 45946, "ĠMessage": 45947, "Ġelector": 45948, "engage": 45949, "ĠForbes": 45950, "ĠCincinnati": 45951, "Ġdifférence": 45952, "df": 45953, "Ġspar": 45954, "Ġawaits": 45955, "ĠUSSR": 45956, "ĠRising": 45957, "ĠHoÅŁ": 45958, "Ġfooting": 45959, "Ġcondiciones": 45960, "ÑĤоÑĢов": 45961, "Ġclinician": 45962, "ĠDiskuss": 45963, "å£ĵ": 45964, "ר×Ĵ": 45965, "×¥": 45966, "iteit": 45967, "gren": 45968, "Ġcharisma": 45969, "Ġleuke": 45970, "Ġirritating": 45971, "Ġcirca": 45972, "ĠRhodes": 45973, "Ġpior": 45974, "Ġhandicap": 45975, "royable": 45976, "Ġvull": 45977, "OG": 45978, "ĠinÃŃcio": 45979, "ieri": 45980, "Ġsplashing": 45981, "Ġdemise": 45982, "Ġassistir": 45983, "ÑĩÑĤо": 45984, "Ġcovert": 45985, "ĠGud": 45986, "à¸ī": 45987, "klär": 45988, "ĠìŀIJ꾸": 45989, "Ġverändert": 45990, "ĠREM": 45991, "ĠConven": 45992, "atge": 45993, "Ġpierwsze": 45994, "Ġclergy": 45995, "lington": 45996, "liv": 45997, "VPN": 45998, "ĠÑģожал": 45999, "ĠHate": 46000, "ãģ¨ãģĵãĤį": 46001, "ÏĨο": 46002, "ĠRespons": 46003, "озд": 46004, "Ġetmek": 46005, "Ġchemin": 46006, "ÙħØ©": 46007, "Ġê°Ģ족": 46008, "Tre": 46009, "Ġumas": 46010, "ĠBurton": 46011, "Ġpatriarch": 46012, "ĠSmithsonian": 46013, "¥ĺ": 46014, "Moon": 46015, "Air": 46016, "Ġmedios": 46017, "Ġeraser": 46018, "Ġwollten": 46019, "Ġpareil": 46020, "ĠBillie": 46021, "æĬ½": 46022, "еÑĢÑĤв": 46023, "Ġparlament": 46024, "Ġagony": 46025, "ĠQUE": 46026, "sequently": 46027, "Another": 46028, "ĠWhew": 46029, "ĠAnnual": 46030, "Ġseben": 46031, "ìĥģìĿĦ": 46032, "values": 46033, "ŀľë§Į": 46034, "Ġsinon": 46035, "ereal": 46036, "ĠEnlight": 46037, "ĠChemistry": 46038, "ĠCatalunya": 46039, "Ġdoctr": 46040, "anton": 46041, "Ġstuk": 46042, "ĠPlate": 46043, "ĠKardashian": 46044, "Ġfilos": 46045, "ĠWet": 46046, "ĠпопÑĭÑĤ": 46047, "Ġunknowns": 46048, "ĠSchon": 46049, "ĠBaldwin": 46050, "Ġtelescopes": 46051, "ĠGucci": 46052, "oxide": 46053, "ĠConservative": 46054, "ìĦ±ìĿĦ": 46055, "Ġhinaus": 46056, "Power": 46057, "Ġê±´ê°ķ": 46058, "Ġprevail": 46059, "orman": 46060, "machine": 46061, "Ġ1946": 46062, "Ġunbel": 46063, "Ġschaut": 46064, "Ġpiel": 46065, "eenth": 46066, "Ġobjectively": 46067, "Ġchakra": 46068, "audio": 46069, "Ġchicos": 46070, "ĠVault": 46071, "å°Ī": 46072, "Ġmedicinal": 46073, "ĠTail": 46074, "While": 46075, "Ġasphalt": 46076, "Ġfroze": 46077, "ĠEK": 46078, "unching": 46079, "nosis": 46080, "2015": 46081, "ĠGri": 46082, "Ġoddly": 46083, "ĠMär": 46084, "ĠAeg": 46085, "colo": 46086, "Par": 46087, "Ġëĵ¤ìĸ´ë": 46088, "Ġvinden": 46089, "ĠOVER": 46090, "Ġiced": 46091, "Ġscorp": 46092, "Ġhac": 46093, "qualified": 46094, "ĠÑĥвидеÑĤÑĮ": 46095, "ermo": 46096, "HEN": 46097, "Ġsoi": 46098, "Ġmultiples": 46099, "Ġlayouts": 46100, "Ġblindness": 46101, "ĠBowser": 46102, "ĠподÑĤ": 46103, "ĠÃİ": 46104, "ventional": 46105, "Ġmata": 46106, "madı": 46107, "Ġgeez": 46108, "Ġcadence": 46109, "Ġważne": 46110, "ĠChristie": 46111, "venge": 46112, "Call": 46113, 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46170, "Ġréussi": 46171, "Ġhypertension": 46172, "ĠManual": 46173, "Ġprophetic": 46174, "Ġhandc": 46175, "ÑĮе": 46176, "Ġrefrain": 46177, "ĠSquid": 46178, "ìŀ¡": 46179, "Ġкоман": 46180, "ällen": 46181, "Ġllegó": 46182, "Ġbash": 46183, "iony": 46184, "ĠÑģклад": 46185, "Ġкаб": 46186, "Ġcareless": 46187, "ĠPool": 46188, "Ġtrás": 46189, "Ġfils": 46190, "ĠSchr": 46191, "Ġsprawd": 46192, "ĠMonaten": 46193, "Ġunforgettable": 46194, "ĠCotton": 46195, "Ġinconvenient": 46196, "ĠRX": 46197, "oris": 46198, "Ġhumbled": 46199, "ת×Ĺ": 46200, "Ġآپ": 46201, "ĠincreÃŃ": 46202, "ĠKommentare": 46203, "èĪĴ": 46204, "ración": 46205, "Ġvantage": 46206, "ĠSeal": 46207, "ĠìĿ´ê±°ë¥¼": 46208, "Ġjoue": 46209, "ãģĿãģĨãģ§ãģĻãģŃ": 46210, "Ġìĺ¤ëŀĺ": 46211, "ĠиÑģпÑĭÑĤ": 46212, "oben": 46213, "Ġgrate": 46214, "Ġcontrole": 46215, "ĠPercy": 46216, "ÅĤada": 46217, "Ġsimultaneous": 46218, "Ġprototy": 46219, "ĠgroÃŁer": 46220, "Ġbewusst": 46221, "inizi": 46222, "Ġpassieren": 46223, "ĠHappiness": 46224, "åīĩ": 46225, "shi": 46226, "geht": 46227, "Ġstationed": 46228, "ĠErgebnis": 46229, "Ġdirectamente": 46230, "Ġsurvives": 46231, "Ġpersones": 46232, "BERG": 46233, "Ġvomiting": 46234, "Ġconhecer": 46235, "Ġadjour": 46236, "ĠCivic": 46237, "pei": 46238, "burst": 46239, "Ġëĭ¤ëĭĪ": 46240, "éı": 46241, "Ġsled": 46242, "Ġplataforma": 46243, "ĠSect": 46244, "ĠDefin": 46245, "çĻ»éĮ²": 46246, "énom": 46247, "chnet": 46248, "Ġprofitability": 46249, "Ġerreicht": 46250, "á»ıi": 46251, "cation": 46252, "Ġì§Ģê¸": 46253, "Ġperdre": 46254, "Ġfelony": 46255, "Ġ1957": 46256, "æĪijå¾Ī": 46257, "Ġunsuccessful": 46258, "Ġnagyon": 46259, "Ġelasticity": 46260, "Ġfacade": 46261, "Ġearthly": 46262, "ĠамеÑĢикан": 46263, "Ġconn": 46264, "cla": 46265, "Du": 46266, "Ġpolitiques": 46267, "Ġhalo": 46268, "iantes": 46269, "Ġмоей": 46270, "ãĥ³ãĥī": 46271, "tones": 46272, "elier": 46273, "è®ļ": 46274, "htaking": 46275, "Ġwichtige": 46276, "Ġanno": 46277, "ĠLok": 46278, "illions": 46279, "Ġviver": 46280, "Ġsolchen": 46281, "Ġsuf": 46282, "ĠSalz": 46283, "ĠNvidia": 46284, "zuge": 46285, "ĠSpike": 46286, "Video": 46287, "Ġtwor": 46288, "ĠAla": 46289, "èijī": 46290, "Ġhanya": 46291, "ĠAdm": 46292, "ìĿµ": 46293, "ĠPatienten": 46294, "ĠOnion": 46295, "ĠKobe": 46296, "ĠScene": 46297, "ĠRash": 46298, "æ¨Ļ": 46299, "ÑĢаÑģÑĤ": 46300, "istani": 46301, "General": 46302, "leye": 46303, "imbap": 46304, "Ġconcealed": 46305, "ĠFridays": 46306, "ĠWool": 46307, "ĠновÑĭÑħ": 46308, "شر": 46309, "Ġê²°ê³¼": 46310, "Ġjedoch": 46311, "´ìĭľ": 46312, "ĵ¤ëıĦ": 46313, "Ġìŀ¥ëĤľ": 46314, "ukt": 46315, "Lou": 46316, "Ġ먹ìĸ´": 46317, "ĠExpect": 46318, "Ġдомой": 46319, "Ġirresponsible": 46320, "Ġacerca": 46321, "ĠZust": 46322, "ר×ĺ": 46323, "UI": 46324, "Ġyoutubers": 46325, "ĠPositive": 46326, "Ġsocioe": 46327, "Ġsnatch": 46328, "èĥĮ": 46329, "Ġrefreshed": 46330, "Ġnominations": 46331, "ĠPatt": 46332, "Ġobsolete": 46333, "ĠdemiÅŁ": 46334, "åı¤": 46335, "ormuÅŁ": 46336, "ĠìĨĶì§ģíŀĪ": 46337, "Ġfla": 46338, "Ġcraziest": 46339, "ĠZie": 46340, "ĠTú": 46341, "zep": 46342, "icem": 46343, "Ġë©ĭìŀĪ": 46344, "Ġcynical": 46345, "ãģĿãĤĵãģª": 46346, "Ġtresp": 46347, "Ġcraz": 46348, "Õ¥Õ": 46349, "Ġnelle": 46350, "Ġmph": 46351, "ĠNered": 46352, "ĠKob": 46353, "ĠEck": 46354, "¨¸ëĭĪ": 46355, "Jan": 46356, "ĠТогда": 46357, "Ġdeci": 46358, "ĠVog": 46359, "Ġbubbling": 46360, "éĢĢ": 46361, "úa": 46362, "Ġproductos": 46363, "iberal": 46364, "Ġreplicated": 46365, "ĠImprove": 46366, "illary": 46367, "Cha": 46368, "Ġrédu": 46369, "ĥIJíķĺë©´": 46370, "Ġconnot": 46371, "ĠKrit": 46372, "ĠдÑĥÑħов": 46373, "Ġtreadmill": 46374, "ĠPW": 46375, "ĠзовÑĥÑĤ": 46376, "Ġclams": 46377, "Ġdrafting": 46378, "Ġ1956": 46379, "unta": 46380, "Ġexpenditures": 46381, "ĠHoover": 46382, "WOO": 46383, "ÑĪее": 46384, "Ġdeduction": 46385, "monary": 46386, "Ġrecib": 46387, "Ġpovo": 46388, "ĠëįĶë": 46389, "ĠPAL": 46390, "ĠBlow": 46391, "Ġwyp": 46392, "Ġdestac": 46393, "deal": 46394, "Graeme": 46395, "Ġnécessaire": 46396, "Ġdamned": 46397, "Ġ1938": 46398, "Ġìĭ¤ìłľë¡ľ": 46399, "Ġtroop": 46400, "Ġinsightful": 46401, "ĠTJ": 46402, "ĠоÑģв": 46403, "Ġfidelity": 46404, "ĠSkip": 46405, "ĠMayo": 46406, "ë§Ŀ": 46407, "appe": 46408, "Ġblas": 46409, "ĠWY": 46410, "ĠGN": 46411, "ctar": 46412, "Su": 46413, "Ġcuent": 46414, "hews": 46415, "Ġcorpses": 46416, "Abs": 46417, "Ġwastewater": 46418, "Ġciek": 46419, "ĠOnu": 46420, "Ġexplosives": 46421, "Ġarma": 46422, "ĠSTEPHAN": 46423, "politik": 46424, "ĠOsaka": 46425, "taÅĤ": 46426, "Ġyapıyor": 46427, "Ġizquier": 46428, "Ġbeleza": 46429, "ĠWyatt": 46430, "åIJ¸": 46431, "Ġsuk": 46432, "Ġspecjal": 46433, "Ġdanke": 46434, "whistle": 46435, "ĠfÃŃsica": 46436, "ĠHarriet": 46437, "ĠìķĦíĮĮ": 46438, "Ġwillkommen": 46439, "iping": 46440, "ĠÑģмоÑĤÑĢиÑĤе": 46441, "ĠможеÑĪÑĮ": 46442, "Ġinaccurate": 46443, "Ġarrogance": 46444, "ĠRemo": 46445, "γά": 46446, "assed": 46447, "Ġdeliveries": 46448, "Ġstinky": 46449, "ĠпеÑĢеж": 46450, "jay": 46451, "Ġtransitional": 46452, "Ġrere": 46453, "ĠNGOs": 46454, "ĠATM": 46455, "خت": 46456, "iology": 46457, "Ġвлад": 46458, "Ġschme": 46459, "ĠShine": 46460, "ìķ¡": 46461, "pants": 46462, "Ġserge": 46463, "Ġsenhor": 46464, "Ġabduct": 46465, "ĠBryant": 46466, "VES": 46467, "Ġawakened": 46468, "ĠLaz": 46469, "ropolis": 46470, "ĠLao": 46471, "è¾Ľèĭ¦": 46472, "Ġvilla": 46473, "Ġsummers": 46474, "Ġenthal": 46475, "Ġ1949": 46476, "Via": 46477, "Ġìĸ´ì¨": 46478, "Ġtendon": 46479, "Ġviolet": 46480, "Ġintellectually": 46481, "Ġbounced": 46482, "araus": 46483, "Ġ1919": 46484, "Ġvraag": 46485, "Ġspel": 46486, "ĠSchwar": 46487, "Scott": 46488, "ĠIndo": 46489, "Ġë§Ŀ": 46490, "Ġcanonical": 46491, "ĠIKE": 46492, "ĠthatÃŃs": 46493, "Ġmellan": 46494, "æ¯Ĵ": 46495, "igmat": 46496, "Could": 46497, "...?)": 46498, "Ġfoarte": 46499, "ĠKumar": 46500, "rendo": 46501, "Ġélé": 46502, "à´": 46503, "valuation": 46504, "cases": 46505, "Ġintuitively": 46506, "hong": 46507, "etted": 46508, "Ġsouven": 46509, "Ġmorb": 46510, "Ġcors": 46511, "ĠNV": 46512, "ĠHasan": 46513, "æĥħåĨµ": 46514, "ieved": 46515, "Ġì§Ģê¸ĪìĿĢ": 46516, "Ġdumpling": 46517, "Ġcontrôle": 46518, "Ġambiguity": 46519, "æ©Łæľĥ": 46520, "Ġcog": 46521, "ĠScriptures": 46522, "Ġcai": 46523, "Ġbever": 46524, "大家éĥ½": 46525, "Ġhuis": 46526, "Ġaime": 46527, "Ġerklären": 46528, "ĠLM": 46529, "ĠFey": 46530, "éļ¾": 46531, "றத": 46532, "Ġsupervised": 46533, "Ġjewe": 46534, "spl": 46535, "ĠÑĨенÑĤÑĢ": 46536, "Ġcollisions": 46537, "ÙĦÙģ": 46538, "ĠHogwarts": 46539, "ĠDurham": 46540, "×ķ×£": 46541, "Ġphosphate": 46542, "Ġoversee": 46543, "Ġinspections": 46544, "Ġbrinc": 46545, "ĠZak": 46546, "Ġpayoff": 46547, "Ġchaud": 46548, "ĠHunger": 46549, "ãos": 46550, "vir": 46551, "Ġfiance": 46552, "Ġboug": 46553, "lived": 46554, "cry": 46555, "åĽŀä¾Ĩ": 46556, "Ġjointly": 46557, "Ġgirlfriends": 46558, "ĠNexus": 46559, "¦¬ê²łìĬµëĭĪëĭ¤": 46560, "ĠKwang": 46561, "åĵĪåĽī": 46562, "å§ij": 46563, "ÅĤÄĻ": 46564, "ĠNeden": 46565, "iece": 46566, "Ġinserting": 46567, "æŁĵ": 46568, "ĠMummy": 46569, "ĠGlobe": 46570, "Ġlee": 46571, "Ġgerman": 46572, "Ġcreams": 46573, 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"Ġchuckling": 48167, "ĠMih": 48168, "Ġrallies": 48169, "Ġmanter": 48170, "Ġearnest": 48171, "super": 48172, "Ġgece": 48173, "ĠRend": 48174, "ĠGerade": 48175, "jenigen": 48176, "ĠVall": 48177, "ĠìŀĪëĤĺ": 48178, "ĠÑģказала": 48179, "Ġtrabalh": 48180, "ĠнаÑĪем": 48181, "ĠмеÑħ": 48182, "ikit": 48183, "Ġnouns": 48184, "Ġneurological": 48185, "Ġmotivational": 48186, "ĠMcMahon": 48187, "ĠFinished": 48188, "Ġë³´ìĿ´": 48189, "ĠFields": 48190, "Ġadolescents": 48191, "ĠTisch": 48192, "ĠNeben": 48193, "ĠFlowers": 48194, "ĠEnerg": 48195, "Ġdiret": 48196, "ĠThi": 48197, "ĠPicas": 48198, "æĥľ": 48199, "æĢİä¹Īæł·": 48200, "Ġavete": 48201, "ĠFors": 48202, "ĠChapel": 48203, "Não": 48204, "Et": 48205, "ĠÑģодеÑĢж": 48206, "reno": 48207, "Ġsven": 48208, "ĠdostÄĻp": 48209, "nee": 48210, "ĠSnapdragon": 48211, "ĠIDs": 48212, "ìķĺëĬĶëį°": 48213, "ר×ļ": 48214, "Ġsunflower": 48215, "Ġperpetual": 48216, "ç³ĸ": 48217, "Ġknights": 48218, "Ġgird": 48219, "ĠTold": 48220, "Ġvolcanoes": 48221, "Ġadversary": 48222, "ĠEconomy": 48223, "Ġextrapol": 48224, "Ġbluetooth": 48225, "Ġzooming": 48226, "Ġskys": 48227, "Ġgenial": 48228, "ÃŃculos": 48229, "ambre": 48230, "ĠмеÑĢ": 48231, "Ġteeny": 48232, "Ġstressing": 48233, "ìķĮ": 48234, "ONY": 48235, "Ġtranslucent": 48236, "Ġrounding": 48237, "Ġgrues": 48238, "×Ļ׳×Ķ": 48239, "après": 48240, "Ġprueba": 48241, "Ġpolygon": 48242, "Ġblueberry": 48243, "ĠProgramm": 48244, "Ġtrenches": 48245, "Ġsebagai": 48246, "Ġpalate": 48247, "Ġlaude": 48248, "Ġbehaved": 48249, "Ġlongitudinal": 48250, "ĠModule": 48251, "Ġadmir": 48252, "λι": 48253, "Greg": 48254, "Ġwyst": 48255, "Ġpropagate": 48256, "Ġmolds": 48257, "ĠTub": 48258, "ĠLoud": 48259, "usto": 48260, "Ġunstoppable": 48261, "Ġreinforcing": 48262, "éĿŀ常çļĦ": 48263, "ĠпÑĢоблема": 48264, "Ġpotencial": 48265, "Ġhemp": 48266, "ìŀĶ": 48267, "य": 48268, "Ġoptic": 48269, "Ġerfolgreich": 48270, "ÑģÑĭ": 48271, "олÑĮÑĪе": 48272, "urst": 48273, "ĠPois": 48274, "Ġrespondents": 48275, "Ġnehme": 48276, "ĠExternal": 48277, "olate": 48278, "Hyun": 48279, "Ġquartz": 48280, "Ġmathematician": 48281, "Ġbásicamente": 48282, "Ġail": 48283, "ìłľë¥¼": 48284, "attutto": 48285, "Ġnooit": 48286, "Ġafflict": 48287, "ĠOlga": 48288, "èŃ·": 48289, "ĠнаÑĤ": 48290, "Ġdites": 48291, "Ġrealidade": 48292, "Ġkän": 48293, "Ġuniqueness": 48294, "Ġpadres": 48295, "Ġsubsidi": 48296, "Ġpigeons": 48297, "βα": 48298, "stad": 48299, "Ġderen": 48300, "ĠСлед": 48301, "doo": 48302, "ĠопиÑģании": 48303, "Ġamber": 48304, "Ġgoosebumps": 48305, "ĠfrÃ¥gor": 48306, "ĠVital": 48307, "ĠIsraelites": 48308, "wasser": 48309, "Isn": 48310, "Ġcommits": 48311, "ĠSTEVEN": 48312, "ĠBevölker": 48313, "uitive": 48314, "Ġlegen": 48315, "Ġbruk": 48316, "иÑĢован": 48317, "ynen": 48318, "helm": 48319, "Ġgenerational": 48320, "ĠLändern": 48321, "οιÏĢÏĮν": 48322, "uzu": 48323, "Ġcaller": 48324, "онÑĮ": 48325, "ümü": 48326, "Ġbesar": 48327, "Ġplats": 48328, "Ġmigrated": 48329, "Ġjap": 48330, "ĠWAR": 48331, "Ġdissect": 48332, "ĠZusch": 48333, "ĠZeiten": 48334, "ĠLions": 48335, "ĠDF": 48336, "âĶ": 48337, "кив": 48338, "Ġpedestrians": 48339, "ĠMarilyn": 48340, "dock": 48341, "Ġyht": 48342, "Ġreincarn": 48343, "ĠSono": 48344, "ĠGrowth": 48345, "ÑĥÑģов": 48346, "Ġdungeons": 48347, "Ġbagus": 48348, "kich": 48349, "ĠÑĥкÑĢаÑĹ": 48350, "éĨ«": 48351, "ĠKeller": 48352, "chemistry": 48353, "Japanese": 48354, "Ġwillst": 48355, "Ġdecomposition": 48356, "ĠÑģÑĤен": 48357, "Ġrevived": 48358, "íķĻêµIJ": 48359, "ĠÅĵ": 48360, "ä½IJ": 48361, "ìĭ¸": 48362, "ippy": 48363, "Ġhourly": 48364, "jän": 48365, "ĠWorkshop": 48366, "Ŀ¼ìĦľ": 48367, "Ġcuarto": 48368, "Ġpatrim": 48369, "ĠBurch": 48370, "ĠìŀĪ기": 48371, "Ġhepat": 48372, "ĠhÃłng": 48373, "ĠëĮĢíķ´": 48374, "ĠваÑĪи": 48375, "Ġrework": 48376, "Ġparse": 48377, "Ġçıktı": 48378, "ĠSax": 48379, "ĠMongo": 48380, "ĠAaah": 48381, "ramble": 48382, "DJ": 48383, "Ġstabilized": 48384, "ĠSpeech": 48385, "Books": 48386, "Ġhurdles": 48387, "ĠWO": 48388, "ĠLamborg": 48389, "Ġ1933": 48390, "Ġvorbere": 48391, "Ġclinically": 48392, "Ġbreathtaking": 48393, "ĠGateway": 48394, "пеÑĢвÑĭÑħ": 48395, "uters": 48396, "Ġë¹µ": 48397, "Ġyeter": 48398, "Ġpulley": 48399, "Ġmuffin": 48400, "ĠPrefer": 48401, "ĠPence": 48402, "Ġinformação": 48403, "ìĬ¤íĬ¸ë": 48404, "ãĤ¸ãĥ£": 48405, "ĠTurtle": 48406, "ĠRegina": 48407, "ĠLoad": 48408, "does": 48409, "panze": 48410, "¸Ķ": 48411, "Ġmina": 48412, "ĠLatinos": 48413, "ammers": 48414, "ĠTort": 48415, "ĠBeyonce": 48416, "имоÑģÑĤи": 48417, "ĠвопÑĢоÑģÑĭ": 48418, "Ġbulun": 48419, "èĢĮå·²": 48420, "inek": 48421, "bereich": 48422, "Ġpasture": 48423, "ĠOA": 48424, "ĠMelt": 48425, "ĠEtt": 48426, "ĠDY": 48427, "Ġobwohl": 48428, "Ġleagues": 48429, "ÑĤеÑģÑĮ": 48430, "ĠкÑĥÑģ": 48431, "Ġvors": 48432, "Ġtopp": 48433, "ographical": 48434, "asst": 48435, "Ġlindo": 48436, "Ġë°ĿíĺĶ": 48437, "Ġréfl": 48438, "Ġclimbs": 48439, "Ġvarsa": 48440, "Ġmethyl": 48441, "ĠKarere": 48442, "Æ°á»Ł": 48443, "Rad": 48444, "Ġpreparedness": 48445, "онÑĩ": 48446, "ĠOD": 48447, "ĠCGI": 48448, "Ġम": 48449, "Ġspeechless": 48450, "Ġlasci": 48451, "Ġbolag": 48452, "ĠÑħоÑĩеÑĤÑģÑı": 48453, "Ġgrieving": 48454, "ĠJohannes": 48455, "ĠCarroll": 48456, "adaki": 48457, "Ī¬ë": 48458, "ĠsÅĤu": 48459, "Ġinnerhalb": 48460, "Ġgymnastics": 48461, "пÑĢи": 48462, "ifiques": 48463, "Ġkarate": 48464, "Ġdomu": 48465, "ãģĿãĤĮãģ§": 48466, "OTHER": 48467, "Ġdemandé": 48468, "Ġbooklet": 48469, "ĠKyoto": 48470, "Ġwoh": 48471, "ĠMarÃŃa": 48472, "violent": 48473, "JE": 48474, "Ġlóg": 48475, "Ġbrutally": 48476, "cot": 48477, "ĠÙħÛĮ": 48478, "ĠWarsz": 48479, "å®Ī": 48480, "wol": 48481, "Ġmikä": 48482, "ĠPronounce": 48483, "ĠBrendan": 48484, "Ġroup": 48485, "Ġitaliano": 48486, "å¦ĤæѤ": 48487, "ĠкомпÑĮÑİÑĤ": 48488, "Ġurging": 48489, "edes": 48490, "Ġcarbono": 48491, "ĠRichardson": 48492, "ĠÐĿаÑĩ": 48493, "ĠTrainer": 48494, "ĠCrimea": 48495, "Ġdiapers": 48496, "Ġcovet": 48497, "ĠMahar": 48498, "ĠHutch": 48499, "ĠAusw": 48500, "berty": 48501, "Ġindifferent": 48502, "кÑĢеÑĤ": 48503, "uldade": 48504, "Ġharms": 48505, "¢ÙĨ": 48506, "lesia": 48507, "Ġgio": 48508, "ĠMistress": 48509, "ĠKnox": 48510, "ĠFREE": 48511, "Ġ루ë": 48512, "ĠнаÑĪа": 48513, "Ġinvincible": 48514, "Ġmaiden": 48515, "ĠJeez": 48516, "Ġbreve": 48517, "pole": 48518, "Ġcriticisms": 48519, "ĠRusia": 48520, "म": 48521, "phin": 48522, "ĠCompare": 48523, "ĠBON": 48524, "Ġsneaking": 48525, "ĠRails": 48526, "ĠGeral": 48527, "Ġ1953": 48528, "Hola": 48529, "ĠопÑĭÑĤ": 48530, "Ġrainforest": 48531, "Ġbelum": 48532, "ĠObi": 48533, "ĠISS": 48534, "ãĤĮãģªãģĦ": 48535, "ĠСв": 48536, "Ġblond": 48537, "Ġwzgl": 48538, "ĠpowiedziaÅĤ": 48539, "Ġchoking": 48540, "ĠSongs": 48541, "ĠBiraz": 48542, "Ġyells": 48543, "Ġstylist": 48544, "ÏĮÏĦε": 48545, "Ġschreiben": 48546, "ĠJaw": 48547, "ĠEleven": 48548, "ĠRif": 48549, "/.": 48550, "Ġìĺ¤ëŀľë§Į": 48551, "Ġtreaties": 48552, "uffed": 48553, "ĠâĪĴ": 48554, "Ġroofs": 48555, "à¹Ģส": 48556, "Ġë»": 48557, "Ġsparkle": 48558, "ĠKiev": 48559, "ĠArgu": 48560, "erecht": 48561, "ĠÐĿадо": 48562, "ĠFIL": 48563, "Ġmolta": 48564, "ĠDevi": 48565, "Ġcampe": 48566, "Ġbenevol": 48567, "ĠTough": 48568, "Ġmoim": 48569, "Ġevacuate": 48570, "Ġerrado": 48571, "å©Ĩ": 48572, "ÑĢÑĥго": 48573, "Ġíİĺ": 48574, "ĠÎĵια": 48575, "Ġweaken": 48576, "Ġilluminated": 48577, "Ġsiglo": 48578, "ĠVacc": 48579, "ией": 48580, "alis": 48581, "ĠÑĥÑģÑĤÑĢой": 48582, "Ġdona": 48583, "ÅĤos": 48584, "üman": 48585, "Ġproducción": 48586, "Ġclot": 48587, "ĠMango": 48588, "Ġuneasy": 48589, "Ġshuts": 48590, "ĠExamples": 48591, "vell": 48592, "ebe": 48593, "Ġpromptly": 48594, "ĠTeles": 48595, "ĠпÑĢоÑĪл": 48596, "Ġpuerta": 48597, "Ġüberzeug": 48598, "Ġcoch": 48599, "social": 48600, "ĠBenson": 48601, "ĠMeth": 48602, "ĠExped": 48603, "Ġsupplemental": 48604, "Ġconceive": 48605, "Ġ×ĺ×ķ×ij": 48606, "Ġcaptivity": 48607, "ıĻìķĪ": 48608, "ĠÑħÑĥд": 48609, "forming": 48610, "Ġuploads": 48611, "Ġturbulence": 48612, "joint": 48613, "Ġsatisfactory": 48614, "ĠAnime": 48615, "Ġwashes": 48616, "Ġliberals": 48617, "ĠSunshine": 48618, "ĠREAL": 48619, "ublik": 48620, "binary": 48621, "Tony": 48622, "Ġpolarized": 48623, "Ġenriched": 48624, "taking": 48625, "ĠëģĿëĤĺ": 48626, "Ġpleasures": 48627, "Ġextermin": 48628, "inese": 48629, "atl": 48630, "vär": 48631, "аÑĢÑĭ": 48632, "ĠmyÅĽ": 48633, "narrator": 48634, "Ġодном": 48635, "ĠnajwiÄĻ": 48636, "Ġmobilize": 48637, "Ġmillor": 48638, "Ġata": 48639, "æ··": 48640, "ĠpolÃŃtico": 48641, "Ġplead": 48642, "Ġpainters": 48643, "ĠSow": 48644, "оÑĦ": 48645, "ĠìĺĽëĤł": 48646, "ĠÑĩÑĤоб": 48647, "Ġsabor": 48648, "ĠUndert": 48649, "ĠJERRY": 48650, "Å¡ÃŃ": 48651, "Ġë°ĸìĹIJ": 48652, "Ġprécéd": 48653, "Ġannotation": 48654, "ĠInaudible": 48655, "Ġtextured": 48656, "Ġfisherman": 48657, "vordan": 48658, "icherung": 48659, "ĠìłģìĿ´": 48660, "Ġgezeigt": 48661, "Ġmandates": 48662, "Ġbeak": 48663, "ĠTWO": 48664, "ĠAkbar": 48665, "ilian": 48666, "Ġtiếp": 48667, "Ġsuperiority": 48668, "inku": 48669, "Ġlys": 48670, "ĠFCC": 48671, "ĠCPA": 48672, "ustering": 48673, "nicos": 48674, "anja": 48675, "Ġchills": 48676, "ĠCage": 48677, "Ġsealing": 48678, "Ġsaç": 48679, "Ġdedans": 48680, "ĠAlger": 48681, "Ġspezie": 48682, "Ġcoloss": 48683, "ıyı": 48684, "clockwise": 48685, "Ġexactamente": 48686, "Ġiemand": 48687, "amı": 48688, "Ġmandar": 48689, "raj": 48690, "faced": 48691, "agua": 48692, "Ġê¹Ķë": 48693, "Ġinsbesondere": 48694, "Ġdrizzle": 48695, "Ġdiminish": 48696, "ĠYoda": 48697, "AI": 48698, "Ġbilmiyorum": 48699, "ĠMMA": 48700, "ategory": 48701, "ĠпеÑĢеп": 48702, "Ġparticipar": 48703, "Ġnormalized": 48704, "Ġcomplexities": 48705, "æ´²": 48706, "æݧ": 48707, "аÑĢов": 48708, "mist": 48709, "icha": 48710, "Group": 48711, "Ġresiliency": 48712, "Ġnogle": 48713, "ĠCNC": 48714, "prü": 48715, "Ġphysicists": 48716, "нок": 48717, "LI": 48718, "Ġstuffs": 48719, "Ġsistemas": 48720, "Ġinterfering": 48721, "ĠMarvin": 48722, "ército": 48723, "ĠìĹĨê³ł": 48724, "Ġsonic": 48725, "Ġequiv": 48726, "Ġabord": 48727, "ĠRamen": 48728, "Ġ09": 48729, "medim": 48730, "atiques": 48731, "ĠделаÑİÑĤ": 48732, "Ġunanimously": 48733, "Ġskirts": 48734, "ĠíĬ¹ë³Ħ": 48735, "ĠPrix": 48736, "kami": 48737, "Ġfruition": 48738, "Ġbirthdays": 48739, "иком": 48740, "Ġinaugural": 48741, "Ġcorrelate": 48742, "ĠTory": 48743, "ĠëĤĺìģ": 48744, "Ġdew": 48745, "ĠPrecis": 48746, "ihi": 48747, "Ġë¬¸ìłľê°Ģ": 48748, "Ġciting": 48749, "ĠLana": 48750, "ĠKag": 48751, "Ġplaythrough": 48752, "ĠProtocol": 48753, "frist": 48754, "hovah": 48755, "Ġmerciful": 48756, "Ġbilingual": 48757, "ĠGuitar": 48758, "rh": 48759, "Ġglamorous": 48760, "ĠVikings": 48761, "ĠOoooh": 48762, "íķĺëĬĶëį°": 48763, "ĠUganda": 48764, "Ġcollapses": 48765, "entry": 48766, "Ġantioxidants": 48767, "ëĤĺë": 48768, "ÑĪаÑı": 48769, "Ġtrivia": 48770, "Ġgäller": 48771, "Ġfungi": 48772, "Ġmilks": 48773, "Ġdicht": 48774, "μη": 48775, "poke": 48776, "ĠвÑĭпÑĥÑģк": 48777, "Ġfeeder": 48778, "ĠAlcohol": 48779, "hower": 48780, "Ġdeserving": 48781, "ĠRebel": 48782, "iosis": 48783, "Ġ103": 48784, "Ġhandout": 48785, "Ġenm": 48786, "Ġlandlords": 48787, "Ġgeology": 48788, "rils": 48789, "Ġcobra": 48790, "ĠVold": 48791, "ĠPanch": 48792, "ĠGREG": 48793, "Ġpross": 48794, "Ġbracelets": 48795, "ĠVega": 48796, "Ġrozum": 48797, "款": 48798, "азд": 48799, "ĠLynd": 48800, "ĠHonors": 48801, "Ġsurrendered": 48802, "Ġlibrarians": 48803, "125": 48804, "ĠÑģиг": 48805, "Ġuniformly": 48806, "ĠEagles": 48807, "ìķĻ": 48808, "иÑĤан": 48809, "andid": 48810, "ĠìłĪëĮĢ": 48811, "Ġض": 48812, "Ġarrests": 48813, "ĠCSV": 48814, "ĠAzerbaijan": 48815, "ortic": 48816, "ĠDX": 48817, "ĠAdventures": 48818, "Ġabus": 48819, "ĠFau": 48820, "Ġschlimm": 48821, "Ġrattling": 48822, "Ġconsumes": 48823, "ĠTolkien": 48824, "Ġresurrected": 48825, "ĠXY": 48826, "íĬ¸ê°Ģ": 48827, "ĠвÑĭÑģÑĤÑĥп": 48828, "ĠAngie": 48829, "żenia": 48830, "Mic": 48831, "ĠSheila": 48832, "achtet": 48833, "Ġoverst": 48834, "Ġlâ": 48835, "Ġineffective": 48836, "æĿ¡": 48837, "æĢİä¹ĪäºĨ": 48838, "å¿Ļ": 48839, "Ġwichtiger": 48840, "Ġvino": 48841, "Ġpum": 48842, "Ġangled": 48843, "ĠPione": 48844, "ĠMỹ": 48845, "ãģĿãĤĮãģ¯": 48846, "woÅĽÄĩ": 48847, "draw": 48848, "ัà¹Ī": 48849, "markets": 48850, "Ġcafes": 48851, "ĠCem": 48852, "âĿ¤": 48853, "ĠSuit": 48854, "MK": 48855, "Ġemphasizes": 48856, "Ġtortilla": 48857, "Ġmejorar": 48858, "ĠSurviv": 48859, "casting": 48860, "Ġeducación": 48861, "ĠGum": 48862, "uely": 48863, "ĠìĹ¬ê¸°ëĬĶ": 48864, "Ġstretchy": 48865, "ença": 48866, "Ġwithhold": 48867, "Ġexiting": 48868, "Ġenthalpy": 48869, "ĠTransit": 48870, "ılmÄ±ÅŁ": 48871, "alies": 48872, "Ġsalvar": 48873, "Ġleaned": 48874, "ĠgroÃŁes": 48875, "Ġfitt": 48876, "аки": 48877, "Sarah": 48878, "Ġhostel": 48879, "Ġfingerna": 48880, "ĠnadziejÄĻ": 48881, "wives": 48882, "Rec": 48883, "Ġspool": 48884, "аÑĤов": 48885, "ĠEnemy": 48886, "Ġfury": 48887, "Ġdetta": 48888, "ĠFay": 48889, "éļ¨": 48890, "ÑıÑİÑĤ": 48891, "Ġaproximadamente": 48892, "Ġsilos": 48893, "Ġmagist": 48894, "Ġcree": 48895, "ĠKrank": 48896, "ĠDOWN": 48897, "Ġstartled": 48898, "Ġreborn": 48899, "ĠUmwelt": 48900, "ĠSuzanne": 48901, "ниÑĨÑĭ": 48902, "outez": 48903, "ĠJAC": 48904, "yards": 48905, "radas": 48906, "rau": 48907, "ipts": 48908, "hail": 48909, "Ġparagraphs": 48910, "Ġmeglio": 48911, "Ġisolating": 48912, "Ġaceite": 48913, "ĠHarsh": 48914, "Ġcyst": 48915, "ĠBlockchain": 48916, "ĠÑħоÑĢоÑĪий": 48917, "Ġvirtuous": 48918, "Ġinvestigación": 48919, "Ġdevoir": 48920, "Ġmasturb": 48921, "ĠSale": 48922, "ÙĬرة": 48923, "ĠΧ": 48924, "ĠStraÃŁen": 48925, "Ġdikk": 48926, "Ġafore": 48927, "ĠJungkook": 48928, "Ġchociaż": 48929, "ĠDebatte": 48930, "Ġweirdly": 48931, "Ġviaje": 48932, "regist": 48933, "Help": 48934, "Ġkinderen": 48935, "Ġformulated": 48936, "Ġenfim": 48937, "ĠTowards": 48938, "коÑĹ": 48939, "ivering": 48940, "ĠдеÑĤи": 48941, "charger": 48942, "Ġpurl": 48943, "Ġacademically": 48944, "ĠNurse": 48945, "Ġdeleting": 48946, "ayo": 48947, "Ġrefusal": 48948, "Ġdepicts": 48949, "ĠDracula": 48950, "Ġtoasted": 48951, "ĠZombie": 48952, "ĠSuperior": 48953, "ĠBold": 48954, "Ġquizzes": 48955, "Ġgle": 48956, "450": 48957, "Ġcomeço": 48958, "ynn": 48959, "Ġverst": 48960, "ĠOlaf": 48961, "Ġpomoc": 48962, "ĠSask": 48963, "ëĺ": 48964, "ĠTCP": 48965, "ĠProperty": 48966, "íķĺì£ł": 48967, "à¸ľà¸¡": 48968, "boom": 48969, "aros": 48970, "ĠÑĢоÑģÑģий": 48971, "ĠбÑĭваеÑĤ": 48972, "åĩºåİ»": 48973, "ĠìĿ´ìķ¼ê¸°ë¥¼": 48974, "Ġcombien": 48975, "vacc": 48976, "Ġebenfalls": 48977, "para": 48978, "Ġзм": 48979, "Ġdesperation": 48980, "ordre": 48981, "Ġש׾×Ļ": 48982, "Ġgenerously": 48983, "ĠÐŀк": 48984, "Ġorbiting": 48985, "> length: + array = array.index_select( + dim=axis, index=torch.arange(length, device=array.device) + ) + + if array.shape[axis] < length: + pad_widths = [(0, 0)] * array.ndim + pad_widths[axis] = (0, length - array.shape[axis]) + array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes]) + else: + if array.shape[axis] > length: + array = array.take(indices=range(length), axis=axis) + + if array.shape[axis] < length: + pad_widths = [(0, 0)] * array.ndim + pad_widths[axis] = (0, length - array.shape[axis]) + array = np.pad(array, pad_widths) + + return array + + +@lru_cache(maxsize=None) +def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor: + """ + load the mel filterbank matrix for projecting STFT into a Mel spectrogram. + Allows decoupling librosa dependency; saved using: + + np.savez_compressed( + "mel_filters.npz", + mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80), + ) + """ + assert n_mels == 80, f"Unsupported n_mels: {n_mels}" + with np.load( + os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz") + ) as f: + return torch.from_numpy(f[f"mel_{n_mels}"]).to(device) + + +def log_mel_spectrogram( + audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS +): + """ + Compute the log-Mel spectrogram of + + Parameters + ---------- + audio: Union[str, np.ndarray, torch.Tensor], shape = (*) + The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz + + n_mels: int + The number of Mel-frequency filters, only 80 is supported + + Returns + ------- + torch.Tensor, shape = (80, n_frames) + A Tensor that contains the Mel spectrogram + """ + if not torch.is_tensor(audio): + if isinstance(audio, str): + audio = load_audio(audio) + audio = torch.from_numpy(audio) + + window = torch.hann_window(N_FFT).to(audio.device) + stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True) + magnitudes = stft[..., :-1].abs() ** 2 + + filters = mel_filters(audio.device, n_mels) + mel_spec = filters @ magnitudes + + log_spec = torch.clamp(mel_spec, min=1e-10).log10() + log_spec = torch.maximum(log_spec, log_spec.max() - 8.0) + log_spec = (log_spec + 4.0) / 4.0 + return log_spec diff --git a/modules/whisper_extractor/decoding.py b/modules/whisper_extractor/decoding.py new file mode 100644 index 0000000000000000000000000000000000000000..82fb9e32418110b69c1aa736dde60a5bb6588459 --- /dev/null +++ b/modules/whisper_extractor/decoding.py @@ -0,0 +1,839 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This module is modified from [Whisper](https://github.com/openai/whisper.git). + +# ## Citations + +# ```bibtex +# @inproceedings{openai-whisper, +# author = {Alec Radford and +# Jong Wook Kim and +# Tao Xu and +# Greg Brockman and +# Christine McLeavey and +# Ilya Sutskever}, +# title = {Robust Speech Recognition via Large-Scale Weak Supervision}, +# booktitle = {{ICML}}, +# series = {Proceedings of Machine Learning Research}, +# volume = {202}, +# pages = {28492--28518}, +# publisher = {{PMLR}}, +# year = {2023} +# } +# ``` +# + +from dataclasses import dataclass, field +from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor +from torch.distributions import Categorical + +from .audio import CHUNK_LENGTH +from .tokenizer import Tokenizer, get_tokenizer +from .utils import compression_ratio + +if TYPE_CHECKING: + from .model import Whisper + + +@torch.no_grad() +def detect_language( + model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None +) -> Tuple[Tensor, List[dict]]: + """ + Detect the spoken language in the audio, and return them as list of strings, along with the ids + of the most probable language tokens and the probability distribution over all language tokens. + This is performed outside the main decode loop in order to not interfere with kv-caching. + + Returns + ------- + language_tokens : Tensor, shape = (n_audio,) + ids of the most probable language tokens, which appears after the startoftranscript token. + language_probs : List[Dict[str, float]], length = n_audio + list of dictionaries containing the probability distribution over all languages. + """ + if tokenizer is None: + tokenizer = get_tokenizer(model.is_multilingual) + if ( + tokenizer.language is None + or tokenizer.language_token not in tokenizer.sot_sequence + ): + raise ValueError( + f"This model doesn't have language tokens so it can't perform lang id" + ) + + single = mel.ndim == 2 + if single: + mel = mel.unsqueeze(0) + + # skip encoder forward pass if already-encoded audio features were given + if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state): + mel = model.encoder(mel) + + # forward pass using a single token, startoftranscript + n_audio = mel.shape[0] + x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1] + logits = model.logits(x, mel)[:, 0] + + # collect detected languages; suppress all non-language tokens + mask = torch.ones(logits.shape[-1], dtype=torch.bool) + mask[list(tokenizer.all_language_tokens)] = False + logits[:, mask] = -np.inf + language_tokens = logits.argmax(dim=-1) + language_token_probs = logits.softmax(dim=-1).cpu() + language_probs = [ + { + c: language_token_probs[i, j].item() + for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes) + } + for i in range(n_audio) + ] + + if single: + language_tokens = language_tokens[0] + language_probs = language_probs[0] + + return language_tokens, language_probs + + +@dataclass(frozen=True) +class DecodingOptions: + task: str = ( + "transcribe" # whether to perform X->X "transcribe" or X->English "translate" + ) + language: Optional[ + str + ] = None # language that the audio is in; uses detected language if None + + # sampling-related options + temperature: float = 0.0 + sample_len: Optional[int] = None # maximum number of tokens to sample + best_of: Optional[ + int + ] = None # number of independent samples to collect, when t > 0 + beam_size: Optional[int] = None # number of beams in beam search, when t == 0 + patience: Optional[ + float + ] = None # patience in beam search (https://arxiv.org/abs/2204.05424) + + # options for ranking generations (either beams or best-of-N samples) + length_penalty: Optional[ + float + ] = None # "alpha" in Google NMT, None defaults to length norm + + # prompt, prefix, and token suppression + prompt: Optional[ + Union[str, List[int]] + ] = None # text or tokens for the previous context + prefix: Optional[ + Union[str, List[int]] + ] = None # text or tokens to prefix the current context + suppress_blank: bool = True # this will suppress blank outputs + + # list of tokens ids (or comma-separated token ids) to suppress + # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()` + suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1" + + # timestamp sampling options + without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only + max_initial_timestamp: Optional[ + float + ] = 1.0 # the initial timestamp cannot be later than this + + # implementation details + fp16: bool = True # use fp16 for most of the calculation + + +@dataclass(frozen=True) +class DecodingResult: + audio_features: Tensor + language: str + language_probs: Optional[Dict[str, float]] = None + tokens: List[int] = field(default_factory=list) + text: str = "" + avg_logprob: float = np.nan + no_speech_prob: float = np.nan + temperature: float = np.nan + compression_ratio: float = np.nan + + +class Inference: + def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: + """Perform a forward pass on the decoder and return per-token logits""" + raise NotImplementedError + + def rearrange_kv_cache(self, source_indices) -> None: + """Update the key-value cache according to the updated beams""" + raise NotImplementedError + + def cleanup_caching(self) -> None: + """Clean up any resources or hooks after decoding is finished""" + pass + + +class PyTorchInference(Inference): + def __init__(self, model: "Whisper", initial_token_length: int): + self.model: "Whisper" = model + self.initial_token_length = initial_token_length + self.kv_cache = {} + self.hooks = [] + + def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: + if not self.kv_cache: + self.kv_cache, self.hooks = self.model.install_kv_cache_hooks() + + if tokens.shape[-1] > self.initial_token_length: + # only need to use the last token except in the first forward pass + tokens = tokens[:, -1:] + + return self.model.decoder(tokens, audio_features, kv_cache=self.kv_cache) + + def cleanup_caching(self): + for hook in self.hooks: + hook.remove() + + self.kv_cache = {} + self.hooks = [] + + def rearrange_kv_cache(self, source_indices): + for module, tensor in self.kv_cache.items(): + # update the key/value cache to contain the selected sequences + self.kv_cache[module] = tensor[source_indices].detach() + + +class SequenceRanker: + def rank( + self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]] + ) -> List[int]: + """ + Given a list of groups of samples and their cumulative log probabilities, + return the indices of the samples in each group to select as the final result + """ + raise NotImplementedError + + +class MaximumLikelihoodRanker(SequenceRanker): + """ + Select the sample with the highest log probabilities, penalized using either + a simple length normalization or Google NMT paper's length penalty + """ + + def __init__(self, length_penalty: Optional[float]): + self.length_penalty = length_penalty + + def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]): + def scores(logprobs, lengths): + result = [] + for logprob, length in zip(logprobs, lengths): + if self.length_penalty is None: + penalty = length + else: + # from the Google NMT paper + penalty = ((5 + length) / 6) ** self.length_penalty + result.append(logprob / penalty) + return result + + # get the sequence with the highest score + lengths = [[len(t) for t in s] for s in tokens] + return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)] + + +class TokenDecoder: + def reset(self): + """Initialize any stateful variables for decoding a new sequence""" + + def update( + self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor + ) -> Tuple[Tensor, bool]: + """Specify how to select the next token, based on the current trace and logits + + Parameters + ---------- + tokens : Tensor, shape = (n_batch, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence tokens + + logits : Tensor, shape = (n_batch, vocab_size) + per-token logits of the probability distribution at the current step + + sum_logprobs : Tensor, shape = (n_batch) + cumulative log probabilities for each sequence + + Returns + ------- + tokens : Tensor, shape = (n_batch, current_sequence_length + 1) + the tokens, appended with the selected next token + + completed : bool + True if all sequences has reached the end of text + + """ + raise NotImplementedError + + def finalize( + self, tokens: Tensor, sum_logprobs: Tensor + ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]: + """Finalize search and return the final candidate sequences + + Parameters + ---------- + tokens : Tensor, shape = (n_audio, n_group, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence + + sum_logprobs : Tensor, shape = (n_audio, n_group) + cumulative log probabilities for each sequence + + Returns + ------- + tokens : Sequence[Sequence[Tensor]], length = n_audio + sequence of Tensors containing candidate token sequences, for each audio input + + sum_logprobs : List[List[float]], length = n_audio + sequence of cumulative log probabilities corresponding to the above + + """ + raise NotImplementedError + + +class GreedyDecoder(TokenDecoder): + def __init__(self, temperature: float, eot: int): + self.temperature = temperature + self.eot = eot + + def update( + self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor + ) -> Tuple[Tensor, bool]: + temperature = self.temperature + if temperature == 0: + next_tokens = logits.argmax(dim=-1) + else: + next_tokens = Categorical(logits=logits / temperature).sample() + + logprobs = F.log_softmax(logits.float(), dim=-1) + current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens] + sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot) + + next_tokens[tokens[:, -1] == self.eot] = self.eot + tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1) + + completed = (tokens[:, -1] == self.eot).all() + return tokens, completed + + def finalize(self, tokens: Tensor, sum_logprobs: Tensor): + # make sure each sequence has at least one EOT token at the end + tokens = F.pad(tokens, (0, 1), value=self.eot) + return tokens, sum_logprobs.tolist() + + +class BeamSearchDecoder(TokenDecoder): + def __init__( + self, + beam_size: int, + eot: int, + inference: Inference, + patience: Optional[float] = None, + ): + self.beam_size = beam_size + self.eot = eot + self.inference = inference + self.patience = patience or 1.0 + self.max_candidates: int = round(beam_size * self.patience) + self.finished_sequences = None + + assert ( + self.max_candidates > 0 + ), f"Invalid beam size ({beam_size}) or patience ({patience})" + + def reset(self): + self.finished_sequences = None + + def update( + self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor + ) -> Tuple[Tensor, bool]: + if tokens.shape[0] % self.beam_size != 0: + raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0") + + n_audio = tokens.shape[0] // self.beam_size + if self.finished_sequences is None: # for the first update + self.finished_sequences = [{} for _ in range(n_audio)] + + logprobs = F.log_softmax(logits.float(), dim=-1) + next_tokens, source_indices, finished_sequences = [], [], [] + for i in range(n_audio): + scores, sources, finished = {}, {}, {} + + # STEP 1: calculate the cumulative log probabilities for possible candidates + for j in range(self.beam_size): + idx = i * self.beam_size + j + prefix = tokens[idx].tolist() + for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)): + new_logprob = (sum_logprobs[idx] + logprob).item() + sequence = tuple(prefix + [token.item()]) + scores[sequence] = new_logprob + sources[sequence] = idx + + # STEP 2: rank the candidates and keep the top beam_size sequences for each audio + saved = 0 + for sequence in sorted(scores, key=scores.get, reverse=True): + if sequence[-1] == self.eot: + finished[sequence] = scores[sequence] + else: + sum_logprobs[len(next_tokens)] = scores[sequence] + next_tokens.append(sequence) + source_indices.append(sources[sequence]) + + saved += 1 + if saved == self.beam_size: + break + + finished_sequences.append(finished) + + tokens = torch.tensor(next_tokens, device=tokens.device) + self.inference.rearrange_kv_cache(source_indices) + + # add newly finished sequences to self.finished_sequences + assert len(self.finished_sequences) == len(finished_sequences) + for previously_finished, newly_finished in zip( + self.finished_sequences, finished_sequences + ): + for seq in sorted(newly_finished, key=newly_finished.get, reverse=True): + if len(previously_finished) >= self.max_candidates: + break # the candidate list is full + previously_finished[seq] = newly_finished[seq] + + # mark as completed if all audio has enough number of samples + completed = all( + len(sequences) >= self.max_candidates + for sequences in self.finished_sequences + ) + return tokens, completed + + def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor): + # collect all finished sequences, including patience, and add unfinished ones if not enough + sum_logprobs = sum_logprobs.cpu() + for i, sequences in enumerate(self.finished_sequences): + if ( + len(sequences) < self.beam_size + ): # when not enough sequences are finished + for j in list(np.argsort(sum_logprobs[i]))[::-1]: + sequence = preceding_tokens[i, j].tolist() + [self.eot] + sequences[tuple(sequence)] = sum_logprobs[i][j].item() + if len(sequences) >= self.beam_size: + break + + tokens: List[List[Tensor]] = [ + [torch.tensor(seq) for seq in sequences.keys()] + for sequences in self.finished_sequences + ] + sum_logprobs: List[List[float]] = [ + list(sequences.values()) for sequences in self.finished_sequences + ] + return tokens, sum_logprobs + + +class LogitFilter: + def apply(self, logits: Tensor, tokens: Tensor) -> None: + """Apply any filtering or masking to logits in-place + + Parameters + ---------- + logits : Tensor, shape = (n_batch, vocab_size) + per-token logits of the probability distribution at the current step + + tokens : Tensor, shape = (n_batch, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence tokens + + """ + raise NotImplementedError + + +class SuppressBlank(LogitFilter): + def __init__(self, tokenizer: Tokenizer, sample_begin: int): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + + def apply(self, logits: Tensor, tokens: Tensor): + if tokens.shape[1] == self.sample_begin: + logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf + + +class SuppressTokens(LogitFilter): + def __init__(self, suppress_tokens: Sequence[int]): + self.suppress_tokens = list(suppress_tokens) + + def apply(self, logits: Tensor, tokens: Tensor): + logits[:, self.suppress_tokens] = -np.inf + + +class ApplyTimestampRules(LogitFilter): + def __init__( + self, + tokenizer: Tokenizer, + sample_begin: int, + max_initial_timestamp_index: Optional[int], + ): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + self.max_initial_timestamp_index = max_initial_timestamp_index + + def apply(self, logits: Tensor, tokens: Tensor): + # suppress <|notimestamps|> which is handled by without_timestamps + if self.tokenizer.no_timestamps is not None: + logits[:, self.tokenizer.no_timestamps] = -np.inf + + # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly + for k in range(tokens.shape[0]): + seq = [t for t in tokens[k, self.sample_begin :].tolist()] + last_was_timestamp = ( + len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin + ) + penultimate_was_timestamp = ( + len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin + ) + + if last_was_timestamp: + if penultimate_was_timestamp: # has to be non-timestamp + logits[k, self.tokenizer.timestamp_begin :] = -np.inf + else: # cannot be normal text tokens + logits[k, : self.tokenizer.eot] = -np.inf + + if tokens.shape[1] == self.sample_begin: + # suppress generating non-timestamp tokens at the beginning + logits[:, : self.tokenizer.timestamp_begin] = -np.inf + + # apply the `max_initial_timestamp` option + if self.max_initial_timestamp_index is not None: + last_allowed = ( + self.tokenizer.timestamp_begin + self.max_initial_timestamp_index + ) + logits[:, last_allowed + 1 :] = -np.inf + + # if sum of probability over timestamps is above any other token, sample timestamp + logprobs = F.log_softmax(logits.float(), dim=-1) + for k in range(tokens.shape[0]): + timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp( + dim=-1 + ) + max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max() + if timestamp_logprob > max_text_token_logprob: + logits[k, : self.tokenizer.timestamp_begin] = -np.inf + + +class DecodingTask: + inference: Inference + sequence_ranker: SequenceRanker + decoder: TokenDecoder + logit_filters: List[LogitFilter] + + def __init__(self, model: "Whisper", options: DecodingOptions): + self.model = model + + language = options.language or "en" + tokenizer = get_tokenizer( + model.is_multilingual, language=language, task=options.task + ) + self.tokenizer: Tokenizer = tokenizer + self.options: DecodingOptions = self._verify_options(options) + + self.n_group: int = options.beam_size or options.best_of or 1 + self.n_ctx: int = model.dims.n_text_ctx + self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2 + + self.sot_sequence: Tuple[int] = tokenizer.sot_sequence + if self.options.without_timestamps: + self.sot_sequence = tokenizer.sot_sequence_including_notimestamps + + self.initial_tokens: Tuple[int] = self._get_initial_tokens() + self.sample_begin: int = len(self.initial_tokens) + self.sot_index: int = self.initial_tokens.index(tokenizer.sot) + + # inference: implements the forward pass through the decoder, including kv caching + self.inference = PyTorchInference(model, len(self.initial_tokens)) + + # sequence ranker: implements how to rank a group of sampled sequences + self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty) + + # decoder: implements how to select the next tokens, given the autoregressive distribution + if options.beam_size is not None: + self.decoder = BeamSearchDecoder( + options.beam_size, tokenizer.eot, self.inference, options.patience + ) + else: + self.decoder = GreedyDecoder(options.temperature, tokenizer.eot) + + # logit filters: applies various rules to suppress or penalize certain tokens + self.logit_filters = [] + if self.options.suppress_blank: + self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin)) + if self.options.suppress_tokens: + self.logit_filters.append(SuppressTokens(self._get_suppress_tokens())) + if not options.without_timestamps: + precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds + max_initial_timestamp_index = None + if options.max_initial_timestamp: + max_initial_timestamp_index = round( + self.options.max_initial_timestamp / precision + ) + self.logit_filters.append( + ApplyTimestampRules( + tokenizer, self.sample_begin, max_initial_timestamp_index + ) + ) + + def _verify_options(self, options: DecodingOptions) -> DecodingOptions: + if options.beam_size is not None and options.best_of is not None: + raise ValueError("beam_size and best_of can't be given together") + if options.temperature == 0: + if options.best_of is not None: + raise ValueError("best_of with greedy sampling (T=0) is not compatible") + if options.patience is not None and options.beam_size is None: + raise ValueError("patience requires beam_size to be given") + if options.length_penalty is not None and not ( + 0 <= options.length_penalty <= 1 + ): + raise ValueError("length_penalty (alpha) should be a value between 0 and 1") + + return options + + def _get_initial_tokens(self) -> Tuple[int]: + tokens = list(self.sot_sequence) + prefix = self.options.prefix + prompt = self.options.prompt + + if prefix: + prefix_tokens = ( + self.tokenizer.encode(" " + prefix.strip()) + if isinstance(prefix, str) + else prefix + ) + if self.sample_len is not None: + max_prefix_len = self.n_ctx // 2 - self.sample_len + prefix_tokens = prefix_tokens[-max_prefix_len:] + tokens = tokens + prefix_tokens + + if prompt: + prompt_tokens = ( + self.tokenizer.encode(" " + prompt.strip()) + if isinstance(prompt, str) + else prompt + ) + tokens = ( + [self.tokenizer.sot_prev] + + prompt_tokens[-(self.n_ctx // 2 - 1) :] + + tokens + ) + + return tuple(tokens) + + def _get_suppress_tokens(self) -> Tuple[int]: + suppress_tokens = self.options.suppress_tokens + + if isinstance(suppress_tokens, str): + suppress_tokens = [int(t) for t in suppress_tokens.split(",")] + + if -1 in suppress_tokens: + suppress_tokens = [t for t in suppress_tokens if t >= 0] + suppress_tokens.extend(self.tokenizer.non_speech_tokens) + elif suppress_tokens is None or len(suppress_tokens) == 0: + suppress_tokens = [] # interpret empty string as an empty list + else: + assert isinstance(suppress_tokens, list), "suppress_tokens must be a list" + + suppress_tokens.extend( + [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm] + ) + if self.tokenizer.no_speech is not None: + # no-speech probability is collected separately + suppress_tokens.append(self.tokenizer.no_speech) + + return tuple(sorted(set(suppress_tokens))) + + def _get_audio_features(self, mel: Tensor): + if self.options.fp16: + mel = mel.half() + + if mel.shape[-2:] == ( + self.model.dims.n_audio_ctx, + self.model.dims.n_audio_state, + ): + # encoded audio features are given; skip audio encoding + audio_features = mel + else: + audio_features = self.model.encoder(mel) + + if audio_features.dtype != ( + torch.float16 if self.options.fp16 else torch.float32 + ): + return TypeError( + f"audio_features has an incorrect dtype: {audio_features.dtype}" + ) + + return audio_features + + def _detect_language(self, audio_features: Tensor, tokens: Tensor): + languages = [self.options.language] * audio_features.shape[0] + lang_probs = None + + if self.options.language is None or self.options.task == "lang_id": + lang_tokens, lang_probs = self.model.detect_language( + audio_features, self.tokenizer + ) + languages = [max(probs, key=probs.get) for probs in lang_probs] + if self.options.language is None: + tokens[:, self.sot_index + 1] = lang_tokens # write language tokens + + return languages, lang_probs + + def _main_loop(self, audio_features: Tensor, tokens: Tensor): + assert audio_features.shape[0] == tokens.shape[0] + n_batch = tokens.shape[0] + sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device) + no_speech_probs = [np.nan] * n_batch + + try: + for i in range(self.sample_len): + logits = self.inference.logits(tokens, audio_features) + + if ( + i == 0 and self.tokenizer.no_speech is not None + ): # save no_speech_probs + probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1) + no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist() + + # now we need to consider the logits at the last token only + logits = logits[:, -1] + + # apply the logit filters, e.g. for suppressing or applying penalty to + for logit_filter in self.logit_filters: + logit_filter.apply(logits, tokens) + + # expand the tokens tensor with the selected next tokens + tokens, completed = self.decoder.update(tokens, logits, sum_logprobs) + + if completed or tokens.shape[-1] > self.n_ctx: + break + finally: + self.inference.cleanup_caching() + + return tokens, sum_logprobs, no_speech_probs + + @torch.no_grad() + def run(self, mel: Tensor) -> List[DecodingResult]: + self.decoder.reset() + tokenizer: Tokenizer = self.tokenizer + n_audio: int = mel.shape[0] + + audio_features: Tensor = self._get_audio_features(mel) # encoder forward pass + tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1) + + # detect language if requested, overwriting the language token + languages, language_probs = self._detect_language(audio_features, tokens) + if self.options.task == "lang_id": + return [ + DecodingResult( + audio_features=features, language=language, language_probs=probs + ) + for features, language, probs in zip( + audio_features, languages, language_probs + ) + ] + + # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling + audio_features = audio_features.repeat_interleave(self.n_group, dim=0) + tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) + + # call the main sampling loop + tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens) + + # reshape the tensors to have (n_audio, n_group) as the first two dimensions + audio_features = audio_features[:: self.n_group] + no_speech_probs = no_speech_probs[:: self.n_group] + assert audio_features.shape[0] == len(no_speech_probs) == n_audio + + tokens = tokens.reshape(n_audio, self.n_group, -1) + sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group) + + # get the final candidates for each group, and slice between the first sampled token and EOT + tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs) + tokens: List[List[Tensor]] = [ + [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] + for s in tokens + ] + + # select the top-ranked sample in each group + selected = self.sequence_ranker.rank(tokens, sum_logprobs) + tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)] + texts: List[str] = [tokenizer.decode(t).strip() for t in tokens] + + sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)] + avg_logprobs: List[float] = [ + lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs) + ] + + fields = ( + texts, + languages, + tokens, + audio_features, + avg_logprobs, + no_speech_probs, + ) + if len(set(map(len, fields))) != 1: + raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}") + + return [ + DecodingResult( + audio_features=features, + language=language, + tokens=tokens, + text=text, + avg_logprob=avg_logprob, + no_speech_prob=no_speech_prob, + temperature=self.options.temperature, + compression_ratio=compression_ratio(text), + ) + for text, language, tokens, features, avg_logprob, no_speech_prob in zip( + *fields + ) + ] + + +@torch.no_grad() +def decode( + model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions() +) -> Union[DecodingResult, List[DecodingResult]]: + """ + Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s). + + Parameters + ---------- + model: Whisper + the Whisper model instance + + mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000) + A tensor containing the Mel spectrogram(s) + + options: DecodingOptions + A dataclass that contains all necessary options for decoding 30-second segments + + Returns + ------- + result: Union[DecodingResult, List[DecodingResult]] + The result(s) of decoding contained in `DecodingResult` dataclass instance(s) + """ + single = mel.ndim == 2 + if single: + mel = mel.unsqueeze(0) + + result = DecodingTask(model, options).run(mel) + + if single: + result = result[0] + + return result diff --git a/modules/whisper_extractor/model.py b/modules/whisper_extractor/model.py new file mode 100644 index 0000000000000000000000000000000000000000..6f0265c9924575d77ebbfd054cbdde6c35df242a --- /dev/null +++ b/modules/whisper_extractor/model.py @@ -0,0 +1,328 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This module is modified from [Whisper](https://github.com/openai/whisper.git). + +# ## Citations + +# ```bibtex +# @inproceedings{openai-whisper, +# author = {Alec Radford and +# Jong Wook Kim and +# Tao Xu and +# Greg Brockman and +# Christine McLeavey and +# Ilya Sutskever}, +# title = {Robust Speech Recognition via Large-Scale Weak Supervision}, +# booktitle = {{ICML}}, +# series = {Proceedings of Machine Learning Research}, +# volume = {202}, +# pages = {28492--28518}, +# publisher = {{PMLR}}, +# year = {2023} +# } +# ``` +# + +from dataclasses import dataclass +from typing import Dict +from typing import Iterable, Optional + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor +from torch import nn + +from .transcribe import transcribe as transcribe_function +from .decoding import ( + detect_language as detect_language_function, + decode as decode_function, +) + + +@dataclass +class ModelDimensions: + n_mels: int + n_audio_ctx: int + n_audio_state: int + n_audio_head: int + n_audio_layer: int + n_vocab: int + n_text_ctx: int + n_text_state: int + n_text_head: int + n_text_layer: int + + +class LayerNorm(nn.LayerNorm): + def forward(self, x: Tensor) -> Tensor: + return super().forward(x.float()).type(x.dtype) + + +class Linear(nn.Linear): + def forward(self, x: Tensor) -> Tensor: + return F.linear( + x, + self.weight.to(x.dtype), + None if self.bias is None else self.bias.to(x.dtype), + ) + + +class Conv1d(nn.Conv1d): + def _conv_forward( + self, x: Tensor, weight: Tensor, bias: Optional[Tensor] + ) -> Tensor: + return super()._conv_forward( + x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype) + ) + + +def sinusoids(length, channels, max_timescale=10000): + """Returns sinusoids for positional embedding""" + assert channels % 2 == 0 + log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) + inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)) + scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] + return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, n_state: int, n_head: int): + super().__init__() + self.n_head = n_head + self.query = Linear(n_state, n_state) + self.key = Linear(n_state, n_state, bias=False) + self.value = Linear(n_state, n_state) + self.out = Linear(n_state, n_state) + + def forward( + self, + x: Tensor, + xa: Optional[Tensor] = None, + mask: Optional[Tensor] = None, + kv_cache: Optional[dict] = None, + ): + q = self.query(x) + + if kv_cache is None or xa is None or self.key not in kv_cache: + # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors; + # otherwise, perform key/value projections for self- or cross-attention as usual. + k = self.key(x if xa is None else xa) + v = self.value(x if xa is None else xa) + else: + # for cross-attention, calculate keys and values once and reuse in subsequent calls. + k = kv_cache[self.key] + v = kv_cache[self.value] + + wv, qk = self.qkv_attention(q, k, v, mask) + return self.out(wv), qk + + def qkv_attention( + self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None + ): + n_batch, n_ctx, n_state = q.shape + scale = (n_state // self.n_head) ** -0.25 + q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale + k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale + v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) + + qk = q @ k + if mask is not None: + qk = qk + mask[:n_ctx, :n_ctx] + qk = qk.float() + + w = F.softmax(qk, dim=-1).to(q.dtype) + return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach() + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, n_state: int, n_head: int, cross_attention: bool = False): + super().__init__() + + self.attn = MultiHeadAttention(n_state, n_head) + self.attn_ln = LayerNorm(n_state) + + self.cross_attn = ( + MultiHeadAttention(n_state, n_head) if cross_attention else None + ) + self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None + + n_mlp = n_state * 4 + self.mlp = nn.Sequential( + Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state) + ) + self.mlp_ln = LayerNorm(n_state) + + def forward( + self, + x: Tensor, + xa: Optional[Tensor] = None, + mask: Optional[Tensor] = None, + kv_cache: Optional[dict] = None, + ): + x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0] + if self.cross_attn: + x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0] + x = x + self.mlp(self.mlp_ln(x)) + return x + + +class AudioEncoder(nn.Module): + def __init__( + self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int + ): + super().__init__() + self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1) + self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1) + self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state)) + + self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( + [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)] + ) + self.ln_post = LayerNorm(n_state) + + def forward(self, x: Tensor): + """ + x : torch.Tensor, shape = (batch_size, n_mels, n_ctx) + the mel spectrogram of the audio + """ + x = F.gelu(self.conv1(x)) + x = F.gelu(self.conv2(x)) + x = x.permute(0, 2, 1) + + assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape" + x = (x + self.positional_embedding).to(x.dtype) + + for block in self.blocks: + x = block(x) + + x = self.ln_post(x) + return x + + +class TextDecoder(nn.Module): + def __init__( + self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int + ): + super().__init__() + + self.token_embedding = nn.Embedding(n_vocab, n_state) + self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state)) + + self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( + [ + ResidualAttentionBlock(n_state, n_head, cross_attention=True) + for _ in range(n_layer) + ] + ) + self.ln = LayerNorm(n_state) + + mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1) + self.register_buffer("mask", mask, persistent=False) + + def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None): + """ + x : torch.LongTensor, shape = (batch_size, <= n_ctx) + the text tokens + xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx) + the encoded audio features to be attended on + """ + offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0 + x = ( + self.token_embedding(x) + + self.positional_embedding[offset : offset + x.shape[-1]] + ) + x = x.to(xa.dtype) + + for block in self.blocks: + x = block(x, xa, mask=self.mask, kv_cache=kv_cache) + + x = self.ln(x) + logits = ( + x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1) + ).float() + + return logits + + +class Whisper(nn.Module): + def __init__(self, dims: ModelDimensions): + super().__init__() + self.dims = dims + self.encoder = AudioEncoder( + self.dims.n_mels, + self.dims.n_audio_ctx, + self.dims.n_audio_state, + self.dims.n_audio_head, + self.dims.n_audio_layer, + ) + self.decoder = TextDecoder( + self.dims.n_vocab, + self.dims.n_text_ctx, + self.dims.n_text_state, + self.dims.n_text_head, + self.dims.n_text_layer, + ) + + def embed_audio(self, mel: torch.Tensor): + return self.encoder(mel) + + def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor): + return self.decoder(tokens, audio_features) + + def forward( + self, mel: torch.Tensor, tokens: torch.Tensor + ) -> Dict[str, torch.Tensor]: + return self.decoder(tokens, self.encoder(mel)) + + @property + def device(self): + return next(self.parameters()).device + + @property + def is_multilingual(self): + return self.dims.n_vocab == 51865 + + def install_kv_cache_hooks(self, cache: Optional[dict] = None): + """ + The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value + tensors calculated for the previous positions. This method returns a dictionary that stores + all caches, and the necessary hooks for the key and value projection modules that save the + intermediate tensors to be reused during later calculations. + + Returns + ------- + cache : Dict[nn.Module, torch.Tensor] + A dictionary object mapping the key/value projection modules to its cache + hooks : List[RemovableHandle] + List of PyTorch RemovableHandle objects to stop the hooks to be called + """ + cache = {**cache} if cache is not None else {} + hooks = [] + + def save_to_cache(module, _, output): + if ( + module not in cache + or output.shape[1] > self.decoder.positional_embedding.shape[0] + ): + cache[ + module + ] = output # save as-is, for the first token or cross attention + else: + cache[module] = torch.cat([cache[module], output], dim=1).detach() + return cache[module] + + def install_hooks(layer: nn.Module): + if isinstance(layer, MultiHeadAttention): + hooks.append(layer.key.register_forward_hook(save_to_cache)) + hooks.append(layer.value.register_forward_hook(save_to_cache)) + + self.decoder.apply(install_hooks) + return cache, hooks + + detect_language = detect_language_function + transcribe = transcribe_function + decode = decode_function diff --git a/modules/whisper_extractor/normalizers/__init__.py b/modules/whisper_extractor/normalizers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/whisper_extractor/normalizers/basic.py b/modules/whisper_extractor/normalizers/basic.py new file mode 100644 index 0000000000000000000000000000000000000000..5b58315beaf001649386cb4db37027255bbd66e4 --- /dev/null +++ b/modules/whisper_extractor/normalizers/basic.py @@ -0,0 +1,104 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This module is modified from [Whisper](https://github.com/openai/whisper.git). + +# ## Citations + +# ```bibtex +# @inproceedings{openai-whisper, +# author = {Alec Radford and +# Jong Wook Kim and +# Tao Xu and +# Greg Brockman and +# Christine McLeavey and +# Ilya Sutskever}, +# title = {Robust Speech Recognition via Large-Scale Weak Supervision}, +# booktitle = {{ICML}}, +# series = {Proceedings of Machine Learning Research}, +# volume = {202}, +# pages = {28492--28518}, +# publisher = {{PMLR}}, +# year = {2023} +# } +# ``` +# + +import re +import unicodedata + +import regex + +# non-ASCII letters that are not separated by "NFKD" normalization +ADDITIONAL_DIACRITICS = { + "œ": "oe", + "Œ": "OE", + "ø": "o", + "Ø": "O", + "æ": "ae", + "Æ": "AE", + "ß": "ss", + "ẞ": "SS", + "đ": "d", + "Đ": "D", + "ð": "d", + "Ð": "D", + "þ": "th", + "Þ": "th", + "ł": "l", + "Ł": "L", +} + + +def remove_symbols_and_diacritics(s: str, keep=""): + """ + Replace any other markers, symbols, and punctuations with a space, + and drop any diacritics (category 'Mn' and some manual mappings) + """ + return "".join( + c + if c in keep + else ADDITIONAL_DIACRITICS[c] + if c in ADDITIONAL_DIACRITICS + else "" + if unicodedata.category(c) == "Mn" + else " " + if unicodedata.category(c)[0] in "MSP" + else c + for c in unicodedata.normalize("NFKD", s) + ) + + +def remove_symbols(s: str): + """ + Replace any other markers, symbols, punctuations with a space, keeping diacritics + """ + return "".join( + " " if unicodedata.category(c)[0] in "MSP" else c + for c in unicodedata.normalize("NFKC", s) + ) + + +class BasicTextNormalizer: + def __init__(self, remove_diacritics: bool = False, split_letters: bool = False): + self.clean = ( + remove_symbols_and_diacritics if remove_diacritics else remove_symbols + ) + self.split_letters = split_letters + + def __call__(self, s: str): + s = s.lower() + s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets + s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis + s = self.clean(s).lower() + + if self.split_letters: + s = " ".join(regex.findall(r"\X", s, regex.U)) + + s = re.sub( + r"\s+", " ", s + ) # replace any successive whitespace characters with a space + + return s diff --git a/modules/whisper_extractor/normalizers/english.json b/modules/whisper_extractor/normalizers/english.json new file mode 100644 index 0000000000000000000000000000000000000000..74a1c3521d9af4a84a0aa65d95b0859138640799 --- /dev/null +++ b/modules/whisper_extractor/normalizers/english.json @@ -0,0 +1,1741 @@ +{ + "accessorise": "accessorize", + "accessorised": "accessorized", + "accessorises": "accessorizes", + "accessorising": "accessorizing", + "acclimatisation": "acclimatization", + "acclimatise": "acclimatize", + "acclimatised": "acclimatized", + "acclimatises": "acclimatizes", + "acclimatising": "acclimatizing", + "accoutrements": "accouterments", + "aeon": "eon", + "aeons": "eons", + "aerogramme": "aerogram", + "aerogrammes": "aerograms", + "aeroplane": "airplane", + "aeroplanes": "airplanes", + "aesthete": "esthete", + "aesthetes": "esthetes", + "aesthetic": "esthetic", + "aesthetically": "esthetically", + "aesthetics": "esthetics", + "aetiology": "etiology", + "ageing": "aging", + "aggrandisement": "aggrandizement", + "agonise": "agonize", + "agonised": "agonized", + "agonises": "agonizes", + "agonising": "agonizing", + "agonisingly": "agonizingly", + "almanack": "almanac", + "almanacks": "almanacs", + "aluminium": "aluminum", + "amortisable": "amortizable", + "amortisation": "amortization", + "amortisations": "amortizations", + "amortise": "amortize", + "amortised": "amortized", + "amortises": "amortizes", + "amortising": "amortizing", + "amphitheatre": "amphitheater", + "amphitheatres": "amphitheaters", + "anaemia": "anemia", + "anaemic": "anemic", + "anaesthesia": "anesthesia", + "anaesthetic": "anesthetic", + "anaesthetics": "anesthetics", + "anaesthetise": "anesthetize", + "anaesthetised": "anesthetized", + "anaesthetises": "anesthetizes", + "anaesthetising": "anesthetizing", + "anaesthetist": "anesthetist", + "anaesthetists": "anesthetists", + "anaesthetize": "anesthetize", + "anaesthetized": "anesthetized", + "anaesthetizes": "anesthetizes", + "anaesthetizing": "anesthetizing", + "analogue": "analog", + "analogues": "analogs", + "analyse": "analyze", + "analysed": "analyzed", + "analyses": "analyzes", + "analysing": "analyzing", + "anglicise": "anglicize", + "anglicised": "anglicized", + "anglicises": "anglicizes", + "anglicising": "anglicizing", + "annualised": "annualized", + "antagonise": "antagonize", + "antagonised": "antagonized", + "antagonises": "antagonizes", + "antagonising": "antagonizing", + "apologise": "apologize", + "apologised": "apologized", + "apologises": "apologizes", + "apologising": "apologizing", + "appal": "appall", + "appals": "appalls", + "appetiser": "appetizer", + "appetisers": "appetizers", + "appetising": "appetizing", + "appetisingly": "appetizingly", + "arbour": "arbor", + "arbours": "arbors", + "archeological": "archaeological", + "archaeologically": "archeologically", + "archaeologist": "archeologist", + "archaeologists": "archeologists", + "archaeology": "archeology", + "ardour": "ardor", + "armour": "armor", + "armoured": "armored", + "armourer": "armorer", + "armourers": "armorers", + "armouries": "armories", + "armoury": "armory", + "artefact": "artifact", + "artefacts": "artifacts", + "authorise": "authorize", + "authorised": "authorized", + "authorises": "authorizes", + "authorising": "authorizing", + "axe": "ax", + "backpedalled": "backpedaled", + "backpedalling": "backpedaling", + "bannister": "banister", + "bannisters": "banisters", + "baptise": "baptize", + "baptised": "baptized", + "baptises": "baptizes", + "baptising": "baptizing", + "bastardise": "bastardize", + "bastardised": "bastardized", + "bastardises": "bastardizes", + "bastardising": "bastardizing", + "battleax": "battleaxe", + "baulk": "balk", + "baulked": "balked", + "baulking": "balking", + "baulks": "balks", + "bedevilled": "bedeviled", + "bedevilling": "bedeviling", + "behaviour": "behavior", + "behavioural": "behavioral", + "behaviourism": "behaviorism", + "behaviourist": "behaviorist", + "behaviourists": "behaviorists", + "behaviours": "behaviors", + "behove": "behoove", + "behoved": "behooved", + "behoves": "behooves", + "bejewelled": "bejeweled", + "belabour": "belabor", + "belaboured": "belabored", + "belabouring": "belaboring", + "belabours": "belabors", + "bevelled": "beveled", + "bevvies": "bevies", + "bevvy": "bevy", + "biassed": "biased", + "biassing": "biasing", + "bingeing": "binging", + "bougainvillaea": "bougainvillea", + "bougainvillaeas": "bougainvilleas", + "bowdlerise": "bowdlerize", + "bowdlerised": "bowdlerized", + "bowdlerises": "bowdlerizes", + "bowdlerising": "bowdlerizing", + "breathalyse": "breathalyze", + "breathalysed": "breathalyzed", + "breathalyser": "breathalyzer", + "breathalysers": "breathalyzers", + "breathalyses": "breathalyzes", + "breathalysing": "breathalyzing", + "brutalise": "brutalize", + "brutalised": "brutalized", + "brutalises": "brutalizes", + "brutalising": "brutalizing", + "busses": "buses", + "bussing": "busing", + "caesarean": "cesarean", + "caesareans": "cesareans", + "calibre": "caliber", + "calibres": "calibers", + "calliper": "caliper", + "callipers": "calipers", + "callisthenics": "calisthenics", + "canalise": "canalize", + "canalised": "canalized", + "canalises": "canalizes", + "canalising": "canalizing", + "cancelation": "cancellation", + "cancelations": "cancellations", + "cancelled": "canceled", + "cancelling": "canceling", + "candour": "candor", + "cannibalise": "cannibalize", + "cannibalised": "cannibalized", + "cannibalises": "cannibalizes", + "cannibalising": "cannibalizing", + "canonise": "canonize", + "canonised": "canonized", + "canonises": "canonizes", + "canonising": "canonizing", + "capitalise": "capitalize", + "capitalised": "capitalized", + "capitalises": "capitalizes", + "capitalising": "capitalizing", + "caramelise": "caramelize", + "caramelised": "caramelized", + "caramelises": "caramelizes", + "caramelising": "caramelizing", + "carbonise": "carbonize", + "carbonised": "carbonized", + "carbonises": "carbonizes", + "carbonising": "carbonizing", + "carolled": "caroled", + "carolling": "caroling", + "catalogue": "catalog", + "catalogued": "cataloged", + "catalogues": "catalogs", + "cataloguing": "cataloging", + "catalyse": "catalyze", + "catalysed": "catalyzed", + "catalyses": "catalyzes", + "catalysing": "catalyzing", + "categorise": "categorize", + "categorised": "categorized", + "categorises": "categorizes", + "categorising": "categorizing", + "cauterise": "cauterize", + "cauterised": "cauterized", + "cauterises": "cauterizes", + "cauterising": "cauterizing", + "cavilled": "caviled", + "cavilling": "caviling", + "centigramme": "centigram", + "centigrammes": "centigrams", + "centilitre": "centiliter", + "centilitres": "centiliters", + "centimetre": "centimeter", + "centimetres": "centimeters", + "centralise": "centralize", + "centralised": "centralized", + "centralises": "centralizes", + "centralising": "centralizing", + "centre": "center", + "centred": "centered", + "centrefold": "centerfold", + "centrefolds": "centerfolds", + "centrepiece": "centerpiece", + "centrepieces": "centerpieces", + "centres": "centers", + "channelled": "channeled", + "channelling": "channeling", + "characterise": "characterize", + "characterised": "characterized", + "characterises": "characterizes", + "characterising": "characterizing", + "cheque": "check", + "chequebook": "checkbook", + "chequebooks": "checkbooks", + "chequered": "checkered", + "cheques": "checks", + "chilli": "chili", + "chimaera": "chimera", + "chimaeras": "chimeras", + "chiselled": "chiseled", + "chiselling": "chiseling", + "circularise": "circularize", + "circularised": "circularized", + "circularises": "circularizes", + "circularising": "circularizing", + "civilise": "civilize", + "civilised": "civilized", + "civilises": "civilizes", + "civilising": "civilizing", + "clamour": "clamor", + "clamoured": "clamored", + "clamouring": "clamoring", + "clamours": "clamors", + "clangour": "clangor", + "clarinettist": "clarinetist", + "clarinettists": "clarinetists", + "collectivise": "collectivize", + "collectivised": "collectivized", + "collectivises": "collectivizes", + "collectivising": "collectivizing", + "colonisation": "colonization", + "colonise": "colonize", + "colonised": "colonized", + "coloniser": "colonizer", + "colonisers": "colonizers", + "colonises": "colonizes", + "colonising": "colonizing", + "colour": "color", + "colourant": "colorant", + "colourants": "colorants", + "coloured": "colored", + "coloureds": "coloreds", + "colourful": "colorful", + "colourfully": "colorfully", + "colouring": "coloring", + "colourize": "colorize", + "colourized": "colorized", + "colourizes": "colorizes", + "colourizing": "colorizing", + "colourless": "colorless", + "colours": "colors", + "commercialise": "commercialize", + "commercialised": "commercialized", + "commercialises": "commercializes", + "commercialising": "commercializing", + "compartmentalise": "compartmentalize", + "compartmentalised": "compartmentalized", + "compartmentalises": "compartmentalizes", + "compartmentalising": "compartmentalizing", + "computerise": "computerize", + "computerised": "computerized", + "computerises": "computerizes", + "computerising": "computerizing", + "conceptualise": "conceptualize", + "conceptualised": "conceptualized", + "conceptualises": "conceptualizes", + "conceptualising": "conceptualizing", + "connexion": "connection", + "connexions": "connections", + "contextualise": "contextualize", + "contextualised": "contextualized", + "contextualises": "contextualizes", + "contextualising": "contextualizing", + "cosier": "cozier", + "cosies": "cozies", + "cosiest": "coziest", + "cosily": "cozily", + "cosiness": "coziness", + "cosy": "cozy", + "councillor": "councilor", + "councillors": "councilors", + "counselled": "counseled", + "counselling": "counseling", + "counsellor": "counselor", + "counsellors": "counselors", + "crenelated": "crenellated", + "criminalise": "criminalize", + "criminalised": "criminalized", + "criminalises": "criminalizes", + "criminalising": "criminalizing", + "criticise": "criticize", + "criticised": "criticized", + "criticises": "criticizes", + "criticising": "criticizing", + "crueller": "crueler", + "cruellest": "cruelest", + "crystallisation": "crystallization", + "crystallise": "crystallize", + "crystallised": "crystallized", + "crystallises": "crystallizes", + "crystallising": "crystallizing", + "cudgelled": "cudgeled", + "cudgelling": "cudgeling", + "customise": "customize", + "customised": "customized", + "customises": "customizes", + "customising": "customizing", + "cypher": "cipher", + "cyphers": "ciphers", + "decentralisation": "decentralization", + "decentralise": "decentralize", + "decentralised": "decentralized", + "decentralises": "decentralizes", + "decentralising": "decentralizing", + "decriminalisation": "decriminalization", + "decriminalise": "decriminalize", + "decriminalised": "decriminalized", + "decriminalises": "decriminalizes", + "decriminalising": "decriminalizing", + "defence": "defense", + "defenceless": "defenseless", + "defences": "defenses", + "dehumanisation": "dehumanization", + "dehumanise": "dehumanize", + "dehumanised": "dehumanized", + "dehumanises": "dehumanizes", + "dehumanising": "dehumanizing", + "demeanour": "demeanor", + "demilitarisation": "demilitarization", + "demilitarise": "demilitarize", + "demilitarised": "demilitarized", + "demilitarises": "demilitarizes", + "demilitarising": "demilitarizing", + "demobilisation": "demobilization", + "demobilise": "demobilize", + "demobilised": "demobilized", + "demobilises": "demobilizes", + "demobilising": "demobilizing", + "democratisation": "democratization", + "democratise": "democratize", + "democratised": "democratized", + "democratises": "democratizes", + "democratising": "democratizing", + "demonise": "demonize", + "demonised": "demonized", + "demonises": "demonizes", + "demonising": "demonizing", + "demoralisation": "demoralization", + "demoralise": "demoralize", + "demoralised": "demoralized", + "demoralises": "demoralizes", + "demoralising": "demoralizing", + "denationalisation": "denationalization", + "denationalise": "denationalize", + "denationalised": "denationalized", + "denationalises": "denationalizes", + "denationalising": "denationalizing", + "deodorise": "deodorize", + "deodorised": "deodorized", + "deodorises": "deodorizes", + "deodorising": "deodorizing", + "depersonalise": "depersonalize", + "depersonalised": "depersonalized", + "depersonalises": "depersonalizes", + "depersonalising": "depersonalizing", + "deputise": "deputize", + "deputised": "deputized", + "deputises": "deputizes", + "deputising": "deputizing", + "desensitisation": "desensitization", + "desensitise": "desensitize", + "desensitised": "desensitized", + "desensitises": "desensitizes", + "desensitising": "desensitizing", + "destabilisation": "destabilization", + "destabilise": "destabilize", + "destabilised": "destabilized", + "destabilises": "destabilizes", + "destabilising": "destabilizing", + "dialled": "dialed", + "dialling": "dialing", + "dialogue": "dialog", + "dialogues": "dialogs", + "diarrhoea": "diarrhea", + "digitise": "digitize", + "digitised": "digitized", + "digitises": "digitizes", + "digitising": "digitizing", + "disc": "disk", + "discolour": "discolor", + "discoloured": "discolored", + "discolouring": "discoloring", + "discolours": "discolors", + "discs": "disks", + "disembowelled": "disemboweled", + "disembowelling": "disemboweling", + "disfavour": "disfavor", + "dishevelled": "disheveled", + "dishonour": "dishonor", + "dishonourable": "dishonorable", + "dishonourably": "dishonorably", + "dishonoured": "dishonored", + "dishonouring": "dishonoring", + "dishonours": "dishonors", + "disorganisation": "disorganization", + "disorganised": "disorganized", + "distil": "distill", + "distils": "distills", + "dramatisation": "dramatization", + "dramatisations": "dramatizations", + "dramatise": "dramatize", + "dramatised": "dramatized", + "dramatises": "dramatizes", + "dramatising": "dramatizing", + "draught": "draft", + "draughtboard": "draftboard", + "draughtboards": "draftboards", + "draughtier": "draftier", + "draughtiest": "draftiest", + "draughts": "drafts", + "draughtsman": "draftsman", + "draughtsmanship": "draftsmanship", + "draughtsmen": "draftsmen", + "draughtswoman": "draftswoman", + "draughtswomen": "draftswomen", + "draughty": "drafty", + "drivelled": "driveled", + "drivelling": "driveling", + "duelled": "dueled", + "duelling": "dueling", + "economise": "economize", + "economised": "economized", + "economises": "economizes", + "economising": "economizing", + "edoema": "edema", + "editorialise": "editorialize", + "editorialised": "editorialized", + "editorialises": "editorializes", + "editorialising": "editorializing", + "empathise": "empathize", + "empathised": "empathized", + "empathises": "empathizes", + "empathising": "empathizing", + "emphasise": "emphasize", + "emphasised": "emphasized", + "emphasises": "emphasizes", + "emphasising": "emphasizing", + "enamelled": "enameled", + "enamelling": "enameling", + "enamoured": "enamored", + "encyclopaedia": "encyclopedia", + "encyclopaedias": "encyclopedias", + "encyclopaedic": "encyclopedic", + "endeavour": "endeavor", + "endeavoured": "endeavored", + "endeavouring": "endeavoring", + "endeavours": "endeavors", + "energise": "energize", + "energised": "energized", + "energises": "energizes", + "energising": "energizing", + "enrol": "enroll", + "enrols": "enrolls", + "enthral": "enthrall", + "enthrals": "enthralls", + "epaulette": "epaulet", + "epaulettes": "epaulets", + "epicentre": "epicenter", + "epicentres": "epicenters", + "epilogue": "epilog", + "epilogues": "epilogs", + "epitomise": "epitomize", + "epitomised": "epitomized", + "epitomises": "epitomizes", + "epitomising": "epitomizing", + "equalisation": "equalization", + "equalise": "equalize", + "equalised": "equalized", + "equaliser": "equalizer", + "equalisers": "equalizers", + "equalises": "equalizes", + "equalising": "equalizing", + "eulogise": "eulogize", + "eulogised": "eulogized", + "eulogises": "eulogizes", + "eulogising": "eulogizing", + "evangelise": "evangelize", + "evangelised": "evangelized", + "evangelises": "evangelizes", + "evangelising": "evangelizing", + "exorcise": "exorcize", + "exorcised": "exorcized", + "exorcises": "exorcizes", + "exorcising": "exorcizing", + "extemporisation": "extemporization", + "extemporise": "extemporize", + "extemporised": "extemporized", + "extemporises": "extemporizes", + "extemporising": "extemporizing", + "externalisation": "externalization", + "externalisations": "externalizations", + "externalise": "externalize", + "externalised": "externalized", + "externalises": "externalizes", + "externalising": "externalizing", + "factorise": "factorize", + "factorised": "factorized", + "factorises": "factorizes", + "factorising": "factorizing", + "faecal": "fecal", + "faeces": "feces", + "familiarisation": "familiarization", + "familiarise": "familiarize", + "familiarised": "familiarized", + "familiarises": "familiarizes", + "familiarising": "familiarizing", + "fantasise": "fantasize", + "fantasised": "fantasized", + "fantasises": "fantasizes", + "fantasising": "fantasizing", + "favour": "favor", + "favourable": "favorable", + "favourably": "favorably", + "favoured": "favored", + "favouring": "favoring", + "favourite": "favorite", + "favourites": "favorites", + "favouritism": "favoritism", + "favours": "favors", + "feminise": "feminize", + "feminised": "feminized", + "feminises": "feminizes", + "feminising": "feminizing", + "fertilisation": "fertilization", + "fertilise": "fertilize", + "fertilised": "fertilized", + "fertiliser": "fertilizer", + "fertilisers": "fertilizers", + "fertilises": "fertilizes", + "fertilising": "fertilizing", + "fervour": "fervor", + "fibre": "fiber", + "fibreglass": "fiberglass", + "fibres": "fibers", + "fictionalisation": "fictionalization", + "fictionalisations": "fictionalizations", + "fictionalise": "fictionalize", + "fictionalised": "fictionalized", + "fictionalises": "fictionalizes", + "fictionalising": "fictionalizing", + "fillet": "filet", + "filleted": "fileted", + "filleting": "fileting", + "fillets": "filets", + "finalisation": "finalization", + "finalise": "finalize", + "finalised": "finalized", + "finalises": "finalizes", + "finalising": "finalizing", + "flautist": "flutist", + "flautists": "flutists", + "flavour": "flavor", + "flavoured": "flavored", + "flavouring": "flavoring", + "flavourings": "flavorings", + "flavourless": "flavorless", + "flavours": "flavors", + "flavoursome": "flavorsome", + "flyer / flier": "flier / flyer", + "foetal": "fetal", + "foetid": "fetid", + "foetus": "fetus", + "foetuses": "fetuses", + "formalisation": "formalization", + "formalise": "formalize", + "formalised": "formalized", + "formalises": "formalizes", + "formalising": "formalizing", + "fossilisation": "fossilization", + "fossilise": "fossilize", + "fossilised": "fossilized", + "fossilises": "fossilizes", + "fossilising": "fossilizing", + "fraternisation": "fraternization", + "fraternise": "fraternize", + "fraternised": "fraternized", + "fraternises": "fraternizes", + "fraternising": "fraternizing", + "fulfil": "fulfill", + "fulfilment": "fulfillment", + "fulfils": "fulfills", + "funnelled": "funneled", + "funnelling": "funneling", + "galvanise": "galvanize", + "galvanised": "galvanized", + "galvanises": "galvanizes", + "galvanising": "galvanizing", + "gambolled": "gamboled", + "gambolling": "gamboling", + "gaol": "jail", + "gaolbird": "jailbird", + "gaolbirds": "jailbirds", + "gaolbreak": "jailbreak", + "gaolbreaks": "jailbreaks", + "gaoled": "jailed", + "gaoler": "jailer", + "gaolers": "jailers", + "gaoling": "jailing", + "gaols": "jails", + "gasses": "gases", + "gage": "gauge", + "gaged": "gauged", + "gages": "gauges", + "gaging": "gauging", + "generalisation": "generalization", + "generalisations": "generalizations", + "generalise": "generalize", + "generalised": "generalized", + "generalises": "generalizes", + "generalising": "generalizing", + "ghettoise": "ghettoize", + "ghettoised": "ghettoized", + "ghettoises": "ghettoizes", + "ghettoising": "ghettoizing", + "gipsies": "gypsies", + "glamorise": "glamorize", + "glamorised": "glamorized", + "glamorises": "glamorizes", + "glamorising": "glamorizing", + "glamor": "glamour", + "globalisation": "globalization", + "globalise": "globalize", + "globalised": "globalized", + "globalises": "globalizes", + "globalising": "globalizing", + "glueing": "gluing", + "goitre": "goiter", + "goitres": "goiters", + "gonorrhoea": "gonorrhea", + "gramme": "gram", + "grammes": "grams", + "gravelled": "graveled", + "grey": "gray", + "greyed": "grayed", + "greying": "graying", + "greyish": "grayish", + "greyness": "grayness", + "greys": "grays", + "grovelled": "groveled", + "grovelling": "groveling", + "groyne": "groin", + "groynes": "groins", + "gruelling": "grueling", + "gruellingly": "gruelingly", + "gryphon": "griffin", + "gryphons": "griffins", + "gynaecological": "gynecological", + "gynaecologist": "gynecologist", + "gynaecologists": "gynecologists", + "gynaecology": "gynecology", + "haematological": "hematological", + "haematologist": "hematologist", + "haematologists": "hematologists", + "haematology": "hematology", + "haemoglobin": "hemoglobin", + "haemophilia": "hemophilia", + "haemophiliac": "hemophiliac", + "haemophiliacs": "hemophiliacs", + "haemorrhage": "hemorrhage", + "haemorrhaged": "hemorrhaged", + "haemorrhages": "hemorrhages", + "haemorrhaging": "hemorrhaging", + "haemorrhoids": "hemorrhoids", + "harbour": "harbor", + "harboured": "harbored", + "harbouring": "harboring", + "harbours": "harbors", + "harmonisation": "harmonization", + "harmonise": "harmonize", + "harmonised": "harmonized", + "harmonises": "harmonizes", + "harmonising": "harmonizing", + "homoeopath": "homeopath", + "homoeopathic": "homeopathic", + "homoeopaths": "homeopaths", + "homoeopathy": "homeopathy", + "homogenise": "homogenize", + "homogenised": "homogenized", + "homogenises": "homogenizes", + "homogenising": "homogenizing", + "honour": "honor", + "honourable": "honorable", + "honourably": "honorably", + "honoured": "honored", + "honouring": "honoring", + "honours": "honors", + "hospitalisation": "hospitalization", + "hospitalise": "hospitalize", + "hospitalised": "hospitalized", + "hospitalises": "hospitalizes", + "hospitalising": "hospitalizing", + "humanise": "humanize", + "humanised": "humanized", + "humanises": "humanizes", + "humanising": "humanizing", + "humour": "humor", + "humoured": "humored", + "humouring": "humoring", + "humourless": "humorless", + "humours": "humors", + "hybridise": "hybridize", + "hybridised": "hybridized", + "hybridises": "hybridizes", + "hybridising": "hybridizing", + "hypnotise": "hypnotize", + "hypnotised": "hypnotized", + "hypnotises": "hypnotizes", + "hypnotising": "hypnotizing", + "hypothesise": "hypothesize", + "hypothesised": "hypothesized", + "hypothesises": "hypothesizes", + "hypothesising": "hypothesizing", + "idealisation": "idealization", + "idealise": "idealize", + "idealised": "idealized", + "idealises": "idealizes", + "idealising": "idealizing", + "idolise": "idolize", + "idolised": "idolized", + "idolises": "idolizes", + "idolising": "idolizing", + "immobilisation": "immobilization", + "immobilise": "immobilize", + "immobilised": "immobilized", + "immobiliser": "immobilizer", + "immobilisers": "immobilizers", + "immobilises": "immobilizes", + "immobilising": "immobilizing", + "immortalise": "immortalize", + "immortalised": "immortalized", + "immortalises": "immortalizes", + "immortalising": "immortalizing", + "immunisation": "immunization", + "immunise": "immunize", + "immunised": "immunized", + "immunises": "immunizes", + "immunising": "immunizing", + "impanelled": "impaneled", + "impanelling": "impaneling", + "imperilled": "imperiled", + "imperilling": "imperiling", + "individualise": "individualize", + "individualised": "individualized", + "individualises": "individualizes", + "individualising": "individualizing", + "industrialise": "industrialize", + "industrialised": "industrialized", + "industrialises": "industrializes", + "industrialising": "industrializing", + "inflexion": "inflection", + "inflexions": "inflections", + "initialise": "initialize", + "initialised": "initialized", + "initialises": "initializes", + "initialising": "initializing", + "initialled": "initialed", + "initialling": "initialing", + "instal": "install", + "instalment": "installment", + "instalments": "installments", + "instals": "installs", + "instil": "instill", + "instils": "instills", + "institutionalisation": "institutionalization", + "institutionalise": "institutionalize", + "institutionalised": "institutionalized", + "institutionalises": "institutionalizes", + "institutionalising": "institutionalizing", + "intellectualise": "intellectualize", + "intellectualised": "intellectualized", + "intellectualises": "intellectualizes", + "intellectualising": "intellectualizing", + "internalisation": "internalization", + "internalise": "internalize", + "internalised": "internalized", + "internalises": "internalizes", + "internalising": "internalizing", + "internationalisation": "internationalization", + "internationalise": "internationalize", + "internationalised": "internationalized", + "internationalises": "internationalizes", + "internationalising": "internationalizing", + "ionisation": "ionization", + "ionise": "ionize", + "ionised": "ionized", + "ioniser": "ionizer", + "ionisers": "ionizers", + "ionises": "ionizes", + "ionising": "ionizing", + "italicise": "italicize", + "italicised": "italicized", + "italicises": "italicizes", + "italicising": "italicizing", + "itemise": "itemize", + "itemised": "itemized", + "itemises": "itemizes", + "itemising": "itemizing", + "jeopardise": "jeopardize", + "jeopardised": "jeopardized", + "jeopardises": "jeopardizes", + "jeopardising": "jeopardizing", + "jewelled": "jeweled", + "jeweller": "jeweler", + "jewellers": "jewelers", + "jewellery": "jewelry", + "judgement": "judgment", + "kilogramme": "kilogram", + "kilogrammes": "kilograms", + "kilometre": "kilometer", + "kilometres": "kilometers", + "labelled": "labeled", + "labelling": "labeling", + "labour": "labor", + "laboured": "labored", + "labourer": "laborer", + "labourers": "laborers", + "labouring": "laboring", + "labours": "labors", + "lacklustre": "lackluster", + "legalisation": "legalization", + "legalise": "legalize", + "legalised": "legalized", + "legalises": "legalizes", + "legalising": "legalizing", + "legitimise": "legitimize", + "legitimised": "legitimized", + "legitimises": "legitimizes", + "legitimising": "legitimizing", + "leukaemia": "leukemia", + "levelled": "leveled", + "leveller": "leveler", + "levellers": "levelers", + "levelling": "leveling", + "libelled": "libeled", + "libelling": "libeling", + "libellous": "libelous", + "liberalisation": "liberalization", + "liberalise": "liberalize", + "liberalised": "liberalized", + "liberalises": "liberalizes", + "liberalising": "liberalizing", + "licence": "license", + "licenced": "licensed", + "licences": "licenses", + "licencing": "licensing", + "likeable": "likable", + "lionisation": "lionization", + "lionise": "lionize", + "lionised": "lionized", + "lionises": "lionizes", + "lionising": "lionizing", + "liquidise": "liquidize", + "liquidised": "liquidized", + "liquidiser": "liquidizer", + "liquidisers": "liquidizers", + "liquidises": "liquidizes", + "liquidising": "liquidizing", + "litre": "liter", + "litres": "liters", + "localise": "localize", + "localised": "localized", + "localises": "localizes", + "localising": "localizing", + "louvre": "louver", + "louvred": "louvered", + "louvres": "louvers", + "lustre": "luster", + "magnetise": "magnetize", + "magnetised": "magnetized", + "magnetises": "magnetizes", + "magnetising": "magnetizing", + "manoeuvrability": "maneuverability", + "manoeuvrable": "maneuverable", + "manoeuvre": "maneuver", + "manoeuvred": "maneuvered", + "manoeuvres": "maneuvers", + "manoeuvring": "maneuvering", + "manoeuvrings": "maneuverings", + "marginalisation": "marginalization", + "marginalise": "marginalize", + "marginalised": "marginalized", + "marginalises": "marginalizes", + "marginalising": "marginalizing", + "marshalled": "marshaled", + "marshalling": "marshaling", + "marvelled": "marveled", + "marvelling": "marveling", + "marvellous": "marvelous", + "marvellously": "marvelously", + "materialisation": "materialization", + "materialise": "materialize", + "materialised": "materialized", + "materialises": "materializes", + "materialising": "materializing", + "maximisation": "maximization", + "maximise": "maximize", + "maximised": "maximized", + "maximises": "maximizes", + "maximising": "maximizing", + "meagre": "meager", + "mechanisation": "mechanization", + "mechanise": "mechanize", + "mechanised": "mechanized", + "mechanises": "mechanizes", + "mechanising": "mechanizing", + "mediaeval": "medieval", + "memorialise": "memorialize", + "memorialised": "memorialized", + "memorialises": "memorializes", + "memorialising": "memorializing", + "memorise": "memorize", + "memorised": "memorized", + "memorises": "memorizes", + "memorising": "memorizing", + "mesmerise": "mesmerize", + "mesmerised": "mesmerized", + "mesmerises": "mesmerizes", + "mesmerising": "mesmerizing", + "metabolise": "metabolize", + "metabolised": "metabolized", + "metabolises": "metabolizes", + "metabolising": "metabolizing", + "metre": "meter", + "metres": "meters", + "micrometre": "micrometer", + "micrometres": "micrometers", + "militarise": "militarize", + "militarised": "militarized", + "militarises": "militarizes", + "militarising": "militarizing", + "milligramme": "milligram", + "milligrammes": "milligrams", + "millilitre": "milliliter", + "millilitres": "milliliters", + "millimetre": "millimeter", + "millimetres": "millimeters", + "miniaturisation": "miniaturization", + "miniaturise": "miniaturize", + "miniaturised": "miniaturized", + "miniaturises": "miniaturizes", + "miniaturising": "miniaturizing", + "minibusses": "minibuses", + "minimise": "minimize", + "minimised": "minimized", + "minimises": "minimizes", + "minimising": "minimizing", + "misbehaviour": "misbehavior", + "misdemeanour": "misdemeanor", + "misdemeanours": "misdemeanors", + "misspelt": "misspelled", + "mitre": "miter", + "mitres": "miters", + "mobilisation": "mobilization", + "mobilise": "mobilize", + "mobilised": "mobilized", + "mobilises": "mobilizes", + "mobilising": "mobilizing", + "modelled": "modeled", + "modeller": "modeler", + "modellers": "modelers", + "modelling": "modeling", + "modernise": "modernize", + "modernised": "modernized", + "modernises": "modernizes", + "modernising": "modernizing", + "moisturise": "moisturize", + "moisturised": "moisturized", + "moisturiser": "moisturizer", + "moisturisers": "moisturizers", + "moisturises": "moisturizes", + "moisturising": "moisturizing", + "monologue": "monolog", + "monologues": "monologs", + "monopolisation": "monopolization", + "monopolise": "monopolize", + "monopolised": "monopolized", + "monopolises": "monopolizes", + "monopolising": "monopolizing", + "moralise": "moralize", + "moralised": "moralized", + "moralises": "moralizes", + "moralising": "moralizing", + "motorised": "motorized", + "mould": "mold", + "moulded": "molded", + "moulder": "molder", + "mouldered": "moldered", + "mouldering": "moldering", + "moulders": "molders", + "mouldier": "moldier", + "mouldiest": "moldiest", + "moulding": "molding", + "mouldings": "moldings", + "moulds": "molds", + "mouldy": "moldy", + "moult": "molt", + "moulted": "molted", + "moulting": "molting", + "moults": "molts", + "moustache": "mustache", + "moustached": "mustached", + "moustaches": "mustaches", + "moustachioed": "mustachioed", + "multicoloured": "multicolored", + "nationalisation": "nationalization", + "nationalisations": "nationalizations", + "nationalise": "nationalize", + "nationalised": "nationalized", + "nationalises": "nationalizes", + "nationalising": "nationalizing", + "naturalisation": "naturalization", + "naturalise": "naturalize", + "naturalised": "naturalized", + "naturalises": "naturalizes", + "naturalising": "naturalizing", + "neighbour": "neighbor", + "neighbourhood": "neighborhood", + "neighbourhoods": "neighborhoods", + "neighbouring": "neighboring", + "neighbourliness": "neighborliness", + "neighbourly": "neighborly", + "neighbours": "neighbors", + "neutralisation": "neutralization", + "neutralise": "neutralize", + "neutralised": "neutralized", + "neutralises": "neutralizes", + "neutralising": "neutralizing", + "normalisation": "normalization", + "normalise": "normalize", + "normalised": "normalized", + "normalises": "normalizes", + "normalising": "normalizing", + "odour": "odor", + "odourless": "odorless", + "odours": "odors", + "oesophagus": "esophagus", + "oesophaguses": "esophaguses", + "oestrogen": "estrogen", + "offence": "offense", + "offences": "offenses", + "omelette": "omelet", + "omelettes": "omelets", + "optimise": "optimize", + "optimised": "optimized", + "optimises": "optimizes", + "optimising": "optimizing", + "organisation": "organization", + "organisational": "organizational", + "organisations": "organizations", + "organise": "organize", + "organised": "organized", + "organiser": "organizer", + "organisers": "organizers", + "organises": "organizes", + "organising": "organizing", + "orthopaedic": "orthopedic", + "orthopaedics": "orthopedics", + "ostracise": "ostracize", + "ostracised": "ostracized", + "ostracises": "ostracizes", + "ostracising": "ostracizing", + "outmanoeuvre": "outmaneuver", + "outmanoeuvred": "outmaneuvered", + "outmanoeuvres": "outmaneuvers", + "outmanoeuvring": "outmaneuvering", + "overemphasise": "overemphasize", + "overemphasised": "overemphasized", + "overemphasises": "overemphasizes", + "overemphasising": "overemphasizing", + "oxidisation": "oxidization", + "oxidise": "oxidize", + "oxidised": "oxidized", + "oxidises": "oxidizes", + "oxidising": "oxidizing", + "paederast": "pederast", + "paederasts": "pederasts", + "paediatric": "pediatric", + "paediatrician": "pediatrician", + "paediatricians": "pediatricians", + "paediatrics": "pediatrics", + "paedophile": "pedophile", + "paedophiles": "pedophiles", + "paedophilia": "pedophilia", + "palaeolithic": "paleolithic", + "palaeontologist": "paleontologist", + "palaeontologists": "paleontologists", + "palaeontology": "paleontology", + "panelled": "paneled", + "panelling": "paneling", + "panellist": "panelist", + "panellists": "panelists", + "paralyse": "paralyze", + "paralysed": "paralyzed", + "paralyses": "paralyzes", + "paralysing": "paralyzing", + "parcelled": "parceled", + "parcelling": "parceling", + "parlour": "parlor", + "parlours": "parlors", + "particularise": "particularize", + "particularised": "particularized", + "particularises": "particularizes", + "particularising": "particularizing", + "passivisation": "passivization", + "passivise": "passivize", + "passivised": "passivized", + "passivises": "passivizes", + "passivising": "passivizing", + "pasteurisation": "pasteurization", + "pasteurise": "pasteurize", + "pasteurised": "pasteurized", + "pasteurises": "pasteurizes", + "pasteurising": "pasteurizing", + "patronise": "patronize", + "patronised": "patronized", + "patronises": "patronizes", + "patronising": "patronizing", + "patronisingly": "patronizingly", + "pedalled": "pedaled", + "pedalling": "pedaling", + "pedestrianisation": "pedestrianization", + "pedestrianise": "pedestrianize", + "pedestrianised": "pedestrianized", + "pedestrianises": "pedestrianizes", + "pedestrianising": "pedestrianizing", + "penalise": "penalize", + "penalised": "penalized", + "penalises": "penalizes", + "penalising": "penalizing", + "pencilled": "penciled", + "pencilling": "penciling", + "personalise": "personalize", + "personalised": "personalized", + "personalises": "personalizes", + "personalising": "personalizing", + "pharmacopoeia": "pharmacopeia", + "pharmacopoeias": "pharmacopeias", + "philosophise": "philosophize", + "philosophised": "philosophized", + "philosophises": "philosophizes", + "philosophising": "philosophizing", + "philtre": "filter", + "philtres": "filters", + "phoney": "phony", + "plagiarise": "plagiarize", + "plagiarised": "plagiarized", + "plagiarises": "plagiarizes", + "plagiarising": "plagiarizing", + "plough": "plow", + "ploughed": "plowed", + "ploughing": "plowing", + "ploughman": "plowman", + "ploughmen": "plowmen", + "ploughs": "plows", + "ploughshare": "plowshare", + "ploughshares": "plowshares", + "polarisation": "polarization", + "polarise": "polarize", + "polarised": "polarized", + "polarises": "polarizes", + "polarising": "polarizing", + "politicisation": "politicization", + "politicise": "politicize", + "politicised": "politicized", + "politicises": "politicizes", + "politicising": "politicizing", + "popularisation": "popularization", + "popularise": "popularize", + "popularised": "popularized", + "popularises": "popularizes", + "popularising": "popularizing", + "pouffe": "pouf", + "pouffes": "poufs", + "practise": "practice", + "practised": "practiced", + "practises": "practices", + "practising": "practicing", + "praesidium": "presidium", + "praesidiums": "presidiums", + "pressurisation": "pressurization", + "pressurise": "pressurize", + "pressurised": "pressurized", + "pressurises": "pressurizes", + "pressurising": "pressurizing", + "pretence": "pretense", + "pretences": "pretenses", + "primaeval": "primeval", + "prioritisation": "prioritization", + "prioritise": "prioritize", + "prioritised": "prioritized", + "prioritises": "prioritizes", + "prioritising": "prioritizing", + "privatisation": "privatization", + "privatisations": "privatizations", + "privatise": "privatize", + "privatised": "privatized", + "privatises": "privatizes", + "privatising": "privatizing", + "professionalisation": "professionalization", + "professionalise": "professionalize", + "professionalised": "professionalized", + "professionalises": "professionalizes", + "professionalising": "professionalizing", + "programme": "program", + "programmes": "programs", + "prologue": "prolog", + "prologues": "prologs", + "propagandise": "propagandize", + "propagandised": "propagandized", + "propagandises": "propagandizes", + "propagandising": "propagandizing", + "proselytise": "proselytize", + "proselytised": "proselytized", + "proselytiser": "proselytizer", + "proselytisers": "proselytizers", + "proselytises": "proselytizes", + "proselytising": "proselytizing", + "psychoanalyse": "psychoanalyze", + "psychoanalysed": "psychoanalyzed", + "psychoanalyses": "psychoanalyzes", + "psychoanalysing": "psychoanalyzing", + "publicise": "publicize", + "publicised": "publicized", + "publicises": "publicizes", + "publicising": "publicizing", + "pulverisation": "pulverization", + "pulverise": "pulverize", + "pulverised": "pulverized", + "pulverises": "pulverizes", + "pulverising": "pulverizing", + "pummelled": "pummel", + "pummelling": "pummeled", + "pyjama": "pajama", + "pyjamas": "pajamas", + "pzazz": "pizzazz", + "quarrelled": "quarreled", + "quarrelling": "quarreling", + "radicalise": "radicalize", + "radicalised": "radicalized", + "radicalises": "radicalizes", + "radicalising": "radicalizing", + "rancour": "rancor", + "randomise": "randomize", + "randomised": "randomized", + "randomises": "randomizes", + "randomising": "randomizing", + "rationalisation": "rationalization", + "rationalisations": "rationalizations", + "rationalise": "rationalize", + "rationalised": "rationalized", + "rationalises": "rationalizes", + "rationalising": "rationalizing", + "ravelled": "raveled", + "ravelling": "raveling", + "realisable": "realizable", + "realisation": "realization", + "realisations": "realizations", + "realise": "realize", + "realised": "realized", + "realises": "realizes", + "realising": "realizing", + "recognisable": "recognizable", + "recognisably": "recognizably", + "recognisance": "recognizance", + "recognise": "recognize", + "recognised": "recognized", + "recognises": "recognizes", + "recognising": "recognizing", + "reconnoitre": "reconnoiter", + "reconnoitred": "reconnoitered", + "reconnoitres": "reconnoiters", + "reconnoitring": "reconnoitering", + "refuelled": "refueled", + "refuelling": "refueling", + "regularisation": "regularization", + "regularise": "regularize", + "regularised": "regularized", + "regularises": "regularizes", + "regularising": "regularizing", + "remodelled": "remodeled", + "remodelling": "remodeling", + "remould": "remold", + "remoulded": "remolded", + "remoulding": "remolding", + "remoulds": "remolds", + "reorganisation": "reorganization", + "reorganisations": "reorganizations", + "reorganise": "reorganize", + "reorganised": "reorganized", + "reorganises": "reorganizes", + "reorganising": "reorganizing", + "revelled": "reveled", + "reveller": "reveler", + "revellers": "revelers", + "revelling": "reveling", + "revitalise": "revitalize", + "revitalised": "revitalized", + "revitalises": "revitalizes", + "revitalising": "revitalizing", + "revolutionise": "revolutionize", + "revolutionised": "revolutionized", + "revolutionises": "revolutionizes", + "revolutionising": "revolutionizing", + "rhapsodise": "rhapsodize", + "rhapsodised": "rhapsodized", + "rhapsodises": "rhapsodizes", + "rhapsodising": "rhapsodizing", + "rigour": "rigor", + "rigours": "rigors", + "ritualised": "ritualized", + "rivalled": "rivaled", + "rivalling": "rivaling", + "romanticise": "romanticize", + "romanticised": "romanticized", + "romanticises": "romanticizes", + "romanticising": "romanticizing", + "rumour": "rumor", + "rumoured": "rumored", + "rumours": "rumors", + "sabre": "saber", + "sabres": "sabers", + "saltpetre": "saltpeter", + "sanitise": "sanitize", + "sanitised": "sanitized", + "sanitises": "sanitizes", + "sanitising": "sanitizing", + "satirise": "satirize", + "satirised": "satirized", + "satirises": "satirizes", + "satirising": "satirizing", + "saviour": "savior", + "saviours": "saviors", + "savour": "savor", + "savoured": "savored", + "savouries": "savories", + "savouring": "savoring", + "savours": "savors", + "savoury": "savory", + "scandalise": "scandalize", + "scandalised": "scandalized", + "scandalises": "scandalizes", + "scandalising": "scandalizing", + "sceptic": "skeptic", + "sceptical": "skeptical", + "sceptically": "skeptically", + "scepticism": "skepticism", + "sceptics": "skeptics", + "sceptre": "scepter", + "sceptres": "scepters", + "scrutinise": "scrutinize", + "scrutinised": "scrutinized", + "scrutinises": "scrutinizes", + "scrutinising": "scrutinizing", + "secularisation": "secularization", + "secularise": "secularize", + "secularised": "secularized", + "secularises": "secularizes", + "secularising": "secularizing", + "sensationalise": "sensationalize", + "sensationalised": "sensationalized", + "sensationalises": "sensationalizes", + "sensationalising": "sensationalizing", + "sensitise": "sensitize", + "sensitised": "sensitized", + "sensitises": "sensitizes", + "sensitising": "sensitizing", + "sentimentalise": "sentimentalize", + "sentimentalised": "sentimentalized", + "sentimentalises": "sentimentalizes", + "sentimentalising": "sentimentalizing", + "sepulchre": "sepulcher", + "sepulchres": "sepulchers", + "serialisation": "serialization", + "serialisations": "serializations", + "serialise": "serialize", + "serialised": "serialized", + "serialises": "serializes", + "serialising": "serializing", + "sermonise": "sermonize", + "sermonised": "sermonized", + "sermonises": "sermonizes", + "sermonising": "sermonizing", + "sheikh": "sheik", + "shovelled": "shoveled", + "shovelling": "shoveling", + "shrivelled": "shriveled", + "shrivelling": "shriveling", + "signalise": "signalize", + "signalised": "signalized", + "signalises": "signalizes", + "signalising": "signalizing", + "signalled": "signaled", + "signalling": "signaling", + "smoulder": "smolder", + "smouldered": "smoldered", + "smouldering": "smoldering", + "smoulders": "smolders", + "snivelled": "sniveled", + "snivelling": "sniveling", + "snorkelled": "snorkeled", + "snorkelling": "snorkeling", + "snowplough": "snowplow", + "snowploughs": "snowplow", + "socialisation": "socialization", + "socialise": "socialize", + "socialised": "socialized", + "socialises": "socializes", + "socialising": "socializing", + "sodomise": "sodomize", + "sodomised": "sodomized", + "sodomises": "sodomizes", + "sodomising": "sodomizing", + "solemnise": "solemnize", + "solemnised": "solemnized", + "solemnises": "solemnizes", + "solemnising": "solemnizing", + "sombre": "somber", + "specialisation": "specialization", + "specialisations": "specializations", + "specialise": "specialize", + "specialised": "specialized", + "specialises": "specializes", + "specialising": "specializing", + "spectre": "specter", + "spectres": "specters", + "spiralled": "spiraled", + "spiralling": "spiraling", + "splendour": "splendor", + "splendours": "splendors", + "squirrelled": "squirreled", + "squirrelling": "squirreling", + "stabilisation": "stabilization", + "stabilise": "stabilize", + "stabilised": "stabilized", + "stabiliser": "stabilizer", + "stabilisers": "stabilizers", + "stabilises": "stabilizes", + "stabilising": "stabilizing", + "standardisation": "standardization", + "standardise": "standardize", + "standardised": "standardized", + "standardises": "standardizes", + "standardising": "standardizing", + "stencilled": "stenciled", + "stencilling": "stenciling", + "sterilisation": "sterilization", + "sterilisations": "sterilizations", + "sterilise": "sterilize", + "sterilised": "sterilized", + "steriliser": "sterilizer", + "sterilisers": "sterilizers", + "sterilises": "sterilizes", + "sterilising": "sterilizing", + "stigmatisation": "stigmatization", + "stigmatise": "stigmatize", + "stigmatised": "stigmatized", + "stigmatises": "stigmatizes", + "stigmatising": "stigmatizing", + "storey": "story", + "storeys": "stories", + "subsidisation": "subsidization", + "subsidise": "subsidize", + "subsidised": "subsidized", + "subsidiser": "subsidizer", + "subsidisers": "subsidizers", + "subsidises": "subsidizes", + "subsidising": "subsidizing", + "succour": "succor", + "succoured": "succored", + "succouring": "succoring", + "succours": "succors", + "sulphate": "sulfate", + "sulphates": "sulfates", + "sulphide": "sulfide", + "sulphides": "sulfides", + "sulphur": "sulfur", + "sulphurous": "sulfurous", + "summarise": "summarize", + "summarised": "summarized", + "summarises": "summarizes", + "summarising": "summarizing", + "swivelled": "swiveled", + "swivelling": "swiveling", + "symbolise": "symbolize", + "symbolised": "symbolized", + "symbolises": "symbolizes", + "symbolising": "symbolizing", + "sympathise": "sympathize", + "sympathised": "sympathized", + "sympathiser": "sympathizer", + "sympathisers": "sympathizers", + "sympathises": "sympathizes", + "sympathising": "sympathizing", + "synchronisation": "synchronization", + "synchronise": "synchronize", + "synchronised": "synchronized", + "synchronises": "synchronizes", + "synchronising": "synchronizing", + "synthesise": "synthesize", + "synthesised": "synthesized", + "synthesiser": "synthesizer", + "synthesisers": "synthesizers", + "synthesises": "synthesizes", + "synthesising": "synthesizing", + "syphon": "siphon", + "syphoned": "siphoned", + "syphoning": "siphoning", + "syphons": "siphons", + "systematisation": "systematization", + "systematise": "systematize", + "systematised": "systematized", + "systematises": "systematizes", + "systematising": "systematizing", + "tantalise": "tantalize", + "tantalised": "tantalized", + "tantalises": "tantalizes", + "tantalising": "tantalizing", + "tantalisingly": "tantalizingly", + "tasselled": "tasseled", + "technicolour": "technicolor", + "temporise": "temporize", + "temporised": "temporized", + "temporises": "temporizes", + "temporising": "temporizing", + "tenderise": "tenderize", + "tenderised": "tenderized", + "tenderises": "tenderizes", + "tenderising": "tenderizing", + "terrorise": "terrorize", + "terrorised": "terrorized", + "terrorises": "terrorizes", + "terrorising": "terrorizing", + "theatre": "theater", + "theatregoer": "theatergoer", + "theatregoers": "theatergoers", + "theatres": "theaters", + "theorise": "theorize", + "theorised": "theorized", + "theorises": "theorizes", + "theorising": "theorizing", + "tonne": "ton", + "tonnes": "tons", + "towelled": "toweled", + "towelling": "toweling", + "toxaemia": "toxemia", + "tranquillise": "tranquilize", + "tranquillised": "tranquilized", + "tranquilliser": "tranquilizer", + "tranquillisers": "tranquilizers", + "tranquillises": "tranquilizes", + "tranquillising": "tranquilizing", + "tranquillity": "tranquility", + "tranquillize": "tranquilize", + "tranquillized": "tranquilized", + "tranquillizer": "tranquilizer", + "tranquillizers": "tranquilizers", + "tranquillizes": "tranquilizes", + "tranquillizing": "tranquilizing", + "tranquilly": "tranquility", + "transistorised": "transistorized", + "traumatise": "traumatize", + "traumatised": "traumatized", + "traumatises": "traumatizes", + "traumatising": "traumatizing", + "travelled": "traveled", + "traveller": "traveler", + "travellers": "travelers", + "travelling": "traveling", + "travelog": "travelogue", + "travelogs": "travelogues", + "trialled": "trialed", + "trialling": "trialing", + "tricolour": "tricolor", + "tricolours": "tricolors", + "trivialise": "trivialize", + "trivialised": "trivialized", + "trivialises": "trivializes", + "trivialising": "trivializing", + "tumour": "tumor", + "tumours": "tumors", + "tunnelled": "tunneled", + "tunnelling": "tunneling", + "tyrannise": "tyrannize", + "tyrannised": "tyrannized", + "tyrannises": "tyrannizes", + "tyrannising": "tyrannizing", + "tyre": "tire", + "tyres": "tires", + "unauthorised": "unauthorized", + "uncivilised": "uncivilized", + "underutilised": "underutilized", + "unequalled": "unequaled", + "unfavourable": "unfavorable", + "unfavourably": "unfavorably", + "unionisation": "unionization", + "unionise": "unionize", + "unionised": "unionized", + "unionises": "unionizes", + "unionising": "unionizing", + "unorganised": "unorganized", + "unravelled": "unraveled", + "unravelling": "unraveling", + "unrecognisable": "unrecognizable", + "unrecognised": "unrecognized", + "unrivalled": "unrivaled", + "unsavoury": "unsavory", + "untrammelled": "untrammeled", + "urbanisation": "urbanization", + "urbanise": "urbanize", + "urbanised": "urbanized", + "urbanises": "urbanizes", + "urbanising": "urbanizing", + "utilisable": "utilizable", + "utilisation": "utilization", + "utilise": "utilize", + "utilised": "utilized", + "utilises": "utilizes", + "utilising": "utilizing", + "valour": "valor", + "vandalise": "vandalize", + "vandalised": "vandalized", + "vandalises": "vandalizes", + "vandalising": "vandalizing", + "vaporisation": "vaporization", + "vaporise": "vaporize", + "vaporised": "vaporized", + "vaporises": "vaporizes", + "vaporising": "vaporizing", + "vapour": "vapor", + "vapours": "vapors", + "verbalise": "verbalize", + "verbalised": "verbalized", + "verbalises": "verbalizes", + "verbalising": "verbalizing", + "victimisation": "victimization", + "victimise": "victimize", + "victimised": "victimized", + "victimises": "victimizes", + "victimising": "victimizing", + "videodisc": "videodisk", + "videodiscs": "videodisks", + "vigour": "vigor", + "visualisation": "visualization", + "visualisations": "visualizations", + "visualise": "visualize", + "visualised": "visualized", + "visualises": "visualizes", + "visualising": "visualizing", + "vocalisation": "vocalization", + "vocalisations": "vocalizations", + "vocalise": "vocalize", + "vocalised": "vocalized", + "vocalises": "vocalizes", + "vocalising": "vocalizing", + "vulcanised": "vulcanized", + "vulgarisation": "vulgarization", + "vulgarise": "vulgarize", + "vulgarised": "vulgarized", + "vulgarises": "vulgarizes", + "vulgarising": "vulgarizing", + "waggon": "wagon", + "waggons": "wagons", + "watercolour": "watercolor", + "watercolours": "watercolors", + "weaselled": "weaseled", + "weaselling": "weaseling", + "westernisation": "westernization", + "westernise": "westernize", + "westernised": "westernized", + "westernises": "westernizes", + "westernising": "westernizing", + "womanise": "womanize", + "womanised": "womanized", + "womaniser": "womanizer", + "womanisers": "womanizers", + "womanises": "womanizes", + "womanising": "womanizing", + "woollen": "woolen", + "woollens": "woolens", + "woollies": "woolies", + "woolly": "wooly", + "worshipped": "worshiped", + "worshipping": "worshiping", + "worshipper": "worshiper", + "yodelled": "yodeled", + "yodelling": "yodeling", + "yoghourt": "yogurt", + "yoghourts": "yogurts", + "yoghurt": "yogurt", + "yoghurts": "yogurts", + "mhm": "hmm", + "mmm": "hmm" +} \ No newline at end of file diff --git a/modules/whisper_extractor/normalizers/english.py b/modules/whisper_extractor/normalizers/english.py new file mode 100644 index 0000000000000000000000000000000000000000..c602d3bc87d2c198d07e325489714751718b983e --- /dev/null +++ b/modules/whisper_extractor/normalizers/english.py @@ -0,0 +1,561 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import os +import re +from fractions import Fraction +from typing import Iterator, List, Match, Optional, Union + +from more_itertools import windowed + +from .basic import remove_symbols_and_diacritics + + +class EnglishNumberNormalizer: + """ + Convert any spelled-out numbers into arabic numbers, while handling: + + - remove any commas + - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc. + - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars` + - spell out `one` and `ones` + - interpret successive single-digit numbers as nominal: `one oh one` -> `101` + """ + + def __init__(self): + super().__init__() + + self.zeros = {"o", "oh", "zero"} + self.ones = { + name: i + for i, name in enumerate( + [ + "one", + "two", + "three", + "four", + "five", + "six", + "seven", + "eight", + "nine", + "ten", + "eleven", + "twelve", + "thirteen", + "fourteen", + "fifteen", + "sixteen", + "seventeen", + "eighteen", + "nineteen", + ], + start=1, + ) + } + self.ones_plural = { + "sixes" if name == "six" else name + "s": (value, "s") + for name, value in self.ones.items() + } + self.ones_ordinal = { + "zeroth": (0, "th"), + "first": (1, "st"), + "second": (2, "nd"), + "third": (3, "rd"), + "fifth": (5, "th"), + "twelfth": (12, "th"), + **{ + name + ("h" if name.endswith("t") else "th"): (value, "th") + for name, value in self.ones.items() + if value > 3 and value != 5 and value != 12 + }, + } + self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal} + + self.tens = { + "twenty": 20, + "thirty": 30, + "forty": 40, + "fifty": 50, + "sixty": 60, + "seventy": 70, + "eighty": 80, + "ninety": 90, + } + self.tens_plural = { + name.replace("y", "ies"): (value, "s") for name, value in self.tens.items() + } + self.tens_ordinal = { + name.replace("y", "ieth"): (value, "th") + for name, value in self.tens.items() + } + self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal} + + self.multipliers = { + "hundred": 100, + "thousand": 1_000, + "million": 1_000_000, + "billion": 1_000_000_000, + "trillion": 1_000_000_000_000, + "quadrillion": 1_000_000_000_000_000, + "quintillion": 1_000_000_000_000_000_000, + "sextillion": 1_000_000_000_000_000_000_000, + "septillion": 1_000_000_000_000_000_000_000_000, + "octillion": 1_000_000_000_000_000_000_000_000_000, + "nonillion": 1_000_000_000_000_000_000_000_000_000_000, + "decillion": 1_000_000_000_000_000_000_000_000_000_000_000, + } + self.multipliers_plural = { + name + "s": (value, "s") for name, value in self.multipliers.items() + } + self.multipliers_ordinal = { + name + "th": (value, "th") for name, value in self.multipliers.items() + } + self.multipliers_suffixed = { + **self.multipliers_plural, + **self.multipliers_ordinal, + } + self.decimals = {*self.ones, *self.tens, *self.zeros} + + self.preceding_prefixers = { + "minus": "-", + "negative": "-", + "plus": "+", + "positive": "+", + } + self.following_prefixers = { + "pound": "£", + "pounds": "£", + "euro": "€", + "euros": "€", + "dollar": "$", + "dollars": "$", + "cent": "¢", + "cents": "¢", + } + self.prefixes = set( + list(self.preceding_prefixers.values()) + + list(self.following_prefixers.values()) + ) + self.suffixers = { + "per": {"cent": "%"}, + "percent": "%", + } + self.specials = {"and", "double", "triple", "point"} + + self.words = set( + [ + key + for mapping in [ + self.zeros, + self.ones, + self.ones_suffixed, + self.tens, + self.tens_suffixed, + self.multipliers, + self.multipliers_suffixed, + self.preceding_prefixers, + self.following_prefixers, + self.suffixers, + self.specials, + ] + for key in mapping + ] + ) + self.literal_words = {"one", "ones"} + + def process_words(self, words: List[str]) -> Iterator[str]: + prefix: Optional[str] = None + value: Optional[Union[str, int]] = None + skip = False + + def to_fraction(s: str): + try: + return Fraction(s) + except ValueError: + return None + + def output(result: Union[str, int]): + nonlocal prefix, value + result = str(result) + if prefix is not None: + result = prefix + result + value = None + prefix = None + return result + + if len(words) == 0: + return + + for prev, current, next in windowed([None] + words + [None], 3): + if skip: + skip = False + continue + + next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next) + has_prefix = current[0] in self.prefixes + current_without_prefix = current[1:] if has_prefix else current + if re.match(r"^\d+(\.\d+)?$", current_without_prefix): + # arabic numbers (potentially with signs and fractions) + f = to_fraction(current_without_prefix) + assert f is not None + if value is not None: + if isinstance(value, str) and value.endswith("."): + # concatenate decimals / ip address components + value = str(value) + str(current) + continue + else: + yield output(value) + + prefix = current[0] if has_prefix else prefix + if f.denominator == 1: + value = f.numerator # store integers as int + else: + value = current_without_prefix + elif current not in self.words: + # non-numeric words + if value is not None: + yield output(value) + yield output(current) + elif current in self.zeros: + value = str(value or "") + "0" + elif current in self.ones: + ones = self.ones[current] + + if value is None: + value = ones + elif isinstance(value, str) or prev in self.ones: + if ( + prev in self.tens and ones < 10 + ): # replace the last zero with the digit + assert value[-1] == "0" + value = value[:-1] + str(ones) + else: + value = str(value) + str(ones) + elif ones < 10: + if value % 10 == 0: + value += ones + else: + value = str(value) + str(ones) + else: # eleven to nineteen + if value % 100 == 0: + value += ones + else: + value = str(value) + str(ones) + elif current in self.ones_suffixed: + # ordinal or cardinal; yield the number right away + ones, suffix = self.ones_suffixed[current] + if value is None: + yield output(str(ones) + suffix) + elif isinstance(value, str) or prev in self.ones: + if prev in self.tens and ones < 10: + assert value[-1] == "0" + yield output(value[:-1] + str(ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + elif ones < 10: + if value % 10 == 0: + yield output(str(value + ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + else: # eleven to nineteen + if value % 100 == 0: + yield output(str(value + ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + value = None + elif current in self.tens: + tens = self.tens[current] + if value is None: + value = tens + elif isinstance(value, str): + value = str(value) + str(tens) + else: + if value % 100 == 0: + value += tens + else: + value = str(value) + str(tens) + elif current in self.tens_suffixed: + # ordinal or cardinal; yield the number right away + tens, suffix = self.tens_suffixed[current] + if value is None: + yield output(str(tens) + suffix) + elif isinstance(value, str): + yield output(str(value) + str(tens) + suffix) + else: + if value % 100 == 0: + yield output(str(value + tens) + suffix) + else: + yield output(str(value) + str(tens) + suffix) + elif current in self.multipliers: + multiplier = self.multipliers[current] + if value is None: + value = multiplier + elif isinstance(value, str) or value == 0: + f = to_fraction(value) + p = f * multiplier if f is not None else None + if f is not None and p.denominator == 1: + value = p.numerator + else: + yield output(value) + value = multiplier + else: + before = value // 1000 * 1000 + residual = value % 1000 + value = before + residual * multiplier + elif current in self.multipliers_suffixed: + multiplier, suffix = self.multipliers_suffixed[current] + if value is None: + yield output(str(multiplier) + suffix) + elif isinstance(value, str): + f = to_fraction(value) + p = f * multiplier if f is not None else None + if f is not None and p.denominator == 1: + yield output(str(p.numerator) + suffix) + else: + yield output(value) + yield output(str(multiplier) + suffix) + else: # int + before = value // 1000 * 1000 + residual = value % 1000 + value = before + residual * multiplier + yield output(str(value) + suffix) + value = None + elif current in self.preceding_prefixers: + # apply prefix (positive, minus, etc.) if it precedes a number + if value is not None: + yield output(value) + + if next in self.words or next_is_numeric: + prefix = self.preceding_prefixers[current] + else: + yield output(current) + elif current in self.following_prefixers: + # apply prefix (dollars, cents, etc.) only after a number + if value is not None: + prefix = self.following_prefixers[current] + yield output(value) + else: + yield output(current) + elif current in self.suffixers: + # apply suffix symbols (percent -> '%') + if value is not None: + suffix = self.suffixers[current] + if isinstance(suffix, dict): + if next in suffix: + yield output(str(value) + suffix[next]) + skip = True + else: + yield output(value) + yield output(current) + else: + yield output(str(value) + suffix) + else: + yield output(current) + elif current in self.specials: + if next not in self.words and not next_is_numeric: + # apply special handling only if the next word can be numeric + if value is not None: + yield output(value) + yield output(current) + elif current == "and": + # ignore "and" after hundreds, thousands, etc. + if prev not in self.multipliers: + if value is not None: + yield output(value) + yield output(current) + elif current == "double" or current == "triple": + if next in self.ones or next in self.zeros: + repeats = 2 if current == "double" else 3 + ones = self.ones.get(next, 0) + value = str(value or "") + str(ones) * repeats + skip = True + else: + if value is not None: + yield output(value) + yield output(current) + elif current == "point": + if next in self.decimals or next_is_numeric: + value = str(value or "") + "." + else: + # should all have been covered at this point + raise ValueError(f"Unexpected token: {current}") + else: + # all should have been covered at this point + raise ValueError(f"Unexpected token: {current}") + + if value is not None: + yield output(value) + + def preprocess(self, s: str): + # replace " and a half" with " point five" + results = [] + + segments = re.split(r"\band\s+a\s+half\b", s) + for i, segment in enumerate(segments): + if len(segment.strip()) == 0: + continue + if i == len(segments) - 1: + results.append(segment) + else: + results.append(segment) + last_word = segment.rsplit(maxsplit=2)[-1] + if last_word in self.decimals or last_word in self.multipliers: + results.append("point five") + else: + results.append("and a half") + + s = " ".join(results) + + # put a space at number/letter boundary + s = re.sub(r"([a-z])([0-9])", r"\1 \2", s) + s = re.sub(r"([0-9])([a-z])", r"\1 \2", s) + + # but remove spaces which could be a suffix + s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s) + + return s + + def postprocess(self, s: str): + def combine_cents(m: Match): + try: + currency = m.group(1) + integer = m.group(2) + cents = int(m.group(3)) + return f"{currency}{integer}.{cents:02d}" + except ValueError: + return m.string + + def extract_cents(m: Match): + try: + return f"¢{int(m.group(1))}" + except ValueError: + return m.string + + # apply currency postprocessing; "$2 and ¢7" -> "$2.07" + s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s) + s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s) + + # write "one(s)" instead of "1(s)", just for the readability + s = re.sub(r"\b1(s?)\b", r"one\1", s) + + return s + + def __call__(self, s: str): + s = self.preprocess(s) + s = " ".join(word for word in self.process_words(s.split()) if word is not None) + s = self.postprocess(s) + + return s + + +class EnglishSpellingNormalizer: + """ + Applies British-American spelling mappings as listed in [1]. + + [1] https://www.tysto.com/uk-us-spelling-list.html + """ + + def __init__(self): + mapping_path = os.path.join(os.path.dirname(__file__), "english.json") + self.mapping = json.load(open(mapping_path)) + + def __call__(self, s: str): + return " ".join(self.mapping.get(word, word) for word in s.split()) + + +class EnglishTextNormalizer: + def __init__(self): + self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b" + self.replacers = { + # common contractions + r"\bwon't\b": "will not", + r"\bcan't\b": "can not", + r"\blet's\b": "let us", + r"\bain't\b": "aint", + r"\by'all\b": "you all", + r"\bwanna\b": "want to", + r"\bgotta\b": "got to", + r"\bgonna\b": "going to", + r"\bi'ma\b": "i am going to", + r"\bimma\b": "i am going to", + r"\bwoulda\b": "would have", + r"\bcoulda\b": "could have", + r"\bshoulda\b": "should have", + r"\bma'am\b": "madam", + # contractions in titles/prefixes + r"\bmr\b": "mister ", + r"\bmrs\b": "missus ", + r"\bst\b": "saint ", + r"\bdr\b": "doctor ", + r"\bprof\b": "professor ", + r"\bcapt\b": "captain ", + r"\bgov\b": "governor ", + r"\bald\b": "alderman ", + r"\bgen\b": "general ", + r"\bsen\b": "senator ", + r"\brep\b": "representative ", + r"\bpres\b": "president ", + r"\brev\b": "reverend ", + r"\bhon\b": "honorable ", + r"\basst\b": "assistant ", + r"\bassoc\b": "associate ", + r"\blt\b": "lieutenant ", + r"\bcol\b": "colonel ", + r"\bjr\b": "junior ", + r"\bsr\b": "senior ", + r"\besq\b": "esquire ", + # prefect tenses, ideally it should be any past participles, but it's harder.. + r"'d been\b": " had been", + r"'s been\b": " has been", + r"'d gone\b": " had gone", + r"'s gone\b": " has gone", + r"'d done\b": " had done", # "'s done" is ambiguous + r"'s got\b": " has got", + # general contractions + r"n't\b": " not", + r"'re\b": " are", + r"'s\b": " is", + r"'d\b": " would", + r"'ll\b": " will", + r"'t\b": " not", + r"'ve\b": " have", + r"'m\b": " am", + } + self.standardize_numbers = EnglishNumberNormalizer() + self.standardize_spellings = EnglishSpellingNormalizer() + + def __call__(self, s: str): + s = s.lower() + + s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets + s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis + s = re.sub(self.ignore_patterns, "", s) + s = re.sub( + r"\s+'", "'", s + ) # standardize when there's a space before an apostrophe + + for pattern, replacement in self.replacers.items(): + s = re.sub(pattern, replacement, s) + + s = re.sub(r"(\d),(\d)", r"\1\2", s) # remove commas between digits + s = re.sub(r"\.([^0-9]|$)", r" \1", s) # remove periods not followed by numbers + s = remove_symbols_and_diacritics( + s, keep=".%$¢€£" + ) # keep some symbols for numerics + + s = self.standardize_numbers(s) + s = self.standardize_spellings(s) + + # now remove prefix/suffix symbols that are not preceded/followed by numbers + s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s) + s = re.sub(r"([^0-9])%", r"\1 ", s) + + s = re.sub( + r"\s+", " ", s + ) # replace any successive whitespace characters with a space + + return s diff --git a/modules/whisper_extractor/tokenizer.py b/modules/whisper_extractor/tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..ba851f686d9a70fb924e480c20678ef978a5482b --- /dev/null +++ b/modules/whisper_extractor/tokenizer.py @@ -0,0 +1,370 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This module is modified from [Whisper](https://github.com/openai/whisper.git). + +# ## Citations + +# ```bibtex +# @inproceedings{openai-whisper, +# author = {Alec Radford and +# Jong Wook Kim and +# Tao Xu and +# Greg Brockman and +# Christine McLeavey and +# Ilya Sutskever}, +# title = {Robust Speech Recognition via Large-Scale Weak Supervision}, +# booktitle = {{ICML}}, +# series = {Proceedings of Machine Learning Research}, +# volume = {202}, +# pages = {28492--28518}, +# publisher = {{PMLR}}, +# year = {2023} +# } +# ``` +# + +import os +from dataclasses import dataclass +from functools import lru_cache +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from transformers import GPT2TokenizerFast + +LANGUAGES = { + "en": "english", + "zh": "chinese", + "de": "german", + "es": "spanish", + "ru": "russian", + "ko": "korean", + "fr": "french", + "ja": "japanese", + "pt": "portuguese", + "tr": "turkish", + "pl": "polish", + "ca": "catalan", + "nl": "dutch", + "ar": "arabic", + "sv": "swedish", + "it": "italian", + "id": "indonesian", + "hi": "hindi", + "fi": "finnish", + "vi": "vietnamese", + "he": "hebrew", + "uk": "ukrainian", + "el": "greek", + "ms": "malay", + "cs": "czech", + "ro": "romanian", + "da": "danish", + "hu": "hungarian", + "ta": "tamil", + "no": "norwegian", + "th": "thai", + "ur": "urdu", + "hr": "croatian", + "bg": "bulgarian", + "lt": "lithuanian", + "la": "latin", + "mi": "maori", + "ml": "malayalam", + "cy": "welsh", + "sk": "slovak", + "te": "telugu", + "fa": "persian", + "lv": "latvian", + "bn": "bengali", + "sr": "serbian", + "az": "azerbaijani", + "sl": "slovenian", + "kn": "kannada", + "et": "estonian", + "mk": "macedonian", + "br": "breton", + "eu": "basque", + "is": "icelandic", + "hy": "armenian", + "ne": "nepali", + "mn": "mongolian", + "bs": "bosnian", + "kk": "kazakh", + "sq": "albanian", + "sw": "swahili", + "gl": "galician", + "mr": "marathi", + "pa": "punjabi", + "si": "sinhala", + "km": "khmer", + "sn": "shona", + "yo": "yoruba", + "so": "somali", + "af": "afrikaans", + "oc": "occitan", + "ka": "georgian", + "be": "belarusian", + "tg": "tajik", + "sd": "sindhi", + "gu": "gujarati", + "am": "amharic", + "yi": "yiddish", + "lo": "lao", + "uz": "uzbek", + "fo": "faroese", + "ht": "haitian creole", + "ps": "pashto", + "tk": "turkmen", + "nn": "nynorsk", + "mt": "maltese", + "sa": "sanskrit", + "lb": "luxembourgish", + "my": "myanmar", + "bo": "tibetan", + "tl": "tagalog", + "mg": "malagasy", + "as": "assamese", + "tt": "tatar", + "haw": "hawaiian", + "ln": "lingala", + "ha": "hausa", + "ba": "bashkir", + "jw": "javanese", + "su": "sundanese", +} + +# language code lookup by name, with a few language aliases +TO_LANGUAGE_CODE = { + **{language: code for code, language in LANGUAGES.items()}, + "burmese": "my", + "valencian": "ca", + "flemish": "nl", + "haitian": "ht", + "letzeburgesch": "lb", + "pushto": "ps", + "panjabi": "pa", + "moldavian": "ro", + "moldovan": "ro", + "sinhalese": "si", + "castilian": "es", +} + + +@dataclass(frozen=True) +class Tokenizer: + """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens""" + + tokenizer: "GPT2TokenizerFast" + language: Optional[str] + sot_sequence: Tuple[int] + + def encode(self, text, **kwargs): + return self.tokenizer.encode(text, **kwargs) + + def decode( + self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs + ): + return self.tokenizer.decode(token_ids, **kwargs) + + def decode_with_timestamps(self, tokens) -> str: + """ + Timestamp tokens are above the special tokens' id range and are ignored by `decode()`. + This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>". + """ + outputs = [[]] + for token in tokens: + if token >= self.timestamp_begin: + timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>" + outputs.append(timestamp) + outputs.append([]) + else: + outputs[-1].append(token) + outputs = [ + s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs + ] + return "".join(outputs) + + @property + @lru_cache() + def eot(self) -> int: + return self.tokenizer.eos_token_id + + @property + @lru_cache() + def sot(self) -> int: + return self._get_single_token_id("<|startoftranscript|>") + + @property + @lru_cache() + def sot_lm(self) -> int: + return self._get_single_token_id("<|startoflm|>") + + @property + @lru_cache() + def sot_prev(self) -> int: + return self._get_single_token_id("<|startofprev|>") + + @property + @lru_cache() + def no_speech(self) -> int: + return self._get_single_token_id("<|nospeech|>") + + @property + @lru_cache() + def no_timestamps(self) -> int: + return self._get_single_token_id("<|notimestamps|>") + + @property + @lru_cache() + def timestamp_begin(self) -> int: + return self.tokenizer.all_special_ids[-1] + 1 + + @property + @lru_cache() + def language_token(self) -> int: + """Returns the token id corresponding to the value of the `language` field""" + if self.language is None: + raise ValueError(f"This tokenizer does not have language token configured") + + additional_tokens = dict( + zip( + self.tokenizer.additional_special_tokens, + self.tokenizer.additional_special_tokens_ids, + ) + ) + candidate = f"<|{self.language}|>" + if candidate in additional_tokens: + return additional_tokens[candidate] + + raise KeyError(f"Language {self.language} not found in tokenizer.") + + @property + @lru_cache() + def all_language_tokens(self) -> Tuple[int]: + result = [] + for token, token_id in zip( + self.tokenizer.additional_special_tokens, + self.tokenizer.additional_special_tokens_ids, + ): + if token.strip("<|>") in LANGUAGES: + result.append(token_id) + return tuple(result) + + @property + @lru_cache() + def all_language_codes(self) -> Tuple[str]: + return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens) + + @property + @lru_cache() + def sot_sequence_including_notimestamps(self) -> Tuple[int]: + return tuple(list(self.sot_sequence) + [self.no_timestamps]) + + @property + @lru_cache() + def non_speech_tokens(self) -> Tuple[int]: + """ + Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech + annotations, to prevent sampling texts that are not actually spoken in the audio, e.g. + + - ♪♪♪ + - ( SPEAKING FOREIGN LANGUAGE ) + - [DAVID] Hey there, + + keeping basic punctuations like commas, periods, question marks, exclamation points, etc. + """ + symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』') + symbols += ( + "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split() + ) + + # symbols that may be a single token or multiple tokens depending on the tokenizer. + # In case they're multiple tokens, suppress the first token, which is safe because: + # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress + # in generations, and in the 3-byte UTF-8 representation they share the first two bytes. + miscellaneous = set("♩♪♫♬♭♮♯") + assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous) + + # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word + result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]} + for symbol in symbols + list(miscellaneous): + for tokens in [ + self.tokenizer.encode(symbol), + self.tokenizer.encode(" " + symbol), + ]: + if len(tokens) == 1 or symbol in miscellaneous: + result.add(tokens[0]) + + return tuple(sorted(result)) + + def _get_single_token_id(self, text) -> int: + tokens = self.tokenizer.encode(text) + assert len(tokens) == 1, f"{text} is not encoded as a single token" + return tokens[0] + + +@lru_cache(maxsize=None) +def build_tokenizer(name: str = "gpt2"): + os.environ["TOKENIZERS_PARALLELISM"] = "false" + path = os.path.join(os.path.dirname(__file__), "assets", name) + tokenizer = GPT2TokenizerFast.from_pretrained(path) + + specials = [ + "<|startoftranscript|>", + *[f"<|{lang}|>" for lang in LANGUAGES.keys()], + "<|translate|>", + "<|transcribe|>", + "<|startoflm|>", + "<|startofprev|>", + "<|nospeech|>", + "<|notimestamps|>", + ] + + tokenizer.add_special_tokens(dict(additional_special_tokens=specials)) + return tokenizer + + +@lru_cache(maxsize=None) +def get_tokenizer( + multilingual: bool, + *, + task: Optional[str] = None, # Literal["transcribe", "translate", None] + language: Optional[str] = None, +) -> Tokenizer: + if language is not None: + language = language.lower() + if language not in LANGUAGES: + if language in TO_LANGUAGE_CODE: + language = TO_LANGUAGE_CODE[language] + else: + raise ValueError(f"Unsupported language: {language}") + + if multilingual: + tokenizer_name = "multilingual" + task = task or "transcribe" + language = language or "en" + else: + tokenizer_name = "gpt2" + task = None + language = None + + tokenizer = build_tokenizer(name=tokenizer_name) + all_special_ids: List[int] = tokenizer.all_special_ids + sot: int = all_special_ids[1] + translate: int = all_special_ids[-6] + transcribe: int = all_special_ids[-5] + + langs = tuple(LANGUAGES.keys()) + sot_sequence = [sot] + if language is not None: + sot_sequence.append(sot + 1 + langs.index(language)) + if task is not None: + sot_sequence.append(transcribe if task == "transcribe" else translate) + + return Tokenizer( + tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence) + ) diff --git a/modules/whisper_extractor/transcribe.py b/modules/whisper_extractor/transcribe.py new file mode 100644 index 0000000000000000000000000000000000000000..b00e909bdb5e0abdec24a80b09ce45a198a72d6c --- /dev/null +++ b/modules/whisper_extractor/transcribe.py @@ -0,0 +1,524 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This module is modified from [Whisper](https://github.com/openai/whisper.git). + +# ## Citations + +# ```bibtex +# @inproceedings{openai-whisper, +# author = {Alec Radford and +# Jong Wook Kim and +# Tao Xu and +# Greg Brockman and +# Christine McLeavey and +# Ilya Sutskever}, +# title = {Robust Speech Recognition via Large-Scale Weak Supervision}, +# booktitle = {{ICML}}, +# series = {Proceedings of Machine Learning Research}, +# volume = {202}, +# pages = {28492--28518}, +# publisher = {{PMLR}}, +# year = {2023} +# } +# ``` +# + +import argparse +import os +import sys +import warnings +from typing import List, Optional, Tuple, Union, TYPE_CHECKING + +import numpy as np +import torch +import tqdm + +from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram +from .decoding import DecodingOptions, DecodingResult +from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer +from .utils import ( + exact_div, + format_timestamp, + optional_int, + optional_float, + str2bool, + write_txt, + write_vtt, + write_srt, +) + +if TYPE_CHECKING: + from .model import Whisper + + +def transcribe( + model: "Whisper", + audio: Union[str, np.ndarray, torch.Tensor], + *, + verbose: Optional[bool] = None, + temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), + compression_ratio_threshold: Optional[float] = 2.4, + logprob_threshold: Optional[float] = -1.0, + no_speech_threshold: Optional[float] = 0.6, + condition_on_previous_text: bool = True, + **decode_options, +): + """ + Transcribe an audio file using Whisper + + Parameters + ---------- + model: Whisper + The Whisper model instance + + audio: Union[str, np.ndarray, torch.Tensor] + The path to the audio file to open, or the audio waveform + + verbose: bool + Whether to display the text being decoded to the console. If True, displays all the details, + If False, displays minimal details. If None, does not display anything + + temperature: Union[float, Tuple[float, ...]] + Temperature for sampling. It can be a tuple of temperatures, which will be successively used + upon failures according to either `compression_ratio_threshold` or `logprob_threshold`. + + compression_ratio_threshold: float + If the gzip compression ratio is above this value, treat as failed + + logprob_threshold: float + If the average log probability over sampled tokens is below this value, treat as failed + + no_speech_threshold: float + If the no_speech probability is higher than this value AND the average log probability + over sampled tokens is below `logprob_threshold`, consider the segment as silent + + condition_on_previous_text: bool + if True, the previous output of the model is provided as a prompt for the next window; + disabling may make the text inconsistent across windows, but the model becomes less prone to + getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. + + decode_options: dict + Keyword arguments to construct `DecodingOptions` instances + + Returns + ------- + A dictionary containing the resulting text ("text") and segment-level details ("segments"), and + the spoken language ("language"), which is detected when `decode_options["language"]` is None. + """ + dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32 + if model.device == torch.device("cpu"): + if torch.cuda.is_available(): + warnings.warn("Performing inference on CPU when CUDA is available") + if dtype == torch.float16: + warnings.warn("FP16 is not supported on CPU; using FP32 instead") + dtype = torch.float32 + + if dtype == torch.float32: + decode_options["fp16"] = False + + mel = log_mel_spectrogram(audio) + + if decode_options.get("language", None) is None: + if not model.is_multilingual: + decode_options["language"] = "en" + else: + if verbose: + print( + "Detecting language using up to the first 30 seconds. Use `--language` to specify the language" + ) + segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype) + _, probs = model.detect_language(segment) + decode_options["language"] = max(probs, key=probs.get) + if verbose is not None: + print( + f"Detected language: {LANGUAGES[decode_options['language']].title()}" + ) + + language = decode_options["language"] + task = decode_options.get("task", "transcribe") + tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task) + + def decode_with_fallback(segment: torch.Tensor) -> DecodingResult: + temperatures = ( + [temperature] if isinstance(temperature, (int, float)) else temperature + ) + decode_result = None + + for t in temperatures: + kwargs = {**decode_options} + if t > 0: + # disable beam_size and patience when t > 0 + kwargs.pop("beam_size", None) + kwargs.pop("patience", None) + else: + # disable best_of when t == 0 + kwargs.pop("best_of", None) + + options = DecodingOptions(**kwargs, temperature=t) + decode_result = model.decode(segment, options) + + needs_fallback = False + if ( + compression_ratio_threshold is not None + and decode_result.compression_ratio > compression_ratio_threshold + ): + needs_fallback = True # too repetitive + if ( + logprob_threshold is not None + and decode_result.avg_logprob < logprob_threshold + ): + needs_fallback = True # average log probability is too low + + if not needs_fallback: + break + + return decode_result + + seek = 0 + input_stride = exact_div( + N_FRAMES, model.dims.n_audio_ctx + ) # mel frames per output token: 2 + time_precision = ( + input_stride * HOP_LENGTH / SAMPLE_RATE + ) # time per output token: 0.02 (seconds) + all_tokens = [] + all_segments = [] + prompt_reset_since = 0 + + initial_prompt = decode_options.pop("initial_prompt", None) or [] + if initial_prompt: + initial_prompt = tokenizer.encode(" " + initial_prompt.strip()) + all_tokens.extend(initial_prompt) + + def add_segment( + *, start: float, end: float, text_tokens: torch.Tensor, result: DecodingResult + ): + text = tokenizer.decode( + [token for token in text_tokens if token < tokenizer.eot] + ) + if len(text.strip()) == 0: # skip empty text output + return + + all_segments.append( + { + "id": len(all_segments), + "seek": seek, + "start": start, + "end": end, + "text": text, + "tokens": text_tokens.tolist(), + "temperature": result.temperature, + "avg_logprob": result.avg_logprob, + "compression_ratio": result.compression_ratio, + "no_speech_prob": result.no_speech_prob, + } + ) + if verbose: + line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}\n" + # compared to just `print(line)`, this replaces any character not representable using + # the system default encoding with an '?', avoiding UnicodeEncodeError. + sys.stdout.buffer.write( + line.encode(sys.getdefaultencoding(), errors="replace") + ) + sys.stdout.flush() + + # show the progress bar when verbose is False (otherwise the transcribed text will be printed) + num_frames = mel.shape[-1] + previous_seek_value = seek + + with tqdm.tqdm( + total=num_frames, unit="frames", disable=verbose is not False + ) as pbar: + while seek < num_frames: + timestamp_offset = float(seek * HOP_LENGTH / SAMPLE_RATE) + segment = pad_or_trim(mel[:, seek:], N_FRAMES).to(model.device).to(dtype) + segment_duration = segment.shape[-1] * HOP_LENGTH / SAMPLE_RATE + + decode_options["prompt"] = all_tokens[prompt_reset_since:] + result: DecodingResult = decode_with_fallback(segment) + tokens = torch.tensor(result.tokens) + + if no_speech_threshold is not None: + # no voice activity check + should_skip = result.no_speech_prob > no_speech_threshold + if ( + logprob_threshold is not None + and result.avg_logprob > logprob_threshold + ): + # don't skip if the logprob is high enough, despite the no_speech_prob + should_skip = False + + if should_skip: + seek += segment.shape[ + -1 + ] # fast-forward to the next segment boundary + continue + + timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin) + consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[ + 0 + ].add_(1) + if ( + len(consecutive) > 0 + ): # if the output contains two consecutive timestamp tokens + last_slice = 0 + for current_slice in consecutive: + sliced_tokens = tokens[last_slice:current_slice] + start_timestamp_position = ( + sliced_tokens[0].item() - tokenizer.timestamp_begin + ) + end_timestamp_position = ( + sliced_tokens[-1].item() - tokenizer.timestamp_begin + ) + add_segment( + start=timestamp_offset + + start_timestamp_position * time_precision, + end=timestamp_offset + end_timestamp_position * time_precision, + text_tokens=sliced_tokens[1:-1], + result=result, + ) + last_slice = current_slice + last_timestamp_position = ( + tokens[last_slice - 1].item() - tokenizer.timestamp_begin + ) + seek += last_timestamp_position * input_stride + all_tokens.extend(tokens[: last_slice + 1].tolist()) + else: + duration = segment_duration + timestamps = tokens[timestamp_tokens.nonzero().flatten()] + if ( + len(timestamps) > 0 + and timestamps[-1].item() != tokenizer.timestamp_begin + ): + # no consecutive timestamps but it has a timestamp; use the last one. + # single timestamp at the end means no speech after the last timestamp. + last_timestamp_position = ( + timestamps[-1].item() - tokenizer.timestamp_begin + ) + duration = last_timestamp_position * time_precision + + add_segment( + start=timestamp_offset, + end=timestamp_offset + duration, + text_tokens=tokens, + result=result, + ) + + seek += segment.shape[-1] + all_tokens.extend(tokens.tolist()) + + if not condition_on_previous_text or result.temperature > 0.5: + # do not feed the prompt tokens if a high temperature was used + prompt_reset_since = len(all_tokens) + + # update progress bar + pbar.update(min(num_frames, seek) - previous_seek_value) + previous_seek_value = seek + + return dict( + text=tokenizer.decode(all_tokens[len(initial_prompt) :]), + segments=all_segments, + language=language, + ) + + +def cli(): + from . import available_models + + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + parser.add_argument( + "audio", nargs="+", type=str, help="audio file(s) to transcribe" + ) + parser.add_argument( + "--model", + default="small", + choices=available_models(), + help="name of the Whisper model to use", + ) + parser.add_argument( + "--model_dir", + type=str, + default=None, + help="the path to save model files; uses ~/.cache/whisper by default", + ) + parser.add_argument( + "--device", + default="cuda" if torch.cuda.is_available() else "cpu", + help="device to use for PyTorch inference", + ) + parser.add_argument( + "--output_dir", + "-o", + type=str, + default=".", + help="directory to save the outputs", + ) + parser.add_argument( + "--verbose", + type=str2bool, + default=True, + help="whether to print out the progress and debug messages", + ) + + parser.add_argument( + "--task", + type=str, + default="transcribe", + choices=["transcribe", "translate"], + help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')", + ) + parser.add_argument( + "--language", + type=str, + default=None, + choices=sorted(LANGUAGES.keys()) + + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), + help="language spoken in the audio, specify None to perform language detection", + ) + + parser.add_argument( + "--temperature", type=float, default=0, help="temperature to use for sampling" + ) + parser.add_argument( + "--best_of", + type=optional_int, + default=5, + help="number of candidates when sampling with non-zero temperature", + ) + parser.add_argument( + "--beam_size", + type=optional_int, + default=5, + help="number of beams in beam search, only applicable when temperature is zero", + ) + parser.add_argument( + "--patience", + type=float, + default=None, + help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search", + ) + parser.add_argument( + "--length_penalty", + type=float, + default=None, + help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default", + ) + + parser.add_argument( + "--suppress_tokens", + type=str, + default="-1", + help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations", + ) + parser.add_argument( + "--initial_prompt", + type=str, + default=None, + help="optional text to provide as a prompt for the first window.", + ) + parser.add_argument( + "--condition_on_previous_text", + type=str2bool, + default=True, + help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop", + ) + parser.add_argument( + "--fp16", + type=str2bool, + default=True, + help="whether to perform inference in fp16; True by default", + ) + + parser.add_argument( + "--temperature_increment_on_fallback", + type=optional_float, + default=0.2, + help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below", + ) + parser.add_argument( + "--compression_ratio_threshold", + type=optional_float, + default=2.4, + help="if the gzip compression ratio is higher than this value, treat the decoding as failed", + ) + parser.add_argument( + "--logprob_threshold", + type=optional_float, + default=-1.0, + help="if the average log probability is lower than this value, treat the decoding as failed", + ) + parser.add_argument( + "--no_speech_threshold", + type=optional_float, + default=0.6, + help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence", + ) + parser.add_argument( + "--threads", + type=optional_int, + default=0, + help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS", + ) + + args = parser.parse_args().__dict__ + model_name: str = args.pop("model") + model_dir: str = args.pop("model_dir") + output_dir: str = args.pop("output_dir") + device: str = args.pop("device") + os.makedirs(output_dir, exist_ok=True) + + if model_name.endswith(".en") and args["language"] not in {"en", "English"}: + if args["language"] is not None: + warnings.warn( + f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead." + ) + args["language"] = "en" + + temperature = args.pop("temperature") + temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback") + if temperature_increment_on_fallback is not None: + temperature = tuple( + np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback) + ) + else: + temperature = [temperature] + + threads = args.pop("threads") + if threads > 0: + torch.set_num_threads(threads) + + from . import load_model + + model = load_model(model_name, device=device, download_root=model_dir) + + for audio_path in args.pop("audio"): + result = transcribe(model, audio_path, temperature=temperature, **args) + + audio_basename = os.path.basename(audio_path) + + # save TXT + with open( + os.path.join(output_dir, audio_basename + ".txt"), "w", encoding="utf-8" + ) as txt: + write_txt(result["segments"], file=txt) + + # save VTT + with open( + os.path.join(output_dir, audio_basename + ".vtt"), "w", encoding="utf-8" + ) as vtt: + write_vtt(result["segments"], file=vtt) + + # save SRT + with open( + os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8" + ) as srt: + write_srt(result["segments"], file=srt) + + +if __name__ == "__main__": + cli() diff --git a/modules/whisper_extractor/utils.py b/modules/whisper_extractor/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..760e5fffb7cfe4e30988ec9b5b943f90989b9a9f --- /dev/null +++ b/modules/whisper_extractor/utils.py @@ -0,0 +1,120 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This module is modified from [Whisper](https://github.com/openai/whisper.git). + +# ## Citations + +# ```bibtex +# @inproceedings{openai-whisper, +# author = {Alec Radford and +# Jong Wook Kim and +# Tao Xu and +# Greg Brockman and +# Christine McLeavey and +# Ilya Sutskever}, +# title = {Robust Speech Recognition via Large-Scale Weak Supervision}, +# booktitle = {{ICML}}, +# series = {Proceedings of Machine Learning Research}, +# volume = {202}, +# pages = {28492--28518}, +# publisher = {{PMLR}}, +# year = {2023} +# } +# ``` +# + +import zlib +from typing import Iterator, TextIO + + +def exact_div(x, y): + assert x % y == 0 + return x // y + + +def str2bool(string): + str2val = {"True": True, "False": False} + if string in str2val: + return str2val[string] + else: + raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}") + + +def optional_int(string): + return None if string == "None" else int(string) + + +def optional_float(string): + return None if string == "None" else float(string) + + +def compression_ratio(text) -> float: + text_bytes = text.encode("utf-8") + return len(text_bytes) / len(zlib.compress(text_bytes)) + + +def format_timestamp( + seconds: float, always_include_hours: bool = False, decimal_marker: str = "." +): + assert seconds >= 0, "non-negative timestamp expected" + milliseconds = round(seconds * 1000.0) + + hours = milliseconds // 3_600_000 + milliseconds -= hours * 3_600_000 + + minutes = milliseconds // 60_000 + milliseconds -= minutes * 60_000 + + seconds = milliseconds // 1_000 + milliseconds -= seconds * 1_000 + + hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" + return ( + f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" + ) + + +def write_txt(transcript: Iterator[dict], file: TextIO): + for segment in transcript: + print(segment["text"].strip(), file=file, flush=True) + + +def write_vtt(transcript: Iterator[dict], file: TextIO): + print("WEBVTT\n", file=file) + for segment in transcript: + print( + f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n" + f"{segment['text'].strip().replace('-->', '->')}\n", + file=file, + flush=True, + ) + + +def write_srt(transcript: Iterator[dict], file: TextIO): + """ + Write a transcript to a file in SRT format. + + Example usage: + from pathlib import Path + from whisper.utils import write_srt + + result = transcribe(model, audio_path, temperature=temperature, **args) + + # save SRT + audio_basename = Path(audio_path).stem + with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt: + write_srt(result["segments"], file=srt) + """ + for i, segment in enumerate(transcript, start=1): + # write srt lines + print( + f"{i}\n" + f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> " + f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n" + f"{segment['text'].strip().replace('-->', '->')}\n", + file=file, + flush=True, + ) diff --git a/modules/whisper_extractor/version.py b/modules/whisper_extractor/version.py new file mode 100644 index 0000000000000000000000000000000000000000..91fba4bb5e6621010b41d23ab9db419bdb7aeb1e --- /dev/null +++ b/modules/whisper_extractor/version.py @@ -0,0 +1,6 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +__version__ = "20230117" diff --git a/optimizer/__init__.py b/optimizer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/optimizer/__pycache__/__init__.cpython-39.pyc b/optimizer/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1f4564e54e3f61ceb8b93dca1c6293bc3e3b0389 Binary files /dev/null and b/optimizer/__pycache__/__init__.cpython-39.pyc differ diff --git a/optimizer/__pycache__/optimizers.cpython-39.pyc b/optimizer/__pycache__/optimizers.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..560b528c8f2bfbd4a60c0607939a5eed46a29c38 Binary files /dev/null and b/optimizer/__pycache__/optimizers.cpython-39.pyc differ diff --git a/optimizer/optimizers.py b/optimizer/optimizers.py new file mode 100644 index 0000000000000000000000000000000000000000..b47420a7c9cc621c16cf29060e9f5542afad8691 --- /dev/null +++ b/optimizer/optimizers.py @@ -0,0 +1,780 @@ +# This module is modified from https://github.com/Plachtaa/VALL-E-X/blob/3faaf8ccadb154d63b38070caf518ce9309ea0f4/modules/optim.py#L836 + +import logging +import contextlib +import torch +from torch import Tensor +from torch.optim.lr_scheduler import _LRScheduler +from torch.optim import Optimizer +from typing import List, Tuple +from collections import defaultdict + + +class NoamLR(_LRScheduler): + """ + Implements the Noam Learning rate schedule. This corresponds to increasing the learning rate + linearly for the first ``num_warmup`` training steps, and decreasing it thereafter proportionally + to the inverse square root of the step number, scaled by the inverse square root of the + dimensionality of the model. Time will tell if this is just madness or it's actually important. + Parameters + ---------- + num_warmup: ``int``, required. + The number of steps to linearly increase the learning rate. + """ + + def __init__(self, optimizer, num_warmup): + self.num_warmup = num_warmup + self.base_lr = optimizer.param_groups[0]["lr"] + super().__init__(optimizer) + + def get_lr(self): + last_epoch = max(1, self.last_epoch) + scale = min(last_epoch ** (-0.5), last_epoch * self.num_warmup ** (-1.5)) + return [scale * self.base_lr] + +class Eve(Optimizer): + """ + Implements Eve algorithm. This is a modified version of AdamW with a special + way of setting the weight-decay / shrinkage-factor, which is designed to make the + rms of the parameters approach a particular target_rms (default: 0.1). This is + for use with networks with 'scaled' versions of modules (see scaling.py), which + will be close to invariant to the absolute scale on the parameter matrix. + + The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_. + The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_. + Eve is unpublished so far. + + Arguments: + params (iterable): iterable of parameters to optimize or dicts defining + parameter groups + lr (float, optional): learning rate (default: 1e-3) + betas (Tuple[float, float], optional): coefficients used for computing + running averages of gradient and its square (default: (0.9, 0.999)) + eps (float, optional): term added to the denominator to improve + numerical stability (default: 1e-8) + weight_decay (float, optional): weight decay coefficient (default: 3e-4; + this value means that the weight would decay significantly after + about 3k minibatches. Is not multiplied by learning rate, but + is conditional on RMS-value of parameter being > target_rms. + target_rms (float, optional): target root-mean-square value of + parameters, if they fall below this we will stop applying weight decay. + + + .. _Adam: A Method for Stochastic Optimization: + https://arxiv.org/abs/1412.6980 + .. _Decoupled Weight Decay Regularization: + https://arxiv.org/abs/1711.05101 + .. _On the Convergence of Adam and Beyond: + https://openreview.net/forum?id=ryQu7f-RZ + """ + + def __init__( + self, + params, + lr=1e-3, + betas=(0.9, 0.98), + eps=1e-8, + weight_decay=1e-3, + target_rms=0.1, + ): + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + if not 0 <= weight_decay <= 0.1: + raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) + if not 0 < target_rms <= 10.0: + raise ValueError("Invalid target_rms value: {}".format(target_rms)) + defaults = dict( + lr=lr, + betas=betas, + eps=eps, + weight_decay=weight_decay, + target_rms=target_rms, + ) + super(Eve, self).__init__(params, defaults) + + def __setstate__(self, state): + super(Eve, self).__setstate__(state) + + @torch.no_grad() + def step(self, closure=None): + """Performs a single optimization step. + + Arguments: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + for group in self.param_groups: + for p in group["params"]: + if p.grad is None: + continue + + # Perform optimization step + grad = p.grad + if grad.is_sparse: + raise RuntimeError("AdamW does not support sparse gradients") + + state = self.state[p] + + # State initialization + if len(state) == 0: + state["step"] = 0 + # Exponential moving average of gradient values + state["exp_avg"] = torch.zeros_like( + p, memory_format=torch.preserve_format + ) + # Exponential moving average of squared gradient values + state["exp_avg_sq"] = torch.zeros_like( + p, memory_format=torch.preserve_format + ) + + exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] + + beta1, beta2 = group["betas"] + + state["step"] += 1 + bias_correction1 = 1 - beta1 ** state["step"] + bias_correction2 = 1 - beta2 ** state["step"] + + # Decay the first and second moment running average coefficient + exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) + exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) + denom = (exp_avg_sq.sqrt() * (bias_correction2**-0.5)).add_( + group["eps"] + ) + + step_size = group["lr"] / bias_correction1 + target_rms = group["target_rms"] + weight_decay = group["weight_decay"] + + if p.numel() > 1: + # avoid applying this weight-decay on "scaling factors" + # (which are scalar). + is_above_target_rms = p.norm() > (target_rms * (p.numel() ** 0.5)) + p.mul_(1 - (weight_decay * is_above_target_rms)) + + p.addcdiv_(exp_avg, denom, value=-step_size) + + # if random.random() < 0.0005: + # step = (exp_avg / denom) * step_size + # logging.info( + # f"Delta rms = {(step**2).mean().item()}, shape = {step.shape}" + # ) + + return loss + +class BatchedOptimizer(Optimizer): + """ + This class adds to class Optimizer the capability to optimize parameters in batches: + it will stack the parameters and their grads for you so the optimizer can work + on tensors with an extra leading dimension. This is intended for speed with GPUs, + as it reduces the number of kernels launched in the optimizer. + + Args: + params: + """ + + def __init__(self, params, defaults): + super(BatchedOptimizer, self).__init__(params, defaults) + + @contextlib.contextmanager + def batched_params(self, param_group, group_params_names): + """ + This function returns (technically, yields) a list of + of tuples (p, state), where + p is a `fake` parameter that is stacked (over axis 0) from real parameters + that share the same shape, and its gradient is also stacked; + `state` is the state corresponding to this batch of parameters + (it will be physically located in the "state" for one of the real + parameters, the last one that has any particular shape and dtype). + + This function is decorated as a context manager so that it can + write parameters back to their "real" locations. + + The idea is, instead of doing: + + for p in group["params"]: + state = self.state[p] + ... + + you can do: + + with self.batched_params(group["params"]) as batches: + for p, state, p_names in batches: + ... + + + Args: + group: a parameter group, which is a list of parameters; should be + one of self.param_groups. + group_params_names: name for each parameter in group, + which is List[str]. + """ + batches = defaultdict( + list + ) # `batches` maps from tuple (dtype_as_str,*shape) to list of nn.Parameter + batches_names = defaultdict( + list + ) # `batches` maps from tuple (dtype_as_str,*shape) to list of str + + assert len(param_group) == len(group_params_names) + for p, named_p in zip(param_group, group_params_names): + key = (str(p.dtype), *p.shape) + batches[key].append(p) + batches_names[key].append(named_p) + + batches_names_keys = list(batches_names.keys()) + sorted_idx = sorted( + range(len(batches_names)), key=lambda i: batches_names_keys[i] + ) + batches_names = [batches_names[batches_names_keys[idx]] for idx in sorted_idx] + batches = [batches[batches_names_keys[idx]] for idx in sorted_idx] + + stacked_params_dict = dict() + + # turn batches into a list, in deterministic order. + # tuples will contain tuples of (stacked_param, state, stacked_params_names), + # one for each batch in `batches`. + tuples = [] + + for batch, batch_names in zip(batches, batches_names): + p = batch[0] + # we arbitrarily store the state in the + # state corresponding to the 1st parameter in the + # group. class Optimizer will take care of saving/loading state. + state = self.state[p] + p_stacked = torch.stack(batch) + grad = torch.stack( + [torch.zeros_like(p) if p.grad is None else p.grad for p in batch] + ) + p_stacked.grad = grad + stacked_params_dict[key] = p_stacked + tuples.append((p_stacked, state, batch_names)) + + yield tuples + + for ((stacked_params, _state, _names), batch) in zip(tuples, batches): + for i, p in enumerate(batch): + p.copy_(stacked_params[i]) + +class ScaledAdam(BatchedOptimizer): + """ + Implements 'Scaled Adam', a variant of Adam where we scale each parameter's update + proportional to the norm of that parameter; and also learn the scale of the parameter, + in log space, subject to upper and lower limits (as if we had factored each parameter as + param = underlying_param * log_scale.exp()) + + + Args: + params: The parameters or param_groups to optimize (like other Optimizer subclasses) + lr: The learning rate. We will typically use a learning rate schedule that starts + at 0.03 and decreases over time, i.e. much higher than other common + optimizers. + clipping_scale: (e.g. 2.0) + A scale for gradient-clipping: if specified, the normalized gradients + over the whole model will be clipped to have 2-norm equal to + `clipping_scale` times the median 2-norm over the most recent period + of `clipping_update_period` minibatches. By "normalized gradients", + we mean after multiplying by the rms parameter value for this tensor + [for non-scalars]; this is appropriate because our update is scaled + by this quantity. + betas: beta1,beta2 are momentum constants for regular momentum, and moving sum-sq grad. + Must satisfy 0 < beta <= beta2 < 1. + scalar_lr_scale: A scaling factor on the learning rate, that we use to update the + scale of each parameter tensor and scalar parameters of the mode.. + If each parameter were decomposed + as p * p_scale.exp(), where (p**2).mean().sqrt() == 1.0, scalar_lr_scale + would be a the scaling factor on the learning rate of p_scale. + eps: A general-purpose epsilon to prevent division by zero + param_min_rms: Minimum root-mean-square value of parameter tensor, for purposes of + learning the scale on the parameters (we'll constrain the rms of each non-scalar + parameter tensor to be >= this value) + param_max_rms: Maximum root-mean-square value of parameter tensor, for purposes of + learning the scale on the parameters (we'll constrain the rms of each non-scalar + parameter tensor to be <= this value) + scalar_max: Maximum absolute value for scalar parameters (applicable if your + model has any parameters with numel() == 1). + size_update_period: The periodicity, in steps, with which we update the size (scale) + of the parameter tensor. This is provided to save a little time + in the update. + clipping_update_period: if clipping_scale is specified, this is the period + """ + + def __init__( + self, + params, + lr=3e-02, + clipping_scale=None, + betas=(0.9, 0.98), + scalar_lr_scale=0.1, + eps=1.0e-08, + param_min_rms=1.0e-05, + param_max_rms=3.0, + scalar_max=10.0, + size_update_period=4, + clipping_update_period=100, + parameters_names=None, + show_dominant_parameters=True, + ): + + assert parameters_names is not None, ( + "Please prepare parameters_names," + "which is a List[List[str]]. Each List[str] is for a group" + "and each str is for a parameter" + ) + defaults = dict( + lr=lr, + clipping_scale=clipping_scale, + betas=betas, + scalar_lr_scale=scalar_lr_scale, + eps=eps, + param_min_rms=param_min_rms, + param_max_rms=param_max_rms, + scalar_max=scalar_max, + size_update_period=size_update_period, + clipping_update_period=clipping_update_period, + ) + + super(ScaledAdam, self).__init__(params, defaults) + assert len(self.param_groups) == len(parameters_names) + self.parameters_names = parameters_names + self.show_dominant_parameters = show_dominant_parameters + + def __setstate__(self, state): + super(ScaledAdam, self).__setstate__(state) + + @torch.no_grad() + def step(self, closure=None): + """Performs a single optimization step. + + Arguments: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + batch = True + + for group, group_params_names in zip(self.param_groups, self.parameters_names): + + with self.batched_params(group["params"], group_params_names) as batches: + + # batches is list of pairs (stacked_param, state). stacked_param is like + # a regular parameter, and will have a .grad, but the 1st dim corresponds to + # a stacking dim, it is not a real dim. + + if ( + len(batches[0][1]) == 0 + ): + clipping_scale = 1 + else: + clipping_scale = self._get_clipping_scale(group, batches) + + for p, state, _ in batches: + # Perform optimization step. + # grad is not going to be None, we handled that when creating the batches. + grad = p.grad + if grad.is_sparse: + raise RuntimeError( + "ScaledAdam optimizer does not support sparse gradients" + ) + # State initialization + if len(state) == 0: + self._init_state(group, p, state) + + self._step_one_batch(group, p, state, clipping_scale) + + return loss + + def _init_state(self, group: dict, p: Tensor, state: dict): + """ + Initializes state dict for parameter 'p'. Assumes that dim 0 of tensor p + is actually the batch dimension, corresponding to batched-together + parameters of a given shape. + + + Args: + group: Dict to look up configuration values. + p: The parameter that we are initializing the state for + state: Dict from string to whatever state we are initializing + """ + size_update_period = group["size_update_period"] + + state["step"] = 0 + + kwargs = {"device": p.device, "dtype": p.dtype} + + # 'delta' implements conventional momentum. There are + # several different kinds of update going on, so rather than + # compute "exp_avg" like in Adam, we store and decay a + # parameter-change "delta", which combines all forms of + # update. this is equivalent to how it's done in Adam, + # except for the first few steps. + state["delta"] = torch.zeros_like(p, memory_format=torch.preserve_format) + + batch_size = p.shape[0] + numel = p.numel() // batch_size + numel = p.numel() + + if numel > 1: + # "param_rms" just periodically records the scalar root-mean-square value of + # the parameter tensor. + # it has a shape like (batch_size, 1, 1, 1, 1) + param_rms = (p**2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt() + state["param_rms"] = param_rms + + state["scale_exp_avg_sq"] = torch.zeros_like(param_rms) + state["scale_grads"] = torch.zeros( + size_update_period, *param_rms.shape, **kwargs + ) + + # exp_avg_sq is the weighted sum of scaled gradients. as in Adam. + state["exp_avg_sq"] = torch.zeros_like(p, memory_format=torch.preserve_format) + + def _get_clipping_scale( + self, group: dict, tuples: List[Tuple[Tensor, dict, List[str]]] + ) -> float: + """ + Returns a scalar factor <= 1.0 that dictates gradient clipping, i.e. we will scale the gradients + by this amount before applying the rest of the update. + + Args: + group: the parameter group, an item in self.param_groups + tuples: a list of tuples of (param, state, param_names) + where param is a batched set of parameters, + with a .grad (1st dim is batch dim) + and state is the state-dict where optimization parameters are kept. + param_names is a List[str] while each str is name for a parameter + in batched set of parameters "param". + """ + assert len(tuples) >= 1 + clipping_scale = group["clipping_scale"] + (first_p, first_state, _) = tuples[0] + step = first_state["step"] + if clipping_scale is None or step == 0: + # no clipping. return early on step == 0 because the other + # parameters' state won't have been initialized yet. + return 1.0 + clipping_update_period = group["clipping_update_period"] + + tot_sumsq = torch.tensor(0.0, device=first_p.device) + for (p, state, param_names) in tuples: + grad = p.grad + if grad.is_sparse: + raise RuntimeError( + "ScaledAdam optimizer does not support sparse gradients" + ) + if p.numel() == p.shape[0]: # a batch of scalars + tot_sumsq += (grad**2).sum() # sum() to change shape [1] to [] + else: + tot_sumsq += ((grad * state["param_rms"]) ** 2).sum() + + tot_norm = tot_sumsq.sqrt() + if "model_norms" not in first_state: + first_state["model_norms"] = torch.zeros( + clipping_update_period, device=p.device + ) + first_state["model_norms"][step % clipping_update_period] = tot_norm + + if step % clipping_update_period == 0: + # Print some stats. + # We don't reach here if step == 0 because we would have returned + # above. + sorted_norms = first_state["model_norms"].sort()[0].to("cpu") + quartiles = [] + for n in range(0, 5): + index = min( + clipping_update_period - 1, + (clipping_update_period // 4) * n, + ) + quartiles.append(sorted_norms[index].item()) + + median = quartiles[2] + threshold = clipping_scale * median + first_state["model_norm_threshold"] = threshold + percent_clipped = ( + first_state["num_clipped"] * 100.0 / clipping_update_period + if "num_clipped" in first_state + else 0.0 + ) + first_state["num_clipped"] = 0 + quartiles = " ".join(["%.3e" % x for x in quartiles]) + logging.info( + f"Clipping_scale={clipping_scale}, grad-norm quartiles {quartiles}, " + f"threshold={threshold:.3e}, percent-clipped={percent_clipped:.1f}" + ) + + if step < clipping_update_period: + return 1.0 # We have not yet estimated a norm to clip to. + else: + try: + model_norm_threshold = first_state["model_norm_threshold"] + except KeyError: + logging.info( + "Warning: model_norm_threshold not in state: possibly " + "you changed config when restarting, adding clipping_scale option?" + ) + return 1.0 + ans = min(1.0, (model_norm_threshold / (tot_norm + 1.0e-20)).item()) + if ans < 1.0: + first_state["num_clipped"] += 1 + if ans < 0.1: + logging.warn( + f"Scaling gradients by {ans}, model_norm_threshold={model_norm_threshold}" + ) + if self.show_dominant_parameters: + assert p.shape[0] == len(param_names) + self._show_gradient_dominating_parameter(tuples, tot_sumsq) + return ans + + def _show_gradient_dominating_parameter( + self, tuples: List[Tuple[Tensor, dict, List[str]]], tot_sumsq: Tensor + ): + """ + Show information of parameter wihch dominanting tot_sumsq. + + Args: + tuples: a list of tuples of (param, state, param_names) + where param is a batched set of parameters, + with a .grad (1st dim is batch dim) + and state is the state-dict where optimization parameters are kept. + param_names is a List[str] while each str is name for a parameter + in batched set of parameters "param". + tot_sumsq: sumsq of all parameters. Though it's could be calculated + from tuples, we still pass it to save some time. + """ + all_sumsq_orig = {} + for (p, state, batch_param_names) in tuples: + # p is a stacked batch parameters. + batch_grad = p.grad + if p.numel() == p.shape[0]: # a batch of scalars + batch_sumsq_orig = batch_grad**2 + # Dummpy values used by following `zip` statement. + batch_rms_orig = torch.ones(p.shape[0]) + else: + batch_rms_orig = state["param_rms"] + batch_sumsq_orig = ((batch_grad * batch_rms_orig) ** 2).sum( + dim=list(range(1, batch_grad.ndim)) + ) + + for name, sumsq_orig, rms, grad in zip( + batch_param_names, batch_sumsq_orig, batch_rms_orig, batch_grad + ): + + proportion_orig = sumsq_orig / tot_sumsq + all_sumsq_orig[name] = (proportion_orig, sumsq_orig, rms, grad) + + assert torch.isclose( + sum([value[0] for value in all_sumsq_orig.values()]).cpu(), + torch.tensor(1.0), + ) + sorted_by_proportion = { + k: v + for k, v in sorted( + all_sumsq_orig.items(), + key=lambda item: item[1][0], + reverse=True, + ) + } + dominant_param_name = next(iter(sorted_by_proportion)) + ( + dominant_proportion, + dominant_sumsq, + dominant_rms, + dominant_grad, + ) = sorted_by_proportion[dominant_param_name] + logging.info( + f"Parameter Dominanting tot_sumsq {dominant_param_name}" + f" with proportion {dominant_proportion:.2f}," + f" where dominant_sumsq=(grad_sumsq*orig_rms_sq)" + f"={dominant_sumsq:.3e}," + f" grad_sumsq = {(dominant_grad**2).sum():.3e}," + f" orig_rms_sq={(dominant_rms**2).item():.3e}" + ) + + def _step_one_batch( + self, group: dict, p: Tensor, state: dict, clipping_scale: float + ): + """ + Do the step for one parameter, which is actually going to be a batch of + `real` parameters, with dim 0 as the batch dim. + Args: + group: dict to look up configuration values + p: parameter to update (actually multiple parameters stacked together + as a batch) + state: state-dict for p, to look up the optimizer state + """ + lr = group["lr"] + size_update_period = group["size_update_period"] + beta1 = group["betas"][0] + + grad = p.grad + if clipping_scale != 1.0: + grad = grad * clipping_scale + step = state["step"] + delta = state["delta"] + + delta.mul_(beta1) + batch_size = p.shape[0] + numel = p.numel() // batch_size + if numel > 1: + # Update the size/scale of p, and set param_rms + scale_grads = state["scale_grads"] + scale_grads[step % size_update_period] = (p * grad).sum( + dim=list(range(1, p.ndim)), keepdim=True + ) + if step % size_update_period == size_update_period - 1: + param_rms = state["param_rms"] # shape: (batch_size, 1, 1, ..) + param_rms.copy_( + (p**2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt() + ) + if step > 0: + # self._size_update() learns the overall scale on the + # parameter, by shrinking or expanding it. + self._size_update(group, scale_grads, p, state) + + if numel == 1: + # For parameters with 1 element we just use regular Adam. + # Updates delta. + self._step_scalar(group, p, state) + else: + self._step(group, p, state) + + state["step"] = step + 1 + + def _size_update( + self, group: dict, scale_grads: Tensor, p: Tensor, state: dict + ) -> None: + """ + Called only where p.numel() > 1, this updates the scale of the parameter. + If we imagine: p = underlying_param * scale.exp(), and we are doing + gradient descent on underlying param and on scale, this function does the update + on `scale`. + + Args: + group: dict to look up configuration values + scale_grads: a tensor of shape (size_update_period, batch_size, 1, 1,...) containing + grads w.r.t. the scales. + p: The parameter to update + state: The state-dict of p + """ + + param_rms = state["param_rms"] + beta1, beta2 = group["betas"] + size_lr = group["lr"] * group["scalar_lr_scale"] + param_min_rms = group["param_min_rms"] + param_max_rms = group["param_max_rms"] + eps = group["eps"] + step = state["step"] + batch_size = p.shape[0] + + size_update_period = scale_grads.shape[0] + # correct beta2 for the size update period: we will have + # faster decay at this level. + beta2_corr = beta2**size_update_period + + scale_exp_avg_sq = state["scale_exp_avg_sq"] # shape: (batch_size, 1, 1, ..) + scale_exp_avg_sq.mul_(beta2_corr).add_( + (scale_grads**2).mean(dim=0), # mean over dim `size_update_period` + alpha=1 - beta2_corr, + ) # shape is (batch_size, 1, 1, ...) + + # The 1st time we reach here is when size_step == 1. + size_step = (step + 1) // size_update_period + bias_correction2 = 1 - beta2_corr**size_step + # we don't bother with bias_correction1; this will help prevent divergence + # at the start of training. + + denom = scale_exp_avg_sq.sqrt() + eps + + scale_step = ( + -size_lr * (bias_correction2**0.5) * scale_grads.sum(dim=0) / denom + ) + + is_too_small = param_rms < param_min_rms + is_too_large = param_rms > param_max_rms + + # when the param gets too small, just don't shrink it any further. + scale_step.masked_fill_(is_too_small, 0.0) + # when it gets too large, stop it from getting any larger. + scale_step.masked_fill_(is_too_large, -size_lr * size_update_period) + delta = state["delta"] + # the factor of (1-beta1) relates to momentum. + delta.add_(p * scale_step, alpha=(1 - beta1)) + + def _step(self, group: dict, p: Tensor, state: dict): + """ + This function does the core update of self.step(), in the case where the members of + the batch have more than 1 element. + + Args: + group: A dict which will be used to look up configuration values + p: The parameter to be updated + grad: The grad of p + state: The state-dict corresponding to parameter p + + This function modifies p. + """ + grad = p.grad + lr = group["lr"] + beta1, beta2 = group["betas"] + eps = group["eps"] + param_min_rms = group["param_min_rms"] + step = state["step"] + + exp_avg_sq = state["exp_avg_sq"] + exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=(1 - beta2)) + + this_step = state["step"] - (state["zero_step"] if "zero_step" in state else 0) + bias_correction2 = 1 - beta2 ** (this_step + 1) + if bias_correction2 < 0.99: + # note: not in-place. + exp_avg_sq = exp_avg_sq * (1.0 / bias_correction2) + + denom = exp_avg_sq.sqrt() + denom += eps + grad = grad / denom + + alpha = -lr * (1 - beta1) * state["param_rms"].clamp(min=param_min_rms) + + delta = state["delta"] + delta.add_(grad * alpha) + p.add_(delta) + + def _step_scalar(self, group: dict, p: Tensor, state: dict): + """ + A simplified form of the core update for scalar tensors, where we cannot get a good + estimate of the parameter rms. + """ + beta1, beta2 = group["betas"] + scalar_max = group["scalar_max"] + eps = group["eps"] + lr = group["lr"] * group["scalar_lr_scale"] + grad = p.grad + + exp_avg_sq = state["exp_avg_sq"] # shape: (batch_size,) + exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) + + # bias_correction2 is like in Adam. Don't bother with bias_correction1; + # slower update at the start will help stability anyway. + bias_correction2 = 1 - beta2 ** (state["step"] + 1) + denom = (exp_avg_sq / bias_correction2).sqrt() + eps + + delta = state["delta"] + delta.add_(grad / denom, alpha=-lr * (1 - beta1)) + p.clamp_(min=-scalar_max, max=scalar_max) + p.add_(delta) + diff --git a/pre-requirements.txt b/pre-requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..0f33cddf08cc80b8fab29d61b8ff40885c112d85 --- /dev/null +++ b/pre-requirements.txt @@ -0,0 +1,46 @@ +setuptools +ruamel.yaml +tqdm +colorama +easydict +tabulate +loguru +json5 +Cython +unidecode +inflect +argparse +g2p_en +tgt +librosa +matplotlib +typeguard +einops +omegaconf +hydra-core +humanfriendly +pandas +tensorboard +tensorboardX +torch==2.0.1 +torchaudio==2.0.2 +torchvision==0.15.2 +transformers==4.30.2 +diffusers==0.19.3 +accelerate +praat-parselmouth +audiomentations +pedalboard +ffmpeg-python==0.2.0 +pyworld +diffsptk +nnAudio +unidecode +inflect +ptwt +torchmetrics +pymcd +openai-whisper +frechet_audio_distance +asteroid +https://github.com/vBaiCai/python-pesq/archive/master.zip diff --git a/preprocessors/__init__.py b/preprocessors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..92514cbbafea25f61bcc4f4bbdce13bfa58b5fd8 --- /dev/null +++ b/preprocessors/__init__.py @@ -0,0 +1,189 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +For source datasets' standard samples +""" + +from collections import defaultdict +import os +import json + +SPEECH_DATASETS = ["vctk", "vctksample"] + +GOLDEN_TEST_SAMPLES = defaultdict(list) +GOLDEN_TEST_SAMPLES["m4singer"] = [ + "Alto-1_美错_0014", + "Bass-1_十年_0008", + "Soprano-2_同桌的你_0018", + "Tenor-5_爱笑的眼睛_0010", +] +GOLDEN_TEST_SAMPLES["svcc"] = [ + # IDF1 + "IDF1_10030", + "IDF1_10120", + "IDF1_10140", + # IDM1 + "IDM1_10001", + "IDM1_10030", + "IDM1_10120", + # CDF1 + "CDF1_10030", + "CDF1_10120", + "CDF1_10140", + # CDM1 + "CDM1_10001", + "CDM1_10030", + "CDM1_10120", +] +GOLDEN_TEST_SAMPLES["svcceval"] = [ + # SF1 + "SF1_30001", + "SF1_30002", + "SF1_30003", + # SM1 + "SM1_30001", + "SM1_30002", + "SM1_30003", +] +GOLDEN_TEST_SAMPLES["popbutfy"] = [ + "Female1#you_are_my_sunshine_Professional#0", + "Female4#Someone_Like_You_Professional#10", + "Male2#Lemon_Tree_Professional#12", + "Male5#can_you_feel_the_love_tonight_Professional#20", +] +GOLDEN_TEST_SAMPLES["opensinger"] = [ + "Man_0_大鱼_10", + "Man_21_丑八怪_14", + "Woman_39_mojito_22", + "Woman_40_易燃易爆炸_12", +] +GOLDEN_TEST_SAMPLES["nus48e"] = [ + "ADIZ_read#01#0000", + "MCUR_sing#10#0000", + "JLEE_read#08#0001", + "SAMF_sing#18#0001", +] +GOLDEN_TEST_SAMPLES["popcs"] = [ + "明天会更好_0004", + "欧若拉_0005", + "虫儿飞_0006", + "隐形的翅膀_0008", +] +GOLDEN_TEST_SAMPLES["kising"] = [ + "421_0040", + "424_0013", + "431_0026", +] +GOLDEN_TEST_SAMPLES["csd"] = [ + "en_004a_0001", + "en_042b_0006", + "kr_013a_0006", + "kr_045b_0004", +] +GOLDEN_TEST_SAMPLES["opera"] = [ + "fem_01#neg_1#0000", + "fem_12#pos_3#0003", + "male_02#neg_1#0002", + "male_11#pos_2#0001", +] +GOLDEN_TEST_SAMPLES["lijian"] = [ + "058矜持_0000", + "079绒花_0000", + "120遥远的天空底下_0000", +] +GOLDEN_TEST_SAMPLES["cdmusiceval"] = ["陶喆_普通朋友", "蔡琴_给电影人的情书"] + +GOLDEN_TRAIN_SAMPLES = defaultdict(list) + + +def get_golden_samples_indexes( + dataset_name, + dataset_dir=None, + cfg=None, + split=None, + min_samples=5, +): + """ + # Get Standard samples' indexes + """ + if dataset_dir is None: + assert cfg is not None + dataset_dir = os.path.join( + cfg.OUTPUT_PATH, + "preprocess/{}_version".format(cfg.PREPROCESS_VERSION), + dataset_name, + ) + + assert split is not None + utt_file = os.path.join(dataset_dir, "{}.json".format(split)) + with open(utt_file, "r", encoding="utf-8") as f: + samples = json.load(f) + + if "train" in split: + golden_samples = GOLDEN_TRAIN_SAMPLES[dataset_name] + if "test" in split: + golden_samples = GOLDEN_TEST_SAMPLES[dataset_name] + + res = [] + for idx, utt in enumerate(samples): + if utt["Uid"] in golden_samples: + res.append(idx) + + if dataset_name == "cdmusiceval": + if "_".join(utt["Uid"].split("_")[:2]) in golden_samples: + res.append(idx) + + if len(res) == 0: + res = [i for i in range(min_samples)] + + return res + + +def get_specific_singer_indexes(dataset_dir, singer_name, split): + utt_file = os.path.join(dataset_dir, "{}.json".format(split)) + with open(utt_file, "r", encoding="utf-8") as f: + samples = json.load(f) + + res = [] + for idx, utt in enumerate(samples): + if utt["Singer"] == singer_name: + res.append(idx) + + assert len(res) != 0 + return res + + +def get_uids_and_wav_paths( + cfg, dataset, dataset_type="train", only_specific_singer=None, return_singers=False +): + dataset_dir = os.path.join( + cfg.OUTPUT_PATH, "preprocess/{}_version".format(cfg.PREPROCESS_VERSION), dataset + ) + dataset_file = os.path.join( + dataset_dir, "{}.json".format(dataset_type.split("_")[-1]) + ) + with open(dataset_file, "r") as f: + utterances = json.load(f) + + indexes = range(len(utterances)) + if "golden" in dataset_type: + # golden_train or golden_test + indexes = get_golden_samples_indexes( + dataset, dataset_dir, split=dataset_type.split("_")[-1] + ) + if only_specific_singer is not None: + indexes = get_specific_singer_indexes( + dataset_dir, only_specific_singer, dataset_type + ) + + uids = [utterances[i]["Uid"] for i in indexes] + wav_paths = [utterances[i]["Path"] for i in indexes] + singers = [utterances[i]["Singer"] for i in indexes] + + if not return_singers: + return uids, wav_paths + else: + return uids, wav_paths, singers diff --git a/preprocessors/__pycache__/__init__.cpython-39.pyc b/preprocessors/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d2188ff45a721e67159ebda688474824923d30de Binary files /dev/null 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b/preprocessors/__pycache__/vocalist.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1995f0799150bd9708dfd94617996df63eecfbed Binary files /dev/null and b/preprocessors/__pycache__/vocalist.cpython-39.pyc differ diff --git a/preprocessors/bigdata.py b/preprocessors/bigdata.py new file mode 100644 index 0000000000000000000000000000000000000000..da541191259e337933ce469503c0d057808ebb85 --- /dev/null +++ b/preprocessors/bigdata.py @@ -0,0 +1,145 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import os +from collections import defaultdict +from tqdm import tqdm + + +def get_uids_and_wav_paths(cfg, dataset, dataset_type): + assert dataset == "bigdata" + dataset_dir = os.path.join( + cfg.OUTPUT_PATH, + "preprocess/{}_version".format(cfg.PREPROCESS_VERSION), + "bigdata/{}".format(cfg.BIGDATA_VERSION), + ) + dataset_file = os.path.join( + dataset_dir, "{}.json".format(dataset_type.split("_")[-1]) + ) + with open(dataset_file, "r") as f: + utterances = json.load(f) + + # Uids + uids = [u["Uid"] for u in utterances] + + # Wav paths + wav_paths = [u["Path"] for u in utterances] + + return uids, wav_paths + + +def take_duration(utt): + return utt["Duration"] + + +def main(output_path, cfg): + datasets = cfg.dataset + + print("-" * 10) + print("Preparing samples for bigdata...") + print("Including: \n{}\n".format("\n".join(datasets))) + + datasets.sort() + bigdata_version = "_".join(datasets) + + save_dir = os.path.join(output_path, bigdata_version) + os.makedirs(save_dir, exist_ok=True) + + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + singer_dict_file = os.path.join(save_dir, cfg.preprocess.spk2id) + utt2singer_file = os.path.join(save_dir, cfg.preprocess.utt2spk) + utt2singer = open(utt2singer_file, "a+") + # We select songs of standard samples as test songs + train = [] + test = [] + + train_total_duration = 0 + test_total_duration = 0 + + # Singer unique names + singer_names = set() + + for dataset in datasets: + dataset_path = os.path.join(output_path, dataset) + train_json = os.path.join(dataset_path, "train.json") + test_json = os.path.join(dataset_path, "test.json") + + with open(train_json, "r", encoding="utf-8") as f: + train_utterances = json.load(f) + + with open(test_json, "r", encoding="utf-8") as f: + test_utterances = json.load(f) + + for utt in tqdm(train_utterances): + train.append(utt) + train_total_duration += utt["Duration"] + singer_names.add("{}_{}".format(utt["Dataset"], utt["Singer"])) + utt2singer.write( + "{}_{}\t{}_{}\n".format( + utt["Dataset"], utt["Uid"], utt["Dataset"], utt["Singer"] + ) + ) + + for utt in test_utterances: + test.append(utt) + test_total_duration += utt["Duration"] + singer_names.add("{}_{}".format(utt["Dataset"], utt["Singer"])) + utt2singer.write( + "{}_{}\t{}_{}\n".format( + utt["Dataset"], utt["Uid"], utt["Dataset"], utt["Singer"] + ) + ) + + utt2singer.close() + + train.sort(key=take_duration) + test.sort(key=take_duration) + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Singer Look Up Table + singer_names = list(singer_names) + singer_names.sort() + singer_lut = {name: i for i, name in enumerate(singer_names)} + print("#Singers: {}\n".format(len(singer_lut))) + + # Save + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) + with open(singer_dict_file, "w") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) + + # Save meta info + meta_info = { + "datasets": datasets, + "train": {"size": len(train), "hours": round(train_total_duration / 3600, 4)}, + "test": {"size": len(test), "hours": round(test_total_duration / 3600, 4)}, + "singers": {"size": len(singer_lut)}, + } + singer2mins = defaultdict(float) + for utt in train: + dataset, singer, duration = utt["Dataset"], utt["Singer"], utt["Duration"] + singer2mins["{}_{}".format(dataset, singer)] += duration / 60 + singer2mins = sorted(singer2mins.items(), key=lambda x: x[1], reverse=True) + singer2mins = dict( + zip([i[0] for i in singer2mins], [round(i[1], 2) for i in singer2mins]) + ) + meta_info["singers"]["training_minutes"] = singer2mins + + with open(os.path.join(save_dir, "meta_info.json"), "w") as f: + json.dump(meta_info, f, indent=4, ensure_ascii=False) + + for singer, min in singer2mins.items(): + print("Singer {}: {} mins".format(singer, min)) + print("-" * 10, "\n") diff --git a/preprocessors/cdmusiceval.py b/preprocessors/cdmusiceval.py new file mode 100644 index 0000000000000000000000000000000000000000..1c4af97c17153196501200811f4e96cfcfa2f4f1 --- /dev/null +++ b/preprocessors/cdmusiceval.py @@ -0,0 +1,174 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from glob import glob +import os +import json +import torchaudio +from tqdm import tqdm +from collections import defaultdict + +from utils.util import has_existed, remove_and_create +from utils.audio_slicer import split_utterances_from_audio + + +def split_to_utterances(input_dir, output_dir): + print("Splitting to utterances for {}...".format(input_dir)) + + files_list = glob("*", root_dir=input_dir) + files_list.sort() + for wav_file in tqdm(files_list): + # # Load waveform + # waveform, fs = torchaudio.load(os.path.join(input_dir, wav_file)) + + # Singer name, Song name + song_name, singer_name = wav_file.split("_")[2].split("-") + save_dir = os.path.join(output_dir, singer_name, song_name) + + split_utterances_from_audio( + os.path.join(input_dir, wav_file), save_dir, max_duration_of_utterance=10 + ) + + # # Split + # slicer = Slicer(sr=fs, threshold=-30.0, max_sil_kept=3000, min_interval=1000) + # chunks = slicer.slice(waveform) + + # for i, chunk in enumerate(chunks): + # save_dir = os.path.join(output_dir, singer_name, song_name) + # os.makedirs(save_dir, exist_ok=True) + + # output_file = os.path.join(save_dir, "{:04d}.wav".format(i)) + # save_audio(output_file, chunk, fs) + + +def _main(dataset_path): + """ + Split to utterances + """ + utterance_dir = os.path.join(dataset_path, "utterances") + remove_and_create(utterance_dir) + split_to_utterances(os.path.join(dataset_path, "vocal"), utterance_dir) + + +def statistics(utterance_dir): + singers = [] + songs = [] + singers2songs = defaultdict(lambda: defaultdict(list)) + + singer_infos = glob(utterance_dir + "/*") + + for singer_info in singer_infos: + singer = singer_info.split("/")[-1] + + song_infos = glob(singer_info + "/*") + + for song_info in song_infos: + song = song_info.split("/")[-1] + + singers.append(singer) + songs.append(song) + + utts = glob(song_info + "/*.wav") + + for utt in utts: + uid = utt.split("/")[-1].split(".")[0] + singers2songs[singer][song].append(uid) + + unique_singers = list(set(singers)) + unique_songs = list(set(songs)) + unique_singers.sort() + unique_songs.sort() + + print( + "Statistics: {} singers, {} utterances ({} unique songs)".format( + len(unique_singers), len(songs), len(unique_songs) + ) + ) + print("Singers: \n{}".format("\t".join(unique_singers))) + return singers2songs, unique_singers + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing samples for CD Music Eval...\n") + + if not os.path.exists(os.path.join(dataset_path, "utterances")): + print("Spliting into utterances...\n") + _main(dataset_path) + + save_dir = os.path.join(output_path, "cdmusiceval") + os.makedirs(save_dir, exist_ok=True) + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + singer_dict_file = os.path.join(save_dir, "singers.json") + utt2singer_file = os.path.join(save_dir, "utt2singer") + if ( + has_existed(train_output_file) + and has_existed(test_output_file) + and has_existed(singer_dict_file) + and has_existed(utt2singer_file) + ): + return + utt2singer = open(utt2singer_file, "w") + + # Load + utt_path = os.path.join(dataset_path, "utterances") + singers2songs, unique_singers = statistics(utt_path) + + # We select songs of standard samples as test songs + train = [] + test = [] + + train_index_count = 0 + test_index_count = 0 + + train_total_duration = 0 + test_total_duration = 0 + + for singer, songs in tqdm(singers2songs.items()): + song_names = list(songs.keys()) + + for chosen_song in song_names: + for chosen_uid in songs[chosen_song]: + res = { + "Dataset": "cdmusiceval", + "Singer": singer, + "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid), + } + res["Path"] = "{}/{}/{}.wav".format(singer, chosen_song, chosen_uid) + res["Path"] = os.path.join(utt_path, res["Path"]) + assert os.path.exists(res["Path"]) + + waveform, sample_rate = torchaudio.load(res["Path"]) + duration = waveform.size(-1) / sample_rate + res["Duration"] = duration + + if duration <= 1e-8: + continue + + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + + utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) + + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save train.json and test.json + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) + + # Save singers.json + singer_lut = {name: i for i, name in enumerate(unique_singers)} + with open(singer_dict_file, "w") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/coco.py b/preprocessors/coco.py new file mode 100644 index 0000000000000000000000000000000000000000..9ac462f32d7a58ed245f58d13a44e02b4efef06b --- /dev/null +++ b/preprocessors/coco.py @@ -0,0 +1,100 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import torchaudio +from tqdm import tqdm +from glob import glob +from collections import defaultdict + +from utils.util import has_existed +from preprocessors import GOLDEN_TEST_SAMPLES + + +def get_test_songs(): + return ["007Di Da Di"] + + +def coco_statistics(data_dir): + song2utts = defaultdict(list) + + song_infos = glob(data_dir + "/*") + + for song in song_infos: + song_name = song.split("/")[-1] + utts = glob(song + "/*.wav") + for utt in utts: + uid = utt.split("/")[-1].split(".")[0] + song2utts[song_name].append(uid) + + print("Coco: {} songs".format(len(song_infos))) + return song2utts + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing datasets for Coco...\n") + + save_dir = os.path.join(output_path, "coco") + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + if has_existed(test_output_file): + return + + # Load + song2utts = coco_statistics(dataset_path) + test_songs = get_test_songs() + + # We select songs of standard samples as test songs + train = [] + test = [] + + train_index_count = 0 + test_index_count = 0 + + train_total_duration = 0 + test_total_duration = 0 + + for song_name, uids in tqdm(song2utts.items()): + for chosen_uid in uids: + res = { + "Dataset": "coco", + "Singer": "coco", + "Song": song_name, + "Uid": "{}_{}".format(song_name, chosen_uid), + } + res["Path"] = "{}/{}.wav".format(song_name, chosen_uid) + res["Path"] = os.path.join(dataset_path, res["Path"]) + assert os.path.exists(res["Path"]) + + waveform, sample_rate = torchaudio.load(res["Path"]) + duration = waveform.size(-1) / sample_rate + res["Duration"] = duration + + if song_name in test_songs: + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + else: + res["index"] = train_index_count + train_total_duration += duration + train.append(res) + train_index_count += 1 + + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save + os.makedirs(save_dir, exist_ok=True) + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/cocoeval.py b/preprocessors/cocoeval.py new file mode 100644 index 0000000000000000000000000000000000000000..3cb3604c9d540d4e8f166f459a1ac992a1bd7a17 --- /dev/null +++ b/preprocessors/cocoeval.py @@ -0,0 +1,99 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import random +import os +import json +import torchaudio +from tqdm import tqdm +from glob import glob +from collections import defaultdict + +from utils.util import has_existed +from utils.audio_slicer import split_utterances_from_audio +from preprocessors import GOLDEN_TEST_SAMPLES + + +def _split_utts(): + raw_dir = "/mnt/chongqinggeminiceph1fs/geminicephfs/wx-mm-spr-xxxx/xueyaozhang/dataset/李玟/cocoeval/raw" + output_root = "/mnt/chongqinggeminiceph1fs/geminicephfs/wx-mm-spr-xxxx/xueyaozhang/dataset/李玟/cocoeval/utterances" + + if os.path.exists(output_root): + os.system("rm -rf {}".format(output_root)) + + vocal_files = glob(os.path.join(raw_dir, "*/vocal.wav")) + for vocal_f in tqdm(vocal_files): + song_name = vocal_f.split("/")[-2] + + output_dir = os.path.join(output_root, song_name) + os.makedirs(output_dir, exist_ok=True) + + split_utterances_from_audio(vocal_f, output_dir, min_interval=300) + + +def cocoeval_statistics(data_dir): + song2utts = defaultdict(list) + + song_infos = glob(data_dir + "/*") + + for song in song_infos: + song_name = song.split("/")[-1] + utts = glob(song + "/*.wav") + for utt in utts: + uid = utt.split("/")[-1].split(".")[0] + song2utts[song_name].append(uid) + + print("Cocoeval: {} songs".format(len(song_infos))) + return song2utts + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing datasets for Cocoeval...\n") + + save_dir = os.path.join(output_path, "cocoeval") + test_output_file = os.path.join(save_dir, "test.json") + if has_existed(test_output_file): + return + + # Load + song2utts = cocoeval_statistics(dataset_path) + + train, test = [], [] + train_index_count, test_index_count = 0, 0 + train_total_duration, test_total_duration = 0.0, 0.0 + + for song_name, uids in tqdm(song2utts.items()): + for chosen_uid in uids: + res = { + "Dataset": "cocoeval", + "Singer": "TBD", + "Song": song_name, + "Uid": "{}_{}".format(song_name, chosen_uid), + } + res["Path"] = "{}/{}.wav".format(song_name, chosen_uid) + res["Path"] = os.path.join(dataset_path, res["Path"]) + assert os.path.exists(res["Path"]) + + waveform, sample_rate = torchaudio.load(res["Path"]) + duration = waveform.size(-1) / sample_rate + res["Duration"] = duration + + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save + os.makedirs(save_dir, exist_ok=True) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/csd.py b/preprocessors/csd.py new file mode 100644 index 0000000000000000000000000000000000000000..645a8b3d1e2bc556ffa7ee36d9b889422e500619 --- /dev/null +++ b/preprocessors/csd.py @@ -0,0 +1,202 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import os +import glob +from tqdm import tqdm +import torchaudio +import pandas as pd +from glob import glob +from collections import defaultdict + +from utils.io import save_audio +from utils.util import has_existed +from preprocessors import GOLDEN_TEST_SAMPLES + + +def save_utterance(output_file, waveform, fs, start, end, overlap=0.1): + """ + waveform: [#channel, audio_len] + start, end, overlap: seconds + """ + start = int((start - overlap) * fs) + end = int((end + overlap) * fs) + utterance = waveform[:, start:end] + save_audio(output_file, utterance, fs) + + +def split_to_utterances(language_dir, output_dir): + print("Splitting to utterances for {}...".format(language_dir)) + wav_dir = os.path.join(language_dir, "wav") + phoneme_dir = os.path.join(language_dir, "txt") + annot_dir = os.path.join(language_dir, "csv") + + pitches = set() + for wav_file in tqdm(glob("{}/*.wav".format(wav_dir))): + # Load waveform + song_name = wav_file.split("/")[-1].split(".")[0] + waveform, fs = torchaudio.load(wav_file) + + # Load utterances + phoneme_file = os.path.join(phoneme_dir, "{}.txt".format(song_name)) + with open(phoneme_file, "r") as f: + lines = f.readlines() + utterances = [l.strip().split() for l in lines] + utterances = [utt for utt in utterances if len(utt) > 0] + + # Load annotation + annot_file = os.path.join(annot_dir, "{}.csv".format(song_name)) + annot_df = pd.read_csv(annot_file) + pitches = pitches.union(set(annot_df["pitch"])) + starts = annot_df["start"].tolist() + ends = annot_df["end"].tolist() + syllables = annot_df["syllable"].tolist() + + # Split + curr = 0 + for i, phones in enumerate(utterances): + sz = len(phones) + assert phones[0] == syllables[curr] + assert phones[-1] == syllables[curr + sz - 1] + + s = starts[curr] + e = ends[curr + sz - 1] + curr += sz + + save_dir = os.path.join(output_dir, song_name) + os.makedirs(save_dir, exist_ok=True) + + output_file = os.path.join(save_dir, "{:04d}.wav".format(i)) + save_utterance(output_file, waveform, fs, start=s, end=e) + + +def _main(dataset_path): + """ + Split to utterances + """ + utterance_dir = os.path.join(dataset_path, "utterances") + + for lang in ["english", "korean"]: + split_to_utterances(os.path.join(dataset_path, lang), utterance_dir) + + +def get_test_songs(): + golden_samples = GOLDEN_TEST_SAMPLES["csd"] + # every item is a tuple (language, song) + golden_songs = [s.split("_")[:2] for s in golden_samples] + # language_song, eg: en_001a + return golden_songs + + +def csd_statistics(data_dir): + languages = [] + songs = [] + languages2songs = defaultdict(lambda: defaultdict(list)) + + folder_infos = glob(data_dir + "/*") + + for folder_info in folder_infos: + folder_info_split = folder_info.split("/")[-1] + + language = folder_info_split[:2] + song = folder_info_split[2:] + + languages.append(language) + songs.append(song) + + utts = glob(folder_info + "/*") + + for utt in utts: + uid = utt.split("/")[-1].split(".")[0] + languages2songs[language][song].append(uid) + + unique_languages = list(set(languages)) + unique_songs = list(set(songs)) + unique_languages.sort() + unique_songs.sort() + + print( + "csd: {} languages, {} utterances ({} unique songs)".format( + len(unique_languages), len(songs), len(unique_songs) + ) + ) + print("Languages: \n{}".format("\t".join(unique_languages))) + return languages2songs + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing test samples for csd...\n") + + if not os.path.exists(os.path.join(dataset_path, "utterances")): + print("Spliting into utterances...\n") + _main(dataset_path) + + save_dir = os.path.join(output_path, "csd") + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + if has_existed(test_output_file): + return + + # Load + csd_path = os.path.join(dataset_path, "utterances") + + language2songs = csd_statistics(csd_path) + test_songs = get_test_songs() + + # We select songs of standard samples as test songs + train = [] + test = [] + + train_index_count = 0 + test_index_count = 0 + + train_total_duration = 0 + test_total_duration = 0 + + for language, songs in tqdm(language2songs.items()): + song_names = list(songs.keys()) + + for chosen_song in song_names: + for chosen_uid in songs[chosen_song]: + res = { + "Dataset": "csd", + "Singer": "Female1_{}".format(language), + "Uid": "{}_{}_{}".format(language, chosen_song, chosen_uid), + } + res["Path"] = "{}{}/{}.wav".format(language, chosen_song, chosen_uid) + res["Path"] = os.path.join(csd_path, res["Path"]) + assert os.path.exists(res["Path"]) + + waveform, sample_rate = torchaudio.load(res["Path"]) + duration = waveform.size(-1) / sample_rate + res["Duration"] = duration + + if [language, chosen_song] in test_songs: + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + else: + res["index"] = train_index_count + train_total_duration += duration + train.append(res) + train_index_count += 1 + + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save + os.makedirs(save_dir, exist_ok=True) + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/custom.py b/preprocessors/custom.py new file mode 100644 index 0000000000000000000000000000000000000000..daa457bb84b7aca70fcd631f25a1678e6e84a333 --- /dev/null +++ b/preprocessors/custom.py @@ -0,0 +1,143 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from glob import glob +import os +import json +import torchaudio +from tqdm import tqdm +from collections import defaultdict + +from utils.util import has_existed + + +def statistics(utterance_dir): + singers = [] + songs = [] + singers2songs = defaultdict(lambda: defaultdict(list)) + + singer_infos = glob(utterance_dir + "/*") + + for singer_info in singer_infos: + singer = singer_info.split("/")[-1] + + song_infos = glob(singer_info + "/*") + + for song_info in song_infos: + song = song_info.split("/")[-1] + + singers.append(singer) + songs.append(song) + + utts = glob(song_info + "/*.wav") + + for utt in utts: + uid = utt.split("/")[-1].split(".")[0] + singers2songs[singer][song].append(uid) + + unique_singers = list(set(singers)) + unique_songs = list(set(songs)) + unique_singers.sort() + unique_songs.sort() + + print( + "Statistics: {} singers, {} utterances ({} unique songs)".format( + len(unique_singers), len(songs), len(unique_songs) + ) + ) + print("Singers: \n{}".format("\t".join(unique_singers))) + return singers2songs, unique_singers + + +def main(output_path, dataset_path, dataset_name): + print("-" * 10) + print("Preparing samples for {}...\n".format(dataset_name)) + + save_dir = os.path.join(output_path, dataset_name) + os.makedirs(save_dir, exist_ok=True) + + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + singer_dict_file = os.path.join(save_dir, "singers.json") + utt2singer_file = os.path.join(save_dir, "utt2singer") + if ( + has_existed(train_output_file) + and has_existed(test_output_file) + and has_existed(singer_dict_file) + and has_existed(utt2singer_file) + ): + return + utt2singer = open(utt2singer_file, "w") + + # Load + singers2songs, unique_singers = statistics(dataset_path) + + # We select songs of standard samples as test songs + train = [] + test = [] + test_songs = set() + + train_index_count = 0 + test_index_count = 0 + + train_total_duration = 0 + test_total_duration = 0 + + for singer, songs in singers2songs.items(): + song_names = list(songs.keys()) + + print("Singer {}...".format(singer)) + for chosen_song in tqdm(song_names): + for chosen_uid in songs[chosen_song]: + res = { + "Dataset": dataset_name, + "Singer": singer, + "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid), + } + res["Path"] = "{}/{}/{}.wav".format(singer, chosen_song, chosen_uid) + res["Path"] = os.path.join(dataset_path, res["Path"]) + assert os.path.exists(res["Path"]) + + waveform, sample_rate = torchaudio.load(res["Path"]) + duration = waveform.size(-1) / sample_rate + res["Duration"] = duration + + # Remove the utterance whose duration is shorter than 0.1s + if duration <= 1e-2: + continue + + # Place into train or test + if "{}_{}".format(singer, chosen_song) not in test_songs: + test_songs.add("{}_{}".format(singer, chosen_song)) + + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + else: + res["index"] = train_index_count + train_total_duration += duration + train.append(res) + train_index_count += 1 + + utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) + + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save train.json and test.json + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) + + # Save singers.json + singer_lut = {name: i for i, name in enumerate(unique_singers)} + with open(singer_dict_file, "w") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/hifitts.py b/preprocessors/hifitts.py new file mode 100644 index 0000000000000000000000000000000000000000..b9ae34d254da0eff542d0c952bd7446373d09545 --- /dev/null +++ b/preprocessors/hifitts.py @@ -0,0 +1,140 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import torchaudio +from tqdm import tqdm +from glob import glob +from collections import defaultdict + +from utils.util import has_existed + +def hifitts_statistics(data_dir): + speakers = [] + distribution2books2utts = defaultdict( + lambda:defaultdict(list) + ) + distribution_infos = glob(data_dir + "/*.json") + + for distribution_info in distribution_infos: + distribution = distribution_info.split("/")[-1].split(".")[0] + speaker_id = distribution.split("_")[0] + speakers.append(speaker_id) + + with open(distribution_info, 'r', encoding='utf-8') as file: + for line in file: + entry = json.loads(line) + text_normalized = entry.get("text_normalized") + audio_path = entry.get("audio_filepath") + book = audio_path.split("/")[-2] + distribution2books2utts[distribution][book].append((text_normalized, audio_path)) + + unique_speakers = list(set(speakers)) + unique_speakers.sort() + + print("Speakers: \n{}".format("\t".join(unique_speakers))) + return distribution2books2utts, unique_speakers + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing samples for hifitts...\n") + + save_dir = os.path.join(output_path, "hifitts") + os.makedirs(save_dir, exist_ok=True) + print('Saving to ', save_dir) + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + valid_output_file = os.path.join(save_dir, "valid.json") + singer_dict_file = os.path.join(save_dir, "singers.json") + utt2singer_file = os.path.join(save_dir, "utt2singer") + if has_existed(train_output_file): + return + utt2singer = open(utt2singer_file, "w") + + # Load + hifitts_path = dataset_path + + distribution2books2utts, unique_speakers = hifitts_statistics( + hifitts_path + ) + + train = [] + test = [] + valid = [] + + train_index_count = 0 + test_index_count = 0 + valid_index_count = 0 + + train_total_duration = 0 + test_total_duration = 0 + valid_total_duration = 0 + + for distribution, books2utts in tqdm( + distribution2books2utts.items(), + desc=f"Distribution" + ): + speaker = distribution.split("_")[0] + book_names = list(books2utts.keys()) + for chosen_book in tqdm(book_names, desc=f"chosen_book"): + for text, utt_path in tqdm(books2utts[chosen_book], desc=f"utterance"): + chosen_uid = utt_path.split("/")[-1].split(".")[0] + res = { + "Dataset":"hifitts", + "Singer":speaker, + "Uid": "{}#{}#{}#{}".format( + distribution, speaker, chosen_book, chosen_uid + ), + "Text": text + } + + res["Path"] = os.path.join(hifitts_path, utt_path) + assert os.path.exists(res["Path"]) + + waveform, sample_rate = torchaudio.load(res["Path"]) + duration = waveform.size(-1) / sample_rate + res["Duration"] = duration + + if "train" in distribution: + res["index"] = train_index_count + train_total_duration += duration + train.append(res) + train_index_count += 1 + + elif 'test' in distribution: + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + + elif 'dev' in distribution: + res["index"] = valid_index_count + valid_total_duration += duration + valid.append(res) + valid_index_count += 1 + + utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) + + print("#Train = {}, #Test = {}, #Valid = {}".format(len(train), len(test), len(valid))) + print( + "#Train hours= {}, #Test hours= {}, #Valid hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600, valid_total_duration / 3600 + ) + ) + + # Save train.json, test.json, valid.json + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) + with open(valid_output_file, "w") as f: + json.dump(valid, f, indent=4, ensure_ascii=False) + + # Save singers.json + singer_lut = {name: i for i, name in enumerate(unique_speakers)} + with open(singer_dict_file, "w") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) \ No newline at end of file diff --git a/preprocessors/kising.py b/preprocessors/kising.py new file mode 100644 index 0000000000000000000000000000000000000000..f81b852aeb05a5d30fe22df07af4c4ab9defda2a --- /dev/null +++ b/preprocessors/kising.py @@ -0,0 +1,116 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import random +import os +import json +import torchaudio +from tqdm import tqdm +from glob import glob +from collections import defaultdict + +from utils.util import has_existed +from preprocessors import GOLDEN_TEST_SAMPLES + + +def get_test_folders(): + golden_samples = GOLDEN_TEST_SAMPLES["kising"] + # every item is a string + golden_folders = [s.split("_")[:1] for s in golden_samples] + # folder, eg: 422 + return golden_folders + + +def KiSing_statistics(data_dir): + folders = [] + folders2utts = defaultdict(list) + + folder_infos = glob(data_dir + "/*") + + for folder_info in folder_infos: + folder = folder_info.split("/")[-1] + + folders.append(folder) + + utts = glob(folder_info + "/*.wav") + + for utt in utts: + uid = utt.split("/")[-1].split(".")[0] + folders2utts[folder].append(uid) + + unique_folders = list(set(folders)) + unique_folders.sort() + + print("KiSing: {} unique songs".format(len(unique_folders))) + return folders2utts + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing test samples for KiSing...\n") + + save_dir = os.path.join(output_path, "kising") + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + if has_existed(test_output_file): + return + + # Load + KiSing_dir = dataset_path + + folders2utts = KiSing_statistics(KiSing_dir) + test_folders = get_test_folders() + + # We select songs of standard samples as test songs + train = [] + test = [] + + train_index_count = 0 + test_index_count = 0 + + train_total_duration = 0 + test_total_duration = 0 + + folder_names = list(folders2utts.keys()) + + for chosen_folder in folder_names: + for chosen_uid in folders2utts[chosen_folder]: + res = { + "Dataset": "kising", + "Singer": "female1", + "Uid": "{}_{}".format(chosen_folder, chosen_uid), + } + res["Path"] = "{}/{}.wav".format(chosen_folder, chosen_uid) + res["Path"] = os.path.join(KiSing_dir, res["Path"]) + assert os.path.exists(res["Path"]) + + waveform, sample_rate = torchaudio.load(res["Path"]) + duration = waveform.size(-1) / sample_rate + res["Duration"] = duration + + if ([chosen_folder]) in test_folders: + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + else: + res["index"] = train_index_count + train_total_duration += duration + train.append(res) + train_index_count += 1 + + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save + os.makedirs(save_dir, exist_ok=True) + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/libritts.py b/preprocessors/libritts.py new file mode 100644 index 0000000000000000000000000000000000000000..a814317df7df0b4a0f8896a35e4e79ab6386cb0c --- /dev/null +++ b/preprocessors/libritts.py @@ -0,0 +1,161 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import torchaudio +from tqdm import tqdm +from glob import glob +from collections import defaultdict + +from utils.util import has_existed + + +def libritts_statistics(data_dir): + speakers = [] + distribution2speakers2pharases2utts = defaultdict( + lambda: defaultdict(lambda: defaultdict(list)) + ) + + distribution_infos = glob(data_dir + "/*") + + for distribution_info in distribution_infos: + distribution = distribution_info.split("/")[-1] + print(distribution) + + speaker_infos = glob(distribution_info + "/*") + + if len(speaker_infos) == 0: + continue + + for speaker_info in speaker_infos: + speaker = speaker_info.split("/")[-1] + + speakers.append(speaker) + + pharase_infos = glob(speaker_info + "/*") + + for pharase_info in pharase_infos: + pharase = pharase_info.split("/")[-1] + + utts = glob(pharase_info + "/*.wav") + + for utt in utts: + uid = utt.split("/")[-1].split(".")[0] + distribution2speakers2pharases2utts[distribution][speaker][ + pharase + ].append(uid) + + unique_speakers = list(set(speakers)) + unique_speakers.sort() + + print("Speakers: \n{}".format("\t".join(unique_speakers))) + return distribution2speakers2pharases2utts, unique_speakers + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing samples for libritts...\n") + + save_dir = os.path.join(output_path, "libritts") + os.makedirs(save_dir, exist_ok=True) + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + valid_output_file = os.path.join(save_dir, "valid.json") + singer_dict_file = os.path.join(save_dir, "singers.json") + utt2singer_file = os.path.join(save_dir, "utt2singer") + if has_existed(train_output_file): + return + utt2singer = open(utt2singer_file, "w") + + # Load + libritts_path = dataset_path + + distribution2speakers2pharases2utts, unique_speakers = libritts_statistics( + libritts_path + ) + + # We select pharases of standard spekaer as test songs + train = [] + test = [] + valid = [] + + train_index_count = 0 + test_index_count = 0 + valid_index_count = 0 + + train_total_duration = 0 + test_total_duration = 0 + valid_total_duration = 0 + + for distribution, speakers2pharases2utts in tqdm( + distribution2speakers2pharases2utts.items() + ): + for speaker, pharases2utts in tqdm(speakers2pharases2utts.items()): + pharase_names = list(pharases2utts.keys()) + + for chosen_pharase in pharase_names: + for chosen_uid in pharases2utts[chosen_pharase]: + res = { + "Dataset": "libritts", + "Singer": speaker, + "Uid": "{}#{}#{}#{}".format( + distribution, speaker, chosen_pharase, chosen_uid + ), + } + res["Path"] = "{}/{}/{}/{}.wav".format( + distribution, speaker, chosen_pharase, chosen_uid + ) + res["Path"] = os.path.join(libritts_path, res["Path"]) + assert os.path.exists(res["Path"]) + + text_file_path = os.path.join(libritts_path, distribution, speaker, chosen_pharase, chosen_uid+'.normalized.txt') + with open(text_file_path, 'r') as f: + lines = f.readlines() + assert len(lines) == 1 + text = lines[0].strip() + res["Text"] = text + + waveform, sample_rate = torchaudio.load(res["Path"]) + duration = waveform.size(-1) / sample_rate + res["Duration"] = duration + + if "test" in distribution: + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + elif 'train' in distribution: + res["index"] = train_index_count + train_total_duration += duration + train.append(res) + train_index_count += 1 + elif 'dev' in distribution: + res["index"] = valid_index_count + valid_total_duration += duration + valid.append(res) + valid_index_count += 1 + + utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) + + print("#Train = {}, #Test = {}, #Valid = {}".format(len(train), len(test), len(valid))) + print( + "#Train hours= {}, #Test hours= {}, #Valid hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600, valid_total_duration / 3600 + ) + ) + + # Save train.json and test.json + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) + with open(valid_output_file, "w") as f: + json.dump(valid, f, indent=4, ensure_ascii=False) + + # Save singers.json + singer_lut = {name: i for i, name in enumerate(unique_speakers)} + with open(singer_dict_file, "w") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/lijian.py b/preprocessors/lijian.py new file mode 100644 index 0000000000000000000000000000000000000000..459986a8d68a5b0dc0811f4f8197cd818fdc263f --- /dev/null +++ b/preprocessors/lijian.py @@ -0,0 +1,151 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import glob +import os +import json +import torchaudio +from tqdm import tqdm +from collections import defaultdict + + +from utils.io import save_audio +from utils.util import has_existed, remove_and_create +from utils.audio_slicer import Slicer +from preprocessors import GOLDEN_TEST_SAMPLES + + +def split_to_utterances(input_dir, output_dir): + print("Splitting to utterances for {}...".format(input_dir)) + + files_list = glob.glob("*.flac", root_dir=input_dir) + files_list.sort() + for wav_file in tqdm(files_list): + # Load waveform + waveform, fs = torchaudio.load(os.path.join(input_dir, wav_file)) + + # Song name + filename = wav_file.replace(" ", "") + filename = filename.replace("(Live)", "") + song_id, filename = filename.split("李健-") + + song_id = song_id.split("_")[0] + song_name = "{:03d}".format(int(song_id)) + filename.split("_")[0].split("-")[0] + + # Split + slicer = Slicer(sr=fs, threshold=-30.0, max_sil_kept=3000) + chunks = slicer.slice(waveform) + + save_dir = os.path.join(output_dir, song_name) + remove_and_create(save_dir) + + for i, chunk in enumerate(chunks): + output_file = os.path.join(save_dir, "{:04d}.wav".format(i)) + save_audio(output_file, chunk, fs) + + +def _main(dataset_path): + """ + Split to utterances + """ + utterance_dir = os.path.join(dataset_path, "utterances") + split_to_utterances(os.path.join(dataset_path, "vocal_v2"), utterance_dir) + + +def get_test_songs(): + golden_samples = GOLDEN_TEST_SAMPLES["lijian"] + golden_songs = [s.split("_")[0] for s in golden_samples] + return golden_songs + + +def statistics(utt_dir): + song2utts = defaultdict(list) + + song_infos = glob.glob(utt_dir + "/*") + song_infos.sort() + for song in song_infos: + song_name = song.split("/")[-1] + utt_infos = glob.glob(song + "/*.wav") + utt_infos.sort() + for utt in utt_infos: + uid = utt.split("/")[-1].split(".")[0] + song2utts[song_name].append(uid) + + utt_sum = sum([len(utts) for utts in song2utts.values()]) + print("Li Jian: {} unique songs, {} utterances".format(len(song2utts), utt_sum)) + return song2utts + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing test samples for Li Jian...\n") + + if not os.path.exists(os.path.join(dataset_path, "utterances")): + print("Spliting into utterances...\n") + _main(dataset_path) + + save_dir = os.path.join(output_path, "lijian") + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + if has_existed(test_output_file): + return + + # Load + lijian_path = os.path.join(dataset_path, "utterances") + song2utts = statistics(lijian_path) + test_songs = get_test_songs() + + # We select songs of standard samples as test songs + train = [] + test = [] + + train_index_count = 0 + test_index_count = 0 + + train_total_duration = 0 + test_total_duration = 0 + + for chosen_song, utts in tqdm(song2utts.items()): + for chosen_uid in song2utts[chosen_song]: + res = { + "Dataset": "lijian", + "Singer": "lijian", + "Uid": "{}_{}".format(chosen_song, chosen_uid), + } + res["Path"] = "{}/{}.wav".format(chosen_song, chosen_uid) + res["Path"] = os.path.join(lijian_path, res["Path"]) + assert os.path.exists(res["Path"]) + + waveform, sample_rate = torchaudio.load(res["Path"]) + duration = waveform.size(-1) / sample_rate + res["Duration"] = duration + + if duration <= 1e-8: + continue + + if chosen_song in test_songs: + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + else: + res["index"] = train_index_count + train_total_duration += duration + train.append(res) + train_index_count += 1 + + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save + os.makedirs(save_dir, exist_ok=True) + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/ljspeech.py b/preprocessors/ljspeech.py new file mode 100644 index 0000000000000000000000000000000000000000..9f4bd4ca386031a7f77e5283eab0129b75b85d54 --- /dev/null +++ b/preprocessors/ljspeech.py @@ -0,0 +1,197 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +from tqdm import tqdm +import os +import torchaudio +from utils import audio +import csv +import random + +from utils.util import has_existed +from text import _clean_text +import librosa +import soundfile as sf +from scipy.io import wavfile + +from pathlib import Path +import numpy as np + + +def textgird_extract( + corpus_directory, + output_directory, + mfa_path=os.path.join("mfa", "montreal-forced-aligner", "bin", "mfa_align"), + lexicon=os.path.join("mfa", "lexicon", "librispeech-lexicon.txt"), + acoustic_model_path=os.path.join( + "mfa", "montreal-forced-aligner", "pretrained_models", "english.zip" + ), + jobs="8", +): + assert os.path.exists( + corpus_directory + ), "Please check the directionary contains *.wav, *.lab" + assert ( + os.path.exists(mfa_path) + and os.path.exists(lexicon) + and os.path.exists(acoustic_model_path) + ), f"Please download the MFA tools to {mfa_path} firstly" + Path(output_directory).mkdir(parents=True, exist_ok=True) + print(f"MFA results are save in {output_directory}") + os.system( + f".{os.path.sep}{mfa_path} {corpus_directory} {lexicon} {acoustic_model_path} {output_directory} -j {jobs} --clean" + ) + + +def get_lines(file): + lines = [] + with open(file, encoding="utf-8") as f: + for line in tqdm(f): + lines.append(line.strip()) + return lines + + +def get_uid2utt(ljspeech_path, dataset, cfg): + index_count = 0 + total_duration = 0 + + uid2utt = [] + for l in tqdm(dataset): + items = l.split("|") + uid = items[0] + text = items[2] + + res = { + "Dataset": "LJSpeech", + "index": index_count, + "Singer": "LJSpeech", + "Uid": uid, + "Text": text, + } + + # Duration in wav files + audio_file = os.path.join(ljspeech_path, "wavs/{}.wav".format(uid)) + + res["Path"] = audio_file + + waveform, sample_rate = torchaudio.load(audio_file) + duration = waveform.size(-1) / sample_rate + res["Duration"] = duration + + uid2utt.append(res) + + index_count = index_count + 1 + total_duration += duration + + return uid2utt, total_duration / 3600 + + +def split_dataset(lines, test_rate=0.05, test_size=None): + if test_size == None: + test_size = int(len(lines) * test_rate) + random.shuffle(lines) + + train_set = [] + test_set = [] + + for line in lines[:test_size]: + test_set.append(line) + for line in lines[test_size:]: + train_set.append(line) + return train_set, test_set + + +max_wav_value = 32768.0 + + +def prepare_align(dataset, dataset_path, cfg, output_path): + in_dir = dataset_path + out_dir = os.path.join(output_path, dataset, cfg.raw_data) + sampling_rate = cfg.sample_rate + cleaners = cfg.text_cleaners + speaker = "LJSpeech" + with open(os.path.join(dataset_path, "metadata.csv"), encoding="utf-8") as f: + for line in tqdm(f): + parts = line.strip().split("|") + base_name = parts[0] + text = parts[2] + text = _clean_text(text, cleaners) + + output_wav_path = os.path.join(out_dir, speaker, "{}.wav".format(base_name)) + output_lab_path = os.path.join(out_dir, speaker, "{}.lab".format(base_name)) + + if os.path.exists(output_wav_path) and os.path.exists(output_lab_path): + continue + + wav_path = os.path.join(in_dir, "wavs", "{}.wav".format(base_name)) + if os.path.exists(wav_path): + os.makedirs(os.path.join(out_dir, speaker), exist_ok=True) + wav, _ = librosa.load(wav_path, sampling_rate) + wav = wav / max(abs(wav)) * max_wav_value + + wavfile.write( + os.path.join(out_dir, speaker, "{}.wav".format(base_name)), + sampling_rate, + wav.astype(np.int16), + ) + + with open( + os.path.join(out_dir, speaker, "{}.lab".format(base_name)), + "w", + ) as f1: + f1.write(text) + # Extract textgird with MFA + textgird_extract( + corpus_directory=out_dir, + output_directory=os.path.join(output_path, dataset, "TextGrid"), + ) + + +def main(output_path, dataset_path, cfg): + print("-" * 10) + print("Dataset splits for {}...\n".format("LJSpeech")) + + dataset = "LJSpeech" + + save_dir = os.path.join(output_path, dataset) + os.makedirs(save_dir, exist_ok=True) + ljspeech_path = dataset_path + + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + singer_dict_file = os.path.join(save_dir, "singers.json") + + speaker = "LJSpeech" + speakers = [dataset + "_" + speaker] + singer_lut = {name: i for i, name in enumerate(sorted(speakers))} + with open(singer_dict_file, "w") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) + + if has_existed(train_output_file) and has_existed(test_output_file): + return + + meta_file = os.path.join(ljspeech_path, "metadata.csv") + lines = get_lines(meta_file) + + train_set, test_set = split_dataset(lines) + + res, hours = get_uid2utt(ljspeech_path, train_set, cfg) + + # Save train + os.makedirs(save_dir, exist_ok=True) + with open(train_output_file, "w") as f: + json.dump(res, f, indent=4, ensure_ascii=False) + + print("Train_hours= {}".format(hours)) + + res, hours = get_uid2utt(ljspeech_path, test_set, cfg) + + # Save test + os.makedirs(save_dir, exist_ok=True) + with open(test_output_file, "w") as f: + json.dump(res, f, indent=4, ensure_ascii=False) + + print("Test_hours= {}".format(hours)) diff --git a/preprocessors/ljspeech_vocoder.py b/preprocessors/ljspeech_vocoder.py new file mode 100644 index 0000000000000000000000000000000000000000..b6ef4fd2c88d85454a939e9fe3329e02b0be949b --- /dev/null +++ b/preprocessors/ljspeech_vocoder.py @@ -0,0 +1,86 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import torchaudio +from tqdm import tqdm +from glob import glob + +from utils.util import has_existed + + +def main(output_path, dataset_path): + print("-" * 10) + print("Dataset splits for ljspeech...\n") + + save_dir = os.path.join(output_path, "ljspeech") + ljspeech_path = dataset_path + + wave_files = glob(ljspeech_path + "/wavs/*.wav") + + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + + if has_existed(train_output_file): + return + + utts = [] + + for wave_file in tqdm(wave_files): + res = { + "Dataset": "ljspeech", + "Singer": "female1", + "Uid": "{}".format(wave_file.split("/")[-1].split(".")[0]), + } + res["Path"] = wave_file + assert os.path.exists(res["Path"]) + + waveform, sample_rate = torchaudio.load(res["Path"]) + duration = waveform.size(-1) / sample_rate + res["Duration"] = duration + + if duration <= 1e-8: + continue + + utts.append(res) + + test_length = len(utts) // 20 + + train_utts = [] + train_index_count = 0 + train_total_duration = 0 + + for i in tqdm(range(len(utts) - test_length)): + tmp = utts[i] + tmp["index"] = train_index_count + train_index_count += 1 + train_total_duration += tmp["Duration"] + train_utts.append(tmp) + + test_utts = [] + test_index_count = 0 + test_total_duration = 0 + + for i in tqdm(range(len(utts) - test_length, len(utts))): + tmp = utts[i] + tmp["index"] = test_index_count + test_index_count += 1 + test_total_duration += tmp["Duration"] + test_utts.append(tmp) + + print("#Train = {}, #Test = {}".format(len(train_utts), len(test_utts))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save + os.makedirs(save_dir, exist_ok=True) + with open(train_output_file, "w") as f: + json.dump(train_utts, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test_utts, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/m4singer.py b/preprocessors/m4singer.py new file mode 100644 index 0000000000000000000000000000000000000000..eb8eb1715ce2af6d712efd9d93c5983dd58ba257 --- /dev/null +++ b/preprocessors/m4singer.py @@ -0,0 +1,138 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import librosa +from tqdm import tqdm +from collections import defaultdict + +from utils.util import has_existed +from preprocessors import GOLDEN_TEST_SAMPLES + + +def get_test_songs(): + golden_samples = GOLDEN_TEST_SAMPLES["m4singer"] + # every item is a tuple (singer, song) + golden_songs = [s.split("_")[:2] for s in golden_samples] + # singer_song, eg: Alto-1_美错 + golden_songs = ["_".join(t) for t in golden_songs] + return golden_songs + + +def m4singer_statistics(meta): + singers = [] + songs = [] + singer2songs = defaultdict(lambda: defaultdict(list)) + for utt in meta: + p, s, uid = utt["item_name"].split("#") + singers.append(p) + songs.append(s) + singer2songs[p][s].append(uid) + + unique_singers = list(set(singers)) + unique_songs = list(set(songs)) + unique_singers.sort() + unique_songs.sort() + + print( + "M4Singer: {} singers, {} utterances ({} unique songs)".format( + len(unique_singers), len(songs), len(unique_songs) + ) + ) + print("Singers: \n{}".format("\t".join(unique_singers))) + return singer2songs, unique_singers + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing test samples for m4singer...\n") + + save_dir = os.path.join(output_path, "m4singer") + os.makedirs(save_dir, exist_ok=True) + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + singer_dict_file = os.path.join(save_dir, "singers.json") + utt2singer_file = os.path.join(save_dir, "utt2singer") + if ( + has_existed(train_output_file) + and has_existed(test_output_file) + and has_existed(singer_dict_file) + and has_existed(utt2singer_file) + ): + return + utt2singer = open(utt2singer_file, "w") + + # Load + m4singer_dir = dataset_path + meta_file = os.path.join(m4singer_dir, "meta.json") + with open(meta_file, "r", encoding="utf-8") as f: + meta = json.load(f) + + singer2songs, unique_singers = m4singer_statistics(meta) + + test_songs = get_test_songs() + + # We select songs of standard samples as test songs + train = [] + test = [] + + train_index_count = 0 + test_index_count = 0 + + train_total_duration = 0 + test_total_duration = 0 + + for singer, songs in tqdm(singer2songs.items()): + song_names = list(songs.keys()) + + for chosen_song in song_names: + chosen_song = chosen_song.replace(" ", "-") + for chosen_uid in songs[chosen_song]: + res = { + "Dataset": "m4singer", + "Singer": singer, + "Song": chosen_song, + "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid), + } + + res["Path"] = os.path.join( + m4singer_dir, "{}#{}/{}.wav".format(singer, chosen_song, chosen_uid) + ) + assert os.path.exists(res["Path"]) + + duration = librosa.get_duration(filename=res["Path"]) + res["Duration"] = duration + + if "_".join([singer, chosen_song]) in test_songs: + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + else: + res["index"] = train_index_count + train_total_duration += duration + train.append(res) + train_index_count += 1 + + utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) + + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save train.json and test.json + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) + + # Save singers.json + singer_lut = {name: i for i, name in enumerate(unique_singers)} + with open(singer_dict_file, "w") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/metadata.py b/preprocessors/metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..ffa5c3e8128856da084307389bcab8f432831985 --- /dev/null +++ b/preprocessors/metadata.py @@ -0,0 +1,138 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +from tqdm import tqdm + + +def cal_metadata(cfg): + """ + Dump metadata (singers.json, meta_info.json, utt2singer) for singer dataset or multi-datasets. + """ + from collections import Counter + + datasets = cfg.dataset + + print("-" * 10) + print("Preparing metadata...") + print("Including: \n{}\n".format("\n".join(datasets))) + + datasets.sort() + + for dataset in tqdm(datasets): + save_dir = os.path.join(cfg.preprocess.processed_dir, dataset) + assert os.path.exists(save_dir) + + # 'train.json' and 'test.json' of target dataset + train_metadata = os.path.join(save_dir, "train.json") + test_metadata = os.path.join(save_dir, "test.json") + + # Sort the metadata as the duration order + with open(train_metadata, "r", encoding="utf-8") as f: + train_utterances = json.load(f) + with open(test_metadata, "r", encoding="utf-8") as f: + test_utterances = json.load(f) + + train_utterances = sorted(train_utterances, key=lambda x: x["Duration"]) + test_utterances = sorted(test_utterances, key=lambda x: x["Duration"]) + + # Write back the sorted metadata + with open(train_metadata, "w") as f: + json.dump(train_utterances, f, indent=4, ensure_ascii=False) + with open(test_metadata, "w") as f: + json.dump(test_utterances, f, indent=4, ensure_ascii=False) + + # Paths of metadata needed to be generated + singer_dict_file = os.path.join(save_dir, cfg.preprocess.spk2id) + utt2singer_file = os.path.join(save_dir, cfg.preprocess.utt2spk) + + # Get the total duration and singer names for train and test utterances + train_total_duration = sum(utt["Duration"] for utt in train_utterances) + test_total_duration = sum(utt["Duration"] for utt in test_utterances) + + singer_names = set( + f"{replace_augment_name(utt['Dataset'])}_{utt['Singer']}" + for utt in train_utterances + test_utterances + ) + + # Write the utt2singer file and sort the singer names + with open(utt2singer_file, "w", encoding="utf-8") as f: + for utt in train_utterances + test_utterances: + f.write( + f"{utt['Dataset']}_{utt['Uid']}\t{replace_augment_name(utt['Dataset'])}_{utt['Singer']}\n" + ) + + singer_names = sorted(singer_names) + singer_lut = {name: i for i, name in enumerate(singer_names)} + + # dump singers.json + with open(singer_dict_file, "w", encoding="utf-8") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) + + meta_info = { + "dataset": dataset, + "statistics": { + "size": len(train_utterances) + len(test_utterances), + "hours": round(train_total_duration / 3600, 4) + + round(test_total_duration / 3600, 4), + }, + "train": { + "size": len(train_utterances), + "hours": round(train_total_duration / 3600, 4), + }, + "test": { + "size": len(test_utterances), + "hours": round(test_total_duration / 3600, 4), + }, + "singers": {"size": len(singer_lut)}, + } + # Use Counter to count the minutes for each singer + total_singer2mins = Counter() + training_singer2mins = Counter() + for utt in train_utterances: + k = f"{replace_augment_name(utt['Dataset'])}_{utt['Singer']}" + training_singer2mins[k] += utt["Duration"] / 60 + total_singer2mins[k] += utt["Duration"] / 60 + for utt in test_utterances: + k = f"{replace_augment_name(utt['Dataset'])}_{utt['Singer']}" + total_singer2mins[k] += utt["Duration"] / 60 + + training_singer2mins = dict( + sorted(training_singer2mins.items(), key=lambda x: x[1], reverse=True) + ) + training_singer2mins = {k: round(v, 2) for k, v in training_singer2mins.items()} + meta_info["singers"]["training_minutes"] = training_singer2mins + + total_singer2mins = dict( + sorted(total_singer2mins.items(), key=lambda x: x[1], reverse=True) + ) + total_singer2mins = {k: round(v, 2) for k, v in total_singer2mins.items()} + meta_info["singers"]["minutes"] = total_singer2mins + + with open(os.path.join(save_dir, "meta_info.json"), "w") as f: + json.dump(meta_info, f, indent=4, ensure_ascii=False) + + for singer, min in training_singer2mins.items(): + print(f"Singer {singer}: {min} mins for training") + print("-" * 10, "\n") + + +def replace_augment_name(dataset: str) -> str: + """Replace the augmented dataset name with the original dataset name. + >>> print(replace_augment_name("dataset_equalizer")) + dataset + """ + if "equalizer" in dataset: + dataset = dataset.replace("_equalizer", "") + elif "formant_shift" in dataset: + dataset = dataset.replace("_formant_shift", "") + elif "pitch_shift" in dataset: + dataset = dataset.replace("_pitch_shift", "") + elif "time_stretch" in dataset: + dataset = dataset.replace("_time_stretch", "") + else: + pass + return dataset diff --git a/preprocessors/nus48e.py b/preprocessors/nus48e.py new file mode 100644 index 0000000000000000000000000000000000000000..e780b3265b3921f21ee0efb5fcd9162e5555c529 --- /dev/null +++ b/preprocessors/nus48e.py @@ -0,0 +1,203 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import torchaudio +from tqdm import tqdm +from glob import glob +from collections import defaultdict + + +from utils.io import save_audio +from utils.util import has_existed +from utils.audio_slicer import Slicer +from preprocessors import GOLDEN_TEST_SAMPLES + + +def split_to_utterances(dataset_path, singer, style, output_dir): + data_dir = os.path.join(dataset_path, singer, style) + + print("Splitting to utterances for {}...".format(data_dir)) + + wave_files = glob(data_dir + "/*.wav") + + for wav_file in tqdm(wave_files): + # Load waveform + song_name = wav_file.split("/")[-1].split(".")[0] + waveform, fs = torchaudio.load(wav_file) + + # Split + slicer = Slicer(sr=fs, threshold=-40.0, max_sil_kept=4000) + chunks = slicer.slice(waveform) + + for i, chunk in enumerate(chunks): + save_dir = os.path.join(output_dir, singer, style, song_name) + os.makedirs(save_dir, exist_ok=True) + + output_file = os.path.join(save_dir, "{:04d}.wav".format(i)) + save_audio(output_file, chunk, fs) + + +def _main(dataset_path): + """ + Split to utterances + """ + utterance_dir = os.path.join(dataset_path, "utterances") + + singer_infos = glob(dataset_path + "/*") + + for singer_info in singer_infos: + singer = singer_info.split("/")[-1] + + for style in ["read", "sing"]: + split_to_utterances(dataset_path, singer, style, utterance_dir) + + +def get_test_songs(): + golden_samples = GOLDEN_TEST_SAMPLES["nus48e"] + # every item is a tuple (singer, song) + golden_songs = [s.split("#")[:2] for s in golden_samples] + # singer_song, eg: Female1#Almost_lover_Amateur + return golden_songs + + +def nus48e_statistics(data_dir): + singers = [] + songs = [] + singer2songs = defaultdict(lambda: defaultdict(list)) + + singer_infos = glob(data_dir + "/*") + + for singer_info in singer_infos: + singer_info_split = singer_info.split("/")[-1] + + style_infos = glob(singer_info + "/*") + + for style_info in style_infos: + style_info_split = style_info.split("/")[-1] + + singer = singer_info_split + "_" + style_info_split + singers.append(singer) + + song_infos = glob(style_info + "/*") + + for song_info in song_infos: + song = song_info.split("/")[-1] + + songs.append(song) + + utts = glob(song_info + "/*.wav") + + for utt in utts: + uid = utt.split("/")[-1].split(".")[0] + singer2songs[singer][song].append(uid) + + unique_singers = list(set(singers)) + unique_songs = list(set(songs)) + unique_singers.sort() + unique_songs.sort() + + print( + "nus_48_e: {} singers, {} utterances ({} unique songs)".format( + len(unique_singers), len(songs), len(unique_songs) + ) + ) + print("Singers: \n{}".format("\t".join(unique_singers))) + return singer2songs, unique_singers + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing test samples for nus48e...\n") + + if not os.path.exists(os.path.join(dataset_path, "utterances")): + print("Spliting into utterances...\n") + _main(dataset_path) + + save_dir = os.path.join(output_path, "nus48e") + os.makedirs(save_dir, exist_ok=True) + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + singer_dict_file = os.path.join(save_dir, "singers.json") + utt2singer_file = os.path.join(save_dir, "utt2singer") + if ( + has_existed(train_output_file) + and has_existed(test_output_file) + and has_existed(singer_dict_file) + and has_existed(utt2singer_file) + ): + return + utt2singer = open(utt2singer_file, "w") + + # Load + nus48e_path = os.path.join(dataset_path, "utterances") + + singer2songs, unique_singers = nus48e_statistics(nus48e_path) + test_songs = get_test_songs() + + # We select songs of standard samples as test songs + train = [] + test = [] + + train_index_count = 0 + test_index_count = 0 + + train_total_duration = 0 + test_total_duration = 0 + + for singer, songs in singer2songs.items(): + song_names = list(songs.keys()) + + for chosen_song in song_names: + for chosen_uid in songs[chosen_song]: + res = { + "Dataset": "nus48e", + "Singer": singer, + "Uid": "{}#{}#{}".format(singer, chosen_song, chosen_uid), + } + res["Path"] = "{}/{}/{}/{}.wav".format( + singer.split("_")[0], singer.split("_")[-1], chosen_song, chosen_uid + ) + res["Path"] = os.path.join(nus48e_path, res["Path"]) + assert os.path.exists(res["Path"]) + + waveform, sample_rate = torchaudio.load(res["Path"]) + duration = waveform.size(-1) / sample_rate + res["Duration"] = duration + + if duration <= 1e-8: + continue + + if ([singer, chosen_song]) in test_songs: + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + else: + res["index"] = train_index_count + train_total_duration += duration + train.append(res) + train_index_count += 1 + + utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) + + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save train.json and test.json + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) + + # Save singers.json + singer_lut = {name: i for i, name in enumerate(unique_singers)} + with open(singer_dict_file, "w") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/opencpop.py b/preprocessors/opencpop.py new file mode 100644 index 0000000000000000000000000000000000000000..d1da6e303cbb06370f3215ce5c2cb4dd22e611b5 --- /dev/null +++ b/preprocessors/opencpop.py @@ -0,0 +1,73 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +from tqdm import tqdm +import os +import librosa + +from utils.util import has_existed + + +def get_lines(file): + with open(file, "r") as f: + lines = f.readlines() + lines = [l.strip() for l in lines] + return lines + + +def get_uid2utt(opencpop_path, dataset, dataset_type): + index_count = 0 + total_duration = 0 + + file = os.path.join(opencpop_path, "segments", "{}.txt".format(dataset_type)) + lines = get_lines(file) + + uid2utt = [] + for l in tqdm(lines): + items = l.split("|") + uid = items[0] + + res = { + "Dataset": dataset, + "index": index_count, + "Singer": "female1", + "Uid": uid, + } + + # Duration in wav files + audio_file = os.path.join(opencpop_path, "segments/wavs/{}.wav".format(uid)) + res["Path"] = audio_file + + duration = librosa.get_duration(filename=res["Path"]) + res["Duration"] = duration + + uid2utt.append(res) + + index_count = index_count + 1 + total_duration += duration + + return uid2utt, total_duration / 3600 + + +def main(dataset, output_path, dataset_path): + print("-" * 10) + print("Dataset splits for {}...\n".format(dataset)) + + save_dir = os.path.join(output_path, dataset) + opencpop_path = dataset_path + for dataset_type in ["train", "test"]: + output_file = os.path.join(save_dir, "{}.json".format(dataset_type)) + if has_existed(output_file): + continue + + res, hours = get_uid2utt(opencpop_path, dataset, dataset_type) + + # Save + os.makedirs(save_dir, exist_ok=True) + with open(output_file, "w") as f: + json.dump(res, f, indent=4, ensure_ascii=False) + + print("{}_{}_hours= {}".format(dataset, dataset_type, hours)) diff --git a/preprocessors/opensinger.py b/preprocessors/opensinger.py new file mode 100644 index 0000000000000000000000000000000000000000..93fc3f648df11a866f6c7017d7c6964c9b36cee6 --- /dev/null +++ b/preprocessors/opensinger.py @@ -0,0 +1,169 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import random +import os +import json +import librosa +from tqdm import tqdm +from glob import glob +from collections import defaultdict + +from utils.util import has_existed +from preprocessors import GOLDEN_TEST_SAMPLES + + +def get_test_songs(): + golden_samples = GOLDEN_TEST_SAMPLES["opensinger"] + # every item is a tuple (singer, song) + golden_songs = [s.split("_")[:3] for s in golden_samples] + # singer_song, eg: Female1#Almost_lover_Amateur + return golden_songs + + +def opensinger_statistics(data_dir): + singers = [] + songs = [] + singer2songs = defaultdict(lambda: defaultdict(list)) + + gender_infos = glob(data_dir + "/*") + + for gender_info in gender_infos: + gender_info_split = gender_info.split("/")[-1][:-3] + + singer_and_song_infos = glob(gender_info + "/*") + + for singer_and_song_info in singer_and_song_infos: + singer_and_song_info_split = singer_and_song_info.split("/")[-1].split("_") + singer_id, song = ( + singer_and_song_info_split[0], + singer_and_song_info_split[1], + ) + singer = gender_info_split + "_" + singer_id + singers.append(singer) + songs.append(song) + + utts = glob(singer_and_song_info + "/*.wav") + + for utt in utts: + uid = utt.split("/")[-1].split("_")[-1].split(".")[0] + singer2songs[singer][song].append(uid) + + unique_singers = list(set(singers)) + unique_songs = list(set(songs)) + unique_singers.sort() + unique_songs.sort() + + print( + "opensinger: {} singers, {} songs ({} unique songs)".format( + len(unique_singers), len(songs), len(unique_songs) + ) + ) + print("Singers: \n{}".format("\t".join(unique_singers))) + return singer2songs, unique_singers + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing test samples for opensinger...\n") + + save_dir = os.path.join(output_path, "opensinger") + os.makedirs(save_dir, exist_ok=True) + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + singer_dict_file = os.path.join(save_dir, "singers.json") + utt2singer_file = os.path.join(save_dir, "utt2singer") + if ( + has_existed(train_output_file) + and has_existed(test_output_file) + and has_existed(singer_dict_file) + and has_existed(utt2singer_file) + ): + return + utt2singer = open(utt2singer_file, "w") + + # Load + opensinger_path = dataset_path + + singer2songs, unique_singers = opensinger_statistics(opensinger_path) + test_songs = get_test_songs() + + # We select songs of standard samples as test songs + train = [] + test = [] + + train_index_count = 0 + test_index_count = 0 + + train_total_duration = 0 + test_total_duration = 0 + + for i, (singer, songs) in enumerate(singer2songs.items()): + song_names = list(songs.keys()) + + for chosen_song in tqdm( + song_names, desc="Singer {}/{}".format(i, len(singer2songs)) + ): + for chosen_uid in songs[chosen_song]: + res = { + "Dataset": "opensinger", + "Singer": singer, + "Song": chosen_song, + "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid), + } + res["Path"] = "{}Raw/{}_{}/{}_{}_{}.wav".format( + singer.split("_")[0], + singer.split("_")[1], + chosen_song, + singer.split("_")[1], + chosen_song, + chosen_uid, + ) + res["Path"] = os.path.join(opensinger_path, res["Path"]) + assert os.path.exists(res["Path"]) + + duration = librosa.get_duration(filename=res["Path"]) + res["Duration"] = duration + + if duration > 30: + print( + "Wav file: {}, the duration = {:.2f}s > 30s, which has been abandoned.".format( + res["Path"], duration + ) + ) + continue + + if ( + [singer.split("_")[0], singer.split("_")[1], chosen_song] + ) in test_songs: + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + else: + res["index"] = train_index_count + train_total_duration += duration + train.append(res) + train_index_count += 1 + + utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) + + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save train.json and test.json + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) + + # Save singers.json + singer_lut = {name: i for i, name in enumerate(unique_singers)} + with open(singer_dict_file, "w") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/opera.py b/preprocessors/opera.py new file mode 100644 index 0000000000000000000000000000000000000000..c421fbb8346945c5dec40b1c872cebcb49f1f606 --- /dev/null +++ b/preprocessors/opera.py @@ -0,0 +1,186 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import os +from tqdm import tqdm +import torchaudio +from glob import glob +from collections import defaultdict + +from utils.util import has_existed +from utils.io import save_audio +from utils.audio_slicer import Slicer +from preprocessors import GOLDEN_TEST_SAMPLES + + +def split_to_utterances(language_dir, output_dir): + print("Splitting to utterances for {}...".format(language_dir)) + + for wav_file in tqdm(glob("{}/*/*".format(language_dir))): + # Load waveform + singer_name, song_name = wav_file.split("/")[-2:] + song_name = song_name.split(".")[0] + waveform, fs = torchaudio.load(wav_file) + + # Split + slicer = Slicer(sr=fs, threshold=-30.0, max_sil_kept=3000) + chunks = slicer.slice(waveform) + + for i, chunk in enumerate(chunks): + save_dir = os.path.join(output_dir, singer_name, song_name) + os.makedirs(save_dir, exist_ok=True) + + output_file = os.path.join(save_dir, "{:04d}.wav".format(i)) + save_audio(output_file, chunk, fs) + + +def _main(dataset_path): + """ + Split to utterances + """ + utterance_dir = os.path.join(dataset_path, "utterances") + + for lang in ["chinese", "western"]: + split_to_utterances(os.path.join(dataset_path, lang), utterance_dir) + + +def get_test_songs(): + golden_samples = GOLDEN_TEST_SAMPLES["opera"] + # every item is a tuple (singer, song) + golden_songs = [s.split("#")[:2] for s in golden_samples] + # singer#song, eg:fem_01#neg_01 + return golden_songs + + +def opera_statistics(data_dir): + singers = [] + songs = [] + singers2songs = defaultdict(lambda: defaultdict(list)) + + singer_infos = glob(data_dir + "/*") + + for singer_info in singer_infos: + singer = singer_info.split("/")[-1] + + song_infos = glob(singer_info + "/*") + + for song_info in song_infos: + song = song_info.split("/")[-1] + + singers.append(singer) + songs.append(song) + + utts = glob(song_info + "/*.wav") + + for utt in utts: + uid = utt.split("/")[-1].split(".")[0] + singers2songs[singer][song].append(uid) + + unique_singers = list(set(singers)) + unique_songs = list(set(songs)) + unique_singers.sort() + unique_songs.sort() + + print( + "opera: {} singers, {} utterances ({} unique songs)".format( + len(unique_singers), len(songs), len(unique_songs) + ) + ) + print("Singers: \n{}".format("\t".join(unique_singers))) + return singers2songs, unique_singers + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing test samples for opera...\n") + + if not os.path.exists(os.path.join(dataset_path, "utterances")): + print("Spliting into utterances...\n") + _main(dataset_path) + + save_dir = os.path.join(output_path, "opera") + os.makedirs(save_dir, exist_ok=True) + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + singer_dict_file = os.path.join(save_dir, "singers.json") + utt2singer_file = os.path.join(save_dir, "utt2singer") + if ( + has_existed(train_output_file) + and has_existed(test_output_file) + and has_existed(singer_dict_file) + and has_existed(utt2singer_file) + ): + return + utt2singer = open(utt2singer_file, "w") + + # Load + opera_path = os.path.join(dataset_path, "utterances") + + singers2songs, unique_singers = opera_statistics(opera_path) + test_songs = get_test_songs() + + # We select songs of standard samples as test songs + train = [] + test = [] + + train_index_count = 0 + test_index_count = 0 + + train_total_duration = 0 + test_total_duration = 0 + + for singer, songs in tqdm(singers2songs.items()): + song_names = list(songs.keys()) + + for chosen_song in song_names: + for chosen_uid in songs[chosen_song]: + res = { + "Dataset": "opera", + "Singer": singer, + "Uid": "{}#{}#{}".format(singer, chosen_song, chosen_uid), + } + res["Path"] = "{}/{}/{}.wav".format(singer, chosen_song, chosen_uid) + res["Path"] = os.path.join(opera_path, res["Path"]) + assert os.path.exists(res["Path"]) + + waveform, sample_rate = torchaudio.load(res["Path"]) + duration = waveform.size(-1) / sample_rate + res["Duration"] = duration + + if duration <= 1e-8: + continue + + if ([singer, chosen_song]) in test_songs: + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + else: + res["index"] = train_index_count + train_total_duration += duration + train.append(res) + train_index_count += 1 + + utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) + + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save train.json and test.json + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) + + # Save singers.json + singer_lut = {name: i for i, name in enumerate(unique_singers)} + with open(singer_dict_file, "w") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/pjs.py b/preprocessors/pjs.py new file mode 100644 index 0000000000000000000000000000000000000000..78d69bc56cae59b3bf512078e365853ccad053ff --- /dev/null +++ b/preprocessors/pjs.py @@ -0,0 +1,135 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +from tqdm import tqdm +import glob +import json +import torchaudio + +from utils.util import has_existed +from utils.io import save_audio + + +def get_splitted_utterances( + raw_wav_dir, trimed_wav_dir, n_utterance_splits, overlapping +): + res = [] + raw_song_files = glob.glob( + os.path.join(raw_wav_dir, "**/pjs*_song.wav"), recursive=True + ) + trimed_song_files = glob.glob( + os.path.join(trimed_wav_dir, "**/*.wav"), recursive=True + ) + + if len(raw_song_files) * n_utterance_splits == len(trimed_song_files): + print("Splitted done...") + for wav_file in tqdm(trimed_song_files): + uid = wav_file.split("/")[-1].split(".")[0] + utt = {"Dataset": "pjs", "Singer": "male1", "Uid": uid, "Path": wav_file} + + waveform, sample_rate = torchaudio.load(wav_file) + duration = waveform.size(-1) / sample_rate + utt["Duration"] = duration + + res.append(utt) + + else: + for wav_file in tqdm(raw_song_files): + song_id = wav_file.split("/")[-1].split(".")[0] + + waveform, sample_rate = torchaudio.load(wav_file) + trimed_waveform = torchaudio.functional.vad(waveform, sample_rate) + trimed_waveform = torchaudio.functional.vad( + trimed_waveform.flip(dims=[1]), sample_rate + ).flip(dims=[1]) + + audio_len = trimed_waveform.size(-1) + lapping_len = overlapping * sample_rate + + for i in range(n_utterance_splits): + start = i * audio_len // 3 + end = start + audio_len // 3 + lapping_len + splitted_waveform = trimed_waveform[:, start:end] + + utt = { + "Dataset": "pjs", + "Singer": "male1", + "Uid": "{}_{}".format(song_id, i), + } + + # Duration + duration = splitted_waveform.size(-1) / sample_rate + utt["Duration"] = duration + + # Save trimed wav + splitted_waveform_file = os.path.join( + trimed_wav_dir, "{}.wav".format(utt["Uid"]) + ) + save_audio(splitted_waveform_file, splitted_waveform, sample_rate) + + # Path + utt["Path"] = splitted_waveform_file + + res.append(utt) + + res = sorted(res, key=lambda x: x["Uid"]) + return res + + +def main(output_path, dataset_path, n_utterance_splits=3, overlapping=1): + """ + 1. Split one raw utterance to three splits (since some samples are too long) + 2. Overlapping of ajacent splits is 1 s + """ + print("-" * 10) + print("Preparing training dataset for PJS...") + + save_dir = os.path.join(output_path, "pjs") + raw_wav_dir = os.path.join(dataset_path, "PJS_corpus_ver1.1") + + # Trim for silence + trimed_wav_dir = os.path.join(dataset_path, "trim") + os.makedirs(trimed_wav_dir, exist_ok=True) + + # Total utterances + utterances = get_splitted_utterances( + raw_wav_dir, trimed_wav_dir, n_utterance_splits, overlapping + ) + total_uids = [utt["Uid"] for utt in utterances] + + # Test uids + n_test_songs = 3 + test_uids = [] + for i in range(1, n_test_songs + 1): + test_uids += [ + "pjs00{}_song_{}".format(i, split_id) + for split_id in range(n_utterance_splits) + ] + + # Train uids + train_uids = [uid for uid in total_uids if uid not in test_uids] + + for dataset_type in ["train", "test"]: + output_file = os.path.join(save_dir, "{}.json".format(dataset_type)) + if has_existed(output_file): + continue + + uids = eval("{}_uids".format(dataset_type)) + res = [utt for utt in utterances if utt["Uid"] in uids] + for i in range(len(res)): + res[i]["index"] = i + + time = sum([utt["Duration"] for utt in res]) + print( + "{}, Total size: {}, Total Duraions = {} s = {:.2f} hour\n".format( + dataset_type, len(res), time, time / 3600 + ) + ) + + # Save + os.makedirs(save_dir, exist_ok=True) + with open(output_file, "w") as f: + json.dump(res, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/popbutfy.py b/preprocessors/popbutfy.py new file mode 100644 index 0000000000000000000000000000000000000000..72ba7bdd60f6d370a7ff39bbb90d695f1157a4b4 --- /dev/null +++ b/preprocessors/popbutfy.py @@ -0,0 +1,153 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import torchaudio +import librosa +from tqdm import tqdm +from glob import glob +from collections import defaultdict + +from utils.util import has_existed +from preprocessors import GOLDEN_TEST_SAMPLES + + +def get_test_songs(): + golden_samples = GOLDEN_TEST_SAMPLES["popbutfy"] + # every item is a tuple (singer, song) + golden_songs = [s.split("#")[:2] for s in golden_samples] + # singer#song, eg: Female1#Almost_lover_Amateur + return golden_songs + + +def popbutfy_statistics(data_dir): + singers = [] + songs = [] + singer2songs = defaultdict(lambda: defaultdict(list)) + + data_infos = glob(data_dir + "/*") + + for data_info in data_infos: + data_info_split = data_info.split("/")[-1].split("#") + + singer, song = data_info_split[0], data_info_split[-1] + singers.append(singer) + songs.append(song) + + utts = glob(data_info + "/*") + + for utt in utts: + uid = utt.split("/")[-1].split("_")[-1].split(".")[0] + singer2songs[singer][song].append(uid) + + unique_singers = list(set(singers)) + unique_songs = list(set(songs)) + unique_singers.sort() + unique_songs.sort() + + print( + "PopBuTFy: {} singers, {} utterances ({} unique songs)".format( + len(unique_singers), len(songs), len(unique_songs) + ) + ) + print("Singers: \n{}".format("\t".join(unique_singers))) + return singer2songs, unique_singers + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing test samples for popbutfy...\n") + + save_dir = os.path.join(output_path, "popbutfy") + os.makedirs(save_dir, exist_ok=True) + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + singer_dict_file = os.path.join(save_dir, "singers.json") + utt2singer_file = os.path.join(save_dir, "utt2singer") + if ( + has_existed(train_output_file) + and has_existed(test_output_file) + and has_existed(singer_dict_file) + and has_existed(utt2singer_file) + ): + return + utt2singer = open(utt2singer_file, "w") + + # Load + popbutfy_dir = dataset_path + + singer2songs, unique_singers = popbutfy_statistics(popbutfy_dir) + test_songs = get_test_songs() + + # We select songs of standard samples as test songs + train = [] + test = [] + + train_index_count = 0 + test_index_count = 0 + + train_total_duration = 0 + test_total_duration = 0 + + for singer, songs in tqdm(singer2songs.items()): + song_names = list(songs.keys()) + + for chosen_song in song_names: + for chosen_uid in songs[chosen_song]: + res = { + "Dataset": "popbutfy", + "Singer": singer, + "Song": chosen_song, + "Uid": "{}#{}#".format(singer, chosen_song, chosen_uid), + } + res["Path"] = "{}#singing#{}/{}#singing#{}_{}.mp3".format( + singer, chosen_song, singer, chosen_song, chosen_uid + ) + if not os.path.exists(os.path.join(popbutfy_dir, res["Path"])): + res["Path"] = "{}#singing#{}/{}#singing#{}_{}.wav".format( + singer, chosen_song, singer, chosen_song, chosen_uid + ) + res["Path"] = os.path.join(popbutfy_dir, res["Path"]) + assert os.path.exists(res["Path"]) + + if res["Path"].split("/")[-1].split(".")[-1] == "wav": + waveform, sample_rate = torchaudio.load(res["Path"]) + duration = waveform.size(-1) / sample_rate + else: + waveform, sample_rate = librosa.load(res["Path"]) + duration = waveform.shape[-1] / sample_rate + res["Duration"] = duration + + if ([singer, chosen_song]) in test_songs: + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + else: + res["index"] = train_index_count + train_total_duration += duration + train.append(res) + train_index_count += 1 + + utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) + + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save train.json and test.json + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) + + # Save singers.json + singer_lut = {name: i for i, name in enumerate(unique_singers)} + with open(singer_dict_file, "w") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/popcs.py b/preprocessors/popcs.py new file mode 100644 index 0000000000000000000000000000000000000000..9acf2127ec27f679d715108495f580a30f75d01a --- /dev/null +++ b/preprocessors/popcs.py @@ -0,0 +1,118 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import torchaudio +from glob import glob +from collections import defaultdict + +from utils.util import has_existed +from preprocessors import GOLDEN_TEST_SAMPLES + + +def get_test_songs(): + golden_samples = GOLDEN_TEST_SAMPLES["popcs"] + # every item is a string + golden_songs = [s.split("_")[:1] for s in golden_samples] + # song, eg: 万有引力 + return golden_songs + + +def popcs_statistics(data_dir): + songs = [] + songs2utts = defaultdict(list) + + song_infos = glob(data_dir + "/*") + + for song_info in song_infos: + song_info_split = song_info.split("/")[-1].split("-")[-1] + + songs.append(song_info_split) + + utts = glob(song_info + "/*.wav") + + for utt in utts: + uid = utt.split("/")[-1].split("_")[0] + songs2utts[song_info_split].append(uid) + + unique_songs = list(set(songs)) + unique_songs.sort() + + print( + "popcs: {} utterances ({} unique songs)".format(len(songs), len(unique_songs)) + ) + print("Songs: \n{}".format("\t".join(unique_songs))) + return songs2utts + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing test samples for popcs...\n") + + save_dir = os.path.join(output_path, "popcs") + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + if has_existed(test_output_file): + return + + # Load + popcs_dir = dataset_path + + songs2utts = popcs_statistics(popcs_dir) + test_songs = get_test_songs() + + # We select songs of standard samples as test songs + train = [] + test = [] + + train_index_count = 0 + test_index_count = 0 + + train_total_duration = 0 + test_total_duration = 0 + + song_names = list(songs2utts.keys()) + + for chosen_song in song_names: + for chosen_uid in songs2utts[chosen_song]: + res = { + "Dataset": "popcs", + "Singer": "female1", + "Song": chosen_song, + "Uid": "{}_{}".format(chosen_song, chosen_uid), + } + res["Path"] = "popcs-{}/{}_wf0.wav".format(chosen_song, chosen_uid) + res["Path"] = os.path.join(popcs_dir, res["Path"]) + assert os.path.exists(res["Path"]) + + waveform, sample_rate = torchaudio.load(res["Path"]) + duration = waveform.size(-1) / sample_rate + res["Duration"] = duration + + if ([chosen_song]) in test_songs: + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + else: + res["index"] = train_index_count + train_total_duration += duration + train.append(res) + train_index_count += 1 + + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save + os.makedirs(save_dir, exist_ok=True) + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/processor.py b/preprocessors/processor.py new file mode 100644 index 0000000000000000000000000000000000000000..845479bc846866680591139347be6b9a1fe70687 --- /dev/null +++ b/preprocessors/processor.py @@ -0,0 +1,105 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import re +from preprocessors import ( + m4singer, + opencpop, + svcc, + pjs, + popbutfy, + opensinger, + popcs, + kising, + csd, + opera, + nus48e, + svcceval, + vctk, + vctksample, + libritts, + lijian, + cdmusiceval, + ljspeech, + coco, + cocoeval, + custom, + vocalist, + ljspeech_vocoder, + hifitts +) + + +def preprocess_dataset( + dataset, dataset_path, output_path, cfg, is_custom_dataset=False +): + """Call specific function to handle specific dataset + Args: + dataset (str): name of a dataset, e.g. opencpop, m4singer + dataset_path (str): path to dataset + output_path (str): path to store preprocessing result files + """ + if is_custom_dataset: + custom.main(output_path, dataset_path, dataset_name=dataset) + return + + if re.match("opencpop*", dataset): + opencpop.main(dataset, output_path, dataset_path) + if dataset == "m4singer": + m4singer.main(output_path, dataset_path) + if dataset == "svcc": + svcc.main(output_path, dataset_path) + if dataset == "pjs": + pjs.main(output_path, dataset_path) + if dataset == "popbutfy": + popbutfy.main(output_path, dataset_path) + if dataset == "opensinger": + opensinger.main(output_path, dataset_path) + if dataset == "popcs": + popcs.main(output_path, dataset_path) + if dataset == "kising": + kising.main(output_path, dataset_path) + if dataset == "csd": + csd.main(output_path, dataset_path) + if dataset == "opera": + opera.main(output_path, dataset_path) + if dataset == "nus48e": + nus48e.main(output_path, dataset_path) + if dataset == "vctk": + vctk.main(output_path, dataset_path) + if dataset == "svcceval": + svcceval.main(output_path, dataset_path) + if dataset == "libritts": + libritts.main(output_path, dataset_path) + if dataset == "lijian": + lijian.main(output_path, dataset_path) + if dataset == "cdmusiceval": + cdmusiceval.main(output_path, dataset_path) + if dataset == "LJSpeech": + ljspeech.main(output_path, dataset_path, cfg) + if dataset == "ljspeech": + ljspeech_vocoder.main(output_path, dataset_path) + if dataset == "coco": + coco.main(output_path, dataset_path) + if dataset == "cocoeval": + cocoeval.main(output_path, dataset_path) + if dataset == "vocalist": + vocalist.main(output_path, dataset_path) + if dataset == "hifitts": + hifitts.main(output_path, dataset_path) + else: + print('The processor for {} is not found'.format(dataset)) + + +def prepare_align(dataset, dataset_path, cfg, output_path): + """Call specific function to handle specific dataset + + Args: + dataset (str): name of a dataset, e.g. ljspeech + dataset_path (str): path to dataset + output_path (str): path to store preprocessing result files + """ + if dataset == "LJSpeech": + ljspeech.prepare_align(dataset, dataset_path, cfg, output_path) diff --git a/preprocessors/svcc.py b/preprocessors/svcc.py new file mode 100644 index 0000000000000000000000000000000000000000..6afee6197146eff9464839ea7844edbb64e76211 --- /dev/null +++ b/preprocessors/svcc.py @@ -0,0 +1,85 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import glob +import librosa +import json + +from utils.util import has_existed +from preprocessors import GOLDEN_TEST_SAMPLES + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing training dataset for svcc...") + + data_dir = os.path.join(dataset_path, "Data") + save_dir = os.path.join(output_path, "svcc") + os.makedirs(save_dir, exist_ok=True) + + singer_dict_file = os.path.join(save_dir, "singers.json") + utt2singer_file = os.path.join(save_dir, "utt2singer") + utt2singer = open(utt2singer_file, "w") + + # Load utterances + train = [] + test = [] + singers = [] + + for wav_file in glob.glob(os.path.join(data_dir, "*/*.wav")): + singer, filename = wav_file.split("/")[-2:] + uid = filename.split(".")[0] + utt = { + "Dataset": "svcc", + "Singer": singer, + "Uid": "{}_{}".format(singer, uid), + "Path": wav_file, + } + + # Duration + duration = librosa.get_duration(filename=wav_file) + utt["Duration"] = duration + + if utt["Uid"] in GOLDEN_TEST_SAMPLES["svcc"]: + test.append(utt) + else: + train.append(utt) + + singers.append(singer) + utt2singer.write("{}\t{}\n".format(utt["Uid"], utt["Singer"])) + + # Save singers.json + unique_singers = list(set(singers)) + unique_singers.sort() + singer_lut = {name: i for i, name in enumerate(unique_singers)} + with open(singer_dict_file, "w") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) + + train_total_duration = sum([utt["Duration"] for utt in train]) + test_total_duration = sum([utt["Duration"] for utt in test]) + + for dataset_type in ["train", "test"]: + output_file = os.path.join(save_dir, "{}.json".format(dataset_type)) + if has_existed(output_file): + continue + + utterances = eval(dataset_type) + utterances = sorted(utterances, key=lambda x: x["Uid"]) + + for i in range(len(utterances)): + utterances[i]["index"] = i + + print("{}: Total size: {}\n".format(dataset_type, len(utterances))) + + # Save + with open(output_file, "w") as f: + json.dump(utterances, f, indent=4, ensure_ascii=False) + + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) diff --git a/preprocessors/svcceval.py b/preprocessors/svcceval.py new file mode 100644 index 0000000000000000000000000000000000000000..871e78956d8e9d323125aaa79770f02176e2426c --- /dev/null +++ b/preprocessors/svcceval.py @@ -0,0 +1,80 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import glob +import librosa +import json + +from utils.util import has_existed + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing training dataset for svcceval...") + + data_dir = os.path.join(dataset_path, "Data") + save_dir = os.path.join(output_path, "svcceval") + os.makedirs(save_dir, exist_ok=True) + + singer_dict_file = os.path.join(save_dir, "singers.json") + utt2singer_file = os.path.join(save_dir, "utt2singer") + utt2singer = open(utt2singer_file, "w") + + # Load utterances + train = [] + test = [] + singers = [] + for wav_file in glob.glob(os.path.join(data_dir, "*/*.wav")): + singer, filename = wav_file.split("/")[-2:] + uid = filename.split(".")[0] + utt = { + "Dataset": "svcceval", + "Singer": singer, + "Uid": "{}_{}".format(singer, uid), + "Path": wav_file, + } + + # Duration + duration = librosa.get_duration(filename=wav_file) + utt["Duration"] = duration + + test.append(utt) + + singers.append(singer) + utt2singer.write("{}\t{}\n".format(utt["Uid"], utt["Singer"])) + + # Save singers.json + unique_singers = list(set(singers)) + unique_singers.sort() + singer_lut = {name: i for i, name in enumerate(unique_singers)} + with open(singer_dict_file, "w") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) + + train_total_duration = sum([utt["Duration"] for utt in train]) + test_total_duration = sum([utt["Duration"] for utt in test]) + + for dataset_type in ["train", "test"]: + output_file = os.path.join(save_dir, "{}.json".format(dataset_type)) + if has_existed(output_file): + continue + + utterances = eval(dataset_type) + utterances = sorted(utterances, key=lambda x: x["Uid"]) + + for i in range(len(utterances)): + utterances[i]["index"] = i + + print("{}: Total size: {}\n".format(dataset_type, len(utterances))) + + # Save + with open(output_file, "w") as f: + json.dump(utterances, f, indent=4, ensure_ascii=False) + + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) diff --git a/preprocessors/vctk.py b/preprocessors/vctk.py new file mode 100644 index 0000000000000000000000000000000000000000..aa4c1c69414561f6e40c69acc653630ba783abd1 --- /dev/null +++ b/preprocessors/vctk.py @@ -0,0 +1,163 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import librosa +from tqdm import tqdm +from glob import glob +from collections import defaultdict + +from utils.util import has_existed + + +def get_lines(file): + with open(file, "r") as f: + lines = f.readlines() + lines = [l.strip() for l in lines] + return lines + + +def vctk_statistics(data_dir): + speakers = [] + speakers2utts = defaultdict(list) + + speaker_infos = glob(data_dir + "/wav48_silence_trimmed" + "/*") + + for speaker_info in speaker_infos: + speaker = speaker_info.split("/")[-1] + + if speaker == "log.txt": + continue + + speakers.append(speaker) + + utts = glob(speaker_info + "/*") + + for utt in utts: + uid = ( + utt.split("/")[-1].split("_")[1] + + "_" + + utt.split("/")[-1].split("_")[2].split(".")[0] + ) + speakers2utts[speaker].append(uid) + + unique_speakers = list(set(speakers)) + unique_speakers.sort() + + print("Speakers: \n{}".format("\t".join(unique_speakers))) + return speakers2utts, unique_speakers + + +def vctk_speaker_infos(data_dir): + file = os.path.join(data_dir, "speaker-info.txt") + lines = get_lines(file) + + ID2speakers = defaultdict() + for l in tqdm(lines): + items = l.replace(" ", "") + + if items[:2] == "ID": + # The header line + continue + + if items[0] == "p": + id = items[:4] + gender = items[6] + elif items[0] == "s": + id = items[:2] + gender = items[4] + + if gender == "F": + speaker = "female_{}".format(id) + elif gender == "M": + speaker = "male_{}".format(id) + + ID2speakers[id] = speaker + + return ID2speakers + + +def main(output_path, dataset_path, TEST_NUM_OF_EVERY_SPEAKER=3): + print("-" * 10) + print("Preparing test samples for vctk...") + + save_dir = os.path.join(output_path, "vctk") + os.makedirs(save_dir, exist_ok=True) + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + singer_dict_file = os.path.join(save_dir, "singers.json") + utt2singer_file = os.path.join(save_dir, "utt2singer") + if has_existed(train_output_file): + return + utt2singer = open(utt2singer_file, "w") + + # Load + vctk_dir = dataset_path + + ID2speakers = vctk_speaker_infos(vctk_dir) + speaker2utts, unique_speakers = vctk_statistics(vctk_dir) + + # We select speakers of standard samples as test utts + train = [] + test = [] + + train_index_count = 0 + test_index_count = 0 + test_speaker_count = defaultdict(int) + + train_total_duration = 0 + test_total_duration = 0 + + for i, speaker in enumerate(speaker2utts.keys()): + for chosen_uid in tqdm( + speaker2utts[speaker], + desc="Speaker {}/{}, #Train = {}, #Test = {}".format( + i + 1, len(speaker2utts), train_index_count, test_index_count + ), + ): + res = { + "Dataset": "vctk", + "Singer": ID2speakers[speaker], + "Uid": "{}#{}".format(ID2speakers[speaker], chosen_uid), + } + res["Path"] = "{}/{}_{}.flac".format(speaker, speaker, chosen_uid) + res["Path"] = os.path.join(vctk_dir, "wav48_silence_trimmed", res["Path"]) + assert os.path.exists(res["Path"]) + + duration = librosa.get_duration(filename=res["Path"]) + res["Duration"] = duration + + if test_speaker_count[speaker] < TEST_NUM_OF_EVERY_SPEAKER: + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + test_speaker_count[speaker] += 1 + else: + res["index"] = train_index_count + train_total_duration += duration + train.append(res) + train_index_count += 1 + + utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) + + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save train.json and test.json + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) + + # Save singers.json + singer_lut = {name: i for i, name in enumerate(unique_speakers)} + with open(singer_dict_file, "w") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/vctkfewsinger.py b/preprocessors/vctkfewsinger.py new file mode 100644 index 0000000000000000000000000000000000000000..e4b6adc76597b18ccbbf065ef339ccff77032a52 --- /dev/null +++ b/preprocessors/vctkfewsinger.py @@ -0,0 +1,175 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import pickle +import glob +from collections import defaultdict +from tqdm import tqdm + + +# Train: male 20 hours, female 10 hours +TRAIN_MALE_MAX_SECONDS = 20 * 3600 +TRAIN_FEMALE_MAX_SECONDS = 10 * 3600 +TEST_MAX_NUM_EVERY_PERSON = 5 + + +def select_sample_idxs(): + chosen_speakers = get_chosen_speakers() + + with open(os.path.join(vctk_dir, "train.json"), "r") as f: + raw_train = json.load(f) + with open(os.path.join(vctk_dir, "test.json"), "r") as f: + raw_test = json.load(f) + + train_idxs, test_idxs = [], [] + + # =========== Test =========== + test_nums = defaultdict(int) + for utt in tqdm(raw_train): + idx = utt["index"] + singer = utt["Singer"] + + if singer in chosen_speakers and test_nums[singer] < TEST_MAX_NUM_EVERY_PERSON: + test_nums[singer] += 1 + test_idxs.append("train_{}".format(idx)) + + for utt in tqdm(raw_test): + idx = utt["index"] + singer = utt["Singer"] + + if singer in chosen_speakers and test_nums[singer] < TEST_MAX_NUM_EVERY_PERSON: + test_nums[singer] += 1 + test_idxs.append("test_{}".format(idx)) + + # =========== Train =========== + for utt in tqdm(raw_train): + idx = utt["index"] + singer = utt["Singer"] + + if singer in chosen_speakers and "train_{}".format(idx) not in test_idxs: + train_idxs.append("train_{}".format(idx)) + + for utt in tqdm(raw_test): + idx = utt["index"] + singer = utt["Singer"] + + if singer in chosen_speakers and "test_{}".format(idx) not in test_idxs: + train_idxs.append("test_{}".format(idx)) + + train_idxs.sort() + test_idxs.sort() + return train_idxs, test_idxs, raw_train, raw_test + + +def statistics_of_speakers(): + speaker2time = defaultdict(float) + sex2time = defaultdict(float) + + with open(os.path.join(vctk_dir, "train.json"), "r") as f: + train = json.load(f) + with open(os.path.join(vctk_dir, "test.json"), "r") as f: + test = json.load(f) + + for utt in train + test: + # minutes + speaker2time[utt["Singer"]] += utt["Duration"] + # hours + sex2time[utt["Singer"].split("_")[0]] += utt["Duration"] + + print( + "Female: {:.2f} hours, Male: {:.2f} hours.\n".format( + sex2time["female"] / 3600, sex2time["male"] / 3600 + ) + ) + + speaker2time = sorted(speaker2time.items(), key=lambda x: x[-1], reverse=True) + for singer, seconds in speaker2time: + print("{}\t{:.2f} mins".format(singer, seconds / 60)) + + return speaker2time + + +def get_chosen_speakers(): + speaker2time = statistics_of_speakers() + + chosen_time = defaultdict(float) + chosen_speaker = defaultdict(list) + train_constrait = { + "male": TRAIN_MALE_MAX_SECONDS, + "female": TRAIN_FEMALE_MAX_SECONDS, + } + + for speaker, seconds in speaker2time: + sex = speaker.split("_")[0] + if chosen_time[sex] < train_constrait[sex]: + chosen_time[sex] += seconds + chosen_speaker[sex].append(speaker) + + speaker2time = dict(speaker2time) + chosen_speaker = chosen_speaker["male"] + chosen_speaker["female"] + print("\n#Chosen speakers = {}".format(len(chosen_speaker))) + for spk in chosen_speaker: + print("{}\t{:.2f} mins".format(spk, speaker2time[spk] / 60)) + + return chosen_speaker + + +if __name__ == "__main__": + root_path = "" + vctk_dir = os.path.join(root_path, "vctk") + fewspeaker_dir = os.path.join(root_path, "vctkfewspeaker") + os.makedirs(fewspeaker_dir, exist_ok=True) + + train_idxs, test_idxs, raw_train, raw_test = select_sample_idxs() + print("#Train = {}, #Test = {}".format(len(train_idxs), len(test_idxs))) + + # There are no data leakage + assert len(set(train_idxs).intersection(set(test_idxs))) == 0 + for idx in train_idxs + test_idxs: + # No test data of raw vctk + assert "test_" not in idx + + for split, chosen_idxs in zip(["train", "test"], [train_idxs, test_idxs]): + print("{}: #chosen idx = {}\n".format(split, len(chosen_idxs))) + + # Select features + feat_files = glob.glob("**/train.pkl", root_dir=vctk_dir, recursive=True) + for file in tqdm(feat_files): + raw_file = os.path.join(vctk_dir, file) + new_file = os.path.join( + fewspeaker_dir, file.replace("train.pkl", "{}.pkl".format(split)) + ) + + new_dir = "/".join(new_file.split("/")[:-1]) + os.makedirs(new_dir, exist_ok=True) + + if "mel_min" in file or "mel_max" in file: + os.system("cp {} {}".format(raw_file, new_file)) + continue + + with open(raw_file, "rb") as f: + raw_feats = pickle.load(f) + + print("file: {}, #raw_feats = {}".format(file, len(raw_feats))) + new_feats = [] + for idx in chosen_idxs: + chosen_split_is_train, raw_idx = idx.split("_") + assert chosen_split_is_train == "train" + new_feats.append(raw_feats[int(raw_idx)]) + + with open(new_file, "wb") as f: + pickle.dump(new_feats, f) + print("New file: {}, #new_feats = {}".format(new_file, len(new_feats))) + + # Utterance re-index + news_utts = [raw_train[int(idx.split("_")[-1])] for idx in chosen_idxs] + for i, utt in enumerate(news_utts): + utt["Dataset"] = "vctkfewsinger" + utt["index"] = i + + with open(os.path.join(fewspeaker_dir, "{}.json".format(split)), "w") as f: + json.dump(news_utts, f, indent=4) diff --git a/preprocessors/vctksample.py b/preprocessors/vctksample.py new file mode 100644 index 0000000000000000000000000000000000000000..476790f3941eb07cfe6ed9051293004b4e588f24 --- /dev/null +++ b/preprocessors/vctksample.py @@ -0,0 +1,108 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import pickle +import glob +from collections import defaultdict +from tqdm import tqdm +from preprocessors import get_golden_samples_indexes + + +TRAIN_MAX_NUM_EVERY_PERSON = 250 +TEST_MAX_NUM_EVERY_PERSON = 25 + + +def select_sample_idxs(): + # =========== Train =========== + with open(os.path.join(vctk_dir, "train.json"), "r") as f: + raw_train = json.load(f) + + train_idxs = [] + train_nums = defaultdict(int) + for utt in tqdm(raw_train): + idx = utt["index"] + singer = utt["Singer"] + + if train_nums[singer] < TRAIN_MAX_NUM_EVERY_PERSON: + train_idxs.append(idx) + train_nums[singer] += 1 + + # =========== Test =========== + with open(os.path.join(vctk_dir, "test.json"), "r") as f: + raw_test = json.load(f) + + # golden test + test_idxs = get_golden_samples_indexes( + dataset_name="vctk", split="test", dataset_dir=vctk_dir + ) + test_nums = defaultdict(int) + for idx in test_idxs: + singer = raw_test[idx]["Singer"] + test_nums[singer] += 1 + + for utt in tqdm(raw_test): + idx = utt["index"] + singer = utt["Singer"] + + if test_nums[singer] < TEST_MAX_NUM_EVERY_PERSON: + test_idxs.append(idx) + test_nums[singer] += 1 + + train_idxs.sort() + test_idxs.sort() + return train_idxs, test_idxs, raw_train, raw_test + + +if __name__ == "__main__": + root_path = "" + vctk_dir = os.path.join(root_path, "vctk") + sample_dir = os.path.join(root_path, "vctksample") + os.makedirs(sample_dir, exist_ok=True) + + train_idxs, test_idxs, raw_train, raw_test = select_sample_idxs() + print("#Train = {}, #Test = {}".format(len(train_idxs), len(test_idxs))) + + for split, chosen_idxs, utterances in zip( + ["train", "test"], [train_idxs, test_idxs], [raw_train, raw_test] + ): + print( + "#{} = {}, #chosen idx = {}\n".format( + split, len(utterances), len(chosen_idxs) + ) + ) + + # Select features + feat_files = glob.glob( + "**/{}.pkl".format(split), root_dir=vctk_dir, recursive=True + ) + for file in tqdm(feat_files): + raw_file = os.path.join(vctk_dir, file) + new_file = os.path.join(sample_dir, file) + + new_dir = "/".join(new_file.split("/")[:-1]) + os.makedirs(new_dir, exist_ok=True) + + if "mel_min" in file or "mel_max" in file: + os.system("cp {} {}".format(raw_file, new_file)) + continue + + with open(raw_file, "rb") as f: + raw_feats = pickle.load(f) + + print("file: {}, #raw_feats = {}".format(file, len(raw_feats))) + new_feats = [raw_feats[idx] for idx in chosen_idxs] + with open(new_file, "wb") as f: + pickle.dump(new_feats, f) + + # Utterance re-index + news_utts = [utterances[idx] for idx in chosen_idxs] + for i, utt in enumerate(news_utts): + utt["Dataset"] = "vctksample" + utt["index"] = i + + with open(os.path.join(sample_dir, "{}.json".format(split)), "w") as f: + json.dump(news_utts, f, indent=4) diff --git a/preprocessors/vocalist.py b/preprocessors/vocalist.py new file mode 100644 index 0000000000000000000000000000000000000000..44de1ac00ebcf10d65e6af37a98afcfe6ae85b89 --- /dev/null +++ b/preprocessors/vocalist.py @@ -0,0 +1,137 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import torchaudio +from tqdm import tqdm +from glob import glob +from collections import defaultdict + +from utils.util import has_existed + + +def vocalist_statistics(data_dir): + singers = [] + songs = [] + global2singer2songs = defaultdict(lambda: defaultdict(lambda: defaultdict(list))) + + global_infos = glob(data_dir + "/*") + + for global_info in global_infos: + global_split = global_info.split("/")[-1] + + singer_infos = glob(global_info + "/*") + + for singer_info in singer_infos: + singer = singer_info.split("/")[-1] + + singers.append(singer) + + song_infos = glob(singer_info + "/*") + for song_info in song_infos: + song = song_info.split("/")[-1] + + songs.append(song) + + utts = glob(song_info + "/*.wav") + + for utt in utts: + uid = utt.split("/")[-1].split(".")[0] + global2singer2songs[global_split][singer][song].append(uid) + + unique_singers = list(set(singers)) + unique_songs = list(set(songs)) + unique_singers.sort() + unique_songs.sort() + + print( + "vocalist: {} singers, {} songs ({} unique songs)".format( + len(unique_singers), len(songs), len(unique_songs) + ) + ) + print("Singers: \n{}".format("\t".join(unique_singers))) + return global2singer2songs, unique_singers + + +def main(output_path, dataset_path): + print("-" * 10) + print("Preparing test samples for vocalist...\n") + + save_dir = os.path.join(output_path, "vocalist") + os.makedirs(save_dir, exist_ok=True) + train_output_file = os.path.join(save_dir, "train.json") + test_output_file = os.path.join(save_dir, "test.json") + singer_dict_file = os.path.join(save_dir, "singers.json") + utt2singer_file = os.path.join(save_dir, "utt2singer") + if ( + has_existed(train_output_file) + and has_existed(test_output_file) + and has_existed(singer_dict_file) + and has_existed(utt2singer_file) + ): + return + utt2singer = open(utt2singer_file, "w") + + # Load + vocalist_path = dataset_path + + global2singer2songs, unique_singers = vocalist_statistics(vocalist_path) + + train = [] + test = [] + + train_index_count = 0 + test_index_count = 0 + + train_total_duration = 0 + test_total_duration = 0 + + for global_info, singer2songs in tqdm(global2singer2songs.items()): + for singer, songs in tqdm(singer2songs.items()): + song_names = list(songs.keys()) + + for chosen_song in song_names: + for chosen_uid in songs[chosen_song]: + res = { + "Dataset": "opensinger", + "Singer": singer, + "Song": chosen_song, + "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid), + } + res["Path"] = "{}/{}/{}/{}.wav".format( + global_info, singer, chosen_song, chosen_uid + ) + res["Path"] = os.path.join(vocalist_path, res["Path"]) + assert os.path.exists(res["Path"]) + + waveform, sample_rate = torchaudio.load(res["Path"]) + duration = waveform.size(-1) / sample_rate + res["Duration"] = duration + + res["index"] = test_index_count + test_total_duration += duration + test.append(res) + test_index_count += 1 + + utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) + + print("#Train = {}, #Test = {}".format(len(train), len(test))) + print( + "#Train hours= {}, #Test hours= {}".format( + train_total_duration / 3600, test_total_duration / 3600 + ) + ) + + # Save train.json and test.json + with open(train_output_file, "w") as f: + json.dump(train, f, indent=4, ensure_ascii=False) + with open(test_output_file, "w") as f: + json.dump(test, f, indent=4, ensure_ascii=False) + + # Save singers.json + singer_lut = {name: i for i, name in enumerate(unique_singers)} + with open(singer_dict_file, "w") as f: + json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/pretrained/README.md b/pretrained/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c2b4c4c8df46c7efea8c243b373eab417a266cef --- /dev/null +++ b/pretrained/README.md @@ -0,0 +1,116 @@ +# Pretrained Models Dependency + +The models dependency of Amphion are as follows (sort alphabetically): + +- [Pretrained Models Dependency](#pretrained-models-dependency) + - [Amphion Singing BigVGAN](#amphion-singing-bigvgan) + - [Amphion Speech HiFi-GAN](#amphion-speech-hifi-gan) + - [ContentVec](#contentvec) + - [WeNet](#wenet) + - [Whisper](#whisper) + - [RawNet3](#rawnet3) + + +The instructions about how to download them is displayed as follows. + +## Amphion Singing BigVGAN + +We fine-tune the official BigVGAN pretrained model with over 120 hours singing voice data. The fine-tuned checkpoint can be downloaded [here](https://cuhko365-my.sharepoint.com/:f:/g/personal/222042021_link_cuhk_edu_cn/EtiHh5JZ0_xGlYbyLLSoqBgBe9kI5q3ROY-SvBqefae-IA?e=dk4Pqa). You need to download the `400000.pt` and `args.json` files into `Amphion/pretrained/bigvgan`: + +``` +Amphion + ┣ pretrained + ┃ ┣ bivgan + ┃ ┃ ┣ 400000.pt + ┃ ┃ ┣ args.json +``` + +## Amphion Speech HiFi-GAN + +We trained our HiFi-GAN pretrained model with 685 hours speech data. Which can be downloaded [here](https://cuhko365-my.sharepoint.com/:f:/g/personal/xueliumeng_cuhk_edu_cn/Ei24hGJO_PVBopjhKje1uzEBqfhV9h89HoLrOoy9K8tzGg?e=ka7MCO). You need to download the whole folder of `hifigan_speech` into `Amphion/pretrained/hifigan`. + +``` +Amphion + ┣ pretrained + ┃ ┣ hifigan + ┃ ┃ ┣ hifigan_speech + ┃ ┃ ┃ ┣ log + ┃ ┃ ┃ ┣ result + ┃ ┃ ┃ ┣ checkpoint + ┃ ┃ ┃ ┣ args.json +``` + +## ContentVec + +You can download the pretrained ContentVec model [here](https://github.com/auspicious3000/contentvec). Note that we use the `ContentVec_legacy-500 classes` checkpoint. Assume that you download the `checkpoint_best_legacy_500.pt` into the `Amphion/pretrained/contentvec`. + +``` +Amphion + ┣ pretrained + ┃ ┣ contentvec + ┃ ┃ ┣ checkpoint_best_legacy_500.pt +``` + +## WeNet + +You can download the pretrained WeNet model [here](https://github.com/wenet-e2e/wenet/blob/main/docs/pretrained_models.md). Take the `wenetspeech` pretrained checkpoint as an example, assume you download the `wenetspeech_u2pp_conformer_exp.tar` into the `Amphion/pretrained/wenet`. Unzip it and modify its configuration file as follows: + +```sh +cd Amphion/pretrained/wenet + +### Unzip the expt dir +tar -xvf wenetspeech_u2pp_conformer_exp.tar.gz + +### Specify the updated path in train.yaml +cd 20220506_u2pp_conformer_exp +vim train.yaml +# TODO: Change the value of "cmvn_file" (Line 2) to the absolute path of the `global_cmvn` file. (Eg: [YourPath]/Amphion/pretrained/wenet/20220506_u2pp_conformer_exp/global_cmvn) +``` + +The final file struture tree is like: + +``` +Amphion + ┣ pretrained + ┃ ┣ wenet + ┃ ┃ ┣ 20220506_u2pp_conformer_exp + ┃ ┃ ┃ ┣ final.pt + ┃ ┃ ┃ ┣ global_cmvn + ┃ ┃ ┃ ┣ train.yaml + ┃ ┃ ┃ ┣ units.txt +``` + +## Whisper + +The official pretrained whisper checkpoints can be available [here](https://github.com/openai/whisper/blob/e58f28804528831904c3b6f2c0e473f346223433/whisper/__init__.py#L17). In Amphion, we use the `medium` whisper model by default. You can download it as follows: + +```bash +cd Amphion/pretrained +mkdir whisper +cd whisper + +wget https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt +``` + +The final file structure tree is like: + +``` +Amphion + ┣ pretrained + ┃ ┣ whisper + ┃ ┃ ┣ medium.pt +``` + +## RawNet3 + +The official pretrained RawNet3 checkpoints can be available [here](https://huggingface.co/jungjee/RawNet3). You need to download the `model.pt` file and put it in the folder. + +The final file structure tree is like: + +``` +Amphion + ┣ pretrained + ┃ ┣ rawnet3 + ┃ ┃ ┣ model.pt +``` + diff --git a/pretrained/bigvgan/args.json b/pretrained/bigvgan/args.json new file mode 100644 index 0000000000000000000000000000000000000000..06878c8cc6e7c667b107836a51c1d577c15fa7b1 --- /dev/null +++ b/pretrained/bigvgan/args.json @@ -0,0 +1,235 @@ +{ + "base_config": "egs/vocoder/gan/exp_config_base.json", + "exp_name": "bigvgan_large", + "inference": { + "batch_size": 1, + }, + "model": { + "bigvgan": { + "activation": "snakebeta", + "resblock": "1", + "resblock_dilation_sizes": [ + [ + 1, + 3, + 5, + ], + [ + 1, + 3, + 5, + ], + [ + 1, + 3, + 5, + ], + ], + "resblock_kernel_sizes": [ + 3, + 7, + 11, + ], + "snake_logscale": true, + "upsample_initial_channel": 1536, + "upsample_kernel_sizes": [ + 8, + 8, + 4, + 4, + 4, + 4, + ], + "upsample_rates": [ + 4, + 4, + 2, + 2, + 2, + 2, + ], + }, + "discriminators": [ + "mpd", + "msstftd", + ], + "generator": "bigvgan", + "mpd": { + "discriminator_channel_multi": 1, + "mpd_reshapes": [ + 2, + 3, + 5, + 7, + 11, + ], + "use_spectral_norm": false, + }, + "mrd": { + "discriminator_channel_multi": 1, + "mrd_override": false, + "resolutions": [ + [ + 1024, + 120, + 600, + ], + [ + 2048, + 240, + 1200, + ], + [ + 512, + 50, + 240, + ], + ], + "use_spectral_norm": false, + }, + "msstftd": { + "filters": 32, + }, + }, + "model_type": "GANVocoder", + "preprocess": { + "audio_dir": "audios", + "bits": 8, + "contentvec_dir": "contentvec", + "cut_mel_frame": 32, + "data_augment": false, + "dur_dir": "durs", + "duration_dir": "duration", + "emo2id": "emo2id.json", + "energy_dir": "energys", + "energy_extract_mode": "from_mel", + "energy_norm": false, + "extract_audio": true, + "extract_contentvec_feature": false, + "extract_duration": false, + "extract_energy": false, + "extract_label": false, + "extract_mcep": false, + "extract_mel": true, + "extract_mert_feature": false, + "extract_one_hot": false, + "extract_pitch": false, + "extract_uv": false, + "extract_wenet_feature": false, + "extract_whisper_feature": false, + "f0_max": 1100, + "f0_min": 50, + "file_lst": "file.lst", + "fmax": 12000, + "fmin": 0, + "hop_size": 256, + "is_mu_law": false, + "lab_dir": "labs", + "label_dir": "labels", + "mcep_dir": "mcep", + "mel_dir": "mels", + "mel_min_max_norm": false, + "min_level_db": -115, + "n_fft": 1024, + "n_mel": 100, + "num_silent_frames": 8, + "phone_seq_file": "phone_seq_file", + "pitch_bin": 256, + "pitch_dir": "pitches", + "pitch_extractor": "parselmouth", + "pitch_max": 1100.0, + "pitch_min": 50.0, + "pitch_norm": false, + "processed_dir": "processed_data", + "ref_level_db": 20, + "sample_rate": 24000, + "spk2id": "singers.json", + "train_file": "train.json", + "trim_fft_size": 512, + "trim_hop_size": 128, + "trim_silence": false, + "trim_top_db": 30, + "trimmed_wav_dir": "trimmed_wavs", + "use_audio": true, + "use_dur": false, + "use_emoid": false, + "use_frame_duration": false, + "use_frame_energy": false, + "use_frame_pitch": false, + "use_lab": false, + "use_label": false, + "use_log_scale_energy": false, + "use_log_scale_pitch": false, + "use_mel": true, + "use_one_hot": false, + "use_phn_seq": false, + "use_phone_duration": false, + "use_phone_energy": false, + "use_phone_pitch": false, + "use_spkid": false, + "use_uv": false, + "use_wav": false, + "use_wenet": false, + "utt2emo": "utt2emo", + "utt2spk": "utt2spk", + "uv_dir": "uvs", + "valid_file": "test.json", + "wav_dir": "wavs", + "wenet_dir": "wenet", + "win_size": 1024, + }, + "supported_model_type": [ + "GANVocoder", + "Fastspeech2", + "DiffSVC", + "Transformer", + "EDM", + "CD", + ], + "train": { + "adamw": { + "adam_b1": 0.8, + "adam_b2": 0.99, + "lr": 0.0002, + }, + "batch_size": 4, + "criterions": [ + "feature", + "discriminator", + "generator", + "mel", + ], + "dataloader": { + "num_worker": 4, + "pin_memory": true, + }, + "ddp": true, + "epochs": 50000, + "exponential_lr": { + "lr_decay": 0.999, + }, + "gradient_accumulation_step": 1, + "keep_checkpoint_max": 5, + "max_epoch": 1000000, + "max_steps": 1000000, + "multi_speaker_training": false, + "random_seed": 114514, + "run_eval": [ + true, + ], + "sampler": { + "drop_last": true, + "holistic_shuffle": true, + }, + "save_checkpoint_stride": [ + 200, + ], + "save_checkpoints_steps": 10000, + "save_summary_steps": 500, + "total_training_steps": 50000, + "tracker": [ + "tensorboard", + ], + "valid_interval": 10000, + }, +} \ No newline at end of file diff --git a/pretrained/contentvec/README.md b/pretrained/contentvec/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6ea10938244c7282355be035c9489efa5bf08bdd --- /dev/null +++ b/pretrained/contentvec/README.md @@ -0,0 +1,5 @@ +# Download + +- [Link](https://github.com/auspicious3000/contentvec) +- Model: `ContentVec_legacy` +- Classes: 500 diff --git a/pretrained/hifigan/README.md b/pretrained/hifigan/README.md new file mode 100644 index 0000000000000000000000000000000000000000..77365668d3d7d4efb084fc68e7dc79f7539f0def --- /dev/null +++ b/pretrained/hifigan/README.md @@ -0,0 +1,5 @@ +# Download + +- [Link](https://cuhko365-my.sharepoint.com/:f:/g/personal/xueliumeng_cuhk_edu_cn/Ei24hGJO_PVBopjhKje1uzEBqfhV9h89HoLrOoy9K8tzGg?e=iDELHE) +- Model: `hifigan_speech` +- Datasets: VCTK + LibriTTS + LJSpeech diff --git a/pretrained/rawnet3/README.md b/pretrained/rawnet3/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8f4d94a9adb6e83e50f30a9d79c44f4ec2f56d42 --- /dev/null +++ b/pretrained/rawnet3/README.md @@ -0,0 +1,4 @@ +# Download + +- [Link](https://huggingface.co/jungjee/RawNet3) +- Pretrained Datasets: `VoxCeleb1`, `VoxCeleb2` diff --git a/pretrained/wenet/README.md b/pretrained/wenet/README.md new file mode 100644 index 0000000000000000000000000000000000000000..34c91e30f4b3a3fae0fc89fbc92f59e3824260c1 --- /dev/null +++ b/pretrained/wenet/README.md @@ -0,0 +1,4 @@ +# Download + +- [Link](https://github.com/wenet-e2e/wenet/blob/main/docs/pretrained_models.md) +- Pretrained Datasets: `wenetspeech` diff --git a/processors/__init__.py b/processors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/processors/__pycache__/__init__.cpython-39.pyc b/processors/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..dc3de3f28ececbd0b9de6bb2cdd42744faaff39d Binary files /dev/null and b/processors/__pycache__/__init__.cpython-39.pyc differ diff --git a/processors/__pycache__/acoustic_extractor.cpython-39.pyc b/processors/__pycache__/acoustic_extractor.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ed25189e4e5779d82065f7cc37cf9397622cc3f5 Binary files /dev/null and b/processors/__pycache__/acoustic_extractor.cpython-39.pyc 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a/processors/acoustic_extractor.py b/processors/acoustic_extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..4d56902b62ffe2244202c694a232dcc3083e85a4 --- /dev/null +++ b/processors/acoustic_extractor.py @@ -0,0 +1,1054 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import torch +import numpy as np +import torchaudio + +import json +from tqdm import tqdm +from sklearn.preprocessing import StandardScaler +from utils.io import save_feature, save_txt, save_torch_audio +from utils.util import has_existed +from utils.tokenizer import extract_encodec_token +from utils.stft import TacotronSTFT +from utils.dsp import compress, audio_to_label +from utils.data_utils import remove_outlier +from preprocessors.metadata import replace_augment_name +from scipy.interpolate import interp1d +from concurrent.futures import ProcessPoolExecutor +from functools import partial + +ZERO = 1e-12 + + +def extract_utt_acoustic_features_parallel(metadata, dataset_output, cfg, num_workers=1): + """Extract acoustic features from utterances using muliprocess + + Args: + metadata (dict): dictionary that stores data in train.json and test.json files + dataset_output (str): directory to store acoustic features + cfg (dict): dictionary that stores configurations + num_workers (int, optional): num of processes to extract features in parallel. Defaults to 1. + + Returns: + list: acoustic features + """ + + executor = ProcessPoolExecutor(max_workers=num_workers) + results = [] + for utt in metadata: + if cfg.task_type == "tts": + results.append( + executor.submit( + partial(extract_utt_acoustic_features_tts, dataset_output, cfg, utt) + ) + ) + if cfg.task_type == "svc": + results.append( + executor.submit( + partial(extract_utt_acoustic_features_svc, dataset_output, cfg, utt) + ) + ) + if cfg.task_type == "vocoder": + results.append( + executor.submit( + partial(extract_utt_acoustic_features_vocoder, dataset_output, cfg, utt) + ) + ) + if cfg.task_type == "tta": + results.append( + executor.submit( + partial(extract_utt_acoustic_features_tta, dataset_output, cfg, utt) + ) + ) + return [result.result() for result in tqdm(results)] + +# def extract_utt_acoustic_features_parallel(metadata, dataset_output, cfg, num_workers=1): +# """Extract acoustic features from utterances using muliprocess + +# Args: +# metadata (dict): dictionary that stores data in train.json and test.json files +# dataset_output (str): directory to store acoustic features +# cfg (dict): dictionary that stores configurations +# num_workers (int, optional): num of processes to extract features in parallel. Defaults to 1. + +# Returns: +# list: acoustic features +# """ +# for utt in tqdm(metadata): +# if cfg.task_type == "tts": +# extract_utt_acoustic_features_tts(dataset_output, cfg, utt) +# if cfg.task_type == "svc": +# extract_utt_acoustic_features_svc(dataset_output, cfg, utt) +# if cfg.task_type == "vocoder": +# extract_utt_acoustic_features_vocoder(dataset_output, cfg, utt) +# if cfg.task_type == "tta": +# extract_utt_acoustic_features_tta(dataset_output, cfg, utt) + + +def avg_phone_feature(feature, duration, interpolation=False): + feature = feature[: sum(duration)] + if interpolation: + nonzero_ids = np.where(feature != 0)[0] + interp_fn = interp1d( + nonzero_ids, + feature[nonzero_ids], + fill_value=(feature[nonzero_ids[0]], feature[nonzero_ids[-1]]), + bounds_error=False, + ) + feature = interp_fn(np.arange(0, len(feature))) + + # Phoneme-level average + pos = 0 + for i, d in enumerate(duration): + if d > 0: + feature[i] = np.mean(feature[pos : pos + d]) + else: + feature[i] = 0 + pos += d + feature = feature[: len(duration)] + return feature + + +def extract_utt_acoustic_features_serial(metadata, dataset_output, cfg): + """Extract acoustic features from utterances (in single process) + + Args: + metadata (dict): dictionary that stores data in train.json and test.json files + dataset_output (str): directory to store acoustic features + cfg (dict): dictionary that stores configurations + + """ + for utt in tqdm(metadata): + if cfg.task_type == "tts": + extract_utt_acoustic_features_tts(dataset_output, cfg, utt) + if cfg.task_type == "svc": + extract_utt_acoustic_features_svc(dataset_output, cfg, utt) + if cfg.task_type == "vocoder": + extract_utt_acoustic_features_vocoder(dataset_output, cfg, utt) + if cfg.task_type == "tta": + extract_utt_acoustic_features_tta(dataset_output, cfg, utt) + + +def __extract_utt_acoustic_features(dataset_output, cfg, utt): + """Extract acoustic features from utterances (in single process) + + Args: + dataset_output (str): directory to store acoustic features + cfg (dict): dictionary that stores configurations + utt (dict): utterance info including dataset, singer, uid:{singer}_{song}_{index}, + path to utternace, duration, utternace index + + """ + from utils import audio, f0, world, duration + + uid = utt["Uid"] + wav_path = utt["Path"] + if os.path.exists(os.path.join(dataset_output, cfg.preprocess.raw_data)): + wav_path = os.path.join( + dataset_output, cfg.preprocess.raw_data, utt["Singer"], uid + ".wav" + ) + + with torch.no_grad(): + # Load audio data into tensor with sample rate of the config file + wav_torch, _ = audio.load_audio_torch(wav_path, cfg.preprocess.sample_rate) + wav = wav_torch.cpu().numpy() + + # extract features + if cfg.preprocess.extract_duration: + durations, phones, start, end = duration.get_duration( + utt, wav, cfg.preprocess + ) + save_feature(dataset_output, cfg.preprocess.duration_dir, uid, durations) + save_txt(dataset_output, cfg.preprocess.lab_dir, uid, phones) + wav = wav[start:end].astype(np.float32) + wav_torch = torch.from_numpy(wav).to(wav_torch.device) + + if cfg.preprocess.extract_linear_spec: + from utils.mel import extract_linear_features + + linear = extract_linear_features(wav_torch.unsqueeze(0), cfg.preprocess) + save_feature( + dataset_output, cfg.preprocess.linear_dir, uid, linear.cpu().numpy() + ) + + if cfg.preprocess.extract_mel: + from utils.mel import extract_mel_features + + if cfg.preprocess.mel_extract_mode == "taco": + _stft = TacotronSTFT( + sampling_rate=cfg.preprocess.sample_rate, + win_length=cfg.preprocess.win_size, + hop_length=cfg.preprocess.hop_size, + filter_length=cfg.preprocess.n_fft, + n_mel_channels=cfg.preprocess.n_mel, + mel_fmin=cfg.preprocess.fmin, + mel_fmax=cfg.preprocess.fmax, + ) + mel = extract_mel_features( + wav_torch.unsqueeze(0), cfg.preprocess, taco=True, _stft=_stft + ) + if cfg.preprocess.extract_duration: + mel = mel[:, : sum(durations)] + else: + mel = extract_mel_features(wav_torch.unsqueeze(0), cfg.preprocess) + save_feature(dataset_output, cfg.preprocess.mel_dir, uid, mel.cpu().numpy()) + + if cfg.preprocess.extract_energy: + if ( + cfg.preprocess.energy_extract_mode == "from_mel" + and cfg.preprocess.extract_mel + ): + energy = (mel.exp() ** 2).sum(0).sqrt().cpu().numpy() + elif cfg.preprocess.energy_extract_mode == "from_waveform": + energy = audio.energy(wav, cfg.preprocess) + elif cfg.preprocess.energy_extract_mode == "from_tacotron_stft": + _stft = TacotronSTFT( + sampling_rate=cfg.preprocess.sample_rate, + win_length=cfg.preprocess.win_size, + hop_length=cfg.preprocess.hop_size, + filter_length=cfg.preprocess.n_fft, + n_mel_channels=cfg.preprocess.n_mel, + mel_fmin=cfg.preprocess.fmin, + mel_fmax=cfg.preprocess.fmax, + ) + _, energy = audio.get_energy_from_tacotron(wav, _stft) + else: + assert cfg.preprocess.energy_extract_mode in [ + "from_mel", + "from_waveform", + "from_tacotron_stft", + ], f"{cfg.preprocess.energy_extract_mode} not in supported energy_extract_mode [from_mel, from_waveform, from_tacotron_stft]" + if cfg.preprocess.extract_duration: + energy = energy[: sum(durations)] + phone_energy = avg_phone_feature(energy, durations) + save_feature( + dataset_output, cfg.preprocess.phone_energy_dir, uid, phone_energy + ) + + save_feature(dataset_output, cfg.preprocess.energy_dir, uid, energy) + + if cfg.preprocess.extract_pitch: + pitch = f0.get_f0(wav, cfg.preprocess) + if cfg.preprocess.extract_duration: + pitch = pitch[: sum(durations)] + phone_pitch = avg_phone_feature(pitch, durations, interpolation=True) + save_feature( + dataset_output, cfg.preprocess.phone_pitch_dir, uid, phone_pitch + ) + save_feature(dataset_output, cfg.preprocess.pitch_dir, uid, pitch) + + if cfg.preprocess.extract_uv: + assert isinstance(pitch, np.ndarray) + uv = pitch != 0 + save_feature(dataset_output, cfg.preprocess.uv_dir, uid, uv) + + if cfg.preprocess.extract_audio: + save_feature(dataset_output, cfg.preprocess.audio_dir, uid, wav) + + if cfg.preprocess.extract_label: + if cfg.preprocess.is_mu_law: + # compress audio + wav = compress(wav, cfg.preprocess.bits) + label = audio_to_label(wav, cfg.preprocess.bits) + save_feature(dataset_output, cfg.preprocess.label_dir, uid, label) + + if cfg.preprocess.extract_acoustic_token: + if cfg.preprocess.acoustic_token_extractor == "Encodec": + codes = extract_encodec_token(wav_path) + save_feature(dataset_output, cfg.preprocess.acoustic_token_dir, uid, codes) + +# TODO: refactor extract_utt_acoustic_features_task function due to many duplicated code +def extract_utt_acoustic_features_tts(dataset_output, cfg, utt): + """Extract acoustic features from utterances (in single process) + + Args: + dataset_output (str): directory to store acoustic features + cfg (dict): dictionary that stores configurations + utt (dict): utterance info including dataset, singer, uid:{singer}_{song}_{index}, + path to utternace, duration, utternace index + + """ + from utils import audio, f0, world, duration + + uid = utt["Uid"] + wav_path = utt["Path"] + if os.path.exists(os.path.join(dataset_output, cfg.preprocess.raw_data)): + wav_path = os.path.join( + dataset_output, cfg.preprocess.raw_data, utt["Singer"], uid + ".wav" + ) + if not os.path.exists(wav_path): + wav_path = os.path.join( + dataset_output, cfg.preprocess.raw_data, utt["Singer"], uid + ".flac" + ) + + assert os.path.exists(wav_path) + + with torch.no_grad(): + # Load audio data into tensor with sample rate of the config file + wav_torch, _ = audio.load_audio_torch(wav_path, cfg.preprocess.sample_rate) + wav = wav_torch.cpu().numpy() + + # extract features + if cfg.preprocess.extract_duration: + durations, phones, start, end = duration.get_duration( + utt, wav, cfg.preprocess + ) + save_feature(dataset_output, cfg.preprocess.duration_dir, uid, durations) + save_txt(dataset_output, cfg.preprocess.lab_dir, uid, phones) + wav = wav[start:end].astype(np.float32) + wav_torch = torch.from_numpy(wav).to(wav_torch.device) + + if cfg.preprocess.extract_linear_spec: + from utils.mel import extract_linear_features + + linear = extract_linear_features(wav_torch.unsqueeze(0), cfg.preprocess) + save_feature( + dataset_output, cfg.preprocess.linear_dir, uid, linear.cpu().numpy() + ) + + if cfg.preprocess.extract_mel: + from utils.mel import extract_mel_features + + if cfg.preprocess.mel_extract_mode == "taco": + _stft = TacotronSTFT( + sampling_rate=cfg.preprocess.sample_rate, + win_length=cfg.preprocess.win_size, + hop_length=cfg.preprocess.hop_size, + filter_length=cfg.preprocess.n_fft, + n_mel_channels=cfg.preprocess.n_mel, + mel_fmin=cfg.preprocess.fmin, + mel_fmax=cfg.preprocess.fmax, + ) + mel = extract_mel_features( + wav_torch.unsqueeze(0), cfg.preprocess, taco=True, _stft=_stft + ) + if cfg.preprocess.extract_duration: + mel = mel[:, : sum(durations)] + else: + mel = extract_mel_features(wav_torch.unsqueeze(0), cfg.preprocess) + save_feature(dataset_output, cfg.preprocess.mel_dir, uid, mel.cpu().numpy()) + + if cfg.preprocess.extract_energy: + if ( + cfg.preprocess.energy_extract_mode == "from_mel" + and cfg.preprocess.extract_mel + ): + energy = (mel.exp() ** 2).sum(0).sqrt().cpu().numpy() + elif cfg.preprocess.energy_extract_mode == "from_waveform": + energy = audio.energy(wav, cfg.preprocess) + elif cfg.preprocess.energy_extract_mode == "from_tacotron_stft": + _stft = TacotronSTFT( + sampling_rate=cfg.preprocess.sample_rate, + win_length=cfg.preprocess.win_size, + hop_length=cfg.preprocess.hop_size, + filter_length=cfg.preprocess.n_fft, + n_mel_channels=cfg.preprocess.n_mel, + mel_fmin=cfg.preprocess.fmin, + mel_fmax=cfg.preprocess.fmax, + ) + _, energy = audio.get_energy_from_tacotron(wav, _stft) + else: + assert cfg.preprocess.energy_extract_mode in [ + "from_mel", + "from_waveform", + "from_tacotron_stft", + ], f"{cfg.preprocess.energy_extract_mode} not in supported energy_extract_mode [from_mel, from_waveform, from_tacotron_stft]" + if cfg.preprocess.extract_duration: + energy = energy[: sum(durations)] + phone_energy = avg_phone_feature(energy, durations) + save_feature( + dataset_output, cfg.preprocess.phone_energy_dir, uid, phone_energy + ) + + save_feature(dataset_output, cfg.preprocess.energy_dir, uid, energy) + + if cfg.preprocess.extract_pitch: + pitch = f0.get_f0(wav, cfg.preprocess) + if cfg.preprocess.extract_duration: + pitch = pitch[: sum(durations)] + phone_pitch = avg_phone_feature(pitch, durations, interpolation=True) + save_feature( + dataset_output, cfg.preprocess.phone_pitch_dir, uid, phone_pitch + ) + save_feature(dataset_output, cfg.preprocess.pitch_dir, uid, pitch) + + if cfg.preprocess.extract_uv: + assert isinstance(pitch, np.ndarray) + uv = pitch != 0 + save_feature(dataset_output, cfg.preprocess.uv_dir, uid, uv) + + if cfg.preprocess.extract_audio: + save_torch_audio(dataset_output, + cfg.preprocess.audio_dir, + uid, + wav_torch, + cfg.preprocess.sample_rate) + + + if cfg.preprocess.extract_label: + if cfg.preprocess.is_mu_law: + # compress audio + wav = compress(wav, cfg.preprocess.bits) + label = audio_to_label(wav, cfg.preprocess.bits) + save_feature(dataset_output, cfg.preprocess.label_dir, uid, label) + + if cfg.preprocess.extract_acoustic_token: + if cfg.preprocess.acoustic_token_extractor == "Encodec": + codes = extract_encodec_token(wav_path) + save_feature(dataset_output, cfg.preprocess.acoustic_token_dir, uid, codes) + +def extract_utt_acoustic_features_svc(dataset_output, cfg, utt): + __extract_utt_acoustic_features(dataset_output, cfg, utt) + + +def extract_utt_acoustic_features_tta(dataset_output, cfg, utt): + __extract_utt_acoustic_features(dataset_output, cfg, utt) + + +def extract_utt_acoustic_features_vocoder(dataset_output, cfg, utt): + """Extract acoustic features from utterances (in single process) + + Args: + dataset_output (str): directory to store acoustic features + cfg (dict): dictionary that stores configurations + utt (dict): utterance info including dataset, singer, uid:{singer}_{song}_{index}, + path to utternace, duration, utternace index + + """ + from utils import audio, f0, world, duration + + uid = utt["Uid"] + wav_path = utt["Path"] + + with torch.no_grad(): + # Load audio data into tensor with sample rate of the config file + wav_torch, _ = audio.load_audio_torch(wav_path, cfg.preprocess.sample_rate) + wav = wav_torch.cpu().numpy() + + # extract features + if cfg.preprocess.extract_mel: + from utils.mel import extract_mel_features + + mel = extract_mel_features(wav_torch.unsqueeze(0), cfg.preprocess) + save_feature(dataset_output, cfg.preprocess.mel_dir, uid, mel.cpu().numpy()) + + if cfg.preprocess.extract_energy: + if ( + cfg.preprocess.energy_extract_mode == "from_mel" + and cfg.preprocess.extract_mel + ): + energy = (mel.exp() ** 2).sum(0).sqrt().cpu().numpy() + elif cfg.preprocess.energy_extract_mode == "from_waveform": + energy = audio.energy(wav, cfg.preprocess) + else: + assert cfg.preprocess.energy_extract_mode in [ + "from_mel", + "from_waveform", + ], f"{cfg.preprocess.energy_extract_mode} not in supported energy_extract_mode [from_mel, from_waveform, from_tacotron_stft]" + + save_feature(dataset_output, cfg.preprocess.energy_dir, uid, energy) + + if cfg.preprocess.extract_pitch: + pitch = f0.get_f0(wav, cfg.preprocess) + save_feature(dataset_output, cfg.preprocess.pitch_dir, uid, pitch) + + if cfg.preprocess.extract_uv: + assert isinstance(pitch, np.ndarray) + uv = pitch != 0 + save_feature(dataset_output, cfg.preprocess.uv_dir, uid, uv) + + if cfg.preprocess.extract_audio: + save_feature(dataset_output, cfg.preprocess.audio_dir, uid, wav) + + if cfg.preprocess.extract_label: + if cfg.preprocess.is_mu_law: + # compress audio + wav = compress(wav, cfg.preprocess.bits) + label = audio_to_label(wav, cfg.preprocess.bits) + save_feature(dataset_output, cfg.preprocess.label_dir, uid, label) + + +def cal_normalized_mel(mel, dataset_name, cfg): + mel_min, mel_max = load_mel_extrema(cfg, dataset_name) + mel_norm = normalize_mel_channel(mel, mel_min, mel_max) + return mel_norm + + +def cal_mel_min_max(dataset, output_path, cfg, metadata=None): + dataset_output = os.path.join(output_path, dataset) + + if metadata is None: + metadata = [] + for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]: + dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type)) + with open(dataset_file, "r") as f: + metadata.extend(json.load(f)) + + tmp_mel_min = [] + tmp_mel_max = [] + for item in metadata: + mel_path = os.path.join( + dataset_output, cfg.preprocess.mel_dir, item["Uid"] + ".npy" + ) + if not os.path.exists(mel_path): + continue + mel = np.load(mel_path) + if mel.shape[0] != cfg.preprocess.n_mel: + mel = mel.T + # mel: (n_mels, T) + assert mel.shape[0] == cfg.preprocess.n_mel + + tmp_mel_min.append(np.min(mel, axis=-1)) + tmp_mel_max.append(np.max(mel, axis=-1)) + + mel_min = np.min(tmp_mel_min, axis=0) + mel_max = np.max(tmp_mel_max, axis=0) + + ## save mel min max data + mel_min_max_dir = os.path.join(dataset_output, cfg.preprocess.mel_min_max_stats_dir) + os.makedirs(mel_min_max_dir, exist_ok=True) + + mel_min_path = os.path.join(mel_min_max_dir, "mel_min.npy") + mel_max_path = os.path.join(mel_min_max_dir, "mel_max.npy") + np.save(mel_min_path, mel_min) + np.save(mel_max_path, mel_max) + + +def denorm_for_pred_mels(cfg, dataset_name, split, pred): + """ + Args: + pred: a list whose every element is (frame_len, n_mels) + Return: + similar like pred + """ + mel_min, mel_max = load_mel_extrema(cfg.preprocess, dataset_name) + recovered_mels = [ + denormalize_mel_channel(mel.T, mel_min, mel_max).T for mel in pred + ] + + return recovered_mels + + +def load_mel_extrema(cfg, dataset_name): + data_dir = os.path.join(cfg.processed_dir, dataset_name, cfg.mel_min_max_stats_dir) + + min_file = os.path.join(data_dir, "mel_min.npy") + max_file = os.path.join(data_dir, "mel_max.npy") + + mel_min = np.load(min_file) + mel_max = np.load(max_file) + + return mel_min, mel_max + + +def denormalize_mel_channel(mel, mel_min, mel_max): + mel_min = np.expand_dims(mel_min, -1) + mel_max = np.expand_dims(mel_max, -1) + return (mel + 1) / 2 * (mel_max - mel_min + ZERO) + mel_min + + +def normalize_mel_channel(mel, mel_min, mel_max): + mel_min = np.expand_dims(mel_min, -1) + mel_max = np.expand_dims(mel_max, -1) + return (mel - mel_min) / (mel_max - mel_min + ZERO) * 2 - 1 + + +def normalize(dataset, feat_dir, cfg): + dataset_output = os.path.join(cfg.preprocess.processed_dir, dataset) + print(f"normalize {feat_dir}") + + max_value = np.finfo(np.float64).min + min_value = np.finfo(np.float64).max + + scaler = StandardScaler() + feat_files = os.listdir(os.path.join(dataset_output, feat_dir)) + + for feat_file in tqdm(feat_files): + feat_file = os.path.join(dataset_output, feat_dir, feat_file) + if not feat_file.endswith(".npy"): + continue + feat = np.load(feat_file) + max_value = max(max_value, max(feat)) + min_value = min(min_value, min(feat)) + scaler.partial_fit(feat.reshape((-1, 1))) + mean = scaler.mean_[0] + std = scaler.scale_[0] + stat = np.array([min_value, max_value, mean, std]) + stat_npy = os.path.join(dataset_output, f"{feat_dir}_stat.npy") + np.save(stat_npy, stat) + return mean, std, min_value, max_value + + +def load_normalized(feat_dir, dataset_name, cfg): + dataset_output = os.path.join(cfg.preprocess.processed_dir, dataset_name) + stat_npy = os.path.join(dataset_output, f"{feat_dir}_stat.npy") + min_value, max_value, mean, std = np.load(stat_npy) + return mean, std, min_value, max_value + + +def cal_pitch_statistics_svc(dataset, output_path, cfg, metadata=None): + # path of dataset + dataset_dir = os.path.join(output_path, dataset) + save_dir = os.path.join(dataset_dir, cfg.preprocess.pitch_dir) + os.makedirs(save_dir, exist_ok=True) + if has_existed(os.path.join(save_dir, "statistics.json")): + return + + if metadata is None: + # load singers and ids + singers = json.load(open(os.path.join(dataset_dir, "singers.json"), "r")) + + # combine train and test metadata + metadata = [] + for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]: + dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type)) + with open(dataset_file, "r") as f: + metadata.extend(json.load(f)) + else: + singers = list(set([item["Singer"] for item in metadata])) + singers = { + "{}_{}".format(dataset, name): idx for idx, name in enumerate(singers) + } + + # use different scalers for each singer + pitch_scalers = [[] for _ in range(len(singers))] + total_pitch_scalers = [[] for _ in range(len(singers))] + + for utt_info in tqdm(metadata, desc="Loading F0..."): + # utt = f'{utt_info["Dataset"]}_{utt_info["Uid"]}' + singer = utt_info["Singer"] + pitch_path = os.path.join( + dataset_dir, cfg.preprocess.pitch_dir, utt_info["Uid"] + ".npy" + ) + # total_pitch contains all pitch including unvoiced frames + if not os.path.exists(pitch_path): + continue + total_pitch = np.load(pitch_path) + assert len(total_pitch) > 0 + # pitch contains only voiced frames + pitch = total_pitch[total_pitch != 0] + spkid = singers[f"{replace_augment_name(dataset)}_{singer}"] + + # update pitch scalers + pitch_scalers[spkid].extend(pitch.tolist()) + # update total pitch scalers + total_pitch_scalers[spkid].extend(total_pitch.tolist()) + + # save pitch statistics for each singer in dict + sta_dict = {} + for singer in tqdm(singers, desc="Singers statistics"): + spkid = singers[singer] + # voiced pitch statistics + mean, std, min, max, median = ( + np.mean(pitch_scalers[spkid]), + np.std(pitch_scalers[spkid]), + np.min(pitch_scalers[spkid]), + np.max(pitch_scalers[spkid]), + np.median(pitch_scalers[spkid]), + ) + + # total pitch statistics + mean_t, std_t, min_t, max_t, median_t = ( + np.mean(total_pitch_scalers[spkid]), + np.std(total_pitch_scalers[spkid]), + np.min(total_pitch_scalers[spkid]), + np.max(total_pitch_scalers[spkid]), + np.median(total_pitch_scalers[spkid]), + ) + sta_dict[singer] = { + "voiced_positions": { + "mean": mean, + "std": std, + "median": median, + "min": min, + "max": max, + }, + "total_positions": { + "mean": mean_t, + "std": std_t, + "median": median_t, + "min": min_t, + "max": max_t, + }, + } + + # save statistics + with open(os.path.join(save_dir, "statistics.json"), "w") as f: + json.dump(sta_dict, f, indent=4, ensure_ascii=False) + + +def cal_pitch_statistics(dataset, output_path, cfg): + # path of dataset + dataset_dir = os.path.join(output_path, dataset) + if cfg.preprocess.use_phone_pitch: + pitch_dir = cfg.preprocess.phone_pitch_dir + else: + pitch_dir = cfg.preprocess.pitch_dir + save_dir = os.path.join(dataset_dir, pitch_dir) + + os.makedirs(save_dir, exist_ok=True) + if has_existed(os.path.join(save_dir, "statistics.json")): + return + # load singers and ids + singers = json.load(open(os.path.join(dataset_dir, "singers.json"), "r")) + + # combine train and test metadata + metadata = [] + for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]: + dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type)) + with open(dataset_file, "r") as f: + metadata.extend(json.load(f)) + + # use different scalers for each singer + pitch_scalers = [[] for _ in range(len(singers))] + total_pitch_scalers = [[] for _ in range(len(singers))] + + for utt_info in metadata: + utt = f'{utt_info["Dataset"]}_{utt_info["Uid"]}' + singer = utt_info["Singer"] + pitch_path = os.path.join(dataset_dir, pitch_dir, utt_info["Uid"] + ".npy") + # total_pitch contains all pitch including unvoiced frames + if not os.path.exists(pitch_path): + continue + total_pitch = np.load(pitch_path) + assert len(total_pitch) > 0 + # pitch contains only voiced frames + # pitch = total_pitch[total_pitch != 0] + if cfg.preprocess.pitch_remove_outlier: + pitch = remove_outlier(total_pitch) + spkid = singers[f"{replace_augment_name(dataset)}_{singer}"] + + # update pitch scalers + pitch_scalers[spkid].extend(pitch.tolist()) + # update total pitch scalers + total_pitch_scalers[spkid].extend(total_pitch.tolist()) + + # save pitch statistics for each singer in dict + sta_dict = {} + for singer in singers: + spkid = singers[singer] + # voiced pitch statistics + mean, std, min, max, median = ( + np.mean(pitch_scalers[spkid]), + np.std(pitch_scalers[spkid]), + np.min(pitch_scalers[spkid]), + np.max(pitch_scalers[spkid]), + np.median(pitch_scalers[spkid]), + ) + + # total pitch statistics + mean_t, std_t, min_t, max_t, median_t = ( + np.mean(total_pitch_scalers[spkid]), + np.std(total_pitch_scalers[spkid]), + np.min(total_pitch_scalers[spkid]), + np.max(total_pitch_scalers[spkid]), + np.median(total_pitch_scalers[spkid]), + ) + sta_dict[singer] = { + "voiced_positions": { + "mean": mean, + "std": std, + "median": median, + "min": min, + "max": max, + }, + "total_positions": { + "mean": mean_t, + "std": std_t, + "median": median_t, + "min": min_t, + "max": max_t, + }, + } + + # save statistics + with open(os.path.join(save_dir, "statistics.json"), "w") as f: + json.dump(sta_dict, f, indent=4, ensure_ascii=False) + + +def cal_energy_statistics(dataset, output_path, cfg): + # path of dataset + dataset_dir = os.path.join(output_path, dataset) + if cfg.preprocess.use_phone_energy: + energy_dir = cfg.preprocess.phone_energy_dir + else: + energy_dir = cfg.preprocess.energy_dir + save_dir = os.path.join(dataset_dir, energy_dir) + os.makedirs(save_dir, exist_ok=True) + print(os.path.join(save_dir, "statistics.json")) + if has_existed(os.path.join(save_dir, "statistics.json")): + return + # load singers and ids + singers = json.load(open(os.path.join(dataset_dir, "singers.json"), "r")) + + # combine train and test metadata + metadata = [] + for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]: + dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type)) + with open(dataset_file, "r") as f: + metadata.extend(json.load(f)) + + # use different scalers for each singer + energy_scalers = [[] for _ in range(len(singers))] + total_energy_scalers = [[] for _ in range(len(singers))] + + for utt_info in metadata: + utt = f'{utt_info["Dataset"]}_{utt_info["Uid"]}' + singer = utt_info["Singer"] + energy_path = os.path.join(dataset_dir, energy_dir, utt_info["Uid"] + ".npy") + # total_energy contains all energy including unvoiced frames + if not os.path.exists(energy_path): + continue + total_energy = np.load(energy_path) + assert len(total_energy) > 0 + # energy contains only voiced frames + # energy = total_energy[total_energy != 0] + if cfg.preprocess.energy_remove_outlier: + energy = remove_outlier(total_energy) + spkid = singers[f"{replace_augment_name(dataset)}_{singer}"] + + # update energy scalers + energy_scalers[spkid].extend(energy.tolist()) + # update total energyscalers + total_energy_scalers[spkid].extend(total_energy.tolist()) + + # save energy statistics for each singer in dict + sta_dict = {} + for singer in singers: + spkid = singers[singer] + # voiced energy statistics + mean, std, min, max, median = ( + np.mean(energy_scalers[spkid]), + np.std(energy_scalers[spkid]), + np.min(energy_scalers[spkid]), + np.max(energy_scalers[spkid]), + np.median(energy_scalers[spkid]), + ) + + # total energy statistics + mean_t, std_t, min_t, max_t, median_t = ( + np.mean(total_energy_scalers[spkid]), + np.std(total_energy_scalers[spkid]), + np.min(total_energy_scalers[spkid]), + np.max(total_energy_scalers[spkid]), + np.median(total_energy_scalers[spkid]), + ) + sta_dict[singer] = { + "voiced_positions": { + "mean": mean, + "std": std, + "median": median, + "min": min, + "max": max, + }, + "total_positions": { + "mean": mean_t, + "std": std_t, + "median": median_t, + "min": min_t, + "max": max_t, + }, + } + + # save statistics + with open(os.path.join(save_dir, "statistics.json"), "w") as f: + json.dump(sta_dict, f, indent=4, ensure_ascii=False) + + +def copy_acoustic_features(metadata, dataset_dir, src_dataset_dir, cfg): + """Copy acoustic features from src_dataset_dir to dataset_dir + + Args: + metadata (dict): dictionary that stores data in train.json and test.json files + dataset_dir (str): directory to store acoustic features + src_dataset_dir (str): directory to store acoustic features + cfg (dict): dictionary that stores configurations + + """ + + if cfg.preprocess.extract_mel: + if not has_existed(os.path.join(dataset_dir, cfg.preprocess.mel_dir)): + os.makedirs( + os.path.join(dataset_dir, cfg.preprocess.mel_dir), exist_ok=True + ) + print( + "Copying mel features from {} to {}...".format( + src_dataset_dir, dataset_dir + ) + ) + for utt_info in tqdm(metadata): + src_mel_path = os.path.join( + src_dataset_dir, cfg.preprocess.mel_dir, utt_info["Uid"] + ".npy" + ) + dst_mel_path = os.path.join( + dataset_dir, cfg.preprocess.mel_dir, utt_info["Uid"] + ".npy" + ) + # create soft-links + if not os.path.exists(dst_mel_path): + os.symlink(src_mel_path, dst_mel_path) + if cfg.preprocess.extract_energy: + if not has_existed(os.path.join(dataset_dir, cfg.preprocess.energy_dir)): + os.makedirs( + os.path.join(dataset_dir, cfg.preprocess.energy_dir), exist_ok=True + ) + print( + "Copying energy features from {} to {}...".format( + src_dataset_dir, dataset_dir + ) + ) + for utt_info in tqdm(metadata): + src_energy_path = os.path.join( + src_dataset_dir, cfg.preprocess.energy_dir, utt_info["Uid"] + ".npy" + ) + dst_energy_path = os.path.join( + dataset_dir, cfg.preprocess.energy_dir, utt_info["Uid"] + ".npy" + ) + # create soft-links + if not os.path.exists(dst_energy_path): + os.symlink(src_energy_path, dst_energy_path) + if cfg.preprocess.extract_pitch: + if not has_existed(os.path.join(dataset_dir, cfg.preprocess.pitch_dir)): + os.makedirs( + os.path.join(dataset_dir, cfg.preprocess.pitch_dir), exist_ok=True + ) + print( + "Copying pitch features from {} to {}...".format( + src_dataset_dir, dataset_dir + ) + ) + for utt_info in tqdm(metadata): + src_pitch_path = os.path.join( + src_dataset_dir, cfg.preprocess.pitch_dir, utt_info["Uid"] + ".npy" + ) + dst_pitch_path = os.path.join( + dataset_dir, cfg.preprocess.pitch_dir, utt_info["Uid"] + ".npy" + ) + # create soft-links + if not os.path.exists(dst_pitch_path): + os.symlink(src_pitch_path, dst_pitch_path) + if cfg.preprocess.extract_uv: + if not has_existed(os.path.join(dataset_dir, cfg.preprocess.uv_dir)): + os.makedirs( + os.path.join(dataset_dir, cfg.preprocess.uv_dir), exist_ok=True + ) + print( + "Copying uv features from {} to {}...".format( + src_dataset_dir, dataset_dir + ) + ) + for utt_info in tqdm(metadata): + src_uv_path = os.path.join( + src_dataset_dir, cfg.preprocess.uv_dir, utt_info["Uid"] + ".npy" + ) + dst_uv_path = os.path.join( + dataset_dir, cfg.preprocess.uv_dir, utt_info["Uid"] + ".npy" + ) + # create soft-links + if not os.path.exists(dst_uv_path): + os.symlink(src_uv_path, dst_uv_path) + if cfg.preprocess.extract_audio: + if not has_existed(os.path.join(dataset_dir, cfg.preprocess.audio_dir)): + os.makedirs( + os.path.join(dataset_dir, cfg.preprocess.audio_dir), exist_ok=True + ) + print( + "Copying audio features from {} to {}...".format( + src_dataset_dir, dataset_dir + ) + ) + for utt_info in tqdm(metadata): + if cfg.task_type == "tts": + src_audio_path = os.path.join( + src_dataset_dir, cfg.preprocess.audio_dir, utt_info["Uid"] + ".wav" + ) + else: + src_audio_path = os.path.join( + src_dataset_dir, cfg.preprocess.audio_dir, utt_info["Uid"] + ".npy" + ) + if cfg.task_type == "tts": + dst_audio_path = os.path.join( + dataset_dir, cfg.preprocess.audio_dir, utt_info["Uid"] + ".wav" + ) + else: + dst_audio_path = os.path.join( + dataset_dir, cfg.preprocess.audio_dir, utt_info["Uid"] + ".npy" + ) + # create soft-links + if not os.path.exists(dst_audio_path): + os.symlink(src_audio_path, dst_audio_path) + if cfg.preprocess.extract_label: + if not has_existed(os.path.join(dataset_dir, cfg.preprocess.label_dir)): + os.makedirs( + os.path.join(dataset_dir, cfg.preprocess.label_dir), exist_ok=True + ) + print( + "Copying label features from {} to {}...".format( + src_dataset_dir, dataset_dir + ) + ) + for utt_info in tqdm(metadata): + src_label_path = os.path.join( + src_dataset_dir, cfg.preprocess.label_dir, utt_info["Uid"] + ".npy" + ) + dst_label_path = os.path.join( + dataset_dir, cfg.preprocess.label_dir, utt_info["Uid"] + ".npy" + ) + # create soft-links + if not os.path.exists(dst_label_path): + os.symlink(src_label_path, dst_label_path) + + +def align_duration_mel(dataset, output_path, cfg): + print("align the duration and mel") + + dataset_dir = os.path.join(output_path, dataset) + metadata = [] + for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]: + dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type)) + with open(dataset_file, "r") as f: + metadata.extend(json.load(f)) + + utt2dur = {} + for index in tqdm(range(len(metadata))): + utt_info = metadata[index] + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + mel_path = os.path.join(dataset_dir, cfg.preprocess.mel_dir, uid + ".npy") + mel = np.load(mel_path).transpose(1, 0) + duration_path = os.path.join( + dataset_dir, cfg.preprocess.duration_dir, uid + ".npy" + ) + duration = np.load(duration_path) + if sum(duration) != mel.shape[0]: + duration_sum = sum(duration) + mel_len = mel.shape[0] + mismatch = abs(duration_sum - mel_len) + assert mismatch <= 5, "duration and mel length mismatch!" + cloned = np.array(duration, copy=True) + if duration_sum > mel_len: + for j in range(1, len(duration) - 1): + if mismatch == 0: + break + dur_val = cloned[-j] + if dur_val >= mismatch: + cloned[-j] -= mismatch + mismatch -= dur_val + break + else: + cloned[-j] = 0 + mismatch -= dur_val + + elif duration_sum < mel_len: + cloned[-1] += mismatch + duration = cloned + utt2dur[utt] = duration + np.save(duration_path, duration) + + return utt2dur \ No newline at end of file diff --git a/processors/content_extractor.py b/processors/content_extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..c034b6bf6aac19eb3ab912661110c8066aa3119b --- /dev/null +++ b/processors/content_extractor.py @@ -0,0 +1,540 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import torch +import numpy as np +import yaml +import copy +from tqdm import tqdm +from torchaudio.compliance import kaldi +from torch.nn.utils.rnn import pad_sequence +from torch.utils.data import DataLoader +from fairseq import checkpoint_utils +from transformers import AutoModel, Wav2Vec2FeatureExtractor + +from utils.io_optim import ( + TorchaudioDataset, + LibrosaDataset, + FFmpegDataset, + collate_batch, +) +from modules import whisper_extractor as whisper +from modules.wenet_extractor.utils.init_model import init_model +from modules.wenet_extractor.utils.checkpoint import load_checkpoint + +""" + Extractor for content features + 1. whisper + 2. contentvec + 3. wenet + 4. mert + + Pipeline: + in preprocess.py: + call extract_utt_content_features() to extract content features for each utterance + extract_utt_content_features() envelopes the following steps: + 1. load the model (whisper, contentvec, wenet) + 2. extract the content features + 3. save the content features into files + in svc_dataset.py: + call offline_align() to align the content features to the given target length + +""" + +""" + Extractor Usage: + 1. initialize an instance of extractor + extractor = WhisperExtractor(cfg) + 2. load the specified model + extractor.load_model() + 3. extract the content features + extractor.extract_content(utt) for single utterance + extractor.extract_content_batch(utts) for batch utterances + 4. save the content features + extractor.save_feature(utt, content_feature) for single utterance +""" + + +class BaseExtractor: + def __init__(self, cfg): + self.cfg = cfg + self.extractor_type = None + self.model = None + + def offline_align(self, content, target_len): + """ + args: + content: (source_len, dim) + target_len: target length + return: + mapped_feature: (target_len, dim) + """ + target_hop = self.cfg.preprocess.hop_size + + assert self.extractor_type in ["whisper", "contentvec", "wenet"] + if self.extractor_type == "whisper": + source_hop = ( + self.cfg.preprocess.whisper_frameshift + * self.cfg.preprocess.whisper_downsample_rate + * self.cfg.preprocess.sample_rate + ) + elif self.extractor_type == "contentvec": + source_hop = ( + self.cfg.preprocess.contentvec_frameshift + * self.cfg.preprocess.sample_rate + ) + elif self.extractor_type == "wenet": + source_hop = ( + self.cfg.preprocess.wenet_frameshift + * self.cfg.preprocess.wenet_downsample_rate + * self.cfg.preprocess.sample_rate + ) + source_hop = int(source_hop) + factor = np.gcd(source_hop, target_hop) + source_hop //= factor + target_hop //= factor + + # (source_len, 256) + _, width = content.shape + # slice the content from padded feature + source_len = min(target_len * target_hop // source_hop + 1, len(content)) + + # const ~= target_len * target_hop + const = source_len * source_hop // target_hop * target_hop + + # (source_len * source_hop, dim) + up_sampling_feats = np.repeat(content, source_hop, axis=0) + # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim) + down_sampling_feats = np.average( + up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1 + ) + + err = abs(target_len - len(down_sampling_feats)) + if err > 8: + # err_log_dir is indeterminate + err_log_dir = os.path.join( + self.cfg.preprocess.processed_dir, "align_max_err.log" + ) + try: + with open(err_log_dir, "r") as f: + err_num = int(f.read()) + except: + with open(err_log_dir, "w") as f: + f.write("0") + err_num = 0 + if err > err_num: + with open(err_log_dir, "w") as f: + f.write(str(err)) + + if len(down_sampling_feats) < target_len: + # (1, dim) -> (err, dim) + end = down_sampling_feats[-1][None, :].repeat(err, axis=0) + down_sampling_feats = np.concatenate([down_sampling_feats, end], axis=0) + + # (target_len, dim) + mapped_feature = down_sampling_feats[:target_len] + + return mapped_feature + + def save_feature(self, utt, content_feature): + """Save a single utternace to path {cfg.preprocess.processed_dir} + + Args: + utt (dict): one item in metadata, containing information for one utterance + content_feature (tensor): content feature of one utterance + """ + uid = utt["Uid"] + assert self.extractor_type != None + out_dir = os.path.join( + self.cfg.preprocess.processed_dir, utt["Dataset"], self.extractor_type + ) + os.makedirs(out_dir, exist_ok=True) + save_path = os.path.join(out_dir, uid + ".npy") + # only keep effective parts + duration = utt["Duration"] + if self.extractor_type == "whisper": + frameshift = ( + self.cfg.preprocess.whisper_frameshift + * self.cfg.preprocess.whisper_downsample_rate + ) # 20ms + elif self.extractor_type == "contentvec": + frameshift = self.cfg.preprocess.contentvec_frameshift # 20ms + elif self.extractor_type == "wenet": + frameshift = ( + self.cfg.preprocess.wenet_frameshift + * self.cfg.preprocess.wenet_downsample_rate + ) # 40ms + elif self.extractor_type == "mert": + frameshift = self.cfg.preprocess.mert_frameshift + else: + raise NotImplementedError + # calculate the number of valid frames + num_frames = int(np.ceil((duration - frameshift) / frameshift)) + 1 + # (num_frames, dim) -> (valid_frames, dim) + assert ( + len(content_feature.shape) == 2 + ), "content feature shape error, it should be (num_frames, dim)" + content_feature = content_feature[:num_frames, :] + np.save(save_path, content_feature.cpu().detach().numpy()) + + +class WhisperExtractor(BaseExtractor): + def __init__(self, config): + super(WhisperExtractor, self).__init__(config) + self.extractor_type = "whisper" + + def load_model(self): + # load whisper checkpoint + print("Loading Whisper Model...") + + checkpoint_file = ( + self.cfg.preprocess.whisper_model_path + if "whisper_model_path" in self.cfg.preprocess + else None + ) + model = whisper.load_model( + self.cfg.preprocess.whisper_model, checkpoint_file=checkpoint_file + ) + if torch.cuda.is_available(): + print("Using GPU...\n") + model = model.cuda() + else: + print("Using CPU...\n") + + self.model = model.eval() + + def extract_content_features(self, wavs, lens): + """extract content features from a batch of dataloader + Args: + wavs: tensor (batch_size, T) + lens: list + """ + # wavs: (batch, max_len) + wavs = whisper.pad_or_trim(wavs) + # batch_mel: (batch, 80, 3000) + batch_mel = whisper.log_mel_spectrogram(wavs).to(self.model.device) + with torch.no_grad(): + # (batch, 1500, 1024) + features = self.model.embed_audio(batch_mel) + return features + + +class ContentvecExtractor(BaseExtractor): + def __init__(self, cfg): + super(ContentvecExtractor, self).__init__(cfg) + self.extractor_type = "contentvec" + + def load_model(self): + assert self.model == None + # Load model + ckpt_path = self.cfg.preprocess.contentvec_file + print("Load Contentvec Model...") + + models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( + [ckpt_path], + suffix="", + ) + model = models[0] + model.eval() + + if torch.cuda.is_available(): + # print("Using GPU...\n") + model = model.cuda() + + self.model = model + + def extract_content_features(self, wavs, lens): + """extract content features from a batch of dataloader + Args: + wavs: tensor (batch, T) + lens: list + """ + device = next(self.model.parameters()).device + wavs = wavs.to(device) # (batch, max_len) + padding_mask = torch.eq(wavs, torch.zeros_like(wavs)).to(device) + with torch.no_grad(): + logits = self.model.extract_features( + source=wavs, padding_mask=padding_mask, output_layer=12 + ) + # feats: (batch, T, 256) + feats = self.model.final_proj(logits[0]) + return feats + + +class WenetExtractor(BaseExtractor): + def __init__(self, config): + super(WenetExtractor, self).__init__(config) + self.extractor_type = "wenet" + + def load_model(self): + wenet_cfg = self.cfg.preprocess.wenet_config + wenet_model_path = self.cfg.preprocess.wenet_model_path + # load Wenet config + with open(wenet_cfg, "r") as w: + wenet_configs = yaml.load(w, Loader=yaml.FullLoader) + self.extract_conf = copy.deepcopy(wenet_configs["dataset_conf"]) + print("Loading Wenet Model...") + self.model = init_model(wenet_configs) + load_checkpoint(self.model, wenet_model_path) + + if torch.cuda.is_available(): + print("Using GPU...\n") + self.model = self.model.cuda() + else: + print("Using CPU...\n") + + self.model = self.model.eval() + + def extract_content_features(self, wavs, lens): + """extract content features from a batch of dataloader + Args: + wavs: tensor + lens: list + """ + feats_list = [] + lengths_list = [] + + device = next(self.model.parameters()).device + # Extract fbank/mfcc features by kaldi + assert self.extract_conf is not None, "load model first!" + feats_type = self.extract_conf.get("feats_type", "fbank") + assert feats_type in ["fbank", "mfcc"] + + for idx, wav in enumerate(wavs): + # wav: (T) + wav = wav[: lens[idx]].to(device) + + # pad one frame to compensate for the frame cut off after feature extraction + pad_tensor = torch.zeros(160, device=wav.device) + wav = torch.cat((wav, pad_tensor), dim=-1) + wav *= 1 << 15 + + wav = wav.unsqueeze(0) # (T) -> (1, T) + if feats_type == "fbank": + fbank_conf = self.extract_conf.get("fbank_conf", {}) + feat = kaldi.fbank( + wav, + sample_frequency=16000, + num_mel_bins=fbank_conf["num_mel_bins"], + frame_length=fbank_conf["frame_length"], + frame_shift=fbank_conf["frame_shift"], + dither=fbank_conf["dither"], + ) + elif feats_type == "mfcc": + mfcc_conf = self.extract_conf.get("mfcc", {}) + feat = kaldi.mfcc( + wav, + sample_frequency=16000, + num_mel_bins=mfcc_conf["num_mel_bins"], + frame_length=mfcc_conf["frame_length"], + frame_shift=mfcc_conf["frame_shift"], + dither=mfcc_conf["dither"], + num_ceps=mfcc_conf.get("num_ceps", 40), + high_freq=mfcc_conf.get("high_freq", 0.0), + low_freq=mfcc_conf.get("low_freq", 20.0), + ) + feats_list.append(feat) + lengths_list.append(feat.shape[0]) + + feats_lengths = torch.tensor(lengths_list, dtype=torch.int32).to(device) + feats_tensor = pad_sequence(feats_list, batch_first=True).to( + device + ) # (batch, len, 80) + + features = self.model.encoder_extractor( + feats_tensor, + feats_lengths, + decoding_chunk_size=-1, + num_decoding_left_chunks=-1, + simulate_streaming=False, + ) + return features + + +class MertExtractor(BaseExtractor): + def __init__(self, cfg): + super(MertExtractor, self).__init__(cfg) + self.extractor_type = "mert" + self.preprocessor = None + + def load_model(self): + assert self.model == None + assert self.preprocessor == None + + print("Loading MERT Model: ...", self.cfg.preprocess.mert_model) + + local_mert_path = "/mnt/workspace/fangzihao/acce/Amphion/pretrained/MERT" + + model_name = self.cfg.preprocess.mert_model + model = AutoModel.from_pretrained(local_mert_path, trust_remote_code=True) + + if torch.cuda.is_available(): + model = model.cuda() + preprocessor = Wav2Vec2FeatureExtractor.from_pretrained( + local_mert_path, trust_remote_code=True + ) + + self.model = model + self.preprocessor = preprocessor + + def extract_content_features(self, wavs, lens): + """extract content features from a batch of dataloader + Args: + wavs: tensor (batch, T) + lens: list + """ + with torch.no_grad(): + sample_rate = self.preprocessor.sampling_rate + device = next(self.model.parameters()).device + assert ( + sample_rate == self.cfg.preprocess.mert_sample_rate + ), "mert sample rate mismatch, expected {}, got {}".format( + self.cfg.preprocess.mert_sample_rate, sample_rate + ) + mert_features = [] + # wav: (len) + for wav in wavs: + # {input_values: tensor, attention_mask: tensor} + inputs = self.preprocessor( + wavs, sampling_rate=sample_rate, return_tensors="pt" + ).to(device) + + outputs = self.model(**inputs, output_hidden_states=True) + # (25 layers, time steps, 1024 feature_dim) + all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze() + # (1, frame_len, 1024) -> (frame_len, 1024) + feature = outputs.hidden_states[ + self.cfg.preprocess.mert_feature_layer + ].squeeze(0) + mert_features.append(feature) + + return mert_features + + +def extract_utt_content_features_dataloader(cfg, metadata, num_workers): + dataset_name = metadata[0]["Dataset"] + + if cfg.preprocess.extract_whisper_feature: + feat_dir = os.path.join(cfg.preprocess.processed_dir, dataset_name, "whisper") + os.makedirs(feat_dir, exist_ok=True) + feat_files_num = len(os.listdir(feat_dir)) + + if feat_files_num != len(metadata): + whisper_waveforms = FFmpegDataset( + cfg, dataset_name, cfg.preprocess.whisper_sample_rate, metadata=metadata + ) + data_loader = DataLoader( + whisper_waveforms, + num_workers=num_workers, + shuffle=False, + pin_memory=cfg.preprocess.pin_memory, + batch_size=cfg.preprocess.content_feature_batch_size, + collate_fn=collate_batch, + drop_last=False, + ) + extractor = WhisperExtractor(cfg) + extractor.load_model() + for batch_idx, items in enumerate(tqdm(data_loader)): + _metadata, wavs, lens = items + + batch_content_features = extractor.extract_content_features( + wavs, + lens, + ) + for index, utt in enumerate(_metadata): + extractor.save_feature(utt, batch_content_features[index]) + + if cfg.preprocess.extract_contentvec_feature: + feat_dir = os.path.join( + cfg.preprocess.processed_dir, dataset_name, "contentvec" + ) + os.makedirs(feat_dir, exist_ok=True) + feat_files_num = len(os.listdir(feat_dir)) + + if feat_files_num != len(metadata): + contentvec_waveforms = LibrosaDataset( + cfg, + dataset_name, + cfg.preprocess.contentvec_sample_rate, + metadata=metadata, + ) + data_loader = DataLoader( + contentvec_waveforms, + num_workers=num_workers, + shuffle=False, + pin_memory=cfg.preprocess.pin_memory, + batch_size=cfg.preprocess.content_feature_batch_size, + collate_fn=collate_batch, + drop_last=False, + ) + extractor = ContentvecExtractor(cfg) + extractor.load_model() + for batch_idx, items in enumerate(tqdm(data_loader)): + _metadata, wavs, lens = items + + batch_content_features = extractor.extract_content_features(wavs, lens) + for index, utt in enumerate(_metadata): + extractor.save_feature(utt, batch_content_features[index]) + + if cfg.preprocess.extract_wenet_feature: + feat_dir = os.path.join(cfg.preprocess.processed_dir, dataset_name, "wenet") + os.makedirs(feat_dir, exist_ok=True) + feat_files_num = len(os.listdir(feat_dir)) + + if feat_files_num != len(metadata): + wenet_waveforms = TorchaudioDataset( + cfg, dataset_name, cfg.preprocess.wenet_sample_rate, metadata=metadata + ) + data_loader = DataLoader( + wenet_waveforms, + num_workers=num_workers, + shuffle=False, + pin_memory=cfg.preprocess.pin_memory, + batch_size=cfg.preprocess.content_feature_batch_size, + collate_fn=collate_batch, + drop_last=False, + ) + extractor = WenetExtractor(cfg) + extractor.load_model() + for batch_idx, items in enumerate(tqdm(data_loader)): + _metadata, wavs, lens = items + + batch_content_features = extractor.extract_content_features( + wavs, + lens, + ) + for index, utt in enumerate(_metadata): + extractor.save_feature(utt, batch_content_features[index]) + + if cfg.preprocess.extract_mert_feature: + feat_dir = os.path.join(cfg.preprocess.processed_dir, dataset_name, "mert") + os.makedirs(feat_dir, exist_ok=True) + feat_files_num = len(os.listdir(feat_dir)) + + if feat_files_num != len(metadata): + mert_waveforms = TorchaudioDataset( + cfg, dataset_name, cfg.preprocess.mert_sample_rate, metadata=metadata + ) + data_loader = DataLoader( + mert_waveforms, + num_workers=num_workers, + shuffle=False, + pin_memory=cfg.preprocess.pin_memory, + batch_size=cfg.preprocess.content_feature_batch_size, + collate_fn=collate_batch, + drop_last=False, + ) + extractor = MertExtractor(cfg) + extractor.load_model() + for batch_idx, items in enumerate(tqdm(data_loader)): + _metadata, wavs, lens = items + + batch_content_features = extractor.extract_content_features( + wavs, + lens, + ) + for index, utt in enumerate(_metadata): + extractor.save_feature(utt, batch_content_features[index]) diff --git a/processors/data_augment.py b/processors/data_augment.py new file mode 100644 index 0000000000000000000000000000000000000000..2fc183361d4bcfd454693ee0b7ffdd9758c09312 --- /dev/null +++ b/processors/data_augment.py @@ -0,0 +1,378 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import random +import os +import json + +import numpy as np +import parselmouth +import torch +import torchaudio +from tqdm import tqdm + +from audiomentations import TimeStretch + +from pedalboard import ( + Pedalboard, + HighShelfFilter, + LowShelfFilter, + PeakFilter, + PitchShift, +) + +from utils.util import has_existed + +PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT = 0.0 +PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT = 1.0 +PRAAT_CHANGEGENDER_PITCHSHIFTRATIO_DEFAULT = 1.0 +PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT = 1.0 +PRAAT_CHANGEGENDER_DURATIONFACTOR_DEFAULT = 1.0 + + +def wav_to_Sound(wav, sr: int) -> parselmouth.Sound: + """Convert a waveform to a parselmouth.Sound object + + Args: + wav (np.ndarray/torch.Tensor): waveform of shape (n_channels, n_samples) + sr (int, optional): sampling rate. + + Returns: + parselmouth.Sound: a parselmouth.Sound object + """ + assert wav.shape == (1, len(wav[0])), "wav must be of shape (1, n_samples)" + sound = None + if isinstance(wav, np.ndarray): + sound = parselmouth.Sound(wav[0], sampling_frequency=sr) + elif isinstance(wav, torch.Tensor): + sound = parselmouth.Sound(wav[0].numpy(), sampling_frequency=sr) + assert sound is not None, "wav must be either np.ndarray or torch.Tensor" + return sound + + +def get_pitch_median(wav, sr: int): + """Get the median pitch of a waveform + + Args: + wav (np.ndarray/torch.Tensor): waveform of shape (n_channels, n_samples) + sr (int, optional): sampling rate. + + Returns: + parselmouth.Pitch, float: a parselmouth.Pitch object and the median pitch + """ + if not isinstance(wav, parselmouth.Sound): + sound = wav_to_Sound(wav, sr) + else: + sound = wav + pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT + + # To Pitch: Time step(s)(standard value: 0.0), Pitch floor (Hz)(standard value: 75), Pitch ceiling (Hz)(standard value: 600.0) + pitch = parselmouth.praat.call(sound, "To Pitch", 0.8 / 75, 75, 600) + # Get quantile: From time (s), To time (s), Quantile(0.5 is then the 50% quantile, i.e., the median), Units (Hertz or Bark) + pitch_median = parselmouth.praat.call(pitch, "Get quantile", 0.0, 0.0, 0.5, "Hertz") + + return pitch, pitch_median + + +def change_gender( + sound, + pitch=None, + formant_shift_ratio: float = PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT, + new_pitch_median: float = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT, + pitch_range_ratio: float = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT, + duration_factor: float = PRAAT_CHANGEGENDER_DURATIONFACTOR_DEFAULT, +) -> parselmouth.Sound: + """Invoke change gender function in praat + + Args: + sound (parselmouth.Sound): a parselmouth.Sound object + pitch (parselmouth.Pitch, optional): a parselmouth.Pitch object. Defaults to None. + formant_shift_ratio (float, optional): formant shift ratio. A value of 1.0 means no change. Greater than 1.0 means higher pitch. Less than 1.0 means lower pitch. + new_pitch_median (float, optional): new pitch median. + pitch_range_ratio (float, optional): pitch range ratio. A value of 1.0 means no change. Greater than 1.0 means higher pitch range. Less than 1.0 means lower pitch range. + duration_factor (float, optional): duration factor. A value of 1.0 means no change. Greater than 1.0 means longer duration. Less than 1.0 means shorter duration. + + Returns: + parselmouth.Sound: a parselmouth.Sound object + """ + if pitch is None: + new_sound = parselmouth.praat.call( + sound, + "Change gender", + 75, + 600, + formant_shift_ratio, + new_pitch_median, + pitch_range_ratio, + duration_factor, + ) + else: + new_sound = parselmouth.praat.call( + (sound, pitch), + "Change gender", + formant_shift_ratio, + new_pitch_median, + pitch_range_ratio, + duration_factor, + ) + return new_sound + + +def apply_formant_and_pitch_shift( + sound: parselmouth.Sound, + formant_shift_ratio: float = PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT, + pitch_shift_ratio: float = PRAAT_CHANGEGENDER_PITCHSHIFTRATIO_DEFAULT, + pitch_range_ratio: float = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT, + duration_factor: float = PRAAT_CHANGEGENDER_DURATIONFACTOR_DEFAULT, +) -> parselmouth.Sound: + """use Praat "Changer gender" command to manipulate pitch and formant + "Change gender": Praat -> Sound Object -> Convert -> Change gender + refer to Help of Praat for more details + # https://github.com/YannickJadoul/Parselmouth/issues/25#issuecomment-608632887 might help + """ + pitch = None + new_pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT + if pitch_shift_ratio != 1.0: + pitch, pitch_median = get_pitch_median(sound, sound.sampling_frequency) + new_pitch_median = pitch_median * pitch_shift_ratio + + # refer to https://github.com/praat/praat/issues/1926#issuecomment-974909408 + pitch_minimum = parselmouth.praat.call( + pitch, "Get minimum", 0.0, 0.0, "Hertz", "Parabolic" + ) + new_median = pitch_median * pitch_shift_ratio + scaled_minimum = pitch_minimum * pitch_shift_ratio + result_minimum = new_median + (scaled_minimum - new_median) * pitch_range_ratio + if result_minimum < 0: + new_pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT + pitch_range_ratio = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT + + if math.isnan(new_pitch_median): + new_pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT + pitch_range_ratio = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT + + new_sound = change_gender( + sound, + pitch, + formant_shift_ratio, + new_pitch_median, + pitch_range_ratio, + duration_factor, + ) + return new_sound + + +# Function used in EQ +def pedalboard_equalizer(wav: np.ndarray, sr: int) -> np.ndarray: + """Use pedalboard to do equalizer""" + board = Pedalboard() + + cutoff_low_freq = 60 + cutoff_high_freq = 10000 + + q_min = 2 + q_max = 5 + + random_all_freq = True + num_filters = 10 + if random_all_freq: + key_freqs = [random.uniform(1, 12000) for _ in range(num_filters)] + else: + key_freqs = [ + power_ratio(float(z) / (num_filters - 1), cutoff_low_freq, cutoff_high_freq) + for z in range(num_filters) + ] + q_values = [ + power_ratio(random.uniform(0, 1), q_min, q_max) for _ in range(num_filters) + ] + gains = [random.uniform(-12, 12) for _ in range(num_filters)] + # low-shelving filter + board.append( + LowShelfFilter( + cutoff_frequency_hz=key_freqs[0], gain_db=gains[0], q=q_values[0] + ) + ) + # peaking filters + for i in range(1, 9): + board.append( + PeakFilter( + cutoff_frequency_hz=key_freqs[i], gain_db=gains[i], q=q_values[i] + ) + ) + # high-shelving filter + board.append( + HighShelfFilter( + cutoff_frequency_hz=key_freqs[9], gain_db=gains[9], q=q_values[9] + ) + ) + + # Apply the pedalboard to the audio + processed_audio = board(wav, sr) + return processed_audio + + +def power_ratio(r: float, a: float, b: float): + return a * math.pow((b / a), r) + + +def audiomentations_time_stretch(wav: np.ndarray, sr: int) -> np.ndarray: + """Use audiomentations to do time stretch""" + transform = TimeStretch( + min_rate=0.8, max_rate=1.25, leave_length_unchanged=False, p=1.0 + ) + augmented_wav = transform(wav, sample_rate=sr) + return augmented_wav + + +def formant_and_pitch_shift( + sound: parselmouth.Sound, fs: bool, ps: bool +) -> parselmouth.Sound: + """ """ + formant_shift_ratio = PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT + pitch_shift_ratio = PRAAT_CHANGEGENDER_PITCHSHIFTRATIO_DEFAULT + pitch_range_ratio = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT + + assert fs != ps, "fs, ps are mutually exclusive" + + if fs: + formant_shift_ratio = random.uniform(1.0, 1.4) + use_reciprocal = random.uniform(-1, 1) > 0 + if use_reciprocal: + formant_shift_ratio = 1.0 / formant_shift_ratio + # only use praat to change formant + new_sound = apply_formant_and_pitch_shift( + sound, + formant_shift_ratio=formant_shift_ratio, + ) + return new_sound + + if ps: + board = Pedalboard() + board.append(PitchShift(random.uniform(-12, 12))) + wav_numpy = sound.values + wav_numpy = board(wav_numpy, sound.sampling_frequency) + # use pedalboard to change pitch + new_sound = parselmouth.Sound( + wav_numpy, sampling_frequency=sound.sampling_frequency + ) + return new_sound + + +def wav_manipulation( + wav: torch.Tensor, + sr: int, + aug_type: str = "None", + formant_shift: bool = False, + pitch_shift: bool = False, + time_stretch: bool = False, + equalizer: bool = False, +) -> torch.Tensor: + assert aug_type == "None" or aug_type in [ + "formant_shift", + "pitch_shift", + "time_stretch", + "equalizer", + ], "aug_type must be one of formant_shift, pitch_shift, time_stretch, equalizer" + + assert aug_type == "None" or ( + formant_shift == False + and pitch_shift == False + and time_stretch == False + and equalizer == False + ), "if aug_type is specified, other argument must be False" + + if aug_type != "None": + if aug_type == "formant_shift": + formant_shift = True + if aug_type == "pitch_shift": + pitch_shift = True + if aug_type == "equalizer": + equalizer = True + if aug_type == "time_stretch": + time_stretch = True + + wav_numpy = wav.numpy() + + if equalizer: + wav_numpy = pedalboard_equalizer(wav_numpy, sr) + + if time_stretch: + wav_numpy = audiomentations_time_stretch(wav_numpy, sr) + + sound = wav_to_Sound(wav_numpy, sr) + + if formant_shift or pitch_shift: + sound = formant_and_pitch_shift(sound, formant_shift, pitch_shift) + + wav = torch.from_numpy(sound.values).float() + # shape (1, n_samples) + return wav + + +def augment_dataset(cfg, dataset) -> list: + """Augment dataset with formant_shift, pitch_shift, time_stretch, equalizer + + Args: + cfg (dict): configuration + dataset (str): dataset name + + Returns: + list: augmented dataset names + """ + # load metadata + dataset_path = os.path.join(cfg.preprocess.processed_dir, dataset) + split = ["train", "test"] if "eval" not in dataset else ["test"] + augment_datasets = [] + aug_types = [ + "formant_shift" if cfg.preprocess.use_formant_shift else None, + "pitch_shift" if cfg.preprocess.use_pitch_shift else None, + "time_stretch" if cfg.preprocess.use_time_stretch else None, + "equalizer" if cfg.preprocess.use_equalizer else None, + ] + aug_types = filter(None, aug_types) + for aug_type in aug_types: + print("Augmenting {} with {}...".format(dataset, aug_type)) + new_dataset = dataset + "_" + aug_type + augment_datasets.append(new_dataset) + new_dataset_path = os.path.join(cfg.preprocess.processed_dir, new_dataset) + + for dataset_type in split: + metadata_path = os.path.join(dataset_path, "{}.json".format(dataset_type)) + augmented_metadata = [] + new_metadata_path = os.path.join( + new_dataset_path, "{}.json".format(dataset_type) + ) + os.makedirs(new_dataset_path, exist_ok=True) + new_dataset_wav_dir = os.path.join(new_dataset_path, "wav") + os.makedirs(new_dataset_wav_dir, exist_ok=True) + + if has_existed(new_metadata_path): + continue + + with open(metadata_path, "r") as f: + metadata = json.load(f) + + for utt in tqdm(metadata): + original_wav_path = utt["Path"] + original_wav, sr = torchaudio.load(original_wav_path) + new_wav = wav_manipulation(original_wav, sr, aug_type=aug_type) + new_wav_path = os.path.join(new_dataset_wav_dir, utt["Uid"] + ".wav") + torchaudio.save(new_wav_path, new_wav, sr) + new_utt = { + "Dataset": utt["Dataset"] + "_" + aug_type, + "index": utt["index"], + "Singer": utt["Singer"], + "Uid": utt["Uid"], + "Path": new_wav_path, + "Duration": utt["Duration"], + } + augmented_metadata.append(new_utt) + new_metadata_path = os.path.join( + new_dataset_path, "{}.json".format(dataset_type) + ) + with open(new_metadata_path, "w") as f: + json.dump(augmented_metadata, f, indent=4, ensure_ascii=False) + return augment_datasets diff --git a/processors/phone_extractor.py b/processors/phone_extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..b0c53a79decf6c8c8e5ee68c5d8e05d878564f6a --- /dev/null +++ b/processors/phone_extractor.py @@ -0,0 +1,142 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +from tqdm import tqdm +from text.g2p_module import G2PModule, LexiconModule +from text.symbol_table import SymbolTable + +''' + phoneExtractor: extract phone from text +''' +class phoneExtractor: + def __init__(self, cfg, dataset_name=None, phone_symbol_file=None): + ''' + Args: + cfg: config + dataset_name: name of dataset + ''' + self.cfg = cfg + + # phone symbols dict + self.phone_symbols = set() + + # phone symbols dict file + if phone_symbol_file is not None: + self.phone_symbols_file = phone_symbol_file + elif dataset_name is not None: + self.dataset_name = dataset_name + self.phone_symbols_file = os.path.join(cfg.preprocess.processed_dir, + dataset_name, + cfg.preprocess.symbols_dict) + + + # initialize g2p module + if cfg.preprocess.phone_extractor in ["espeak", "pypinyin", "pypinyin_initials_finals"]: + self.g2p_module = G2PModule(backend=cfg.preprocess.phone_extractor) + elif cfg.preprocess.phone_extractor == 'lexicon': + assert cfg.preprocess.lexicon_path != "" + self.g2p_module = LexiconModule(cfg.preprocess.lexicon_path) + else: + print('No suppert to', cfg.preprocess.phone_extractor) + raise + + + def extract_phone(self, text): + ''' + Extract phone from text + Args: + + text: text of utterance + + Returns: + phone_symbols: set of phone symbols + phone_seq: list of phone sequence of each utterance + ''' + + if self.cfg.preprocess.phone_extractor in ["espeak", "pypinyin", "pypinyin_initials_finals"]: + text = text.replace("”", '"').replace("“", '"') + phone = self.g2p_module.g2p_conversion(text=text) + self.phone_symbols.update(phone) + phone_seq = [phn for phn in phone] + + elif self.cfg.preprocess.phone_extractor == 'lexicon': + phone_seq = self.g2p_module.g2p_conversion(text) + phone = phone_seq + if not isinstance(phone_seq, list): + phone_seq = phone_seq.split() + + return phone_seq + + def save_dataset_phone_symbols_to_table(self): + # load and merge saved phone symbols + if os.path.exists(self.phone_symbols_file): + phone_symbol_dict_saved = SymbolTable.from_file(self.phone_symbols_file)._sym2id.keys() + self.phone_symbols.update(set(phone_symbol_dict_saved)) + + # save phone symbols + phone_symbol_dict = SymbolTable() + for s in sorted(list(self.phone_symbols)): + phone_symbol_dict.add(s) + phone_symbol_dict.to_file(self.phone_symbols_file) + + +def extract_utt_phone_sequence(cfg, metadata): + ''' + Extract phone sequence from text + Args: + cfg: config + metadata: list of dict, each dict contains "Uid", "Text" + + ''' + + dataset_name = cfg.dataset[0] + + # output path + out_path = os.path.join(cfg.preprocess.processed_dir, dataset_name, cfg.preprocess.phone_dir) + os.makedirs(out_path, exist_ok=True) + + phone_extractor = phoneExtractor(cfg, dataset_name) + + for utt in tqdm(metadata): + uid = utt["Uid"] + text = utt["Text"] + + phone_seq = phone_extractor.extract_phone(text) + + phone_path = os.path.join(out_path, uid+'.phone') + with open(phone_path, 'w') as fin: + fin.write(' '.join(phone_seq)) + + if cfg.preprocess.phone_extractor != 'lexicon': + phone_extractor.save_dataset_phone_symbols_to_table() + + + +def save_all_dataset_phone_symbols_to_table(self, cfg, dataset): + # phone symbols dict + phone_symbols = set() + + for dataset_name in dataset: + phone_symbols_file = os.path.join(cfg.preprocess.processed_dir, + dataset_name, + cfg.preprocess.symbols_dict) + + # load and merge saved phone symbols + assert os.path.exists(phone_symbols_file) + phone_symbol_dict_saved = SymbolTable.from_file(phone_symbols_file)._sym2id.keys() + phone_symbols.update(set(phone_symbol_dict_saved)) + + # save all phone symbols to each dataset + phone_symbol_dict = SymbolTable() + for s in sorted(list(phone_symbols)): + phone_symbol_dict.add(s) + for dataset_name in dataset: + phone_symbols_file = os.path.join(cfg.preprocess.processed_dir, + dataset_name, + cfg.preprocess.symbols_dict) + phone_symbol_dict.to_file(phone_symbols_file) + + \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..630a405d4b17d5a2615edee3cfde7a4c52de5a32 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,5 @@ +fairseq +git+https://github.com/lhotse-speech/lhotse +encodec +phonemizer==3.2.1 +pypinyin==0.48.0 \ No newline at end of file diff --git a/schedulers/__init__.py b/schedulers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/schedulers/__pycache__/__init__.cpython-39.pyc b/schedulers/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..31cb075c1cdce25b10392e3ff956ee006c33108e Binary files /dev/null and b/schedulers/__pycache__/__init__.cpython-39.pyc differ diff --git a/schedulers/__pycache__/scheduler.cpython-39.pyc b/schedulers/__pycache__/scheduler.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a4e262ecd56a8f5281fdd9052293ebd29f751969 Binary files /dev/null and b/schedulers/__pycache__/scheduler.cpython-39.pyc differ diff --git a/schedulers/scheduler.py b/schedulers/scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..ead0efdd4d7e00f1239852a36ce3ff5151d83c19 --- /dev/null +++ b/schedulers/scheduler.py @@ -0,0 +1,173 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch.optim import Optimizer +from typing import List, Optional, Tuple, Union + + +def calc_lr(step, dim_embed, warmup_steps): + return dim_embed ** (-0.5) * min(step ** (-0.5), step * warmup_steps ** (-1.5)) + +# The function is modified from +# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/modules/scheduler.py +class NoamScheduler(torch.optim.lr_scheduler._LRScheduler): + def __init__( + self, + base_lr: float, + optimizer: torch.optim.Optimizer, + dim_embed: int, + warmup_steps: int, + last_epoch: int = -1, + verbose: bool = False, + ) -> None: + + self.dim_embed = dim_embed + self.base_lr = base_lr + self.warmup_steps = warmup_steps + self.num_param_groups = len(optimizer.param_groups) + + super().__init__(optimizer, last_epoch, verbose) + + def get_lr(self) -> float: + lr = self.base_lr * calc_lr(self._step_count, self.dim_embed, self.warmup_steps) + return [lr] * self.num_param_groups + + def set_step(self, step: int): + self._step_count = step + + +class LRScheduler(object): + """ + Base-class for learning rate schedulers where the learning-rate depends on both the + batch and the epoch. + """ + + def __init__(self, optimizer: Optimizer, verbose: bool = False): + # Attach optimizer + if not isinstance(optimizer, Optimizer): + raise TypeError("{} is not an Optimizer".format(type(optimizer).__name__)) + self.optimizer = optimizer + self.verbose = verbose + + for group in optimizer.param_groups: + group.setdefault("base_lr", group["lr"]) + + self.base_lrs = [group["base_lr"] for group in optimizer.param_groups] + + self.epoch = 0 + self.batch = 0 + + def state_dict(self): + """Returns the state of the scheduler as a :class:`dict`. + + It contains an entry for every variable in self.__dict__ which + is not the optimizer. + """ + return { + "base_lrs": self.base_lrs, + "epoch": self.epoch, + "batch": self.batch, + } + + def load_state_dict(self, state_dict): + """Loads the schedulers state. + + Args: + state_dict (dict): scheduler state. Should be an object returned + from a call to :meth:`state_dict`. + """ + self.__dict__.update(state_dict) + + def get_last_lr(self) -> List[float]: + """Return last computed learning rate by current scheduler. Will be a list of float.""" + return self._last_lr + + def get_lr(self): + # Compute list of learning rates from self.epoch and self.batch and + # self.base_lrs; this must be overloaded by the user. + # e.g. return [some_formula(self.batch, self.epoch, base_lr) for base_lr in self.base_lrs ] + raise NotImplementedError + + def step_batch(self, batch: Optional[int] = None) -> None: + # Step the batch index, or just set it. If `batch` is specified, it + # must be the batch index from the start of training, i.e. summed over + # all epochs. + # You can call this in any order; if you don't provide 'batch', it should + # of course be called once per batch. + if batch is not None: + self.batch = batch + else: + self.batch = self.batch + 1 + self._set_lrs() + + def step_epoch(self, epoch: Optional[int] = None): + # Step the epoch index, or just set it. If you provide the 'epoch' arg, + # you should call this at the start of the epoch; if you don't provide the 'epoch' + # arg, you should call it at the end of the epoch. + if epoch is not None: + self.epoch = epoch + else: + self.epoch = self.epoch + 1 + self._set_lrs() + + def _set_lrs(self): + values = self.get_lr() + assert len(values) == len(self.optimizer.param_groups) + + for i, data in enumerate(zip(self.optimizer.param_groups, values)): + param_group, lr = data + param_group["lr"] = lr + self._last_lr = [group["lr"] for group in self.optimizer.param_groups] + + +class Eden(LRScheduler): + """ + Eden scheduler. + The basic formula (before warmup) is: + lr = base_lr * (((batch**2 + lr_batches**2) / lr_batches**2) ** -0.25 * + (((epoch**2 + lr_epochs**2) / lr_epochs**2) ** -0.25)) * warmup + where `warmup` increases from linearly 0.5 to 1 over `warmup_batches` batches + and then stays constant at 1. + + + E.g. suggest base_lr = 0.04 (passed to optimizer) if used with ScaledAdam + + Args: + optimizer: the optimizer to change the learning rates on + lr_batches: the number of batches after which we start significantly + decreasing the learning rate, suggest 5000. + lr_epochs: the number of epochs after which we start significantly + decreasing the learning rate, suggest 6 if you plan to do e.g. + 20 to 40 epochs, but may need smaller number if dataset is huge + and you will do few epochs. + """ + + def __init__( + self, + optimizer: Optimizer, + lr_batches: Union[int, float], + lr_epochs: Union[int, float], + warmup_batches: Union[int, float] = 500.0, + verbose: bool = False, + ): + super(Eden, self).__init__(optimizer, verbose) + self.lr_batches = lr_batches + self.lr_epochs = lr_epochs + self.warmup_batches = warmup_batches + + def get_lr(self): + factor = ( + (self.batch**2 + self.lr_batches**2) / self.lr_batches**2 + ) ** -0.25 * ( + ((self.epoch**2 + self.lr_epochs**2) / self.lr_epochs**2) ** -0.25 + ) + warmup_factor = ( + 1.0 + if self.batch >= self.warmup_batches + else 0.5 + 0.5 * (self.batch / self.warmup_batches) + ) + + return [x * factor * warmup_factor for x in self.base_lrs] diff --git a/text/__init__.py b/text/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a4db9a6a2c8dbb20fb4188c322a7cc32afe722e2 --- /dev/null +++ b/text/__init__.py @@ -0,0 +1,79 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" This code is modified from https://github.com/keithito/tacotron """ +import re +from text import cleaners +from text.symbols import symbols + + +# Mappings from symbol to numeric ID and vice versa: +_symbol_to_id = {s: i for i, s in enumerate(symbols)} +_id_to_symbol = {i: s for i, s in enumerate(symbols)} + +# Regular expression matching text enclosed in curly braces: +_curly_re = re.compile(r"(.*?)\{(.+?)\}(.*)") + + +def text_to_sequence(text, cleaner_names): + """Converts a string of text to a sequence of IDs corresponding to the symbols in the text. + + The text can optionally have ARPAbet sequences enclosed in curly braces embedded + in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street." + + Args: + text: string to convert to a sequence + cleaner_names: names of the cleaner functions to run the text through + + Returns: + List of integers corresponding to the symbols in the text + """ + sequence = [] + + # Check for curly braces and treat their contents as ARPAbet: + while len(text): + m = _curly_re.match(text) + + if not m: + sequence += _symbols_to_sequence(_clean_text(text, cleaner_names)) + break + sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names)) + sequence += _arpabet_to_sequence(m.group(2)) + text = m.group(3) + return sequence + + +def sequence_to_text(sequence): + """Converts a sequence of IDs back to a string""" + result = "" + for symbol_id in sequence: + if symbol_id in _id_to_symbol: + s = _id_to_symbol[symbol_id] + # Enclose ARPAbet back in curly braces: + if len(s) > 1 and s[0] == "@": + s = "{%s}" % s[1:] + result += s + return result.replace("}{", " ") + + +def _clean_text(text, cleaner_names): + for name in cleaner_names: + cleaner = getattr(cleaners, name) + if not cleaner: + raise Exception("Unknown cleaner: %s" % name) + text = cleaner(text) + return text + + +def _symbols_to_sequence(symbols): + return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)] + + +def _arpabet_to_sequence(text): + return _symbols_to_sequence(["@" + s for s in text.split()]) + + +def _should_keep_symbol(s): + return s in _symbol_to_id and s != "_" and s != "~" diff --git a/text/__pycache__/__init__.cpython-39.pyc b/text/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9e60bcc32b3aea4b4908abfa04bdb86aa6b3d7c1 Binary files /dev/null and b/text/__pycache__/__init__.cpython-39.pyc differ diff --git a/text/__pycache__/cleaners.cpython-39.pyc b/text/__pycache__/cleaners.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1791a2bfbb7a5732cc402c8da91117ce533c1674 Binary files /dev/null and b/text/__pycache__/cleaners.cpython-39.pyc differ diff --git a/text/__pycache__/cmudict.cpython-39.pyc b/text/__pycache__/cmudict.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..672e705fd3e7e9f14470bbc1dfd601ef29c5d90f Binary files /dev/null and b/text/__pycache__/cmudict.cpython-39.pyc differ diff --git a/text/__pycache__/g2p_module.cpython-39.pyc b/text/__pycache__/g2p_module.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9af7beb8b32d5af9f24594cd66ba9ae439ea87bb Binary files /dev/null and b/text/__pycache__/g2p_module.cpython-39.pyc differ diff --git a/text/__pycache__/numbers.cpython-39.pyc b/text/__pycache__/numbers.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fc757d4b5c609fedfc4b0065a7c73c10edd6673b Binary files /dev/null and b/text/__pycache__/numbers.cpython-39.pyc differ diff --git a/text/__pycache__/pinyin.cpython-39.pyc b/text/__pycache__/pinyin.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6e9e63fdb5aefbe51e6bfe18b5da6ce0199c797a Binary files /dev/null and b/text/__pycache__/pinyin.cpython-39.pyc differ diff --git a/text/__pycache__/symbol_table.cpython-39.pyc b/text/__pycache__/symbol_table.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5a4479f95578a5f8e48cca74b136451f7e31e6b3 Binary files /dev/null and b/text/__pycache__/symbol_table.cpython-39.pyc differ diff --git a/text/__pycache__/symbols.cpython-39.pyc b/text/__pycache__/symbols.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4683b07c5bb9d3555217f01dc45716868b5cb34c Binary files /dev/null and b/text/__pycache__/symbols.cpython-39.pyc differ diff --git a/text/__pycache__/text_token_collation.cpython-39.pyc b/text/__pycache__/text_token_collation.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2b4bc9a1ea2d0563b98fab0e2a5b3bbe5c46a695 Binary files /dev/null and b/text/__pycache__/text_token_collation.cpython-39.pyc differ diff --git a/text/cleaners.py b/text/cleaners.py new file mode 100644 index 0000000000000000000000000000000000000000..10df96dd400efa0cceb9283cbab0371196ff3c29 --- /dev/null +++ b/text/cleaners.py @@ -0,0 +1,98 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" This code is modified from https://github.com/keithito/tacotron """ + +""" +Cleaners are transformations that run over the input text at both training and eval time. + +Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners" +hyperparameter. Some cleaners are English-specific. You'll typically want to use: + 1. "english_cleaners" for English text + 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using + the Unidecode library (https://pypi.python.org/pypi/Unidecode) + 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update + the symbols in symbols.py to match your data). +""" + + +# Regular expression matching whitespace: +import re +from unidecode import unidecode +from .numbers import normalize_numbers + +_whitespace_re = re.compile(r"\s+") + +# List of (regular expression, replacement) pairs for abbreviations: +_abbreviations = [ + (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) + for x in [ + ("mrs", "misess"), + ("mr", "mister"), + ("dr", "doctor"), + ("st", "saint"), + ("co", "company"), + ("jr", "junior"), + ("maj", "major"), + ("gen", "general"), + ("drs", "doctors"), + ("rev", "reverend"), + ("lt", "lieutenant"), + ("hon", "honorable"), + ("sgt", "sergeant"), + ("capt", "captain"), + ("esq", "esquire"), + ("ltd", "limited"), + ("col", "colonel"), + ("ft", "fort"), + ] +] + + +def expand_abbreviations(text): + for regex, replacement in _abbreviations: + text = re.sub(regex, replacement, text) + return text + + +def expand_numbers(text): + return normalize_numbers(text) + + +def lowercase(text): + return text.lower() + + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text) + + +def convert_to_ascii(text): + return unidecode(text) + + +def basic_cleaners(text): + """Basic pipeline that lowercases and collapses whitespace without transliteration.""" + text = lowercase(text) + text = collapse_whitespace(text) + return text + + +def transliteration_cleaners(text): + """Pipeline for non-English text that transliterates to ASCII.""" + text = convert_to_ascii(text) + text = lowercase(text) + text = collapse_whitespace(text) + return text + + +def english_cleaners(text): + """Pipeline for English text, including number and abbreviation expansion.""" + text = convert_to_ascii(text) + text = lowercase(text) + text = expand_numbers(text) + text = expand_abbreviations(text) + text = collapse_whitespace(text) + return text diff --git a/text/cmudict.py b/text/cmudict.py new file mode 100644 index 0000000000000000000000000000000000000000..684e63a52f84c4942d5be2a6fac80b6abc004eda --- /dev/null +++ b/text/cmudict.py @@ -0,0 +1,145 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" This code is modified from https://github.com/keithito/tacotron """ + +import re + + +valid_symbols = [ + "AA", + "AA0", + "AA1", + "AA2", + "AE", + "AE0", + "AE1", + "AE2", + "AH", + "AH0", + "AH1", + "AH2", + "AO", + "AO0", + "AO1", + "AO2", + "AW", + "AW0", + "AW1", + "AW2", + "AY", + "AY0", + "AY1", + "AY2", + "B", + "CH", + "D", + "DH", + "EH", + "EH0", + "EH1", + "EH2", + "ER", + "ER0", + "ER1", + "ER2", + "EY", + "EY0", + "EY1", + "EY2", + "F", + "G", + "HH", + "IH", + "IH0", + "IH1", + "IH2", + "IY", + "IY0", + "IY1", + "IY2", + "JH", + "K", + "L", + "M", + "N", + "NG", + "OW", + "OW0", + "OW1", + "OW2", + "OY", + "OY0", + "OY1", + "OY2", + "P", + "R", + "S", + "SH", + "T", + "TH", + "UH", + "UH0", + "UH1", + "UH2", + "UW", + "UW0", + "UW1", + "UW2", + "V", + "W", + "Y", + "Z", + "ZH", +] + +_valid_symbol_set = set(valid_symbols) + + +class CMUDict: + """Thin wrapper around CMUDict data. http://www.speech.cs.cmu.edu/cgi-bin/cmudict""" + + def __init__(self, file_or_path, keep_ambiguous=True): + if isinstance(file_or_path, str): + with open(file_or_path, encoding="latin-1") as f: + entries = _parse_cmudict(f) + else: + entries = _parse_cmudict(file_or_path) + if not keep_ambiguous: + entries = {word: pron for word, pron in entries.items() if len(pron) == 1} + self._entries = entries + + def __len__(self): + return len(self._entries) + + def lookup(self, word): + """Returns list of ARPAbet pronunciations of the given word.""" + return self._entries.get(word.upper()) + + +_alt_re = re.compile(r"\([0-9]+\)") + + +def _parse_cmudict(file): + cmudict = {} + for line in file: + if len(line) and (line[0] >= "A" and line[0] <= "Z" or line[0] == "'"): + parts = line.split(" ") + word = re.sub(_alt_re, "", parts[0]) + pronunciation = _get_pronunciation(parts[1]) + if pronunciation: + if word in cmudict: + cmudict[word].append(pronunciation) + else: + cmudict[word] = [pronunciation] + return cmudict + + +def _get_pronunciation(s): + parts = s.strip().split(" ") + for part in parts: + if part not in _valid_symbol_set: + return None + return " ".join(parts) diff --git a/text/g2p.py b/text/g2p.py new file mode 100644 index 0000000000000000000000000000000000000000..2368c1e97b4018abde395b190a9bbec76ba8ede0 --- /dev/null +++ b/text/g2p.py @@ -0,0 +1,38 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import re +from g2p_en import G2p +from string import punctuation + + +def read_lexicon(lex_path): + lexicon = {} + with open(lex_path) as f: + for line in f: + temp = re.split(r"\s+", line.strip("\n")) + word = temp[0] + phones = temp[1:] + if word.lower() not in lexicon: + lexicon[word.lower()] = phones + return lexicon + + +def preprocess_english(text, lexicon): + text = text.rstrip(punctuation) + + g2p = G2p() + phones = [] + words = re.split(r"([,;.\-\?\!\s+])", text) + for w in words: + if w.lower() in lexicon: + phones += lexicon[w.lower()] + else: + phones += list(filter(lambda p: p != " ", g2p(w))) + phones = "}{".join(phones) + phones = re.sub(r"\{[^\w\s]?\}", "{sp}", phones) + phones = phones.replace("}{", " ") + + return phones diff --git a/text/g2p_module.py b/text/g2p_module.py new file mode 100644 index 0000000000000000000000000000000000000000..39a1ab3c701bbfd20cccca0047fcad4caa09c41d --- /dev/null +++ b/text/g2p_module.py @@ -0,0 +1,221 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import re +from g2p_en import G2p +from string import punctuation +from typing import Any, Dict, List, Optional, Pattern, Union + +from phonemizer.backend import EspeakBackend +from phonemizer.backend.espeak.language_switch import LanguageSwitch +from phonemizer.backend.espeak.words_mismatch import WordMismatch +from phonemizer.punctuation import Punctuation +from phonemizer.separator import Separator + +try: + from pypinyin import Style, pinyin + from pypinyin.style._utils import get_finals, get_initials +except Exception: + pass + + +# This code is modified from +# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/data/tokenizer.py + +class PypinyinBackend: + """PypinyinBackend for Chinese. Most codes is referenced from espnet. + There are two types pinyin or initials_finals, one is + just like "ni1 hao3", the other is like "n i1 h ao3". + """ + + def __init__( + self, + backend="initials_finals", + punctuation_marks: Union[str, Pattern] = Punctuation.default_marks(), + ) -> None: + self.backend = backend + self.punctuation_marks = punctuation_marks + + def phonemize( + self, text: List[str], separator: Separator, strip=True, njobs=1 + ) -> List[str]: + assert isinstance(text, List) + phonemized = [] + for _text in text: + _text = re.sub(" +", " ", _text.strip()) + _text = _text.replace(" ", separator.word) + phones = [] + if self.backend == "pypinyin": + for n, py in enumerate( + pinyin( + _text, style=Style.TONE3, neutral_tone_with_five=True + ) + ): + if all([c in self.punctuation_marks for c in py[0]]): + if len(phones): + assert phones[-1] == separator.syllable + phones.pop(-1) + + phones.extend(list(py[0])) + else: + phones.extend([py[0], separator.syllable]) + elif self.backend == "pypinyin_initials_finals": + for n, py in enumerate( + pinyin( + _text, style=Style.TONE3, neutral_tone_with_five=True + ) + ): + if all([c in self.punctuation_marks for c in py[0]]): + if len(phones): + assert phones[-1] == separator.syllable + phones.pop(-1) + phones.extend(list(py[0])) + else: + if py[0][-1].isalnum(): + initial = get_initials(py[0], strict=False) + if py[0][-1].isdigit(): + final = ( + get_finals(py[0][:-1], strict=False) + + py[0][-1] + ) + else: + final = get_finals(py[0], strict=False) + phones.extend( + [ + initial, + separator.phone, + final, + separator.syllable, + ] + ) + else: + assert ValueError + else: + raise NotImplementedError + phonemized.append( + "".join(phones).rstrip(f"{separator.word}{separator.syllable}") + ) + return phonemized + + +class G2PModule: + """Phonemize Text.""" + + def __init__( + self, + language="en-us", + backend="espeak", + separator=Separator(word="_", syllable="-", phone="|"), + preserve_punctuation=True, + punctuation_marks: Union[str, Pattern] = Punctuation.default_marks(), + with_stress: bool = False, + tie: Union[bool, str] = False, + language_switch: LanguageSwitch = "keep-flags", + words_mismatch: WordMismatch = "ignore", + ) -> None: + + self.backend = self._initialize_backend( + backend, language, punctuation_marks, preserve_punctuation, + with_stress, tie, language_switch, words_mismatch + ) + self.separator = separator + + def _initialize_backend( + self, backend, language, punctuation_marks, preserve_punctuation, + with_stress, tie, language_switch, words_mismatch + ): + if backend == "espeak": + return EspeakBackend( + language, + punctuation_marks=punctuation_marks, + preserve_punctuation=preserve_punctuation, + with_stress=with_stress, + tie=tie, + language_switch=language_switch, + words_mismatch=words_mismatch, + ) + elif backend in ["pypinyin", "pypinyin_initials_finals"]: + return PypinyinBackend( + backend=backend, + punctuation_marks=punctuation_marks + self.separator.word, + ) + else: + raise NotImplementedError(f"{backend}") + + def to_list(self, phonemized: str) -> List[str]: + fields = [] + for word in phonemized.split(self.separator.word): + pp = re.findall(r"\w+|[^\w\s]", word, re.UNICODE) + fields.extend( + [p for p in pp if p != self.separator.phone] + + [self.separator.word] + ) + assert len("".join(fields[:-1])) == len(phonemized) - phonemized.count( + self.separator.phone + ) + return fields[:-1] + + + def phonemization(self, text, strip=True) -> List[List[str]]: + if isinstance(text, str): + text = [text] + + phonemized = self.backend.phonemize( + text, separator=self.separator, strip=strip, njobs=1 + ) + phonemes = [self.to_list(p) for p in phonemized] + return phonemes + + def g2p_conversion(self, text: str) -> List[str]: + phonemes = self.phonemization([text.strip()]) + return phonemes[0] + + +class LexiconModule: + def __init__(self, lex_path, language="en-us") -> None: + + # todo: check lexicon derivation, merge with G2PModule? + lexicon = {} + with open(lex_path) as f: + for line in f: + temp = re.split(r"\s+", line.strip("\n")) + word = temp[0] + phones = temp[1:] + if word.lower() not in lexicon: + lexicon[word.lower()] = phones + self.lexicon = lexicon + self.language = language + + def g2p_conversion(self, text): + phone = None + + # todo: preprocess with other languages + if self.language == 'en-us': + phone = self.preprocess_english(text) + else: + print('No support to', self.language) + raise + + return phone + + + def preprocess_english(self, text): + text = text.rstrip(punctuation) + + g2p = G2p() + phones = [] + words = re.split(r"([,;.\-\?\!\s+])", text) + for w in words: + if w.lower() in self.lexicon: + phones += self.lexicon[w.lower()] + else: + phones += list(filter(lambda p: p != " ", g2p(w))) + phones = "}{".join(phones) + phones = re.sub(r"\{[^\w\s]?\}", "{sp}", phones) + phones = phones.replace("}{", " ") + + + return phones diff --git a/text/lexicon/librispeech-lexicon.txt b/text/lexicon/librispeech-lexicon.txt new file mode 100644 index 0000000000000000000000000000000000000000..1091e15b860e3ce6dd9c785ba62076fa0ea20b6f --- /dev/null +++ b/text/lexicon/librispeech-lexicon.txt @@ -0,0 +1,206508 @@ +A AH0 +A EY1 +A''S EY1 Z +A'BODY EY1 B AA2 D IY0 +A'COURT EY1 K AO2 R T +A'D EY1 D +A'GHA EY1 G AH0 +A'GOIN EY1 G OY1 N +A'LL EY1 L +A'M EY1 M +A'MIGHTY EY1 M AY1 T IY0 +A'MIGHTY'S EY1 M AY1 T IY0 Z +A'MOST EY1 M OW2 S T +A'N'T EY1 AH0 N T +A'PENNY EY1 P EH2 N IY0 +A'READY EY1 R IY1 D IY0 +A'RIGHT EY1 R AY2 T +A'RONY EY1 R OW1 N IY0 +A'S EY1 Z +A'TER EY1 T ER0 +A'TERNOON EY1 T ER0 N UW1 N +A'TERWARDS EY1 T ER0 W ER0 D Z +A'THEGITHER EY1 DH AH0 JH IH1 DH ER0 +A'THING EY1 DH IH0 NG +A'TIM EY1 T IH2 M +A'VE AH0 V +AA AA1 +AAANTHOR T R IH2 P AH0 L EY1 N TH ER0 +AACHEN AA1 K AH0 N +AAD AA1 D +AAGE AA1 ZH +AAGE'S IH0 JH IH0 Z +AAGOT AA1 G AH0 T +AAGOT'S AA1 G AH0 T S +AAH AA1 +AAHMES AA1 M Z +AAKRE AA1 K ER0 +AAL AA1 L +AALBOM AA1 L B AO2 M +AALST AA1 L S T +AAMASH AA1 M AE2 SH +AAN AA1 N +AANA AA1 N AH0 +AANY EH1 N IY0 +AAR AA1 R +AARAAF AA1 R AA0 F +AARAU AA1 R AW1 +AARD AA0 R D +AARON EH1 R AH0 N +AARON'S EH1 R AH0 N Z +AARONIC EH1 R AH0 N IH2 K +AARONS EH1 R AH0 N Z +AARONSON AA1 R AH0 N S AH0 N +AARONSON EH1 R AH0 N S AH0 N +AASA AA1 S AH0 +AASE AA1 S +AASVOGEL AA1 Z V OW2 G AH0 L +AASVOGELS AA1 Z V OW2 G AH0 L Z +AAT AA1 T +AB AE1 B +AB EY1 B IY1 +AB'S AE1 B Z +ABA AA1 B AH0 +ABAAT AH0 B AA1 T +ABAB'DEH AH0 B AA1 B D AH0 +ABABA AA1 B AH0 B AH0 +ABABA AH0 B AA1 B AH0 +ABABDE AA1 B AH0 B D +ABABDEH AH0 B AE1 B D EH2 +ABACA AE1 B AH0 K AH0 +ABACK AH0 B AE1 K +ABACO AE1 B AH0 K OW2 +ABACUS AE1 B AH0 K AH0 S +ABAD AH0 B AA1 D +ABADAN AH0 B AE1 D AH0 N +ABADDON AH0 B AE1 D AH0 N +ABAFT AH0 B AE1 F T +ABAHT AH0 B AA1 T +ABAJO AE1 B AH0 JH OW0 +ABALONE AE2 B AH0 L OW1 N IY0 +ABALONES AE2 B AH0 L OW1 N Z +ABANA AH0 B AE1 N AH0 +ABANAZAR AH0 B AE1 N AA0 Z AA1 R +ABANDON AH0 B AE1 N D AH0 N +ABANDON'D AH0 B AE1 N D AH0 N D +ABANDONED AH0 B AE1 N D AH0 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IY1 N AH0 +ARGENTINE AA1 R JH AH0 N T IY2 N +ARGENTINES AA1 R JH AH0 N T IY2 N Z +ARGENTON AA2 R JH EH1 N T AH0 N +ARGENTUM AA2 R JH EH1 N T AH0 M +ARGETTI AA2 R JH EH1 T IY0 +ARGHOUSE AA1 R G HH AW2 S +ARGIA AA2 R JH AH0 +ARGIER AA1 R G IY0 ER0 +ARGIFY AA2 R JH AH0 F AY2 +ARGILE AA2 R JH AH0 L +ARGILE'S AA2 R JH AH0 L Z +ARGILIUS AA2 R JH IY1 L IY0 IH0 S +ARGILLACEOUS AA1 R G IH2 L EY1 SH AH0 S +ARGIOPE AA0 R JH OW1 P +ARGIVE AA1 R G IH1 V +ARGIVES AA1 R G IH1 V Z +ARGLE AA1 R G AH0 L +ARGO AA1 R G OW0 +ARGO'S AA1 R G OW0 Z +ARGOB AA1 R G AA2 B +ARGOBBA AA1 R G AA2 B AH0 +ARGOL AA1 R G AH0 L +ARGOLANDER AA1 R G AH0 L AE1 N D ER0 +ARGOLIS AA1 R G AH0 L IH0 S +ARGON AA1 R G AA2 N +ARGONAUT AA1 R G AH0 N AO2 T +ARGONAUTIC AA1 R G AH0 N AO1 T IH0 K +ARGONAUTS AA1 R G AH0 N AO2 T S +ARGONNE AA1 R G AA2 N +ARGONNE AA1 R G OW2 N +ARGONO AA1 R G AH0 N OW2 +ARGOS AA1 R G OW2 Z +ARGOSIES AA1 R G AH0 S IY0 Z +ARGOSY AA1 R G AH0 S IY0 +ARGOT AA1 R G AH0 T +ARGOUGES AA1 R G AA2 JH IH0 Z +ARGUABLE 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AH0 N T +ARHAT AA1 R HH AE2 T +ARHATS AA1 R HH AE2 T S +ARI AA1 R IY0 +ARIA AA1 R IY0 AH0 +ARIADNE EH2 R IY0 AE1 D N IY0 +ARIADNE'S EH2 R IY0 AE1 D N IY0 Z +ARIAEUS ER0 AY1 EY1 AH0 S +ARIAHS ER0 AY1 AH0 Z +ARIAL AA1 R IY0 AH0 L +ARIAN AE1 R IY0 AH0 N +ARIANE EH2 R IY0 AE1 N +ARIANISM AE1 R IY0 AH0 N IH2 Z AH0 M +ARIANS EH1 R IY0 AH0 N Z +ARIAS AA1 R IY0 AH0 Z +ARIBERT ER0 IH1 B ER0 T +ARICA AA0 R IY1 K AH0 +ARICHAT ER0 IH1 CH AH0 T +ARICIA ER0 IH1 SH AH0 +ARICIE ER0 IH1 S IY0 +ARICK EH2 R IH0 K +ARICKAREE ER0 IH1 K ER0 IY1 +ARID AE1 R AH0 D +ARID EH1 R AH0 D +ARIDAEUS AE1 R AH0 D AE2 IY0 AH0 S +ARIDITY ER0 IH1 D AH0 T IY0 +ARIE EH1 R IY0 +ARIEL EH1 R IY0 AH0 L +ARIEL'S EH1 R IY0 AH0 L Z +ARIELS AA0 R IY0 EH1 L Z +ARIES EH1 R IY0 Z +ARIGHT AA0 R AY2 T +ARII AE1 R IY0 +ARIMA AA0 R IY1 M AH0 +ARIMANES AA0 R IY0 M AA1 N IY0 Z +ARIMATHAEA AA0 R IY0 M AA1 T AH2 IY0 AH0 +ARIMATHEA AA0 R IY0 M AA1 DH IY0 AH0 +ARIMBALDO ER0 IH1 M B AH0 L D OW0 +ARIMINUM ER0 IH2 M AH0 N AH0 M +ARINA AA0 R IY1 N 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AH0 S AH0 D AA1 R IY0 +BALLISTA B AH0 L IH1 S T AH0 +BALLISTAE B AH0 L IH1 S T AH0 +BALLISTAS B AH0 L IH1 S T AH0 Z +BALLISTIC B AH0 L IH1 S T IH0 K +BALLISTICS B AH0 L IH1 S T IH0 K S +BALLIUM B AE1 L IY0 AH0 M +BALLMAN B AO1 L M AH0 N +BALLMEYER B AE1 L M AY0 ER0 +BALLO B AE1 L OW0 +BALLOBA B AE1 L AH0 B AH0 +BALLOCH B AE1 L AH0 K +BALLOCHGRAY B AE1 L AH0 K G R EY0 +BALLOCHMYLE B AE1 L AH0 K M AY2 L +BALLON B AE1 L AH0 N +BALLONETS B AE1 L AH0 N EH1 T S +BALLOON B AH0 L UW1 N +BALLOON'S B AH0 L UW1 N Z +BALLOONED B AH0 L UW1 N D +BALLOONING B AH0 L UW1 N IH0 NG +BALLOONIST B AH0 L UW1 N IH0 S T +BALLOONISTS B AH0 L UW1 N IH0 S T S +BALLOONS B AH0 L UW1 N Z +BALLOQUET B AE1 L AH0 K W AH0 T +BALLOQUET'S B AE1 L AH0 K W AH0 T S +BALLORAY B AO1 L ER0 EY2 +BALLOT B AE1 L AH0 T +BALLOTED B AE1 L AH0 T IH0 D +BALLOTING B AE1 L AH0 T IH0 NG +BALLOTS B AE1 L AH0 T S +BALLOTTI B AH0 L OW1 T IY0 +BALLOU B AH0 L UW1 +BALLOU'S B AH0 L UW1 Z +BALLPLATZ B AO1 L P L AE1 T S +BALLPLAYER B AO1 L P L 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R EY2 D +BALUSTRADED B AE1 L AH0 S T R EY1 D IH0 D +BALUSTRADES B AE1 L AH0 S T R EY1 D Z +BALUSTRADING B AE1 L AH0 S T R EY1 D IH0 NG +BALUZZI B AH0 L UW1 T S IY0 +BALUZZI'S B AH0 L UW1 Z IY0 Z +BALVANYOSSI B AA0 L V AA1 N Y OW1 S IY0 +BALWHIDDER B AO1 L W IH1 D ER0 +BALWIN B AE1 L W IH0 N +BALYIKA B AE2 L IY0 IH0 K AH0 +BALYUZ B AA1 L Y UW0 Z +BALZA B AA0 L Z AH0 +BALZAC B AA0 L Z AE0 K +BALZAC'S B AA0 L Z AE2 K S +BALZAJETTE B AA0 L Z AA1 ZH EH1 T +BALZER B EY1 L Z ER0 +BAM B AE1 M +BAMANGWATO B AE1 M AH0 NG W AA1 T OW0 +BAMBABEF B AE1 M B AH0 B AH0 F +BAMBAEV B AE1 M B EY1 V +BAMBARA B AE1 M B AA1 R AH0 +BAMBARRA B AE1 M B AA1 R AH0 +BAMBATSE B AE1 M B AE1 T S +BAMBER B AE1 M B ER0 +BAMBER'S B AE1 M B ER0 Z +BAMBERG B AE1 M B ER0 G +BAMBERGER B AE1 M B ER0 G ER0 +BAMBI B AE1 M B IY0 +BAMBI'S B AE2 M B IY1 Z +BAMBILIO B AE2 M B IY1 L IY0 OW0 +BAMBINA B AE2 M B IY1 N AH0 +BAMBINI B AE2 M B IY1 N IY0 +BAMBINO B AE2 M B IY1 N OW0 +BAMBINOS B AE2 M B IY1 N OW0 Z +BAMBLE B AE1 M B AH0 L 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IH0 N +BANGING B AE1 NG IH0 NG +BANGINGS B AE1 NG IH0 NG Z +BANGKOK B AE0 NG K AA1 K +BANGKOK B AE1 NG K AA0 K +BANGLE B AE1 NG G AH0 L +BANGLED B AE1 NG G AH0 L D +BANGLES B AE1 NG G AH0 L Z +BANGLETOP B AE1 NG G AH0 L T AA2 P +BANGLETOPS B AE1 NG G AH0 L T AA2 P S +BANGO B AA1 NG G OW0 +BANGOR B AE1 NG G AO2 R +BANGOR B AE1 NG G ER0 +BANGS B AE1 NG Z +BANGS'S B AE1 NG Z IH0 Z +BANGU B AE1 NG UW1 +BANGWAN B AE1 NG G W AA0 N +BANI B AA1 N IY0 +BANIAN B AE1 N IY0 AH0 N +BANIANS B AE1 N IY0 AH0 N Z +BANIM B AE1 N IH0 M +BANION B AE1 N Y AH0 N +BANION'S B AE1 N Y AH0 N Z +BANISH B AE1 N IH0 SH +BANISH'D B AE1 N IH0 SH D +BANISHED B AE1 N IH0 SH T +BANISHES B AE1 N IH0 SH IH0 Z +BANISHING B AE1 N IH0 SH IH0 NG +BANISHMENT B AE1 N IH0 SH M AH0 N T +BANISHMENTS B AE1 N IH0 SH M AH0 N T S +BANISSAT B AE1 N IH0 S AE0 T +BANISTER B AE1 N IH0 S T ER0 +BANISTERS B AE1 N IH0 S T ER0 Z +BANIYA B AE1 N IH0 Y AH0 +BANJARA B AA1 N Y AA1 R AH0 +BANJER B AE1 N JH ER0 +BANJO B AE1 N JH OW2 +BANJO'S B AE1 N JH OW2 Z +BANJOES B AE1 N JH OW2 Z +BANJOIST B AE1 N JH OW2 AH0 S T +BANJOS B AE1 N JH OW2 Z +BANK B AE1 NG K +BANK'S B AE1 NG K S +BANKA B AE1 NG K AH0 +BANKBILLS B AE1 NG K B IH2 L Z +BANKBOOK B AE1 NG K B UH2 K +BANKBOYS B AE1 NG K B OY2 Z +BANKCLERK B AE1 NG K L ER1 K +BANKCLERKS B AE1 NG K L ER1 K S +BANKED B AE1 NG K T +BANKEI B AE1 NG K IY0 +BANKER B AE1 NG K ER0 +BANKER'S B AE1 NG K ER0 Z +BANKERS B AE1 NG K ER0 Z +BANKES B AE1 NG K S +BANKES'S B AE1 NG K S IH0 Z +BANKET B AE1 NG K AH0 T +BANKHEAD B AE1 NG K HH EH2 D +BANKIM B AE0 NG K IY1 M +BANKIN B AE1 NG K IH0 N +BANKING B AE1 NG K IH0 NG +BANKNOTE B AE1 NG K N OW2 T +BANKNOTES B AE1 NG K N OW2 T S +BANKOK B AE1 NG K AH0 K +BANKROLL B AE1 NG K R OW2 L +BANKRUPT B AE1 NG K R AH0 P T +BANKRUPT'S B AE1 NG K R AH0 P T S +BANKRUPTCIES B AE1 NG K R AH0 P T S IY0 Z +BANKRUPTCY B AE1 NG K R AH0 P S IY0 +BANKRUPTCY B AE1 NG K R AH0 P T S IY0 +BANKRUPTED B AE1 NG K R AH0 P T IH0 D +BANKRUPTING B AE1 NG K R AH2 P T IH0 NG +BANKRUPTS B AE1 NG K R AH0 P S +BANKS B AE1 NG K S +BANKS'S B AE1 NG K S IH0 Z +BANKSIA B AE1 NG K S IY0 AH0 +BANKSIAN B AE1 NG ZH IH0 N +BANKSIAS B AE1 NG K S IY1 AH0 Z +BANKSIDE B AE1 NG K S AY2 D +BANLIEUE B AE0 N L UW2 +BANMANN B AE1 N M AH0 N +BANN B AE1 N +BANN'D B AE1 N D +BANNACK B AE1 N AH0 K +BANNACKS B AE1 N AE2 K S +BANNADONNA B AE1 N AH0 D AA1 N AH0 +BANNALEC B AE1 N AH0 L EH2 K +BANNATINE B AE1 N AH0 T AY2 N +BANNATYNE B AE1 N AH0 T AY2 N +BANNE B AE1 N +BANNED B AE1 N D +BANNEKER B AE1 N IH0 K ER0 +BANNER B AE1 N ER0 +BANNER'S B AE1 N ER0 Z +BANNERBRIDGE B AE1 N ER0 B R IH2 JH +BANNERDALE B AE1 N ER0 D EY2 L +BANNERED B AE1 N ER0 D +BANNERET B AE1 N ER0 EY1 +BANNERETS B AE1 N ER0 EY1 Z +BANNERHALL B AE1 N ER0 HH AO2 L +BANNERMAN B AE1 N ER0 M AH0 N +BANNERMAN'S B AE1 N ER0 M AH0 N Z +BANNERS B AE1 N ER0 Z +BANNERT B AE1 N ER0 T +BANNERTAIL B AE1 N ER0 T EY1 L +BANNET B AE1 N IH0 T +BANNIER B AE1 N IY0 ER0 +BANNING B AE1 N IH0 NG +BANNING'S B AE1 N IH0 NG Z +BANNINGTON'S B AE1 N IH0 NG T AH0 N Z +BANNISDALE B AE1 N IH0 S D EY2 L +BANNISTER B AE1 N AH0 S T ER0 +BANNISTER B AE1 N IH0 S T ER0 +BANNISTER'S B AE1 N IH0 S T ER0 Z +BANNISTERS B AE1 N IH0 S T ER0 Z +BANNOCK B AE1 N AH0 K +BANNOCKBURN B AE1 N AH0 K B ER0 N +BANNOCKS B AE1 N AH0 K S +BANNON B AE1 N AH0 N +BANNON'S B AE1 N AH0 N Z +BANNS B AE1 N Z +BANNU B AE1 N UW0 +BANO B AA1 N OW0 +BANOS B AA1 N OW0 Z +BANOU B AH0 N UW1 +BANQUE B AE1 NG K +BANQUET B AE1 NG K W AH0 T +BANQUET'S B AE1 NG K W AH0 T S +BANQUETA B AE1 NG K W EY1 T AH0 +BANQUETED B AE1 NG K W AH0 T IH0 D +BANQUETER B AE1 NG K W AH0 T ER0 +BANQUETERS B AE1 NG K W AH0 T ER0 Z +BANQUETING B AE1 NG K W AH0 T IH0 NG +BANQUETINGS B AE1 NG K W AH0 T IH0 NG Z +BANQUETS B AE1 NG K W AH0 T S +BANQUETTE B AE1 NG K EH1 T +BANQUETTES B AE1 NG K EH1 T S +BANQUETTING B AE1 NG K EH1 T IH0 NG +BANQUIER B AE1 NG K W AY1 ER0 +BANQUO B AE1 NG K W OW0 +BANQUO'S B AE1 NG K W OW0 Z +BANS B AE1 N Z +BANSHEE B AE0 N SH IY1 +BANSHEE B AE1 N SH IY0 +BANSHEE'S B AE0 N SH IY0 Z +BANSHEES B AE0 N SH IY0 Z +BANSKIAN B AE1 N S K IY0 AH0 N +BANSTEAD B AE1 N S T EH2 D +BANT B AE1 N T +BANTA B AE1 N T AH0 +BANTAM B AE1 N T AH0 M +BANTAM'S B AE1 N T AH0 M Z +BANTAMS B AE1 N T AH0 M Z +BANTEM B AE1 N T AH0 M +BANTER B AE1 N T ER0 +BANTERED B AE1 N T ER0 D +BANTERER B AE1 N T ER0 ER0 +BANTERING B AE1 N T ER0 IH0 NG +BANTERINGLY B AE1 N T ER0 IH0 NG L IY0 +BANTERINGS B AE1 N T ER0 IH0 NG Z +BANTERS B AE1 N T ER0 Z +BANTH B AE1 N TH +BANTHS B AE1 N TH S +BANTING B AE1 N T IH0 NG +BANTISON B AE1 N T IH0 S AH0 N +BANTLING B AE1 N T L IH0 NG +BANTLINGS B AE1 N T L IH0 NG Z +BANTO B AE1 N T OW0 +BANTOC B AE1 N T AH0 K +BANTOM B AE1 N T AH0 M +BANTOOM B AE1 N T UW2 M +BANTORNYI B AE1 N T ER0 N Y IY0 +BANTORNYI'S B AE1 N T ER0 N Y IY1 Z +BANTRY B AE1 N T R IY0 +BANTU B AE1 N T UW0 +BANTUS B AE1 N T AH0 S +BANTY B AE1 N T IY0 +BANU B AA1 N UW0 +BANVILLE B AA1 N V IH0 L +BANVILLE'S B AA1 N V IH0 L Z +BANYA B AA1 N Y AH0 +BANYA B AE1 N Y AH0 +BANYAN B AE1 N Y AH0 N 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AE0 P T AY1 Z D +BAPTIZED B AE1 P T AY2 Z D +BAPTIZES B AE1 P T AY2 Z IH0 Z +BAPTIZIN B AE0 P T AY1 Z IH0 N +BAPTIZING B AE1 P T AY2 Z IH0 NG +BAPUTI B AA0 P UW1 T IY0 +BAR B AA1 R +BAR'D B ER0 D +BAR'L B ER0 AH0 L +BAR'LS B ER0 L Z +BAR'S B AA1 R Z +BARA B AA1 R AH0 +BARABA B AA0 R AA1 B AH0 +BARABBAS B AE1 R AH0 B AA1 S +BARACH B AH0 R AA1 K +BARACH B ER0 AA1 K +BARADLAY B AA0 R AA1 D L IY0 +BARAGAR B AE1 R AH0 G AA1 R +BARAGAT B AH0 R AA1 G AH0 T +BARAGUAN B ER0 AA1 G W AH0 N +BARAK B AA1 R AH0 K +BARAKA B AA1 R AH0 K AH0 +BARAKA'S B AA1 R AH0 K AH0 Z +BARAKOFF B AA1 R AH0 K AO2 F +BARAL B AE1 R AH0 L +BARALONG B ER0 AO1 L AO0 NG +BARALONGS B AE1 R AH0 L AO1 NG Z +BARAMULA B EH1 R AE0 M Y AH0 L AH0 +BARAN B AA0 R AA1 N +BARANCY B ER0 AH0 N S IY0 +BARANG B AA0 R AA1 NG +BARANG'S B AA0 R AA1 NG Z +BARANGAY B AA0 R AA1 NG G EY1 +BARANHOV B EH1 R AE0 N HH AH1 V +BARANOFF B ER0 AH0 N AO0 F +BARANOV B ER0 AH0 N AA0 V +BARANTE B AA0 R AA1 N T IY0 +BARAQUAN B AA0 R AA1 K W AH0 N +BARAT B ER0 AA1 +BARATARIA B AE1 R AH0 T AA1 R IY0 AH0 +BARATARIAN B ER0 AA1 T EH1 R IY0 AH0 N +BARATARIANS B ER0 AA1 T EH1 R IY0 AH0 N Z +BARATHRUM B AE1 R AH0 TH R AH0 M +BARATOWSKI B AE1 R AH0 T AO1 F S K IY0 +BARB B AA1 R B +BARB'ROUS B AA1 R B R AH0 S +BARB'RY B AA1 R B R IY0 +BARBA B AA1 R B AH0 +BARBACAN B AA1 R B AH0 K AA2 N +BARBACZY B AA1 R B AH0 CH IY0 +BARBADIAN B AA0 R B AA1 D IY0 AH0 N +BARBADIANS B AA0 R B AA1 D IY0 AH0 N Z +BARBADOES B AA0 R B AA1 D OW0 Z +BARBADOS B AA0 R B EY1 D OW0 S +BARBAIK B AA1 R B EY1 K +BARBAJA B AA0 R B AA0 Y AH0 +BARBANCON B AA0 R B AE1 NG K AH0 N +BARBAR B AA1 R B ER0 +BARBARA B AA1 R B ER0 AH0 +BARBARA B AA1 R B R AH0 +BARBARA'S B AA1 R B ER0 AH0 Z +BARBARE B AA0 R B EH1 R +BARBAREE B AA1 R B ER0 IY1 +BARBARELLA B AA1 R B ER0 EH1 L AH0 +BARBARES B AA0 R B EH1 R Z +BARBARI B AA0 R B AA1 R IY0 +BARBARIAN B AA0 R B EH1 R IY0 AH0 N +BARBARIAN'S B AA0 R B EH1 R IY0 AH0 N Z +BARBARIANS B AA0 R B EH1 R IY0 AH0 N Z +BARBARIC B AA0 R B AE1 R IH0 K +BARBARIC B AA0 R B EH1 R IH0 K +BARBARICALLY B AA0 R B AE1 R IH0 K L IY0 +BARBARICO B AA0 R B AA0 R IY1 K OW0 +BARBARIE B AA0 R B EH1 R IY0 +BARBARIGO B AA0 R B AA0 R IY1 G OW0 +BARBARISI B AA0 R B AA0 R IY1 S IY0 +BARBARISM B AA1 R B ER0 IH2 Z AH0 M +BARBARISMS B AA1 R B ER0 IH2 Z AH0 M Z +BARBARITIES B AA0 R B EH1 R AH0 T IY0 Z +BARBARITY B AA0 R B AE1 R AH0 T IY0 +BARBARITY B AA0 R B EH1 R AH0 T IY0 +BARBARIZED B AA1 R B ER0 AY2 Z D +BARBARO B AA0 R B AA1 R OW0 +BARBAROS B AA0 R B AA1 R OW0 Z +BARBAROSSA B AA1 R B ER0 AA1 S AH0 +BARBAROSSA'S B AA1 R B ER0 AA1 S AH0 Z +BARBAROUS B AA1 R B ER0 AH0 S +BARBAROUSLY B AA1 R B ER0 AH0 S L IY0 +BARBAROUX B AA1 R B ER0 UW1 +BARBARUS B AA1 R B ER0 AH0 S +BARBARY B AA1 R B ER0 IY0 +BARBARY'S B AA1 R B ER0 IY0 Z +BARBASCO B AA0 R B AA1 S K OW0 +BARBASSOU B AA1 R B AH0 S UW0 +BARBATO B AA0 R B AA1 T OW0 +BARBATUS B AA0 R B AE1 T AH0 S +BARBAULD B AA0 R B AA0 L D +BARBAULD'S B AA1 R B AO1 L D Z +BARBAY B AA0 R B EY2 +BARBAZURE B AA0 R B AA1 ZH ER0 +BARBE B AA1 R B +BARBEAU B AA0 R B OW1 +BARBECUE B AA1 R B IH0 K Y UW2 +BARBECUED B AA1 R B IH0 K Y UW2 D +BARBECUES B AA1 R B IH0 K Y UW2 Z +BARBED B AA1 R B D +BARBEE B AA1 R B IY1 +BARBEE'S B AA1 R B IY0 Z +BARBEL B AA1 R B AH0 L +BARBELL B AA1 R B EH2 L +BARBELLE B AA1 R B EH2 L +BARBELS B AA1 R B AH0 L Z +BARBEMOUCHE B AA1 R B IH0 M UW1 SH +BARBEMUCHE B AA1 R B IH0 M AH1 CH +BARBER B AA1 R B ER0 +BARBER'S B AA1 R B ER0 Z +BARBERED B AA1 R B ER0 D +BARBERIE B AA0 R B EH1 R IY0 +BARBERIN B AA0 R B EH1 R IH0 N +BARBERIN'S B AA0 R B EH1 R AH0 N Z +BARBERING B AA1 R B ER0 IH0 NG +BARBERINI B AA1 R B ER0 IY1 N IY0 +BARBERRIES B AA1 R B EH2 R IY0 Z +BARBERRY B AA1 R B EH2 R IY0 +BARBERS B AA1 R B ER0 Z +BARBERSHOP B AA1 R B ER0 SH AA2 P +BARBERTON B AA1 R B ER0 T AH0 N +BARBES B AA1 R B Z +BARBET B AA1 R B IH0 T +BARBETS B AA1 R B IH0 T S +BARBETTE B AA0 R B EH1 T +BARBEY B AA1 R B IY0 +BARBEZIEUX B AA0 R B EH1 Z IY0 OW0 +BARBI B AA1 R B IY0 +BARBICAN B AA1 R B IY0 K AH0 N +BARBICANE B AA0 R B AH0 K EY2 N +BARBICANE'S B AA1 R B IY0 K AH0 N Z +BARBICANS B AA1 R B IY0 K AH0 N Z +BARBIE B AA1 R B IY0 +BARBIER B AA1 R B IY0 ER0 +BARBIER'S B AA0 R B IH1 R Z +BARBIERE B AA0 R B IH1 R +BARBILLE B AA0 R B IH2 L +BARBILLES B AA0 R B IH1 L IY0 Z +BARBILLON B AA0 R B IH1 L AH0 N +BARBIN B AA1 R B IH0 N +BARBITON B AA0 R B IH1 T AH0 N +BARBIZON B AA0 R B IH1 Z AH0 N +BARBLESS B AA1 R B L IH0 S +BARBO B AA1 R B OW0 +BARBORIN B AA0 R B AO1 R IH0 N +BARBOROUGH B AA1 R B ER2 OW0 +BARBOUR B AA1 R B ER0 +BARBOUR'S B AA1 R B ER0 Z +BARBOUX B AA1 R B UW1 +BARBOX B AA1 R B AA2 K S +BARBOZA B AA0 R B OW1 Z AH0 +BARBRA B AA1 R B R AH0 +BARBRAN B AA1 R B R AH0 N +BARBRECK B AA1 R B R IH0 K +BARBRO B AA1 R B R OW0 +BARBRO'S B AA1 R B R OW0 Z +BARBS B AA1 R B Z +BARBUL B AA0 R B UW1 L +BARBURY B AA1 R B EH2 R IY0 +BARBUSSE B AA1 R B AH0 S +BARBUZZI B AA0 R B UW1 T S IY0 +BARBY B AA1 R B IY0 +BARBY'S B AA1 R B IY0 Z +BARCA B AA1 R K AH0 +BARCAROLE B AA1 R K EH1 R AH0 L +BARCAROLLE B AA1 R K AA0 R OW1 L +BARCAS B AA1 R K AH0 S +BARCE B AA2 R S +BARCELLO B AA2 R S EH1 L OW0 +BARCELONA B AA2 R S IH0 L OW1 N AH0 +BARCH B AA1 R K +BARCHESTER B AA1 R CH EH2 S T ER0 +BARCHON B AA0 CH AH0 N +BARCINE B AA2 R S AH0 N +BARCLAY B AA1 R K L EY2 +BARCLAY B AA1 R K L IY2 +BARCLAY'S B AA1 R K L EY0 Z +BARCLAY'S B AA1 R K L IY0 Z +BARCLAYS B AA1 R K L EY0 Z +BARCLAYS B AA1 R K L IY0 Z +BARCOE B AA1 R K OW0 +BARCOMBE B AA1 R K AH0 M +BARCOO B AA1 R K UW1 +BARCREEK B AA1 R K R IY2 K +BARCROFT B AA1 R K R AO2 F T +BARCROFT'S B AA1 R K R AO2 F T S +BARCUS B AA1 R K AH0 S +BARCY B AA1 R S IY0 +BARD B AA1 R D +BARD'S B AA1 R D Z +BARDAS B AA1 R D AH0 S +BARDD B AA1 R D +BARDEEN B AA1 R D IY1 N +BARDEL B AA1 R D EH2 L +BARDELL B AA0 R D EH1 L +BARDELL'S B AA2 R D EH1 L Z +BARDELYS B AA0 R D EH1 L IY0 Z +BARDEN B AA1 R D AH0 N +BARDI B AA1 R D IY0 +BARDIANNA B AA1 R D IY0 AE1 N AH0 +BARDIC B AA1 R D IH0 K +BARDIYA B ER0 D IY2 AH0 +BARDO B AA1 R D OW0 +BARDO'S B AA1 R D OW0 Z +BARDOLF B AA1 R D OW2 L F 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EH2 D IH0 D +BAREILLY B EH1 R AY1 L IY0 +BARELA B AA0 R EH1 L AH0 +BARELEGGED B EH1 R AH0 L EH1 G D +BARELLAN B AA0 R EH1 L AH0 N +BARELY B EH1 R L IY0 +BARENESS B EH1 R N AH0 S +BARENNA B AA0 R EH1 N AH0 +BARENT B EH1 R AH0 N T +BARENTZ B EH1 R AH0 N T S +BAREPOLES B EH1 R P OW2 L Z +BARER B EH1 R ER0 +BARERE B AA0 R EH0 R +BARES B EH1 R Z +BARESARK B AE1 R IH0 S AA1 R K +BARESHANKS B EH1 R SH AE1 NG K S +BAREST B EH1 R AH0 S T +BARETTA B ER0 EH1 T AH0 +BARETTI B AA0 R EH1 T IY0 +BARETTI'S B AA0 R EH1 T IY0 Z +BAREY B EH1 R IY0 +BAREZZI B AA0 R EH0 Z IY0 +BARFIELD B AA1 R F IY2 L D +BARFLEUR B AA1 R F L ER1 +BARFOOT B AA1 R F UH2 T +BARFOOT'S B AA1 R F UH2 T S +BARFORD B AA1 R F ER0 D +BARFORDSHIRE B AA1 R F ER0 D SH AY2 ER0 +BARFORTH B AA1 R F AO2 R TH +BARGA B AA1 R G AH0 +BARGAIN B AA1 R G AH0 N +BARGAIN B AA1 R G IH0 N +BARGAIN'S B AA1 R G AH0 N Z +BARGAINED B AA1 R G AH0 N D +BARGAINER B AA1 R G IH0 N ER0 +BARGAINERS B AA1 R G IH0 N ER0 Z +BARGAINING B AA1 R G IH0 N IH0 NG +BARGAININGS B AA1 R G IH0 N IH0 NG Z +BARGAINS B AA1 R G AH0 N Z +BARGAINS B AA1 R G IH0 N Z +BARGE B AA1 R JH +BARGE'S B AA1 R JH AH0 Z +BARGED B AA1 R JH D +BARGEE B AA1 R JH IY0 +BARGEE'S B AA1 R JH IY0 Z +BARGEES B AA1 R JH IY0 Z +BARGELLO B AA1 R JH EH1 L OW0 +BARGEMAN B AA1 R JH M AH0 N +BARGEMAN'S B AA1 R JH M AH0 N Z +BARGEMEN B AA1 R JH M AH0 N +BARGER B AA1 R JH ER0 +BARGES B AA1 R JH AH0 Z +BARGES B AA1 R JH IH0 Z +BARGETON B AA1 R G AH0 T AH0 N +BARGETON'S B AA1 R G AH0 T AH0 N Z +BARGIN B AA1 R JH AH0 N +BARGING B AA1 R JH IH0 NG +BARGLE B AA1 R G AH0 L +BARGO B AA1 R G OW2 +BARGRAVE B AA1 R G R EY2 V +BARGRAVE'S B AA1 R G R EY2 V Z +BARGROVE B AA1 R G R OW2 V +BARHA B AA1 R HH AH0 +BARHAM B AA1 R HH AH0 M +BARHOLM B AA1 R HH OW0 L M +BARHOP B AA1 R HH AO0 P +BARHWI B AA1 R HH Y UW1 AY1 +BARI B AA1 R IY0 +BARILLERIE B EH2 R IH0 L ER0 IY0 +BARILLON B EH2 R IH0 L AA0 N +BARILLOT B ER0 IH1 L AH0 T +BARIN B AE1 R IH0 N +BARINE B AA0 R IY1 N IY0 +BARINE'S B ER0 IY1 N Z +BARING B EH1 R IH0 NG +BARING'S B EH1 R IH0 NG Z +BARINGO B ER0 IH1 NG G OW0 +BARINGS B EH1 R IH0 NG Z +BARIS B AA1 R IY0 Z +BARITONE B EH1 R AH0 T OW2 N +BARITONES B EH1 R AH0 T OW2 N Z +BARIUM B EH1 R IY0 AH0 M +BARIZY B ER0 IH1 Z IY0 +BARJOLS B AA1 R JH AH0 L Z +BARJONA B AA1 R JH OW1 N AH0 +BARJONAS B AA1 R JH OW1 N AH0 S +BARK B AA1 R K +BARK'D B AA1 R K D +BARK'S B AA1 R K S +BARKA B AA1 R K AH0 +BARKAL B AA1 R K AH0 L +BARKDALE B AA1 R K D EY2 L +BARKE B AA1 R K +BARKED B AA1 R K T +BARKEEP B AA1 R K IY2 P +BARKEEPER B AA1 R K IY2 P ER0 +BARKEEPER'S B AA1 R K IY2 P ER0 Z +BARKEEPERS B AA1 R K IY2 P ER0 Z +BARKENTINE B AA1 R K EH0 N T IY1 N IY0 +BARKER B AA1 R K ER0 +BARKER'S B AA1 R K ER0 Z +BARKERS B AA1 R K ER0 Z +BARKET B AA1 R K IH0 T +BARKEWORTH B AA1 R K W ER2 TH +BARKHAM B AA1 R K AH0 M +BARKHOUSE B AA1 R K HH AW2 S +BARKIE B AA1 R K IY0 +BARKIN B AA1 R K IH0 N +BARKING B AA1 R K IH0 NG +BARKINGHAM B AA1 R K IH0 NG HH AE2 M +BARKINGS B AA1 R K IH0 NG Z +BARKINGTON B AA1 R K IH0 NG T AH0 N +BARKIS B AA1 R K IH0 S +BARKLAND B AA1 R K L AH0 N D +BARKLAY B AA1 R K L EY0 +BARKLESS B AA1 R K L AH0 S +BARKLEY B AA1 R K L IY0 +BARKLEY'S B AA1 R K L IY0 Z +BARKLY B AA1 R K L IY0 +BARKMAN B AA1 R K M AH0 N +BARKOO B AA1 R K UW2 +BARKOVIS B AA1 R K AH0 V IH0 S +BARKS B AA1 R K S +BARKSDALE B AA1 R K S D EY2 L +BARKSDALE'S B AA1 R K S D EY2 L Z +BARKWELL B AA1 R K W EH2 L +BARKWORTH B AA1 R K W ER2 TH +BARKWORTH'S B AA1 R K W ER2 TH S +BARKY B AA1 R K IY0 +BARL B AA1 R L +BARLAAM B AA1 R L AA1 M +BARLACCHIA B AA1 R L AA1 K IY0 AH0 +BARLAIMONT B AA1 R L EY2 M AA2 N T +BARLASCH B AA1 R L AE1 SH +BARLCOMB B AA1 R L K AH0 M +BARLEY B AA1 R L IY0 +BARLEY'S B AA1 R L IY0 Z +BARLEYCORN B AA1 R L IY0 K AO2 R N +BARLEYCORN'S B AA1 R L IY1 K AO0 R N Z +BARLEYFIELD B AA1 R L IY0 F IY2 L D +BARLIGHT B AA1 R L AY2 T +BARLING B AA1 R L IH0 NG +BARLINGFORD B AA1 R L IH0 NG F ER0 D +BARLOW B AA1 R L OW2 +BARLOW'S B AA1 R L OW2 Z +BARLOWE B AA1 R L OW2 +BARLOWE'S B AA1 R L OW2 Z +BARLOWS B AA1 R L OW2 Z +BARLY B AA1 R L IY0 +BARM B AA1 R M +BARMAID B AA1 R M EY2 D +BARMAID'S B AA1 R M EY2 D Z +BARMAIDS B AA1 R M EY2 D Z +BARMAN B AA1 R M AH0 N +BARMBERRY B AA1 R M B EH2 R IY0 +BARMBY B AA1 R M B IY0 +BARMBY'S B AA1 R M B IY0 Z +BARMBYS B AA1 R M B IY0 Z +BARMECIDE B AA1 R M AH0 S AY2 D +BARMEN B AA1 R M AH0 N +BARMESYDE B AA1 R M AH0 Z AY1 D +BARMINSTER B AA1 R M IH2 N S T ER0 +BARMITZVAH B AA1 R M IH0 T S V AH0 +BARMONT B AA1 R M AA2 N T +BARMOUTH B AA1 R M AH0 TH +BARMSTON B AA1 R M S T AH0 N +BARMY B AA1 R M IY0 +BARN B AA1 R N +BARN'S B AA1 R N Z +BARNABAS B AA1 R N AH0 B AH0 S +BARNABAS'S B AA1 R N AH0 B AH0 S IH0 Z +BARNABETTA B AA1 R N AH0 B EH1 T AH0 +BARNABITE B AA1 R N AH0 B AY2 T +BARNABITES B AA1 R N AH0 B AY2 T S +BARNABO B AA1 R N AH0 B OW0 +BARNABY B AA1 R N AH0 B IY0 +BARNABY'S B AA1 R N AH0 B IY0 Z +BARNACLE B AA1 R N AH0 K AH0 L +BARNACLED B AA1 R N AH0 K AH0 L D +BARNACLES B AA1 R N AH0 K AH0 L Z +BARNARD B AA1 R N AA0 R D +BARNARD'S B AA1 R N AA0 R D Z +BARNARDINE B AA1 R N AA0 R D IY1 N IY0 +BARNARDO B AA1 R N AA0 R D OW0 +BARNARDO'S B AA1 R N AA0 R D OW0 Z +BARNARDS B AA1 R N ER0 D Z +BARNATO B AA1 R N AA1 T OW0 +BARNAVE B AA1 R N AH0 V +BARNBY B AA1 R N B IY0 +BARNCASTLE B AA1 R N K AE2 S AH0 L +BARNCASTLE'S B AA1 R N K AE2 S AH0 L Z +BARNDALE B AA1 R N D EY2 L +BARNDALE'S B AA1 R N D EY2 L Z +BARNDOLLAR B AA1 R N D AA2 L ER0 +BARNDOOR B AA1 R N D AO2 R +BARNE B AA1 R N +BARNEA B AA1 R N AH0 +BARNEGAT B AA1 R N IH0 G AE0 T +BARNES B AA1 R N Z +BARNES'S B AA1 R N Z IH0 Z +BARNESS B AA1 R N AH0 S +BARNESVILLE B AA1 R N Z V IH2 L +BARNET B AA1 R N IH0 T +BARNET'S B AA1 R N AH0 T S +BARNETT B AA0 R N EH1 T +BARNETT'S B AA0 R N EH1 T S +BARNETTE B AA1 R N EH1 T +BARNEVELD B AA1 R N IH0 V IH0 L D +BARNEVELD'S B AA1 R N IH0 V IH0 L D Z +BARNEVELDT B AA1 R N AH0 V EH2 L T +BARNEWITZ B AA1 R N IH0 W IH0 T S +BARNEY B AA1 R N IY0 +BARNEY'S B AA1 R N IY0 Z +BARNHAM B AA1 R N HH AH0 M +BARNHARDT B AA1 R N HH AA2 R T +BARNHART B AA1 R N HH 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L AY2 D Z +BASILIKE B AA0 S IY1 L AY2 K +BASILIO B AA0 S IY1 L IY0 OW0 +BASILISCUS B AA0 S IY1 L IY0 S K AH0 S +BASILISK B AA1 S AH0 L IH1 S K +BASILISK'S B AH0 Z IH1 L AH0 S K S +BASILISKOS B AH0 Z IH1 L AH0 S K OW0 Z +BASILISKS B AA1 S AH0 L IH1 S K S +BASILIUS B AA0 S IY1 L IY0 IH0 S +BASILIVITCH B AA0 S IY1 L IY0 V IH0 CH +BASIN B EY1 S AH0 N +BASIN'S B EY1 S AH0 N Z +BASINE B AA0 S IY1 N IY0 +BASINE'S B EY1 S AH0 N Z +BASINFUL B AH0 S IH1 N F AH0 L +BASING B EY1 S IH0 NG +BASINGHALL B AH0 S IH1 NG AH0 L +BASINGSTEAD B EY1 S IH0 NG S T EH2 D +BASINGSTOKE B EY1 S IH0 NG S T OW1 K +BASINGWELL B EY1 S IH0 NG W EH2 L +BASINS B EY1 S AH0 N Z +BASIS B EY1 S AH0 S +BASIS B EY1 S IH0 S +BASK B AE1 S K +BASKA B AE1 S K AH0 +BASKED B AE1 S K T +BASKELETT B AH0 S K EH1 L AH0 T +BASKELETT'S B AH0 S K EH1 L AH0 T S +BASKERVILLE B AE1 S K ER0 V IH2 L +BASKERVILLES B AE1 S K ER0 V IH2 L Z +BASKET B AE1 S K AH0 T +BASKET'S B AE1 S K AH0 T S +BASKETBALL B AE1 S K AH0 T B AO2 L +BASKETED B AE1 S K 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P EY1 K +BESPANGLED B IH0 S P AE1 NG G AH0 L D +BESPATTER B IH0 S P AE1 T ER0 +BESPATTERED B IH0 S P AE1 T ER0 D +BESPATTERING B IH0 S P AE1 T ER0 IH0 NG +BESPATTERS B IH0 S P AE1 T ER0 Z +BESPEAK B IH0 S P IY1 K +BESPEAKING B IH0 S P IY1 K IH0 NG +BESPEAKS B IH0 S P IY1 K S +BESPECTACLED B IH0 S P EH1 K T AH0 K AH0 L D +BESPOKE B IH0 S P OW1 K +BESPOKEN B IH0 S P OW1 K AH0 N +BESPREAD B IH0 S P R EH1 D +BESPRENT B IH0 S P R AH0 N T +BESPRINKLE B IH0 S P R IH1 NG K AH0 L +BESPRINKLED B IH0 S P R IH1 NG K AH0 L D +BESPRINKLING B IH0 S P R IH1 NG K L IH0 NG +BESS B EH1 S +BESS'S B EH1 S IH0 Z +BESSACRE B EH1 S AH0 K ER0 +BESSARABIA B EH1 S ER0 EY1 B IY0 AH0 +BESSAS B EH1 S AH0 S +BESSBOROUGH B EH1 S B ER0 OW0 +BESSE B EH1 S +BESSEE B EH0 S IY1 +BESSEL B EH1 S AH0 L +BESSEMER B EH1 S AH0 M ER0 +BESSENDIUM B EH1 S AH0 N D IY0 AH0 M +BESSER B EH1 S ER0 +BESSERDICH B EH1 S ER0 D IH0 K +BESSIE B EH1 S IY0 +BESSIE'S B EH1 S IY0 Z +BESSIERES B EH1 S IY0 EH0 R Z +BESSIN B EH1 S IH0 N +BESSINGTON 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AH0 L Z +BIRRENSWORK B ER1 AH0 N Z W ER2 K +BIRROO B ER1 UW2 +BIRS B ER1 Z +BIRSAY B IH1 R S EY1 +BIRSE B ER1 S +BIRT B ER1 T +BIRTH B ER1 TH +BIRTH'S B ER1 TH S +BIRTHA B ER1 TH AH0 +BIRTHDAY B ER1 TH D EY2 +BIRTHDAY'S B ER1 TH D EY2 Z +BIRTHDAYS B ER1 TH D EY2 Z +BIRTHLAND B ER1 TH L AE2 N D +BIRTHMARK B ER1 TH M AA2 R K +BIRTHMARKS B ER1 TH M AA2 R K S +BIRTHNIGHT B ER1 TH N AY2 T +BIRTHPLACE B ER1 TH P L EY2 S +BIRTHPLACES B ER1 TH P L EY2 S IH0 Z +BIRTHRATE B ER1 TH R EY2 T +BIRTHRIGHT B ER1 TH R AY2 T +BIRTHRIGHTS B ER1 TH R AY2 T S +BIRTHS B ER1 TH S +BIRTING B ER1 T IH0 NG +BIRTLEY B ER1 T L IY0 +BIRTON B ER1 T AH0 N +BIRTWICK B ER1 T W IH2 K +BIS B IH1 S +BIS'NESS B IH1 N AH0 S +BISARA B IH0 S AA1 R AH0 +BISARRE B IH0 S AA1 R IY0 +BISBEE B IH1 S B IY0 +BISCARRAT B IH0 S K EH1 R AH0 T +BISCAY B IH0 S K EY1 +BISCAYAN B IH0 S K EY1 AH0 N +BISCAYANS B IH0 S K EY1 AH0 N Z +BISCHEN B IH1 SH AH0 N +BISCHOFF B IH1 S K HH AO0 F +BISCLAVARET B IH0 S K L AH0 V AA1 R EY0 T +BISCLAVERET B 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NG Z +BISSINGEN B IH1 S IH0 NG G EH0 N +BISSON B IH1 S AH0 N +BISSONNETTE B IH1 S AH0 N EH2 T +BISSY B IH1 S IY0 +BIST B IH1 S T +BISTER B IH1 S T ER0 +BISTOURY B IH1 S T UH0 R IY0 +BISTRE B IH1 S T ER0 +BISTRITZ B IH1 S T R IH0 T S +BISULPHIDE B IY2 S UW0 L F IH0 D +BISVAS B IH1 S V AH0 Z +BIT B IH1 T +BITCH B IH1 CH +BITCH'S B IH1 CH IH0 Z +BITCHE B IH1 CH +BITCHES B IH1 CH IH0 Z +BITE B AY1 T +BITED B AY1 T IH0 D +BITER B AY1 T ER0 +BITEROLF B AY1 T ER0 OW0 L F +BITERRES B AY1 T ER0 Z +BITERS B AY1 T ER0 Z +BITES B AY1 T S +BITETH B IH1 T TH +BITHA B IH1 DH AH0 +BITHERSTONE B IH1 DH ER0 S T OW2 N +BITHOOR B IH1 DH UH1 R +BITHRI B IH1 DH R IY1 +BITHYNIA B IH1 DH IH1 N IY0 AH0 +BITHYNIAN B IH1 DH IH1 N IY0 AH0 N +BITHYNIANS B IH1 DH IH1 N IY0 AH0 N Z +BITIAS B IH1 SH AH0 Z +BITIN B AY1 T IH0 N +BITING B AY1 T IH0 NG +BITINGLY B AY1 T IH0 NG L IY0 +BITINGS B AY1 T IH0 NG Z +BITIOU B IH1 T IY0 UW0 +BITLIS B AY1 T L IH0 S +BITOU B IH1 CH UW0 +BITPIN B IH1 T P IH2 N +BITS B IH1 T S +BITT B IH1 T +BITTACY B IH1 T AH0 S IY0 +BITTE B IH1 T +BITTED B IH1 T IH0 D +BITTEN B IH1 T AH0 N +BITTER B IH1 T ER0 +BITTERER B IH1 T ER0 ER0 +BITTEREST B IH1 T ER0 AH0 S T +BITTERISH B IH1 T ER0 IH0 SH +BITTERLY B IH1 T ER0 L IY0 +BITTERN B IH1 T ER0 N +BITTERN'S B IH1 T ER0 N Z +BITTERNESS B IH1 T ER0 N AH0 S +BITTERNESSES B IH1 T ER0 N EH2 S IH0 Z +BITTERNS B IH1 T ER0 N Z +BITTERS B IH1 T ER0 Z +BITTERSWEET B IH1 T ER0 S W IY2 T +BITTHER B IH1 T DH ER0 +BITTIE B IH1 T IY0 +BITTING B IH1 T IH0 NG +BITTOCK B IH1 T AH0 K +BITTRA B IH1 T R AH0 +BITTRIDGE B IH1 T R IH0 JH +BITTS B IH1 T S +BITTY B IH1 T IY0 +BITUMEN B AY2 T UW1 M AH0 N +BITUMEN B IH2 T UW1 M AH0 N +BITUMINOUS B IH0 T UW1 M AH0 N AH0 S +BITWADDAD B IH1 T W AA0 D AH0 D +BITZER B IH1 T Z ER0 +BIVALVE B AY1 V AE2 L V +BIVALVES B AY1 V AE2 L V Z +BIVAR B IH1 V ER0 +BIVOUAC B IH1 V W AE0 K +BIVOUAC'S B IH1 V W AE0 K S +BIVOUACK B IH1 V W AE0 K +BIVOUACKED B IH1 V W AE0 K T +BIVOUACKING B IH1 V W AE0 K IH0 NG +BIWA B AY1 W AH0 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+BJORNSON'S B Y AO1 R N S AH0 N Z +BJORNSTAM B Y AO1 R N S T AE0 M +BJORNSTJERNE B Y AO1 R N S T Y ER1 N +BJRN B Y R N +BL B L +BLA B L AH0 +BLAAUWILDEBEESTEFONTEIN B L AA2 V IH1 L D IH0 B IY0 S T EH0 F OW1 N T AY2 N +BLAB B L AE1 B +BLABBED B L AE1 B D +BLABBER B L AE1 B ER0 +BLABBIN B L AE1 B IH0 N +BLABBING B L AE1 B IH0 NG +BLABS B L AE1 B Z +BLACAS B L AE1 K AH0 Z +BLACE B L AA1 CH IY0 +BLACHERNAE B L AA1 CH ER0 N EY2 +BLACHERNE B L AA1 CH ER0 N +BLACHINGTON B L AE1 CH IH0 NG T AH0 N +BLACK B L AE1 K +BLACK'S B L AE1 K S +BLACKADDER B L AE1 K AE2 D ER0 +BLACKADDER'S B L AE1 K AE2 D ER0 Z +BLACKAMOOR B L AE1 K AH0 M UH2 R +BLACKAMOORS B L AE1 K AH0 M UW1 R Z +BLACKAMORE B L AE1 K AH0 M AO2 R +BLACKANA B L AE1 K AA1 N AH0 +BLACKBALL B L AE1 K B AO2 L +BLACKBALLED B L AE1 K B AO2 L D +BLACKBEAR B L AE1 K B EH2 R +BLACKBEARD B L AE1 K B ER0 D +BLACKBEARD'S B L AE1 K B IH1 R D Z +BLACKBEETLE B L AE1 K B IY1 T AH0 L +BLACKBEETLES B L AE1 K B IY1 T AH0 L Z +BLACKBERRIES B L AE1 K B EH2 R IY0 Z +BLACKBERRY B L AE1 K B EH2 R IY0 +BLACKBERRYING B L AE1 K B EH2 R IY0 IH0 NG +BLACKBIRD B L AE1 K B ER0 D +BLACKBIRD'S B L AE1 K B ER0 D Z +BLACKBIRDER B L AE1 K B ER0 D ER0 +BLACKBIRDS B L AE1 K B ER0 D Z +BLACKBOARD B L AE1 K B AO2 R D +BLACKBOARDS B L AE1 K B AO2 R D Z +BLACKBOROUGH B L AE1 K B ER0 OW0 +BLACKBOROUGH'S B L AE1 K B ER0 OW0 Z +BLACKBOURNE B L AE1 K B ER0 N +BLACKBOY B L AE1 K B OY2 +BLACKBURN B L AE1 K B ER0 N +BLACKBURN'S B L AE1 K B ER0 N Z +BLACKBURNE B L AE1 K B ER0 N +BLACKBURNIAN B L AE1 K B ER0 N IY0 AH0 N +BLACKBURNS B L AE1 K B ER1 N Z +BLACKCAP B L AE1 K K AE2 P +BLACKCAPS B L AE1 K K AE2 P S +BLACKCOCK B L AE1 K K AA2 K +BLACKDEED B L AE1 K D IY1 D +BLACKDEEP B L AE1 K D IY1 P +BLACKDOWN B L AE1 K D AW2 N +BLACKE B L AE1 K +BLACKED B L AE1 K T +BLACKEE B L AE1 K IY0 +BLACKEN B L AE1 K AH0 N +BLACKEN'D B L AE1 K AH0 N D +BLACKENED B L AE1 K AH0 N D +BLACKENING B L AE1 K AH0 N IH0 NG +BLACKENING B L AE1 K N IH0 NG +BLACKENS B L AE1 K AH0 N Z +BLACKER B L AE1 K ER0 +BLACKEST B L AE1 K AH0 S T +BLACKETT B L AE1 K IH0 T +BLACKETT'S B L AE1 K IH0 T S +BLACKEY B L AE1 K IY0 +BLACKFACED B L AE1 K F AH0 S T +BLACKFEATHER B L AE1 K F EH1 DH ER0 +BLACKFEET B L AE1 K F IY1 T +BLACKFELLOW B L AE1 K F EH2 L OW0 +BLACKFELLOWS B L AE1 K F EH2 L OW0 Z +BLACKFISH B L AE1 K F IH2 SH +BLACKFOOT B L AE1 K F UH2 T +BLACKFORD B L AE1 K F ER0 D +BLACKFORD'S B L AE1 K F ER0 D Z +BLACKFRIARS B L AE1 K F R AY1 ER0 Z +BLACKGUARD B L AE1 K G AA2 R D +BLACKGUARD'S B L AE1 K G AA2 R D Z +BLACKGUARDED B L AE1 K G AA1 R D IH0 D +BLACKGUARDING B L AE1 K G AA1 R D IH0 NG +BLACKGUARDISM B L AE1 K G AA2 R D IH0 Z AH0 M +BLACKGUARDLY B L AE1 K G AA1 R D L IY0 +BLACKGUARDS B L AE1 K G AA2 R D Z +BLACKHAMPTON B L AE1 K HH AE1 M P T AH0 N +BLACKHAWK B L AE1 K HH AO2 K +BLACKHEAD B L AE1 K HH EH2 D +BLACKHEADS B L AE1 K HH EH2 D Z +BLACKHEARTED B L AE1 K HH AA2 R T IH0 D +BLACKHEATH B L AE1 K HH IY2 TH +BLACKHOUSE B L AE1 K HH AW2 S +BLACKHURST B L AE1 K HH ER0 S T +BLACKIE B L AE1 K IY0 +BLACKIE'S B L AE1 K IY0 Z +BLACKIES B L AE1 K IY0 Z +BLACKIN B L AE1 K IH0 N +BLACKING B L AE1 K IH0 NG +BLACKISH B L AE1 K IH0 SH +BLACKJACK B L AE1 K JH AE2 K +BLACKJACKS B L AE1 K JH AE2 K S +BLACKLADIES B L AE1 K L AE2 D IY0 Z +BLACKLAND B L AE1 K L AH0 N D +BLACKLEAD B L AE1 K L EH0 D +BLACKLEADED B L AE1 K L EH1 D AH0 D +BLACKLEG B L AE1 K L EH2 G +BLACKLEGS B L AE1 K L EH2 G Z +BLACKLER B L AE1 K L ER0 +BLACKLETTER B L AE1 K L EH2 T ER0 +BLACKLIST B L AE1 K L IH2 S T +BLACKLISTED B L AE1 K L IH2 S T IH0 D +BLACKLISTING B L AE1 K L IH2 S T IH0 NG +BLACKLOCK B L AE1 K L AA2 K +BLACKLY B L AE1 K L IY0 +BLACKMAIL B L AE1 K M EY2 L +BLACKMAILED B L AE1 K M EY2 L D +BLACKMAILER B L AE1 K M EY2 L ER0 +BLACKMAILER'S B L AE1 K M EY2 L ER0 Z +BLACKMAILERS B L AE1 K M EY2 L ER0 Z +BLACKMAILING B L AE1 K M EY2 L IH0 NG +BLACKMAN B L AE1 K M AH0 N +BLACKMAN'S B L AE1 K M AH0 N Z +BLACKMOOR B L AE1 K M UH2 R +BLACKMORE B L AE1 K M AO0 R +BLACKMORE'S B L AE1 K M AO0 R Z +BLACKNESS B L AE1 K N AH0 S +BLACKNESSES B L AE1 K N AH0 S IH0 Z +BLACKOUT B L AE1 K AW2 T +BLACKPOOL B L AE1 K P UW2 L +BLACKROCK B L AE1 K R AA2 K +BLACKS B L AE1 K S +BLACKSHEEP B L AE1 K SH IY1 P +BLACKSMITH B L AE1 K S M IH2 TH +BLACKSMITH'S B L AE1 K S M IH2 TH S +BLACKSMITHING B L AE1 K S M IH2 DH IH0 NG +BLACKSMITHS B L AE1 K S M IH2 TH S +BLACKSNAKE B L AE1 K S N EY2 K +BLACKSNAKES B L AE1 K S N EY2 K S +BLACKSTABLE B L AE1 K S T AH0 B AH0 L +BLACKSTAFFE B L AE1 K S T AE2 F +BLACKSTEAD B L AE1 K S T EH2 D +BLACKSTICK B L AE1 K S T IH2 K +BLACKSTOCK B L AE1 K S T AA2 K +BLACKSTOCK'S B L AE1 K S T AA2 K S +BLACKSTONE B L AE1 K S T OW2 N +BLACKSTONE'S B L AE1 K S T OW2 N Z +BLACKSTRAP B L AE1 K S T R AE2 P +BLACKTAIL B L AE1 K T EY2 L +BLACKTHORN B L AE1 K TH AO2 R N +BLACKTHORNE B L AE1 K TH AO2 R N +BLACKTHORNS B L AE1 K TH AO2 R N Z +BLACKTON B L AE1 K T AH0 N +BLACKTON'S B L AE1 K T AH0 N Z +BLACKTONS B L AE1 K T AH0 N Z +BLACKTOOTH B L AE1 K T UW2 TH +BLACKWALL B L AE1 K W AO2 L +BLACKWATER B L AE1 K W AO2 T ER0 +BLACKWELL B L AE1 K W EH2 L +BLACKWELL'S B L AE1 K W EH2 L Z +BLACKWINGS B L AE1 K W IH0 NG Z +BLACKWOOD B L AE1 K W UH2 D +BLACKWOOD'S B L AE1 K W UH2 D Z +BLACKWOODS B L AE1 K W UH2 D Z +BLACKY B L AE1 K IY0 +BLACKY'S B L AE1 K IY0 Z +BLACQUAIRE B L AH0 K EH1 R +BLAD B L AE1 D +BLADDER B L AE1 D ER0 +BLADDERS B L AE1 D ER0 Z +BLADDERWORT B L AE1 D ER0 W ER0 T +BLADDERY B L AE1 D ER0 IY0 +BLADE B L EY1 D +BLADE'S B L EY1 D Z +BLADED B L EY1 D IH0 D +BLADEN B L EY1 D AH0 N +BLADENSBURG B L EY1 D AH0 N Z B ER0 G +BLADERGRASS B L EY1 D ER0 G R AE2 S +BLADES B L EY1 D Z +BLADESOVER B L EY1 D IH0 S OW2 V ER0 +BLADON B L AA0 D AO1 N +BLADUD B L EY1 D AH0 D +BLAENAU B L AE1 N AW0 +BLAESKAEK B L EH1 S K IY1 K +BLAETTER B L EH1 T ER0 +BLAGDEN B L AE1 G D AH0 N +BLAGDEN'S B L AE1 G D AH0 N Z +BLAGDON B L AE1 G D AH0 N +BLAGG B L AE1 G +BLAGGARD B L AE1 G ER0 D +BLAGGARDS B L AE1 G ER0 D Z +BLAGOVO B L AA0 G OW1 V OW0 +BLAGUARD B L AH0 G W AA1 R D +BLAGUE B L EY1 G +BLAGUEUR B L EY1 G ER0 +BLAH B L AA1 +BLAIKIE B L EY1 K IY0 +BLAIN B L EY1 N +BLAINE B L EY1 N +BLAINE'S B L EY1 N Z +BLAINES B L EY1 N Z +BLAINEY B L EY1 N IY0 +BLAINS B L EY1 N Z +BLAINVILLE B L EY1 N V IH2 L +BLAIR B L EH1 R +BLAIR'S B L EH1 R Z +BLAIRE B L EH1 R +BLAIRS B L EH1 R Z +BLAIRSDALE B L EH1 R S D EY2 L +BLAIRTOWN B L EH1 R T AW2 N +BLAISDELL B L EY1 S D AH0 L +BLAISDELL'S B L EY1 S D AH0 L Z +BLAISDELLS B L EY1 S D AH0 L Z +BLAISE B L EY1 Z +BLAISE'S B L EY1 Z IH0 Z +BLAISETTE B L EY1 Z EH1 T +BLAISOIS B L EY1 S W AA2 +BLAITIERE B L EY1 T Y EH1 R +BLAIZE B L EY1 Z +BLAK B L AE1 K +BLAKE B L EY1 K +BLAKE'S B L EY1 K S +BLAKEFORD B L EY1 K F AO0 R D +BLAKEFORD'S B L EY1 K F ER0 D Z +BLAKELEY B L EY1 K L IY0 +BLAKELEY'S B L EY1 K L IY0 Z +BLAKELY B L EY1 K L IY0 +BLAKELY'S B L EY1 K L IY0 Z +BLAKEMAN B L EY1 K M AH0 N +BLAKEMORE B L EY1 K M AO0 R +BLAKEN B L EY1 K AH0 N +BLAKENEY B L EY1 K N IY0 +BLAKENEY'S B L EY1 K N IY0 Z +BLAKENEYS B L EY1 K N IY0 Z +BLAKES B L EY1 K S +BLAKESLEE B L EY1 K S L IY0 +BLAKESLEY B L EY1 K S L IY0 +BLAKESLY B L EY1 K S L IY0 +BLAKESON B L EY1 K S AH0 N +BLAKESTON B L EY1 K S T AH0 N +BLAKEVILLE B L EY1 K V IH0 L +BLAKEY B L EY1 K IY0 +BLAKISTON B L AE1 K IH0 S T AA0 N +BLALOK B L AE1 L AA0 K +BLAM B L AE1 M +BLAM'D B L EY1 M D +BLAMABLE B L AE1 M AH0 B AH0 L +BLAME B L EY1 M +BLAMEABLE B L EY1 M AH0 B AH0 L +BLAMED B L EY1 M D +BLAMEDEST B L EY1 M D AH0 S T +BLAMEFUL B L EY1 M F AH0 L +BLAMELESS B L EY1 M L AH0 S +BLAMELESSLY B L EY1 M L AH0 S L IY0 +BLAMELESSNESS B L EY1 M L AH0 S N AH0 S +BLAMES B L EY1 M Z +BLAMEWORTHY B L EY1 M W ER2 DH IY0 +BLAMIN B L AE1 M IH0 N +BLAMING B L EY1 M IH0 NG +BLAMIRE B L AA0 M IH1 R IY0 +BLAMONT B L EY1 M AH0 N T +BLAMOR B L AE1 M ER0 +BLAMORE B L AA1 M AO0 R +BLAN B L AE1 N +BLANC B L AE1 NG K +BLANC'S B L AE1 NG K S +BLANCA B L AA1 NG K AH0 +BLANCA'S B L AE1 NG K AH0 Z +BLANCANDRIN B L AE1 NG K AH0 N D R IH0 N +BLANCAS B L AE1 NG K AH0 Z +BLANCH B L AE1 N CH +BLANCH'D B L AE1 N CH D +BLANCHARD B L AE1 N CH ER0 D +BLANCHARD'S B L AE1 N CH ER0 D Z +BLANCHARDS B L AE1 N CH ER0 D Z +BLANCHE B L AE1 N CH +BLANCHE'S B L AE1 N CH IY0 Z +BLANCHED B L AE1 N CH T +BLANCHEFLEUR B L AE1 N CH IH0 F L ER1 +BLANCHELANDE B L AE1 N CH EH0 L AA1 N D IY0 +BLANCHES B L AE1 N CH IH0 Z +BLANCHET B L AE1 N K IH0 T +BLANCHETTE B L AH0 N SH EH1 T +BLANCHFLOWER B L AE1 N CH F L AW2 ER0 +BLANCHFLOWER'S B L AE1 N CH F L AW2 ER0 Z +BLANCHIDINE B L AE1 N CH IH0 D AY2 N +BLANCHING B L AE1 N CH IH0 NG +BLANCHISSEUSE B L AE1 N CH AY2 Z S UW1 S +BLANCHISSEUSES B L AE1 N CH AY2 Z S IY0 AH0 S IH0 Z +BLANCHITA B L AE1 N CH IY1 T AH0 +BLANCHMINSTER B L AE1 N CH M IH2 N S T ER0 +BLANCMANGE B L AE1 NG K M EY1 N JH +BLANCMANGES B L AE1 NG K M EY1 N JH IH0 Z +BLANCMESNIL B L AE1 NG K M EH0 S N IH1 L +BLANCO B L AE1 NG K OW0 +BLANCO'S B L AE1 NG K OW0 Z +BLANCOS B L AE1 NG K OW0 Z +BLANCS B L AE1 NG K S +BLAND B L AE1 N D +BLAND'S B L AE1 N D Z +BLANDA B L AE1 N D AH0 +BLANDAMER B L AE1 N D AH0 M ER0 +BLANDAMER'S B L AE1 N D AE2 M ER0 Z +BLANDAMERS B L AE1 N D AE1 M ER0 Z +BLANDANO B L AA0 N D AA1 N OW0 +BLANDEN B L AE1 N D AH0 N +BLANDEN'S B L AE1 N D AH0 N Z +BLANDER B L AE1 N D ER0 +BLANDEROCKS B L AE1 N D ER0 AH0 K S +BLANDEST B L AE1 N D AH0 S T +BLANDEVILLE B L AE1 N D AH0 V IH2 L +BLANDFIELD B L AE1 N D F IY2 L D +BLANDFORD B L AE1 N D F AO0 R D +BLANDINA B L AA0 N D IY1 N AH0 +BLANDING B L AE1 N D IH0 NG +BLANDINGS B L AE1 N D IH0 NG Z +BLANDISH B L AE1 N D IH0 SH +BLANDISHING B L AE1 N D IH0 SH IH0 NG +BLANDISHMENT B L AE1 N D IH0 SH M AH0 N T +BLANDISHMENTS B L AE1 N D IH0 SH M AH0 N T S +BLANDLY B L AE1 N D L IY0 +BLANDNESS B L AE1 N D N AH0 S +BLANDOIS B L AE1 N D OW2 IH0 Z +BLANDULA B L AE1 N D UW1 L AH0 +BLANDY B L AE1 N D IY0 +BLANE B L EY1 N +BLANEY B L EY1 N IY0 +BLANEY'S B L EY1 N IY0 Z +BLANEYS B L EY1 N IY0 Z +BLANG B L AE1 NG +BLANGIN B L AE1 NG G IH0 N +BLANGY B L AE1 N JH IY0 +BLANID B L AE1 N IH0 D +BLANK B L AE1 NG K +BLANK'S B L AE1 NG K S +BLANKA B L AE1 NG K AH0 +BLANKA'S B L AE1 NG K AH0 Z +BLANKED B L AE1 NG K T +BLANKENBERG B L AE1 NG K AH0 N B ER0 G +BLANKENBERGHE B L AE1 NG K AH0 N B ER0 G +BLANKENBURG B L AE1 NG K AH0 N B ER0 G +BLANKENSHIP B L AE1 NG K AH0 N SH IH2 P +BLANKER B L AE1 NG K ER0 +BLANKEST B L AE1 NG K IH0 S T +BLANKET B L AE1 NG K AH0 T +BLANKET B L AE1 NG K IH0 T +BLANKETED B L AE1 NG K AH0 T IH0 D +BLANKETING B L AE1 NG K AH0 T IH0 NG +BLANKETS B L AE1 NG K AH0 T S +BLANKETY B L AE1 NG K AH0 T IY0 +BLANKINSHIP B L AE1 NG K IH0 N SH IH0 P +BLANKLEY'S B L AE1 NG K L IY0 Z +BLANKLY B L AE1 NG K L IY0 +BLANKNESS B L AE1 NG K N AH0 S +BLANKS B L AE1 NG K S +BLANKSHIRE B L AE1 NG K SH AY2 R +BLANKTON B L AE1 NG K T AH0 N +BLANKY B L AE1 NG K IY0 +BLANLEY B L AE1 N L IY0 +BLANQUET B L AE1 NG K W AH0 T +BLANQUETTE B L AE1 NG K EH1 T +BLANQUETTE'S B L AE1 NG K EH1 T S +BLANQUI B L AE1 NG K W IY0 +BLANSAC B L AE1 N S AE0 K +BLANSTON B L AE1 N S T AH0 N +BLANT B L AE1 N T +BLANT'S B L AE1 N T S +BLANTOCK B L AE1 N T AH0 K +BLANTON B L AE1 N T AH0 N +BLANTYRE B L AE1 N T AY2 R +BLARCUM B L AA1 R K AH0 M +BLARDEN B L AA1 R D AH0 N +BLARDEN'S B L AA1 R D AH0 N Z +BLARE B L EH1 R +BLARED B L EH1 R D +BLARES B L EH1 R Z +BLARING B L EH1 R IH0 NG +BLARNEY B L AA1 R N IY0 +BLARNEYED B L AA1 R N IY0 D +BLARNEYING B L AA1 R N IY0 IH0 NG +BLARST B L AA1 R S T +BLARSTED B L AA1 R S T AH0 D +BLAS B L AA1 S +BLASCO B L AA1 S K OW0 +BLASDELL B L AE1 S D AH0 L +BLASE B L EY1 Z +BLASEE B L EY1 Z IY1 +BLASENGAME B L EY1 S AH0 N G EY2 M +BLASES B L EY1 S IH0 Z +BLASH B L AA1 SH +BLASHFIELD B L AA1 SH F IY0 L D +BLASI B L EY1 Z IY0 +BLASIUS B L EY1 S IY0 IH0 S +BLASPHEME B L AE1 S F AH0 M +BLASPHEMED B L AE1 S F AH0 M D +BLASPHEMER B L AE1 S F AH0 M ER0 +BLASPHEMERS B L AE1 S F AH0 M ER0 Z +BLASPHEMES B L AE1 S F AH0 M Z +BLASPHEMETH B L AE1 S F AH0 M IH0 TH +BLASPHEMIES B L AE1 S F AH0 M IY0 Z +BLASPHEMIN B L AE1 S F AH0 M IH2 N +BLASPHEMING B L AE1 S F AH0 M IH0 NG +BLASPHEMOUS B L AE1 S F AH0 M AH0 S +BLASPHEMOUSLY B L AE1 S F AH0 M AH0 S L IY0 +BLASPHEMY B L AE1 S F AH0 M IY0 +BLASQUITO B L AE1 S K IY1 T OW0 +BLASS B L AE1 S +BLASSEMARE B L AE1 S AH0 M EH1 R +BLAST B L AE1 S T +BLASTBLOW B L AE1 S T B L OW2 +BLASTED B L AE1 S T AH0 D +BLASTED B L AE1 S T IH0 D +BLASTER B L AE1 S T ER0 +BLASTERS B L AE1 S T ER0 Z +BLASTIN B L AE1 S T IH0 N +BLASTING B L AE1 S T IH0 NG +BLASTODERM B L AE1 S T AH0 D ER0 M +BLASTOFF B L AE1 S T AO2 F +BLASTS B L AE1 S T S +BLAT B L AE1 T +BLATANCY B L EY1 T AH0 N S IY0 +BLATANT B L EY1 T AH0 N T +BLATANTLY B L EY1 T AH0 N T L IY0 +BLATCH B L AE1 CH +BLATCH'S B L AE1 CH IH0 Z +BLATCHFORD B L AE1 CH F ER0 D +BLATCHFORD'S B L AE1 CH F ER0 D Z +BLATCHLEY B L AE1 CH L IY0 +BLATE B L EY1 T +BLATHENOY B L AE1 TH IH0 N OY0 +BLATHER B L AE1 DH ER0 +BLATHERIN B L AE1 DH ER0 IH0 N +BLATHERING B L AE1 DH ER0 IH0 NG +BLATHERS B L AE1 DH ER0 Z +BLATHERSKITE B L AE1 DH ER0 S K AY2 T +BLATHERSKITES B L AE1 DH ER0 S K AY2 T S +BLATHERUMSKITE B L AE1 DH ER0 AH1 M S K AY2 T +BLATHERWICK B L AE1 DH ER0 W IH2 K +BLATHERWYCKE B L AE1 DH ER0 W IH1 K +BLATHWAYT B L AE1 TH W EY1 T +BLATT B L AE1 T +BLATTED B L AE1 T AH0 D +BLATTER B L AE1 T ER0 +BLATTERGOWL B L AE1 T ER0 G AW1 L +BLATTING B L AE1 T IH0 NG +BLAU B L AW1 +BLAUBEUREN B L AW1 B OY0 R AH0 N +BLAUE B L AW1 IY0 +BLAUNCH B L AO1 N CH +BLAUNCHEFLOUR B L AO1 N CH EH1 F L AW1 ER0 +BLAUSSER B L AW1 S ER0 +BLAUVELT B L AW1 V IH0 L T +BLAUW B L AW1 +BLAVATSKY B L AA0 V AA1 T S K IY0 +BLAVATSKY'S B L AA0 V AA1 T S K IY0 Z +BLAW B L AO1 +BLAWIN B L AW1 IH0 N +BLAWING B L AO1 IH0 NG +BLAWN B L AO2 N +BLAWS B L AO2 Z +BLAWSTED B L AW1 S T IH0 D +BLAY B L EY1 +BLAYDES B L EY1 D Z +BLAYE B L EY1 +BLAYLOCK B L EY1 L AH0 K +BLAYNEY B L EY1 N IY0 +BLAYTHWAITE B L EY1 TH W EY2 T +BLAZ'D B L EY1 Z AH0 D +BLAZE B L EY1 Z +BLAZED B L EY1 Z D +BLAZER B L EY1 Z ER0 +BLAZER'S B L EY1 Z ER0 Z +BLAZERS B L EY1 Z ER0 Z +BLAZES B L EY1 Z IH0 Z +BLAZIN B L AA0 Z IY1 N +BLAZING B L EY1 Z IH0 NG +BLAZINGLY B L EY1 Z IH0 NG L IY0 +BLAZIUS B L EY1 Z IY0 IH0 S +BLAZON B L EY1 Z AH0 N +BLAZON'D B L EY1 Z AH0 N D +BLAZONED B L EY1 Z AH0 N D +BLAZONING B L EY1 Z AH0 N IH0 NG +BLAZONINGS B L EY1 Z AH0 N IH0 NG Z +BLAZONRIES B L EY1 Z AH0 N R IY0 Z +BLAZONRY B L EY1 Z AH0 N R IY0 +BLAZONS B L EY1 Z AH0 N Z +BLE B AH0 L +BLEACH B L IY1 CH +BLEACH'D B L IY1 CH AH0 D +BLEACHED B L IY1 CH T +BLEACHER B L IY1 CH ER0 +BLEACHERS B L IY1 CH ER0 Z +BLEACHES B L IY1 CH IH0 Z +BLEACHIN B L IY1 CH IH0 N +BLEACHING B L IY1 CH IH0 NG +BLEACRES B L IY1 K ER0 Z +BLEAD B L IY1 D +BLEAK B L IY1 K +BLEAKER B L IY1 K ER0 +BLEAKEST B L IY1 K AH0 S T +BLEAKIRK B L IY1 K ER0 K +BLEAKLY B L IY1 K L IY0 +BLEAKNESS B L IY1 K N AH0 S +BLEAKRIDGE B L IY1 K R IH2 JH +BLEAKS B L IY1 K S +BLEAR B L IH1 R +BLEARED B L IH1 R D +BLEARILY B L IY1 R AH0 L IY0 +BLEARY B L IH1 R IY0 +BLEASE B L IY1 S +BLEAT B L IY1 T +BLEATED B L IY1 T IH0 D +BLEATIN B L IY1 T IH0 N +BLEATING B L IY1 T IH0 NG +BLEATINGS B L IY1 T IH0 NG Z +BLEATS B L IY1 T S +BLECKER B L EH1 K ER0 +BLED B L EH1 D +BLEDSHAW B L EH1 D SH AO2 +BLEDSOE B L EH1 D S OW0 +BLEDSOE'S B L EH1 D S OW0 Z +BLEE B L IY1 +BLEECKER B L IY1 K ER0 +BLEED B L IY1 D +BLEED'N B L IY1 D AH0 N +BLEEDEM B L IY1 D AH0 M +BLEEDER B L IY1 D ER0 +BLEEDIN B L IY1 D IH0 N +BLEEDING B L IY1 D IH0 NG +BLEEDINGS B L IY1 D IH0 NG Z +BLEEDS B L IY1 D Z +BLEEGED B L IY1 JH D +BLEEKER B L IY1 K ER0 +BLEEVE B L IY1 V +BLEEZE B L IY1 Z +BLEEZING B L IY1 Z IH0 NG +BLEFUSCU B L EH1 F AH0 S K Y UW2 +BLEIBEN B L AY1 B AH0 N +BLEIBT B L AY1 B IY1 T IY1 +BLEIBTREU B L AY1 B T R UW1 +BLEIS B L AY1 Z +BLEKE B L IY1 K +BLEKEKE B L EH1 K AH0 K IY0 +BLEKINGE B L EH1 K IH1 N JH +BLEMISH B L EH1 M IH0 SH +BLEMISHED B L EH1 M IH0 SH T +BLEMISHES B L EH1 M IH0 SH IH0 Z +BLEMISHLESS B L EH1 M IH0 SH L AH0 S +BLEMMYES B L EH1 M IY0 Z +BLEMONT B L EH1 M AH0 N T +BLENAU B L EH1 N AW0 +BLENAU'S B L IH0 N OW0 Z +BLENAVON B L EH1 N AH0 V AA0 N +BLENCATHRA B L EH1 N S EH2 TH R AH0 +BLENCH B L EH1 N CH +BLENCHED B L EH1 N CH T +BLENCHES B L EH1 N CH IH0 Z +BLENCHING B L EH1 N CH IH0 NG +BLEND B L EH1 N D +BLENDE B L EH1 N D +BLENDED B L EH1 N D AH0 D +BLENDED B L EH1 N D IH0 D +BLENDERSHIN B L EH1 N D ER0 SH IH0 N +BLENDING B L EH1 N D IH0 NG +BLENDINGS B L EH1 N D IH0 NG Z +BLENDS B L EH1 N D Z +BLENHAM B L EH1 N HH AH0 M +BLENHAM'S B L EH1 N AH0 M Z +BLENHEIM B L EH1 N HH AY2 M +BLENKER B L EH1 NG K ER0 +BLENKERS B L EH1 NG K ER0 Z +BLENKINS B L EH1 NG K IH0 N Z +BLENKINSOP B L EH1 NG K IH0 N S AH0 P +BLENKINSOPP B L EH1 NG K IH0 N S AH0 P +BLENKIRON B L EH1 NG K AY2 ER0 N +BLENKIRON'S B L EH1 N K AY1 R AH0 N Z +BLENNERHASSET B L EH1 N ER0 HH AE1 S EH0 T +BLENNERHASSETT B L EH1 N ER0 HH AE1 S IH0 T +BLENNERHASSETT'S B L EH1 N ER0 HH AE1 S IH0 T S +BLENNERHASSETTS B L EH1 N ER0 HH AE1 S IH0 T S +BLENNY B L EH1 N IY0 +BLENSOP B L EH1 N S AA2 P +BLENT B L EH1 N T +BLENTY B L EH1 N T IY0 +BLENTZ B L EH1 N T S +BLEOBERIS B L IY1 AH0 B ER0 IH0 S +BLERIOT B L IY1 R IY0 AH0 T +BLES B L Z +BLESBOK B L Z B AA0 K +BLESBOKS B L Z B AA0 K S +BLESH B L EH1 SH +BLESHUGH B L EH1 SH +BLESS B L EH1 S +BLESS'D B L EH1 S AH0 D +BLESSAC B L EH1 S AE0 K +BLESSE B L EH1 S +BLESSED B L EH1 S T +BLESSEDEST B L EH1 S AH0 D AH0 S T +BLESSEDLY B L EH1 S AH0 D L IY0 +BLESSEDNESS B L EH1 S AH0 D N AH0 S +BLESSES B L EH1 S IH0 Z +BLESSEST B L EH1 S AH0 S T +BLESSETH B L EH1 S IH0 TH +BLESSID B L EH1 S IH0 D +BLESSIN B L EH1 S IH0 N +BLESSIN'S B L EH1 S IH0 N Z +BLESSING B L EH1 S IH0 NG +BLESSINGS B L EH1 S IH0 NG Z +BLESSINGTON B L EH1 S IH0 NG T AH0 N +BLESSINGTON'S B L EH1 S IH0 NG T AH0 N Z +BLESSOM B L EH1 S AH0 M +BLEST B L EH1 S T +BLESUS B L EH1 S AH0 S +BLETCHLEY B L EH1 CH L IY0 +BLETHER B L EH1 TH ER0 +BLETHERIN B L EH1 TH ER0 AH0 N +BLETHERING B L EH1 TH ER0 IH0 NG +BLETHERS B L EH1 TH ER0 Z +BLETSO B L EH1 T S OW0 +BLETSOE B L EH1 T S OW0 +BLETSON B L EH1 T S AH0 N +BLEU B L UW1 +BLEUE B L UW1 +BLEURY B L UW1 R IY0 +BLEUS B L UW1 S +BLEVENS B L IY1 V AH0 N Z +BLEVINS B L EH1 V IH0 N Z +BLEW B L UW1 +BLEWET B L UW1 IH0 T +BLEWETT B L UW1 IH0 T +BLEWFORTH B L UW1 F AO2 R TH +BLEWITT B L UW1 IH0 T +BLEYER B L EY1 ER0 +BLEYS B L EY1 Z +BLI B L AY1 +BLI'ME B L AY1 M +BLICK B L IH1 K +BLICK'S B L IH1 K S +BLICKLEY B L IH1 K L IY0 +BLICKY B L IH1 K IY0 +BLID B L IH1 D +BLIDAH B L AY1 D AH0 +BLIEVE B L IY1 V +BLIFIL B L IH1 F AH0 L +BLIFIL'S B L IH1 F AH0 L Z +BLIGE B L AY1 JH +BLIGED B L AY1 JH D +BLIGH B L AY1 +BLIGH'S B L AY1 Z +BLIGHT B L AY1 T +BLIGHT'S B L AY1 T S +BLIGHTED B L AY1 T IH0 D +BLIGHTER B L AY1 T ER0 +BLIGHTER'S B L AY1 T ER0 Z +BLIGHTERS B L AY1 T ER0 Z +BLIGHTING B L AY1 T IH0 NG +BLIGHTS B L AY1 T S +BLIGHTY B L AY1 T IY0 +BLIGNY B L IH1 G N IY0 +BLIKSEM B L IH1 K S IH0 M +BLIMBER B L IH1 M B ER0 +BLIMBER'S B L IH1 M B ER0 Z +BLIME B L AY1 M +BLIMEY B L AY1 M IY0 +BLIMP B L IH1 M P +BLIMY B L AY1 M IY0 +BLIN B L IH0 N +BLIND B L AY1 N D +BLINDBECK B L AY1 N D B EH0 K +BLINDE B L AY1 N D +BLINDED B L AY1 N D IH0 D +BLINDER B L AY1 N D ER0 +BLINDER'S B L AY1 N D ER0 Z 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N T +BLIP B L IH1 P +BLIPPER B L IH1 P ER0 +BLIPS B L IH1 P S +BLIROS B L IH1 R OW0 Z +BLISH B L IH1 SH +BLISLAND B L AY1 L AH0 N D +BLISS B L IH1 S +BLISS'S B L IH1 S IH0 Z +BLISSAM B L IH1 S AH0 M +BLISSE B L IH1 S +BLISSED B L IH1 S T +BLISSES B L IH1 S IH0 Z +BLISSFUL B L IH1 S F AH0 L +BLISSFULLY B L IH1 S F AH0 L IY0 +BLISSFULNESS B L IH1 S F AH0 L N AH0 S +BLISSID B L IH1 S AH0 D +BLISSINGTON B L IH1 S IH0 NG T AH0 N +BLISTER B L IH1 S T ER0 +BLISTER'D B L IH1 S T ER0 D +BLISTER'S B L IH1 S T ER0 Z +BLISTERED B L IH1 S T ER0 D +BLISTERING B L IH1 S T ER0 IH0 NG +BLISTERINGLY B L IH1 S T ER0 IH0 NG L IY0 +BLISTERS B L IH1 S T ER0 Z +BLITH B L AY1 TH +BLITHE B L AY1 DH +BLITHEDALE B L AY1 TH D EY2 L +BLITHELY B L AY1 TH L IY0 +BLITHENESS B L AY1 TH N AH0 S +BLITHER B L AY1 DH ER0 +BLITHERIN B L AY1 DH ER0 IH0 N +BLITHERING B L AY1 DH ER0 IH0 NG +BLITHERS B L AY1 DH ER0 Z +BLITHESOME B L AY1 TH S AH0 M +BLITHESOMENESS B L AY1 TH S AH0 M N AH0 S +BLITHEST B L AY1 DH AH0 S T +BLITZ B L IH1 T S +BLITZEN B L IH1 T S AH0 N +BLITZENBERG B L IH1 T Z AH0 N B ER0 G +BLIVEN B L AY1 V AH0 N +BLIVENS B L AY1 V AH0 N Z +BLIX B L IH1 K S +BLIXIE B L IH1 K S IY0 +BLIZZARD B L IH1 Z ER0 D +BLIZZARD'S B L IH1 Z ER0 D Z +BLIZZARDS B L IH1 Z ER0 D Z +BLIZZARDY B L IH1 Z ER0 D IY0 +BLO B L OW0 +BLOAT B L OW1 T +BLOATED B L OW1 T IH0 D +BLOATER B L OW1 T ER0 +BLOATERS B L OW1 T ER0 Z +BLOATING B L OW1 T IH0 NG +BLOB B L AA1 B +BLOB'S B L AA1 B Z +BLOBBER B L AA1 B ER0 +BLOBBS B L AA1 B Z +BLOBBY B L AA1 B IY0 +BLOBS B L AA1 B Z +BLOC B L AA1 K +BLOCH B L AA1 K +BLOCH'S B L AA1 K S +BLOCK B L AA1 K +BLOCK'D B L AA1 K D +BLOCK'S B L AA1 K S +BLOCKADE B L AA2 K EY1 D +BLOCKADED B L AA2 K EY1 D IH0 D +BLOCKADER B L AA2 K EY1 D ER0 +BLOCKADERS B L AA2 K EY1 D ER0 Z +BLOCKADES B L AA2 K EY1 D Z +BLOCKADING B L AA2 K EY1 D IH0 NG +BLOCKED B L AA1 K T +BLOCKER B L AA1 K ER0 +BLOCKHEAD B L AA1 K HH EH2 D +BLOCKHEADS B L AA1 K HH EH2 D Z +BLOCKHOUSE B L AA1 K HH AW2 S +BLOCKHOUSES B L AA1 K HH AW2 S AH0 Z +BLOCKIN B L AA1 K IH0 N +BLOCKING B L AA1 K IH0 NG +BLOCKISH B L AA1 K IH0 SH +BLOCKS B L AA1 K S +BLOCKSBERG B L AA1 K S B ER0 G +BLOCKULA B L AA1 K UW1 L AH0 +BLOCKY B L AA1 K IY0 +BLOCS B L AA1 K S +BLODGETT B L AA1 JH IH0 T +BLODGETT'S B L AA1 JH IH0 T S +BLODSOMBRE B L AA1 D S AH0 M B ER0 +BLODWELL B L AA1 D W EH2 L +BLOECKMAN B L OW1 K M AH0 N +BLOEDEL B L OW1 D AH0 L +BLOEMFONTEIN B L OW1 M F AO0 N T AY2 N +BLOFELD B L AA1 F EH2 L D +BLOG B L AO1 G +BLOGG B L AO1 G +BLOIS B L W AA1 +BLOK B L AA1 K +BLOKE B L OW1 K +BLOKE'S B L OW1 K S +BLOKES B L OW1 K S +BLOM B L AA1 M +BLOMBERG B L AA1 M B ER0 G +BLOME B L OW1 M +BLOME'S B L OW1 M Z +BLOMFIELD B L AA1 M F IY2 L D +BLOMIDON B L AA1 M IH0 D IH0 N +BLOMMERS B L AA1 M ER0 Z +BLOMSBERRY B L AA1 M S B EH2 R IY0 +BLONAY B L AA1 N EY0 +BLOND B L AA1 N D +BLONDE B L AA1 N D +BLONDE'S B L AA1 N D Z +BLONDEL B L AA1 N D AH0 L +BLONDEL'S B L AA1 N D AH0 L Z +BLONDER B L AA1 N D ER0 +BLONDES B L AA1 N D Z +BLONDET B L AA1 N D EH1 T +BLONDET'S B L AA1 N D AH0 T S +BLONDHEIM B L AA1 N D HH AY2 M +BLONDIE B L AA1 N D IY0 +BLONDIN B L AA1 N D IH0 N +BLONDINA B L AA1 N D IH0 N AH0 +BLONDINE B L AA1 N D AY2 N +BLONDINE'S B L AA1 N D AY2 N Z +BLONDNESS B L AA1 N D N AH0 S +BLONDS B L AA1 N D Z +BLONDY B L AA1 N D IY0 +BLONDY'S B L AA1 N D IY0 Z +BLONG B L AO1 NG +BLOO B L UW1 +BLOOD B L AH1 D +BLOOD' B L AH1 D +BLOOD'S B L AH1 D Z +BLOODCURDLING B L AH1 D K ER1 D AH0 L IH0 NG +BLOODED B L AH1 D IH0 D +BLOODEDLY B L AH1 D IH0 D L IY0 +BLOODEDNESS B L AH1 D AH0 D N AH0 S +BLOODGOOD B L AH1 D G UH2 D +BLOODGOOD'S B L AH1 D G UH2 D Z +BLOODGUILTINESS B L AH1 D G W IH1 T IY0 N AH0 S +BLOODHOUND B L AH1 D HH AW0 N D +BLOODHOUND'S B L AH1 D HH AW0 N D Z +BLOODHOUNDS B L AH1 D HH AW0 N D Z +BLOODHOUNDS B L AH1 D HH AW0 N Z +BLOODIED B L AH1 D IY0 D +BLOODIER B L AH1 D IY0 ER0 +BLOODIEST B L AH1 D IY0 AH0 S T +BLOODILY B L AH1 D AH0 L IY0 +BLOODING B L AH1 D IH0 NG +BLOODLESS B L AH1 D L AH0 S +BLOODLESSLY B L AH1 D L AH0 S L IY0 +BLOODLESSNESS B L AH1 D L AH0 S N AH0 S +BLOODLETTING B L AH1 D L EH2 T IH0 NG +BLOODROOT B L AH1 D R UW2 T +BLOODS B L AH1 D Z +BLOODSHED B L AH1 D SH EH2 D +BLOODSHEDDING B L AH1 D SH EH1 D IH0 NG +BLOODSHOT B L AH1 D SH AA2 T +BLOODSON B L AH1 D S AH0 N +BLOODSTAIN B L AH1 D S T EY2 N +BLOODSTAINED B L AH1 D S T EY2 N D +BLOODSTAINS B L AH1 D S T EY2 N Z +BLOODSTONE B L AH1 D S T OW2 N +BLOODSTREAM B L AH1 D S T R IY2 M +BLOODSUCK B L AH1 D S AH2 K +BLOODSUCKER B L AH1 D S AH2 K ER0 +BLOODSUCKERS B L AH1 D S AH2 K ER0 Z +BLOODSUCKING B L AH1 D S AH2 K IH0 NG +BLOODTHIRSTINESS B L AH1 D TH ER2 S T IY0 N AH0 S +BLOODTHIRSTY B L AH1 D TH ER2 S T IY0 +BLOODVESSEL B L AH1 D V EH1 S AH0 L +BLOODVESSELS B L AH1 D V EH1 S AH0 L Z +BLOODWOOD B L AH1 D W UH2 D +BLOODWOODS B L AH1 D W UH2 D Z +BLOODWORTH B L AH1 D W ER2 TH +BLOODY B L AH1 D IY0 +BLOOEY B L UW1 IY0 +BLOOIE B L UW1 IY0 +BLOOKER B L UH1 K ER0 +BLOOM B L UW1 M +BLOOM'D B L UW1 M D +BLOOM'S B L UW1 M Z +BLOOMDALE B L UW1 M D EY2 L +BLOOME B L UW1 M +BLOOMED B L UW1 M D +BLOOMER B L UW1 M ER0 +BLOOMER'S B L UW1 M ER0 Z +BLOOMERS B L UW1 M ER0 Z +BLOOMETH B L UW1 M IH0 TH +BLOOMFIELD B L UW1 M F IY2 L D +BLOOMFIELD'S B L UW1 M F IY2 L D Z +BLOOMIN B L UW1 M IH0 N +BLOOMING B L UW1 M IH0 NG +BLOOMINGDALE B L UW1 M IH0 NG D EY2 L +BLOOMINGSBURG B L UW1 M IH0 NG Z B ER0 G +BLOOMINGTON B L UW1 M IH0 NG T AH0 N +BLOOMLESS B L UW1 M L AH0 S +BLOOMS B L UW1 M Z +BLOOMSBURY B L UW1 M Z B EH2 R IY0 +BLOOMY B L UW1 M IY0 +BLOOR B L UH1 R +BLOOTCH B L UW1 CH +BLORE B L AO1 R +BLORENGE B L ER0 EH1 N JH +BLOS B L OW0 S +BLOSBERG B L AA1 S B ER0 G +BLOSS B L AO1 S +BLOSSER B L AO1 S ER0 +BLOSSETT B L AA1 S IH0 T +BLOSSOM B L AA1 S AH0 M +BLOSSOM'D B L AA1 S AH0 M D +BLOSSOM'S B L AA1 S AH0 M Z +BLOSSOMED B L AA1 S AH0 M D +BLOSSOMING B L AA1 S AH0 M IH0 NG +BLOSSOMINGS B L AA1 S AH0 M IH0 NG Z +BLOSSOMLESS B L AA1 S AH0 M L AH0 S +BLOSSOMNOSE B L AA1 S AH0 M N OW2 Z +BLOSSOMS B L AA1 S AH0 M Z +BLOSSOMY B L AA1 S AH0 M IY0 +BLOT B L AA1 T +BLOTCH B L AA1 T CH +BLOTCHED B L AA1 T CH T +BLOTCHES B L AA1 T CH AH0 Z +BLOTCHING B L AA1 T CH IH0 NG +BLOTCHY B L AA1 T CH IY0 +BLOTS B L AA1 T S +BLOTT B L AA1 T +BLOTTED B L AA1 T AH0 D +BLOTTER B L AA1 T ER0 +BLOTTERS B L AA1 T ER0 Z +BLOTTING B L AA1 T IH0 NG +BLOTTO B L AA1 T OW0 +BLOTTON B L AA1 T AH0 N +BLOUD B L AW1 D +BLOUDY B L AW1 D IY0 +BLOUGRAM B L AW1 G R AE2 M +BLOUGRAM'S B L AW1 G R AE2 M Z +BLOUNDELL B L AW1 N D AH0 L +BLOUNT B L AW1 N T +BLOUNT'S B L AW1 N T S +BLOUNTS B L AW1 N T S +BLOUSE B L AW1 S +BLOUSED B L AW1 S T +BLOUSES B L AW1 S AH0 Z +BLOUSES B L AW1 S IH0 Z +BLOUSY B L AW1 S IY0 +BLOW B L OW1 +BLOW'D B L OW1 D +BLOW'S B L OW1 Z +BLOWED B L OW1 D +BLOWELL B L OW1 AH0 L +BLOWEN B L OW1 AH0 N +BLOWER B L OW1 ER0 +BLOWER'S B L OW1 ER0 Z +BLOWERS B L OW1 ER0 Z +BLOWES B L OW1 Z +BLOWETH B L OW1 IH0 TH +BLOWFEN B L OW1 F AH0 N +BLOWFIELD B L OW1 F IY2 L D +BLOWGUN B L OW1 G AH2 N +BLOWGUNS B L OW1 G AH2 N Z +BLOWHARD B L OW1 HH AA2 R D 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+BLUDGEON B L AH1 JH AH0 N +BLUDGEONED B L AH1 JH AH0 N D +BLUDGEONHEAD B L AH1 JH AH0 N HH EH2 D +BLUDGEONING B L AH1 JH AH0 N IH0 NG +BLUDGEONINGS B L AH1 JH AH0 N IH0 NG Z +BLUDGEONS B L AH1 JH AH0 N Z +BLUDGER B L AH1 JH ER0 +BLUDIRON B L AH1 D AY2 ER0 N +BLUDOFFSKI B L UW1 D AO1 F S K IY0 +BLUDSO B L AH1 D S OW0 +BLUDSOE B L AH1 D S OW0 +BLUDSTON B L AH1 D S T AH0 N +BLUDYER B L AH1 D IY0 ER0 +BLUE B L UW1 +BLUE'S B L UW1 Z +BLUEBACK B L UW1 B AE2 K +BLUEBACKS B L UW1 B AE2 K S +BLUEBEARD B L UW1 B IY2 R D +BLUEBEARD'S B L UW1 B IY2 R D Z +BLUEBEARDS B L UW1 B IY2 R D Z +BLUEBELL B L UW1 B EH2 L +BLUEBELLS B L UW1 B EH2 L Z +BLUEBERRIES B L UW1 B EH2 R IY0 Z +BLUEBERRY B L UW1 B EH2 R IY0 +BLUEBIRD B L UW1 B ER2 D +BLUEBIRD'S B L UW1 B ER2 D Z +BLUEBIRDS B L UW1 B ER2 D Z +BLUEBOOK B L UW1 B UH2 K +BLUEBOTTLE B L UW1 B AO2 T AH0 L +BLUEBOTTLE'S B L UW1 B AO2 T AH0 L Z +BLUEBOTTLES B L UW1 B AO2 T AH0 L Z +BLUECHER B L UW1 CH ER0 +BLUECHER'S B L UW1 CH ER0 Z +BLUECOAT B L UW1 K OW2 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AH0 Z +BRAHMACHARYA B R AA1 M AA0 K EH0 R Y AH0 +BRAHMADAITYA B R AA1 M AA0 D EY1 T Y AH0 +BRAHMAN B R AA1 M AH0 N +BRAHMAN'S B R AA1 M AH0 N Z +BRAHMANA B R AA1 M AA1 N AH0 +BRAHMANAS B R AA1 M AA1 N AH0 Z +BRAHMANI B R AA1 M AA1 N IY0 +BRAHMANIC B R AA1 M AE1 N IH0 K +BRAHMANICAL B R AA1 M AE1 N IH0 K AH0 L +BRAHMANISM B R AA1 M AH0 N IH2 Z AH0 M +BRAHMANS B R AA1 M AH0 N Z +BRAHMAPUTRA B R AA1 M AH0 P UW1 T R AH0 +BRAHMAS B R AA1 M AH0 Z +BRAHMIN B R AA1 M IH0 N +BRAHMIN'S B R AA1 M IH0 N Z +BRAHMINICAL B R AA1 M IH0 N IH0 K AH0 L +BRAHMINISM B R AA1 M IH0 N IH2 Z AH0 M +BRAHMINS B R AA1 M IH0 N Z +BRAHMO B R AA1 M OW0 +BRAHMS B R AA1 M Z +BRAID B R EY1 D +BRAID'S B R EY1 D Z +BRAIDED B R EY1 D IH0 D +BRAIDING B R EY1 D IH0 NG +BRAIDINGS B R EY1 D IH0 NG Z +BRAIDS B R EY1 D Z +BRAIDWOOD B R EY1 D W UH2 D +BRAIGH B R EY1 +BRAIKENS B R EY1 K IH0 N Z +BRAIL B R EY1 L +BRAILE B R EY1 L +BRAILED B R EY1 L D +BRAILLE B R EY1 L +BRAILS B R EY1 L Z +BRAILSFORD B R EY1 L S F ER0 D 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EY1 K S +BRAKED B R EY1 K T +BRAKELY B R EY1 K L IY0 +BRAKEMAN B R EY1 K M AH0 N +BRAKEMAN'S B R EY1 K M AH0 N Z +BRAKEMEN B R EY1 K M EH0 N +BRAKEN B R EY1 K AH0 N +BRAKES B R EY1 K S +BRAKESMAN B R EY1 K S M AH0 N +BRAKESMEN B R EY1 K S M IH0 N +BRAKESPEARE B R EY1 K S P IY2 R +BRAKFAST B R EY1 K F AH0 S T +BRAKFONTEIN B R EY1 K F AO0 N T AY2 N +BRAKIE B R AE1 K IY0 +BRAKIN B R EY1 K IH0 N +BRAKING B R EY1 K IH0 NG +BRALEY B R AE1 L IY0 +BRALLIGAN B R AE1 L IH0 G AH0 N +BRAM B R AE1 M +BRAM'S B R AE1 M Z +BRAMA B R AE1 M AH0 +BRAMAH B R AA1 M AH0 +BRAMAN B R EY1 M AH0 N +BRAMAN'S B R EY1 M AH0 N Z +BRAMANTE B R AH0 M AA1 N T IY0 +BRAMBER B R AE1 M B ER0 +BRAMBERGER B R AE1 M B ER0 G ER0 +BRAMBLE B R AE1 M B AH0 L +BRAMBLE'S B R AE1 M B AH0 L Z +BRAMBLED B R AE1 M B AH0 L D +BRAMBLEDOWN B R AE1 M B AH0 L D AW2 N +BRAMBLES B R AE1 M B AH0 L Z +BRAMBLETON B R AE1 M B L IH0 T AA0 N +BRAMBLY B R AE1 M B L IY0 +BRAMBOURG B R AE1 M B AO2 R G +BRAMFELL B R AE1 M F EH2 L +BRAMHALL B R AE1 M HH AH0 L +BRAMHAM B R AE1 M HH AH0 M +BRAMHAM'S B R AE1 M HH AH0 M Z +BRAMIMUNDE B R AE1 M IH0 M UW1 N D +BRAMIN B R AE1 M IH0 N +BRAMINICAL B R AE1 M AH0 N IH0 K AH0 L +BRAMINS B R AE1 M IH0 N Z +BRAMINTHO B R AE1 M IH0 N TH OW0 +BRAMLEIGH B R AE1 M L AH0 +BRAMLEIGH'S B R AE1 M L IY0 Z +BRAMLEIGHS B R AE1 M L IY0 Z +BRAMLETT B R AE1 M L IH0 T +BRAMLETT'S B R AE1 M L IH0 T S +BRAMLEY B R AE1 M L IY0 +BRAMMEL B R AE1 M AH0 L +BRAMMLE B R AE1 M L +BRAMMO B R AE1 M OW0 +BRAMPTON B R AE1 M P T AH0 N +BRAMSDEAN B R AE1 M S D IY0 AH0 N +BRAMSEN B R AE1 M S AH0 N +BRAMSTON B R AE1 M S T AH0 N +BRAMWELL B R AE1 M W EH2 L +BRAN B R AE1 N +BRAN' B R AE1 N +BRAN'S B R AE1 N Z +BRANASKO B R AA0 N AA1 S K OW0 +BRANASKO'S B R AA0 N AA1 S K OW0 Z +BRANCA B R AE1 NG K AH0 +BRANCACCIA B R AA0 N K AA1 CH AH0 +BRANCADORI B R AA0 N K AA0 D AO1 R IY0 +BRANCALEONE B R AA0 N K AA1 L IY0 OW2 N +BRANCARDIERS B R AA0 N K AA1 R D IY0 ER0 Z +BRANCAS B R AE1 NG K AH0 Z +BRANCASTER B R AE1 N K AE2 S T ER0 +BRANCEPETH B R AE1 N CH EH2 P IH0 TH +BRANCH B R AE1 N CH +BRANCH'D B R AE1 N CH D +BRANCH'S B R AE1 N CH IH0 Z +BRANCHE B R AE1 N CH +BRANCHED B R AE1 N CH T +BRANCHELL B R AE1 N CH AH0 L +BRANCHES B R AE1 N CH AH0 Z +BRANCHES B R AE1 N CH IH0 Z +BRANCHIAE B R AE1 N CH IY0 AH0 +BRANCHIMONT B R AE1 N CH IH0 M AA2 N T +BRANCHING B R AE1 N CH IH0 NG +BRANCHINGS B R AE1 N CH IH0 NG Z +BRANCHLESS B R AE1 N CH L AH0 S +BRANCHLET B R AE1 N CH L AH0 T +BRANCHLETS B R AE1 N CH L AH0 T S +BRANCHSPELL B R AE1 N CH S P EH1 L +BRANCHVILLE B R AE1 N CH V IH0 L +BRANCHY B R AE1 N CH IY0 +BRANCIANI B R AE1 N CH AH0 N IY0 +BRANCO B R AE1 NG K OW0 +BRAND B R AE1 N D +BRAND'S B R AE1 N D Z +BRANDAN B R AE1 N D AH0 N +BRANDAN'S B R AE1 N D AH0 N Z +BRANDANES B R AE1 N D EY1 N Z +BRANDE B R AE1 N D +BRANDE'S B R AE1 N D Z +BRANDED B R AE1 N D IH0 D +BRANDEGEE B R AE1 N D AH0 G IY0 +BRANDEIS B R AE1 N D AY0 S +BRANDEIS'S B R AE1 N D AY0 S IH0 Z +BRANDENBURG B R AE1 N D AH0 N B ER0 G +BRANDENBURGER B R AE1 N D AH0 N B ER0 G ER0 +BRANDENBURGERS B R AE1 N D AH0 N B ER0 G ER0 Z +BRANDENBURGH B R AE1 N D AH0 N B ER0 G +BRANDENBURGHERS B R AE1 N D AH0 N B ER0 G ER0 Z +BRANDER B R AE1 N D ER0 +BRANDER'S B R AE1 N D ER0 Z +BRANDERS B R AE1 N D ER0 Z +BRANDES B R AE1 N D Z +BRANDFORT B R AE1 N D F ER0 T +BRANDIED B R AE1 N D IY0 D +BRANDIES B R AE1 N D IY0 Z +BRANDILES B R AE1 N D AY2 L Z +BRANDIMART B R AE1 N D AY2 M AA2 R T +BRANDIN B R AE1 N D IH0 N +BRANDING B R AE1 N D IH0 NG +BRANDINI B R AE1 N D IY1 N IY0 +BRANDIR B R AE1 N D AY2 ER0 +BRANDIS B R AE1 N D IH0 S +BRANDISH B R AE1 N D IH0 SH +BRANDISH'D B R AE1 N D IH0 SH D +BRANDISHED B R AE1 N D IH0 SH T +BRANDISHES B R AE1 N D IH0 SH IH0 Z +BRANDISHING B R AE1 N D IH0 SH IH0 NG +BRANDLING B R AE1 N D L IH0 NG +BRANDOLINI B R AE1 N D OW0 L IY1 N IY0 +BRANDON B R AE1 N D AH0 N +BRANDON'S B R AE1 N D AH0 N Z +BRANDONS B R AE1 N D AH0 N Z +BRANDOW B R AE1 N D AW2 +BRANDOW'S B R AE1 N D AW2 Z +BRANDRAM B R AE1 N D R AH0 M +BRANDRETH B R AE1 N D R IH0 TH +BRANDRETH'S B R AE1 N D R IH0 TH S +BRANDS B R AE1 N D Z +BRANDT B R AE1 N T +BRANDT'S B R AE2 N T S +BRANDUR B R AE1 N D ER0 +BRANDY B R AE1 N D IY0 +BRANDY'S B R AE1 N D IY0 Z +BRANDYWINE B R AE1 N D IY0 W AY2 N +BRANEHOG B R EY1 N HH AA2 G +BRANEY B R EY1 N IY0 +BRANFORD B R AE1 N F ER0 D +BRANGAENA B R AE1 N G IY1 N AH0 +BRANGAENE B R AE1 N G IY1 N +BRANGEANE B R EY1 N JH AH0 N +BRANGHTON B R AE1 NG T AH0 N +BRANGHTONS B R AE1 NG T AH0 N Z +BRANGIEN B R AE1 N JH IY0 N +BRANGWEN B R AE1 NG W AH0 N +BRANGWEN'S B R AE1 NG W AH0 N Z +BRANGWENS B R AE1 NG W AH0 N Z +BRANGWYN B R AE1 NG W IH0 N +BRANHAM B R AE1 N HH AH0 M +BRANIGAN B R AE1 N IH0 G AH0 N +BRANKOVITCH B R AE1 NG K AH0 V IH0 CH +BRANKS B R AE1 NG K S +BRANKSOME B R AE1 NG K S AH0 M +BRANLING B R AE1 N L IH0 NG +BRANLY B R AE1 N L IY0 +BRANN B R AE1 N +BRANNAD B R AE1 N AH0 D +BRANNAGAN B R AE1 N AH0 G AE0 N +BRANNAN B R AE1 N AH0 N +BRANNAN'S B R AE1 N AH0 N Z +BRANNHARD B R AE1 N HH AA2 R D +BRANNIGAN B R AE1 N IH0 G AH0 N +BRANNING B R AE1 N IH0 NG +BRANNITON B R AE1 N IH0 T AH0 N +BRANNON B R AE1 N AH0 N +BRANNY B R AE1 N IY0 +BRANSBY B R AE1 N S B IY0 +BRANSBY'S B R AE1 N S B IY0 Z +BRANSCOMBE B R AE1 N S K AH0 M +BRANSCOMBE'S B R AE1 N S K AH0 M Z +BRANSCOME B R AE1 N Z K AH2 M +BRANSCOME'S B R AE1 N Z K AH2 M Z +BRANSFORD B R AE1 N S F ER0 D +BRANSFORD'S B R AE1 N S F ER0 D Z +BRANSHAW B R AE1 N SH AO2 +BRANSOME B R AE1 N S AH0 M +BRANSON B R AE1 N S AH0 N +BRANSTOCK B R AE1 N S T AA2 K +BRANSTON B R AE1 N S T AH0 N +BRANSTON'S B R AE1 N S T AH0 N Z +BRANT B R AE1 N T +BRANT'S B R AH0 N T S +BRANTEFIELD B R AE1 N T AH0 F IY0 L D +BRANTFORD B R AE1 N T F ER0 D +BRANTLY B R AE1 N T L IY0 +BRANTOME B R AE1 N T AH0 M IY0 +BRANTS B R AH0 N T S +BRANTWOOD B R AE1 N T W UH2 D +BRANWELL B R AE1 N W EH2 L +BRANWELL'S B R AE1 N W EH2 L Z +BRANWEN B R AE1 N W AH0 N +BRAOSE B R AA0 OW1 Z +BRAQUE B R AE1 K +BRAS B R AA1 S +BRAS B R AE1 S +BRASCHI B R AH0 S K IY0 +BRASCHON B R AE1 SH AH0 N +BRASEN B R AE1 S AH0 N +BRASENOSE B R AH0 S EH1 N OW2 Z +BRASERO B R AH0 S EH1 R OW0 +BRASH B R AE1 SH +BRASHEAR B R AE1 SH IY0 R +BRASHEARS B R AE1 SH IY0 R Z +BRASHER B R AE1 SH ER0 +BRASIDAS B R AH0 S IY1 D AH0 S +BRASIER B R EY1 ZH ER0 +BRASIL B R AE1 S AH0 L +BRASIL B R AH0 S IY1 L +BRASK B R AE1 S K +BRASKA B R AE1 S K AH0 +BRASS B R AE1 S +BRASS'S B R AE1 S IH0 Z +BRASSARD B R AE1 S ER0 D +BRASSARDS B R AE1 S ER0 D Z +BRASSBOUND B R AE1 S B AW2 N D +BRASSBOUND'S B R AE1 S B AW2 N D Z +BRASSE B R AE1 S +BRASSERIE B R AE1 S ER0 IY0 +BRASSERIES B R AE1 S ER0 IY0 Z +BRASSES B R AE1 S IH0 Z +BRASSET B R AE1 S EH0 T +BRASSEUR B R AE1 S ER0 +BRASSEY B R AE1 S IY0 +BRASSEY'S B R AE1 S IY0 Z +BRASSICA B R AE1 S IH0 K AH0 +BRASSIE B R AE1 S IY0 +BRASSIERE B R AE1 S IY0 EH0 R +BRASSING B R AE1 S IH0 NG +BRASSINGTON B R AE1 S IH0 NG T AH0 N +BRASSWARE B R AE1 S W EH2 R +BRASSWORK B R AE1 S W ER2 K +BRASSY B R AE1 S IY0 +BRAST B R AE1 S T +BRASTED B R AE1 S T AH0 D +BRASTER B R AE1 S T ER0 +BRASTIAS B R AE1 S T IY0 AH0 Z +BRASYER B R AE1 S AY0 ER0 +BRAT B R AE1 T +BRAT'S B R AE1 T S +BRATA B R AA1 T AH0 +BRATEN B R AE1 T AH0 N +BRATHAM B R AE1 T AH0 M +BRATHWAITE B R AE1 TH W EY2 T +BRATHWAITE'S B R AE1 TH W EY2 T S +BRATLEY B R AE1 T L IY0 +BRATS B R AE1 T S +BRATTEN B R AE1 T AH0 N +BRATTI B R AE1 T IY0 +BRATTLE B R AE1 T AH0 L +BRATTLE'S B R AE1 T AH0 L Z +BRATTLEBORO B R AE1 T AH0 L B ER0 OW0 +BRATTLES B R AE1 T AH0 L Z +BRATTLESBY B R AE1 T AH0 L S B IY0 +BRATTLING B R AE1 T AH0 L IH0 NG +BRATTON B R AE1 T AH0 N +BRATVOLD B R AA2 T V OW2 L D +BRAUER B R AW1 ER0 +BRAULARD B R AO1 L ER0 D +BRAULT B R AO1 L T +BRAUN B R AO1 N +BRAUN'S B R AO1 N Z +BRAUNAU B R AO1 N AW0 +BRAUNER B R AO1 N ER0 +BRAUNFELS B R AO1 N F AH0 L Z +BRAUNWORTH B R AO1 N W ER2 TH +BRAUT B R AO1 T +BRAUWS B R AW1 Z +BRAV B R AA1 V +BRAVA B R AH0 V AH0 +BRAVADO B R AH0 V AA1 D OW0 +BRAVADOES B R AH0 V AA1 D OW0 Z +BRAVAS B R AA1 V AA0 Z +BRAVE B R EY1 V +BRAVE'S B R EY1 V Z +BRAVED B R EY1 V D +BRAVEES B R EY1 V IY0 Z +BRAVELY B R EY1 V L IY0 +BRAVENESS B R EY1 V N AH0 S +BRAVER B R EY1 V ER0 +BRAVERIES B R EY1 V ER0 IY0 Z +BRAVERY B R EY1 V ER0 IY0 +BRAVES B R EY1 V Z +BRAVEST B R EY1 V AH0 S T +BRAVI B R AA1 V IY0 +BRAVIDA B R AH0 V IY1 D AH0 +BRAVING B R EY1 V IH0 NG +BRAVISSIMA B R EY1 V IY0 S IY1 M AH0 +BRAVISSIMO B R EY1 V IY0 S IY1 M OW0 +BRAVO B R AA1 V OW0 +BRAVO'S B R AA1 V OW0 Z +BRAVOES B R AA1 V OW0 Z +BRAVOS B R AA1 V OW0 S +BRAVURA B R AH0 V Y UH1 R AH0 +BRAVURAS B R AH0 V Y UH1 R AH0 S +BRAW B R AO1 +BRAWER B R AO1 ER0 +BRAWL B R AO1 L +BRAWLED B R AO1 L D +BRAWLER B R AO1 L ER0 +BRAWLERS B R AO1 L ER0 Z +BRAWLING B R AO1 L IH0 NG +BRAWLS B R AO1 L Z +BRAWLY B R AO1 L IY0 +BRAWN B R AO1 N +BRAWN'S B R AO1 N Z +BRAWNE B R AO1 N +BRAWNIEST B R AO1 N IY0 AH0 S T +BRAWNSTON B R AO1 N S T AH0 N +BRAWNY B R AO1 N IY0 +BRAWS B R AO1 Z +BRAXFIELD B R AE1 K S F IY0 L D +BRAXMAR B R AE1 K S M AA2 R +BRAXOME B R AE1 K S OW1 M +BRAXTON B R AE1 K S T AH0 N +BRAXTON'S B R AE1 K S T AH0 N Z +BRAXY B R AE1 K S IY0 +BRAY B R EY1 +BRAY'S B R EY1 Z +BRAYBRIDGE B R EY1 B R IH2 JH +BRAYBROOK B R EY1 B R UH2 K +BRAYBROOKE B R EY1 B R UH0 K +BRAYBROOKE'S B R EY1 B R UH0 K S +BRAYDER B R EY1 D ER0 +BRAYDON B R EY1 D AH0 N +BRAYDON'S B R EY1 D AH0 N Z +BRAYE B R EY1 +BRAYED B R EY1 D +BRAYER B R EY1 ER0 +BRAYFIELD B R EY1 F IY2 L D +BRAYING B R EY1 IH0 NG +BRAYINGS B R EY1 IH0 NG Z +BRAYLE B R EY1 L +BRAYLEY B R EY1 L IY0 +BRAYNE B R EY1 N +BRAYS B R EY1 Z +BRAYTON B R EY1 T AH0 N +BRAYTON'S B R EY1 T AH0 N Z +BRAYTREE B R EY1 T R IY2 +BRAYVO B R EY1 V OW0 +BRAZE B R AH0 Z +BRAZED B R EY1 Z D +BRAZEN B R EY1 Z AH0 N +BRAZENED B R EY1 Z AH0 N D +BRAZENFACE B R EY1 Z AH0 F EY2 S +BRAZENING B R EY1 Z AH0 N IH0 NG +BRAZENLY B R EY1 Z AH0 N L IY0 +BRAZENNESS B R EY1 Z AH0 N AH0 S +BRAZENOSE B R AE1 Z AH0 N OW2 Z +BRAZIER B R EY1 Z IY0 ER0 +BRAZIER'S B R EY1 Z IY0 ER0 Z +BRAZIERS B R EY1 ZH ER0 Z +BRAZIL B R AH0 Z IH1 L +BRAZIL'S B R AH0 Z IH1 L Z +BRAZILIAN B R AH0 Z IH1 L Y AH0 N +BRAZILIAN'S B R AH0 Z IH1 L Y AH0 N Z +BRAZILIANS B R AH0 Z IH1 L Y AH0 N Z +BRAZILS B R AH0 Z IH1 L Z +BRAZOS B R AA1 Z OW0 S +BRAZZA B R AE1 Z AH0 +BRDA B R D AH0 +BRE B R +BRE'ER B R IY1 ER0 +BRE'K B R IY1 K +BRE'R B R ER0 +BREA B R IY1 +BREACH B R IY1 CH +BREACHED B R IY1 CH T +BREACHES B R IY1 CH IH0 Z +BREACHING B R IY1 CH IH0 NG +BREACHY B R IY1 CH IY0 +BREAD B R EH1 D +BREAD'S B R EH1 D Z +BREADALBANE B R EH1 D AA1 L B AH0 N +BREADBASKET B R EH1 D B AE2 S K AH0 T +BREADCRUMBS B R EH1 D K R AH1 M Z +BREADED B R EH1 D IH0 D +BREADEN B R EH1 D AH0 N +BREADFRUIT B R EH1 D F R UW2 T +BREADLESS B R EH1 D L AH0 S +BREADS B R EH1 D Z +BREADSTUFF B R EH1 D S T AH2 F +BREADSTUFFS B R EH1 D S T AH2 F S +BREADTH B R EH1 D TH +BREADTHS B R EH1 D TH S +BREADWINNER B R EH1 D W IH2 N ER0 +BREADWINNERS B R EH1 D W IH2 N ER0 Z +BREAK B R EY1 K +BREAKABLE B R EY1 K AH0 B AH0 L +BREAKAGE B R EY1 K IH0 JH +BREAKAGES B R EY1 K IH0 JH IH0 Z +BREAKAWAY B R EY1 K AH0 W EY2 +BREAKDOWN B R EY1 K D AW2 N +BREAKDOWNS B R EY1 K D AW2 N Z +BREAKE B R EY1 K +BREAKER B R EY1 K ER0 +BREAKER'S B R EY1 K ER0 Z +BREAKERS B R EY1 K ER0 Z +BREAKEST B R EY1 K AH0 S T +BREAKETH B R EY1 K AH0 TH +BREAKFAS B R EH1 K F AH0 S +BREAKFAST B R EH1 K F AH0 S T +BREAKFAST'S B R EH1 K F AH0 S T S +BREAKFASTED B R EH1 K F AH0 S T IH0 D +BREAKFASTERS B R EH1 K F AH0 S T ER0 Z +BREAKFASTING B R EH1 K F AH0 S T IH0 NG +BREAKFASTLESS B R EH1 K F AH0 S T L AH0 S +BREAKFASTS B R EH1 K F AH0 S T S +BREAKFUS B R EH1 K F AH0 S +BREAKFUSS B R EH1 K F AH0 S +BREAKFUST B R EH1 K F AH2 S T +BREAKIN B R EY1 K IH0 N +BREAKING B R EY1 K IH0 NG +BREAKINGLY B R EY1 K IH0 NG L IY0 +BREAKINGS B R EY1 K IH0 NG Z +BREAKNECK B R EY1 K N EH2 K +BREAKOUT B R EY1 K AW2 T +BREAKS B R EY1 K S +BREAKSTONE B R EY1 K S T OW2 N +BREAKSTONE'S B R EY1 K S T OW2 N Z +BREAKTHROUGH B R EY1 K TH R UW2 +BREAKUP B R EY1 K AH2 P +BREAKWATER B R EY1 K W AO2 T ER0 +BREAKWATERS B R EY1 K W AO2 T ER0 Z +BREAM B R IY1 M +BREAM'S B R IY1 M Z +BREAMS B R IY1 M Z +BREARLEY B R ER1 L IY0 +BREAS B R IY1 Z +BREAST B R EH1 S T +BREAST'S B R EH1 S T S +BREASTBONE B R EH1 S T B OW2 N +BREASTED B R EH1 S T AH0 D +BREASTED B R EH1 S T IH0 D +BREASTING B R EH1 S T IH0 NG +BREASTPIN B R EH1 S T P AE1 N +BREASTPINS B R EH1 S T P IH2 N Z +BREASTPLATE B R EH1 S T P L EY2 T +BREASTPLATES B R EH1 S T P L EY2 T S +BREASTPOCKET B R EH1 S T P AA2 K AH0 T +BREASTS B R EH1 S T S +BREASTWORK B R IY1 S T W ER2 K +BREASTWORKS B R IY1 S T W ER2 K S +BREATH B R EH1 TH +BREATH'D B R EH1 TH D +BREATH'S B R EH1 TH S +BREATHABLE B R IY1 DH AH0 B AH0 L +BREATHE B R IY1 DH +BREATHED B R IY1 DH D +BREATHER B R IY1 DH ER0 +BREATHERS B R IY1 DH ER0 Z +BREATHES B R IY1 DH Z +BREATHEST B R IY1 DH AH0 S T +BREATHETH B R EH1 TH IH0 TH +BREATHIN B R EH1 TH IH0 N +BREATHING B R IY1 DH IH0 NG +BREATHINGS B R IY1 DH IH0 NG Z +BREATHLESS B R EH1 TH L AH0 S +BREATHLESSLY B R EH1 TH L AH0 S L IY0 +BREATHLESSNESS B R EH1 TH L AH0 S N AH0 S +BREATHS B R EH1 TH S +BREATHTAKING B R 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B R AO1 D S AO2 R D +BROADSWORDS B R AO1 D S AO2 R D Z +BROADUS B R OW1 D AH0 S +BROADWATER B R AO1 D W AO2 T ER0 +BROADWAY B R AO1 D W EY2 +BROADWAY'S B R AO1 D W EY2 Z +BROADWAYS B R AO1 D W EY2 Z +BROADWELL B R AO1 D W EH2 L +BROADWOOD B R AO1 D W UH2 D +BROADWOOD'S B R AO1 D W UH2 D Z +BROBDIGNAG B R AO2 B D IH0 G N AE1 G +BROBDIGNAGIAN B R AO2 B D IH0 G N AE1 G IY0 AH0 N +BROBDINGNAG B R AO2 B D IH0 G N AE1 G +BROBDINGNAGIAN B R AO2 B D IH0 G N AE1 G IY0 AH0 N +BROBSON B R AA1 B S AH0 N +BROBY B R OW1 B IY0 +BROC B R AA1 K +BROCA B R OW1 K AH0 +BROCADE B R OW0 K EY1 D +BROCADED B R OW0 K EY1 D IH0 D +BROCADES B R OW0 K EY1 D Z +BROCAS B R OW1 K AH0 Z +BROCATELLE B R OW0 K AA0 T EH1 L +BROCCOLI B R AA1 K AH0 L IY0 +BROCCOLI B R AA1 K L IY0 +BROCELIANDE B R OW1 S IY1 L IY0 IH0 N D +BROCHE B R OW1 SH +BROCHET B R OW0 SH EY1 +BROCHETTE B R OW0 SH EH1 T +BROCHT B R AA1 K T +BROCHURE B R OW0 SH UH1 R +BROCHURES B R OW0 SH UH1 R Z +BROCK B R AA1 K +BROCK'S B R AA1 K S +BROCKDEN B R AA1 K D AH0 N +BROCKELSBY B R AA1 K IH0 L S B IY0 +BROCKEN B R AA1 K AH0 N +BROCKENHAM B R AA1 K AH0 N HH AE2 M +BROCKENHURST B R AA1 K AH0 N HH ER2 S T +BROCKET B R AA1 K AH0 T +BROCKETT B R AA1 K IH0 T +BROCKETT'S B R AA1 K AH0 T S +BROCKHAUS B R AA1 K HH AW2 S +BROCKLEBANK B R AA1 K AH0 L B AE2 NG K +BROCKLEHURST B R AA1 K AH0 L HH ER0 S T +BROCKLESBY B R AA1 K L S B IY0 +BROCKLEY B R AA1 K L IY0 +BROCKMAN B R AA1 K M AH0 N +BROCKS B R AA1 K S +BROCKTON B R AA1 K T AH0 N +BROCKVILLE B R AA1 K V IH2 L +BROCKWAY B R AA1 K W EY2 +BROCKY B R AA1 K IY0 +BROCKY'S B R AA1 K IY0 Z +BROCOLI B R OW0 K OW1 L IY0 +BROCQ B R AA1 K +BROCQ'S B R AA1 K S +BROCTON B R AO1 K T AA0 N +BROD B R AA1 D +BRODAA B R AA1 D AH0 +BRODDER B R AA1 D ER0 +BRODDI B R AA1 D IY0 +BRODER B R OW1 D ER0 +BRODERICK B R AA1 D ER0 IH0 K +BRODERICK B R AA1 D R IH0 K +BRODERICK'S B R AA1 D ER0 IH0 K Z +BRODERICK'S B R AA1 D R IH0 K Z +BRODERS B R OW1 D ER0 Z +BRODERSON B R AA1 D ER0 S AH0 N +BRODEUR B R AA1 D ER0 +BRODHEAD B R AA1 D HH EH2 D +BRODIE B R OW1 T IY0 +BRODIE'S B R OW1 T IY0 Z +BRODIUM B R OW1 D IY0 AH0 M +BRODNAX B R AA1 D N AE0 K S +BRODNO B R AA1 D N AH0 +BRODNYX B R AA1 D N IH0 K S +BRODRICK B R AA1 D R IH0 K +BRODRICK'S B R AA1 D R IH0 K S +BRODRICKS B R AA1 D R IH0 K S +BRODY B R OW1 D IY0 +BROECKLYN B R OW1 K L IH0 N +BROEK B R OW1 K +BROEKHUYSEN B R OW1 K HH AY1 S AH0 N +BROENLUND B R OW1 N L AH0 N D +BROER B R OW1 ER0 +BROFFIN B R AA1 F IH0 N +BROFFIN'S B R AA1 F IH0 N Z +BROG B R AA1 G +BROGAN B R OW1 G AH0 N +BROGANS B R OW1 G AH0 N Z +BROGARD B R AA1 G ER0 D +BROGDEN B R AA1 G D AH0 N +BROGHILL B R AA1 G HH IH2 L +BROGLEY B R AA1 G L IY0 +BROGLIE B R AA1 G L IY0 +BROGLIO B R OW1 L Y OW0 +BROGTEN B R OW1 D AH0 N +BROGTEN'S B R OW1 T AH0 N Z +BROGUE B R OW1 G +BROGUES B R OW1 G Z +BROHAN B R OW1 AH0 N +BROHL B R OW1 L +BROIDER B R OY1 D ER0 +BROIDER'D B R OY1 D ER0 D +BROIDERED B R OY1 D ER0 D +BROIDERIES B R OY1 D ER0 IY0 Z +BROIDERING B R OY1 D ER0 IH0 NG +BROIDERY B R OY1 D ER0 IY0 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+CIVILITIES S AH0 V IH1 L AH0 T IY0 Z +CIVILITY S AH0 V IH1 L AH0 T IY0 +CIVILIZATION S IH2 V AH0 L IH0 Z EY1 SH AH0 N +CIVILIZATION'S S IH2 V AH0 L IH0 Z EY1 SH AH0 N Z +CIVILIZATIONS S IH2 V AH0 L IH0 Z EY1 SH AH0 N Z +CIVILIZE S IH1 V AH0 L AY2 Z +CIVILIZED S IH1 V AH0 L AY2 Z D +CIVILIZER S IH1 V AH0 L AY2 Z ER0 +CIVILIZERS S IH1 V AH0 L AY2 Z ER0 Z +CIVILIZES S IH1 V AH0 L AY2 Z IH0 Z +CIVILIZING S IH1 V AH0 L AY2 Z IH0 NG +CIVILL S IH1 V IH0 L +CIVILLER S IH1 V IH0 L ER0 +CIVILLEST S AH0 V IH1 L IH0 S T +CIVILLY S IH1 V IH0 L IY0 +CIVIQUE S IH2 V AH0 K EY2 +CIVIS S IH2 V AH0 S +CIVISM S AH0 V IH2 Z AH0 M +CIVITA CH IY0 V IY1 T AH0 +CIVITAS S IH2 V AH0 T AH0 S +CIVITATE S IH2 V AH0 T EY2 T +CIVITATEM S IH2 V AH0 T EY2 T M +CIVITATIS S IH2 V AH0 T EY1 T IH0 S +CIVITELLA S IH1 V IH0 T EH1 L AH0 +CIVIUM S IY2 V IY0 AH0 M +CIVS S AH0 V Z +CIVVIES S IH1 V IY0 Z +CIX S IH2 K S +CIZMEASZ CH IH1 Z M AA1 SH +CK K +CL S IY1 EH1 L +CL'AR K L AA1 R +CLA K L AH0 +CLABBER K L AE1 B ER0 +CLAC K L AE1 K +CLACHAN K L AE1 CH AH0 N +CLACK K L AE1 K +CLACKAMAS K L AE1 K AH0 M AH0 S +CLACKED K L AE1 K T +CLACKETY K L AE1 K AH0 T IY0 +CLACKING K L AE1 K IH0 NG +CLACKS K L AE1 K S +CLACTON K L AE1 K T AH0 N +CLAD K L AE1 D +CLADDAGH K L AE1 D AH2 +CLADEL K L AE1 D AH0 L +CLADICH K L AA1 D IH0 CH +CLADS K L AE2 D Z +CLADY K L AH0 D IY0 +CLAELIA K L EY1 L IY0 AH0 +CLAES K L EY1 Z +CLAES'S K L EY1 Z IH0 Z +CLAFLIN K L AE1 F L IH0 N +CLAFLIN'S K L AE1 F L IH0 N Z +CLAGETT K L AE1 JH IH0 T +CLAGGET K L AE1 G IH0 T +CLAGGETT K L AE1 G IH0 T +CLAGHORN K L AE1 G HH ER0 N +CLAGHORN'S K L AE1 G HH AO0 R N Z +CLAGHORNS K L AE1 G HH AO0 R N Z +CLAGNY K L AE1 G N IY0 +CLAH K L AH0 +CLAIB K L EY1 B +CLAIBORNE K L EY1 B ER0 N +CLAIBORNE'S K L EY1 B AO0 R N Z +CLAIBORNE'S K L EY1 B ER0 N Z +CLAIBORNES K L EY1 B AO0 R N Z +CLAIM K L EY1 M +CLAIM'D K L EY1 M D +CLAIM'S K L EY1 M Z +CLAIMANT K L EY1 M AH0 N T +CLAIMANT'S K L EY1 M AH0 N T S +CLAIMANTS K L EY1 M AH0 N T S +CLAIMED K L EY1 M D +CLAIMETH K L EY1 M IH0 TH +CLAIMIN K L EY1 M IH0 N +CLAIMING K L EY1 M IH0 NG +CLAIMS K L EY1 M Z +CLAIN K L EY1 N +CLAIR K L EH1 R +CLAIR'S K L EH1 R Z +CLAIRAUDIENCE K L EH0 R AO1 D IY0 AH0 N S +CLAIRAUDIENT K L EH0 R AO1 D IY0 AH0 N T +CLAIRDELUNE K L EH0 R D EH0 L UW1 N +CLAIRDYCE K L EH0 R D AY2 S +CLAIRE K L EH1 R +CLAIRE'S K L EH1 R Z +CLAIRES K L EH1 R Z +CLAIRETTE K L ER0 EH1 T +CLAIRETTE'S K L ER0 EH1 T S +CLAIRFAIT K L EH0 R F EY1 +CLAIRIERE K L EH0 R IH1 R IY0 +CLAIRIN K L EH0 R IH0 N +CLAIRMONT K L EH1 R M AH0 N T +CLAIROIX K L EH0 R W AH0 +CLAIRON K L EH1 R AA0 N +CLAIRS K L EH1 R Z +CLAIRVAL K L EH0 R V AH0 L +CLAIRVAUX K L EH0 R V OW1 +CLAIRVEND K L EH0 R V AH0 N D +CLAIRVILLE K L EH0 R V IH0 L +CLAIRVILLE'S K L EH0 R V IH0 L Z +CLAIRVOYANCE K L EH0 R V OY1 AH0 N S +CLAIRVOYANT K L EH0 R V OY1 AH0 N T +CLAIRVOYANTE K L EH0 R V OY1 AH0 N T IY0 +CLAIRVOYANTLY K L EH0 R V OY1 AH0 N T L IY0 +CLAIRVOYANTS K L EH0 R V OY1 AH0 N T S +CLAIRY K L EH1 R IY0 +CLAITH K L EY1 TH +CLAK K L AH0 K +CLAKS K L AE1 K S +CLALLAM K L AE1 L AH0 M +CLAM K L AE1 M +CLAMADOS K L AA0 M AA1 D OW0 Z +CLAMANT K L AE1 M AH0 N T +CLAMART K L AE1 M AA2 R T +CLAMAVI K L AH0 M EY1 V IY0 +CLAMBAKE K L AE1 M B EY2 K +CLAMBAKES K L AE1 M B EY2 K S +CLAMBER K L AE1 M B ER0 +CLAMBERED K L AE1 M B ER0 D +CLAMBERING K L AE1 M B ER0 IH0 NG +CLAMBERS K L AE1 M B ER0 Z +CLAMERAN K L AE1 M ER0 AH0 N +CLAMMER K L AE1 M ER0 +CLAMMERS K L AE1 M ER0 Z +CLAMMILY K L AE1 M AH0 L IY0 +CLAMMINESS K L AE1 M IY0 N AH0 S +CLAMMING K L AE1 M IH0 NG +CLAMMY K L AE1 M IY0 +CLAMOR K L AE1 M ER0 +CLAMORED K L AE1 M ER0 D +CLAMORING K L AE1 M ER0 IH0 NG +CLAMORINGS K L AE1 M ER0 IH0 NG Z +CLAMOROUS K L AE1 M ER0 AH0 S +CLAMOROUSLY K L AE1 M ER0 AH0 S L IY0 +CLAMORS K L AE1 M ER0 Z +CLAMOUR K L AE1 M UH0 R +CLAMOURED K L AE1 M UH0 R D +CLAMOURING K L AE1 M UH0 R IH0 NG +CLAMOURINGS K L AE1 M UH0 R IH0 NG Z +CLAMOUROUS K L AE1 M UH0 R AH0 S +CLAMOURS K L AE1 M UH0 R Z +CLAMP K L AE1 M P +CLAMPED K L AE1 M P T +CLAMPING K L AE1 M P IH0 NG +CLAMPS K L AE1 M P S +CLAMS K L AE1 M Z +CLAMSHELL K L AE1 M SH EH2 L +CLAN K L AE1 N +CLAN'S K L AE1 N Z +CLANAHAN K L AE1 N AH0 HH AE0 N +CLANBRASSIL K L AE1 N B R AE0 S IY1 L +CLANCARTY K L AE1 NG K AA2 R T IY0 +CLANCEHURST K L AE1 N S HH ER0 S T +CLANCEY K L AE1 N S IY0 +CLANCY K L AE1 N S IY0 +CLANCY'S K L AE1 N S IY0 Z +CLANCYS K L AE1 N S IY0 Z +CLANDESTINE K L AE0 N D EH1 S T IH0 N +CLANDESTINELY K L AE0 N D EH1 S T AH0 N L IY0 +CLANDEVIL K L AE0 N D EH1 V AH0 L +CLANDON K L AE1 N D AH0 N +CLANDON'S K L AE1 N D AH0 N Z +CLANE K L EY1 N +CLANE'S K L EY1 N Z +CLANED K L EY1 N D +CLANG K L AE1 NG +CLANGED K L AE1 NG D +CLANGER K L AE1 NG ER0 +CLANGING K L AE1 NG IH0 NG +CLANGINGS K L AE1 NG IH0 NG Z +CLANGOR K L AE1 NG G ER0 +CLANGOROUS K L AE1 NG G ER0 AH0 S +CLANGOUR K L AE1 NG ER0 +CLANGS K L AE1 NG Z +CLANHOME K L AE1 N HH OW2 M +CLANK K L AE1 NG K +CLANKED K L AE1 NG K T +CLANKING K L AE1 NG K IH0 NG +CLANKINGS K L AE1 NG K IH0 NG Z +CLANKS K L AE1 NG K S +CLANKWOOD K L AE1 NG K W UH2 D +CLANMORNE K L AE1 N M AO0 R N +CLANN K L AE1 N +CLANNISH K L AE1 N IH0 SH +CLANNISHNESS K L AE1 N IH0 SH N AH0 S +CLANRANALD K L AE1 N R AH0 N AA1 L D +CLANRICARDE K L AE1 N R IH0 K AA1 R D +CLANS K L AE1 N Z +CLANSFORD K L AE1 N S F ER0 D +CLANSHIP K L AE1 N SH IH2 P +CLANSMAN K L AE1 N Z M AH0 N +CLANSMEN K L AE1 N Z M AH0 N +CLANTON K L AE1 N T AH0 N +CLANYA K L AE1 N Y AH0 +CLAP K L AE1 P +CLAP'S K L AE1 P S +CLAPARON K L AE1 P R AH0 N +CLAPART K L AE1 P AA2 R T +CLAPBOARD K L AE1 P B AO2 R D +CLAPBOARDED K L AE1 P B AO2 R D IH0 D +CLAPBOARDS K L AE1 P B AO2 R D Z +CLAPHAM K L AE1 F AH0 M +CLAPHURST K L AE1 F ER0 S T +CLAPP K L AE1 P +CLAPP'D K L AE1 P D +CLAPP'S K L AE1 P S +CLAPPED K L AE1 P T +CLAPPER K L AE1 P ER0 +CLAPPERS K L AE1 P ER0 Z +CLAPPERTON K L AE1 P ER0 T AH0 N +CLAPPERTON'S K L AE1 P ER0 T AH0 N Z +CLAPPIN K L AE1 P IH0 N +CLAPPING K L AE1 P IH0 NG +CLAPPINGS K L AE1 P IH0 NG Z +CLAPS K L AE1 P S +CLAPSADDLE K L AE1 P S AE2 D AH0 L +CLAPT K L AE1 P T +CLAPTON K L AE1 P T AH0 N +CLAPTRAP K L AE1 P T R AE2 P +CLAQUE K L AE1 K +CLAQUEURS K L AE1 K ER1 Z +CLAR K L AA1 R +CLARA K L AE1 R AH0 +CLARA K L EH1 R AH0 +CLARA'S K L AE1 R AH0 Z +CLARA'S K L EH1 R AH0 Z +CLARE K L EH1 R +CLARE'S K L EH1 R Z +CLAREBOROUGH K L AE1 R B ER0 OW0 +CLARED K L EH1 R D +CLAREHAVEN K L AA0 R EH1 HH AH0 V AH0 N +CLAREHAVEN'S K L AA0 R EH1 HH AH0 V AH0 N Z +CLAREMONT K L EH1 R M AA2 N T +CLAREMONT'S K L AE1 R M AA2 N T S +CLAREMORRIS K L AE1 R AH0 M AO1 R IH0 S +CLAREN K L AE1 R AH0 N +CLARENBACH K L EH1 R AH0 N B AA2 K +CLARENCE K L EH1 R AH0 N S +CLARENCE'S K L EH1 R AH0 N S IH0 Z +CLARENCES K L EH1 R AH0 N S IH0 Z +CLARENCEUX K L EH1 R AH0 N S OW1 +CLARENDEL K L AA0 R EH1 N D AH0 L +CLARENDON K L EH1 R AH0 N D AH0 N +CLARENDON'S K L EH1 R AH0 N D AH0 N Z +CLARENHAM K L EH1 R AH0 N HH AE2 M +CLARENS K L EH1 R AH0 N Z +CLARES K L EH1 R Z +CLARET K L EH1 R AH0 T +CLARETIE K L ER0 EH1 T IY0 +CLARETS K L AE1 R AH0 T S +CLARETTE K L ER0 EH1 T +CLARGES K L AA1 R JH IH0 Z +CLARGY K L AA1 R JH IY0 +CLARIAN K L EH1 R IY0 AH0 N +CLARIAN'S K L EH1 R IY0 AH0 N Z +CLARIBEL K L EH1 R AH0 B EH2 L +CLARIBEL'S K L EH1 R AH0 B EH2 L Z +CLARICE K L ER0 IY1 S +CLARICE'S K L AA1 R IH0 S IH0 Z +CLARIDGE K L EH1 R IH0 JH +CLARIDGE'S K L EH1 R IH0 JH AH0 Z +CLARIE K L EH1 R IY0 +CLARIFICATION K L EH2 R AH0 F AH0 K EY1 SH AH0 N +CLARIFIED K L EH1 R AH0 F AY2 D +CLARIFIES K L EH1 R AH0 F AY2 Z +CLARIFY K L EH1 R AH0 F AY2 +CLARIFYING K L EH1 R AH0 F AY2 IH0 NG +CLARIMONDE K L AE1 R IH0 M AH0 N D +CLARIN K L EH2 R AH0 N +CLARINA K L AA0 R IY1 N AH0 +CLARINDA K L ER0 IH1 N D AH0 +CLARINDA'S K L ER0 IH1 N D AH0 Z +CLARINET K L EH2 R AH0 N EH1 T +CLARINETS K L EH2 R AH0 N EH1 T S +CLARION K L EH1 R IY0 AH0 N +CLARION'S K L EH1 R IY0 AH0 N Z +CLARIONET K L EH1 R IY0 AH0 N EH1 T +CLARIONETS K L EH1 R IY0 AH0 N EH1 T S +CLARIONS K L EH1 R IY0 AH0 N Z +CLARIS K L EH1 R IH0 S +CLARISS K L ER0 IH1 S +CLARISSA K L ER0 IH1 S AH0 +CLARISSA'S K L ER0 IH1 S AH0 Z +CLARISSE K L AE1 R IH0 S +CLARISSE K L AH0 R IY1 S +CLARISSE'S K L AA0 R IY1 S IH0 Z +CLARITA K L AA0 R IY1 T AH0 +CLARITY K L EH1 R AH0 T IY0 +CLARITY K L EH1 R IH0 T IY0 +CLARK K L AA1 R K +CLARK'S K L AA1 R K S +CLARKE K L AA1 R K +CLARKE'S K L AA1 R K S +CLARKES K L AA1 R K S +CLARKEVILLE K L AA1 R K V IH0 L +CLARKS K L AA1 R K S +CLARKSBURG K L AA1 R K S B ER0 G +CLARKSON K L AA1 R K S AH0 N +CLARKSON'S K L AA1 R K S AH0 N Z +CLARKSVILLE K L AA1 R K S V IH2 L +CLARO K L AA1 R OW0 +CLAROS K L AA1 R OW0 Z +CLARRIE K L AE1 R IY0 +CLARRY K L AE1 R IY0 +CLARTE K L AA1 R T IY0 +CLARUS K L AA1 R AH0 S +CLARY K L EH1 R IY0 +CLARY'S K L EH1 R IY0 Z +CLAS K L AE1 S +CLASFEMPTA K L AE1 S F EH2 M P T AH0 +CLASH K L AE1 SH +CLASH'D K L AE1 SH IH0 D +CLASHED K L AE1 SH T +CLASHES K L AE1 SH IH0 Z +CLASHING K L AE1 SH IH0 NG +CLASHINGS K L AE1 SH IH0 NG Z +CLASHNICHD K L AE1 SH N IH0 CH D +CLASON K L AE1 S AH0 N +CLASP K L AE1 S P +CLASP'D K L AE1 S P D +CLASPED K L AE1 S P T +CLASPER K L AE1 S P ER0 +CLASPERS K L AE1 S P ER0 Z +CLASPING K L AE1 S P IH0 NG +CLASPINGS K L AE1 S P IH0 NG Z +CLASPS K L AE1 S P S +CLASPT K L AE1 S P T +CLASS K L AE1 S +CLASS'S K L AE1 S IH0 Z +CLASSE K L AE1 S +CLASSED K L AE1 S T +CLASSES K L AE1 S AH0 Z +CLASSES K L AE1 S IH0 Z +CLASSIC K L AE1 S IH0 K +CLASSICAL K L AE1 S IH0 K AH0 L +CLASSICALISM K L AE1 S IH0 K AH0 L IH2 Z AH0 M +CLASSICALITY K L AE1 S IH0 K AE1 L AH0 T IY0 +CLASSICALLY K L AE1 S IH0 K L IY0 +CLASSICISM K L AE1 S IH0 S IH2 Z AH0 M +CLASSICIST K L AE1 S AH0 S AH0 S T +CLASSICISTS K L AE1 S IH0 S IH0 S T S +CLASSICS K L AE1 S IH0 K S +CLASSICUS K L AE1 S IH0 K AH0 S +CLASSIFIABLE K L AE1 S AH0 F AY2 AH0 B AH0 L +CLASSIFICATION K L AE2 S AH0 F AH0 K EY1 SH AH0 N +CLASSIFICATIONS K L AE2 S AH0 F AH0 K EY1 SH AH0 N Z +CLASSIFICATORY K L AE1 S AH0 F AH0 K EY1 T ER0 IY0 +CLASSIFIED K L AE1 S AH0 F AY2 D +CLASSIFIER K L AE1 S AH0 F AY2 ER0 +CLASSIFIERS K L AE1 S AH0 F AY2 ER0 Z +CLASSIFIES K L AE1 S AH0 F AY2 Z +CLASSIFY K L AE1 S AH0 F AY2 +CLASSIFYING K L AE1 S AH0 F AY2 IH0 NG +CLASSING K L AE1 S IH0 NG +CLASSIQUE K L AE1 S IY1 K +CLASSIS K L AE1 S IH0 S +CLASSMAN K L AE1 S M AH0 N +CLASSMATE K L AE1 S M EY2 T +CLASSMATE'S K L AE1 S M EY2 T S +CLASSMATES K L AE1 S M EY2 T S +CLASSMEN K L AE1 S M EH0 N +CLASSON K L AE1 S AH0 N +CLASSROOM K L AE1 S R UW2 M +CLASSROOMS K L AE1 S R UW2 M Z +CLASSY K L AE1 S IY0 +CLAT K L AE1 T +CLATFORD K L AE1 T F ER0 D +CLATSOP K L AE1 T S AA2 P +CLATSOPS K L AE1 T S AA2 P S +CLATTER K L AE1 T ER0 +CLATTERBY K L AE1 T ER0 B IY0 +CLATTERED K L AE1 T ER0 D +CLATTERIN K L AE1 T ER0 IH0 N +CLATTERING K L AE1 T ER0 IH0 NG +CLATTERINGS K L AE1 T ER0 IH0 NG Z +CLATTERMOUTH K L AE1 T ER0 M AH0 TH +CLATTERS K L AE1 T ER0 Z +CLAUD K L AO1 D +CLAUD'S K L AO1 D Z +CLAUDAS K L AO1 D AH0 Z +CLAUDE K L AO1 D +CLAUDE'S K L AO1 D Z +CLAUDES K L AO1 D Z +CLAUDET K L AO0 D EH1 T +CLAUDET'S K L AO0 D EH1 T S +CLAUDIA K L AO1 D IY0 AH0 +CLAUDIA'S K L AO1 D IY0 AH0 Z +CLAUDIAN K L AO1 D IY0 AH0 N +CLAUDIE K L AO1 D IY0 +CLAUDIEUSE K L AO1 D Y UW1 S +CLAUDII K L AO0 D IY1 IY0 +CLAUDINE K L AO0 D IY1 N +CLAUDINE'S K L AO0 D IY1 N Z +CLAUDIO K L AO1 D IY0 OW2 +CLAUDIO'S K L AO1 D IY0 OW0 Z +CLAUDIS K L AO1 D IH0 S +CLAUDIUS K L AO1 D IY0 AH0 S +CLAUDIUS'S K L AO1 D IY0 AH0 S IH0 Z +CLAUDO K L AO1 D OW0 +CLAUGHTON K L AO1 T AH0 N +CLAUS K L AO1 Z +CLAUS'S K L AO1 Z IH0 Z +CLAUSE K L AO1 Z +CLAUSEL K L AO1 Z AH0 L +CLAUSEN K L AW1 S AH0 N +CLAUSEN'S K L AO1 Z AH0 N Z +CLAUSES K L AO1 Z AH0 Z +CLAUSES K L AO1 Z IH0 Z +CLAUSEWITZ K L AO1 Z W IH0 T S +CLAUSIN K L AO1 Z AH0 N +CLAUSIUS K L AO1 Z IY0 IH0 S +CLAUSTHAL K L AO2 S T AH0 L +CLAUSTRAL K L AO2 S T R AH0 L +CLAUSTROPHOBIA K L AO2 S T R AH0 F OW1 B IY0 AH0 +CLAUSUM K L AO1 Z AH0 M +CLAVE K L EY2 V +CLAVECIN K L AH0 V EH1 S IH0 N +CLAVEL K L AE1 V AH0 L +CLAVEL'S K L AH0 V EH1 L Z +CLAVER K L AH0 V ER0 +CLAVER'SE K L AH0 V ER1 Z +CLAVERHOUSE K L AE1 V ER0 HH AW2 S +CLAVERHOUSE'S K L AE1 V ER0 HH AW2 S IH0 Z +CLAVERIAS K L AH0 V EH1 R IY0 AH0 Z +CLAVERING K L AE1 V ER0 IH0 NG +CLAVERING'S K L AE1 V ER0 IH0 NG Z +CLAVERINGS K L AE1 V ER0 IH0 NG Z +CLAVERS K L AE1 V ER0 Z +CLAVERTON K L AE1 V ER0 T AH0 N +CLAVERTON'S K L AE1 V ER0 T AH0 N Z +CLAVES K L EY2 V Z +CLAVICHORD K L AE1 V AH0 K AO2 R D +CLAVICLE K L AE1 V AH0 K AH0 L +CLAVICLE K L AE1 V IH0 K AH0 L +CLAVICLES K L AE1 V AH0 K AH0 L Z +CLAVICULAR K L AE1 V AH0 K Y AH0 L ER0 +CLAVIER K L AE1 V Y ER0 +CLAVIERE K L AE1 V IY0 EH0 R +CLAVIGERA K L AE1 V AH0 JH EH1 R AH0 +CLAVIGERO K L AE1 V AH0 JH EH1 R OW0 +CLAVIS K L AE1 V IH0 S +CLAVIUS K L EY1 V IY0 IH0 S +CLAVO K L AA1 V OW0 +CLAVUS K L AH0 V AH0 S +CLAVVY K L AE1 V IY0 +CLAW K L AO1 +CLAW'S K L AO1 Z +CLAWED K L AO1 D +CLAWHAMMER K L AO1 HH AE2 M ER0 +CLAWIN K L AO1 IY0 N +CLAWING K L AO1 IH0 NG +CLAWLIKE K L AO1 L AY2 K +CLAWS K L AO1 Z +CLAWSON K L AO1 S AH0 N +CLAWY K L AO1 IY0 +CLAXON K L AE1 K S AH0 N +CLAXTON K L AE1 K S T AH0 N +CLAY K L EY1 +CLAY'S K L EY1 Z +CLAYBANK K L EY1 B AE2 NG K 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IY1 V Z +CLEEVER K L IY1 V ER0 +CLEF K L EH1 F +CLEFS K L EH1 F S +CLEFT K L EH1 F T +CLEFTS K L EH1 F T S +CLEGG K L EH1 G +CLEGG'S K L EH1 G Z +CLEGGETT K L EH1 G IH0 T +CLEGGETT'S K L EH1 G EH1 T S +CLEGGS K L EH1 G Z +CLEGHORN K L EH1 G HH ER0 N +CLEGHORNE K L EH1 G HH AO0 R N +CLEIGH K L AH0 +CLEIGH'S K L IY0 Z +CLEIKUM K L IY1 K AH0 M +CLEINIAS K L EY0 IY1 N IY0 AH0 Z +CLEISHBOTHAM K L IY1 SH B AH0 TH AH0 M +CLEISTHENES K L IY1 S TH IY1 N Z +CLEITOPHON K L IY1 T AA1 F AH0 N +CLEL K L EH1 L +CLELAND K L EH1 L AH0 N D +CLELIA K L IY1 L Y AH0 +CLELIA'S K L IY1 L Y AH0 Z +CLELIE K L EH1 L IY0 +CLELLAN K L EH1 L AH0 N +CLELLAN'S K L EH1 L AH0 N Z +CLELLAND K L EH1 L AH0 N D +CLEM K L EH1 M +CLEM'S K L AH0 M Z +CLEMATIS K L AH0 M AE1 T IH0 S +CLEMATIS K L EH1 M AH0 T IH0 S +CLEMENCE K L EH1 M AH0 N S +CLEMENCE'S K L EH1 M AH0 N S IH0 Z +CLEMENCEAU K L EH1 M AH0 N S OW1 +CLEMENCY K L EH1 M AH0 N S IY0 +CLEMENS K L EH1 M AH0 N Z +CLEMENS'S K L EH1 M AH0 N Z IH0 Z +CLEMENSES K L EH1 M AH0 N S IH0 Z +CLEMENT K L EH1 M AH0 N T +CLEMENT'S K L EH1 M AH0 N T S +CLEMENTE K L AH0 M EH1 N T EY0 +CLEMENTE K L AH0 M EH1 N T IY0 +CLEMENTE'S K L AH0 M EH1 N T EY0 Z +CLEMENTE'S K L AH0 M EH1 N T IY0 Z +CLEMENTI K L EY0 M EY1 N T IY0 +CLEMENTIA K L EY0 M EY1 N SH AH0 +CLEMENTINA K L AH0 M EH1 N T AH0 N AH0 +CLEMENTINA'S K L AH0 M EH1 N T AH0 N AH0 Z +CLEMENTINE K L EH1 M AH0 N T AY2 N +CLEMENTINE K L EH1 M AH0 N T IY2 N +CLEMENTINE'S K L AH0 M EH1 N T IY0 N Z +CLEMENTINI K L AH0 M EH1 N T IY1 N IY0 +CLEMENTS K L EH1 M AH0 N T S +CLEMENTS'S K L AH0 M EH1 N T S AH0 Z +CLEMENZA K L AH0 M EH1 N Z AH0 +CLEMM K L EH1 M +CLEMM'S K L EH1 M Z +CLEMMED K L EH1 M D +CLEMMER K L EH1 M ER0 +CLEMMIE K L EH1 M IY0 +CLEMMY K L EH1 M IY0 +CLEN K L EH1 N +CLENA K L EH1 N AH0 +CLENARVON K L EH1 N ER0 V AA0 N +CLENCH K L EH1 N CH +CLENCH'D K L EH1 N CH D +CLENCHED K L EH1 N CH T +CLENCHES K L EH1 N CH AH0 Z +CLENCHING K L EH1 N CH IH0 NG +CLENDENIN K L EH1 N D IH0 N IH0 N +CLENDENNING K L EH2 N D EH1 N 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+CLERE'S K L EH1 R Z +CLEREMONT K L EH1 R AH0 M AA1 N T +CLERESTORY K L EH1 R IH0 S T AO1 R IY0 +CLERGEOT K L ER1 JH IY0 AA1 T +CLERGY K L ER1 JH IY0 +CLERGY'S K L ER1 JH IY0 Z +CLERGYMAN K L ER1 JH IY0 M AE2 N +CLERGYMAN K L ER1 JH IY0 M AH0 N +CLERGYMAN'S K L ER1 JH IY0 M AH0 N Z +CLERGYMEN K L ER1 JH IY0 M EH2 N +CLERGYMEN K L ER1 JH IY0 M IH0 N +CLERGYMEN'S K L ER1 JH IY0 M IH0 N Z +CLERGYWOMAN K L ER1 JH IY0 W UH2 M AH0 N +CLERIC K L EH1 R IH0 K +CLERIC'S K L EH1 R IH0 K S +CLERICAL K L EH1 R AH0 K AH0 L +CLERICAL K L EH1 R IH0 K AH0 L +CLERICALISM K L EH1 R IH0 K AH0 L IH2 Z AH0 M +CLERICALLY K L EH1 R IH0 K L IY0 +CLERICALS K L EH1 R IH0 K AH0 L Z +CLERICO K L ER0 IY1 K OW0 +CLERICO'S K L EH1 R IH0 K OW2 Z +CLERICS K L EH1 R IH0 K S +CLERICY K L EH1 R AH0 S IY0 +CLERICY'S K L EH1 R AH0 S IY0 Z +CLERIHEW K L EH1 R AH0 HH Y UW0 +CLERIMONT K L EH1 R IH0 M AA2 N T +CLERIOS K L EH1 R IY0 OW0 Z +CLERK K L ER1 K +CLERK'S K L ER1 K S +CLERKE K L ER1 K +CLERKE'S K L ER1 K S +CLERKED K L ER1 K T +CLERKENWELL K L ER1 K AH0 N W EH2 L +CLERKIN K L ER1 K IH0 N +CLERKING K L ER1 K IH0 NG +CLERKLAND K L ER1 K L AH0 N D +CLERKLY K L ER1 K L IY0 +CLERKS K L ER1 K S +CLERKSHIP K L ER1 K SH IH2 P +CLERKSHIPS K L ER1 K SH IH2 P S +CLERMONT K L EH1 R M AA2 N T +CLERMONT'S K L EH1 R M AA2 N T S +CLERNAND K L ER1 N AH0 N D +CLERNAND'S K L ER1 N IH0 N D Z +CLERRON K L EH1 R AH0 N +CLERTE K L ER1 T +CLERVAL K L EH1 R V AH0 L +CLERY K L EH1 R IY0 +CLERY'S K L EH1 R IY0 Z +CLETA K L EH1 T AH0 +CLETHRA K L IY1 TH R AH0 +CLEUCH K L IY1 AH0 K +CLEUGH K L +CLEVAH K L IY1 V AH0 +CLEVAIRE K L AH0 V EH2 R +CLEVE K L IY1 V +CLEVE'S K L IY1 V Z +CLEVEDEN K L EH1 V D AH0 N +CLEVEDON K L IY1 V AH0 D AO2 N +CLEVELAND K L IY1 V L AH0 N D +CLEVELAND'S K L IY1 V L AH0 N D Z +CLEVELANDS K L IY1 V L AH0 N D Z +CLEVENGER K L EH1 V IH0 N JH ER0 +CLEVER K L EH1 V ER0 +CLEVERER K L EH1 V ER0 ER0 +CLEVEREST K L EH1 V R AH0 S T +CLEVERING K L EH1 V ER0 IH0 NG +CLEVERING'S K L EH1 V ER0 IH0 NG Z +CLEVERISH K L EH1 V R IH0 SH +CLEVERLY K L EH1 V ER0 L IY0 +CLEVERNESS K L EH1 V ER0 N AH0 S +CLEVES K L IY1 V Z +CLEVI K L IY1 V IY0 +CLEVIS K L EH1 V IH0 S +CLEW K L UW1 +CLEWE K L UW1 +CLEWE'S K L EH1 W EY2 Z +CLEWED K L UW1 D +CLEWER K L UW1 ER0 +CLEWES K L UW1 Z +CLEWING K L UW1 IH0 NG +CLEWLINE K L UW1 L AY0 N +CLEWLINES K L UW1 L AY2 N Z +CLEWS K L UW1 Z +CLEYRE K L EH1 R +CLI K L IY0 +CLIA K L IY0 AH0 +CLIANTHA K L IY0 AE1 N TH AH0 +CLIBOROUGH K L AY1 B ER0 OW0 +CLICHE K L IY0 SH EY1 +CLICHES K L IY0 SH EY1 Z +CLICHY K L IY0 SH IY0 +CLICK K L IH1 K +CLICKED K L IH1 K T +CLICKER K L IH1 K ER0 +CLICKETY K L IH1 K AH0 T IY0 +CLICKIN K L IH1 K IH0 N +CLICKING K L IH1 K IH0 NG +CLICKINGS K L IH1 K IH0 NG Z +CLICKITS K L IH1 K IH0 T S +CLICKITY K L IH1 K IH0 T IY0 +CLICKS K L IH1 K S +CLICQUOT K L IH1 K W OW1 +CLIENT K L AY1 AH0 N T +CLIENT'S K L AY1 AH0 N T S +CLIENTAGE K L AY1 AH0 N T IH0 JH +CLIENTELE K L AY2 AH0 N T EH1 L +CLIENTS K L AY1 AH0 N T S +CLIF K L IH1 F +CLIFDEN K L IH1 F D AH0 N +CLIFF K L IH1 F +CLIFF'S K L IH1 F S +CLIFFE K L IH1 F +CLIFFE'S K L IH1 F S +CLIFFED K L IH1 F T +CLIFFMORE K L IH1 F M AO0 R +CLIFFORD K L IH1 F ER0 D +CLIFFORD'S K L IH1 F ER0 D Z +CLIFFORDS K L IH1 F ER0 D Z +CLIFFS K L IH1 F S +CLIFFSIDE K L IH1 F S AY2 D +CLIFFY K L IH1 F IY0 +CLIFT K L IH1 F T +CLIFTON K L IH1 F T AH0 N +CLIFTON'S K L IH1 F T AH0 N Z +CLIFTS K L IH1 F T S +CLIFTY K L IH1 F T IY0 +CLIGES K L AY1 JH IH0 Z +CLIGNANCOURT K L IH1 G N AH0 N K AO2 R T +CLII K L IY0 +CLIM K L IH1 M +CLIMACTERIC K L AY0 M AE1 K T EH1 R IH0 K +CLIMACTIC K L AY0 M AE1 K T IH0 K +CLIMAT K L AY1 M AE0 T +CLIMATAL K L AY2 M AH0 T AA1 L +CLIMATE K L AY1 M AH0 T +CLIMATE K L AY1 M IH0 T +CLIMATE'S K L AY1 M IH0 T S +CLIMATES K L AY1 M AH0 T S +CLIMATES K L AY1 M IH0 T S +CLIMATIC K L AY0 M AE1 T IH0 K +CLIMATICALLY K L AY0 M AE1 T IH0 K L IY0 +CLIMATOLOGY K L AY2 M AH0 T AA1 L AH0 JH IY0 +CLIMAX K L AY1 M AE2 K S +CLIMAXED K L AY1 M AE2 K S T +CLIMAXES K L AY1 M AE0 K S IH0 Z +CLIMAXES K L IH1 M AE0 K S IH0 Z +CLIMAXING K L AY1 M AE2 K S IH0 NG +CLIMB K L AY1 M +CLIMB'D K L AY1 M D +CLIMBABLE K L AY1 M B AH0 L +CLIMBED K L AY1 M D +CLIMBER K L AY1 M ER0 +CLIMBER'S K L AY1 M ER0 Z +CLIMBERKIN K L AY1 M ER0 K IH0 N +CLIMBERS K L AY1 M ER0 Z +CLIMBETH K L AY1 M TH +CLIMBIN K L AY1 M IH0 N +CLIMBING K L AY1 M IH0 NG +CLIMBINGS K L AY1 M IH0 NG Z +CLIMBS K L AY1 M Z +CLIME K L AY1 M +CLIMENE K L AY1 M IY2 N +CLIMES K L AY1 M Z +CLIMO K L AY1 M OW0 +CLIMOE K L AY1 M OW0 +CLINABARIANS K L AH0 N EY1 B EH1 R IY0 AH0 N Z +CLINCH K L IH1 N CH +CLINCH'S K L IH1 N CH IH0 Z +CLINCHED K L IH1 N CH T +CLINCHER K L IH1 N CH ER0 +CLINCHES K L IH1 N CH AH0 Z +CLINCHES K L IH1 N CH IH0 Z +CLINCHING K L IH1 N CH IH0 NG +CLINE K L AY1 N +CLING K L IH1 NG +CLINGAN K L IH1 NG G AH0 N +CLINGEST K L IH1 NG G IH0 S T +CLINGETH K L IH1 NG G IH0 TH +CLINGIN K L IH1 NG AH0 N +CLINGING K L IH1 NG IH0 NG +CLINGINGLY K L IH1 NG IH0 NG L IY0 +CLINGMAN K L IH1 NG M AH0 N +CLINGS K L IH1 NG Z +CLINGY K L IH1 NG IY0 +CLINIAS K L IH1 N IY0 AH0 Z +CLINIC K L IH1 N IH0 K +CLINICAL K L IH1 N AH0 K AH0 L +CLINICAL K L IH1 N IH0 K AH0 L +CLINICS K L IH1 N IH0 K S +CLINIQUE K L AH0 N IY1 K +CLINK K L IH1 NG K +CLINKED K L IH1 NG K T +CLINKER K L IH1 NG K ER0 +CLINKERS K L IH1 NG K ER0 Z +CLINKIN K L IH1 NG K IH0 N +CLINKING K L IH1 NG K IH0 NG +CLINKS K L IH1 NG K S +CLINOCK K L IH1 N AH0 K +CLINT K L IH1 N T +CLINT'S K L IH1 N T S +CLINTOCK K L IH1 N T AH0 K +CLINTOCK'S K L IH1 N T AH0 K S +CLINTON K L IH1 N T AH0 N +CLINTON'S K L IH1 N T AH0 N Z +CLINTONIA K L IH2 N T OW1 N IY0 AH0 +CLINTONS K L IH1 N T AH0 N Z +CLIO K L IY1 OW0 +CLIO'S K L IY1 OW0 Z +CLIP K L IH1 P +CLIPBOARD K L IH1 P B AO2 R D +CLIPCLAP K L IH1 P K L AE2 P +CLIPP'D K L IH1 P D +CLIPPED K L IH1 P T +CLIPPER K L IH1 P ER0 +CLIPPER'S K L IH1 P ER0 Z +CLIPPERS K L IH1 P ER0 Z +CLIPPIN K L IH1 P IH0 N +CLIPPING K L IH1 P IH0 NG +CLIPPINGS K L IH1 P IH0 NG Z +CLIPS K L IH1 P S +CLIPSTON K L IH1 P S T AH0 N +CLIPSTONE K L IH1 P S T OW2 N +CLIPT K L IH1 P T +CLIPTON K L IH1 P T AH0 N +CLIQUE K L IY1 K +CLIQUES K L IH1 K S +CLIQUOT K L IY1 K W OW1 +CLISSON K L IH1 S AH0 N +CLISTHENES K L IH2 S TH IY1 N Z +CLITANDRE K L AY0 T AE1 N D ER0 +CLITHERING K L AY1 DH ER0 IH0 NG +CLITHEROE K L IH1 TH ER0 OW0 +CLITHEROW K L IH1 TH ER0 OW0 +CLITORIS K L AY0 T AO1 R IH0 S +CLITTER K L IH1 T ER0 +CLITUMNUS K L AY0 T AH1 M N AH0 S +CLITUS K L AY1 T AH0 S +CLIVE K L AY1 V +CLIVE'S K L AY1 V Z +CLIVEDEN K L AY1 V D AH0 N +CLIVER K L AY1 V ER0 +CLIX K L IH0 K S +CLO K L OW1 +CLO'ES K L OW1 EH0 S +CLO'S K L OW0 Z +CLO'SE K L OW1 S +CLOACA K L OW1 K AH0 +CLOACINA K L OW1 CH IY1 N AH0 +CLOAK K L OW1 K +CLOAK'S K L OW1 K S +CLOAKE K L OW1 K +CLOAKED K L OW1 K T +CLOAKING K L OW1 K IH0 NG +CLOAKROOM K L OW1 K R UW2 M +CLOAKROOMS K L OW1 K R UW2 M Z +CLOAKS K L OW1 K S +CLOAM K L OW1 M +CLOANTHUS K L OW1 N TH AH0 S +CLOAREK K L OW1 ER0 IH0 K +CLOAREK'S K L OW1 ER0 EH2 K S +CLOATH K L OW1 TH +CLOATHED K L OW1 DH D +CLOATHES K L OW1 DH Z +CLOATHING K L OW1 TH IH0 NG +CLOATHS K L OW1 TH S +CLOBBER K L AA1 B ER0 +CLOCA K L OW1 K AH0 +CLOCHE K L OW1 K IY0 +CLOCHEGOURDE K L AA1 K IH0 G AO1 R D +CLOCHES K L AA1 K S +CLOCHETTE K L AA1 K EH1 T +CLOCHONNE K L AA1 K AH0 N +CLOCK K L AA1 K +CLOCK'S K L AA1 K S +CLOCKE K L AA1 K +CLOCKED K L AA1 K T +CLOCKLIKE K L AA1 K L AY2 K +CLOCKMAKER K L AA1 K M EY0 K ER0 +CLOCKMAKER'S K L AA1 K M EY2 K ER0 Z +CLOCKMAKERS K L AA1 K M EY2 K ER0 Z +CLOCKS K L AA1 K S +CLOCKWISE K L AA1 K W AY2 Z +CLOCKWORK K L AA1 K W ER2 K +CLOD K L AA1 D +CLODAGH K L AA1 D AH0 G +CLODAGH'S K L AA1 D AH2 Z +CLODD K L AA1 D +CLODDED K L AA1 D IH0 D +CLODDISH K L AA1 D IH0 SH +CLODDY K L AA1 D IY0 +CLODE K L OW1 D +CLODE'S K L AA1 D Z +CLODFELTER K L AA1 D F EH2 L T ER0 +CLODHOPPER K L AA1 D HH AA2 P ER0 +CLODHOPPERS K L AA1 D HH AA2 P ER0 Z +CLODIA K L OW1 D IY0 AH0 +CLODION K L OW1 D IY0 AH0 N +CLODIUS K L OW1 D IY0 AH0 S +CLODIUS'S K L OW1 D IY0 AH0 S IH0 Z +CLODMAN K L AA1 D M AH0 N +CLODOCHE K L OW0 D OW1 SH +CLODOMIRO K L OW0 D OW1 M IH1 R OW0 +CLODPOLE K L AA1 D P OW2 L +CLODS K L AA1 D Z +CLODWIG K L AA1 D W IH0 G +CLODWIG'S K L AA1 D W IH0 G Z +CLOE K L OW1 +CLOELIA K L OW1 L IY0 AH0 +CLOES K L OW1 Z +CLOETE K L OW1 T IY0 +CLOG K L AA1 G +CLOGG K L AA1 G +CLOGG'D K L AO1 G D +CLOGGED K L AO1 G D +CLOGGING K L AA1 G IH0 NG +CLOGGING K L AO1 G IH0 NG +CLOGHER K L AA1 G ER0 +CLOGS K L AA1 G Z +CLOHERTY K L AA1 HH ER0 T IY0 +CLOISONNE K L OY1 S AH0 N +CLOISTER K L OY1 S T ER0 +CLOISTER'D K L OY1 S T R AH0 D +CLOISTER'S K L OY1 S T ER0 Z +CLOISTERED K L OY1 S T ER0 D +CLOISTERHAM K L OY1 S T ER0 HH AE2 M +CLOISTERS K L OY1 S T ER0 Z +CLOISTRAL K L OY1 S T R AH0 L +CLOITRE K L OY1 T ER0 +CLOKE K L OW1 K +CLOMB K L AA1 M +CLOMBER K L AA1 M B ER0 +CLON K L AA1 N +CLONAKILTY K L OW1 N AH0 K IH1 L T IY0 +CLONARD K L AA1 N ER0 D +CLONARTY K L OW1 N AA0 R T IY0 +CLONBREE K L AA1 N B R IY1 +CLONBRONY K L AA1 N B R OW2 N IY0 +CLONBRONY'S K L AA1 N B R OW1 N IY0 Z +CLONCURRY K L AA1 N K ER1 IY0 +CLONE K L OW1 N +CLONES K L OW1 N Z +CLONMACNOISE K L AA1 N M AE1 K N W AA1 Z +CLONMEL K L AA1 N M AH0 L +CLONMELL K L AA1 N M EY1 L +CLONTARF K L AA1 N T AA1 R F +CLOOM K L UW2 M +CLOOMBER K L UW1 M B ER0 +CLOON K L UW1 N +CLOOSTEDD K L UW1 S T IH0 D +CLOOTZ K L UW1 T S +CLOP K L AA1 P +CLOPIN K L OW1 P IH0 N +CLOPIN'S K L OW1 P IH0 N Z +CLOPPER K L AA1 P ER0 +CLOPPERTY K L AA1 P ER0 T IY0 +CLOPTON K L AA1 P T AH0 N +CLOPTON'S K L AA1 P T AH0 N Z +CLOPTONS K L AA1 P T AH0 N Z +CLOQUET K L OW1 K EY1 +CLORAN K L AO1 R AH0 N +CLORIDAN K L AO1 R IH0 D AH0 N +CLORINDA K L AO0 R IY1 N D AH0 +CLORINDA'S K L AO0 R IY1 N D AH0 Z +CLORINDE K L AO0 R IY1 N D +CLORINDY K L AO0 R IY1 N D IY0 +CLORIS K L AO1 R IH0 S +CLOS K L AA1 S +CLOS'D K L AA1 Z D +CLOS'T K L AA1 Z T +CLOSANA K L AA1 Z AA1 N AH0 +CLOSE K L OW1 S +CLOSE K L OW1 Z +CLOSE'S K L OW1 S IH0 Z +CLOSEBURN K L OW1 S B ER2 N +CLOSED K L OW1 Z D +CLOSELY K L OW1 S L IY0 +CLOSENESS K L OW1 S N IH0 S +CLOSER K L OW1 S ER0 +CLOSER K L OW1 Z ER0 +CLOSER'N K L OW1 S ER2 AH0 N +CLOSERIE K L OW1 S ER0 IY0 +CLOSERS K L OW1 Z ER0 Z +CLOSERSTIL K L OW1 S ER0 S T IH1 L +CLOSES K L OW1 Z IH0 Z +CLOSEST K L OW1 S AH0 S T +CLOSET K L AA1 Z AH0 T +CLOSETED K L AA1 Z AH0 T IH0 D +CLOSETER K L AA1 Z AH0 T ER0 +CLOSETH K L AA1 S IH0 TH +CLOSETS K L AA1 Z AH0 T S +CLOSIN K L AA1 Z AH0 N +CLOSING K L OW1 Z IH0 NG +CLOSKEY K L AO1 S K IY0 +CLOSKEY'S K L AO1 S K IY0 Z +CLOSKY K L AO1 S K IY0 +CLOSS K L AO1 S +CLOST K L AO1 S T +CLOSTE K L OW1 S T +CLOSTER K L AA1 S T ER0 +CLOSURE K L OW1 ZH ER0 +CLOT K L AA1 T +CLOTAIRE K L AA1 T EH1 R +CLOTALDO K L AA1 T AA1 L D OW0 +CLOTE K L OW1 T IY0 +CLOTELLE K L AA1 T EH2 L +CLOTEN K L OW1 T AH0 N +CLOTH K L AO1 TH +CLOTH'D K L OW1 DH D +CLOTH'S K L AO1 TH S +CLOTHE K L OW1 DH +CLOTHED K L OW1 DH D +CLOTHES K L OW1 DH Z +CLOTHES K L OW1 Z +CLOTHESLINE K L OW1 DH Z L AY0 N +CLOTHESLINES K L OW1 DH Z L AY2 N Z +CLOTHESMAN K L OW1 DH Z M AH0 N +CLOTHESMEN K L OW1 DH Z M IH0 N +CLOTHESPIN K L AO1 TH EH1 S P IH0 N +CLOTHESPINS K L AO1 TH EH1 S P IH0 N Z +CLOTHESPRESS K L OW1 DH S P R EH1 S +CLOTHETH K L OW1 DH TH +CLOTHIER K L OW1 DH Y ER0 +CLOTHIER'S K L OW1 DH Y ER0 Z +CLOTHIERS K L OW1 DH Y ER0 Z +CLOTHILDE K L AA1 TH IH0 L D +CLOTHIN K L AA1 TH AH0 N +CLOTHING K L OW1 DH IH0 NG +CLOTHINGS K L OW1 DH IH0 NG Z +CLOTHO K L AA1 TH OW0 +CLOTHS K L AO1 TH S +CLOTHWORKERS K L AO1 TH W ER2 K ER0 Z +CLOTHYARD K L OW1 DH Y AA2 R D +CLOTILDA K L AH0 T IH1 L D AH0 +CLOTILDA'S K L AH0 T IH1 L D AH0 Z +CLOTILDE K L AH0 T IH1 L D +CLOTILDE'S K L AH0 T IH1 L D Z +CLOTS K L AA1 T S +CLOTT K L AA1 T +CLOTTED K L AA1 T AH0 D +CLOTTED K L AA1 T IH0 D +CLOTTING K L AA1 T IH0 NG +CLOTURE K L OW1 CH ER0 +CLOU K L OW1 +CLOUD K L AW1 D +CLOUD'S K L AW1 D Z +CLOUDBANK K L AW1 D B AE2 NG K +CLOUDBERRY K L AW1 D B EH2 R IY0 +CLOUDBURST K L AW1 D B ER2 S T +CLOUDBURSTS K L AW1 D B ER2 S T S +CLOUDED K L AW1 D IH0 D +CLOUDESLEY K L AW1 D AH0 S L IY0 +CLOUDIER K L AW1 D IY0 ER0 +CLOUDIEST K L AW1 D IY0 AH0 S T +CLOUDILY K L AW1 D AH0 L IY0 +CLOUDINESS K L AW1 D IY0 N IH0 S +CLOUDING K L AW1 D IH0 NG +CLOUDINGS K L AW1 D IH0 NG Z +CLOUDLAND K L AW1 D L AH0 N D +CLOUDLESS K L AW1 D L AH0 S +CLOUDLESSLY K L AW1 D L AH0 S L IY0 +CLOUDLET K L AW1 D L EH2 T +CLOUDLETS K L AW1 D L AH0 T S +CLOUDLIKE K L AW1 D L AY2 K +CLOUDS K L AW1 D Z +CLOUDSDALE K L AW1 D Z D EY2 L +CLOUDY K L AW1 D IY0 +CLOUET K L OW1 EY0 T +CLOUGH K L AW1 +CLOUGH'S K L AW1 Z +CLOUR K L AW1 R +CLOUSTON K L AW1 S T AH0 N +CLOUT K L AW1 T +CLOUT'S K L AW1 T S +CLOUTED K L AW1 T IH0 D +CLOUTING K L AW1 T IH0 NG +CLOUTS K L AW1 T S +CLOVE K L OW1 V +CLOVELLY K L OW1 V AH0 L IY0 +CLOVEN K L OW1 V AH0 N +CLOVER K L OW1 V ER0 +CLOVER'S K L OW1 V ER0 Z +CLOVERDALE K L OW1 V ER0 D EY2 L +CLOVERLEA K L OW1 V ER0 L IY1 +CLOVERLEAF K L OW1 V ER0 L IY2 F +CLOVERNOOK K L OW1 V ER0 N UH1 K +CLOVERS K L OW1 V ER0 Z +CLOVERTON K L OW1 V ER0 T AH0 N +CLOVERTOWN K L OW1 V ER0 T AW2 N +CLOVERY K L OW1 V ER0 IY0 +CLOVES K L OW1 V Z +CLOVIO K L OW1 V IY0 OW0 +CLOVIS K L OW1 V IH0 S +CLOVIS'S K L AA1 V IH0 S IH0 Z +CLOVY K L OW1 V IY0 +CLOW K L OW1 +CLOWES K L AW1 Z +CLOWES'S K L AW1 Z IH0 Z +CLOWN K L AW1 N +CLOWN'S K L AW1 N Z +CLOWNING K L AW1 N IH0 NG +CLOWNISH K L AW1 N IH0 SH +CLOWNISHNESS K L AW1 N IH0 SH N AH0 S +CLOWNS K L AW1 N Z +CLOWRIE K L AO1 R IY0 +CLOY K L OY1 +CLOY'D K L OY1 D +CLOYD K L OY1 D +CLOYED K L OY1 D +CLOYING K L OY1 IH0 NG +CLOYINGLY K L OY1 IH0 NG L IY0 +CLOYNE K L OY1 N +CLOYS K L OY1 Z +CLOYSTER K L OY1 S T ER0 +CLOYSTER'S K L OY1 S T ER0 Z +CLOZE K L OW1 Z +CLOZEL K L OW1 Z AH0 L +CLU K L UW1 +CLUB K L AH1 B +CLUB'S K L AH1 B Z +CLUBABLE K L AH1 B AH0 B AH0 L +CLUBBE K L AH1 B +CLUBBED K L AH1 B D +CLUBBER K L AH1 B ER0 +CLUBBING K L AH1 B IH0 NG +CLUBBY K L AH1 B IY0 +CLUBFOOT K L AH1 B F UH2 T +CLUBFOOT'S K L AH1 B F UH2 T S +CLUBFOOTED K L AH1 B F UH1 T AH0 D +CLUBHOUSE K L AH1 B HH AW2 S +CLUBHOUSES K L AH1 B HH AW2 S IH0 Z +CLUBIN K L AH1 B IH0 N +CLUBIN'S K L AH1 B AH0 N Z +CLUBLAND K L AH1 B L AE2 N D +CLUBMAN K L AH1 B M AH0 N +CLUBMAN'S K L AH1 B M AH0 N Z +CLUBMEN K L AH1 B M AH0 N +CLUBROOM K L AH1 B R UW2 M +CLUBROOMS K L AH1 B R UW2 M Z +CLUBS K L AH1 B Z +CLUCK K L AH1 K +CLUCKED K L AH1 K T +CLUCKING K L AH1 K IH0 NG +CLUCKINGS K L AH1 K IH0 NG Z +CLUCKS K L AH1 K S +CLUD K L AH1 D +CLUDED K L UW1 D IH0 D +CLUDEN K L UW1 D AH0 N +CLUE K L UW1 +CLUENTIUS K L UW1 N T IY2 IH0 S +CLUES K L UW1 Z +CLUFF K L AH1 F +CLUFFE K L AH1 F +CLUFFE'S K L AH1 F S +CLUFFINS K L AH1 F IH0 N Z +CLUGNET K L AH1 G N IH0 T +CLUHIR K L AH1 IH2 R +CLUKE K L UW1 K +CLUKE'S K L UW1 K S +CLUM K L AH1 M +CLUMB K L AH1 M +CLUMBER K L AH1 M B ER0 +CLUMP K L AH1 M P +CLUMP'S K L AH1 M P S +CLUMPED K L AH1 M P T +CLUMPING K L AH1 M P IH0 NG +CLUMPS K L AH1 M P S +CLUMPY K L AH1 M P IY0 +CLUMS K L AH1 M Z +CLUMSIER K L AH1 M Z IY0 ER0 +CLUMSIEST K L AH1 M Z IY0 AH0 S T +CLUMSILY K L AH1 M S AH0 L IY0 +CLUMSINESS K L AH1 M Z IY0 N AH0 S +CLUMSY K L AH1 M Z IY0 +CLUN K L UW1 N +CLUNAS K L UW1 N AH0 Z +CLUNE K L UW1 N +CLUNES K L UW1 N Z +CLUNG K L AH1 NG +CLUNIAC K L UW1 N IY0 AE2 K +CLUNIE K L UW1 N IY0 +CLUNIE'S K L UW1 N IY0 Z +CLUNK K L AH1 NG K +CLUNY K L UW1 N IY0 +CLUNY'S K L UW1 N IY0 Z +CLUPP K L AH1 P +CLUPPINS K L AH1 P IH0 N Z +CLUR K L UW1 R +CLURE K L UW1 R +CLURE'S K L UW1 R Z +CLURG K L ER1 G +CLUS K L AH1 S +CLUSERET K L UW1 Z ER0 EY1 +CLUSIUM K L AH1 S IY0 AH0 M +CLUSIUS K L AH1 S IY0 AH0 S +CLUSKY K L AH1 S K IY0 +CLUST'RING K L AH1 S T IH0 R IH0 NG +CLUSTER K L AH1 S T ER0 +CLUSTER'D K L AH1 S T ER0 D +CLUSTERED K L AH1 S T ER0 D +CLUSTERING K L AH1 S T ER0 IH0 NG +CLUSTERS K L AH1 S T ER0 Z +CLUTCH K L AH1 CH +CLUTCH'D K L AH1 CH D +CLUTCHED K L AH1 CH T +CLUTCHES K L AH1 CH AH0 Z +CLUTCHES K L AH1 CH IH0 Z +CLUTCHING K L AH1 CH IH0 NG +CLUTCHINGS K L AH1 CH IH0 NG Z +CLUTSAM K L AH1 T S AH0 M +CLUTTER K L AH1 T ER0 +CLUTTERBUCK K L AH1 T ER0 B AH2 K +CLUTTERBUCK'S K L AH1 T ER0 B AH2 K S +CLUTTERED K L AH1 T ER0 D +CLUTTERIN K L AH1 T ER0 IH0 N +CLUTTERING K L AH1 T ER0 IH0 NG +CLUTTERS K L AH1 T ER0 Z +CLUTTON K L AH1 T AH0 N +CLUYME K L AY1 M +CLXI S IY1 EH1 L EH1 K S AY1 +CLXII K L K S IY1 IY0 +CLXIX K L K S IH0 K S +CLXVI K L K S V IY0 +CLXXI K L K S IH0 +CLXXIII K L K S IH0 IY0 +CLXXIV K L K S IH0 V +CLXXIX K L K S IH0 K S +CLXXV K L K S V +CLXXVI K L K S V IY0 +CLXXVII K L K S V IY0 +CLXXXIX K L Z K S IH0 K S +CLXXXVI K L Z K S V IY0 +CLY K L AY1 +CLYDE K L AY1 D +CLYDE'S K L AY1 D Z +CLYDESDALE K L AY1 D Z D EY2 L +CLYDESDALE'S K L AY1 D Z D EY2 L Z +CLYKES K L AY1 K S +CLYM K L IH1 M +CLYM'S K L AY1 M Z +CLYMENE K L IH0 M IY1 N +CLYMER K L AY1 M ER0 +CLYMER'S K L IH0 M ER0 Z +CLYNE K L AY1 N +CLYNES K L AY1 N Z +CLYNESWORTH K L AY1 N Z W ER0 TH +CLYNTON K L IH1 N T AH0 N +CLYONE K L AY1 AH0 N +CLYST K L IH1 S T +CLYSTER K L IH1 S T ER0 +CLYSTERS K L IH1 S T ER0 Z +CLYTAEMNESTRA K L AY1 T EY1 M N AH0 S T R AH0 +CLYTEMNESTRA K L AY1 T M N AH0 S T R AH0 +CLYTEMNESTRA'S K L AY1 T M N AH0 S T R AH0 Z +CLYTIA K L IH1 T IY0 AH0 +CLYTIE K L IH1 T IY0 +CLYTIE'S K L IH1 T IY0 Z +CNAEUS K N EY1 AH0 S +CNEIUS S IY1 EH1 N IY1 IH0 S +CNEMON S IY1 EH1 N IY1 M AH0 N +CNIDIAN S IY1 EH1 N IH1 D IY0 AH0 N +CNIDOS S IY1 EH1 N IH0 D OW0 Z +CNIDUS S IY1 EH1 N IH0 D IH0 S +CNOSIANS S IY1 EH1 N OW1 Z IY0 AH0 N Z +CNOSUS S IY1 EH1 N OW1 S AH0 S +CNTS S IY1 EH1 N T S +CNUT S IY1 EH1 N AH2 T +CO K OW1 +CO'N K OW0 AH0 N +CO'NNLE K OW0 N AH0 L +CO'S K OW0 Z +CO'SE K Y UW1 Z +CO'T K OW0 T +CO'TE K OW0 T +COA K OW1 AH0 +COACH K OW1 CH +COACH'S K OW1 CH IH0 Z +COACHED K OW1 CH T +COACHEE K OW1 CH IY0 +COACHER K OW1 CH ER0 +COACHERS K OW1 CH ER0 Z +COACHES K OW1 CH IH0 Z +COACHFUL K OW1 CH F AH0 L +COACHHOUSE K OW1 CH HH AW2 S +COACHIN K OW1 CH IH0 N +COACHING K OW1 CH IH0 NG +COACHMAKER K OW1 CH M EY2 K ER0 +COACHMAKER'S K OW1 CH M EY2 K ER0 Z +COACHMAKERS K OW1 CH M EY2 K ER0 Z +COACHMAN K OW1 CH M AH0 N +COACHMAN'S K OW1 CH M AH0 N Z +COACHMEN K OW1 CH M AH0 N +COACHMEN'S K OW1 CH M AH0 N Z +COACHY K OW1 CH IY0 +COACOOCHEE K OW0 AE1 K UW1 CH IY0 +COADJUTEUR K OW2 AH0 JH AH1 T UW1 R +COADJUTOR K OW0 AE1 JH AH0 T ER0 +COADJUTOR'S K OW2 AH0 JH UW0 T ER0 Z +COADJUTORS K OW0 AE1 JH AH0 T ER0 Z +COADLEY K OW1 D L IY0 +COAGULATE K OW0 AE1 G Y AH0 L EY2 T +COAGULATED K OW0 AE1 G Y AH0 L EY2 T IH0 D +COAGULATES K OW0 AE1 G Y AH0 L EY2 T S +COAGULATING K OW0 AE1 G Y AH0 L EY2 T IH0 NG +COAGULATION K OW0 AE1 G Y AH0 L EY1 SH AH0 N +COAGULUM K OW0 AE1 G Y AH0 L AH0 M +COAHUILA K OW1 AA1 W IY1 L AH0 +COAITA K OW1 EY1 T AH0 +COAK K OW1 K +COAKLEY K OW1 K L IY0 +COAL K OW1 L +COAL'S K OW1 L Z +COALBROOKDALE K OW1 L B R UH0 K D EY2 L +COALCHESTER K OW2 AH0 L CH EH2 S T ER0 +COALE K OW1 L +COALED K OW2 AH0 L D +COALESCE K OW2 AH0 L EH1 S +COALESCED K OW2 AH0 L EH1 S T +COALESCENCE K OW2 AH0 L EH1 S AH0 N S +COALESCES K OW2 AH0 L EH1 S IH0 Z +COALESCING K OW2 AH0 L EH1 S IH0 NG +COALEY K OW1 L IY0 +COALFIELD K OW1 L F IY2 L D +COALFIELDS K OW1 L F IY2 L D Z +COALHEAVER K OW1 L HH IY1 V ER0 +COALHEAVERS K OW1 L HH IY1 V ER0 Z +COALING K OW1 L IH0 NG +COALITION K OW2 AH0 L IH1 SH AH0 N +COALITIONS K OW2 AH0 L IH1 SH AH0 N Z +COALMAN K OW1 L M AH0 N +COALPITS K OW1 L P IH0 T S +COALS K OW1 L Z +COALSCUTTLE K OW1 L S K AH1 T AH0 L +COALVILLE K OW1 L V IH2 L +COALY K OW1 L IY0 +COAMING K OW1 M IH0 NG +COAMINGS K OW1 M IH0 NG Z +COAMO K OW1 AA1 M OW0 +COAN K OW1 N +COANZA K OW1 AA1 N Z AH0 +COARRAZE K OW2 AA0 R AA1 Z IY0 +COARSE K AO1 R S +COARSELY K AO1 R S L IY0 +COARSEN K AO1 R S N +COARSENED K AO1 R S N D +COARSENESS K AO1 R S N AH0 S +COARSENESSES K AO1 R S N AH0 S IH0 Z +COARSENING K AO1 R S IH0 N IH0 NG +COARSENS K AO1 R S N Z +COARSER K AO1 R S ER0 +COARSEST K AO1 R S IH0 S T +COAST K OW1 S T +COAST'S K OW1 S T S +COASTAL K OW1 S T AH0 L +COASTED K OW1 S T IH0 D +COASTER K OW1 S T ER0 +COASTERS K OW1 S T ER0 Z +COASTGUARD K OW1 S T G AA2 R D +COASTGUARD'S K OW1 S T G AA2 R D Z +COASTGUARDS K OW1 S T G AA2 R D Z +COASTGUARDSMAN K OW1 S T G AA1 R D Z M AH0 N +COASTGUARDSMEN K OW1 S T G AA1 R D Z M IH0 N +COASTIN K OW1 S 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AO2 R AH0 S P AA1 N D AH0 N S +CORRESPONDED K AO2 R AH0 S P AA1 N D IH0 D +CORRESPONDENCE K AO2 R AH0 S P AA1 N D AH0 N S +CORRESPONDENCES K AO2 R AH0 S P AA1 N D AH0 N S IH0 Z +CORRESPONDENCIES K AO2 R AH0 S P AA1 N D AH0 N S IY0 Z +CORRESPONDENCY K AO2 R AH0 S P AA1 N D AH0 N S IY0 +CORRESPONDENT K AO2 R AH0 S P AA1 N D AH0 N T +CORRESPONDENT'S K AO2 R AH0 S P AA1 N D AH0 N T S +CORRESPONDENTS K AO2 R AH0 S P AA1 N D AH0 N T S +CORRESPONDING K AO2 R AH0 S P AA1 N D IH0 NG +CORRESPONDINGLY K AO2 R AH0 S P AA1 N D IH0 NG L IY0 +CORRESPONDS K AO2 R AH0 S P AA1 N D Z +CORREY K ER0 EY1 +CORREY'S K AO1 R IY0 Z +CORREZE K ER0 EH1 Z +CORRI K AO2 R IY0 +CORRIA K AO2 R Y AH0 +CORRIB K AO1 R IH0 B +CORRICHATACHIN K AO1 R IH0 K AH0 T AE1 CH IH0 N +CORRICOLO K AO1 R AH0 K OW1 L OW0 +CORRIDA K AO1 R AH0 D AH0 +CORRIDAN K AO1 R IH0 D AH0 N +CORRIDAS K AO1 R AH0 D AH0 Z +CORRIDOR K AO1 R AH0 D ER0 +CORRIDOR K AO1 R IH0 D ER0 +CORRIDORS K AO1 R AH0 D ER0 Z +CORRIDORS K AO1 R IH0 D ER0 Z +CORRIE K AO1 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Z +CORSAR K ER0 S AA1 R +CORSCADDEN K ER0 S K AE1 D AH0 N +CORSE K AO1 R S +CORSE'S K AO1 R S AH0 Z +CORSELET K AO1 R S L AH0 T +CORSELETS K AO1 R S L AH0 T S +CORSER K AO1 R S ER0 +CORSES K AO1 R S IH0 Z +CORSET K AO1 R S AH0 T +CORSETED K AO1 R S AH0 T IH0 D +CORSETIERE K ER0 S EH1 T IY0 EH0 R +CORSETS K ER0 S EH1 T S +CORSI K AO1 R S IY0 +CORSICA K AO1 R S IH0 K AH0 +CORSICAN K AO1 R S AH0 K AH0 N +CORSICAN'S K AO1 R S IH0 K AH0 N Z +CORSICANS K AO1 R S IH0 K AH0 N Z +CORSINI K ER0 S IY1 N IY0 +CORSINI'S K ER0 S IY1 N IY0 Z +CORSLET K AO1 R S L IH0 T +CORSLETS K AO1 R S L IH0 T S +CORSO K AO1 R S OW0 +CORSON K AO1 R S AH0 N +CORSON'S K AO1 R S AH0 N Z +CORSTORPHINE K AO1 R S T ER0 F IY2 N +CORT K AO1 R T +CORTA K AO1 R T AH0 +CORTACHY K AO0 R T AA1 CH IY0 +CORTE K AO1 R T +CORTEGE K AO0 R T EH1 ZH +CORTEGES K AO0 R T EH1 ZH +CORTELYON K AO1 R T EH0 L Y AH0 N +CORTELYOU K AO1 R T EH0 L Y UW2 +CORTENAEKEN K AO1 R T N AH0 K AH0 N +CORTES K AO2 R T EH1 Z +CORTESE K ER0 T EY1 Z IY0 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N S AH0 L D +COUNSELLETH K AW1 N S AH0 L EH2 TH +COUNSELLING K AW1 N S L IH0 NG +COUNSELLINGS K AW1 N S L IH0 NG Z +COUNSELLOR K AW1 N S AH0 L ER0 +COUNSELLOR K AW1 N S L ER0 +COUNSELLOR'S K AW1 N S L ER0 Z +COUNSELLORS K AW1 N S AH0 L ER0 Z +COUNSELLORS K AW1 N S L ER0 Z +COUNSELOR K AW1 N S AH0 L ER0 +COUNSELOR'S K AW1 N S AH0 L ER0 Z +COUNSELORS K AW1 N S AH0 L ER0 Z +COUNSELS K AW1 N S AH0 L Z +COUNT K AW1 N T +COUNT'S K AW2 N T S +COUNTABLE K AW1 N T AH0 B AH0 L +COUNTDOWN K AW1 N T D AW2 N +COUNTED K AW1 N AH0 D +COUNTED K AW1 N IH0 D +COUNTED K AW1 N T AH0 D +COUNTED K AW1 N T IH0 D +COUNTENANCE K AW1 N T AH0 N AH0 N S +COUNTENANCED K AW1 N T AH0 N AH0 N S T +COUNTENANCES K AW1 N T AH0 N AH0 N S IH0 Z +COUNTENANCING K AW1 N T AH0 N AH0 N S IH0 NG +COUNTER K AW1 N T ER0 +COUNTERACT K AW1 N T ER0 AE2 K T +COUNTERACTED K AW1 N T ER0 AE2 K T IH0 D +COUNTERACTING K AW2 N T ER0 AE1 K T IH0 NG +COUNTERACTION K AW1 N ER0 AE1 K SH AH0 N +COUNTERACTS K AW1 N ER0 AE1 K T S +COUNTERAGENT K 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D +COWDEN K AW1 D AH0 N +COWDENKNOWES K AW1 D AH0 N OW1 Z +COWDER K AW1 D ER0 +COWDERY K AW1 D ER0 IY0 +COWDIN K AW1 D IH0 N +COWDRAY K AW1 D R EY0 +COWDUNG K AW1 D AH0 NG +COWE K AW1 +COWED K AW1 D +COWELL K AA1 W EH0 L +COWELS K AW1 AH0 L Z +COWELS'S K AW1 AH0 L Z IH0 Z +COWEN K AW1 AH0 N +COWEN K OW1 AH0 N +COWEN'S K OW1 AH0 N Z +COWER K AW1 ER0 +COWERED K AW1 ER0 D +COWERING K AW1 ER0 IH0 NG +COWERS K AW1 ER0 Z +COWES K AW1 Z +COWEY K AW1 IY0 +COWFOLD K AW1 F OW2 L D +COWGATE K AW1 G EY2 T +COWGIRL K AW1 G ER2 L +COWGIRLS K AW1 G ER2 L Z +COWHAND K AW1 HH AE2 N D +COWHANDS K AW1 HH AE2 N D Z +COWHERD K AW1 HH ER2 D +COWHERD'S K AW1 HH ER2 D Z +COWHERDS K AW1 HH ER2 D Z +COWHIDE K AW1 HH AY2 D +COWHIDED K AA1 W AY2 D IH0 D +COWHIDES K AA1 W AY2 D Z +COWHIDING K AA1 W AY2 D IH0 NG +COWHOUSE K AW1 HH AW2 S +COWIE K AW1 IY0 +COWING K AW1 IH0 NG +COWL K AW1 L +COWLASSES K AW1 L AE2 S IH0 Z +COWLD K AW1 L D +COWLED K AW1 L D +COWLER K AW1 L ER0 +COWLES K AW1 AH0 L Z +COWLES'S K AW1 L Z IH0 Z +COWLEY K AW1 L IY0 +COWLEY'S K AW1 L IY0 Z +COWLICK K AW1 L IH0 K +COWLIK K AW1 L IH0 K +COWLIKE K AW1 L AY2 K +COWLING K AW1 L IH0 NG +COWLISHAW K AW1 L IH0 SH AO2 +COWLITZ K AW1 L IH0 T S +COWLS K AW1 L Z +COWMAN K AW1 M AH0 N +COWMAN'S K AW1 M AH0 N Z +COWMEN K AW1 M AH0 N +COWORKER K OW1 W ER1 K ER0 +COWORKERS K OW1 W ER1 K ER0 Z +COWPATH K AW1 P AE2 TH +COWPEAS K AW1 P IY1 Z +COWPENS K AW1 P AH0 N Z +COWPER K AW1 P ER0 +COWPER K UW1 P ER0 +COWPER'S K AW1 P ER0 Z +COWPERWOOD K AW1 P ER0 W UH2 D +COWPERWOOD'S K AW1 P ER0 W UH2 D Z +COWPERWOODS K AW1 P ER0 W UH2 D Z +COWPONIES K AW1 P OW1 N IY0 Z +COWPONY K AW1 P OW1 N IY0 +COWPUNCHER K AW1 P AH1 N CH ER0 +COWPUNCHER'S K AW1 P AH1 N CH ER0 Z +COWPUNCHERS K AW1 P AH1 N CH ER0 Z +COWRIE K AW1 R IY0 +COWRIES K AW1 R IY0 Z +COWRY K AW1 R IY0 +COWS K AW1 Z +COWSHED K OW1 SH T +COWSHEDS K AW1 SH EH2 D Z +COWSKIN K AW1 S K IH0 N +COWSLIP K AW1 S L IH0 P +COWSLIP'S K AW1 S L IH0 P S +COWSLIPS K AW1 S L IH0 P S +COWYARD K AW1 Y AA2 R D 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K AY1 OW0 T EH1 R OW0 +COYOTEROS K AY1 OW0 T EH1 R OW0 Z +COYOTES K AY0 OW1 T IY0 S +COYOTES K AY1 OW0 T S +COYTMORE K OY1 T M AO2 R +COZ K AA1 Z +COZEN K OW1 Z AH0 N +COZEN'D K OW1 Z AH0 N D +COZENAGE K OW1 Z IH0 N IH0 JH +COZENED K OW1 Z AH0 N D +COZENER K AA1 Z IY0 N ER0 +COZENING K OW1 Z AH0 N IH0 NG +COZENS K OW1 Z AH0 N Z +COZIER K OW1 Z IY0 ER0 +COZIEST K OW1 Z IY0 AH0 S T +COZILY K AA1 Z AH0 L IY0 +COZINESS K OW1 Z IY0 N AH0 S +COZMA K OW1 Z M AH0 +COZUMEL K AA1 Z UW2 M EH1 L +COZY K OW1 Z IY0 +COZZENS K AA1 Z AH0 N Z +CR K R +CRA K R AH0 +CRAB K R AE1 B +CRAB'S K R AE1 B Z +CRABAPPLE K R AE1 B AH0 P AH0 L +CRABB K R AE1 B +CRABB'S K R AE1 B Z +CRABBE K R AE1 B +CRABBE'S K R AE1 B Z +CRABBED K R AE1 B D +CRABBEDLY K R AE1 B D L IY0 +CRABBEDNESS K R AE1 B IH0 D N AH0 S +CRABBER K R AE1 B ER0 +CRABBIN K R AE1 B AH0 N +CRABBING K R AE1 B IH0 NG +CRABBY K R AE1 B IY0 +CRABLIKE K R AE1 B L AY2 K +CRABS K R AE1 B Z +CRABSHAW K R AE1 B SH AO2 +CRABSTICK K R AE1 B S T IH0 K +CRABTREE K R AE1 B T R IY2 +CRABTREE'S K R AE1 B T R IY2 Z +CRABWISE K R AE1 B W AY2 Z +CRAC K R AE1 K +CRACHE K R EY1 K IY0 +CRACHIT K R AA1 K IH2 T +CRACHITS K R AA1 K AH0 T S +CRACIOUS K R EY1 SH AH0 S +CRACIS K R EY1 S IH0 S +CRACK K R AE1 K +CRACK'D K R AE1 K D +CRACKAJACK K R AE1 K AA0 JH AE2 K +CRACKANTHORPE K R AE1 K AH0 N TH AO0 R P +CRACKBRAINED K R AE1 K B R EY2 N D +CRACKED K R AE1 K T +CRACKEN K R AE1 K AH0 N +CRACKENTHORP K R AE1 K AH0 N TH AO0 R P +CRACKER K R AE1 K ER0 +CRACKER'S K R AE1 K ER0 Z +CRACKERJACK K R AE1 K ER0 JH AE2 K +CRACKERS K R AE1 K ER0 Z +CRACKEY K R AE1 K IY0 +CRACKIN K R AE1 K IH0 N +CRACKING K R AE1 K IH0 NG +CRACKINGS K R AE1 K IH0 NG Z +CRACKIT K R AE1 K IH0 T +CRACKLE K R AE1 K AH0 L +CRACKLED K R AE1 K AH0 L D +CRACKLES K R AE1 K AH0 L Z +CRACKLIN K R AE1 K L IH0 N +CRACKLING K R AE1 K L IH0 NG +CRACKLINGS K R AE1 K L IH0 NG Z +CRACKLY K R AE1 K L IY0 +CRACKNELL K R AE1 K N AH0 L +CRACKNELS K R AE1 K N AH0 L Z +CRACKPOT K R AE1 K P AA2 T +CRACKPOTS K R AE1 K P AA2 T S +CRACKS K R AE1 K S +CRACKSMAN K R AE1 K S M AH0 N +CRACKSMEN K R AE1 K S M EH0 N +CRACKT K R AE1 K T +CRACKY K R AE1 K IY0 +CRACOW K R AA1 K OW2 +CRADD K R AE1 D +CRADDOCK K R AE1 D AH0 K +CRADDOCK'S K R AE1 D AH0 K S +CRADLE K R EY1 D AH0 L +CRADLE'S K R EY1 D AH0 L Z +CRADLEBAUGH'S K R EY1 D AH0 L B AO0 Z +CRADLED K R EY1 D AH0 L D +CRADLES K R EY1 D AH0 L Z +CRADLING K R EY1 D AH0 L IH0 NG +CRADLING K R EY1 D L IH0 NG +CRADOCK K R AE1 D AH0 K +CRADOCK'S K R AE1 D AH0 K S +CRADOCKE'S K R AE1 D AH0 K S +CRAE K R EY1 +CRAE'S K R EY1 Z +CRAEKE K R IY1 K +CRAFFROE K R AE1 F R OW0 +CRAFORD K R AE1 F ER0 D +CRAFT K R AE1 F T +CRAFT'S K R AE1 F T S +CRAFTIE K R AE1 F T IY0 +CRAFTIER K R AE1 F T IY0 ER0 +CRAFTIEST K R AE1 F T IY0 AH0 S T +CRAFTILY K R AE1 F T AH0 L IY0 +CRAFTINESS K R AE1 F T IY0 N AH0 S +CRAFTS K R AE1 F S +CRAFTS K R AE1 F T S +CRAFTSMAN K R AE1 F S M AH0 N +CRAFTSMAN K R AE1 F T S M AH0 N +CRAFTSMAN'S K R AE1 F T S M AH0 N Z +CRAFTSMANSHIP K R AE1 F S M AH0 N SH IH2 P +CRAFTSMANSHIP K R AE1 F T S M AH0 N SH IH2 P +CRAFTSMEN K R AE1 F S M EH0 N +CRAFTSMEN K R AE1 F T S M EH0 N +CRAFTY K R AE1 F T IY0 +CRAFTY'S K R AE1 F T IY0 Z +CRAG K R AE1 G +CRAG'S K R AE1 G Z +CRAGG K R AE1 G +CRAGG'S K R AE1 G Z +CRAGGE K R AE1 G +CRAGGED K R AE1 G D +CRAGGIE K R AE1 G IY0 +CRAGGS K R AE1 G Z +CRAGGY K R AE1 G IY0 +CRAGIEMUIR K R AE1 G IY1 M Y UH1 R +CRAGIN K R AE1 JH IH0 N +CRAGLEVIN K R AE1 G L EH1 V IH0 N +CRAGLEY K R AE1 G L IY0 +CRAGLEY'S K R AE1 G L IY0 Z +CRAGMIRE K R AE1 G M AY2 ER0 +CRAGS K R AE1 G Z +CRAGSIDE K R AE1 G S AY2 D +CRAGSMAN K R AE1 G Z M AH0 N +CRAGSNOOK K R AE1 G S N UH1 K +CRAGSTONE K R AE1 G S T OW2 N +CRAIG K R EY1 G +CRAIG'S K R EY1 G Z +CRAIGBURNFOOT K R EY1 G B ER0 N F UH2 T +CRAIGDALLIE K R EY1 G D AE1 L IY0 +CRAIGENDARROCH K R EY1 G EH0 N D AA0 R AH0 K +CRAIGENPUTTOCK K R EY1 G EH1 N P AH1 T AH0 K +CRAIGHEAD K R EY1 G HH EH2 D +CRAIGIE K R EY1 G IY0 +CRAIGIE'S K R EY1 G IY0 Z +CRAIGLETHORPE K R EY1 G L TH AO0 R P +CRAIGMILE K R EY1 G M AY2 L +CRAIGMILLAR K R EY1 G M IH0 L ER0 +CRAIGS K R EY1 G Z +CRAIGSWOLD K R EY1 G S W OW2 L D +CRAIK K R EY1 K +CRAIK'S K R EY1 K S +CRAIL K R EY1 L +CRAILEY K R EY1 L IY0 +CRAILEY'S K R EY1 L IY0 Z +CRAILING K R EY1 L IH0 NG +CRAILLOCH K R EY1 L AH0 K +CRAIN K R EY1 N +CRAINDRE K R EY1 N D R AH0 +CRAINS K R EY1 N Z +CRAINT K R EY1 N T +CRAINTE K R EY1 N T +CRAITUR K R EY1 T ER0 +CRAKE K R EY1 K +CRAKES K R EY1 K S +CRALINE K R AA0 L IY1 N IY0 +CRALO K R AA1 L OW0 +CRAM K R AE1 M +CRAMAHE K R AE1 M EY1 HH IY0 +CRAMB K R AE1 M +CRAMBAMBULI K R AE1 M B AA0 M B UW1 L IY0 +CRAMBE K R AE1 M B +CRAMBO K R AE1 M B OW0 +CRAMBOY K R AE1 M B OY2 +CRAMBRY K R AE1 M B R IY0 +CRAME K R EY1 M +CRAMER K R EY1 M ER0 +CRAMER'S K R EY1 M ER0 Z +CRAMERTOWN K R EY1 M ER0 T AW2 N +CRAMLIN K R AE1 M L IH0 N +CRAMM'D K R AE1 M D +CRAMMED K R AE1 M D +CRAMMER K R AE1 M ER0 +CRAMMER'S K R AE1 M ER0 Z +CRAMMERS K R AE1 M ER0 Z +CRAMMING K R AE1 M IH0 NG +CRAMOISIE K R AE1 M W AA2 Z IY1 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EY1 N Z +CRANED K R EY1 N D +CRANEMAN K R EY1 N M AH0 N +CRANES K R EY1 N Z +CRANESBILL K R EY1 N Z B IH2 L +CRANEY K R EY1 N IY0 +CRANEY'S K R AE1 N IY0 Z +CRANFIELD K R AE1 N F IY2 L D +CRANFORD K R AE1 N F ER0 D +CRANG K R AE1 NG +CRANIA K R EY1 N IY0 AH0 +CRANIAL K R EY1 N Y AH0 L +CRANING K R EY1 N IH0 NG +CRANIOLOGICAL K R EY1 N IY0 AH0 L AA1 JH IH0 K AH0 L +CRANIOLOGIST K R EY1 N IY0 AA1 L AH0 JH IH0 S T +CRANIOLOGY K R EY1 N IY0 AA1 L AH0 JH IY0 +CRANIUM K R EY1 N IY0 AH0 M +CRANIUMS K R EY1 N IY0 AH0 M Z +CRANK K R AE1 NG K +CRANKCASE K R AE1 NG K K EY2 S +CRANKED K R AE1 NG K T +CRANKIE K R AE1 NG K IY0 +CRANKIE'S K R AE1 NG K IY0 Z +CRANKIEST K R AE1 NG K IY0 AH0 S T +CRANKINESS K R AE1 NG K IY0 N AH0 S +CRANKING K R AE1 NG K IH0 NG +CRANKS K R AE1 NG K S +CRANKY K R AE1 NG K IY0 +CRANLEIGH K R AE1 N L AH0 +CRANLEY K R AE1 N L IY0 +CRANLEY'S K R AE1 N L IY0 Z +CRANMER K R AE1 N M ER0 +CRANMER'S K R AE1 N M ER0 Z +CRANMERE K R AE1 N M ER0 +CRANMERE'S K R AE1 N M IH2 R Z +CRANNIED K R AE1 N IY0 D +CRANNIES K R AE1 N IY0 Z +CRANNON K R AE1 N AH0 N +CRANNY K R AE1 N IY0 +CRANO K R AA1 N OW0 +CRANSTON K R AE1 N S T AH0 N +CRANSTON'S K R AE1 N S T AH0 N Z +CRANSTONS K R AE1 N S T AH0 N Z +CRANSTOUN K R AE1 N S T AW1 N +CRANSTOUN'S K R AE1 N S T AW1 N Z +CRANWORTH K R AE1 N W ER2 TH +CRANZE K R AE1 N Z +CRAOIBHIN K R AW1 IH0 B HH IH0 N +CRAON K R OW0 N +CRAONNE K R EY1 AH1 N +CRAP K R AE1 P +CRAPAUD K R AE1 P AO0 D +CRAPAUDS K R AE1 P AO1 D Z +CRAPE K R EY1 P +CRAPEAU K R AE1 P OW1 +CRAPED K R EY1 P T +CRAPES K R EY1 P S +CRAPHOUND K R AE1 P HH AW2 N D +CRAPO K R AA1 P OW2 +CRAPPY K R AE1 P IY2 +CRAPS K R AE1 P S +CRAPSEY K R AE1 P S IY0 +CRAPULOUS K R AE1 P Y AH0 L AH0 S +CRARY K ER1 EH0 R IY2 +CRAS K R AE2 S +CRASH K R AE1 SH +CRASH'D K R AE1 SH IH0 D +CRASHAW K R AE1 SH AO2 +CRASHAW'S K R AE1 SH AO1 Z +CRASHED K R AE1 SH T +CRASHES K R AE1 SH IH0 Z +CRASHFORD K R AE1 SH F ER0 D +CRASHIN K R AE1 SH IH0 N +CRASHING K R AE1 SH IH0 NG +CRASHINGLY K R AE1 SH IH0 NG L IY0 +CRASHINGS K R AE1 SH IH0 NG Z +CRASKE K R EY1 S K +CRASKY K R AE2 S K IY0 +CRASS K R AE1 S +CRASS'S K R AE1 S IH0 Z +CRASSES K R AE1 S IH0 Z +CRASSEST K R AE1 S AH0 S T +CRASSLY K R AE1 S L IY0 +CRASSNESS K R AE1 S N AH0 S +CRASSUS K R AE1 S AH0 S +CRATCHIT K R AE1 CH IH0 T +CRATCHIT'S K R AE1 CH IH0 T S +CRATCHITS K R AE1 CH IH0 T S +CRATE K R EY1 T +CRATED K R EY1 T IH0 D +CRATER K R EY1 T ER0 +CRATER'S K R EY1 T ER0 Z +CRATERED K R EY1 T ER0 D +CRATERS K R EY1 T ER0 Z +CRATERUS K R EY1 T ER0 AH0 S +CRATERVILLE K R EY1 T ER0 V IH0 L +CRATES K R EY1 T S +CRATHER K R AE1 DH ER0 +CRATHIS K R AE1 TH IH0 S +CRATHUR K R AE1 TH ER0 +CRATHUR'S K R AE1 TH ER0 Z +CRATHURS K R AE1 TH ER0 Z +CRATI K R AA1 T IY0 +CRATING K R EY1 T IH0 NG +CRATINUS K R AE1 T AH0 N AH0 S +CRATO K R AE1 T OW0 +CRATUR K R EY1 T ER0 +CRATUR'S K R EY1 T ER0 Z +CRATURE K R EY1 CH ER0 +CRATURES K R EY1 CH ER0 Z +CRATURS K R EY1 T ER0 Z +CRATYLUS K R AE1 T AH0 L AH0 S +CRAU K R AW1 +CRAUFORD K R AO1 F ER0 D 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ER0 +CRAWLERS K R AO1 L ER0 Z +CRAWLEY K R AO1 L IY0 +CRAWLEY'S K R AO1 L IY0 Z +CRAWLEYS K R AO1 L IY0 Z +CRAWLIE K R AO1 L IY0 +CRAWLIN K R AO1 L IH0 N +CRAWLING K R AO1 L IH0 NG +CRAWLS K R AO1 L Z +CRAWLY K R AO1 L IY0 +CRAWS K R AO1 Z +CRAWSHAW K R AO1 SH AO2 +CRAWSHAY K R AO1 SH EY0 +CRAWSHAY'S K R AO1 SH EY0 Z +CRAX K R AE1 K S +CRAY K R EY1 +CRAY'S K R EY1 Z +CRAYCROFT K R EY1 K R AO2 F T +CRAYDOCKE K R EY1 D AH0 K +CRAYDOCKE'S K R EY1 D AH0 K S +CRAYE K R EY1 +CRAYE'S K R EY1 Z +CRAYFISH K R EY1 F IH0 SH +CRAYFORD K R EY1 F ER0 D +CRAYFORD'S K R EY1 F ER0 D Z +CRAYNE K R EY1 N +CRAYON K R EY1 AA2 N +CRAYONS K R EY1 AA2 N Z +CRAYSHAW K R EY1 SH AO2 +CRAYSHAW'S K R EY1 SH AO1 Z +CRAYTHER K R EY1 TH ER0 +CRAYTHUR K R EY1 TH ER0 +CRAYTHURS K R EY1 TH ER0 Z +CRAYTON K R EY1 T AH0 N +CRAYTURE K R EY1 T UH1 R +CRAYTURES K R EY1 CH ER0 Z +CRAZE K R EY1 Z +CRAZED K R EY1 Z D +CRAZEE K R EY1 Z IY0 +CRAZES K R EY1 Z IH0 Z +CRAZIER K R EY1 Z IY0 ER0 +CRAZIEST K R EY1 Z IY0 AH0 S T +CRAZILY K R EY1 Z AH0 L IY0 +CRAZINESS K R EY1 Z IY0 N AH0 S +CRAZING K R EY1 Z IH0 NG +CRAZY K R EY1 Z IY0 +CRE K R +CREA K R IY1 +CREA'S K R IY1 AH0 Z +CREADY K R IY1 D IY0 +CREADY'S K R IY1 D IY0 Z +CREAG K R IY1 G +CREAGAN K R IY1 G AH0 N +CREAGH K R IY1 G +CREAK K R IY1 K +CREAKE K R IY1 K +CREAKED K R IY1 K T +CREAKER K R IY1 K ER0 +CREAKILY K R IY1 K IH0 L IY0 +CREAKIN K R IY1 K IH0 N +CREAKING K R IY1 K IH0 NG +CREAKINGLY K R IY1 K IH0 NG L IY0 +CREAKINGS K R IY1 K IH0 NG Z +CREAKLE K R IY1 K AH0 L +CREAKLE'S K R IY1 K AH0 L Z +CREAKS K R IY1 K S +CREAKY K R IY1 K IY0 +CREAL K R IY1 L +CREAM K R IY1 M +CREAMED K R IY1 M D +CREAMER K R IY1 M ER0 +CREAMERIES K R IY1 M ER0 IY0 Z +CREAMERY K R IY1 M ER0 IY0 +CREAMINESS K R IY1 M IY0 N AH0 S +CREAMING K R IY1 M IH0 NG +CREAMS K R IY1 M Z +CREAMY K R IY1 M IY0 +CREAN K R IY1 N +CREARY K R IH1 R IY0 +CREASE K R IY1 S +CREASED K R IY1 S T +CREASELESS K R IY1 S L IH0 S +CREASES K R IY1 S IH0 Z +CREASING K R IY1 S IH0 NG +CREASY K R IY1 S IY0 +CREAT K R IY0 EY1 T +CREATE K R IY0 EY1 T +CREATED K R IY0 EY1 T AH0 D +CREATED K R IY0 EY1 T IH0 D +CREATES K R IY0 EY1 T S +CREATETH K R IY0 EY1 T IH0 TH +CREATIN K R IY0 EY1 T IH0 N +CREATING K R IY0 EY1 T IH0 NG +CREATION K R IY0 EY1 SH AH0 N +CREATION'S K R IY0 EY1 SH AH0 N Z +CREATIONAL K R IY0 EY1 SH AH0 N AH0 L +CREATIONS K R IY0 EY1 SH AH0 N Z +CREATIVE K R IY0 EY1 T IH0 V +CREATIVELY K R IY0 EY1 T IH0 V L IY0 +CREATIVENESS K R IY0 EY1 T IH0 V N AH0 S +CREATIVITY K R IY2 EY0 T IH1 V AH0 T IY0 +CREATOR K R IY0 EY1 T ER0 +CREATOR'S K R IY0 EY1 T ER0 Z +CREATORS K R IY0 EY1 T ER0 Z +CREATRIX K R IY1 T R IH0 K S +CREATUR K R IY1 CH ER0 +CREATUR'S K R IY1 CH ER0 Z +CREATURAL K R IY1 CH ER0 AH0 L +CREATURE K R IY1 CH ER0 +CREATURE'S K R IY1 CH ER0 Z +CREATURELY K R IY1 CH ER0 L IY0 +CREATURES K R IY1 CH ER0 Z +CREATURS K R IY1 CH ER0 Z +CREBICHE K R IY1 B IH0 SH +CREBILLON K R IH0 B IH1 L AH0 N +CRECHE K R EH1 SH +CRECHES K R EH1 SH AH0 Z +CRECKHOLT K R EH1 K HH OW2 L T +CRECY K R AH0 S IY0 +CREDAT K R EH1 D AH0 T +CREDDLE K R EH1 D AH0 L +CREDDLE'S K R EH1 D AH0 L Z +CREDE K R IY1 D +CREDENCE K R IY1 D AH0 N S +CREDENTIAL K R AH0 D EH1 N SH AH0 L +CREDENTIAL K R IH0 D EH1 N CH AH0 L +CREDENTIALS K R AH0 D EH1 N CH AH0 L Z +CREDENTIALS K R AH0 D EH1 N SH AH0 L Z +CREDERE K R IH0 D IH1 R +CREDI K R EH1 D IY0 +CREDIBILITY K R EH2 D AH0 B IH1 L IH0 T IY0 +CREDIBLE K R EH1 D AH0 B AH0 L +CREDIBLY K R EH1 D AH0 B L IY0 +CREDIT K R EH1 D AH0 T +CREDIT K R EH1 D IH0 T +CREDIT'S K R EH1 D IH0 T S +CREDITABLE K R EH1 D AH0 T AH0 B AH0 L +CREDITABLY K R EH1 D AH0 T AH0 B L IY0 +CREDITED K R EH1 D AH0 T AH0 D +CREDITED K R EH1 D IH0 T IH0 D +CREDITING K R EH1 D AH0 T IH0 NG +CREDITOR K R EH1 D AH0 T ER0 +CREDITOR K R EH1 D IH0 T ER0 +CREDITOR'S K R EH1 D IH0 T ER0 Z +CREDITORS K R EH1 D IH0 T ER0 Z +CREDITS K R EH1 D IH0 T S +CREDO K R EY1 D OW0 +CREDO K R IY1 D OW0 +CREDOS K R IY1 D OW0 Z +CREDULITIES K R IH0 D UW1 L AH0 T IY0 Z +CREDULITY K R IH0 D UW1 L AH0 T IY0 +CREDULOUS K R EH1 JH AH0 L AH0 S +CREDULOUSLY K R EH1 JH AH0 L AH0 S L IY0 +CREDULOUSNESS K R EH1 JH AH0 L AH0 S N AH0 S +CREE K R IY1 +CREE'S K R IY1 Z +CREECH K R IY1 CH +CREECH'S K R IY1 CH IH0 Z +CREECHES K R IY1 CH IH0 Z +CREED K R IY1 D +CREED'S K R IY1 D Z +CREEDAL K R IY1 D AH0 L +CREEDE K R IY1 D +CREEDE'S K R IY1 D Z +CREEDLE K R IY1 D AH0 L +CREEDLESS K R IY1 D L AH0 S +CREEDON K R IY1 D AH0 N +CREEDS K R IY1 D Z +CREEGAN K R IY1 G AH0 N +CREEK K R IY1 K +CREEK'S K R IY1 K S +CREEKS K R IY1 K S +CREEL K R IY1 L +CREELMAN K R IY1 L M AH0 N +CREELS K R IY1 L Z +CREEM K R IY1 M +CREEP K R IY1 P +CREEPED K R IY1 P T +CREEPER K R IY1 P ER0 +CREEPERS K R IY1 P ER0 Z +CREEPETH K R IY1 P IH0 TH +CREEPIE K R IY1 P IY0 +CREEPIN K R IY1 P IY0 N +CREEPINESS K R IY1 P IY0 N IH0 S +CREEPING K R IY1 P IH0 NG +CREEPINGS K R IY1 P IH0 NG Z +CREEPIT K R IY1 P IH0 T +CREEPS K R IY1 P S +CREEPY K R IY1 P IY0 +CREERY K R IH1 R IY0 +CREES K R IY1 Z +CREESE K R IY1 Z +CREESES K R IY1 Z IH0 Z +CREETER K R IY1 T ER0 +CREETERS K R IY1 T ER0 Z +CREETUR K R IY1 T ER0 +CREETUR'S K R IY1 T ER0 Z +CREETURS K R IY1 T ER0 Z +CREEVEY K R IY1 V IY0 +CREEVY K R IY1 V IY0 +CREEVY'S K R IY1 V IY0 Z +CREFELD K R IH0 F EH0 L D +CREFTON K R EH1 F T AH0 N +CREGAN K R IY1 G AH0 N +CREGEEN K R IY1 JH IY0 N +CREGO K R EH1 G OW0 +CREIGHTON K R EY1 T AH0 N +CREIGHTON'S K R EY1 T AH0 N Z +CREIL K R IY1 L +CRELINE K R L AY2 N +CRELLAN K R EH1 L AH0 N +CRELLIN K R EH1 L IH0 N +CRELLOCK K R EH1 L AH0 K +CREMA K R IY1 M AH0 +CREMATE K R IY1 M EY0 T +CREMATED K R IY1 M EY0 T IH0 D +CREMATING K R IY1 M EY0 T IH0 NG +CREMATION K R IY0 M EY1 SH AH0 N +CREMATORIUM K R IY0 M AH0 T AO1 R IY0 AH0 M +CREMATORY K R IY0 M AH0 T AO2 R IY0 +CREME K R IY1 M +CREMER K R IY1 M ER0 +CREMER'S K R IY1 M ER0 Z +CREMERIE K R IY1 M ER0 IY0 +CREMIERE K R IY0 M IH1 R +CREMIEUX K R IY1 M Y UW1 +CREMMEN K R EH1 M AH0 N +CREMONA K R IY0 M OW1 N AH0 +CREMORNE K R EH1 M ER0 N +CRENATE K R EH1 N IH0 T +CRENELATED K R EH1 N AH0 L EY2 T AH0 D +CRENELLATED K R EH1 N AH0 L EY2 T IH0 D +CRENELLATIONS K R EH1 N AH0 L EY1 SH AH0 N Z +CRENMITZ K R EH0 N M IH1 T S +CRENNELL K R EH1 N AH0 L +CRENO K R EH1 N OW0 +CRENSHAW K R EH1 N SH AO2 +CRENSHAW'S K R EH1 N SH AO2 Z +CREOLE K R IY1 OW0 L +CREOLE'S K R IY0 OW1 L Z +CREOLES K R IY0 OW1 L Z +CREON K R IY1 AH0 N +CREON'S K R IY1 AH0 N Z +CREOSOTE K R IY1 AH0 S OW2 T +CREOSOTED K R IY1 AH0 S OW2 T IH0 D +CREP K R EH1 P +CREPE K R EY1 P +CREPES K R EY1 P S +CREPIDAM K R EH1 P AH0 D AH0 M +CREPIN K R EH1 P IH0 N +CREPITATING K R EH1 P IH0 T EY2 T IH0 NG +CREPITATION K R EH1 P IH0 T EY1 SH AH0 N +CREPITUDE K R EH1 P IH0 T UW2 D +CREPON K R EY1 P OW0 N +CREPT K R EH1 P T +CREPUSCULAR K R EY1 P AH2 S K Y AH0 L ER0 +CREPUSCULE K R EH1 P AH0 S K Y UW2 L +CREPY K R EY1 P IY0 +CREQUI K R EH1 K W IY0 +CREQUY K R EH1 K AY2 +CRERAR K R EH1 R AA0 R +CRESAP K R EH1 S AH0 P +CRESAP K R IY1 S AH0 P +CRESAP'S K R EH1 S AH0 P S +CRESCENCE K R EH1 S AH0 N S +CRESCENDO K R IH0 SH EH1 N D OW0 +CRESCENDOS K R IH0 SH EH1 N D OW0 Z +CRESCENT K R EH1 S AH0 N T +CRESCENT'S K R EH1 S AH0 N T S +CRESCENTI K R EH0 S CH EH1 N T IY0 +CRESCENTIC K R IH0 SH EH1 N IH0 K +CRESCENTINI K R EH1 S AH0 N T IY1 N IY0 +CRESCENTIUS K R EH0 S CH EH1 N T IY0 AH0 S +CRESCENTS K R EH1 S AH0 N T S +CRESCENZI K R EH0 S CH EH1 N Z IY0 +CRESCO K R EH1 S K OW0 +CRESCY K R EH1 S IY0 +CRESPHONTES K R EH1 S P OW1 N T EH0 S +CRESPI K R EH1 S P IY0 +CRESPIGNY K R EH1 S P N IY0 +CRESPIGNY'S K R EH1 S P N IY0 Z +CRESPO K R EH1 S P OW0 +CRESS K R EH1 S +CRESSAGE K R EH1 S AH0 JH +CRESSEMER K R EH1 S AH0 M ER0 +CRESSES K R EH1 S AH0 Z +CRESSES K R EH1 S IH0 Z +CRESSET K R EH1 S AH0 T +CRESSET'S K R EH1 S IH0 T S +CRESSETS K R EH1 S IH0 T S +CRESSETT K R EH1 S IH0 T +CRESSIDA K R EH1 S IY1 D AH0 +CRESSIDA'S K R EH1 S IH0 D AH0 Z +CRESSINGHAM K R EH1 S IH0 NG HH AE2 M +CRESSLEIGH K R EH1 S L AH0 +CRESSLEY K R EH1 S L IY0 +CRESSOGNO K R EH1 S OW2 G N OW0 +CRESSON K R EH1 S AH0 N +CRESSWELL K R EH1 S W EH2 L 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AH0 S +CREVASSED K R EH1 V AH0 S T +CREVASSES K R EH1 V AH0 S IH0 Z +CREVE K R IY1 V +CREVECOEUR K R EH1 V IH0 K ER0 +CREVEL K R EH1 V AH0 L +CREVEL'S K R EH1 V AH0 L Z +CREVICE K R EH1 V AH0 S +CREVICED K R EH1 V AH0 S T +CREVICES K R EH1 V AH0 S IH0 Z +CREW K R UW1 +CREW'S K R UW1 Z +CREWDSON K R UW1 D S AH0 N +CREWE K R UW1 +CREWE'S K R UW1 Z +CREWEL K R UW1 AH0 L +CREWELS K R UW1 AH0 L Z +CREWES K R UW1 Z +CREWMAN K R UW1 M AH0 N +CREWMEN K R UW1 M IH0 N +CREWS K R UW1 Z +CRI K R IY0 +CRIADO K R IY0 AA1 D OW0 +CRIADOS K R IY0 AA1 D OW0 Z +CRIANAN K R IY0 AA1 N AH0 N +CRIB K R IH1 B +CRIB'S K R IH1 B Z +CRIBB K R IH1 B +CRIBB'S K R IH1 B Z +CRIBBAGE K R IH1 B IH0 JH +CRIBBED K R IH1 B D +CRIBBENS K R IH1 B AH0 N Z +CRIBBER K R IH1 B ER0 +CRIBBING K R IH1 B IH0 NG +CRIBS K R IH1 B Z +CRIBSERTH K R IH1 B Z ER2 TH +CRIC K R IH1 K +CRICH K R IH1 CH +CRICHELL K R IH1 CH AH0 L +CRICHTON K R IH1 CH T AH0 N +CRICHTON'S K R IH1 CH T AH0 N Z +CRICHTONS K R IH1 CH T AH0 N Z +CRICK K R IH1 K +CRICK'S K R IH1 K S +CRICKET K R IH1 K AH0 T +CRICKET K R IH1 K IH0 T +CRICKET'S K R IH1 K AH0 T S +CRICKETER K R IH1 K AH0 T ER0 +CRICKETER'S K R IH1 K AH0 T ER0 Z +CRICKETERS K R IH1 K AH0 T ER0 Z +CRICKETING K R IH1 K AH0 T IH0 NG +CRICKETS K R IH1 K AH0 T S +CRICKETY K R IH1 K AH0 T IY0 +CRICKEY K R IH1 K IY0 +CRICKGELLY K R IH1 K G AH0 L IY0 +CRICKLEDON K R IH1 K AH0 L D AH0 N +CRICKLEDON'S K R IH1 K AH0 L D AH0 N Z +CRICKLEWICK K R IH1 K AH0 L W IH2 K +CRICKLEWOOD K R IH1 K AH0 L W UH2 D +CRICKS K R IH1 K S +CRICKY K R IH1 K IY0 +CRICQ K R IH1 K +CRIDA K R IY1 D AH0 +CRIDA'S K R IH0 D AH0 Z +CRIDER K R AY1 D ER0 +CRIE K R AY1 +CRIED K R AY1 D +CRIEDIR K R AY1 D ER0 +CRIEFF K R IY2 F +CRIER K R AY1 ER0 +CRIER'S K R AY1 ER0 Z +CRIERS K R AY1 ER0 Z +CRIES K R AY1 Z +CRIEST K R AY1 IH0 S T +CRIETH K R AY1 IH0 TH +CRIFFEL K R IH1 F AH0 L +CRIK K R IH0 K +CRIKEY K R AY1 K IY0 +CRIKSWICH K R IH1 K S W IH0 CH +CRILL K R IH1 L +CRILLON K R IH1 L AH0 N +CRILLON'S K R IH1 L AH0 N Z +CRIM K R IH1 M +CRIMBLE K R IH1 M B AH0 L +CRIMBLE'S K R IH1 M B AH0 L Z +CRIME K R AY1 M +CRIME'S K R AY1 M Z +CRIMEA K R AY0 M IY1 AH0 +CRIMEAN K R AY2 M IY1 AH0 N +CRIMEAN K R IH0 M IY1 N +CRIMEN K R IH1 M IH0 N +CRIMES K R AY1 M Z +CRIMINAL K R IH1 M AH0 N AH0 L +CRIMINAL'S K R IH1 M AH0 N AH0 L Z +CRIMINALITY K R IH2 M AH0 N AE1 L IH0 T IY0 +CRIMINALLY K R IH1 M AH0 N AH0 L IY0 +CRIMINALS K R IH1 M AH0 N AH0 L Z +CRIMINATE K R IH1 M AH0 N EY2 T +CRIMINATED K R IH1 M AH0 N EY2 T AH0 D +CRIMINATING K R IH1 M AH0 N EY2 T IH0 NG +CRIMINATION K R IH2 M AH0 N EY1 SH AH0 N +CRIMINATIONS K R IH2 M AH0 N EY1 SH AH0 N Z +CRIMINEL K R IH1 M AH0 N AH0 L +CRIMINIS K R IH1 M AH0 N IY0 Z +CRIMINOLOGICAL K R IH2 M IH0 N AA1 L AH0 JH IH0 K AH0 L +CRIMINOLOGIST K R IH2 M IH0 N AA1 L AH0 JH IH0 S T +CRIMINOLOGISTS K R IH2 M IH0 N AA1 L AH0 JH IH0 S T S +CRIMINOLOGY K R IH2 M IH0 N AA1 L AH0 JH IY0 +CRIMINOUS K R IH1 M AH0 N AH0 S +CRIMM K R IH1 M +CRIMMEEN K R IH1 M IY0 N +CRIMMETART K R IH1 M IH0 T ER0 T 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IH1 NG K L IY0 +CRINOID K R IH0 N OY0 D +CRINOIDS K R IH0 N OY0 D Z +CRINOLINE K R AH0 N AA1 L AY0 N +CRINOLINES K R AH0 N AA1 L AY0 N Z +CRIP K R IH1 P +CRIPES K R AY1 P S +CRIPP K R IH1 P +CRIPPEN K R IH1 P AH0 N +CRIPPLE K R IH1 P AH0 L +CRIPPLE'S K R IH1 P AH0 L Z +CRIPPLED K R IH1 P AH0 L D +CRIPPLEGATE K R IH1 P AH0 L G EY2 T +CRIPPLES K R IH1 P AH0 L Z +CRIPPLESTRAW K R IH1 P AH0 L S T R AO2 +CRIPPLING K R IH1 P AH0 L IH0 NG +CRIPPLING K R IH1 P L IH0 NG +CRIPPS K R IH1 P S +CRIPPS'S K R IH1 P S IH0 Z +CRIPPSES K R IH1 P S IH0 Z +CRIPS K R IH1 P S +CRIQUET K R IH1 K EY1 +CRIS K R IH1 S +CRISE K R AY1 Z +CRISES K R AY1 S IY0 Z +CRISIS K R AY1 S AH0 S +CRISMORE K R IH1 S M AO0 R +CRISMUS K R IH1 Z M AH0 S +CRISOSTOMO K R IY0 S OW0 S T OW1 M OW0 +CRISP K R IH1 S P +CRISPARKLE K R IH1 S P AA1 R K AH0 L +CRISPARKLE'S K R IH1 S P AA1 R K AH0 L Z +CRISPED K R IH1 S P T +CRISPER K R IH1 S P ER0 +CRISPEST K R IH1 S P AH0 S T +CRISPI K R IH1 S P IY0 +CRISPIN K R IH1 S P IH0 N +CRISPIN'S K R IH1 S P IH0 N Z +CRISPINA K R IY0 S P IY1 N AH0 +CRISPING K R IH1 S P IH0 NG +CRISPINILLA K R IH1 S P IH0 N IH1 L AH0 +CRISPINILLUS K R IH1 S P IH0 N IH1 L AH0 S +CRISPINO K R IY0 S P IY1 N OW0 +CRISPINUS K R IY0 S P IY1 N AH0 S +CRISPLY K R IH1 S P L IY0 +CRISPNESS K R IH1 S P N AH0 S +CRISPS K R IH1 S P S +CRISPUS K R IH1 S P AH0 S +CRISPY K R IH1 S P IY0 +CRISS K R IH1 S +CRISSCROSS K R IH1 S K R AO2 S +CRISSCROSSED K R IH1 S K R AO2 S T +CRISSCROSSING K R IH1 S K R AO2 S IH0 NG +CRISSOBELLA K R IH1 S OW0 B EH1 L AH0 +CRIST K R IH1 S T +CRISTAL K R IH1 S T AH0 L +CRISTATA K R IH2 S T AA1 T AH0 +CRISTEL K R IH1 S T AH0 L +CRISTEL'S K R IH1 S T EH0 L Z +CRISTI K R IH1 S T IY0 +CRISTIANO K R IY0 S T IY0 AA1 N OW0 +CRISTINA K R IH0 S T IY1 N AH0 +CRISTINA'S K R IH0 S T IY1 N AH0 Z +CRISTINO K R IH0 S T IY1 N OW0 +CRISTO K R IH1 S T OW2 +CRISTO'S K R IH1 S T OW2 Z +CRISTOBAL K R IH1 S T AH0 B AH0 L +CRISTOBAL'S K R IH1 S T AH0 B AH0 L Z +CRISTOFANO K R IY0 S T OW0 F AA1 N OW0 +CRISTOFORO K R IY0 S T OW0 F AO1 R OW0 +CRISTOVAL K R IY0 S T OW0 V AA1 L +CRISTY K R IH1 S T IY0 +CRISWELL K R IH1 S W EH0 L +CRIT K R IH1 T +CRITCH K R IH1 CH +CRITCHET K R IH1 CH AH0 T +CRITCHFIELD K R IH1 CH F IY0 L D +CRITCHLOW K R IH1 CH L OW0 +CRITCHLOW'S K R IH1 CH L OW0 Z +CRITERIA K R AY0 T IH1 R IY0 AH0 +CRITERION K R AY0 T IH1 R IY0 AH0 N +CRITERIONS K R AY0 T IH1 R IY0 AH0 N Z +CRITIAS K R IH1 T IY0 AH0 Z +CRITIC K R IH1 T IH0 K +CRITIC'S K R IH1 T IH0 K S +CRITICA K R IH1 T IH0 K AH0 +CRITICAL K R IH1 T IH0 K AH0 L +CRITICALLY K R IH1 T IH0 K AH0 L IY0 +CRITICALLY K R IH1 T IH0 K L IY0 +CRITICISE K R IH1 T IH0 S AY0 Z +CRITICISED K R IH1 T IH0 S AY2 Z D +CRITICISES K R IH1 T IH0 S AY2 Z IH0 Z +CRITICISING K R IH1 T IH0 S AY2 Z IH0 NG +CRITICISM K R IH1 T IH0 S IH2 Z AH0 M +CRITICISMS K R IH1 T IH0 S IH2 Z AH0 M Z +CRITICIZE K R IH1 T IH0 S AY2 Z +CRITICIZED K R IH1 T AH0 S AY2 Z D +CRITICIZES K R IH1 T IH0 S AY2 Z IH0 Z +CRITICIZING K R IH1 T IH0 S AY2 Z IH0 NG +CRITICK K R IH1 T IH0 K +CRITICKS K R IH1 T IH0 K S +CRITICS K R IH1 T IH0 K S +CRITIQUE K R AH0 T IY1 K +CRITIQUE K R IH0 T IY1 K +CRITIQUES K R IH0 T IY1 K S +CRITO K R IY1 T OW0 +CRITTENDEN K R IH0 T EH1 N D AH0 N +CRITTENDEN'S K R IH1 T AH0 N D AH0 N Z +CRITTENDENS K R IH1 T AH0 N D AH0 N Z +CRITTER K R IH1 T ER0 +CRITTER'S K R IH1 T ER0 Z +CRITTERS K R IH1 T ER0 Z +CRITTUR K R IH0 T ER0 +CRITTUR'S K R IH0 T ER0 Z +CRITTURS K R IH0 T ER0 Z +CRITZ K R IH1 T S +CRIVELLI K R IH0 V EH1 L IY0 +CRO K R OW1 +CROAGH K R OW1 AA2 G +CROAK K R OW1 K +CROAKED K R OW1 K T +CROAKER K R OW1 K ER0 +CROAKERS K R OW1 K ER0 Z +CROAKIN K R OW1 K IH0 N +CROAKING K R OW1 K IH0 NG +CROAKINGS K R OW1 K IH0 NG Z +CROAKS K R OW1 K S +CROAKY K R OW1 K IY0 +CROALE K R OW0 EY1 L +CROALE'S K R OW0 EY1 L Z +CROASAN K R OW0 EY1 Z AH0 N +CROAT K R OW1 AA2 T +CROATAN K R OW1 AA2 T AH0 N +CROATIA K R OW0 EY1 SH AH0 +CROATIAN K R OW0 EY1 SH AH0 N +CROATIANS K R OW0 EY1 SH AH0 N Z +CROATS K R OW1 AA2 T S +CROBBLE K R AA1 B AH0 L +CROC K R AA1 K +CROCANS K R AA1 K AH0 N Z +CROCE K R OW1 CH IY0 +CROCE'S K R OW1 CH IY0 Z +CROCHARD K R OW0 SH AA1 R D +CROCHARD'S K R AA1 CH ER0 D Z +CROCHE K R OW1 SH +CROCHET K R OW0 SH EY1 +CROCHETED K R OW0 SH EY1 D +CROCHETING K R OW0 SH EY1 IH0 NG +CROCHETS K R OW0 SH EY1 Z +CROCHETY K R OW2 SH AH0 T IY0 +CROCI K R OW1 S IY0 +CROCK K R AA1 K +CROCKED K R AA1 K T +CROCKER K R AA1 K ER0 +CROCKER'S K R AA1 K ER0 Z +CROCKERS K R AA1 K ER0 Z +CROCKERY K R AA1 K ER0 IY0 +CROCKERYWARE K R AA1 K ER0 IY0 W EH2 R +CROCKET K R AA1 K AH0 T +CROCKETED K R AA1 K AH0 T IH0 D +CROCKETS K R AA1 K AH0 T S +CROCKETT K R AA1 K IH0 T +CROCKETT'S K R AA1 K AH0 T S +CROCKFORD K R AA1 K F ER0 D +CROCKFORD'S K R AA1 K F ER0 D Z +CROCKS K R AA1 K S +CROCKSTON K R AA1 K S T AH0 N +CROCKY K R AA1 K IY0 +CROCODILE K R AA1 K AH0 D AY2 L +CROCODILE'S K R AA1 K AH0 D AY2 L Z +CROCODILES K R AA1 K AH0 D AY2 L Z +CROCODILIAN K R AA2 K AH0 D IH1 L Y AH0 N +CROCS K R AA1 K S +CROCUS K R OW1 K AH0 S +CROCUSES K R OW1 K Y UW0 S IH0 Z +CROESUS K R OW1 S AH0 S +CROESUS'S K R OW1 S AH0 S IH0 Z +CROESUSES K R OW1 S AH0 S IH0 Z +CROFF K R AO1 F +CROFFUT K R AA1 F AH0 +CROFT K R AO1 F T +CROFT'S K R AO1 F T S +CROFTANGRY K R AO1 F T AA0 NG G R IY0 +CROFTER K R AO1 F T ER0 +CROFTER'S K R AO1 F T ER0 Z +CROFTERS K R AO1 F T ER0 Z +CROFTLY K R AO1 F T L IY0 +CROFTON K R AA1 F T AH0 N +CROFTON'S K R AA1 F T AH0 N Z +CROFTONS K R AA1 F T AH0 N Z +CROFTS K R AO1 F T S +CROGHAN K R AA1 G AH0 N +CROGHAN'S K R AA1 G AH0 N Z +CROIA K R OW1 Y AH0 +CROIL K R OY1 L +CROIRE K W AA2 R +CROIS K W AA2 +CROISETTE K R AH0 Z EH1 T +CROISEZ K R OY1 Z EY2 +CROISIC K R OY1 Z IH0 K +CROISIER K R OY1 Z IY0 ER0 +CROISIER'S K R OY1 Z IY0 ER0 Z +CROISILLES K R OY1 S AY1 L Z +CROISNEL K R OY1 S N EH1 L +CROISSANT K R AH0 S AA1 N T +CROISSANT K W AA2 S AA1 N T +CROISSET K W AA2 S EH0 T +CROISSET'S K W AA2 S EH0 T S +CROIT K R OY1 T +CROIX K R OY1 +CROIX'S K R OY1 Z +CROIXILIA K R OY1 IY1 L IY0 AH0 +CROIXILIAN K R OY1 IH1 L Y AH0 N +CROIXILIANS K R OY1 IH1 L Y AH0 N Z +CROIXMARE K R OY1 M EH1 R +CROIZETTE K R AH0 Z EH1 T +CROJACK K R OW1 JH AE2 K +CROKE K R OW1 K +CROKER K R OW1 K ER0 +CROKER'S K R OW1 K ER0 Z +CROLL K R OW1 L +CROLY K R OW1 L IY0 +CROLY'S K R OW1 L IY0 Z +CROM K R AA1 M +CROMA K R OW1 M AH0 +CROMARTIE K R AA1 M AA0 R T IY0 +CROMARTY K R AA1 M AA0 R T IY0 +CROMBIE K R AA1 M B IY0 +CROMBIE'S K R AA1 M B IY0 Z +CROME K R OW1 M +CROMER K R OW1 M ER0 +CROMERING K R OW1 M ER0 IH0 NG +CROMFORD K R AA1 M F ER0 D +CROMLECH K R AA1 M L EH1 K +CROMLECHS K R AA1 M L EH1 K S +CROMMELIN K R AA1 M IH0 L IH0 N +CROMPTON K R AA1 M P T AH0 N +CROMPTON'S K R AA1 M P T AH0 N Z +CROMPTONS K R AA1 M P T AH0 N Z +CROMWEL K R AA1 M W AH0 L +CROMWELL K R AA1 M W AH0 L +CROMWELL'S K R AA1 M W IH0 L Z +CROMWELLIAN K R AA1 M W IH0 L IY0 AH0 N +CROMWELLIANS K R AA1 M W IH0 L Y AH0 N Z +CROMWELLS K R AA1 M W EH2 L Z +CRON K R AA1 N +CRONACA K R AA1 N AH0 K AH0 +CRONAN K R OW1 N AH0 N +CRONDALL K R AA1 N D AH0 L +CRONDALL'S K R AA1 N D AH0 L Z +CRONE K R OW1 N +CRONE'S K R OW1 N Z +CRONENBORG K R OW1 N AH0 N B AO0 R G +CRONES K R OW1 N Z +CRONICA K R AA1 N IH0 K AH0 +CRONIES K R OW1 N IY0 Z +CRONIN K R OW1 N IH0 N +CRONING K R OW1 N IH0 NG +CRONJE K R AA1 N JH +CRONJE'S K R AA1 N JH Z +CRONK K R AA1 NG K +CRONNIN K R AA1 N IH0 N +CRONOS K R OW1 N OW0 S +CRONSHAW K R AA1 N SH AO2 +CRONSHAW'S K R AA1 N SH AO1 Z +CRONSTADT K R AA1 N S T AE2 T +CRONUS K R OW1 N AH0 S +CRONY K R OW1 N IY0 +CRONY'S K R OW1 N IY0 Z +CRONYN K R AH0 N IH0 N +CROOD K R UW1 D +CROOD'S K R UW1 D Z +CROODLIN K R UH1 D L IH0 N +CROOK K R UH1 K +CROOK'D K R UH1 K D +CROOK'S K R UH1 K S +CROOKBACK K R UH1 K B AE2 K +CROOKE K R UH1 K +CROOKED K R UH1 K AH0 D +CROOKEDER K R UH1 K AH0 D ER0 +CROOKEDEST K R UH1 K AH0 D AH0 S T +CROOKEDLY K R UH1 K AH0 D L IY0 +CROOKEDNESS K R UH1 K AH0 D N AH0 S +CROOKENDEN K R UH1 K EH2 N D AH0 N +CROOKER K R UH1 K ER0 +CROOKES K R UH1 K S +CROOKHORN K R UH1 K HH AO2 R N +CROOKING K R UH1 K IH0 NG +CROOKIT K R UH1 K IH0 T +CROOKLEG K R UH1 K L EH2 G +CROOKLYN K R UH1 K L IH2 N +CROOKS K R UH1 K S +CROOKS'S K R UH1 K S IH0 Z +CROOKSHANK K R UH1 K SH AE2 NG K +CROOKSHANKS K R UH1 K SH AE2 NG K S +CROOL K R UW1 L +CROOM K R UW1 M +CROOME K R UW1 M +CROON K R UW1 N +CROONAH K R UW1 N AH0 +CROONED K R UW1 N D +CROONING K R UW1 N IH0 NG +CROONINGS K R UW1 N IH0 NG Z +CROONS K R UW1 N Z +CROOP K R UW1 P +CROOPER K R UW1 P ER0 +CROP K R AA1 P +CROPE K R OW1 P +CROPP'D K R AA1 P D +CROPPED K R AA1 P T +CROPPER K R AA1 P ER0 +CROPPERS K R AA1 P ER0 Z +CROPPIES K R AA1 P IY0 Z +CROPPING K R AA1 P IH0 NG +CROPPINGS K R AA1 P IH0 NG Z +CROPPO K R AA1 P OW0 +CROPPY K R AA1 P IY0 +CROPS K R AA1 P S +CROPSEY K R AA1 P S IY0 +CROPT K R AA1 P T +CROQUART K R OW0 K AO1 R T +CROQUE K R OW0 K +CROQUEMITAINE K R OW0 K M IH0 T EY2 N +CROQUET K R OW0 K EY1 +CROQUETS K R OW0 K EY1 Z +CROQUETTE K R OW0 K EH1 T +CROQUETTES K R OW0 K EH1 T S +CRORE K R AO1 R +CRORES K R AO1 R Z +CRORIDGE K R AA1 R IH0 JH +CROS K R AO1 S +CROSBEIGH K R AA1 S B AY0 +CROSBER K R AA1 S B ER0 +CROSBEY K R AA1 S B IY0 +CROSBIE K R AO1 Z B IY0 +CROSBIE'S K R AO1 Z B IY0 Z +CROSBY K R AO1 Z B IY0 +CROSBY'S K R AO1 Z B IY0 Z +CROSBYS K R AO1 Z B IY0 Z +CROSFIELD K R AO1 Z F IY2 L D +CROSIER K R OW1 Z IY0 ER0 +CROSIERS K R OW1 SH IY0 ER0 Z +CROSLAND K R AA1 S L AH0 N D +CROSNE K R AA1 S N +CROSS K R AO1 S +CROSS'D K R AA1 S D +CROSS'S K R AO1 S IH0 Z +CROSSAN K R AA1 S AH0 N +CROSSBAR K R AO1 S B AA2 R +CROSSBARS K R AO1 S B AA2 R Z +CROSSBEAM K R AO1 S B IY2 M +CROSSBEAMS K R AO1 S B IY1 M Z +CROSSBILL K R AO1 S B IH2 L +CROSSBILLS K R AO1 S B IH2 L Z +CROSSBONES K R AO1 S B OW2 N Z +CROSSBOW K R AO1 S B OW2 +CROSSBOWMEN K R AO1 S B OW2 M AH0 N +CROSSBOWS K R AO1 S B OW2 Z +CROSSCRAB K R AO1 S K R AE1 B +CROSSCUT K R AO1 S K T +CROSSE K R AA1 S +CROSSED K R AO1 S T +CROSSER K R AO1 S ER0 +CROSSES K R AO1 S IH0 Z +CROSSEST K R AO1 S AH0 S T +CROSSETT K R AA1 S IH0 T +CROSSFIELD K R AO1 S F IY2 L D +CROSSFIRE K R AO1 S F AY0 R +CROSSFIRE K R AO1 S F AY2 R +CROSSI K R AO1 S IY0 +CROSSIN K R AA1 S IH0 N +CROSSING K R AO1 S IH0 NG +CROSSINGS K R AO1 S IH0 NG Z +CROSSJAY K R AO1 S JH EY0 +CROSSJAY'S K R AO1 S JH EY0 Z +CROSSKEY K R AO1 S K IY0 +CROSSLAND K R AO1 S L AE2 N D +CROSSLANDS K R AO1 S L AE2 N D Z +CROSSLEGGED K R AA1 S L EH1 G D +CROSSLEY K R AA1 S L IY0 +CROSSLEY'S K R AA1 S L IY0 Z +CROSSLEYS K R AA1 S L IY0 Z +CROSSLY K R AA1 S L IY0 +CROSSMAN K R AO1 S M AH0 N +CROSSNESS K R AA1 S N AH0 S +CROSSON K R AA1 S AH0 N +CROSSPATCH K R AO1 S P AE2 CH +CROSSPIECE K R AO1 S P IY2 S +CROSSPIECES K R AO1 S P IY2 S AH0 Z +CROSSPIECES K R AO1 S P IY2 S IH0 Z +CROSSROAD K R AO1 S R OW2 D +CROSSROADS K R AO1 S R OW2 D Z +CROSSTHWAITE K R AO1 S TH W EY2 T +CROSSTOWN K R AO1 S T AW2 N +CROSSTREES K R AO1 S T R IY1 Z +CROSSWATER K R AA1 S W AA2 T ER0 +CROSSWAY K R AO1 S W EY2 +CROSSWAYS K R AO1 S W EY2 Z +CROSSWISE K R AO1 S W AY2 Z +CROST K R AA1 S T +CROSTHWAITE K R AA1 S TH W AH0 T +CROSWELL K R AA1 S W EH0 L +CROTALUS K R AA1 T AH0 L AH0 S +CROTCH K R AA1 CH +CROTCHED K R AA1 CH T +CROTCHES K R AA1 CH IH0 Z +CROTCHET K R AA1 CH AH0 T +CROTCHETS K R AA1 CH AH0 T S +CROTCHETTY K R AA1 CH IH0 T IY0 +CROTCHETY K R AA1 CH AH0 T IY0 +CROTHERS K R AH1 DH ER0 Z +CROTIN K R AA1 T IH0 N +CROTON K R OW1 T AH0 N +CROTONA K R OW1 T AH0 N AH0 +CROTONS K R OW1 T AH0 N Z +CROTOY K R AA1 T OY0 +CROTTAT K R AA1 T AA1 T +CROTTY K R AA1 T IY0 +CROTUS K R OW1 T AH0 S +CROUBEL K R UW1 B AH0 L +CROUCH K R AW1 CH +CROUCH'D K R AW1 CH D +CROUCH'S K R AW1 CH IH0 Z +CROUCHED K R AW1 CH T +CROUCHER K R AW1 CH ER0 +CROUCHES K R AW1 CH IH0 Z +CROUCHIN K R AW1 CH IH0 N +CROUCHING K R AW1 CH IH0 NG +CROUCHINGLY K R AW1 CH IH0 NG L IY0 +CROUD K R AW1 D +CROUDED K R AW1 D IH0 D +CROUDS K R AW1 D Z +CROUDY K R AW1 D IY0 +CROUP K R UW1 P +CROUPE K R UW1 P +CROUPIER K R UW1 P IY0 ER0 +CROUPIER'S K R UW1 P IY0 ER0 Z +CROUPIERS K R UW1 P IY0 ER0 Z +CROUPS K R UW1 P S +CROUPY K R UW1 P IY0 +CROUSAZ K R AW1 S AE2 Z +CROUSE K R AW1 S +CROUSTADE K R AW1 S T EY1 D +CROUSTILLAC K R AW1 S T IY1 AH0 K +CROUSTILLAC'S K R AW1 S T IY1 AH0 K S +CROUT K R AW1 T +CROUTE K R AW1 T +CROUTEF K R AW1 T F +CROUTHA K R AW1 TH AH0 +CROUTONS K R AW1 T AH0 N Z +CROUY K R AA1 AY0 +CROW K R OW1 +CROW'D K R AW1 D +CROW'S K R OW1 Z +CROWBAIT K R OW1 B EY1 T +CROWBAR K R OW1 B AA2 R +CROWBARS K R OW1 B AA2 R Z +CROWBOROUGH K R OW1 B ER0 OW0 +CROWD K R AW1 D +CROWD'S K R AW1 D Z +CROWDED K R AW1 D AH0 D +CROWDED K R AW1 D IH0 D +CROWDEN K R AW1 D AH0 N +CROWDER K R AW1 D ER0 +CROWDER'S K R AW1 D ER0 Z +CROWDEY K R AW1 D IY0 +CROWDIE K R AW1 D IY0 +CROWDIN K R AW1 D IY0 N +CROWDING K R AW1 D IH0 NG +CROWDS K R AW1 D Z +CROWDY K R AW1 D IY0 +CROWE K R OW1 +CROWE'S K R OW1 Z +CROWED K R OW1 D +CROWELL K R OW1 AH0 L +CROWEN K R OW1 AH0 N +CROWER K R OW1 ER0 +CROWFIELD K R OW1 F IY2 L D +CROWFOOT K R OW1 F UH2 T +CROWFOOT'S K R OW1 F UH2 T S +CROWHEART K R AW1 HH AA2 R T +CROWHEART'S K R AW1 HH AA1 R T S +CROWHURST K R OW1 HH ER0 S T +CROWIN K R OW1 IH0 N +CROWING K R OW1 IH0 NG +CROWINGS K R OW1 IH0 NG Z +CROWL K R AW1 L +CROWL'S K R AW1 L Z +CROWLAND K R OW1 L AH0 N D +CROWLE K R AW1 L +CROWLEIGH K R AW1 L AH0 +CROWLEY K R AW1 L IY0 +CROWLEY'S K R AW1 L IY0 Z +CROWN K R AW1 N +CROWN'D K R AW1 N D +CROWN'S K R AW1 N Z +CROWN'ST K R AW1 N S T +CROWNE K R AW1 N +CROWNED K R AW1 N D +CROWNER K R AW1 N ER0 +CROWNER'S K R AW1 N ER0 Z +CROWNES K R AW1 N Z +CROWNETH K R AW1 N IH0 TH +CROWNHEART K R AW1 N HH AA2 R T +CROWNING K R AW1 N IH0 NG +CROWNINGSHIELD K R AW1 N IH0 NG SH IY2 L D +CROWNINSHIELD K R AW1 N IH0 N SH IY2 L D +CROWNLANDS K R AW1 N L AE2 N D Z +CROWNLESS K R AW1 N L AH0 S +CROWNS K R AW1 N Z +CROWNWALL K R AW1 N W AO0 L +CROWQUILL K R AW1 K IY1 L +CROWS K R OW1 Z +CROWS' K R OW1 Z +CROWSFEATHER K R AW1 S F EH1 DH ER0 +CROWSFEET K R AW1 S F IY1 T +CROWSFOOT K R AW1 S F UH2 T +CROWSWOOD K R AW1 S W UH2 D +CROWTHER K R AW1 DH ER0 +CROXALL K R AA1 K S AO2 L +CROXLEY K R AA1 K S L IY0 +CROXTON K R AA1 K S T AH0 N +CROY K R OY1 +CROYANT K R OY1 AH0 N T +CROYDEN K R OY1 D AH0 N +CROYDEN'S K R OY1 D AH0 N Z +CROYDON K R OY1 D AH0 N +CROYE K R OY1 +CROYEZ K R OY1 EH0 Z +CROYLAND K R OY1 L AH0 N D +CROYLE K R OY1 L +CROYLE'S K R OY1 L Z +CROZ K R AA1 Z +CROZAT K R OW1 Z AE0 T +CROZER K R OW1 Z ER0 +CROZIER K R OW1 ZH ER0 +CROZIER'S K R OW1 ZH ER0 Z +CROZIERS K R OW1 ZH ER0 Z +CROZON K R OW1 Z AH0 N +CRSUS K R S AH0 S +CRU K R UW1 +CRUA K R UW1 AH0 +CRUACH K R UW1 AE0 K +CRUACHAN K R UW0 AE1 CH AH0 N +CRUACHMAA K R UW0 AE1 K M AH0 +CRUAGAN K R UW0 EY1 G AH0 N +CRUCE K R UW1 S +CRUCEM K R UW1 S AH0 M +CRUCES K R UW1 S IY0 Z +CRUCHE K R AH1 CH +CRUCHO K R AH1 CH OW0 +CRUCHOT K R UW1 K AH0 T +CRUCHOTS K R UW1 K AA1 T S +CRUCIAL K R UW1 SH AH0 L +CRUCIAN K R UW1 SH AH0 N +CRUCIANS K R UW1 SH AH0 N Z +CRUCIBLE K R UW1 S AH0 B AH0 L +CRUCIBLES K R UW1 S AH0 B AH0 L Z +CRUCIFIED K R UW1 S AH0 F AY2 D +CRUCIFIERS K R UW1 S AH0 F AY2 ER0 Z +CRUCIFIES K R UW1 S AH0 F AY2 Z +CRUCIFIX K R UW1 S AH0 F IH2 K S +CRUCIFIXES K R UW1 S AH0 F IH2 K S IH0 Z +CRUCIFIXION K R UW2 S IH0 F IH1 K SH AH0 N +CRUCIFIXIONS K R UW2 S IH0 F IH1 K SH AH0 N Z +CRUCIFORM K R UW1 S AH0 F AO2 R M +CRUCIFY K R UW1 S AH0 F AY2 +CRUCIFYING K R UW1 S AH0 F AY2 IH0 NG +CRUCIS K R UW1 S AH0 S +CRUDE K R UW1 D +CRUDELY K R UW1 D L IY0 +CRUDEN K R UW1 D AH0 N +CRUDEN'S K R UW1 D AH0 N Z +CRUDENESS K R UW1 D N AH0 S +CRUDENS K R UW1 D AH0 N Z +CRUDER K R UW1 D ER0 +CRUDEST K R UW1 D AH0 S T +CRUDITIES K R UW1 D AH0 T IY0 Z +CRUDITY K R UW1 D IH0 T IY0 +CRUDUP K R AH1 D AH2 P +CRUEL K R UW1 AH0 L +CRUEL K R UW1 L +CRUELER K R UW1 L ER0 +CRUELEST K R UW1 L AH0 S T +CRUELL K R UW1 L +CRUELLE K R UW1 L +CRUELLER K R UW1 L ER0 +CRUELLEST K R UW1 L AH0 S T +CRUELLY K R UW1 L IY0 +CRUELTIES K R UW1 AH0 L T IY0 Z +CRUELTIES K R UW1 L T IY0 Z +CRUELTY K R UW1 AH0 L T IY0 +CRUELTY K R UW1 L T IY0 +CRUELTY'S K R UW1 AH0 L T IY0 Z +CRUET K R UW1 T +CRUETS K R UW1 AH0 T S +CRUFT K R AH1 F T +CRUFT'S K R AH1 F T S +CRUGER K R UW1 JH ER0 +CRUGER'S K R UW1 JH ER0 Z +CRUICKSHANK K R UH1 K SH AE2 NG K +CRUICKSHANKS K R UH1 K SH AE2 NG K S +CRUIKSHANK K R UW1 IH0 K SH AE2 NG K +CRUIKSHANK'S K R UW1 IH0 K SH AE2 NG K S +CRUIKSHANKS K R UW1 IH0 K SH AE2 NG K S +CRUISE K R UW1 Z +CRUISED K R UW1 Z D +CRUISER K R UW1 Z ER0 +CRUISER'S K R UW1 Z ER0 Z +CRUISERS K R UW1 Z ER0 Z +CRUISES K R UW1 Z IH0 Z +CRUISIE K R UW1 Z IY1 +CRUISIN K R UW1 Z IH0 N +CRUISING K R UW1 Z IH0 NG +CRUISINGS K R UW1 Z IH0 NG Z +CRUISKEEN K R UW1 Z K IY2 N +CRUIZE K R UW1 Z +CRUIZERS K R UW1 Z ER0 Z +CRUIZING K R UW1 Z IH0 NG +CRULE K R UW1 L +CRULL K R AH1 L +CRULL'S K R AH1 L Z +CRULLER K R AH1 L ER0 +CRULLERS K R AH1 L ER0 Z +CRUM K R AH1 M +CRUM'S K R AH0 M Z +CRUMB K R AH1 M +CRUMBAUGH K R AH1 M B AO2 +CRUMBED K R AH1 M D +CRUMBLE K R AH1 M B AH0 L +CRUMBLED K R AH1 M B AH0 L D +CRUMBLER K R AH1 M B L ER0 +CRUMBLES K R AH1 M B AH0 L Z +CRUMBLING K R AH1 M B AH0 L IH0 NG +CRUMBLING K R AH1 M B L IH0 NG +CRUMBLINGS K R AH1 M B L IH0 NG Z 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+DAMORIDE D EY1 M AO2 R AY2 D +DAMOSEL D AA0 M OW1 S AH0 L +DAMOSELS D EY1 M AH0 S AH0 L Z +DAMOT D EY1 M AH0 T +DAMOUR D AH0 M UH1 R +DAMOZEL D AA0 M OW1 Z AH0 L +DAMP D AE1 M P +DAMPED D AE1 M P T +DAMPEN D AE1 M P AH0 N +DAMPENED D AE1 M P AH0 N D +DAMPENING D AE1 M P AH0 N IH0 NG +DAMPENING D AE1 M P N IH0 NG +DAMPER D AE1 M P ER0 +DAMPERS D AE1 M P ER0 Z +DAMPEST D AE1 M P AH0 S T +DAMPFER D AE1 M P F ER0 +DAMPHOOL D AE1 M F UW1 L +DAMPIER D AE1 M P IY0 ER0 +DAMPIER'S D AE1 M P IY0 ER0 Z +DAMPIERRE D AE1 M P IY0 EH1 R +DAMPING D AE1 M P IH0 NG +DAMPISH D AE2 M P IH0 SH +DAMPLY D AE1 M P L IY0 +DAMPNESS D AE1 M P N IH0 S +DAMPS D AE1 M P S +DAMROSCH D AE1 M R AO1 SH +DAMS D AE1 M Z +DAMSEL D AE1 M Z AH0 L +DAMSEL'S D AE1 M Z AH0 L Z +DAMSELS D AE1 M Z AH0 L Z +DAMSON D AE1 M S AH0 N +DAMSONS D AE1 M S AH0 N Z +DAMTIDAM D AE1 M T AY1 D AH0 M +DAMVILLE D AE1 M V IH2 L +DAN D AE1 N +DAN'EL D AE1 N AH0 L +DAN'L D AE1 N AH0 L +DAN'L'S D AE1 N L Z +DAN'LL D AE1 N L +DAN'S D AE1 N Z +DANA D EY1 N AH0 +DANA'S D EY1 N AH0 Z +DANAAN D EY1 N AH0 N +DANAANS D EY1 N AH0 N Z +DANAE D AE1 N EY2 +DANAE'S D AE2 N AH0 Z +DANAID D EY1 N EY2 D +DANAIDES D EY1 N AY2 D Z +DANAIDS D EY1 N EY1 D Z +DANAKIL D AE1 N AH0 K IY1 L +DANANN D AE1 N AE0 N +DANAOS D AH0 N EY1 OW0 S +DANAUS D EY1 N AO1 Z +DANAVAS D AA0 N AA1 V AH0 S +DANBRIDGE D AE1 N B R IH2 JH +DANBURY D AE1 N B ER0 IY0 +DANBY D AE1 N B IY0 +DANBY'S D AE1 N B IY0 Z +DANC'D D AE2 NG K D +DANCAIRE D AE1 N K EH1 R +DANCE D AE1 N S +DANCE'S D AE1 N S IH0 Z +DANCED D AE1 N S T +DANCEING D AE1 N S IH0 NG +DANCER D AE1 N S ER0 +DANCER'S D AE1 N S ER0 Z +DANCERS D AE1 N S ER0 Z +DANCES D AE1 N S AH0 Z +DANCES D AE1 N S IH0 Z +DANCETH D AE1 N S IH0 TH +DANCEY D AE1 N S IY0 +DANCIN D AE1 N S IH0 N +DANCIN' D AE1 N S IH0 N +DANCING D AE1 N S IH0 NG +DANCING'S D AE1 N S IH0 NG Z +DANCINGS D AE1 N S IH0 NG Z +DANCLA D AE1 N K L AH0 +DANCOX D AE1 N K AA0 K S +DANCY D AE1 N S IY0 +DANCY'S D AE1 N S IY0 Z +DAND D AE1 N D +DANDALOO D AE1 N D AH0 L UW2 +DANDELINE D AE1 N D AH0 L AY2 N +DANDELION D AE1 N D AH0 L AY2 AH0 N +DANDELIONS D AE1 N D AH0 L AY2 AH0 N Z +DANDENONG D AE1 N D AH0 N AO2 NG +DANDER D AE1 N D ER0 +DANDIACAL D AE1 N D AY1 AH0 K AH0 L +DANDIE D AE1 N D IY0 +DANDIES D AE1 N D IY0 Z +DANDIEST D AE1 N D IY0 AH0 S T +DANDIFIED D AE1 N D IH0 F AY2 D +DANDIN D AE1 N D IH0 N +DANDLE D AE1 N D AH0 L +DANDLED D AE1 N D AH0 L D +DANDLES D AE1 N D AH0 L Z +DANDLING D AE1 N D L IH0 NG +DANDO D AE1 N D OW0 +DANDOINS D AE1 N D OY1 N Z +DANDOLO D AE1 N D OW0 L OW0 +DANDRIDGE D AE1 N D R IH0 JH +DANDRIK D AE1 N D R IH0 K +DANDRUFF D AE1 N D R AH0 F +DANDTAN D AE1 N D T AE2 N +DANDTAN'S D AE1 N D T AE2 N Z +DANDY D AE1 N D IY0 +DANDY'S D AE1 N D IY0 Z +DANDYFIED D AE1 N D IY0 F AY2 D +DANDYISH D AE1 N D IY0 IH2 SH +DANDYISM D AE1 N D IY0 IH2 Z AH0 M +DANE D EY1 N +DANE'S D EY1 N Z +DANEFORD D AE1 N IH0 F ER0 D +DANEGRE D AH0 N EH1 G ER0 +DANEL D AE1 N AH0 L +DANELAGH D AE1 N IH0 L AH0 +DANES D EY1 N Z +DANESBURY D EY1 N Z B EH2 R IY0 +DANESBURY'S D EY1 N Z B EH2 R IY0 Z +DANESFORD D EY1 N Z F ER0 D +DANESH D AE1 N IH0 SH +DANESLY D AH0 N IY1 Z L IY0 +DANEY D EY1 N IY0 +DANEY'S D EY1 N IY0 Z +DANFORD D AE1 N F ER0 D +DANFORTH D AE1 N F ER0 TH +DANFORTH'S D AE1 N F ER0 TH S +DANFORTHS D AE1 N F ER0 TH S +DANG D AE1 NG +DANG'ROUS D AE1 NG R AH0 S +DANGAN D AE1 NG G AH0 N +DANGDEST D AE1 NG D AH0 S T +DANGEAU D EY1 NG G OW1 +DANGEAU'S D EY1 N JH OW1 Z +DANGED D EY1 N JH D +DANGER D EY1 N JH ER0 +DANGER'S D EY1 N JH ER0 Z +DANGEREUSES D EY1 N JH ER0 EY1 AH0 S IH0 Z +DANGEREUX D EY1 N JH ER0 OW0 +DANGERFIELD D EY1 N JH ER0 F IY2 L D +DANGERFIELD'S D EY1 N JH ER0 F IY2 L D Z +DANGEROUS D EY1 N JH ER0 AH0 S +DANGEROUSLY D EY1 N JH ER0 AH0 S L IY0 +DANGEROUSNESS D EY1 N JH ER0 AH0 S N AH0 S +DANGERS D EY1 N JH ER0 Z +DANGERSOME D AE1 NG G ER0 S AH0 M +DANGLAR D AE1 NG G L ER0 +DANGLAR'S D AE1 NG G L ER0 Z +DANGLARS D AE1 NG G L ER0 Z +DANGLE D AE1 NG G AH0 L +DANGLED D AE1 NG G AH0 L D +DANGLER D AE1 NG G AH0 L ER0 +DANGLER D AE1 NG G L ER0 +DANGLERS D AE1 NG G AH0 L ER0 Z +DANGLES D AE1 NG G AH0 L Z +DANGLIN D AE1 NG G L IH0 N +DANGLING D AE1 NG G AH0 L IH0 NG +DANGLING D AE1 NG G L IH0 NG +DANGLOSS D AE1 NG G L AA2 S +DANGO D AE1 NG G OW0 +DANHASCH D AE1 N HH AE1 SH +DANICAN D AE1 N AH0 K AH0 N +DANIDOFF D EY1 N IH0 D AO0 F +DANIEL D AE1 N Y AH0 L +DANIEL'S D AE1 N Y AH0 L Z +DANIELE D AE0 N Y EH1 L +DANIELI D AE0 N Y EH1 L IY0 +DANIELL D AE1 N IY0 L +DANIELLA D AE2 N Y EH1 L AH0 +DANIELLE D AE2 N IY0 EH1 L +DANIELLE D AE2 N Y EH1 L +DANIELS D AE1 N Y AH0 L Z +DANIELSON D AE1 N Y AH0 L S AH0 N +DANIIL D AH0 N IY1 L +DANILO D AE1 N AH0 L OW2 +DANILOVITCH D AE1 N AH0 L AA1 V IH0 CH +DANIRA D AH0 N R AH0 +DANIRA'S D AH0 N ER0 AH0 Z +DANISH D EY1 N IH0 SH +DANITE D AE1 N AH0 T +DANITES D AH0 N AY2 T S +DANIVER D AE1 N AH0 V ER0 +DANJURO D AE1 N JH UW0 R OW0 +DANJURO'S D AE1 N JH UW0 R OW0 Z +DANK D AE1 NG K +DANKALI D AE1 NG K AA1 L IY0 +DANKE D AE1 NG K +DANKLY D AE1 NG K L IY0 +DANKMERE D AE1 NG K M IH2 R +DANKNESS D AE1 NG K N AH0 S +DANKO D AE1 NG K OW0 +DANKS D AE1 NG K S +DANKSHIRE D AE1 NG K SH AY2 R +DANKVILLE D AE1 NG K V IH2 L +DANKWART D AE1 NG K W AO0 R T +DANLEY D AE1 N L IY0 +DANMOUTH D AE1 N M AH0 TH +DANN D AE1 N +DANNA D AE1 N AH0 +DANNEBROG D AE1 N AH0 B R AH0 G +DANNEL D AE1 N AH0 L +DANNER D AE1 N ER0 +DANNER'S D AE1 N ER0 Z +DANNIE D AE1 N IY0 +DANNISBURGH D AE1 N IH0 S B ER0 G +DANNISBURGH'S D AE1 N IH0 S B ER0 G Z +DANNUX D AA0 N UW1 +DANNY D AE1 N IY0 +DANNY'S D AE1 N IY0 Z +DANO D AA1 N OW0 +DANS D AE1 N Z +DANSANT D AE1 N S AH0 N T +DANSANTS D AE1 N S AH0 N T S +DANSBURG D AE1 N S B ER0 G +DANSE D AE1 N S +DANSER D AE1 N S ER0 +DANSEUSE D AE1 N S UW1 S +DANSEUSES D AE1 N S IY0 AH0 S IH0 Z +DANSEY D AE1 N S IY0 +DANSKE D AE1 N S K +DANSON D AE1 N S AH0 N +DANSON'S D AE1 N S AH0 N Z +DANSONS D AE1 N S AH0 N Z +DANSTER D AE1 N S T ER0 +DANSVILLE D AE1 N Z V IH0 L +DANT D AE1 N T +DANTAN D AE1 N T AH0 N +DANTE D AA1 N T EY0 +DANTE'S D AE1 N T IY0 Z 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+DANZON D AE1 N Z AO1 N +DAON'T D AA0 OW1 N T +DAOUD D AW1 D +DAOULAS D AW1 L AH0 Z +DAOWN D AA0 OW1 N +DAP D AH0 P +DAPH D AE1 F +DAPHNE D AE1 F N IY0 +DAPHNE'S D AE1 F N IY0 Z +DAPHNES D AE1 F N IY0 Z +DAPHNIS D AE1 F N AH0 S +DAPICHO D AA1 P IH0 CH OW0 +DAPITAN D AA0 P IY0 T AA1 N +DAPPER D AE1 P ER0 +DAPPLE D AE1 P AH0 L +DAPPLED D AE1 P AH0 L D +DAPPLEGRIM D AE1 P AH0 L G R IH0 M +DAPPLEMERE D AE1 P L AH0 M EH2 R +DAPPLES D AE1 P AH0 L Z +DAPPLING D AE1 P L IH0 NG +DAPSUL D AE1 P S AH0 L +DAR D AA1 R +DAR'D D AA2 D +DAR'LL D AA1 R AH0 L +DAR'N'T D AA2 AH0 N T +DAR'S D AA1 R Z +DAR'ST D ER0 S T +DAR'TER D AA2 T ER0 +DARA D AE1 R AH0 +DARA'S D EH1 R AH0 Z +DARAGH D AE1 R AH0 G +DARAGH'S D AE1 R AH0 Z +DARAI D AA1 R EY0 +DARANTE D AA0 R AA1 N T IY0 +DARB D AA1 R B +DARBAR D AA1 R B AA2 R +DARBEY D AA1 R B IY0 +DARBIE D AA1 R B IY0 +DARBIES D AA1 R B IY0 Z +DARBOR D AA1 R B ER0 +DARBY D AA1 R B IY0 +DARBY'S D AA1 R B IY0 Z +DARBYSHIRE D AA1 R B IH0 SH AY2 R +DARBYVILLE D AA1 R B IY0 V IH2 L +DARC D AA1 R K +DARCEY D AA1 R S IY0 +DARCH D AA1 R K +DARCIEUX D AA1 R K IY0 OW0 +DARCY D AA1 R S IY0 +DARCY'S D AA1 R S IY0 Z +DARCYS D AA1 R S IY0 Z +DARD D AA1 R D +DARD'S D AA1 R D Z +DARDALE D AA1 R D EY2 L +DARDAN D AA1 R D AH0 N +DARDANELLES D AA1 R D AH0 N EH1 L Z +DARDANELOV D AA1 R D AE1 N AH0 L AA0 V +DARDANIAN D AA1 R D EY1 N IY0 AH0 N +DARDANIANS D AA1 R D EY1 N IY0 AH0 N Z +DARDANUS D AA1 R D EY1 N AH0 S +DARDAVAN D AA0 R D AA1 V AH0 N +DARDINEL D AA0 R D IY0 N EH1 L +DARDONVILLE D AA0 R D OW1 N V IH0 L +DARDURR D AA1 R D ER1 +DARDUS D AA1 R D AH0 S +DARE D EH1 R +DARE'S D EH1 R Z +DARED D EH1 R D +DAREDEVIL D EH1 R D EH2 V AH0 L +DAREDEVILS D EH1 R D EH2 V AH0 L Z +DARELL D AA0 R EH1 L +DAREM D AE1 R AH0 M +DAREN'T D AE1 R AH0 N T +DARENA D AE1 R IH0 N AH0 +DARENA'S D AE1 R IH0 N AH0 Z +DAREPORT D AE1 R IH0 P AO1 R T +DARES D EH1 R Z +DARES'S D EH1 R S IH0 Z +DARESAY D EH1 R S EY0 +DARESN'T D EH1 R Z AH0 N T +DAREST D AE1 R AH0 S T +DARETH D AE1 R IH0 TH +DAREVILLE D EH1 R V IH2 L +DAREWELL D EH1 R W EH2 L +DARF D AA1 R F +DARFOUR D AA2 F AO0 R +DARFUR D AA2 F UW1 R +DARG D AA1 R G +DARGAI D AA1 R G AY1 +DARGAN D AA1 R G AH0 N +DARGENTAL D AA1 R JH EH1 N T AH0 L +DARGENTAL'S D AA1 R JH EH1 N T AH0 L Z +DARGLE D AA1 R G AH0 L +DARI D AA1 R IY0 +DARIA D AA1 R IY0 AH0 +DARIAN D EH1 R IY0 AH0 N +DARIANS D EH1 R IY0 AH0 N Z +DARIC D AE1 R IH0 K +DARICS D AE1 R IH0 K S +DARIEL D EH1 R IY0 AH0 L +DARIEL'S D EH1 R IY0 AH0 L Z +DARIEN D EH1 R IY0 AH0 N +DARIN D AE1 R IH0 N +DARING D EH1 R IH0 NG +DARINGLY D EH1 R IH0 NG L IY0 +DARINGS D EH1 R IH0 NG Z +DARIO D EH1 R IY0 OW2 +DARIO'S D AA1 R IY0 OW0 Z +DARION D EH1 R IY0 AH0 N +DARIUS D ER0 AY1 AH0 S +DARIUS'S D ER0 AY1 AH0 S IH0 Z +DARJEELING D AA2 R JH IY1 L IH0 NG +DARJEELING D AA2 R ZH IY1 L IH0 NG +DARJILING D AA2 R ZH L IH0 NG +DARK D AA1 R K +DARK'NING D AA1 R K AH0 N IH0 NG +DARK'S D AA1 R K S +DARKE D AA1 R K +DARKEN D AA1 R K AH0 N +DARKEN'D D AA1 R K AH0 N D +DARKENED D AA1 R K AH0 N D +DARKENING D AA1 R K AH0 N IH0 NG +DARKENING D AA1 R K N IH0 NG +DARKENS D AA1 R K AH0 N Z +DARKER D AA1 R K ER0 +DARKEST D AA1 R K AH0 S T +DARKEY D AA1 R K IY0 +DARKEY'S D AA1 R K IY2 Z +DARKEYE D AA1 R K AY2 +DARKEYS D AA1 R K IY0 Z +DARKHAN D AA1 R K HH AE0 N +DARKIE D AA1 R K IY0 +DARKIE'S D AA1 R K IY0 Z +DARKIES D AA1 R K IY0 Z +DARKINGHAM D AA1 R K IH0 NG HH AE2 M +DARKISH D AA1 R K IH0 SH +DARKLE D AA1 R K AH0 L +DARKLING D AA1 R K L IH0 NG +DARKLY D AA1 R K L IY0 +DARKNESS D AA1 R K N AH0 S +DARKNESSE D AA1 R K N IH0 S +DARKNESSES D AA1 R K N AH0 S IH0 Z +DARKNIGHT D AA1 R K N AY2 T +DARKOVAN D AA1 R K AH0 V AH0 N +DARKOVANS D AA1 R K AH0 V AH0 N Z +DARKOVER D AA1 R K AH0 V ER0 +DARKROOM D AA1 R K R UW2 M +DARKS D AA1 R K S +DARKSOME D AA1 R K S AH0 M +DARKUSH D AA1 R K UH1 SH +DARKY D AA1 R K IY0 +DARKY'S D AA1 R K IY0 Z +DARL D AA1 R L +DARL'S D AA1 R L Z +DARLES D AA1 R L Z +DARLETON D AA1 R L T AH0 N +DARLEY D AA1 R L IY0 +DARLEY'S D AA1 R L IY0 Z +DARLFORD D AA1 R L F ER0 D +DARLIN D AA1 R L IH0 N +DARLIN' D AA1 R L IH0 N +DARLIN'S D AA1 R L IH0 N Z +DARLING D AA1 R L IH0 NG +DARLING'S D AA1 R L IH0 NG Z +DARLINGEST D AA1 R L IH0 NG AH0 S T +DARLINGFORD D AA1 R L IH0 NG F ER0 D +DARLINGS D AA1 R L IH0 NG Z +DARLINGTON D AA1 R L IH0 NG T AH0 N +DARLINGTON'S D AA1 R L IH0 NG T AH0 N Z +DARLINK D AA1 R L IH2 NG K +DARLINS D AA1 R L AH0 N Z +DARLINT D AA1 R L IH0 N T +DARLINTS D AA1 R L IH0 N T S +DARLOT D AA1 R L AH0 T +DARMESTETER D AA1 R M IH0 S T T ER0 +DARMSTADT D AA1 R M S T AE2 T +DARN D AA1 R N +DARN'D D AA1 R N D +DARN'T D AA1 R N T +DARNA D AA1 R N AH0 +DARNATION D AA1 R N EY1 SH AH0 N +DARNAY D AA1 R N EY1 +DARNBOROUGH D AA1 R N B ER0 OW0 +DARNCE D AA1 R N S +DARNDEST D AA1 R N D AH0 S T +DARNED D AA1 R N D +DARNEDEST D AA1 R N D EH0 S T +DARNEDEST D AA1 R N EH0 S T +DARNEL D AA1 R N AH0 L +DARNEL'S D AA1 R N AH0 L Z +DARNELL D AA0 R N EH1 L +DARNER D AA1 R N ER0 +DARNFORD D AA1 R N F ER0 D +DARNFORD'S D AA1 R N F ER0 D Z +DARNICK D AA1 R N IH0 K +DARNIN D AA1 R N IH0 N +DARNING D AA1 R N IH0 NG +DARNLEY D AA1 R N L IY0 +DARNLEY'S D AA1 R N L IY0 Z +DARNLINVARACH D AA1 R N L IH2 N V AA1 R AH0 CH +DARNS D AA1 R N Z +DAROGA D AA0 R OW1 G AH0 +DARPORE D AA0 R P AO0 R +DARQUELNOY D AA0 R K EH1 L N OY0 +DARRACH D AE1 R AH0 K +DARRACOTT D AE1 R AH0 K AA0 T +DARRAGH D EH1 R AH0 +DARRAGH'S D EH1 R AH0 Z +DARRAGON D EH1 R AH0 G AA2 N +DARRAH D AE1 R AH0 +DARRAH'S D AE1 R AH0 Z +DARRANT D AE1 R AH0 N T +DARRAS D AE1 R AH0 Z +DARREL D EH1 R AH0 L +DARREL'S D EH1 R AH0 L Z +DARRELL D EH1 R AH0 L +DARRELL'S D EH1 R IH0 L Z +DARRELLS D EH1 R AH0 L Z +DARREUCH D ER0 AH0 K +DARRIE D EH1 R IY0 +DARRIG D AE1 R IH0 G +DARRIN D AE1 R IH0 N +DARRIN'S D AE1 R IH0 N Z +DARRINGTON D AE1 R IH0 NG T AH0 N +DARRO D AA1 R OW0 +DARROW D EH1 R OW0 +DARROW'S D EH1 R OW0 Z +DARROWTOWN D EH1 R OW0 T AW2 N +DARRY D EH1 R IY0 +DARRY'S D EH1 R IY0 Z +DARRYLL D EH1 R AH0 L +DARS D ER0 Z +DARSAY D AA1 R S EY1 +DARSEN'T D AA1 R S AH0 N T +DARSENA D AA1 R S AH0 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G AH0 L +DISENTANGLED D IH0 S IH0 N T AE1 NG G AH0 L D +DISENTANGLEMENT D IH0 S IH0 N T AE1 NG G AH0 L M AH0 N T +DISENTANGLERS D IH0 S IH0 N T AE1 NG G AH0 L ER0 Z +DISENTANGLES D IH0 S IH0 N T AE1 NG G AH0 L Z +DISENTANGLING D IH0 S IH0 N T AE1 NG G AH0 L IH0 NG +DISENTHRALLED D IH0 S IH0 N TH R AO1 L D +DISENTHRALMENT D IH0 S IH0 N TH R AO1 L M AH0 N T +DISENTOMBED D IH0 S IH0 N T UW1 M D +DISESTABLISH D IH0 S IH0 S T AE1 B L IH0 SH +DISESTABLISHED D IH0 S IH0 S T AE1 B L IH0 SH T +DISESTABLISHING D IH0 S IH0 S T AE1 B L IH0 SH IH0 NG +DISESTABLISHMENT D IH0 S IH0 S T AE1 B L IH0 SH M AH0 N T +DISESTEEM D IH0 S IH0 S T IY1 M +DISESTEEMED D IH0 S IH0 S T IY1 M D +DISFAVOR D IH0 S F EY1 V ER0 +DISFAVOUR D IH0 S F EY1 V ER0 +DISFIGURATION D IH0 S F IH1 G Y ER0 EY1 SH AH0 N +DISFIGURE D IH0 S F IH1 G Y ER0 +DISFIGURED D IH0 S F IH1 G Y ER0 D +DISFIGUREMENT D IH0 S F IH1 G Y ER0 M EH0 N T +DISFIGUREMENTS D IH0 S F IH1 G Y ER0 M AH0 N T S +DISFIGURES D IH0 S F IH1 G Y ER0 Z +DISFIGURING D 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AE2 M AH0 T AH0 Z EY1 SH AH0 N +DRAMATISE D R AE1 M AH0 T AY2 Z +DRAMATISED D R AE1 M AH0 T AY2 Z D +DRAMATISING D R AE1 M AH0 T AY2 Z IH0 NG +DRAMATIST D R AA1 M AH0 T IH0 S T +DRAMATIST'S D R AA1 M AH0 T IH0 S T S +DRAMATISTS D R AA1 M AH0 T IH0 S T S +DRAMATIZATION D R AE2 M AH0 T AH0 Z EY1 SH AH0 N +DRAMATIZATIONS D R AE2 M AH0 T AH0 Z EY1 SH AH0 N Z +DRAMATIZE D R AA1 M AH0 T AY2 Z +DRAMATIZE D R AE1 M AH0 T AY2 Z +DRAMATIZED D R AE1 M AH0 T AY2 Z D +DRAMATIZES D R AE1 M AH0 T AY2 Z IH0 Z +DRAMATIZING D R AE1 M AH0 T AY2 Z IH0 NG +DRAMATURGIC D R AA1 M AH0 T ER1 JH IH0 K +DRAMATURGIE D R AA1 M AH0 T ER0 JH IY0 +DRAMATURGY D R AA1 M AH0 T ER0 JH IY0 +DRAME D R EY1 M +DRAMED D R AH0 M EH2 D +DRAMES D R EY1 M Z +DRAMMEN D R AE1 M AH0 N +DRAMS D IY1 R AE2 M Z +DRAMS D R AE1 M Z +DRAMSHOP D R AE1 M SH AA2 P +DRAN D R AH0 N +DRANCES D R AH0 N S IH0 Z +DRANE D R EY1 N +DRANE'S D R EY1 N Z +DRANEY D R EY1 N IY0 +DRANG D R AE1 NG +DRANGEY D R EY1 N JH IY0 +DRANIGO D R EY1 N IY1 G OW0 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N +DRAUGHTY D R AO1 T IY0 +DRAUPADI D R AO1 P AA1 D IY0 +DRAUPNIR D R AO1 P N ER0 +DRAV D R AE1 V +DRAVE D R EY1 V +DRAVIDIAN D R AH0 V IH1 D IY0 AH0 N +DRAVOT D R AE1 V OW0 +DRAW D R AO1 +DRAW'D D R AO1 D +DRAWBACK D R AO1 B AE2 K +DRAWBACKS D R AO1 B AE2 K S +DRAWBRIDGE D R AO1 B R IH2 JH +DRAWBRIDGES D R AO1 B R IH2 JH IH0 Z +DRAWCANSIR D R AO1 K AH0 N S IH1 R +DRAWD D R AO1 D +DRAWED D R AO1 D +DRAWEN D R AO1 AH0 N +DRAWER D R AO1 R +DRAWERFUL D R AO1 R F AH0 L +DRAWERS D R AO1 R Z +DRAWEST D R AO1 AH0 S T +DRAWETH D R AO1 TH +DRAWFORD D R AO1 F ER0 D +DRAWIN D R AO1 IH0 N +DRAWIN' D R AO1 IH0 N +DRAWING D R AO1 IH0 NG +DRAWING'S D R AO1 IH0 NG Z +DRAWINGROOM D R AO1 IH0 NG R UW2 M +DRAWINGS D R AO1 IH0 NG Z +DRAWL D R AO1 L +DRAWLED D R AO1 L D +DRAWLING D R AO1 L IH0 NG +DRAWLINGLY D R AO1 L IH0 NG L IY0 +DRAWLOCK D R AO1 L AA2 K +DRAWLS D R AO1 L Z +DRAWLY D R AO1 L IY0 +DRAWN D R AO1 N +DRAWNE D R AO1 N +DRAWS D R AO1 Z +DRAWSTRING D R AO1 S T R IH2 NG +DRAWWELL D R AO1 W EH0 L +DRAX D R AE1 K S +DRAXY D R AE1 K S IY0 +DRAXY'S D R AE1 K S IY0 Z +DRAY D R EY1 +DRAYCOTT D R EY1 K AH0 T +DRAYCOTT'S D R EY1 K AH0 T S +DRAYMAN D R EY1 M AH0 N +DRAYMAN'S D R EY1 M AH0 N Z +DRAYMEN D R EY1 M AH0 N +DRAYMOOR D R EY1 M UH2 R +DRAYNE D R EY1 N +DRAYNE'S D R EY1 N Z +DRAYS D R EY1 Z +DRAYSON D R EY1 S AH0 N +DRAYTON D R EY1 T AH0 N +DRAYTON'S D R EY1 T AH0 N Z +DRE D R EY2 +DREA D R IY0 AH0 +DREAD D R EH1 D +DREADED D R EH1 D IH0 D +DREADEST D R EH1 D AH0 S T +DREADFU D R EH1 D F UW0 +DREADFUL D R EH1 D F AH0 L +DREADFULEST D R EH1 D F AH0 L AH0 S T +DREADFULL D R EH1 D F AH0 L +DREADFULLEST D R EH1 D F AH0 L AH0 S T +DREADFULLY D R EH1 D F AH0 L IY0 +DREADFULNESS D R EH1 D F AH0 L N AH0 S +DREADFULS D R EH1 D F AH0 L Z +DREADIN D R EH1 D IY0 N +DREADING D R EH1 D IH0 NG +DREADLESS D R EH1 D L AH0 S +DREADNAUGHT D R EH1 D N AO1 T +DREADNAUGHTS D R EH1 D N AO0 T S +DREADNOUGHT D R EH1 D N AO2 T +DREADNOUGHTS D R EH1 D N AO2 T S +DREADS D R EH1 D Z +DREAM D R IY1 M +DREAM'D D R IY1 M D +DREAM'S D R IY1 M Z +DREAM'ST D R IY1 M S T +DREAME D R IY1 M +DREAMED D R IY1 M D +DREAMER D R IY1 M ER0 +DREAMER'S D R IY1 M ER0 Z +DREAMERIE D R IY1 M ER0 IY0 +DREAMERS D R IY1 M ER0 Z +DREAMEST D R EH1 M AH0 S T +DREAMETH D R IY1 M IH0 TH +DREAMFUL D R IY1 M F UH2 L +DREAMFULLY D R IY1 M F AH0 L IY0 +DREAMIER D R IY1 M IY0 ER0 +DREAMIEST D R IY1 M IY0 IH0 S T +DREAMILY D R IY1 M AH0 L IY0 +DREAMIN D R IY1 M IH0 N +DREAMINESS D R IY1 M IY0 N AH0 S +DREAMING D R IY1 M IH0 NG +DREAMINGLY D R IY1 M IH0 NG L IY0 +DREAMINGS D R IY1 M IH0 NG Z +DREAMLAND D R IY1 M L AE2 N D +DREAMLESS D R IY1 M L AH0 S +DREAMLESSLY D R IY1 M L AH0 S L IY0 +DREAMLIKE D R IY1 M L AY2 K +DREAMPT D R IY1 M T +DREAMS D R IY1 M Z +DREAMT D R EH1 M T +DREAMY D R IY1 M IY0 +DREAR D R IY1 R +DREARIER D R IY1 R IY0 ER0 +DREARIEST D R IY1 R IY0 AH0 S T +DREARILY D R IY1 R AH0 L IY0 +DREARINESS D R IY1 R IY0 N AH0 S +DREARY D R IH1 R IY0 +DREAULOND D R UW1 L AH0 N D +DREAZURE D R IY1 ZH ER0 +DREBBER D R EH1 B ER0 +DRECHSLER D R EH1 K S AH0 L ER0 +DRECHSLER D R EH1 K S L ER0 +DRED D R EH1 D +DREDA D R EY1 D AH0 +DREDA'S D R EH1 D AH0 Z +DREDDLINGTON D R EH1 D L IH0 NG T AH0 N +DREDDLINGTON'S D R EH1 D L IH0 NG T AH0 N Z +DREDE D R IY1 D +DREDFUL D R EH1 D F AH0 L +DREDGE D R EH1 JH +DREDGE'S D R EH1 JH IH0 Z +DREDGED D R EH1 JH D +DREDGER D R EH1 JH ER0 +DREDGERS D R EH1 JH ER0 Z +DREDGES D R EH1 JH AH0 Z +DREDGES D R EH1 JH IH0 Z +DREDGING D R EH1 JH IH0 NG +DREDGINGS D R EH1 JH IH0 NG Z +DREDLINTON D R EH1 D L IH0 N T AH0 N +DREDLINTON'S D R EH1 D L IH0 N T AH0 N Z +DREE D R IY1 +DREED D R IY1 D +DREEME D R IY2 M +DREEN D R IY1 N +DREEPDAILY D R IY1 P D EY1 L IY0 +DREER D R IH1 R +DREEVER D R IY1 V ER0 +DREEVER'S D R IY1 V ER0 Z +DREFFLE D R EH1 F AH0 L +DREFFUL D R EH1 F AH0 L +DREFFULLY D R EH1 F AH0 L IY0 +DREG D R EH1 G +DREGHORN D R EH1 G HH ER0 N +DREGS D R EH1 G Z +DREI D R AY1 +DREIBERG D R AY1 B ER0 G +DREICH D R AY1 K +DREIER D R AY1 ER0 +DREISER D R AY1 S ER0 +DREISSIGER D R AY1 S IH0 G ER0 +DREISSIGER'S D R AY1 S IH0 G ER0 Z +DRELICH D R EH1 L IH0 K +DRELINCOURT D R EH1 L AH0 N K AO2 R T +DRELINCOURT'S D R EH1 L AH0 N K AO2 R T S +DRELMER D R EH1 L M ER0 +DREM D R EH1 M +DREMMEL D R EH1 M AH0 L +DREMMEL'S D R EH1 M AH0 L Z +DREMP D R EH1 M P +DREN D R EH1 N +DRENCH D R EH1 N CH +DRENCH'D D R EH1 N CH D +DRENCHED D R EH1 N CH T +DRENCHES D R EH1 N CH AH0 Z +DRENCHING D R EH1 N CH IH0 NG +DRENCHINGS D R EH1 N CH IH0 NG Z +DRENGO D R EH1 NG G OW0 +DRENNAN D R EH1 N AH0 N +DRENNEN D R EH1 N AH0 N +DRENNEN'S D R EH1 N AH0 N Z +DRENTELL D R EH1 N T AH0 L +DRENTHE D R EH1 N TH IY0 +DRESDEN D R EH1 Z D IH0 N +DRESDENER D R EH1 Z D IH0 N ER0 +DRESHAM D R EH1 SH AH0 M +DRESS D R EH1 S +DRESS'D D R EH1 S D +DRESSCOAT D R EH1 S K OW2 T +DRESSE D R EH1 S +DRESSED D R EH1 S T +DRESSEL D R EH1 S AH0 L +DRESSEL'S D R EH1 S AH0 L Z +DRESSER D R EH1 S ER0 +DRESSER'S D R EH1 S ER0 Z +DRESSERS D R EH1 S ER0 Z +DRESSES D R EH1 S AH0 Z +DRESSES D R EH1 S IH0 Z +DRESSIER D R EH1 S IY0 ER0 +DRESSIN D R EH1 S IH0 N +DRESSIN' D R EH1 S IH0 N +DRESSING D R EH1 S IH0 NG +DRESSINGS D R EH1 S IH0 NG Z +DRESSLER D R EH1 S L ER0 +DRESSLIN D R EH1 S L IH0 N +DRESSMAKER D R EH1 S M EY2 K ER0 +DRESSMAKER'S D R EH1 S M EY2 K ER0 Z +DRESSMAKERS D R EH1 S M EY2 K ER0 Z +DRESSMAKING D R EH1 S M EY2 K IH0 NG +DRESSY D R EH1 S IY0 +DREST D R EH1 S T +DRETFUL D R EH1 T F AH0 L +DRETFULLY D R EH1 T F AH0 L IY0 +DREUX D R OW0 +DREVE D R IY1 V +DREVENOFF D R EH1 V IH0 N AO0 F +DREVER D R EH1 V ER0 +DREW D R UW1 +DREW'S D R UW1 Z +DREWITT D R UW1 IH0 T +DREWS D R UW1 Z +DREWYER D R UW1 IY0 ER0 +DREXEL D R EH1 K S AH0 L +DREXELL D R EH1 K S AH0 L +DREXLEY D R EH1 K S L IY0 +DREXLEY'S D R EH1 K S L IY0 Z +DREYER D R EY1 ER0 +DREYFUS D R AY1 F AH0 S +DREYFUS D R EY1 F AH0 S +DREYMARSH D R EY1 M AA2 R SH +DRI D R IY0 +DRIBBLE D R IH1 B AH0 L +DRIBBLED D R IH1 B AH0 L D +DRIBBLES D R IH1 B AH0 L Z +DRIBBLING D R IH1 B AH0 L IH0 NG +DRIBBLING D R IH1 B L IH0 NG +DRIBE D R AY1 B +DRIBLET D R IH1 B L AH0 T +DRIBLETS D R IH1 B L AH0 T S +DRIBS D R IH1 B Z +DRIE D R AY1 +DRIEBERGEN D R IY1 B ER0 G AH0 N +DRIED D R AY1 D +DRIEL D R IY1 L +DRIER D R AY1 ER0 +DRIERS D R AY1 ER0 Z +DRIES D R AY1 Z +DRIESBACH D R IY1 S B AA0 K +DRIEST D R AY1 AH0 S T +DRIETH D R AY1 IH0 TH +DRIF D R IH1 F +DRIFFIELD D R IH1 F IY0 L D +DRIFT D R IH1 F T +DRIFTAGE D R IH1 F T AH0 JH +DRIFTED D R IH1 F T AH0 D +DRIFTED D R IH1 F T IH0 D +DRIFTER D R IH1 F T ER0 +DRIFTERS D R IH1 F T ER0 Z +DRIFTIN D R IH1 F T IH0 N +DRIFTING D R IH1 F T IH0 NG +DRIFTINGS D R IH1 F T IH0 NG Z +DRIFTS D R IH1 F T S +DRIFTWOOD D R IH1 F T W UH2 D +DRIFTY D R IH1 F T IY0 +DRIGGS D R IH1 G Z +DRILGO D R IH1 L G OW2 +DRILGOES D R IH1 L G OW2 Z +DRILL D R IH1 L +DRILLED D R IH1 L D +DRILLER D R IH1 L ER0 +DRILLERS D R IH1 L ER0 Z +DRILLFORD D R IH1 L F ER0 D +DRILLIN D R IH1 L IH0 N +DRILLING D R IH1 L IH0 NG +DRILLINGS D R IH1 L IH0 NG Z +DRILLOT D R IH1 L AH0 T +DRILLS D R IH1 L Z +DRILY D R AH0 L IY0 +DRIMDARROCH D R IH1 M D AA2 R AH0 K +DRIN D R IH0 N +DRINA D IY1 N AH0 +DRING D R IH1 NG +DRINGENSTADT D R IH1 NG AH0 N S T AE2 T +DRINK D R IH1 NG K +DRINK'S D R IH1 NG K S +DRINKABLE D R IH1 N K AH0 B AH0 L +DRINKABLES D R IH1 N K AH0 B AH0 L Z +DRINKE D R IH1 NG K +DRINKED D R IH1 NG K T +DRINKER D R IH1 NG K ER0 +DRINKER'S D R IH1 NG K ER0 Z +DRINKERS D R IH1 NG K ER0 Z +DRINKEST D R IH1 NG K IH0 S T +DRINKETH D R IH1 NG K IH0 TH +DRINKIN D R IH1 NG K IH0 N +DRINKIN' D R IH1 NG K IH0 N +DRINKING D R IH1 NG K IH0 NG +DRINKINGS D R IH1 NG K IH0 NG Z +DRINKS D R IH1 NG K S +DRINKWATER D R IH1 NG K W AO2 T ER0 +DRINKWATER'S D R IH1 NG K W AO2 T ER0 Z +DRINKWOTTER D R IH1 NG K W AA1 T ER0 +DRIP D R IH1 P +DRIPPED D R IH1 P T +DRIPPIN D R IH1 P IH0 N +DRIPPING D R IH1 P IH0 NG +DRIPPINGLY D R IH1 P IH0 NG L IY0 +DRIPPINGS D R IH1 P IH0 NG Z +DRIPPY D R IH1 P IY0 +DRIPS D R IH1 P S +DRIPSTONE D R IH1 P S T OW2 N +DRISCOE D R IH1 S K OW0 +DRISCOL D R IH1 S K AA0 L +DRISCOLD D R IH1 S K OW1 L D +DRISCOLL D R IH1 S K AH0 L +DRISCOLL'S D R IH1 S K AA0 L Z +DRISCOLLS D R IH1 S K AA0 L Z +DRISHNA D R IH1 SH N AH0 +DRISKOL D R IH1 S K AO0 L +DRITTO D R IH1 T OW0 +DRIV D R IH1 V +DRIV'N D R AY1 V AH0 N +DRIVE D R AY1 V +DRIVEL D R IH1 V AH0 L +DRIVELFORD D R IH1 V AH0 L F ER0 D +DRIVELING D R IH1 V AH0 L IH0 NG +DRIVELLED D R IH1 V AH0 L D +DRIVELLER D R IH1 V AH0 L ER0 +DRIVELLERS D R IH1 V AH0 L ER0 Z +DRIVELLING D R IH1 V AH0 L IH0 NG +DRIVELS D R IH1 V AH0 L Z +DRIVEN D R IH1 V AH0 N +DRIVER D R AY1 V ER0 +DRIVER'S D R AY1 V ER0 Z +DRIVERS D R AY1 V ER0 Z +DRIVES D R AY1 V Z +DRIVEST D R IH1 V AH0 S T +DRIVETH D R IH1 V IH0 TH +DRIVEWAY D R AY1 V W EY2 +DRIVEWAYS D R AY1 V W EY2 Z +DRIVIN D R AY1 V IH0 N +DRIVING D R AY1 V IH0 NG +DRIZZLE D R IH1 Z AH0 L +DRIZZLED D R IH1 Z AH0 L D +DRIZZLES D R IH1 Z AH0 L Z +DRIZZLING D R IH1 Z AH0 L IH0 NG +DRIZZLING D R IH1 Z L IH0 NG +DRIZZLY D R IH1 Z L IY0 +DRO D R OW0 +DROCH D R AA1 K +DROCK D R AA2 K +DROFFS D R AO2 F S +DROGHEDA D R OW0 G EY1 D AH0 +DROGUE D R OW1 G +DROGUET D R OW0 G AH0 T +DROGUET'S D R OW1 G EY1 Z +DROI D R OY0 +DROIT D R OY1 T +DROITE D R OY2 T +DROITS D R OY1 T S +DROITWICH D R OY1 T W IH2 CH +DROITWICHES D R OY1 T W IH0 CH IH0 Z +DROLATIQUES D R OW2 L AH0 T IY1 K S +DROLE D R OW1 L +DROLETTE D R OW0 L EH1 T +DROLL D R OW1 L +DROLLER D R OW1 L ER0 +DROLLERIES D R OW1 L ER0 IY0 Z +DROLLERY D R OW1 L ER0 IY0 +DROLLEST D R OW1 L AH0 S T +DROLLING D R OW1 L IH0 NG +DROLLNESS D R OW1 L N AH0 S +DROLLO D R OW1 L OW2 +DROLLS D R OW1 L Z +DROLLY D R OW1 L IY0 +DROM D R AA1 M +DROMARD D R AA1 M ER0 D +DROMARDS D R AA1 M AA2 R D Z +DROMAS D R OW1 M AH0 Z +DROME D R OW2 M +DROMEDARIES D R AA1 M AH0 D EH2 R IY0 Z +DROMEDARY D R AA1 M AH0 D EH2 R IY0 +DROMEDARY'S D R AA1 M AH0 D EH2 R IY0 Z +DROMIO D R AA1 M IY0 OW0 +DROMIOS D R AA1 M IY0 OW2 Z +DROMO D R OW1 M OW0 +DROMON D R OW1 M AH0 N +DROMORE D R AA1 M AO0 R +DROMOS D R OW2 M OW0 Z +DROMUND D R OW1 M AH0 N D +DROMY D R OW2 M IY0 +DRONA D R OW1 N AH0 +DRONA'S D R OW1 N AH0 Z +DRONE D R OW1 N +DRONE'S D R OW1 N Z +DRONED D R OW1 N D +DRONES D R OW1 N Z +DRONGO D R OW1 NG G OW0 +DRONING D R OW1 N IH0 NG +DRONK D R AA1 NG K +DRONNE D R AA1 N +DRONTHEIM D R AA1 N T HH AY0 M +DROO D R UW1 +DROOCE D R OW1 CH IY0 +DROOD D R UW1 D +DROOD'S D R UW1 D Z +DROOL D R UW1 L +DROOLED D R UW1 L D +DROOLING D R UW1 L IH0 NG +DROOM D R UW2 M +DROON D R UW1 N +DROONED D R UW1 N D +DROOP D R UW1 P +DROOP'D D R UW1 P D +DROOPED D R UW1 P T +DROOPIN D R UW1 P IH0 N +DROOPING D R UW1 P IH0 NG +DROOPINGLY D R UW1 P IH0 NG L IY0 +DROOPS D R UW1 P S +DROOPT D R UW1 P T +DROOPY D R UW1 P IY0 +DROP D R AA1 P +DROP D R AO1 P +DROP'D D R AA1 P D +DROP'S D R AA1 P S +DROPED D R OW1 P T +DROPLET D R AA1 P L AH0 T +DROPLETS D R AA1 P L AH0 T S +DROPLIGHT D R AA1 P L AY2 T +DROPMORE D R AA1 P M AO0 R +DROPP'D D R AA1 P D +DROPPED D R AA1 P T +DROPPER D R AA1 P ER0 +DROPPERS D R AA1 P ER0 Z +DROPPETH D R AA1 P IH0 TH +DROPPIN D R AA1 P IH0 N +DROPPING D R AA1 P IH0 NG +DROPPINGS D R AA1 P IH0 NG Z +DROPS D R AA1 P S +DROPSICAL D R AA1 P S IH0 K AH0 L +DROPSIES D R AA1 P S IY0 Z +DROPSY D R AA1 P S IY0 +DROPT D R AA1 P T +DROQVILLE D R OW1 K V IH2 L +DROQVILLE'S D R OW1 K V IH2 L Z +DROR D R ER0 +DRORED D R ER0 D +DRORIN D R AO1 R IH0 N +DRORIN' D R AO1 R IH0 N +DRORING D R AO1 R IH0 NG +DROSCHKE D R AO1 SH K +DROSCHKY D R AO1 SH K IY0 +DROSERA D R AA1 S EH1 R AH0 +DROSHKY D R OW1 SH K IY0 +DROSKY D R OW1 S K IY0 +DROSS D R AO1 S +DROSSDELL D R AO1 S D AH0 L +DROSSE D R AA1 S +DROSSY D R AO1 S IY0 +DROST D R AA1 S T +DROST'S D R OW1 S T S +DROSTE D R AA1 S T +DROSTE D R AA1 S T AH0 +DROSTE D R OW1 S T +DROT D R OW1 +DROUBLE D R AH1 B AH0 L +DROUET D R UW1 T +DROUGH D R AW1 +DROUGHT D R AW1 T +DROUGHTS D R AW1 T S +DROUGHTY D R AW1 T IY0 +DROUILLARD D R W IY0 L AA1 R D +DROUOT D R W OW1 +DROUTH D R AW1 TH +DROUTHIELAW D R AW1 TH IY0 L AO2 +DROUTHS D R AW1 TH S +DROUTHY D R AW1 TH IY0 +DROUVA D R UW1 V AH0 +DROUYN D R UW1 IH0 N +DROVE D R OW1 V +DROVER D R OW1 V ER0 +DROVER'S D R OW1 V ER0 Z +DROVERS D R OW1 V ER0 Z +DROVES D R OW1 V Z +DROVING D R OW1 V IH0 NG +DROW D R OW2 +DROWN D R AW1 N +DROWN'D D R AW1 N D +DROWND D R AW1 N D +DROWNDED D R AW1 N D IH0 D +DROWNDIN D R AW1 N D IH0 N +DROWNDING D R AW1 N D IH0 NG +DROWNE D R AW1 N +DROWNE'S D R AW1 N Z +DROWNED D R AW1 N D +DROWNIN D R AW1 N IH0 N +DROWNING D R AW1 N IH0 NG +DROWNINGS D R AW1 N IH0 NG Z +DROWNS D R AW1 N Z +DROWS D R AW1 Z +DROWSE D R AW1 Z +DROWSED D R AW1 Z D +DROWSES D R AW1 Z IH0 Z +DROWSIER D R AW1 Z IY0 ER0 +DROWSIEST D R AW1 Z IY0 AH0 S T +DROWSILY D R AW1 Z AH0 L IY0 +DROWSINESS D R AW1 Z IY0 N AH0 S +DROWSING D R AW1 Z IH0 NG +DROWSY D R AW1 Z IY0 +DROZ D R AA1 Z +DROZHKI D R AA1 Z K IY0 +DRS D R Z +DRU D R UW1 +DRU'S D R UW1 Z +DRUB D R AH1 B +DRUBB'D D R AH1 B D +DRUBBED D R AH1 B D +DRUBBING D R AH1 B IH0 NG +DRUBBINGS D R AH1 B IH0 NG Z +DRUCE D R UW1 S +DRUCE'S D R UW1 S IH0 Z +DRUCKEN D R AH1 K AH0 N +DRUCOUR D R AH1 K UH1 R +DRUDGE D R AH1 JH +DRUDGED D R AH1 JH D +DRUDGERIES D R AH1 JH ER0 IY0 Z +DRUDGERY D R AH1 JH ER0 IY0 +DRUDGES D R AH1 JH IH0 Z +DRUDGING D R AH1 JH IH0 NG +DRUE D R UW1 +DRUEL D R UW1 L +DRUFF D R AH1 F +DRUG D R AH1 G +DRUG'S D R AH1 G Z +DRUGA D R UW1 G AH0 +DRUGGED D R AH1 G D +DRUGGER D R AH1 G ER0 +DRUGGET D R AH1 G IH0 T +DRUGGETS D R AH1 G AH0 T S +DRUGGING D R AH1 G IH0 NG +DRUGGIST D R AH1 G IH0 S T +DRUGGIST'S D R AH1 G AH0 S T S +DRUGGIST'S D R AH1 G IH0 S T S +DRUGGISTS D R AH1 G AH0 S T S +DRUGGISTS D R AH1 G IH0 S T S +DRUGLESS D R AH1 G L AH0 S +DRUGS D R AH1 G Z +DRUGSTORE D R AH1 G S T AO2 R +DRUID D R UW1 IH0 D +DRUID'S D R UW1 IH0 D Z +DRUIDIC D R UW1 IH0 D IH0 K +DRUIDICAL D R UW1 IH0 D IH0 K AH0 L +DRUIDISM D R UW1 AH0 D IH2 Z AH0 M +DRUIDS D R UW1 IH0 D Z +DRUILLETES D R UW1 AH0 L EY1 S +DRUIM D R UW1 AH0 M +DRUITT D R UW1 IH0 T +DRUKKER D R UW1 K ER0 +DRUM D R AH1 M +DRUM'S D R AH1 M Z +DRUMAH D R AH1 M AH0 +DRUMBARROW D R AH1 M B EH2 R OW0 +DRUMBEAT D R AH1 M B IY2 T +DRUMBEATS D R AH1 M B IY2 T S +DRUMBLADE D R AH1 M B L EY2 D +DRUMCALLER D R AH1 M K AO1 L ER0 +DRUMCLIFF D R AH1 M K L IH0 F +DRUMCLOG D R AH1 M K L AO2 G +DRUMCURRAN D R AH1 M K UH1 R AH0 N +DRUMFIRE D R AH1 M F AY2 R +DRUMHEAD D R AH1 M HH EH2 D +DRUMLANRIG D R AH1 M L AE1 N R IH0 G +DRUMLE D R AH1 M L +DRUMLEY D R AH1 M L IY0 +DRUMLY D R AH1 M L IY0 +DRUMM D R AH1 M +DRUMMED D R AH1 M D +DRUMMER D R AH1 M ER0 +DRUMMER'S D R AH1 M ER0 Z +DRUMMERS D R AH1 M ER0 Z +DRUMMIN D R AH1 M IH0 N +DRUMMING D R AH1 M IH0 NG +DRUMMLE D R AH1 M L +DRUMMOND D R AH1 M AH0 N D +DRUMMOND'S D R AH1 M AH0 N D Z +DRUMMONDS D R AH1 M AH0 N D Z +DRUMMUIR D R AH1 M Y UH1 R +DRUMQUHAT D R AH1 M K W AH0 T +DRUMS D R AH1 M Z +DRUMSHEUGH D R AH1 M S HH Y UW1 +DRUMSTICK D R AH1 M S T IH0 K +DRUMSTICKS D R AH1 M S T IH2 K S +DRUMTHWACKET D R AH1 M TH W AA1 K AH0 T +DRUMTOCHTY D R AH1 M T AH0 K T IY0 +DRUNK D R AH1 NG K +DRUNKARD D R AH1 NG K ER0 D +DRUNKARD'S D R AH1 NG K ER0 D Z +DRUNKARDS D R AH1 NG K ER0 D Z +DRUNKEN D R AH1 NG K AH0 N +DRUNKENLY D R AH1 NG K AH0 N L IY0 +DRUNKENNESS D R AH1 NG K AH0 N N AH0 S +DRUNKER D R AH1 NG K ER0 +DRUNKEST D R AH1 NG K AH0 S T +DRUNKS D R AH1 NG K S +DRUPADA D R UW1 P AA1 D AH0 +DRUPE D R UW1 P +DRUPES D R UW1 P S +DRURIOLANUS D R UW1 R IY0 OW1 L AH0 N IH0 S +DRURY D R UW1 R IY0 +DRURY'S D R UW1 R IY0 Z +DRUSE D R UW1 Z +DRUSE'S D R UW1 Z IH0 Z +DRUSES D R UW1 Z IH0 Z +DRUSHNEVNA D R AH1 SH N AH0 V N AH0 +DRUSIE D R AH1 S IY0 +DRUSILLA D R UW2 S IH1 L AH0 +DRUSILLA'S D R UW1 S IH1 L AH0 Z +DRUSILLY D R UW1 Z AH0 L IY0 +DRUSUS D R UW1 S AH0 S +DRUTHER D R AH1 DH ER0 +DRUV D R UW1 V +DRUZE D R UW1 Z +DRUZES D R UW1 Z IH0 Z +DRY D R AY1 +DRY'D D R AY1 D +DRYAD D R AY1 AE0 D +DRYAD'S D R AY1 AE0 D Z +DRYADES D R AY1 AH0 D IY2 Z +DRYADS D R AY1 AE2 D Z +DRYAS D R AY1 AH0 S +DRYASDUST D R AY1 AH0 Z D AH2 S T +DRYBONE D R AY1 B OW2 N +DRYBROOK D R AY1 B R UH2 K +DRYBURGH D R AY1 B ER0 G +DRYDEN D R AY1 D AH0 N +DRYDEN'S D R AY1 D AH0 N Z +DRYDOCK 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L AH0 K +DUANE D W EY1 N +DUANE'S D W EY1 N Z +DUAR D W AA1 R +DUARS D W AA1 R Z +DUART D UW1 ER0 T +DUARTE D W AA1 R T EY2 +DUARTE'S D W AA1 R T EY2 Z +DUAS D UW2 AH0 Z +DUAT D UW1 AE0 T +DUATERRA D UW2 AH0 T EH1 R AH0 +DUB D AH1 B +DUBAR D UW1 B ER0 +DUBARLE D UW0 B AA1 R AH0 L +DUBARRE D UW0 B AA1 R +DUBARRY D UW1 B EH2 R IY0 +DUBARRY'S D UW1 B EH2 R IY0 Z +DUBASH D AH1 B AE0 SH +DUBBE D AH1 B +DUBBED D AH1 B D +DUBBIN D AH1 B IH0 N +DUBBING D AH1 B IH0 NG +DUBBLE D AH1 B AH0 L +DUBBLEY D AH1 B L IY0 +DUBBS D AH1 B Z +DUBBULDIDEERY D AH1 B AH0 L D IY1 D IH1 R IY0 +DUBCEK D AH1 B CH EH2 K +DUBCEK D UW1 B CH EH2 K +DUBEC D UW0 B EH1 K +DUBECHNIA D UW1 B EH0 K N IY1 AH0 +DUBEDAT D UW1 B EH0 D AE2 T +DUBERSOME D UW1 B ER0 S AH0 M +DUBETCHNYA D UW0 B EH1 CH N IY0 AH0 +DUBH D UW1 B +DUBIETY D AH1 B IY0 T IY0 +DUBIOUS D UW1 B IY0 AH0 S +DUBIOUSLY D UW1 B IY0 AH0 S L IY0 +DUBIOUSNESS D UW1 B IY0 AH0 S N AH0 S +DUBITATION D UW2 B AH0 T EY1 SH AH0 N +DUBKOFF D AH1 B K AO0 F +DUBLIN D AH1 B L 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N HH EY2 V AH0 N +DUNHOLM D AH1 N HH OW2 L M +DUNHOLMS D AH1 N HH OW2 L M Z +DUNIN D AH1 N IH0 N +DUNK D AH1 NG K +DUNK'S D AH1 NG K S +DUNKARD D AH1 NG K ER0 D +DUNKARDS D AH1 NG K ER0 D Z +DUNKE D AH1 NG K +DUNKELBERG D AH1 NG K AH0 L B ER0 G +DUNKELBERGS D AH1 NG K AH0 L B ER0 G Z +DUNKELD D AH1 NG K EH0 L D +DUNKER D AH1 NG K ER0 +DUNKERLEY D AH1 NG K ER0 L IY0 +DUNKERQUE D AH1 NG K ER0 K IY0 +DUNKERS D AH1 NG K ER0 Z +DUNKERY D AH1 NG K ER0 IY0 +DUNKIN D AH1 NG K IH0 N +DUNKING D AH1 NG K IH0 NG +DUNKIRK D AH1 N K ER0 K +DUNKSWELL D AH1 NG K S W EH2 L +DUNLAP D AH1 N L AE0 P +DUNLAP'S D AH1 N L AE0 P S +DUNLAVEY D AH0 N L AH0 V EY1 +DUNLAVEY'S D AH1 N L AH0 V EY1 Z +DUNLEARY D UW1 N L IH1 R IY0 +DUNLEAVY D UW1 N L AH0 V IY0 +DUNLEE D UW1 N L IY0 +DUNLEIGH D UW1 N L AH0 +DUNLOE D AH1 N L OW2 +DUNLOP D AH1 N L AA2 P +DUNLOP'S D AH1 N L AA1 P S +DUNLOPPEL D AH1 N L AA1 P AH0 L +DUNLUCE D AH1 N L UW2 S +DUNMAIL D UW0 N M EY2 L +DUNMORE D UW0 N M AO0 R +DUNMORE'S D UW0 N M AO2 R Z 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AH0 N IH1 R IH0 N +ENGINEERING EH1 N JH AH0 N IH1 R IH0 NG +ENGINEERS EH1 N JH AH0 N IH1 R Z +ENGINEMAN EH1 N JH AH0 N M AH0 N +ENGINEMEN EH1 N JH AH0 N M IH0 N +ENGINERY EH1 N JH AH0 N ER0 IY0 +ENGINES EH1 N JH AH0 N Z +ENGIRDLED EH1 NG G ER1 D AH0 L D +ENGIRDLING EH1 NG G ER1 D AH0 L IH0 NG +ENGIRT EH1 NG G ER1 T +ENGIS EH1 N JH AH0 S +ENGLAENDER IH1 NG G L AE1 N D ER0 +ENGLAENDERIN IH1 NG G L AE1 N D ER0 IH0 N +ENGLAN IH1 NG G L AH0 N +ENGLAND IH1 NG G L AH0 N D +ENGLAND'S IH1 NG G L AH0 N D Z +ENGLANDER IH1 NG G L AH0 N D ER0 +ENGLANDER'S IH1 NG G L AH0 N D ER0 Z +ENGLANDERS IH1 NG G L AH0 N D ER0 Z +ENGLANDISM IH1 NG G L AH0 N D IH2 Z AH0 M +ENGLANDS IH1 NG G L AH0 N D Z +ENGLE EH1 NG G AH0 L +ENGLE'S IH1 NG AH0 L Z +ENGLEBOURN EH1 NG G AH0 L B UH1 R N +ENGLEDEW IH1 NG L D UW2 +ENGLEDUE IH1 NG L D UW2 +ENGLEESH IH1 NG L IY1 SH +ENGLEFIELD IH1 NG AH0 L F IY2 L D +ENGLEFIELD'S IH1 NG AH0 L F IY2 L D Z +ENGLEHART IH1 NG AH0 L HH AA0 R T +ENGLER EH1 NG G AH0 L ER0 +ENGLER EH1 NG G L 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IH0 R EY1 S +ERASE IY0 R EY1 S +ERASED IH0 R EY1 S T +ERASED IY0 R EY1 S T +ERASER IH0 R EY1 S ER0 +ERASER IY0 R EY1 S ER0 +ERASERS IH0 R EY1 S ER0 Z +ERASERS IY0 R EY1 S ER0 Z +ERASES IH0 R EY1 S IH0 Z +ERASES IY0 R EY1 S IH0 Z +ERASING IH0 R EY1 S IH0 NG +ERASING IY0 R EY1 S IH0 NG +ERASISTRATUS IH0 R EY1 S IH0 S T R AE1 T AH0 S +ERASMUS IH0 R AE1 Z M AH0 S +ERASMUS'S IH0 R AE1 Z M AH0 S IH0 Z +ERASTE IH0 R EY1 S T +ERASTIAN IH0 R AE1 S T IY0 AH0 N +ERASTIANISM IH0 R AE1 S T IY0 AH0 N IH0 Z AH0 M +ERASTIANS IH0 R EY1 S CH AH0 N Z +ERASTO IH0 R AE1 S T OW0 +ERASTUS IH0 R AE1 S T AH0 S +ERASURE IH0 R EY1 ZH ER0 +ERASURES IH0 R EY1 ZH ER0 Z +ERAT IH0 R AE1 T +ERATH IH1 R AH0 TH +ERATO EH0 R AA1 T OW0 +ERATOSTHENES EH1 R AH0 T AO2 S TH IY1 N Z +ERB ER1 B +ERBA ER1 B AH0 +ERBACH ER1 B AA0 K +ERBE ER1 B +ERBERT ER1 B ER0 T +ERBIN ER1 B IH0 N +ERBISDORF ER1 B IH0 S D AO0 R F +ERBOUT ER1 B AW2 T +ERBPRINCESSIN ER1 B P R IH1 N S EH0 S IH0 N +ERCILDOUN ER1 S IH0 L D UW0 N +ERCILDOUNE ER1 S IH0 L D UW0 N +ERCILDOWN ER1 S IH0 L D AW2 N +ERCILLA ER1 S IH1 L AH0 +ERCKMANN ER1 K M AH0 N +ERCLES ER1 K AH0 L Z +ERCOLE ER0 K OW1 L IY0 +ERCOLE'S ER0 K OW1 L Z +ERD ER1 D +ERDA EH1 R D AH0 +ERDBLATT ER1 D B L AH0 T +ERDE ER1 D +ERDMANN ER1 D M AH0 N +ERE EH1 R +ERE'S EH1 R Z +EREBUS EH1 R IH0 B IH0 S +ERECH IH0 R EH1 K +ERECHTHEUM IH0 R EH1 K TH IY1 AH0 M +ERECHTHEUS IH0 R EH1 K TH IY0 AH0 S +ERECT IH0 R EH1 K T +ERECTED IH0 R EH1 K T AH0 D +ERECTED IH0 R EH1 K T IH0 D +ERECTILE IH0 R EH1 K T AH0 L +ERECTING IH0 R EH1 K T IH0 NG +ERECTION IH0 R EH1 K SH AH0 N +ERECTIONS IH0 R EH1 K SH AH0 N Z +ERECTLY IH0 R EH1 K L IY0 +ERECTNESS IH0 R EH1 K N AH0 S +ERECTOR IH0 R EH1 K T ER0 +ERECTS IH0 R EH1 K T S +ERELONG EH1 R AH0 L AO0 NG +EREMA ER0 IY1 M AH0 +EREMITE EH1 R AH0 M AY2 T +EREMITES EH1 R AH0 M AY2 T S +EREMITESS IH0 R EH1 M AH0 T AH0 S +ERENTZ ER0 EH1 N T S +ERES IY1 R Z +ERETRIA IH0 R EH1 T R IY0 AH0 +ERETRIANS IH0 R EH1 T R IY0 AH0 N Z +EREVATO EH1 R EY0 V AA1 T OW0 +EREWHILE EH1 R W AY2 L +EREWHON EH1 R W AA0 N +EREWHONIAN EH1 R W OW1 N IY0 AH0 N +EREWHONIANS EH1 R W OW1 N IY0 AH0 N Z +ERFURT ER1 F ER0 T +ERFURTH ER1 F ER0 TH +ERG ER1 G +ERGA ER1 G AH0 +ERGALON ER1 G AH0 L AA2 N +ERGASTE ER1 G AH0 S T +ERGASTULA ER1 G AA0 S T UW1 L AH0 +ERGASTULUM ER0 G AE2 S T UW0 L AH0 M +ERGIMO ER1 G IH0 M OW2 +ERGIN ER1 G IH0 N +ERGO ER1 G OW0 +ERGOT ER1 G AH0 T +ERGUSHOV ER1 G AH0 SH AA0 V +ERH ER1 HH +ERHALTE ER0 HH AO2 L T +ERHARDT ER1 HH AA0 R T +ERHART ER1 HH AA0 R T +ERHAUPT EH1 R HH AO1 P T +ERI EH1 R IY0 +ERI'S EH0 R IY0 Z +ERIC EH1 R IH0 K +ERIC'S EH1 R IH0 K S +ERICA EH1 R IH0 K AH0 +ERICA'S EH1 R IH0 K AH0 Z +ERICH EH1 R IH0 K +ERICH'S EH1 R IH0 K S +ERICHSEN EH1 R IH0 K S AH0 N +ERICHT EH1 R IH0 K T +ERICK EH1 R IH0 K +ERICK'S EH1 R IH0 K S +ERICKSEN EH1 R IH0 K S AH0 N +ERICKSON EH1 R IH0 K S AH0 N +ERICKSON'S EH1 R IH0 K S AH0 N Z +ERICSON EH1 R IH0 K S AH0 N +ERICSON'S EH1 R IH0 K S AH0 N Z +ERICSONS EH1 R IH0 K S AH0 N Z +ERICSSEN EH1 R IH0 K S AH0 N +ERICSSON EH1 R IH0 K S AH0 N +ERICSSON'S EH1 R IH0 K S AH0 N Z +ERIDANUS EH2 R IH0 D AE1 N AH0 S +ERIDU EH2 R IH0 D UW1 +ERIE IH1 R IY0 +ERIE'S IH1 R IY0 Z +ERIECREEK IH1 R IY0 K R IY2 K +ERIES IH1 R IY0 Z +ERIGENA EH1 R IH0 JH IY1 N AH0 +ERIK EH1 R IH0 K +ERIK'S EH1 R IH0 K S +ERIKA EH1 R IH0 K AH0 +ERIKA'S EH1 R IH0 K AH0 Z +ERIKSEN EH1 R IH0 K S AH0 N +ERIKSON EH1 R IH0 K S AH0 N +ERIKSSON EH1 R IH0 K S AH0 N +ERIN EH1 R IH0 N +ERIN'S EH1 R IH0 N Z +ERINACEUS EH1 R AH0 N EY1 S IY0 AH0 S +ERINN EH1 R IH0 N +ERINNA EH1 R IH0 N AH0 +ERINNYES EH1 R IH0 N AY1 Z +ERINNYS EH1 R IH0 N IY0 Z +ERINTOFF EH1 R IH0 N T AO0 F +ERINYES EH0 R IY1 N Y Z +ERIPHYLE EH1 R IH0 F AY1 L +ERIS EH1 R IH0 S +ERISICHTHON EH2 R AH0 S AH0 TH AH0 N +ERISTIC EH1 R IH0 S T IH0 K +ERIT EH2 R IH0 T +ERITH EH1 R IH0 TH +ERITH'S EH1 R IH0 TH S +ERIVAN EH1 R IH0 V AH0 N +ERKEL ER1 K AH0 L +ERL ER1 L +ERLACH ER1 L AA2 K +ERLAND ER1 L AH0 N D +ERLANGEN ER0 L AE1 NG G AH0 N +ERLANGER EH1 R L AE0 NG ER0 +ERLANGER EH1 R L AE0 NG G ER0 +ERLAU EH1 R L AW0 +ERLAU'S ER1 L AA2 Z +ERLCORT ER1 L K AO1 R T +ERLE EH1 R L AH0 +ERLE'S ER1 L Z +ERLICH ER1 L IH0 K +ERLICHS ER1 L IH0 K S +ERLIN ER1 L IH0 N +ERLING ER1 L IH0 NG +ERLING'S ER1 L IH0 NG Z +ERLINGSEN ER1 L IH0 NG S AH0 N +ERLINGSEN'S ER1 L IH0 NG S AH0 N Z +ERLKING ER1 L K IH0 NG +ERLON ER1 L AH0 N +ERLONE EH1 R L OW1 N IY0 +ERLONG ER1 L AO0 NG +ERM ER1 M +ERMA ER1 M AH0 +ERMA'S ER1 M AH0 Z +ERMAN ER1 M AH0 N +ERMANCE ER1 M AH0 N S +ERME ER1 M +ERMELIN ER1 M AH0 L IH0 N +ERMELO ER1 M AH0 L OW2 +ERMENGARDE ER1 M AH0 N G AA2 R D +ERMENGARDE'S ER1 M AH0 N G AA2 R D Z +ERMENONVILLE ER1 M EH0 N OW1 N V IH0 L +ERMENRICH ER0 M EH1 N R IH0 K +ERMENTRUDE ER0 M EH1 N T R UW0 D +ERMETYNE ER1 M T AY1 N +ERMIE ER1 M IY0 +ERMINE ER1 M AH0 N +ERMINE'S ER1 M IH0 N Z +ERMINED ER1 M IH0 N D +ERMINES ER1 M AH0 N Z +ERMINIA ER0 M IY1 N IY0 AH0 +ERMINIE ER1 M IH0 N IY0 +ERMINIO ER0 M IY1 N IY0 OW0 +ERMINIO'S ER1 M IH0 N IY0 OW0 Z +ERMITA ER0 M IY1 T AH0 +ERMOLAI ER1 M AH0 L EY0 +ERMSTED ER1 M S T IH0 D +ERMSTED'S ER1 M S T EH0 D Z +ERMSTEIN ER1 M S T AY2 N +ERMYN ER1 M IH0 N +ERMYNTRUDE ER1 M IH0 N T R UW1 D +ERN ER1 N +ERNA ER1 N AH0 +ERNA'S ER1 N AH0 Z +ERNANI EH0 R N AA1 N IY0 +ERNE ER1 N +ERNE'S ER1 N Z +ERNEMONT ER1 N AH0 M AA2 N T +ERNEST ER1 N AH0 S T +ERNEST ER1 N IH0 S T +ERNEST'S ER1 N AH0 S T S +ERNESTA ER1 N AH0 S T AH0 +ERNESTINA ER1 N AH0 S T IY1 N AH0 +ERNESTINE ER1 N IH0 S T IY2 N +ERNESTINE'S ER1 N IH0 S T IY2 N Z +ERNESTO ER0 N EH1 S T OW0 +ERNIE ER1 N IY0 +ERNIE'S ER1 N IY0 Z +ERNOL ER1 N AO2 L +ERNOLF ER1 N OW0 L F +ERNSHAW ER1 N SH AO2 +ERNSLEY ER1 N S L IY0 +ERNST ER1 N S T +ERNST'S ER1 N S T S +ERNSTHAUSEN ER1 N S T HH AW0 Z AH0 N +ERNSTORFF ER1 N S T AO2 R F +ERNUFF ER1 N AH2 F +ERO IH1 R OW0 +ERODE IH0 R OW1 D +ERODE IY1 R OW0 D +ERODED IH0 R OW1 D AH0 D +ERODED IH0 R OW1 D IH0 D +ERODING IH0 R OW1 D IH0 NG +ERODING IY0 R OW1 D IH0 NG +EROES IY1 R OW0 Z +EROICA ER0 OY1 K AH0 +EROPEGOFF IH0 R OW1 P IY0 G AO1 F +EROS IH1 R AA0 S +EROSHKA IH0 R OW1 SH K AH0 +EROSION IH0 R OW1 ZH AH0 N +EROSIONS IH0 R OW1 ZH AH0 N Z +EROSIVE IH0 R OW1 S IH0 V +EROSTRATUS IH1 R AA0 S T R AE1 T AH0 S +EROTIC IH0 R AA1 T IH0 K +EROTICINE IH0 R AA1 T IH0 S IY2 N +EROTICISM ER0 AA1 T IH0 S IH2 Z AH0 M +EROTICS IH0 R AA1 T IH0 K S +EROTION IH0 R OW1 SH AH0 N +ERP ER1 P +ERPATR ER1 P AE1 T ER0 +ERPINGHAM ER1 P IH0 NG HH AE2 M +ERPINGHAM'S ER1 P IH0 NG HH AE2 M Z +ERPS ER1 P S +ERR EH1 R +ERR ER1 +ERR'D ER1 D +ERRA EH1 R AH0 +ERRAND EH1 R AH0 N D +ERRANDS EH1 R AH0 N D Z +ERRANT EH1 R AH0 N T +ERRANT'S EH1 R AH0 N T S +ERRANTE ER0 AA1 N T IY0 +ERRANTRY EH1 R AH0 N T R IY0 +ERRANTS EH1 R AH0 N T S +ERRARE ER0 AA1 R IY0 +ERRATA EH0 R AA1 T AH0 +ERRATIC IH0 R AE1 T IH0 K +ERRATICALLY EH0 R AE1 T IH0 K L IY0 +ERRATUM EH1 R AH0 T AH0 M +ERRED EH1 R D +ERRENT ER1 AH0 N T +ERRENTS EH1 R AH0 N T S +ERREST EH1 R IH0 S T +ERREUR EH1 R ER1 +ERREURS EH1 R ER1 Z +ERRICKER EH1 R IH0 K ER0 +ERRING EH1 R IH0 NG +ERRINGTON EH1 R IH0 NG T AH0 N 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ER1 TH +ERTHE ER1 DH +ERU EH1 R UW0 +ERUCTATION IY1 R AH0 K T EY1 SH AH0 N +ERUCTATIONS IY1 R AH0 K T EY1 SH AH0 N Z +ERUDITE EH1 R AH0 D AY2 T +ERUDITION EH2 R AH0 D IH1 SH AH0 N +ERUDITUS EH1 R AH0 D AY2 T AH0 S +ERUNT IY2 R AH1 N T +ERUPT IH0 R AH1 P T +ERUPT IY1 R AH0 P T +ERUPTED IH2 R AH1 P T IH0 D +ERUPTED IY2 R AH1 P T IH0 D +ERUPTING IH2 R AH1 P T IH0 NG +ERUPTING IY2 R AH1 P T IH0 NG +ERUPTION IH2 R AH1 P SH AH0 N +ERUPTION IY2 R AH1 P SH AH0 N +ERUPTIONS IH2 R AH1 P SH AH0 N Z +ERUPTIONS IY2 R AH1 P SH AH0 N Z +ERUPTIVE IH2 R AH1 P T IH0 V +ERUPTIVE IY2 R AH1 P T IH0 V +ERVENG ER1 V AH2 NG +ERVENGS ER1 V AH2 N G Z +ERVIC ER1 V IH0 K +ERVIN ER1 V IH2 N +ERVINE ER1 V AY0 N +ERVING ER1 V IH2 NG +ERWALD ER1 W AO0 L D +ERWAY ER1 W EY2 +ERWIN ER1 W IH2 N +ERWIN'S ER1 W IH2 N Z +ERWING ER1 W IH2 NG +ERY EH1 R IY0 +ERYCINA EH0 R IH2 CH IY1 N AH0 +ERYMANTH EH1 R IY0 M AE2 N TH +ERYMANTHUS EH1 R IY0 M AA0 N TH AH0 S +ERYSIPELAS EH0 R IH2 S AH0 P EH1 L AH0 S +ERYTHRAE EH0 R IH2 TH R EY2 +ERYTHRINA EH0 R IH2 TH R IY1 N AH0 +ERYXIMACHUS EH0 R IH2 K S IH0 M AE1 CH AH0 S +ERZEROUM ER1 Z ER0 OW1 M +ERZERUM ER0 Z EH1 R AH0 M +ERZGEBIRGE ER0 Z G IH0 B ER1 JH +ERZINGAN ER0 Z IY1 NG G AH0 N +ES EH1 S +ESA EH1 S AH0 +ESAIAS EY0 S AA1 Y AH0 Z +ESARHADDON EH1 S ER0 HH AE1 D AH0 N +ESARON EH1 S ER0 AA0 N +ESAU IY1 S AO2 +ESAU'S IY1 S AO2 Z +ESBEN EH1 S B AH0 N +ESBERN EH1 S B ER0 N +ESBLY EH1 S B L IY0 +ESBUL EH1 S B AH0 L +ESCADRILLE EH0 S K AA0 D R IH0 L +ESCALADE EH2 S K AH0 L AA1 D +ESCALADED EH1 S K AH0 L EY1 D IH0 D +ESCALADING EH1 S K AH0 L EY1 D IH0 NG +ESCALANTE EH0 S K AA0 L AA1 N T IY0 +ESCALATOR EH1 S K AH0 L EY2 T ER0 +ESCALATORS EH1 S K AH0 L EY2 T ER0 Z +ESCALERA EH0 S K AA0 L EH1 R AH0 +ESCALIER EH0 S K AA0 L IY0 ER0 +ESCALLOPED EH0 S K AA1 L AH0 P T +ESCALOP EH1 S K AH0 L AA1 P +ESCALOPED EH0 S K AA0 L OW1 P T +ESCALUS EH1 S K AH0 L AH0 S +ESCAMBIA EH2 S K AE1 M B IY0 AH0 +ESCAMILLO EH0 S K AA0 M IH1 L OW0 +ESCAP'D IH0 S K EY1 P IY1 D +ESCAPADE EH1 S K AH0 P EY2 D +ESCAPADES EH1 S K AH0 P EY2 D Z +ESCAPE IH0 S K EY1 P +ESCAPED IH0 S K EY1 P T +ESCAPEES IH0 S K EY2 P IY1 Z +ESCAPEMENT IH0 S K EY1 P M AH0 N T +ESCAPES IH0 S K EY1 P S +ESCAPETH IH0 S K EY1 P IH0 TH +ESCAPIN IH0 S K EY1 P IH2 N +ESCAPING IH0 S K EY1 P IH0 NG +ESCARPED EH0 S K AA0 R P T +ESCARPMENT EH0 S K AA1 R P M AH0 N T +ESCARPMENTS EH0 S K AA1 R P M AH0 N T S +ESCAUPIL EH0 S K AA0 UW1 P AH0 L +ESCH EH1 SH +ESCHATOLOGY EH2 S K AH0 T AA1 L AH0 JH IY0 +ESCHEAT EH1 SH IY2 T +ESCHEATED EH1 SH IY0 EY2 T IH0 D +ESCHELLE EH2 S K EH1 L +ESCHELLES EH2 S K EH1 L Z +ESCHENBACH EH1 SH IH0 N B AA0 K +ESCHEW EH0 S CH UW1 +ESCHEWED EH2 S K Y UW1 D +ESCHEWED EH2 SH UW1 D +ESCHEWING EH2 S K Y UW1 IH0 NG +ESCHEWING EH2 SH UW1 IH0 NG +ESCHEWS EH0 S CH UW1 Z +ESCHINE EH0 S K IY1 N IY0 +ESCHINES EH0 S K IY1 N Z +ESCHMOUN EH1 SH M UW0 N +ESCHOL EH2 S K AH0 L +ESCHTAH EH0 S CH T AH0 +ESCHTAH'S EH2 S K T AA0 Z +ESCHYLUS EH1 SH AY1 L AH0 S +ESCLAIRMONDE EH0 S K L EH1 R M AH0 N D +ESCLANDRE EH0 S K L AE2 N D ER0 +ESCLARMONDE EH0 S K L AA1 R M AH0 N D +ESCLAVE EH0 S K L EY2 V +ESCOBAN EH1 S K AH0 B AA2 N +ESCOBAR EH1 S K AH0 B AA2 R +ESCOBAR EH1 S K OW0 B AA2 R +ESCOBAR'S EH1 S K AH0 B AA2 R Z +ESCOBAR'S EH1 S K OW0 B AA2 R Z +ESCOBARS EH1 S K AH0 B AA2 R Z +ESCOBEDO EH0 S K OW0 B EY1 D OW0 +ESCOLTA EH0 S K OW1 L T AH0 +ESCOMBE EH0 S K OW1 M B IY0 +ESCOMBE'S EH0 S K OW1 M B IY0 Z +ESCONDIDO EH2 S K AA0 N D IY1 D OW0 +ESCORIAL EH0 S K AO1 R IY0 AH0 L +ESCORT EH0 S K AO1 R T +ESCORT EH1 S K AO0 R T +ESCORT'S EH1 S K AO0 R T S +ESCORTED EH0 S K AO1 R T IH0 D +ESCORTING EH1 S K AO0 R T IH0 NG +ESCORTS EH1 S K AO0 R T S +ESCORVAL EH0 S K AO1 R V AH0 L +ESCOSURA EH1 S K AH0 S ER1 AH0 +ESCOT EH1 S K AH0 T +ESCOTT EH1 S K AH0 T +ESCOUMAINS EH0 S K OW1 M EY1 N Z +ESCOURT EH1 S K AO0 R T +ESCOVEDO EH0 S K OW1 V EY1 D OW0 +ESCOVEDO'S EH0 S K AA1 V AH0 D OW0 Z +ESCRIBANO EH1 S K R IY0 B AA1 N OW0 +ESCRITOIR EH1 S K R IH0 T OY1 R +ESCRITOIRE EH1 S K R IH2 T W AA1 R +ESCRITOIRES EH1 S K R IH2 T W AA2 Z +ESCROW EH0 S K R OW1 +ESCROW EH1 S K R OW0 +ESCULAPIAN EH0 S K UW1 L AA1 P IY0 AH0 N +ESCULAPIUS EH0 S K UW1 L AH0 P AY1 AH0 S +ESCULENT EH0 S K UW1 L AH0 N T +ESCULENTA EH0 S K UW1 L AH0 N T AH0 +ESCULENTS EH0 S K UW1 L AH0 N T S +ESCURIAL EH0 S K Y UH1 R IY0 AH0 L +ESCUTCHEON EH0 S K UW1 CH IY0 AH0 N +ESCUTCHEONED EH0 S K AH1 CH AH0 N D +ESCUTCHEONS EH0 S K AH1 CH AH0 N Z +ESDAILE EH1 Z D EY1 L +ESDAILE'S EH1 Z D AY2 L Z +ESDALE EH1 Z D EY2 L +ESDRAELON EH1 Z D R EY1 L AO0 N +ESDRALON EH0 S D AE1 L AH0 N +ESDRAS EH1 Z D R AH0 S +ESE IY2 S +ESEK EH1 S EH0 K +ESEL EH1 S AH0 L +ESENS EH1 S AH0 N Z +ESENWEIN EH1 S IH0 N W AY0 N +ESERIN EH1 S ER0 IH0 N +ESH EH1 SH +ESHCOL EH1 SH K AA2 L +ESHELBY EH1 SH EH1 L B IY0 +ESHER EH1 SH ER0 +ESHMIADZINI EH1 SH AH0 M AY0 AE1 D Z IY1 N IY0 +ESHTON EH1 SH T AH0 N +ESHWELL EH1 SH W EH2 L +ESIR IH0 S IH1 R +ESK EH1 S K +ESKAQUA EH1 S K AA1 K AH0 +ESKDALE EH1 S K D EY2 L +ESKE EH1 S K +ESKELL EH0 S K AH0 L +ESKELTA EH1 S K IH0 L T AH0 +ESKER EH1 S K ER0 +ESKI EH1 S K IY0 +ESKIMO EH1 S K AH0 M OW2 +ESKIMO'S EH1 S K AH0 M OW2 Z +ESKIMOS EH1 S K AH0 M OW2 Z +ESKUROLA EH1 S K ER0 OW1 L AH0 +ESLE EH1 S AH0 L +ESMAY EH1 S M EY1 +ESME EH1 Z M +ESME'S EH0 S M Z +ESMEE EH0 S M IY0 +ESMERALD EH1 Z M ER0 R AH0 L D +ESMERALDA EH0 S M ER0 AA1 L D AH0 +ESMERALDA'S EH1 Z M ER0 R AH0 L D AH0 Z +ESMERALDY EH1 Z M ER0 R AH0 L D IY0 +ESMERELDA EH0 S M ER0 EH1 L D AH0 +ESMO EH1 Z M OW0 +ESMO'S EH1 Z M OW0 Z +ESMOND EH1 Z M AH0 N D +ESMOND'S EH1 Z M AH0 N D Z +ESMONDE EH1 Z M AH0 N D +ESMONDS EH1 Z M AH0 N D Z +ESMUN EH1 Z M AH0 N +ESNEH EH1 S N EH1 +ESO EH1 S OW0 +ESON IH0 S AH0 N +ESOON EY0 S UW1 N +ESOP IH0 S AA1 P +ESOP'S IY1 S AA2 P S +ESOPHAGUS IH0 S AA1 F AH0 G AH0 S +ESOPUS EH0 S OW1 P AH0 S +ESORA EH0 S AO1 R AH0 +ESOTERIC EH2 S AH0 T EH1 R IH0 K +ESOTERIC EH2 S OW0 T EH1 R IH0 K +ESOTERICALLY EH2 S AH0 T EH1 R IH0 K L IY0 +ESOTERICISM EH2 S AH0 T EH1 R IH0 S IH2 Z AH0 M +ESPADA EY0 S P AA1 D AH0 +ESPADANA EY0 S P AA1 D AE1 N AH0 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F L AE1 T B OW2 T +FLATBOATS F L AE1 T B OW2 T S +FLATBUSH F L AE1 T B UH2 SH +FLATCH F L AE1 CH +FLATEAR F L AE1 T IH2 R +FLATEN F L AE1 T AH0 N +FLATFISH F L AE1 T F IH2 SH +FLATFOOT F L AE1 T F UH2 T +FLATFOOTED F L AE1 T F UH1 T AH0 D +FLATHEAD F L AE1 T HH EH2 D +FLATHEADS F L AE1 T HH EH2 D Z +FLATHOOTLY F L AE1 T HH UW1 T L IY0 +FLATIRON F L AE1 T AY2 ER0 N +FLATIRONS F L AE1 T AH0 R AA0 N Z +FLATLAND F L AE1 T L AE2 N D +FLATLANDS F L AE1 T L AE0 N D Z +FLATLY F L AE1 T L IY0 +FLATNESS F L AE1 T N AH0 S +FLATNOSE F L AE1 T N OW2 Z +FLATS F L AE1 T S +FLATT'RING F L AE1 T ER0 IH0 NG +FLATT'RY F L AE1 T R IY0 +FLATTE F L AE1 T +FLATTED F L AE1 T AH0 D +FLATTEN F L AE1 T AH0 N +FLATTENED F L AE1 T AH0 N D +FLATTENING F L AE1 T AH0 N IH0 NG +FLATTENING F L AE1 T N IH0 NG +FLATTENS F L AE1 T AH0 N Z +FLATTER F L AE1 T ER0 +FLATTER'D F L AE1 T ER0 D +FLATTERED F L AE1 T ER0 D +FLATTERER F L AE1 T ER0 ER0 +FLATTERER'S F L AE1 T ER0 ER0 Z +FLATTERERS F L AE1 T ER0 ER0 Z +FLATTERIES F L 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F L EY1 V IY0 AH0 N Z +FLAVIE F L EY1 V IY0 +FLAVIGNY F L AE1 V IH1 G N IY0 +FLAVILLA F L AE1 V IH1 L AH0 +FLAVIN F L EY1 V IH0 N +FLAVIO F L AA1 V IY0 OW0 +FLAVIUS F L EY1 V IY0 IH0 S +FLAVOR F L EY1 V ER0 +FLAVORED F L EY1 V ER0 D +FLAVORING F L EY1 V ER0 IH0 NG +FLAVORLESS F L EY1 V ER2 L EH0 S +FLAVOROUS F L EY1 V ER0 AH0 S +FLAVORS F L EY1 V ER0 Z +FLAVOUR F L EY1 V ER0 +FLAVOURED F L EY1 V AW1 ER0 D +FLAVOURING F L EY1 V AW1 ER0 IH0 NG +FLAVOURLESS F L EY1 V AO1 R L AH0 S +FLAVOURS F L EY1 V AO1 R Z +FLAVY F L EY1 V IY0 +FLAW F L AO1 +FLAW'D F L AO1 D +FLAWED F L AO1 D +FLAWLESS F L AO1 L AH0 S +FLAWLESSLY F L AO1 L AH0 S L IY0 +FLAWLESSNESS F L AO1 L AH0 S N AH0 S +FLAWS F L AO1 Z +FLAX F L AE1 K S +FLAXDRESSER F L AE1 K S D R EH2 S ER0 +FLAXEN F L AE1 K S AH0 N +FLAXIE F L AE1 K S IY0 +FLAXIE'S F L AE1 K S IY0 Z +FLAXILOCKS F L AE1 K S IH0 L AA2 K S +FLAXIUS F L AE1 K S IY0 IH0 S +FLAXMAN F L AE1 K S M AH0 N +FLAXMAN'S F L AE1 K S M AH0 N Z +FLAXSEED F L AE1 K S IY2 D +FLAY F L 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+GARAT G ER0 AA1 +GARATSH G ER0 AA1 CH +GARAY G AE1 R EY0 +GARB G AA1 R B +GARBAGE G AA1 R B IH0 JH +GARBANZOS G AA0 R B AA0 N Z OW0 Z +GARBED G AA1 R B D +GARBER G AA1 R B ER0 +GARBETTS G AA1 R B IH0 T S +GARBHA G AA1 R B HH AH0 +GARBING G AA1 R B IH0 NG +GARBITSCH G AA1 R B IH1 CH +GARBLE G AA1 R B AH0 L +GARBLED G AA1 R B AH0 L D +GARBLING G AA1 R B L IH0 NG +GARBOARD G AA1 R B AO2 R D +GARBORG G AA1 R B AO0 R G +GARBS G AA1 R B Z +GARBY G AA1 R B IY0 +GARCES G AA1 R S EH0 S +GARCHES G AA2 R CH AH0 Z +GARCHESTER G AA2 R CH EH2 S T ER0 +GARCI G AA0 R S IY1 +GARCIA G AA2 R S IY1 AH0 +GARCIA'S G AA0 R S IY1 AH0 Z +GARCIAS G AA0 R S IY1 AH0 Z +GARCIDE G AA0 R S AY2 D +GARCILASO G AA0 R S IY1 L AA1 S OW0 +GARCILASSO G AA0 R S IY1 L AA1 S OW0 +GARCINDE G AA0 R S IH1 N D +GARCON G AA1 R K AH0 N +GARCONNET G AA1 R K AH0 N IH0 T +GARCONS G AA1 R K AH0 N Z +GARD G AA1 R D +GARD'NER G AA0 R D N ER0 +GARD'S G AA1 R D Z +GARDA G AA1 R D AH0 +GARDANA G AA1 R D AE1 N AH0 +GARDANNE G AA1 R D AH0 N +GARDANNES G AA1 R D AH0 N Z +GARDE G AA1 R D +GARDED G AA1 R D IH0 D +GARDEE G AA0 R D IY1 +GARDEEN G AA0 R D IY1 N +GARDEN G AA1 R D AH0 N +GARDEN'S G AA1 R D AH0 N Z +GARDENCOURT G AA0 R D IY1 N K AO2 R T +GARDENED G AA1 R D AH0 N D +GARDENER G AA1 R D AH0 N ER0 +GARDENER'S G AA1 R D AH0 N ER0 Z +GARDENERS G AA1 R D AH0 N ER0 Z +GARDENERS G AA1 R D N ER0 Z +GARDENESS G AA1 R D N AH0 S +GARDENIA G AA0 R D IY1 N Y AH0 +GARDENIAS G AA0 R D IY1 N Y AH0 Z +GARDENIN G AA0 R D IY1 N AH0 N +GARDENING G AA1 R D AH0 N IH0 NG +GARDENING G AA1 R D N IH0 NG +GARDENS G AA1 R D AH0 N Z +GARDER G AA1 R D ER0 +GARDES G AA1 R D Z +GARDETTE G AA0 R D EH1 T +GARDEZ G AA1 R D EH0 Z +GARDIE G AA1 R D IY0 +GARDIEN G AA1 R D IY1 N +GARDIN G AA1 R D IH0 N +GARDINE G AA0 R D IY1 N IY0 +GARDINER G AA1 R D N ER0 +GARDINER'S G AA1 R D N ER0 Z +GARDINERS G AA1 R D N ER0 Z +GARDING G AA1 R D IH0 NG +GARDINGS G AA1 R D IH0 NG Z +GARDIS G AA1 R D IH0 S +GARDISTON G AA1 R D IH0 S T AA0 N +GARDNER G AA1 R D N ER0 +GARDNER'S G AA1 R D N ER0 Z +GARDNERS G AA1 R D N ER0 Z +GARDON G AA0 R D AO1 N +GARDONE G AA0 R D OW1 N IY0 +GARDONI G AA0 R D OW1 N IY0 +GARDOQUI G AA1 R D OW0 K W IY1 +GARDS G AA1 R D Z +GARDY G AA1 R D IY0 +GARE G EH1 R +GAREE G ER0 IY1 +GAREN G AE1 R AH0 N +GARENGANZE G AE1 R IH0 NG G AA1 N Z +GARENNE G AE1 R IH0 N +GARET G ER0 IH0 T +GARETH G EH1 R IH0 TH +GARETH'S G AE1 R IH0 TH S +GARFAGNANA G AA1 R F AA1 G N AE1 N AH0 +GARFIAS G AA1 R F IY0 AH0 Z +GARFIELD G AA1 R F IY2 L D +GARFIELD'S G AA1 R F IY2 L D Z +GARFIELDS G AA1 R F IY2 L D Z +GARFINKEL G AA1 R F IH0 NG K AH0 L +GARFORD G AA1 R F ER0 D +GARGANO G AA0 R G AA1 N OW0 +GARGANTUA G AA0 R G AE1 N CH UW0 AH0 +GARGANTUAN G AA0 R G AE1 N CH UW0 AH0 N +GARGE G AA1 R JH +GARGERY G AA1 R JH ER0 IY0 +GARGLE G AA1 R G AH0 L +GARGLED G AA1 R G AH0 L D +GARGLES G AA1 R G AH0 L Z +GARGLING G AA1 R G AH0 L IH0 NG +GARGOUF G AA1 R G UW1 F +GARGOUSSE G AA1 R G UW1 S +GARGOYLE G AA1 R G OY2 L +GARGOYLES G AA1 R G OY2 L Z +GARGUILLE G AA0 R G W IY1 L +GARIBALDI G AE2 R AH0 B AO1 L D IY0 +GARIBALDI'S G AE2 R AH0 B AO1 L D IY0 Z +GARIBALDIAN G AE2 R AH0 B AO1 L D IY0 AH0 N +GARIBALDIANS G AE2 R AH0 B AO1 L D IY0 AH0 N Z +GARIBALDINO G AE2 R AH0 B AO1 L D IY1 N OW0 +GARIE G EH1 R IY0 +GARIE'S G EH1 R IY0 Z +GARIEP G ER0 IY1 P +GARIES G ER0 IY0 Z +GARIET G ER0 IY1 T +GARIGLIANO G ER0 IY0 G L IY0 AA1 N OW0 +GARILLAS G AA0 R IH1 L AH0 Z +GARIN G EH1 R IH0 N +GARIN'S G AE1 R AH0 N Z +GARINS G AE1 R AH0 N Z +GARIS G AE1 R IH0 S +GARISH G EH1 R IH0 SH +GARISHLY G EH1 R IH0 SH L IY0 +GARISHNESS G EH1 R IH0 SH N AH0 S +GARITA G EH0 R IY1 T AH0 +GARLACH G AA1 R L AH0 K +GARLAN G AA1 R L AH0 N +GARLAND G AA1 R L AH0 N D +GARLAND'S G AA1 R L AH0 N D Z +GARLANDED G AA1 R L AE1 N D IH0 D +GARLANDING G AA1 R L AE1 N D IH0 NG +GARLANDS G AA1 R L AH0 N D Z +GARLIC G AA1 R L IH0 K +GARLICK G AA1 R L IH0 K +GARLICK'S G AA1 R L IH0 K S +GARLICKY G AA1 R L IH0 K IY0 +GARLINGE G AA1 R L IH0 NG +GARLON G AA1 R L AH0 N +GARLS G AA1 R L Z +GARM G AA1 R M +GARMAN G AA1 R M AH0 N +GARMANS G AA1 R M AH0 N Z +GARMENT G AA1 R M AH0 N T +GARMENT'S G AA1 R M AH0 N T S +GARMENTED G AA1 R M AH0 N IH0 D +GARMENTS G AA1 R M AH0 N T S +GARMENTURE G AA1 R M EH1 N CH ER0 +GARMO G AA1 R M OW0 +GARMON G AA1 R M AH0 N +GARN G AA1 R N +GARNEAU G AA0 R N OW1 +GARNER G AA1 R N ER0 +GARNER'D G AA1 R N ER0 D +GARNER'S G AA1 R N ER0 Z +GARNERED G AA1 R N ER0 D +GARNERIN G AA1 R N ER0 IH0 N +GARNERING G AA1 R N ER0 IH0 NG +GARNERS G AA1 R N ER0 Z +GARNESK G AA0 R N EH1 S K +GARNET G AA1 R N AH0 T +GARNET'S G AA1 R N AH0 T S +GARNETS G AA1 R N AH0 T S +GARNETT G AA1 R N IH0 T +GARNETT'S G AA1 R N AH0 T S +GARNI G AA1 R N IY0 +GARNIER G AA1 R N IY0 ER0 +GARNIER'S G AA0 R N IH1 R Z +GARNINGHAM G AA1 R N IH0 NG HH AE2 M +GARNISH G AA1 R N IH0 SH +GARNISH'D G AA1 R N IH0 SH D +GARNISHED G AA1 R N IH0 SH T +GARNISHES G AA1 R N IH0 SH AH0 Z +GARNISHING G AA1 R N IH0 SH IH0 NG +GARNISHINGS G AA1 R N IH0 SH IH0 NG Z +GARNISHMENT G AA1 R N IH0 SH M AH0 N T +GARNITURE G AA1 R N IH0 CH ER0 +GARNITURES G AA1 R N IH0 CH ER0 Z +GARNON G AA1 R N AH0 N +GARNY G AA1 R N IY0 +GAROCHE G ER0 OW1 SH +GAROFFI G AE1 R AO0 F IY0 +GAROFOLI G EH0 R AH0 F OW1 L IY0 +GARON G AA0 R AO1 N +GARONNE G ER0 AA1 N +GAROTE G AE1 R AH0 T +GAROTTE G ER0 AO1 T +GAROTTED G ER0 AA1 T IH0 D +GAROTTER G ER0 AA1 T ER0 +GAROTTERS G ER0 AA1 T ER0 Z +GAROU G ER0 UW1 +GAROUPE G ER0 UW1 P +GAROUX G ER0 UW1 +GARPEL G AA1 R P AH0 L +GARPLE G AA1 R P AH0 L +GARPLEFOOT G AA1 R P L F UH2 T +GARR G AE1 R +GARR'D G ER0 D +GARRA G AA1 R AH0 +GARRABRANT G AA0 R AA1 B R AH0 N T +GARRAGHTY G AE1 R AH0 T IY0 +GARRAL G AE1 R AH0 L +GARRAN G AE1 R AH0 N +GARRAPATAS G AE1 R AH0 P AA1 T AH0 Z +GARRARD G AE1 R ER0 D +GARRARD'S G AE1 R ER0 D Z +GARRATT G EH1 R AH0 T +GARRAVEEN G AE1 R AH0 V IY1 N +GARRAWAY G AE1 R AH0 W EY0 +GARRAWAY'S G AE1 R AH0 W EY2 Z +GARRED G AA1 R D +GARRET G EH1 R IH0 T +GARRET'S G EH1 R AH0 T S +GARRETS G EH1 R AH0 T S +GARRETSON G AE1 R IH0 T S AH0 N +GARRETT G AE1 R IH0 T +GARRETT G EH1 R IH0 T +GARRETT'S G AE1 R IH0 T Z +GARRETT'S G EH1 R IH0 T Z +GARRIBARDINE G AE1 R AH0 B ER0 D IY2 N +GARRIBARDINE'S G AE1 R AH0 B ER0 D IY2 N Z +GARRICK G EH1 R IH0 K +GARRICK'S G AE1 R IH0 K S +GARRIDGE G AE1 R IH0 JH +GARRIGAN G AE1 R IH0 G AH0 N +GARRIGOU G AE1 R IH0 G UW1 +GARRIGUES G AA0 R IY1 G EH0 S +GARRION G AE1 R IY0 AH0 N +GARRISON G AE1 R IH0 S AH0 N +GARRISON'S G AE1 R IH0 S AH0 N Z +GARRISONED G AE1 R AH0 S AH0 N D +GARRISONIAN G AE1 R IH0 S OW1 N IY0 AH0 N +GARRISONIANS G AE1 R IH0 S OW1 N IY0 AH0 N Z +GARRISONING G AE1 R AH0 S AH0 N IH0 NG +GARRISONS G AE1 R IH0 S AH0 N Z +GARRITY G EH1 R IH0 T IY0 +GARROD G AE1 R AH0 D +GARROFAT G AE1 R AH0 F AE2 T +GARROID G AE1 R OY0 D +GARRON G AE1 R AH0 N +GARRONE G AA0 R OW1 N IY0 +GARRONS G AE1 R AH0 N Z +GARROTE G AE1 R AH0 T +GARROTED G AE1 R AH0 T IH0 D +GARROTER G AE1 R AH0 T ER0 +GARROTERS G AE1 R AH0 T ER0 Z +GARROTING G AE1 R AH0 T IH0 NG +GARROTT G AE1 R AH0 T +GARROTTE G AE1 R AH0 T +GARROTTED G AE1 R AH0 T IH0 D +GARROTTERS G AE1 R AH0 T ER0 Z +GARROTTING G AE1 R AH0 T IH0 NG +GARROW G EH1 R OW0 +GARRUCHOLO G EH1 R AH0 HH OW1 L OW0 +GARRULITY G EH1 R AH0 L AH0 T IY0 +GARRULOUS G EH1 R AH0 L AH0 S +GARRULOUSLY G EH1 R AH0 L AH0 S L IY0 +GARRULOUSNESS G EH1 R AH0 L AH0 S N AH0 S +GARRY G AE1 R IY0 +GARRY G EH1 R IY0 +GARRY'S G EH1 R IY0 Z +GARRYHORN G EH1 R IY0 HH ER0 N +GARRYOWEN G EH1 R Y OW2 AH0 N +GARS G AA1 R Z +GARSCHATTACHIN G AA1 R SH AA2 T AH0 CH IH0 N +GARSETT G AA1 R S IH0 T +GARSH G AA1 R SH +GARSHIN G AA1 R SH IH0 N +GARSIDE G AA1 R S AY2 D +GARSINGTON G AA1 R S IH0 NG T AH0 N +GARSON G AA1 R S AH0 N +GARSON'S G AA1 R S AH0 N Z +GARSTANG G AA1 R S T AE2 NG +GARSTANG'S G AA1 R S T AE2 NG Z +GARSTIN G AA1 R S T AH0 N +GARSTIN'S G AA1 R S T AH0 N Z +GARSTOFF G AA1 R S T AO0 F +GARSTON G AA1 R S T AH0 N +GART G AA0 R T +GARTA G AA1 R T AH0 +GARTEN G AA1 R T AH0 N +GARTENHAUS G AA1 R T AH0 N HH AW2 S +GARTER G AA1 R T ER0 +GARTERED G AA1 R T ER0 D +GARTERS G AA1 R T ER0 Z +GARTH G AA1 R TH +GARTH'S G AA1 R TH S +GARTHDALE G AA1 R TH D EY2 L +GARTHIE G AA1 R TH IY0 +GARTHMYLE G AA1 R TH M AY2 L +GARTHORNE G AA2 R TH AO0 R N +GARTHORNE'S G AA2 R TH AO0 R N Z +GARTHOWEN G AA1 R TH AH0 W AH0 N +GARTHS G AA1 R TH S +GARTHWAITE G AA1 R TH W EY2 T +GARTLAND G AA1 R T L AH0 N D +GARTLAUBEN G AA1 R T L AW1 B AH0 N +GARTLEY G AA1 R T L IY0 +GARTNER G AA1 R T N ER0 +GARTNEY G AA0 R T N IY0 +GARTNEY'S G AA0 R T N IY0 Z +GARTOK G AA1 R T AH0 K +GARTON G AA1 R T AH0 N +GARTRAM G AA1 R T R AE2 M +GARTRAM'S G AA1 R T R AE2 M Z +GARTSIDE G AA1 R T S AY2 D +GARTSIDE'S G AA1 R T S AY2 D Z +GARUDA G AH0 R UW1 D AH0 +GARULY G AH0 R UW1 L IY0 +GARUM G AE1 R AH0 M +GARV G AA1 R V +GARVACE G AA1 R V AA1 CH IY0 +GARVAGE G AA1 R V IH0 JH +GARVALD G AA1 R V AA1 L D +GARVAN G AA1 R V AH0 N +GARVELL G AA0 R V EY1 L +GARVER G AA1 R V ER0 +GARVESTAD G AA1 R V AH0 S T AE2 D +GARVET G AA1 R V T +GARVEY G AA1 R V IY0 +GARVEY'S G AA1 R V IY0 Z +GARVEZ G AA1 R V EH0 Z +GARVIAN G AA1 R V IY0 AH0 N +GARVICE G AA1 R V AH0 S +GARVIE G AA1 R V IY0 +GARVIN G AA1 R V IH0 N +GARVIN'S G AA1 R V IH0 N Z +GARVINGTON G AA1 R V IH0 NG T AH0 N +GARVINGTON'S G AA1 R V IH0 NG T AH0 N Z +GARWOOD G AA1 R W UH2 D +GARWOOD'S G AA1 R W UH2 D Z +GARY G AE1 R IY0 +GARY G EH1 R IY0 +GARY'S G AE1 R IY0 Z +GARY'S G EH1 R IY0 Z +GARYVILLE G EH1 R IY0 V IH2 L +GARZA G AA1 R Z AH0 +GARZIA G AA1 R Z IY0 AH0 +GARZIN G AA1 R Z IH0 N +GAS G AE1 S +GASCA G AA1 S K AH0 +GASCOGNE G AE1 S K OW1 N +GASCOIGNE G AE1 S K OY1 N Y +GASCOIGNE'S G AE1 S K OY1 N Z +GASCON G AE1 S K AH0 N +GASCON'S G AE1 S K AH0 N Z +GASCONADE G AE1 S K AH0 N EY1 D +GASCONADES G AE1 S K AH0 N EY1 D Z +GASCONADING G AE1 S K AH0 N EY1 D IH0 NG +GASCONS G AE1 S K AH0 N Z +GASCONY G AE1 S K AH0 N IY0 +GASCOYNE G AE1 S K OY0 N +GASCOYNE'S G AE1 S K OY1 N Z +GASCOYNES G AE1 S K OY1 N Z +GASELEE G AE1 S IH0 L IY0 +GASELIER G EY1 Z L IY0 ER0 +GASEOUS G AE1 S IY0 AH0 S +GASES G AE1 S AH0 Z +GASES G AE1 S IH0 Z +GASFITTER G AE1 S F IH1 T ER0 +GASGOYNE G AE1 S G OY1 N +GASH G AE1 SH +GASH'D G AE1 SH D +GASHA G AE1 SH AH0 +GASHED G AE1 SH T +GASHES G AE1 SH AH0 Z +GASHFORD G AE1 SH F ER0 D +GASHING G AE1 SH IH0 NG +GASHIRA G AA0 SH IH1 R AH0 +GASHLEIGH G AE1 SH L AH0 +GASHLY G AE1 SH L IY0 +GASHMU G AE1 SH M UW1 +GASHWILER G AE1 SH W AY2 L ER0 +GASKELL G AE1 S K AH0 L +GASKELL'S G AE1 S K AH0 L Z +GASKET G AE1 S K AH0 T +GASKETS G AE1 S K AH0 T S +GASKETTE G AE1 S K EH1 T +GASKHO G AE1 S K OW0 +GASKILL G AE1 S K IH2 L +GASKINS G AE1 S K IH0 N Z +GASKO G AE1 S K OW0 +GASLIGHT G AE1 S L AY0 T +GASLIGHTS G AE1 S L AY0 T S +GASLIT G AE1 S L AY0 T +GASOLENE G AE1 S AH0 L IY2 N +GASOLINE G AE1 S AH0 L IY2 N +GASOMETER G AE1 S AH0 M IY2 T ER0 +GASOMETERS G AE1 S AH0 M IY2 T ER0 Z +GASOWASHINE G AE1 S AH0 W AA2 SH AY2 N +GASP G AE1 S P +GASP'D G AA0 S P D +GASPACHO G AA0 S P AA1 CH OW0 +GASPAR G AE1 S P ER0 +GASPAR'S G AH0 S P AA1 R Z +GASPARA G AH0 S P AA1 R AH0 +GASPARD G AH0 S P AA1 R D +GASPARD'S G AE1 S P ER0 D Z +GASPARE G AH0 S P EH1 R +GASPARE'S G AH0 S P EH1 R Z +GASPARIN G AE1 S P ER0 IH0 N +GASPARO G AH0 S P AA1 R OW0 +GASPE G AE1 S P IY0 +GASPED G AE1 S P T +GASPEE G AA0 S P IY0 +GASPER G AE1 S P ER0 +GASPEREAU G AE1 S P ER0 OW2 +GASPIN G AE1 S P IH0 N +GASPING G AE1 S P IH0 NG +GASPINGLY G AE1 S P IH0 NG L IY0 +GASPINGS G AE1 S P IH0 NG Z +GASPIPE G AA0 S P AY2 P +GASPON G AA0 S P AH0 N +GASPS G AE1 S P S +GASPY G AE1 S P IY0 +GASQUET G AE1 S K EY1 +GASS G AE1 S +GASSE G AE1 S +GASSED G AE1 S T +GASSEN G AE1 S AH0 N +GASSENDI G AE1 S IH0 N D IY0 +GASSER G AE1 S ER0 +GASSES G AE1 S IH0 Z +GASSIN G AE1 S IH0 N +GASSING G AE1 S IH0 NG +GASSION G AE1 SH AH0 N +GASSNER G AE1 S N ER0 +GASSY G AE1 S IY0 +GAST G AE1 S T +GASTANG G AH0 S T AE2 NG +GASTEIN G AH0 S T IY0 N +GASTELL G AH0 S T EH1 L +GASTER G AE1 S T ER0 +GASTERINI G AE1 S T ER0 IY1 N IY0 +GASTEROPODA G AE1 S T ER0 AA2 P AH0 D AH0 +GASTHAUS G AE1 S T HH AW2 S +GASTHOF G AE1 S T HH AO0 F +GASTON G AE1 S T AH0 N +GASTON'S G AE1 S T AH0 N Z +GASTONG G AE1 S T AO0 NG +GASTRALGIA G AE1 S T R AH0 L JH AH0 +GASTRELL G AE1 S T R AH0 L +GASTRELL'S G AE1 S T R AH0 L Z +GASTRIC G AE1 S T R IH0 K +GASTRITIS G AE0 S T R AY1 T AH0 S +GASTRO G AE1 S T R OW0 +GASTRON'S G AE1 S T R AH0 N Z +GASTRONOME G AE0 S T R OW0 N OW1 M +GASTRONOMIC G AH0 S T R AA2 N AA1 M IH0 K +GASTRONOMICAL G AE1 S T R AH0 N AA1 M IH0 K AH0 L +GASTRONOMY G AE0 S T R AA1 N AH0 M IY0 +GASWORKS G AE1 S W ER2 K S +GAT G AE1 T +GATA G AA1 T AH0 +GATACRE G AE1 T AH0 K ER0 +GATACRE'S G AE1 T AH0 K ER0 Z +GATE G EY1 T +GATE'S G EY1 T S +GATEAU G AA0 T OW1 +GATEAUX G AA0 T OW1 +GATEBY G EY1 T B IY0 +GATED G EY1 T IH0 D +GATEHOUSE G EY1 T HH AW2 S +GATEKEEPER G EY1 T K IY2 P ER0 +GATEKEEPER'S G EY1 T K IY2 P ER0 Z +GATEKEEPERS G EY1 T K IY2 P ER0 Z +GATELESS G EY1 T L IH0 S +GATELY G EY1 T L IY0 +GATEMAN G EY1 T M AH0 N +GATEMEN G EY1 T M AH0 N +GATEPOST G EY1 T P OW2 S T +GATEPOSTS G EY1 T P OW2 S T S +GATER G EY1 T ER0 +GATES G EY1 T S +GATES'S G EY1 T S +GATES'S G EY1 T S IH0 Z +GATESBORO G EY1 T S B ER0 OW0 +GATESHEAD G EY1 T Z HH EH2 D +GATESTONE G EY1 T S T OW2 N +GATEWARD G EY1 T W ER0 D +GATEWAY G EY1 T W EY2 +GATEWAYS G EY1 T W EY2 Z +GATEWOOD G EY1 T W UH2 D +GATEWOOD'S G EY1 T W UH2 D Z +GATH G AE1 TH +GATH'RING G AE1 TH R IH0 NG +GATHAS G AE1 TH AH0 Z +GATHBROKE G AE1 TH B R OW2 K +GATHER G AE1 DH ER0 +GATHER'D G AE1 DH ER0 D +GATHERCOLE G AE1 DH ER0 K OW1 L IY0 +GATHERED G AE1 DH ER0 D +GATHERER G AE1 DH ER0 ER0 +GATHERER'S G AE1 DH ER0 ER0 Z +GATHERERS G AE1 DH ER0 ER0 Z +GATHEREST G AE1 DH ER0 IH0 S T +GATHERETH G AE1 DH ER0 IY1 TH +GATHERGOLD G AE1 DH ER0 G OW2 L D +GATHERIN G AE1 DH ER0 IH0 N +GATHERING G AE1 DH ER0 IH0 NG +GATHERINGS G AE1 DH ER0 IH0 NG Z +GATHERS G AE1 DH ER0 Z +GATHERUM G AE1 DH ER0 AH0 M +GATHO G AE1 TH OW0 +GATHOL G AE1 TH AH0 L +GATHOLIAN G AE1 TH L IY0 AH0 N +GATHON G AE1 TH AH0 N +GATHORNE G AE1 TH AO0 R N +GATIEN G EY1 T Y EH0 N +GATIEN'S G AE1 SH AH0 N Z +GATIENNE G AE1 T IY0 EH0 N +GATINAIS G EY1 T IH0 N EY0 +GATINEAU G AE1 T IH0 N OW2 +GATING G EY1 T IH0 NG +GATLIFFE G AE1 T L IH0 F +GATLIN G AE1 T L IH0 N +GATLING G AE1 T L IH0 NG +GATLINGS G AE1 T L IH0 NG Z +GATO G AA1 T OW2 +GATOR G EY1 T ER0 +GATORS G EY1 T ER0 Z +GATOS G AA1 T OW2 S +GATOS G AE1 T OW2 S +GATOS G EY1 T OW0 S +GATOS G EY1 T OW2 S +GATS G AE1 T S +GATTE G AE1 T +GATTI G AE1 T IY0 +GATTI'S G AE1 T IY0 Z +GATTLE G AE1 T AH0 L +GATTLETON G AE1 T AH0 L T AH0 N +GATTO G AE1 T OW0 +GATTON G AE1 T AH0 N +GATTON'S G AE1 T AH0 N Z +GATTRIE G AE1 T R IY0 +GATTY G AE1 T IY0 +GATTY'S G AE1 T IY0 Z +GATUN G AE1 T AH0 N +GATWICK G AE1 T W IH2 K +GATWICK G EY1 T W IH2 K +GATWORTH G AE1 T W ER2 TH +GATZI G AE1 T S IY0 +GAU G OW1 +GAUBE G AO1 B +GAUBERTIN G AW1 B ER0 T IY1 N +GAUBERTIN'S G AW1 B ER0 T IH0 N Z +GAUCHE G OW1 SH +GAUCHER G OW1 SH ER0 +GAUCHERIE G AW1 CH ER0 IY0 +GAUCHERIES G AW1 CH ER0 IY0 Z +GAUCHO G AW1 CH OW0 +GAUCHO'S G AW1 CH OW0 Z +GAUCHOS G AW1 CH OW0 Z +GAUCOURT G OW0 K AO2 R T +GAUD G AO1 D 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OW0 +GIROD ZH ER0 AA1 D +GIRODET ZH ER0 AA1 D IH0 T +GIROFLA JH IH0 R OW0 F L AH0 +GIROFLE G IH0 R OW1 F AH0 L +GIROFLEE JH IH0 R OW0 F L IY0 +GIROLAMO JH IH0 R OW0 L AA1 M OW0 +GIROLAMO'S JH IH0 R OW0 L AA1 M OW0 Z +GIRON ZH IH1 R AH0 N +GIRONAC JH ER0 AO1 N AE0 K +GIRONAC'S JH ER0 AO1 N AE2 K S +GIRONACS JH ER0 AO1 N AE2 K S +GIROND JH ER0 AO1 N D +GIRONDE JH ER0 AO1 N D +GIRONDIN JH ER0 AO1 N D IH0 N +GIRONDINS JH ER0 AO1 N D IH0 N Z +GIRONDIST JH ER0 AO1 N D AH0 S T +GIRONDISTS JH ER0 AO1 N D AH0 S T S +GIRONE G IH0 R OW1 N +GIROUARD ZH AY1 R UW0 ER0 D +GIROUDEAU ZH IH0 R UW2 D OW1 +GIROUETTE ZH AY1 R UW0 EH1 T +GIROUX ZH IH0 R UW1 +GIRRL G ER1 L +GIRRUL G ER1 AH0 L +GIRSHA G ER1 SH AH0 +GIRSU G ER1 S UW0 +GIRT G ER1 T +GIRTED G ER1 T AH0 D +GIRTH G ER1 TH +GIRTHED G ER1 TH T +GIRTHING G ER1 TH IH0 NG +GIRTHS G ER1 TH S +GIRTIN G ER1 T IH0 N +GIRTLE G ER1 T AH0 L +GIRTLEY G ER1 T L IY0 +GIRTON G ER1 T AH0 N +GIRTY G ER1 T IY0 +GIRTY'S G ER1 T IY0 Z +GIRTYS G ER1 T IY0 Z +GIRVAN G 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+GLADSTEIN G L AE1 D S T AY2 N +GLADSTEIN G L AE1 D S T IY2 N +GLADSTONE G L AE1 D S T OW2 N +GLADSTONE'S G L AE1 D S T OW2 N Z +GLADSTONES G L AE1 D S T OW2 N Z +GLADSTONIAN G L AE1 D S T OW1 N IY0 AH0 N +GLADWIN G L AE1 D W IH0 N +GLADWIN'S G L AE1 D W AH0 N Z +GLADWYN G L AE1 D W IH0 N +GLADY G L AE1 D IY0 +GLADYS G L AE1 D IH0 S +GLADYS'S G L AE1 D IH0 S IH0 Z +GLAFIRA G L AA0 F IY1 R AH0 +GLAHN G L AE1 N +GLAID G L EY2 D +GLAIKIT G L EY1 K IH0 T +GLAIRE G L EH1 R +GLAIRE'S G L EH2 R Z +GLAIRY G L EH1 R IY0 +GLAISHER G L EY1 SH ER0 +GLAIVE G L AY1 V +GLAIVES G L AY1 V Z +GLAIZE G L EY1 Z +GLAM G L AE1 M +GLAM'S G L AE1 M Z +GLAMBECK G L AE1 M B EH2 K +GLAMBECKS G L AE1 M B EH2 K S +GLAMIS G L AE1 M IH0 S +GLAMMIS G L AE1 M IH0 S +GLAMOR G L AE1 M ER0 +GLAMORGAN G L AE1 M ER0 G AH0 N +GLAMORGAN'S G L AE1 M ER0 G AH0 N Z +GLAMORGANSHIRE G L AE1 M ER0 G AH0 N SH IH2 R +GLAMOROUS G L AE1 M ER0 AH0 S +GLAMORYS G L AE1 M ER0 IY0 Z +GLAMOUR G L AE1 M ER0 +GLAMOURED G L AE1 M UH0 R D +GLAMOUROUS G L AE1 M UH0 R AH0 S +GLAMOURS G L AE1 M UH0 R Z +GLAMR G L AE1 M R +GLAN G L AE1 N +GLANC'D G L AE1 N S D +GLANCE G L AE1 N S +GLANCED G L AE1 N S T +GLANCES G L AE1 N S IH0 Z +GLANCIN G L AE1 N S IH0 N +GLANCING G L AE1 N S IH0 NG +GLANCINGLY G L AE1 N S IH0 NG L IY0 +GLAND G L AE1 N D +GLANDERED G L AE1 N D ER0 D +GLANDERS G L AE1 N D ER0 Z +GLANDIER G L AE1 N D IY0 ER0 +GLANDORE G L AE1 N D ER0 +GLANDORES G L AE1 N D ER0 Z +GLANDS G L AE1 N D Z +GLANDULAR G L AE1 N JH AH0 L ER0 +GLANEDALE G L EY1 N D EY2 L +GLANI G L AA1 N IY0 +GLANS G L AE1 N Z +GLANTON G L AE1 N T AH0 N +GLANTON'S G L AE1 N T AH0 N Z +GLANVIL G L AE1 N V IH0 L +GLANVILL G L AE1 N V IH0 L +GLANVILLE G L AE1 N V IH2 L +GLANVILS G L AE1 N V AH0 L Z +GLANWERN G L AE1 N W ER1 N +GLANYRAVON G L AE1 N IH1 R AH0 V AA0 N +GLANZ G L AE1 N Z +GLARE G L EH1 R +GLARED G L EH1 R D +GLARES G L EH1 R Z +GLARIN G L EH1 R IH0 N +GLARING G L EH1 R IH0 NG +GLARINGLY G L EH1 R IH0 NG L IY0 +GLARUS G L EH1 R AH0 S +GLARY G L EH1 R IY0 +GLAS G L AE1 S +GLASBY G L AE1 S B IY0 +GLASCOCK G L AE1 S K AH0 K +GLASCOW G L AE1 S K OW2 +GLASDALE G L AO1 S D EY2 L +GLASENAPP G L AE1 S IH0 N AE1 P +GLASER G L EY1 Z ER0 +GLASGIE G L AE1 S G IY0 +GLASGOW G L AE1 S G OW2 +GLASGOW G L AE1 S K OW2 +GLASGOW'S G L AE1 S K OW2 Z +GLASHA G L AE1 SH AH0 +GLASHAN G L AE1 SH AH0 N +GLASHBURN G L AE1 SH B ER2 N +GLASHER G L AE1 SH ER0 +GLASHGAR G L AE1 SH G AA2 R +GLASHRUACH G L AH0 SH R UW1 AH0 K +GLASLYN G L AE1 S L IH0 N +GLASNABINNIE G L AE1 S N AH0 B IH1 N IY0 +GLASNEVIN G L AE1 S N EH1 V AH0 N +GLASPELL G L AE1 S P EH1 L +GLASPYTH G L AE1 S P IH0 TH +GLASS G L AE1 S +GLASS'D G L AE1 S IH0 D +GLASS'S G L AE1 S IH0 Z +GLASSDALE G L AE1 S D EY2 L +GLASSE G L AE1 S +GLASSED G L AE1 S T +GLASSER G L AE1 S ER0 +GLASSES G L AE1 S AH0 Z +GLASSES G L AE1 S IH0 Z +GLASSFELL G L AE1 S F AH0 L +GLASSFORD G L AE1 S F AO0 R D +GLASSFUL G L AE1 S F AH0 L +GLASSFULS G L AE1 S F AH0 L Z +GLASSHOUSE G L AE1 S HH AW2 S +GLASSILY G L AE1 S AH0 L IY0 +GLASSINESS G L AE1 S IY0 N AH0 S +GLASSING G L AE1 S IH0 NG +GLASSITE G L AE1 S AY0 T +GLASSLESS G L AE1 S L AH0 S +GLASSLIKE G L AE1 S L AY2 K +GLASSMANIKIN G L AE1 S M AE2 N IH0 K IH0 N +GLASSON G L AE1 S AH0 N +GLASSON'S G L AE1 S AH0 N Z +GLASSWARE G L AE1 S W EH2 R +GLASSY G L AE1 S IY0 +GLASTER G L AE1 S T ER0 +GLASTON G L AE1 S T AH0 N +GLASTONBURY G L AE1 S T AH0 N B EH2 R IY0 +GLAT G L AE1 T +GLATHION G L AE1 TH IY0 AO0 N +GLATIGNY G L AE1 T AH0 N IY0 +GLATZ G L AE1 T S +GLAUBE G L AO1 B +GLAUBEN G L AW1 B AH0 N +GLAUBER G L AW1 B ER0 +GLAUBER'S G L AW1 B ER0 Z +GLAUBUS G L AO1 B AH0 S +GLAUCA G L AO0 K AH0 +GLAUCIA G L AO1 SH AH0 +GLAUCIAS G L AO1 S IY0 AH0 Z +GLAUCOMA G L AO0 K OW1 M AH0 +GLAUCOMATOUS G L AO0 K OW1 M AH0 T AH0 S +GLAUCON G L AO0 K AA2 N +GLAUCON'S G L AO0 K AH0 N Z +GLAUCOUS G L AO1 K AH0 S +GLAUCUS G L AO1 K AH0 S +GLAUKE G L AO1 K +GLAUKIAS G L AO1 K IY1 AH0 Z +GLAUM G L AO1 M +GLAUR G L AO1 R +GLAVE G L EY1 V +GLAVIS G L AE1 V IH0 S +GLAZ'D G L EY1 Z AH0 D +GLAZE G L EY1 Z +GLAZEBROOK G L EY1 Z B R UH2 K +GLAZED G L EY1 Z D +GLAZES G L EY1 Z AH0 Z +GLAZES G L EY1 Z IH0 Z +GLAZIER G L EY1 Z IY0 ER0 +GLAZIER'S G L EY1 Z IY0 ER0 Z +GLAZIERS G L EY1 Z IY0 ER0 Z +GLAZING G L EY1 Z IH0 NG +GLAZOUNOV G L EY1 Z UW0 N AA0 V +GLAZY G L EY1 Z IY0 +GLE G AH0 L +GLEAM G L IY1 M +GLEAM'D G L IY1 M D +GLEAMED G L IY1 M D +GLEAMEIL G L IY1 M IY1 L +GLEAMIN G L IY1 M IH0 N +GLEAMING G L IY1 M IH0 NG +GLEAMINGLY G L IY1 M IH0 NG L IY0 +GLEAMINGS G L IY1 M IH0 NG Z +GLEAMS G L IY1 M Z +GLEAMY G L IY1 M IY0 +GLEAN G L IY1 N +GLEAN'D G L IY1 N D +GLEANED G L IY1 N D +GLEANER G L IY1 N ER0 +GLEANER'S G L IY1 N ER0 Z +GLEANERS G L IY1 N ER0 Z +GLEANING G L IY1 N IH0 NG +GLEANINGS G L IY1 N IH0 NG Z +GLEANS G L IY1 N Z +GLEAR G L IY1 R +GLEASON G L IY1 S AH0 N +GLEASON'S G L IY1 S AH0 N Z +GLEAT G L IY1 T +GLEAZEN G L IY1 Z AH0 N +GLEAZEN'S G L IY1 Z AH0 N Z +GLEBAE G L IY1 B AY1 +GLEBE G L IY1 B +GLEBES G L IY1 B Z +GLEBESHIRE G L EH1 B IH0 SH AY2 R +GLECK G L EH1 K +GLED G AH0 L D +GLEDE G L IY1 D +GLEDWARE G L EH1 D W EH2 R +GLEDWARE'S G L EH1 D W EH2 R Z +GLEE G L IY1 +GLEED G L IY1 D +GLEEFUL G L IY1 F AH0 L +GLEEFULLY G L IY1 F AH0 L IY0 +GLEEK G L IY1 K +GLEEMAIDEN G L IY1 M EY1 D AH0 N +GLEEMAN G L IY1 M AH0 N +GLEEMEN G L IY1 M EH0 N +GLEER G L IH1 R +GLEES G L IY1 Z +GLEESOME G L IY1 S AH0 M +GLEESON G L IY1 Z AH0 N +GLEESON'S G L IY1 Z AH0 N Z +GLEG G L EH1 G +GLEGG G L EH1 G +GLEGG'S G L EH1 G Z +GLEICESTER G L EH1 S T ER0 +GLEICH G L AY1 K +GLEICHEN G L AY1 K AH0 N +GLEIG G L IY1 G +GLEIM G L IY1 M +GLEISSERT G L AY1 S ER0 T +GLEIWITZ G L AY1 W IH0 T S +GLEK G L EH1 K +GLEN G L EH1 N +GLEN'S G L EH1 N Z +GLENAIRE G L EH2 N EH1 R +GLENALADALE G L EH1 N AH0 L AA1 D EY2 L +GLENALBERT G L EH2 N AE1 L B ER0 T +GLENALDER G L EH1 N AO1 L D ER0 +GLENALLA G L EH2 N AE1 L AH0 +GLENALLAN G L EH2 N AE1 L AH0 N +GLENALLAN'S G L EH2 N AE1 L AH0 N Z +GLENALTA G L EH1 N AH0 L T AH0 +GLENALVON G L EH1 N AE0 L V AO1 N +GLENARK G L EH1 N ER0 K +GLENARM G L EH2 N EH1 R M +GLENARM'S G L EH2 N EH1 R M Z +GLENARTNEY G L EH1 N ER0 T N IY0 +GLENARVAN G L EH2 N EH1 R V AH0 N +GLENBARTH G L EH1 N B AA0 R TH +GLENBOGIE G L EH1 N B UH1 G IY0 +GLENBURY G L EH1 N B EH2 R IY0 +GLENBY G L EH1 N B IY0 +GLENCAID G L EH1 N K EY1 D +GLENCAIRN G L EH1 N K EH1 R N +GLENCAIRN'S G L EH1 N K EH1 R N Z +GLENCARYLL G L EH1 N K EH1 R AH0 L +GLENCOE G L EH1 N K OW1 +GLENCORA G L EH1 N K AO2 R AH0 +GLENCORA'S G L EH1 N K AO2 R AH0 Z +GLENCORE G L EH1 N K AO2 R +GLENCORE'S G L EH1 N K AO2 R Z +GLENDALE G L EH1 N D EY2 L +GLENDALOUGH G L EH1 N D AH0 L AW0 +GLENDAY G L EH1 N D EY2 +GLENDENNING G L EH1 N D IH0 N IH0 NG +GLENDIN G L EH1 N D IH0 N +GLENDINNING G L EH1 N D IH0 N IH0 NG +GLENDINNING'S G L EH1 N D IH0 N IH0 NG Z +GLENDIVE G L EH1 N D AH0 V +GLENDON G L EH1 N D OW0 N +GLENDON'S G L EH1 N D AH0 N Z +GLENDORA G L EH1 N D AO1 R AH0 +GLENDORE G L EH1 N D ER0 +GLENDOW G L EH1 N D OW0 +GLENDOWER G L EH1 N D OW0 ER0 +GLENDOWER'S G L EH1 N D OW0 ER0 Z +GLENEESH G L EH1 N IY1 SH +GLENELG G L EH1 N IH0 L G +GLENFALLEN G L EH1 N F AO2 L AH0 N +GLENFALLEN'S G L EH1 N F AO2 L AH0 N Z +GLENFERN G L EH1 N F ER1 N +GLENFERNIE G L EH1 N F ER1 N IY0 +GLENFERNIE'S G L EH1 N F ER1 N IY0 Z +GLENFIELD G L EH1 N F IY0 L D +GLENFINLAS G L EH1 N F IH2 N L AH0 Z +GLENFINLASS G L EH1 N F IH2 N L AE0 S +GLENFINNAN G L EH1 N F IH1 N AH0 N +GLENGALL G L EH2 N G AH0 L +GLENGARIFF G L EH2 N G EH1 R AH0 F +GLENGARRY G L EH2 N G EH1 R IY0 +GLENGARY G L EH2 N G EH1 R IY0 +GLENGOWRA G L EH2 N G AO1 R AH0 +GLENGYLE G L EH2 N G AY2 L +GLENHAM G L EH1 N AH0 M +GLENHAM G L EH1 N HH AH0 M +GLENHAVEN G L EH1 N HH EY2 V AH0 N +GLENINCH G L EH1 N IH0 N CH +GLENISTER G L EH1 N IH0 S T ER0 +GLENISTER'S G L EH1 N IH0 S T ER0 Z +GLENKENS G L EH1 NG K AH0 N Z +GLENLIVAT G L EH1 N L IH1 V AH0 T +GLENLIVET G L EH1 N L IH1 V AH0 T +GLENLOCH G L EH1 N L AH0 K +GLENLYON G L EH1 N L IY0 AA0 N +GLENMORE G L EH1 N M AO2 R +GLENMORISTON G L EH1 N M AO2 R IH0 S T AA0 N +GLENMUIR G L EH1 N M Y UH1 R +GLENMUTCHKIN G L EH1 N M AH1 CH K IH0 N +GLENN G L EH1 N +GLENN'S G L EH1 N Z +GLENNA G L EH1 N AH0 +GLENNACLACH G L EH1 N AA0 K L AH0 K +GLENNARD G L EH1 N ER0 D +GLENNARD'S G L EH1 N ER0 D Z +GLENNEY G L EH1 N IY0 +GLENNIE G L EH1 N IY0 +GLENNIE'S G L EH1 N IY0 Z +GLENNY G L EH1 N IY0 +GLENOGIE G L EH1 N OW0 JH IY0 +GLENOGLE G L EH1 N OW1 G AH0 L +GLENORA G L AH0 N AO1 R AH0 +GLENORCHY G L AH0 N AO1 R K IY0 +GLENORO G L AH0 N AO1 R OW0 +GLENS G L EH1 N Z +GLENSHEE G L EH1 N SH IY0 +GLENSIDE G L EH2 N S AY2 D +GLENTHIRSK G L EH1 N TH IH1 R S K +GLENTHORN G L EH1 N TH AO2 R N +GLENTHORPE G L EH1 N TH AO0 R P +GLENTHORPE'S G L EH1 N TH AO0 R P S +GLENTROOM G L EH1 N T R UW2 M +GLENTWORTH G L EH1 N T W ER2 TH +GLENTWORTHY G L EH1 N T W ER2 DH IY0 +GLENURE G L EH1 N Y ER0 +GLENUSKIE G L EH1 N AH2 S K IY0 +GLENVARLOCH G L EH1 N V AA1 R L AH0 K +GLENVARLOCH'S G L EH1 N V ER0 L AA1 K S +GLENVIEW G L EH1 N V Y UW2 +GLENVILLE G L EH1 N V IH0 L +GLENVOIE G L EH1 N V W AA1 +GLENWILLIAM G L EH1 N W IH1 L Y AH0 M +GLENWILLIAM'S G L EH1 N W IH1 L Y AH0 M Z +GLENWITH G L EH1 N W IH2 TH +GLENWOOD G L EH1 N W UH2 D +GLESCA G L EH1 S K AH0 +GLEW G L UW1 +GLEY G L IY0 +GLEYRE G L EH1 R +GLI G L IY0 +GLIB G L IH1 B +GLIBLY G L IH1 B L IY0 +GLIBNESS G L IH1 B N AH0 S +GLICKHICAN G L IH1 K HH IY1 K AH0 N +GLIDDEN G L IH1 D AH0 N +GLIDDERY G L IH1 D ER0 IY0 +GLIDDON G L IH1 D AH0 N +GLIDE G L AY1 D +GLIDED G L AY1 D IH0 D +GLIDER G L AY1 D ER0 +GLIDERS G L AY1 D ER0 Z +GLIDES G L AY1 D Z +GLIDEST G L AY1 D AH0 S T +GLIDETH G L AY1 D IH0 TH +GLIDIN G L AY1 D IH0 N +GLIDING G L AY1 D IH0 NG +GLIDINGLY G L AY1 D IH0 NG L IY0 +GLIEVE G L IY1 V +GLIFF G L IH0 F +GLIM G L IH1 M +GLIMM'RING G L IH1 M R IH0 NG +GLIMMER G L IH1 M ER0 +GLIMMER'D G L IH1 M ER0 AH0 D +GLIMMERED G L IH1 M ER0 D +GLIMMERIN G L IH1 M ER0 IH0 N +GLIMMERING G L IH1 M ER0 IH0 NG +GLIMMERINGLY G L IH1 M ER0 IH0 NG L IY0 +GLIMMERINGS G L IH1 M ER0 IH0 NG Z +GLIMMERS G L IH1 M ER0 Z +GLIMMINGE G L IH1 M IH0 NG +GLIMPSE G L IH1 M P S +GLIMPSED G L IH1 M P S T +GLIMPSES G L IH1 M P S IH0 Z +GLIMPSING G L IH1 M P S IH0 NG +GLIN G L IH0 N +GLINDA G L IH1 N D AH0 +GLINDA'S G L IH1 N D AH0 Z +GLINDS G L IH1 N D Z +GLINKA G L IH1 NG K AH0 +GLINKOMOK G L IH1 NG K OW0 M AA1 K +GLINN G L IH1 N +GLINSKI G L IH1 N S K IY0 +GLINT G L IH1 N T +GLINTED G L IH1 N T IH0 D +GLINTIN G L IH1 N T IH0 N +GLINTING G L IH1 N T IH0 NG +GLINTINGS G L IH1 N T IH0 NG Z +GLINTS G L IH1 N T S +GLION G L Y AH0 N +GLISK G L IH1 S K +GLISPIN G L IH1 S P IH0 N +GLISSADE G L AH0 S AA1 D +GLIST'NING G L AH0 AH0 N IH0 NG +GLISTEN G L IH1 S AH0 N +GLISTENED G L IH1 S AH0 N D +GLISTENIN G L IH1 S N IH0 N +GLISTENING G L IH1 S AH0 N IH0 NG +GLISTENING G L IH1 S N IH0 NG +GLISTENINGLY G L IH1 S N IH0 NG L IY0 +GLISTENS G L IH1 S AH0 N Z +GLISTER G L IH1 S T ER0 +GLISTERED G L IH1 S T ER0 D +GLISTERING G L IH1 S T ER0 IH0 NG +GLISTERS G L IH1 S T ER0 Z +GLITT'RING G L IH1 T ER0 IH0 NG +GLITTER G L IH1 T ER0 +GLITTER'D G L IH1 T ER0 D +GLITTERED G L IH1 T ER0 D +GLITTERIN G L IH1 T ER0 IH0 N +GLITTERING G L IH1 T ER0 IH0 NG +GLITTERINGLY G L IH1 T ER0 IH0 NG L IY0 +GLITTERINGS G L IH1 T ER0 IH0 NG Z +GLITTERS G L IH1 T ER0 Z +GLITTERY G L IH1 T ER0 IY0 +GLO G L OW1 +GLO'STER G L OW0 S T ER0 +GLOAK G L OW1 K +GLOAM G L OW1 M +GLOAMIN G L OW1 M IH0 N +GLOAMING G L OW1 M IH0 NG +GLOAMINGS G L OW1 M IH0 NG Z +GLOAT G L OW1 T +GLOATED G L OW1 T IH0 D +GLOATING G L OW1 T IH0 NG +GLOATINGLY G L OW1 T IH0 NG L IY0 +GLOATS G L OW1 T S +GLOB G L AA1 B +GLOBAL G L OW1 B AH0 L +GLOBE G L OW1 B +GLOBE'S G L OW1 B Z +GLOBED G L OW1 B D +GLOBER G L OW1 B ER0 +GLOBES G L OW1 B Z +GLOBESKA G L OW1 B EH1 S K AH0 +GLOBESKI G L OW1 B EH1 S K IY0 +GLOBIGERINA G L OW1 B AH0 JH EH0 R IY1 N AH0 +GLOBIGERINAE G L OW1 B AH0 JH EH1 R IH0 N EY2 +GLOBO G L OW1 B OW0 +GLOBOSE G L OW1 B OW0 S +GLOBULAR G L AA1 B Y AH0 L ER0 +GLOBULE G L AA1 B Y AH0 L +GLOBULES G L AA1 B Y AH0 L Z +GLOCESTER G L AW1 S T ER0 +GLOCKEN G L AA1 K AH0 N +GLOCKNER G L AA1 K N ER0 +GLOGAU G L AH0 G OW1 +GLOGOVA G L AH0 G OW1 V AH0 +GLOGOWSKI G L AH0 G AO1 F S K IY0 +GLOIRE G L OY1 R +GLOM G L AH0 M +GLOMAX G L OW1 M AE2 K S +GLONGLIM G L AA1 NG G L IH0 M +GLOODY G L UW1 D IY0 +GLOOM G L UW1 M +GLOOMED G L UW1 M D +GLOOMIER G L UW1 M IY0 ER0 +GLOOMIEST G L UW1 M IY0 IH0 S T +GLOOMILY G L UW1 M AH0 L IY0 +GLOOMINESS G L UW1 M IY0 N AH0 S +GLOOMING G L UW1 M IH0 NG +GLOOMS G L UW1 M Z +GLOOMSTER G L UW1 M S T ER0 +GLOOMY G L UW1 M IY0 +GLOOS G L UW1 Z +GLOOSCAP G L UW1 S K AE2 P +GLOOSKAP G L UW1 S K AE2 P +GLOOSKAP'S G L UW1 S K AE1 P S +GLORA G L AO1 R AH0 +GLORIA G L AO1 R IY0 AH0 +GLORIA'S G L AO1 R IY0 AH0 Z +GLORIAE G L AO1 R IY0 AH0 +GLORIAM G L AO1 R IY0 AH0 M +GLORIANA G L AO2 R IY0 AE1 N AH0 +GLORIANA'S G L AO2 R IY0 AE1 N AH0 Z +GLORIANDE G L AO2 R IY0 AE1 N D +GLORIANI G L AO2 R IY0 AE1 N IY0 +GLORIANI'S G L AO1 R IY0 AA1 N IY0 Z +GLORIANNA G L AO2 R IY0 AE1 N AH0 +GLORIAS G L AO1 R IY0 AH0 Z +GLORIE G L AO1 R IY0 +GLORIED G L AO1 R IY0 D +GLORIES G L AO1 R IY0 Z +GLORIETA G L AO2 R IY1 T AH0 +GLORIETH G L AO1 R IY0 IH0 TH +GLORIETTE G L AO1 R IY0 EH0 T +GLORIEUSE G L AO1 R IY0 UW1 S +GLORIEUX G L AO1 R IY0 UW2 +GLORIFICATION G L AO2 R AH0 F IH0 K EY1 SH AH0 N +GLORIFICATION G L AO2 R IH0 F IH0 K EY1 SH AH0 N +GLORIFICATIONS G L AO1 R AH0 F AH0 K EY1 SH AH0 N Z +GLORIFIED G L AO1 R AH0 F AY2 D +GLORIFIER G L AO1 R AH0 F AY2 ER0 +GLORIFIES G L AO1 R AH0 F AY2 Z +GLORIFY G L AO1 R AH0 F AY2 +GLORIFYING G L AO1 R AH0 F AY2 IH0 NG +GLORIOSA G L AO0 R IY0 OW1 S AH0 +GLORIOSO G L AO0 R IY0 OW1 S OW0 +GLORIOUS G L AO1 R IY0 AH0 S +GLORIOUSLY G L AO1 R IY0 AH0 S L IY0 +GLORIOUSNESS G L AO1 R IY0 AH0 S N AH0 S +GLOROREM G L ER0 AO1 R IH0 M +GLORVINA G L AO2 R V IY1 N AH0 +GLORY G L AO1 R IY0 +GLORY'S G L AO1 R IY0 Z +GLORYING G L AO1 R IY0 IH0 NG +GLOS G L OW0 Z +GLOSE G L OW2 S +GLOSING G L OW1 Z IH0 NG +GLOSS G L AO1 S +GLOSSARIAL G L AO1 S EH1 R IY0 AH0 L +GLOSSARIES G L AO1 S EH2 R IY0 Z +GLOSSARY G L AO1 S ER0 IY0 +GLOSSED G L AO1 S T +GLOSSES G L AO1 S IH0 Z +GLOSSIE G L AO1 S IY0 +GLOSSIER G L AO1 S IY0 ER0 +GLOSSIEST G L AO1 S IY0 AH0 S T +GLOSSILY G L AA1 S AH0 L IY0 +GLOSSIN G L AO1 S IH0 N +GLOSSIN'S G L AO1 S AH0 N Z +GLOSSINESS G L AO1 S IY0 N AH0 S +GLOSSING G L AO1 S IH0 NG +GLOSSOP G L AA1 S AH0 P +GLOSSOP'S G L AA1 S AH0 P S +GLOSSY G L AO1 S IY0 +GLOSTER G L AA1 S T ER0 +GLOSTER'S G L AA1 S T ER0 Z +GLOSTERVILLE G L AA1 S T ER0 V IH0 L +GLOTTENBERG G L AA1 T AH0 N B ER0 G +GLOTTIS G L AA1 T AH0 S +GLOTZBOURG G L AA1 T S B AO2 R G +GLOUCESTER G L AO1 S T ER0 +GLOUCESTER'S G L AO1 S T ER0 Z +GLOUCESTERS G L AO1 S T ER0 Z +GLOUCESTERSHIRE G L AO1 S T ER0 SH ER0 +GLOUGLOU G L UW2 G L UW1 +GLOVE G L AH1 V +GLOVED G L AH1 V D +GLOVELESS G L AH1 V L AH0 S +GLOVEMAKER G L AH1 V M EY0 K ER0 +GLOVER G L AH1 V ER0 +GLOVER'S G L AH1 V ER0 Z +GLOVERS G L AH1 V ER0 Z +GLOVERSVILLE G L AH1 V ER0 Z V IH2 L +GLOVES G L AH1 V Z +GLOVING G L AH1 V IH0 NG +GLOW G L OW1 +GLOW'D G L AW1 D +GLOWACZ G 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G ER0 IH1 L AH0 Z +GORILLAS G ER0 IH1 L AH0 Z +GORING G AO1 R IH0 NG +GORING'S G AO1 R IH0 NG Z +GORINGS G AO1 R IH0 NG Z +GORIOT G AO1 R IY0 AH0 T +GORIOT'S G AO1 R IY0 AH0 T S +GORITZ G AO1 R IH0 T S +GORITZA G AO0 R IH1 T Z AH0 +GORIZIA G ER0 IH1 Z IY0 AH0 +GORKA G AO1 R K AH0 +GORKA'S G AO1 R K AH0 Z +GORKI G AO1 R K IY0 +GORKRINK G AO1 R K R IH2 NG K +GORKY G AO1 R K IY0 +GORKY'S G AO1 R K IY0 Z +GORLA G AO1 R L AH0 +GORLAT G AO1 R L AE0 T +GORLESTON G AO1 R AH0 L S T AH0 N +GORLEY G AO1 R L IY0 +GORLIAS G AO1 R L IY0 AH0 Z +GORLITZ G AO1 R L IH0 T S +GORLMYN G AO1 R L M IH0 N +GORLOIS G AO1 R L W AA1 +GORM G AO1 R M +GORMALA G AO1 R M AH0 L AH0 +GORMALA'S G AO1 R M AH0 L AH0 Z +GORMAN G AO1 R M AH0 N +GORMAN'S G AO1 R M AH0 N Z +GORMAND G AO1 R M AH0 N D +GORMANDISING G AO1 R M AH0 N D AY2 Z IH0 NG +GORMANDIZE G AO1 R M AH0 N D AY2 Z +GORMANDIZED G AO1 R M AH0 N D AY2 Z D +GORMANDIZER G AO1 R M AH0 N D AY2 Z ER0 +GORMANDIZERS G AO1 R M AH0 N D AY2 Z ER0 Z +GORMANDIZING G AO1 R M 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EY1 S F AH0 L IY0 +GRACEFULNESS G R EY1 S F AH0 L N AH0 S +GRACELESS G R EY1 S L AH0 S +GRACELESSLY G R EY1 S L AH0 S L IY0 +GRACELESSNESS G R EY1 S L AH0 S N AH0 S +GRACELIUS G R EY1 S L IY0 IH0 S +GRACES G R EY1 S IH0 Z +GRACEY G R EY1 S IY0 +GRACHT G R AE1 K T +GRACIA G R AA1 CH AH0 +GRACIAN G R EY1 SH AH0 N +GRACIAS G R AA1 CH AH0 Z +GRACIE G R EY1 S IY0 +GRACIE'S G R EY1 S IY0 Z +GRACIEUSE G R AA1 CH UW1 S +GRACIEUX G R EY1 S IY0 OW0 +GRACILE G R AE1 S AH0 L +GRACILIS G R AE1 S AH0 L IH0 S +GRACING G R EY1 S IH0 NG +GRACIOSA G R EY1 S IY0 OW1 S AH0 +GRACIOSO G R EY1 S IY0 OW1 S OW0 +GRACIOUS G R EY1 SH AH0 S +GRACIOUSLY G R EY1 SH AH0 S L IY0 +GRACIOUSNESS G R EY1 SH AH0 S N AH0 S +GRACK G R AE1 K +GRACKLE G R AE1 K AH0 L +GRACKLES G R AE1 K AH0 L Z +GRACY G R EY1 S IY0 +GRAD G R AE1 D +GRADASSO G R AA0 D AA1 S OW0 +GRADATED G R EY1 D EY0 T AH0 D +GRADATION G R EY0 D EY1 SH AH0 N +GRADATIONS G R EY0 D EY1 SH AH0 N Z +GRADDY G R AE1 D IY0 +GRADE G R EY1 D +GRADED G R EY1 D AH0 D 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AE1 JH UW0 EY2 T IH0 D +GRADUATES G R AE1 JH AH0 W AH0 T S +GRADUATES G R AE1 JH AH0 W EY2 T S +GRADUATES G R AE1 JH UW0 EY2 T S +GRADUATES G R AE1 JH UW0 W AH0 T S +GRADUATING G R AE1 JH AH0 W EY2 T IH0 NG +GRADUATING G R AE1 JH UW0 EY2 T IH0 NG +GRADUATION G R AE2 JH AH0 W EY1 SH AH0 N +GRADUATION G R AE2 JH UW0 EY1 SH AH0 N +GRADUATIONS G R AE2 JH AH0 W EY1 SH AH0 N Z +GRADUATIONS G R AE2 JH UW0 EY1 SH AH0 N Z +GRADUM G R EY1 D AH0 M +GRADUS G R EY1 D AH0 S +GRADY G R EY1 D IY0 +GRADY'S G R EY1 D IY0 Z +GRAECA G R EH1 K AH0 +GRAECIA G R IY1 SH AH0 +GRAECINA G R EH0 CH IY1 N AH0 +GRAECO G R IY1 K OW0 +GRAEF G R AE1 F +GRAEF G R IY1 F +GRAEFE G R AE1 F +GRAEFE G R IY1 F +GRAEFIN G R IY1 F IH0 N +GRAELENT G R AE1 L AH0 N T +GRAEM G R AE1 M +GRAEME G R AE1 M +GRAEME G R EY1 M +GRAEME'S G R AE1 M Z +GRAEMER G R AE1 M ER0 +GRAEMES G R AE1 M Z +GRAETZ G R IY1 T S +GRAEVENITZ G R EY1 V IY1 N IH0 T S +GRAEVENITZ'S G R IY1 V AH0 N IH2 T S IH0 Z +GRAEVENITZIN G R IY1 V AH0 N IH1 T Z IH0 N 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K AH0 L +GRAMMATICALLY G R AH0 M AE1 T IH0 K L IY0 +GRAMMATICUS G R AH0 M AE1 T IH0 K AH0 S +GRAMME G R AE1 M +GRAMMELIN'S G R AE1 M AH0 L IH0 N Z +GRAMMER G R AE1 M ER0 +GRAMMERCY G R AE1 M ER0 S IY0 +GRAMMES G R AE1 M Z +GRAMMONT G R AE1 M AH0 N T +GRAMMONT'S G R AE1 M AA2 N T S +GRAMONT G R AH0 M AA1 N T +GRAMONT'S G R AA0 M OW1 N T S +GRAMOPHONE G R EY1 M OW0 F OW2 N +GRAMOPHONES G R EY1 M OW0 F OW2 N Z +GRAMP G R AE1 M P +GRAMPA G R AE1 M P AH0 +GRAMPAW G R AE1 M P AO0 +GRAMPER G R AE1 M P ER0 +GRAMPIAN G R AE1 M P IY0 AH0 N +GRAMPIANS G R AE1 M P IY0 AH0 N Z +GRAMPOUND G R AE1 M P AW1 N D +GRAMPS G R AE1 M P S +GRAMPUS G R AE1 M P AH0 S +GRAMPUSES G R AE1 M P AH0 S IH0 Z +GRAMS G R AE1 M Z +GRAN G R AE1 N +GRAN' G R AE1 N +GRAN'DAD G R AE1 N D AE2 D +GRAN'FAITHER G R AE1 N F EY1 TH ER0 +GRAN'FATHAH G R AE1 N F AE1 TH AH0 +GRAN'FATHER G R AE1 N F AA2 DH ER0 +GRAN'MA G R AE1 N M AH0 +GRAN'MA'S G R AE1 N M AH0 Z +GRAN'MERE G R AE1 N M IH2 R +GRAN'MOTHER G R AE1 N M AH2 DH ER0 +GRAN'PA G R AE1 N P AH0 +GRAN'PAP G R AE1 N P AE0 P +GRAN'S G R AE1 N Z +GRAN'SIR G R AE1 N S IH1 R +GRAN'SON G R AE1 N S AH0 N +GRAN'THER G R AE1 N DH ER0 +GRAN'THER'S G R AE1 N DH ER0 Z +GRAN'VAITHER G R AE1 N V EY1 TH ER0 +GRANA G R AE1 N AH0 +GRANACCI G R AA0 N AA1 CH IY0 +GRANADA G R AH0 N AA1 D AH0 +GRANADA'S G R AH0 N AA1 D AH0 Z +GRANADILLA G R AE1 N AH0 D IH1 L AH0 +GRANADILLAS G R AE1 N AH0 D IH1 L AH0 Z +GRANADOS G R AA0 N AA1 D OW0 Z +GRANAHAN G R AE1 N AH0 HH AE0 N +GRANARD G R AH0 N AA1 R D +GRANARIES G R AH0 N EH2 R IY0 Z +GRANARY G R AE1 N ER0 IY0 +GRANBERRY G R AE1 N B EH2 R IY0 +GRANBURY G R AE1 N B EH2 R IY0 +GRANBY G R AE1 N B IY0 +GRANBY'S G R AE1 N B IY0 Z +GRANCEY G R AE1 N S IY0 +GRANCHESTER G R AE1 N CH EH2 S T ER0 +GRANCY G R AH0 N S IY0 +GRANCY'S G R AE1 N S IY0 Z +GRAND G R AE1 N D +GRAND' G R AE1 N +GRAND'MERE G R AE1 N M IH2 R +GRAND'RUE G R AE1 N R UW0 +GRAND'S G R AE1 N D Z +GRAND'THER G R AE1 N DH ER0 +GRANDAD G R AE1 N D AE2 D +GRANDAD'S G R AE1 N D AE2 D Z +GRANDADA G R AE1 N D AE2 D AH0 +GRANDADDY G R AE1 N D AE1 D IY0 +GRANDADDY'S G R AE1 N D AE1 D IY0 Z +GRANDAIRS G R AE0 N D EH1 R Z +GRANDALE G R AE1 N D EY2 L +GRANDALL G R AE1 N D AH0 L +GRANDALL'S G R AE1 N D AH0 L Z +GRANDAM G R AE1 N D AE2 M +GRANDAM'S G R AE1 N D AE2 M Z +GRANDAME G R AE0 N D EY1 M +GRANDAMES G R AE0 N D EY1 M Z +GRANDAMS G R AE1 N D AE2 M Z +GRANDAUNT G R AE1 N D AO1 N T +GRANDAUNTS G R AE1 N D AO1 N T S +GRANDCHILD G R AE1 N CH AY2 L D +GRANDCHILD G R AE1 N D CH AY2 L D +GRANDCHILD'S G R AE1 N D CH AY2 L D Z +GRANDCHILDREN G R AE1 N CH IH2 L D R AH0 N +GRANDCHILDREN G R AE1 N D CH IH2 L D R AH0 N +GRANDCHILDREN'S G R AE1 N CH IH2 L D R AH0 N Z +GRANDCHILDREN'S G R AE1 N D CH IH2 L D R AH0 N Z +GRANDCOURT G R AE1 N D K AO2 R T +GRANDCOURT'S G R AE1 N D K AO1 R T S +GRANDDAD G R AE1 N D AH0 D +GRANDDAD'S G R AE1 N D AO2 D Z +GRANDDADDY G R AE1 N D AE2 D IY0 +GRANDDAM G R AE1 N D AA1 M +GRANDDAUGHTER G R AE1 N D AO2 T ER0 +GRANDDAUGHTER'S G R AE1 N D AO2 T ER0 Z +GRANDDAUGHTERS G R AE1 N D AO2 T ER0 Z +GRANDE G R AE1 N D +GRANDE'S G R AA1 N D EY2 Z +GRANDEE G R AE1 N D IY0 +GRANDEE'S G R AE1 N D IY0 Z +GRANDEES G R AE1 N D IY1 Z +GRANDEMONT G R AE1 N D AH0 M AA1 N T +GRANDER G R AE1 N D ER0 +GRANDES G R AE1 N D EH0 S +GRANDEST G R AE1 N D AH0 S T +GRANDET G R AE1 N D EH1 T +GRANDET'S G R AE1 N D EH0 T S +GRANDETS G R AE1 N D IH0 T S +GRANDEUR G R AE0 N D UW1 R +GRANDEUR'S G R AE0 N D UW1 R Z +GRANDEURS G R AE0 N D UW1 R Z +GRANDFATHER G R AE1 N D F AA2 DH ER0 +GRANDFATHER G R AE1 N F AA2 DH ER0 +GRANDFATHER'S G R AE1 N D F AA2 DH ER0 Z +GRANDFATHER'S G R AE1 N F AA2 DH ER0 Z +GRANDFATHERLY G R AE1 N D F AA2 DH ER0 L IY0 +GRANDFATHERLY G R AE1 N F AA2 DH ER0 L IY0 +GRANDFATHERS G R AE1 N D F AA2 DH ER0 Z +GRANDFATHERS G R AE1 N F AA2 DH ER0 Z +GRANDFER G R AE1 N D F ER0 +GRANDFEYTHER G R AE1 N D F AY1 DH ER0 +GRANDI G R AE1 N D IY0 +GRANDIDIER G R AE0 N D IH1 D IY0 ER0 +GRANDIE G R AE1 N D IY0 +GRANDIER G R AE1 N D IY0 ER0 +GRANDIER'S G R AE1 N D IY0 ER0 Z +GRANDIFLORA G R AE1 N D IH0 F L AO1 R AH0 +GRANDILOQUENCE G R AE0 N D IH1 L AH0 K W AH0 N S +GRANDILOQUENT G R AE0 N D IH1 L AH0 K W AH0 N T +GRANDILOQUENTLY G R AE0 N D IH1 L AH0 K W AH0 N T L IY0 +GRANDIN G R AE1 N D IH2 N +GRANDIOSE G R AE1 N D IY0 OW2 S +GRANDIOSE G R AE2 N D IY0 OW1 S +GRANDIOSELY G R AE1 N D IY0 OW2 S L IY0 +GRANDIOSITY G R AE2 N D IY0 AA1 S AH0 T IY0 +GRANDIS G R AE1 N D IH0 S +GRANDISON G R AE1 N D IH0 S AH0 N +GRANDISON'S G R AE1 N D IH0 S AH0 N Z +GRANDISONIAN G R AE1 N D IH0 S OW1 N IY0 AH0 N +GRANDISSIME G R AE0 N D IH1 S IH0 M +GRANDISSIMES G R AE0 N D IH1 S IH0 M Z +GRANDISSIMO G R AE0 N D IH1 S IH0 M OW2 +GRANDJON G R AE1 N JH AA0 N +GRANDLIEU G R AE1 N L Y UH1 +GRANDLIEU'S G R AE1 N L AY2 UW2 Z +GRANDLIEUS G R AE1 N L AY2 UW1 S +GRANDLY G R AE1 N D L IY0 +GRANDMA G R AE1 M AA0 +GRANDMA G R AE1 N D M AA0 +GRANDMA'S G R AE1 M AA2 Z +GRANDMA'S G R AE1 N D M AA2 Z +GRANDMAISON G R AE1 N D M AY2 Z AA1 N +GRANDMAISON G R AE1 N D M EY1 S AH0 N +GRANDMAMA G R AE1 M AA2 M AH0 +GRANDMAMA'S G R AE1 N M AA1 M AH0 Z +GRANDMAMMA G R AE1 N M AE1 M AH0 +GRANDMAMMA'S G R AE1 N M AE1 M AH0 Z +GRANDMAMMAS G R AE1 N M AE1 M AH0 Z +GRANDMARM G R AE1 M AA2 R M +GRANDMAS G R AE1 M AA2 Z +GRANDMAS G R AE1 N D M AA2 Z +GRANDMAS G R AE1 N M AA2 Z +GRANDMERE G R AE1 N D M IH2 R +GRANDMOTHER G R AE1 M AH2 DH ER0 +GRANDMOTHER G R AE1 N D M AH2 DH ER0 +GRANDMOTHER G R AE1 N M AH2 DH ER0 +GRANDMOTHER'S G R AE1 M AH2 DH ER0 Z +GRANDMOTHER'S G R AE1 N D M AH2 DH ER0 Z +GRANDMOTHER'S G R AE1 N M AH2 DH ER0 Z +GRANDMOTHERLY G R AE1 M AH2 DH ER0 L IY0 +GRANDMOTHERLY G R AE1 N D M AH2 DH ER0 L IY0 +GRANDMOTHERLY G R AE1 N M AH2 DH ER0 L IY0 +GRANDMOTHERS G R AE1 M AH2 DH ER0 Z +GRANDMOTHERS G R AE1 N D M AH2 DH ER0 Z +GRANDMOTHERS G R AE1 N M AH2 DH ER0 Z +GRANDNEPHEW G R AE1 N D N EH1 F Y UW0 +GRANDNESS G R AE1 N D N AH0 S +GRANDNIECE G R AE1 N D N IY1 S +GRANDNIECES G R AE1 N D N IY1 S IH0 Z +GRANDOIS G R AE1 N W AA1 +GRANDOLPH G R AE1 N D AA0 L F +GRANDON G R AE1 N D AA0 N +GRANDON'S G R AE1 N D AH0 N Z +GRANDONI G R AE1 N D OW1 N IY0 +GRANDPA G R AE1 M P AA2 +GRANDPA G R AE1 N D P AA2 +GRANDPA G R AE1 N P AA2 +GRANDPA'S G R AE1 N P AH0 Z +GRANDPAP G R AE1 N D P AE2 P +GRANDPAP'S G R AE1 N P AA2 P S +GRANDPAPA G R AE1 N P AA2 P AH0 +GRANDPAPA'S G R AE1 N P AA2 P AH0 Z +GRANDPAPAS G R AE1 N P AA2 P AH0 Z +GRANDPARENT G R AE1 N D P EH2 R AH0 N T +GRANDPARENT G R AE1 N P EH2 R AH0 N T +GRANDPARENTS G R AE1 N D P EH2 R AH0 N T S +GRANDPARENTS G R AE1 N P EH2 R AH0 N T S +GRANDPAS G R AE1 N P AA2 S +GRANDPAW G R AE1 N P AA2 +GRANDPERE G R AE1 N P IH1 R +GRANDPOP G R AE1 N P AA2 P +GRANDPRE G R AE1 N D P R EY2 +GRANDS G R AE1 N D Z +GRANDSIR G R AE1 N S IH1 R +GRANDSIRE G R AE1 N S AY0 R +GRANDSIRE'S G R AE1 N S AY0 R Z +GRANDSIRES G R AE1 N S AY1 R Z +GRANDSON G R AE1 N D S AH2 N +GRANDSON G R AE1 N S AH2 N +GRANDSON'S G R AE1 N D S AH2 N Z +GRANDSON'S G R AE1 N S AH2 N Z +GRANDSONS G R AE1 N D S AH2 N Z +GRANDSONS G R AE1 N S AH2 N Z +GRANDSTAND G R AE1 N D S T AE2 N D +GRANDSTAND G R AE1 N S T AE2 N D +GRANDSTANDER G R AE1 N S T AE2 N D ER0 +GRANDSTANDS G R AE1 N S T AE2 N D Z +GRANDUNCLE G R AE1 N D AH1 NG K AH0 L +GRANDVAL G R AE1 N D V AH0 L +GRANDVILAIN G R AE1 N D V IH2 L EY2 N +GRANDVILAIN'S G R AE1 N D V IH2 L AH0 N Z +GRANDVILLE G R AE1 N D V IH2 L +GRANDY G R AE1 N D IY0 +GRANDY'S G R AE1 N D IY0 Z +GRANE G R EY1 N +GRANET G R AE1 N IH0 T +GRANET'S G R AE1 N AH0 T S +GRANEY G R EY1 N IY0 +GRANEY'S G R AE1 N IY0 Z +GRANFA G R AE1 N F AH0 +GRANFER G R AE1 N F ER0 +GRANGE G R EY1 N JH +GRANGE'S G R EY1 N JH IH0 Z +GRANGER G R EY1 N JH ER0 +GRANGER'S G R EY1 N JH ER0 Z +GRANGERS G R EY1 N JH ER0 Z +GRANGES G R EY1 N JH IH0 Z +GRANGEVILLE G R EY1 N JH V IH0 L +GRANGEWOOD G R EY1 N JH W UH2 D +GRANGIOIA G R AA0 N JH OW1 Y AH0 +GRANI G R AE1 N IY0 +GRANIA G R EY1 N IY0 AH0 +GRANIANI G R AA0 N IY0 AA1 N IY0 +GRANICE G R AA0 N IY1 S +GRANICE'S G R AE2 N IH0 S IH0 Z +GRANICUS G R AE1 N AH0 K AH0 S +GRANIER G R EY1 N IY0 ER0 +GRANIGER G R AE1 N IH0 G ER0 +GRANIT G R AH0 N IH1 T +GRANITE G R AE1 N AH0 T +GRANITE G R AE1 N IH0 T +GRANITES G R AE1 N AH0 T S +GRANITIC G R AH0 N IH1 T IH0 K +GRANJA G R AE1 N JH AH0 +GRANK G R AE1 NG K +GRANMA G R AE1 N M AH0 +GRANMAR G R AE1 N M AA0 R +GRANNAM G R AE1 N AH0 M +GRANNIE G R AE1 N IY0 +GRANNIE'S G R AE1 N IY0 Z +GRANNIES G R AE1 N IY0 Z +GRANNING G R AE1 N IH0 NG +GRANNIS G R AE1 N IH0 S +GRANNIS'S G R AE1 N AH0 S IH0 Z +GRANNOCH G R AE1 N AH0 K +GRANNONIA G R AA0 N OW1 N IY0 AH0 +GRANNY G R AE1 N IY0 +GRANNY'S G R AE1 N IY0 Z +GRANNYMOTHER G R AE1 N IY0 M AH2 DH ER0 +GRANO G R AA1 N OW0 +GRANOUX G R AE1 N UW1 +GRANOW G R AE1 N OW0 +GRANP G R AE1 N P +GRANPA G R AE1 N P AH0 +GRANPERE G R AE1 N T P IY1 R +GRANS G R AE1 N Z +GRANSER G R AE1 N S ER0 +GRANSIERE G R AE1 N S IY0 EH0 R +GRANSON G R AE1 N S AH0 N +GRANT G R AE1 N T +GRANT'S G R AE1 N T S +GRANTA G R AE1 N T AH0 +GRANTCHESTER G R AE1 N T CH EH2 S T ER0 +GRANTED G R AE1 N AH0 D +GRANTED G R AE1 N IH0 D +GRANTED G R AE1 N T AH0 D +GRANTED G R AE1 N T IH0 D +GRANTEE G R AH0 N T IY1 +GRANTEES G R AH0 N T IY1 Z +GRANTER G R AE1 N T ER0 +GRANTEST G R AE1 N T AH0 S T +GRANTETH G R AH0 N T EH2 TH +GRANTHAM G R AE1 N TH AH0 M +GRANTHAM'S G R AE1 N TH AH0 M Z +GRANTHAMS G R AE1 N TH AH0 M Z +GRANTHER G R AE1 N DH ER0 +GRANTHUM G R AE1 N TH AH0 M +GRANTIE G R AE1 N T IY0 +GRANTIN G R AE1 N T IH0 N +GRANTING G R AE1 N IH0 NG +GRANTING G R AE1 N T IH0 NG +GRANTLEY G R AE1 N T L IY0 +GRANTLEY'S G R AE1 N T L IY0 Z +GRANTLINE G R AE1 N T L AY2 N +GRANTLINE'S G R AE1 N T L AY2 N Z +GRANTLY G R AH0 N T L IY0 +GRANTMESNIL G R AE1 N T M AH0 S N IH1 L +GRANTON G R AE1 N T AH0 N +GRANTOR G R AE1 N T ER0 +GRANTOWN G R AH0 N T AW2 N +GRANTS G R AE1 N T S +GRANTVILLE G R AH0 N T V IH2 L +GRANUA G R AE1 N Y UW0 AH0 +GRANULAR G R AE1 N Y AH0 L ER0 +GRANULATED G R AE1 N Y AH0 L EY2 T IH0 D +GRANULATION G R AE2 N Y AH0 L EY1 SH AH0 N +GRANULATIONS G R AE1 N Y AH0 L EY1 SH AH0 N Z +GRANULE G R AE1 N Y AH0 L +GRANULES G R AE1 N Y AH0 L Z +GRANVELLA G R AE1 N V EH1 L AH0 +GRANVELLE G R AE1 N V AH0 L +GRANVILLE G R AE1 N V IH0 L +GRANVILLE'S G R AE1 N V IH0 L Z +GRANVILLES G R AE1 N V IH0 L Z +GRAOUND G R AA0 AW2 N D +GRAP G R AE1 P +GRAPE G R EY1 P +GRAPE'S G R EY1 P S +GRAPEFRUIT G R EY1 P F R UW2 T +GRAPERIES G R EY1 P ER0 IY0 Z +GRAPERY G R EY1 P ER0 IY0 +GRAPES G R EY1 P S +GRAPESHOT G R EY1 P SH AA2 T +GRAPEVIMES G R EY1 P V AY2 M Z +GRAPEVINE G R EY1 P V AY2 N +GRAPEVINES G R EY1 P V AY2 N Z +GRAPH G R AE1 F +GRAPHIC G R AE1 F IH0 K +GRAPHICAL G R AE1 F IH0 K AH0 L +GRAPHICALLY G R AE1 F IH0 K L IY0 +GRAPHITE G R AE1 F AY2 T +GRAPHOPHONE G R AE1 F AH0 F OW2 N +GRAPHS G R AE1 F S +GRAPION G R EY1 P IY0 AH0 N +GRAPNEL G R AE1 P N AH0 L +GRAPNELS G R AE1 P N AH0 L Z +GRAPPLE G R AE1 P AH0 L +GRAPPLED G R AE1 P AH0 L D +GRAPPLES G R AE1 P AH0 L Z +GRAPPLING G R AE1 P AH0 L IH0 NG +GRAPPLING G R AE1 P L IH0 NG +GRAPPLINGS G R AE1 P AH0 L IH0 NG Z +GRAS G R AA1 +GRAS G R AE1 S +GRASCOUR G R AE1 S K UH1 R +GRASHUS G R AE1 SH AH0 S +GRASMERE G R AE1 Z M IH2 R +GRASNAGALLIPAS G R AE1 S N AH0 G AE1 L IH0 P AH0 Z +GRASP G R AE1 S P +GRASP'D G R AE1 S P D +GRASPAN G R AE1 S P AE2 N +GRASPED G R AE1 S P T +GRASPER G R AE1 S P ER0 +GRASPIN G R AE1 S P IH0 N +GRASPING G R AE1 S P IH0 NG +GRASPINGS G R AE1 S P IH0 NG Z +GRASPS G R AE1 S P S +GRASS G R AE1 S +GRASS'S G R AE1 S IH0 Z +GRASSDALE G R AE1 S D EY2 L +GRASSE G R AE1 S +GRASSED G R AE1 S T +GRASSES G R AE1 S AH0 Z +GRASSES G R AE1 S IH0 Z +GRASSET G R AE1 S EH0 T +GRASSETTE G R AE1 S EH1 T +GRASSETTE'S G R AE1 S EH1 T S +GRASSFORD G R AE1 S F ER0 D +GRASSHOPPER G R AE1 S HH AA2 P ER0 +GRASSHOPPER'S G R AE1 S HH AA2 P ER0 Z +GRASSHOPPERS G R AE1 S HH AA2 P ER0 Z +GRASSI G R AE1 S IY0 +GRASSINGFORD G R AE1 S IH0 NG F ER0 D +GRASSINI G R AE1 S IY1 N IY0 +GRASSINS G R AE1 S AH0 N Z +GRASSLAND G R AE1 S L AE2 N D +GRASSLANDS G R AE1 S L AE2 N D Z +GRASSLESS G R AE1 S L AH0 S +GRASSMARKET G R AE1 S M AA2 R K IH0 T +GRASSMERE G R AE1 S M IH2 R +GRASSO G R AE1 S OW0 +GRASSOU G R AE1 S UW0 +GRASSPLOT G R AE1 S P L AA0 T +GRASSY G R AE1 S IY0 +GRASTY G R AE1 S T IY0 +GRAT G R AE2 T +GRATA G R AA1 T AH0 +GRATACAP G R AE1 T AH0 K AE2 P +GRATCHER G R AE1 CH ER0 +GRATE G R EY1 T +GRATED G R EY1 T IH0 D +GRATEFUL G R EY1 T F AH0 L +GRATEFULLY G R EY1 T F AH0 L IY0 +GRATEFULNESS G R EY1 T F AH0 L N AH0 S +GRATER G R EY1 T ER0 +GRATERS G R EY1 T ER0 Z +GRATES G R EY1 T S +GRATH G R AE1 TH +GRATH'S G R AE1 TH S +GRATIA G R EY1 SH IY0 AH0 +GRATIAE G R EY1 SH IY0 AH0 +GRATIAN G R EY1 SH AH0 N +GRATIAN'S G R EY1 SH AH0 N Z +GRATIANO G R EY1 SH IY0 AA1 N OW0 +GRATIANUS G R EY1 SH AH0 N IH0 S +GRATIAS G R AA1 SH AH0 Z +GRATIEN G R EY1 T Y EH1 N +GRATIENNE G R EY1 SH IY0 EH2 N +GRATIFICATION G R AE2 T AH0 F AH0 K EY1 SH AH0 N +GRATIFICATIONS G R AE1 T AH0 F AH0 K EY1 SH AH0 N Z +GRATIFIE G R AE1 T AH0 F AY2 +GRATIFIED G R AE1 T AH0 F AY2 D +GRATIFIES G R AE1 T AH0 F AY2 Z +GRATIFY G R AE1 T AH0 F AY2 +GRATIFYED G R AE1 T AH0 F AY2 D +GRATIFYING G R AE1 T AH0 F AY2 IH0 NG +GRATIFYINGLY G R AE1 T AH0 F AY2 IH0 NG L IY0 +GRATIN G R AE1 T AH0 N +GRATING G R EY1 T IH0 NG +GRATINGLY G R EY1 T IH0 NG L IY0 +GRATINGS G R EY1 T IH0 NG Z +GRATIOT G R EY1 SH IY0 OW0 +GRATIOUS G R EY1 SH AH0 S +GRATIS G R AE1 T AH0 S +GRATITOOD G R AE1 T AH0 T UH1 D +GRATITUDE G R AE1 T AH0 T UW2 D +GRATITUDE'S G R AE1 T AH0 T UW2 D Z +GRATITUDES G R AE1 T AH0 T UW2 D Z +GRATTAN G R AE1 T AH0 N +GRATTAN'S G R AE1 T AH0 N Z +GRATTON G R AE1 T AH0 N +GRATTON'S G R AE1 T AH0 N Z +GRATUITIES G R AH0 T UW1 IH0 T IY0 Z +GRATUITOUS G R AH0 T UW1 AH0 T AH0 S +GRATUITOUSLY G R AH0 T UW1 AH0 T AH0 S L IY0 +GRATUITY G R AH0 T UW1 IH0 T IY0 +GRATULATE G R AE1 CH AH0 L EY2 T +GRATULATING G R AE1 CH AH0 L EY2 T IH0 NG +GRATULATION G R AE2 CH AH0 L EY1 SH AH0 N +GRATULATIONS G R AE2 CH AH0 L EY1 SH AH0 N Z +GRATULATORY G R AE1 CH AH0 L AH0 T AO2 R IY0 +GRATUS G R AE1 T AH0 S +GRATZ G R AE1 T S +GRATZEN G R AE1 T Z AH0 N +GRATZEN'S G R AE1 T S AH0 N Z +GRAU G R AW1 +GRAUB G R AW1 B +GRAUBLE G R AO1 B AH0 L +GRAUBLE'S G R AO1 B AH0 L Z +GRAUBUENDEN G R AW1 B Y UW0 EH1 N D AH0 N +GRAUDENZ G R AW1 D IH0 N S +GRAUFFIS G R AO1 F AH0 S +GRAUL G R AO1 L +GRAUMANN G R AO1 M AH0 N +GRAUN G R AO1 N +GRAUND G R AO1 N D +GRAUNT G R AO1 N T +GRAUSTARK G R AO1 S T AA2 R K +GRAUSTARK'S G R AO1 S T AA1 R K S +GRAV G R AE2 V +GRAV'D G R AE2 V D +GRAVAMEN G R AH0 V EY1 M AH0 N +GRAVANI G R AH0 V AA1 N IY0 +GRAVATANA G R AE1 V AH0 T AE1 N AH0 +GRAVE G R EY1 V +GRAVE'S G R EY1 V Z +GRAVEAIRS G R EY1 V EH2 R Z +GRAVECLOTHES G R AH0 V EH1 K L OW1 Z +GRAVED G R EY1 V D +GRAVEDIGGER G R AE1 V IH0 D IH1 G ER0 +GRAVEDIGGER'S G R AE1 V IH0 D IH1 G ER0 Z +GRAVEDIGGERS G R AE1 V IH0 D IH1 G ER0 Z +GRAVEL G R AE1 V AH0 L +GRAVELED G R AE1 V AH0 L D +GRAVELEIGH G R AE1 V AH0 L AH0 +GRAVELINES G R EY1 V L AY2 N Z +GRAVELLED G R AE1 V AH0 L D +GRAVELLO G R AH0 V EH1 L OW0 +GRAVELLY G R AE1 V AH0 L IY0 +GRAVELOTTE G R AE1 V AH0 L AA1 T +GRAVELS G R AE1 V AH0 L Z +GRAVELY G R EY1 V L IY0 +GRAVEN G R EY1 V AH0 N +GRAVENARD G R AE1 V IH0 N ER0 D +GRAVENESS G R AE1 V IH0 N EH0 S +GRAVENITZ G R AE1 V IH0 N IH0 T S +GRAVER G R EY1 V ER0 +GRAVER'S G R EY1 V ER0 Z +GRAVERS G R EY1 V ER0 Z +GRAVES G R EY1 V Z +GRAVES'S G R EY1 V Z IH0 Z +GRAVESEND G R EY1 V S EH2 N D +GRAVESIDE G R EY1 V S AY2 D +GRAVESON G R EY1 V S AH0 N +GRAVEST G R AE1 V AH0 S T +GRAVESTONE G R EY1 V S T OW2 N +GRAVESTONES G R EY1 V S T OW2 N Z +GRAVEYARD G R EY1 V Y AA2 R D +GRAVEYARDS G R EY1 V Y AA2 R D Z +GRAVI G R EY1 V IY0 +GRAVID G R AE2 V AH0 D +GRAVIEL G R EY1 V IY0 L +GRAVIER G R AE1 V Y ER0 +GRAVIER'S G R AE1 V Y ER0 Z +GRAVIES G R EY1 V IY0 Z +GRAVILLE G R AE2 V IH0 L +GRAVINA G R AA0 V IY1 N AH0 +GRAVING G R EY1 V IH0 NG +GRAVIS G R AE1 V IH0 S +GRAVITATE G R AE1 V IH0 T EY2 T +GRAVITATED G R AE1 V AH0 T EY2 T IH0 D +GRAVITATES G R AE1 V IH0 T EY2 T S +GRAVITATING G R AE1 V IH0 T EY2 T IH0 NG +GRAVITATION G R AE2 V IH0 T EY1 SH AH0 N +GRAVITATIONAL G R AE2 V IH0 T EY1 SH AH0 N AH0 L +GRAVITATIONS G R AE2 V IH0 T EY1 SH AH0 N Z +GRAVITATIVE G R AE1 V IH0 T EY2 T IH0 V +GRAVITER G R AE1 V IH0 T ER0 +GRAVITIES G R AE1 V IH0 T IY0 Z +GRAVITY G R AE1 V AH0 T IY0 +GRAVITY G R AE1 V IH0 T IY0 +GRAVOIS G R AH0 V W AA1 +GRAVOSA G R AA0 V OW1 S AH0 +GRAVY G R EY1 V IY0 +GRAW G R AO1 +GRAW'S G R AO1 Z +GRAWED G R AO1 D +GRAWLER G R AO1 L ER0 +GRAWSON G R AO1 S AH0 N +GRAY G R EY1 +GRAY'S G R EY1 Z +GRAYBACK G R EY1 B AE2 K +GRAYBACKS G R EY1 B AE2 K S +GRAYBEARD G R EY1 B IY0 R D +GRAYBEARDS G R EY1 B IY0 R D Z +GRAYBRIDGE G R EY1 B R IH2 JH +GRAYBROOK G R EY1 B R UH2 K +GRAYBROOKE G R EY1 B R UH0 K +GRAYBULL G R EY1 B AH2 L +GRAYCOAT G R EY1 K OW2 T +GRAYCOATS G R EY1 K OW2 T S +GRAYDON G R EY1 D AH0 N +GRAYDON'S G R EY1 D AH0 N Z +GRAYED G R EY1 D +GRAYER G R EY1 ER0 +GRAYEST G R EY1 IH0 S T +GRAYHEADED G R EY1 HH EH2 D IH0 D +GRAYHOUND G R EY1 HH AW2 N D +GRAYHOUNDE G R EY1 HH AW2 N D +GRAYHOUNDS G R EY1 HH AW2 N D Z +GRAYING G R EY1 IH0 NG +GRAYISH G R EY1 IH0 SH +GRAYLE G R EY1 L +GRAYLEES G R EY1 L IY0 Z +GRAYLING G R EY1 L IH0 NG +GRAYLING'S G R EY1 L IH0 NG Z +GRAYLOCK G R EY1 L AH0 K +GRAYLY G R EY1 L IY0 +GRAYMAN G R EY1 M AH0 N +GRAYNE G R EY1 N +GRAYNESS G R EY1 N AH0 S +GRAYNOR G R EY1 N ER0 +GRAYROCK G R EY1 R AA2 K +GRAYS G R EY1 Z +GRAYSBROOKE G R EY1 S B R UH0 K +GRAYSKIN G R EY1 S K IH0 N +GRAYSON G R EY1 S AH0 N +GRAYSON'S G R EY1 S AH0 N Z +GRAYSONS G R EY1 S AH0 N Z +GRAYSQUIRREL G R EY1 S K W ER1 AH0 L +GRAYSTEEL G R EY1 S T IY2 L +GRAYSTONE G R EY1 S T OW2 N +GRAYSVILLE G R EY1 S V IH2 L +GRAYTHORNE G R EY1 TH AO0 R N +GRAYTHORPE G R EY1 TH AO0 R P +GRAYTON G R EY1 T AH0 N +GRAYVILLE G R IY0 V IH2 L +GRAYWATER G R EY1 W AO2 T ER0 +GRAZ G R AA0 Z +GRAZE G R EY1 Z +GRAZED G R EY1 Z D +GRAZER G R EY1 Z ER0 +GRAZES G R EY1 Z IH0 Z +GRAZIA G R AA1 Z IY0 AH0 +GRAZIANI G R AA0 Z IY0 AA1 N IY0 +GRAZIANO G R AA0 T S IY0 AA1 N OW0 +GRAZIE G R EY1 Z IY0 +GRAZIELLA G R AA0 T S IY0 EH1 L AH0 +GRAZIER G R EY1 Z IY0 ER0 +GRAZIER'S G R EY1 Z IY0 ER0 Z +GRAZIERS G R EY1 Z IY0 ER0 Z +GRAZIN G R EY1 Z IH0 N +GRAZING G R EY1 Z IH0 NG +GRAZINGLANDS G R EY1 Z IH0 NG G L AH0 N D Z +GRAZIOSA G R EY1 Z IY0 OW1 S AH0 +GRAZIOSO G R EY1 Z IY0 OW1 S OW0 +GRAZY G R EY1 Z IY0 +GRDZNTH G R JH N TH +GRE G R +GRE'T G R T +GREA G R EY1 +GREAL G R IY1 L +GREANE G R IY1 N +GREASE G R IY1 S +GREASED G R IY1 S T +GREASER G R IY1 S ER0 +GREASER'S G R IY1 S ER0 Z +GREASERS G R IY1 S ER0 Z +GREASES G R IY1 S IH0 Z +GREASEWOOD G R IY1 S W UH2 D +GREASIER G R EY1 Z IY0 ER0 +GREASIEST G R IY1 Z IY0 AH0 S T +GREASILY G R IY1 Z AH0 L IY0 +GREASIN G R IY1 S IH0 N +GREASINESS G R IY1 Z IY0 N AH0 S +GREASING G R IY1 S IH0 NG +GREASY G R IY1 S IY0 +GREAT G R EY1 T +GREAT'S G R EY1 T S +GREATAUK G R EY1 T AO1 K +GREATCOAT G R EY1 T K OW2 T +GREATCOATS G R EY1 T K OW2 T S +GREATE G R EY1 T +GREATEN G R EY1 T AH0 N +GREATENED G R EY1 T AH0 N D +GREATENING G R EY1 T AH0 N IH0 NG +GREATENS G R EY1 T AH0 N Z +GREATER G R EY1 T ER0 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G R IH1 F IH0 N +GRIFFIN'S G R IH1 F IH0 N Z +GRIFFINS G R IH1 F IH0 N Z +GRIFFIS G R IH1 F IH0 S +GRIFFITH G R IH1 F AH0 TH +GRIFFITH G R IH1 F IH0 TH +GRIFFITH'S G R IH1 F IH0 TH S +GRIFFITHS G R IH1 F IH0 TH S +GRIFFITHS'S G R IH1 F IH0 TH S IH0 Z +GRIFFON G R IH1 F AH0 N +GRIFFONS G R IH1 F AH0 N Z +GRIFLET G R IH1 F L IH0 T +GRIFONI G R IH1 F OW1 N IY0 +GRIG G R IH0 G +GRIG'S G R IH0 G Z +GRIGG G R IH1 G +GRIGGINS G R IH1 G IH0 N Z +GRIGGS G R IH1 G Z +GRIGGS'S G R IH1 G Z IH0 Z +GRIGNAN G R IH0 G N AH0 N +GRIGNON G R IH1 G N AH0 N +GRIGORI G R IH0 G AO1 R IY0 +GRIGORIEVITCH G R IH0 G AO1 R Y AH0 V IH2 CH +GRIGORIEVNA G R IH0 G AO1 R IY2 V N AH0 +GRIGORITCH G R IH0 G AO1 R IH0 CH +GRIGOROVITCH G R IH0 G AO1 R AH0 V IH0 CH +GRIGORY G R EH1 G ER0 IY0 +GRIGORY G R IY1 G ER0 IY0 +GRIGORY'S G R IY1 G ER0 IY0 Z +GRIGOSIE G R IH0 G OW1 Z IY0 +GRIGS G R IH1 G Z +GRIGSBY G R IH1 G Z B IY0 +GRIGSBY'S G R IH1 G Z B IY0 Z +GRIJALVA G R IY0 Y AA1 L V AH0 +GRIL G R IH1 L +GRILL G R IH1 L +GRILLE G R IH1 L +GRILLED G R IH1 L D +GRILLES G R IH1 L IY0 Z +GRILLING G R IH1 L IH0 NG +GRILLO G R IH1 L OW0 +GRILLON G R IH1 L AH0 N +GRILLONNE G R IH1 L Y AH0 N +GRILLPARZER G R IH1 L P AA2 R Z ER0 +GRILLROOM G R IH1 L R UW2 M +GRILLS G R IH1 L Z +GRILLWORK G R IH1 L W ER2 K +GRILLY G R IH1 L IY0 +GRILLYER G R IH1 L IY0 ER0 +GRILSE G R IH1 L S +GRILSTON G R IH1 L S T AH0 N +GRIM G R IH1 M +GRIM'S G R IH1 M Z +GRIMACE G R IH1 M AH0 S +GRIMACED G R IH1 M AH0 S T +GRIMACES G R IH1 M AH0 S IH0 Z +GRIMACING G R IH1 M AH0 S IH0 NG +GRIMALDI G R IY0 M AA1 L D IY0 +GRIMALDI'S G R IY0 M AA1 L D IY0 Z +GRIMALDIS G R IY0 M AA1 L D IH0 S +GRIMALDO G R IY0 M AA1 L D OW0 +GRIMALKIN G R IY0 M AA1 L K IH0 N +GRIMALKIN'S G R IY0 M AA1 L K IH0 N Z +GRIMALSON G R IH1 M AH0 L S AH0 N +GRIMANI G R IY0 M AA1 N IY0 +GRIMAUD G R IY0 M AA1 D +GRIMAUD'S G R IY0 M AA1 D Z +GRIMBAL G R IH1 M B AH0 L +GRIMBAL'S G R IH1 M B AH0 L Z +GRIMBALD G R IH1 M B AH0 L D +GRIMBARD G R IH1 M B ER0 D +GRIMCKE G R IH1 M K IY0 +GRIME G R AY1 M +GRIMED G R AY1 M D +GRIMERS G R AY1 M ER0 Z +GRIMES G R AY1 M Z +GRIMES'S G R AY1 M Z IH0 Z +GRIMESBY G R AY1 M S B IY0 +GRIMESES G R AY1 M Z IH0 Z +GRIMHILD G R IH1 M HH AY2 L D +GRIMIER G R IH1 M IY0 ER0 +GRIMIEST G R IH1 M IY0 IH0 S T +GRIMINESS G R IH1 M IY0 N AH0 S +GRIMITI G R IH1 M IH0 T IY0 +GRIMJAW G R IH1 M Y AA1 +GRIMKE G R IH1 M K IY0 +GRIMKE'S G R IH1 M K S +GRIMLY G R IH1 M L IY0 +GRIMM G R IH1 M +GRIMM'S G R IH1 M Z +GRIMME G R IH1 M +GRIMMER G R IH1 M ER0 +GRIMMEST G R IH1 M AH0 S T +GRIMMS G R IH1 M Z +GRIMMY G R IH1 M IY0 +GRIMNESS G R IH1 M N AH0 S +GRIMOD G R IH1 M AH0 D +GRIMPEN G R IH1 M P AH0 N +GRIMPS G R IH1 M P S +GRIMROSS G R IH1 M R AA2 S +GRIMSBY G R IH1 M Z B IY0 +GRIMSBY'S G R IH1 M Z B IY0 Z +GRIMSEL G R IH1 M S AH0 L +GRIMSEY G R IH1 M Z IY0 +GRIMSHAW G R IH1 M SH AO2 +GRIMSHAW'S G R IH1 M SH AO2 Z +GRIMSHAWE G R IH1 M SH AO2 +GRIMSHAWE'S G R IH1 M SH AO2 Z +GRIMSTAD G R IH1 M S T AH0 D +GRIMSTON G R IH1 M S T AH0 N +GRIMSTON'S G R IH1 M S T AH0 N Z +GRIMSTONE G R IH1 M S T OW0 N +GRIMSTONE'S G R IH1 M S T OW2 N Z +GRIMSWITCH G R IH1 M S W IH1 CH +GRIMWIG G R IH1 M W IH0 G +GRIMWOOD G R IH1 M W UH2 D +GRIMWOOD'S G R IH1 M W UH2 D Z +GRIMWORTH G R IH1 M W ER0 TH +GRIMY G R AY1 M IY0 +GRIN G R IH1 N +GRINALDI G R IH0 N AA1 L D IY0 +GRINBY'S G R IH1 N B IY0 Z +GRIND G R AY1 N D +GRINDAL G R IH1 N D AH0 L +GRINDALL'S G R IH1 N D AH0 L Z +GRINDED G R AY1 N D IH0 D +GRINDELWALD G R IH1 N D AH0 L W AO0 L D +GRINDEM G R AY1 N D AH0 M +GRINDER G R AY1 N D ER0 +GRINDER'S G R AY1 N D ER0 Z +GRINDERS G R AY1 N D ER0 Z +GRINDETH G R AY1 N D IH0 TH +GRINDHUSEN G R AY1 N D HH UW0 S AH0 N +GRINDIN G R IH1 N D IH0 N +GRINDING G R AY1 N D IH0 NG +GRINDINGS G R AY1 N D IH0 NG Z +GRINDLAY G R AY1 N D L EY2 +GRINDLAY G R IH1 N D L IY2 +GRINDLE G R IH1 N D AH0 L +GRINDLEJERKIN G R IH1 N D AH0 L JH ER1 K IH0 N +GRINDORF G R AY1 N D AO0 R F +GRINDOT G R AY1 N D AH0 T +GRINDS G R AY1 N D Z +GRINDSTANE G R AY1 N D S T AE2 N +GRINDSTONE G R AY1 N D S T OW2 N +GRINDSTONES G R AY1 N D S T OW2 N Z +GRINDUM G R AY1 N D AH0 M +GRINELL G R IH1 N AH0 L +GRINGALET G R IH1 NG G AH0 L EY1 +GRINGAMOR G R IH1 NG G AH0 M AO2 R +GRINGAMORE G R IH1 NG G AH0 M AO2 R +GRINGAMORS G R IH1 NG G AH0 M ER0 Z +GRINGO G R IH1 NG G OW0 +GRINGO'S G R IH1 NG G OW0 Z +GRINGOES G R IH1 NG G OW0 Z +GRINGOIRE G R IH1 NG G W AA1 R +GRINGOIRE'S G R IH1 NG G W AA1 R Z +GRINGOS G R IH1 NG G OW0 Z +GRINLING G R IH1 N L IH0 NG +GRINN'D G R IH0 N D +GRINNED G R IH1 N D +GRINNELL G R IH0 N EH1 L +GRINNELL'S G R IH0 N EH1 L Z +GRINNER G R IH1 N ER0 +GRINNIN G R IH1 N IH0 N +GRINNING G R IH1 N IH0 NG +GRINNINGLY G R IH1 N IH0 NG L IY0 +GRINOBLE G R IH1 N OW1 B AH0 L +GRINS G R IH1 N Z +GRINSELHOF G R IH1 N S AH0 L HH AO0 F +GRINSELL G R IH1 N S AH0 L +GRINSON G R IH1 N S AH0 N +GRINSON'S G R IH1 N S AH0 N Z +GRINSTEAD G R IH1 N S T EH2 D +GRIO G R IY0 OW0 +GRIO'S G R IY0 OW0 Z +GRIP G R IH1 P +GRIP'S G R IH1 P S +GRIPE G R AY1 P +GRIPED G R AY1 P T +GRIPES G R AY1 P S +GRIPING G R AY1 P IH0 NG +GRIPPA G R IH1 P AH0 +GRIPPE G R IH1 P +GRIPPED G R IH1 P T +GRIPPER G R IH1 P ER0 +GRIPPERS G R IH1 P ER0 Z +GRIPPIN G R IH1 P IH0 N +GRIPPING G R IH1 P IH0 NG +GRIPPIT G R IH1 P IH0 T +GRIPPY G R IH1 P IY0 +GRIPS G R IH1 P S +GRIPSACK G R IH1 P S AE2 K +GRIPSACKS G R IH1 P S AE2 K S +GRIPT G R IH1 P T +GRIQUA G R IH1 K W AH0 +GRIQUALAND G R IY1 K AH0 L AH0 N D +GRIQUAS G R IH1 K W AH0 Z +GRIS G R IH0 S +GRISAILLE G R IH1 S EY1 L +GRISBEN G R IH1 S B AH0 N +GRISBEN'S G R IH1 S B IH0 N Z +GRISCOM G R IH1 S K AH0 M +GRISCOM'S G R IH1 S K AA0 M Z +GRISE G R AY1 Z +GRISEL G R AY1 Z AH0 L +GRISEL'S G R IH1 S AH0 L Z +GRISELDA G R IH1 S EH1 L D AH0 +GRISELDA'S G R IH1 S IH0 L D AH0 Z +GRISELDIS G R IH1 S IH0 L D IH0 S +GRISETTE G R IH0 S EH0 T +GRISETTE'S G R IH0 S EH0 T S +GRISETTES G R IH0 S EH0 T S +GRISH G R IH1 SH +GRISHA G R IH1 SH AH0 +GRISHA'S G R IH1 SH AH0 Z +GRISHKA G R IH1 SH K AH0 +GRISI G R IY1 S IY0 +GRISKIN G R IH1 S K IH0 N +GRISLEY G R IH1 Z L IY0 +GRISLIES G R IH1 Z L IY0 Z +GRISLIEST G R IH1 Z L IY0 AH0 S T +GRISLY G R IH1 Z L IY0 +GRISNEZ G R IH1 S N EH1 Z +GRISON G R IH1 S AH0 N +GRISONS G R IH1 S AH0 N Z +GRISSEL G R IH1 S AH0 L +GRISSOM G R IH1 S AH0 M +GRISSON G R IH1 S AH0 N +GRIST G R IH1 S T +GRISTLE G R IH1 S AH0 L +GRISTLY G R IH1 S T L IY0 +GRISTMILL G R IH1 S T M IH2 L +GRISTS G R IH0 S T S +GRISWOLD G R IH1 S W OW2 L D +GRISWOLD'S G R IH1 S W OW2 L D Z +GRIT G R IH1 T +GRIT'S G R IH1 T S +GRITO G R IY1 T OW0 +GRITS G R IH1 T S +GRITTE G R IH1 T +GRITTED G R IH1 T AH0 D +GRITTING G R IH1 T IH0 NG +GRITTY G R IH1 T IY0 +GRITZ G R IH1 T S +GRIVET G R IH1 V AH0 T +GRIVOIS G R IY1 V W AA1 +GRIVOTTE G R IH1 V AH0 T +GRIZ G R IH1 Z +GRIZEK G R IH1 Z EH0 K +GRIZEL G R IY0 Z EH1 L +GRIZEL'S G R IY0 Z EH1 L Z +GRIZELL G R IH1 Z AH0 L +GRIZLY G R IH1 Z AH0 L IY0 +GRIZZEL G R IH1 Z AH0 L +GRIZZEL'S G R IH1 Z AH0 L Z +GRIZZLE G R IH1 Z AH0 L +GRIZZLE'S G R IH1 Z AH0 L Z +GRIZZLEBEARD G R IH1 Z AH0 L B IY2 R D +GRIZZLED G R IH1 Z AH0 L D +GRIZZLIES G R IH1 Z L IY0 Z +GRIZZLING G R IH1 Z L IH0 NG +GRIZZLY G R IH1 Z L IY0 +GRIZZLY'S G R IH1 Z L IY0 Z +GRIZZY G R IH1 Z IY0 +GRIZZY'S G R IH1 Z IY0 Z +GRO G R OW1 +GROA G R OW1 AH0 +GROA'S G R OW1 AH0 Z +GROAC'H G R OW1 +GROAN G R OW1 N +GROAN'D G R OW1 N D +GROANED G R OW1 N D +GROANER G R OW1 N ER0 +GROANETH G R OW1 N TH +GROANIN G R OW1 N IH0 N +GROANING G R OW1 N IH0 NG +GROANINGLY G R OW1 N IH0 NG L IY0 +GROANINGS G R OW1 N IH0 NG Z +GROANS G R OW1 N Z +GROAR G R AO1 R +GROARTY G R AO1 R T IY0 +GROAT G R OW1 T +GROAT'S G R OW1 T S +GROATS G R OW1 T S +GROB G R AA1 B +GROBBLE G R AA1 B AH0 L +GROBLAAR G R AA1 B L AA1 R +GROBLER G R AA1 B L ER0 +GROBLER'S G R AA1 B L ER0 Z +GROBSTOCK G R AA1 B S T AA2 K +GROBSTOCK'S G R AA1 B S T AA2 K S +GROBY G R OW1 B IY0 +GROCER G R OW1 S ER0 +GROCER'S G R OW1 S ER0 Z +GROCERIES G R OW1 S ER0 IY0 Z +GROCERIES G R OW1 S R IY0 Z +GROCERS G R OW1 S ER0 Z +GROCERY G R OW1 S ER0 IY0 +GROCERY G R OW1 S R IY0 +GROCERYMAN G R OW1 S R IY0 M AH0 N +GROCHOWSKI G R AH0 CH AO1 F S K IY0 +GROCOTT G R OW1 K AH0 T +GROCOTT'S G R OW1 K AH0 T S +GROCYN G R OW1 S IH0 N +GRODEK G R OW1 D IH0 K +GRODMAN G R AA1 D M AH0 N +GRODMAN'S G R AA1 D M AH0 N Z +GRODNO G R AA1 D N AH0 +GRODZITSKI G R AH0 JH IH1 T S K IY0 +GROEN G R OW1 N +GROENENDAEL G R OW1 N IH0 N D EY0 L +GROENER G R OW1 N ER0 +GROENFONTEIN G R OW1 N F AO0 N T AY2 N +GROENING G R AA1 AH0 N IH0 NG +GROESBECK G R OW1 S B EH0 K +GROF G R AH0 F +GROFF G R AO1 F +GROFT G R AA1 F T +GROG G R AA1 G +GROG'S G R AA1 G Z +GROGAN G R OW1 G AH0 N +GROGAN'S G R OW1 G AH0 N Z +GROGGERIES G R AA1 G ER0 IY0 Z +GROGGERY G R AA1 G ER0 IY0 +GROGGILY G R AA1 G AH0 L IY0 +GROGGS G R AA1 G Z +GROGGY G R AA1 G IY0 +GROGOFF G R AA1 G AO0 F +GROGOFF'S G R AA1 G AO0 F S +GROGRAM G R OW1 G R AE2 M +GROGS G R AA1 G Z +GROGSHOP G R AA1 G SH AA2 P +GROGZWIG G R AA1 G Z W IH0 G +GROIN G R OY1 N +GROINED G R OY1 N D +GROINING G R OY1 N IH0 NG +GROINS G R OY1 N Z +GROISON G R OY1 Z AH0 N +GROIX G R OY1 +GROLES G R OW1 L Z +GROLIER G R OW1 L Y ER0 +GROLLE G R OW1 L +GROLLMANN G R OW1 L M AH0 N +GROM G R AA1 M +GROM'S G R AA1 M Z +GROMMER G R AA1 M ER0 +GRONAU G R OW1 N AW0 +GRONAU'S G R OW1 N AW0 Z +GRONER G R OW1 N ER0 +GRONG G R AO1 NG +GRONINGEN G R OW1 N IH0 NG AH0 N +GRONOW G R AA1 N OW0 +GRONVOLD G R AA1 N V OW2 L D +GROOM G R UW1 M +GROOM'S G R UW1 M Z +GROOMBRIDE G R UW1 M B R AY1 D +GROOMBRIDGE G R UW1 M B R IH2 JH +GROOMBRIDGE'S G R UW1 M B R IH2 JH IH0 Z +GROOME G R UW1 M +GROOME'S G R UW1 M Z +GROOMED G R UW1 M D +GROOMING G R UW1 M IH0 NG +GROOMS G R UW1 M Z +GROOMSMAN G R UW1 M Z M AH0 N +GROOMSMEN G R UW1 M Z M IH0 N +GROOS G R UW1 Z +GROOT G R UW1 T +GROOTE G R UW1 T +GROOTENHEIMER G R UW1 T AH0 N HH AY2 M ER0 +GROOTON G R UW1 T AH0 N +GROOTZ G R UW1 T S +GROOTZ'S G R UW1 T S IH0 Z +GROOVE G R UW1 V +GROOVED G R UW1 V D +GROOVES G R UW1 V Z +GROOVING G R UW1 V IH0 NG +GROPE G R OW1 P +GROPED G R OW1 P T +GROPER G R OW1 P ER0 +GROPERS G R OW1 P ER0 Z +GROPES G R OW1 P S +GROPIN G R OW1 P IH0 N +GROPING G R OW1 P IH0 NG +GROPINGLY G R OW1 P IH0 NG L IY0 +GROPINGS G R OW1 P IH0 NG Z +GROPPHUSEN G R AA1 P HH UW0 S AH0 N +GROS G R OW1 S +GROSART G R OW1 Z ER0 T +GROSBEAK G R OW1 S B IY2 K +GROSBEAKS G R OW1 S B IY2 K S +GROSBOIS G R OW1 S B W AA1 +GROSCHEN G R AO1 SH AH0 N +GROSCHENS G R AO1 SH AH0 N Z +GROSCHUT G R AO1 SH AH0 T +GROSE G R OW1 Z +GROSE'S G R OW1 Z IH0 Z +GROSEMAN G R OW1 Z M AH0 N +GROSKET G R OW1 S K AH0 T +GROSLOW G R AA1 S L OW0 +GROSMONT G R AO1 Z M AH0 N T +GROSNOFF G R AO1 S N AO0 F +GROSPIERRE G R OW1 S P IY0 EH1 R +GROSS G R OW1 S +GROSS'S G R OW1 S IH0 Z +GROSSBEAK G R OW1 S B IY2 K +GROSSBECK G R OW1 S B EH2 K +GROSSBY G R OW1 S B IY0 +GROSSE G R AA1 S +GROSSE'S G R OW1 S IH0 Z +GROSSEN G R OW1 S AH0 N +GROSSENSTECK G R OW1 S EH0 N S T IH0 K +GROSSER G R OW1 S ER0 +GROSSES G R OW1 S IH0 Z +GROSSEST G R OW1 S AH0 S T +GROSSET G R OW1 S IH0 T +GROSSI G R OW1 S IY0 +GROSSIER G R OW1 S IY0 ER0 +GROSSIERETE G R OW1 S Y EY1 IY2 T +GROSSLY G R OW1 S L IY0 +GROSSMAN G R OW1 S M AH0 N +GROSSMANN G R AO1 S M AH0 N +GROSSMITH G R OW1 S M IH2 TH +GROSSMUTTER G R OW1 S M AH1 T ER0 +GROSSNESS G R OW1 S N AH0 S +GROSSNESSES G R OW1 S N AH0 S IH0 Z +GROSSO G R OW1 S OW0 +GROSSWARDEIN G R AA1 S W ER0 D AY0 N +GROST G R AA1 S T +GROSTESTE G R OW1 S T +GROSVENOR G R OW1 V N ER0 +GROSVENOR'S G R OW1 V N ER0 Z +GROT G R AA1 T +GROTAIT G R OW0 T EY1 T +GROTE G R OW1 T +GROTE'S G R OW1 T S +GROTESQUE G R OW0 T EH1 S K +GROTESQUELY G R OW0 T EH1 S K L IY0 +GROTESQUENESS G R OW0 T EH1 S K N AH0 S +GROTESQUERIE G R OW0 T EH1 S K ER0 IY0 +GROTESQUERIES G R OW0 T EH1 S K ER0 IY0 Z +GROTESQUERY G R OW0 T EH1 S K ER0 IY0 +GROTESQUES G R OW0 T EH1 S K S +GROTEWELL G R OW0 T EH1 W EH0 L +GROTH G R AA1 TH +GROTHUS G R AO1 TH AH0 S +GROTIUS G R OW1 T IY0 AH0 S +GROTKAU G R AA1 T K AA1 UW0 +GROTON G R AA1 T AH0 N +GROTS G R AA1 T S +GROTTA G R OW1 T AH0 +GROTTE G R AA1 T +GROTTKAU G R AA1 T K AA1 UW0 +GROTTO G R AA1 T OW2 +GROTTO'S G R AA1 T OW2 Z +GROTTOES G R OW1 T OW0 Z +GROTTOS G R AA1 T OW0 Z +GROUARD G R UW0 ER0 D +GROUCH G R AW1 CH +GROUCHE'S G R AW1 CH IH0 Z +GROUCHED G R AW1 CH T +GROUCHES G R AW1 CH IH0 Z +GROUCHILY G R AW1 CH AH0 L IY0 +GROUCHING G R AW1 CH IH0 NG +GROUCHY G R AW1 CH IY0 +GROUCHY'S G R AW1 CH IY0 Z +GROUN G R AW1 N +GROUN' G R AW1 N +GROUN'S G R AW1 N Z +GROUND G R AW1 N D +GROUND'S G R AW1 N D Z +GROUNDCAR G R AW1 N D K AA2 R +GROUNDCARS G R AW1 N D K AA2 R Z +GROUNDE G R AW1 N D +GROUNDED G R AW1 N D IH0 D +GROUNDER G R AW1 N D ER0 +GROUNDERS G R AW1 N D ER0 Z +GROUNDHOG G R AW1 N D HH AA2 G +GROUNDING G R AW1 N D IH0 NG +GROUNDLESS G R AW1 N D L AH0 S +GROUNDLESSLY G R AW1 N D L AH0 S L IY0 +GROUNDLESSNESS G R AW1 N D L AH0 S N AH0 S +GROUNDLING G R AW1 N D L IH0 NG +GROUNDLINGS G R AW1 N D L IH0 NG Z +GROUNDNUTS G R AW1 N D N AH2 T S +GROUNDS G R AW1 N D Z +GROUNDS G R AW1 N Z +GROUNDSEL G R AW1 N D S AH0 L +GROUNDSWELL G R AW1 N D S W EH2 L +GROUNDWARD G R AW1 N D W ER0 D +GROUNDWOOD G R AW1 N D W UH2 D +GROUNDWORK G R AW1 N D W ER2 K +GROUNSELL G R AW1 N S AH0 L +GROUNSELL'S G R AW1 N S AH0 L Z +GROUP G R UW1 P +GROUP'D G R UW1 P D +GROUP'S G R UW1 P S +GROUPE G R UW1 P +GROUPED G R UW1 P T +GROUPER G R UW1 P ER0 +GROUPES G R UW1 P S +GROUPING G R UW1 P IH0 NG +GROUPINGS G R UW1 P IH0 NG Z +GROUPS G R UW1 P S +GROUSE G R AW1 S +GROUSE'S G R AW1 S IH0 Z +GROUSED G R AW1 S T +GROUSER G R AW1 S ER0 +GROUSES G R AW1 S IH0 Z +GROUSING G R AW1 S IH0 NG +GROUSKI G R AW1 S K IY0 +GROUSSET G R UW1 S EH0 T +GROUT G R AW1 T +GROUTS G R AW1 T S +GROUTY G R AW1 T IY0 +GROUVILLE G R UW1 V IH2 L +GROV'LING G R AO1 V AH0 L IH0 NG +GROVE G R OW1 V +GROVE'S G R OW1 V Z +GROVEBURY G R OW1 V B EH2 R IY0 +GROVEL G R AA1 V AH0 L +GROVELAND G R AO1 V L AH0 N D +GROVELED G R AO1 V AH0 L D +GROVELING G R AO1 V AH0 L IH0 NG +GROVELING G R AO1 V L IH0 NG +GROVELLED G R AO1 V AH0 L D +GROVELLER G R AO1 V AH0 L ER0 +GROVELLERS G R AO1 V AH0 L ER0 Z +GROVELLING G R AO1 V AH0 L IH0 NG +GROVELS G R AO1 V AH0 L Z +GROVER G R OW1 V ER0 +GROVER'S G R OW1 V ER0 Z +GROVES G R OW1 V Z +GROVETON G R OW1 V T AH0 N +GROVEVILLE G R OW1 V V IH0 L +GROVNO G R AA1 V N OW0 +GROW G R OW1 +GROW'D G R AW1 D +GROWD G R AW1 D +GROWED G R OW1 D +GROWER G R OW1 ER0 +GROWER'S G R OW1 ER0 Z +GROWERS G R OW1 ER0 Z +GROWEST G R OW1 AH0 S T +GROWETH G R AW1 IH0 TH +GROWIN G R OW1 IH0 N +GROWING G R OW1 IH0 NG +GROWINGLY G R OW1 IH0 NG L IY0 +GROWL G R AW1 L +GROWL'D G R AW1 L D +GROWLED G R AW1 L D +GROWLER G R AW1 L ER0 +GROWLER'S G R AW1 L ER0 Z +GROWLERS G R AW1 L ER0 Z +GROWLERY G R AW1 L ER0 IY0 +GROWLES G R OW1 L Z +GROWLEYWOGS G R AW1 L IY0 W AO1 G Z +GROWLIN G R AW1 L IH0 N +GROWLING G R OW1 L IH0 NG +GROWLINGLY G R AW1 L IH0 NG L IY0 +GROWLINGS G R AW1 L IH0 NG Z +GROWLS G R AW1 L Z +GROWLY G R OW1 L IY0 +GROWN G R OW1 N +GROWND G R OW1 N D +GROWNFOLKS G R OW1 N F OW1 K S +GROWNUP G R OW1 N AH2 P +GROWNUPS G R OW1 N AH2 P S +GROWS G R OW1 Z +GROWTH 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IY0 +GUSTAV G UH1 S T AA0 F +GUSTAV'S G UW0 S T AA1 V Z +GUSTAVA G UW0 S T AA1 V AH0 +GUSTAVE G UW0 S T AA1 V IY0 +GUSTAVE'S G UW0 S T AA1 V IY0 Z +GUSTAVING G UW0 S T AA1 V IH0 NG +GUSTAVO G AH0 S T AA1 V OW0 +GUSTAVO'S G UW0 S T AA1 V OW0 Z +GUSTAVUS G AH1 S T AH0 V AH0 S +GUSTAVUS'S G AH1 S T AH0 V AH0 S IH0 Z +GUSTED G AH1 S T IH0 D +GUSTEL G AH1 S T AH0 L +GUSTERSON G AH1 S T ER0 S AH0 N +GUSTERSON'S G AH1 S T ER0 S AH0 N Z +GUSTI G AH1 S T IY0 +GUSTIBUS G AH1 S T AH0 B AH0 S +GUSTILY G AH1 S T AH0 L IY0 +GUSTING G AH1 S T IH0 NG +GUSTISE G UW0 S T IY1 Z +GUSTO G AH1 S T OW2 +GUSTS G AH1 S T S +GUSTUS G AH1 S T AH0 S +GUSTUS'S G AH1 S T AH0 S IH0 Z +GUSTY G AH1 S T IY0 +GUT G AH1 T +GUTA G UW1 T AH0 +GUTCH G AH1 CH +GUTCHALL G AH1 CH AH0 L +GUTCHKOFF G AH1 CH K AO2 F +GUTE G Y UW1 T +GUTEN G UW1 T AH0 N +GUTENBERG G UW1 T AH0 N B ER0 G +GUTENBERG'S G UW1 T AH0 N B ER0 G Z +GUTENFELS G AH1 T IH0 N F AH0 L Z +GUTER G Y UW1 T ER0 +GUTH G UW1 TH +GUTHERIDGE G AH1 TH ER0 IH2 JH 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AH0 N ER0 +GUV'NOR'S G UW0 V AH0 N ER0 Z +GUV'NORS G UW0 V AH0 N ER0 Z +GUVENER G UW0 V IY0 N ER0 +GUVIE G UW1 V IY0 +GUVNER G UW0 V N ER0 +GUVNOR G UW0 V N ER0 +GUVNOR'S G UW0 V N ER0 Z +GUWERGIS G UW1 W ER1 G IH0 S +GUY G AY1 +GUY'LL G AY1 L +GUY'S G AY1 Z +GUYANA G AY2 AA1 N AH0 +GUYAU G AY1 OW1 +GUYED G AY1 D +GUYENNE G AY2 EH1 N +GUYIN G AY1 IH0 N +GUYING G AY1 IH0 NG +GUYLER G AY1 L ER0 +GUYOMAR G AY1 AH0 M AA0 R +GUYON G AY1 AH0 N +GUYON'S G AY1 AH0 N Z +GUYOT G AY1 AH0 T +GUYRION G AY1 R IY0 AH0 N +GUYS G AY1 Z +GUZ G AH1 Z +GUZERAT G UW1 Z EH0 R AE2 T +GUZLA G Y UW1 Z L AH0 +GUZMAN G AH1 Z M AH0 N +GUZMAN G UW1 Z M AA2 N +GUZMAN'S G UW1 Z M AA2 N Z +GUZMANS G AH1 Z M AH0 N Z +GUZRA G AH1 Z R AH0 +GUZZLE G AH1 Z AH0 L +GUZZLED G AH1 Z AH0 L D +GUZZLER G AH1 Z L ER0 +GUZZLIN G AH1 Z L IH0 N +GUZZLING G AH1 Z AH0 L IH0 NG +GUZZLING G AH1 Z L IH0 NG +GUZZUH G UW0 T S UW1 +GWA G W AH0 +GWAHLUR G W AO1 L ER1 +GWAI G W AY1 +GWAIN G W EY2 N +GWAINE G W EY0 N +GWALCHMAI G W AO1 L CH M AY2 +GWALIOR G W AO1 L Y ER0 +GWAN G W AA0 N +GWANDA G W AA1 N D AH0 +GWARJAK G W AO1 R Y AH0 K +GWATHMEY G W AO1 M IY0 +GWAW G W AO1 +GWAWL G W AO1 L +GWE G W +GWEAT G W EH1 T +GWEI G W EY1 +GWELO G W EH1 L OW0 +GWEN G W EH1 N +GWEN'S G W EH1 N Z +GWENDA G W EH1 N D AH0 +GWENDA'S G W EH1 N D AH0 Z +GWENDOLAINE G W EH1 N D AH0 L EY0 N +GWENDOLEN G W EH1 N D OW1 L AH0 N +GWENDOLEN'S G W EH1 N D AH0 L AH0 N Z +GWENDOLINE G W EH1 N D OW0 L IY1 N IY0 +GWENDOLINE'S G W EH1 N D OW0 L IY1 N Z +GWENDOLYN G W EH1 N D AH0 L IH0 N +GWENDOLYN'S G W EH1 N D AH0 L IH0 N Z +GWENEVERE G W EH1 N AH0 V IH1 R +GWENLYN G W EH1 N L IH0 N +GWENNA G W EH1 N AH0 +GWENNA'S G W EH1 N AH0 Z +GWENNAP G W EH1 N AE2 P +GWENNAS G W EH1 N AH0 Z +GWENNIE G W EH1 N IY0 +GWENNY G W EH1 N IY0 +GWENNY'S G W EH1 N IY0 Z +GWENT G W EH1 N T +GWENTHIAN G W EH1 N TH IY0 AH0 N +GWENWYN G W EH1 N W IH0 N +GWETHYN G W EH1 TH AH0 N +GWETHYN'S G W EH1 TH AH0 N Z +GWEVA G W EH1 V AH0 +GWILLIM G W IH1 L IH0 M +GWILLYM G W IH0 L IH1 M 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Y AA2 R D +GYARDEN G Y AA2 R D AH0 N +GYARDIN G Y AA1 R D IH0 N +GYARDNER G Y AA2 R D N ER0 +GYARNIT JH AY1 ER0 N IH0 T +GYAS G Y AH0 S +GYBE JH IH1 B +GYDA JH IH1 D AH0 +GYDA'S JH IH1 D AH0 Z +GYE JH AY1 +GYGES JH IH1 JH IH0 Z +GYGI JH IH1 JH IY0 +GYIRL G IY1 ER1 L +GYLDENLOVE JH IH1 D AH0 N L AH2 V +GYLES JH AY1 L Z +GYLIPPUS JH AY1 L IH0 P AH0 S +GYLLE JH IH1 L +GYLSTON JH IH1 L S T AH0 N +GYM JH IH1 M +GYME JH IH1 M +GYMER'S JH IH1 M ER0 Z +GYMKHANA JH IH1 M AH0 K AE1 N AH0 +GYMKHANAS JH IH1 M AH0 K AE1 N AH0 Z +GYMNASE JH IH0 M N EY1 Z +GYMNASIA JH IH0 M N EY1 Z IY0 AH0 +GYMNASIARCH JH IH0 M N EY1 Z IY0 AA2 R K +GYMNASIUM JH IH0 M N EY1 Z IY0 AH0 M +GYMNASIUMS JH IH0 M N EY1 Z IY0 AH0 M Z +GYMNAST JH IH1 M N AH0 S T +GYMNASTIC JH IH0 M N AE1 S T IH0 K +GYMNASTICS JH IH0 M N AE1 S T IH0 K S +GYMNASTIQUE JH IH1 M N AE0 S T IY1 K +GYMNASTS JH IH1 M N AE0 S T S +GYMNOSOPHISTS JH IH0 M N AA1 S AH0 F IH1 S T S +GYMNOTI JH IH0 M N OW1 T IY0 +GYMNOTUS JH IH0 M N AA1 T AH0 S +GYNAECEUM JH IH1 N AH0 S IY1 AH0 M +GYNECOLOGY G AY2 N AH0 K AA1 L AH0 JH IY0 +GYNT JH IH1 N T +GYONGYOM G Y AO1 NG Y AO2 M +GYP JH IH1 P +GYP'S JH IH1 P S +GYPO JH IH1 P OW0 +GYPS JH IH1 P S +GYPSEY JH IH1 P S IY0 +GYPSIES JH IH1 P S IY0 Z +GYPSOPHILA JH IH1 P S AH0 F IY1 L AH0 +GYPSUM JH IH1 P S AH0 M +GYPSY JH IH1 P S IY0 +GYPSY'S JH IH1 P S IY0 Z +GYPSYING JH IH1 P S IY0 IH0 NG +GYPT JH IH1 P T +GYPTZIS JH IH1 P T Z IY0 Z +GYRATE JH AY1 R EY2 T +GYRATED JH AY1 R EY2 T IH0 D +GYRATING JH AY1 R EY2 T IH0 NG +GYRATION JH AY0 R EY1 SH AH0 N +GYRATIONS JH AY0 R EY1 SH AH0 N Z +GYRATORY JH AY1 R AH0 T AO2 R IY0 +GYRE JH AY1 R +GYRES JH AY1 R Z +GYRL JH AY1 R L +GYRO JH AY1 R OW2 +GYROS JH AY1 R OW2 Z +GYROSCOPE JH AY1 R AH0 S K OW2 P +GYROSCOPES JH AY1 R AH0 S K OW2 P S +GYROSCOPIC JH AY2 R AH0 S K AA1 P IH0 K +GYS JH IY0 Z +GYT JH IH1 T +GYTE JH AY1 T +GYTHA JH AY1 DH AH0 +GYU G Y UW2 +GYURI G Y UH1 R IY0 +GYURI'S G Y UW2 R IY0 Z +GYURICZA G Y UW2 R IH2 CH AH0 +GYURL G Y UW2 R L +GYURLS G Y UW2 R L Z +GYVE JH AY1 V +GYVED JH AY1 V D +GYVES JH AY1 V Z +H EY1 CH +H'AIN'T EY1 CH IH0 EY1 N T +H'AINT EY1 CH IH0 AE1 N +H'ALL HH AO2 L +H'AND EY1 CH IH0 AO2 N D +H'ARD HH AA1 R D +H'ARE EY1 CH IH0 EH0 R +H'AURA HH AO1 R AH0 +H'EVER EY1 CH IH0 EH1 V ER0 +H'EYES EY1 CH IH0 AY1 Z +H'I'LL EY1 CH IH0 AY1 L +H'IST EY1 CH IH0 Z IH2 S T +H'ISTED EY1 CH IH0 Z IH1 S T AH0 D +H'IT EY1 CH IH0 Z IH0 T +H'LO EY1 CH IH0 L OW0 +H'M EY1 CH IH0 M +H'OUR EY1 CH IH0 AO0 R +H'OUT EY1 CH IH0 AW2 T +H'S EY1 CH IH0 Z +H'UP EY1 CH IH0 AH2 P +H'YAR EY1 CH IH0 Y AA2 R +H'YER EY1 CH IH0 IY0 ER0 +HA EY1 CH EY1 +HA HH AA1 +HA' HH +HA'AF HH AH0 AA1 F +HA'D HH D +HA'E HH AH0 EH1 +HA'F HH F +HA'N'T HH AH0 N T +HA'NT HH AH0 N T +HA'NTED HH AH0 N IH0 D +HA'NTING HH AH0 N IH0 NG +HA'NTS HH AH0 N T S +HA'O HH OW0 +HA'O'S HH OW0 Z +HA'P'ORTH HH AO2 R TH +HA'PENCE HH P EH1 N S +HA'PENNIES HH P EH1 N IY0 Z +HA'PENNY HH P EH2 N IY0 +HA'PENNY'S HH P EH1 N IY0 Z +HA'PORTH HH P ER0 TH +HA'R HH R +HA'S HH AH0 Z +HA'SH HH SH +HAA HH AA1 +HAAB HH AA1 B +HAABUNAI HH AA1 B AH0 N AY2 +HAAF HH AA1 F +HAAK HH AA1 K +HAAKON HH AA1 K AA0 N +HAAMDAANEE HH AA1 M D AA1 N IY1 +HAAMOURA HH AA1 M UH1 R AH0 +HAAN HH AA1 N +HAAR HH AA1 R +HAARFAGER HH AA1 R F EY1 G ER0 +HAARFAGR HH AA1 R F AA1 G R +HAARLEM HH AA1 R L AH0 M +HAARLEM'S HH AA1 R L AH0 M Z +HAARLEMER HH AA1 R AH0 L M ER0 +HAARLEMMER HH AA2 R L EH1 M ER0 +HAAS HH AA1 S +HAASE HH AA1 S +HAASE'S HH AA1 S IH0 Z +HAAVE HH AA2 V +HAB HH AE2 B +HABAKKUK HH AH0 B AE1 K AH0 K +HABAKUK HH AE1 B AH0 K AH0 K +HABAL HH AE1 B AH0 L +HABAL'S HH AE1 B AH0 L Z +HABANA HH AH0 B AE1 N AH0 +HABANERA HH EY1 B AH0 N ER1 AH0 +HABBERTON HH AE1 B ER0 T AH0 N +HABBY HH AE1 B IY0 +HABE HH EY1 B +HABEAS HH AE1 B IY0 AH0 S +HABEEBAH HH AE1 B IY0 B AH1 +HABELSCHWERT HH AA0 B EH1 L SH W ER1 T +HABEMUS HH AE1 B IH0 M IH0 S +HABEN HH AE1 B AH0 N +HABENECK HH AE1 B IH0 N EH0 K +HABENT HH AE1 B IH0 N T +HABEO HH AE1 B IY0 OW0 +HABER HH EY1 B ER0 +HABERDASHER HH AE1 B ER0 D AE2 SH ER0 +HABERDASHER'S HH AE1 B ER0 D AE2 SH ER0 Z +HABERDASHERS HH AE1 B ER0 D AE2 SH ER0 Z +HABERDASHERY HH AE1 B ER0 D AE2 SH ER0 IY0 +HABERDEN HH AE1 B ER0 D AH0 N +HABERE HH AE1 B ER0 +HABERECHT HH AE1 B ER0 EH0 K T +HABERGEON HH AE1 B ER0 JH IH0 N +HABERGEONS HH AE1 B ER0 JH AH0 N Z +HABERI HH AA0 B EH1 R IY0 +HABERLAND HH AE1 B ER0 L AH0 N D +HABERMANN HH EY1 B ER0 M AH0 N +HABERMANN'S HH AE1 B ER0 M AH0 N Z +HABERSHAM HH AE1 B ER0 SH AE2 M +HABERSHAW HH AE1 B ER0 SH AO2 +HABERSTRO HH AA0 B EH1 R S T R OW0 +HABERT HH AE1 B ER0 T +HABES HH EY1 B Z +HABESH HH AE1 B IH0 SH +HABET HH AE1 B IH0 T +HABIB HH AH0 B IY1 B +HABILE HH AE1 B AH0 L +HABILIMENT HH AH0 B IH1 L AH0 M AH0 N T +HABILIMENTS HH AH0 B IH1 L AH0 M AH0 N T S +HABIN HH AE1 B IH0 N +HABINAS HH AH0 B IY1 N AH0 Z +HABINGTON HH AE1 B IH0 NG T AH0 N +HABINNAS HH AE1 B IH0 N AH0 Z +HABIT HH AE1 B AH0 T +HABIT'S HH AE1 B AH0 T S +HABITA HH AE1 B AH0 T AH0 +HABITABILITY HH AE1 B IH0 T AH0 B IH1 L IH0 T IY0 +HABITABLE HH AE1 B AH0 T AH0 B AH0 L +HABITACION HH AE2 B AH0 T EY1 SH AH0 N +HABITAN HH AE1 B IH0 T AH0 N +HABITANS HH AE1 B AH0 T AH0 N Z +HABITANT HH AE1 B IH0 T AH0 N T +HABITANTS HH AE1 B IH0 T AH0 N T S +HABITAT HH AE1 B AH0 T AE2 T +HABITATION HH AE2 B AH0 T EY1 SH AH0 N +HABITATIONS HH AE2 B AH0 T EY1 SH AH0 N Z +HABITATS HH AE1 B AH0 T AE2 T S +HABITE HH AE1 B IH0 T +HABITED HH AE1 B AH0 T AH0 D +HABITING HH AE1 B IH0 T IH0 NG +HABITS HH AE1 B AH0 T S +HABITUAL HH AH0 B IH1 CH UW0 AH0 L +HABITUALLY HH AH0 B IH1 CH UW0 AH0 L IY0 +HABITUALLY HH AH0 B IH1 CH UW0 L IY0 +HABITUATE HH AH0 B IH1 CH UW0 EY2 T +HABITUATED HH AH0 B IH1 CH UW0 EY2 T IH0 D +HABITUATES HH AH0 B IH1 CH UW0 EY2 T S +HABITUATING HH AH0 B IH1 CH UW0 EY2 T IH0 NG +HABITUATION HH AH0 B IH1 CH UW0 EY1 SH AH0 N +HABITUDE HH AE1 B AH0 T UW2 D +HABITUDES HH AE1 B AH0 T UW2 D Z +HABITUE HH AE1 B IH0 CH UW0 +HABITUEE HH AE1 B IH0 CH UW0 IY0 +HABITUES HH AE1 B IH0 CH UW0 Z +HABITUS HH AE1 B AH0 T AH0 S +HABLA HH AE1 B L AH0 +HABLE HH EY1 B AH0 L +HABLETON HH EY1 B AH0 L T AH0 N +HABLOT HH AE1 B L AH0 T +HABN'T HH EY1 B AH0 N T +HABOGI HH AE1 B OW1 G IY0 +HABR HH AE1 B R +HABRUNT HH AE1 B R AH1 N T +HABRUNT'S HH AE1 B R AH0 N T S +HABSBURG HH AE1 P S B ER0 G +HABSBURGS HH AE1 P S B ER0 G Z +HABTI HH AE1 B T IY0 +HABU HH AE1 B UW0 +HABUIT HH AE1 B UW0 IH0 T +HAC HH AH0 K +HACCIDENT HH AE1 K S AH0 D AH0 N T +HACE HH AA1 CH IY0 +HACENDADO HH AA0 CH EH0 N D AA1 D OW0 +HACENDERO HH AE2 S AH0 N D EH1 R OW0 +HACENDERO'S HH AE2 S AH0 N D EH1 R OW0 Z +HACENDEROS HH AE2 S AH0 N D EH1 R OW0 Z +HACHA HH AE1 CH AH0 +HACHE HH AE1 CH +HACHESTO HH AH0 SH EH1 S T OW0 +HACHETTE HH AH0 SH EH1 T +HACHIMAN HH AE1 K IH0 M AH0 N +HACHIRO HH AE1 CH IH1 R OW0 +HACHMONI HH AH0 SH M OW1 N IY0 +HACHOVNIK HH AE1 CH OW1 V N IH0 K +HACIENDA HH AE2 S IY0 EH1 N D AH0 +HACIENDADO HH AE2 S IY0 EH1 N D AA1 D OW0 +HACIENDAS HH AE2 S IY0 EH1 N D AH0 Z +HACK HH AE1 K +HACK'D HH AE1 K D +HACK'S HH AE1 K S +HACKAMORE HH AE1 K AH0 M AO2 R +HACKBERRY HH AE1 K B EH2 R IY0 +HACKBUTT HH AE1 K B AH0 T +HACKE HH AE1 K +HACKEBORN HH AE1 K IH0 B ER0 N +HACKED HH AE1 K T +HACKELBERG HH AE1 K AH0 L B ER0 G +HACKENSACK HH AE1 K AH0 N S AE2 K +HACKER HH AE1 K ER0 +HACKER'S HH AE1 K ER0 Z +HACKERIS HH AE1 K ER0 IH0 S +HACKERS HH AE1 K ER0 Z +HACKET HH AE1 K IH0 T +HACKET'S HH AE1 K AH0 T S +HACKETT HH AE1 K IH0 T +HACKETT'S HH AE1 K IH0 T S +HACKING HH AE1 K IH0 NG +HACKIT HH AE1 K IH0 T +HACKLE HH AE1 K AH0 L +HACKLED HH AE1 K AH0 L D +HACKLEFORD HH AE1 K AH0 L F ER0 D +HACKLER HH AE1 K AH0 L ER0 +HACKLER HH AE1 K L ER0 +HACKLES HH AE1 K AH0 L Z +HACKLEY HH AE1 K L IY0 +HACKLING HH AE1 K L IH0 NG +HACKMAN HH AE1 K M AE2 N +HACKMAN HH AE1 K M AH0 N +HACKMATACK HH AE1 K M AH0 T AE2 K +HACKMEN HH AE1 K M EH2 N +HACKNEY HH AE1 K N IY0 +HACKNEYED HH AE1 K N IY0 D +HACKNEYS HH AE1 K N IY0 Z +HACKNIED HH AE1 K N IY0 D +HACKS HH AE1 K S +HACKSTON HH AE1 K S T AH0 N +HACKSTOUN HH AE1 K S T AW1 N +HACKTON HH AE1 K T AH0 N +HACO HH AA1 K OW0 +HACON HH EY1 K AH0 N +HACT HH AE1 K T +HACTENUS HH AE1 K T IY1 N AH0 S +HACTION HH AE1 K SH AH0 N +HAD HH AE1 D +HAD'NT HH AE1 D AH0 N T +HAD'ST HH AE1 D S T +HADAD HH AE1 D AH0 D +HADASSAH HH AE1 D AE2 S AH0 +HADASSAH'S HH AE1 D AH0 S AA1 Z +HADD HH AE1 D +HADDA HH AE1 D AH0 +HADDAD HH AE1 D AH0 D +HADDAM HH AE1 D AH0 M +HADDAN HH AE1 D AH0 N +HADDE HH AE1 D +HADDEN HH AE1 D AH0 N +HADDEN'S HH AE1 D AH0 N Z +HADDENS HH AE1 D AH0 N Z +HADDICK HH AE1 D IH0 K +HADDIE HH AE1 D IY0 +HADDIES HH AE1 D IY0 Z +HADDINGTON HH AE1 D IH0 NG T AH0 N +HADDO HH AE1 D OW0 +HADDO'S HH AE1 D OW0 Z +HADDOCK HH AE1 D AH0 K +HADDOCK'S HH AE1 D AH0 K S +HADDOCKS HH AE1 D AH0 K S +HADDON HH AE1 D AH0 N +HADDON'S HH AE1 D AH0 N Z +HADDONFIELD HH AE1 D AH0 N F IY0 L D +HADDS HH AE1 D Z +HADE HH EY1 D +HADEN HH EY1 D AH0 N +HADEN'S HH EY1 D AH0 N Z +HADENDOA HH AE1 D EH0 N D OW1 AH0 +HADENDOWAS HH AE1 D EH0 N D AW1 AH0 Z +HADENT HH EY1 D AH0 N T +HADES HH EY1 D IY0 Z +HADES HH EY1 D Z +HADFIELD HH AE1 D F IY2 L D +HADGER HH AE1 JH ER0 +HADGI HH AE1 JH IY0 +HADI HH AA1 D IY0 +HADIDJA HH AE1 D AH0 D Y AH0 +HADING HH EY1 D IH0 NG +HADITH HH EY1 D IH0 TH +HADJ HH AE1 JH +HADJ'S HH AE1 JH EY1 Z +HADJI HH AE1 JH IY0 +HADJI'S HH AE1 JH IY0 Z +HADJIS HH AE1 JH IY0 Z +HADLAI HH AE1 D L AA1 IY0 +HADLEIGH HH AE1 D L AH0 +HADLEY HH AE1 D L IY0 +HADLEY'S HH AE1 D L IY0 Z +HADLEYBURG HH AE1 D L IY0 B ER0 G +HADLEYS HH AE1 D L IY0 Z +HADLOW HH AE1 D L OW2 +HADLOW'S HH AE1 D L OW2 Z +HADLOWS HH AE1 D L OW2 Z +HADN HH AE1 D AH0 N +HADN'T HH AE1 D AH0 N +HADN'T HH AE1 D AH0 N T +HADNA HH AE1 D N AH0 +HADNOCK HH AE1 D N AH0 K +HADNT HH AE1 D N T +HADOW HH AE1 D OW0 +HADOWAY HH AE1 D OW0 W EY2 +HADRATHUS HH AE1 D R AH0 TH AH0 S +HADRIA HH AE1 D R IY0 AH0 +HADRIA'S HH EY1 D R IY0 AH0 Z +HADRIAN HH AE1 D R IY0 AH0 N +HADRIAN HH EY1 D R IY0 AH0 N +HADRIAN'S HH EY1 D R IY0 AH0 N Z +HADRIATIC HH EY1 D R IY0 AE1 T IH0 K +HADRON HH AE1 D R AO2 N +HADST HH AE1 D S T +HADSTONE HH AE1 D S T OW2 N +HADUMOTH HH AE1 D AH0 M AH0 TH +HADWELL HH AE1 D W EH2 L +HADWELL'S HH AE1 D W EH2 L Z +HADWIG HH AE1 D W IH0 G +HADWIG'S HH AE1 D W IH0 G Z +HADWIN HH AE1 D W IH0 N +HADWIN'S HH AE1 D W AH0 N Z +HADWINS HH AE1 D W IH0 N Z +HADYN HH AE1 D IH0 N +HADYN'S HH AE1 D IH0 N Z +HAE HH EY1 +HAE'T HH IY1 T +HAEC HH EH1 K +HAECKEL HH EH1 K AH0 L +HAECKEL'S HH EH1 K AH0 L Z +HAECKELHEIMER HH EH1 K AH0 L HH AY2 M ER0 +HAEIN HH IY1 N +HAEL HH EH1 L +HAELEN HH EH1 L AH0 N +HAEMATITE HH EH1 M AH0 T AY2 T +HAEMON HH IY1 M AH0 N +HAEMORRHAGE HH EH1 M ER0 IH0 JH +HAEMORRHOIDS HH EH1 M ER0 OY2 D Z +HAEMUS HH IY1 M AH0 S +HAENA HH IY1 N AH0 +HAENDEL HH EH1 N D AH0 L +HAENDEL'S HH EH1 N D AH0 L Z +HAENNCHEN HH EH1 N K AH0 N +HAENSEL HH EH1 N S AH0 L +HAER HH EH1 R +HAER'S HH AA1 ER0 Z +HAERE HH EH1 R +HAERET HH EH1 R AH0 T +HAERLEM HH EH1 R L AH0 M +HAES HH AE1 +HAESIT HH EH1 Z IH0 T +HAESPELE HH EH1 S P EH1 L EY0 +HAEUNING HH AW1 N IH0 NG +HAF HH AE1 F +HAFBUR HH AE1 F B ER0 +HAFE HH EY1 F +HAFED HH EY1 F T +HAFELA HH AE1 F AH0 L AH0 +HAFEN HH AE1 F AH0 N +HAFER HH EY1 F ER0 +HAFF HH AE1 F +HAFFEN HH AE1 F AH0 N +HAFFETS HH AE1 F AH0 T S +HAFFGO HH AE1 F G OW2 +HAFI HH AA1 F IY0 +HAFID HH AA0 F IY1 D +HAFIR HH AA0 F IY1 R +HAFITZ HH AE1 F IH0 T S +HAFIZ HH AA0 F IY1 Z +HAFLIDI HH AE1 F L IY1 D IY0 +HAFNER HH AE1 F N ER0 +HAFNER'S HH AE1 F N ER0 Z +HAFOD HH AE1 F AH0 D +HAFRAN HH AE1 F R AH0 N +HAFSA HH AE1 F S AH0 +HAFT HH AE1 F T +HAFTA HH AE1 F T AH0 +HAFTED HH AE1 F T IH0 D +HAFTER HH AE1 F T ER0 +HAFTS HH AE1 F S +HAFTS HH AE1 F T S +HAG HH AE1 G +HAG'S HH AE1 G Z +HAGA HH AA1 G AH0 +HAGADORN HH AE1 G AH0 D AO0 R N +HAGAN HH EY1 G AH0 N +HAGAN'S HH EY1 G AH0 N Z +HAGAR HH EY1 G AA0 R +HAGAR HH EY1 G ER0 +HAGAR'S HH EY1 G ER0 Z +HAGARS HH EY1 G ER0 Z +HAGBERD HH AE1 G B ER0 D +HAGBURN HH AE1 G B ER0 N +HAGE HH EY1 JH +HAGEB HH AE1 G IH0 B +HAGEDORN HH AE1 G IH0 D ER0 N +HAGEL HH AE1 G AH0 L +HAGEN HH EY1 G AH0 N +HAGEN'S HH EY1 G AH0 N Z +HAGENAU HH EY1 G AH0 N OW1 +HAGENBACH HH AE1 G IH0 N B AA0 K +HAGENBECK HH AE1 G IH0 N B EH0 K +HAGENHOF HH AE1 G IH0 N HH AH0 F +HAGER HH EY1 G ER0 +HAGERSTOWN HH EY1 G ER0 Z T AW2 N +HAGERUP HH EY1 G ER0 AH2 P +HAGG HH AE1 G +HAGGAGE HH AE1 G AH0 JH +HAGGAI HH AE1 G AY2 +HAGGARD HH AE1 G ER0 D +HAGGARD'S HH AE1 G ER0 D Z +HAGGARDLY HH AE1 G ER0 D L IY0 +HAGGARDNESS HH AE1 G ER0 D N AH0 S +HAGGART HH AE1 G ER0 T +HAGGART'S HH AE1 G ER0 T S +HAGGARTY HH AE1 G AA2 R T IY0 +HAGGER HH AE1 G ER0 +HAGGERDY HH AE1 G ER0 D IY0 +HAGGERSTONE HH AE1 G ER0 S T OW2 N +HAGGERT HH AE1 G ER0 T +HAGGERTY HH AE1 G ER0 T IY0 +HAGGERTY'S HH AE1 G ER0 T IY0 Z +HAGGIS HH AE1 G AH0 S +HAGGITH HH AE1 G AH0 TH +HAGGLE HH AE1 G AH0 L +HAGGLED HH AE1 G AH0 L D +HAGGLER HH AE1 G AH0 L ER0 +HAGGLES HH AE1 G AH0 L Z +HAGGLING HH AE1 G AH0 L IH0 NG +HAGGLING HH AE1 G L IH0 NG +HAGHAR HH AE1 G AA2 R +HAGHARS HH AE1 G AA2 R Z +HAGI HH AE1 G IY0 +HAGIA HH AE1 G IY0 AH0 +HAGIOLOGY HH AE1 G IY0 AA1 L AH0 JH IY0 +HAGIOS HH AE1 G IY0 OW2 Z +HAGIWARA HH AA2 G IH0 W AA1 R AH0 +HAGLEY HH AE1 G L IY0 +HAGNON HH AE1 G N AH0 N +HAGON HH AE1 G AH0 N +HAGS HH AE1 G Z +HAGSTROM HH AE1 G S T R AH0 M +HAGTHORPE HH AE1 G TH AO0 R P +HAGUE HH EY1 G +HAGUENIN HH EY1 G EY0 N IY1 N +HAGUNA HH AH0 G UW1 N AH0 +HAGWORTH HH AE1 G W ER0 TH +HAH HH AA1 +HAHA HH AA0 HH AA0 +HAHD HH AA1 D +HAHN HH AA1 N +HAHN'S HH AA1 N Z +HAHNEMANN HH AA1 N M AH0 N +HAHNEMANN'S HH AA1 N M AH0 N Z +HAHNKE HH AE1 NG K +HAHNSOME HH AA1 N S AH0 M +HAI HH AY1 +HAIATALNEFOUS HH AY1 AH0 T AA1 L N EH0 F AH0 S +HAICK HH AY1 K +HAICKS HH AY1 K S +HAID HH EY1 D +HAIDA HH AY1 D AH0 +HAIDAR HH AY1 D AA2 R +HAIDAS HH AY1 D AH0 S +HAIDE HH EY1 D +HAIDED HH EY1 D IH0 D +HAIDEE HH EY1 D IY0 +HAIDEE'S HH EY1 D IY0 Z +HAIDEMAKEN HH EY1 D M EY1 K AH0 N +HAIDEN HH EY1 D AH0 N +HAIDIA HH EY1 D Y AH0 +HAIDIA'S HH EY1 D IY0 AH0 Z +HAIDPLATZ HH EY1 D P L AE1 T S +HAIDS HH EY1 D Z +HAIDUK HH EY1 D AH0 K +HAIDUKS HH EY1 D AH1 K S +HAIE HH AY1 +HAIEN HH AY1 AH0 N +HAIFA HH AY1 F AH0 +HAIG HH EY1 G +HAIG'S HH EY1 G Z +HAIGH HH EY1 +HAIGH'S HH EY1 Z +HAIGHT HH EY1 T +HAIGHT'S HH EY1 T S +HAIK HH EY1 K +HAIKS HH EY1 K S +HAIL HH EY1 L +HAIL'D HH EY1 L D +HAILE HH AY1 L IY0 +HAILE HH EY1 L +HAILED HH EY1 L D +HAILES HH AY1 L IY0 Z +HAILES HH EY1 L Z +HAILEY HH EY1 L IY0 +HAILEY'S HH EY1 L IY0 Z +HAILEYBURY HH EY1 L IY0 B EH2 R IY0 +HAILIN HH EY1 L IH0 N +HAILING HH EY1 L IH0 NG +HAILL HH EY1 L +HAILLON HH EY1 L AH0 N +HAILO HH AY1 L OW0 +HAILS HH EY1 L Z +HAILSHAM HH EY1 L S HH AH0 M +HAILSTONE HH EY1 L S T OW2 N +HAILSTONES HH EY1 L S T OW2 N Z +HAILSTORM HH EY1 L S T AO2 R M +HAILSTORMS HH EY1 L S T AO2 R M Z +HAIM HH AY1 M +HAIM HH EY1 M +HAIM'S HH EY1 M Z +HAIMANOT HH EY1 M AH0 N AA0 T +HAIMANOUT HH EY1 M AH0 N AW2 T +HAIN HH EY1 N +HAIN'T HH EY1 N T +HAINAN HH EY1 N AH0 N +HAINAULT HH EY1 N OW1 +HAINBURG HH EY1 N B ER0 G +HAINE HH EY1 N +HAINES HH EY1 N Z +HAINES'S HH EY1 N Z IH0 Z +HAINS HH EY1 N Z +HAINT HH EY1 N T +HAINTEROH HH EY1 N T EH1 R OW0 +HAIR HH EH1 R +HAIR'D HH EH1 D +HAIR'S HH EH1 R Z +HAIRBRAINED HH EH1 R B R EY2 N D +HAIRBREADTH HH EH1 R B R EH1 D TH +HAIRBRUSH HH EH1 R B R AH2 SH +HAIRBRUSHES HH EH1 R B R AH2 SH IH0 Z +HAIRCLOTH HH EH1 R K L AO2 TH +HAIRCUT HH EH1 R K AH2 T +HAIRCUTTING HH EH1 R K AH2 T IH0 NG +HAIRD HH EH1 R D +HAIRDRESSER HH EH1 R D R EH2 S ER0 +HAIRDRESSER'S HH EH1 R D R EH2 S ER0 Z +HAIRDRESSERS HH EH1 R D R EH2 S ER0 Z +HAIRDRESSING HH EH1 R D R EH2 S IH0 NG +HAIRE HH EH1 R +HAIRED HH EH1 R D +HAIRES HH EH1 R Z +HAIRIER HH EH1 R IY0 ER0 +HAIRINESS HH EH1 R IY0 N AH0 S +HAIRLESS HH EH1 R L AH0 S +HAIRLESSNESS HH EH1 R L AH0 S N AH0 S +HAIRLIKE HH EH1 R L AY2 K +HAIRLINE HH EH1 R L AY2 N +HAIRPIN HH EH1 R P IH0 N +HAIRPINS HH EH1 R P IH0 N Z +HAIRS HH EH1 R Z +HAIRSBREADTH HH AY1 R S B R EH1 D TH +HAIRST HH EH1 R S T +HAIRSTON HH AY1 R S T AH0 N +HAIRT HH EH1 R T +HAIRY HH EH1 R IY0 +HAIS HH EY1 +HAIT HH EY1 T +HAITH HH EY1 TH +HAITI HH EY1 T IY0 +HAITIAN HH EY1 SH AH0 N +HAITIANS HH EY1 SH AH0 N Z +HAITIEN HH EY1 SH AH0 N +HAITIENS HH EY1 SH AH0 N Z +HAIUTO HH EY1 UW1 T OW0 +HAJ HH AY1 +HAJA HH AY1 AH0 +HAJI HH AE1 JH IY0 +HAJJ HH AE1 JH +HAJJAJ HH AA0 Y AA1 JH +HAJJEE HH AA0 Y EY1 +HAJJI HH AA0 Y IY0 +HAJO HH AA1 Y OW0 +HAJUS HH AY1 IH0 S +HAK HH AH0 K +HAKA HH AA1 K AH0 +HAKADAH HH AH0 K AA1 D AH0 +HAKAMA HH AA0 K AA1 M AH0 +HAKAU HH AH0 K AA1 UW0 +HAKE HH EY1 K +HAKE'S HH EY1 K S +HAKEEM HH AA0 K IY1 M +HAKEM HH EY1 K AH0 M +HAKEMOUTH HH AA0 K IY1 M AH0 TH +HAKIM AA0 K IY1 M +HAKIM HH AA0 K IY1 M +HAKIM'S AA0 K IY1 M Z +HAKIM'S HH AA0 K IY1 M Z +HAKIMA HH AA0 K IY1 M AH0 +HAKKUT HH AE1 K AH0 T +HAKLUYT HH AE1 K L AY1 T +HAKLUYT'S HH AE1 K L AY1 T S +HAKM HH AA0 K M +HAKO HH AA1 K OW0 +HAKODATE HH AE1 K AH0 D EY2 T +HAKON HH AE1 K AH0 N +HAKON'S HH AE1 K AH0 N Z +HAKONE HH AE1 K AH0 N +HAKONSON HH AE1 K AH0 N S AH0 N +HAL HH AE1 L +HAL'S HH AE1 L Z +HALA HH AA1 L AH0 +HALAKAZI HH AA1 L AH0 K AA1 Z IY0 +HALB HH AE1 L B +HALBACH HH AE1 L B AA0 K +HALBAN HH AE1 L B AH0 N +HALBE HH AE1 L B +HALBECK HH AE1 L B EH2 K +HALBER HH AE1 L B ER0 +HALBERD HH AE1 L B ER0 D +HALBERDIER HH AE1 L B ER0 D IH1 R +HALBERDIERS HH AE1 L B ER0 D IH1 R Z +HALBERDS HH AE1 L B ER0 D Z +HALBERG HH AE1 L B ER0 G +HALBERSTADT HH AE1 L B ER0 SH T AE0 T +HALBERT HH AE1 L B ER0 T +HALBERT'S HH AE1 L B ER0 T S +HALBERTS HH AE1 L B ER0 T S +HALBOROUGH HH AE1 L B ER0 OW0 +HALBPFENNIG HH AE1 L B F EH1 N IH0 G +HALBRANE HH AE1 L B R EY2 N +HALBY HH AE1 L B IY0 +HALCHEE HH AE1 L CH IY0 +HALCOMBE HH AE1 L K AH0 M +HALCOMBE'S HH AE1 L K AH0 M Z +HALCON HH AE1 L K AH0 N +HALCRO HH AE1 L K R OW0 +HALCROW HH AE1 L K R OW0 +HALCYON HH AE1 L S IY0 AH0 N +HALCYON'S HH AE1 L S IY0 AH0 N Z +HALCYONDALE HH AE1 L S IY0 AH0 N D EY2 L +HALCYONE HH AE1 L S IY0 OW0 N +HALCYONS HH AE1 L S IY0 AH0 N Z +HALD HH AO1 L D +HALDANE HH AH0 L D AE1 N +HALDANE'S HH AE1 L D AH0 N Z +HALDEMAN HH AA1 L D AH0 M AH0 N +HALDEMAN HH AA1 L D M AH0 N +HALDEN HH AO1 L D AH0 N +HALDENBRUNN HH AO1 L D AH0 N B R AH1 N +HALDER HH AO1 L D ER0 +HALDERSCHRODT HH AO1 L D ER0 SH R AA1 T +HALDGREN HH AO1 L D G R EH0 N +HALDIMAND HH AO1 L D AH0 M AH0 N D +HALDIMAR HH AO1 L D IY2 M AA0 R +HALDIMAR'S HH AO1 L D IH0 M AA2 R Z +HALDIN HH AO1 L D IH0 N +HALDIN'S HH AO1 L D AH0 N Z +HALDING HH AO1 L D IH0 NG +HALDON HH AE1 L D AH0 N +HALDOR HH AO1 L D ER0 +HALDOR'S HH AO1 L D ER0 Z +HALDORSTEDE HH AO1 L D ER0 S T IY1 D +HALDWELL HH AO1 L D W EH0 L +HALE HH EY1 L +HALE'S HH EY1 L Z +HALEAKALA HH EY1 L IY0 AH0 K AA1 L AH0 +HALED HH EY1 L D +HALEEL HH AE1 L IY0 L +HALEEMA HH AE1 L IY0 M AH0 +HALEINE HH AH0 L IY1 N +HALER HH EY1 L ER0 +HALES HH EY1 L Z +HALES'S HH EY1 L Z IH0 Z +HALESOME HH EY1 L S AH0 M +HALESUS HH AH0 L IY1 S AH0 S +HALESWORTH HH EY1 L Z W ER0 TH +HALET HH AH0 L EY1 +HALET'S HH AH0 L EY1 Z +HALEVI HH AH0 L EY1 V IY0 +HALEVI HH AH0 L IY1 V IY0 +HALEVY HH AH0 L IY1 V IY0 +HALEVY'S HH AH0 L IY1 V IY0 Z +HALEWITZ HH AE1 L AH0 W IH0 T S +HALEWYN HH AE1 L AH0 W IH0 N +HALEY HH EY1 L IY0 +HALEY'S HH EY1 L IY0 Z +HALEYVILLE HH EY1 L IY0 V IH2 L +HALF HH AE1 F +HALF'S HH AE1 F S +HALFA HH AE1 L F AH0 +HALFBACK HH AE1 F B AE2 K +HALFBREED HH AE1 F B R IY1 D +HALFBREED'S HH AE1 F B R IY1 D Z +HALFBREEDS HH AE1 F B R IY1 D Z +HALFDAN HH AE1 L F D AH0 N +HALFDAN'S HH AE1 L F D AH0 N Z +HALFE HH AE1 F +HALFHEARTED HH AE1 F HH AA2 R T IH0 D +HALFHEARTEDLY HH AE1 F HH AA2 R T IH0 D L IY0 +HALFLIN HH AE1 F L IH0 N +HALFMAN HH AE1 F M AH0 N +HALFMAN'S HH AE1 F M AH0 N Z +HALFMOON HH AE1 F M UW2 N +HALFNESS HH AE1 F N AH0 S +HALFOFF HH AE1 F AO0 F +HALFONT HH AE1 F AA1 N T +HALFORD HH AE1 F ER0 D +HALFORD'S HH AE1 F ER0 D Z +HALFPAST HH AE1 F P S T +HALFPENCE HH AE1 F P EH1 N S +HALFPENNIES HH AE1 F P EH1 N IY0 Z +HALFPENNY HH AE1 F P EH2 N IY0 +HALFPENNY'S HH AE1 F P EH1 N IY0 Z +HALFPENNYWORTH HH AE1 F P EH1 N IY0 W ER0 TH +HALFRED HH AE1 L F R IH0 D +HALFRED'S HH AE1 L F R IH0 D Z +HALFS HH AE1 F S +HALFTONE HH AE1 F T OW2 N +HALFVORSON HH AE1 F V AO1 R S AH0 N +HALFWAY HH AE1 F W EY1 +HALHED HH AE1 L T +HALI HH AA1 L IY0 +HALIBURTON HH AE1 L IH0 B ER2 T AH0 N +HALIBURTON'S HH AE1 L IH0 B ER2 T AH0 N Z +HALIBUT HH AE1 L AH0 B AH0 T +HALICARNASSUS HH AE1 L IH0 K AA2 R N AE1 S AH0 S +HALICTI HH AE1 L IH0 K T IY0 +HALICTUS HH AE1 L IH0 K T AH0 S +HALID HH AA1 L IH0 D +HALIDAME HH AE2 L AH0 D EY1 M +HALIDOM HH AH0 L IH1 D AH0 M +HALIDOME HH AE1 L AY2 D OW2 M +HALIDON HH AE1 L AY2 D AH0 N +HALIEUS HH EY1 L Y UW1 S +HALIFAX HH AE1 L IH0 F AE2 K S +HALIFAX'S HH AH0 L IY1 F AE2 K S IH0 Z +HALIL HH AE1 L AH0 L +HALIL'S HH AE1 L AH0 L Z +HALIM HH AH0 L IY1 M +HALIM'S HH AH0 L IY1 M Z +HALIMA HH AH0 L IY1 M AH0 +HALIMAH HH AH0 L IY1 M AH0 +HALING HH EY1 L IH0 NG +HALINGRE HH AE1 L IH0 N G R IY1 +HALIOTIS HH AA0 L IY0 OW1 T IH0 S +HALITHERSES HH AH0 L AY1 DH ER0 S AH0 Z +HALJAN HH AE1 L JH AH0 N +HALKET HH AE1 L K IH0 T +HALKETT HH AE1 L K IH0 T +HALKETT'S HH AE1 L K AH0 T S +HALKIN HH AE1 L K IH0 N +HALKON HH AE1 L K AO0 N +HALL HH AO1 L +HALL'S HH AO1 L Z +HALLA HH AE1 L AH0 +HALLAJ HH AE1 L AY2 +HALLAM HH AE1 L AH0 M +HALLAM'S HH AE1 L AH0 M Z +HALLAMS HH AE1 L AH0 M Z +HALLAN HH AE1 L AH0 N +HALLAND HH AE1 L AH0 N D +HALLARD HH AE1 L ER0 D +HALLASON HH AE1 L AH0 S AH0 N +HALLAYS HH AH0 L EY1 Z +HALLBERG HH AO1 L B ER0 G +HALLBIORN HH AO1 L B IY0 AO0 R N +HALLBLITHE HH AO1 L B L AY1 DH +HALLBLITHE'S HH AO1 L B L AY1 TH IH0 Z +HALLBOY HH AO1 L B OY2 +HALLDOOR HH AE1 L D AO2 R +HALLDOR HH AE1 L D AO0 R +HALLE HH AE1 L +HALLE HH AE1 L IY0 +HALLECK HH AE1 L IH0 K +HALLECK'S HH AE1 L IH0 K S +HALLELUIAH HH AE2 L AH0 L UW1 AH0 +HALLELUJAH HH AE2 L AH0 L UW1 Y AH0 +HALLELUJAHS HH AE2 L AH0 L UW1 Y AH0 Z +HALLEN HH AO1 L AH0 N +HALLEN'S HH AO1 L AH0 N Z +HALLER HH AO1 L ER0 +HALLERPFORT HH AE1 L ER0 P F ER0 T +HALLES HH EY1 L Z +HALLET HH AE1 L IH0 T +HALLET'S HH AE1 L IH0 T S +HALLETT HH AE1 L IH0 T +HALLETT'S HH AE1 L AH0 T S +HALLETTS HH AE1 L IH0 T S +HALLEY HH AE1 L IY0 +HALLEY HH EY1 L IY0 +HALLEY'S HH AE1 L IY0 Z +HALLFRED HH AE1 L F ER0 D +HALLGERD HH AE1 L G ER0 D +HALLGERD'S HH AE1 L G ER0 D Z +HALLGERDA HH AE1 L G ER1 D AH0 +HALLGROVE HH AE1 L G R OW2 V +HALLIARD HH AE1 L Y ER0 D +HALLIARDS HH AE1 L Y ER0 D Z +HALLIBURN HH AE1 L IH0 B ER2 N +HALLIBURN'S HH AE1 L IH0 B ER0 N Z +HALLIBURTON HH AE1 L IH0 B ER2 T AH0 N +HALLIBURTON'S HH AE1 L IH0 B ER2 T AH0 N Z +HALLIBURTONS HH AE1 L IH0 B ER2 T AH0 N Z +HALLIBURTT HH AE1 L IH0 B ER1 T +HALLIDAY HH AE1 L IH0 D EY2 +HALLIDAY'S HH AE1 L IH0 D EY2 Z +HALLIDAYS HH AE1 L IH0 D EY2 Z +HALLIE HH AO1 L IY0 +HALLIE'S HH AE1 L IY0 Z +HALLIFAX HH AE1 L IH0 F AE2 K S +HALLIFORD HH AE1 L IH0 F ER0 D +HALLIGAN HH AE1 L IH0 G AH0 N +HALLIJOHN HH AE1 L IH0 JH AA0 N +HALLIJOHN'S HH AE1 L AY1 JH AA2 N Z +HALLIN HH AE1 L IH0 N +HALLIN'S HH AE1 L IH0 N Z +HALLING HH AO1 L IH0 NG +HALLINGDAL HH AO1 L IH0 NG G D AH0 L +HALLISS HH AE1 L IH0 S +HALLIWAY HH AE1 L AH0 W EY2 +HALLIWAY'S HH AE1 L AH0 W EY2 Z +HALLIWELL HH AE1 L AH0 W EH0 L +HALLMAN HH AO1 L M AH0 N +HALLMARK HH AA1 L M AA2 R K +HALLMARKS HH AO1 L M AA2 R K S +HALLMUND HH AO1 L M AH0 N D +HALLO HH AE1 L OW0 +HALLOA HH AE1 L OW1 AH0 +HALLOAED HH AE1 L OW0 AH0 D +HALLOAING HH AE1 L OW0 EY1 NG +HALLOCK HH AE1 L AH0 K +HALLOCK'S HH AE2 L AA1 K S +HALLOED HH AE1 L OW0 D +HALLOH HH AE2 L AH0 +HALLOING HH AE1 L OW0 IH0 NG +HALLOO HH AE1 L UW2 +HALLOO'D HH AE1 L UW0 D +HALLOOED HH AH0 L UW1 D +HALLOOING HH AH0 L UW1 IH0 NG +HALLOOS HH AH0 L UW1 Z +HALLORAN HH AE1 L ER0 AH0 N +HALLORAN'S HH AE1 L ER0 AH0 N Z +HALLOW HH AE1 L OW0 +HALLOW'D HH AE1 L OW0 D +HALLOWAY HH AE1 L OW0 W EY2 +HALLOWAY'S HH AE1 L OW0 W EY2 Z +HALLOWE'EN HH AE2 L AH0 W IY1 N +HALLOWED HH AE1 L OW0 D +HALLOWEEN HH AE2 L AH0 W IY1 N +HALLOWELL HH AE1 L AH0 W EH0 L +HALLOWELL'S HH AE1 L OW0 IH0 L Z +HALLOWELLS HH AE2 L AH0 W EH0 L Z +HALLOWING HH AE1 L OW0 IH0 NG +HALLOWS HH AE1 L OW0 Z +HALLS HH AO1 L Z +HALLSTOCK HH AE1 L S T AA2 K +HALLTON HH AO1 L T AH0 N +HALLTOWN HH AO1 L T AW2 N +HALLUCINATED HH AH0 L UW1 S AH0 N EY0 T IH0 D +HALLUCINATING HH AH0 L UW1 S AH0 N EY0 T IH0 NG +HALLUCINATING HH AH0 L UW1 S IH0 N EY0 T IH0 NG +HALLUCINATION HH AH0 L UW2 S AH0 N EY1 SH AH0 N +HALLUCINATIONS HH AH0 L UW2 S AH0 N EY1 SH AH0 N Z +HALLUCINATORY HH AH0 L UW1 S AH0 N AH0 T AO2 R IY0 +HALLUS HH AE1 L AH0 S +HALLVARD HH AE1 L V ER0 D +HALLWARD HH AO1 L W ER0 D +HALLWARD'S HH AO1 L W ER0 D Z +HALLWAY HH AO1 L W EY2 +HALLWAYS HH AO1 L W EY2 Z +HALLY HH AE1 L IY0 +HALM HH AA1 M +HALMA HH AA1 L M AH0 +HALMAR HH AO1 L M ER0 +HALNAKEHAM HH AE1 L N AH0 K AH0 M +HALO HH EY1 L OW0 +HALOED HH EY1 L OW0 D +HALOES HH EY1 L OW0 Z +HALOO HH AH0 L UW2 +HALOS HH EY1 L OW0 Z +HALP HH AE1 L P +HALPIN HH AE1 L P IH0 N +HALS HH AH0 L Z +HALSA HH AO1 L S AH0 +HALSBURY HH AO1 L Z B EH0 R IY0 +HALSE HH AE1 L S +HALSE'S HH AE1 L S IH0 Z +HALSELL HH AE1 L S AH0 L +HALSEY HH AE1 L S IY0 +HALSEY'S HH AE1 L S IY0 Z +HALSING HH AE1 L S IH0 NG +HALSON HH AE1 L S AH0 N +HALSTEAD HH AE1 L S T EH0 D +HALSTEAD'S HH AE1 L S T EH2 D Z +HALSTED HH AE1 L S T IH0 D +HALSWOOD HH AE1 L S W UH2 D +HALT HH AO1 L T +HALTE HH EY1 L T +HALTED HH AO1 L T AH0 D +HALTED HH AO1 L T IH0 D +HALTER HH AO1 L T ER0 +HALTERED HH AO1 L T ER0 D +HALTERING HH AO1 L T ER0 IH0 NG +HALTERS HH AO1 L T ER0 Z +HALTING HH AO1 L T IH0 NG +HALTINGLY HH AO1 L T IH0 NG L IY0 +HALTINGS HH AO1 L T IH0 NG Z +HALTON HH AE1 L T AH0 N +HALTREN HH AO1 L T R AH0 N +HALTS HH AO1 L T S +HALTWHISTLE HH AO1 L T W IH1 S AH0 L +HALVARD HH AE1 L V ER0 D +HALVDAN HH AE1 V D AH0 N +HALVE HH AE1 V +HALVED HH AE1 V D +HALVERTON HH AE1 L V ER0 T AH0 N +HALVES HH AE1 V Z +HALVEY HH AE1 L V IY0 +HALVING HH AE1 V IH0 NG +HALVOR HH AE1 L V ER0 +HALVORSEN HH AE0 L V AO1 R S AH0 N +HALWAYS HH AO1 L W EY2 Z +HALY HH AH0 L IY0 +HALYARD HH AO1 L Y ER0 D +HALYARDS HH AE1 L Y AA0 R D Z +HALYS HH AE1 L IY0 Z +HAM HH AE1 M +HAM'S HH AE1 M Z +HAMA HH AA1 M AH0 +HAMA'S HH AA1 M AH0 Z +HAMADAH HH AE1 M AH0 D AH0 +HAMADAN HH AE1 M AH0 D AH0 N +HAMADCHAS HH AE1 M AH0 D CH AH0 Z +HAMADI HH AH0 M AA1 D IY0 +HAMADIAN HH AH0 M AA1 D IY0 AH0 N +HAMADRYAD HH AE1 M AH0 D R AY1 AE0 D +HAMADRYADS HH AE1 M AH0 D R AY1 AE2 D Z +HAMAGE HH AE1 M IH0 JH +HAMAKO HH AE1 M AH0 K OW0 +HAMAKUA HH AE1 M AH0 K Y UW0 AH0 +HAMAL HH EY1 M AH0 L +HAMALS HH EY1 M AH0 L Z +HAMAN HH EY1 M AH0 N +HAMAN'S HH EY1 M AH0 N Z +HAMANDO HH AH0 M AA1 N D OW0 +HAMANN HH AA1 M AH0 N +HAMAR HH AH0 M AA1 R +HAMARI HH AA0 M AA1 R IY0 +HAMATH HH AE1 M AH0 TH +HAMBA HH AE1 M B AH0 +HAMBARD HH AE1 M B ER0 D +HAMBLE HH AE1 M B AH0 L +HAMBLEBY HH AE1 M B AH0 L B IY0 +HAMBLEDON HH AE1 M B AH0 L D AH0 N +HAMBLETON HH AE1 M B AH0 L T AA0 N +HAMBLETONIAN HH AE1 M B L IH0 T OW1 N IY0 AH0 N +HAMBLIN HH AE1 M B L IH0 N +HAMBLIN'S HH AE1 M B L IH0 N Z +HAMBLY HH AE1 M B L IY0 +HAMBLYN HH AE1 M B L IH0 N +HAMBO HH AE1 M B OW0 +HAMBOURG HH AE1 M B AO2 R G +HAMBURG HH AE1 M B ER0 G +HAMBURG'S HH AE1 M B ER0 G Z +HAMBURGER HH AE1 M B ER0 G ER0 +HAMBURGERS HH AE1 M B ER0 G ER0 Z +HAMBURGH HH AE1 M B ER0 G +HAMBURGS HH AE1 M B ER0 G Z +HAMBY HH AE1 M B IY0 +HAMDEN HH AE1 M D AH0 N +HAMDI HH AE1 M D IY0 +HAMDIR HH AE1 M D ER0 +HAME HH EY1 M +HAMEAU HH AE1 M OW1 +HAMED HH AE1 M D +HAMED'S HH AA1 M EH0 D Z +HAMEL HH AE1 M AH0 L +HAMEL'S HH AE1 M AH0 L Z +HAMELIN HH AE1 M AH0 L IH0 N +HAMELIN HH AE1 M L IH2 N +HAMELINE HH AE1 M L AY2 N +HAMELN HH AE1 M AH0 L N +HAMELY HH AE1 M L IY0 +HAMELYN HH AE1 M IH0 L IH0 N +HAMER HH AE1 M ER0 +HAMER'S HH AE1 M ER0 Z +HAMERS HH AE1 M ER0 Z +HAMERSLEY HH AE1 M ER0 S L IY0 +HAMERSLEY'S HH AE1 M ER0 S L IY0 Z +HAMERTON HH AE1 M ER0 T AH0 N +HAMERTON'S HH AE1 M ER0 T AH0 N Z +HAMES HH EY1 M Z +HAMET HH AE1 M IH0 T +HAMET'S HH AE1 M AH0 T S +HAMEWARTS HH AE1 M W AO1 R T S +HAMID HH AH0 M IY1 D +HAMID'S HH AE1 M IH0 D Z +HAMIDA HH AH0 M IY1 D AH0 +HAMIL HH AE1 M AH0 L +HAMILCAR HH AE1 M AH0 L K AA2 R +HAMILCAR'S HH AE1 M AH0 L K AA1 R Z +HAMILL HH AE1 M AH0 L +HAMILT HH AE1 M AH0 L T +HAMILTON HH AE1 M AH0 L T AH0 N +HAMILTON'S HH AE1 M AH0 L T AH0 N Z +HAMILTONIAN HH AE1 M AH0 L T OW1 N IY0 AH0 N +HAMILTONS HH AE1 M AH0 L T AH0 N Z +HAMIR HH AA0 M IH1 R +HAMISH HH AE1 M IH0 SH +HAMISH'S HH AE1 M IH0 SH IH0 Z +HAMITIC HH AE0 M IH1 T IH0 K +HAMLEN HH AE1 M L AH0 N +HAMLEN'S HH AE1 M L AH0 N Z +HAMLET HH AE1 M L AH0 T +HAMLET HH AE1 M L IH0 T +HAMLET'S HH AE1 M L AH0 T S +HAMLETS HH AE1 M L AH0 T S +HAMLEY HH AE1 M L IY0 +HAMLIKE HH AE1 M L AY2 K +HAMLIN HH AE1 M L IH0 N +HAMLIN'S HH AE1 M L IH0 N Z +HAMLYN HH AE1 M L IH0 N +HAMLYN'S HH AE1 M L IH0 N Z +HAMLYNS HH AE1 M L IH0 N Z +HAMM HH AE1 M +HAMMA HH AE1 M AH0 +HAMMAL HH AE1 M AH0 L +HAMMAM HH AE1 M AH0 M +HAMMAMAT HH AE1 M AH0 M AE0 T +HAMMED HH AE1 M D +HAMMER HH AE1 M ER0 +HAMMER'D HH AE1 M ER0 AH0 D +HAMMER'S HH AE1 M ER0 Z +HAMMERCLOTH HH AE1 M ER0 K L AO2 TH +HAMMERED HH AE1 M ER0 D +HAMMERER HH AE1 M ER0 ER0 +HAMMERER'S HH AE1 M ER0 ER0 Z +HAMMERFEST HH AE1 M ER0 F EH2 S T +HAMMERFIELD HH AE1 M ER0 F IY2 L D +HAMMERIN HH AE1 M ER0 IH0 N +HAMMERING HH AE1 M ER0 IH0 NG +HAMMERINGS HH AE1 M ER0 IH0 NG Z +HAMMERLESS HH AE1 M ER0 L AH0 S +HAMMERLOCK HH AE1 M ER0 L AA2 K +HAMMERMAN HH AE1 M ER0 M AH0 N +HAMMERMEN HH AE1 M ER0 M IH0 N +HAMMERPOND HH AE1 M ER0 P AH0 N D +HAMMERS HH AE1 M ER0 Z +HAMMERSLEY HH AE1 M ER0 S L IY0 +HAMMERSLEY'S HH AE1 M ER0 S L IY0 Z +HAMMERSLY HH AE1 M ER0 S L IY0 +HAMMERSMITH HH AE1 M ER0 S M IH2 TH +HAMMERSMITH'S HH AE1 M ER0 S M IH2 TH S +HAMMERSTEIN HH AE1 M ER0 S T AY2 N +HAMMERSTEIN HH AE1 M ER0 S T IY2 N +HAMMERSTEIN'S HH AE1 M ER0 S T AY2 N Z +HAMMERSTEIN'S HH AE1 M ER0 S T IY2 N Z +HAMMERTON HH AE1 M ER0 T AH0 N +HAMMET HH AE1 M IH0 T +HAMMETH HH AE1 M IH0 TH +HAMMETT HH AE1 M IH0 T +HAMMICK HH AE1 M IH0 K +HAMMOCK HH AE1 M AH0 K +HAMMOCKS HH AE1 M AH0 K S +HAMMON HH AE1 M AH0 N +HAMMON'S HH AE1 M AH0 N Z +HAMMOND HH AE1 M AH0 N D +HAMMOND'S HH AE1 M AH0 N D Z +HAMMONDS HH AE1 M AH0 N D Z +HAMMONTON HH AE1 M AH0 N T AH0 N +HAMMURABI HH AE1 M UH0 R AA1 B IY0 +HAMMURABI'S HH AE1 M UH0 R AA0 B IY1 Z +HAMMY HH AE1 M IY0 +HAMN HH AE1 M N +HAMNER HH AE1 M N ER0 +HAMNET HH AE1 M N EH2 T +HAMO HH AA1 M OW0 +HAMOAZE HH AE1 M EY1 Z +HAMON HH AE1 M AH0 N +HAMON'S HH EY1 M AH0 N Z +HAMOND HH AE1 M AH0 N D +HAMOOD HH AE1 M UW2 D +HAMOR HH AE1 M ER0 +HAMP HH AE1 M P +HAMPDEN HH AE1 M P D AH0 N +HAMPDEN'S HH AE1 M P D AH0 N Z +HAMPDENS HH AE1 M P D AH0 N Z +HAMPDENSHIRE HH AE1 M P D IH0 N SH AY2 R +HAMPER HH AE1 M P ER0 +HAMPERED HH AE1 M P ER0 D +HAMPERING HH AE1 M P ER0 IH0 NG +HAMPERS HH AE1 M P ER0 Z +HAMPOLE HH AE1 M P OW2 L +HAMPS HH AE1 M P S +HAMPS'S HH AE1 M P S IH0 Z +HAMPSHIRE HH AE1 M P SH AY0 ER0 +HAMPSHIRE HH AE1 M P SH ER0 +HAMPSHIRE HH AE1 M SH AY0 ER0 +HAMPSHIRE HH AE1 M SH ER0 +HAMPSHIRE'S HH AE1 M P SH AY0 ER0 Z +HAMPSHIRE'S HH AE1 M P SH ER0 Z +HAMPSHIRE'S HH AE1 M SH AY0 ER0 Z +HAMPSHIRE'S HH AE1 M SH ER0 Z +HAMPSHIRES HH AE1 M P SH AY0 ER0 Z +HAMPSON HH AE1 M P S AH0 N +HAMPSON'S HH AE1 M P S AH0 N Z +HAMPSTEAD HH AE1 M P S T EH2 D +HAMPSTEAD'S HH AE1 M P S T EH2 D Z +HAMPTON HH AE1 M P T AH0 N +HAMPTON'S HH AE1 M P T AH0 N Z +HAMPTONS HH AE1 M P T AH0 N Z +HAMPTONSHIRE HH AE1 M P T AH0 N SH ER0 +HAMRAN HH AE1 M R AH0 N +HAMS HH AE1 M Z +HAMSHAW HH AE1 M SH AO2 +HAMSON HH AE1 M S AH0 N +HAMSTEAD HH AE1 M S T EH2 D +HAMSTER HH AE1 M S T ER0 +HAMSTERS HH AE1 M S T ER0 Z +HAMSTRING HH AE1 M S T R IH2 NG +HAMSTRINGING HH AE1 M S T R IH2 NG IH0 NG +HAMSTRINGS HH AE1 M S T R IH2 NG Z +HAMSTRUNG HH AE1 M S T R AH0 NG +HAMSUN HH AE1 M S AH2 N +HAMSUN'S HH AE1 M S AH0 N Z +HAMTHWAITE HH AE1 M TH W EY2 T +HAMTON HH AE0 M T AH0 N +HAMVERT HH AE1 M V ER1 T +HAMYLTON HH AE1 M IH0 L T AH0 N +HAMZA HH AE1 M Z AH0 +HAMZAH HH AE1 M Z AH0 +HAN HH AA1 N +HAN HH AE1 N +HAN' HH AE1 N +HAN'FUL HH AE1 N F AH0 L +HAN'KERCHIEF HH AE1 N K ER1 CH AH0 F +HAN'LE HH AE1 N AH0 L +HAN'S HH AA1 N Z +HAN'S HH AE1 N Z +HAN'SOME HH AE1 N S AH0 M +HAN'SOMER HH AE1 N S AH0 M ER0 +HAN'T HH AE1 N T +HANA HH AE1 N AH0 +HANADRA HH AA0 N AA1 D R AH0 +HANAFORD HH AE1 N AH0 F AO0 R D +HANAK HH AA1 N AH0 K +HANALEI HH AE1 N AH0 L IY0 +HANAN HH EY1 N AH0 N +HANANI HH AA0 N AA1 N IY0 +HANANIAH HH AE1 N AH0 N AY1 AH0 +HANAP HH AE1 N AH0 P +HANARURO HH AE1 N ER0 UW1 R OW0 +HANAU HH AE1 N AW0 +HANAUD HH AE1 N OW1 +HANAUD'S HH AE1 N AW0 D Z +HANAVAVE HH AE1 N AH0 V AE2 V +HANBOROUGH HH AE1 N B ER0 OW0 +HANBRIDGE HH AE1 N B R IH2 JH +HANBURY HH AE1 N B EH2 R IY0 +HANBURY'S HH AE1 N B EH2 R IY0 Z +HANBURYS HH AE1 N B EH2 R IY0 Z +HANBY HH AE1 N B IY0 +HANC HH AE1 NG K +HANCE HH AE1 N S +HANCHEN HH AE1 N CH AH0 N +HANCHETT HH AE1 N CH IH0 T +HANCKEL HH AE1 NG K AH0 L +HANCOCK HH AE1 N K AA2 K +HANCOCK'S HH AE1 N K AA2 K S +HANCOCKS HH AE1 N K AA2 K S +HAND HH AE1 N D +HAND' HH AE1 N +HAND'S HH AE1 N D Z +HANDASSAH HH AE1 N D AE2 S AH0 +HANDBAG HH AE1 N D B AE2 G +HANDBAGS HH AE1 N D B AE2 G Z +HANDBALL HH AE1 N D B AO2 L +HANDBARROW HH AE1 N D B EH2 R OW0 +HANDBELL HH AE1 N D B EH0 L +HANDBILL HH AE1 N D B IH2 L +HANDBILLS HH AE1 N D B IH2 L Z +HANDBOOK HH AE1 N D B UH2 K +HANDBOOKS HH AE1 N D B UH2 K S +HANDBREADTH HH AE1 N D B R EH2 D TH +HANDBY HH AE1 N D B IY0 +HANDCAR HH AE1 N D K AA2 R +HANDCART HH AE1 N D K AA2 R T +HANDCARTS HH AE1 N D K AA2 R T S +HANDCLAPPING HH AE1 N D K L AE2 P IH0 NG +HANDCLASP HH AE1 N D K L AE2 S P +HANDCLASPS HH AE1 N D K L AE2 S P S +HANDCOCK HH AE1 N D K AA2 K +HANDCRAFT HH AE1 N D K R AE2 F T +HANDCUFF HH AE1 N D K AH2 F +HANDCUFFED HH AE1 N D K AH2 F T +HANDCUFFING HH AE1 N D K AH2 F IH0 NG +HANDCUFFS HH AE1 N D K AH2 F S +HANDE HH AE1 N D +HANDED HH AE1 N D AH0 D +HANDED HH AE1 N D IH0 D +HANDEDLY HH AE1 N D IH0 D L IY0 +HANDEDNESS HH AE1 N D AH0 D N AH0 S +HANDEL HH AE1 N D AH0 L +HANDEL'S HH AE1 N D AH0 L Z +HANDELS HH AE1 N D AH0 L Z +HANDER HH AE1 N D ER0 +HANDERS HH AE1 N D ER0 Z +HANDES HH AE1 N D IY0 Z +HANDFAST HH AE1 N D F AE2 S T +HANDFORD HH AE1 N D F ER0 D +HANDFU HH AE1 N D F UW0 +HANDFUL HH AE1 N D F UH2 L +HANDFULL HH AE1 N D F AH0 L +HANDFULLS HH AE1 N D F AH0 L Z +HANDFULS HH AE1 N D F UH2 L Z +HANDGLASS HH AE1 N D G L AE2 S +HANDGRIP HH AE1 N D G R IH0 P +HANDGRIPS HH AE1 N D G R IH1 P S +HANDGUN HH AE1 N D G AH2 N +HANDHOLD HH AE1 N D HH OW2 L D +HANDHOLDS HH AE1 N D HH OW2 L D Z +HANDI HH AE1 N D IY0 +HANDICAP HH AE1 N D IY0 K AE2 P +HANDICAPPED HH AE1 N D IY0 K AE2 P T +HANDICAPPER HH AE1 N D IY0 K AE2 P ER0 +HANDICAPPING HH AE1 N D IY0 K AE2 P IH0 NG +HANDICAPS HH AE1 N D IY0 K AE2 P S +HANDICRAFT HH AE1 N D IY0 K R AE2 F T +HANDICRAFTS HH AE1 N D IY0 K R AE2 F T S +HANDICRAFTSMAN HH AE1 N D IY0 K R AE2 F T S M AH0 N +HANDICRAFTSMEN HH AE1 N D IY0 K R AE2 F T S M EH0 N +HANDIER HH AE1 N D IY0 ER0 +HANDIES HH AE1 N D IY0 Z +HANDIEST HH AE1 N D IY0 AH0 S T +HANDILY HH AE1 N D AH0 L IY0 +HANDIN HH AE1 N D IH0 N +HANDINESS HH AE1 N D IY0 N AH0 S +HANDING HH AE1 N D IH0 NG +HANDIWORK HH AE1 N D IY0 W ER2 K +HANDIWORKS HH AE1 N D IY0 W ER2 K S +HANDJAR HH AE1 N JH ER0 +HANDKERCHEIF HH AE1 NG K ER0 CH IY1 F +HANDKERCHER HH AE1 NG K ER0 CH ER0 +HANDKERCHERS HH AE1 NG K ER0 CH ER0 Z +HANDKERCHIEF HH AE1 NG K ER0 CH IH0 F +HANDKERCHIEF HH AE1 NG K ER0 CH IY0 F +HANDKERCHIEF'S HH AE1 NG K ER0 CH IY1 F S +HANDKERCHIEFS HH AE1 NG K ER0 CH AH0 F S +HANDKERCHIEFS HH AE1 NG K ER0 CH IY0 F S +HANDKERCHY HH AE1 NG K ER0 CH IY0 +HANDLE HH AE1 N D AH0 L +HANDLE'S HH AE1 N D AH0 L Z +HANDLEBARS HH AE1 N D AH0 L B AA2 R Z +HANDLED HH AE1 N D AH0 L D +HANDLELESS HH AE1 N D AH0 L L AH0 S +HANDLER HH AE1 N D AH0 L ER0 +HANDLER HH AE1 N D L ER0 +HANDLERS HH AE1 N D AH0 L ER0 Z +HANDLERS HH AE1 N D L ER0 Z +HANDLES HH AE1 N D AH0 L Z +HANDLESS HH AE1 N D L AH0 S +HANDLETH HH AE1 N D AH0 L TH +HANDLEY HH AE1 N D L IY0 +HANDLIKE HH AE1 N D L AY2 K +HANDLIN HH AE1 N D L IH0 N +HANDLING HH AE1 N D AH0 L IH0 NG +HANDLING HH AE1 N D L IH0 NG +HANDLINGS HH AE1 N D L IH0 NG Z +HANDLON HH AE1 N D L AH0 N +HANDLOOM HH AE1 N D L UW2 M +HANDMADE HH AE1 N D M EY1 D +HANDMADE HH AE1 N M EY1 D +HANDMAID HH AE1 N D M EY2 D +HANDMAIDEN HH AE1 N D M EY1 D AH0 N +HANDMAIDEN'S HH AE1 N D M EY1 D AH0 N Z +HANDMAIDENS HH AE1 N D M EY1 D AH0 N Z +HANDMAIDS HH AE1 N D M EY2 D Z +HANDMILL HH AE1 N D M IH2 L +HANDOUT HH AE1 N D AW2 T +HANDOUTS HH AE1 N D AW2 T S +HANDPHONE HH AE1 N D F OW2 N +HANDRAIL HH AE1 N D R EY2 L +HANDRAILS HH AE1 N D R EY2 L Z +HANDROSAN HH AE1 N D R OW0 S AA1 N +HANDS HH AE1 N D Z +HANDS HH AE1 N Z +HANDS' HH AE1 N D Z +HANDS'LL HH AE1 N S AH0 L +HANDSAW HH AE1 N D S AO2 +HANDSBREADTH HH AE1 N D Z B R EH1 D TH +HANDSCOMBE HH AE1 N D S K OW1 M B IY0 +HANDSEL HH AE1 N D S AH0 L +HANDSELL HH AE1 N D S AH0 L +HANDSELLED HH AE1 N D Z EH1 L D +HANDSET HH AE1 N D S EH2 T +HANDSFUL HH AE1 N S F AH0 L +HANDSHAKE HH AE1 N D SH EY2 K +HANDSHAKES HH AE1 N D SH EY2 K S +HANDSHAKING HH AE1 N D SH EY2 K IH0 NG +HANDSHAKINGS HH AE1 N D SH EY2 K IH0 NG Z +HANDSLEY HH AE1 N D S L IY0 +HANDSOM HH AE1 N S AH0 M +HANDSOME HH AE1 N S AH0 M +HANDSOMEBODY HH AE1 N D S AH2 M B AA2 D IY0 +HANDSOMEBODY'S HH AE1 N D S AH2 M B AA2 D IY0 Z +HANDSOMELY HH AE1 N S AH0 M L IY0 +HANDSOMENESS HH AE1 N S AH0 M N AH0 S +HANDSOMER HH AE1 N S AH0 M ER0 +HANDSOMEST HH AE1 N D S AH2 M AH0 S T +HANDSOMLY HH AE1 N S AH0 M L IY0 +HANDSPIKE HH AE1 N D S P IH0 K +HANDSPIKES HH AE1 N D S P AY1 K S +HANDSPRING HH AE1 N D S P R IH0 NG +HANDSPRINGS HH AE1 N D S P R IH1 NG Z +HANDSTONE HH AE1 N D S T OW2 N +HANDSUM HH AE1 N Z AH0 M +HANDSWORTH HH AE1 N D Z W ER2 TH +HANDWORK HH AE1 N D W ER2 K +HANDWRITE HH AE1 N D R AY2 T +HANDWRITIN HH AE1 N D R AY2 T IH0 N +HANDWRITING HH AE1 N D R AY2 T IH0 NG +HANDWRITINGS HH AE1 N D R AY2 T IH0 NG Z +HANDWRITTEN HH AE1 N D R IH2 T AH0 N +HANDY HH AE1 N D IY0 +HANDY'S HH AE1 N D IY0 Z +HANDYCOCK HH AE1 N D IY0 K AO2 K +HANDYMAN HH AE1 N D IY0 M AE2 N +HANDYSIDE HH AE1 N D IY0 S AY2 D +HANDYSIDE'S HH AE1 N D IY0 S AY2 D Z +HANDYWORK HH AE1 N D IY0 W ER2 K +HANE HH EY1 N +HANEEMAR HH AH0 N EY1 EY0 M AA1 R +HANEM HH AE1 N IH0 M +HANENWINKEL HH AE1 N IH0 N W IH1 NG K AH0 L +HANER HH EY1 N ER0 +HANES HH EY1 N Z +HANEY HH EY1 N IY0 +HANEY'S HH EY1 N IY0 Z +HANEYS HH EY1 N IY0 Z +HANFIELD HH AE1 N F IY2 L D +HANFORD HH AE1 N F ER0 D +HANFORD'S HH AE1 N F ER0 D Z +HANFSTAENGL HH AE1 N F S T AE0 IH1 NG AH0 L +HANG HH AE1 NG +HANG'D HH AE1 NG D +HANGAR HH AE1 NG ER0 +HANGARS HH AE1 NG ER0 Z +HANGCHOW HH AE1 NG CH OW0 +HANGDOG HH AE1 NG D AO2 G +HANGED HH AE1 NG D +HANGEL HH AE1 NG G AH0 L +HANGELINA HH AE1 N JH AH0 L IY1 N AH0 +HANGER HH AE1 NG ER0 +HANGER'S HH AE1 NG ER0 Z +HANGERS HH AE1 NG ER0 Z +HANGEST HH AE1 NG G AH0 S T +HANGETH HH AE1 NG AH0 TH +HANGIN HH AE1 NG G IH0 N +HANGIN'S HH AE1 NG G IH0 N Z +HANGING HH AE1 NG G IH0 NG +HANGING HH AE1 NG IH0 NG +HANGING'S HH AE1 NG G IH0 NG Z +HANGINGS HH AE1 NG G IH0 NG Z +HANGIT HH AE1 NG G IH0 T +HANGLIP HH AE1 NG L IH0 P +HANGMAN HH AE1 NG M AH0 N +HANGMAN'S HH AE1 NG M AH0 N Z +HANGMEN HH AE1 NG M AH0 N +HANGOUT HH AE1 NG AW2 T +HANGOVER HH AE1 NG OW2 V ER0 +HANGS HH AE1 NG Z +HANGT HH AE1 NG T +HANGTOWN HH AE1 NG T AW2 N +HANGWAN HH AE1 NG G W AA0 N +HANIERRI HH AE1 N Y EH1 R IY0 +HANIMAL HH AE1 N AH0 M AH0 L +HANIMALS HH AE1 N AH0 M AH0 L Z +HANK HH AE1 NG K +HANK'S HH AE1 N K S +HANKA HH AE1 NG K AH0 +HANKER HH AE1 NG K ER0 +HANKERCHER HH AE1 NG K ER1 K ER0 +HANKERCHIEF HH AE1 NG K ER1 CH AH0 F +HANKERED HH AE1 NG K ER0 D +HANKERIN HH AE1 NG K ER0 IH0 N +HANKERING HH AE1 NG K ER0 IH0 NG +HANKERINGS HH AE1 NG K ER0 IH0 NG Z +HANKERS HH AE1 NG K ER0 Z +HANKES HH AE1 NG K S +HANKES'S HH AE1 NG K S IH0 Z +HANKEY HH AE1 NG K IY0 +HANKEY'S HH AE1 NG K IY0 Z +HANKIES HH AE1 NG K IY0 Z +HANKIN HH AE1 NG K IH0 N +HANKINS HH AE1 NG K IH0 N Z +HANKINSON HH AE1 NG K IH0 N S AH0 N +HANKLY HH AE1 NG K L IY0 +HANKOS HH AE1 NG K OW0 Z +HANKOW HH AE1 NG K OW0 +HANKS HH AE1 NG K S +HANKY HH AE1 NG K IY0 +HANKY'S HH AE1 NG K IY0 Z +HANKYCHY HH AE1 NG K Y CH IY0 +HANLEY HH AE1 N L IY0 +HANLEY'S HH AE1 N L IY0 Z +HANLIN HH AE1 N L IH0 N +HANLON HH AE1 N L AH0 N +HANLON'S HH AE1 N L AH0 N Z +HANLY HH AE1 N L IY0 +HANMER HH AE1 N M ER0 +HANMER'S HH AE1 N M ER0 Z +HANN HH AE1 N +HANNA HH AE1 N AH0 +HANNA'S HH AE1 N AH0 Z +HANNAFORD HH AE1 N AH0 F ER0 D +HANNAFORD'S HH AE1 N AH0 F ER0 D Z +HANNAH HH AE1 N AH0 +HANNAH'S HH AE1 N AH0 Z +HANNAN HH AE1 N AH0 N +HANNAN'S HH AE1 N AH0 N Z +HANNAS HH AE1 N AH0 Z +HANNASYDE HH AE1 N AH0 S AY1 D +HANNAWAY HH AE1 N AH0 W EY0 +HANNAY HH AE1 N EY0 +HANNE HH AE1 N +HANNE'S HH AE1 N Z +HANNEH HH AE1 N IH0 HH +HANNELE HH AE1 N AH0 L +HANNEN HH AE1 N AH0 N +HANNEQUIN HH AE1 N AH0 K IH0 N +HANNER HH AE1 N ER0 +HANNES HH AE1 N Z +HANNIBAL HH AE1 N IH0 B AH0 L +HANNIBAL'S HH AE1 N AH0 B AH0 L Z +HANNIGAN HH AE1 N IH0 G AH0 N +HANNING HH AE1 N IH0 NG +HANNINGTON HH AE1 N IH0 NG T AH0 N +HANNINGTON'S HH AE1 N IH0 NG T AH0 N Z +HANNO HH AE1 N OW0 +HANNO'S HH AE1 N OW0 Z +HANNOCK HH AE1 N AH0 K +HANNOCK'S HH AE1 N AH0 K S +HANNON HH AE1 N AH0 N +HANNOVER HH AE1 N OW2 V ER0 +HANNS HH AE1 N Z +HANNY HH AE1 N IY0 +HANNY'S HH AE1 N IY0 Z +HANOTAUX HH AE1 N OW0 T OW1 +HANOTEAU HH AE1 N AH0 T OW1 +HANOUM HH AH0 N UW1 M +HANOVER HH AE1 N OW0 V ER0 +HANOVERIAN HH AE2 N OW0 V IH1 R IY0 AH0 N +HANOVERIANS HH AE2 N OW0 V IH1 R IY0 AH0 N Z +HANOVERTOWN HH AE1 N OW0 V ER0 T AW2 N +HANOVRE HH AE1 N OW0 V ER0 +HANRAHAN HH AE1 N R AH0 HH AE0 N +HANRAHAN'S HH AE1 N R AH0 HH AE0 N Z +HANS HH AA1 N S +HANS HH AE1 N Z +HANS'S HH AE1 N Z IH0 Z +HANSA HH AE1 N S AH0 +HANSAG HH AE1 N S AE0 G +HANSARD HH AE1 N S ER0 D +HANSARD'S HH AE1 N S ER0 D Z +HANSCHA HH AE1 N SH AH0 +HANSCOM HH AE1 N S K AH0 M +HANSE HH AE1 N S +HANSEATIC HH AE2 N S IY0 AE1 T IH0 K +HANSEI HH AA1 N S EY2 +HANSEL HH AE1 N S AH0 L +HANSEL'S HH AE1 N S AH0 L Z +HANSELL HH AE1 N S AH0 L +HANSEN HH AE1 N S AH0 N +HANSEN'S HH AE1 N S AH0 N Z +HANSENS HH AE1 N S AH0 N Z +HANSERL HH AE1 N S ER0 L +HANSERL'S HH AE1 N S ER0 L Z +HANSFORD HH AE1 N S F ER0 D +HANSFORD'S HH AE1 N S F ER0 D Z +HANSGEORGE HH AE1 N Z JH AO1 R JH +HANSHEW HH AE1 N SH UW0 +HANSI HH AE1 N S IY0 +HANSKA HH AE1 N S K AH0 +HANSKA'S HH AE1 N S K AH0 Z +HANSKE HH AE1 N S K +HANSKI HH AE1 N S K IY0 +HANSLEIN HH AE1 N S L AY2 N +HANSLICK HH AE1 N S L IH0 K +HANSOM HH AE1 N S AH0 M +HANSOM'S HH AE1 N S AH0 M Z +HANSOMBODY HH AE1 N S AH0 M B AA1 D IY0 +HANSOME HH AE1 N S AH0 M +HANSOMS HH AE1 N S AH0 M Z +HANSON HH AE1 N S AH0 N +HANSON'S HH AE1 N S AH0 N Z +HANSONS HH AE1 N S AH0 N Z +HANSSON HH AE1 N S AH0 N +HANSTEEN HH AE1 N S T IY2 N +HANSUM HH AE1 N S AH0 M +HANSWER HH AE1 N S ER0 +HANT HH AE1 N T +HANTE HH AA1 N T IY0 +HANTED HH AE1 N IH0 D +HANTEE HH AE1 N T IY0 +HANTLE HH AE1 N T AH0 L +HANTS HH AE1 N T S +HANU HH AA1 N UW0 +HANUM HH AA1 N AH0 M +HANUMAN HH AA1 N AH0 M AH0 N +HANUN HH AA1 N AH0 N +HANWAY HH AE1 N W EY2 +HANWAY'S HH AE1 N W EY2 Z +HANWAYS HH AE1 N W EY2 Z +HANWELL HH AE1 N W EH2 L +HANWOOD HH AE1 N W UH2 D +HANY HH EY1 N IY0 +HANYTHING HH EY1 N IY0 TH IH2 NG +HANYTHINK HH EY1 N IY0 TH IH2 NG K +HANZLI HH AE1 N Z L IY0 +HAO HH AW1 +HAOKAH HH AA0 OW1 K AH0 +HAOLE HH EY1 OW2 L +HAOMA HH AA0 OW1 M AH0 +HAORI HH AW1 R IY0 +HAOUSE HH AW1 Z +HAOW HH EY1 OW0 +HAP HH AE1 P +HAP'ORTH HH AE1 P AO2 R TH +HAP'S HH AE1 P S +HAPAA HH AE1 P AH0 +HAPAAS HH AE1 P AA1 Z +HAPARANDA HH AE1 P ER0 AE1 N D AH0 +HAPE HH EY1 P +HAPENDEKI HH AE1 P AH0 N D EH1 K IY0 +HAPGOOD HH AE1 P G UH2 D +HAPGOOD'S HH AE1 P G UH2 D Z +HAPHAZARD HH AE0 P HH AE1 Z ER0 D +HAPHAZARDLY HH AE0 P HH AE1 Z ER0 D L IY0 +HAPHAZARDLY HH AE1 F AH0 Z ER0 D L IY0 +HAPI HH AE1 P IY0 +HAPLESS HH AE1 P L AH0 S +HAPLEY HH AE1 P L IY0 +HAPLY HH AE1 P L IY0 +HAPNED HH AE1 P N D +HAPORTH HH EY1 P ER0 TH +HAPP HH AE1 P +HAPP'D HH AE1 P D +HAPPAR HH AE1 P ER0 +HAPPARS HH AE1 P ER0 Z +HAPPED HH AE1 P T +HAPPEN HH AE1 P AH0 N +HAPPEN'D HH AE1 P AH0 N D +HAPPENCHANCE HH AE1 P IH0 N CH AE1 N S +HAPPENED HH AE1 P AH0 N D +HAPPENETH HH AE1 P AH0 N EH1 TH +HAPPENIN HH AE1 P AH0 N IH1 N +HAPPENIN'S HH AE1 P AH0 N IH0 N Z +HAPPENING HH AE1 P AH0 N IH0 NG +HAPPENING HH AE1 P N IH0 NG +HAPPENINGS HH AE1 P AH0 N IH0 NG Z +HAPPENINGS HH AE1 P N IH0 NG Z +HAPPENS HH AE1 P AH0 N Z +HAPPENSTANCE HH AE1 P AH0 N S T AE2 N S +HAPPENT HH AE1 P AH0 N T +HAPPER HH AE1 P ER0 +HAPPERSETT HH AE1 P ER0 S AH0 T +HAPPERTON HH AE1 P ER0 T AH0 N +HAPPETITE HH AE1 P AH0 T AY2 T +HAPPIE HH AE1 P IY0 +HAPPIER HH AE1 P IY0 ER0 +HAPPIEST HH AE1 P IY0 AH0 S T +HAPPILY HH AE1 P AH0 L IY0 +HAPPINESS HH AE1 P IY0 N AH0 S +HAPPINESSE HH AE1 P IY0 N IH0 S +HAPPINESSES HH AE1 P IY0 N AH0 S IH0 Z +HAPPUCH HH AE1 P AH0 K +HAPPY HH AE1 P IY0 +HAPPY'S HH AE1 P IY0 Z +HAPPYHEART HH AE1 P IY0 HH AA2 R T +HAPPYLAND HH AE1 P IY0 L AE2 N D +HAPS HH AE1 P S +HAPSBURG HH AE1 P S B ER0 G +HAPSBURGERS HH AE1 P S B ER0 G ER0 Z +HAPSBURGS HH AE1 P S B ER0 G Z +HAPU HH AE1 P Y UW0 +HAQ HH AE1 K +HAR HH AA1 R +HARA HH EH1 R AH0 +HARAB HH AE1 R AH0 B +HARADEN HH AH0 R AE1 D AH0 N +HARAK HH AE1 R AH0 K +HARAKIRI HH AE1 R AH0 K IH1 R IY0 +HARALD HH AA1 R AH0 L D +HARALD'S HH AA1 R AH0 L D Z +HARALSON HH AE1 R AH0 L S AH0 N +HARAM HH AE1 R AH0 M +HARAMOK HH AA0 R AA1 M AA0 K +HARAMONT HH AE1 R AH0 M AA1 N T +HARAN HH AE1 R AH0 N +HARANGUE HH ER0 AE1 NG +HARANGUED HH ER0 AE1 NG D +HARANGUES HH ER0 AE1 NG Z +HARANGUING HH ER0 AE1 NG IH0 NG +HARAR HH ER0 AA1 R +HARARI HH ER0 AA1 R IY0 +HARAS HH AA1 R AH0 Z +HARASS HH ER0 AE1 S +HARASS'D HH ER0 AE1 SH D +HARASSED HH ER0 AE1 S T +HARASSES HH ER0 AE1 S IH0 Z +HARASSING HH ER0 AE1 S IH0 NG +HARASSMENT HH ER0 AE1 S M AH0 N T +HARASSMENTS HH ER0 AE1 S M AH0 N T S +HARB HH AA1 R B +HARBELL HH AA1 R B EH2 L +HARBEN HH AA1 R B AH0 N +HARBER HH AA1 R B ER0 +HARBERG HH AA1 R B ER0 G +HARBERTON HH AA1 R B ER0 T AH0 N +HARBIN HH AA1 R B IH0 N +HARBINGER HH AA1 R B IH0 N JH ER0 +HARBINGERS HH AA1 R B IH0 NG ER0 Z +HARBISON HH AA1 R B IH0 S AH0 N +HARBISON'S HH AA1 R B IH0 S AH0 N Z +HARBOR HH AA1 R B ER0 +HARBOR'S HH AA1 R B ER0 Z +HARBORAGE HH AA1 R B ER0 AH0 JH +HARBORD HH AA1 R B ER0 D +HARBORED HH AA1 R B ER0 D +HARBORING HH AA1 R B ER0 IH0 NG +HARBORNE HH AA1 R B ER0 N +HARBORNE'S HH AA1 R B ER0 N Z +HARBOROUGH HH AA1 R B ER2 OW0 +HARBOROUGH'S HH AA1 R B ER2 OW0 Z +HARBORS HH AA1 R B ER0 Z +HARBOTTLE HH AA1 R B AO2 T AH0 L +HARBOTTLE'S HH AA1 R B AO2 T AH0 L Z +HARBOTTLES HH AA1 R B AO2 T AH0 L Z +HARBOUR HH AA1 R B ER0 +HARBOUR'D HH AA1 R B ER0 D +HARBOUR'S HH AA1 R B ER0 Z +HARBOURAGE HH AA1 R B ER0 AA1 ZH +HARBOURED HH AA1 R B ER0 D +HARBOURER HH AA1 R B ER0 ER0 +HARBOURING HH AA1 R B ER0 IH0 NG +HARBOURLESS HH AA1 R B ER0 L AH0 S +HARBOURS HH AA1 R B ER0 Z +HARBURG HH AA1 R B ER0 G +HARBURG'S HH AA1 R B ER0 G Z +HARBURGER HH AA1 R B ER0 G ER0 +HARBURY HH AA1 R B ER0 IY0 +HARBUTT HH AA1 R B AH0 T +HARBY HH AA1 R B IY0 +HARCH HH AA1 R K +HARCLAY HH AA1 R K L EY0 +HARCOMB HH AA1 R K AH0 M +HARCOMBE HH AA1 R K AH0 M +HARCOURT HH AA1 R K AO2 R T +HARCOURT'S HH AA1 R K ER0 T S +HARCOURTS HH AA1 R K ER1 T S +HARCOUT HH AA1 R K AW0 T +HARCRAFT HH AA1 R K R AE2 F T +HARD HH AA1 R D +HARD'S HH AA1 R D Z +HARDA HH AA1 R D AH0 +HARDACRE HH AA1 R D EY2 K ER0 +HARDAGE HH AA1 R D IH0 JH +HARDANGER HH AA1 R D EY1 N JH ER0 +HARDAWAY HH AA1 R D AH0 W EY2 +HARDBAKE HH AA1 R D B EY2 K +HARDBOTTLE HH AA1 R D B AO2 T AH0 L +HARDBROOKE HH AA1 R D B R UH0 K +HARDCAP HH AA1 R D K AE2 P +HARDCASE HH AA1 R D K EY2 S +HARDCASTLE HH AA1 R D K AE2 S AH0 L +HARDCASTLE'S HH AA1 R D K AE2 S AH0 L Z +HARDCOME HH AA1 R D K AH2 M +HARDCOME'S HH AA1 R D K AH2 M Z +HARDE HH AA1 R D +HARDEE HH AA1 R D IY1 +HARDEE'S HH AA1 R D IY1 Z +HARDEN HH AA1 R D AH0 N +HARDEN'D HH AA1 R D AH0 N D +HARDEN'S HH AA1 R D AH0 N Z +HARDENBERG HH AA1 R D AH0 N B ER0 G +HARDENBERG'S HH AA1 R D AH0 N B ER0 G Z +HARDENBURGH HH AA1 R D AH0 N B ER0 G +HARDENED HH AA1 R D AH0 N D +HARDENETH HH AA1 R D N IH0 TH +HARDENING HH AA1 R D AH0 N IH0 NG +HARDENING HH AA1 R D N IH0 NG +HARDENOW HH AA1 R D N OW0 +HARDENS HH AA1 R D AH0 N Z +HARDER HH AA1 R D ER0 +HARDER'N HH AA1 R D ER0 AH0 N +HARDEST HH AA1 R D AH0 S T +HARDESTY HH AA1 R D AH0 S T IY0 +HARDFELLOW HH AA1 R D F EH2 L OW0 +HARDGRAVE HH AA1 R D G R EY2 V +HARDHACK HH AA1 R D HH AE0 K +HARDHACK'S HH AA1 R D HH AE2 K S +HARDHAND HH AA1 R D HH AE2 N D +HARDHEADED HH AA1 R D HH EH2 D IH0 D +HARDHEARTED HH AA1 R D HH AA2 R T IH0 D +HARDHEARTEDNESS HH AA1 R D HH AA2 R T IH0 D N AH0 S +HARDHOLD HH AA1 R D HH OW2 L D +HARDI HH AA1 R D IY0 +HARDICANUTE HH AA1 R D IH0 K AH0 N UW1 T +HARDIE HH AA1 R D IY0 +HARDIE'S HH AA1 R D IY2 Z +HARDIER HH AA1 R D IY0 ER0 +HARDIES HH AA1 R D IY0 Z +HARDIEST HH AA1 R D IY0 AH0 S T +HARDIHOOD HH AA1 R D IY0 HH UH2 D +HARDILY HH AA1 R D IH0 L IY0 +HARDIMAN HH AA1 R D IH0 M AH0 N +HARDIMAN'S HH AA1 R D IH0 M AH0 N Z +HARDIMENT HH AA1 R D IH0 M AH0 N T +HARDIN HH AA1 R D IH0 N +HARDIN'S HH AA1 R D IH0 N Z +HARDINESS HH AA1 R D IY0 N AH0 S +HARDING HH AA1 R D IH0 NG +HARDING'S HH AA1 R D IH0 NG S +HARDINGE HH AA1 R D IH0 N G +HARDINGE'S HH AA1 R D IH0 N G Z +HARDINGHAM HH AA1 R D IH0 NG HH AE2 M +HARDINGS HH AA1 R D IH0 NG Z +HARDISH HH AA1 R D IH0 SH +HARDLANDS HH AA1 R D L AE2 N D Z +HARDLEY HH AA1 R D L IY0 +HARDLINES HH AA1 R D L AY2 N Z +HARDLY HH AA1 R D L IY0 +HARDMAN HH AA1 R D M AH0 N +HARDMAN'S HH AA1 R D M AH0 N Z +HARDNED HH AA1 R D N D +HARDNESS HH AA1 R D N AH0 S +HARDNESSES HH AA1 R D N AH0 S IH0 Z +HARDOCK HH AA1 R D AA0 K +HARDON HH AA1 R D AH0 N +HARDON'S HH AA1 R D AH0 N Z +HARDOUIN HH AA1 R D OW0 AH0 N +HARDPAN HH AA1 R D P AE2 N +HARDRAADE HH AA1 R D R AA1 D +HARDRADA HH AA1 R D R AA1 D AH0 +HARDRAT HH AA1 R D R AH0 T +HARDRAT'S HH AA1 R D R AE2 T S +HARDRAW HH AA1 R D R AO1 +HARDRESS HH AA1 R D R EH2 S +HARDRESS'S HH AA1 R D R AH0 S IH0 Z +HARDROSS HH AA1 R D R AO1 S +HARDS HH AA1 R D Z +HARDSCRABBLE HH AA1 R D S K R AE2 B AH0 L +HARDSETT HH AA1 R D S EH2 T +HARDSHAW HH AA1 R D SH AO2 +HARDSHELL HH AA1 R D SH AH0 L +HARDSHIP HH AA1 R D SH IH0 P +HARDSHIPS HH AA1 R D SH IH0 P S +HARDT HH AA1 R T +HARDTACK HH AA1 R D T AE2 K +HARDUP HH AA1 R D AH2 P +HARDWAR HH AA1 R D W AO0 R +HARDWARE HH AA1 R D W EH2 R +HARDWAY HH AA1 R D W EY2 +HARDWICK HH AA1 R D W IH2 K +HARDWICK'S HH AA1 R D W IH0 K S +HARDWICKE HH AA1 R D W IH0 K +HARDWICKE'S HH AA1 R D W IH0 K S +HARDWOOD HH AA1 R D W UH2 D +HARDWOODS HH AA1 R D W UH2 D Z +HARDWORKED HH AA1 R D W ER2 K T +HARDWORKING HH AA1 R D W ER2 K IH0 NG +HARDY HH AA1 R D IY0 +HARDY'S HH AA1 R D IY0 Z +HARDYMAN HH AA1 R D IY0 M AE2 N +HARDYMAN'S HH AA1 R D IY0 M AH0 N Z +HARDYNGE HH AA1 R D AY2 N JH +HARDYS HH AA1 R D IY0 Z +HARE HH EH1 R +HARE'S HH EH1 R Z +HAREAU HH EH1 R OW1 +HAREBELL HH EH1 R B EH2 L +HAREBELLS HH EH1 R B EH2 L Z +HAREBRAINED HH EH1 R B R EY2 N D +HAREDALE HH EH1 R D EY2 L +HAREDALE'S HH EH1 R D EY2 L Z +HAREEM HH ER0 IY1 M +HAREEMS HH ER0 IY1 M Z +HAREFIELD HH AE1 R IH0 F IY0 L D +HAREFIELD'S HH AE1 R IH0 F IY0 L D Z +HAREFOOT HH EH1 R F UH2 T +HAREK HH AA1 R EH0 K +HAREL HH EH1 R AH0 L +HARELIP HH AE1 R IH0 L IH0 P +HARELIPPED HH AE1 R IH0 L IH1 P T +HAREM HH EH1 R AH0 M +HAREM'S HH EH1 R AH0 M Z +HAREMLIK HH EH1 R AH0 M L IH0 K +HAREMS HH EH1 R AH0 M Z +HARERO HH EH0 R EH1 R OW0 +HARES HH EH1 R Z +HARET HH EH1 R AH0 T +HARET'S HH EH1 R AH0 T S +HARETON HH AE1 R IH0 T AA0 N +HAREVILLE HH EH1 R AH0 V IH2 L +HAREWOOD HH EH1 R W UH2 D +HAREWOOD'S HH EH1 R W UH2 D Z +HARF HH AA1 R F +HARFINK HH AA1 R F IH2 NG K +HARFLEUR HH AA1 R F L ER1 +HARFORD HH AA1 R F ER0 D +HARFORD'S HH AA1 R F ER0 D Z +HARG HH AA1 R G +HARGATE HH AA1 R G EY2 T +HARGER HH AA1 R G ER0 +HARGIS HH AA1 R G IH0 S +HARGRAVE HH AA1 R G R EY2 V +HARGRAVE'S HH AA1 R G R EY2 V Z +HARGRAVES HH AA1 R G R EY2 V Z +HARGREAVE HH AA1 R G R IY1 V +HARGREAVE'S HH AA1 R G R IY1 V Z +HARGREAVES HH AA1 R G R IY2 V Z +HARGREW HH AA1 R G R UW1 +HARGROVE HH AA1 R G R OW2 V +HARGROVE'S HH AA1 R G R AH0 V Z +HARGUS HH AA1 R G AH0 S +HARI HH AA1 R IY0 +HARI'S HH AA1 R IY0 Z +HARICOT HH EH1 R IH0 K AA2 T +HARICOTS HH EH1 R IH0 K AA2 T S +HARIDAS HH EH1 R IH0 D AH0 Z +HARIETTA HH AA1 R IY0 EH1 T AH0 +HARIETTA'S HH AA1 R IY0 EH1 T AH0 Z +HARIKAR HH AE1 R IH0 K AA2 R +HARIM HH EH0 R IY1 M +HARINGTON HH EH1 R IH0 NG T AH0 N +HARIOT HH EH1 R IY0 AH0 T +HARIRI HH ER0 IH1 R IY0 +HARIS HH AA1 R IY0 S +HARIS HH EH1 R IH0 S +HARISARMAN HH EH1 R IH0 S AA1 R M AH0 N +HARISCHANDRA HH EH1 R IH0 S CH AA1 N D R AH0 +HARISH HH EH1 R IH0 SH +HARISON HH EH1 R IH0 S AH0 N +HARISWAMI HH AA1 R IY0 S W AA1 M IY0 +HARITH HH ER0 IH1 TH +HARJES HH AA1 R Y AH0 S +HARK HH AA1 R K +HARK'E HH AA1 R K EY2 +HARK'EE HH AA1 R K IY0 +HARKAMAN HH AA1 R K AH0 M AH0 N +HARKAMAN'S HH AA1 R K AH0 M AH0 N Z +HARKAVY HH AA1 R K AH0 V IY0 +HARKAWAY HH AA1 R K AH0 W EY2 +HARKAWAY'S HH AA1 R K AH0 W EY2 Z +HARKED HH AA1 R K T +HARKEE HH AA1 R K IY1 +HARKEN HH AA1 R K AH0 N +HARKENED HH AA1 R K AH0 N D +HARKENING HH AA1 R K N IH0 NG +HARKER HH AA1 R K ER0 +HARKER'S HH AA1 R K ER0 Z +HARKEY HH AA1 R K IY0 +HARKIN HH AA1 R K IH0 N +HARKING HH AA1 R K IH0 NG +HARKINGS HH AA1 R K IH0 NG Z +HARKINS HH AA1 R K IH0 N Z +HARKNESS HH AA1 R K N AH0 S +HARKNESS'S HH AA1 R K N AH0 S IH0 Z +HARKNESSES HH AA1 R K N AH0 S IH0 Z +HARKOV HH AA1 R K AO2 V +HARKS HH AA1 R K S +HARKUTT HH AA1 R K AH0 T +HARKUTT'S HH AA1 R K UH1 T S +HARKVILLE HH AA1 R K V IH2 L +HARKY HH AA1 R K IY0 +HARKY'S HH AA1 R K IY0 Z +HARKYE HH AA1 R K IY0 +HARL HH AA1 R L +HARLAEM HH AA1 R L EY1 M +HARLAN HH AA1 R L AH0 N +HARLAN'S HH AA1 R L AH0 N Z +HARLAND HH AA1 R L AH0 N D +HARLAND'S HH AA1 R L AH0 N D Z +HARLAW HH AA1 R L AO2 +HARLAY HH AA1 R L EY0 +HARLE HH AA1 R AH0 L +HARLECH HH AA1 R AH0 L CH +HARLED HH AA1 R L D +HARLEIAN HH AA1 R L IY2 AH0 N +HARLEIGH HH AA1 R L AH0 +HARLEM HH AA1 R L AH0 M +HARLEQUIN HH AA1 R L AH0 K W AH0 N +HARLEQUIN'S HH AA1 R L AH0 K IH0 N Z +HARLEQUINADE HH AA1 R L AH0 K IH0 N EY1 D +HARLEQUINS HH AA1 R L AH0 K IH0 N Z +HARLESDEN HH AA1 R L Z D AH0 N +HARLESTON HH AA1 R L S T AH0 N +HARLESTON'S HH AA1 R L S T AH0 N Z +HARLETH HH AA1 R L AH0 TH +HARLEY HH AA1 R L IY0 +HARLEY'S HH AA1 R L IY0 Z +HARLEYS HH AA1 R L IY0 Z +HARLINGEN HH AA1 R L IH0 NG AH0 N +HARLINGTON HH AA1 R L IH0 NG T AH0 N +HARLOT HH AA1 R L AH0 T +HARLOT'S HH AA1 R L AH0 T S +HARLOTRY HH AA1 R L AH0 T R IY0 +HARLOTS HH AA1 R L AH0 T S +HARLOW HH AA1 R L OW0 +HARLOW'S HH AA1 R L OW2 Z +HARLOWE HH AA1 R L OW2 +HARLOWE'S HH AA1 R L OW2 Z +HARLOWES HH AA1 R L OW2 Z +HARM HH AA1 R M +HARM'D HH AA1 R M D +HARM'S HH AA1 R M Z +HARMAC HH AA2 R M AH0 K +HARMACHIS HH AA2 R M AH0 K IH1 Z +HARMAKHIS HH AA2 R M AH0 K IH0 S +HARMAN HH AA1 R M AH0 N +HARMAN'S HH AA1 R M AH0 N Z +HARMANS HH AA1 R M AH0 N Z +HARMAR HH AA0 R M AA1 R +HARMAS HH AA1 R M AH0 Z +HARMATTAN HH AA2 R M AH0 T AE1 N +HARME HH AA1 R M +HARMED HH AA1 R M D +HARMER HH AA1 R M ER0 +HARMER'S HH AA1 R M ER0 Z +HARMERS HH AA1 R M ER0 Z +HARMES HH AA1 R M Z +HARMFUL HH AA1 R M F AH0 L +HARMFULLY HH AA1 R M F AH0 L IY0 +HARMFULNESS HH AA1 R M F AH0 L N AH0 S +HARMHABI HH AA1 R M HH AE2 B IY0 +HARMING HH AA1 R M IH0 NG +HARMLESS HH AA1 R M L AH0 S +HARMLESSLY HH AA1 R M L AH0 S L IY0 +HARMLESSNESS HH AA1 R M L AH0 S N AH0 S +HARMODIUS HH AA0 R M OW1 D IY0 AH0 S +HARMON HH AA1 R M AH0 N +HARMON'S HH AA1 R M AH0 N Z +HARMONIA HH AA0 R M OW1 N IY0 AH0 +HARMONIC HH AA0 R M AA1 N IH0 K +HARMONICA HH AA0 R M AA1 N IH0 K AH0 +HARMONICAL HH AA0 R M AA1 N IH0 K AH0 L +HARMONICALLY HH AA0 R M AA1 N IH0 K L IY0 +HARMONICAS HH AA0 R M AA1 N IH0 K AH0 Z +HARMONICON HH AA0 R M AA1 N IH0 K AA2 N +HARMONICS HH AA0 R M AA1 N IH0 K S +HARMONIE HH AA1 R M AH0 N IY0 +HARMONIES HH AA1 R M AH0 N IY0 Z +HARMONIOUS HH AA0 R M OW1 N IY0 AH0 S +HARMONIOUSLY HH AA0 R M OW1 N IY0 AH0 S L IY0 +HARMONISE HH AA1 R M AH0 N IH0 Z +HARMONISED HH AA1 R M AH0 N IH0 Z D +HARMONISES HH AA1 R M AH0 N IH0 Z IH0 Z +HARMONISING HH AA1 R M AH0 N IH0 Z IH0 NG +HARMONIST HH AA1 R M AH0 N IH0 S T +HARMONISTS HH AA1 R M AH0 N IH0 S T S +HARMONIUM HH AA0 R M OW1 N IY0 AH0 M +HARMONIUMS HH AA0 R M OW1 N IY0 AH0 M Z +HARMONIZATION HH AA2 R M AH0 N IH0 Z EY1 SH AH0 N +HARMONIZE HH AA1 R M AH0 N AY2 Z +HARMONIZED HH AA1 R M AH0 N AY2 Z D +HARMONIZES HH AA1 R M AH0 N AY2 Z IH0 Z +HARMONIZING HH AA1 R M AH0 N AY2 Z IH0 NG +HARMONS HH AA1 R M AH0 N Z +HARMONY HH AA1 R M AH0 N IY0 +HARMONY'S HH AA1 R M AH0 N IY0 Z +HARMOST HH AA1 R M OW2 S T +HARMOUTH HH AA1 R M AH0 TH +HARMS HH AA1 R M Z +HARMSWORTH HH AA1 R M S W ER0 TH +HARMY HH AA1 R M IY0 +HARN HH AA1 R N +HARNACH HH AA1 R N AH0 K +HARNACK HH AA1 R N AH0 K +HARNED HH AA1 R N D +HARNESS HH AA1 R N AH0 S +HARNESS HH AA1 R N IH0 S +HARNESS'D HH AA1 R N AH0 S D +HARNESSED HH AA1 R N AH0 S T +HARNESSES HH AA1 R N AH0 S AH0 Z +HARNESSES HH AA1 R N AH0 S IH0 Z +HARNESSING HH AA1 R N AH0 S IH0 NG +HARNEY HH AA1 R N IY0 +HARNEY'S HH AA1 R N IY0 Z +HARNHAM HH AA1 R N AH0 M +HARNISH HH AA1 R N IH0 SH +HARNISS HH AA1 R N IH0 S +HARNOSH HH AA1 R N AH0 SH +HARNS HH AA1 R N Z +HARNSOME HH AA1 R N S AH0 M +HARNT HH AA1 R N T +HARO HH AA1 R OW0 +HAROL HH EH1 R AH0 L +HAROLD HH EH1 R AH0 L D +HAROLD'S HH EH1 R AH0 L D Z +HAROLDS HH EH1 R AH0 L D Z +HAROOD HH ER0 UW1 D +HAROON HH ER0 UW1 N +HAROUN HH AA1 R UW1 N +HAROUN'S HH ER0 AW1 N Z +HARP HH AA1 R P +HARP'S HH AA1 R P S +HARPAGAS HH AA1 R P AA1 G AH0 Z +HARPAGON HH AA1 R P AH0 G AA2 N +HARPAGUS HH AA1 R P AH0 G AH0 S +HARPALUS HH AA1 R P AH0 L AH0 S +HARPAR HH AA1 R P AA2 R +HARPE HH AA1 R P +HARPE'S HH AA1 R P S +HARPED HH AA1 R P T +HARPEN HH AA1 R P AH0 N +HARPENDEN HH AA0 R P EH1 N D AH0 N +HARPENDING HH AA0 R P EH1 N D IH0 NG +HARPER HH AA1 R P ER0 +HARPER'S HH AA1 R P ER0 Z +HARPERS HH AA1 R P ER0 Z +HARPETH HH AA1 R P IH0 TH +HARPIES HH AA1 R P IY0 Z +HARPIGNIES HH AA1 R P IH0 G N IY0 Z +HARPIN HH AA0 R P AE1 N +HARPING HH AA1 R P IH0 NG +HARPINGS HH AA1 R P IH0 NG Z +HARPIST HH AA1 R P IH0 S T +HARPISTS HH AA1 R P IH0 S T S +HARPIT HH AA1 R P IH0 T +HARPOCRATES HH AA1 R P AH0 K R EY2 T S +HARPOON HH AA0 R P UW1 N +HARPOONED HH AA0 R P UW1 N D +HARPOONEER HH AA0 R P UW1 N IH1 R +HARPOONEERS HH AA0 R P UW1 N IH1 R Z +HARPOONER HH AA0 R P UW1 N ER0 +HARPOONERS HH AA0 R P UW1 N ER0 Z +HARPOONING HH AA0 R P UW1 N IH0 NG +HARPOONS HH AA0 R P UW1 N Z +HARPS HH AA1 R P S +HARPSICHORD HH AA1 R P S AH0 K AO2 R D +HARPSICHORDS HH AA1 R P S AH0 K AO2 R D Z +HARPSTENAH HH AA1 R P S T EY1 N AH0 +HARPSTRING HH AA1 R P S T R IH2 NG +HARPSTRINGS HH AA1 R P S T R IH1 NG Z +HARPSWELL HH AA1 R P S W EH2 L +HARPUR HH AA1 R P ER0 +HARPY HH AA1 R P IY0 +HARQUEBUS HH AA1 R K W AH0 B AH0 S +HARQUEBUSS HH AA1 R K W AH0 B AH0 S +HARRACLES HH AE1 R AH0 K AH0 L Z +HARRADEN HH AE1 R AH0 D EH2 N +HARRAN HH AE1 R AH0 N +HARRAN'S HH AE1 R AH0 N Z +HARRAP HH AE1 R AH0 P +HARRAS HH AE1 R AH0 Z +HARRASS HH EH1 R AH0 S +HARRASSED HH EH1 R AH0 S T +HARRASSING HH EH1 R AH0 S IH0 NG +HARRD HH AE1 R D +HARREL HH AE1 R AH0 L +HARREL'S HH AE1 R AH0 L Z +HARRELS HH AE1 R AH0 L Z +HARRER HH AA1 R ER0 +HARRICANE HH AE1 R AH0 K EY2 N +HARRICUTT HH AE1 R AH0 K AH0 T +HARRIDAN HH EH1 R IH0 D AH0 N +HARRIDANS HH EH1 R IH0 D AH0 N Z +HARRIE HH EH1 R IY0 +HARRIE'S HH EH1 R IY0 Z +HARRIED HH EH1 R IY0 D +HARRIER HH EH1 R IY0 ER0 +HARRIERS HH EH1 R IY0 ER0 Z +HARRIES HH EH1 R IY0 Z +HARRIET HH EH1 R IY0 AH0 T +HARRIET'S HH EH1 R IY0 AH0 T S +HARRIETT HH AE1 R IY0 EH0 T +HARRIETT'S HH AE1 R IY0 EH0 T S +HARRIETTE HH AE1 R IY0 EH0 T +HARRIGAN HH EH1 R IH0 G AH0 N +HARRIGAN'S HH AE1 R IH0 G AH0 N Z +HARRIGANS HH AE1 R IH0 G AH0 N Z +HARRIGATE HH AE1 R AH0 G EY2 T +HARRIHAN HH EH1 R IH0 HH AE0 N +HARRIK HH AE1 R IH0 K +HARRIMAN HH EH1 R IH0 M AH0 N +HARRIMAN'S HH EH1 R IH0 M AH0 N Z +HARRINGAY HH EH1 R IH0 NG EY2 +HARRINGFORD HH EH1 R IH0 NG F ER0 D +HARRINGTON HH EH1 R IH0 NG T AH0 N +HARRINGTON'S HH EH1 R IH0 NG T AH0 N Z +HARRINGTONS HH EH1 R IH0 NG T AH0 N Z +HARRIOT HH AE1 R IY0 AH0 T +HARRIOT'S HH AE1 R IY0 AH0 T S +HARRIOTT HH AE1 R IY0 AH0 T +HARRIS HH EH1 R IH0 S +HARRIS'S HH EH1 R IH0 S IH0 Z +HARRISBURG HH AE1 R IH0 S B ER0 G +HARRISBURG HH EH1 R IH0 S B ER0 G +HARRISES HH AE1 R IH0 S IH0 Z +HARRISMITH HH EH1 R IH0 S M IH2 TH +HARRISON HH EH1 R IH0 S AH0 N +HARRISON'S HH EH1 R IH0 S AH0 N Z +HARRISONBURG HH EH1 R IH0 S AH0 N B ER0 G +HARRISONS HH EH1 R IH0 S AH0 N Z +HARRISSE HH AE1 R IH0 S +HARRISSON HH AE1 R IH0 S AH0 N +HARRITY HH AE1 R IH0 T IY0 +HARRIWELL HH EH1 R IH0 W EH0 L +HARROD HH EH1 R AH0 D +HARROD'S HH EH1 R AH0 D Z +HARRODS HH EH1 R AH0 D Z +HARRODSBURG HH EH1 R AH0 D Z B ER0 G +HARRODSBURGH HH EH1 R AH0 D Z B ER0 G +HARROGATE HH EH1 R AH0 G EY2 T +HARROP HH EH1 R AH0 P +HARROR HH AE1 R ER0 +HARROVIAN HH AE1 R OW0 V IY1 AH0 N +HARROVIANS HH AE1 R OW1 V IY0 AH0 N Z +HARROW HH AE1 R OW0 +HARROW'S HH AE1 R OW0 Z +HARROWBY HH AE1 R OW0 B IY0 +HARROWBY'S HH AE1 R OW0 B IY0 Z +HARROWDEN HH AE1 R OW0 D AH0 N +HARROWED HH AE1 R OW0 D +HARROWELL'S HH AE1 R OW0 W EH2 L Z +HARROWFIELD HH AE1 R OW0 F IY2 L D +HARROWFORK HH AE1 R OW0 F AO2 R K +HARROWGATE HH AE1 R OW0 G EY2 T +HARROWIN HH AE1 R OW0 IH0 N +HARROWING HH EH1 R OW0 IH0 NG +HARROWINGLY HH AE1 R OW0 IH0 NG L IY0 +HARROWS HH AE1 R OW0 Z +HARRUM HH AE1 R AH0 M +HARRY HH EH1 R IY0 +HARRY'S HH EH1 R IY0 Z +HARRYING HH EH1 R IY0 IH0 NG +HARRYMAN HH AE1 R IY0 M AH0 N +HARRYS HH AE1 R IY0 Z +HARSANYI HH AA1 R S AA1 N Y IY0 +HARSE HH AA1 R S +HARSES HH AA1 R S IH0 Z +HARSH HH AA1 R SH +HARSHA HH AA1 R SH AH0 +HARSHAW HH AA1 R SH AO2 +HARSHENED HH AA1 R SH AH0 N D +HARSHER HH AA1 R SH ER0 +HARSHEST HH AA1 R SH AH0 S T +HARSHLY HH AA1 R SH L IY0 +HARSHNESS HH AA1 R SH N AH0 S +HARSHNESSES HH AA1 R SH N AH0 S IH0 Z +HARSON HH AA1 R S AH0 N +HARSTON HH AA1 R S T AH0 N +HART HH AA1 R T +HART'S HH AA1 R T S +HARTE HH AA1 R T +HARTE'S HH AA1 R T EY2 Z +HARTEBEEST HH AA1 R T AH0 B IY1 IH0 S T +HARTEBEESTE HH AA1 R T AH0 B IY1 S T +HARTEBEESTS HH AA1 R T AH0 B AH0 S T S +HARTENSTEIN HH AA1 R T AH0 N S T AY2 N +HARTENSTEIN HH AA1 R T AH0 N S T IY2 N +HARTER HH AA1 R T ER0 +HARTES HH AA1 R T IY0 Z +HARTFIELD HH AA1 R T F IY2 L D +HARTFORD HH AA1 R T F ER0 D +HARTFORD'S HH AA1 R T F ER0 D Z +HARTHBOROUGH HH AA1 R TH B ER2 OW0 +HARTHE HH AA1 R T +HARTHILD HH AA1 R T AY2 L D +HARTHOVER HH AA1 R T OW0 V ER0 +HARTIGAN HH AA1 R T IH0 G AH0 N +HARTIGAN'S HH AA1 R T IH0 G AH0 N Z +HARTILL HH AA1 R T IH0 L +HARTILL'S HH AA1 R T IH0 L Z +HARTILY HH AA1 R T AH0 L IY0 +HARTING HH AA1 R T IH0 NG +HARTINGTON HH AA1 R T IH0 NG T AH0 N +HARTINGTON'S HH AA1 R T IH0 NG T AH0 N Z +HARTIST HH AA1 R T IH0 S T +HARTLAND HH AA1 R T L AH0 N D +HARTLEDON HH AA1 R T AH0 L D AH0 N +HARTLEDON'S HH AA1 R T AH0 L D AH0 N Z +HARTLEPOD HH AA1 R T L AH0 P AA2 D +HARTLEPOOL HH AA1 R T L AH0 P UW2 L +HARTLETOP HH AA1 R T AH0 L T AA2 P +HARTLEY HH AA1 R T L IY0 +HARTLEY'S HH AA1 R T L IY0 Z +HARTLEYS HH AA1 R T L IY0 Z +HARTLY HH AA1 R T L IY0 +HARTMAN HH AA1 R T M AH0 N +HARTMAN'S HH AA1 R T M AH0 N Z +HARTMANN HH AA1 R T M AH0 N +HARTMANN'S HH AA1 R T M AH0 N Z +HARTNESS HH AA1 R T N AH0 S +HARTNETT HH AA1 R T N IH0 T +HARTNOLL HH AA1 R T N OW2 L +HARTOG HH AA1 R T AH0 G +HARTON HH AA1 R T AH0 N +HARTOP HH AA1 R T AO0 P +HARTOPP HH AA1 R T AH0 P +HARTOPP'S HH AA1 R T AH0 P S +HARTRATH HH AA1 R T R AH0 TH +HARTREY HH AA1 R T R IY0 +HARTRICH HH AA1 R T R IH0 K +HARTRIDGE HH AA1 R T R IH0 JH +HARTRIGHT HH AA1 R T R AY2 T +HARTRIGHT'S HH AA1 R T R AY2 T S +HARTROTT HH AA1 R T R AA1 T +HARTS HH AA1 R T S +HARTSCHWERT HH AA1 R CH W ER0 T +HARTSED HH AA1 R T S T +HARTSEL HH AA1 R T S AH0 L +HARTSEL'S HH AA1 R T S AH0 L Z +HARTSHORN HH AA1 R T S HH AO2 R N +HARTSHORNE HH AA1 R CH ER0 N +HARTSON HH AA1 R T S AH0 N +HARTSOOK HH AA1 R T S UH0 K +HARTSTEIN HH AA1 R T S T AY2 N +HARTSTEIN HH AA1 R T S T IY2 N +HARTSTONGUE HH AA1 R T S T AA1 NG G +HARTSUFF HH AA1 R CH AH0 F +HARTSVILLE HH AA1 R T S V IH0 L +HARTVIG HH AA1 R T V IH0 G +HARTVIK HH AA1 R T V IH0 K +HARTWELL HH AA1 R T W EH2 L +HARTWELL'S HH AA1 R T W EH2 L Z +HARTWICH HH AA1 R T W IH0 CH +HARTWICH'S HH AA1 R T W IH0 CH IH0 Z +HARTWIG HH AA1 R T W IH0 K +HARTWIG'S HH AA1 R T W IH0 G Z +HARTY HH AA1 R T IY0 +HARTZ HH AA1 R T S +HARTZENBOSCH HH AA1 R T S AH0 N B AO2 SH +HARTZENBUSCH HH AA1 R T S AH0 N B UH0 SH +HARTZMANN HH AA1 R T S M AH0 N +HARU HH AA1 R UW0 +HARU'S HH AA1 R UW0 Z +HARUHIKU HH AA0 R UW0 HH IY1 K UW2 +HARUM HH EH1 R AH0 M +HARUN HH ER0 UW1 N +HARUSPEX HH AA0 R UW1 S P EH2 K S +HARUSPICES HH AA0 R UW1 S P AH0 S IH0 Z +HARUT HH AA0 R UW1 T +HARV HH AA1 R V +HARVARD HH AA1 R V ER0 D +HARVARD'S HH AA1 R V ER0 D Z +HARVE HH AA1 R V +HARVEST HH AA1 R V AH0 S T +HARVEST'S HH AA1 R V AH0 S T S +HARVESTED HH AA1 R V AH0 S T AH0 D +HARVESTED HH AA1 R V AH0 S T IH0 D +HARVESTER HH AA1 R V AH0 S T ER0 +HARVESTER'S HH AA1 R V AH0 S T ER0 Z +HARVESTERS HH AA1 R V AH0 S T ER0 Z +HARVESTIN HH AA1 R V AH0 S T IH0 N +HARVESTING HH AA1 R V AH0 S T IH0 NG +HARVESTMAN HH AA1 R V AH0 S T M AH0 N +HARVESTMEN HH AA1 R V EH1 S M AH0 N +HARVESTON HH AA1 R V AH0 S T AH0 N +HARVESTS HH AA1 R V AH0 S T S +HARVEY HH AA1 R V IY0 +HARVEY'S HH AA1 R V IY0 Z +HARVEYS HH AA1 R V EY1 Z +HARVIE HH AA1 R V IY0 +HARVILLE HH AA1 R V IH2 L +HARVILLE'S HH AA1 R V IH2 L Z +HARVILLES HH AA1 R V IH2 L Z +HARVINGTON HH AA1 R V IH0 NG T AH0 N +HARVISS HH AA1 R V IH0 S +HARVY HH AA1 R V IY0 +HARWELL HH AA1 R W EH2 L +HARWICH HH AA1 R W IH0 CH +HARWOOD HH AA1 R W UH2 D +HARWOOD'S HH AA1 R W UH2 D Z +HARZ HH ER0 Z +HARZA HH AA1 R Z AH0 +HAS HH AE1 Z +HAS HH AH0 Z +HAS'T HH AE1 S AH0 T +HASAN HH EY1 Z AH0 N +HASARD HH AE1 S ER0 D +HASAYAMPA HH AE1 S AA0 Y AA1 M P AH0 +HASBROOK HH AE1 S B R UH0 K +HASBROOK'S HH AE1 Z B R UH0 K S +HASBROUCK HH AE1 Z B R UH2 K +HASBROUCK'S HH AE1 Z B R UH2 K S +HASCHICH HH AE1 SH IH0 K +HASCHISCH HH AE1 SH IH0 SH +HASCHISH HH AE1 SH IH0 SH +HASDRUBAL HH AE1 S D R UW0 B AE1 L +HASDRUBAL'S HH AE1 S D R AH2 B AH0 L Z +HASE HH EY1 Z +HASEBROEK HH AE1 S IH0 B R OW0 K +HASELFORD HH AE1 S IH0 L F ER0 D +HASELTINE HH AE1 S IH0 L T IY0 N +HASELTON HH AH0 S EH1 L T AH0 N +HASENCLEVER HH AE1 S IH0 N K L IY1 V ER0 +HASH HH AE1 SH +HASHA HH AE1 SH AH0 +HASHED HH AE1 SH T +HASHEESH HH AH0 SH IY1 SH +HASHEM HH AE1 SH IH0 M +HASHES HH AE1 SH AH0 Z +HASHI HH AE1 SH IY2 +HASHING HH AE1 SH IH0 NG +HASHISH HH AE1 SH IH0 SH +HASHISH HH AH0 SH IY1 SH +HASHISHIN HH AH0 SH IY1 SH IH0 N +HASHMI HH AA1 SH M IY0 +HASHMI HH AE1 SH M IY0 +HASHNU HH AE1 SH N UW0 +HASHOLOM HH AE1 SH AH0 L AH0 M +HASIDIM HH AH0 S IH1 D IH0 M +HASIDIM HH AH0 S IY1 D IH0 M +HASIDISM HH AH0 S IY1 D IH0 Z AH0 M +HASISADRA HH EY1 S IH0 S AA1 D R AH0 +HASJELTI HH AE1 S JH EH1 L T IY0 +HASK HH AE1 S K +HASKALAH HH AE1 S K AA1 L AH0 +HASKELL HH AE1 S K AH0 L +HASKELL'S HH AE1 S K AH0 L Z +HASKERS HH AE1 S K ER0 Z +HASKET HH AE1 S K AH0 T +HASKETT HH AE1 S K IH0 T +HASKETT'S HH AE1 S K IH0 T S +HASKINS HH AE1 S K IH0 N Z +HASLAM HH AE1 S L AH0 M +HASLAM'S HH AE1 S L AH0 M Z +HASLAR HH AE1 S L ER0 +HASLEM HH AE1 S L IH0 M +HASLEMERE HH AE1 S L IH0 M IH2 R +HASLETT HH AE1 S L IH0 T +HASLUCK HH AE1 S L AH0 K +HASN HH AE1 Z AH0 N +HASN'T HH AE1 Z AH0 N T +HASNA HH AE1 S N AH0 +HASNIP HH AE1 S N IH1 P +HASNT HH AE1 Z N T +HASP HH AE1 S P +HASPARREN HH AE1 S P AA1 R AH0 N +HASPED HH AE1 S P T +HASPS HH AE1 S P S +HASS HH AE1 S +HASSAL HH AE1 S AH0 L +HASSALL HH AE1 S AH0 L +HASSAM HH AE1 S AH0 M +HASSAN HH AH0 S AA1 N +HASSAN'S HH AE1 S AA0 N Z +HASSARD HH AE1 S ER0 D +HASSAYAMP HH AE1 S AY1 AE0 M P +HASSAYAMPA HH AE1 S IY0 AA1 M P AH0 +HASSE HH AA1 S +HASSEBU HH AE1 S AH0 B UW0 +HASSEEBOO HH AE1 S IY0 B UW2 +HASSEL HH AE1 S AH0 L +HASSELL HH AE1 S AH0 L +HASSELT HH AE1 S IH0 L T +HASSEN HH AE1 S AH0 N +HASSENREUTER HH AE1 S AH0 N R OY1 T ER0 +HASSETT HH AE1 S IH0 T +HASSIEBROCK HH AE1 S IY1 B R AH0 K +HASSIM HH AE1 S IH0 M +HASSIM'S HH AE1 S IH0 M Z +HASSLE HH AE1 S AH0 L +HASSLER HH AE1 S L ER0 +HASSLING HH AE1 S AH0 L IH0 NG +HASSLING HH AE1 S L IH0 NG +HASSOCK HH AE1 S AH0 K +HASSOCKS HH AE1 S AH0 K S +HAST HH AE1 S T +HASTA AA1 S T AH0 +HASTA HH AE1 S T AH0 +HASTAIN HH EY1 S T EY1 N +HASTATI HH EY1 S T AA1 T IY0 +HASTE HH EY1 S T +HASTED HH EY1 S T IH0 D +HASTEN HH EY1 S AH0 N +HASTEN'D HH EY1 S AH0 N D +HASTENBROOK HH EY1 S AH0 N B R UH2 K +HASTENED HH EY1 S AH0 N D +HASTENETH HH EY1 S AH0 N EH1 TH +HASTENING HH EY1 S AH0 N IH0 NG +HASTENING HH EY1 S N IH0 NG +HASTENS HH EY1 S AH0 N Z +HASTES HH EY1 S T S +HASTETH HH EY1 S T IH0 TH +HASTIE HH EY1 S T IY0 +HASTIE'S HH EY1 S T IY0 Z +HASTIER HH EY1 S T IY0 ER0 +HASTIEST HH EY1 S T IY0 AH0 S T +HASTILY HH EY1 S T AH0 L IY0 +HASTINESS HH EY1 S T IY0 N AH0 S +HASTING HH EY1 S T IH0 NG +HASTING'S HH EY1 S T IH0 NG Z +HASTINGS HH EY1 S T IH0 NG Z +HASTINGS'S HH EY1 S T IH0 NG Z IH0 Z +HASTOR HH AE1 S T ER0 +HASTUR HH AE1 S T ER0 +HASTURS HH AE1 S T ER0 Z +HASTY HH EY1 S T IY0 +HASWELL HH AE1 S W EH0 L +HASWELL'S HH AE1 S W EH2 L Z +HAT HH AE1 T +HAT'S HH AE1 T S +HATA HH AA1 T AH0 +HATAMOTO HH AE1 T AH0 M OW1 T OW0 +HATASOO HH AE1 T AH0 S UW1 +HATASU HH AE1 T AH0 S UW1 +HATBAND HH AE1 T B AE2 N D +HATBANDS HH AE1 T B AE2 N D Z +HATBORO HH AE1 T B ER0 OW0 +HATBOX HH AE1 T B AA2 K S +HATBRIM HH AE1 T B R IH1 M +HATBURN HH AE1 T B ER2 N +HATBURNS HH AE1 T B ER2 N Z +HATCH HH AE1 CH +HATCH'D HH AE1 CH IH0 D +HATCH'S HH AE1 CH IH0 Z +HATCHAM HH AE1 CH AH0 M +HATCHARD HH AE1 CH ER0 D +HATCHARD'S HH AE1 CH ER0 D Z +HATCHED HH AE1 CH T +HATCHELL HH AE1 CH AH0 L +HATCHER HH AE1 CH ER0 +HATCHER'S HH AE1 CH ER0 Z +HATCHERIES HH AE1 CH ER0 IY0 Z +HATCHERY HH AE1 CH ER0 IY0 +HATCHES HH AE1 CH IH0 Z +HATCHET HH AE1 CH AH0 T +HATCHETS HH AE1 CH AH0 T S +HATCHETT HH AE1 CH IH0 T +HATCHIE HH AE1 CH IY0 +HATCHIN HH AE1 CH IH0 N +HATCHING HH AE1 CH IH0 NG +HATCHINGS HH AE1 CH IH0 NG Z +HATCHMENT HH AE1 CH M AH0 N T +HATCHMENTS HH AE1 CH M AH0 N T S +HATCHWAY HH AE1 CH W EY2 +HATCHWAY'S HH AE1 CH W EY2 Z +HATCHWAYS HH AE1 CH W EY2 Z +HATE HH EY1 T +HATE'S HH EY1 T S +HATED HH EY1 T AH0 D +HATED HH EY1 T IH0 D +HATEETA HH EY1 T IY1 T AH0 +HATEETAH HH EY1 T IY1 T AH0 +HATEFUL HH EY1 T F AH0 L +HATEFULEST HH EY1 T F AH0 L AH0 S T +HATEFULLEST HH EY1 T F AH0 L AH0 S T +HATEFULLY HH EY1 T F AH0 L IY0 +HATEFULNESS HH EY1 T F AH0 L N AH0 S +HATER HH EY1 T ER0 +HATERIUS HH AE1 T IY1 R IY0 AH0 S +HATERS HH EY1 T ER0 Z +HATES HH EY1 T S +HATEST HH EY1 T AH0 S T +HATETH HH AE1 T IH0 TH +HATFIELD HH AE1 T F IY2 L D +HATFIELD'S HH AE1 T F IY2 L D Z +HATFIELDS HH AE1 T F IY2 L D Z +HATFUL HH AE1 T F AH0 L +HATFULS HH AE1 T F AH0 L Z +HATH HH AE1 TH +HATHA HH AE1 TH AH0 +HATHAWAY HH AE1 TH AH0 W EY2 +HATHAWAY'S HH AE1 TH AH0 W EY2 Z +HATHAWAYS HH AE1 TH AH0 W EY2 Z +HATHE HH EY1 DH +HATHELSBOROUGH HH AE1 TH AH0 L Z B ER0 OW0 +HATHEN HH AE1 TH AH0 N +HATHERCLEUGH HH AE1 TH ER0 K L +HATHERELL HH AE1 TH ER0 AH0 L +HATHERLEY HH EY1 DH ER0 L IY0 +HATHERLY HH EY1 DH ER0 L IY0 +HATHERSAGE HH EY1 DH ER0 S IH0 JH +HATHERTON HH AE1 TH ER0 T AH0 N +HATHI HH AE1 TH IY0 +HATHI'S HH AE1 TH IY0 Z +HATHIPOTA HH AE1 TH IH0 P OW1 T AH0 +HATHN'T HH AE1 TH N T +HATHNIT HH AE1 TH N IH1 T +HATHOR HH AE1 TH ER0 +HATHORN HH AA1 TH AO0 R N +HATHORN'S HH AA1 TH AO0 R N Z +HATHORNE HH AE1 TH ER0 N +HATHORNE'S HH AA1 TH AO0 R N Z +HATHORNES HH AA1 TH AO0 R N Z +HATHORS HH AE1 TH ER0 Z +HATI HH AA1 T IY0 +HATIF HH AA0 T IY1 F +HATIHEU AE2 T IH0 HH Y UW0 +HATIM HH AE1 T IH2 M +HATIN HH AE1 T AH0 N +HATING HH EY1 T IH0 NG +HATLESS HH EY1 T L AH0 S +HATO HH AE1 T OW0 +HATOR HH EY1 T ER0 +HATORASK HH AE1 T AO2 R AE1 S K +HATOS HH AE1 T OW0 Z +HATOUL HH AE1 T UW1 L +HATPIN HH EY1 T P IH0 N +HATPINS HH EY1 T P IH2 N Z +HATRACK HH AE1 T R AE2 K +HATREAUMONT HH AE1 T R OW1 M AO0 N T +HATRED HH EY1 T R AH0 D +HATRED'S HH EY1 T R AH0 D Z +HATREDS HH EY1 T R AH0 D Z +HATRY HH EY1 T R IY0 +HATS HH AE1 T S +HATSHEPSU HH EY1 T SH EH2 P S UW0 +HATSHEPSU'S HH EY1 T SH EH2 P S UW2 Z +HATSHEPSUT HH EY1 T SH EH2 P S AH0 T +HATSHOPSITU HH T SH AA2 P S IH0 T UW0 +HATSTAND HH AE1 T S T AE2 N D +HATT HH AE1 T +HATTA HH AE1 T AH0 +HATTE HH AE1 T +HATTED HH AE1 T IH0 D +HATTER HH AE1 T ER0 +HATTER'S HH AE1 T ER0 Z +HATTERAICK HH AE1 T ER0 IH0 K +HATTERAICK'S HH AE1 T ER0 IH0 K S +HATTERAS HH AE1 T ER0 AH0 S +HATTERS HH AE1 T ER0 Z +HATTERSLEY HH AE1 T ER0 Z L IY0 +HATTHA HH AE1 T AH0 +HATTI HH AE1 T IY0 +HATTIE HH AE1 T IY0 +HATTIE'S HH AE1 T IY0 Z +HATTIN HH AE1 T IH0 N +HATTINGH HH AE1 T IH0 NG +HATTO HH AE1 T OW0 +HATTO'S HH AE1 T OW0 Z +HATTOCK HH AE1 T AH0 K +HATTON HH AE1 T AH0 N +HATTON'S HH AE1 T AH0 N Z +HATTOY HH AE1 T OY1 +HATTY HH AE1 T IY0 +HATTY'S HH AE1 T IY0 Z +HATYK HH AH0 T IH0 K +HATZE HH AE1 T Z +HATZFELD HH AE1 T S F EH0 L D +HATZFELDT HH AE1 T S F EH0 L T +HAU HH AW1 +HAUB HH AO1 B +HAUBERK HH AW1 B ER0 K +HAUBERK'S HH AW1 B ER0 K S +HAUBERKS HH AW1 B ER2 K S +HAUCK HH AO1 K +HAUCK'S HH AO1 K S +HAUD HH AO1 D +HAUDEN HH AO1 D AH0 N +HAUDIN HH OW0 D AE1 N +HAUDING HH AW1 D IH0 NG +HAUDRY HH AO1 D R IY0 +HAUDS HH AO1 D Z +HAUE HH AW1 +HAUENSTEIN HH AW1 AH0 N S T AY0 N +HAUENSTEIN HH AW1 AH0 N S T IY0 N +HAUEY HH AO1 IY0 +HAUF HH AO1 F +HAUFF HH AO1 F +HAUFF'S HH AO1 F S +HAUFFE HH AO1 F +HAUG HH AO1 G +HAUGE HH AO1 JH +HAUGH HH AO1 +HAUGHS HH AO1 Z +HAUGHT HH AO1 T +HAUGHT'S HH AO1 T S +HAUGHTIER HH AO1 T IY0 ER0 +HAUGHTIEST HH AO1 T IY0 AH0 S T +HAUGHTILY HH AO1 T IH0 L IY0 +HAUGHTINESS HH AO1 T IY0 N AH0 S +HAUGHTON HH AO1 T AH0 N +HAUGHTON'S HH AO1 T AH0 N Z +HAUGHTONS HH AO1 T AH0 N Z +HAUGHTS HH AO1 T S +HAUGHTY HH AO1 T IY0 +HAUGIANS HH AO1 JH AH0 N Z +HAUGWITZ HH AO1 G W IH0 T S +HAUK HH AO1 K +HAUKE HH AO1 K +HAUKE'S HH AO1 K S +HAUKSBEE HH AO1 K S B IY2 +HAUL HH AO1 L +HAUL'D HH AO1 L D +HAULAGE HH AO1 L IH0 JH +HAULD HH AO1 L D +HAULED HH AO1 L D +HAULER HH AO1 L ER0 +HAULERS HH AO1 L ER0 Z +HAULIN HH AO1 L IH0 N +HAULING HH AO1 L IH0 NG +HAULM HH AO1 M +HAULS HH AO1 L Z +HAULYARDS HH AO1 L Y AA2 R D Z +HAUN HH AO1 N +HAUNCH HH AO1 N CH +HAUNCHED HH AO1 N CH T +HAUNCHES HH AO1 N CH IH0 Z +HAUND HH AO1 N D +HAUNS HH AO1 N Z +HAUNT HH AO1 N T +HAUNTE HH AO1 N T +HAUNTED HH AO1 N T AH0 D +HAUNTED HH AO1 N T IH0 D +HAUNTER HH AO1 N T ER0 +HAUNTERS HH AO1 N T ER0 Z +HAUNTETH HH AO1 N T IH0 TH +HAUNTIN HH AO1 N T IH0 N +HAUNTING HH AO1 N T IH0 NG +HAUNTINGLY HH AO1 N T IH0 NG L IY0 +HAUNTINGS HH AO1 N T IH0 NG Z +HAUNTS HH AO1 N T S +HAUPT HH AO1 P T +HAUPTMANN HH AW1 P T M AH0 N +HAUPTMANN'S HH AW1 P T M AH0 N Z +HAURAKI HH AO1 R AA1 K IY0 +HAURAN HH AO1 R AH0 N +HAUS HH AW1 S +HAUSA HH AW1 S AH0 +HAUSAS HH AW1 S AH0 S +HAUSBERG HH AW1 S B ER0 G +HAUSE HH AW1 S +HAUSEN HH AW1 Z AH0 N +HAUSER HH AW1 Z ER0 +HAUSER'S HH AW1 Z ER0 Z +HAUSERMAN HH AW1 Z ER0 M AH0 N +HAUSFRAU HH AW1 S F R AW1 +HAUSKULD HH AO1 S K AH0 L D +HAUSMANN HH AW1 S M AH0 N +HAUSSA HH AW1 S AH0 +HAUSSAS HH AW1 S AH0 S +HAUSSET HH AW1 S EH0 T +HAUSSMANN HH AW1 S M AH0 N +HAUSTUS HH AO1 S T AH0 S +HAUT 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Z +HAWKERS HH AO1 K ER0 Z +HAWKES HH AO1 K S +HAWKESBURY HH AO1 K S B EH0 R IY0 +HAWKESBY HH AO1 K S B IY0 +HAWKESEYE HH AO1 K Z AY0 +HAWKESLEY HH AO1 K S L IY0 +HAWKESWORTH HH AO1 K S W ER0 TH +HAWKESWORTH'S HH AO1 K S W ER0 TH S +HAWKEYE HH AO1 K AY2 +HAWKEYE'S HH AO1 K AY2 Z +HAWKHURST HH AO1 K HH ER0 S T +HAWKIE HH AO1 K IY0 +HAWKING HH AO1 K IH0 NG +HAWKINS HH AO1 K IH0 N Z +HAWKINS'S HH AO1 K IH0 N Z IH0 Z +HAWKINSES HH AO1 K IH0 N S IH0 Z +HAWKINSON HH AO1 K IH0 N S AH0 N +HAWKISH HH AO1 K IH0 SH +HAWKLIKE HH AO1 K L AY2 K +HAWKS HH AO1 K S +HAWKSBY HH AO1 K S B IY0 +HAWKSGLEN HH AO1 K S G AH0 L AH0 N +HAWKSHAW HH AO1 K SH AO2 +HAWKSHAWE HH AO1 K SH AO1 +HAWKSHAWE'S HH AO1 K SH AH0 W Z +HAWKSHEAD HH AO1 K S HH EH2 D +HAWKSLEY HH AO1 K S L IY0 +HAWKSLEY'S HH AO1 K S L IY0 Z +HAWKSTONE HH AO1 K S T OW2 N +HAWKSWING HH AO1 K S W IH2 NG +HAWKSWORTH HH AO1 K S W ER0 TH +HAWKWEED HH AO1 K W IY2 D +HAWKWOOD HH AO1 K W UH2 D +HAWL HH AO1 L +HAWLEY HH AO1 L IY0 +HAWLEY'S HH AO1 L IY0 Z 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+HAZLEWOOD'S HH EY1 Z AH0 L W UH2 D Z +HAZLIT HH AE1 Z L IH2 T +HAZLIT'S HH AE1 Z L IH0 T S +HAZLITT HH AE1 Z L IH0 T +HAZLITT'S HH AE1 Z L IH0 T S +HAZNA HH EY1 Z N AH0 +HAZOR HH EY1 Z ER0 +HAZRI HH EY1 Z R IY0 +HAZUR HH AE1 Z ER0 +HAZY HH EY1 Z IY0 +HAZZARD HH AE1 Z ER0 D +HAZZARD'S HH AE1 Z ER0 D Z +HAZZARDS HH AE1 Z ER0 D Z +HBLE EY1 CH B AH0 L +HC HH IH0 K +HE HH IY1 +HE' HH IY1 +HE'A HH IY1 AH0 +HE'D HH IY1 D +HE'E HH IY1 +HE'L HH IY1 +HE'LD HH IY1 D +HE'LL HH IY1 L +HE'M HH IY1 M +HE'N HH IY1 AH0 N +HE'P HH IY1 P +HE'PED HH IY1 P T +HE'RT HH IY1 R T +HE'S HH IY1 Z +HE'SA HH IY1 S AH0 +HE'SH HH IY1 SH +HE'TH HH IY1 TH +HE'VE HH IY1 V +HEA HH IY1 +HEA'T HH IY1 T +HEABEN HH IY1 B AH0 N +HEABENLY HH IY1 B AH0 N L IY0 +HEAD HH EH1 D +HEAD'LL HH EH1 D L +HEAD'S HH EH1 D Z +HEADACH HH EH1 D EY2 K +HEADACHE HH EH1 D EY2 K +HEADACHES HH EH1 D EY2 K S +HEADACHY HH EH1 D EY2 K IY0 +HEADBAND HH EH1 D B AE2 N D +HEADBANDS HH EH1 D B AE2 N D Z +HEADBOARD HH EH1 D B AO2 R D +HEADBOARDS HH EH1 D B AO2 R D Z +HEADBOROUGH HH EH1 D B ER0 OW0 +HEADDRESS HH EH1 D R EH2 S +HEADDRESSES HH EH1 D D R EH2 S AH0 Z +HEADE HH EH1 D +HEADED HH EH1 D AH0 D +HEADED HH EH1 D IH0 D +HEADEDLY HH EH1 D AH0 D L IY0 +HEADEDNESS HH EH2 D AH0 D N AH0 S +HEADER HH EH1 D ER0 +HEADERS HH EH1 D ER0 Z +HEADERTON HH EH1 D ER0 T AH0 N +HEADFIRST HH EH1 D F ER1 S T +HEADFORD HH EH1 D F ER0 D +HEADFOREMOST HH EH1 D F AO1 R M OW2 S T +HEADFORT HH EH1 D F ER0 T +HEADFORT'S HH EH1 D F ER0 T S +HEADFORTS HH EH1 D F ER0 T S +HEADGEAR HH EH1 D G IH2 R +HEADIER HH EH1 D IY0 ER0 +HEADIN HH EH1 D AH0 N +HEADINESS HH EH1 D IY0 N AH0 S +HEADING HH EH1 D IH0 NG +HEADINGLY HH EH1 D IH0 NG L IY0 +HEADINGS HH EH1 D IH0 NG Z +HEADINGTON HH EH1 D IH0 NG T AH0 N +HEADKERCHIEF HH EH1 K ER0 CH AH0 F +HEADLAM HH EH1 D L AH0 M +HEADLAMP HH EH1 D L AE1 M P +HEADLAMPS HH EH1 D L AE1 M P S +HEADLAND HH EH1 D L AH0 N D +HEADLAND'S HH EH1 D L AH0 N D Z +HEADLANDS HH EH1 D L AE2 N D Z +HEADLESS HH EH1 D L AH0 S +HEADLEY HH EH1 D L IY0 +HEADLEY'S HH EH1 D L IY0 Z +HEADLIGHT HH EH1 D L AY2 T +HEADLIGHTS HH EH1 D L AY2 T S +HEADLINE HH EH1 D L AY2 N +HEADLINED HH EH1 D L AY2 N D +HEADLINER HH EH1 D L AY2 N ER0 +HEADLINERS HH EH1 D L AY2 N ER0 Z +HEADLINES HH EH1 D L AY2 N Z +HEADLONG HH EH1 D L AO2 NG +HEADMAN HH EH1 D M AH0 N +HEADMAN'S HH EH1 D M AH0 N Z +HEADMASTER HH EH1 D M AE1 S T ER0 +HEADMASTER'S HH EH1 D M AE1 S T ER0 Z +HEADMASTERS HH EH1 D M AE1 S T ER0 Z +HEADMEN HH EH1 D M AH0 N +HEADMISTRESS HH EH1 D M IH2 S T R AH0 S +HEADMOST HH EH1 D M OW2 S T +HEADON HH EH1 D AH0 N +HEADPHONE HH EH1 D F OW2 N +HEADPHONES HH EH1 D F OW2 N Z +HEADPIECE HH EH1 D P IY2 S +HEADPIECES HH EH1 D P IY2 S IH0 Z +HEADQUARTER HH EH1 D K AO2 R T ER0 +HEADQUARTER HH EH1 D K W AO2 R T ER0 +HEADQUARTERS HH EH1 D K AO2 R T ER0 Z +HEADQUARTERS HH EH1 D K W AO2 R T ER0 Z +HEADRIGG HH EH1 D R IH0 G +HEADROOM HH EH1 D R UW2 M +HEADS HH EH1 D Z +HEADSAIL HH EH1 D S EY2 L +HEADSAILS HH EH1 D S EY1 L Z +HEADSET HH EH1 D S EH2 T +HEADSETS HH EH1 D S EH2 T S +HEADSHAKE HH EH1 D SH EY2 K +HEADSHAKES HH EH1 D SH EY2 K S +HEADSHIP HH EH1 D SH IH2 P +HEADSIZE HH EH1 D S AY2 Z +HEADSMAN HH EH1 D Z M AH0 N +HEADSMAN'S HH EH1 D Z M AH0 N Z +HEADSMEN HH EH1 D Z M IH0 N +HEADSTALL HH EH1 D S T AO1 L +HEADSTALLS HH EH1 D S T AO1 L Z +HEADSTONE HH EH1 D S T OW2 N +HEADSTONE'S HH EH1 D S T OW2 N Z +HEADSTONES HH EH1 D S T OW2 N Z +HEADSTRONG HH EH1 D S T R AO2 NG +HEADWAITER HH EH1 D W EY0 T ER0 +HEADWATER HH EH1 D W AO2 T ER0 +HEADWATERS HH EH1 D W AO2 T ER0 Z +HEADWAY HH EH1 D W EY2 +HEADWIND HH EH1 D W IH2 N D +HEADWINDS HH EH1 D W IH2 N D Z +HEADWORK HH EH1 D W ER2 K +HEADY HH EH1 D IY0 +HEAH HH IY1 AH0 +HEAH'S HH IY1 Z +HEAHS HH IY1 Z +HEAL HH IY1 L +HEAL'D HH IY1 L D +HEALD HH IY1 L D +HEALE HH IY1 L +HEALE'S HH IY1 L Z +HEALED HH IY1 L D +HEALER HH IY1 L ER0 +HEALER'S HH IY1 L ER0 Z +HEALERS HH IY1 L ER0 Z +HEALEST HH IY1 L IH0 S T +HEALETH HH IY1 L EH2 TH +HEALEY HH IY1 L IY0 +HEALIN HH IY1 L IH0 N +HEALING HH IY1 L IH0 NG +HEALINGLY HH IY1 L IH0 NG L IY0 +HEALINGS HH IY1 L IH0 NG Z +HEALS HH IY1 L Z +HEALT HH EH1 L T +HEALTH HH EH1 L TH +HEALTH'S HH EH1 L TH S +HEALTHFUL HH EH1 L TH F AH0 L +HEALTHFULLY HH EH1 L TH F AH0 L IY0 +HEALTHFULNESS HH EH1 L TH F AH0 L N AH0 S +HEALTHIER HH EH1 L TH IY0 ER0 +HEALTHIEST HH EH1 L TH IY0 IH0 S T +HEALTHILY HH EH1 L TH AH0 L IY0 +HEALTHINESS HH EH1 L TH IY0 N AH0 S +HEALTHS HH EH1 L TH S +HEALTHY HH EH1 L TH IY0 +HEALY HH IY1 L IY0 +HEALY'S HH IY1 L IY0 Z +HEAP HH IY1 P +HEAP'D HH IY1 P D +HEAPED HH IY1 P T +HEAPETH HH IY1 P IH0 TH +HEAPIN HH IY1 P IH0 N +HEAPING HH IY1 P IH0 NG +HEAPS HH IY1 P S +HEAPT HH IY1 P T +HEAR HH IY1 R +HEAR'D HH ER1 D +HEAR'N HH ER1 AH0 N +HEAR'ST HH ER1 S T +HEAR'T HH ER1 T +HEARD HH ER1 D +HEARD'S HH ER1 D Z +HEARD'ST HH ER1 D S T +HEARDE HH AA1 R D +HEARDEST HH AA1 R D AH0 S T +HEARDST HH ER1 D S T +HEARE HH IY1 R +HEARED HH IY1 R D +HEARER HH IY1 R ER0 +HEARER'S HH IY1 R ER0 Z +HEARERS HH IY1 R ER0 Z +HEAREST HH IH1 R AH0 S T +HEARETH HH IY1 R IH0 TH +HEARIN HH IH1 R IH0 N +HEARING HH IY1 R IH0 NG +HEARINGS HH IY1 R IH0 NG Z +HEARK HH AA1 R K +HEARKEN HH AA1 R K AH0 N +HEARKENED HH AA1 R K AH0 N D +HEARKENETH HH AA1 R K AH0 N EH1 TH +HEARKENING HH AA1 R K AH0 N IH0 NG +HEARKENS HH AA1 R K AH0 N Z +HEARL HH ER1 L +HEARN HH ER1 N +HEARN'S HH ER1 N Z +HEARNE HH ER1 N +HEARNE'S HH ER1 N Z +HEARS HH IY1 R Z +HEARSAY HH IY1 R S EY2 +HEARSAYS HH IY1 R S EY2 Z +HEARSE HH ER1 S +HEARSED HH ER1 S T +HEARSES HH ER1 S IH0 Z +HEARST HH ER1 S T +HEARST'S HH ER1 S T S +HEART HH AA1 R T +HEART' HH AA1 R T +HEART'LL HH AA1 R T AH0 L +HEART'S HH AA1 R T S +HEARTACHE HH AA1 R T EY2 K +HEARTACHES HH AA1 R T EY2 K S +HEARTALL HH AA1 R T AH0 L +HEARTBEAT HH AA1 R T B IY2 T +HEARTBEATS HH AA1 R T B IY2 T S +HEARTBREAK HH AA1 R T B R EY2 K +HEARTBREAKING HH AA1 R T B R EY2 K IH0 NG +HEARTBREAKINGLY HH AA1 R T B R EY2 K IH0 NG L IY0 +HEARTBREAKS HH AA1 R T B R EY2 K S +HEARTBROKEN HH AA1 R T B R OW2 K AH0 N +HEARTBROKENLY HH AA1 R T B R OW2 K AH0 N L IY0 +HEARTBURN HH AA1 R T B ER2 N +HEARTBURNING HH AA1 R T B ER2 N IH0 NG +HEARTBURNINGS HH AA1 R T B ER2 N IH0 NG Z +HEARTED HH AA1 R T AH0 D +HEARTED HH AA1 R T IH0 D +HEARTEDLY HH AA1 R T IH0 D L IY0 +HEARTEDNESS HH AA1 R T IH0 D N AH0 S +HEARTEN HH AA1 R T AH0 N +HEARTENED HH AA1 R T AH0 N D +HEARTENING HH AA1 R T AH0 N IH0 NG +HEARTENING HH AA1 R T N IH0 NG +HEARTENINGLY HH AA1 R T AH0 N IH0 NG L IY0 +HEARTENS HH AA1 R T AH0 N Z +HEARTFELT HH AA1 R T F EH2 L T +HEARTFREE HH AA1 R T F R IY2 +HEARTFREE'S HH AA1 R T F R IY2 Z +HEARTFUL HH AA1 R T F AH0 L +HEARTFULLY HH AA1 R T F AH0 L IY0 +HEARTH HH AA1 R TH +HEARTH'S HH AA1 R TH S +HEARTHRUG HH AA1 R T R AH1 G +HEARTHRUGS HH AA1 R T R AH1 G Z +HEARTHS HH AA1 R TH S +HEARTHSIDE HH AA1 R TH S AY2 D +HEARTHSTONE HH AA1 R TH S T OW2 N +HEARTHSTONES HH AA1 R TH S T OW2 N Z +HEARTIER HH AA1 R T IY0 ER0 +HEARTIES HH AA1 R T IY0 Z +HEARTIEST HH AA1 R T IY0 AH0 S T +HEARTILY HH AA1 R T AH0 L IY0 +HEARTINESS HH AA1 R T IH0 N IH0 S +HEARTING HH AA1 R T IH0 NG +HEARTLESS HH AA1 R T L AH0 S +HEARTLESSLY HH AA1 R T L AH0 S L IY0 +HEARTLESSNESS HH AA1 R T L AH0 S N AH0 S +HEARTLY HH AA1 R T L IY0 +HEARTRENDING HH AA1 R T R EH1 N D IH0 NG +HEARTS HH AA1 R T S +HEARTSEASE HH AA1 R T S IY1 Z +HEARTSELL HH AA1 R T S AH0 L +HEARTSICK HH AA1 R T S IH0 K +HEARTSICKNESS HH AA1 R T S IH2 K N AH0 S +HEARTSOME HH AA1 R T S AH0 M +HEARTSORE HH AA1 R T S ER0 +HEARTSTRINGS HH AA1 R T S T R IH1 NG Z +HEARTWELL HH AA1 R T W EH2 L +HEARTY HH AA1 R T IY0 +HEARTY'S HH AA1 R T IY0 Z +HEARTYS HH AA1 R T IY0 Z +HEASLIP HH IY1 S L IH0 P +HEASY HH IY1 S IY0 +HEAT HH IY1 T +HEAT'S HH IY1 T S +HEATE HH IY1 T +HEATED HH IY1 T AH0 D +HEATED HH IY1 T IH0 D +HEATEDLY HH IY1 T IH0 D L IY0 +HEATER HH IY1 T ER0 +HEATERS HH IY1 T ER0 Z +HEATGUN HH IY1 T G AH2 N +HEATGUNS HH IY1 T G AH2 N Z +HEATH HH IY1 TH +HEATH'S HH IY1 TH S +HEATHBRIDGE HH IY1 TH B R IH2 JH +HEATHCLIFF HH EH1 TH K L IH0 F +HEATHCLIFF'S HH EH1 TH K L IH0 F S +HEATHCOCK HH EH1 TH K AH0 K +HEATHCOTE HH EH1 TH K AH0 T +HEATHCOTE'S HH EH1 TH K AH0 T S +HEATHCOTES HH EH1 TH K AH0 T S +HEATHCOTT HH EH1 TH K AH0 T +HEATHCROFT HH EH1 TH K R AO2 F T +HEATHEN HH IY1 DH AH0 N +HEATHEN'S HH IY1 DH AH0 N Z +HEATHENDOM HH IY1 DH AH0 N D AH0 M +HEATHENESSE HH IY1 DH AH0 N EH1 S +HEATHENISH HH IY1 TH EH1 N IH0 SH +HEATHENISM HH IY1 DH AH0 N IH2 Z AH0 M +HEATHENRY HH IY1 TH EH1 N R IY0 +HEATHENS HH IY1 TH AH0 N Z +HEATHER HH EH1 DH ER0 +HEATHER'S HH EH1 DH ER0 Z +HEATHERBY HH EH1 DH ER0 B IY0 +HEATHERCLIFFE HH EH1 DH ER0 K L IH0 F +HEATHERDALE HH EH1 DH ER0 D EY2 L +HEATHERED HH EH1 DH ER0 D +HEATHERFORD HH EH1 DH ER0 F ER0 D +HEATHERLAND HH EH1 DH ER0 L AH0 N D +HEATHERLANDS HH EH1 DH ER0 L AE2 N D Z +HEATHERLEGH HH EH1 DH ER0 L IH0 G +HEATHERLEIGH HH EH1 DH ER0 L AH0 +HEATHERLY HH EH1 DH ER0 L IY0 +HEATHERPOINT HH EH1 DH ER0 P OY2 N T +HEATHERSTONE HH EH1 DH ER0 S T OW2 N +HEATHERTON HH EH1 DH ER0 T AH0 N +HEATHERY HH EH1 DH ER0 IY0 +HEATHERY'S HH EH1 DH ER0 IY0 Z +HEATHFIELD HH IY1 TH F IY2 L D +HEATHFIELD'S HH IY1 TH F IY2 L D Z +HEATHLAND HH IY1 TH L AE2 N D +HEATHMAN HH IY1 TH M AH0 N +HEATHS HH IY1 TH S +HEATHWELL HH IY1 TH W EH2 L +HEATHY HH IY1 TH IY0 +HEATIN HH IY1 T IH0 N +HEATING HH IY1 T IH0 NG +HEATLESS HH IY1 T L AH0 S +HEATON HH IY1 T AH0 N +HEATON'S HH IY1 T AH0 N Z +HEATS HH IY1 T S +HEAV HH EH1 V +HEAV'D HH EH1 V D +HEAV'N HH EH1 V AH0 N +HEAV'N'S HH EH1 V AH0 N Z +HEAV'NLY HH EH1 V AH0 N L IY0 +HEAV'NS HH EH1 V AH0 N Z +HEAVE HH IY1 V +HEAVED HH IY1 V D +HEAVEN HH EH1 V AH0 N +HEAVEN' HH EH1 V AH0 N +HEAVEN'S HH EH1 V AH0 N Z +HEAVENBORN HH IY1 V IH0 N B ER0 N +HEAVENLIER HH EH1 V AH0 N L IY0 ER0 +HEAVENLIES HH EH1 V AH0 N L IY0 Z +HEAVENLIEST HH EH1 V AH0 N L IY0 AH0 S T +HEAVENLINESS HH EH1 V AH0 N L IY0 N AH0 S +HEAVENLY HH EH1 V AH0 N L IY0 +HEAVENS HH EH1 V AH0 N Z +HEAVENWARD HH EH1 V AH0 N W ER0 D +HEAVENWARDS HH EH1 V AH0 N W ER0 D Z +HEAVER HH IY1 V ER0 +HEAVERS HH IY1 V ER0 Z +HEAVES HH IY1 V Z +HEAVIE HH EH1 V IY0 +HEAVIER HH EH1 V IY0 ER0 +HEAVIES HH EH1 V IY0 Z +HEAVIEST HH EH1 V IY0 AH0 S T +HEAVILY HH EH1 V AH0 L IY0 +HEAVIN HH EH1 V IH0 N +HEAVINESS HH EH1 V IY0 N IH0 S +HEAVING HH IY1 V IH0 NG +HEAVINGS HH IY1 V IH0 NG Z +HEAVISIDE HH EH1 V AH0 S AY2 D +HEAVN HH IY1 V N +HEAVY HH EH1 V IY0 +HEAVY'S HH EH1 V IY0 Z +HEAVYASBRICKS HH EH1 V IY0 AE1 Z B R IH2 K S +HEAVYISH HH EH1 V IY0 IH0 SH +HEAVYSET HH EH1 V IY0 S EH2 T +HEAVYSTERNE HH EH1 V IY0 S T ER1 N +HEAVYSTONE HH EH1 V IY0 S T OW2 N +HEAVYWEIGHT HH EH1 V IY0 W EY2 T +HEAVYWEIGHTS HH EH1 V IY0 W EY2 T S +HEAW HH AO1 +HEB HH EH1 B +HEBBEL HH EH1 B AH0 L +HEBBEN HH EH1 B AH0 N +HEBBERS HH EH1 B ER0 Z +HEBBLETHWAITE HH EH1 B AH0 L TH W EY2 T +HEBBY HH EH1 B IY0 +HEBDOMADAL HH EH1 B D AH0 M AE2 D AH0 L +HEBE HH IY1 B +HEBE'S HH IY1 B Z +HEBEL HH EH1 B AH0 L +HEBEL'S HH EH1 B AH0 L Z +HEBEN HH EH1 B AH0 N +HEBENSTREIT HH EH1 B IH0 N S T R AY0 T +HEBER HH IY1 B ER0 +HEBER'S HH EH1 B ER0 Z +HEBERDEN HH EH1 B ER0 D AH0 N +HEBERLAUF HH EH1 B ER0 L AW0 F +HEBERT HH EH1 B ER0 T +HEBERT'S HH EH1 B ER0 T S +HEBERTISTS HH EH1 B ER0 T IH0 S T S +HEBERTS HH EH1 B ER0 T S +HEBES HH IY1 B Z +HEBETUDE HH EH1 B AH0 T UW2 D +HEBLER HH EH1 B L ER0 +HEBR HH EH1 B R +HEBRAIC HH AH0 B R EY1 IH0 K +HEBRAISM HH EH1 B R EY0 IH2 Z AH0 M +HEBRAIST HH EH1 B R AA1 IH0 S T +HEBRAISTS HH EH1 B R AH0 S +HEBREW HH IY1 B R UW0 +HEBREW'S HH IY1 B R UW2 Z +HEBREWS HH IY1 B R UW2 Z +HEBRID HH EH1 B R IH0 D +HEBRIDEAN HH EH1 B R IH0 D IY0 AH0 N +HEBRIDES HH EH1 B R IH0 D IY0 Z +HEBRON HH EH1 B R AH0 N +HEBRON HH EH1 B R AO2 N +HEBRON'S HH EH1 B R AO2 N Z +HEBRUS HH EH1 B R AH0 S +HEC HH EH1 K +HECATE HH EH1 K EY0 T +HECATE'S HH EH1 K AH0 T S +HECATEUS HH EH1 K AH0 T IY0 AH0 S +HECATOMB HH EH1 K AH0 T UW1 M +HECATOMBS HH EH1 K AH0 T UW1 M Z +HECH HH EH1 K +HECHAM HH EH1 CH AH0 M +HECHNAHOUL HH EH1 K N AH0 UW1 L +HECHO HH EH1 K OW0 +HECHT HH EH1 K T +HECHT'S HH EH1 K T S +HECK HH EH1 K +HECKEPFENNIG HH EH1 K EH0 F EH1 N IH0 G +HECKER HH EH1 K ER0 +HECKEWELDER HH EH0 K Y UW1 W EH2 L D ER0 +HECKEWELDER'S HH EH0 K Y UW1 W EH2 L D ER0 Z +HECKLE HH EH1 K AH0 L +HECKLED HH EH1 K AH0 L D +HECKLEFIELD HH EH1 K AH0 L F IY2 L D +HECKLER HH EH1 K L ER0 +HECKLESTON HH EH1 K AH0 L S T AH0 N +HECKLING HH EH1 K L IH0 NG +HECKSCHER HH EH1 K SH ER0 +HECKSHER HH EH1 K SH ER0 +HECLA HH EH1 K L AH0 +HECTARE HH EH1 K T AA2 R +HECTARES HH EH1 K T AA2 R Z +HECTIC HH EH1 K T IH0 K +HECTICALLY HH EH1 K T IH0 K L IY0 +HECTOGRAPH HH EH1 K T AH0 G R AE2 F +HECTOR HH EH1 K T ER0 +HECTOR'S HH EH1 K T ER0 Z +HECTORED HH EH1 K T ER0 D +HECTORIN HH EH1 K T AH0 R IH0 N +HECTORING HH EH1 K T ER0 IH0 NG +HECTORS HH EH1 K T ER0 Z +HECUBA HH EH1 K Y AH0 B AH0 +HECUBA HH EH1 K Y UW0 B AH0 +HED HH EH1 D +HEDDA HH EH1 D AH0 +HEDDEGAN HH EH1 D AH0 G AH0 N +HEDDEN HH EH1 D AH0 N +HEDDINGTON HH EH1 D IH0 NG T AH0 N +HEDDON HH EH1 D AH0 N +HEDE HH IY1 D +HEDER HH EH1 D ER0 +HEDGE HH EH1 JH +HEDGE'S HH EH1 JH IH0 Z +HEDGED HH EH1 JH D +HEDGEFLOWER HH EH1 JH F L AW2 ER0 +HEDGEHOG HH EH1 JH HH AA2 G +HEDGEHOG'S HH EH1 JH HH AA2 G Z +HEDGEHOGS HH EH1 JH HH AA2 G Z +HEDGELESS HH EH1 JH L AH0 S +HEDGER HH EH1 JH ER0 +HEDGER'S HH EH1 JH ER0 Z +HEDGEROW HH EH1 JH ER0 OW0 +HEDGEROWS HH EH1 JH ER0 OW0 Z +HEDGERS HH EH1 JH ER0 Z +HEDGES HH EH1 JH IH0 Z +HEDGESIDE HH EH1 JH S AY2 D +HEDGEVILLE HH EH1 JH V IH0 L +HEDGING HH EH1 JH IH0 NG +HEDIGER HH EH1 D IH0 G ER0 +HEDIN HH EH1 D IH0 N +HEDINGHAM HH EH1 D IH0 NG HH AE2 M +HEDJAZ HH EH1 JH AA1 Z +HEDLEY HH EH1 D L IY0 +HEDN'T HH EH1 D AH0 N T +HEDON HH IY1 D AH0 N +HEDONISM HH IY1 D AH0 N IH2 Z AH0 M +HEDONIST HH IY1 D AH0 N IH0 S T +HEDONISTIC HH IY2 D AH0 N IH1 S T IH0 K +HEDONISTS HH IY1 D AH0 N IH0 S T S +HEDRICK HH EH1 D R IH0 K +HEDRICK'S HH EH1 D R IH0 K S +HEDRIN HH EH1 D R IH0 N +HEDULIO HH EH1 D UW1 L IY0 OW0 +HEDULIO'S HH EH1 D UW1 L IY0 OW0 Z +HEDVIG HH EH1 D V IH2 G +HEDWIG HH EH1 D W IH0 G +HEDWIG'S HH EH1 D W IH0 G Z +HEDWIGE HH EH1 D W IH0 G +HEDWORTH HH EH1 D W ER2 TH +HEDZOFF HH EH1 D Z AO0 F +HEE HH IY1 +HEE'S HH IY1 Z +HEEAD HH IY1 EH0 D +HEEARD HH IY1 ER0 D +HEED HH IY1 D +HEEDED HH IY1 D AH0 D +HEEDED HH IY1 D IH0 D +HEEDEST HH IY1 D AH0 S T +HEEDETH HH IY1 D IH0 TH +HEEDFUL HH IY1 D F AH0 L +HEEDFULLY HH IY1 D F AH0 L IY0 +HEEDFULNESS HH IY1 D F AH0 L N AH0 S +HEEDIN HH IY1 D IH0 N +HEEDING HH IY1 D IH0 NG +HEEDLESS HH IY1 D L AH0 S +HEEDLESSLY HH IY1 D L AH0 S L IY0 +HEEDLESSNESS HH IY1 D L AH0 S N AH0 S +HEEDS HH IY1 D Z +HEEF HH IY1 F +HEEL HH IY1 L +HEEL'D HH IY1 L D +HEELBARE HH IY1 L B EH2 R +HEELED HH IY1 L D +HEELER HH IY1 L ER0 +HEELER'S HH IY1 L ER0 Z +HEELERS HH IY1 L ER0 Z +HEELES HH IY1 L Z +HEELEY HH IY1 L IY0 +HEELING HH IY1 L IH0 NG +HEELLESS HH IY1 L L AH0 S +HEELS HH IY1 L Z +HEELS'S HH IY1 L Z IH0 Z +HEELTAP HH IY1 L T AE2 P +HEELTAPS HH IY1 L T AE2 P S +HEEM HH IY1 M +HEEMSELF HH IY1 M S EH1 L F +HEEMSKERK HH IY1 M S K ER1 K +HEENAN HH IY1 N AH0 N +HEENY HH IY1 N IY0 +HEENY'S HH IY1 N IY0 Z +HEEP HH IY1 P +HEEP'S HH IY1 P S +HEEPS HH IY1 P S +HEER HH IY1 ER0 +HEER'D HH IH1 R D +HEERA HH IH1 R AH0 +HEERBRAND HH IH1 R B R AE2 N D +HEERD HH IH1 R D +HEERDEN HH IH1 R D AH0 N +HEERDEN'S HH IH1 R D AH0 N Z +HEERE HH IH1 R +HEERED HH IH1 R D +HEEREN HH IH1 R AH0 N +HEERN HH IH1 R N +HEES HH IY1 Z +HEET HH IY1 T +HEF HH EH1 F +HEFFER HH EH1 F ER0 +HEFFERNAN HH EH1 F ER0 N AH0 N +HEFFERNAN'S HH EH1 F ER0 N AH0 N Z +HEFFEROM HH EH1 F ER0 AA1 M +HEFFLEFINGER HH EH1 F AH0 L F IH2 NG G ER0 +HEFFNER HH EH1 F N ER0 +HEFFTERDINGT HH EH1 F T ER0 D IH0 NG T +HEFFY HH EH1 F IY0 +HEFT HH EH1 F T +HEFTED HH EH1 F T IH0 D +HEFTING HH EH1 F T IH0 NG +HEFTS HH EH1 F T S +HEFTY HH EH1 F T IY0 +HEG HH EH1 G +HEGAN HH EH1 G AH0 N +HEGAN'S HH EY1 G AH0 N Z +HEGARTY HH EH1 G AA0 R T IY0 +HEGARTY'S HH EH1 G AA0 R T IY0 Z +HEGAU HH EH1 G OW1 +HEGEL HH EH1 G AH0 L +HEGEL'S HH EH1 G AH0 L Z +HEGELIAN HH IY0 JH IY1 L IY0 AH0 N +HEGELIANISM HH IY0 JH IY1 L IY0 AH0 N IH0 Z AH0 M +HEGELIANS HH IY0 JH IY1 L IY0 AH0 N Z +HEGELMANN HH EH1 G AH0 L M AH0 N +HEGEMONY HH IY0 JH EH1 M AH0 N IY0 +HEGER HH IY1 G ER0 +HEGERMANN HH EH1 G ER0 M AH0 N +HEGESIPPUS HH EH1 G IH0 S IH1 P AH0 S +HEGG HH EH1 G +HEGGIE HH EH1 G IY0 +HEGGS HH EH1 G Z +HEGH HH EH1 G +HEGIRA HH IY0 JH IH1 R AH0 +HEGNER HH EH1 G N ER0 +HEGUMEN HH EH1 G Y AH0 M AH0 N +HEGUMEN'S HH EH1 G Y AH0 M AH0 N Z +HEH HH EH1 +HEHRMANN HH EH1 R M AH0 N +HEHRMANN'S HH EH1 R M AH0 N Z +HEI HH AY1 +HEIAU HH AY1 AW0 +HEIBER HH AY1 B ER0 +HEIBERG HH AY1 B ER0 G +HEID HH AY1 D +HEIDE HH AY1 D +HEIDECK HH AY1 D IH0 K +HEIDEGARDS HH AY1 D IH0 G AA1 R D Z +HEIDEGGER HH AY1 D IH0 G ER0 +HEIDEGGER'S HH AY1 D IH0 G ER0 Z +HEIDEL HH AY1 D AH0 L +HEIDELBERG HH AY1 D AH0 L B ER0 G +HEIDELMANN HH AY1 D AH0 L M AH0 N +HEIDENDORF HH AY1 D IH0 N D AO0 R F +HEIDI HH AY1 D IY0 +HEIDLEMANN HH AY1 D AH0 L M AH0 N +HEIDLEMANNS HH AY1 D AH0 L M AH0 N Z +HEIDREK HH AY1 D R EH0 K +HEIDREK'S HH AY1 D R EH2 K S +HEIDS HH AY1 D Z +HEIDSIECK HH AY1 D S IY1 K +HEIDT HH AY1 D T +HEIF HH IY1 F +HEIFER HH AY1 F ER0 +HEIFER HH EH1 F ER0 +HEIFER'S HH EH1 F ER0 Z +HEIFERS HH AY1 F ER0 Z +HEIFERS HH EH1 F ER0 Z +HEIFETZ HH AY1 F IH0 T S +HEIGH HH AY1 +HEIGHAM HH AY1 AH0 M +HEIGHO HH AY1 OW0 +HEIGHT HH AY1 T +HEIGHTEN HH AY1 T AH0 N +HEIGHTENED HH AY1 T AH0 N D +HEIGHTENING HH AY1 T AH0 N IH0 NG +HEIGHTENING HH AY1 T N IH0 NG +HEIGHTENS HH AY1 T AH0 N Z +HEIGHTH HH AY1 TH +HEIGHTS HH AY1 T S +HEIGHTY HH AY1 T IY0 +HEIKE HH AY1 K +HEIL HH AY1 L +HEILBORN HH AY1 L B ER0 N +HEILBRON HH AY1 L B R AH0 N +HEILBRONN HH AY1 L B R AA1 N +HEILIG HH AY1 L IH0 G +HEILIGE HH AY1 L IH0 JH +HEILIGEN HH AY1 L IH0 JH EH0 N +HEILIGENBERG HH AY1 L IH0 G AH0 N B ER0 G +HEILIGENGELT HH AY1 L IH0 G EH1 NG G IH0 L T +HEILIGENSTADT HH AY1 L IH0 G AH0 N S T AE2 T +HEILIGENSTERN HH AY1 L IH0 G AH0 N S T ER0 N +HEILMANN HH AY1 L M AH0 N +HEILSBERG HH AY1 L Z B ER0 G +HEIM HH AY1 M +HEIM'S HH AY2 M Z +HEIMA HH AY1 M AH0 +HEIMAN HH AY1 M AH0 N +HEIMAT HH AY1 M AE0 T +HEIMBERT HH AY1 M B ER0 T +HEIMBURG HH AY1 M B ER0 G +HEIMCHEN HH AY1 M CH AH0 N +HEIMDAL HH AY1 M D AH0 L +HEIMDALL HH AY1 M D AH0 L +HEIMERT HH AY1 M ER0 T +HEIMRA HH AY1 M R AH0 +HEIMSKRINGLA HH AY1 M S K R IH1 NG G L AH0 +HEIMWEH HH AY1 M W EH2 +HEIN HH AY1 N +HEINDE HH AY1 N D +HEINE HH AY1 N +HEINE'S HH AY1 N Z +HEINECKE HH AY1 N IH0 K +HEINECKEN HH AY1 N IH0 K AH0 N +HEINEMAN HH AY1 N M AH0 N +HEINEMANN HH AY1 N M AH0 N +HEINEMANN'S HH AY1 N M AH0 N Z +HEINFETTER HH AY1 N F EH1 T ER0 +HEINHOLD HH AY1 N HH OW0 L D +HEINIE HH AY1 N IY0 +HEINIES HH AY1 N IY0 Z +HEINK HH AY1 NG K +HEINOUS HH EY1 N AH0 S +HEINOUSLY HH EY1 N AH0 S L IY0 +HEINOUSNESS HH EY1 N AH0 S N AH0 S +HEINRICH HH AY1 N R IH0 K +HEINRICH'S HH AY1 N R IH0 K S +HEINRICK HH AY1 N R IH0 K +HEINSIUS HH AY1 N S IY0 AH0 S +HEINTZELMAN HH AY1 N T Z AH0 L M AH0 N +HEINTZELMAN'S HH AY1 N T Z AH0 L M AH0 N Z +HEINY HH AY1 N IY0 +HEINZ HH AY1 N Z +HEINZ'S HH AY1 N Z IH0 Z +HEINZE HH AY1 N Z +HEINZEL HH AY1 N Z AH0 L +HEINZLEMAN HH AY1 N Z AH0 L M AH0 N +HEINZMAN HH AY1 N Z M AH0 N +HEINZMAN'S HH AY1 N Z M AH0 N Z +HEIR EH1 R +HEIR'S EH1 R Z +HEIRE HH AY0 R +HEIRED EH1 R D +HEIRESS EH1 R AH0 S +HEIRESS'S EH1 R AH0 S IH0 Z +HEIRESSES EH1 R AH0 S IH0 Z +HEIRI EH1 R IY0 +HEIRI'S EH1 R IY0 Z +HEIRLESS EH1 R L AH0 S +HEIRLOOM EH1 R L UW2 M +HEIRLOOMS EH1 R L UW2 M Z +HEIRS EH1 R Z +HEIRSHIP EH1 R SH IH2 P +HEISE HH AY1 S +HEISH HH IY1 IH0 SH +HEISSE HH AY1 S +HEISSLER HH AY1 S L ER0 +HEITMAN HH AY1 T M AH0 N +HEITZ HH AY1 T S +HEIZER HH AY1 Z ER0 +HEJ HH EH1 JH +HEJAZ HH EH1 JH AA1 Z +HEK HH EH0 K +HEKALU HH EH2 K AA1 L UW0 +HEKALU'S HH EH2 K AH0 L UW2 Z +HEKE HH IH0 K +HEKE'S HH EH2 K S +HEKEKIAN HH EH2 K EY1 K IY0 AH0 N +HEKEKYAN HH EH1 K AH0 K Y AH0 N +HEKKA HH EH1 K AH0 +HEKKADOR HH EH1 K AH0 D AO2 R +HEKKI HH EH1 K IY0 +HEKT HH EH2 K T +HEL HH EH1 L +HEL'S HH EH1 L Z +HELA HH EH1 L AH0 +HELA'S HH EH1 L AH0 Z +HELAS HH EH1 L AH0 S +HELBECK HH EH1 L B EH2 K +HELBECK'S HH EH1 L B EH2 K S +HELBERSON HH EH1 L B ER0 S AH0 N +HELBON HH EH1 L B AH0 N +HELCA HH EH1 L K AH0 +HELD HH EH1 L D +HELDA HH EH1 L D AH0 +HELDAR HH EH1 L D AA2 R +HELDBURG HH EH1 L D B ER0 G +HELDEN HH EH1 L D AH0 N +HELDENFELD HH EH1 L D IH0 N F EH0 L D +HELDER HH EH1 L D ER0 +HELDEREN HH EH1 L D IH1 R AH0 N +HELDERS HH EH1 L D ER0 Z +HELDEST HH EH1 L D AH0 S T +HELDING HH EH1 L D IH0 NG +HELDON HH EH1 L D AH0 N +HELDRE HH EH1 L D R +HELDRE'S HH EH1 L D R Z +HELE HH EH1 L +HELEIGH HH EH1 L AH0 +HELEN HH EH1 L AH0 N +HELEN'S HH EH1 L IH0 N Z +HELENA HH EH1 L AH0 N AH0 +HELENA'S HH EH1 L IH0 N AH0 Z +HELENE HH AH0 L IY1 N +HELENE'S HH AH0 L IY1 N Z +HELENS HH EH1 L AH0 N Z +HELENSVILLE HH EH1 L AH0 N Z V IH2 L +HELENUS HH AH0 L IY1 N AH0 S +HELEPHANT HH EH1 L AH0 F AH0 N T +HELEPHANTS HH EH1 L AH0 F AH0 N T S +HELFEN HH EH1 L F AH0 N +HELFFERICH HH EH1 L F ER0 IH0 K +HELFORD HH EH1 L F ER0 D +HELGA HH EH1 L G AH0 +HELGA'S HH EH1 L G AH0 Z +HELGE HH EH1 L JH +HELGE'S HH EH1 L G Z +HELGELAND HH EH1 L G IH0 L AH0 N D +HELGERS HH EH1 L G ER0 Z +HELGI HH EH1 L G IY0 +HELGI'S HH EH1 L G IY0 Z +HELGOLAND HH EH1 L G OW1 L AH0 N D +HELHEIM HH EH1 L HH AY2 M +HELI HH EH1 L IY0 +HELIACAL HH IY1 L IY0 AE2 K AH0 L +HELIANTHUS HH IY1 L IY0 AH0 N TH AH0 S +HELICAL HH EH1 L IH0 K AH0 L +HELICANUS HH EH1 L IH0 K AH0 N AH0 S +HELICES HH EH1 L IH0 S IH0 Z +HELICON HH EH1 L IH0 K AA2 N +HELICON'S HH EH1 L IH0 K AA2 N Z +HELICONIAN HH EH1 L IH0 K OW1 N IY0 AH0 N +HELICONIAS HH EH1 L IH0 K AA2 N IY0 AH0 Z +HELICOPTER HH EH1 L IH0 K AA2 P T ER0 +HELICOPTERS HH EH1 L IH0 K AA2 P T ER0 Z +HELIDOR HH IY1 L IY0 D AO1 R +HELIE HH EH1 L IY0 +HELIER HH IY1 L IY0 ER0 +HELIER'S HH IY1 L IY0 ER0 Z +HELIERS HH EH1 L Y ER0 Z +HELIET HH IY1 L IY0 EH2 T +HELIET'S HH IY1 L IY0 EH2 T S +HELIGOLAND HH EH1 L IH0 G AA1 L AH0 N D +HELIM HH EH1 L AH0 M +HELIO HH IY1 L IY0 OW0 +HELIOBAS HH IY1 L IY0 AA2 B AH0 Z +HELIOCENTRIC HH IY2 L IY0 OW0 S EH1 N T R IH0 K +HELIODORA HH IY1 L IY0 AH0 D AO1 R AH0 +HELIODORE HH IY1 L IY0 AH0 D AO2 R +HELIODORUS HH IY1 L IY0 AH0 D AO1 R AH0 S +HELIOGABALUS HH IY2 L IY0 AA1 G AH0 B AH0 L AH0 S +HELIOGRAPH HH IY1 L IY0 AH0 G R AE2 F +HELIOGRAPHED HH IY1 L IY0 AH0 G R AE2 F T +HELIOGRAPHIC HH IY1 L IY0 AH0 G R AE1 F IH0 K +HELIOGRAPHING HH IY2 L IY0 AA1 G R AH0 F IH0 NG +HELIOGRAPHS HH IY1 L IY0 AH0 G R AE2 F S +HELIOPOLIS HH IY2 L IY0 AA1 P AH0 L AH0 S +HELIOPOLITAN HH IY2 L IY0 AA1 P AH0 L IH1 T AH0 N +HELIOS HH IY1 L IY0 AA2 S +HELIOT HH IY1 L IY0 AH0 T +HELIOTROPE HH IY1 L IY0 AH0 T R OW2 P +HELIOTROPES HH IY1 L IY0 AH0 T R OW2 P S +HELIUM HH IY1 L IY0 AH0 M +HELIUM'S HH IY1 L IY0 AH0 M Z +HELIUMITE HH IY1 L IY0 AH0 M AY2 T +HELIUS HH IY1 L IY0 IH0 S +HELIX HH IY1 L IH0 K S +HELKA HH EH1 L K AH0 +HELL HH EH1 L +HELL' HH EH1 L +HELL'S HH EH1 L Z +HELLA HH EH1 L AH0 +HELLAJ HH EH1 L AY2 +HELLANICUS HH EH1 L AE1 N AH0 K AH0 S +HELLAR HH EH1 L ER0 +HELLAS HH EH1 L AH0 Z +HELLBEAM HH EH1 L B IY2 M +HELLBEAM'S HH EH1 L B IY2 M Z +HELLDORF HH EH1 L D AO0 R F +HELLE HH EH1 L +HELLEBERGENE HH EH1 L AH0 B ER0 JH IY2 N +HELLEBORE HH EH1 L AH0 B AO0 R +HELLELIL HH EH1 L AH0 L IH1 L +HELLEN HH EH1 L AH0 N +HELLENA HH EH1 L IH0 N AH0 +HELLENE HH EH1 L IY2 N +HELLENES HH EH1 L IY2 N Z +HELLENIC HH AH0 L EH1 N IH0 K +HELLENISM HH EH1 L AH0 N IH2 Z AH0 M +HELLENIST HH EH1 L AH0 N IH0 S T +HELLENISTIC HH EH2 L AH0 N IH1 S T IH0 K +HELLENISTS HH EH1 L AH0 N IH0 S T S +HELLENIZED HH EH1 L AH0 N AY2 Z D +HELLENS HH EH1 L AH0 N Z +HELLENY HH EH1 L AH0 N IY0 +HELLER HH EH1 L ER0 +HELLERAU HH EH1 L ER0 AW0 +HELLERS HH EH1 L ER0 Z +HELLES HH EH1 L Z +HELLESPONT HH EH1 L AH0 S P AA2 N T +HELLESTON HH EH1 L AH0 S T AH0 N +HELLFDE HH EH1 L F D +HELLFIRE HH EH1 L F AY2 R +HELLHOUND HH EH1 L HH AW2 N D +HELLHOUNDS HH EH1 L HH AW2 N D Z +HELLICANUS HH EH1 L IH0 K AH0 N AH0 S +HELLIFIELD HH EH1 L IH0 F IY2 L D +HELLING HH EH1 L IH0 NG +HELLINGFORD HH EH1 L IH0 NG F ER0 D +HELLINGSLEY HH EH1 L IH0 NG Z L IY0 +HELLION HH EH1 L Y AH0 N +HELLIONS HH EH1 L Y AH0 N Z +HELLISH HH EH1 L IH0 SH +HELLISHLY HH EH1 L IH0 SH L IY0 +HELLISHNESS HH EH1 L IH0 SH N AH0 S +HELLNER HH EH1 L N ER0 +HELLO HH AH0 L OW1 +HELLO HH EH0 L OW1 +HELLOA HH AH0 L OW1 AH0 +HELLOS HH EH1 L OW0 Z +HELLS HH EH1 L Z +HELLUM HH EH1 L AH0 M +HELLUP HH EH1 L AH0 P +HELLUVA HH EH2 L UW1 V AH0 +HELLWARD HH EH1 L W ER0 D +HELLWARDS HH EH1 L W ER0 D Z +HELLWIG HH EH1 L W IH0 G +HELLY HH EH1 L IY0 +HELLYER HH EH1 L IY0 ER0 +HELM HH EH1 L M +HELM'S HH EH1 L M Z +HELMA HH EH1 L M AH0 +HELMA'S HH EH1 L M AH0 Z +HELMAN HH EH1 L M AH0 N +HELMANOFF HH EH1 L M AH0 N AO0 F +HELMAR HH EH1 L M ER0 +HELMAS HH EH1 L M AH0 Z +HELMBOE HH EH1 L M B OW0 +HELMBRECHT HH EH1 L M B R IH0 K T +HELMED HH EH1 L M D +HELMER HH EH1 L M ER0 +HELMER'S HH EH1 L M ER0 Z +HELMET HH EH1 L M AH0 T +HELMET'S HH EH1 L M AH0 T S +HELMETED HH EH1 L M AH0 T IH0 D +HELMETH HH EH1 L M AH0 TH +HELMETS HH EH1 L M AH0 T S +HELMHOLTZ HH EH1 L M HH OW2 L T S +HELMINE HH EH1 L M IH0 N +HELMLESS HH EH1 L M L AH0 S +HELMONT HH EH1 L M AA2 N T +HELMS HH EH1 L M Z +HELMSDALE HH EH1 L M Z D EY2 L +HELMSLEY HH EH1 L M Z L IY0 +HELMSLEY'S HH EH1 L M Z L IY0 Z +HELMSMAN HH EH1 L M Z M AE2 N +HELMSMAN'S HH EH1 L M Z M AE2 N Z +HELMSMEN HH EH1 L M Z M IH0 N +HELMSTONE HH EH1 L M S T OW2 N +HELMSTONE'S HH EH1 L M S T OW2 N Z +HELMSTONES HH EH1 L M S T OW2 N Z +HELMSWOMAN HH EH1 L M Z W UH2 M AH0 N +HELMSWORTH HH EH1 L M Z W ER2 TH +HELMUND HH EH1 L M AH0 N D +HELMUT HH EH1 L M AH0 T +HELMUTH HH EH1 L M UW2 TH +HELMY HH IH0 L M IY0 +HELOISE HH EH1 L OW0 IY0 Z +HELOISE'S HH EH1 L OW0 IY0 Z IH0 Z +HELON HH EH1 L AH0 N +HELOT HH EH1 L AH0 T +HELOTS HH EH1 L AH0 T S +HELOUAN HH EH1 L AH0 W AA1 N +HELOUIN HH EH1 L W IY1 N +HELP HH EH1 L P +HELP'D HH EH1 L P D +HELP'S HH EH1 L P S +HELPE HH EH1 L P +HELPED HH EH1 L P T +HELPER HH EH1 L P ER0 +HELPER'S HH EH1 L P ER0 Z +HELPERS HH EH1 L P ER0 Z +HELPEST HH EH1 L P AH0 S T +HELPETH HH EH1 L P IH0 TH +HELPFUL HH EH1 L P F AH0 L +HELPFULLY HH EH1 L P F AH0 L IY0 +HELPFULNESS HH EH1 L P F AH0 L N AH0 S +HELPIN HH EH1 L P IH0 N +HELPING HH EH1 L P IH0 NG +HELPINGS HH EH1 L P IH0 NG Z +HELPIT HH EH1 L P IH0 T +HELPLESS HH EH1 L P L AH0 S +HELPLESSLY HH EH1 L P L AH0 S L IY0 +HELPLESSNESS HH EH1 L P L AH0 S N AH0 S +HELPMAKAAR HH EH1 L P M AH0 K AA1 R +HELPMAN HH EH1 L P M AH0 N +HELPMATE HH EH1 L P M EY2 T +HELPMATES HH EH1 L P M EY2 T S +HELPMEET HH EH1 L P M IY1 T +HELPMEETS HH EH1 L P M IY1 T S +HELPS HH EH1 L P S +HELPSTON HH EH1 L P S T AH0 N +HELPSTONE HH EH1 L P S T OW2 N +HELPT HH EH1 L P T +HELSA HH EH1 L S AH0 +HELSETH HH EH1 L S IH0 TH +HELSING HH EH1 L S IH0 NG +HELSING'S HH EH1 L S IH0 NG Z +HELSINGBORG HH EH1 L S IH0 NG B AO0 R G +HELSINGFORS HH EH1 L S IH0 NG F ER0 Z +HELSINGHAM HH EH1 L S IH0 NG HH AE2 M +HELST HH EH1 L S T +HELSTON HH EH1 L S T AH0 N +HELSTONE HH EH1 L S T OW2 N +HELSTONLEIGH HH EH1 L S OW2 N L AH0 +HELT HH EH1 L T +HELTER HH EH1 L T ER0 +HELTH HH EH1 L TH +HELU HH EH1 L UW0 +HELUVA HH EH1 L UW1 V AH0 +HELVA HH EH1 L V AH0 +HELVE HH EH1 L V +HELVELLYN HH EH1 L V EH1 L IH0 N +HELVES HH EH1 L V Z +HELVETIA HH EH1 L V AH0 T Y AH0 +HELVETIAN HH EH1 L V IY1 SH AH0 N +HELVETIANS HH EH1 L V IY1 SH Y IH0 N Z +HELVETIC HH EH1 L V AH0 T IH2 K +HELVETII HH EH1 L V AH0 T IY0 +HELVETIUS HH EH1 L V AH0 T IY0 IH0 S +HELVIDIUS HH EH1 L V IH0 D IY0 AH0 S +HELVOETSLUYS HH EH1 L V OW1 S L AY1 Z +HELWYSE HH EH1 L W AY0 S +HELWYSE'S HH EH1 L W AY1 Z IH0 Z +HELY HH EH1 L IY0 +HELZEPHRON HH EH1 L Z EH1 F R AA0 N +HELZEPHRON'S HH EH1 L Z EH1 F R AA0 N Z +HEM HH EH1 M +HEM'D HH EH1 M D +HEMAN HH IY1 M AH0 N +HEMAN'S HH EH1 M AH0 N Z +HEMANGINI HH EH1 M AA0 NG G IY1 N IY0 +HEMANS HH EH1 M AH0 N Z +HEMANS'S HH EH1 M AH0 N Z IH0 Z +HEMANTA HH AH0 M AA1 N T AH0 +HEMATITE HH EH1 M AH0 T AY2 T +HEMBURG HH EH1 M B ER0 G +HEMENWAY HH EH1 M AH0 N W EY2 +HEMERLINGUE HH EH1 M ER0 L IH0 NG +HEMERLINGUE'S HH EH1 M ER0 L IH0 NG Z +HEMI HH EH1 M IY0 +HEMICYCLE HH EH2 M AH0 S AY2 K AH0 L +HEMING HH EH1 M IH0 NG +HEMINGE HH EH1 M IH0 NG +HEMINGWAY HH EH1 M IH0 NG W EY2 +HEMINGWAY'S HH EH1 M IH0 NG W EY2 Z +HEMINGWAYS HH EH1 M IH0 NG W EY2 Z +HEMINWAY HH EH1 M IH0 N W EY2 +HEMIPLEGIA HH EH2 M AH0 P L IY1 JH IY0 AH0 +HEMIPLEGIA HH EH2 M AH0 P L IY1 JH Y AH0 +HEMIPO HH EH2 M AH0 P OW0 +HEMIPTERA HH EH2 M AH0 P T EH1 R AH0 +HEMISPHERE HH EH1 M IH0 S F IH2 R +HEMISPHERES HH EH2 M AH0 S F IH1 R Z +HEMISPHERIC HH EH2 M AH0 S F IH1 R IH0 K +HEMISPHERICAL HH EH2 M AH0 S F EH1 R IH0 K AH0 L +HEMLOCK HH EH1 M L AA2 K +HEMLOCKS HH EH1 M L AA2 K S +HEMM'D HH EH1 M D +HEMMED HH EH1 M D +HEMMELINE HH EH1 M IH0 L AY2 N +HEMMENSHAW HH EH1 M IH0 N SH AO2 +HEMMING HH EH1 M IH0 NG +HEMMING'S HH EH1 M IH0 NG Z +HEMMINGS HH EH1 M IH0 NG Z +HEMMINGWAY HH EH1 M IH0 NG W EY2 +HEMMINGWELL HH EH1 M IH0 NG W EH2 L +HEMMY HH EH1 M IY0 +HEMOGLOBIN HH IY2 M AH0 G L OW1 B AH0 N +HEMORRHAGE HH EH1 M ER0 IH0 JH +HEMORRHAGE HH EH1 M R AH0 JH +HEMORRHAGES HH EH1 M ER0 IH0 JH IH0 Z +HEMORRHOIDS HH EH1 M ER0 OY2 D Z +HEMP HH EH1 M P +HEMPEL HH EH1 M P AH0 L +HEMPEL'S HH EH1 M P AH0 L Z +HEMPEN HH EH1 M P AH0 N +HEMPFIELD HH EH1 M P F IY2 L D +HEMPHILL HH EH1 M P HH IH2 L +HEMPIE HH EH1 M P IY0 +HEMPSEED HH EH1 M P S IY0 D +HEMPSTEAD HH EH1 M P S T EH0 D +HEMS HH EH1 M Z +HEMSON HH EH1 M S AH0 N +HEMSTITCH HH EH1 M S T IH0 CH +HEMSTITCHED HH EH1 M S T IH1 CH T +HEMSTITCHING HH EH1 M S T IH1 CH IH0 NG +HEMTI HH EH1 M T IY0 +HEMU HH EH1 M UW0 +HEMYOCK HH EH1 M IY0 AH0 K +HEN HH EH1 N +HEN'S HH EH1 N Z +HENA HH EH1 N AH0 +HENARES HH EH1 N ER0 Z +HENAREZ HH EH1 N ER0 EH2 Z +HENAULT HH EH1 N AW0 L T +HENBANE HH EH1 N B EY2 N +HENCASTLE HH EH1 N K AE2 S AH0 L +HENCE HH EH1 N S +HENCEFORTH HH EH1 N S F AO1 R TH +HENCEFORWARD HH EH1 N S F AO1 R W ER0 D +HENCEFORWARDS HH EH1 N S F AO1 R W ER0 D Z +HENCHARD HH EH1 N CH ER0 D +HENCHARD'S HH EH1 N CH ER0 D Z +HENCHMAN HH EH1 N CH M AH0 N +HENCHMAN'S HH EH1 N CH M AH0 N Z +HENCHMEN HH EH1 N CH M AH0 N +HENCHY HH EH1 N CH IY0 +HENCKEL HH EH1 N K AH0 L +HENCKLEY HH EH1 NG K L IY0 +HENCOOP HH EH1 N K UW1 P +HENCOOPS HH EH1 N K UW1 P S +HEND HH EH1 N D +HENDAYE HH EH1 N D EY1 +HENDER HH EH1 N D ER0 +HENDER'S HH EH1 N D ER0 Z +HENDERLAND HH EH1 N D ER0 L AH0 N D +HENDERLEY HH EH1 N D ER0 L IY0 +HENDERLIN HH EH1 N D ER0 L IH0 N +HENDERS HH EH1 N D ER0 Z +HENDERSON HH EH1 N D ER0 S AH0 N +HENDERSON'S HH EH1 N D ER0 S AH0 N Z +HENDERSONS HH EH1 N D ER0 S AH0 N Z +HENDERSONVILLE HH EH1 N D ER0 S AH0 N V IH2 L +HENDIE HH EH1 N D IY0 +HENDON HH EH1 N D OW0 N +HENDREN HH EH1 N D ER0 AH0 N +HENDRIC HH EH1 N D R IH0 K +HENDRICK HH EH1 N D R IH0 K +HENDRICK'S HH EH1 N D R IH0 K S +HENDRICKS HH EH1 N D R IH0 K S +HENDRIE HH EH1 N D ER0 IY0 +HENDRIE'S HH EH1 N D ER0 IY0 Z +HENDRIK HH EH1 N D R IH0 K +HENDRIK'S HH EH1 N D R IH0 K S +HENDRIKA HH EH1 N D R IY1 K AH0 +HENDRIKSEN HH EH1 N D R IH0 K S AH0 N +HENDRIX HH EH1 N D R IH0 K S +HENDRY HH EH1 N D R IY0 +HENDRY'S HH EH1 N D R IY0 Z +HENDRYX HH EH1 N D R IH0 K S +HENDY HH EH1 N D IY0 +HENEAGE HH EH1 N IY0 AH0 JH +HENERY HH EH1 N ER0 IY0 +HENERY'S HH EH1 N ER0 IY0 Z +HENEY HH EH1 N IY0 +HENEY'S HH EH1 N IY0 Z +HENFREY HH EH1 N F R IY0 +HENG HH EH1 NG +HENGEST HH EH1 NG G AH0 S T +HENGIST HH EH1 N JH IH0 S T +HENGLAND HH EH1 NG G L AH0 N D +HENGLER'S HH EH1 NG L ER0 Z +HENGLISH HH EH1 NG G AH0 L IH0 SH +HENGLY HH EH1 NG L IY0 +HENGO HH EH1 NG G OW0 +HENGSTENBERG HH EH1 NG G S T AH0 N B ER0 G +HENHOUSE HH EH1 N HH AW2 S +HENKE HH EH1 NG K +HENKEL HH EH1 NG K AH0 L +HENKEL'S HH EH1 NG K AH0 L Z +HENKER HH EH1 NG K ER0 +HENLE HH EH1 N AH0 L +HENLEIGH HH EH1 N L AH0 +HENLEY HH EH1 N L IY0 +HENLEY'S HH EH1 N L IY0 Z +HENLOPEN HH EH1 N L OW1 P AH0 N +HENLY HH EH1 N L IY0 +HENN HH EH1 N +HENNA HH EH1 N AH0 +HENNE HH EH1 N +HENNE HH EH1 N IY0 +HENNEBON HH EH1 N IH0 B AO0 N +HENNELEINLEIN HH EH1 N AH0 L AY1 N L AY2 N +HENNELL HH EH1 N AH0 L +HENNEPIN HH EH1 N IH0 P IH0 N +HENNEPIN'S HH EH1 N IH0 P IH0 N Z +HENNEQUIN HH EH1 N AH0 K IH0 N +HENNER HH EH1 N ER0 +HENNERY HH EH1 N ER0 IY0 +HENNESEY HH EH1 N IH0 S IY0 +HENNESSEY HH EH1 N AH0 S IY0 +HENNESSEY'S HH EH1 N AH0 S IY0 Z +HENNESSY HH EH1 N AH0 S IY0 +HENNESSY'S HH EH1 N IH0 S IY0 Z +HENNESY HH EH1 N AH0 S IY0 +HENNEY HH EH1 N IY0 +HENNIBUL HH EH1 N IY0 B UW1 L +HENNIE HH EH1 N IY0 +HENNIFER HH EH1 N IH0 F ER0 +HENNIKER HH EH1 N IH0 K ER0 +HENNIKER'S HH EH1 N IH0 K ER0 Z +HENNIKERS HH EH1 N IH0 K ER0 Z +HENNING HH EH1 N IH0 NG +HENNINGER HH EH1 N IH0 NG ER0 +HENNINGS HH EH1 N IH0 NG Z +HENNINGSEN HH EH1 N IH0 NG S AH0 N +HENNION HH EH1 N Y AH0 N +HENNO HH EH1 N OW0 +HENNY HH EH1 N IY0 +HENNY'S HH EH1 N IY0 Z +HENO HH EH1 N OW0 +HENORY HH EH1 N ER0 IY0 +HENPECK HH EH1 N P EH2 K +HENPECKED HH EH1 N P EH2 K T +HENRI AA2 N R IY1 +HENRI AO2 R IY1 +HENRI HH EH1 N R IY0 +HENRI'S HH EH2 N R IY0 Z +HENRIADE HH EH2 N R IY0 EY2 D +HENRICA HH EH1 N R IH0 K AH0 +HENRICA'S HH EH1 N R IH0 K AH0 Z +HENRICI AA0 N R IY1 CH IY0 +HENRICO AA0 N R IY1 K OW0 +HENRICUS HH EH1 N R IH0 K AH0 S +HENRIE HH EH1 N ER0 IY0 +HENRIES HH EH1 N ER0 IY0 Z +HENRIET HH EH2 N R IY0 EH1 T +HENRIETTA HH EH2 N R IY0 EH1 T AH0 +HENRIETTA'S HH EH2 N R IY0 EH1 T AH0 Z +HENRIETTE HH EH2 N R IY0 EH1 T +HENRIETTE'S HH EH2 N R IY0 EH1 T S +HENRIK HH EH1 N R IH0 K +HENRIK'S HH EH1 N R IH0 K S +HENRIKSEN HH EH1 N R IH0 K S AH0 N +HENRIKSEN'S HH EH1 N R IH0 K S AH0 N Z +HENRIOT HH EH2 N R IY0 AH0 T +HENRIOT'S HH EH2 N R IY0 AH0 T S +HENRIQUE AA0 N R IY1 K +HENRIQUES AA0 N R IY1 K +HENRIQUES HH EH0 N R IY1 K EH0 Z +HENRIQUEZ AA0 N R IY1 K +HENRIQUEZ HH EH0 N R IY1 K EH0 Z +HENROOST HH EH1 N R UW1 S T +HENROOSTS HH EH1 N R UW1 S T S +HENRY HH EH1 N R IY0 +HENRY'LL HH EH1 N ER0 IY0 AH0 L +HENRY'S HH EH1 N R IY0 Z +HENRYK HH EH1 N ER0 IH0 K +HENRYS HH EH1 N ER0 IY0 Z +HENS HH EH1 N Z +HENSCHEL HH EH1 N SH AH0 L +HENSE HH EH1 N S +HENSEL HH EH1 N S AH0 L +HENSELT HH EH1 N S AH0 L T +HENSEN HH EH1 N S AH0 N +HENSHAW HH EH1 N SH AO2 +HENSHAW'S HH EH1 N SH AO2 Z +HENSHAWS HH EH1 N SH AO2 Z +HENSLAER HH EH1 N Z L IH1 R +HENSLER HH EH1 N S AH0 L ER0 +HENSLER HH EH1 N S L ER0 +HENSLEY HH EH1 N Z L IY0 +HENSLOW HH EH1 N S L OW2 +HENSLOW'S HH EH1 N S L OW2 Z +HENSLOWE HH EH1 N S L OW2 +HENSLOWE'S HH EH1 N S L OW1 Z +HENSON HH EH1 N S AH0 N +HENSON'S HH EH1 N S AH0 N Z +HENSOR HH EH1 N S ER0 +HENSTEAD HH EH1 N S T EH2 D +HENT HH EH1 N T +HENTIG HH EH1 N T IH0 G +HENTLEY HH EH1 N T L IY0 +HENTY HH EH1 N T IY0 +HENTY'S HH EH1 N IY0 Z +HENTYS HH EH1 N IY0 Z +HENTZ HH EH1 N T S +HENTZAU HH EH1 N T S OW1 +HENTZI HH EH1 N T Z IY0 +HENTZI'S HH EH1 N T Z IY0 Z +HENWIFE HH EH1 N W AY2 F +HENYARD HH EH1 N Y AA2 R D +HENZEL HH EH1 N Z AH0 L +HENZY HH EH1 N Z IY0 +HENZY'S HH EH1 N Z IY0 Z +HEOROT HH IY2 AH0 R OW1 +HEOW HH IY1 OW0 +HEP HH EH1 P +HEPATIC HH AH0 P AE1 T IH0 K +HEPATICA HH AH0 P AE1 T IH0 K AH0 +HEPATICAS HH AH0 P AE1 T IH0 K AH0 S +HEPBURN HH EH1 P B ER0 N +HEPBURN'S HH EH1 P B ER0 N Z +HEPE HH EH1 P IY0 +HEPE'S HH EH1 P S +HEPHAESTION HH EH1 P EH1 S CH AH0 N +HEPHAESTUS HH EH1 P EH1 S T AH0 S +HEPHAISTOS HH EH1 F AY0 IY1 S T OW0 Z +HEPHZIBAH HH EH1 F Z IY1 B AH0 +HEPHZIBAH'S HH EH1 F Z IY1 B AH0 Z +HEPHZY HH EH1 F Z IY0 +HEPHZY'S HH EH1 F Z IY0 Z +HEPNER HH EH1 P N ER0 +HEPPEL HH EH1 P AH0 L +HEPPELWHITE HH EH1 P AH0 L W AY2 T +HEPPIE HH EH1 P IY0 +HEPPLEWHITE HH EH1 P AH0 L W AY2 T +HEPPNER HH EH1 P N ER0 +HEPPNER'S HH EH1 P N ER0 Z +HEPPY HH EH1 P IY0 +HEPSA HH EH1 P S AH0 +HEPSEBA HH EH1 P S IY1 B AH0 +HEPSEY HH EH1 P S IY0 +HEPSEY'S HH EH1 P S IY0 Z +HEPSIE HH EH1 P S IY0 +HEPSY HH EH1 P S IY0 +HEPSY'S HH EH1 P S IY0 Z +HEPTAGON HH EH0 P T AE1 G AH0 N +HEPTAMERON HH EH0 P T AE1 M ER0 AH0 N +HEPTARCHY HH EH1 P T AA2 R K IY0 +HEPTONSTALL HH EH0 P T AA1 N S T AH0 L +HEPWORTH HH EH1 P W ER0 TH +HEPWORTH'S HH EH1 P W ER0 TH S +HEPWORTHS HH EH1 P W ER0 TH S +HEPZEBIAH HH EH1 P Z AH0 B AY1 AH0 +HEPZIBAH HH EH1 P Z IY1 B AH0 +HEPZIBAH'S HH EH1 P Z IY1 B AH0 Z +HEQUET EH2 K EY1 +HER HH ER0 +HER HH ER1 +HER' HH ER1 +HER'D HH ER1 D +HER'LL HH ER1 L +HER'N HH ER1 AH0 N +HER'S HH ER1 Z +HERA HH IH1 R AH0 +HERA'S HH EH1 R AH0 Z +HERACLEA HH ER1 AH0 K L IY0 AH0 +HERACLEIDAE HH ER1 AH0 K L IY1 D EY2 +HERACLEIDES HH EH1 R AH0 K AH0 L AY2 D Z +HERACLEITEAN HH ER1 AH0 K L IY1 T IY1 N +HERACLEITUS HH ER1 AH0 K L IY1 T AH0 S +HERACLES HH EH1 R AH0 K AH0 L Z +HERACLIDAE HH ER1 AH0 K L IH0 D EY0 +HERACLIDES HH ER1 AH0 K L AY2 D Z +HERACLITUS HH ER1 AH0 K L AY1 T AH0 S +HERACLIUS HH ER1 AH0 K L IY0 IH0 S +HERAIA HH EH0 R AA1 Y AH0 +HERAKLEOPHORBIA HH EH0 R AE1 K L IY0 OW0 F ER0 B IY0 AH0 +HERAKLEOPOLIS HH EH0 R AE1 K L IY0 AO1 P AH0 L IH0 S +HERAKLES HH ER1 AH0 K AH0 L Z +HERALD HH EH1 R AH0 L D +HERALD'S HH EH1 R AH0 L D Z +HERALDED HH EH1 R AH0 L D IH0 D +HERALDIC HH EH0 R AE1 L D IH0 K +HERALDICALLY HH EH0 R AE1 L D IH0 K L IY0 +HERALDING HH EH1 R AH0 L D IH0 NG +HERALDRY HH EH1 R AH0 L D R IY0 +HERALDS HH EH1 R AH0 L D Z +HERAPATH HH ER1 AH0 P AE2 TH +HERAPATH'S HH ER1 AH0 P AE1 TH S +HERAT HH ER0 AA1 T +HERATY HH ER1 AH0 T IY0 +HERAUD HH ER0 AO1 D +HERAUGIERE HH ER0 AO0 G IH1 R +HERAULT HH ER0 OW0 +HERB ER1 B +HERB HH ER1 B +HERB'S ER1 B Z +HERB'S HH ER1 B Z +HERBA HH ER1 B AH0 +HERBACEOUS ER0 B EY1 SH AH0 S +HERBAGE HH ER1 B IH0 JH +HERBAL ER1 B AH0 L +HERBAL HH ER1 B AH0 L +HERBALIST ER1 B AH0 L AH0 S T +HERBALIST HH ER1 B AH0 L AH0 S T +HERBALIST'S ER1 B AH0 L AH0 S T S +HERBALIST'S HH ER1 B AH0 L AH0 S T S +HERBALISTS ER1 B AH0 L AH0 S +HERBALISTS ER1 B AH0 L AH0 S T S +HERBALISTS HH ER1 B AH0 L AH0 S +HERBALISTS HH ER1 B AH0 L AH0 S T S +HERBALS HH ER1 B AH0 L Z +HERBARIUM ER0 B EH1 R IY0 AH0 M +HERBARIUM HH ER0 B EH1 R IY0 AH0 M +HERBART HH ER1 B AA2 R T +HERBART'S ER1 B AA0 R T S +HERBARTIAN HH ER0 B EH1 R SH AH0 N +HERBARTIANS HH ER0 B EH1 R SH Y IH0 N Z +HERBAUT HH ER1 B AO1 T +HERBAUT'S HH ER1 B AO0 T S +HERBE HH ER1 B +HERBECK HH ER1 B EH0 K +HERBEGG HH ER1 B IH0 G +HERBENFELDER HH ER1 B IH0 N F EH0 L D ER0 +HERBERGE HH ER1 B ER0 JH +HERBERT HH ER1 B ER0 T +HERBERT'S HH ER1 B ER0 T S +HERBERTS HH ER1 B ER0 T S +HERBERTSON HH ER1 B ER0 T S AH0 N +HERBES HH ER1 B Z +HERBEY HH ER1 B IY0 +HERBIE HH ER1 B IY0 +HERBIN HH ER1 B IH0 N +HERBIVORA HH ER1 B IH0 V AO2 R AH0 +HERBIVORES HH ER1 B IH0 V AO2 R Z +HERBIVOROUS ER0 B IH1 V ER0 AH0 S +HERBIVOROUS HH ER0 B IH1 V ER0 AH0 S +HERBLESS HH ER1 B L AH0 S +HERBOIS HH ER1 B W AA1 +HERBOLD HH ER1 B OW0 L D +HERBS ER1 B Z +HERBUX ER1 B +HERBY HH ER1 B IY0 +HERC HH ER1 K +HERCULANEUM HH ER1 K Y AH0 L AH0 N IY1 AH0 M +HERCULE HH ER0 K Y UW2 L +HERCULEAN HH ER0 K Y UW1 L IY0 AH0 N +HERCULEM HH ER1 K Y AH0 L AH0 M +HERCULES HH ER1 K Y AH0 L IY2 Z +HERCULES'S HH ER0 K Y UW2 L Z IH0 Z +HERCULIS HH ER1 K Y AH0 L IH0 S +HERCUS HH ER1 K AH0 S +HERCYNIAN HH EH2 R T S IH1 N IY0 AH0 N +HERD HH ER1 D +HERD'S HH ER1 D Z +HERDAL HH EH1 R D AH0 L +HERDBOY HH ER1 D B OY2 +HERDED HH ER1 D IH0 D +HERDEGEN HH ER1 D IH0 G AH0 N +HERDEGEN'S HH ER1 D IH0 JH EH2 N Z +HERDER HH EH1 R D ER0 +HERDER HH ER1 D ER0 +HERDER'S HH ER1 D ER0 Z +HERDERS HH ER1 D ER0 Z +HERDHOLT HH ER1 D HH OW2 L T +HERDIC HH ER1 D IH0 K +HERDICKER HH ER1 D IH0 K ER0 +HERDIN HH ER1 D IH0 N +HERDING HH ER1 D IH0 NG +HERDMAN HH ER1 D M AH0 N +HERDMANSTONE HH ER1 D M AH0 N S T OW2 N +HERDMEN HH ER1 D M AH0 N +HERDS HH ER1 D Z +HERDSMAN HH ER1 D Z M AH0 N +HERDSMAN'S HH ER1 D Z M AH0 N Z +HERDSMEN HH ER1 D Z M IH0 N +HERDSMEN'S HH ER1 D Z M IH0 N Z +HERE HH IY1 R +HERE' HH IH1 R +HERE'D HH EH1 R D +HERE'LL HH IH1 R AH0 L +HERE'S HH IH1 R Z +HEREABOUT HH IH1 R AH0 B AW2 T +HEREABOUTS HH IH1 R AH0 B AW2 T S +HEREAFTER HH IH0 R AE1 F T ER0 +HEREAT HH IH0 R AE1 T +HEREAWAY HH IH1 R AH0 W EY2 +HEREAWAYS HH IH1 R AH0 W EY2 Z +HEREBY HH IH0 R B AY1 +HEREDIA HH ER0 EH1 D IY0 AH0 +HEREDITAMENTS HH ER0 EH1 D AH0 T AH0 M AH0 N T S +HEREDITARILY HH ER0 EH1 D AH0 T EH1 R AH0 L IY0 +HEREDITARY HH ER0 EH1 D AH0 T EH2 R IY0 +HEREDITIES HH ER0 EH1 D AH0 T IY0 Z +HEREDITY HH ER0 EH1 D AH0 T IY0 +HEREFORD HH EH1 R AH0 F ER0 D +HEREFORD'S HH IH0 R F ER0 D Z +HEREFORDS HH IH0 R F ER0 D Z +HEREFORDSHIRE HH IH0 R F ER0 D SH AY2 ER0 +HEREFROM HH IH0 R F R AA2 M +HEREGAR HH IH0 R G ER0 +HEREGAR'S HH IH0 R G ER0 Z +HEREIN HH IH0 R IH1 N +HEREINAFTER HH IH0 R IH1 N AE2 F T ER0 +HEREINBEFORE HH IH0 R IH1 N B IY2 F AO1 R +HEREM HH EH1 R AH0 M +HERENDEN HH ER0 EH1 N D AH0 N +HERENNIUS HH ER0 EH1 N IY0 AH0 S +HEREOF HH EH2 R AH1 V +HEREON HH EH0 R AO1 N +HERERO HH ER0 EH1 R OW0 +HERES HH IH1 R Z +HERESIARCH HH ER0 EH1 S IY0 AA2 R K +HERESIARCHS HH ER0 EH1 S IY0 AA2 R K S +HERESIE HH ER0 EH1 S IY0 +HERESIES HH EH1 R AH0 S IY0 Z +HERESY HH EH1 R AH0 S IY0 +HERETIC HH EH1 R AH0 T IH0 K +HERETIC'S HH ER0 EH1 T IH0 K S +HERETICAL HH ER0 EH1 T IH0 K AH0 L +HERETICK HH EH1 R AH0 T IH2 K +HERETICKS HH EH1 R AH0 T IH2 K S +HERETICO HH ER0 EH1 T IH0 K OW2 +HERETICS HH ER0 EH1 T IH0 K S +HERETO HH ER0 EH1 T OW0 +HERETOFORE HH IH2 R T AH0 F AO1 R +HEREUNDER HH IH0 R OY1 N D ER0 +HEREUNTO HH IH0 R UW1 N T OW2 +HEREUPON HH IH0 R AH0 P AA1 N +HEREWARD HH IH1 R W ER0 D +HEREWARD'S HH IH1 R W ER0 D Z +HEREWITH HH IH1 R W IH1 TH +HEREWITHIN HH IH1 R W IH0 DH IH1 N +HEREZUELO HH EH1 R IH0 Z W EY1 L OW0 +HERFORD HH ER1 F ER0 D +HERFRIDA HH ER1 F R IY1 D AH0 +HERGESHEIMER HH ER1 G IH0 S HH AY0 M ER0 +HERGESHEIMER'S HH ER1 G Z HH AY2 M ER0 Z +HERGEST HH ER1 G IH0 S T +HERHOR HH ER1 HH AO0 R +HERI HH EH1 R IY0 +HERIBALD HH EH1 R AH0 B AO2 L D +HERIBERT HH EH1 R IH0 B ER0 T +HERICOURT HH ER1 IH0 K AO2 R T +HERING HH ER1 IH0 NG +HERIOT HH IH1 R IY0 AA0 T +HERIOT'S HH IH1 R IY0 AH0 T S +HERIOTS HH IH1 R IY0 AH0 T S +HERISAU HH EH1 R IH0 S AO0 +HERITABLE HH EH1 R AH0 T AH0 B AH0 L +HERITAGE HH EH1 R AH0 T AH0 JH +HERITAGE HH EH1 R IH0 T IH0 JH +HERITAGE'S HH EH1 R AH0 T IH0 JH IH0 Z +HERITAGES HH EH1 R IH0 T IH0 JH AH0 Z +HERITOR HH EH1 R AH0 T ER0 +HERITORS HH EH1 R AH0 T ER0 Z +HERITZBURG HH EH1 R IH0 T S B ER0 G +HERIULF HH ER1 IY0 AH0 L F +HERKIMER HH ER1 K IH0 M ER0 +HERKIMER'S HH ER1 K AY2 M ER0 Z +HERKOMER HH ER1 K OW1 M ER0 +HERKU HH ER1 K UW0 +HERKUS HH ER1 K IH0 S +HERKY HH ER1 K IY0 +HERLAND HH ER1 L AH0 N D +HERLIHY HH ER1 L IH0 HH IY0 +HERLUIN HH ER1 L UW1 IH0 N +HERM HH ER1 M +HERMAE HH ER1 M IY2 +HERMAN HH ER1 M AH0 N +HERMAN'S HH ER1 M AH0 N Z +HERMANA HH ER0 M AA1 N AH0 +HERMANCE HH ER1 M AH0 N S +HERMANDAD HH ER1 M AH0 N D AA1 D +HERMANITOS HH ER0 M AE1 N IH0 T OW0 Z +HERMANN HH ER1 M AH0 N +HERMANN'S HH ER1 M AH0 N Z +HERMANNSBURG HH ER1 M AA0 N Z B ER0 G +HERMANNSON HH ER1 M AA0 N S AH0 N +HERMANNUS HH ER1 M AA0 N AH0 S +HERMANO HH ER0 M AA1 N OW0 +HERMANOS HH ER0 M EY1 N OW0 Z +HERMANRIC HH ER1 M AH0 N R IH0 K +HERMANRIC'S HH ER1 M AH0 N R IH0 K S +HERMANSEN HH ER1 M AH0 N S AH0 N +HERMANSTADT HH ER1 M AH0 N S T AE2 T +HERMANUS HH ER0 M EY1 N IH0 S +HERMAPHRODITE HH ER0 M AE1 F R AH0 D AY2 T +HERMAPHRODITES HH ER0 M AE1 F R AH0 D AY1 T IY0 Z +HERMAPHRODITIC HH ER0 M AE2 F R AH0 D IH1 T IH0 K +HERMAS HH ER1 M AH0 Z +HERMENEGILDO HH ER0 M EH1 N IH0 G IH1 L D OW0 +HERMENEUTICS HH ER0 M EH1 N Y UW1 T IH0 K S +HERMES HH ER1 M IY0 Z +HERMETIC HH ER0 M EH1 T IH0 K +HERMETICALLY HH ER0 M EH1 T IH0 K AH0 L IY0 +HERMETICALLY HH ER0 M EH1 T IH0 K L IY0 +HERMETICS HH ER0 M EH1 T IH0 K S +HERMETISTS HH ER0 M EH1 T IH0 S T S +HERMIA HH ER1 M IY0 AH0 +HERMIA'S HH ER1 M IY0 AH0 Z +HERMIE HH ER1 M IY0 +HERMIES HH ER1 M IY0 Z +HERMIL HH ER1 M IH0 L +HERMINE HH ER1 M IH0 N +HERMINE'S HH ER1 M AH0 N Z +HERMING HH ER1 M IH0 NG +HERMINIA HH EH0 R M IY1 N IY0 AH0 +HERMINIA'S HH ER1 M IH0 N IY0 AH0 Z +HERMINIE HH ER1 M IH0 N IY0 +HERMINIE'S HH ER1 M IH0 N IY0 Z +HERMINIUS HH ER1 M IH0 N IY0 IH0 S +HERMIONE HH ER1 M IY0 OW1 N IY0 +HERMIONE'S HH ER1 M IY0 OW1 N Z +HERMIPPUS HH ER1 M IY0 P AH0 S +HERMISTON HH ER1 M IH0 S T AA0 N +HERMIT HH ER1 M AH0 T +HERMIT'S HH ER1 M IH0 T S +HERMITAGE HH ER1 M AH0 T AH0 JH +HERMITAGES HH ER1 M IH0 T IH0 JH IH0 Z +HERMITE HH ER1 M AY0 T +HERMITS HH ER1 M AH0 T S +HERMNAEN HH ER1 M N EY2 N +HERMOCRATES HH ER0 M AH0 K R EY2 T S +HERMOD HH ER1 M AH0 D +HERMOGEN HH ER0 M AH0 JH AH0 N +HERMOGENES HH ER0 M AH0 JH IY1 N Z +HERMOISES HH ER1 M OY1 Z IH0 Z +HERMON HH ER1 M AH0 N +HERMON'S HH ER0 M AH0 N Z +HERMONTHIS HH ER0 M AH0 N TH IH0 S +HERMOPOLIS HH ER1 M AO1 P AH0 L IH0 S +HERMOSA HH EH0 R M OW1 S AH0 +HERMOSILLO HH ER0 M AH0 S IH1 L OW0 +HERMOTIMUS HH EH0 R M OW1 T IH0 M IH0 S +HERMSDORF HH ER1 M S D AO0 R F +HERMUND HH EH0 R M AH0 N D +HERMUS HH EH0 R M AH0 S +HERMY HH EH0 R M IY0 +HERN HH ER1 N +HERN'S HH ER1 N Z +HERNAN HH ER1 N AH0 N +HERNAN'S HH ER1 N IH0 N Z +HERNANDEZ HH ER0 N AE1 N D EH0 Z +HERNANDO HH ER0 N AA1 N D OW0 +HERNANI HH ER0 N AA1 N IY0 +HERNCASTLE HH ER1 N K AE2 S AH0 L +HERNDA HH ER1 N D AH0 +HERNDON HH ER1 N D AH0 N +HERNDON'S HH ER1 N D AH0 N Z +HERNE HH ER1 N +HERNE'S HH ER1 N Z +HERNHUT HH ER1 N HH AH0 T +HERNIA HH ER1 N IY0 AH0 +HERNICIANS HH ER0 N IH1 SH AH0 N Z +HERNSHAW HH ER1 N SH AO2 +HERO HH IH1 R OW0 +HERO HH IY1 R OW0 +HERO'S HH IH1 R OW0 Z +HERO'S HH IY1 R OW0 Z +HEROD HH EH1 R AH0 D +HEROD'S HH EH1 R AH0 D Z +HERODE HH IY1 R OW0 D +HERODE'S HH EH1 R OW2 D Z +HERODED HH IH0 R OW1 D AH0 D +HERODES HH IY1 R OW0 D Z +HERODIADE HH EH1 R AH0 D AY1 AH0 D +HERODIAN HH IH0 R OW1 D IY0 AH0 N +HERODIANS HH IH0 R OW1 D IY0 AH0 N Z +HERODIAS HH IY1 R OW0 D IY1 AH0 Z +HERODICUS HH EH1 R AH0 D IH0 K AH0 S +HERODING HH IH0 R OW1 D IH0 NG +HERODOTUS HH EH1 R AH0 D OW1 T AH0 S +HERODOTUS'S HH EH1 R AH0 D OW1 T AH0 S IH0 Z +HERODS HH EH1 R AH0 D Z +HEROE HH IY1 R OW0 +HEROES HH IH1 R OW0 Z +HEROES HH IY1 R OW0 Z +HEROIC HH IH0 R OW1 IH0 K +HEROICA HH IH0 R OW1 IH0 K AH0 +HEROICAL HH IH0 R OW1 IH0 K AH0 L +HEROICALLY HH IH2 R OW1 IH0 K L IY0 +HEROICK HH IH0 R OW1 IH0 K +HEROICS HH IH0 R OW1 IH0 K S +HEROIN HH EH1 R OW0 AH0 N +HEROINE HH EH1 R OW0 AH0 N +HEROINE'S HH EH1 R OW0 AH0 N Z +HEROINES HH EH1 R OW2 AH0 N Z +HEROIQUE HH EH1 R OW0 AH0 K EY2 +HEROISM HH EH1 R OW0 IH2 Z AH0 M +HEROISMS HH IH0 R OW1 IH0 Z AH0 M Z +HEROLD HH EH1 R AH0 L D +HERON HH EH1 R AH0 N +HERON'S HH EH1 R AH0 N Z +HERONDALE HH EH1 R AH0 N D EY2 L +HERONHURST HH EH1 R AH0 N HH ER0 S T +HERONRIES HH EH1 R AH0 N R IY0 Z +HERONRY HH EH1 R AH0 N R IY0 +HERONS HH EH1 R AH0 N Z +HERONSMERE HH EH1 R AH0 N Z M IH2 R +HEROPHILUS HH EH1 R AH0 F AY0 AH0 L AH0 S +HEROS HH IH1 R OW0 Z +HEROUVILLE HH ER0 UW1 V IH2 L +HERPES HH ER1 P IY0 Z +HERR HH EH1 R +HERR'S HH EH1 R Z +HERRAN HH EH1 R AH0 N +HERRAN'S HH EH1 R AH0 N Z +HERRARA HH ER0 AA1 R AH0 +HERRARA'S HH ER0 AA1 R AH0 Z +HERRE HH AH1 R IY0 +HERRE HH EH1 R +HERREGUD HH EH1 R AH0 G AH0 D +HERREN HH EH1 R AH0 N +HERRENDEEN HH ER0 EH1 N D IY0 N +HERRENHAUSEN HH EH1 R AH0 N HH AW2 Z AH0 N +HERRERA HH ER0 EH1 R AH0 +HERRERA'S HH ER0 EH1 R AH0 Z +HERREROS HH EH0 R EH1 R OW0 Z +HERRESFORD HH EH1 R Z F ER0 D +HERRESFORD'S HH EH1 R Z F ER0 D Z +HERRETS HH EH1 R AH0 T S +HERRICK HH EH1 R IH0 K +HERRICK'S HH EH1 R IH0 K S +HERRICKS HH EH1 R IH0 K S +HERRIES HH EH1 R IY0 Z +HERRIN HH EH1 R IH0 N +HERRING HH EH1 R IH0 NG +HERRING'S HH EH1 R IH0 NG Z +HERRINGBONE HH EH1 R IH0 NG B OW2 N +HERRINGHAME HH EH1 R IH0 NG AH0 M +HERRINGS HH EH1 R IH0 NG Z +HERRINGTON HH EH1 R IH0 NG T AH0 N +HERRION HH EH1 R IY0 AH0 N +HERRIOT HH EH1 R IY0 AA0 T +HERRIOT'S HH EH1 R IY0 AH0 T S +HERRIOTT HH EH1 R IY0 AA0 T +HERRIOTT HH EH1 R IY0 AH0 T +HERRIOTT'S HH EH1 R IY0 AH0 T S +HERRIT HH EH1 R IH0 T +HERRITON HH EH1 R IH0 T AO1 N +HERRMANN HH EH1 R M AH0 N +HERRN HH EH1 R AH0 N +HERRNHUT HH ER0 N HH AH0 T +HERRON HH EH1 R AH0 N +HERRON'S HH EH1 R AH0 N Z +HERRSCHAFT HH EH1 R AH0 SH AE0 F T +HERRSCHAFTEN HH EH1 R AH0 SH AE0 F T AH0 N +HERS HH ER0 Z +HERS HH ER1 Z +HERSANT HH ER1 S AH0 N T +HERSCHEL HH ER1 SH AH0 L +HERSCHEL'S HH ER1 SH AH0 L Z +HERSCHELL HH ER1 SH AH0 L +HERSCHELS HH ER1 SH AH0 L Z +HERSE HH ER1 S +HERSE'F HH ER1 S IH0 F +HERSE'S HH ER1 S IH0 Z +HERSEBOM HH ER1 S B AA2 M +HERSEL HH ER1 S AH0 L +HERSELF HH ER0 S EH1 L F +HERSELF'S HH ER0 S EH1 L F S +HERSELFE HH ER0 S EH1 L F +HERSELL HH ER0 S EH2 L +HERSEN HH ER0 S AH0 N +HERSEY HH ER1 S IY0 +HERSEY'S HH ER1 Z IY0 Z +HERSHEIMMER HH ER1 SH IH1 M ER0 +HERSHEL HH ER1 SH AH0 L +HERSHEY HH ER1 SH IY0 +HERSHEY'S HH ER1 SH IY0 Z +HERSHIE HH ER1 SH IY0 +HERSHIE'S HH ER1 SH IY0 Z +HERSILF HH ER1 S IH1 L F +HERSILIE HH ER0 S IY1 L IY0 +HERSIR HH ER1 S IH1 R +HERT HH ER1 T +HERTE HH ER1 T +HERTEL HH ER1 T AH0 L +HERTER HH ER1 T ER0 +HERTER'S HH ER1 T ER0 Z +HERTFORD HH ER1 T F ER0 D +HERTFORD'S HH ER1 T F ER0 D Z +HERTFORDSHIRE HH ER1 T F ER0 D SH AY2 ER0 +HERTHA HH ER1 TH AH0 +HERTHA'S HH ER1 TH AH0 Z +HERTIG HH ER1 T IH0 G +HERTLING HH ER1 T L IH0 NG +HERTS HH ER1 T S +HERTUG HH ER1 T AH0 G +HERTZ HH EH1 R T S +HERTZ HH ER1 T S +HERTZIAN HH ER1 T Z IY0 AH0 N +HERTZOG HH ER1 T Z AA2 G +HERTZOG HH ER1 T Z AH0 G +HERU HH EH1 R UW0 +HERULES HH ER0 UW1 L Z +HERULI HH ER0 UW1 L IY0 +HERULIAN HH ER0 UW1 L IY0 AH0 N +HERULIANS HH ER0 UW1 L Y AH0 N Z +HERVAGAULT HH ER1 V AH0 G OW1 +HERVAL HH ER0 V AH0 L +HERVART HH ER1 V ER0 T +HERVART'S HH ER1 V ER0 T S +HERVE HH ER1 V +HERVE HH ER1 V EY0 +HERVE'S HH ER1 V Z +HERVEY HH ER0 V EY1 +HERVEY'S HH ER0 V EY1 Z +HERVEYS HH ER0 V EY1 Z +HERVIEU HH ER1 V Y UW1 +HERWARTH HH ER1 W ER0 TH +HERWARTH'S HH ER1 W ER0 TH S +HERWEGH HH ER1 W IY0 G +HERWIG HH ER1 W IH0 G +HERZ HH ER1 Z +HERZBERG HH ER1 Z B ER0 G +HERZCHEN HH ER1 Z K AH0 N +HERZEGOVINA HH EH2 R T S AH0 G OW0 V IY1 N AH0 +HERZEGOVINA HH ER2 T S AH0 G OW0 V IY1 N AH0 +HERZEGOVINIAN HH ER2 T S AH0 G OW0 V IY1 N IY0 AH0 N +HERZEN HH ER1 Z AH0 N +HERZENSTUBE HH ER2 T S AH0 N S T UW1 B +HERZL HH ER1 Z L +HERZL'S HH ER1 Z L Z +HERZLICH HH ER1 Z L IH0 K +HERZMANN HH ER1 Z M AH0 N +HERZMANN'S HH ER1 Z M AH0 N Z +HERZOG HH ER1 Z AA0 G +HERZOG'S HH ER1 Z AA0 G Z +HES HH EH1 Z +HESA HH EH1 S AH0 +HESBA HH EH1 S B AH0 +HESCOCK'S HH EH1 S K AA2 K S +HESDEN HH EH1 Z D AH0 N +HESDEN'S HH EH1 Z D AH0 N Z +HESDIN HH EH1 S D IH0 N +HESEA HH IY1 S IY0 AH0 +HESED HH EH1 S T +HESELF HH IH0 S EH1 L F +HESELRIGGE HH EH1 S AH0 L R IH0 G +HESELTINE HH EH1 S AH0 L T AY2 N +HESELTINE HH EH1 S AH0 L T IY2 N +HESELTON HH IH0 S EH1 L T AH0 N +HESH HH EH1 SH +HESHBON HH EH1 SH B AH0 N +HESIOD HH IY1 S IY0 AH0 D +HESIOD'S HH IY1 S IY0 AH0 D Z +HESITANCE HH EH1 Z IH0 T AH0 N S +HESITANCIES HH EH1 Z IH0 T AH0 N S IY0 Z +HESITANCY HH EH1 Z IH0 T AH0 N S IY0 +HESITANT HH EH1 Z IH0 T AH0 N T +HESITANTLY HH EH1 Z IH0 T AH0 N T L IY0 +HESITATE HH EH1 Z AH0 T EY2 T +HESITATED HH EH1 Z IH0 T EY2 T IH0 D +HESITATES HH EH1 Z AH0 T EY2 T S +HESITATIN HH EH2 Z AH0 T EY1 T IH0 N +HESITATING HH EH1 Z AH0 T EY2 T IH0 NG +HESITATINGLY HH EH1 Z AH0 T EY2 T IH0 NG L IY0 +HESITATION HH EH2 Z AH0 T EY1 SH AH0 N +HESITATIONS HH EH2 Z AH0 T EY1 SH AH0 N Z +HESKETH HH EH1 S K IH0 TH +HESKETH'S HH EH1 S K IH0 TH S +HESKETT HH EH1 S K IH0 T +HESPER HH EH1 S P ER0 +HESPERIA HH EY0 S P EH1 R IY0 AH0 +HESPERIAN HH EY0 S P EH1 R IY0 AH0 N +HESPERIDES HH EH1 S P ER0 AY2 D Z +HESPERIDUM HH EY0 S P EH1 R IH0 D AH0 M +HESPERUS HH EY0 S P EH1 R AH0 S +HESS HH EH1 S +HESSE HH EH1 S +HESSE'S HH EH1 S IH0 Z +HESSEL HH EH1 S AH0 L +HESSELIUS HH EH1 S AH0 L IY0 IH0 S +HESSELS HH EH1 S AH0 L Z +HESSEN HH EH1 S AH0 N +HESSET HH EH1 S AH0 T +HESSEY HH EH1 S IY0 +HESSIA HH EH1 SH AH0 +HESSIAN HH EH1 SH AH0 N +HESSIANS HH EH1 SH AH0 N Z +HESSIE HH EH1 S IY0 +HESSLEIN HH EH1 S L AY2 N +HEST HH EH1 S T +HESTER HH EH1 S T ER0 +HESTER'S HH EH1 S T ER0 Z +HESTIA HH EH1 S T IY0 AH0 +HESTON HH EH1 S T AH0 N +HESTRI HH EH1 S T R IY0 +HESTS HH EH1 S T S +HESUS HH EH1 S AH0 S +HET HH EH1 T +HET'S HH EH1 T S +HETAERA HH EH1 T EH1 R AH0 +HETAERAE HH EH1 T EH2 R EY2 +HETAIRA HH EH1 T AA0 IH1 R AH0 +HETAIRAE HH AH0 T EH1 R EY2 +HETAIRAI HH EH2 T EH2 R AA1 IY0 +HETAIRISM HH EH1 T ER0 IH2 Z AH0 M +HETCH HH EH1 CH +HETCHY HH EH1 CH IY0 +HETEP HH EH2 T P +HETERO HH EH2 T ER0 OW0 +HETERODOX HH EH2 T ER0 AH0 D AA2 K S +HETERODOXIES HH EH2 T ER0 AH0 D AA2 K S IY0 Z +HETERODOXY HH EH1 T ER0 AH0 D AA2 K S IY0 +HETERODYNED HH EH2 T ER0 AH0 D AY2 N D +HETEROGENEITY HH EH2 T ER0 AH0 JH IH0 N EY1 AH0 T IY0 +HETEROGENEITY HH EH2 T ER0 AH0 JH IH0 N IY1 AH0 T IY0 +HETEROGENEOUS HH EH2 T ER0 AH0 JH IY1 N Y AH0 S +HETEROGENEOUSLY HH EH2 T ER0 AH0 JH IY1 N IY0 AH0 S L IY0 +HETEROGENOUS HH EH2 T ER0 AH0 JH AH0 N AH0 S +HETERONOMY HH EH2 T ER0 AA1 N AH0 M IY0 +HETFALU HH EH1 T F AA1 L UW0 +HETFALUSY HH EH1 T F AE1 L IH0 S IY0 +HETH HH EH1 TH +HETH'S HH EH1 TH S +HETHCOTE HH EH2 TH K AH0 T +HETHENCOURT HH EH1 TH AH0 N K AO2 R T +HETHER HH EH1 DH ER0 +HETHERFORD HH EH1 DH ER0 F ER0 D +HETHERIDGE HH EH1 TH ER0 IH2 JH +HETHERINGTON HH EH1 DH ER0 IH0 NG T AH0 N +HETHERINGTON'S HH EH1 DH ER0 IH0 NG T AH0 N Z +HETHERTON HH EH1 DH ER0 T AH0 N +HETLEY HH EH1 T L IY0 +HETMAN HH EH1 T M AH0 N +HETMAN'S HH EH1 T M AH0 N Z +HETMANS HH EH1 T M AH0 N Z +HETON HH EH1 T AH0 N +HETT HH EH1 T +HETTA HH EH1 T AH0 +HETTERMAN HH EH1 T ER0 M AH0 N +HETTIE HH EH1 T IY0 +HETTIE'S HH EH1 T IY0 Z +HETTY HH EH1 T IY0 +HETTY'S HH EH1 T IY0 Z +HEU HH OY1 +HEUGEL HH Y UW1 G AH0 L +HEUGH HH Y UW1 +HEUKBANE HH Y UW1 K B EY2 N +HEUMANN HH Y UW1 M AH0 N +HEURE HH Y UW1 R +HEURES HH Y UH0 R Z +HEUREUSE HH Y UH0 R UW1 Z +HEUREUSEMENT HH Y UH0 R UW1 Z M AH0 N T +HEUREUX HH Y UW2 R UH1 +HEURISTIC HH Y UH0 R IH1 S T IH0 K +HEURNE HH Y UH0 R N +HEURTEBISE HH Y UH0 R T IH0 B AY0 Z +HEUS HH Y UW1 Z +HEUSCHRECKE HH OY1 SH R EH1 K +HEUTE HH Y UW1 T +HEUZEY HH Y UW1 Z IY0 +HEV HH EH1 V +HEVA HH EY1 V AH0 +HEVE HH EH1 V IY0 +HEVEA HH EH1 V IY0 AH0 +HEVELIUS HH IH0 V EY1 L IY0 IH0 S +HEVEN HH EH1 V AH0 N +HEVEN'T HH EH1 V AH0 N T +HEVENIN HH EH1 V IY0 N IH0 N +HEVENING HH IY1 V N IH0 NG +HEVER HH EH1 V ER0 +HEVERY HH EH1 V R IY0 +HEVERYTHING HH EH1 V R IY0 TH IH2 NG +HEVERYTHINK HH EH1 V R IY0 TH IH2 NG K +HEVEYTH HH IH0 V EY1 TH +HEVIN HH EH1 V IH0 N +HEVN'T HH EH1 V N T +HEVY HH EH1 V IY0 +HEW HH Y UW1 +HEW'D HH Y UW1 D +HEWBY HH Y UW1 B IY0 +HEWED HH Y UW1 D +HEWEN HH Y UW1 AH0 N +HEWER HH Y UW1 ER0 +HEWERS HH Y UW1 ER0 Z +HEWES HH Y UW1 Z +HEWET HH Y UW1 IH0 T +HEWET'S HH Y UW1 IH0 T S +HEWETT HH Y UW1 IH0 T +HEWETT'S HH Y UW1 IH0 T S +HEWETTS HH Y UW1 IH0 T S +HEWING HH Y UW1 IH0 NG +HEWIT HH Y UW1 IH0 T +HEWITSON HH Y UW1 IH0 T S AH0 N +HEWITT HH Y UW1 IH0 T +HEWITT'S HH Y UW1 IH0 T S +HEWLETT HH Y UW1 L IH0 T +HEWLETT'S HH Y UW1 L IH0 T S +HEWLEY HH Y UW1 L IY0 +HEWLITT HH Y UW1 L IH0 T +HEWN HH Y UW1 N +HEWS HH Y UW1 Z +HEWSON HH Y UW1 S AH0 N +HEWSON'S HH Y UW1 S AH0 N Z +HEWSTON HH Y UW1 S T AH0 N +HEX HH EH1 K S +HEXAGON HH EH1 K S AH0 G AA2 N +HEXAGONAL HH EH0 K S AE1 G AH0 N AH0 L +HEXAGONS HH EH0 K S AE1 G AH0 N Z +HEXAM HH EH1 K S AH0 M +HEXAM'S HH EH1 K S AH0 M Z +HEXAMETER HH EH0 K S AE1 M AH0 T ER0 +HEXAMETERS HH EH0 K S AE1 M AH0 T ER0 Z +HEXAN HH EH1 K S AH0 N +HEXANS HH EH1 K S AH0 N Z +HEXAPODE HH EH1 K S AH0 P OW1 D +HEXCUSE HH EH1 K S K Y UW1 Z +HEXHAM HH EH1 K S AH0 M +HEXIE HH EH1 K S IY0 +HEXOXEN HH EH1 K S AA1 K S AH0 N +HEXTON HH EH1 K S T AH0 N +HEXTRA HH EH1 K S T R AH0 +HEXTRY HH EH1 K S T R IY0 +HEY HH EY1 +HEYDAY HH EY1 D EY2 +HEYDE HH EY1 D +HEYDECK HH EY1 D EH0 K +HEYDECK'S HH EY1 D EH0 K S +HEYDEN HH EY1 D AH0 N +HEYDEN'S HH EY1 D AH0 N Z +HEYDEY HH EY1 D IY0 +HEYDINGER HH EY1 D IH0 NG ER0 +HEYDOCKE HH EY1 D AA1 K +HEYDON HH EY1 D AH0 N +HEYDUKE HH EY1 D UW2 K +HEYDUKES HH EY1 D UW1 K S +HEYE HH AY1 +HEYERDAHL HH AY1 ER1 D AA0 L +HEYES HH AY1 Z +HEYL HH EY1 L +HEYLER HH EY1 L ER0 +HEYLIGER HH EY1 L IH0 G ER0 +HEYLING HH EY1 L IH0 NG +HEYM HH EY1 M +HEYMANN HH EY1 M AH0 N +HEYMES HH EY1 M Z +HEYNE HH EY1 N +HEYNES HH EY1 N Z +HEYSE HH EY1 S +HEYST HH EY1 S T +HEYST'S HH EY1 S T S +HEYTESBURY HH EY1 T S B EH0 R IY0 +HEYTHORP HH EY1 TH AO0 R P +HEYTHORP'S HH EY1 TH AO0 R P S +HEYTON HH EY1 T AH0 N +HEYTON'S HH EY1 T AH0 N Z +HEYWARD HH EY1 W ER0 D +HEYWOOD HH EY1 W UH2 D +HEYWOOD'S HH EY1 W UH2 D Z +HEYWOODS HH EY1 W UH2 D Z +HEYWORTH HH EY1 W ER2 TH +HEZ HH EH0 Z +HEZ'S HH EH0 Z IH0 Z +HEZEKIAH HH EH1 Z AH0 K AY1 AH0 +HEZEKIAH'S HH EH1 Z AH0 K AY1 AH0 Z +HEZILO HH EH0 Z IY1 L OW0 +HEZZY HH EH1 Z IY0 +HH HH +HHETT HH IH0 T +HI HH AY1 +HI'LL HH AY1 L +HI'M HH AY1 M +HI'S HH AY1 Z +HI'VE HH AY1 V +HIA HH IY0 AH0 +HIALMAR HH AY2 AH0 L M ER0 +HIALTI HH AY2 AH0 L T IY0 +HIAN HH AY1 AH0 N +HIAQUA HH AY2 AH0 K W AH0 +HIARANDI HH AY1 ER0 AE1 N D IY0 +HIATUS HH AY0 EY1 T AH0 S +HIATUSES HH AY1 AH0 T AH0 S IH0 Z +HIAWASSEE HH AY2 AH0 W AA1 S IY0 +HIAWATHA HH AY2 AH0 W AA1 TH AH0 +HIAWATHA'S HH AY2 AH0 W AA1 TH AH0 Z +HIB HH IH1 B +HIBACHI HH AH0 B AA1 CH IY0 +HIBACHI HH IY0 B AA1 CH IY0 +HIBBARD HH IH1 B ER0 D +HIBBEN HH IH1 B AH0 N +HIBBERT HH IH1 B ER0 T +HIBBERT'S HH IH1 B ER0 T S +HIBBERTY HH IH1 B ER0 T IY0 +HIBBINS HH IH1 B IH0 N Z +HIBBS HH IH1 B Z +HIBERNATE HH AY1 B ER0 N EY2 T +HIBERNATED HH AY2 B ER0 N EY2 T AH0 D +HIBERNATES HH AY2 B ER0 N EY2 T S +HIBERNATING HH AY2 B ER0 N EY2 T IH0 NG +HIBERNATION HH AY2 B ER0 N EY1 SH AH0 N +HIBERNIA HH AY2 B ER1 N IY0 AH0 +HIBERNIAN HH AY2 B ER1 N IY0 AH0 N +HIBERNIANS HH AY2 B ER1 N IY0 AH0 N Z +HIBERNICISM HH AY2 B ER1 N IH0 S IH2 Z AH0 M +HIBISCUS HH AY2 B IH1 S K AH0 S +HIC HH IH1 K +HICCOUGH HH IH1 K AH0 P +HICCOUGHED HH IH1 K AH1 F T +HICCOUGHING HH IH1 K AO1 F IH0 NG +HICCOUGHS HH IH1 K AH0 P S +HICCUP HH IH1 K AH0 P +HICCUPED HH IH1 K AH0 P T +HICCUPING HH IH1 K AH0 P IH0 NG +HICCUPPED HH IH1 K AH0 P T +HICCUPPING HH IH1 K AH0 P IH0 NG +HICCUPS HH IH1 K AH0 P S +HICH HH IH0 CH +HICHENS HH IH1 K AH0 N Z +HICK HH IH1 K +HICK'RY HH IH1 R IY0 +HICKATHRIFT HH IH1 K AH0 TH R IH1 F T +HICKATHRIFT'S HH IH1 K AH0 TH R IH2 F T S +HICKERY HH IH1 K ER0 IY0 +HICKES HH IH1 K S +HICKETY HH IH1 K AH0 T IY0 +HICKEY HH IH1 K IY0 +HICKEY'S HH IH1 K IY0 Z +HICKIE HH IH1 K IY0 +HICKLEY HH IH1 K L IY0 +HICKLEYBROW HH IH1 K L IY0 B R AW0 +HICKLING HH IH1 K L IH0 NG +HICKMAN HH IH1 K M AH0 N +HICKMAN'S HH IH1 K M AH0 N Z +HICKMANS HH IH1 K M AH0 N Z +HICKNEY HH IH1 K N IY0 +HICKOCK HH IH1 K AA0 K +HICKOCK'S HH IH1 K AA0 K S +HICKOK HH IH1 K AH0 K +HICKORIES HH IH1 K ER0 IY0 Z +HICKORY HH IH1 K ER0 IY0 +HICKORY HH IH1 K R IY0 +HICKORY'S HH IH1 K ER0 IY0 Z +HICKOX HH IH1 K AA0 K S +HICKOX'S HH IH1 K AA0 K S IH0 Z +HICKS HH IH1 K S +HICKS'S HH IH1 K S IH0 Z +HICKSES HH IH1 K S IH0 Z +HICKSEY HH IH1 K S IY0 +HICKSITE HH IH1 K S AY2 T +HICKSITES HH IH1 K S AY2 T S +HICKSON HH IH1 K S AH0 N +HICKSON'S HH IH1 K S AH0 N Z +HICKSWORTHY HH IH1 K S W ER0 DH IY0 +HICKTON HH IH1 K T AH0 N +HICKWOOD HH IH1 K W UH2 D +HICKY HH IH1 K IY0 +HID HH IH1 D +HIDALGO HH AH0 D AE1 L G OW2 +HIDALGO'S HH AH0 D AE1 L G OW2 Z +HIDALGOS HH AH0 D AE1 L G OW2 Z +HIDDEKEL HH IH1 D AH0 K AH0 L +HIDDEN HH IH1 D AH0 N +HIDDIGEIGEI HH IH1 D IH0 G EY1 G EY1 +HIDE HH AY1 D +HIDE'S HH AY1 D Z +HIDEAWAY HH AY1 D AH0 W EY2 +HIDEBOUND HH AY1 D B AW2 N D +HIDED HH AY1 D IH0 D +HIDEOUS HH IH1 D IY0 AH0 S +HIDEOUSLY HH IH1 D IY0 AH0 S L IY0 +HIDEOUSNESS HH IH1 D IY0 AH0 S N AH0 S +HIDEOUT HH AY1 D AW2 T +HIDER HH AY1 D ER0 +HIDERS HH AY1 D ER0 Z +HIDES HH AY1 D Z +HIDESATO HH IY2 D EY0 S AA1 T OW0 +HIDEST HH AY1 D AH0 S T +HIDETADA HH IH1 D IY0 T AA1 D AH0 +HIDETH HH AY1 D IH0 TH +HIDEYOSHI HH IH0 D EY1 Y OW1 SH IY0 +HIDIN HH AY1 D IH0 N +HIDIN' HH AY1 D IH0 N +HIDING HH AY1 D IH0 NG +HIDINGS HH AY1 D IH0 NG Z +HIDY HH AY1 D IY0 +HIE HH AY1 +HIED HH AY1 D +HIEING HH AY1 IH0 NG +HIEL HH IY1 L +HIELAN HH IY1 L AH0 N +HIELAN'MAN HH IY1 L AE0 N M AH0 N +HIELAN'S HH IY1 L AH0 N Z +HIELAND HH IY1 L AH0 N D +HIELANDERS HH IY1 L AH0 N D ER0 Z +HIELANDMAN HH AY1 L AE1 N D M AH0 N +HIELANDMEN HH AY1 L AE1 N D M AH0 N +HIELANDS HH IY1 L AH0 N D Z +HIELEN HH IY1 L AH0 N +HIEMPSAL HH IY1 M P S AH0 L +HIEN HH IY1 N +HIENS HH IY1 N Z +HIER HH AY1 ER0 +HIERA HH IY1 R AH0 +HIERAKONPOLIS HH AY1 ER0 AA2 K AA2 N P AH0 L IH0 S +HIERAPOLIS HH AY1 R AE1 P AH0 L IH0 S +HIERARCH HH AY1 ER0 AA2 R K +HIERARCHIC HH AY1 ER0 AA2 R K IH0 K +HIERARCHICAL HH AY2 R AA1 R K AH0 K AH0 L +HIERARCHIES HH AY1 R AA2 R K IY0 Z +HIERARCHS HH AY1 ER0 AA2 R K S +HIERARCHY HH AY1 ER0 AA2 R K IY0 +HIERARCHY HH AY1 R AA2 R K IY0 +HIERATIC HH AY1 ER0 AE1 T IH0 K +HIERAX HH AY1 R AE0 K S +HIERIE HH AY1 ER0 IY0 +HIERO HH AY2 R OW0 +HIERO'S HH AY2 R OW0 Z +HIEROCLES HH AY2 R OW0 K AH0 L Z +HIERODULES HH AY1 ER0 AA1 JH UW0 L Z +HIEROGLYPH HH AY2 R OW0 G L IH1 F +HIEROGLYPHIC HH AY2 R OW0 G L IH1 F IH0 K +HIEROGLYPHICAL HH AY2 R OW0 G L IH1 F IH0 K AH0 L +HIEROGLYPHICS HH AY2 R OW0 G L IH1 F IH0 K S +HIEROGLYPHS HH AY2 R OW0 G L IH1 F S +HIEROME HH AY2 R OW0 M +HIERON HH AY1 ER0 AH0 N +HIERONIMO HH AY1 ER0 OW0 N IY1 M OW0 +HIERONYMO HH AY2 R OW0 N AH0 M OW0 +HIERONYMUS HH AY2 R OW0 N AH0 M AH0 S +HIEROPHANT HH AY2 R OW0 F AH0 N T +HIEROPHANTIC HH AY2 R OW0 F AE1 N T IH0 K +HIEROPHANTS HH AY2 R OW0 F AH0 N T S +HIERUSALEM HH AY1 ER0 UW1 S AH0 L AH0 M +HIES HH AY1 Z +HIF HH AY1 F +HIFALUTIN HH AY1 F AH0 L UW1 T IH0 N +HIGBEE HH IH1 G B IY2 +HIGBEE'S HH IH1 G B IY2 Z +HIGBIE HH IH1 G B IY0 +HIGBY HH IH1 G B IY0 +HIGDEN HH IH1 G D AH0 N +HIGG HH IH1 G +HIGG'S HH IH1 G Z +HIGGINBOTHAM HH IH0 G IH0 N B AA1 TH AH0 M +HIGGINBOTHAM'S HH IH1 G IH0 N B AA1 TH AH0 M Z +HIGGINBOTTOM HH IH0 G IH0 N B AA1 T AH0 M +HIGGINS HH IH1 G IH0 N Z +HIGGINS'S HH IH1 G IH0 N Z IH0 Z +HIGGINSON HH IH1 G IH0 N S AH0 N +HIGGINSON'S HH IH1 G IH0 N S AH0 N Z +HIGGLE HH IH1 G AH0 L +HIGGLEBY'S HH IH1 G AH0 L B IY0 Z +HIGGLED HH IH1 G AH0 L D +HIGGLEDEE HH IH1 G AH0 L D IY0 +HIGGLEDY HH IH1 G AH0 L D IY0 +HIGGLER HH IH1 G AH0 L ER0 +HIGGLESTON HH IH1 G AH0 L S T AH0 N +HIGGLING HH IH1 G L IH0 NG +HIGGS HH IH1 G Z +HIGGS'S HH IH1 G Z IH0 Z +HIGH HH AY1 +HIGH'S HH AY1 Z +HIGHAM HH AY1 AH0 M +HIGHBALL HH AY1 B AO2 L +HIGHBALLS HH AY1 B AO2 L Z +HIGHBINDERS HH AY1 B AY2 N D ER0 Z +HIGHBORN HH AY1 B ER0 N +HIGHBOURNE HH AY1 B ER0 N +HIGHBOY HH AY1 B OY2 +HIGHBOYS HH AY1 B OY2 Z +HIGHBRED HH AY1 B R EH1 D +HIGHBROW HH AY1 B R AW2 +HIGHBROWS HH AY1 B R AW2 Z +HIGHBURY HH AY1 B EH0 R IY0 +HIGHCAMP HH AY1 K AE1 M P +HIGHCLIFF HH AY1 K L IH0 F +HIGHCLIFFE HH AY1 K L IH0 F +HIGHCLOSE HH AY1 K L OW1 Z +HIGHCOMBE HH AY1 K AH0 M +HIGHCOURT HH AY1 K AO2 R T +HIGHER HH AY1 ER0 +HIGHER'N HH AY1 AH0 AH0 N +HIGHEST HH AY1 AH0 S T +HIGHFALUTIN HH AY2 F AH0 L UW1 T IH0 N +HIGHFALUTING HH AY1 F AH0 L UW1 T IH0 NG +HIGHFIELD HH AY1 F IY2 L D +HIGHFLIER HH AY1 F L AY2 ER0 +HIGHFLOWN HH AY1 F L OW1 N +HIGHFLYER HH AY1 F L AY2 ER0 +HIGHFLYERS HH AY1 F L AY2 ER0 Z +HIGHGATE HH AY1 G EY2 T +HIGHGRADER HH AY1 G R EY1 D ER0 +HIGHGRADING HH AY1 G R EY2 D IH0 NG +HIGHHANDED HH AY1 HH AE2 N D IH0 D +HIGHLAND HH AY1 L AH0 N D +HIGHLANDER HH AY1 L AE2 N D ER0 +HIGHLANDER'S HH AY1 L AE2 N D ER0 Z +HIGHLANDERS HH AY1 L AE2 N D ER0 Z +HIGHLANDMAN HH AY1 L AE2 N D M AH0 N +HIGHLANDMEN HH AY1 L AE2 N D M AH0 N +HIGHLANDS HH AY1 L AH0 N D Z +HIGHLIGHT HH AY1 L AY2 T +HIGHLIGHTS HH AY1 L AY2 T S +HIGHLOWS HH AY1 L OW0 Z +HIGHLY HH AY1 L IY0 +HIGHMARKET HH AY1 M AA2 R K IH0 T +HIGHMINDED HH AY1 M AY1 N D IH0 D +HIGHMINDEDNESS HH AY1 M AY1 N D IH0 D N AH0 S +HIGHMORE HH AY1 M AO0 R +HIGHNESS HH AY1 N AH0 S +HIGHNESS'S HH AY1 N AH0 S IH0 Z +HIGHNESSES HH AY1 N AH0 S IH0 Z +HIGHPOCKETS HH AY1 P AA2 K AH0 T S +HIGHPOINT HH AY1 P OY2 N T +HIGHROAD HH AY1 R OW1 D +HIGHROADS HH AY1 R OW2 D Z +HIGHROB HH AY1 R AA2 B +HIGHS HH AY1 Z +HIGHSTRUNG HH AY1 S T R AH0 NG +HIGHT HH AY1 T +HIGHTOFT HH AY1 T AA1 F T +HIGHTON HH AY1 T AH0 N +HIGHTOWER HH AY1 T AW2 ER0 +HIGHTOWN HH AY1 T AW2 N +HIGHTS HH AY1 T S +HIGHTUM HH AY1 T AH0 M +HIGHTY HH AY1 T IY0 +HIGHWATER HH AY1 W AO2 T ER0 +HIGHWAY HH AY1 W EY2 +HIGHWAYMAN HH AY1 W EY1 M AH0 N +HIGHWAYMAN'S HH AY1 W EY2 M AH0 N Z +HIGHWAYMEN HH AY1 W EY2 M AH0 N +HIGHWAYS HH AY1 W EY2 Z +HIGLEY HH IH1 G L IY0 +HIGLI HH IY0 G L IY0 +HIGLINSON HH IH1 G L IH0 N S AH0 N +HIGNETT HH IH1 G N IH0 T +HIGNETT'S HH IH1 G N AH0 T S +HIGNORANT HH IH1 G N ER0 AH0 N T +HIGUEROTA HH IY0 G EH0 R OW1 T AH0 +HIH HH IH1 +HIJACKERS HH AY1 JH AE2 K ER0 Z +HIJAZ HH AY1 JH AE2 Z +HIJO HH AY1 JH OW0 +HIJOS HH AY1 JH OW0 Z +HIJRAH HH AY1 JH R AH0 +HIKE HH AY1 K +HIKED HH AY1 K T +HIKER HH AY1 K ER0 +HIKERS HH AY1 K ER0 Z +HIKES HH AY1 K S +HIKIN HH AY1 K IH0 N +HIKING HH AY1 K IH0 NG +HIKOROKU HH AY1 K AH0 R OW1 K UW2 +HIKOZAEMON HH AY1 K AA2 Z EY1 M AH0 N +HIKUERU HH AY1 K UW0 EY1 R UW0 +HIL HH IH1 L +HIL'RY HH IH1 L R IY0 +HILAIRE HH IH0 L EH1 R +HILAIRE'S HH IH0 L EY1 R Z +HILAL HH IH1 L AH0 L +HILAND HH IH1 L AH0 N D +HILARIA HH IY0 L AA1 R IY0 AH0 +HILARIO HH IY0 L AA1 R IY0 OW0 +HILARIO'S HH IH0 L EH1 R IY0 OW0 Z +HILARION HH IH0 L EH1 R IY0 AH0 N +HILARION'S HH IH0 L EH1 R IY0 AH0 N Z +HILARIOUS HH IH0 L EH1 R IY0 AH0 S +HILARIOUSLY HH IH0 L EH1 R IY0 AH0 S L IY0 +HILARIOUSNESS HH IH0 L EH1 R IY0 AH0 S N AH0 S +HILARITIES HH IH0 L EH1 R AH0 T IY0 Z +HILARITY HH IH0 L EH1 R AH0 T IY0 +HILARIUS HH IH0 L EH1 R IY0 AH0 S +HILARO HH IY0 L AA1 R OW0 +HILARUS HH IY0 L AA1 R AH0 S +HILARY HH IH1 L ER0 IY0 +HILARY'S HH IH0 L EH1 R IY0 Z +HILARYS HH IH0 L EH1 R IY0 Z +HILAS HH IH1 L AH0 Z +HILBERRY HH IH1 L B EH2 R IY0 +HILBERT HH IH1 L B ER0 T +HILBERY HH IH1 L B ER0 IY0 +HILBERY'S HH IH1 L B ER0 IY0 Z +HILBERYS HH IH1 L B ER0 IY0 Z +HILBOROUGH HH IH1 L B ER0 OW0 +HILBROUGH HH IH1 L B R AW0 +HILBROUGH'S HH IH1 L B R AW0 Z +HILBROUGHS HH IH1 L B R AW0 Z +HILBRUN HH IH1 L B R AH0 N +HILCHESTER HH IH1 L CH EH2 S T ER0 +HILD HH IH1 L D +HILDA HH IH1 L D AH0 +HILDA'S HH IH1 L D AH0 Z +HILDEBRAND HH IH1 L D IH0 B R AE2 N D +HILDEBRAND'S HH IH1 L D IH0 B R AE2 N D Z +HILDEBRANDS HH IH1 L D IH0 B R AE2 N D Z +HILDEBRANDT HH IH1 L D IH0 B R AH0 N T +HILDEBRANT HH IH1 L D IH0 B R AH0 N T +HILDEBROD HH IH1 L D IH0 B R AH0 D +HILDEGARD HH IH1 L D AH0 G AA2 R D +HILDEGARDE HH IH1 L D IH0 G AA2 R D +HILDEGARDE'S HH IH1 L D AH0 G AA2 R D Z +HILDEGARDIS HH IH1 L D AH0 G AA2 R D IH0 S +HILDEGUND HH IH1 L D AH0 G AH0 N D +HILDEGUNDE HH IH1 L D IH0 G AH1 N D +HILDEMALLER HH IH1 L D IH0 M AO1 L ER0 +HILDEMALLER'S HH IH1 L D IH0 M AO1 L ER0 Z +HILDEN HH AY1 L D AH0 N +HILDENHEIM HH IH1 L D IH0 N HH AY0 M +HILDERIC HH IH1 L D ER0 IH0 K +HILDERMAN HH IH1 L D ER0 M AH0 N +HILDERMAN'S HH IH1 L D ER0 M AH0 N Z +HILDERTON HH IH1 L D ER0 T AH0 N +HILDESHEIM HH IH1 L D Z HH AY2 M +HILDGUND HH IH1 L D G AH0 N D +HILDING HH IH1 L D IH0 NG +HILDITCH HH IH1 L D IH0 CH +HILDITCH'S HH IH1 L D IH1 CH IH0 Z +HILDRED HH IH1 L D R IH0 D +HILDRETH HH IH1 L D R IH0 TH +HILDRETH'S HH IH1 L D R IH0 TH S +HILDUR HH IH1 L D UH1 R +HILDY HH IH1 L D IY0 +HILDYARD HH IH1 L D Y ER0 D +HILDYARD'S HH IH1 L D Y ER0 D Z +HILES HH AY1 L Z +HILGARD HH IH1 L G AA2 R D +HILGARDE HH IH1 L G AA2 R D +HILGENDORF HH IH1 L G IH0 N D AO0 R F +HILI HH IH1 L IY0 +HILKIAH HH IH1 L K AY1 AH0 +HILL HH IH1 L +HILL'S HH IH1 L Z +HILLA HH IH1 L AH0 +HILLAH HH IH1 L AH0 +HILLARD HH IH1 L ER0 D +HILLARD'S HH IH1 L ER0 D Z +HILLARY HH IH1 L ER0 IY0 +HILLAS HH IH1 L AH0 S +HILLATON HH IH1 L AH0 T AA1 N +HILLBILLY HH IH1 L B IH0 L IY0 +HILLBRIDGE HH IH1 L B R IH0 JH +HILLCREST HH IH1 L K R EH0 S T +HILLCREST'S HH IH1 L K R EH0 S T S +HILLCRIST HH IH1 L K R IH2 S T +HILLEBOE HH IH1 L IH0 B OW1 +HILLEBRANT HH IH1 L IH0 B R AH0 N T +HILLED HH IH1 L D +HILLEL HH IH2 L EH1 L +HILLER HH IH1 L ER0 +HILLER'S HH IH1 L ER0 Z +HILLERN HH IH1 L ER0 N +HILLERS HH IH1 L ER0 Z +HILLERTON HH IH1 L ER0 T AH0 N +HILLES HH IH1 L IY0 Z +HILLEY HH IH1 L IY0 +HILLFORD HH IH1 L F ER0 D +HILLGROVE HH IH1 L G R OW2 V +HILLHOUSE HH IH1 L HH AW2 S +HILLIARD HH IH1 L Y AA0 R D +HILLIARD'S HH IH1 L Y ER0 D Z +HILLIER HH IH1 L IY0 ER0 +HILLIGOSS HH IH1 L IH0 G AA0 S +HILLING HH IH1 L IH0 NG +HILLINGSWORTH HH IH1 L IH0 NG Z W ER2 TH +HILLIS HH IH1 L IH0 S +HILLISTON HH IH1 L IH0 S T AA0 N +HILLISTON'S HH IH1 L IH0 S T AA0 N Z +HILLITES HH IH1 L AY0 T S +HILLMAN HH IH1 L M AE2 N +HILLMAN'S HH IH1 L M AH0 N Z +HILLMEN HH IH1 L M EH0 N +HILLMER HH IH1 L M ER0 +HILLMER'S HH IH1 L M ER0 Z +HILLNER HH IH1 L N ER0 +HILLO HH IH1 L OW0 +HILLOA HH IH1 L OW1 AH0 +HILLOAH HH IH1 L OW1 AH0 +HILLOCK HH IH1 L AH0 K +HILLOCKS HH IH1 L AH0 K S +HILLOCKY HH IH1 L AH0 K IY0 +HILLOH HH IH1 L AH0 +HILLPORT HH IH1 L P AO2 R T +HILLS HH IH1 L Z +HILLSBORO HH IH1 L Z B ER0 OW0 +HILLSBOROUGH HH IH1 L Z B ER0 OW0 +HILLSBRO HH IH1 L Z B R OW0 +HILLSCOMBE HH IH1 L S K OW1 M B IY0 +HILLSDALE HH IH1 L Z D EY2 L +HILLSIDE HH IH1 L S AY2 D +HILLSIDE'S HH IH1 L S AY2 D Z +HILLSIDES HH IH1 L S AY2 D Z +HILLSLOPE HH IH1 L S L OW1 P +HILLSMAN HH IH1 L S M AH0 N +HILLSMEN HH IH1 L S M IH0 N +HILLSOVER HH IH1 L S AH0 V ER0 +HILLSTOKE HH IH1 L S OW2 K +HILLSVILLE HH IH1 L S V IH2 L +HILLTON HH IH1 L T AH0 N +HILLTONIANS HH IH1 L T OW1 N IY0 AH0 N Z +HILLTOP HH IH1 L T AA2 P +HILLTOPS HH IH1 L T AA2 P S +HILLTOWN HH IH1 L T AW2 N +HILLVIEW HH IH1 L V Y UW2 +HILLWARD HH IH1 L W ER0 D +HILLY HH IH1 L IY0 +HILLYAR HH IH1 L Y AA2 R +HILLYARD HH IH1 L Y AA2 R D +HILLYARD'S HH IH1 L Y AA2 R D Z +HILLYER HH IH1 L IY0 ER0 +HILMA HH IH1 L M AH0 +HILMA'S HH IH1 L M AH0 Z +HILMAN HH IH1 L M AH0 N +HILMAR HH IH1 L M ER0 +HILNARIC HH IH1 L N ER0 IH0 K +HILO HH IY1 L OW0 +HILP HH IH1 L P +HILS HH IH1 L Z +HILSBORN HH IH1 L Z B ER0 N +HILSE HH IH1 L S +HILSENHOFF HH IH1 L S IH0 N HH AO0 F +HILSEY HH IH1 L S IY0 +HILSON HH IH1 L S AH0 N +HILST HH IH1 L S T +HILT HH IH1 L T +HILTED HH IH1 L T IH0 D +HILTNER HH IH1 L T N ER0 +HILTNERS HH IH1 L T N ER0 Z +HILTON HH IH1 L T AH0 N +HILTON'S HH IH1 L T AH0 N Z +HILTONS HH IH1 L T AH0 N Z +HILTS HH IH1 L T S +HILTZE HH IH1 L T S +HILVERSUM HH IH1 L V ER0 S AH0 M +HILYARD HH AH0 L Y AA1 R D +HILYARD'S HH AH0 L Y AA1 R D Z +HIM HH IH1 M +HIM IH0 M +HIM' HH IH1 M +HIM'LL HH IH1 M AH0 L +HIM'S HH IH1 M Z +HIMAGE HH IH2 M IH0 JH +HIMALAYA HH IH2 M AH0 L AY1 AH0 +HIMALAYA HH IH2 M AH0 L EY1 AH0 +HIMALAYAN HH IH2 M AH0 L AY1 AH0 N +HIMALAYAN HH IH2 M AH0 L EY1 AH0 N +HIMALAYAS HH IH2 M AH0 L AY1 AH0 S +HIMALAYAS HH IH2 M AH0 L EY1 AH0 S +HIMATION HH AH0 M EY1 SH AH0 N +HIMAVAT HH IH2 M AH0 V AA1 T +HIME HH AY1 M +HIMEGIMI HH IH1 M IH0 JH IY1 M IY0 +HIMEJI HH AY1 M JH IY0 +HIMENE HH IH0 M IY2 N +HIMENES HH AY1 M N Z +HIMERA IH0 M EH1 R AH0 +HIMILCO HH IH0 M IH1 L K OW0 +HIMILTRUDE HH IH0 M IH1 L T R UW0 D +HIMINE HH IH2 M AH0 N +HIMKOF HH IH1 M K AO0 F +HIMMEL HH IH1 M AH0 L +HIMMELS HH IH1 M AH0 L Z +HIMS HH IH1 M Z +HIMSE'F HH IH0 M S EH1 F +HIMSEEMED HH IH0 M S IY1 M D +HIMSEL HH IH0 M S EH1 L +HIMSELF HH IH0 M S EH1 L F +HIMSELF'S HH IH0 M S EH1 L F S +HIMSELFE HH IH0 M S EH1 L F +HIMSELL HH IH0 M S EH1 L +HIMSILF HH IH0 M S AH0 L F +HIN HH IH1 N +HINA HH AY1 N AH0 +HINAUS HH IH1 N AO1 Z +HINAYANA HH IH1 N AH0 Y AA1 N AH0 +HINC HH IH1 NG K +HINCH HH IH1 N CH +HINCHCLIFFE HH IH1 N CH K L IH0 F +HINCHEL HH IH1 N K AH0 L +HINCHEY HH IH1 N CH IY0 +HINCHFORD HH IH1 N CH F ER0 D +HINCHFORD'S HH IH1 N CH F ER0 D Z +HINCHFORDS HH IH1 N CH F ER0 D Z +HINCHINBROOK HH IH1 N CH IH0 N B R UH2 K +HINCHINGBROKE HH IH1 N CH IH0 NG B R OW2 K +HINCHMAN HH IH1 NG K M AH0 N +HINCKELDEYM HH IH1 NG K AH0 L D IH2 IH1 M +HINCKLEY HH IH1 NG K L IY0 +HINCKLEY'S HH IH1 NG K L IY0 Z +HINCKMAN HH IH1 NG K M AH0 N +HINCKS HH IH1 NG K S +HINCMAR HH IH1 NG K M AA0 R +HIND HH AY1 N D +HIND'S HH AY1 N D Z +HINDAL HH IH1 N D AH0 L +HINDBAD HH AY1 N D B AE0 D +HINDE HH IH1 N D +HINDELOOPEN HH IH1 N D AH0 L UW2 P AH0 N +HINDENBERG HH IH1 N D AH0 N B ER0 G +HINDENBURG HH IH1 N D AH0 N B ER0 G +HINDENBURG'S HH IH1 N D AH0 N B ER0 G Z +HINDER HH IH1 N D ER0 +HINDERANCE HH IH1 N D ER0 AH0 N S +HINDERANCES HH IH1 N D ER0 AH0 N S AH0 Z +HINDERED HH IH1 N D ER0 D +HINDERER HH IH1 N D ER0 ER0 +HINDERERS HH IH1 N D ER0 ER0 Z +HINDERETH HH IH1 N D ER0 IH0 TH +HINDERIN HH IH1 N D ER0 IH0 N +HINDERING HH IH1 N D ER0 IH0 NG +HINDERMOST HH IH1 N D ER0 M OW2 S T +HINDERS HH IH1 N D ER0 Z +HINDES HH IH1 N D Z +HINDFORD HH IH1 N D F ER0 D +HINDHEAD HH IH1 N D HH EH2 D +HINDI HH IH1 N D IY0 +HINDLE HH IH1 N D AH0 L +HINDLEGS HH IH1 N D L EH2 G Z +HINDLEY HH IH1 N D L IY0 +HINDLIP HH IH1 N D L IH2 P +HINDMAN HH AY1 N D M AH0 N +HINDMAN'S HH IH1 N D M AH0 N Z +HINDMOST HH IH1 N D M OW2 S T +HINDOO HH AY1 N D UW2 +HINDOO'S HH IH1 N JH UW1 Z +HINDOOISM HH IH1 N D AO2 IH2 Z AH0 M +HINDOOS HH IH1 N JH UW1 Z +HINDOOSTAN HH IH1 N D AO2 S T AE2 N +HINDOOSTANEE HH IH1 N D AO2 S T AH0 N IY1 +HINDOSTAN HH IH1 N D OW0 S T AA1 N +HINDOSTANEE HH IH1 N D OW0 S T AA1 N IY1 +HINDOSTANI HH IH1 N D OW0 S T AA1 N IY0 +HINDQUARTER HH AY1 N D K AO2 R T ER0 +HINDQUARTER HH AY1 N D K W AO2 R T ER0 +HINDQUARTERS HH AY1 N D K AO2 R T ER0 Z +HINDQUARTERS HH AY1 N D K W AO2 R T ER0 Z +HINDRANCE HH IH1 N D R AH0 N S +HINDRANCES HH IH1 N D R AH0 N S IH0 Z +HINDRED HH IH1 N D R IH0 D +HINDRICKSON HH IH1 N D R IH0 K S AH0 N +HINDS HH AY1 N D Z +HINDSIDE HH AY1 N D S AY2 D +HINDSIGHT HH AY1 N D S AY2 T +HINDSIGHT HH AY1 N S AY2 T +HINDU HH IH1 N D UW2 +HINDU'S HH IH1 N D UW2 Z +HINDUISM HH IH1 N JH UW0 IH2 Z AH0 M +HINDUS HH IH1 N D UW2 Z +HINDUSTAN HH IH1 N D UW0 S T AE2 N +HINDUSTANEE HH IH1 N D UW0 S T AE2 N IY1 +HINDUSTANI HH IH1 N D UW0 S T AA1 N IY0 +HINE HH AY1 N +HINE'S HH AY1 N Z +HINEIN HH AY1 N AY2 N +HINES HH AY1 N Z +HINFANT HH IH1 N F AH0 N T +HING HH IH1 NG +HINGE HH IH1 N JH +HINGED HH IH1 N JH D +HINGES HH IH1 N JH IH0 Z +HINGHAM HH IH1 NG AH0 M +HINGIN HH IH1 NG AH0 N +HINGING HH IH1 NG IH0 NG +HINGLAND HH IH1 NG G L AH0 N D +HINGLISH HH IH1 NG G L IH0 SH +HINGS HH IH1 NG Z +HINGSTON HH IH1 NG S T AH0 N +HINGSTON'S HH IH1 NG S T AH0 N Z +HINKLE HH IH1 NG K AH0 L +HINKLE'S HH IH1 NG K AH0 L Z +HINKLING HH IH1 NG K L IH0 NG +HINKMAN HH IH1 NG K M AH0 N +HINKS HH IH1 NG K S +HINKSEY HH IH1 NG K S IY0 +HINKSON HH IH1 NG K S AH0 N +HINKSVILLE HH IH1 NG K S V IH2 L +HINMAN HH IH1 N M AH0 N +HINNA HH IH1 N AH0 +HINNERCENT HH IH1 N ER0 S EH1 N T +HINNOCENT HH IH1 N AH0 S AH0 N T +HINNOM HH IH1 N AH0 M +HINNY HH IH1 N IY0 +HINOE HH IY0 N OW0 +HINOMISAKI HH IH0 N OW1 M IY0 S AA1 K IY0 +HINPOHA HH IH1 N P OW1 HH AH0 +HINPOHA'S HH IH1 N P OW2 AH0 Z +HINRICH HH IH1 N R IH0 K +HINS HH IH1 N Z +HINSDALE HH IH1 N S D EY2 L +HINSETH HH IH1 N S IH0 TH +HINSHAW HH IH1 N SH AO2 +HINSON HH IH1 N S AH0 N +HINT HH IH1 N T +HINTED HH IH1 N IH0 D +HINTED HH IH1 N T AH0 D +HINTED HH IH1 N T IH0 D +HINTER HH IH1 N T ER0 +HINTEREST HH IH1 N T R AH0 S T +HINTERLAND HH IH1 N T ER0 L AE2 N D +HINTERLANDS HH IH1 N T ER0 L AE2 N D Z +HINTIN HH IH1 N T IH0 N +HINTING HH IH1 N T IH0 NG +HINTINGS HH IH1 N T IH0 NG Z +HINTOCK HH IH1 N T AH0 K +HINTON HH IH1 N T AH0 N +HINTON'S HH IH1 N T AH0 N Z +HINTS HH IH1 N T S +HINTZA HH IH1 N T Z AH0 +HINZ HH IH1 N Z +HINZA HH IH1 N Z AH0 +HINZE HH IH1 N Z +HINZELMANN HH IH1 N Z AH0 L M AH0 N +HIO HH AY1 OW0 +HIORBA HH AO1 R B AH0 +HIORDIS HH AO1 R D IH0 S +HIP HH IH1 P +HIPCROFT HH IH1 P K R AO2 F T +HIPGRAVE HH IH1 P G R EY2 V +HIPOLITO IY1 P OW0 L IY1 T OW0 +HIPP HH IH1 P +HIPPALCA HH IH1 P AE1 L K AH0 +HIPPANTHIGH HH IH1 P AH0 N TH AY1 +HIPPARCHIA HH IH1 P AA1 R K IY0 AH0 +HIPPARCHUS HH IH1 P ER0 CH AH0 S +HIPPARETE HH IH1 P ER0 IY2 T +HIPPARION HH IH1 P EH2 R IY0 AH0 N +HIPPASUS HH IH1 P AH0 S AH0 S +HIPPE HH IH1 P +HIPPED HH IH1 P T +HIPPEL HH IH1 P AH0 L +HIPPERTY HH IH1 P ER0 T IY0 +HIPPESLEY HH IH1 P IH0 S L IY0 +HIPPETTS HH IH1 P IH0 T S +HIPPETY HH IH1 P AH0 T IY0 +HIPPI HH IH1 P IY0 +HIPPIAS HH IH1 P IY0 AH0 S +HIPPICUS HH IH1 P IH0 K AH0 S +HIPPISLEY HH IH1 P IY0 Z L IY0 +HIPPITY HH IH1 P AH0 T IY0 +HIPPLE HH IH1 P AH0 L +HIPPO HH IH1 P OW0 +HIPPO'S HH IH2 P OW0 Z +HIPPOCAMPUS HH IH2 P OW0 K AE1 M P AH0 S +HIPPOCRAS HH IH0 P AO1 K R AH0 Z +HIPPOCRATES HH IH0 P AO1 K R AH0 T IY0 Z +HIPPOCRATES HH IH1 P AH0 K R EY2 T S +HIPPOCRATIC HH IH0 P AH0 K R AE1 T IH0 K +HIPPOCRENE HH IH1 P AH0 K R IY0 N +HIPPODAMIA HH IH1 P AH0 D EY1 M IY0 AH0 +HIPPODROME HH IH1 P AH0 D R OW2 M +HIPPODROME'S HH IH1 P AH0 D R OW2 M Z +HIPPODROMES HH IH1 P AH0 D R OW2 M Z +HIPPOGRIF HH IH1 P AH0 G R IH0 F +HIPPOGRIFF HH IH1 P AH0 G R IH0 F +HIPPOGRIFFS HH IH1 P AH0 G R IH0 F S +HIPPOGRYPH HH IH1 P AH0 G R IH1 F +HIPPOI HH IH1 P OY0 +HIPPOLITA HH IH2 P OW0 L IY1 T AH0 +HIPPOLITA'S HH IH2 P OW0 L IY1 T AH0 Z +HIPPOLITO HH IH2 P OW0 L IY1 T OW0 +HIPPOLITO'S HH IH2 P OW0 L IY1 T OW0 Z +HIPPOLITUS HH IH2 P OW0 L IY1 T AH0 S +HIPPOLOCHUS HH IH1 P AH0 L AA1 K AH0 S +HIPPOLYTA HH IH2 P AA2 L IH0 T AH0 +HIPPOLYTE HH IH1 P AH0 L AY2 T +HIPPOLYTE'S HH IH1 P AH0 L AY2 T S +HIPPOLYTUS HH AH0 P AA1 L AH0 T AH0 S +HIPPOMENES HH IH1 P AH0 M IY2 N Z +HIPPONAX HH IH1 P AH0 N AE0 K S +HIPPONICUS HH IH2 P AA1 N AH0 K AH0 S +HIPPOPOTAMI HH IH2 P AH0 P AA1 T AH0 M IY0 +HIPPOPOTAMUS HH IH2 P AH0 P AA1 T AH0 M AH0 S +HIPPOPOTAMUSES HH IH2 P AH0 P AA1 T AH0 M AH0 S IH0 Z +HIPPOS HH IH1 P OW0 Z +HIPPS HH IH1 P S +HIPPUS HH IH1 P AH0 S +HIPPY HH IH1 P IY0 +HIPPY'S HH IH1 P IY0 Z +HIPS HH IH1 P S +HIPSLEY HH IH1 P S L IY0 +HIR HH IH2 R +HIR'D HH ER1 D +HIRA HH IH1 R AH0 +HIRABUL HH IH0 R AH0 B UW1 L +HIRAM HH AY1 R AH0 M +HIRAM'S HH IH0 R AA1 M Z +HIRAMAN HH IH0 R AA1 M AH0 N +HIRAMANI HH IH0 R AH0 M AA1 N IY0 +HIRANYA HH IH0 R AA1 N Y AH0 +HIRATA HH IH0 R AA1 T AH0 +HIRD HH ER1 D +HIRE HH AY1 ER0 +HIRE HH AY1 R +HIRED HH AY1 ER0 D +HIRELING HH AY1 ER0 L IH0 NG +HIRELING'S HH AY1 ER0 L IH0 NG Z +HIRELINGS HH AY1 ER0 L IH0 NG Z +HIRER HH AY1 ER0 R +HIRERS HH AY1 ER0 ER0 Z +HIRES HH AY1 ER0 Z +HIRES HH AY1 R Z +HIRIN HH IH1 R IH0 N +HIRING HH AY1 R IH0 NG +HIRISH HH AY1 R IH0 SH +HIRLAJ HH ER1 L AY2 +HIRLAJI HH ER1 L AA1 JH IY0 +HIRNIO HH ER1 N IY0 OW0 +HIRO HH IH1 R OW0 +HIROM HH IH2 R OW0 M +HIRONDELLE HH IH2 R OW0 N D EH1 L +HIROSHIMA HH IH2 R OW0 SH IY1 M AH0 +HIROSHIMA HH IH2 R OW1 SH IH0 M AH0 +HIROSHIMI HH IH2 R OW0 SH IY1 M IY0 +HIRPINGTON HH ER1 P IH0 NG T AH0 N +HIRPINGTON'S HH ER1 P IH0 NG T AH0 N Z +HIRPLE HH ER1 P AH0 L +HIRPLING HH ER1 P AH0 L IH0 NG +HIRSCH HH ER1 SH +HIRSCH'S HH ER1 SH IH0 Z +HIRSCHBERG HH ER1 SH B ER0 G +HIRSCHHORN HH ER1 SH HH ER0 N +HIRSCHVOGEL HH ER1 SH V OW2 G AH0 L +HIRSFELD HH IH1 R S F EH2 L D +HIRST HH ER1 S T +HIRST'S HH ER1 S T S +HIRSUTE HH ER0 S UW1 T +HIRTIUS HH ER1 T IY0 IH0 S +HIRUNDO HH IH1 R UW2 N D OW0 +HIS HH IH0 Z +HIS HH IH1 Z +HIS'N HH IH1 AH0 N +HISAR HH IH0 S AA1 R +HISCOCK HH IH1 S K AH0 K +HISEN HH IH1 Z AH0 N +HISGINS HH IH0 S G IH0 N Z +HISH HH IH1 SH +HISHAM HH IH1 SH AH0 M +HISI HH IH1 S IY0 +HISLOP HH IH1 S L AH0 P +HISN HH IH1 S N +HISPAL HH IH1 S P AH0 L +HISPANIA HH IH0 S P AE1 N Y AH0 +HISPANIC HH IH0 S P AE1 N IH0 K +HISPANIOLA HH IH0 S P AA2 NG OW1 L AH0 +HISPANO HH IH0 S P AA1 N OW0 +HISS HH IH1 S +HISS'D HH IH1 SH D +HISSAR HH IH1 S ER0 +HISSE'F HH IH1 S IH0 F +HISSED HH IH1 S T +HISSEF HH IH1 S EH0 F +HISSEFF HH IH2 S EH1 F +HISSEL HH IH1 S AH0 L +HISSELF HH IH2 S EH1 L F +HISSEN HH IH1 S AH0 N +HISSES HH IH1 S IH0 Z +HISSILF HH IH1 S AH0 L F +HISSIN HH IH1 S IH0 N +HISSING HH IH1 S IH0 NG +HISSINGLY HH IH1 S IH0 NG L IY0 +HISSINGS HH IH1 S IH0 NG Z +HISSODECHA HH IH1 S AO2 D EH1 CH AH0 +HIST HH IH0 S T +HIST'RY HH IH1 S T R IY0 +HISTAH HH IH1 S T AH0 +HISTED HH IH1 S T IH0 D +HISTER HH IH0 S T ER0 +HISTIAEUS HH IH1 S T IY0 EY1 AH0 S +HISTIN HH IH1 S T IH0 N +HISTOIRE HH IH1 S T OY2 R +HISTOIRES HH IH0 S T W AA2 Z +HISTOLOGICAL HH IH1 S T AH0 L AA1 JH IH0 K AH0 L +HISTOLOGY HH IH0 S T AA1 L AH0 JH IY0 +HISTORIA HH IH0 S T AO1 R IY0 AH0 +HISTORIAE HH IH0 S T AO1 R IY0 AH0 +HISTORIAN HH IH0 S T AO1 R IY0 AH0 N +HISTORIAN'S HH IH0 S T AO1 R IY0 AH0 N Z +HISTORIANS HH IH0 S T AO1 R IY0 AH0 N Z +HISTORIC HH IH0 S T AO1 R IH0 K +HISTORICAL HH IH0 S T AO1 R IH0 K AH0 L +HISTORICALLY HH IH0 S T AO1 R IH0 K AH0 L IY0 +HISTORICALLY HH IH0 S T AO1 R IH0 K L IY0 +HISTORICITY HH IH0 S T AO1 R IH0 S IH2 T IY0 +HISTORICO HH IH0 S T AO1 R IH0 K OW2 +HISTORIE HH IH0 S T AO1 R IY0 +HISTORIES HH IH1 S T ER0 IY0 Z +HISTORIES HH IH1 S T R IY0 Z +HISTORIETTES HH IH0 S T AO1 R IY0 EH1 T S +HISTORIOGRAPHER HH IH0 S T AO2 R IY0 AA1 G R AH0 F ER0 +HISTORIOGRAPHERS HH IH0 S T AO2 R IY0 AA1 G R AH0 F ER0 Z +HISTORIQUE HH IH1 S T ER0 IY1 K +HISTORIQUES HH IH1 S T ER0 IY1 K S +HISTORY HH IH1 S T ER0 IY0 +HISTORY HH IH1 S T R IY0 +HISTORY'S HH IH1 S T ER0 IY0 Z +HISTORY'S HH IH1 S T R IY0 Z +HISTRIO HH IH1 S T R IY0 OW0 +HISTRION HH IH1 S T R IY0 AH0 N +HISTRIONIC HH IH2 S T R IY0 AA1 N IH0 K +HISTRIONICALLY HH IH1 S T R IY0 AA1 N IH0 K L IY0 +HISTRIONICS HH IH2 S T R IY0 AA1 N IH0 K S +HISTRIONISM HH IH1 S T R IY0 AH0 N IH2 Z AH0 M +HIT HH IH1 T +HIT'LL HH AY1 T AH0 L +HIT'S HH IH0 T S +HITACHI HH IH0 T AA1 CH IY0 +HITANI HH IH0 T AA1 N IY0 +HITCH HH IH1 CH +HITCHCOCK HH IH1 CH K AA2 K +HITCHCOCK'S HH IH1 CH K AA2 K S +HITCHED HH IH1 CH T +HITCHENER HH IH1 CH N ER0 +HITCHENER'S HH IH1 CH N ER0 Z +HITCHER HH IH1 CH ER0 +HITCHES HH IH1 CH IH0 Z +HITCHIN HH IH1 CH IH0 N +HITCHIN'S HH IH1 CH IH0 N Z +HITCHING HH IH1 CH IH0 NG +HITCHINS HH IH1 CH IH0 N Z +HITCHY HH IH1 CH IY0 +HITE HH AY1 T +HITE'S HH AY1 T S +HITH HH IH1 TH +HITHE HH AY1 DH +HITHER HH IH1 DH ER0 +HITHERTO HH IH1 DH ER2 T UW1 +HITHERWARD HH IH1 DH ER0 W ER0 D +HITHERWARDS HH IH1 DH ER0 W ER0 D Z +HITLE HH AY1 T AH0 L +HITLER HH IH1 T L ER0 +HITLER'S HH IH1 T L ER0 Z +HITO HH IY1 T OW0 +HITOPADESA HH IH0 T OW1 P AH0 D EH1 S AH0 +HITS HH IH1 T S +HITT HH IH1 T +HITTAWAY HH IH1 T AH0 W EY0 +HITTAWAY'S HH IH1 T AH0 W EY2 Z +HITTELL HH IH1 T AH0 L +HITTER HH IH1 T ER0 +HITTERS HH IH1 T ER0 Z +HITTIM HH IH1 T AY2 M +HITTIN HH IH1 T IH0 N +HITTING HH IH1 T IH0 NG +HITTITE HH IH1 T AY0 T +HITTITE HH IH1 T AY2 T +HITTITES HH IH1 T AY2 T S +HITTY HH IH1 T IY0 +HITTY'S HH IH1 T IY0 Z +HITZ HH IH1 T S +HITZIG HH IH1 T Z IH0 G +HIU UW1 +HIV HH AY1 V +HIVA HH AY1 V AH0 +HIVE HH AY1 V +HIVED HH AY1 V D +HIVEN HH AY1 V AH0 N +HIVER HH AY1 V ER0 +HIVES HH AY1 V Z +HIVIN HH IH1 V IH0 N +HIVING HH AY1 V IH0 NG +HIVINS HH IH1 V IH0 N Z +HIVITES HH AY1 V AY2 T S +HIVOHITEE HH AY1 V OW1 HH IH0 T IY0 +HIXLEY HH IH1 K S L IY0 +HIXON HH IH1 K S AH0 N +HIYA HH IY1 AH0 +HIYU HH Y UW1 +HIZ HH IH0 Z +HIZZIE HH IH1 Z IY0 +HIZZIES HH IH1 Z IY0 Z +HIZZY HH IH1 Z IY0 +HJALMAR HH Y AA1 L M ER0 +HJALTI HH Y AA1 L T IY0 +HJORTH HH AH0 Y AO1 R TH +HLABULANA HH L AE1 B Y UW0 L AE1 N AH0 +HLALA HH L AA1 L AH0 +HLANGANI HH L AA1 NG G AH0 N IY0 +HLANGWANE HH L AE1 NG G W EY0 N +HLAWA HH L AA1 W AH0 +HLO HH L OW0 +HLOMA HH L OW1 M AH0 +HLORG HH L AO0 R G +HLUBI HH L AH0 B IY0 +HLUBIS HH L UW1 B IH0 S +HM HH AH0 M +HMELNITSKI HH AH0 M EH1 L N IH0 T S K IY0 +HMM HH M +HMMM HH M M +HN N +HNOSSA HH N OW1 S AH0 +HO HH OW1 +HO'D HH OW1 D +HO'S HH OW1 Z +HOA HH OW1 AH0 +HOAD HH OW1 D +HOADLEY HH OW1 D L IY0 +HOADLY HH OW1 D L IY0 +HOAEM HH OW1 AH0 M +HOAG HH OW1 G +HOAK HH OW1 K +HOAM HH OW1 M +HOAN HH OW1 N +HOANG HH OW1 NG +HOANGTI HH OW1 NG K T IY2 +HOAPE HH OW1 P +HOAR HH AO1 R +HOAR'S HH AO1 R Z +HOARD HH AO1 R D +HOARDED HH AO1 R D IH0 D +HOARDER HH AO1 R D ER0 +HOARDERS HH AO1 R D ER0 Z +HOARDING HH AO1 R D IH0 NG +HOARDINGS HH AO1 R D IH0 NG Z +HOARDS HH AO1 R D Z +HOARE HH AO1 R +HOARFROST HH AO1 R F R AO1 S T +HOARHOUND HH AO1 R HH AW2 N D +HOARIEST HH AO1 R IY0 AH0 S T +HOARINESS HH AO1 R IY0 N AH0 S +HOARSE HH AO1 R S +HOARSELY HH AO1 R S L IY0 +HOARSENED HH AO1 R S N D +HOARSENESS HH AO1 R S N AH0 S +HOARSER HH AO1 R S ER0 +HOARSEST HH AO1 R S IH0 S T +HOARY HH AO1 R IY0 +HOAST HH OW1 S T +HOATZIN HH OW1 T S IH0 N +HOATZINS HH OW1 T S IH0 N Z +HOAX HH OW1 K S +HOAXED HH OW1 K S T +HOAXER HH OW1 K S ER0 +HOAXES HH OW1 K S IH0 Z +HOAXING HH OW1 K S IH0 NG +HOB HH AA1 B +HOB'S HH AA1 B Z +HOBART HH OW1 B AA2 R T +HOBART'S HH OW1 B AA2 R T S +HOBARTON HH OW1 B AA2 R T AH0 N +HOBBAMOCK HH AA1 B AH0 M AA2 K +HOBBEMA HH AA1 B IY0 M AH0 +HOBBES HH AA1 B IY0 Z +HOBBES HH AA1 B Z +HOBBES'S HH AA1 B IY0 Z IH0 Z +HOBBIE HH AA1 B IY0 +HOBBIE'S HH AA1 B IY0 Z +HOBBIES HH AA1 B IY0 Z +HOBBIMA HH AA1 B IY1 M AH0 +HOBBLE HH AA1 B AH0 L +HOBBLED HH AA1 B AH0 L D +HOBBLEDEHOY HH AA1 B AH0 L D EH1 HH OY0 +HOBBLEDEHOYS HH AA1 B AH0 L D EH1 HH OY0 Z +HOBBLES HH AA1 B AH0 L Z +HOBBLIN HH AA1 B L IH0 N +HOBBLING HH AA1 B AH0 L IH0 NG +HOBBLING HH AA1 B L IH0 NG +HOBBS HH AA1 B Z +HOBBS'S HH AA1 B Z IH0 Z +HOBBY HH AA1 B IY0 +HOBBY'S HH AA1 B IY0 Z +HOBBYHORSE HH AA1 B IY0 HH AO2 R S +HOBDAY HH AA1 B D EY2 +HOBDEN HH AA1 B D AH0 N +HOBDEN'S HH AA1 B D AH0 N Z +HOBGOBLIN HH AA1 B G AA1 B L IH0 N +HOBGOBLINS HH AA1 B G AA1 B L IH0 N Z +HOBHOUSE HH AA1 B HH AW2 S +HOBHOUSE'S HH AA1 B HH AW2 S IH0 Z +HOBIN HH OW1 B IH0 N +HOBKIRK HH AA1 B K ER0 K +HOBLER HH OW1 B L ER0 +HOBNAIL HH AA1 B N EY2 L +HOBNAILED HH AA1 B N EY2 L D +HOBNAILS HH AA1 B N EY2 L Z +HOBNELL HH AA1 B N AH0 L +HOBNOB HH AA1 B N AA2 B +HOBNOBBED HH AA1 B N AA2 B D +HOBNOBBING HH AA1 B N AA2 B IH0 NG +HOBO HH OW1 B OW0 +HOBO'S HH OW1 B OW0 Z +HOBOES HH OW1 B OW0 Z +HOBOING HH OW1 B OW0 IH0 NG +HOBOKEN HH OW1 B OW0 K AH0 N +HOBOLAND HH OW1 B OW0 L AH0 N D +HOBOMOK HH OW1 B OW0 M AA1 K +HOBOS HH OW1 B OW0 Z +HOBOTOV HH OW1 B OW0 T AA2 F +HOBS HH AA1 B Z +HOBSON HH AA1 B S AH0 N +HOBSON'S HH AA1 B S AH0 N Z +HOBY HH OW1 B IY0 +HOC HH AA1 K +HOCH HH AA1 K +HOCHBERG HH AA1 K B ER0 G +HOCHBURG HH AA1 K B ER0 G +HOCHE HH AA1 CH +HOCHELAGA HH AA1 K AH0 L AA1 G AH0 +HOCHENHEIMER HH AO1 K AH0 N HH AY2 M ER0 +HOCHFELS HH AA1 K F AH0 L Z +HOCHHEIM HH AA1 K HH AY0 M +HOCHHEIMER HH AA1 K HH AY0 M ER0 +HOCHKIRCH HH AA1 K K ER0 CH +HOCHMULLER HH AA1 K M AH1 L ER0 +HOCHON HH AA1 CH AH0 N +HOCHON'S HH AA1 K AH0 N Z +HOCHS HH AA1 K S +HOCHST HH AA1 K S T +HOCHSTADT HH AA1 K S T AE0 T +HOCHSTEDT HH AA1 K S T IH0 T +HOCHSTEIN HH AA1 K S T AY0 N +HOCHSTEIN HH AA1 K S T IY0 N +HOCHSTETTEN HH AA1 K S T EH0 T AH0 N +HOCHSTETTER HH AA1 K S T IH0 T ER0 +HOCHWALD HH AA1 K W AO0 L D +HOCHWALD'S HH AA1 K W AO2 L D Z +HOCHWOHLGEBOREN HH AA1 K W OW2 L G IH0 B AO1 R AH0 N +HOCK HH AA1 K +HOCKED HH AA1 K T +HOCKER HH AA1 K ER0 +HOCKEY HH AA1 K IY0 +HOCKHEIMER HH AA1 K HH AY2 M ER0 +HOCKHOCKING HH AA1 K HH AA1 K IH0 NG +HOCKIN HH AA1 K IH0 N +HOCKING HH AA1 K IH0 NG +HOCKING'S HH AA1 K IH0 NG Z +HOCKLEY HH AA1 K L IY0 +HOCKLING HH AA1 K L IH0 NG +HOCKS HH AA1 K S +HOCOMO HH AA1 K OW1 M OW0 +HOCUS HH OW1 K AH0 S +HOCUSSED HH OW1 K IH0 S T +HOD HH AA1 D +HODCARRIERS HH AA1 D K AE2 R Y ER0 Z +HODD HH AA1 D +HODDAM HH AA1 D AA1 M +HODDAN HH AA1 D AH0 N +HODDAN'S HH AA1 D AH0 N Z +HODDEN HH AA1 D AH0 N +HODDEN'S HH AA1 D AH0 N Z +HODDER HH AA1 D ER0 +HODDER'S HH AA1 D ER0 Z +HODDIDODDI HH AA1 D IH0 D AA1 D IY0 +HODDON HH AA1 D AH0 N +HODDY HH AA1 D IY0 +HODE HH OW1 D +HODEN HH OW1 D AH0 N +HODEN'S HH OW1 D AH0 N Z +HODENOSAUNEE HH AA1 D EH1 N AH0 S AO1 N IY0 +HODER HH OW1 D ER0 +HODGE HH AA1 JH +HODGE'S HH AA1 JH IH0 Z +HODGEN HH AA1 JH AH0 N +HODGEPODGE HH AA1 JH P AA2 JH +HODGES HH AA1 JH IH0 Z +HODGES'S HH AA1 JH IH0 Z IH0 Z +HODGESON HH AA1 JH S AH0 N +HODGETT HH AA1 JH IH0 T +HODGKIN HH AA1 JH K IH0 N +HODGKINS HH AA1 JH K IH0 N Z +HODGKINSON HH AA1 JH K IH0 N S AH0 N +HODGMAN HH AA1 JH M AH0 N +HODGSON HH AA1 JH S AH0 N +HODGSON'S HH AA1 JH S AH0 N Z +HODIE HH OW1 D IY0 +HODJA HH AA1 D Y AH0 +HODMAN HH AA1 D M AH0 N +HODMEN HH AA1 D M AH0 N +HODNIG HH AA1 D N IH0 G +HODO HH OW1 D OW0 +HODO'S HH OW1 D OW0 Z +HODS HH AA1 D Z +HODSHON HH AA1 D SH AH0 N +HODSON HH AA1 D S AH0 N +HODSON'S HH AA1 D S AH0 N Z +HODULF HH OW1 D AH0 L F +HODULF'S HH OW1 D AH0 L F S +HODUR HH AA1 D ER0 +HOE HH OW1 +HOE'S HH OW1 Z +HOECAKE HH OW1 K EY2 K +HOED HH OW1 D +HOEDOWN HH OW1 D AW2 N +HOEDUR HH OW1 D ER0 +HOEFER HH OW1 F ER0 +HOEGNI HH OW1 G N IY0 +HOEGSTAD HH OW1 G S T AH0 D +HOEIN HH OW1 IH0 N +HOEING HH OW1 IH0 NG +HOEK HH OW1 K +HOEL HH OW1 L +HOEL'S HH OW1 L Z +HOENIR HH OW1 N ER0 +HOER HH OW1 ER0 +HOEREN HH AO1 R AH0 N +HOERSELBERG HH AO1 R S EH1 L B ER0 G +HOERSTEL HH AO1 R S T AH0 L +HOES HH OW1 Z +HOEY HH OW1 IY0 +HOF HH AA1 F +HOFBAU HH AA1 F B AW0 +HOFBAUER HH AA1 F B AW0 ER0 +HOFBURG HH AA1 F B ER0 G +HOFE HH AA2 F +HOFER HH OW1 F ER0 +HOFER'S HH OW1 F ER0 Z +HOFF HH AO1 F +HOFFER HH AA1 F ER0 +HOFFERMAN HH AO1 F ER0 M AH0 N +HOFFICER HH AO1 F IH0 S ER0 +HOFFLAND HH AO1 F L AH0 N D +HOFFMAN HH AO1 F M AH0 N +HOFFMAN'S HH AO1 F M AH0 N Z +HOFFMANN HH AO1 F M AH0 N +HOFFMANN'S HH AO1 F M AH0 N Z +HOFFMEIER HH AO1 F M AY0 ER0 +HOFFMEISTER HH AO1 F M AY2 S T ER0 +HOFFMEYER HH AO1 F M AY0 ER0 +HOFFNER HH AO1 F N ER0 +HOFFNUNG HH AO1 F N AH0 NG +HOFFORD HH AA1 F ER0 D +HOFGARTEN HH AA1 F G AA0 R T AH0 N +HOFLAND HH AA1 F L AH0 N D +HOFMAN HH AA1 F M AH0 N +HOFMANN HH AA1 F M AH0 N +HOFMANN'S HH AA1 F M AH0 N Z +HOFMANNSTHAL HH AA1 F M AH0 N Z TH AH0 L +HOFMANNSTHAL'S HH AA1 F M AH0 N Z TH AH0 L Z +HOFMEISTER HH AA1 F M AY0 S T ER0 +HOFMYER HH AA1 F M IY0 ER0 +HOFRA HH AA1 F R AH0 +HOFRAT HH AA1 F R AE2 T +HOFRATH HH AA1 F R AH0 TH +HOFUS HH AA1 F AH0 S +HOFWYL HH AA1 F W IH0 L +HOG HH AA1 G +HOG'EM HH AA1 G EH1 M +HOG'S HH AA1 G Z +HOGAN HH OW1 G AA2 N +HOGAN HH OW1 G AH0 N +HOGAN'S HH OW1 G AA2 N Z +HOGANS HH OW1 G AA2 N Z +HOGANS HH OW1 G AH0 N Z +HOGARTH HH OW1 G AA2 R TH +HOGARTH'S HH OW1 G AA2 R TH S +HOGARTHIAN HH OW1 G AA2 R TH IY0 AH0 N +HOGARTHS HH OW1 G AA2 R TH S +HOGBACK HH AA1 G B AE2 K +HOGBACKS HH AA1 G B AE2 K S +HOGBEN HH AA1 G B AH0 N +HOGBIN HH AA1 G B IH0 N +HOGE HH OW1 JH +HOGEN HH AA1 G AH0 N +HOGENDORP HH AA1 G IH0 N D AO0 R P +HOGES HH AA1 JH IH0 Z +HOGG HH AA1 G +HOGG'S HH AA1 G Z +HOGGAN HH AA1 G AH0 N +HOGGARTY HH AA1 G ER0 T IY0 +HOGGE HH AA1 G +HOGGED HH AA1 G D +HOGGENBACK HH AA1 G IH0 N B AE2 K +HOGGETT HH AA1 G IH0 T +HOGGETT'S HH AA1 G IH0 T S +HOGGIE HH AA1 G IY0 +HOGGIN HH AA1 G IH0 N +HOGGINARMO HH AA1 G IH0 N AA1 R M OW0 +HOGGING HH AO1 G IH0 NG +HOGGINS HH AA1 G IH0 N Z +HOGGISH HH AA1 G IH0 SH +HOGGISHNESS HH AA1 G IH0 SH N AH0 S +HOGGS HH AA1 G Z +HOGHTON HH AA1 T AH0 N +HOGJAW HH AA1 G JH AO1 +HOGMANAY HH AA1 G M AE2 N EY0 +HOGNI HH AA1 G N IY0 +HOGS HH AA1 G Z +HOGSDEN HH AA1 G Z D AH0 N +HOGSFLESH HH AA1 G S F L EH2 SH +HOGSHEAD HH AA1 G S HH EH2 D +HOGSHEADS HH AA1 G S HH EH2 D Z +HOGSON HH AA1 G S AH0 N +HOGSTEAD HH AA1 G S T EH2 D +HOGSTRATEN HH AA1 G S T R EY1 T AH0 N +HOGTIE HH AA1 G T IY0 +HOGUE HH OW1 G +HOGVARDT HH OW1 G AH0 V EH0 R T +HOGWASH HH AA1 G W AA2 SH +HOH HH OW1 +HOHASTILLPILP HH OW1 HH AA0 S T IH1 L P IH0 L P +HOHE HH OW1 +HOHEN HH OW1 AH0 N +HOHENASPERG HH OW1 AH0 N AE1 S P ER0 G +HOHENAU HH OW1 AH0 N OW1 +HOHENBERG HH OW1 AH0 N B ER0 G +HOHENBURG HH OW1 AH0 N B ER0 G +HOHENBURGERHOF HH OW1 AH0 N B ER0 G ER0 HH AO0 F +HOHENDAHL HH OW1 AH0 N D AA2 L +HOHENECK HH OW1 AH0 N EH2 K +HOHENFELS HH AA0 HH IH0 N F AH0 L Z +HOHENHEIM HH OW1 AH0 N HH AY2 M +HOHENHOERN HH OW1 AH0 N HH AO1 R N +HOHENKRAEHEN HH OW1 AH0 N K R EY1 AH0 N +HOHENLINDEN HH OW1 AH0 N L IH2 N D AH0 N +HOHENLOHE HH OW1 AH0 N L OW1 +HOHENLOHE'S HH OW1 AH0 N L OW1 AH0 Z +HOHENSTAUFEN HH OW1 AH0 N S T AO2 F AH0 N +HOHENSTAUFENS HH OW1 AH0 N S T AO2 F AH0 N Z +HOHENSTAUFFEN HH OW1 AH0 N S T AO1 F AH0 N +HOHENSTEIN HH OW1 AH0 N S T AY0 N +HOHENSTEIN HH OW1 AH0 N S T IY0 N +HOHENSTIEL HH OW1 AH0 N S T IY2 L +HOHENTWIEL HH AA0 HH IH0 N T W IY1 L +HOHENWALD HH OW1 AH0 N W AO2 L D +HOHENWALDS HH OW1 AH0 N W AO2 L D Z +HOHENZOLLERN HH OW1 AH0 N Z AA1 L ER0 N +HOHENZOLLERNS HH OW1 AH0 N Z AA1 L ER0 N Z +HOHLAKOV HH OW1 L AH0 K AO0 V +HOHLAKOV'S HH OW1 L AH0 K AA2 V Z +HOHO HH OW1 HH OW1 +HOHSMANN HH OW1 S M AH0 N +HOI HH OY1 +HOICHI HH OW0 IY1 CH IY0 +HOICK HH OY1 K +HOIDEN HH OY1 D AH0 N +HOIDENISH HH OY1 D EH1 N IH0 SH +HOIGH HH OY1 +HOIGHTY HH OY1 T IY0 +HOIL HH OY1 L +HOIST HH OY1 S T +HOISTED HH OY1 S T AH0 D +HOISTED HH OY1 S T IH0 D +HOISTING HH OY1 S T IH0 NG +HOISTS HH OY1 S T S +HOIT HH OY1 T +HOITH HH OY1 TH +HOITLE HH OY1 T AH0 L +HOITY HH OY1 T IY0 +HOJIA HH AH0 Y IY0 AH0 +HOJO HH AH0 Y OW0 +HOJOS HH AH0 Y OW0 Z +HOK HH AO1 K +HOKE HH OW1 K +HOKE'S HH OW1 K S +HOKEE HH OW1 K IY0 +HOKEIS HH OW1 K AY0 Z +HOKEY HH OW1 K IY0 +HOKI HH OW1 K IY0 +HOKIANGA HH OW1 K IY0 AA1 NG G AH0 +HOKITIKA HH OW1 K IH0 T IH1 K AH0 +HOKOSA HH AH0 K OW1 S AH0 +HOKOTAN HH AH0 K OW1 T AH0 N +HOKUM HH OW1 K AH0 M +HOKUS HH OW1 K AH0 S +HOKUSAI HH AA1 K Y UW0 S AY2 +HOKY HH OW1 K IY0 +HOL HH OW1 L +HOLA HH OW1 L AH0 +HOLABIRD HH OW1 L AH0 B ER2 D +HOLAR HH OW1 L ER0 +HOLATA HH OW0 L AA1 T AH0 +HOLATI HH OW0 L AA1 T IY0 +HOLBACH HH OW1 L B AA2 K +HOLBEACH HH OW1 L B IY2 CH +HOLBEAUT HH OW1 L B OW1 T +HOLBEIN HH OW1 L B AY0 N +HOLBEIN'S HH OW1 L B AY0 N Z +HOLBEINS HH OW1 L B AY0 N Z +HOLBERG HH OW1 L B ER0 G +HOLBERG'S HH OW1 L B ER0 G Z +HOLBERTON HH OW1 L B ER0 T AH0 N +HOLBORN HH OW1 L B ER0 N +HOLBORNE HH OW1 L B AO0 R N +HOLBOROUGH HH OW1 L B ER0 OW0 +HOLBROOK HH OW1 L B R UH2 K +HOLBROOK'S HH OW1 L B R UH0 K S +HOLBROOKS HH OW1 L B R UH0 K S +HOLBURY HH OW1 L B EH2 R IY0 +HOLBY HH OW1 L B IY0 +HOLCHESTER HH OW1 L CH EH2 S T ER0 +HOLCHESTER'S HH OW1 L CH EH2 S T ER0 Z +HOLCOMB HH OW1 L K AH0 M +HOLCOMB'S HH OW1 L K AH0 M Z +HOLCOMBE HH OW1 L K AH0 M +HOLCOMBE'S HH OW1 L K AH0 M Z +HOLCROFT HH OW1 L K R AO2 F T +HOLCROFT'S HH OW1 L K R AO2 F T S +HOLCUS HH OW1 L K AH0 S +HOLD HH OW1 L D 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AH0 S +HUNYAD HH AH1 N Y AE0 D +HUNYADES HH AH1 N Y AA1 D Z +HUNYADI HH AH1 N Y AA1 D IY0 +HUNYADY HH AH1 N Y AA1 D IY0 +HUNYADY'S HH AH1 N Y AA1 D IY0 Z +HUNZA HH AH1 N Z AH0 +HUON HH Y UW1 AH0 N +HUON'S HH Y UW1 AH0 N Z +HUP HH AH1 P +HUPEH HH AH1 P EH2 +HUPFELD HH AH1 P F EH2 L D +HUPFELDT HH AH1 P F IH0 L T +HUPFELDT'S HH AH1 P F IH0 L T S +HUPP HH AH1 P +HUPP'S HH AH1 P S +HUPPER HH AH1 P ER0 +HUQ HH UW1 K +HUR HH ER1 +HUR'S HH ER1 Z +HURD HH ER1 D +HURD'S HH ER1 D Z +HURDA HH ER1 D AH0 +HURDIES HH ER1 D IY0 Z +HURDLE HH ER1 D AH0 L +HURDLED HH ER1 D AH0 L D +HURDLES HH ER1 D AH0 L Z +HURDLESTONE HH ER1 D AH0 L S T OW2 N +HURDLESTONE'S HH ER1 D AH0 L S T OW2 N Z +HURDLING HH ER1 D AH0 L IH0 NG +HURDLING HH ER1 D L IH0 NG +HURDLY HH ER1 D L IY0 +HURDLY'S HH ER1 D L IY0 Z +HURDON HH ER1 D AH0 N +HURDS HH ER1 D Z +HURDWAR HH ER1 D W AO0 R +HURDY HH ER1 D IY0 +HURE HH Y UH1 R +HURFORD HH ER1 F ER0 D +HURIERE HH ER1 IY0 EH0 R +HURKARU HH ER1 K ER0 UW1 +HURL HH ER1 L +HURL'D HH ER1 L D 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AH0 +HYGELAC HH AY2 JH EY1 L AH0 K +HYGIENE HH AY1 JH IY2 N +HYGIENIC HH AY2 G IY1 N IH0 K +HYGIENICALLY HH AY2 G IY1 N IH0 K L IY0 +HYGIENIST HH AY2 G EH1 N IH0 S T +HYGIENIST HH AY2 G IY1 N IH0 S T +HYGIENISTS HH AY2 G EH1 N IH0 S +HYGIENISTS HH AY2 G EH1 N IH0 S T S +HYGIENISTS HH AY2 G IY1 N IH0 S +HYGIENISTS HH AY2 G IY1 N IH0 S T S +HYGROMETER HH AY0 G R AA1 M AH0 T ER0 +HYGROMETERS HH AY2 G R AA1 M AH0 T ER0 Z +HYGROMETRIC HH AY2 G R OW0 M EH1 T R IH0 K +HYKE HH AY1 K +HYKSOS HH AY1 K S OW2 Z +HYLA HH AY1 L AH0 +HYLACTOR HH IH0 L AE1 K T ER0 +HYLAND HH AY1 L AH0 N D +HYLAND'S HH AY1 L AH0 N D Z +HYLAS HH IH1 L AH0 Z +HYLAX HH AY1 L AE2 K S +HYLDA HH AY1 L D AH0 +HYLDEBRAND HH AY1 L D IH0 B R AE2 N D +HYLE HH AY1 L +HYLEG HH AY1 L AH0 G +HYLLUS HH IH1 L AH0 S +HYLTON HH IH1 L T AH0 N +HYM HH IH1 M +HYMAN HH AY1 M AH0 N +HYMBERCOURT HH IH1 M B ER0 K AO2 R T +HYMEN HH AY1 M AH0 N +HYMEN'S HH AY1 M AH0 N Z +HYMENAEUS HH AY1 M AH0 N IY1 AH0 S +HYMENEAL HH AY1 M AH0 N IY1 L +HYMENOP HH AY1 M AH0 N AA2 P +HYMENOPS HH AY1 M AH0 N AA2 P S +HYMENOPTERA HH AY1 M AH0 N AA2 P T EH1 R AH0 +HYMENOPTERON HH AY1 M AH0 N AA2 P T ER0 AH0 N +HYMETTUS HH AY1 M EH1 T AH0 S +HYMIE HH AY1 M IY0 +HYMIR HH IH1 M IH0 R +HYMIR'S HH AY1 M IH1 R Z +HYMN HH IH1 M +HYMNAL HH IH1 M N AH0 L +HYMNALS HH IH1 M N AH0 L Z +HYMNBOOK HH IH1 M N B UH2 K +HYMNE HH IH1 M +HYMNED HH IH1 M D +HYMNING HH IH1 M IH0 NG +HYMNODY HH IH1 M N AH0 D IY0 +HYMNOLOGY HH IH0 M N AA1 L AH0 JH IY0 +HYMNS HH IH1 M Z +HYMNWRITER HH IH1 M N R AY2 T ER0 +HYMNWRITERS HH IH1 M N R AY2 T ER0 Z +HYND HH IH1 N D +HYNDE HH IH1 N D +HYNDMAN HH IH1 N D M AH0 N +HYNE HH AY1 N +HYNES HH AY1 N Z +HYOSCYAMUS HH Y AO1 S AY1 AH0 M IH0 S +HYP HH AY1 P +HYPAETHRAL HH AY2 P IY1 TH R AH0 L +HYPATIA HH AY2 P AA1 SH AH0 +HYPATIA'S HH AY1 P EY2 SH AH0 Z +HYPE HH AY1 P +HYPER HH AY1 P ER0 +HYPERAEMIA HH AY2 P ER0 AE0 IY1 M IY0 AH0 +HYPERAESTHESIA HH AY2 P ER0 AE0 S TH IY1 ZH AH0 +HYPERAESTHETIC HH AY2 P ER0 AE0 S TH EH1 T IH0 K +HYPERBOLA HH AY0 P ER1 B AH0 L AH0 +HYPERBOLE HH AY0 P ER1 B AH0 L IY2 +HYPERBOLES HH AY0 P ER1 B AH0 L IY2 Z +HYPERBOLIC HH AY2 P ER0 B AA1 L IH0 K +HYPERBOLICAL HH AY2 P ER0 B AA1 L IH0 K AH0 L +HYPERBOLICALLY HH AY2 P ER0 B AA1 L IH0 K L IY0 +HYPERBOREAN HH AY2 P ER0 B AO1 R IY0 AH0 N +HYPERBOREANS HH AY2 P ER0 B AO1 R IY0 AH0 N Z +HYPERCRITICAL HH AY2 P ER0 K R IH1 T IH0 K AH0 L +HYPERCRITICISM HH AY2 P ER0 K R IH1 T IH0 S IH2 Z AH0 M +HYPERDRIVE HH AY2 P ER0 D R AY2 V +HYPERFILM HH AY2 P ER0 F IH2 L M +HYPERICUM HH AY0 P ER1 IH0 K AH0 M +HYPERIDES HH AY2 P ER0 AY2 D Z +HYPERION HH AY0 P IH1 R IY0 AH0 N +HYPERION'S HH AY0 P IH1 R IY0 AH0 N Z +HYPERKRITE HH AY2 P ER0 K R AY2 T +HYPERMNESTRA HH AY1 P ER0 M N AH0 S T R AH0 +HYPERPHYSICAL HH AY2 P ER0 F IH1 Z IH0 K AH0 L +HYPERSENSITIVE HH AY2 P ER0 S EH1 N S IH0 T IH0 V +HYPERSENSITIVENESS HH AY2 P ER0 S EH1 N S AH0 T IH0 V N AH0 S +HYPERSHIP HH AY2 P ER0 SH IH2 P +HYPERSPACE HH AY2 P ER0 S P EY2 S +HYPERTHYROID HH AY2 P ER0 TH AY1 R OY0 D 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EY2 T ER0 Z +INCORPOREAL IH0 N K AO1 R P R IY0 AH0 L +INCORRECT IH0 N K ER0 EH1 K T +INCORRECTLY IH0 N K ER0 EH1 K T L IY0 +INCORRECTNESS IH0 N K ER0 EH1 K N AH0 S +INCORRIGIBILITY IH0 NG K AO1 R AH0 JH AH0 B IH1 L AH0 T IY0 +INCORRIGIBLE IH0 N K AA1 R AH0 JH AH0 B AH0 L +INCORRIGIBLES IH0 NG K AO1 R AH0 JH AH0 B AH0 L Z +INCORRIGIBLY IH0 NG K AO1 R AH0 JH AH0 B L IY0 +INCORRUPT IH0 N K ER0 AH1 P T +INCORRUPTIBILITY IH0 N K ER0 AH1 P T AH0 B IH1 L AH0 T IY0 +INCORRUPTIBLE IH0 N K ER0 AH1 P T AH0 B AH0 L +INCORRUPTIBLY IH0 N K ER0 AH1 P T IH0 B L IY0 +INCORRUPTION IH0 N K ER0 AH1 P SH AH0 N +INCOS IH1 NG K OW0 Z +INCOURAGEMENT IH0 N K ER1 IH0 JH M AH0 N T +INCREAS'D IH0 N K R IY2 S AH0 D +INCREASE IH0 N K R IY1 S +INCREASE IH1 N K R IY2 S +INCREASED IH0 N K R IY1 S T +INCREASED IH1 N K R IY2 S T +INCREASES IH0 N K R IY1 S AH0 Z +INCREASES IH0 N K R IY1 S IH0 Z +INCREASES IH1 N K R IY0 S AH0 Z +INCREASETH IH0 N K R IY2 S IH0 TH +INCREASIN IH0 N K R IY2 S IH0 N +INCREASING IH0 N K R IY1 S 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R AH0 TH +KAMO K AA1 M OW0 +KAMON K EY1 M AH0 N +KAMP K AE1 M P +KAMP'S K AE1 M P S +KAMPALA K AH0 M P AA1 L AH0 +KAMPEN K AE1 M P AH0 N +KAMPF K AE1 M P F +KAMPONG K AE1 M P AO1 NG +KAMPONGS K AE1 M P AO1 NG Z +KAMRAN K AE1 M R AH0 N +KAMRASI K AE1 M R AH0 S IY0 +KAMRASI'S K AE1 M R AH0 S IY0 Z +KAMSCHATKA K AE1 M S K AA1 T K AH0 +KAMSKATKA K AE1 M S K AA1 T K AH0 +KAMTCHADALES K AA2 M CH AH0 D AA1 L EH0 S +KAMTCHATKA K AA2 M CH AA1 T K AH0 +KAMTSCHATKA K AA2 M CH AE1 T K AH0 +KAMUSO K AA2 M UW1 S OW0 +KAMWORTH K AE1 M W ER0 TH +KAMYAKA K AA2 M Y AA1 K AH0 +KAN K AE1 N +KAN'T K AE1 N T +KANA K AE1 N AH0 +KANAB K AH0 N AA1 B +KANADAY K AE1 N AH0 D EY2 +KANAGAWA K AA2 N AA0 G AA1 W AH0 +KANAKA K AA0 N AA1 K AH0 +KANAKA'S K AA0 N AA1 K AH0 Z +KANAKAS K AA0 N AA1 K AH0 Z +KANANA K AH0 N AE1 N AH0 +KANANA'S K AA2 N AA1 N AH0 Z +KANARI K AH0 N AA1 R IY0 +KANARIS K AE1 N ER0 IH0 S +KANARY K AE1 N ER0 IY0 +KANATS K AE1 N AH0 T S +KANAUJ K AA2 N AA0 UW1 JH +KANAWHA K AH0 N AO1 HH AH0 +KANAYA K AH0 N AY1 AH0 +KANCABA K AA0 N K AA1 B AH0 +KANDA K AE1 N D AH0 +KANDAHAR K AE1 N D AH0 HH AA0 R +KANDAR K AE1 N D ER0 +KANDAR'S K AE1 N D ER0 Z +KANDARIAN K AH0 N D EH1 R IY0 AH0 N +KANDAULES K AE1 N D AW0 L Z +KANDAVU K AA0 N D AA1 V UW2 +KANDERSTEG K AE1 N D ER0 S T EH2 G +KANDIAN K AE1 N D IY0 AH0 N +KANDIDAT K AE1 N D AH0 D AH0 T +KANDIN K AE2 N D IH0 N +KANDY K AE1 N D IY0 +KANE K EY1 N +KANE'S K EY1 N Z +KANELE K AE1 N AH0 L +KANEM K AE2 N EH0 M +KANEMY K AE1 N AH0 M IY0 +KANENGE K AE1 N AH0 N JH +KANESVILLE K EY1 N Z V IH2 L +KANG K AA1 NG +KANG K AE1 NG +KANGA K AE1 NG G AH0 +KANGAROO K AE2 NG G ER0 UW1 +KANGAROO'S K AE2 NG G ER0 UW1 Z +KANGAROOS K AE2 NG G ER0 UW1 Z +KANGERTLUKSOAK K AE2 NG G ER0 T L AH0 K S OW1 K +KANGHI K AE1 N G IY0 +KANGMA K AE1 NG M AH0 +KANGOUROU K AE2 NG G UW1 R UW2 +KANGWE K AE1 NG W +KANIKI K AA0 N IY1 K IY0 +KANINE K AA0 N IY1 N IY0 +KANISHKA K AA0 N IY1 SH K AH0 +KANKA K AE1 N K AH0 +KANKA K AE1 NG K AH0 +KANKAD K AA2 N K AA1 D +KANKAD'S K AA2 N K AA1 D Z +KANKAKEE K AE1 NG K IH0 K IY0 +KANKER K AE1 NG K ER0 +KANN K AE1 N +KANNAKA K AE1 N AA1 K AH0 +KANNEBERG K AE1 N B ER0 G +KANNOT K AE1 N AA0 T +KANNUSHI K AE1 N UW1 SH IY0 +KANO K AA1 N OW0 +KANO'S K AA1 N OW0 Z +KANONSIONNI K EY1 N AH0 N CH AH0 N IY0 +KANOPIC K EY1 N AH0 P IH0 K +KANOPUS K AH0 N OW1 P AH0 S +KANOU K AA0 N OW0 UW1 +KANOURI K AE1 N UH1 R IY0 +KANOWIT K AE1 N AH0 W IH0 T +KANOWITS K AE1 N AH0 W IH0 T S +KANS K AH0 N Z +KANSA K AE1 N Z AH0 +KANSAN K AE1 N Z AH0 N +KANSANS K AE1 N Z AH0 N Z +KANSAS K AE1 N Z AH0 S +KANSU K AE1 N Z UW0 +KANT K AE1 N T +KANT'S K AE1 N T S +KANTARA K AE1 N T AA1 R AH0 +KANTELE K AA0 N T EH1 L +KANTIAN K AE1 N T IY0 AH0 N +KANTIANS K AE1 N T IY0 AH0 N Z +KANTO K AE1 N T OW0 +KANTOR K AE1 N T ER0 +KANTOS K AE1 N T OW0 Z +KANTURK K AE1 N T ER2 K +KANU K AA1 N UW0 +KANUS K EY1 N IH0 S +KANYA K AA1 N Y AH0 +KANZAS K AE1 N Z AH0 Z +KANZLER K AE1 N Z L ER0 +KAO K AW1 +KAO K EY1 OW2 +KAOHA K EY2 OW1 HH AH0 +KAOL K AW1 L +KAOLIAN K EY2 OW1 L IY0 AH0 N +KAOLIANS K AW1 L Y AH0 N Z +KAOLIN K AW1 L IH0 N +KAOLIN K EY1 OW0 L IH0 N +KAOR K AO1 R +KAOTSOU K AW1 T S UW1 +KAOU K EY1 OW2 +KAOUES K AW1 EH0 S +KAP K AE1 P +KAPA K AA1 P AH0 +KAPAA'A K AE1 P AA1 +KAPALI K AH0 P AA1 L IY0 +KAPE K EY1 P +KAPELLE K AH0 P EH1 L +KAPELLMEISTER K AH0 P EH1 L M AY0 S T ER0 +KAPERTON K AE1 P ER0 T AH0 N +KAPES K EY1 P S +KAPHENGST K AE1 P HH EH1 NG G S T +KAPI K AE1 P IY0 +KAPIK K AE1 P IH0 K +KAPILA K AE1 P IH0 L AH0 +KAPILAVASTU K AE1 P IH0 L AA1 V AH0 S T UW0 +KAPILAWOT K AE1 P AH0 L AO2 AO1 T +KAPIN K AE1 P IH0 N +KAPING K AE1 P IH0 NG +KAPIOLANI K AE1 P IY0 OW0 L AA1 N IY0 +KAPIT K AE1 P IH0 T +KAPITAL K AE1 P IH0 T AH0 L +KAPITAN K AE1 P IH0 T AH0 N +KAPITEIN K AE1 P IH0 T AY2 N +KAPITI K AH0 P IY1 T IY0 +KAPITOLINA K AE1 P IH0 T AH0 L IY1 N AH0 +KAPITON K AE1 P IH0 T AH0 N +KAPJE K AE1 P Y EH2 +KAPLAN K AE1 P L AH0 N +KAPOIOLANI K AH0 P OW1 IY0 OW0 L AA1 N IY0 +KAPOK K AH0 P AA1 K +KAPOLNA K AH0 P OW1 L N AH0 +KAPOSIA K AH0 P OW1 S IY0 AH0 +KAPOTAI K AE1 P OW0 T AA1 IY0 +KAPOU K AH0 P UW1 +KAPP K AE1 P +KAPPA K AE1 P AH0 +KAPPAN K AE1 P AH0 N +KAPPANS K AE1 P AH0 N Z +KAPSTAAD K AE1 P S T AA1 D +KAPTCHITCH K AE1 P T SH IH0 CH +KAPU K AE1 P +KAPUDAN K AH0 P AH1 D AH0 N +KAPUT K AE1 P AH0 T +KAR K AA2 R +KARA K EH1 R AH0 +KARA'S K EH1 R AH0 Z +KARABOGHAZJI K ER0 AE1 B AH0 G AH0 Z JH IY0 +KARACHI K AA0 R AA1 CH IY0 +KARACSAY K ER0 AE0 K S EY0 +KARADEUCQ K ER0 AA1 D UW1 K +KARAGEORGE K AE1 R AH0 JH AO1 R JH +KARAGEORGES K AE1 R AH0 JH AO1 R JH IH0 Z +KARAGEORGEVITCH K AE1 R AH0 JH AO1 R JH AH0 V IH0 CH +KARAGWAH K AA0 R AA1 G W AH0 +KARAGWE K AA0 R AA1 G W +KARAIN K ER0 EY1 N +KARAIT K ER0 EY1 T +KARAITES K EH2 R IY0 AY2 T S +KARAITI K ER0 EY1 T IY0 +KARAIYO K EH2 R IY0 IH0 Y OW0 +KARAKA K AA1 R AH0 K AH0 +KARAKALPAKS K AA1 R AH0 K AA2 L P AE0 K S +KARAKOEE K AA1 R AH0 K AO2 IY0 +KARAKOROM K AA1 R AH0 K AO2 R AH0 M +KARAKORUM K AA1 R AH0 K AO2 R AH0 M +KARAM K AE1 R AH0 M +KARAMANEH K AE1 R AH0 M AA1 N EH2 +KARAMANEH'S K AE1 R AH0 M AA1 N EH2 Z +KARAMAZOV K AE1 R AH0 M AE1 Z AA0 V +KARAMAZOVS K ER0 AE1 M AH0 Z AA2 F S +KARAMZIN K ER0 AE1 M Z IH0 N +KARAN K EH1 R AH0 N +KARARA K AA0 R AA1 R AH0 +KARARA'S K ER0 AA1 R AH0 Z +KARASJOK K ER0 AA1 S JH AH0 K +KARASOWSKI K AA2 R AH0 S AO1 F S K IY0 +KARAT K EH1 R AH0 T +KARATAYEFF K ER0 AA1 T AH0 Y EH1 F +KARAZ K AA1 R AA0 Z +KARB K AA1 R B +KARDAR K AA1 R D AA2 R +KARDELJ K AA1 D EH1 L JH +KARDON K AA1 R D AH0 N +KARE K EH1 R +KAREE K ER0 IY1 +KAREEMA K ER0 IY1 M AA0 +KAREL K EH1 R AH0 L +KAREL'S K EH1 R AH0 L Z +KARELA K ER0 IY1 L AH0 +KARELY K EH1 R L IY0 +KAREMA K AH0 R EH1 M AH0 +KAREMAKU K AH0 R EH1 M AH0 K UW0 +KAREN K EH1 R AH0 N +KAREN'S K EH1 R AH0 N Z +KARENIN K EH2 R EH0 N IY1 N +KARENINA K AH0 R EH1 N IH0 N AH0 +KARENINA K EH2 R EH0 N IY1 N AH0 +KARENNA K AH0 R EH1 N AH0 +KARENS K EH1 R AH0 N Z +KARETSKY K AH0 R EH1 T S K IY0 +KAREWENDA K EH2 R W EH1 N D AH0 +KARF K AA1 R F +KARF'S K AA1 R F S +KARFA K AA1 R F AH0 +KARFA'S K AA1 R F AH0 Z +KARFEDIX K AA1 R F EH1 D IH0 K S +KARFFARD K AA1 R F AA1 R D +KARG K AA1 R G +KARGOPOL K AA1 R G AH0 P AO1 L +KARI K EH1 R IY0 +KARIBIB K EH2 R IY1 B IY1 B +KARIBU K EH2 R IY1 B UW0 +KARIHWA K ER0 IY1 W AH0 +KARIL K EH2 R AH0 L +KARIM K ER0 IY1 M +KARIM'S K ER0 IY1 M Z +KARIN K EH1 R IH0 N +KARIN'S K EH1 R IH0 N Z +KARINA K EH2 R IY1 N AH0 +KARINE K EH2 R IY1 N IY0 +KARINE'S K ER0 IY1 N Z +KARIOL K EH1 R IY0 AH0 L +KARIZE K ER0 AY2 Z +KARJALA K AA1 R Y AA1 L AH0 +KARKAPAHA K AA1 R K AA2 P AH0 HH AA0 +KARKEEK K AA1 R K IY1 K +KARKOWSKY K ER0 K AW1 S K IY0 +KARL K AA1 R L +KARL'ITCH K AA1 R L IH0 CH +KARL'S K AA1 R L Z +KARLA K AA1 R L AH0 +KARLCHEN K AA1 R L K AH0 N +KARLCHEN'S K AA1 R L CH AH0 N Z +KARLI K AA1 R L IY0 +KARLIE K AA1 R L IY0 +KARLKAMMER K AA1 R L K AE1 M ER0 +KARLON K AA1 R L AH0 N +KARLOV K AA1 R L AA0 V +KARLOV'S K AA1 R L AO2 V Z +KARLSBAD K AA1 R L S B AE0 D +KARLSBURG K AA1 R L Z B ER0 G +KARLSEFNI K AA1 R L S AH0 F N IY0 +KARLSON K AA1 R L S AH0 N +KARLSON'S K AA1 R L S AH0 N Z +KARLSRUHE K AA1 R L Z R UW2 +KARLSTADT K AA1 R L S T AE2 T +KARLSTRASSE K AA1 R L S T R AE1 S +KARLUK K AA1 R L AH0 K +KARMA K AA1 R M AH0 +KARMIC K AA1 R M IH0 K +KARMOS K AA1 R M OW0 Z +KARN K AA1 R N +KARNA K AA1 R N AH0 +KARNA'S K AA1 R N AH0 Z +KARNAC K AA1 R N AH0 K +KARNAK K AA1 R N AH0 K +KARNAL K AA1 R N AH0 L +KARNEGIE K AA1 R N AH0 G IY0 +KARNER K AA1 R N ER0 +KARNER'S K AA1 R N ER0 Z +KARNES K AA1 R N Z +KARNIS K AA1 R N IY0 Z +KARNOS K AA1 R N OW0 Z +KARNS K AA1 R N Z +KARNSTEIN K AA1 R N S T AY2 N +KARO K AA1 R OW0 +KAROL K EH1 R AO0 L +KAROLIDES K EH2 R AH0 L AY1 D Z +KAROLINE K EH1 R AH0 L AY2 N +KARONYA K ER0 OW1 N Y AH0 +KAROO K ER0 UW1 +KAROON K ER0 UW1 N +KAROSS K EH1 R AO0 S +KAROSSES K EH2 R AH0 Z IH0 Z +KARP K AA1 R P +KARPATFALVA K AA2 R P AE1 T F AA1 L V AH0 +KARPATHY K AA1 R P AE2 TH IY0 +KARPATHY'S K AA2 R P AE1 TH IY0 Z +KARPIK K AA1 R P IH0 K +KARPIN K AA1 R P IH0 N +KARPIN'S K AA1 R P IH0 N Z +KARPOVNA K AA1 R P AA2 V N AH0 +KARR K AA1 R +KARR'S K AA1 R Z +KARRACK K AE1 R AH0 K +KARRAJE K AE1 R AH0 JH +KARRAJE'S K AE1 R AH0 JH Z +KARRI K AA1 R IY0 +KARRO K AA1 R OW0 +KARROO K AA1 R UW0 +KARS K AA1 R Z +KARSAVINA K AA1 R S AH0 V IY1 N AH0 +KARSCHOFF K AA1 R S K HH AO0 F +KARSHISH K AA1 R SH IY1 SH +KARSKI K AA1 R S K IY0 +KARSLAKE K AA1 R S L EY2 K +KARST K AA1 R S T +KARSTEN K AA1 R S T AH0 N +KARSTEN'S K AA1 R S T EH0 N Z +KARSWELL K AA1 R S W EH2 L +KARTA K AA1 R T AH0 +KARTAUS K AA0 R T AE2 IH0 S +KARTEL K AA1 R T AH0 L +KARTHLOS K AA1 R TH L OW0 Z +KARTIK K AA1 R T IH0 K +KARTINKIN K AA2 R T IH1 NG K IH0 N +KARTOUM K AA1 R T UW1 M +KARUMA K ER0 UW1 M AH0 +KARUN K ER0 UW1 N +KARUSKA K ER0 AH1 S K AH0 +KARVA K AA1 R V AH0 +KARVALL K AA1 R V AA1 L +KARVARINA K AA1 R V ER0 IY1 N AH0 +KARVAY K AA1 R V EY0 +KAS K AH0 S +KASA K AA1 S AH0 +KASAI K AH0 S AA1 IY0 +KASAN K AH0 S AA1 N +KASANA K AH0 S AA1 N AH0 +KASANA'S K AH0 S AA1 N AH0 Z +KASAR K AH0 S AA1 R +KASATSKY K AH0 S AA1 T S K IY0 +KASBA K AE1 S B AH0 +KASBAH K AE1 S B AH0 +KASCHTA K AE1 SH T AH0 +KASE K EY1 Z +KASEGARA K AE1 S EH0 G AA1 R AH0 +KASER K EY1 Z ER0 +KASGI K AE1 S G IY0 +KASH K AE1 SH +KASHA K AA1 SH AH0 +KASHALLA K AH0 SH AA1 L AH0 +KASHAN K AE1 SH AH0 N +KASHAQUA K AH0 SH AA1 K AH0 +KASHGAR K AE1 SH G AA2 R +KASHI K AA1 SH IY0 +KASHIKU K AA2 SH IY0 K UW1 +KASHIRIN K AA0 SH IH1 R AH0 N +KASHMIR K AE1 SH M IH0 R +KASHMIR K AE1 SH M IY0 R +KASHMIRI K AE0 SH M IY1 R IY0 +KASHMIRIS K AE0 SH M IY1 R IY0 Z +KASHNA K AE1 SH N AH0 +KASHTANKA K AA1 SH T AE1 NG K AH0 +KASHTANOV K AA1 SH T AA0 N AA0 V +KASHTSHEI K AA1 SH T SH AY0 +KASI K AA1 Z IY0 +KASIA K EY1 ZH AH0 +KASIDAH K AA1 S IH0 D AH0 +KASIH K AH0 S IH1 +KASIM K AE1 S IH0 M +KASIM'S K AE1 S IH0 M Z +KASIN K AE1 S IH0 N +KASKASKIA K AA1 S K AH0 S K IY0 AH0 +KASKASKIAS K AA1 S K AH0 S K IY1 AH0 Z +KASKER K AE1 S K ER0 +KASKER'S K AE1 S K ER0 Z +KASKISOON K AE1 S K IH0 S UW1 N +KASLO K AA1 S L OW0 +KASPAR K AE1 S P ER0 +KASPAR'S K AE1 S P ER0 Z +KASPER K AE1 S P ER0 +KASR K AE1 S R +KASS K AE1 S +KASSA K AE1 S AH0 +KASSALA K AE1 S AH0 L AH0 +KASSANDANE K AE1 S AA0 N D EY1 N +KASSANDRA K AE1 S AA1 N D R AH0 +KASSIM K AE1 S IH0 M +KASSIMERE K AE1 S IH0 M IH2 R +KASSITE K AE1 S AY0 T +KASSON K AE1 S AH0 N +KAST K AE1 S T +KASTEEL K AH0 S T IY1 L +KASTELIZ K AH0 S T EH1 L IH0 Z +KASTEN K AE1 S T AH0 N +KASTNER K AE1 S T N ER0 +KASUKAH K AA1 S UW0 K AH0 +KASVIN K AE1 S V IH0 N +KASYA K AE1 S IY0 AH0 +KASYAPA K AE1 S AY0 AA1 P AH0 +KAT K AE1 T +KAT'S K AE2 T S +KATA K AA1 T AH0 +KATADA K AH0 T AA1 D AH0 +KATAHDIN K AA0 T AA0 D IH0 N +KATAHDIN'S K AA0 T AA0 D AH0 N Z +KATAK K AE1 T AH0 K +KATAKI K AH0 T AA1 K IY0 +KATALAMBULA K AA0 T AA0 L AA0 M B UW1 L AH0 +KATALAMBULA'S K AA0 T AA0 L AA0 M B UW1 L AH0 Z +KATALLA K AA0 T AA0 L AH0 +KATANA K AA0 T AA1 N AH0 +KATANGA K AA0 T AA0 NG G AH0 +KATARINA K AA2 T ER0 IY1 N AH0 +KATCH K AE1 CH +KATCHA K AE1 CH AH0 +KATE K EY1 T +KATE'S K EY1 T S +KATEL K AH0 T EH1 L +KATENKA K AA0 T EH1 NG K AH0 +KATER K EY1 T ER0 +KATER'S K EY1 T ER0 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P S IH0 Z +KIPPUR K IH1 P ER0 +KIPPY K IH1 P IY0 +KIPS K IH1 P S +KIPT K IH1 P T +KIR K IH1 R +KIRANGOZI K IH1 R AE0 NG G OW1 Z IY0 +KIRBEY K ER1 B IY0 +KIRBY K ER1 B IY0 +KIRBY'S K ER1 B IY0 Z +KIRBYS K ER1 B IY0 Z +KIRCHE K ER1 K +KIRCHEISEN K ER1 K AY0 S AH0 N +KIRCHER K ER1 K ER0 +KIRCHHEIM K ER1 K HH AY0 M +KIRCHHOFF K ER1 K HH AO0 F +KIRCHNER K ER1 K N ER0 +KIRCHOFF K ER1 K HH AO0 F +KIRCONNELL K ER1 K AA1 N AH0 L +KIRDALL K ER1 D AA0 L +KIRDY K ER1 D IY0 +KIRDY'S K ER1 D IY0 Z +KIRGAN K ER1 G AH0 N +KIRGHIS K ER1 G IH0 S +KIRGHIZ K ER1 G IY1 Z +KIRI K IH1 R IY0 +KIRIL K IH1 R IH0 L +KIRILIN K IH1 R IH0 L IH0 N +KIRILL K IH1 R AH0 L +KIRILLA K IH2 R IH1 L AH0 +KIRILLOVITCH K IH2 R IH1 L AH0 V IH0 CH +KIRILOV K IH1 R IH0 L AA0 V +KIRIS K IH1 R IH0 S +KIRITIN K IH1 R IH0 T IH0 N +KIRJATH K ER1 JH AE2 TH +KIRJIPA K ER1 JH IY1 P AH0 +KIRK K ER1 K +KIRK'S K ER1 K S +KIRKALDY K ER1 K AA1 L D IY0 +KIRKANDREW K ER1 K AH0 N D R UW2 +KIRKBRIDE K ER1 K B R AY0 D +KIRKBURN K ER1 K B ER2 N 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+KIRKWOODS K ER1 K W UH2 D Z +KIRKYAIRD K ER1 K Y ER0 D +KIRKYARD K ER1 K Y AA2 R D +KIRKYARDS K ER1 K Y AA2 R D Z +KIRMANSHAH K ER1 M AH0 N SH AA1 +KIRMENBACH K ER1 M IH0 N B AA0 K +KIRN K ER1 N +KIROS K IH1 R OW0 Z +KIRPO K IH1 R P OW0 +KIRREE K ER1 IY0 +KIRRY K ER1 IY0 +KIRSCH K ER1 SH +KIRSCHEN K ER1 SH AH0 N +KIRSCHNER K ER1 SH N ER0 +KIRSCHWASSER K ER1 SH W AA1 S ER0 +KIRSHA K ER1 SH AH0 +KIRSTEN K ER1 S T AH0 N +KIRSTIN K ER1 S T IH0 N +KIRSTY K ER1 S T IY0 +KIRSTY'S K ER1 S T IY0 Z +KIRTAN K ER1 T AH0 N +KIRTELL K ER1 T AH0 L +KIRTLAND K ER1 T L AH0 N D +KIRTLE K ER1 T AH0 L +KIRTLED K ER1 T AH0 L D +KIRTLES K ER1 T AH0 L Z +KIRTLEY K ER1 T L IY0 +KIRTLEY'S K ER1 T L IY0 Z +KIRTON K ER1 T AH0 N +KIRTON'S K ER1 T AH0 N Z +KIRU K IH1 R UW0 +KIRV K ER1 V +KIRWAN K ER1 W AO0 N +KIRWAN'S K ER1 W AO0 N Z +KIRWIN K ER1 W IH0 N +KIRWIN'S K ER1 W AH0 N Z +KIRYAK K IH1 R Y AE0 K +KIRYAKOV K IH2 R Y AA1 K AO2 V +KIRYLO K ER0 IH1 L OW0 +KIS K IH1 S +KISA K IH1 S AH0 +KISARI K IY0 S AA1 R IY0 +KISCADDEN K IH1 S K AE1 D AH0 N +KISCO K IH1 S K OW0 +KISER K AY1 Z ER0 +KISESA K IH1 S EH1 S AH0 +KISH K IH1 SH +KISHIMOTO K IY1 SH IY0 M OW1 T OW0 +KISHINEV K IY1 SH IH0 N EH0 F +KISHLAK K IH1 SH L AE0 K +KISHLAKI K IH1 SH L AA1 K IY0 +KISHNU K IH1 SH N UW0 +KISHON K IH1 SH AH0 N +KISHRA K IH1 SH R AH0 +KISHU K IY1 SH UW2 +KISINGTON K AY1 Z IH0 NG T AH0 N +KISKA K IH1 S K AH0 +KISKADDEN K IH1 S K AE1 D AH0 N +KISLAR K IH1 S L ER0 +KISLIAKOV K IH1 S L AY2 AH0 K AA0 V +KISLOVODSK K IH1 S L OW0 V AA1 D S K +KISMET K IH1 Z M IH0 T +KISMINE K IH1 Z M AH0 N +KISS K IH1 S +KISS'D K IH1 SH D +KISSABLE K IH1 S AH0 B AH0 L +KISSAM K IH1 S AH0 M +KISSE K IH1 S +KISSED K IH1 S T +KISSEL K IH1 S AH0 L +KISSER K IH1 S ER0 +KISSES K IH1 S AH0 Z +KISSES K IH1 S IH0 Z +KISSETH K IH1 S IH0 TH +KISSIMMEE K IH0 S IH1 M IY0 +KISSIN K IH1 S IH0 N +KISSING K IH1 S IH0 NG +KISSINGEN K IH1 S IH0 NG G EH0 N +KISSINGS K IH1 S IH0 NG Z +KISSOCK K IH1 S AH0 K +KISSOFF K IH1 S AO0 F +KIST K IH1 S T +KISTNAGHERRY K IH1 S T N AH0 G EH1 R IY0 +KISTS K IH1 S T S +KISTUNOV K IH1 S T UW0 N AA0 V +KISUMU K IH1 S UW1 M UW0 +KISWAH K IH1 S W AH0 +KISWAHILI K IH1 S W AA0 HH IY1 L IY0 +KIT K IH1 T +KIT'S K IH1 T S +KITAB K IH1 T AH0 B +KITBAG K IH1 T B AE2 G +KITCAT K IH1 T K AE2 T +KITCH K IH1 CH +KITCHED K IH1 CH T +KITCHELL K IH1 CH AH0 L +KITCHEN K IH1 CH AH0 N +KITCHEN'S K IH1 CH AH0 N Z +KITCHENER K IH1 CH AH0 N ER0 +KITCHENER K IH1 CH N ER0 +KITCHENER'S K IH1 CH N ER0 Z +KITCHENETTE K IH2 CH AH0 N EH1 T +KITCHENMAID K IH1 CH AH0 N M EY2 D +KITCHENMAIDS K IH1 CH AH0 N M EY2 D Z +KITCHENS K IH1 CH AH0 N Z +KITCHENWARD K IH1 CH AH0 N W ER0 D +KITCHENWARE K IH1 CH AH0 N W EH2 R +KITCHI K IH1 CH IY0 +KITCHIN K IH1 CH IH0 N +KITCHINER K IH1 CH IH0 N ER0 +KITCHING K IH1 CH IH0 NG +KITE K AY1 T +KITE'S K AY1 T S +KITED K AY1 T IH0 D +KITELY K AY1 T L IY0 +KITELY'S K AY1 T L IY0 Z +KITES K AY1 T S +KITH K IH1 TH +KITHAERON K AY1 TH EH1 R AH0 N +KITHNEB K IH1 TH N AH0 B +KITI K IH1 T IY0 +KITIN K AY1 T IH0 N +KITING K AY1 T IH0 NG +KITPOOSEAGUNOW K IH1 P UW1 S IY1 G AH2 N OW0 +KITS K IH1 T S +KITSIE K IH1 T S IY0 +KITSOMAX K IH1 T S AH0 M AE2 K S +KITSON K IH1 T S AH0 N +KITSUNE K IH1 T S UW0 N +KITT K IH1 T +KITT'S K IH1 T S +KITTELHAUS K IH1 T IH0 L HH AW2 S +KITTEN K IH1 T AH0 N +KITTEN'S K IH1 T AH0 N Z +KITTENED K IH1 T AH0 N D +KITTENHOOD K IH1 T AH0 N HH UH2 D +KITTENISH K IH1 T AH0 N IH0 SH +KITTENS K IH1 T AH0 N Z +KITTERBELL K IH1 T ER0 B EH2 L +KITTERICK K IH1 T ER0 IH0 K +KITTERIDGE K IH1 T ER0 IH0 JH +KITTERING K IH1 T ER0 IH0 NG +KITTERY K IH1 T ER0 IY0 +KITTICUT K IH1 T IH0 K AH2 T +KITTIE K IH1 T IY0 +KITTIE'S K IH1 T IY0 Z +KITTIES K IH1 T IY0 Z +KITTIWAKE K IH1 T IH0 W EY2 K +KITTIWAKES K IH1 T IH0 W EY2 K S +KITTIWYNK K IH1 T IH0 W IH1 NG K +KITTLE K IH1 T AH0 L +KITTLES K IH1 T AH0 L Z +KITTLEWAKE K IH1 T AH0 L W EY1 K +KITTLEWEEM K IH1 T AH0 L W IY1 M +KITTO K IH1 T OW0 +KITTON K IH1 T AH0 N +KITTREDGE K IH1 T R IH0 JH +KITTREDGE'S K IH1 T R IH0 JH IH0 Z +KITTRICK K IH1 T R IH0 K +KITTRIDGE K IH1 T R IH0 JH +KITTRIDGE'S K IH1 T R IH2 JH IH0 Z +KITTS K IH1 T S +KITTY K IH1 T IY0 +KITTY'S K IH1 T IY0 Z +KITTYWAKE K IH1 T IY0 W EY2 K +KITUTA K IY0 T UW1 T AH0 +KITUWIA K IH1 CH UW0 V IY0 AH0 +KITZUKI K IH1 T Z UW1 K IY0 +KIU K IY1 UW0 +KIUKIANG K AY1 AH0 K Y AA1 NG +KIUPRILE K AY1 AH0 P R AH0 L +KIUSHIU K AY1 AH0 SH UW1 +KIVA K IY1 V AH0 +KIVAS K IY1 V AH0 Z +KIVELSON K IH1 V IH0 L S AH0 N +KIVELSON'S K IH1 V IH0 L S AH0 N Z +KIVER K AY1 V ER0 +KIVERED K IH1 V ER0 D +KIVERS K AY1 V ER0 Z +KIVI K IH1 V IY0 +KIVIL K IH1 V AH0 L +KIWI K IY1 W IY0 +KIWYEH K IY1 W Y AH0 +KIYI K IY2 IY0 +KIYO K IY2 OW0 +KIYOMORI K IY2 OW0 M AO1 R IY0 +KIYOSAI K IY2 OW0 S AA1 IY0 +KIZIL K AY1 Z AH0 L +KIZLAR K AY1 Z L ER0 +KIZZIE K IH1 Z IY0 +KIZZY K IH1 Z IY0 +KJARTAN K Y AA1 R T AH0 N +KJARTAN'S K Y AA1 R T AH0 N Z +KJELGAARD K Y EH1 L G AA2 R D +KJELLIN K Y EH1 L IH0 N +KJERSTI K Y ER1 S T IY0 +KJOEGE K Y OW1 JH +KLA K L AH0 +KLA'UNS K L AH0 N Z +KLAAS K L AA1 S +KLAAS'S K L AA1 S IH0 Z +KLAE K L EY1 +KLAEDEN K L IY1 D AH0 N +KLAERE K L IH1 R +KLAHOWYA K L AE1 AW0 Y AH0 +KLAKEE K L EY1 K IY0 +KLAMATH K L AE1 M AH0 TH +KLAN K L AE1 N +KLANG K L AE1 NG +KLANNER K L AE1 N ER0 +KLANNER'S K L AE1 N ER0 Z +KLANS K L AE1 N Z +KLANSMAN K L AE1 N Z M AH0 N +KLANSMEN K L AE1 N Z M AH0 N +KLAPKA K L EY1 P K AH0 +KLAPKA'S K L EY1 P K AH0 Z +KLAPPAN K L AE1 P AH0 N +KLAPROTH K L AE1 P R AO2 TH +KLARA K L AA1 R AH0 +KLARNOOD K L AA1 R N UH0 D +KLAS K L AE1 S +KLASSAN K L AE1 S AH0 N +KLATAWA K L AE1 T AH0 W AA2 +KLATSCH K L AE1 CH +KLAUBER K L AW1 B ER0 +KLAUS K L AW1 S +KLAUS'S K L AH0 S IH0 Z +KLAUSEN K L AW1 S AH0 N +KLAUSEN'S K L AW1 S AH0 N Z +KLAUSENBERG K L AW1 S AH0 N B ER0 G +KLAUSENBURG K L AW1 S AH0 N B ER0 G +KLAUSNER K L AW1 S N ER0 +KLAUSOFF K L AW1 S AO0 F +KLAUSS K L AW1 S +KLAV K L EY1 V +KLAVAN K L EY1 V AH0 N +KLAVS K L AA1 V Z +KLAW K L AO2 +KLAWS K L AO2 Z +KLAXON K L AE1 K S AH0 N +KLAYU K L EY1 UW0 +KLE K AH0 L +KLEA K L IY1 AH0 +KLEA'S K L IY1 AH0 Z +KLEAGLE K L IY1 G AH0 L +KLEAGLES K L IY1 G AH0 L Z +KLEBER K L IY1 B ER0 +KLEBER'S K L IY1 B ER0 Z +KLEBS K L EH1 B Z +KLECZYNSKI K L EH1 CH IH1 N S K IY0 +KLEEF K L IY1 F +KLEEK K L IY1 K +KLEESMAN K L IY1 Z M AH0 N +KLEESMAN'S K L IY1 S M AH0 N Z +KLEGG K L EH1 G +KLEIG K L IY1 G +KLEIMAN K L AY1 M AH0 N +KLEIN K L AY1 N +KLEIN'S K L AY1 N Z +KLEINE K L AY1 N +KLEINER K L AY1 N ER0 +KLEINERT K L AY1 N ER0 T +KLEINMICHEL K L AY1 N M IH2 K AH0 L +KLEINSEITE K L AY1 N S AY2 T +KLEINTEICH K L AY1 N T AY0 K +KLEINWALDE K L AY1 N W AO2 L D +KLEINWORT K L AY1 N W ER0 T +KLEINWORT'S K L AY1 N W ER0 T S +KLEISER K L AY1 S ER0 +KLEIST K L AY1 S T +KLEIST'S K L AY1 S T S +KLEM K L EH1 M +KLEMENTI K L EH1 M AH0 N T IY0 +KLEMM K L EH1 M +KLEMM'S K L EH1 M Z +KLEON K L IY1 AH0 N +KLEOPATRA K L IY2 AH0 P AE1 T R AH0 +KLEPPER K L EH1 P ER0 +KLEPTOMANIA K L EH2 P T AH0 M EY1 N IY0 AH0 +KLEPTOMANIAC K L EH2 P T AH0 M EY1 N IY0 AE0 K +KLEPTOMANIACS K L EH2 P T AH0 M EY1 N IY0 AE2 K S +KLERK K L ER1 K +KLERKSDORP K L ER1 K S D AO0 R P +KLESMER K L EH1 S M ER0 +KLESMER'S K L EH1 S M ER0 Z +KLETTE K L EH1 T +KLETZINGK K L EH1 T S IH2 NG K +KLEW K L UW0 +KLICK K L IH1 K +KLIEWER K L IY1 W ER0 +KLIK K L IH0 K +KLIM K L IH1 M +KLIMOV K L IH0 M AA0 V +KLINDWORTH K L IH1 N D W ER0 TH +KLINE K L AY1 N +KLING K L IH1 NG +KLING'S K L IH1 NG Z +KLINGENBERG K L IH1 NG AH0 N B ER0 G +KLINGENSMITH K L IH1 NG G IH0 N S M IH0 TH +KLINGENSPIEL K L IH1 NG AH0 N S P IY2 L +KLINGER K L IH1 NG ER0 +KLINGSOHR K L IH1 NG Z AO1 R +KLINGSOR K L IH1 NG Z ER0 +KLINGSOR'S K L IH1 NG Z ER0 Z +KLINK K L IH1 NG K +KLINKAERT K L IH1 NG K EH1 R T +KLINKER K L IH1 NG K ER0 +KLINKER'S K L IH1 NG K ER0 Z +KLIP K L IH1 P +KLIPSPRINGER K L IH1 P S P R IH1 NG ER0 +KLIPSPRINGERS K L IH1 P S P R IH1 NG ER0 Z +KLITZ K L IH1 T S +KLITZING K L IH1 T Z IH0 NG +KLOCK K L AA1 K +KLOED K L OW1 D +KLOENG K L OW1 NG +KLOFE K L AA1 F +KLOH K L OW1 +KLONDIKE K L AA1 N D AY2 K +KLONDIKER K L AA1 N D AY2 K ER0 +KLONDIKERS K L AA1 N D AY2 K ER0 Z +KLONDYKE K L AA1 N D AY2 K +KLONDYKE'S K L AA1 N D AY2 K S +KLOOCH K L UW1 CH +KLOOCHMAN K L UW1 CH M AH0 N +KLOOF K L UW1 F +KLOOFS K L UW1 F S +KLOOMIRIA K L UW1 M IH0 R IY1 AH0 +KLOON K L UW1 N +KLOON'S K L UW1 N Z +KLOOTCHMAN K L UW1 CH M AH0 N +KLOOTS K L UW1 T S +KLOP K L AA1 P +KLOPEN K L OW1 P AH0 N +KLOPP K L AA1 P +KLOPSTOCK K L AA1 P S T AA2 K +KLOPSTOCK'S K L AA1 P S T AA2 K S +KLOPTON K L AA1 P T AH0 N +KLORAN K L ER0 AH0 N +KLOSHE K L AA1 SH +KLOSKING K L AA1 S K IH0 NG +KLOSKING'S K L AA1 S K IH0 NG Z +KLOSTER K L AO1 S T ER0 +KLOTA K L OW1 T AH0 +KLOTCHKOV K L AA1 T CH K AO2 V +KLOTZ K L AA1 T S +KLOW K L AW0 +KLU K L UW1 +KLUCK K L AH1 K +KLUCK'S K L AH1 K S +KLUCKERS K L AH1 K ER0 Z +KLUCKHUHN K L AH1 K HH AH1 N +KLUEBER K L UH1 B ER0 +KLUECKING K L UH1 K IH0 NG +KLUG K L AH1 G +KLUGE K L UW1 JH +KLUMPKE K L AH1 M P K +KLUTZ K L AH1 T S +KLUX K L AH1 K S +KLUXES K L AH1 K S AH0 Z +KLUXISM K L AH1 K S IH0 Z AH0 M +KLYDA K L IH1 D AH0 +KMITA K AH0 M IY1 T AH0 +KMITA'S K AH0 M IY1 T AH0 Z +KMOCH K AH0 M AA1 K +KN K EY1 EH1 N +KNABE N EY1 B +KNACK N AE1 K +KNACKER N AE1 K ER0 +KNACKER'S N AE1 K ER0 Z +KNACKERS N AE1 K ER0 Z +KNACKS N AE1 K S +KNAG N AE1 G +KNAGGS N AE1 G Z +KNAKIAS N EY1 K IY0 AH0 Z +KNAOW N EY1 OW0 +KNAOWED N EY1 OW0 D +KNAP N AE1 P +KNAPDALE N AE1 P D EY2 L +KNAPF N AE1 P F +KNAPP N AE1 P +KNAPP'S N AE1 P S +KNAPPE N AE1 P +KNAPPING N AE1 P IH0 NG +KNAPPS N AE1 P S +KNAPSACK N AE1 P S AE2 K +KNAPSACKS N AE1 P S AE2 K S +KNAPWEED N EY1 P W IY2 D +KNAPWURST N EY1 P W ER0 S T +KNARESBOROUGH N AE1 R Z B ER0 OW0 +KNAT N AE1 T +KNAT'S N AE1 T S +KNATCHBULL N AE1 CH B AH0 L +KNATCHETT N AE1 CH IH0 T +KNATS N AE1 T S +KNAUS N AO1 Z +KNAVE N EY1 V +KNAVE'S N EY1 V Z +KNAVERIES N EY1 V ER0 IY0 Z +KNAVERY N EY1 V ER0 IY0 +KNAVES N EY1 V Z +KNAVISH N EY1 V IH0 SH +KNAVISHLY N EY1 V IH0 SH L IY0 +KNAW N AO2 +KNAW'D N AW1 D +KNAWED N AO1 D +KNAWIN N AE1 W IH0 N +KNAWN N AO1 N +KNAWS N AO1 Z +KNEAD N IY1 D +KNEADED N IY1 D IH0 D +KNEADING N IY1 D IH0 NG +KNEADS N IY1 D Z +KNEBWORTH N EH1 B W ER0 TH +KNECHT N EH1 K T +KNECHTS N EH1 K T S +KNEE N IY1 +KNEE'D N IY1 D +KNEE'S N IY1 Z +KNEEBONE N IY1 B OW2 N +KNEEBONE'S N IY1 B OW2 N Z +KNEECAP N IY1 K AE2 P +KNEECAPS N IY1 K AE2 P S +KNEED N IY1 D +KNEEDROCK N IY1 D R AA2 K +KNEEDROCK'S N IY1 D R AA2 K S +KNEEL N IY1 L +KNEEL'D N IY1 L D +KNEELAND N IY1 L AH0 N D +KNEELED N IY1 L D +KNEELER N IY1 L ER0 +KNEELERS N IY1 L ER0 Z +KNEELETH N IY1 L EH0 TH +KNEELIN N IY1 L IH0 N +KNEELING N IY1 L IH0 NG +KNEELINGS N IY1 L IH0 NG Z +KNEELS N IY1 L Z +KNEES N IY1 Z +KNEIP N IY1 P +KNEIPE N IY1 P +KNEIPP N IY1 P +KNEIPS N IY1 P S +KNEISEL N AY1 S AH0 L +KNELL N EH1 L +KNELL'S N EH1 L Z +KNELLED N EH1 L D +KNELLER N EH1 L ER0 +KNELLING N EH1 L IH0 NG +KNELLS N EH1 L Z +KNELT N EH1 L T +KNEOW N IY1 OW0 +KNEPH N EH1 F +KNEPP N EH1 P +KNERR N EH1 R +KNES N Z +KNESEBECK N EH1 S AH0 B EH2 K +KNEVET N V EH2 T +KNEW N UW1 +KNEW N Y UW1 +KNEW'ST N UW1 S T +KNEWE N Y UW1 +KNEWED N UW1 D +KNEWEST N Y UW1 AH0 S T +KNIAZ N AY1 AE0 Z +KNIBBS N IH1 B Z +KNICHT N IH1 K T +KNICK N IH1 K +KNICKER N IH1 K ER0 +KNICKERBOCKER N IH1 K ER0 B AA2 K ER0 +KNICKERBOCKER'S N IH1 K ER0 B AA2 K ER0 Z +KNICKERBOCKERED N IH1 K ER0 B AA2 K ER0 D +KNICKERBOCKERS N IH1 K ER0 B AA2 K ER0 Z +KNICKERS N IH1 K ER0 Z +KNICKKNACK N IH1 K N AE2 K +KNICKKNACKS N IH1 K N AE2 K S +KNICKNACKS N IH1 K N AE2 K S +KNICKS N IH1 K S +KNIEBRECHE N IY1 B R IH0 K +KNIEPP N EH1 P +KNIFE N AY1 F +KNIFE'S N AY1 F S +KNIFEBOARD N AY1 F B AO2 R D +KNIFED N AY1 F T +KNIFELIKE N AY1 F L AY2 K +KNIFES N AY1 F S +KNIFIN N AY1 F IH0 N +KNIFING N AY1 F IH0 NG +KNIGHT N AY1 T +KNIGHT'S N AY1 T S +KNIGHTE N AY1 T +KNIGHTED N AY1 T IH0 D +KNIGHTES N AY1 T AH0 S +KNIGHTHEADS N AY1 T HH EH2 D Z +KNIGHTHOOD N AY1 T HH UH2 D +KNIGHTHOOD'S N AY1 T HH UH2 D Z +KNIGHTHOODS N AY1 T HH UH2 D Z +KNIGHTING N AY1 T IH0 NG +KNIGHTLEY N AY1 T L IY0 +KNIGHTLEY'S N AY1 T L IY0 Z +KNIGHTLEYS N AY1 T L IY0 Z +KNIGHTLIEST N AY1 T L IY0 IH0 S T +KNIGHTLINESS N AY1 T L AY2 N AH0 S +KNIGHTLY N AY1 T L IY0 +KNIGHTON N AY1 T AH0 N +KNIGHTS N AY1 T S +KNIGHTSBRIDGE N AY1 T S B R IH2 JH +KNILL N IH1 L +KNIN K EY1 EH1 N AY1 EH1 N +KNIN K N IH1 N +KNIN N IH1 N +KNIPE N AY1 P +KNIPP N IH1 P +KNIPPEL N IH1 P AH0 L +KNIPPERDOLLING N IH1 P ER0 D OW1 L IH0 NG +KNIPS N IH1 P S +KNISTENEAUX N IH1 S T IH0 N OW0 +KNIT N IH1 T +KNITS N IH1 T S +KNITTED N IH1 T AH0 D +KNITTED N IH1 T IH0 D +KNITTER N IH1 T ER0 +KNITTERS N IH1 T ER0 Z +KNITTIN N IH1 T IH0 N +KNITTING N IH1 T IH0 NG +KNIVES N AY1 V Z +KNNI K N IY0 +KNO N OW1 +KNOA N OW1 AH0 +KNOB N AA1 B +KNOBBE N AA1 B +KNOBBED N AA1 B D +KNOBBLY N AA1 B AH0 L IY0 +KNOBBY N AA1 B IY0 +KNOBELSDORF N OW1 B AH0 L Z D AO0 R F +KNOBKERRIE N AA1 B K EH1 R IY0 +KNOBKERRIES N AA1 B K EH1 R IY0 Z +KNOBLAUCH N AA1 B L AW0 K +KNOBLOCH N AA1 B L AH0 K +KNOBLOCK N AA1 B L AA0 K +KNOBS N AA1 B Z +KNOBSTICK N AA1 B S T IH2 K +KNOBSTICKS N AA1 B S T IH2 K S +KNOCK N AA1 K +KNOCK'D N AA1 K D +KNOCKABOUT N AA1 K AH0 B AW2 T +KNOCKDANE N AA1 K D EY2 N +KNOCKDOWN N AA1 K D AW2 N +KNOCKDUNDER N AA1 K D AH0 N D ER0 +KNOCKED N AA1 K T +KNOCKER N AA1 K ER0 +KNOCKERS N AA1 K ER0 Z +KNOCKESPOTCH N AA1 K IH0 S P AA1 CH +KNOCKETH N AA1 K AH0 TH +KNOCKIN N AA1 K IH0 N +KNOCKING N AA1 K IH0 NG +KNOCKINGS N AA1 K IH0 NG Z +KNOCKIT N AA1 K IH0 T +KNOCKMANY N AA1 K M AH0 N IY0 +KNOCKMAROON N AA1 K M ER0 UW1 N +KNOCKNAREA N AA1 K N ER0 IY1 AH0 +KNOCKOUT N AA1 K AW2 T +KNOCKOWEN N AA1 K AW1 AH0 N +KNOCKS N AA1 K S +KNOCKT N AA1 K T +KNOCKTARLITIE N AA1 K T ER1 L T IY0 +KNOCKWINNOCK N AA1 K W IH1 N AH0 K +KNOLE N OW1 L +KNOLL N OW1 L +KNOLLED N OW1 L D +KNOLLES N OW1 L Z +KNOLLS N OW1 L Z +KNOLLSEA N OW1 L S IY0 AH0 +KNOLLWOOD N OW1 L W UH2 D +KNOLLYS N OW1 L IY0 Z +KNOO N UW1 +KNOOK N UH1 K +KNOOKS N UH1 K S +KNOP N AA1 P +KNOPF N AA1 F +KNOPF N AA1 P F +KNOPF'S N AA1 P F S +KNOPS N AA1 P S +KNORR N AO1 R +KNOSSOS N OW1 S OW2 Z +KNOT N AA1 T +KNOT'S N AA1 T S +KNOTEN N 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AO0 R IY1 N D AH0 +KORINNA K AO0 R IH1 N AH0 +KORINNA'S K AO1 R IH0 N AH0 Z +KORITTO K AO0 R IY1 T OW0 +KORITZA K AO0 R IH1 T Z AH0 +KORKYRA K AO1 R K AY1 R AH0 +KORLIN K AO1 R L IH0 N +KORMORAN K AO1 R M ER0 AH0 N +KORMT K AO1 R M T +KORN K AO1 R N +KORNER K AO1 R N ER0 +KORNER'S K AO1 R N ER0 Z +KORNICKER K AO1 R N IH0 K ER0 +KORNILOFF K AO1 R N AH0 L AO0 F +KORNIS K AO1 R N IY0 Z +KORO K AO1 R OW0 +KOROBOTCHKA K AO1 R OW0 B AA1 CH K AH0 +KOROLENKO K AO0 R AA2 L EH1 NG K OW0 +KOROLEVICH K AO0 R AA2 L IH0 V IH0 CH +KOROLEVITCH K AO0 R AA2 L AH0 V IH0 CH +KOROLEVNA K AO0 R AA2 L AH0 V N AH0 +KOROLYOV K AO0 R AA2 L Y AH0 V +KORONG K AO1 R AO0 NG +KORORAREKA K AO1 R AO0 R AA0 R EH1 K AH0 +KORORARIKA K AO1 R AO0 R AA0 R IY1 K AH0 +KOROS K AO1 R OW0 Z +KOROSKO K AO0 R OW1 S K OW0 +KORPANOFF K ER0 P AH0 N AO0 F +KORPER K AO1 R P ER0 +KORPS K AO1 R P S +KORRIGAN K AO1 R IH0 G AH0 N +KORRIGANS K AO1 R IH0 G AH0 N Z +KORSAKOFF K AO0 R S EY1 K AO0 F +KORSAKOV K AO0 R S EY1 K AA0 V +KORSAKOVSK K AO0 R S EY1 K AA1 F S K +KORSE K AO1 R S +KORSOER K AO1 R S OW0 ER0 +KORTE K AO1 R T +KORTI K AO1 R T IY0 +KORTLANDT K AO1 R T L AH0 N T +KORUS K AO1 R AH0 S +KORUST K AO1 R AH0 S T +KORVAN K AO1 R V AH0 N +KORVIN K AO1 R V IH0 N +KORWSKY K AO1 R W S K IY0 +KORY K AO1 R IY0 +KORYNTHIA K AO1 R IH1 N TH IY0 AH0 +KOS K AA1 S +KOSAKA K AH0 S AH0 K AH0 +KOSALA K AH0 S AA1 L AH0 +KOSATO K AH0 S AA1 T OW0 +KOSCAM K OW1 S K AE0 M +KOSCIEY K AH0 S CH IY0 +KOSCIUSKO K AH0 S CH UW1 S K OW0 +KOSCIUSKO'S K AH0 S CH UW1 S K OW0 Z +KOSCIUSZKO K AH0 S CH UW1 SH K OW0 +KOSCZIELNA K AH0 S CH IH0 L N AA1 +KOSEK K OW1 S EH0 K +KOSEKIN K AA1 S IH0 K IH0 N +KOSERITZ K OW1 S EH0 R IH0 T S +KOSERITZES K OW1 Z ER0 IH1 T S IH0 Z +KOSH K AA1 SH +KOSHAR K OW1 SH AA2 R +KOSHARE K OW1 SH EH2 R +KOSHCHEI K OW1 SH AY0 +KOSHEH K OW1 SH +KOSHER K OW1 SH ER0 +KOSHIN K OW1 SH IH0 N +KOSHINGA K OW1 SH IY1 NG G AH0 +KOSHIU K OW1 SH UW1 +KOSHNILI K OW1 SH N IY1 L IY0 +KOSIS K OW1 S IH0 S +KOSLOV K AH0 S L AA2 V +KOSMA K AH0 S M AH0 +KOSMAROFF K AH0 S M AA1 R AO0 F +KOSMAROFF'S K AA2 Z M ER0 AO0 F S +KOSMOS K AA2 Z M OW0 Z +KOSO K OW1 S OW0 +KOSOGOL K AH0 S AO1 G AH0 L +KOSOVO K OW1 S OW0 V OW2 +KOSRU K AA1 Z R UW2 +KOSSOVO K AA1 S OW0 V OW2 +KOSSUTH K AA1 S AH0 TH +KOSSUTH'S K AA1 S AH0 TH S +KOST K AA1 S T +KOSTANZHOGLO K AH0 S T AA1 N Z AH0 G L OW1 +KOSTER K AA1 S T ER0 +KOSTI K AA1 S T IY0 +KOSTIA K AA1 S T IY0 AH0 +KOSTIEI K AA1 S T IY0 EY2 +KOSTKA K AA1 S T K AH0 +KOSTROMA K AH0 S T R OW1 M AH0 +KOSTUILIN K AA1 S T UW0 L IH0 N +KOSTYA K OW1 S T Y AH0 +KOSUKE K AH0 S UW0 K +KOSUTH K AH0 S UW1 TH +KOSWELL K AA1 S W EH0 L +KOSZTA K AA1 S T AH0 +KOT K AA1 T +KOTA K OW1 T AH0 +KOTAH K AH0 T AA1 +KOTAL K OW1 T AH0 L +KOTCH K AA1 CH +KOTCHED K AA1 CH T +KOTCHEVOY K AA1 CH IH0 V OY2 +KOTE K OW1 T +KOTEI K OW1 T EY2 +KOTH K AA1 TH +KOTI K OW1 T IY0 +KOTICK K AH0 T IH2 K +KOTIK K AA1 T IH0 K +KOTKELL K AA1 T K AH0 L +KOTLICKI K AA1 T L IH1 T S K IY0 +KOTO K OW1 T OW0 +KOTOK K OW1 T AA2 K +KOTOWING K AH0 T OW1 IH0 NG +KOTRINA K AH0 T R IY1 N AH0 +KOTTAH K AA1 T AH0 +KOTTENNER K AA1 T EH1 N ER0 +KOTTWITZ K AA1 T W IH0 T S +KOTUKO K AH0 T UW1 K OW0 +KOTUL K AH0 T UW1 L +KOTWAL K AA1 T W AH0 L +KOTZE K OW1 T Z +KOTZEBUE K AA1 T S AH0 B Y UW2 +KOTZEBUE'S K AA1 T S IH0 B Y UW2 Z +KOU K UW1 +KOUA K UW1 AH0 +KOUAGA K W AA1 G AH0 +KOUANG K W AA1 NG +KOUBAN K UW1 B AH0 N +KOUBBA K AW1 B AH0 +KOUIDAR K UW1 AH0 D AA2 R +KOUKA K UW1 K AH0 +KOULIKOFF K AW1 L IH0 K AO2 F +KOUM K OW1 M +KOUME K UW1 M +KOUMISS K UW1 M IH0 S +KOUMONGOE K UW1 M AA2 NG G OW0 +KOUN K AW1 N +KOUPRIANE K UW1 P R IY0 AE1 N +KOUPRIANE'S K UW1 P R IY0 AE1 N Z +KOURAGINE K UW1 R AA0 JH IY1 N IY0 +KOURAKIN K UW1 R AH0 K IH0 N +KOURATA K UW1 R AA1 T AH0 +KOURBASH K AW1 R B AE2 SH +KOUROPATKINE K UW1 R AH0 P AE1 T K AH0 N +KOURSK K UW1 R S K +KOUS K AH0 S +KOUSKI K UW1 S K IY0 +KOUSKOUS K UW1 S K AH0 S +KOUSMA K AW1 S M AH0 +KOUSMINSKI K AW1 S M IH1 N S K IY0 +KOUSSEVITZKY K UW1 S EH0 V IH1 T S K IY0 +KOUTONEPI K AW1 T AH0 N IY1 P IY0 +KOVAK K OW1 V AE0 K +KOVALCHUK K AH0 V AA1 L HH AH0 K +KOVALENKO K OW1 V AA0 L EH1 NG K OW0 +KOVALEVSKY K OW1 V AA0 L EH1 V S K IY0 +KOVALOFF K AH0 V AA1 L AO0 F +KOVALSKI K AH0 V AA1 L S K IY0 +KOVANDASWAMY K AH0 V AA1 D AH0 S W AA1 M IY0 +KOVEN K OW1 V AH0 N +KOVNO K AA1 V N OW0 +KOVRIN K OW1 V R IH0 N +KOVUDOO K OW1 V AH0 D UW2 +KOW K AW0 +KOWA K AW1 AH0 +KOWALSKI K AH0 W AA1 L S K IY0 +KOWALT K AW1 AH0 L T +KOWEN K AW1 AH0 N +KOWFAT K OW1 F AE2 T +KOWLIANG K OW1 L Y AE1 NG +KOWNO K AW0 N OW0 +KOWRIES K AW1 R IY0 Z +KOWSHING K OW1 SH IH0 NG +KOWTERSKI K AW1 T IH1 R S K IY0 +KOWTOW K AW1 T AW1 +KOWTOW K OW1 T OW1 +KOWTOWED K OW1 T OW1 D +KOWTOWING K OW1 T OW1 IH0 NG +KOY K OY1 +KOYALA K OW0 Y AA1 L AH0 +KOYALA'S K OW0 Y AA1 L AH0 Z +KOYOKUK K OY1 OW0 K AH0 K +KOYUK K OW0 Y UW1 K +KOYUKUK K OW0 Y UW1 K AH0 K +KOZAK K OW1 Z AH0 K +KOZO K OW1 Z OW0 +KR K R +KRAAL K R AA1 L +KRAALBOOM K R AA1 L B UW2 M +KRAALED K R AH1 L AH0 D +KRAALS K R AA1 L Z +KRADEN K R EY1 D AH0 N +KRAEVESK K R EY1 V EH0 S K +KRAFFT K R AE1 F T +KRAFFT'S K R AE1 F T S +KRAFT K R AE1 F T +KRAG K R AE1 G +KRAGAN K R EY1 G AH0 N +KRAGANS K R EY1 G AH0 N Z +KRAGLIEVIC K R AE1 G L Y AH0 V IH0 K +KRAILL K R AH0 L +KRAILL'S K R EY2 L Z +KRAKA K R AA1 K AH0 +KRAKATAU K R AA1 K AH0 T AW0 +KRAKATOA K R AA1 K AH0 T OW0 AH0 +KRAKAUER K R AE1 K AW0 R +KRAKEN K R EY1 K AH0 N +KRAKER K R EY1 K ER0 +KRAKOW K R AA1 K AA0 V +KRAKOW K R AA1 K AW0 +KRAKOW K R AE1 K AW0 +KRAKOWITZ K R AE1 K AH0 W IH0 T S +KRALAHOME K R AE1 L AH0 HH OW1 M +KRALINSKY K R AH0 L IH1 N S K IY0 +KRALITZ K R AA1 L IH0 T S +KRAMENIN K R AE1 M IH0 N IH0 N +KRAMER K R EY1 M ER0 +KRAMER'S K R EY1 M ER0 Z +KRAMM K R AE1 M +KRAN K R AH0 N +KRANATH K R AE1 N AH0 TH +KRANATH'S K R AE1 N AH0 TH S +KRANITSKI K R AH0 N IH1 T S K IY0 +KRANK K R AE1 NG K +KRANNON K R AE1 N AH0 N +KRANS K R AE1 N Z +KRANSBERG K R AE1 N Z B ER0 G +KRANT K R AE1 N T +KRANTZ K R AE1 N T S +KRANTZER K R AE1 N T Z ER0 +KRANTZES K R AE1 N T S IH0 Z +KRANZ K R AE1 N Z +KRANZLER K R AE1 N Z L ER0 +KRAP K R AE1 P +KRAPACH K R AA1 P AH0 CH +KRAPAK K R AE1 P AH0 K +KRAPF K R AE1 P F +KRAPP K R AE1 P +KRAPP'S K R AE1 P S +KRASINSKA K R AH0 S IH1 N S K AH0 +KRASINSKI K R AH0 S IH1 N S K IY0 +KRASIPPE K R AH0 S IH1 P +KRASNOI K R AE0 S N OY0 +KRASNOIARSK K R AA1 S N OW0 IY0 AA1 R S K +KRASNOYARSK K R AE0 S N OY1 AA0 R S K +KRASSIN K R AE1 S IH0 N +KRASSNOV K R AE1 S N AA0 V +KRASSOTKIN K R AE1 S OW0 T K IH1 N +KRATES K R EY1 T S +KRATON K R AE1 T AH0 N +KRAUS K R AW1 S +KRAUSE K R AO1 S +KRAUSE'S K R AW1 S IH0 Z +KRAUSS K R AW1 S +KRAUT K R AW1 T +KRAYBO K R EY1 B OW2 +KRAYENHOFF K R EY1 AH0 N HH AO2 F +KRAYTON K R EY1 T AH0 N +KRAZINSKI K R AE1 Z IH1 N S K IY0 +KRAZY K R EY1 Z IY0 +KREBS K R EH1 B Z +KREGER K R IY1 G ER0 +KREHBIEL K R EH1 B IY0 L +KREIGER K R AY1 G ER0 +KREIS K R IY1 Z +KREISGERICHTSRATH K R AY1 S G ER0 IH0 K T S R AH0 TH +KREISLER K R AY1 S L ER0 +KREISLER'S K R AY1 S L ER0 Z +KREISLERIANA K R AY1 S L ER0 IY0 AA1 N AH0 +KREISPHYSIKUS K R AY1 S F AH0 Z IH1 K AH0 S +KREISS K R AY1 S +KRELI K R EH1 L IY0 +KRELI'S K R EH1 L IY0 Z +KREMLIN K R EH1 M L AH0 N +KREMLIN K R EH1 M L IH0 N +KREMNITZ K R EH1 M N IH0 T S +KREMPE K R EH1 M P +KRENNER K R EH1 N ER0 +KRENOJ K R EH1 N OW0 JH +KRENSKA K R EH1 N S K AH0 +KRENSKI K R EH1 N S K IY0 +KREPETSKI K R EH0 P EH1 T S K IY0 +KREPETSKIS K R EH0 P EH1 T S K IY0 Z +KRESPEL K R EH1 S P AH0 L +KRESPEL'S K R EH1 S P AH0 L Z +KRESS K R EH1 S +KRESTOWSKY K R EH2 S T AW1 S K IY0 +KRETA K R EH1 T AH0 +KRETSCHMER K R EH1 CH M ER0 +KREUTZ K R UW1 T S +KREUTZER K R OY1 T Z ER0 +KREUTZERS K R OY1 T Z ER0 Z +KREUTZKAMMER K R OY1 T S K AE1 M ER0 +KREUZ K R UW1 Z +KREUZER K R UW1 Z ER0 +KREUZERS K R UW1 Z ER0 Z +KREVIN K R EH1 V AH0 N +KREWE K R UW1 +KREYMBORG K R IH2 M B AO0 R G +KREYNBORG K R EY1 N B AO0 R G +KRIEFF K R IY2 F +KRIEG K R IY1 G +KRIEGER K R IY1 G ER0 +KRIEGSPIEL K R IY1 G S P IY0 L +KRIEMHILD K R IY1 AY2 L D +KRIEMHILD'S K R IY1 M HH IH0 L D Z +KRIEMHILDA K R IY1 M HH IY1 L D AH0 +KRIGE K R IH0 JH +KRIGE'S K R AY1 JH IH0 Z +KRIGES K R AY1 JH IH0 Z +KRIGGER K R IH1 G ER0 +KRIIJORL K R IY1 Y AO1 R L +KRIJGSRAAD K R IH0 JH Z R AA1 D +KRILOFF K R IH1 L AO0 F +KRIM K R IH1 M +KRIMGIRAI K R IH0 M JH IH0 R AA1 IY0 +KRINGLE K R IH1 NG G AH0 L +KRINK K R IH1 NG K +KRINKEN K R IH1 NG K AH0 N +KRIPA K R IY1 P AH0 +KRIS K R IH1 S +KRISBAY K R IH1 Z B EY2 +KRISCHAN K R IH1 SH AH0 N +KRISES K R AY1 Z IH0 Z +KRISHNA K R IH1 SH N AH0 +KRISHNA K R IY1 SH N AH0 +KRISHNA'S K R IH1 SH N AH0 Z +KRISHNOS K R IH1 SH N OW0 S +KRISHT K R IH1 SH T +KRISHTS K R IH1 SH T S +KRISS K R IH1 S +KRISSED K R IH1 S T +KRISSES K R IH1 S IH0 Z +KRIST K R IH1 S T +KRISTEN K R IH1 S T AH0 N +KRISTIAN K R IH1 S T IY0 AH0 N +KRISTOPO K R IH0 S T AA1 P OW2 +KRISTY K R IH1 S T IY0 +KRISTY'S K R IH1 S T IY0 Z +KRITA K R IY1 T AH0 +KRITHIA K R IH1 TH IY0 AH0 +KRITIK K R IH1 T IH0 K +KRITZINGER K R IH1 T Z IH0 NG ER0 +KRIVER K R IH1 V ER0 +KRIVOSCIA K R IH1 V OW1 S CH AH0 +KRIZSANOWSKI K R IH1 Z S AH0 N AO1 F S K IY0 +KRO K R OW1 +KROCHMAL K R AA1 K M AE1 L +KROEKER K R OW1 K ER0 +KROG K R AA1 G +KROGSKOGEN K R AA1 G S K AA1 G AH0 N +KROGSTAD K R AA1 G S T AH0 D +KROLL K R AO1 L +KROLL'S K R AO1 L Z +KROLLER K R OW1 L ER0 +KROM K R AA1 M +KROMITZKA K R OW1 M IH1 T S K AH0 +KROMITZKI K R OW1 M IH1 T S K IY0 +KROMITZKI'S K R OW1 M IH1 T S K IY0 Z +KROMODEOR K R OW1 M AH0 D IY0 ER0 +KRON K R AA1 N +KRONAS K R OW1 N AH0 Z +KRONBERG K R AA1 N B ER0 G +KRONBORG K R AA1 N B AO0 R G +KRONBORG'S K R AA1 N B AO0 R G Z +KRONBORGS K R AA1 N B AO0 R G Z +KRONE K R OW1 N AH0 +KRONEN K R OW1 N AH0 N +KRONER K R OW1 N ER0 +KRONERS K R OW1 N ER0 Z +KRONES K R OW1 N Z +KRONION K R OW1 N Y AH0 N +KRONISCHE K R AA1 N IH0 SH IY0 +KRONK K R AA1 NG K +KRONOS K R OW1 N OW0 S +KRONPRINZ K R AA1 N P R IH1 N Z +KRONSTADT K R AA1 N S T AE2 T +KROO K R UW0 +KROOBOYS K R UW1 B OY2 Z +KROOK K R UH2 K +KROOMAN K R UW1 M AH0 N +KROOMEN K R UW2 M AH0 N +KROONSTAD K R UW1 N S T AE2 D +KROPFSBERG K R AA1 P F S B ER0 G +KROPOTKIN K R AA1 P AA1 T K IH0 N +KROPOTKIN'S K R AA1 P AA1 T K IH0 N Z +KROSSON K R AA1 S AH0 N +KROSSOW K R AA1 S OW0 +KROTEN K R OW1 T AH0 N +KROUMEN K R UW1 M AH0 N +KROUT K R AW1 T +KROVAC K R OW1 V AH0 K +KROVITCH K R OW1 V IH0 CH +KROVITZER K R OW1 V IH0 T S ER0 +KROVITZERS K R OW1 V IH0 T S ER0 Z +KRU K R UW0 +KRUBOY K R AH1 B OY2 +KRUBOYS K R AH1 B OY2 Z +KRUDENER K R UW1 D N ER0 +KRUEGER K R UW1 G ER0 +KRUG K R AH1 G +KRUGEN K R AH1 G AH0 N +KRUGER K R UW1 G ER0 +KRUGER'S K R UW1 G ER0 Z +KRUGERSDORP K R UW1 G ER0 S D AO0 R P +KRULLERS K R AH1 L ER0 Z +KRUMAN K R UW1 M AH0 N +KRUMEN K R UW1 M AH0 N +KRUMERWEG K R AH1 M ER0 W EH2 G +KRUMM K R AH1 M +KRUMMACHER K R AH1 M AH0 K ER0 +KRUMMHORN K R AH1 M HH ER0 N +KRUNTZ K R AH1 N T S +KRUPP K R AH1 P +KRUPP'S K R AH1 P S +KRUPPS K R AH1 P S +KRUSE K R UW1 Z +KRUSENSTERN K R UW1 Z AH0 N S T ER0 N +KRUSTACH K R AH1 S T AE2 SH +KRUTZ K R AH1 T S +KRUTZER K R AH1 T Z ER0 +KRYCHINSKI K R AY1 CH IH1 N S K IY0 +KRYLTZOFF K R IH1 L T Z AO0 F +KRYN K R IH1 N +KRYSIA K R IH1 S IY0 AH0 +KRYSIA'S K R IH1 S IY0 AH0 Z +KRYUKOV K R Y UW0 K AA2 V +KRZESNIA K R EH1 Z N IY0 AH0 +KRZYZAK K R IH0 Z AH0 K +KRZYZAKS K R IH0 Z AE0 K S +KS K S +KSEW K S UW0 +KSHATRIYA K SH AH0 T R IY1 AH0 +KSHATRIYAS K SH AE1 T R IH0 Y AA1 S +KSHATTRIYA K SH AA2 T R IY1 AH0 +KSHIR K SH ER0 +KSIONDZ K SH AH0 D Z +KT K T +KU K UW1 +KU'S K UW2 Z +KUA K Y UW1 AH0 +KUAN K W AA1 N +KUBA K Y UW1 B AH0 +KUBAN K Y UW1 B AH0 N +KUBAR K AH0 B AA1 R +KUBAYS K UW0 B EY2 Z +KUBBADAR K AH1 B AH0 D AA1 R +KUBBEE K AH1 B IY2 +KUBBELING K AH1 B AH0 L IH0 NG +KUBEL K UW1 B AH0 L +KUBELIK K UW1 B EH2 L IH0 K +KUBI K UW1 B IY0 +KUBIK K Y UW1 B IH0 K +KUBIN K Y UW1 B IH0 N +KUBLA K AH1 B L AH0 +KUBLAI K AH1 B L AA1 IY0 +KUBO K Y UW1 B OW0 +KUBOKO K UW0 B OW1 K OW0 +KUBOKO'S K UW0 B OW1 K OW0 Z +KUBWA K AH1 B W AH0 +KUC K AH1 K +KUCHEN K AH1 K AH0 N +KUCHENG K UW2 CH EH1 NG +KUCHIN K AH0 CH IH1 N +KUCHING K AH1 CH IH0 NG +KUCSUK K AH1 K S AH1 K +KUD K AH1 D +KUDAN K UW1 D AH0 N +KUDOS K UW1 D OW0 S +KUDRAT K AH1 D R AH0 T +KUDRIASH K AH1 D R IY0 AE0 SH +KUDU K AH1 D UW0 +KUEELO K UH1 IY1 L OW0 +KUEHLEBORN K UH1 L B ER0 N +KUEHLMANN K UH1 L M AH0 N +KUEI K Y UW0 IY0 +KUEMMEL K W EH1 M AH0 L +KUENEN K UH1 N AH0 N +KUENGOLT K UH1 G OW0 L T +KUENGOLT'S K UH1 G OW1 L T S +KUESSNACHT K UW1 S N AH0 K T +KUESTRIN K UH1 S T R IH0 N +KUFA K UW1 F AH0 +KUFFSTEIN K AH1 F S T IY0 N +KUFNER K AH1 F N ER0 +KUGAUL K UW1 G AO1 L +KUGE K Y UW1 JH +KUGELBLITZ K AH1 G AH0 L B L IH0 T S +KUGES K Y UW1 JH IH0 Z +KUGLER K UW1 G AH0 L ER0 +KUGLER K UW1 G L ER0 +KUGLUTUK K UW1 G L UW2 T AH0 K +KUH K AH1 +KUHL K AH1 L +KUHL'S K UW1 L Z +KUHLEBORN K UW1 AH0 L B ER0 N +KUHLMANN K UW1 L M AH0 N +KUHN K UW1 N +KUHNE K AH1 N +KUHNE'S K UW1 N Z +KUI K UW1 IY0 +KUK K AH1 K +KUKA K UW1 K AH0 +KUKLINOVSKI K AH0 K L IH1 N AA1 F S K IY0 +KUKOR K AH0 K AO1 R +KUKOR'S K UW1 K ER0 Z +KUKRI K UW1 K R IY0 +KUKRIS K UW1 K R IH0 S +KUKUANA K UW1 K UW0 AE1 N AH0 +KUKUANALAND K UW1 K UW0 AE1 N AH0 L AH0 N D +KUKUANAS K UW1 K UW0 AE1 N AH0 Z +KUKUI K UW0 K UW1 IY0 +KUKULEYO K UW1 K AH0 L EY1 OW0 +KUKUSHKIN K UW1 K AH0 SH K IH0 N +KUL K AH0 L +KULAK K UW1 L AH0 K +KULAN K Y UW1 L AH0 N +KULANCHE K Y UW1 L AH0 N SH +KULE K Y UW0 L +KULI K Y UW1 L IY0 +KULIGIN K Y UW0 L IH0 G IH0 N +KULIN K Y UW1 L IH0 N +KULISO K Y UW0 L IY1 S OW0 +KULISO'S K UW1 L IH0 S OW0 Z +KULL K AH1 L +KULLA K AH1 L AH0 +KULLABERG K AH1 L AA1 B ER0 G +KULLAK K AH1 L AH0 K +KULLAK'S K AH1 L AE0 K S +KULLERS K AH1 L ER0 Z +KULLERVO K AH1 L EH1 R V OW0 +KULLERWOINEN K AH1 L ER0 V AW1 IH0 N AH0 N +KULLRICH K AH1 L R IH0 K +KULM K AH1 L M +KULMAN K AH1 L M AH0 N +KULMITTEN K AH1 L M IH1 T AH0 N +KULSUM K AH1 L S AH0 M +KULTUR K AH1 L CH ER0 +KULU K UW1 L UW0 +KULUN K AH0 L AH0 N +KULVYETS K AH1 L V Y EH1 T S +KUM K AH0 M +KUMA K Y UW1 M ER0 +KUMAMOTO K UW0 M AA0 M OW1 T OW0 +KUMANO K UW0 M AA1 N OW0 +KUMAON K UW0 M AA0 OW1 N +KUMAR K UW0 M AA1 R +KUMARA K UW0 M AA1 R AH0 +KUMASAKA K UW0 M AA0 S AA1 K AH0 +KUMBELWA 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AH0 N IY0 Z +LARCENOUS L AA1 R S AH0 N AH0 S +LARCENY L AA1 R S AH0 N IY0 +LARCH L AA1 R CH +LARCHER L AA1 R CH ER0 +LARCHER'S L AA1 R CH ER0 Z +LARCHES L AA1 R CH IH0 Z +LARCHMONT L AA1 R CH M AA2 N T +LARCOM L AA1 R K AA2 M +LARCOM L AA1 R K AH0 M +LARD L AA1 R D +LARD'S L AA1 R D Z +LARDED L AA1 R D IH0 D +LARDER L AA1 R D ER0 +LARDER'S L AA1 R D ER0 Z +LARDERS L AA1 R D ER0 Z +LARDING L AA1 R D IH0 NG +LARDNER L AA1 R D N ER0 +LARDNER'S L AA1 R D N ER0 Z +LARDY L AA1 R D IY0 +LARE L EH1 R +LAREAU L ER0 OW1 +LAREDO L ER0 EY1 D OW0 +LAREEN L AE1 R IY0 N +LAREN L AE1 R AH0 N +LAREN'S L AA1 R AH0 N Z +LARENS L AE1 R AH0 N Z +LARENZ L AA0 R EH1 N Z +LARES L EH1 R Z +LARF L AA1 R F +LARFED L AA1 R F T +LARFIN L AA1 R F IH0 N +LARFING L AA1 R F IH0 NG +LARFS L AA1 R F S +LARGA L AA1 R G AH0 +LARGANI L AA0 R G AA1 N IY0 +LARGE L AA1 R JH +LARGELY L AA1 R JH L IY0 +LARGENESS L AA1 R JH N IH0 S +LARGENTIERE L AA1 R JH EH0 N T IH1 R +LARGER L AA1 R JH ER0 +LARGESS L AA1 R JH AH0 S +LARGESSE L AA0 R G EH1 S +LARGESSES L AA0 R G EH1 S IH0 Z +LARGEST L AA1 R JH AH0 S T +LARGHETTO L AA1 R G EH1 T OW0 +LARGISH L AA1 R JH IH0 SH +LARGNEN L AA1 R G N AH0 N +LARGO L AA1 R G OW0 +LARGS L AA1 R G Z +LARI L AA1 R IY0 +LARIAN L EH1 R IY0 AH0 N +LARIAT L EH1 R IY0 AH0 T +LARIATED L EH1 R IY0 EY2 T IH0 D +LARIATS L EH1 R IY0 IH0 T S +LARIBOISIERE L AE1 R IH0 B OY1 S IY0 EH0 R +LARICH L AE1 R IH0 K +LARIMER L AE1 R IH0 M ER0 +LARIMORE L AA0 R IY1 M AO0 R +LARINSKI L ER0 IH1 N S K IY0 +LARISSA L AA0 R IY1 S AH0 +LARISSE L AA0 R IY1 S +LARIVAUDIERE L AE1 R IH0 V AO0 D IY0 EH0 R +LARIVE L AE1 R IH0 V +LARK L AA1 R K +LARK'S L AA1 R K S +LARKED L AA1 R K T +LARKEN L AA1 R K EH0 N +LARKEY L AA1 R K IY0 +LARKHALL L AA1 R K HH AO2 L +LARKIE L AA1 R K IY0 +LARKIN L AA1 R K IH0 N +LARKIN'S L AA1 R K IH0 N Z +LARKINBURG L AA1 R K IH0 N B ER0 G +LARKING L AA1 R K IH0 NG +LARKINGTON L AA1 R K IH0 NG T AH0 N +LARKINS L AA1 R K IH0 N Z +LARKINS'S L AA1 R K IH0 N Z IH0 Z +LARKS L AA1 R K S +LARKSPUR L AA1 R K S P ER2 +LARKSPURS L AA1 R K S P ER2 Z +LARKUM L AA1 R K AH0 M +LARKUM'S L AA1 R K AH0 M Z +LARKWELL L AA1 R K W EH2 L +LARKWELL'S L AA1 R K W EH2 L Z +LARKY L AA1 R K IY0 +LARKYN L AA1 R K IH0 N +LARKYNS L AA1 R K IH0 N Z +LARM L AA1 R M +LARMER L AA1 R M ER0 +LARMES L AA1 R M Z +LARMOR L AA1 R M ER0 +LARMORE L AA1 R M AO0 R +LARN L AA1 R N +LARNAGE L AA1 R N IH0 JH +LARNE L AA1 R N +LARNED L AA1 R N D +LARNER L AA1 R N ER0 +LARNIGAN L AA1 R N IH0 G AE0 N +LARNIN L AA1 R N IH0 N +LARNING L AA1 R N IH0 NG +LARNS L AA1 R N Z +LARNT L AA1 R N T +LARO L AA1 R OW0 +LAROCHE L AA0 R OW1 SH +LAROCHE L ER0 OW1 SH +LAROCHEJAQUELIN L AA0 R OW1 SH EY1 AH0 K AH0 L IH0 N +LAROCQUE L ER0 OW1 K +LAROLLES L AA0 R OW1 L Z +LARONDE L ER0 AO1 N D +LAROQUE L ER0 OW1 K +LAROQUE'S L ER0 OW1 K S +LAROSE L AA0 R OW1 S IY0 +LAROUGE L AA0 R UW1 ZH +LAROUSSE L AA0 R UW1 S +LAROUSSEL L AA0 R UW1 S AH0 L +LARPENT L AA1 R P AH0 N T +LARPENT'S L AA1 R P AH0 N T S +LARR L AE1 R +LARRABBEE L AE1 R AH0 B IY2 +LARRABEE L AE1 R AH0 B IY0 +LARRABEE'S L AE1 R AH0 B IY0 Z +LARRABIE L AE1 R AH0 B IY0 +LARRALDE L AE1 R AH0 L D +LARRANCE L AE1 R AH0 N S +LARREE L AA0 R IY1 +LARREY L AE1 R IY0 +LARRIAN L AE1 R IY0 AH0 N +LARRIE L AE1 R IY0 +LARRIGANS L AE1 R IH0 G AH0 N Z +LARRIKIN L AE1 R IH0 K IH0 N +LARRIKINS L AE1 R IH0 K AH0 N Z +LARRIMER L AE1 R IH0 M ER0 +LARRO L AA2 R OW0 +LARRONE L AA0 R OW1 N IY0 +LARRUP L AE1 R AH0 P +LARRUPED L AE1 R UW2 P T +LARRUPING L AA0 R UW1 P IH0 NG +LARRY L EH1 R IY0 +LARRY'S L EH1 R IY0 Z +LARS L AA1 R Z +LARS'S L ER0 Z IH0 Z +LARSAGNY L AA1 R S EY1 N IY0 +LARSAM L AA1 R S AE0 M +LARSAN L AA1 R Z AH0 N +LARSAN'S L AA0 R S AA1 N Z +LARSEN L AA1 R S AH0 N +LARSEN'S L AA1 R S AH0 N Z +LARSON L AA1 R S AH0 N +LARSON'S L AA1 R S AH0 N Z +LARSSEN L AA1 R S AH0 N +LARSSEN'S L AA1 R S AH0 N Z +LARSSON L AA1 R S AH0 N +LARST L AA1 R S T +LARTET L AA0 R T EH1 T +LARTIUS L AA1 R SH Y IH0 S +LARUE L ER0 UW1 +LARUE'S L ER0 UW1 Z +LARUJA L AA0 R UW1 Y AH0 +LARUM L AE1 R AH0 M +LARUS L AA1 R AH0 S +LARUT L AA0 R UW1 T +LARVA L AA1 R V AH0 +LARVA'S L AA1 R V AH0 Z +LARVAE L AA1 R V IY0 +LARVAL L AA1 R V AH0 L +LARVIG L AA1 R V IH0 G +LARWOOD L AA1 R W UH2 D +LARYER L AA0 R AY1 ER0 +LARYNGEAL L AA2 R IH1 N JH IY2 AH0 L +LARYNGITIS L AA2 R IH0 N JH AY1 T AH0 S +LARYNGOSCOPE L AA0 R IH1 N G AH0 S K OW2 P +LARYNGOSCOPE L AA0 R IH1 N JH AH0 S K OW2 P +LARYNX L EH1 R IH0 NG K S +LAS EH1 L EY1 EH1 S +LAS L AA1 S +LASAK L AA1 S AH0 K +LASALLE L AH0 S AE1 L +LASAR L AH0 Z AA1 R +LASAR'S L AH0 Z AA1 R Z +LASAREVICH L AA0 S AA1 R EH0 V IH0 CH +LASBERG L AE1 S B ER0 G +LASCA L AA1 S K AH0 +LASCALLA L AA0 S K AA1 L AH0 +LASCAR L AE1 S K ER0 +LASCAR'S L AE1 S K ER0 Z +LASCARIS L AA0 S K AA1 R IY0 Z +LASCARO L AA0 S K AA1 R OW0 +LASCARS L AA0 S K AA1 R Z +LASCELLES L AH0 S EH1 L Z +LASCIA L AA0 S CH AH0 +LASCIATE L AH0 S IY0 EY2 T +LASCIVIOUS L AH0 S IH1 V IY0 AH0 S +LASCIVIOUSLY L AH0 S IH1 V IY0 AH0 S L IY0 +LASCIVIOUSNESS L AH0 S IH1 V IY0 AH0 S N AH0 S +LASE L EY1 Z +LASELLE L AH0 S EH1 L +LASER L EY1 Z ER0 +LASETTE L AH0 S EH1 T +LASEYNE L AA0 S EY1 N +LASGIRD L AE1 S G ER2 D +LASH L AE1 SH +LASH'D L AE1 SH IH0 D +LASHAM L AE1 SH AH0 M +LASHCAIRN L AE1 SH K EH1 R N +LASHED L AE1 SH T +LASHER L AE1 SH ER0 +LASHER'S L AE1 SH ER0 Z +LASHES L AE1 SH IH0 Z +LASHIN L AE1 SH IH0 N +LASHIN'S L AE1 SH IH0 N Z +LASHING L AE1 SH IH0 NG +LASHINGS L AE1 SH IH0 NG Z +LASHINS L AE1 SH IH0 N Z +LASHKAR L AE1 SH K AA2 R +LASHLESS L AE1 SH L AH0 S +LASHLEY L AE1 SH L IY0 +LASHMAR L AE1 SH M ER0 +LASHMORE L AE1 SH M AO0 R +LASHMORE'S L AE1 SH M AO0 R Z +LASHNAGAR L AE1 SH N AH0 G AA2 R +LASHT L AE1 SH T +LASKA L AA1 S K AH0 +LASKER L AE1 S K ER0 +LASKI L AA1 S K IY0 +LASKOWSKI L AH0 S K AO1 F S K IY0 +LASKOY L AE1 S K OY1 +LASKY L AE1 S K IY0 +LASNE L AH0 N +LASO L AA1 S OW0 +LASPARA L AA0 S P AA1 R AH0 +LASPUR L AA0 S P ER2 +LASS L AE1 S +LASS'S L AE1 S IH0 Z +LASSA L AA1 S AH0 +LASSALLE L AE1 S AE1 L +LASSALLE'S L AE1 S AE1 L Z +LASSAN L AE1 S AH0 N +LASSAN'S L AE1 S AA0 N Z +LASSANS L AE1 S AA0 N Z +LASSE L AE1 S +LASSE'S L AE1 S IH0 Z +LASSELLE L AE1 S AH0 L +LASSELLES L AE1 S AH0 L Z +LASSEN L AE1 S AH0 N +LASSEN'S L AE1 S AH0 N Z +LASSER L AE1 S ER0 +LASSEROE L AE1 S ER0 OW2 +LASSES L AE1 S IH0 Z +LASSETT L AE1 S IH0 T +LASSIE L AE1 S IY0 +LASSIE'S L AE1 S IY0 Z +LASSIES L AE1 S IY0 Z +LASSIGNY L AH0 S AY1 N IY0 +LASSIGNY'S L AH0 S AY1 N IY0 Z +LASSITER L AE1 S AY0 T ER0 +LASSITER'S L AE1 S AY0 T ER0 Z +LASSITUDE L AE1 S IH0 T UW2 D +LASSITUDES L AE1 S IH0 T UW0 D Z +LASSO L AE1 S OW0 +LASSOED L AE1 S OW0 D +LASSOES L AE1 S OW0 Z +LASSOING L AE1 S OW0 IH0 NG +LASSOIS L AE1 S W AA2 +LASSOO L AH0 S UW1 +LASSOS L AA1 S OW0 S +LASST L AE1 S T +LASSUS L AE1 S AH0 S +LASSWADE L AE1 S W EY1 D +LASSY L AE1 S IY0 +LAST L AE1 S +LAST L AE1 S T +LAST L AO1 S T +LAST' L AE1 S T +LAST'S L AE1 S T S +LASTA L AE1 S T AH0 +LASTAOLA L AE1 S T AA0 OW1 L AH0 +LASTE L AE1 S T +LASTED L AE1 S T AH0 D +LASTED L AE1 S T IH0 D +LASTER L AE1 S T ER0 +LASTETH L AE1 S T IH0 TH +LASTIC L AE1 S T IH0 K +LASTIN L AE1 S T IH0 N +LASTING L AE1 S T IH0 NG +LASTINGLY L AE1 S T IH0 NG L IY0 +LASTINGNESS L AE1 S T IH0 NG N IH0 S +LASTLY L AE1 S T L IY0 +LASTS L AE1 S T S +LASUEN L AH0 S UW1 N +LASUER L AH0 S UW1 ER0 +LASWAREE L AE1 S W ER0 IY1 +LAT L AA1 T +LAT L AE1 T +LAT'S L AE1 T S +LATA L AA1 T AH0 +LATAKIA L AH0 T AA1 K IY0 AH0 +LATCH L AE1 CH +LATCHED L AE1 CH T +LATCHES L AE1 CH AH0 Z +LATCHES L AE1 CH IH0 Z +LATCHET L AE1 CH AH0 T +LATCHETS L AE1 CH AH0 T S +LATCHFORD L AE1 CH F ER0 D +LATCHING L AE1 CH IH0 NG +LATCHKEY L AE1 CH K IY2 +LATCHKEYS L AE1 CH K IY0 Z +LATCHLEY L AE1 CH L IY0 +LATCHSTRING L AE1 CH S T R IH2 NG +LATCHWORTH L AE1 CH W ER2 TH +LATE L EY1 T +LATECOMER L EY1 T K AH0 M ER0 +LATEEN L EY1 T IY2 N +LATELY L EY1 T L IY0 +LATENCY L EY1 T AH0 N S IY0 +LATENESS L EY1 T N AH0 S +LATENT L EY1 T AH0 N T +LATENTLY L EY1 T AH0 N T L IY0 +LATER L EY1 T ER0 +LATERAL L AE1 T ER0 AH0 L +LATERALLY L AE1 T ER0 AH0 L IY0 +LATERALS L AE1 T ER0 AH0 L Z +LATERAN L AE1 T ER0 AH0 N +LATERANO L AE1 T ER0 AH0 N OW0 +LATERANUS L AE1 T ER0 AH0 N IH0 S +LATERE L AA0 T EH1 R +LATERLY L AE1 T ER0 L IY0 +LATEST L EY1 T AH0 S T +LATET L AA0 T EH1 T +LATEX L EY1 T EH2 K S +LATH L AE1 TH +LATHAM L AE1 TH AH0 M +LATHAM'S L AE1 TH AH0 M Z +LATHBUN L AE1 TH B AH0 N +LATHBUNS L AE1 TH B AH0 N Z +LATHBURY L AE1 TH B ER0 IY0 +LATHE L EY1 DH +LATHED L EY1 DH D +LATHER L AE1 DH ER0 +LATHERED L AE1 DH ER0 D +LATHERING L AE1 DH ER0 IH0 NG +LATHERS L AE1 DH ER0 Z +LATHERY L AE1 DH ER0 IY0 +LATHES L EY1 DH Z +LATHI L AE1 TH IY0 +LATHING L EY1 DH IH0 NG +LATHOM L AE1 DH AH0 M +LATHROP L AE1 TH R AH0 P +LATHROP'S L AE1 TH R AH0 P S +LATHS L AE1 TH S +LATHT L AE1 TH T +LATHY L AE1 TH IY0 +LATHYRUS L AE1 TH AY1 R AH0 S +LATIAN L EY1 SH AH0 N +LATIANS L EY1 SH AH0 N Z +LATIARIS L AH0 T IY1 ER0 IH0 S +LATIFOLIA L AE1 T AH0 F OW1 L IY0 AH0 +LATIGO L AH0 T IY1 G OW0 +LATIMER L AE1 T AH0 M ER0 +LATIMER'S L AE1 T AH0 M ER0 Z +LATIMERS L AE1 T AH0 M ER0 Z +LATIN L AE1 T AH0 N +LATIN'S L AE1 T AH0 N Z +LATINA L AA0 T IY1 N AH0 +LATINA L AH0 T IY1 N AH0 +LATINE L AA0 T IY1 N IY0 +LATINES L AH0 T IY1 N Z +LATINI L AA0 T IY1 N IY0 +LATINISED L AE1 T AH0 N AY2 Z D +LATINISM L AE1 T AH0 N IH2 Z AH0 M +LATINISMS L AE1 T AH0 N IH2 Z AH0 M Z +LATINIST L AE1 T AH0 N AH0 S T +LATINISTS L AE1 T AH0 N AH0 S T S +LATINITY L AH0 T IH1 N AH0 T IY0 +LATINIZED L AE1 T AH0 N AY2 Z D +LATINO L AA0 T IY1 N OW0 +LATINO L AH0 T IY1 N OW0 +LATINS L AE1 T AH0 N Z +LATINUS L AE1 T AH0 N AH0 S +LATIONS L EY1 SH AH0 N Z +LATISH L AH0 T IH1 SH +LATISSIMA L AE1 T IY0 S IY1 M AH0 +LATITUDE L AE1 T AH0 T UW2 D +LATITUDES L AE1 T AH0 T UW2 D Z +LATITUDINAL L AE2 T AH0 T UW1 D AH0 N AH0 L +LATITUDINARIAN L AE1 T AH0 T UW1 D AH0 N EH1 R IY0 AH0 N +LATITUDINARIANISM L AE1 T AH0 T UW1 D AH0 N EH1 R IY0 AH0 N IH2 Z AH0 M +LATITUDINARIANS L AE1 T AH0 T UW1 D AH0 N EH1 R IY0 AH0 N Z +LATIUM L AE1 T IY0 AH0 M +LATKIN L AA1 T K IH0 N +LATMOS L AE1 T M AH0 S +LATONA L AA0 T OW1 N AH0 +LATONA'S L AA0 T OW1 N AH0 Z +LATONIA L AA0 T OW1 N IY0 AH0 +LATOOKA L AH0 T UW1 K AH0 +LATOOKAS L AH0 T UW1 K AH0 Z +LATOUCHE L AH0 T UW1 SH +LATOUR L AH0 T UH1 R +LATOUR'S L AH0 T UH1 R Z +LATOURNELLE L AE1 T UH0 R N EH1 L +LATOURNELLES L AE1 T UH0 R N EH1 L Z +LATRANS L AH0 T R AE2 N Z +LATREILLE L AH0 T R AY1 L +LATREILLE'S L AH0 T R AY1 L Z +LATREZZI L AH0 T R EH1 Z IY0 +LATRINE L AH0 T R IY1 N +LATRINES L AH0 T R IY1 N Z +LATROBE L AA0 T R OW1 B +LATROBE'S L AA0 T R OW1 B Z +LATRON L AH0 T R AO0 N +LATRON'S L AH0 T R AO0 N Z +LATROP L AE1 T R AH0 P +LATTA L AE1 T AH0 +LATTAKER L AE1 T AH0 K ER0 +LATTE L AA1 T EY2 +LATTEEN L AE1 T IY2 N +LATTEINER L AE1 T AY0 N ER0 +LATTEN L AE1 T AH0 N +LATTER L AE1 T ER0 +LATTER'S L AE1 T ER0 Z +LATTERDAY L AE1 T ER0 D EY2 +LATTERLY L AE1 T ER0 L IY0 +LATTERMAN L AE1 T ER0 M AH0 N +LATTERS L AE1 T ER0 Z +LATTERY L AE1 T ER0 IY0 +LATTICE L AE1 T AH0 S +LATTICED L AE1 T AH0 S T +LATTICES L AE1 T AH0 S IH0 Z +LATTICEWORK L AE1 T AH0 S W ER2 K +LATTIMER L AE1 T AH0 M ER0 +LATTIMORE L AA0 T IY1 M AO0 R +LATTIN L AE1 T IH0 N +LATTITUDE L AE1 T AH0 T UW2 D +LATUDE L AA0 T UW2 D +LATUS L AE1 T AH0 S +LATY L EY1 T IY0 +LATZ L AE1 T S +LAU L AW1 +LAUATI L AW1 AA1 T IY0 +LAUBARDEMONT L AW1 B AA2 R D EH1 M AA2 N T +LAUBE L AO1 B +LAUBEPIN L AW1 B IH0 P IH0 N +LAUCH L AA1 CH +LAUCHIE L AA1 CH IY0 +LAUCHIE'S L AA1 CH IY0 Z +LAUCHIN L AW1 K IH0 N +LAUCHLAN L AO1 K L AH0 N +LAUCHLANE L AO1 K L AH0 N +LAUD L AO1 D +LAUD'S L AO1 D Z +LAUDA L AO1 D AH0 +LAUDABLE L AO1 D AH0 B AH0 L +LAUDABLY L AO1 D AH0 B L IY0 +LAUDAMUS L AO1 D AH0 M IH0 S +LAUDANUM L AO1 D AH0 N AH0 M +LAUDATE L AO1 D AH0 T +LAUDATION L AO1 D EY1 SH AH0 N +LAUDATIONS L AO1 D EY1 SH AH0 N Z +LAUDATOR L AO1 D AH0 T ER0 +LAUDATORY L AO1 D AH0 T AO2 R IY0 +LAUDE L AO1 D +LAUDED L AO1 D IH0 D +LAUDER L AO1 D ER0 +LAUDER L AW1 D ER0 +LAUDER'S L AW1 D ER0 Z +LAUDERDALE L AO1 D ER0 D EY2 L +LAUDERSDALE L AO1 D ER0 Z D EY2 L +LAUDES L AO1 D Z +LAUDHER L AO1 D HH ER0 +LAUDHER'S L AO1 D ER0 Z +LAUDI L AO1 D IY0 +LAUDIAN L AO1 D IY0 AH0 N +LAUDING L AO1 D IH0 NG +LAUDOHN L AO1 D AA1 N +LAUDON L AO1 D AH0 N +LAUDONNIERE L AO1 D AA1 N IY0 EH0 R +LAUDONNIERE'S L AO1 D AH0 N Y EH1 R Z +LAUDS L AO1 D Z +LAUE L AW1 IY0 +LAUE'S L AW1 IY0 Z +LAUENBURG L AW1 AH0 N B ER0 G +LAUER L AW1 ER0 +LAUFEN L AW1 F AH0 N +LAUFER L AO1 F ER0 +LAUFFER L AO1 F ER0 +LAUGAR L AW1 G ER0 +LAUGH L AE1 F +LAUGH'D L AE1 F D +LAUGH'S L AE1 F Z +LAUGHABLE L AE1 F AH0 B AH0 L +LAUGHABLY L AE1 F AH0 B L IY0 +LAUGHED L AE1 F T +LAUGHER L AA1 K ER0 +LAUGHERS L AE1 F ER0 Z +LAUGHEST L AE1 F AH0 S T +LAUGHETH L AE1 F EH1 TH +LAUGHIN L AE1 F IH0 N +LAUGHIN' L AE1 F IH0 N +LAUGHING L AE1 F IH0 NG +LAUGHINGLY L AE1 F IH0 NG L IY0 +LAUGHINGSTOCK L AE1 F IH0 NG S T AA2 K +LAUGHLIN L AA1 F L IH0 N +LAUGHLIN L AA1 K L IH0 N +LAUGHLIN'S L AE1 F L IH0 N Z +LAUGHS L AE1 F S +LAUGHT L AO1 T +LAUGHTER L AE1 F T ER0 +LAUGHTER'S L AE1 F T ER0 Z +LAUGHTERS L AE1 F T ER0 Z +LAUGHTON L AO1 T AH0 N +LAUK L AO1 K 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+MIGHTY M AY1 T IY0 +MIGNARD M IY0 G N AA1 R D +MIGNET M IH1 G N IH0 T +MIGNIONETTE M IY0 G N IY0 OW0 N EH1 T +MIGNON M IH1 G N AH0 N +MIGNON'S M IH1 G N AH0 N Z +MIGNONETTE M IH1 G N AH0 N EH0 T +MIGNONETTES M IH1 G N AH0 N EH1 T S +MIGNONNE M IH1 G N AH0 N +MIGNONNE'S M IH1 G N AH0 N Z +MIGNONNET M IH1 G N AH0 N IH0 T +MIGNONNETTE M IH1 G N AH0 N EH1 T +MIGNONS M IH1 G N AH0 N Z +MIGNOT M IH1 G N AH0 T +MIGNOTTE M IY0 G N AO1 T +MIGOTT M IH1 G AH0 T +MIGRAINE M AY1 G R EY2 N +MIGRANT M AY1 G R AH0 N T +MIGRANTS M AY1 G R AH0 N T S +MIGRATE M AY1 G R EY2 T +MIGRATED M AY1 G R EY2 T IH0 D +MIGRATES M AY1 G R EY2 T S +MIGRATING M AY1 G R EY2 T IH0 NG +MIGRATION M AY0 G R EY1 SH AH0 N +MIGRATIONS M AY0 G R EY1 SH AH0 N Z +MIGRATORIUS M AY1 G R AH0 T AO1 R IY0 IH0 S +MIGRATORY M AY1 G R AH0 T AO2 R IY0 +MIGUEL M IH0 G EH1 L +MIGUEL'S M IH0 G EH1 L Z +MIGUELITO M IY1 G EH0 L IY1 T OW0 +MIGUEV M IY0 G EH1 V +MIGUL M IH0 G L +MIGUNOV M IH1 G UW0 N AA0 V +MIGWAN M IH1 G W AA0 N +MIGWAN'S M IH1 G W AA1 N Z +MIHAIL M IY2 HH AY1 L +MIHAILITCH M IY2 HH AY1 L IH0 CH +MIHAILO M IY2 HH AY1 L OW0 +MIHAILOVICH M IY2 HH AY1 L AH0 V IH0 CH +MIHAILOVITCH M IY2 HH AY1 L AH0 V IH0 CH +MIHAILOVNA M IY2 HH AY1 L OW0 V N AH0 +MIHAILOVNA'S M IY2 HH AY1 L OW0 V N AH0 Z +MIHAL M AY1 HH AH0 L +MIHALEVITCH M IH1 HH AH0 L IY1 V IH0 CH +MIHALITCH M IH1 HH AH0 L IH1 CH +MIHALOVITCH M IH1 HH AH0 L AA1 V IH0 CH +MIHALOVNA M IH1 HH AH0 L AA1 V N AH0 +MIHI M IH1 HH IY0 +MIHIEL M IH1 HH IY0 L +MIHRAB M IH1 R AE1 B +MIHUL M IH1 HH UW1 L +MIJAUREE M IY0 Y AA1 R IY1 +MIJNHEER M IY0 Y N HH IH1 R +MIJOK M IH0 JH AA0 K +MIJOK'S M IY0 Y OW0 K S +MIK M IH0 K +MIKA M IY1 K AH0 +MIKA'PI M IY0 K P IY0 +MIKADO M IH0 K AA1 D OW0 +MIKADO'S M IH0 K AA1 D OW0 Z +MIKADOS M IH0 K AA1 D OW0 Z +MIKAEL M AH0 K EY1 L +MIKAH M IY1 K AH0 +MIKAH'S M IY1 K AH0 Z +MIKAHIL M IY1 K AH0 HH IY1 L +MIKAIL M IY0 K EY1 L +MIKAILOVITCH M IY0 K AY0 L AH0 V IH0 CH +MIKAK M IY0 K AA1 K +MIKCHICH M IH0 K IH0 K +MIKE M AY1 K +MIKE'S M AY1 K S +MIKEEN M AY1 K IY2 N +MIKEL M IH1 K AH0 L +MIKES M AY1 K S +MIKEY M AY1 K IY0 +MIKHAIL M IH0 K AY1 L +MIKHAIL M IH0 K EY1 L +MIKHAILOFF M IH0 K AY1 L AO0 F +MIKHAILOVICH M IH0 K AY1 L AH0 V IH0 CH +MIKHAILOVNA M IH0 K AY1 L OW0 V N AH0 +MIKHALEVICH M IH0 K AH1 L AH0 V IH0 CH +MIKHAYLOVNA M IH0 K EY1 L AA2 V N AH0 +MIKHYL M IH0 K AH0 L +MIKI M IH0 K IY1 +MIKI'S M IH1 K IY0 Z +MIKITA M IH0 K IY1 T AH0 +MIKKO M IH0 K OW0 +MIKKY M IH0 K IY0 +MIKKY'S M IH0 K IY0 Z +MIKLOS M IY1 K L OW0 S +MIKO M AY1 K OW0 +MIKO'S M AY1 K OW0 Z +MIKOLAJ M IH0 K AA1 L AY0 +MIKOLKA M IH0 K AA1 L K AH0 +MIKONAREE M IH0 K AA1 N ER0 IY1 +MIKOTO M IH0 K OW1 T OW0 +MIKSZATH M IH1 K Z AE2 TH +MIKULI M IH0 K UW1 L IY0 +MIKULIN M IH0 K UW1 L IH0 N +MIKUMWESS M IH0 K AH1 M W IH0 S +MIL M IH1 L +MILA M IH1 L AH0 +MILADI M IH0 L AA1 D IY0 +MILADI'S M IH0 L AA1 D IY0 Z +MILADY M IH0 L AA1 D IY0 +MILADY'S M IH0 L AA1 D IY0 Z +MILAGRO M IH0 L AE1 G R OW0 +MILAGROS M IH0 L AE1 G R OW0 Z +MILAGROSA M IH0 L AE1 G R OW1 S AH0 +MILAM M IH1 L AH0 M +MILAN M AH0 L AA1 N +MILAN M AY1 L AE2 N +MILAN M IH0 L AA1 N +MILAN'S M IH0 L AA1 N Z +MILANESE M IH0 L AH0 N IY1 Z +MILANO M IY0 L AA1 N OW0 +MILANS M IH0 L AH0 N Z +MILAUD M IH0 L OW1 +MILBANK M IH1 L B AE2 NG K +MILBANKE M IH1 L B AE2 NG K +MILBANKE'S M IH1 L B AE2 NG K S +MILBE M IH1 L B +MILBOROUGH M IH1 L B ER0 OW0 +MILBOROUGH'S M IH1 L B ER0 OW0 Z +MILBOURN M IH0 L B UH1 R N +MILBREY M IH1 L B R IY0 +MILBREYS M IH1 L B R IY0 Z +MILBURGH M IH1 L B ER0 G +MILBURGH'S M IH1 L B ER0 G Z +MILBURN M IH1 L B ER2 N +MILBURN'S M IH1 L B ER0 N Z +MILBURNS M IH1 L B ER0 N Z +MILBY M IH1 L B IY0 +MILCAH M IH1 L K AH0 +MILCASTER M IH1 L K AE2 S T ER0 +MILCH M IH1 L CH +MILCHER M IH1 L CH ER0 +MILCHESTER M IH1 L CH EH2 S T ER0 +MILCHO M IH1 L CH OW0 +MILCOM M IH1 L K AA0 M +MILD M AY1 L D +MILDAU M AY1 L D AW0 +MILDE M IH1 L D +MILDENHAM M IH1 L D IH0 N HH AH0 M +MILDER M AY1 L D ER0 +MILDEST M AY1 L D AH0 S T +MILDEW M IH1 L D UW2 +MILDEWED M IH1 L D UW2 D +MILDEWS M IH1 L D UW2 Z +MILDEWY M AY1 L D UW1 IY0 +MILDLY M AY1 L D L IY0 +MILDMAY M AY1 L D M EY2 +MILDMAY'S M AY1 L D M EY2 Z +MILDMAYS M AY1 L D M EY2 Z +MILDNESS M IH1 L D N AH0 S +MILDRED M IH1 L D R IH0 D +MILDRED'S M IH1 L D R IH0 D Z +MILDS M AY1 L D Z +MILDUME M IH1 L D AH0 M +MILE M AY1 L +MILE'S M AY1 L Z +MILEAGE M AY1 L AH0 JH +MILEAGE M AY1 L IH0 JH +MILENA M IH1 L IH0 N AH0 +MILENA'S M IH0 L IY1 N AH0 Z +MILENKO M IH0 L EH1 NG K OW0 +MILENKO'S M IH0 L EH1 NG K OW0 Z +MILER M AY1 L ER0 +MILES M AY1 AH0 L Z +MILES M AY1 L Z +MILES'S M AY1 L Z IH0 Z +MILESIAN M IH0 L IY1 Z AH0 N +MILESIANS M IH0 L IY1 Z AH0 N Z +MILESTONE M AY1 L S T OW2 N +MILESTONES M AY1 L S T OW2 N Z +MILETTE M IH0 L EH1 T +MILETUS M IH0 L IY1 T AH0 S +MILEY M AY1 L IY0 +MILFOIL M IH0 L F OY1 L +MILFORD M IH1 L F ER0 D +MILFORD'S M IH1 L F ER0 D Z +MILGRAVE M IH1 L G R EY2 V +MILHAU M IH1 L HH AW0 +MILHAUD M IH1 L HH AW0 D +MILHAUD'S M IH1 L HH AW0 D Z +MILHOLLAND M IH1 L HH AH0 L AH0 N D +MILIANA M IH2 L IY0 AE1 N AH0 +MILIANAH M IH2 L IY0 AE1 N AH0 +MILICENT M IH1 L IH0 S IH0 N T +MILICHUS M IY0 L IY1 CH AH0 S +MILIEU M IH0 L Y UH1 +MILIEVA M IH0 L IY2 V AH0 +MILITA M IY0 L IY1 T AH0 +MILITAIRE M IH2 L AH0 T EH1 R +MILITAIRES M IH2 L AH0 T EH1 R Z +MILITANCY M IH1 L AH0 T AH0 N S IY0 +MILITANT M IH1 L AH0 T AH0 N T +MILITANTLY M IH1 L IH0 T AH0 N T L IY0 +MILITANTS M IH1 L AH0 T AH0 N T S +MILITARI M IH2 L AH0 T EH1 R IY0 +MILITARILY M IH2 L AH0 T EH1 R AH0 L IY0 +MILITARISM M IH1 L AH0 T ER0 IH2 Z AH0 M +MILITARIST M IH1 L AH0 T ER0 IH0 S T +MILITARISTIC M IH2 L IH0 T ER0 IH1 S T IH0 K +MILITARISTS M IH1 L AH0 T ER0 IH0 S T S +MILITARIZATION M IH1 L AH0 T ER0 AH0 Z EY1 SH AH0 N +MILITARIZED M IH1 L AH0 T ER0 AY2 Z D +MILITARY M IH1 L AH0 T EH2 R IY0 +MILITARY M IH1 L IH0 T EH2 R IY0 +MILITATE M IH1 L IH0 T EY2 T +MILITATED M IH1 L AH0 T EY2 T IH0 D +MILITATES M IH1 L AH0 T EY2 T S +MILITATING M IH1 L AH0 T EY2 T IH0 NG +MILITCH M IH0 L IH1 CH +MILITES M IY0 L IY1 T S +MILITIA M AH0 L IH1 SH AH0 +MILITIA M IH0 L IH1 SH AH0 +MILITIAMAN M AH0 L IH1 SH AH0 M AH0 N +MILITIAMEN M AH0 L IH1 SH AH0 M IH0 N +MILITRISA M IH1 L AH0 T R IY1 S AH0 +MILITUM M IH1 L AH0 T AH0 M +MILITZA M IH0 L IH1 T Z AH0 +MILIUKOFF M AY1 L IY0 IH0 K AO0 F +MILIUKOV M IY0 L IY0 UW1 K AA0 V +MILK M IH1 L K +MILK'S M IH1 L K S +MILKA M IH1 L K AH0 +MILKE M IH1 L K +MILKED M IH1 L K T +MILKER M IH1 L K ER0 +MILKERS M IH1 L K ER0 Z +MILKIN M IH1 L K IH0 N +MILKINESS M IH1 L K IY0 N AH0 S +MILKING M IH1 L K IH0 NG +MILKLESS M IH1 L K L AH0 S +MILKMAID M IH1 L K M EY2 D +MILKMAID'S M IH1 L K M EY2 D Z +MILKMAIDS M IH1 L K M EY2 D Z +MILKMAN M IH1 L K M AE2 N +MILKMAN'S M IH1 L K M AE2 N Z +MILKMEN M IH1 L K M EH2 N +MILKS M IH1 L K S +MILKSOP M IH1 L K S AA2 P +MILKSOPS M IH1 L K S AA2 P S +MILKSTAND M IH1 L K S T AE2 N D +MILKWEED M IH1 L K W IY2 D +MILKWHITE M IH1 L K W AY2 T +MILKWOMAN M IH1 L K W UH2 M AH0 N +MILKWOMEN M IH1 L K W IH2 M AH0 N +MILKWORT M IH1 L K W ER0 T +MILKY M IH1 L K IY0 +MILL M IH1 L +MILL'CENT M IH1 L S AH0 N T +MILL'S M IH1 L Z +MILLA M IH1 L AH0 +MILLAIRD M IH1 L ER0 D +MILLAIS M IH1 L EY1 +MILLAIS'S M IH1 L AH0 S IH0 Z +MILLAMANT M IH1 L AH0 M AH0 N T +MILLAN M IH1 L AH0 N +MILLAN'S M IH1 L AH0 N Z +MILLAR M IH1 L ER0 +MILLAR'S M IH1 L ER0 Z +MILLARCA M IH1 L AA1 R K AH0 +MILLARD M IH1 L ER0 D +MILLARD'S M IH1 L ER0 D Z +MILLARS M IH1 L ER0 Z +MILLAU M IH1 L AW0 +MILLAY M IH0 L EY1 +MILLBANK M IH1 L B AE2 NG K +MILLBORNE M IH1 L B ER0 N +MILLBOURNE M IH1 L B ER0 N +MILLBROOK M IH1 L B R UH0 K +MILLCOTE M IH1 L K OW2 T +MILLDAM M IH1 L D AH0 M +MILLDEAN M IH1 L D IY0 AH0 N +MILLE M IH1 L +MILLED M IH1 L D +MILLEDGEVILLE M IH1 L IH0 JH V IH0 L +MILLEFLEUR M IH1 L IH0 F L ER1 +MILLEFLEURS M IH1 L IH0 F L ER1 Z +MILLEN M IH1 L AH0 N +MILLENARIAN M IH1 L AH0 N EH1 R IY0 AH0 N +MILLENARY M IH1 L AH0 N EH2 R IY0 +MILLENIA M IH1 L EH0 N IY1 AH0 +MILLENIAL M IH1 L EH0 N Y AH0 L +MILLENIUM M IH1 L IY1 N IY0 AH0 M +MILLENIUMS M IH1 L IY1 N IY0 AH0 M Z +MILLENNIA M AH0 L EH1 N IY0 AH0 +MILLENNIAL M IH0 L EH1 N IY0 AH0 L +MILLENNIUM M AH0 L EH1 N IY0 AH0 M +MILLENNIUMS M AH0 L EH1 N IY0 AH0 M Z +MILLER M IH1 L ER0 +MILLER'S M IH1 L ER0 Z +MILLERAND M IH1 L ER0 AH0 N D +MILLERISM M IH1 L ER0 IH2 Z AH0 M +MILLERITE M IH1 L ER0 AY2 T +MILLERITES M IH1 L ER0 IY1 T S +MILLERS M IH1 L ER0 Z +MILLERSTOWN M IH1 L ER0 Z T AW2 N +MILLERSVILLE M IH1 L ER0 Z V IH2 L +MILLES M IH1 L IY0 Z +MILLESCHAUER M AH0 L EH1 S K AW1 ER0 +MILLESIMO M IH1 L EH0 S IY1 M OW0 +MILLET M IH1 L AH0 T +MILLET'S M IH1 L AH0 T S +MILLETS M IH1 L AH0 T S +MILLETT M IH1 L IH0 T +MILLEVOYE M IH1 L AH0 V OY1 +MILLHAVEN M IH1 L HH EY2 V AH0 N +MILLIA M IH1 L Y AH0 +MILLIARD M IH1 L Y AA0 R D +MILLIARDS M IH1 L Y ER0 D Z +MILLICENT M IH1 L IH0 S IH0 N T +MILLICENT'S M IH1 L IH0 S IH0 N T S +MILLIDEW M IH1 L AH0 D UW2 +MILLIE M IH1 L IY0 +MILLIE'S M IH1 L IY0 Z +MILLIERE M IH1 L IY0 EH0 R +MILLIES M IH1 L IY0 Z +MILLIET M IH1 L IY0 EH2 T +MILLIGAN M IH1 L IH0 G AH0 N +MILLIGAN'S M IH1 L IH0 G AH0 N Z +MILLIGANS M IH1 L IH0 G AH0 N Z +MILLIGRAM M IH1 L AH0 G R AE2 M +MILLIGRAMS M IH1 L AH0 G R AE2 M Z +MILLIKAN M IH1 L AH0 K AH0 N +MILLIKEN M IH1 L IH0 K AH0 N +MILLIKEN'S M IH1 L IH0 K AH0 N Z +MILLIKIN M IH1 L IH0 K IH0 N +MILLIKINS M IH1 L AH0 K AH0 N Z +MILLIMETER M IH1 L AH0 M IY2 T ER0 +MILLIMETERS M IH1 L AH0 M IY2 T ER0 Z +MILLIMETRE M IH1 L AH0 M IY2 T ER0 +MILLIMETRES M IH1 L AH0 M IY2 T ER0 Z +MILLIN M IH1 L AH0 N +MILLINBORN M IH1 L IH0 N B ER0 N +MILLINER M IH1 L IH0 N ER0 +MILLINER'S M IH1 L AY2 N ER0 Z +MILLINERS M IH1 L IH0 N ER0 Z +MILLINERY M IH1 L IH0 N ER0 IY0 +MILLING M IH1 L IH0 NG +MILLINGER M IH1 L IH0 NG ER0 +MILLINGHAM M IH1 L IH0 NG HH AE2 M +MILLINGHAM'S M IH1 L IH0 NG HH AE2 M Z +MILLINGS M IH1 L IH0 NG Z +MILLINGTON M IH1 L IH0 NG T AH0 N +MILLINGTON'S M IH1 L IH0 NG T AH0 N Z +MILLION M IH1 L Y AH0 N +MILLION'S M IH1 L Y AH0 N Z +MILLIONAIRE M IH2 L Y AH0 N EH1 R +MILLIONAIRE'S M IH2 L Y AH0 N EH1 R Z +MILLIONAIRES M IH2 L Y AH0 N EH1 R Z 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AA0 B EH1 L AH0 +MIRABELLE M IH1 R AH0 B EH2 L +MIRABELLE'S M IH0 R AA0 B EH1 L Z +MIRABILE M IH0 R AA1 B AH0 L +MIRABILIS M IH0 R AA1 B AH0 L IH0 S +MIRAC M IH1 R AH0 K +MIRACASTLE M IH1 R AH0 K AE2 S AH0 L +MIRACLE M IH1 R AH0 K AH0 L +MIRACLES M IH1 R AH0 K AH0 L Z +MIRACULA M ER0 AE1 K Y AH0 L AH0 +MIRACULOUS M ER0 AE1 K Y AH0 L AH0 S +MIRACULOUSLY M ER0 AE1 K Y AH0 L AH0 S L IY0 +MIRACULOUSNESS M ER0 AE1 K Y AH0 L AH0 S N AH0 S +MIRADOR M IH1 R AH0 D AO2 R +MIRAFLORES M IH1 R AH0 F L ER0 Z +MIRAGE M ER0 AA1 ZH +MIRAGES M ER0 AA1 ZH AH0 Z +MIRAH M IH1 R AH0 +MIRAH'S M IH1 R AH0 Z +MIRAKHOR M IH1 R AH0 K ER0 +MIRAKOFF M IH0 R AE1 K AO2 F +MIRALDA M IH0 R AA1 L D AH0 +MIRAMAR M IH1 R AH0 M AA2 R +MIRAMBO M ER0 AE1 M B OW0 +MIRAMBO'S M ER0 AA1 M B OW0 Z +MIRAMICHI M IH1 R AH0 M IY1 CH IY0 +MIRAMON M IH1 R AH0 M AH0 N +MIRAN M IH0 R AA1 N +MIRANDA M ER0 AE1 N D AH0 +MIRANDA'S M ER0 AE1 N D AH0 Z +MIRANDO M IH0 R AE1 N D OW0 +MIRANDOL M ER0 AE1 N D AH0 L +MIRANDOLA M ER0 AE1 N D AH0 L 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AH0 F L ER0 Z +MIRMIDONS M ER1 M IH0 D IH0 N Z +MIRO M IH0 R OW1 +MIRO M IH1 R OW0 +MIROBOLANT M IH0 R OW1 B OW0 L AH0 N T +MIROIR M AY0 R W AA1 R +MIROMESNIL M IH0 R OW1 M EH0 S N IH1 L +MIRON M IH0 R AO1 N +MIRONDA M ER0 AA1 N D AH0 +MIRONOFF M IH1 R AH0 N AO0 F +MIRONOV M IH2 R OW0 N OW1 V +MIRONSAC M IH2 R OW0 N S AE0 K +MIRONTAINE M IH2 R OW0 N T EY1 N +MIRONTON M AY1 R N T AH0 N +MIROSLAV M IH1 R AH0 S L AA0 V +MIROUET M IH2 R UW0 EH1 T +MIROV M IH1 R AA0 V +MIRPAH M ER1 P AH0 +MIRRAB M IH1 R AH0 B +MIRRABLE M IH1 R AH0 B AH0 L +MIRRAM M IH1 R AH0 M +MIRREN M IH1 R AH0 N +MIRROR M IH1 R ER0 +MIRROR'D M IH1 R ER0 D +MIRROR'S M IH1 R ER0 Z +MIRRORED M IH1 R ER0 D +MIRRORING M IH1 R ER0 IH0 NG +MIRRORLIKE M IH1 R ER0 L AY2 K +MIRRORS M IH1 R ER0 Z +MIRROUR M IH1 R AW0 R +MIRRY M IH1 R IY0 +MIRSKY M ER1 S K IY0 +MIRST M ER1 S T +MIRTH M ER1 TH +MIRTH'S M ER1 TH S +MIRTHFUL M ER1 TH F AH0 L +MIRTHFULLY M ER1 TH F AH0 L IY0 +MIRTHFULNESS M ER1 TH F AH0 L N AH0 S +MIRTHLESS M ER1 TH L AH0 S +MIRTHLESSLY M ER1 TH L AH0 S L IY0 +MIRU M IH1 R UW0 +MIRUM M IH1 R AH0 M +MIRVAN M ER1 V AH0 N +MIRVAN'S M ER1 V AH0 N Z +MIRY M IH1 R IY0 +MIRZA M ER1 Z AH0 +MIRZA'S M ER1 Z AH0 Z +MIS M IH1 S +MIS'ABLE M IH0 S IH0 AH0 B AH0 L +MIS'ESS M IH0 S IH0 Z AH0 S +MIS'R M IH2 S R +MIS'RABLE M IH2 S R AH0 B AH0 L +MIS'RY M IH2 S R IY0 +MISADVENTURE M IH0 S AH0 D V EH1 N CH ER0 +MISADVENTURES M IH0 S AH0 D V EH1 N CH ER0 Z +MISAIL M IH1 S EY2 L +MISALLIANCE M IH0 S AE1 L IY0 AH0 N S +MISALLIANCES M IH0 S AE1 L IY0 AH0 N S IH0 Z +MISAMIS M IH0 S AH0 M IH0 S +MISANTHROPE M IH1 S AH0 N TH R OW2 P +MISANTHROPES M IH1 S AH0 N TH R OW2 P S +MISANTHROPIC M IH1 S AH0 N TH R AH0 P IH0 K +MISANTHROPICAL M IH1 S AH0 N TH R AH0 P AH0 K AH0 L +MISANTHROPICALLY M IH1 S AH0 N TH R AH0 P IH0 K L IY0 +MISANTHROPIST M IH1 S AH0 N TH R AH0 P IH0 S T +MISANTHROPISTS M IH1 S AH0 N TH R AH0 P IH0 S T S +MISANTHROPY M IH1 S AH0 N TH R AH0 P IY0 +MISAPPLICATION M IH0 S AE2 P L AH0 K EY1 SH AH0 N 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S K EH1 R IY0 +MISCARRYING M IH0 S K EH1 R IY0 IH0 NG +MISCAST M IH0 S K AE1 S T +MISCEGENATION M IH0 S CH EH1 G AH0 N EY1 SH AH0 N +MISCELLANEA M IH2 S AH0 L EY1 N IY0 AH0 +MISCELLANEOUS M IH2 S AH0 L EY1 N IY0 AH0 S +MISCELLANEOUSLY M IH2 S AH0 L EY1 N IY0 AH0 S L IY0 +MISCELLANIES M IH2 S AH0 L EY1 N IY0 Z +MISCELLANY M IH1 S AH0 L EY2 N IY0 +MISCET M IH2 S AH0 T +MISCHA M IH1 SH AH0 +MISCHANCE M IH0 S K AE2 N S +MISCHANCES M IH0 S K AE2 N S IH0 Z +MISCHANCY M IH0 S K AE2 N S IY0 +MISCHIANZA M IH0 S K IY0 AA1 N Z AH0 +MISCHIEF M IH1 S CH AH0 F +MISCHIEF'S M IH1 S CH IY1 F S +MISCHIEFE M IH1 S CH AH0 F +MISCHIEFS M IH1 S CH AH0 F S +MISCHIENZA M IH1 S CH EH1 N Z AH0 +MISCHIEVOUS M IH1 S CH AH0 V AH0 S +MISCHIEVOUSLY M IH1 S CH AH0 V AH0 S L IY0 +MISCHIEVOUSNESS M IH1 S CH AH0 V AH0 S N AH0 S +MISCOMPREHENSION M IH0 S K AA2 M P R IY0 HH EH1 N SH AH0 N +MISCONCEIVE M IH0 S K AH0 N S IY1 V +MISCONCEIVED M IH0 S K AH0 N S IY1 V D +MISCONCEIVES M IH0 S K AH0 N S IY1 V Z +MISCONCEIVING M IH0 S K AH0 N S IY1 V IH0 NG +MISCONCEPTION M IH0 S K AH0 N S EH1 P SH AH0 N +MISCONCEPTIONS M IH0 S K AH0 N S EH1 P SH AH0 N Z +MISCONDUCT M IH0 S K AA1 N D AH0 K T +MISCONDUCTED M IH0 S K AH0 N D AH1 K T IH0 D +MISCONDUCTING M IH0 S K AH0 N D AH1 K T IH0 NG +MISCONNA M IH0 S K AH0 N AH0 +MISCONSTRUCTION M IH0 S K AH0 N S T R AH1 K SH AH0 N +MISCONSTRUCTIONS M IH0 S K AH0 N S T R AH1 K SH AH0 N Z +MISCONSTRUE M IH0 S K AH0 N S T R UW1 +MISCONSTRUED M IH2 S K AH0 N S T R UW1 D +MISCONSTRUING M IH0 S K AH0 N S T R UW1 IH0 NG +MISCOUNTED M IH1 S K AW2 N T IH0 D +MISCREANT M IH1 S K R IY0 AH0 N T +MISCREANT'S M IH1 S K R IY0 AH0 N T S +MISCREANTS M IH1 S K R IY0 AH0 N T S +MISDEAL M IH1 S D IY1 AH0 L +MISDEED M IH1 S D IY1 D +MISDEEDS M IH0 S D IY1 D Z +MISDEEM M IH1 S D IY1 M +MISDEMEANANT M IH2 S D AH0 M IY1 N AH0 N T +MISDEMEANANTS M IH2 S D AH0 M IY1 N AH0 N T S +MISDEMEANOR M IH2 S D AH0 M IY1 N ER0 +MISDEMEANORS M IH2 S D AH0 M IY1 N ER0 Z +MISDEMEANOUR M IH2 S D AH0 M IY1 N ER0 +MISDEMEANOURS M IH2 S D AH0 M IY1 N AO1 R Z +MISDER M IH0 S D ER0 +MISDIRECT M IH0 S D IH0 R EH1 K T +MISDIRECTED M IH0 S D IH0 R EH1 K T IH0 D +MISDIRECTING M IH0 S D IH0 R EH1 K T IH0 NG +MISDIRECTION M IH0 S D IH0 R EH1 K SH AH0 N +MISDOER M IH0 S D UW1 R +MISDOING M IH0 S D UW1 IH0 NG +MISDOINGS M IH0 S D UW1 IH0 NG Z +MISDONE M IH0 S D AH1 N +MISDOUBT M IH0 S D AW1 T +MISDOUBTED M IH0 S D AW1 T IH0 D +MISDOUBTING M IH0 S D AW1 T IH0 NG +MISDOUBTS M IH0 S D AW1 T S +MISE M AY1 Z +MISEASE M IH0 Z IY1 Z +MISELLE M IH0 S EH1 L +MISEMPLOYED M IH1 S IH0 M P L OY1 D +MISENUM M IH1 S IY0 N AH0 M +MISENUS M IH1 S IY0 N AH0 S +MISER M AY1 Z ER0 +MISER'BLE M IH1 Z R B AH0 L +MISER'S M IH1 Z ER0 Z +MISERA M IH1 Z ER0 AH0 +MISERABLE M IH1 Z ER0 AH0 B AH0 L +MISERABLE M IH1 Z R AH0 B AH0 L +MISERABLENESS M IH1 Z ER0 AH0 B AH0 L N AH0 S +MISERABLES M IH1 Z ER0 AH0 B AH0 L Z +MISERABLES M IH1 Z R AH0 B AH0 L Z +MISERABLES M IH2 Z ER0 AA1 B +MISERABLEST M IH1 Z ER0 AH0 B AH0 L S T +MISERABLY M IH1 Z ER0 AH0 B L IY0 +MISERABLY M IH1 Z R AH0 B L IY0 +MISERE M IH1 Z ER0 +MISERERE M IH1 Z ER0 IH2 R +MISERERES M IH1 Z R IY1 R Z +MISERES M IH1 Z R Z +MISERIA M IH1 Z ER0 IY1 AH0 +MISERICORDE M IH1 Z R IH0 K AO2 R D +MISERICORDIA M IH1 Z R IH0 K AO1 R D IY0 AH0 +MISERICORDIAE M IH1 Z R IH0 K AO1 R D IY0 AH0 +MISERICORDIAM M IH1 Z R IH0 K AO1 R D IY0 AH0 M +MISERIE M IH1 Z ER0 IY0 +MISERIES M IH1 Z ER0 IY0 Z +MISERIS M IH1 Z R IH0 S +MISERLINESS M AY1 Z ER0 L IY0 N AH0 S +MISERLY M AY1 Z ER0 L IY0 +MISERO M IH1 Z R OW0 +MISERRIMUS M IH1 Z ER0 R AY1 M AH0 S +MISERS M AY1 Z ER0 Z +MISERUM M IH1 Z R AH0 M +MISERY M IH1 Z ER0 IY0 +MISERY'S M IH1 Z R IY0 Z +MISES M AY1 Z IH0 Z +MISFIRE M IH0 S F AY1 ER0 +MISFIRED M IH0 S F AY1 ER0 D +MISFIRING M IH0 S F AY1 ER0 IH0 NG +MISFIT M IH1 S F IH2 T +MISFITS M IH1 S F IH2 T S +MISFITTING M IH1 S F IH2 T IH0 NG +MISFORMED M IH0 S F AO1 R M D +MISFORT'NIT M IH0 S F AO1 R CH AH0 N IH0 T +MISFORTIN M IH0 S F AO1 R T IH0 N +MISFORTUN M IH0 S F AO1 R CH AH0 N +MISFORTUNATE M IH0 S F AO1 R CH UW0 N AH0 T +MISFORTUNE M IH0 S F AO1 R CH AH0 N +MISFORTUNE'S M IH0 S F AO1 R CH UW0 N Z +MISFORTUNES M IH0 S F AO1 R CH AH0 N Z +MISGAVE M IH0 S G EY1 V +MISGIVE M IH0 S G IH1 V +MISGIVES M IH0 S G IH1 V Z +MISGIVING M IH0 S G IH1 V IH0 NG +MISGIVINGS M IH0 S G IH1 V IH0 NG Z +MISGOVERN M IH0 S G AH1 V ER0 N +MISGOVERNED M IH0 S G AH1 V ER0 N D +MISGOVERNMENT M IH0 S G AH1 V ER0 N M AH0 N T +MISGUIDANCE M IH0 S G AY1 D AH0 N S +MISGUIDE M IH0 S G AY1 D +MISGUIDED M IH0 S G AY1 D IH0 D +MISGUIDING M IH0 S G AY1 D IH0 NG +MISH M IH1 SH +MISHA M IH1 SH AH0 +MISHA'S M IH1 SH AH0 Z +MISHAEL M IH1 SH AH0 L +MISHANDLE M IH0 S HH AE1 N D AH0 L +MISHANDLED M IH0 S HH AE1 N D AH0 L D +MISHANDLING M IH0 S HH AE1 N D L IH0 NG +MISHAP M IH1 S HH AE2 P +MISHAPS M IH1 S HH AE2 P S +MISHAUMOK M IH1 SH OW0 M AA1 K +MISHCHA M IH1 SH HH AH0 +MISHE M IY1 SH +MISHEARD M IH2 S HH ER1 D +MISHELL M IH1 SH AH0 L +MISHKA M IY1 SH K AH0 +MISHKA'S M IY1 SH K AH0 Z 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AE1 N JH IY0 +NANJIVELL N AE1 N JH IH0 V EH1 L +NANJULIAN N AE1 N JH UW1 L IY0 AH0 N +NANKEEN N AE1 NG K IY0 N +NANKEENS N AE1 NG K IY1 N Z +NANKI N AE1 NG K IY0 +NANKIN N AE1 NG K IH0 N +NANKING N AE1 NG K IH0 NG +NANLO N AE1 N L OW0 +NANN N AE1 N +NANN'S N AE1 N Z +NANNA N AE1 N AH0 +NANNA'S N AE1 N AH0 Z +NANNAR N AE1 N ER0 +NANNERL N AE1 N ER0 L +NANNETTE N AA0 N EH1 T +NANNI N AE1 N IY0 +NANNI'S N AE1 N IY0 Z +NANNICHETTE N AA0 N IY1 SH EH1 T +NANNIE N AE1 N IY0 +NANNIE'S N AE1 N IY0 Z +NANNIES N AE1 N IY0 Z +NANNIN N AE1 N IH0 N +NANNO N AE1 N OW0 +NANNOCK N AE1 N AH0 K +NANNON N AE1 N AH0 N +NANNOOK N AE1 N UH1 K +NANNY N AE1 N IY0 +NANNY'S N AE1 N IY0 Z +NANO N AA1 N OW0 +NANO N AE1 N OW0 +NANOMAGA N AE1 N OW0 M AA1 G AH0 +NANON N AE1 N AH0 N +NANON'S N AE1 N AH0 N Z +NANOS N AA1 N OW0 Z +NANOS N AE1 N OW0 Z +NANPI N AE1 N P IY0 +NANS N AH0 N Z +NANSE N AE1 N S +NANSEMOND N AE1 N S EH1 M AH0 N D +NANSEMONDS N AE1 N S EH1 M AH0 N D Z +NANSEN N AE1 N S AH0 N +NANSEN'S N AE1 N S AH0 N Z +NANSIE N AE1 N S IY0 +NANSIE'S N AE1 N S IY0 Z +NANSOUTY N AE1 N S AW1 T IY0 +NANT N AH0 N T +NANTAIS N AE2 N T EY0 +NANTAQUAS N AE0 N T AA1 K W AH0 Z +NANTASKET N AE0 N T AE1 S K AH0 T +NANTAUQUAS N AE2 N T AO1 K W AH0 Z +NANTBROOK N AE1 N T B R UH2 K +NANTERRE N AE1 N T ER0 +NANTES N AE1 N T S +NANTEUIL N AE1 N T UW0 L +NANTEUIL'S N AE1 N T OY0 AH0 L Z +NANTGWILLT N AE2 N T G W IH2 L T +NANTICOKE N AE1 N IH0 K OW2 K +NANTICOKES N AE1 N IH0 K OW1 K S +NANTILLONS N AE0 N T IH1 L AH0 N Z +NANTING N AE1 N T IH0 NG +NANTUCKET N AE0 N T AH1 K IH0 T +NANTUCKETER N AE2 N T AH1 K IH0 T ER0 +NANTUCKETERS N AE2 N T AH1 K IH0 T ER0 Z +NANTWICH N AE1 N T W IH0 CH +NANTY N AA1 N T IY0 +NANTZ N AE1 N T S +NANUK N AE1 N AH0 K +NANUK'S N AE1 N AH0 K S +NANUNGAMANOO N AE1 N AH0 NG G AH0 M AH0 N UW1 +NANZELA N AE1 N Z AH0 L AH0 +NANZICELE N AE2 N Z IY0 CH EH1 L IY0 +NAO N AW1 +NAOIS N AA0 W AA1 +NAOMI N EY0 OW1 M IY0 +NAOMI'S N EY0 OW1 M IY0 Z +NAOS N EY0 OW1 Z +NAOW N EY0 OW1 +NAP N AE1 P 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AA2 R AH0 S AA1 R AH0 +NARR N AE1 R +NARRA N EH1 R AH0 +NARRACOMBE N EH0 R EY1 K AH0 M +NARRAGANSET N EH1 R AH0 G AH0 N S EH2 T +NARRAGANSETT N EH2 R AH0 G AE1 N S AH0 T +NARRAGANSETTS N EH1 R AH0 G AE1 N S AH0 T S +NARRAMORE N AA0 R AA1 M AO0 R +NARRAN N EH1 R AH0 N +NARRATE N EH1 R EY2 T +NARRATED N EH1 R EY2 T IH0 D +NARRATES N EH0 R EY1 T S +NARRATES N EH1 R EY0 T S +NARRATING N EH1 R EY2 T IH0 NG +NARRATION N EH0 R EY1 SH AH0 N +NARRATIONS N EH0 R EY1 SH AH0 N Z +NARRATIVE N AE1 R AH0 T IH0 V +NARRATIVE N EH1 R AH0 T IH0 V +NARRATIVES N AE1 R AH0 T IH0 V Z +NARRATIVES N EH1 R AH0 T IH0 V Z +NARRATOR N EH1 R EY0 T ER0 +NARRATOR'S N EH1 R EY2 T ER0 Z +NARRATORS N EH1 R EY2 T ER0 Z +NARRATUR N EH1 R AH0 T ER0 +NARRER N AE1 R ER0 +NARROBOURNE N EH1 R OW0 B ER0 N +NARROW N AE1 R OW0 +NARROW N EH1 R OW0 +NARROWED N EH1 R OW0 D +NARROWER N EH1 R OW0 ER0 +NARROWEST N EH1 R OW0 AH0 S T +NARROWING N EH1 R OW0 IH0 NG +NARROWISH N EH1 R OW0 IH0 SH +NARROWLY N EH1 R OW0 L IY0 +NARROWNESS N EH1 R OW0 N AH0 S +NARROWNESSES N EH1 R OW0 N AH0 S IH0 Z +NARROWPATH N EH1 R OW0 P AE2 TH +NARROWS N EH1 R OW0 Z +NARRY N EH1 R IY0 +NARSAC N AA0 R S AE0 K +NARSES N AA0 R S IH0 Z +NARSISI N AA0 R S IY1 S IY0 +NARSTY N AA0 R S T IY0 +NARTH N AA2 R TH +NARTHEX N AA2 R TH EH1 K S +NARU N AA0 R UW0 +NARUHODO N AA0 R UW0 HH OW1 D OW0 +NARVA N AA1 R V AH0 +NARVA'S N AA0 R V AH0 Z +NARVAEZ N AA0 R V AA1 EH0 Z +NARVES N AA1 R V Z +NARVI N AA0 R V IY0 +NARVOUS N AA0 R V AH0 S +NARWAL N AA1 R W AH0 L +NARWHAL N AA2 R W AO2 L +NARWHAL'S N AA2 R W AH0 L Z +NARWHALE N AA2 R W EY2 L +NARWHALS N AA2 R W AH0 L Z +NARY N EH1 R IY0 +NAS N AH0 Z +NASAKCHI N AE1 S AH0 K CH IY0 +NASAL N EY1 Z AH0 L +NASALITY N EY1 Z AH0 L AH0 T IY0 +NASALLY N EY1 Z AH0 L IY0 +NASALS N EY1 Z AH0 L Z +NASANSKI N EY1 Z AH0 N S K IY0 +NASANSKI'S N EY1 Z AH0 N S K IY0 Z +NASBY N AE1 S B IY0 +NASCAUPEE N AE1 S K OW0 P IY1 +NASCENT N EY1 S AH0 N T +NASCITUR N AE2 S IH0 T ER0 +NASCOPIES N AE1 S K AA2 P IY0 Z +NASEBY N AE1 S IH0 B IY0 +NASEEB N AE1 S IY1 B +NASELTON N AH0 S EH1 L T AH0 N +NASELTON'S N AH0 S EH1 L T AH0 N Z +NASH N AE1 SH +NASH'S N AE1 SH IH0 Z +NASHA N AE1 SH AH0 +NASHBY N AE1 SH B IY0 +NASHI N AE1 SH IY0 +NASHOLA N AE1 SH OW1 L AH0 +NASHTY N AE1 SH T IY0 +NASHUA N AE1 SH UW0 AH0 +NASHVILLE N AE1 SH V IH0 L +NASI N AA1 S IY0 +NASIB N AE1 S IH0 B +NASIBU N AE2 S IY0 B UW1 +NASICA N EY1 Z IH0 K AH0 +NASICORNIS N EY1 Z IH0 K AO2 R N IH0 S +NASIE N AE2 S IY0 +NASIK N AA1 S IH0 K +NASIR N AA1 S ER0 +NASIRABAD N AE1 S IH0 R AH0 B AE2 D +NASIRI'D N AE1 S IH0 R IY1 D +NASIRIYEH N AE1 S IH1 R IH0 Y AH0 +NASMITH N AE1 S M IH2 TH +NASMYTH N AE1 S M IH1 TH +NASMYTH'S N AE1 S M IH1 TH S +NASO N AA1 S OW0 +NASON N AE1 S AH0 N +NASPA N AE1 S P AH0 +NASR N AA1 S ER0 +NASRULLA N AH0 Z R UW1 L AH0 +NASS N AE1 S +NASSALINE N AE1 S AA0 L IY1 N IY0 +NASSAU N AE1 S AO0 +NASSE N AE1 S +NASSER N AE1 S ER0 +NASSI N AE1 S IY0 +NASSIK N AE1 S IH0 K +NASSINGTON N AE1 S IH0 NG T AH0 N +NASSY N AE1 S IY0 +NAST N AE1 S T +NAST'S N AE1 S T S +NASTA N AE1 S T AH0 +NASTA'S N AE1 S T AH0 Z +NASTAGIO N AA0 S T AA1 ZH IY0 OW2 +NASTASA N AA0 S T AA1 S AH0 +NASTASIA N AA0 S T AA1 S IY0 AH0 +NASTASIA'S N AH0 S T EY1 ZH AH0 Z +NASTASIE N AA0 S T AA1 S IY0 +NASTASIO N AA0 S T AA1 S IY0 OW0 +NASTASYA N AE1 S T AH0 S IY0 AH0 +NASTER N AE1 S T ER0 +NASTIER N AE1 S T IY0 ER0 +NASTIEST N AE1 S T IY0 AH0 S T +NASTILY N AE1 S T AH0 L IY0 +NASTINESS N AE1 S T IY0 N AH0 S +NASTIRSEVITCH N AE1 S T ER1 Z AH0 V IH0 CH +NASTURTIUM N AE1 S T UH1 R SH IY0 AH0 M +NASTURTIUMS N AE1 S T UH1 R SH AH0 M Z +NASTY N AE1 S T IY0 +NASTYA N AE1 S T Y AH0 +NAT N AE1 T +NAT'RAL N EY1 T R AH0 L +NAT'RALLY N EY1 T ER0 AH0 L IY0 +NAT'S N AE2 T S +NATA N AA1 T AH0 +NATACHA N AH0 T AA1 CH AH0 +NATACHA'S N AH0 T AA1 CH AH0 Z +NATAH N AH0 T AA1 +NATAL N AH0 T AA1 L +NATAL N EY1 T AH0 L +NATALE N AA0 T AA1 L IY0 +NATALENKO N EY1 T AH0 L EH1 NG K OW0 +NATALIA N AH0 T AA1 L Y AH0 +NATALIA'S N AH0 T AA1 L Y AH0 Z +NATALIE N AE1 T AH0 L IY0 +NATALIE'S N AA0 T AA1 L IY0 Z +NATALINE N EY1 T AH0 L IY2 N +NATALIS N AA0 T AA1 L IY0 Z +NATALY N AH0 T AA1 L IY0 +NATALY'S N AH0 T AA1 L IY0 Z +NATALYA N AH0 T AA1 L Y AH0 +NATALYA'S N AH0 T AA1 L Y AH0 Z +NATAN N EY1 T AH0 N +NATASCHA N AH0 T AE1 SH AH0 +NATASHA N AH0 T AA1 SH AH0 +NATASHA N AH0 T AE1 SH AH0 +NATATION N AH0 T EY1 SH AH0 N +NATATORIUM N EY1 T AH0 T AO1 R IY0 AH0 M +NATCH N AE1 CH +NATCHALLY N AE1 CH AH0 L IY0 +NATCHALNIK N AE1 CH AO2 L N IH0 K +NATCHEL N AE1 CH AH0 L +NATCHELLY N AE1 CH AH0 L IY0 +NATCHERAL N AE1 CH ER0 AH0 L +NATCHERALLY N AE1 CH ER0 AH0 L IY0 +NATCHERLY N AE1 CH ER0 L IY0 +NATCHEZ N AE1 CH EH2 Z +NATCHITOCHES N AE0 K AH0 T AO1 SH +NATE N EY1 T +NATE'S N EY1 T S +NATELY N AH0 T L IY0 +NATER N EY1 T ER0 +NATERA N AA0 T EH1 R AH0 +NATERAL N AE1 T ER0 AH0 L +NATERALLY N AE1 T ER0 AH0 L IY0 +NATERED N EY1 T ER0 D +NATH N AE1 TH +NATH'S N AH0 TH S +NATHALIE N AE1 T AH0 L IY0 +NATHALIE'S N AH0 TH AE1 L IY0 Z +NATHAN N EY1 TH AH0 N +NATHAN'S N EY1 TH AH0 N Z 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SH +PANTHERLIKE P AE1 N TH ER0 L AY2 K +PANTHERS P AE1 N TH ER0 Z +PANTHOUS P AE1 N TH AW1 Z +PANTHRY P AE1 N TH R IY0 +PANTIER P AE1 N T IY0 ER0 +PANTIES P AE1 N T IY0 Z +PANTILE P AE1 N T IH0 L +PANTILES P AE1 N T AY2 L Z +PANTIN P AE1 N T IH0 N +PANTIN'S P AE1 N T AH0 N Z +PANTING P AE1 N T IH0 NG +PANTINGLY P AE1 N T IH0 NG L IY0 +PANTINGS P AE1 N T IH0 NG Z +PANTISOCRACY P AE1 N T AY0 S AA1 K R AH0 S IY0 +PANTLER P AE1 N T L ER0 +PANTO P AE1 N T OW0 +PANTOGRAPH P AE1 N T AH0 G R AE2 F +PANTOMIME P AE1 N T AH0 M AY2 M +PANTOMIMED P AE1 N T AH0 M AY2 M D +PANTOMIMES P AE1 N T AH0 M AY2 M Z +PANTOMIMI P AE1 N T AH0 M AY2 M IY0 +PANTOMIMIC P AE1 N T AH0 M AY2 M IH0 K +PANTOMIMICAL P AE1 N T AH0 M AY2 M IH0 K AH0 L +PANTOMIMICALLY P AE1 N T AH0 M AY2 M IH0 K L IY0 +PANTOMIMING P AE1 N T AH0 M AY2 M IH0 NG +PANTOMIMIST P AE1 N T AH0 M AY2 M AH0 S T +PANTOMIMISTS P AE1 N T AH0 M AY2 M AH0 S T S +PANTOMINE P AE1 N T AH0 M AY2 N +PANTON P AE1 N T AH0 N +PANTOUFLES P AE1 N T AH0 F AH0 L Z 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+PAPERING P EY1 P ER0 IH0 NG +PAPERL P EY1 P ER0 L +PAPERS P EY1 P ER0 Z +PAPERWEIGHT P EY1 P ER0 W EY2 T +PAPERWORK P EY1 P ER0 W ER2 K +PAPERY P EY1 P ER0 IY0 +PAPES P EY1 P S +PAPHIAN P AE1 F IY0 AH0 N +PAPHLAGONIA P AE1 F L AH0 G OW1 N IY0 AH0 +PAPHLAGONIAN P AE1 F L AH0 G OW1 N IY0 AH0 N +PAPHLAGONIANS P AE1 F L AH0 G OW1 N IY0 AH0 N Z +PAPHNUTIUS P AE1 F N UW1 SH AH0 S +PAPHOS P AE1 F OW0 Z +PAPIAS P AA0 P IY1 AH0 S +PAPIER P EY1 P Y ER0 +PAPIERS P EY1 P Y ER0 Z +PAPIK P AE2 P IH0 K +PAPILETTE P AE1 P AH0 L EH1 T +PAPILIO P AA0 P IY1 L IY0 OW0 +PAPILIOS P AH0 P IH1 L Y AH0 S +PAPILLAE P AE2 P IH0 L EY2 +PAPILLON P AH0 P IH1 L AH0 N +PAPILLONS P AH0 P IH1 L AH0 N Z +PAPILLOTE P AE2 P IH0 L OW1 T IY0 +PAPILLOTES P AE2 P IH0 L OW1 T S +PAPIN P AA0 P IY1 N +PAPIN'S P AE2 P IH0 N Z +PAPINEAU P AE1 P IH0 N OW2 +PAPINEAU'S P AE2 P IH0 N OW1 Z +PAPINI P AA0 P IY1 N IY0 +PAPINIAN P AA0 P IY1 N IY0 AH0 N +PAPIOL P EY1 P Y AH0 L +PAPIRIUS P EY1 P ER0 IY0 IH0 S +PAPIS P AE2 P IH0 S +PAPISH P AE2 P IH0 SH +PAPISHERS P AH0 P IH1 SH ER0 Z +PAPISHES P AE2 P IH0 SH IH0 Z +PAPIST P AE2 P IH0 S T +PAPISTICAL P AH0 P IH1 S T IH0 K AH0 L +PAPISTRY P AE2 P IH0 S T R IY0 +PAPISTS P AE2 P IH0 S T S +PAPOOSE P AH0 P UW1 Z +PAPOOSES P AH0 P UW1 Z IH0 Z +PAPOOSH P AH0 P UW1 SH +PAPPA P AE1 P AH0 +PAPPAN P AE1 P AH0 N +PAPPAS P AE1 P AH0 S +PAPPENHEIM P AE1 P AH0 N HH AY2 M +PAPPENHEIM'S P AE1 P AH0 N HH AY2 M Z +PAPPENHEIMERS P AE1 P AH0 N HH AY2 M ER0 Z +PAPPLEWORTH P AE1 P AH0 L W ER2 TH +PAPPOOSE P AE1 P UW1 Z +PAPPOOSES P AE1 P UW1 Z IH0 Z +PAPPUS P AE1 P AH0 S +PAPPY P AE1 P IY0 +PAPPY'S P AE1 P IY0 Z +PAPRICA P AE0 P R IY1 K AH0 +PAPRIKA P AE0 P R IY1 K AH0 +PAPRIKA P AH0 P R IY1 K AH0 +PAPS P AE1 P S +PAPSON P AE1 P S AH0 N +PAPSON'S P AE1 P S AH0 N Z +PAPUA P AE1 P Y UW0 AH0 +PAPUAN P AA0 P UW0 AA1 N +PAPUANS P AA0 P UW0 AA1 N Z +PAPUS P AH0 P AH0 S +PAPWITH P AE1 P W IH2 TH +PAPYRI P AE1 P ER0 IY0 +PAPYRUS P AE1 P AY0 R AH0 S +PAQUES P AE1 K S +PAQUET P AA1 K EY0 T +PAQUETAGE P AA1 K EY0 T IH0 JH +PAQUETTE P AH0 K EH1 T +PAQUIN P AE1 K W IH0 N +PAQUIN'S P AE1 K W IH0 N Z +PAQUITA P AE1 K W IY1 T AH0 +PAQUITA'S P AH0 K IY1 T AH0 Z +PAR P AA1 R +PAR'S P AA1 R Z +PARA P EH1 R AH0 +PARABERE P AE2 R AH0 B IY1 R +PARABLE P EH1 R AH0 B AH0 L +PARABLES P EH1 R AH0 B AH0 L Z +PARABLIST P EH1 R AH0 B L IH0 S T +PARABOLA P ER0 AE1 B AH0 L AH0 +PARABOLAS P ER0 AE1 B AH0 L AH0 S +PARABOLIC P EH2 R AH0 B AA1 L IH0 K +PARABOLICAL P EH2 R AH0 B AA1 L IH0 K AH0 L +PARABOLICALLY P EH2 R AH0 B AA1 L IH0 K L IY0 +PARABRAHM P EH1 R AH0 B R AA1 M +PARACELSUS P EH1 R AH0 S AH0 L S AH0 S +PARACHUTE P EH1 R AH0 SH UW2 T +PARACHUTES P EH1 R AH0 SH UW2 T S +PARACLETE P ER0 AE1 K L IY2 T +PARACOSMA P ER0 AE1 K AA2 Z M AH0 +PARACUTA P ER0 AE1 K Y UW0 T AH0 +PARADAY P EH1 R AH0 D EY2 +PARADAY'S P EH1 R AH0 D EY2 Z +PARADE P ER0 EY1 D +PARADED P ER0 EY1 D IH0 D +PARADERS P ER0 EY1 D ER0 Z +PARADES P ER0 EY1 D Z +PARADICE P EH1 R AH0 D AY2 S +PARADIES P EH1 R AH0 D IY0 Z +PARADIGM P EH1 R AH0 D AY2 M +PARADIGMATIC P EH1 R AH0 D AY2 M AE1 T IH0 K +PARADIGMS P EH1 R AH0 D AY2 M Z +PARADIN P EH1 R AH0 D IH0 N +PARADINE P EH1 R AH0 D AY2 N +PARADING P ER0 EY1 D IH0 NG +PARADIS P ER0 AE1 D IH0 S +PARADISAIC P EH1 R AH0 D AY2 S EY1 IH0 K +PARADISAICAL P EH1 R AH0 D AY2 Z EY1 IH0 K AH0 L +PARADISAL P EH1 R AH0 D AY2 Z AH0 L +PARADISE P EH1 R AH0 D AY2 S +PARADISEA P EH1 R AH0 D AY2 S IY1 +PARADISES P EH1 R AH0 D AY2 Z IH0 Z +PARADISIACAL P EH1 R AH0 D AY2 S AY1 AH0 K AH0 L +PARADISO P ER0 AH0 D IY1 S OW0 +PARADOL P EH1 R AH0 D AA2 L +PARADOS P EH2 R AH0 D AA1 S +PARADOX P EH1 R AH0 D AA2 K S +PARADOXES P EH1 R AH0 D AA2 K S IH0 Z +PARADOXICAL P EH2 R AH0 D AA1 K S IH0 K AH0 L +PARADOXICALLY P EH2 R AH0 D AA1 K S AH0 K AH0 L IY0 +PARADOXICALLY P EH2 R AH0 D AA1 K S AH0 K L IY0 +PARADOXUS P EH2 R AH0 D AA1 K S AH0 S +PARAENSE P ER0 AE1 N S +PARAFFIN P EH1 R AH0 F AH0 N +PARAFFINE P EH1 R AH0 F AY1 N +PARAGON P EH1 R AH0 G AA2 N +PARAGON'S P EH1 R AH0 G AA2 N Z +PARAGONS P EH1 R AH0 G AA2 N Z +PARAGOT P EH1 R AH0 G AA2 T +PARAGOT'S P EH1 R AH0 G AA2 T S +PARAGRAPH P AE1 R AH0 G R AE2 F +PARAGRAPH P EH1 R AH0 G R AE2 F +PARAGRAPHED P EH1 R AH0 G R AE2 F T +PARAGRAPHER P EH1 R AH0 G R AE2 F ER0 +PARAGRAPHERS P EH1 R AH0 G R AE2 F ER0 Z +PARAGRAPHIC P EH1 R AH0 G R AE2 F IH0 K +PARAGRAPHING P EH1 R AH0 G R AE2 F IH0 NG +PARAGRAPHIST P EH1 R AH0 G R AE2 F IH0 S T +PARAGRAPHISTS P EH1 R AH0 G R AE2 F IH0 S +PARAGRAPHS P AE1 R AH0 G R AE2 F S +PARAGRAPHS P EH1 R AH0 G R AE2 F S +PARAGUA P ER0 AA1 G W AH0 +PARAGUAY P EH1 R AH0 G W EY2 +PARAGUAYAN P EH2 R AH0 G W AY1 AH0 N +PARAGUAYANS P EH1 R AH0 G W EY1 AH0 N Z +PARAHUARI P EH2 R AH0 HH UW1 ER0 IY0 +PARAISO P AA0 R AA0 IY1 S OW0 +PARAIT P ER0 EY1 T +PARAITRE P AA1 R EY1 T ER0 +PARAKEET P EH1 R AH0 K IY2 T +PARAKEETS P EH1 R AH0 K IY2 T S +PARALIPOMENA P EH2 R AH0 L IH1 P AH0 M IY1 N AH0 +PARALIZED P EH2 R AH0 L AY2 Z D +PARALLAX P EH1 R AH0 L AE2 K S +PARALLEL P EH1 R AH0 L EH2 L +PARALLELED P EH1 R AH0 L EH2 L D +PARALLELING P EH1 R AH0 L EH2 L IH0 NG +PARALLELISM P EH1 R AH0 L EH2 L IH2 Z AH0 M +PARALLELISMS P EH1 R AH0 L EH2 L IH2 Z AH0 M Z +PARALLELLED P EH1 R AH0 L EH2 L D +PARALLELOGRAM P EH1 R AH0 L EH2 L AH0 G R AE2 M +PARALLELOGRAMS P EH1 R AH0 L EH2 L AH0 G R AE2 M Z +PARALLELOPIPED P EH1 R AH0 L EH2 L AH0 P AY2 P T +PARALLELOPIPEDON P EH1 R AH0 L EH2 L AH0 P AY2 P D AH0 N +PARALLELS P EH1 R AH0 L EH2 L Z +PARALO P ER0 AA1 L OW0 +PARALOGISM P EH2 R AH0 L AA0 G IH0 Z AH0 M +PARALOGISMS P EH2 R AH0 L AA0 G IH0 Z AH0 M Z +PARALUS P EH2 R AH0 L AH0 S +PARALYSE P EH2 R AH0 L IH1 S IY0 +PARALYSED P ER0 AE1 L AH0 S T +PARALYSES P ER0 AE1 L AH0 S IY2 Z +PARALYSING P ER0 AE1 L AH0 S IH0 NG +PARALYSIS P ER0 AE1 L AH0 S AH0 S +PARALYSIS P ER0 AE1 L IH0 S IH0 S +PARALYTIC P EH2 R AH0 L IH1 T IH0 K +PARALYTICS P EH2 R AH0 L IH1 T IH0 K S +PARALYZE P EH1 R AH0 L AY2 Z +PARALYZED P EH1 R AH0 L AY2 Z D +PARALYZER P EH1 R AH0 L AY2 Z ER0 +PARALYZERS P EH1 R AH0 L AY2 Z ER0 Z +PARALYZES P EH1 R AH0 L AY2 Z IH0 Z +PARALYZING P EH1 R AH0 L AY2 Z IH0 NG +PARAMARIBO P EH2 R AH0 M EH1 R IH0 B OW0 +PARAMATTA P EH2 R AH0 M AA1 T AH0 +PARAME P EH2 R AH0 M +PARAMECIUM P EH2 R AH0 M IY1 S IY0 AH0 M +PARAMENDR P EH2 R AH0 M EH1 N D ER0 +PARAMO P ER0 AE1 M OW0 +PARAMON P EH2 R AH0 M AH0 N +PARAMORE P EH1 AH0 M AO1 R +PARAMOS P ER0 AE1 M OW0 Z +PARAMOUNT P EH1 R AH0 M AW2 N T +PARAMOUR P EH1 R AH0 M UH1 R +PARAMOURS P EH1 R AH0 M UH1 R Z +PARAMUS P ER0 AE1 M AH0 S +PARAN P EH1 R AH0 N +PARANA P ER0 AA1 N AH0 +PARANG P ER0 AE1 NG +PARANGS P ER0 AE1 NG Z +PARANIS P ER0 AA1 N IY0 Z +PARANOIA P EH2 R AH0 N OY1 AH0 +PARANOIAC P EH2 R AH0 N OY1 AE2 K +PARANOID P EH1 R AH0 N OY2 D +PARANOYA P EH2 R AH0 N OY1 AH0 +PARAO P AA1 R AW0 +PARAOS P ER0 EY1 OW0 S +PARAPARA P EH2 R AH0 P AA1 R AH0 +PARAPET P EH1 R AH0 P EH2 T +PARAPETED P EH1 R AH0 P EH2 T IH0 D +PARAPETS P EH1 R AH0 P EH2 T S +PARAPHERNALIA P EH2 R AH0 F AH0 N EY1 L Y AH0 +PARAPHRASE P EH1 R AH0 F R EY2 Z +PARAPHRASED P EH1 R AH0 F R EY2 Z D +PARAPHRASES P EH1 R AH0 F R EY1 Z IH0 Z +PARAPHRASING P EH1 R AH0 F R EY2 Z IH0 NG +PARAPHRASTIC P EH1 R AH0 F R AE1 S T IH0 K +PARAPLEGIA P EH2 R AH0 P L IY1 JH IY0 AH0 +PARAPLUIE P EH2 R AH0 P L UW0 IY1 +PARAPSYCHOLOGY P ER0 AE1 S AY0 K AA1 L AH0 JH IY0 +PARARUMA P EH2 R AH0 R UW1 M AH0 +PARAS P EH2 R AH0 S +PARASANG P EH2 R AH0 S AE1 NG +PARASANGS P EH2 R AH0 S AE1 NG Z +PARASCHITES P EH2 R AH0 S K IY1 T S +PARASHA P ER0 AA1 SH AH0 +PARASHINO P EH2 R AH0 SH IY1 N OW0 +PARASITE P EH1 R AH0 S AY2 T +PARASITE'S P EH1 R AH0 S AY2 T S +PARASITES P EH1 R AH0 S AY2 T S +PARASITIC P EH2 R AH0 S IH1 T IH0 K +PARASITICAL P EH2 R AH0 S IH1 T IH0 K AH0 L +PARASITICALLY P EH2 R AH0 S IH1 T IH0 K L IY0 +PARASITISM P EH1 R AH0 S AY2 T IH2 Z AH0 M +PARASOL P EH2 R AH0 S OW1 L +PARASOLS P EH2 R AH0 S OW1 L Z +PARASURAMA P EH2 R AH0 S UH0 R AA1 M AH0 +PARASYTE P EH2 R AH0 S AY1 T +PARASYTE'S P EH2 R AH0 S IH2 T S +PARATEMPORAL P EH1 R AH0 T EH2 M P ER0 AH0 L +PARATHYROIDS P EH1 R AH0 TH AY1 R OY0 D Z +PARATIME P ER0 AH0 T AY2 M +PARATIMERS P EH1 R AH0 T AY1 M ER0 Z +PARATUS P ER0 AE1 T AH0 S +PARAVAS P ER0 AA1 V AA0 Z +PARAVID P AA1 R V AH0 D +PARAYEARS P EH2 R AH0 Y IH1 R Z +PARBAT P AA1 R B AE0 T +PARBLEU P AA1 R B L UW0 +PARBOIL P AA1 R B OY2 L +PARBOILED P AA1 R B OY2 L D +PARBOILING P AA1 R B OY2 L IH0 NG +PARBUCKLE P AA1 R B AH0 K AH0 L +PARBURY P AA1 R B EH2 R IY0 +PARBURY'S P AA1 R B EH2 R IY0 Z +PARBUTTI P AA1 R B AH2 T IY0 +PARBUTTI'S P AA1 R B AH2 T IY0 Z +PARC P AA1 R K +PARCAE P AA1 R K EY2 +PARCE P AA1 R S +PARCEL P AA1 R S AH0 L +PARCELED P AA1 R S AH0 L D +PARCELING P AA1 R S AH0 L IH0 NG +PARCELING P AA1 R S L IH0 NG +PARCELLED P AA1 R S AH0 L D +PARCELLING P AA1 R S AH0 L IH0 NG +PARCELS P AA1 R S AH0 L Z +PARCEQUE P AA1 R S AH0 K +PARCERE P AA1 R S ER0 +PARCH P AA1 R CH +PARCH'D P AA1 R CH D +PARCHED P AA1 R CH T +PARCHER P AA1 R CH ER0 +PARCHER'S P AA1 R CH ER0 Z +PARCHERS P AA1 R CH ER0 Z +PARCHES P AA1 R CH IH0 Z +PARCHESI P AA1 R K EH1 S IY0 +PARCHING P AA1 R CH IH0 NG +PARCHMENT P AA1 R CH M AH0 N T +PARCHMENTS P AA1 R CH M AH0 N T S +PARCIVAL P AA1 R S IH0 V AH0 L +PARD P AA1 R D +PARDALOE P AA1 R D AA0 L OW1 +PARDEE P AA1 R D IY0 +PARDESSUS P AA0 R D EH1 S AH0 S +PARDI P AA1 R D IY0 +PARDIE P AA1 R D IY0 +PARDIEU P AA0 R D UW1 +PARDIGGLE P AA1 R D IH1 G AH0 L +PARDIN P AA1 R D IH0 N +PARDING P AA1 R D IH0 NG +PARDNER P AA1 R D N ER0 +PARDNER'S P AA1 R D N ER0 Z +PARDNERS P AA1 R D N ER0 Z +PARDNERSHIP P AA1 R D N ER0 SH IH2 P +PARDO P AA1 R D OW0 +PARDO'S P AA1 R D OW0 Z +PARDOE P AA1 R D OW2 +PARDOE'S P AA1 R D OW2 AH0 Z +PARDON P AA1 R D AH0 N +PARDON'D P AA1 R D AH0 N D +PARDON'S P AA1 R D AH0 N Z +PARDONABLE P AA1 R D AH0 N AH0 B AH0 L +PARDONABLY P AA1 R D AH0 N AH0 B L IY0 +PARDONED P AA1 R D AH0 N D +PARDONER P AA1 R D AH0 N ER0 +PARDONETH P AA1 R D AH0 N EH0 TH +PARDONING P AA1 R D AH0 N IH0 NG +PARDONNE P AA1 R D AH0 N +PARDONNER P AA0 R D AA1 N ER0 +PARDONNEZ P AA0 R D AA1 N EH0 Z +PARDONS P AA1 R D AH0 N Z +PARDRIFF P AA1 R D R IH0 F +PARDS P AA1 R D Z +PARDUE P AA1 R D UW2 +PARDUS P AA1 R D AH0 S +PARDY P AA1 R D IY0 +PARE P EH1 R +PAREA P ER0 IY1 AH0 +PARECIS P AA0 R EY1 S IY2 +PARED P EH1 R D +PAREDES P AA0 R EY1 D EH0 S +PAREDES'S P AA0 R EY1 D EH0 S IH0 Z +PAREE P ER0 IY1 +PAREGORIC P EH1 R AH0 G AO1 R IH0 K +PAREIL P AA0 R EY1 L +PAREILLE P EH1 R EY1 L +PARELL P AA0 R EH1 L +PARENI P ER0 EH1 N IY0 +PARENS P EH1 R AH0 N Z +PARENT P EH1 R AH0 N T +PARENT'S P EH1 R AH0 N T S +PARENTAGE P EH1 R AH0 N T AH0 JH +PARENTAL P ER0 EH1 N T AH0 L +PARENTALLY P EH1 R AH0 N T AH0 L IY0 +PARENTES P AA0 R EH1 N T EH0 S +PARENTHESE P ER0 EH0 N TH IY2 S +PARENTHESES P ER0 EH1 N TH AH0 S IY2 Z +PARENTHESIS P ER0 EH1 N TH AH0 S IH0 S +PARENTHESISED P ER0 EH1 N TH AH0 S AY2 Z D +PARENTHETIC P ER0 EH0 N TH EH1 T IH0 K +PARENTHETICAL P ER0 EH0 N TH EH1 T IH0 K AH0 L +PARENTHETICALLY P ER0 EH0 N TH EH1 T IH0 K AH0 L IY0 +PARENTHETICALLY P ER0 EH0 N TH EH1 T IH0 K L IY0 +PARENTHOOD P EH1 R AH0 N T HH UH2 D +PARENTI P AA0 R EH1 N T IY0 +PARENTIS P EH1 R AH0 N T IH0 S +PARENTLESS P AA0 R EH1 N T L AH0 S +PARENTS P EH1 R AH0 N T S +PARENTUM P EH1 R AH0 N T AH0 M +PARENZO P ER0 EH1 N Z OW0 +PAREO P AA0 R EY1 OW0 +PAREPA P AA0 R EY1 P AH0 +PARER P EH1 R ER0 +PARERGA P ER0 EH1 R G AH0 +PARERS P EH1 R ER0 Z +PARES P EH1 R Z +PARESIS P EH1 R AH0 S IH0 S +PARET P EH1 R AH0 T +PARETIC P ER0 EH1 T IH0 K +PAREU P EH1 R UW1 +PAREUS P AA0 R EY1 AH0 S +PARFAIT P AA2 R F EY1 +PARFAITE P AA2 R F EY1 T +PARFAITEMENT P AA2 R F EY1 M AH0 N T +PARFECT P ER0 F EH0 K T +PARFEN P AA1 R F AH0 N +PARFENOVITCH P AA2 R F EH1 N AH0 V IH0 CH +PARFIT P AA1 R F IH0 T +PARFITT P AA1 R F IH0 T +PARFITTS P AA1 R F IH0 T S +PARFLECHE P AA2 R F L EH1 CH +PARFLECHES P AA2 R F L CH IH0 Z +PARFOIS P AA2 R F W AA1 +PARFORCE P ER0 F AO2 R S +PARFUM P ER0 F Y UW1 M +PARGA P AA1 R G AH0 +PARGANIOTES P AA1 R G AH0 N AY2 OW1 T S +PARGETER P AA1 R G AH0 T ER0 +PARGETER'S P AA1 R G AH0 T ER0 Z +PARHAM P AA1 R HH AH0 M +PARI P AA1 R IY0 +PARIA P AA1 R IY0 AH0 +PARIAH P ER0 AY1 AH0 +PARIAH'S P ER0 AY1 AH0 Z +PARIAHS P ER0 AY1 AH0 Z +PARIAN P EH1 R IY0 AH0 N +PARIAS P ER0 AY1 AH0 S +PARIBUS P AE1 R IH0 B AH0 S +PARICHHA P ER0 IH1 CH HH AH0 +PARICLES P AE1 R IH0 K AH0 L Z +PARIENT P AA0 R IY0 AH0 N T +PARIENTS P ER0 AY1 AH0 N T S +PARIETAL P ER0 AY1 AH0 T AH0 L +PARIETES P ER0 AY1 AH0 T S +PARIGI P ER0 IH1 JH IY0 +PARIMA P AA0 R IY1 M AH0 +PARIME P AA0 R IY1 M +PARING P EH1 R IH0 NG +PARINGS P EH1 R IH0 NG Z +PARINI P AA0 R IY1 N IY0 +PARIS P AE1 R IH0 S +PARIS P EH1 R IH0 S +PARIS'S P EH1 R IH0 S IH0 Z +PARISER P AE1 R IH0 S ER0 +PARISET P AE1 R IH0 S EH2 T +PARISH P AE1 R IH0 SH +PARISH P EH1 R IH0 SH +PARISH'S P ER0 IH1 SH IH0 Z +PARISHES P AE1 R IH0 SH IH0 Z +PARISHES P EH1 R IH0 SH IH0 Z +PARISHIONER P ER0 IH1 SH AH0 N ER0 +PARISHIONER'S P ER0 IH1 SH AH0 N ER0 Z +PARISHIONERS P ER0 IH1 SH AH0 N ER0 Z +PARISIAN P ER0 IH1 ZH AH0 N +PARISIAN'S P ER0 IH1 S IY0 AH0 N Z +PARISIANS P ER0 IH1 S IY0 AH0 N Z +PARISIEN P ER0 IH1 S IY0 EH2 N +PARISIENNE P ER0 IH1 S IY0 EH2 N +PARISIENNES P ER0 IH1 S IY0 EH2 N Z +PARISIENS P ER0 IH1 S IY0 AH0 N Z +PARISINA P ER0 IH1 S IY0 N AH0 +PARISIS P ER0 IH1 S IY0 Z +PARISMUS P ER0 IH1 Z M AH0 S +PARISOT P AE1 R IH0 S AO1 T +PARISSE P AA0 R IY1 S +PARITER P ER0 AY1 T ER0 +PARITY P EH1 R AH0 T IY0 +PARK P AA1 R K +PARK'S P AA1 R K S +PARKA P AA1 R K AH0 +PARKAS P AA1 R K AH0 Z +PARKE P AA1 R K +PARKE'S P AA1 R K S +PARKED P AA1 R K T +PARKER P AA1 R K ER0 +PARKER'S P AA1 R K ER0 Z +PARKERS P AA1 R K ER0 Z +PARKERSBURG P AA1 R K ER0 Z B ER0 G +PARKES P AA1 R K S +PARKFORD P AA1 R K F ER0 D +PARKGATE P AA1 R K G EY2 T +PARKHILL P AA1 R K HH IH2 L +PARKHURST P AA1 R K HH ER0 S T +PARKHURST'S P AA1 R K HH ER0 S T S +PARKI P AA1 R K IY0 +PARKIE P AA1 R K IY0 +PARKIES P AA1 R K IY0 Z +PARKIN P AA1 R K IH0 N +PARKING P AA1 R K IH0 NG +PARKINS P AA1 R K IH0 N Z +PARKINS'S P AA1 R K IH0 N Z IH0 Z +PARKINSON P AA1 R K IH0 N S AH0 N +PARKINSON'S P AA1 R K IH0 N S AH0 N Z +PARKLAND P AA1 R K L AE2 N D +PARKLE P AA1 R K AH0 L +PARKLEY P AA1 R K L IY0 +PARKLIKE P AA1 R K L AY2 K +PARKMAN P AA1 R K M AH0 N +PARKMAN'S P AA1 R K M AH0 N Z +PARKNEY P AA1 R K N IY0 +PARKS P AA1 R K S +PARKS'S P AA1 R K S IH0 Z +PARKSIDES P AA1 R K S AY2 D Z +PARKSON P AA1 R K S AH0 N +PARKSON'S P AA1 R K S AH0 N Z +PARKVILLE P AA1 R K V IH2 L +PARKWAY P AA1 R K W EY2 +PARKY P AA1 R K IY0 +PARKYN P AA1 R K IH0 N +PARL P AA1 R L +PARLA P AA1 R L AH0 +PARLANCE P AA1 R L AH0 N S +PARLANT P AA1 R L AH0 N T +PARLAR P AA1 R L ER0 +PARLAY P AA1 R L EY0 +PARLE P AA1 R L +PARLEMENT P AA1 R L AH0 M AH0 N T +PARLEMENTAIRE P AA1 R L AH0 M AH0 N T EH1 R +PARLEMENTO P AA1 R L AH0 M EH1 N T OW0 +PARLER P AA1 R L ER0 +PARLEY P AA1 R L IY0 +PARLEY'S P AA1 R L IY0 Z +PARLEYED P AA1 R L IY0 D +PARLEYING P AA1 R L IY0 IH0 NG +PARLEYINGS P AA1 R L IY0 IH0 NG Z +PARLEYS P AA1 R L IY0 Z +PARLEYVOO P AA1 R L IY0 V UW2 +PARLEZ P AA2 R L EH1 Z +PARLIAMENT P AA1 R L AH0 M AH0 N T +PARLIAMENT'S P AA1 R L AH0 M AH0 N T S 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P AE1 S D AH0 L UW1 P +PASE P EY1 S +PASEAR P AA1 Z IH2 R +PASED P EY1 S T +PASEO P AA0 S EY1 OW2 +PASEOS P AA0 S EY1 OW2 Z +PASES P EY1 S IH0 Z +PASEY P AE1 S IY0 +PASH P AE1 SH +PASHA P AA1 SH AH0 +PASHA P AH0 SH AA1 +PASHA'S P AE1 SH AH0 Z +PASHALIC P AH0 SH AE1 L IH0 K +PASHALIK P AE1 SH AH0 L IH0 K +PASHAR P AH0 SH AA1 R +PASHAS P AH0 SH AA1 S +PASHCHUR P AH0 SH ER0 +PASHENKA P AE1 SH EH1 NG K AH0 +PASHEPAHO P AE1 SH EH2 P AH0 HH OW2 +PASHHUR P AE1 SH ER0 +PASHINT P AE1 SH IH0 N T +PASHITCH P AE1 SH IH0 CH +PASHKA P AE1 SH K AH0 +PASHT P AA1 SH T +PASHUR P AE1 SH ER0 +PASHY P AE1 SH IY0 +PASI P AA1 S IY0 +PASIANCE P AA0 ZH IY1 AH0 N S +PASIG P AE1 S IH0 G +PASIMONDAS P AE1 S IH0 M AA1 N D AH0 Z +PASINKOV P EY1 S IH0 NG K AA0 V +PASINKOV'S P EY1 S IH0 NG K AA2 V Z +PASIPHAE P AA0 S IH1 F IY2 +PASKEWITCH P AE1 S K Y UW1 IH0 CH +PASKOU P AE1 S K UW0 +PASLEW P AE1 S L UW1 +PASLEY P AE1 S L IY0 +PASMER P AE1 S AH0 M ER0 +PASMER'S P AE1 S AH0 M ER0 Z +PASMERS P AE1 S AH0 M ER0 Z 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IH0 JH +PASSAGES P AE1 S AH0 JH AH0 Z +PASSAGES P AE1 S IH0 JH IH0 Z +PASSAGEWAY P AE1 S AH0 JH W EY2 +PASSAGEWAY P AE1 S IH0 JH W EY2 +PASSAGEWAYS P AE1 S IH0 JH W EY2 Z +PASSAI P AE1 S AY1 +PASSAIC P AH0 S EY1 IH0 K +PASSAMAQUODDIES P AA0 S AA0 M AA0 K W AA1 D IY0 Z +PASSAMAQUODDY P AA0 S AA0 M AA0 K W AA1 D IY0 +PASSANHA P AA0 S AA0 N HH AH0 +PASSANT P AE1 S AH0 N T +PASSARO P AA0 S AA1 R OW0 +PASSATO P AA0 S AA1 T OW0 +PASSAU P AE1 S AO0 +PASSAUF P AE1 S AO0 F +PASSAVANT P AE1 S AH0 V AH0 N T +PASSBOOK P AE1 S B UH2 K +PASSCHENDAELE P AE1 S CH EH1 N D EH1 L +PASSD P AE1 S D +PASSE P AE2 S EY1 +PASSED P AE1 S T +PASSEE P AE1 S IY0 +PASSEES P AE1 S IY0 Z +PASSEL P AE1 S IH0 L +PASSEMENTERIE P AE1 S AH0 M AA1 N T IH1 R IY0 +PASSENGER P AE1 S AH0 N JH ER0 +PASSENGER'S P AE1 S AH0 N JH ER0 Z +PASSENGERS P AE1 S AH0 N JH ER0 Z +PASSEPARTOUT P AE1 S IH0 P AA1 R CH AW2 T +PASSEPARTOUT'S P AA0 S EH1 P ER0 T AW1 T S +PASSER P AE1 S ER0 +PASSER'S P AE1 S ER0 Z +PASSERA P AA0 S EH1 R AH0 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SH AH0 N L AH0 S +PASSIONLESSLY P AE1 SH AH0 N L AH0 S L IY0 +PASSIONS P AE1 SH AH0 N Z +PASSIVE P AE1 S IH0 V +PASSIVELY P AE1 S IH0 V L IY0 +PASSIVENESS P AE1 S IH0 V N AH0 S +PASSIVITY P AH0 S IH1 V IH0 T IY0 +PASSKEY P AE1 S K IY0 +PASSLE P AE1 S AH0 L +PASSMAN P AE1 S M AH0 N +PASSMAQUADDY P AE1 S M AH0 K W AE1 D IY0 +PASSMORE P AA1 S M AO0 R +PASSO P AE1 S OW0 +PASSON P AE1 S AH0 N +PASSONIANS P AA0 S OW1 N IY0 AH0 N Z +PASSONS P AE1 S AH0 N Z +PASSOS P AA1 S OW0 S +PASSOVER P AE1 S OW2 V ER0 +PASSPORT P AE1 S P AO2 R T +PASSPORTS P AE1 S P AO2 R T S +PASSU P AA0 S UW1 +PASSUK P AH0 S UW1 K +PASSUS P AE1 S AH0 S +PASSWAY P AE1 S W EY2 +PASSWORD P AE1 S W ER2 D +PASSWORDS P AE1 S W ER2 D Z +PASSY P AE1 S IY0 +PAST P AE1 S T +PAST'S P AE1 S T S +PASTA P AA1 S T AH0 +PASTE P EY1 S T +PASTEBOARD P EY1 S T B AO2 R D +PASTEBOARDS P EY1 S T B AO2 R D Z +PASTED P EY1 S T IH0 D +PASTEL P AE0 S T EH1 L +PASTELS P AE0 S T EH1 L Z +PASTER P AE1 S T ER0 +PASTERN P AE1 S T ER0 N +PASTERNS P AE1 S T ER0 N Z +PASTERS P AE1 S T ER0 Z +PASTES P EY1 S T S +PASTEUR P AH0 S T UW1 R +PASTEUR'S P AE1 S CH ER0 Z +PASTEURIZED P AE1 S CH ER0 AY2 Z D +PASTICCIO P AE2 S T IY1 CH IY0 OW0 +PASTICHE P AE2 S T IY1 SH +PASTIES P EY1 S T IY0 Z +PASTILES P AE1 S T AY2 L Z +PASTILLE P AE2 S T IY1 L +PASTILLES P AE2 S T IY1 +PASTIME P AE1 S T AY2 M +PASTIMES P AE1 S T AY2 M Z +PASTING P EY1 S T IH0 NG +PASTIRI P AE1 S CH ER0 IY0 +PASTO P AA1 S T OW0 +PASTON P AE1 S T AH0 N +PASTOPHORI P AE1 S T AO0 F AO1 R IY0 +PASTOR P AE1 S T ER0 +PASTOR'S P AE1 S T ER0 Z +PASTORA P AE0 S T AO1 R AH0 +PASTORAL P AE1 S T ER0 AH0 L +PASTORALE P AE1 S T ER0 AE1 L +PASTORALISTS P AE1 S T ER0 AH0 L IH0 S T S +PASTORALS P AE1 S T ER0 AH0 L Z +PASTORATE P AE1 S T ER0 AH0 T +PASTORATES P AE1 S T ER0 AH0 T S +PASTORE P AE1 S T AO2 R +PASTORELLA P AE1 S T AO0 R EH1 L AH0 +PASTORET P AE0 S T AO1 R T +PASTORIN P AE0 S T AO1 R IH0 N +PASTORIN'S P AE1 S T AO0 R IY1 N Z +PASTORIUS P AE1 S T AO0 R IY0 IH0 S +PASTORS P AE1 S T ER0 Z +PASTORSHIP P AE1 S T ER0 SH IH2 P +PASTOUREAU P AE1 S T UH0 R OW0 +PASTOURELLES P AE1 S T UH0 R EH1 L Z +PASTRENGO P EY1 S T R EH1 NG G OW0 +PASTRIES P EY1 S T R IY0 Z +PASTRY P EY1 S T R IY0 +PASTRYCOOK P EY1 S T R IH1 K UH1 K +PASTRYCOOK'S P EY1 S T R IH1 K UH1 K S +PASTRYCOOKS P EY1 S T R IH1 K UH1 K S +PASTS P AE1 S T S +PASTUR P AE1 S CH ER0 +PASTURAGE P AE1 S CH ER0 AA1 ZH +PASTURAGES P AE1 S CH ER0 AA1 ZH AH0 Z +PASTURE P AE1 S CH ER0 +PASTURE'S P AE1 S CH ER0 Z +PASTURED P AE1 S CH ER0 D +PASTURELAND P AE1 S CH ER0 L AH0 N D +PASTURES P AE1 S CH ER0 Z +PASTURING P AE1 S CH ER0 IH0 NG +PASTY P EY1 S T IY0 +PASUR P AE1 S ER0 +PAT P AE1 T +PAT'S P AE1 T S +PATA P AE1 T AH0 +PATACHE P AE1 T AH0 SH EY1 +PATAGONES P AE2 T AH0 G OW1 N Z +PATAGONIA P AE2 T AH0 G OW1 N IY0 AH0 +PATAGONIAN P AE2 T AH0 G OW1 N IY0 AH0 N +PATAGONIANS P AE2 T AH0 G OW1 N IY0 AH0 N Z +PATALA P AH0 T AA1 L AH0 +PATALOLO P AA0 T AA0 L OW1 L OW0 +PATAN P AE1 T AH0 N +PATANJALI P AA0 T AA0 N Y AA1 L IY0 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+PATENTEE P AE1 T AH0 N T IY1 +PATENTEES P AE1 T AH0 N T IY1 Z +PATENTING P AE1 T AH0 N T IH0 NG +PATENTLY P AE1 T AH0 N T L IY0 +PATENTS P AE1 T AH0 N T S +PATER P EY1 T ER0 +PATER'S P EY1 T ER0 Z +PATERA P AA0 T EH1 R AH0 +PATERCULUS P AH0 T ER2 K Y AH0 L AH0 S +PATERFAMILIAS P AH0 T ER2 F AH0 M IH2 L Y AH0 Z +PATERNAL P AH0 T ER1 N AH0 L +PATERNALISM P AH0 T ER1 N AH0 L IH2 Z AH0 M +PATERNALISTIC P AH0 T ER2 N AH0 L IH1 S T IH0 K +PATERNALLY P AH0 T ER1 N AH0 L IY0 +PATERNE P AH0 T ER1 N +PATERNITY P AH0 T ER1 N IH0 T IY0 +PATERNO P AA0 T EH1 R N OW0 +PATERNOSTER P AA0 T EH1 R N AA1 S T ER0 +PATERNOSTERS P AA0 T EH1 R N AA1 S T ER0 Z +PATEROFF P AA0 T ER0 AO0 F +PATERS P EY1 T ER0 Z +PATERSON P AE1 T ER0 S AH0 N +PATERSON'S P AE1 T ER0 S AH0 N Z +PATES P EY1 T S +PATESHULLE P AH0 T IY1 S HH AH2 L +PATESI P AH0 T IY1 S IY0 +PATESVILLE P EY1 T S V IH2 L +PATET P AH0 T EH1 T +PATEY P EY1 T IY0 +PATH P AE1 TH +PATH'S P AE1 TH S +PATHAN P AE1 TH AH0 N +PATHAN'S P AE1 TH AH0 N Z +PATHANS P AE1 TH AH0 N Z +PATHE P AE1 TH +PATHETIC P AH0 TH EH1 T IH0 K +PATHETICAL P AH0 TH EH1 T IH0 K AH0 L +PATHETICALLY P AH0 TH EH1 T IH0 K AH0 L IY0 +PATHETICALLY P AH0 TH EH1 T IH0 K L IY0 +PATHETICK P AH0 TH EH1 T IH0 K +PATHETIQUE P AH0 TH EH1 T AH0 K EY2 +PATHFINDER P AE1 TH F AY2 N D ER0 +PATHFINDER'S P AE1 TH F AY2 N D ER0 Z +PATHFINDERS P AE1 TH F AY2 N D ER0 Z +PATHIS P AE1 TH IH0 S +PATHLESS P AE1 TH L AH0 S +PATHOGENIC P AE2 TH AH0 JH EH1 N IH0 K +PATHOLOGIC P AE2 TH AH0 L AA1 JH IH0 K +PATHOLOGICAL P AE2 TH AH0 L AA1 JH IH0 K AH0 L +PATHOLOGICALLY P AE2 TH AH0 L AA1 JH IH0 K L IY0 +PATHOLOGIST P AH0 TH AA1 L AH0 JH AH0 S T +PATHOLOGISTS P AH0 TH AA1 L AH0 JH AH0 S T S +PATHOLOGY P AH0 TH AA1 L AH0 JH IY0 +PATHOS P EY1 TH AA0 S +PATHRICK P AE1 TH R IH0 K +PATHS P AE1 DH Z +PATHS P AE1 TH S +PATHURST P AE1 TH ER0 S T +PATHWAY P AE1 TH W EY2 +PATHWAYS P AE1 TH W EY2 Z +PATI P AA1 T IY0 +PATIE P AE1 T IY0 +PATIENCE P EY1 SH AH0 N S +PATIENCE'S P EY1 SH AH0 N S IH0 Z +PATIENCES P EY1 SH AH0 N S IH0 Z +PATIENT P EY1 SH AH0 N T +PATIENT'S P EY1 SH AH0 N T S +PATIENTEST P EY1 SH AH0 N T AH0 S T +PATIENTIA P EY1 SH AH0 N T IY0 AH0 +PATIENTLY P EY1 SH AH0 N T L IY0 +PATIENTS P EY1 SH AH0 N T S +PATIENTS P EY1 SH AH0 N Z +PATIN P AE1 T IH0 N +PATINA P AH0 T IY1 N AH0 +PATINE P AA0 T IY1 N IY0 +PATINES P AA0 T IY1 N Z +PATINO P AA0 T IY1 N OW0 +PATIO P AE1 T IY0 OW2 +PATIOS P AE1 T IY0 OW2 Z +PATISSERIE P AE1 T IY0 S ER0 IY0 +PATISSOT P AE1 T IY0 S AH0 T +PATJARAM P AE1 T JH AE2 R AH0 M +PATKASA P AE1 T K AA1 S AH0 +PATLANDER P AE1 T L AH0 N D ER0 +PATLY P AE1 T L IY0 +PATMADHAVRANI P AE1 T M AA2 D HH AH0 V R AE1 N IY0 +PATMAN P AE1 T M AH0 N +PATMORE P AE1 T M AO0 R +PATMORE'S P AE1 T M AO0 R Z +PATMOS P AE1 T M AH0 S +PATNA P AE1 T N AH0 +PATOFA P AE1 T OW0 F AH0 +PATOFF P AH0 T AO0 F +PATOFF'S P AE1 T AH0 F S +PATOIS P AA0 T W AA1 +PATON P AE1 T AH0 N +PATON'S P AE1 T AH0 N Z +PATOO P AH0 T UW2 +PATOS P AE1 T OW0 Z +PATOU P AH0 T UW1 +PATOUCHKI P AH0 T AH1 CH K IY0 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EY2 T +PATRICIDE P AE1 T R AH0 S AY2 D +PATRICIO P AH0 T R IH1 S IY0 OW0 +PATRICIUS P AH0 T R IH1 SH AH0 S +PATRICK P AE1 T R IH0 K +PATRICK'S P AE1 T R IH0 K S +PATRICKSON P AE1 T R IH0 K S AH0 N +PATRICO P AA0 T R IY1 K OW0 +PATRIE P AE1 T ER0 IY0 +PATRIGNO P AA0 T R IY1 G N OW0 +PATRIMONIAL P AE1 T R AH0 M OW2 N IY0 AH0 L +PATRIMONIAL P AE1 T R AH0 M OW2 N Y AH0 L +PATRIMONIES P AE1 T R AH0 M OW2 N IY0 Z +PATRIMONY P AE1 T R AH0 M OW2 N IY0 +PATRINGTON P AE1 T R IH0 NG T AH0 N +PATRIOT P EY1 T R IY0 AH0 T +PATRIOT'S P EY1 T R IY0 AH0 T S +PATRIOTE P EY1 T R IY0 AH0 T +PATRIOTIC P EY2 T R IY0 AA1 T IH0 K +PATRIOTICALLY P EY2 T R IY0 AA1 T IH0 K L IY0 +PATRIOTISM P EY1 T R IY0 AH0 T IH2 Z AH0 M +PATRIOTISMS P EY1 T R IY0 AH0 T IH2 Z AH0 M Z +PATRIOTS P EY1 T R IY0 AH0 T S +PATRIS P AE1 T R AH0 S +PATRISTIC P AH0 T R IH1 S T IH0 K +PATRO P AE1 T R OW0 +PATROCINIO P AH0 T R AA1 S AH0 N IY0 OW0 +PATROCLES P EY1 T R AH0 K AH0 L Z +PATROCLUS P EY1 T R AH0 K L AH0 S +PATROKLES P EY1 T R AH0 K AH0 L Z +PATROKLOS P EY1 T R AH0 K L OW0 S +PATROL P AH0 T R OW1 L +PATROL'S P AH0 T R OW1 L Z +PATROLE P AH0 T R OW1 L IY0 +PATROLED P AH0 T R OW1 L D +PATROLES P AH0 T R OW1 L Z +PATROLING P AH0 T R OW1 L IH0 NG +PATROLLED P AH0 T R OW1 L D +PATROLLERS P AH0 T R OW1 L ER0 Z +PATROLLING P AH0 T R OW1 L IH0 NG +PATROLMAN P AH0 T R OW1 L M AE2 N +PATROLMAN'S P AH0 T R OW1 L M AE2 N Z +PATROLMEN P AH0 T R OW0 L M EH1 N +PATROLS P AH0 T R OW1 L Z +PATRON P EY1 T R AH0 N +PATRON'S P EY1 T R AH0 N Z +PATRONA P AH0 T R OW1 N AH0 +PATRONA'S P AH0 T R OW1 N AH0 Z +PATRONAGE P AE1 T R AH0 N IH0 JH +PATRONAGE P EY1 T R AH0 N AH0 JH +PATRONAGE P EY1 T R AH0 N IH0 JH +PATRONE P AA0 T R OW1 N IY0 +PATRONESS P EY1 T R AH0 N AH0 S +PATRONESS'S P EY1 T R AH0 N AH0 S IH0 Z +PATRONESSES P EY1 T R AH0 N AH0 S IH0 Z +PATRONISE P EY1 T R AH0 N AY2 Z +PATRONISED P EY1 T R AH0 N AY2 Z D +PATRONISES P EY1 T R AH0 N AY2 Z IH0 Z +PATRONISING P EY1 T R AH0 N AY2 Z IH0 NG +PATRONISINGLY P EY1 T R AH0 N AY2 Z IH0 NG L IY0 +PATRONIZE P AE1 T R AH0 N AY2 Z +PATRONIZE P EY1 T R AH0 N AY2 Z +PATRONIZED P AE1 T R AH0 N AY2 Z D +PATRONIZED P EY1 T R AH0 N AY2 Z D +PATRONIZES P EY1 T R AH0 N AY2 Z AH0 Z +PATRONIZIN P EY1 T R AH0 N AY2 Z IH0 N +PATRONIZING P EY1 T R AH0 N AY2 Z IH0 NG +PATRONIZINGLY P EY1 T R AH0 N AY2 Z IH0 NG L IY0 +PATRONNE P EY1 T R AH0 N +PATRONS P EY1 T R AH0 N Z +PATRONUS P AH0 T R OW1 N AH0 S +PATRONYMIC P AE2 T R AH0 N IH1 M IH0 K +PATRONYMICS P EY1 T R AH0 N IH1 M IH0 K S +PATROON P EY1 T R UW1 N +PATROON'S P EY1 T R UW1 N Z +PATROONS P EY1 T R UW2 N Z +PATRUM P AE1 T R AH0 M +PATS P AE1 T S +PATSEY P AE1 T S IY0 +PATSEY'S P AE1 T S IY0 Z +PATSIE P AE1 T S IY0 +PATSIE'S P AE1 T S IY0 Z +PATSY P AE1 T S IY0 +PATSY'S P AE1 T S IY0 Z +PATTA P AE1 T AH0 +PATTAQUASSET P AA0 T AA0 K W AE1 S EH0 T +PATTE P AE1 T +PATTED P AE1 T AH0 D +PATTED P AE1 T IH0 D +PATTEE P AE1 T IY1 +PATTEN P AE1 T AH0 N +PATTEN'S P AE1 T AH0 N Z +PATTENHALL P AE1 T AH0 N HH AO2 L +PATTENS P AE1 T AH0 N Z +PATTER P AE1 T ER0 +PATTERAN P AE1 T ER0 AH0 N +PATTERDALE P AE1 T ER0 D EY2 L +PATTERED P AE1 T ER0 D +PATTERER P AE1 T ER0 ER0 +PATTERERS P AE1 T ER0 ER0 Z +PATTERING P AE1 T ER0 IH0 NG +PATTERINGS P AE1 T ER0 IH0 NG Z +PATTERN P AE1 T ER0 N +PATTERNE P AE1 T ER0 N +PATTERNED P AE1 T ER0 N D +PATTERNING P AE1 T ER0 N IH0 NG +PATTERNINGS P AE1 T ER0 N IH0 NG Z +PATTERNLESS P AE1 T ER0 N L AH0 S +PATTERNS P AE1 T ER0 N Z +PATTEROLLERS P AE1 T ER0 OW0 L ER0 Z +PATTERS P AE1 T ER0 Z +PATTERSON P AE1 T ER0 S AH0 N +PATTERSON'S P AE1 T ER0 S AH0 N Z +PATTERSONS P AE1 T ER0 S AH0 N Z +PATTES P AE1 T S +PATTESON P AE1 T IH0 S AH0 N +PATTESON'S P AE1 T IH0 S AH0 N Z +PATTHERN P AE1 T DH ER0 N +PATTI P AE1 T IY0 +PATTI'S P AE1 T IY0 Z +PATTIE P AE1 T IY0 +PATTIE'S P AE1 T IY0 Z +PATTIES P AE1 T IY0 Z +PATTILLO P AA0 T IH1 L OW0 +PATTIN P AE1 T IH0 N +PATTING P AE1 T IH0 NG +PATTINGS P AE1 T IH0 NG Z +PATTIPANS P AE1 T IH0 P AE1 N Z +PATTISON P AE1 T IH0 S AH0 N +PATTISON'S P AE1 T IH0 S AH0 N Z +PATTMORE P AE1 T M AO2 R +PATTMORE'S P AE1 T M AO2 R Z +PATTON P AE1 T AH0 N +PATTON'S P AE1 T AH0 N Z +PATTY P AE1 T IY0 +PATTY'S P AE1 T IY0 Z +PATTYCAKE P AE1 T IY0 K EY2 K +PATUIT P AH0 T UW1 AH0 T +PATUONE P AE1 CH AH0 W AH2 N +PATUSAN P AA0 T UW1 Z AH0 N +PATUXENT P AH0 T AH1 K S AH0 N T +PATUXET P AH0 T AH1 K S AH0 T +PATZAPATI P EY1 T Z AA0 P AA1 T IY0 +PAU P AW1 +PAUCA P AO1 K AH0 +PAUCI P AO1 S IY0 +PAUCIS P AO1 S AH0 S +PAUCITY P AO1 S AH0 T IY0 +PAUDEEN P AO1 D IY1 N +PAUER P AW1 ER0 +PAUGUS P AO1 G AH0 S +PAUL P AO1 L +PAUL'S P AO1 L Z +PAULA P AO1 L AH0 +PAULA'S P AO1 L AH0 Z +PAULCHEN P AO1 L CH AH0 N +PAULDING P AO1 L D IH0 NG +PAULE P AO1 L +PAULET P AO1 L IH0 T +PAULETT P AO0 L EH1 T +PAULETTA P AA0 L EH1 T AH0 +PAULETTE P AO0 L EH1 T +PAULETTE'S P AO0 L EH1 T S +PAULI P AO1 L IY0 +PAULIAN P AO1 L IY0 AH0 N +PAULICIANS P AO0 L IY1 SH AH0 N Z +PAULIN P AO1 L IH0 N +PAULINA P AO2 L IY1 N AH0 +PAULINA'S P AO0 L IY1 N AH0 Z +PAULINE P AO0 L IY1 N +PAULINE'S P AO0 L 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P EH0 L IY0 Z +PELOFF P EH1 L AO0 F +PELONE P EH0 L OW1 N IY0 +PELOPAEUS P EH2 L AH0 P EY1 AH0 S +PELOPIDAS P EH0 L OW1 P IH0 D AH0 Z +PELOPONNESE P EH2 L AH0 P AH0 N IY1 Z +PELOPONNESIAN P EH2 L AH0 P AH0 N IY1 ZH AH0 N +PELOPONNESIANS P EH2 L AH0 P AH0 N IY1 ZH AH0 N Z +PELOPONNESUS P EH2 L AH0 P AH0 N IY1 S AH0 S +PELOPS P EH2 L AH0 P S +PELORUS P EH1 L OW0 R AH1 S +PELOTA P EH0 L OW1 T AH0 +PELOTI P EH0 L OW1 T IY0 +PELOTON P EH0 L OW1 T AH0 N +PELOUSE P EH1 L AW1 S +PELOUX P EH1 L UW1 +PELS P EH1 L Z +PELSALL P EH1 L S AH0 L +PELT P EH1 L T +PELT'S P EH1 L T S +PELTASTS P EH1 L T AE2 S +PELTED P EH1 L T IH0 D +PELTER P EH1 L T ER0 +PELTER'S P EH1 L T ER0 Z +PELTERS P EH1 L T ER0 Z +PELTIER P EH1 L T IY0 ER0 +PELTIN P EH1 L T IH0 N +PELTING P EH1 L T IH0 NG +PELTINGS P EH1 L T IH0 NG Z +PELTON P EH1 L T AH0 N +PELTON'S P EH1 L T AH0 N Z +PELTRIE P EH1 L T ER0 IY0 +PELTRIES P EH1 L T R IY0 Z +PELTRY P EH1 L T R IY0 +PELTS P EH1 L T S +PELTY P EH1 L T IY0 +PELTZER P EH1 L T Z ER0 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+PENALIZE P EH1 N AH0 L AY2 Z +PENALIZE P IY1 N AH0 L AY2 Z +PENALIZED P IY1 N AH0 L AY2 Z D +PENALIZING P IY1 N AH0 L AY2 Z IH0 NG +PENALLY P EH1 N AH0 L IY0 +PENALTA P EH1 N AH0 L T AH0 +PENALTIES P EH1 N AH0 L T IY0 Z +PENALTY P EH1 N AH0 L T IY0 +PENALUNA P EH1 N AH0 L UW1 N AH0 +PENALVA P EH0 N AA0 L V AH0 +PENANCE P EH1 N AH0 N S +PENANCES P EH1 N AH0 N S IH0 Z +PENANG P EH1 N AE0 NG +PENARROW P EH2 N EH1 R OW0 +PENARTH P EH0 N AA0 R TH +PENARVON P EH0 N AA0 R V AA0 N +PENAS P IY1 N AH0 Z +PENATES P EH1 N AH0 T S +PENAUMBE P IH0 N OW0 M B +PENCARROW P EH1 NG K AE1 R OW0 +PENCE P EH1 N S +PENCE'S P EH1 N S IH0 Z +PENCES P EH1 N S AH0 Z +PENCHANT P EH1 N CH AH0 N T +PENCHARD P EH1 N CH ER0 D +PENCIL P EH1 N S AH0 L +PENCIL'S P EH1 N S AH0 L Z +PENCILED P EH1 N S AH0 L D +PENCILING P EH1 N S IH0 L IH0 NG +PENCILINGS P EH1 N S IH0 L IH0 NG Z +PENCILL'D P EH2 N S AH0 L D +PENCILLED P EH2 N S AH0 L D +PENCILLING P EH2 N S AH0 L IH0 NG +PENCILLINGS P EH2 N S AH0 L IH0 NG Z +PENCILS P 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EH1 N F AA0 N T AA1 N Y UW1 +PENFIELD P EH1 N F IY2 L D +PENFIELD'S P EH1 N F IY0 L D Z +PENFOLD P EH1 N F OW2 L D +PENFOLD'S P EH1 N F OW2 L D Z +PENFOLDS P EH1 N F OW2 L D Z +PENFORD P EH1 N F ER0 D +PENG P EH1 NG +PENGARTH P EH1 NG G AA2 R TH +PENGARVAN P EH1 NG G AA0 R V AH0 N +PENGE P EH1 N JH +PENGELLY P EH1 NG G AH0 L IY0 +PENGELLY'S P EH1 NG G AH0 L IY0 Z +PENGER P EH1 NG G ER0 +PENGILLY P EH1 NG G AH0 L IY0 +PENGOWAN P EH1 NG G AW1 AH0 N +PENGUIN P EH1 NG G W AH0 N +PENGUIN'S P EH1 NG G W AH0 N Z +PENGUINIA P EH1 NG G W AH0 N IY0 AH0 +PENGUINS P EH1 NG G W AH0 N Z +PENH P EH1 N +PENHALIGON P EH1 N HH AH0 L IH1 G AH0 N +PENHALLOW P EH1 N HH AH0 L AW1 +PENHALLOW'S P EH1 N HH AH0 L AW1 Z +PENHALLOWS P EH1 N HH AH0 L AW1 Z +PENHEIM P AH0 N HH AY2 M +PENHOLDER P EH1 N HH OW2 L D ER0 +PENHOLDERS P EH1 N HH OW2 L D ER0 Z +PENIBLE P EH2 N AH0 B AH0 L +PENICILLIUM P EH2 N AH0 S IH1 L IY0 AH0 M +PENICUIK P EH2 N AH0 S IH0 K +PENIEL P EH2 N IY1 L +PENIMAN P EH1 N IH0 M AH0 N +PENIMAN'S P EH1 N IH0 M AH0 N Z +PENINAH P AH0 N IH1 N AH0 +PENINE P EH2 N AH0 N +PENINGTON P EH1 N IH0 NG T AH0 N +PENINNAH P AH0 N IH1 N AH0 +PENINSULA P AH0 N IH1 N S AH0 L AH0 +PENINSULAR P AH0 N IH1 N S AH0 L ER0 +PENINSULAS P AH0 N IH1 N S AH0 L AH0 Z +PENIS P IY1 N IH0 S +PENISTON P EH1 N IH0 S T AA0 N +PENISTON'S P IY1 N IH0 S T AA0 N Z +PENISTONE P IY1 N IH0 S T OW2 N +PENITENCE P EH1 N IH0 T IH0 N S +PENITENCES P EH2 N IH0 T EH1 N S AH0 Z +PENITENT P EH1 N IH0 T IH0 N T +PENITENT'S P EH1 N IH0 T IH0 N T S +PENITENTE P EH1 N IH0 T IH0 N T +PENITENTES P EH1 N IH0 T IH0 N T EH0 S +PENITENTIAL P EH2 N IH0 T EH1 N CH AH0 L +PENITENTIALLY P EH2 N IH0 T EH1 N CH AH0 L IY0 +PENITENTIARIES P EH2 N IH0 T EH1 N CH ER0 IY0 Z +PENITENTIARY P EH2 N IH0 T EH1 N CH ER0 IY0 +PENITENTLY P EH1 N IH0 T IH0 N T L IY0 +PENITENTS P EH1 N IH0 T IH0 N T S +PENITUS P EH1 N IH0 T AH0 S +PENKAWR P EH0 NG K AA1 R +PENKETHMAN P EH0 NG K EH1 TH M AH0 N +PENKNIFE P AH0 N AY2 F +PENKNIVES P AH0 N AY1 V Z +PENKRIDGE P EH0 NG K R IH2 JH +PENLEY P EH1 N L IY0 +PENLOGAN P EH1 N L OW1 G AH0 N +PENLOGANS P EH1 N L OW1 G AH0 N Z +PENLOP P EH1 N L AH0 P +PENMAENMAWR P EH1 N M EH1 N M AA1 R +PENMAN P EH1 N M AH0 N +PENMANSHIP P EH1 N M AH0 N SH IH2 P +PENMEN P EH1 N M EH0 N +PENMORGAN P EH1 N M AO2 R G AH0 N +PENN P EH1 N +PENN'D P EH1 N D +PENN'ORTH P EH2 N S AO2 R TH +PENN'ORTHS P EH2 N S AO0 R TH S +PENN'S P EH1 N Z +PENNA P EH1 N AH0 +PENNA P EH2 N S IH0 L V EY1 N Y AH0 +PENNACOOK P EH1 N AH0 K UH1 K +PENNANT P EH1 N AH0 N T +PENNANT'S P EH1 N AH0 N T S +PENNANTS P EH1 N AH0 N T S +PENNE P EH1 N +PENNED P EH1 N D +PENNEFATHER P EH1 N IH0 F AA2 DH ER0 +PENNEL P EH1 N AH0 L +PENNEL'S P EH1 N AH0 L Z +PENNELL P EH1 N AH0 L +PENNELL'S P EH2 N EH1 L Z +PENNELLINI P EH1 N AH0 L IY1 N IY0 +PENNEY P EH1 N IY0 +PENNI P EH1 N IY0 +PENNICOTE P EH1 N IY0 K OW1 T +PENNICUT P EH1 N IH0 K AH0 T +PENNIE P EH1 N IY0 +PENNIE'S P EH1 N IY0 Z +PENNIES P EH1 N IY0 Z +PENNIFEATHER P EH1 N IH0 F EH1 DH ER0 +PENNIKET P EH1 N IH0 K AH0 T +PENNILESS P EH1 N IY0 L AH0 S +PENNILESSNESS P EH1 N IY0 L AH0 S N AH0 S +PENNIMAN P EH1 N IH0 M AH0 N +PENNINE P EH0 N IY1 N IY0 +PENNINES P AH0 N IY2 N Z +PENNING P EH1 N IH0 NG +PENNINGTON P EH1 N IH0 NG T AH0 N +PENNINGTON'S P EH1 N IH0 NG T AH0 N Z +PENNINGTONS P EH1 N IH0 NG T AH0 N Z +PENNOCK P EH1 N AH0 K +PENNOCK'S P EH1 N AH0 K S +PENNOCKS P EH1 N AH0 K S +PENNON P EH1 N AH0 N +PENNONED P EH1 N AH0 N D +PENNONS P EH1 N AH0 N Z +PENNORTH P EH2 N AO1 R TH +PENNOYER P EH2 N OY0 ER0 +PENNS P EH1 N Z +PENNSYLVANIA P EH2 N S AH0 L V EY1 N Y AH0 +PENNSYLVANIA'S P EH2 N S AH0 L V EY1 N Y AH0 Z +PENNSYLVANIAN P EH2 N S IH0 L V EY1 N IY0 AH0 N +PENNSYLVANIANS P EH2 N S IH0 L V EY1 N IY0 AH0 N Z +PENNSYLVANY P EH2 N S AH0 L V EY1 N IY0 +PENNY P EH1 N IY0 +PENNY'S P EH1 N IY0 Z +PENNYBET P EH1 N IY0 B EH2 T +PENNYCHERRY P EH1 N IH0 HH EH0 R IY0 +PENNYCUICK P EH1 N IH0 K UW1 K +PENNYCUICKS P EH1 N IH0 K Y UW1 IH0 K S +PENNYFEATHER P EH1 N IH0 F EH1 DH ER0 +PENNYLESS P EH1 N IH0 L AH0 S +PENNYLOAF P EH1 N IH0 L OW0 F +PENNYLOAF'S P EH1 N IH0 L OW0 F S +PENNYPACKER P EH1 N IH0 P AE0 K ER0 +PENNYPACKER P EH1 N IY0 P AE0 K ER0 +PENNYPACKER'S P EH1 N IH0 P AE0 K ER0 Z +PENNYROYAL P EH1 N IH0 R OY1 AH0 L +PENNYS P EH1 N IH0 S +PENNYWAIT P EH1 N IY0 W EY0 T +PENNYWEIGHT P EH1 N IY0 W EY2 T +PENNYWEIGHTS P EH1 N IY0 W EY2 T S +PENNYWISE P EH1 N IY0 W AY2 Z +PENNYWORTH P EH1 N IY0 W ER0 TH +PENNYWORTHS P EH1 N IY0 W ER1 TH S +PENO P EH1 N OW0 +PENOBSCOT P AH0 N AA1 B S K AA0 T +PENOBSCOTS P AH0 N AA1 B S K AA0 T S +PENOLOGY P IY0 N AA1 L AH0 JH IY0 +PENON P EH1 N AH0 N +PENREATH P EH1 N R EH1 TH +PENREATH'S P EH1 N R EH1 TH S +PENREIFE P EH1 N R AY2 F +PENRHYN P EH1 N R IH1 N +PENRHYS P EH1 N R IY1 S +PENRI P EH1 N R IY0 +PENRICE P EH1 N R IH0 S +PENRITH P EH1 N R IH0 TH +PENROD P EH1 N R AA2 D +PENROD'S P EH1 N R AA2 D Z +PENROOF P EH1 N R UW2 F +PENROSE P EH1 N R OW2 Z +PENROSE'S P EH1 N R OW2 Z IH0 Z +PENRUDDOCK P EH1 N R AH1 D AH0 K +PENRUN P EH1 N R AH2 N +PENRUTHYN P EH1 N R UW2 TH IH0 N +PENRYN P EH1 N R IH1 N +PENS P EH1 N Z +PENSA P EH1 N S AH0 +PENSACOLA P EH2 N S AH0 K OW1 L AH0 +PENSE P EH1 N S +PENSEE P EH2 N S IY1 +PENSEES P EH2 N S IY2 Z +PENSER P EH1 N S ER0 +PENSEROSO P EH1 N S ER0 OW1 S OW0 +PENSEUR P EH1 N S ER1 +PENSEZ P EH1 N S IH0 Z +PENSHURST P EH1 N SH ER0 S T +PENSI P EH2 N S IY0 +PENSIER P EH2 N S IY0 ER0 +PENSIERI P EH1 N SH IH1 R IY0 +PENSIERO P EH1 N SH IH1 R OW0 +PENSIL P EH1 N S AH0 L +PENSILE P EH1 N S AH0 L +PENSION P EH1 N SH AH0 N +PENSIONARY P EH1 N SH AH0 N EH2 R IY0 +PENSIONED P EH1 N SH AH0 N D +PENSIONER P EH1 N SH AH0 N ER0 +PENSIONER'S P EH1 N SH AH0 N ER0 Z +PENSIONERS P EH1 N SH AH0 N ER0 Z +PENSIONING P EH1 N SH AH0 N IH0 NG +PENSIONNAIRE P EH1 N SH AH0 N EH2 R +PENSIONNAIRES P EH1 N SH AH0 N EH2 R Z +PENSIONNAT P EH1 N SH AH0 N AE1 T +PENSIONS P EH1 N SH AH0 N Z +PENSIVE P EH1 N S IH0 V +PENSIVELY P EH1 N S IH0 V L IY0 +PENSIVENESS P EH1 N S IH0 V N AH0 S +PENSTOCK P EH1 N S T AA2 K +PENSYLVANIA 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AH1 JH D +PREJUDGING P R IY0 JH AH1 JH IH0 NG +PREJUDGMENT P R IY0 JH AH1 JH M AH0 N T +PREJUDICE P R EH1 JH AH0 D IH0 S +PREJUDICED P R EH1 JH AH0 D AH0 S T +PREJUDICES P R EH1 JH AH0 D IH0 S IH0 Z +PREJUDICIAL P R EH2 JH AH0 D IH1 SH AH0 L +PREJUDICIALLY P R EH1 JH AH0 D IH1 SH AH0 L IY0 +PREJUDICING P R EH1 JH AH0 D IH0 S IH0 NG +PREJUGES P R IY0 JH UW1 JH IH0 Z +PRELACY P R EH1 L IH0 S IY0 +PRELATE P R EH1 L IH0 T +PRELATE P R IY1 L EY2 T +PRELATE'S P R EH1 L IH0 T S +PRELATES P R EH1 L IH0 T S +PRELATI P R IH0 L AA1 T IY0 +PRELATIC P R EH1 L AH0 T IH2 K +PRELATICAL P R EH1 L AH0 T AH0 K AH0 L +PRELATIST P R EH1 L AH0 T IH0 S T +PRELATISTS P R EH1 L AH0 T IH0 S T S +PRELECTION P R IH0 L EH2 K SH AH0 N +PRELECTIONS P R IH0 L EH2 K SH AH0 N Z +PRELIMINARIES P R IH0 L IH1 M AH0 N EH2 R IY0 Z +PRELIMINARIES P R IY0 L IH1 M AH0 N EH2 R IY0 Z +PRELIMINARILY P R IH0 L IH2 M AH0 N EH1 R IH0 L IY0 +PRELIMINARY P R IH0 L IH1 M AH0 N EH2 R IY0 +PRELIMINARY P R IY0 L IH1 M AH0 N EH2 R IY0 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AH0 N T +RADIANT R EY1 D Y AH0 N T +RADIANTLY R EY1 D Y AH0 N T L IY0 +RADIATA R EY2 D IY0 AA1 T AH0 +RADIATE R EY1 D IY0 AH0 T +RADIATE R EY1 D IY0 EY2 T +RADIATED R EY1 D IY0 EY2 T AH0 D +RADIATED R EY1 D IY0 EY2 T IH0 D +RADIATES R EY1 D IY0 EY2 T S +RADIATING R EY1 D IY0 EY2 T IH0 NG +RADIATION R EY2 D IY0 EY1 SH AH0 N +RADIATIONS R EY2 D IY0 EY1 SH AH0 N Z +RADIATOR R EY1 D IY0 EY2 T ER0 +RADIATORS R EY1 D IY0 EY2 T ER0 Z +RADICAL R AE1 D AH0 K AH0 L +RADICAL R AE1 D IH0 K AH0 L +RADICALISM R AE1 D IH0 K AH0 L IH2 Z AH0 M +RADICALLY R AE1 D IH0 K L IY0 +RADICALS R AE1 D AH0 K AH0 L Z +RADICALS R AE1 D IH0 K AH0 L Z +RADICLE R AE1 D IH0 K AH0 L +RADICLES R AE1 D IH0 K AH0 L Z +RADICOFANI R AE1 D IH0 K OW2 F AA1 N IY0 +RADII R EY2 D IY0 IY2 +RADIO R EY1 D IY0 OW2 +RADIO'S R EY1 D IY0 OW2 Z +RADIOACTIVE R EY2 D IY0 OW0 AE1 K T IH0 V +RADIOACTIVES R EY2 D IY0 OW0 AE1 K T IH0 V Z +RADIOACTIVITY R EY1 D IY0 OW0 AE0 K T IH1 V AH0 T IY0 +RADIOACTIVITY R EY2 D IY0 OW0 AE0 K T IH1 V AH0 T 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+RADWAY'S R AE1 D W EY2 Z +RADWIN R AE1 D W IH0 N +RADY R EY1 D IY0 +RADZAGIO R AE1 D Z EY1 JH IY0 OW0 +RADZEYOVSKI R AA1 JH IH0 AO1 V S K IY0 +RADZIMINSKI R AA1 JH IH0 M IH1 N S K IY0 +RADZIVIL R AE1 D Z IH1 V AH0 L +RADZIVILL R AA1 JH IH0 V AH0 L +RADZIVILLS R AA1 JH IH0 V IH1 L Z +RADZIWILL R AA1 JH IH0 W IH2 L +RAE R EY1 +RAE'S R EY1 Z +RAEBURN R EY1 B ER0 N +RAEBURN'S R EH1 B ER0 N Z +RAEBURNS R EH1 B ER1 N Z +RAED R EH1 D +RAEL R EY1 L +RAEMAEKERS R EY1 M EH1 K ER0 Z +RAES R EY1 Z +RAETHIN R EH1 DH IH1 N +RAF R AE1 F +RAF'S R AH0 F S +RAFA R AE1 F AH0 +RAFAEL R AA2 F AY0 EH1 L +RAFAEL'S R AA2 F AY0 EH1 L Z +RAFAELA R AA2 F AY0 EH1 L AH0 +RAFAELE R AA2 F AY0 EH1 L EY0 +RAFAELE'S R AA2 F AY0 EH1 L EY0 Z +RAFAELLA R AA2 F AY0 EH1 L AH0 +RAFAELLE R AA2 F AY0 EH1 L +RAFALSKI R AH0 F AA1 L S K IY0 +RAFE R EY1 F +RAFE'S R EY1 F S +RAFEL R AE1 F AH0 L +RAFF R AE1 F +RAFF'S R AA0 F S +RAFFAEL R AA0 F AY0 EH1 L +RAFFAELE R AA0 F AY0 EH1 L EY0 +RAFFAELLE R AA0 F AA0 EH1 L +RAFFAELLE'S R AA0 F AA0 EH1 L Z +RAFFAELLI R AA0 F AA0 EH1 L IY0 +RAFFAELLO R AA0 F AA0 EH1 L OW0 +RAFFARTY R AA0 F AA0 R T IY0 +RAFFER R AE1 F ER0 +RAFFERTY R AE1 F ER0 T IY0 +RAFFERTY'S R AE1 F ER0 T IY0 Z +RAFFET R AE1 F AH0 T +RAFFIA R AE1 F IY0 AH0 +RAFFIN R AE1 F IH0 N +RAFFISH R AE1 F IH0 SH +RAFFLE R AE1 F AH0 L +RAFFLED R AE1 F AH0 L D +RAFFLES R AE1 F AH0 L Z +RAFFLES'S R AE1 F AH0 L Z IH0 Z +RAFFLESIA R AE1 F L EY1 S IY0 AH0 +RAFFLING R AE1 F AH0 L IH0 NG +RAFFOLD R AE1 F AH0 L D +RAFFONI R AA0 F OW1 N IY0 +RAFN R AE1 F N +RAFT R AE1 F T +RAFT'S R AE1 F T S +RAFTE R AE1 F T +RAFTED R AE1 F T AH0 D +RAFTED R AE1 F T IH0 D +RAFTER R AE1 F T ER0 +RAFTERED R AE1 F T ER0 D +RAFTERS R AE1 F T ER0 Z +RAFTERY R AE1 F T ER0 IY0 +RAFTERY'S R AE1 F T ER0 IY0 Z +RAFTING R AE1 F T IH0 NG +RAFTS R AE1 F T S +RAFTSMAN R AE1 F T S M AH0 N +RAFTSMEN R AE1 F T S M EH0 N +RAG R AE1 G +RAG'D R AE1 G D +RAG'S R AE1 G Z +RAGA R AA1 G AH0 +RAGAMUFFIN R AA1 G AH0 M AH1 F AH0 N +RAGAMUFFINS R AA1 G AH0 M AH1 F AH0 N Z +RAGASTEIN R AA1 G AH0 S T IY0 N +RAGATZ R AA1 G AH0 T S +RAGBAG R AE1 G B AE2 G +RAGDEALER R AE1 G D IY1 L ER0 +RAGE R EY1 JH +RAGED R EY1 JH D +RAGEFUL R EY1 JH F AH0 L +RAGEING R EY1 JH IH0 NG +RAGES R EY1 JH IH0 Z +RAGETH R EY1 JH IH0 TH +RAGG R AE1 G +RAGGAMUFFINS R AE2 G AH0 M AH1 F AH0 N Z +RAGGED R AE1 G AH0 D +RAGGEDER R AE1 G D ER0 +RAGGEDEST R AE1 G AH0 D AH0 S T +RAGGEDLY R AE1 G AH0 D L IY0 +RAGGEDNESS R AE1 G AH0 D N AH0 S +RAGGEDY R AE1 G AH0 D IY0 +RAGGETT R AE1 G IH0 T +RAGGETT'S R AE1 G IH0 T S +RAGGING R AE1 G IH0 NG +RAGGIO R AA1 JH IY0 OW0 +RAGGLES R AE1 G AH0 L Z +RAGGLES'S R AE1 G AH0 L Z IH0 Z +RAGGLESFORD R AE1 G AH0 L S F ER0 D +RAGGS R AE1 G Z +RAGGY R AE1 G IY0 +RAGGYLUG R AE1 G IY0 L AH1 G +RAGHAV R AH0 G AA2 V +RAGHE R AH0 G +RAGHIB R AH0 G IY1 B +RAGHT R AH0 T +RAGHU R AE1 G HH UW0 +RAGHUNATH R AH0 G UW2 N AH0 TH +RAGIDEAU R AE1 JH IH0 D OW2 +RAGIN R AE1 JH IH0 N +RAGING R EY1 JH IH0 NG +RAGINGLY R EY1 JH IH0 NG L IY0 +RAGINGS R EY1 JH IH0 NG Z +RAGLAN R AE1 G L AH0 N +RAGLAN'S R AE1 G L AH0 N Z +RAGLAND R AE1 G L AH0 N D +RAGLAND'S R AE1 G L AH0 N D Z +RAGMAN R AE1 G M AH0 N +RAGNA R AA1 N Y AH0 +RAGNA'S R AA1 N Y AH0 Z +RAGNALL R AE1 G N AH0 L +RAGNALL'S R AA1 N Y AH0 L Z +RAGNAR R AA0 G N AA1 R +RAGNAR'S R AA0 G N AA1 R Z +RAGNAROEK R AA0 G N AA1 R OW0 K +RAGNAROK R AA0 G N AA1 R AH0 K +RAGNHILD R AA0 G N HH IH0 L D +RAGNI R AE1 G N IY0 +RAGNI'S R AA0 G N IY0 Z +RAGON R AA0 G AO1 N +RAGONS R AE1 G AH0 N Z +RAGOUT R AE1 G AW2 T +RAGOUTS R AE1 G AW2 T S +RAGPICKER R AE1 G P IH1 K ER0 +RAGPICKERS R AE1 G P IH1 K ER0 Z +RAGS R AE1 G Z +RAGSDALE R AE1 G Z D EY2 L +RAGSTAFF R AE1 G S T AE2 F +RAGSTEAD R AE1 G S T EH2 D +RAGTAG R AE1 G T AE2 G +RAGTIME R AE1 G T AY2 M +RAGTOWN R AE1 G T AW2 N +RAGTOWN'S R AE1 G T AW2 N Z +RAGUENEAU R AE1 G IH0 N OW0 +RAGUENEAU'S R AE1 G IH0 N OW0 Z +RAGUET R AE1 G AH0 T +RAGUSA R AA0 G UW1 S AH0 +RAGUSAN R AA0 G UW1 S AH0 N +RAGWEED R AE1 G W IY2 D +RAGWORT R AE1 G W ER0 T +RAH R AA1 +RAHAB R AH0 HH AE2 B +RAHAB'S R AH0 HH AA1 B Z +RAHAN R AH0 HH AE0 N +RAHANA R AH0 HH AA1 N AH0 +RAHANDA R AH0 HH AA1 N D AH0 +RAHANDAS R AH0 HH AE1 N D AH0 Z +RAHANS R AE1 HH AH0 N Z +RAHAT R AH0 HH AE0 T +RAHDEN R AA1 D AH0 N +RAHDEN'S R AA1 D AH0 N Z +RAHEEL R AH0 HH IY1 L +RAHEEM R AH0 HH IY1 M +RAHEL R EY1 HH IY0 L +RAHERE R AH0 HH IY1 R +RAHERE'S R AH0 HH IH1 R Z +RAHERO R AH0 HH IY1 R OW0 +RAHIM R AH0 HH IY1 M +RAHL R AA1 L +RAHMAN R AA1 M AH0 N +RAHMUT R AA1 M AH0 T +RAHN R AE1 N +RAHND R AA1 N D +RAHNSTADT R AA1 N S T AE2 T +RAHNSTADTERS R AA1 N S T AE2 T ER0 Z +RAHNSTAEDT R AA1 N S T AE0 T +RAHS R AA1 Z +RAHU R AH0 HH UW1 +RAHULA R AH0 HH UW1 L AH0 +RAHWAY R AA1 W EY2 +RAI R AA1 IY0 +RAIATEA R AA1 Y AH0 T IY1 AH0 +RAIBAR R EY1 B AA2 R +RAICHARAN R AY1 K AE1 R AH0 N +RAICHI R AA0 IY1 CH IY0 +RAID R EY1 D +RAIDED R EY1 D IH0 D +RAIDER R EY1 D ER0 +RAIDER'S R EY1 D ER0 Z +RAIDERS R EY1 D ER0 Z +RAIDIN R EY1 D IH0 N +RAIDING R EY1 D IH0 NG +RAIDINGS R EY1 D IH0 NG Z +RAIDMASTER R EY1 D M AE1 S T ER0 +RAIDS R EY1 D Z +RAIERE R AA1 Y EH1 R +RAIFE R EY1 F +RAIFE'S R EY1 F S +RAIGHT R EY1 T +RAIGNE R EY1 K N +RAIKES R EY1 K S +RAIKES'S R EY1 K S IH0 Z +RAIKO R EY1 K OW0 +RAIL R EY1 L +RAIL'D R EY1 L D +RAIL'S R EY1 L Z +RAILED R EY1 L D +RAILER R EY1 L ER0 +RAILERS R EY1 L ER0 Z +RAILEY R EY1 L IY0 +RAILHEAD R EY1 L HH EH2 D +RAILIN R EY1 L IH0 N +RAILING R EY1 L IH0 NG +RAILINGS R EY1 L IH0 NG Z +RAILLARD R AH0 L AA1 R D +RAILLERIES R EY1 L ER0 IY0 Z +RAILLERY R EY1 L ER0 IY0 +RAILLY R EY1 L IY0 +RAILROAD R EY1 L R OW2 D +RAILROAD'S R EY1 L R OW2 D Z +RAILROADED R EY1 L R OW2 D IH0 D +RAILROADER R EY1 L R OW2 D ER0 +RAILROADERS R EY1 L R OW2 D ER0 Z +RAILROADING R EY1 L R OW2 D IH0 NG +RAILROADS R EY1 L R OW2 D Z +RAILRUD R EY1 L R AH0 D +RAILS R EY1 L Z +RAILTON R EY1 L T AH0 N +RAILTON'S R EY1 L T AH0 N Z +RAILWAY R EY1 L W EY2 +RAILWAY'S R EY1 L W EY2 Z +RAILWAYMAN R EY1 L W EY1 M AH0 N +RAILWAYMEN R EY1 L W EY2 M AH0 N +RAILWAYS R EY1 L W EY2 Z +RAIMBAUT R EY2 M B AO1 T 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N JH +RANGED R EY1 N JH D +RANGELAND R EY1 N JH L AH0 N D +RANGELEY R EY1 N JH L IY0 +RANGELY R EY1 N JH L IY0 +RANGEMAN R EY1 N JH M AH0 N +RANGEMENT R EY1 N JH M AH0 N T +RANGEMENTS R EY1 N JH M AH0 N T S +RANGER R EY1 N JH ER0 +RANGER'S R EY1 N JH ER0 Z +RANGERS R EY1 N JH ER0 Z +RANGES R EY1 N JH AH0 Z +RANGES R EY1 N JH IH0 Z +RANGIN R AE0 NG G IH0 N +RANGING R EY1 N JH IH0 NG +RANGO R AA1 NG G OW0 +RANGON R AE0 NG G AH0 N +RANGOON R AE0 NG G UW1 N +RANGOON'S R AE0 NG G UW1 N Z +RANGY R EY1 N JH IY0 +RANI R AA1 N IY0 +RANI'S R AA1 N IY0 Z +RANIER R AH0 N IY0 ER0 +RANIERI R AE2 N IY0 EH1 R IY0 +RANIERI R AH0 N IY0 EH1 R IY0 +RANIERO R AH0 N IY0 EH1 R OW0 +RANIERO'S R AH0 N IY0 EH1 R OW0 Z +RANILD R AA0 N IY1 L D +RANIPUR R AE1 N AH0 P ER0 +RANIS R AA1 N IY0 Z +RANJAN R AE1 N JH AH0 N +RANJAN'S R AE1 N JH AA1 N Z +RANJI R AE1 N JH IY0 +RANJIT R AE1 N JH IH0 T +RANJITSINHJI R AE1 N JH IH0 T S IH1 N JH IY0 +RANJOOR R AE1 N JH UH1 R +RANK R AE1 NG K +RANK'D R AE1 NG K D +RANK'S R AE1 NG K S +RANKE R AE1 NG K +RANKE'S R AE1 NG K S +RANKED R AE1 NG K T +RANKEILLOR R AE1 NG K IY1 L ER0 +RANKEN R AE1 NG K AH0 N +RANKER R AE1 NG K ER0 +RANKERS R AE1 NG K ER0 Z +RANKEST R AE1 NG K AH0 S T +RANKIN R AE1 NG K IH0 N +RANKIN'S R AE1 NG K IH0 N Z +RANKINE R AE1 NG K AY2 N +RANKING R AE1 NG K IH0 NG +RANKINS R AE1 NG K IH0 N Z +RANKLE R AE1 NG K AH0 L +RANKLED R AE1 NG K AH0 L D +RANKLES R AE1 NG K AH0 L Z +RANKLING R AE1 NG K L IH0 NG +RANKLY R AE1 NG K L IY0 +RANKNESS R AE1 NG K N AH0 S +RANKS R AE1 NG K S +RANLEIGH R AE1 N L AH0 +RANN R AE1 N +RANN'S R AE1 N Z +RANNA R AE1 N AH0 +RANNAG R AE1 N AH0 G +RANNE R AE1 N +RANNEY R AE1 N IY0 +RANNIE R AE1 N IY0 +RANNION R AE1 N Y AH0 N +RANNOCH R AE1 N AH0 K +RANNS R AE1 N Z +RANNU R AE1 N UW0 +RANNVEIG R AE1 N V IY1 G +RANNY R AE1 N IY0 +RANNY'S R AE1 N IY0 Z +RANOVICA R AE1 N AH0 V IH0 K AH0 +RANQUIST R AE1 N K W IH2 S T +RANS R AE1 N Z +RANSACK R AE1 N S AE2 K +RANSACKED R AE1 N S AE2 K T +RANSACKING R AE1 N S AE2 K IH0 NG +RANSACKS R AE1 N S AE2 K S +RANSAY R AE1 N S EY0 +RANSCOMB R AE1 N S K AH0 M +RANSDELL R AE1 N Z D EH1 L +RANSE R AE1 N S +RANSFORD R AE1 N S F ER0 D +RANSFORD'S R AE1 N S F ER0 D Z +RANSIE R AE1 N S IY0 +RANSOM R AE1 N S AH0 M +RANSOM'D R AE1 N S AH0 M D +RANSOM'S R AE1 N S AH0 M Z +RANSOME R AE1 N S AH0 M +RANSOME'S R AE1 N S AH0 M Z +RANSOMED R AE1 N S AH0 M D +RANSOMING R AE1 N S AH0 M IH0 NG +RANSOMS R AE1 N S AH0 M Z +RANSON R AE1 N S AH0 N +RANT R AE1 N T +RANTAINE R AE1 N T EY1 N +RANTED R AE1 N T AH0 D +RANTED R AE1 N T IH0 D +RANTER R AE1 N T ER0 +RANTERS R AE1 N T ER0 Z +RANTH R AE1 N TH +RANTHAR R AE2 N T AA2 R +RANTHOOR R AE1 N T HH UH0 R +RANTHORPE R AE1 N TH ER0 P +RANTIN R AE1 N T IH0 N +RANTING R AE1 N T IH0 NG +RANTINGS R AE1 N T IH0 NG Z +RANTIPOLE R AE1 N T IH0 P OW2 L +RANTOUL R AE2 N T UW1 L +RANTREMLY R AE1 N T R EH2 M L IY0 +RANTS R AE1 N T S +RANTUM R AE2 N T AH0 M +RANTZAU R AE1 N T S OW1 +RANTZAU'S R AE1 N T S OW0 Z +RANTZOW R AE1 N T Z OW0 +RANULF R AE1 N AH0 L F +RANULPH R AE1 N AH0 L F +RANULPH'S R AE1 N AH0 L F S +RANUNCULUS R AE1 N AH0 NG K Y AH0 L AH0 S +RANUNCULUSES R AE1 N AH0 NG K Y AH0 L AH0 S IH0 Z +RANUZI R AA0 N UW1 Z IY0 +RANWORTH R AE1 N W ER2 TH +RANZ R AE1 N Z +RANZA R AE1 N Z AH0 +RANZO R AE1 N Z OW0 +RAO R AW1 +RAO'S R AW1 Z +RAOUL R AA0 UW1 L +RAOUL'S R AA0 UW1 L Z +RAOULA R AA0 UW1 L AH0 +RAOUN R AA0 UW1 N +RAOUND R AA0 AW2 N D +RAP R AE1 P +RAPACIOUS R AH0 P AE1 SH IH0 S +RAPACIOUS R AH0 P EY1 SH IH0 S +RAPACIOUSLY R AH0 P EY1 SH AH0 S L IY0 +RAPACIOUSNESS R AH0 P EY1 SH AH0 S N AH0 S +RAPACITY R AH0 P AE1 S AH0 T IY0 +RAPAHOE R AE1 P AH0 HH OW2 +RAPAJU R AH0 P EY1 JH UW0 +RAPALLO R AH0 P AE1 L OW0 +RAPALLY R AE1 P AH0 L IY0 +RAPAUD R AE1 P AO0 D +RAPE R EY1 P +RAPED R EY1 P T +RAPER R EY1 P ER0 +RAPES R EY1 P S +RAPET R AH0 P EH2 T +RAPHAEL R AA2 F AY0 EH1 L +RAPHAEL R AA2 F Y EH1 L +RAPHAEL R EY1 F IY0 AH0 L +RAPHAEL'S R EY1 F IY0 AH0 L Z +RAPHAELE R AA2 F AY0 EH1 L EY0 +RAPHAELESQUE R EY1 F IY0 AH0 L EH1 S K +RAPHAELITE R AE0 F Y EH1 L AY0 T +RAPHAELITES R AA2 F Y EH1 L AY0 T S +RAPHAELITISM R AA2 F AY0 EH1 L IH0 T IH2 Z AH0 M +RAPHAELS R EY1 F IY0 AH0 L Z +RAPHIA R AA2 F IY0 AH0 +RAPID R AE1 P AH0 D +RAPID R AE1 P IH0 D +RAPIDAN R AE1 P IH0 D AH0 N +RAPIDE R AE1 P IH0 D +RAPIDES R AE1 P IH0 D AH0 S +RAPIDEST R AE1 P IH0 D AH0 S T +RAPIDITY R AH0 P IH1 D AH0 T IY0 +RAPIDLY R AE1 P AH0 D L IY0 +RAPIDS R AE1 P AH0 D Z +RAPIDS R AE1 P IH0 D Z +RAPIER R EY1 P IY0 ER0 +RAPIER'S R EY1 P IY0 ER0 Z +RAPIERS R EY1 P IY0 ER0 Z +RAPIN R AE1 P IH0 N +RAPIN'S R AE1 P IH0 N Z +RAPINE R AA0 P IY1 N IY0 +RAPINES R AH0 P AY2 N Z +RAPING R EY1 P IH0 NG +RAPIST R EY1 P IH0 S T +RAPKIN R AE1 P K IH0 N +RAPLEY R AE1 P L IY0 +RAPOPORT R AE1 P AH0 P AO0 R T +RAPOPORT R AH0 P AA1 P AO0 R T +RAPOSA R AA0 P OW1 S AH0 +RAPP R AE1 P +RAPP'S R AE1 P S +RAPPAHANNOCK R AE2 P AH0 HH AE1 N AH0 K +RAPPAREE R AE2 P ER0 IY1 +RAPPAREES R AE2 P ER0 IY1 Z +RAPPED R AE1 P T +RAPPEE R AE1 P IY1 +RAPPEL R AE1 P AH0 L +RAPPELER R AE1 P AH0 L ER0 +RAPPELLE R AH0 P EH1 L +RAPPER R AE1 P ER0 +RAPPERS R AE1 P ER0 Z +RAPPERSCHWYLL R AE1 P ER0 SH W IH0 L +RAPPID R AE1 P IH0 D +RAPPIDS R AE1 P IH0 D Z +RAPPIN R AE1 P IH0 N +RAPPING R AE1 P IH0 NG +RAPPINGS R AE1 P IH0 NG Z +RAPPORT R AE0 P AO1 R +RAPPORTE R AE0 P AO1 R T +RAPPORTS R AE0 P AO1 R T S +RAPPROCHEMENT R AE2 P R OW2 SH M AA1 N +RAPS R AE1 P S +RAPSCALLION R AE1 P S K AE1 L Y AH0 N +RAPSCALLIONS R AE1 P S K AE1 L Y AH0 N Z +RAPT R AE1 P T +RAPTLY R AE1 P T L IY0 +RAPTUR'D R AE1 P CH ER0 D +RAPTURE R AE1 P CH ER0 +RAPTURE'S R AE1 P CH ER0 Z +RAPTURED R AE1 P CH ER0 D +RAPTURES R AE1 P CH ER0 Z +RAPTUROUS R AE1 P CH ER0 AH0 S +RAPTUROUSLY R AE1 P CH ER0 AH0 S L IY0 +RAPUNZEL R AH0 P AH1 N Z AH0 L +RAQUEL R AH0 K EH1 L +RAQUETTE R AH0 K EH1 T +RAQUETTES R AH0 K EH1 T S +RAQUIN R AH0 K IY1 N +RAR R AA2 R +RARA R AA1 R AH0 +RARAHU R AE1 R AH0 HH UW1 +RARATONGA R AA1 R AH0 T AA1 N G AH0 +RARD R ER0 D +RARE R EH1 R +RAREBIT R EH1 R B IH0 T +RAREBITS R EH1 R AH0 B AH0 T S +RARED R EH1 R D +RAREE R EH1 R IY1 +RAREFACTION R EH1 R AH0 F AE1 K SH AH0 N +RAREFIED R EH1 R AH0 F AY0 D +RAREFIES R EH1 R AH0 F AY0 Z +RAREFY R EH1 R AH0 F AY0 +RARELY R EH1 R L IY0 +RAREMENT R EH1 R AH0 M AH0 N T +RARENESS R EH1 R N IH0 S +RARER R EH1 R ER0 +RARES R EH1 R Z +RAREST R EH1 R AH0 S T +RAREY R EH1 R IY0 +RAREY'S R EH1 R IY0 Z +RARI R AE1 R EY2 +RARIFIED R EH1 R AH0 F AY2 D +RARIFY'D R EH1 R IH0 F AY1 D +RARIK R AE1 R IH0 K +RARIN R EH1 R IH0 N +RARING R EH1 R IH0 NG +RARITAN R EH1 R IH0 T AH0 N +RARITAN'S R EH1 R IH0 T AH0 N Z +RARITIES R EH1 R IH0 T IY0 Z +RARITY R EH1 R AH0 T IY0 +RARITY R EH1 R IH0 T IY0 +RARO R AA1 R OW0 +RAROTONGA R AE1 R AH0 T AA1 N G AH0 +RARX R AA1 R K S +RAS R AE1 S +RAS'S R AH0 S IH0 Z +RASA R AA1 S AH0 +RASAY R AE1 S EY0 +RASBERRIES R AE1 S B EH2 R IY0 Z +RASCAL R AE1 S K AH0 L +RASCAL'S R AE1 S K AH0 L Z +RASCALDOM R AE1 S K AH0 L D AH0 M +RASCALITIES R AE0 S K AE1 L AH0 T IY0 Z +RASCALITY R AE0 S K AE1 L AH0 T IY0 +RASCALLY R AE1 S K AH0 L IY0 +RASCALS R AE1 S K AH0 L Z +RASCHI R AE1 SH IY0 +RASCHID R AE1 SH IH0 D +RASCHKE R AE1 SH K +RASCIAN R AE1 S K AH0 N +RASCZINSKY R AE1 S K Z IH1 N S K IY0 +RASE R EY1 Z +RASED R EY1 S T +RASELLI R AH0 S EH1 L IY0 +RASH R AE1 SH +RASHBOROUGH R AE1 SH B ER2 OW0 +RASHBURN R AE1 SH B ER2 N +RASHCLIFFE R AE1 SH K L IH0 F +RASHER R AE1 SH ER0 +RASHERS R AE1 SH ER0 Z +RASHES R AE1 SH IH0 Z +RASHEST R AH0 SH AH0 S T +RASHEVITCH R AE1 SH V IH2 CH +RASHID R AA0 SH IY1 D +RASHID R AH0 SH IY1 D +RASHID'S R AA0 SH IY1 D Z +RASHIE R AE1 SH IY0 +RASHKOFF R AH0 SH K AO2 F +RASHLEIGH R AE1 SH L AH0 +RASHLEIGH'S R AE1 SH L IY0 Z +RASHLY R AH0 SH L IY0 +RASHNESS R AE1 SH N AH0 S +RASKA R AA1 S K AH0 +RASKIL R AE1 S K AH0 L +RASKILS R AE1 S K AH0 L Z +RASKOL R AE1 S K AO0 L +RASKOLNIKOV R AE1 S K OW1 L N IH0 K AO2 V +RASKOLNIKOV'S R AE1 S K OW1 L N IH0 K AA2 V Z +RASKOLNIKS R AE1 S K OW1 L N IH0 K S +RASLANEI R AE1 S L AA0 N EY0 +RASMER R AE1 S AH0 M ER0 +RASMITH R AE1 S M IH2 TH +RASMUS R AE1 Z M IH0 S +RASMUS'S R AE1 Z M AH0 S IH0 Z +RASMUSSEN R AE1 S M AH0 S AH0 N +RASON R AE1 S AH0 N +RASONABLE R EY1 Z N AH0 B AH0 L +RASP R AE1 S P +RASP'S R AE1 S P S +RASPAIL R AE1 S P EY2 L +RASPBERRIES R AE1 Z B EH2 R IY0 Z +RASPBERRY R AE1 Z B EH2 R IY0 +RASPE R AE1 S P IY0 +RASPED R AE1 S P T +RASPER R AE1 S P ER0 +RASPIN R AE1 S P IH0 N +RASPING R AE1 S P IH0 NG +RASPINGLY R AE1 S P IH0 NG L IY0 +RASPINGS R AE1 S P IH0 NG Z +RASPS R AE1 S P S +RASPUL R AE1 S P AH0 L +RASPUTIN R AH0 S P Y UW1 T IH0 N +RASPUTIN'S R AE0 S P Y UW1 T AH0 N Z +RASPY R AE1 S P IY0 +RASS R AE0 S +RASSAK R AA1 S AH0 K +RASSAM R AA1 S AH0 M +RASSAM'S R AA1 S AH0 M Z +RASSEDIN R AE1 S IH0 D IH0 N +RASSELAS R AE1 S IH0 L AH0 Z +RASSELYER R AE1 S IH0 L IY0 ER0 +RASSEN R AE1 S AH0 N +RASSENDYLL R AE1 S IH0 N D IH0 L +RASSENDYLL'S R AE1 S IH0 N D IH0 L Z +RASSEULGER R AE0 S AH1 L G ER0 +RASSLE R AE1 S AH0 L +RAST R AE1 S T +RAST'S R AE1 S T S +RASTA R AE1 S T AH0 +RASTADT R AE1 S T AE2 T +RASTEKAIS R EY1 S T IH0 K EY1 +RASTIGNAC R AE1 S T IH0 N Y AE2 K +RASTIGNAC'S R AE1 S T IH0 N Y AE2 K S +RASTLE R AE1 S AH0 L +RASTLIN R AE1 S T L IH0 N +RASTRO R AE1 S T R OW0 +RASTUS R AE1 S T AH0 S +RASUL R AE1 S AH0 L +RAT R AE1 T +RAT'S R AE1 T S +RATA R AE1 T AH0 +RATAFIA R AE1 T AH0 F IY0 AH0 +RATAN R AE1 T AH0 N +RATANS R AE1 T AH0 N Z +RATAPLAN R AA1 T AH0 P L AH0 N +RATAZIAEV R AE1 T AH0 Z IY1 EY1 V +RATCATCHER R AE1 T K AE2 CH ER0 +RATCATCHER'S R AE1 T K AE2 CH ER0 Z +RATCHAM R AE1 CH AH0 M +RATCHCALI R AE0 T CH K AA1 L IY0 +RATCHET R AE0 T CH AH0 T +RATCHET R AE1 CH AH0 T +RATCHETS R AE1 CH AH0 T S +RATCHETT R AE1 CH IH0 T +RATCLIFF R AE1 T K L IH0 F +RATCLIFFE R AE1 T K L IH0 F +RATCLIFFE'S R AE1 T K L IH0 F S +RATE R EY1 T +RATEABLE R AE1 T AH0 B AH0 L +RATEAU R AE0 T OW1 +RATED R EY1 T AH0 D +RATED R EY1 T IH0 D +RATEL R AE1 T AH0 L +RATEPAYER R EY1 T P EY2 ER0 +RATEPAYERS R EY1 T P EY2 ER0 Z +RATER R EY1 T ER0 +RATERS R EY1 T ER0 Z +RATES R EY1 T S +RATH R AE1 TH +RATH'S R AE1 TH S +RATHA R AE1 TH AH0 +RATHAH R AA1 T AH2 +RATHAUS R AE1 T HH AW2 S +RATHBONE R AE1 TH B OW2 N +RATHBONE'S R AE1 TH B OW2 N Z +RATHBORNE R AE1 TH B AO2 R N +RATHBUN R AE1 TH B AH0 N +RATHBURN R AE1 TH B ER0 N +RATHBURN'S R AE1 TH B ER0 N Z +RATHBURY R AE1 TH B ER0 IY0 +RATHBY R AE1 TH B IY0 +RATHCLARE R AE1 TH K L EH1 R +RATHDALE R AE1 TH D EY2 L +RATHE R EY1 DH +RATHEE R AE1 TH IY1 +RATHEES R AE1 TH IY0 Z +RATHENAU R AE1 TH IH0 N OW1 +RATHENOW R AE1 TH AH0 N OW0 +RATHER R AE1 DH ER0 +RATHER R AH1 DH ER0 +RATHER'N R AE1 DH ER0 AH0 N +RATHERBY R AE1 DH ER0 B IY0 +RATHHAUS R AE1 TH HH AW2 S +RATHIE R AE1 TH IY0 +RATHIE'S R AE1 TH IY0 Z +RATHILLET R AE1 TH IY0 L EH1 T +RATHLIN R AE1 TH L IH0 N +RATHMINES R AE1 TH M AH0 N Z +RATHMORE R AE1 TH M AO2 R +RATHMULLAN R AE1 TH M AH1 L AH0 N +RATHOLE R AE1 T HH OW2 L +RATHS R AE1 TH S +RATHSHERR R AE1 TH SH EH1 R +RATHSKELLER R AE1 TH S K EH2 L ER0 +RATI R AA1 T IY0 +RATIBOR R AE1 T IH0 B ER0 +RATICHON R AE1 T IH0 CH AH0 N +RATIE R EY1 T IY0 +RATIFIA R AE1 T AH0 F AY2 AH0 +RATIFICATION R AE2 T AH0 F AH0 K EY1 SH AH0 N +RATIFICATIONS R AE2 T AH0 F AH0 K EY1 SH AH0 N Z +RATIFIED R AE1 T AH0 F AY2 D +RATIFIES R AE1 T AH0 F AY2 Z +RATIFY R AE1 T AH0 F AY2 +RATIFYING R AE1 T AH0 F AY2 IH0 NG +RATIGNOLLE R EY1 SH IY0 N Y OW1 L +RATIN R AE1 T IH0 N +RATING R EY1 T IH0 NG +RATINGA R AA0 T IY1 NG G AH0 +RATINGS R EY1 T IH0 NG Z +RATIO R EY1 SH IY0 OW2 +RATIOCINATION R EY1 SH IY0 OW0 S AH0 N EY1 SH AH0 N +RATIOCINATIONS R EY1 SH IY0 OW0 S AH0 N EY1 SH AH0 N Z +RATIOCINATIVE R EY1 SH IY0 OW0 S AH0 N AH0 T IH0 V +RATION R AE1 SH AH0 N +RATION R EY1 SH AH0 N +RATIONAL R AE1 SH AH0 N AH0 L +RATIONAL R AE1 SH N AH0 L +RATIONALE R AE2 SH AH0 N AE1 L +RATIONALIS R AE1 SH N AH0 L AH0 S +RATIONALISE R AE1 SH N AH0 L AY0 Z +RATIONALISED R AE1 SH N AH0 L AH0 S T +RATIONALISING R AE1 SH N AH0 L AH0 S IH0 NG +RATIONALISM R AE1 SH AH0 N AH0 L IH2 Z AH0 M +RATIONALIST R AE1 SH N AH0 L IH0 S T +RATIONALISTIC R AE1 SH N AH0 L IH1 S T IH0 K +RATIONALISTS R AE1 SH N AH0 L IH0 S T S +RATIONALITY R AE2 SH AH0 N AE1 L IH0 T IY0 +RATIONALIZATION R AE2 SH AH0 N AH0 L IH0 Z EY1 SH AH0 N +RATIONALIZATION R AE2 SH N AH0 L IH0 Z EY1 SH AH0 N +RATIONALIZE R AE1 SH AH0 N AH0 L AY2 Z +RATIONALIZED R AE1 SH AH0 N AH0 L AY2 Z D +RATIONALIZING R AE1 SH AH0 N AH0 L AY2 Z IH0 NG +RATIONALLY R AE1 SH AH0 N AH0 L IY0 +RATIONALLY R AE1 SH N AH0 L IY0 +RATIONALS R AE1 SH N AH0 L Z +RATIONE R AE1 SH N +RATIONED R AE1 SH AH0 N D +RATIONED R EY1 SH AH0 N D +RATIONEM R AE1 SH N AH0 M +RATIONING R AE1 SH AH0 N IH0 NG +RATIONING R AE1 SH N IH0 NG +RATIONING R EY1 SH AH0 N IH0 NG +RATIONIS R AE1 SH AH0 N IH0 S +RATIONS R AE1 SH AH0 N Z +RATIOS R EY1 SH IY0 OW2 Z +RATISBON R AH0 T IH1 Z B AH0 N +RATISBONNE R AE1 T IH0 S B AH0 N +RATLER R AE1 T L ER0 +RATLIKE R AE1 T L AY2 K +RATLIN R AE1 T L IH0 N +RATLINE R AE1 T L AY2 N +RATLINES R AE1 T L AY2 N Z +RATLINS R AE1 T L IH0 N Z +RATMIROV R AE1 T M IH1 R AA0 V +RATNA R AE1 T N AH0 +RATNAPOORA R AE1 T N AE2 P AO1 R AH0 +RATNAPURA R AE1 T N AE2 P UH1 R AH0 +RATON R AH0 T OW1 N +RATONA R AA0 T OW1 N AH0 +RATONEAU R AE1 T AH0 N OW0 +RATONEAU'S R AE1 T AH0 N OW0 Z +RATS R AE1 T S +RATSBANE R AE1 T S B EY2 N +RATSEY R AE1 T S IY0 +RATTAM R AE1 T AH0 M +RATTAN R AE0 T AE1 N +RATTANS R AE1 T AH0 N Z +RATTAZZI R AE1 T AH0 Z IY0 +RATTED R AE1 T IH0 D +RATTEN R AE1 T AH0 N +RATTENBURY R AE1 T AH0 N B ER0 IY0 +RATTENING R AE1 T N IH0 NG +RATTER R AE1 T ER0 +RATTING R AE1 T IH0 NG +RATTLE R AE1 T AH0 L +RATTLED R AE1 T AH0 L D +RATTLER R AE1 T AH0 L ER0 +RATTLER R AE1 T L ER0 +RATTLER'S R AE1 T L ER0 Z +RATTLERS R AE1 T AH0 L ER0 Z +RATTLES R AE1 T AH0 L Z +RATTLESNAKE R AE1 T AH0 L S N EY2 K +RATTLESNAKE'S R AE1 T AH0 L S N EY2 K S +RATTLESNAKES R AE1 T AH0 L S N EY2 K S +RATTLETON R AE1 T AH0 L T AH0 N +RATTLETON'S R AE1 T AH0 L T AH0 N Z +RATTLETRAP R AE1 T AH0 L T R AE2 P +RATTLETRAPS R AE1 T AH0 L T R AE2 P S +RATTLETY R AE1 T AH0 L T IY0 +RATTLIN R AE1 T L IH0 N +RATTLING R AE1 T AH0 L IH0 NG +RATTLING R AE1 T L IH0 NG +RATTLINGER R AE1 T AH0 L IH0 NG ER0 +RATTLINGS R AE1 T AH0 L IH0 NG Z +RATTLINS R AE1 T L IH0 N Z +RATTLY R AE1 T L IY0 +RATTO R AE1 T OW0 +RATTRAY R AE1 T R EY0 +RATTRAY'S R AE1 T R EY0 Z +RATTY R AE1 T IY0 +RATTY'S R AE1 T IY0 Z +RATURA R AH0 T UH1 R AH0 +RATURANS R AE1 CH ER0 AH0 N Z +RATZ R AE1 T S +RAU R AW1 +RAUB R AO1 B +RAUCH R AO1 CH +RAUCHAD R AO1 CH AA1 D +RAUCHEN R AW1 K AH0 N +RAUCHHAUPT R AO1 CH AW0 P T +RAUCHING R AO1 CH IH0 NG +RAUCOURT R AO1 K AO2 R T +RAUCOUS R AO1 K AH0 S +RAUCOUSLY R AO1 K AH0 S L IY0 +RAUD R AO1 D +RAUDAL R AO1 D AH0 L +RAUDALES R AO1 D EY2 L Z +RAUDNITZ R AW1 D N IH0 T S +RAUGHT R AO1 T +RAULECOURT R AA0 UW1 L IH0 K AO2 R T +RAUM R AO1 M +RAUMER R AW1 M ER0 +RAUNE R AO1 N +RAUPARAHA R AO1 P AA1 R AH0 HH AA0 +RAUS R AW1 S +RAUSCH R AW1 SH +RAUT R AW1 T +RAUTENKRON R AW1 T AH0 N K R AH0 N +RAUTHGUNDIS R AW1 TH G AH2 N D IH0 S +RAUTSCHIN R AW1 CH IH0 N +RAV R AA2 V +RAVA R AH0 V AH0 +RAVAGE R AE1 V IH0 JH +RAVAGED R AE1 V IH0 JH D 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IY0 +REFUGEED R EH1 F Y UW0 JH IY0 D +REFUGEES R EH1 F Y UW2 JH IY0 Z +REFUGES R EH1 F Y UW0 JH IH0 Z +REFUGIES R EH1 F Y UW0 JH IY0 Z +REFUGIO R AH0 F Y UW1 JH IY0 OW0 +REFUGIUM R EH1 F Y UW0 JH IY0 AH0 M +REFULGENCE R IH0 F AH1 L JH AH0 N S +REFULGENT R IH0 F AH1 L JH AH0 N T +REFUND R IH0 F AH1 N D +REFUND R IY1 F AH2 N D +REFUNDED R IH0 F AH1 N D IH0 D +REFUNDING R IH0 F AH1 N D IH0 NG +REFURBISHED R IY0 F ER1 B IH0 SH T +REFURBISHING R IY0 F ER1 B IH0 SH IH0 NG +REFURNISH R IY0 F ER1 N IH0 SH +REFURNISHED R IY0 F ER1 N IH0 SH T +REFURNISHING R IY0 F ER1 N IH0 SH IH0 NG +REFUS'D R EH1 F Y UW2 Z D +REFUSAL R AH0 F Y UW1 Z AH0 L +REFUSAL R IH0 F Y UW1 Z AH0 L +REFUSALS R IH0 F Y UW1 Z AH0 L Z +REFUSE R AH0 F Y UW1 Z +REFUSE R EH1 F Y UW2 Z +REFUSE R IH0 F Y UW1 Z +REFUSED R AH0 F Y UW1 Z D +REFUSED R IH0 F Y UW1 Z D +REFUSER R IH0 F Y UW1 Z ER0 +REFUSERS R IH0 F Y UW1 Z ER0 Z +REFUSES R AH0 F Y UW1 Z AH0 Z +REFUSES R IH0 F Y UW1 Z IH0 Z +REFUSEST R IH0 F Y UW1 Z AH0 S T +REFUSETH R 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AH0 L D IH0 NG +REGILLUS R EH1 JH AH0 L AH0 S +REGIMBART R EH1 JH AH0 M B AA2 R T +REGIME R AH0 ZH IY1 M +REGIME R EY0 ZH IY1 M +REGIMEN R EH1 JH AH0 M AH0 N +REGIMENT R EH1 JH AH0 M AH0 N T +REGIMENT'S R EH1 JH AH0 M AH0 N T S +REGIMENTAL R EH2 JH AH0 M EH1 N T AH0 L +REGIMENTALS R EH2 JH AH0 M EH1 N AH0 L Z +REGIMENTATION R EH2 JH AH0 M EH0 N T EY1 SH AH0 N +REGIMENTED R EH1 JH AH0 M EH2 N T IH0 D +REGIMENTING R EH1 JH AH0 M EH2 N T IH0 NG +REGIMENTS R EH1 JH AH0 M AH0 N T S +REGIMES R AH0 ZH IY1 M Z +REGIMES R EY0 ZH IY1 M Z +REGIN R EH1 JH AH0 N +REGINA R IH0 JH IY1 N AH0 +REGINA'S R IH0 JH IY1 N AH0 Z +REGINAE R EH1 JH AH0 N EY0 +REGINALD R EH1 JH AH0 N AH0 L D +REGINALD'S R EH1 JH AH0 N AH0 L D Z +REGINCHEN R EH1 JH AH0 N K AH0 N +REGINCHEN'S R EH1 JH AH0 N CH AH0 N Z +REGINE R EH1 JH AH0 N +REGIO R IY1 JH IY0 OW0 +REGIOMONTANUS R IY1 JH AH0 M AA0 N T EY1 N IH0 S +REGION R IY1 JH AH0 N +REGION'S R IY1 JH AH0 N Z +REGIONAL R IY1 JH AH0 N AH0 L +REGIONIC R IY1 G IY0 AA1 N IH0 K 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+REGULA R EH0 G UW1 L AH0 +REGULAR R EH1 G Y AH0 L ER0 +REGULAR R EY1 G Y AH0 L ER0 +REGULARISE R EH1 G Y AH0 L EH1 R AH0 S +REGULARISED R EH1 G Y AH0 L EH1 R AH0 S T +REGULARITIES R EH1 G Y AH0 L EH1 R AH0 T IY0 Z +REGULARITY R EH2 G Y AH0 L EH1 R AH0 T IY0 +REGULARIZE R EH1 G Y AH0 L ER0 AY2 Z +REGULARIZED R EH1 G Y AH0 L ER0 AY2 Z D +REGULARIZING R EH1 G Y AH0 L ER0 AY2 Z IH0 NG +REGULARLY R EH1 G Y AH0 L ER0 L IY0 +REGULARS R EH1 G Y AH0 L ER0 Z +REGULATE R EH1 G Y AH0 L EY2 T +REGULATED R EH1 G Y AH0 L EY2 T AH0 D +REGULATED R EH1 G Y AH0 L EY2 T IH0 D +REGULATES R EH1 G Y AH0 L EY2 T S +REGULATING R EH1 G Y AH0 L EY2 T IH0 NG +REGULATION R EH2 G Y AH0 L EY1 SH AH0 N +REGULATIONS R EH2 G Y AH0 L EY1 SH AH0 N Z +REGULATIVE R EH1 G Y AH0 L EY2 T IH0 V +REGULATOR R EH1 G Y AH0 L EY2 T ER0 +REGULATORS R EH1 G Y AH0 L EY2 T ER0 Z +REGULATORY R EH1 G Y AH0 L AH0 T AO2 R IY0 +REGULES R EH1 G Y AH0 L Z +REGULUS R EH1 G Y AH0 L AH0 S +REGULUS'S R EH1 G Y AH0 L AH0 S IH0 Z +REGUM R EH1 G Y AH0 M +REGURGITATION R IH2 G ER1 JH AH0 T EY1 SH AH0 N +REGY R EH2 JH IY0 +REH R EH1 +REHAB R IY0 HH AE0 B +REHABILITATE R IY2 AH0 B IH1 L AH0 T EY2 T +REHABILITATE R IY2 HH AH0 B IH1 L AH0 T EY2 T +REHABILITATED R IY2 AH0 B IH1 L AH0 T EY2 T IH0 D +REHABILITATED R IY2 HH AH0 B IH1 L AH0 T EY2 T IH0 D +REHABILITATING R IY2 AH0 B IH1 L AH0 T EY2 T IH0 NG +REHABILITATING R IY2 HH AH0 B IH1 L AH0 T EY2 T IH0 NG +REHABILITATION R IY2 AH0 B IH2 L AH0 T EY1 SH AH0 N +REHABILITATION R IY2 HH AH0 B IH2 L AH0 T EY1 SH AH0 N +REHAN R EH1 HH AH0 N +REHASH R IY0 HH AE1 SH +REHASHED R IY0 HH AE1 SH T +REHBOCK R EH1 B AA2 K +REHEARING R IY0 HH IY1 R IH0 NG +REHEARSAL R IH0 HH ER1 S AH0 L +REHEARSAL R IY0 HH ER1 S AH0 L +REHEARSALS R IH0 HH ER1 S AH0 L Z +REHEARSE R IY0 HH ER1 S +REHEARSED R IY0 HH ER1 S T +REHEARSES R IY0 HH ER1 S IH0 Z +REHEARSETH R IY0 HH ER1 S IH0 TH +REHEARSIN R IY0 HH ER1 S IH0 N +REHEARSING R IH0 HH ER1 S IH0 NG +REHEAT R IY0 HH IY1 T +REHEATED R IY0 HH IY1 T IH0 D +REHEATING R IY0 HH IY1 T IH0 NG +REHNHJELM R EH1 N HH Y EH1 L M +REHOBOAM R IY0 HH AH0 B OW1 AH0 M +REHOBOTH R IY0 HH AA1 B AH0 TH +REHOISTED R EH1 HH OY1 S T IH0 D +REHU R IY0 HH UW0 +REHUNG R IY0 HH AH0 NG +REI R EY1 +REICH R AY1 K +REICHA R AY1 K AH0 +REICHARDT R AY1 K AA0 R T +REICHENAU R AY1 K IH0 N OW1 +REICHENBACH R AY1 K AH0 N B AA2 K +REICHENBERG R AY1 K AH0 N B ER0 G +REICHERT R AY1 K ER0 T +REICHSRATH R AY1 K S R AH0 TH +REICHSTADT R AY1 K S T AE0 T +REICHSTAG R AY1 K S T AE0 G +REICHT R AY1 K T +REID R IY1 D +REID'S R IY1 D Z +REIDESEL R IY1 D Z AH0 L +REIDIE R IY1 D IY0 +REIDMAR R IY1 D M AA0 R +REIDS R IY1 D Z +REIF R IY1 F +REIFENSTEIN R AY1 F AH0 N S T AY2 N +REIFFERSCHEID R AY1 F ER0 SH AY2 D +REIGART R IY1 G AA0 R T +REIGATE R IY0 IH0 G EY2 T +REIGELHEIMER'S R AY1 G AH0 L HH AY2 M ER0 Z +REIGHT R EY1 T +REIGN R EY1 N +REIGN'D R EY1 N D +REIGN'ST R EY1 N S T +REIGNED R EY1 N D +REIGNEST R IY0 IH0 G N AH0 S T +REIGNETH R IY0 IH0 G N IH0 TH +REIGNING R EY1 N IH0 NG +REIGNS R EY1 N Z +REIJER R IY1 JH ER0 +REIKJAVIK R EY1 K Y AH0 V IH0 K +REILLAGHAN R AY1 L AH0 G AH0 N +REILLAGHAN'S R AY1 L AH0 G AH0 N Z +REILLE R AY1 L +REILLY R AY1 L IY0 +REILLY'S R AY1 L IY0 Z +REIM R IY1 M +REIMANN R AY1 M AH0 N +REIMARUS R AY1 M AA0 R AH0 S +REIMBURSE R IY2 IH0 M B ER1 S +REIMBURSED R IY2 IH0 M B ER1 S T +REIMBURSEMENT R IY2 IH0 M B ER1 S M AH0 N T +REIMBURSING R IY2 IH0 M B ER1 S IH0 NG +REIMERS R AY1 M ER0 Z +REIMS R IY1 M Z +REIN R EY1 N +REIN'D R EY1 N D +REINA R EY0 IY1 N AH0 +REINACH R AY1 N AH0 CH +REINALDO R EY2 N AA1 L D OW0 +REINAULT R AY1 N AW0 L T +REINBERG R AY1 N B ER0 G +REINCARNATE R IY2 IH0 N K AA1 R N EY2 T +REINCARNATED R IY2 IH0 N K AA1 R N EY2 T IH0 D +REINCARNATING R IY2 IH0 N K AA1 R N EY2 T IH0 NG +REINCARNATION R IY0 IH0 N K AA0 R N EY1 SH AH0 N +REINCARNATIONS R IY0 IH0 N K AA0 R N EY1 SH AH0 N Z +REINDEER R EY1 N D IH2 R +REINDEER'S R EY1 N D IH2 R Z +REINDEERS R EY1 N D IH2 R Z +REINE R EY1 N +REINE'S R EY1 N Z +REINECKE R AY1 N IH0 K IY0 +REINECKE'S R AY1 N IH0 K IY0 Z +REINECOURT R AY1 N IH0 K AO0 R T +REINED R EY1 N D +REINEKE R AY1 N IH0 K +REINEN R AY1 N AH0 N +REINES R EY1 N Z +REINET R AY1 N AH0 T +REINFORCE R IY2 IH0 N F AO1 R S +REINFORCED R IY2 IH0 N F AO1 R S T +REINFORCEMENT R IY2 IH0 N F AO1 R S M AH0 N T +REINFORCEMENTS R IY2 IH0 N F AO1 R S M AH0 N T S +REINFORCES R IY2 IH0 N F AO1 R S IH0 Z +REINFORCING R IY2 IH0 N F AO1 R S IH0 NG +REINGELDER R AY1 NG G IH0 L D ER0 +REINHARD R AY1 N HH AA2 R D +REINHARD'S R AY1 N HH AA2 R D Z +REINHARDT R AY1 N HH AA2 R T +REINHART R AY1 N HH AA2 R T +REINHART'S R AY1 N HH AA0 R T S +REINHEIT R AY1 N HH AY2 T +REINHOLD R AY1 N HH OW0 L T +REINHOLD'S R AY1 N HH OW2 L D Z +REINING R EY1 N IH0 NG +REINKEN R AY1 NG K AH0 N +REINLESS R AY1 N L AH0 S +REINO R AY1 N OW0 +REINOLD R AY1 N OW0 L D +REINS R EY1 N Z +REINSERTED R IY2 IH0 N S ER1 T IH0 D +REINSFELD R AY1 N Z F EH2 L D +REINSPIRED R IY0 IH0 N S P AY1 ER0 D +REINSTALLED R IY2 IH0 N S T AA1 L D +REINSTATE R IY2 IH0 N S T EY1 T +REINSTATED R IY2 IH0 N S T EY1 T IH0 D +REINSTATEMENT R IY2 IH0 N S T EY1 T M AH0 N T +REINSTATES R IY2 IH0 N S T EY1 T S +REINSTATING R IY2 IH0 N S T EY1 T IH0 NG +REINSTROM R IY2 IH0 N S T R AH0 M +REINSURANCE R IY2 IH0 N SH UH1 R AH0 N S +REINTEGRATION R IY0 IH1 N T AH0 G R EY2 SH AH0 N +REINTERPRETED R IY0 IH0 N T ER1 P R AH0 T IH0 D +REINTERRED R IY0 IH0 N T ER1 D +REINTRODUCE R IY0 IH0 N T R AH0 D UW1 S +REINTRODUCED R IY0 IH0 N T R AH0 D UW1 S T +REINTRODUCING R IY0 IH0 N T R AH0 D UW1 S IH0 NG +REINTRODUCTION R IY0 IH0 N T R AH0 D AH1 K SH AH0 N +REINVEST R IY2 IH0 N V EH1 S T +REINVESTED R IY2 IH0 N V EH1 S T IH0 D +REINVESTMENT R IY2 IH0 N V EH1 S M AH0 N T +REINVESTMENT R IY2 IH0 N V EH1 S T M AH0 N T +REINVIGORATE R IY2 IH0 N V IH1 G ER0 EY2 T +REINVIGORATED R IY2 IH0 N V IH1 G ER0 EY2 T IH0 D +REINVIGORATING R IY2 IH0 N V IH1 G ER0 EY2 T IH0 NG +REINVIGORATION R IY0 IH0 N V IH2 G ER0 EY1 SH AH0 N +REIPUBLICAE R IY1 P AH1 B L IH0 K EY1 +REIS R IY1 Z +REISACHER R AY1 S AE2 K ER0 +REISCHACH R AY1 SH AA2 CH +REISE R IY1 S +REISEBILDER R AY1 S AH0 B AY1 L D ER0 +REISEN R AY1 S AH0 N +REISENAUER R AY1 S IH0 N AW0 ER0 +REISENBURG R AY1 S AH0 N B ER0 G +REISING R AY1 S IH0 NG +REISS R AY1 S +REISSMAN R AY1 S M AH0 N +REISSOUSE R AY1 S AW1 S +REISSUE R IY0 IH1 SH UW0 +REISSUED R IY0 IH1 SH UW0 D +REISZ R AY1 SH +REIT R AY1 T +REITAN R AY1 T AH0 N +REITBERG R AY1 T B ER0 G +REITBOK R AY1 T B AA0 K +REITER R AY1 T ER0 +REITER'S R IY0 IH1 T ER0 Z +REITERANT R IY0 IH1 T ER0 AH0 N T +REITERATE R IY0 IH1 T ER0 EY2 T +REITERATED R IY0 IH1 T ER0 EY2 T IH0 D +REITERATES R IY0 IH1 T ER0 EY2 S +REITERATING R IY0 IH1 T ER0 EY2 T IH0 NG +REITERATION R IY0 IH2 T ER0 EY1 SH AH0 N +REITERATIONS R IY0 IH1 T ER0 EY2 SH AH0 N Z +REITERATIVE R IY0 IH1 T ER0 EY2 T IH0 V +REITERS R AY1 T ER0 Z +REITH R IY1 TH +REITZ R AY1 T S +REITZENSTEIN R AY1 T S AH0 N S T AY2 N +REIVE R IY0 IH0 V +REIVER R AY1 V ER0 +REIVERS R AY1 V ER0 Z +REIVING R AY1 V IH0 NG +REJ R IH0 JH +REJA R IY1 JH AH0 +REJANE R IY0 JH EY0 N +REJANG R IH0 JH AE1 NG +REJEB R IY0 JH EH1 B +REJEB'S R IY0 JH EH1 B Z +REJECT R IH0 JH EH1 K T +REJECT R IY1 JH EH0 K T +REJECTED R IH0 JH EH1 K T IH0 D +REJECTED R IY0 JH EH1 K T AH0 D +REJECTED R IY0 JH EH1 K T IH0 D +REJECTERS R IH0 JH EH1 K T ER0 Z +REJECTETH R IH0 JH EH1 K T IH0 TH +REJECTING R IH0 JH EH1 K T IH0 NG +REJECTING R IY0 JH EH1 K T IH0 NG +REJECTION R IH0 JH EH1 K SH AH0 N +REJECTION R IY0 JH EH1 K SH AH0 N +REJECTIONS R IH0 JH EH1 K SH AH0 N Z +REJECTIONS R IY0 JH EH1 K SH AH0 N Z +REJECTORS R IH0 JH EH1 K T ER0 Z +REJECTS R IH0 JH EH1 K S +REJECTS R IH0 JH EH1 K T S +REJECTS R IY1 JH EH0 K S +REJECTS R IY1 JH EH0 K T S +REJJY R IY0 JH JH IY0 +REJOICE R IH0 JH OY1 S +REJOICED R IH0 JH OY1 S T +REJOICEFUL R IH0 JH OY1 S F AH0 L +REJOICERS R IH0 JH OY1 S ER0 Z +REJOICES R IH0 JH OY1 S IH0 Z +REJOICEST R IH0 JH OY1 S AH0 S T +REJOICETH R IH0 JH OY1 S IH0 TH +REJOICIN R IH0 JH OY1 S IH0 N +REJOICING R IH0 JH OY1 S IH0 NG +REJOICINGLY R 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R IH0 M AA1 R K AH0 B AH0 L +REMARKABLE R IY0 M AA1 R K AH0 B AH0 L +REMARKABLY R IH0 M AA1 R K AH0 B L IY0 +REMARKABLY R IY0 M AA1 R K AH0 B L IY0 +REMARKED R IH0 M AA1 R K T +REMARKED R IY0 M AA1 R K T +REMARKIN R IY0 M AA1 R K IH0 N +REMARKING R IH0 M AA1 R K IH0 NG +REMARKS R IH0 M AA1 R K S +REMARKS R IY0 M AA1 R K S +REMARQUABLE R IH0 M AA1 R K W AH0 B AH0 L +REMARQUE R IH0 M AA1 R K +REMARRIAGE R IY0 M EH1 R IH0 JH +REMARRIED R IY0 M EH1 R IY0 D +REMARRY R IY0 M EH1 R IY0 +REMARRYING R IY0 M EH1 R IY0 IH0 NG +REMATE R IY0 M EY2 T +REMBAS R EH1 M B AH0 Z +REMBOLD R EH1 M B OW2 L D +REMBRANDT R EH1 M B R AE2 N T +REMBRANDT'S R EH1 M B R AE2 N T S +REMBRANDTESQUE R EH1 M B R AE2 N T EH1 S K +REMBRANDTS R EH1 M B R AE2 N T S +REMBWE R EH1 M B W IY0 +REMEDE R IH0 M IY2 D +REMEDIABLE R IY0 M IY1 D IY0 AH0 B AH0 L +REMEDIAL R IH0 M IY1 D IY0 AH0 L +REMEDIE R EH1 M AH0 D IY0 +REMEDIED R EH1 M AH0 D IY0 D +REMEDIES R EH1 M AH0 D IY0 Z +REMEDILESS R IH0 M IY2 D IY0 L AH0 S +REMEDIOS R IH0 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EH2 M AH0 N IH1 S AH0 N S IH0 Z +REMINISCENT R EH2 M AH0 N IH1 S AH0 N T +REMINISCENTIAL R EH2 M AH0 N IH1 S EH1 N CH AH0 L +REMINISCENTLY R EH2 M AH0 N IH1 S AH0 N T L IY0 +REMINISCING R EH2 M AH0 N IH1 S IH0 NG +REMINITSKY'S R EH2 M AH0 N IH1 T S K IY0 Z +REMIREMONT R IY0 M AY1 R M AA2 N T +REMIS R EY1 M IY0 Z +REMISE R EH1 M IH0 S +REMISS R IY0 M IH1 S +REMISSION R IY0 M IH1 SH AH0 N +REMISSIONS R IY0 M IH1 SH AH0 N Z +REMISSNESS R IY0 M IH1 S N AH0 S +REMIT R IY0 M IH1 T +REMITS R IY0 M IH1 T S +REMITTANCE R AH0 M IH1 T AH0 N S +REMITTANCE R IY0 M IH1 T AH0 N S +REMITTANCES R AH0 M IH1 T AH0 N S IH0 Z +REMITTANCES R IY0 M IH1 T AH0 N S IH0 Z +REMITTED R IY0 M IH1 T IH0 D +REMITTENT R IY0 M IH1 T AH0 N T +REMITTING R IY0 M IH1 T IH0 NG +REMM R EH1 M +REMNANT R EH1 M N AH0 N T +REMNANTS R EH1 M N AH0 N T S +REMO R EH1 M OW0 +REMODEL R IY0 M AA1 D AH0 L +REMODELED R IY0 M AA1 D AH0 L D +REMODELING R IH0 M AA1 D AH0 L IH0 NG +REMODELING R IH0 M AA1 D L IH0 NG +REMODELLED R IY0 M AA1 D AH0 L D +REMODELLING R IY0 M AA1 D AH0 L IH0 NG +REMODELS R IY0 M AA1 D AH0 L Z +REMOLD R IY0 M OW1 L D +REMOLDED R IY0 M OW1 L D IH0 D +REMOND R EH1 M AH0 N D +REMONENCQ R EH1 M OW0 N EH1 NG K +REMONETIZATION R IY0 M AA1 N AH0 T IH0 Z EY1 SH AH0 N +REMONSTRANCE R EH1 M AH0 N S T R AH0 N S +REMONSTRANCES R EH1 M AH0 N S T R AH0 N S IH0 Z +REMONSTRANT R EH1 M AH0 N S T R AH0 N T +REMONSTRANTLY R EH1 M AH0 N S T R AH0 N T L IY0 +REMONSTRANTS R EH1 M AH0 N S T R AH0 N T S +REMONSTRATE R EH1 M AH0 N S T R EY2 T +REMONSTRATED R EH1 M AH0 N S T R EY2 T IH0 D +REMONSTRATES R EH1 M AH0 N S T R EY2 T S +REMONSTRATING R EH1 M AH0 N S T R EY2 T IH0 NG +REMONSTRATINGLY R EH1 M AH0 N S T R EY2 T IH0 NG L IY0 +REMONSTRATIVE R EH1 M AH0 N S T R AH0 T IH0 V +REMONSTRATIVELY R EH1 M AH0 N S T R AH0 T IH0 V L IY0 +REMORA R IH0 M AO1 R AH0 +REMORSE R IH0 M AO1 R S +REMORSEFUL R IH0 M AO1 R S F AH0 L +REMORSEFULLY R IH0 M AO1 R S F AH0 L IY0 +REMORSELESS R IH0 M AO1 R S L AH0 S +REMORSELESSLY R IH0 M AO1 R S L AH0 S L IY0 +REMORSELESSNESS R IH0 M AO1 R S L AH0 S N AH0 S +REMORSES R IH0 M AO1 R S AH0 Z +REMOTE R IH0 M OW1 T +REMOTE R IY0 M OW1 T +REMOTELY R IY0 M OW1 T L IY0 +REMOTENESS R IY0 M OW1 T N AH0 S +REMOTENESSES R IY0 M OW1 T N AH0 S IH0 Z +REMOTER R IY0 M OW1 T ER0 +REMOTEST R IH0 M OW1 T AH0 S T +REMOULD R IY0 M OW1 L D +REMOULDED R IY0 M OW1 L D IH0 D +REMOULDING R IY0 M OW1 L D IH0 NG +REMOUNT R IH0 M AW2 N T +REMOUNTED R IH0 M AW2 N T IH0 D +REMOUNTING R IH0 M AW2 N T IH0 NG +REMOUNTS R IH0 M AW2 N T S +REMOV'D R IY0 M UW1 V D +REMOVABLE R IH0 M UW1 V AH0 B AH0 L +REMOVAL R IH0 M UW1 V AH0 L +REMOVALS R IH0 M UW1 V AH0 L Z +REMOVE R IY0 M UW1 V +REMOVED R IY0 M UW1 V D +REMOVER R IH0 M UW1 V ER0 +REMOVERS R IY0 M UW1 V ER0 Z +REMOVES R IY0 M UW1 V Z +REMOVETH R IY0 M UW1 V IH0 TH +REMOVIN R IY0 M UW1 V IH0 N +REMOVING R IY0 M UW1 V IH0 NG +REMPLUME R EH1 M P L UW1 M +REMSEN R EH1 M S AH0 N +REMSEN'S R EH1 M S AH0 N Z +REMSON R EH1 M S AH0 N +REMUDA R IY0 M Y UW1 D AH0 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UW1 D +RENEWED R IY0 N UW1 D +RENEWEDLY R IY0 N UW1 D L IY0 +RENEWER R IY0 N UW1 ER0 +RENEWERS R IY0 N UW1 ER0 Z +RENEWING R IH0 N UW1 IH0 NG +RENEWS R IH0 N UW1 Z +RENFIELD R EH1 N F IY0 L D +RENFORD R EH1 N F ER0 D +RENFREW R EH1 N F R UW0 +RENFREW'S R EH1 N F R UW2 Z +RENFREWSHIRE R EH1 N F R UW0 SH AY2 R +RENGADE R EH1 NG G AH0 D +RENGETEGI R AH0 N G EH1 T AH0 JH AY0 +RENI R IY1 N IY0 +RENI'S R IY1 N IY0 Z +RENIE R IY1 N IY0 +RENIE'S R IY1 N IY0 Z +RENIER R IY1 N IY0 ER0 +RENIG R EH1 N IH0 G +RENINE R IY1 N AH0 N +RENINE'S R IY1 N AH0 N Z +RENMARK R EH0 N M AA2 R K +RENNEL R EH1 N AH0 L +RENNELL R EH1 N AH0 L +RENNENKAMPF R EH1 N IH0 N K AE0 M P F +RENNEPONT R EH1 N IH0 P AA2 N T +RENNEPONTS R EH1 N AH0 P AA1 N T S +RENNER R EH1 N ER0 +RENNES R EH1 N Z +RENNET R EH1 N AH0 T +RENNETT R EH1 N IH0 T +RENNEY R EH1 N IY0 +RENNICK R EH1 N IH0 K +RENNIE R EH1 N IY0 +RENNIE'S R EH1 N IY0 Z +RENNIES R EH1 N IY0 Z +RENNIKER R EH1 N IH0 K ER0 +RENNSCHMIDT R EH1 N SH M IH2 T +RENNSDALE R EH1 N Z D EY2 L +RENNULS R EH1 N UW1 L Z +RENNY R EH1 N IY0 +RENO R IY1 N OW0 +RENO'S R IY1 N OW0 Z +RENOIR R EH0 N W AA1 R +RENOMINATED R IY0 N AA1 M AH0 N EY2 T IH0 D +RENOMINATION R IY1 N AA2 M AH0 N EY1 SH AH0 N +RENOMME R EH1 N AH0 M +RENOMMEE R EH1 N AH0 M IY0 +RENOMMIST R EH1 N AH0 M AH0 S T +RENOUARD R AH0 N UW1 ER0 D +RENOUARD'S R AH0 N UW1 ER0 D Z +RENOUF R AH0 N UW1 F +RENOUF'S R AH0 N UW1 F S +RENOUNCE R IH0 N AW1 N S +RENOUNCED R IH0 N AW1 N S T +RENOUNCEMENT R IH0 N AW1 N S M AH0 N T +RENOUNCEMENTS R IH0 N AW1 N S M AH0 N T S +RENOUNCES R IH0 N AW1 N S IH0 Z +RENOUNCING R IH0 N AW1 N S IH0 NG +RENOUVIER R IH0 N UW2 V IY0 EY1 +RENOVALES R EH1 N AH0 V EY2 L Z +RENOVARE R EH1 N AH0 V EY1 R +RENOVATE R EH1 N AH0 V EY2 T +RENOVATED R EH1 N AH0 V EY2 T IH0 D +RENOVATES R EH1 N AH0 V EY2 T S +RENOVATING R EH1 N AH0 V EY2 T IH0 NG +RENOVATION R EH2 N AH0 V EY1 SH AH0 N +RENOVATIONS R EH1 N AH0 V EY2 SH AH0 N Z +RENOVATOR R EH1 N AH0 V EY2 T ER0 +RENOVATORS R EH1 N AH0 V EY2 T ER0 Z +RENOWN R IH0 N AW1 N +RENOWN'D R IH0 N AW1 N D +RENOWNED R IH0 N AW1 N D +RENS R EH1 N Z +RENSALAER R EH1 N S AA0 L AA1 ER0 +RENSBURG R EH1 N S B ER0 G +RENSDYR R EH1 N Z D IH0 R +RENSHAW R EH1 N SH AO2 +RENSHAW'S R EH1 N SH AO2 Z +RENSHAWE R EH1 N SH AO2 +RENSSELAER R EH2 N S AH0 L IH1 R +RENSSELAER'S R EH2 N S AH0 L IH1 R Z +RENSSELAERS R EH2 N S AH0 L IH1 R Z +RENSTOKE R EH1 N S T OW1 K +RENT R EH1 N T +RENT'S R EH1 N T S +RENTAL R EH1 N T AH0 L +RENTALS R EH1 N T AH0 L Z +RENTE R EH1 N T EY0 +RENTED R EH1 N T AH0 D +RENTED R EH1 N T IH0 D +RENTER R EH1 N T ER0 +RENTERIA R EH0 N T EH1 R IY0 AH0 +RENTERS R EH1 N T ER0 Z +RENTES R EH1 N T EH0 S +RENTHEIM R EH1 N T HH AY0 M +RENTIER R EH1 N T IY0 ER0 +RENTIERS R EH1 N T IH1 R Z +RENTIN R EH1 N T IH0 N +RENTING R EH1 N T IH0 NG +RENTON R EH1 N T AH0 N +RENTON'S R EH1 N T AH0 N Z +RENTOUL R EH1 N T UW1 L +RENTRE R EH1 N T ER0 +RENTREE R EH1 N T R IY2 +RENTS R EH1 N T S +RENTY R EH1 N T IY0 +RENTZEL R EH1 N T Z AH0 L +RENUMBERED R IH0 N AH1 M B ER0 D +RENUNCIATION R IH0 N AH2 N S IY0 EY1 SH AH0 N +RENUNCIATIONS R IH0 N AH2 N S IY0 EY1 SH AH0 N Z +RENVILLE R EH1 N V IH0 L +RENWICK R EH1 N W IH0 K +RENWICK'S R EH1 N W IH2 K S +RENZIE R EH1 N Z IY0 +RENZIE'S R EH1 N Z IY0 Z +RENZO R EH1 N Z OW0 +RENZOLLA R EH0 N Z OW1 L AH0 +REO R IY1 OW0 +REOCCUPATION R IY0 AA1 K Y AH0 P EY1 SH AH0 N +REOCCUPIED R IY0 AA1 K Y AH0 P AY2 D +REOCCUPY R IY0 AO1 K Y UW2 P AY0 +REOH R IY0 OW1 +REOLE R IY0 OW0 L +REOPEN R IY0 OW1 P AH0 N +REOPENED R IY0 OW1 P AH0 N D +REOPENING R IY0 OW1 P AH0 N IH0 NG +REOPENS R IY0 OW1 P AH0 N Z +REOR R IY0 AO1 R +REORGANISATION R IY0 AO1 R G AH0 N IH0 Z EY1 SH AH0 N +REORGANISE R IY0 AO1 R G AH0 N AH0 S +REORGANISED R IY0 AO1 R G AH0 N IH0 Z D +REORGANISING R IY0 AO1 R G AH0 N IH1 Z IH0 NG +REORGANIZATION R IY2 AO0 R G AH0 N AH0 Z EY1 SH AH0 N +REORGANIZATIONS R IY0 AO2 R G AH0 N IH0 Z EY1 SH AH0 N Z +REORGANIZE R IY0 AO1 R G AH0 N AY2 Z +REORGANIZED R IY0 AO1 R G AH0 N AY2 Z D +REORGANIZES R IY0 AO1 R G AH0 N AY2 Z IH0 Z +REORGANIZING R IY0 AO1 R G AH0 N AY2 Z IH0 NG +REORIENTATION R IY0 AO1 R IY0 EH0 N T EY1 SH AH0 N +REP R EH0 P R IY0 Z EH1 T AH0 T IH0 V +REP R EH1 P +REPACK R IY0 P AE1 K +REPACKED R IY0 P AE1 K T +REPACKING R IY2 P AE1 K IH0 NG +REPAID R IY0 P EY1 D +REPAINT R IY0 P EY1 N T +REPAINTED R IY0 P EY1 N T AH0 D +REPAINTING R IY0 P EY1 N T IH0 NG +REPAIR R IH0 P EH1 R +REPAIR'D R IH0 P EH1 R D +REPAIRE R IH0 P EH1 R +REPAIRED R IH0 P EH1 R D +REPAIRED R IY0 P EH1 R D +REPAIRER R IH0 P EH1 R ER0 +REPAIRERS R IH0 P EH1 R ER0 Z +REPAIRIN R IH0 P EH1 R IH0 N +REPAIRING R IH0 P EH1 R IH0 NG +REPAIRING R IY0 P EH1 R IH0 NG +REPAIRMAN R IH0 P EH1 R M AE2 N +REPAIRMAN R IY0 P EH1 R M AE2 N +REPAIRS R IH0 P EH1 R Z +REPAIRS R IY0 P EH1 R Z +REPAPERED R IY0 P EY1 P ER0 D +REPARABLE R EH1 P ER0 AH0 B AH0 L +REPARATA R EH2 P ER0 AA1 T AH0 +REPARATION R EH2 P ER0 EY1 SH AH0 N +REPARATIONS R EH2 P ER0 EY1 SH AH0 N Z +REPARATIVE R EH1 P R AH0 T IH0 V +REPARE R IY0 P EH1 R +REPARED R IY0 P EH1 R D +REPARTEE R EH2 P ER0 T IY1 +REPARTEES R EH2 P ER0 T IY1 Z +REPARTIMIENTO R EH2 P ER0 T IH1 M IY0 EH1 N T OW0 +REPARTIMIENTOS R EH2 P ER0 T IH1 M IY1 N T OW0 Z +REPARTITION R EH2 P ER0 T IH1 SH AH0 N +REPAS R IH0 P AA1 S +REPASS R IY0 P AE1 S +REPASSED R IY0 P AE1 S T +REPASSES R IY0 P AE1 S IH0 Z +REPASSING R IY0 P AE1 S IH0 NG +REPAST R IY1 P AE0 S T +REPASTS R IY0 P AE1 S +REPATE R IY0 P EY1 T +REPATRIATED R IY0 P EY1 T R IY0 EY2 T IH0 D +REPATRIATION R IY0 P EY2 T R IY0 EY1 SH AH0 N +REPATRIES R IY0 P EY1 T R IY0 Z +REPAY R IY0 P EY1 +REPAYING R IY0 P EY1 IH0 NG +REPAYMENT R IY0 P EY1 M AH0 N T +REPAYMENTS R IY0 P EY1 M AH0 N T S +REPAYS R IY2 P EY1 Z +REPEAL R IH0 P IY1 L +REPEAL R IY0 P IY1 L +REPEALED R IH0 P IY1 L D +REPEALED R IY0 P IY1 L D +REPEALER R IY0 P IY1 L ER0 +REPEALERS R IY0 P IY1 L ER0 Z +REPEALING R IY0 P IY1 L IH0 NG +REPEALS R IY0 P IY1 L Z +REPEAT R IH0 P IY1 T +REPEAT R IY0 P IY1 T +REPEATED R IH0 P IY1 T IH0 D +REPEATED R IY0 P IY1 T AH0 D +REPEATED R IY0 P IY1 T IH0 D +REPEATEDLY R IH0 P IY1 T IH0 D L IY0 +REPEATER R IH0 P IY1 T ER0 +REPEATERS R IH0 P IY1 T ER0 Z +REPEATIN R IH0 P IY1 T IH0 N +REPEATING R IH0 P IY1 T IH0 NG +REPEATING R IY0 P IY1 T IH0 NG +REPEATS R IH0 P IY1 T S +REPEATS R IY0 P IY1 T S +REPEETED R IH0 P IY1 T IH0 D +REPEL R IH0 P EH1 L +REPELL'D R IH0 P EH1 L D +REPELLANT R IH0 P EH1 L AH0 N T +REPELLED R AH0 P EH1 L D +REPELLED R IH0 P EH1 L D +REPELLENT R IH0 P EH1 L AH0 N T +REPELLENTLY R IH0 P EH1 L AH0 N T L IY0 +REPELLER R IH0 P EH1 L ER0 +REPELLERS R IH0 P EH1 L ER0 Z +REPELLIER R IH0 P EH1 L IY0 ER0 +REPELLIER'S R IH0 P EH1 L Y ER0 Z +REPELLING R AH0 P EH1 L IH0 NG +REPELLOR R IH0 P EH1 L ER0 +REPELS R IH0 P EH1 L Z +REPENS R EH2 P AH0 N Z +REPENT R IH0 P EH1 N T +REPENTANCE R IH0 P EH1 N T AH0 N S +REPENTANCES R IH0 P EH1 N T AH0 N S IH0 Z +REPENTANT R IH0 P EH1 N T AH0 N T +REPENTANTLY R IH0 P EH1 N T AH0 N T L IY0 +REPENTE R IH0 P EH1 N T EY0 +REPENTED R IH0 P EH1 N T IH0 D +REPENTENT R IH0 P EH1 N T AH0 N T +REPENTETH R IH0 P EH1 N T AH0 TH +REPENTIGNY R IH0 P EH1 N T AY0 N IY0 +REPENTIN R IH0 P EH1 N T IH0 N +REPENTINA R IH0 P EH1 N T AH0 N AH0 +REPENTING R IH0 P EH1 N T IH0 NG +REPENTS R IH0 P EH1 N T S +REPEOPLE R IY0 P IY2 P AH0 L +REPEOPLED R IY0 P IY2 P AH0 L D +REPEOPLING R IY0 P IY2 P AH0 L IH0 NG +REPERCUSSION R IY2 P ER0 K AH1 SH AH0 N +REPERCUSSION R IY2 P R AH0 K AH1 SH AH0 N +REPERCUSSIONS R IY2 P ER0 K AH1 SH AH0 N Z +REPERCUSSIONS R IY2 P R AH0 K AH1 SH AH0 N Z +REPERTOIRE R EH1 P ER0 T W AA2 R +REPERTORIES R EH1 P ER0 T AO2 R IY0 Z +REPERTORY R EH1 P ER0 T AO2 R IY0 +REPERUSAL R EH1 P ER0 UW1 Z AH0 L +REPERUSE R EH1 P ER0 UW1 Z +REPERUSED R EH1 P ER0 UW1 Z D +REPERUSING R EH1 P ER0 Y UW1 Z IH0 NG +REPETE R IY0 P IY0 T +REPETITA R EH2 P AH0 T IY1 T AH0 +REPETITION R EH2 P AH0 T IH1 SH AH0 N +REPETITIONS R EH2 P AH0 T IH1 SH AH0 N Z +REPETITIOUS R EH2 P AH0 T IH1 SH AH0 S +REPETITIVE R IH0 P EH1 T IH0 T IH0 V +REPETTO R EH0 P EH1 T OW0 +REPHAIM R IY0 P HH AY1 IH0 M +REPHAN R EH1 F AH0 N +REPHIDIM R IY0 F IY1 D IH0 M +REPINE R EH0 P IY1 N IY0 +REPINED R IH0 P IH1 N D +REPINES R EH0 P IY1 N Z +REPINGTON R IY2 P IH0 NG T AH0 N +REPINING R IY0 P AY1 N IH0 NG +REPININGS R IY0 P AY1 N IH0 NG Z +REPITATION R EH1 P IH0 T EY1 SH AH0 N +REPLACE ER0 P L EY1 S +REPLACE R IY2 P L EY1 S +REPLACEABLE R IY2 P L EY1 S AH0 B AH0 L +REPLACED R IY2 P L EY1 S T +REPLACEMENT R IH0 P L EY1 S M AH0 N T +REPLACEMENTS R IY0 P L EY1 S M AH0 N T S +REPLACERS R IH0 P L EY1 S ER0 Z +REPLACES R IH0 P L EY1 S IH0 Z +REPLACING R IH0 P L EY1 S IH0 NG +REPLANT R IY0 P L AE1 N T +REPLANTED R IY0 P L AE1 N IH0 D +REPLANTED R IY0 P L AE1 N T IH0 D +REPLANTING R IY0 P L AE1 N T IH0 NG +REPLENISH R IY0 P L EH1 N IH0 SH +REPLENISH'D R IY0 P L EH1 N IH0 SH D +REPLENISHED R IY0 P L EH1 N IH0 SH T +REPLENISHES R IY0 P L EH1 N IH0 SH IH0 Z +REPLENISHING R IY0 P L EH1 N IH0 SH IH0 NG +REPLENISHMENT R IH0 P L EH1 N IH0 SH M AH0 N T +REPLETE R IY0 P L IY1 T +REPLETED R IY0 P L IY1 T 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AH0 S K AE0 T +REQUIN R IH0 K W IH0 N +REQUIR'D R IY0 K W AY1 ER0 D +REQUIRE R IH0 K W AY1 ER0 +REQUIRE R IY0 K W AY1 R +REQUIRE R IY2 K W AY1 ER0 +REQUIRED R IY0 K W AY1 ER0 D +REQUIRED R IY0 K W AY1 R D +REQUIREMENT R IH0 K W AY1 R M AH0 N T +REQUIREMENTS R IH0 K W AY1 R M AH0 N T S +REQUIRES R IY0 K W AY1 ER0 Z +REQUIRES R IY0 K W AY1 R Z +REQUIREST R IY0 K W AY1 R AH0 S T +REQUIRETH R IY0 K W AY1 R IH0 TH +REQUIRING R IY0 K W AY1 ER0 IH0 NG +REQUIRING R IY0 K W AY1 R IH0 NG +REQUISITE R EH1 K W AH0 Z AH0 T +REQUISITES R EH1 K W AH0 Z AH0 T S +REQUISITION R EH2 K W AH0 Z IH1 SH AH0 N +REQUISITIONED R EH2 K W AH0 Z IH1 SH AH0 N D +REQUISITIONING R EH2 K W AH0 Z IH1 SH AH0 N IH0 NG +REQUISITIONS R EH2 K W AH0 Z IH1 SH AH0 N Z +REQUITAL R EH1 K W AH0 T AH0 L +REQUITE R IY0 K W AY1 T +REQUITED R IY0 K W AY1 T IH0 D +REQUITES R IY0 K W AY1 T S +REQUITING R IY0 K W AY1 T IH0 NG +RER R ER0 +RERE R IY1 R +REREAD R IY1 R IY1 D +REREADING R IY1 R IY1 D IH0 NG +RERECHILD R IY0 R EH0 CH AY2 L D 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Z +RESEARCHES R IY0 S ER1 CH IH0 Z +RESEARCHING R IY0 S ER1 CH IH0 NG +RESEAT R IY0 S IY1 T +RESEATED R IY0 S IY1 T IH0 D +RESEATING R IY0 S IY1 T IH0 NG +RESEATS R IY0 S IY1 T S +RESEDA R EH0 S EY1 D AH0 +RESELL R IY0 S EH1 L +RESELLING R IY0 S EH1 L IH0 NG +RESEMBLANCE R IH0 Z EH1 M B L AH0 N S +RESEMBLANCE R IY0 Z EH1 M B L AH0 N S +RESEMBLANCES R IY0 Z EH1 M B L AH0 N S AH0 Z +RESEMBLE R IH0 Z EH1 M B AH0 L +RESEMBLE R IY0 Z EH1 M B AH0 L +RESEMBLED R IH0 Z EH1 M B AH0 L D +RESEMBLED R IY0 Z EH1 M B AH0 L D +RESEMBLES R IH0 Z EH1 M B AH0 L Z +RESEMBLES R IY0 Z EH1 M B AH0 L Z +RESEMBLEST R IY0 Z EH1 M B AH0 L AH0 S T +RESEMBLETH R IY0 Z EH1 M B AH0 L TH +RESEMBLING R IH0 Z EH1 M B AH0 L IH0 NG +RESEMBLING R IY0 Z EH1 M B AH0 L IH0 NG +RESEMBLING R IY0 Z EH1 M B L IH0 NG +RESENT R IH0 Z EH1 N T +RESENT R IY0 Z EH1 N T +RESENTED R IY0 Z EH1 N AH0 D +RESENTED R IY0 Z EH1 N T AH0 D +RESENTFUL R IH0 Z EH1 N T F AH0 L +RESENTFULLY R IH0 Z EH1 N T F AH0 L IY0 +RESENTFULNESS R IH0 Z EH1 N T F AH0 L N AH0 S +RESENTING R IH0 Z EH1 N IH0 NG +RESENTING R IH0 Z EH1 N T IH0 NG +RESENTING R IY0 Z EH1 N IH0 NG +RESENTING R IY0 Z EH1 N T IH0 NG +RESENTMENT R IH0 Z EH1 N M AH0 N T +RESENTMENT R IH0 Z EH1 N T M AH0 N T +RESENTMENTS R IH0 Z EH1 N M AH0 N T S +RESENTMENTS R IH0 Z EH1 N T M AH0 N T S +RESENTS R IH0 Z EH1 N T S +RESERV'D R EH2 Z ER0 V D +RESERVATION R EH2 Z ER0 V EY1 SH AH0 N +RESERVATIONS R EH2 Z ER0 V EY1 SH AH0 N Z +RESERVE R IH0 Z ER1 V +RESERVE R IY0 Z ER1 V +RESERVED R IH0 Z ER1 V D +RESERVED R IY0 Z ER1 V D +RESERVEDLY R IH0 Z ER1 V AH0 D L IY0 +RESERVEDNESS R IH0 Z ER1 V AH0 D N AH0 S +RESERVES R IH0 Z ER1 V Z +RESERVES R IY0 Z ER1 V Z +RESERVING R IH0 Z ER1 V IH0 NG +RESERVING R IY0 Z ER1 V IH0 NG +RESERVIST R IH0 Z ER1 V IH0 S T +RESERVISTS R IH0 Z ER1 V IH0 S T S +RESERVOIR R EH1 Z AH0 V W AA2 R +RESERVOIR R EH1 Z ER0 V W AA2 R +RESERVOIRS R EH1 Z ER0 V W AA2 R Z +RESET R IY0 S EH1 T +RESET R IY1 S EH0 T +RESETTING R IY0 S EH1 T IH0 NG +RESETTLE R IY0 S EH1 T AH0 L +RESETTLED R IY0 S EH1 T AH0 L D +RESETTLEMENT R IY0 S EH1 T AH0 L M AH0 N T +RESETTLING R IY0 S EH1 T AH0 L IH0 NG +RESH R EH1 SH +RESHAPE R IY0 SH EY1 P +RESHAPED R IY0 SH EY1 P T +RESHAPING R IY0 SH EY1 P IH0 NG +RESHID R IY0 SH IY1 D +RESHIP R IY0 SH IH2 P +RESHIPPED R IY0 SH IH1 P T +RESHT R IY0 SH T +RESIDE R IH0 Z AY1 D +RESIDE R IY0 Z AY1 D +RESIDED R IH0 Z AY1 D IH0 D +RESIDENCE R EH1 Z IH0 D AH0 N S +RESIDENCES R EH1 Z IH0 D AH0 N S IH0 Z +RESIDENCIA R EH2 Z AH0 D EH1 N CH AH0 +RESIDENCIAS R EH1 Z IH0 D AH0 N S IY0 AH0 Z +RESIDENCIES R EH1 Z IH0 D EH2 N S IY0 Z +RESIDENCY R EH1 Z IH0 D AH0 N S IY0 +RESIDENT R EH1 Z IH0 D AH0 N T +RESIDENT'S R EH1 Z IH0 D AH0 N T S +RESIDENTER R EH1 Z IH0 D AH0 N T ER0 +RESIDENTERS R EH1 Z IH0 D AH0 N T ER0 Z +RESIDENTIAL R EH2 Z IH0 D EH1 N CH AH0 L +RESIDENTIARY R EH2 Z AH0 D EH1 N CH IY0 EH0 R IY0 +RESIDENTS R EH1 Z IH0 D AH0 N T S +RESIDENZ R EH2 Z AH0 D EH1 N Z +RESIDES R IH0 Z AY1 D Z +RESIDES R IY0 Z AY1 D Z +RESIDING R IH0 Z 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S +RESISTS R IY0 Z IH1 S T S +RESK R EH1 S K +RESKS R EH1 S K S +RESKY R EH1 S K IY0 +RESMITH R IY0 S M IH2 TH +RESNEL R EH1 S N AH0 L +RESOLD R IY0 S OW1 L D +RESOLED R IY0 S OW1 L D +RESOLUTE R EH1 Z AH0 L UW2 T +RESOLUTELY R EH1 S AH0 L UW2 T L IY0 +RESOLUTELY R EH1 Z AH0 L UW2 T L IY0 +RESOLUTENESS R EH1 Z AH0 L UW2 T N AH0 S +RESOLUTES R EH1 Z AH0 L UW2 T S +RESOLUTION R EH2 Z AH0 L UW1 SH AH0 N +RESOLUTION'S R EH2 Z AH0 L UW1 SH AH0 N Z +RESOLUTIONERS R EH2 Z AH0 L UW1 SH AH0 N ER0 Z +RESOLUTIONS R EH2 Z AH0 L UW1 SH AH0 N Z +RESOLV'D R IY0 Z AA1 L V D +RESOLVABLE R IY0 Z AA1 L V AH0 B AH0 L +RESOLVE R IY0 Z AA1 L V +RESOLVED R IY0 Z AA1 L V D +RESOLVES R IY0 Z AA1 L V Z +RESOLVING R IY0 Z AA1 L V IH0 NG +RESONANCE R EH1 Z AH0 N AH0 N S +RESONANCES R EH1 Z AH0 N AH0 N S IH0 Z +RESONANT R EH1 Z AH0 N AH0 N T +RESONANTLY R EH1 Z AH0 N AH0 N T L IY0 +RESONATING R EH1 Z AH0 N EY2 T IH0 NG +RESOOM R IY0 S UW0 M +RESORT R IH0 Z AO1 R T +RESORT R IY0 S AO1 R T +RESORT R IY0 Z AO1 R T +RESORTED R IH0 Z AO1 R T IH0 D +RESORTED R IY0 S AO1 R T IH0 D +RESORTED R IY0 Z AO1 R T IH0 D +RESORTERS R IY0 S AO1 R T ER0 Z +RESORTING R IH0 Z AO1 R T IH0 NG +RESORTING R IY0 S AO1 R T IH0 NG +RESORTING R IY0 Z AO1 R T IH0 NG +RESORTS R IH0 Z AO1 R T S +RESORTS R IY0 S AO1 R T S +RESORTS R IY0 Z AO1 R T S +RESOUGHT R IY0 S AO1 T +RESOUND R IY2 S AW1 N D +RESOUND R IY2 Z AW1 N D +RESOUNDED R IY0 S AW1 N D IH0 D +RESOUNDING R IY0 S AW1 N D IH0 NG +RESOUNDINGLY R IH0 Z AW1 N D IH0 NG L IY0 +RESOUNDS R IY0 S AW1 N D Z +RESOURCE R IY1 S AO0 R S +RESOURCEFUL R IY0 S AO1 R S F AH0 L +RESOURCEFULLY R IY0 S AO1 R S F AH0 L IY0 +RESOURCEFULNESS R IY0 S AO1 R S F AH0 L N AH0 S +RESOURCELESS R IY0 S AO1 R S L AH0 S +RESOURCES R IY1 S AO0 R S IH0 Z +RESPEC R IH0 S P EH1 K +RESPECK R IY0 S P EH2 K +RESPECKIT R EY1 S P IH0 K IH1 T +RESPECKS R AH0 S P EH1 K S +RESPECT R IH0 S P EH1 K T +RESPECT R IY0 S P EH1 K T +RESPECTABILITIES R IH0 S P EH1 K T AH0 B IH1 L AH0 T IY0 Z +RESPECTABILITY R IY0 S P EH2 K T AH0 B IH1 L IH0 T IY0 +RESPECTABLE R IH0 S P EH1 K T AH0 B AH0 L +RESPECTABLE R IY0 S P EH1 K T AH0 B AH0 L +RESPECTABLES R IH0 S P EH1 K T AH0 B AH0 L Z +RESPECTABLY R IY0 S P EH1 K T AH0 B L IY0 +RESPECTE R IH0 S P EH1 K T +RESPECTED R IH0 S P EH1 K T IH0 D +RESPECTED R IY0 S P EH1 K T AH0 D +RESPECTED R IY0 S P EH1 K T IH0 D +RESPECTER R IH0 S P EH1 K T ER0 +RESPECTERBLE R IH0 S P EH1 K T ER0 B AH0 L +RESPECTERS R IH0 S P EH1 K T ER0 Z +RESPECTETH R IH0 S P EH1 K T IH0 TH +RESPECTFUL R IH0 S P EH1 K T F AH0 L +RESPECTFULLY R IH0 S P EH1 K T F AH0 L IY0 +RESPECTFULNESS R IH0 S P EH1 K T F AH0 L N AH0 S +RESPECTIN R IH0 S P EH1 K T IH0 N +RESPECTING R IY0 S P EH1 K T IH0 NG +RESPECTIT R IH0 S P EH1 K T IH0 T +RESPECTIVE R IH0 S P EH1 K T IH0 V +RESPECTIVE R IY0 S P EH1 K T IH0 V +RESPECTIVELY R IH0 S P EH1 K T IH0 V L IY0 +RESPECTS R AH0 S P EH1 K S +RESPECTS R IH0 S P EH1 K T S +RESPECTS R IY0 S P EH1 K S +RESPECTS R IY0 S P EH1 K T S +RESPICE R IY0 S P AH0 S +RESPIRABLE R EH2 S P ER0 AH0 B AH0 L +RESPIRATION R EH2 S P ER0 EY1 SH AH0 N +RESPIRATIONS R EH2 S P ER0 EY1 SH AH0 N Z +RESPIRATOR R EH1 S P ER0 EY2 T ER0 +RESPIRATORS R EH1 S P ER0 EY2 T ER0 Z +RESPIRATORY R EH1 S P ER0 AH0 T AO2 R IY0 +RESPIRE R IY0 S P AY1 R +RESPIRED R IY0 S P AY1 ER0 D +RESPIRING R IY0 S P AY1 R IH0 NG +RESPITE R EH1 S P IH0 T +RESPITED R EH1 S P IH0 T IH0 D +RESPITES R EH1 S P IH0 T S +RESPLENDANT R IY0 S P L EH1 N D AH0 N T +RESPLENDENCE R IY0 S P L EH1 N D AH0 N S +RESPLENDENCY R IY0 S P L EH1 N D AH0 N S IY0 +RESPLENDENT R IY0 S P L EH1 N D AH0 N T +RESPLENDENTLY R IY0 S P L EH1 N D AH0 N T L IY0 +RESPOND R IH0 S P AA1 N D +RESPOND R IY0 S P AA1 N D +RESPONDED R IH0 S P AA1 N D IH0 D +RESPONDED R IY0 S P AA1 N D AH0 D +RESPONDED R IY0 S P AA1 N D IH0 D +RESPONDENT R IH0 S P AA1 N D AH0 N T +RESPONDENT'S R IH0 S P AA1 N D AH0 N T S +RESPONDENTS R IH0 S P AA1 N D AH0 N T S +RESPONDING R IH0 S P AA1 N D IH0 NG +RESPONDING R IY0 S P AA1 N D IH0 NG +RESPONDS R IH0 S P AA1 N D Z +RESPONDS R IY0 S P AA1 N D Z +RESPONSE R IH0 S P AA1 N S +RESPONSE R IY0 S P AA1 N S +RESPONSES R IH0 S P AA1 N S IH0 Z +RESPONSES R IY0 S P AA1 N S AH0 Z +RESPONSES R IY0 S P AA1 N S IH0 Z +RESPONSIBILITIES R IY0 S P AA2 N S AH0 B IH1 L AH0 T IY0 Z +RESPONSIBILITY R IY0 S P AA2 N S AH0 B IH1 L AH0 T IY0 +RESPONSIBLE R IY0 S P AA1 N S AH0 B AH0 L +RESPONSIBLENESS R IY0 S P AA2 N S AH0 B AH0 L N AH0 S +RESPONSIBLY R IH0 S P AA1 N S AH0 B L IY0 +RESPONSIONS R IH0 S P AA1 N SH AH0 N Z +RESPONSIVE R IH0 S P AA1 N S IH0 V +RESPONSIVELY R IH0 S P AA1 N S IH0 V L IY0 +RESPONSIVENESS R IH0 S P AA1 N S IH0 V N AH0 S +RESPUBLICA R IY0 S P AH1 B L IH0 K AH0 +RESS R EH1 S +RESSAIDAR R EH1 S AY1 D AA2 R +RESSALDAR R EH1 S AH0 L D AA2 R +RESSEMBLANCE R EH2 S EH1 M B L AH0 N S +RESSEMBLE R EH2 S EH1 M B AH0 L +RESSEMBLENT R EH2 S EH1 M B AH0 L AH0 N T +RESSORT R EH1 S ER0 T +RESSOURCE R EH1 S AO2 R S +RESSOURCES R EH1 S AO1 R S AH0 Z +REST R EH1 S T +REST' R IY0 S T IH0 Z +REST'LL R IY0 S T AH0 L +REST'S R EH1 S T S +RESTA R EH1 S T AH0 +RESTABIT R IY0 S T EY2 B AH0 T +RESTALRIG R IY0 S T AA1 L R IH0 G +RESTANTE R EH0 S T AA0 N T +RESTART R IY0 S T AA1 R T +RESTARTED R IY0 S T AA1 R T IH0 D +RESTARTING R IY0 S T AA1 R T IH0 NG +RESTATE R IY0 S T EY1 T +RESTATED R IY0 S T EY1 T IH0 D +RESTATEMENT R IY0 S T EY1 T M AH0 N T +RESTATING R IY0 S T EY1 T IH0 NG +RESTAUD R EH1 S T D +RESTAUD'S R EH1 S T D Z +RESTAURANT R EH1 S T ER0 AA2 N T +RESTAURANT R EH1 S T R AA2 N T +RESTAURANT'S R EH1 S T ER0 AA2 N T S +RESTAURANT'S R EH1 S T R AA2 N T S +RESTAURANTS R EH1 S T ER0 AA2 N T S +RESTAURANTS R EH1 S T R AA2 N T S +RESTAURATEUR R EH2 S T ER0 AH0 T ER1 +RESTAURATEUR R EH2 S T R AH0 T ER1 +RESTAURATEUR'S R EH1 S T R AH0 T ER1 Z +RESTAURATEURS R EH2 S T ER0 AH0 T ER1 Z +RESTAURATEURS R EH2 S T R AH0 T ER1 Z +RESTAURATION R EH1 S T ER0 EY1 SH AH0 N +RESTE R EH1 S T +RESTED R EH1 S T AH0 D +RESTED R EH1 S T IH0 D +RESTER R EH1 S T ER0 +RESTES R EH1 S T IY0 Z +RESTETH R IH0 S T IH0 TH +RESTEZ R IY0 S T EH0 Z +RESTFUL R EH1 S T F AH0 L +RESTFULLY R EH1 S T F AH0 L IY0 +RESTFULNESS R EH1 S T F AH0 L N AH0 S +RESTIFF R EH1 S T AH0 F +RESTIGOUCHE R EH1 S T AH0 G UW1 SH +RESTIN R EH1 S T IH0 N +RESTING R EH1 S T IH0 NG +RESTITUTION R EH2 S T IH0 T UW1 SH AH0 N +RESTITUTIONS R EH1 S T AH0 T UW1 SH AH0 N Z +RESTIVE R EH1 S T IH0 V +RESTIVELY R EH1 S T IH0 V L IY0 +RESTIVENESS R EH1 S T IH0 V N AH0 S +RESTLESS R EH1 S T L AH0 S +RESTLESSLY R EH1 S T L AH0 S L IY0 +RESTLESSNESS R EH1 S T L AH0 S N AH0 S +RESTOCK R IY0 S T AA1 K +RESTOCKED R IY0 S T AA1 K T +RESTOCKING R IY0 S T AA1 K IH0 NG +RESTON R EH1 S T AH0 N +RESTOR'D R EH1 S T ER0 D +RESTORATION R EH2 S T ER0 EY1 SH AH0 N +RESTORATIONS R EH2 S T ER0 EY1 SH AH0 N Z +RESTORATIVE R AH0 S T AO1 R AH0 T IH0 V +RESTORATIVES R EH2 S T ER0 AH0 T IH0 V Z +RESTORE R IH0 S T AO1 R +RESTORED R IH0 S T AO1 R D +RESTORER R IH0 S T AO1 R ER0 +RESTORERS R IH0 S T AO1 R ER0 Z +RESTORES R IH0 S T AO1 R Z +RESTORETH R IH0 S T AO1 R TH +RESTORING R IH0 S T AO1 R IH0 NG +RESTRAIN R IY0 S T R EY1 N +RESTRAIN'D R IY0 S T R EY1 N D +RESTRAINED R IY0 S T R EY1 N D +RESTRAINEDLY R IY0 S T R EY1 N D L IY0 +RESTRAINER R IY0 S T R EY1 N ER0 +RESTRAINING R IY0 S T R EY1 N IH0 NG +RESTRAININGLY R IY0 S T R EY1 N IH0 NG L IY0 +RESTRAINS R IY0 S T R EY1 N Z +RESTRAINT R IH0 S T R EY1 N T +RESTRAINT R IY0 S T R EY1 N T +RESTRAINTS R IH0 S T R EY1 N T S +RESTRAINTS R IY0 S T R EY1 N T S +RESTRICT R IY0 S T R IH1 K T +RESTRICTED R IY0 S T R IH1 K T AH0 D +RESTRICTED R IY0 S T R IH1 K T IH0 D +RESTRICTING R IY0 S T R IH1 K T IH0 NG +RESTRICTION R IY0 S T R IH1 K SH AH0 N +RESTRICTIONS R IY0 S T R IH1 K SH AH0 N Z +RESTRICTIVE R IY0 S T R IH1 K T IH0 V +RESTRICTS R IY0 S T R IH1 K T S +RESTRONGUET R IY0 S T R AO1 NG W EH2 T +RESTRONGUET'S R IY0 S T R AO1 NG W EH2 T S +RESTRUNG R IY0 S T R AH1 NG +RESTS R EH1 S T S +RESTU R IY0 S T UW0 +RESTY R EH1 S T IY0 +RESULT R IH0 Z AH1 L T +RESULT R IY0 Z AH1 L T +RESULT'S R IY0 Z AH1 L T S +RESULTANT R IY0 Z AH1 L T AH0 N T +RESULTANTS R IY0 Z AH1 L T AH0 N T S +RESULTED R IH0 Z AH1 L T IH0 D +RESULTED R IY0 Z AH1 L T AH0 D +RESULTED R IY0 Z AH1 L T IH0 D +RESULTING R IH0 Z AH1 L T IH0 NG +RESULTING R IY0 Z AH1 L T IH0 NG +RESULTLESS R IY0 Z AH1 L T L AH0 S +RESULTS R IH0 Z AH1 L T S +RESULTS R IY0 Z AH1 L T S +RESUM'D R IY0 Z AH1 M D +RESUME R EH1 Z AH0 M EY2 +RESUME R IH0 Z UW1 M +RESUME R IY0 Z UW1 M +RESUMED R IH0 Z UW1 M D +RESUMED R IY0 Z UW1 M D +RESUMES R EH1 Z AH0 M EY2 Z +RESUMES R IH0 Z UW1 M Z +RESUMES R IY0 Z UW1 M Z +RESUMING R IH0 Z UW1 M IH0 NG +RESUMING R IY0 Z UW1 M IH0 NG +RESUMPTION R IH0 Z AH1 M P SH AH0 N +RESUMPTION R IH0 Z AH1 M SH AH0 N +RESUMPTION R IY0 Z AH1 M P SH AH0 N +RESUMPTION R IY0 Z AH1 M SH AH0 N +RESURGAM R IY0 S ER1 G AH0 M +RESURGED R IY0 S ER1 JH D +RESURGENCE R IY0 S ER1 JH AH0 N S +RESURGENT R IH0 S ER1 JH AH0 N T +RESURGENT R IY0 S ER1 JH AH0 N T +RESURGING R IY0 S ER1 JH IH0 NG +RESURRECT R EH2 Z ER0 EH1 K T +RESURRECTED R EH2 Z ER0 EH1 K T IH0 D +RESURRECTING R EH2 Z ER0 EH1 K T IH0 NG +RESURRECTION R EH2 Z ER0 EH1 K SH AH0 N +RESURRECTIONIST R EH2 Z ER0 EH1 K SH AH0 N IH0 S T +RESURRECTIONISTS R EH2 Z ER0 EH1 K SH AH0 N IH0 S T S +RESURRECTIONS R EH2 Z ER0 EH1 K SH AH0 N Z +RESUSCITATE R IH0 S AH1 S IH0 T EY2 T +RESUSCITATE R IY0 S AH1 S IH0 T EY2 T +RESUSCITATED R IH0 S AH1 S IH0 T EY2 T IH0 D +RESUSCITATES R IH0 S AH1 S IH0 T EY2 T S +RESUSCITATING R IH0 S AH1 S IH0 T EY2 T IH0 NG +RESUSCITATION R IH0 S AH2 S IH0 T EY1 SH AH0 N +RESZKE R IY0 SH K IY0 +RET R EH1 T +RETAIL R IY1 T EY2 L +RETAILED R IY1 T EY2 L D +RETAILER R IY1 T EY2 L ER0 +RETAILERS R IY1 T EY2 L ER0 Z +RETAILING R IY1 T EY2 L IH0 NG +RETAILS R IY1 T EY2 L Z +RETAIN R IH0 T EY1 N +RETAIN R IY0 T EY1 N +RETAIN'D R IY1 T EY2 N D +RETAINED R IH0 T EY1 N D +RETAINED R IY0 T EY1 N D +RETAINER R IH0 T EY1 N ER0 +RETAINER R IY0 T EY1 N ER0 +RETAINER'S R IH0 T EY1 N ER0 Z +RETAINERS R IH0 T EY1 N ER0 Z +RETAINETH R IH0 T EY1 N TH +RETAINING R IH0 T EY1 N IH0 NG +RETAINING R IY0 T EY1 N IH0 NG +RETAINS R IH0 T EY1 N Z +RETAINS R IY0 T EY1 N Z +RETAKE R IY0 T EY1 K +RETAKE R IY1 T EY1 K +RETAKEN R IY0 T EY1 K AH0 N +RETAKES R IY0 T EY1 K S +RETAKING R IY0 T EY1 K IH0 NG +RETALIATE R IH0 T AE1 L IY0 EY2 T +RETALIATE R IY0 T AE1 L IY0 EY2 T +RETALIATED R IH0 T AE1 L IY0 EY2 T IH0 D +RETALIATED R IY0 T AE1 L IY0 EY2 T IH0 D +RETALIATES R IH0 T AE1 L IY0 EY2 T S +RETALIATING R IH0 T AE1 L IY0 EY2 T IH0 NG +RETALIATION R IY0 T AE2 L IY0 EY1 SH AH0 N +RETALIATIONS R IY0 T AE2 L IY0 EY1 SH AH0 N Z +RETALIATORY R IY0 T AE1 L Y AH0 T AO2 R IY0 +RETAMOSO R EH1 T AH0 M OW1 S OW0 +RETARD R IH0 T AA1 R D +RETARD R IY0 T AA1 R D +RETARDATION R IY0 T AA0 R D EY1 SH AH0 N +RETARDED R IH0 T AA1 R D IH0 D +RETARDED R IY0 T AA1 R D AH0 D +RETARDED R IY0 T AA1 R D IH0 D +RETARDER R IY0 T AA1 R D ER0 +RETARDING R IH0 T AA1 R D IH0 NG +RETARDING R IY0 T AA1 R D IH0 NG +RETARDS R IH0 T AA1 R D Z +RETARDS R IY0 T AA1 R D Z +RETAUX R IY1 T AO1 K S +RETCH R EH1 CH +RETCH'S R EH1 CH IH0 Z +RETCHED R EH1 CH T +RETCHING R EH1 CH IH0 NG +RETCHINGS R EH1 CH IH0 NG Z +RETCLIFFE R EH1 T K L IH0 F +RETE R IY0 T +RETEAU R IY2 T OW1 +RETELL R IY0 T EH1 L +RETELLING R IY0 T EH1 L IH0 NG +RETENTION R IY0 T EH1 N SH AH0 N +RETENTIVE R IY0 T EH1 N T IH0 V +RETENTIVENESS R IY0 T EH1 N T IH0 V N AH0 S +RETENUE R IY0 T EH1 N Y UW0 +RETFORD R EH1 T F ER0 D +RETHEL R EH1 TH AH0 L +RETHER R EH1 DH ER0 +RETHERTON R IH0 TH ER1 T AH0 N +RETIARIUS R IY0 T IY0 EH1 R IY0 AH0 S +RETICENCE R EH1 T IH0 S AH0 N S +RETICENCES R EH1 T IH0 S AH0 N S IH0 Z +RETICENT R EH1 T IH0 S AH0 N T +RETICENTLY R EH1 T IH0 S AH0 N T L IY0 +RETICULATED R IH0 T IH1 K Y AH0 L EY2 T IH0 D +RETICULATION R IH0 T IH1 K Y AH0 L EY1 SH AH0 N +RETICULATIONS R IH0 T IH1 K Y AH0 L EY1 SH AH0 N Z +RETICULE R EH1 T AH0 K Y UW2 L +RETICULES R EH1 T AH0 K Y UW2 L Z +RETIE R EH1 T IY0 +RETIED R IH0 T AY1 D +RETIEF R IH0 T AY1 F +RETIEF'S R IH0 T AY1 F S +RETINA R EH1 T AH0 N AH0 +RETINAL R EH1 T AH0 N AH0 L +RETINAS R IH0 T AY1 N AH0 Z +RETINUE R EH1 T AH0 N UW2 +RETINUES R EH1 T AH0 N UW2 Z +RETIR'D R IY0 T AY1 ER0 D +RETIRACY R IH0 T AY1 R AH0 S IY0 +RETIRE R IH0 T AY1 R +RETIRE R IY0 T AY1 R +RETIRE R IY2 T AY1 ER0 +RETIRED R IH0 T AY1 R D +RETIRED R IY0 T AY1 ER0 D +RETIRED R IY0 T AY1 R D +RETIREMENT R IH0 T AY1 ER0 M AH0 N T +RETIREMENT R IY0 T AY1 ER0 M AH0 N T +RETIREMENTS R IH0 T AY1 R M AH0 N T S +RETIREMENTS R IY0 T AY1 R M AH0 N T S +RETIRER R IY0 T AY1 ER0 R +RETIRES R IH0 T AY1 R Z +RETIRES R IY0 T AY1 ER0 Z +RETIRES R IY0 T AY1 R Z +RETIRIN R IH0 T AY1 R IH0 N +RETIRING R IH0 T AY1 R IH0 NG +RETIRING R IY0 T AY1 ER0 IH0 NG +RETIRING R IY0 T AY1 R IH0 NG +RETIRO R IH0 T AY1 R OW0 +RETOC R IY0 T AO1 K +RETOC'S R IY0 T AO1 K S +RETOLD R EH1 T OW0 L D +RETOOK R IY0 T UH1 K +RETORT R IY1 T AO2 R T +RETORTED R IY0 T AO1 R T IH0 D +RETORTII R IY1 T AO2 R T IY1 IY0 +RETORTING R IY0 T AO1 R T IH0 NG +RETORTS R IH0 T AO1 R T S +RETORTS R IY0 T AO1 R T S +RETOUCH R IY0 T AH1 CH +RETOUCHED R IY0 T AH1 CH T +RETOUCHER R IY0 T AH1 CH ER0 +RETOUCHING R IY0 T AH1 CH IH0 NG +RETOUR R IY0 T UH1 R +RETOURNE R IY0 T UH1 R N +RETOURNER R IY0 T ER1 N ER0 +RETRACE R IY0 T R EY1 S +RETRACED R IY0 T R EY1 S T +RETRACES R IY0 T R EY1 S IH0 Z +RETRACING R IY0 T R EY1 S IH0 NG +RETRACT R IY0 T R AE1 K T +RETRACTATION R IY0 T R AE1 K T EY1 SH AH0 N +RETRACTATIONS R IY0 T R AE1 K T EY1 SH AH0 N Z +RETRACTED R IY0 T R AE1 K T AH0 D +RETRACTILE R IY0 T R AE1 K T AH0 L +RETRACTING R IY0 T R AE1 K T IH0 NG +RETRACTION R IY0 T R AE1 K SH AH0 N +RETRACTIONS R IY0 T R AE1 K SH AH0 N Z +RETRACTS R IY0 T R AE1 K T S +RETRAITE R IY0 T R EY1 T +RETRANSLATED R IY2 T R AE0 N Z L EY1 T AH0 D +RETRANSLATION R IY2 T R AE0 N Z L EY1 SH AH0 N +RETREAD R IY0 T R EH1 D +RETREAT R IY0 T R IY1 T +RETREATED R IY0 T R IY1 T AH0 D +RETREATED R IY0 T R IY1 T IH0 D +RETREATIN R IY0 T R IY1 T AH0 N +RETREATING R IY0 T R IY1 T IH0 NG +RETREATS R IY0 T R IY1 T S +RETRENCH R IY0 T R EH1 N CH 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N IH0 S T +RETURNETH R IY0 T ER1 N AH0 TH +RETURNIN R IY0 T ER1 N IH0 N +RETURNING R IH0 T ER1 N IH0 NG +RETURNING R IY0 T ER1 N IH0 NG +RETURNINGS R IY0 T ER1 N IH0 NG Z +RETURNLESS R IY0 T ER1 N L AH0 S +RETURNS R IH0 T ER1 N Z +RETURNS R IY0 T ER1 N Z +RETY R IH0 T IY0 +RETY'S R IY0 T IY0 Z +RETYING R IY0 T IY0 IH0 NG +RETYS R IY0 T IY0 Z +RETZ R EH1 T S +RETZ'S R EH1 T S IH0 Z +RETZOW R EH1 T Z OW0 +REUBEN R UW1 B AH0 N +REUBEN'S R UW1 B AH0 N Z +REUBENS R UW1 B AH0 N Z +REUBIN R UW1 B IH0 N +REUBY R UW1 B IY0 +REUCH'S R OY1 K S +REUCHLIN R OY1 K L IH0 N +REUEL R UW1 AH0 L +REUILLY R IY0 UW1 L IY0 +REULAH R UW1 L AH0 +REUMONT R UW1 M AA2 N T +REUNION R IY0 UW1 N Y AH0 N +REUNIONS R IY0 UW1 N Y AH0 N Z +REUNIS R IY0 UW1 N AH0 S +REUNITE R IY2 UW0 N AY1 T +REUNITED R IY2 UW0 N AY1 T IH0 D +REUNITES R IY2 UW0 N AY1 T S +REUNITING R IY2 UW0 N AY1 T IH0 NG +REUS R IY0 UW1 Z +REUSS R UW1 S +REUTER R OY1 T ER0 +REUTER'S R OY1 T ER0 Z +REUTHER R OY1 DH ER0 +REUTHER'S R OY1 DH ER0 Z 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AH0 L IH0 NG +REVIENDRA R IY0 V Y EH1 N D R AH0 +REVIENS R EH1 V IY0 AH0 N Z +REVIENT R IY0 V Y AH0 N T +REVIERE R IY0 V Y EH1 R +REVIEW R IY2 V Y UW1 +REVIEWED R IY0 V Y UW1 D +REVIEWER R IY0 V Y UW1 ER0 +REVIEWER'S R IY0 V Y UW1 ER0 Z +REVIEWERS R IY0 V Y UW1 ER0 Z +REVIEWING R IY0 V Y UW1 IH0 NG +REVIEWS R AH0 V Y UW1 Z +REVIEWS R IY0 V Y UW1 Z +REVILE R IY0 V AY1 L +REVILED R IY0 V AY1 L D +REVILEMENT R IY0 V AY1 L M AH0 N T +REVILER R IY0 V AY1 L ER0 +REVILERS R IY0 V AY1 L ER0 Z +REVILES R IY0 V AY1 L Z +REVILING R IY0 V AY1 L IH0 NG +REVILINGS R IY0 V AY1 L IH0 NG Z +REVILLAGIGEDO R EH0 V IH1 L IH0 JH IH0 G EY1 D OW0 +REVILLE R EH1 V IH2 L +REVILLON R EH0 V IH1 L AH0 N +REVINGE R IY0 V IH1 N JH +REVINGTON R EH1 V IH0 NG T AH0 N +REVINGTON'S R EH1 V IH0 NG T AH0 N Z +REVISAL R IY0 V IH1 Z AH0 L +REVISE R IH0 V AY1 Z +REVISE R IY0 V AY1 Z +REVISED R IH0 V AY1 Z D +REVISED R IY0 V AY1 Z D +REVISER R IY0 V AY1 Z ER0 +REVISERS R IY0 V AY1 Z ER0 Z +REVISES R IH0 V AY1 Z IH0 Z +REVISES 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AH1 L SH AH0 N +REVULSIONS R IY0 V AH1 L SH AH0 N Z +REVYNGTON R IY0 V IH0 NG T AH0 N +REW R UW1 +REWA R IY0 W AH0 +REWARD R IH0 W AO1 R D +REWARD R IY0 W AO1 R D +REWARDED R IH0 W AO1 R D IH0 D +REWARDED R IY0 W AO1 R D AH0 D +REWARDED R IY0 W AO1 R D IH0 D +REWARDER R IY0 W AO1 R D ER0 +REWARDETH R IY0 W AO1 R D IH0 TH +REWARDING R IH0 W AO1 R D IH0 NG +REWARDING R IY0 W AO1 R D IH0 NG +REWARDS R IH0 W AO1 R D Z +REWARDS R IY0 W AO1 R D Z +REWBELL R UW1 B EH2 L +REWBLE R UW1 B AH0 L +REWBUSH R UW1 B UH2 SH +REWI R IY0 W AY1 +REWIND R IY1 W AY0 N D +REWOUND R IY0 W AW1 N D +REWRITE R IY0 R AY1 T +REWRITE R IY1 R AY2 T +REWRITING R IY0 R AY1 T IH0 NG +REWRITTEN R IY0 R IH1 T AH0 N +REWROTE R IY0 R OW1 T +REWTHAM R IY0 TH AH0 M +REX R EH1 K S +REX'S R EH1 K S IH0 Z +REXFORD R EH1 K S F ER0 D +REXFORD'S R EH1 K S F ER0 D Z +REXFORDS R EH1 K S F ER0 D Z +REXHILL R EH1 K S HH IH2 L +REXHILL'S R EH1 K S HH IH0 L Z +REXHILLS R EH1 K S HH IH2 L Z +REXIN R EH1 K S IH0 N +REXOW R EH1 K S OW0 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IH0 S +RHADAMANDASPES R AH0 D AE1 M AH0 N D AA1 S P S +RHADAMANTHINE R EY1 D AH0 M AE1 N TH IY2 N +RHADAMANTHUS R EY1 D AH0 M AE1 N TH AH0 S +RHADAMES R AH0 D EY1 M Z +RHADEN R EY1 D AH0 N +RHAETIA R IY1 SH AH0 +RHAETIAN R IY1 SH AH0 N +RHAGES R AH0 JH AH0 Z +RHAINT R EY1 N T +RHAL R AH0 L +RHAMADAN R AA1 M AH0 D AA2 N +RHAMDA R AE0 M D AH0 +RHAMDAS R AE0 M D AH0 Z +RHAMNUS R AE0 M N AH0 S +RHANGABE R AA0 NG G AA1 B EY0 +RHANTO R AE1 N T OW0 +RHAPSODE R AE1 P S AH0 D +RHAPSODES R AE1 P S AH0 D Z +RHAPSODIC R AE0 P S AA1 D IH0 K +RHAPSODICAL R AE0 P S AA1 D IH0 K AH0 L +RHAPSODICALLY R AE0 P S AA1 D IH0 K L IY0 +RHAPSODIE R AE1 P S AH0 D IY0 +RHAPSODIES R AE1 P S AH0 D IY0 Z +RHAPSODISE R AE1 P S AH0 D AY2 Z +RHAPSODISED R AE1 P S AH0 D AY2 Z D +RHAPSODISING R AE1 P S AH0 D AY2 Z IH0 NG +RHAPSODIST R AE1 P S AH0 D AH0 S T +RHAPSODISTS R AE1 P S AH0 D AH0 S T S +RHAPSODIZE R AE1 P S AH0 D AY2 Z +RHAPSODIZED R AE1 P S AH0 D AY2 Z D +RHAPSODIZING R AE1 P S AH0 D AY2 Z IH0 NG +RHAPSODY R AE1 P S AH0 D IY0 +RHASIS R AH0 S AH0 S +RHAYADER R EY1 AE1 D ER0 +RHE R +RHEA R IY1 AH0 +RHEA'S R IY1 AH0 Z +RHEAD R HH EH2 D +RHEDA R IY1 D AH0 +RHEDERN R D ER0 N +RHEE R IY1 +RHEES R IY1 Z +RHEGIUM R IY1 JH IY0 AH0 M +RHEIM R AY0 M +RHEIMS R AY1 M Z +RHEIN R AY1 N +RHEINFELDEN R AY1 N F EH2 L D AH0 N +RHEINFELS R AY1 N F AH0 L Z +RHEINFRID R AY1 N F R IH0 D +RHEINGAU R AY1 N G OW1 +RHEINGOLD R AY1 N G OW2 L D +RHEINHARD R AY1 N HH AA2 R D +RHEINHARDT R AY1 N HH AA2 R T +RHEINHART R AY1 N HH AA2 R T +RHEINSBERG R AY1 N S B ER0 G +RHEINSTEIN R AY1 N S T AY2 N +RHENISH R IY1 N IH0 SH +RHENOSTER R IY0 N AA1 S T ER0 +RHEOSTAT R IY0 AO1 S T AE0 T +RHEOU R IY1 UW2 +RHES R Z +RHESUS R IY1 S AH0 S +RHETOR R EH2 T ER0 +RHETORE R IH0 T AO1 R IY0 +RHETORIC R EH1 T ER0 IH0 K +RHETORICAL R IH0 T AO1 R IH0 K AH0 L +RHETORICALLY R AH0 T AO1 R IH0 K L IY0 +RHETORICIAN R EH2 T ER0 IH1 SH AH0 N +RHETORICIAN'S R EH2 T ER0 IH1 SH AH0 N Z +RHETORICIANS R EH2 T ER0 IH1 SH AH0 N Z +RHETORICK R AH0 T AO1 R IH0 K +RHETORICS R AH0 T AO1 R IH0 K S +RHETORS R EH2 T ER0 Z +RHETT R EH1 T +RHETT'S R EH1 T S +RHETTA R EH1 T AH0 +RHETTS R EH1 T S +RHEUM R UW0 M +RHEUMATIC R UW0 M AE1 T IH0 K +RHEUMATICALLY R UW0 M AE1 T IH0 K L IY0 +RHEUMATICKY R UW2 M AH0 T IH1 K IY0 +RHEUMATICS R UW0 M AE1 T IH0 K S +RHEUMATIS R UW0 M AE1 T IH0 S +RHEUMATISM R UW1 M AH0 T IH2 Z AH0 M +RHEUMATISMS R UW1 M AH0 T IH2 Z AH0 M Z +RHEUMATIZ R UW2 M AH0 T IY1 Z +RHEUMS R UW1 M Z +RHEUMY R UW2 M IY0 +RHEYN R EY1 N +RHIANNON R AY0 AE1 N AH0 N +RHIME R AY1 M +RHIMES R AY1 M Z +RHIMESON R AY1 M S AH0 N +RHIN R IH1 N +RHIND R AY2 N D +RHINDS R AY1 N D Z +RHINE R AY1 N +RHINE'S R AY1 N Z +RHINEBECK R AY1 N B EH2 K +RHINEGOLD R AY1 N G OW2 L D +RHINEGRAVE R AY1 N G R EY2 V +RHINEHART R AY1 N HH AA2 R T +RHINELAND R AY1 N L AE2 N D +RHINELANDER R AY1 N L AH0 N D ER0 +RHINES R AY1 N Z +RHINESTONE R AY1 N S T OW2 N +RHINESTONES R AY1 N S T OW2 N Z +RHINEWARD R AY1 N W ER0 D +RHINITIS R AY1 N AY1 T IH0 S +RHINO R AY1 N OW2 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CH IH0 Z +RICHAMBEAU R IH0 SH AH0 M B OW1 +RICHARD R IH1 CH ER0 D +RICHARD'S R IH1 CH ER0 D Z +RICHARDET R IH1 CH ER0 D EH1 T +RICHARDETTO R IH1 CH ER0 D EH1 T OW0 +RICHARDO R IH1 CH ER0 D OW0 +RICHARDOT R IH1 CH ER0 D AA2 T +RICHARDS R IH1 CH ER0 D Z +RICHARDS'S R IH1 CH ER0 D Z IH0 Z +RICHARDSON R IH1 CH ER0 D S AH0 N +RICHARDSON'S R IH1 CH ER0 D S AH0 N Z +RICHARDSONIAN R IH1 CH ER0 D S OW1 N IY0 AH0 N +RICHARDSONS R IH1 CH ER0 D S AH0 N Z +RICHARN R IH1 CH ER0 N +RICHART R IH1 K HH AA0 R T +RICHBERTA R IH1 CH B EH1 R T AH0 +RICHE R IH1 CH +RICHEBOURG R IH1 CH AH0 B AO2 R G +RICHEFORT R IH1 CH F ER0 T +RICHELIEU R IH1 SH AH0 L UW2 +RICHELIEU'S R IH1 SH AH0 L UW2 Z +RICHELIEUS R IH1 SH AH0 L UW2 Z +RICHEMONT R IH1 CH AH0 M AA2 N T +RICHENDA R IH1 K IH0 N D AH0 +RICHEPIN R IH1 CH IH0 P IH0 N +RICHER R IH1 CH ER0 +RICHER'N R IH1 CH ER0 AH0 N +RICHES R IH1 CH AH0 Z +RICHES R IH1 CH IH0 Z +RICHESSE R IH1 CH EH1 S +RICHESSES R IH1 CH EH1 S IH0 Z +RICHEST R IH1 CH AH0 S T +RICHET R IH1 CH 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AH0 +RICKABY R IH1 K AH0 B IY0 +RICKARD R IH1 K ER0 D +RICKARD'S R IH1 K ER0 D Z +RICKARDS R IH1 K ER0 D Z +RICKART R IH1 K ER0 T +RICKARTS R IH1 K ER0 T S +RICKED R IH1 K T +RICKER R IH1 K ER0 +RICKER'S R IH1 K ER0 Z +RICKERBY R IH1 K ER0 B IY0 +RICKERT R IH1 K ER0 T +RICKETS R IH1 K IH0 T S +RICKETT R IH1 K IH0 T +RICKETT'S R IH1 K IH0 T S +RICKETTS R IH1 K IH0 T S +RICKETTS'S R IH1 K IH0 T S IH0 Z +RICKETTY R IH1 K IH0 T IY0 +RICKETY R IH1 K AH0 T IY0 +RICKEY R IH1 K IY0 +RICKEYBOCKEY R IH1 K IY0 B AA2 K IY0 +RICKIE R IH1 K IY0 +RICKIE'S R IH1 K IY0 Z +RICKITY R IH1 K IH0 T IY0 +RICKLE R IH1 K AH0 L +RICKMAN R IH1 K M AH0 N +RICKMAN'S R IH1 K M AH0 N Z +RICKMANN R IH1 K M AH0 N +RICKMANSWORTH R IH1 K M AE2 N Z W ER0 TH +RICKON R IH1 K AH0 N +RICKROSE R IH1 K R OW2 Z +RICKS R IH1 K S +RICKSBOROUGH R IH1 K S B ER0 OW0 +RICKSDAG R IH1 K S D AE0 G +RICKSHA R IH1 K SH AA2 +RICKSHAS R IH1 K SH AH0 Z +RICKSHAW R IH1 K SH AO2 +RICKSHAWS R IH1 K SH AO1 Z +RICKWELL R IH1 K W EH2 L +RICKY R 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JH W EY2 +RIDGWAY'S R IH1 JH W EY2 Z +RIDGWELL R IH1 JH W EH0 L +RIDGY R IH1 JH IY0 +RIDI R IY1 D IY0 +RIDIC'LOUS R IH1 D AH0 K AH0 L AH0 S +RIDICULE R IH1 D AH0 K Y UW2 L +RIDICULED R IH1 D AH0 K Y UW2 L D +RIDICULES R IH1 D AH0 K Y UW2 L Z +RIDICULING R IH1 D AH0 K Y UW2 L IH0 NG +RIDICULOUS R IH0 D IH1 K Y AH0 L AH0 S +RIDICULOUSLY R AH0 D IH1 K Y AH0 L AH0 S L IY0 +RIDICULOUSNESS R IH0 D IH1 K Y AH0 L AH0 S N AH0 S +RIDICULUS R IH0 D IH1 K Y AH0 L AH0 S +RIDIN R IH1 D AH0 N +RIDIN' R IH1 D IH0 N +RIDING R AY1 D IH0 NG +RIDINGHOOD R AY1 D IH0 NG HH UH2 D +RIDINGS R AY1 D IH0 NG Z +RIDLET R IH1 D L EH2 T +RIDLEY R IH1 D L IY0 +RIDLEY'S R IH1 D L IY0 Z +RIDLEYS R IH1 D L IY0 Z +RIDOLFI R IY0 D OW1 L F IY0 +RIDOTTO R IY0 D OW1 T OW0 +RIDOTTOS R IY0 D OW1 T OW0 Z +RIDOUT R IH1 D AW2 T +RIDPATH R IH1 D P AE2 TH +RIDS R IH1 D Z +RIDSDALE R IH1 D Z D EY2 L +RIDSON R IH1 D S AH0 N +RIDVAN R IH1 D V AH0 N +RIE R IY0 +RIEBEEK R IY1 B IY1 K +RIED R IY1 D +RIED'S R IY1 D Z +RIEDEL R IY1 D AH0 L +RIEDESEL R IY1 D IH0 S AH0 L +RIEDT R IY1 D T +RIEGEL R IY1 G AH0 L +RIEGO R IY1 G OW0 +RIEGO'S R IY1 G OW0 Z +RIEHL R IY1 L +RIEKA R IY1 K AH0 +RIEKE R IY1 K +RIEKJE R IY1 K JH IY2 +RIEL R IY1 L +RIEL'S R IY1 L Z +RIELLE R IY1 L +RIEM R IY1 M +RIEMANN R IY1 M AH0 N +RIEMANN'S R IY1 M AH0 N Z +RIEMS R IY1 M Z +RIEN R IY1 N +RIENAECKER R IY1 N EH1 K ER0 +RIENCE R IY0 AH0 N S +RIENS R IY1 N Z +RIENZI R IY1 N Z IY0 +RIENZI'S R IY1 N Z IY0 Z +RIENZO R IY1 N Z OW0 +RIEPPE R AY1 IH0 P +RIERA R IY1 R AH0 +RIES R AY1 Z +RIESE R IY1 Z +RIESENECK R IY1 S AH0 N EH2 K +RIESENECK'S R IY1 S IH0 N EH0 K S +RIESENGEBIRGE R IY1 S AH0 N JH B ER1 JH +RIESLING R IY1 Z L IH0 NG +RIESSER R IY1 S ER0 +RIESSER'S R IY1 S ER0 Z +RIET R IY1 T +RIETBUCK R IY1 T B AH2 K +RIETFONTEIN R IY1 T F AO0 N T AY2 N +RIETI R IY1 T IY2 +RIETTE R IY1 T +RIEUX R IY0 UW2 +RIEVAULX R IY1 V AW1 L K S +RIEVER R IY1 V ER0 +RIEVERS R IY1 V ER0 Z +RIEVING R IY1 V IH0 NG +RIEZ R AY1 IH0 Z +RIF R IH1 F +RIFE R AY1 F +RIFER R AY1 F ER0 +RIFF R IH1 F +RIFFEL R IH1 F AH0 L +RIFFELBERG R IH1 F IH0 L B ER0 G +RIFFI R IH1 F IY0 +RIFFIS R IH1 F IH0 S +RIFFLE R IH1 F AH0 L +RIFFLED R IH1 F AH0 L D +RIFFLES R IH1 F AH0 L Z +RIFFLING R IH1 F L IH0 NG +RIFFRAFF R IH1 F R AE2 F +RIFFRATH R IH1 F R AE2 TH +RIFKIN R IH1 F K IH0 N +RIFLA R AY1 F L AH0 +RIFLE R AY1 F AH0 L +RIFLE'S R AY1 F AH0 L Z +RIFLED R AY1 F AH0 L D +RIFLEMAN R AY1 F AH0 L M AH0 N +RIFLEMAN'S R AY1 F AH0 L M AH0 N Z +RIFLEMEN R AY1 F AH0 L M IH0 N +RIFLES R AY1 F AH0 L Z +RIFLING R AY1 F L IH0 NG +RIFT R IH1 F T +RIFTED R IH1 F T IH0 D +RIFTING R IH1 F T IH0 NG +RIFTS R IH1 F T S +RIG R IH1 G +RIG'LAR R IH1 L ER0 +RIG'LARS R IH1 L ER0 Z +RIG'LATIONS R IH1 L EY1 SH AH0 N Z +RIG'MINT R IH1 M IH2 N T +RIGA R IY1 G AH0 +RIGADOON R IH2 G AH0 D UW1 N +RIGAMAROLE R AY1 G AH0 M ER0 OW2 L +RIGAUD R IH1 G OW1 +RIGAULT R IH1 G OW1 +RIGAUX R IH1 G OW1 +RIGBY R IH1 G B IY0 +RIGBY'S R IH1 G B IY0 Z +RIGDALE R IH1 G D EY2 L +RIGDEN R IH1 G D AH0 N +RIGDEN'S R IH1 G D AH0 N Z 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AY1 T L IY0 +RIGHTNESS R AY1 T N AH0 S +RIGHTO R AY1 T OW0 +RIGHTS R AY1 T S +RIGHTWISE R AY1 T W AY2 Z +RIGHTY R AY1 T IY0 +RIGI R IH1 JH IY0 +RIGID R IH1 JH AH0 D +RIGID R IH1 JH IH0 D +RIGIDITIES R IH0 JH IH1 D IH0 T IY0 Z +RIGIDITY R IH0 JH IH1 D AH0 T IY0 +RIGIDLY R IH1 JH IH0 D L IY0 +RIGIDNESS R IH1 JH AH0 D N AH0 S +RIGIMENT R IH1 JH AH0 M AH0 N T +RIGIMINT R IH1 JH AH0 M IH0 N T +RIGINAL R IH1 JH AH0 N AH0 L +RIGLAR R IH1 G L ER0 +RIGMAROLE R IH1 G M ER0 OW2 L +RIGMAROLES R IH1 G M ER0 OW2 L Z +RIGNAUT R IH1 G N AO2 T +RIGNOLD R IH1 G N OW0 L D +RIGOLET R IH2 G AH0 L EH1 T +RIGOLETS R IH2 G AH0 L EH1 T S +RIGOLETTE R IH2 G AH0 L EH1 T +RIGOLETTO R IH2 G AH0 L EH1 T OW0 +RIGOLOT R IH2 G AH0 L AA1 T +RIGOR R IH1 G ER0 +RIGORISTS R IH1 G ER0 IH0 S T S +RIGOROUS R IH1 G ER0 AH0 S +RIGOROUSLY R IH1 G ER0 AH0 S L IY0 +RIGORS R IH1 G ER0 Z +RIGOU R IH1 G UW0 +RIGOU'S R IH1 G UW1 Z +RIGOUR R IH1 G ER0 +RIGOURS R IH1 G AO1 R Z +RIGS R IH1 G Z +RIGSDAK R IH1 G Z D AE0 K +RIGSWORTH R IH1 G Z W ER2 TH +RIGUEUR R IH0 G Y UW1 R +RIGUEUR R IH1 G ER0 +RII R IY0 +RIIS R IY1 Z +RIJEKA R IH0 JH EH1 K AH0 +RIJN R IY1 AH0 N +RIK R IH1 K +RIKA R IY1 K AH0 +RIKCHEN R IH1 K AH0 N +RIKE R AY1 K +RIKER R AY1 K ER0 +RIKI R IH1 K IY0 +RIKIU R IH1 K Y UW0 +RIKKI R IH1 K IY0 +RIKLI R IH1 K L IY0 +RIKOR R IH1 K ER0 +RIKSDAG R IH1 K S D AE0 G +RIL R IH0 L +RILAS R AY1 L AH0 Z +RILE R AY1 L +RILED R AY1 L D +RILES R AY1 L Z +RILEY R AY1 L IY0 +RILEY'S R AY1 L IY0 Z +RILIEVO R IH1 L Y AH0 V OW0 +RILIN R IH0 L IH0 N +RILING R AY1 L IH0 NG +RILKE R IH1 L K +RILKE'S R IH1 L K S +RILL R IH1 L +RILLA R IH1 L AH0 +RILLA'S R IH1 L AH0 Z +RILLED R IH1 L D +RILLER R IH1 L ER0 +RILLET R IH1 L IH0 T +RILLETTE R IH0 L EH1 T +RILLING R IH1 L IH0 NG +RILLINGDON R IH1 L IH0 NG D AH0 N +RILLS R IH1 L Z +RILLY R IH1 L IY0 +RILSTON R AY1 L S T AH0 N +RILTH R IH1 L TH +RILTON R IH1 L T AH0 N +RILY R AH0 L IY0 +RIM R IH1 M +RIMA R IY1 M AH0 +RIMA'S R IY1 M AH0 Z +RIMAC R IH1 M AE0 K +RIMBAUD R IH1 M B OW1 +RIME R AY1 M +RIMED R AY1 M D +RIMES R AY1 M Z +RIMINGTON R AY1 M IH0 NG T AH0 N +RIMINGTON'S R AY1 M IH0 NG T AH0 N Z +RIMINI R IH1 M AH0 N IY0 +RIMLESS R IH1 M L AH0 S +RIMMED R IH1 M D +RIMMEL R IH1 M AH0 L +RIMMER R IH1 M ER0 +RIMMING R IH1 M IH0 NG +RIMMON R IH1 M AH0 N +RIMMON'S R IH1 M AH0 N Z +RIMN R IH1 M N +RIMOUSKI R IH1 M AH1 S K IY0 +RIMPI R IH2 M P IY0 +RIMPIS R IH1 M P IH0 S +RIMROCK R IH1 M R AA2 K +RIMROCK'S R IH1 M R AA2 K S +RIMS R IH1 M Z +RIMSKY R IH1 M S K IY0 +RIMU R IH1 M UW0 +RIMY R AY1 M IY0 +RIN R IH0 N +RINA R IY1 N AH0 +RINALDI R IY0 N AA1 L D IY0 +RINALDINI R IH0 N AA1 L D IY1 N IY0 +RINALDO R IH0 N AA1 L D OW0 +RINALDO'S R IH0 N AA1 L D OW0 Z +RINCER R IH1 N S ER0 +RINCON R IH1 NG K AH0 N +RINCONA R IH1 NG K OW1 N AH0 +RINCONADA R IH1 NG K AH0 N AA1 D AH0 +RIND R AY1 N D +RINDA R IH1 N D AH0 +RINDED R AY1 N D IH0 D +RINDERPEST R IH1 N D ER0 P AH0 S T +RINDGE R IH1 N JH +RINDING R AY1 N D IH0 NG +RINDS R AY1 N D Z +RINDY R IH1 N D IY0 +RINE R AY1 N +RINEHART R AY1 N HH AA2 R T +RINEHART'S R AY1 N HH AA2 R T S +RINELDO R AY1 N L D OW0 +RINEY R AY1 N IY0 +RING R IH1 NG +RING'D R IH1 NG D +RING'S R IH1 NG Z +RINGAMY R IH1 NG G AH0 M IY0 +RINGAN R IH1 NG G AH0 N +RINGAN'S R IH1 NG G AH0 N Z +RINGBOLT R IH1 NG B OW2 L T +RINGBOLTS R IH1 NG B OW2 L T S +RINGBONE R IH1 NG B OW2 N +RINGDOVE R IH1 NG D AH0 V +RINGDOVE'S R IH1 NG D AH0 V Z +RINGDOVES R IH1 NG D UW1 V Z +RINGE R IH1 NG +RINGED R IH1 NG D +RINGER R IH1 NG ER0 +RINGER'S R IH1 NG ER0 Z +RINGERS R IH1 NG ER0 Z +RINGETH R IH1 NG G IH0 TH +RINGFIELD R IH1 NG F IY2 L D +RINGFIELD'S R IH1 NG F IY2 L D Z +RINGG R IH1 NG G +RINGG'S R IH1 NG G Z +RINGGAN R IH1 NG G AH0 N +RINGGAN'S R IH1 NG G AH0 N Z +RINGGOLD R IH1 NG G OW2 L D +RINGGOLDS R IH1 NG G OW2 L D Z +RINGHEIM R IH1 NG HH AY2 M +RINGIN R IH1 NG AH0 N +RINGING R IH1 NG IH0 NG +RINGINGLY R IH1 NG IH0 NG L IY0 +RINGINGS R IH1 NG IH0 NG Z +RINGLEADER R IH1 NG L IY2 D ER0 +RINGLEADERS R IH1 NG L IY2 D ER0 Z +RINGLESS R IH1 NG L AH0 S +RINGLET R IH1 NG G AH0 L T +RINGLETED R IH1 NG G AH0 L T IH0 D +RINGLETS R IH1 NG G AH0 L AH0 T S +RINGLETTED R IH1 NG G L EH1 T IH0 D +RINGLEY R IH1 NG G L IY0 +RINGMASTER R IH1 NG M AE2 S T ER0 +RINGO R IY1 NG G OW0 +RINGO'S R IH1 NG G OW0 Z +RINGOLD R IH1 NG G OW0 L D +RINGROPE R IH1 NG G R OW1 P +RINGROSE R IH1 NG G R OW2 Z +RINGS R IH1 NG Z +RINGSEND R IH1 NG Z AH0 N D +RINGSIDE R IH1 NG S AY2 D +RINGSTETTEN R IH1 NG S T IH0 T AH0 N +RINGSTRASSE R IH1 NG S T R AE1 S +RINGTAIL R IH1 NG T EY2 L +RINGTOP R IH1 NG T AA2 P +RINGTOWN R IH1 NG T AW2 N +RINGWAAK R IH1 NG W AA1 K +RINGWOOD R IH1 NG W UH2 D +RINGWORM R IH1 NG G W ER0 M +RINK R IH1 NG K +RINKA R IH1 NG K AH0 +RINKELMANN R IH1 NG K AH0 L M AH0 N +RINKING R IH1 NG K IH0 NG +RINKITINK R IH1 NG K IH0 T IH0 NG K +RINKITINK'S R IH1 NG K IH0 T IH1 NG K S +RINKS R IH1 NG K S +RINKTUM R IH1 NG K T AH0 M +RINNIN R IH1 N IH0 N +RINNING R IH1 N IH0 NG +RINOLFO R IY0 N OW1 L F OW0 +RINS R IH1 N Z +RINSE R IH1 N S +RINSED R IH1 N S T +RINSES R IH1 N S AH0 Z +RINSING R IH1 N S IH0 NG +RINSINGS R IH1 N S IH0 NG Z +RINT R IH0 N T +RINTOUL R IH1 N T UW1 L +RINTOUL'S R IH1 N T UW1 L Z +RINTS R IH0 N T S +RINTZEVA R IH1 N T Z EY1 V AH0 +RIO R IY1 OW0 +RIO'S R IY1 OW0 Z +RIOBAMBA R IY1 OW0 B AE1 M B AH0 +RIOJA R IY1 OW0 Y AH0 +RIOL R IY0 AH0 L +RIOLAMA R IY0 AH0 L AA1 M AH0 +RIOM R IY1 AH0 M +RION R AY1 AH0 N +RIONGA R IY1 OW0 NG G AH0 +RIORDAN R IH1 R D AH0 N +RIORDON R IH1 R D AH0 N +RIOS R IY1 OW0 S +RIOS'S R IY1 OW0 S IH0 Z +RIOSA R IY0 OW1 S AH0 +RIOT R AY1 AH0 T +RIOT'S R AY1 AH0 T S +RIOTED R AY1 AH0 T IH0 D +RIOTER R AY1 AH0 T ER0 +RIOTERS R AY1 AH0 T ER0 Z +RIOTING R AY1 AH0 T IH0 NG +RIOTINGS R AY1 AH0 T IH0 NG Z +RIOTOUS R AY1 AH0 T AH0 S +RIOTOUSLY R AY1 AH0 T AH0 S L IY0 +RIOTOUSNESS R AY1 AH0 T AH0 S N AH0 S +RIOTS R AY1 AH0 T S +RIOU R IY1 UW0 +RIOWAG R IY1 OW0 W AE2 G +RIP R IH1 P +RIP'S R IH1 P S +RIPA R IY1 P AH0 +RIPARIAN R IH1 P EH1 R IY0 AH0 N +RIPE R AY1 P +RIPELY R AY1 P AH0 L IY0 +RIPEN R AY1 P AH0 N +RIPEN'D R AY1 P AH0 N D +RIPENED R AY1 P AH0 N D +RIPENESS R AY1 P N AH0 S +RIPENIN R AY1 P AH0 N IH1 N +RIPENING R AY1 P AH0 N IH0 NG +RIPENING R AY1 P N IH0 NG +RIPENS R AY1 P AH0 N Z +RIPER R AY1 P ER0 +RIPER'S R AY1 P ER0 Z +RIPES R AY1 P S +RIPEST R AY1 P AH0 S T +RIPETTA R IH1 P EH1 T AH0 +RIPLEY R IH1 P L IY0 +RIPLEY'S R IH1 P L IY0 Z +RIPLEYS R IH1 P L IY0 Z +RIPOGENUS R IH1 P AH0 JH IY1 N AH0 S +RIPOLI R IH1 P AH0 L IY0 +RIPON R IH1 P AA2 N +RIPOSO R IH0 P OW1 S OW0 +RIPOSTE R IH0 P OW1 S T +RIPOSTED R IH0 P OW1 S T IH0 D +RIPOSTES R IH0 P AO1 S T S +RIPPED R IH1 P T +RIPPENGER R IH1 P IH0 N JH ER0 +RIPPENGER'S R IH1 P IH0 N JH ER0 Z +RIPPER R IH1 P ER0 +RIPPERS R IH1 P ER0 Z +RIPPIN R IH1 P IH0 N +RIPPING R IH1 P IH0 NG +RIPPINGILLE R IH1 P IH0 NG G IY1 L IY0 +RIPPINGLY R IH1 P IH0 NG L IY0 +RIPPLE R IH1 P AH0 L +RIPPLE'S R IH1 P AH0 L Z +RIPPLED R IH1 P AH0 L D +RIPPLELESS R IH1 P AH0 L L AH0 S +RIPPLES R IH1 P AH0 L Z +RIPPLETON R IH1 P AH0 L T AH0 N +RIPPLIN R IH1 P L IH0 N +RIPPLING R IH1 P AH0 L IH0 NG +RIPPLING R IH1 P L IH0 NG +RIPPLINGLY R IH1 P AH0 L IH0 NG L IY0 +RIPPLINGS R IH1 P AH0 L IH0 NG Z +RIPPLY R IH1 P L AY1 +RIPPON R IH1 P AH0 N +RIPS R IH1 P S +RIPSY R IH1 P S IY0 +RIPT R IH1 P T +RIPTON R IH1 P T AH0 N +RIPTON'S R IH1 P T AH0 N Z +RIQU'L R IY1 G AH0 L +RIQUET R IY1 K EY0 T +RIQUET'S R IY1 K EH0 T S +RIQUETTE R IY1 K EH1 T +RIQUETTI R IY1 K EH1 T IY0 +RIQUIER R IH1 K Y ER0 +RIRE R AY1 ER0 +RIS R IH1 S +RIS'N R IH1 S AH0 N +RISALDAR R AY1 Z AH0 L D AA2 R +RISALDAR'S R AY1 Z AH0 L D AA2 R Z +RISCHENHEIM R IH1 SH AH0 N HH AY2 M +RISCHENHEIM'S R IH1 SH AH0 N HH AY2 M Z +RISE R AY1 Z +RISEHOLME R IH1 Z AH0 HH OW0 L M +RISEING R AY1 Z IH0 NG +RISEN R IH1 Z AH0 N +RISER R AY1 Z ER0 +RISERS R AY1 Z ER0 Z +RISES R AY1 Z AH0 Z +RISES R AY1 Z IH0 Z +RISEST R AY1 Z AH0 S T +RISETH R IH1 S IH0 TH +RISH R IH1 SH +RISHI R IY1 SH IY0 +RISHIS R IH0 SH IH0 S +RISHYAS R IH1 SH Y AH0 S +RISIBILITIES R IH1 S IH0 B IH1 L IH0 T IY0 Z +RISIBILITY R IH1 S IH0 B IH1 L IH0 T IY0 +RISIBLE R IH1 Z AH0 B AH0 L +RISIBLES R IH1 Z AH0 B AH0 L Z +RISIN R AY1 Z IH0 N +RISING R AY1 Z IH0 NG +RISINGHAM R AY1 Z IH0 NG HH AE2 M +RISINGS R AY1 Z IH0 NG Z +RISK R IH1 S K +RISK'D R IH1 S K D +RISK'S R IH1 S K S +RISKED R IH1 S K T +RISKIEST R IH1 S K IY0 AH0 S T +RISKIN R IH1 S K IH0 N +RISKINESS R IH1 S K IY0 N AH0 S +RISKING R IH1 S K IH0 NG +RISKS R IH1 S K S +RISKY R IH1 S K IY0 +RISLER R IH1 S L ER0 +RISLEY R IH1 Z L IY0 +RISLEY'S R IH1 Z L IY0 Z +RISO R IY1 S OW0 +RISORGIMENTO R IY2 S AO1 R JH AH0 M EH1 N T OW0 +RISOTTO R IY2 S AO1 T OW2 +RISPECT R IY0 S P EH2 K T +RISPECTS R IY0 S P EH2 K T S +RISPETTO R IY0 S P EH1 T OW0 +RISPONDI R IY0 S P OW1 N D IY0 +RISPOSE R IY0 S P OW1 Z +RISQUE R IH2 S K EY1 +RISQUED R IH2 S K D +RISQUES R IH2 S K S +RISSALA R IH1 S AH0 L AH0 +RISSALDAR R IH1 S AH0 L D AA2 R +RISSOLE R IH0 S OW1 L +RISSOLES R IH0 S OW1 L Z +RIST R IH1 S T +RISTAL R IH1 S T AH0 L +RISTIGOUCHE R IH1 S T IH0 G UW1 SH +RISTOFALO R IY0 S T OW0 F AA1 L OW0 +RISTON R IH1 S T AH0 N +RISTORANTE R IH0 S T ER0 AA1 N T EY0 +RISTORI R IH1 S T AO0 R IY0 +RISU R IH1 S UW2 +RISUM R IH1 Z AH0 M +RISUS R IH1 S AH0 S +RIT R IH1 T +RITA R IY1 T AH0 +RITA'S R IY1 T AH0 Z +RITCHEY R IH1 CH IY0 +RITCHIE R IH1 CH IY0 +RITCHIE'S R IH1 CH IY0 Z +RITCHING R IH1 CH IH0 NG +RITE R AY1 T +RITES R AY1 T S +RITHMETIC R IH0 TH M EH1 T IH0 K +RITIN R IH1 T IH0 N +RITING R AY1 T IH0 NG +RITO R IY1 T OW0 +RITORNELLO R IH0 T AO2 R N EH1 L OW0 +RITSON R IH1 T S AH0 N +RITSON'S R IH1 T S AH0 N Z +RITT R IH1 T +RITTA R IH1 T AH0 +RITTEN R IH1 T AH0 N +RITTENHEIM R IH1 T AH0 N HH AY2 M +RITTENHEIM'S R IH1 T AH0 N HH AY2 M Z +RITTENHOUSE R IH1 T AH0 N HH AW2 S +RITTER R IH1 T ER0 +RITTER'S R IH1 T ER0 Z +RITTERS R IH1 T ER0 Z +RITTERSAAL R IH1 T ER0 S AA1 L +RITTERSHEIM R IH1 T ER0 S HH AY0 M +RITTI R IY1 T IY0 +RITTMEISTER R IH1 T M AY0 S T ER0 +RITUAL R IH1 CH UW0 AH0 L +RITUALISM R IH2 CH UW0 AH0 L IH2 Z AH0 M +RITUALIST R IH0 CH AH0 W AH0 L IH0 S T +RITUALISTIC R IH2 CH UW0 AH0 L IH1 S T IH0 K +RITUALISTS R IH0 CH AH0 W AH0 L IH0 S T S +RITUALLY R IH1 CH UW0 AH0 L IY0 +RITUALS R IH1 CH UW0 AH0 L Z +RITUEL R IH2 CH UW0 AH0 L +RITZ R IH1 T S +RITZ'S R IH1 T S IH0 Z +RITZER R IH1 T Z ER0 +RITZMORE R IH1 T S M AO0 R +RITZTON R IH1 T S T AH0 N +RIV R IH1 V +RIV'RENCE R IH1 V R AH0 N S +RIVA R IY1 V AH0 +RIVAGE R AY1 V AH0 JH +RIVAL R AY1 V AH0 L +RIVAL'S R AY1 V AH0 L Z +RIVALED R AY1 V AH0 L D +RIVALING R AY1 V AH0 L IH0 NG +RIVALING R AY1 V L IH0 NG +RIVALLED R AY1 V AH0 L D +RIVALLING R AY1 V AH0 L IH0 NG +RIVALRIES R AY1 V AH0 L R IY0 Z +RIVALRY R AY1 V AH0 L R IY0 +RIVALS R AY1 V AH0 L Z +RIVALSHIP R AY1 V AH0 L SH IH2 P +RIVALSHIPS R AY1 V AH0 L SH IH2 P S +RIVANNA R IH0 V AE1 N AH0 +RIVAR R IH1 V ER0 +RIVARDI R IH1 V ER0 D IY0 +RIVAREZ R IH1 V ER0 EH2 Z +RIVAROL R IH0 V AE1 R AH0 L +RIVAROL'S R IH0 V AE1 R AH0 L Z +RIVAS R IY1 V AA0 Z +RIVAZ R IH0 V AA2 Z +RIVE R AY1 V +RIVED R AY1 V D +RIVEDERCI R IH1 V IH0 D IH1 R CH IY0 +RIVEH R IH1 V AH0 +RIVEN R IH1 V AH0 N +RIVENOAK R IH1 V IH0 N OW1 K +RIVER R IH1 V ER0 +RIVER'S R IH1 V ER0 Z +RIVERA R IH0 V EH1 R AH0 +RIVERA'S R IH1 V ER0 AH0 Z +RIVERAIN R IH1 V ER0 EY1 N +RIVERBANK R IH1 V ER0 B AE2 NG K +RIVERBANKS R IH1 V ER0 B AE2 NG K S +RIVERBED R IH1 V ER0 B EH2 D +RIVERBEDS R IH1 V ER0 B EH2 D Z +RIVERBEND R IH1 V ER0 B EH2 N D +RIVERBORO R IH1 V ER0 B ER0 OW0 +RIVERBY R IH1 V ER0 B IY0 +RIVERCOURT R IH1 V ER0 K AO2 R T +RIVERDALE R IH1 V ER0 D EY2 L +RIVERDENE R IH0 V EH1 R D IY0 N +RIVEREDGE R IH1 V ER0 EH0 JH +RIVERENCE R IH1 V ER0 AH0 N S +RIVERENZA R IH1 V ER0 EH1 N Z AH0 +RIVERFIELD R IH1 V ER0 F IY2 L D +RIVERFRONT R IH1 V ER0 F R AH2 N T +RIVERHEAD R IH1 V ER0 HH EH2 D +RIVERINA R IH1 V ER0 IY1 N AH0 +RIVERINCE R IH1 V ER0 IH0 N S +RIVERINE R IH1 V ER0 IY1 N IY0 +RIVERLAWN R IH1 V ER0 L AO2 N +RIVERLAWNS R IH1 V ER0 L AO1 N Z +RIVERMAN R IH1 V ER0 M AH0 N +RIVERMAN'S R IH1 V ER0 M AH0 N Z +RIVERMEAD R IH1 V ER0 M EH2 D +RIVERMEN R IH1 V ER0 M IH0 N +RIVERMOUTH R IH1 V ER0 M AH0 TH +RIVERO R IY0 V EH1 R OW0 +RIVEROLA R IH1 V ER0 OW1 L AH0 +RIVEROS R IY0 V EH1 R OW0 Z +RIVERPORT R IH1 V ER0 P AO2 R T +RIVERS R IH1 V ER0 Z +RIVERS'S R IH1 V ER0 Z IH0 Z +RIVERSBOROUGH R IH1 V ER0 Z B ER0 OW0 +RIVERSBROOK R IH1 V ER0 S B R UH2 K +RIVERSDALE R IH1 V ER0 S D EY2 L +RIVERSEDGE R IH1 V ER0 S EH2 JH +RIVERSIDE R IH1 V ER0 S AY2 D +RIVERSLEY R IH1 V ER0 S L IY0 +RIVERSMOUTH R IH1 V ER0 S M AH0 TH +RIVERSTOWN R IH1 V ER0 S T AW2 N +RIVERTON R IH1 V ER0 T AH0 N +RIVERTOWN R IH1 V ER0 T AW2 N +RIVERVALE R IH1 V ER0 V EY2 L +RIVERVIEW R IH1 V ER0 V Y UW2 +RIVERWARD R IH1 V ER0 W ER0 D +RIVERWARDS R IH1 V ER0 W ER0 D Z +RIVERWOOD R IH1 V ER0 W UH2 D +RIVES R AY1 V Z +RIVET R IH1 V AH0 T +RIVETED R IH1 V AH0 T IH0 D +RIVETER R IH1 V AH0 T ER0 +RIVETERS R IH1 V AH0 T ER0 Z +RIVETING R IH1 V AH0 T IH0 NG +RIVETS R IH1 V AH0 T S +RIVETTED R IH0 V EH1 T IH0 D +RIVETTING R IH0 V EH1 T IH0 NG +RIVI R IH2 V IY0 +RIVIERA R IH2 V IY0 EH1 R AH0 +RIVIERE R IH1 V IY0 EH0 R +RIVIERE'S R IH0 V Y EH1 R Z +RIVIERES R IH0 V Y IH1 R EH0 Z +RIVING R AY1 V IH0 NG +RIVINGTON R AY1 V IH0 NG T AH0 N +RIVINGTON'S R AY1 V IH0 NG T AH0 N Z +RIVO R IY1 V OW0 +RIVOLI R IH0 V OW1 L IY0 +RIVOLI'S R IH1 V AH0 L IY0 Z +RIVULET R IH1 V AH1 L IH0 T +RIVULET'S R IH1 V AH1 L EY1 Z +RIVULETS R IH1 V AH1 L AH0 T S +RIX R IH1 K S +RIXA R IH1 K S AH0 +RIXDOLLARS R IH1 K S D AO2 L ER0 Z +RIYA R IY0 AH0 +RIZ R IH1 Z +RIZA R IY1 Z AH0 +RIZAL R IY0 Z AA1 L +RIZAL'S R IY0 Z AA1 L Z +RIZOFF R IH1 Z AO0 F +RIZPAH R IH1 T S P AA2 +RIZZI R IH1 Z IY0 +RIZZIO R IH1 Z IY0 OW0 +RIZZIO'S R IY0 T S IY0 OW0 Z +RIZZO R IH1 Z OW0 +RIZZON R IH0 Z OW1 N +RJUKAN R JH AH0 K AH0 N +RJUKANFOS R JH AH0 K AE1 N F OW0 Z +RK R K +RL R L +RM R M +RN R N +RNER R N ER0 +RNSON R N S AH0 N +RNSTJERNE R N S T JH ER1 N +RO R OW1 +RO'D R OW1 D +ROA R OW1 AH0 +ROAB R OW1 AE0 B +ROABE R OW0 EY1 B +ROACH R OW1 CH +ROACH'S R OW1 CH IH0 Z +ROACHBACK R OW1 CH B AE2 K +ROACHED R OW1 CH T +ROACHES R OW1 CH IH0 Z +ROAD R OW1 D +ROAD'LL R OW1 D L +ROAD'S R OW1 D Z +ROADBED R OW1 D B EH2 D +ROADBEDS R OW1 D B EH2 D Z +ROADE R OW1 D +ROADHOUSE R OW1 D HH AW2 S +ROADHOUSES R OW1 D HH AW2 S IH0 Z +ROADLESS R OW1 D L AH0 S +ROADMAN R OW1 D M AH0 N +ROADMASTER R OW1 D M AE2 S T ER0 +ROADMASTERS R OW1 D M AE2 S T ER0 Z +ROADMEN R OW1 D M AH0 N +ROADMENDER R OW1 D M EH1 N D ER0 +ROADS R OW1 D Z +ROADSIDE R OW1 D S AY2 D +ROADSIDES R OW1 D S AY2 D Z +ROADSTEAD R OW1 D S T EH2 D +ROADSTEADS R OW1 D S T EH2 D Z +ROADSTER R OW1 D S T ER0 +ROADSTER'S R OW1 D S T ER0 Z +ROADSTERS R OW1 D S T ER0 Z +ROADWAY R OW1 D W EY2 +ROADWAYS R OW1 D W EY2 Z +ROADY R OW1 D IY0 +ROAG R OW1 G +ROAM R OW1 M +ROAM'D R OW1 M D +ROAMED R OW1 M D +ROAMER R OW1 M ER0 +ROAMERS R OW1 M ER0 Z +ROAMIN R OW1 M IH0 N +ROAMING R OW1 M IH0 NG +ROAMINGS R OW1 M IH0 NG Z +ROAMS R OW1 M Z +ROAN R OW1 N +ROAN'S R OW1 N Z +ROANE R OW1 N +ROANNE R OW2 AE1 N +ROANNES R OW2 AE1 N Z +ROANOAK R OW1 AH0 N OW2 K +ROANOKE R OW1 AH0 N OW2 K 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AA1 B ER0 T S AH0 N +ROBERTSON'S R AA1 B ER0 T S AH0 N Z +ROBERTSONS R AA1 B ER0 T S AH0 N Z +ROBERTUS R AA1 B ER1 T AH0 S +ROBERVAL R AA1 B ER0 V AH0 L +ROBERVAL'S R AA1 B ER0 V AH0 L Z +ROBES R OW1 B Z +ROBESART R OW1 B IH0 S AA0 R T +ROBESON R OW1 B IH0 S AH0 N +ROBESONS R OW1 B IH0 S AH0 N Z +ROBESPIERRE R OW0 B EH1 S P IY0 EH1 R +ROBESPIERRE'S R OW0 B EH1 S P IY0 EH1 R Z +ROBESPIERRES R OW0 B EH1 S P IY0 EH1 R Z +ROBEY R OW1 B IY0 +ROBEY'S R OW1 B IY0 Z +ROBIDEAU R AA1 B IH0 D OW2 +ROBIE R OW1 B IY0 +ROBIN R AA1 B AH0 N +ROBIN R AA1 B IH0 N +ROBIN'S R AA1 B AH0 N Z +ROBIN'S R AA1 B IH0 N Z +ROBINA R AH0 B IY1 N AH0 +ROBINA'S R OW0 B IY1 N AH0 Z +ROBINE R AA1 B AH0 N +ROBINEAU R AA1 B IH0 N OW2 +ROBINET R AA1 B IH0 N EH2 T +ROBINETTA R AA1 B IH0 N EH1 T AH0 +ROBINETTE R AA1 B IH0 N EH2 T +ROBING R AA1 B IH0 NG +ROBINGS R AA1 B IH0 NG Z +ROBINIA R OW0 B IY1 N IY0 AH0 +ROBINS R AA1 B AH0 N Z +ROBINSBURG R AA1 B AH0 N Z B ER0 G +ROBINSON R AA1 B AH0 N S AH0 N +ROBINSON'S R AA1 B AH0 N S AH0 N Z +ROBINSONS R AA1 B AH0 N S AH0 N Z +ROBINSTEIN R AA1 B IH0 N S T AY2 N +ROBINVAST R AA1 B IH0 N V AH0 S T +ROBISCH R AA1 B IH0 SH +ROBISON R AA1 B IH0 S AH0 N +ROBLADO R AA1 B L AA1 D OW0 +ROBLADO'S R AA1 B L AE1 D OW0 Z +ROBLEDO R OW0 B L EY1 D OW0 +ROBLES R OW1 B AH0 L Z +ROBLEY R AA1 B L IY0 +ROBLEZ R OW0 B L EY1 Z +ROBLING R OW1 B AH0 L IH0 NG +ROBLING R OW1 B L IH0 NG +ROBO R OW1 B OW0 +ROBOT R OW1 B AA2 T +ROBOT R OW1 B AH2 T +ROBOT'S R OW1 B AA2 T S +ROBOT'S R OW1 B AH2 T S +ROBOTCAB R OW1 B AH2 T K AE2 B +ROBOTIC R OW2 B AA1 T IH0 K +ROBOTICS R OW2 B AA1 T IH0 K S +ROBOTRON R OW2 B AA1 T R AH0 N +ROBOTS R OW1 B AA2 T S +ROBOTS R OW1 B AH2 T S +ROBS R AA1 B Z +ROBSART R AA1 B S ER0 T +ROBSART'S R AA1 B S ER0 T S +ROBSON R AA1 B S AH0 N +ROBSON'S R AA1 B S AH0 N Z +ROBSONS R AA1 B S AH0 N Z +ROBT R AA1 T +ROBUR R AO2 B UH1 R +ROBUST R OW0 B AH1 S T +ROBUSTA R AO2 B UH1 S T AH0 +ROBUSTER R OW0 B AH1 S T ER0 +ROBUSTI R OW0 B AH1 S T IY0 +ROBUSTIOUS R AO2 B UH1 S SH AH0 S +ROBUSTLY R OW2 B AH1 S T L IY0 +ROBUSTNESS R OW0 B AH1 S T N AH0 S +ROBUSTO R OW0 B AH1 S T OW0 +ROBY R OW1 B IY0 +ROBY'S R OW1 B IY0 Z +ROBYN R AA1 B IH0 N +ROC R AA1 K +ROC'S R AA1 K S +ROCA R OW1 K AH0 +ROCAMBEAU R OW1 K AH0 M B OW1 +ROCCA R AA1 K AH0 +ROCCA'S R AA1 K AH0 Z +ROCCABRUNA R AA1 K AH0 B R UW1 N AH0 +ROCCANERA R AA1 K AH0 N EH1 R AH0 +ROCCHI R OW1 K IY0 +ROCCO R AA1 K OW0 +ROCCO'S R AA1 K OW0 Z +ROCDIANE R OW1 K D IY0 AE1 N +ROCH R AA1 K +ROCH'S R AA1 CH IH0 Z +ROCHA R OW1 K AH0 +ROCHAIGUE R AA1 CH AY1 G +ROCHAIGUE'S R AA1 CH AY1 G Z +ROCHAL R AA1 CH AH0 L +ROCHALLY R AA1 CH AH0 L IY0 +ROCHAMBEAU R AA1 SH AH0 M B OW1 +ROCHAMBEAU'S R AA1 SH AH0 M B OW2 Z +ROCHARD R AA1 CH ER0 D +ROCHAS R OW1 K AH0 Z +ROCHCLIFFE R AA1 CH K L IH0 F +ROCHDALE R AA1 CH D EY2 L +ROCHE R OW1 CH +ROCHE R OW1 SH +ROCHE'S R OW1 SH IH0 Z +ROCHEBLAVE R OW0 SH EH1 B L EY1 V +ROCHEBRIANT R AA1 SH IH0 B R AY1 AH0 N T +ROCHEBRUNE R AA1 CH EH1 B R AH0 N +ROCHECHOUART R AH0 CH EH2 HH UW1 AA0 R T +ROCHECLIFFE R AA1 CH EH2 K L IH0 F +ROCHECLIFFE'S R AA1 CH EH2 K L IH0 F S +ROCHEFIDE R AA1 SH F AY2 D +ROCHEFORD R AA1 SH F ER0 D +ROCHEFORT R AA1 K F ER0 T +ROCHEFORT R AA1 K IH0 F ER0 T +ROCHEFORT R AA1 SH F ER0 T +ROCHEFORT'S R AA1 SH F ER0 T S +ROCHEFOUCAULD R AA1 SH F UW0 K AA1 L D +ROCHEFOUCAULD'S R AA1 SH F UW0 K AA1 L D Z +ROCHEFOUCAULT R AA1 SH F AW1 K OW1 +ROCHEJAQUELEIN R OW0 SH EY1 Y AA1 K W IH0 L AY2 N +ROCHEJAQUELIN R OW0 SH EY1 Y AA0 K W AH0 L IH0 N +ROCHEJHONE R OW1 SH EY1 OW1 N +ROCHELLAIS R OW0 SH EH1 L EY1 +ROCHELLE R AH2 SH EH1 L +ROCHELLE R OW0 SH EH1 L +ROCHELLOIS R OW0 SH EH1 L W AA1 +ROCHEMAURE R AA1 K IH0 M AO1 R +ROCHEMONT R OW2 SH M AA2 N T +ROCHEPOMMIER R AA1 SH IH0 P AA1 M IY0 ER0 +ROCHEPOT R AA1 CH EH2 P AA2 T +ROCHER R OW1 CH ER0 +ROCHER R OW1 SH ER0 +ROCHERS R OW1 CH ER0 Z +ROCHES R AA1 SH IH0 Z +ROCHESTER R AA1 CH EH2 S T ER0 +ROCHESTER'S R AA1 CH EH2 S T ER0 Z +ROCHESTERS R AA1 CH EH2 S T ER0 Z +ROCHET R OW0 SH EY1 +ROCHETS R OW0 SH EY1 Z +ROCHETTE R AH0 SH EH1 T +ROCHEVERTE R AA1 SH IH0 V ER1 T +ROCHEVILLE R OW1 SH V IH2 L +ROCHEZ R AA1 CH EH2 Z +ROCHFORD R AA1 CH F ER0 D +ROCHFORT R AA1 CH F ER0 T +ROCHLITZ R AA1 K L IH0 T S +ROCHOLL R AA1 CH AH0 L +ROCHOW R OW1 CH AW0 +ROCHUS R AA1 CH AH0 S +ROCJEAN R OW1 K JH IY1 N +ROCK R AA1 K +ROCK'D R AA1 K D +ROCK'S R AA1 K S +ROCKA R AA1 K AH0 +ROCKABY R AA1 K AH0 B IY0 +ROCKAWAY R AA1 K AH0 W EY2 +ROCKBOUND R AA1 K B AW2 N D +ROCKBRIDGE R AA1 K B R IH0 JH +ROCKBURG R AA1 K B ER0 G +ROCKDALE R AA1 K D EY2 L +ROCKE R AA1 K +ROCKED R AA1 K T +ROCKEFELLER R AA1 K AH0 F EH2 L ER0 +ROCKEFELLER'S R AA1 K AH0 F EH2 L ER0 Z +ROCKEFELLERS R AA1 K AH0 F EH2 L ER0 Z +ROCKER R AA1 K ER0 +ROCKERIES R AA1 K ER0 IY0 Z +ROCKERS R AA1 K ER0 Z +ROCKERY R AA1 K ER0 IY0 +ROCKET R AA1 K AH0 T +ROCKET'S R AA1 K AH0 T S +ROCKETED R AA1 K AH0 T IH0 D +ROCKETING R AA1 K AH0 T IH0 NG +ROCKETPORT R AA1 K AH0 T P AO2 R T +ROCKETS R AA1 K AH0 T S +ROCKETSHIP R AA1 K AH0 T SH IH2 P +ROCKETT R AA1 K IH0 T +ROCKEY R AA1 K IY0 +ROCKFACE R AA1 K F EY2 S +ROCKFISH R AA1 K F IH2 SH +ROCKFORD R AA1 K F ER0 D +ROCKFORD'S R AA1 K F ER0 D Z +ROCKHAM R AA1 K HH AH0 M +ROCKHAMMER R AA1 K HH AE2 M ER0 +ROCKHAVEN R AA1 K HH EY2 V AH0 N +ROCKHILL R AA1 K HH IH2 L +ROCKIER R AA1 K IY0 ER0 +ROCKIES R AA1 K IY0 Z +ROCKIEST R AA1 K IY0 AH0 S T +ROCKIN R AA1 K IH0 N +ROCKIN' R AA1 K IH0 N +ROCKINESS R AA1 K IY0 N AH0 S +ROCKING R AA1 K IH0 NG +ROCKINGHAM R AA1 K IH0 NG HH AE2 M +ROCKINGS R AA1 K IH0 NG Z +ROCKLAND R AA1 K L AH0 N D +ROCKLAND'S R AA1 K L AH0 N D Z +ROCKLEDGE R AA1 K L EH2 JH +ROCKLEY R AA1 K L IY0 +ROCKLEY'S R AA1 K L IY0 Z +ROCKLIKE R AA1 K L AY2 K +ROCKLIN R AA1 K L IH0 N +ROCKMETTELLER R AA1 K M T AH0 L ER0 +ROCKMINSTER R AA1 K M IH2 N S T ER0 +ROCKMINSTER'S R AA1 K M IH2 N S T ER0 Z +ROCKNEY R AA1 K N IY0 +ROCKNEY'S R AA1 K N IY0 Z +ROCKPIER R AA1 K P AY2 ER0 +ROCKPOINT R AA1 K P OY2 N T +ROCKPORT R AA1 K P AO2 R T +ROCKQUAY R AA1 K K EY1 +ROCKS R AA1 K S +ROCKSAND R AA1 K S AE2 N D +ROCKSHIRE R AA1 K SH AY2 R +ROCKSTONE R AA1 K S T OW2 N +ROCKSTRO R AA1 K S T R OW0 +ROCKSWORTH R AA1 K S W ER0 TH +ROCKTON R AA1 K T AH0 N +ROCKVILLE R AA1 K V IH2 L +ROCKVILLES R AA1 K V IH2 L Z +ROCKWEED R AA1 K W IY2 D +ROCKWELL R AA1 K W EH2 L +ROCKWELL'S R AA1 K W EH2 L Z +ROCKWOOD R AA1 K W UH2 D +ROCKWORK R AA1 K W ER2 K +ROCKY R AA1 K IY0 +ROCKY'S R AA1 K IY0 Z +ROCKYMOUTH R AA1 K IY0 M AH0 TH +ROCLIFFE R AH0 K L IH0 F +ROCLIFFES R AH0 K L IH0 F S +ROCNABAD R OW1 K N AH0 B AE2 D +ROCOCO R AH0 K OW1 K OW2 +ROCQUAINE R AA1 K EY0 N +ROCQUE R AA1 K +ROCREUSE R OW1 K R IY0 Y UW1 Z +ROCROI R AA1 K R OY1 +ROCROY R AA1 K R OY1 +ROCS R AA1 K S +ROCUS R OW1 K AH0 S +ROD R AA1 D +ROD'S R AA1 D Z +RODA R OW1 D AH0 +RODADERO R AA1 D AH0 D EH1 R OW0 +RODAINE R AA1 D EY0 N +RODAINES R AA1 D EY1 N Z +RODAN R OW1 D AH0 N +RODAN'S R OW1 D AH0 N Z +RODBER R AA1 D B ER0 +RODBURD R AA1 D B ER2 D +RODCHURCH R AA1 D CH ER1 CH +RODD R AA1 D +RODD'S R AA1 D Z +RODDAM R AA1 D AA1 M +RODDICE R AA1 D IH0 S +RODDY R AA1 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S AA0 CH EH1 R D OW1 T AH0 L +SACERDOTALISM S AA0 CH EH1 R D OW1 T AH0 L IH2 Z AH0 M +SACERDOTIS S AA0 CH EH1 R D OW1 T IH0 S +SACH S AE1 CH +SACHA S AE1 CH AH0 +SACHARISSA S AE1 K AA0 R IY1 S AH0 +SACHE S AA1 SH +SACHEL S AE1 CH AH0 L +SACHEM S AE1 CH AH0 M +SACHEM'S S AE1 K EH2 M Z +SACHEMS S AE1 CH AH0 M Z +SACHEPRIS S AE0 SH EY1 P R IH0 S +SACHER S AA1 SH ER0 +SACHER S AE1 K ER0 +SACHET S AE0 SH EY1 +SACHETS S AE1 CH AH0 T S +SACHEVEREL S AE1 CH EH1 V ER0 AH0 L +SACHEVERELL S AE1 CH IH0 V EH0 R EY1 L +SACHIGO S AE1 CH IY1 G OW0 +SACHINESSA S AE1 CH AH0 N EH1 S AH0 +SACHO S AE1 CH OW0 +SACHS S AA1 K S +SACHS S AE1 K S +SACHS'S S AE1 K S IH0 Z +SACHSE S AE1 CH S +SACHSEN S AE1 K S AH0 N +SACHSEN'S S AE1 K S AH0 N Z +SACHSENHAUSEN S AE1 K S AH0 N HH AW2 Z AH0 N +SACIAN S AA1 K AH0 N +SACK S AE1 K +SACK'D S AE1 K D +SACK'S S AE1 K S +SACKBUT S AE1 K B AH0 T +SACKBUTS S AE1 K B T S +SACKCLOTH S AE1 K K L AO2 TH +SACKED S AE1 K T +SACKEN S AE1 K AH0 N +SACKER S AE1 K ER0 +SACKETT S AE1 K IH0 T +SACKETT'S S AE1 K IH0 T S +SACKFUL S AE1 K F AH0 L +SACKFULS S AE1 K F AH0 L Z +SACKIMA S AE1 K IY1 M AH0 +SACKING S AE1 K IH0 NG +SACKINGS S AE1 K IH0 NG Z +SACKLESS S AE1 K L AH0 S +SACKS S AE1 K S +SACKVILLE S AE1 K V IH2 L +SACKVILLE'S S AE1 K V IH2 L Z +SACNOTH S AE1 K N AA1 TH +SACO S EY1 K OW0 +SACQUE S AE1 K +SACQUES S AE1 K S +SACR S AE1 K R +SACRA S AE1 K R AH0 +SACRAE S AE1 K R EY1 +SACRAL S AE1 K R AH0 L +SACRAMENT S AE1 K R AH0 M AH0 N T +SACRAMENTAL S AE1 K R AH0 M AH0 N T AH0 L +SACRAMENTALISM S AE1 K R AH0 M EH1 N AH0 L IH2 Z AH0 M +SACRAMENTALLY S AE1 K R AH0 M EH1 N AH0 L IY0 +SACRAMENTARIANS S AE1 K R AH0 M AH0 N T EH1 R IY0 AH0 N Z +SACRAMENTO S AE2 K R AH0 M EH1 N T OW0 +SACRAMENTOS S AE1 K R AH0 M EH1 N T OW0 Z +SACRAMENTS S AE1 K R AH0 M AH0 N T S +SACRAMENTUM S AE1 K R AH0 M EH1 N T AH0 M +SACRE S AH0 K ER0 +SACREBLEU S AE1 K R EH1 B L UW0 +SACRED S EY1 K R AH0 D +SACRED S EY1 K R IH0 D +SACREDEST S EY1 K R IH0 D AH0 S T +SACREDLY S EY1 K R IH0 D L IY0 +SACREDNESS S EY1 K R IH0 D N IH0 S +SACREE S EY1 K R IY1 +SACREMENT S EY1 K R AH0 M AH0 N T +SACRES S EY1 K R Z +SACRET S EY1 K R IH0 T +SACRI S AE1 K R IY0 +SACRIFICE S AE1 K R AH0 F AY2 S +SACRIFICED S AE1 K R AH0 F AY2 S T +SACRIFICER S AE1 K R AH0 F AH0 S ER0 +SACRIFICERS S AE1 K R AH0 F AH0 S ER0 Z +SACRIFICES S AE1 K R AH0 F AY2 S IH0 Z +SACRIFICETH S AE1 K R AH0 F AY2 S IH0 TH +SACRIFICIAL S AE2 K R AH0 F IH1 SH AH0 L +SACRIFICIN S AE1 K R AH0 F AY2 S IH0 N +SACRIFICING S AE1 K R AH0 F AY2 S IH0 NG +SACRIFICINGLY S AE1 K R AH0 F AY2 S IH0 NG L IY0 +SACRILEGE S AE1 K R AH0 L AH0 JH +SACRILEGES S AE1 K R AH0 L AH0 JH AH0 Z +SACRILEGIOUS S AE2 K R AH0 L EH1 JH IH0 S +SACRILEGIOUSLY S AE1 K R AH0 L EH1 JH IH0 S L IY0 +SACRING S AE1 K R IH0 NG +SACRIPANT S AE1 K R AH0 P AH0 N T +SACRIPANTE S AE1 K R AH0 P AA1 N T IY0 +SACRIS S AE1 K R AH0 S +SACRIST S AE1 K R AH0 S T +SACRISTAN S AE1 K R AH0 S T AE2 N +SACRISTAN'S S AE1 K R AH0 S T AE2 N Z +SACRISTANS S AE1 K R AH0 S T AE2 N Z +SACRISTI S AE1 K R IY0 S T IY0 +SACRISTIES S AE1 K R IY0 S T IY0 Z +SACRISTY S AE1 K R AH0 S T IY0 +SACRO S AE1 K R OW0 +SACROSANCT S AE1 K R OW0 S AE0 NG K T +SACROVIR S AE1 K R OW0 V IH2 R +SACRUM S AE1 K R AH0 M +SACS S AE1 K S +SACY S EY1 S IY0 +SAD S AE1 D +SADA S AA1 D AH0 +SADA'S S AE1 D AH0 Z +SADAGORA S AH0 D AE1 G ER0 AH0 +SADAI S AH0 D AY1 +SADAKO S AA0 D AA1 K OW0 +SADAKO'S S AA0 D AA1 K OW0 Z +SADAU S AH0 D AW0 +SADDELL S AE1 D AH0 L +SADDEN S AE1 D AH0 N +SADDEN'D S AE1 D AH0 N D +SADDENED S AE1 D AH0 N D +SADDENING S AE1 D AH0 N IH0 NG +SADDENING S AE1 D N IH0 NG +SADDENS S AE1 D AH0 N Z +SADDER S AE1 D ER0 +SADDEST S AE1 D AH0 S T +SADDHU S AE1 D HH UW0 +SADDLE S AE1 D AH0 L +SADDLE'S S AE1 D AH0 L Z +SADDLEBACK S AE1 D AH0 L B AE2 K +SADDLEBAG S AE1 D AH0 L B AE2 G +SADDLEBAGS S AE1 D AH0 L B AE2 G Z +SADDLEBANK S AE1 D AH0 L B AE2 NG K +SADDLEBOW S AE1 D AH0 L B OW2 +SADDLED S AE1 D AH0 L D +SADDLEHORN S AE1 D AH0 L HH AO2 R N +SADDLELESS S AE1 D AH0 L L AH0 S +SADDLER S AE1 D AH0 L ER0 +SADDLER S AE1 D L ER0 +SADDLER'S S AE1 D AH0 L ER0 Z +SADDLERS S AE1 D AH0 L ER0 Z +SADDLERY S AE1 D AH0 L ER0 IY0 +SADDLES S AE1 D AH0 L Z +SADDLETREE S AE1 D AH0 L T R IY2 +SADDLIN S AE1 D L IH0 N +SADDLING S AE1 D AH0 L IH0 NG +SADDLING S AE1 D L IH0 NG +SADDOC S AE1 D AH0 K +SADDUCEAN S AE1 D AH0 SH AH0 N +SADDUCEE S AE1 D AH0 IY0 +SADDUCEEISM S AE1 D AH0 S IY1 IH0 Z AH0 M +SADDUCEES S AE1 D AH0 S IY1 Z +SADE S EY1 D +SADEN S EY1 D AH0 N +SADENWATER S AA0 D EH1 N W AA2 T ER0 +SADESCHEO S AA0 D EH1 SH IY0 OW0 +SADGROVE S AE1 D G R OW2 V +SADHANA S AA1 D AA1 N AH0 +SADHU S AE1 D HH UW0 +SADHU'S S AE1 D HH UW0 Z +SADHUS S AE1 D HH IH0 S +SADI S AE1 D IY0 +SADIC S EY1 D IH0 K +SADIE S AE1 D IY0 +SADIE'S S EY1 D IY0 Z +SADIK S EY1 D IH0 K +SADISM S EY1 D IH0 Z AH0 M +SADIST S EY1 D AH0 S T +SADISTIC S AH0 D IH1 S T IH0 K +SADKO S AE1 D K OW0 +SADLEIR S AE1 D L EH1 R +SADLER S AE1 D L ER0 +SADLER'S S AE1 D L ER0 Z +SADLERS S AE1 D L ER0 Z +SADLIER S AE1 D L IY0 ER0 +SADLY S AE1 D L IY0 +SADNESS S AE1 D N AH0 S +SADNESSE S AE1 D N AH0 S +SADNESSES S AE1 D N AH0 S IH0 Z +SADO S EY1 D OW0 +SADOC S AE1 D AH0 K +SADONG S AH0 D AO1 NG +SADOVSKI S AH0 D AO1 V S K IY0 +SADOWA S AH0 D AW1 AH0 +SADR S AE1 D R +SADS S AE1 D Z +SADU S AA1 D UW0 +SADUKO S AA0 D UW1 K OW0 +SADUKO'S S AA0 D UW1 K OW0 Z +SADY S EY1 D IY0 +SAE S EY1 +SAECLUM S IY1 CH L AH0 M +SAECULA S EH1 K Y UW0 L AH0 +SAECULORUM S EH1 K Y UW0 L AO1 R AH0 M +SAEED S AA0 IY1 D +SAEGER S EH1 G ER0 +SAEGLEK S EH1 G L EH0 K +SAEKKINGEN S EH1 K IH0 NG AH0 N +SAELINGSDALE S EH1 L IH0 NG G Z D EY2 L +SAEMUND S EY1 M AH0 N D +SAEMUND'S S EY1 M AH0 N D Z +SAENGER S EH1 N JH ER0 +SAENGERFEST S EH1 N JH ER0 F EH2 S T +SAENS S EY1 N Z +SAENS'S S EY1 N Z IH0 Z +SAENTIS S EY1 N T IH0 S +SAEPE S IY1 P +SAETER S IY1 T ER0 +SAETERS S IY1 T ER0 Z +SAETZEMA S IY1 T S AH0 M AH0 +SAETZEMAS S IY1 T S M AH0 Z +SAEVA S EY1 V AH0 +SAEVUNA S EY1 V UW1 N AH0 +SAFA S AE1 F AH0 +SAFAL S AH0 F AA1 L +SAFAR S AH0 F AA1 R +SAFARI S AH0 F AA1 R IY0 +SAFARIS S AH0 F AA1 R IY0 Z +SAFE S EY1 F +SAFE'S S EY1 F S +SAFED S EY1 F T +SAFEGUARD S EY1 F G AA2 R D +SAFEGUARDED S EY1 F G AA2 R D IH0 D +SAFEGUARDING S EY1 F G AA2 R D IH0 NG +SAFEGUARDS S EY1 F G AA2 R D Z +SAFEKEEPING S EY1 F K IY1 P IH0 NG +SAFEL S EY1 F AH0 L +SAFELY S EY1 F L IY0 +SAFENESS S EY1 F N AH0 S +SAFER S EY1 F ER0 +SAFES S EY1 F S +SAFEST S EY1 F AH0 S T +SAFETIES S EY1 F T IY0 Z +SAFETY S EY1 F T IY0 +SAFETY'S S EY1 F T IY0 Z +SAFFED S AE1 F T +SAFFI S AE1 F IY0 +SAFFIR S AH0 F IY1 R +SAFFIR'S S AH0 F IY1 R Z +SAFFLOWER S AE1 F L AW2 ER0 +SAFFORD S AE1 F ER0 D +SAFFRAN S AE1 F R AH0 N +SAFFREN S AE1 F R IH0 N +SAFFRON S AE1 F R AH0 N +SAFFRON'S S AE1 F R AH0 N Z +SAFFY S AE1 F IY0 +SAFIE S EY1 F IY0 +SAFONOFF S AH0 F AA1 N AO0 F +SAFT S AE1 F T +SAFTI S AE1 F T IY0 +SAFTLY S AE1 F T L IY0 +SAFTY S AE1 F T IY0 +SAG S AE1 G +SAGA S AA1 G AH0 +SAGACIOUS S AE1 G EY1 SH AH0 S +SAGACIOUSLY S AE1 G EY1 SH AH0 S L IY0 +SAGACITIES S AH0 G AE1 S IH0 T IY0 Z +SAGACITY S AH0 G AE1 S AH0 T IY0 +SAGALAC S AA0 G AA1 L AH0 K +SAGALIE S AA1 G AH0 L IY0 +SAGAMI S AE1 G AH0 M IY0 +SAGAMITE S AA1 G AH0 M AY2 T +SAGAMORE S AA0 G AA1 M AO0 R +SAGAMORE'S S AA1 G AH0 M AO1 R EY0 Z +SAGAMORES S AA1 G AH0 M AO2 R Z +SAGAN S EY1 G AH0 N +SAGAN'S S EY1 G AH0 N Z +SAGANA S AA0 G AE1 N AH0 +SAGANAW S AH0 G AE1 N AO2 +SAGAR S AA0 G AA1 R +SAGARD S AE1 G ER0 D +SAGARIKA S AA0 G AA1 R IH0 K AH0 +SAGAS S AA1 G AH0 Z +SAGASTA S AH0 G AE1 S T AH0 +SAGASTAO S AH0 G AE1 S T AW1 +SAGE S EY1 JH +SAGE'S S EY1 JH AH0 Z +SAGEBRUSH S EY1 JH B R AH2 SH +SAGEBUSH S EY1 JH B UH2 SH +SAGELAND S EY1 G AH0 L AE0 N D +SAGELY S EY1 JH L IY0 +SAGEMEN S EY1 JH M AH0 N +SAGEN S AE1 G AH0 N +SAGER S EY1 G ER0 +SAGES S EY1 JH IH0 Z +SAGESSE S AE1 G EH0 S +SAGEST S EY1 JH AH0 S T +SAGET S AE1 JH AH0 T +SAGETOWN S EY1 JH T AW2 N +SAGGART S AE1 G ER0 T +SAGGED S AE1 G D +SAGGER S AE1 G ER0 +SAGGERS S AE1 G ER0 Z +SAGGIA S AE1 G IY0 AH0 +SAGGING S AE1 G IH0 NG +SAGGIO S AA1 JH IY0 OW0 +SAGGOTT S AE1 G AH0 T +SAGGS S AE1 G Z +SAGGY S AE1 G IY0 +SAGHALIEN S AE1 G EY2 L IY0 AH0 N +SAGHARAWITE S AE1 G HH AA1 R AH0 W AY2 T +SAGI S AA1 G IY0 +SAGILA S AA0 JH IY1 L AH0 +SAGIMAU S AE1 G IH0 M AW0 +SAGINAW S AE1 G IH0 N AO2 +SAGINAW'S S AE1 G IH0 N AO2 Z +SAGITTA S AE1 JH AH0 T AH0 +SAGITTAL S AE1 JH AH0 T AH0 L +SAGITTARIA S AE1 JH AH0 T AA1 R IY0 AH0 +SAGITTARIUS S AE1 JH AH0 T ER0 AY1 AH0 S +SAGNIER S AE1 G N IY0 ER0 +SAGNIER'S S AE1 G N IY0 ER0 Z +SAGO S EY1 G OW0 +SAGON S AH0 G AA2 N +SAGOSTO S AA0 G OW1 S T OW0 +SAGOTH S AE1 G AH0 TH +SAGOTHS S AE1 G AH0 TH S +SAGRA S AE1 G R AH0 +SAGRAMOR S AA0 G R AH0 M AO2 R +SAGRAMORE S AA0 G R AA1 M AO0 R +SAGRAMOUR S AA0 G R AA1 M AO0 R +SAGRARIO S AA0 G R AA1 R IY0 OW0 +SAGRES S AA0 G R Z +SAGRESTIA S AA0 G R EH1 S T IY0 AH0 +SAGS S AE1 G Z +SAGT S AE1 G T +SAGUA S AA1 G W AH0 +SAGUARO S AH0 G W AO1 R OW0 +SAGUENAY S AE1 G AH0 N EY0 +SAGUM S AE1 G AH0 M +SAGUNTINE S AE1 G N T IY1 N IY0 +SAGUNTINES S AE1 G N T IY2 N Z +SAGUNTUM S AE1 G AH0 N T AH0 M +SAH S AA1 +SAHADEVA S AE1 HH AH0 D EY1 V AH0 +SAHAGUN S AE1 HH AH0 G AH0 N +SAHAI S AE1 HH AY1 +SAHALIN S AE1 HH AH0 L IH0 N +SAHAMIN S AE1 HH AH0 M AH0 N +SAHARA S AH0 HH EH1 R AH0 +SAHARAN S AE1 HH ER0 AH0 N +SAHARAS S AE1 HH ER0 AH0 Z +SAHARUNPORE S AE1 HH ER0 AH1 N P AO0 R +SAHASRA S AH0 HH IY1 Z R AH0 +SAHATOF S AH0 HH IY1 T AO0 F +SAHDE S AA1 D +SAHEB S AE1 HH EH2 B +SAHEBS S AE1 HH EH2 B Z +SAHEL S AA1 AH0 L +SAHELA S AH0 HH EY1 L AH0 +SAHIB S AH0 HH IH1 B +SAHIB'S S AE1 HH AH0 B Z +SAHIBA S AA0 HH IY1 B AH0 +SAHIBA'S S AA0 HH IY1 B AH0 Z +SAHIBS S AH0 HH IH1 B Z +SAHLMANN S AA1 L M AH0 N +SAHO S AA1 HH OW0 +SAHUARO S AA1 W AA1 R OW0 +SAHUAROS S AA1 W AA1 R OW0 Z +SAHWAH S AA1 W AH0 +SAHWAH'S S AA1 W AH0 Z +SAI EH1 S EY1 AY1 +SAI S AY1 +SAIB S EY1 B +SAICE S AY2 S +SAICRET S EY1 K R IH0 T +SAID S EH1 D +SAID' S EY1 D +SAID'S S EY1 D Z +SAID'ST S EY1 D S T +SAIDA S EY1 D AH0 +SAIDE S EY1 D +SAIDEE S EY1 D IY0 +SAIDEE'S S EY1 D IY0 Z +SAIDIE S EY1 D IY0 +SAIDJAH S EY1 D Y AH0 +SAIDS S EY1 D Z +SAIDST S EY1 D S T +SAIES S EY1 Z +SAIGNE S EY1 N Y +SAIGO S EY1 G OW0 +SAIGON S AY2 G AA1 N +SAIGON'S S AY2 G AA1 N Z +SAIL S EY1 L +SAIL'D S EY1 L D +SAIL'S S EY1 L Z +SAILBOAT S EY1 L B OW2 T +SAILBOATS S EY1 L B OW2 T S +SAILCLOTH S EY1 L K L AO2 TH +SAILE S EY1 L +SAILED S EY1 L D +SAILER S EY1 L ER0 +SAILERS S EY1 L ER0 Z +SAILEST S EY1 L AH0 S T +SAILETH S EY1 L EH2 TH +SAILFISH S EY1 L F IH2 SH +SAILIN S EY1 L IH0 N +SAILIN' S EY1 L IH0 N +SAILING S EY1 L IH0 NG +SAILINGS S EY1 L IH0 NG Z +SAILLANT S EY1 L AH0 N T +SAILLARD S EY1 L ER0 D +SAILLARDS S EY1 L ER0 D Z +SAILLESS S EY1 L AH0 S +SAILLY S EY1 L IY0 +SAILMAKER S EY1 L M EY2 K ER0 +SAILMAKER'S S EY1 L M EY2 K ER0 Z +SAILMAKERS S EY1 L M EY2 K ER0 Z +SAILOR S EY1 L ER0 +SAILOR'S S EY1 L ER0 Z +SAILORING S EY1 L ER0 IH0 NG +SAILORLY S EY1 L ER0 L IY0 +SAILORMAN S EY1 L ER0 M AH0 N +SAILORMAN'S S EY1 L ER0 M AH0 N Z +SAILORMEN S EY1 L ER0 M IH0 N +SAILORS S EY1 L ER0 Z +SAILPLANE S EY1 L P L EY2 N +SAILS S EY1 L Z +SAIN S EY1 N +SAINA S EY1 N AH0 +SAINFOIN S EY1 N F OY2 N +SAINFOY S EY1 N F OY0 +SAINFOY'S S EY1 N F OY2 Z +SAINSBURY S EY1 N S B EH0 R IY0 +SAINSBURY'S S EY1 N Z B ER2 IY0 Z +SAINT S EY1 N T +SAINT'S S EY1 N T S +SAINTE S EY1 N T +SAINTED S EY1 N T IH0 D +SAINTES S EY1 N T S +SAINTEVILLE S EY1 N T AH0 V IH2 L +SAINTHOOD S EY1 N T HH UH2 D +SAINTLIER S EY1 N T L AY2 ER0 +SAINTLIEST S EY1 N T L IY0 IH0 S T +SAINTLIKE S EY1 N T L AY2 K +SAINTLINESS S EY1 N T L IY0 N AH0 S +SAINTLY S EY1 N T L IY0 +SAINTONGE S EY1 N T AA1 N JH +SAINTS S EY1 N T S +SAINTS' S EY1 N T S +SAINTSBURY S EY1 N T S B EH0 R IY0 +SAINTSBURY'S S EY1 N T S B EH2 R IY0 Z +SAINTSHIP S EY1 N CH IH2 P +SAIR S EH1 R +SAIRER S AY1 ER0 R +SAIREY S EY1 R IY0 +SAIRINDHRI S EY1 R IY0 N D R IY1 +SAIRLY S EH1 R L IY0 +SAIRMEUSE S EH1 R M UW1 S +SAIRVICE S EY1 R V AH0 S +SAIRY S EH1 R IY0 +SAIS S EY1 Z +SAISES S EY1 Z AH0 Z +SAISHO S EY1 SH OW0 +SAISIAZ S EY1 Z IY0 AE2 Z +SAISON S EY1 S AA0 N +SAISONS S EY1 S AA0 N Z +SAIT S EY1 T +SAITE S EY1 T +SAITH S EY1 TH +SAITO S EY1 T OW2 +SAIVA S EY1 V AH0 +SAIZE S EY1 Z +SAJ S AY1 +SAJANE S AE1 JH EY0 N +SAJIPONA S AE1 JH IH0 P OW1 N AH0 +SAJIPONA'S S AE1 JH IH0 P OW1 N AH0 Z +SAK S AE1 K +SAKA S AA1 K AH0 +SAKAE S AA0 K AA0 +SAKAI S AA0 K AA1 IY0 +SAKARI S AA0 K AA1 R IY0 +SAKARRAN S AA0 K AA1 R AH0 N +SAKAY S AA0 K EY1 +SAKE S EY1 K +SAKE'S S EY1 K S +SAKECHAK S EY1 K AH0 K +SAKELDE S AA0 K EH2 L D +SAKER S EY1 K ER0 +SAKERS S EY1 K ER0 Z +SAKES S EY1 K S +SAKEWAWIN S EY1 K W AO1 IY0 N +SAKHALIN S AE1 K AH0 L IH0 N +SAKHYA S AE1 K Y AH0 +SAKI S AA1 K IY0 +SAKINA S AH0 K IY1 N AH0 +SAKIS S AA1 K IH0 S +SAKKA S AE1 K AH0 +SAKKARA S AE1 K ER0 AH0 +SAKKARAH S AE1 K ER0 AH0 +SAKKATOU S AE1 K AH0 T UW1 +SAKKIA S AE1 K IY0 AH0 +SAKO S AA1 K OW0 +SAKON S AE1 K AH0 N +SAKOONTALA S AE1 K UW1 N T AA1 L AH0 +SAKOVICH S AE1 K AH0 V IH0 CH +SAKOVICH'S S AE1 K AH0 V IH0 CH IH0 Z +SAKR S AE1 K R +SAKRA S AE1 K R AH0 +SAKSIN S AA2 K S IY1 N +SAKTA S AA1 K T AH0 +SAKTI S AA0 K T IY0 +SAKU S AA0 K UW1 +SAKUNI S AA0 K UW1 N IY0 +SAKUNTALA S AE0 K UH1 N T AA1 L AH0 +SAKURA S AE0 K UH1 R AH0 +SAKURAI S AA0 K UH0 R AA1 IY0 +SAKWASEW S AA0 K W AA1 Z UW0 +SAKYA S AA0 K Y AH0 +SAKYAMUNI S AA0 K Y AA0 M UW1 N IY0 +SAKYAS S AE2 K Y AA1 S +SAL S AE1 L +SAL'S S AE1 L Z +SALA S AA1 L AH0 +SALA'S S AE1 L AH0 Z +SALAAM S AH0 L AA1 M +SALAAMED S AH0 L AA1 M EH0 D +SALAAMING S AH0 L AA1 M IH0 NG +SALAAMS S AH0 L AA1 M Z +SALABAETTO S AE1 L AH0 B AE0 EH1 T OW0 +SALABERRY S EY1 L AH0 B EH2 R IY0 +SALABERT S AH0 L EY1 B ER0 T +SALABLE S EY1 L AH0 B AH0 L +SALACIOUS S AH0 L EY1 SH AH0 S +SALAD S AE1 L AH0 D +SALADE S AH0 L AA1 D +SALADIERS S AE1 L AH0 D IY0 ER0 Z +SALADIN S AE1 L AH0 D IH0 N +SALADIN'S S AE1 L AH0 D IH0 N Z +SALADO S AH0 L AA1 D OW0 +SALADS S AE1 L AH0 D Z +SALAERATUS S AE1 L EH1 R AH0 T AH0 S +SALAGUA S AA0 L AA1 G W AH0 +SALAH S AE1 L AH0 +SALAK S AE1 L AH0 K +SALAKOT S AE1 L AH0 K AO0 T +SALAL S EY1 L AH0 L +SALAM S AA0 L AA1 M +SALAMA S AA0 L AA1 M AH0 +SALAMAN S AE2 L AH0 M AH0 N +SALAMANCA S AE2 L AH0 M AE1 NG K AH0 +SALAMANDER S AE2 L AH0 M AE1 N D ER0 +SALAMANDER'S S AE1 L AH0 M AE2 N D ER0 Z +SALAMANDERS S AE2 L AH0 M AE1 N D ER0 Z +SALAME S AE1 L AH0 M +SALAMI S AH0 L AA1 M IY0 +SALAMINIAN S AE2 L AH0 M IH1 N IY0 AH0 N +SALAMIS S AH0 L AA1 M IY0 Z +SALAMMBO S AA1 L AA0 M B OW0 +SALAMMBO'S S AA1 L AA0 M B OW0 Z +SALAMON S AE1 L AH0 M AH0 N +SALAMS S AE1 L AH0 M Z +SALANDRA S AA0 L AA1 N D R AH0 +SALANDRA'S S AA0 L AA1 N D R AH0 Z +SALANOVA S AA0 L AA0 N OW1 V AH0 +SALAR S AE1 L ER0 +SALARIA S AA0 L AA1 R IY0 AH0 +SALARIAN S AH0 L EH1 R IY0 AH0 N +SALARIED S AE1 L ER0 IY0 D +SALARIES S AE1 L ER0 IY0 Z +SALARIK S AA0 L AA1 R IH0 K +SALARIKI S AA0 L AA0 R IY1 K IY0 +SALARY S AE1 L ER0 IY0 +SALARY'S S AE1 L ER0 IY0 Z +SALAS S AA1 L AA0 Z +SALAT S AA0 L AA1 T +SALATHIEL S AE1 L AH0 TH IY1 L +SALATHIEL'S S AE1 L AH0 TH IY1 L Z +SALAUN S AE1 L AW0 N +SALAVEIRA S AE1 L AH0 V EY1 R AH0 +SALAZAR S AE1 L AH0 Z AA0 R +SALAZAR'S S AE1 L AH0 Z AA0 R Z +SALCEDO S AA0 L CH EY1 D OW0 +SALCHEN S AA0 L CH AH0 N +SALCOMBE S AE1 L K AH0 M +SALDANA S AA0 L D AE1 N AH0 +SALDANHA S AA0 L D AE1 N HH AH0 +SALDAR S AA0 L D AA2 R +SALDEN S AA0 L D AH0 N +SALDERN S AA0 L D ER0 N +SALDIBAR S AA0 L D IH0 B AA1 R +SALE S EY1 L +SALE'S S EY1 L Z +SALEABLE S EY1 L AH0 B AH0 L +SALEE S AE1 L IY0 +SALEEBY S AE1 L IY0 B IY0 +SALEH S AA1 L EH0 HH +SALEK S AA1 L EH0 K +SALEM S EY1 L AH0 M +SALEM'S S EY1 L AH0 M Z +SALEMINA S EY1 L AH0 M IY1 N AH0 +SALEMINA'S S EY1 L AH0 M IY1 N AH0 Z +SALEN S EY1 L AH0 N +SALENSUS S AH0 L EH1 N S AH0 S +SALERATUS S AH0 L EH1 R AH0 T AH0 S +SALERNO S AH0 L EH1 R N OW0 +SALEROOM S AH0 L IY1 R UW2 M +SALES S EY1 L Z +SALESA S AH0 L EH1 S AH0 +SALESBURY S EY1 L Z B EH2 R IY0 +SALESGIRL S EY1 L Z G ER2 L +SALESGIRLS S EY1 L Z G ER2 L Z +SALESLADIES S EY1 L Z L AH0 D IY0 Z +SALESLADY S EY1 L Z L AH0 D IY0 +SALESMAN S EY1 L Z M AH0 N +SALESMAN'S S EY1 L Z M AH0 N Z +SALESMANSHIP S EY1 L Z M AH0 N SH IH2 P +SALESMEN S EY1 L Z M IH0 N +SALESPEOPLE S EY1 L Z P IY2 P AH0 L +SALESPERSON S EY1 L Z P ER2 S AH0 N +SALESROOM S EY1 L Z R UW2 M +SALESROOMS S EY1 L Z R UW2 M Z +SALESWOMAN S EY1 L Z W UH2 M AH0 N +SALESWOMEN S EY1 L Z W IH2 M AH0 N +SALETA S AE1 L IH0 T AH0 +SALETTE S AA1 L EH0 T +SALEVE S AH0 L IY1 V +SALFORD S AE1 L F ER0 D +SALGAR S AA0 L G AA1 R +SALGATH S AA0 L G AA1 TH +SALI S AA1 L IY0 +SALIAN S EY1 L IY0 AH0 N +SALIC S AE1 L IH0 K +SALICETTI S AA0 L IY0 CH EH1 T IY0 +SALICYLATE S AE1 L IH0 S AH0 L IH0 T +SALICYLIC S AH0 L IY1 S IH1 L IH0 K +SALIDA S AA0 L IY1 D AH0 +SALIENCE S EY1 L Y AH0 N S +SALIENCY S EY1 L Y AH0 N S IY0 +SALIENS S EY1 L IY0 AH0 N Z +SALIENT S EY1 L IY0 AH0 N T +SALIENT S EY1 L Y AH0 N T +SALIENTLY S EY1 L Y AH0 N T L IY0 +SALIENTS S EY1 L IY0 AH0 N T S +SALIERI S AE1 L IH1 R IY0 +SALIH S AH0 L IY1 +SALII S AE1 L IY0 +SALIM S AA0 L IY0 M +SALIM S AE1 L IH0 M +SALIM'S S AE1 L IH0 M Z +SALIMBA S AE1 L IH0 M B AH0 +SALINA S AH0 L IY1 N AH0 +SALINAS S AH0 L IY1 N AH0 S +SALINAS S AH0 L IY1 N AH0 Z +SALINATOR S AE1 L AH0 N EY2 T ER0 +SALINE S AH0 L IY1 N +SALINES S AH0 L IY1 N Z +SALINGEN S AE1 L IH0 NG G EH0 N +SALINGER S AE1 L IH0 N JH ER0 +SALINGER S EY1 L IH0 NG ER0 +SALINS S AE1 L IH0 N Z +SALIQUE S AH0 L IY1 K +SALIS S AA1 L IH0 S +SALISBURY S AE1 L Z B ER0 IY0 +SALISBURY'S S AE1 L Z B EH2 R IY0 Z +SALISSA S AH0 L IH1 S AH0 +SALISTA S AH0 L IH1 S T AH0 +SALIUS S EY1 L Y AH0 S +SALIVA S AH0 L AY1 V AH0 +SALIVARY S AE1 L AH0 V ER0 IY0 +SALIVATE S AE1 L AH0 V EY2 T +SALIVATED S AE1 L AH0 V EY2 T AH0 D +SALIVATION S AE1 L AH0 V EY1 SH AH0 N +SALIVE S AE1 L AH0 V +SALIX S AE1 L IH0 K S +SALKARS S AO1 K ER0 Z +SALKELD S AE1 L K IH0 L D +SALL S AO1 L +SALL'S S AO1 L Z +SALLA S AA1 L AH0 +SALLAD S AE1 L AH0 D +SALLADS S AE1 L AH0 D Z +SALLAZAR S AA1 L AA0 Z AA1 R +SALLE S EY1 L +SALLE'S S AE1 L Z +SALLEE S AE1 L IY0 +SALLENAUVE S AE1 L IH0 N OW1 V +SALLENAUVE'S S AE1 L IH0 N OW1 V Z +SALLES S AA1 L EH0 S +SALLET S AE1 L IH0 T +SALLETS S AO1 L AH0 T S +SALLEY S AE1 L IY0 +SALLIANNA S AE1 L IY0 AE1 N AH0 +SALLIANNA'S S AE1 L IY0 AE1 N AH0 Z +SALLIE S AE1 L IY0 +SALLIE'S S AE1 L IY0 Z +SALLIED S AE1 L IY0 D +SALLIES S AE1 L IY0 Z +SALLINBEG S AE1 L IH0 N B AH0 G +SALLORSEN S AE1 L ER0 S AH0 N +SALLOW S AA1 L OW0 +SALLOWED S AA1 L OW0 D +SALLOWER S AA1 L OW0 ER0 +SALLOWNESS S AA1 L OW0 N AH0 S +SALLOWS S AA1 L OW0 Z +SALLUS S AE1 L AH0 S +SALLUST S AE1 L AH0 S T +SALLUST'S S AE1 L AH0 S T S +SALLUSTIUS S AE1 L AH0 S T IY0 IH0 S +SALLY S AE1 L IY0 +SALLY'D S AH0 L IY2 D +SALLY'S S AE1 L IY0 Z +SALLYING S AE1 L IY0 IH0 NG +SALLYKIN S AA1 L IH1 K AH0 N +SALLYPORT S AH0 L IY2 P AO2 R T +SALM S AA1 M +SALMACIS S AE1 M AH0 S IH0 S +SALMAGUNDI S AE1 M AH0 G AH0 N D IY0 +SALMAN S AE1 M AH0 N +SALMANAZAR S AE1 M AH0 N AA0 Z AA1 R +SALMASIUS S AE2 L M AE1 S IY0 AH0 S +SALME S AA0 L M +SALMERON S AA0 L M EH0 R AO1 N +SALMI S AA1 L M IY0 +SALMIS S AA0 L M IY0 S +SALMO S AA0 L M OW0 +SALMON S AE1 M AH0 N +SALMON'S S 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Z +SALVADRO S AE1 L V AH0 D R OW0 +SALVAGE S AE1 L V AH0 JH +SALVAGE S AE1 L V IH0 JH +SALVAGED S AE1 L V IH0 JH D +SALVAGES S AE1 L V IH0 JH IH0 Z +SALVAGING S AE1 L V IH0 JH IH0 NG +SALVAIN S AA0 L V EY2 N +SALVAIR S AA0 L V EH1 R +SALVAT S AA0 L V AA1 T +SALVAT'S S AA0 L V AA1 T S +SALVATIERRA S AA0 L V AA0 T IH1 R AH0 +SALVATION S AE0 L V EY1 SH AH0 N +SALVATION'S S AA0 L V EY1 SH AH0 N Z +SALVATIONIST S AA0 L V EY1 SH AH0 N IH0 S T +SALVATIONISTS S AA0 L V EY1 SH AH0 N IH0 S T S +SALVATIONS S AA0 L V EY1 SH AH0 N Z +SALVATOR S AA0 L V AA0 T AO1 R +SALVATOR'S S AE1 L V AH0 D AO2 R Z +SALVATORE S AE0 L V AH0 T AO1 R IY0 +SALVATORE S AE1 L V AH0 D AO2 R +SALVATORE'S S AE1 L V AH0 D AO2 R Z +SALVATORI S AA0 L V AA0 T AO1 R IY0 +SALVE S AA1 V +SALVED S AA1 V D +SALVELINUS S AA1 L V EH0 L IY1 N AH0 S +SALVER S AE1 L V ER0 +SALVERS S AE1 L V ER0 Z +SALVES S AA1 V Z +SALVESTRO S AA0 L V EY1 S T R OW0 +SALVEY S AE1 L V IY0 +SALVI S AA1 L V IY0 +SALVIA S AE1 L V IY0 AH0 +SALVIAN S AA1 L V IY0 AH0 N +SALVIAS S AA0 L V IY1 AH0 Z +SALVIATI S AA1 L V IY0 AA1 T IY0 +SALVIERDERRA S AA1 L V IY0 EH1 R D EH1 R AH0 +SALVIERDERRA'S S AA1 L V IY0 EH1 R D EH1 R AH0 Z +SALVIN S AE1 L V IH0 N +SALVING S AE1 L V IH0 NG +SALVINI S AA0 L V IY1 N IY0 +SALVINI'S S AA0 L V IY1 N IY0 Z +SALVINSKI S AA0 L V IH1 N S K IY0 +SALVIUS S AA1 L V IY0 IH0 S +SALVO S AE1 L V OW0 +SALVOES S AE1 L V OW0 Z +SALVOLIO S AE1 L V OW0 L IY0 OW0 +SALVOR S AE0 L V AO1 R +SALVORS S AE1 L V ER0 Z +SALVOS S AE1 L V OW0 Z +SALVUM S AA0 L V UW1 M +SALWATTY S AE1 L W AA0 T IY0 +SALWEN S AE1 L W AH0 N +SALYER S AA1 L IY0 ER0 +SALYERS S AA1 L IY0 ER0 Z +SALZ S AO1 L Z +SALZA S AE1 L Z AH0 +SALZANO S AA0 L Z AA1 N OW0 +SALZAR S AA0 L Z AA1 R +SALZBRUECK S AO1 L Z B R AH2 K +SALZBURG S AO1 L T S B ER0 G +SALZBURG S AO1 L Z B ER0 G +SALZBURGERS S AO1 L T S B ER0 G ER0 Z +SALZKAMMERGUT S AO1 L T S K AE1 M ER0 G AH0 T +SAM S AE1 M +SAM'D S AE1 M D +SAM'L S AE1 M AH0 L +SAM'L'S S AE1 M L Z +SAM'LL S AE1 M AH0 L +SAM'S S AE1 M Z +SAMA S AA1 M AH0 +SAMADHI S AA0 M AA1 D IY0 +SAMAEL S AE1 M EY0 L +SAMAIN S AE1 M AY0 N +SAMAJ S AA0 M AA1 JH +SAMAN S AH0 M AH0 N +SAMANA S AA0 M AA1 N AH0 +SAMANAS S AA0 M AA1 N AH0 Z +SAMANEANS S AH0 M AA1 N IY0 AH0 N Z +SAMANON S AA0 M AA0 N AO1 N +SAMANTHA S AH0 M AE1 N TH AH0 +SAMANTHY S AH0 M AE1 N TH IY0 +SAMAR S AE1 M AA0 R +SAMARA S AE1 M ER0 AH0 +SAMARAH S AE1 M ER0 AH0 +SAMARAK S AE1 M ER0 AE2 K +SAMARANG S AE1 M ER0 AE2 NG +SAMARCAND S AA0 M AA1 R K AH0 N D +SAMARENDRA S AE1 M ER0 EH1 N D R AH0 +SAMARENDRA'S S AE1 M ER0 EH1 N D R AH0 Z +SAMARIA S EY2 M ER0 IY1 AH0 +SAMARINDA S AE1 M ER0 IH1 N D AH0 +SAMARITAINE S AH0 M EH1 R IH0 T EY2 N +SAMARITAN S AH0 M EH1 R IH0 T AH0 N +SAMARITAN'S S AH0 M EH1 R AH0 T AH0 N Z +SAMARITANS S AH0 M EH1 R IH0 T AH0 N Z +SAMARKAN S AE1 M AA0 R K AE2 N +SAMARKAND S AE1 M AA0 R K AE2 N D +SAMAROV S AE1 M ER0 AA0 V +SAMAROVA S AE1 M AA0 R OW1 V AH0 +SAMARRA S AA0 M AA1 R AH0 +SAMAS S AA1 M AH0 Z +SAMATAU S AH0 M AE1 T AW0 +SAMAVIA S AA0 M 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+SARACINESCA S AA0 R AA0 CH IY0 N EH1 S K AH0 +SARACINESCA'S S AA0 R AA0 CH IY0 N EH1 S K AH0 Z +SARADINE S EH1 R AH0 D AY2 N +SARADOKIS S AA2 R AH0 D OW1 K IY0 Z +SARAGOSSA S AA0 R AA0 G OW1 S AH0 +SARAH S EH1 R AH0 +SARAH'S S EH1 R AH0 Z +SARAHCUSE S EH1 R AH0 K Y UW1 Z +SARAI S AA0 R AA1 IY0 +SARAJEVO S AA2 R AH0 Y EY1 V OW0 +SARAKOFF S AA1 R AH0 K AO2 F +SARAKOFF'S S AA1 R AH0 K AO2 F S +SARALTHIA S ER0 EY1 L DH IY0 AH0 +SARANAC S ER0 AE1 N AE0 K +SARANACS S EH1 R AH0 N AE2 K S +SARANOFF S EH1 R AH0 N AO0 F +SARAPE S ER0 EY1 P +SARAPES S ER0 EY1 P S +SARAPIS S EH1 R AH0 P AH0 S +SARASATE S EH1 R IH0 S EY2 T +SARASWATI S EH2 R AH0 S W AA1 T IY0 +SARATOFF S EH1 R AH0 T AO2 F +SARATOGA S EH2 R AH0 T OW1 G AH0 +SARATOV S EH1 R AH0 T AO2 F +SARATOV S EH1 R AH0 T AO2 V +SARATOVSKY S EH1 R AH0 T AO2 F S K IY0 +SARAWAK S EH1 R AH0 W AA0 K +SARAWIA S AA0 R AA0 V IY0 AH0 +SARAYASHIKI S AA0 R AA0 Y AA0 SH IY1 K IY0 +SARAZEN S EH1 R AH0 Z AH0 N +SARBALAND S AA1 R B AE1 L AH0 N D +SARCASM S AA1 R K AE2 Z AH0 M +SARCASMS S AA1 R K AE2 Z AH0 M Z +SARCASTIC S AA0 R K AE1 S T IH0 K +SARCASTICAL S AA0 R K AE1 S T IH0 K AH0 L +SARCASTICALLY S AA0 R K AE1 S T IH0 K L IY0 +SARCE S AA1 R S +SARCEE S AA1 R S EY2 +SARCEES S AA0 R S IY1 Z +SARCENET S AA1 R S N EH2 T +SARCEY S AA1 R S IY0 +SARCH S AA0 R K +SARCHED S AA1 R CH T +SARCHEDON S AA0 R SH EH1 D AH0 N +SARCHIN S AA0 R K IH0 N +SARCI S AA0 R CH IY0 +SARCO S AA1 R K OW0 +SARCOGNOMY S AA0 R K AA1 G N AH0 M IY0 +SARCOMA S AA0 R K OW1 M ER0 +SARCOPHAGI S AA0 R K AA1 F AH0 G IY0 +SARCOPHAGUS S AA0 R K AA1 F AH0 G AH0 S +SARCUMSTANCE S AA0 R K AH0 M S T AE2 N S +SARCUMSTANCES S AA0 R K AH0 M S T AE2 N S IH0 Z +SARCUS S AA0 R K IH0 S +SARCY S AA0 R K IY0 +SARD S AA1 R D +SARD'S S AA1 R D Z +SARDANAPALUS S AA1 R D AH0 N AE2 P AH0 L AH0 S +SARDAR S AA0 R D AA1 R +SARDATHRION S AA1 R D AH0 TH R IY0 AH0 N +SARDES S AA0 R D Z +SARDIANS S AA1 R D IY0 AH0 N Z +SARDINA S AA0 R D IY1 N AH0 +SARDINE S AA0 R D IY1 N +SARDINES S AA0 R D IY1 N Z +SARDINIA S AA0 R D IY1 N IY0 AH0 +SARDINIA'S S AA0 R D IY1 N IY0 AH0 Z +SARDINIAN S AA0 R D IY1 N IY0 AH0 N +SARDINIANS S AA0 R D IY1 N IY0 AH0 N Z +SARDIS S AA1 R D IH0 S +SARDIUS S AA1 R D IY0 AH0 S +SARDONIC S AA0 R D AA1 N IH0 K +SARDONICALLY S AA0 R D AA1 N IH0 K AH0 L IY0 +SARDONICALLY S AA0 R D AA1 N IH0 K L IY0 +SARDONICUS S AA0 R D AA1 N IH0 K AH0 S +SARDONYX S AA0 R D AA1 N IH0 K S +SARDOTOPOLIS S AA0 R D AA1 T AA2 P AH0 L IH0 S +SARDOU S AA0 R D AA1 +SARDOU'S S AA0 R D AA1 Z +SARDS S AA1 R D Z +SARE S EH1 R +SAREBAS S ER0 IY1 B AH0 Z +SAREBBE S ER0 EH1 B +SAREBUS S ER0 IY1 B AH0 S +SAREE S ER0 IY1 +SAREL S AA2 R AH0 L +SARELLI S AA0 R EH1 L IY0 +SAREPTA S AA2 R AH0 P T AH0 +SARET S AA2 R AH0 T +SARGA S AA1 R G AH0 +SARGASSO S AA0 R G AE1 S OW0 +SARGE S AA1 R JH +SARGEANT S AA1 R JH AH0 N T +SARGENT S AA1 R JH AH0 N T +SARGENT'S S AA1 R JH AH0 N T S +SARGENTO S AA1 R JH EH1 N T OW0 +SARGENTS S AA1 R JH AH0 N T S +SARGINT S AA1 R G IH0 N T +SARGOL S AA1 R G AH0 L +SARGOLIAN S AA0 R G OW1 L IY0 AH0 N +SARGON S AA1 R G AH0 N +SARGYLL S AA0 R G AY1 L +SARI S AA1 R IY0 +SARIA S AA1 R IY0 AH0 +SARIANS S EH1 R IY0 AH0 N Z +SARIETTE S EH2 R IY0 EH1 T +SARIKAMISH S AE1 R IH0 K AA1 M IH0 SH +SARILI S AH0 R IY1 L IY0 +SARIOLA S AA1 R IY0 OW1 L AH0 +SARIOUS S EH1 R IY0 AH0 S +SARIS S AA1 R IY0 Z +SARISHAN S EH1 R IH0 SH AH0 N +SARISSAS S AA0 R IY1 S AH0 S +SARJINT S AA1 R JH IH2 N T +SARK S AA1 R K +SARKA S AA1 R K AH0 +SARKA'S S AA1 R K AH0 Z +SARKANTYUS S AA1 R K AA2 N T Y UW1 S +SARKAS S AA1 R K AH0 Z +SARKEE S AA1 R K IY1 +SARKELD S AA1 R K EH0 L D +SARKEY S AA1 R K IY0 +SARKIES S AA1 R K IY0 Z +SARKOJA S AA0 R K OW1 Y AH0 +SARKS S AA1 R K S +SARLAT S AA1 R L AE0 T +SARMA S AA1 R M AH0 +SARMAN S AA0 R M AH0 N +SARMATIA S AA0 R M EY1 SH AH0 +SARMATIAN S AA0 R M EY1 SH AH0 N +SARMATIANS S AA0 R M EY1 SH AH0 N Z +SARMIENTO S AA0 R M IY1 N T OW0 +SARMON S AA0 R M AO1 N +SARMONS S AA0 R M AH0 N Z +SARN S AA1 R N +SARNAK S AA1 R N AH0 K +SARNAX S AA1 R N AE0 K S +SARNEN S AA1 R N AH0 N +SARNIA S AA1 R N IY0 AH0 +SARNIDAC S AA1 R N IH0 D AE2 K +SARNO S AA1 R N OW0 +SARO S AA1 R OW0 +SAROJINI S ER0 AO1 JH IY1 N IY0 +SARONG S ER0 AO1 NG +SARONGS S ER0 AO1 NG Z +SARONIA S AA0 R OW1 N IY0 AH0 +SARONIC S ER0 AO1 N IH0 K +SARONY S ER0 AO1 N IY0 +SAROS S AA1 R OW0 S +SARP S AA1 R P +SARPEDON S AA0 R P EH1 D AH0 N +SARPENT S AA1 R P AH0 N T +SARPENTS S AA1 R P AH0 N T S +SARPI S AA1 R P IY0 +SARPINT S AA0 R P IY0 N T +SARPINTS S ER0 P IH1 N T S +SARPY S AA1 R P IY0 +SARRA S AA1 R AH0 +SARRACENIA S AA1 R AA0 CH EH1 N IY0 AH0 +SARRADET S AA1 R AH0 D EH1 T +SARRADET'S S AA1 R AH0 D EH1 T S +SARRADETS S AA1 R AH0 D EH1 T S +SARRAF S AA1 R AH0 F +SARRAGUCE S ER0 AA1 ZH S +SARRAS S AE1 R AH0 Z +SARRASIN S ER0 AE1 S IH0 N +SARRASIN'S S AA1 R AH0 S AH0 N Z +SARRASINE S AA1 R AH0 S IY2 N +SARRASINS S AA1 R AH0 S IH0 N Z +SARRATT S ER0 AE1 T +SARRATT'S S ER0 AE1 T S +SARRAZIN S AA1 R AH0 Z IH0 N +SARRAZINS S AA1 R AH0 Z IH0 N Z +SARRE S AA1 R IY0 +SARREBOURG S AE1 R IH0 B AO2 R G +SARREO S AA1 R IY0 OW0 +SARRIA S AE1 R IY0 AH0 +SARRIA'S S AE1 R IY0 AH0 Z +SARRIETTE S AE1 R IY0 EH0 T +SARRIO S AA1 R IY0 OW0 +SARRION S AE1 R IY0 AH0 N +SARRION'S S AE1 R IY0 AH0 N Z +SARRIONS S AE1 R IY0 AH0 N Z +SARRY S AE1 R IY0 +SARS S AA1 R Z +SARSAPARILLA S AA1 R S AE2 P ER0 IH1 L AH0 +SARSBY S AA1 R Z B IY0 +SARSE S AA1 R S +SARSEN S AA1 R S AH0 N +SARSENET S AA1 R S AH0 N EH2 T +SARSENS S AA1 R S AH0 N Z +SARSER S AA1 R S ER0 +SARSFIELD S AA1 R S F IY0 L D +SARSON S AA1 R S AH0 N +SARSUTI S AA1 R S UW1 T IY0 +SART S AA0 R T +SART'IN S AA0 R T AE1 N +SARTAIN S AA0 R T EY1 N +SARTAIN'S S AA0 R T EY1 N Z +SARTAINLY S AA0 R T EY1 N L IY0 +SARTAINTY S AA0 R T EY1 N T IY0 +SARTEN S AA1 R T AH0 N +SARTHE S AA1 R T +SARTI S AA1 R T IY0 +SARTIGES S AA1 R T IH0 JH IH0 Z +SARTIN S AA1 R T IH0 N +SARTINE S AA0 R T IY1 N +SARTINES S AA0 R T IY1 N Z +SARTINLY S AA1 R T IH0 N L IY0 +SARTINT S AA1 R T IH0 N T +SARTINTY S AA1 R T IH0 N T IY0 +SARTLIFF S AA1 R T L IH0 F +SARTO S AA1 R T OW0 +SARTO'S S AA1 R T OW0 Z +SARTOR S AA1 R T ER0 +SARTORIAL S AA0 R T AO1 R IY0 AH0 L +SARTORIALLY S AA0 R T AO1 R IY0 AH0 L IY0 +SARTORIAN S AA0 R T AO1 R IY0 AH0 N +SARTORIS S AA0 R T AO1 R IH0 S +SARTORIS'S S AA0 R T AO1 R IH0 S IH0 Z +SARTORIUS S AA0 R T AO1 R IY0 IH0 S +SARU S AA1 R UW0 +SARUDINE S AE1 R AH0 D AY2 N +SARUDINE'S S AE1 R AH0 D AY2 N Z +SARUM S AE1 R AH0 M +SARVABHAUMA S AA1 R V AE1 B HH UW1 M AH0 +SARVANT S AA1 R V AH0 N T +SARVANTS S AA1 R V AH0 N T S +SARVE S AA1 R V +SARVED S AA1 R V D +SARVENT S AA1 R V AH0 N T +SARVES S AA1 R V Z +SARVICE S AA1 R V AH0 S +SARVICES S AA1 R V AH0 S IH0 Z +SARVIN S AA1 R V IH0 N +SARVING S AA1 R V IH0 NG +SARVINT S AA1 R V IH0 N T +SARVINTS S AA1 R V IH0 N T S +SARVIS S AA1 R V IH0 S +SARWAK S AA1 R W AA0 K +SARY S EH2 R IY0 +SARY'S S EH2 R IY0 Z +SARZAN S AA1 R Z AH0 N +SARZANA S AA0 R Z AA1 N AH0 +SARZEAU S AA0 R Z OW1 +SARZEC S AA1 R Z IH0 K +SAS S AE1 S +SASA S AE1 S AH0 +SASABONSUM S AE2 S AH0 B AA2 N S AH0 M +SASAKI S AA0 S AA1 K IY0 +SASASQUIT S AA0 S AA1 S K W IH0 T +SASEBO S AA0 S EH1 B OW0 +SASEBO'S S AE1 S IH0 B OW0 Z +SASELLANO S AA0 S EH0 L AA1 N OW0 +SASERKOPEE S AE1 S ER0 K AO1 P IY1 +SASH S AE1 SH +SASHA S AE1 SH AH0 +SASHA'S S AE1 SH AH0 Z +SASHAY S AE1 SH EY0 +SASHED S AE1 SH T +SASHENKA S AE1 SH EH1 NG K AH0 +SASHES S AE1 SH IH0 Z +SASHKA S AE1 SH K AH0 +SASHLESS S AE1 SH L AH0 S +SASKASHAWAN S AE0 S K AE1 SH AH0 W AA2 N +SASKATCHEWAN S AE0 S K AE1 CH AH0 W AO2 N +SASKATOON S AE1 S K AH0 T UW2 N +SASKATOONS S AE1 S K AH0 T UW2 N Z +SASKI S AE1 S K IY0 +SASKIA S AE0 S K IY0 AH0 +SASKIA'S S AE0 S K IY0 AH0 Z +SASNETT S AE1 S N IH0 T +SASS S AE1 S +SASSACUS S AE1 S AH0 K AH0 S +SASSAFRAS S AE1 S AH0 F R AE2 S +SASSAGE S AE1 S AH0 JH +SASSAGES S AE1 S AH0 JH AH0 Z +SASSANIAN S AA0 S AA1 N IY0 AH0 N +SASSED S AE1 S T +SASSENACH S AE1 S EH0 N AA0 K +SASSENACHS S AE1 S EH0 N AA0 K S +SASSER S AE1 S ER0 +SASSETTO S AA0 S EH1 T OW2 +SASSI S AE1 S IY0 +SASSI'S S AE1 S IY0 Z +SASSIETY S AE1 S IY0 T IY0 +SASSIN S AE1 S AH0 N +SASSING S AE1 S IH0 NG +SASSNILTZ S AE1 S N IH1 L T S +SASSO S AE1 S OW0 +SASSOFERRATO S AH0 S OW1 F ER0 AA1 T OW0 +SASSOON S AH0 S UW1 N +SASSOON'S S AH0 S UW1 N Z +SASSY S AE1 S IY0 +SASTRAS S AE1 S T R AH0 Z +SASTRE S AE1 S T ER0 +SASTROW S AE1 S T R OW0 +SAT S AE1 T +SAT'DAY S AE1 T IH0 D EY2 +SAT'ST S AE1 T IH0 S T +SATAN S EY1 T AH0 N +SATAN'S S EY1 T AH0 N Z +SATANAS S AA2 T AA0 N AA1 S +SATANIC S AH0 T AE1 N IH0 K +SATANICAL S AH0 T AE1 N IH0 K AH0 L +SATANISM S EY1 T AH0 N IH2 Z AH0 M +SATANIST S EY1 T AH0 N IH0 S T +SATANISTS S AH0 T AE1 N IH0 S T S +SATANK S AH0 T AE1 NG K +SATANS S EY1 T AH0 N Z +SATANSTOE S EY1 T AH0 N S T OW2 +SATANSTOWN S EY1 T AH0 N Z T AW2 N +SATANTA S AH0 T AE1 N AH0 +SATCHEL S AE1 CH AH0 L +SATCHELL S AE1 CH AH0 L +SATCHELS S AE1 CH AH0 L Z +SATE S EY1 T +SATED S EY1 T IH0 D +SATEEN S EY1 T IY2 N +SATELLITE S AE1 T AH0 L AY2 T +SATELLITE'S S AE1 T AH0 L AY2 T S +SATELLITES S AE1 T AH0 L AY2 T S +SATELLOID S AE1 T AH0 L OY0 D +SATERLEE S AE1 T ER0 L IY1 +SATES S EY1 T S +SATHAN S AE1 TH AH0 N +SATHANAS S AE1 TH AH0 N AH0 Z +SATHURDAY S AE1 TH ER0 D EY0 +SATI S AA1 T IY0 +SATIABLE S EY1 SH AH0 B AH0 L +SATIATE S EY1 SH IY0 EY2 T +SATIATED S EY1 SH IY0 EY2 T IH0 D +SATIATES S EY1 SH IY0 EY2 T S +SATIATING S EY1 SH IY0 EY2 T IH0 NG +SATIATION S EY1 SH IY0 EY1 SH AH0 N +SATIETY S AH0 T AY1 AH0 T IY0 +SATIN S AE1 T AH0 N +SATIN'S S AE1 T AH0 N Z +SATINE S AE1 T AY2 N +SATING S EY1 T IH0 NG +SATINS S AE1 T AH0 N Z +SATINWOOD S AH0 T IH1 N W UH2 D +SATINY S AE1 T AH0 N IY0 +SATIRE S AE1 T AY2 ER0 +SATIRE'S S AE1 T AY2 ER0 Z +SATIRES S AE1 T AY2 ER0 Z +SATIRIC S AH0 T IH1 R IH0 K +SATIRICAL S AH0 T IH1 R AH0 K AH0 L +SATIRICAL S AH0 T IH1 R IH0 K AH0 L +SATIRICALLY S AH0 T IH1 R IH0 K L IY0 +SATIRISE S AE1 T ER0 AH0 S +SATIRISED S AE1 T ER0 AY2 Z D +SATIRISES S AE1 T ER0 AH0 S AH0 Z +SATIRISING S AE1 T ER0 AY2 Z IH0 NG +SATIRIST S AE1 T ER0 AH0 S T +SATIRIST'S S AE1 T ER0 AH0 S T S +SATIRISTS S AE1 T ER0 AH0 S T S +SATIRIZE S AE1 T ER0 AY2 Z +SATIRIZED S AE1 T ER0 AY2 Z D +SATIRIZES S AE1 T ER0 AY2 Z IH0 Z +SATIRIZING S AE1 T ER0 AY2 Z IH0 NG +SATIROMASTIX S AH0 T IH1 R AH0 M AE1 S T IH0 K S +SATIS S AE2 T IH0 S +SATISFACTION S AE2 T AH0 S F AE1 K SH AH0 N +SATISFACTION S AE2 T IH0 S F AE1 K SH AH0 N +SATISFACTIONS S AE2 T AH0 S F AE1 K SH AH0 N Z +SATISFACTORILY S AE2 T IH0 S F AE1 K T R AH0 L IY0 +SATISFACTORINESS S AE1 T AH0 S F AE1 K T R IY0 N AH0 S +SATISFACTORY S AE2 T AH0 S F AE1 K T R IY0 +SATISFACTORY S AE2 T IH0 S F AE1 K T ER0 IY0 +SATISFAX S AE1 T AH0 S F AE2 K S +SATISFIE S AE1 T AH0 S F AY2 +SATISFIED S AE1 T AH0 S F AY2 D +SATISFIED S AE1 T IH0 S F AY2 D +SATISFIES S AE1 T IH0 S F AY2 Z +SATISFIETH S AE1 T AH0 S F AY2 TH +SATISFY S AE1 T AH0 S F AY2 +SATISFY S AE1 T IH0 S F AY2 +SATISFY'D S AE1 T AH0 S F AY2 D +SATISFYED S AE1 T AH0 S F AY2 D +SATISFYIN S AE1 T AH0 S F AY2 IH0 N +SATISFYING S AE1 T IH0 S F AY2 IH0 NG +SATISFYINGLY S AE1 T AH0 S F AY2 IH0 NG L IY0 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ER0 EY2 T AH0 D +SATURATED S AE1 CH ER0 EY2 T IH0 D +SATURATES S AE1 CH ER0 AH0 T S +SATURATING S AE1 CH ER0 EY2 T IH0 NG +SATURATION S AE2 CH ER0 EY1 SH AH0 N +SATURDAY S AE1 T ER0 D IY0 +SATURDAY S AE1 T IH2 D EY2 +SATURDAY'S S AE1 T ER0 D EY0 Z +SATURDAY'S S AE1 T ER0 D IY0 Z +SATURDAYS S AE1 T ER0 D EY0 Z +SATURDAYS S AE1 T ER0 D IY0 Z +SATURIUS S AE1 T ER0 IY0 IH0 S +SATURN S AE1 T ER0 N +SATURN'S S AE1 T ER0 N Z +SATURNALIA S AE1 T ER0 N EY1 L Y AH0 +SATURNALIAN S AE1 T ER0 N EY1 L Y AH0 N +SATURNALIAS S AE1 T ER0 N EY1 L Y AH0 Z +SATURNIA S AE1 T ER0 N IY0 AH0 +SATURNIAN S AE1 T ER0 N IY0 AH0 N +SATURNIANS S AE1 T ER0 N IY0 AH0 N Z +SATURNIN S AE1 T ER0 N IH1 N +SATURNINE S AE1 T ER0 N AY2 N +SATURNINO S AE1 T ER0 N IY1 N OW0 +SATURNINUS S AE1 T ER0 N IH1 N AH0 S +SATURNUS S AE1 T ER0 N AH0 S +SATYA S AA1 T Y AH0 +SATYABHAMA S AA1 T Y AH0 B HH AA1 M AH0 +SATYAGRAHA S AA2 T Y AA1 G R AH0 HH AA0 +SATYAVAN S AA2 T Y AA1 V AH0 N +SATYAVANA S AA1 T Y AH0 V AE1 N AH0 +SATYR S AE1 T 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AE1 K S AH0 N Z +SAXONBY S AE1 K S AH0 N B IY0 +SAXONDOM S AE1 K S AH0 N D AH0 M +SAXONHOLME S AE1 K S AH0 N HH OW2 L M +SAXONISM S AE1 K S AH0 N IH2 Z AH0 M +SAXONS S AE1 K S AH0 N Z +SAXONSTEADE S AE1 K S AH0 N S T EH1 D +SAXONY S AE1 K S AH0 N IY0 +SAXONY'S S AE1 K S AH0 N IY0 Z +SAXOPHONE S AE1 K S AH0 F OW2 N +SAXOPHONES S AE1 K S AH0 F OW2 N Z +SAXPENCE S AE1 K S P EH1 N S +SAXTEEN S AE1 K S T IY1 N +SAXTHORPE S AE1 K S TH AO0 R P +SAXTON S AE1 K S T AH0 N +SAXTON'S S AE1 K S T AH0 N Z +SAXTORPH S AE1 K S T ER0 F +SAXTY S AE1 K S T IY0 +SAXUM S AE1 K S AH0 M +SAXUMA S AE1 K S AH0 M AH0 +SAY S EY1 +SAY'D S AY1 D +SAY'S S EY1 Z +SAY'ST S EY1 S T +SAY'T S EY1 T +SAYA S AY1 AH0 +SAYA'S S AY1 AH0 Z +SAYAH S AY1 AH0 +SAYBROOK S EY1 B R UH2 K +SAYCE S AY1 S +SAYCHASE S AY1 CH EY2 S +SAYCRET S AY0 K R IH0 T +SAYD S EY1 D +SAYDA S EY1 D AH0 +SAYDE S EY1 D +SAYE S EY1 +SAYED S AA2 Y EH1 D +SAYED S EY1 D +SAYED S EY2 Y IH1 D +SAYEE S EY1 IY1 +SAYER S EY1 ER0 +SAYERIUS S EY2 Y IH1 R IY0 IH0 S +SAYERS S EY1 ER0 Z +SAYES S EY1 Z +SAYEST S EY1 IH0 S T +SAYETH S EY1 IH0 TH +SAYIN S EY1 IH0 N +SAYIN' S EY1 IH0 N +SAYIN'S S EY1 IH0 N Z +SAYING S EY1 IH0 NG +SAYINGS S EY1 IH0 NG Z +SAYIS S EY1 IH0 Z +SAYLE S EY1 L +SAYLES S EY1 L Z +SAYLO S EY1 L OW0 +SAYMER S EY1 M ER0 +SAYN S EY1 N +SAYNT S IH1 N T +SAYONARA S EY1 OW0 N AA1 R AH0 +SAYOTKATTA S EY1 AA2 K AA1 T AH0 +SAYRE S EH1 R +SAYS S EH1 Z +SAYS S IH1 Z +SAYSON S EY1 Z AH0 N +SAYSS S IH1 S +SAYST S IH1 S T +SAYT S EY1 T +SAYTH S IH1 TH +SAYTHER S AY0 DH ER0 +SAYWARD S EY1 W ER0 D +SAYYE S AY1 +SAYYID S AY1 IH0 D +SAZONOF S AA1 Z OW0 N OW1 F +SAZONOFF S AA0 Z AO1 N AO0 F +SBIRRI S B IH1 R IY0 +SBIRRO S B IH1 R OW0 +SBLOOD S B L AH2 D +SC S K +SCA S K AH0 +SCA'CE S K AA0 S +SCA'CELY S K AA0 S L IY0 +SCAB S K AE1 B +SCABBARD S K AE1 B ER0 D +SCABBARDED S K AE1 B ER0 D IH0 D +SCABBARDS S K AE1 B ER0 D Z +SCABBED S K AE1 B D +SCABBY S K AE1 B IY0 +SCABIES S K AE1 B IY0 Z +SCABIOUS S K AE1 B IY0 AH0 S +SCABROUS S K AE1 B R AH0 S +SCABS S K AE1 B Z +SCACCIATI S K AA1 CH AA1 T IY0 +SCADDER S K AE1 D ER0 +SCADDHAN S K AE1 D AH0 N +SCADS S K AE1 D Z +SCAEAN S K IY1 AH0 N +SCAEVOLA S K AA0 EH0 V OW1 L AH0 +SCAFELL S K AA0 F AH0 L +SCAFFOLD S K AE1 F AH0 L D +SCAFFOLDED S K AE1 F AH0 L D IH0 D +SCAFFOLDING S K AE1 F AH0 L D IH0 NG +SCAFFOLDINGS S K AE1 F AH0 L D IH0 NG Z +SCAFFOLDS S K AE1 F AH0 L D Z +SCAGLIOLA S K AE2 G L IY0 OW1 L AH0 +SCAIFE S K EY1 F +SCAIFE'S S K EY1 F S +SCAIRT S K ER0 T +SCAIS S K EY1 Z +SCAIT S K EY1 T +SCAITH S K EY1 TH +SCAL'D S K EY1 L D +SCALA S K AA1 L AH0 +SCALARIA S K AA0 L AA1 R IY0 AH0 +SCALASTRO S K AA0 L AA1 S T R OW0 +SCALAWAG S K AA1 L AH0 W AE2 G +SCALAWAGS S K AA1 L AH0 W AE1 G Z +SCALD S K AO1 L D +SCALDED S K AO1 L D IH0 D +SCALDIN S K AO1 L D IH0 N +SCALDING S K AO1 L D IH0 NG +SCALDINO S K AO1 L D IY1 N OW0 +SCALDS S K AO1 L D Z +SCALE S K EY1 L +SCALE'S S K EY1 L Z +SCALED S K EY1 L D +SCALELESS S K EY1 L L AH0 S +SCALENE S K EY1 L IY0 N +SCALER S K EY1 L ER0 +SCALERY S K AE1 L ER0 IY0 +SCALES S K EY1 L Z +SCALES'S S K EY1 L Z IH0 Z +SCALEY S K EY1 L IY0 +SCALI S K AA1 L IY0 +SCALI S K EY1 L IY0 +SCALIGER S K EY1 L IH0 G ER0 +SCALIGERS S K AA1 L AY0 G ER0 Z +SCALING S K EY1 L IH0 NG +SCALLAMERA S K AE1 L AH0 M EH1 R AH0 +SCALLAWAG S K AE1 L AH0 W AE2 G +SCALLAWAGS S K AE1 L AH0 W AE1 G Z +SCALLOP S K AE1 L AH0 P +SCALLOPED S K AA1 L AH0 P T +SCALLOPING S K AE1 L AH0 P IH0 NG +SCALLOPS S K AE1 L AH0 P S +SCALLOWA S K AE1 L AW1 AH0 +SCALLY S K AE1 L IY0 +SCALLYWAG S K AE1 L IY0 W AE2 G +SCALLYWAGS S K AE1 L IY0 W AE1 G Z +SCALP S K AE1 L P +SCALPA S K AE1 L P AH0 +SCALPED S K AE1 L P T +SCALPEL S K AE1 L P AH0 L +SCALPELS S K AE1 L P AH0 L Z +SCALPER S K AE1 L P ER0 +SCALPER'S S K AE1 L P ER0 Z +SCALPERS S K AE1 L P ER0 Z +SCALPIN S K AE1 L P IH0 N +SCALPING S K AE1 L P IH0 NG +SCALPINGS S K AE1 L P IH0 NG Z +SCALPLESS S K AE1 L P L AH0 S +SCALPLOCK S K AE1 L P L AH0 K +SCALPS S K AE1 L P S +SCALY S K EY1 L IY0 +SCAMANDER S K AE1 M AH0 N D ER0 +SCAMANDER'S S K AE1 M AH0 N D ER0 Z +SCAMMEL S K AE1 M AH0 L +SCAMMON S K AE1 M AH0 N +SCAMMONY S K AE1 M AH0 N IY0 +SCAMP S K AE1 M P +SCAMP'S S K AE1 M P S +SCAMPED S K AE1 M P T +SCAMPER S K AE1 M P ER0 +SCAMPER'D S K AE1 M P ER0 D +SCAMPERDALE S K AE1 M P ER0 D EY2 L +SCAMPERDALE'S S K AE1 M P ER0 D EY2 L Z +SCAMPERED S K AE1 M P ER0 D +SCAMPERING S K AE1 M P ER0 IH0 NG +SCAMPERINGS S K AE1 M P ER0 IH0 NG Z +SCAMPERS S K AE1 M P ER0 Z +SCAMPFORD S K AE1 M P F ER0 D +SCAMPING S K AE1 M P IH0 NG +SCAMPISH S K AE1 M P IH0 SH +SCAMPS S K AE1 M P S +SCAN S K AE1 N +SCAN'LOUS S K AE1 N L AH0 S +SCAND'LOUS S K AE1 N L AH0 S +SCANDAL S K AE1 N D AH0 L +SCANDAL'S S K AE1 N D AH0 L Z +SCANDALE S K AE1 N D EY2 L +SCANDALEUSE S K AE1 N D AH0 L UW1 S +SCANDALISE S K AE1 N D AH0 L IY1 S +SCANDALISED S K AE1 N D AH0 L AY2 Z D +SCANDALISING S K AE1 N D AH0 L AY2 Z IH0 NG +SCANDALIZE S K AE1 N D AH0 L AY2 Z +SCANDALIZED S K AE1 N D AH0 L AY2 Z D +SCANDALIZES S K AE1 N D AH0 L AY2 Z IH0 Z +SCANDALIZING S K AE1 N D AH0 L AY2 Z IH0 NG +SCANDALMONGER S K AE1 N D AH0 L M AA2 NG G ER0 +SCANDALMONGERING S K AE1 N D AH0 L M AA2 NG G ER0 IH0 NG +SCANDALMONGERS S K AE1 N D AH0 L M AA2 NG G ER0 Z +SCANDALOUS S K AE1 N D AH0 L AH0 S +SCANDALOUSLY S K AE1 N D AH0 L AH0 S L IY0 +SCANDALS S K AE1 N D AH0 L Z +SCANDALUM S K AE1 N D AH0 L AH0 M +SCANDENS S K AE1 N D AH0 N Z +SCANDERBEG S K AE1 N D ER0 B EH2 G +SCANDERBEG'S S K AE1 N D ER0 B EH2 G Z +SCANDEROON S K AE1 N D ER0 UW2 N +SCANDIANO S K AE1 N D IY0 AA1 N OW0 +SCANDINAVIA S K AE2 N D IH0 N EY1 V IY0 AH0 +SCANDINAVIAN S K AE2 N D IH0 N EY1 V IY0 AH0 N +SCANDINAVIANS S K AE2 N D IH0 N EY1 V IY0 AH0 N Z +SCANIA S K AA1 N IY0 AH0 +SCANLAN S K AE1 N L AH0 N +SCANLAN'S S K AE1 N L AH0 N Z +SCANLON S K AE1 N L AH0 N +SCANLON'S S K AE1 N L AH0 N Z +SCANN'D S K AE1 N D +SCANNED S K AE1 N D +SCANNELL S K AE1 N AH0 L +SCANNER S K AE1 N ER0 +SCANNERS S K AE1 N ER0 Z +SCANNING S K AE1 N IH0 NG +SCANS S K AE1 N Z +SCANSION S K AE1 N SH AH0 N +SCANT S K AE1 N T +SCANTED S K AE1 N T IH0 D +SCANTER S K AE1 N T ER0 +SCANTEST S K AE1 N T AH0 S T +SCANTIER S K AE1 N T IY0 ER0 +SCANTIEST S K AE1 N T IY0 AH0 S T +SCANTILY S K AE1 N T AH0 L IY0 +SCANTINESS S K AE1 N T IY0 N AH0 S +SCANTLEBRAY S K AE1 N T L AH0 B R EY2 +SCANTLEBRAY'S S K AE1 N T L AH0 B R EY2 Z +SCANTLEBURY S K AE1 N T AH0 L B EH2 R IY0 +SCANTLING S K AE1 N T L IH0 NG +SCANTLINGS S K AE1 N T L IH0 NG Z +SCANTLY S K AE1 N T L IY0 +SCANTNESS S K AE1 N T N AH0 S +SCANTY S K AE1 N T IY0 +SCAP'D S K EY1 P IY1 D +SCAPA S K AA1 P AH0 +SCAPE S K EY1 P +SCAPED S K EY1 P T +SCAPEGOAT S K EY1 P G OW2 T +SCAPEGOATS S K EY1 P G OW2 T S +SCAPEGRACE S K EY1 P G R EY1 S +SCAPEGRACES S K EY1 P G R EY1 S IH0 Z +SCAPES S K EY1 P S +SCAPHIO S K AE1 F IY0 OW0 +SCAPIN S K EY1 P IH2 N +SCAPING S K EY1 P IH0 NG +SCAPULA S K AE1 P Y AH0 L AH0 +SCAPULAR S K AE1 P Y AH0 L ER0 +SCAPULARIES S K AE1 P Y AH0 L ER0 IY0 Z +SCAPULARS S K AE1 P Y AH0 L ER0 Z +SCAPULARY S K AE1 P Y AH0 L EH2 R IY0 +SCAR S K AA1 R +SCAR'D S K AA0 R D +SCAR'S S K AA1 R Z +SCARAB S K AE1 R AH0 B +SCARABAEI S K EH1 R AH0 B AE0 EY2 +SCARABAEUS S K AE1 R AH0 B EY1 AH0 S +SCARABEE S K AE1 R AH0 B IY0 +SCARABEUS S K AA0 R AA1 B EY0 IH0 S +SCARABS S K AE1 R AH0 B Z +SCARAMMUCCIA S K EH1 R AH0 M Y UW0 CH AH0 +SCARAMOUCH S K AE1 R AH0 M AW2 CH +SCARAMOUCHE S K EH1 R AH0 M AW2 CH +SCARAMOUCHES S K EH1 R AH0 M AW2 CH IH0 Z +SCARBERRY S K AA1 R B EH2 R IY0 +SCARBERRY'S S K AA1 R B EH2 R IY0 Z +SCARBORO S K AA1 R B ER0 OW0 +SCARBOROUGH S K AA1 R B ER2 OW0 +SCARBOROUGH'S S K AA1 R B ER2 OW0 Z +SCARBY S K AA1 R B IY0 +SCARCE S K EH1 R S +SCARCELY S K EH1 R S L IY0 +SCARCENESS S K EH1 R S N AH0 S +SCARCER S K EH1 R S ER0 +SCARCEST S K EH1 R S AH0 S T +SCARCITY S K EH1 R S IH0 T IY0 +SCARCLIFF S K AA0 R K L IH2 F +SCARCLIFFS S K AA0 R K L IH2 F S +SCARDALE S K AA1 R D EY2 L +SCARE S K EH1 R +SCARECROW S K AE1 R K R OW0 +SCARECROW'S S K AE1 R K R OW0 Z +SCARECROWS S K AE1 R K R OW0 Z +SCARED S K EH1 R D +SCAREHEAD S K EH1 R HH EH0 D +SCAREHEADS S K EH1 R HH EH2 D Z +SCARER S K EH1 R ER0 +SCARES S K EH1 R Z +SCARESBY S K AA0 R EH1 S B IY0 +SCARET S K AA0 R EH1 T +SCAREY S K EH1 R IY0 +SCARF S K AA1 R F +SCARFACE S K AA1 R F EY2 S +SCARFE S K AA1 R F +SCARFED S K AA1 R F T +SCARFIELD S K AA1 R F IY2 L D +SCARFORD S K AA1 R F ER0 D +SCARFPIN S K AA1 R F P AE1 N +SCARFS S K AA1 R F S +SCARGATE S K AA1 R G EY2 T +SCARGILL S K AA1 R G IH2 L +SCARHAVEN S K AA1 R HH EY2 V AH0 N +SCARIEST S K EH1 R IY0 AH0 S T +SCARIFF S K EH1 R IH0 F +SCARIFICATION S K EH1 R IH0 F IH0 K EY1 SH AH0 N +SCARIFIED S K EH1 R AH0 F AY2 D +SCARIFY S K EH1 R AH0 F AY2 +SCARIFYING S K EH1 R AH0 F AY2 IH0 NG +SCARIN S K AE1 R IH0 N +SCARING S K EH1 R IH0 NG +SCARISBRICK S K EH1 R IH0 S B R IH2 K +SCARISBRICKS S K EH1 R IH0 S B R IH2 K S +SCARLATINA S K AA1 R L AH0 T IY1 N AH0 +SCARLATTI S K AA0 R L AA1 T IY0 +SCARLATTI'S S K AA1 R L AH0 T IY0 Z +SCARLET S K AA1 R L AH0 T +SCARLET'S S K AA1 R L AH0 T S +SCARLETS S K AA1 R L AH0 T S +SCARLETT S K AA1 R L IH0 T +SCARLETT'S S K AA1 R L AH0 T S +SCARMANN S K AA1 R M AH0 N +SCARMELLI S K AA0 R M EH1 L IY0 +SCARNHAM S K AA1 R N AH0 M +SCAROOYADI S K EH1 R UW0 AA1 D IY0 +SCARP S K AA1 R P +SCARPA S K AA1 R P AH0 +SCARPE S K AA1 R P +SCARPED S K AA1 R P T +SCARPIA S K AA0 R P IY0 AH0 +SCARPIA'S S K AA0 R P IY0 AH0 Z +SCARPS S K AA1 R P S +SCARR'D S K AA1 R D +SCARRED S K AA1 R D +SCARREN S K AA1 R AH0 N +SCARRING S K AA1 R IH0 NG +SCARRON S K AE1 R AH0 N +SCARRON'S S K AE1 R AH0 N Z +SCARROW S K AE1 R OW0 +SCARROWBY S K AE1 R OW0 B IY0 +SCARROWMANIA S K AE1 R OW0 M EY1 N IY0 AH0 +SCARS S K AA1 R Z +SCARSDALE S K AA1 R Z D EY2 L +SCARSELLA S K AA2 R S EH1 L AH0 +SCARSMERE S K AA1 R Z M IH2 R +SCART S K AA2 R T +SCARTH S K AA0 R TH +SCARTHEY S K AA1 R DH IY0 +SCARTHOE S K AA1 R TH OW2 +SCARUM S K EH1 R AH0 M +SCARVES S K AA1 R V Z +SCARY S K EH1 R IY0 +SCAT S K AE1 T +SCATCHARD S K AE1 CH ER0 D +SCATCHERD S K AE1 CH ER0 D +SCATCHERD'S S K AE1 CH ER0 D Z +SCATCHERDS S K AE1 CH ER0 D Z +SCATCHY S K AE1 CH IY0 +SCATE S K EY2 T +SCATES S K EY1 T S +SCATH S K AE1 TH +SCATHE S K EY1 DH +SCATHED S K EY1 DH D +SCATHELESS S K EY1 DH L AH0 S +SCATHES S K EY1 DH Z +SCATHING S K EY1 DH IH0 NG +SCATHINGLY S K EY1 DH IH0 NG L IY0 +SCATHLESS S K AE1 TH L AH0 S +SCATT S K AE1 T +SCATTER S K AE1 T ER0 +SCATTER'D S K AE1 T ER0 D +SCATTERALL S K AE1 T ER0 AO2 L +SCATTERATION S K AE1 T ER0 EY1 SH AH0 N +SCATTERBRAIN S K AE1 T ER0 B R EY2 N +SCATTERBRAIN'S S K AE1 T ER0 B R EY1 N Z +SCATTERBRAINED S K AE1 T ER0 B R EY2 N D +SCATTERBRAINS S K AE1 T ER0 B R EY1 N Z +SCATTERCASH S K AE1 T ER0 K AE2 SH +SCATTERCASH'S S K AE1 T ER0 K AE2 SH IH0 Z +SCATTERED S K AE1 T ER0 D +SCATTERER S K AE1 T ER0 ER0 +SCATTEREST S K AE1 T ER0 AH0 S T +SCATTERETH S K AE1 T ER0 EH1 TH +SCATTERGOOD S K AE1 T ER0 G UH2 D +SCATTERIN S K AE1 T ER0 IH0 N +SCATTERING S K AE1 T ER0 IH0 NG +SCATTERINGLY S K AE1 T ER0 IH0 NG L IY0 +SCATTERINGS S K AE1 T ER0 IH0 NG Z +SCATTERS S K AE1 T ER0 Z +SCAUR S K AA1 R +SCAURS S K AO2 R Z +SCAURUS S K AO1 R AH0 S +SCAVAIG S K AH0 V EY1 G +SCAVENGED S K AE1 V AH0 N JH D +SCAVENGER S K AE1 V AH0 N JH ER0 +SCAVENGER'S S K AE1 V AH0 N JH ER0 Z +SCAVENGERS S K AE1 V AH0 N JH ER0 Z +SCAVENGING S K AE1 V AH0 N JH IH0 NG +SCAVOIR S K AH0 V W AA1 R +SCAW S K AO1 +SCAWFELL S K AO1 F AH0 L +SCAWTHORNE S K AO1 TH AO0 R N +SCEAUX S OW1 +SCELECTED S IH0 L EH1 K T IH0 D +SCELERAT S EH2 L ER0 EY1 T +SCELUS S K EH1 L AH0 S +SCENA S IY1 N AH0 +SCENARIO S IH0 N EH1 R IY0 OW0 +SCENARIOS S IH0 N EH1 R IY0 OW0 Z +SCENE S IY1 N +SCENE'S S IY1 N Z +SCENERIES S IY1 N ER0 IY0 Z +SCENERY S IY1 N ER0 IY0 +SCENES S IY1 N Z +SCENIC S IY1 N IH0 K +SCENICAL S IY1 N IH0 K AH0 L +SCENICALLY S IY1 N IH0 K L IY0 +SCENT S EH1 N T +SCENTED S EH1 N T IH0 D +SCENTIN S EH1 N T IH0 N +SCENTING S EH1 N T IH0 NG +SCENTLESS S EH1 N T L AH0 S +SCENTS S EH1 N T S +SCENTW S IH0 N T W +SCENTWELL S EH1 N T W EH2 L +SCEPTER S K EH1 P T ER0 +SCEPTERED S K EH1 P T ER0 D +SCEPTERS S K EH1 P T ER0 Z +SCEPTIC S EH1 P T IH0 K +SCEPTIC'S S EH1 P T IH0 K S +SCEPTICAL S EH1 P T IH0 K AH0 L +SCEPTICALLY S EH1 P T IH0 K L IY0 +SCEPTICISM S EH1 P T IH0 S IH2 Z AH0 M +SCEPTICISMS S EH1 P T IH0 S IH2 Z AH0 M Z +SCEPTICS S EH1 P T IH0 K S +SCEPTRE S EH1 P T ER0 +SCEPTRE S K EH1 P T ER0 +SCEPTRE'S S K EH1 P T ER0 Z +SCEPTRED S K EH1 P T ER0 D +SCEPTRES S K EH1 P T ER0 Z +SCERCE S K EH1 R S +SCERCELY S K EH1 R S L IY0 +SCERNI S K EH1 R N IY0 +SCEVINUS S K EH1 V AH0 N IH0 S +SCH SH +SCHABELITZ SH AE1 B AH0 L IH0 T S +SCHACABAC SH AE1 K AH0 B AE0 K +SCHACH SH AE1 K +SCHADE SH EY1 D +SCHADECK SH AE1 D EH0 K +SCHADOW SH AE1 D OW0 +SCHAEFER SH EY1 F ER0 +SCHAEFER'S SH EY1 F ER0 Z +SCHAEFFER SH EH1 F ER0 +SCHAERTLIN SH EH1 R T L IH0 N +SCHAFF SH AE1 F +SCHAFFER SH EY1 F ER0 +SCHAFFHAUSEN SH AE1 F HH AW2 Z AH0 N +SCHAFFNER SH AE1 F N ER0 +SCHAFROFF SH AE1 F R AO0 F +SCHAH SH AA1 +SCHAHABARIM SH EY1 HH AE2 B ER0 IY1 M +SCHAIBAR S K EY1 B AA2 R +SCHAICK SH AY1 K +SCHAL SH AE1 L +SCHALCKENBERG SH AE1 L K AH0 N B ER0 G +SCHALCKENBERG'S SH AE1 L K AH0 N B ER0 G Z +SCHALISCHIM SH AE1 L IH0 SH IH0 M +SCHALK SH AO1 K +SCHALK'S SH AO1 K S +SCHALKEN SH AO1 K AH0 N +SCHALL SH AO1 L +SCHALLBERG SH AO1 L B ER0 G +SCHALLIBAUM SH AE1 L AH0 B AA2 M +SCHAME SH EY1 M +SCHAMING SH EY1 M IH0 NG +SCHAMIR SH AH0 M IH1 R +SCHAMLE SH AE1 M AH0 L +SCHAMYL SH AE1 M IH0 L +SCHANDAU SH AE1 N D AW0 +SCHANTZE SH AE1 N T Z +SCHANVOCH SH AE1 N V AH0 K +SCHANZES SH AE1 N Z IH0 Z +SCHAPOUR SH EY1 P AO1 R +SCHARLACH SH AA1 R L AH0 K +SCHARNHOFF SH AA1 R N HH AO0 F +SCHARNHORST SH AA1 R N HH AO0 R S T +SCHARWENKA SH AA1 R W EH1 NG K AH0 +SCHATZ SH AE1 T S +SCHATZHAUSER SH AE1 T S HH AW0 Z ER0 +SCHATZLAR SH AE1 T Z L ER0 +SCHAU SH OW1 +SCHAUFFLER SH OW1 F L ER0 +SCHAUMBERG SH OW1 M B ER0 G +SCHAUMBURG SH AW1 M B ER0 G +SCHAUNARD SH AW1 N ER0 D +SCHAUNARD'S SH AW1 N ER0 D Z +SCHE SH +SCHEDULE S K EH1 JH UH0 L +SCHEDULE S K EH1 JH UW0 L +SCHEDULED S K EH1 JH UH0 L D +SCHEDULED S K EH1 JH UW0 L D +SCHEDULES S K EH1 JH UH0 L Z +SCHEDULES S K EH1 JH UW0 L Z +SCHEEDAM SH IY1 D AH0 M +SCHEEL SH IY1 L +SCHEEPERS SH IY1 P ER0 Z +SCHEER SH IH1 R +SCHEERAU SH IH1 R AW1 +SCHEFF SH EH1 F +SCHEFFAUER SH EH1 F AW1 ER0 +SCHEFFEL SH EH1 F AH0 L +SCHEFFER SH EH1 F ER0 +SCHEFFER'S SH EH1 F ER0 Z +SCHEHERAZADE SH EH1 HH ER0 AA1 Z AH0 D +SCHEHEREZADE SH EH1 HH ER0 EY1 Z AH0 D +SCHEIBNER SH AY1 B N ER0 +SCHEICH SH AY0 K +SCHEIDECK SH AY1 D IH0 K +SCHEIDEMANN SH AY1 D M AH0 N +SCHEIH SH AY1 +SCHEIK SH IY1 K +SCHEIMER SH AY1 M ER0 +SCHEIMER'S SH AY1 M ER0 Z +SCHEIN SH AY1 N +SCHELD SH EH1 L D +SCHELDE S K IH0 L D +SCHELDT SH EH1 L T +SCHELL S K EH1 L +SCHELLEN SH EH1 L AH0 N +SCHELLENBERG SH EH1 L AH0 N B ER0 G +SCHELLER S K EH1 L ER0 +SCHELLING S K EH1 L IH0 NG +SCHELLING'S SH EH1 L IH0 NG Z +SCHELM SH EH1 L M +SCHELMS SH EH1 L M Z +SCHEM S K IH0 M +SCHEMA S K IY1 M AH0 +SCHEMATA S K IH0 M AE1 T AH0 +SCHEMATIC S K IH0 M AE1 T IH0 K +SCHEME S K IY1 M +SCHEME'S S K IY1 M Z +SCHEMED S K IY1 M D +SCHEMER S K IY1 M ER0 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+SCHNAPS SH N AE1 P S +SCHNEE SH N IY1 +SCHNEEBERG SH N IY1 B ER0 G +SCHNEEBURG SH N IY1 B ER0 G +SCHNEEKOPPE SH N IY1 K AA1 P +SCHNEEWEISS SH N IY1 W AY1 S +SCHNEIDEKIND SH N AY1 D IH0 K IH0 N D +SCHNEIDEKOUPON SH N AY1 D EH2 K UW1 P AO2 N +SCHNEIDER SH N AY1 D ER0 +SCHNEIDER'S SH N AY1 D ER0 Z +SCHNELL SH N EH1 L +SCHNELLEST SH N EH1 L AH0 S T +SCHNELLING SH N EH1 L IH0 NG +SCHNETZ SH N EH1 T S +SCHNETZEN SH N EH1 T Z AH0 N +SCHNITT SH N IH2 T +SCHNITZEL SH N IH2 T S AH0 L +SCHNITZELHAMMERSTEIN SH N IH2 T S AH0 L HH AE2 M ER0 S T AY2 N +SCHNITZER SH N IH1 T Z ER0 +SCHNITZLER SH N IH1 T S L ER0 +SCHNORR SH N AO1 R +SCHNORRER SH N AO1 R ER0 +SCHNORRER'S SH N AO1 R ER0 Z +SCHNORRERS SH N AO1 R ER0 Z +SCHNOUFFIUS SH N OW1 F IY0 IH0 S +SCHNOZZLE SH N AA1 Z AH0 L +SCHNURRI SH N ER1 IY0 +SCHO SH OW1 +SCHOBER SH OW1 B ER0 +SCHODACK SH AA1 D AE0 K +SCHOEFFER SH OW1 F ER0 +SCHOEN SH OW1 N +SCHOENAU SH OW1 N AW0 +SCHOENBERG SH OW1 N B ER0 G +SCHOENBERG'S SH OW1 N B ER0 G Z +SCHOENBRUNN SH OW1 N B R AH1 N +SCHOENDEL SH OW1 N D AH0 L +SCHOENE SH AA1 IY0 N +SCHOENEN SH OW1 N AH0 N +SCHOENER SH OW1 N ER0 +SCHOENFELD SH OW1 N F EH2 L D +SCHOENHERR SH OW1 N HH ER0 +SCHOENLEBEN SH AA1 AH0 N L EY2 B AH0 N +SCHOENMAKERS SH OW1 N M EY2 K ER0 Z +SCHOENSTEIN SH OW1 N S T AY2 N +SCHOENSTROM SH OW1 N S T R AH0 M +SCHOENTHAL SH OW1 N TH AO2 L +SCHOENUS SH OW1 N AH0 S +SCHOENVORTS SH OW1 N V AO1 R T S +SCHOEPF SH OW1 P +SCHOFIELD S K OW1 F IY0 L D +SCHOFIELD'S S K OW1 F IY0 L D Z +SCHOFIELDS S K OW1 F IY0 L D Z +SCHOHARIE SH OW1 HH AA0 R IY0 +SCHOL S K AH0 L +SCHOLA S K OW1 L AH0 +SCHOLAR S K AA1 L ER0 +SCHOLAR'S S K AA1 L ER0 Z +SCHOLARD S K AA1 L ER0 D +SCHOLARDS S K AA1 L ER0 D Z +SCHOLARIUS S K AA1 L ER0 AY1 AH0 S +SCHOLARLIKE S K AA1 L ER0 L AY2 K +SCHOLARLINESS S K AA1 L ER0 L IY0 N AH0 S +SCHOLARLY S K AA1 L ER0 L IY0 +SCHOLARS S K AA1 L ER0 Z +SCHOLARSHIP S K AA1 L ER0 SH IH2 P +SCHOLARSHIPS S K AA1 L ER0 SH IH2 P S +SCHOLASTIC S K AH0 L AE1 S T IH0 K +SCHOLASTICA S K AH0 L AE1 S T IH0 K AH0 +SCHOLASTICALLY S K AH0 L AE1 S T IH0 K L IY0 +SCHOLASTICISM S K AH0 L AE1 S T IH0 S IH2 Z AH0 M +SCHOLASTICK S K AH0 L AE1 S T IH0 K +SCHOLASTICS S K AH0 L AE1 S T IH0 K S +SCHOLASTICUS S K AH0 L AE1 S T IH0 K AH0 S +SCHOLASTIQUE S K AH0 L AH0 S T IY1 K +SCHOLE SH OW1 L +SCHOLFIELD S K OW1 L F IY2 L D +SCHOLIA S K OW1 L IY0 AH0 +SCHOLIAST S K OW1 L IY0 AE0 S T +SCHOLIASTS SH OW1 L Y AE1 S +SCHOLIUM S K OW1 L IY0 AH0 M +SCHOLLARD SH AA1 L ER0 D +SCHOLLER SH AA1 L ER0 +SCHOLLERS SH AA1 L ER0 Z +SCHOLTZ SH OW1 L T S +SCHOMBERG SH AA1 M B ER0 G +SCHOMBERG'S SH AA1 M B ER0 G Z +SCHOMBURGK SH AA1 M B ER0 G K +SCHOMMER SH AA1 M ER0 +SCHON SH AA1 N +SCHONBERG SH AA1 N B ER0 G +SCHONBOCK SH AA1 N B AA2 K +SCHONBRUNN SH AA1 N B R AH1 N +SCHONE SH OW1 N +SCHONEN SH AA1 N AH0 N +SCHONHAUSEN SH AA1 N HH AW2 Z AH0 N +SCHONTZ SH AA1 N T S +SCHONWALDT SH AA1 N W AO2 L D T +SCHOO SH UW1 +SCHOOL S K UW1 L +SCHOOL'D S K UW1 L D +SCHOOL'S S K UW1 L Z +SCHOOLBOOK S K UW1 L B UH2 K +SCHOOLBOOKS S 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ER0 +SCHOOLMASTER'S S K UW1 L M AE2 S T ER0 Z +SCHOOLMASTERING S K UW1 L M AE2 S T ER0 IH0 NG +SCHOOLMASTERS S K UW1 L M AE2 S T ER0 Z +SCHOOLMATE S K UW1 L M EY2 T +SCHOOLMATE'S S K UW1 L M EY2 T S +SCHOOLMATES S K UW1 L M EY2 T S +SCHOOLMEASTHER S K UW1 L M IY1 S T ER0 +SCHOOLMEN S K UW1 L M EH1 N +SCHOOLMISTRESS S K UW1 L M IH0 S T R EH1 S +SCHOOLMISTRESS'S S K UW1 L M IH0 S T R EH1 S IH0 Z +SCHOOLMISTRESSES S K UW1 L M IH0 S T R EH1 S IH0 Z +SCHOOLROOM S K UW1 L R UW2 M +SCHOOLROOMS S K UW1 L R UW2 M Z +SCHOOLS S K UW1 L Z +SCHOOLTEACHER S K UW1 L T IY2 CH ER0 +SCHOOLTEACHER'S S K UW1 L T IY2 CH ER0 Z +SCHOOLTEACHERS S K UW1 L T IY2 CH ER0 Z +SCHOOLTIME S K UW1 L T AY2 M +SCHOOLWARD S K UW1 L W ER0 D +SCHOOLWORK S K UW1 L W ER2 K +SCHOOLYARD S K UW1 L Y AA2 R D +SCHOONER S K UW1 N ER0 +SCHOONER'S S K UW1 N ER0 Z +SCHOONERS S K UW1 N ER0 Z +SCHOONOVER SH UW1 N AH0 V ER0 +SCHOPENHAUER SH OW1 P AH0 N HH AW0 ER0 +SCHOPENHAUER'S SH OW1 P AH0 N HH AW0 ER0 Z +SCHOPFLOCHER SH AA1 P F L AA1 K ER0 +SCHOPPER SH AA1 P ER0 +SCHOPPERS SH AA1 P ER0 Z +SCHORLIN SH AO1 R L IH0 N +SCHORLIN'S SH AO1 R L IH0 N Z +SCHORN SH AO1 R N +SCHORN'S SH AO1 R N Z +SCHOTT SH AA1 T +SCHOTTISCHE SH AA1 T IH1 SH +SCHOTZ SH AA1 T S +SCHOULER SH UW1 L ER0 +SCHOULER'S SH UW1 L ER0 Z +SCHOUT SH AW2 T +SCHOUTEN SH AA1 UW0 T AH0 N +SCHOUWEN SH UW1 AH0 N +SCHOVERLING SH OW1 V ER0 L IH0 NG +SCHOVILLE SH AA1 V IH0 L +SCHRADER SH R EY1 D ER0 +SCHRAMM SH R AE1 M +SCHRANDEN SH R AE1 N D AH0 N +SCHRANDENERS SH R AE1 N D AH0 N ER0 Z +SCHRANK SH R AE1 NG K +SCHRANK'S SH R AE1 NG K S +SCHRAPPE SH R AE1 P +SCHRATT SH R AE1 T +SCHRAUBE SH R AO1 B +SCHRECKENHEIM SH R EH1 K AH0 N HH AY2 M +SCHRECKENSTEIN SH R EH1 K AH0 N S T AY2 N +SCHRECKENWALD SH R EH1 K IH0 N W AO0 L D +SCHRECKHORN SH R EH1 K HH AO2 R N +SCHRECKLICH SH R EH1 K L IH0 K +SCHRECKLICHKEIT SH R EH1 K L IH1 CH K AY0 T +SCHREIBER SH R AY1 B ER0 +SCHREIDERLING SH R AY1 D ER0 L IH0 NG +SCHREINER SH R AY1 N ER0 +SCHREINER'S SH R AY1 N ER0 Z +SCHRIEBER SH R 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+SCRAPINGS S K R EY1 P IH0 NG Z +SCRAPPED S K R AE1 P T +SCRAPPER S K R AE1 P ER0 +SCRAPPERS S K R AE1 P ER0 Z +SCRAPPIE S K R AE1 P IY2 +SCRAPPIN S K R AE1 P IH0 N +SCRAPPING S K R AE1 P IH0 NG +SCRAPPLE S K R AE1 P AH0 L +SCRAPPLEHEAD S K R AE1 P AH0 L HH EH2 D +SCRAPPY S K R AE1 P IY0 +SCRAPS S K R AE1 P S +SCRAT S K R AE1 T +SCRATCH S K R AE1 CH +SCRATCH'D S K R AE1 CH IH0 D +SCRATCHARD S K R AE1 CH ER0 D +SCRATCHED S K R AE1 CH T +SCRATCHER S K R AE1 CH ER0 +SCRATCHERS S K R AE1 CH ER0 Z +SCRATCHES S K R AE1 CH AH0 Z +SCRATCHES S K R AE1 CH IH0 Z +SCRATCHIN S K R AE1 CH IH0 N +SCRATCHING S K R AE1 CH IH0 NG +SCRATCHINGS S K R AE1 CH IH0 NG Z +SCRATCHY S K R AE1 CH IY0 +SCRATTED S K R AE1 T IH0 D +SCRATTON S K R AE1 T AH0 N +SCRAWL S K R AO1 L +SCRAWLED S K R AO1 L D +SCRAWLING S K R AO1 L IH0 NG +SCRAWLS S K R AO1 L Z +SCRAWLY S K R AO1 L IY0 +SCRAWNY S K R AO1 N IY0 +SCRAWS S K R AO1 Z +SCRE S K R +SCREAKING S K R IY1 K IH0 NG +SCREAM S K R IY1 M +SCREAM'D S K R IY1 M D +SCREAMED S K R IY1 M D +SCREAMER S K R IY1 M ER0 +SCREAMERS S K R IY1 M ER0 Z +SCREAMIN S K R IY1 M IH0 N +SCREAMING S K R IY1 M IH0 NG +SCREAMINGLY S K R IY1 M IH0 NG L IY0 +SCREAMINGS S K R IY1 M IH0 NG Z +SCREAMS S K R IY1 M Z +SCREE S K R IY1 +SCREECH S K R IY1 CH +SCREECHED S K R IY1 CH T +SCREECHER S K R IY1 CH ER0 +SCREECHES S K R IY1 CH IH0 Z +SCREECHIN S K R IY1 CH AH0 N +SCREECHING S K R IY1 CH IH0 NG +SCREECHINGS S K R IY1 CH IH0 NG Z +SCREECHY S K R IY1 CH IY0 +SCREED S K R IY1 D +SCREEDS S K R IY1 D Z +SCREEN S K R IY1 N +SCREEN'D S K R IY1 N D +SCREEN'S S K R IY1 N Z +SCREENED S K R IY1 N D +SCREENING S K R IY1 N IH0 NG +SCREENINGS S K R IY1 N IH0 NG Z +SCREENS S K R IY1 N Z +SCREES S K R IY1 Z +SCREMERSTON S K R IY1 M ER0 S T AH0 N +SCREW S K R UW1 +SCREW'D S K R UW1 D +SCREW'S S K R UW1 Z +SCREWBALL S K R UW1 B AO2 L +SCREWDRIVER S K R UW1 D R AY2 V ER0 +SCREWDRIVERS S K R UW1 D R AY2 V ER0 Z +SCREWED S K R UW1 D +SCREWIN S K R UW1 IH0 N +SCREWING S K R UW1 IH0 NG +SCREWS S K R UW1 Z +SCREWSTOWN S K R UW1 Z T AW2 N +SCREWY S K R UW1 IY0 +SCRIABINE S K R AY1 AH0 B IY1 N +SCRIBBLE S K R IH1 B AH0 L +SCRIBBLED S K R IH1 B AH0 L D +SCRIBBLER S K R IH1 B L ER0 +SCRIBBLERS S K R IH1 B L ER0 Z +SCRIBBLES S K R IH1 B AH0 L Z +SCRIBBLING S K R IH1 B AH0 L IH0 NG +SCRIBBLING S K R IH1 B L IH0 NG +SCRIBBLINGS S K R IH1 B L IH0 NG Z +SCRIBBO S K R IH1 B OW0 +SCRIBE S K R AY1 B +SCRIBE'S S K R AY1 B Z +SCRIBED S K R AY1 B D +SCRIBENDI S K R AY1 B N D IY0 +SCRIBERE S K R AY1 B ER0 +SCRIBES S K R AY1 B Z +SCRIBLERS S K R IH1 B L ER0 Z +SCRIBLERUS S K R IH1 B L ER0 AH0 S +SCRIBNER S K R IH1 B N ER0 +SCRIBNER'S S K R IH1 B N ER0 Z +SCRIBNERS S K R IH1 B N ER0 Z +SCRIDAIN S K R IH1 D EY1 N +SCRIECK S K R IY1 K +SCRIM S K R IH1 M +SCRIMMAGE S K R IH1 M IH0 JH +SCRIMMAGES S K R IH1 M IH0 JH AH0 Z +SCRIMMAGING S K R IH1 M IH0 JH IH0 NG +SCRIMP S K R IH1 M P +SCRIMPED S K R IH1 M P T +SCRIMPING S K R IH1 M P IH0 NG +SCRIMSHAW S K R IH1 M SH AO2 +SCRINGE S K R IH1 N JH +SCRIP S K R IH1 P +SCRIPIO S K R IH1 P IY0 OW0 +SCRIPPS S K R IH1 P S +SCRIPS S K R IH1 P S +SCRIPSIT S K R IH1 P S IH0 T +SCRIPT S K R IH1 P T +SCRIPTA S K R IH1 P T AH0 +SCRIPTER S K R IH1 P T ER0 +SCRIPTERS S K R IH1 P T ER0 Z +SCRIPTION S K R IH1 P SH AH0 N +SCRIPTIS S K R IH1 P T IH0 S +SCRIPTORES S K R IH1 P T AO2 R Z +SCRIPTORIUM S K R IH1 P T AO1 R IY0 AH0 M +SCRIPTS S K R IH1 P S +SCRIPTS S K R IH1 P T S +SCRIPTUM S K R IH1 P T AH0 M +SCRIPTUR S K R IH1 P T ER0 +SCRIPTURAL S K R IH1 P CH ER0 AH0 L +SCRIPTURALLY S K R IH1 P CH ER0 AH0 L IY0 +SCRIPTURE S K R IH1 P CH ER0 +SCRIPTURES S K R IH1 P CH ER0 Z +SCRITCH S K R IH1 CH +SCRITTO S K R IH1 T OW0 +SCRIVEN S K R IH1 V IH0 N +SCRIVENER S K R IH1 V N ER0 +SCRIVENER'S S K R IH1 V IH0 N ER0 Z +SCRIVENERS S K R AY1 V AH0 N ER0 Z +SCRIVER S K R AY1 V ER0 +SCRO S K R OW1 +SCROB S K R AA1 B +SCROBBY S K R AA1 B IY0 +SCROBBY'S S K R AA1 B IY0 Z +SCROFULA S K R AA1 F UW1 L AH0 +SCROFULOUS S K R AA1 F AH0 L AH0 S +SCROG S K R AA1 G +SCROGGIE S K R AA1 G IY0 +SCROGGINS S K R AA1 G IH0 N Z +SCROGGLES S K R AA1 G AH0 L Z +SCROGGS S K R AA1 G Z +SCROGIE S K R AA1 G IY0 +SCROLL S K R OW1 L +SCROLLED S K R OW1 L D +SCROLLS S K R OW1 L Z +SCROLLWORK S K R OW1 L W ER2 K +SCROOBY S K R UW1 B IY0 +SCROOCH S K R UW1 CH +SCROOGE S K R UW1 JH +SCROOGE'S S K R UW1 JH IH0 Z +SCROOGED S K R UW1 JH D +SCROOP S K R UW1 P +SCROOPE S K R UW1 P +SCROOPE'S S K R UW1 P S +SCROPE S K R OW1 P +SCROPE'S S K R AO1 P S +SCROPES S K R OW1 P S +SCROTTON S K R AO1 T AH0 N +SCROTTON'S S K R AO1 T AH0 N Z +SCROTUM S K R AO1 T AH0 M +SCROUGE S K R AW1 JH +SCROUGED S K R AW1 JH D +SCROUGING S K R AW1 JH IH0 NG +SCROUNGE S K R AW1 N JH +SCROUNGING S K R AW1 N JH IH0 NG +SCRUB S K R AH1 B +SCRUB'S S K R AH1 B Z +SCRUBBED S K R AH1 B D +SCRUBBER S K R AH1 B ER0 +SCRUBBERS S K R AH1 B ER0 Z +SCRUBBIN S K R AH1 B IH0 N +SCRUBBING S K R AH1 B IH0 NG +SCRUBBINGS S K R AH1 B IH0 NG Z +SCRUBBS S K R AH1 B Z +SCRUBBY S K R AH1 B IY0 +SCRUBS S K R AH1 B Z +SCRUBWOMAN S K R AH1 B W UH2 M AH0 N +SCRUBWOMEN S K R AH1 B W IH2 M AH0 N +SCRUFF S K R AH1 F +SCRUFF'S S K R AH1 F S +SCRUGGS S K R AH1 G Z +SCRUM S K R AH1 M +SCRUMMAGE S K R AH1 M IH0 JH +SCRUMPTIOUS S K R AH1 M SH AH0 S +SCRUMPYDIKE S K R AH1 M P IY0 D AY2 K +SCRUNCH S K R AH1 N CH +SCRUNCHED S K R AH1 N CH T +SCRUNCHING S K R AH1 N CH IH0 NG +SCRUPLE S K R UW1 P AH0 L +SCRUPLED S K R UW1 P AH0 L D +SCRUPLES S K R UW1 P AH0 L Z +SCRUPLING S K R UW1 P AH0 L IH0 NG +SCRUPULOSITY S K R UW1 P Y AH0 L AA1 S AH0 T IY0 +SCRUPULOUS S K R UW1 P Y AH0 L AH0 S +SCRUPULOUSLY S K R UW1 P Y AH0 L AH0 S L IY0 +SCRUPULOUSNESS S K R UW1 P Y AH0 L AH0 S N AH0 S +SCRUT S K R UW1 T +SCRUTATOR S K R UW1 T AH0 T ER0 +SCRUTINEERS S K R UW1 T AH0 N IH1 R Z +SCRUTINIES S K R UW1 T AH0 N IY0 Z +SCRUTINISE S K R UW1 T AH0 N IY1 S +SCRUTINISED S K R UW1 T AH0 N AY2 Z D +SCRUTINISES S K R UW1 T AH0 N AY2 Z IH0 Z +SCRUTINISING S K R UW1 T AH0 N AY2 Z IH0 NG +SCRUTINISINGLY S K R UW1 T AH0 N AY2 Z IH0 NG L IY0 +SCRUTINIZE S K R UW1 T AH0 N AY2 Z +SCRUTINIZED S K R UW1 T AH0 N AY2 Z D +SCRUTINIZES S K R UW1 T AH0 N AY2 Z IH0 Z +SCRUTINIZING S K R UW1 T AH0 N AY2 Z IH0 NG +SCRUTINIZINGLY S K R UW1 T AH0 N AY2 Z IH0 NG L IY0 +SCRUTINY S K R UW1 T AH0 N IY0 +SCRUTOIRE S K R UW1 T W AA1 R +SCRUTON S K R UW1 T AH0 N +SCRUTS S K R UW1 T S +SCRYMGEOUR S K R IY0 M G AO0 R +SCUBA S K UW1 B AH0 +SCUD S K AH1 D +SCUDAMORE S K UW0 D AA1 M AO0 R +SCUDAMORE'S S K AH1 D AH0 M AO1 R EY0 Z +SCUDAMORES S K AH1 D AH0 M AO2 R Z +SCUDAWAY S K AH1 D AH0 W EY2 +SCUDDED S K AH1 D IH0 D +SCUDDER S K AH1 D ER0 +SCUDDER'S S K AH1 D ER0 Z +SCUDDING S K AH1 D IH0 NG +SCUDDS S K AH1 D Z +SCUDDY S K AH1 D IY0 +SCUDERI S K UW0 D EH1 R IY0 +SCUDERI'S S K UW0 D EH1 R IY0 Z +SCUDERY S K UW0 D EH1 R IY0 +SCUDI S K AH1 D IY0 +SCUDMORE S K AH1 D M AO0 R +SCUDO S K UW1 D OW0 +SCUDS S K AH1 D Z +SCUFF S K AH1 F +SCUFFED S K AH1 F T +SCUFFING S K AH1 F IH0 NG +SCUFFLE S K AH1 F AH0 L +SCUFFLED S K AH1 F AH0 L D +SCUFFLES S K AH1 F AH0 L Z +SCUFFLIN S K AH1 F L IH0 N +SCUFFLING S K AH1 F AH0 L IH0 NG +SCUFFLINGS S K AH1 F AH0 L IH0 NG Z +SCULCO S K AH1 L K OW0 +SCULDUDDERY S K AH1 L D AH1 D ER0 IY0 +SCULL S K AH1 L +SCULLED S K AH1 L D +SCULLER S K AH1 L ER0 +SCULLERIES S K AH1 L ER0 IY0 Z +SCULLERS S K AH1 L ER0 Z +SCULLERY S K AH1 L ER0 IY0 +SCULLERYMAID S K AH1 L ER0 IY0 M EY2 D +SCULLING S K AH1 L IH0 NG +SCULLION S K AH1 L Y AH0 N +SCULLION'S S K AH1 L Y AH0 N Z +SCULLIONS S K AH1 L Y AH0 N Z +SCULLS S K AH1 L Z +SCULLY S K AH1 L IY0 +SCULLY'S S K AH1 L IY0 Z +SCULP S K AH1 L P +SCULPED S K AH1 L P T +SCULPIN S K AH1 L P IH0 N +SCULPING S K AH1 L P IH0 NG +SCULPINS S K AH1 L P IH1 N Z +SCULPS S K AH1 L P S +SCULPTED S K AH1 L P T IH0 D +SCULPTEURS S K AH1 L P CH ER1 Z +SCULPTOR S K AH1 L P T ER0 +SCULPTOR'S S K AH1 L P T ER0 Z +SCULPTORS S K AH1 L P T ER0 Z +SCULPTRESS S K AH1 L P T R IH0 S +SCULPTURAL S K AH1 L P CH ER0 AH0 L +SCULPTURE S K AH1 L P CH ER0 +SCULPTURED S K AH1 L P CH ER0 D +SCULPTURES S K AH1 L P CH ER0 Z +SCULPTURESQUE S K AH1 L P CH ER0 AH0 S K +SCULPTURING S K AH1 L P CH ER0 IH0 NG +SCULPTURING S K AH1 L P T ER0 IH0 NG +SCUM S K AH1 M +SCUMBERG'S S K AH1 M B ER0 G Z +SCUMMED S K AH1 M D +SCUMMY S K AH1 M IY0 +SCUNNER S K AH1 N ER0 +SCUOLA S K W OW1 L AH0 +SCUP S K AH1 P +SCUPHAM S K AH1 P AH0 M +SCUPPER S K AH1 P ER0 +SCUPPERED S K AH1 P ER0 D +SCUPPERNON S K AH1 P ER1 N AH0 N +SCUPPERNONG S K AH1 P ER0 N AO2 NG +SCUPPERS S K AH1 P ER0 Z +SCURATOFF S K ER1 AH0 T AO2 F +SCURCE S K ER1 S +SCURCELY S K ER1 S L IY0 +SCURF S K ER1 F +SCURFY S K ER1 F IY0 +SCURO S K ER1 OW0 +SCURRIED S K ER1 IY0 D +SCURRIES S K ER1 IY0 Z +SCURRIL S K ER1 AH0 L +SCURRILITIES S K ER1 AH0 L AH0 T IY0 Z +SCURRILITY S K ER1 IH0 L IH0 T IY0 +SCURRILOUS S K ER1 AH0 L AH0 S +SCURRILOUSLY S K ER1 AH0 L AH0 S L IY0 +SCURRY S K ER1 IY0 +SCURRYIN S K ER1 IY0 IH0 N +SCURRYING S K ER1 IY0 IH0 NG +SCURRYINGS S K ER1 IY0 IH0 NG Z +SCURVILY S K ER1 V IH0 L IY0 +SCURVY S K ER1 V IY0 +SCUSE S K UW1 S IY0 +SCUSI S K UW1 S IY0 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EH1 M P S T R EH1 S +SEMPSTRESS'S S EH1 M P S T R EH1 S IH0 Z +SEMPSTRESSES S EH1 M P S T R EH1 S IH0 Z +SEMPTER S EH1 M P T ER0 +SEMSA S EH1 M S AH0 +SEMUR S IY1 M ER0 +SEMYON S EH1 M Y AO1 N +SEMYONITCH S EH1 M Y AO1 N IH0 CH +SEMYONITCH'S S EH1 M Y AH0 N IH1 CH IH0 Z +SEMYONOV S EH1 M Y AO1 N AA0 V +SEMYONOV'S S EH1 M Y AO1 N AA0 V Z +SEMYONOVITCH S EH1 M Y AO1 N AH0 V IH0 CH +SEMYONOVNA S EH1 M Y AO1 N AH0 V N AH0 +SEN S EH1 N +SEN S EH1 N AH0 T ER0 +SEN'S S AH0 N Z +SENA S EH1 N AH0 +SENAAR S IH0 N AA0 R +SENACHIE S EH0 N AA0 K IY1 +SENANCOUR S EH2 N AH0 N K UH1 R +SENART S EH0 N AA0 R T +SENAT S EH1 N AH0 T +SENATE S EH1 N AH0 T +SENATE S EH1 N IH0 T +SENATE'S S EH1 N IH0 T S +SENATES S EH1 N IH0 T S +SENATEUR S EH1 N AH0 T ER1 +SENATOR S EH1 N AH0 T ER0 +SENATOR'S S EH1 N AH0 T ER0 Z +SENATORIAL S EH2 N AH0 T AO1 R IY0 AH0 L +SENATORS S EH1 N AH0 T ER0 Z +SENATORSHIP S EH1 N AH0 T ER0 SH IH2 P +SENATORSKA S EH1 N AH0 T ER0 S K AH0 +SENATUS S EH1 N AH0 T AH0 S +SENBORN S IH0 N B ER0 N +SENBY S EH1 N B IY0 +SENCE S EH1 N S +SENCES S EH1 N S AH0 Z +SENCI S EH1 N S IY0 +SENCI'S S EH1 N S IY0 Z +SEND S EH1 N D +SENDA S EH1 N D AH0 +SENDAI S EH1 N D AY1 +SENDAL S EH1 N D AH0 L +SENDE S EH1 N D EY0 +SENDED S EH1 N D IH0 D +SENDEN S EH1 N D AH0 N +SENDENNIS S EH0 N D EH1 N IH0 S +SENDER S EH1 N D ER0 +SENDER'S S EH1 N D ER0 Z +SENDERS S EH1 N D ER0 Z +SENDEST S EH0 N D AH0 S T +SENDETH S EH1 N D IH0 TH +SENDIN S EH1 N D IH0 N +SENDING S EH1 N D IH0 NG +SENDLINGEN S EH1 N D L IH0 NG AH0 N +SENDLINGEN'S S EH1 N D L IH0 NG G EH0 N Z +SENDS S EH1 N D Z +SENE S IY2 N +SENECA S EH1 N AH0 K AH0 +SENECA'S S EH1 N AH0 K AH0 Z +SENECAL S EH1 N IH0 K AH0 L +SENECAS S EH1 N AH0 K AH0 Z +SENECHAL S EH1 N IH0 K AH0 L +SENECIO S EH1 N AH0 S IY0 OW0 +SENECTUS S EH1 N IH0 K T AH0 S +SENECTUTE S EH1 N IH0 K CH UW0 T +SENEFERU S EH1 N IH0 F ER0 UW1 +SENEGAL S EH2 N AH0 G AO1 L +SENEGALESE S EH2 N AH0 G AH0 L IY1 Z +SENEGAMBIA S EH2 N AH0 G AE1 M B IY0 AH0 +SENEGAMBIAN S EH2 N AH0 G AE1 M B IY0 AH0 N +SENEKAL S EH1 N AH0 K AH0 L +SENESCENCE S AH0 N EH1 S AH0 N S +SENESCENT S EH0 N EH1 S AH0 N T +SENESCHAL S EH1 N AH0 SH AH0 L +SENESCHAL'S S EH1 N AH0 SH AH0 L Z +SENESCHALS S EH1 N AH0 SH AH0 L Z +SENESINO S EH1 N IH0 S IY1 N OW0 +SENESTRO S EH1 N AH0 S T R OW0 +SENEX S EH1 N AH0 K S +SENEZ S EH0 N EH1 Z +SENF S EH1 N F +SENFFT S EH1 N F T +SENFT S EH1 N F T +SENG S EH1 NG +SENGA S EH1 NG G AH0 +SENGSTACK S EH1 NG S T AE2 K +SENHIME S EH1 N HH AY2 M +SENHOR S IH0 N HH ER0 +SENHORA S EH1 N ER0 AH0 +SENHORA'S S IH0 N HH ER0 AH0 Z +SENHORES S EH1 N HH AO2 R Z +SENHORINA S EH1 N HH AO2 R IY1 N AH0 +SENI S IY1 N IY0 +SENIAS S IY1 N Y AH0 Z +SENILE S IY1 N AY2 L +SENILIS S AH0 N IH1 L AH0 S +SENILITY S AH0 N IH1 L AH0 T IY0 +SENIOR S IY1 N Y ER0 +SENIOR'S S IY1 N Y ER0 Z +SENIORITY S IY0 N Y AO1 R IH0 T IY0 +SENIORS S IY1 N Y ER0 Z +SENIORSHIP S IY1 N Y ER0 SH IH2 P +SENKA S EH1 NG K AH0 +SENLABOR S EH1 N L EY1 B ER0 +SENLAC S EH1 N AH0 L AE2 K +SENLIS S EH1 N AH0 L IH0 S +SENN S EH1 N +SENNA S EH1 N AH0 +SENNAAR S EH1 N AA1 R +SENNACHERIB S EH1 N AH0 K ER0 IY1 B +SENNACHERIB'S S EH1 N AH0 K ER0 IY1 B Z +SENNANS S EH1 N AH0 N Z +SENNAR S EH1 N ER0 +SENNEN S EH1 N AH0 N +SENNER S EH1 N ER0 +SENNET S EH1 N AH0 T +SENNETERRE S EH1 N IH0 T EH0 R +SENNETERRE'S S EH1 N IH0 T ER1 AH0 Z +SENNETT S EH1 N IH0 T +SENNIER S EH1 N IY0 ER0 +SENNIER'S S EH1 N IY0 ER0 Z +SENNIERS S EH1 N IY0 ER0 Z +SENNIGHT S EH1 N AY2 T +SENNIT S EH1 N AH0 T +SENNITT S EH1 N IH0 T +SENNOURES S EH1 N AH0 ER0 Z +SENNY S EH1 N IY0 +SENONCHES S IY2 N Y AO1 N CH IH0 Z +SENOR S IY2 N Y AO1 R +SENOR'S S EH1 N ER0 Z +SENORA S IY2 N Y AO1 R AH0 +SENORA'S S IY2 N Y AO1 R AH0 Z +SENORAS S IY2 N Y AO1 R AH0 Z +SENORES S EH0 N AO1 R Z +SENORIA S EH0 N AO1 R IY0 AH0 +SENORITA S EH1 N AO0 R IY1 T AH0 +SENORITA'S S EH1 N AO0 R IY1 T AH0 Z +SENORITAS S EH1 N AO0 R IY1 T AH0 S +SENORITO S EH1 N AO0 R IY1 T OW0 +SENORS S EH1 N ER0 Z +SENOTE S EH0 N OW1 T +SENOTT S EH1 N AH0 T +SENOUSYA S EH1 N AW1 S IY0 AH0 +SENS S EH1 N Z +SENSAR S EH1 N S ER0 +SENSATION S EH0 N S EY1 SH AH0 N +SENSATIONAL S EH0 N S EY1 SH AH0 N AH0 L +SENSATIONALISM S EH0 N S EY1 SH AH0 N AH0 L IH2 Z AH0 M +SENSATIONALIST S EH0 N S EY1 SH AH0 N AH0 L IH0 S T +SENSATIONALLY S EH0 N S EY1 SH AH0 N AH0 L IY0 +SENSATIONS S EH0 N S EY1 SH AH0 N Z +SENSE S EH1 N S +SENSE'S S EH1 N S IH0 Z +SENSED S EH1 N S T +SENSEI S EH1 N S EY2 +SENSELESS S EH1 N S L AH0 S +SENSELESSLY S EH1 N S L AH0 S L IY0 +SENSELESSNESS S EH1 N S L AH0 S N AH0 S +SENSES S EH1 N S IH0 Z +SENSI S EH2 N S IY0 +SENSIBILITE S EH1 N S AH0 B IH1 L AH0 T +SENSIBILITIES S EH2 N S IH0 B IH1 L IH0 T IY0 Z +SENSIBILITY S EH2 N S IH0 B IH1 L IH0 T IY0 +SENSIBLE S EH1 N S AH0 B AH0 L +SENSIBLENESS S EH1 N S AH0 B AH0 L N AH0 S +SENSIBLEST S EH1 N S AH0 B AH0 L S T +SENSIBLY S EH1 N S AH0 B L IY0 +SENSIBRI S EH2 N S IH0 B R IY0 +SENSING S EH1 N S IH0 NG +SENSITISED S EH1 N S AH0 T AY2 Z D +SENSITIVE S EH1 N S AH0 T IH0 V +SENSITIVE S EH1 N S IH0 T IH0 V +SENSITIVELY S EH1 N S IH0 T IH0 V L IY0 +SENSITIVENESS S EH1 N S AH0 T IH0 V N AH0 S +SENSITIVES S EH1 N S AH0 T IH0 V Z +SENSITIVITY S EH2 N S IH0 T IH1 V IH0 T IY0 +SENSITIZED S EH1 N S AH0 T AY2 Z D +SENSITIZER S EH1 N S AH0 T AY2 Z ER0 +SENSOR S EH1 N S ER0 +SENSORIAL S EH1 N S ER0 AY1 AH0 L +SENSORIUM S EH1 N S ER0 IY0 AH0 M +SENSORY S EH1 N S ER0 IY0 +SENSU S EH1 N S UW0 +SENSUA S EH1 N SH UW0 AH0 +SENSUAL S EH1 N SH AH0 W AH0 L +SENSUALISM S EH1 N SH AH0 W AH0 L IH2 Z AH0 M +SENSUALIST S EH1 N SH AH0 W AH0 L IH0 S T +SENSUALISTS S EH1 N SH AH0 W AH0 L IH0 S T S +SENSUALITIES S EH2 N SH UW2 AE1 L AH0 T IY0 Z +SENSUALITY S EH2 N SH UW2 AE1 L AH0 T IY2 +SENSUALLY S EH1 N SH AH0 W AH0 L IY0 +SENSUOUS S EH1 N SH AH0 W AH0 S +SENSUOUSLY S EH1 N SH AH0 W AH0 S L IY0 +SENSUOUSNESS S EH1 N SH AH0 W AH0 S N AH0 S +SENSUS S EH1 N S AH0 S +SENT S EH1 N T +SENTA S EH1 N T AH0 +SENTA'S S EH1 N T AH0 Z +SENTARO S EH0 N T AA1 R OW0 +SENTE S EH1 N T +SENTENC'D S EH2 N T AH0 N S D +SENTENCE S EH1 N T AH0 N S +SENTENCED S EH1 N T AH0 N S T +SENTENCES S EH1 N T AH0 N S AH0 Z +SENTENCES S EH1 N T AH0 N S IH0 Z +SENTENCING S EH1 N T AH0 N S IH0 NG +SENTENTIA S EH0 N T EH1 N SH AH0 +SENTENTIAE S EH0 N T EH1 N SH IY0 AH0 +SENTENTIOUS S EH0 N T EH1 N SH AH0 S +SENTENTIOUSLY S EH0 N T EH1 N SH AH0 S L IY0 +SENTENTIOUSNESS S EH0 N T EH1 N SH AH0 S N AH0 S +SENTER S EH1 N T ER0 +SENTER'S S EH1 N T ER0 Z +SENTEST S EH1 N T EH0 S T +SENTI S EH1 N T IY0 +SENTIENCE S EH1 N IY0 AH0 N S +SENTIENCY S EH1 N SH AH0 N S IY0 +SENTIENT S EH1 N SH AH0 N T +SENTIER S EH2 N T IY0 ER0 +SENTIMENS S EH2 N AH0 M AH0 N Z +SENTIMENT S EH1 N AH0 M AH0 N T +SENTIMENT S EH1 N T AH0 M AH0 N T +SENTIMENT'S S EH2 N AH0 M AH0 N T S +SENTIMENTAL S EH2 N AH0 M EH1 N AH0 L +SENTIMENTAL S EH2 N AH0 M EH1 N T AH0 L +SENTIMENTAL S EH2 N T AH0 M EH1 N AH0 L +SENTIMENTAL S EH2 N T AH0 M EH1 N T AH0 L +SENTIMENTALE S EH2 N AH0 M EH1 N T AH0 L +SENTIMENTALISE S EH2 N AH0 M EH1 N AH0 L IY1 S +SENTIMENTALISED S EH2 N AH0 M EH1 N AH0 L IH0 Z D +SENTIMENTALISING S EH2 N AH0 M EH1 N AH0 L AY2 Z IH0 NG +SENTIMENTALISM S EH2 N AH0 M EH1 N AH0 L IH2 Z AH0 M +SENTIMENTALISMS S EH2 N AH0 M EH1 N AH0 L IH2 Z AH0 M Z +SENTIMENTALIST S EH2 N AH0 M EH1 N AH0 L IH0 S T +SENTIMENTALISTS S EH2 N AH0 M EH1 N AH0 L IH0 S T S +SENTIMENTALITIES S EH2 N AH0 M EH0 N T AE1 L IH0 T IY0 Z +SENTIMENTALITY S EH2 N AH0 M EH0 N T AE1 L IH0 T IY0 +SENTIMENTALITY S EH2 N T AH0 M EH0 N T AE1 L IH0 T IY0 +SENTIMENTALIZE S EH2 N AH0 M EH1 N AH0 L AY2 Z +SENTIMENTALIZED S EH2 N AH0 M EH1 N AH0 L AY2 Z D +SENTIMENTALIZING S EH2 N AH0 M EH1 N AH0 L AY2 Z IH0 NG +SENTIMENTALLY S EH2 N AH0 M EH1 N AH0 L IY0 +SENTIMENTS S EH1 N AH0 M AH0 N T S +SENTIMENTS S EH1 N T AH0 M AH0 N T S +SENTINAL S EH1 N T AH0 N AH0 L +SENTINEL S EH1 N T AH0 N AH0 L +SENTINEL'S S EH1 N T AH0 N AH0 L Z +SENTINELED S EH1 N T AH0 N AH0 L D +SENTINELLED S EH1 N T IH0 N EH1 L D +SENTINELS S EH1 N T AH0 N AH0 L Z +SENTINUM S EH1 N T AH0 N AH0 M +SENTIR S EH1 N IH0 R +SENTIRE S EH0 N T AY2 R +SENTO S EH1 N T OW0 +SENTRIES S EH1 N T R IY0 Z +SENTRY S EH1 N T R IY0 +SENTRY'S S EH1 N T R IY0 Z +SENTS S EH1 N T S +SENUSRET S EH1 N Y UW0 Z R AH0 T +SENUSSI S IY2 N Y UW1 S IY0 +SENZA S EH1 N Z AH0 +SENZANGACONA S EY0 N Z AA1 NG G AH0 K OW1 N AH0 +SENZANGAKONA S EY0 N Z AA1 NG G AH0 K OW1 N AH0 +SEOUL S OW1 L +SEP EH1 S IY1 P IY1 +SEP S EH1 P +SEP'RATE S EH0 ER0 IH0 T +SEPAILOFF S EH1 P EY1 L AO0 F +SEPALS S EH1 P AH0 L Z +SEPAR S EH1 P ER0 +SEPARABLE S EH1 P ER0 AH0 B AH0 L +SEPARATE S EH1 P ER0 EY2 T +SEPARATE S EH1 P ER0 IH0 T +SEPARATE S EH1 P R AH0 T +SEPARATED S EH1 P ER0 EY2 T AH0 D +SEPARATED S EH1 P ER0 EY2 T IH0 D +SEPARATELY S EH1 P ER0 AH0 T L IY0 +SEPARATELY S EH1 P R AH0 T L IY0 +SEPARATENESS S EH1 P ER0 AH0 T N AH0 S +SEPARATES S EH1 P ER0 EY2 T S +SEPARATES S EH1 P ER0 IH0 T S +SEPARATETH S EH1 P ER0 IH0 T TH +SEPARATIN S EH1 P ER0 AH0 T IH0 N +SEPARATING S EH1 P ER0 EY2 T IH0 NG +SEPARATION S EH2 P ER0 EY1 SH AH0 N +SEPARATIONS S EH2 P ER0 EY1 SH AH0 N Z +SEPARATISM S EH1 P ER0 AH0 T IH2 Z AH0 M +SEPARATIST S EH1 P ER0 AH0 T IH0 S T +SEPARATISTS S EH1 P ER0 AH0 T IH0 S T S +SEPARATISTS S EH1 P R AH0 T IH0 S T S +SEPARATOR S EH1 P ER0 EY2 T ER0 +SEPARATORS S EH1 P ER0 EY2 T ER0 Z +SEPASTIAN S EH1 P AE2 S CH AH0 N +SEPERATE S EH1 P ER0 AH0 T +SEPERATED S EH1 P ER0 EY2 T IH0 D +SEPERATELY S EH1 P ER0 AH0 T L IY0 +SEPERATES S EH1 P ER0 EY2 T S +SEPET S EY0 P EY1 +SEPH S AH0 F +SEPHAR S IH0 F AA1 R +SEPHAR'S S EH0 F AA2 R Z +SEPHARDI S IH0 F AA1 R D IY0 +SEPHARDIC S AH0 F AA1 R D IH0 K +SEPHARDIM S EH0 F AA2 R D IY1 M +SEPHARDIM S IH0 F AA1 R D IH0 M +SEPHARIAN S IH0 F AA1 R IY0 AH0 N +SEPHARIANS S IH0 F AA1 R IY0 AH0 N Z +SEPHER S EH1 F ER0 +SEPHORA S EH1 F ER0 AH0 +SEPHORAH S EH1 F ER0 AH0 +SEPIA S IY1 P IY0 AH0 +SEPOLCRO S EH1 P AH0 L K R OW0 +SEPOS S IY1 P OW2 Z +SEPOY S EH1 P OY0 +SEPOY'S S EH1 P OY0 Z +SEPOYS S EH1 P OY0 Z +SEPP S EH1 P +SEPPER S EH1 P ER0 +SEPPERL S EH1 P ER0 L +SEPPHORIS S EH0 F AO1 R IH0 S +SEPPI S EH1 P IY0 +SEPPINGS S EH1 P IH0 NG Z +SEPPUKU S EH0 P UW1 K UW0 +SEPPY S EH1 P IY0 +SEPTA S EH1 P T AH0 +SEPTEM S EH0 P T EH1 M +SEPTEMBER S EH0 P T EH1 M B ER0 +SEPTEMBER'S S EH0 P T EH1 M B ER0 Z +SEPTEMBERS S EH0 P T EH1 M B ER0 Z +SEPTEMBRE S EH0 P T EH1 M B R AH0 +SEPTEMBRISTS S EH0 P T EH1 M B R IH0 S T S +SEPTENARY S EH0 P T EH1 N ER0 IY0 +SEPTENNIAL S EH0 P T EH1 N IY0 AH0 L +SEPTENTRIONAL S EH0 P T EH1 N T R IY0 OW1 N AH0 L +SEPTENTRIONALE S EH0 P T EH1 N T R IY0 AH0 N AA1 L IY0 +SEPTIC S EH1 P T IH0 K +SEPTIMA S EH0 P T IY1 M AH0 +SEPTIMINE S EH1 P T IH0 M IY1 N +SEPTIMIUS S EH1 P T IH0 M IY0 IH0 S +SEPTIMIUS'S S EH1 P T IH0 M Y AH0 S IH0 Z +SEPTIMO S EH1 P T IH0 M OW2 +SEPTIMUS S EH1 P T IH0 M IH0 S +SEPTR S EH1 P T ER0 +SEPTS S EH1 P T S +SEPTUAGENARIAN S EH0 P CH UW2 AH0 JH AH0 N EH1 R IY0 AH0 N +SEPTUAGENARIANS S EH0 P CH UW2 AH0 JH AH0 N EH1 R IY0 AH0 N Z +SEPTUAGINT S EH0 P CH UW2 AH0 JH IH1 N T +SEPTUM S EH1 P T AH0 M +SEPULCHER S EY0 P UW0 L CH ER0 +SEPULCHERS S EY0 P UW0 L CH ER0 Z +SEPULCHRAL S EY0 P UW0 L CH R AH0 L +SEPULCHRALLY S EY0 P UW0 L CH R AH0 L IY0 +SEPULCHRE S EY0 P UW0 L K ER0 +SEPULCHRE'S S EY0 P UW0 L CH R Z +SEPULCHRED S EY0 P UW0 L K ER0 D +SEPULCHRES S EY0 P UW0 L CH R Z +SEPULTURE S EH1 P AH0 L CH ER0 +SEPULTURED S EH1 P AH0 L CH ER0 D +SEPULVIDA S EY0 P UW0 L V IY1 D AH0 +SEQ S EH2 K +SEQUANI S EH1 K W AH0 N IY0 +SEQUARD S IY1 K W AA1 R D +SEQUEL S IY1 K W AH0 L +SEQUELAE S IY1 K W AH0 L EY2 +SEQUELS S IY1 K W AH0 L Z +SEQUENCE S IY1 K W AH0 N S +SEQUENCES S IY1 K W AH0 N S AH0 Z +SEQUENCES S IY1 K W AH0 N S IH0 Z +SEQUENT S IY1 K W AH0 N T +SEQUENTIAL S AH0 K W EH1 N CH AH0 L +SEQUENTS S IY1 K W AH0 N T S +SEQUESTER S IH0 K W EH1 S T ER0 +SEQUESTER'D S IH0 K W EH1 S T ER0 D +SEQUESTERED S IH0 K W EH1 S T ER0 D +SEQUESTERING S IH0 K W EH1 S T ER0 IH0 NG +SEQUESTRATE S IH0 K W EH1 S T R EY2 T +SEQUESTRATED S IH0 K W EH1 S T R EY2 T IH0 D +SEQUESTRATING S IH0 K W EH1 S T R EY2 T IH0 NG +SEQUESTRATION S EH2 K W AH0 S T R EY1 SH AH0 N +SEQUESTRATIONS S IH0 K W EH1 S T R EY1 SH AH0 N Z +SEQUESTRATORS S IH0 K W EH1 S T R EY2 T ER0 Z +SEQUIN S IY1 K W AH0 N +SEQUIN S IY1 K W IH0 N +SEQUINED S IY1 K W AH0 N D +SEQUINS S IY1 K W AH0 N Z +SEQUINS S IY1 K W IH0 N Z +SEQUITUR S EH1 K W IH0 T ER0 +SEQUOIA S IH0 K W OY1 AH0 +SEQUOIAS S IH0 K W OY1 AH0 Z +SEQUOR S IH0 K W ER0 +SEQUUNTUR S IY1 K W UH1 N T ER0 +SER S ER0 +SERA S IH1 R AH0 +SERAC S EH1 R AH0 K +SERACS S ER0 AA0 K S +SERAFIN S EH1 R AH0 F IH0 N +SERAFIN'S S ER0 AA0 F IY1 N Z +SERAFINA S ER0 AA0 F IY1 N AH0 +SERAFINA'S S ER0 AA0 F IY1 N AH0 Z +SERAFINO S ER0 AA0 F IY1 N OW0 +SERAGLIO S EH0 R AA1 G L IY0 OW0 +SERAGLIOS S ER0 AA0 G L IY0 OW1 S +SERAGUNTING S EH1 R AH0 G AH2 N T IH0 NG +SERAI S EH0 R AA1 IY0 +SERAIAH S ER0 AA0 IY1 AH0 +SERAIEVO S ER0 AA0 IY1 EY1 V OW0 +SERAIL S R EY2 L +SERAINE S ER0 AA0 IY1 N IY0 +SERAINE'S S ER0 AA0 IY1 N Z +SERAING S EH0 R AA1 IH0 NG +SERAIS S ER0 EY1 +SERAIT S ER0 AA0 IY1 T +SERAJEVO S ER0 AA0 Y EY1 V OW0 +SERAMINTA S EH1 R AH0 M IH1 N T AH0 +SERAMPORE S ER0 AA0 M P AO0 R +SERANG S ER0 AA0 NG +SERANG'S S ER0 AA0 NG Z +SERANO S EH0 R AA1 N OW0 +SERAO S EH0 R AA1 OW0 +SERAPA S EH0 R AA1 P AH0 +SERAPE S EH0 R AA1 P +SERAPES S EH0 R AA1 P EH0 S +SERAPEUM S ER0 AA0 P IY1 AH0 M +SERAPH S ER0 AA0 F +SERAPH'S S ER0 AA0 F S +SERAPHAEL S ER0 AA0 F AY0 EH1 L +SERAPHAEL'S S ER0 AA0 F Y EH1 L Z +SERAPHIA S ER0 AA0 F IY1 AH0 +SERAPHIC S ER0 AA0 F IH0 K +SERAPHICALLY S ER0 AA0 F IH0 K L IY0 +SERAPHICK S ER0 AA0 F IY1 K +SERAPHIM S ER0 AA0 F IY1 M +SERAPHIMS S ER0 AA0 F IY1 M Z +SERAPHIN S ER0 AA0 F IY1 N +SERAPHIN'S S ER0 AA0 F IY1 N Z +SERAPHINA S ER0 AA0 F IY1 N AH0 +SERAPHINA'S S ER0 AA0 F IY1 N AH0 Z +SERAPHINE S ER0 AA0 F IY1 N IY0 +SERAPHINE'S S ER0 AA0 F IY1 N Z +SERAPHITA S ER0 AA0 F IY1 T AH0 +SERAPHITUS S ER0 AA0 F IY1 T AH0 S +SERAPHS S ER0 AA0 F S +SERAPIO S ER0 AA0 P IY0 OW0 +SERAPION S ER0 AA0 P IY0 AH0 N +SERAPION'S S ER0 AA0 P IY0 AH0 N Z +SERAPIONTIC S ER0 AA0 P IY0 AA1 N T IH0 K +SERAPIS S EH1 R AH0 P IH0 S +SERAS S EH1 R AH0 Z +SERASKIER S ER0 AA1 S K IY0 ER0 +SERATO S EH0 R AA1 T OW0 +SERAWOOLLI S EH0 R AA1 W UH1 L IY0 +SERAWOOLLIES S EH0 R AA1 W UH1 L IY0 Z +SERAYEVO S ER0 AA0 Y EH2 V OW0 +SERB S ER1 B +SERBELLONI S ER1 B EH0 L OW1 N IY0 +SERBERINE S ER1 B ER0 IH0 N +SERBIA S ER1 B IY0 AH0 +SERBIA'S S ER1 B IY0 AH0 Z +SERBIAN S ER1 B IY0 AH0 N +SERBIANS S ER1 B IY0 AH0 N Z +SERBO S ER1 B OW0 +SERBONIAN S ER1 B OW0 N IY0 AH0 N +SERBS S ER1 B Z +SERCH S ER1 K +SERCHIO S EH1 R K IY0 OW0 +SERCOMBE S ER0 K OW1 M B IY0 +SERDAB S EH1 R D AH0 B +SERDAN S EH1 R D AH0 N +SERDAR S EH1 R D AA2 R +SERE S IH1 R +SEREDA S EH0 R EY1 D AH0 +SEREJKA S EH0 R EY1 K AH0 +SERENA S AH0 R IY1 N AH0 +SERENA'S S ER0 IY1 N AH0 Z +SERENADE S EH2 R AH0 N EY1 D +SERENADED S EH2 R AH0 N EY1 D IH0 D +SERENADER S EH2 R AH0 N EY1 D ER0 +SERENADERS S EH2 R AH0 N EY1 D ER0 Z +SERENADES S EH2 R AH0 N EY1 D Z +SERENADING S EH2 R AH0 N EY1 D IH0 NG +SERENAS S ER0 IY1 N AH0 Z +SERENATA S EH2 R AH0 N AA1 T AH0 +SERENDIB 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JH AH0 N T S IY0 +SERGEANTS S AA1 R JH AH0 N T S +SERGEAUX S EH1 R JH OW1 +SERGEI S EH1 R G EY2 +SERGEI S ER0 G EY1 +SERGEITCH S EH1 R JH AY1 CH +SERGENT S ER1 JH AH0 N T +SERGENTE S EH1 R JH EH1 N T EY0 +SERGENTS S ER1 JH AH0 N T S +SERGEOT S ER1 JH IY0 AA1 T +SERGES S ER1 JH IH0 Z +SERGESTUS S EH1 R JH EH1 S T AH0 S +SERGEY S EH1 R G EY0 +SERGEY S EH1 R JH IY0 +SERGEYEVITCH S EH1 R JH AY1 AH0 V IH0 CH +SERGEYEVNA S EH1 R G EY0 EY1 V N AH0 +SERGEYITCH S EH1 R G EY0 IH0 CH +SERGI S EH1 R JH IY0 +SERGIA S ER1 JH Y AH0 +SERGIEVITCH S ER1 JH Y AH0 V IH2 CH +SERGIEVNA S ER1 JH Y AH0 V N AH0 +SERGINT S EH1 R JH IY0 N T +SERGIUS S ER1 JH Y AH0 S +SERGIUS'S S ER1 JH Y AH0 S IH0 Z +SERGOI S EH1 R G OY1 +SERGT S EH1 R G T +SERGTS S EH1 R G T S +SERGUEYEVNA S EH1 R G AY1 AH0 V N AH0 +SERGYEITCH S EH1 R JH IY0 AY1 CH +SERGYEVNA S EH1 R JH Y EH2 V N AH0 +SERI S EH1 R IY0 +SERIA S IH1 R IY0 AH0 +SERIAL S IH1 R IY2 AH0 L +SERIALIZED S IH1 R IY2 AH0 L AY2 Z D +SERIALLY S IH1 R IY0 AH0 L IY0 +SERIALS S IH1 R IY0 AH0 L Z +SERIATIM S IH1 R IY2 AH0 T IH2 M +SERICANE S EH2 R AH0 K EY2 N +SERIE S IH1 R IY0 +SERIES S IH1 R IY0 Z +SERIEUSE S IH1 R IY2 UW1 S +SERIEUX S EH1 R IY0 UW2 +SERIGNAN S EH2 R IH0 G N AH0 N +SERIGNY S EH0 R AY0 N IY0 +SERIGNY'S S EH0 R AY0 N IY0 Z +SERINA S ER0 IY1 N AH0 +SERINGA S ER0 IY1 NG G AH0 +SERINGAPATAM S ER1 IH0 NG G AH0 P AE2 T AH0 M +SERIO S IY1 R IY0 OW0 +SERIOSHA S IH1 R IY2 OW1 SH AH0 +SERIOUS S IH1 R IY0 AH0 S +SERIOUSLY S IH1 R IY0 AH0 S L IY0 +SERIOUSNESS S IH1 R IY0 AH0 S N AH0 S +SERIPHOS S IH1 R IY2 F OW0 Z +SERIPHUS S IH1 R IY2 F AH0 S +SERIS S ER0 IY1 +SERIZY S IH1 R IY0 Z IY0 +SERIZY'S S IH1 R IY0 Z IY0 Z +SERJEANT S ER1 JH AH0 N T +SERJEANT'S S ER1 JH IY1 N T S +SERJEANTS S ER1 JH AH0 N T S +SERJT S ER1 JH T +SERK S ER1 K +SERKO S ER1 K OW0 +SERLE S ER1 AH0 L +SERMAISE S ER1 M EY2 S +SERMO S EH1 R M OW0 +SERMON S ER1 M AH0 N +SERMON'S S ER1 M AH0 N Z +SERMONE S ER1 M OW2 N +SERMONETA S ER1 M AH0 N EH1 T AH0 +SERMONISING S ER1 M AH0 N IH0 Z 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SH UH1 D AH0 N T +SHOULDNA SH UH1 D AH0 N AH0 +SHOULDNT SH UH1 D AH0 N T +SHOULDST SH UH1 D AH0 S T +SHOUSTOVA SH UW2 S T OW1 V AH0 +SHOUT SH AW1 T +SHOUTED SH AW1 T AH0 D +SHOUTED SH AW1 T IH0 D +SHOUTER SH AW1 T ER0 +SHOUTERS SH AW1 T ER0 Z +SHOUTHER SH UW1 TH ER0 +SHOUTHERS SH UW1 TH ER0 Z +SHOUTIN SH AW1 T IH0 N +SHOUTING SH AW1 T IH0 NG +SHOUTINGS SH AW1 T IH0 NG Z +SHOUTS SH AW1 T S +SHOVE SH AH1 V +SHOVED SH AH1 V D +SHOVEL SH AH1 V AH0 L +SHOVEL'S SH AH1 V AH0 L Z +SHOVELED SH AH1 V AH0 L D +SHOVELER SH AH1 V AH0 L ER0 +SHOVELERS SH AH1 V AH0 L ER0 Z +SHOVELFUL SH AH1 V AH0 L F AH0 L +SHOVELFULS SH AH1 V AH0 L F AH0 L Z +SHOVELIN SH AH1 V AH0 L IH0 N +SHOVELING SH AH1 V L IH0 NG +SHOVELLED SH AH1 V AH0 L D +SHOVELLER SH AH1 V AH0 L ER0 +SHOVELLERS SH AH1 V AH0 L ER0 Z +SHOVELLIN SH AH1 V AH0 L IH0 N +SHOVELLING SH AH1 V L IH0 NG +SHOVELS SH AH1 V AH0 L Z +SHOVER SH AH1 V ER0 +SHOVES SH AH1 V Z +SHOVIN SH AH1 V IH0 N +SHOVING SH AH1 V IH0 NG +SHOW SH OW1 +SHOW'D SH AW1 D +SHOW'R 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+SITTAH S IH1 T AH0 +SITTEN S IH1 T AH0 N +SITTER S IH1 T ER0 +SITTER'S S IH1 T ER0 Z +SITTERS S IH1 T ER0 Z +SITTEST S IH1 T AH0 S T +SITTETH S IH1 T AH0 TH +SITTI S IH1 T IY0 +SITTIN S IH1 T IH0 N +SITTIN' S IH1 T AH0 N +SITTINA S IH1 T IY1 N AH0 +SITTING S IH1 T IH0 NG +SITTINGBOURNE S IH1 T IH0 NG B ER0 N +SITTINGROOM S IH1 T IH0 NG R UW2 M +SITTINGS S IH1 T IH0 NG Z +SITTMANN S IH1 T M AH0 N +SITU S IH2 CH UW0 +SITUATE S IH1 CH UW0 EY2 T +SITUATED S IH1 CH UW0 EY2 T IH0 D +SITUATION S IH2 CH UW0 EY1 SH AH0 N +SITUATION'S S IH2 CH UW0 EY1 SH AH0 N Z +SITUATIONS S IH2 CH UW0 EY1 SH AH0 N Z +SITUS S AY1 T AH0 S +SITWELL S IH1 T W EH2 L +SITZ S IH1 T S +SITZKY S IH1 T S K IY0 +SIU S IY1 UW0 +SIUR S IY0 UW1 R +SIURD S IY1 UW0 R D +SIVA S AY1 V AH0 +SIVA'S S IH1 V AH0 Z +SIVACHAR S IH1 V AH0 K AA1 R +SIVAS S IH1 V AH0 Z +SIVE S IH0 V +SIVELY S IH0 V L IY0 +SIVEN S IH1 V AH0 N +SIVERAL S IH1 V ER0 AH0 L +SIVERT S IH1 V ER0 T +SIVI S IH1 V IY0 +SIVIANO S IY2 V IY0 AA1 N OW0 +SIVIN S IH1 V AH0 N +SIVINTY S IH1 V AH0 N T IY0 +SIVORI S AY0 V AO1 R IY0 +SIWANOIS S IH1 W AO0 N W AA1 +SIWARD S IH1 W ER0 D +SIWARD'S S IH1 W ER0 D Z +SIWARDS S IH1 W ER0 D Z +SIWASH S IH1 W AA2 SH +SIWASHES S IH1 W AA2 SH AH0 Z +SIX S IH1 K S +SIX'S S IH1 K S IH0 Z +SIXE S IH1 K S +SIXER S IH1 K S ER0 +SIXERS S IH1 K S ER0 Z +SIXES S IH1 K S IH0 Z +SIXFOLD S IH1 K S F OW2 L D +SIXPENCE S IH1 K S P EH1 N S +SIXPENCES S IH1 K S P AH0 N S IH0 Z +SIXPENN'ORTH S IH1 K S P EH2 N S AO2 R TH +SIXPENNY S IH1 K S P EH2 N IY0 +SIXPENNYWORTH S IH1 K S P EH1 N IY0 W ER0 TH +SIXSCORE S IH1 K S K AO1 R +SIXSHOOTER S IH1 K S SH UW2 T ER0 +SIXT S IH1 K S T +SIXTE S IH1 K S T +SIXTEEN S IH0 K S T IY1 N +SIXTEEN S IH1 K S T IY1 N +SIXTEENS S IH1 K S T IY1 N Z +SIXTEENTH S IH0 K S T IY1 N TH +SIXTEENTH S IH1 K S T IY1 N TH +SIXTEENTHS S IH1 K S T IY1 N TH S +SIXTH S IH1 K S TH +SIXTH'S S IH1 K S TH S +SIXTHLY S IH1 K S TH L IY0 +SIXTHS S IH1 K S TH S +SIXTIES S IH1 K S T IY0 Z +SIXTIETH S IH1 K S T IY0 IH0 TH +SIXTINE S IH1 K S T IY0 N +SIXTUS S IH1 K S T AH0 S +SIXTY S IH1 K S T IY0 +SIXTYFOLD S IH1 K S T IY0 F OW2 L D +SIYAH S IY1 AH0 +SIYAL S IY0 Y AA1 L +SIYALA S IY0 Y AA1 L AH0 +SIYARO S IY0 Y AA1 R OW0 +SIYYID S IY1 IH0 D +SIZ S IH1 Z +SIZABLE S AY1 Z AH0 B AH0 L +SIZAR S AY1 Z AA0 R +SIZARS S AY1 Z AA2 R Z +SIZE S AY1 Z +SIZEABLE S AY1 Z AH0 B AH0 L +SIZED S AY1 Z D +SIZER S AY1 Z ER0 +SIZERS S AY1 Z ER0 Z +SIZES S AY1 Z AH0 Z +SIZES S AY1 Z IH0 Z +SIZIN S AY1 Z IH0 N +SIZING S AY1 Z IH0 NG +SIZZ S IH1 Z +SIZZLE S IH1 Z AH0 L +SIZZLED S IH1 Z AH0 L D +SIZZLIN S IH1 Z L IH0 N +SIZZLING S IH1 Z AH0 L IH0 NG +SIZZLING S IH1 Z L IH0 NG +SJAMBAK S JH AA1 M B AH0 K +SJAMBAKS S JH AE2 M B AE0 K S +SJAMBOCK S JH AE2 M B AH0 K +SJAMBOK S JH AE2 M B AA0 K +SJOEHOLM SH OW1 HH OW2 L M +SK S K +SKA S K AH0 +SKAAL S K AA1 L +SKADI S K AA1 D IY0 +SKAFTASON S K AE1 F T AH0 S AH0 N +SKAG S K AE1 G +SKAG'S S K AE1 G Z +SKAGEN S K AE1 G AH0 N +SKAGER S K EY1 G ER0 +SKAGGS S K AE1 G Z +SKAGGSY S K AE1 G S IY0 +SKAGIT S K AE1 G IH0 T +SKAGUAY S K AE1 G EY0 +SKAGWAY S K AE1 G W EY2 +SKAIFE S K EY1 F +SKAIFE'S S K AY1 F S +SKAILL S K EY1 L +SKAIRFUL S K EH1 R F AH0 L +SKAIRT S K ER0 T +SKAITH S K EY1 TH +SKAKTAVL S K AE2 K T AE1 V L +SKAL S K AH0 L +SKALD S K AA1 L D +SKALDS S K AA1 L D Z +SKALLAGRIM S K AE1 L AH0 G R IH0 M +SKAMKELL S K AE1 M K AH0 L +SKAMMERHORN S K AE1 M ER0 HH ER0 N +SKANDA S K AE1 N D AH0 +SKANDINAVIA S K AE2 N D IH0 N EY1 V IY0 AH0 +SKANDINAVIA'S S K AE2 N D IH0 N EY1 V IY0 AH0 Z +SKANE S K EY1 N +SKANEATELES S K AE1 N AH0 T EH1 L Z +SKANOR S K AE1 N ER0 +SKANSEN S K AE1 N S AH0 N +SKAPTI S K AE1 P T IY0 +SKARBEK S K AA1 R B IH0 K +SKARBOLOV'S S K AA1 R B AA2 L AA0 V Z +SKARES S K EH1 R Z +SKARL S K AA1 R L +SKARLIE S K AA1 R L IY0 +SKAROFF S K EH1 R AO0 F +SKARPEDIN S K AA1 R P AH0 D IH0 N +SKARPHEDINN S K AA1 R F AH0 D IH1 N +SKARTS S K AA1 R T S +SKAT S K EY1 T +SKATE S K EY1 T +SKATE'S S K EY1 T S +SKATED S K EY1 T IH0 D +SKATER S K EY1 T ER0 +SKATER'S S K EY1 T ER0 Z +SKATERS S K EY1 T ER0 Z +SKATES S K EY1 T S +SKATIN S K EY1 T IH0 N +SKATING S K EY1 T IH0 NG +SKAVINSKI S K AH0 V IH1 N S K IY0 +SKAW S K AA1 +SKE S K +SKEAN S K IY1 N +SKEAR S K IY1 R +SKEARED S K IY1 R D +SKEART S K EH1 ER0 T +SKEARY S K IH1 R IY0 +SKEAT S K IY1 T +SKEATS S K IY1 T S +SKEBAWN S K IY1 B AO1 N +SKEDADDLE S K IY1 D AE1 D AH0 L +SKEDADDLED S K IY1 D AE1 D AH0 L D +SKEDADDLING S K IY1 D AE1 D L IH0 NG +SKEE S K IY1 +SKEEL S K IY1 L +SKEELS S K IY1 L Z +SKEELTY S K IY1 L T IY0 +SKEELY S K IY1 L IY0 +SKEENA S K IY1 N AH0 +SKEENY S K IY1 N IY0 +SKEER S K IY1 ER0 +SKEER'D S K IY1 R D +SKEERCE S K IY1 R S +SKEERED S K IY1 R D +SKEERIN S K IY1 R IH0 N +SKEERS S K IY1 ER0 Z +SKEERT S K IY1 R T +SKEERY S K IY1 R IY0 +SKEES S K IY1 Z +SKEESICKS S K IY1 S IH2 K S +SKEET S K IY1 T +SKEET'S S K IY1 T S +SKEETER S K IY1 T ER0 +SKEETER'S S K IY1 T ER0 Z +SKEETERS S K IY1 T ER0 Z +SKEETS S K IY1 T S +SKEEZER S K IY1 Z ER0 +SKEEZERS S K IY1 Z ER0 Z +SKEEZICKS S K IY1 Z IH0 K S +SKEEZICS S K IY1 Z IH0 K S +SKEFF S K EH1 F +SKEFFINGTON S K EH1 F IH0 NG T AH0 N +SKEFFINGTON'S S K EH1 F IH0 NG T AH0 N Z +SKEFFY S K EH1 F IY0 +SKEGGI S K EH1 JH IY0 +SKEGGS S K EH1 G Z +SKEGNESS S K IY1 G N AH0 S +SKEHAN S K EY1 AH0 N +SKEIGHAN S K EY1 G AH0 N +SKEIN S K EY1 N +SKEINS S K EY1 N Z +SKELDING S K EH1 L D IH0 NG +SKELETAL S K EH1 L AH0 T AH0 L +SKELETON S K EH1 L AH0 T AH0 N +SKELETON'S S K EH1 L AH0 T AH0 N Z +SKELETONIZED S K EH1 L AH0 T AH0 N AY2 Z D +SKELETONS S K EH1 L AH0 T AH0 N Z +SKELL S K EH1 L +SKELLIGS S K EH1 L IH0 G Z +SKELLING S K EH1 L IH0 NG +SKELLORN S K EH1 L AO0 R N +SKELLUM S K EH1 L AH0 M +SKELLY S K EH1 L IY0 +SKELMERSDALE S K EH1 L M EH0 R Z D EY2 L +SKELMERTON S K EH1 L M ER0 T AH0 N +SKELP S K EH1 L P +SKELPED S K EH1 L P T +SKELPING S K EH1 L P IH0 NG +SKELPS S K EH1 L P S +SKELT S K EH1 L T +SKELT'S S K EH1 L T S +SKELTER S K EH1 L T ER0 +SKELTON S K EH1 L T AH0 N +SKELTON'S S K EH1 L T AH0 N Z +SKELTY S K EH1 L T IY0 +SKELTY'S S K EH1 L T IY0 Z +SKELWICK S K EH1 L W IH0 K +SKEM S K EH1 M +SKENE S K IY1 N +SKENES S K IY1 N Z +SKENESBOROUGH S K IY1 N Z B ER0 OW0 +SKENY S K IY1 N IY0 +SKEOCH S K IY1 AA0 CH +SKEP S K EH1 P +SKEPS S K EH1 P S +SKEPSEY S K EH1 P S IY0 +SKEPSEY'S S K EH1 P S IY0 Z +SKEPTIC S K EH1 P T IH0 K +SKEPTIC'S S K EH1 P T IH0 K S +SKEPTICAL S K EH1 P T AH0 K AH0 L +SKEPTICAL S K EH1 P T IH0 K AH0 L +SKEPTICALLY S K EH1 P T IH0 K AH0 L IY0 +SKEPTICALLY S K EH1 P T IH0 K L IY0 +SKEPTICISM S K EH1 P T IH0 S IH2 Z AH0 M +SKEPTICS S K EH1 P T IH0 K S +SKERM S K EH1 R M +SKERNFORD S K EH1 R N F ER0 D +SKERRETT S K EH1 R IH0 T +SKERRIES S K EH1 R IY0 Z +SKERRY S K EH1 R IY0 +SKERRYVORE S K EH1 R IY0 V AO2 R +SKERSTYMONE S K EH1 R S T IH0 M OW2 N +SKERT S K ER0 T +SKETCH S K EH1 CH +SKETCHBOOK S K EH1 CH B UH2 K +SKETCHED S K EH1 CH T +SKETCHER S K EH1 CH ER0 +SKETCHER'S S K EH1 CH ER0 Z +SKETCHERS S K EH1 CH ER0 Z +SKETCHES S K EH1 CH AH0 Z +SKETCHES S K EH1 CH IH0 Z +SKETCHILY S K EH1 CH AH0 L IY0 +SKETCHINESS S K EH1 CH IY0 N AH0 S +SKETCHING S K EH1 CH IH0 NG +SKETCHLEY S K EH1 CH L IY0 +SKETCHY S K EH1 CH IY0 +SKETTLE S K EH1 T AH0 L +SKETTLES S K EH1 T AH0 L Z +SKEW S K Y UW1 +SKEWBALD S K Y UW1 B AO2 L D +SKEWED S K Y UW1 D +SKEWER S K Y UW1 ER0 +SKEWERED S K Y UW1 ER0 D +SKEWERING S K Y UW1 ER0 IH0 NG +SKEWERS S K Y UW1 ER0 Z +SKEWTON S K Y UW1 T AH0 N +SKEWTON'S S K Y UW1 T AH0 N Z +SKI S K IY1 +SKI'S S K IY0 Z +SKIAGRAPH S K AY1 AH0 G R AE2 F +SKIBBEREEN S K IH1 B ER0 IY1 N +SKIBO S K AY1 B OW0 +SKIBO S K IY1 B OW0 +SKID S K IH1 D +SKIDARS S K IH1 D ER0 Z +SKIDBLADNIR S K IH1 D B L AH0 D N ER0 +SKIDDAW S K IH1 D AO2 +SKIDDED S K IH1 D AH0 D +SKIDDED S K IH1 D IH0 D +SKIDDER S K IH1 D ER0 +SKIDDING S K IH1 D IH0 NG +SKIDDOO S K IH1 D UW2 +SKIDDY S K IH1 D IY0 +SKIDDY'S S K IH1 D IY0 Z +SKIDEGATE S K IH1 D AH0 G EY2 T +SKIDMORE S K IH1 D M AO0 R +SKIDMORE'S S K IH1 D M AO2 R Z +SKIDOO S K IH1 D UW2 +SKIDS S K IH1 D Z +SKIDWAY S K IH1 D W EY2 +SKIDWAYS S K IH1 D W EY2 Z +SKIE S K IY1 +SKIED S K IY1 D +SKIEN S K AY1 N +SKIES S K AY1 Z +SKIEY S K AY1 IY0 +SKIFF S K IH1 F +SKIFF'S S K IH1 F S +SKIFFINS S K IH1 F IH0 N Z +SKIFFS S K IH1 F S +SKIFT S K IH1 F T +SKIHI S K IY1 IY0 +SKIING S K IY1 IH0 NG +SKIL S K IH1 L +SKILES S K AY1 L Z +SKILFUL S K IH1 L F AH0 L +SKILFULLEST S K IH1 L F AH0 L AH0 S T +SKILFULLY S K IH1 L F AH0 L IY0 +SKILFULNESS S K IH1 L F AH0 L N AH0 S +SKILK S K IH1 L K +SKILL S K IH1 L +SKILL'D S K IH1 L D +SKILLCORN S K IH1 L K AO1 R N +SKILLED S K IH1 L D +SKILLERY S K IH1 L ER0 IY0 +SKILLESS S K IH1 L IH0 S +SKILLET S K IH1 L AH0 T +SKILLETS S K IH1 L AH0 T S +SKILLFUL S K IH1 L F AH0 L +SKILLFULLY S K IH1 L F AH0 L IY0 +SKILLFULNESS S K IH1 L F AH0 L N AH0 S +SKILLING S K IH1 L IH0 NG +SKILLINGS S K IH1 L IH0 NG Z +SKILLION S K IH1 L Y AH0 N +SKILLOOTS S K IH1 L UW2 T S +SKILLS S K IH1 L Z +SKILLUTES S K IH1 L UW1 T S +SKILLY S K IH1 L IY0 +SKIM S K IH1 M +SKIMM'D S K IH1 M D +SKIMMED S K IH1 M D +SKIMMER S K IH1 M ER0 +SKIMMERHORN S K IH1 M ER0 HH ER0 N +SKIMMERS S K IH1 M ER0 Z +SKIMMIN S K IH1 M IH0 N +SKIMMING S K IH1 M IH0 NG +SKIMMINGS S K IH1 M IH0 NG Z +SKIMP S K IH1 M P +SKIMPED S K IH1 M P T +SKIMPIN S K IH1 M P IY0 N +SKIMPING S K IH1 M P IH0 NG +SKIMPOLE S K IH1 M P OW2 L +SKIMPY S K IH1 M P IY0 +SKIMS S K IH1 M Z +SKIN S K IH1 N +SKIN'S S K IH1 N Z +SKINADRE S K IH1 N AH0 D R AH0 +SKINADRE'S S K IH1 N AH0 D R Z +SKINDEEP S K IH1 N D IY1 P +SKINFLINT S K IH1 N F L IH0 N T +SKINFLINTS S K IH1 N F L IH1 N T S +SKINFUL S K IH1 N F AH0 L +SKINK S K IH1 NG K +SKINLESS S K IH1 N L AH0 S +SKINLIN S K IH1 N L IH0 N +SKINN'D S K IH1 N D +SKINNE S K IH1 N +SKINNED S K IH1 N D +SKINNER S K IH1 N ER0 +SKINNER'S S K IH1 N ER0 Z +SKINNERS S K IH1 N ER0 Z +SKINNES S K IH1 N Z +SKINNIER S K IH1 N IY0 ER0 +SKINNIN S K IH1 N IH0 N +SKINNING S K IH1 N IH0 NG +SKINNY S K IH1 N IY0 +SKINNY'S S K IH1 N IY0 Z +SKINS S K IH1 N Z +SKINSKI S K IH1 N S K IY0 +SKINSKY S K IH1 N S K IY0 +SKINYER S K IH1 N Y ER0 +SKIP S K IH1 P +SKIPETARS S K IH1 P EH0 T AA1 R Z +SKIPJACK S K IH1 P JH AE2 K +SKIPP S K IH1 P +SKIPPED S K IH1 P T +SKIPPER S K IH1 P ER0 +SKIPPER'S S K IH1 P ER0 Z +SKIPPERED S K IH1 P ER0 D +SKIPPERING S K IH1 P ER0 IH0 NG +SKIPPERS S K IH1 P ER0 Z +SKIPPIN S K IH1 P IH0 N +SKIPPING S K IH1 P IH0 NG +SKIPPITY S K IH1 P AH0 T IY0 +SKIPPON S K IH1 P AH0 N +SKIPPY S K IH1 P IY0 +SKIPS S K IH1 P S +SKIPT S K IH1 P T +SKIPTON S K IH1 P T AH0 N +SKIPWITH S K IH1 P W IH2 TH +SKIPWORTH S K IH1 P W ER2 TH +SKIRL S K ER1 L +SKIRLED S K ER1 L D +SKIRLIN S K ER1 L IH0 N +SKIRLING S K ER1 L IH0 NG +SKIRLS S K ER1 L Z +SKIRMISH S K ER1 M IH0 SH +SKIRMISHED S K ER1 M IH0 SH T +SKIRMISHER S K ER1 M IH0 SH ER0 +SKIRMISHERS S K ER1 M IH0 SH ER0 Z +SKIRMISHES S K ER1 M IH0 SH IH0 Z +SKIRMISHIN S K ER1 M IH0 SH IH0 N +SKIRMISHING S K ER1 M IH0 SH IH0 NG +SKIRMISHINGS S K ER1 M IH0 SH IH0 NG Z +SKIRNIR S K ER1 N ER0 +SKIRR S K ER1 +SKIRRYWINKS S K ER1 IY0 W IH0 NG K S +SKIRT S K ER1 T +SKIRT'S S K ER1 T S +SKIRTED S K ER1 T IH0 D +SKIRTING S K ER1 T IH0 NG +SKIRTLESS S K ER1 T L AH0 S +SKIRTS S K ER1 T S +SKIRVING S K ER1 V IH0 NG +SKIRWOILLA S K ER1 W UH2 IH1 L AH0 +SKIS S K IY1 Z +SKIT S K IH1 T +SKITE S K AY1 T +SKITS S K IH1 T S +SKITTER S K IH1 T ER0 +SKITTERED S K IH1 T ER0 D +SKITTERING S K IH1 T ER0 IH0 NG +SKITTISH S K IH1 T IH0 SH +SKITTISHLY S K IH1 T IH0 SH L IY0 +SKITTISHNESS S K IH1 T IH0 SH N AH0 S +SKITTLE S K IH1 T AH0 L +SKITTLES S K IH1 T AH0 L Z +SKITZLAND S K IH1 T Z L AH0 N D +SKIVER S K AY1 V ER0 +SKIVERED S K AY1 V ER0 D +SKLARZ S K L AA1 R Z +SKOAL S K OW1 L +SKOBELEFF S K OW1 B AH0 L EH1 F +SKOELL S K OW1 L +SKOFF S K AO1 F +SKOODOOWABSKOOK S K UW1 D UW2 W AO1 B S K UH2 K +SKOODOOWABSKOOKSIS S K UW1 D UW2 W AO1 B S K UW1 K S AH0 S +SKOOKUM S K UW1 K AH0 M +SKOOKUM'S S K UW1 K AH0 M Z +SKOONLY S K UW2 N L IY0 +SKOOT S K UW2 T +SKOP S K AA1 P +SKOPTZI S K AO1 P T Z IY0 +SKORDRAN S K ER0 D R AH0 N +SKORVSKY S K AO1 R V S K IY0 +SKOWHEGAN S K OW1 HH IY2 G AH0 N +SKRAE S K R EY2 +SKRATDJ S K R AE2 T JH +SKREBENSKY S K R EH0 B EH1 N S K IY0 +SKREEN S K R IY0 N +SKREIGH S K R EY1 +SKRIMMAGE S K R IH1 M IH0 JH +SKROELINGAR S K R AA1 AH0 L IH1 NG G ER0 +SKRYMIR S K R IH0 M IH1 R +SKRYMNER S K R IH0 M N ER0 +SKUA S K AH0 +SKUAS S K AH1 AH0 Z +SKULD S K AH0 L D +SKULDA S K AH0 L D AH0 +SKULDUGGERY S K AH0 L D AH1 G ER0 IY0 +SKULE S K AH0 L +SKULI S K AH1 L IY0 +SKULISON S K AH0 L IH0 S AH0 N +SKULK S K AH1 L K +SKULKED S K AH1 L K T +SKULKER S K AH1 L K ER0 +SKULKERS S K AH1 L K ER0 Z +SKULKIN S K AH1 L K IH0 N +SKULKING S K AH1 L K IH0 NG +SKULKS S K AH1 L K S +SKULL S K AH1 L +SKULL'D S K AH1 L D +SKULL'S S K AH1 L Z +SKULLCAP S K AH1 L K AE2 P +SKULLDUGGERY S K AH1 L D AH1 G ER0 IY0 +SKULLED S K AH1 L D +SKULLS S K AH1 L Z +SKULP S K AH1 L P +SKULPIT S K AH1 L P IH0 T +SKUN S K AH1 N +SKUNK S K AH1 NG K +SKUNK'S S K AH1 NG K S +SKUNKED S K AH1 NG K T +SKUNKS S K AH1 NG K S +SKURCE S K ER1 S +SKURGE S K ER1 JH +SKURRIED S K ER1 IY0 D +SKURRY S K ER1 IY0 +SKURRYING S K ER1 IY0 IH0 NG +SKUYTERCLIFF S K AY1 T ER0 K L IH0 F +SKVORTSOV S K V AO1 R T S AA2 V +SKWESS S K W AH0 S +SKY S K AY1 +SKY'S S K AY1 Z +SKYBLUE S K AY1 B L UW2 +SKYBROW S K AY1 B R OW2 +SKYDS S K AY1 D Z +SKYE S K AY1 +SKYED S K AY1 D +SKYEMAN S K AY1 M AH0 N +SKYES S K AY1 Z +SKYEY S K AY1 IY0 +SKYFFINGTON S K AY1 F IH0 NG T AH0 N +SKYING S K AY1 IH0 NG +SKYLAND S K AY1 L AH0 N D +SKYLARK S K AY1 L AA2 R K +SKYLARK'S S K AY1 L AA2 R K S +SKYLARKED S K AY1 L AA2 R K T +SKYLARKIN S K AY1 L AA2 R K IH0 N +SKYLARKING S K AY1 L AA2 R K IH0 NG +SKYLARKS S K AY1 L AA2 R K S +SKYLIGHT S K AY1 L AY2 T +SKYLIGHTS S K AY1 L AY2 T S +SKYLINE S K AY1 L AY2 N +SKYLINES S K AY1 L AY2 N Z +SKYNSES S K IH0 N Z IH0 Z +SKYRMIR S K AY1 R M IH0 R +SKYROCKET S K AY1 R AA2 K AH0 T +SKYROCKETS S K AY1 R AA2 K AH0 T S +SKYSAIL S K AY1 S EY2 L +SKYSAILS S K AY1 S EY1 L Z +SKYSCRAPER S K AY1 S K R EY2 P ER0 +SKYSCRAPERS S K AY1 S K R EY2 P ER0 Z +SKYSTEIN S K AY1 S T IY0 N +SKYWARD S K AY1 W ER0 D +SKYWARDS S K AY1 W ER0 D Z +SL S L +SLAAG S L AA1 G +SLAB S L AE1 B +SLABBED S L AE1 B D +SLABBERING S L AE1 B ER0 IH0 NG +SLABBERT'S S L AE1 B ER0 T S +SLABE S L EY1 B +SLABES S L EY1 B Z +SLABS S L AE1 B Z +SLABSIDES S L AE1 B S AY2 D Z +SLABTOWN S L AE1 B T AW2 N +SLACK S L AE1 K +SLACK'S S L AE1 K S +SLACKED S L AE1 K T +SLACKEN S L AE1 K AH0 N +SLACKEN'D S L AE1 K AH0 N D +SLACKENED S L AE1 K AH0 N D +SLACKENING S L AE1 K AH0 N IH0 NG +SLACKENS S L AE1 K AH0 N Z +SLACKER S L AE1 K ER0 +SLACKERS S L AE1 K ER0 Z +SLACKEST S L AE1 K AH0 S T +SLACKING S L AE1 K IH0 NG +SLACKLY S L AE1 K L IY0 +SLACKNESS S L AE1 K N AH0 S +SLACKS S L AE1 K S +SLADDEN S L AE1 D AH0 N +SLADDER S L AE1 D ER0 +SLADE S L EY1 D +SLADE'S S L AE1 D Z +SLADEN S L EY1 D AH0 N +SLADY S L AE1 D IY0 +SLAFE S L EY1 F +SLAG S L AE1 G +SLAGELSE S L AE1 G AH0 L S +SLAGFID S L AE1 G F IH0 D +SLAGG S L AE1 G +SLAGG'S S L AE1 G Z +SLAGHAMMER S L AE1 HH AE2 M ER0 +SLAIN S L EY1 N +SLAINE S L EY1 N +SLAINS S L EY1 N Z +SLAINTE S L EY1 N T +SLAKE S L EY1 K +SLAKED S L EY1 K T +SLAKES S L EY1 K S +SLAKING S L EY1 K IH0 NG +SLAM S L AE1 M +SLAMMED S L AE1 M D +SLAMMER S L AE1 M ER0 +SLAMMIN S L AE1 M IH0 N +SLAMMING S L AE1 M IH0 NG +SLAMS S L AE1 M Z +SLAN S L AE1 N +SLANDER S L AE1 N D ER0 +SLANDER'S S L AE1 N D ER0 Z +SLANDERED S L AE1 N D ER0 D +SLANDERER S L AE1 N D ER0 ER0 +SLANDERER'S S L AE1 N D ER0 ER0 Z +SLANDERERS S L AE1 N D ER0 ER0 Z +SLANDERIN S L AE1 N D ER0 IH0 N +SLANDERING S L AE1 N D ER0 IH0 NG +SLANDEROUS S L AE1 N D ER0 AH0 S +SLANDEROUSLY S L AE1 N D ER0 AH0 S L IY0 +SLANDERS S L AE1 N D ER0 Z +SLANE S L EY1 N +SLANEY S L EY1 N IY0 +SLANG S L AE1 NG +SLANGED S L AE1 NG D +SLANGILY S L AE1 NG G AH0 L IY0 +SLANGINESS S L AE1 NG IY0 N AH0 S +SLANGING S L AE1 NG IH0 NG +SLANGS S L AE1 NG Z +SLANGY S L EY1 N JH IY0 +SLANK S L AE1 NG K +SLANT S L AE1 N T +SLANTED S L AE1 N AH0 D +SLANTED S L AE1 N IH0 D +SLANTED S L AE1 N T AH0 D +SLANTED S L AE1 N T IH0 D +SLANTIN S L AE1 N T IH0 N +SLANTINDICULAR S L AE1 N T IH0 N D IH1 K Y AH0 L ER0 +SLANTING S L AE1 N T IH0 NG +SLANTINGLY S L AE1 N T IH0 NG L IY0 +SLANTS S L AE1 N T S +SLANTWAYS S L AE1 N T W EY2 Z +SLANTWISE S L AE1 N T W AY2 Z +SLAP S L AE1 P +SLAPDASH S L AE1 P D AE2 SH +SLAPE S L EY1 P +SLAPIN S L AE1 P IH0 N +SLAPJACK S L AE1 P JH AE2 K +SLAPJACKS S L AE1 P JH AE2 K S +SLAPMAN S L AE1 P M AH0 N +SLAPMAN'S S L AE1 P M AH0 N Z +SLAPP S L AE1 P +SLAPPED S L AE1 P T +SLAPPIN S L AE1 P IH0 N +SLAPPING S L AE1 P IH0 NG +SLAPPINGS S L AE1 P IH0 NG Z +SLAPS S L AE1 P S +SLAPSTICK S L AE1 P S T IH2 K +SLASH S L AE1 SH +SLASHAWAY S L AE1 SH AH0 W EY2 +SLASHED S L AE1 SH T +SLASHER S L AE1 SH ER0 +SLASHERS S L AE1 SH ER0 Z +SLASHES S L AE1 SH IH0 Z +SLASHIN S L AE1 SH IH0 N +SLASHING S L AE1 SH IH0 NG +SLASHINGS S L AE1 SH IH0 NG Z +SLAT S L AE1 T +SLATE S L EY1 T +SLATED S L EY1 T IH0 D +SLATEE S L EY1 T IY0 +SLATEES S L EY1 T IY0 Z +SLATER S L EY1 T ER0 +SLATER'S S L EY1 T ER0 Z +SLATERS S L EY1 T ER0 Z +SLATES S L EY1 T S +SLATEY S L EY1 T IY0 +SLATHER S L AE1 DH ER0 +SLATHERED S L AE1 DH ER0 D +SLATHERS S L AE1 DH ER0 Z +SLATIN S L AE1 T AH0 N +SLATING S L EY1 T IH0 NG +SLATOR S L EY1 T ER0 +SLATS S L AE1 T S +SLATTED S L AE1 T AH0 D +SLATTER S L AE1 T ER0 +SLATTERN S L AE1 T ER0 N +SLATTERNLINESS S L AE1 T ER0 N L IY0 N AH0 S +SLATTERNLY S L AE1 T ER0 N L IY0 +SLATTERNS S L AE1 T ER0 N Z +SLATTERY S L AE1 T ER0 IY0 +SLATTIN S L AE1 T IH0 N +SLATTIN'S S L AE1 T IH0 N Z +SLATTING S L AE1 T IH0 NG +SLATTOX S L AE1 T AA2 K S +SLATY S L EY1 T IY0 +SLATZANEK S L AE1 T Z AH0 N IH0 K +SLAUGHTER S L AO1 T ER0 +SLAUGHTER'D S L AO1 T ER0 D +SLAUGHTER'S S L AO1 T ER0 Z +SLAUGHTERED S L AO1 T ER0 D +SLAUGHTERER S L AO1 T ER0 ER0 +SLAUGHTERERS S L AO1 T ER0 ER0 Z +SLAUGHTERHOUSE S L AO1 T ER0 HH AW2 S +SLAUGHTERHOUSES S L AO1 T ER0 HH AW2 S IH0 Z +SLAUGHTERING S L AO1 T ER0 IH0 NG +SLAUGHTERINGS S L AO1 T ER0 IH0 NG Z +SLAUGHTEROUS S L AO1 T ER0 AH0 S +SLAUGHTERS S L AO1 T ER0 Z +SLAV S L AA1 V +SLAV'RY S L AA1 V R IY0 +SLAVA S L AA1 V AH0 +SLAVATA S L AH0 V AA1 T AH0 +SLAVE S L EY1 V +SLAVE'S S L EY1 V Z +SLAVED S L EY1 V D +SLAVEDOM S L EY1 V D AH0 M +SLAVEHOLDER S L EY1 V HH OW2 L D ER0 +SLAVEHOLDER'S S L EY1 V HH OW2 L D ER0 Z +SLAVEHOLDERS S L EY1 V HH OW2 L D ER0 Z +SLAVEHOLDING S L EY1 V HH OW2 L D IH0 NG +SLAVEMASTER S L EY1 V M AE2 S T ER0 +SLAVENA S L AE1 V IH0 N AH0 +SLAVEOWNERS S L EY1 V OW2 N ER0 Z +SLAVER S L EY1 V ER0 +SLAVER'S S L EY1 V ER0 Z +SLAVERED S L EY1 V ER0 D +SLAVERIES S L EY1 V ER0 IY0 Z +SLAVERING S L EY1 V ER0 IH0 NG +SLAVERS S L EY1 V ER0 Z +SLAVERY S L EY1 V ER0 IY0 +SLAVERY'S S L EY1 V ER0 IY0 Z +SLAVES S L EY1 V Z +SLAVEY S L EY1 V IY0 +SLAVEYS S L AH0 V EY1 Z +SLAVI S L AA1 V IY0 +SLAVIA S L AA1 V IY0 AH0 +SLAVIC S L AA1 V IH0 K +SLAVIN S L AE1 V IH0 N +SLAVIN'S S L AE1 V IH0 N Z +SLAVING S L EY1 V IH0 NG +SLAVISH S L EY1 V IH0 SH +SLAVISHLY S L AE1 V IH0 SH L IY0 +SLAVISHNESS S L AE1 V IH0 SH N AH0 S +SLAVISM S L AA1 V IH0 Z AH0 M +SLAVKOVSKY S L AA1 V K AA1 V S K IY0 +SLAVONIA S L AH0 V OW1 N IY0 AH0 +SLAVONIA S L AH0 V OW1 N Y AH0 +SLAVONIAN S L AH0 V OW1 N IY0 AH0 N +SLAVONIANS S L AH0 V OW1 N IY0 AH0 N Z +SLAVONIC S L AH0 V OW1 N IH0 K +SLAVOPHIL S L AA1 V OW0 F IH1 L +SLAVOPHILS S L AA1 V OW0 F IH1 L Z +SLAVOVITCH S L AH0 V OW1 V IH0 CH +SLAVOWITZ S L AE1 V AH0 W IH0 T S +SLAVS S L AA1 V Z +SLAW S L AO1 +SLAWA S L AA0 V AH0 +SLAWSON S L AO1 S AH0 N +SLAY S L EY1 +SLAYBACK S L EY1 B AE2 K +SLAYBACK'S S L EY1 B AE2 K S +SLAYED S L EY1 D +SLAYER S L EY1 ER0 +SLAYER'S S L EY1 ER0 Z +SLAYERS S L EY1 ER0 Z +SLAYEST S L EY1 IH0 S T +SLAYETH S L EY1 IH0 TH +SLAYFORTH S L EY1 F AO2 R TH +SLAYING S L EY1 IH0 NG +SLAYINGS S L EY1 IH0 NG Z +SLAYNE S L EY1 N +SLAYS S L EY1 Z +SLAYTON S L EY1 T AH0 N +SLEAFORD S L IY1 F ER0 D +SLEAFORD'S S L IY1 F ER0 D Z +SLEAFORDS S L IY1 F ER0 D Z +SLEARY S L IH1 R IY0 +SLEAT S L IY1 T +SLEAVE S L IY1 V +SLEAZY S L IY1 Z IY0 +SLED S L EH1 D +SLEDD S L EH1 D +SLEDDED S L EH1 D AH0 D +SLEDDIN S L EH1 D IH0 N +SLEDDING S L EH1 D IH0 NG +SLEDGE S L EH1 JH +SLEDGEHAMMER S L EH1 JH HH AE2 M ER0 +SLEDGEHOUSE S L EH1 JH HH AW2 S +SLEDGEMEN S L EH1 JH M AH0 N +SLEDGES S L EH1 JH IH0 Z +SLEDGING S L EH1 JH IH0 NG +SLEDS S L EH1 D Z +SLEE S L IY1 +SLEE'S S L IY1 Z +SLEECH S L IY1 CH +SLEECH'S S L IY1 CH IH0 Z +SLEEK S L IY1 K +SLEEKED S L IY1 K T +SLEEKER S L IY1 K ER0 +SLEEKEST S L IY1 K AH0 S T +SLEEKING S L IY1 K IH0 NG +SLEEKIT S L IY1 K IH0 T +SLEEKLY S L IY1 K L IY0 +SLEEKNESS S L IY1 K N AH0 S +SLEEMAN S L IY1 M AH0 N +SLEEP S L IY1 P +SLEEP'S S L IY1 P S +SLEEP'ST S L IY1 P S T +SLEEPE S L IY1 P +SLEEPED S L IY1 P T +SLEEPER S L IY1 P ER0 +SLEEPER'S S L IY1 P ER0 Z +SLEEPERS S L IY1 P ER0 Z +SLEEPEST S L IY1 P AH0 S T +SLEEPETH S L IY1 P IH0 TH +SLEEPIER S L IY1 P IY0 ER0 +SLEEPIEST S L IY1 P IY0 AH0 S T +SLEEPILY S L IY1 P AH0 L IY0 +SLEEPIN S L IY1 P IH0 N +SLEEPIN' S L IY1 P IH0 N +SLEEPINBUFF S L IY1 P IY0 N B AH1 F +SLEEPINESS S L IY1 P IY0 N AH0 S +SLEEPING S L IY1 P IH0 NG +SLEEPIT S L IY1 P IH0 T +SLEEPLESS S L IY1 P L AH0 S +SLEEPLESSLY S L IY1 P L AH0 S L IY0 +SLEEPLESSNESS S L IY1 P L AH0 S N AH0 S +SLEEPS S L IY1 P S +SLEEPUM S L IY1 P AH0 M +SLEEPWALKER S L IY1 P W AO2 K ER0 +SLEEPWALKING S L IY1 P W AO2 K IH0 NG +SLEEPY S L IY1 P IY0 +SLEEPYHEAD S L IY1 P IY0 HH EH2 D +SLEEPYHORN S L IY1 P IY0 HH ER0 N +SLEET S L IY1 T +SLEETED S L IY1 T AH0 D +SLEETING S L IY1 T IH0 NG +SLEETS S L IY1 T S +SLEETY S L IY1 T IY0 +SLEEVE S L IY1 V +SLEEVED S L IY1 V D +SLEEVELESS S L IY1 V L IH0 S +SLEEVES S L IY1 V Z +SLEGGE S L EH1 G +SLEGGE'S S L EH1 JH IH0 Z +SLEIGH S L EY1 +SLEIGHBELLS S L EY1 B EH2 L Z +SLEIGHING S L EY1 IH0 NG +SLEIGHRIDE S L AY1 R AY2 D +SLEIGHS S L EY1 Z +SLEIGHT S L AY1 T +SLEIGHTS S L AY1 T S +SLEIPNER S L AY1 P N ER0 +SLEIPNIR S L EY1 P N ER0 +SLEMMER S L EH1 M ER0 +SLEMMICK S L EH1 M IH0 K +SLEMON S L EH1 M AH0 N +SLENCY S L EH1 N S IY0 +SLENDER S L EH1 N D ER0 +SLENDERER S L EH1 N D ER0 ER0 +SLENDEREST S L EH1 N D ER0 IH0 S T +SLENDERLY S L EH1 N D ER0 L IY0 +SLENDERNESS S L EH1 N D ER0 N AH0 S +SLEP S L IY1 P +SLEPT S L EH1 P T +SLESSOR S L EH1 S ER0 +SLESVIG S L EH1 V IH2 G +SLESWICK S L EH1 S W IH0 K +SLETH S L EH1 TH +SLEUT S L UW1 T +SLEUTH S L UW1 TH +SLEUTH'S S L UW1 TH S +SLEUTHHOUND S L UW1 TH HH AW2 N D +SLEUTHHOUNDS S L UW1 TH HH AW2 N D Z +SLEUTHING S L UW1 TH IH0 NG +SLEUTHS S L UW1 TH S +SLEVIN S L EH1 V IH0 N +SLEW S L UW1 +SLEWED S L UW1 D +SLEWEST S L UW1 AH0 S T +SLEWING S L UW1 IH0 NG +SLEWS S L UW1 Z +SLEY S L IY0 +SLEYBELLS S L IY0 B EH2 L Z +SLIAB S L AY1 AH0 B +SLICE S L AY1 S +SLICED S L AY1 S T +SLICER S L AY1 S ER0 +SLICES S L AY1 S AH0 Z +SLICES S L AY1 S IH0 Z +SLICIN S L AY1 S IH0 N +SLICING S L AY1 S IH0 NG +SLICK S L IH1 K +SLICK'S S L IH1 K S +SLICKED S L IH1 K T +SLICKENS S L IH1 K AH0 N Z +SLICKER S L IH1 K ER0 +SLICKERS S L IH1 K ER0 Z +SLICKEST S L IH1 K AH0 S T +SLICKIN S L IH1 K IH0 N +SLICKING S L IH1 K IH0 NG +SLICKLY S L IH1 K L IY0 +SLICKNESS S L IH1 K N AH0 S +SLICKO S L IH1 K OW0 +SLICKS S L IH1 K S +SLICKVILLE S L IH1 K V IH2 L +SLID S L IH1 D +SLIDDEN S L IH1 D AH0 N +SLIDDER S L IH1 D ER0 +SLIDDIM S L IH1 D IH0 M +SLIDE S L AY1 D +SLIDE'S S L AY1 D Z +SLIDED S L AY1 D IH0 D +SLIDELL S L AY1 D AH0 L +SLIDER S L AY1 D ER0 +SLIDERS S L AY1 D ER0 Z +SLIDERSKEW S L AY1 D ER1 S K Y UW1 +SLIDES S L AY1 D Z +SLIDESTAIRS S L AY1 D S T EH1 R Z +SLIDEWALK S L AY1 D W AO2 K +SLIDIN S L AY1 D IH0 N +SLIDING S L AY1 D IH0 NG +SLIDINGS S L AY1 D IH0 NG Z +SLIEU S L Y UH1 +SLIEVE S L IY1 V +SLIFE S L AY1 F +SLIFER S L AY1 F ER0 +SLIFERS S L AY1 F ER0 Z +SLIGH S L AY1 +SLIGHEST S L AY1 AH0 S T +SLIGHT S L AY1 T +SLIGHTED S L AY1 T IH0 D +SLIGHTER S L AY1 T ER0 +SLIGHTEST S L AY1 T AH0 S T +SLIGHTING S L AY1 T IH0 NG +SLIGHTINGLY S L AY1 T IH0 NG L IY0 +SLIGHTLY S L AY1 T L IY0 +SLIGHTLY'S S L AY1 T L IY0 Z +SLIGHTNESS S L AY1 T N AH0 S +SLIGHTS S L AY1 T S +SLIGO S L AY1 G OW2 +SLIKE S L AY1 K +SLIKE'S S L AY1 K S +SLILY S L IH1 L IY0 +SLIM S L IH1 M +SLIM'S S L IH1 M Z +SLIMAK S L IH1 M AH0 K +SLIMAK'S S L AY1 M AE0 K S +SLIMAKOWA S L AY1 M AH0 K OW1 AH0 +SLIMAN S L AY1 M AH0 N +SLIMBROEK S L IH1 M B R OW0 K +SLIME S L AY1 M +SLIMED S L AY1 M D +SLIMEY S L AY1 M IY0 +SLIMILY S L AY1 M AH0 L IY0 +SLIMINESS S L IH1 M IY0 N AH0 S +SLIMLY S L IH1 M L IY0 +SLIMMER S L IH1 M ER0 +SLIMMEST S L IH1 M AH0 S T +SLIMMY S L IH1 M IY0 +SLIMNESS S L IH1 M N AH0 S +SLIMPSY S L IH1 M P S IY0 +SLIMSON S L IH1 M S AH0 N +SLIMY S L AY1 M IY0 +SLING S L IH1 NG +SLINGER S L IH1 NG ER0 +SLINGERS S L IH1 NG ER0 Z +SLINGIN S L IH1 NG AH0 N +SLINGING S L IH1 NG IH0 NG +SLINGS S L IH1 NG Z +SLINGSBY S L IH1 NG Z B IY0 +SLINGSBY'S S L IH1 NG Z B IY0 Z +SLINGSHOT S L IH1 NG SH AA2 T +SLINK S L IH1 NG K +SLINKARD S L IH1 NG K ER0 D +SLINKED S L IH1 NG K T +SLINKIN S L IH1 NG K IH0 N +SLINKING S L IH1 NG K IH0 NG +SLINKS S L IH1 NG K S +SLINKY S L IH1 NG K IY0 +SLIP S L IH1 P +SLIPED S L AY1 P T +SLIPKNOT S L IH1 P N AA2 T +SLIPP'D S L IH1 P D +SLIPP'RY S L IH1 P R IY0 +SLIPPED S L IH1 P T +SLIPPER S L IH1 P ER0 +SLIPPER'S S L IH1 P ER0 Z +SLIPPERED S L IH1 P ER0 D +SLIPPERIEST S L IH1 P IH1 R IY0 AH0 S T +SLIPPERINESS S L IH1 P ER0 IH0 N AH0 S +SLIPPERLESS S L IH1 P ER0 L AH0 S +SLIPPERS S L IH1 P ER0 Z +SLIPPERY S L IH1 P ER0 IY0 +SLIPPERY S L IH1 P R IY0 +SLIPPERY'S S L IH1 P ER0 IY0 Z +SLIPPIN S L IH1 P IH0 N +SLIPPING S L IH1 P IH0 NG +SLIPPIT S L IH1 P IH0 T +SLIPPY S L IH1 P IY0 +SLIPRAILS S L IH1 P R EY2 L Z +SLIPS S L IH1 P S +SLIPSHAW S L IH1 P SH AO2 +SLIPSHOD S L IH1 P SH AA2 D +SLIPSLOP S L IH1 P S L AH0 P +SLIPT S L IH1 P T +SLIPWAY S L IH1 P W EY2 +SLISS S L IH1 S +SLIT S L IH1 T +SLITH S L IH1 DH +SLITHER S L IH1 DH ER0 +SLITHERED S L IH1 DH ER0 D +SLITHERING S L IH1 DH ER0 IH0 NG +SLITHERS S L IH1 DH ER0 Z +SLITHERY S L IH1 DH ER0 IY0 +SLITHY S L IH1 DH IY0 +SLITS S L IH1 T S +SLITTED S L IH1 T IH0 D +SLITTING S L IH1 T IH0 NG +SLIVA S L IY1 V AH0 +SLIVER S L IH1 V ER0 +SLIVERED S L IH1 V ER0 D +SLIVERS S L IH1 V ER0 Z +SLIVERSTONE S L IH1 V ER0 S T OW2 N +SLO S L OW0 +SLOAN S L OW1 N +SLOAN'S S L OW1 N Z +SLOANE S L OW1 N +SLOANE'S S L OW1 N Z +SLOANEHURST S L OW1 N HH ER0 S T +SLOANES S L OW1 N Z +SLOAT S L OW1 T +SLOB S L AA1 B +SLOBBER S L AA1 B ER0 +SLOBBERED S L AA1 B ER0 D +SLOBBERIN S L AA1 B ER0 IH0 N +SLOBBERING S L AA1 B ER0 IH0 NG +SLOBBERS S L AA1 B ER0 Z +SLOBBERY S L AA1 B ER0 IY0 +SLOBS S L AA1 B Z +SLOCOMB S L OW1 K AH0 M +SLOCOMBE S L OW1 K AH0 M +SLOCUM S L OW1 K AH0 M +SLOCUM'S S L OW1 K AH0 M Z +SLOCUMB S L OW1 K AH0 M +SLOCUMS S L OW1 K AH0 M Z +SLOE S L OW1 +SLOES S L OW1 Z +SLOG S L AA1 G +SLOGAN S L OW1 G AH0 N +SLOGANS S L OW1 G AH0 N Z +SLOGGED S L AA1 G D +SLOGGER S L AA1 G ER0 +SLOGGERS S L AA1 G ER0 Z +SLOGGETT S L AA1 G IH0 T +SLOGGING S L AA1 G IH0 NG +SLOGWELL S L AA1 G W EH0 L +SLOICH S L OY1 CH +SLOMAN S L OW1 M AH0 N +SLOMAN'S S L OW1 M AH0 N Z +SLOME S L OW1 M +SLOME'S S L OW1 M Z +SLONE S L OW1 N +SLONE'S S L OW1 N Z +SLOO S L UW1 +SLOOP S L UW1 P +SLOOP'S S L UW1 P S +SLOOPS S L UW1 P S +SLOOT S L UW1 T +SLOOTS S L UW1 T S +SLOP S L AA1 P +SLOPE S L OW1 P +SLOPE'S S L OW1 P S +SLOPED S L OW1 P T +SLOPER S L OW1 P ER0 +SLOPER'S S L OW1 P ER0 Z +SLOPERTON S L OW1 P ER0 T AH0 N +SLOPES S L OW1 P S +SLOPHAM S L AA1 F AH0 M +SLOPHAM'S S L AA1 F AH0 M Z +SLOPIN S L OW1 P IH0 N +SLOPING S L OW1 P IH0 NG +SLOPPED S L AA1 P T +SLOPPER S L AA1 P ER0 +SLOPPILY S L AA1 P AH0 L IY0 +SLOPPIN S L AA1 P IH0 N +SLOPPINESS S L AA1 P IY0 N AH0 S +SLOPPING S L AA1 P IH0 NG +SLOPPY S L AA1 P IY0 +SLOPS S L AA1 P S +SLOSH S L AA1 SH +SLOSHED S L AA1 SH T +SLOSHER S L AA1 SH ER0 +SLOSHES S L AA1 SH IH0 Z +SLOSHING S L AA1 SH IH0 NG +SLOSHY S L AA1 SH IY0 +SLOSSON S L AA1 S AH0 N +SLOSSON'S S L AA1 S AH0 N Z +SLOSSONS S L AA1 S AH0 N Z +SLOT S L AA1 T +SLOTE S L OW1 T IY0 +SLOTH S L OW1 TH +SLOTH'S S L AA1 TH S +SLOTHFUL S L AA1 TH F AH0 L +SLOTHFULLY S L AA1 TH F AH0 L IY0 +SLOTHFULNESS S L AA1 TH F AH0 L N AH0 S +SLOTHS S L AA1 TH S +SLOTMAN S L AA1 T M AH0 N +SLOTMAN'S S L AA1 T M AH0 N Z +SLOTS S L AA1 T S +SLOTT S L AA1 T +SLOTTED S L AA1 T IH0 D +SLOUCH S L AW1 CH +SLOUCHED S L AW1 CH T +SLOUCHES S L AW1 CH IH0 Z +SLOUCHING S L AW1 CH IH0 NG +SLOUCHY S L AW1 CH IY0 +SLOUGH S L AH1 F +SLOUGHED S L AH1 F T +SLOUGHING S L AH1 F IH0 NG +SLOUGHS S L AH1 F S +SLOVAK S L OW1 V AA0 K +SLOVAK S L OW1 V AE0 K +SLOVAKIA S L OW2 V AA1 K IY0 AH0 +SLOVAKIAN S L OW0 V AA1 K IY0 AH0 N +SLOVAKS S L OW1 V AA0 K S +SLOVAKS S L OW1 V AE0 K S +SLOVEN S L AH1 V AH0 N +SLOVENE S L OW2 V IY1 N +SLOVENES S L OW2 V IY1 N Z +SLOVENLINESS S L AH1 V AH0 N L IY0 N AH0 S +SLOVENLY S L AH1 V AH0 N L IY0 +SLOVENS S L AH1 V AH0 N Z +SLOVER S L OW1 V ER0 +SLOW S L OW1 +SLOW'S S L OW1 Z +SLOWBOY S L OW1 B OY2 +SLOWBRIDGE S L OW1 B R IH0 JH +SLOWBURGH S L OW1 B ER0 G +SLOWCOACH S L OW1 K OW2 CH +SLOWE S L OW1 +SLOWED S L OW1 D +SLOWER S L OW1 ER0 +SLOWEST S L OW1 AH0 S T +SLOWFOOT S L OW1 F UH2 T +SLOWIN S L OW1 IH0 N +SLOWING S L OW1 IH0 NG +SLOWLY S L OW1 L IY0 +SLOWNESS S L OW1 N AH0 S +SLOWS S L OW1 Z +SLOY S L OY0 +SLUBBER S L AH1 B ER0 +SLUDGE S L AH1 JH +SLUDGY S L AH1 JH IY0 +SLUE S L UW1 +SLUED S L UW1 D +SLUG S L AH1 G +SLUG'S S L AH1 G Z +SLUGBY S L AH1 G B IY0 +SLUGGARD S L AH1 G ER0 D +SLUGGARD'S S L AH1 G ER0 D Z +SLUGGARDS S L AH1 G ER0 D Z +SLUGGED S L AH1 G D +SLUGGER S L AH1 G ER0 +SLUGGERS S L AH1 G ER0 Z +SLUGGING S L AH1 G IH0 NG +SLUGGISH S L AH1 G IH0 SH +SLUGGISHLY S L AH1 G IH0 SH L IY0 +SLUGGISHNESS S L AH1 G IH0 SH N AH0 S +SLUGS S L AH1 G Z +SLUICE S L UW1 S +SLUICED S L UW1 S T +SLUICES S L UW1 S AH0 Z +SLUICEWAY S L UW1 S W EY2 +SLUICEWAYS S L UW1 S W EY2 Z +SLUICING S L UW1 S IH0 NG +SLUIT S L UW1 T +SLUITS S L UW1 T S +SLUM S L AH1 M +SLUMB'RING S L AH1 M R IH0 NG +SLUMBER S L AH1 M B ER0 +SLUMBER'D S L AH1 M B ER0 D +SLUMBER'S S L AH1 M B ER0 Z +SLUMBERED S L AH1 M B ER0 D +SLUMBERER S L AH1 M B ER0 ER0 +SLUMBERER'S S L AH1 M B ER0 ER0 Z +SLUMBERERS S L AH1 M B ER0 ER0 Z +SLUMBEREST S L AH1 M B ER0 IH0 S T +SLUMBERETH S L AH1 M B ER0 TH +SLUMBERING S L AH1 M B ER0 IH0 NG +SLUMBERINGS S L AH1 M B ER0 IH0 NG Z +SLUMBERLAND S L AH1 M B ER0 L AH0 N D +SLUMBEROUS S L AH1 M B ER0 AH0 S +SLUMBEROUSLY S L AH1 M B ER0 AH0 S L IY0 +SLUMBERS S L AH1 M B ER0 Z +SLUMBROUS S L AH1 M B R AH0 S +SLUMGULLION S L AH1 M G AH1 L Y AH0 N +SLUMKEY S L AH1 M K IY0 +SLUMMER S L AH1 M ER0 +SLUMMERS S L AH1 M ER0 Z +SLUMMING S L AH1 M IH0 NG +SLUMMY S L AH1 M IY0 +SLUMP S L AH1 M P +SLUMP'S S L AH1 M P S +SLUMPED S L AH1 M P T +SLUMPER S L AH1 M P ER0 +SLUMPING S L AH1 M P IH0 NG +SLUMPS S L AH1 M P S +SLUMPY S L AH1 M P IY0 +SLUMS S L AH1 M Z +SLUNG S L AH1 NG +SLUNGSHOT S L AH1 NG SH AA2 T +SLUNK S L AH1 NG K +SLUR S L ER1 +SLURK S L ER1 K +SLURRED S L ER1 D +SLURRING S L ER1 IH0 NG +SLURS S L ER1 Z +SLUS'UHR S L AH1 UH1 R +SLUSH S L AH1 SH +SLUSHED S L AH1 SH T +SLUSHING S L AH1 SH IH0 NG +SLUSHY S L AH1 SH IY0 +SLUSS S L AH1 S +SLUSUHR S L AH1 SH UH1 R +SLUT S L AH1 T +SLUT'S S L AH1 T S +SLUTS S L AH1 T S +SLUTTISH S L AH1 T IH0 SH +SLUTTISHNESS S L AH1 T IH0 SH N AH0 S +SLUYS S L AY1 Z +SLY S L AY1 +SLY'S S L AY1 Z +SLYBOOTS S L AY1 B UW2 T S +SLYCK S L AY1 K +SLYCK'S S L AY1 K S +SLYE'S S L AY1 Z +SLYER S L AY1 ER0 +SLYEST S L AY1 AH0 S T +SLYLY S L AY1 L IY0 +SLYME S L AY1 M +SLYME'S S L AY1 M Z +SLYNE S L AY1 N +SLYNE'S S L AY1 N Z +SLYNESS S L AY1 N AH0 S +SM S M +SMA S M AH0 +SMA'T S M EY1 T +SMACK S M AE1 K +SMACK'S S M AE1 K S +SMACKED S M AE1 K T +SMACKIN S M AE1 K AH0 N +SMACKING S M AE1 K IH0 NG +SMACKS S M AE1 K S +SMACKSMAN S M AE1 K S M AH0 N +SMACKSMEN S M AE1 K S M EH0 N +SMAHT S M AA1 T +SMAIN S M EY1 N +SMAIN'S S M EY1 N Z +SMAITH S M EY1 TH +SMAL S M AE1 L +SMALA S M AA1 L AH0 +SMALAND S M EY1 L AH0 N D +SMALE S M EY1 L +SMALKALDIC S M AE1 L K AH0 L D IH0 K +SMALL S M AO1 L +SMALL'S S M AO1 L Z +SMALLAGE S M AO1 L IH0 JH +SMALLBONES S M AO1 L B OW2 N Z +SMALLCLOTHES S M AO1 L K L OW1 Z +SMALLER S M AO1 L ER0 +SMALLEST S M AO1 L AH0 S T +SMALLEY S M AA1 L IY0 +SMALLEY'S S M AO1 L IY0 Z +SMALLHEAD S M AO1 L HH EH2 D +SMALLING S M AO1 L IH0 NG +SMALLISH S M AO1 L IH0 SH +SMALLNESS S M AO1 L N AH0 S +SMALLNESSES S M AO1 L N AH0 S IH0 Z +SMALLPEACE S M AO1 L P IY0 S +SMALLPOX S M AO1 L P AA2 K S +SMALLS S M AO1 L Z +SMALLSOLE S M AO1 L S AA2 L +SMALLWAYS S M AO1 L W EY2 Z +SMALLWEED S M AO1 L W IY2 D +SMALLWOOD S M AO1 L W UH2 D +SMALT S M AO1 L T +SMANGLE S M AE1 NG G AH0 L +SMARAGD S M AA1 R AE2 G D +SMARLINGHUE S M AA1 R L IH0 NG HH Y UW0 +SMARLINGHUE'S S M AA1 R L IH0 NG HH Y UW0 Z +SMART S M AA1 R T +SMART'S S M AA1 R T S +SMARTED S M AA1 R T AH0 D +SMARTEN S M AA1 R T AH0 N +SMARTENED S M AA1 R T AH0 N D +SMARTENING S M AA1 R T AH0 N IH0 NG +SMARTER S M AA1 R T ER0 +SMARTER'N S M AA1 R T ER0 AH0 N +SMARTEST S M AA1 R T AH0 S T +SMARTIE S M AA1 R T IY0 +SMARTIES S M AA1 R T IY0 Z +SMARTING S M AA1 R T IH0 NG +SMARTISH S M AA1 R T IH0 SH +SMARTLE S M AA1 R T AH0 L +SMARTLY S M AA1 R T L IY0 +SMARTNESS S M AA1 R T N IH0 S +SMARTS S M AA1 R T S +SMARTWEED S M AA1 R T W IY2 D +SMARTY S M AA1 R T IY0 +SMASH S M AE1 SH +SMASH'D S M AE1 SH D +SMASHED S M AE1 SH T +SMASHER S M AE1 SH ER0 +SMASHERS S M AE1 SH ER0 Z +SMASHES S M AE1 SH IH0 Z +SMASHIN S M AE1 SH IH0 N +SMASHING S M AE1 SH IH0 NG +SMASHUP S M AE1 SH AH2 P +SMATCH S M AE1 CH +SMATHE S M AE1 DH +SMATHERS S M AE1 DH ER0 Z +SMATTER S M AE1 T ER0 +SMATTERER S M AE1 T ER0 ER0 +SMATTERERS S M AE1 T ER0 ER0 Z +SMATTERING S M AE1 T ER0 IH0 NG +SMATTERINGS S M AE1 T ER0 IH0 NG Z +SMATTERTON S M AE1 T ER0 T AH0 N +SMAUKER S M AW1 K ER0 +SMAWL S M AO1 L +SMAYLE S M EY1 L +SMEAD S M IY1 D +SMEAR S M IH1 R +SMEAR'D S M IH1 R D +SMEARCASE S M IH1 R K EY2 S +SMEARED S M IH1 R D +SMEARER S M IH1 R ER0 +SMEARING S M IH1 R IH0 NG +SMEARS S M IH1 R Z +SMEARY S M IH1 R IY0 +SMEATH S M IY1 TH +SMEATON S M IY1 T AH0 N +SMEATON'S S M IY1 T AH0 N Z +SMEDDUM S M EH1 D AH0 M +SMEDLEY S M EH1 D L IY0 +SMEDLEY'S S M EH1 D L IY0 Z +SMEE S M IY1 +SMEED S M IY1 D +SMEER S M IY1 ER0 +SMEES S M IY1 Z +SMEET S M IY1 T +SMEETH S M IY1 TH +SMEETON S M IY1 T AH0 N +SMELFUNGUS S M EH1 L F AH1 NG G AH0 S +SMELKOFF S M EH1 L K AO0 F +SMELKOFF'S S M EH1 L K AO0 F S +SMELL S M EH1 L +SMELLED S M EH1 L D +SMELLER S M EH1 L ER0 +SMELLERS S M EH1 L ER0 Z +SMELLETH S M EH1 L IH0 TH +SMELLIE S M EH1 L IY0 +SMELLIE'S S M EH1 L IY0 Z +SMELLIN S M EH1 L IH0 N +SMELLING S M EH1 L IH0 NG +SMELLS S M EH1 L Z +SMELLY S M EH1 L IY0 +SMELT S M EH1 L T +SMELTED S M EH1 L T IH0 D +SMELTER S M EH1 L T ER0 +SMELTERS S M EH1 L T ER0 Z +SMELTING S M EH1 L T IH0 NG +SMELTS S M EH1 L T S +SMERDIS S M EH1 R D IH0 S +SMERDYAKOV S M EH1 R D Y AA1 K AO2 V +SMERDYAKOV'S S M ER1 D AY0 AE1 K AA2 V Z +SMERNOFF S M ER1 N AO0 F +SMERWICK S M ER1 W IH0 K +SMET S M EH1 T +SMETANA S M EH1 T AH0 N AH0 +SMETHURST S M EH1 TH ER0 S T +SMETSE S M EH1 T S +SMIBERT S M IH1 B ER0 T +SMIDDY S M IH1 D IY0 +SMIDGKIN S M IH1 JH K IH0 N +SMIDMORE S M IH1 D M AO2 R +SMIFF S M IH1 F +SMIKE S M AY1 K +SMIKE'S S M AY1 K S +SMIL'D S M IH1 L D +SMILASH S M AY1 L AE2 SH +SMILAX S M AY1 L AE2 K S +SMILE S M AY1 L +SMILE'S S M AY1 L Z +SMILED S M AY1 L D +SMILELESS S M AY1 L L AH0 S +SMILELESSLY S M AY1 L L AH0 S L IY0 +SMILER S M AY1 L ER0 +SMILER'S S M AY1 L ER0 Z +SMILES S M AY1 L Z +SMILES'S S M AY1 L Z IH0 Z +SMILEST S M AY1 L S T +SMILETH S M IH1 L IH0 TH +SMILEY S M AY1 L IY0 +SMILEY'S S M AY1 L IY0 Z +SMILIN S M AY1 L IH0 N +SMILINDA S M IH1 L IY1 N D AH0 +SMILINDA'S S M AY1 L IH0 N D AH0 Z +SMILING S M AY1 L IH0 NG +SMILINGLY S M AY1 L IH0 NG L IY0 +SMILINGS S M AY1 L IH0 NG Z +SMILLIE S M IH1 L IY0 +SMILY S M L IY0 +SMINTHEUS S M IH1 N DH OY0 Z +SMIRCH S M ER1 CH +SMIRCHED S M ER1 CH T +SMIRCHES S M ER1 CH IH0 Z +SMIRCHING S M ER1 CH IH0 NG +SMIRK S M ER1 K +SMIRKE S M ER1 K +SMIRKE'S S M ER1 K S +SMIRKED S M ER1 K T +SMIRKIE S M ER1 K IY0 +SMIRKING S M ER1 K IH0 NG +SMIRKS S M ER1 K S +SMIRNOV S M ER1 N AA0 F +SMIRRE S M ER1 +SMIT S M IH1 T +SMITCH S M IH0 CH +SMITE S M AY2 T +SMITER S M AY1 T ER0 +SMITERS S M AY1 T ER0 Z +SMITES S M AY1 T S +SMITEST S M AY1 T AH0 S T +SMITETH S M IH1 T EH0 TH +SMITH S M IH1 TH +SMITH'S S M IH1 TH S +SMITHE S M IH1 TH +SMITHER S M IH1 DH ER0 +SMITHEREENS S M IH2 DH ER0 IY1 N Z +SMITHERS S M IH1 DH ER0 Z +SMITHERS'S S M IH1 DH ER0 Z IH0 Z +SMITHERTON S M IH1 TH ER0 T AH0 N +SMITHERY S M IH1 TH ER0 IY0 +SMITHFIELD S M IH1 TH F IY0 L D +SMITHICK S M IH1 TH IH0 K +SMITHIE S M IH1 TH IY0 +SMITHIE'S S M IH1 TH IY0 Z +SMITHIES S M IH1 TH IY0 Z +SMITHING S M IH1 TH IH0 NG +SMITHS S M IH1 TH S +SMITHSON S M IH1 TH S AH0 N +SMITHSON'S S M IH1 TH S AH0 N Z +SMITHSONIAN S M IH0 S OW1 N IY0 AH0 N +SMITHSONIAN S M IH0 TH S OW1 N IY0 AH0 N +SMITHSONS S M IH1 TH S AH0 N Z +SMITHTOWN S M IH1 TH T AW2 N +SMITHVILLE S M IH1 TH V IH2 L +SMITHWICK S M IH1 TH W IH2 K +SMITHY S M IH1 TH IY0 +SMITHY'S S M IH1 TH IY0 Z +SMITING S M AH0 T IH0 NG +SMITINGS S M AH0 T IH0 NG Z +SMITTEN S M IH1 T AH0 N +SMITZ S M IH1 T S +SMOAK S M OW1 K +SMOAKED S M OW1 K T +SMOAKING S M OW1 K IH0 NG +SMOAKY S M OW1 K IY0 +SMOCK S M AA1 K +SMOCKED S M AA1 K T +SMOCKFROCK S M AA1 K F R AA2 K +SMOCKS S M AA1 K S +SMOG S M AA1 G +SMOIT S M OY1 T +SMOK'D S M OW1 K D +SMOKE S M OW1 K +SMOKE'S S M OW1 K S +SMOKED S M OW1 K T +SMOKEHOUSE S M OW1 K HH AW2 S +SMOKEING S M OW1 K IH0 NG +SMOKELESS S M OW1 K L AH0 S +SMOKER S M OW1 K ER0 +SMOKER'S S M OW1 K ER0 Z +SMOKERS S M OW1 K ER0 Z +SMOKES S M OW1 K S +SMOKESTACK S M OW1 K S T AE2 K +SMOKESTACKS S M OW1 K S T AE2 K S +SMOKEY S M OW1 K IY0 +SMOKIER S M OW1 K IY0 ER0 +SMOKIES S M OW1 K IY0 Z +SMOKILY S M OW1 K IH0 L IY0 +SMOKIN S M OW1 K IH0 N +SMOKIN' S M OW1 K IH0 N +SMOKINESS S M OW1 K IY0 N AH0 S +SMOKING S M OW1 K IH0 NG +SMOKOVNIKOV S M OW1 K AH0 V N IH0 K AA2 V +SMOKY S M OW1 K IY0 +SMOKY'S S M OW1 K IY0 Z +SMOLDER S M OW1 L D ER0 +SMOLDERED S M OW1 L D ER0 D +SMOLDERING S M OW1 L D ER0 IH0 NG +SMOLENSK S M OW0 L EH1 N S K +SMOLENSKIN S M AH0 L EH1 N S K IH0 N +SMOLENSKIN'S S M AH0 L EH1 N S K IH0 N Z +SMOLENSKO S M AH0 L EH1 N S K OW0 +SMOLICHECK S M AH0 L IH1 CH EH2 K +SMOLIN S M OW1 L IH0 N +SMOLLET S M AA1 L IH0 T +SMOLLETT S M AA1 L IH0 T +SMOLLETT'S S M AA1 L IH0 T S +SMOLLEY S M AA1 L IY0 +SMOLNY S M OW1 L N IY0 +SMOLTS S M OW1 L T S +SMOOCH S M UW1 CH +SMOOK S M UW1 K +SMOORED S M AO1 R D +SMOOT S M UW1 T +SMOOTH S M UW1 DH +SMOOTH'D S M UW1 DH D +SMOOTHBORE S M UW1 DH B AO0 R +SMOOTHE S M UW1 DH +SMOOTHED S M UW1 DH D +SMOOTHER S M UW1 DH ER0 +SMOOTHES S M UW1 DH Z +SMOOTHEST S M UW1 DH AH0 S T +SMOOTHIN S M UW1 DH IH1 N +SMOOTHING S M UW1 DH IH0 NG +SMOOTHLY S M UW1 DH L IY0 +SMOOTHNESS S M UW1 DH N AH0 S +SMOOTHS S M UW1 DH Z +SMOTE S M OW1 T +SMOTH S M AA1 TH +SMOTHE S M AH1 DH +SMOTHER S M AH1 DH ER0 +SMOTHER'D S M AH1 DH ER0 D +SMOTHERED S M AH1 DH ER0 D +SMOTHERIN S M AH1 DH ER0 IH0 N +SMOTHERING S M AH1 DH ER0 IH0 NG +SMOTHERINGLY S M AH1 DH ER0 IH0 NG L IY0 +SMOTHERS S M AH1 DH ER0 Z +SMOTHERY S M AH1 DH ER0 IY0 +SMOUCH S M AW1 CH +SMOUCHED S M AW1 CH T +SMOULDER S M OW1 L D ER0 +SMOULDERED S M OW1 L D ER0 D +SMOULDERING S M OW1 L D ER0 IH0 NG +SMOULDERS S M OW1 L D ER0 Z +SMOUTCHIES S M AW1 CH IY0 Z +SMOUTCHY S M AW1 CH IY0 +SMU S M UW1 +SMUDGE S M AH1 JH +SMUDGE'S S M AH1 JH IH0 Z +SMUDGED S M AH1 JH D +SMUDGES S M AH1 JH IH0 Z +SMUDGING S M AH1 JH IH0 NG +SMUDGY S M AH1 JH IY0 +SMUG S M AH1 G +SMUGG S M AH1 G +SMUGGLE S M AH1 G AH0 L +SMUGGLED S M AH1 G AH0 L D +SMUGGLER S M AH1 G AH0 L ER0 +SMUGGLER S M AH1 G L ER0 +SMUGGLER'S S M AH1 G L ER0 Z +SMUGGLERS S M AH1 G AH0 L ER0 Z +SMUGGLERS S M AH1 G L ER0 Z +SMUGGLES S M AH1 G AH0 L Z +SMUGGLIN S M AH1 G L IH0 N +SMUGGLING S M AH1 G AH0 L IH0 NG +SMUGGLING S M AH1 G L IH0 NG +SMUGLY S M AH1 G L IY0 +SMUGNESS S M AH1 G N AH0 S +SMUGS S M AH1 G Z +SMULLYAN S M AH1 L Y AH0 N +SMUROV S M ER0 AO2 V +SMUSH S M AH1 SH +SMUT S M AH1 T +SMUTCH S M AH1 CH +SMUTCHED S M AH1 CH T +SMUTS S M AH1 T S +SMUTTED S M AH1 T IH0 D +SMUTTY S M AH1 T IY0 +SMYLIE S M IH1 L IY0 +SMYRNA S M ER1 N AH0 +SMYRNIOTE S M ER1 N IY0 OW0 T +SMYTH S M AY1 TH +SMYTH S M IH1 TH +SMYTH'S S M AY1 TH S +SMYTHE S M AY1 DH +SMYTHE'S S M AY1 DH AH0 Z +SMYTHES S M AY1 DH Z +SMYTHJE S M AY1 TH JH AH0 +SMYTHJE'S S M AY1 TH JH AH0 Z +SN S N +SNACK S N AE1 K +SNACKS S N AE1 K S +SNAEFELL S N IY1 F AH0 L +SNAFFLE S N AE1 F AH0 L +SNAFFLE'S S N AE1 F AH0 L Z +SNAFFLED S N AE1 F AH0 L D +SNAFFLES S N AE1 F AH0 L Z +SNAG S N AE1 G +SNAGG S N AE1 G +SNAGGED S N AE1 G D +SNAGGLE S N AE1 G AH0 L +SNAGGS S N AE1 G Z +SNAGGY S N AE1 G IY0 +SNAGLEY S N AE1 G L IY0 +SNAGS S N AE1 G Z +SNAGSBY S N AE1 G Z B IY0 +SNAGTOWN S N AE1 G T AW2 N +SNAIL S N EY1 L +SNAIL'S S N EY1 L Z +SNAILED S N EY1 L D +SNAILS S N EY1 L Z +SNAITH S N EY1 TH +SNAKE S N EY1 K +SNAKE'S S N EY1 K S +SNAKEBITE S N EY1 K B AY2 T +SNAKED S N EY1 K T +SNAKELIKE S N EY1 K L AY2 K +SNAKEROOT S N EY1 K R UW2 T +SNAKES S N EY1 K S +SNAKESHIP S N EY1 K SH IH2 P +SNAKESKIN S N EY1 K S K IH2 N +SNAKEY S N EY1 K IY0 +SNAKILY S N EY1 K AH0 L IY0 +SNAKIN S N EY1 K IH0 N +SNAKING S N EY1 K IH0 NG +SNAKISH S N EY1 K IH0 SH +SNAKIT S N EY1 K IH0 T +SNAKY S N EY1 K IY0 +SNANA S N AE1 N AH0 +SNANDULIA S N AE1 N JH AH0 L Y AH0 +SNAP S N AE1 P +SNAP'S S N AE1 P S +SNAPDRAGON S N AE1 P D R AE2 G AH0 N +SNAPDRAGONS S N AE1 P D R AE2 G AH0 N Z +SNAPE S N EY1 P +SNAPHANCE S N AE1 F AH0 N S +SNAPP'D S N AE1 P D +SNAPPED S N AE1 P T +SNAPPER S N AE1 P ER0 +SNAPPERS S N AE1 P ER0 Z +SNAPPIER S N AE1 P IY0 ER0 +SNAPPIEST S N AE1 P IY0 AH0 S T +SNAPPILY S N AE1 P AH0 L IY0 +SNAPPIN S N AE1 P IH0 N +SNAPPING S N AE1 P IH0 NG +SNAPPINGS S N AE1 P IH0 NG Z +SNAPPISH S N AE1 P IH0 SH +SNAPPISHLY S N AE1 P IH0 SH L IY0 +SNAPPISHNESS S N AE1 P IH0 SH N AH0 S +SNAPPS S N AE1 P S +SNAPPY S N AE1 P IY0 +SNAPS S N AE1 P S +SNAPSHOT S N AE1 P SH AA2 T +SNAPSHOTS S N AE1 P SH AA2 T S +SNAPSHOTTED S N AE1 P SH AA2 T IH0 D +SNAPT S N AE1 P T +SNARBI S N AA1 R B IY0 +SNARE S N EH1 R +SNARED S N EH1 R D +SNARER S N EH1 R ER0 +SNARES S N EH1 R Z +SNARING S N EH1 R IH0 NG +SNARK S N AA1 R K +SNARL S N AA1 R L +SNARLED S N AA1 R AH0 L D +SNARLER S N AA1 R L ER0 +SNARLEY S N AA1 R L IY0 +SNARLEY'S S N AA1 R L IY0 Z +SNARLEYOW S N AA1 R L IY0 AW0 +SNARLEYYOW S N AA1 R L IY0 Y AW1 +SNARLIE S N AA1 R L IY0 +SNARLIN S N AA1 R L IH0 N +SNARLING S N AA1 R L IH0 NG +SNARLINGLY S N AA1 R L IH0 NG L IY0 +SNARLINGS S N AA1 R L IH0 NG Z +SNARLS S N AA1 R L Z +SNARLY S N AA1 R L IY0 +SNASS S N AE1 S +SNATCH S N AE1 CH +SNATCH'D S N AE1 CH IH0 D +SNATCHED S N AE1 CH T +SNATCHER S N AE1 CH ER0 +SNATCHERS S N AE1 CH ER0 Z +SNATCHES S N AE1 CH IH0 Z +SNATCHIN S N AE1 CH IH0 N +SNATCHING S N AE1 CH IH0 NG +SNATCHT S N AE1 CH T +SNATH S N AE1 TH +SNATTY S N AE1 T IY0 +SNAVELY S N EY1 V L IY0 +SNAW S N AO2 +SNAWDOR S N AA1 D ER0 +SNAWDOR'S S N AA1 D ER0 Z +SNAWDOUN S N AA1 D UW0 N +SNAWLEY S N AO1 L IY0 +SNEAD S N IY1 D +SNEAK S N IY1 K +SNEAK'S S N IY1 K S +SNEAKED S N IY1 K T +SNEAKER S N IY1 K ER0 +SNEAKERS S N IY1 K ER0 Z +SNEAKIN S N IY1 K IH0 N +SNEAKING S N IY1 K IH0 NG +SNEAKINGLY S N IY1 K IH0 NG L IY0 +SNEAKISH S N IY1 K IH0 SH +SNEAKS S N IY1 K S +SNEAKY S N IY1 K IY0 +SNEATH S N EH1 TH +SNEATHY S N EH1 TH IY0 +SNEATHY'S S N EH1 TH IY0 Z +SNECK S N EH1 K +SNECKER S N EH1 K ER0 +SNECKY S N EH1 T S K IY0 +SNEDDEN S N EH1 D AH0 N +SNEDECKER S N EH1 D IH0 K ER0 +SNEDEKER S N EH1 D IH0 K ER0 +SNEDEKER'S S N EH1 D IH0 K ER0 Z +SNEED S N IY1 D +SNEED'S S N IY1 D Z +SNEEK S N IY1 K +SNEEKINS S N IY1 K IH0 N Z +SNEER S N IH1 R +SNEERED S N IH1 R D +SNEERER S N IH1 R ER0 +SNEERERS S N IH1 R ER0 Z +SNEERIN S N IH1 R IH0 N +SNEERING S N IH1 R IH0 NG +SNEERINGLY S N IH1 R IH0 NG L IY0 +SNEERS S N IH1 R Z +SNEERWELL S N IH1 R W EH2 L +SNEEZE S N IY1 Z +SNEEZED S N IY1 Z D +SNEEZER S N IY1 Z ER0 +SNEEZES S N IY1 Z IH0 Z +SNEEZIN S N IY1 Z IH0 N +SNEEZING S N IY1 Z IH0 NG +SNEEZIT S N IY1 Z IH0 T +SNEEZUM S N IY1 Z AH0 M +SNEFERU S N EH1 F R UW0 +SNEFRU S N EH1 F R UW0 +SNEGIRYOV S N EH1 JH IH0 R Y AH0 V +SNELGROVE S N EH1 L G R OW2 V +SNELL S N EH1 L +SNELL'S S N EH1 L Z +SNELLGROVE S N EH1 L G R OW2 V +SNELLING S N EH1 L IH0 NG +SNELLING'S S N EH1 L IH0 NG Z +SNELLINGS S N EH1 L IH0 NG Z +SNELLIUS S N EH1 L IY0 IH0 S +SNELLS S N EH1 L Z +SNELSON S N EH1 L S AH0 N +SNEVELLICCI S N EH1 V AH0 L IY1 CH IY0 +SNEVELLICCI'S S N EH1 V AH0 L IY1 CH IY0 Z +SNGI S N JH IY0 +SNIATYNSKA S N AY1 AH0 T IH1 N S K AH0 +SNIATYNSKI S N AY1 AH0 T IH1 N S K IY0 +SNIATYNSKIS S N AY1 AH0 T IH1 N S K IY0 Z +SNICK S N IH0 K +SNICKED S N IH1 K T +SNICKER S N IH1 K ER0 +SNICKER'S S N IH1 K ER0 Z +SNICKERED S N IH1 K ER0 D +SNICKERIN S N IH1 K ER0 IH0 N +SNICKERING S N IH1 K ER0 IH0 NG +SNICKERS S N IH1 K ER0 Z +SNICKERSNEE S N IH1 K ER0 S N IY1 +SNICKING S N IH1 K IH0 NG +SNIDE S N AY1 D +SNIDER S N AY1 D ER0 +SNIDER'S S N AY1 D ER0 Z +SNIDERS S N AY1 D ER0 Z +SNIFF S N IH1 F +SNIFFED S N IH1 F T +SNIFFEN S N IH1 F AH0 N +SNIFFER S N IH1 F ER0 +SNIFFERS S N IH1 F ER0 Z +SNIFFIN S N IH1 F IH0 N +SNIFFING S N IH1 F IH0 NG +SNIFFINS S N IH1 F IH0 N Z +SNIFFLE S N IH1 F AH0 L +SNIFFLED S N IH1 F AH0 L D +SNIFFLES S N IH1 F AH0 L Z +SNIFFLIN S N IH1 F L IH0 N +SNIFFLING S N IH1 F L IH0 NG +SNIFFS S N IH1 F S +SNIFFY S N IH1 F IY0 +SNIFTER S N IH1 F T ER0 +SNIG S N IH1 G +SNIGGER S N IH1 G ER0 +SNIGGERED S N IH1 G ER0 D +SNIGGERING S N IH1 G ER0 IH0 NG +SNIGGERS S N IH1 G ER0 Z +SNIGGLE S N IH1 G AH0 L +SNIGGS S N IH1 G Z +SNIGSWORTH S N IH1 G Z W ER2 TH +SNIP S N IH1 P +SNIP'S S N IH1 P S +SNIPE S N AY1 P +SNIPE'S S N AY1 P S +SNIPED S N AY1 P T +SNIPER S N AY1 P ER0 +SNIPER'S S N AY1 P ER0 Z +SNIPERS S N AY1 P ER0 Z +SNIPES S N AY1 P S +SNIPING S N AY1 P IH0 NG +SNIPPED S N IH1 P T +SNIPPER S N IH1 P ER0 +SNIPPET S N IH1 P AH0 T +SNIPPETS S N IH1 P AH0 T S +SNIPPEY S N IH1 P IY0 +SNIPPING S N IH1 P IH0 NG +SNIPPINGS S N IH1 P IH0 NG Z +SNIPPY S N IH1 P IY0 +SNIPS S N IH1 P S +SNITCH S N IH1 CH +SNITCHED S N IH1 CH T +SNITCHEY S N IH1 CH IY0 +SNITKO S N IH1 T K OW0 +SNITTERFIELD S N IH1 T ER0 F IY2 L D +SNIVEL S N IH1 V AH0 L +SNIVELED S N IH1 V AH0 L D +SNIVELING S N IH1 V L IH0 NG +SNIVELLED S N IH1 V AH0 L D +SNIVELLER S N IH1 V AH0 L ER0 +SNIVELLIN S N IH1 V AH0 L IH0 N +SNIVELLING S N AY1 V L IH0 NG +SNIVELS S N IH1 V AH0 L Z +SNIVELY S N AY1 V L IY0 +SNIVEY S N AY1 V IY0 +SNJOLFUR S N JH OW0 L F ER0 +SNO S N OW1 +SNOB S N AA1 B +SNOB'S S N AA1 B Z +SNOBBE S N AA1 B +SNOBBERIES S N AA1 B ER0 IY0 Z +SNOBBERY S N AA1 B ER0 IY0 +SNOBBISH S N AA1 B IH0 SH +SNOBBISHLY S N AA1 B IH0 SH L IY0 +SNOBBISHNESS S N AA1 B IH0 SH N AH0 S +SNOBBISM S N AA1 B IH0 Z AH0 M +SNOBBS S N AA1 B Z +SNOBBY S N AA1 B IY0 +SNOBS S N AA1 B Z +SNOCKERTY S N AA1 K ER0 T IY0 +SNOD S N AA1 D +SNODGRASS S N AA1 D G R AE2 S +SNODGRASS'S S N AA1 D G R AE2 S IH0 Z +SNODSBURY S N AA1 D Z B EH2 R IY0 +SNOFRU S N AO1 F R UW0 +SNOHOMISH S N AA1 HH AH0 M IH0 SH +SNOILSKY S N OY2 L S K IY0 +SNOOD S N UW1 D +SNOODED S N UW1 D IH0 D +SNOODS S N UW1 D Z +SNOOK S N UH1 K +SNOOKER S N UH1 K ER0 +SNOOKIE S N UH1 K IY0 +SNOOKS S N UH1 K S +SNOOKSY S N UH1 K S IY0 +SNOOKUMS S N UH1 K AH0 M Z +SNOOKY S N UH1 K IY0 +SNOOP S N UW1 P +SNOOP'S S N UW1 P S +SNOOPED S N UW1 P T +SNOOPER S N UW1 P ER0 +SNOOPERS S N UW1 P ER0 Z +SNOOPIN S N UW1 P IH0 N +SNOOPING S N UW1 P IH0 NG +SNOOPOPATHIC S N UW1 P AH0 P AE1 TH AH0 K +SNOOPY S N UW1 P IY0 +SNOOT S N UW1 T +SNOOTS S N UW1 T S +SNOOTY S N UW1 T IY0 +SNOOZE S N UW1 Z +SNOOZED S N UW1 Z D +SNOOZER S N UW1 Z ER0 +SNOOZES S N UW1 Z IH0 Z +SNOOZIN S N UW1 Z IH0 N +SNOOZING S N UW1 Z IH0 NG +SNOOZLEUMS S N UW1 Z L IY1 AH0 M Z +SNOPPER S N AA1 P ER0 +SNOQUALMIE S N OW1 K W AA1 L M IY0 +SNOQUALMIES S N OW1 K W AA1 L M IY0 Z +SNORE S N AO1 R +SNORED S N AO1 R D +SNORER S N AO1 R ER0 +SNORERS S N AO1 R ER0 Z +SNORES S N AO1 R Z +SNORIN S N AO1 R IH0 N +SNORING S N AO1 R IH0 NG +SNORINGS S N AO1 R IH0 NG Z +SNORKEL S N AO1 R K AH0 L +SNORRI S N AO1 R IY0 +SNORRO S N AO1 R OW0 +SNORRO'S S N AO1 R OW0 Z +SNORT S N AO1 R T +SNORTED S N AO1 R T IH0 D +SNORTER S N AO1 R T ER0 +SNORTIN S N AO1 R T IH0 N +SNORTING S N AO1 R T IH0 NG +SNORTINGS S N AO1 R T IH0 NG Z +SNORTS S N AO1 R T S +SNORTY S N AO1 R T IY0 +SNOT S N AO1 T +SNOTTIES S N AA1 T IY0 Z +SNOTTY S N AA1 T IY0 +SNOUT S N AW1 T +SNOUTED S N AW1 T IH0 D +SNOUTS S N AW1 T S +SNOW S N OW1 +SNOW'LL S N OW1 L +SNOW'S S N OW1 Z +SNOWBALL S N OW1 B AO2 L +SNOWBALL'S S N OW1 B AO2 L Z +SNOWBALLED S N OW1 B AO2 L D +SNOWBALLING S N OW1 B AO2 L IH0 NG +SNOWBALLS S N OW1 B AO2 L Z +SNOWBANK S N OW1 B AE2 NG K +SNOWBANKS S N OW1 B AH0 NG K S +SNOWBERRY S N OW1 B EH2 R IY0 +SNOWBIRD S N OW1 B ER2 D +SNOWBIRD'S S N OW1 B ER2 D Z +SNOWBIRDS S N OW1 B ER2 D Z +SNOWBOUND S N OW1 B AW2 N D +SNOWCAPPED S N OW1 K AE2 P T +SNOWCLAD S N OW1 K L AE2 D +SNOWDEN S N OW1 D AH0 N +SNOWDEN'S S N OW1 D AH0 N Z +SNOWDON S N OW1 D AH0 N +SNOWDON'S S N OW1 D AH0 N Z +SNOWDOWN S N OW1 D AW2 N +SNOWDRIFT S N OW1 D R IH2 F T +SNOWDRIFTS S N OW1 D R IH1 F T S +SNOWDROP S N OW1 D R AA2 P +SNOWDROP'S S N OW1 D R AA1 P S +SNOWDROPS S N OW1 D R AA2 P S +SNOWE S N OW1 +SNOWED S N OW1 D +SNOWES S N OW1 Z +SNOWEY S N OW1 IY0 +SNOWFALL S N OW1 F AA0 L +SNOWFALLS S N OW1 F AA0 L Z +SNOWFIELD S N OW1 F IY2 L D +SNOWFIELDS S N OW1 F IY2 L D Z +SNOWFLAKE S N OW1 F L EY2 K +SNOWFLAKES S N OW1 F L EY2 K S +SNOWFLOWER S N OW1 F L AW2 ER0 +SNOWFOOT S N OW1 F UH2 T +SNOWFORD S N OW1 F ER0 D +SNOWHOUSE S N OW1 HH AW2 S +SNOWIE S N OW1 IY0 +SNOWIEST S N OW1 IY0 AH0 S T +SNOWILY S N OW1 AH0 L IY0 +SNOWIN S N OW1 IH0 N +SNOWING S N OW1 IH0 NG +SNOWLESS S N OW1 L AH0 S +SNOWLINE S N OW1 L AY0 N +SNOWMAN S N OW1 M AE2 N +SNOWPLOW S N OW1 P L AW2 +SNOWS S N OW1 Z +SNOWSHOE S N OW1 SH UW2 +SNOWSHOED S N OW1 SH UW2 D +SNOWSHOEING S N OW1 SH UW2 IH0 NG +SNOWSHOES S N OW1 SH UW2 Z +SNOWSLIDE S N OW1 S L AY2 D +SNOWSLIDES S N OW1 S L AY2 D Z +SNOWSTORM S N OW1 S T AO2 R M +SNOWSTORMS S N OW1 S T AO2 R M Z +SNOWY S N OW1 IY0 +SNUB S N AH1 B +SNUBBED S N AH1 B D +SNUBBIN S N AH1 B IH0 N +SNUBBING S N AH1 B IH0 NG +SNUBBINGS S N AH1 B IH0 NG Z +SNUBBINS S N AH1 B IH0 N Z +SNUBBLE S N AH1 B AH0 L +SNUBBY S N AH1 B IY0 +SNUBS S N AH1 B Z +SNUCK S N AH1 K 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+SOUTHWORTH'S S AW1 TH W ER0 TH S +SOUTIEN S AW1 SH AH0 N +SOUVAROFF S UW2 V EH0 R AO0 F +SOUVENEZ S UW2 V AH0 N EH1 Z +SOUVENIR S UW2 V AH0 N IH1 R +SOUVENIRS S UW2 V AH0 N IH1 R Z +SOUVENT S UW2 V AH0 N T +SOUVERAIN S UW1 V ER0 EY1 N +SOUVESTRE S UW2 V EH1 S T ER0 +SOUVIENS S UW2 V IY0 AH0 N Z +SOUWANAS S AW1 W AA1 N AH0 Z +SOUX S UW1 +SOUYOU S OW1 AY1 UW0 +SOUZA S UW1 Z AH0 +SOUZA'S S UW1 Z AH0 Z +SOV S AA1 V +SOV'REIGN S OW2 V R AH0 N +SOVERAIGN S OW2 V ER0 EY1 N +SOVERAIGNE S AA1 V R EY2 N +SOVEREIGN S AA1 V R AH0 N +SOVEREIGN'S S AA1 V R AH0 N Z +SOVEREIGNLY S AA1 V R AH0 N L IY0 +SOVEREIGNS S AA1 V R AH0 N Z +SOVEREIGNTIES S AA1 V R AH0 N T IY0 Z +SOVEREIGNTY S AA1 V R AH0 N T IY0 +SOVIET S OW1 V IY0 AH0 T +SOVIET S OW1 V IY0 EH2 T +SOVIETS S OW1 V IY0 EH2 T S +SOVRAN S AA1 V R AH0 N +SOVRANTY S AA1 V R AH0 N T IY0 +SOVS S AA1 V Z +SOW S AW1 +SOW S OW1 +SOW'D S AW1 D +SOW'S S OW1 Z +SOWANS S OW1 AH0 N Z +SOWAR S OW1 ER0 +SOWARS S OW1 ER0 Z +SOWBELLY S AW1 B EH2 L IY0 +SOWED S AW1 D +SOWED S OW1 D +SOWELL S AA1 W EH0 L +SOWENS S OW1 AH0 N Z +SOWER S OW1 ER0 +SOWER'S S OW1 ER0 Z +SOWERBERRY S AW1 ER0 B EH2 R IY0 +SOWERBY S AW1 ER0 B IY0 +SOWERBY'S S AW1 ER0 B IY0 Z +SOWERS S OW1 ER0 Z +SOWEST S OW1 AH0 S T +SOWETH S AW1 IH0 TH +SOWIN S OW1 IH0 N +SOWING S OW1 IH0 NG +SOWINGS S OW1 IH0 NG Z +SOWINSKA S OW0 IH1 N S K AH0 +SOWINSKI S OW0 IH1 N S K IY0 +SOWINSKI'S S OW0 IH1 N S K IY0 Z +SOWLER S OW1 L ER0 +SOWLES S OW1 L Z +SOWLS S OW1 L Z +SOWN S OW1 N +SOWNDS S OW1 N D Z +SOWNE S OW1 N +SOWS S OW1 Z +SOWSHIP S OW1 SH IH2 P +SOWWY S OW1 UW1 IY0 +SOX S AA1 K S +SOY S OY1 +SOYER S OY1 ER0 +SOYERA S OY1 IH1 R AH0 +SOYEZ S OY1 EH0 Z +SOYLAND S OY1 L AH0 N D +SOYLE S OY1 L +SOYONS S OY1 AH0 N Z +SOYOT S OY1 OW0 +SOYOTS S OY1 AH0 T S +SOZANA S OW1 Z AE1 N AH0 +SOZOMEN S OW1 Z OW1 M AH0 N +SOZONT S OW1 Z OW0 N T +SP S P +SP'ILED S P IH0 Z AY2 L D +SP'ILIN S P L IH0 N +SP'ILT S P IH2 L T +SPA S P AA1 +SPAAKENBERG S P AA1 K AH0 N B ER0 G +SPABOROUGH S P EY1 B ER0 OW0 +SPACE S P EY1 S +SPACEBOAT S P EY1 S B OW2 T +SPACEBOY S P EY1 S B OY2 +SPACECRAFT S P EY1 S K R AE2 F T +SPACED S P EY1 S T +SPACEDRIVE S P EY1 S D R AY2 V +SPACEFIELD S P EY1 S F IY2 L D +SPACEFORCE S P EY1 S F AO0 R S +SPACEHOUND S P EY1 S HH AW2 N D +SPACEHOUNDS S P EY1 S HH AW2 N D Z +SPACELAND S P EY1 S L AE0 N D +SPACELANES S P EY1 S L EY2 N Z +SPACELINES S P EY1 S L AY2 N Z +SPACEMAN S P EY1 S M AE2 N +SPACEMAN'S S P EY1 S M AH0 N Z +SPACEMEN S P EY1 S M AH0 N +SPACEPORT S P EY1 S P AO2 R T +SPACEPORTS S P EY1 S P AO2 R T S +SPACER S P EY1 S ER0 +SPACER'S S P EY1 S ER0 Z +SPACERS S P EY1 S ER0 Z +SPACERTOWN S P EY1 S ER0 T AW2 N +SPACES S P EY1 S AH0 Z +SPACES S P EY1 S IH0 Z +SPACESHIP S P EY1 S SH IH2 P +SPACESHIP'S S P EY1 S SH IH2 P S +SPACESHIPS S P EY1 S SH IH2 P S +SPACESUIT S P EY1 S UW2 T +SPACESUITED S P EY1 S UW2 T AH0 D +SPACESUITS S P EY1 S UW2 T S +SPACEWAYS S P EY1 S W EY2 Z +SPACHE S P AE1 CH IY0 +SPACING S P EY1 S IH0 NG +SPACIOUS S P EY1 SH AH0 S +SPACIOUSLY S P EY1 SH AH0 S L IY0 +SPACIOUSNESS S P EY1 SH AH0 S N AH0 S +SPAD S P AE0 D +SPADA S P AA1 D AH0 +SPADASSIN S P AA0 D AA0 S IH0 N +SPADASSINS S P AA0 D AA0 S IY0 N Z +SPADDEN S P AE1 D AH0 N +SPADE S P EY1 D +SPADE'S S P EY1 D Z +SPADED S P EY1 D IH0 D +SPADEFUL S P EY1 D F AH0 L +SPADEFULS S P EY1 D F AH0 L Z +SPADES S P EY1 D Z +SPADEVIL S P AA0 D EH1 V AH0 L +SPADILLE S P AA0 D IH1 L +SPADINA S P AA0 D IY1 N AH0 +SPADING S P EY1 D IH0 NG +SPADS S P AA0 D Z +SPAE S P EY1 +SPAEWIFE S P EY1 W AY2 F +SPAFF S P AE1 F +SPAFFORD S P AE1 F ER0 D +SPAFIELDS S P AE1 F IY2 L D Z +SPAGHETTI S P AH0 G EH1 T IY0 +SPAGNA S P AE1 G N AH0 +SPAGNOLETTO S P AA0 G N OW1 L EH1 T OW0 +SPAHI S P AA1 HH IY0 +SPAHIS S P AA1 IH0 S +SPAIGHT S P EY1 T +SPAIN S P EY1 N +SPAIN'S S P EY1 N Z +SPAINS S P EY1 N Z +SPAK S P AE1 K +SPAKE S P EY1 K +SPAKES S P EY1 K S +SPAKEST S P EY1 K AH0 S T +SPAKIN S P AE1 K IH0 N +SPAKING S P EY1 K IH0 NG +SPALATIN S P AE1 L AH0 T AH0 N +SPALATO S P AA0 L AA1 T OW0 +SPALATRO S P AE1 L AH0 T R OW0 +SPALDING S P AO1 L D IH0 NG +SPALDING'S S P AO1 L D IH0 NG Z +SPALL S P AO1 L +SPALLANZANI S P AA0 L AA0 N Z AA1 N IY0 +SPALPANE S P AA0 L P AA1 N EY0 +SPALPEEN S P AA1 L P IY0 N +SPALPEENS S P AA1 L P IY1 N Z +SPALTON S P AO1 L T AH0 N +SPALTON'S S P AO1 L T AH0 N Z +SPAN S P AE1 N +SPAN'S S P AE1 N Z +SPANA S P AE1 N Y AH0 +SPANCIOC S P AE1 NG K IH0 AA0 K +SPANDAU S P AE1 N D AW0 +SPANDRELS S P AE1 N D R AH0 L Z +SPANDRILS S P AE1 D R AH0 L Z +SPANDY S P AE1 N D IY0 +SPANG S P AE1 NG +SPANGENBERG S P AE1 NG AH0 N B ER0 G +SPANGENBERG'S S P AE1 NG AH0 N B ER0 G Z +SPANGLE S P AE1 NG G AH0 L +SPANGLED S P AE1 NG G AH0 L D +SPANGLER S P AE1 NG G AH0 L ER0 +SPANGLER S P AE1 NG G L ER0 +SPANGLER'S S P AE1 NG G AH0 L ER0 Z +SPANGLES S P AE1 NG G AH0 L Z +SPANGLING S P AE1 NG G AH0 L IH0 NG +SPANGLY S P AE1 NG L IY0 +SPANHEIM S P AE1 N HH AY0 M +SPANIARD S P AE1 N Y ER0 D +SPANIARD'S S P AE1 N Y ER0 D Z +SPANIARDS S P AE1 N Y ER0 D Z +SPANIEL S P AE1 N Y AH0 L +SPANIEL'S S P AE1 N Y AH0 L Z +SPANIELS S P AE1 N Y AH0 L Z +SPANISCHE S P AE1 N IH0 SH IY0 +SPANISH S P AE1 N IH0 SH +SPANISHERS S P AE1 N IH0 SH ER0 Z +SPANK S P AE1 NG K +SPANKED S P AE1 NG K T +SPANKER S P AE1 NG K ER0 +SPANKIN S P AE1 NG K IH0 N +SPANKING S P AE1 NG K IH0 NG +SPANKINGS S P AE1 NG K IH0 NG Z +SPANKS S P AE1 NG K S +SPANN'D S P AE1 N D +SPANNED S P AE1 N D +SPANNER S P AE1 N ER0 +SPANNERS S P AE1 N ER0 Z +SPANNESS S P AE1 N AH0 S +SPANNING S P AE1 N IH0 NG +SPANO S P AA1 N OW0 +SPANS S P AE1 N Z +SPANTZ S P AE1 N T S +SPAR S P AA1 R +SPAR'D S P ER0 D +SPARE S P EH1 R +SPARED S P EH1 R D +SPARELY S P EH1 R L IY0 +SPARENECK S P EH1 R AH0 N EH2 K +SPARENESS S P EH1 R N AH0 S +SPARER S P EH1 R ER0 +SPARERIB S P ER0 IH0 R IH1 B +SPARERIBS S P ER0 IH0 R IH1 B Z +SPARES S P EH1 R Z +SPAREST S P ER0 IH0 S T +SPARETH S P AA1 R IH0 TH +SPARFLIGHT S P AA1 R F L AY2 T +SPARGO S P AA1 R G OW0 +SPARGO'S S P AA1 R G OW0 Z +SPARHAWK S P AA1 R HH AO2 K +SPARHAWK'S S P AA1 R HH AO2 K S +SPARICIO S P ER0 IH1 S IY0 OW0 +SPARIN S P ER0 IH0 N +SPARING S P EH1 R IH0 NG +SPARINGLY S P EH1 R IH0 NG L IY0 +SPARK S P AA1 R K +SPARK'S S P AA1 R K S +SPARKE S P AA1 R K +SPARKED S P AA1 R K T +SPARKEION S P AA1 R K IY0 AH0 N +SPARKES S P AA1 R K S +SPARKIN S P AA1 R K IH0 N +SPARKING S P AA1 R K IH0 NG +SPARKINS S P AA1 R K IH0 N Z +SPARKISH S P AA1 R K IH0 SH +SPARKLE S P AA1 R K AH0 L +SPARKLE'S S P AA1 R K AH0 L Z +SPARKLED S P AA1 R K AH0 L D +SPARKLER S P AA1 R K AH0 L ER0 +SPARKLER'S S P AA1 R K AH0 L ER0 Z +SPARKLERS S P AA1 R K AH0 L ER0 Z +SPARKLES S P AA1 R K AH0 L Z +SPARKLET S P AA1 R K L IH0 T +SPARKLIN S P AA1 R K L IH0 N +SPARKLING S P AA1 R K AH0 L IH0 NG +SPARKLING S P AA1 R K L IH0 NG +SPARKLINGLY S P AA1 R K AH0 L IH0 NG L IY0 +SPARKLINGS S P AA1 R K AH0 L IH0 NG Z +SPARKLY S P AA1 R K L IY0 +SPARKS S P AA1 R K S +SPARKS'S S P AA1 R K S IH0 Z +SPARKY S P AA1 R K IY0 +SPARLING S P AA1 R L IH0 NG +SPARMIENTO S P AA1 R M IY0 EH1 N T OW0 +SPARNHEIM S P AA1 R N HH AY0 M +SPARRE S P AA1 R +SPARRED S P AA1 R D +SPARRELL S P EH1 R AH0 L +SPARRER S P AA1 R ER0 +SPARRERS S P AA1 R ER0 Z +SPARRIN S P EH1 R IH0 N +SPARRING S P AA1 R IH0 NG +SPARROW S P EH1 R OW0 +SPARROW'S S P EH1 R OW0 Z +SPARROWGRASS S P EH1 R OW0 G R AE2 S +SPARROWHAWK S P EH1 R OW0 HH AO2 K +SPARROWHAWKS S P EH1 R OW0 HH AO2 K S +SPARROWS S P EH1 R OW0 Z +SPARRY S P EH1 R IY0 +SPARS S P AA1 R Z +SPARSE S P AA1 R S +SPARSELY S P AA1 R S L IY0 +SPARSENESS S P AA1 R S N AH0 S +SPARSER S P AA1 R S ER0 +SPARSHOT S P AA1 R SH AA2 T +SPARSITY S P AA1 R S IH0 T IY0 +SPARTA S P AA1 R T AH0 +SPARTA'S S P AA1 R T AH0 Z +SPARTACUS S P AA1 R T AH0 K AH0 S +SPARTAN S P AA1 R T AH0 N +SPARTAN'S S P AA1 R T AH0 N Z +SPARTANBURG S P AA1 R T AH0 N B ER0 G +SPARTANE S P AA1 R T AH0 N +SPARTANS S P AA1 R T AH0 N Z +SPARTEL S P AA1 R T AH0 L +SPARTIATE S P AA2 R SH IY0 EY2 T +SPARTIVENTO S P AA1 R T AH0 V EY0 N T OW0 +SPAS S P AA1 Z +SPASH S P AE1 SH +SPASM S P AE1 Z AH0 M +SPASMODIC S P AE0 Z M AO1 D IH0 K +SPASMODICALLY S P AE0 Z M AO1 D IH0 K L IY0 +SPASMS S P AE1 Z AH0 M Z +SPASSKII S P AE1 S K IY0 +SPASSO S P AE1 S OW0 +SPAT S P AE1 T +SPATCH S P AE1 CH +SPATE S P EY1 T +SPATES S P EY1 T S +SPATH S P AE1 TH +SPATHE S P EY1 DH +SPATHES S P EY1 DH Z +SPATIAL S P EY1 SH AH0 L +SPATIALLY S P AA1 SH AH0 L IY0 +SPATOLA S P AA0 T OW1 L AH0 +SPATOLINO S P EY1 T OW0 L IY1 N OW0 +SPATS S P AE1 T S +SPATTED S P AE1 T IH0 D +SPATTER S P AE1 T ER0 +SPATTERDASHES S P AE1 T ER0 D AE2 SH AH0 Z +SPATTERED S P AE1 T ER0 D +SPATTERING S P AE1 T ER0 IH0 NG +SPATTERINGS S P AE1 T ER0 IH0 NG Z +SPATTERS S P AE1 T ER0 Z +SPATTING S P AE1 T IH0 NG +SPATULA S P AE1 CH UH0 L AH0 +SPATULATE S P AE1 CH AH0 L EY2 T +SPATZIG S P AE1 T Z IH0 G +SPAULDING S P AO1 L D IH0 NG +SPAULDING'S S P AO1 L D IH0 NG Z +SPAULDINGS S P AO1 L D IH0 NG Z +SPAVIN S P EY1 V IH0 N +SPAVINED S P AE1 V AH0 N D +SPAVINHITCH S P AA0 V IH1 N HH IH1 CH +SPAVINS S P AE1 V IH0 N Z +SPAW S P AO1 +SPAWN S P AA1 N +SPAWN S P AO1 N +SPAWN'S S P AO1 N Z +SPAWNED S P AO1 N D +SPAWNING S P AA1 N IH0 NG +SPAWNING S P AO1 N IH0 NG +SPAWNS S P AA1 N Z +SPAWNS S P AO1 N Z +SPAYED S P EY1 D +SPAZZO S P AA1 Z OW0 +SPE S P +SPEACH S P IY1 CH +SPEAIGHT S P IY1 AY2 T +SPEAK S P IY1 K +SPEAK'ST S P IY1 K S T +SPEAKABLE S P IY1 K AH0 B AH0 L +SPEAKE S P IY1 K +SPEAKEASY S P IY1 K IY1 S IY0 +SPEAKEE S P IY1 K IY0 +SPEAKER S P IY1 K ER0 +SPEAKER'S S P IY1 K ER0 Z +SPEAKERS S P IY1 K ER0 Z +SPEAKERSHIP S P IY1 K ER0 SH IH2 P +SPEAKEST S P IY1 K AH0 S T +SPEAKETH S P IY1 K AH0 TH +SPEAKIN S P IY1 K IH0 N +SPEAKIN' S P IY1 K IH0 N +SPEAKING S P IY1 K IH0 NG +SPEAKINGLY S P IY1 K IH0 NG L IY0 +SPEAKINGS S P IY1 K IH0 NG Z +SPEAKS S P IY1 K S +SPEAKUM S P IY1 K AH0 M +SPEAN S P IY1 AH0 N +SPEAR S P IH1 R +SPEAR'S S P IH1 R Z +SPEARE S P IY1 R +SPEARED S P IH1 R D +SPEARES S P IH1 R Z +SPEARHEAD S P IH1 R HH EH2 D +SPEARHEADS S P IH1 R HH EH2 D Z +SPEARING S P IH1 R IH0 NG +SPEARMAN S P IH1 R M AH0 N +SPEARMAN'S S P IH1 R M AH0 N Z +SPEARMEN S P IH1 R M AH0 N +SPEARMINT S P IH1 R M IH0 N T +SPEARS S P IH1 R Z +SPEARSMAN S P IH1 R S M AH0 N +SPEARSMEN S P IH1 R Z M IH0 N +SPEARWOMEN S P IH1 R W IH2 M EH0 N +SPEC S P EH1 K +SPEC'S S P EH0 K S +SPECCHI S P EH1 K IY0 +SPECEIS S P IH0 S IY1 Z +SPECES S P EH1 S IH0 Z +SPECHT S P EH1 K T +SPECIAL S P EH1 SH AH0 L +SPECIAL'S S P EH1 SH AH0 L Z +SPECIALE S P EH1 CH AH0 L IY0 +SPECIALISATION S P EH1 SH AH0 L IH0 Z EY1 SH AH0 N +SPECIALISE S P EH1 SH AH0 L IY1 S +SPECIALISED S P EH1 SH AH0 L AH0 S T +SPECIALISES S P EH1 SH AH0 L AH0 S AH0 Z +SPECIALISING S P EH1 SH AH0 L AY2 Z IH0 NG +SPECIALISM S P EH1 SH AH0 L IH2 Z AH0 M +SPECIALIST S P EH1 SH AH0 L AH0 S T +SPECIALIST S P EH1 SH AH0 L IH0 S T +SPECIALIST'S S P EH1 SH AH0 L AH0 S T S +SPECIALISTIC S P EH1 SH AH0 L IH1 S T IH0 K +SPECIALISTS S P EH1 SH AH0 L AH0 S T S +SPECIALISTS S P EH1 SH AH0 L IH0 S T S +SPECIALITE S P EH1 SH AH0 L AY2 T +SPECIALITIES S P EH1 SH AH0 L T IY0 Z +SPECIALITY S P EH2 SH IY0 AE1 L IH0 T IY0 +SPECIALIZATION S P EH2 SH AH0 L AH0 Z EY1 SH AH0 N +SPECIALIZATIONS S P EH1 SH AH0 L IH0 Z EY1 SH AH0 N Z +SPECIALIZE S P EH1 SH AH0 L AY2 Z +SPECIALIZED S P EH1 SH AH0 L AY2 Z D +SPECIALIZES S P EH1 SH AH0 L AY2 Z AH0 Z +SPECIALIZES S P EH1 SH AH0 L AY2 Z IH0 Z +SPECIALIZING S P EH1 SH AH0 L AY2 Z IH0 NG +SPECIALL S P EH1 SH AH0 L +SPECIALLY S P EH1 SH AH0 L IY0 +SPECIALLY S P EH1 SH L IY0 +SPECIALS S P EH1 SH AH0 L Z +SPECIALTIES S P EH1 SH AH0 L T IY0 Z +SPECIALTY S P EH1 SH AH0 L T IY0 +SPECIALTY S P EY1 SH AH0 L T IY0 +SPECIE S P IY1 SH IY0 +SPECIES S P IY1 SH IY0 Z +SPECIFIC S P AH0 S IH1 F IH0 K +SPECIFIC S P IH0 S IH1 F IH0 K +SPECIFICALLY S P AH0 S IH1 F IH0 K L IY0 +SPECIFICATION S P EH2 S IH0 F IH0 K EY1 SH AH0 N +SPECIFICATIONS S P EH2 S AH0 F AH0 K EY1 SH AH0 N Z +SPECIFICK S P EH2 S AH0 F IH1 K +SPECIFICS S P IH0 S IH1 F IH0 K S +SPECIFIED S P EH1 S AH0 F AY2 D +SPECIFIES S P EH1 S AH0 F AY2 Z +SPECIFY S P EH1 S AH0 F AY2 +SPECIFYING S P EH1 S AH0 F AY2 IH0 NG +SPECIMEN S P EH1 S AH0 M AH0 N +SPECIMENS S P EH1 S AH0 M AH0 N Z +SPECIOSA S P IY1 SH IY0 OW1 S AH0 +SPECIOSUM S P IY1 SH IY0 OW2 Z AH0 M +SPECIOUS S P IY1 SH AH0 S +SPECIOUSLY S P EH1 SH AH0 S L IY0 +SPECIOUSNESS S P EH1 SH AH0 S N AH0 S +SPECIS S P EH1 S AH0 S +SPECK S P EH1 K +SPECKED S P EH1 K T +SPECKLE S P EH1 K AH0 L +SPECKLED S P EH1 K AH0 L D +SPECKLEMS S P EH1 K L AH0 M Z +SPECKLES S P EH1 K AH0 L Z +SPECKLESS S P EH1 K L AH0 S +SPECKLESSLY S P EH1 K L AH0 S L IY0 +SPECKLING S P EH1 K AH0 L IH0 NG +SPECKLY S P EH1 K L IY0 +SPECKPORT S P EH1 K P AO2 R T +SPECKS S P EH1 K S +SPECKSIONEER S P EH1 K S AY2 AH0 N IH1 R +SPECO S P EH1 K OW0 +SPECS S P EH1 K S +SPECT S P EH1 K T +SPECTABILIS S P EH1 K T AH0 B IH1 L AH0 S +SPECTABLE S P EH1 K T AH0 B AH0 L +SPECTACLE S P EH1 K T AH0 K AH0 L +SPECTACLED S P EH1 K T AH0 K AH0 L D +SPECTACLES S P EH1 K T AH0 K AH0 L Z +SPECTACULAR S P EH0 K T AE1 K Y AH0 L ER0 +SPECTACULARLY S P EH0 K T AE1 K Y AH0 L ER0 L IY0 +SPECTATOR S P EH1 K T EY0 T ER0 +SPECTATOR'S S P EH1 K T EY0 T ER0 Z +SPECTATORIAL S P EH1 K T AH0 T AO1 R IY0 AH0 L +SPECTATORS S P EH1 K T EY0 T ER0 Z +SPECTATORSHIP S P EH1 K T EY0 T ER0 SH IH2 P +SPECTATRESS S P EH0 K T AE1 T R IH0 S +SPECTED S P EH1 K T IH0 D +SPECTER S P EH1 K T ER0 +SPECTERS S P EH1 K T ER0 Z +SPECTIN S P EH1 K T IH0 N +SPECTIONEER S P EH1 K SH AH0 N IH1 R +SPECTRA S P EH1 K T R AH0 +SPECTRAL S P EH1 K T R AH0 L +SPECTRALLY S P EH1 K T R AH0 L IY0 +SPECTRE S P EH1 K T ER0 +SPECTRE'S S P EH1 K T ER0 Z +SPECTRES S P EH1 K T ER0 Z +SPECTRO S P EH1 K T R OW0 +SPECTROSCOPE S P EH1 K T R AH0 S K OW2 P +SPECTROSCOPIC S P EH1 K T R AH0 S K AA1 P IH0 K +SPECTRUM S P EH1 K T R AH0 M +SPECTS S P EH1 K S +SPECULA S P EH1 K Y AH0 L AH0 +SPECULATE S P EH1 K Y AH0 L EY2 T +SPECULATED S P EH1 K Y AH0 L EY2 T AH0 D +SPECULATED S P EH1 K Y AH0 L EY2 T IH0 D +SPECULATES S P EH1 K Y AH0 L EY2 T S +SPECULATIN S P EH1 K Y AH0 L AH0 T IH0 N +SPECULATING S P EH1 K Y AH0 L EY2 T IH0 NG +SPECULATION S P EH2 K Y AH0 L EY1 SH AH0 N +SPECULATIONS S P EH2 K Y AH0 L EY1 SH AH0 N Z +SPECULATIST S P EH1 K Y AH0 L AH0 T IH0 S T +SPECULATISTS S P EH1 K Y AH0 L AH0 T IH0 S T S +SPECULATIVE S P EH1 K Y AH0 L AH0 T IH0 V +SPECULATIVELY S P EH1 K Y AH0 L EY2 T IH0 V L IY0 +SPECULATOR S P EH1 K Y AH0 L EY2 T ER0 +SPECULATOR'S S P EH1 K Y AH0 L EY2 T ER0 Z +SPECULATORS S P EH1 K Y AH0 L EY2 T ER0 Z +SPECULUM S P EH1 K Y AH0 L AH0 M +SPED S P EH1 D +SPEDDING S P EH1 D IH0 NG +SPEDDING'S S P EH1 D IH0 NG Z +SPEE S P IY1 +SPEE'S S P IY1 Z +SPEECH S P IY1 CH +SPEECHES S P IY1 CH AH0 Z +SPEECHES S P IY1 CH IH0 Z +SPEECHIFY S P IY1 CH AH0 F AY2 +SPEECHIFYING S P IY1 CH AH0 F AY2 IH0 NG +SPEECHLESS S P IY1 CH L AH0 S +SPEECHLESSLY S P IY1 CH L AH0 S L IY0 +SPEECHLESSNESS S P IY1 CH L AH0 S N AH0 S +SPEECHMAKER S P IY1 CH M EY2 K ER0 +SPEECHMAKING S P IY1 CH M EY2 K IH0 NG +SPEED S P IY1 D +SPEED'S S P IY1 D Z +SPEEDAWAY S P IY1 D AH0 W EY2 +SPEEDBOAT S P IY1 D B OW2 T +SPEEDE S P IY1 D +SPEEDED S P IY1 D IH0 D +SPEEDER S P IY1 D ER0 +SPEEDERS S P IY1 D ER0 Z +SPEEDETH S P IY1 D IH0 TH +SPEEDIER S P IY1 D IY0 ER0 +SPEEDIEST S P IY1 D IY0 IH0 S T +SPEEDILIE S P IY1 D AH0 L IY0 +SPEEDILY S P IY1 D AH0 L IY0 +SPEEDIN S P IY1 D AH0 N +SPEEDINESS S P IY1 D IY0 N AH0 S +SPEEDING S P IY1 D IH0 NG +SPEEDOMETER S P IY0 D AA1 M AH0 T ER0 +SPEEDS S P IY1 D Z +SPEEDSTER S P IY1 D S T ER0 +SPEEDSTERS S P IY1 D S T ER0 Z +SPEEDWAY S P IY1 D W EY2 +SPEEDWELL S P IY1 D W EH2 L +SPEEDWELL'S S P IY1 D W EH2 L Z +SPEEDWELLS S P IY1 D W EH2 L Z +SPEEDY S P IY1 D IY0 +SPEEDY'S S P IY1 D IY0 Z +SPEEK S P IY1 K +SPEEL S P IY1 L +SPEEN S P IY1 N +SPEER S P IH1 R +SPEERED S P IH1 R D +SPEERIN S P IH1 R IH0 N +SPEERING S P IH1 R IH0 NG +SPEERIT S P IY1 R IH0 T +SPEERITS S P IY1 R IH0 T S +SPEERITUAL S P IY1 R IH0 CH AH0 W AH0 L +SPEERS S P IH1 R Z +SPEIDEL S P AY1 D AH0 L +SPEIGHT S P EY1 T +SPEIK S P AY1 K +SPEIR S P IH1 R +SPEIRED S P AY1 ER0 D +SPEIRIN S P IH1 R IH0 N +SPEIRING S P AY1 R IH0 NG +SPEIRS S P IH1 R Z +SPEKE S P IY1 K +SPEKE'S S P EH1 K S +SPELING S P AH0 L IH0 NG +SPELL S P EH1 L +SPELL'S S P EH1 L Z +SPELLBINDER S P EH1 L B AY2 N D ER0 +SPELLBOUND S P EH1 L B AW2 N D +SPELLED S P EH1 L D +SPELLER S P EH1 L ER0 +SPELLERS S P EH1 L ER0 Z +SPELLIN S P EH1 L IH0 N +SPELLIN' S P EH1 L IH0 N +SPELLING S P EH1 L IH0 NG +SPELLINGS S P EH1 L IH0 NG Z +SPELLMAN S P EH1 L M AH0 N +SPELLO S P EH1 L OW0 +SPELLS S P EH1 L Z +SPELMAN S P EH1 L M AH0 N +SPELT S P EH1 L T +SPELTER S P EH1 L T ER0 +SPELTZ S P EH1 L T S +SPELVIN S P EH1 L V IH0 N +SPELW S P IY1 L +SPELWANE S P AH0 L W EY0 N +SPEM S P EH0 M +SPEN S P AH0 N +SPENCE S P EH1 N S +SPENCE'S S P EH1 N S IH0 Z +SPENCER S P EH1 N S ER0 +SPENCER'S S P EH1 N S ER0 Z +SPENCERIAN S P EH1 N S EH1 R IY0 AH0 N +SPENCERS S P EH1 N S ER0 Z +SPENCERVALE S P EH1 N S ER0 V EY2 L +SPEND S P EH1 N D +SPENDALL S P EH1 N D AH0 L +SPENDED S P EH1 N D AH0 D +SPENDER S P EH1 N D ER0 +SPENDERS S P EH1 N D ER0 Z +SPENDETH S P EH1 N D IH0 TH +SPENDIN S P EH1 N D IH0 N +SPENDING S P EH1 N D IH0 NG +SPENDINGS S P EH1 N D IH0 NG Z +SPENDIUS S P EH1 N D IY0 AH0 S +SPENDQUICK S P EH1 N D K W IH2 K +SPENDS S P EH1 N D Z +SPENDS S P EH1 N Z +SPENDTHRIFT S P EH1 N D TH R IH2 F T +SPENDTHRIFT'S S P EH1 N D TH R IH2 F T S +SPENDTHRIFTS S P EH1 N D TH R IH1 F T S +SPENER S P AH0 N ER0 +SPENER'S S P EH1 N ER0 Z +SPENERSBERG S P EH1 N ER0 S B ER0 G +SPENGLER S P IH1 NG AH0 L ER0 +SPENGLER S P IH1 NG L ER0 +SPENLOVE S P EH1 N L AH2 V +SPENLOVE'S S P EH1 N L AH1 V Z +SPENLOW S P EH1 N L OW0 +SPENLOW'S S P EH1 N L OW2 Z +SPENNIE S P EH1 N IY0 +SPENS S P EH1 N S +SPENSARY S P EH0 N S EH2 R IY0 +SPENSER S P EH1 N S ER0 +SPENSER'S S P EH1 N S ER0 Z +SPENSERIAN S P EH1 N S EH1 R IY0 AH0 N +SPENSERS S P EH1 N S ER0 Z +SPENT S P EH1 N T +SPENTOLI S P EH1 N T OW1 L IY0 +SPERA S P EH1 R AH0 +SPERANSKY S P ER0 AE1 N S K IY0 +SPERANZA S P ER0 AA1 N Z AH0 +SPERAVI S P ER0 AA1 V IY0 +SPERELLI S P ER0 EH1 L IY0 +SPERET S P ER0 IH0 T +SPERIENCE S P IH1 R IY0 AH0 N S +SPERIENCED S P IH1 R IY0 AH0 N S T +SPERIMENT S P EH1 R AH0 M AH0 N T +SPERIT S P EH1 R AH0 T +SPERITED S P EH1 R AH0 T IH0 D +SPERITS S P EH1 R AH0 T S +SPERITUAL S P EH1 R IH0 CH AH0 W AH0 L +SPERLING S P ER1 L IH0 NG +SPERM S P ER1 M +SPERMACETI S P ER1 M AH0 S EH1 T IY0 +SPERMATAZOA S P ER2 M AH0 T AH0 Z OW1 AH0 +SPERMATIC S P ER0 M AE1 T IH0 K +SPERMATOZOA S P ER2 M AH0 T AH0 Z OW1 AH0 +SPERMATOZOON S P ER2 M AH0 T AH0 Z UW2 N +SPERMS S P ER1 M Z +SPERNER S P ER1 N ER0 +SPERO S P EH1 R OW0 +SPERO'S S P EH1 R OW0 Z +SPERONARE S P EH1 R OW0 N AA1 R IY0 +SPERRIT S P EH1 R IH0 T +SPERRITED S P EH1 R IH0 T IH0 D +SPERRITS S P EH1 R IH0 T S +SPERRY S P EH1 R IY0 +SPERRY'S S P EH1 R IY0 Z +SPERVER S P ER1 V ER0 +SPES S P S +SPESHALLY S P EH1 SH AH0 L IY0 +SPESHUL S P EH1 SH UW0 L +SPESSART S P EH1 S ER0 T +SPETTIGEW S P EH1 T IH0 G UW0 +SPEUSIPPUS S P Y UW2 S IH1 P AH0 S +SPEW S P Y UW1 +SPEWED S P Y UW1 D +SPEWING S P Y UW1 IH0 NG +SPEWS S P Y UW1 Z +SPEY S P EY1 +SPEYER S P EY1 ER0 +SPEYK S P IH0 K +SPEZIA S P EH0 Z IY0 AH0 +SPEZZIA S P EH0 T S IY0 AH0 +SPHACTERIA S F AE0 K T IH1 R IY0 AH0 +SPHAGNUM S F AA1 G N AH0 M +SPHENOID S F AH0 N OY2 D +SPHERAL S F ER0 AH0 L +SPHERE S F IH1 R +SPHERE'S S F IH2 R Z +SPHERED S F IH1 R D +SPHERES S F IH1 R Z +SPHERIC S F IH1 R IH0 K +SPHERICAL S F EH1 R IH0 K AH0 L +SPHERICALLY S F IH1 R IH0 K L IY0 +SPHERICITY S F IH1 R IH0 S IH2 T IY0 +SPHEROID S F IH1 R OY2 D +SPHEROIDAL S F IH1 R OY2 D AH0 L +SPHEROIDS S F IH1 R OY2 D Z +SPHEX S F EH1 K S +SPHINCTER S F IH1 NG K T ER0 +SPHINX S F IH1 NG K S +SPHINX'S S F IH1 NG K S IH0 Z +SPHINXES S F IH1 NG K S IH0 Z +SPHINXLIKE S F IH1 NG K S L AY2 K +SPHYGMOGRAPH S F IH1 G M AH0 G R AE1 F +SPHYNX S F IH0 NG K S +SPHYNXES S F IH0 NG K S IH0 Z +SPI S P IY0 +SPIC S P IH1 K +SPICA S P IH1 K AH0 +SPICCA S P IH1 K AH0 +SPICCATO S P IH1 K AA1 T OW0 +SPICE S P AY1 S +SPICED S P AY1 S T +SPICER S P AY1 S ER0 +SPICER'S S P AY1 S ER0 Z +SPICERIES S P AY1 S R IY0 Z +SPICERS S P AY1 S ER0 Z +SPICERY S P AY1 S ER0 IY0 +SPICES S P AY1 S AH0 Z +SPICES S P AY1 S IH0 Z +SPICEWOOD S P AY1 S W UH2 D +SPICIER S P AY1 S IY0 ER0 +SPICIEST S P AY1 S IY0 AH0 S T +SPICILY S P AY1 S IH0 L IY0 +SPICINESS S P AY1 S IY0 N AH0 S +SPICING S P AY1 S IH0 NG +SPICION S P IH1 SH AH0 N +SPICIOUS S P IH1 SH AH0 S +SPICK S P IH1 K +SPICULA S P IH1 K Y AH0 L AH0 +SPICULAE S P IH1 K Y AH0 L EY2 +SPICULES S P IH1 K Y AH0 L IY2 Z +SPICY S P AY1 S IY0 +SPIDEL S P IH1 D AH0 L +SPIDER S P AY1 D ER0 +SPIDER'S S P AY1 D ER0 Z +SPIDERLIKE S P AY1 D ER0 L AY2 K +SPIDERMONKEY S P AY1 D ER0 M AH1 NG K IY0 +SPIDERS S P AY1 D ER0 Z +SPIDERWEB S P AY1 D ER0 W EH2 B +SPIDERWEBS S P AY1 D ER0 W EH2 B Z +SPIDERWITZ S P AY1 D ER1 W IH0 T S +SPIDERWORT S P AY1 D ER0 W ER0 T +SPIDERY S P AY1 D ER0 IY0 +SPIED S P AY1 D +SPIEGEL S P IY1 G AH0 L +SPIEGELBERG S P IY1 G AH0 L B ER0 G +SPIEGELER S P IY1 G AH0 L ER0 +SPIEL S P IY1 L +SPIELBERG S P IY1 L B ER0 G +SPIELEN S P IY1 L AH0 N +SPIELER S P IY1 L ER0 +SPIELERS S P IY1 L ER0 Z +SPIELHAGEN S P IY1 L HH EY2 G AH0 N +SPIELHAGEN'S S P IY1 L HH EY2 G AH0 N Z +SPIELIN S P IY1 L IH0 N +SPIELING S P IY1 L IH0 NG +SPIELMAN'S S P IY1 L M AH0 N Z +SPIELMANN S P IY1 L M AH0 N +SPIER S P AY1 ER0 +SPIERING S P AY1 ER0 IH0 NG +SPIERS S P AY1 ER0 Z +SPIES S P AY1 Z +SPIESZ S P IY1 SH +SPIEWELL S P IY1 W EH0 L +SPIFFING S P IH1 F IH0 NG +SPIFFINGTON S P IH1 F IH0 NG T AH0 N +SPIFFY S P IH1 F IY0 +SPIFFY'S S P IH1 F IY0 Z +SPIFLICATE S P AY1 F L IH0 K EY2 T +SPIFLICATED S P AY1 F L IH0 K EY2 T IH0 D +SPIGG S P IH1 G +SPIGGOT S P IH1 G AH0 T +SPIGHT S P AY1 T +SPIGOT S P IH1 G AH0 T +SPIGOTS S P IH1 G AH0 T S +SPIK S P IH0 K +SPIKE S P AY1 K +SPIKE'S S P AY1 K S +SPIKED S P AY1 K T +SPIKELET S P AY1 K L AH0 T +SPIKELETS S P AY1 K L AH0 T S +SPIKEMAN S P AY1 K M AH0 N +SPIKENARD S P AY1 K AH0 N ER0 D +SPIKER S P AY1 K ER0 +SPIKES S P AY1 K S +SPIKEY S P AY1 K IY0 +SPIKING S P AY1 K IH0 NG +SPIKY S P AY1 K IY0 +SPILE S P AY1 L +SPILED S P AY1 L D +SPILES S P AY1 L Z +SPILETT S P IH1 L IH0 T +SPILIN S P IH1 L IH0 N +SPILING S P AY1 L IH0 NG +SPILL S P IH1 L +SPILLANE S P IH1 L AH0 N +SPILLED S P IH1 L D +SPILLER S P IH1 L ER0 +SPILLER'S S P IH1 L ER0 Z +SPILLIKINS S P IH1 L AH0 K AH0 N Z +SPILLIN S P IH1 L IH0 N +SPILLING S P IH1 L IH0 NG +SPILLINGS S P IH1 L IH0 NG Z +SPILLINI S P IH1 L IY1 N IY0 +SPILLKINS S P IH1 L K IH0 N Z +SPILLS S P IH1 L Z +SPILLSBURY S P IH1 L Z B EH2 R IY0 +SPILLWAY S P IH1 L W EY2 +SPILLWAYS S P IH1 L W EY2 Z +SPILMAN S P IH1 L M AH0 N +SPILSBY S P IH1 L S B IY0 +SPILT S P IH1 L T +SPILTH S P IH1 L TH +SPIN S P IH1 N +SPINA S P IY1 N AH0 +SPINACH S P IH1 N AH0 CH +SPINAGE S P IH1 N AH0 JH +SPINAGEBERD S P IY0 N AA1 G IH0 B ER0 D +SPINAL S P AY1 N AH0 L +SPINALIS S P AY1 N AH0 L IH0 S +SPINBRONN S P IH1 N B R AA1 N +SPIND S P IH1 N D +SPINDLE S P IH1 N D AH0 L +SPINDLED S P IH1 N D AH0 L D +SPINDLER S P IH1 N D AH0 L ER0 +SPINDLER S P IH1 N D L ER0 +SPINDLER'S S P IH1 N D L ER0 Z +SPINDLES S P IH1 N D AH0 L Z +SPINDLESTON S P IH1 N D AH0 L S T AH0 N +SPINDLEWOOD S P IH1 N D AH0 L W UH2 D +SPINDLIN S P IH1 N D L IH0 N +SPINDLING S P IH1 N D L IH0 NG +SPINDLY S P IH1 N D L IY0 +SPINDRIFT S P IH1 N D R IH0 F T +SPINDRIFTERS S P IH1 N D R IH1 F T ER0 Z +SPINE S P AY1 N +SPINED S P AY1 N D +SPINELESS S P AY1 N L AH0 S +SPINELLOCCIO S P IH0 N EH1 L OW1 CH IY0 OW0 +SPINES S P AY1 N Z +SPINET S P IH1 N IH0 T +SPINETS S P IH0 N EH1 T S +SPINETT S P IH0 N EH1 T +SPINGLE S P IH1 NG G AH0 L +SPINI S P IY1 N IY0 +SPINIFEX S P IH1 N IH0 F EH0 K S +SPINK S P IH1 NG K +SPINK'S S P IH1 NG K S +SPINKS S P IH1 NG K S +SPINKS'S S P IH1 NG K S IH0 Z +SPINKY S P IH1 NG K IY0 +SPINNAKER S P IH1 N AH0 K ER0 +SPINNER S P IH1 N ER0 +SPINNER'S S P IH1 N ER0 Z +SPINNERET S P IH1 N ER0 EY1 +SPINNERETS S P IH1 N ER0 EY1 Z +SPINNERS S P IH1 N ER0 Z +SPINNET S P IH1 N IH0 T +SPINNEY S P IH1 N IY0 +SPINNEY'S S P IH1 N IY0 Z +SPINNEYS S P IH1 N IY0 Z +SPINNIES S P IH1 N IY0 Z +SPINNIN S P IH1 N IH0 N +SPINNIN' S P IH1 N IH0 N +SPINNING S P IH1 N IH0 NG +SPINNY S P IH1 N IY0 +SPINOLA S P IY0 N OW1 L AH0 +SPINOSA S P IY0 N OW1 S AH0 +SPINOUS S P IH1 N AH0 S +SPINOZA S P IH0 N OW1 Z AH0 +SPINOZA'S S P IY0 N OW1 Z AH0 Z +SPINOZISM S P IY0 N OW1 Z IH0 Z AH0 M +SPINS S P IH1 N Z +SPINSTER S P IH1 N S T ER0 +SPINSTER'S S P IH1 N S T ER0 Z +SPINSTERHOOD S P IH1 N S T ER0 HH UH2 D +SPINSTERISH S P IH1 N S T ER0 IH0 SH +SPINSTERS S P IH1 N S T ER0 Z +SPINSTRESS S P IH1 N S T R EH1 S +SPINT S P IH1 N T +SPINVILLE S P IH1 N V IH2 L +SPINY S P AY1 N IY0 +SPION S P IY0 AH0 N +SPIR S P IH1 R +SPIRA S P IH1 R AH0 +SPIRACLE S P IH1 R AH0 K AH0 L +SPIRACLES S P IH1 R AH0 K AH0 L Z +SPIRAEA S P IH1 R IY1 AH0 +SPIRAL S P AY1 R AH0 L +SPIRALED S P AY1 R AH0 L D +SPIRALING S P AY1 R AH0 L IH0 NG +SPIRALLED S P AY1 R AH0 L D +SPIRALLING S P AY1 R AH0 L IH0 NG +SPIRALLY S P AY1 R AH0 L IY0 +SPIRALS S P AY1 R AH0 L Z +SPIRE S P AY1 R +SPIREA S P AY1 R EY1 +SPIREAS S P AY1 R IY1 Z +SPIRED S P AY1 R D +SPIRES S P AY1 R Z +SPIRIDION S P IH1 R IH0 D IY0 AH0 N +SPIRIDIONE S P IH1 R IH0 D IY0 OW1 N IY0 +SPIRING S P AY1 R IH0 NG +SPIRIT S P IH1 R AH0 T +SPIRIT S P IH1 R IH0 T +SPIRIT' S P IH1 R IH0 T +SPIRIT'S S P IH1 R IH0 T S +SPIRITE S P IH1 R IH0 T +SPIRITED S P IH1 R IH0 T AH0 D +SPIRITEDLY S P IH1 R IH0 T AH0 D L IY0 +SPIRITEDNESS S P IH1 R IH0 T IH0 D N AH0 S +SPIRITING S P IH1 R IH0 T IH0 NG +SPIRITISM S P IH1 R IH0 T IH2 Z AH0 M +SPIRITIST S P IH1 R IH0 T IH0 S T +SPIRITISTIC S P IH1 R IH0 T IH1 S T IH0 K +SPIRITISTS S P IH1 R IH0 T IH0 S T S +SPIRITLAND S P IH1 R IH0 T L AH0 N D +SPIRITLESS S P IH1 R IH0 T L AH0 S +SPIRITLESSLY S P IH1 R IH0 T L AH0 S L IY0 +SPIRITO S P IH0 R IY1 T OW2 +SPIRITS S P IH1 R IH0 T S +SPIRITS S P IH1 R IH0 T S +SPIRITU S P IH0 R IY1 CH UW0 +SPIRITUAL S P IH1 R IH0 CH AH0 W AH0 L +SPIRITUAL S P IH1 R IH0 CH W AH0 L +SPIRITUALISATION S P IH1 R IH0 CH AH0 W AH0 L IH0 Z EY1 SH AH0 N +SPIRITUALISE S P IH1 R IH0 CH W AH0 L IY1 S +SPIRITUALISED S P IH1 R IH0 CH AH0 W AH0 L IH0 Z D +SPIRITUALISING S P IH1 R IH0 CH AH0 W AH0 L IH0 Z IH0 NG +SPIRITUALISM S P IH1 R IH0 CH AH0 W AH0 L IH0 Z M +SPIRITUALISM S P IH1 R IH0 CH W AH0 L IH2 Z M +SPIRITUALIST S P IH1 R IH0 CH AH0 W AH0 L IH0 S T +SPIRITUALISTIC S P IH1 R IH0 CH W AH0 L IH1 S T IH0 K +SPIRITUALISTS S P IH1 R IH0 CH AH0 W AH0 L IH0 S T S +SPIRITUALITIES S P IH1 R IH0 CH AH0 W AE1 L AH0 T IY0 Z +SPIRITUALITY S P IH2 R IH0 CH AH0 W AE1 L AH0 T IY0 +SPIRITUALIZATION S P IH1 R IH0 CH W AH0 L IH0 Z EY1 SH AH0 N +SPIRITUALIZE S P IH1 R IH0 CH W AH0 L AY2 Z +SPIRITUALIZED S P IH1 R IH0 CH W AH0 L AY2 Z D +SPIRITUALIZES S P IH1 R IH0 CH W AH0 L AY2 Z IH0 Z +SPIRITUALIZING S P IH1 R IH0 CH W AH0 L AY2 Z IH0 NG +SPIRITUALL S P IH1 R IH0 CH AH0 W AH0 L +SPIRITUALLY S P IH1 R IH0 CH AH0 W AH0 L IY0 +SPIRITUALS S P IH1 R IH0 CH AH0 W AH0 L Z +SPIRITUALS S P IH1 R IH0 CH W AH0 L Z +SPIRITUEL S P IH1 R IH0 CH UW0 AH0 L +SPIRITUELLE S P IH1 R IH0 CH W EH1 L +SPIRITUM S P IH1 R IH0 CH AH0 M +SPIRITUOUS S P IH1 R IH0 CH AH0 W AH0 S +SPIRITUS S P IH1 R AH0 T AH0 S +SPIRITY S P IH1 R IH0 T IY0 +SPIRO S P IH1 R OW0 +SPIRT S P IH1 R T +SPIRTED S P IH1 R T IH0 D +SPIRTING S P IH1 R T IH0 NG +SPIRTO S P IH1 R T OW0 +SPIRTS S P IH1 R T S +SPIRY S P AY1 R IY0 +SPISE S P AY1 Z +SPIT S P IH1 T +SPITAL S P IH0 T AH0 L +SPITALFIELDS S P IY0 T AA1 L F IY2 L D Z +SPITBALL S P IH1 T B AO2 L +SPITE S P AY1 T +SPITED S P AY1 T IH0 D +SPITEFUL S P AY1 T F AH0 L +SPITEFULLY S P AY1 T F AH0 L IY0 +SPITEFULNESS S P AY1 T F AH0 L N AH0 S +SPITEHAHN S P IY0 T EH1 HH AH0 N +SPITERI S P IY0 T EH1 R IY0 +SPITES S P AY1 T S +SPITFIRE S P IH1 T F AY2 R +SPITFIRES S P IH1 T F AY2 ER0 Z +SPITHEAD S P IH1 T HH EH2 D +SPITING S P AY1 T IH0 NG +SPITS S P IH1 T S +SPITTA S P IH1 T AH0 +SPITTAL S P IH1 T AH0 L +SPITTED S P IH1 T IH0 D +SPITTER S P IH1 T ER0 +SPITTIN S P IH1 T IH0 N +SPITTING S P IH1 T IH0 NG +SPITTINGS S P IH1 T IH0 NG Z +SPITTLE S P IH1 T AH0 L +SPITTLES S P IH1 T AH0 L Z +SPITTOON S P IH1 T UW1 N +SPITTOONS S P IH1 T UW1 N Z +SPITZ S P IH1 T S +SPITZBERGEN S P IH1 T S B ER0 G AH0 N +SPITZE S P IH1 T Z +SPITZER S P IH1 T Z ER0 +SPITZHASE S P IH1 T S HH EY2 Z +SPITZKOPF S P IH1 T S K AO0 F +SPIVEYCOMB S P IH1 V IY0 K OW2 M +SPIVIN S P IH1 V IH0 N +SPIX S P IH2 K S +SPIZEY S P AY1 Z IY0 +SPIZZA S P IY0 T S AH0 +SPLAIGHTON S P L EY1 T AH0 N +SPLAIN S P L EY1 N +SPLAINED S P L EY1 N D +SPLANN S P L AE1 N +SPLASH S P L AE1 SH +SPLASH'S S P L AE1 SH IH0 Z +SPLASHBOARD S P L AE1 SH B AO2 R D +SPLASHED S P L AE1 SH T +SPLASHER S P L AE1 SH ER0 +SPLASHES S P L AE1 SH AH0 Z +SPLASHES S P L AE1 SH IH0 Z +SPLASHIN S P L AE1 SH IH0 N +SPLASHING S P L AE1 SH IH0 NG +SPLASHINGS S P L AE1 SH IH0 NG Z +SPLASHY S P L AE1 SH IY0 +SPLAT S P L AE1 T +SPLATCHETT'S S P L AE1 CH IH0 T S +SPLATTER S P L AE1 T ER0 +SPLATTERED S P L AE1 T ER0 D +SPLATTERING S P L AE1 T ER0 IH0 NG +SPLAY S P L EY2 +SPLAY'S S P L EY1 Z +SPLAYED S P L EY1 D +SPLAYFOOT S P L EY1 F UH2 T +SPLAYING S P L EY1 IH0 NG +SPLEEN S P L IY1 N +SPLEENFUL S P L IY1 N F AH0 L +SPLEENS S P L IY1 N Z +SPLEENWORT S P L IY1 N W ER0 T +SPLEN S P L EH1 N +SPLENDENS S P L EH1 N D AH0 N Z +SPLENDENT S P L EH1 N D AH0 N T +SPLENDEUR S P L EH1 N D UW1 R +SPLENDEURS S P L EH1 N D ER1 Z +SPLENDIANO S P L EH1 N D IY0 AA1 N OW0 +SPLENDID S P L EH1 N D AH0 D +SPLENDID S P L EH1 N D IH0 D +SPLENDID'S S P L EH1 N D AH0 D Z +SPLENDIDA S P L EH1 N D AH0 D AH0 +SPLENDIDE S P L EH1 N D AH0 D +SPLENDIDEST S P L EH1 N D AH0 D AH0 S T +SPLENDIDLY S P L EH1 N D AH0 D L IY0 +SPLENDIFEROUS S P L EH1 N D IH1 F ER0 AH0 S +SPLENDOR S P L EH1 N D ER0 +SPLENDORS S P L EH1 N D ER0 Z +SPLENDOUR S P L EH1 N D AO2 R +SPLENDOURS S P L EH1 N D AO2 R Z +SPLENETIC S P L EH1 N EH1 T IH0 K +SPLENIC S P L EH1 N IH0 K +SPLENIC S P L IY1 N IH0 K +SPLICE S P L AY1 S +SPLICED S P L AY1 S T +SPLICES S P L AY1 S IH0 Z +SPLICING S P L AY1 S IH0 NG +SPLINT S P L IH1 N T +SPLINTED S P L IH1 N T IH0 D +SPLINTER S P L IH1 N T ER0 +SPLINTER'D S P L IH1 N T R AH0 D +SPLINTERED S P L IH1 N T ER0 D +SPLINTERING S P L IH1 N T ER0 IH0 NG +SPLINTERS S P L IH1 N T ER0 Z +SPLINTERY S P L IH1 N T ER0 IY0 +SPLINTS S P L IH1 N T S +SPLISH S P L IH1 SH +SPLIT S P L IH1 T +SPLITS S P L IH1 T S +SPLITTER S P L IH1 T ER0 +SPLITTERS S P L IH1 T ER0 Z +SPLITTIN S P L IH1 T IH0 N +SPLITTING S P L IH1 T IH0 NG +SPLITTINGS S P L IH1 T IH0 NG Z +SPLORE S P L AO1 R +SPLOSH S P L AA1 SH +SPLOTCH S P L AA1 T CH +SPLOTCHED S P L AA1 T CH T +SPLOTCHES S P L AA1 T CH AH0 Z +SPLOTCHY S P L AA1 T CH IY0 +SPLUEGEN S P L UH1 G AH0 N +SPLUETHNER S P L EH1 TH N ER0 +SPLUGEN S P L G AH0 N +SPLURGE S P L ER1 JH +SPLURGED S P L ER1 JH D +SPLURGES S P L ER1 JH IH0 Z +SPLURGING S P L ER1 JH IH0 NG +SPLUTTER S P L AH1 T ER0 +SPLUTTERED S P L AH1 T ER0 D +SPLUTTERING S P L AH1 T ER0 IH0 NG +SPLUTTERINGS S P L AH1 T ER0 IH0 NG Z +SPLUTTERS S P L AH1 T ER0 Z +SPO'TIN S P OW0 T IH0 N +SPOASE S P AH1 EY2 Z +SPODE S P OW0 D +SPOELBERCH S P OW1 L B ER1 K +SPOFFORD S P AA1 F ER0 D +SPOFFORTH S P AA1 F ER0 TH +SPOHF S P AA1 F +SPOHR S P AA1 R +SPOHR'S S P AA1 R Z +SPOIL S P OY1 L +SPOIL'D S P OY1 L D +SPOILATION S P OY1 L EY1 SH AH0 N +SPOILED S P OY1 L D +SPOILER S P OY1 L ER0 +SPOILER'S S P OY1 L ER0 Z +SPOILERS S P OY1 L ER0 Z +SPOILIN S P OY1 L IH0 N +SPOILING S P OY1 L IH0 NG +SPOILS S P OY1 L Z +SPOILSMAN S P OY1 L Z M AH0 N +SPOILSMEN S P OY1 L Z M IH0 N +SPOILSPORT S P OY1 L S P AO2 R T +SPOILT S P OY1 L T +SPOKANE S P OW0 K AE1 N +SPOKANE S P OW0 K EY1 N +SPOKE S P OW1 K +SPOKED S P OW1 K T +SPOKEN S P OW1 K AH0 N +SPOKENNESS S P OW1 K AH0 N AH0 S +SPOKER S P OW1 K ER0 +SPOKES S P OW1 K S +SPOKESHAVE S P OW1 K SH EY1 V +SPOKESLY S P OW1 K S L IY0 +SPOKESLY'S S P OW1 K S L IY0 Z +SPOKESMAN S P OW1 K S M AH0 N +SPOKESMAN'S S P OW1 K S M AH0 N Z +SPOKESMEN S P OW1 K S M IH0 N +SPOKESWOMAN S P OW1 K S W UH2 M AH0 N +SPOLETO S P OW0 L EY1 T OW0 +SPOLIA S P OW1 L IY0 AH0 +SPOLIATION S P OW0 L IY0 EY1 SH AH0 N +SPOLIATIONS S P OW0 L IY0 EY1 SH AH0 N Z +SPOLIATOR S P OW0 L IY0 EY2 T ER0 +SPOLIATORS S P OW0 L IY0 EY2 T ER0 Z +SPON S P AA0 N +SPONDAIC S P AA1 N D EY1 IH0 K +SPONDE S P AA1 N D +SPONDEE S P AA1 N D IY1 +SPONDEES S P AA1 N D IY1 Z +SPONDULICKS S P AA1 N D Y UW1 L IH0 K S +SPONECK S P AH1 N EH2 K +SPONG S P AO1 NG +SPONGE S P AH1 N JH +SPONGE'S S P AH1 N JH IH0 Z +SPONGECAKE S P AH1 N JH K EY2 K +SPONGED S P AH1 N JH D +SPONGER S P AH1 N JH ER0 +SPONGERS S P AH1 N JH ER0 Z +SPONGES S P AH1 N JH AH0 Z +SPONGING S P AH1 N JH IH0 NG +SPONGY S P AH1 N JH IY0 +SPONSA S P AA1 N S AH0 +SPONSIBILITY S P AA1 N S AH0 B IH1 L AH0 T IY0 +SPONSIBLE S P AA1 N S AH0 B AH0 L +SPONSILIER S P AA1 N S AH0 L IY0 ER0 +SPONSILIER'S S P AA1 N S AH0 L IY0 ER0 Z +SPONSON S P AA1 N S AH0 N +SPONSONS S P AA1 N S AH0 N Z +SPONSOR S P AA1 N S ER0 +SPONSORED S P AA1 N S ER0 D +SPONSORS S P AA1 N S ER0 Z +SPONSORSHIP S P AA1 N S ER0 SH IH2 P +SPONTANEITY S P AA2 N T AH0 N IY1 AH0 T IY0 +SPONTANEOUS S P AA0 N T EY1 N IY0 AH0 S +SPONTANEOUSLY S P AA0 N T EY1 N IY0 AH0 S L IY0 +SPONTANEOUSNESS S P AA0 N T EY1 N IY0 AH0 S N AH0 S +SPONTINI S P AA0 N T IY1 N IY0 +SPONTINI'S S P AA0 N T IY1 N IY0 Z +SPONTOON S P AA0 N T UW1 N +SPOOF S P UW1 F +SPOOF'S S P UW1 F S +SPOOFED S P UW1 F T +SPOOFING S P UW1 F IH0 NG +SPOOK S P UW1 K +SPOOKED S P UW1 K T +SPOOKS S P UW1 K S +SPOOKY S P UW1 K IY0 +SPOOL S P UW1 L +SPOOLING S P UW1 L IH0 NG +SPOOLS S P UW1 L Z +SPOON S P UW1 N +SPOONBILL S P UW1 N B AH0 L +SPOONBILLS S P UW1 N B IH2 L Z +SPOONDRIFT S P UW1 N D R IH2 F T +SPOONED S P UW1 N D +SPOONER S P UW1 N ER0 +SPOONER'S S P UW1 N ER0 Z +SPOONEY S P UW1 N IY0 +SPOONFUL S P UW1 N F UH2 L +SPOONFULS S P UW1 N F UH2 L Z +SPOONIES S P UW1 N IY0 Z +SPOONIN S P UW1 N IH0 N +SPOONING S P UW1 N IH0 NG +SPOONS S P UW1 N Z +SPOONSFUL S P UW1 N Z F AH0 L +SPOONY S P UW1 N IY0 +SPOOR S P UH1 R +SPOOR'EM S P UH1 R EH1 M +SPOORED S P UH1 R D +SPOORING S P UH1 R IH0 NG +SPOORS S P UH1 R Z +SPOR S P ER0 +SPORADIC S P ER0 AE1 D IH0 K +SPORADICALLY S P ER0 AE1 D IH0 K L IY0 +SPORANGIA S P AO1 R AH0 N JH AH0 +SPORE S P AO1 R +SPORES S P AO1 R Z +SPORRAN S P AO1 R AH0 N +SPORRANS S P AO1 ER0 AH0 N Z +SPORT S P AO1 R T +SPORT'S S P AO1 R T S +SPORTED S P AO1 R T IH0 D +SPORTFUL S P AO1 R T F AH0 L +SPORTIN S P AO1 R T IH0 N +SPORTING S P AO1 R T IH0 NG +SPORTINGLY S P AO1 R T IH0 NG L IY0 +SPORTIVE S P AO1 R T IH0 V +SPORTIVELY S P AO1 R T IH0 V L IY0 +SPORTIVENESS S P AO1 R T IH0 V N AH0 S +SPORTMAN S P AO1 R T M AH0 N +SPORTMEN S P AO1 R T M AH0 N +SPORTS S P AO1 R T S +SPORTSMAN S P AO1 R T S M AH0 N +SPORTSMAN'S S P AO1 R T S M AH0 N Z +SPORTSMANLIKE S P AO1 R T S M AH0 N L AY2 K +SPORTSMANSHIP S P AO1 R T S M AH0 N SH IH2 P +SPORTSMEN S P AO1 R T S M IH0 N +SPORTSMEN'S S P AO1 R T S M IH0 N Z +SPORTSWOMAN S P AO1 R T S W UH2 M AH0 N +SPORTY S P AO1 R T IY0 +SPORUS S P AO1 R AH0 S +SPOSA S P OW1 S AH0 +SPOSALIZIO S P OW1 Z AH0 L IH1 Z IY0 OW0 +SPOSE S P OW1 Z +SPOSED S P OW1 Z D +SPOSEN S P OW0 S AH0 N +SPOSI S P OW1 S IY0 +SPOSIN S P OW0 S IH0 N +SPOSO S P OW1 S OW0 +SPOT S P AA1 T +SPOT'S S P AA1 T S +SPOTKIRK S P AA1 T K ER0 K +SPOTLESS S P AA1 T L AH0 S +SPOTLESSLY S P AA1 T L AH0 S L IY0 +SPOTLESSNESS S P AA1 T L AH0 S N AH0 S +SPOTLIGHT S P AA1 T L AY2 T +SPOTLIGHTS S P AA1 T L AY2 T S +SPOTS S P AA1 T S +SPOTSWOOD S P AA1 T S W UH2 D +SPOTSWOOD'S S P AA1 T S W UH2 D Z +SPOTSYLVANIA S P AA1 T S IH0 L V EY1 N Y AH0 +SPOTTED S P AA1 T AH0 D +SPOTTED S P AA1 T IH0 D +SPOTTER S P AA1 T ER0 +SPOTTERS S P AA1 T ER0 Z +SPOTTIN S P AA1 T IH0 N +SPOTTING S P AA1 T IH0 NG +SPOTTISWOODE S P AA1 T AH0 S W UH0 D +SPOTTISWOODE'S S P AA1 T AH0 S W UH0 D Z +SPOTTLEOVER S P AA1 T L OW2 V ER0 +SPOTTLETOE S P AA1 T AH0 L T OW2 +SPOTTS S P AA1 T S +SPOTTSWOOD S P AA1 T S W UH2 D +SPOTTSYLVANIA S P AA1 T S IH0 L V EY1 N Y AH0 +SPOTTY S P AA1 T IY0 +SPOTTY'S S P AA1 T IY0 Z +SPOUSAL S P AW1 Z AH0 L +SPOUSALS S P AW1 Z AH0 L Z +SPOUSE S P AW1 S +SPOUSE'S S P AW1 S IH0 Z +SPOUSELESS S P AW1 S L AH0 S +SPOUSES S P AW1 S AH0 Z +SPOUSES S P AW1 S IH0 Z +SPOUT S P AW1 T +SPOUTED S P AW1 T AH0 D +SPOUTER S P AW1 T ER0 +SPOUTER'S S P AW1 T ER0 Z +SPOUTERS S P AW1 T ER0 Z +SPOUTIN S P AW1 T IH0 N +SPOUTING S P AW1 T IH0 NG +SPOUTINGS S P AW1 T IH0 NG Z +SPOUTS S P AW1 T S +SPOYLE S P OY0 L +SPOZE S P OW1 Z +SPOZED S P OW1 Z D +SPRACH S P R AE1 CH +SPRACHE S P R EY1 K IY0 +SPRADDLE S P R AE1 D AH0 L +SPRADDLED S P R AE1 D AH0 L D +SPRAGG S P R AE1 G +SPRAGG'S S P R AE1 G Z +SPRAGGE S P R AE1 G +SPRAGGINS S P R AE1 G IH0 N Z +SPRAGGON S P R AE1 G AH0 N +SPRAGGS S P R AE1 G Z +SPRAGUE S P R EY1 G +SPRAGUE'S S P R EY1 G Z +SPRAGUES S P R EY1 G Z +SPRAIN S P R EY1 N +SPRAINED S P R EY1 N D +SPRAINING S P R EY1 N IH0 NG +SPRAINS S P R EY1 N Z +SPRANG S P R AE1 NG +SPRAT S P R AE1 T +SPRAT'S S P R AE1 T S +SPRATS S P R AE1 T S +SPRATT S P R AE1 T +SPRATT'S S P R AE1 T S +SPRATTS S P R AE1 T S +SPRAWL S P R AO1 L +SPRAWLED S P R AO1 L D +SPRAWLEY S P R AO1 L IY0 +SPRAWLIN S P R AO1 L IH0 N +SPRAWLING S P R AO1 L IH0 NG +SPRAWLINGLY S P R AO1 L IH0 NG L IY0 +SPRAWLS S P R AO1 L Z +SPRAWLY S P R AO1 L IY0 +SPRAY S P R EY1 +SPRAY'S S P R EY1 Z +SPRAYED S P R EY1 D +SPRAYER S P R EY1 ER0 +SPRAYING S P R EY1 IH0 NG +SPRAYS S P R EY1 Z +SPREAD S P R EH1 D +SPREADEAGLED S P R EH1 D IY1 G AH0 L D +SPREADER S P R EH1 D ER0 +SPREADERS S P R EH1 D ER0 Z +SPREADEST S P R EH1 D AH0 S T +SPREADETH S P R EH1 D IH0 TH +SPREADIN S P R EH1 D IY0 N +SPREADING S P R EH1 D IH0 NG +SPREADS S P R EH1 D Z +SPRECHEN S P R EH1 K AH0 N +SPRECHER S P R EH1 K ER0 +SPRECKDAL S P R EH1 K D AA2 L +SPRECKELS S P R EH1 K AH0 L Z +SPRED S P R D +SPREE S P R IY1 +SPREEING S P R IY1 IH0 NG +SPREES S P R IY1 Z +SPRENGEL S P R EH1 NG G AH0 L +SPRENGER S P R EH1 N JH ER0 +SPRENT S P R AH0 N T +SPRESS S P R EH1 S +SPRETAE S P R IH0 T EY1 +SPREUWS S P R IY1 UW0 Z +SPRIG S P R IH1 G +SPRIGG S P R IH1 G +SPRIGGAN S P R IH1 G AH0 N +SPRIGGANS S P R IH1 G AH0 N Z +SPRIGGED S P R IH1 G D +SPRIGGINS S P R IH1 G IH0 N Z +SPRIGGS S P R IH1 G Z +SPRIGHT S P R AY1 T +SPRIGHTLIER S P R AY1 T L AY2 ER0 +SPRIGHTLIEST S P R AY1 T L IY0 AH0 S T +SPRIGHTLINESS S P R AY1 T L AY2 N AH0 S +SPRIGHTLY S P R AY1 T L IY0 +SPRIGS S P R IH1 G Z +SPRING S P ER0 IH1 NG +SPRING S P R IH1 NG +SPRING'S S P R IH1 NG Z +SPRINGALD S P R IH1 NG G AH0 L D +SPRINGALL S P R IH1 NG G AH0 L +SPRINGBOARD S P R IH1 NG B AO2 R D +SPRINGBOK S P R IH1 NG B AA0 K +SPRINGBOKS S P R IH1 NG B AA0 K S +SPRINGBROOK S P R IH1 NG B R UH2 K +SPRINGBUCK S P R IH1 NG B AH2 K +SPRINGBUCKS S P R IH1 NG B AH2 K S +SPRINGDALE S P R IH1 NG D EY2 L +SPRINGE S P R IH1 NG +SPRINGED S P R IH1 NG D +SPRINGER S P R IH1 NG ER0 +SPRINGER'S S P R IH1 NG ER0 Z +SPRINGERS S P R IH1 NG ER0 Z +SPRINGES S P R IH1 N JH IH0 Z +SPRINGEST S P R IH1 NG AH0 S T +SPRINGETH S P R IH1 NG G IH0 TH +SPRINGETT S P R IH1 NG G IH0 T +SPRINGFIELD S P R IH1 NG F IY2 L D +SPRINGFIELD'S S P R IH1 NG F IY2 L D Z +SPRINGFIELDS S P R IH1 NG F IY2 L D Z +SPRINGHILL S P R IH1 NG HH IH2 L +SPRINGHOUSE S P R IH1 NG HH AW2 S +SPRINGILY S P R IH1 NG AH0 L IY0 +SPRINGIN S P R IH1 NG AH0 N +SPRINGINESS S P R IH1 NG IY0 N AH0 S +SPRINGING S P R IH1 NG IH0 NG +SPRINGLE S P R IH1 NG G AH0 L +SPRINGLESS S P R IH1 NG L AH0 S +SPRINGLIKE S P R IH1 NG L AY2 K +SPRINGS S P ER0 IH1 NG Z +SPRINGS S P R IH1 NG Z +SPRINGTIDE S P R IH1 NG T AY2 D +SPRINGTIDES S P R IH1 NG T AY2 D Z +SPRINGTIME S P R IH1 NG T AY2 M +SPRINGTIME'S S P R IH1 NG T AY2 M Z +SPRINGTIMES S P R IH1 NG T AY2 M Z +SPRINGTON S P R IH1 NG T AH0 N +SPRINGTOWN S P R IH1 NG T AW2 N +SPRINGVALE S P R IH1 NG V EY2 L +SPRINGVILLE S P R IH1 NG V IH2 L +SPRINGWHEAT S P R IH1 NG W IY2 T +SPRINGY S P R IH1 NG IY0 +SPRINKLE S P R IH1 NG K AH0 L +SPRINKLED S P R IH1 NG K AH0 L D +SPRINKLER S P R IH1 NG K AH0 L ER0 +SPRINKLER S P R IH1 NG K L ER0 +SPRINKLERS S P R IH1 NG K AH0 L ER0 Z +SPRINKLERS S P R IH1 NG K L ER0 Z +SPRINKLES S P R IH1 NG K AH0 L Z +SPRINKLIN S P R IH1 NG K L IH0 N +SPRINKLING S P R IH1 NG K AH0 L IH0 NG +SPRINKLING S P R IH1 NG K L IH0 NG +SPRINKLINGS S P R IH1 NG K L IH0 NG Z +SPRINT S P R IH1 N T +SPRINTED S P R IH1 N T IH0 D +SPRINTER S P R IH1 N T ER0 +SPRINTERS S P R IH1 N T ER0 Z +SPRINTING S P R IH1 N T IH0 NG +SPRINTS S P R IH1 N T S +SPRIT S P R IY1 +SPRITE S P R AY1 T +SPRITE'S S P R AY1 T S +SPRITELY S P R AY1 T L IY0 +SPRITES S P R AY1 T S +SPRITS S P R IH1 T S +SPRITSAIL S P R IY1 T S EY2 L +SPROAT S P R OW1 T +SPROAT'S S P R OW1 T S +SPROATLY S P R OW1 T L IY0 +SPROATLY'S S P R OW1 T L IY0 Z +SPROCKET S P R AA1 K AH0 T +SPROCKETS S P R AA1 K AH0 T S +SPROCKETT S P R AA1 K IH0 T +SPROCKETT'S S P R AA1 K AH0 T S +SPRONG S P R AO1 NG +SPROTSFIELD S P R AA1 T S F IY0 L D +SPROTT S P R AA1 T +SPROTTS S P R AA1 T S +SPROUD S P R AW1 D +SPROUL S P R AW1 L +SPROULE S P R UW1 L +SPROUT S P R AW1 T +SPROUTED S P R AW1 T AH0 D +SPROUTED S P R AW1 T IH0 D +SPROUTIN S P R AW1 T IH0 N +SPROUTING S P R AW1 T IH0 NG +SPROUTINGS S P R AW1 T IH0 NG Z +SPROUTS S P R AW1 T S +SPROWL S P R AW1 L +SPROWL'S S P R AW1 L Z +SPROWLE S P R AW1 AH0 L +SPROWLE'S S P R AW1 L Z +SPRUCE S P R UW1 S +SPRUCED S P R UW1 S T +SPRUCELY S P R UW1 S L IY0 +SPRUCENESS S P R UW1 S N AH0 S +SPRUCER S P R UW1 S ER0 +SPRUCES S P R UW1 S IH0 Z +SPRUCH S P R AH1 CH +SPRUDEL S P R UW1 D AH0 L +SPRUGEON S P R UW1 JH IH0 N +SPRUGGINS S P R AH1 G IH0 N Z +SPRUIT S P R UW1 T +SPRUITS S P R UW1 T S +SPRUNG S P R AH1 NG +SPRY S P R AY1 +SPRYER S P R AY1 ER0 +SPRYEST S P R AY1 AH0 S T +SPRYLY S P R AY1 L IY0 +SPRYNESS S P R AY1 N AH0 S +SPUD S P AH1 D +SPUD'S S P AH1 D Z +SPUDD S P AH1 D +SPUDS S P AH1 D Z +SPUE S P Y UW2 +SPUGG S P AH1 G +SPUMANTE S P Y UW0 M AA1 N T IY0 +SPUME S P UW1 M +SPUMING S P UW1 M IH0 NG +SPUMY S P AH1 M IY0 +SPUN S P AH1 N +SPUNE S P AH1 N +SPUNGE S P AH1 N JH +SPUNGING S P AH1 N JH IH0 NG +SPUNGY S P AH1 N JH IY0 +SPUNK S P AH1 NG K +SPUNKED S P AH1 NG K T +SPUNKIE S P AH1 NG K IY0 +SPUNKIES S P AH1 NG K IY0 Z +SPUNKS S P AH1 NG K S +SPUNKY S P AH1 NG K IY0 +SPUNYARN S P AH1 N Y AA1 R N +SPUR S P ER1 +SPUREY S P Y UH1 R IY0 +SPURGE S P ER1 JH +SPURGEON S P ER1 JH IH0 N +SPURGEON'S S P ER1 JH AH0 N Z +SPURGEONS S P ER1 JH AH0 N Z +SPURIOUS S P ER1 IY0 AH0 S +SPURIOUS S P Y UH1 R IY0 AH0 S +SPURIOUSLY S P ER1 IY0 AH0 S L IY0 +SPURIOUSNESS S P ER1 IY0 AH0 S N AH0 S +SPURIUS S P ER1 IY0 IH0 S +SPURLESS S P ER1 L AH0 S +SPURLING S P ER1 L IH0 NG +SPURLOCK S P ER1 L AA2 K +SPURLOCK'S S P ER1 L AA2 K S +SPURN S P ER1 N +SPURN'D S P ER1 N D +SPURNED S P ER1 N D +SPURNING S P ER1 N IH0 NG +SPURNS S P ER1 N Z +SPURR S P ER1 +SPURR'D S P ER1 D +SPURRE S P ER1 +SPURRED S P ER1 D +SPURREL S P ER1 L +SPURRELL S P ER1 EY1 L +SPURRIER S P ER1 IY0 ER0 +SPURRIN S P ER1 IH0 N +SPURRING S P ER1 IH0 NG +SPURS S P ER1 Z +SPURSTOW S P ER1 S T OW0 +SPURT S P ER1 T +SPURTED S P ER1 T IH0 D +SPURTING S P ER1 T IH0 NG +SPURTS S P ER1 T S +SPURZHEIM S P ER1 Z HH AY2 M +SPUT S P AH1 T +SPUTIN S P Y UW1 T AH0 N +SPUTTER S P AH1 T ER0 +SPUTTERED S P AH1 T ER0 D +SPUTTERING S P AH1 T ER0 IH0 NG +SPUTTERINGS S P AH1 T ER0 IH0 NG Z +SPUTTERS S P AH1 T ER0 Z +SPUTUM S P Y UW1 T AH0 M +SPUYTEN S P AY0 T AH0 N +SPY S P AY1 +SPY'D S P AY1 D +SPY'S S P AY1 Z +SPYCHOW S P IH0 HH AW0 +SPYCK S P IH0 K +SPYE S P AY1 +SPYERS S P AY1 ER0 Z +SPYGLASS S P AY1 G L AE2 S +SPYGLASSES S P AY1 G L AE2 S IH0 Z +SPYHOLE S P AY1 HH OW2 L +SPYIN S P AY1 IH0 N +SPYING S P AY1 IH0 NG +SPYKE S P AY1 K +SPYRI S P AY1 ER0 IY0 +SQ S K +SQQ S K K +SQUAB S K W AA1 +SQUABBLE S K W AA1 B AH0 L +SQUABBLED S K W AA1 B AH0 L D +SQUABBLES S K W AA1 B AH0 L Z +SQUABBLIN S K W AA1 B L IH0 N +SQUABBLING S K W AA1 B AH0 L IH0 NG +SQUABBLING S K W AA1 B L IH0 NG +SQUABBLINGS S K W AA1 B AH0 L IH0 NG Z +SQUABS S K W AA1 Z +SQUAD S K W AA1 D +SQUAD'S S K W AA1 D Z +SQUADRA S K W AA1 D R AH0 +SQUADRON S K W AA1 D R AH0 N +SQUADRON'S S K W AA1 D R AH0 N Z +SQUADRONS S K W AA1 D R AH0 N Z +SQUADS S K W AA1 D Z +SQUAIR S K W EH1 R +SQUAK S K W AA1 K +SQUALID S K W AA1 L AH0 D +SQUALIDLY S K W AA1 L AH0 D L IY0 +SQUALIDNESS S K W AA1 L AH0 D N AH0 S +SQUALL S K W AO1 L +SQUALLED S K W AO1 L D +SQUALLER S K W AO1 L ER0 +SQUALLERS S K W AO1 L ER0 Z +SQUALLIN S K W AO1 L IH0 N +SQUALLING S K W AO1 L IH0 NG +SQUALLS S K W AO1 L Z +SQUALLY S K W AO1 L IY0 +SQUALOR S K W AA1 L ER0 +SQUALORS S K W AA1 L ER0 Z +SQUALOUR S K W AA1 L ER0 +SQUAM S K W AA1 M +SQUAMISH S K W AA1 M IH0 SH +SQUANDER S K W AA1 N D ER0 +SQUANDER'D S K W AA1 N D ER0 D +SQUANDERED S K W AA1 N D ER0 D +SQUANDERER S K W AA1 N D ER0 ER0 +SQUANDERING S K W AA1 N D ER0 IH0 NG +SQUANDERS S K W AA1 N D ER0 Z +SQUANKO S K W AA1 NG K OW0 +SQUANTO S K W AA1 N T OW0 +SQUANTOWN S K W AO1 N T AW2 N +SQUANTUM S K W AA1 N AH0 M +SQUAR S K W AA1 R +SQUARCI S K W AA0 R CH IY0 +SQUARE S K W EH1 R +SQUARE'S S K W EH1 R Z +SQUARED S K W EH1 R D +SQUAREFACE S K W EH1 R F EY2 S +SQUAREHEAD S K W EH1 R HH EH0 D +SQUARELY S K W EH1 R L IY0 +SQUARENESS S K W EH1 R N AH0 S +SQUARER S K W EH1 R ER0 +SQUARES S K W EH1 R Z +SQUARESAIL S K W EH1 R S EY0 L +SQUAREST S K W EH1 R AH0 S T +SQUARETOES S K W EH1 R IH0 T OW0 Z +SQUARING S K W EH1 R IH0 NG +SQUARISH S K W EH1 R IH0 SH +SQUARS S K W AA1 R Z +SQUASH S K W AA1 SH +SQUASHED S K W AA1 SH T +SQUASHES S K W AA1 SH IH0 Z +SQUASHING S K W AA1 SH IH0 NG +SQUASHY S K W AA1 SH IY0 +SQUAT S K W AA1 T +SQUATS S K W AA1 T S +SQUATTED S K W AA1 T IH0 D +SQUATTER S K W AA1 T ER0 +SQUATTER'S S K W AA1 T ER0 Z +SQUATTERED S K W AA1 T ER0 D +SQUATTERING S K W AA1 T ER0 IH0 NG +SQUATTERS S K W AA1 T ER0 Z +SQUATTIN S K W AA1 T IH0 N +SQUATTING S K W AA1 T IH0 NG +SQUATTY S K W AA1 T IY0 +SQUAW S K W AO1 +SQUAW'S S K W AO1 Z +SQUAWK S K W AO1 K +SQUAWKED S K W AO1 K T +SQUAWKER S K W AO1 K ER0 +SQUAWKIN S K W AO1 K IH0 N +SQUAWKING S K W AO1 K IH0 NG +SQUAWKS S K W AO1 K S +SQUAWMAN S K W AO1 M AH0 N +SQUAWS S K W AO1 Z +SQUAZE S K W AA1 Z IY0 +SQUEAK S K W IY1 K +SQUEAKED S K W IY1 K T +SQUEAKER S K W IY1 K ER0 +SQUEAKERS S K W IY1 K ER0 Z +SQUEAKILY S K W IY1 K AH0 L IY0 +SQUEAKIN S K W IY1 K IH0 N +SQUEAKING S K W IY1 K IH0 NG +SQUEAKINGS S K W IY1 K IH0 NG Z +SQUEAKNIBBLE S K W IY1 K N IH1 B AH0 L +SQUEAKS S K W IY1 K S +SQUEAKY S K W IY1 K IY0 +SQUEAL S K W IY1 L +SQUEALED S K W IY1 L D +SQUEALER S K W IY1 L ER0 +SQUEALERS S K W IY1 L ER0 Z +SQUEALIN S K W IY1 L IH0 N +SQUEALING S K W IY1 L IH0 NG +SQUEALINGS S K W IY1 L IH0 NG Z +SQUEALS S K W IY1 L Z +SQUEAMISH S K W IY1 M IH0 SH +SQUEAMISHLY S K W IY1 M IH0 SH L IY0 +SQUEAMISHNESS S K W IY1 M IH0 SH N AH0 S +SQUEE S K W IY1 +SQUEEGEE S K W IY1 JH IY0 +SQUEERS S K W IH1 R Z +SQUEERS'S S K W IH1 R Z IH0 Z +SQUEEZ'D S K W IY1 Z D +SQUEEZABLE S K W IY1 Z AH0 B AH0 L +SQUEEZE S K W IY1 Z +SQUEEZED S K W IY1 Z D +SQUEEZER S K W IY1 Z ER0 +SQUEEZES S K W IY1 Z IH0 Z +SQUEEZIN S K W IY1 Z IH0 N +SQUEEZING S K W IY1 Z IH0 NG +SQUELCH S K W EH1 L CH +SQUELCHED S K W EH1 L CH T +SQUELCHING S K W EH1 L CH IH0 NG +SQUEM S K W AH0 M +SQUENCH S K W EH1 N CH +SQUENCHED S K W EH1 N CH T +SQUIB S K W IH1 B +SQUIBBER S K W IH1 B ER0 +SQUIBBS S K W IH1 B Z +SQUIBS S K W IH1 B Z +SQUID S K W IH1 D +SQUIDS S K W IH1 D Z +SQUIER S K W AY1 ER0 +SQUIFF S K W IH1 F +SQUIFFY S K W IH1 F IY0 +SQUIGG S K W IH1 G +SQUIGGLES S K W IH1 G AH0 L Z +SQUILL S K W IH1 L +SQUILLACE S K W IH1 L AH0 S +SQUILLS S K W IH1 L Z +SQUINCH S K W IH1 N CH +SQUINCHED S K W IH1 N CH T +SQUINNY S K W IH1 N IY0 +SQUINT S K W IH1 N T +SQUINTED S K W IH1 N T IH0 D +SQUINTIN S K W IH1 N T IH0 N +SQUINTING S K W IH1 N T IH0 NG +SQUINTS S K W IH1 N T S +SQUINTY S K W IH1 N T IY0 +SQUINTY'S S K W IH1 N T IY0 Z +SQUIR S K W ER1 +SQUIRE S K W AY1 R +SQUIRE'LL S K W AY2 R AH0 L +SQUIRE'S S K W AY2 R Z +SQUIREARCHY S K W AY1 ER0 AA2 R K IY0 +SQUIRED S K W AY1 R D +SQUIREDOM S K W AY1 R D AH0 M +SQUIREEN S K W AY1 R IY0 N +SQUIREENS S K W AY1 R IY2 N Z +SQUIREL S K W IH1 R AH0 L +SQUIRELS S K W AY1 ER0 L Z +SQUIRES S K W AY1 ER0 Z +SQUIRESS S K W AY1 ER0 AH0 S +SQUIRING S K W AY1 ER0 IH0 NG +SQUIRL S K W ER1 L +SQUIRM S K W ER1 M +SQUIRMED S K W ER1 M D +SQUIRMIN S K W ER1 M IH0 N +SQUIRMING S K W ER1 M IH0 NG +SQUIRMS S K W ER1 M Z +SQUIRMY S K W ER1 M IY0 +SQUIRRADICAL S K W ER1 AE1 D AH0 K AH0 L +SQUIRREL S K W ER1 AH0 L +SQUIRREL'S S K W ER1 AH0 L Z +SQUIRRELS S K W ER1 AH0 L Z +SQUIRT S K W ER1 T +SQUIRTED S K W ER1 T AH0 D +SQUIRTED S K W ER1 T IH0 D +SQUIRTER S K W ER1 T ER0 +SQUIRTING S K W ER1 T IH0 NG +SQUIRTS S K W ER1 T S +SQUISH S K W IH1 SH +SQUISHING S K W IH1 SH IH0 NG +SQUITTY S K W IH1 T IY0 +SQUOIRE S K W OY2 R +SQUOZE S K W OW1 Z +SR S IH1 S T ER0 +SR S IY1 N Y ER0 +SRAHMANDAZI SH R AA1 M AH0 N D AA1 Z IY0 +SRALIK SH R AA1 L IH0 K +SRAMANA SH R AH0 M AA1 N AH0 +SRANKO SH R AE1 NG K OW0 +SRAVASTI SH R AH0 V AE1 S T IY0 +SREE S R IY1 +SRI EH1 S AA1 R AY1 +SRI S R IY1 +SRI SH R IY1 +SRIA SH R IY1 AH0 +SRIBATSA SH R IY1 B AE1 T S AH0 +SRIJUT SH R IY1 JH AH0 T +SRIKANTHA SH R IY2 K AA1 N DH AH0 +SRINAGAR S R IY1 N AH0 G AA2 R +SRINAGAR SH R IY1 N AH0 G AA2 R +SRINGA SH R IY1 NG G AH0 +SRIPATI S R IY0 P AA1 T IY0 +SRUB S R AH1 B +SRUTI SH R UW1 T IY0 +SS EH1 S EH1 S +SSASSAROR S AE1 S ER0 ER0 +SSEMA S IY1 M AH0 +SSH SH +SSHH SH +SSIDI S IY1 D IY0 +SSS EH1 S EH1 S EH1 S +SSSH S SH +SSSHHH S SH +SSSURI S SH ER0 IY0 +SSSURI'S S SH ER0 AY0 Z +SST S T +SSU EH1 S EH1 S Y UW1 +ST S EY1 N T +ST S T R IY1 T +STA EH1 S T IY1 EY1 +STA S T AH1 +STAAL S T AA1 L +STAARBRUCKER S T AA1 R B R AH0 K ER0 +STAAT S T AA1 T +STAATEN S T AA1 T AH0 N +STAATS S T AA1 T S +STAATSRAETHIN S T AA1 T S R EH0 TH AH0 N +STAATSRAETHIN'S S T AA1 T S R EH0 TH IH0 N Z +STAB S T AE1 B +STABAT S T AE1 B AH0 T +STABB'D S T AE1 B D +STABBED S T AE1 B D +STABBER S T AE1 B ER0 +STABBER'S S T AE1 B ER0 Z +STABBERS S T AE1 B ER0 Z +STABBING S T AE1 B IH0 NG +STABBINGS S T AE1 B IH0 NG Z +STABIAE S T EY1 B IY0 AH0 +STABILITY S T AH0 B IH1 L IH0 T IY0 +STABILIZATION S T EY2 B AH0 L AH0 Z EY1 SH AH0 N +STABILIZE S T EY1 B AH0 L AY2 Z +STABILIZED S T EY1 B AH0 L AY2 Z D +STABILIZER S T EY1 B AH0 L AY2 Z ER0 +STABILIZERS S T EY1 B AH0 L AY2 Z ER0 Z +STABILIZING S T EY1 B AH0 L AY2 Z IH0 NG +STABLE S T EY1 B AH0 L +STABLE'S S T EY1 B AH0 L Z +STABLEBOY S T EY1 B AH0 L B OY2 +STABLED S T EY1 B AH0 L D +STABLEMAN S T EY1 B AH0 L M AH0 N +STABLEMEN S T EY1 B AH0 L M IH0 N +STABLER S T EY1 B AH0 L ER0 +STABLER S T EY1 B L ER0 +STABLES S T EY1 B AH0 L Z +STABLEYARD S T EY1 B AH0 L Y AA2 R D +STABLING S T EY1 B AH0 L IH0 NG +STABLISH S T AE1 B L IH0 SH +STABLISHED S T AE1 B L IH0 SH T +STABLY S T AH0 B L IY0 +STABROEK S T EY1 B R OW0 K +STABS S T AE1 B Z +STABSARZT S T AE1 B Z AA1 R Z T +STACCATO S T AH0 K AA1 T OW2 +STACCATOS S T AH0 K AA1 T OW2 Z +STACE S T EY1 S +STACEY S T EY1 S IY0 +STACEY'S S T EY1 S IY0 Z +STACIA S T AA1 CH AH0 +STACK S T AE1 K +STACKED S T AE1 K T +STACKELBERG S T AE1 K AH0 L B ER0 G +STACKER S T AE1 K ER0 +STACKERS S T AE1 K ER0 Z +STACKHOUSE S T AE1 K HH AW2 S +STACKIN S T AE1 K IH0 N +STACKING S T AE1 K IH0 NG +STACKPOLE S T AE1 K P OW2 L +STACKPOLE'S S T AE1 K P OW2 L Z +STACKPOOLE S T AE1 K P UW2 L +STACKS S T AE1 K S +STACKYARD S T AE1 K Y AA2 R D +STACKYARDS S T AE1 K Y AA2 R D Z +STACPOLE S T AE1 K P OW2 L +STACPOOLE S T AE1 K P UW2 L +STACY S T EY1 S IY0 +STACY'S S T EY1 S IY0 Z +STAD S T AE2 D +STADACONA S T AE1 D AH0 K OW1 N AH0 +STADDLES S T AE1 D AH0 L Z +STADE S T EY1 D +STADES S T EY1 D Z +STADHOLDER S T AE1 D HH OW2 L D ER0 +STADHUIS S T AE1 D HH UW0 IH0 Z +STADIA S T EY1 D IY0 AH0 +STADION S T EY1 D IY0 AH0 N +STADIUM S T EY1 D IY0 AH0 M +STADT S T AE1 D T +STADTHOLDER SH T AE1 T HH OW2 L D ER0 +STADTHOUSE SH T AE1 T HH AW2 S +STAEDEL S T EH1 D AH0 L +STAEL S T EY1 L +STAEL'S S T EY1 L Z +STAEMER S T EY1 M ER0 +STAEMER'S S T EY1 M ER0 Z +STAFF S T AE1 F +STAFF'S S T AE1 F S +STAFFA S T AA1 F AH0 +STAFFE S T AE1 F +STAFFED S T AE1 F T +STAFFELAER S T AE1 F AH0 L IH1 R +STAFFELAER'S S T AE1 F AH0 L IH1 R Z +STAFFER S T AE1 F ER0 +STAFFER'S S T AE1 F ER0 Z +STAFFING S T AE1 F IH0 NG +STAFFORD S T AE1 F ER0 D +STAFFORD'S S T AE1 F ER0 D Z +STAFFORDS S T AE1 F ER0 D Z +STAFFORDSHIRE S T AE1 F ER0 D SH AY2 ER0 +STAFFORDSHIRE S T AE1 F ER0 D SH ER0 +STAFFORTH S T AE1 F ER0 TH +STAFFS S T AE1 F S +STAFFY S T AE1 F IY0 +STAG S T AE1 G +STAG'S S T AE1 G Z +STAGE S T EY1 JH +STAGECOACH S T EY1 JH K OW2 CH +STAGECOACHES S T EY1 JH K OW2 CH IH0 Z +STAGECRAFT S T EY1 JH K R AE2 F T +STAGED S T EY1 JH D +STAGELAND S T EY1 JH L AH0 N D +STAGER S T EY1 JH ER0 +STAGERS S T EY1 JH ER0 Z +STAGES S T EY1 JH AH0 Z +STAGES S T EY1 JH IH0 Z +STAGEY S T EY1 JH IY0 +STAGG S T AE1 G +STAGG'S S T AE1 G Z +STAGGED S T AE1 G D +STAGGER S T AE1 G ER0 +STAGGER'D S T AE1 G ER0 D +STAGGERED S T AE1 G ER0 D +STAGGERER S T AE1 G ER0 ER0 +STAGGERERS S T AE1 G ER0 ER0 Z +STAGGERIN S T AE1 G ER0 IH0 N +STAGGERING S T AE1 G ER0 IH0 NG +STAGGERINGLY S T AE1 G ER0 IH0 NG L IY0 +STAGGERS S T AE1 G ER0 Z +STAGGERY S T AE1 G ER0 IY0 +STAGGS S T AE1 G Z +STAGGS'S S T AE1 G Z IH0 Z +STAGHORN S T AE1 G HH ER0 N +STAGHOUND S T AE1 G HH AW2 N D +STAGHOUNDS S T AE1 G HH AW2 N D Z +STAGHOUSE S T AE1 G HH AW2 S +STAGINESS S T EY1 JH IY0 N AH0 S +STAGING S T EY1 JH IH0 NG +STAGINGS S T EY1 JH IH0 NG Z +STAGIRA S T EY1 JH IH1 R AH0 +STAGIRITE S T EY1 JH ER0 AY2 T +STAGNANCY S T AE1 G N AH0 N S IY0 +STAGNANT S T AE1 G N AH0 N T +STAGNATE S T AE1 G N EY2 T +STAGNATED S T AE1 G N EY2 T IH0 D +STAGNATES S T AE1 G N EY2 T S +STAGNATING S T AE1 G N EY2 T IH0 NG +STAGNATION S T AE0 G N EY1 SH AH0 N +STAGS S T AE1 G Z +STAGY S T EY1 JH IY0 +STAGYRITE S T EY1 JH IY0 R AY2 T +STAH S T AA1 +STAHEL S T AE1 AH0 L +STAHL S T AA1 L +STAHL'S S T AA1 L Z +STAHLECK S T AA1 L IH0 K +STAHOV S T AE1 AA0 V +STAHOVS S T AE1 AO0 F S +STAHT S T AA1 T +STAID S T EY1 D +STAIDER S T EY1 D ER0 +STAIDEST S T EY1 D AH0 S T +STAIDLY S T EY1 D L IY0 +STAIDNESS S T EY1 D N AH0 S +STAIN S T EY1 N +STAIN'D S T EY2 N D +STAINED S T EY1 N D +STAINER S T EY1 N ER0 +STAINERS S T EY1 N ER0 Z +STAINES S T EY1 N Z +STAINES'S S T EY1 N Z IH0 Z +STAINING S T EY1 N IH0 NG +STAINLESS S T EY1 N L AH0 S +STAINS S T EY1 N Z +STAINTON S T EY1 N T AH0 N +STAINTON'S S T EY1 N T AH0 N Z +STAINVILLE S T EY1 N V IH2 L +STAINVILLE'S S T EY1 N V IH2 L Z +STAIR S T EH1 R +STAIR'S S T EH1 R Z +STAIRCASE S T EH1 R K EY2 S +STAIRCASES S T EH1 R K EY2 S IH0 Z +STAIRFOOT S T EH1 R F UH2 T +STAIRHEAD S T EH1 R HH EH2 D +STAIRS S T EH1 R Z +STAIRS'S S T EH1 R Z IH0 Z +STAIRWAY S T EH1 R W EY2 +STAIRWAYS S T EH1 R W EY2 Z +STAIRWELL S T EH1 R W EH2 L +STAITHE S T AY1 DH +STAITHES S T EY1 TH IY0 Z +STAKE S T EY1 K +STAKE'S S T EY1 K S +STAKED S T EY1 K T +STAKEHOLDER S T EY1 K HH OW2 L D ER0 +STAKER S T EY1 K ER0 +STAKERS S T EY1 K ER0 Z +STAKES S T EY1 K S +STAKIN S T EY1 K IH0 N +STAKING S T EY1 K IH0 NG +STALACTICAL S T AH0 L AE1 K T IH0 K AH0 L +STALACTITE S T AH0 L AE1 K T AY2 T +STALACTITES S T AH0 L AE1 K T AY2 T S +STALACTITIC S T AO1 L AE0 K T IH1 T IH0 K +STALAGMITE S T AA0 L AA1 G M AY2 T +STALAGMITES S T AA0 L AA1 G M AY2 T S +STALAGMITIC S T AA0 L AA1 G M IH1 T IH0 K +STALDEN S T AO1 L D AH0 N +STALE S T EY1 L +STALED S T EY1 L D +STALEMATE S T EY1 L M EY2 T +STALENESS S T EY1 L N AH0 S +STALER S T EY1 L ER0 +STALES S T EY1 L Z +STALEST S T EY1 L AH0 S T +STALEY S T EY1 L IY0 +STALHAM S T AO1 L HH AH0 M +STALHEIM S T AE1 L HH AY0 M +STALIN S T AA1 L AH0 N +STALING S T AE1 L IH0 NG +STALK S T AO1 K +STALK'D S T AO1 K D +STALKED S T AO1 K T +STALKENBERG S T AO1 K AH0 N B ER0 G +STALKER S T AO1 K ER0 +STALKER'S S T AO1 K ER0 Z +STALKERS S T AO1 K ER0 Z +STALKIN S T AO1 K IH0 N +STALKING S T AO1 K IH0 NG +STALKS S T AO1 K S +STALKY S T AO1 K IY0 +STALKY'S S T AO1 K IY0 Z +STALL S T AO1 L +STALLED S T AO1 L D +STALLING S T AO1 L IH0 NG +STALLINGS S T AO1 L IH0 NG Z +STALLION S T AE1 L Y AH0 N +STALLION'S S T AE1 L Y AH0 N Z +STALLIONS S T AE1 L Y AH0 N Z +STALLMAN S T AO1 L M AH0 N +STALLS S T AO1 L Z +STALWART S T AO1 L W ER0 T +STALWARTH S T AO1 L W ER0 TH +STALWARTLY S T AO1 L W ER0 T L IY0 +STALWARTS S T AO1 L W ER0 T S +STALWORTH S T AO1 L W ER0 TH +STAM S T AE1 M +STAMACH S T AE1 M AH0 K +STAMBOUL S T AE1 M B UW1 L +STAMBUL S T AE0 M B UW1 L +STAMEN S T EY1 M AH0 N +STAMENS S T EY1 M AH0 N Z +STAMER S T EY1 M ER0 +STAMER'S S T EY1 M ER0 Z +STAMFORD S T AE1 M F ER0 D +STAMFORDHAM S T AE1 M F AO1 R D AH0 M +STAMFORDHAM'S S T AE1 M F AO1 R D AH0 M Z +STAMINA S T AE1 M AH0 N AH0 +STAMINATE S T AE1 M AH0 N EY2 T +STAMMARK S T AE1 M AA2 R K +STAMMER S T AE1 M ER0 +STAMMER'D S T AE1 M ER0 AH0 D +STAMMERED S T AE1 M ER0 D +STAMMERER S T AE1 M ER0 ER0 +STAMMERER'S S T AE1 M ER0 ER0 Z +STAMMERERS S T AE1 M ER0 ER0 Z +STAMMERING S T AE1 M ER0 IH0 NG +STAMMERINGLY S T AE1 M ER0 IH0 NG L IY0 +STAMMERINGS S T AE1 M ER0 IH0 NG Z +STAMMERS S T AE1 M ER0 Z +STAMP S T AE1 M P +STAMP'D S T AE1 M P D +STAMPA S T AE1 M P AH0 +STAMPA'S S T AE1 M P AH0 Z +STAMPED S T AE1 M P T +STAMPEDE S T AE0 M P IY1 D +STAMPEDE'S S T AE0 M P IY1 D Z +STAMPEDED S T AE0 M P IY1 D AH0 D +STAMPEDED S T AE0 M P IY1 D IH0 D +STAMPEDER S T AE0 M P IY1 D ER0 +STAMPEDERS S T AE0 M P IY1 D ER0 Z +STAMPEDES S T AE0 M P IY1 D Z +STAMPEDIN S T AE0 M P IY1 D IH0 N +STAMPEDING S T AE0 M P IY1 D IH0 NG +STAMPER S T AE1 M P ER0 +STAMPERS S T AE1 M P ER0 Z +STAMPIN S T AE1 M P IH0 N +STAMPING S T AE1 M P IH0 NG +STAMPINGS S T AE1 M P IH0 NG Z +STAMPS S T AE1 M P S +STAMPS'S S T AE1 M P S IH0 Z +STAMPT S T AE0 M P T +STAN S T AE1 N +STAN'IN S T AE1 N AE1 N +STAN'S S T AE1 N Z +STANAPAT S T AE1 N AH0 P AE2 T +STANARD S T AE1 N ER0 D +STANBERY S T AE1 N B ER0 IY0 +STANBOROUGH S T AE1 N B ER0 OW0 +STANBURY S T AE1 N B EH2 R IY0 +STANBY S T AE1 N B IY0 +STANCE S T AE1 N S +STANCES S T AE1 N S IH0 Z +STANCH S T AE1 N CH +STANCHED S T AE1 N CH T +STANCHELLS S T AE1 N CH AH0 L Z +STANCHEONS S T AE1 N CH IH0 N Z +STANCHER S T AE1 N CH ER0 +STANCHEST S T AE1 N CH AH0 S T +STANCHESTER S T AE1 N CH EH2 S T ER0 +STANCHING S T AE1 N CH IH0 NG +STANCHION S T AE1 N CH IY1 N +STANCHIONS S T AE1 N CH AH0 N Z +STANCHLY S T AE1 N CH L IY0 +STANCHNESS S T AE1 N CH N AH0 S +STANCHON S T AE1 N CH AH0 N +STANCY S T AE1 N S IY0 +STAND S T AE1 N D +STAND'ST S T AE1 N D IH0 S T +STANDALE S T AE1 N D EY2 L +STANDARD S T AE1 N D ER0 D +STANDARD'S S T AE1 N D ER0 D Z +STANDARDISED S T AE1 N D ER0 D AY2 Z D +STANDARDIZATION S T AE0 N D ER0 D IH0 Z EY1 SH AH0 N +STANDARDIZE S T AE1 N D ER0 D AY2 Z +STANDARDIZED S T AE1 N D ER0 D AY2 Z D +STANDARDIZING S T AE1 N D ER0 D AY2 Z IH0 NG +STANDARDS S T AE1 N D ER0 D Z +STANDBY S T AE1 N D B AY1 +STANDBYS S T AE1 N D B AY2 Z +STANDE S T AE1 N D +STANDEN S T AE1 N D AH0 N +STANDER S T AE1 N D ER0 +STANDERS S T AE1 N D ER0 Z +STANDERTON S T AE1 N D ER0 T AH0 N +STANDERTON'S S T AE1 N D ER0 T AH0 N Z +STANDEST S T AE1 N D AH0 S T +STANDETH S T AE1 N D IH0 TH +STANDFAST S T AE1 N D F AE2 S T +STANDI S T AE1 N D IY0 +STANDIFER S T AE1 N D IH0 F ER0 +STANDIN S T AE1 N D IH0 N +STANDING S T AE1 N D IH0 NG +STANDING'S S T AE1 N D IH0 NG Z +STANDINGS S T AE1 N D IH0 NG Z +STANDISH S T AE1 N D IH0 SH +STANDISH'S S T AE1 N D IH0 SH IH0 Z +STANDISHES S T AE1 N D IH0 SH IH0 Z +STANDOFFISH S T AE1 N D AO2 F IH2 SH +STANDON S T AE1 N D AH0 N +STANDPOINT S T AE1 N D P OY2 N T +STANDPOINTS S T AE1 N D P OY2 N T S +STANDRING S T AE1 N D ER0 IH0 NG +STANDS S T AE1 N D Z +STANDSTILL S T AE1 N D S T IH2 L +STANDUP S T AE1 N D AH2 P +STANE S T AE1 N +STANEHOLME S T AE1 N IH0 HH OW0 L M +STANEHOLME'S S T AE1 N IH0 HH OW0 L M Z +STANES S T EY1 N Z +STANESBY S T EY1 N Z B IY0 +STANFIELD S T AE1 N F IY2 L D +STANFIELD'S S T AE1 N F IY2 L D Z +STANFORD S T AE1 N F ER0 D +STANFORD'S S T AE1 N F ER0 D Z +STANFORDS S T AE1 N F ER0 D Z +STANFORTH S T AE1 N F ER0 TH +STANFORTH'S S T AE1 N F ER0 TH S +STANFORTHS S T AE1 N F ER0 TH S +STANG S T AE1 NG +STANGATE S T AE1 N G EY2 T +STANGEIST S T EY1 NG G AY2 S T +STANGEIST'S S T EY1 NG G AY2 S T S +STANGER S T AE1 NG ER0 +STANGERSON S T EY1 NG G ER0 S AH0 N +STANGERSON'S S T EY1 NG G ER0 S AH0 N Z +STANGO S T AA1 NG G OW0 +STANGRAVE S T AA0 NG G R EY2 V +STANHOPE S T AE1 N AH0 P +STANHOPE'S S T AE1 N AH0 P S +STANHOPES S T AE1 N AH0 P S +STANIEF S T AE1 N IY0 F +STANIEF'S S T AE1 N IY0 EH0 F S +STANIFORD S T AE1 N IH0 F ER0 D +STANIFORD'S S T AE1 N IH0 F ER0 D Z +STANIFORTH S T AE1 N IH0 F AO2 R TH +STANILAND S T AH0 N IH1 L AH0 N D +STANISLAO S T AE1 N IH0 S L AW0 +STANISLAS S T AH0 N IH1 S L AH0 S +STANISLASS S T AE1 N IH0 S L AE2 S +STANISLAUS S T AE1 N IH0 S L AO2 S +STANISLAV S T AE1 N IH0 S L AA0 V +STANISLAVSKY S T AE1 N IH0 S L AH0 V S K IY0 +STANISLAW S T AE1 N IH0 S L AO2 +STANISLAWSKI S T AH0 N IH0 S L AA1 F S K IY0 +STANISLOFF S T AE1 N IH0 S L AO0 F +STANISLOVAS S T AE1 N IH0 S L AA2 V AH0 Z +STANISTREET S T AE1 N IH0 S T R IY2 T +STANISTREET'S S T AE1 N IH0 S T R IY2 T S +STANITSA S T AH0 N IH1 T S AH0 +STANITSAS S T AE1 N IH0 T S AA2 Z +STANK S T AE1 NG K +STANLEIGH S T AE1 N L AH0 +STANLEY S T AE1 N L IY0 +STANLEY'S S T AE1 N L IY0 Z +STANLEYS S T AE1 N L IY0 Z +STANLOCK S T AE1 N L AA2 K +STANLOCK'S S T AE1 N L AA2 K S +STANLY S T AE1 N L IY0 +STANMORE S T AE1 N M AO0 R +STANMORE'S S T AE1 N M AO0 R Z +STANN S T AE1 N +STANNARD S T AE1 N ER0 D +STANNARD'S S T AE1 N ER0 D Z +STANNARY S T AE1 N ER0 IY0 +STANNER S T AE1 N ER0 +STANNIFORD S T AE1 N IH0 F ER0 D +STANNIN S T AE1 N IH0 N +STANNING S T AE1 N IH0 NG +STANNUM S T AE1 N AH0 M +STANNY S T AE1 N IY0 +STANOVOI S T AE1 N AH0 V OY2 +STANS S T AH0 N Z +STANSBURY S T AE1 N S B EH0 R IY0 +STANSBY S T AE1 N S B IY0 +STANSE S T AE1 N S +STANSFELD S T AE1 N S F EH2 L D +STANSFIELD S T AE1 N S F IY0 L D +STANSFORD S T AE1 N S F ER0 D +STANSHY S T AE1 N SH IY0 +STANSHY'S S T AE1 N SH IY0 Z +STANSMORE S T AE1 N S M AO0 R +STANSON S T AE1 N S AH0 N +STANSTEAD S T AE1 N S T EH2 D +STANT S T AE1 N T +STANTILOUP S T AE1 N T AH0 L UW1 P +STANTON S T AE1 N T AH0 N +STANTON'S S T AE1 N T AH0 N Z +STANTONS S T AE1 N T AH0 N Z +STANWAY S T AE1 N W EY2 +STANWELL S T AE1 N W EH2 L +STANWELL'S S T AE1 N W EH2 L Z +STANWICK S T AE1 N W IH0 K +STANWIX S T AE1 N W IH0 K S +STANWOOD S T AE1 N W UH2 D +STANZ S T AE1 N Z +STANZA S T AE1 N Z AH0 +STANZAS S T AE1 N Z AH0 Z +STANZE S T AE1 N Z +STAP S T AE1 P +STAPFER S T AE1 P F ER0 +STAPHYLOCOCCUS S T AE2 F AH0 L AH0 K AO1 K AH0 S +STAPIL S T AE1 P AH0 L +STAPLE S T EY1 P AH0 L +STAPLED S T EY1 P AH0 L D +STAPLEDEAN S T EY1 P AH0 L D EH1 N +STAPLEGROVE S T EY1 P AH0 L G R OW2 V +STAPLEHURST S T EY1 P AH0 L HH ER0 S T +STAPLER S T EY1 P AH0 L ER0 +STAPLER S T EY1 P L ER0 +STAPLES S T EY1 P AH0 L Z +STAPLETON S T EY1 P AH0 L T AH0 N +STAPLETON'S S T EY1 P AH0 L T AH0 N Z +STAPLETONS S T EY1 P AH0 L T AH0 N Z +STAPLEWICK S T EY1 P AH0 L W IH2 K +STAPPIT S T AE1 P IH0 T +STAPS S T AE1 P S +STAPYLTON S T EY1 P AH0 L T AH0 N +STAR S T AA1 R +STAR'D S T AA1 R D +STAR'S S T AA1 R Z +STARBEAM S T AA1 R B IY2 M +STARBEAMS S T AA1 R B IY1 M Z +STARBIRD S T AA1 R B ER2 D +STARBLOSSOM S T AA1 R B L AA1 S AH0 M +STARBOARD S T AA1 R B ER0 D +STARBOARDED S T AA1 R B AO1 R D IH0 D +STARBOTTLE S T AA1 R B AO2 T AH0 L +STARBOTTLE'S S T AA1 R B AO2 T AH0 L Z +STARBRIGHT S T AA1 R B R AY2 T +STARBUCK S T AA1 R B AH2 K +STARBUCK'S S T AA1 R B AH2 K S +STARCH S T AA1 R CH +STARCHED S T AA1 R CH T +STARCHER S T AA1 R CH ER0 +STARCHERS S T AA1 R CH ER0 Z +STARCHES S T AA1 R CH IH0 Z +STARCHING S T AA1 R CH IH0 NG +STARCHY S T AA1 R CH IY0 +STARCK S T AA1 R K +STARCUS S T AA1 R K AH0 S +STARD S T AA1 R D +STARDEN S T AA1 R D AH0 N +STARDI S T AA1 R D IY0 +STARDOM S T AA1 R D AH0 M +STARDUST S T AA1 R D AH2 S T +STARE S T EH1 R +STARED S T EH1 R D +STARELEIGH S T EH1 R L AH0 +STAREMBERG S T AE1 R AH0 M B ER0 G +STARER S T EH1 R ER0 +STARERS S T EH1 R ER0 Z +STARES S T EH1 R Z +STARETS S T AE1 R AH0 T S +STARETTA S T ER0 EH1 T AH0 +STARFALL S T AA1 R F AO2 L +STARFISH S T AA1 R F IH2 SH +STARFISHES S T AA1 R F IH2 SH IH0 Z +STARGAZERS S T AA1 R G EY2 Z ER0 Z +STARGESHINA S T AA1 R G IH0 SH IY1 N AH0 +STARHURST S T AA1 R HH ER0 S T +STARIN S T AE1 R IH0 N +STARING S T EH1 R IH0 NG +STARINGLY S T EH1 R IH0 NG L IY0 +STARK S T AA1 R K +STARK'S S T AA1 R K S +STARKE S T AA1 R K +STARKE'S S T AA1 R K S +STARKEST S T AA1 R K AH0 S T +STARKEY S T AA1 R K IY2 +STARKFIELD S T AA1 R K F IY0 L D +STARKIE S T AA1 R K IY0 +STARKLY S T AA1 R K L IY0 +STARKNESS S T AA1 R K N IH0 S +STARKS S T AA1 R K S +STARKWEATHER S T AA1 R K W EH2 DH ER0 +STARKWEATHER'S S T AA1 R K W EH2 DH ER0 Z +STARLAND S T AA1 R L AH0 N D +STARLEIN S T AA1 R L AY0 N +STARLESS S T AA1 R L IH0 S +STARLETT S T AA1 R L IH0 T +STARLIGHT S T AA1 R L AY2 T +STARLIGHT'S S T AA1 R L AY2 T S +STARLIGHTS S T AA1 R L AY2 T S +STARLIKE S T AA1 R L AY2 K +STARLIN S T AA1 R L IH0 N +STARLING S T AA1 R L IH0 NG +STARLING'S S T AA1 R L IH0 NG Z +STARLINGS S T AA1 R L IH0 NG Z +STARLIT S T AA1 R L IH0 T +STARMAN S T AA1 R M AH0 N +STARMEN S T AA1 R M AH0 N +STARMIDGE S T AA1 R M IH0 JH +STARN S T AA1 R N +STARNA S T AA1 R N AH0 +STARNBERG S T AA1 R N B ER0 G +STARNS S T AA1 R N Z +STARNWORTH S T AA1 R N W ER0 TH +STAROST S T AE1 R AH0 S T +STAROSTA S T AA1 R AO1 S T AH0 +STAROSTA'S S T AA1 R AO1 S T AH0 Z +STAROSTE S T AA0 R OW1 S T +STAROSTINE S T AE1 R AH0 S T IY1 N IY0 +STARPHA S T AA1 R F AH0 +STARR S T AA1 R +STARR'D S T AA1 R D +STARR'S S T AA1 R Z +STARRE S T AA1 R +STARRED S T AA1 R D +STARRES S T AA1 R Z +STARRETT S T AE1 R IH0 T +STARRIE S T AE1 R IY0 +STARRING S T AA1 R IH0 NG +STARRS S T AA1 R Z +STARRY S T AA1 R IY0 +STARS S T AA1 R Z +STARSHINE S T AA1 R SH AY2 N +STARSHIP S T AA1 R SH IH2 P +STARSHIPS S T AA1 R SH IH2 P S +STARSKY S T AA1 R S K IY0 +START S T AA1 R T +STARTED S T AA1 R T AH0 D +STARTED S T AA1 R T IH0 D +STARTER S T AA1 R T ER0 +STARTER'S S T AA1 R T ER0 Z +STARTERS S T AA1 R T ER0 Z +STARTIN S T AA1 R T IH0 N +STARTING S T AA1 R T IH0 NG +STARTINGS S T AA1 R T IH0 NG Z +STARTIT S T AA1 R T IH0 T +STARTLE S T AA1 R T AH0 L +STARTLED S T AA1 R T AH0 L D +STARTLEDLY S T AA1 R T L AH0 D L IY0 +STARTLEMENT S T AA1 R T AH0 L M AH0 N T +STARTLER S T AA1 R T L ER0 +STARTLES S T AA1 R T AH0 L Z +STARTLIN S T AA1 R T L IH0 N +STARTLING S T AA1 R T L IH0 NG +STARTLINGLY S T AA1 R T L IH0 NG L IY0 +STARTOP S T AA1 R T AA2 P +STARTS S T AA1 R T S +STARTSEV S T AA1 R T S EH0 V +STARTUP S T AA1 R T AH2 P +STARTUPS S T AA1 R T AH2 P S +STARV'D S T AA1 R V D +STARVATION S T AA0 R V EY1 SH AH0 N +STARVATIONS S T AA0 R V EY1 SH AH0 N Z +STARVE S T AA1 R V +STARVED S T AA1 R V D +STARVELING S T AA1 R V AH0 L IH0 NG +STARVELINGS S T AA1 R V AH0 L IH0 NG Z +STARVEM S T AA1 R V M +STARVES S T AA1 R V Z +STARVIN S T AA1 R V IH0 N +STARVING S T AA1 R V IH0 NG +STARWARD S T AA1 R W ER0 D +STARWOOD S T AA1 R W UH2 D +STARWORT S T AA1 R W ER0 T +STARY S T EH1 R IY0 +STARZA S T AA1 R Z AH0 +STARZA'S S T AA1 R Z AH0 Z +STARZL S T AA1 R Z AH0 L +STAS S T AH0 S +STASH S T AE1 SH +STASHED S T AE1 SH T +STASHIE S T AE1 SH IY0 +STASHKO S T AA1 SH K OW0 +STASHUN S T AE1 SH AH0 N +STASIEK S T AA1 S IY0 EH0 K +STASIS S T EY1 S AH0 S +STASU S T AA1 S UW0 +STAT S T AE1 T +STATA S T AA1 T AH0 +STATE S T EY1 T +STATE'S S T EY1 T S +STATECRAFT S T EY1 T K R AE2 F T +STATED S T EY1 T AH0 D +STATED S T EY1 T IH0 D +STATEDLY S T EY1 T AH0 D L IY0 +STATEHOOD S T EY1 T HH UH2 D +STATEHOUSE S T EY1 T HH AW2 S +STATELIER S T EY1 T L IY0 ER0 +STATELIEST S T EY1 T L IY0 AH0 S T +STATELILY S T AH0 T EH1 L AH0 L IY0 +STATELINESS S T EY1 T L IY0 N AH0 S +STATELY S T EY1 T L IY0 +STATEMENT S T EY1 T M AH0 N T +STATEMENTS S T EY1 T M AH0 N T S +STATEN S T AE1 T AH0 N +STATER S T EY1 T ER0 +STATEROOM S T EY1 T R UW2 M +STATEROOMS S T EY1 T R UW2 M Z +STATERS S T EY1 T ER0 Z +STATES S T EY1 T S +STATES' S T EY1 T S +STATESMAN S T EY1 T S M AH0 N +STATESMAN'S S T EY1 T S M AH0 N Z +STATESMANLIKE S T AH0 T EH1 S M AH0 N L AY2 K +STATESMANSHIP S T EY1 T S M AH0 N SH IH2 P +STATESMEN S T EY1 T S M IH0 N +STATESMEN'S S T EY1 T S M IH0 N Z +STATHAM S T AE1 TH AH0 M +STATHAM'S S T AE1 TH AH0 M Z +STATIA S T AE1 T IY0 AH0 +STATIC S T AE1 T IH0 K +STATICAL S T AE1 T IH0 K AH0 L +STATICALLY S T AE1 T IH0 K L IY0 +STATICS S T AE1 T IH0 K S +STATILIUS S T EY1 T IH2 L IY0 IH0 S +STATIM S T AH0 T IH1 M +STATIN S T EY1 T IH0 N +STATING S T EY1 T IH0 NG +STATION S T EY1 SH AH0 N +STATION'D S T EY1 SH AH0 N D 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+STEAKS S T EY1 K S +STEAL S T IY1 L +STEALE S T IY1 L +STEALER S T IY1 L ER0 +STEALER'S S T IY1 L ER0 Z +STEALERS S T IY1 L ER0 Z +STEALEST S T IY1 L IH0 S T +STEALETH S T IY1 L EH2 TH +STEALIN S T EH1 L IH0 N +STEALING S T IY1 L IH0 NG +STEALINGS S T IY1 L IH0 NG Z +STEALS S T IY1 L Z +STEALTH S T EH1 L TH +STEALTHIER S T EH1 L TH IY0 ER0 +STEALTHILY S T EH1 L TH AH0 L IY0 +STEALTHINESS S T EH1 L TH IY0 N AH0 S +STEALTHY S T EH1 L TH IY0 +STEAM S T IY1 M +STEAM'S S T IY1 M Z +STEAMBOAT S T IY1 M B OW2 T +STEAMBOAT'S S T IY1 M B OW2 T S +STEAMBOATING S T IY1 M B OW2 T IH0 NG +STEAMBOATMAN S T IY1 M B OW2 T M AH0 N +STEAMBOATMEN S T IY1 M B OW2 T M EH0 N +STEAMBOATS S T IY1 M B OW2 T S +STEAMED S T IY1 M D +STEAMER S T IY1 M ER0 +STEAMER'S S T IY1 M ER0 Z +STEAMERS S T IY1 M ER0 Z +STEAMIN S T IY1 M AH0 N +STEAMING S T IY1 M IH0 NG +STEAMROLLER S T IY1 M R OW2 L ER0 +STEAMS S T IY1 M Z +STEAMSHIP S T IY1 M SH IH2 P +STEAMSHIP'S S T IY1 M SH IH2 P S +STEAMSHIPS S T IY1 M SH IH2 P S 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AY1 N IH0 G ER0 +STEINITZ S T AY1 N IH0 T S +STEINKIRK S T AY1 NG K ER0 K +STEINMAN S T AY1 N M AH0 N +STEINMANN S T AY1 N M AH0 N +STEINMARC S T AY1 N M AA1 R K +STEINMARC'S S T AY1 N M AA1 R K S +STEINMETZ S T AY1 N M EH0 T S +STEINMETZ'S S T AY1 N M EH0 T S IH0 Z +STEINN S T AY1 N AH0 N +STEINRUECK S T AY1 N R UW1 K +STEINRUECK'S S T AY1 N R AH2 K S +STEINRUECKS S T AY1 N R AH2 K S +STEINS S T AY1 N Z +STEINTHAL S T AY1 N TH AH0 L +STEINTHOR S T AY1 N TH ER0 +STEINVOR S T AY1 N V ER0 +STEINWAY S T AY1 N W EY2 +STEINWEG S T AY1 N W IH0 G +STEINWITZ S T AY1 N W IH0 T S +STEKEL S T EH1 K AH0 L +STEL S T EH1 L +STELA S T EH1 L AH0 +STELAE S T EH1 L EY2 +STELE S T EH1 L +STELFOX S T EH1 L F AA2 K S +STELIS S T EH1 L IH0 S +STELL S T EH1 L +STELLA S T EH1 L AH0 +STELLA'S S T EH1 L AH0 Z +STELLAMARE S T EH1 L AH0 M EH1 R +STELLAR S T EH1 L ER0 +STELLATO S T EH0 L AA1 T OW0 +STELLE S T EH1 L +STELLENBOSCH S T EH1 L AH0 N B AO2 SH +STELLENE S T EH1 L IY2 N +STELLING S T EH1 L IH0 NG +STELLING'S S T EH1 L IH0 NG Z +STELTON S T EH1 L T AH0 N +STELTON'S S T EH1 L T AH0 N Z +STELVIO S T EH1 L V IY0 OW0 +STELZE S T EH1 L Z +STELZNER S T EH1 L Z N ER0 +STEM S T EH1 M +STEMHAGEN S T EH1 M HH AA2 G AH0 N +STEMLESS S T EH1 M L AH0 S +STEMM S T EH1 M +STEMM'D S T EH1 M D +STEMMED S T EH1 M D +STEMMING S T EH1 M IH0 NG +STEMP S T EH1 M P +STEMPLES S T EH1 M P AH0 L Z +STEMPLES'S S T EH1 M P AH0 L Z IH0 Z +STEMS S T EH1 M Z +STEN S T EH1 N +STENAY S T IY1 N EY0 +STENCH S T EH1 N CH +STENCHES S T EH1 N CH IH0 Z +STENCHING S T EH1 N CH IH0 NG +STENCIL S T EH1 N S IH0 L +STENCILED S T EH1 N S IH0 L D +STENCILING S T EH1 N S IH0 L IH0 NG +STENCILING S T EH1 N S L IH0 NG +STENCILLED S T EH1 N S IH1 L D +STENCILLING S T EH1 N S IH0 L IH0 NG +STENCILS S T EH1 N S L Z +STENDAHL S T EH1 N D AA2 L +STENDEL S T EH1 N D AH0 L +STENDHAL S T EH1 N D AA0 L +STENDHAL'S S T EH1 N D AA0 L Z +STENER S T EH1 N ER0 +STENER'S S T EH1 N ER0 Z +STENGEL S T EH1 NG G AH0 L +STENGER S T EH1 N JH ER0 +STENHOUSE S T EH1 N HH AW2 S +STENIO S T IY1 N IY0 OW0 +STENIUS S T IY1 N IY0 AH0 S +STENKA S T EH1 NG K AH0 +STENNIS S T EH1 N IH0 S +STENNIS'S S T EH1 N AH0 S IH0 Z +STENO S T EH1 N OW0 +STENO'S S T EH1 N OW0 Z +STENOG S T EH0 N AH1 G +STENOGRAPHER S T EH0 N AH1 G R AH0 F ER0 +STENOGRAPHER'S S T EH0 N AH1 G R AH0 F ER0 Z +STENOGRAPHERS S T EH0 N AH1 G R AH0 F ER0 Z +STENOGRAPHIC S T EH2 N AH0 G R AE1 F IH0 K +STENOGRAPHY S T EH0 N AH1 G R AH0 F IY0 +STENSON S T EH1 N S AH0 N +STENSON'S S T EH1 N S AH0 N Z +STENSSON S T EH1 N S AH0 N +STENT S T EH1 N T +STENT'S S T EH1 N T S +STENTERELLO S T EH1 N ER0 EH1 L OW0 +STENTOR S T EH1 N T ER0 +STENTORIAN S T EH1 N T AO1 R IY0 AH0 N +STENTORIOUSLY S T EH1 N T AO1 R IY0 AH0 S L IY0 +STENTORS S T EH1 N T ER0 Z +STEP S T EH1 P +STEP'S S T EH1 P S +STEPAN S T EH1 P AH0 N +STEPAN'S S T EH1 P AH0 N Z +STEPANIDA S T EH1 P AH0 N IY1 D AH0 +STEPANITCH S T EH1 P AH0 N IH0 CH +STEPANOFF S T EH1 P AH0 N AO0 F +STEPANOVITCH S T IH0 P AE1 N AH0 V AH0 CH +STEPANOVNA S T EH1 P AH0 N AA1 V N AH0 +STEPANUICH S T EH1 P AH0 N UW1 IH0 CH +STEPANUITCH S T EH1 P AH0 N UW1 IH0 CH +STEPASIDE S T EH1 P AH0 S AY2 D +STEPASIDE'S S T EH1 P AH0 S AY2 D Z +STEPBROTHER S T EH1 P B R AH1 DH ER0 +STEPCHILD S T EH1 P CH AY2 L D +STEPCHILDREN S T EH1 P CH IH1 L D R AH0 N +STEPDADDY S T EH1 P D AE1 D IY0 +STEPDAME S T EH1 P D EY1 M +STEPDAUGHTER S T EH1 P D AO2 T ER0 +STEPDAUGHTER'S S T EH1 P D AO2 T ER0 Z +STEPDAUGHTERS S T EH1 P D AO2 T ER0 Z +STEPFATHER S T EH1 P F AA2 DH ER0 +STEPFATHER'S S T EH1 P F AA2 DH ER0 Z +STEPH S T EH1 F +STEPHAN S T EH1 F AH0 N +STEPHANE S T EH0 F AA1 N +STEPHANI S T EH1 F AH0 N IY0 +STEPHANIA S T IH0 F AE1 N IY0 AH0 +STEPHANIA'S S T EH1 F AH0 N IY0 AH0 Z +STEPHANIE S T EH1 F AH0 N IY0 +STEPHANIE'S S T EH1 F AH0 N IY0 Z +STEPHANO S T EH0 F AA1 N OW0 +STEPHANO'S S T EH0 F AA1 N OW0 Z +STEPHANOPULOS S T EH1 F AH0 N AA1 P UW1 L OW0 Z +STEPHANOS S T EH0 F AA1 N OW0 S +STEPHANOTIS S T EH1 F AH0 N OW1 T IH0 S +STEPHANU S T EH0 F AA1 N UW0 +STEPHANUS S T EH1 F AH0 N IH0 S +STEPHEN S T IY1 V AH0 N +STEPHEN'S S T IY1 V AH0 N Z +STEPHENS S T EH1 F AH0 N Z +STEPHENS S T IY1 V AH0 N Z +STEPHENS'S S T IY1 V IH0 N Z IH0 Z +STEPHENSON S T IY1 V AH0 N S AH0 N +STEPHENSON'S S T IY1 V AH0 N S AH0 N Z +STEPHENSONS S T IY1 V AH0 N S AH0 N Z +STEPKA S T EH1 P K AH0 +STEPLADDER S T EH1 P L AE2 D ER0 +STEPLADDERS S T EH1 P L AE2 D ER0 Z +STEPMOTHER S T EH1 P M AH2 DH ER0 +STEPMOTHER'S S T IY1 V M AH2 DH ER0 Z +STEPMOTHERS S T IY1 V M AH2 DH ER0 Z +STEPNEY S T EH1 P N IY0 +STEPNEY'S S T EH1 P N IY0 Z +STEPNEYS S T EH1 P N IY0 Z +STEPNIAK S T EH1 P N IY0 AE0 K +STEPP'D S T EH1 P D +STEPPE S T EH1 P +STEPPED S T EH1 P T +STEPPER S T EH1 P ER0 +STEPPERS S T EH1 P ER0 Z +STEPPES S T EH1 P S +STEPPETH S T EH1 P IH0 TH +STEPPIN S T EH1 P IH0 N +STEPPING S T EH1 P IH0 NG +STEPPINGS S T EH1 P IH0 NG Z +STEPS S T EH1 P S +STEPSISTER S T EH1 P S IH2 S T ER0 +STEPSISTERS S T EH1 P S IH2 S T ER0 Z +STEPSON S T EH1 P S AH2 N +STEPSON'S S T EH1 P S AH2 N Z +STEPSONS S T EH1 P S AH2 N Z +STEPT S T EH1 P T +STEPTOE S T EH1 P T OW2 +STEPUNCLE S T EH1 P AH2 NG K AH0 L +STER S T ER0 +STEREO S T EH1 R IY0 OW2 +STEREOGRAPH S T EH2 R IY0 AH0 G R AE2 F +STEREOPTICON S T EH1 R IY0 AA1 P T IH0 K AA2 N +STEREOS S T EH1 R IY0 OW2 Z +STEREOSCOPE S T EH2 R IY0 AH0 S K OW2 P +STEREOSCOPIC S T EH2 R IY0 AH0 S K AA1 P IH0 K +STEREOTYPE S T EH1 R IY0 AH0 T AY2 P +STEREOTYPE S T EH1 R IY0 OW0 T AY2 P +STEREOTYPED S T EH1 R IY0 AH0 T AY2 P T +STEREOTYPED S T EH1 R IY0 OW0 T AY2 P T +STEREOTYPER S T EH1 R IY0 AH0 T AY2 P ER0 +STEREOTYPERS S T EH1 R IY0 AH0 T AY2 P ER0 Z +STEREOTYPES S T EH1 R IY0 AH0 T AY2 P S +STEREOTYPES S T EH1 R IY0 OW0 T AY2 P S +STEREOTYPING S T EH1 R IY0 AH0 T AY2 P IH0 NG +STEREOTYPING S T EH1 R IY0 OW0 T AY2 P IH0 NG +STERILE S T EH1 R AH0 L +STERILISATION S T EH2 R AH0 L IH0 Z EY1 SH AH0 N +STERILISE S T EH2 R AH0 L AH0 S +STERILISED S T EH2 R AH0 L AH0 S T +STERILISING S T EH2 R AH0 L IH0 S IH0 NG +STERILITY S T ER0 IH1 L IH0 T IY0 +STERILIZATION S T EH2 R AH0 L AH0 Z EY1 SH AH0 N +STERILIZATION S T EH2 R AH0 L IH0 Z EY1 SH AH0 N +STERILIZE S T EH1 R AH0 L AY2 Z +STERILIZED S T EH1 R AH0 L AY2 Z D +STERILIZER S T EH1 R AH0 L AY2 Z ER0 +STERILIZING S T EH1 R AH0 L AY2 Z IH0 NG +STERLIN S T ER1 L IH0 N +STERLING S T ER1 L IH0 NG +STERLING'S S T ER1 L IH0 NG Z +STERLINGS S T ER1 L IH0 NG Z +STERN S T ER1 N +STERN'S S T ER1 N Z +STERNA S T ER1 N AH0 +STERNBALD S T ER1 N B AO2 L D +STERNBERG S T ER1 N B ER0 G +STERNBERGER S T ER1 N B ER0 G ER0 +STERNBURG S T ER1 N B ER0 G +STERNDALE S T ER1 N D EY2 L +STERNE S T ER1 N +STERNE'S S T ER1 N Z +STERNED S T ER1 N D +STERNER S T ER1 N ER0 +STERNEST S T ER1 N AH0 S T +STERNFORD S T ER1 N F ER0 D +STERNHEIM S T ER1 N HH AY0 M +STERNHOLD S T ER1 N HH OW0 L D +STERNHOLD'S S T ER1 N HH OW2 L D Z +STERNLY S T ER1 N L IY0 +STERNMOST S T ER0 N M OW2 S T +STERNNESS S T ER1 N AH0 S +STERNNESS S T ER1 N N AH0 S +STERNPOST S T ER1 N P OW2 S T +STERNROYD S T ER1 N R OY2 D +STERNS S T ER1 N Z +STERNSHEETS S T ER1 N SH IY2 T S +STERNUM S T ER1 N AH0 M +STERNUTATION S T ER1 N Y UW0 T EY1 SH AH0 N +STERNWARD S T ER1 N W ER0 D +STERNWAY S T ER1 N W EY2 +STERNY S T ER1 N IY0 +STERNY'S S T ER1 N IY0 Z +STERRETT S T EH1 R IH0 T +STERRY S T EH1 R IY0 +STERT S T ER1 T +STERTINIUS S T ER1 SH IY0 N IY0 IH0 S +STERTOROUS S T EH1 R T ER0 AH0 S +STERTOROUSLY S T EH1 R T ER0 AH0 S L IY0 +STERZER S T ER1 Z ER0 +STERZL S T ER1 Z AH0 L +STESICHORUS S T IY1 S IH0 K AO1 R AH0 S +STESILAUS S T EH1 S IH0 L AW0 Z +STESSO S T EH1 S OW0 +STET S T EH1 T +STETHOSCOPE S T EH1 TH AH0 S K OW2 P +STETHOSCOPES S T EH1 TH AH0 S K OW2 P S +STETS S T EH1 T S +STETSON S T EH1 T S AH0 N +STETSON'S S T EH1 T S AH0 N Z +STETSONS S T EH1 T S AH0 N Z +STETTEN S T EH1 T AH0 N +STETTIN S T EH1 T IH0 N +STEUART S T OY1 AA0 R T +STEUBEN S T UW1 B IH0 N +STEUBEN S T Y UW1 B IH0 N +STEUBENVILLE S T UW1 B AH0 N V IH2 L +STEUER S T OY1 ER0 +STEUERMANN S T OY1 ER0 M AH0 N +STEUERRATH S T OY1 ER0 R AH0 TH +STEVAN S T EH1 V AH0 N +STEVE S T IY1 V +STEVE'S S T IY1 V Z +STEVEDORE S T IY1 V AH0 D AO2 R +STEVEDORE'S S T IY1 V AH0 D AO2 R Z +STEVEDORES S T IY1 V AH0 D AO2 R Z +STEVEN S T IY1 V AH0 N +STEVEN'S S T IY1 V AH0 N Z +STEVENAGE S T IY1 V AH0 N IH0 JH +STEVENS S T IY1 V AH0 N Z +STEVENS'S S T IY1 V AH0 N Z AH0 Z +STEVENS'S S T IY1 V AH0 N Z IH0 Z +STEVENSBURG S T IY1 V AH0 N Z B ER0 G +STEVENSES S T IY1 V AH0 N S IH0 Z +STEVENSON S T IY1 V AH0 N S AH0 N +STEVENSON'S S T IY1 V AH0 N S AH0 N Z +STEVENSONIAN S T IY1 V AH0 N S OW1 N IY0 AH0 N +STEVENSONS S T IY1 V AH0 N S AH0 N Z +STEVENTON S T IY1 V AH0 N T AH0 N +STEVENYNE S T EH1 V IH0 N AY1 N +STEVEY S T IY1 V IY0 +STEVIE S T IY1 V IY0 +STEVIE'S S T IY1 V IY0 Z +STEVY S T EH1 V IY0 +STEW S T UW1 +STEW'D S T UW1 D +STEW'S S T UW1 Z +STEWARD S T UW1 ER0 D +STEWARD'S S T UW1 ER0 D Z +STEWARDESS S T UW1 ER0 D AH0 S +STEWARDESSES S T UW1 ER0 D AH0 S IH0 Z +STEWARDS S T UW1 ER0 D Z +STEWARDSHIP S T UW1 ER0 D SH IH2 P +STEWART S T UW1 ER0 T +STEWART'S S T UW1 ER0 T S +STEWARTS S T UW1 ER0 T S +STEWED S T UW1 D +STEWIN S T UW1 IH0 N +STEWING S T UW1 IH0 NG +STEWPAN S T UW1 P AH0 N +STEWPANS S T UW1 P AE2 N Z +STEWPOT S T UW1 P AA2 T +STEWS S T UW1 Z +STEY S T IY0 +STEYN S T EY1 N +STEYN'S S T EY1 N Z +STEYNE S T EY1 N +STEYNE'S S T EY1 N Z +STEYNHAM S T EY1 N AH0 M +STEYNING S T EY1 N IH0 NG +STHAND S T HH AE1 N D +STHANTO S T HH AE1 N T OW0 +STHENELUS S TH IY1 N AH0 L AH0 S +STHETIC S TH EH1 T IH0 K +STHOP S TH AA1 P +STHOR S TH ER0 +STHRAIGHT S TH R EY2 T +STHRAME S TH R EY2 M +STHRANGE S TH R EY2 N JH +STHRANGER S TH R EY1 N JH ER0 +STHRANGERS S TH R EY1 N JH ER0 Z +STHRAW S TH R AO1 +STHREET S TH R IY1 T +STHRIKE S TH R AY2 K +STHROKE S TH R OW2 K +STHRONG S TH R AO1 NG +STHRUCK S TH R AH0 K +STI S T AY1 +STIBBS S T IH1 B Z +STICH S T IH1 CH +STICK S T IH1 K +STICK'S S T IH1 K S +STICKATIT S T IH1 K AH0 T IH2 T +STICKCHI S T IH1 K CH IY0 +STICKED S T IH1 K T +STICKEEN S T IH1 K IY2 N +STICKELBACK S T IH1 K AH0 L B AE2 K +STICKEN S T IH1 K AH0 N +STICKER S T IH1 K ER0 +STICKERS S T IH1 K ER0 Z +STICKERY S T IH1 K ER0 IY0 +STICKETH S T IH1 K IH0 TH +STICKIER S T IH1 K IY0 ER0 +STICKIEST S T IH1 K IY0 AH0 S T +STICKILY S T IH1 K AH0 L IY0 +STICKIN S T IH1 K IH0 N +STICKINESS S T IH1 K IY0 N AH0 S +STICKING S T IH1 K IH0 NG +STICKINGS S T IH1 K IH0 NG Z +STICKIT S T IH1 K IH0 T +STICKLE S T IH1 K AH0 L +STICKLEBACK S T IH1 K AH0 L B AE2 K +STICKLEBACKS S T IH1 K AH0 L B AE2 K S +STICKLEFORD S T IH1 K AH0 L F ER0 D +STICKLER S T IH1 K AH0 L ER0 +STICKLER S T IH1 K L ER0 +STICKLERS S T IH1 K L ER0 Z +STICKLES S T IH1 K AH0 L Z +STICKLING S T IH1 K L IH0 NG +STICKLY S T IH1 K L IY0 +STICKNEY S T IH1 K N IY0 +STICKNEY'S S T IH1 K N IY0 Z +STICKPIN S T IH1 K P AE1 N +STICKS S T IH1 K S +STICKY S T IH1 K IY0 +STID S T IH1 D +STIDDIER S T IH1 D IY0 ER0 +STIDDY S T IH1 D IY0 +STIDMANN S T IH1 D M AH0 N +STIEFBACH S T IY1 F B AA0 K +STIEFBACH'S S T IY1 F B AA2 K S +STIEFEL S T IY1 F AH0 L +STIEGLITZ S T IY1 G L IH0 T S +STIELOW S T IY1 L OW0 +STIEPAN S T IY1 P AH0 N +STIER S T AY1 ER0 +STIES S T AY1 Z +STIETENCRON S T IY1 T IH0 N K R AA2 N +STIF S T AY1 F +STIFF S T IH1 F +STIFF'S S T IH1 F S +STIFFE S T IH1 F +STIFFED S T IH1 F T +STIFFELKIND S T IH1 F IH0 L K AY1 N D +STIFFEN S T IH1 F AH0 N +STIFFEN'D S T IH1 F AH0 N D +STIFFENED S T IH1 F AH0 N D +STIFFENER S T IH1 F AH0 N ER0 +STIFFENIN S T IH1 F AH0 N IH0 N +STIFFENING S T IH1 F AH0 N IH0 NG +STIFFENING S T IH1 F N IH0 NG +STIFFENS S T IH1 F AH0 N Z +STIFFER S T IH1 F ER0 +STIFFEST S T IH1 F AH0 S T +STIFFISH S T IH1 F IH0 SH +STIFFLEG S T IH1 F L IH0 G +STIFFLY S T IH1 F L IY0 +STIFFNECKED S T IH1 F N EH0 K T +STIFFNER S T IH1 F N ER0 +STIFFNER'S S T IH1 F N ER0 Z +STIFFNESS S T IH1 F N AH0 S +STIFFORD S T IH1 F ER0 D +STIFFS S T IH1 F S +STIFFSON S T IH1 F S AH0 N +STIFFSON'S S T IH1 F S AH0 N Z +STIFFY S T IH1 F IY0 +STIFLE S T AY1 F AH0 L +STIFLED S T AY1 F AH0 L D +STIFLES S T AY1 F AH0 L Z +STIFLING S T AY1 F AH0 L IH0 NG +STIFLING S T AY1 F L IH0 NG +STIFLINGLY S T AY1 F L IH0 NG L IY0 +STIG S T IH1 G +STIG'S S T IH0 G Z +STIGAND S T IH1 G AH0 N D +STIGANDI S T IH1 G AH0 N D IY0 +STIGE S T IH1 JH +STIGGINS S T IH1 G IH0 N Z +STIGGS S T IH1 G Z +STIGMA S T IH1 G M AH0 +STIGMAS S T IH1 G M AH0 Z +STIGMATA S T IH0 G M AA1 T AH0 +STIGMATISE S T IH1 G M AH0 T AY2 Z +STIGMATISED S T IH1 G M AH0 T AY2 Z D +STIGMATISES S T IH1 G M AH0 T AY2 Z IH0 Z +STIGMATISING S T IH1 G M AH0 T AY2 Z IH0 NG +STIGMATIZATION S T IH1 G M AH0 T AH0 Z EY1 SH AH0 N +STIGMATIZE S T IH1 G M AH0 T AY2 Z +STIGMATIZED S T IH1 G M AH0 T AY2 Z D +STIGMATIZES S T IH1 G M AH0 T AY2 Z IH0 Z +STIGMATIZING S T IH1 G M AH0 T AY2 Z IH0 NG +STIKEEN S T IH1 K IY2 N +STIKINE S T IH1 K IY0 N +STIL S T IH1 L +STILE S T AY1 L +STILED S T AY1 L D +STILES S T AY1 L Z +STILES'S S T AY1 L Z IH0 Z +STILETTO S T AH0 L EH1 T OW0 +STILETTOES S T AH0 L EH1 T OW0 Z +STILETTOS S T AH0 L EH1 T OW0 Z +STILICHO S T IH1 L AH0 K OW0 +STILL S T IH1 L +STILL'D S T IH1 L D +STILL'S S T IH1 L Z +STILLBORN S T IH1 L B AO1 R N +STILLBOTTLE S T IH1 L B AO2 T AH0 L +STILLBROOK S T IH1 L B R UH0 K +STILLBURY S T IH1 L B ER0 IY0 +STILLE S T IH1 L +STILLED S T IH1 L D +STILLER S T IH1 L ER0 +STILLEST S T IH1 L IH0 S T +STILLING S T IH1 L IH0 NG +STILLINGER S T IH1 L IH0 NG ER0 +STILLINGFLEET S T IH1 L IH0 NG F L IY2 T +STILLINGHAST S T IH1 L IH0 NG HH AE2 S T +STILLINGHAST'S S T IH1 L IH0 NG HH S T S +STILLINGS S T IH1 L IH0 NG Z +STILLMAN S T IH1 L M AH0 N +STILLMAN'S S T IH1 L M AH0 N Z +STILLNESS S T IH1 L N AH0 S +STILLNESSES S T IH1 L N AH0 S IH0 Z +STILLROOM S T IH1 L R UW2 M +STILLS S T IH1 L Z +STILLSON S T IH1 L S AH0 N +STILLWATER S T IH1 L W AO2 T ER0 +STILLWELL S T IH1 L W EH2 L +STILLWELL'S S T IH1 L W EH2 L Z +STILLWOOD S T IH1 L W UH2 D +STILLY S T IH1 L IY0 +STILMAN S T IH1 L M AH0 N +STILO S T AY1 L OW0 +STILPO S T IH1 L P OW0 +STILT S T IH1 L T +STILTED S T IH1 L T IH0 D +STILTEDNESS S T IH1 L T IH0 D N AH0 S +STILTON S T IH1 L T AH0 N +STILTS S T IH1 L T S +STILTSTALKING S T IH1 L T S T AO1 K IH0 NG +STILUS S T AH0 L AH0 S +STILWELL S T IH1 L W EH2 L +STILWELL'S S T IH1 L W EH2 L Z +STIM S T IH1 M +STIMCOE S T IH1 M K OW2 +STIMCOE'S S T IH1 M K OW2 Z +STIMPCETT S T IH1 M P S IH0 T +STIMPSON S T IH1 M P S AH0 N +STIMPSON'S S T IH1 M P S AH0 N Z +STIMSON S T IH1 M S AH0 N +STIMSON'S S T IH1 M S AH0 N Z +STIMULANT S T IH1 M Y AH0 L AH0 N T +STIMULANTS S T IH1 M Y AH0 L AH0 N T S +STIMULATE S T IH1 M Y AH0 L EY2 T +STIMULATED S T IH1 M Y AH0 L EY2 T AH0 D +STIMULATED S T IH1 M Y AH0 L EY2 T IH0 D +STIMULATES S T IH1 M Y AH0 L EY2 T S +STIMULATING S T IH1 M Y AH0 L EY2 T IH0 NG +STIMULATION S T IH2 M Y AH0 L EY1 SH AH0 N +STIMULATIONS S T IH1 M Y AH0 L EY1 SH AH0 N Z +STIMULATIVE S T IH1 M Y AH0 L EY2 T IH0 V +STIMULATOR S T IH1 M Y AH0 L EY2 T ER0 +STIMULI S T IH1 M Y AH0 L AY2 +STIMULUS S T IH1 M Y AH0 L AH0 S +STINA S T IY1 N AH0 +STINCHE S T IH1 N CH +STINE S T AY1 N +STING S T IH1 NG +STINGAREE S T IH1 NG G ER0 IY1 +STINGAREE'S S T IH1 NG G ER0 IY1 Z +STINGAREES S T IH1 NG G ER0 IY1 Z +STINGER S T IH1 NG ER0 +STINGERS S T IH1 NG ER0 Z +STINGETH S T IH1 NG G IH0 TH +STINGEY S T IH1 NG G IY0 +STINGIER S T IH1 N JH IY0 ER0 +STINGIEST S T IH1 N JH IY0 AH0 S T +STINGILY S T IH1 NG G AH0 L IY0 +STINGIN S T IH1 NG G AH0 N +STINGINESS S T IH1 N JH IY0 N IH0 S +STINGING S T IH1 NG IH0 NG +STINGINGLY S T IH1 NG IH0 NG L IY0 +STINGLESS S T IH1 NG L AH0 S +STINGO S T IH1 NG G OW0 +STINGO'S S T IH1 NG G OW0 Z +STINGS S T IH1 NG Z +STINGWING S T IH1 NG G W IH2 NG +STINGY S T IH1 N JH IY0 +STINK S T IH1 NG K +STINKARD S T IH1 NG K ER0 D +STINKCAT S T IH1 NG K K AE2 T +STINKER S T IH1 NG K ER0 +STINKERS S T IH1 NG K ER0 Z +STINKETH S T IH1 NG K IH0 TH +STINKFOOT S T IH1 NG K F UH2 T +STINKFOOTS S T IH1 NG K F UH1 T S +STINKIN S T IH1 NG K IH0 N +STINKING S T IH1 NG K IH0 NG +STINKS S T IH1 NG K S +STINKY S T IH1 NG K IY0 +STINSON S T IH1 N S AH0 N +STINT S T IH1 N T +STINTED S T IH1 N T IH0 D +STINTING S T IH1 N T IH0 NG +STINTS S T IH1 N T S +STIORNA S T Y AO1 R N AH0 +STIOSHKA S T AY1 OW0 SH K AH0 +STIP S T IH2 P +STIPEND S T AY1 P AH0 N D +STIPENDIARIES S T AY1 P AH0 N D IY0 EH2 R IY0 Z +STIPENDIARY S T AY1 P AH0 N D IY0 EH0 R IY0 +STIPENDS S T AY1 P AH0 N D Z +STIPP S T IH1 P +STIPPLE S T IH1 P AH0 L +STIPPLED S T IH1 P AH0 L D +STIPPLING S T IH1 P AH0 L IH0 NG +STIPULATE S T IH1 P Y AH0 L EY2 T +STIPULATED S T IH1 P Y AH0 L EY2 T IH0 D +STIPULATES S T IH1 P Y AH0 L EY2 T S +STIPULATING S T IH1 P Y AH0 L EY2 T IH0 NG +STIPULATION S T IH2 P Y AH0 L EY1 SH AH0 N +STIPULATIONS S T IH2 P Y AH0 L EY1 SH AH0 N Z +STIR S T ER1 +STIRABOUT S T ER1 AH0 B AW2 T +STIRBACKS S T ER1 B AE2 K S +STIRES S T AY1 R Z +STIRHAMPTON S T ER1 HH AE1 M P T AH0 N +STIRK S T ER1 K +STIRKS S T ER1 K S +STIRLESS S T ER1 L AH0 S +STIRLING S T ER1 L IH0 NG +STIRLING'S S T ER1 L IH0 NG Z +STIRLINGSHIRE S T ER1 L IH0 NG SH AY2 R +STIRMINGHAM S T ER1 M IH0 NG HH AE2 M +STIRN S T ER1 N +STIRNER S T ER1 N ER0 +STIRR'D S T ER1 D +STIRRE S T ER1 +STIRRED S T ER1 D +STIRRER S T ER1 ER0 +STIRRERS S T ER1 R Z +STIRREST S T ER1 AH0 S T +STIRRETH S T ER1 AH0 TH +STIRRIN S T ER1 IH0 N +STIRRING S T ER1 IH0 NG +STIRRINGLY S T ER1 IH0 NG L IY0 +STIRRINGS S T ER1 IH0 NG Z +STIRRUP S T ER1 AH0 P +STIRRUPED S T ER1 AH0 P T +STIRRUPS S T ER1 AH0 P S +STIRS S T ER1 Z +STISTED S T IH1 S T IH0 D +STISTICK S T IH1 S T IH0 K +STITCH S T IH1 CH +STITCHED S T IH1 CH T +STITCHER S T IH1 CH ER0 +STITCHERY S T IH1 CH ER0 IY0 +STITCHES S T IH1 CH IH0 Z +STITCHIN S T IH1 CH IH0 N +STITCHING S T IH1 CH IH0 NG +STITCHINGS S T IH1 CH IH0 NG Z +STITCHLEY S T IH1 CH L IY0 +STITCHLEY'S S T IH1 CH L IY0 Z +STITCHWORT S T IH1 CH W ER0 T +STITH S T IH1 TH +STITHY S T IH1 TH IY0 +STITT S T IH1 T +STITTS S T IH1 T S +STITZ S T IH1 T S +STIVED S T AY1 V D +STIVER S T AY1 V ER0 +STIVERS S T AY1 V ER0 Z +STIXON S T IH1 K S AH0 N +STIZUS S T IH1 Z AH0 S +STJERNHOEK S EY1 N T JH ER1 N HH OW2 K +STLONG S T L AO0 NG +STO S T OW0 +STOA S T OW1 AH0 +STOAT S T OW1 T +STOATS S T OW1 T S +STOB S T AA1 B +STOBAEUS S T AA1 B EY1 AH0 S +STOBALT S T OW1 B AO2 L T +STOBART S T AA1 B AA2 R T +STOBART'S S T AA1 B ER0 T S +STOBB S T AA1 B +STOBBS S T AA1 B Z +STOBELL S T OW1 B AH0 L +STOBELL'S S T OW1 B AH0 L Z +STOBO S T OW1 B OW0 +STOCK S T AA1 K +STOCK'D S T AA1 K D +STOCK'S S T AA1 K S +STOCKADE S T AA1 K AH0 D +STOCKADED S T AA1 K EY1 D IH0 D +STOCKADER S T AA1 K EY1 D ER0 +STOCKADERS S T AA1 K EY1 D ER0 Z +STOCKADES S T AA1 K EY1 D Z +STOCKARD S T AA1 K ER0 D +STOCKBRIDGE S T AA1 K B R IH0 JH +STOCKBROKER S T AA1 K B R OW2 K ER0 +STOCKBROKER'S S T AA1 K B R OW2 K ER0 Z +STOCKBROKERS S T AA1 K B R OW2 K ER0 Z +STOCKBROKING S T AA1 K B R OW2 K IH0 NG +STOCKDALE S T AA1 K D EY2 L +STOCKDALE'S S T AA1 K D EY2 L Z +STOCKDOVE S T AA1 K D AH0 V +STOCKED S T AA1 K T +STOCKENS S T AA1 K AH0 N Z +STOCKFISH S T AA1 K F IH2 SH +STOCKHAM S T AA1 K HH AH0 M +STOCKHAUSEN S T AA1 K HH AW2 Z AH0 N +STOCKHEATH S T AA1 K HH IY2 TH +STOCKHOLDER S T AA1 K HH OW2 L D ER0 +STOCKHOLDERS S T AA1 K HH OW2 L D ER0 Z +STOCKHOLM S T AA1 K HH OW2 L M +STOCKIE S T AA1 K IY0 +STOCKIER S T AA1 K IY0 ER0 +STOCKILY S T AA1 K AH0 L IY0 +STOCKIN S T AA1 K IH0 N +STOCKIN' S T AA1 K IH0 N +STOCKIN'S S T AA1 K IH0 N Z +STOCKINET S T AA1 K IH0 N EH2 T +STOCKING S T AA1 K IH0 NG +STOCKINGED S T AA1 K IH0 NG D +STOCKINGLESS S T AA1 K IH0 NG L AH0 S +STOCKINGS S T AA1 K IH0 NG Z +STOCKINGTON S T AA1 K IH0 NG T AH0 N +STOCKINGTONIANS S T AA1 K IH0 NG T OW1 N IY0 AH0 N Z +STOCKINS S T AA1 K IH0 N Z +STOCKJOBBER S T AA1 K JH AA1 B ER0 +STOCKJOBBING S T AA1 K JH AA1 B IH0 NG +STOCKLEY S T AA1 K L IY0 +STOCKMAN S T AA1 K M AH0 N +STOCKMAN'S S T AA1 K M AE2 N Z +STOCKMAR S T AA1 K M AA2 R +STOCKMAR'S S T AA1 K M AA2 R Z +STOCKMEN S T AA1 K M EH2 N +STOCKMEN'S S T AA1 K M EH0 N Z +STOCKPILE S T AA1 K P AY2 L +STOCKPORT S T AA1 K P AO2 R T +STOCKRIDER S T AA1 K R AY2 D ER0 +STOCKROOM S T AA1 K R UW2 M +STOCKS S T AA1 K S +STOCKSTILL S T AA1 K S T IH2 L +STOCKTAKING S T AA1 K T EY2 K IH0 NG +STOCKTON S T AA1 K T AH0 N +STOCKTON'S S T AA1 K T AH0 N Z +STOCKWELL S T AA1 K W EH2 L +STOCKWHIP S T AA1 K W IH2 P +STOCKWHIPS S T AA1 K W IH2 P S +STOCKY S T AA1 K IY0 +STOCKYARD S T AA1 K Y AA2 R D +STOCKYARDS S T AA1 K Y AA2 R D Z +STODDARD S T AA1 D ER0 D +STODDARD'S S T AA1 D ER0 D Z +STODDARDS S T AA1 D ER0 D Z +STODDART S T AA1 D AA0 R T +STODDART'S S T AA1 D ER0 T S +STODE S T OW0 D +STODGE S T AA1 JH +STODGER S T AA1 JH ER0 +STODGER'S S T AA1 JH ER0 Z +STODGINESS S T AA1 JH IY0 N AH0 S +STODGY S T AA1 JH IY0 +STOEFFI S T OW1 F IY0 +STOELL'S S T OW1 L Z +STOEP S T OW1 P +STOEPS S T OW1 P S +STOFF S T AO1 F +STOFFEL S T AA1 F AH0 L +STOFFLES S T AA1 F AH0 L Z +STOFFLET S T AO1 F L IH0 T +STOGBUCHNER S T AA1 G B K N ER0 +STOGIE S T AA1 G IY0 +STOGIES S T AA1 G IY0 Z +STOGY S T AA1 G IY0 +STOHL S T OW1 L +STOIC S T OW1 IH0 K +STOIC'S S T OW1 IH0 K S +STOICAL S T OW1 IH0 K AH0 L +STOICALLY S T OW1 IH0 K L IY0 +STOICISM S T OW1 AH0 S IH2 Z AH0 M +STOICK S T OW1 IH0 K +STOICS S T OW1 IH0 K S +STOK S T AA1 K +STOKE S T OW1 K +STOKED S T OW1 K T +STOKEHOLD S T OW1 K HH OW2 L D +STOKEHOLE S T OW1 K HH OW2 L +STOKELY S T OW1 K L IY0 +STOKER S T OW1 K ER0 +STOKER'S S T OW1 K ER0 Z +STOKERS S T OW1 K ER0 Z +STOKES S T OW1 K S +STOKES'S S T OW1 K S IH0 Z +STOKESLEY S T OW1 K S L IY0 +STOKH S T AA0 K +STOKIMATIS S T OW1 K IY0 M AA1 T IH0 S +STOKING S T OW1 K IH0 NG +STOKOWSKI S T OW1 K AO1 F S K IY0 +STOKTON S T AA1 K T AH0 N +STOL'N S T OW1 L AH0 N +STOLA S T OW1 L AH0 +STOLATZ S T OW1 L AH0 T S +STOLBERG S T OW1 L B ER0 G +STOLD S T OW1 L D +STOLE S T OW1 L +STOLED S T OW1 L D +STOLEN S T OW1 L AH0 N +STOLES S T OW1 L Z +STOLGOLAND S T OW1 L G OW0 L AH0 N D +STOLID S T AA1 L AH0 D +STOLIDITY S T AH0 L IH1 D AH0 T IY0 +STOLIDLY S T AA1 L AH0 D L IY0 +STOLIDNESS S T OW1 L AH0 D N AH0 S +STOLIKER S T OW2 L IH0 K ER0 +STOLKJAERRE S T OW1 L K JH EH1 R +STOLL S T OW1 L +STOLLEN S T AA1 L AH0 N +STOLLER S T OW1 L ER0 +STOLLER'S S T AA1 L ER0 Z +STOLO S T OW1 L OW0 +STOLONS S T OW1 L AH0 N Z +STOLPE S T OW1 L P +STOLPEN S T OW1 L P EH2 N +STOLPHE S T AA1 L F IY0 +STOLT S T OW1 L T +STOLTOR S T OW1 L T ER0 +STOLYPIN S T AA1 L IH0 P AE1 N +STOLZ S T OW1 L Z +STOLZEN S T OW1 L Z AH0 N +STOLZENBERG S T OW1 L Z AH0 N B ER0 G +STOLZENFELS S T OW1 L Z IH0 N F AH0 L Z +STOMACH S T AH1 M AH0 K +STOMACH'S S T AH1 M AH0 K S +STOMACHACHE S T AH1 M AH0 K EY2 K +STOMACHED S T AH1 M AH0 K T +STOMACHER S T AH1 M AH0 K ER0 +STOMACHERS S T AH1 M AH0 K ER0 Z +STOMACHFUL S T AH1 M AH0 K F AH0 L +STOMACHIC S T AH1 M AH0 K IH0 K +STOMACHS S T AH1 M AH0 K S +STOMACK S T AH1 M AH0 K +STOMACKE S T AH1 M AH0 K +STOMICK S T AA1 M IH0 K +STOMMICK S T AA1 M IH0 K +STOMP S T AA1 M P +STOMPED S T AA1 M P T +STOMPIN S T AA1 M P IH0 N +STOMPING S T AA1 M P IH0 NG +STON S T AH0 N +STOND S T AA1 N D +STONE S T OW1 N +STONE'S S T OW1 N Z +STONEBREAKER S T OW1 N B R EY2 K ER0 +STONEBRIDGE S T OW1 N B R IH2 JH +STONECHAT S T AA1 N IH0 K AH0 T +STONECROP S T OW1 N K R AA2 P +STONECROSS S T OW1 N K R AO2 S +STONECUTTER S T OW1 N K AH2 T ER0 +STONECUTTER'S S T OW1 N K AH2 T ER0 Z +STONECUTTERS S T OW1 N K AH2 T ER0 Z +STONED S T OW1 N D +STONEGATE S T AA1 N IH0 G EY1 T +STONEHAM S T OW1 N AH0 M +STONEHAVEN S T OW1 N HH EY2 V AH0 N +STONEHEART S T OW1 N HH AA0 R T +STONEHENGE S T OW1 N HH EH2 N JH +STONEHOUSE S T OW1 N HH AW2 S +STONELAND S T OW1 N L AH0 N D +STONELEIGH S T OW1 N L AH0 +STONELESS S T OW1 N L AH0 S +STONELIKE S T OW1 N L AY2 K +STONEMAN S T OW1 N M AH0 N +STONEMAN'S S T OW1 N M AH0 N Z +STONEMASON S T OW1 N M EY1 S AH0 N +STONEMASON'S S T OW1 N M EY1 S AH0 N Z +STONEMASONS S T OW1 N M EY1 S AH0 N Z +STONER S T OW1 N ER0 +STONER'S S T OW1 N ER0 Z +STONES S T OW1 N Z +STONEST S T OW1 N S T +STONESTUDY S T OW1 N S T AH2 D IY0 +STONEWALL S T OW1 N W AO2 L +STONEWALL'S S T OW1 N W AO2 L Z +STONEWARE S T OW1 N W EH2 R +STONEWELL S T OW1 N W EH2 L +STONEWELL'S S T OW1 N W EH2 L Z +STONEWORK S T OW1 N W ER2 K +STONEY S T OW1 N IY0 +STONHAWON S T AA1 N HH AH0 W AA0 N +STONIE S T OW1 N IY0 +STONIER S T OW1 N IY0 ER0 +STONIES S T OW1 N IY0 Z +STONIEST S T OW1 N IY0 AH0 S T +STONILY S T AA1 N AH0 L IY0 +STONINESS S T OW1 N IY0 N AH0 S +STONING S T OW1 N IH0 NG +STONINGTON S T OW1 N IH0 NG T AH0 N +STONISH S T AA1 N IH0 SH +STONISHED S T AA1 N IH0 SH T +STONISHING S T AA1 N IH0 SH IH0 NG +STONO S T OW1 N OW0 +STONOR S T AA1 N ER0 +STONOR'S S T AA1 N ER0 Z +STONY S T OW1 N IY0 +STONYHURST S T OW1 N IY0 HH ER0 S T +STOO S T UW1 +STOOARD S T UW1 AA0 R D +STOOD S T UH1 D +STOOD'ST S T UW1 D S T +STOODE S T UW1 D +STOODEST S T UH1 D AH0 S T +STOODLES S T UW1 D AH0 L Z +STOODST S T UH1 D S T +STOOF S T UW1 F +STOOGE S T UW1 JH +STOOK S T UH1 K +STOOKED S T UW1 K T +STOOKING S T UW1 K IH0 NG +STOOKS S T UW1 K S +STOOL S T UW1 L +STOOLS S T UW1 L Z +STOOM S T UW1 M +STOOP S T UW1 P +STOOP'D S T UW1 P D +STOOPED S T UW1 P T +STOOPID S T UW1 P IH0 D +STOOPIN S T UW1 P IH0 N +STOOPING S T UW1 P IH0 NG +STOOPS S T UW1 P S +STOOPT S T UW1 P T +STOOPY S T UW1 P IY0 +STOORWORM S T UW1 R W ER0 M +STOOT S T UW1 T +STOP S T AA1 P +STOP'D S T AA1 P D +STOPCHASE S T AA1 P CH EY2 S +STOPCOCK S T AA1 P K AA2 K +STOPE S T OW1 P +STOPED S T OW1 P T +STOPES S T OW1 P S +STOPFOOT S T AA1 P F UH2 T +STOPFORD S T AA1 P F ER0 D +STOPGAP S T AA1 P G AE2 P +STOPING S T OW1 P IH0 NG +STOPOVER S T AA1 P OW2 V ER0 +STOPP'D S T AA1 P D +STOPPA S T AA1 P AH0 +STOPPAGE S T AA1 P IH0 JH +STOPPAGES S T AA1 P IH0 JH IH0 Z +STOPPED S T AA1 P T +STOPPER S T AA1 P ER0 +STOPPERED S T AA1 P ER0 D +STOPPERS S T AA1 P ER0 Z +STOPPETH S T AA1 P IH0 TH +STOPPIN S T AA1 P IH0 N +STOPPING S T AA1 P IH0 NG +STOPPINGS S T AA1 P IH0 NG Z +STOPPIT S T AA1 P IH0 T +STOPPLE S T AA1 P AH0 L +STOPPUM S T AA1 P AH0 M +STOPS S T AA1 P S +STOPT S T AA1 P T +STOPWATCH S T AA1 P W AA2 CH +STOR S T AO1 R +STOR'D S T AA1 D +STORACE S T AO1 R AH0 S +STORAGE S T AO1 R AH0 JH +STORAGE S T AO1 R IH0 JH +STORAX S T AO1 R AE2 K S +STORBORG S T AO1 R B AO0 R G +STORBUK S T AO1 R B UH2 K +STORBY S T AO1 R B IY0 +STORCK S T AO1 R K +STORDALEN S T AO1 R D AA0 L EY1 N +STORE S T AO1 R +STORE'S S T AO1 R Z +STORED S T AO1 R D +STOREFIELD S T AO1 R F IY2 L D +STOREHOUSE S T AO1 R HH AW2 S +STOREHOUSES S T AO1 R HH AW2 Z AH0 Z +STOREKEEPER S T AO1 R K IY2 P ER0 +STOREKEEPER'S S T AO1 R K IY2 P ER0 Z +STOREKEEPERS S T AO1 R K IY2 P ER0 Z +STOREKEEPING S T AO1 R K IY2 P IH0 NG +STOREMAN S T AO1 R M AH0 N +STORER S T AO1 R ER0 +STOREROOM S T AO1 R R UW2 M +STOREROOMS S T AO1 R R UW2 M Z +STORES S T AO1 R Z +STORESHIP S T AO1 R SH IH2 P +STORESHIPS S T AO1 R SH IH2 P S +STOREY S T AO1 R IY0 +STOREY'S S T AO1 R IY0 Z +STOREYED S T AO1 R IY0 D +STOREYS S T AO1 R IY0 Z +STORIA S T AO1 R IY0 AH0 +STORIE S T AO1 R IY0 +STORIED S T AO1 R IY0 D +STORIES S T AO1 R IY0 Z +STORIETTES S T AO1 R IY0 EH1 T S +STORING S T AO1 R IH0 NG +STORISENDE S T AO1 R IH0 S EH1 N D EY0 +STORK S T AO1 R K +STORK'S S T AO1 R K S +STORKS S T AO1 R K S +STORM S T AO1 R M +STORM'D S T AO1 R M D +STORM'S S T AO1 R M Z +STORMBERG S T AO1 R M B ER0 G +STORMBOUND S T AO1 R M B AW2 N D +STORMCOCK S T AO1 R M K AA2 K +STORME S T AO1 R M +STORMED S T AO1 R M D +STORMER S T AO1 R M ER0 +STORMERS S T AO1 R M ER0 Z +STORMES S T AO1 R M Z +STORMFIELD S T AO1 R M F IY2 L D +STORMFIELD'S S T AO1 R M F IY2 L D Z +STORMFUL S T AO1 R M F AH0 L +STORMIER S T AO1 R M IY0 ER0 +STORMIEST S T AO1 R M IY0 IH0 S T +STORMILY S T AO1 R M AH0 L IY0 +STORMIN S T AO1 R M IY0 N +STORMING S T AO1 R M IH0 NG +STORMINGS S T AO2 R M IH0 NG Z +STORMLESS S T AO1 R M L AH0 S +STORMONT S T AO1 R M OW0 N T +STORMONT'S S T AO1 R M AA2 N T S +STORMONTH S T AO1 R M AH0 N TH +STORMPOINT S T AO1 R M P OY2 N T +STORMS S T AO1 R M Z +STORMWAYS S T AO1 R M W EY2 Z +STORMY S T AO1 R M IY0 +STORMY'S S T AO1 R M IY0 Z +STORNAWAY S T AO1 R N AH0 W EY2 +STORNDALE S T AO1 R N D EY2 L +STORNELLI S T AO1 R N EH1 L IY0 +STORNHAM S T AO1 R N HH AH0 M +STORNOWAY S T AO1 R N OW0 W EY2 +STORR S T AO1 R +STORRE S T AO1 R IY0 +STORRINGTON S T AO1 R IH0 NG T AH0 N +STORROW S T AA1 R OW0 +STORRS S T AO1 R Z +STORTHING S T AO1 R DH IH0 NG +STORY S T AO1 R IY0 +STORY'S S T AO1 R IY0 Z +STORYBOOK S T AO1 R IY0 B UH2 K +STORYBOOKS S T AO1 R IY0 B UH2 K S +STORYS S T AO1 R IY0 Z +STORYTELLER S T AO1 R IY0 T EH2 L ER0 +STORYTELLERS S T AO1 R IY0 T EH2 L ER0 Z +STORYTELLING S T AO1 R IY0 T EH2 L IH0 NG +STOSS S T AA1 S +STOT S T AA1 T +STOTFORD S T AA1 T F ER0 D +STOTHARD S T AA1 TH ER0 D +STOTS S T AA1 T S +STOTT S T AA1 T +STOTT'S S T AA1 T S +STOTTS S T AA1 T S +STOTZ S T AA1 T S +STOUGHTON S T OW1 T AH0 N +STOUGHTON'S S T OW1 T AH0 N Z +STOUND S T AW1 N D +STOUP S T UW1 P +STOUPS S T UW1 P S +STOUR S T AW1 R +STOUR'S S T UH1 R Z +STOURBRIDGE S T OW1 ER1 B R IH2 JH +STOUSH S T AW1 SH +STOUT S T AW1 T +STOUT'S S T AW1 T S +STOUTENBURGH S T AW1 T AH0 N B ER0 G +STOUTENBURGH'S S T AW1 T AH0 N B ER0 G Z +STOUTER S T AW1 T ER0 +STOUTEST S T AW1 T AH0 S T +STOUTHEART S T AW1 T HH AA2 R T +STOUTHEARTED S T AW1 T HH AA2 R T IH0 D +STOUTISH S T AW1 T IH0 SH +STOUTLY S T AW1 T L IY0 +STOUTNESS S T AW1 T N AH0 S +STOUTS S T AW1 T S +STOVE S T OW1 V +STOVE'S S T OW1 V Z +STOVED S T OW1 V D +STOVEPIPE S T OW1 V P AY2 P +STOVEPIPES S T OW1 V P AY2 P S +STOVER S T OW1 V ER0 +STOVER'S S T OW1 V ER0 Z +STOVES S T OW1 V Z +STOVEY S T OW1 V IY0 +STOVIK S T OW1 V IH0 K +STOVIN S T OW1 V IH0 N +STOW S T OW1 +STOW'D S T AW1 D +STOW'S S T OW1 Z +STOWAGE S T OW1 AH0 JH +STOWAWAY S T OW1 AH0 W EY2 +STOWAWAYS S T OW1 AH0 W EY2 Z +STOWBODY S T OW1 B AA2 D IY0 +STOWE S T OW1 +STOWE'S S T OW1 Z +STOWED S T OW1 D +STOWEL S T AW1 AH0 L +STOWELL S T AA1 W EH0 L +STOWER S T OW1 ER0 +STOWER'S S T OW1 ER0 Z +STOWEY S T OW1 IY0 +STOWIN S T OW1 IH0 N +STOWING S T OW1 IH0 NG +STOWMARKET S T OW1 M AA2 R K IH0 T +STOWN S T AW2 N +STOWORTH S T AA1 W ER0 TH +STOWS S T OW1 Z +STOWSHER S T OW1 SH ER0 +STOWTE S T OW1 T +STOWTING S T OW1 T IH0 NG +STOY S T OY1 +STOYAN S T OY1 AH0 N +STOYANOVITCH S T OY1 AH0 N AA1 V IH0 CH +STR S T R +STRA S T R AH0 +STRAANGE S T R AA1 EY0 N JH +STRAANGER S T R AA1 EY0 N JH ER0 +STRAAT S T R AA1 T +STRABANE S T R AH0 B EY2 N +STRABISMUS S T R AH0 B IH1 Z M AH0 S +STRABO S T R AA1 B OW0 +STRABO'S S T R AA1 B OW0 Z +STRACCHINO S T R AE1 K IY1 N OW0 +STRACHAN S T R AE1 CH AH0 N +STRACHEY S T R AE1 CH IY0 +STRACHEY'S S T R AE1 CH IY0 Z +STRACHINSKY S T R AE1 CH IH1 N S K IY0 +STRACHINSKY'S S T R AE1 CH IH1 N S K IY0 Z +STRACTED S T R AE1 K T IH0 D +STRAD S T R AE2 D +STRADA S T R AA1 D AH0 +STRADDLE S T R AE1 D AH0 L +STRADDLED S T R AE1 D AH0 L D +STRADDLES S T R AE1 D AH0 L Z +STRADDLING S T R AE1 D L IH0 NG +STRADELLA S T R AE1 D EH1 L AH0 +STRADELLA'S S T R AE1 D EH1 L AH0 Z +STRADIOTES S T R EY1 D IY0 OW2 T S +STRADIOTS S T R AE1 D IY0 AH0 T S +STRADIVARI S T R AE2 D IH0 V EH1 R IY0 +STRADIVARIUS S T R AE2 D IH0 V EH1 R IY0 AH0 S +STRADLING S T R AE1 D L IH0 NG +STRAE S T R EY1 +STRAET S T R EH0 T +STRAFE S T R EY1 F +STRAFED S T R EY1 F T +STRAFFORD S T R AE1 F ER0 D +STRAFFORD'S S T R AE1 F ER0 D Z +STRAFING S T R EY1 F IH0 NG +STRAGGALLS S T R AE1 G AO2 L Z +STRAGGLE S T R AE1 G AH0 L +STRAGGLED S T R AE1 G AH0 L D +STRAGGLER S T R AE1 G L ER0 +STRAGGLERS S T R AE1 G L ER0 Z +STRAGGLES S T R AE1 G AH0 L Z +STRAGGLING S T R AE1 G AH0 L IH0 NG +STRAGGLY S T R AE1 G AH0 L IY0 +STRAHAN S T R AE1 HH AH0 N +STRAHAN'S S T R AE1 HH AH0 N Z +STRAHL S T R AA1 L +STRAIGHT S T R EY1 T +STRAIGHT'S S T R EY1 T S +STRAIGHTAWAY S T R EY1 T AH0 W EY2 +STRAIGHTEN S T R EY1 T AH0 N +STRAIGHTENED S T R EY1 T AH0 N D +STRAIGHTENERS S T R EY1 T AH0 N ER0 Z +STRAIGHTENIN S T R EY1 T N IH0 N +STRAIGHTENING S T R EY1 T AH0 N IH0 NG +STRAIGHTENING S T R EY1 T N IH0 NG +STRAIGHTENS S T R EY1 T AH0 N Z +STRAIGHTER S T R EY1 T ER0 +STRAIGHTEST S T R EY1 T AH0 S T +STRAIGHTFORWARD S T R EY1 T F AO1 R W ER0 D +STRAIGHTFORWARDLY S T R EY2 T F AO1 R W ER0 D L IY0 +STRAIGHTFORWARDNESS S T R EY1 T F AO1 R W ER0 D N AH0 S +STRAIGHTLY S T R EY1 T L IY0 +STRAIGHTNESS S T R EY1 T N AH0 S +STRAIGHTS S T R EY1 T S +STRAIGHTWAY S T R EY1 T W EY2 +STRAIGHTWAYS S T R EY1 T W EY2 Z +STRAIK S T R EY1 K +STRAIN S T R EY1 N +STRAIN'D S T R EY1 N D +STRAIN'S S T R EY1 N Z +STRAINED S T R EY1 N D +STRAINER S T R EY1 N ER0 +STRAINERS S T R EY1 N ER0 Z +STRAININ S T R EY1 N IH0 N +STRAINING S T R EY1 N IH0 NG +STRAININGLY S T R EY1 N IH0 NG L IY0 +STRAININGS S T R EY1 N IH0 NG Z +STRAINS S T R EY1 N Z +STRAIT S T R EY1 T +STRAITEN S T R EY1 T AH0 N +STRAITENED S T R EY1 T AH0 N D +STRAITENING S T R EY1 T N IH0 NG +STRAITER S T R EY1 T ER0 +STRAITEST S T R EY1 T AH0 S T +STRAITHWAITE S T R EY1 TH W EY2 T +STRAITJACKET S T R EY1 T JH AE2 K AH0 T +STRAITLACED S T R EY1 T L AH0 S T +STRAITLY S T R EY1 T L IY0 +STRAITNESS S T R EY1 T N AH0 S +STRAITS S T R EY1 T S +STRAKE S T R EY1 K +STRAKED S T R EY1 K T +STRAKENCZ S T R AA1 K IH0 N CH +STRAKER S T R EY1 K ER0 +STRAKES S T R EY1 K S +STRAKOSCH S T R AA1 K AO1 SH +STRALEY S T R AE1 L IY0 +STRALSUND S T R AE1 L S AH0 N D +STRAMASH S T R AE1 M AE2 SH +STRAME S T R EY1 M +STRAMMFEST S T R AE1 M F EH2 S T +STRAMONIUM S T R AH0 M OW1 N IY0 AH0 M +STRAN S T R AE1 N +STRAND S T R AE1 N D +STRANDED S T R AE1 N D AH0 D +STRANDED S T R AE1 N D IH0 D +STRANDING S T R AE1 N D IH0 NG +STRANDS S T R AE1 N D Z +STRANDVIK S T R AE1 N D V IH0 K +STRANG S T R AE1 NG +STRANG'S S T R AE1 NG Z +STRANGE S T R EY1 N JH +STRANGE'S S T R EY1 N JH IH0 Z +STRANGELY S T R EY1 N JH L IY0 +STRANGENESS S T R EY1 N JH N AH0 S +STRANGENESSES S T R EY1 N JH N AH0 S IH0 Z +STRANGER S T R EY1 N JH ER0 +STRANGER'S S T R EY1 N JH ER0 Z +STRANGERS S T R EY1 N JH ER0 Z +STRANGERSHIP S T R EY1 N JH ER0 SH IH2 P +STRANGES S T R EY1 N JH IH0 Z +STRANGEST S T R EY1 N JH IH0 S T +STRANGEWAYS S T R EY1 N JH W EY2 Z +STRANGFORD S T R AE1 NG F ER0 D +STRANGLE S T R AE1 NG G AH0 L +STRANGLED S T R AE1 NG G AH0 L D +STRANGLEHOLD S T R AE1 NG G AH0 L HH OW2 L D +STRANGLER S T R AE1 NG G L ER0 +STRANGLER'S S T R AE1 NG G L ER0 Z +STRANGLERS S T R AE1 NG G L ER0 Z +STRANGLES S T R AE1 NG G AH0 L Z +STRANGLING S T R AE1 NG G AH0 L IH0 NG +STRANGLING S T R AE1 NG G L IH0 NG +STRANGMAN S T R AE1 NG M AH0 N +STRANGULATED S T R AE2 NG G Y AH0 L EY2 T IH0 D +STRANGULATION S T R AE2 NG G Y AH0 L EY1 SH AH0 N +STRANGWAY S T R AE1 NG W EY2 +STRANGWAYS S T R AE1 NG W EY2 Z +STRANGWISE S T R AE1 NG W AY2 Z +STRANGWISE'S S T R AE1 NG W AY2 Z IH0 Z +STRANGWYN S T R AE1 NG W IH0 N +STRANN S T R AE1 N +STRANNGER S T R AE1 N JH ER0 +STRANOR S T R AE1 N ER0 +STRANRAER S T R AE1 N R EY1 R +STRANSOM S T R AE1 N S AH0 M +STRAP S T R AE1 P +STRAP'S S T R AE1 P S +STRAPPADO S T R AE1 P AA1 D OW0 +STRAPPED S T R AE1 P T +STRAPPER S T R AE1 P ER0 +STRAPPERS S T R AE1 P ER0 Z +STRAPPIN S T R AE1 P IH0 N +STRAPPING S T R AE1 P IH0 NG +STRAPPINGS S T R AE1 P IH0 NG Z +STRAPS S T R AE1 P S +STRASBOURG S T R AE1 S B AO2 R G +STRASBURG S T R AE1 S B ER0 G +STRASBURGH S T R AE1 S B ER0 G +STRASOLDA S T R AE1 S OW1 L D AH0 +STRASS S T R AE1 S +STRASSBURG S T R AE1 S B ER0 G +STRASSE S T R AE1 S +STRATA S T R AE1 T AH0 +STRATAGEM S T R AE1 T AH0 JH AH0 M +STRATAGEMS S T R AE1 T AH0 JH AH0 M Z +STRATAS S T R AE1 T AH0 Z +STRATE S T R EY1 T +STRATEGEM S T R AE1 T IH0 JH EH0 M +STRATEGETIC S T R AE1 T IH0 JH EH1 T IH0 K +STRATEGI S T R AE1 T IH0 JH IY0 +STRATEGIAN S T R AE1 T IH0 JH IY0 AH0 N +STRATEGIANS S T R AH0 T IY1 JH AH0 N Z +STRATEGIC S T R AH0 T IY1 JH IH0 K +STRATEGICAL S T R AH0 T IY1 JH IH0 K AH0 L +STRATEGICALLY S T R AH0 T IY1 JH IH0 K L IY0 +STRATEGIES S T R AE1 T AH0 JH IY0 Z +STRATEGIST S T R AE1 T IH0 JH IH0 S T +STRATEGISTS S T R AE1 T IH0 JH IH0 S T S +STRATEGUS S T R AE1 T IH0 JH IH0 S +STRATEGY S T R AE1 T AH0 JH IY0 +STRATEMEYER S T R AE1 T IH0 M AY0 ER0 +STRATES S T R EY1 T S +STRATFIELD S T R AE1 T F IY2 L D +STRATFORD S T R AE1 T F ER0 D +STRATFORD'S S T R AE1 T F ER0 D Z +STRATFORDTOWN S T R AE1 T F AO2 R D T AW2 N +STRATH S T R AE1 TH +STRATHAIRLIE S T R AE1 T HH EH1 R L IY0 +STRATHAIVRON S T R AE1 TH EY1 V R AH0 N +STRATHBOGIE S T R AE1 TH B UH1 G IY0 +STRATHBOGY S T R AE1 TH B OW2 JH IY0 +STRATHCLYDE S T R AE1 TH K L AY1 D +STRATHCONA S T R AE1 TH K OW1 N AH0 +STRATHCONA'S S T R AE1 TH K AH0 N AH0 Z +STRATHDENE S T R AE1 TH D IY0 N +STRATHEARN S T R AE1 TH ER0 N +STRATHEDEN S T R AE1 TH IY1 D AH0 N +STRATHFIELDSAYE S T R AE1 TH F IY2 L D Z EY0 +STRATHGOVAN S T R AE1 TH G OW1 V AH0 N +STRATHMOIRA S T R AE1 TH M OY1 R AH0 +STRATHMORE S T R AE1 TH M AO2 R +STRATHMUIR S T R AE1 TH M Y UH1 R +STRATHORAN S T R AE1 TH AO0 R AE1 N +STRATHPEFFER S T R AE1 TH P EH1 F ER0 +STRATHS S T R AE1 TH S +STRATHSAY S T R AE1 TH S EY0 +STRATHSAY'S S T R AE1 TH S EY2 Z +STRATHSEY S T R AE1 TH S IY0 +STRATHSPEY S T R AE1 TH S P IY0 +STRATHSPEYS S T R AE1 TH S P EY1 Z +STRATHYRE S T R AE1 TH AY1 R +STRATIFICATION S T R AE1 T AH0 F AH0 K EY1 SH AH0 N +STRATIFICATIONS S T R AE1 T AH0 F AH0 K EY1 SH AH0 N Z +STRATIFIED S T R AE1 T AH0 F AY2 D +STRATIMOJEFF S T R AE1 T AH0 M AH0 JH EH1 F +STRATO S T R AE1 T OW0 +STRATON S T R AE1 T AH0 N +STRATONICE S T R AE1 T AH0 N IH0 S +STRATOSPHERE S T R AE1 T AH0 S F IH2 R +STRATTON S T R AE1 T AH0 N +STRATTON'S S T R AE1 T AH0 N Z +STRATUM S T R AE1 T AH0 M +STRATUS S T R AE1 T AH0 S +STRAUCHT S T R AW1 K T +STRAUDENHEIM S T R AW1 D IH0 N HH AY0 M +STRAUGHT S T R AO1 T +STRAUMEY S T R AW1 M IY0 +STRAUNGE S T R AW1 N JH +STRAUNGER S T R AW1 NG ER0 +STRAUS S T R AW1 S +STRAUSS S T R AW1 S +STRAUSS'S S T R AW1 S IH0 Z +STRAVINSKY S T R AH0 V IH1 N S K IY0 +STRAW S T R AO1 +STRAW'S S T R AO1 Z +STRAWBERRIES S T R AO1 B EH2 R IY0 Z +STRAWBERRY S T R AO1 B EH2 R IY0 +STRAWED S T R AO1 D +STRAWINSKY S T R AA1 V IH1 N S K IY0 +STRAWN S T R AO1 N +STRAWN'S S T R AO1 N Z +STRAWS S T R AO1 Z +STRAWSTACK S T R AO1 S T AE2 K +STRAWY S T R AO1 IY0 +STRAWYERS S T R AO1 Y ER0 Z +STRAY S T R EY1 +STRAY'D S T R EY1 D +STRAYED S T R EY1 D +STRAYER S T R EY1 ER0 +STRAYHORN S T R EY1 HH ER0 N +STRAYIN S T R EY1 IH0 N +STRAYING S T R EY1 IH0 NG +STRAYINGS S T R EY1 IH0 NG Z +STRAYS S T R EY1 Z +STREAK S T R IY1 K +STREAK'D S T R IY1 K D +STREAKED S T R IY1 K T +STREAKIN S T R IY1 K IH0 N +STREAKING S T R IY1 K IH0 NG +STREAKINGS S T R IY1 K IH0 NG Z +STREAKS S T R IY1 K S +STREAKY S T R IY1 K IY0 +STREAM S T R IY1 M +STREAM'D S T R IY2 M D +STREAM'S S T R IY2 M Z +STREAMED S T R IY1 M D +STREAMER S T R IY1 M ER0 +STREAMERS S T R IY1 M ER0 Z +STREAMETH S T R IY2 M IH0 TH +STREAMIN S T R IY2 M IH0 N +STREAMING S T R IY1 M IH0 NG +STREAMLET S T R IY1 M L AH0 T +STREAMLET'S S T R IY1 M L AH0 T S +STREAMLETS S T R IY1 M L AH0 T S +STREAMLINE S T R IY1 M L AY2 N +STREAMLINED S T R IY1 M L AY2 N D +STREAMS S T R IY1 M Z +STREAMSIDE S T R IY2 M S AY2 D +STREAMY S T R IY1 M IY0 +STREATFEILD S T R IY1 T F IY1 L D +STREATFIELD S T R IY1 T F IY2 L D +STREATHAM S T R IY1 TH AH0 M +STREATHMERE S T R IY1 TH M IH2 R +STREATLEY S T R IY1 T L IY0 +STREATOR S T R IY1 T ER0 +STREBELOW S T R EH1 B AH0 L OW0 +STREBER S T R IY1 B ER0 +STRECKFUSS S T R EH1 K F AH0 S +STRECKMANN S T R EH1 K M AH0 N +STREEK S T R IY2 K +STREESES S T R IY1 Z IH0 Z +STREET S T R IY1 T +STREET'S S T R IY1 T S +STREETCAR S T R IY1 T K AA2 R +STREETCARS S T R IY1 T K AA2 R Z +STREETE S T R IY1 T +STREETED S T R IY1 T IH0 D +STREETER S T R IY1 T ER0 +STREETER'S S T R IY1 T ER0 Z +STREETS S T R IY1 T S +STREETWALKER S T R IY1 T W AO2 K ER0 +STREFF S T R EH1 F +STREFFORD S T R EH1 F ER0 D +STREFFORD'S S T R EH1 F ER0 D Z +STREFFY S T R EH1 F IY0 +STREGA S T R IY1 G AH0 +STREHLA S T R EH1 L AH0 +STREIGHT S T R EY1 T +STREIGHT'S S T R AY1 T S +STREIGHTS S T R AY1 T S +STRELINSKI S T R EH0 L IH1 N S K IY0 +STRELITSKI S T R EH1 L IH1 T S K IY0 +STRELITSKI'S S T R EH1 L IH1 T S K IY0 Z +STRELITZ S T R EH1 L IH0 T S +STRELLENHAUS S T R EH1 L AH0 N HH AW2 S +STRELLEY S T R EH1 L IY0 +STRELSA S T R EH1 L S AH0 +STRELSA'S S T R EH1 L S AH0 Z +STRELSAU S T R EH1 L S AO0 +STRELTSI S T R EH1 L T S IY0 +STREN'TH S T R IY1 N TH +STRENG S T R EH1 NG +STRENGTH S T R EH1 NG K TH +STRENGTH S T R EH1 NG TH +STRENGTH'S S T R EH1 NG TH S +STRENGTHEN S T R EH1 NG TH AH0 N +STRENGTHEN'D S T R EH1 NG TH AH0 N D +STRENGTHENED S T R EH1 NG TH AH0 N D +STRENGTHENER S T R EH1 NG TH AH0 N ER0 +STRENGTHENETH S T R EH1 NG TH AH0 N EH1 TH +STRENGTHENING S T R EH1 NG TH AH0 N IH0 NG +STRENGTHENS S T R EH1 NG TH AH0 N Z +STRENGTHLESS S T R EH1 NG TH L AH0 S +STRENGTHS S T R EH1 NG K TH S +STRENGTHS S T R EH1 NG TH S +STRENUOSITY S T R EH1 N Y UW0 S AH0 T IY0 +STRENUOUS S T R EH1 N Y UW0 AH0 S +STRENUOUSLY S T R EH1 N Y UW0 AH0 S L IY0 +STRENUOUSNESS S T R EH1 N Y UW0 AH0 S N AH0 S +STREPHON S T R EH1 F AH0 N +STREPHON'S S T R EH1 F AH0 N Z +STREPSIADES S T R EH1 P S IY2 EY0 D Z +STREPSICEROS S T R EH1 P S IH0 S EH1 R OW0 Z +STRESA S T R EH1 S AH0 +STRESS S T R EH1 S +STRESSED S T R EH1 S T +STRESSES S T R EH1 S AH0 Z +STRESSES S T R EH1 S IH0 Z +STRESSFUL S T R EH1 S F AH0 L +STRESSING S T R EH1 S IH0 NG +STRET S T R EH1 T +STRETCH S T R EH1 CH +STRETCH'D S T R EH1 CH D +STRETCHED S T R EH1 CH T +STRETCHER S T R EH1 CH ER0 +STRETCHERS S T R EH1 CH ER0 Z +STRETCHES S T R EH1 CH AH0 Z +STRETCHES S T R EH1 CH IH0 Z +STRETCHEST S T R EH1 CH AH0 S T +STRETCHETH S T R EH1 CH IH0 TH +STRETCHIN S T R EH1 CH IH0 N +STRETCHING S T R EH1 CH IH0 NG +STRETCHINGS S T R EH1 CH IH0 NG Z +STRETCHT S T R EH1 CH T +STRETCHY S T R EH1 CH IY0 +STRETHER S T R EH1 DH ER0 +STRETHER'S S T R EH1 DH ER0 Z +STRETT S T R EH1 T +STRETTA S T R EH1 T AH0 +STRETTI S T R EH1 T IY0 +STRETTO S T R EH1 T OW2 +STRETTON S T R EH1 T AH0 N +STRETTON'S S T R EH1 T AH0 N Z +STRETTS S T R EH1 T S +STREUSS S T R UW1 S +STREVA S T R EH1 V AH0 +STREW S T R UW1 +STREW'D S T R UW1 D +STREWED S T R UW1 D +STREWING S T R UW1 IH0 NG +STREWN S T R UW1 N +STREWS S T R UW1 Z +STRIAE S T R AY1 IY0 +STRIATA S T R IY0 AA1 T AH0 +STRIATED S T R IY0 EY2 T IH0 D +STRIATIONS S T R IY0 EY1 SH AH0 N Z +STRIBBLE S T R IH1 B AH0 L +STRIBLING S T ER1 AH0 B AH0 L IH0 NG +STRIBLING S T R IH1 B L IH0 NG +STRIC'LY S T R IH1 K AH0 L IY0 +STRICK S T R IH1 K +STRICKEN S T R IH1 K AH0 N +STRICKENLY S T R IH1 K AH0 N L IY0 +STRICKLAND S T R IH1 K L AH0 N D +STRICKLAND'S S T R IH1 K L AH0 N D Z +STRICKLANDS S T R IH1 K L AH0 N D Z +STRICKLY S T R IH1 K L IY0 +STRICT S T R IH1 K T +STRICTER S T R IH1 K T ER0 +STRICTEST S T R IH1 K T AH0 S T +STRICTLY S T R IH1 K T L IY0 +STRICTNESS S T R IH1 K T N AH0 S +STRICTURE S T R IH1 K CH ER0 +STRICTURES S T R IH1 K CH ER0 Z +STRID S T R IH1 D +STRIDDEN S T R IH1 D AH0 N +STRIDE S T R AY1 D +STRIDE'S S T R AY1 D Z +STRIDED S T R AY1 D IH0 D +STRIDENCY S T R AY1 D AH0 N S IY0 +STRIDENT S T R AY1 D AH0 N T +STRIDENTLY S T R AY1 D AH0 N T L IY0 +STRIDER S T R AY1 D ER0 +STRIDERS S T R AY1 D ER0 Z +STRIDES S T R AY1 D Z +STRIDING S T R AY1 D IH0 NG +STRIDOR S T R AY1 D ER0 +STRIDULATION S T R IH1 JH AH0 L EY1 SH AH0 N +STRIDULATIONS S T R IH1 JH AH0 L EY1 SH AH0 N Z +STRIDULOUS S T R IH1 JH AH0 L AH0 S +STRIEM S T R IY1 M +STRIFE S T R AY1 F +STRIFE'S S T R AY1 F S +STRIFES S T R AY1 F S +STRIK'ST S T R AY1 K S T +STRIKE S T R AY1 K +STRIKE'S S T R AY1 K S +STRIKEBREAKERS S T R AY1 K B R EY2 K ER0 Z +STRIKEN S T R AY1 K AH0 N +STRIKER S T R AY1 K ER0 +STRIKER'S S T R AY1 K ER0 Z +STRIKERS S T R AY1 K ER0 Z +STRIKES S T R AY1 K S +STRIKEST S T R AY1 K AH0 S T +STRIKETH S T R AY1 K TH +STRIKIN S T R AY1 K IH0 N +STRIKING S T R AY1 K IH0 NG +STRIKINGLY S T R AY1 K IH0 NG L IY0 +STRINDBERG S T R AY1 N D B ER0 G +STRINDBERG'S S T R AY1 N D B ER0 G Z +STRING S T R IH1 NG +STRING'S S T R IH1 NG Z +STRINGED S T R IH1 NG D +STRINGENCY S T R IH1 N JH AH0 N S IY0 +STRINGENT S T R IH1 N JH AH0 N T +STRINGENTLY S T R IH1 N JH AH0 N T L IY0 +STRINGER S T R IH1 NG ER0 +STRINGER'S S T R IH1 NG ER0 Z +STRINGERS S T R IH1 NG ER0 Z +STRINGFELLOW S T R IH1 NG F EH2 L OW0 +STRINGHAM S T R IH1 NG HH AE2 M +STRINGHAM'S S T R IH1 NG AH0 M Z +STRINGIN S T R IH1 NG AH0 N +STRINGING S T R IH1 NG IH0 NG +STRINGLESS S T R IH1 NG L AH0 S +STRINGPIECE S T R IH1 NG P IY2 S +STRINGS S T R IH1 NG Z +STRINGY S T R IH1 NG IY0 +STRINT S T R IH1 N T +STRIP S T R IH1 P +STRIPE S T R AY1 P +STRIPED S T R AY1 P T +STRIPER S T R AY1 P ER0 +STRIPES S T R AY1 P S +STRIPEY S T R AY1 P IY0 +STRIPING S T R AY1 P IH0 NG +STRIPINGS S T R AY1 P IH0 NG Z +STRIPLING S T R IH1 P L IH0 NG +STRIPLING'S S T R IH1 P L IH0 NG Z +STRIPLINGS S T R IH1 P L IH0 NG Z +STRIPP'D S T R IH1 P D +STRIPPED S T R IH1 P T +STRIPPER S T R IH1 P ER0 +STRIPPERS S T R IH1 P ER0 Z +STRIPPING S T R IH1 P IH0 NG +STRIPPINGS S T R IH1 P IH0 NG Z +STRIPS S T R IH1 P S +STRIPT S T R IH1 P T +STRIPY S T R IH1 P IY0 +STRITE S T R AY1 T +STRIVE S T R AY1 V +STRIVED S T R AY1 V D +STRIVEN S T R IH1 V AH0 N +STRIVER S T R AY1 V ER0 +STRIVERS S T R AY1 V ER0 Z +STRIVES S T R AY1 V Z +STRIVEST S T R AY1 V EH2 S T +STRIVETH S T R AY1 V EH2 TH +STRIVIN S T R AY1 V IH0 N +STRIVING S T R AY1 V IH0 NG +STRIVINGS S T R AY1 V IH0 NG Z +STRIX S T R IH1 K S +STROBELL S T R OW1 B AH0 L +STROBIK S T R OW1 B IH0 K +STROBRIDGE S T R AA1 B R IH0 JH +STROBRIDGE'S S T R AA1 B R IH0 JH AH0 Z +STROCK S T R AA1 K +STRODE S T R OW1 D +STROEBEL S T R OW1 B AH0 L +STROEBEL'S S T R OW1 B AH0 L Z +STROEHEN S T R OW1 AH0 N +STROEM S T R OW1 M +STROEVE S T R OW1 V +STROEVE'S S T R OW1 V Z +STROGANOFF S T R OW1 G AH0 N AO2 F +STROGANOFFS S T R OW1 G AH0 N AO2 F S +STROGOFF S T R AA1 G AO0 F +STROGUE S T R OW1 G +STROICI S T R OY1 CH IY0 +STROIKE S T R OY1 K +STROKE S T R OW1 K +STROKE'S S T R OW2 K S +STROKED S T R OW1 K T +STROKES S T R OW1 K S +STROKHR S T R OW1 K R +STROKING S T R OW1 K IH0 NG +STROKINGS S T R OW1 K IH0 NG Z +STROKOR S T R OW1 K ER0 +STROLL S T R OW1 L +STROLL'D S T R OW1 L D +STROLLED S T R OW1 L D +STROLLER S T R OW1 L ER0 +STROLLER'S S T R OW1 L ER0 Z +STROLLERS S T R OW1 L ER0 Z +STROLLIN S T R AA1 L IH0 N +STROLLING S T R OW1 L IH0 NG +STROLLS S T R OW1 L Z +STROM S T R AA1 M +STROMA S T R OW1 M AH0 +STROMBERG S T R AA1 M B ER0 G +STROMBERG'S S T R AA1 M B ER0 G Z +STROMBICHIDES S T R AA1 M B IH1 CH AY1 D Z +STROMBOLI S T R AA2 M B OW1 L IY0 +STROMER S T R OW1 M ER0 +STROMLING S T R AA1 M L IH0 NG +STROMNESS S T R AA1 M N AH0 S +STROMSTAD S T R AA1 M S T AE2 D +STRONE S T R OW1 N IY0 +STRONG S T R AO1 NG +STRONG'S S T R AO1 NG Z +STRONGARA S T R AO1 NG G AA1 R AH0 +STRONGBOW S T R AO1 NG B OW2 +STRONGBOW'S S T R AO1 NG B OW2 Z +STRONGBOX S T R AO1 NG B AA2 K S +STRONGE S T R AO1 NG +STRONGER S T R AO1 NG ER0 +STRONGER S T R AO1 NG G ER0 +STRONGER'N S T R AO1 NG ER0 AH0 N +STRONGEST S T R AO1 NG G AH0 S T +STRONGHEART S T R AO1 NG HH AA2 R T +STRONGHOLD S T R AO1 NG HH OW2 L D +STRONGHOLDS S T R AO1 NG HH OW2 L D Z +STRONGISH S T R AO1 NG IH0 SH +STRONGITHARM S T R AO1 NG G IH0 TH AA1 R M +STRONGLY S T R AO1 NG L IY0 +STRONGNESS S T R AO1 NG N AH0 S +STRONGROOM S T R AO1 NG R UW2 M +STRONGS S T R AO1 NG Z +STRONGTHARM S T R AO1 NG TH AA1 R M +STRONTIUM S T R AA1 N T IY0 AH0 M +STROOD S T R UW1 D +STROOK S T R UH1 K +STROOM S T R UW2 M +STROP S T R AA1 P +STROPHE S T R AA1 F AH0 +STROPHES S T R AA1 F AH0 S +STROPHIUS S T R OW1 F IY0 IH0 S +STROPPED S T R AA1 P T +STROPPING S T R AA1 P IH0 NG +STROPS S T R AA1 P S +STRORLEY S T R AO1 R L IY0 +STROTHER S T R AA1 DH ER0 +STROTHERS S T R AH1 DH ER0 Z +STROUD S T R AW1 D +STROUDS S T R AW1 D Z +STROUDSBURG S T R AW1 D Z B ER0 G +STROUSS S T R AW1 S +STROUT S T R AW1 T +STROUT'S S T R AW1 T S +STROVE S T R OW1 V +STROW S T R OW1 +STROW'D S T R AW1 D +STROWED S T R OW1 D +STROWN S T R OW1 N +STROWS S T R OW1 Z +STROYAN S T R OY1 AH0 N +STROYER S T R OY1 ER0 +STROZZA S T R AA1 Z AH0 +STROZZI S T R AA1 Z IY0 +STRUBBER S T R AH1 B ER0 +STRUBLE S T R UW1 B AH0 L +STRUCK S T R AH1 K +STRUCKEN S T R AH1 K AH0 N +STRUCTION S T R AH1 K SH AH0 N +STRUCTURAL S T R AH1 K CH ER0 AH0 L +STRUCTURALLY S T R AH1 K CH ER0 AH0 L IY0 +STRUCTURE S T R AH1 K CH ER0 +STRUCTURE'S S T R AH1 K CH ER0 Z +STRUCTURED S T R AH1 K CH ER0 D +STRUCTURELESS S T R AH1 K CH ER0 L AH0 S +STRUCTURES S T R AH1 K CH ER0 Z +STRUDWICK S T R AH1 D W IH0 K +STRUENSEE S T R UH1 N S IY1 +STRUGGLE S T R AH1 G AH0 L +STRUGGLE'S S T R AH1 G AH0 L Z +STRUGGLED S T R AH1 G AH0 L D +STRUGGLER S T R AH1 G AH0 L ER0 +STRUGGLERS S T R AH1 G L ER0 Z +STRUGGLES S T R AH1 G AH0 L Z +STRUGGLIN S T R AH1 G AH0 L IH0 N +STRUGGLING S T R AH1 G AH0 L IH0 NG +STRUGGLING S T R AH1 G L IH0 NG +STRUGGLINGS S T R AH1 G AH0 L IH0 NG Z +STRULDBRUGS S T R AH1 L D B R AH1 G Z +STRULL S T R AH1 L +STRUM S T R AH1 M +STRUMLEY S T R AH1 M L IY0 +STRUMMED S T R AH1 M D +STRUMMERY S T R AH1 M ER0 IY0 +STRUMMIN S T R AH1 M IH0 N +STRUMMING S T R AH1 M IH0 NG +STRUMP S T R AH1 M P +STRUMPET S T R AH1 M P AH0 T +STRUMPETS S T R AH1 M P AH0 T S +STRUMS S T R AH1 M Z +STRUNG S T R AH1 NG +STRUNSKY S T R AH1 N S K IY0 +STRUT S T R AH1 T +STRUTABOUT S T R AH1 T AH0 B AW2 T +STRUTH S T R UW1 TH +STRUTHERS S T R AH1 DH ER0 Z +STRUTHERS'S S T R AH1 DH ER0 Z IH0 Z +STRUTHIO S T R UW1 TH IY0 OW0 +STRUTS S T R AH1 T S +STRUTT S T R AH1 T +STRUTT'S S T R AH1 T S +STRUTTED S T R AH1 T IH0 D +STRUTTER S T R AH1 T ER0 +STRUTTIN S T R AH1 T IH0 N +STRUTTING S T R AH1 T IH0 NG +STRUTTINGS S T R AH1 T IH0 NG Z +STRUTWELL S T R AH1 T W EH2 L +STRUVE S T R UW1 V +STRUVE'S S T R AH1 V Z +STRUVELIUS S T R UW1 V L IY0 IH0 S +STRUWWELPETER S T R UW1 W W EH1 L P IY0 T ER0 +STRYCHNIA S T R IH1 K N IY0 AH0 +STRYCHNINE S T R IH1 K N AY2 N +STRYCHNOS S T R IH1 K N OW0 S +STRYKER S T R AY1 K ER0 +STRYKER'S S T R AY1 K ER0 Z +STRYMON S T R IH1 M AH0 N +STRYPE S T R IH1 P +STRYPE'S S T R IH1 P S +STRYVER S T R AY1 V ER0 +STS S T S +STU S T UW1 +STUART S T AO1 R T +STUART S T UW1 ER0 T +STUART S T Y UW1 ER0 T +STUART'S S T AO1 R T S +STUART'S S T UW1 ER0 T S +STUART'S S T Y UW1 ER0 T S +STUARTS S T UW1 ER0 T S +STUB S T AH1 B +STUBB S T AH1 B +STUBB'S S T AH1 B Z +STUBBED S T AH1 B D +STUBBER S T AH1 B ER0 +STUBBERD S T AH1 B ER0 D +STUBBES S T AH1 B Z +STUBBING S T AH1 B IH0 NG +STUBBINGS S T AH1 B IH0 NG Z +STUBBINS S T AH1 B IH0 N Z +STUBBLE S T AH1 B AH0 L +STUBBLED S T AH1 B AH0 L D +STUBBLEFIELD S T AH1 B AH0 L F IY2 L D +STUBBLES S T AH1 B AH0 L Z +STUBBLY S T AH1 B L IY0 +STUBBORN S T AH1 B ER0 N +STUBBORNER S T AH1 B ER0 N ER0 +STUBBORNESS S T AH1 B ER0 N AH0 S +STUBBORNEST S T AH1 B ER0 N AH0 S T +STUBBORNLY S T AH1 B ER0 N L IY0 +STUBBORNNESS S T AH1 B ER0 N AH0 S +STUBBORNNESS S T AH1 B ER0 N N AH0 S +STUBBS S T AH1 B Z +STUBBS'S S T AH1 B Z IH0 Z +STUBBY S T AH1 B IY0 +STUBBY'S S T AH1 B IY0 Z +STUBE S T UW1 B +STUBEN S T UW1 B IH0 N +STUBLEY S T AH1 B L IY0 +STUBS S T AH1 B Z +STUBTAIL S T AH1 T EY2 L +STUCCO S T AH1 K OW0 +STUCCOED S T AH1 K OW0 D +STUCK S T AH1 K +STUCK'S S T AH1 K S +STUCKER S T AH1 K ER0 +STUCKUP S T AH1 K AH2 P +STUCKY S T AH1 K IY0 +STUD S T AH1 D +STUDD S T AH1 D +STUDDED S T AH1 D IH0 D +STUDDENHAM S T AH1 D IH0 N HH AH0 M +STUDDING S T AH1 D IH0 NG +STUDDINGSAIL S T AH1 D IH0 NG Z EY2 L +STUDDINGSAILS S T AH1 D IH0 N S EY1 L Z +STUDDY S T AH1 D IY0 +STUDE S T UW1 D +STUDEBAKER S T UW1 D AH0 B EY2 K ER0 +STUDENT S T UW1 D AH0 N T +STUDENT'S S T UW1 D AH0 N T S +STUDENTS S T UW1 D AH0 N T S +STUDENTSHIP S T UW1 D AH0 N T SH IH2 P +STUDHOLME S T AH1 D HH OW2 L M +STUDIA S T UW1 D IY0 AH0 +STUDIE S T AH1 D IY0 +STUDIED S T AH1 D IY0 D +STUDIEDLY S T AH1 D IY0 D L IY0 +STUDIEN S T AH1 D IY0 N +STUDIES S T AH1 D IY0 Z +STUDII S T UW1 D IY0 IY2 +STUDIIS S T AH1 D IY2 Z +STUDIO S T UW1 D IY0 OW2 +STUDIORUM S T UW1 D IY0 AO1 R AH0 M +STUDIOS S T UW1 D IY0 OW2 Z +STUDIOSUS S T UW1 D IY0 OW2 Z AH0 S +STUDIOUS S T UW1 D IY0 AH0 S +STUDIOUSLY S T UW1 D IY0 AH0 S L IY0 +STUDIOUSNESS S T UW1 D IY0 AH0 S N AH0 S +STUDIUM S T UW1 D IY0 AH0 M +STUDLAND S T AH1 D L AH0 N D +STUDLEIGH S T AH1 D L AH0 +STUDLEIGHS S T AH1 D L IY0 Z +STUDLEY S T AH1 D L IY0 +STUDS S T AH1 D Z +STUDWICK S T AH1 D W IH0 K +STUDWICK'S S T AH1 D W IH0 K S +STUDY S T AH1 D IY0 +STUDY'S S T AH1 D IY0 Z +STUDYIN S T AH1 D IY0 IH0 N +STUDYING S T AH1 D IY0 IH0 NG +STUDZIANKA S T AH0 JH IY0 AA1 NG K AH0 +STUEBEL S T UH1 B AH0 L +STUECK S T UW1 K +STUERMER S T UW1 ER1 M ER0 +STUERMER'S S T UW1 ER0 M ER0 Z +STUF S T AH1 F +STUFF S T AH1 F +STUFF'D S T AH1 F D +STUFF'LL S T AH1 F L +STUFF'S S T AH1 F S +STUFFE S T AH1 F +STUFFED S T AH1 F T +STUFFER S T AH1 F ER0 +STUFFERS S T AH1 F ER0 Z +STUFFIER S T AH1 F IY0 ER0 +STUFFILY S T AH1 F AH0 L IY0 +STUFFIN S T AH1 F IH0 N +STUFFINESS S T AH1 F IH0 N IH0 S +STUFFING S T AH1 F IH0 NG +STUFFINGS S T AH1 F IH0 NG Z +STUFFS S T AH1 F S +STUFFY S T AH1 F IY0 +STUFFY'S S T AH1 F IY0 Z +STUFT S T AH1 F T +STUHK S T UW1 K +STUKA S T UW1 K AH0 +STUKAS S T UW1 K AH0 Z +STUKELEY S T UW1 K AH0 L IY0 +STUKELEY'S S T UW1 K AH0 L IY0 Z +STUKELY S T AH1 K L IY0 +STUKELY'S S T AH1 K L IY0 Z +STUKEY S T AH1 K IY0 +STULPNAGEL S T AH1 L P N AH0 G AH0 L +STULTA S T AH1 L T AH0 +STULTIFICATION S T AH1 L T AH0 F AH0 K EY1 SH AH0 N +STULTIFIED S T AH1 L T AH0 F AY2 D +STULTIFIES S T AH1 L T AH0 F AY2 Z +STULTIFY S T AH1 L T AH0 F AY2 +STULTIFYING S T AH1 L T AH0 F AY2 IH0 NG +STULTITIA S T AH1 L T AH0 T Y AH0 +STULTS S T AH1 L T S +STULTUS S T AH1 L T AH0 S +STULTZ S T AH1 L T S +STUMBLE S T AH1 M B AH0 L +STUMBLED S T AH1 M B AH0 L D +STUMBLER S T AH1 M B L ER0 +STUMBLES S T AH1 M B AH0 L Z +STUMBLETH S T AH1 M B AH0 L TH +STUMBLIN S T AH1 M B L IH0 N +STUMBLIN' S T AH1 M B L IH0 N +STUMBLING S T AH1 M B AH0 L IH0 NG +STUMBLING S T AH1 M B L IH0 NG +STUMBLINGLY S T AH1 M B L IH0 NG L IY0 +STUMBLINGS S T AH1 M B L IH0 NG Z +STUMFOLD S T AH1 M F OW2 L D +STUMFOLD'S S T AH1 M F OW2 L D Z +STUMFOLDIAN S T AH1 M F OW2 L D IY0 AH0 N +STUMM S T AH1 M +STUMM'S S T AH1 M Z +STUMMICK S T AH1 M IH0 K +STUMMICKS S T AH1 M IH0 K S +STUMMIK S T AH1 M IH0 K +STUMP S T AH1 M P +STUMP'S S T AH1 M P S +STUMPAGE S T AH1 M P IH0 JH +STUMPED S T AH1 M P T +STUMPER S T AH1 M P ER0 +STUMPF S T AH1 M P F +STUMPH S T AH1 M F +STUMPIN S T AH1 M P IH0 N +STUMPING S T AH1 M P IH0 NG +STUMPINGHAME S T AH1 M P IH0 NG AH0 M +STUMPS S T AH1 M P S +STUMPTAIL S T AH1 M P T EY2 L +STUMPTON S T AH1 M P T AH0 N +STUMPY S T AH1 M P IY0 +STUMPY'S S T AH1 M P IY0 Z +STUN S T AH1 N +STUN' S T AH1 N +STUNDEN S T AH1 N D AH0 N +STUNG S T AH1 NG +STUNK S T AH1 NG K +STUNN'D S T AH1 N D +STUNNED S T AH1 N D +STUNNENBERG S T AH1 N AH0 N B ER0 G +STUNNER S T AH1 N ER0 +STUNNERS S T AH1 N ER0 Z +STUNNIN S T AH1 N IH0 N +STUNNING S T AH1 N IH0 NG +STUNNINGLY S T AH1 N IH0 NG L IY0 +STUNS S T AH1 N Z +STUNSAIL S T AH1 N S EY2 L +STUNSAILS S T AH1 N S EY2 L Z +STUNT S T AH1 N T +STUNTED S T AH1 N T IH0 D +STUNTING S T AH1 N T IH0 NG +STUNTS S T AH1 N T S +STUPE S T UW1 P +STUPEFACTION S T UW1 P AH0 F AE1 K SH AH0 N +STUPEFIED S T UW1 P F AY2 D +STUPEFIES S T UW1 P F AY2 Z +STUPEFY S T UW0 P IY1 F IY0 +STUPEFYING S T UW0 P EH1 F AY2 IH0 NG +STUPENDIOUS S T UW0 P EH1 N D IY0 AH0 S +STUPENDOUS S T UW0 P EH1 N D AH0 S +STUPENDOUSLY S T UW0 P EH1 N D AH0 S L IY0 +STUPENDOUSNESS S T UW0 P EH1 N D AH0 S N AH0 S +STUPID S T UW1 P AH0 D +STUPID S T UW1 P IH0 D +STUPIDE S T UW1 P IH0 D +STUPIDER S T UW1 P IH0 D ER0 +STUPIDEST S T UW1 P IH0 D AH0 S T +STUPIDITIES S T UW0 P IH1 D IH0 T IY0 Z +STUPIDITY S T UW0 P IH1 D IH0 T IY0 +STUPIDLY S T UW1 P AH0 D L IY0 +STUPIDNESS S T UW0 P IH1 D N AH0 S +STUPIDS S T UW1 P IH0 D Z +STUPIFIED S T UW1 P AH0 F AY2 D +STUPIFY S T UW1 P AH0 F AY2 +STUPIFYING S T UW1 P AH0 F AY2 IH0 NG +STUPOR S T UW1 P ER0 +STUPORS S T UW1 P ER0 Z +STUPOUR S T UW1 P AO1 R +STUPPENY S T AH1 P IH0 N IY0 +STURATZBERG S T ER1 AH0 T S B ER0 G +STURB S T ER1 B +STURCH S T ER1 K +STURDEE S T ER1 D IY0 +STURDEVANT S T ER1 D IH0 V AH0 N T +STURDIER S T ER1 D IY0 ER0 +STURDIEST S T ER1 D IY0 AH0 S T +STURDILY S T ER1 D AH0 L IY0 +STURDINESS S T ER1 D IY0 N AH0 S +STURDY S T ER1 D IY0 +STURE S T UH1 R +STURE'S S CH ER0 Z +STURGE S T ER1 JH +STURGEON S T ER1 JH AH0 N +STURGEON S T ER1 JH IH0 N +STURGEON'S S T ER1 JH AH0 N Z +STURGEONS S T ER1 JH AH0 N Z +STURGES S T ER1 JH IH0 Z +STURGESS S T ER1 G IH0 S +STURGIS S T ER1 JH IH0 S +STURGIS'S S T ER1 JH IH0 S IH0 Z +STURGISES S T ER1 G AH0 S AH0 Z +STURK S T ER1 K +STURK'S S T ER2 K S +STURLA S T ER1 L AH0 +STURLUSON S T ER1 L AH0 S AH0 N +STURM S T ER1 M +STURM'S S T ER1 M Z +STURMER S T ER1 M ER0 +STURMFEDER S T ER1 M F EH2 D ER0 +STURMI S T ER1 M IY0 +STURRY S T ER1 IY0 +STURT S T ER1 T +STURT'S S T ER1 T S +STURTEVANT S T ER1 T AH0 V AH0 N T +STURTEVANT'S S T ER1 T AH0 V AH0 N T S +STURTON S T ER1 T AH0 N +STUSS S T AH1 S +STUT S T AH1 T +STUTELY S T UW1 T L IY0 +STUTEVILL S T UW1 T V IH0 L +STUTSMAN S T AH1 T S M AH0 N +STUTSMAN'S S T AH1 T S M AH0 N Z +STUTTER S T AH1 T ER0 +STUTTERED S T AH1 T ER0 D +STUTTERER S T AH1 T ER0 ER0 +STUTTERERS S T AH1 T ER0 ER0 Z +STUTTERIN S T AH1 T ER0 IH0 N +STUTTERING S T AH1 T ER0 IH0 NG +STUTTERINGLY S T AH1 T ER0 IH0 NG L IY0 +STUTTERS S T AH1 T ER0 Z +STUTTGARD S T UW1 T G AA2 R D +STUTTGARDT S T UW1 T G AA2 R T +STUTTGART S T AH1 T G ER0 T +STUTTGART S T UW1 T G AA2 R T +STUTTGART SH T UW1 T G AA2 R T +STUVIC S T UW1 V IH0 K +STUVIC'S S T UW1 V IH2 CH IH0 Z +STUYLER S T AY1 L ER0 +STUYVESANT S T AY1 V AH0 S AH0 N T +STUYVESANT'S S T AY1 V AH0 S AH0 N T S +STUYVESANTS S T AY1 V AH0 S AH0 N T S +STUYVIE S T AY1 V IY0 +STWUNS S T W AH1 N Z +STY S T AY1 +STYE S T AY1 +STYES S T AY1 Z +STYF S T AY1 F +STYGIA S T IH1 JH IY0 AH0 +STYGIAN S T IH1 JH IY0 AH0 N +STYGIANS S T IH1 JH IY0 AH0 N Z +STYLE S T AY1 L +STYLE'S S T AY1 L Z +STYLED S T AY1 L D +STYLEHURST S T AY1 AH0 L HH ER0 S T +STYLES S T AY1 L Z +STYLING S T AY1 L IH0 NG +STYLISH S T AY1 L IH0 SH +STYLISHLY S T AY1 L IH0 SH L IY0 +STYLISHNESS S T AY1 L IH0 SH N AH0 S +STYLIST S T AY1 L IH0 S T +STYLISTIC S T AY0 L IH1 S T IH0 K +STYLISTS S T AY1 L IH0 S T S +STYLITES S T IH0 L AY1 T S +STYLIZED S T AY1 L AY2 Z D +STYLO S T AY1 L OW0 +STYLOBATE S T AY1 L AO0 B EY0 T +STYLOGRAPH S T AY1 L AH0 G R AE2 F +STYLOGRAPHIC S T AY1 L AH0 G R AE1 F IH0 K +STYLUS S T AY1 L AH0 S +STYMIE S T AY1 M IY0 +STYMIED S T AY1 M IY0 D +STYMPHALIAN S T AY1 M F EY1 L IY0 AH0 N +STYMPSON S T IH1 M P S AH0 N +STYMPSONS S T IH1 M P S AH0 N Z +STYNE S T AY1 N +STYPTIC S T IH1 P T IH0 K +STYPTICS S T IH1 P T IH0 K S +STYRAX S T AY1 R AE0 K S +STYRIA S T IH1 R IY0 AH0 +STYRIAN S T IH1 R IY0 AH0 N +STYX S T IH1 K S +SU EH1 S Y UW1 +SU S UW1 +SU'PRISE S UW1 P R AY1 Z +SU'PRISED S UW1 P R AY1 Z D +SU'S S UW0 Z +SU'THIN S UW1 TH AH0 N +SUA S W AA1 +SUABIA S W AA1 B IY0 AH0 +SUABIAN S W EY1 B IY0 AH0 N +SUABIANS S W EY1 B IY0 AH0 N Z +SUABY S W EY1 B IY0 +SUAE S W EY1 +SUAGE S W EY1 JH +SUAH S UW1 AA0 +SUAKIM S W AA0 K IY1 M +SUAKIN S W EY1 K IH0 N +SUALTAIM ZH W AH0 L T EY1 M +SUAM S W AA1 M +SUAN S W AA1 N +SUAQUEM S W AA1 K W AH0 M +SUARD S UW1 AA0 R D +SUARD S W AA1 R D +SUARDA S W AA1 R D AH0 +SUAREZ S W AA0 R EH1 Z +SUAS S W AA0 Z +SUASION S W EY1 ZH AH0 N +SUASIVE S W EY1 S IH0 V +SUAVE S W AA1 V +SUAVELY S W AA1 V L IY0 +SUAVEST S W AA1 V AH0 S T +SUAVITER S W AA1 V AY0 T ER0 +SUAVITIES S W AA1 V IH0 T IY0 Z +SUAVITY S W AA1 V IH0 T IY0 +SUB S AH1 B +SUB'S S AH1 B Z +SUBA S UW1 B AH0 +SUBACID S AH0 B AE1 S AH0 D +SUBADAR S AH2 B AH0 D AA1 R +SUBAH S AH2 B AH0 +SUBAHDAR S AH0 B AA1 D AA0 R +SUBALTERN S AH2 B AH0 L T ER0 N +SUBALTERN'S S AH2 B AH0 L T ER0 N Z +SUBALTERNS S AH2 B AH0 L T ER0 N Z +SUBAQUEOUS S AH1 B EY1 K W IY0 AH0 S +SUBBY S AH1 B IY0 +SUBCHIEF S AH1 B CH AH0 F +SUBCHIEFS S AH1 B CH AH0 F S +SUBCOMMITTEE S AH1 B K AH0 M IH1 T IY0 +SUBCOMMITTEES S AH1 B K AH0 M IH1 T IY0 Z +SUBCONSCIOUS S AH0 B K AA1 N SH AH0 S +SUBCONSCIOUSLY S AH0 B K AA1 N SH AH0 S L IY0 +SUBCONSCIOUSNESS S AH0 B K AA1 N SH AH0 S N AH0 S +SUBCONTINENT S AH0 B K AA1 N T IH0 N AH0 N T +SUBCUTANEOUS S AH1 B K Y UW0 T EY1 N IY0 AH0 S +SUBDEACON S AH2 B D IY1 K AH0 N +SUBDEAN S AH2 B D IY0 AH0 N +SUBDIVIDE S AH2 B D AH0 V AY1 D +SUBDIVIDED S AH2 B D IH0 V AY1 D IH0 D +SUBDIVIDES S AH1 B D IH0 V AY1 D Z +SUBDIVIDING S AH1 B D IH0 V AY1 D IH0 NG +SUBDIVISION S AH1 B D IH0 V IH2 ZH AH0 N +SUBDIVISIONS S AH1 B D IH0 V IH2 ZH AH0 N Z +SUBDOMINANT S AH2 B D AA1 M AH0 N AH0 N T +SUBDU'D S AH0 B D UW1 D +SUBDUAL S AH0 B D UW1 AH0 L +SUBDUE S AH0 B D UW1 +SUBDUED S AH0 B D UW1 D +SUBDUEDLY S AH0 B D UW1 D L IY0 +SUBDUER S AH0 B D UW1 ER0 +SUBDUERS S AH0 B D UW1 ER0 Z +SUBDUES S AH0 B D UW1 Z +SUBDUING S AH0 B D UW1 IH0 NG +SUBE S UW1 B +SUBE'S S AH0 B Z +SUBEDAR S AH1 B IH0 D AA1 R +SUBHA S AH2 B HH AH0 +SUBHADRA S AH1 B AE1 D R AH0 +SUBHAN S AH2 B HH AH0 N +SUBHEDAR S AH2 B HH IY1 D ER0 +SUBHUMAN S AH2 B HH Y UW1 M AH0 N +SUBIACO S UW0 B IY1 AH0 K OW0 +SUBIENKOW S AH1 B IH0 NG K OW0 +SUBITO S UW0 B IY1 T OW0 +SUBJACENT S AH1 B JH EY1 S AH0 N T +SUBJEC S AH1 B JH IH0 K +SUBJECK S AH1 B JH IH0 K +SUBJECT S AH0 B JH EH1 K T +SUBJECT S AH1 B JH IH0 K T +SUBJECT'S S AH1 B JH IH0 K T S +SUBJECTED S AH0 B JH EH1 K T IH0 D +SUBJECTING S AH0 B JH EH1 K T IH0 NG +SUBJECTION S AH0 B JH EH1 K SH AH0 N +SUBJECTIS S AH1 B JH IH0 K T IH0 S +SUBJECTIVE S AH0 B JH EH1 K T IH0 V +SUBJECTIVELY S AH0 B JH EH1 K T IH0 V L IY0 +SUBJECTIVISM S AH0 B JH EH1 K T IH0 V IH2 Z AH0 M +SUBJECTIVITY S AH0 B JH EH0 K T IH1 V IH0 T IY0 +SUBJECTS S AH0 B JH EH1 K S +SUBJECTS S AH0 B JH EH1 K T S +SUBJECTS S AH1 B JH IH0 K T S +SUBJICK S 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T +SUBMITS S AH2 B M IH1 T S +SUBMITTED S AH0 B M IH1 T AH0 D +SUBMITTING S AH0 B M IH1 T IH0 NG +SUBNORMAL S AH1 B N AO1 R M AH0 L +SUBORDINATE S AH0 B AO1 R D AH0 N AH0 T +SUBORDINATE S AH0 B AO1 R D AH0 N EY2 T +SUBORDINATE'S S AH0 B AO1 R D AH0 N IH0 T S +SUBORDINATED S AH0 B AO1 R D AH0 N EY2 T IH0 D +SUBORDINATELY S AH0 B AO1 R D AH0 N AH0 T L IY0 +SUBORDINATES S AH0 B AO1 R D AH0 N AH0 T S +SUBORDINATES S AH0 B AO1 R D AH0 N EY2 T S +SUBORDINATING S AH0 B AO1 R D AH0 N EY2 T IH0 NG +SUBORDINATION S AH0 B AO2 R D AH0 N EY1 SH AH0 N +SUBORDINATIONS S AH0 B AO1 R D AH0 N EY1 SH AH0 N Z +SUBORN S AH0 B AO2 R N +SUBORNATION S AH0 B AO2 R N EY1 SH AH0 N +SUBORNED S AH0 B AO2 R N D +SUBORNER S AH0 B AO1 R N ER0 +SUBORNING S AH0 B AO2 R N IH0 NG +SUBPOENA S AH0 P IY1 N AH0 +SUBPOENAED S AH0 P IY1 N AH0 D +SUBPOENAS S AH0 P IY1 N AH0 Z +SUBPOSE S AH0 B P OW1 Z +SUBPRIOR S AH0 B P R IY0 ER0 +SUBRAHMANYA S UW0 B R AA1 M AA1 N Y AH0 +SUBRECTOR S AH1 B R EH1 K T ER0 +SUBROSA S UW0 B R OW1 S AH0 +SUBS S AH1 B Z +SUBSCRIBE S AH0 B S K R AY1 B +SUBSCRIBED S AH0 B S K R AY1 B D +SUBSCRIBER S AH0 B S K R AY1 B ER0 +SUBSCRIBERS S AH0 B S K R AY1 B ER0 Z +SUBSCRIBES S AH0 B S K R AY1 B Z +SUBSCRIBING S AH0 B S K R AY1 B IH0 NG +SUBSCRIPT S AH0 B S K R IH1 P T +SUBSCRIPTION S AH0 B S K R IH1 P SH AH0 N +SUBSCRIPTIONS S AH0 B S K R IH1 P SH AH0 N Z +SUBSECIVAE S AH1 B S EH0 K IH0 V EY1 +SUBSECTION S AH1 B S EH0 K SH AH0 N +SUBSEQUENT S AH1 B S AH0 K W AH0 N T +SUBSEQUENTLY S AH1 B S AH0 K W AH0 N T L IY0 +SUBSERVE S AH0 B S ER1 V +SUBSERVED S AH0 B S ER1 V D +SUBSERVES S AH0 B S ER1 V Z +SUBSERVIENCE S AH0 B S ER1 V IY0 AH0 N S +SUBSERVIENCY S AH0 B S ER1 V IY0 AH0 N S IY0 +SUBSERVIENT S AH0 B S ER1 V IY0 AH0 N T +SUBSERVIENTLY S AH0 B S ER1 V IY0 AH0 N T L IY0 +SUBSERVING S AH0 B S ER1 V IH0 NG +SUBSIDE S AH0 B S AY1 D +SUBSIDED S AH0 B S AY1 D IH0 D +SUBSIDENCE S AH0 B S AY1 D AH0 N S +SUBSIDENCES S AH1 B S AH0 D AH0 N S IH0 Z +SUBSIDES S AH0 B S AY1 D Z +SUBSIDIARIES S AH0 B S IH1 D IY0 EH2 R IY0 Z +SUBSIDIARY S AH0 B S IH1 D IY0 EH2 R IY0 +SUBSIDIES S AH1 B S AH0 D IY0 Z +SUBSIDIES S AH1 B S IH0 D IY0 Z +SUBSIDING S AH0 B S AY1 D IH0 NG +SUBSIDISE S AH1 B S IH0 D AY2 Z +SUBSIDISED S AH1 B S IH0 D AY2 Z D +SUBSIDIZE S AH1 B S IH0 D AY2 Z +SUBSIDIZED S AH1 B S IH0 D AY2 Z D +SUBSIDIZING S AH1 B S IH0 D AY2 Z IH0 NG +SUBSIDY S AH1 B S IH0 D IY0 +SUBSIST S AH0 B S IH1 S T +SUBSISTANCE S AH0 B S IH1 S T AH0 N S +SUBSISTED S AH0 B S IH1 S T AH0 D +SUBSISTENCE S AH0 B S IH1 S T AH0 N S +SUBSISTENT S AH0 B S IH1 S T AH0 N T +SUBSISTING S AH0 B S IH1 S T IH0 NG +SUBSISTS S AH0 B S IH1 S T S +SUBSOIL S AH1 B S OY2 L +SUBSOILING S AH1 B S OY2 L IH0 NG +SUBSPACE S AH1 B S P EY2 S +SUBSPECIES S AH1 B S P IY1 SH IY0 Z +SUBSTANCE S AH1 B S T AH0 N S +SUBSTANCELESS S AH0 B S T AE1 N S L AH0 S +SUBSTANCES S AH1 B S T AH0 N S AH0 Z +SUBSTANCES S AH1 B S T AH0 N S IH0 Z +SUBSTANTIA S AH0 B S T AE1 N SH AH0 +SUBSTANTIAL S AH0 B S T AE1 N CH AH0 L +SUBSTANTIAL S AH0 B S T AE1 N SH AH0 L +SUBSTANTIALITY S AH0 B S T AE1 N CH IY0 AE1 L AH0 T IY0 +SUBSTANTIALLY S AH0 B S T AE1 N CH AH0 L IY0 +SUBSTANTIALLY S AH0 B S T AE1 N SH AH0 L IY0 +SUBSTANTIALS S AH0 B S T AE1 N CH AH0 L Z +SUBSTANTIATE S AH0 B S T AE1 N CH IY0 EY2 T +SUBSTANTIATE S AH0 B S T AE1 N SH IY0 EY2 T +SUBSTANTIATED S AH0 B S T AE1 N CH IY0 EY2 T IH0 D +SUBSTANTIATED S AH0 B S T AE1 N SH IY0 EY2 T IH0 D +SUBSTANTIATES S AH0 B S T AE1 N CH IY0 EY2 T S +SUBSTANTIATES S AH0 B S T AE1 N SH IY0 EY2 T S +SUBSTANTIATING S AH0 B S T AE1 N SH IY0 EY2 T IH0 NG +SUBSTANTIATION S AH0 B S T AE2 N CH IY0 EY1 SH AH0 N +SUBSTANTIATION S AH0 B S T AE2 N SH IY0 EY1 SH AH0 N +SUBSTANTIVE S AH1 B S T AH0 N T IH0 V +SUBSTANTIVES S AH1 B S T AH0 N T IH0 V Z +SUBSTATION S AH1 B S T EY2 SH AH0 N +SUBSTITUT S AH1 B S T IH0 T UW0 T +SUBSTITUTE S AH1 B S T AH0 T UW2 T +SUBSTITUTED S AH1 B S T AH0 T UW2 T AH0 D +SUBSTITUTES S AH1 B S T AH0 T UW2 T S +SUBSTITUTING S AH1 B S T IH0 T UW2 T IH0 NG +SUBSTITUTION S AH2 B S T IH0 T UW1 SH AH0 N +SUBSTITUTIONARY S AH1 B S T AH0 T UW1 SH AH0 N EH2 R IY0 +SUBSTITUTIONS S AH2 B S T IH0 T Y UW1 SH AH0 N Z +SUBSTRATA S AH1 B S T R AE1 T AH0 +SUBSTRATUM S AH1 B S T R AE1 T AH0 M +SUBSTRUCTIONS S AH1 B S T R AH1 K SH AH0 N Z +SUBSTRUCTURE S AH1 B S T R AH1 K CH ER0 +SUBSTRUCTURES S AH1 B S T R AH1 K CH ER0 Z +SUBSUMED S AH1 B S UW1 M D +SUBSUMPTION S AH1 B S AH1 M P SH AH0 N +SUBSURFACE S AH1 B S ER2 F AH0 S +SUBTEND S AH1 B T EH2 N D +SUBTENDED S AH1 B T EH1 N D IH0 D +SUBTENDS S AH1 B T EH2 N D Z +SUBTER S AH1 B T ER0 +SUBTERFUGE S AH1 B T ER0 F Y UW2 JH +SUBTERFUGES S AH1 B T ER0 F Y UW2 JH IH0 Z +SUBTERRANE S AH1 B T ER0 EY1 N +SUBTERRANEAN S AH0 B T ER0 EY1 N IY0 AH0 N +SUBTERRANEANS S AH1 B T ER0 EY1 N IY0 AH0 N Z +SUBTERRANEOUS S AH1 B T ER0 EY1 N IY0 AH0 S +SUBTERRANEOUSLY S AH1 B T ER0 EY1 N IY0 AH0 S L IY0 +SUBTERRENE S AH1 B T ER0 IY2 N +SUBTHYROID S AH1 B T AY1 R OY0 D +SUBTIL S AH1 B T IH0 L +SUBTILE S AH1 B T AY2 L +SUBTILELY S AH1 B T AY2 L L IY0 +SUBTILER S AH1 B T AY2 L ER0 +SUBTILEST S AH1 B T AY2 L AH0 S T +SUBTILISED S AH1 B T AY2 L AY2 Z D +SUBTILITY S AH1 B T IH1 L IH0 T IY0 +SUBTILIZED S AH1 B T AH0 L AY2 Z D +SUBTILIZING S AH1 B T AH0 L AY2 Z IH0 NG +SUBTILL S AH1 B T IH2 L +SUBTILLY S AH1 B T IH1 L IY0 +SUBTILTIES S AH1 B T AY2 L T IY0 Z +SUBTILTY S AH1 B T IH1 L T IY0 +SUBTITLE S AH1 B T AY2 T AH0 L +SUBTITLES S AH1 B T AY2 T AH0 L Z +SUBTLE S AH1 T AH0 L +SUBTLELY S AH1 T L L IY0 +SUBTLENESS S AH1 T AH0 L N AH0 S +SUBTLER S AH1 T AH0 L ER0 +SUBTLER S AH1 T L ER0 +SUBTLEST S AH1 T AH0 L S T +SUBTLETIES S AH1 T AH0 L T IY0 Z +SUBTLETY S AH1 T AH0 L T IY0 +SUBTLY S AH1 T AH0 L IY0 +SUBTRACT S AH0 B T R AE1 K T +SUBTRACTED S AH0 B T R AE1 K T IH0 D +SUBTRACTING S AH0 B T R AE1 K T IH0 NG +SUBTRACTION S AH0 B T R AE1 K SH AH0 N +SUBTRACTIONS S AH0 B T R AE1 K SH AH0 N Z +SUBTRACTS S AH0 B T R AE1 K T S +SUBTREASURY S AH0 B T R EH1 ZH ER0 IY0 +SUBTROPICAL S AH0 B T R AA1 P IH0 K AH0 L +SUBUDDHI S AH1 B UW1 D IY0 +SUBULATE S AH0 B Y UW0 L EY0 T +SUBURA S AH0 B ER1 AH0 +SUBURB S AH1 B ER0 B +SUBURBAN S AH0 B ER1 B AH0 N +SUBURBANITE S AH0 B ER1 B AH0 N AY2 T +SUBURBANITES S AH0 B ER1 B AH0 N AY2 T S +SUBURBIA S AH0 B ER1 B IY0 AH0 +SUBURBS S AH1 B ER0 B Z +SUBURRA S AH0 B ER1 AH0 +SUBVENTION S AH0 B V EH1 N SH AH0 N +SUBVERSION S AH0 B V ER1 ZH AH0 N +SUBVERSIVE S AH0 B V ER1 S IH0 V +SUBVERSIVES S AH0 B V ER1 S IH0 V Z +SUBVERT S AH0 B V ER1 T +SUBVERTED S AH0 B V ER1 T IH0 D +SUBVERTER S AH0 B V ER1 T ER0 +SUBVERTERS S AH0 B V ER1 T ER0 Z +SUBVERTING S AH0 B V ER1 T IH0 NG +SUBVERTS S AH0 B V ER1 T S +SUBVOCALS S AH0 B V OW1 K AH0 L Z +SUBWAY S AH1 B W EY2 +SUBWAYS S AH1 B W EY2 Z +SUC S AH1 K +SUCCEDANEUM S AH0 K S IY1 D AH0 N IY1 AH0 M +SUCCEED S AH0 K S IY1 D +SUCCEEDED S AH0 K S IY1 D AH0 D +SUCCEEDED S AH0 K S IY1 D IH0 D +SUCCEEDING S AH0 K S IY1 D IH0 NG +SUCCEEDS S AH0 K S IY1 D Z +SUCCES S AH0 K S IH0 Z +SUCCESS S AH0 K S EH1 S +SUCCESSE S AH0 K S EH1 S +SUCCESSES S AH0 K S EH1 S AH0 Z +SUCCESSES S AH0 K S EH1 S IH0 Z +SUCCESSFUL S AH0 K S EH1 S F AH0 L +SUCCESSFULLY S AH0 K S EH1 S F AH0 L IY0 +SUCCESSION S AH0 K S EH1 SH AH0 N +SUCCESSIONS S AH0 K S EH1 SH AH0 N Z +SUCCESSIVE S AH0 K S EH1 S IH0 V +SUCCESSIVELY S AH0 K S EH1 S IH0 V L IY0 +SUCCESSLESS S AH0 K S EH1 S L AH0 S +SUCCESSOR S AH0 K S EH1 S ER0 +SUCCESSOR'S S AH0 K S EH1 S ER0 Z +SUCCESSORS S AH0 K S EH1 S ER0 Z +SUCCESSORSHIP S AH0 K S EH1 S ER0 SH IH2 P +SUCCINCT S AH0 K S IH1 NG K T +SUCCINCTLY S AH0 K S IH1 NG K T L IY0 +SUCCINCTNESS S AH0 K S IH1 NG K T N AH0 S +SUCCOR S AH1 K ER0 +SUCCORED S AH1 K ER0 D +SUCCORER S AH1 K ER0 ER0 +SUCCORING S AH1 K ER0 IH0 NG +SUCCORS S AH1 K ER0 Z +SUCCORY S AH1 K ER0 IY0 +SUCCOTASH S AH1 K OW0 T AE2 SH +SUCCOTH S AH1 K AA2 TH +SUCCOUR S AH1 K UH1 R +SUCCOURED S AH1 K AW1 ER0 D +SUCCOURER S AH1 K AO1 R ER0 +SUCCOURING S AH1 K AO1 R IH0 NG +SUCCOURS S AH1 K AO1 R Z +SUCCUBI S AH1 K Y AH0 B AY2 +SUCCUBUS S AH1 K Y AH0 B IH0 S +SUCCULENCE S AH1 K Y AH0 L AH0 N S +SUCCULENT S AH1 K Y AH0 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N T +SUIVEZ S W IY1 V EH0 Z +SUIVI S W IY1 V IY0 +SUIVRE S W IY1 V ER0 +SUJAH S UW1 Y AH0 +SUJAH'S S UW1 Y AH0 Z +SUJET S UW0 JH EH2 T +SUJETS S UW1 JH EH2 T S +SUJIN S UW1 JH IH0 N +SUK S AH1 K +SUKA S UW1 K AH0 +SUKE S UW1 K +SUKE'S S UW1 K S +SUKES S UW1 K S +SUKET S UW1 K AH0 T +SUKEY S UW1 K IY0 +SUKEY'S S UW1 K IY0 Z +SUKHOMLINOFF S UW2 K OW0 M L IH0 N AO0 F +SUKHOTIN S UW2 K AH0 T IH0 N +SUKIE S UW0 K IY0 +SUKKESTAD S UW0 K K S T AE2 D +SUKKUR S UW0 K K ER0 +SUKMANA S AH1 K M AA1 N AH0 +SUKY S UW0 K IY0 +SUL S AH1 L +SULA S UW1 L AH0 +SULACO S AH1 L AA1 K OW0 +SULAIMAN S UW1 L EY1 M AH0 N +SULALI S AH1 L AA1 L IY0 +SULAMITH S UW1 L AH0 M IH1 TH +SULBY S AH1 L B IY0 +SULD S AH1 L D +SULDNA S AH1 L D N AH0 +SULEAU S UW1 L OW1 +SULEIMAN S UW1 L AY1 M AH0 N +SULEIMAN'S S UW1 L AY1 M AH0 N Z +SULEYM S UW1 L IY0 M +SULEYMAN S UW1 L IY0 M AH0 N +SULFUR S AH1 L F ER0 +SULGOSTOVSKIS S AH1 L G AH0 S T AA1 F S K IY0 Z +SULGOSTOW S AH1 L G OW1 S T OW0 +SULI S UW1 L IY0 +SULI'S S UW1 L IY0 Z 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+SULLYING S AH1 L IY0 IH0 NG +SULPHATE S AH1 L F EY2 T +SULPHATES S AH1 L F EY2 T S +SULPHER S AH1 L F ER0 +SULPHIDE S AH1 L F IH0 D +SULPHITE S AH1 L F AY2 T +SULPHITES S AH1 L F AY2 T S +SULPHITIC S AH1 L F IH1 T IH0 K +SULPHO S AH1 L F OW0 +SULPHONAL S AH1 L F AH0 N AH0 L +SULPHUR S AH1 L F ER0 +SULPHUREOUS S AH1 L F ER0 AH0 S +SULPHURET S AH1 L F ER0 EH1 T +SULPHURETS S AH1 L F ER0 EH1 T S +SULPHURETTED S AH1 L F ER0 EH1 T IH0 D +SULPHURIC S AH1 L F ER0 IH0 K +SULPHUROUS S AH1 L F ER0 AH0 S +SULPHUROUSLY S AH1 L F ER0 AH0 S L IY0 +SULPHURS S AH1 L F ER0 Z +SULPHURY S AH1 L F ER0 IY0 +SULPICE S UW0 L P IH0 S +SULPICIAN S UW0 L P IH1 SH AH0 N +SULPICIANS S UW0 L P IH1 SH AH0 N Z +SULPICIUS S UW0 L P IH0 S IY0 IH0 S +SULT S AH1 L T +SULTAN S AH1 L T AH0 N +SULTAN'S S AH1 L T AH0 N Z +SULTANA S AH1 L T AH0 N AH0 +SULTANA'S S AH1 L T AH0 N AH0 Z +SULTANAS S AH1 L T AH0 N AH0 Z +SULTANATE S AH1 L T AH0 N AH0 T +SULTANESS S AH1 L T AH0 N AH0 S +SULTANI S AH1 L T AH0 N IY0 +SULTANIC S AH1 L T AH0 N IH0 K +SULTANIEH S AH1 L T AH0 N IY1 +SULTANS S AH1 L T AH0 N Z +SULTAUN S AH1 L T AW0 N +SULTON S AH1 L T AH0 N +SULTRIER S AH1 L T R IY0 ER0 +SULTRIEST S AH1 L T R IY0 AH0 S T +SULTRINESS S AH1 L T R IY0 N AH0 S +SULTRY S AH1 L T R IY0 +SULU S UW1 L UW0 +SULZER S AH1 L Z ER0 +SUM S AH1 M +SUMA S UW1 M AH0 +SUMAC S UW1 M AE0 K +SUMACH S UW2 M AH0 K +SUMACHS S UW2 M AH0 K S +SUMACS S UW1 M AE0 K S +SUMATRA S UW2 M AA1 T R AH0 +SUMATRAN S UW2 M AA1 T R AH0 N +SUMBAT S AH1 M B AE0 T +SUMBAWA S AH1 M B AA1 W AH0 +SUME S UW1 M +SUMER S AH1 M ER0 +SUMERIAN S AH0 M ER1 IY0 AH0 N +SUMERIANS S AH0 M ER1 IY0 AH0 N Z +SUMERO S UW2 M EH1 R OW0 +SUMFIN S AH1 M F IH0 N +SUMHOW S AH1 M HH AW2 +SUMI S UW1 M IY0 +SUMITRA S UW0 M IY1 T R AH0 +SUMM S AH1 M +SUMM'AT S AH1 M AE1 T +SUMM'D S AH1 M D +SUMMA S UW1 M AH0 +SUMMAH S AH2 M AH0 +SUMMANUS S AH0 M EY1 N IH0 S +SUMMARIES S AH1 M ER0 IY0 Z +SUMMARILY S AH0 M EH1 R IH0 L IY0 +SUMMARISE S AH1 M ER0 AY2 Z +SUMMARISED S AH1 M ER0 AY2 Z D 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+SUPPLEMENT S AH1 P L AH0 M EH1 N T +SUPPLEMENTAL S AH2 P L AH0 M EH1 N AH0 L +SUPPLEMENTAL S AH2 P L AH0 M EH1 N T AH0 L +SUPPLEMENTARY S AH2 P L AH0 M EH1 N CH R IY0 +SUPPLEMENTARY S AH2 P L AH0 M EH1 N ER0 IY0 +SUPPLEMENTARY S AH2 P L AH0 M EH1 N T ER0 IY0 +SUPPLEMENTARY S AH2 P L AH0 M EH1 N T R IY0 +SUPPLEMENTED S AH1 P L AH0 M EH2 N T AH0 D +SUPPLEMENTING S AH1 P L AH0 M AH0 N T IH0 NG +SUPPLEMENTING S AH1 P L AH0 M EH1 N T IH0 NG +SUPPLEMENTS S AH1 P L AH0 M AH0 N T S +SUPPLEMENTS S AH1 P L AH0 M EH1 N T S +SUPPLENESS S AH1 P L N AH0 S +SUPPLER S AH1 P L ER0 +SUPPLIANCE S AH0 P L AY1 AH0 N S +SUPPLIANT S AH0 P L AY1 AH0 N T +SUPPLIANT'S S AH0 P L AY1 AH0 N T S +SUPPLIANTS S AH0 P L AY1 AH0 N T S +SUPPLICANT S AH1 P L AH0 K AH0 N T +SUPPLICANTS S AH1 P L AH0 K AH0 N T S +SUPPLICATE S AH1 P L AH0 K EY2 T +SUPPLICATED S AH1 P L AH0 K EY2 T IH0 D +SUPPLICATES S AH1 P L AH0 K EY2 T S +SUPPLICATING S AH1 P L AH0 K EY2 T IH0 NG +SUPPLICATINGLY S AH1 P L AH0 K EY2 T IH0 NG L IY0 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OW1 Z IH0 Z +SUPPOSETH S AH0 P OW1 Z AH0 TH +SUPPOSIN S AH2 P AH0 Z AH0 N +SUPPOSING S AH0 P OW1 Z IH0 NG +SUPPOSITION S AH2 P AH0 Z IH1 SH AH0 N +SUPPOSITIONS S AH2 P AH0 Z IH1 SH AH0 N Z +SUPPOSITIOUS S AH2 P AH0 Z IH1 SH AH0 S +SUPPOSITITIOUS S AH2 P AH0 Z AH0 T IH1 SH AH0 S +SUPPOSITORIES S AH2 P AH0 Z AH0 T AO2 R IY0 Z +SUPPOSITORY S AH2 P AH0 Z AH0 T AO2 R IY0 +SUPPRESS S AH0 P R EH1 S +SUPPRESS'D S AH0 P R EH1 SH D +SUPPRESSED S AH0 P R EH1 S T +SUPPRESSES S AH0 P R EH1 S IH0 Z +SUPPRESSING S AH0 P R EH1 S IH0 NG +SUPPRESSIO S AH0 P R EH1 S IY0 OW0 +SUPPRESSION S AH0 P R EH1 SH AH0 N +SUPPRESSIONS S AH0 P R EH1 SH AH0 N Z +SUPPREST S AH0 P R EH1 S T +SUPPRISE S AH0 P R AY1 Z +SUPPRISED S AH0 P R AY1 Z D +SUPPURATE S AH0 P ER0 AH0 T +SUPPURATING S AH0 P ER0 EY2 T IH0 NG +SUPPURATION S AH0 P ER0 EY1 SH AH0 N +SUPRA S UW1 P R AH0 +SUPRACILIARY S UW2 P R AH0 S IH1 L IY0 EH0 R IY0 +SUPRARENAL S UW2 P R AH0 R IY1 N AH0 L +SUPREAM S UW0 P R IY2 M +SUPREMA S UW0 P R EH1 M AH0 +SUPREMACIES S UW0 P R EH1 M AH0 S IY0 Z +SUPREMACY S AH0 P R EH1 M AH0 S IY0 +SUPREME S AH0 P R IY1 M +SUPREME S ER0 P R IY1 M +SUPREMELY S UW0 P R IY1 M AH0 L IY0 +SUPREMER S UW0 P R IY1 M ER0 +SUPREMES S UW0 P R IY1 M Z +SUPREMEST S UW0 P R EH1 M AH0 S T +SUPREMO S UW0 P R EH1 M OW0 +SUPRISED S UW2 P R AY1 Z D +SUPS S AH0 P S +SUPT S AH0 P T +SUR S ER1 +SURA S UH1 R AH0 +SURABAYA S UH1 R AH0 B AY0 AA1 +SURABHI S UH0 R AA1 B IY0 +SURAH S UH0 R AA1 +SURAI S UH0 R AA1 IY0 +SURAJ S UH0 R AA1 ZH +SURAJA S ER0 AA1 JH AH0 +SURAJAH S ER0 AA1 JH AH0 +SURAJAH'S S ER0 AA1 JH AH0 Z +SURAS S UH1 R AH0 S +SURAT S ER0 AA1 T +SURBER S ER1 B ER0 +SURBITON S ER1 B IH1 T AH0 N +SURBITON'S S ER1 B IH0 T AH0 N Z +SURBRIDGE S ER1 B R IH2 JH +SURBUS S ER1 B IH0 S +SURCEASE S ER1 S IY1 S +SURCH S ER1 CH +SURCHARGE S ER0 CH AA1 R JH +SURCHARGE S ER1 CH AA2 R JH +SURCHARGED S ER1 CH AA2 R JH D +SURCHARGES S ER1 CH AA2 R JH IH0 Z +SURCHARGING S ER0 CH AA1 R JH IH0 NG +SURCINGLE S ER0 CH IH1 NG G AH0 L 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ER0 M AY1 Z IH0 NG Z +SURMIZE S ER0 M AY1 Z +SURMOUNT S ER0 M AW1 N T +SURMOUNTABLE S ER0 M AW1 N T AH0 B AH0 L +SURMOUNTED S ER0 M AW1 N T IH0 D +SURMOUNTING S ER0 M AW1 N T IH0 NG +SURMOUNTS S ER0 M AW1 N T S +SURNAME S ER1 N EY2 M +SURNAMED S ER1 N EY2 M D +SURNAMES S ER1 N EY2 M Z +SUROK S UH2 R AH0 K +SURPASS S ER0 P AE1 S +SURPASS'D S ER0 P AE1 SH D +SURPASSED S ER0 P AE1 S T +SURPASSES S ER0 P AE1 S IH0 Z +SURPASSETH S ER0 P AE1 S IH0 TH +SURPASSING S ER0 P AE1 S IH0 NG +SURPASSINGLY S ER0 P AE1 S IH0 NG L IY0 +SURPLICE S ER1 P L IH0 S +SURPLICED S ER1 P L AY1 S T +SURPLICES S ER1 P L AH0 S AH0 Z +SURPLUS S ER1 P L AH0 S +SURPLUSAGE S ER1 P L AH0 S IH0 JH +SURPLUSES S ER1 P L AH0 S IH0 Z +SURPRENANT S ER1 P R IH0 N AH0 N T +SURPRIS'D S ER0 P R AY1 Z D +SURPRISAL S ER0 P R AY1 Z AH0 L +SURPRISALS S ER0 P R AY1 Z AH0 L Z +SURPRISE S AH0 P R AY1 Z +SURPRISE S ER0 P R AY1 Z +SURPRISED S AH0 P R AY1 Z D +SURPRISED S ER0 P R AY1 Z D +SURPRISEDLY S ER0 P R AY1 Z AH0 D L IY0 +SURPRISES S AH0 P R AY1 Z IH0 Z +SURPRISES S ER0 P R AY1 Z IH0 Z +SURPRISIN S ER0 P R AY1 Z IH0 N +SURPRISING S AH0 P R AY1 Z IH0 NG +SURPRISING S ER0 P R AY1 Z IH0 NG +SURPRISINGLY S AH0 P R AY1 Z IH0 NG L IY0 +SURPRISINGLY S ER0 P R AY1 Z IH0 NG L IY0 +SURPRIZ'D S AH0 P R IH1 Z AH0 D +SURPRIZE S ER0 P R AY1 Z +SURPRIZED S ER0 P R AY1 Z D +SURPRIZES S ER0 P R AY1 Z IH0 Z +SURPRIZING S ER0 P R AY1 Z IH0 NG +SURPRIZINGLY S ER0 P R AY1 Z IH0 NG L IY0 +SURR S ER1 +SURRATT S AO1 R AH0 T +SURRATT'S S AO1 R AH0 T S +SURRATTSVILLE S AO1 R AH0 T S V IH0 L +SURRENDER S ER0 EH1 N D ER0 +SURRENDER'D S ER0 EH1 N D ER0 D +SURRENDERED S ER0 EH1 N D ER0 D +SURRENDERING S ER0 EH1 N D ER0 IH0 NG +SURRENDERS S ER0 EH1 N D ER0 Z +SURREPTITIOUS S ER2 AH0 P T IH1 SH AH0 S +SURREPTITIOUSLY S ER2 AH0 P T IH1 SH AH0 S L IY0 +SURREPTITIOUSNESS S ER2 AH0 P T IH1 SH AH0 S N AH0 S +SURREY S ER1 IY0 +SURREY'S S ER0 IY0 Z +SURREYS S ER0 IY0 Z +SURRIAGE S AO1 R IH0 JH +SURROGATE S ER1 AH0 G AH0 T +SURROGATE S ER1 AH0 G EY2 T 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V AY1 V AH0 L +SURVIVALS S ER0 V AY1 V AH0 L Z +SURVIVE S ER0 V AY1 V +SURVIVED S ER0 V AY1 V D +SURVIVES S ER0 V AY1 V Z +SURVIVIN S ER0 V AY1 V IH0 N +SURVIVING S ER0 V AY1 V IH0 NG +SURVIVOR S ER0 V AY1 V ER0 +SURVIVOR'S S ER0 V AY1 V ER0 Z +SURVIVORS S ER0 V AY1 V ER0 Z +SURVIVORSHIP S ER0 V AY1 V ER0 SH IH2 P +SURYA S UH1 R Y AH0 +SUS S AH1 S +SUSA S UW1 S AH0 +SUSAG S UW2 Z AE1 G +SUSAN S UW1 Z AH0 N +SUSAN'S S UW1 Z AH0 N Z +SUSANA S UW0 S AA1 N AH0 +SUSANAH S UW0 S AA1 N AH0 +SUSANGATA S UW2 S AE1 NG G AA1 T AH0 +SUSANI S UW0 S AA1 N IY0 +SUSANNA S UW0 Z AE1 N AH0 +SUSANNA'S S UW2 Z AE1 N AH0 Z +SUSANNAH S UW2 S AE1 N AH0 +SUSANNAH'S S UW2 Z AE1 N AH0 Z +SUSANNE S UW2 Z AE1 N +SUSANS S UW1 Z AH0 N Z +SUSANVILLE S UW0 S AA1 N V IH0 L +SUSARMAN S UW0 S AA1 R M AH0 N +SUSCEPTIBILITIES S AH0 S EH2 P T AH0 B IH1 L AH0 T IY0 Z +SUSCEPTIBILITY S AH0 S EH2 P T AH0 B IH1 L AH0 T IY0 +SUSCEPTIBLE S AH0 S EH1 P T AH0 B AH0 L +SUSE S UW1 Z +SUSETTE S UW2 Z EH1 T +SUSH S AH1 SH +SUSHAI S UW0 SH AY1 +SUSI S UW1 S IY0 +SUSI'S S UW1 Z IY0 Z +SUSIANA S UW2 Z IY0 AE1 N AH0 +SUSIANIANS S UW2 Z IY0 AE1 N IY0 AH0 N Z +SUSIE S UW1 Z IY0 +SUSIE'S S UW1 Z IY0 Z +SUSINI S UW2 S IY1 N IY0 +SUSKIND S AH1 S K IH0 N D +SUSKO S AH1 S K OW0 +SUSO S UW1 S OW0 +SUSPECT S AH0 S P EH1 K T +SUSPECT S AH1 S P EH2 K T +SUSPECTED S AH0 S P EH1 K T AH0 D +SUSPECTED S AH0 S P EH1 K T IH0 D +SUSPECTFUL S AH0 S P EH1 K T F AH0 L +SUSPECTIN S AH0 S P EH1 K T IH0 N +SUSPECTING S AH0 S P EH1 K T IH0 NG +SUSPECTS S AH0 S P EH1 K S +SUSPECTS S AH0 S P EH1 K T S +SUSPECTS S AH1 S P EH2 K S +SUSPECTS S AH1 S P EH2 K T S +SUSPENCE S AH0 S P EH1 N S +SUSPEND S AH0 S P EH1 N D +SUSPENDED S AH0 S P EH1 N D AH0 D +SUSPENDED S AH0 S P EH1 N D IH0 D +SUSPENDER S AH0 S P EH1 N D ER0 +SUSPENDERS S AH0 S P EH1 N D ER0 Z +SUSPENDING S AH0 S P EH1 N D IH0 NG +SUSPENDS S AH0 S P EH1 N D Z +SUSPENSE S AH0 S P EH1 N S +SUSPENSES S AH0 S P EH1 N S IH0 Z +SUSPENSION S AH0 S P EH1 N SH AH0 N +SUSPENSIONS S AH0 S P EH1 N SH AH0 N Z +SUSPENSIVE S AH0 S P EH1 N S IH0 V +SUSPENSORY S AH0 S P EH1 N S ER0 IY0 +SUSPICION S AH0 S P IH1 SH AH0 N +SUSPICION'S S AH0 S P IH1 SH AH0 N Z +SUSPICIONED S AH0 S P IH1 SH AH0 N D +SUSPICIONING S AH0 S P IH1 SH AH0 N IH0 NG +SUSPICIONS S AH0 S P IH1 SH AH0 N Z +SUSPICIOUS S AH0 S P IH1 SH AH0 S +SUSPICIOUSLY S AH0 S P IH1 SH AH0 S L IY0 +SUSPICIOUSNESS S AH0 S P IH1 SH AH0 S N AH0 S +SUSPIRATION S AH0 S P ER0 EY1 SH AH0 N +SUSPIRE S AH0 S P AY1 R +SUSPIRIA S AH0 S P IH0 R IY1 AH0 +SUSPITION S AH0 S P IH1 SH AH0 N +SUSQUEHANNA S UW2 S K W EH0 HH AE1 N AH0 +SUSQUEHANNAH S UW2 S K W EH0 HH AE1 N AH0 +SUSQUESUS S UW2 S K W EH0 S AH0 S +SUSS S AH1 S +SUSSA S UW1 S AH0 +SUSSEX S AH1 S IH0 K S +SUSSEX'S S AH1 S IH0 K S IH0 Z +SUSSKIND S AH1 S K IH0 N D +SUSTAIN S AH0 S T EY1 N +SUSTAIN'D S AH0 S T EY2 N D +SUSTAINABLE S AH0 S T EY1 N AH0 B AH0 L +SUSTAINED S AH0 S T EY1 N D +SUSTAINER S AH0 S T EY1 N ER0 +SUSTAINETH S AH0 S T EY1 N TH +SUSTAINING S AH0 S T EY1 N IH0 NG +SUSTAINMENT S AH0 S T EY1 N M AH0 N T +SUSTAINS S AH0 S T EY1 N Z +SUSTENANCE S AH1 S T AH0 N AH0 N S +SUSTENTATION S AH1 S T AH0 N T EY1 SH AH0 N +SUSUHUNAN S UW2 S UW1 HH UW1 N AH0 N +SUSUKI S UW2 S UW1 K IY0 +SUSURRUS S UW2 S UH1 R AH0 S +SUSY S UW1 Z IY0 +SUSY'S S UW2 Z IY0 Z +SUSYJAN S UW2 S AY1 JH AE2 N +SUT S AH1 T +SUT'NY S UW1 T AH0 N IY0 +SUTA S UW1 T AH0 +SUTA'S S UW1 T AH0 Z +SUTAS S UW1 T AH0 S +SUTCH S AH1 CH +SUTCLIFFE S AH1 T K L IH0 F +SUTCLIFFE'S S AH1 T K L IH0 F S +SUTCLIFFES S AH1 T K L IH0 F S +SUTCOMBE S AH1 T K AH0 M +SUTE S UW1 T +SUTER S UW1 T ER0 +SUTHEN S AH1 TH AH0 N +SUTHERLAND S AH1 DH ER0 L AH0 N D +SUTHERLAND'S S AH1 DH ER0 L AH0 N D Z +SUTHERLANDS S AH1 DH ER0 L AE2 N D Z +SUTHERLANDSHIRE S AH1 DH ER0 L AE0 N D SH ER0 +SUTHERLANDTOWN S AH1 DH ER0 L AE0 N D T AW2 N +SUTHIN S AH1 TH AH0 N +SUTHIN'S S AH1 TH IH0 N Z +SUTHING S AH1 TH IH0 NG +SUTKHU S AH1 T K UW1 +SUTLEJ S AH1 T L EY1 +SUTLEMEME S AH1 T L IH0 M IY2 M +SUTLER S AH1 T L ER0 +SUTLER'S S AH1 T L ER0 Z +SUTLERS S AH1 T L ER0 Z +SUTON S UW1 T AH0 N +SUTOR S UW1 T ER0 +SUTOTO S UW0 T OW1 T OW0 +SUTPHEN S AH1 T F AH0 N +SUTRA S UW1 T R AH0 +SUTRAS S UW1 T R AH0 Z +SUTRI S UW1 T R IY0 +SUTRIUM S UW1 T R IY0 AH0 M +SUTRO S UW1 T R OW0 +SUTT'NLY S AH1 T AH0 N L IY0 +SUTTEE S AH1 T IY1 +SUTTENLY S AH1 T AH0 N L IY0 +SUTTER S AH1 T ER0 +SUTTER'S S AH1 T ER0 Z +SUTTERBY S AH1 T ER0 B IY0 +SUTTINLY S AH1 T AH0 N L IY0 +SUTTON S AH1 T AH0 N +SUTTON'S S AH1 T AH0 N Z +SUTTONS S AH1 T AH0 N Z +SUTTU S AH1 T UW1 +SUTTUNG S AH1 T AH0 NG +SUTURE S UW1 CH ER0 +SUTURES S UW1 CH ER0 Z +SUUM S UW1 M +SUVA S UW1 V AH0 +SUVALKI S UW0 V AA1 L K IY0 +SUVARNDRUG S UW0 V AA1 R D R AH2 G +SUVAROFF S UW1 V AH0 R AO0 F +SUVLA S UW1 V L AH0 +SUVORIN S UW1 V AO1 R IH0 N +SUVOROFF S UW1 V AO1 R AO0 F +SUVY S UW1 V IY0 +SUVY'S S UW1 V IY0 Z +SUWALKI S UW1 W AO0 K IY0 +SUWANEE S UW1 W AA1 N IY1 +SUWANNEE S UW0 AA1 N IY1 +SUWARNA S UW0 AA1 R N AH0 +SUWARNDRUG S UW0 W ER0 D R AH2 G +SUWAROW S UW0 W ER0 OW0 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W AA1 NG G AH0 +SWANHAVEN S W AO1 N HH EY2 V AH0 N +SWANHILD S W AO1 N HH IH0 L D +SWANHILD'S S W AO1 N HH IH0 L D Z +SWANING S W AA1 N IH0 NG +SWANK S W AE1 NG K +SWANKER S W AO1 NG K ER0 +SWANKI S W AA1 NG K IY0 +SWANKIE S W AA1 NG K IY0 +SWANKING S W AE1 NG K IH0 NG +SWANKS S W AA1 NG K S +SWANKY S W AA1 NG K IY0 +SWANLAND S W AA1 N L AH0 N D +SWANLAND'S S W AA1 N L AH0 N D Z +SWANLEY S W AA1 N L IY0 +SWANLIKE S W AA1 N L AY2 K +SWANN S W AA1 N +SWANN'S S W AA1 N Z +SWANNANOA S W AA1 N AH0 N OW1 AH0 +SWANNE S W AA1 N +SWANNS S W AA1 N Z +SWANNY S W AA1 N IY0 +SWANS S W AA1 N Z +SWANS S W AO1 N Z +SWANSDOWN S W AO1 N Z D AW0 N +SWANSEA S W AO1 N Z IY1 AH0 +SWANSKIN S W AA1 N S K IH0 N +SWANSON S W AA1 N S AH0 N +SWANSON'S S W AA1 N S AH0 N Z +SWANSTON S W AA1 N S T AH0 N +SWANTON S W AA1 N T AH0 N +SWANWHITE S W AO1 N W AY2 T +SWANWICK S W AO1 N W IH0 K +SWAP S W AA1 P +SWAPPED S W AA1 P T +SWAPPED S W AO1 P T +SWAPPIN S W AA1 P IH0 N +SWAPPING S W AA1 P IH0 NG +SWAPS S W AA1 P S 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NG +SWASTIKA S W AA1 S T IH0 K AH0 +SWAT S W AA1 T +SWATCH S W AA1 CH +SWATCHES S W AA1 CH AH0 Z +SWATCHES S W AA1 CH IH0 Z +SWATE S W AA1 T EY2 +SWATH S W AA1 TH +SWATHE S W AA1 DH +SWATHE S W EY1 DH +SWATHED S W AA1 DH D +SWATHES S W EY1 DH Z +SWATHING S W EY1 DH IH0 NG +SWATHINGLEA S W EY1 DH IH0 NG L IY2 +SWATHINGS S W EY1 DH IH0 NG Z +SWATHS S W AA1 DH Z +SWATOW S W AA1 T OW2 +SWATRIDGE S W AA1 T R IH2 JH +SWATS S W AA1 T S +SWATTED S W AA1 T IH0 D +SWATTER S W AA1 T ER0 +SWATTING S W AA1 T IH0 NG +SWATTY S W AA1 T IY0 +SWATTY'S S W AA1 T IY0 Z +SWAY S W EY1 +SWAY'D S W EY1 D +SWAYED S W EY1 D +SWAYETH S W EY1 IH0 TH +SWAYING S W EY1 IH0 NG +SWAYINGLY S W EY1 IH0 NG L IY0 +SWAYINGS S W EY1 IH0 NG Z +SWAYLANDS S W EY1 L AH0 N D Z +SWAYNE S W EY1 N +SWAYNE'S S W EY1 N Z +SWAYNY S W EY1 N IY0 +SWAYS S W EY1 Z +SWAZI S W AA1 Z IY0 +SWAZIE S W AA1 Z IY0 +SWAZIES S W AA1 Z IY0 Z +SWAZILAND S W AA1 Z IH0 L AH0 N D +SWAZIS S W AA1 Z IY0 Z +SWE S W IY1 +SWEAR S W EH1 R +SWEARAH S W EH1 R 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+SWEETPEAS S W IY1 T P IY1 Z +SWEETS S W IY1 T S +SWEETSBURG S W IY1 T S B ER0 G +SWEETSER S W IY1 T S ER0 +SWEETSIR S W IY1 T S ER0 +SWEETSTUFF S W IY1 T S T AH2 F +SWEETVOICE S W IY1 T V OY1 S +SWEETWATER S W IY1 T W AA2 T ER0 +SWEETWATER'S S W IY1 T W AA2 T ER0 Z +SWEETY S W IY1 T IY0 +SWEIR S W IY1 R +SWELDING S W EH1 L D IH0 NG +SWELL S W EH1 L +SWELL'D S W EH2 L D +SWELL'S S W EH2 L Z +SWELLDOM S W EH1 L D AH0 M +SWELLED S W EH1 L D +SWELLER S W EH1 L ER0 +SWELLEST S W EH1 L AH0 S T +SWELLETH S W EH1 L EH0 TH +SWELLIN S W EH1 L IH0 N +SWELLING S W EH1 L IH0 NG +SWELLINGS S W EH1 L IH0 NG Z +SWELLS S W EH1 L Z +SWELP S W EH1 L P +SWELTER S W EH1 L T ER0 +SWELTERED S W EH1 L T ER0 D +SWELTERING S W EH1 L T ER0 IH0 NG +SWELTERS S W EH1 L T ER0 Z +SWEN S W EH1 N +SWEND S W EH1 N D +SWENDON S W EH1 N D AH0 N +SWENO S W EH1 N OW0 +SWENSON S W EH1 N S AH0 N +SWENSTER S W EH1 N S T ER0 +SWENSTER'S S W EH1 N S T ER0 Z +SWEP S W EH1 P +SWEPT S W EH1 P T +SWERVE S W ER1 V +SWERVED S W ER1 V 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S W IH1 SH T +SWISHES S W IH1 SH IH0 Z +SWISHFORD S W IH1 SH F ER0 D +SWISHING S W IH1 SH IH0 NG +SWISHTAIL S W IH1 SH T EY2 L +SWISS S W IH1 S +SWISSVALE S W IH1 S V EY2 L +SWITCH S W IH1 CH +SWITCHBACK S W IH1 CH B AE2 K +SWITCHBACKS S W IH1 CH B AE2 K S +SWITCHBLADE S W IH1 CH B L EY2 D +SWITCHBOARD S W IH1 CH B AO2 R D +SWITCHBOARDS S W IH1 CH B AO2 R D Z +SWITCHED S W IH1 CH T +SWITCHEL S W IH1 CH AH0 L +SWITCHER S W IH1 CH ER0 +SWITCHES S W IH1 CH AH0 Z +SWITCHES S W IH1 CH IH0 Z +SWITCHIN S W IH1 CH IH0 N +SWITCHING S W IH1 CH IH0 NG +SWITCHMAN S W IH1 CH M AH0 N +SWITCHMAN'S S W IH1 CH M AH0 N Z +SWITCHMEN S W IH1 CH M EH0 N +SWITHER S W IH1 DH ER0 +SWITHERED S W IH1 DH ER0 D +SWITHERING S W IH1 DH ER0 IH0 NG +SWITHIN S W IH1 TH IH0 N +SWITHIN'S S W IH1 TH IH0 N Z +SWITHUN S W IH1 TH AH0 N +SWITHUN'S S W IH1 TH AH0 N Z +SWITZER S W IH1 T S ER0 +SWITZER'S S W IH1 T S ER0 Z +SWITZERLAND S W IH1 T S ER0 L AH0 N D +SWITZERLAND'S S W IH1 T S ER0 L AH0 N D Z +SWITZERS S W IH1 T Z ER0 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IH1 L AH0 +SYLLA'S S IH1 L AH0 Z +SYLLABARY S IH1 L AH0 B ER0 IY0 +SYLLABIC S AH0 L AE1 B IH0 K +SYLLABIFY S IH1 L AH0 B AH0 F AY2 +SYLLABLE S IH1 L AH0 B AH0 L +SYLLABLED S IH1 L AH0 B AH0 L D +SYLLABLES S IH1 L AH0 B AH0 L Z +SYLLABUB S IH1 L AH0 B AH0 B +SYLLABUBS S IH1 L AH0 B AH0 B Z +SYLLABUS S IH1 L AH0 B AH0 S +SYLLOGISM S IH1 L AA0 G IH0 Z AH0 M +SYLLOGISMS S IH1 L AH0 G IH2 Z AH0 M Z +SYLLOGISTIC S IH1 L AH0 JH IH1 S T IH0 K +SYLLOGISTICALLY S IH1 L AH0 JH IH1 S T IH0 K L IY0 +SYLOSON S IH1 L AO1 S AH0 N +SYLPH S IH1 L F +SYLPHID S IH1 L F IH0 D +SYLPHIDE S IH1 L F IH0 D +SYLPHIDES S IH1 L F IH0 D Z +SYLPHLIKE S IH1 L F L AY2 K +SYLPHS S IH1 L F S +SYLUM S AY1 L AH0 M +SYLVA S IH1 L V AH0 +SYLVA'S S IH1 L V AH0 Z +SYLVAE S IH0 L V EY1 +SYLVAIN S IH0 L V EY1 N +SYLVAN S IH1 L V AH0 N +SYLVANE S IH0 L V EY1 N +SYLVANIA S IH0 L V EY1 N IY0 AH0 +SYLVANNE S IH0 L V AE1 N +SYLVANS S IH1 L V AH0 N Z +SYLVANUS S IH1 L V AH0 N IH0 S +SYLVARUM S IH0 L V ER0 AH1 M +SYLVEST S IH0 L V EH1 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T AH0 N +SYMPHONIA S IH0 M F OW1 N IY0 AH0 +SYMPHONIC S IH0 M F AA1 N IH0 K +SYMPHONIE S IH1 M F AH0 N IY0 +SYMPHONIES S IH1 M F AH0 N IY0 Z +SYMPHONIOUS S IH0 M F OW1 N IY0 AH0 S +SYMPHONIST S IH1 M F AH0 N IH0 S T +SYMPHONY S IH1 M F AH0 N IY0 +SYMPHORIEN S IH1 M F AH0 R IY0 AH0 N +SYMPLEGADES S IH0 M P AH0 L G EY2 D Z +SYMPOSIA S IH0 M P OW1 S IY0 AH0 +SYMPOSIUM S IH0 M P OW1 Z IY0 AH0 M +SYMPOSIUMS S IH0 M P OW1 Z IY0 AH0 M Z +SYMPSON S IH1 M P S AH0 N +SYMPTOM S IH1 M P T AH0 M +SYMPTOMATIC S IH2 M P T AH0 M AE1 T IH0 K +SYMPTOMS S IH1 M P T AH0 M Z +SYMS S IH1 M Z +SYN S IH1 N +SYNAGOGUE S IH1 N AH0 G AO2 G +SYNAGOGUES S IH1 N AH0 G AO2 G Z +SYNAPSE S IH1 AE0 P S +SYNAPTIC S IH1 AE0 P T IH0 K +SYNC S IH1 NG K +SYNCHRONISE S IH1 NG K R AH0 N IH1 S IY0 +SYNCHRONISED S IH1 NG K R AH0 N AY2 Z D +SYNCHRONISM S IH1 NG K R AH0 N IH2 Z AH0 M +SYNCHRONIZE S IH1 NG K R AH0 N AY2 Z +SYNCHRONIZED S IH1 NG K R AH0 N AY2 Z D +SYNCHRONIZES S IH1 NG K R AH0 N AY2 Z IH0 Z +SYNCHRONIZING S IH1 NG 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SH +TAISEZ T EY1 S EH0 Z +TAISY T EY1 Z IY0 +TAISY'S T EY1 Z IY0 Z +TAIT T EY1 T +TAIT'S T EY1 T S +TAITA T EY1 T AH0 +TAITBOUT T EY1 T B AW2 T +TAITHU T EY1 TH UW0 +TAITSAN T EY1 Z AH0 N +TAITSONG T EY1 T S AO0 NG +TAIWAN T AY1 W AA1 N +TAIYUEANFU T AY1 Y UW1 AH0 N F UW0 +TAJ T AA1 ZH +TAJAPORE T AA1 JH AH0 P AO2 R +TAJEN T AA2 JH AH0 N +TAJETTERAT T AA1 ZH EH1 T ER0 AE2 T +TAJI T AA1 JH IY0 +TAJIMA T AA2 JH IY1 M AH0 +TAJO T AA1 JH OW0 +TAJOORAH T AA1 ZH AO1 R AH0 +TAJURA T AA1 ZH UH1 R AH0 +TAJURRAH T AA1 ZH UH1 R AH0 +TAK T AE1 K +TAK'S T AA2 K S +TAK'ST T AA2 K S T +TAKA T AA1 K AH0 +TAKA'S T AE1 K AH0 Z +TAKAHASHI T AA0 K AA0 HH AA1 SH IY0 +TAKAHIRO T AA0 K AA0 HH IY1 R OW0 +TAKAI T AA0 K AA1 IY0 +TAKANE T AA0 K AA1 N +TAKATA T AA0 K AA1 T AH0 +TAKBIR T AA2 K B ER0 +TAKE T EY1 K +TAKEE T EY1 K IY0 +TAKEING T EY1 K IH0 NG +TAKEN T EY1 K AH0 N +TAKEO T AA2 K EY1 OW0 +TAKEOFF T EY1 K AO2 F +TAKER T EY1 K ER0 +TAKER'S T EY1 K ER0 Z +TAKERN T EY1 K ER0 N +TAKERS T EY1 K ER0 Z +TAKES T EY1 K S +TAKESHI T AH0 K EH1 SH IY0 +TAKEST T AA2 K AH0 S T +TAKETH T EY1 K AH0 TH +TAKHAAR T AA2 K HH AA0 R +TAKHT T AE1 K T +TAKI T AE1 K IY0 +TAKIM T AA0 K IY1 M +TAKIN T EY1 K IH0 N +TAKING T EY1 K IH0 NG +TAKINGS T EY1 K IH0 NG Z +TAKKEN T AE1 K AH0 N +TAKKIN T AE1 K IH0 N +TAKO T AA2 K OW0 +TAKO'S T AA2 K OW0 Z +TAKRURIES T AA2 K R UH1 R IY0 Z +TAKS T AA2 K S +TAKSALI T AA2 K S AA1 L IY0 +TAKSHONY T AA2 K SH AH0 N IY0 +TAKU T AA1 K UW2 +TAKUAN T AA2 K UW0 AA1 N +TAKUR T AA2 K ER0 +TAL T AA1 L +TALA T AA1 L AH0 +TALAAT T AA0 L AA1 T +TALABAS T AA0 L AA0 B AA1 S +TALABOR T AA0 L AA0 B AO1 R +TALAGOUGA T AA0 L AA1 G UW1 G AH0 +TALALOO T AH0 L AA1 L UW2 +TALANA T AA0 L AA1 N AH0 +TALAPA T AA0 L AA1 P AH0 +TALAPOINS T AE2 L AH0 P OY1 N Z +TALAPUS T AA0 L AA0 P AH0 S +TALAVERA T AA0 L AA0 V EH1 R AH0 +TALAYOT T AA0 L AA0 Y AH0 T +TALB T AE1 L B +TALBERT T AE1 L B ER0 T +TALBOT T AE1 L B AH0 T +TALBOT'S T AE1 L B AH0 T S +TALBOTHAYS T AE1 L B AH0 TH EY1 Z +TALBOTS T AE1 L B AH0 T S 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T AH0 N +TALLINGTON'S T AO1 L IH0 NG T AH0 N Z +TALLIS T AE1 L IH0 S +TALLISH T AE1 L IH0 SH +TALLMADGE T AE1 L M AE0 JH +TALLMAN T AO1 L M AH0 N +TALLMAN'S T AO1 L M AH0 N Z +TALLNESS T AO1 L N AH0 S +TALLONS T AE1 L AH0 N Z +TALLOW T AE1 L OW0 +TALLOWAX T AE2 L AH0 W AE2 K S +TALLOWED T AE1 L OW0 D +TALLOWY T AE2 L AH0 W IY0 +TALLUSHATCHEE T AE2 L UW1 SH AE2 CH IY0 +TALLY T AE1 L IY0 +TALLY'S T AE1 L IY0 Z +TALLYHO T AE2 L IY0 HH OW1 +TALLYING T AE1 L IY0 IH0 NG +TALLYMAN T AE2 L IY0 M AE2 N +TALMA T AE1 L M AH0 +TALMA'S T AE1 L M AH0 Z +TALMADGE T AE1 L M AE0 JH +TALMAGE T AE1 L M IH0 JH +TALMAGE'S T AE1 L M IH0 JH IH0 Z +TALMASH T AE1 L M AE0 SH +TALMONT T AE1 L M AA2 N T +TALMUD T AE1 L M AH0 D +TALMUDIC T AE1 L M UW1 D IH0 K +TALMUDICAL T AE1 L M UW1 D AH0 K AH0 L +TALMUDIST T AE1 L M UW1 D AH0 S T +TALMUDISTS T AE1 L M UW1 D AH0 S T S +TALMUDS T AE1 L M AH1 D Z +TALOFA T AE1 L AH0 F AH0 +TALOLO T AE1 L AH0 L OW0 +TALON T AE1 L AH0 N +TALON'S T AE1 L AH0 N Z +TALONED T AE1 L AH0 N D +TALONS T AE1 L AH0 N Z +TALOO T AH0 L UW2 +TALOUEL T AE1 L UW1 AH0 L +TALPERS T AE1 L P ER0 Z +TALPERS'S T AE1 L P ER0 Z IH0 Z +TALS T AH0 L Z +TALSO T AA1 L S OW0 +TALSONIAN T AA1 L S OW1 N IY0 AH0 N +TALT T AO1 L T +TALTAVULL T AA1 L T AH0 V AH0 L +TALTHYBIUS T AE1 L TH IH0 B IY0 IH0 S +TALTON T AE1 L T AH0 N +TALU T AA1 L UW0 +TALUS T AE1 L IH0 S +TALUSES T AE1 L AH0 S IH0 Z +TALUTA T AA0 L UW1 T AH0 +TALVIG T AE1 L V IH0 G +TAM T AE1 M +TAM'D T AE2 M D +TAM'S T AE2 M Z +TAMA T AA1 M AH0 +TAMADA T AH0 M AA1 D AH0 +TAMADA'S T AH0 M AA1 D AH0 Z +TAMAHAY T AA0 M AA0 HH EY0 +TAMAHNAWUS T AA0 M AA0 N AO1 IH0 S +TAMAHNOUS T AA0 M AA0 N AH0 S +TAMAHU T AA0 M AA1 HH UW0 +TAMAI T AA0 M AA1 IY0 +TAMAITI T AA0 M AA0 IY1 T IY0 +TAMALA T AH0 M AA1 L AH0 +TAMALE T AH0 M AA1 L IY0 +TAMALES T AH0 M AA1 L IY0 Z +TAMALPAIS T AH0 M AA1 L P EY1 +TAMAN T EY1 M AH0 N +TAMANAC T AE1 M AH0 N AE2 K +TAMANACS T AE1 M AH0 N AE2 K S +TAMANDUA T AA0 M AA0 N D UW0 AH0 +TAMANES T AH0 M AA1 N IY0 Z +TAMANOIR T AA0 M AA0 N OY1 R +TAMANOUS T AA0 M AA0 N AH0 S +TAMAR T AE1 M ER0 +TAMAR'S T AH0 M AA1 R Z +TAMARA T AH0 M AA1 R AH0 +TAMARA'S T AE1 M ER0 AH0 Z +TAMARAC T AE1 M ER0 AE0 K +TAMARACK T AE1 M ER0 AE0 K +TAMARACKS T AE1 M ER0 AE2 K S +TAMARIND T AE1 M ER0 IH0 N D +TAMARINDS T AE1 M ER0 IH0 N D Z +TAMARISK T AE1 M ER0 IH0 S K +TAMARISKS T AE1 M ER0 IH0 S K S +TAMAROCHKA T AE1 M ER0 OW1 SH K AH0 +TAMAS T AA1 M AH0 Z +TAMASESE T AA0 M AA0 S EY1 Z IY0 +TAMASESE'S T AA0 M AA0 S EY1 Z IY0 Z +TAMASESES T AA0 M AA0 S IY2 Z +TAMASHA T AA0 M AA0 SH AH0 +TAMASJO T AA0 M AA0 SH Y OW0 +TAMASP T AA0 M AA1 S P +TAMATAVE T AA0 M AA0 T AA1 V IY0 +TAMATEA T AE1 M AH0 T IY1 AH0 +TAMAULIPAS T AE1 M AW1 L IH0 P AH0 Z +TAMAVILI T AA0 M AA0 V IY1 L IY0 +TAMB T AE1 M +TAMBA T AE1 M B AH0 +TAMBLYN T AE1 M B L IH0 N +TAMBO T AE1 M B OW0 +TAMBOOKIE T AE2 M B UH1 K IY0 +TAMBORINE T AE2 M B ER0 IY2 N +TAMBOS T AE1 M B OW0 Z +TAMBOUR T AE2 M B ER0 +TAMBOURIN T AE2 M B UH0 R AE0 N +TAMBOURINE T AE2 M B ER0 IY1 N 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IH0 NG +TAMIYA T AA1 M IY0 AH0 +TAMLANE T AE1 M L EY2 N +TAMLYN T AE1 M L IH0 N +TAMLYN'S T AE1 M L IH0 N Z +TAMMAND T AE1 M AH0 N D +TAMMANY T AE1 M AH0 N IY0 +TAMMANY'S T AE1 M AH0 N IY0 Z +TAMMAS T AE1 M AH0 Z +TAMMAS'S T AA0 M AA1 S IH0 S +TAMMIE T AE1 M IY0 +TAMMIE'S T AE1 M IY0 Z +TAMMIS T AE1 M IH0 S +TAMMOCK T AE1 M AH0 K +TAMMSAN T AE1 M Z AH0 N +TAMMUZ T AE1 M UW0 Z +TAMMY T AE1 M IY0 +TAMN T AE2 M +TAMNA T AE2 M N AH0 +TAMNED T AE2 M D +TAMON T AA0 M AO1 N +TAMOSZIUS T AE2 M AA1 SH IY0 IH0 S +TAMOYOS T AH0 M OY0 OW0 Z +TAMP T AE1 M P +TAMPA T AE1 M P AH0 +TAMPED T AE1 M P T +TAMPER T AE1 M P ER0 +TAMPERED T AE1 M P ER0 D +TAMPERING T AE1 M P ER0 IH0 NG +TAMPERINGS T AE1 M P ER0 IH0 NG Z +TAMPERS T AE1 M P ER0 Z +TAMPICO T AA0 M P IY1 K OW0 +TAMPING T AE1 M P IH0 NG +TAMPION T AE1 M P IY0 AH0 N +TAMPLIN T AE1 M P L IH0 N +TAMROY T AE1 M R OY2 +TAMS T AE1 M Z +TAMS'S T AH0 M Z IH0 Z +TAMSIE T AE1 M Z IY0 +TAMSIN T AE1 M S AH0 N +TAMSON T AE1 M S AH0 N +TAMSUI T AE1 M S UW1 IY0 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+TANDE T AE1 N D +TANDE'S T AE1 N D Z +TANDEM T AE1 N D AH0 M +TANDEMS T AE1 N D AH0 M Z +TANDIS T AE1 N D IH0 S +TANDY T AE1 N D IY0 +TANE T EY1 N +TANEE T AH0 N EY1 +TANELKUM T AH0 N EY1 K AH0 M +TANELKUMS T AH0 N EY1 K AH0 M Z +TANERTON T EY1 N ER0 T AH0 N +TANEY T EY1 N IY0 +TANEYTOWN T EY1 N IY0 T AW2 N +TANFIELD T AE1 N F IY2 L D +TANG T AA1 NG +TANG T AE1 NG +TANGA T AA1 NG G AH0 +TANGALOA T AE1 NG G AH0 L OW1 AH0 +TANGANIKA T AA0 NG G AA0 N IY1 K AH0 +TANGANYIKA T AA0 NG G AA1 N Y AY1 K AH0 +TANGARI T AA0 NG G AA1 R IY0 +TANGED T AE1 NG D +TANGENT T AE1 N JH AH0 N T +TANGENTIAL T AE0 N JH EH1 N CH AH0 L +TANGENTS T AE1 N JH AH0 N T S +TANGERE T AE1 NG G ER0 +TANGERINE T AE2 N JH ER0 IY1 N +TANGERINES T AE1 N JH ER0 IY2 N Z +TANGI T AE1 N JH IY0 +TANGIBILITY T AE1 N JH AH0 B IH1 L AH0 T IY0 +TANGIBLE T AE1 N JH AH0 B AH0 L +TANGIBLY T AE1 N JH AH0 B L IY0 +TANGIER T AE1 N JH IY0 ER0 +TANGIERS T AE2 N JH IH1 R Z +TANGIKANO T AE1 N JH AH0 K AA1 N OW0 +TANGING T AE1 NG IH0 NG +TANGLE T AE1 NG G AH0 L +TANGLED T AE1 NG G AH0 L D +TANGLEFOOT T AE1 NG G AH0 L F UH2 T +TANGLEMENT T AE1 NG G AH0 L M AH0 N T +TANGLES T AE1 NG G AH0 L Z +TANGLEWOOD T AE1 NG G AH0 L W UH2 D +TANGLEY T AE1 NG G L IY0 +TANGLING T AE1 NG G L IH0 NG +TANGO T AE1 NG G OW0 +TANGOED T AE1 NG G OW0 D +TANGOING T AE1 N G OW2 IH0 NG +TANGROUILLE T AE1 NG R UW1 IY0 +TANGS T AE1 NG Z +TANGY T AE1 N JH IY0 +TANGYE T AE1 N JH Y +TANI T AA1 N IY0 +TANIA T AA1 N Y AH0 +TANIERA T AA0 N IY0 EH1 R AH0 +TANIS T AE1 N IH0 S +TANITH T AA2 N IH0 TH +TANIWHA T AA2 N IH0 W AH0 +TANJE T AE1 N JH +TANJONG T AE1 N JH AO1 NG +TANJORE T AE1 N JH AO2 R +TANK T AE1 NG K +TANK'S T AE1 NG K S +TANKA T AE1 NG K AH0 +TANKADERE T AE1 NG K AH0 D IH1 R +TANKARD T AE1 NG K ER0 D +TANKARDEW T AE1 NG K ER0 D UW2 +TANKARDEW'S T AE1 NG K ER0 D UW2 Z +TANKARDS T AE1 NG K ER0 D Z +TANKED T AE1 NG K T +TANKEE T AE1 NG K IY0 +TANKELOW T AE1 NG K AH0 L OW0 +TANKER T AE1 NG K ER0 +TANKERS T AE1 NG K ER0 Z +TANKERTON T AE1 NG K ER0 T AH0 N +TANKERU T AE1 NG K ER0 UW1 +TANKERVILLE T AE1 NG K ER0 V IH2 L +TANKETTE T AE1 NG K EH1 T +TANKFUL T AE1 NG K F AH0 L +TANKING T AE1 NG K IH0 NG +TANKLE T AE1 NG K AH0 L +TANKRED T AE1 NG K ER0 D +TANKS T AE1 NG K S +TANLAY T AE1 N L EY2 +TANN T AE1 N +TANN'D T AE1 N D +TANNA T AE1 N AH0 +TANNAHILL T AE1 N AH0 HH IH2 L +TANNBERG T AE1 N B ER0 G +TANNEBERGER T AE1 N B ER0 G ER0 +TANNED T AE1 N D +TANNENBAUM T AE1 N AH0 N B AW2 M +TANNENBERG T AE1 N AH0 N B ER0 G +TANNENBUEHL T AE1 N AH0 N B UH0 L +TANNENBURG T AE1 N AH0 N B ER0 G +TANNENEGG T AE1 N IH0 N EH1 G +TANNENWALD T AE1 N AH0 N W AO2 L D +TANNER T AE1 N ER0 +TANNER'S T AE1 N ER0 Z +TANNERIES T AE1 N ER0 IY0 Z +TANNERS T AE1 N ER0 Z +TANNERY T AE1 N ER0 IY0 +TANNHAEUSER T AE1 N HH AW2 Z ER0 +TANNHAEUSER'S T AE1 N HH AW2 Z ER0 Z +TANNHAUSER T AE1 N HH AW2 Z ER0 +TANNIAN T AE1 N IY0 AH0 N +TANNIC T AE1 N IH0 K +TANNIN T AE1 N IH0 N +TANNING T AE1 N IH0 NG +TANNIS T AE1 N IH0 S +TANNO T AE1 N OW0 +TANNO'S T AE1 N OW0 Z +TANNY T AE1 N IY0 +TANOA T AA0 N OW1 AH0 +TANOFIR T AA0 N OW1 F ER0 +TANQUAM T AE1 NG K W AA1 M +TANQUERAY T AE1 NG K W ER0 EY2 +TANQUERAY'S T AE1 NG K W ER0 EY2 Z +TANRADE T AE1 N R EY1 D +TANRADE'S T AE1 N R EY1 D Z +TANS T AE1 N Z +TANSEY T AE1 N Z IY0 +TANSEY'S T AE1 N Z IY0 Z +TANSIE T AE1 N Z IY0 +TANSILLO T AE1 N S IH1 L OW0 +TANSLEY T AE1 N S L IY0 +TANSONVILLE T AE1 N S AH0 N V IH2 L +TANSY T AE1 N Z IY0 +TANT T AE1 N T +TANTA T AE1 N AH0 +TANTAH T AE1 N AH0 +TANTALISE T AE1 N T AH0 L AY0 Z +TANTALISED T AE1 N AH0 L IH0 Z D +TANTALISING T AE1 N AH0 L AY2 Z IH0 NG +TANTALISINGLY T AE1 N AH0 L AY2 Z IH0 NG L IY0 +TANTALIZATION T AE1 N T AH0 L AH0 Z EY1 SH AH0 N +TANTALIZE T AE1 N AH0 L AY2 Z +TANTALIZE T AE1 N T AH0 L AY2 Z +TANTALIZED T AE1 N AH0 L AY2 Z D +TANTALIZED T AE1 N T AH0 L AY2 Z D +TANTALIZES T AE1 N AH0 L AY2 Z AH0 Z +TANTALIZES T AE1 N T AH0 L AY2 Z IH0 Z +TANTALIZING T AE1 N AH0 L AY2 Z IH0 NG +TANTALIZING T AE1 N T AH0 L AY2 Z IH0 NG +TANTALIZINGLY T AE1 N AH0 L AY2 Z IH0 NG L IY0 +TANTALIZINGLY T AE1 N T AH0 L AY2 Z IH0 NG L IY0 +TANTALLAN T AE1 N T AH0 L AH0 N +TANTALLON T AE1 N T AH0 L AA0 N +TANTALUS T AE1 N T AH0 L AH0 S +TANTAMOUNT T AE1 N T AH0 M AW2 N T +TANTARA T AA0 N T AA1 R AH0 +TANTE T AA1 N T IY0 +TANTE'S T AE1 N T IY0 Z +TANTI T AE1 N T IY0 +TANTIA T AE1 N SH AH0 +TANTIBBA T AE1 N T IH0 B AH0 +TANTIE T AE1 N T IY0 +TANTILLION T AA0 N T IH1 L Y AH0 N +TANTIVY T AE1 N T IH0 V IY0 +TANTO T AE1 N T OW0 +TANTON T AE1 N T AH0 N +TANTOR T AE1 N T ER0 +TANTOR'S T AE1 N T ER0 Z +TANTOT T AE1 N T AH0 T +TANTRA T AE1 N T R AH0 +TANTRAMAR T AE1 N T R AH0 M AA0 R +TANTRAS T AE1 N T R AH0 Z +TANTRELLA T AE0 N T R EH1 L AH0 +TANTRIL T AE1 N T R IH0 L +TANTRIL'S T AE1 N T R AH0 L Z +TANTRIPP T AE1 N T R IH0 P +TANTROY T AE1 N T R OY2 +TANTRUM T AE1 N T R AH0 M +TANTRUMS T AE1 N T R AH0 M Z +TANTUM T AE1 N T AH0 M +TANTUS T AE1 N T AH0 S +TANTY T AE1 N T IY0 +TANTY'S T AE1 N IY0 Z +TANUKI T AA0 N UW1 K IY0 +TANY T EY1 N IY0 +TANYA T AA1 N Y AH0 +TANYA'S T AA1 N Y AH0 Z +TANYARD T AA1 N Y AA2 R D +TANYRALLT T AA1 N Y R AO1 L T +TANYUSHA T AA1 N Y UW1 SH AH0 +TANZ T AE1 N Z +TAO D AW1 +TAO T AW1 +TAOISM D AW1 IH0 Z AH0 M +TAOIST D AW1 IH0 S T +TAOISTS D AW1 AH0 S T S +TAORMINA T AA0 AO0 R M IY1 N AH0 +TAOS T AW1 Z +TAOU D AW1 +TAOWN T AA0 OW1 N +TAP T AE1 P +TAPA T AA1 P AH0 +TAPADEROS T AH1 P AH0 D EH1 R OW0 Z +TAPAGE T AA1 P IH0 JH +TAPAJOS T AH1 P AH0 HH OW2 Z +TAPE T EY1 P +TAPEALL T EY1 P IY1 L +TAPED T EY1 P T +TAPEISM T EY1 P IH0 Z AH0 M +TAPER T EY1 P ER0 +TAPER'S T EY1 P ER0 Z +TAPERED T EY1 P ER0 D +TAPERING T EY1 P ER0 IH0 NG +TAPERS T EY1 P ER0 Z +TAPES T EY1 P S +TAPESTRIED T AE1 P AH0 S T R IY0 D +TAPESTRIES T AE1 P AH0 S T R IY0 Z +TAPESTRY T AE1 P AH0 S T R IY0 +TAPEWORM T EY1 P W ER0 M +TAPEWORMS T EY1 P W ER2 M Z +TAPIA T AA1 P IY0 AH0 +TAPILOW T EY1 P IH0 L OW0 +TAPING T EY1 P IH0 NG +TAPIO T AE1 P IY0 OW0 +TAPIO'S T AE1 P IY0 OW0 Z +TAPIOCA T AE1 P IY0 OW1 K AH0 +TAPIR T EY1 P IH0 R +TAPIRS T EY1 P IH0 R Z +TAPIS T AE1 P IY0 Z +TAPISM T EY1 P IH2 Z AH0 M +TAPKINS T AE1 P K IH0 N Z +TAPLEY T AE1 P L IY0 +TAPLEY'S T AE1 P L IY0 Z +TAPLOW T AE1 P L OW0 +TAPOTTE T AE1 P AO1 T +TAPP T AE1 P +TAPP'D T AE1 P D +TAPP'S T AE1 P S +TAPPA T AE1 P AH0 +TAPPAHANNOCK T AE1 P AH0 HH AE1 N AH0 K +TAPPAN T AE1 P AH0 N +TAPPAN'S T AE1 P AH0 N Z +TAPPARIANS T AE1 P EH1 R IY0 AH0 N Z +TAPPAU T AE1 P AW0 +TAPPED T AE1 P T +TAPPEN T AE1 P AH0 N +TAPPENHAM T AE1 P AH0 N HH AE2 M +TAPPER T AE1 P ER0 +TAPPERIJ T AE1 P ER0 IH2 JH +TAPPERS T AE1 P ER0 Z +TAPPERTIT T AE1 P ER0 T IY1 T +TAPPEUR T AE1 P ER0 +TAPPIN T AE1 P IH0 N +TAPPING T AE1 P IH0 NG +TAPPINGHAM T AE1 P IH0 NG HH AE2 M +TAPPINGS T AE1 P IH0 NG Z +TAPPINGTON T AE1 P IH0 NG T AH0 N +TAPPINGTON'S T AE1 P IH0 NG T AH0 N Z +TAPPIT T AE1 P IH0 T +TAPPITT T AE1 P IH0 T +TAPPITT'S T AE1 P IH0 T S +TAPPITTS T AE1 P IH0 T S +TAPPLETON T AE1 P AH0 L T AH0 N +TAPPY T AE1 P IY0 +TAPROBANE T AE1 P R AH0 B EY1 N +TAPROOM T AE1 P R UW2 M +TAPROOT T AE1 P R UW1 T +TAPS T AE1 P S +TAPSTER T AE1 P S T ER0 +TAPSTER'S T AE1 P S T ER0 Z +TAPSTERS T AE1 P S T ER0 Z +TAPU T AE1 P Y UW0 +TAPUS T AE1 P AH0 S +TAPUYO T AE1 P UW0 Y OW0 +TAPWATER T AE1 P W AO2 T ER0 +TAQUAMENAW T AA2 K W AH0 M EH0 N AO2 +TAQUISARA T AE1 K W AH0 S AA1 R AH0 +TAQUISARA'S T AE1 K W AH0 Z AA1 R AH0 Z +TAR T AA1 R +TAR'S T AA1 R Z +TARA T AA1 R AH0 +TARA T EH1 R AH0 +TARA'S T AA1 R AH0 Z +TARA'S T EH1 R AH0 Z +TARADIDDLE T AA0 R AH0 D IH1 D AH0 L +TARADIDDLES T AA0 R AH0 D IH1 D AH0 L Z +TARAG T AH0 R AE1 G +TARAH T EH1 R AH0 +TARAMIS T EH1 R AH0 M IH0 S +TARANAKA T AA0 R AA0 N AA1 K AH0 +TARANAKI T AA0 R AA0 N AA1 K IY0 +TARANTARA T AA0 R AA0 N T AA1 R AH0 +TARANTASS T AA0 R AA0 N T AE1 S +TARANTELLA T AA0 R AA0 N T EH1 L AH0 +TARANTIA T AH0 R AE1 N CH IY0 AH0 +TARANTISM T AA0 R AA0 N T IH2 Z AH0 M +TARANTO T ER0 AE1 N T OW0 +TARANTULA T AH0 R AE1 N CH AH0 L AH0 +TARANTULA T AH0 R AE1 N CH UW0 L AH0 +TARANTULA'S T AH0 R AE1 N CH UW0 L AH0 Z +TARANTULAS T AH0 R AE1 N CH AH0 L AH0 Z +TARANTULAS T AH0 R AE1 N CH UW0 L AH0 Z +TARAPATAPOUM T AA0 R AA0 P AA1 T AH0 P AH0 M +TARARA T AA0 R AA1 R AH0 +TARARO T AA0 R AA1 R OW0 +TARAS T AA1 R AH0 Z +TARASCON T AH0 R AE1 S K AH0 N +TARASCONAIS T AE2 R AH0 S K AH0 N EY1 +TARASCONESE T AE2 R AH0 S K OW1 N IY0 Z +TARASCONIAN T AE2 R AH0 S K OW1 N IY0 AH0 N +TARASCUS T AE2 R AH0 S K AH0 S +TARASOVICH T EH1 R IH0 S AH0 V IH0 CH +TARASQUE T EH1 R IH0 S K +TARATA T AA0 R AA1 T AH0 +TARAUTAS T AA0 R AA0 UW1 T AH0 S +TARAVAO T AE2 R AH0 V OW1 +TARAWA T AA0 R AA1 W AH0 +TARAWERA T AA0 R AA0 V EH1 R AH0 +TARBABY T AA1 R B EY2 B IY0 +TARBELL T AA1 R B EH2 L +TARBELL'S T AA1 R B EH2 L Z +TARBERT T AA1 R B ER0 T +TARBES T AA1 R B Z +TARBET T AA1 R B IH0 T +TARBILL T AA1 R B IH2 L +TARBOE T AA1 R B OW2 +TARBOE'S T AA1 R B OW2 Z +TARBOLT T AA1 R B OW2 L T +TARBOLTON T AA1 R B OW2 L T AH0 N +TARBOOSH T AA1 R B UW1 SH +TARBOOSHES T AA1 R B UH1 SH AH0 Z +TARBOX T AA1 R B AA2 K S +TARBOX'S T AA1 R B AA2 K S IH0 Z +TARBRO T AA1 R B R OW0 +TARBUSH T AA1 R B UH2 SH +TARCHON T AA1 R CH AH0 N +TARD T AA1 R D +TARDA T AA1 R D AH0 +TARDE T AA1 R D +TARDES T AA2 R D Z +TARDI T AA1 R D IY0 +TARDIER T AA1 R D IY0 ER0 +TARDIEST T AA1 R D IY0 AH0 S T +TARDIEU T AA1 R D UW1 +TARDIF T AA1 R D IH0 F +TARDIGRADA T AA1 R D IH0 G R AA1 D AH0 +TARDILY T AA2 R D AH0 L IY0 +TARDINESS T AA1 R D IY0 N AH0 S +TARDOS T AA1 R D OW0 Z +TARDREW T AA1 R D R UW0 +TARDY T AA1 R D IY0 +TARE T AA1 R IY0 +TAREELA T AA1 R IY1 L AH0 +TARENTA T EH1 R AH0 N T AH0 +TARENTINE T EH1 R AH0 N T IY2 N +TARENTINES T EH1 R AH0 N T IY2 N Z +TARENTO T AA0 R EH1 N T OW0 +TARENTUM T EH1 R AH0 N T AH0 M +TARES T EH1 R Z +TARFAG T AA0 R F AA1 G +TARFE T AA1 R F +TARGAI T ER1 G AY1 +TARGATT T AA1 R G AH0 T +TARGE T AA1 R JH +TARGES T AA1 R JH IH0 Z +TARGET T AA1 R G AH0 T +TARGET T ER1 G AH0 T +TARGET'S T AA1 R G AH0 T S +TARGETED T AA1 R G AH0 T IH0 D +TARGETEERS T AA1 R G AH0 T IY1 R Z +TARGETING T AA1 R G AH0 T IH0 NG +TARGETS T AA1 R G AH0 T S +TARGHEE T ER1 G IY1 +TARGIA T ER1 G IY0 AH0 +TARGO T AA1 R G OW0 +TARGO'S T AA1 R G OW2 Z +TARGUI T AA0 R G W IY1 +TARGUM T AA1 R G AH0 M +TARGUMS T AA1 R G Y AH0 M Z +TARHALMY T AA1 R HH AA0 M IY0 +TARHAY T AA1 R HH EY0 +TARHE T AA1 R HH +TARHOV T AA1 R HH AO0 V +TARI T AA1 R IY0 +TARIC T AE1 R IH0 K +TARICHEAE T AE1 R IH0 K EY1 AA0 +TARIF T EH1 R AH0 +TARIFA T AH0 R IY1 F AH0 +TARIFF T EH1 R AH0 F +TARIFFS T EH1 R AH0 F S +TARIK T AA1 R IH0 K +TARIN T AA1 R IY0 N +TARIO T AA1 R IY0 OW0 +TARITA T AA0 R IY1 T AH0 +TARITAI T AA1 R IY0 T AA1 IY0 +TARK T ER0 K +TARKAS T AA1 R K AH0 Z +TARKHAN T AA1 R K HH AE0 N +TARKINGTON T AA1 R K IH0 NG T AH0 N +TARKINGTON'S T AA1 R K IH0 NG T AH0 N Z +TARKOWSKI T ER0 K AO1 F S K IY0 +TARLAC T AA1 R L AE0 K +TARLAC'S T AA1 R L AE0 K S +TARLATAN T AA1 R L AH0 T AH0 N +TARLEIGH T AA1 R L AH0 +TARLENHEIM T AA1 R L AH0 N HH AY2 M +TARLETAN T AA1 R L IH0 T AH0 N +TARLETON T AA1 R L T AH0 N +TARLETON'S T AA1 R L T AH0 N Z +TARLING T AA1 R L IH0 NG +TARLING'S T AA1 R L IH0 NG Z +TARLINGFORD T AA1 R L IH0 NG F ER0 D +TARLOK T AA1 R L AA2 K +TARLTON T AA1 R L T AH0 N +TARLTON'S T AA1 R L T AH0 N Z +TARMAC T AA1 R M AE2 K +TARMANGANI T AA1 R M AE2 NG G AA1 N IY0 +TARMILLAN T AA1 R M IH1 L AH0 N +TARMS T AA1 R M Z +TARN T AA1 R N +TARN'T T AA1 R N T +TARNAL T AA1 R N AH0 L +TARNALLY T AA1 R N AH0 L IY0 +TARNATION T AA1 R N EY1 SH AH0 N +TARNAUD T AA1 R N OW1 +TARNE T AA1 R N +TARNEL T AA1 R N AH0 L +TARNHELM T AA1 R N HH EH0 L M +TARNHORST T AA1 R N HH AO0 R S T +TARNISH T AA1 R N IH0 SH +TARNISH'D T AA1 R N IH0 SH D +TARNISHED T AA1 R N IH0 SH T +TARNISHES T AA1 R N IH0 SH IH0 Z +TARNISHING T AA1 R N IH0 SH IH0 NG +TARNOW T AA1 R N OW0 +TARNOWSKI T ER0 N AO1 F S K IY0 +TARNOWSY T AA1 R N AW0 S IY0 +TARNS T AA1 R N Z +TARO T EH1 R OW0 +TAROCCHI T AE2 R OW1 K IY0 +TAROK T EH1 R AH0 K +TARONIARA T AA0 R OW1 N IY0 AA1 R AH0 +TAROT T AE2 R OW1 +TAROW T EH1 R OW0 +TARP T AA1 R P +TARPAPER T AA1 R P EY2 P ER0 +TARPAULIN T AA1 R P AO1 L IH0 N +TARPAULINED T AA1 R P AO0 L IY1 N D +TARPAULING T AA1 R P AO1 L IH0 NG +TARPAULINS T AA1 R P AO1 L IH0 N Z +TARPEIA T AA1 R P EY1 AH0 +TARPEIAN T AA1 R P IY1 AH0 N +TARPEY T AA1 R P IY0 +TARPON T AA1 R P AH0 N +TARPOT T AA1 R P AH0 T +TARQUIN T AA0 R K W IY1 N +TARQUIN'S T AA0 R K W IY1 N Z +TARQUINII T AA0 R K W IY1 N IY0 +TARQUINIUS T AA0 R K W IY1 N IY0 IH0 S +TARQUINS T AA0 R K W AH0 N Z +TARR T AA1 R +TARR'BLE T AA1 R B AH0 L +TARRA T AA1 R AH0 +TARRACO T AA0 R AA1 K OW0 +TARRADIDDLE T AA1 R AH0 D IH1 D AH0 L +TARRAGON T EH1 R AH0 G AA2 N +TARRAGONA T AA1 R AA0 G OW1 N AH0 +TARRANT T AA1 R AH0 N T +TARRANT'S T AA1 R AH0 N T S +TARRAPIN T AA1 R AH0 P AE1 N +TARRAR T ER0 AA1 R +TARRAS T AA1 R AH0 Z +TARRED T AA1 R D +TARRELL T EH1 R AH0 L +TARRENGOWER T AA1 R EH1 NG G OW0 ER0 +TARRIANCE T AA1 R IY0 AH0 N S +TARRIBLE T AA1 R AH0 B AH0 L +TARRIED T EH1 R IY0 D +TARRIER T EH1 R IY0 ER0 +TARRIERS T EH1 R IY0 ER0 Z +TARRIES T EH1 R IY0 Z +TARRIEST T AA1 R IY0 AH0 S T +TARRIETH T AA1 R IY0 IH0 TH +TARRING T AA1 R IH0 NG +TARRINGTON T AA1 R IH0 NG T AH0 N +TARRION T EH1 R IY0 AH0 N +TARRONG T AA1 R AO0 NG +TARRY T EH1 R IY0 +TARRYALL T EH1 R IY0 AA1 L +TARRYING T EH1 R IY0 IH0 NG +TARRYPIN T EH1 R IY0 P AE1 N +TARRYTOWN T EH1 R IY0 T AW2 N +TARS T AA1 R Z +TARSAL T AA1 R S AH0 L +TARSH T AA1 R SH +TARSHISH T AA1 R SH IY1 SH +TARSI T AA1 R S IY0 +TARSIA T AA1 R S IY0 AH0 +TARSILO T AA1 R S IY1 L OW0 +TARSUS T AA1 R S AH0 S +TART T AA1 R T +TART'RI T AA1 R T R IY0 +TARTAGLIA T AA0 R T AE1 G L IY0 AH0 +TARTAN T AA1 R T AH0 N +TARTAN'S T AA1 R T AH0 N Z +TARTANA T AA0 R T AE0 N AH0 +TARTANS T AA1 R T AH0 N Z +TARTAR T AA1 R T ER0 +TARTAR'S T AA1 R T ER0 Z +TARTARE T AA1 R T ER0 +TARTAREAN T AA1 R T ER0 IY2 N +TARTARI T AA0 R T AA1 R IY0 +TARTARIAN T AA0 R T EH1 R IY0 AH0 N +TARTARIC T AA1 R T ER0 IH0 K +TARTARIN T AA1 R T ER0 IH0 N +TARTARIN'S T AA1 R T ER0 IH2 N Z +TARTAROFF T AA1 R T AH0 R AO0 F +TARTARS T AA1 R T ER0 Z +TARTARUS T AA1 R T ER0 AH0 S +TARTARY T AA1 R T ER0 IY0 +TARTARY'S T AA1 R T ER0 IY0 Z +TARTAS T AA1 R T AH0 Z +TARTE T AA1 R T +TARTER T AA1 R T ER0 +TARTESSUS T AA1 R T S AH0 S +TARTH T AA1 R TH +TARTINES T AA0 R T IY1 N Z +TARTINI T AA0 R T IY1 N IY0 +TARTINI'S T AA0 R T IY1 N IY0 Z +TARTLET T AA1 R T L AH0 T +TARTLETS T AA1 R T L AH0 T S +TARTLY T AA1 R T L IY0 +TARTNESS T AA1 R T N AH0 S +TARTRATE T AA1 R T R EY0 T +TARTS T AA1 R T S +TARTUFE T AA1 R T AH2 F +TARTUFFE T AA1 R T AH2 F +TARTUFFES T AA1 R T AH2 F S +TARTY T AA1 R T IY0 +TARVEN T AA1 R V AH0 N +TARVER T AA1 R V ER0 +TARVISIUM T AA1 R V IH0 S IY0 AH0 M +TARWEED T AA1 R W IY2 D +TARY T EH1 R IY0 +TARYA T AA1 R Y AH0 +TARZAN T AA1 R Z AE2 N +TARZAN T AA1 R Z AH0 N +TARZAN'S T AA1 R Z AE2 N Z +TARZAN'S T AA1 R Z AH0 N Z +TAS T AH0 S +TASAJARA T AE1 S AH0 HH AA1 R AH0 +TASAJO T AE1 S AH0 HH OW2 +TASCALUCA T AE1 S K AH0 L UW1 K AH0 +TASCELA T AA0 S CH EH1 L AH0 +TASCHER T AE1 SH ER0 +TASCHEREAU T AE1 SH IH1 R OW1 +TASCOSA T AE1 S K OW1 S AH0 +TASEK T AA1 S EH0 K +TASH T AE1 SH +TASHI T AA1 SH IY0 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T AH0 M IH1 S T AH0 K AH0 L +TEMOIN T IH0 M OY1 N +TEMOINS T IH0 M OY1 N Z +TEMP T EH1 M P +TEMP'RANCE T EH0 M R AH0 N S +TEMP'RY T EH0 M R IY0 +TEMPE T EH1 M P IY0 +TEMPE'S T EH1 M P S +TEMPELHOF T EH1 M P AH0 L HH AO0 F +TEMPER T EH1 M P ER0 +TEMPER'D T EH1 M P ER0 D +TEMPER'S T EH1 M P ER0 Z +TEMPERA T EH1 M P ER0 AH0 +TEMPERAMENT T EH1 M P ER0 M AH0 N T +TEMPERAMENT T EH1 M P R AH0 M AH0 N T +TEMPERAMENTAL T EH2 M P ER0 M EH1 N T AH0 L +TEMPERAMENTAL T EH2 M P R AH0 M EH1 N T AH0 L +TEMPERAMENTALLY T EH2 M P ER0 M EH1 N AH0 L IY0 +TEMPERAMENTALLY T EH2 M P ER0 M EH1 N T AH0 L IY0 +TEMPERAMENTALLY T EH2 M P R AH0 M EH1 N AH0 L IY0 +TEMPERAMENTALLY T EH2 M P R AH0 M EH1 N T AH0 L IY0 +TEMPERAMENTS T EH1 M P ER0 M AH0 N T S +TEMPERAMENTS T EH1 M P R AH0 M AH0 N T S +TEMPERANCE T EH1 M P ER0 AH0 N S +TEMPERANCE T EH1 M P R AH0 N S +TEMPERATE T EH1 M P ER0 AH0 T +TEMPERATE T EH1 M P R AH0 T +TEMPERATELY T EH1 M P R AH0 T L IY0 +TEMPERATENESS T EH1 M P ER0 AH0 T N AH0 S +TEMPERATURE T 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M P L AH0 M AO2 R Z +TEMPLES T EH1 M P AH0 L Z +TEMPLESTOWE T EH1 M P L IH0 S T OW1 +TEMPLETON T EH1 M P AH0 L T AH0 N +TEMPLETON'S T EH1 M P AH0 L T AH0 N Z +TEMPLEWOOD T EH1 M P AH0 L W UH2 D +TEMPLO T EH1 M P L OW0 +TEMPLUM T EH1 M P L AH0 M +TEMPO T EH1 M P OW2 +TEMPORA T EH1 M P ER0 AH0 +TEMPORAL T EH1 M P ER0 AH0 L +TEMPORALITIES T EH1 M P ER0 AE1 L IH0 T IY0 Z +TEMPORALITY T EH1 M P ER0 AE1 L AH0 T IY0 +TEMPORALLY T EH1 M P ER0 AH0 L IY0 +TEMPORALS T EH1 M P ER0 AH0 L Z +TEMPORARILY T EH2 M P ER0 EH1 R AH0 L IY0 +TEMPORARY T EH1 M P ER0 EH2 R IY0 +TEMPORE T EH1 M P AO0 R +TEMPORE T EH2 M P AO1 R IY0 +TEMPORIBUS T EH2 M P AO1 R AH0 B AH0 S +TEMPORIS T EH1 M P ER0 IH0 S +TEMPORISE T EH1 M P ER0 AY1 Z +TEMPORISED T EH1 M P ER0 AY1 Z D +TEMPORISING T EH1 M P ER0 AY1 Z IH0 NG +TEMPORIZE T EH1 M P ER0 AY2 Z +TEMPORIZED T EH1 M P ER0 AY2 Z D +TEMPORIZING T EH1 M P ER0 AY2 Z IH0 NG +TEMPORUM T EH2 M P AO1 R AH0 M +TEMPORY T EH1 M P ER0 IY0 +TEMPS T EH1 M P S +TEMPT T EH1 M P T +TEMPTATION T EH0 M T EY1 SH AH0 N +TEMPTATION'S T EH0 M T EY1 SH AH0 N Z +TEMPTATIONS T EH0 M T EY1 SH AH0 N Z +TEMPTED T EH1 M P T AH0 D +TEMPTED T EH1 M P T IH0 D +TEMPTER T EH1 M P T ER0 +TEMPTER'S T EH1 M P T ER0 Z +TEMPTERS T EH1 M P T ER0 Z +TEMPTETH T EH1 M P T IH0 TH +TEMPTIN T EH1 M P T IH0 N +TEMPTING T EH1 M P T IH0 NG +TEMPTINGLY T EH1 M P T IH0 NG L IY0 +TEMPTINGS T EH1 M P T IH0 NG Z +TEMPTRESS T EH1 M P T R IH0 S +TEMPTS T EH1 M P T S +TEMPURA T EH1 M P UH1 R AH0 +TEMPUS T EH1 M P AH0 S +TEMPY T EH1 M P IY0 +TEMS T EH1 M Z +TEMUJIN T EH1 M Y UW1 JH IH0 N +TEMUJIN'S T EH1 M Y UW1 JH IH0 N Z +TEN T EH1 N +TEN'S T EH1 N Z +TEN'T T EH1 N T +TENABLE T EH1 N AH0 B AH0 L +TENACEM T EH1 N AH0 S AH0 M +TENACIOUS T AH0 N EY1 SH AH0 S +TENACIOUSLY T AH0 N EY1 SH AH0 S L IY0 +TENACIOUSNESS T AH0 N EY1 SH AH0 S N AH0 S +TENACITY T AH0 N AE1 S IH0 T IY0 +TENAFLY T AH0 N AE1 F L IY0 +TENANCIES T EH1 N AH0 N S IY0 Z +TENANCY T EH1 N AH0 N S IY0 +TENANT T EH1 N AH0 N T +TENANT'S T EH1 N AH0 N T S +TENANTABLE T EH1 N AH0 N T AH0 B AH0 L +TENANTED T EH1 N AH0 N T IH0 D +TENANTING T EH1 N AH0 N T IH0 NG +TENANTLESS T EH1 N AH0 N T L AH0 S +TENANTRY T EH1 N AH0 N T R IY0 +TENANTS T EH1 N AH0 N T S +TENAS T EH1 N AH0 Z +TENASSERIM T AH0 N AE1 S ER0 AH0 M +TENAWA T EH0 N AA1 W AH0 +TENAWAS T EH0 N AA1 W AH0 Z +TENAX T EH1 N AE0 K S +TENAYA T EH0 N AA1 Y AH0 +TENBRUGGEN T EH1 N B R UW1 G AH0 N +TENBRUGGEN'S T EH1 N B R UW1 G AH0 N Z +TENBY T EH1 N B IY0 +TENCH T EH1 N CH +TENCIN T EH1 N S IH0 N +TENCZYN T EH1 N CH IH0 N +TEND T EH1 N D +TEND'REST T EH1 N R AH0 S T +TENDA T EH1 N D AH0 +TENDANCE T EH1 N D AH0 N S +TENDE T EH1 N D +TENDED T EH1 N D AH0 D +TENDED T EH1 N D IH0 D +TENDENCIES T EH1 N D AH0 N S IY0 Z +TENDENCY T EH1 N D AH0 N S IY0 +TENDER T EH1 N D ER0 +TENDER'S T EH1 N D ER0 Z +TENDERED T EH1 N D ER0 D +TENDERER T EH1 N D ER0 ER0 +TENDEREST T EH1 N D ER0 IH0 S T +TENDERFEET T EH1 N D ER0 F IY2 T +TENDERFOOT T EH1 N D ER0 F UH2 T +TENDERFOOT'S T EH1 N D ER0 F UH2 T S +TENDERFOOTS T EH1 N D ER0 F UH1 T S +TENDERFUT T EH1 N D ER0 F AH0 T +TENDERHEART T EH1 N D ER0 HH AA2 R T +TENDERHEARTED T EH1 N D ER0 HH AA2 R T IH0 D +TENDERING T EH1 N D ER0 IH0 NG +TENDERLOIN T EH1 N D ER0 L OY2 N +TENDERLOINS T EH1 N D ER0 L OY1 N Z +TENDERLY T EH1 N D ER0 L IY0 +TENDERNESS T EH1 N D ER0 N AH0 S +TENDERNESSES T EH1 N D ER0 N AH0 S IH0 Z +TENDERS T EH1 N D ER0 Z +TENDETH T EH1 N D IH0 TH +TENDHER T EH1 N D ER0 +TENDIN T EH1 N D IH0 N +TENDING T EH1 N D IH0 NG +TENDINOUS T EH1 N D IH0 N AH0 S +TENDON T EH1 N D AH0 N +TENDONS T EH1 N D AH0 N Z +TENDRE T AA1 N D R AH0 +TENDRESSE T EH1 N D R EH0 S +TENDRIL T EH1 N D R IH0 L +TENDRILLED T EH0 N D R IH1 L D +TENDRILS T EH1 N D R IH0 L Z +TENDS T EH1 N D Z +TENE T IY1 N +TENEBRAE T EH1 N AH0 B R EY2 +TENEBRARUM T EH1 N AH0 B R AE1 R AH0 M +TENEBREUSE T EH1 N AH0 B R UW1 Z +TENEBRIS T EH1 N AH0 B R IY1 +TENEBROUS T EH1 N AH0 B R AH0 S +TENEDOS T EH1 N AH0 D OW0 +TENEMENT T EH1 N AH0 M AH0 N T +TENEMENTS T EH1 N AH0 M AH0 N T S +TENENS T EH1 N AH0 N Z +TENENTE T EH1 N AH0 N T +TENER T EH1 N ER0 +TENERE T EH1 N ER0 +TENERIFE T EH1 N AH0 R AY2 F +TENERIFFE T EH1 N ER0 IH0 F +TENESCO T EH0 N EH1 S K OW0 +TENET T EH1 N AH0 T +TENETS T EH1 N AH0 T S +TENEZ T EH0 N EH1 Z +TENFOLD T EH1 N F OW2 L D +TENGA T EH1 NG G AH0 +TENGELYI T EH1 NG G AH0 L Y IY0 +TENGELYI'S T EH1 NG G AH0 L Y IY1 Z +TENGGA T EH1 NG G AH0 +TENGGA'S T EH1 NG G AH0 Z +TENGGER T EH1 NG G ER0 +TENGO T EH1 NG G OW0 +TENGU T EH1 NG UW1 +TENGYUEH T EH1 NG Y W EH1 +TENHAM T EH1 N AH0 M +TENIE T EH1 N IY0 +TENIENTE T EH1 N IY0 EH1 N T EY0 +TENIERS T IY1 N IY0 ER0 Z +TENIET T EH1 N IY0 AH0 T +TENIR T AH0 N IH1 R +TENISE T EH1 N AY0 Z +TENISON T EH1 N IH0 S AH0 N +TENISON'S T EH1 N IH0 S AH0 N Z +TENJIN T EH1 N JH IH0 N +TENLOW T EH1 N L OW0 +TENN T EH1 N +TENNANT T EH1 N AH0 N T +TENNANT'S T EH1 N AH0 N T S +TENNANTS T EH1 N AH0 N T S +TENNE T EH1 N +TENNELLY T EH1 N AH0 L IY0 +TENNELLY'S T EH1 N AH0 L IY0 Z +TENNENT T EH1 N AH0 N T +TENNER T EH1 N ER0 +TENNESSEAN T EH2 N IH0 S IY1 AH0 N +TENNESSEANS T EH2 N IH0 S IY1 AH0 N Z +TENNESSEE T EH2 N AH0 S IY1 +TENNESSEE'S T EH2 N AH0 S IY1 Z +TENNESSEEAN T EH2 N AH0 S IY1 AH0 N +TENNESSEEANS T EH2 N IH0 S IY0 AH0 N Z +TENNEY T EH1 N IY0 +TENNEY'S T EH1 N IY0 Z +TENNIEL T EH1 N IY0 L +TENNIEL'S T EH1 N IY0 AH0 L Z +TENNIN T EH1 N IH0 N +TENNINGTON T EH1 N IH0 NG T AH0 N +TENNIS T EH1 N AH0 S +TENNIS T EH1 N IH0 S +TENNISON T EH1 N IH0 S AH0 N +TENNO T EH1 N OW0 +TENNY T EH1 N IY0 +TENNY'S T EH1 N IY0 Z +TENNYRATE T EH1 N IH0 R EY0 T +TENNYSON T EH1 N IH0 S AH0 N +TENNYSON'S T EH1 N IH0 S AH0 N Z +TENNYSONIAN T EH1 N IH0 S OW1 N IY0 AH0 N +TENNYSONS T EH1 N IH0 S AH0 N Z +TENOCHTITLAN T EH1 N AA1 K T IY2 T L AH0 N +TENON T EH1 N AH0 N +TENONS T EH1 N AH0 N Z +TENOR T EH1 N ER0 +TENOR'S T EH1 N ER0 Z +TENORE T EH1 N AO2 R +TENORINA T EH1 N AO2 R IY1 N AH0 +TENORIO T EH0 N AO1 R IY0 OW0 +TENORS T EH1 N ER0 Z +TENOS T IY1 N OW0 Z +TENOUR T EH1 N ER0 +TENPENCE T EH1 N P EH1 N S +TENPENNY T EH1 N P EH2 N IY0 +TENPIN T EH1 N P AE1 N +TENPINS T EH1 N P IH2 N Z +TENS T EH1 N Z +TENSAS T EH1 N S AH0 S +TENSAW T EH2 N S AO2 +TENSCORE T EH2 N S K AO1 R +TENSE T EH1 N S +TENSED T EH1 N S T +TENSELY T EH1 N S L IY0 +TENSENESS T EH1 N S N AH0 S +TENSER T EH1 N S ER0 +TENSES T EH1 N S IH0 Z +TENSEST T EH1 N S AH0 S T +TENSILE T EH1 N S AH0 L +TENSING T EH1 N S IH0 NG +TENSION T EH1 N SH AH0 N +TENSIONED T EH1 N SH AH0 N D +TENSIONS T EH1 N CH AH0 N Z +TENSITY T EH1 N S AH0 T IY0 +TENSKWATAWA T EH1 N S K V AH0 T AA1 W AH0 +TENSON T EH1 N S AH0 N +TENT T EH1 N T +TENT'S T EH1 N T S +TENTACLE T EH1 N T AH0 K AH0 L +TENTACLED T EH1 N T AH0 K AH0 L D +TENTACLES T EH1 N T AH0 K AH0 L Z +TENTACULA T EH1 N T AH0 K Y UW0 L AH0 +TENTACULAR T EH1 N T AH0 K Y AH0 L ER0 +TENTAGE T EH1 N T AH0 JH +TENTATION T EH0 N T EY1 SH AH0 N +TENTATIVE T EH1 N AH0 T IH0 V +TENTATIVE T EH1 N T AH0 T IH0 V +TENTATIVELY T EH1 N T AH0 T IH0 V L IY0 +TENTATIVELY T EH1 N T AH0 V L IY0 +TENTATIVENESS T EH1 N T AH0 T IH0 V N AH0 S +TENTATIVES T EH1 N T AH0 T IH0 V Z +TENTE T EH1 N T EY0 +TENTED T EH1 N T IH0 D +TENTER T EH1 N T ER0 +TENTERDEN T EH1 N T ER0 D AH0 N +TENTERHOOKS T EH1 N T ER0 HH UH2 K S +TENTERS T EH1 N T ER0 Z +TENTH T EH1 N TH +TENTHLY T EH1 N TH L IY0 +TENTHOLM T EH1 N TH AH0 M +TENTHS T EH1 N TH S +TENTING T EH1 N T IH0 NG +TENTION T EH1 N SH AH0 N +TENTLESS T EH1 N T L AH0 S +TENTMAKER T EH1 N T M EY2 K ER0 +TENTMATE T EH1 N T M EY2 T +TENTORIUM T EH1 N T AO1 R IY0 AH0 M +TENTS T EH1 N T S +TENU T EH0 N UW1 +TENUE T EH1 N Y UW0 +TENUES T EH1 N Y UW0 Z +TENUI T EH0 N UW1 IY0 +TENUITY T EH0 N UW1 AH0 T IY0 +TENUOUS T EH1 N Y AH0 W AH0 S +TENURE T EH1 N Y ER0 +TENURES T EH1 N Y ER0 Z +TENWAY T EH1 N W EY2 +TEO T EY1 OW0 +TEOCALI T IY1 AH0 K AA1 L IY0 +TEOCALLI T EY1 OW0 K AE1 L IY0 +TEOCALLIS T EY1 OW0 K AE1 L IH0 S +TEODORA T IY2 AH0 D AO1 R AH0 +TEODORO T IY2 OW0 D AO1 R OW0 +TEODORO'S T IY2 AH0 D AO1 R OW0 Z +TEOFAN T EY1 OW0 F AE2 N +TEOS T IY1 OW0 Z +TEOTUCTLI T IY1 OW0 T AH2 K T L IY0 +TEP T IH0 P +TEPE T IY1 P +TEPEACA T IY1 P IY1 K AH0 +TEPEE T IY1 P IY1 +TEPEES T IY1 P IY0 Z +TEPELEN T IY1 P L AH0 N +TEPELENI T EH1 P AH0 L IY1 N IY0 +TEPELENTI T IY1 P IH0 L EH1 N T IY0 +TEPELENTI'S T IY1 P IH0 L EH1 N T IY0 Z +TEPENACASCO T EH2 P EH2 N AH0 K AE1 S K OW0 +TEPETL T IY1 P T L +TEPHANY T EH1 F AH0 N IY0 +TEPI T EH1 P IY0 +TEPIC T EH1 P IH0 K +TEPID T EH1 P IH0 D +TEPIDARIUM T EH1 P IH0 D EH1 R IY0 AH0 M +TEPIDITY T IH0 P IH1 D IH0 T IY0 +TEPIDLY T EH1 P IH0 D L IY0 +TEPL T EH1 P AH0 L +TEPLITZ T EH1 P L IH0 T S +TEPPING T EH1 P IH0 NG +TEPUS T EH1 P AH0 S +TEQUILA T AH0 K IY1 L AH0 +TER T ER1 +TER'BLE T ER1 B AH0 L +TER'S T ER0 Z +TERA T EH1 R AH0 +TERAH T EH2 R AH0 +TERAI T EH0 R AA1 IY0 +TERAIN T ER0 EY1 N +TERALBAY T ER0 AA1 L B EY2 +TERAMACHI T EH1 R AH0 M AA1 CH IY0 +TERAMORE T EH0 R AA1 M AO0 R +TERAPHIM T ER0 AA0 F IY1 M +TERAUCHI T EH1 R AA0 UW1 CH IY0 +TERBACCER T ER1 B AE0 K S ER0 +TERBACCO T ER1 B AA1 K OW0 +TERBACKER T ER0 B AE2 K ER0 +TERBLE T ER1 B AH0 L +TERBURG T ER0 B ER0 G +TERCANBURY T ER0 K AE1 N B EH2 R IY0 +TERCE T ER1 S +TERCEIRA T ER0 CH EH1 R AH0 +TERCEL T ER1 S AH0 L +TERCENTENARY T ER0 S EH1 N T IH0 N EH2 R IY0 +TERDAY T ER0 D EY2 +TERE T EH1 R IY0 +TEREBINTH T ER0 EY1 B IH2 N TH +TEREBRATULA T EH1 R AH0 B R AH0 T UW1 L AH0 +TEREDO T ER0 EY1 D OW0 +TEREK T ER0 EH0 K +TEREKEZA T ER0 EH0 K EY1 Z AH0 +TERELAS T ER0 EY1 L AH0 Z +TERELCHE T ER0 EH1 L CH +TERENCE T EH1 R AH0 N S +TERENCE'S T EH1 R AH0 N S IH0 Z +TERENTIA T ER0 EH1 N SH AH0 +TERENTIEFF T EH1 R AH0 N T IY2 F +TERENTIUS T ER0 EH1 N CH IY0 IH0 S +TERENTY T EH1 R AH0 N T IY0 +TERES T ER0 EH0 S +TERESA T ER0 EY1 S AH0 +TERESA T ER0 IY1 S AH0 +TERESA'S T ER0 EY1 S AH0 Z +TERESA'S T ER0 IY1 S AH0 Z +TERESIANI T ER0 IY1 S IY0 AA1 N IY0 +TERESINA T ER0 EH0 S IY1 N AH0 +TERESITA T ER0 EH0 S IY1 T AH0 +TEREUS T ER0 EY1 AH0 S +TERGEDDER T ER1 G EH1 D ER0 +TERGETHER T ER1 G EH1 DH ER0 +TERGIVERSATION T ER0 G IH1 V ER0 S EY1 SH AH0 N +TERGIVERSATIONS T ER0 G IH1 V ER0 S EY1 SH AH0 N Z +TERGO T ER1 G OW0 +TERGOES T ER0 G OW0 Z +TERGOU T ER0 G UW0 +TERHUNE T ER0 HH Y UW1 N +TERIG T EH2 R IH0 G +TERIS T EH2 R IH0 S +TERLAKE T ER0 L EY2 K +TERLI T ER0 L IY0 +TERLINCTHUN T ER0 L IH1 N K TH AH0 N +TERLIZZI T ER0 L IY1 T S IY0 +TERM T ER1 M +TERM'D T ER1 M D +TERM'S T ER1 M Z +TERMAGANT T ER1 M AH0 G AH0 N T +TERMAGANTS T ER1 M AH0 G AH0 N T S +TERMAGAUNT T ER1 M AH0 G AO1 N T +TERME T ER1 M +TERMED T ER1 M D +TERMER T ER1 M ER0 +TERMES T ER1 M IY0 Z +TERMINABLE T ER1 M AH0 N AH0 B AH0 L +TERMINAL T ER1 M AH0 N AH0 L +TERMINALS T ER1 M AH0 N AH0 L Z +TERMINATE T ER1 M AH0 N EY2 T +TERMINATED T ER1 M AH0 N EY2 T AH0 D +TERMINATED T ER1 M AH0 N EY2 T IH0 D +TERMINATES T ER1 M IH0 N EY2 T S +TERMINATING T ER1 M AH0 N EY2 T IH0 NG +TERMINATION T ER0 M AH0 N EY1 SH AH0 N +TERMINATIONS T ER2 M AH0 N EY1 SH AH0 N Z +TERMINATOR T ER1 M AH0 N EY2 T ER0 +TERMINE T ER1 M IH0 N +TERMINER T ER1 M AH0 N ER0 +TERMING T ER1 M IH0 NG +TERMINI T ER1 M IH0 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IY0 +TIMOCRATES T IH1 M AH0 K R EY2 T S +TIMOFEEVNA T IH1 M AH0 F IY2 V N AH0 +TIMOFEITCH T IH1 M AH0 F AY1 CH +TIMOFEY T IH1 M AH0 F IY0 +TIMOFEYITCH T IH1 M AH0 F IY0 IH0 CH +TIMOFYEVNA T IH1 M AH0 F Y EH2 V N AH0 +TIMOLEON T IH1 M AH0 L IY0 AH0 N +TIMOLEON'S T IH1 M AH0 L IY0 AH0 N Z +TIMON T AY1 M AH0 N +TIMON'S T AY1 M AH0 N Z +TIMOR T IY0 M AO1 R +TIMORESE T IY2 M AO0 R IY1 Z +TIMOROUS T IH1 M ER0 AH0 S +TIMOROUSLY T IH1 M ER0 AH0 S L IY0 +TIMOROUSNESS T IH1 M ER0 AH0 S N AH0 S +TIMOTEO T IH1 M AH0 T EY1 OW0 +TIMOTHEUS T IH0 M AH0 DH OY0 Z +TIMOTHY T IH1 M AH0 TH IY0 +TIMOTHY'S T IH1 M AH0 TH IY0 Z +TIMOUR T IH1 M UH1 R +TIMPANY T IH1 M P AH0 N IY0 +TIMPER T IH1 M P ER0 +TIMPSON T IH1 M P S AH0 N +TIMPSON'S T IH1 M P S AH0 N Z +TIMROD T IH1 M R AA0 D +TIMROD'S T IH1 M R AA0 D Z +TIMS T IH1 M Z +TIMS'S T IH1 M Z IH0 Z +TIMSON T IH1 M S AH0 N +TIMSON'S T IH1 M S AH0 N Z +TIMTOM T IH1 M T AH0 M +TIMUR T IH1 M ER0 +TIMUR'S T IH1 M ER0 Z +TIMURTASCH T IH1 M ER0 T AE1 SH +TIMWORTH T 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IH0 NG +TINGLING T IH1 NG G L IH0 NG +TINGLINGLY T IH1 NG G L IH0 NG L IY0 +TINGLINGS T IH1 NG G L IH0 NG Z +TINGLY T IH1 NG G L IY0 +TINGOCCIO T IH1 NG G OW1 CH IY0 OW0 +TINGS T IH1 NG Z +TINHORN T IH1 N HH AO2 R N +TINI T IY1 N IY0 +TINIER T AY1 N IY0 ER0 +TINIEST T AY1 N IY0 AH0 S T +TINILY T IH1 N AH0 L IY0 +TININESS T AY1 N IY0 N AH0 S +TINK T IH1 NG K +TINKA T IH1 NG K AH0 +TINKEE T IH1 NG K IY0 +TINKELES T IH1 NG K AH0 L IY2 Z +TINKER T IH1 NG K ER0 +TINKER'S T IH1 NG K ER0 Z +TINKERED T IH1 NG K ER0 D +TINKERIN T IH1 NG K R IH0 N +TINKERING T IH1 NG K ER0 IH0 NG +TINKERING T IH1 NG K R IH0 NG +TINKERS T IH1 NG K ER0 Z +TINKHAM T IH1 NG K AH0 M +TINKIN T IH1 NG K IH0 N +TINKING T IH1 NG K IH0 NG +TINKLE T IH1 NG K AH0 L +TINKLEBY T IH1 NG K AH0 L B IY0 +TINKLED T IH1 NG K AH0 L D +TINKLER T IH1 NG K AH0 L ER0 +TINKLER T IH1 NG K L ER0 +TINKLER'S T IH1 NG K L ER0 Z +TINKLERS T IH1 NG K L ER0 Z +TINKLES T IH1 NG K AH0 L Z +TINKLETOWN T IH1 NG K AH0 L T AW2 N +TINKLING T IH1 NG K 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N S M IH0 TH +TINSMITH'S T IH1 N S M IH2 TH S +TINSMITHS T IH1 N S M IH0 S +TINT T IH1 N T +TINTACKER T IH1 N T AE1 K ER0 +TINTACKS T IH1 N T AE2 K S +TINTAGEL T IH1 N T AH0 G AH0 L +TINTAGGON T IH1 N T AE2 G AO0 N +TINTAGHODA T IH1 N T AH0 G OW1 D AH0 +TINTAGIL T IH1 N T AH0 JH AH0 L +TINTALOUS T IH1 N T AH0 L AH0 S +TINTAMARRE T IH1 N T AH0 M AA1 R +TINTED T IH1 N T IH0 D +TINTEFLECK T IH1 N T AH0 F L IH0 K +TINTER T IH1 N T ER0 +TINTERN T IH1 N T ER0 N +TINTI T IH1 N T IY0 +TINTIC T IH1 N T IH0 K +TINTING T IH1 N T IH0 NG +TINTINGS T IH1 N T IH0 NG Z +TINTINNABULATION T IH1 N T IH1 N AH0 B Y AH0 L EY1 SH AH0 N +TINTO T IH1 N T OW0 +TINTOP T IH1 N T AA2 P +TINTORET T IH1 N T OW0 R IH0 T +TINTORET'S T IH1 N T OW0 R IH0 T S +TINTORETTO T IH1 N T OW0 R EH1 T OW0 +TINTORETTO'S T IH1 N T OW0 R EH1 T OW0 Z +TINTORETTOS T IH1 N T OW0 R EH1 T OW0 Z +TINTRENALE T IH1 N T R EH1 N AH0 L +TINTS T IH1 N T S +TINTYE T IH1 N T AY2 +TINTYPE T IH1 N T AY2 P +TINTYPES T IH1 N T AY2 P S +TINVILLE T IH1 N V IH2 L +TINVILLE'S T IH1 N V IH0 L Z +TINWALD T IH1 N W AO2 L D +TINWARE T IH1 N W EH2 R +TINY T AY1 N IY0 +TINY'S T AY1 N IY0 Z +TINYBOY T AY1 N IY0 B OY2 +TIO T Y OW0 +TIOGA T IY0 OW1 G AH0 +TION SH AH0 N +TIONS SH AH0 N Z +TIOR T IY1 ER0 +TIP T IH1 P +TIP'S T IH1 P S +TIPE T AY1 P +TIPED T AY1 P T +TIPENE T AY1 P AH0 N +TIPHAINE T IH1 P EY1 N +TIPHAINE'S T IH1 P EY1 N Z +TIPHAINES T IH1 P EY1 N Z +TIPHYS T IH1 P IY0 Z +TIPKINS T IH1 P K IH0 N Z +TIPP T IH1 P +TIPP'D T IH1 P D +TIPPECANOE T IH1 P IH0 K AE1 N OW0 +TIPPED T IH1 P T +TIPPER T IH1 P ER0 +TIPPERARY T IH1 P ER0 EH2 R IY0 +TIPPET T IH1 P AH0 T +TIPPETS T IH1 P IH0 T S +TIPPIN T IH1 P IH0 N +TIPPING T IH1 P IH0 NG +TIPPING'S T IH1 P IH0 NG Z +TIPPINGS T IH1 P IH0 NG Z +TIPPINS T IH1 P IH0 N Z +TIPPIPAHS T IH1 P IY0 P AA1 Z +TIPPIT T IH1 P IH0 T +TIPPITT T IH1 P IH0 T +TIPPLE T IH1 P AH0 L +TIPPLED T IH1 P AH0 L D +TIPPLER T IH1 P AH0 L ER0 +TIPPLER'S T IH1 P L ER0 Z +TIPPLERS T IH1 P L ER0 Z +TIPPLES T IH1 P AH0 L Z 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S AY2 T +TOWNSLEY T AW1 N S L IY0 +TOWNSMAN T AW1 N Z M AH0 N +TOWNSMAN'S T AW1 N Z M AH0 N Z +TOWNSMEN T AW1 N Z M AH0 N +TOWNSMEN'S T AW1 N Z M AH0 N Z +TOWNSPEOPLE T AW1 N Z P IY2 P AH0 L +TOWNSVILLE T AW1 N Z V IH0 L +TOWNSWOMAN T AW1 N S W UH2 M AH0 N +TOWNSWOMEN T AW1 N S W IH2 M AH0 N +TOWNWARD T AW1 N W ER0 D +TOWNWARDS T AW1 N W ER0 D Z +TOWNY T AW1 N IY0 +TOWPATH T OW1 P AE2 TH +TOWROPE T AO1 R OW1 P +TOWS T OW1 Z +TOWSE T OW1 S +TOWSER T OW1 Z ER0 +TOWSER'S T OW1 Z ER0 Z +TOWSEY T OW1 S IY0 +TOWSIE T OW1 S IY0 +TOWSLED T OW1 S L D +TOWSY T OW1 S IY0 +TOWTON T OW1 T AH0 N +TOWY T OW1 IY0 +TOWZER T AW1 Z ER0 +TOWZER'S T AW1 Z ER0 Z +TOWZLED T AW1 Z AH0 L D +TOX T AA2 K S +TOX'S T AA2 K S IH0 Z +TOXARIS T AA2 K S ER0 IH0 S +TOXIC T AA1 K S IH0 K +TOXICOLOGICAL T AA2 K S AH0 K AH0 L AA1 JH IH0 K AH0 L +TOXICOLOGIST T AA2 K S IH0 K AA1 L AH0 JH IH0 S T +TOXICOLOGISTS T AA2 K S IH0 K AA1 L AH0 JH IH0 S T S +TOXICOLOGY T AA2 K S IH0 K AA1 L AH0 JH IY0 +TOXIN T AA1 K S AH0 N +TOXINS T AA1 K S AH0 N Z +TOY T OY1 +TOY'S T OY1 Z +TOYAH T OY1 AH0 +TOYATTE T OY1 AH0 T +TOYE T OY1 +TOYE'S T OY1 Z +TOYED T OY1 D +TOYING T OY1 IH0 NG +TOYLAND T OY1 L AH0 N D +TOYLE T OY1 L +TOYLIKE T OY1 L AY2 K +TOYMAKER T OY1 M EY2 K ER0 +TOYMAN T OY1 M AH0 N +TOYMAN'S T OY1 M AH0 N Z +TOYNBEE T OY1 N B IY0 +TOYNBEE'S T OY1 N B IY0 Z +TOYO T OW1 Y OW0 +TOYON T OY1 AH0 N +TOYS T OY1 Z +TOYSHOP T OY1 SH AA2 P +TOYSHOPS T OY1 SH AA2 P S +TOZA T OW1 Z AH0 +TOZER T OW1 Z ER0 +TOZER'S T OW1 Z ER0 Z +TR T R +TR'EN T ER0 N +TRA T R AH0 +TRABB T R AE1 B +TRABB'S T R AE1 B Z +TRABUCOS T R AA0 B UW1 K OW0 Z +TRABUE T R AA1 B W EH0 +TRAC T R AE1 K +TRAC'D T R EY1 K D +TRACADIE T R EY1 K AE1 D IY0 +TRACASSIER T R EY1 K AH0 S IY0 ER0 +TRACE T R EY1 S +TRACEABLE T R EY1 S AH0 B AH0 L +TRACED T R EY1 S T +TRACELESS T R EY1 S L AH0 S +TRACER T R EY1 S ER0 +TRACERIED T R EY1 S R IY0 D +TRACERIES T R EY1 S ER0 IY0 Z +TRACERS T R EY1 S ER0 Z +TRACERY T R EY1 S ER0 IY0 +TRACES T R EY1 S AH0 Z +TRACES T R EY1 S IH0 Z +TRACEY T R EY1 S IY0 +TRACEY'S T R EY1 S IY0 Z +TRACHEA T R EY1 K IY0 AH0 +TRACHEOTOMY T R EY1 K IY0 AH0 T OW2 M IY0 +TRACHIS T R EY1 K IY0 Z +TRACHOMA T R EY1 K OW1 M AH0 +TRACHONITIS T R EY1 K AH0 N AY1 T IH0 S +TRACHYTE T R EY1 K AY2 T +TRACHYTIC T R EY1 K IH1 T IH0 K +TRACING T R EY1 S IH0 NG +TRACINGS T R EY1 S IH0 NG Z +TRACK T R AE1 K +TRACK'D T R AE1 K D +TRACK'S T R AE1 K S +TRACKED T R AE1 K T +TRACKER T R AE1 K ER0 +TRACKERS T R AE1 K ER0 Z +TRACKIN T R AE1 K IH0 N +TRACKING T R AE1 K IH0 NG +TRACKLESS T R AE1 K L AH0 S +TRACKMEN T R AE1 K M EH2 N +TRACKS T R AE1 K S +TRACKSIDE T R AE1 K S AY2 D +TRACKWAY T R AE1 K W EY2 +TRACKWAYS T R AE1 K W EY2 Z +TRACT T R AE1 K T +TRACTABILITY T R AE1 K T AH0 B IH1 L IH0 T IY0 +TRACTABLE T R AE1 K T AH0 B AH0 L +TRACTARIAN T R AE1 K T EH1 R IY0 AH0 N +TRACTARIANISM T R AE1 K T EH1 R IY0 AH0 N IH2 Z AH0 M +TRACTARIANS T R AE1 K T EH1 R IY0 AH0 N Z +TRACTATE T R AE1 K T EY2 T +TRACTATES T R AE1 K T EY2 T S +TRACTATUS T R AE1 K T EY1 T AH0 S +TRACTION T R AE1 K SH AH0 N +TRACTIONS T R AE1 K SH AH0 N Z +TRACTIVE T R AE1 K T IH0 V +TRACTOR T R AE1 K T ER0 +TRACTORS T R AE1 K T ER0 Z +TRACTS T R AE1 K T S +TRACY T R EY1 S IY0 +TRACY'S T R EY1 S IY0 Z +TRACYS T R EY1 S IY0 Z +TRADDLES T R AE1 D AH0 L Z +TRADE T R EY1 D +TRADE'S T R EY1 D Z +TRADED T R EY1 D IH0 D +TRADEING T R EY1 D IH0 NG +TRADEMARK T R EY1 D M AA2 R K +TRADEMARKS T R EY1 D M AA2 R K S +TRADER T R EY1 D ER0 +TRADER'S T R EY1 D ER0 Z +TRADERS T R EY1 D ER0 Z +TRADES T R EY1 D Z +TRADESFOLK T R EY1 D Z F OW2 K +TRADESMAN T R EY1 D Z M AH0 N +TRADESMAN'S T R EY1 D Z M AH0 N Z +TRADESMEN T R EY1 D Z M AH0 N +TRADESMEN'S T R EY1 D Z M AH0 N Z +TRADESPEOPLE T R EY1 D Z P IY2 P AH0 L +TRADESWOMAN T R EY1 D S W UH2 M AH0 N +TRADEWINDS T R EY1 D W IH2 N D Z +TRADGEDY T R AE0 JH AH0 D IY0 +TRADGIC T R AE1 JH IH0 K +TRADIN T R EY1 D IH0 N +TRADING T R EY1 D IH0 NG +TRADITION T R AH0 D IH1 SH AH0 N +TRADITION'S T R AH0 D IH1 SH AH0 N Z +TRADITIONAL T R AH0 D IH1 SH AH0 N AH0 L +TRADITIONALISM T R AH0 D IH1 SH AH0 N AH0 L IH2 Z AH0 M +TRADITIONALISTS T R AH0 D IH1 SH N AH0 L AH0 S T S +TRADITIONALLY T R AH0 D IH1 SH AH0 N AH0 L IY0 +TRADITIONALLY T R AH0 D IH1 SH N AH0 L IY0 +TRADITIONARY T R AH0 D IH1 SH AH0 N EH2 R IY0 +TRADITIONS T R AH0 D IH1 SH AH0 N Z +TRADITORE T R AH0 D AY2 T AO1 R IY0 +TRADMOS T R EY1 D IY1 EH1 M OW1 EH1 S +TRADUCE T R AH0 D UW1 CH IY0 +TRADUCED T R AH0 D UW1 S T +TRADUCER T R AH0 D UW1 S ER0 +TRADUCERS T R AH0 D UW1 S ER0 Z +TRADUCES T R AH0 D UW1 S IH0 Z +TRADUCING T R AH0 D UW1 S IH0 NG +TRADUCTION T R EY1 D AH1 K SH AH0 N +TRADUIT T R EY1 D UW0 IH0 T +TRAEGER T R EH1 G ER0 +TRAEUMEREI T R IY1 AH0 M EH0 R EY2 +TRAFALGAR T R AH0 F AE1 L G ER0 +TRAFFIC T R AE1 F IH0 K +TRAFFIC'S T R AE1 F IH0 K S +TRAFFICK T R AE1 F IH0 K +TRAFFICK'S T R AE1 F IH0 K S +TRAFFICKED T R AE1 F IH0 K T +TRAFFICKER T R AE1 F IH0 K ER0 +TRAFFICKERS T R AE1 F IH0 K ER0 Z +TRAFFICKING T R AE1 F IH0 K IH0 NG +TRAFFICKS T R AE1 F IH0 K S +TRAFFICS T R AE1 F IH0 K S +TRAFFORD T R AE1 F ER0 D +TRAFFORD'S T R AE1 F ER0 D Z +TRAFFORDS T R AE1 F ER0 D Z +TRAFTON T R AE1 F T AH0 N +TRAFTON'S T R AE1 F T AH0 N Z +TRAGACANTH T R AH0 G AH0 K AE2 N TH +TRAGEDIAN T R AE1 JH AH0 D IY0 AH0 N +TRAGEDIAN'S T R AE1 JH AH0 D IY0 AH0 N Z +TRAGEDIANS T R AE1 JH AH0 D IY0 AH0 N Z +TRAGEDIE T R AE1 JH AH0 D IY0 +TRAGEDIENNE T R AE1 JH AH0 D IY0 EH2 N +TRAGEDIES T R AE1 JH AH0 D IY0 Z +TRAGEDY T R AE1 JH AH0 D IY0 +TRAGEDY'S T R AE1 JH AH0 D IY0 Z +TRAGHETTO T R AH0 G EH1 T OW0 +TRAGI T R AA1 G IY0 +TRAGIC T R AE1 JH IH0 K +TRAGICAL T R AE1 JH IH0 K AH0 L +TRAGICALLY T R AE1 JH IH0 K L IY0 +TRAGICK T R AE1 JH IH0 K +TRAGICO T R AE1 JH IH0 K OW2 +TRAGICOMEDY T R AE2 JH IH0 K AA1 M AH0 D IY0 +TRAHERNE T R AE1 HH ER0 N +TRAHISON T R AE1 HH IH0 S AH0 N +TRAHIT T R AE1 HH IH0 T +TRAIDOR T R EY1 D ER0 +TRAIL T R EY1 L +TRAIL'D T R EY1 L D +TRAIL'S T R EY1 L Z +TRAILED T R EY1 L D +TRAILER T R EY1 L ER0 +TRAILERS T R EY1 L ER0 Z +TRAILIN T R EY1 L IH0 N +TRAILING T R EY1 L IH0 NG +TRAILL T R EY1 L +TRAILL'S T R EY1 L Z +TRAILLES T R EY1 L Z +TRAILLESS T R EY1 L AH0 S +TRAILMEN T R EY1 L M AH0 N +TRAILMEN'S T R EY1 L M AH0 N Z +TRAILS T R EY1 L Z +TRAIN T R EY1 N +TRAIN'D T R EY1 N D +TRAIN'S T R EY1 N Z +TRAINARD T R EY1 N ER0 D +TRAINBANDS T R EY1 N B AE2 N D Z +TRAINE T R EY1 N +TRAINED T R EY1 N D +TRAINEE T R EY1 N IY1 +TRAINEES T R EY1 N IY1 Z +TRAINER T R EY1 N ER0 +TRAINER'S T R EY1 N ER0 Z +TRAINERS T R EY1 N ER0 Z +TRAINFUL T R EY1 N F AH0 L +TRAININ T R EY1 N IH0 N +TRAINING T R EY1 N IH0 NG +TRAININGS T R EY1 N IH0 NG Z +TRAINLOAD T R EY1 N L OW2 D +TRAINLOADS T R EY1 N L OW2 D Z +TRAINMAN T R EY1 N M AH0 N +TRAINMASTER T R EY1 N M AE2 S T ER0 +TRAINMASTER'S T R EY1 N M AE2 S T ER0 Z +TRAINMEN T R EY1 N M AH0 N +TRAINOR T R EY1 N ER0 +TRAINOR'S T R EY1 N ER0 Z +TRAINORS T R EY1 N ER0 Z +TRAINS T R EY1 N Z +TRAIPSE T R EY1 P S +TRAIPSED T R EY1 P S T +TRAIPSIN T R EY1 P S IH0 N +TRAIPSING T R EY1 P S IH0 NG +TRAIT T R EY1 T +TRAIT'ROUS T R EY1 T R AH0 S +TRAITE T R EY1 T +TRAITER T R EY1 T ER0 +TRAITEUR T R EY1 T UW1 R +TRAITI T R EY1 T IY0 +TRAITOR T R EY1 T ER0 +TRAITOR'S T R EY1 T ER0 Z +TRAITORESS T R EY1 T ER0 EH1 S +TRAITOROUS T R EY1 T ER0 AH0 S +TRAITOROUSLY T R EY1 T ER0 AH0 S L IY0 +TRAITORS T R EY1 T ER0 Z +TRAITRE T R EY1 T ER0 +TRAITRESS T R EY1 T R AH0 S +TRAITS T R EY1 T S +TRAJAN T R EY1 JH AH0 N +TRAJAN'S T R EY1 JH AH0 N Z +TRAJECTORIES T R AH0 JH EH1 K T ER0 IY0 Z +TRAJECTORY T R AH0 JH EH1 K T ER0 IY0 +TRAKTIR T R AE1 K T ER0 +TRALALA T R AA0 L AA1 L AH0 +TRALEE T R AE1 L IY0 +TRALEIGH T R EY1 L IY0 +TRAM T R AE1 M +TRAMCAR T R AE1 M K AA2 R +TRAMCARS T R AE1 M K AA2 R Z +TRAMLINE T R AE1 M L AY2 N +TRAMLINES T R AE1 M L AY2 N Z +TRAMMEL T R AE1 M AH0 L +TRAMMELED T R AE1 M AH0 L D +TRAMMELLED T R AE1 M AH0 L D +TRAMMELLING T R AE1 M AH0 L IH0 NG +TRAMMELS T R AE1 M AH0 L Z +TRAMONTANA T R AA0 M OW0 N T AE1 N AH0 +TRAMONTANE T R AA0 M OW1 N T EY2 N +TRAMP T R AE1 M P +TRAMP'S T R AE1 M P S +TRAMPAS T R AE1 M P AH0 Z +TRAMPED T R AE1 M P T +TRAMPER T R AE1 M P ER0 +TRAMPERS T R AE1 M P ER0 Z +TRAMPIN T R AE1 M P IH0 N +TRAMPING T R AE1 M P IH0 NG +TRAMPINGS T R AE1 M P IH0 NG Z +TRAMPISH T R AE1 M P IH0 SH +TRAMPLE T R AE1 M P AH0 L +TRAMPLEASURE T R AE1 M P L EH1 ZH ER0 +TRAMPLED T R AE1 M P AH0 L D +TRAMPLER T R AE1 M P L ER0 +TRAMPLES T R AE1 M P AH0 L Z +TRAMPLIN T R AE1 M P L IH0 N +TRAMPLING T R AE1 M P L IH0 NG +TRAMPLINGS T R AE1 M P L IH0 NG Z +TRAMPS T R AE1 M P Z +TRAMS T R AE1 M Z +TRAMTRIST T R AE1 M T R IH0 S T +TRAMU T R AA0 M UW1 +TRAMWAY T R AE1 M W EY2 +TRAMWAYS T R AE1 M W EY2 Z +TRAMWORTH T R AE1 M W ER0 TH +TRANA T R AE1 N AH0 +TRANCE T R AE1 N S +TRANCED T R AE1 N S T +TRANCELIKE T R AE1 N S L AY2 K +TRANCES T R AE1 N S IH0 Z +TRANCHANT T R AE1 N CH AH0 N T +TRANCHE T R AE1 N CH +TRANCHEES T R AE1 N CH IY2 Z +TRANDAFIR T R AE1 N D AH0 F ER0 +TRANEST T R AE2 N IH0 S T +TRANGOIL T R AE1 NG G OY2 L +TRANI T R AA1 N IY0 +TRANK T R AE1 NG K +TRANKWILLITATIN T R AE1 NG K W IH2 L AH0 T EY1 T IH0 N +TRANNEL T R AE1 N AH0 L +TRANQUIL T R AE1 NG K W AH0 L +TRANQUIL T R AE1 NG K W IH0 L +TRANQUILITY T R AE0 NG K W IH1 L IH0 T IY0 +TRANQUILIZE T R AE1 NG K W AH0 L AY2 Z +TRANQUILIZED T R AE1 NG K W AH0 L AY2 Z D +TRANQUILIZER T R AE1 NG K W AH0 L AY2 Z ER0 +TRANQUILIZING T R AE1 NG K W AH0 L AY2 Z IH0 NG +TRANQUILLA T R AE1 NG K W IH1 L AH0 +TRANQUILLE T R AE1 NG K W IH0 L +TRANQUILLISE T R AE1 NG K W IY0 L IY1 S +TRANQUILLISED T R AE1 NG K W IH1 L IH0 S T +TRANQUILLISING T R AE1 NG K W AH0 L L AY2 Z IH0 NG +TRANQUILLITIES T R AE1 NG K W IH1 L AH0 T IY0 Z +TRANQUILLITY T R AE1 NG K W IH1 L AH0 T IY0 +TRANQUILLIZE T R AE1 NG K W AH0 L L AY2 Z +TRANQUILLIZED T R AE1 NG K W AH0 L L AY2 Z D +TRANQUILLIZING T R AE1 NG K W AH0 L L AY2 Z IH0 NG +TRANQUILLO T R AE1 NG K W IH1 L OW0 +TRANQUILLUS T R AE1 NG K W IH1 L AH0 S +TRANQUILLY T R AE1 NG K W AH0 L IY0 +TRANS T R AE1 N Z +TRANSACT T R AE0 N Z AE1 K T +TRANSACTED T R AE0 N S AE1 K T IH0 D +TRANSACTED T R AE0 N Z AE1 K T IH0 D +TRANSACTING T R AE0 N Z AE1 K T IH0 NG +TRANSACTION T R AE0 N Z AE1 K SH AH0 N +TRANSACTIONS T R AE0 N Z AE1 K SH AH0 N Z +TRANSACTS T R AE0 N Z AE1 K T S +TRANSALPINE T R AE0 N Z AE1 L P AY1 N +TRANSATLANTIC T R AE2 N Z AH0 T L AE1 N IH0 K +TRANSATLANTIC T R AE2 N Z AH0 T L AE1 N T IH0 K +TRANSATLANTIQUE T R AE2 N Z AH0 T L AE1 N T AH0 K EY2 +TRANSBAIKAL T R AE1 N Z B AA2 IY1 K AH0 L +TRANSBAIKALIA T R AE1 N Z B AA2 IH0 K AA1 L Y AH0 +TRANSCAUCASIA T R AE2 N Z K AO0 K EY1 ZH AH0 +TRANSCEIVER T R AE2 N S IY1 V ER0 +TRANSCEND T R AE0 N S EH1 N D +TRANSCENDANT T R AE0 N S EH1 N D AH0 N T +TRANSCENDED T R AE0 N S EH1 N D IH0 D +TRANSCENDENCE T R AE0 N S EH1 N D AH0 N S +TRANSCENDENCY T R AE0 N S EH1 N D AH0 N S IY0 +TRANSCENDENT T R AE0 N S EH1 N D AH0 N T +TRANSCENDENTAL T R AE2 N S AH0 N D EH1 N AH0 L +TRANSCENDENTAL T R AE2 N S AH0 N D EH1 N T AH0 L +TRANSCENDENTALISM T R AE2 N S AH0 N D EH1 N AH0 L IH2 Z AH0 M +TRANSCENDENTALIST T R AE2 N S AH0 N D EH1 N AH0 L IH0 S T +TRANSCENDENTALISTS T R AE2 N S AH0 N D EH1 N AH0 L IH0 S T S +TRANSCENDENTALLY T R AE2 N S AH0 N D EH1 N AH0 L IY0 +TRANSCENDENTLY T R AE0 N S EH1 N D AH0 N T L IY0 +TRANSCENDETH T R AE0 N S EH1 N D IH0 TH +TRANSCENDING T R AE0 N S EH1 N D IH0 NG +TRANSCENDS T R AE0 N S EH1 N D Z +TRANSCONTINENTAL T R AE2 N Z K AA2 N IH0 N EH1 N AH0 L +TRANSCONTINENTAL T R AE2 N Z K AA2 N IH0 N EH1 N T AH0 L +TRANSCONTINENTAL T R AE2 N Z K AA2 N T IH0 N EH1 N AH0 L +TRANSCONTINENTAL T R AE2 N Z K AA2 N T IH0 N EH1 N T AH0 L +TRANSCRIBE T R AE0 N S K R AY1 B +TRANSCRIBED T R AE0 N S K R AY1 B D +TRANSCRIBER T R AE0 N S K R AY1 B ER0 +TRANSCRIBER'S T R AE0 N S K R AY1 B ER0 Z +TRANSCRIBERS T R AE0 N S K R AY1 B ER0 Z +TRANSCRIBES T R AE0 N S K R AY1 B Z +TRANSCRIBING T R AE0 N S K R AY1 B IH0 NG +TRANSCRIPT T R AE1 N S K R IH2 P T +TRANSCRIPTION T R AE2 N S K R IH1 P SH AH0 N +TRANSCRIPTIONS T R AE2 N S K R IH1 P SH AH0 N Z +TRANSCRIPTS T R AE1 N S K R IH2 P T S +TRANSDUCERS T R AE0 N S D UW1 S ER0 Z +TRANSEPT T R AE0 N S EH1 P T +TRANSEPTS T R AE1 N S EH2 P T S +TRANSFER T R AE0 N S F ER1 +TRANSFER T R AE1 N S F ER0 +TRANSFERABLE T R AE0 N S F ER1 AH0 B AH0 L +TRANSFERENCE T R AE0 N S F ER1 AH0 N S +TRANSFERMENT T R AE0 N S F ER1 M AH0 N T +TRANSFERR'D T R AE0 N S F ER1 D +TRANSFERRED T R AE0 N S F ER1 D +TRANSFERRED T R AE1 N S F ER0 D +TRANSFERRENCE T R AE0 N S F ER1 AH0 N S +TRANSFERRING T R AE0 N S F ER1 IH0 NG +TRANSFERS T R AE0 N S F ER1 Z +TRANSFERS T R AE1 N S F ER0 Z +TRANSFIGURATION T R AE0 N S F IH1 G Y ER0 EY1 SH AH0 N +TRANSFIGURATIONS T R AE0 N S F IH1 G Y ER0 EY1 SH AH0 N Z +TRANSFIGURE T R AE0 N S F IH1 G Y ER0 +TRANSFIGURED T R AE0 N S F IH1 G Y ER0 D +TRANSFIGURES T R AE0 N S F IH1 G Y ER0 Z +TRANSFIGURING T R AE0 N S F IH1 G Y ER0 IH0 NG +TRANSFIX T R AE0 N S F IH1 K S +TRANSFIX'D T R AE0 N S F IH1 K S D +TRANSFIXED T R AE0 N S F IH1 K S T +TRANSFIXES T R AE0 N S F IH1 K S IH0 Z +TRANSFIXING T R AE0 N S F IH1 K S IH0 NG +TRANSFORM T R AE0 N S F AO1 R M +TRANSFORM T R AE1 N S F AO0 R M +TRANSFORM'D T R AE0 N S F AO1 R M D +TRANSFORMATION T R AE2 N S F ER0 M EY1 SH AH0 N +TRANSFORMATIONS T R AE2 N S F ER0 M EY1 SH AH0 N Z +TRANSFORMED T R AE0 N S F AO1 R M D +TRANSFORMER T R AE0 N S F AO1 R M ER0 +TRANSFORMERS T R AE0 N S F AO1 R M ER0 Z +TRANSFORMING T R AE0 N S F AO1 R M IH0 NG +TRANSFORMISM T R AE0 N S F AO1 R M IH0 Z AH0 M +TRANSFORMS T R AE0 N S F AO1 R M Z +TRANSFUSE T R AE0 N S F Y UW1 Z +TRANSFUSED T R AE0 N S F Y UW1 Z D +TRANSFUSING T R AE0 N S F Y UW1 Z IH0 NG +TRANSFUSION T R AE0 N S F Y UW1 ZH AH0 N +TRANSGRESS T R AE0 N Z G R EH1 S +TRANSGRESSED T R AE0 N Z G R EH1 S T +TRANSGRESSES T R AE0 N Z G R EH1 S IH0 S +TRANSGRESSING T R AE0 N Z G R EH1 S IH0 NG +TRANSGRESSION T R AE0 N Z G R EH1 SH AH0 N +TRANSGRESSIONS T R AE0 N Z G R EH1 SH AH0 N Z +TRANSGRESSOR T R AE0 N Z G R EH1 S ER0 +TRANSGRESSORS T R AE0 N Z G R EH1 S ER0 Z +TRANSHIP T R AE1 N SH IH2 P +TRANSHIPMENT T R AE1 N SH IH1 P M AH0 N T +TRANSHIPPED T R AE1 N ZH IH1 P T +TRANSHIPPING T R AE1 N ZH IH1 P IH0 NG +TRANSIENCE T R AE1 N Z IY0 AH0 N S +TRANSIENCY T R AE1 N Z IY0 AH0 N S IY0 +TRANSIENT T R AE1 N ZH AH0 N T +TRANSIENTLY T R AE1 N ZH AH0 N T L IY0 +TRANSIENTS T R AE1 N Z IY0 AH0 N T S +TRANSISTOR T R AE0 N Z IH1 S T ER0 +TRANSISTORS T R AE0 N Z IH1 S T ER0 Z +TRANSIT T R AE1 N Z IH0 T +TRANSITA T R AE0 N S IY1 T AH0 +TRANSITION T R AE0 N Z IH1 SH AH0 N +TRANSITIONAL T R AE0 N S IH1 SH AH0 N AH0 L +TRANSITIONAL T R AE0 N Z IH1 SH AH0 N AH0 L +TRANSITIONS T R AE0 N Z IH1 SH AH0 N Z +TRANSITIVE T R AE1 N Z IH0 T IH0 V +TRANSITORILY T R AE1 N Z AH0 T AO2 R AH0 L IY0 +TRANSITORINESS T R AE1 N Z AH0 T AO2 R IY0 N AH0 S +TRANSITORY T R AE1 N Z AH0 T AO2 R IY0 +TRANSITS T R AE1 N Z IH0 T S +TRANSITU T R AE1 N Z IH0 T UW0 +TRANSKEI T R AE1 N Z K EY2 +TRANSL T R AE0 N S L +TRANSLATABLE T R AE0 N Z L EY1 T AH0 B AH0 L +TRANSLATE T R AE0 N S L EY1 T +TRANSLATE T R AE0 N Z L EY1 T +TRANSLATED T R AE0 N S L EY1 T IH0 D +TRANSLATED T R AE0 N Z L EY1 T AH0 D +TRANSLATES T R AE0 N Z L EY1 T S +TRANSLATES T R AE1 N S L EY2 T S +TRANSLATING T R AE0 N Z L EY1 T IH0 NG +TRANSLATING T R AE1 N S L EY2 T IH0 NG +TRANSLATION T R AE0 N S L EY1 SH AH0 N +TRANSLATION T R AE0 N Z L EY1 SH AH0 N +TRANSLATIONS T R AE0 N S L EY1 SH AH0 N Z +TRANSLATIONS T R AE0 N Z L EY1 SH AH0 N Z +TRANSLATOR T R AE0 N S L EY1 T ER0 +TRANSLATOR T R AE0 N Z L EY1 T ER0 +TRANSLATOR'S T R AE0 N Z L EY1 T ER0 Z +TRANSLATORS T R AE0 N S L EY1 T ER0 Z +TRANSLATORS T R AE0 N Z L EY1 T ER0 Z +TRANSLITERATED T R AE0 N S L IH1 T ER0 EY2 T IH0 D +TRANSLITERATION T R AE0 N S L IH2 T ER0 EY1 SH AH0 N +TRANSLUCENCE T R AE0 N S L UW1 S AH0 N S +TRANSLUCENCY T R AE0 N S L UW1 S AH0 N S IY0 +TRANSLUCENT T R AE0 N S L UW1 S AH0 N T +TRANSLUCENTLY T R AE0 N S L UW1 S AH0 N T L IY0 +TRANSLUCID T R AE0 N S L UW1 S AH0 D +TRANSLUNAR T R AE0 N S L UW1 N ER0 +TRANSMARINE T R AE0 N Z M ER0 IY1 N +TRANSMIGRATE T R AE0 N Z M AH0 G R EY2 T +TRANSMIGRATED T R AE0 N Z M AH0 G R EY2 T IH0 D +TRANSMIGRATES T R AE0 N Z M AH0 G R EY2 T S +TRANSMIGRATION T R AE0 N Z M AH0 G R EY1 SH AH0 N +TRANSMIGRATIONS T R AE0 N Z M AH0 G R EY1 SH AH0 N Z +TRANSMISSIBLE T R AE0 N S M IH1 S AH0 B AH0 L +TRANSMISSION T R AE0 N S M IH1 SH AH0 N +TRANSMISSION T R AE0 N Z M IH1 SH AH0 N +TRANSMISSIONS T R AE0 N Z M IH1 SH AH0 N Z +TRANSMIT T R AE0 N Z M IH1 T +TRANSMITS T R AE0 N Z M IH1 T S +TRANSMITTABLE T R AE0 N Z M IH1 T AH0 B AH0 L +TRANSMITTAL T R AE0 N S M IH1 T AH0 L +TRANSMITTED T R AE0 N S M IH1 T IH0 D +TRANSMITTED T R AE0 N Z M IH1 T AH0 D +TRANSMITTER T R AE0 N S M IH1 T ER0 +TRANSMITTERS T R AE0 N S M IH1 T ER0 Z +TRANSMITTING T R AE0 N S M IH1 T IH0 NG +TRANSMOGRIFICATION T R AE0 N Z M OW0 G R IH0 F IH0 K EY1 SH AH0 N +TRANSMOGRIFIED T R AE0 N Z M OW0 G R AH0 F AY2 D +TRANSMUTABLE T R AE0 N S M Y UW1 T AH0 B AH0 L +TRANSMUTATION T R AE0 N S M Y UW0 T EY1 SH AH0 N +TRANSMUTATIONS T R AE0 N S M Y UW0 T EY1 SH AH0 N Z +TRANSMUTE T R AE0 N S M Y UW2 T +TRANSMUTED T R AE0 N S M Y UW2 T IH0 D +TRANSMUTES T R AE0 N S M Y UW2 T S +TRANSMUTING T R AE0 N S M Y UW2 T IH0 NG +TRANSNATIONAL T R AE0 N S N AE1 SH AH0 N AH0 L +TRANSOCEANIC T R AE2 N Z OW0 SH IY0 AE1 N IH0 K +TRANSOM T R AE1 N S AH0 M +TRANSOME T R AE1 N S AH0 M +TRANSOMS T R AE1 N S AH0 M Z +TRANSPARENCE T R AE0 N S P EH1 R AH0 N S +TRANSPARENCIES T R AE0 N S P EH1 R AH0 N S IY0 Z +TRANSPARENCY T R AE0 N S P EH1 R AH0 N S IY0 +TRANSPARENT T R AE0 N S P EH1 R AH0 N T +TRANSPARENTLY T R AE0 N S P EH1 R AH0 N T L IY0 +TRANSPIERCE T R AE0 N S P IH1 R S +TRANSPIERCED T R AE0 N S P IH1 R S T +TRANSPIRATION T R AE0 N S P ER0 EY1 SH AH0 N +TRANSPIRE T R AE0 N S P AY1 ER0 +TRANSPIRED T R AE0 N S P AY1 ER0 D +TRANSPIRES T R AE0 N S P AY1 ER0 Z +TRANSPIRING T R AE0 N S P AY1 ER0 IH0 NG +TRANSPLANT T R AE0 N S P L AE1 N T +TRANSPLANTATION T R AE2 N Z P L AE0 N T EY1 SH AH0 N +TRANSPLANTATIONS T R AE2 N Z P L AE0 N T EY1 SH AH0 N Z +TRANSPLANTED T R AE0 N S P L AE1 N T IH0 D +TRANSPLANTING T R AE0 N S P L AE1 N T IH0 NG +TRANSPLANTS T R AE0 N S P L AE1 N T S +TRANSPONTINE T R AE0 N S P AA1 N T IY2 N +TRANSPORT T R AE0 N S P AO1 R T +TRANSPORT T R AE1 N S P AO0 R T +TRANSPORT'S T R AE0 N S P AO1 R T S +TRANSPORTABLE T R AE0 N S P AO1 R T AH0 B AH0 L +TRANSPORTATION T R AE2 N S P ER0 T EY1 SH AH0 N +TRANSPORTATIONS T R AE2 N S P ER0 T EY1 SH AH0 N Z +TRANSPORTED T R AE0 N S P AO1 R T AH0 D +TRANSPORTER T R AE0 N S P AO1 R T ER0 +TRANSPORTING T R AE0 N S P AO1 R T IH0 NG +TRANSPORTS T R AE0 N S P AO1 R T S +TRANSPORTS T R AE1 N S P AO0 R T S +TRANSPOSE T R AE0 N S P OW1 Z +TRANSPOSED T R AE0 N S P OW1 Z D +TRANSPOSES T R AE0 N S P OW1 Z IH0 Z +TRANSPOSING T R AE0 N S P OW1 Z IH0 NG +TRANSPOSITION T R AE0 N S P AH0 Z IH1 SH AH0 N +TRANSPOSITIONS T R AE0 N S P AH0 Z IH1 SH AH0 N Z +TRANSSHIP T R AE0 N SH IH2 P +TRANSSHIPMENT T R AE0 N SH IH1 P M AH0 N T +TRANSSHIPPED T R AE0 N SH IH1 P T +TRANSUBSTANTIATION T R AE0 N S AH0 B S T AE2 N SH IY0 EY1 SH AH0 N +TRANSVAAL T R AE0 N Z V AA1 L +TRANSVAALERS T R AE0 N Z V AA1 L ER0 Z +TRANSVALUATION T R AE0 N Z V AE2 L Y UW0 EY1 SH AH0 N +TRANSVERSAL T R AE0 N Z V ER1 S AH0 L +TRANSVERSE T R AE0 N Z V ER1 S +TRANSVERSELY T R AE0 N Z V ER1 S L IY0 +TRANSVERSLY T R AE0 N Z V ER1 S L IY0 +TRANSYLVANIA T R AE2 N S IH0 L V EY1 N Y AH0 +TRANSYLVANIA T R AE2 N Z IY0 L V EY1 N IY0 AH0 +TRANSYLVANIAN T R AE2 N S IH0 L V EY1 N IY0 AH0 N +TRANSYLVANIANS T R AE2 N S IH0 L V EY1 N IY0 AH0 N Z +TRANT T R AE1 N T +TRANT'S T R AE1 N T S +TRANTA T R AE1 N T AH0 +TRANTER T R AE1 N T ER0 +TRANTER'S T R AE1 N T ER0 Z +TRANTHAM T R AE1 N TH AH0 M +TRANTO T R AE1 N T OW0 +TRANTOW T R AE1 N T OW0 +TRANTOWITZ T R AE1 N T AH0 W IH0 T S +TRANTRIDGE T R AE1 N T R IH0 JH +TRANYON T R AE1 N Y AH0 N +TRAP T R AE1 P +TRAP'S T R AE1 P S +TRAPANI T R AA0 P AA1 N IY0 +TRAPBOIS T R AE1 P B W AA1 +TRAPDOOR T R AE1 P D AO2 R +TRAPDOORS T R AE1 P D AO2 R Z +TRAPESIN T R AA1 P EH0 S IH0 N +TRAPESING T R AH0 P EH1 Z IH0 NG +TRAPEZE T R AH0 P IY1 Z +TRAPEZES T R AH0 P EH1 Z IH0 Z +TRAPEZIUM T R AA1 P EH0 Z IY0 AH0 M +TRAPEZOID T R AH0 P EH1 Z OY2 D +TRAPEZUS T R AH0 P EH1 Z AH0 S +TRAPLINE T R AE1 P L IH0 N +TRAPP T R AE1 P +TRAPPE T R AE1 P +TRAPPED T R AE1 P T +TRAPPEME T R AE1 P M +TRAPPER T R AE1 P ER0 +TRAPPER'S T R AE1 P ER0 Z +TRAPPERS T R AE1 P ER0 Z +TRAPPIN T R AE1 P IH0 N +TRAPPING T R AE1 P IH0 NG +TRAPPINGS T R AE1 P IH0 NG Z +TRAPPIST T R AE1 P IH0 S T +TRAPPISTS T R AE1 P IH0 S T S +TRAPS T R AE1 P S +TRAPSE T R AE1 P S +TRAPT T R AE1 P T +TRAQUAIR T R AE1 K W EH1 R +TRAR T R AA2 R +TRASCOTT T R AE1 S K AH0 T +TRASH T R AE1 SH +TRASHY T R AE1 SH IY0 +TRASIMENE T R AE1 S IH0 M IY2 N +TRASIMENUS T R AH0 S IH1 M IH0 N AH0 S +TRASK T R AE1 S K +TRASK'S T R AE1 S K S +TRASKON T R AE1 S AA0 N +TRAST T R AE1 S T +TRASTAMARE T R AE1 S T AH0 M EH1 R +TRASTEVERE T R AE1 S T EY0 V EH1 R EY0 +TRATE T R EY2 T +TRATED T R EY2 T IH0 D +TRATEMENT T R EY1 T M AH0 N T +TRATH T R AH0 TH +TRATTORIA T R AA0 T AO1 R IY0 AH0 +TRAUBEL T R AW1 B AH0 L +TRAUE T R AW1 +TRAUGOTT T R AW1 G AH0 T +TRAUM T R AO1 M +TRAUMA T R AO1 M AH0 +TRAUMATIC T R AO0 M AE1 T IH0 K +TRAUMEREI T R AO1 M EH0 R EY2 +TRAUN T R AO1 N +TRAUNBERG T R AO1 N B ER0 G +TRAUT T R AO1 T +TRAUTTENBERG T R AW1 T AH0 N B ER0 G +TRAUTVETTER T R AW1 T V EH1 T ER0 +TRAV'LLER T R AA0 V L ER0 +TRAVAIL T R AH0 V EY1 L +TRAVAILE T R AH0 V EY1 L +TRAVAILED T R AH0 V EY1 L D +TRAVAILETH T R AH0 V EY1 L EH2 TH +TRAVAILING T R AH0 V EY1 L IH0 NG +TRAVAILLE T R AH0 V EY1 L +TRAVAILLER T R AH0 V EY1 L ER0 +TRAVAILLEURS T R AH0 V EY1 L ER1 Z +TRAVAILS T R AH0 V EY1 L Z +TRAVANCORE T R AA0 V AA1 N K AO2 R +TRAVANN T R AH0 V AE1 N +TRAVAUX T R AH0 V OW1 +TRAVE T R AE1 V +TRAVEL T R AE1 V AH0 L +TRAVEL'D T R AE1 V AH0 L D +TRAVEL'S T R AE1 V AH0 L Z +TRAVELED T R AE1 V AH0 L D +TRAVELER T R AE1 V AH0 L ER0 +TRAVELER T R AE1 V L ER0 +TRAVELER'S T R AE1 V AH0 L ER0 Z +TRAVELERS T R AE1 V AH0 L ER0 Z +TRAVELERS T R AE1 V L ER0 Z +TRAVELIN T R AE1 V AH0 L IH0 N +TRAVELING T R AE1 V AH0 L IH0 NG +TRAVELING T R AE1 V L IH0 NG +TRAVELL T R AE1 V AH0 L +TRAVELL'D T R AE1 V AH0 L D +TRAVELLED T R AE1 V AH0 L D +TRAVELLER T R AE1 V AH0 L ER0 +TRAVELLER'S T R AE1 V AH0 L ER0 Z +TRAVELLERS T R AE1 V AH0 L ER0 Z +TRAVELLIN T R AE1 V AH0 L IH0 N +TRAVELLING T R AE1 V AH0 L IH0 NG +TRAVELLING T R AE1 V L IH0 NG +TRAVELLINGS T R AE1 V L IH0 NG Z +TRAVELOGUE T R AE1 V AH0 L AO2 G +TRAVELS T R AE1 V AH0 L Z +TRAVENION T R AE1 V AH0 N Y AH0 N +TRAVENION'S T R AA0 V EH1 N IY0 AH0 N Z +TRAVERS T R AE1 V ER0 Z +TRAVERS'S T R AE1 V ER0 Z IH0 Z +TRAVERSABLE T R AH0 V ER1 S AH0 B AH0 L +TRAVERSE T R AE1 V ER0 S +TRAVERSE T R AH0 V ER1 S +TRAVERSE'S T R AH0 V ER1 S IH0 Z +TRAVERSED T R AE1 V ER0 S T +TRAVERSED T R AH0 V ER1 S T +TRAVERSER T R AH0 V ER1 S ER0 +TRAVERSERS T R AH0 V ER1 S ER0 Z +TRAVERSES T R AH0 V ER1 S IH0 Z +TRAVERSING T R AH0 V ER1 S IH0 NG +TRAVERTINE T R AE1 V ER0 T AY2 N +TRAVESIA T R AE1 V AH0 S IY0 AH0 +TRAVESTIE T R AE1 V AH0 S T IY0 +TRAVESTIED T R AE1 V AH0 S T IY0 D +TRAVESTIES T R AE1 V AH0 S T IY0 Z +TRAVESTY T R AE1 V AH0 S T IY0 +TRAVESTYING T R AE1 V AH0 S T IY0 IH0 NG +TRAVIATA T R AA0 V IY0 AA1 T AH0 +TRAVICE T R AE1 V AH0 S +TRAVILLA T R AA0 V IH1 L AH0 +TRAVILLA'S T R AA0 V IH1 L AH0 Z +TRAVILLAS T R AA0 V IH1 L AH0 Z +TRAVINO T R AA0 V IY1 N OW0 +TRAVIS T R AE1 V IH0 S +TRAVIS'S T R AE1 V IH0 S IH0 Z +TRAVNIK T R AE1 V N IH0 K +TRAVOIS T R AH0 V W AA1 +TRAVOY T R AH0 V OY1 +TRAWL T R AO1 L +TRAWLER T R AO1 L ER0 +TRAWLER'S T R AO1 L ER0 Z +TRAWLERS T R AO1 L ER0 Z +TRAWLING T R AO1 L IH0 NG +TRAWLS T R AO1 L Z +TRAXALLA T R AE1 K S AH0 L AH0 +TRAY T R EY1 +TRAY'S T R EY1 Z +TRAYFUL T R EY1 F AH0 L +TRAYLOR T R EY1 L ER0 +TRAYLORS T R EY1 L ER0 Z +TRAYNE T R EY1 N +TRAYNOR T R EY1 N ER0 +TRAYNOR'S T R EY1 N ER0 Z +TRAYS T R EY1 Z +TRAYTOR T R EY1 T ER0 +TRE T R EY1 +TREACH'ROUS T R EH1 CH ER0 AH0 S +TREACHERIES T R EH1 CH ER0 IY0 Z +TREACHEROUS T R EH1 CH ER0 AH0 S +TREACHEROUSLY T R EH1 CH ER0 AH0 S L IY0 +TREACHERY T R EH1 CH ER0 IY0 +TREACLE T R IY1 K AH0 L +TREACLY T R EH1 CH L IY0 +TREAD T R EH1 D +TREAD'ST T R EH1 D S T +TREADED T R EH1 D IH0 D +TREADER T R EH1 D ER0 +TREADERS T R EH1 D ER0 Z +TREADEST T R EH1 D AH0 S T +TREADETH T R EH1 D IH0 TH +TREADIN T R EH1 D IY0 N +TREADING T R EH1 D IH0 NG +TREADLE T R EH1 D AH0 L +TREADLES T R EH1 D AH0 L Z +TREADMAN T R EH1 D M AH0 N +TREADMILL T R EH1 D M IH2 L +TREADMILLS T R EH1 D M IH2 L Z +TREADS T R EH1 D Z +TREADWAY T R EH1 D W EY2 +TREADWELL T R EH1 D W EH2 L +TREADWELL'S T R EH1 D W EH2 L Z +TREADWELLS T R EH1 D W EH2 L Z +TREADWYN T R EH1 D W IH0 N +TREAS T R IY1 Z +TREASON T R IY1 Z AH0 N +TREASON'S T R IY1 Z AH0 N Z +TREASONABLE T R IY1 Z AH0 N AH0 B AH0 L +TREASONABLY T R IY1 Z AH0 N AH0 B L IY0 +TREASONOUS T R IY1 Z AH0 N AH0 S +TREASONS T R IY1 Z AH0 N Z +TREASUR'D T R EH1 ZH ER0 D +TREASURE T R EH1 ZH ER0 +TREASURE'S T R EH1 ZH ER0 Z +TREASURED T R EH1 ZH ER0 D +TREASURER T R EH1 ZH ER0 ER0 +TREASURER'S T R EH1 ZH ER0 ER0 Z +TREASURERS T R EH1 ZH ER0 ER0 Z +TREASURERSHIP T R EH1 ZH ER0 ER0 SH IH2 P +TREASURES T R EH1 ZH ER0 Z +TREASURIES T R EH1 ZH ER0 IY0 Z +TREASURING T R EH1 ZH ER0 IH0 NG +TREASURY T R EH1 ZH ER0 IY0 +TREASURY'S T R EH1 ZH ER0 IY0 Z +TREAT T R IY1 T +TREAT'S T R IY1 T S +TREATED T R IY1 T AH0 D +TREATED T R IY1 T IH0 D +TREATETH T R IY1 T IH0 TH +TREATIES T R IY1 T IY0 Z +TREATIN T R IY1 T AH0 N +TREATING T R IY1 T IH0 NG +TREATISE T R IY1 T AH0 S +TREATISES T R IY1 T AH0 S AH0 Z +TREATMENT T R IY1 T M AH0 N T +TREATMENTS T R IY1 T M AH0 N T S +TREATS T R IY1 T S +TREATY T R IY1 T IY0 +TREATY'S T R IY1 T IY0 Z +TREB T R EH1 B +TREBASI T R EH0 B AA1 S IY0 +TREBASSOF T R EH0 B AA1 S AH0 F +TREBASSOF'S T R EH0 B AA1 S OW0 F S +TREBATIUS T R EH0 B EY1 SH AH0 S +TREBELLIUS T R EH0 B EH1 L Y AH0 S +TREBERON T R EH0 B EH1 R AH0 N +TREBIA T R EH0 B IY0 AH0 +TREBINJE T R IH0 B IH1 N JH +TREBISKEN T R IH0 B IH1 S K AH0 N +TREBIZOND T R EH1 B IH0 Z AA1 N D +TREBIZONDE T R EH1 B IH0 Z AA1 N D +TREBLE T R EH1 B AH0 L +TREBLE'S T R EH1 B AH0 L Z +TREBLED T R EH1 B AH0 L D +TREBLES T R EH1 B AH0 L Z +TREBLING T R EH1 B AH0 L IH0 NG +TREBLY T R EH0 B L IY0 +TREBON T R EH1 B AH0 N +TREBONIUS T R EH0 B OW1 N IY0 AH0 S +TREBOOZE T R IH0 B UW1 Z +TREBY T R B AY1 +TRECARREL T R IH0 K EH1 R AH0 L +TRED T R D +TREDEGAR T R EH1 D IH0 G ER0 +TREDEGAR'S T R EH1 D IH0 G ER0 Z +TREDGOLD T R EH1 D G OW2 L D +TREDGOLD'S T R EH1 D G OW2 L D Z +TREDNOKE T R EH1 N OW1 K +TREDOWEN T R EH1 D AW1 AH0 N +TREDS T R EH1 D Z +TREDWAY T R EH1 D W EY2 +TREDWELL T R EH1 D W EH2 L +TREDWORTH T R EH1 D W ER2 TH +TREE T R IY1 +TREE'D T R IY2 D +TREE'S T R IY2 Z +TREED T R IY1 D +TREEING T R IY1 IH0 NG +TREELESS T R IY1 L AH0 S +TREEN T R IY1 N +TREES T R IY1 Z +TREETON T R IY1 T AH0 N +TREETOP T R IY1 T AO2 P +TREETOPS T R IY1 T AO2 P S +TREETRUNKS T R IY1 T R AH1 NG K S +TREFETHAN T R EH1 F IH0 TH AH0 N +TREFETHEN T R EH1 F IH0 TH AH0 N +TREFETHEN'S T R EH1 F IH0 TH AH0 N Z +TREFF T R EH1 F +TREFFINGER T R EH1 F IH0 NG ER0 +TREFFINGER'S T R EH1 F IH0 NG ER0 Z +TREFFRY T R EH1 F R IY0 +TREFFRY'S T R EH1 F R IY0 Z +TREFFY T R EH1 F IY0 +TREFFY'S T R EH1 F IY0 Z +TREFOIL T R EH1 F OY2 L +TREFOIL'S T R EH1 F OY2 L Z +TREFOILS T R EH1 F OY2 L Z +TREFOYLE T R EH1 F OY0 L +TREFUSIS T R EH1 F Y UW0 S IH0 S +TREFUSIS'S T R EH1 F Y UW0 S IH0 S IH0 Z +TREGANNON T R EH1 G AE1 N AH0 N +TREGAR T R EH1 G ER0 +TREGARRICK T R EH1 G EH1 R IH0 K +TREGARS T R EH1 G ER0 Z +TREGARTHEN T R EH1 G AA2 R TH AH0 N +TREGARTHEN'S T R EH1 G AA2 R TH AH0 N Z +TREGEAGLE T R EH1 G IY1 G AH0 L +TREGEAR T R EH1 G IH2 R +TREGEAR'S T R EH1 G IH1 R Z +TREGELLIS T R EH1 G IH0 L IH0 S +TREGENNA T R EH0 G EH1 N AH0 +TREGENNA'S T R EH0 G EH1 N AH0 Z +TREGENNAS T R EH0 G EH1 N AH0 Z +TREGERAINT T R EH1 G ER0 EY1 N T +TREGETHNER T R EH1 G IH0 T N ER0 +TREGO T R EH1 G OW0 +TREGO'S T R EH1 G OW0 Z +TREGONNING T R EH1 G AH0 N IH0 NG +TREGONY T R EH1 G AH0 N IY0 +TREGONYS T R EH1 G AH0 N IY0 Z +TREGUIER T R EY1 G W IY0 ER0 +TREGUNC T R EH1 G AH2 NG K +TREGUNTER T R EH1 G AH1 N T ER0 +TREHARNE T R EH1 HH AA0 R N +TREHARNE'S T R EH1 HH AA0 R N Z +TREHAWKE T R EH1 HH AO2 K +TREHEARN T R IH0 HH ER1 N +TREHEARNE T R IH0 HH ER1 N +TREHERNE T R EH1 HH ER1 N +TREHERNE'S T R EH1 HH ER0 N Z +TREIG T R IY1 G +TREILHARD T R AY1 L HH AA2 R D +TREILLE T R AY1 L +TREISURE T R EH1 ZH ER0 +TREITSCHKE T R AY1 CH K +TREIZE T R AY1 Z +TREJAGO T R EY1 Y AA1 G OW0 +TREK T R EH1 K +TREKED T R EH1 K T +TREKKED T R EH1 K T +TREKKERS T R EH1 K ER0 Z +TREKKING T R EH1 K IH0 NG +TREKS T R EH1 K S +TREKSCHUIT T R EH1 K S CH UW0 IH0 T +TREKSCHUYT T R EH1 K S K AY1 T +TRELASKE T R EH1 L EY1 S K +TRELAWNEY T R EH1 L AO1 N IY0 +TRELAWNEY'S T R EH1 L AW1 N IY0 Z +TRELAWNY T R EH1 L AO2 N IY0 +TRELAWNY'S T R EH1 L AO2 N IY0 Z +TRELEAVEN T R EH0 L EH1 V AH0 N +TRELISKEY T R IH0 L IH1 S K IY0 +TRELISS T R EH1 L IH0 S +TRELITZ T R EH1 L IH0 T S +TRELLA T R EH1 L AH0 +TRELLICE T R EH1 L AH0 S +TRELLIS T R EH1 L AH0 S +TRELLISED T R EH1 L AH0 S T +TRELLISES T R EH1 L AH0 S AH0 Z +TRELLISWORK T R EH1 L AH0 S W ER2 K +TRELOAR T R EH1 L AO0 R +TRELUAN T R EH1 L UW0 AA1 N +TRELYON T R EH1 L IY0 AA0 N +TREM T R EH1 M +TREMADOC T R EH1 M AE1 D AH0 K +TREMAIL T R EH1 M EY2 L +TREMAIN T R EH1 M AY0 N +TREMAIN'S T R IH0 M EY1 N Z +TREMAINE T R IH0 M EY1 N +TREMAINE'S T R IH0 M EY1 N Z +TREMAYNE T R EH1 M EY0 N +TREMAZAN T R EH1 M AA0 Z AA1 N +TREMBATH T R EH1 M B AH0 TH +TREMBLANT T R EH1 M B L AH0 N T +TREMBLAY T R EH1 M B L EY0 +TREMBLAYE T R EH1 M B L EY1 +TREMBLE T R EH1 M B AH0 L +TREMBLED T R EH1 M B AH0 L D +TREMBLEMENT T R EH1 M B AH0 L M AH0 N T +TREMBLER T R EH1 M B L ER0 +TREMBLERS T R EH1 M B L ER0 Z +TREMBLES T R EH1 M B AH0 L Z +TREMBLEST T R EH1 M B AH0 L AH0 S T +TREMBLETH T R EH1 M B AH0 L TH +TREMBLETON T R EH1 M B AH0 L T AH0 N +TREMBLEY T R EH1 M B L IY0 +TREMBLIN T R EH1 M B L IH0 N +TREMBLING T R EH1 M B AH0 L IH0 NG +TREMBLING T R EH1 M B L IH0 NG +TREMBLINGLY T R EH1 M B AH0 L IH0 NG L IY0 +TREMBLINGS T R EH1 M B AH0 L IH0 NG Z +TREMBLY T R EH1 M B L IY0 +TREMEAU T R EH1 M OW1 +TREMENDIOUS T R AH0 M EH1 N D IY0 AH0 S +TREMENDOUS T R AH0 M EH1 N D AH0 S +TREMENDOUS T R IH0 M EH1 N D AH0 S +TREMENDOUSLY T R AH0 M EH1 N D AH0 S L IY0 +TREMENDOUSLY T R IH0 M EH1 N D AH0 S L IY0 +TREMENDOUSNESS T R AH0 M EH1 N D AH0 S N AH0 S +TREMENHERE T R EH1 M AH0 N HH IH1 R +TREMENHERE'S T R EH1 M AH0 N HH IH1 R Z +TREMENJOUS T R AH0 M EH1 N JH AH0 S +TREMENJUS T R AH0 M EH1 N JH AH0 S +TREMENS T R EH1 M AH0 N Z +TREMERE T R EH1 M ER0 +TREMLETT T R EH1 M AH0 L IH0 T +TREMNELL T R EH1 M N AH0 L +TREMOILLE T R EH1 M AH0 L +TREMOLINO T R EH1 M OW0 L IY1 N OW0 +TREMOLO T R EH1 M OW0 L OW0 +TREMOLOS T R EH1 M AH0 L OW0 Z +TREMONCEAU T R EY1 M AA0 N S OW1 +TREMONT T R EH1 M AH0 N T +TREMONT'S T R EH1 M AA2 N T S +TREMONTES T R EY1 M AA0 N T EH0 S +TREMONTS T R EY1 M AA0 N T S +TREMOR T R EH1 M ER0 +TREMOREL T R EH1 M ER0 EH1 L +TREMOREL'S T R EH1 M ER0 EH1 L Z +TREMORS T R EH1 M ER0 Z +TREMOUILLE T R EH1 M UW1 IY0 +TREMOUR T R EH1 M UH1 R +TREMOURS T R EH1 M ER0 Z +TREMP T R EH1 M P +TREMPE T R EH1 M P +TREMUKJI T R IH0 M UW0 K JH IY0 +TREMULO T R EH1 M Y AH0 L OW0 +TREMULOUS T R EH1 M Y AH0 L AH0 S +TREMULOUSLY T R EH1 M Y AH0 L AH0 S L IY0 +TREMULOUSNESS T R EH1 M Y AH0 L AH0 S N AH0 S +TRENAILS T R EH1 N EH0 L Z +TRENBY T R EH1 N B IY0 +TRENCH T R EH1 N CH +TRENCH'S T R EH1 N CH IH0 Z +TRENCHANCY T R EH1 N CH AE1 N S IY0 +TRENCHANT T R EH1 N CH AH0 N T +TRENCHANTLY T R EH1 N CH AH0 N T L IY0 +TRENCHARD T R EH1 NG K ER0 D +TRENCHARD'S T R EH1 N CH ER0 D Z +TRENCHARDS T R EH1 N CH ER0 D Z +TRENCHED T R EH1 N CH T +TRENCHER T R EH1 N CH ER0 +TRENCHERMAN T R EH1 N CH ER0 M AH0 N +TRENCHERMEN T R EH1 N CH ER0 M IH0 N +TRENCHERS T R EH1 N CH ER0 Z +TRENCHES T R EH1 N CH IH0 Z +TRENCHING T R EH1 N CH IH0 NG +TRENCHON T R EH1 N CH AH0 N +TRENCHS T R EH1 N CH S +TRENCK T R EH1 NG K +TRENCK'S T R EH1 NG K S +TREND T R EH1 N D +TRENDALL T R EH1 N D AH0 L +TRENDED T R EH1 N D IH0 D +TRENDELLSOHN T R EH1 N D AH0 L S AH0 N +TRENDELLSOHNS T R EH1 N D AH0 L S AH0 N Z +TRENDING T R EH1 N D IH0 NG +TRENDLE T R EH1 N D AH0 L +TRENDLEY T R EH1 N D L IY0 +TRENDON T R EH1 N D AH0 N +TRENDS T R EH1 N D Z +TRENDS T R EH1 N Z +TRENHOLM T R EH1 N HH OW2 L M +TRENHOLME T R EH1 N HH OW2 L M +TRENHOLME'S T R EH1 N HH OW2 L M Z +TRENNAHAN T R EH1 N AH0 HH AE0 N +TRENNAHAN'S T R EH1 N AH0 HH AE0 N Z +TRENOR T R EH1 N ER0 +TRENOR'S T R EH1 N ER0 Z +TRENORS T R EH1 N ER0 Z +TRENOW T R EH1 N OW0 +TRENOWETH T R EH1 N AH0 W EH0 TH +TRENT T R EH1 N T +TRENT'S T R EH1 N T S +TRENTA T R EH1 N T AH0 +TRENTA'S T R EH1 N T AH0 Z +TRENTANOVE T R EH1 N T AH0 N OW2 V +TRENTE T R EH1 N T EY0 +TRENTHAM T R EH1 N TH AH0 M +TRENTHAM'S T R EH1 N TH AH0 M Z +TRENTINO T R EH0 N T IY1 N OW0 +TRENTON T R EH1 N T AH0 N +TRENTON'S T R EH1 N T AH0 N Z +TRENTS T R EH1 N T S +TRENWITH T R EH1 N W IH2 TH +TREPAN T R EH1 P AH0 N +TREPANG T R EH1 P AH0 NG +TREPANNED T R EH1 P AE1 N D +TREPANNING T R EH1 P AE1 N IH0 NG +TREPHINE T R EH1 F IY2 N +TREPHINED T R IH0 F AY1 N D +TREPHINING T R EH1 F AY2 N IH0 NG +TREPIDATION T R EH2 P IH0 D EY1 SH AH0 N +TREPIDATIONS T R EH1 P AH0 D EY1 SH AH0 N Z +TREPLIEFF T R EH1 P L IY2 F +TREPLIN T R EH1 P L IH0 N +TREPORT T R EH1 P AO2 R T +TREPTOW T R EH1 P T OW0 +TRES T ER0 Z +TRESAWNA T R S AO1 N AH0 +TRESCO T R EH1 S K OW0 +TRESCO'S T R EH1 S K OW0 Z +TRESCORRE T R EH1 S K AO1 R IY0 +TRESCORRE'S T R EH1 S K AO1 R IY0 Z +TRESCOTT T R EH1 S K AH0 T +TRESCOTT'S T R EH1 S K AH0 T S +TRESHAM T R EH1 SH AH0 M +TRESHAM'S T R EH1 SH AH0 M Z +TRESILLIAN T R EH1 S IH1 L IY0 AH0 N +TRESILLO T R EH1 S IH1 L OW0 +TRESILYAN T R IH0 Z IH1 L IY0 AH0 N +TRESILYAN'S T R IH0 Z IH1 L IY0 AH0 N Z +TRESLER T R EH1 S L ER0 +TRESLER'S T R EH1 S L ER0 Z +TRESLONG T R EH0 S L AO1 NG +TRESOR T R EH1 S ER0 +TRESPASS T R EH1 S P AE2 S +TRESPASS T R EH1 S P AH0 S +TRESPASSED T R EH1 S P AE2 S T +TRESPASSER T R EH1 S P AH0 S ER0 +TRESPASSERS T R EH1 S P AH0 S ER0 Z +TRESPASSES T R EH1 S P AE2 S IH0 Z +TRESPASSIN T R EH1 S P AE2 S IH0 N +TRESPASSING T R EH1 S P AE2 S IH0 NG +TRESPASSING T R EH1 S P AH0 S IH0 NG +TRESQU T R EH1 S K +TRESS T R EH1 S +TRESSA T R EH1 S AH0 +TRESSA'S T R EH1 S AH0 Z +TRESSADY T R EH1 S AH0 D IY0 +TRESSADY'S T R EH1 S AH0 D IY0 Z +TRESSAMER T R EH1 S AH0 M ER0 +TRESSAMER'S T R EH1 S AH0 M ER0 Z +TRESSAN T R EH1 S AH0 N +TRESSED T R EH1 S T +TRESSEL T R EH1 S AH0 L +TRESSELS T R EH1 S AH0 L Z +TRESSES T R EH1 S IH0 Z +TRESSIDAR T R EH1 S IH0 D AA1 R +TRESSIDY T R EH1 S IH0 D IY0 +TRESSILIAN T R EH0 S IH1 L Y AH0 N +TRESSILIAN'S T R EH0 S IH1 L Y AH0 N Z +TRESSITER T R EH1 S IH0 T ER0 +TRESTLE T R EH1 S AH0 L +TRESTLES T R EH1 S AH0 L Z +TRESUMPSCOTT T R IH0 Z AH1 M P S K AH0 T +TRET T R EH1 T +TRETA T R EY1 T AH0 +TRETHAWAY T R EH1 TH AH0 W EY2 +TRETHERICK T R EH1 DH ER0 IH0 K +TRETHERICK'S T R EH1 DH ER0 IH0 K S +TRETHEWAY T R EH1 DH W EY0 +TRETHICK T R EH1 TH IH0 K +TRETHICK'S T R EH1 TH IH0 K S +TRETHOWEN T R EH1 TH AW1 AH0 N +TRETS T R AH0 T S +TRETTON T R EH1 T AH0 N +TREU T R UW1 +TREUE T R UW1 +TREUHAFT T R UW2 HH AE1 F T +TREUMANN T R UW1 M AH0 N +TREURENBERG T R UW1 R AH0 N B ER0 G +TREUTH T R UW1 TH +TREV T R AH0 V +TREVANION T R AH0 V AE1 N Y AH0 N +TREVANION'S T R AH0 V AE1 N Y AH0 N Z +TREVANIONS T R AH0 V AE1 N Y AH0 N Z +TREVANNION T R EH0 V AA1 N Y AH0 N +TREVANNION'S T R EH0 V AA1 N Y AH0 N Z +TREVARTHEN T R EH0 V AA1 R TH AH0 N +TREVE T R IY1 V +TREVEC T R AH0 V EH1 K +TREVECCA T R AH0 V EH1 K AH0 +TREVELE T R AH0 V EH1 L +TREVELLE T R AH0 V EH1 L +TREVELLYAN T R EH1 V AH0 L IY0 AH0 N +TREVELLYAN'S T R AH0 V EH1 L IY0 EY2 N Z +TREVELLYN T R AH0 V EH1 L IH0 N +TREVELYAN T R AH0 V EH1 L Y AH0 N +TREVELYAN'S T R AH0 V EH1 L Y AH0 N Z +TREVENA T R EH1 V IH0 N AH0 +TREVENNA T R AH0 V EH1 N AH0 +TREVENNACK T R AH0 V EH1 N AH0 K +TREVENNACK'S T R AH0 V EH1 N AH0 K S +TREVENNACKS T R AH0 V EH1 N AE2 K S +TREVERT T R EH1 V ER0 T +TREVERT'S T R EH1 V ER0 T S +TREVERTON T R EH1 V ER0 T AH0 N +TREVERTON'S T R EH1 V ER0 T AH0 N Z +TREVES T R IY1 V Z +TREVES'S T R AH0 V Z IH0 Z +TREVET T R AH0 V EH1 T +TREVETHICK T R AH0 V EH1 TH IH0 K +TREVETHICK'S T R AH0 V EH1 TH IH0 K S +TREVETHLAN T R AH0 V EH1 TH L AH0 N +TREVETHLAN'S T R AH0 V EH1 TH L AH0 N Z +TREVI T R EH0 V IY1 +TREVIGNAC T R AH0 V IH1 G N AH0 K +TREVILIAN T R AH0 V IH1 L Y AH0 N +TREVILLE T R AH0 V IH2 L +TREVILLE'S T R AH0 V IH2 L Z +TREVILLIAN T R EH0 V IH1 L Y AH0 N +TREVINO T R AH0 V IY1 N OW0 +TREVIS T R EH1 V IH0 S +TREVISA T R EH0 V IY1 S AH0 +TREVISA'S T R AH0 V IY1 Z AH0 Z +TREVISE T R EH0 V IY1 S +TREVISE'S T R EH0 V IY1 S IH0 Z +TREVISO T R EH0 V IY1 S OW0 +TREVISON T R EH1 V IH0 S AH0 N +TREVISON'S T R EH1 V IH0 S AH0 N Z +TREVITHICK T R EH0 V IY1 TH IH0 K +TREVITT T R EH1 V IH0 T +TREVLYN T R EH1 V L AH0 N +TREVLYN'S T R EH1 V L AH0 N Z +TREVLYNS T R EH1 V L AH0 N Z +TREVNA T R AH0 V N AH0 +TREVOR T R EH1 V ER0 +TREVOR'S T R EH1 V ER0 Z +TREVORS T R EH1 V ER0 Z +TREVORS'S T R EH1 V ER0 Z IH0 Z +TREVOSE T R EY1 V OW0 S +TREVOUX T R EH1 V UW1 +TREVYLLIAN T R AH0 V IH0 L IY0 AH0 N +TREW T R UW1 +TREWE T R UW1 +TREWHELLA T R IY1 HH W EH1 L AH0 +TREWIN T R UW1 IH0 N +TREWINION T R IY1 W AY0 N Y AH0 N +TREWINNARD T R UW1 IH0 N ER0 D +TREWITT T R UW1 IH0 T +TREWLY T R UW1 L IY0 +TREWS T R UW1 Z +TREWTH T R UW1 TH +TREY T R EY1 +TREYF T R EY1 F +TREYMES T R EY1 M Z +TREYS T R EY1 Z +TREYSA T R EY1 S AH0 +TREZAC T R EH1 Z AH0 K +TREZE T R EH1 Z +TRI T R AY1 +TRIA T R IY0 AH0 +TRIAD T R AY1 AE2 D +TRIADS T R AY1 AE2 D Z +TRIAL T R AY1 AH0 L +TRIAL T R AY1 L +TRIAL'S T R AY1 AH0 L Z +TRIALL T R AY1 L +TRIALS T R AY1 AH0 L Z +TRIALS T R AY1 L Z +TRIAMOND T R AY1 M AH0 N D +TRIANA T R IY0 AE1 N AH0 +TRIANGLE T R AY1 AE2 NG G AH0 L +TRIANGLES T R AY1 AE2 NG G AH0 L Z +TRIANGULAR T R AY0 AE1 NG G Y AH0 L ER0 +TRIANGULARLY T R AY0 AE1 NG G Y AH0 L ER0 L IY0 +TRIANGULATION T R AY0 AE2 NG G Y UW0 L EY1 SH AH0 N +TRIANON T R AY2 AE1 N AH0 N +TRIANONS T R AY1 AE2 N AH0 N Z +TRIARII T R AY2 AE1 R IY0 +TRIAS T R IY1 AH0 Z +TRIASSIC T R AY2 AE1 S IH2 K +TRIB T R IH1 B +TRIBAL T R AY1 B AH0 L +TRIBB T R IH1 B +TRIBBLE T R IH1 B AH0 L +TRIBBYLATION T R IH1 B AH0 L EY1 SH AH0 N +TRIBE T R AY1 B +TRIBE'S T R AY1 B Z +TRIBEFOLK T R IH0 B EH1 F OW2 K +TRIBELESS T R AY1 B L AH0 S +TRIBES T R AY1 B Z +TRIBESFOLK T R AY1 B Z F OW2 K +TRIBESMAN T R AY1 B Z M AE0 N +TRIBESMEN T R AY1 B Z M IH0 N +TRIBONIAN T R IH2 B AH0 N IY1 AH0 N +TRIBOULET T R AY1 B UW0 L EH1 T +TRIBOULET'S T R AY1 B UW0 L EH1 T S +TRIBU T R IH1 B Y UW0 +TRIBULATION T R IH2 B Y AH0 L EY1 SH AH0 N +TRIBULATIONS T R IH2 B Y AH0 L EY1 SH AH0 N Z +TRIBUNA T R AY2 B Y UW1 N AH0 +TRIBUNAL T R AH0 B Y UW1 N AH0 L +TRIBUNALS T R AY2 B Y UW1 N AH0 L Z +TRIBUNATE T R IH1 B Y UW0 N AH0 T +TRIBUNAUX T R IH1 B Y UW0 N OW1 +TRIBUNE T R IH1 B Y UW0 N +TRIBUNE'S T R IH1 B Y UW0 N Z +TRIBUNES T R IH1 B Y UW0 N Z +TRIBUNESHIP T R IH1 B Y UW0 N SH IH2 P +TRIBUS T R IH1 B AH0 S +TRIBUTA T R IH1 B Y AH0 T AH0 +TRIBUTARIES T R IH1 B Y AH0 T EH2 R IY0 Z +TRIBUTARIOS T R IH1 B Y AH0 T EH2 R IY0 OW0 Z +TRIBUTARY T R IH1 B Y AH0 T EH2 R IY0 +TRIBUTE T R IH1 B Y UW0 T +TRIBUTES T R IH1 B Y UW0 T S +TRIC T R IH0 K +TRICASSE T R IY0 K AA0 S EY1 +TRICE T R AY1 S +TRICED T R AY1 S T +TRICEPS T R AY1 S EH2 P S +TRICERATOPS T R AY2 S EH1 R AH0 T AO2 P S +TRICHET T R IH1 CH IH0 T +TRICHINA T R IH1 CH AY1 N AH0 +TRICHINOPOLI T R IH1 CH IH2 N OW0 P OW1 L IY0 +TRICHINOPOLY T R IH1 CH IH0 N AA1 P AH0 L IY0 +TRICHINOSIS T R IH1 CH IH0 N OW1 S AH0 S +TRICHY T R IH1 CH IY0 +TRICING T R AY1 S IH0 NG +TRICK T R IH1 K +TRICK'D T R IH1 K D +TRICK'S T R IH1 K S +TRICKED T R IH1 K T +TRICKERIES T R IH1 K ER0 IY0 Z +TRICKERY T R IH1 K ER0 IY0 +TRICKEY T R IH1 K IY0 +TRICKIER T R IH1 K IY0 ER0 +TRICKIEST T R IH1 K IY0 AH0 S T +TRICKINESS T R IH1 K IY0 N AH0 S +TRICKING T R IH1 K IH0 NG +TRICKLE T R IH1 K AH0 L +TRICKLED T R IH1 K AH0 L D +TRICKLES T R IH1 K AH0 L Z +TRICKLIN T R IH1 K L IH0 N +TRICKLING T R IH1 K L IH0 NG +TRICKLINGS T R IH1 K L IH0 NG Z +TRICKS T R IH1 K S +TRICKSEY T R IH1 K S IY0 +TRICKSOME T R IH1 K S AH0 M +TRICKSTER T R IH1 K S T ER0 +TRICKSTER'S T R IH1 K S T ER0 Z +TRICKSTERS T R IH1 K S T ER0 Z +TRICKSY T R IH1 K S IY0 +TRICKSY'S T R IH1 K S IY0 Z +TRICKY T R IH1 K IY0 +TRICLINIUM T R AY0 K L IH1 N IY0 AH0 M +TRICOLOR T R AY0 K AH2 L ER0 +TRICOLORED T R IY0 K AH1 L ER0 D +TRICOLOUR T R IY0 K AH1 L ER0 +TRICOLOURED T R IY0 K AH1 L ER0 D +TRICON T R IH1 K AH0 N +TRICORNE T R AY0 K AO2 R N +TRICOT T R AY1 K AA0 T +TRICOTEUSES T R AY0 K AH0 T UW1 Z IH0 Z +TRICOTRIN T R AY0 K AA1 T R IH0 N +TRICYCLE T R IH1 S IH0 K AH0 L +TRICYCLES T R IH1 S IH0 K AH0 L Z +TRIDACNA T R AY0 D AE1 K N AH0 +TRIDDELFITZ T R IH1 D AH0 L F IH0 T S +TRIDDELSITZ T R IH1 D AH0 L S IH0 T S +TRIDE T R AY0 D +TRIDENT T R AY1 D AH0 N T +TRIDENTATA T R AY1 D AH0 N T AA1 T AH0 +TRIDENTINE T R AY1 D AH0 N T AY2 N +TRIDENTS T R AY1 D AH0 N T S +TRIDGE T R IH1 JH +TRIDON T R AY1 D AH0 N +TRIE T R AY1 +TRIED T R AY1 D +TRIEL T R IY0 AH0 L +TRIENNIAL T R AY0 EH1 N IY0 AH0 L +TRIENT T R IY0 AH0 N T +TRIENTJE T R AY1 N T JH +TRIER T R AY1 ER0 +TRIERARCH T R AY1 ER0 AA2 R K +TRIERMAIN T R AY1 ER0 M EY1 N +TRIERS T R AY1 ER0 Z +TRIES T R AY1 Z +TRIEST T R IY0 AH0 S T +TRIESTE T R IY1 S T +TRIETH T R AY1 IH0 TH +TRIEVE T R IY1 V +TRIFFITT T R IH1 F IH0 T +TRIFFITT'S T R IH1 F IH0 T S +TRIFINA T R AY0 F IY1 N AH0 +TRIFLE T R AY1 F AH0 L +TRIFLED T R AY1 F AH0 L D +TRIFLER T R AY1 F AH0 L ER0 +TRIFLERS T R AY1 F AH0 L ER0 Z +TRIFLES T R AY1 F AH0 L Z +TRIFLIN T R AY1 F L IH0 N +TRIFLING T R AY1 F L IH0 NG +TRIFLINGLY T R AY1 F L IH0 NG L IY0 +TRIFLINGS T R AY1 F L IH0 NG Z +TRIFOLIUM T R AY1 F OW2 L IY0 AH0 M +TRIFON T R AY1 F AO0 N +TRIFORIUM T R AY1 F AO2 R IY0 AH0 M +TRIFORMIS T R AY1 F AO1 R M AH0 S +TRIG T R IH1 G +TRIGAMY T R AY0 G AH0 M IY0 +TRIGARTAS T R AY0 G AA0 R T AH0 S +TRIGEDGO T R AY0 G EH1 D G OW2 +TRIGEMINAL T R AY0 JH EH1 M AH0 N AH0 L +TRIGEMINUS T R AY0 G EH0 M IY1 N AH0 S +TRIGG T R IH1 G +TRIGGE T R IH1 G +TRIGGED T R IH1 G D +TRIGGER T R IH1 G ER0 +TRIGGER'S T R IH1 G ER0 Z +TRIGGERED T R IH1 G ER0 D +TRIGGERS T R IH1 G ER0 Z +TRIGGS T R IH1 G Z +TRIGGS'S T R IH1 G Z IH0 Z +TRIGILLGUS T R IH1 JH AH0 L G AH0 S +TRIGLYPHS T R AY0 G L IH1 F S +TRIGONOCEPHALUS T R IY0 G OW1 N OW0 S EH2 F AH0 L AH0 S +TRIGONOMETRICAL T R AY0 G AA2 N AH0 M EH1 T R IH0 K AH0 L +TRIGONOMETRY T R IH1 G AH0 N AA1 M AH0 T R IY0 +TRIGORIN T R AY0 G AO1 R IH0 N +TRIKE T R AY2 K +TRILBY T R IH1 L B IY0 +TRILBY'S T R IH1 L B IY0 Z +TRILE T R AY0 L +TRILITHON T R AY0 L IH1 TH AH0 N +TRILITHONS T R AY0 L IH1 TH AA2 N Z +TRILL T R IH1 L +TRILLED T R IH1 L D +TRILLER T R IH1 L ER0 +TRILLING T R IH1 L IH0 NG +TRILLION T R IH1 L Y AH0 N +TRILLIONS T R IH1 L Y AH0 N Z +TRILLIUM T R IH1 L IY0 AH0 M +TRILLIUMS T R IH1 L IY0 AH0 M Z +TRILLS T R IH1 L Z +TRILOBITE T R IH1 L AH0 B AY2 T +TRILOBITES T R IH1 L AH0 B AY2 T S +TRILOGIES T R IH1 L AH0 JH IY0 Z +TRILOGY T R IH1 L AH0 JH IY0 +TRIM T R IH1 M +TRIM'S T R IH1 M Z +TRIMALCHIO T R IY0 M AA1 L K IY0 OW0 +TRIMALCHIO'S T R IY0 M AA1 L K IY0 OW2 Z +TRIMBLE T R IH1 M B AH0 L +TRIMBLE'S T R IH1 M B AH0 L Z +TRIMETER T R IH1 M AH0 T ER0 +TRIMLEY T R IH1 M L IY0 +TRIMLY T R IH1 M L IY0 +TRIMM T R IH1 M +TRIMM'D T R IH1 M D +TRIMM'S T R IH1 M Z +TRIMMED T R IH1 M D +TRIMMER T R IH1 M ER0 +TRIMMER'S T R IH1 M ER0 Z +TRIMMERS T R IH1 M ER0 Z +TRIMMEST T R IH1 M AH0 S T +TRIMMIN T R IH1 M IH0 N +TRIMMIN'S T R IH1 M IH0 N Z +TRIMMING T R IH1 M IH0 NG +TRIMMINGS T R IH1 M IH0 NG Z +TRIMMINS T R IH1 M IH0 N Z +TRIMMLE T R IH1 M L +TRIMMY T R IH1 M IY0 +TRIMNESS T R IH1 M N AH0 S +TRIMOUILLE T R IH1 M UW1 IY0 +TRIMS T R IH1 M Z +TRIMURTI T R IY0 M UH1 R T IY0 +TRIN T R IH0 N +TRINA T R IY1 N AH0 +TRINA'S T R IY1 N AH0 Z +TRINACRIA T R AY2 N AE1 K R IY0 AH0 +TRINACRIAN T R AY2 N AE1 K R IY0 AH0 N +TRINAL T R AH0 N AH0 L +TRINC T R IH0 NG K +TRINCALO T R IH0 NG K AA1 L OW0 +TRINCOMALEE T R IH0 NG K OW1 M AH0 L IY0 +TRINCULO T R IH0 NG K UW1 L OW0 +TRINDER T R IH1 N D ER0 +TRINE T R AY1 N +TRING T R IH0 NG +TRINGHAM T R IH1 NG HH AE2 M +TRINGLE T R IH1 NG G AH0 L +TRINGLE'S T R IH1 NG L Z +TRINGLES T R IH1 NG G AH0 L Z +TRINGLO T R IH1 NG L OW0 +TRINIDAD T R IH1 N IH0 D AE2 D +TRINIDAD'S T R IH1 N IH0 D AE2 D Z +TRINIDADA T R IH1 N IH0 D AE2 D AH0 +TRINIDADO T R IH1 N IH0 D AE2 D OW0 +TRINITA T R IY0 N IY1 T AH0 +TRINITARIAN T R IH1 N AH0 T EH1 R IY0 AH0 N +TRINITARIANISM T R IH1 N AH0 T EH1 R IY0 AH0 N IH2 Z AH0 M +TRINITARIANS T R IH1 N AH0 T EH1 R IY0 AH0 N Z +TRINITATIS T R IH1 N IH0 T EY1 T AH0 S +TRINITE T R IH1 N IH0 T +TRINITIES T R IH1 N IH0 T IY0 Z +TRINITY T R IH1 N AH0 T IY0 +TRINITY T R IH1 N IH0 T IY0 +TRINITY'S T R IH1 N IH0 T IY0 Z +TRINK T R IH1 NG K +TRINKEN T R IH1 NG K AH0 N +TRINKET T R IH1 NG K AH0 T +TRINKETRY T R IH1 NG K AH0 T R IY0 +TRINKETS T R IH1 NG K AH0 T S +TRINKETT T R IH1 NG K IH0 T +TRINKGELD T R IH1 NG K G EH2 L D +TRINOBANTES T R AY2 N OW1 B AE1 N T S +TRINQUIER T R IH1 N K W IH1 R +TRIO T R IY1 OW2 +TRIOLET T R IY0 OW1 L IH0 T +TRIOLETS T R IY0 OW0 L EY1 Z +TRIOMED T R IY0 OW1 M D +TRIOMPHANTE T R IY0 OW1 M F AE2 N T +TRIOMPHE T R IY0 OW1 M F +TRIONAL T R IY0 OW1 N AH0 L +TRIONFO T R IY0 OW1 N F OW0 +TRIOS T R IY0 OW0 Z +TRIP T R IH1 P +TRIP'S T R IH1 P S +TRIPANG T R AY0 P AA1 NG +TRIPARTITE T R AY0 P AA1 R T AY2 T +TRIPATALY T R AY0 P AA1 T AH0 L IY0 +TRIPE T R AY1 P +TRIPEAUD T R AY1 P OW1 D +TRIPES T R AY1 P S +TRIPHAMMER T R IH1 P HH AE2 M ER0 +TRIPLANE T R IH1 P L EY2 N +TRIPLANES T R IH1 P L EY2 N Z +TRIPLANETARY T R IH1 P L AE1 N AH0 T EH2 R IY0 +TRIPLANETARY'S T R IH1 P L AE1 N AH0 T EH2 R IY0 Z +TRIPLE T R IH1 P AH0 L +TRIPLED T R IH1 P AH0 L D +TRIPLES T R IH1 P AH0 L Z +TRIPLET T R IH1 P L AH0 T +TRIPLET'S T R IH1 P L AH0 T S +TRIPLETS T R IH1 P L AH0 T S +TRIPLETT T R IH1 P L IH0 T +TRIPLEX T R IH0 P L EH1 K S +TRIPLICATE T R IH1 P L IH0 K AH0 T +TRIPLICITY T R IH1 P L IH1 S AH0 T IY0 +TRIPLING T R IH1 P AH0 L IH0 NG +TRIPLING T R IH1 P L IH0 NG +TRIPLY T R IH1 P L IY0 +TRIPOD T R AY1 P AA2 D +TRIPODS T R AY1 P AA2 D Z +TRIPOLI T R IH1 P AH0 L IY0 +TRIPOLIS T R IH1 P AH0 L IH0 S +TRIPOLITAN T R IH1 P AH0 L IH1 T AH0 N +TRIPOLITANS T R IH1 P AH0 L IH1 T AH0 N Z +TRIPOS T R IY1 P OW0 Z +TRIPP T R IH1 P +TRIPP'D T R IH1 P D +TRIPP'S T R IH1 P S +TRIPPE T R IH1 P +TRIPPED T R IH1 P T +TRIPPELLI T R IH1 P EH1 L IY0 +TRIPPER T R IH1 P ER0 +TRIPPERS T R IH1 P ER0 Z +TRIPPET T R IH1 P AH0 T +TRIPPIN T R IH1 P IH0 N +TRIPPING T R IH1 P IH0 NG +TRIPPINGLY T R IH1 P IH0 NG L IY0 +TRIPPINGS T R IH1 P IH0 NG Z +TRIPPLE T R IH1 P AH0 L +TRIPPS T R IH1 P S +TRIPS T R IH1 P S +TRIPT T R IH1 P T +TRIPTOLEMUS T R IH1 P T AA1 L AH0 M AH0 S +TRIPTON T R IH1 P T AH0 N +TRIPTYCH T R IH1 P T IH0 K +TRIREME T R AY1 R IY2 M +TRIREMES T R AY1 R IY2 M Z +TRIRODOV T R IH1 R AH0 D AO2 V +TRIRODOV'S T R IH1 R AH0 D AA0 V Z +TRIS T R IH1 S +TRISAGION T R IH1 Z AH0 JH AH0 N +TRISCOE T R IH1 S K OW0 +TRISCOE'S T R IH1 S K OW0 Z +TRISCOES T R IH1 S K OW0 Z +TRISMEGISTUS T R IH1 S M IH0 JH IY1 S T AH0 S +TRISMUS T R IH1 Z M AH0 S +TRISSOTIN T R IH0 S OW1 T IH0 N +TRIST T R AY1 S T +TRISTAM T R IH1 S T AH0 M +TRISTAN T R IH1 S T AE2 N +TRISTAN'S T R IH1 S T AH0 N Z +TRISTE T R AY1 S T IY0 +TRISTEMENT T R IH1 S T AH0 M AH0 N T +TRISTES T R AY1 S T IY0 Z +TRISTESSE T R AY1 S T EH0 S +TRISTEZA T R AY1 S T EY1 Z AH0 +TRISTFUL T R IH1 S T F AH0 L +TRISTIA T R IH1 S T IY0 AH0 +TRISTIS T R IH1 S T IH0 S +TRISTRAM T R IH1 S T R AH0 M +TRISTRAM'S T R IH1 S T R AH0 M Z +TRISTRAMS T R IH1 S T R AH0 M Z +TRISTRAN T R IH1 S T R AH0 N +TRISTREM T R IH1 S T R IH0 M +TRISTREM'S T R IH1 S T R AH0 M Z +TRISYLLABIC T R AY1 S IH0 L AE1 B IH0 K +TRISYLLABLE T R AY1 S IH1 L AH0 B AH0 L +TRISYSTEM T R AY1 S IH2 S T AH0 M +TRIT T R IH1 T +TRITE T R AY1 T +TRITELY T R AY1 T L IY0 +TRITENESS T R AY1 T N AH0 S +TRITEST T R AY1 T IH0 S T +TRITILL T R IH1 T AH0 L +TRITON T R AY1 T AH0 N +TRITON'S T R AY1 T AH0 N Z +TRITONIA T R AY1 T AH0 N IY0 AH0 +TRITONS T R AY1 T AH0 N Z +TRITTON T R IH1 T AH0 N +TRITU T R IY1 CH UW0 +TRITURATE T R IH1 CH ER0 AH0 T +TRITURATED T R IY1 CH ER0 EY2 T IH0 D +TRITURATION T R IH0 CH UH0 R EY1 SH AH0 N +TRIUM T R AY1 AH0 M +TRIUMPH T R AY1 AH0 M F +TRIUMPH'D T R AY1 AH0 M F D +TRIUMPH'S T R AY1 AH0 M F S +TRIUMPHAL T R AY0 AH1 M F AH0 L +TRIUMPHANS T R AY0 AH1 M F AH0 N Z +TRIUMPHANT T R AY0 AH1 M F AH0 N T +TRIUMPHANTLY T R AY0 AH1 M F AH0 N T L IY0 +TRIUMPHATOR T R AY1 AH0 M F EY2 T ER0 +TRIUMPHE T R AY1 AH0 M F +TRIUMPHED T R AY1 AH0 M F T +TRIUMPHER T R AY1 AH0 M F ER0 +TRIUMPHING T R AY1 AH0 M F IH0 NG +TRIUMPHS T R AY1 AH0 M F S +TRIUMVIR T R AY0 AH1 M V ER0 +TRIUMVIRATE T R AY0 AH1 M V ER0 AH0 T +TRIUMVIRI T R AY0 AH1 M V ER0 IY0 +TRIUMVIRS T R AY0 AH1 M V ER0 Z +TRIUNE T R AY1 AH0 N +TRIUS T R AY1 AH0 S +TRIVET T R IH1 V AH0 T +TRIVETT T R IH1 V IH0 T +TRIVETT'S T R IH0 V EH1 T S +TRIVIA T R IH1 V IY0 AH0 +TRIVIAL T R IH1 V IY0 AH0 L +TRIVIALITIES T R IH1 V IY0 AE1 L IH0 T IY0 Z +TRIVIALITY T R IH2 V IY0 AE1 L AH0 T IY0 +TRIVIALLY T R IH1 V IY0 AH0 L IY0 +TRIVIUM T R IH1 V IY0 AH0 M +TRIVULCE T R IH1 V AH1 L S +TRIX T R IH1 K S +TRIX'S T R IH1 K S IH0 Z +TRIXIE T R IH1 K S IY0 +TRIXIE'S T R IH1 K S IY0 Z +TRIXTON T R IH1 K S T AH0 N +TRIXY T R IH1 K S IY0 +TRIXY'S T R IH1 K S IY0 Z +TRO T R OW0 +TROAD T R OW1 D +TROAS T R OW1 AH0 Z +TROAT T R OW1 T +TROATS T R OW1 T S +TROBE T R OW1 B +TROCADERO T R AA1 K AH0 D EH1 R OW0 +TROCERO T R OW0 CH EH1 R OW0 +TROCHA T R AA1 CH AH0 +TROCHAIC T R AA1 CH EY1 IH0 K +TROCHANTER T R AA1 CH AE1 N T ER0 +TROCHEE T R AA1 CH IY0 +TROCHEES T R AA1 CH IY2 Z +TROCHES T R AA1 CH IH0 Z +TROCHILUS T R AA1 CH IY1 L UW0 Z +TROCHU T R OW1 K UW0 +TROCHU'S T R AA1 CH UW2 Z +TROCHUS T R AA1 CH AH0 S +TROD T R AA1 D +TRODDEN T R AA1 D AH0 N +TRODE T R OW1 D +TROEZEN T R OW1 Z AH0 N +TROEZENE T R OW1 Z IY0 N +TROFAST T R OW0 F AE2 S T +TROFFATER T R AA1 F EY2 T ER0 +TROFIMOV T R AH0 F IY1 M AA0 V +TROGLODYTE T R AA1 G L AH0 D AY2 T +TROGLODYTES T R AA1 G L AH0 D AY2 T S +TROGLODYTIC T R AA1 G L AH0 D IH1 T IH0 K +TROGNON T R AA1 G N AH0 N +TROGON T R AA1 G AO0 N +TROGONS T R AA1 G AH0 N Z +TROGZMONDOFF T R OW1 Z M OW0 N D AO1 F +TROIA T R OW1 Y AH0 +TROIKA T R OY1 K AH0 +TROIKAS T R OY1 K AH0 Z +TROIL T R OY1 L +TROILUS T R OY1 L AH0 S +TROIS T W AA1 +TROISIEME T R OY1 S IY1 M +TROISVILLE T R OY1 Z V IH2 L +TROISVILLES T R OY1 Z V IH2 L Z +TROITZ T R OY1 T S +TROJA T R OW1 JH AH0 +TROJAN T R OW1 JH AH0 N +TROJAN'S T R OW1 JH AH0 N Z +TROJANS T R OW1 JH AH0 N Z +TROKOFF T R OW1 K AO0 F +TROLD T R OW2 L D +TROLDS T R OW2 L D Z +TROLL T R OW1 L +TROLL'S T R OW1 L Z +TROLLE T R OW1 L +TROLLED T R OW1 L D +TROLLER T R OW1 L ER0 +TROLLERS T R OW1 L ER0 Z +TROLLEY T R AA1 L IY0 +TROLLEYS T R AA1 L IY0 Z +TROLLHAETTA T R OW1 L HH EH1 T AH0 +TROLLIES T R AA1 L IY0 Z +TROLLING T R OW1 L IH0 NG +TROLLOLOP T R AA1 L AH0 L AA1 P +TROLLOP T R AA1 L AH0 P +TROLLOPE T R OW1 L OW2 P +TROLLOPE'S T R AA1 L AH0 P S +TROLLOPING T R AA1 L AH0 P IH0 NG +TROLLOPS T R AA1 L AH0 P S +TROLLS T R OW1 L Z +TROLLY T R AA1 L IY0 +TROLOO T R OW0 L UW2 +TROMBA T R AA1 M B AH0 +TROMBONE T R AA0 M B OW1 N +TROMBONES T R AA0 M B OW1 N Z +TROMP T R AA1 M P +TROMP'S T R AA1 M P S +TROMPE T R AA1 M P IY0 +TROMPED T R AA1 M P T +TROMPERE T R AA1 M P IY1 R +TROMPETTE T R AA1 M P EH2 T +TROMPIN T R AA1 M P IH0 N +TROMPLE T R AA1 M P AH0 L +TROMPLED T R AA1 M P AH0 L D +TROMPS T R AA1 M P S +TROMSO T R AA1 M S OW0 +TROMSOE T R AA1 M S OW0 +TROMSOEE T R AA1 M S OW0 IY0 +TRON T R AA1 N +TRONCHET T R OW0 N K IH0 T +TRONCHIN T R OW0 N K IH0 N +TRONCKH T R AA1 NG K +TROND T R AA1 N D +TRONDHJEM T R OW0 N D JH AH0 M +TRONE T R OW1 N +TRONEG T R OW0 N EH1 G +TRONES T R OW1 N Z +TRONG T R AO0 NG +TRONK T R AA1 NG K +TRONKA T R AA1 NG K AH0 +TRONYEM T R AA1 N Y AH0 M +TROO T R UW1 +TROOBLE T R UW1 B AH0 L +TROODOS T R UW1 D OW0 Z +TROOF T R UW1 F +TROOL T R UW1 L +TROOLY T R UW0 L IY0 +TROON T R UW1 N +TROOP T R UW1 P +TROOP'S T R UW1 P S +TROOPED T R UW1 P T +TROOPER T R UW1 P ER0 +TROOPER'S T R UW1 P ER0 Z +TROOPERS T R UW1 P ER0 Z +TROOPIN T R UW1 P IH0 N +TROOPING T R UW1 P IH0 NG +TROOPS T R UW1 P S +TROOPSHIP T R UW1 P SH IH2 P +TROOPSHIPS T R UW1 P SH IH2 P S +TROOST T R UW1 S T +TROOT T R UW1 T +TROOTH T R UW1 TH +TROOZ T R UW1 Z +TROP T R AA2 P +TROPAEOLUM T R AA1 P IY0 OW1 L AH0 M +TROPE T R OW2 P +TROPES T R OW2 P S +TROPHIC T R OW1 F IH0 K +TROPHIED T R OW1 F IY0 D +TROPHIES T R OW1 F IY0 Z +TROPHIME T R OW1 F AY2 M +TROPHIMUS T R OW1 F IH0 M IH0 S +TROPHONIUS T R OW1 F AO1 N IY0 IH0 S +TROPHY T R OW1 F IY0 +TROPIC T R AA1 P IH0 K +TROPICAL T R AA1 P IH0 K AH0 L +TROPICALLY T R AA1 P IH0 K L IY0 +TROPICK T R AA1 P IH0 K +TROPICS T R AA1 P IH0 K S +TROPININ T R AA2 P IH0 N IH0 N +TROPISM T R AA2 P IH0 Z AH0 M +TROPLONG T R AA1 P L AO0 NG +TROPPO T R AA1 P OW0 +TROS T R OW0 Z +TROSACHS T R AA1 S AE1 K S +TROSSACHS T R AA1 S AE1 K S +TROSTLE'S T R AA1 S AH0 L Z +TROT T R AA1 T +TROT'S T R AA1 T S +TROTH T R AO1 TH +TROTH T R OW1 TH +TROTHS T R AO1 TH S +TROTKINS T R AA1 T K IH0 N Z +TROTMAN T R AA1 T M AH0 N +TROTS T R AA1 T S +TROTSKY T R AA1 T S K IY2 +TROTSKY'S T R AA1 T S K IY0 Z +TROTT T R AA1 T +TROTT'S T R AA1 T S +TROTTED T R AA1 T AH0 D +TROTTED T R AA1 T IH0 D +TROTTER T R AA1 T ER0 +TROTTER'S T R AA1 T ER0 Z +TROTTERS T R AA1 T ER0 Z +TROTTIE T R AA1 T IY0 +TROTTIN T R AA1 T IH0 N +TROTTING T R AA1 T IH0 NG +TROTTLE T R AA1 T AH0 L +TROTTLE'S T R AA1 T AH0 L Z +TROTTMAN T R AA1 T M AH0 N +TROTTOIR T R AA1 T W AA2 R +TROTTOIRS T R AA1 T W AA2 R Z +TROTTY T R AA1 T IY0 +TROTTY'S T R AA1 T IY0 Z +TROTWOOD T R AA1 T W UH2 D +TROTZKY T R AA1 T S K IY0 +TROU T R UW1 +TROUB T R AH1 B +TROUBADOR T R UW1 B AH0 D ER0 +TROUBADOUR T R UW1 B AH0 D AO2 R +TROUBADOUR'S T R UW1 B AH0 D UH1 R Z +TROUBADOURS T R UW1 B AH0 D AO2 R Z +TROUBERT T R UW1 B ER0 T +TROUBETZKOY T R AH1 B EH1 T S K OY1 +TROUBLE T R AH1 B AH0 L +TROUBLE'S T R AH1 B AH0 L Z +TROUBLED T R AH1 B AH0 L D +TROUBLEMAKER T R AH1 B AH0 L M EY2 K ER0 +TROUBLER T R AH1 B AH0 L ER0 +TROUBLERS T R AH1 B AH0 L ER0 Z +TROUBLES T R AH1 B AH0 L Z +TROUBLESOM T R AH1 B AH0 L S AH0 M +TROUBLESOME T R AH1 B AH0 L S AH0 M +TROUBLESOMELY T R AH1 B AH0 L S AH0 M L IY0 +TROUBLESOMENESS T R AH1 B AH0 L S AH0 M N AH0 S +TROUBLETH T R AH1 B AH0 L TH +TROUBLIN T R AH1 B L IH0 N +TROUBLING T R AH1 B AH0 L IH0 NG +TROUBLING T R AH1 B L IH0 NG +TROUBLOUS T R AH1 B L AH0 S +TROUBRIDGE T R AH1 B R IH2 JH +TROUBRIDGE'S T R AH1 B R IH2 JH IH0 Z +TROUFFLE T R AH1 F AH0 L +TROUGH T R AO1 F +TROUGHS T R AO1 F S +TROUILLEFOU T R UW1 IY0 F UW0 +TROUIN T R W IY1 N +TROUKHATCHEVSKY T R AW1 K AE1 CH IH0 V S K IY0 +TROUNCE T R AW1 N S +TROUNCED T R AW1 N S T +TROUNCING T R AW1 N S IH0 NG +TROUP T R UW1 P +TROUP'S T R UW1 P S +TROUPE T R UW1 P +TROUPER T R UW1 P ER0 +TROUPES T R UW1 P S +TROUSER T R AW1 Z ER0 +TROUSER'S T R AW1 Z ER0 Z +TROUSERED T R AW1 Z ER0 D +TROUSERINGS T R AW1 S R IH0 NG Z +TROUSERLESS T R AW1 Z ER0 L AH0 S +TROUSERS T R AW1 Z ER0 Z +TROUSIES T ER1 AH0 S IY0 Z +TROUSSEAU T R AW1 S OW1 +TROUSSEAUX T R AW1 S OW1 +TROUT T R AW1 T +TROUT'S T R AW1 T S +TROUTBECK T R AW1 T B EH2 K +TROUTENAU T R AW1 T IH0 N OW0 +TROUTH T R AW1 TH +TROUTING T R AW1 T IH0 NG +TROUTLINGS T R AW1 T L IH0 NG Z +TROUTS T R AW1 T S +TROUTT T R AW1 T +TROUVA T R UW1 V AH0 +TROUVAILLE T R UW1 V EY1 L +TROUVE T R UW1 V +TROUVENT T R UW2 V AH0 N T +TROUVER T R UW1 V ER0 +TROUVERA T R UW1 V ER0 AH0 +TROUVERE T R UW1 V ER0 +TROUVERES T R UW1 V ER0 Z +TROUVEREZ T R UW2 V EH1 R EH0 Z +TROUVES T R AW1 V Z +TROUVEZ T R UW1 V EH0 Z +TROUVILLE T R UW1 V IH2 L +TROVAR T R OW0 V AA1 R +TROVATO T R OW0 V AA1 T OW0 +TROVATORE T R OW1 V AH0 T AO2 R +TROVE T R OW1 V +TROVER T R OW1 V ER0 +TROVES T R OW1 V Z +TROVO T R OW1 V OW0 +TROW T R OW1 +TROW'S T R OW1 Z +TROWBRIDGE T R OW1 B R IH0 JH +TROWBRIDGE'S T R OW1 B R IH0 JH AH0 Z +TROWBRIDGES T R OW1 B R IH0 JH IH0 Z +TROWED T R AW1 D +TROWEL T R AW1 W EH0 L +TROWELFUL T R AA1 W EH0 L F AH0 L +TROWELS T R AW1 L Z +TROWER T R AW1 ER0 +TROWL T R AW1 L +TROWS T R OW1 Z +TROWSER T R AW1 Z ER0 +TROWSERS T R AW1 Z ER0 Z +TROWSIS T R AW1 S IH0 S +TROWTH T R OW1 TH +TROY T R OY1 +TROY'S T R OY1 Z +TROYANO'S T R OY2 AA1 N OW0 Z +TROYAS T R OY2 AA1 Z +TROYES T R OY1 Z +TROYLAND T R OY1 L AH0 N D +TROYON T R OY1 AH0 N +TROYON'S T R OY1 AH0 N Z +TRR T R +TRU T R UW0 +TRUAGE T R UW1 AH0 JH +TRUAGH T R UW1 AH0 +TRUANCIES T R UW1 AH0 N S IY0 Z +TRUANCY T R UW1 AH0 N S IY0 +TRUANT T R UW1 AH0 N T +TRUANT'S T R UW1 AH0 N T S +TRUANTRY T R UW1 AH0 N T R IY0 +TRUANTS T R UW1 AH0 N T S +TRUAX T R UW1 AE0 K S +TRUBA T R UW1 B AH0 +TRUBBLE T R AH1 B AH0 L +TRUBERT T R UW1 B ER0 T +TRUBLET T R AH1 B L EH2 T +TRUBNER T R AH1 B N ER0 +TRUBY T R UW1 B IY0 +TRUC T R AH1 K +TRUCE T R UW1 S +TRUCELESS T R UW1 S L AH0 S +TRUCES T R UW1 S IH0 Z +TRUCHSES T R UW1 K S AH0 Z +TRUCHSESS T R AH1 CH AH0 S +TRUCHSZ T R UW1 SH +TRUCK T R AH1 K +TRUCK'S T R AH1 K S +TRUCKED T R AH1 K T +TRUCKEE T R AH1 K IY0 +TRUCKER T R AH1 K ER0 +TRUCKERS T R AH1 K ER0 Z +TRUCKING T R AH1 K IH0 NG +TRUCKLE T R AH1 K AH0 L +TRUCKLED T R AH1 K AH0 L D +TRUCKLEFORD T R AH1 K L IH0 F ER0 D +TRUCKLEFORD'S T R AH1 K L IH0 F ER0 D Z +TRUCKLES T R AH1 K AH0 L Z +TRUCKLING T R AH1 K L IH0 NG +TRUCKLOAD T R AH1 K L OW2 D +TRUCKLOADS T R AH1 K L OW2 D Z +TRUCKMAN T R AH1 K M AH0 N +TRUCKMEN T R AH1 K M EH2 N +TRUCKS T R AH1 K S +TRUCULENCE T R AH1 K Y AH0 L AH0 N S +TRUCULENT T R AH1 K Y AH0 L AH0 N T +TRUCULENTLY T R AH1 K Y AH0 L AH0 N T L IY0 +TRUD T R AH1 D +TRUDA T R UW1 D AH0 +TRUDAINE T R UW1 D EY0 N +TRUDAINE'S T R UW1 D EY1 N Z +TRUDCHEN T R UW1 D CH AH0 N +TRUDE T R UW1 D +TRUDE'S T R UW1 D Z +TRUDEAU T R UW0 D OW1 +TRUDEL T R UW1 D AH0 L +TRUDEL'S T R UW1 D AH0 L Z +TRUDER T R UW1 D ER0 +TRUDGE T R AH1 JH +TRUDGED T R AH1 JH D +TRUDGEON T R AH1 JH AH0 N +TRUDGES T R AH1 JH IH0 Z +TRUDGIN T R AH1 JH IH0 N +TRUDGING T R AH1 JH IH0 NG +TRUDI T R UW1 D IY0 +TRUDI'S T R UW1 D IY0 Z +TRUDOLYUBOV T R UW1 D AA2 L Y UW1 B AO2 V +TRUDY T R UW1 D IY0 +TRUDY'S T R UW1 D IY0 Z +TRUE T R UW1 +TRUE'S T R UW1 Z +TRUEBA T R UH1 B AH0 +TRUEBLOOD T R UW1 B L AH2 D +TRUEBNER T R UW1 B N ER0 +TRUEBOY T R UH1 B OY2 +TRUEFIT T R UW1 F IH2 T +TRUEFITT T R UW1 AH0 F IH0 T +TRUEFITT'S T R UW1 AH0 F IH0 T S +TRUEHEART T R UW1 HH AA0 R T +TRUEHEARTED T R UW1 HH AA0 R T IH0 D +TRUELLA T R UW2 EH1 L AH0 +TRUELOVE T R UW1 L AH2 V +TRUELOVE'S T R UW1 L AH2 V Z +TRUELY T R UW1 L IY0 +TRUEMAN T R UH1 M AH0 N +TRUEMAN'S T R UH1 M AH0 N Z +TRUENESS T R UW1 N AH0 S +TRUEPENNY T R UW1 EH2 P EH2 N IY0 +TRUER T R UW1 ER0 +TRUESDALE T R UW1 Z D EY2 L +TRUESDALE'S T R UW1 Z D EY2 L Z +TRUEST T R UW1 IH0 S T +TRUETT T R UW1 T +TRUEWIT T R UW1 W IH0 T +TRUEY T R UW1 IY0 +TRUF T R AH1 F +TRUFALDIN T R UW0 F AE1 L D IH0 N +TRUFE T R UW1 F +TRUFF T R AH1 F +TRUFFES T R UW0 F S +TRUFFLE T R AH1 F AH0 L +TRUFFLED T R AH1 F AH0 L D +TRUFFLES T R AH1 F AH0 L Z +TRUG T R AH1 G +TRUIDE T R UW1 IH0 D +TRUIE T R UW0 IY1 +TRUISM T R UW1 IH0 Z AH0 M +TRUISMS T R UW1 IH0 Z AH0 M Z +TRUITJE T R UW1 T JH +TRUJANO T R UW0 Y AA1 N OW0 +TRUJILLO T R UW0 HH IY1 OW0 +TRULL T R AH1 L +TRULLIBER T R AH1 L IH0 B ER0 +TRULLS T R AH1 L Z +TRULS T R AH1 L Z +TRULY T R UW1 L IY0 +TRUM T R AH1 M +TRUMAN T R UW1 M AH0 N +TRUMAN'S T R UW1 M AH0 N Z +TRUMBLE T R AH1 M B AH0 L +TRUMBULL T R AH1 M B AH0 L +TRUMBULL'S T R AH1 M B AH0 L Z +TRUMBULLSES T R AH1 M B AH0 L S IH0 Z +TRUMEAU T R UW1 M OW1 +TRUMELLE T R UW0 M EH1 L +TRUMENCE T R UW1 M AH0 N S +TRUMET T R AH1 M IH0 T +TRUMILLY T R UW1 M IH0 L IY0 +TRUMKARD T R AH1 M K ER0 D +TRUMP T R AH1 M P +TRUMPED T R AH1 M P T +TRUMPER T R AH1 M P ER0 +TRUMPERIES T R AH1 M P ER0 IY0 Z +TRUMPERY T R AH1 M P ER0 IY0 +TRUMPET T R AH1 M P AH0 T +TRUMPET'S T R AH1 M P AH0 T S +TRUMPETED T R AH1 M P AH0 T IH0 D +TRUMPETER T R AH1 M P AH0 T ER0 +TRUMPETER'S T R AH1 M P AH0 T ER0 Z +TRUMPETERS T R AH1 M P AH0 T ER0 Z +TRUMPETING T R AH1 M P AH0 T IH0 NG +TRUMPETINGS T R AH1 M P AH0 T IH0 NG Z +TRUMPETS T R AH1 M P AH0 T S +TRUMPING T R AH1 M P IH0 NG +TRUMPINGTON T R AH1 M P IH0 NG T AH0 N +TRUMPLER T R AH1 M P AH0 L ER0 +TRUMPS T R AH1 M P S +TRUNCATED T R AH1 NG K EY2 T IH0 D +TRUNCHEON T R AH1 N CH IH0 N +TRUNCHEONS T R AH1 N CH IH0 N Z +TRUNDLE T R AH1 N D AH0 L +TRUNDLE'S T R AH1 N D AH0 L Z +TRUNDLEBEN T R AH1 N D AH0 L B AH0 N +TRUNDLED T R AH1 N D AH0 L D +TRUNDLES T R AH1 N D AH0 L Z +TRUNDLING T R AH1 N D AH0 L IH0 NG +TRUNELLA T R UW2 N EH1 L AH0 +TRUNION T R AH1 N Y AH0 N +TRUNK T R AH1 NG K +TRUNK'S T R AH1 NG K S +TRUNKED T R AH1 NG K T +TRUNKFUL T R AH1 NG K F AH0 L +TRUNKFULS T R AH1 NG K F AH0 L Z +TRUNKLESS T R AH1 NG K L AH0 S +TRUNKMAKER T R AH1 NG K M EY2 K ER0 +TRUNKS T R AH1 NG K S +TRUNNELL T R AH1 N AH0 L +TRUNNELL'S T R AH1 N AH0 L Z +TRUNNION T R AH1 N Y AH0 N +TRUNNION'S T R AH1 N Y AH0 N Z +TRUNNIONS T R AH1 N Y AH0 N Z +TRURIE T R UH1 R IY0 +TRURO T R UW1 R OW0 +TRURO'S T R UH1 R OW0 Z +TRUS T R AH1 S +TRUSCOMB T R AH1 S K AH0 M +TRUSCOMB'S T R AH1 S K AH0 M Z +TRUSCOTT T R AH1 S K AH0 T +TRUSCOTT'S T R AH1 S K AH0 T S +TRUSIA T R UW1 ZH AH0 +TRUSIA'S T R UW1 ZH AH0 Z +TRUSLOVE T R AH1 S L AH2 V +TRUSLOW T R AH1 S L OW0 +TRUSOTSKY T R AH1 S AA0 T S K IY0 +TRUSS T R AH1 S +TRUSSED T R AH1 S T +TRUSSER T R AH1 S ER0 +TRUSSES T R AH1 S IH0 Z +TRUSSING T R AH1 S IH0 NG +TRUSSIT T R AH1 S IH0 T +TRUST T R AH1 S T +TRUST'S T R AH1 S T S +TRUSTAFORD T R AH1 S T AH0 F ER0 D +TRUSTCOTT T R AH1 S T K AA2 T +TRUSTED T R AH1 S T IH0 D +TRUSTEE T R AH2 S T IY1 +TRUSTEE'S T R AH2 S T IY1 Z +TRUSTEES T R AH2 S T IY1 Z +TRUSTEESHIP T R AH2 S T IY1 SH IH2 P +TRUSTEN T R AH1 S T AH0 N +TRUSTERS T R AH1 S T ER0 Z +TRUSTEST T R AH1 S T AH0 S T +TRUSTETH T R AH1 S T IH0 TH +TRUSTFUL T R AH1 S T F AH0 L +TRUSTFULLY T R AH1 S T F AH0 L IY0 +TRUSTFULNESS T R AH1 S T F AH0 L N AH0 S +TRUSTIER T R AH1 S T IY0 ER0 +TRUSTIES T R AH1 S T IY0 Z +TRUSTIEST T R AH1 S T IY0 AH0 S T +TRUSTIN T R AH1 S T IH0 N +TRUSTING T R AH1 S T IH0 NG +TRUSTINGLY T R AH1 S T IH0 NG L IY0 +TRUSTLESS T R AH1 S T L AH0 S +TRUSTRAM T R AH1 S T R AH0 M +TRUSTS T R AH1 S T S +TRUSTWORTHINESS T R AH1 S T W ER2 DH IY0 N AH0 S +TRUSTWORTHY T R AH1 S T W ER2 DH IY0 +TRUSTY T R AH1 S T IY0 +TRUSTY'S T R AH1 S T IY0 Z +TRUT T R AH1 T +TRUTE T R UW1 T +TRUTH T R UW1 TH +TRUTH'S T R UW1 TH S +TRUTHFUL T R UW1 TH F AH0 L +TRUTHFULLY T R UW1 TH F AH0 L IY0 +TRUTHFULNESS T R UW1 TH F AH0 L N AH0 S +TRUTHLESS T R UW1 TH L AH0 S +TRUTHS T R UW1 TH S +TRUTHSENSE T R UW1 TH S EH2 N S +TRUTZ T R AH1 T S +TRUXILLO T R AH2 K S IH1 L OW0 +TRUXTON T R AH1 K S T AH0 N +TRUXTON'S T R AH1 K S T AH0 N Z +TRUXTUN T R AH2 K S CH AH0 N +TRUYN T R AY1 N +TRUYN'S T R UW1 IH0 N Z +TRY T R AY1 +TRY'D T R AY1 D +TRYAL T R AY1 AA2 L +TRYALL T R AY1 AA2 L +TRYALS T R AY1 AH0 L Z +TRYAN T R AY1 AH0 N +TRYAN'S T R AY1 AH0 N Z +TRYANITE T R AY1 AH0 N AY2 T +TRYED T R AY1 D +TRYER T R AY1 ER0 +TRYETH T R AY1 IH0 TH +TRYGGVASON T R IH1 G V AH0 S AH0 N +TRYGGVESON T R IH1 G V IH0 S AH0 N +TRYGGVESON'S T R IH1 G V EY1 S AH0 N Z +TRYIN T R AY1 IH0 N +TRYING T R AY1 IH0 NG +TRYING T R AY1 NG +TRYON T R AY1 AH0 N +TRYON'S T R AY1 AH0 N Z +TRYOUT T R AY1 AW2 T +TRYOUTS T R AY1 AW2 T S +TRYPHAENA T R IH1 F EY1 N AH0 +TRYPHENA T R IH1 F IH0 N AH0 +TRYPHENA'S T R IH1 F IH0 N AH0 Z +TRYPHO T R IH1 F OW0 +TRYPHOENA T R IH1 F IY1 N AH0 +TRYPHON T R IH1 F AH0 N +TRYPHOSA T R IH1 F OW1 S AH0 +TRYPHOSA'S T R IH1 F OW1 S AH0 Z +TRYS T R IH1 S +TRYSAIL T R IH1 S EY2 L +TRYSAILS T R IH1 S EY1 L Z +TRYST T R AY1 S T +TRYST T R IH1 S T +TRYSTE T R AY1 S T +TRYSTED T R AY1 S T IH0 D +TRYSTING T R AY1 S T IH0 NG +TRYSTS T R IH1 S T S +TS T IY1 EH1 S +TS'AO S AW1 +TS'I S IY0 +TS'IN S AE1 N +TS'U S UW0 +TSA S AH0 +TSAD T S AA1 D +TSAI S AY1 +TSAI T S AY1 +TSALAL S EY1 L AH0 L +TSAMANNI T S AA1 M AE1 N IY0 +TSAMPA T S AA1 M P AH0 +TSAN T S AA1 N +TSANDA T S AA1 N D AH0 +TSANG S AE1 NG +TSANG T S AE1 NG +TSANGPO S AE1 NG P OW0 +TSAO S AW1 +TSAO T S AW1 +TSAR T S AA1 R +TSAR Z AA1 R +TSAR'S T S ER0 Z +TSARDOM T S ER0 D AH0 M +TSAREVICH T S AA1 R IH0 V IH0 CH +TSAREVITCH T S ER0 AH0 V IH0 CH +TSAREVNA Z AA1 R EY0 V N AH0 +TSARGRAD T S AA1 R G R AE2 D +TSARINA T S ER0 IY1 N AH0 +TSARISM T S ER0 IH2 Z AH0 M +TSARIST T S ER0 IH0 S T +TSARITSA T S AA1 R IH0 T S AH0 +TSARITSINO T S AA1 R IH0 T S IY1 N OW0 +TSARITZA T S AA1 R IH1 T Z AH0 +TSARITZA'S T S AA1 R IH1 T Z AH0 Z +TSARPI T S AA1 R P IY0 +TSARS T S AA1 R Z +TSARSKOE T S AA1 R S K OW1 +TSARSKOYE T S AA1 R S K OY1 +TSAVO T S AA1 V OW0 +TSCHADKURRS CH AE1 D K UH0 R Z +TSCHAIKOVSKI CH AY2 K AA1 F S K IY0 +TSCHAIKOVSKY CH AY2 K AA1 V S K IY0 +TSCHAIKOVSKY'S CH AY2 K AA1 F S K IY0 Z +TSCHAIKOWSKY CH EY2 K AW1 S K IY0 +TSCHAIKOWSKY'S CH EY2 K AW1 S K IY0 Z +TSCHERI CH EH1 R IY0 +TSCHERKASKI CH ER0 K AA1 S K IY0 +TSCHEU CH UW0 +TSCHOLENS CH OW1 L AH0 N Z +TSCHUDI CH UW1 D IY0 +TSCHWI CH W IY0 +TSE S IY1 +TSE T S IY1 +TSEE T S IY1 +TSENG S EH1 NG +TSENG T S EH1 NG +TSEPITSO T S EH1 P IH0 T S OW1 +TSERETELLI T S EH1 R EH0 T EH1 L IY0 +TSERIN T S EH1 R IH0 N +TSETSE T IY1 T S IY0 +TSETSE T S IY1 T S IY0 +TSH SH +TSHAKA SH AA1 K AH0 +TSHAKA'S SH AA1 K AH0 Z +TSHE SH +TSHOZA SH OW1 Z AH0 +TSHUEN SH W EH1 N +TSHUEN'S SH UW0 EH1 N Z +TSI S IY0 +TSIEN T S Y AH0 N +TSIETSE T S IY1 T S IY0 +TSIGANE S IH1 G AH0 N +TSIGANES S AH0 G EY1 N Z +TSIGANOK S IH1 G AH0 N AA0 K +TSIN S IH0 N +TSING S IH1 NG +TSINGPU S IH1 NG P UW0 +TSISKWA S IH1 S K V AH0 +TSITH S IH1 TH +TSITSITH S IH1 T S IH0 TH +TSIWARAN S IH1 W EH1 R AH0 N +TSK S K +TSKI S K IY0 +TSO S OW1 +TSO T S OW1 +TSOAY S OW2 IY0 +TSOLO T S OW1 L OW0 +TSOMASS S AH0 M AE0 S +TSONG T S AO1 NG +TSOO S UW1 +TSS T IY1 EH1 S EH1 S +TST T S T +TSU T S UW1 +TSUKI S UW1 K IY0 +TSUNG S UW1 NG +TSURE T S UW0 R +TSUTAYOSHI T S UW0 T AA0 Y OW1 SH IY0 +TSUYU T S UW0 Y UW1 +TSYBUKIN S IH0 B UW1 K IH0 N +TSYPRIANOVITCH S IH1 P R IY0 AE1 N AH0 V AH0 CH +TSZ SH +TSZE T Z +TT T +TTERBUSCH T ER0 B UH2 SH +TU T UW1 +TU'NED T UW1 N D +TU'S T UW0 Z +TUA T UW1 AH0 +TUAE T UW0 EY2 +TUAI T UW0 AA1 IY0 +TUAM T W AA1 M +TUAMASANGA T W AA2 M AA0 S AA1 NG G AH0 +TUAMINI T UW0 AA0 M IY1 N IY0 +TUAN T UW0 AA1 N +TUAREG T Y UW1 ER0 AH0 G +TUAREGS T Y UW1 ER0 AH0 G Z 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ER0 B IH1 D N AH0 S +TURBIE T ER1 B IY0 +TURBIN T ER1 B IH0 N +TURBINATED T ER1 B IH0 N EY2 T IH0 D +TURBINE T ER1 B AY0 N +TURBINES T ER1 B AY0 N Z +TURBLE T ER1 B AH0 L +TURBO T ER1 B OW0 +TURBOT T ER1 B AH2 T +TURBOT T ER2 B OW1 +TURBOTS T ER1 B AH2 T S +TURBULENCE T ER1 B Y AH0 L AH0 N S +TURBULENCY T ER1 B Y AH0 L AH0 N S IY0 +TURBULENT T ER1 B Y AH0 L AH0 N T +TURBULENTLY T ER1 B Y AH0 L AH0 N T L IY0 +TURC T ER1 K +TURCARET T ER1 K EH1 R AH0 T +TURCHIN T ER1 CH IH0 N +TURCICA T ER1 CH IY1 K AH0 +TURCK T ER1 K +TURCKEMS T ER1 K AH0 M Z +TURCO T ER1 K OW0 +TURCOMAN T ER1 K AH0 M AH0 N +TURCOMANS T ER1 K AH0 M AH0 N Z +TURCOS T ER1 K OW0 Z +TURD T ER1 D +TURDETANI T ER1 D IH0 T AA1 N IY0 +TURDUS T ER1 D AH0 S +TURE T UH1 R +TUREEN T UH1 R IY0 N +TUREENS T UH1 R IY2 N Z +TURENNE T Y UW1 R IH0 N +TURENNE'S T Y UW1 R IH0 N Z +TURF T ER1 F +TURFED T ER1 F T +TURFMAN T ER1 F M AH0 N +TURFMEN T ER1 F M EH0 N +TURFS T ER1 F S +TURFY T ER1 F IY0 +TURGENEV T ER1 JH EH2 N EH0 F +TURGENEV'S T ER1 JH EH2 N EH0 F S +TURGENIEFF T ER1 JH EH0 N Y EH1 F +TURGENIEFF'S T ER1 JH EH0 N Y EH1 F S +TURGENIEV T ER1 JH IH0 N IY0 EH0 V +TURGESCENCE T ER1 JH EH1 S AH0 N S +TURGID T ER1 JH IH0 D +TURGIDITY T ER0 JH IH1 D AH0 T IY0 +TURGIS T ER1 JH IH0 S +TURGOT T ER1 G AH0 T +TURGUENIEF T ER1 G W EY2 N IY1 F +TURGUENIEF'S T ER1 G W EY2 N IY1 F S +TURIDDU T UH1 R IH0 D UW1 +TURIN T UH1 R IH0 N +TURIN T Y UH1 R IH0 N +TURK T ER1 K +TURK'S T ER2 K S +TURKE T ER1 K +TURKE'S T ER1 K S +TURKESTAN T ER1 K S T AE2 N +TURKEY T ER1 K IY0 +TURKEY'S T ER1 K IY0 Z +TURKEYFOOT T ER1 K IY0 F UH2 T +TURKEYS T ER1 K IY0 Z +TURKHI T ER1 K HH IY0 +TURKHOMAN T ER1 K HH OW1 M AH0 N +TURKI T ER1 K IY0 +TURKIE T ER1 K IY0 +TURKIES T ER1 K IY0 Z +TURKINS T ER1 K IH0 N Z +TURKIS T ER1 K IH0 S +TURKISH T ER1 K IH0 SH +TURKISTAN T ER1 K IH0 S T AE2 N +TURKLE T ER1 K AH0 L +TURKLES T ER1 K AH0 L Z +TURKLETAUB T ER1 K AH0 L T AO1 B +TURKO T ER1 K OW0 +TURKOIS T ER1 K W AA1 +TURKOMAN T ER1 K OW0 M AH0 N +TURKOMANS T ER1 K OW1 M AH0 N Z +TURKS T ER1 K S +TURKY T ER1 K IY0 +TURL T ER1 L +TURLEY T ER1 L IY0 +TURLIGOOD T ER1 L IH0 G UH2 D +TURLINGTON T ER1 L IH0 NG T AH0 N +TURLINGTON'S T ER1 L IH0 NG T AH0 N Z +TURLOCK T ER1 L AH0 K +TURLOUGH T ER1 L AW0 +TURLOUGH T ER1 L OW0 +TURLOUGH'S T ER1 L OW0 Z +TURLUPIN T ER1 L UW1 P AH0 N +TURLURETTE T ER1 L ER0 EH1 T +TURLUTU T ER1 L UW1 T UW2 +TURMAE T ER1 M IY2 +TURMERIC T ER1 M ER0 IH0 K +TURMOIL T ER1 M OY2 L +TURMOILING T ER1 M OY1 L IH0 NG +TURMOILS T ER1 M OY1 L Z +TURMUT T ER1 M AH0 T +TURMUTS T ER1 M AH0 T S +TURN T ER1 N +TURN'D T ER1 N D +TURN'LL T ER1 N L +TURN'S T ER0 N Z +TURN'ST T ER1 N S T +TURNABOUT T ER1 N AH0 B AW2 T +TURNBERRY T ER1 N B EH2 R IY0 +TURNBULL T ER1 N B UH2 L +TURNBULL'S T ER1 N B UH2 L Z +TURNCOAT T ER1 N K OW2 T +TURNCOATS T ER1 N K OW2 T S +TURNCOCK T ER1 N K AA2 K +TURNDOWN T ER1 N D AW2 N +TURNE T ER1 N +TURNED T ER1 N D +TURNER T ER1 N ER0 +TURNER'S T ER1 N ER0 Z +TURNERESQUE T ER1 N ER0 AH0 S K +TURNERS T ER1 N ER0 Z +TURNEST T ER1 N IH0 S T +TURNETH T ER1 N AH0 TH +TURNEY T ER1 N IY0 +TURNHAM T ER1 N HH AH0 M +TURNHAM'S T ER1 N HH AE0 M Z +TURNHILL T ER1 N HH IH2 L +TURNIN T ER1 N IH0 N +TURNING T ER1 N IH0 NG +TURNINGS T ER1 N IH0 NG Z +TURNIP T ER1 N AH0 P +TURNIPS T ER1 N AH0 P S +TURNKEY T ER1 N K IY2 +TURNKEY'S T ER1 N K IY2 Z +TURNKEYS T ER1 N K IY0 Z +TURNOUR T ER1 N ER0 +TURNOUR'S T ER1 N AW0 R Z +TURNOURS T ER1 N AW2 ER0 Z +TURNOUT T ER1 N AW2 T +TURNOUTS T ER1 N AW2 T S +TURNOVER T ER1 N OW2 V ER0 +TURNOVERS T ER1 N OW2 V ER0 Z +TURNPENNY T ER1 N P EH2 N IY0 +TURNPENNY'S T ER1 N P EH1 N IY0 Z +TURNPIKE T ER1 N P AY2 K +TURNPIKES T ER1 N P AY2 K S +TURNS T ER1 N Z +TURNSCREW T ER1 N S K R UW1 +TURNSPIT T ER1 N S P IH0 T +TURNSPITS T ER1 N Z P IH2 T S +TURNSTILE T ER1 N S T AY2 L +TURNSTILES T ER1 N S T AY2 L Z +TURNT T ER1 N T +TURNTABLE T ER1 N T EY2 B AH0 L +TURNUS T ER1 N AH0 S +TURNVEREIN T ER1 N V EH0 R IH1 N +TUROLD T UH1 R OW0 L D +TUROLD'S T UH1 R AH0 L D Z +TURON T Y UW1 R AA0 N +TURPE T ER1 P +TURPENAY T ER1 P AH0 N EY0 +TURPENTINE T ER1 P AH0 N T AY2 N +TURPIN T ER1 P IH0 N +TURPIN'S T ER1 P IH0 N Z +TURPINS T ER1 P IH0 N Z +TURPIO T ER1 P IY0 OW0 +TURPITUDE T ER1 P IH0 T UW2 D +TURPITUDES T ER1 P IH0 T UW2 D Z +TURPS T ER1 P S +TURPSICHOR T ER1 P S IH0 K AO2 R +TURQUAND T ER1 K W AH0 N D +TURQUE T ER1 K +TURQUET T ER1 K EY1 +TURQUIE T ER1 K W IY0 +TURQUIETTE T ER1 K W IY1 T +TURQUINE T ER1 K W AY2 N +TURQUOIS T ER1 K W OY0 Z +TURQUOISE T ER1 K W OY0 Z +TURQUOISES T ER1 K W OY0 Z IH0 Z +TURR T ER1 +TURR'BLE T ER1 B AH0 L +TURRALD T AO1 R AH0 L D +TURRENTINE T UH0 R EH0 N T IY1 N IY0 +TURRENTINE'S T ER1 AH0 N T AY2 N Z +TURRENTINES T ER1 AH0 N T AY2 N Z +TURRET T ER1 AH0 T +TURRET T ER1 T +TURRET'S T AO1 R AH0 T S +TURRETED T AO1 R AH0 T IH0 D +TURRETS T ER1 AH0 T S +TURRIBLE T AO1 R AH0 B AH0 L +TURRIBLY T AO1 R AH0 B L IY0 +TURRIF T UH0 R IY1 F +TURRIFS T ER1 AH0 F S +TURRILL T AO1 R AH0 L +TURRIPARVA T AO1 R AY0 P AA1 R V AH0 +TURRIS T UH0 R IY1 Z +TURRITELLA T AO1 R IH0 T EH1 L AH0 +TURRN T ER1 N +TURRON T AO1 R AH0 N +TURRUBLE T AO1 R UW1 B AH0 L +TURTLE T ER1 T AH0 L +TURTLE'S T ER1 T AH0 L Z +TURTLEDOVE T ER1 T AH0 L D AH0 V +TURTLEDOVES T ER1 T AH0 L D AH0 V Z +TURTLES T ER1 T AH0 L Z +TURTON T ER1 T AH0 N +TURVES T ER1 V Z +TURVEY T ER0 V EY1 +TURVEYDOM T ER1 V EY2 D AH0 M +TURVEYDROP T ER1 V EY2 D R AA2 P +TURVY T ER1 V IY0 +TURVYDOM T ER1 V IY0 D AH0 M +TUS T AH0 S +TUSCALOOSA T AH2 S K AH0 L UW1 S AH0 +TUSCAN T AH1 S K AH0 N +TUSCANIA T AH1 S K AH0 N IY1 AH0 +TUSCANS T AH1 S K AH0 N Z +TUSCANY T AH1 S K AH0 N IY0 +TUSCAR T AH2 S K ER0 +TUSCARAWAS T AH2 S K EH1 R AH0 W AA0 Z +TUSCARORA T AH2 S K AA0 R AO1 R AH0 +TUSCARORAS T AH2 S K AA0 R AO1 R AH0 Z +TUSCOLEE T AH2 S K OW1 L IY1 +TUSCULAN T AH1 S K Y AH0 L AH0 N +TUSCULUM T AH1 S K Y AH0 L AH0 M +TUSCUMBIA T AH2 S K AH1 M B IY0 AH0 +TUSH T UH1 SH +TUSHEGOUN T UH1 SH IH0 G UW1 N +TUSHEPAWS T UH1 SH IH0 P AO1 Z +TUSHER T UH1 SH ER0 +TUSHER'S T UH1 SH ER0 Z +TUSHES T UH1 SH IH0 Z +TUSHIELAW T UH1 SH IY2 L AO2 +TUSHIN T UH1 SH IH0 N +TUSHY T UH1 SH IY0 +TUSITALA T UW1 S IH0 T AA1 L AH0 +TUSK T AH1 S K +TUSKAMUCK T AH1 S K AH0 M AH1 K +TUSKED T AH1 S K T +TUSKEGEE T AH0 S K IY1 G IY2 +TUSKEGEE T AH1 S K AH0 G IY2 +TUSKER T AH1 S K ER0 +TUSKER'S T AH1 S K ER0 Z +TUSKERS T AH1 S K ER0 Z +TUSKINGUM T AH1 S K IH2 NG G AH0 M +TUSKS T AH1 S K S +TUSKY T AH1 S K IY0 +TUSSAC T UW1 S AE0 K +TUSSAUD T AH0 S OW1 +TUSSAUD'S T AH0 S OW1 Z +TUSSAUD'S T UW0 S OW1 Z +TUSSEL T AH1 S AH0 L +TUSSER T AH1 S ER0 +TUSSIE T AH1 S IY0 +TUSSIE'S T AH1 S IY0 Z +TUSSLE T AH1 S AH0 L +TUSSLED T AH1 S AH0 L D +TUSSLES T AH1 S AH0 L Z +TUSSLING T AH1 S AH0 L IH0 NG +TUSSMANN T AH1 S M AH0 N +TUSSOCK T AH1 S AH0 K +TUSSOCKS T AH1 S AH0 K S +TUSSOCKY T AH1 S AH0 K IY0 +TUSSORE T AH1 S AO2 R +TUT T AH1 T +TUTA T UW1 T AH0 +TUTANEKAI T UW1 T AH0 N AH0 K AY1 +TUTANKHAMON T UW0 T AA1 NG K AH0 M AH0 N +TUTBURY T AH1 T B EH2 R IY0 +TUTE T Y UW1 T +TUTELAGE T Y UW1 T IH0 L IH0 JH +TUTELAR T Y UW1 T IH0 L ER0 +TUTELARY T Y UW1 T IH0 L EH2 R IY0 +TUTELU T Y UW1 T IH0 L UW0 +TUTHER T AH1 DH ER0 +TUTHILL T AH1 T HH IH2 L +TUTISSIMUS T AH1 T IY0 S IY1 M AH0 S +TUTMOSIS T AH1 T M OW2 S AH0 S +TUTO T UW1 T OW0 +TUTONIAN T UW0 T OW1 N IY0 AH0 N +TUTOR T UW1 T ER0 +TUTOR'S T UW1 T ER0 Z +TUTORAGE T UW0 T AO1 R AH0 JH +TUTORED T UW1 T ER0 D +TUTORESS T UW0 T AO1 R IH0 S +TUTORIAL T UW0 T AO1 R IY0 AH0 L +TUTORING T UW1 T ER0 IH0 NG +TUTORS T UW1 T ER0 Z +TUTORSHIP T UW1 T ER0 SH IH2 P +TUTOYER T UW1 T OY1 ER0 +TUTS T AH1 T S +TUTT T AH1 T +TUTTA T AH1 T AH0 +TUTTE T UW1 T +TUTTI T UW1 T IY0 +TUTTLE T AH1 T AH0 L +TUTTLE'S T AH1 T AH0 L Z +TUTTO T UW1 T OW0 +TUTTS T AH1 T S +TUTTU T AH1 T UW1 +TUTU T UW1 T UW2 +TUTUILA T UW1 T UW2 IY1 L AH0 +TUTUL T AH1 T AH0 L +TUTUS T UW1 T AH0 S +TUUM T UW0 AH0 M +TUUS T UW0 IH0 S +TUVACHE T UW1 V AA0 CH +TUVVY T UW1 V IY0 +TUVVY'S T UW1 V IY0 Z +TUXBURY T AH1 K S B EH2 R IY0 +TUXEDO T AH2 K S IY1 D OW0 +TUXEDOS T AH0 K S IY1 D OW2 Z +TUXFORD T AH1 K S F ER0 D +TUXPAM T AH1 K S P AE0 M +TUXTON T AH1 K S T AH0 N +TUY T UW1 +TUYL T UW1 L +TUYN T UW1 IH0 N +TUYN'S T UW1 IH0 N Z +TUZENBACH T UW1 Z IH0 N B AA0 K +TVENTY T V EH1 N T IY0 +TVER T V ER0 +TW T +TWA T W AH0 +TWA'N'T T W AH1 AH0 N T +TWACHTMAN T W AA0 K T M AH0 N +TWAD T W AA1 D +TWADDLE T W AA1 D AH0 L +TWADDLER T W AA1 D AH0 L ER0 +TWADDLING T W AA1 D AH0 L IH0 NG +TWAE T W IY0 +TWAIN T W EY1 N +TWAIN'S T W EY1 N Z +TWAL T W AH0 L +TWALA T W AA1 L AH0 +TWALA'S T W AO1 L AH0 Z +TWALMONTH T W AA1 M AH0 N TH +TWAN'T T W AA1 N T +TWANG T W AA1 NG +TWANGED T W AA1 N JH D +TWANGING T W AA1 N JH IH0 NG +TWANGLING T W AA1 NG L IH0 NG +TWANGS T W AA1 NG Z +TWANGY T W AA1 N JH IY0 +TWANKAY T W AA1 NG K EY1 +TWANT T W AO1 N T +TWAR T W AO1 R +TWARDOWSKI T W ER0 D AW1 S K IY0 +TWARDOWSKY T W ER0 D AW1 S K IY0 +TWARN'T T W AO1 R N T +TWAS T W AH1 Z +TWASN'T T W AA1 Z AH0 N T +TWATT T W AA2 T +TWAY T W EY2 +TWEAK T W IY1 K +TWEAKED T W IY1 K T +TWEAKING T W IY1 K IH0 NG +TWEAKS T W IY1 K S +TWEATY T W IY1 T IY0 +TWEDDLE T W EH1 D AH0 L +TWEE T W IY1 +TWEED T W IY1 D +TWEED'S T W IY1 D Z +TWEEDALE T W IY1 D EY2 L +TWEEDDALE T W IY1 D D EY2 L +TWEEDIE T W IY1 D IY0 +TWEEDIE'S T W IY1 D IY0 Z +TWEEDIES T W IY1 D IY0 Z +TWEEDLE T W IY1 D AH0 L +TWEEDLEDEE T W IY1 D AH0 L D IY0 +TWEEDLEDUM T W IY1 D AH0 L D AH0 M +TWEEDLES T W IY1 D AH0 L Z +TWEEDMOUTH T W IY1 D M AH0 TH +TWEEDS T W IY1 D Z +TWEEDSIDE T W IY1 D S AY2 D +TWEEDY T W IY1 D IY0 +TWEEN T W IY1 N +TWEENDECKS T W IY1 N D IH0 K S +TWEENY T W IY1 N IY0 +TWEET T W IY1 T +TWEET'S T W IY1 T S +TWEETY T W IY1 T IY0 +TWEEZERS T W IY1 Z ER0 Z +TWEEZY T W IY1 Z IY0 +TWEEZY'S T W IY1 Z IY0 Z +TWEL T W EH1 L +TWELF T W EH1 L F +TWELFTH T W EH1 L F TH +TWELFTHS T W EH1 L F TH S +TWELL T W EH2 L +TWELLS T W EH1 L Z +TWELVE T W EH1 L V +TWELVEFOLD T W EH1 L V F OW2 L D +TWELVEMONTH T W EH1 L V M AH0 N TH +TWELVEMONTH'S T W EH1 L V M AH0 N TH S +TWELVEMONTHS T W EH1 L V M AH0 N TH S +TWELVEMOUGH T W EH1 L V M OW0 +TWELVEPENCE T W EH1 L V EH0 P EH1 N S +TWELVES T W EH1 L V Z +TWELVETREES T W EH1 L V AH0 T R IY1 Z +TWEMLOW T W EH1 M L OW0 +TWEMLOW'S T W EH1 M L OW2 Z +TWEN T W EH1 N +TWENT T W EH1 N T +TWENTIE T W EH1 N T IY0 +TWENTIES T W EH1 N IY0 Z +TWENTIES T W EH1 N T IY0 Z +TWENTIETH T W EH1 N IY0 AH0 TH +TWENTIETH T W EH1 N IY0 IH0 TH +TWENTIETH T W EH1 N T IY0 AH0 TH +TWENTIETH T W EH1 N T IY0 IH0 TH +TWENTIETHS T W EH1 N IY0 AH0 TH S +TWENTY T W EH1 N IY0 +TWENTY T W EH1 N T IY0 +TWENTY'S T W EH1 N IY0 Z +TWENTY'S T W EH1 N T IY0 Z +TWENTYFIVE T W EH2 N IY0 F AY1 V +TWENTYFOLD T W EH2 N IY0 F OW2 L D +TWENTYFOUR T W EH2 N IY0 F AO0 R +TWENTYMAN T W EH2 N IY0 M AE2 N +TWENTYMAN'S T W EH2 N IY0 M AH0 N Z +TWENTYTWO T W EH2 N IY0 T UW1 +TWER T W ER0 +TWERE T W ER0 +TWEREN'T T W ER1 AH0 N T +TWETTENHAM T W EH1 T IH0 N HH AH0 M +TWETTENHAM'S T W EH1 T AH0 N AH0 M Z +TWEXBY T W EH2 K S B IY0 +TWI T W AY1 +TWICE T W AY1 S +TWICED T W AY1 S T +TWICET T W AY1 S AH0 T +TWICHELL T W IH1 CH AH0 L +TWICHELL'S T W IH1 CH AH0 L Z +TWICK T W IH2 K +TWICKENHAM T W IH1 K AH0 N HH AE2 M +TWICT T W IH1 K T +TWIDDEL T W IH1 D AH0 L +TWIDDLE T W IH1 D AH0 L +TWIDDLED T W IH1 D AH0 L D +TWIDDLER T W IH1 D L ER0 +TWIDDLES T W IH1 D AH0 L Z +TWIDDLING T W IH1 D L IH0 NG +TWIFORD'S T W IH1 F ER0 D Z +TWIG T W IH1 G +TWIG'S T W IH1 G Z +TWIGG T W IH1 G +TWIGGED T W IH1 G D +TWIGGER T W IH1 G ER0 +TWIGGING T W IH1 G IH0 NG +TWIGGS T W IH1 G Z +TWIGGY T W IH1 G IY0 +TWIGS T W IH1 G Z +TWILIGHT T W AY1 L AY2 T +TWILIGHT'S T W AY1 L AY2 T S +TWILIGHTED T W AY1 L AY2 T IH0 D +TWILIGHTS T W AY1 L AY2 T S +TWILIT T W AY1 L IH2 T +TWILL T W IH1 L +TWILLED T W IH1 L D +TWILLER T W IH1 L ER0 +TWILLINGATE T W IH1 L IH0 NG G EY2 T +TWILLY T W IH1 L IY0 +TWIN T W IH1 N +TWIN'D T W IH1 N D +TWIN'S T W IH1 N Z +TWINE T W AY1 N +TWINE'S T W AY1 N Z +TWINEALL T W AY1 N IY1 L +TWINED T W AY1 N D +TWINES T W AY1 N Z +TWINETH T W IH1 N IH0 TH +TWINGE T W IH1 N JH +TWINGED T W IH1 N JH D +TWINGES T W IH1 N JH IH0 Z +TWINGING T W IH1 NG IH0 NG +TWINING T W AY1 N IH0 NG +TWINING'S T W AY1 N IH0 NG Z +TWININGS T W AY1 N IH0 NG Z +TWINK T W IH1 NG K +TWINKLE T W IH1 NG K AH0 L +TWINKLED T W IH1 NG K AH0 L D +TWINKLEHEELS T W IH1 NG K AH0 L HH IY2 L Z +TWINKLERS T W IH1 NG K L ER0 Z +TWINKLES T W IH1 NG K AH0 L Z +TWINKLETON T W IH1 NG K AH0 L T AH0 N +TWINKLETON'S T W IH1 NG K AH0 L T AH0 N Z +TWINKLIN T W IH1 NG K L IH0 N +TWINKLING T W IH1 NG K AH0 L IH0 NG +TWINKLING T W IH1 NG K L IH0 NG +TWINKLINGS T W IH1 NG K AH0 L IH0 NG Z +TWINKLY T W IH1 NG K L IY0 +TWINNED T W IH1 N D +TWINNIES T W IH1 N IY0 Z +TWINS T W IH1 N Z +TWINTY T W IH1 N T IY0 +TWIRL T W ER1 L +TWIRL'D T W ER1 L D +TWIRLED T W ER1 L D +TWIRLER T W ER1 L ER0 +TWIRLERS T W ER1 L ER0 Z +TWIRLING T W ER1 L IH0 NG +TWIRLS T W ER1 L Z +TWIRLY T W ER1 L IY0 +TWISDEN T W IH1 S D AH0 N +TWISE T W AY2 Z +TWISEGOOD T W IH1 S IH0 G UH0 D +TWISS T W IH1 S +TWIST T W IH1 S T +TWIST'S T W IH1 S T S +TWISTE T W IH1 S T +TWISTED T W IH1 S T AH0 D +TWISTED T W IH1 S T IH0 D +TWISTER T W IH1 S T ER0 +TWISTERS T W IH1 S T ER0 Z +TWISTIFIED T W IH1 S T AH0 F AY2 D +TWISTIN T W IH1 S T IH0 N +TWISTING T W IH1 S T IH0 NG +TWISTINGS T W IH1 S T IH0 NG Z +TWISTS T W IH1 S T S +TWISTY T W IH1 S T IY0 +TWIT T W IH1 T +TWITCH T W IH1 CH +TWITCHED T W IH1 CH T +TWITCHEL T W IH1 CH AH0 L +TWITCHELL T W IH1 CH AH0 L +TWITCHER T W IH1 CH ER0 +TWITCHES T W IH1 CH IH0 Z +TWITCHIN T W IH1 CH IH0 N +TWITCHING T W IH1 CH IH0 NG +TWITCHINGS T W IH1 CH IH0 NG Z +TWITCHIT T W IH1 CH IH0 T +TWITCHY T W IH1 CH IY0 +TWITE T W AY1 T +TWITS T W IH1 T S +TWITTED T W IH1 T IH0 D +TWITTER T W IH1 T ER0 +TWITTERED T W IH1 T ER0 D +TWITTERIN T W IH1 T ER0 IH0 N +TWITTERING T W IH1 T ER0 IH0 NG +TWITTERINGS T W IH1 T ER0 IH0 NG Z +TWITTERS T W IH1 T ER0 Z +TWITTING T W IH1 T IH0 NG +TWIX T W IH1 K S +TWIXT T W IH1 K S T +TWIZZLE T W IH1 Z AH0 L +TWO T UW1 +TWO' T UW1 +TWO'D T UW1 D +TWO'LL T UW1 L +TWO'S T UW1 Z +TWOAD T UW1 AE0 D +TWOFOLD T UW1 F OW1 L D +TWOFORK T UW1 F 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AH0 N B R AY1 D AH0 L D +UNBRIGHTENED AH0 N B R AY1 T AH0 N D +UNBROACHED AH0 N B R OW1 CH T +UNBROKE AH0 N B R OW1 K +UNBROKEN AH0 N B R OW1 K AH0 N +UNBROKENLY AH0 N B R OW1 K AH0 N L IY0 +UNBROTHERLY AH0 N B R AH1 DH ER0 L IY0 +UNBRUISED AH0 N B R UW1 Z D +UNBRUSHED AH0 N B R AH2 SH T +UNBUCKLE AH0 N B AH0 K AH0 L +UNBUCKLED AH0 N B AH0 K AH0 L D +UNBUCKLING AH0 N B AH1 K L IH0 NG +UNBUILD AH0 N B IH1 L D +UNBUILDING AH0 N B IH1 L D IH0 NG +UNBUILT AH2 N B IH1 L T +UNBURDEN AH0 N B ER1 D AH0 N +UNBURDENED AH0 N B ER1 D AH0 N D +UNBURDENING AH0 N B ER1 D AH0 N IH0 NG +UNBURDENS AH0 N B ER1 D AH0 N Z +UNBURIED AH0 N B ER1 IY0 D +UNBURNED AH0 N B ER1 N D +UNBURNISHED AH0 N B ER1 N IH0 SH T +UNBURNT AH0 N B ER1 N T +UNBURTHEN AH0 N B ER1 TH AH0 N +UNBURTHENED AH0 N B ER1 TH AH0 N D +UNBURTHENING AH0 N B ER1 TH AH0 N IH0 NG +UNBUSINESS AH0 N B IH1 Z N IH0 S +UNBUSINESSLIKE AH0 N B IH1 Z N IH0 S L AY2 K +UNBUTTERED AH0 N B AH1 T ER0 D +UNBUTTON AH0 N B AH1 T AH0 N +UNBUTTONED AH0 N B AH1 T AH0 N D +UNBUTTONING AH0 N B AH1 T AH0 N IH0 NG +UNBUTTONS AH0 N B AH1 T AH0 N Z +UNC AH1 NG K +UNCAGED AH0 N K EY1 JH D +UNCALCULATED AH0 N K AE1 L K Y AH0 L EY2 T IH0 D +UNCALCULATING AH0 N K AE1 L K Y AH0 L EY2 T IH0 NG +UNCALLED AH0 N K AO1 L D +UNCANCELLED AH0 N K AE1 N S AH0 L D +UNCANDID AH0 N K AE1 N D AH0 D +UNCANDOR AH0 N K AE1 N D ER0 +UNCANNILY AH0 N K AE1 N AH0 L IY0 +UNCANNINESS AH0 N K AE1 N IY0 N AH0 S +UNCANNON AH0 N K AE1 N AH0 N +UNCANNY AH0 N K AE1 N IY0 +UNCANONICAL AH1 N K AH0 N AA1 N AH0 K AH0 L +UNCAPABLE AH0 N K EY1 P AH0 B AH0 L +UNCAPAPA AH0 N K AE1 P AA1 P AH0 +UNCAPAPAS AH0 N K AE1 P AA1 P AH0 Z +UNCAPPED AH0 N K AE1 P T +UNCAPTURABLE AH0 N K AE1 P CH ER0 AH0 B AH0 L +UNCAPTURED AH0 N K AE1 P CH ER0 D +UNCARED AH0 N K EH1 R D +UNCARING AH0 N K EH1 R IH0 NG +UNCARPETED AH0 N K AA1 R P AH0 T IH0 D +UNCARVED AH0 N K AA2 R V D +UNCAS AH1 N K AH0 Z +UNCASE AH0 N K EY1 S +UNCASED AH0 N K EY1 S T +UNCATALOGUED AH0 N K AE1 T AH0 L AO2 G D +UNCAUGHT AH0 N K AO1 T 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CH EY1 N JH AH0 B AH0 L N AH0 S +UNCHANGEABLY AH0 N CH EY1 N JH AH0 B L IY0 +UNCHANGED AH0 N CH EY1 N JH D +UNCHANGING AH0 N CH EY1 N JH IH0 NG +UNCHANGINGLY AH0 N CH EY1 N JH IH0 NG L IY0 +UNCHAPERONED AH0 N CH AE1 P ER0 OW2 N D +UNCHARACTERISTIC AH2 N K EH2 R IH0 K T ER0 IH1 S T IH0 K +UNCHARACTERISTICALLY AH2 N K EH2 R IH0 K T ER0 IH1 S T IH0 K L IY0 +UNCHARGED AH0 N CH AA1 R JH D +UNCHARITABLE AH2 N K EH2 R AH0 T AH0 B AH0 L +UNCHARITABLENESS AH2 N K EH2 R AH0 T AH0 B AH0 L N AH0 S +UNCHARITABLY AH2 N K EH2 R IH0 T AH0 B L IY0 +UNCHARITY AH2 N K EH2 R IH0 T IY0 +UNCHARTED AH0 N CH AA1 R T IH0 D +UNCHARTERED AH0 N CH AA1 R T ER0 D +UNCHASTE AH0 N CH EY1 S T +UNCHASTENED AH0 N CH EY1 S AH0 N D +UNCHASTISED AH0 N CH AE0 S T AY1 Z D +UNCHASTITY AH0 N CH AE1 S T AH0 T IY0 +UNCHECK'D AH0 N CH EH1 K D +UNCHECKED AH0 N CH EH1 K T +UNCHECKERED AH0 N CH EH1 K ER0 D +UNCHEEDAH AH0 N CH IY2 D AH0 +UNCHEERED AH2 N K IH1 R D +UNCHEERFUL AH2 N K IH1 R F AH0 L +UNCHEERING AH2 N K IH1 R IH0 NG 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NG +UNCLASPS AH0 N K L AE1 S P S +UNCLASSED AH0 N K L AE1 S T +UNCLASSIC AH0 N K L AE1 S IH0 K +UNCLASSICAL AH0 N K L AE1 S IH0 K AH0 L +UNCLASSIFIABLE AH0 N K L AE1 S AH0 F AY2 AH0 B AH0 L +UNCLASSIFIED AH0 N K L AE1 S IH0 F AY2 D +UNCLE AH1 NG K AH0 L +UNCLE'S AH1 NG K AH0 L Z +UNCLEAN AH0 N K L IY1 N +UNCLEANED AH0 N K L IY1 N D +UNCLEANLINESS AH0 N K L IY1 N L IY0 N AH0 S +UNCLEANLY AH0 N K L IY1 N L IY0 +UNCLEANNESS AH0 N K L IY1 N IH0 S +UNCLEANSED AH0 N K L EH1 N Z D +UNCLEAR AH0 N K L IH1 R +UNCLEARED AH0 N K L IH1 R D +UNCLENCH AH0 N K L EH1 N CH +UNCLENCHED AH0 N K L EH1 N CH T +UNCLENCHING AH0 N K L EH1 N CH IH0 NG +UNCLERICAL AH0 N K L EH1 R IH0 K AH0 L +UNCLES AH1 NG K AH0 L Z +UNCLIMBABLE AH0 N K L IH1 M B AH0 B AH0 L +UNCLINCHED AH0 N K L IH1 N CH T +UNCLIPPED AH0 N K L IH1 P T +UNCLOAKED AH0 N K L OW1 K T +UNCLOGGED AH0 N K L AO1 G D +UNCLOSE AH0 N K L OW1 Z +UNCLOSED AH0 N K L OW1 Z D +UNCLOSES AH0 N K L OW1 Z IH0 Z +UNCLOSING AH0 N K L OW1 Z IH0 NG +UNCLOTHED AH0 N K L AO2 TH T +UNCLOUD AH0 N K L AW1 D +UNCLOUDED AH0 N K L AW1 D IH0 D +UNCLOYING AH0 N K L OY1 IH0 NG +UNCO AH0 N K OW0 +UNCOCKED AH0 N K AA1 K T +UNCOCKING AH0 N K AA1 K IH0 NG +UNCOFFINED AH0 N K AA1 F AY1 N D +UNCOIL AH2 N K OY1 L +UNCOILED AH2 N K OY1 L D +UNCOILING AH2 N K OY1 L IH0 NG +UNCOILS AH2 N K OY1 L Z +UNCOINED AH2 N K OY1 N D +UNCOLLECTED AH0 N K AH0 L EH1 K T IH0 D +UNCOLORED AH0 N K AH1 L ER0 D +UNCOLOURED AH0 N K AH1 L ER0 D +UNCOMBED AH2 N K AH0 M D +UNCOMBINED AH2 N K AH0 M B AY1 N D +UNCOMELINESS AH0 N K AH1 M L IY0 N AH0 S +UNCOMELY AH0 N K AH1 M L IY0 +UNCOMFORTABLE AH0 N K AH1 M F ER0 T AH0 B AH0 L +UNCOMFORTABLENESS AH0 N K AH1 M F ER0 T AH0 B AH0 L N AH0 S +UNCOMFORTABLY AH0 N K AH1 M F T AH0 B L IY0 +UNCOMFORTED AH0 N K AH1 M F ER0 T IH0 D +UNCOMFORTING AH0 N K AH1 M F ER0 T IH0 NG +UNCOMFY AH0 N K AH1 M F IY0 +UNCOMMANDED AH2 N K AH0 M AE1 N D IH0 D +UNCOMMENTED AH0 N K AA1 M EH0 N T IH0 D +UNCOMMERCIAL AH2 N K AH0 M ER1 SH AH0 L +UNCOMMITTED AH2 N K AH0 M IH1 T IH0 D +UNCOMMON AH0 N K AA1 M AH0 N +UNCOMMONLY AH2 N K AA1 M AH0 N L IY0 +UNCOMMONNESS AH0 N K AA1 M AH0 N N AH0 S +UNCOMMUNICATED AH2 N K AH0 M Y UW1 N AH0 K EY2 T IH0 D +UNCOMMUNICATIVE AH2 N K AH0 M Y UW1 N AH0 K EY2 T IH0 V +UNCOMMUNICATIVENESS AH2 N K AH0 M Y UW1 N AH0 K EY2 T IH0 V N AH0 S +UNCOMPAHGRE AH0 N K AH1 M P AH0 HH G ER0 +UNCOMPANIONABLE AH2 N K AH0 M P AE1 N Y AH0 N AH0 B AH0 L +UNCOMPANIONED AH2 N K AH0 M P AE1 N Y AH0 N D +UNCOMPELLED AH2 N K AH0 M P EH1 L D +UNCOMPENSATED AH0 N K AA1 M P AH0 N S EY2 T IH0 D +UNCOMPLAINING AH2 N K AH0 M P L EY1 N IH0 NG +UNCOMPLAININGLY AH2 N K AH0 M P L EY1 N IH0 NG L IY0 +UNCOMPLETED AH2 N K AH0 M P L IY1 T IH0 D +UNCOMPLEX AH0 N K AA1 M P L EH0 K S +UNCOMPLICATED AH0 N K AA1 M P L AH0 K EY2 T IH0 D +UNCOMPLIMENTARY AH0 N K AA1 M P L AH0 M EH1 N ER0 IY0 +UNCOMPOUNDED AH2 N K AH0 M P AW1 N D IH0 D +UNCOMPREHENDED AH2 N K AA2 M P R IH0 HH EH1 N D IH0 D +UNCOMPREHENDING AH2 N K AA2 M P R IH0 HH EH1 N D IH0 NG +UNCOMPREHENDINGLY 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AH0 N F Y UW1 Z D +UNCONGENIAL AH2 N K AH0 N JH IY1 N Y AH0 L +UNCONGENIALITY AH2 N K AH0 N JH IY2 N IY0 AE1 L AH0 T IY0 +UNCONJECTURED AH2 N K AH0 N JH EH1 K CH ER0 D +UNCONNECTED AH2 N K AH0 N EH1 K T IH0 D +UNCONNECTEDLY AH2 N K AH0 N EH1 K T IH0 D L IY0 +UNCONQUER'D AH0 N K AA1 NG K ER0 D +UNCONQUERABLE AH0 N K AA1 NG K ER0 AH0 B AH0 L +UNCONQUERABLY AH0 N K AA1 NG K ER0 AH0 B L IY0 +UNCONQUERED AH0 N K AA1 NG K ER0 D +UNCONSCIENTIOUS AH2 N K AA1 N CH IY0 EH1 N CH AH0 S +UNCONSCIONABLE AH0 N K AA1 N SH AH0 N AH0 B AH0 L +UNCONSCIONABLY AH2 N K AA1 N SH AH0 N AH0 B L IY0 +UNCONSCIOUS AH2 N K AA1 N SH AH0 S +UNCONSCIOUSLY AH2 N K AA1 N SH AH0 S L IY0 +UNCONSCIOUSNESS AH2 N K AA1 N SH AH0 S N IH0 S +UNCONSECRATED AH2 N K AA2 N S AH0 K R EY1 T IH0 D +UNCONSIDERED AH2 N K AH0 N S IH1 D ER0 D +UNCONSOLED AH2 N K AH0 N S OW1 L D +UNCONSTANT AH2 N K AA2 N S T AH0 N T +UNCONSTITUTIONAL AH2 N K AA2 N S T AH0 T UW1 SH AH0 N AH0 L +UNCONSTITUTIONALITY AH2 N K AA2 N S T IH0 T UW1 SH AH0 N AE1 L 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AH0 N S EH1 P ER0 EY2 T IH0 D +UNSER AH1 N S ER0 +UNSERE AH0 N S EH1 R +UNSERIOUS AH0 N S EH1 R IY0 AH0 S +UNSERN AH0 N S EH1 R N +UNSERVED AH0 N S ER1 V D +UNSERVICEABLE AH0 N S ER1 V AH0 S AH0 B AH0 L +UNSERVICEABLE AH1 N S ER1 V AH0 S AH0 B AH0 L +UNSET AH0 N S EH1 T +UNSET AH1 N S EH1 T +UNSETTING AH0 N S EH1 T IH0 NG +UNSETTLE AH0 N S EH1 T AH0 L +UNSETTLED AH0 N S EH1 T AH0 L D +UNSETTLEDNESS AH0 N S EH1 T L AH0 D N AH0 S +UNSETTLEMENT AH0 N S EH1 T AH0 L M AH0 N T +UNSETTLES AH0 N S EH1 T AH0 L Z +UNSETTLING AH0 N S EH1 T AH0 L IH0 NG +UNSETTLING AH0 N S EH1 T L IH0 NG +UNSEVERED AH0 N S AH0 V IH1 R D +UNSEWED AH0 N S OW1 D +UNSEWN AH0 N S OW1 N +UNSEX AH0 N S EH2 K S +UNSEXED AH0 N S EH1 K S T +UNSEXING AH0 N S EH1 K S IH0 NG +UNSHACKLE AH0 N SH AE1 K AH0 L +UNSHACKLED AH0 N SH AE1 K AH0 L D +UNSHADED AH0 N SH EY1 D IH0 D +UNSHADOWED AH0 N SH AE1 D OW0 D +UNSHAKABLE AH0 N SH EY1 K AH0 B AH0 L +UNSHAKABLY AH0 N SH EY1 K AH0 B L IY0 +UNSHAKEABLE AH0 N SH EY1 K AH0 B AH0 L 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+VAIKUNTHA V EY1 K UW0 N TH AH0 +VAIL V EY1 L +VAIL'S V EY1 L Z +VAILE V EY1 L +VAILED V EY1 L D +VAILELE V EY1 L EY1 L IY0 +VAILIMA V EY1 L IY1 M AH0 +VAILING V EY1 L IH0 NG +VAILLANT V EY1 L AH0 N T +VAILLANT'S V EY1 L AH0 N T S +VAILLIANT V EY1 L IY0 AH0 N T +VAILS V EY1 L Z +VAIN V EY1 N +VAINCU V EY1 NG K UW1 +VAINE V EY1 N +VAINER V EY1 N ER0 +VAINEST V EY1 N AH0 S T +VAINGLORIOUS V EY1 N G L AO1 R IY0 AH0 S +VAINGLORIOUSLY V EY1 N G L AO1 R IY0 AH0 S L IY0 +VAINGLORIOUSNESS V EY1 N G L AO1 R IY0 AH0 S N AH0 S +VAINGLORY V EY1 N G L AO1 R IY0 +VAINLOVE V EY1 N L AH2 V +VAINLY V EY1 N L IY0 +VAINQUEUR V EY1 NG K ER1 +VAINS V EY1 N Z +VAIR V EH2 R +VAIRDELAN V EH2 R D EH1 L AH0 N +VAIRE V EH2 R +VAIREE V EY1 R IY1 +VAIRY V EH1 R IY0 +VAIS V EY1 +VAISAMPAYANA V EY1 S AH0 M P AY2 AA1 N AH0 +VAISHNAV V EY1 SH N AH0 V +VAISHNAVA V EY1 SH N AA1 V AH0 +VAISHNAVISM V EY1 SH N AH0 V IH2 Z AH0 M +VAISHNAVS V EY1 SH N AH0 V Z +VAISINGANO V EY2 S IH0 NG G AA1 N OW0 +VAISON V EY1 Z AH0 N 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AH0 +VALENCIA'S V AH0 L EH1 N S IY0 AH0 Z +VALENCIAN V AH0 L EH1 N S IY0 AH0 N +VALENCIAS V AH0 L EH1 N S IY0 AH0 Z +VALENCIENNES V AH0 L EH1 N S IY0 EH2 N Z +VALENGLAY V AH0 L IH1 NG AH0 L EY0 +VALENKA V AA0 L EH1 NG K AH0 +VALENS V AE1 L AH0 N Z +VALENSOLLE V AH0 L EH1 N S OW0 L +VALENTI V AA0 L EH1 N T IY2 +VALENTIA V AA0 L EH1 N SH AH0 +VALENTIA'S V AA0 L EH1 N SH AH0 Z +VALENTIN V AA2 L EH1 N T IY2 N +VALENTIN'S V AA0 L EH1 N T AH0 N Z +VALENTINA V AA2 L EH0 N T IY1 N AH0 +VALENTINE V AE1 L AH0 N T AY2 N +VALENTINE'S V AE1 L AH0 N T AY2 N Z +VALENTINES V AE1 L AH0 N T AY2 N Z +VALENTINI V AE2 L AH0 N T IY1 N IY0 +VALENTINI'S V AA0 L EH0 N T IY1 N IY0 Z +VALENTINIAN V AA0 L EH0 N T IY1 N IY0 AH0 N +VALENTINIANS V AA0 L EH0 N T IY1 N IY0 AH0 N Z +VALENTINO V AE2 L AH0 N T IY1 N OW0 +VALENTINOIS V AA0 L EH1 N T IH0 N OY0 Z +VALENTINUS V AA0 L EH1 N T AH0 N AH0 S +VALENZIA V AE2 L EH0 N Z Y AH0 +VALENZUELA V AA0 L EH0 N Z UW1 L AH0 +VALER V AE1 L ER0 +VALERA V AH0 L EH1 R AH0 +VALERE 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L AH0 M B R OW1 S AH0 +VALONA V AH0 L OW1 N AH0 +VALOOABLE V AE1 L UW1 AH0 B AH0 L +VALOR V AE1 L ER0 +VALOR'S V AE1 L ER0 Z +VALOREM V AH0 L AO1 R IH0 M +VALORI V AH0 L AO1 R IY0 +VALORO V AA0 L AO1 R OW0 +VALOROSO V AE1 L ER0 OW1 S OW0 +VALOROUS V AE1 L ER0 AH0 S +VALOROUSLY V AE1 L ER0 AH0 S L IY0 +VALORS V AE1 L ER0 Z +VALORSAY V AH0 L AO1 R S EY1 +VALORSAY'S V AE1 L ER0 S EY2 Z +VALORY V AE1 L ER0 IY0 +VALOUR V AE1 L ER0 +VALOUR'S V AE1 L ER0 Z +VALOURS V AE1 L AW1 ER0 Z +VALPARAISO V AE2 L P ER0 EY1 S OW0 +VALPERGA V AA0 L P ER1 G AH0 +VALPINSON V AO0 L P IH1 N S AH0 N +VALPRE V AA0 L P ER0 +VALPY V AA0 L P IY0 +VALQUEYRAS V AE1 L K W EH1 R AH0 Z +VALROSA V AA0 L R OW1 S AH0 +VALSCH V AA0 L SH +VALSE V AE1 L S +VALSECCA V AO0 L S EH1 K AH0 +VALSES V AE1 L S IH0 Z +VALSIN V AE1 L S IH0 N +VALSOLDA V AE1 L S OW1 L D AH0 +VALTELLINE V AO1 L T EH2 L IY1 N IY0 +VALTESI V AA0 L T EH1 S IY0 +VALUABLE V AE1 L Y AH0 B AH0 L +VALUABLE V AE1 L Y UW0 B AH0 L +VALUABLES V AE1 L Y AH0 B AH0 L Z +VALUABLES V AE1 L Y UW0 B AH0 L Z +VALUABLY V AE1 L Y AH0 B L IY0 +VALUATION V AE0 L Y UW0 EY1 SH AH0 N +VALUATIONS V AE0 L Y UW0 EY1 SH AH0 N Z +VALUE V AE1 L Y UW0 +VALUED V AE1 L Y UW0 D +VALUELESS V AE1 L Y UW0 L AH0 S +VALUER V AE1 L Y UW2 ER0 +VALUERS V AE1 L Y UW2 ER0 Z +VALUES V AE1 L Y UW0 Z +VALUEST V AH0 L UW1 IH0 S T +VALUING V AE1 L Y UW0 IH0 NG +VALVE V AE1 L V +VALVED V AE1 L V D +VALVERDE V AA0 L V EH1 R D EY0 +VALVERT V AE1 L V ER0 T +VALVES V AE1 L V Z +VALVULAR V AE1 L V AH0 L ER0 +VAMADEVA V AE2 M AH0 D EY1 V AH0 +VAMBERY V AE1 M B ER0 IY0 +VAMI V AE1 M IY0 +VAMOOSE V AH0 M UW1 S +VAMOOSED V AH0 M UW1 S T +VAMOS V AA1 M OW0 Z +VAMOSE V AA1 M OW0 Z +VAMOSED V AA1 M OW0 Z D +VAMP V AE1 M P +VAMPA V AE1 M P AH0 +VAMPED V AE1 M P T +VAMPING V AE1 M P IH0 NG +VAMPIRE V AE1 M P AY0 R +VAMPIRE'S V AE1 M P AY0 R Z +VAMPIRES V AE1 M P AY0 R Z +VAMPIRISM V AE1 M P AY0 R IH2 Z AH0 M +VAMPS V AE1 M P S +VAMPYRE V AE1 M P AY2 R +VAMPYRES V AE1 M P AY1 ER0 Z +VAN V AE1 N +VAN'S V AH0 N Z +VANA V AE1 N AH0 +VANADIUM V AH0 N EY1 D IY0 AH0 M +VANAMEE V AE2 N AH0 M IY0 +VANAMEE'S V AE2 N AH0 M IY0 Z +VANARDY V AA0 N AA1 R D IY0 +VANAS V AA1 N AH0 Z +VANATOR V AE0 N EY2 T ER0 +VANBEEST V AE2 N B IY1 S T +VANBLOEM V AE2 N B L OW1 M +VANBOEREN V AE2 N B AO1 R AH0 N +VANBOROUGH V AE2 N B ER0 OW0 +VANBOROUGH'S V AE2 N B ER0 OW0 Z +VANBRUGH V AE2 N B R AH1 +VANBRUGH'S V AE2 N B R AH1 Z +VANBRUNT V AE2 N B R AH1 N T +VANBURGH V AE2 N B ER0 G +VANBURGH'S V AE2 N B ER0 G Z +VANCE V AE1 N S +VANCE'S V AE1 N S IH0 Z +VANCEY V AE1 N S IY0 +VANCOUVER V AE0 N K UW1 V ER0 +VANCOUVER'S V AE0 N K UW1 V ER0 Z +VAND V AE2 N D +VAND'S V AE2 N D Z +VANDA V AE1 N D AH0 +VANDA'S V AE1 N D AH0 Z +VANDAIH V AE0 N D EY2 +VANDAL V AE1 N D AH0 L +VANDAL'S V AE1 N D AH0 L Z +VANDALIA V AE2 N D AE1 L IY0 AH0 +VANDALISM V AE1 N D AH0 L IH0 Z AH0 M +VANDALS V AE1 N D AH0 L Z +VANDAM V AE2 N D AE1 M +VANDAMME V AE2 N D AE1 M +VANDEFORD V AE1 N D IH0 F ER0 D +VANDEFORD'S V AE1 N D IH0 F ER0 D Z +VANDEGRIFT V AE1 N D AH0 G R IH0 F T +VANDEL V AE1 N D AH0 L +VANDELEUR V AE1 N D AH0 L ER1 +VANDELEUR'S V AE1 N D AH0 L ER0 Z +VANDELOUP V AE1 N D AH0 L UW1 P +VANDELOUP'S V AE1 N D AH0 L UW1 P S +VANDEMAN V AE1 N D AH0 M AH0 N +VANDEMAN'S V AE1 N D AH0 M AH0 N Z +VANDEMAR V AE1 N D AH0 M AA2 R +VANDEMARK V AE1 N D AH0 M AA2 R K +VANDEMARK'S V AE1 N D AH0 M AA2 R K S +VANDEMEYER V AE1 N D AH0 M AY2 R +VANDEMEYER'S V AE1 N D AH0 M AY2 ER0 Z +VANDEMONIAN V AE1 N D AH0 M OW1 N IY0 AH0 N +VANDEN V AE1 N D AH0 N +VANDENESSE V AE1 N D AH0 N AH0 S +VANDENHOFF V AE1 N D AH0 N HH AO2 F +VANDENHUTEN V AE1 N D AH0 N HH AH2 T AH0 N +VANDER V AE1 N D ER0 +VANDERBANK V AE1 N D ER0 B AE2 NG K +VANDERBANK'S V AE1 N D ER0 B AE2 NG K S +VANDERBECK V AE1 N D ER0 B EH2 K +VANDERBILT V AE1 N D ER0 B IH0 L T +VANDERBILT'S V AE1 N D ER0 B IH0 L T S +VANDERBILTS V AE1 N D ER0 B IH0 L T S +VANDERBURGH V AE1 N D ER0 B ER0 G +VANDERCOOK V AE1 N D ER0 K UH2 K +VANDERDECKEN V AE1 N D ER0 D EH2 K AH0 N +VANDERDYKE V AE1 N D ER0 D AY2 K +VANDERFORD V AE1 N D ER0 F ER0 D +VANDERGILT V AE1 N D ER0 G IH1 L T +VANDERGRIFF V AE1 N D ER0 G R IH0 F +VANDERHAUSEN V AE1 N D ER0 HH AW2 Z AH0 N +VANDERHECK V AE1 N D ER0 HH EH1 K +VANDERHOEK V AE1 N D ER0 HH OW2 K +VANDERHOFF V AE1 N D ER0 HH AO2 F +VANDERHOFFEN V AE1 N D ER0 HH AO2 F AH0 N +VANDERHOLT V AE1 N D ER0 HH OW0 L T +VANDERHOLT'S V AE1 N D ER0 HH OW0 L T S +VANDERHOOF V AE1 N D ER0 HH UH2 F +VANDERHOOK V AE1 N D ER0 HH UH2 K +VANDERKISTE V AE1 N D ER0 K IH2 S T +VANDERLEY V AE1 N D ER0 L IY0 +VANDERLIP V AE1 N D ER0 L IH2 P +VANDERLYN V AE1 N D ER0 L IH0 N +VANDERLYN'S V AE1 N D ER0 L IH0 N Z +VANDERMACLIN V AE1 N D ER0 M AE1 K L AH0 N +VANDERMAN V AE1 N D ER0 M AH0 N +VANDERMEER V AE1 N D ER0 M IH2 R +VANDERMERE V AE1 N D ER0 M IH2 R +VANDERNOODT V AE1 N D ER0 N UW1 D T +VANDERPOEL V AE1 N D ER0 P OW2 L +VANDERPOEL'S V AE1 N D ER0 P OW2 L Z +VANDERPOLE V AE1 N D ER0 P OW2 L +VANDERPOOL V AE1 N D ER0 P UW2 L +VANDERSCAMP V AE1 N D ER0 S K AE1 M P +VANDERSCHOOF V AE1 N D ER0 SH UH1 F +VANDERSEE V AE1 N D ER0 S IY2 +VANDERSEE'S V AE1 N D ER0 S IY2 Z +VANDERSLOOSH V AE1 N D ER0 S L UW1 SH +VANDERVEER V AE1 N D ER0 V IH2 R +VANDERVELDE V AE1 N D ER0 V EH2 L D +VANDERVELDE'S V AE1 N D ER0 V EH2 L D Z +VANDERVELT V AE1 N D ER0 V IH0 L T +VANDERWALL V AE1 N D ER0 W AA2 L +VANDERWATER V AE1 N D ER0 W AO2 T ER0 +VANDERWELT V AE1 N D ER0 W EH2 L T +VANDERWILLER V AE1 N D ER0 W IH2 L ER0 +VANDERZEE V AE1 N D ER0 Z IY2 +VANDEUVRES V AE1 N D V Z +VANDEVELDE V AE1 N D AH0 V EH2 L D +VANDIERES V AE2 N D IH1 R Z +VANDORY V AE0 N D AO1 R IY0 +VANDORY'S V AE0 N D AO1 R IY0 Z +VANDRAD V AE2 N D R AA1 D +VANDS V AE2 N D Z +VANDUZEN V AE2 N D UW1 Z AH0 N +VANDYCK V AE0 N D IH1 K +VANDYCK'S V AE0 N D IH1 K S +VANDYK V AE1 N D IH0 K +VANDYKE V AE2 N D AY1 K +VANDYKE'S V AE2 N D AY1 K S +VANDYKED V AE2 N D AY1 K T +VANDYKES V AE2 N D AY1 K S +VANDYNE V AE1 N D AY2 N +VANE V EY1 N +VANE'S V EY1 N Z +VANEAU V AE1 N OW1 +VANEBURY V AE0 N EH1 B EH2 R IY0 +VANEMAN V AE1 N M AH0 N +VANES V EY1 N Z +VANESKI V AE0 N EH1 S K IY0 +VANESSA V AH0 N EH1 S AH0 +VANESSA'S V AH0 N EH1 S AH0 Z +VANEY V EY1 N IY0 +VANG V AE1 NG +VANGE V AE1 N JH +VANGILT V AE0 N G IH2 L T +VANGUARD V AE1 N G AA2 R D +VANGUARDS V AE1 N G AA2 R D Z +VANGY V AE1 N JH IY0 +VANHANSEN V AE2 N HH AE1 N S AH0 N +VANHEIMERT V AE2 N HH AY0 M ER0 T +VANHOMRIGH V AE0 N HH AA1 M R AY2 +VANHORN V AE0 N HH AO1 R N +VANHORN'S V AE0 N HH AO1 R N Z +VANIA V AA1 N IY0 AH0 +VANIKORO V AA1 N IH0 K AO1 R OW0 +VANILLA V AH0 N IH1 L AH0 +VANILLE V AH0 N IH1 L IY0 +VANINATH V AE1 N IH0 N AE0 TH +VANINI V AH0 N IY1 N IY0 +VANIR V AH0 N IH1 R +VANISH V AE1 N IH0 SH +VANISH'D V AE1 N IH0 SH D +VANISHED V AE1 N IH0 SH T +VANISHES V AE1 N IH0 SH IH0 Z +VANISHETH V AE1 N IH0 SH IH0 TH +VANISHING V AE1 N IH0 SH IH0 NG +VANISHINGS V AE1 N IH0 SH IH0 NG Z +VANISHMENT V AE1 N IH0 SH M AH0 N T +VANITAS V AE0 N IY1 T AH0 S +VANITATUM V AE2 N AH0 T EY1 T AH0 M +VANITE V AE1 N AH0 T +VANITIE V AE1 N IH0 T IY0 +VANITIES V AE1 N AH0 T IY0 Z +VANITY V AE1 N AH0 T IY0 +VANITY V AE1 N IH0 T IY0 +VANITY'S V AE1 N IH0 T IY0 Z +VANKA V AE1 NG K AH0 +VANKIN V AE1 NG K IH0 N +VANLEIGH V AE1 N L AH0 +VANLIEF V AE0 N L IY1 F +VANLOADS V AE1 N L OW2 D Z +VANLOO V AE1 N L UW2 +VANN V AE1 N +VANNA V AE1 N AH0 +VANNEAU V AE0 N OW1 +VANNECK V AE0 N EH0 K +VANNECKS V AE0 N EH2 K S +VANNES V AE0 N Z +VANNI V AE1 N IY0 +VANNICOCK V AE0 N AH0 K AA2 K +VANNIER V AE1 N IY0 ER0 +VANNO V AE1 N OW0 +VANNO'S V AE1 N OW0 Z +VANNOZZA V AE0 N AA1 Z AH0 +VANNUCCI V AA0 N UW1 CH IY0 +VANNY V AE1 N IY0 +VANO V AA1 N OW0 +VANOC V AE0 N AA1 K +VANOUSHKA V AE1 N UH1 SH K AH0 +VANOZZA V AE0 N AA1 Z AH0 +VANQUISH V AE1 NG K W IH0 SH +VANQUISH'D V AE1 NG K W IH0 SH D +VANQUISHED V AE1 NG K W IH0 SH T +VANQUISHER V AE1 NG K W IH0 SH ER0 +VANQUISHERS V AE1 NG K W IH0 SH ER0 Z +VANQUISHES V AE1 NG K W IH0 SH IH0 Z +VANQUISHING V AE1 NG K W IH0 SH IH0 NG +VANQUISHMENT V AE1 NG K W IH0 SH M AH0 N T +VANREVEL V AE2 N R EH1 V AH0 L +VANS V AE1 N Z +VANSITTART V AE2 N S IH1 T ER0 T +VANSITTART'S V AE2 N S IH1 T ER0 T S +VANSLYPERKEN V AE2 N S L AY1 P ER0 K AH0 N +VANSLYPERKEN'S V AE2 N S L AY1 P ER0 K AH0 N Z +VANSTON V AE2 N S T AH0 N +VANSTONE V AE2 N S T OW1 N +VANSTONE'S V AE2 N S T OW2 N Z +VANSTRUTHER V AE2 N S T R AH1 DH ER0 +VANT V AH0 N T +VANTAGE V AE1 N T AH0 JH +VANTAGE V AE1 N T IH0 JH +VANTAGES V AE1 N IH0 JH IH0 Z +VANTAGES V AE1 N T IH0 JH IH0 Z +VANTED V AE1 N T AH0 D +VANTER V AE1 N T ER0 +VANTINE V AA0 N T IY1 N IY0 +VANTINE'S V AA0 N T IY1 N Z +VANTRASSON V AE2 N T R AE1 S AH0 N +VANTS V AE2 N T S +VANUA V AE2 N W AH0 +VANUCCI V AH0 N UW1 CH IY0 +VANULM V AE2 N W M +VANUS V AH0 N IH0 S +VANUXEM V AE0 N AH1 K S AH0 M +VANVES V AE0 N V EH0 Z +VANWARD V AE0 N W ER0 D +VANYA V AA1 N Y AH0 +VANYA'S V AA1 N Y AH0 Z +VANYUSHA V AA1 N Y UW1 SH AH0 +VANZETTI V AE2 N Z EH1 T IY0 +VAORNIA V AO1 R N IY0 AH0 +VAPEUR V EY1 P ER0 +VAPID V AE1 P IH0 D +VAPIDITY V AH0 P IH1 D AH0 T IY0 +VAPOOR V EY1 P UH1 R +VAPOR V EY1 P ER0 +VAPORED V EY1 P ER0 D +VAPORING V EY1 P ER0 IH0 NG +VAPORINGS V EY1 P ER0 IH0 NG Z +VAPORIZATION V EY0 P ER0 AH0 Z EY1 SH AH0 N +VAPORIZE V EY1 P ER0 AY2 Z +VAPORIZED V EY1 P ER0 AY2 Z D +VAPOROUS V EY1 P ER0 AH0 S +VAPOROUSLY V EY1 P ER0 AH0 S L IY0 +VAPORS V EY1 P ER0 Z +VAPORY V EY1 P ER0 IY0 +VAPOUR V EY1 P AO1 R +VAPOURED V EY1 P AW1 ER0 D +VAPOURING V EY1 P AO1 R IH0 NG +VAPOURINGS V EY1 P AO1 R IH0 NG Z +VAPOURISH V EY1 P ER1 IH0 SH +VAPOUROUS V EY1 P ER0 AH0 S +VAPOURS V EY1 P AO1 R Z +VAPOURY V EY1 P UH0 R IY0 +VAPP V AE1 P +VAQUERO V AA0 K W EH1 R OW0 +VAQUERO'S V AA0 K W EH1 R OW0 Z +VAQUEROS V AA0 K W EH1 R OW0 Z +VAQUIERAS V AA0 K W IH0 R AA1 Z +VAR V AA0 R +VARA V AA1 R AH0 +VARACHON V AA1 R AH0 CH AH0 N +VARAJOU V ER0 AA1 ZH UW2 +VARALLO V ER0 AA1 L OW0 +VARANDEUIL V EH1 R AE0 N D UW1 AH0 L +VARANGIANS V ER0 AA1 N JH AH0 N Z +VARAS V AA1 R AH0 Z +VARBARRIERE V AA0 R B AA0 R IH1 R IY0 +VARCEK V AA0 R S IH0 K +VARCHI V AA1 R K IY0 +VARCO V AA1 R K OW0 +VARCOE V AA1 R K OW0 +VARD V AA1 R D +VARD'S V ER0 D Z +VARDARELLI V AA0 R D AA0 R EH1 L IY0 +VARDAS V AA1 R D AH0 S +VARDE V AA1 R D +VARDEMAN V AA1 R D M AH0 N +VARDEN V AA1 R D AH0 N +VARDEN'S V AA1 R D AH0 N Z +VARDIELLO V AA0 R D IY0 EH1 L OW0 +VARDIENO V AA0 R D IY0 EH1 N OW0 +VARDIN V AA1 R D IH0 N +VARDON V AA0 R D AO1 N +VARDRI V AA1 R D R IY0 +VARDRI'S V AA1 R D R IY0 Z +VARE V EY1 R +VARELA V AA0 R EY1 L AH0 +VARENIKA V AA0 R EH1 N IH0 K AH0 +VARENKA V AA0 R EH1 NG K AH0 +VARENNA V AA0 R EH1 N AH0 +VARENNE V AA0 R EH1 N +VARENNES V AA0 R EH1 N Z +VARENS V EH1 R AH0 N Z +VARESE V AA0 R EY1 Z IY0 +VARGA V AA1 R G AH0 +VARGAS V AA1 R G AH0 S +VARGERS V AA1 R G ER0 Z +VARGIN V AA1 R G IH0 N +VARGNES V AA1 R G N Z +VARGRAVE V AA1 R G R EY2 V +VARGUS V AA1 R G AH0 S +VARI V AA1 R IY0 +VARIA V EH1 R IY0 AH0 +VARIA'S V EH1 R IY0 AH0 Z +VARIABILIS V EH1 R IY0 AH0 B IH1 L AH0 S +VARIABILITY V EH0 R IY0 AH0 B IH1 L IH0 T IY0 +VARIABLE V EH1 R IY0 AH0 B AH0 L +VARIABLENESS V EH1 R IY0 AH0 B AH0 L N AH0 S +VARIABLES V EH1 R IY0 AH0 B AH0 L Z +VARIABLY V EH1 R IY0 AH0 B L IY0 +VARIAE V EH2 R IY0 EY1 +VARIAGATED V ER0 AY1 AH0 G EY2 T IH0 D +VARIAN V EH1 R IY0 AH0 N +VARIANCE V EH1 R IY0 AH0 N S +VARIANCES V EH1 R IY0 AH0 N S IH0 Z +VARIANI V EH2 R IY0 AA1 N IY0 +VARIANT V EH1 R IY0 AH0 N T +VARIANTS V EH1 R IY0 AH0 N T S +VARIAS V EH1 R IY0 AH0 Z +VARIATION V EH2 R IY0 EY1 SH AH0 N +VARIATIONS V EH2 R IY0 EY1 SH AH0 N Z +VARICK V EH1 R IH0 K +VARICK'S V EH1 R IH0 K S +VARICKS V EH1 R IH0 K S +VARICOLORED V EH1 R IY0 K AH1 L ER0 D +VARICOSE V EH1 R AH0 K OW2 S +VARIE V EH1 R IY0 +VARIED V EH1 R IY0 D +VARIEDLY V ER0 AY1 AH0 D L IY0 +VARIEGATA V EH1 R IH0 G AA1 T AH0 +VARIEGATE V EH1 R IH0 G EY0 T +VARIEGATED V EH1 R IH0 G EY0 T AH0 D +VARIEGATION V EH1 R IH0 G EY1 SH AH0 N +VARIEGATIONS V EH1 R IH0 G EY1 SH AH0 N Z +VARIES V EH1 R IY0 Z +VARIETE V ER0 AY1 AH0 T +VARIETES V ER0 AY1 AH0 T S +VARIETIES V ER0 AY1 AH0 T IY0 Z +VARIETY V ER0 AY1 AH0 T IY0 +VARIETY'S V ER0 AY1 AH0 T IY0 Z +VARIGATED V EH1 R IH0 G EY2 T IH0 D +VARIN V AA0 R IY1 N +VARINA V AA0 R IY1 N AH0 +VARINAS V AA0 R IY1 N AH0 Z +VARIOLOID V EH1 R IY0 AH0 L OY2 D +VARION V EH1 R IY0 AH0 N +VARIORUM V EH1 R IY0 AO1 R AH0 M +VARIOS V EH1 R IY0 OW0 Z +VARIOUS V EH1 R IY0 AH0 S +VARIOUSLY V EH1 R IY0 AH0 S L IY0 +VARIOUSNESS V EH1 R IY0 AH0 S N AH0 S +VARIUM V EH1 R IY0 AH0 M +VARIUS V EH1 R IY0 AH0 S +VARK V AA0 R K +VARKA V AA1 R K AH0 +VARL V AA1 R L +VARLAAM V AA1 R L AA1 M +VARLET V AA1 R L AH0 T +VARLET'S V AA1 R L AH0 T S +VARLETS V AA1 R L AH0 T S +VARLEY V AA1 R L IY0 +VARLEY'S V AA1 R L IY0 Z +VARLEYS V AA1 R L IY0 Z +VARM V AA1 R M +VARMAN V AA1 R M AH0 N +VARMER V AA1 R M ER0 +VARMIN V AA1 R M AH0 N +VARMINT V AA1 R M IH0 N T +VARMINT'S V AA1 R M IH0 N T S +VARMINTS V AA1 R M IH0 N T S +VARMONT V AA1 R M AA2 N T +VARMOUNT V AA1 R M AW2 N T +VARN V AA1 R N +VARNA V AA1 R N AH0 +VARNE V AA1 R N +VARNER V AA1 R N ER0 +VARNEY V AA1 R N IY0 +VARNEY'S V AA1 R N IY0 Z +VARNHAGEN V AA1 R N HH AA2 G AH0 N +VARNHAGEN'S V AA1 R N HH AA2 G AH0 N Z +VARNIER V AA1 R N IY0 ER0 +VARNISH V AA1 R N IH0 SH +VARNISHED V AA1 R N IH0 SH T +VARNISHES V AA1 R N IH0 SH AH0 Z +VARNISHES V AA1 R N IH0 SH IH0 Z +VARNISHING V AA1 R N IH0 SH IH0 NG +VARNUM V AA1 R N AH0 M +VARO V AA1 R OW0 +VARO V EH1 R OW0 +VARONA V AA0 R OW1 N AH0 +VAROS V AA1 R OW0 Z +VARRA V AA1 R AH0 +VARRILLAT V AA0 R IH0 L AE0 T +VARRO V AA1 R OW0 +VARRO'S V AA1 R OW0 Z +VARRY V AA0 R IY0 +VARS V AA1 R Z +VARSAL V AA1 R S AH0 L +VARSE V AA1 R S +VARSES V AA1 R S IH0 Z +VARSIEFF V AA1 R S IY2 F +VARSINI V AA0 R S IY1 N IY0 +VARSITIES V AA1 R S IH0 T IY0 Z +VARSITY V AA1 R S IH0 T IY0 +VARSOVIE V AA1 R S AH0 V IY0 +VARTUE V AA1 R CH UW0 +VARUNA V AA0 R UW1 N AH0 +VARUS V EH1 R AH0 S +VARVARA V AA0 R V AA1 R AH0 +VARVARA'S V AA0 R V AA1 R AH0 Z +VARY V EH1 R IY0 +VARYA V EH0 R Y AH0 +VARYING V EH1 R IY0 IH0 NG +VARZIN V AA1 R Z IH0 N +VAS V AA0 Z +VASA V AA1 S AH0 +VASANTAKA V AA0 S AA1 N T AA1 K AH0 +VASANTASENA V AA0 S AA1 N T AH0 S EH1 N AH0 +VASARHELY V AA0 S AA1 R HH L IY0 +VASARI V AA0 S AA1 R IY0 +VASARI'S V AA0 S AA1 R IY0 Z +VASAVA V AE1 S AH0 V AH0 +VASAVADATTA V AE1 S AH0 V AH0 D AA1 T AH0 +VASCO V AE1 S K OW0 +VASCON V AE0 S K AH0 N +VASCONS V AE0 S K AH0 N Z +VASCULAR V AE1 S K Y AH0 L ER0 +VASE V AA1 Z +VASE V EY1 S +VASELINE V AE1 S AH0 L IY2 N +VASES V AA1 Z IH0 Z +VASES V EY1 S AH0 Z +VASH V AE1 SH +VASHON V AE1 SH AH0 N +VASHTAR V AE1 SH T AA0 R +VASHTI V AE1 SH T IY0 +VASHTI'S V AE1 SH T IY0 Z +VASILE V AA1 S AH0 L +VASILI V AA0 S IY1 L IY0 +VASILICI V AE1 S IY0 L IY1 CH IY0 +VASILICI'S V AA0 S IY1 L IY0 CH IY0 Z +VASILIEVICH V AH0 Z IH1 L Y AH0 V IH2 CH +VASILIEVNA V AH0 Z IH1 L Y AH0 V N AH0 +VASILIEVSKOE V AE1 S AH0 L IY2 V S K OW1 +VASILISA V AE1 S AH0 L IY1 S AH0 +VASILISSA V AE1 S AH0 L IH1 S AH0 +VASILKOVSKI V AE1 S AH0 L K AA1 V S K IY0 +VASILLI V AA0 S IH1 L IY0 +VASILOVICH V AE1 S AH0 L AH0 V IH0 CH +VASILY V AE1 S AH0 L IY0 +VASISHTHA V AA0 S IY1 SH TH AH0 +VASKA V AA1 S K AH0 +VASLING V AE1 S L IH0 NG +VASN'T V AA1 Z AH0 N T +VASO V AA1 S OW0 +VASOMOTOR V AE1 S AH0 M OW1 T ER0 +VASQUES V AA1 S K W EH0 S +VASQUEZ V AE0 S K EH1 Z +VASS V AE1 S +VASSAL V AE1 S AH0 L +VASSAL'S V AE1 S AH0 L Z +VASSALAGE V AE1 S AH0 L IH0 JH +VASSALARO V AE1 S AH0 L AA1 R OW0 +VASSALL V AE1 S AH0 L +VASSALS V AE1 S AH0 L Z +VASSAR V AE1 S ER0 +VASSE V AE1 S +VASSELITCH V AE1 S IH0 L IH0 CH +VASSELOT V AE1 S IH0 L AH0 T +VASSELOT'S V AE1 S AH0 L AA2 T S +VASSEUR V AE1 S ER0 +VASSIE V AE1 S IY0 +VASSIE'S V AE1 S IY0 Z +VASSILAN V AE2 S IH1 L AH0 N +VASSILEVITCH V AE2 S IH1 L AH0 V IH0 CH +VASSILI V AE2 S IH1 L IY0 +VASSILI'S V AE2 S IH1 L IY0 Z +VASSILIEVITCH V AE2 S IH1 L Y AH0 V IH2 CH +VASSILIEVITCH'S V AE2 S IH1 L Y AH0 V IH2 CH IH0 Z +VASSILIEVNA V AE2 S IH1 L Y AH0 V N AH0 +VASSILISSA V AE2 S IH1 L IH1 S AH0 +VASSILITCH V AE2 S IH1 L IH0 CH +VASSILITZI V AE2 S IH1 L IH1 T S IY0 +VASSILY V AH0 S IH1 L IY0 +VASSILYEV V AE2 S IH1 L IY0 EH0 V 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UW1 +WHERETO W EH0 R EH1 T OW0 +WHEREUNDER W EH1 R AH0 N D ER0 +WHEREUNTO W EH1 R AH0 N T OW0 +WHEREUPON HH W EH1 R AH0 P AA1 N +WHEREUPON W EH1 R AH0 P AA1 N +WHEREVER HH W EH0 R EH1 V ER0 +WHEREVER W EH0 R EH1 V ER0 +WHEREWITH W EH1 R W IH0 DH +WHEREWITHAL HH W EH1 R W IH0 DH AO2 L +WHEREWITHAL W EH1 R W IH0 DH AO2 L +WHERFORE W ER1 F AO2 R +WHERIVER W EH0 R AY2 V ER0 +WHERNSIDE W ER1 N S AY2 D +WHERO HH W EH1 R OW0 +WHERO'S HH W IH1 R OW0 Z +WHEROF W EH0 R AH0 F +WHERRIES HH W EH1 R IY0 Z +WHERRIT HH W EH1 R IH0 T +WHERRY HH W EH1 R IY0 +WHERRY W EH1 R IY0 +WHERRY'S HH W EH1 R IY0 Z +WHERWELL W ER1 W EH0 L +WHET HH W EH1 T +WHET W EH1 T +WHETHEN HH W EH1 TH AH0 N +WHETHER HH W EH1 DH ER0 +WHETHER W EH1 DH ER0 +WHETS HH W EH1 T S +WHETSTONE HH W EH1 T S T OW2 N +WHETSTONE W EH1 T S T OW2 N +WHETSTONES HH W EH1 T S T OW2 N Z +WHETTED HH W EH1 T IH0 D +WHETTED W EH1 T IH0 D +WHETTING HH W EH1 T IH0 NG +WHETZEL HH W EH1 T Z AH0 L +WHETZEL W EH1 T Z AH0 L +WHEUGH HH W +WHEW HH W UW1 +WHEW HH Y UW1 +WHEW W UW1 +WHEWELL HH W EH1 W EH0 L +WHEWELL'S HH W UW1 AH0 L Z +WHEY HH W EY1 +WHEY W EY1 +WHI HH W AY1 +WHIBLEY HH W IH1 B L IY0 +WHIBLEY'S HH W IH1 B L IY0 Z +WHICH HH W IH1 CH +WHICH W IH1 CH +WHICH'LL HH W IH1 CH L +WHICH'S HH W IH1 CH IH0 Z +WHICH'S W IH1 CH IH0 Z +WHICHE HH W IH1 CH +WHICHEHALSE HH W IH1 CH EH0 HH AE1 L S +WHICHELLO HH W IH0 CH EH1 L OW0 +WHICHELLO'S HH W IH0 CH EH1 L OW0 Z +WHICHELO HH W IH1 CH IH0 L OW0 +WHICHEVER HH W IH0 CH EH1 V ER0 +WHICHEVER W IH0 CH EH1 V ER0 +WHICHSOEVER HH W IH1 CH S OW0 EH1 V ER0 +WHICK HH W IH1 K +WHICKER HH W IH1 K ER0 +WHICKER W IH1 K ER0 +WHICKERED HH W IH1 K ER0 D +WHIDAH HH W IH1 D AH0 +WHIDBY HH W IH1 D B IY0 +WHIDBY W IH1 D B IY0 +WHIDDEN HH W IH1 D AH0 N +WHIDDEN W IH1 D AH0 N +WHIDDON HH W IH1 D AH0 N +WHIDDON W IH1 D AH0 N +WHIFF HH W IH1 F +WHIFF W IH1 F +WHIFFED HH W IH1 F T +WHIFFET HH W IH1 F IH0 T +WHIFFING HH W IH1 F IH0 NG +WHIFFLE HH W IH1 F AH0 L +WHIFFLER HH W IH1 F L ER0 +WHIFFLETIT HH W IH1 F L IH0 T IH1 T +WHIFFLETREE HH W IH1 F AH0 L T R IY2 +WHIFFLETREES HH W IH1 F AH0 L T R IY2 Z +WHIFFLING HH W IH1 F L IH0 NG +WHIFFS HH W IH1 F S +WHIG HH W IH1 G +WHIG W IH1 G +WHIGGERY HH W IH1 G ER0 IY0 +WHIGGISH HH W IH1 G IH0 SH +WHIGGISM HH W IH1 G IH0 Z AH0 M +WHIGS HH W IH1 G Z +WHIGS W IH1 G Z +WHIL'ST W AY1 L S T +WHILDEN HH W AY1 L D AH0 N +WHILDEN W AY1 L D AH0 N +WHILE HH W AY1 L +WHILE W AY1 L +WHILE'S W AY1 L Z +WHILED W AY1 L D +WHILERE W AY1 L ER0 +WHILES HH W AY1 L Z +WHILES W AY1 L Z +WHILEST W AY1 L AH0 S T +WHILING W AY1 L IH0 NG +WHILK HH W IH1 L K +WHILOM W AY1 L AH0 M +WHILOME W AY1 L OW1 M +WHILSE W AY1 L S +WHILST W AY1 L S T +WHIM HH W IH1 M +WHIM W IH1 M +WHIMPER HH W IH1 M P ER0 +WHIMPER W IH1 M P ER0 +WHIMPERED HH W IH1 M P ER0 D +WHIMPERIN HH W IH1 M P ER0 IH0 N +WHIMPERING HH W IH1 M P ER0 IH0 NG +WHIMPERING W IH1 M P ER0 IH0 NG +WHIMPERINGLY HH W IH1 M P ER0 IH0 NG L IY0 +WHIMPERINGS HH W IH1 M P ER0 IH0 NG Z +WHIMPERS HH W IH1 M P ER0 Z +WHIMPLE HH W IH1 M P AH0 L +WHIMPLE'S HH W IH1 M P AH0 L Z +WHIMPOLE HH W IH1 M P OW2 L +WHIMS HH W IH1 M Z +WHIMS W IH1 M Z +WHIMSEY HH W IH1 M Z IY0 +WHIMSEYS HH W IH1 M Z IY0 Z +WHIMSICAL HH W IH1 M Z IH0 K AH0 L +WHIMSICAL W IH1 M Z IH0 K AH0 L +WHIMSICALITIES HH W IH1 M Z IH0 K AE1 L AH0 T IY0 Z +WHIMSICALITY HH W IH1 M Z IH0 K AE1 L AH0 T IY0 +WHIMSICALLY HH W IH1 M Z IH0 K L IY0 +WHIMSICALLY W IH1 M Z IH0 K L IY0 +WHIMSIES HH W IH1 M Z IY0 Z +WHIMSY HH W IH1 M S IY0 +WHIMSY W IH1 M S IY0 +WHIN HH W AY1 N +WHINBOROUGH HH W IH1 N B ER0 OW0 +WHINCHER HH W IH1 N CH ER0 +WHINDALE HH W AY1 N D EY2 L +WHINE HH W AY1 N +WHINE W AY1 N +WHINED HH W AY1 N D +WHINER HH W AY1 N ER0 +WHINER W AY1 N ER0 +WHINERS HH W AY1 N ER0 Z +WHINERS W AY1 N ER0 Z +WHINES HH W AY1 N Z +WHINES W AY1 N Z +WHINEY HH W AY1 N IY0 +WHINFIELD HH W IH1 N F IY2 L D +WHING HH W IH1 NG +WHINGER HH W IH1 NG ER0 +WHINGS HH W IH1 NG Z +WHININ HH W AY1 N IH0 N +WHINING HH W AY1 N IH0 NG +WHINING W AY1 N IH0 NG +WHININGLY HH W AY1 N IH0 NG L IY0 +WHININGS HH W AY1 N IH0 NG Z +WHINIVER HH W AY1 N IH0 V ER0 +WHINNER W IH1 N ER0 +WHINNEY HH W IH1 N IY0 +WHINNEY W IH1 N IY0 +WHINNEYED W IH1 N IY0 D +WHINNEYING W IH1 N IY0 IH0 NG +WHINNIE W IH1 N IY0 +WHINNIED W IH1 N IY0 D +WHINNIES W IH1 N IY0 Z +WHINNY W IH1 N IY0 +WHINNYFOLD W IH1 N IY0 F OW2 L D +WHINNYING W IH1 N IY0 IH0 NG +WHINS HH W IH1 N Z +WHINSTANE HH W IH1 N S T AH0 N +WHINSTONE HH W IH1 N S T OW2 N +WHINTHORPE HH W AY1 N TH AO0 R P +WHINY HH W AY1 N IY0 +WHINY W AY1 N IY0 +WHIP HH W IH1 P +WHIP W IH1 P +WHIP'S HH W IH1 P S +WHIPCORD HH W IH1 P K AO0 R D +WHIPCORDS HH W IH1 P K AO1 R D Z +WHIPHAND HH W IH1 P HH AE2 N D +WHIPLASH HH W IH1 P L AE2 SH +WHIPLASH W IH1 P L AE2 SH +WHIPLASHES HH W IH1 P L AE2 SH AH0 Z +WHIPLASHES W IH1 P L AE2 SH AH0 Z +WHIPLIKE W IH1 P L AY2 K +WHIPP HH W IH1 P +WHIPP W IH1 P +WHIPP'D HH W IH1 P D +WHIPPED HH W IH1 P T +WHIPPED W IH1 P T +WHIPPER HH W IH1 P ER0 +WHIPPERS HH W IH1 P ER0 Z +WHIPPERSNAPPER HH W IH1 P ER0 S N AE1 P ER0 +WHIPPERSNAPPERS HH W IH1 P ER0 S N AE1 P ER0 Z +WHIPPET HH W IH1 P AH0 T +WHIPPETS HH W IH1 P IH0 T S +WHIPPIN HH W IH1 P IH0 N +WHIPPING HH W IH1 P IH0 NG +WHIPPING W IH1 P IH0 NG +WHIPPINGS HH W IH1 P IH0 NG Z +WHIPPLE HH W IH1 P AH0 L +WHIPPLE W IH1 P AH0 L +WHIPPLE'S HH W IH1 P AH0 L Z +WHIPPLE'S W IH1 P AH0 L Z +WHIPPLEBY HH W IH1 P AH0 L B IY0 +WHIPPLETON HH W IH1 P AH0 L T AH0 N +WHIPPLETON'S HH W IH1 P AH0 L T AH0 N Z +WHIPPOORWILL HH W IH1 P ER0 W IH2 L +WHIPPOORWILL'S HH W IH1 P ER0 W IH2 L Z +WHIPPOORWILLS HH W IH1 P ER0 W IH2 L Z +WHIPPOORWILLS W IH1 P ER0 W IH2 L Z +WHIPS HH W IH1 P S +WHIPS W IH1 P S +WHIPSAW HH W IH1 P S AO2 +WHIPSAW W IH1 P S AO2 +WHIPSAWED HH W IH1 P S AO2 D +WHIPSAWED W IH1 P S AO2 D +WHIPSTAFF HH W IH1 P S T AE2 F +WHIPSTER HH W IH1 P S T ER0 +WHIPSTOCK HH W IH1 P S T AA2 K +WHIPT HH W IH1 P T +WHIR W ER1 +WHIRK W ER1 K +WHIRL HH W ER1 L +WHIRL W ER1 L +WHIRL'D HH W ER1 L D +WHIRLED HH W ER1 L D +WHIRLED W ER1 L D +WHIRLER HH W ER1 L ER0 +WHIRLIGIG HH W ER1 L IH0 JH IH0 G +WHIRLIGIGS HH W ER1 L IH0 JH IH1 G Z +WHIRLIN HH W ER1 L IH0 N +WHIRLING HH W ER1 L IH0 NG +WHIRLING W ER1 L IH0 NG +WHIRLINGS HH W ER1 L IH0 NG Z +WHIRLPOOL HH W ER1 L P UW2 L +WHIRLPOOL W ER1 L P UW2 L +WHIRLPOOL'S HH W ER1 L P UW2 L Z +WHIRLPOOL'S W ER1 L P UW2 L Z +WHIRLPOOLERS HH W ER1 L P UW2 L ER0 Z +WHIRLPOOLS HH W ER1 L P UW2 L Z +WHIRLPOOLS W ER1 L P UW2 L Z +WHIRLS HH W ER1 L Z +WHIRLWIND HH W ER1 L W IH2 N D +WHIRLWIND W ER1 L W IH2 N D +WHIRLWIND'S HH W ER1 L W IH2 N D Z +WHIRLWINDS HH W ER1 L W IH2 N D Z +WHIRLY HH W ER1 L IY0 +WHIRR W ER1 +WHIRR'S W ER1 Z +WHIRRED W ER1 D +WHIRRING HH W ER1 IH0 NG +WHIRRING W ER1 IH0 NG +WHIRRINGS W ER1 IH0 NG Z +WHIRRS W ER1 Z +WHIRS W ER1 Z +WHIRTER W ER1 T ER0 +WHIS W IH1 S +WHISH HH W IH1 SH +WHISHAW HH W IH1 SH AO2 +WHISHING HH W IH1 SH IH0 NG +WHISHT HH W IH1 SH T +WHISK HH W IH1 S K +WHISK W IH1 S K +WHISKED HH W IH1 S K T +WHISKED W IH1 S K T +WHISKER HH W IH1 S K ER0 +WHISKER W IH1 S K ER0 +WHISKERED W IH1 S K ER0 D +WHISKERLESS W IH1 S K ER0 L AH0 S +WHISKERS HH W IH1 S K ER0 Z +WHISKERS W IH1 S K ER0 Z +WHISKERY W IH1 S K ER0 IY0 +WHISKEY HH W IH1 S K IY0 +WHISKEY W IH1 S K IY0 +WHISKEY'S W IH1 S K IY0 Z +WHISKEYS HH W IH1 S K IY0 Z +WHISKEYS W IH1 S K IY0 Z +WHISKIES W IH1 S K IY0 Z +WHISKING W IH1 S K IH0 NG +WHISKS HH W IH1 S K S +WHISKS W IH1 S K S +WHISKY HH W IH1 S K IY0 +WHISKY W IH1 S K IY0 +WHISKY'S W IH1 S K IY0 Z +WHISP W IH1 S P +WHISP'RING W IH1 S P R IH0 NG +WHISPER HH W IH1 S P ER0 +WHISPER W IH1 S P ER0 +WHISPER'D W IH1 S P ER0 D +WHISPERED HH W IH1 S P ER0 D +WHISPERED W IH1 S P ER0 D +WHISPERER W IH1 S P ER0 ER0 +WHISPERERS W IH1 S P ER0 ER0 Z +WHISPERETH W IH1 S P ER0 IH0 TH +WHISPERFOOT W IH1 S P ER1 F UH0 T +WHISPERIN W IH1 S P ER0 IH0 N +WHISPERING HH W IH1 S P ER0 IH0 NG +WHISPERING W IH1 S P ER0 IH0 NG +WHISPERINGLY W IH1 S P ER0 IH0 NG L IY0 +WHISPERINGS W IH1 S P ER0 IH0 NG Z +WHISPERS HH W IH1 S P ER0 Z +WHISPERS W IH1 S P ER0 Z +WHISPERY W IH1 S P ER0 IY0 +WHISPS W IH1 S P S +WHIST W IH1 S T +WHISTLE HH W IH1 S AH0 L +WHISTLE W IH1 S AH0 L +WHISTLE'S HH W IH1 S AH0 L Z +WHISTLEBINKIE HH W IH1 S AH0 L B IH0 NG K IY0 +WHISTLED HH W IH1 S AH0 L D +WHISTLED W IH1 S AH0 L D +WHISTLEING HH W IH1 S AH0 L IH0 NG +WHISTLER HH W IH1 S L ER0 +WHISTLER W IH1 S L ER0 +WHISTLER'S W IH1 S L ER0 Z +WHISTLERIAN W IH1 S L ER0 IY1 AH0 N +WHISTLERS HH W IH1 S L ER0 Z +WHISTLERS W IH1 S L ER0 Z +WHISTLES HH W IH1 S AH0 L Z +WHISTLES W IH1 S AH0 L Z +WHISTLIN W IH1 S L IH0 N +WHISTLING HH W IH1 S L IH0 NG +WHISTLING W IH1 S L IH0 NG +WHISTLINGS W IH1 S L IH0 NG Z +WHISTON HH W IH1 S T AH0 N +WHISTON W IH1 S T AH0 N +WHISTON'S W IH1 S T AH0 N Z +WHIT HH W IH1 T +WHIT W IH1 T +WHITADDER HH W IH1 T AH0 D ER0 +WHITAKER HH W IH1 T AH0 K ER0 +WHITAKER W IH1 T AH0 K ER0 +WHITAKER'S HH W IH1 T AH0 K ER0 Z +WHITALL HH W IH1 T AH0 L +WHITBREAD HH W IH1 T B R EH2 D +WHITBREAD W IH1 T B R EH2 D +WHITBURN HH W IH1 T B ER2 N +WHITBURN'S HH W IH1 T B ER0 N Z +WHITBURY HH W IH1 T B EH2 R IY0 +WHITBY HH W IH1 T B IY0 +WHITBY W IH1 T B IY0 +WHITBY'S HH W IH1 T B IY0 Z +WHITCHURCH HH W IH1 T CH ER2 CH +WHITCHURCH W IH1 T CH ER2 CH +WHITCLIFFE HH W IH1 T K L IH0 F +WHITCOMB HH W IH1 T K AH0 M +WHITCOMB W IH1 T K AH0 M +WHITCOMB'S HH W IH1 T K AH0 M Z +WHITCOMBE HH W IH1 T K AH0 M +WHITE HH W AY1 T +WHITE W AY1 T +WHITE'S HH W AY1 T S +WHITE'S W AY1 T S +WHITEAWAY HH W IH1 T AH0 W EY2 +WHITEBAIT HH W AY1 T B EY1 T +WHITEBEAR HH W AY1 T B EH2 R +WHITEBECK HH W AY1 T B EH0 K +WHITEBOY HH W AY1 T B OY2 +WHITEBOYS HH W AY1 T B OY2 Z +WHITECAP HH W AY1 T K AE2 P +WHITECAPPED HH W AY1 T K AE2 P T +WHITECAPS HH W AY1 T K AE2 P S +WHITECHAPEL HH W AY1 T K AE1 P AH0 L +WHITECHOKER HH W AY1 T K AH0 K ER0 +WHITECLETT HH W AY1 T K L IH0 T +WHITECLIFF HH W AY1 T K L IH0 F +WHITECLIFFE HH W AY1 T K L IH0 F +WHITECRAIGS HH W AY1 T K R EY1 G Z +WHITECROSS HH W AY1 T K R AO2 S +WHITED HH W AY1 T IH0 D +WHITED W AY1 T IH0 D +WHITEFACE HH W AY1 T F EY2 S +WHITEFACED HH W AY1 T F AH0 S T +WHITEFIELD HH W AY1 T F IY2 L D +WHITEFIELD W AY1 T F IY2 L D +WHITEFIELD'S HH W AY1 T F IY2 L D Z +WHITEFIRE HH W AY1 T F AY2 R +WHITEFISH HH W AY1 T F IH2 SH +WHITEFISH W AY1 T F IH2 SH +WHITEFOOT HH W AY1 T F UH2 T +WHITEFRIARS HH W AY1 T F R AY1 ER0 Z +WHITEHAIRED HH W AY1 T HH EH1 R D +WHITEHALL HH W AY1 T HH AO2 L +WHITEHALL W AY1 T HH AO2 L +WHITEHALL'S HH W AY1 T HH AO2 L Z +WHITEHALL'S W AY1 T HH AO2 L Z +WHITEHAVEN HH W AY1 T HH EY2 V AH0 N +WHITEHEAD HH W AY1 T HH EH2 D +WHITEHEAD W AY1 T HH EH2 D +WHITEHEAD'S HH W AY1 T HH EH2 D Z +WHITEHEAD'S W AY1 T HH EH2 D Z +WHITEHEADED HH W AY1 T HH EH2 D IH0 D +WHITEHILL HH W AY1 T HH IH2 L +WHITEHILL W AY1 T HH IH2 L +WHITEHORSE HH W AY1 T HH AO2 R S +WHITEHORSE W AY1 T HH AO2 R S +WHITEHOUSE HH W AY1 T HH AW2 S +WHITEHOUSE W AY1 T HH AW2 S +WHITEISH HH W AY1 T IH0 SH +WHITELADIES HH W AY1 T EY2 D IY0 Z +WHITELAND HH W AY1 T L AH0 N D +WHITELAW HH W AY1 T L AO2 +WHITELAW W AY1 T L AO2 +WHITELAW'S HH W AY1 T L AO2 Z 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AY1 T S AY2 D Z +WHITESIDES W AY1 T S AY2 D Z +WHITEST HH W AY1 T IH0 S T +WHITEST W AY1 T IH0 S T +WHITESTONE HH W AY1 T S T OW2 N +WHITESTONE W AY1 T S T OW2 N +WHITETAIL HH W AY1 T EY2 L +WHITETAIL HH W AY1 T T EY2 L +WHITETAIL W AY1 T T EY2 L +WHITETHORN HH W AY1 T T HH ER0 N +WHITETHROAT HH W AY1 T IH0 TH R OW1 T +WHITETHROATS HH W AY1 T IH0 TH R OW1 T S +WHITEWASH HH W AY1 T W AA2 SH +WHITEWASH W AY1 T W AA2 SH +WHITEWASHED HH W AY1 T W AA2 SH T +WHITEWASHED W AY1 T W AA2 SH T +WHITEWASHER HH W AY1 T W AA2 SH ER0 +WHITEWASHERS HH W AY1 T W AA2 SH ER0 Z +WHITEWASHES HH W AY1 T W AA2 SH AH0 Z +WHITEWASHIN HH W AY1 T W AA2 SH IH0 N +WHITEWASHING HH W AY1 T W AA2 SH IH0 NG +WHITEWATER HH W AY1 T W AO2 T ER0 +WHITEWATER W AY1 T W AO2 T ER0 +WHITEWAYS HH W AY1 T W EY2 Z +WHITEWEBS HH W AY1 T W EH2 B Z +WHITEWING HH W AY1 T W IH2 NG +WHITEWOOD HH W AY1 T W UH2 D +WHITEY HH W AY1 T IY0 +WHITEY HH W AY1 T IY2 +WHITEY W AY1 T IY2 +WHITEY'S HH W AY1 T IY2 Z +WHITFIELD HH W IH1 T F IY0 L D 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AA1 R K +YARKAND Y AA0 R K AE2 N D +YARKANDI Y AA0 R K AE1 N D IY0 +YARKER Y AA1 R K ER0 +YARKHUN Y AA1 R K HH AH0 N +YARLETT Y AA1 R L IH0 T +YARLEYS Y AA1 R L IY0 Z +YARLOO Y AA1 R L UW2 +YARMOUTH Y AA1 R M AH0 TH +YARN Y AA1 R N +YARN'S Y AA1 R N Z +YARNALL Y AA1 R N AH0 L +YARNED Y AA1 R N D +YARNELL Y AA1 R N AH0 L +YARNI Y AA1 R N IY0 +YARNIN Y AA1 R N IH0 N +YARNING Y AA1 R N IH0 NG +YARNINGDALE Y AA1 R N IH0 NG D EY2 L +YARNITH Y AA1 R N IH0 TH +YARNOVA Y AA1 R N OW1 V AH0 +YARNS Y AA1 R N Z +YAROSLAV Y AE1 R AH0 S L AA0 V +YAROSLAVL Y AE1 R AH0 S L AA0 V L +YARPE Y AA1 R P +YARRA Y AA1 R AH0 +YARRAGERH Y ER0 AA1 ZH ER0 HH +YARRAHAPPINI Y AE1 R AH0 HH AE2 P IY1 N IY0 +YARRELL Y AE1 R AH0 L +YARRIBA Y AA1 R IY1 B AH0 +YARRO Y AA1 R OW0 +YARROW Y AA1 R OW0 +YARROW'S Y AA1 R OW0 Z +YARRY Y AE1 R IY0 +YARTSEV Y AA1 R T S EH0 V +YARZA Y AA1 R Z AH0 +YAS Y AA2 S +YAS'M Y AA2 S M +YASALA Y AA0 S AA1 L AH0 +YASE Y EY1 Z +YASH Y AE2 SH +YASHA Y AE1 SH AH0 +YASHIKI Y AA0 SH IY1 K IY0 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T ER0 N AY2 T +YESTERYEAR Y EH1 S T ER0 Y IH2 R +YESTHERDAY Y EH1 S DH ER0 D EY2 +YESTIDDY Y EH1 S T IH1 D IY0 +YESTREEN Y EH1 S T R IY2 N +YET Y EH1 T +YET' Y EY1 +YETEL Y EH1 T AH0 L +YETEVE Y EH1 T AH0 V +YETH Y EH1 TH +YETHOLM Y EH1 T HH OW2 L M +YETIVE Y AH0 T IH0 V +YETIVE'S Y AH0 T IH0 V Z +YETMORE Y EH1 T M AO0 R +YETMORE'S Y EH1 T M AO0 R Z +YETSKO Y EH1 T S K OW0 +YETT Y EH1 T +YETTA Y EH1 T AH0 +YETTA'S Y EH1 T AH0 Z +YETTER Y EH1 T ER0 +YETTS Y EH1 T S +YEU Y UW1 +YEUX Y UW1 +YEVER Y EH2 V ER0 +YEVGENY Y EH2 V G EH1 N IY0 +YEW Y UW1 +YEW'D Y UW1 D +YEW'LL Y UW1 L +YEW'RE Y UW1 R +YEW'VE Y UW1 V +YEWDALE Y UW1 D EY2 L +YEWR Y UW1 R +YEWS Y UW1 Z +YEZ Y EH1 Z +YEZ'LL Y EH0 Z AH0 L +YEZD Y EH1 Z D +YEZDI Y EH1 Z D IY0 +YEZIBABA Y EH1 Z IY0 B AA1 B AH0 +YEZIBABA'S Y EH1 Z IY0 B AA1 B AH0 Z +YEZIDEES Y EH1 Z AY2 D IY1 Z +YEZO Y EH1 Z OW0 +YEZONKAI Y EH1 Z AO1 NG K AY1 +YEZSIR Y EH1 S IH1 R +YFFIM Y F IY1 M +YGBERG IY0 G B ER0 G +YGDRASIL IY0 G D R AH0 S IY1 L +YGDRASSIL IY0 G D 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AO0 R +ZZ Z +ZZZ Z Z +ZZZZ Z AH0 Z diff --git a/text/lexicon/pinyin-lexicon-r.txt b/text/lexicon/pinyin-lexicon-r.txt new file mode 100644 index 0000000000000000000000000000000000000000..3ccd50dd74df47c8e36e250661feabe734195cc9 --- /dev/null +++ b/text/lexicon/pinyin-lexicon-r.txt @@ -0,0 +1,4120 @@ +a1 a1 +a2 a2 +a3 a3 +a4 a4 +a5 a5 +ai1 ai1 +ai2 ai2 +ai3 ai3 +ai4 ai4 +ai5 ai5 +an1 an1 +an2 an2 +an3 an3 +an4 an4 +an5 an5 +ang1 ang1 +ang2 ang2 +ang3 ang3 +ang4 ang4 +ang5 ang5 +ao1 ao1 +ao2 ao2 +ao3 ao3 +ao4 ao4 +ao5 ao5 +ba1 b a1 +ba2 b a2 +ba3 b a3 +ba4 b a4 +ba5 b a5 +bai1 b ai1 +bai2 b ai2 +bai3 b ai3 +bai4 b ai4 +bai5 b ai5 +ban1 b an1 +ban2 b an2 +ban3 b an3 +ban4 b an4 +ban5 b an5 +bang1 b ang1 +bang2 b ang2 +bang3 b ang3 +bang4 b ang4 +bang5 b ang5 +bao1 b ao1 +bao2 b ao2 +bao3 b ao3 +bao4 b ao4 +bao5 b ao5 +bei1 b ei1 +bei2 b ei2 +bei3 b ei3 +bei4 b ei4 +bei5 b ei5 +ben1 b en1 +ben2 b en2 +ben3 b en3 +ben4 b en4 +ben5 b en5 +beng1 b eng1 +beng2 b eng2 +beng3 b eng3 +beng4 b 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k u5 rr +kuar1 k ua1 rr +kuar2 k ua2 rr +kuar3 k ua3 rr +kuar4 k ua4 rr +kuar5 k ua5 rr +kuair1 k uai1 rr +kuair2 k uai2 rr +kuair3 k uai3 rr +kuair4 k uai4 rr +kuair5 k uai5 rr +kuanr1 k uan1 rr +kuanr2 k uan2 rr +kuanr3 k uan3 rr +kuanr4 k uan4 rr +kuanr5 k uan5 rr +kuangr1 k uang1 rr +kuangr2 k uang2 rr +kuangr3 k uang3 rr +kuangr4 k uang4 rr +kuangr5 k uang5 rr +kuir1 k uei1 rr +kuir2 k uei2 rr +kuir3 k uei3 rr +kuir4 k uei4 rr +kuir5 k uei5 rr +kunr1 k uen1 rr +kunr2 k uen2 rr +kunr3 k uen3 rr +kunr4 k uen4 rr +kunr5 k uen5 rr +kuor1 k uo1 rr +kuor2 k uo2 rr +kuor3 k uo3 rr +kuor4 k uo4 rr +kuor5 k uo5 rr +lar1 l a1 rr +lar2 l a2 rr +lar3 l a3 rr +lar4 l a4 rr +lar5 l a5 rr +lair1 l ai1 rr +lair2 l ai2 rr +lair3 l ai3 rr +lair4 l ai4 rr +lair5 l ai5 rr +lanr1 l an1 rr +lanr2 l an2 rr +lanr3 l an3 rr +lanr4 l an4 rr +lanr5 l an5 rr +langr1 l ang1 rr +langr2 l ang2 rr +langr3 l ang3 rr +langr4 l ang4 rr +langr5 l ang5 rr +laor1 l ao1 rr +laor2 l ao2 rr +laor3 l ao3 rr +laor4 l ao4 rr +laor5 l ao5 rr +ler1 l e1 rr +ler2 l e2 rr +ler3 l e3 rr +ler4 l e4 rr +ler5 l e5 rr +leir1 l ei1 rr +leir2 l ei2 rr +leir3 l ei3 rr +leir4 l ei4 rr +leir5 l ei5 rr +lengr1 l eng1 rr +lengr2 l eng2 rr +lengr3 l eng3 rr +lengr4 l eng4 rr +lengr5 l eng5 rr +lir1 l i1 rr +lir2 l i2 rr +lir3 l i3 rr +lir4 l i4 rr +lir5 l i5 rr +liar1 l ia1 rr +liar2 l ia2 rr +liar3 l ia3 rr +liar4 l ia4 rr +liar5 l ia5 rr +lianr1 l ian1 rr +lianr2 l ian2 rr +lianr3 l ian3 rr +lianr4 l ian4 rr +lianr5 l ian5 rr +liangr1 l iang1 rr +liangr2 l iang2 rr +liangr3 l iang3 rr +liangr4 l iang4 rr +liangr5 l iang5 rr +liaor1 l iao1 rr +liaor2 l iao2 rr +liaor3 l iao3 rr +liaor4 l iao4 rr +liaor5 l iao5 rr +lier1 l ie1 rr +lier2 l ie2 rr +lier3 l ie3 rr +lier4 l ie4 rr +lier5 l ie5 rr +linr1 l in1 rr +linr2 l in2 rr +linr3 l in3 rr +linr4 l in4 rr +linr5 l in5 rr +lingr1 l ing1 rr +lingr2 l ing2 rr +lingr3 l ing3 rr +lingr4 l ing4 rr +lingr5 l ing5 rr +liur1 l iou1 rr +liur2 l iou2 rr +liur3 l iou3 rr +liur4 l iou4 rr +liur5 l iou5 rr +lor1 l o1 rr +lor2 l o2 rr +lor3 l o3 rr +lor4 l o4 rr +lor5 l o5 rr +longr1 l ong1 rr +longr2 l ong2 rr +longr3 l ong3 rr +longr4 l ong4 rr +longr5 l ong5 rr +lour1 l ou1 rr +lour2 l ou2 rr +lour3 l ou3 rr +lour4 l ou4 rr +lour5 l ou5 rr +lur1 l u1 rr +lur2 l u2 rr +lur3 l u3 rr +lur4 l u4 rr +lur5 l u5 rr +luanr1 l uan1 rr +luanr2 l uan2 rr +luanr3 l uan3 rr +luanr4 l uan4 rr +luanr5 l uan5 rr +luer1 l ve1 rr +luer2 l ve2 rr +luer3 l ve3 rr +luer4 l ve4 rr +luer5 l ve5 rr +lver1 l ve1 rr +lver2 l ve2 rr +lver3 l ve3 rr +lver4 l ve4 rr +lver5 l ve5 rr +lunr1 l uen1 rr +lunr2 l uen2 rr +lunr3 l uen3 rr +lunr4 l uen4 rr +lunr5 l uen5 rr +luor1 l uo1 rr +luor2 l uo2 rr +luor3 l uo3 rr +luor4 l uo4 rr +luor5 l uo5 rr +lvr1 l v1 rr +lvr2 l v2 rr +lvr3 l v3 rr +lvr4 l v4 rr +lvr5 l v5 rr +mar1 m a1 rr +mar2 m a2 rr +mar3 m a3 rr +mar4 m a4 rr +mar5 m a5 rr +mair1 m ai1 rr +mair2 m ai2 rr +mair3 m ai3 rr +mair4 m ai4 rr +mair5 m ai5 rr +manr1 m an1 rr +manr2 m an2 rr +manr3 m an3 rr +manr4 m an4 rr +manr5 m an5 rr +mangr1 m ang1 rr +mangr2 m ang2 rr +mangr3 m ang3 rr +mangr4 m ang4 rr +mangr5 m ang5 rr +maor1 m ao1 rr +maor2 m ao2 rr +maor3 m ao3 rr +maor4 m ao4 rr +maor5 m ao5 rr +mer1 m e1 rr +mer2 m e2 rr +mer3 m e3 rr +mer4 m e4 rr +mer5 m e5 rr +meir1 m ei1 rr +meir2 m ei2 rr +meir3 m ei3 rr +meir4 m ei4 rr +meir5 m ei5 rr +menr1 m en1 rr +menr2 m en2 rr +menr3 m en3 rr +menr4 m en4 rr +menr5 m en5 rr +mengr1 m eng1 rr +mengr2 m eng2 rr +mengr3 m eng3 rr +mengr4 m eng4 rr +mengr5 m eng5 rr +mir1 m i1 rr +mir2 m i2 rr +mir3 m i3 rr +mir4 m i4 rr +mir5 m i5 rr +mianr1 m ian1 rr +mianr2 m ian2 rr +mianr3 m ian3 rr +mianr4 m ian4 rr +mianr5 m ian5 rr +miaor1 m iao1 rr +miaor2 m iao2 rr +miaor3 m iao3 rr +miaor4 m iao4 rr +miaor5 m iao5 rr +mier1 m ie1 rr +mier2 m ie2 rr +mier3 m ie3 rr +mier4 m ie4 rr +mier5 m ie5 rr +minr1 m in1 rr +minr2 m in2 rr +minr3 m in3 rr +minr4 m in4 rr +minr5 m in5 rr +mingr1 m ing1 rr +mingr2 m ing2 rr +mingr3 m ing3 rr +mingr4 m ing4 rr +mingr5 m ing5 rr +miur1 m iou1 rr +miur2 m iou2 rr +miur3 m iou3 rr +miur4 m iou4 rr +miur5 m iou5 rr +mor1 m o1 rr +mor2 m o2 rr +mor3 m o3 rr +mor4 m o4 rr +mor5 m o5 rr +mour1 m ou1 rr +mour2 m ou2 rr +mour3 m ou3 rr +mour4 m ou4 rr +mour5 m ou5 rr +mur1 m u1 rr +mur2 m u2 rr +mur3 m u3 rr +mur4 m u4 rr +mur5 m u5 rr +nar1 n a1 rr +nar2 n a2 rr +nar3 n a3 rr +nar4 n a4 rr +nar5 n a5 rr +nair1 n ai1 rr +nair2 n ai2 rr +nair3 n ai3 rr +nair4 n ai4 rr +nair5 n ai5 rr +nanr1 n an1 rr +nanr2 n an2 rr +nanr3 n an3 rr +nanr4 n an4 rr +nanr5 n an5 rr +nangr1 n ang1 rr +nangr2 n ang2 rr +nangr3 n ang3 rr +nangr4 n ang4 rr +nangr5 n ang5 rr +naor1 n ao1 rr +naor2 n ao2 rr +naor3 n ao3 rr +naor4 n ao4 rr +naor5 n ao5 rr +ner1 n e1 rr +ner2 n e2 rr +ner3 n e3 rr +ner4 n e4 rr +ner5 n e5 rr +neir1 n ei1 rr +neir2 n ei2 rr +neir3 n ei3 rr +neir4 n ei4 rr +neir5 n ei5 rr +nenr1 n en1 rr +nenr2 n en2 rr +nenr3 n en3 rr +nenr4 n en4 rr +nenr5 n en5 rr +nengr1 n eng1 rr +nengr2 n eng2 rr +nengr3 n eng3 rr +nengr4 n eng4 rr +nengr5 n eng5 rr +nir1 n i1 rr +nir2 n i2 rr +nir3 n i3 rr +nir4 n i4 rr +nir5 n i5 rr +nianr1 n ian1 rr +nianr2 n ian2 rr +nianr3 n ian3 rr +nianr4 n ian4 rr +nianr5 n ian5 rr +niangr1 n iang1 rr +niangr2 n iang2 rr +niangr3 n iang3 rr +niangr4 n iang4 rr +niangr5 n iang5 rr +niaor1 n iao1 rr +niaor2 n iao2 rr +niaor3 n iao3 rr +niaor4 n iao4 rr +niaor5 n iao5 rr +nier1 n ie1 rr +nier2 n ie2 rr +nier3 n ie3 rr +nier4 n ie4 rr +nier5 n ie5 rr +ninr1 n in1 rr +ninr2 n in2 rr +ninr3 n in3 rr +ninr4 n in4 rr +ninr5 n in5 rr +ningr1 n ing1 rr +ningr2 n ing2 rr +ningr3 n ing3 rr +ningr4 n ing4 rr +ningr5 n ing5 rr +niur1 n iou1 rr +niur2 n iou2 rr +niur3 n iou3 rr +niur4 n iou4 rr +niur5 n iou5 rr +nongr1 n ong1 rr +nongr2 n ong2 rr +nongr3 n ong3 rr +nongr4 n ong4 rr +nongr5 n ong5 rr +nour1 n ou1 rr +nour2 n ou2 rr +nour3 n ou3 rr +nour4 n ou4 rr +nour5 n ou5 rr +nur1 n u1 rr +nur2 n u2 rr +nur3 n u3 rr +nur4 n u4 rr +nur5 n u5 rr +nuanr1 n uan1 rr +nuanr2 n uan2 rr +nuanr3 n uan3 rr +nuanr4 n uan4 rr +nuanr5 n uan5 rr +nuer1 n ve1 rr +nuer2 n ve2 rr +nuer3 n ve3 rr +nuer4 n ve4 rr +nuer5 n ve5 rr +nver1 n ve1 rr +nver2 n ve2 rr +nver3 n ve3 rr +nver4 n ve4 rr +nver5 n ve5 rr +nuor1 n uo1 rr +nuor2 n uo2 rr +nuor3 n uo3 rr +nuor4 n uo4 rr +nuor5 n uo5 rr +nvr1 n v1 rr +nvr2 n v2 rr +nvr3 n v3 rr +nvr4 n v4 rr +nvr5 n v5 rr +or1 o1 rr +or2 o2 rr +or3 o3 rr +or4 o4 rr +or5 o5 rr +our1 ou1 rr +our2 ou2 rr +our3 ou3 rr +our4 ou4 rr +our5 ou5 rr +par1 p a1 rr +par2 p a2 rr +par3 p a3 rr +par4 p a4 rr +par5 p a5 rr +pair1 p ai1 rr +pair2 p ai2 rr +pair3 p ai3 rr +pair4 p ai4 rr +pair5 p ai5 rr +panr1 p an1 rr +panr2 p an2 rr +panr3 p an3 rr +panr4 p an4 rr +panr5 p an5 rr +pangr1 p ang1 rr +pangr2 p ang2 rr +pangr3 p ang3 rr +pangr4 p ang4 rr +pangr5 p ang5 rr +paor1 p ao1 rr +paor2 p ao2 rr +paor3 p ao3 rr +paor4 p ao4 rr +paor5 p ao5 rr +peir1 p ei1 rr +peir2 p ei2 rr +peir3 p ei3 rr +peir4 p ei4 rr +peir5 p ei5 rr +penr1 p en1 rr +penr2 p en2 rr +penr3 p en3 rr +penr4 p en4 rr +penr5 p en5 rr +pengr1 p eng1 rr +pengr2 p eng2 rr +pengr3 p eng3 rr +pengr4 p eng4 rr +pengr5 p eng5 rr +pir1 p i1 rr +pir2 p i2 rr +pir3 p i3 rr +pir4 p i4 rr +pir5 p i5 rr +pianr1 p ian1 rr +pianr2 p ian2 rr +pianr3 p ian3 rr +pianr4 p ian4 rr +pianr5 p ian5 rr +piaor1 p iao1 rr +piaor2 p iao2 rr +piaor3 p iao3 rr +piaor4 p iao4 rr +piaor5 p iao5 rr +pier1 p ie1 rr +pier2 p ie2 rr +pier3 p ie3 rr +pier4 p ie4 rr +pier5 p ie5 rr +pinr1 p in1 rr +pinr2 p in2 rr +pinr3 p in3 rr +pinr4 p in4 rr +pinr5 p in5 rr +pingr1 p ing1 rr +pingr2 p ing2 rr +pingr3 p ing3 rr +pingr4 p ing4 rr +pingr5 p ing5 rr +por1 p o1 rr +por2 p o2 rr +por3 p o3 rr +por4 p o4 rr +por5 p o5 rr +pour1 p ou1 rr +pour2 p ou2 rr +pour3 p ou3 rr +pour4 p ou4 rr +pour5 p ou5 rr +pur1 p u1 rr +pur2 p u2 rr +pur3 p u3 rr +pur4 p u4 rr +pur5 p u5 rr +qir1 q i1 rr +qir2 q i2 rr +qir3 q i3 rr +qir4 q i4 rr +qir5 q i5 rr +qiar1 q ia1 rr +qiar2 q ia2 rr +qiar3 q ia3 rr +qiar4 q ia4 rr +qiar5 q ia5 rr +qianr1 q ian1 rr +qianr2 q ian2 rr +qianr3 q ian3 rr +qianr4 q ian4 rr +qianr5 q ian5 rr +qiangr1 q iang1 rr +qiangr2 q iang2 rr +qiangr3 q iang3 rr +qiangr4 q iang4 rr +qiangr5 q iang5 rr +qiaor1 q iao1 rr +qiaor2 q iao2 rr +qiaor3 q iao3 rr +qiaor4 q iao4 rr +qiaor5 q iao5 rr +qier1 q ie1 rr +qier2 q ie2 rr +qier3 q ie3 rr +qier4 q ie4 rr +qier5 q ie5 rr +qinr1 q in1 rr +qinr2 q in2 rr +qinr3 q in3 rr +qinr4 q in4 rr +qinr5 q in5 rr +qingr1 q ing1 rr +qingr2 q ing2 rr +qingr3 q ing3 rr +qingr4 q ing4 rr +qingr5 q ing5 rr +qiongr1 q iong1 rr +qiongr2 q iong2 rr +qiongr3 q iong3 rr +qiongr4 q iong4 rr +qiongr5 q iong5 rr +qiur1 q iou1 rr +qiur2 q iou2 rr +qiur3 q iou3 rr +qiur4 q iou4 rr +qiur5 q iou5 rr +qur1 q v1 rr +qur2 q v2 rr +qur3 q v3 rr +qur4 q v4 rr +qur5 q v5 rr +quanr1 q van1 rr +quanr2 q van2 rr +quanr3 q van3 rr +quanr4 q van4 rr +quanr5 q van5 rr +quer1 q ve1 rr +quer2 q ve2 rr +quer3 q ve3 rr +quer4 q ve4 rr +quer5 q ve5 rr +qunr1 q vn1 rr +qunr2 q vn2 rr +qunr3 q vn3 rr +qunr4 q vn4 rr +qunr5 q vn5 rr +ranr1 r an1 rr +ranr2 r an2 rr +ranr3 r an3 rr +ranr4 r an4 rr +ranr5 r an5 rr +rangr1 r ang1 rr +rangr2 r ang2 rr +rangr3 r ang3 rr +rangr4 r ang4 rr +rangr5 r ang5 rr +raor1 r ao1 rr +raor2 r ao2 rr +raor3 r ao3 rr +raor4 r ao4 rr +raor5 r ao5 rr +rer1 r e1 rr +rer2 r e2 rr +rer3 r e3 rr +rer4 r e4 rr +rer5 r e5 rr +renr1 r en1 rr +renr2 r en2 rr +renr3 r en3 rr +renr4 r en4 rr +renr5 r en5 rr +rengr1 r eng1 rr +rengr2 r eng2 rr +rengr3 r eng3 rr +rengr4 r eng4 rr +rengr5 r eng5 rr +rir1 r iii1 rr +rir2 r iii2 rr +rir3 r iii3 rr +rir4 r iii4 rr +rir5 r iii5 rr +rongr1 r ong1 rr +rongr2 r ong2 rr +rongr3 r ong3 rr +rongr4 r ong4 rr +rongr5 r ong5 rr +rour1 r ou1 rr +rour2 r ou2 rr +rour3 r ou3 rr +rour4 r ou4 rr +rour5 r ou5 rr +rur1 r u1 rr +rur2 r u2 rr +rur3 r u3 rr +rur4 r u4 rr +rur5 r u5 rr +ruar1 r ua1 rr +ruar2 r ua2 rr +ruar3 r ua3 rr +ruar4 r ua4 rr +ruar5 r ua5 rr +ruanr1 r uan1 rr +ruanr2 r uan2 rr +ruanr3 r uan3 rr +ruanr4 r uan4 rr +ruanr5 r uan5 rr +ruir1 r uei1 rr +ruir2 r uei2 rr +ruir3 r uei3 rr +ruir4 r uei4 rr +ruir5 r uei5 rr +runr1 r uen1 rr +runr2 r uen2 rr +runr3 r uen3 rr +runr4 r uen4 rr +runr5 r uen5 rr +ruor1 r uo1 rr +ruor2 r uo2 rr +ruor3 r uo3 rr +ruor4 r uo4 rr +ruor5 r uo5 rr +sar1 s a1 rr +sar2 s a2 rr +sar3 s a3 rr +sar4 s a4 rr +sar5 s a5 rr +sair1 s ai1 rr +sair2 s ai2 rr +sair3 s ai3 rr +sair4 s ai4 rr +sair5 s ai5 rr +sanr1 s an1 rr +sanr2 s an2 rr +sanr3 s an3 rr +sanr4 s an4 rr +sanr5 s an5 rr +sangr1 s ang1 rr +sangr2 s ang2 rr +sangr3 s ang3 rr +sangr4 s ang4 rr +sangr5 s ang5 rr +saor1 s ao1 rr +saor2 s ao2 rr +saor3 s ao3 rr +saor4 s ao4 rr +saor5 s ao5 rr +ser1 s e1 rr +ser2 s e2 rr +ser3 s e3 rr +ser4 s e4 rr +ser5 s e5 rr +senr1 s en1 rr +senr2 s en2 rr +senr3 s en3 rr +senr4 s en4 rr +senr5 s en5 rr +sengr1 s eng1 rr +sengr2 s eng2 rr +sengr3 s eng3 rr +sengr4 s eng4 rr +sengr5 s eng5 rr +shar1 sh a1 rr +shar2 sh a2 rr +shar3 sh a3 rr +shar4 sh a4 rr +shar5 sh a5 rr +shair1 sh ai1 rr +shair2 sh ai2 rr +shair3 sh ai3 rr +shair4 sh ai4 rr +shair5 sh ai5 rr +shanr1 sh an1 rr +shanr2 sh an2 rr +shanr3 sh an3 rr +shanr4 sh an4 rr +shanr5 sh an5 rr +shangr1 sh ang1 rr +shangr2 sh ang2 rr +shangr3 sh ang3 rr +shangr4 sh ang4 rr +shangr5 sh ang5 rr +shaor1 sh ao1 rr +shaor2 sh ao2 rr +shaor3 sh ao3 rr +shaor4 sh ao4 rr +shaor5 sh ao5 rr +sher1 sh e1 rr +sher2 sh e2 rr +sher3 sh e3 rr +sher4 sh e4 rr +sher5 sh e5 rr +sheir1 sh ei1 rr +sheir2 sh ei2 rr +sheir3 sh ei3 rr +sheir4 sh ei4 rr +sheir5 sh ei5 rr +shenr1 sh en1 rr +shenr2 sh en2 rr +shenr3 sh en3 rr +shenr4 sh en4 rr +shenr5 sh en5 rr +shengr1 sh eng1 rr +shengr2 sh eng2 rr +shengr3 sh eng3 rr +shengr4 sh eng4 rr +shengr5 sh eng5 rr +shir1 sh iii1 rr +shir2 sh iii2 rr +shir3 sh iii3 rr +shir4 sh iii4 rr +shir5 sh iii5 rr +shour1 sh ou1 rr +shour2 sh ou2 rr +shour3 sh ou3 rr +shour4 sh ou4 rr +shour5 sh ou5 rr +shur1 sh u1 rr +shur2 sh u2 rr +shur3 sh u3 rr +shur4 sh u4 rr 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+yuer1 y ve1 rr +yuer2 y ve2 rr +yuer3 y ve3 rr +yuer4 y ve4 rr +yuer5 y ve5 rr +yunr1 y vn1 rr +yunr2 y vn2 rr +yunr3 y vn3 rr +yunr4 y vn4 rr +yunr5 y vn5 rr +zar1 z a1 rr +zar2 z a2 rr +zar3 z a3 rr +zar4 z a4 rr +zar5 z a5 rr +zair1 z ai1 rr +zair2 z ai2 rr +zair3 z ai3 rr +zair4 z ai4 rr +zair5 z ai5 rr +zanr1 z an1 rr +zanr2 z an2 rr +zanr3 z an3 rr +zanr4 z an4 rr +zanr5 z an5 rr +zangr1 z ang1 rr +zangr2 z ang2 rr +zangr3 z ang3 rr +zangr4 z ang4 rr +zangr5 z ang5 rr +zaor1 z ao1 rr +zaor2 z ao2 rr +zaor3 z ao3 rr +zaor4 z ao4 rr +zaor5 z ao5 rr +zer1 z e1 rr +zer2 z e2 rr +zer3 z e3 rr +zer4 z e4 rr +zer5 z e5 rr +zeir1 z ei1 rr +zeir2 z ei2 rr +zeir3 z ei3 rr +zeir4 z ei4 rr +zeir5 z ei5 rr +zenr1 z en1 rr +zenr2 z en2 rr +zenr3 z en3 rr +zenr4 z en4 rr +zenr5 z en5 rr +zengr1 z eng1 rr +zengr2 z eng2 rr +zengr3 z eng3 rr +zengr4 z eng4 rr +zengr5 z eng5 rr +zhar1 zh a1 rr +zhar2 zh a2 rr +zhar3 zh a3 rr +zhar4 zh a4 rr +zhar5 zh a5 rr +zhair1 zh ai1 rr +zhair2 zh ai2 rr +zhair3 zh ai3 rr +zhair4 zh ai4 rr +zhair5 zh ai5 rr +zhanr1 zh an1 rr +zhanr2 zh an2 rr +zhanr3 zh an3 rr +zhanr4 zh an4 rr +zhanr5 zh an5 rr +zhangr1 zh ang1 rr +zhangr2 zh ang2 rr +zhangr3 zh ang3 rr +zhangr4 zh ang4 rr +zhangr5 zh ang5 rr +zhaor1 zh ao1 rr +zhaor2 zh ao2 rr +zhaor3 zh ao3 rr +zhaor4 zh ao4 rr +zhaor5 zh ao5 rr +zher1 zh e1 rr +zher2 zh e2 rr +zher3 zh e3 rr +zher4 zh e4 rr +zher5 zh e5 rr +zheir1 zh ei1 rr +zheir2 zh ei2 rr +zheir3 zh ei3 rr +zheir4 zh ei4 rr +zheir5 zh ei5 rr +zhenr1 zh en1 rr +zhenr2 zh en2 rr +zhenr3 zh en3 rr +zhenr4 zh en4 rr +zhenr5 zh en5 rr +zhengr1 zh eng1 rr +zhengr2 zh eng2 rr +zhengr3 zh eng3 rr +zhengr4 zh eng4 rr +zhengr5 zh eng5 rr +zhir1 zh iii1 rr +zhir2 zh iii2 rr +zhir3 zh iii3 rr +zhir4 zh iii4 rr +zhir5 zh iii5 rr +zhongr1 zh ong1 rr +zhongr2 zh ong2 rr +zhongr3 zh ong3 rr +zhongr4 zh ong4 rr +zhongr5 zh ong5 rr +zhour1 zh ou1 rr +zhour2 zh ou2 rr +zhour3 zh ou3 rr +zhour4 zh ou4 rr +zhour5 zh ou5 rr +zhur1 zh u1 rr +zhur2 zh u2 rr +zhur3 zh u3 rr +zhur4 zh u4 rr +zhur5 zh u5 rr +zhuar1 zh ua1 rr +zhuar2 zh ua2 rr +zhuar3 zh ua3 rr +zhuar4 zh ua4 rr +zhuar5 zh ua5 rr +zhuair1 zh uai1 rr +zhuair2 zh uai2 rr +zhuair3 zh uai3 rr +zhuair4 zh uai4 rr +zhuair5 zh uai5 rr +zhuanr1 zh uan1 rr +zhuanr2 zh uan2 rr +zhuanr3 zh uan3 rr +zhuanr4 zh uan4 rr +zhuanr5 zh uan5 rr +zhuangr1 zh uang1 rr +zhuangr2 zh uang2 rr +zhuangr3 zh uang3 rr +zhuangr4 zh uang4 rr +zhuangr5 zh uang5 rr +zhuir1 zh uei1 rr +zhuir2 zh uei2 rr +zhuir3 zh uei3 rr +zhuir4 zh uei4 rr +zhuir5 zh uei5 rr +zhunr1 zh uen1 rr +zhunr2 zh uen2 rr +zhunr3 zh uen3 rr +zhunr4 zh uen4 rr +zhunr5 zh uen5 rr +zhuor1 zh uo1 rr +zhuor2 zh uo2 rr +zhuor3 zh uo3 rr +zhuor4 zh uo4 rr +zhuor5 zh uo5 rr +zir1 z ii1 rr +zir2 z ii2 rr +zir3 z ii3 rr +zir4 z ii4 rr +zir5 z ii5 rr +zongr1 z ong1 rr +zongr2 z ong2 rr +zongr3 z ong3 rr +zongr4 z ong4 rr +zongr5 z ong5 rr +zour1 z ou1 rr +zour2 z ou2 rr +zour3 z ou3 rr +zour4 z ou4 rr +zour5 z ou5 rr +zur1 z u1 rr +zur2 z u2 rr +zur3 z u3 rr +zur4 z u4 rr +zur5 z u5 rr +zuanr1 z uan1 rr +zuanr2 z uan2 rr +zuanr3 z uan3 rr +zuanr4 z uan4 rr +zuanr5 z uan5 rr +zuir1 z uei1 rr +zuir2 z uei2 rr +zuir3 z uei3 rr +zuir4 z uei4 rr +zuir5 z uei5 rr +zunr1 z uen1 rr +zunr2 z uen2 rr +zunr3 z uen3 rr +zunr4 z uen4 rr +zunr5 z uen5 rr +zuor1 z uo1 rr +zuor2 z uo2 rr +zuor3 z uo3 rr +zuor4 z uo4 rr +zuor5 z uo5 rr diff --git a/text/numbers.py b/text/numbers.py new file mode 100644 index 0000000000000000000000000000000000000000..660a2964b10a273c0c0b7e8998c8111d7e4aa3be --- /dev/null +++ b/text/numbers.py @@ -0,0 +1,77 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" This code is modified from https://github.com/keithito/tacotron """ + +import inflect +import re + +_inflect = inflect.engine() +_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])") +_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)") +_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)") +_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)") +_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)") +_number_re = re.compile(r"[0-9]+") + + +def _remove_commas(m): + return m.group(1).replace(",", "") + + +def _expand_decimal_point(m): + return m.group(1).replace(".", " point ") + + +def _expand_dollars(m): + match = m.group(1) + parts = match.split(".") + if len(parts) > 2: + return match + " dollars" # Unexpected format + dollars = int(parts[0]) if parts[0] else 0 + cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 + if dollars and cents: + dollar_unit = "dollar" if dollars == 1 else "dollars" + cent_unit = "cent" if cents == 1 else "cents" + return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit) + elif dollars: + dollar_unit = "dollar" if dollars == 1 else "dollars" + return "%s %s" % (dollars, dollar_unit) + elif cents: + cent_unit = "cent" if cents == 1 else "cents" + return "%s %s" % (cents, cent_unit) + else: + return "zero dollars" + + +def _expand_ordinal(m): + return _inflect.number_to_words(m.group(0)) + + +def _expand_number(m): + num = int(m.group(0)) + if num > 1000 and num < 3000: + if num == 2000: + return "two thousand" + elif num > 2000 and num < 2010: + return "two thousand " + _inflect.number_to_words(num % 100) + elif num % 100 == 0: + return _inflect.number_to_words(num // 100) + " hundred" + else: + return _inflect.number_to_words( + num, andword="", zero="oh", group=2 + ).replace(", ", " ") + else: + return _inflect.number_to_words(num, andword="") + + +def normalize_numbers(text): + text = re.sub(_comma_number_re, _remove_commas, text) + text = re.sub(_pounds_re, r"\1 pounds", text) + text = re.sub(_dollars_re, _expand_dollars, text) + text = re.sub(_decimal_number_re, _expand_decimal_point, text) + text = re.sub(_ordinal_re, _expand_ordinal, text) + text = re.sub(_number_re, _expand_number, text) + return text diff --git a/text/pinyin.py b/text/pinyin.py new file mode 100644 index 0000000000000000000000000000000000000000..bb4b41b5c3990d0c9026beb288a60c1edc86528e --- /dev/null +++ b/text/pinyin.py @@ -0,0 +1,218 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +initials = [ + "b", + "c", + "ch", + "d", + "f", + "g", + "h", + "j", + "k", + "l", + "m", + "n", + "p", + "q", + "r", + "s", + "sh", + "t", + "w", + "x", + "y", + "z", + "zh", +] +finals = [ + "a1", + "a2", + "a3", + "a4", + "a5", + "ai1", + "ai2", + "ai3", + "ai4", + "ai5", + "an1", + "an2", + "an3", + "an4", + "an5", + "ang1", + "ang2", + "ang3", + "ang4", + "ang5", + "ao1", + "ao2", + "ao3", + "ao4", + "ao5", + "e1", + "e2", + "e3", + "e4", + "e5", + "ei1", + "ei2", + "ei3", + "ei4", + "ei5", + "en1", + "en2", + "en3", + "en4", + "en5", + "eng1", + "eng2", + "eng3", + "eng4", + "eng5", + "er1", + "er2", + "er3", + "er4", + "er5", + "i1", + "i2", + "i3", + "i4", + "i5", + "ia1", + "ia2", + "ia3", + "ia4", + "ia5", + "ian1", + "ian2", + "ian3", + "ian4", + "ian5", + "iang1", + "iang2", + "iang3", + "iang4", + "iang5", + "iao1", + "iao2", + "iao3", + "iao4", + "iao5", + "ie1", + "ie2", + "ie3", + "ie4", + "ie5", + "ii1", + "ii2", + "ii3", + "ii4", + "ii5", + "iii1", + "iii2", + "iii3", + "iii4", + "iii5", + "in1", + "in2", + "in3", + "in4", + "in5", + "ing1", + "ing2", + "ing3", + "ing4", + "ing5", + "iong1", + "iong2", + "iong3", + "iong4", + "iong5", + "iou1", + "iou2", + "iou3", + "iou4", + "iou5", + "o1", + "o2", + "o3", + "o4", + "o5", + "ong1", + "ong2", + "ong3", + "ong4", + "ong5", + "ou1", + "ou2", + "ou3", + "ou4", + "ou5", + "u1", + "u2", + "u3", + "u4", + "u5", + "ua1", + "ua2", + "ua3", + "ua4", + "ua5", + "uai1", + "uai2", + "uai3", + "uai4", + "uai5", + "uan1", + "uan2", + "uan3", + "uan4", + "uan5", + "uang1", + "uang2", + "uang3", + "uang4", + "uang5", + "uei1", + "uei2", + "uei3", + "uei4", + "uei5", + "uen1", + "uen2", + "uen3", + "uen4", + "uen5", + "uo1", + "uo2", + "uo3", + "uo4", + "uo5", + "v1", + "v2", + "v3", + "v4", + "v5", + "van1", + "van2", + "van3", + "van4", + "van5", + "ve1", + "ve2", + "ve3", + "ve4", + "ve5", + "vn1", + "vn2", + "vn3", + "vn4", + "vn5", +] +valid_symbols = initials + finals + ["rr"] diff --git a/text/symbol_table.py b/text/symbol_table.py new file mode 100644 index 0000000000000000000000000000000000000000..8b3945510ea8f029aa2b567d35ce05240c02112c --- /dev/null +++ b/text/symbol_table.py @@ -0,0 +1,284 @@ +# Copyright 2020 Mobvoi Inc. (authors: Fangjun Kuang) +# +# See ../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from dataclasses import field +from typing import Dict +from typing import Generic +from typing import List +from typing import Optional +from typing import TypeVar +from typing import Union + +Symbol = TypeVar('Symbol') + +# SymbolTable is copied from +# https://github.com/k2-fsa/k2/blob/master/k2/python/k2/symbol_table.py + +''' + SymbolTable: map symbol to id +''' +@dataclass(repr=False) +class SymbolTable(Generic[Symbol]): + '''SymbolTable that maps symbol IDs, found on the FSA arcs to + actual objects. These objects can be arbitrary Python objects + that can serve as keys in a dictionary (i.e. they need to be + hashable and immutable). + + The SymbolTable can only be read to/written from disk if the + symbols are strings. + ''' + _id2sym: Dict[int, Symbol] = field(default_factory=dict) + '''Map an integer to a symbol. + ''' + + _sym2id: Dict[Symbol, int] = field(default_factory=dict) + '''Map a symbol to an integer. + ''' + + _next_available_id: int = 1 + '''A helper internal field that helps adding new symbols + to the table efficiently. + ''' + + eps: Symbol = '' + '''Null symbol, always mapped to index 0. + ''' + + def __post_init__(self): + assert all(self._sym2id[sym] == idx for idx, sym in self._id2sym.items()) + assert all(self._id2sym[idx] == sym for sym, idx in self._sym2id.items()) + assert 0 not in self._id2sym or self._id2sym[0] == self.eps + + self._next_available_id = max(self._id2sym, default=0) + 1 + self._id2sym.setdefault(0, self.eps) + self._sym2id.setdefault(self.eps, 0) + + + @staticmethod + def from_str(s: str) -> 'SymbolTable': + '''Build a symbol table from a string. + + The string consists of lines. Every line has two fields separated + by space(s), tab(s) or both. The first field is the symbol and the + second the integer id of the symbol. + + Args: + s: + The input string with the format described above. + Returns: + An instance of :class:`SymbolTable`. + ''' + id2sym: Dict[int, str] = dict() + sym2id: Dict[str, int] = dict() + + for line in s.split('\n'): + fields = line.split() + if len(fields) == 0: + continue # skip empty lines + assert len(fields) == 2, \ + f'Expect a line with 2 fields. Given: {len(fields)}' + sym, idx = fields[0], int(fields[1]) + assert sym not in sym2id, f'Duplicated symbol {sym}' + assert idx not in id2sym, f'Duplicated id {idx}' + id2sym[idx] = sym + sym2id[sym] = idx + + eps = id2sym.get(0, '') + + return SymbolTable(_id2sym=id2sym, _sym2id=sym2id, eps=eps) + + @staticmethod + def from_file(filename: str) -> 'SymbolTable': + '''Build a symbol table from file. + + Every line in the symbol table file has two fields separated by + space(s), tab(s) or both. The following is an example file: + + .. code-block:: + + 0 + a 1 + b 2 + c 3 + + Args: + filename: + Name of the symbol table file. Its format is documented above. + + Returns: + An instance of :class:`SymbolTable`. + + ''' + with open(filename, 'r', encoding='utf-8') as f: + return SymbolTable.from_str(f.read().strip()) + + def to_str(self) -> str: + ''' + Returns: + Return a string representation of this object. You can pass + it to the method ``from_str`` to recreate an identical object. + ''' + s = '' + for idx, symbol in sorted(self._id2sym.items()): + s += f'{symbol} {idx}\n' + return s + + def to_file(self, filename: str): + '''Serialize the SymbolTable to a file. + + Every line in the symbol table file has two fields separated by + space(s), tab(s) or both. The following is an example file: + + .. code-block:: + + 0 + a 1 + b 2 + c 3 + + Args: + filename: + Name of the symbol table file. Its format is documented above. + ''' + with open(filename, 'w') as f: + for idx, symbol in sorted(self._id2sym.items()): + print(symbol, idx, file=f) + + def add(self, symbol: Symbol, index: Optional[int] = None) -> int: + '''Add a new symbol to the SymbolTable. + + Args: + symbol: + The symbol to be added. + index: + Optional int id to which the symbol should be assigned. + If it is not available, a ValueError will be raised. + + Returns: + The int id to which the symbol has been assigned. + ''' + # Already in the table? Return its ID. + if symbol in self._sym2id: + return self._sym2id[symbol] + # Specific ID not provided - use next available. + if index is None: + index = self._next_available_id + # Specific ID provided but not available. + if index in self._id2sym: + raise ValueError(f"Cannot assign id '{index}' to '{symbol}' - " + f"already occupied by {self._id2sym[index]}") + self._sym2id[symbol] = index + self._id2sym[index] = symbol + + # Update next available ID if needed + if self._next_available_id <= index: + self._next_available_id = index + 1 + + return index + + def get(self, k: Union[int, Symbol]) -> Union[Symbol, int]: + '''Get a symbol for an id or get an id for a symbol + + Args: + k: + If it is an id, it tries to find the symbol corresponding + to the id; if it is a symbol, it tries to find the id + corresponding to the symbol. + + Returns: + An id or a symbol depending on the given `k`. + ''' + if isinstance(k, int): + return self._id2sym[k] + else: + return self._sym2id[k] + + def merge(self, other: 'SymbolTable') -> 'SymbolTable': + '''Create a union of two SymbolTables. + Raises an AssertionError if the same IDs are occupied by + different symbols. + + Args: + other: + A symbol table to merge with ``self``. + + Returns: + A new symbol table. + ''' + self._check_compatible(other) + return SymbolTable( + _id2sym={**self._id2sym, **other._id2sym}, + _sym2id={**self._sym2id, **other._sym2id}, + eps=self.eps + ) + + def _check_compatible(self, other: 'SymbolTable') -> None: + # Epsilon compatibility + assert self.eps == other.eps, f'Mismatched epsilon symbol: ' \ + f'{self.eps} != {other.eps}' + # IDs compatibility + common_ids = set(self._id2sym).intersection(other._id2sym) + for idx in common_ids: + assert self[idx] == other[idx], f'ID conflict for id: {idx}, ' \ + f'self[idx] = "{self[idx]}", ' \ + f'other[idx] = "{other[idx]}"' + # Symbols compatibility + common_symbols = set(self._sym2id).intersection(other._sym2id) + for sym in common_symbols: + assert self[sym] == other[sym], f'ID conflict for id: {sym}, ' \ + f'self[sym] = "{self[sym]}", ' \ + f'other[sym] = "{other[sym]}"' + + def __getitem__(self, item: Union[int, Symbol]) -> Union[Symbol, int]: + return self.get(item) + + def __contains__(self, item: Union[int, Symbol]) -> bool: + if isinstance(item, int): + return item in self._id2sym + else: + return item in self._sym2id + + def __len__(self) -> int: + return len(self._id2sym) + + def __eq__(self, other: 'SymbolTable') -> bool: + if len(self) != len(other): + return False + + for s in self.symbols: + if self[s] != other[s]: + return False + + return True + + @property + def ids(self) -> List[int]: + '''Returns a list of integer IDs corresponding to the symbols. + ''' + ans = list(self._id2sym.keys()) + ans.sort() + return ans + + @property + def symbols(self) -> List[Symbol]: + '''Returns a list of symbols (e.g., strings) corresponding to + the integer IDs. + ''' + ans = list(self._sym2id.keys()) + ans.sort() + return ans + diff --git a/text/symbols.py b/text/symbols.py new file mode 100644 index 0000000000000000000000000000000000000000..a75cebd57c090c0827284a3e8885e17b42831ed2 --- /dev/null +++ b/text/symbols.py @@ -0,0 +1,34 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" This code is modified from https://github.com/keithito/tacotron """ + +""" +Defines the set of symbols used in text input to the model. + +The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. """ + +from text import cmudict, pinyin + +_pad = "_" +_punctuation = "!'(),.:;? " +_special = "-" +_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_silences = ["@sp", "@spn", "@sil"] + +# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters): +_arpabet = ["@" + s for s in cmudict.valid_symbols] +_pinyin = ["@" + s for s in pinyin.valid_symbols] + +# Export all symbols: +symbols = ( + [_pad] + + list(_special) + + list(_punctuation) + + list(_letters) + + _arpabet + + _silences + # + _pinyin # for chinese +) diff --git a/text/text_token_collation.py b/text/text_token_collation.py new file mode 100644 index 0000000000000000000000000000000000000000..b64a93fa0189070a6f9e6a3b97a94a4083c1d40c --- /dev/null +++ b/text/text_token_collation.py @@ -0,0 +1,131 @@ + # Copyright (c) 2023 Amphion. + + # This source code is licensed under the MIT license found in the + # LICENSE file in the root directory of this source tree. + +from pathlib import Path +from typing import List, Tuple +import os +import numpy as np +import torch +from text.symbol_table import SymbolTable +from text import text_to_sequence + + +''' + TextToken: map text to id +''' +# TextTokenCollator is modified from +# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/data/collation.py +class TextTokenCollator: + def __init__( + self, + text_tokens: List[str], + add_eos: bool = True, + add_bos: bool = True, + pad_symbol: str = "", + bos_symbol: str = "", + eos_symbol: str = "", + ): + self.pad_symbol = pad_symbol + self.add_eos = add_eos + self.add_bos = add_bos + self.bos_symbol = bos_symbol + self.eos_symbol = eos_symbol + + unique_tokens = [pad_symbol] + if add_bos: + unique_tokens.append(bos_symbol) + if add_eos: + unique_tokens.append(eos_symbol) + unique_tokens.extend(sorted(text_tokens)) + + self.token2idx = {token: idx for idx, token in enumerate(unique_tokens)} + self.idx2token = unique_tokens + + def index( + self, tokens_list: List[str] + ) -> Tuple[torch.Tensor, torch.Tensor]: + seqs, seq_lens = [], [] + for tokens in tokens_list: + assert ( + all([True if s in self.token2idx else False for s in tokens]) + is True + ) + seq = ( + ([self.bos_symbol] if self.add_bos else []) + + list(tokens) + + ([self.eos_symbol] if self.add_eos else []) + ) + seqs.append(seq) + seq_lens.append(len(seq)) + + max_len = max(seq_lens) + for k, (seq, seq_len) in enumerate(zip(seqs, seq_lens)): + seq.extend([self.pad_symbol] * (max_len - seq_len)) + + tokens = torch.from_numpy( + np.array( + [[self.token2idx[token] for token in seq] for seq in seqs], + dtype=np.int64, + ) + ) + tokens_lens = torch.IntTensor(seq_lens) + + return tokens, tokens_lens + + def __call__(self, text): + tokens_seq = [p for p in text] + seq = ( + ([self.bos_symbol] if self.add_bos else []) + + tokens_seq + + ([self.eos_symbol] if self.add_eos else []) + ) + + token_ids = [self.token2idx[token] for token in seq] + token_lens = len(tokens_seq) + self.add_eos + self.add_bos + + return token_ids, token_lens + + +def get_text_token_collater(text_tokens_file: str) -> TextTokenCollator: + text_tokens_path = Path(text_tokens_file) + unique_tokens = SymbolTable.from_file(text_tokens_path) + collater = TextTokenCollator( + unique_tokens.symbols, add_bos=True, add_eos=True + ) + token2idx = collater.token2idx + return collater, token2idx + + +class phoneIDCollation: + def __init__(self, cfg, dataset=None, symbols_dict_file=None) -> None: + + if cfg.preprocess.phone_extractor != 'lexicon': + ### get text token collator + if symbols_dict_file is None: + assert dataset is not None + symbols_dict_file = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.symbols_dict + ) + self.text_token_colloator, token2idx = get_text_token_collater(symbols_dict_file) + # # unique_tokens = SymbolTable.from_file(symbols_dict_path) + # # text_tokenizer = TextToken(unique_tokens.symbols, add_bos=True, add_eos=True) + + # # update phone symbols dict file with pad_symbol or optional tokens (add_bos and add_eos) in TextTokenCollator + # phone_symbol_dict = SymbolTable() + # for s in sorted(list(set(token2idx.keys()))): + # phone_symbol_dict.add(s) + # phone_symbol_dict.to_file(symbols_dict_file) + + def get_phone_id_sequence(self, cfg, phones_seq): + + if cfg.preprocess.phone_extractor == 'lexicon': + phones_seq = ' '.join(phones_seq) + sequence = text_to_sequence(phones_seq, cfg.preprocess.text_cleaners) + else: + sequence, seq_len = self.text_token_colloator(phones_seq) + return sequence + \ No newline at end of file diff --git a/utils/HyperParams/__init__.py b/utils/HyperParams/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..706e31b1c499f11548d08d38e1c3091aeb2dadaa --- /dev/null +++ b/utils/HyperParams/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .hps import HyperParams diff --git a/utils/HyperParams/hps.py b/utils/HyperParams/hps.py new file mode 100644 index 0000000000000000000000000000000000000000..cc6f474c4e28d092ea8ba2cdcf7233322c5ff281 --- /dev/null +++ b/utils/HyperParams/hps.py @@ -0,0 +1,43 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +class HyperParams: + """The class to store hyperparameters. The key is case-insensitive. + + Args: + *args: a list of dict or HyperParams. + **kwargs: a list of key-value pairs. + """ + + def __init__(self, **kwargs): + for k, v in kwargs.items(): + if type(v) == dict: + v = HyperParams(**v) + self[k] = v + + def keys(self): + return self.__dict__.keys() + + def items(self): + return self.__dict__.items() + + def values(self): + return self.__dict__.values() + + def __len__(self): + return len(self.__dict__) + + def __getitem__(self, key): + return getattr(self, key) + + def __setitem__(self, key, value): + return setattr(self, key, value) + + def __contains__(self, key): + return key in self.__dict__ + + def __repr__(self): + return self.__dict__.__repr__() diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/utils/__pycache__/__init__.cpython-39.pyc b/utils/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 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into torch tensor + + Args: + wave_file (str): path to wave file + fs (int): sample rate + + Returns: + audio (tensor): audio data in tensor + fs (int): sample rate + """ + + audio, sample_rate = librosa.load(wave_file, sr=fs, mono=True) + # audio: (T,) + assert len(audio) > 2 + + # Check the audio type (for soundfile loading backbone) - float, 8bit or 16bit + if np.issubdtype(audio.dtype, np.integer): + max_mag = -np.iinfo(audio.dtype).min + else: + max_mag = max(np.amax(audio), -np.amin(audio)) + max_mag = ( + (2**31) + 1 + if max_mag > (2**15) + else ((2**15) + 1 if max_mag > 1.01 else 1.0) + ) + + # Normalize the audio + audio = torch.FloatTensor(audio.astype(np.float32)) / max_mag + + if (torch.isnan(audio) | torch.isinf(audio)).any(): + return [], sample_rate or fs or 48000 + + # Resample the audio to our target samplerate + if fs is not None and fs != sample_rate: + audio = torch.from_numpy( + librosa.core.resample(audio.numpy(), orig_sr=sample_rate, target_sr=fs) + ) + sample_rate = fs + + return audio, fs + + +def _stft(y, cfg): + return librosa.stft( + y=y, n_fft=cfg.n_fft, hop_length=cfg.hop_size, win_length=cfg.win_size + ) + + +def energy(wav, cfg): + D = _stft(wav, cfg) + magnitudes = np.abs(D).T # [F, T] + return LA.norm(magnitudes, axis=1) + + +def get_energy_from_tacotron(audio, _stft): + audio = torch.clip(torch.FloatTensor(audio).unsqueeze(0), -1, 1) + audio = torch.autograd.Variable(audio, requires_grad=False) + mel, energy = _stft.mel_spectrogram(audio) + energy = torch.squeeze(energy, 0).numpy().astype(np.float32) + return mel, energy diff --git a/utils/audio_slicer.py b/utils/audio_slicer.py new file mode 100644 index 0000000000000000000000000000000000000000..28474596b42c8f8215b878a80967112960d0c9e0 --- /dev/null +++ b/utils/audio_slicer.py @@ -0,0 +1,476 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import json +import numpy as np +from tqdm import tqdm +import torch +import torchaudio + +from utils.io import save_audio +from utils.audio import load_audio_torch + + +# This function is obtained from librosa. +def get_rms( + y, + *, + frame_length=2048, + hop_length=512, + pad_mode="constant", +): + padding = (int(frame_length // 2), int(frame_length // 2)) + y = np.pad(y, padding, mode=pad_mode) + + axis = -1 + # put our new within-frame axis at the end for now + out_strides = y.strides + tuple([y.strides[axis]]) + # Reduce the shape on the framing axis + x_shape_trimmed = list(y.shape) + x_shape_trimmed[axis] -= frame_length - 1 + out_shape = tuple(x_shape_trimmed) + tuple([frame_length]) + xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides) + if axis < 0: + target_axis = axis - 1 + else: + target_axis = axis + 1 + xw = np.moveaxis(xw, -1, target_axis) + # Downsample along the target axis + slices = [slice(None)] * xw.ndim + slices[axis] = slice(0, None, hop_length) + x = xw[tuple(slices)] + + # Calculate power + power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True) + + return np.sqrt(power) + + +class Slicer: + """ + Copy from: https://github.com/openvpi/audio-slicer/blob/main/slicer2.py + """ + + def __init__( + self, + sr: int, + threshold: float = -40.0, + min_length: int = 5000, + min_interval: int = 300, + hop_size: int = 10, + max_sil_kept: int = 5000, + ): + if not min_length >= min_interval >= hop_size: + raise ValueError( + "The following condition must be satisfied: min_length >= min_interval >= hop_size" + ) + if not max_sil_kept >= hop_size: + raise ValueError( + "The following condition must be satisfied: max_sil_kept >= hop_size" + ) + min_interval = sr * min_interval / 1000 + self.threshold = 10 ** (threshold / 20.0) + self.hop_size = round(sr * hop_size / 1000) + self.win_size = min(round(min_interval), 4 * self.hop_size) + self.min_length = round(sr * min_length / 1000 / self.hop_size) + self.min_interval = round(min_interval / self.hop_size) + self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size) + + def _apply_slice(self, waveform, begin, end): + begin = begin * self.hop_size + if len(waveform.shape) > 1: + end = min(waveform.shape[1], end * self.hop_size) + return waveform[:, begin:end], begin, end + else: + end = min(waveform.shape[0], end * self.hop_size) + return waveform[begin:end], begin, end + + # @timeit + def slice(self, waveform, return_chunks_positions=False): + if len(waveform.shape) > 1: + # (#channle, wave_len) -> (wave_len) + samples = waveform.mean(axis=0) + else: + samples = waveform + if samples.shape[0] <= self.min_length: + return [waveform] + rms_list = get_rms( + y=samples, frame_length=self.win_size, hop_length=self.hop_size + ).squeeze(0) + sil_tags = [] + silence_start = None + clip_start = 0 + for i, rms in enumerate(rms_list): + # Keep looping while frame is silent. + if rms < self.threshold: + # Record start of silent frames. + if silence_start is None: + silence_start = i + continue + # Keep looping while frame is not silent and silence start has not been recorded. + if silence_start is None: + continue + # Clear recorded silence start if interval is not enough or clip is too short + is_leading_silence = silence_start == 0 and i > self.max_sil_kept + need_slice_middle = ( + i - silence_start >= self.min_interval + and i - clip_start >= self.min_length + ) + if not is_leading_silence and not need_slice_middle: + silence_start = None + continue + # Need slicing. Record the range of silent frames to be removed. + if i - silence_start <= self.max_sil_kept: + pos = rms_list[silence_start : i + 1].argmin() + silence_start + if silence_start == 0: + sil_tags.append((0, pos)) + else: + sil_tags.append((pos, pos)) + clip_start = pos + elif i - silence_start <= self.max_sil_kept * 2: + pos = rms_list[ + i - self.max_sil_kept : silence_start + self.max_sil_kept + 1 + ].argmin() + pos += i - self.max_sil_kept + pos_l = ( + rms_list[ + silence_start : silence_start + self.max_sil_kept + 1 + ].argmin() + + silence_start + ) + pos_r = ( + rms_list[i - self.max_sil_kept : i + 1].argmin() + + i + - self.max_sil_kept + ) + if silence_start == 0: + sil_tags.append((0, pos_r)) + clip_start = pos_r + else: + sil_tags.append((min(pos_l, pos), max(pos_r, pos))) + clip_start = max(pos_r, pos) + else: + pos_l = ( + rms_list[ + silence_start : silence_start + self.max_sil_kept + 1 + ].argmin() + + silence_start + ) + pos_r = ( + rms_list[i - self.max_sil_kept : i + 1].argmin() + + i + - self.max_sil_kept + ) + if silence_start == 0: + sil_tags.append((0, pos_r)) + else: + sil_tags.append((pos_l, pos_r)) + clip_start = pos_r + silence_start = None + # Deal with trailing silence. + total_frames = rms_list.shape[0] + if ( + silence_start is not None + and total_frames - silence_start >= self.min_interval + ): + silence_end = min(total_frames, silence_start + self.max_sil_kept) + pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start + sil_tags.append((pos, total_frames + 1)) + # Apply and return slices. + if len(sil_tags) == 0: + return [waveform] + else: + chunks = [] + chunks_pos_of_waveform = [] + + if sil_tags[0][0] > 0: + chunk, begin, end = self._apply_slice(waveform, 0, sil_tags[0][0]) + chunks.append(chunk) + chunks_pos_of_waveform.append((begin, end)) + + for i in range(len(sil_tags) - 1): + chunk, begin, end = self._apply_slice( + waveform, sil_tags[i][1], sil_tags[i + 1][0] + ) + chunks.append(chunk) + chunks_pos_of_waveform.append((begin, end)) + + if sil_tags[-1][1] < total_frames: + chunk, begin, end = self._apply_slice( + waveform, sil_tags[-1][1], total_frames + ) + chunks.append(chunk) + chunks_pos_of_waveform.append((begin, end)) + + return ( + chunks + if not return_chunks_positions + else ( + chunks, + chunks_pos_of_waveform, + ) + ) + + +def split_utterances_from_audio( + wav_file, + output_dir, + max_duration_of_utterance=10.0, + min_interval=300, + db_threshold=-40, +): + """ + Split a long audio into utterances accoring to the silence (VAD). + + max_duration_of_utterance (second): + The maximum duration of every utterance (seconds) + min_interval (millisecond): + The smaller min_interval is, the more sliced audio clips this script is likely to generate. + """ + print("File:", wav_file.split("/")[-1]) + waveform, fs = torchaudio.load(wav_file) + + slicer = Slicer(sr=fs, min_interval=min_interval, threshold=db_threshold) + chunks, positions = slicer.slice(waveform, return_chunks_positions=True) + + durations = [(end - begin) / fs for begin, end in positions] + print( + "Slicer's min silence part is {}ms, min and max duration of sliced utterances is {}s and {}s".format( + min_interval, min(durations), max(durations) + ) + ) + + res_chunks, res_positions = [], [] + for i, chunk in enumerate(chunks): + if len(chunk.shape) == 1: + chunk = chunk[None, :] + + begin, end = positions[i] + assert end - begin == chunk.shape[-1] + + max_wav_len = max_duration_of_utterance * fs + if chunk.shape[-1] <= max_wav_len: + res_chunks.append(chunk) + res_positions.append(positions[i]) + else: + # TODO: to reserve overlapping and conduct fade-in, fade-out + + # Get segments number + number = 2 + while chunk.shape[-1] // number >= max_wav_len: + number += 1 + seg_len = chunk.shape[-1] // number + + # Split + for num in range(number): + s = seg_len * num + t = min(s + seg_len, chunk.shape[-1]) + + seg_begin = begin + s + seg_end = begin + t + + res_chunks.append(chunk[:, s:t]) + res_positions.append((seg_begin, seg_end)) + + # Save utterances + os.makedirs(output_dir, exist_ok=True) + res = {"fs": int(fs)} + for i, chunk in enumerate(res_chunks): + filename = "{:04d}.wav".format(i) + res[filename] = [int(p) for p in res_positions[i]] + save_audio(os.path.join(output_dir, filename), chunk, fs) + + # Save positions + with open(os.path.join(output_dir, "positions.json"), "w") as f: + json.dump(res, f, indent=4, ensure_ascii=False) + return res + + +def is_silence( + wavform, + fs, + threshold=-40.0, + min_interval=300, + hop_size=10, + min_length=5000, +): + """ + Detect whether the given wavform is a silence + + wavform: (T, ) + """ + threshold = 10 ** (threshold / 20.0) + + hop_size = round(fs * hop_size / 1000) + win_size = min(round(min_interval), 4 * hop_size) + min_length = round(fs * min_length / 1000 / hop_size) + + if wavform.shape[0] <= min_length: + return True + + # (#Frame,) + rms_array = get_rms(y=wavform, frame_length=win_size, hop_length=hop_size).squeeze( + 0 + ) + return (rms_array < threshold).all() + + +def split_audio( + wav_file, target_sr, output_dir, max_duration_of_segment=10.0, overlap_duration=1.0 +): + """ + Split a long audio into segments. + + target_sr: + The target sampling rate to save the segments. + max_duration_of_utterance (second): + The maximum duration of every utterance (second) + overlap_duraion: + Each segment has "overlap duration" (second) overlap with its previous and next segment + """ + # (#channel, T) -> (T,) + waveform, fs = torchaudio.load(wav_file) + waveform = torchaudio.functional.resample( + waveform, orig_freq=fs, new_freq=target_sr + ) + waveform = torch.mean(waveform, dim=0) + + # waveform, _ = load_audio_torch(wav_file, target_sr) + assert len(waveform.shape) == 1 + + assert overlap_duration < max_duration_of_segment + length = int(max_duration_of_segment * target_sr) + stride = int((max_duration_of_segment - overlap_duration) * target_sr) + chunks = [] + for i in range(0, len(waveform), stride): + # (length,) + chunks.append(waveform[i : i + length]) + if i + length >= len(waveform): + break + + # Save segments + os.makedirs(output_dir, exist_ok=True) + results = [] + for i, chunk in enumerate(chunks): + uid = "{:04d}".format(i) + filename = os.path.join(output_dir, "{}.wav".format(uid)) + results.append( + {"Uid": uid, "Path": filename, "Duration": len(chunk) / target_sr} + ) + save_audio( + filename, + chunk, + target_sr, + turn_up=not is_silence(chunk, target_sr), + add_silence=False, + ) + + return results + + +def merge_segments_torchaudio(wav_files, fs, output_path, overlap_duration=1.0): + """Merge the given wav_files (may have overlaps) into a long audio + + fs: + The sampling rate of the wav files. + output_path: + The output path to save the merged audio. + overlap_duration (float, optional): + Each segment has "overlap duration" (second) overlap with its previous and next segment. Defaults to 1.0. + """ + + waveforms = [] + for file in wav_files: + # (T,) + waveform, _ = load_audio_torch(file, fs) + waveforms.append(waveform) + + if len(waveforms) == 1: + save_audio(output_path, waveforms[0], fs, add_silence=False, turn_up=False) + return + + overlap_len = int(overlap_duration * fs) + fade_out = torchaudio.transforms.Fade(fade_out_len=overlap_len) + fade_in = torchaudio.transforms.Fade(fade_in_len=overlap_len) + fade_in_and_out = torchaudio.transforms.Fade(fade_out_len=overlap_len) + + segments_lens = [len(wav) for wav in waveforms] + merged_waveform_len = sum(segments_lens) - overlap_len * (len(waveforms) - 1) + merged_waveform = torch.zeros(merged_waveform_len) + + start = 0 + for index, wav in enumerate( + tqdm(waveforms, desc="Merge for {}".format(output_path)) + ): + wav_len = len(wav) + + if index == 0: + wav = fade_out(wav) + elif index == len(waveforms) - 1: + wav = fade_in(wav) + else: + wav = fade_in_and_out(wav) + + merged_waveform[start : start + wav_len] = wav + start += wav_len - overlap_len + + save_audio(output_path, merged_waveform, fs, add_silence=False, turn_up=True) + + +def merge_segments_encodec(wav_files, fs, output_path, overlap_duration=1.0): + """Merge the given wav_files (may have overlaps) into a long audio + + fs: + The sampling rate of the wav files. + output_path: + The output path to save the merged audio. + overlap_duration (float, optional): + Each segment has "overlap duration" (second) overlap with its previous and next segment. Defaults to 1.0. + """ + + waveforms = [] + for file in wav_files: + # (T,) + waveform, _ = load_audio_torch(file, fs) + waveforms.append(waveform) + + if len(waveforms) == 1: + save_audio(output_path, waveforms[0], fs, add_silence=False, turn_up=False) + return + + device = waveforms[0].device + dtype = waveforms[0].dtype + shape = waveforms[0].shape[:-1] + + overlap_len = int(overlap_duration * fs) + segments_lens = [len(wav) for wav in waveforms] + merged_waveform_len = sum(segments_lens) - overlap_len * (len(waveforms) - 1) + + sum_weight = torch.zeros(merged_waveform_len, device=device, dtype=dtype) + out = torch.zeros(*shape, merged_waveform_len, device=device, dtype=dtype) + offset = 0 + + for frame in waveforms: + frame_length = frame.size(-1) + t = torch.linspace(0, 1, frame_length + 2, device=device, dtype=torch.float32)[ + 1:-1 + ] + weight = 0.5 - (t - 0.5).abs() + weighted_frame = frame * weight + + cur = out[..., offset : offset + frame_length] + cur += weighted_frame[..., : cur.size(-1)] + out[..., offset : offset + frame_length] = cur + + cur = sum_weight[offset : offset + frame_length] + cur += weight[..., : cur.size(-1)] + sum_weight[offset : offset + frame_length] = cur + + offset += frame_length - overlap_len + + assert sum_weight.min() > 0 + merged_waveform = out / sum_weight + save_audio(output_path, merged_waveform, fs, add_silence=False, turn_up=True) diff --git a/utils/data_utils.py b/utils/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4a104b56760d239e533acd414e7e542feac47968 --- /dev/null +++ b/utils/data_utils.py @@ -0,0 +1,581 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import os + +import numpy as np +from scipy.interpolate import interp1d +from tqdm import tqdm +from sklearn.preprocessing import StandardScaler + + + +def intersperse(lst, item): + result = [item] * (len(lst) * 2 + 1) + result[1::2] = lst + return result + +def load_content_feature_path(meta_data, processed_dir, feat_dir): + utt2feat_path = {} + for utt_info in meta_data: + utt = utt_info["Dataset"] + "_" + utt_info["Uid"] + feat_path = os.path.join( + processed_dir, utt_info["Dataset"], feat_dir, f'{utt_info["Uid"]}.npy' + ) + utt2feat_path[utt] = feat_path + + return utt2feat_path + + +def load_source_content_feature_path(meta_data, feat_dir): + utt2feat_path = {} + for utt in meta_data: + feat_path = os.path.join(feat_dir, f"{utt}.npy") + utt2feat_path[utt] = feat_path + + return utt2feat_path + + +def get_spk_map(spk2id_path, utt2spk_path): + utt2spk = {} + with open(spk2id_path, "r") as spk2id_file: + spk2id = json.load(spk2id_file) + with open(utt2spk_path, encoding="utf-8") as f: + for line in f.readlines(): + utt, spk = line.strip().split("\t") + utt2spk[utt] = spk + return spk2id, utt2spk + + +def get_target_f0_median(f0_dir): + total_f0 = [] + for utt in os.listdir(f0_dir): + if not utt.endswith(".npy"): + continue + f0_feat_path = os.path.join(f0_dir, utt) + f0 = np.load(f0_feat_path) + total_f0 += f0.tolist() + + total_f0 = np.array(total_f0) + voiced_position = np.where(total_f0 != 0) + return np.median(total_f0[voiced_position]) + + +def get_conversion_f0_factor(source_f0, target_median, source_median=None): + """Align the median between source f0 and target f0 + + Note: Here we use multiplication, whose factor is target_median/source_median + + Reference: Frequency and pitch interval + http://blog.ccyg.studio/article/be12c2ee-d47c-4098-9782-ca76da3035e4/ + """ + if source_median is None: + voiced_position = np.where(source_f0 != 0) + source_median = np.median(source_f0[voiced_position]) + factor = target_median / source_median + return source_median, factor + + +def transpose_key(frame_pitch, trans_key): + # Transpose by user's argument + print("Transpose key = {} ...\n".format(trans_key)) + + transed_pitch = frame_pitch * 2 ** (trans_key / 12) + return transed_pitch + + +def pitch_shift_to_target(frame_pitch, target_pitch_median, source_pitch_median=None): + # Loading F0 Base (median) and shift + source_pitch_median, factor = get_conversion_f0_factor( + frame_pitch, target_pitch_median, source_pitch_median + ) + print( + "Auto transposing: source f0 median = {:.1f}, target f0 median = {:.1f}, factor = {:.2f}".format( + source_pitch_median, target_pitch_median, factor + ) + ) + transed_pitch = frame_pitch * factor + return transed_pitch + + +def load_frame_pitch( + meta_data, + processed_dir, + pitch_dir, + use_log_scale=False, + return_norm=False, + interoperate=False, + utt2spk=None, +): + utt2pitch = {} + utt2uv = {} + if utt2spk is None: + pitch_scaler = StandardScaler() + for utt_info in meta_data: + utt = utt_info["Dataset"] + "_" + utt_info["Uid"] + pitch_path = os.path.join( + processed_dir, utt_info["Dataset"], pitch_dir, f'{utt_info["Uid"]}.npy' + ) + pitch = np.load(pitch_path) + assert len(pitch) > 0 + uv = pitch != 0 + utt2uv[utt] = uv + if use_log_scale: + nonzero_idxes = np.where(pitch != 0)[0] + pitch[nonzero_idxes] = np.log(pitch[nonzero_idxes]) + utt2pitch[utt] = pitch + pitch_scaler.partial_fit(pitch.reshape(-1, 1)) + + mean, std = pitch_scaler.mean_[0], pitch_scaler.scale_[0] + if return_norm: + for utt_info in meta_data: + utt = utt_info["Dataset"] + "_" + utt_info["Uid"] + pitch = utt2pitch[utt] + normalized_pitch = (pitch - mean) / std + utt2pitch[utt] = normalized_pitch + pitch_statistic = {"mean": mean, "std": std} + else: + spk2utt = {} + pitch_statistic = [] + for utt_info in meta_data: + utt = utt_info["Dataset"] + "_" + utt_info["Uid"] + if not utt2spk[utt] in spk2utt: + spk2utt[utt2spk[utt]] = [] + spk2utt[utt2spk[utt]].append(utt) + + for spk in spk2utt: + pitch_scaler = StandardScaler() + for utt in spk2utt[spk]: + dataset = utt.split("_")[0] + uid = "_".join(utt.split("_")[1:]) + pitch_path = os.path.join( + processed_dir, dataset, pitch_dir, f"{uid}.npy" + ) + pitch = np.load(pitch_path) + assert len(pitch) > 0 + uv = pitch != 0 + utt2uv[utt] = uv + if use_log_scale: + nonzero_idxes = np.where(pitch != 0)[0] + pitch[nonzero_idxes] = np.log(pitch[nonzero_idxes]) + utt2pitch[utt] = pitch + pitch_scaler.partial_fit(pitch.reshape(-1, 1)) + + mean, std = pitch_scaler.mean_[0], pitch_scaler.scale_[0] + if return_norm: + for utt in spk2utt[spk]: + pitch = utt2pitch[utt] + normalized_pitch = (pitch - mean) / std + utt2pitch[utt] = normalized_pitch + pitch_statistic.append({"spk": spk, "mean": mean, "std": std}) + + return utt2pitch, utt2uv, pitch_statistic + + +# discard +def load_phone_pitch( + meta_data, + processed_dir, + pitch_dir, + utt2dur, + use_log_scale=False, + return_norm=False, + interoperate=True, + utt2spk=None, +): + print("Load Phone Pitch") + utt2pitch = {} + utt2uv = {} + if utt2spk is None: + pitch_scaler = StandardScaler() + for utt_info in tqdm(meta_data): + utt = utt_info["Dataset"] + "_" + utt_info["Uid"] + pitch_path = os.path.join( + processed_dir, utt_info["Dataset"], pitch_dir, f'{utt_info["Uid"]}.npy' + ) + frame_pitch = np.load(pitch_path) + assert len(frame_pitch) > 0 + uv = frame_pitch != 0 + utt2uv[utt] = uv + phone_pitch = phone_average_pitch(frame_pitch, utt2dur[utt], interoperate) + if use_log_scale: + nonzero_idxes = np.where(phone_pitch != 0)[0] + phone_pitch[nonzero_idxes] = np.log(phone_pitch[nonzero_idxes]) + utt2pitch[utt] = phone_pitch + pitch_scaler.partial_fit(remove_outlier(phone_pitch).reshape(-1, 1)) + + mean, std = pitch_scaler.mean_[0], pitch_scaler.scale_[0] + max_value = np.finfo(np.float64).min + min_value = np.finfo(np.float64).max + if return_norm: + for utt_info in meta_data: + utt = utt_info["Dataset"] + "_" + utt_info["Uid"] + pitch = utt2pitch[utt] + normalized_pitch = (pitch - mean) / std + max_value = max(max_value, max(normalized_pitch)) + min_value = min(min_value, min(normalized_pitch)) + utt2pitch[utt] = normalized_pitch + phone_normalized_pitch_path = os.path.join( + processed_dir, + utt_info["Dataset"], + "phone_level_" + pitch_dir, + f'{utt_info["Uid"]}.npy', + ) + pitch_statistic = { + "mean": mean, + "std": std, + "min_value": min_value, + "max_value": max_value, + } + else: + spk2utt = {} + pitch_statistic = [] + for utt_info in tqdm(meta_data): + utt = utt_info["Dataset"] + "_" + utt_info["Uid"] + if not utt2spk[utt] in spk2utt: + spk2utt[utt2spk[utt]] = [] + spk2utt[utt2spk[utt]].append(utt) + + for spk in spk2utt: + pitch_scaler = StandardScaler() + for utt in spk2utt[spk]: + dataset = utt.split("_")[0] + uid = "_".join(utt.split("_")[1:]) + pitch_path = os.path.join( + processed_dir, dataset, pitch_dir, f"{uid}.npy" + ) + frame_pitch = np.load(pitch_path) + assert len(frame_pitch) > 0 + uv = frame_pitch != 0 + utt2uv[utt] = uv + phone_pitch = phone_average_pitch( + frame_pitch, utt2dur[utt], interoperate + ) + if use_log_scale: + nonzero_idxes = np.where(phone_pitch != 0)[0] + phone_pitch[nonzero_idxes] = np.log(phone_pitch[nonzero_idxes]) + utt2pitch[utt] = phone_pitch + pitch_scaler.partial_fit(remove_outlier(phone_pitch).reshape(-1, 1)) + + mean, std = pitch_scaler.mean_[0], pitch_scaler.scale_[0] + max_value = np.finfo(np.float64).min + min_value = np.finfo(np.float64).max + + if return_norm: + for utt in spk2utt[spk]: + pitch = utt2pitch[utt] + normalized_pitch = (pitch - mean) / std + max_value = max(max_value, max(normalized_pitch)) + min_value = min(min_value, min(normalized_pitch)) + utt2pitch[utt] = normalized_pitch + pitch_statistic.append( + { + "spk": spk, + "mean": mean, + "std": std, + "min_value": min_value, + "max_value": max_value, + } + ) + + return utt2pitch, utt2uv, pitch_statistic + + +def phone_average_pitch(pitch, dur, interoperate=False): + pos = 0 + + if interoperate: + nonzero_ids = np.where(pitch != 0)[0] + interp_fn = interp1d( + nonzero_ids, + pitch[nonzero_ids], + fill_value=(pitch[nonzero_ids[0]], pitch[nonzero_ids[-1]]), + bounds_error=False, + ) + pitch = interp_fn(np.arange(0, len(pitch))) + phone_pitch = np.zeros(len(dur)) + + for i, d in enumerate(dur): + d = int(d) + if d > 0 and pos < len(pitch): + phone_pitch[i] = np.mean(pitch[pos : pos + d]) + else: + phone_pitch[i] = 0 + pos += d + return phone_pitch + + +def load_energy( + meta_data, + processed_dir, + energy_dir, + use_log_scale=False, + return_norm=False, + utt2spk=None, +): + utt2energy = {} + if utt2spk is None: + for utt_info in meta_data: + utt = utt_info["Dataset"] + "_" + utt_info["Uid"] + energy_path = os.path.join( + processed_dir, utt_info["Dataset"], energy_dir, f'{utt_info["Uid"]}.npy' + ) + if not os.path.exists(energy_path): + continue + energy = np.load(energy_path) + assert len(energy) > 0 + + if use_log_scale: + nonzero_idxes = np.where(energy != 0)[0] + energy[nonzero_idxes] = np.log(energy[nonzero_idxes]) + utt2energy[utt] = energy + + if return_norm: + with open( + os.path.join( + processed_dir, utt_info["Dataset"], energy_dir, "statistics.json" + ) + ) as f: + stats = json.load(f) + mean, std = ( + stats[utt_info["Dataset"] + "_" + utt_info["Singer"]][ + "voiced_positions" + ]["mean"], + stats["LJSpeech_LJSpeech"]["voiced_positions"]["std"], + ) + for utt in utt2energy.keys(): + energy = utt2energy[utt] + normalized_energy = (energy - mean) / std + utt2energy[utt] = normalized_energy + + energy_statistic = {"mean": mean, "std": std} + else: + spk2utt = {} + energy_statistic = [] + for utt_info in meta_data: + utt = utt_info["Dataset"] + "_" + utt_info["Uid"] + if not utt2spk[utt] in spk2utt: + spk2utt[utt2spk[utt]] = [] + spk2utt[utt2spk[utt]].append(utt) + + for spk in spk2utt: + energy_scaler = StandardScaler() + for utt in spk2utt[spk]: + dataset = utt.split("_")[0] + uid = "_".join(utt.split("_")[1:]) + energy_path = os.path.join( + processed_dir, dataset, energy_dir, f"{uid}.npy" + ) + if not os.path.exists(energy_path): + continue + frame_energy = np.load(energy_path) + assert len(frame_energy) > 0 + + if use_log_scale: + nonzero_idxes = np.where(frame_energy != 0)[0] + frame_energy[nonzero_idxes] = np.log(frame_energy[nonzero_idxes]) + utt2energy[utt] = frame_energy + energy_scaler.partial_fit(frame_energy.reshape(-1, 1)) + + mean, std = energy_scaler.mean_[0], energy_scaler.scale_[0] + if return_norm: + for utt in spk2utt[spk]: + energy = utt2energy[utt] + normalized_energy = (energy - mean) / std + utt2energy[utt] = normalized_energy + energy_statistic.append({"spk": spk, "mean": mean, "std": std}) + + return utt2energy, energy_statistic + + +def load_frame_energy( + meta_data, + processed_dir, + energy_dir, + use_log_scale=False, + return_norm=False, + interoperate=False, + utt2spk=None, +): + utt2energy = {} + if utt2spk is None: + energy_scaler = StandardScaler() + for utt_info in meta_data: + utt = utt_info["Dataset"] + "_" + utt_info["Uid"] + energy_path = os.path.join( + processed_dir, utt_info["Dataset"], energy_dir, f'{utt_info["Uid"]}.npy' + ) + frame_energy = np.load(energy_path) + assert len(frame_energy) > 0 + + if use_log_scale: + nonzero_idxes = np.where(frame_energy != 0)[0] + frame_energy[nonzero_idxes] = np.log(frame_energy[nonzero_idxes]) + utt2energy[utt] = frame_energy + energy_scaler.partial_fit(frame_energy.reshape(-1, 1)) + + mean, std = energy_scaler.mean_[0], energy_scaler.scale_[0] + if return_norm: + for utt_info in meta_data: + utt = utt_info["Dataset"] + "_" + utt_info["Uid"] + energy = utt2energy[utt] + normalized_energy = (energy - mean) / std + utt2energy[utt] = normalized_energy + energy_statistic = {"mean": mean, "std": std} + + else: + spk2utt = {} + energy_statistic = [] + for utt_info in meta_data: + utt = utt_info["Dataset"] + "_" + utt_info["Uid"] + if not utt2spk[utt] in spk2utt: + spk2utt[utt2spk[utt]] = [] + spk2utt[utt2spk[utt]].append(utt) + + for spk in spk2utt: + energy_scaler = StandardScaler() + for utt in spk2utt[spk]: + dataset = utt.split("_")[0] + uid = "_".join(utt.split("_")[1:]) + energy_path = os.path.join( + processed_dir, dataset, energy_dir, f"{uid}.npy" + ) + frame_energy = np.load(energy_path) + assert len(frame_energy) > 0 + + if use_log_scale: + nonzero_idxes = np.where(frame_energy != 0)[0] + frame_energy[nonzero_idxes] = np.log(frame_energy[nonzero_idxes]) + utt2energy[utt] = frame_energy + energy_scaler.partial_fit(frame_energy.reshape(-1, 1)) + + mean, std = energy_scaler.mean_[0], energy_scaler.scale_[0] + if return_norm: + for utt in spk2utt[spk]: + energy = utt2energy[utt] + normalized_energy = (energy - mean) / std + utt2energy[utt] = normalized_energy + energy_statistic.append({"spk": spk, "mean": mean, "std": std}) + + return utt2energy, energy_statistic + + +def align_length(feature, target_len, pad_value=0.0): + feature_len = feature.shape[-1] + dim = len(feature.shape) + # align 1-D data + if dim == 2: + if target_len > feature_len: + feature = np.pad( + feature, + ((0, 0), (0, target_len - feature_len)), + constant_values=pad_value, + ) + else: + feature = feature[:, :target_len] + # align 2-D data + elif dim == 1: + if target_len > feature_len: + feature = np.pad( + feature, (0, target_len - feature_len), constant_values=pad_value + ) + else: + feature = feature[:target_len] + else: + raise NotImplementedError + return feature + + +def align_whisper_feauture_length( + feature, target_len, fast_mapping=True, source_hop=320, target_hop=256 +): + factor = np.gcd(source_hop, target_hop) + source_hop //= factor + target_hop //= factor + # print( + # "Mapping source's {} frames => target's {} frames".format( + # target_hop, source_hop + # ) + # ) + + max_source_len = 1500 + target_len = min(target_len, max_source_len * source_hop // target_hop) + + width = feature.shape[-1] + + if fast_mapping: + source_len = target_len * target_hop // source_hop + 1 + feature = feature[:source_len] + + else: + source_len = max_source_len + + # const ~= target_len * target_hop + const = source_len * source_hop // target_hop * target_hop + + # (source_len * source_hop, dim) + up_sampling_feats = np.repeat(feature, source_hop, axis=0) + # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim) + down_sampling_feats = np.average( + up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1 + ) + assert len(down_sampling_feats) >= target_len + + # (target_len, dim) + feat = down_sampling_feats[:target_len] + + return feat + + +def align_content_feature_length(feature, target_len, source_hop=320, target_hop=256): + factor = np.gcd(source_hop, target_hop) + source_hop //= factor + target_hop //= factor + # print( + # "Mapping source's {} frames => target's {} frames".format( + # target_hop, source_hop + # ) + # ) + + # (source_len, 256) + source_len, width = feature.shape + + # const ~= target_len * target_hop + const = source_len * source_hop // target_hop * target_hop + + # (source_len * source_hop, dim) + up_sampling_feats = np.repeat(feature, source_hop, axis=0) + # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim) + down_sampling_feats = np.average( + up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1 + ) + + err = abs(target_len - len(down_sampling_feats)) + if err > 4: ## why 4 not 3? + print("target_len:", target_len) + print("raw feature:", feature.shape) + print("up_sampling:", up_sampling_feats.shape) + print("down_sampling_feats:", down_sampling_feats.shape) + exit() + if len(down_sampling_feats) < target_len: + # (1, dim) -> (err, dim) + end = down_sampling_feats[-1][None, :].repeat(err, axis=0) + down_sampling_feats = np.concatenate([down_sampling_feats, end], axis=0) + + # (target_len, dim) + feat = down_sampling_feats[:target_len] + + return feat + + +def remove_outlier(values): + values = np.array(values) + p25 = np.percentile(values, 25) + p75 = np.percentile(values, 75) + lower = p25 - 1.5 * (p75 - p25) + upper = p75 + 1.5 * (p75 - p25) + normal_indices = np.logical_and(values > lower, values < upper) + return values[normal_indices] diff --git a/utils/distribution.py b/utils/distribution.py new file mode 100644 index 0000000000000000000000000000000000000000..de3000e99194f7e848712d8b4cb77c988f098fd2 --- /dev/null +++ b/utils/distribution.py @@ -0,0 +1,270 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +import torch.nn.functional as F + +from torch.distributions import Normal + + +def log_sum_exp(x): + """numerically stable log_sum_exp implementation that prevents overflow""" + # TF ordering + axis = len(x.size()) - 1 + m, _ = torch.max(x, dim=axis) + m2, _ = torch.max(x, dim=axis, keepdim=True) + return m + torch.log(torch.sum(torch.exp(x - m2), dim=axis)) + + +def discretized_mix_logistic_loss( + y_hat, y, num_classes=256, log_scale_min=-7.0, reduce=True +): + """Discretized mixture of logistic distributions loss + + Note that it is assumed that input is scaled to [-1, 1]. + + Args: + y_hat (Tensor): Predicted output (B x C x T) + y (Tensor): Target (B x T x 1). + num_classes (int): Number of classes + log_scale_min (float): Log scale minimum value + reduce (bool): If True, the losses are averaged or summed for each + minibatch. + + Returns + Tensor: loss + """ + assert y_hat.dim() == 3 + assert y_hat.size(1) % 3 == 0 + nr_mix = y_hat.size(1) // 3 + + # (B x T x C) + y_hat = y_hat.transpose(1, 2) + + # unpack parameters. (B, T, num_mixtures) x 3 + logit_probs = y_hat[:, :, :nr_mix] + means = y_hat[:, :, nr_mix : 2 * nr_mix] + log_scales = torch.clamp(y_hat[:, :, 2 * nr_mix : 3 * nr_mix], min=log_scale_min) + + # B x T x 1 -> B x T x num_mixtures + y = y.expand_as(means) + + centered_y = y - means + inv_stdv = torch.exp(-log_scales) + plus_in = inv_stdv * (centered_y + 1.0 / (num_classes - 1)) + cdf_plus = torch.sigmoid(plus_in) + min_in = inv_stdv * (centered_y - 1.0 / (num_classes - 1)) + cdf_min = torch.sigmoid(min_in) + + # log probability for edge case of 0 (before scaling) + # equivalent: torch.log(torch.sigmoid(plus_in)) + log_cdf_plus = plus_in - F.softplus(plus_in) + + # log probability for edge case of 255 (before scaling) + # equivalent: (1 - torch.sigmoid(min_in)).log() + log_one_minus_cdf_min = -F.softplus(min_in) + + # probability for all other cases + cdf_delta = cdf_plus - cdf_min + + mid_in = inv_stdv * centered_y + # log probability in the center of the bin, to be used in extreme cases + # (not actually used in our code) + log_pdf_mid = mid_in - log_scales - 2.0 * F.softplus(mid_in) + + # tf equivalent + """ + log_probs = tf.where(x < -0.999, log_cdf_plus, + tf.where(x > 0.999, log_one_minus_cdf_min, + tf.where(cdf_delta > 1e-5, + tf.log(tf.maximum(cdf_delta, 1e-12)), + log_pdf_mid - np.log(127.5)))) + """ + # TODO: cdf_delta <= 1e-5 actually can happen. How can we choose the value + # for num_classes=65536 case? 1e-7? not sure.. + inner_inner_cond = (cdf_delta > 1e-5).float() + + inner_inner_out = inner_inner_cond * torch.log( + torch.clamp(cdf_delta, min=1e-12) + ) + (1.0 - inner_inner_cond) * (log_pdf_mid - np.log((num_classes - 1) / 2)) + inner_cond = (y > 0.999).float() + inner_out = ( + inner_cond * log_one_minus_cdf_min + (1.0 - inner_cond) * inner_inner_out + ) + cond = (y < -0.999).float() + log_probs = cond * log_cdf_plus + (1.0 - cond) * inner_out + + log_probs = log_probs + F.log_softmax(logit_probs, -1) + + if reduce: + return -torch.sum(log_sum_exp(log_probs)) + else: + return -log_sum_exp(log_probs).unsqueeze(-1) + + +def to_one_hot(tensor, n, fill_with=1.0): + # we perform one hot encore with respect to the last axis + one_hot = torch.FloatTensor(tensor.size() + (n,)).zero_() + if tensor.is_cuda: + one_hot = one_hot.cuda() + one_hot.scatter_(len(tensor.size()), tensor.unsqueeze(-1), fill_with) + return one_hot + + +def sample_from_discretized_mix_logistic(y, log_scale_min=-7.0, clamp_log_scale=False): + """ + Sample from discretized mixture of logistic distributions + + Args: + y (Tensor): B x C x T + log_scale_min (float): Log scale minimum value + + Returns: + Tensor: sample in range of [-1, 1]. + """ + assert y.size(1) % 3 == 0 + nr_mix = y.size(1) // 3 + + # B x T x C + y = y.transpose(1, 2) + logit_probs = y[:, :, :nr_mix] + + # sample mixture indicator from softmax + temp = logit_probs.data.new(logit_probs.size()).uniform_(1e-5, 1.0 - 1e-5) + temp = logit_probs.data - torch.log(-torch.log(temp)) + _, argmax = temp.max(dim=-1) + + # (B, T) -> (B, T, nr_mix) + one_hot = to_one_hot(argmax, nr_mix) + # select logistic parameters + means = torch.sum(y[:, :, nr_mix : 2 * nr_mix] * one_hot, dim=-1) + log_scales = torch.sum(y[:, :, 2 * nr_mix : 3 * nr_mix] * one_hot, dim=-1) + if clamp_log_scale: + log_scales = torch.clamp(log_scales, min=log_scale_min) + # sample from logistic & clip to interval + # we don't actually round to the nearest 8bit value when sampling + u = means.data.new(means.size()).uniform_(1e-5, 1.0 - 1e-5) + x = means + torch.exp(log_scales) * (torch.log(u) - torch.log(1.0 - u)) + + x = torch.clamp(torch.clamp(x, min=-1.0), max=1.0) + + return x + + +# we can easily define discretized version of the gaussian loss, however, +# use continuous version as same as the https://clarinet-demo.github.io/ +def mix_gaussian_loss(y_hat, y, log_scale_min=-7.0, reduce=True): + """Mixture of continuous gaussian distributions loss + + Note that it is assumed that input is scaled to [-1, 1]. + + Args: + y_hat (Tensor): Predicted output (B x C x T) + y (Tensor): Target (B x T x 1). + log_scale_min (float): Log scale minimum value + reduce (bool): If True, the losses are averaged or summed for each + minibatch. + Returns + Tensor: loss + """ + assert y_hat.dim() == 3 + C = y_hat.size(1) + if C == 2: + nr_mix = 1 + else: + assert y_hat.size(1) % 3 == 0 + nr_mix = y_hat.size(1) // 3 + + # (B x T x C) + y_hat = y_hat.transpose(1, 2) + + # unpack parameters. + if C == 2: + # special case for C == 2, just for compatibility + logit_probs = None + means = y_hat[:, :, 0:1] + log_scales = torch.clamp(y_hat[:, :, 1:2], min=log_scale_min) + else: + # (B, T, num_mixtures) x 3 + logit_probs = y_hat[:, :, :nr_mix] + means = y_hat[:, :, nr_mix : 2 * nr_mix] + log_scales = torch.clamp( + y_hat[:, :, 2 * nr_mix : 3 * nr_mix], min=log_scale_min + ) + + # B x T x 1 -> B x T x num_mixtures + y = y.expand_as(means) + + centered_y = y - means + dist = Normal(loc=0.0, scale=torch.exp(log_scales)) + # do we need to add a trick to avoid log(0)? + log_probs = dist.log_prob(centered_y) + + if nr_mix > 1: + log_probs = log_probs + F.log_softmax(logit_probs, -1) + + if reduce: + if nr_mix == 1: + return -torch.sum(log_probs) + else: + return -torch.sum(log_sum_exp(log_probs)) + else: + if nr_mix == 1: + return -log_probs + else: + return -log_sum_exp(log_probs).unsqueeze(-1) + + +def sample_from_mix_gaussian(y, log_scale_min=-7.0): + """ + Sample from (discretized) mixture of gaussian distributions + Args: + y (Tensor): B x C x T + log_scale_min (float): Log scale minimum value + Returns: + Tensor: sample in range of [-1, 1]. + """ + C = y.size(1) + if C == 2: + nr_mix = 1 + else: + assert y.size(1) % 3 == 0 + nr_mix = y.size(1) // 3 + + # B x T x C + y = y.transpose(1, 2) + + if C == 2: + logit_probs = None + else: + logit_probs = y[:, :, :nr_mix] + + if nr_mix > 1: + # sample mixture indicator from softmax + temp = logit_probs.data.new(logit_probs.size()).uniform_(1e-5, 1.0 - 1e-5) + temp = logit_probs.data - torch.log(-torch.log(temp)) + _, argmax = temp.max(dim=-1) + + # (B, T) -> (B, T, nr_mix) + one_hot = to_one_hot(argmax, nr_mix) + + # Select means and log scales + means = torch.sum(y[:, :, nr_mix : 2 * nr_mix] * one_hot, dim=-1) + log_scales = torch.sum(y[:, :, 2 * nr_mix : 3 * nr_mix] * one_hot, dim=-1) + else: + if C == 2: + means, log_scales = y[:, :, 0], y[:, :, 1] + elif C == 3: + means, log_scales = y[:, :, 1], y[:, :, 2] + else: + assert False, "shouldn't happen" + + scales = torch.exp(log_scales) + dist = Normal(loc=means, scale=scales) + x = dist.sample() + + x = torch.clamp(x, min=-1.0, max=1.0) + return x diff --git a/utils/dsp.py b/utils/dsp.py new file mode 100644 index 0000000000000000000000000000000000000000..18f9466f6b12e5539ce221f86030f5114ccdb503 --- /dev/null +++ b/utils/dsp.py @@ -0,0 +1,97 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch + +# ZERO = 1e-12 + + +def gaussian_normalize_mel_channel(mel, mu, sigma): + """ + Shift to Standorm Normal Distribution + + Args: + mel: (n_mels, frame_len) + mu: (n_mels,), mean value + sigma: (n_mels,), sd value + Return: + Tensor like mel + """ + mu = np.expand_dims(mu, -1) + sigma = np.expand_dims(sigma, -1) + return (mel - mu) / sigma + + +def de_gaussian_normalize_mel_channel(mel, mu, sigma): + """ + + Args: + mel: (n_mels, frame_len) + mu: (n_mels,), mean value + sigma: (n_mels,), sd value + Return: + Tensor like mel + """ + mu = np.expand_dims(mu, -1) + sigma = np.expand_dims(sigma, -1) + return sigma * mel + mu + + +def decompress(audio_compressed, bits): + mu = 2**bits - 1 + audio = np.sign(audio_compressed) / mu * ((1 + mu) ** np.abs(audio_compressed) - 1) + return audio + + +def compress(audio, bits): + mu = 2**bits - 1 + audio_compressed = np.sign(audio) * np.log(1 + mu * np.abs(audio)) / np.log(mu + 1) + return audio_compressed + + +def label_to_audio(quant, bits): + classes = 2**bits + audio = 2 * quant / (classes - 1.0) - 1.0 + return audio + + +def audio_to_label(audio, bits): + """Normalized audio data tensor to digit array + + Args: + audio (tensor): audio data + bits (int): data bits + + Returns: + array: digit array of audio data + """ + classes = 2**bits + # initialize an increasing array with values from -1 to 1 + bins = np.linspace(-1, 1, classes) + # change value in audio tensor to digits + quant = np.digitize(audio, bins) - 1 + return quant + + +def label_to_onehot(x, bits): + """Converts a class vector (integers) to binary class matrix. + Args: + x: class vector to be converted into a matrix + (integers from 0 to num_classes). + num_classes: total number of classes. + Returns: + A binary matrix representation of the input. The classes axis + is placed last. + """ + classes = 2**bits + + result = torch.zeros((x.shape[0], classes), dtype=torch.float32) + for i in range(x.shape[0]): + result[i, x[i]] = 1 + + output_shape = x.shape + (classes,) + output = torch.reshape(result, output_shape) + return output diff --git a/utils/duration.py b/utils/duration.py new file mode 100644 index 0000000000000000000000000000000000000000..c9544b40b88c68b4e1df33ab1c81a6196a43111e --- /dev/null +++ b/utils/duration.py @@ -0,0 +1,86 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import os +import tgt + + +def get_alignment(tier, cfg): + sample_rate = cfg["sample_rate"] + hop_size = cfg["hop_size"] + + sil_phones = ["sil", "sp", "spn"] + + phones = [] + durations = [] + start_time = 0 + end_time = 0 + end_idx = 0 + + for t in tier._objects: + s, e, p = t.start_time, t.end_time, t.text + + # Trim leading silences + if phones == []: + if p in sil_phones: + continue + else: + start_time = s + + if p not in sil_phones: + # For ordinary phones + phones.append(p) + end_time = e + end_idx = len(phones) + else: + # For silent phones + phones.append(p) + + durations.append( + int( + np.round(e * sample_rate / hop_size) + - np.round(s * sample_rate / hop_size) + ) + ) + + # Trim tailing silences + phones = phones[:end_idx] + durations = durations[:end_idx] + + return phones, durations, start_time, end_time + + +def get_duration(utt, wav, cfg): + speaker = utt["Singer"] + basename = utt["Uid"] + dataset = utt["Dataset"] + sample_rate = cfg["sample_rate"] + + # print(cfg.processed_dir, dataset, speaker, basename) + wav_path = os.path.join( + cfg.processed_dir, dataset, "raw_data", speaker, "{}.wav".format(basename) + ) + text_path = os.path.join( + cfg.processed_dir, dataset, "raw_data", speaker, "{}.lab".format(basename) + ) + tg_path = os.path.join( + cfg.processed_dir, dataset, "TextGrid", speaker, "{}.TextGrid".format(basename) + ) + + # Read raw text + with open(text_path, "r") as f: + raw_text = f.readline().strip("\n") + + # Get alignments + textgrid = tgt.io.read_textgrid(tg_path) + phone, duration, start, end = get_alignment( + textgrid.get_tier_by_name("phones"), cfg + ) + text = "{" + " ".join(phone) + "}" + if start >= end: + return None + + return duration, text, int(sample_rate * start), int(sample_rate * end) diff --git a/utils/f0.py b/utils/f0.py new file mode 100644 index 0000000000000000000000000000000000000000..fcd95396a6b08d8ffa1a310c5bee6f0d8b556796 --- /dev/null +++ b/utils/f0.py @@ -0,0 +1,299 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import librosa +import numpy as np +import torch +import parselmouth +import torchcrepe +import pyworld as pw + + +def get_bin_index(f0, m, M, n_bins, use_log_scale): + """ + WARNING: to abandon! + + Args: + raw_f0: tensor whose shpae is (N, frame_len) + Returns: + index: tensor whose shape is same to f0 + """ + raw_f0 = f0.clone() + raw_m, raw_M = m, M + + if use_log_scale: + f0[torch.where(f0 == 0)] = 1 + f0 = torch.log(f0) + m, M = float(np.log(m)), float(np.log(M)) + + # Set normal index in [1, n_bins - 1] + width = (M + 1e-7 - m) / (n_bins - 1) + index = (f0 - m) // width + 1 + # Set unvoiced frames as 0, Therefore, the vocabulary is [0, n_bins- 1], whose size is n_bins + index[torch.where(f0 == 0)] = 0 + + # TODO: Boundary check (special: to judge whether 0 for unvoiced) + if torch.any(raw_f0 > raw_M): + print("F0 Warning: too high f0: {}".format(raw_f0[torch.where(raw_f0 > raw_M)])) + index[torch.where(raw_f0 > raw_M)] = n_bins - 1 + if torch.any(raw_f0 < raw_m): + print("F0 Warning: too low f0: {}".format(raw_f0[torch.where(f0 < m)])) + index[torch.where(f0 < m)] = 0 + + return torch.as_tensor(index, dtype=torch.long, device=f0.device) + + +def f0_to_coarse(f0, pitch_bin, pitch_min, pitch_max): + ## TODO: Figure out the detail of this function + + f0_mel_min = 1127 * np.log(1 + pitch_min / 700) + f0_mel_max = 1127 * np.log(1 + pitch_max / 700) + + is_torch = isinstance(f0, torch.Tensor) + f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700) + f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (pitch_bin - 2) / ( + f0_mel_max - f0_mel_min + ) + 1 + + f0_mel[f0_mel <= 1] = 1 + f0_mel[f0_mel > pitch_bin - 1] = pitch_bin - 1 + f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int32) + assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, ( + f0_coarse.max(), + f0_coarse.min(), + ) + return f0_coarse + + +def interpolate(f0): + """Interpolate the unvoiced part. Thus the f0 can be passed to a subtractive synthesizer. + Args: + f0: A numpy array of shape (seq_len,) + Returns: + f0: Interpolated f0 of shape (seq_len,) + uv: Unvoiced part of shape (seq_len,) + """ + uv = f0 == 0 + if len(f0[~uv]) > 0: + # interpolate the unvoiced f0 + f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv]) + uv = uv.astype("float") + uv = np.min(np.array([uv[:-2], uv[1:-1], uv[2:]]), axis=0) + uv = np.pad(uv, (1, 1)) + return f0, uv + + +def get_log_f0(f0): + f0[np.where(f0 == 0)] = 1 + log_f0 = np.log(f0) + return log_f0 + + +# ========== Methods ========== + + +def get_f0_features_using_pyin(audio, cfg): + """Using pyin to extract the f0 feature. + Args: + audio + fs + win_length + hop_length + f0_min + f0_max + Returns: + f0: numpy array of shape (frame_len,) + """ + f0, voiced_flag, voiced_probs = librosa.pyin( + y=audio, + fmin=cfg.f0_min, + fmax=cfg.f0_max, + sr=cfg.sample_rate, + win_length=cfg.win_size, + hop_length=cfg.hop_size, + ) + # Set nan to 0 + f0[voiced_flag == False] = 0 + return f0 + + +def get_f0_features_using_parselmouth(audio, cfg, speed=1): + """Using parselmouth to extract the f0 feature. + Args: + audio + mel_len + hop_length + fs + f0_min + f0_max + speed(default=1) + Returns: + f0: numpy array of shape (frame_len,) + pitch_coarse: numpy array of shape (frame_len,) + """ + hop_size = int(np.round(cfg.hop_size * speed)) + + # Calculate the time step for pitch extraction + time_step = hop_size / cfg.sample_rate * 1000 + + f0 = ( + parselmouth.Sound(audio, cfg.sample_rate) + .to_pitch_ac( + time_step=time_step / 1000, + voicing_threshold=0.6, + pitch_floor=cfg.f0_min, + pitch_ceiling=cfg.f0_max, + ) + .selected_array["frequency"] + ) + + # Pad the pitch to the mel_len + # pad_size = (int(len(audio) // hop_size) - len(f0) + 1) // 2 + # f0 = np.pad(f0, [[pad_size, mel_len - len(f0) - pad_size]], mode="constant") + + # Get the coarse part + pitch_coarse = f0_to_coarse(f0, cfg.pitch_bin, cfg.f0_min, cfg.f0_max) + return f0, pitch_coarse + + +def get_f0_features_using_dio(audio, cfg): + """Using dio to extract the f0 feature. + Args: + audio + mel_len + fs + hop_length + f0_min + f0_max + Returns: + f0: numpy array of shape (frame_len,) + """ + # Get the raw f0 + _f0, t = pw.dio( + audio.astype("double"), + cfg.sample_rate, + f0_floor=cfg.f0_min, + f0_ceil=cfg.f0_max, + channels_in_octave=2, + frame_period=(1000 * cfg.hop_size / cfg.sample_rate), + ) + # Get the f0 + f0 = pw.stonemask(audio.astype("double"), _f0, t, cfg.sample_rate) + return f0 + + +def get_f0_features_using_harvest(audio, mel_len, fs, hop_length, f0_min, f0_max): + """Using harvest to extract the f0 feature. + Args: + audio + mel_len + fs + hop_length + f0_min + f0_max + Returns: + f0: numpy array of shape (frame_len,) + """ + f0, _ = pw.harvest( + audio.astype("double"), + fs, + f0_floor=f0_min, + f0_ceil=f0_max, + frame_period=(1000 * hop_length / fs), + ) + f0 = f0.astype("float")[:mel_len] + return f0 + + +def get_f0_features_using_crepe( + audio, mel_len, fs, hop_length, hop_length_new, f0_min, f0_max, threshold=0.3 +): + """Using torchcrepe to extract the f0 feature. + Args: + audio + mel_len + fs + hop_length + hop_length_new + f0_min + f0_max + threshold(default=0.3) + Returns: + f0: numpy array of shape (frame_len,) + """ + # Currently, crepe only supports 16khz audio + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + audio_16k = librosa.resample(audio, orig_sr=fs, target_sr=16000) + audio_16k_torch = torch.FloatTensor(audio_16k).unsqueeze(0).to(device) + + # Get the raw pitch + f0, pd = torchcrepe.predict( + audio_16k_torch, + 16000, + hop_length_new, + f0_min, + f0_max, + pad=True, + model="full", + batch_size=1024, + device=device, + return_periodicity=True, + ) + + # Filter, de-silence, set up threshold for unvoiced part + pd = torchcrepe.filter.median(pd, 3) + pd = torchcrepe.threshold.Silence(-60.0)(pd, audio_16k_torch, 16000, hop_length_new) + f0 = torchcrepe.threshold.At(threshold)(f0, pd) + f0 = torchcrepe.filter.mean(f0, 3) + + # Convert unvoiced part to 0hz + f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0) + + # Interpolate f0 + nzindex = torch.nonzero(f0[0]).squeeze() + f0 = torch.index_select(f0[0], dim=0, index=nzindex).cpu().numpy() + time_org = 0.005 * nzindex.cpu().numpy() + time_frame = np.arange(mel_len) * hop_length / fs + f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1]) + return f0 + + +def get_f0(audio, cfg): + if cfg.pitch_extractor == "dio": + f0 = get_f0_features_using_dio(audio, cfg) + elif cfg.pitch_extractor == "pyin": + f0 = get_f0_features_using_pyin(audio, cfg) + elif cfg.pitch_extractor == "parselmouth": + f0, _ = get_f0_features_using_parselmouth(audio, cfg) + # elif cfg.data.f0_extractor == 'cwt': # todo + + return f0 + + +def get_cents(f0_hz): + """ + F_{cent} = 1200 * log2 (F/440) + + Reference: + APSIPA'17, Perceptual Evaluation of Singing Quality + """ + voiced_f0 = f0_hz[f0_hz != 0] + return 1200 * np.log2(voiced_f0 / 440) + + +def get_pitch_derivatives(f0_hz): + """ + f0_hz: (,T) + """ + f0_cent = get_cents(f0_hz) + return f0_cent[1:] - f0_cent[:-1] + + +def get_pitch_sub_median(f0_hz): + """ + f0_hz: (,T) + """ + f0_cent = get_cents(f0_hz) + return f0_cent - np.median(f0_cent) diff --git a/utils/hparam.py b/utils/hparam.py new file mode 100644 index 0000000000000000000000000000000000000000..c5dd35c6a3158b0aaf8d936dba139a030a48bc62 --- /dev/null +++ b/utils/hparam.py @@ -0,0 +1,659 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This code is modified from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/training/python/training/hparam.py pylint: disable=line-too-long +"""Hyperparameter values.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import json +import numbers +import re +import six + +# Define the regular expression for parsing a single clause of the input +# (delimited by commas). A legal clause looks like: +# []? = +# where is either a single token or [] enclosed list of tokens. +# For example: "var[1] = a" or "x = [1,2,3]" +PARAM_RE = re.compile( + r""" + (?P[a-zA-Z][\w\.]*) # variable name: "var" or "x" + (\[\s*(?P\d+)\s*\])? # (optional) index: "1" or None + \s*=\s* + ((?P[^,\[]*) # single value: "a" or None + | + \[(?P[^\]]*)\]) # list of values: None or "1,2,3" + ($|,\s*)""", + re.VERBOSE, +) + + +def _parse_fail(name, var_type, value, values): + """Helper function for raising a value error for bad assignment.""" + raise ValueError( + "Could not parse hparam '%s' of type '%s' with value '%s' in %s" + % (name, var_type.__name__, value, values) + ) + + +def _reuse_fail(name, values): + """Helper function for raising a value error for reuse of name.""" + raise ValueError("Multiple assignments to variable '%s' in %s" % (name, values)) + + +def _process_scalar_value(name, parse_fn, var_type, m_dict, values, results_dictionary): + """Update results_dictionary with a scalar value. + + Used to update the results_dictionary to be returned by parse_values when + encountering a clause with a scalar RHS (e.g. "s=5" or "arr[0]=5".) + + Mutates results_dictionary. + + Args: + name: Name of variable in assignment ("s" or "arr"). + parse_fn: Function for parsing the actual value. + var_type: Type of named variable. + m_dict: Dictionary constructed from regex parsing. + m_dict['val']: RHS value (scalar) + m_dict['index']: List index value (or None) + values: Full expression being parsed + results_dictionary: The dictionary being updated for return by the parsing + function. + + Raises: + ValueError: If the name has already been used. + """ + try: + parsed_value = parse_fn(m_dict["val"]) + except ValueError: + _parse_fail(name, var_type, m_dict["val"], values) + + # If no index is provided + if not m_dict["index"]: + if name in results_dictionary: + _reuse_fail(name, values) + results_dictionary[name] = parsed_value + else: + if name in results_dictionary: + # The name has already been used as a scalar, then it + # will be in this dictionary and map to a non-dictionary. + if not isinstance(results_dictionary.get(name), dict): + _reuse_fail(name, values) + else: + results_dictionary[name] = {} + + index = int(m_dict["index"]) + # Make sure the index position hasn't already been assigned a value. + if index in results_dictionary[name]: + _reuse_fail("{}[{}]".format(name, index), values) + results_dictionary[name][index] = parsed_value + + +def _process_list_value(name, parse_fn, var_type, m_dict, values, results_dictionary): + """Update results_dictionary from a list of values. + + Used to update results_dictionary to be returned by parse_values when + encountering a clause with a list RHS (e.g. "arr=[1,2,3]".) + + Mutates results_dictionary. + + Args: + name: Name of variable in assignment ("arr"). + parse_fn: Function for parsing individual values. + var_type: Type of named variable. + m_dict: Dictionary constructed from regex parsing. + m_dict['val']: RHS value (scalar) + values: Full expression being parsed + results_dictionary: The dictionary being updated for return by the parsing + function. + + Raises: + ValueError: If the name has an index or the values cannot be parsed. + """ + if m_dict["index"] is not None: + raise ValueError("Assignment of a list to a list index.") + elements = filter(None, re.split("[ ,]", m_dict["vals"])) + # Make sure the name hasn't already been assigned a value + if name in results_dictionary: + raise _reuse_fail(name, values) + try: + results_dictionary[name] = [parse_fn(e) for e in elements] + except ValueError: + _parse_fail(name, var_type, m_dict["vals"], values) + + +def _cast_to_type_if_compatible(name, param_type, value): + """Cast hparam to the provided type, if compatible. + + Args: + name: Name of the hparam to be cast. + param_type: The type of the hparam. + value: The value to be cast, if compatible. + + Returns: + The result of casting `value` to `param_type`. + + Raises: + ValueError: If the type of `value` is not compatible with param_type. + * If `param_type` is a string type, but `value` is not. + * If `param_type` is a boolean, but `value` is not, or vice versa. + * If `param_type` is an integer type, but `value` is not. + * If `param_type` is a float type, but `value` is not a numeric type. + """ + fail_msg = "Could not cast hparam '%s' of type '%s' from value %r" % ( + name, + param_type, + value, + ) + + # Some callers use None, for which we can't do any casting/checking. :( + if issubclass(param_type, type(None)): + return value + + # Avoid converting a non-string type to a string. + if issubclass(param_type, (six.string_types, six.binary_type)) and not isinstance( + value, (six.string_types, six.binary_type) + ): + raise ValueError(fail_msg) + + # Avoid converting a number or string type to a boolean or vice versa. + if issubclass(param_type, bool) != isinstance(value, bool): + raise ValueError(fail_msg) + + # Avoid converting float to an integer (the reverse is fine). + if issubclass(param_type, numbers.Integral) and not isinstance( + value, numbers.Integral + ): + raise ValueError(fail_msg) + + # Avoid converting a non-numeric type to a numeric type. + if issubclass(param_type, numbers.Number) and not isinstance(value, numbers.Number): + raise ValueError(fail_msg) + + return param_type(value) + + +def parse_values(values, type_map, ignore_unknown=False): + """Parses hyperparameter values from a string into a python map. + + `values` is a string containing comma-separated `name=value` pairs. + For each pair, the value of the hyperparameter named `name` is set to + `value`. + + If a hyperparameter name appears multiple times in `values`, a ValueError + is raised (e.g. 'a=1,a=2', 'a[1]=1,a[1]=2'). + + If a hyperparameter name in both an index assignment and scalar assignment, + a ValueError is raised. (e.g. 'a=[1,2,3],a[0] = 1'). + + The hyperparameter name may contain '.' symbols, which will result in an + attribute name that is only accessible through the getattr and setattr + functions. (And must be first explicit added through add_hparam.) + + WARNING: Use of '.' in your variable names is allowed, but is not well + supported and not recommended. + + The `value` in `name=value` must follows the syntax according to the + type of the parameter: + + * Scalar integer: A Python-parsable integer point value. E.g.: 1, + 100, -12. + * Scalar float: A Python-parsable floating point value. E.g.: 1.0, + -.54e89. + * Boolean: Either true or false. + * Scalar string: A non-empty sequence of characters, excluding comma, + spaces, and square brackets. E.g.: foo, bar_1. + * List: A comma separated list of scalar values of the parameter type + enclosed in square brackets. E.g.: [1,2,3], [1.0,1e-12], [high,low]. + + When index assignment is used, the corresponding type_map key should be the + list name. E.g. for "arr[1]=0" the type_map must have the key "arr" (not + "arr[1]"). + + Args: + values: String. Comma separated list of `name=value` pairs where + 'value' must follow the syntax described above. + type_map: A dictionary mapping hyperparameter names to types. Note every + parameter name in values must be a key in type_map. The values must + conform to the types indicated, where a value V is said to conform to a + type T if either V has type T, or V is a list of elements of type T. + Hence, for a multidimensional parameter 'x' taking float values, + 'x=[0.1,0.2]' will parse successfully if type_map['x'] = float. + ignore_unknown: Bool. Whether values that are missing a type in type_map + should be ignored. If set to True, a ValueError will not be raised for + unknown hyperparameter type. + + Returns: + A python map mapping each name to either: + * A scalar value. + * A list of scalar values. + * A dictionary mapping index numbers to scalar values. + (e.g. "x=5,L=[1,2],arr[1]=3" results in {'x':5,'L':[1,2],'arr':{1:3}}") + + Raises: + ValueError: If there is a problem with input. + * If `values` cannot be parsed. + * If a list is assigned to a list index (e.g. 'a[1] = [1,2,3]'). + * If the same rvalue is assigned two different values (e.g. 'a=1,a=2', + 'a[1]=1,a[1]=2', or 'a=1,a=[1]') + """ + results_dictionary = {} + pos = 0 + while pos < len(values): + m = PARAM_RE.match(values, pos) + if not m: + raise ValueError("Malformed hyperparameter value: %s" % values[pos:]) + # Check that there is a comma between parameters and move past it. + pos = m.end() + # Parse the values. + m_dict = m.groupdict() + name = m_dict["name"] + if name not in type_map: + if ignore_unknown: + continue + raise ValueError("Unknown hyperparameter type for %s" % name) + type_ = type_map[name] + + # Set up correct parsing function (depending on whether type_ is a bool) + if type_ == bool: + + def parse_bool(value): + if value in ["true", "True"]: + return True + elif value in ["false", "False"]: + return False + else: + try: + return bool(int(value)) + except ValueError: + _parse_fail(name, type_, value, values) + + parse = parse_bool + else: + parse = type_ + + # If a singe value is provided + if m_dict["val"] is not None: + _process_scalar_value( + name, parse, type_, m_dict, values, results_dictionary + ) + + # If the assigned value is a list: + elif m_dict["vals"] is not None: + _process_list_value(name, parse, type_, m_dict, values, results_dictionary) + + else: # Not assigned a list or value + _parse_fail(name, type_, "", values) + + return results_dictionary + + +class HParams(object): + """Class to hold a set of hyperparameters as name-value pairs. + + A `HParams` object holds hyperparameters used to build and train a model, + such as the number of hidden units in a neural net layer or the learning rate + to use when training. + + You first create a `HParams` object by specifying the names and values of the + hyperparameters. + + To make them easily accessible the parameter names are added as direct + attributes of the class. A typical usage is as follows: + + ```python + # Create a HParams object specifying names and values of the model + # hyperparameters: + hparams = HParams(learning_rate=0.1, num_hidden_units=100) + + # The hyperparameter are available as attributes of the HParams object: + hparams.learning_rate ==> 0.1 + hparams.num_hidden_units ==> 100 + ``` + + Hyperparameters have type, which is inferred from the type of their value + passed at construction type. The currently supported types are: integer, + float, boolean, string, and list of integer, float, boolean, or string. + + You can override hyperparameter values by calling the + [`parse()`](#HParams.parse) method, passing a string of comma separated + `name=value` pairs. This is intended to make it possible to override + any hyperparameter values from a single command-line flag to which + the user passes 'hyper-param=value' pairs. It avoids having to define + one flag for each hyperparameter. + + The syntax expected for each value depends on the type of the parameter. + See `parse()` for a description of the syntax. + + Example: + + ```python + # Define a command line flag to pass name=value pairs. + # For example using argparse: + import argparse + parser = argparse.ArgumentParser(description='Train my model.') + parser.add_argument('--hparams', type=str, + help='Comma separated list of "name=value" pairs.') + args = parser.parse_args() + ... + def my_program(): + # Create a HParams object specifying the names and values of the + # model hyperparameters: + hparams = tf.HParams(learning_rate=0.1, num_hidden_units=100, + activations=['relu', 'tanh']) + + # Override hyperparameters values by parsing the command line + hparams.parse(args.hparams) + + # If the user passed `--hparams=learning_rate=0.3` on the command line + # then 'hparams' has the following attributes: + hparams.learning_rate ==> 0.3 + hparams.num_hidden_units ==> 100 + hparams.activations ==> ['relu', 'tanh'] + + # If the hyperparameters are in json format use parse_json: + hparams.parse_json('{"learning_rate": 0.3, "activations": "relu"}') + ``` + """ + + _HAS_DYNAMIC_ATTRIBUTES = True # Required for pytype checks. + + def __init__(self, model_structure=None, **kwargs): + """Create an instance of `HParams` from keyword arguments. + + The keyword arguments specify name-values pairs for the hyperparameters. + The parameter types are inferred from the type of the values passed. + + The parameter names are added as attributes of `HParams` object, so they + can be accessed directly with the dot notation `hparams._name_`. + + Example: + + ```python + # Define 3 hyperparameters: 'learning_rate' is a float parameter, + # 'num_hidden_units' an integer parameter, and 'activation' a string + # parameter. + hparams = tf.HParams( + learning_rate=0.1, num_hidden_units=100, activation='relu') + + hparams.activation ==> 'relu' + ``` + + Note that a few names are reserved and cannot be used as hyperparameter + names. If you use one of the reserved name the constructor raises a + `ValueError`. + + Args: + model_structure: An instance of ModelStructure, defining the feature + crosses to be used in the Trial. + **kwargs: Key-value pairs where the key is the hyperparameter name and + the value is the value for the parameter. + + Raises: + ValueError: If both `hparam_def` and initialization values are provided, + or if one of the arguments is invalid. + + """ + # Register the hyperparameters and their type in _hparam_types. + # This simplifies the implementation of parse(). + # _hparam_types maps the parameter name to a tuple (type, bool). + # The type value is the type of the parameter for scalar hyperparameters, + # or the type of the list elements for multidimensional hyperparameters. + # The bool value is True if the value is a list, False otherwise. + self._hparam_types = {} + self._model_structure = model_structure + for name, value in six.iteritems(kwargs): + self.add_hparam(name, value) + + def add_hparam(self, name, value): + """Adds {name, value} pair to hyperparameters. + + Args: + name: Name of the hyperparameter. + value: Value of the hyperparameter. Can be one of the following types: + int, float, string, int list, float list, or string list. + + Raises: + ValueError: if one of the arguments is invalid. + """ + # Keys in kwargs are unique, but 'name' could the name of a pre-existing + # attribute of this object. In that case we refuse to use it as a + # hyperparameter name. + if getattr(self, name, None) is not None: + raise ValueError("Hyperparameter name is reserved: %s" % name) + if isinstance(value, (list, tuple)): + if not value: + raise ValueError( + "Multi-valued hyperparameters cannot be empty: %s" % name + ) + self._hparam_types[name] = (type(value[0]), True) + else: + self._hparam_types[name] = (type(value), False) + setattr(self, name, value) + + def set_hparam(self, name, value): + """Set the value of an existing hyperparameter. + + This function verifies that the type of the value matches the type of the + existing hyperparameter. + + Args: + name: Name of the hyperparameter. + value: New value of the hyperparameter. + + Raises: + KeyError: If the hyperparameter doesn't exist. + ValueError: If there is a type mismatch. + """ + param_type, is_list = self._hparam_types[name] + if isinstance(value, list): + if not is_list: + raise ValueError( + "Must not pass a list for single-valued parameter: %s" % name + ) + setattr( + self, + name, + [_cast_to_type_if_compatible(name, param_type, v) for v in value], + ) + else: + if is_list: + raise ValueError( + "Must pass a list for multi-valued parameter: %s." % name + ) + setattr(self, name, _cast_to_type_if_compatible(name, param_type, value)) + + def del_hparam(self, name): + """Removes the hyperparameter with key 'name'. + + Does nothing if it isn't present. + + Args: + name: Name of the hyperparameter. + """ + if hasattr(self, name): + delattr(self, name) + del self._hparam_types[name] + + def parse(self, values): + """Override existing hyperparameter values, parsing new values from a string. + + See parse_values for more detail on the allowed format for values. + + Args: + values: String. Comma separated list of `name=value` pairs where 'value' + must follow the syntax described above. + + Returns: + The `HParams` instance. + + Raises: + ValueError: If `values` cannot be parsed or a hyperparameter in `values` + doesn't exist. + """ + type_map = {} + for name, t in self._hparam_types.items(): + param_type, _ = t + type_map[name] = param_type + + values_map = parse_values(values, type_map) + return self.override_from_dict(values_map) + + def override_from_dict(self, values_dict): + """Override existing hyperparameter values, parsing new values from a dictionary. + + Args: + values_dict: Dictionary of name:value pairs. + + Returns: + The `HParams` instance. + + Raises: + KeyError: If a hyperparameter in `values_dict` doesn't exist. + ValueError: If `values_dict` cannot be parsed. + """ + for name, value in values_dict.items(): + self.set_hparam(name, value) + return self + + def set_model_structure(self, model_structure): + self._model_structure = model_structure + + def get_model_structure(self): + return self._model_structure + + def to_json(self, indent=None, separators=None, sort_keys=False): + """Serializes the hyperparameters into JSON. + + Args: + indent: If a non-negative integer, JSON array elements and object members + will be pretty-printed with that indent level. An indent level of 0, or + negative, will only insert newlines. `None` (the default) selects the + most compact representation. + separators: Optional `(item_separator, key_separator)` tuple. Default is + `(', ', ': ')`. + sort_keys: If `True`, the output dictionaries will be sorted by key. + + Returns: + A JSON string. + """ + + def remove_callables(x): + """Omit callable elements from input with arbitrary nesting.""" + if isinstance(x, dict): + return { + k: remove_callables(v) + for k, v in six.iteritems(x) + if not callable(v) + } + elif isinstance(x, list): + return [remove_callables(i) for i in x if not callable(i)] + return x + + return json.dumps( + remove_callables(self.values()), + indent=indent, + separators=separators, + sort_keys=sort_keys, + ) + + def parse_json(self, values_json): + """Override existing hyperparameter values, parsing new values from a json object. + + Args: + values_json: String containing a json object of name:value pairs. + + Returns: + The `HParams` instance. + + Raises: + KeyError: If a hyperparameter in `values_json` doesn't exist. + ValueError: If `values_json` cannot be parsed. + """ + values_map = json.loads(values_json) + return self.override_from_dict(values_map) + + def values(self): + """Return the hyperparameter values as a Python dictionary. + + Returns: + A dictionary with hyperparameter names as keys. The values are the + hyperparameter values. + """ + return {n: getattr(self, n) for n in self._hparam_types.keys()} + + def get(self, key, default=None): + """Returns the value of `key` if it exists, else `default`.""" + if key in self._hparam_types: + # Ensure that default is compatible with the parameter type. + if default is not None: + param_type, is_param_list = self._hparam_types[key] + type_str = "list<%s>" % param_type if is_param_list else str(param_type) + fail_msg = ( + "Hparam '%s' of type '%s' is incompatible with " + "default=%s" % (key, type_str, default) + ) + + is_default_list = isinstance(default, list) + if is_param_list != is_default_list: + raise ValueError(fail_msg) + + try: + if is_default_list: + for value in default: + _cast_to_type_if_compatible(key, param_type, value) + else: + _cast_to_type_if_compatible(key, param_type, default) + except ValueError as e: + raise ValueError("%s. %s" % (fail_msg, e)) + + return getattr(self, key) + + return default + + def __contains__(self, key): + return key in self._hparam_types + + def __str__(self): + return str(sorted(self.values().items())) + + def __repr__(self): + return "%s(%s)" % (type(self).__name__, self.__str__()) + + @staticmethod + def _get_kind_name(param_type, is_list): + """Returns the field name given parameter type and is_list. + + Args: + param_type: Data type of the hparam. + is_list: Whether this is a list. + + Returns: + A string representation of the field name. + + Raises: + ValueError: If parameter type is not recognized. + """ + if issubclass(param_type, bool): + # This check must happen before issubclass(param_type, six.integer_types), + # since Python considers bool to be a subclass of int. + typename = "bool" + elif issubclass(param_type, six.integer_types): + # Setting 'int' and 'long' types to be 'int64' to ensure the type is + # compatible with both Python2 and Python3. + typename = "int64" + elif issubclass(param_type, (six.string_types, six.binary_type)): + # Setting 'string' and 'bytes' types to be 'bytes' to ensure the type is + # compatible with both Python2 and Python3. + typename = "bytes" + elif issubclass(param_type, float): + typename = "float" + else: + raise ValueError("Unsupported parameter type: %s" % str(param_type)) + + suffix = "list" if is_list else "value" + return "_".join([typename, suffix]) diff --git a/utils/hubert.py b/utils/hubert.py new file mode 100644 index 0000000000000000000000000000000000000000..84b509fb9fde8485cfb504a675e5d3b7d27622ff --- /dev/null +++ b/utils/hubert.py @@ -0,0 +1,155 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This code is modified from https://github.com/svc-develop-team/so-vits-svc/blob/4.0/preprocess_hubert_f0.py + +import os +import librosa +import torch +import numpy as np +from fairseq import checkpoint_utils +from tqdm import tqdm +import torch + + +def load_hubert_model(hps): + # Load model + ckpt_path = hps.hubert_file + print("Load Hubert Model...") + + models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( + [ckpt_path], + suffix="", + ) + model = models[0] + model.eval() + + if torch.cuda.is_available(): + model = model.cuda() + + return model + + +def get_hubert_content(hmodel, wav_16k_tensor): + feats = wav_16k_tensor + if feats.dim() == 2: # double channels + feats = feats.mean(-1) + assert feats.dim() == 1, feats.dim() + feats = feats.view(1, -1) + padding_mask = torch.BoolTensor(feats.shape).fill_(False) + inputs = { + "source": feats.to(wav_16k_tensor.device), + "padding_mask": padding_mask.to(wav_16k_tensor.device), + "output_layer": 9, # layer 9 + } + with torch.no_grad(): + logits = hmodel.extract_features(**inputs) + feats = hmodel.final_proj(logits[0]).squeeze(0) + + return feats + + +def content_vector_encoder(model, audio_path, default_sampling_rate=16000): + """ + # content vector default sr: 16000 + """ + + wav16k, sr = librosa.load(audio_path, sr=default_sampling_rate) + device = next(model.parameters()).device + wav16k = torch.from_numpy(wav16k).to(device) + + # (1, 256, frame_len) + content_feature = get_hubert_content(model, wav_16k_tensor=wav16k) + + return content_feature.cpu().detach().numpy() + + +def repeat_expand_2d(content, target_len): + """ + content : [hubert_dim(256), src_len] + target: [hubert_dim(256), target_len] + """ + src_len = content.shape[-1] + target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to( + content.device + ) + temp = torch.arange(src_len + 1) * target_len / src_len + current_pos = 0 + for i in range(target_len): + if i < temp[current_pos + 1]: + target[:, i] = content[:, current_pos] + else: + current_pos += 1 + target[:, i] = content[:, current_pos] + + return target + + +def get_mapped_features(raw_content_features, mapping_features): + """ + Content Vector: frameshift = 20ms, hop_size = 480 in 24k + + Now it's only used for mapping to bigvgan's mels (sr = 24k, hop_size = 256, frameshift ~= 10.7 ms) + """ + source_hop = 480 + target_hop = 256 + + factor = np.gcd(source_hop, target_hop) + source_hop //= factor + target_hop //= factor + print( + "Mapping source's {} frames => target's {} frames".format( + target_hop, source_hop + ) + ) + + results = [] + for index, mapping_feat in enumerate(tqdm(mapping_features)): + # mappping_feat: (mels_frame_len, n_mels) + target_len = len(mapping_feat) + + # (source_len, 256) + raw_feats = raw_content_features[index][0].cpu().numpy().T + source_len, width = raw_feats.shape + + # const ~= target_len * target_hop + const = source_len * source_hop // target_hop * target_hop + + # (source_len * source_hop, dim) + up_sampling_feats = np.repeat(raw_feats, source_hop, axis=0) + # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim) + down_sampling_feats = np.average( + up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1 + ) + + err = abs(target_len - len(down_sampling_feats)) + if err > 3: + print("index:", index) + print("mels:", mapping_feat.shape) + print("raw content vector:", raw_feats.shape) + print("up_sampling:", up_sampling_feats.shape) + print("down_sampling_feats:", down_sampling_feats.shape) + exit() + if len(down_sampling_feats) < target_len: + # (1, dim) -> (err, dim) + end = down_sampling_feats[-1][None, :].repeat(err, axis=0) + down_sampling_feats = np.concatenate([down_sampling_feats, end], axis=0) + + # (target_len, dim) + feats = down_sampling_feats[:target_len] + results.append(feats) + + return results + + +def extract_hubert_features_of_dataset(datasets, model, out_dir): + for utt in tqdm(datasets): + uid = utt["Uid"] + audio_path = utt["Path"] + + content_vector_feature = content_vector_encoder(model, audio_path) # (T, 256) + + save_path = os.path.join(out_dir, uid + ".npy") + np.save(save_path, content_vector_feature) diff --git a/utils/io.py b/utils/io.py new file mode 100644 index 0000000000000000000000000000000000000000..7aa9691587d1b255b99a5495d45331f596263c1c --- /dev/null +++ b/utils/io.py @@ -0,0 +1,163 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import numpy as np +import torch +import torchaudio + + +def save_feature(process_dir, feature_dir, item, feature, overrides=True): + """Save features to path + + Args: + process_dir (str): directory to store features + feature_dir (_type_): directory to store one type of features (mel, energy, ...) + item (str): uid + feature (tensor): feature tensor + overrides (bool, optional): whether to override existing files. Defaults to True. + """ + process_dir = os.path.join(process_dir, feature_dir) + os.makedirs(process_dir, exist_ok=True) + out_path = os.path.join(process_dir, item + ".npy") + + if os.path.exists(out_path): + if overrides: + np.save(out_path, feature) + else: + np.save(out_path, feature) + + +def save_txt(process_dir, feature_dir, item, feature, overrides=True): + process_dir = os.path.join(process_dir, feature_dir) + os.makedirs(process_dir, exist_ok=True) + out_path = os.path.join(process_dir, item + ".txt") + + if os.path.exists(out_path): + if overrides: + f = open(out_path, "w") + f.writelines(feature) + f.close() + else: + f = open(out_path, "w") + f.writelines(feature) + f.close() + + +def save_audio(path, waveform, fs, add_silence=False, turn_up=False, volume_peak=0.9): + if turn_up: + # continue to turn up to volume_peak + ratio = volume_peak / max(waveform.max(), abs(waveform.min())) + waveform = waveform * ratio + + if add_silence: + silence_len = fs // 20 + silence = np.zeros((silence_len,), dtype=waveform.dtype) + result = np.concatenate([silence, waveform, silence]) + waveform = result + + waveform = torch.as_tensor(waveform, dtype=torch.float32, device="cpu") + if len(waveform.size()) == 1: + waveform = waveform[None, :] + elif waveform.size(0) != 1: + # Stereo to mono + waveform = torch.mean(waveform, dim=0, keepdim=True) + torchaudio.save(path, waveform, fs, encoding="PCM_S", bits_per_sample=16) + +def save_torch_audio(process_dir, feature_dir, item, wav_torch, fs, overrides=True): + if wav_torch.shape != 2: + wav_torch = wav_torch.unsqueeze(0) + + process_dir = os.path.join(process_dir, feature_dir) + os.makedirs(process_dir, exist_ok=True) + out_path = os.path.join(process_dir, item + ".wav") + + torchaudio.save(out_path, wav_torch, fs) + + +async def async_load_audio(path, sample_rate: int = 24000): + r""" + Args: + path: The source loading path. + sample_rate: The target sample rate, will automatically resample if necessary. + + Returns: + waveform: The waveform object. Should be [1 x sequence_len]. + """ + + async def use_torchaudio_load(path): + return torchaudio.load(path) + + waveform, sr = await use_torchaudio_load(path) + waveform = torch.mean(waveform, dim=0, keepdim=True) + + if sr != sample_rate: + waveform = torchaudio.functional.resample(waveform, sr, sample_rate) + + if torch.any(torch.isnan(waveform) or torch.isinf(waveform)): + raise ValueError("NaN or Inf found in waveform.") + return waveform + + +async def async_save_audio( + path, + waveform, + sample_rate: int = 24000, + add_silence: bool = False, + volume_peak: float = 0.9, +): + r""" + Args: + path: The target saving path. + waveform: The waveform object. Should be [n_channel x sequence_len]. + sample_rate: Sample rate. + add_silence: If ``true``, concat 0.05s silence to beginning and end. + volume_peak: Turn up volume for larger number, vice versa. + """ + + async def use_torchaudio_save(path, waveform, sample_rate): + torchaudio.save( + path, waveform, sample_rate, encoding="PCM_S", bits_per_sample=16 + ) + + waveform = torch.as_tensor(waveform, device="cpu", dtype=torch.float32) + shape = waveform.size()[:-1] + + ratio = abs(volume_peak) / max(waveform.max(), abs(waveform.min())) + waveform = waveform * ratio + + if add_silence: + silence_len = sample_rate // 20 + silence = torch.zeros((*shape, silence_len), dtype=waveform.type()) + waveform = torch.concatenate((silence, waveform, silence), dim=-1) + + if waveform.dim() == 1: + waveform = waveform[None] + + await use_torchaudio_save(path, waveform, sample_rate) + + +def load_mel_extrema(cfg, dataset_name, split): + dataset_dir = os.path.join( + cfg.OUTPUT_PATH, + "preprocess/{}_version".format(cfg.data.process_version), + dataset_name, + ) + + min_file = os.path.join( + dataset_dir, + "mel_min_max", + split.split("_")[-1], + "mel_min.npy", + ) + max_file = os.path.join( + dataset_dir, + "mel_min_max", + split.split("_")[-1], + "mel_max.npy", + ) + mel_min = np.load(min_file) + mel_max = np.load(max_file) + return mel_min, mel_max diff --git a/utils/io_optim.py b/utils/io_optim.py new file mode 100644 index 0000000000000000000000000000000000000000..942619d625d4c8e2d00ae1255421ceaf6ab39986 --- /dev/null +++ b/utils/io_optim.py @@ -0,0 +1,123 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torchaudio +import json +import os +import numpy as np +import librosa +from torch.nn.utils.rnn import pad_sequence +from modules import whisper_extractor as whisper + + +class TorchaudioDataset(torch.utils.data.Dataset): + def __init__(self, cfg, dataset, sr, accelerator=None, metadata=None): + """ + Args: + cfg: config + dataset: dataset name + + """ + assert isinstance(dataset, str) + + self.sr = sr + self.cfg = cfg + + if metadata is None: + self.train_metadata_path = os.path.join( + cfg.preprocess.processed_dir, dataset, cfg.preprocess.train_file + ) + self.valid_metadata_path = os.path.join( + cfg.preprocess.processed_dir, dataset, cfg.preprocess.valid_file + ) + self.metadata = self.get_metadata() + else: + self.metadata = metadata + + if accelerator is not None: + self.device = accelerator.device + elif torch.cuda.is_available(): + self.device = torch.device("cuda") + else: + self.device = torch.device("cpu") + + def get_metadata(self): + metadata = [] + with open(self.train_metadata_path, "r", encoding="utf-8") as t: + metadata.extend(json.load(t)) + with open(self.valid_metadata_path, "r", encoding="utf-8") as v: + metadata.extend(json.load(v)) + return metadata + + def __len__(self): + return len(self.metadata) + + def __getitem__(self, index): + utt_info = self.metadata[index] + wav_path = utt_info["Path"] + + wav, sr = torchaudio.load(wav_path) + + # resample + if sr != self.sr: + wav = torchaudio.functional.resample(wav, sr, self.sr) + # downmixing + if wav.shape[0] > 1: + wav = torch.mean(wav, dim=0, keepdim=True) + assert wav.shape[0] == 1 + wav = wav.squeeze(0) + # record the length of wav without padding + length = wav.shape[0] + # wav: (T) + return utt_info, wav, length + + +class LibrosaDataset(TorchaudioDataset): + def __init__(self, cfg, dataset, sr, accelerator=None, metadata=None): + super().__init__(cfg, dataset, sr, accelerator, metadata) + + def __getitem__(self, index): + utt_info = self.metadata[index] + wav_path = utt_info["Path"] + + wav, _ = librosa.load(wav_path, sr=self.sr) + # wav: (T) + wav = torch.from_numpy(wav) + + # record the length of wav without padding + length = wav.shape[0] + return utt_info, wav, length + + +class FFmpegDataset(TorchaudioDataset): + def __init__(self, cfg, dataset, sr, accelerator=None, metadata=None): + super().__init__(cfg, dataset, sr, accelerator, metadata) + + def __getitem__(self, index): + utt_info = self.metadata[index] + wav_path = utt_info["Path"] + + # wav: (T,) + wav = whisper.load_audio(wav_path) # sr = 16000 + # convert to torch tensor + wav = torch.from_numpy(wav) + # record the length of wav without padding + length = wav.shape[0] + + return utt_info, wav, length + + +def collate_batch(batch_list): + """ + Args: + batch_list: list of (metadata, wav, length) + """ + metadata = [item[0] for item in batch_list] + # wavs: (B, T) + wavs = pad_sequence([item[1] for item in batch_list], batch_first=True) + lens = [item[2] for item in batch_list] + + return metadata, wavs, lens diff --git a/utils/mel.py b/utils/mel.py new file mode 100644 index 0000000000000000000000000000000000000000..d32d38226dfe6c0162527b3136a392b9168c7f06 --- /dev/null +++ b/utils/mel.py @@ -0,0 +1,283 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from librosa.filters import mel as librosa_mel_fn + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def extract_linear_features(y, cfg, center=False): + if torch.min(y) < -1.0: + print("min value is ", torch.min(y)) + if torch.max(y) > 1.0: + print("max value is ", torch.max(y)) + + global hann_window + hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device) + + y = torch.nn.functional.pad( + y.unsqueeze(1), + (int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)), + mode="reflect", + ) + y = y.squeeze(1) + + # complex tensor as default, then use view_as_real for future pytorch compatibility + spec = torch.stft( + y, + cfg.n_fft, + hop_length=cfg.hop_size, + win_length=cfg.win_size, + window=hann_window[str(y.device)], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + spec = torch.view_as_real(spec) + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) + spec = torch.squeeze(spec, 0) + return spec + + +def mel_spectrogram_torch(y, cfg, center=False): + if torch.min(y) < -1.0: + print("min value is ", torch.min(y)) + if torch.max(y) > 1.0: + print("max value is ", torch.max(y)) + + global mel_basis, hann_window + if cfg.fmax not in mel_basis: + mel = librosa_mel_fn( + sr=cfg.sample_rate, + n_fft=cfg.n_fft, + n_mels=cfg.n_mel, + fmin=cfg.fmin, + fmax=cfg.fmax, + ) + mel_basis[str(cfg.fmax) + "_" + str(y.device)] = ( + torch.from_numpy(mel).float().to(y.device) + ) + hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device) + + y = torch.nn.functional.pad( + y.unsqueeze(1), + (int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)), + mode="reflect", + ) + y = y.squeeze(1) + + spec = torch.stft( + y, + cfg.n_fft, + hop_length=cfg.hop_size, + win_length=cfg.win_size, + window=hann_window[str(y.device)], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + + spec = torch.view_as_real(spec) + spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) + + spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec) + spec = spectral_normalize_torch(spec) + + return spec + + +mel_basis = {} +hann_window = {} + + +def extract_mel_features( + y, + cfg, + center=False + # n_fft, n_mel, sampling_rate, hop_size, win_size, fmin, fmax, center=False +): + """Extract mel features + + Args: + y (tensor): audio data in tensor + cfg (dict): configuration in cfg.preprocess + center (bool, optional): In STFT, whether t-th frame is centered at time t*hop_length. Defaults to False. + + Returns: + tensor: a tensor containing the mel feature calculated based on STFT result + """ + if torch.min(y) < -1.0: + print("min value is ", torch.min(y)) + if torch.max(y) > 1.0: + print("max value is ", torch.max(y)) + + global mel_basis, hann_window + if cfg.fmax not in mel_basis: + mel = librosa_mel_fn( + sr=cfg.sample_rate, + n_fft=cfg.n_fft, + n_mels=cfg.n_mel, + fmin=cfg.fmin, + fmax=cfg.fmax, + ) + mel_basis[str(cfg.fmax) + "_" + str(y.device)] = ( + torch.from_numpy(mel).float().to(y.device) + ) + hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device) + + y = torch.nn.functional.pad( + y.unsqueeze(1), + (int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)), + mode="reflect", + ) + y = y.squeeze(1) + + # complex tensor as default, then use view_as_real for future pytorch compatibility + spec = torch.stft( + y, + cfg.n_fft, + hop_length=cfg.hop_size, + win_length=cfg.win_size, + window=hann_window[str(y.device)], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + spec = torch.view_as_real(spec) + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) + + spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec) + spec = spectral_normalize_torch(spec) + + return spec.squeeze(0) + + +def extract_mel_features_tts( + y, + cfg, + center=False, + taco=False, + _stft=None, +): + """Extract mel features + + Args: + y (tensor): audio data in tensor + cfg (dict): configuration in cfg.preprocess + center (bool, optional): In STFT, whether t-th frame is centered at time t*hop_length. Defaults to False. + taco: use tacotron mel + + Returns: + tensor: a tensor containing the mel feature calculated based on STFT result + """ + if not taco: + if torch.min(y) < -1.0: + print("min value is ", torch.min(y)) + if torch.max(y) > 1.0: + print("max value is ", torch.max(y)) + + global mel_basis, hann_window + if cfg.fmax not in mel_basis: + mel = librosa_mel_fn( + sr=cfg.sample_rate, + n_fft=cfg.n_fft, + n_mels=cfg.n_mel, + fmin=cfg.fmin, + fmax=cfg.fmax, + ) + mel_basis[str(cfg.fmax) + "_" + str(y.device)] = ( + torch.from_numpy(mel).float().to(y.device) + ) + hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device) + + y = torch.nn.functional.pad( + y.unsqueeze(1), + (int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)), + mode="reflect", + ) + y = y.squeeze(1) + + # complex tensor as default, then use view_as_real for future pytorch compatibility + spec = torch.stft( + y, + cfg.n_fft, + hop_length=cfg.hop_size, + win_length=cfg.win_size, + window=hann_window[str(y.device)], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + spec = torch.view_as_real(spec) + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) + + spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec) + spec = spectral_normalize_torch(spec) + spec = spec.squeeze(0) + else: + audio = torch.clip(y, -1, 1) + audio = torch.autograd.Variable(audio, requires_grad=False) + spec, energy = _stft.mel_spectrogram(audio) + spec = torch.squeeze(spec, 0) + + spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec) + spec = spectral_normalize_torch(spec) + + return spec.squeeze(0) + + +def amplitude_phase_spectrum(y, cfg): + hann_window = torch.hann_window(cfg.win_size).to(y.device) + + y = torch.nn.functional.pad( + y.unsqueeze(1), + (int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)), + mode="reflect", + ) + y = y.squeeze(1) + + stft_spec = torch.stft( + y, + cfg.n_fft, + hop_length=cfg.hop_size, + win_length=cfg.win_size, + window=hann_window, + center=False, + return_complex=True, + ) + + stft_spec = torch.view_as_real(stft_spec) + if stft_spec.size()[0] == 1: + stft_spec = stft_spec.squeeze(0) + + if len(list(stft_spec.size())) == 4: + rea = stft_spec[:, :, :, 0] # [batch_size, n_fft//2+1, frames] + imag = stft_spec[:, :, :, 1] # [batch_size, n_fft//2+1, frames] + else: + rea = stft_spec[:, :, 0] # [n_fft//2+1, frames] + imag = stft_spec[:, :, 1] # [n_fft//2+1, frames] + + log_amplitude = torch.log( + torch.abs(torch.sqrt(torch.pow(rea, 2) + torch.pow(imag, 2))) + 1e-5 + ) # [n_fft//2+1, frames] + phase = torch.atan2(imag, rea) # [n_fft//2+1, frames] + + return log_amplitude, phase, rea, imag diff --git a/utils/mert.py b/utils/mert.py new file mode 100644 index 0000000000000000000000000000000000000000..4181429feb36f5013bafafff9505b0b9571485b4 --- /dev/null +++ b/utils/mert.py @@ -0,0 +1,139 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This code is modified from https://huggingface.co/m-a-p/MERT-v1-330M + +import torch +from tqdm import tqdm +import numpy as np + +from transformers import Wav2Vec2FeatureExtractor +from transformers import AutoModel +import torchaudio +import torchaudio.transforms as T +from sklearn.preprocessing import StandardScaler + + +def mert_encoder(model, processor, audio_path, hps): + """ + # mert default sr: 24000 + """ + with torch.no_grad(): + resample_rate = processor.sampling_rate + device = next(model.parameters()).device + + input_audio, sampling_rate = torchaudio.load(audio_path) + input_audio = input_audio.squeeze() + + if sampling_rate != resample_rate: + resampler = T.Resample(sampling_rate, resample_rate) + input_audio = resampler(input_audio) + + inputs = processor( + input_audio, sampling_rate=resample_rate, return_tensors="pt" + ).to( + device + ) # {input_values: tensor, attention_mask: tensor} + + outputs = model(**inputs, output_hidden_states=True) # list: len is 25 + + # [25 layer, Time steps, 1024 feature_dim] + # all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze() + # mert_features.append(all_layer_hidden_states) + + feature = outputs.hidden_states[ + hps.mert_feature_layer + ].squeeze() # [1, frame len, 1024] -> [frame len, 1024] + + return feature.cpu().detach().numpy() + + +def mert_features_normalization(raw_mert_features): + normalized_mert_features = list() + + mert_features = np.array(raw_mert_features) + scaler = StandardScaler().fit(mert_features) + for raw_mert_feature in raw_mert_feature: + normalized_mert_feature = scaler.transform(raw_mert_feature) + normalized_mert_features.append(normalized_mert_feature) + return normalized_mert_features + + +def get_mapped_mert_features(raw_mert_features, mapping_features, fast_mapping=True): + source_hop = 320 + target_hop = 256 + + factor = np.gcd(source_hop, target_hop) + source_hop //= factor + target_hop //= factor + print( + "Mapping source's {} frames => target's {} frames".format( + target_hop, source_hop + ) + ) + + mert_features = [] + for index, mapping_feat in enumerate(tqdm(mapping_features)): + # mapping_feat: (mels_frame_len, n_mels) + target_len = mapping_feat.shape[0] + + # (frame_len, 1024) + raw_feats = raw_mert_features[index].cpu().numpy() + source_len, width = raw_feats.shape + + # const ~= target_len * target_hop + const = source_len * source_hop // target_hop * target_hop + + # (source_len * source_hop, dim) + up_sampling_feats = np.repeat(raw_feats, source_hop, axis=0) + # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim) + down_sampling_feats = np.average( + up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1 + ) + + err = abs(target_len - len(down_sampling_feats)) + if err > 3: + print("index:", index) + print("mels:", mapping_feat.shape) + print("raw mert vector:", raw_feats.shape) + print("up_sampling:", up_sampling_feats.shape) + print("const:", const) + print("down_sampling_feats:", down_sampling_feats.shape) + exit() + if len(down_sampling_feats) < target_len: + # (1, dim) -> (err, dim) + end = down_sampling_feats[-1][None, :].repeat(err, axis=0) + down_sampling_feats = np.concatenate([down_sampling_feats, end], axis=0) + + # (target_len, dim) + feats = down_sampling_feats[:target_len] + mert_features.append(feats) + + return mert_features + + +def load_mert_model(hps): + print("Loading MERT Model: ", hps.mert_model) + + # Load model + model_name = hps.mert_model + model = AutoModel.from_pretrained(model_name, trust_remote_code=True) + + if torch.cuda.is_available(): + model = model.cuda() + + # model = model.eval() + + preprocessor = Wav2Vec2FeatureExtractor.from_pretrained( + model_name, trust_remote_code=True + ) + return model, preprocessor + + +# loading the corresponding preprocessor config +# def load_preprocessor (model_name="m-a-p/MERT-v1-330M"): +# print('load_preprocessor...') +# preprocessor = Wav2Vec2FeatureExtractor.from_pretrained(model_name,trust_remote_code=True) +# return preprocessor diff --git a/utils/model_summary.py b/utils/model_summary.py new file mode 100644 index 0000000000000000000000000000000000000000..ec72b0d17869dc0886a9eb665efedb70bb307bbf --- /dev/null +++ b/utils/model_summary.py @@ -0,0 +1,74 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import humanfriendly +import numpy as np +import torch + + +def get_human_readable_count(number: int) -> str: + """Return human_readable_count + + Originated from: + https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pytorch_lightning/core/memory.py + + Abbreviates an integer number with K, M, B, T for thousands, millions, + billions and trillions, respectively. + Examples: + >>> get_human_readable_count(123) + '123 ' + >>> get_human_readable_count(1234) # (one thousand) + '1 K' + >>> get_human_readable_count(2e6) # (two million) + '2 M' + >>> get_human_readable_count(3e9) # (three billion) + '3 B' + >>> get_human_readable_count(4e12) # (four trillion) + '4 T' + >>> get_human_readable_count(5e15) # (more than trillion) + '5,000 T' + Args: + number: a positive integer number + Return: + A string formatted according to the pattern described above. + """ + assert number >= 0 + labels = [" ", "K", "M", "B", "T"] + num_digits = int(np.floor(np.log10(number)) + 1 if number > 0 else 1) + num_groups = int(np.ceil(num_digits / 3)) + num_groups = min(num_groups, len(labels)) + shift = -3 * (num_groups - 1) + number = number * (10**shift) + index = num_groups - 1 + return f"{number:.2f} {labels[index]}" + + +def to_bytes(dtype) -> int: + return int(str(dtype)[-2:]) // 8 + + +def model_summary(model: torch.nn.Module) -> str: + message = "Model structure:\n" + message += str(model) + tot_params = sum(p.numel() for p in model.parameters()) + num_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + percent_trainable = "{:.1f}".format(num_params * 100.0 / tot_params) + tot_params = get_human_readable_count(tot_params) + num_params = get_human_readable_count(num_params) + message += "\n\nModel summary:\n" + message += f" Class Name: {model.__class__.__name__}\n" + message += f" Total Number of model parameters: {tot_params}\n" + message += ( + f" Number of trainable parameters: {num_params} ({percent_trainable}%)\n" + ) + num_bytes = humanfriendly.format_size( + sum( + p.numel() * to_bytes(p.dtype) for p in model.parameters() if p.requires_grad + ) + ) + message += f" Size: {num_bytes}\n" + dtype = next(iter(model.parameters())).dtype + message += f" Type: {dtype}" + return message diff --git a/utils/prompt_preparer.py b/utils/prompt_preparer.py new file mode 100644 index 0000000000000000000000000000000000000000..dba833ee72cd661bbc178ffc4735e05a4e4949c9 --- /dev/null +++ b/utils/prompt_preparer.py @@ -0,0 +1,73 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +class PromptPreparer: + def prepare_prompts(self, y, y_lens, codes, nar_stage, y_prompts_codes): + if self.prefix_mode == 0: + y_emb, prefix_len = self._handle_prefix_mode_0(y, codes, nar_stage) + elif self.prefix_mode == 1: + y_emb, prefix_len = self._handle_prefix_mode_1(y, y_lens, codes, nar_stage) + elif self.prefix_mode in [2, 4]: + y_emb, prefix_len = self._handle_prefix_mode_2_4(y, y_lens, codes, nar_stage, y_prompts_codes) + else: + raise ValueError("Invalid prefix mode") + + return y_emb, prefix_len + + def _handle_prefix_mode_0(self, y, codes, nar_stage): + prefix_len = 0 + y_emb = self.nar_audio_embeddings[0](y) + for j in range(1, nar_stage): + y_emb = y_emb + self.nar_audio_embeddings[j](codes[..., j]) + return y_emb, 0 + + def _handle_prefix_mode_1(self, y, y_lens, codes, nar_stage): + int_low = (0.25 * y_lens.min()).type(torch.int64).item() + prefix_len = torch.randint(int_low, int_low * 2, size=()).item() + prefix_len = min(prefix_len, 225) + + y_prompts = self.nar_audio_embeddings[0](y[:, :prefix_len]) + y_emb = self.nar_audio_embeddings[0](y[:, prefix_len:]) + for j in range(1, self.num_quantizers): + y_prompts += self.nar_audio_embeddings[j]( + codes[:, :prefix_len, j] + ) + if j < nar_stage: + y_emb += self.nar_audio_embeddings[j]( + codes[:, prefix_len:, j] + ) + y_emb = torch.concat([y_prompts, y_emb], axis=1) + return y_emb, prefix_len + + def _handle_prefix_mode_2_4(self, y, y_lens, codes, nar_stage, y_prompts_codes): + if self.prefix_mode == 2: + prefix_len = min(225, int(0.25 * y_lens.min().item())) + + y_prompts_codes = [] + for b in range(codes.shape[0]): + start = self.rng.randint(0, y_lens[b].item() - prefix_len) + y_prompts_codes.append( + torch.clone(codes[b, start : start + prefix_len]) + ) + codes[ + b, start : start + prefix_len, nar_stage + ] = self.audio_token_num + y_prompts_codes = torch.stack(y_prompts_codes, dim=0) + else: + prefix_len = y_prompts_codes.shape[1] + + y_prompts = self.nar_audio_embeddings[0](y_prompts_codes[..., 0]) + y_emb = self.nar_audio_embeddings[0](y) + for j in range(1, self.num_quantizers): + y_prompts += self.nar_audio_embeddings[j]( + y_prompts_codes[..., j] + ) + if j < nar_stage: + y_emb += self.nar_audio_embeddings[j](codes[..., j]) + y_emb = torch.concat([y_prompts, y_emb], axis=1) + + return y_emb, prefix_len diff --git a/utils/ssim.py b/utils/ssim.py new file mode 100644 index 0000000000000000000000000000000000000000..a0b95007b2c225f3cae869f0653a733d1d92043a --- /dev/null +++ b/utils/ssim.py @@ -0,0 +1,80 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This code is modified from https://github.com/Po-Hsun-Su/pytorch-ssim + +import torch +import torch.nn.functional as F +from torch.autograd import Variable +from math import exp + + +def gaussian(window_size, sigma): + gauss = torch.Tensor( + [ + exp(-((x - window_size // 2) ** 2) / float(2 * sigma**2)) + for x in range(window_size) + ] + ) + return gauss / gauss.sum() + + +def create_window(window_size, channel): + _1D_window = gaussian(window_size, 1.5).unsqueeze(1) + _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) + window = Variable( + _2D_window.expand(channel, 1, window_size, window_size).contiguous() + ) + return window + + +def _ssim(img1, img2, window, window_size, channel, size_average=True): + mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) + mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) + + mu1_sq = mu1.pow(2) + mu2_sq = mu2.pow(2) + mu1_mu2 = mu1 * mu2 + + sigma1_sq = ( + F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq + ) + sigma2_sq = ( + F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq + ) + sigma12 = ( + F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) + - mu1_mu2 + ) + + C1 = 0.01**2 + C2 = 0.03**2 + + ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ( + (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2) + ) + + if size_average: + return ssim_map.mean() + else: + return ssim_map.mean(1) + + +class SSIM(torch.nn.Module): + def __init__(self, window_size=11, size_average=True): + super(SSIM, self).__init__() + self.window_size = window_size + self.size_average = size_average + self.channel = 1 + self.window = create_window(window_size, self.channel) + + def forward(self, fake, real, bias=6.0): + fake = fake[:, None, :, :] + bias # [B, 1, T, n_mels] + real = real[:, None, :, :] + bias # [B, 1, T, n_mels] + self.window = self.window.to(dtype=fake.dtype, device=fake.device) + loss = 1 - _ssim( + fake, real, self.window, self.window_size, self.channel, self.size_average + ) + return loss diff --git a/utils/stft.py b/utils/stft.py new file mode 100644 index 0000000000000000000000000000000000000000..bcec4c84ace0cc40d65361316222b090428cc391 --- /dev/null +++ b/utils/stft.py @@ -0,0 +1,278 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +import numpy as np +from scipy.signal import get_window +from librosa.util import pad_center, tiny +from librosa.filters import mel as librosa_mel_fn + +import torch +import numpy as np +import librosa.util as librosa_util +from scipy.signal import get_window + + +def window_sumsquare( + window, + n_frames, + hop_length, + win_length, + n_fft, + dtype=np.float32, + norm=None, +): + """ + # from librosa 0.6 + Compute the sum-square envelope of a window function at a given hop length. + + This is used to estimate modulation effects induced by windowing + observations in short-time fourier transforms. + + Parameters + ---------- + window : string, tuple, number, callable, or list-like + Window specification, as in `get_window` + + n_frames : int > 0 + The number of analysis frames + + hop_length : int > 0 + The number of samples to advance between frames + + win_length : [optional] + The length of the window function. By default, this matches `n_fft`. + + n_fft : int > 0 + The length of each analysis frame. + + dtype : np.dtype + The data type of the output + + Returns + ------- + wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))` + The sum-squared envelope of the window function + """ + if win_length is None: + win_length = n_fft + + n = n_fft + hop_length * (n_frames - 1) + x = np.zeros(n, dtype=dtype) + + # Compute the squared window at the desired length + win_sq = get_window(window, win_length, fftbins=True) + win_sq = librosa_util.normalize(win_sq, norm=norm) ** 2 + win_sq = librosa_util.pad_center(win_sq, n_fft) + + # Fill the envelope + for i in range(n_frames): + sample = i * hop_length + x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))] + return x + + +def griffin_lim(magnitudes, stft_fn, n_iters=30): + """ + PARAMS + ------ + magnitudes: spectrogram magnitudes + stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods + """ + + angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size()))) + angles = angles.astype(np.float32) + angles = torch.autograd.Variable(torch.from_numpy(angles)) + signal = stft_fn.inverse(magnitudes, angles).squeeze(1) + + for i in range(n_iters): + _, angles = stft_fn.transform(signal) + signal = stft_fn.inverse(magnitudes, angles).squeeze(1) + return signal + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + """ + PARAMS + ------ + C: compression factor + """ + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression(x, C=1): + """ + PARAMS + ------ + C: compression factor used to compress + """ + return torch.exp(x) / C + + +class STFT(torch.nn.Module): + """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft""" + + def __init__(self, filter_length, hop_length, win_length, window="hann"): + super(STFT, self).__init__() + self.filter_length = filter_length + self.hop_length = hop_length + self.win_length = win_length + self.window = window + self.forward_transform = None + scale = self.filter_length / self.hop_length + fourier_basis = np.fft.fft(np.eye(self.filter_length)) + + cutoff = int((self.filter_length / 2 + 1)) + fourier_basis = np.vstack( + [np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])] + ) + + forward_basis = torch.FloatTensor(fourier_basis[:, None, :]) + inverse_basis = torch.FloatTensor( + np.linalg.pinv(scale * fourier_basis).T[:, None, :] + ) + + if window is not None: + assert filter_length >= win_length + # get window and zero center pad it to filter_length + fft_window = get_window(window, win_length, fftbins=True) + fft_window = pad_center(fft_window, filter_length) + fft_window = torch.from_numpy(fft_window).float() + + # window the bases + forward_basis *= fft_window + inverse_basis *= fft_window + + self.register_buffer("forward_basis", forward_basis.float()) + self.register_buffer("inverse_basis", inverse_basis.float()) + + def transform(self, input_data): + num_batches = input_data.size(0) + num_samples = input_data.size(1) + + self.num_samples = num_samples + + # similar to librosa, reflect-pad the input + input_data = input_data.view(num_batches, 1, num_samples) + input_data = F.pad( + input_data.unsqueeze(1), + (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0), + mode="reflect", + ) + input_data = input_data.squeeze(1) + + forward_transform = F.conv1d( + input_data.cuda(), + torch.autograd.Variable(self.forward_basis, requires_grad=False).cuda(), + stride=self.hop_length, + padding=0, + ).cpu() + + cutoff = int((self.filter_length / 2) + 1) + real_part = forward_transform[:, :cutoff, :] + imag_part = forward_transform[:, cutoff:, :] + + magnitude = torch.sqrt(real_part**2 + imag_part**2) + phase = torch.autograd.Variable(torch.atan2(imag_part.data, real_part.data)) + + return magnitude, phase + + def inverse(self, magnitude, phase): + recombine_magnitude_phase = torch.cat( + [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1 + ) + + inverse_transform = F.conv_transpose1d( + recombine_magnitude_phase, + torch.autograd.Variable(self.inverse_basis, requires_grad=False), + stride=self.hop_length, + padding=0, + ) + + if self.window is not None: + window_sum = window_sumsquare( + self.window, + magnitude.size(-1), + hop_length=self.hop_length, + win_length=self.win_length, + n_fft=self.filter_length, + dtype=np.float32, + ) + # remove modulation effects + approx_nonzero_indices = torch.from_numpy( + np.where(window_sum > tiny(window_sum))[0] + ) + window_sum = torch.autograd.Variable( + torch.from_numpy(window_sum), requires_grad=False + ) + window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum + inverse_transform[:, :, approx_nonzero_indices] /= window_sum[ + approx_nonzero_indices + ] + + # scale by hop ratio + inverse_transform *= float(self.filter_length) / self.hop_length + + inverse_transform = inverse_transform[:, :, int(self.filter_length / 2) :] + inverse_transform = inverse_transform[:, :, : -int(self.filter_length / 2) :] + + return inverse_transform + + def forward(self, input_data): + self.magnitude, self.phase = self.transform(input_data) + reconstruction = self.inverse(self.magnitude, self.phase) + return reconstruction + + +class TacotronSTFT(torch.nn.Module): + def __init__( + self, + filter_length, + hop_length, + win_length, + n_mel_channels, + sampling_rate, + mel_fmin, + mel_fmax, + ): + super(TacotronSTFT, self).__init__() + self.n_mel_channels = n_mel_channels + self.sampling_rate = sampling_rate + self.stft_fn = STFT(filter_length, hop_length, win_length) + mel_basis = librosa_mel_fn( + sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax + ) + mel_basis = torch.from_numpy(mel_basis).float() + self.register_buffer("mel_basis", mel_basis) + + def spectral_normalize(self, magnitudes): + output = dynamic_range_compression(magnitudes) + return output + + def spectral_de_normalize(self, magnitudes): + output = dynamic_range_decompression(magnitudes) + return output + + def mel_spectrogram(self, y): + """Computes mel-spectrograms from a batch of waves + PARAMS + ------ + y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1] + + RETURNS + ------- + mel_output: torch.FloatTensor of shape (B, n_mel_channels, T) + """ + assert torch.min(y.data) >= -1 + assert torch.max(y.data) <= 1 + + magnitudes, phases = self.stft_fn.transform(y) + magnitudes = magnitudes.data + mel_output = torch.matmul(self.mel_basis, magnitudes) + mel_output = self.spectral_normalize(mel_output) + energy = torch.norm(magnitudes, dim=1) + + return mel_output, energy diff --git a/utils/symbol_table.py b/utils/symbol_table.py new file mode 100644 index 0000000000000000000000000000000000000000..730ffe7a8018f80f662e260542a859a6f6f74a47 --- /dev/null +++ b/utils/symbol_table.py @@ -0,0 +1,313 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This code is modified from +# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/utils/symbol_table.py + +from dataclasses import dataclass +from dataclasses import field +from typing import Dict +from typing import Generic +from typing import List +from typing import Optional +from typing import TypeVar +from typing import Union + +Symbol = TypeVar('Symbol') + + +@dataclass(repr=False) +class SymbolTable(Generic[Symbol]): + '''SymbolTable that maps symbol IDs, found on the FSA arcs to + actual objects. These objects can be arbitrary Python objects + that can serve as keys in a dictionary (i.e. they need to be + hashable and immutable). + + The SymbolTable can only be read to/written from disk if the + symbols are strings. + ''' + _id2sym: Dict[int, Symbol] = field(default_factory=dict) + '''Map an integer to a symbol. + ''' + + _sym2id: Dict[Symbol, int] = field(default_factory=dict) + '''Map a symbol to an integer. + ''' + + _next_available_id: int = 1 + '''A helper internal field that helps adding new symbols + to the table efficiently. + ''' + + eps: Symbol = '' + '''Null symbol, always mapped to index 0. + ''' + + def __post_init__(self): + assert all(self._sym2id[sym] == idx for idx, sym in self._id2sym.items()) + assert all(self._id2sym[idx] == sym for sym, idx in self._sym2id.items()) + assert 0 not in self._id2sym or self._id2sym[0] == self.eps + + self._next_available_id = max(self._id2sym, default=0) + 1 + self._id2sym.setdefault(0, self.eps) + self._sym2id.setdefault(self.eps, 0) + + + @staticmethod + def from_str(s: str) -> 'SymbolTable': + '''Build a symbol table from a string. + + The string consists of lines. Every line has two fields separated + by space(s), tab(s) or both. The first field is the symbol and the + second the integer id of the symbol. + + Args: + s: + The input string with the format described above. + Returns: + An instance of :class:`SymbolTable`. + ''' + id2sym: Dict[int, str] = dict() + sym2id: Dict[str, int] = dict() + + for line in s.split('\n'): + fields = line.split() + if len(fields) == 0: + continue # skip empty lines + assert len(fields) == 2, \ + f'Expect a line with 2 fields. Given: {len(fields)}' + sym, idx = fields[0], int(fields[1]) + assert sym not in sym2id, f'Duplicated symbol {sym}' + assert idx not in id2sym, f'Duplicated id {idx}' + id2sym[idx] = sym + sym2id[sym] = idx + + eps = id2sym.get(0, '') + + return SymbolTable(_id2sym=id2sym, _sym2id=sym2id, eps=eps) + + @staticmethod + def from_file(filename: str) -> 'SymbolTable': + '''Build a symbol table from file. + + Every line in the symbol table file has two fields separated by + space(s), tab(s) or both. The following is an example file: + + .. code-block:: + + 0 + a 1 + b 2 + c 3 + + Args: + filename: + Name of the symbol table file. Its format is documented above. + + Returns: + An instance of :class:`SymbolTable`. + + ''' + with open(filename, 'r', encoding='utf-8') as f: + return SymbolTable.from_str(f.read().strip()) + + def to_str(self) -> str: + ''' + Returns: + Return a string representation of this object. You can pass + it to the method ``from_str`` to recreate an identical object. + ''' + s = '' + for idx, symbol in sorted(self._id2sym.items()): + s += f'{symbol} {idx}\n' + return s + + def to_file(self, filename: str): + '''Serialize the SymbolTable to a file. + + Every line in the symbol table file has two fields separated by + space(s), tab(s) or both. The following is an example file: + + .. code-block:: + + 0 + a 1 + b 2 + c 3 + + Args: + filename: + Name of the symbol table file. Its format is documented above. + ''' + with open(filename, 'w') as f: + for idx, symbol in sorted(self._id2sym.items()): + print(symbol, idx, file=f) + + def add(self, symbol: Symbol, index: Optional[int] = None) -> int: + '''Add a new symbol to the SymbolTable. + + Args: + symbol: + The symbol to be added. + index: + Optional int id to which the symbol should be assigned. + If it is not available, a ValueError will be raised. + + Returns: + The int id to which the symbol has been assigned. + ''' + # Already in the table? Return its ID. + if symbol in self._sym2id: + return self._sym2id[symbol] + # Specific ID not provided - use next available. + if index is None: + index = self._next_available_id + # Specific ID provided but not available. + if index in self._id2sym: + raise ValueError(f"Cannot assign id '{index}' to '{symbol}' - " + f"already occupied by {self._id2sym[index]}") + self._sym2id[symbol] = index + self._id2sym[index] = symbol + + # Update next available ID if needed + if self._next_available_id <= index: + self._next_available_id = index + 1 + + return index + + def get(self, k: Union[int, Symbol]) -> Union[Symbol, int]: + '''Get a symbol for an id or get an id for a symbol + + Args: + k: + If it is an id, it tries to find the symbol corresponding + to the id; if it is a symbol, it tries to find the id + corresponding to the symbol. + + Returns: + An id or a symbol depending on the given `k`. + ''' + if isinstance(k, int): + return self._id2sym[k] + else: + return self._sym2id[k] + + def merge(self, other: 'SymbolTable') -> 'SymbolTable': + '''Create a union of two SymbolTables. + Raises an AssertionError if the same IDs are occupied by + different symbols. + + Args: + other: + A symbol table to merge with ``self``. + + Returns: + A new symbol table. + ''' + self._check_compatible(other) + return SymbolTable( + _id2sym={**self._id2sym, **other._id2sym}, + _sym2id={**self._sym2id, **other._sym2id}, + eps=self.eps + ) + + def _check_compatible(self, other: 'SymbolTable') -> None: + # Epsilon compatibility + assert self.eps == other.eps, f'Mismatched epsilon symbol: ' \ + f'{self.eps} != {other.eps}' + # IDs compatibility + common_ids = set(self._id2sym).intersection(other._id2sym) + for idx in common_ids: + assert self[idx] == other[idx], f'ID conflict for id: {idx}, ' \ + f'self[idx] = "{self[idx]}", ' \ + f'other[idx] = "{other[idx]}"' + # Symbols compatibility + common_symbols = set(self._sym2id).intersection(other._sym2id) + for sym in common_symbols: + assert self[sym] == other[sym], f'ID conflict for id: {sym}, ' \ + f'self[sym] = "{self[sym]}", ' \ + f'other[sym] = "{other[sym]}"' + + def __getitem__(self, item: Union[int, Symbol]) -> Union[Symbol, int]: + return self.get(item) + + def __contains__(self, item: Union[int, Symbol]) -> bool: + if isinstance(item, int): + return item in self._id2sym + else: + return item in self._sym2id + + def __len__(self) -> int: + return len(self._id2sym) + + def __eq__(self, other: 'SymbolTable') -> bool: + if len(self) != len(other): + return False + + for s in self.symbols: + if self[s] != other[s]: + return False + + return True + + @property + def ids(self) -> List[int]: + '''Returns a list of integer IDs corresponding to the symbols. + ''' + ans = list(self._id2sym.keys()) + ans.sort() + return ans + + @property + def symbols(self) -> List[Symbol]: + '''Returns a list of symbols (e.g., strings) corresponding to + the integer IDs. + ''' + ans = list(self._sym2id.keys()) + ans.sort() + return ans + + +class TextToken: + def __init__( + self, + text_tokens: List[str], + add_eos: bool = True, + add_bos: bool = True, + pad_symbol: str = "", + bos_symbol: str = "", + eos_symbol: str = "", + ): + self.pad_symbol = pad_symbol + self.add_eos = add_eos + self.add_bos = add_bos + self.bos_symbol = bos_symbol + self.eos_symbol = eos_symbol + + unique_tokens = [pad_symbol] + if add_bos: + unique_tokens.append(bos_symbol) + if add_eos: + unique_tokens.append(eos_symbol) + unique_tokens.extend(sorted(text_tokens)) + + self.token2idx = {token: idx for idx, token in enumerate(unique_tokens)} + self.idx2token = unique_tokens + + + def get_token_id_seq(self, text): + tokens_seq = [p for p in text] + seq = ( + ([self.bos_symbol] if self.add_bos else []) + + tokens_seq + + ([self.eos_symbol] if self.add_eos else []) + ) + + token_ids = [self.token2idx[token] for token in seq] + token_lens = len(tokens_seq) + self.add_eos + self.add_bos + + return token_ids, token_lens + + \ No newline at end of file diff --git a/utils/tokenizer.py b/utils/tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..8971432bdcc2f3f2920775bb3397c90f2a91f8b8 --- /dev/null +++ b/utils/tokenizer.py @@ -0,0 +1,151 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# This code is modified from +# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/data/tokenizer.py + +import re +from typing import Any, Dict, List, Optional, Pattern, Union + +import torch +import torchaudio +from encodec import EncodecModel +from encodec.utils import convert_audio + + + +class AudioTokenizer: + """EnCodec audio tokenizer for encoding and decoding audio. + + Attributes: + device: The device on which the codec model is loaded. + codec: The pretrained EnCodec model. + sample_rate: Sample rate of the model. + channels: Number of audio channels in the model. + """ + + def __init__(self, device: Any = None) -> None: + model = EncodecModel.encodec_model_24khz() + model.set_target_bandwidth(6.0) + remove_encodec_weight_norm(model) + + if not device: + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda:0") + + self._device = device + + self.codec = model.to(device) + self.sample_rate = model.sample_rate + self.channels = model.channels + + @property + def device(self): + return self._device + + def encode(self, wav: torch.Tensor) -> torch.Tensor: + """Encode the audio waveform. + + Args: + wav: A tensor representing the audio waveform. + + Returns: + A tensor representing the encoded audio. + """ + return self.codec.encode(wav.to(self.device)) + + def decode(self, frames: torch.Tensor) -> torch.Tensor: + """Decode the encoded audio frames. + + Args: + frames: A tensor representing the encoded audio frames. + + Returns: + A tensor representing the decoded audio waveform. + """ + return self.codec.decode(frames) + + + +def tokenize_audio(tokenizer: AudioTokenizer, audio_path: str): + """ + Tokenize the audio waveform using the given AudioTokenizer. + + Args: + tokenizer: An instance of AudioTokenizer. + audio_path: Path to the audio file. + + Returns: + A tensor of encoded frames from the audio. + + Raises: + FileNotFoundError: If the audio file is not found. + RuntimeError: If there's an error processing the audio data. + """ + # try: + # Load and preprocess the audio waveform + wav, sr = torchaudio.load(audio_path) + wav = convert_audio(wav, sr, tokenizer.sample_rate, tokenizer.channels) + wav = wav.unsqueeze(0) + + # Extract discrete codes from EnCodec + with torch.no_grad(): + encoded_frames = tokenizer.encode(wav) + return encoded_frames + + # except FileNotFoundError: + # raise FileNotFoundError(f"Audio file not found at {audio_path}") + # except Exception as e: + # raise RuntimeError(f"Error processing audio data: {e}") + + + +def remove_encodec_weight_norm(model): + from encodec.modules import SConv1d + from encodec.modules.seanet import SConvTranspose1d, SEANetResnetBlock + from torch.nn.utils import remove_weight_norm + + encoder = model.encoder.model + for key in encoder._modules: + if isinstance(encoder._modules[key], SEANetResnetBlock): + remove_weight_norm(encoder._modules[key].shortcut.conv.conv) + block_modules = encoder._modules[key].block._modules + for skey in block_modules: + if isinstance(block_modules[skey], SConv1d): + remove_weight_norm(block_modules[skey].conv.conv) + elif isinstance(encoder._modules[key], SConv1d): + remove_weight_norm(encoder._modules[key].conv.conv) + + decoder = model.decoder.model + for key in decoder._modules: + if isinstance(decoder._modules[key], SEANetResnetBlock): + remove_weight_norm(decoder._modules[key].shortcut.conv.conv) + block_modules = decoder._modules[key].block._modules + for skey in block_modules: + if isinstance(block_modules[skey], SConv1d): + remove_weight_norm(block_modules[skey].conv.conv) + elif isinstance(decoder._modules[key], SConvTranspose1d): + remove_weight_norm(decoder._modules[key].convtr.convtr) + elif isinstance(decoder._modules[key], SConv1d): + remove_weight_norm(decoder._modules[key].conv.conv) + + +def extract_encodec_token(wav_path): + model = EncodecModel.encodec_model_24khz() + model.set_target_bandwidth(6.0) + + wav, sr = torchaudio.load(wav_path) + wav = convert_audio(wav, sr, model.sample_rate, model.channels) + wav = wav.unsqueeze(0) + if torch.cuda.is_available(): + model = model.cuda() + wav = wav.cuda() + with torch.no_grad(): + encoded_frames = model.encode(wav) + codes_ = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1) # [B, n_q, T] + codes = codes_.cpu().numpy()[0,:,:].T # [T, 8] + + return codes \ No newline at end of file diff --git a/utils/topk_sampling.py b/utils/topk_sampling.py new file mode 100644 index 0000000000000000000000000000000000000000..03f405c15506609bbd571818123cf7f0db7f1f4b --- /dev/null +++ b/utils/topk_sampling.py @@ -0,0 +1,87 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import torch +import torch.nn.functional as F + + +# This function is modified from https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py +def top_k_top_p_filtering( + logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1 +): + """ + Filter a distribution of logits using top-k and/or nucleus (top-p) filtering. + + Args: + logits (torch.Tensor): Logits distribution with shape (batch size, vocabulary size). + top_k (int, optional): Keep only top k tokens with highest probability (top-k filtering). + Set to 0 to disable. Defaults to 0. + top_p (float, optional): Keep the top tokens with a cumulative probability >= top_p (nucleus filtering). + Must be between 0 and 1, inclusive. Defaults to 1.0. + filter_value (float, optional): The value to assign to filtered logits. Defaults to -float('Inf'). + min_tokens_to_keep (int, optional): Ensure that at least this number of tokens are kept per batch example. + Defaults to 1. + + Returns: + torch.Tensor: The filtered logits. + """ + """ + Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751) + Make sure we keep at least min_tokens_to_keep per batch example in the output + From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 + """ + if top_k > 0: + # Apply top-k filtering + top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1)) + indices_to_remove = logits < torch.topk(logits, top_k).values[..., -1, None] + logits[indices_to_remove] = filter_value + + if top_p < 1.0: + # Apply top-p filtering + sorted_logits, sorted_indices = torch.sort(logits, descending=True) + cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) + + # Create a mask to remove tokens with cumulative probability above the top_p threshold + sorted_indices_to_remove = cumulative_probs > top_p + if min_tokens_to_keep > 1: + sorted_indices_to_remove[..., :min_tokens_to_keep] = 0 + sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() + sorted_indices_to_remove[..., 0] = 0 + + # Scatter sorted tensors back to original indexing + indices_to_remove = sorted_indices.scatter(1, sorted_indices, sorted_indices_to_remove) + logits[indices_to_remove] = filter_value + + return logits + + +def topk_sampling(logits, top_k=50, top_p=1.0, temperature=1.0): + """ + Perform top-k and top-p sampling on logits. + + Args: + logits (torch.Tensor): The logits to sample from. + top_k (int, optional): The number of highest probability tokens to keep for top-k filtering. + Must be a positive integer. Defaults to 50. + top_p (float, optional): The cumulative probability threshold for nucleus sampling. + Must be between 0 and 1. Defaults to 1.0. + temperature (float, optional): The scaling factor to adjust the logits distribution. + Must be strictly positive. Defaults to 1.0. + + Returns: + torch.Tensor: The sampled token. + """ + + # Adjust logits using temperature + if temperature != 1.0: + logits = logits / temperature + + # Top-p/top-k filtering + logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p) + + # Sample from the filtered distribution + token = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1) + return token diff --git a/utils/trainer_utils.py b/utils/trainer_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e5d9ad794864aa3ee0c49a86e9be293f69442886 --- /dev/null +++ b/utils/trainer_utils.py @@ -0,0 +1,16 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + + +def check_nan(logger, loss, y_pred, y_gt): + if torch.any(torch.isnan(loss)): + logger.info("out has nan: ", torch.any(torch.isnan(y_pred))) + logger.info("y_gt has nan: ", torch.any(torch.isnan(y_gt))) + logger.info("out: ", y_pred) + logger.info("y_gt: ", y_gt) + logger.info("loss = {:.4f}\n".format(loss.item())) + exit() diff --git a/utils/util.py b/utils/util.py new file mode 100644 index 0000000000000000000000000000000000000000..096d9129cd0dab4798f205ab7fdc690303ef5d37 --- /dev/null +++ b/utils/util.py @@ -0,0 +1,688 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import collections +import glob +import os +import random +import time +import argparse +from collections import OrderedDict + +import json5 +import numpy as np +import glob +from torch.nn import functional as F + + +try: + from ruamel.yaml import YAML as yaml +except: + from ruamel_yaml import YAML as yaml + +import torch + +from utils.hparam import HParams +import logging +from logging import handlers + + +def str2bool(v): + """Used in argparse.ArgumentParser.add_argument to indicate + that a type is a bool type and user can enter + + - yes, true, t, y, 1, to represent True + - no, false, f, n, 0, to represent False + + See https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse # noqa + """ + if isinstance(v, bool): + return v + if v.lower() in ("yes", "true", "t", "y", "1"): + return True + elif v.lower() in ("no", "false", "f", "n", "0"): + return False + else: + raise argparse.ArgumentTypeError("Boolean value expected.") + + +def find_checkpoint_of_mapper(mapper_ckpt_dir): + mapper_ckpts = glob.glob(os.path.join(mapper_ckpt_dir, "ckpts/*.pt")) + + # Select the max steps + mapper_ckpts.sort() + mapper_weights_file = mapper_ckpts[-1] + return mapper_weights_file + + +def pad_f0_to_tensors(f0s, batched=None): + # Initialize + tensors = [] + + if batched == None: + # Get the max frame for padding + size = -1 + for f0 in f0s: + size = max(size, f0.shape[-1]) + + tensor = torch.zeros(len(f0s), size) + + for i, f0 in enumerate(f0s): + tensor[i, : f0.shape[-1]] = f0[:] + + tensors.append(tensor) + else: + start = 0 + while start + batched - 1 < len(f0s): + end = start + batched - 1 + + # Get the max frame for padding + size = -1 + for i in range(start, end + 1): + size = max(size, f0s[i].shape[-1]) + + tensor = torch.zeros(batched, size) + + for i in range(start, end + 1): + tensor[i - start, : f0s[i].shape[-1]] = f0s[i][:] + + tensors.append(tensor) + + start = start + batched + + if start != len(f0s): + end = len(f0s) + + # Get the max frame for padding + size = -1 + for i in range(start, end): + size = max(size, f0s[i].shape[-1]) + + tensor = torch.zeros(len(f0s) - start, size) + + for i in range(start, end): + tensor[i - start, : f0s[i].shape[-1]] = f0s[i][:] + + tensors.append(tensor) + + return tensors + + +def pad_mels_to_tensors(mels, batched=None): + """ + Args: + mels: A list of mel-specs + Returns: + tensors: A list of tensors containing the batched mel-specs + mel_frames: A list of tensors containing the frames of the original mel-specs + """ + # Initialize + tensors = [] + mel_frames = [] + + # Split mel-specs into batches to avoid cuda memory exceed + if batched == None: + # Get the max frame for padding + size = -1 + for mel in mels: + size = max(size, mel.shape[-1]) + + tensor = torch.zeros(len(mels), mels[0].shape[0], size) + mel_frame = torch.zeros(len(mels), dtype=torch.int32) + + for i, mel in enumerate(mels): + tensor[i, :, : mel.shape[-1]] = mel[:] + mel_frame[i] = mel.shape[-1] + + tensors.append(tensor) + mel_frames.append(mel_frame) + else: + start = 0 + while start + batched - 1 < len(mels): + end = start + batched - 1 + + # Get the max frame for padding + size = -1 + for i in range(start, end + 1): + size = max(size, mels[i].shape[-1]) + + tensor = torch.zeros(batched, mels[0].shape[0], size) + mel_frame = torch.zeros(batched, dtype=torch.int32) + + for i in range(start, end + 1): + tensor[i - start, :, : mels[i].shape[-1]] = mels[i][:] + mel_frame[i - start] = mels[i].shape[-1] + + tensors.append(tensor) + mel_frames.append(mel_frame) + + start = start + batched + + if start != len(mels): + end = len(mels) + + # Get the max frame for padding + size = -1 + for i in range(start, end): + size = max(size, mels[i].shape[-1]) + + tensor = torch.zeros(len(mels) - start, mels[0].shape[0], size) + mel_frame = torch.zeros(len(mels) - start, dtype=torch.int32) + + for i in range(start, end): + tensor[i - start, :, : mels[i].shape[-1]] = mels[i][:] + mel_frame[i - start] = mels[i].shape[-1] + + tensors.append(tensor) + mel_frames.append(mel_frame) + + return tensors, mel_frames + + +def load_model_config(args): + """Load model configurations (in args.json under checkpoint directory) + + Args: + args (ArgumentParser): arguments to run bins/preprocess.py + + Returns: + dict: dictionary that stores model configurations + """ + if args.checkpoint_dir is None: + assert args.checkpoint_file is not None + checkpoint_dir = os.path.split(args.checkpoint_file)[0] + else: + checkpoint_dir = args.checkpoint_dir + config_path = os.path.join(checkpoint_dir, "args.json") + print("config_path: ", config_path) + + config = load_config(config_path) + return config + + +def remove_and_create(dir): + if os.path.exists(dir): + os.system("rm -r {}".format(dir)) + os.makedirs(dir, exist_ok=True) + + +def has_existed(path, warning=False): + if not warning: + return os.path.exists(path) + + if os.path.exists(path): + answer = input( + "The path {} has existed. \nInput 'y' (or hit Enter) to skip it, and input 'n' to re-write it [y/n]\n".format( + path + ) + ) + if not answer == "n": + return True + + return False + + +def remove_older_ckpt(saved_model_name, checkpoint_dir, max_to_keep=5): + if os.path.exists(os.path.join(checkpoint_dir, "checkpoint")): + with open(os.path.join(checkpoint_dir, "checkpoint"), "r") as f: + ckpts = [x.strip() for x in f.readlines()] + else: + ckpts = [] + ckpts.append(saved_model_name) + for item in ckpts[:-max_to_keep]: + if os.path.exists(os.path.join(checkpoint_dir, item)): + os.remove(os.path.join(checkpoint_dir, item)) + with open(os.path.join(checkpoint_dir, "checkpoint"), "w") as f: + for item in ckpts[-max_to_keep:]: + f.write("{}\n".format(item)) + + +def set_all_random_seed(seed: int): + random.seed(seed) + np.random.seed(seed) + torch.random.manual_seed(seed) + + +def save_checkpoint( + args, + generator, + g_optimizer, + step, + discriminator=None, + d_optimizer=None, + max_to_keep=5, +): + saved_model_name = "model.ckpt-{}.pt".format(step) + checkpoint_path = os.path.join(args.checkpoint_dir, saved_model_name) + + if discriminator and d_optimizer: + torch.save( + { + "generator": generator.state_dict(), + "discriminator": discriminator.state_dict(), + "g_optimizer": g_optimizer.state_dict(), + "d_optimizer": d_optimizer.state_dict(), + "global_step": step, + }, + checkpoint_path, + ) + else: + torch.save( + { + "generator": generator.state_dict(), + "g_optimizer": g_optimizer.state_dict(), + "global_step": step, + }, + checkpoint_path, + ) + + print("Saved checkpoint: {}".format(checkpoint_path)) + + if os.path.exists(os.path.join(args.checkpoint_dir, "checkpoint")): + with open(os.path.join(args.checkpoint_dir, "checkpoint"), "r") as f: + ckpts = [x.strip() for x in f.readlines()] + else: + ckpts = [] + ckpts.append(saved_model_name) + for item in ckpts[:-max_to_keep]: + if os.path.exists(os.path.join(args.checkpoint_dir, item)): + os.remove(os.path.join(args.checkpoint_dir, item)) + with open(os.path.join(args.checkpoint_dir, "checkpoint"), "w") as f: + for item in ckpts[-max_to_keep:]: + f.write("{}\n".format(item)) + + +def attempt_to_restore( + generator, g_optimizer, checkpoint_dir, discriminator=None, d_optimizer=None +): + checkpoint_list = os.path.join(checkpoint_dir, "checkpoint") + if os.path.exists(checkpoint_list): + checkpoint_filename = open(checkpoint_list).readlines()[-1].strip() + checkpoint_path = os.path.join(checkpoint_dir, "{}".format(checkpoint_filename)) + print("Restore from {}".format(checkpoint_path)) + checkpoint = torch.load(checkpoint_path, map_location="cpu") + if generator: + if not list(generator.state_dict().keys())[0].startswith("module."): + raw_dict = checkpoint["generator"] + clean_dict = OrderedDict() + for k, v in raw_dict.items(): + if k.startswith("module."): + clean_dict[k[7:]] = v + else: + clean_dict[k] = v + generator.load_state_dict(clean_dict) + else: + generator.load_state_dict(checkpoint["generator"]) + if g_optimizer: + g_optimizer.load_state_dict(checkpoint["g_optimizer"]) + global_step = 100000 + if discriminator and "discriminator" in checkpoint.keys(): + discriminator.load_state_dict(checkpoint["discriminator"]) + global_step = checkpoint["global_step"] + print("restore discriminator") + if d_optimizer and "d_optimizer" in checkpoint.keys(): + d_optimizer.load_state_dict(checkpoint["d_optimizer"]) + print("restore d_optimizer...") + else: + global_step = 0 + return global_step + + +class ExponentialMovingAverage(object): + def __init__(self, decay): + self.decay = decay + self.shadow = {} + + def register(self, name, val): + self.shadow[name] = val.clone() + + def update(self, name, x): + assert name in self.shadow + update_delta = self.shadow[name] - x + self.shadow[name] -= (1.0 - self.decay) * update_delta + + +def apply_moving_average(model, ema): + for name, param in model.named_parameters(): + if name in ema.shadow: + ema.update(name, param.data) + + +def register_model_to_ema(model, ema): + for name, param in model.named_parameters(): + if param.requires_grad: + ema.register(name, param.data) + + +class YParams(HParams): + def __init__(self, yaml_file): + if not os.path.exists(yaml_file): + raise IOError("yaml file: {} is not existed".format(yaml_file)) + super().__init__() + self.d = collections.OrderedDict() + with open(yaml_file) as fp: + for _, v in yaml().load(fp).items(): + for k1, v1 in v.items(): + try: + if self.get(k1): + self.set_hparam(k1, v1) + else: + self.add_hparam(k1, v1) + self.d[k1] = v1 + except Exception: + import traceback + + print(traceback.format_exc()) + + # @property + def get_elements(self): + return self.d.items() + + +def override_config(base_config, new_config): + """Update new configurations in the original dict with the new dict + + Args: + base_config (dict): original dict to be overridden + new_config (dict): dict with new configurations + + Returns: + dict: updated configuration dict + """ + for k, v in new_config.items(): + if type(v) == dict: + if k not in base_config.keys(): + base_config[k] = {} + base_config[k] = override_config(base_config[k], v) + else: + base_config[k] = v + return base_config + + +def get_lowercase_keys_config(cfg): + """Change all keys in cfg to lower case + + Args: + cfg (dict): dictionary that stores configurations + + Returns: + dict: dictionary that stores configurations + """ + updated_cfg = dict() + for k, v in cfg.items(): + if type(v) == dict: + v = get_lowercase_keys_config(v) + updated_cfg[k.lower()] = v + return updated_cfg + + +def _load_config(config_fn, lowercase=False): + """Load configurations into a dictionary + + Args: + config_fn (str): path to configuration file + lowercase (bool, optional): whether changing keys to lower case. Defaults to False. + + Returns: + dict: dictionary that stores configurations + """ + with open(config_fn, "r") as f: + data = f.read() + config_ = json5.loads(data) + if "base_config" in config_: + # load configurations from new path + p_config_path = os.path.join(os.getenv("WORK_DIR"), config_["base_config"]) + p_config_ = _load_config(p_config_path) + config_ = override_config(p_config_, config_) + if lowercase: + # change keys in config_ to lower case + config_ = get_lowercase_keys_config(config_) + return config_ + + +def load_config(config_fn, lowercase=False): + """Load configurations into a dictionary + + Args: + config_fn (str): path to configuration file + lowercase (bool, optional): _description_. Defaults to False. + + Returns: + JsonHParams: an object that stores configurations + """ + config_ = _load_config(config_fn, lowercase=lowercase) + # create an JsonHParams object with configuration dict + cfg = JsonHParams(**config_) + return cfg + + +def save_config(save_path, cfg): + """Save configurations into a json file + + Args: + save_path (str): path to save configurations + cfg (dict): dictionary that stores configurations + """ + with open(save_path, "w") as f: + json5.dump( + cfg, f, ensure_ascii=False, indent=4, quote_keys=True, sort_keys=True + ) + + +class JsonHParams: + def __init__(self, **kwargs): + for k, v in kwargs.items(): + if type(v) == dict: + v = JsonHParams(**v) + self[k] = v + + def keys(self): + return self.__dict__.keys() + + def items(self): + return self.__dict__.items() + + def values(self): + return self.__dict__.values() + + def __len__(self): + return len(self.__dict__) + + def __getitem__(self, key): + return getattr(self, key) + + def __setitem__(self, key, value): + return setattr(self, key, value) + + def __contains__(self, key): + return key in self.__dict__ + + def __repr__(self): + return self.__dict__.__repr__() + + +class ValueWindow: + def __init__(self, window_size=100): + self._window_size = window_size + self._values = [] + + def append(self, x): + self._values = self._values[-(self._window_size - 1) :] + [x] + + @property + def sum(self): + return sum(self._values) + + @property + def count(self): + return len(self._values) + + @property + def average(self): + return self.sum / max(1, self.count) + + def reset(self): + self._values = [] + + +class Logger(object): + def __init__( + self, + filename, + level="info", + when="D", + backCount=10, + fmt="%(asctime)s : %(message)s", + ): + self.level_relations = { + "debug": logging.DEBUG, + "info": logging.INFO, + "warning": logging.WARNING, + "error": logging.ERROR, + "crit": logging.CRITICAL, + } + if level == "debug": + fmt = "%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s" + self.logger = logging.getLogger(filename) + format_str = logging.Formatter(fmt) + self.logger.setLevel(self.level_relations.get(level)) + sh = logging.StreamHandler() + sh.setFormatter(format_str) + th = handlers.TimedRotatingFileHandler( + filename=filename, when=when, backupCount=backCount, encoding="utf-8" + ) + th.setFormatter(format_str) + self.logger.addHandler(sh) + self.logger.addHandler(th) + self.logger.info( + "==========================New Starting Here==============================" + ) + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + + +def slice_segments(x, ids_str, segment_size=4): + ret = torch.zeros_like(x[:, :, :segment_size]) + for i in range(x.size(0)): + idx_str = ids_str[i] + idx_end = idx_str + segment_size + ret[i] = x[i, :, idx_str:idx_end] + return ret + + +def rand_slice_segments(x, x_lengths=None, segment_size=4): + b, d, t = x.size() + if x_lengths is None: + x_lengths = t + ids_str_max = x_lengths - segment_size + 1 + ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) + ret = slice_segments(x, ids_str, segment_size) + return ret, ids_str + + +def subsequent_mask(length): + mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) + return mask + + +@torch.jit.script +def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): + n_channels_int = n_channels[0] + in_act = input_a + input_b + t_act = torch.tanh(in_act[:, :n_channels_int, :]) + s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) + acts = t_act * s_act + return acts + + +def convert_pad_shape(pad_shape): + l = pad_shape[::-1] + pad_shape = [item for sublist in l for item in sublist] + return pad_shape + + +def sequence_mask(length, max_length=None): + if max_length is None: + max_length = length.max() + x = torch.arange(max_length, dtype=length.dtype, device=length.device) + return x.unsqueeze(0) < length.unsqueeze(1) + + +def generate_path(duration, mask): + """ + duration: [b, 1, t_x] + mask: [b, 1, t_y, t_x] + """ + device = duration.device + + b, _, t_y, t_x = mask.shape + cum_duration = torch.cumsum(duration, -1) + + cum_duration_flat = cum_duration.view(b * t_x) + path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) + path = path.view(b, t_x, t_y) + path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] + path = path.unsqueeze(1).transpose(2, 3) * mask + return path + + +def clip_grad_value_(parameters, clip_value, norm_type=2): + if isinstance(parameters, torch.Tensor): + parameters = [parameters] + parameters = list(filter(lambda p: p.grad is not None, parameters)) + norm_type = float(norm_type) + if clip_value is not None: + clip_value = float(clip_value) + + total_norm = 0 + for p in parameters: + param_norm = p.grad.data.norm(norm_type) + total_norm += param_norm.item() ** norm_type + if clip_value is not None: + p.grad.data.clamp_(min=-clip_value, max=clip_value) + total_norm = total_norm ** (1.0 / norm_type) + return total_norm + + +def get_current_time(): + pass + + +def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor: + """ + Args: + lengths: + A 1-D tensor containing sentence lengths. + max_len: + The length of masks. + Returns: + Return a 2-D bool tensor, where masked positions + are filled with `True` and non-masked positions are + filled with `False`. + + >>> lengths = torch.tensor([1, 3, 2, 5]) + >>> make_pad_mask(lengths) + tensor([[False, True, True, True, True], + [False, False, False, True, True], + [False, False, True, True, True], + [False, False, False, False, False]]) + """ + assert lengths.ndim == 1, lengths.ndim + max_len = max(max_len, lengths.max()) + n = lengths.size(0) + seq_range = torch.arange(0, max_len, device=lengths.device) + expaned_lengths = seq_range.unsqueeze(0).expand(n, max_len) + + return expaned_lengths >= lengths.unsqueeze(-1) + diff --git a/utils/whisper.py b/utils/whisper.py new file mode 100644 index 0000000000000000000000000000000000000000..16462c7e84f2ce71d4fd5e57832d705fd07b95ca --- /dev/null +++ b/utils/whisper.py @@ -0,0 +1,165 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import os +import pickle +from tqdm import tqdm +import numpy as np + +from modules import whisper_extractor as whisper + + +def whisper_encoder_batch(model, audio_paths): + batch = len(audio_paths) + batch_mel = torch.zeros((batch, 80, 3000), dtype=torch.float32, device=model.device) + + for i, audio_path in enumerate(audio_paths): + # (48000,) + audio = whisper.load_audio(str(audio_path)) + audio = whisper.pad_or_trim(audio) + + # (80, 3000) + mel = whisper.log_mel_spectrogram(audio).to(model.device) + batch_mel[i] = mel + + with torch.no_grad(): + # (batch, 1500, 1024) + features = model.embed_audio(batch_mel) + + return features.cpu().detach().numpy() + + +def whisper_encoder(model, audio_path): + audio = whisper.load_audio(str(audio_path)) + audio = whisper.pad_or_trim(audio) + + # (80, 3000) + mel = whisper.log_mel_spectrogram(audio).to(model.device).unsqueeze(0) + + with torch.no_grad(): + # (1, 1500, 1024) -> # (1500, 1024) + features = model.embed_audio(mel).squeeze(0) + + return features.cpu().detach().numpy() + + +def get_mapped_whisper_features( + raw_whisper_features, mapping_features, fast_mapping=True +): + """ + Whisper: frameshift = 20ms (30s audio -> 1500 frames), hop_size = 480 in 24k + # Ref: https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/model.py#L136 + + Now it's only used for mapping to bigvgan's mels (sr = 24k, hop_size = 256, frameshift ~= 10.7 ms) + """ + source_hop = 480 + target_hop = 256 + + factor = np.gcd(source_hop, target_hop) + source_hop //= factor + target_hop //= factor + print( + "Mapping source's {} frames => target's {} frames".format( + target_hop, source_hop + ) + ) + + max_source_len = 1500 + whisper_features = [] + for index, mapping_feat in enumerate(tqdm(mapping_features)): + # mapping_feat: (mels_frame_len, n_mels) + target_len = mapping_feat.shape[0] + # The max target_len is 2812 + target_len = min(target_len, max_source_len * source_hop // target_hop) + + # (1500, dim) + raw_feats = raw_whisper_features[index] + width = raw_feats.shape[-1] + + if fast_mapping: + source_len = target_len * target_hop // source_hop + 1 + raw_feats = raw_feats[:source_len] + else: + source_len = max_source_len + + # const ~= target_len * target_hop + const = source_len * source_hop // target_hop * target_hop + + # (source_len * source_hop, dim) + up_sampling_feats = np.repeat(raw_feats, source_hop, axis=0) + # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim) + down_sampling_feats = np.average( + up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1 + ) + assert len(down_sampling_feats) >= target_len + + # (target_len, dim) + feats = down_sampling_feats[:target_len] + whisper_features.append(feats) + + return whisper_features + + +def load_whisper_model(hps): + print("Loading Whisper Model: ", hps.whisper_model) + model = whisper.load_model(hps.whisper_model) + if torch.cuda.is_available(): + model = model.cuda() + + model = model.eval() + return model + + +def load_target_acoustic_features( + output_path, dataset, acoustic_features_name, acoustic_features_fs, dataset_type +): + mapping_dir = os.path.join( + output_path, + dataset, + "{}/{}".format(acoustic_features_name, acoustic_features_fs), + ) + with open(os.path.join(mapping_dir, "{}.pkl".format(dataset_type)), "rb") as f: + mapping_features = pickle.load(f) + + # Mels: (n_mels, frame_len) -> (frame_len, n_mels) + if acoustic_features_name == "mels": + print("Transposing mel features...") + mapping_features = [feat.T for feat in mapping_features] + + print( + "Mapping to the acoustic features {}, #sz = {}, feats[0] is {}".format( + acoustic_features_name, len(mapping_features), mapping_features[0].shape + ) + ) + return mapping_features + + +def extract_whisper_features_of_dataset( + datasets, + model, + batch_size, + out_dir, +): + audio_paths = [utt["Path"] for utt in datasets] + if len(audio_paths) < batch_size: + batch_size = len(audio_paths) + + start, end = 0, 0 + while end < len(audio_paths): + # Raw features: (batch_size, 1500, dim) + start = end + end = start + batch_size + tmp_raw_whisper_features = whisper_encoder_batch(model, audio_paths[start:end]) + + # Mapping to acoustic features' lengths + for index, utt in enumerate(tqdm(datasets[start:end])): + uid = utt["Uid"] + raw_whisper_feature = tmp_raw_whisper_features[index] + + save_path = os.path.join(out_dir, uid + ".npy") + np.save(save_path, raw_whisper_feature) + + print("{}/{} Done...".format(end, len(audio_paths))) diff --git a/utils/world.py b/utils/world.py new file mode 100644 index 0000000000000000000000000000000000000000..ce5f61bd9b571607fd83da6b22283757e67201da --- /dev/null +++ b/utils/world.py @@ -0,0 +1,92 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# 1. Extract WORLD features including F0, AP, SP +# 2. Transform between SP and MCEP +import torchaudio +import pyworld as pw +import numpy as np +import torch +import diffsptk +import os +from tqdm import tqdm +import pickle +import torchaudio + + +def get_mcep_params(fs): + """Hyperparameters of transformation between SP and MCEP + + Reference: + https://github.com/CSTR-Edinburgh/merlin/blob/master/misc/scripts/vocoder/world_v2/copy_synthesis.sh + + """ + if fs in [44100, 48000]: + fft_size = 2048 + alpha = 0.77 + if fs in [16000]: + fft_size = 1024 + alpha = 0.58 + return fft_size, alpha + + +def extract_world_features(waveform, frameshift=10): + # waveform: (1, seq) + # x: (seq,) + x = np.array(waveform, dtype=np.double) + + _f0, t = pw.dio(x, fs, frame_period=frameshift) # raw pitch extractor + f0 = pw.stonemask(x, _f0, t, fs) # pitch refinement + sp = pw.cheaptrick(x, f0, t, fs) # extract smoothed spectrogram + ap = pw.d4c(x, f0, t, fs) # extract aperiodicity + + return f0, sp, ap, fs + + +def sp2mcep(x, mcsize, fs): + fft_size, alpha = get_mcep_params(fs) + x = torch.as_tensor(x, dtype=torch.float) + + tmp = diffsptk.ScalarOperation("SquareRoot")(x) + tmp = diffsptk.ScalarOperation("Multiplication", 32768.0)(tmp) + mgc = diffsptk.MelCepstralAnalysis( + cep_order=mcsize - 1, fft_length=fft_size, alpha=alpha, n_iter=1 + )(tmp) + return mgc.numpy() + + +def mcep2sp(x, mcsize, fs): + fft_size, alpha = get_mcep_params(fs) + x = torch.as_tensor(x, dtype=torch.float) + + tmp = diffsptk.MelGeneralizedCepstrumToSpectrum( + alpha=alpha, + cep_order=mcsize - 1, + fft_length=fft_size, + )(x) + tmp = diffsptk.ScalarOperation("Division", 32768.0)(tmp) + sp = diffsptk.ScalarOperation("Power", 2)(tmp) + return sp.double().numpy() + + +def f0_statistics(f0_features, path): + print("\nF0 statistics...") + + total_f0 = [] + for f0 in tqdm(f0_features): + total_f0 += [f for f in f0 if f != 0] + + mean = sum(total_f0) / len(total_f0) + print("Min = {}, Max = {}, Mean = {}".format(min(total_f0), max(total_f0), mean)) + + with open(path, "wb") as f: + pickle.dump([mean, total_f0], f) + + +def world_synthesis(f0, sp, ap, fs, frameshift): + y = pw.synthesize( + f0, sp, ap, fs, frame_period=frameshift + ) # synthesize an utterance using the parameters + return y