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| from multiprocess.pool import ThreadPool | |
| from speaker_encoder.params_data import * | |
| from speaker_encoder.config import librispeech_datasets, anglophone_nationalites | |
| from datetime import datetime | |
| from speaker_encoder import audio | |
| from pathlib import Path | |
| from tqdm import tqdm | |
| import numpy as np | |
| class DatasetLog: | |
| """ | |
| Registers metadata about the dataset in a text file. | |
| """ | |
| def __init__(self, root, name): | |
| self.text_file = open(Path(root, "Log_%s.txt" % name.replace("/", "_")), "w") | |
| self.sample_data = dict() | |
| start_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M")) | |
| self.write_line("Creating dataset %s on %s" % (name, start_time)) | |
| self.write_line("-----") | |
| self._log_params() | |
| def _log_params(self): | |
| from speaker_encoder import params_data | |
| self.write_line("Parameter values:") | |
| for param_name in (p for p in dir(params_data) if not p.startswith("__")): | |
| value = getattr(params_data, param_name) | |
| self.write_line("\t%s: %s" % (param_name, value)) | |
| self.write_line("-----") | |
| def write_line(self, line): | |
| self.text_file.write("%s\n" % line) | |
| def add_sample(self, **kwargs): | |
| for param_name, value in kwargs.items(): | |
| if not param_name in self.sample_data: | |
| self.sample_data[param_name] = [] | |
| self.sample_data[param_name].append(value) | |
| def finalize(self): | |
| self.write_line("Statistics:") | |
| for param_name, values in self.sample_data.items(): | |
| self.write_line("\t%s:" % param_name) | |
| self.write_line("\t\tmin %.3f, max %.3f" % (np.min(values), np.max(values))) | |
| self.write_line("\t\tmean %.3f, median %.3f" % (np.mean(values), np.median(values))) | |
| self.write_line("-----") | |
| end_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M")) | |
| self.write_line("Finished on %s" % end_time) | |
| self.text_file.close() | |
| def _init_preprocess_dataset(dataset_name, datasets_root, out_dir) -> (Path, DatasetLog): | |
| dataset_root = datasets_root.joinpath(dataset_name) | |
| if not dataset_root.exists(): | |
| print("Couldn\'t find %s, skipping this dataset." % dataset_root) | |
| return None, None | |
| return dataset_root, DatasetLog(out_dir, dataset_name) | |
| def _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, extension, | |
| skip_existing, logger): | |
| print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs))) | |
| # Function to preprocess utterances for one speaker | |
| def preprocess_speaker(speaker_dir: Path): | |
| # Give a name to the speaker that includes its dataset | |
| speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts) | |
| # Create an output directory with that name, as well as a txt file containing a | |
| # reference to each source file. | |
| speaker_out_dir = out_dir.joinpath(speaker_name) | |
| speaker_out_dir.mkdir(exist_ok=True) | |
| sources_fpath = speaker_out_dir.joinpath("_sources.txt") | |
| # There's a possibility that the preprocessing was interrupted earlier, check if | |
| # there already is a sources file. | |
| if sources_fpath.exists(): | |
| try: | |
| with sources_fpath.open("r") as sources_file: | |
| existing_fnames = {line.split(",")[0] for line in sources_file} | |
| except: | |
| existing_fnames = {} | |
| else: | |
| existing_fnames = {} | |
| # Gather all audio files for that speaker recursively | |
| sources_file = sources_fpath.open("a" if skip_existing else "w") | |
| for in_fpath in speaker_dir.glob("**/*.%s" % extension): | |
| # Check if the target output file already exists | |
| out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts) | |
| out_fname = out_fname.replace(".%s" % extension, ".npy") | |
| if skip_existing and out_fname in existing_fnames: | |
| continue | |
| # Load and preprocess the waveform | |
| wav = audio.preprocess_wav(in_fpath) | |
| if len(wav) == 0: | |
| continue | |
| # Create the mel spectrogram, discard those that are too short | |
| frames = audio.wav_to_mel_spectrogram(wav) | |
| if len(frames) < partials_n_frames: | |
| continue | |
| out_fpath = speaker_out_dir.joinpath(out_fname) | |
| np.save(out_fpath, frames) | |
| logger.add_sample(duration=len(wav) / sampling_rate) | |
| sources_file.write("%s,%s\n" % (out_fname, in_fpath)) | |
| sources_file.close() | |
| # Process the utterances for each speaker | |
| with ThreadPool(8) as pool: | |
| list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs), | |
| unit="speakers")) | |
| logger.finalize() | |
| print("Done preprocessing %s.\n" % dataset_name) | |
| # Function to preprocess utterances for one speaker | |
| def __preprocess_speaker(speaker_dir: Path, datasets_root: Path, out_dir: Path, extension: str, skip_existing: bool): | |
| # Give a name to the speaker that includes its dataset | |
| speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts) | |
| # Create an output directory with that name, as well as a txt file containing a | |
| # reference to each source file. | |
| speaker_out_dir = out_dir.joinpath(speaker_name) | |
| speaker_out_dir.mkdir(exist_ok=True) | |
| sources_fpath = speaker_out_dir.joinpath("_sources.txt") | |
| # There's a possibility that the preprocessing was interrupted earlier, check if | |
| # there already is a sources file. | |
| # if sources_fpath.exists(): | |
| # try: | |
| # with sources_fpath.open("r") as sources_file: | |
| # existing_fnames = {line.split(",")[0] for line in sources_file} | |
| # except: | |
| # existing_fnames = {} | |
| # else: | |
| # existing_fnames = {} | |
| existing_fnames = {} | |
| # Gather all audio files for that speaker recursively | |
| sources_file = sources_fpath.open("a" if skip_existing else "w") | |
| for in_fpath in speaker_dir.glob("**/*.%s" % extension): | |
| # Check if the target output file already exists | |
| out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts) | |
| out_fname = out_fname.replace(".%s" % extension, ".npy") | |
| if skip_existing and out_fname in existing_fnames: | |
| continue | |
| # Load and preprocess the waveform | |
| wav = audio.preprocess_wav(in_fpath) | |
| if len(wav) == 0: | |
| continue | |
| # Create the mel spectrogram, discard those that are too short | |
| frames = audio.wav_to_mel_spectrogram(wav) | |
| if len(frames) < partials_n_frames: | |
| continue | |
| out_fpath = speaker_out_dir.joinpath(out_fname) | |
| np.save(out_fpath, frames) | |
| # logger.add_sample(duration=len(wav) / sampling_rate) | |
| sources_file.write("%s,%s\n" % (out_fname, in_fpath)) | |
| sources_file.close() | |
| return len(wav) | |
| def _preprocess_speaker_dirs_vox2(speaker_dirs, dataset_name, datasets_root, out_dir, extension, | |
| skip_existing, logger): | |
| # from multiprocessing import Pool, cpu_count | |
| from pathos.multiprocessing import ProcessingPool as Pool | |
| # Function to preprocess utterances for one speaker | |
| def __preprocess_speaker(speaker_dir: Path): | |
| # Give a name to the speaker that includes its dataset | |
| speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts) | |
| # Create an output directory with that name, as well as a txt file containing a | |
| # reference to each source file. | |
| speaker_out_dir = out_dir.joinpath(speaker_name) | |
| speaker_out_dir.mkdir(exist_ok=True) | |
| sources_fpath = speaker_out_dir.joinpath("_sources.txt") | |
| existing_fnames = {} | |
| # Gather all audio files for that speaker recursively | |
| sources_file = sources_fpath.open("a" if skip_existing else "w") | |
| wav_lens = [] | |
| for in_fpath in speaker_dir.glob("**/*.%s" % extension): | |
| # Check if the target output file already exists | |
| out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts) | |
| out_fname = out_fname.replace(".%s" % extension, ".npy") | |
| if skip_existing and out_fname in existing_fnames: | |
| continue | |
| # Load and preprocess the waveform | |
| wav = audio.preprocess_wav(in_fpath) | |
| if len(wav) == 0: | |
| continue | |
| # Create the mel spectrogram, discard those that are too short | |
| frames = audio.wav_to_mel_spectrogram(wav) | |
| if len(frames) < partials_n_frames: | |
| continue | |
| out_fpath = speaker_out_dir.joinpath(out_fname) | |
| np.save(out_fpath, frames) | |
| # logger.add_sample(duration=len(wav) / sampling_rate) | |
| sources_file.write("%s,%s\n" % (out_fname, in_fpath)) | |
| wav_lens.append(len(wav)) | |
| sources_file.close() | |
| return wav_lens | |
| print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs))) | |
| # Process the utterances for each speaker | |
| # with ThreadPool(8) as pool: | |
| # list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs), | |
| # unit="speakers")) | |
| pool = Pool(processes=20) | |
| for i, wav_lens in enumerate(pool.map(__preprocess_speaker, speaker_dirs), 1): | |
| for wav_len in wav_lens: | |
| logger.add_sample(duration=wav_len / sampling_rate) | |
| print(f'{i}/{len(speaker_dirs)} \r') | |
| logger.finalize() | |
| print("Done preprocessing %s.\n" % dataset_name) | |
| def preprocess_librispeech(datasets_root: Path, out_dir: Path, skip_existing=False): | |
| for dataset_name in librispeech_datasets["train"]["other"]: | |
| # Initialize the preprocessing | |
| dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir) | |
| if not dataset_root: | |
| return | |
| # Preprocess all speakers | |
| speaker_dirs = list(dataset_root.glob("*")) | |
| _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "flac", | |
| skip_existing, logger) | |
| def preprocess_voxceleb1(datasets_root: Path, out_dir: Path, skip_existing=False): | |
| # Initialize the preprocessing | |
| dataset_name = "VoxCeleb1" | |
| dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir) | |
| if not dataset_root: | |
| return | |
| # Get the contents of the meta file | |
| with dataset_root.joinpath("vox1_meta.csv").open("r") as metafile: | |
| metadata = [line.split("\t") for line in metafile][1:] | |
| # Select the ID and the nationality, filter out non-anglophone speakers | |
| nationalities = {line[0]: line[3] for line in metadata} | |
| # keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items() if | |
| # nationality.lower() in anglophone_nationalites] | |
| keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items()] | |
| print("VoxCeleb1: using samples from %d (presumed anglophone) speakers out of %d." % | |
| (len(keep_speaker_ids), len(nationalities))) | |
| # Get the speaker directories for anglophone speakers only | |
| speaker_dirs = dataset_root.joinpath("wav").glob("*") | |
| speaker_dirs = [speaker_dir for speaker_dir in speaker_dirs if | |
| speaker_dir.name in keep_speaker_ids] | |
| print("VoxCeleb1: found %d anglophone speakers on the disk, %d missing (this is normal)." % | |
| (len(speaker_dirs), len(keep_speaker_ids) - len(speaker_dirs))) | |
| # Preprocess all speakers | |
| _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "wav", | |
| skip_existing, logger) | |
| def preprocess_voxceleb2(datasets_root: Path, out_dir: Path, skip_existing=False): | |
| # Initialize the preprocessing | |
| dataset_name = "VoxCeleb2" | |
| dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir) | |
| if not dataset_root: | |
| return | |
| # Get the speaker directories | |
| # Preprocess all speakers | |
| speaker_dirs = list(dataset_root.joinpath("dev", "aac").glob("*")) | |
| _preprocess_speaker_dirs_vox2(speaker_dirs, dataset_name, datasets_root, out_dir, "m4a", | |
| skip_existing, logger) | |