diff --git a/.gitignore b/.gitignore
index b174072d5944c7044abfdde417bb3ec9eb33521e..913f061d749498929b9d9da5a5c4e3a2fdad85a6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,9 @@
 __pycache__
 flagged
 result
+source_audios
+ckpts/svc/vocalist_l1_contentvec+whisper/data
+!ckpts/svc/vocalist_l1_contentvec+whisper/data/vocalist_l1
 
 # Developing mode
 _*.sh
diff --git a/app.py b/app.py
index 49d1a413ec53285bb9a4ec813702c8fab99d9d1d..a026f70b12b8bbb0c8815cc74d4f46e8cbbda091 100644
--- a/app.py
+++ b/app.py
@@ -1,5 +1,11 @@
-import gradio as gr
+# Copyright (c) 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 gradio as gr
+import os
+import inference
 
 SUPPORTED_TARGET_SINGERS = {
     "Adele": "vocalist_l1_Adele",
@@ -21,33 +27,58 @@ SUPPORTED_TARGET_SINGERS = {
 
 
 def svc_inference(
-    source_audio,
+    source_audio_path,
     target_singer,
-    diffusion_steps=1000,
-    key_shift_mode="auto",
+    key_shift_mode="Auto Shift",
     key_shift_num=0,
+    diffusion_steps=1000,
 ):
-    pass
+    #### Prepare source audio file ####
+    print("source_audio_path: {}".format(source_audio_path))
+    audio_file = source_audio_path.split("/")[-1]
+    audio_name = audio_file.split(".")[0]
+    source_audio_dir = source_audio_path.replace(audio_file, "")
+
+    ### Target Singer ###
+    target_singer = SUPPORTED_TARGET_SINGERS[target_singer]
+
+    ### Inference ###
+    if key_shift_mode == "Auto Shift":
+        key_shift = "autoshift"
+    else:
+        key_shift = key_shift_num
+
+    args_list = ["--config", "ckpts/svc/vocalist_l1_contentvec+whisper/args.json"]
+    args_list += ["--acoustics_dir", "ckpts/svc/vocalist_l1_contentvec+whisper"]
+    args_list += ["--vocoder_dir", "pretrained/bigvgan"]
+    args_list += ["--target_singer", target_singer]
+    args_list += ["--trans_key", str(key_shift)]
+    args_list += ["--diffusion_inference_steps", str(diffusion_steps)]
+    args_list += ["--source", source_audio_dir]
+    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(audio_name, audio_name, target_singer)
+    )
+    return result_file
 
 
 demo_inputs = [
     gr.Audio(
         sources=["upload", "microphone"],
         label="Upload (or record) a song you want to listen",
+        type="filepath",
     ),
     gr.Radio(
         choices=list(SUPPORTED_TARGET_SINGERS.keys()),
         label="Target Singer",
         value="Jian Li 李健",
     ),
-    gr.Slider(
-        1,
-        1000,
-        value=1000,
-        step=1,
-        label="Diffusion Inference Steps",
-        info="As the step number increases, the synthesis quality will be better while the inference speed will be lower",
-    ),
     gr.Radio(
         choices=["Auto Shift", "Key Shift"],
         value="Auto Shift",
@@ -62,6 +93,14 @@ demo_inputs = [
         label="Key Shift Values",
         info='How many semitones you want to transpose.	This parameter will work only if you choose "Key Shift"',
     ),
+    gr.Slider(
+        1,
+        1000,
+        value=1000,
+        step=1,
+        label="Diffusion Inference Steps",
+        info="As the step number increases, the synthesis quality will be better while the inference speed will be lower",
+    ),
 ]
 
 demo_outputs = gr.Audio(label="")
diff --git a/ckpts/svc/vocalist_l1_contentvec+whisper/args.json b/ckpts/svc/vocalist_l1_contentvec+whisper/args.json
index 836d5e81420921d4ec096d3445c0ff5964e13b73..d5271e5d962a1141615548fcbb00304f6082e237 100755
--- a/ckpts/svc/vocalist_l1_contentvec+whisper/args.json
+++ b/ckpts/svc/vocalist_l1_contentvec+whisper/args.json
@@ -1,5 +1,5 @@
 {
-    "base_config": "config/diffusion.json",
+    "task_type": "svc",
     "dataset": [
         "vocalist_l1",
     ],
@@ -195,6 +195,7 @@
         "whisper_frameshift": 0.01,
         "whisper_model": "medium",
         "whisper_model_path": "pretrained/whisper/medium.pt",
+        "whisper_sample_rate": 16000,
         "win_size": 1024,
     },
     "supported_model_type": [
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..d46670ebe858f478e3699e49b88e87ec7c0e34d4
--- /dev/null
+++ b/config/tts.json
@@ -0,0 +1,23 @@
+{
+  "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,
+  },
+  "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 <BOS> 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..d8de83ab4641f5c4efef7752e7017d0733a7ddac
--- /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": 0, // 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/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:
+
+<br>
+<div align="center">
+  <img src="../../../imgs/vocoder/gan/pipeline.png" width="40%">
+</div>
+<br>
+
+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-<COLOR>.svg)](https://arxiv.org/abs/2311.14957)
+[![demo](https://img.shields.io/badge/Vocoder-Demo-red)](https://vocodexelysium.github.io/MS-SB-CQTD/)
+
+<br>
+<div align="center">
+<img src="../../../../imgs/vocoder/gan/MSSBCQTD.png" width="80%">
+</div>
+<br>
+
+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/inference.py b/inference.py
index 729bbd0058b1ed399c793231ef645db106a071cf..7d4298c3507f225b3d097792c386e5a38b539f52 100644
--- a/inference.py
+++ b/inference.py
@@ -208,9 +208,9 @@ def build_parser():
     return parser
 
 
-def main():
+def main(args_list):
     ### Parse arguments and config
-    args = build_parser().parse_args()
+    args = build_parser().parse_args(args_list)
     cfg = load_config(args.config)
 
     # CUDA settings
@@ -256,3 +256,7 @@ def main():
     else:
         ### Infer from dataset
         infer(args, cfg, infer_type="from_dataset")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/modules/__init__.py b/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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/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 <https://arxiv.org/pdf/1606.05328.pdf>`_.
+
+    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/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/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
+    <https://github.com/Stability-AI/generative-models/blob/main/sgm/modules/attention.py#L531>`_.
+
+    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/standard_duration_predictor.py b/modules/duration_predictor/standard_duration_predictor.py
new file mode 100644
index 0000000000000000000000000000000000000000..9924e70a7571cef0359019d6d083db7d9bd6e9cf
--- /dev/null
+++ 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/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/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 BalancedDoubleSwish
+from .utils import Transpose
\ No newline at end of file
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/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/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/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/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 = "<blank>"
+UNK_WORD = "<unk>"
+BOS_WORD = "<s>"
+EOS_WORD = "</s>"
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 = "<blank>"
+UNK_WORD = "<unk>"
+BOS_WORD = "<s>"
+EOS_WORD = "</s>"
+
+
+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
+++ b/modules/transformer/__init__.py
@@ -0,0 +1,2 @@
+from .mh_attention import MultiheadAttention
+from .position_embedding import SinePositionalEmbedding
\ No newline at end of file
diff --git a/modules/transformer/attentions.py b/modules/transformer/attentions.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b0cfc54ae038d4ca35ba32732dc69c776f4c4f0
--- /dev/null
+++ b/modules/transformer/attentions.py
@@ -0,0 +1,416 @@
+# Copyright (c) 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
+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 <https://arxiv.org/abs/1706.03762>`_.
+
+    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 <https://pytorch.org/docs/stable/nested.html>`_ is passed, neither ``key_padding_mask``
+      nor ``attn_mask`` is passed
+
+    If the optimized implementation is in use, a
+    `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ can be passed for
+    ``query``/``key``/``value`` to represent padding more efficiently than using a
+    padding mask. In this case, a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_
+    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/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 100755
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 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/wenet_extractor/bin/alignment.py b/modules/wenet_extractor/bin/alignment.py
new file mode 100755
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 100755
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 100755
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 100755
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 100755
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  # <sos>
+    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 100755
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 100755
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 100755
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 100755
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/predictor.py b/modules/wenet_extractor/cif/predictor.py
new file mode 100755
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 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/wenet_extractor/dataset/dataset.py b/modules/wenet_extractor/dataset/dataset.py
new file mode 100755
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 100755
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<int>) 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<int>) 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<float>) 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<vector<tuple<int,float> > >
+# - 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<vec<int,float>>) = 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<vector<tuple<int,float> > >',
+    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<tuple<float,float>>) = 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<tuple<float,float> >.
+    (begin/end times of bins at the confusion network).
+
+    Binary layout is '<num-bins> <beg1> <end1> <beg2> <end2> ...'
+
+    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 : '<utt> <rec> <t-beg> <t-end>'
+    - 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 100755
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 "<unk>" in symbol_table:
+                label.append(symbol_table["<unk>"])
+
+        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 100755
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 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/wenet_extractor/efficient_conformer/attention.py b/modules/wenet_extractor/efficient_conformer/attention.py
new file mode 100755
index 0000000000000000000000000000000000000000..96f5ac10d56e44c0873e6d659ed501fdedf43192
--- /dev/null
+++ b/modules/wenet_extractor/efficient_conformer/attention.py
@@ -0,0 +1,273 @@
+# 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, 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 100755
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 100755
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 100755
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 100755
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/paraformer.py b/modules/wenet_extractor/paraformer/paraformer.py
new file mode 100755
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/beam_search.py b/modules/wenet_extractor/paraformer/search/beam_search.py
new file mode 100755
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 <eos> 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 <eos> 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 100755
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 100755
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(<sos>) and r_t^b(<sos>), 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 100755
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 100755
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 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/wenet_extractor/squeezeformer/attention.py b/modules/wenet_extractor/squeezeformer/attention.py
new file mode 100755
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 100755
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 100755
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 100755
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 100755
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 100755
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 100755
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 100755
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 100755
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 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/wenet_extractor/transducer/joint.py b/modules/wenet_extractor/transducer/joint.py
new file mode 100755
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/greedy_search.py b/modules/wenet_extractor/transducer/search/greedy_search.py
new file mode 100755
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 100755
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 100755
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 <sos> 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 forward_predictor_init_state(self) -> List[torch.Tensor]:
+        return self.predictor.init_state(1, device=torch.device("cpu"))
diff --git a/modules/wenet_extractor/transformer/__init__.py b/modules/wenet_extractor/transformer/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/wenet_extractor/transformer/asr_model.py 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 <sos> 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] == "<sos/eos>":
+                    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="<UNK>",
+            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 <sos> 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 <sos> token, we need minus it.
+        r_hyps_lens = hyps_lens - 1
+        # this hyps has included <sos> 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 100755
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 100755
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 100755
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 100755
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 100755
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 100755
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 100755
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 100755
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 100755
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 100755
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 100755
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 100755
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 100755
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 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/wenet_extractor/utils/checkpoint.py b/modules/wenet_extractor/utils/checkpoint.py
new file mode 100755
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 100755
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 100755
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 <blank>
+
+    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 <sos> and <eos> labels.
+
+    Args:
+        ys_pad (torch.Tensor): batch of padded target sequences (B, Lmax)
+        sos (int): index of <sos>
+        eos (int): index of <eeos>
+        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 100755
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 100755
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 100755
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 100755
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>/[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 100755
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 100755
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 <eos>
+
+    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 100755
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 100755
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/assets/gpt2/merges.txt b/modules/whisper_extractor/assets/gpt2/merges.txt
new file mode 100755
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
+. "
+b er
+i z
+a ct
+Ġon e
+Ġsa id
+Ġ -
+a re
+Ġyou r
+c c
+ĠT h
+Ġc l
+e p
+a ke
+ab le
+i p
+Ġcon t
+Ġwh ich
+i a
+Ġ im
+Ġab out
+Ġwe re
+ver y
+u b
+Ġh ad
+Ġ en
+Ġcom p
+, "
+ĠI n
+Ġu n
+Ġa g
+i re
+ac e
+a u
+ar y
+Ġw ould
+as s
+r y
+Ġ âĢ
+c l
+o ok
+e re
+s o
+Ġ V
+ig n
+i b
+Ġof f
+Ġt e
+v en
+Ġ Y
+i le
+o se
+it e
+or m
+Ġ2 01
+Ġre s
+Ġm an
+Ġp er
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+or d
+ul t
+Ġbe en
+Ġl ike
+as e
+an ce
+k s
+ay s
+ow n
+en ce
+Ġd is
+ct ion
+Ġan y
+Ġa pp
+Ġs p
+in t
+res s
+ation s
+a il
+Ġ 4
+ic al
+Ġthe m
+Ġhe r
+ou nt
+ĠC h
+Ġa r
+Ġ if
+Ġthe re
+Ġp e
+Ġy ear
+a v
+Ġm y
+Ġs ome
+Ġwhe n
+ou gh
+ac h
+Ġth an
+r u
+on d
+ic k
+Ġo ver
+ve l
+Ġ qu
+Ċ Ċ
+Ġs c
+re at
+re e
+ĠI t
+ou nd
+p ort
+Ġal so
+Ġp art
+f ter
+Ġk n
+Ġbe c
+Ġt ime
+en s
+Ġ 5
+op le
+Ġwh at
+Ġn o
+d u
+m er
+an g
+Ġn ew
+-- --
+Ġg et
+or y
+it ion
+ing s
+Ġj ust
+Ġint o
+Ġ 0
+ent s
+o ve
+t e
+Ġpe ople
+Ġp re
+Ġit s
+Ġre c
+Ġt w
+i an
+ir st
+ar k
+or s
+Ġwor k
+ad e
+o b
+Ġs he
+Ġo ur
+w n
+in k
+l ic
+Ġ1 9
+ĠH e
+is h
+nd er
+au se
+Ġh im
+on s
+Ġ [
+Ġ ro
+f orm
+i ld
+at es
+ver s
+Ġon ly
+o ll
+Ġs pe
+c k
+e ll
+am p
+Ġa cc
+Ġb l
+i ous
+ur n
+f t
+o od
+Ġh ow
+he d
+Ġ '
+Ġa fter
+a w
+Ġat t
+o v
+n e
+Ġpl ay
+er v
+ic t
+Ġc ould
+it t
+Ġa m
+Ġf irst
+Ġ 6
+Ġa ct
+Ġ $
+e c
+h ing
+u al
+u ll
+Ġcom m
+o y
+o ld
+c es
+at er
+Ġf e
+Ġbe t
+w e
+if f
+Ġtw o
+oc k
+Ġb ack
+) .
+id ent
+Ġu nder
+rou gh
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+==== ==
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+Ġ( /
+âĢ¦ ."
+Com par
+Ġampl ification
+om inated
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+ĠColl ider
+Ġinform ants
+Ġg azed
diff --git a/modules/whisper_extractor/assets/gpt2/special_tokens_map.json b/modules/whisper_extractor/assets/gpt2/special_tokens_map.json
new file mode 100755
index 0000000000000000000000000000000000000000..817762d631ad6f9c799f6b9dc713c46420e65546
--- /dev/null
+++ b/modules/whisper_extractor/assets/gpt2/special_tokens_map.json
@@ -0,0 +1 @@
+{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}
\ No newline at end of file
diff --git a/modules/whisper_extractor/assets/gpt2/tokenizer_config.json b/modules/whisper_extractor/assets/gpt2/tokenizer_config.json
new file mode 100755
index 0000000000000000000000000000000000000000..c92208ce39324aadabc239d73dd9bb3ec1cd45ca
--- /dev/null
+++ b/modules/whisper_extractor/assets/gpt2/tokenizer_config.json
@@ -0,0 +1 @@
+{"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "gpt2", "tokenizer_class": "GPT2Tokenizer"}
\ No newline at end of file
diff --git a/modules/whisper_extractor/assets/gpt2/vocab.json b/modules/whisper_extractor/assets/gpt2/vocab.json
new file mode 100755
index 0000000000000000000000000000000000000000..84ef7fb594b5c0979e48bdeddb60a0adef33df0b
--- /dev/null
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+{"<|endoftext|>": 50257}
diff --git a/modules/whisper_extractor/assets/multilingual/merges.txt b/modules/whisper_extractor/assets/multilingual/merges.txt
new file mode 100755
index 0000000000000000000000000000000000000000..afde9f4fa8bcf6d4762c5780977825666dc49eac
--- /dev/null
+++ b/modules/whisper_extractor/assets/multilingual/merges.txt
@@ -0,0 +1,50000 @@
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diff --git a/modules/whisper_extractor/assets/multilingual/special_tokens_map.json b/modules/whisper_extractor/assets/multilingual/special_tokens_map.json
new file mode 100755
index 0000000000000000000000000000000000000000..817762d631ad6f9c799f6b9dc713c46420e65546
--- /dev/null
+++ b/modules/whisper_extractor/assets/multilingual/special_tokens_map.json
@@ -0,0 +1 @@
+{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}
\ No newline at end of file
diff --git a/modules/whisper_extractor/assets/multilingual/tokenizer_config.json b/modules/whisper_extractor/assets/multilingual/tokenizer_config.json
new file mode 100755
index 0000000000000000000000000000000000000000..0235ccff90efbc022fbe30360220375c5bad68da
--- /dev/null
+++ b/modules/whisper_extractor/assets/multilingual/tokenizer_config.json
@@ -0,0 +1 @@
+{"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "multilingual", "errors": "replace", "tokenizer_class": "GPT2Tokenizer"}
\ No newline at end of file
diff --git a/modules/whisper_extractor/assets/multilingual/vocab.json b/modules/whisper_extractor/assets/multilingual/vocab.json
new file mode 100755
index 0000000000000000000000000000000000000000..406c3f2600089389bd8bf3920c2fa3770d11789a
--- /dev/null
+++ b/modules/whisper_extractor/assets/multilingual/vocab.json
@@ -0,0 +1 @@
+{"!": 0, "\"": 1, "#": 2, "$": 3, "%": 4, "&": 5, "'": 6, "(": 7, ")": 8, "*": 9, "+": 10, ",": 11, "-": 12, ".": 13, "/": 14, "0": 15, "1": 16, "2": 17, "3": 18, "4": 19, "5": 20, "6": 21, "7": 22, "8": 23, "9": 24, ":": 25, ";": 26, "<": 27, "=": 28, ">": 29, "?": 30, "@": 31, "A": 32, "B": 33, "C": 34, "D": 35, "E": 36, "F": 37, "G": 38, "H": 39, "I": 40, "J": 41, "K": 42, "L": 43, "M": 44, "N": 45, "O": 46, "P": 47, "Q": 48, "R": 49, "S": 50, "T": 51, "U": 52, "V": 53, "W": 54, "X": 55, "Y": 56, "Z": 57, "[": 58, "\\": 59, "]": 60, "^": 61, "_": 62, "`": 63, "a": 64, "b": 65, "c": 66, "d": 67, "e": 68, "f": 69, "g": 70, "h": 71, "i": 72, "j": 73, "k": 74, "l": 75, "m": 76, "n": 77, "o": 78, "p": 79, "q": 80, "r": 81, "s": 82, "t": 83, "u": 84, "v": 85, "w": 86, "x": 87, "y": 88, "z": 89, "{": 90, "|": 91, "}": 92, "~": 93, "¡": 94, "¢": 95, "£": 96, "¤": 97, "¥": 98, "¦": 99, "§": 100, "¨": 101, "©": 102, "ª": 103, "«": 104, "¬": 105, "®": 106, "¯": 107, "°": 108, "±": 109, "²": 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"ĠPerry": 17334, "Ġmamy": 17335, "Ġbackgrounds": 17336, "Ġà®İன": 17337, "Ġpendant": 17338, "ĠColumbia": 17339, "Ġinverse": 17340, "ĠÑĩеÑĢез": 17341, "Ġsv": 17342, "Ġdigging": 17343, "41": 17344, "chem": 17345, "Ġnavigation": 17346, "ĠShin": 17347, "ĠFront": 17348, "PD": 17349, "Ġbearing": 17350, "ĠWasser": 17351, "Ġwax": 17352, "ĠCHRIS": 17353, "ching": 17354, "Ġpressed": 17355, "El": 17356, "ĠDal": 17357, "onsin": 17358, "Ġbinding": 17359, "Ñģкой": 17360, "poons": 17361, "Ġmock": 17362, "arest": 17363, "кÑĢа": 17364, "MM": 17365, "Ġcorrupt": 17366, "storm": 17367, "Ġrefres": 17368, "ĠCoach": 17369, "llä": 17370, "ĠTHIS": 17371, "Ġparag": 17372, "Ġìĵ°": 17373, "pool": 17374, "Ġbillions": 17375, "Ġê¹Ģ": 17376, "group": 17377, "Ġwelcoming": 17378, "cellence": 17379, "ĠDuke": 17380, "긴": 17381, "Ġprimera": 17382, "ìł¸": 17383, "Ġpond": 17384, "Ġstatue": 17385, "Ġ구ë": 17386, "Ġhatch": 17387, "Ġinstrumental": 17388, "Ġresidential": 17389, "커": 17390, "Ġaccepting": 17391, "oshi": 17392, "date": 17393, "ĠìĶ¨": 17394, "Ġplanted": 17395, "Ġjoking": 17396, "ĠìĦľ": 17397, "Ġhated": 17398, "ĠÑĢаÑģÑģк": 17399, "Ġslept": 17400, "Ġpackages": 17401, "Ġislands": 17402, "esen": 17403, "ģı": 17404, "Ġdiagon": 17405, "ĠOsc": 17406, "Ġmesh": 17407, "Ġscales": 17408, "arity": 17409, "ĠDefense": 17410, "ãģ¡ãĤĩ": 17411, "ĠLewis": 17412, "ĠÑģегоднÑı": 17413, "Ġflies": 17414, "uinely": 17415, "ĠConsider": 17416, "Ġstark": 17417, "hew": 17418, "ĠAsÃŃ": 17419, "³´ë": 17420, "Ġpropose": 17421, "Ġíķĺë©´": 17422, "odo": 17423, "ĠNormally": 17424, "Ġheeft": 17425, "ĠHarris": 17426, "gro": 17427, "ĠBlood": 17428, "base": 17429, "ĠiOS": 17430, "Ġtouches": 17431, "Ġinspir": 17432, "Ġ×ĵ": 17433, "Ġbinary": 17434, "Ġì¶Ķ": 17435, "Ġserial": 17436, "Ġion": 17437, "Ġunemployment": 17438, "Ġodds": 17439, "ĠFab": 17440, "ĠFBI": 17441, "BRUN": 17442, "Ġweights": 17443, "νο": 17444, "atile": 17445, "Ġnurses": 17446, "Ġinvolvement": 17447, "ĠíĶ¼": 17448, "Ġgovernance": 17449, "ĠâĤ¬": 17450, "ÑĢÑĥп": 17451, "ierra": 17452, "íĺķ": 17453, "ĠJerry": 17454, "Ġbeard": 17455, "Ġsalvation": 17456, "ĠAlong": 17457, "gentle": 17458, "ĠKi": 17459, "bol": 17460, "ĠPlat": 17461, "Ġhasht": 17462, "è¿ij": 17463, "Ġware": 17464, "Ġpartie": 17465, "ycz": 17466, "Ġintr": 17467, "Fih": 17468, "nent": 17469, "Ġcheat": 17470, "ilen": 17471, "Ġë¯": 17472, "orie": 17473, "Ġfácil": 17474, "etric": 17475, "Ġaffecting": 17476, "unciation": 17477, "Ġaffairs": 17478, "Ġbee": 17479, "Ġviewing": 17480, "Ġorang": 17481, "ĠLan": 17482, "ĠСÑĤ": 17483, "ä¸ĸ": 17484, "ĠMes": 17485, "ĥģ": 17486, "erie": 17487, "Ġespa": 17488, "Ġinterpre": 17489, "Ġpossess": 17490, "Ġpurely": 17491, "rito": 17492, "found": 17493, "asma": 17494, "ìłģìĿ¸": 17495, "Ġexamine": 17496, "ĠÑĥм": 17497, "Ġbesch": 17498, "ĠTomorrow": 17499, "ĠBlock": 17500, "Ġvariant": 17501, "Ġpreference": 17502, "Ġcoaches": 17503, "Ġmedications": 17504, "ĠíĺĦ": 17505, "Ġempire": 17506, "ëĦ¤": 17507, "ĠIllinois": 17508, "Ġcrispy": 17509, "Ġthì": 17510, "Ġbees": 17511, "77": 17512, "Ġglow": 17513, "èº": 17514, "ĠStudies": 17515, "åIJĦ": 17516, "ĠChallenge": 17517, "Ġunlikely": 17518, "Ч": 17519, "ıyorsun": 17520, "DIE": 17521, "Ġminimize": 17522, "izard": 17523, "Ġún": 17524, "Ġencontrar": 17525, "ĠKill": 17526, "å»": 17527, "Ġvanilla": 17528, "ĠGrant": 17529, "ĠGT": 17530, "sea": 17531, "Ġsought": 17532, "вод": 17533, "Ġnäm": 17534, "ĠAunt": 17535, "OWN": 17536, "Ġpumpkin": 17537, "stellen": 17538, "Ġrag": 17539, "егда": 17540, "Ġstoryt": 17541, "Ġforum": 17542, "æ©Ł": 17543, "Ġestaba": 17544, "uche": 17545, "Ġcongress": 17546, "ĠRey": 17547, "Ġdramatically": 17548, "ĠSport": 17549, "ĠYellow": 17550, "Ġê³ĦìĨį": 17551, "Ġdisgusting": 17552, "ĠRecent": 17553, "Ġacquired": 17554, "Ġcables": 17555, "çĶļ": 17556, "din": 17557, "Ġvisto": 17558, "Ġcommunicating": 17559, "ÑģÑĤавлÑı": 17560, "еÑģÑĤо": 17561, "ãĥ»ãĥ»ãĥ»": 17562, "Ġrég": 17563, "Ġsocks": 17564, "Ġproces": 17565, "because": 17566, "Ġutter": 17567, "Ġcolocar": 17568, "Ġnewest": 17569, "Ġgramm": 17570, "表": 17571, "ä¸įçŁ¥éģĵ": 17572, "Ġshifting": 17573, "Ġcarrier": 17574, "ĠÑģкоÑĢ": 17575, "ĠSchw": 17576, "Ġexecuted": 17577, "Ġmaintained": 17578, "ĠÏĨ": 17579, "ĠMoses": 17580, "Ġdisse": 17581, "Ġhorr": 17582, "ãĢľ": 17583, "Ġrally": 17584, "Ġallem": 17585, "ĠEventually": 17586, "Ġdiyor": 17587, "lvania": 17588, "Ġschnell": 17589, "Ġê³¼": 17590, "Ġ매": 17591, "Ġstruggles": 17592, "late": 17593, "Ġclarify": 17594, "ément": 17595, "Ġmultiplic": 17596, "ибо": 17597, "Ġjourn": 17598, "Ġfragr": 17599, "Ġsurprisingly": 17600, "Ġdesperate": 17601, "52": 17602, "Ġsul": 17603, "ĠRead": 17604, "ĠFried": 17605, "Ġmond": 17606, "woo": 17607, "Ġorganizing": 17608, "ãģĹãĤĩãģĨ": 17609, "ĠSoon": 17610, "ĠвопÑĢоÑģ": 17611, "ĠNur": 17612, "ĠÐĹд": 17613, "Ġspider": 17614, "еÑģÑı": 17615, "Ġtutorials": 17616, "Ġnutrients": 17617, "orer": 17618, "Ġcoefficient": 17619, "Ġarrangement": 17620, "Ġpricing": 17621, "nan": 17622, "yu": 17623, "BL": 17624, "Ġtribe": 17625, "ĠHoward": 17626, "unks": 17627, "Ġnewer": 17628, "Ġprovin": 17629, "Ġprediction": 17630, "hos": 17631, "Ġolsun": 17632, "ĠAround": 17633, "Ġvier": 17634, "ĠÑģÑĤоÑĢон": 17635, "Ġvalley": 17636, "ĠEla": 17637, "ifi": 17638, "Ġgalaxy": 17639, "Ġtranqu": 17640, "Ġadvers": 17641, "ĠTemple": 17642, "iffs": 17643, "igence": 17644, "èĩªå·±": 17645, "Ġkönnte": 17646, "ĠÄijó": 17647, "Did": 17648, "Ġphotographs": 17649, "ĠAWS": 17650, "ÑĨиÑı": 17651, "Ġguards": 17652, "Ġappointed": 17653, "ĠGil": 17654, "Ġмом": 17655, "Ġcod": 17656, "ĠUnlike": 17657, "Ġevenly": 17658, "isconsin": 17659, "Ġestou": 17660, "Ġmnie": 17661, "ĠExec": 17662, "ĠMV": 17663, "ĠEine": 17664, "ä¿¡": 17665, "ĠRoger": 17666, "ĠFac": 17667, "ĠList": 17668, "Ġfuer": 17669, "аеÑĤе": 17670, "omed": 17671, "Ġattraction": 17672, "èī²": 17673, "Ġterrain": 17674, "ĠDrop": 17675, "Ġcorporations": 17676, "Ġsciences": 17677, "Ġthrone": 17678, "ãģĦãģŁ": 17679, "Ġaj": 17680, "ĠRot": 17681, "çī¹": 17682, "Ġsupporters": 17683, "ĠBere": 17684, "Here": 17685, "Ġdiferentes": 17686, "Ġsignificance": 17687, "Ïĥη": 17688, "æĪij覺å¾Ĺ": 17689, "Ġclamp": 17690, "ĠëĮĢë": 17691, "Ġfabulous": 17692, "rez": 17693, "æĮģ": 17694, "Ġassumptions": 17695, "uther": 17696, "wid": 17697, "pot": 17698, "è¿İ": 17699, "Ġyan": 17700, "ulin": 17701, "ÑĢÑĭв": 17702, "ĠSlow": 17703, "ĠPennsy": 17704, "Ġíķ´ìĦľ": 17705, "Ġmeio": 17706, "Ġwealthy": 17707, "ĠEight": 17708, "Ġpulse": 17709, "Ġfriction": 17710, "idity": 17711, "ĠHoll": 17712, "iyorum": 17713, "Ġsounded": 17714, "ĠCarr": 17715, "Ġfork": 17716, "âĺ": 17717, "ĠPA": 17718, "Ġconspir": 17719, "Ġcoding": 17720, "rt": 17721, "ĠTyp": 17722, "Ġìĸij": 17723, "Ġпог": 17724, "Ġmiser": 17725, "ĠÑģмоÑĤÑĢ": 17726, "ĠSweden": 17727, "Ġolarak": 17728, "ĠZhang": 17729, "ĠChi": 17730, "ĠTitan": 17731, "Ġscreening": 17732, "ĠSpider": 17733, "ĠÅŀimdi": 17734, "Ġobstacles": 17735, "lara": 17736, "Ġchallenged": 17737, "pse": 17738, "TON": 17739, "ụ": 17740, "ĠPi": 17741, "Ġlagi": 17742, "ieurs": 17743, "Ġhurting": 17744, "Ġneglect": 17745, "Ġgenerating": 17746, "Ġyoungest": 17747, "Ġaudit": 17748, "ĠÑĢез": 17749, "Ïģά": 17750, "Ġdonate": 17751, "ĠPDF": 17752, "Ġvisits": 17753, "Ġcruise": 17754, "PP": 17755, "aser": 17756, "Ġwsp": 17757, "backs": 17758, "ivals": 17759, "ãģĨãĤĵ": 17760, "Ġdeve": 17761, "Ġproport": 17762, "Ġcath": 17763, "ĠEffect": 17764, "Ġwinds": 17765, "ĠìĻĶ": 17766, "Ġcharts": 17767, "Ġsama": 17768, "Ġautomation": 17769, "Ġпока": 17770, "Ġolan": 17771, "Ġboats": 17772, "Ġcafe": 17773, "Ġdenied": 17774, "ĠMama": 17775, "Ġblocking": 17776, "ĠThor": 17777, "Ġphenomenal": 17778, "Ġstakeholders": 17779, "Ġunos": 17780, "ÑĥеÑĤ": 17781, "ĠAbraham": 17782, "ãģ§ãĤĤ": 17783, "Ġdetection": 17784, "Ġjuris": 17785, "Ġpowered": 17786, "zial": 17787, "Ġwelfare": 17788, "Ġupgrad": 17789, "Ġmożna": 17790, "ĠCase": 17791, "cular": 17792, "ĶìĿ´": 17793, "ãĥģ": 17794, "ĠGuess": 17795, "Ġcycles": 17796, "ä¾ĭ": 17797, "給": 17798, "rock": 17799, "umi": 17800, "Ġelite": 17801, "Ġquè": 17802, "åł±": 17803, "ÑĤом": 17804, "Ġshore": 17805, "gunta": 17806, "Ġku": 17807, "Ġfaithful": 17808, "ĠJeremy": 17809, "aid": 17810, "à·": 17811, "ugal": 17812, "å°įåķĬ": 17813, "ĠVel": 17814, "Ġvrai": 17815, "stell": 17816, "¨¸": 17817, "Ġkol": 17818, "è½": 17819, "Ġquanto": 17820, "ĠзаÑĢ": 17821, "Ġ2002": 17822, "esy": 17823, "Ġreserve": 17824, "ĠмоменÑĤ": 17825, "Ġdeployed": 17826, "Ġdefining": 17827, "Ġsau": 17828, "Ġgaat": 17829, "\")": 17830, "Ġtransmit": 17831, "Ġpublishing": 17832, "Ġranking": 17833, "Ġoffense": 17834, "Ġ46": 17835, "pin": 17836, "ĠTaking": 17837, "Ġentitled": 17838, "Ġgenuinely": 17839, "Ġvariations": 17840, "Ġfinde": 17841, "Ġtau": 17842, "Ġunfortunate": 17843, "ĠRah": 17844, "ports": 17845, "ĠcÅ": 17846, "Ġmonkey": 17847, "Ġbrac": 17848, "wei": 17849, "lung": 17850, "Ġartif": 17851, "Ġsyrup": 17852, "ĠÐĶав": 17853, "Ġlifted": 17854, "Ġchez": 17855, "ĠAdvent": 17856, "ĠStock": 17857, "Ġdol": 17858, "мен": 17859, "иÑĪÑĮ": 17860, "Ġyn": 17861, "gio": 17862, "det": 17863, "Ġdesse": 17864, "Ġgri": 17865, "ĠChairman": 17866, "çħ": 17867, "Ġcuenta": 17868, "anim": 17869, "Ġcrab": 17870, "Ġescal": 17871, "Ġpremière": 17872, "ĠGef": 17873, "Ġdining": 17874, "Ġseventh": 17875, "Ġchasing": 17876, "ĠTower": 17877, "Ġbrutal": 17878, "Ġfundamentally": 17879, "ãģ¨ãģĨ": 17880, "лениÑı": 17881, "stage": 17882, "Ġacquis": 17883, "Ġcylinder": 17884, "Ġcommander": 17885, "mem": 17886, "ĠUV": 17887, "happy": 17888, "Ġepsilon": 17889, "Ġinvitation": 17890, "Ġfarmer": 17891, "chair": 17892, "Ġdestiny": 17893, "Ġsovere": 17894, "ĠHebrew": 17895, "Ġservant": 17896, "Ġbew": 17897, "Ġgast": 17898, "uties": 17899, "Ġadministrative": 17900, "ĠCommand": 17901, "éta": 17902, "Ġnitrogen": 17903, "ê·¼": 17904, "Ġabi": 17905, "Ġvillain": 17906, "Ġblanket": 17907, "ĠSend": 17908, "Ġbeaten": 17909, "²Ħ": 17910, "Ġvolunt": 17911, "Ġscholar": 17912, "ĠEmperor": 17913, "Ġ43": 17914, "vable": 17915, "ĠDus": 17916, "ĠGU": 17917, "Ġtargeting": 17918, "www": 17919, "Ġamendment": 17920, "ìĨĮë": 17921, "Ġting": 17922, "Ġnasty": 17923, "Ġgauge": 17924, "ĠÑĢод": 17925, "ĠHans": 17926, "Your": 17927, "αν": 17928, "Ġprojet": 17929, "ĠHawaii": 17930, "Ġsuspicious": 17931, "Ġschw": 17932, "Ġremoval": 17933, "Ġintrig": 17934, "ĠMU": 17935, "Ġponto": 17936, "ा": 17937, "ĠобÑĢаз": 17938, "Ġguessing": 17939, "pace": 17940, "Ġmothers": 17941, "Ġmillimeter": 17942, "ление": 17943, "没æľī": 17944, "Ġavailability": 17945, "icz": 17946, "æѤ": 17947, "Ġfract": 17948, "Ġbases": 17949, "km": 17950, "ĠBTS": 17951, "ĠField": 17952, "Ġdzie": 17953, "Ġsegundo": 17954, "ĠëĤĺëĬĶ": 17955, "Ġlegitimate": 17956, "imas": 17957, "Ġвн": 17958, "Ġcorruption": 17959, "Ġsmash": 17960, "ĠValent": 17961, "Ġaligned": 17962, "ĠPennsylvania": 17963, "Ġgab": 17964, "ĠEun": 17965, "enth": 17966, "ĠMorning": 17967, "Ġcandle": 17968, "Ġbackpack": 17969, "ĠIslamic": 17970, "ações": 17971, "Ġencry": 17972, "Ġmushrooms": 17973, "íĮĮ": 17974, "dit": 17975, "Ġtransit": 17976, "ĠWisconsin": 17977, "Ġparticipated": 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"Ġcivilization": 18036, "Ġaccomp": 18037, "ĠVM": 18038, "émon": 18039, "Ġdeze": 18040, "Ġgrades": 18041, "Ġsollte": 18042, "Ġstaring": 18043, "×IJת": 18044, "arnt": 18045, "Ġhorizon": 18046, "Ġtravail": 18047, "hour": 18048, "第ä¸Ģ": 18049, "ĠED": 18050, "ĠDak": 18051, "Ġny": 18052, "Ġconve": 18053, "ĠCham": 18054, "Ġfirms": 18055, "ĠLiu": 18056, "ĠÑģÑĤÑĢан": 18057, "Ġlibert": 18058, "Ġlenses": 18059, "Ġintake": 18060, "ĠвÑĭб": 18061, "Ġmensen": 18062, "hel": 18063, "Ġpractition": 18064, "Ġ350": 18065, "ãĤ³": 18066, "FO": 18067, "Ġbeds": 18068, "Ġancestors": 18069, "ĠìĹĦì²Ń": 18070, "Ġdisturb": 18071, "ĠLastly": 18072, "ĠSupport": 18073, "ีà¹ī": 18074, "ĠCorona": 18075, "Ġenthusi": 18076, "Ġвозм": 18077, "ĠìĤ¬ëŀĮë": 18078, "Ġ52": 18079, "bird": 18080, "Ġreduces": 18081, "ĠìŀĪìĿĦ": 18082, "ĠGene": 18083, "êµIJ": 18084, "ÄĻp": 18085, "ĠÃľber": 18086, "Ġconcerning": 18087, "user": 18088, "Ġconcentrate": 18089, "ĠWHAT": 18090, "ishop": 18091, "onymous": 18092, "nold": 18093, "Ġsuggesting": 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20630, "Ġhonour": 20631, "orig": 20632, "INA": 20633, "ĠNathan": 20634, "ва": 20635, "ĠÑģказаÑĤÑĮ": 20636, "tır": 20637, "Ġexclusively": 20638, "Ġshades": 20639, "ĠпÑĢоÑĨ": 20640, "Ġoccasions": 20641, "ija": 20642, "çļĦæĻĤåĢĻ": 20643, "åݲ": 20644, "æħ¢": 20645, "fig": 20646, "Ġtus": 20647, "Ġremem": 20648, "ĠChristopher": 20649, "Ġslime": 20650, "Ġalguna": 20651, "ĠFortunately": 20652, "Ġlors": 20653, "voll": 20654, "aver": 20655, "Ġoutlet": 20656, "ĠLinkedIn": 20657, "ĠExecutive": 20658, "Ġorgans": 20659, "ĠBegin": 20660, "ĠíĻĶ": 20661, "Ġtransplant": 20662, "ragen": 20663, "VO": 20664, "ĠFör": 20665, "ĠباÙĦ": 20666, "ĠAndre": 20667, "isine": 20668, "Ġlasts": 20669, "Ġhistória": 20670, "Ġluz": 20671, "Ġcollar": 20672, "Ġkidna": 20673, "Ġoptical": 20674, "iov": 20675, "Ġtob": 20676, "Ġexterior": 20677, "Ġmetric": 20678, "ieur": 20679, "Ġtroll": 20680, "ĠÑĢоз": 20681, "æĺŁ": 20682, "Ġtô": 20683, "ĠìĺĪìģ": 20684, "ĠGesetz": 20685, "Ġед": 20686, "Ġdenominator": 20687, "ì³": 20688, 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"Ġhomemade": 23336, "amba": 23337, "Ġpronunciation": 23338, "Ġalphabet": 23339, "анÑĮ": 23340, "owany": 23341, "iras": 23342, "idency": 23343, "OME": 23344, "ĠÑĢаÑģÑģ": 23345, "arak": 23346, "viamente": 23347, "Ġnonprofit": 23348, "ĠYouTuber": 23349, "Ġparenth": 23350, "ĠBoo": 23351, "vat": 23352, "ĠStir": 23353, "Ġprecip": 23354, "Ġants": 23355, "Ġally": 23356, "ĠMaori": 23357, "ĠëĮĢíķľ": 23358, "åı¯æĺ¯": 23359, "ogene": 23360, "ĠLabour": 23361, "arette": 23362, "Ġrecycling": 23363, "ensa": 23364, "Ġpursuit": 23365, "Ġsak": 23366, "ĠÐĹдеÑģÑĮ": 23367, "Ġtolerance": 23368, "Ġsaat": 23369, "Ġclicked": 23370, "âĻ¥": 23371, "Ġfacebook": 23372, "ĠInto": 23373, "Ġincentives": 23374, "기ëĬĶ": 23375, "ĠDennis": 23376, "ĠWik": 23377, "gesch": 23378, "à¹Ģà¸Ľ": 23379, "ĠÏĢα": 23380, "ĠWhoo": 23381, "Ġrounded": 23382, "Ġdope": 23383, "Ġcapturing": 23384, "ĠWarri": 23385, "Ġcivilian": 23386, "Ġcharming": 23387, "Ġesas": 23388, "Ġsustained": 23389, "Ġleaning": 23390, "Ġabundance": 23391, "ÃŃlia": 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23746, "ĠìĹĦë§Ī": 23747, "dest": 23748, "Ġkolej": 23749, "Ġtemporarily": 23750, "Ġdetermining": 23751, "ĠGlass": 23752, "ÑĢон": 23753, "olan": 23754, "Ġdominated": 23755, "åĮĸ": 23756, "____": 23757, "ĠÙĩذا": 23758, "ĠDana": 23759, "Ġdinheiro": 23760, "aqu": 23761, "민": 23762, "ĠÃłs": 23763, "ĠJoey": 23764, "ĠGriff": 23765, "Ġattain": 23766, "Ġtransitions": 23767, "ĠLiterally": 23768, "енд": 23769, "ĠHaven": 23770, "Ġgrabbing": 23771, "Ġcrystals": 23772, "ĠFourth": 23773, "Ġcandles": 23774, "ĠÑģлÑĥÑĩа": 23775, "rico": 23776, "Ġ5000": 23777, "etto": 23778, "Ġundo": 23779, "Ġkto": 23780, "Ġdivert": 23781, "Ġchir": 23782, "Ġpersec": 23783, "Ġhiking": 23784, "Ġannouncements": 23785, "çĶ±": 23786, "зÑĭ": 23787, "Ġauc": 23788, "Ġsystemic": 23789, "ĠRM": 23790, "Ïĥα": 23791, "ĠÐĶж": 23792, "Ġyar": 23793, "ĠWard": 23794, "Ġpissed": 23795, "Ġcarn": 23796, "Ġautonomous": 23797, "ãħİãħİ": 23798, "sover": 23799, "æ²ĴéĮ¯": 23800, "å¾Ī好": 23801, "Ġreflex": 23802, "Ġgardens": 23803, "Ġdated": 23804, "ì±": 23805, "amiÄĻ": 23806, "Ġcontinuity": 23807, "Ġcitizenship": 23808, "Ġschwer": 23809, "Ġzak": 23810, "table": 23811, "ĠÑģÑĩ": 23812, "è§ģ": 23813, "ĠÏĥε": 23814, "Ġgenerates": 23815, "구ëĤĺ": 23816, "öh": 23817, "óm": 23818, "alam": 23819, "ĠJUDY": 23820, "ĠBug": 23821, "Ġãģ¦": 23822, "Ġdrones": 23823, "Ġágua": 23824, "acaks": 23825, "æļ": 23826, "ĠÐļон": 23827, "×ĸ×Ķ": 23828, "Ġstrive": 23829, "ĠAltern": 23830, "Ġnearest": 23831, "Ġproyect": 23832, "tera": 23833, "ĠASHLEY": 23834, "Ġworm": 23835, "Ġreplay": 23836, "Ġtara": 23837, "ĠIndians": 23838, "ãĤ°": 23839, "icaid": 23840, "ĠìĪľ": 23841, "Ġappealing": 23842, "ĠWes": 23843, "Ġmentions": 23844, "Ġделе": 23845, "Ġkw": 23846, "Ġfragile": 23847, "isz": 23848, "ków": 23849, "hang": 23850, "color": 23851, "Ġpresidente": 23852, "87": 23853, "еÑĦ": 23854, "çĪ¸": 23855, "Ġдобав": 23856, "ĠNelson": 23857, "áfic": 23858, "ĠMICHAEL": 23859, "Ġmechanic": 23860, "Ġmetres": 23861, "ĠoczywiÅĽcie": 23862, "ĠCind": 23863, "ĠogsÃ¥": 23864, "Ġlandsca": 23865, "ACE": 23866, "Ġheadlines": 23867, "Ġcatalyst": 23868, "ĠCatch": 23869, "inkles": 23870, "Ġpills": 23871, "ordo": 23872, "Ġimmigrant": 23873, "Ġexamination": 23874, "Ġaccidents": 23875, "zÄħd": 23876, "Ġquiere": 23877, "Ġnella": 23878, "Ġ67": 23879, "Ġpassa": 23880, "Ġsuperfic": 23881, "istor": 23882, "Ġnov": 23883, "ëĭµ": 23884, "Ġmandate": 23885, "isons": 23886, "ĠVirtual": 23887, "Ġselber": 23888, "Ġcounseling": 23889, "ĠNBA": 23890, "Ġsept": 23891, "Ġbeliever": 23892, "Ġmarvel": 23893, "ĠIntegr": 23894, "ĠмÑĸ": 23895, "Ġorph": 23896, "Ġbackward": 23897, "ĠGeneration": 23898, "ĠPict": 23899, "ĠÑĤоÑĤ": 23900, "Ġtapi": 23901, "prochen": 23902, "Ġhallway": 23903, "hte": 23904, "ĠÛģÛĴ": 23905, "ĠZum": 23906, "èĢģ師": 23907, "achment": 23908, "iquer": 23909, "folg": 23910, "ĠEddie": 23911, "ĠKil": 23912, "Ġwellness": 23913, "stock": 23914, "è¼ĥ": 23915, "Ġkaç": 23916, "Ġterrorism": 23917, "Ġpointer": 23918, "Of": 23919, "heric": 23920, "ĠUltimately": 23921, "Ġmeses": 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"Ġsynt": 23980, "Joe": 23981, "Ġgeg": 23982, "Ġrevealing": 23983, "ĠEthan": 23984, "outer": 23985, "Ġyay": 23986, "é«Ķ": 23987, "лаÑĢ": 23988, "Ġreportedly": 23989, "Ġihrer": 23990, "Ġrecognise": 23991, "Ġbumper": 23992, "ĠRandy": 23993, "ĠVenus": 23994, "tles": 23995, "Ġappetite": 23996, "Ġglucose": 23997, "Ġchodzi": 23998, "ĠFurthermore": 23999, "tir": 24000, "Ġconta": 24001, "Ġintuition": 24002, "Ġaltitude": 24003, "Ġchunks": 24004, "ĠJoshua": 24005, "ıģım": 24006, "rylic": 24007, "leans": 24008, "ĠíĶ¼ë": 24009, "LL": 24010, "Que": 24011, "Ġgor": 24012, "ĠзнаÑĩиÑĤ": 24013, "Ġpoems": 24014, "Ġexcel": 24015, "Ġexplored": 24016, "Ġpopul": 24017, "Ġincluso": 24018, "stä": 24019, "ĠGavin": 24020, "alling": 24021, "ĠÏĦον": 24022, "é©": 24023, "arbeit": 24024, "ĠGas": 24025, "Ġglorious": 24026, "rieben": 24027, "Ġspam": 24028, "Ġindoor": 24029, "Ġthrust": 24030, "ĠAld": 24031, "ĠPrior": 24032, "Ġonboard": 24033, "ãģłãģķãģĦ": 24034, "oca": 24035, "ASH": 24036, "£ł": 24037, "ĠChristine": 24038, "Ġdrawer": 24039, "Ġnoon": 24040, "Ġìŀĺë": 24041, "Ġpermanently": 24042, "æ·±": 24043, "ĠнапÑĢимеÑĢ": 24044, "Ġpodcasts": 24045, "erapeut": 24046, "prit": 24047, "Ġstainless": 24048, "ĠÚ©ÛĴ": 24049, "Ġfamilia": 24050, "ĠÑĢазÑĢ": 24051, "unto": 24052, "ĠÑģÑĤол": 24053, "Ġhä": 24054, "ĠHai": 24055, "ĠPB": 24056, "izon": 24057, "Ġkonnte": 24058, "Ġbüyük": 24059, "Ġutilizar": 24060, "ÚĨ": 24061, "Ġaquesta": 24062, "Ġmixer": 24063, "udent": 24064, "лекÑģ": 24065, "ÅĤu": 24066, "ĠÑģиÑģÑĤем": 24067, "ĠноÑĢм": 24068, "Ġfatal": 24069, "Ġconsiderations": 24070, "Ġvalidation": 24071, "Ġoli": 24072, "ĠkardeÅŁ": 24073, "ĠGLORIA": 24074, "Ġpall": 24075, "еÑģÑĤе": 24076, "Ġrectang": 24077, "Ġmedieval": 24078, "allahi": 24079, "asti": 24080, "ĠSyrian": 24081, "Ġshear": 24082, "Ġdebug": 24083, "ĠMai": 24084, "Ġknocking": 24085, "ĠLex": 24086, "ardan": 24087, "rov": 24088, "Ġmemorial": 24089, "æ°£": 24090, "ooky": 24091, "Ġstuffed": 24092, "Ġpassé": 24093, "Ġwig": 24094, "Ĥł": 24095, "Ġpróxima": 24096, "Ġ1991": 24097, "ĠмеждÑĥ": 24098, "Ġnuestros": 24099, "ĠBeast": 24100, "Ġsmo": 24101, "atched": 24102, "ologia": 24103, "Ġмод": 24104, "Ġgee": 24105, "Ġconceptual": 24106, "Ġô": 24107, "Ġdecreases": 24108, "Ġqueries": 24109, "олÑĮÑĪ": 24110, "ĠApart": 24111, "Ġexempl": 24112, "å±±": 24113, "Ġfled": 24114, "ĠOFF": 24115, "ggak": 24116, "Ġbead": 24117, "hir": 24118, "lies": 24119, "ĠClearly": 24120, "ılar": 24121, "Ġchess": 24122, "Ġwhichever": 24123, "Ġ96": 24124, "ằ": 24125, "Ġrespects": 24126, "ĠмоÑĢ": 24127, "Ġorganism": 24128, "Ġgrandpa": 24129, "ĠVie": 24130, "è·Łä½ł": 24131, "Ġflooding": 24132, "Ġupgraded": 24133, "ÑijÑĢ": 24134, "Ġcheeks": 24135, "Ġconquer": 24136, "Ġstubborn": 24137, "Ġpuzzles": 24138, "Ġauction": 24139, "Ġrelying": 24140, "ĠPROF": 24141, "ĠEsper": 24142, "ĠÐľÐ£": 24143, "Ġhype": 24144, "Ġpossibil": 24145, "Ġimprison": 24146, "ĠErn": 24147, "ìĹĪìĬµëĭĪëĭ¤": 24148, "Ġenvie": 24149, "Ġresurrection": 24150, "ä¸įè¡Į": 24151, "Ġsper": 24152, "ĠVenezuela": 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26716, "urm": 26717, "ĠAdventure": 26718, "ãĥĬ": 26719, "cis": 26720, "emark": 26721, "Ġnego": 26722, "Ġinappropriate": 26723, "ĠпÑĢиз": 26724, "ĠÑĢол": 26725, "Ġdreamed": 26726, "Bry": 26727, "Ġshuttle": 26728, "Ġpillars": 26729, "Ġbik": 26730, "inum": 26731, "ĠÑĥÑģ": 26732, "ĠNebr": 26733, "Ġperpendicular": 26734, "Ġbooked": 26735, "bery": 26736, "Ġvikt": 26737, "bear": 26738, "esus": 26739, "Ġвозможно": 26740, "¨¹": 26741, "Ġpresumably": 26742, "ĠMemphis": 26743, "Ġambulance": 26744, "×ķ×ŀר": 26745, "Ġthumbnail": 26746, "Ġmodification": 26747, "éĩı": 26748, "Ġinterpreted": 26749, "Ġpromo": 26750, "Ġκά": 26751, "ĠεÏĢ": 26752, "Ġacoustic": 26753, "ĠDB": 26754, "åĵİ": 26755, "Ġnonetheless": 26756, "oule": 26757, "Ġpequ": 26758, "Ġknob": 26759, "ãĤ£": 26760, "ĠëıĮìķĦ": 26761, "Ġpurchases": 26762, "ĠÃĩünkü": 26763, "Ġdividing": 26764, "perform": 26765, "raction": 26766, "healthy": 26767, "ĠTitle": 26768, "Ġuk": 26769, "Ġcerca": 26770, "Ġarguably": 26771, "Ġfale": 26772, "ë³µ": 26773, 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27008, "Ġamendments": 27009, "Ġöz": 27010, "Ġdigits": 27011, "urai": 27012, "Ġchaotic": 27013, "ĠMasters": 27014, "eon": 27015, "ĠCash": 27016, "ĠCuz": 27017, "Ġbedeutet": 27018, "Ġscanning": 27019, "Ġжд": 27020, "неÑĤ": 27021, "Ġcertainty": 27022, "jek": 27023, "Ġdijo": 27024, "ĠClimate": 27025, "Ġrinse": 27026, "Ġkrij": 27027, "veland": 27028, "Ġsoundtrack": 27029, "ĠSafe": 27030, "ĠNova": 27031, "94": 27032, "Ġathe": 27033, "ĠVerb": 27034, "oler": 27035, "ìĿ´ì£ł": 27036, "Ġvin": 27037, "Ġrespiratory": 27038, "ĠStudy": 27039, "ĠCAM": 27040, "Ġavocado": 27041, "ĠZhen": 27042, "Ġlatency": 27043, "Ġfeathers": 27044, "Ġcontar": 27045, "ĠвеÑī": 27046, "Ġfark": 27047, "Ġblended": 27048, "Ġexploded": 27049, "ĠXX": 27050, "ĠBenim": 27051, "Ġalguém": 27052, "istoire": 27053, "Ġconfidential": 27054, "Ġmast": 27055, "Ġì¿": 27056, "geh": 27057, "Ġdisrespect": 27058, "ĠSystems": 27059, "Æ°a": 27060, "Ed": 27061, "Ġwys": 27062, "Ġexotic": 27063, "Ġglowing": 27064, "ùng": 27065, "ounge": 27066, "èĦ": 27067, "аниз": 27068, "Ġpalav": 27069, "ĠSword": 27070, "Ġgim": 27071, "ĠCrow": 27072, "Ġpotent": 27073, "bish": 27074, "Ġabused": 27075, "ĠJed": 27076, "Ġgambling": 27077, "ĠSpect": 27078, "Ġinvestigators": 27079, "æĻļ": 27080, "Ġratt": 27081, "Ġdob": 27082, "ĠDES": 27083, "hog": 27084, "ĠоÑĤкÑĢÑĭ": 27085, "íĮħ": 27086, "ĠденÑĮги": 27087, "Ġíĺ¹": 27088, "Ġ머리": 27089, "Ġsaturation": 27090, "Ġinherited": 27091, "ĠInnovation": 27092, "ìĹĪëįĺ": 27093, "Ġtangible": 27094, "Ġdepri": 27095, "hed": 27096, "Ġпомог": 27097, "Ġsliced": 27098, "à¥į": 27099, "Ġthế": 27100, "Å¥": 27101, "68": 27102, "Ġcorona": 27103, "Ġgifted": 27104, "Ġsoir": 27105, "Ġhumility": 27106, "ĠìĿ´ê±¸": 27107, "Ġflaws": 27108, "ĠпÑĢакÑĤи": 27109, "Ġkald": 27110, "waż": 27111, "yw": 27112, "ãĤĵãģ§ãģĻ": 27113, "irteen": 27114, "Ġcrochets": 27115, "¦¬ê°Ģ": 27116, "ĠìłĦìĹIJ": 27117, "Ġdese": 27118, "æ¥Ń": 27119, "Ġмаг": 27120, "ĠdziaÅĤ": 27121, "Ġlég": 27122, "changing": 27123, "Ġllev": 27124, "ÅĦsk": 27125, "çĶ»": 27126, "Ġ1984": 27127, "orns": 27128, "ĠWelsh": 27129, "Ġpharmaceutical": 27130, "Ġpumping": 27131, "ĠShaw": 27132, "punk": 27133, "Ġvault": 27134, "Ġkinetic": 27135, "Ġhurricane": 27136, "ĠIncluding": 27137, "ức": 27138, "ĠGrandpa": 27139, "anship": 27140, "é¦Ļ港": 27141, "ĠвÑĭÑħод": 27142, "нож": 27143, "ľł": 27144, "utta": 27145, "Ġê²ģëĭĪëĭ¤": 27146, "Ġbaz": 27147, "ĠпоÑĪ": 27148, "Ġpeculiar": 27149, "zyÄĩ": 27150, "ĠEllie": 27151, "Ġlearns": 27152, "ĠKrishna": 27153, "Ġconsecut": 27154, "Ġempath": 27155, "ĠDin": 27156, "Ġtraded": 27157, "ĠBoris": 27158, "uggage": 27159, "olla": 27160, "Ġназв": 27161, "Ġeternity": 27162, "Ġвп": 27163, "èmes": 27164, "Ġgrapp": 27165, "bé": 27166, "ĠпÑĢедÑģÑĤав": 27167, "ĠFC": 27168, "įëĭĪëĭ¤": 27169, "even": 27170, "ĠNebraska": 27171, "ortune": 27172, "Ġkarena": 27173, "ĠAgent": 27174, "Ġsting": 27175, "ĠPI": 27176, "Ġmunicipal": 27177, "powered": 27178, "Ġconsegue": 27179, "ĠManchester": 27180, "Ġrainy": 27181, "Ġbli": 27182, "Ġkost": 27183, 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"ĠLegends": 28103, "ĠTiffany": 28104, "ализ": 28105, "ĠпеÑĢев": 28106, "ĠогÑĢом": 28107, "Ġcros": 28108, "ĠCE": 28109, "Bu": 28110, "Ġensures": 28111, "Ġgrandchildren": 28112, "Ġacuerdo": 28113, "Ġprisoner": 28114, "Ġthirsty": 28115, "bane": 28116, "Ġë¹ł": 28117, "Ġúltima": 28118, "ĠLaunch": 28119, "nity": 28120, "Ġcombustion": 28121, "Ġunicorn": 28122, "Ġfamille": 28123, "Ġlowering": 28124, "ĠYing": 28125, "building": 28126, "Ġduo": 28127, "ĠMéxico": 28128, "astian": 28129, "Ġ먹ìĿĦ": 28130, "ĠRalph": 28131, "Ġrewrite": 28132, "Ġglam": 28133, "ifique": 28134, "Er": 28135, "ĠRunning": 28136, "онов": 28137, "Ġmeanings": 28138, "Ġchewy": 28139, "ĠLeslie": 28140, "Ġfinest": 28141, "Ġhahaha": 28142, "ĠSTEP": 28143, "Ġloneliness": 28144, "rians": 28145, "Ġquestioned": 28146, "Ġesque": 28147, "Ġsinking": 28148, "Ġpeso": 28149, "ĠWrong": 28150, "asmine": 28151, "Ġdefinitive": 28152, "Ġbuys": 28153, "Ġcruc": 28154, "cool": 28155, "ĠëłĪ": 28156, "Ġpó": 28157, "Ġutilized": 28158, "Ġworthwhile": 28159, "ĠDylan": 28160, "ESE": 28161, "Ġvertex": 28162, "tı": 28163, "ĠFir": 28164, "Ġzaw": 28165, "ĠGed": 28166, "ĠÐĿап": 28167, "dz": 28168, "Ġcursor": 28169, "Ġswipe": 28170, "Ġinevitably": 28171, "Ġposters": 28172, "Ġinclined": 28173, "Ġgreeting": 28174, "Ġdisappointment": 28175, "ãģ¾ãģ§": 28176, "Ġrelação": 28177, "TT": 28178, "Ġrabb": 28179, "ĠMaine": 28180, "Ġanalyzed": 28181, "FE": 28182, "ĠÐŁÐ¾Ð»": 28183, "ĠSandra": 28184, "Ġplague": 28185, "ARE": 28186, "Ġvär": 28187, "ĠViv": 28188, "umed": 28189, "hando": 28190, "houette": 28191, "ĠBailey": 28192, "ä¸įéģİ": 28193, "yson": 28194, "Ġsemua": 28195, "Ġhardcore": 28196, "âĤ¬": 28197, "Ñĸм": 28198, "éra": 28199, "OTH": 28200, "Ġforeigners": 28201, "ĠPalestinian": 28202, "Ġproprio": 28203, "аний": 28204, "Ġmyths": 28205, "WH": 28206, "Ġninth": 28207, "ĠCreator": 28208, "лом": 28209, "ĠFlip": 28210, "Ġeman": 28211, "ĠkiÅŁ": 28212, "zieh": 28213, "ĠEarnest": 28214, "system": 28215, "ĸìĹIJ": 28216, "Ġarmies": 28217, "ĠOutside": 28218, "Ġharus": 28219, "æºĸ": 28220, "одаÑĢ": 28221, "Ġvisitor": 28222, "çŃĶ": 28223, "Ġstrengthening": 28224, "Ġ92": 28225, "vio": 28226, "Ġ리": 28227, "Ġgreedy": 28228, "Ġpoquito": 28229, "uder": 28230, "ĠKopf": 28231, "Ġëĭ¤ìĿĮìĹIJ": 28232, "Ġseis": 28233, "ático": 28234, "Ġtrusting": 28235, "ÃŃp": 28236, "ĠEmm": 28237, "leen": 28238, "ĠاÙĦÙĨ": 28239, "Ġrecruitment": 28240, "ĠFilip": 28241, "ĠÙĥÙĦ": 28242, "Clint": 28243, "ĠвеÑģ": 28244, "auft": 28245, "Ġdominate": 28246, "Ġresto": 28247, "Ġkra": 28248, "ái": 28249, "ĠCait": 28250, "rows": 28251, "Ġcountryside": 28252, "Ġ1945": 28253, "аÑĨиÑİ": 28254, "Ġди": 28255, "Ġkernel": 28256, "lov": 28257, "Ġcalculating": 28258, "دا": 28259, "ĠWalt": 28260, "Ġempowering": 28261, "Ġchassis": 28262, "linear": 28263, "гÑĥ": 28264, "Ġnova": 28265, "Ġuy": 28266, "Ġ69": 28267, "Ġencompass": 28268, "trl": 28269, "Ġcomputational": 28270, "Ġworms": 28271, "Ġnhiá»ģu": 28272, "Ġastronauts": 28273, "Ġves": 28274, "Ġsytu": 28275, "Ġdemanded": 28276, "Ġcs": 28277, "ĠMol": 28278, "Ġ`": 28279, "Ġchant": 28280, "Ġthereby": 28281, "Ġpenis": 28282, "Ġemoc": 28283, "wyn": 28284, "Ñĥже": 28285, "Ġtread": 28286, "óle": 28287, "Ġdeepest": 28288, "Ġmache": 28289, "ĠVent": 28290, "ĠAmsterdam": 28291, "ãĥĽ": 28292, "Ġrebel": 28293, "Ġ61": 28294, "ĠвкÑĥÑģ": 28295, "uffs": 28296, "ĠdoÄŁru": 28297, "ĠNapole": 28298, "ήÏĥ": 28299, "Ġworkouts": 28300, "ĠGlad": 28301, "неÑģ": 28302, "Ġtensions": 28303, "ĠShift": 28304, "ĠGuer": 28305, "íĮIJ": 28306, "Ġì¹ľêµ¬": 28307, "Ðĸ": 28308, "Ġimplant": 28309, "êu": 28310, "ê¸Ģ": 28311, "Ġauthorized": 28312, "CER": 28313, "ĠRV": 28314, "Ġhil": 28315, "lev": 28316, "cimento": 28317, "ĠUFO": 28318, "ìĥĪ": 28319, "è¨Ĥ": 28320, "wor": 28321, "Ġdances": 28322, "ĠPixel": 28323, "çľĭä¸Ģä¸ĭ": 28324, "Ġtrotzdem": 28325, "Ġobten": 28326, "ĠAlfred": 28327, "Ġcostly": 28328, "ĠStanley": 28329, "Ġterrorists": 28330, "ĠWid": 28331, "ħëĭĪëĭ¤": 28332, "Ġleicht": 28333, "ìĿ´ìĬ¤": 28334, "Ġdobrze": 28335, "Ġhesit": 28336, "Ġerzäh": 28337, "Ġeinige": 28338, "Ġhebt": 28339, "Ñģе": 28340, "Ġunpredict": 28341, "Cómo": 28342, "remos": 28343, "ĠThankfully": 28344, "Ġpurse": 28345, "chs": 28346, "ancer": 28347, "ulos": 28348, "stud": 28349, "æľīæ²Ĵæľī": 28350, "Ġneurolog": 28351, "ĠAncient": 28352, "Out": 28353, "awsze": 28354, "Ġoppose": 28355, "Ġantibodies": 28356, "ĠSomehow": 28357, "ropolitan": 28358, "ktor": 28359, "ĠÑģÑĤоÑĢонÑĭ": 28360, "Ġrockets": 28361, "Ġdisable": 28362, "Ġcatastroph": 28363, "´ìŀ": 28364, "Ġcyn": 28365, "ĠдÑĢÑĥзÑĮÑı": 28366, "Ġinstructors": 28367, "emaal": 28368, "Ġetwa": 28369, "Ġyuan": 28370, "ĠGround": 28371, "Ġpremiere": 28372, "Ñĩив": 28373, "Ġsaint": 28374, "yba": 28375, "Ġkok": 28376, "Ġcontractors": 28377, "Ġê°ģ": 28378, "Ġ×IJ׾": 28379, "Ġheadline": 28380, "Ġcompletamente": 28381, "Ġinexpensive": 28382, "Ġviu": 28383, "ĠGrande": 28384, "Ġbleed": 28385, "물": 28386, "Ġ73": 28387, "ĠtodavÃŃa": 28388, "ĠRush": 28389, "ĠElder": 28390, "ê°ĢëĬĶ": 28391, "ĠRou": 28392, "ĠженÑī": 28393, "ĠMira": 28394, "Ġdeine": 28395, "Ġkarma": 28396, "Ġumm": 28397, "Ġentsche": 28398, "ĠHolocaust": 28399, "Ġdiscoveries": 28400, "aments": 28401, "Ġraison": 28402, "Ġburgers": 28403, "Back": 28404, "Ġgdy": 28405, "ĠAG": 28406, "ĠDaw": 28407, "ìķł": 28408, "headed": 28409, "ĠClar": 28410, "Inst": 28411, "ĠLieutenant": 28412, "ĠAfD": 28413, "ĠCes": 28414, "Ġpersonalized": 28415, "Ġinterfaces": 28416, "à¸Īะ": 28417, "ĠÑĢеж": 28418, "Ġsuic": 28419, "Ġstarving": 28420, "Ġoxide": 28421, "Ġdecorated": 28422, "ĠDU": 28423, "ĠìĺĪìģĺ": 28424, "Ġquo": 28425, "Ġdistortion": 28426, "段": 28427, "Ġ먹ìĸ´ë": 28428, "Ġstakes": 28429, "æĺİçĻ½": 28430, "Ġsyntax": 28431, "Ġbiết": 28432, "thy": 28433, "icie": 28434, "Ġbrasile": 28435, "isis": 28436, "RC": 28437, "Ġshook": 28438, "Ġdepths": 28439, "ĠCosta": 28440, "Ġvocals": 28441, "Ġcoaster": 28442, "Ġfalou": 28443, "ettle": 28444, "Ġkennen": 28445, "Ġderive": 28446, "Ġaids": 28447, "ĠÐĿик": 28448, "Ġentwic": 28449, "Ġvertically": 28450, "ĠÍ": 28451, "ĠSUV": 28452, "Ġfireworks": 28453, "Ġspecifics": 28454, "交": 28455, "Ġinsisted": 28456, "Ġdeshalb": 28457, "ĠGonz": 28458, "love": 28459, "ĠMilitary": 28460, "ĠPierre": 28461, "ĠâĪ": 28462, "ĠWhose": 28463, "Ġperfume": 28464, "ĠÏĢε": 28465, "Ġlowered": 28466, "Ġcrosses": 28467, "Ġtranslates": 28468, "Ġarriba": 28469, "ÃŃdo": 28470, "ĠLev": 28471, "åħ§": 28472, "ĠCiao": 28473, "Ġscholarships": 28474, "Ġgestures": 28475, "ĠÑĢезÑĥлÑĮÑĤаÑĤ": 28476, "Ġquestão": 28477, "ĠColonel": 28478, "ĠBott": 28479, "رÙģ": 28480, "NING": 28481, "ĠWatching": 28482, "ĠPurple": 28483, "ÑģÑĤÑĢан": 28484, "Ġexecutives": 28485, "ĠKris": 28486, "orneys": 28487, "еннÑĭй": 28488, "Ġcoated": 28489, "Ä©": 28490, "Ġparked": 28491, "ĠÑģвеÑĤ": 28492, "!!!!!": 28493, "ĠFloyd": 28494, "ısı": 28495, "ziÄĩ": 28496, "Ġmotivate": 28497, "ĠElon": 28498, "lean": 28499, "Ĩĵ": 28500, "Ġip": 28501, "Ġniż": 28502, "ĠExperience": 28503, "ĠTina": 28504, "ĠKollege": 28505, "ĠAmbassador": 28506, "inya": 28507, "Ġtheft": 28508, "Ġheures": 28509, "ĠMyst": 28510, "Ġmaison": 28511, "leb": 28512, "Ġbowls": 28513, "ĠBürger": 28514, "ĠRoosevelt": 28515, "RP": 28516, "ê°ĢìļĶ": 28517, "ĠDelicious": 28518, "erdings": 28519, "ĠAssociate": 28520, "ousse": 28521, "ĠCort": 28522, "ĠRepeat": 28523, "ĠGlory": 28524, "Ġcontag": 28525, "à¹Ģล": 28526, "ĠParad": 28527, "ĠKerry": 28528, "Ġê¿": 28529, "ĠWave": 28530, "å¿ħ": 28531, "Ġgateway": 28532, "çIJĥ": 28533, "!ãĢį": 28534, "Ġtranscend": 28535, "Ġdamages": 28536, "Ġtails": 28537, "Ġgravitational": 28538, "ĠShield": 28539, "Ġprimitive": 28540, "Ġcarriers": 28541, "ĠHuawei": 28542, "ÙĤد": 28543, "Ġfeliz": 28544, "ĠMia": 28545, "åĥķ": 28546, "ĠпÑĢÑıмо": 28547, "ĠпÑĢоиÑģÑħодиÑĤ": 28548, "ĠMurphy": 28549, "ĠActiv": 28550, "ãĥĥãĤ¯": 28551, "Ġdiscomfort": 28552, "×ij×Ķ": 28553, "ĠKell": 28554, "ĠCentury": 28555, "Ġspaghetti": 28556, "ĠDurch": 28557, "Ġcierto": 28558, "ĠEmpress": 28559, "Ġguts": 28560, "neg": 28561, "ĠдоÑģÑĤаÑĤоÑĩно": 28562, "Ġvoluntary": 28563, "失": 28564, "Ġsquirrel": 28565, "欢": 28566, "ãģ¡ãĤī": 28567, "ĠMaz": 28568, "´ìĭ¬": 28569, "Ġви": 28570, "ãĤ§": 28571, "ĠÑĤакиÑħ": 28572, "ĠSharon": 28573, "Ġenthusiastic": 28574, "irement": 28575, "Ġíŀĺëĵ¤": 28576, "Ġpotrze": 28577, "Ġinitiated": 28578, "ãĥ§": 28579, "ĠÅĽrod": 28580, "ĠìĿ´ë¦Ħ": 28581, "Ġremake": 28582, "Ġculmin": 28583, "Ġconfuse": 28584, "miyor": 28585, "urar": 28586, "CTOR": 28587, "Ġbunny": 28588, "Ġ大": 28589, "ä¸įèĥ½": 28590, "elp": 28591, "Ġvampire": 28592, "Ġillumin": 28593, "ĠHend": 28594, "ĠкаÑĩе": 28595, "ĠSalv": 28596, "Ġканал": 28597, "Ġporta": 28598, "Ġasshole": 28599, "Ġsupporter": 28600, "Ġskeptical": 28601, "Ġknead": 28602, "Ġìĺ¬": 28603, "eza": 28604, "Ġquê": 28605, "ĠDH": 28606, "Ġrodz": 28607, "owners": 28608, "Ġplots": 28609, "Ġdelays": 28610, "Ġbelonged": 28611, "Ġahh": 28612, "Ġcarved": 28613, "Ġrisen": 28614, "Ġorden": 28615, "phony": 28616, "issy": 28617, "!!!!!!!!": 28618, "ĠolduÄŁunu": 28619, "Ġroses": 28620, "Ġintrins": 28621, "ĠAngst": 28622, "Ġfinalement": 28623, "ì§Ŀ": 28624, "SOUND": 28625, "Ġindul": 28626, "°Į": 28627, "Ġ×ķ×Ķ": 28628, "chy": 28629, "акÑģим": 28630, "Ġnggak": 28631, "Ġliz": 28632, "Ġelectoral": 28633, "ĠShawn": 28634, "ricia": 28635, "Ġarsen": 28636, "ĠPep": 28637, "Ġ2030": 28638, "Ġtrophy": 28639, "Ġsmoother": 28640, "Ġerre": 28641, "Ġcrashes": 28642, "Ġschne": 28643, "Ġasi": 28644, "ĠMaÃŁ": 28645, "Ñĥли": 28646, "ÑĩеÑģки": 28647, "ieves": 28648, "REAM": 28649, "Ġstirring": 28650, "ãĥĢ": 28651, "usta": 28652, "Ġinver": 28653, "sight": 28654, "ordu": 28655, "oor": 28656, "ĠÄĥn": 28657, "Ġpermitted": 28658, "ÑĢÑĮ": 28659, "Ġchalk": 28660, "ãĤĪãģĹ": 28661, "Ġtattoos": 28662, "ĠRelations": 28663, "ĠHoy": 28664, "ksam": 28665, "Ġdentist": 28666, "Ġ미êµŃ": 28667, "Ġsofa": 28668, "ĠÑĶ": 28669, "Ġforme": 28670, "ÙĤØ©": 28671, "Ġë²ł": 28672, "Ġembraced": 28673, "mil": 28674, "Ġsunglasses": 28675, "Ġê°Ķ": 28676, "Ġseamless": 28677, "Ġbeep": 28678, "ächst": 28679, "Ġsweets": 28680, "Ġsemaine": 28681, "Ġirrelevant": 28682, "Ġdesenvol": 28683, "ÏģÏī": 28684, "ĠпÑĢоизвод": 28685, "angs": 28686, "Ġaroma": 28687, "Ġpools": 28688, "Ġgiá»Ŀ": 28689, "ĠUg": 28690, "Ġclimbed": 28691, "Ġtrending": 28692, "Ġseperti": 28693, "ĠBarr": 28694, "ĠpÅĤ": 28695, "ĠOriginally": 28696, "ĠÚ¯": 28697, "utto": 28698, "Ĭ¸ë": 28699, "ĠкоÑĤоÑĢÑĭÑħ": 28700, "ĠзаÑħ": 28701, "Ġeigenen": 28702, "Ġmurderer": 28703, "ername": 28704, "Åŀ": 28705, "Ġannouncing": 28706, "ĠPlatform": 28707, "Ġexplanations": 28708, "Ġpresente": 28709, "ĠNasıl": 28710, "Ġorphan": 28711, "ĠFortnite": 28712, "rospect": 28713, "eredith": 28714, "ĠìĹĨìĸ´": 28715, "ĠNIH": 28716, "wagen": 28717, "Ġremed": 28718, "§Ģë": 28719, "mont": 28720, "ĠJeffrey": 28721, "prom": 28722, "Ġfünf": 28723, "Ġназад": 28724, "Ġcucumber": 28725, "ĠSummit": 28726, "åĪĿ": 28727, "§¤": 28728, "ÐĿÐIJЯ": 28729, "ĠJet": 28730, "Ġcambio": 28731, "ÑĥйÑĤе": 28732, "Ġcubic": 28733, "Ġdisproportion": 28734, "erez": 28735, "Ġmadness": 28736, "çĹĽ": 28737, "Ġtint": 28738, "Ġfueron": 28739, "Ġky": 28740, "Ġbipart": 28741, "ãģ¾ãģĽ": 28742, "Sam": 28743, "Ġë½": 28744, "Ġriv": 28745, "ĠTank": 28746, "ĠëĨĵ": 28747, "Ġrendered": 28748, "ÅĽlÄĻ": 28749, "conds": 28750, "Ġdisruption": 28751, "Ġinconven": 28752, "Ġquiser": 28753, "Ġdenial": 28754, "Ġgalaxies": 28755, "Ġsovereign": 28756, "Ġpolsk": 28757, "ÏģÏİ": 28758, "Ġmex": 28759, "Ġcaracter": 28760, "ĠLego": 28761, "anden": 28762, ".'\"": 28763, "ĠíĶĮë": 28764, "Ġcompressor": 28765, "ĠMovie": 28766, "Ġapplicants": 28767, "ziehen": 28768, "Ġvegetation": 28769, "Ġbelle": 28770, "ĠGOOD": 28771, "ĠBau": 28772, "Ġresent": 28773, "sex": 28774, "amentos": 28775, "Ġ×Ķ×ĸ×Ķ": 28776, "Ġoverload": 28777, "Ġsilicone": 28778, "еÑģÑĤно": 28779, "Ġdenken": 28780, "Ġdefinit": 28781, "ĠWasn": 28782, "Ġaltered": 28783, "ĠSoo": 28784, "ĠWing": 28785, "indre": 28786, "ĠNPC": 28787, "ÏģÎŃ": 28788, "ĠTwenty": 28789, "ĠLiebe": 28790, "Ġhomelessness": 28791, "oulder": 28792, "ĠÐĺÑĤак": 28793, "ÑģкаÑı": 28794, "Ġcuatro": 28795, "ĠHarvey": 28796, "Ġphilan": 28797, "ĠBeet": 28798, "Ġpolicing": 28799, "ĠAlexand": 28800, "Ġмолод": 28801, "Ġmüs": 28802, "Ġhizo": 28803, "ë³´ëĭ¤": 28804, "Ġпозвол": 28805, "ĠпÑĭÑĤ": 28806, "оÑĩемÑĥ": 28807, "Ġíĥľ": 28808, "Ġcryptocurrency": 28809, "Ġloro": 28810, "Ġsummation": 28811, "Ġbakalım": 28812, "Ġneuros": 28813, "Ø¥": 28814, "Ġможем": 28815, "Ġüst": 28816, "Ġpreliminary": 28817, "Ġhorns": 28818, "ĠTI": 28819, "ÙĥÙĦ": 28820, "YO": 28821, "Ġhinge": 28822, "Ġrepairs": 28823, "Ġbonding": 28824, "Ġbize": 28825, "ĠÑĪÑĤ": 28826, "Ġmotive": 28827, "ĠNigeria": 28828, "120": 28829, "block": 28830, "Ġaviation": 28831, "ĠKommun": 28832, "Ġоказ": 28833, "Ġtenha": 28834, "Ġeducating": 28835, "Ġstaat": 28836, "æ¶Ī": 28837, "ĠÑģколÑĮко": 28838, "Ġfrightened": 28839, "Ġseeks": 28840, "ÑĢÑĥÑĪ": 28841, "quent": 28842, "ĠNou": 28843, "Ġprat": 28844, "ĠShot": 28845, "Work": 28846, "karang": 28847, "ĠLightning": 28848, "nolds": 28849, "rolled": 28850, "glass": 28851, "Ġcredibility": 28852, "ITY": 28853, "Ġatmospheric": 28854, "Ġhavia": 28855, "ändern": 28856, "cheers": 28857, "These": 28858, "ĠCell": 28859, "Ġmagnes": 28860, "ĠBravo": 28861, "season": 28862, "ĠÅŁeyler": 28863, "ðŁİ": 28864, "white": 28865, "ĠMB": 28866, "Ġstacked": 28867, "Ġ74": 28868, "Ġдавай": 28869, "Ġpave": 28870, "ĠоÑħ": 28871, "Ġdataset": 28872, "Ġretour": 28873, "Ġmaturity": 28874, "Ġquase": 28875, "Ġ93": 28876, "ĠSym": 28877, "Ġbriefing": 28878, "Ġculturally": 28879, "Ġì·¨": 28880, "inhas": 28881, "Ġmadam": 28882, "Ġajudar": 28883, "ĠTibet": 28884, "Ġleaks": 28885, "cile": 28886, "Ġtheaters": 28887, "ìĺ¨": 28888, "ãĥĸ": 28889, "72": 28890, "ĠWash": 28891, "ĠQuality": 28892, "ĠIvan": 28893, "ĠBent": 28894, "igator": 28895, "ĠGeschichte": 28896, "Ġreactive": 28897, "Ġ1900": 28898, "æ¡Ī": 28899, "Ġcontradict": 28900, "Ġziemlich": 28901, "Ġcohort": 28902, "ủ": 28903, "Ġpestic": 28904, "Ġoraz": 28905, "Ġtellement": 28906, "é¾": 28907, "ĠNowadays": 28908, "crew": 28909, "Steve": 28910, "Ġfictional": 28911, "Ġilk": 28912, "ãģĤãģ£": 28913, "Ġgasoline": 28914, "zam": 28915, "Ġpancake": 28916, "ència": 28917, "Ġmuitos": 28918, "Ġbury": 28919, "Ġkop": 28920, "ĠIQ": 28921, "Ġreservation": 28922, "ĠUpdate": 28923, "Ġjej": 28924, "ĠEyes": 28925, "åıij": 28926, "Ġvive": 28927, "Ġchce": 28928, "ĠIni": 28929, "respons": 28930, "Ġreflective": 28931, "ĠWan": 28932, "Ñĸз": 28933, "Ġenca": 28934, "Ġembod": 28935, "ĠBurger": 28936, "Ġacademia": 28937, "ĠCirc": 28938, "ĠпÑĢек": 28939, "Ġanlam": 28940, "Ġphilanthrop": 28941, "ĠBaÅŁ": 28942, "ĠAudi": 28943, "Ġvost": 28944, "ä½łçŁ¥éģĵ": 28945, "Ġreper": 28946, "Peter": 28947, "Ġconsoles": 28948, "Ġscrut": 28949, "ĠTurner": 28950, "ĠбÑĭв": 28951, "III": 28952, "訴": 28953, "ĠFlight": 28954, "à¸ĸ": 28955, "ĠRaven": 28956, "Ġcorros": 28957, "fern": 28958, "Ġprova": 28959, "ĠSev": 28960, "Ġrecipro": 28961, "Ġ1985": 28962, "Ġnueva": 28963, "Ġdab": 28964, "ãĢģãĢĮ": 28965, "Ġmez": 28966, "ĠStark": 28967, "ppings": 28968, "оÑģÑĤи": 28969, "ì¦Ŀ": 28970, "Ġframing": 28971, "ĠÐłÐ°Ð·": 28972, "Ġpostp": 28973, "ĠShannon": 28974, "ĠкÑĥÑĢ": 28975, "Ġjakby": 28976, "iennent": 28977, "ĠMaps": 28978, "ĠRevelation": 28979, "ĠÑģÑĤал": 28980, "ìļ´ëį°": 28981, "Ġdevant": 28982, "ĠGiving": 28983, "ĠWAS": 28984, "Ġкого": 28985, "Ġrema": 28986, "ĠRC": 28987, "nÃŃ": 28988, "Ġslipped": 28989, "ĠRams": 28990, "Ġweet": 28991, "Ġmasculine": 28992, "ĠEc": 28993, "Ġreop": 28994, "ĠPlant": 28995, "ĠMAY": 28996, "Ġspikes": 28997, "Ġnozzle": 28998, "ĠWikipedia": 28999, "ĠCoh": 29000, "ISSA": 29001, "chlossen": 29002, "ì§Ģ를": 29003, "Ġ미ë": 29004, "ĠNeder": 29005, "Josh": 29006, "ĠÐłÐ¾ÑģÑģии": 29007, "Ġ1987": 29008, "ĠTheory": 29009, "ekk": 29010, "Ġutan": 29011, "Ġдома": 29012, "chu": 29013, "ĠÑģб": 29014, "Ġaprove": 29015, "VEN": 29016, "ueprint": 29017, "Ġ84": 29018, "æ¼Ĥ亮": 29019, "Cor": 29020, "Ġricher": 29021, "Ġsandwiches": 29022, "atsu": 29023, "ÑĪиÑħ": 29024, "Ġlatt": 29025, "~~~~": 29026, "friends": 29027, "Ġdernière": 29028, "Ġstereo": 29029, "ĠÑįкÑģп": 29030, "Ġprotections": 29031, "Ġhaut": 29032, "Everyone": 29033, "Ġenterprises": 29034, "ĠMostly": 29035, "ĠSpotify": 29036, "ĠSex": 29037, "Ġung": 29038, "Į를": 29039, "Ġactivism": 29040, "ctica": 29041, "original": 29042, "ĠпÑĢогÑĢам": 29043, "Ġbroccoli": 29044, "à¦": 29045, "огÑĢаÑĦ": 29046, "Ġsekarang": 29047, "Ġcrafting": 29048, "Ġбан": 29049, "ãģ»ãģ©": 29050, "ĠRaz": 29051, "Ġnaive": 29052, "Ġscrolling": 29053, "Ġnumerical": 29054, "Ġscheduling": 29055, "Ġapartments": 29056, "çį": 29057, "Ġstretches": 29058, "acey": 29059, "ĠHER": 29060, "ãĤº": 29061, "Ġzinc": 29062, "Ġdarn": 29063, "Ġcél": 29064, "Ġwardrobe": 29065, "Ġredirect": 29066, "Ġjum": 29067, "ĠStrange": 29068, "ĠnÃło": 29069, "Ġexperimenting": 29070, "éré": 29071, "Ġvoulez": 29072, "Ġgebe": 29073, "ĠKann": 29074, "ĠÄijá»Ļ": 29075, "ĠMaxim": 29076, "ĠKön": 29077, "ĠGlas": 29078, "Ġpolished": 29079, "Ġnuma": 29080, "Ich": 29081, "Ġrituals": 29082, "ĠSI": 29083, "иÑĤели": 29084, "Ġinfilt": 29085, "Ġscarf": 29086, "ophy": 29087, "Ġyine": 29088, "Ġcivic": 29089, "ĠMeng": 29090, "änge": 29091, "Õ¥": 29092, "histoire": 29093, "ĠOke": 29094, "ĠìĺĨ": 29095, "Ġsollten": 29096, "Ġ82": 29097, "馬": 29098, "Ġprescribed": 29099, "ĠDubai": 29100, "ĠEltern": 29101, "Ġnationwide": 29102, "Ġskating": 29103, "iary": 29104, "Ġrewarded": 29105, "Ġmorality": 29106, "ĠMaggie": 29107, "ĠOhhh": 29108, "ĠFahren": 29109, "olved": 29110, "æŶåĢĻ": 29111, "Ġdeuxième": 29112, "techn": 29113, "role": 29114, "Ġleider": 29115, "ĠJAY": 29116, "ĠинÑĦоÑĢм": 29117, "Ġcaffe": 29118, "reichen": 29119, "Ġkart": 29120, "ĠCute": 29121, "ffective": 29122, "Ġbully": 29123, "agar": 29124, "Ġcommodity": 29125, "Ġobrig": 29126, "OUR": 29127, "Ġunpleasant": 29128, "nox": 29129, "Jul": 29130, "olith": 29131, "ÑĤоÑıÑī": 29132, "ĠBella": 29133, "Ġdolls": 29134, "ĠHoff": 29135, "Ġadvisors": 29136, "Ġtransfers": 29137, "ĠGoku": 29138, "Ġ1200": 29139, "inhos": 29140, "Pal": 29141, "Ġëĺij": 29142, "Ġrept": 29143, "Ġaccomplishment": 29144, "Ġweave": 29145, "Ġoversight": 29146, "Ġunhealthy": 29147, "Ġfilt": 29148, "Ġpudding": 29149, "ĠMiguel": 29150, "Ġchuckles": 29151, "åı°çģ£": 29152, "version": 29153, "Ġconfession": 29154, "value": 29155, "Ġtriumph": 29156, "Ġsair": 29157, "Ġëħ¸": 29158, "Ġarte": 29159, "ĠMaterial": 29160, "uti": 29161, "Ġliquor": 29162, "ĠBayern": 29163, "ĠMail": 29164, "Ġíĸ¥": 29165, "Ñģком": 29166, "Ġcheapest": 29167, "ĠÑĩаÑģÑĤи": 29168, "ĠJobs": 29169, "ĠCanyon": 29170, "harma": 29171, "aley": 29172, "andro": 29173, "Ġappearances": 29174, "prof": 29175, "Ġоз": 29176, "lagen": 29177, "Ġ//": 29178, "ĠлиÑĪÑĮ": 29179, "Ġrecovering": 29180, "дж": 29181, "psy": 29182, "ãĥ¢": 29183, "Ġswift": 29184, "ĠSpin": 29185, "å¸Ī": 29186, "Ġseinem": 29187, "Ġdolph": 29188, "führ": 29189, "ât": 29190, "Ġaltijd": 29191, "ĠMarty": 29192, "ĠHoch": 29193, "Ġpredators": 29194, "Ġvorher": 29195, "ĠÐĶавай": 29196, "Ġfragments": 29197, "Ġpastry": 29198, "Ġcommen": 29199, "ĠSana": 29200, "Ġê±´ëį°": 29201, "ussen": 29202, "Ġtela": 29203, "ĠNina": 29204, "lek": 29205, "Ġcries": 29206, "Ġthighs": 29207, "ĠFlex": 29208, "ĠBuzz": 29209, "ãĦ": 29210, "Us": 29211, "Ġpaso": 29212, "Ġdeclined": 29213, "ĠNy": 29214, "balance": 29215, "Ġmasa": 29216, "Ġjos": 29217, "ãģªãĤĭ": 29218, "ĠСпаÑģибо": 29219, "achu": 29220, "loud": 29221, "Ġpena": 29222, "ĠWald": 29223, "Ġelimination": 29224, "ĠвеÑģÑĮ": 29225, "orage": 29226, "Ġmisunderstanding": 29227, "Ġendorse": 29228, "Ġogóle": 29229, "Ġgreed": 29230, "Ġklein": 29231, "׾×Ķ": 29232, "REY": 29233, "ĠEating": 29234, "Ġseminar": 29235, "ĠBirthday": 29236, "Ġquelle": 29237, "ĠMulti": 29238, "Ġtirar": 29239, "Ġperch": 29240, "Ġlavor": 29241, "ĠJia": 29242, "Ġmutations": 29243, "Ġcigarettes": 29244, "ÙĪج": 29245, "Ġcousins": 29246, "Ġcapsule": 29247, "Ġhorrific": 29248, "Ġstur": 29249, "Ġzeigt": 29250, "nuts": 29251, "Ġmeanwhile": 29252, "ĠColin": 29253, "Ġgobierno": 29254, "Ġgw": 29255, "Ġuhh": 29256, "ĠJER": 29257, "specific": 29258, "Ġallegations": 29259, "Ġë©ĭ": 29260, "ĠElla": 29261, "ooked": 29262, "ĠFit": 29263, "affle": 29264, "ĠAprès": 29265, "ĠDuck": 29266, "Ġcellular": 29267, "ców": 29268, "ĠÑĩÑĥвÑģÑĤв": 29269, "genommen": 29270, "ìĬ¤íĬ¸": 29271, "Ġlain": 29272, "isol": 29273, "Ġholders": 29274, "Ġbooster": 29275, "ĠSasha": 29276, "ÑĭваеÑĤ": 29277, "ģ¼": 29278, "Ġseparating": 29279, "Ġreinforcement": 29280, "Ġодной": 29281, "ìĹĨ": 29282, "IDE": 29283, "ĠOption": 29284, "phon": 29285, "Ġplais": 29286, "ĠCamb": 29287, "ĠíĻĺ": 29288, "Ġuncommon": 29289, "\":": 29290, "miyorum": 29291, "moi": 29292, "acje": 29293, "ажÑĥ": 29294, "Õ¶": 29295, "Ġgems": 29296, "üler": 29297, "ools": 29298, "Ġenzymes": 29299, "Ġkidnapped": 29300, "Ġketchup": 29301, "talk": 29302, "Ġzach": 29303, "Ġwasher": 29304, "ãĢĤãĢĤ": 29305, "ĠArchitect": 29306, "venue": 29307, "ĠPlanning": 29308, "éĢģ": 29309, "ĠSavior": 29310, "ĠгÑĢÑĥпп": 29311, "íĬ¼": 29312, "arya": 29313, "Ġproceso": 29314, "Ġlimbs": 29315, "Ġrealizes": 29316, "iander": 29317, "FS": 29318, "aji": 29319, "Ġunite": 29320, "ĠìĿĺë": 29321, "ĠpossÃŃvel": 29322, "raits": 29323, "ĠAgre": 29324, "ÛĮÚ©": 29325, "ìĦľëıĦ": 29326, "æİī": 29327, "Ġвел": 29328, "ĠмеÑģÑı": 29329, "anor": 29330, "Pat": 29331, "Ġdernier": 29332, "ÏĥÏĦε": 29333, "ĠкакаÑı": 29334, "Ġlässt": 29335, "æİ°": 29336, "ĠMeh": 29337, "Ġngh": 29338, "Ġamateur": 29339, "è«ĸ": 29340, "Fe": 29341, "Ġê¶ģ": 29342, "Ġsituación": 29343, "Ġsedan": 29344, "Ġcleansing": 29345, "lasting": 29346, "Ġcommunist": 29347, "ANE": 29348, "Ġirregular": 29349, "Ġsout": 29350, "ĠCarney": 29351, "Ġallemaal": 29352, "ĠmuchÃŃs": 29353, "Ġlibro": 29354, "ÐŃÑĤо": 29355, "Ġап": 29356, "Ġcontinuation": 29357, "ĠLor": 29358, "?\",": 29359, "quin": 29360, "Ġcharacterized": 29361, "ajes": 29362, "Ġsights": 29363, "ĠÑıзÑĭ": 29364, "ĠUhh": 29365, "è·³": 29366, "birth": 29367, "dong": 29368, "Ġhablando": 29369, "Ġsymptom": 29370, "çµĤ": 29371, "Ġcapacitor": 29372, "Ġtransported": 29373, "Ġignorant": 29374, "Ġникогда": 29375, "Ġdrip": 29376, "ĠEva": 29377, "Ġadject": 29378, "Ġmassively": 29379, "ĠEthi": 29380, "ĠCircle": 29381, "Ġrainfall": 29382, "ĠMouse": 29383, "Ġrefund": 29384, "ĠZw": 29385, "assemb": 29386, "Ġ220": 29387, "ĠOrd": 29388, "è§Ĵ": 29389, "Ġveins": 29390, "ĠGiant": 29391, "Ġmãe": 29392, "Ġvap": 29393, "Ġmisses": 29394, "οÏħÏĤ": 29395, "Mo": 29396, "ĠEntwick": 29397, "INT": 29398, "ÙĨت": 29399, "Ġtheoretically": 29400, "Ġtearing": 29401, "Ġtroubled": 29402, "prem": 29403, "Ġrepetitive": 29404, "Ġâĸ": 29405, "Ġheavenly": 29406, "ĠAmber": 29407, "Ġполож": 29408, "Ġíķ´ì¤": 29409, "Ġvowel": 29410, "anking": 29411, "ĠWirtschaft": 29412, "Ġirr": 29413, "Ġcozy": 29414, "Ġunfamiliar": 29415, "ĠPors": 29416, "Ġë§ŀìķĦ": 29417, "ĠTimothy": 29418, "ÑģолÑİÑĤ": 29419, "pex": 29420, "ĠVIS": 29421, ")(": 29422, "Ġsuperst": 29423, "Ġimprov": 29424, "ĠBeng": 29425, "Ġdisconnected": 29426, "Ġapt": 29427, "ÑĢен": 29428, "ĠExtra": 29429, "Ġбел": 29430, "shop": 29431, "dings": 29432, "ĠConnecticut": 29433, "ì°¬": 29434, "ĠGC": 29435, "åıĸ": 29436, "beh": 29437, "Jeremy": 29438, "ĠBatt": 29439, "ãģ¸": 29440, "atha": 29441, "ĠZusammen": 29442, "screams": 29443, "Ġgras": 29444, "afft": 29445, "ĠInitially": 29446, "ĠBrett": 29447, "Ġspecifications": 29448, "Ġseaweed": 29449, "Ġoath": 29450, "Ġfountain": 29451, "ĠкоÑĤоÑĢой": 29452, "ĠStein": 29453, "èģ²": 29454, "ĠCorinth": 29455, "Ġconjug": 29456, "å·¦åı³": 29457, "Ġcompensate": 29458, "ĠëĬIJëĤĮìĿ´": 29459, "Ġonze": 29460, "Ġskincare": 29461, "Brian": 29462, "Ġservir": 29463, "}}": 29464, "ĠVik": 29465, "Ġunint": 29466, "Ġsuppliers": 29467, "Ġbalcony": 29468, "Ġenergia": 29469, "ometric": 29470, "зÑı": 29471, "Ġsigh": 29472, "ĠTOM": 29473, "ĠPure": 29474, "ytt": 29475, "ÑĭÑģ": 29476, "ĠRainbow": 29477, "ĠPitts": 29478, "×Ļ×ŀ": 29479, "Ġstatues": 29480, "heads": 29481, "Ġcoupled": 29482, "éĮ¢": 29483, "Ġherd": 29484, "ä½ĵ": 29485, "Ġexcluded": 29486, "Ġgilt": 29487, "ĠÑİ": 29488, "Ġswoje": 29489, "ĠSver": 29490, "63": 29491, "issant": 29492, "Ġdürfen": 29493, "łĪë": 29494, "Ġkissing": 29495, "oof": 29496, "以ä¸Ĭ": 29497, "Ġcursed": 29498, "Ġshowers": 29499, "Ġswinging": 29500, "Ġreproduce": 29501, "ãģ¨ãģĦãģĨãģĵãģ¨": 29502, "Ġsätt": 29503, "elcome": 29504, "Ġfundamentals": 29505, "Ġalmond": 29506, "Ġpé": 29507, "Ġwellbeing": 29508, "Ġhunters": 29509, "å¾Ģ": 29510, "Sec": 29511, "ĵľë¦´": 29512, "Ġemission": 29513, "Ġpsychologist": 29514, "Ġbetrayed": 29515, "ĠReynolds": 29516, "LES": 29517, "Ġpolling": 29518, "Ġnegatively": 29519, "Ġcombines": 29520, "׾×IJ": 29521, "аÑĢа": 29522, "λλά": 29523, "ĠTurns": 29524, "OTT": 29525, "Ġ×Ķ×Ļ": 29526, "aison": 29527, "Ġairline": 29528, "Ġrestriction": 29529, "wal": 29530, "Ġaurait": 29531, "ĠLebanon": 29532, "ĠMOR": 29533, "Ġmonkeys": 29534, "éner": 29535, "ÑĸÑĹ": 29536, "Ġmotherf": 29537, "ĠÙĩØ°Ùĩ": 29538, "Ġfeu": 29539, "ühren": 29540, "Ġhygiene": 29541, "enteen": 29542, "Des": 29543, "Ġdissip": 29544, "Est": 29545, "Ġsaints": 29546, "Ġpotassium": 29547, "Ġreckon": 29548, "Clintus": 29549, "Ġmanifestation": 29550, "ĠAppro": 29551, "ĠInspect": 29552, "Ġventilation": 29553, "Ġhelm": 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"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, "Ġkomplett": 32261, "Ġdüny": 32262, "James": 32263, "Ġreceptor": 32264, "Ġoneself": 32265, "guru": 32266, "Ġmerchant": 32267, "liness": 32268, "Ġoverlooked": 32269, "Ġharmonic": 32270, "éķ¿": 32271, "ieso": 32272, "×ķ×ŀ": 32273, "colm": 32274, "ĠпÑĢоекÑĤ": 32275, "ĠAda": 32276, "اس": 32277, "Tim": 32278, "Ġrecurring": 32279, "Ġproceeds": 32280, "ĠParticularly": 32281, "ĠDownload": 32282, "etrical": 32283, "Ġmatrices": 32284, "Ġproyecto": 32285, "ancies": 32286, "ĠUhm": 32287, "Ġcaves": 32288, "Ġìĸ´ëł¤": 32289, "ĠLeaf": 32290, "ĠобÑĭÑĩ": 32291, "ĠìĿ´ìľł": 32292, "Europe": 32293, "ĠtÄħ": 32294, "Ġpuls": 32295, "Ġtakiego": 32296, "ÐĿе": 32297, "GU": 32298, "Ġfors": 32299, "Ïģγ": 32300, "Ġfotos": 32301, "Ġ))": 32302, "Ġ멤ë": 32303, "Ġaquilo": 32304, "ĠKurd": 32305, "ï¸ı": 32306, "ptic": 32307, "ĠDort": 32308, "Ġmisery": 32309, "auso": 32310, "åĬŁ": 32311, "chuckling": 32312, "ĠRidge": 32313, "ĠíĸĪìĬµëĭĪëĭ¤": 32314, "Ġ***": 32315, "客": 32316, "ĠHmmm": 32317, "Ġgeographic": 32318, "Ġanys": 32319, "Ġtalvez": 32320, "Ġskelet": 32321, "Ġsignatures": 32322, "Ġliters": 32323, "IJë©´": 32324, "ĠÑģвоего": 32325, "Ġskiing": 32326, "ĠÐľÐ¾Ñģ": 32327, "Ġadopting": 32328, "Ġhaft": 32329, "Ġsymmetric": 32330, "ĠLiqu": 32331, "Ġthyroid": 32332, "Ġmisin": 32333, "lude": 32334, "Ġhull": 32335, "ĠXD": 32336, "ĠGust": 32337, "zeich": 32338, "Ġvibrations": 32339, "Ġesemp": 32340, "ĠвÑģÑİ": 32341, "ĠQuem": 32342, "Ġübrig": 32343, "ĠSke": 32344, "ĠLynch": 32345, "rooms": 32346, "artet": 32347, "fest": 32348, "Ġfrüher": 32349, "Ġlure": 32350, "ä¸į好æĦıæĢĿ": 32351, "ĠìķĮìķĦ": 32352, "ĠWIN": 32353, "ĠRYAN": 32354, "ĠкоÑĤоÑĢÑĥÑİ": 32355, "ĠKash": 32356, "Ġ×Ķ×ŀ": 32357, "Ġsafeg": 32358, "ĠHallelujah": 32359, "ĠдвÑĥÑħ": 32360, "Ġstaple": 32361, "Ġsediment": 32362, "ĠActs": 32363, "Ġblaming": 32364, "Ġmainland": 32365, "Ġsporting": 32366, "Ġdecorations": 32367, "Ġexecuting": 32368, "Ġparan": 32369, "ĠDollar": 32370, "Ġprojections": 32371, "Ġcommissioned": 32372, "Ġbour": 32373, "öm": 32374, "Ġsteamed": 32375, "ĠëŃĺ": 32376, "Ġpetrol": 32377, "Ġcelular": 32378, "帶": 32379, "ĠHungary": 32380, "Ġrented": 32381, "ĠваÑĢи": 32382, "bbie": 32383, "Ġsécur": 32384, "üll": 32385, "Ġswings": 32386, "between": 32387, "ĠиÑĤ": 32388, "estro": 32389, "Ġniemand": 32390, "ĠìĤ¼": 32391, "ĠPardon": 32392, "esses": 32393, "ĠMID": 32394, "Ġcentralized": 32395, "ĠAlien": 32396, "culos": 32397, "Ġcrise": 32398, "裡éĿ¢": 32399, "Ġclasse": 32400, "beitet": 32401, "iÄŁi": 32402, "Ġwhales": 32403, "Ġperimeter": 32404, "Ġtying": 32405, "Ġstrony": 32406, "Ġlikewise": 32407, "ĠPunch": 32408, "Da": 32409, "ĠBaptist": 32410, "Ġsorting": 32411, "Ġiv": 32412, "Ġíķ©": 32413, "Ġrehab": 32414, "Ġeta": 32415, "river": 32416, "Ġsai": 32417, "ãģĦãģŁãģł": 32418, "odus": 32419, "ãģĬé¡ĺãģĦãģĹãģ¾ãģĻ": 32420, "Ġessayer": 32421, "Ġturtles": 32422, "ĠHazrat": 32423, "Ġfabrics": 32424, "Ġcavity": 32425, "Ġponieważ": 32426, "Ġschlecht": 32427, "Ġsalsa": 32428, "ÅŁekkür": 32429, "Ġseating": 32430, "Ġeconomists": 32431, "Ġmang": 32432, "Ġseguinte": 32433, "Ġrang": 32434, "Ġratios": 32435, "Ġconstell": 32436, "Ġlongtemps": 32437, "uating": 32438, "Ġspoiled": 32439, "Ġrecipients": 32440, "Ġsniper": 32441, "ä¹ĭåīį": 32442, "ìĬµëĭĪê¹Į": 32443, "Ġwp": 32444, "ĠLINKE": 32445, "Ġflare": 32446, "ĠAdri": 32447, "ñas": 32448, "Ġbackl": 32449, "mÃ¤ÃŁ": 32450, "ĠBend": 32451, "Ġworkloads": 32452, 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33328, "หà¸Ļ": 33329, "Ġappli": 33330, "ா?": 33331, "üyorum": 33332, "ĠинÑĤеÑĢеÑģно": 33333, "Ġdisinfect": 33334, ">-": 33335, "Ġchampagne": 33336, "Ġkla": 33337, "opers": 33338, "Trans": 33339, "ĠDesert": 33340, "Ġcultivate": 33341, "ĠFucking": 33342, "idelity": 33343, "ĠÑĤан": 33344, "Ġincub": 33345, "Ġtemu": 33346, "Ġlearner": 33347, "founder": 33348, "ĠSyl": 33349, "ãĤĢ": 33350, "Ġfato": 33351, "zier": 33352, "ĠìĹĨìĿ´": 33353, "ĠìĪ¨": 33354, "Ġpsycho": 33355, "ĠÑĤелеÑĦ": 33356, "Ġregarde": 33357, "Ġrepresentations": 33358, "Ġlitigation": 33359, "Ġspann": 33360, "ults": 33361, "bior": 33362, "è¦ĭãģ¦": 33363, "ä¸įå¤ļ": 33364, "ĠSurvey": 33365, "ĠLEDs": 33366, "Ġträ": 33367, "Ġlên": 33368, "Ġantioxid": 33369, "еÑĢом": 33370, "Ġinduction": 33371, "Ġfooled": 33372, "ätzlich": 33373, "ĠговоÑĢÑıÑĤ": 33374, "ĠFact": 33375, "umbai": 33376, "Ġwiggle": 33377, "NOUN": 33378, "Ġdévelopp": 33379, "ĠClaro": 33380, "Ġì¸": 33381, "ë¬": 33382, "ãģªãĤĵãģł": 33383, "Ġaccumulate": 33384, "Ġmaintains": 33385, "ëĦ": 33386, "ĠFighter": 33387, "íĨł": 33388, "Ġmatin": 33389, "Ġcoupon": 33390, "Ġstunt": 33391, "Ġdebuted": 33392, "å¾ħãģ£ãģ¦": 33393, "Ġprag": 33394, "иваем": 33395, "73": 33396, "Ġexpres": 33397, "Ġìĺ¤ë¹ł": 33398, "ĠпеÑĢÑģон": 33399, "Ġcalculus": 33400, "Ġabrupt": 33401, "ĠInspector": 33402, "ourt": 33403, "æĸĻ": 33404, "źniej": 33405, "intense": 33406, "Ba": 33407, "Ġlounge": 33408, "Ġasthma": 33409, "ĠHiç": 33410, "ª»": 33411, "Ġeditorial": 33412, "Ġseize": 33413, "Ġkır": 33414, "Ġmouve": 33415, "Ġtierra": 33416, "Ġtestosterone": 33417, "Ġrh": 33418, "ĠKingston": 33419, "ELLE": 33420, "ĠRepresentative": 33421, "Ġ1974": 33422, "Ġiba": 33423, "Ts": 33424, "Ġsorta": 33425, "Ġ(?)": 33426, "ĠتÙĪ": 33427, "ĠëĤ´ëł¤": 33428, "Ġbekommt": 33429, "Ġspiritually": 33430, "Ġdistorted": 33431, "Mad": 33432, "Ġreim": 33433, "ánh": 33434, "ĠOttoman": 33435, "ĠRelig": 33436, "ĠEls": 33437, "Ġretained": 33438, "ĠLaughs": 33439, "æĢ»": 33440, "ĠSAS": 33441, "ĠколиÑĩеÑģÑĤво": 33442, "×ķתר": 33443, "Ġinnovate": 33444, "Ġkork": 33445, "ĠÑĢаÑģÑģказÑĭв": 33446, "ondere": 33447, "ivi": 33448, "aye": 33449, "ounty": 33450, "ĠполÑĥÑĩаеÑĤÑģÑı": 33451, "Ġbuns": 33452, "åħ«": 33453, "Ġyüzden": 33454, "Ġsurgeries": 33455, "Ø£ÙĨ": 33456, "Ġbankruptcy": 33457, "welt": 33458, "Ġsiamo": 33459, "Ġdarkest": 33460, "ĠHann": 33461, "gga": 33462, "Ġformas": 33463, "ĠDj": 33464, "named": 33465, "Ġshields": 33466, "ueller": 33467, "ĠFew": 33468, "Ġlace": 33469, "Ġfurious": 33470, "ĠYU": 33471, "Ġsocietal": 33472, "Ġjudgement": 33473, "ĠDos": 33474, "Ġjab": 33475, "laws": 33476, "Ġreinvent": 33477, "ĠKatherine": 33478, "ĠChoi": 33479, "adows": 33480, "Ġrans": 33481, "oden": 33482, "ĠMidwest": 33483, "nın": 33484, "Ġdeport": 33485, "ĠDip": 33486, "ç´ħ": 33487, "Ġatención": 33488, "ĠCourtney": 33489, "ividad": 33490, "ĠÚ©Ûģ": 33491, "Ġefficacy": 33492, "ĠBrooks": 33493, "Ġreferral": 33494, "ĠконÑĨ": 33495, "Ġmalicious": 33496, "Ġkir": 33497, "ĠGoddess": 33498, "Ġfunky": 33499, "Ġinterim": 33500, "ĠKörper": 33501, "Ġìĸ¼ë§": 33502, "kur": 33503, "Ġкли": 33504, "Ġtrucs": 33505, "gesetz": 33506, "Ġzug": 33507, "ĠGlück": 33508, "ĠMinute": 33509, "Ġprestigious": 33510, "Ġniez": 33511, "Ġconcentrations": 33512, "лаÑģÑĤи": 33513, "ĠSis": 33514, "ĠVitamin": 33515, "kov": 33516, "ĠPBS": 33517, "Ġнее": 33518, "Ġretailers": 33519, "Ġconventions": 33520, "ĠSamantha": 33521, "Ġproudly": 33522, "Jordan": 33523, "ĠJASON": 33524, "atk": 33525, "Ġtriste": 33526, "Ġstär": 33527, "Ġreiterate": 33528, "Ġposterior": 33529, "Ġ1973": 33530, "ĠPine": 33531, "ĠJuliet": 33532, "Ġpedir": 33533, "kil": 33534, "Ġoverlapping": 33535, "Ġexclude": 33536, "Ġeconóm": 33537, "Ġaccepts": 33538, "ĠSter": 33539, "決": 33540, "Ġìļ´ëıĻ": 33541, "estab": 33542, "Ġtug": 33543, "arg": 33544, "Ġlivro": 33545, "اص": 33546, "Ġseams": 33547, "Ġburaya": 33548, "Ġello": 33549, "ĠTM": 33550, "ĠPaw": 33551, "ĠIndex": 33552, "Exc": 33553, "Ġinspirational": 33554, "Ġdunk": 33555, "è°ģ": 33556, "akter": 33557, "Ġconditioner": 33558, "ĠSalut": 33559, "ÅĤec": 33560, "Ġìī½": 33561, "ĠÑĥзна": 33562, "ĠRomeo": 33563, "fruit": 33564, "ĠYO": 33565, "Ġchá»ī": 33566, "бÑĥ": 33567, "bons": 33568, "Ġreproductive": 33569, "Ġorada": 33570, "Ġíļ¨": 33571, "Ġtentar": 33572, "Ġmañana": 33573, "ãĤ¬": 33574, "Ġsolvent": 33575, "Jessica": 33576, "ĠLegal": 33577, "Ġtua": 33578, "Ġsic": 33579, "ĠEQ": 33580, "aukee": 33581, "ìĭľëĭ¤": 33582, "ĠÅŀu": 33583, "Ġadhere": 33584, "ĠTul": 33585, "Ġà®Ĩ": 33586, "Ġtextbooks": 33587, "ĠFifth": 33588, "Ġexperi": 33589, "Ġchic": 33590, "Ġheap": 33591, "inely": 33592, "atra": 33593, "Two": 33594, "Ġhelemaal": 33595, "Ġfren": 33596, "æݨ": 33597, "Ġbisher": 33598, "اش": 33599, "ĠìĦłìĥĿ": 33600, "ĠTages": 33601, "Ġsá»±": 33602, "Ġbullied": 33603, "ؤ": 33604, "Ġbenefited": 33605, "ĠPreviously": 33606, "ĠÑįÑĦÑĦ": 33607, "Ùį": 33608, "Ġsenate": 33609, "ĠMorm": 33610, "ijke": 33611, "ĠFlu": 33612, "Ġincorporating": 33613, "jack": 33614, "ĠпиÑĤ": 33615, "Ġimply": 33616, "Ġhacks": 33617, "ĠRICH": 33618, "ĠкваÑĢ": 33619, "ĠпÑĢекÑĢаÑģ": 33620, "Ġdependency": 33621, "Ġìļ©": 33622, "Ġì±ħ": 33623, "Ġwährend": 33624, "Ġsulla": 33625, "ĠPittsburgh": 33626, "Ġesempio": 33627, "¼ë¡ľ": 33628, "prot": 33629, "ĠRosen": 33630, "ĠIndependence": 33631, "Ġparsley": 33632, "iegen": 33633, "Ġhaw": 33634, "Ġaquell": 33635, "ĠCAP": 33636, "ĠÑĢабоÑĤаÑĤÑĮ": 33637, "ĠCliff": 33638, "ionar": 33639, "Ġsecuring": 33640, "æĪijåĢijçļĦ": 33641, "νε": 33642, "Ġutilis": 33643, "Ġcoule": 33644, "ĠPing": 33645, "Ġtrek": 33646, "Ġfak": 33647, "Ġenorme": 33648, "Ġìĭ«": 33649, "让": 33650, "Ġdoubling": 33651, "ĠнÑĢавиÑĤÑģÑı": 33652, "Ġhed": 33653, "hoven": 33654, "ĠStanding": 33655, "ĠmÃŃn": 33656, "ĠJimin": 33657, "Ġmonarch": 33658, "Ġcoke": 33659, "Ġmr": 33660, "Ġclic": 33661, "Ãį": 33662, "Ġimpeachment": 33663, "Ġdurability": 33664, "Ġvarios": 33665, "Ġcommercials": 33666, "Ġgreetings": 33667, "ĠRi": 33668, "ĠAppreci": 33669, "ìŀĪëĬĶ": 33670, "Ġrésult": 33671, "ért": 33672, "Ġsalute": 33673, "Ġpoderia": 33674, "Ġsunrise": 33675, "veck": 33676, "Ġreluctant": 33677, "Ġcommissioner": 33678, "念": 33679, "âte": 33680, "ĠKenny": 33681, "ĠSiri": 33682, "ãĥĥãĥĹ": 33683, "ĠëĬĺ": 33684, "ĠEE": 33685, "Ġunch": 33686, "кон": 33687, "ĠاÙĦØ¥": 33688, "Ġbelts": 33689, "Ġhass": 33690, "ĠмоÑı": 33691, "Ġdisplaced": 33692, "Ġabra": 33693, "ÎŃλ": 33694, "Ġscratches": 33695, "Ġcomet": 33696, "Ġauthorization": 33697, "ĠLLC": 33698, "Ġproduk": 33699, "Ġrehabilitation": 33700, "åŀ": 33701, "ÑĸÑĩ": 33702, "uding": 33703, "olit": 33704, "Ġ105": 33705, "Ġexpands": 33706, "Ġaltri": 33707, "ĠKomment": 33708, "Ġanf": 33709, "Pl": 33710, "ĠMana": 33711, "fed": 33712, "Ġbri": 33713, "Ġora": 33714, "Gs": 33715, "ĠGur": 33716, "uckland": 33717, "Ġjunction": 33718, "Ġironic": 33719, "ĠFeed": 33720, "Ġprakt": 33721, "ĠHammer": 33722, "ĮëıĦ": 33723, "ĠTracy": 33724, "çµ±": 33725, "ĠAside": 33726, "него": 33727, "ĠиÑģполÑĮзоваÑĤÑĮ": 33728, "Ġzaj": 33729, "Ġequitable": 33730, "Ġcurb": 33731, "ĠãģĵãĤĮ": 33732, "Ġderivatives": 33733, "Ġpuppies": 33734, "ĠKenneth": 33735, "ĠCompl": 33736, "igram": 33737, "ĠGarcia": 33738, ")\"": 33739, "ĠHarbor": 33740, "estial": 33741, "Ġä¾Ĩ": 33742, "Ġers": 33743, "æ¹": 33744, "Ġunwanted": 33745, "Ġbelang": 33746, "аго": 33747, "emb": 33748, "dos": 33749, "ĠìĻľë": 33750, "ĠBudget": 33751, "Ġbattling": 33752, "ØŃت": 33753, "kok": 33754, "наÑĩала": 33755, "Ġplag": 33756, "Ġcantidad": 33757, "Ġgrupos": 33758, "Ġplugins": 33759, "lerini": 33760, "ĠимееÑĤ": 33761, "Ġsozusagen": 33762, "olics": 33763, "Ġpueblo": 33764, "Ġreminis": 33765, "rän": 33766, "ĠMorrison": 33767, "Ġlinha": 33768, "Ġbreaths": 33769, "ĠTaste": 33770, "Ġenfrent": 33771, "ĠDocker": 33772, "Ġден": 33773, "Ġethnicity": 33774, "Ġwob": 33775, "Ġsuffers": 33776, "Ġtransitioning": 33777, "ĠRange": 33778, "ÄĻdzy": 33779, "ĠкаÑĤ": 33780, "Ġsyner": 33781, "Ġdonut": 33782, "Ġprobabilities": 33783, "ĠOmar": 33784, "Which": 33785, "uish": 33786, "isin": 33787, "Ġdemos": 33788, "ĠìłĢ기": 33789, "Ġëĺijê°Ļ": 33790, "Ġедин": 33791, "Ġcerve": 33792, "Ġjoka": 33793, "IAN": 33794, "Ġkilometer": 33795, "Ġhorizontally": 33796, "ĠBhag": 33797, "Ġ->": 33798, "ĠMonitor": 33799, "Ġknowledgeable": 33800, "Ġfav": 33801, "Ġpinned": 33802, "ĠeBay": 33803, "icker": 33804, "Ġìŀłê¹IJë§Į": 33805, "ĠXiaomi": 33806, "Ġcapit": 33807, "Ġnp": 33808, "Ġ1965": 33809, "hoe": 33810, "Ġnok": 33811, "ĠSage": 33812, "ĠнелÑĮзÑı": 33813, "ĠTow": 33814, "gam": 33815, "Ġdicen": 33816, "ĠSUBSCRIBE": 33817, "Ġreboot": 33818, "Ġpaj": 33819, "Ġë³´ìŬë": 33820, "Ġthicken": 33821, "ĠReality": 33822, "idän": 33823, "Na": 33824, "Ġê²ĥìĿĢ": 33825, "!!)": 33826, "Ġroutines": 33827, "Ġодного": 33828, "Ġexting": 33829, "Ġì¦Ŀ": 33830, "Ġsulfur": 33831, "Ġcarve": 33832, "Ġasteroid": 33833, "ĠWarrior": 33834, "Ġphotographers": 33835, "Ġpell": 33836, "Ġcrossover": 33837, "æĪijçŁ¥éģĵ": 33838, "Ġhacemos": 33839, "ĠNej": 33840, "Ġsettling": 33841, "Ġirm": 33842, "ĠBooks": 33843, "ientôt": 33844, "Ġespacio": 33845, "ĠScholars": 33846, "Ġdoomed": 33847, "ĠIRS": 33848, "wohl": 33849, "Ġsegue": 33850, "ĠëĪĦê°Ģ": 33851, "Ġpratic": 33852, "BT": 33853, "ĠConsidering": 33854, "ĠBuffalo": 33855, "Ġtrainings": 33856, "Ġgebru": 33857, "ĠGleich": 33858, "Ġpirates": 33859, "Ġenvelop": 33860, "Ġreopen": 33861, "imat": 33862, "Ġtee": 33863, "Ġsued": 33864, "feh": 33865, "Ġ×Ķק": 33866, "Ġdiets": 33867, "Ġjuntos": 33868, "asto": 33869, "Ġmisunderstood": 33870, "Ġruim": 33871, "Ġclassify": 33872, "ĠпÑĢодÑĥк": 33873, "Ġinse": 33874, "Ġillustrated": 33875, "Ġcorrosion": 33876, "Ġaccred": 33877, "ĠAuntie": 33878, "ĠпÑĢивеÑĤ": 33879, "ĠLIVE": 33880, "Ġrek": 33881, "Ġreceipt": 33882, "åĪ°åºķ": 33883, "ĠBarbie": 33884, "ĠSnake": 33885, "turn": 33886, "Jeff": 33887, "ãģĬãģĬ": 33888, "ķĦ": 33889, "VOICEOVER": 33890, "coll": 33891, "Ġrunners": 33892, "ìłľë": 33893, "osos": 33894, "moon": 33895, "Ġkeynote": 33896, "ĠInstit": 33897, "SPEAK": 33898, "Ġplugs": 33899, "Ġcurv": 33900, "ĠYuri": 33901, "ĠTheres": 33902, "ĠPs": 33903, "ĠμÏĢο": 33904, "Ġconverter": 33905, "Ġrefine": 33906, "Ġbadass": 33907, "Ġοι": 33908, "Ġregen": 33909, "azzi": 33910, "ÙĬÙģ": 33911, "Ġseized": 33912, "Ġiçer": 33913, "ilee": 33914, "Ġupstream": 33915, "Ġbuds": 33916, "Ġpim": 33917, "Ġíķĺ루": 33918, "Ġalluded": 33919, "Ġthemed": 33920, "Ġconsisting": 33921, "Ġbons": 33922, "unuz": 33923, "ĠпÑĢовод": 33924, "ĠLovely": 33925, "à¥ĭ": 33926, "Ġparach": 33927, "ĠStaats": 33928, "éļĬ": 33929, "Ġselective": 33930, "Ġfase": 33931, "ĠGeorget": 33932, "Ġcocaine": 33933, "Ġreproduction": 33934, "ĠLara": 33935, "ĠLD": 33936, "Ġgh": 33937, "Jon": 33938, "ĠlÃ¥": 33939, "ĠëijIJë": 33940, "Ġtyped": 33941, "ĠBana": 33942, "ëĵľë": 33943, "Ġsavory": 33944, "ĠZomb": 33945, "standen": 33946, "Ġpedestrian": 33947, "Ġdifférents": 33948, "Ġìĭ¸": 33949, "èī¯": 33950, "Ġcomplained": 33951, "ç¦ı": 33952, "ĠÐļÑĤо": 33953, "Ġ׾פ": 33954, "aliÅĽmy": 33955, "Ġmortar": 33956, "Ġverdict": 33957, "Ġsuficiente": 33958, "ĠMillion": 33959, "mittel": 33960, "inals": 33961, "ĠاÙĦØ®": 33962, "аÑİÑģÑĮ": 33963, "ĠmiÄĻdzy": 33964, "ĠOle": 33965, "Ġinvert": 33966, "czyÄĩ": 33967, "озможно": 33968, "starter": 33969, "Ġauditor": 33970, "ĠScout": 33971, "chien": 33972, "ĠSverige": 33973, "uffled": 33974, "Ġzehn": 33975, "ĠAuckland": 33976, "Ġargent": 33977, "Ġ1976": 33978, "ĠHoe": 33979, "Ġbothers": 33980, "Ġsocialist": 33981, "Ġpliers": 33982, "Ġemergen": 33983, "ĠXP": 33984, "еÑĢов": 33985, "More": 33986, "ĠLevi": 33987, "ĠAnders": 33988, "ibilidad": 33989, "ĠParents": 33990, "Ġinduced": 33991, "ìĸ´ì¤": 33992, "Ġbalances": 33993, "ĠвÑĭÑĪ": 33994, "Ġsubmarine": 33995, "Start": 33996, "Ġdries": 33997, "Ġvolver": 33998, "Ġticking": 33999, "cott": 34000, "Ġfaj": 34001, "prés": 34002, "ĠSabb": 34003, "ĠзаÑĩ": 34004, "ĠпокÑĥп": 34005, "Ġbaptized": 34006, "ĠBrilliant": 34007, "ĠÐijог": 34008, "Ġmots": 34009, "bits": 34010, "Ġlattice": 34011, "æĪijè·Łä½ł": 34012, "Ġcoriander": 34013, "Ġresidency": 34014, "ync": 34015, "Ġpierwszy": 34016, "ĠKnock": 34017, "ĠZap": 34018, "ĠÐķв": 34019, "견": 34020, "å°ıå¿ĥ": 34021, "Ġuneven": 34022, "ĠJas": 34023, "odor": 34024, "ç¿Ĵ": 34025, "74": 34026, "ĠSite": 34027, "Ġaconteceu": 34028, "ympt": 34029, "Ġtrilogy": 34030, "Ġlantern": 34031, "ĠZucker": 34032, "vari": 34033, "welling": 34034, "ĠPotato": 34035, "gomery": 34036, "Ġreacted": 34037, "ĠChron": 34038, "Ġjede": 34039, "beeld": 34040, "Ġtwent": 34041, "Ġlact": 34042, "æ¨Ĥ": 34043, "Ġrése": 34044, "Ġrelent": 34045, "Ġfurnace": 34046, "Ġwidget": 34047, "Ġearthquakes": 34048, "ĠAdjust": 34049, "ilit": 34050, "ĠØ£ÙĪ": 34051, "Ġhearings": 34052, "Ġdefendant": 34053, "irsiniz": 34054, "Ġbask": 34055, "cja": 34056, "ľ¨": 34057, "Ġrifles": 34058, "Ġinstal": 34059, "ĠForgive": 34060, "pical": 34061, "ĠÐŀÑĩенÑĮ": 34062, "Ġpetites": 34063, "Ġhp": 34064, "Ġrenowned": 34065, "ĠInn": 34066, "Ġ주ìĦ¸ìļĶ": 34067, "Ġemphasized": 34068, "éĹ®é¢ĺ": 34069, "ĠìŀĪì£ł": 34070, "Ġê²ĥìľ¼ë¡ľ": 34071, "ãĤĨ": 34072, "Åĵ": 34073, "gili": 34074, "Dave": 34075, "Ġexhausting": 34076, "ÅĤug": 34077, "Ġschema": 34078, "μά": 34079, "cycl": 34080, "Ġautant": 34081, "Ġparcel": 34082, "Ġmateria": 34083, "ĠBerry": 34084, "ĠÑģами": 34085, "Ġextracted": 34086, "ĠSaying": 34087, "ismatic": 34088, "ĠпопÑĢоб": 34089, "Ġneuron": 34090, "graph": 34091, "ľë©´": 34092, "Ġenclosure": 34093, "ĠJohann": 34094, "Ġaftermath": 34095, "ÑĤоб": 34096, "Ġuży": 34097, "Ġsamp": 34098, "360": 34099, "ĠMei": 34100, "Ġtaco": 34101, "Ġreceptors": 34102, "Ġpunches": 34103, "ĠHoje": 34104, "ĠÙĩÙĨا": 34105, "=\"#": 34106, "ĠAngular": 34107, "Ġmusique": 34108, "Ġrol": 34109, "Ġñ": 34110, "sterreich": 34111, "Ġclam": 34112, "ĠTreasury": 34113, "chemical": 34114, "Ġapar": 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, "ĠElite": 34404, "Ġallons": 34405, "Ġmayonnaise": 34406, "ĠSustain": 34407, "prisingly": 34408, "Ġsupervis": 34409, "Ġê·¸ëłĩì£ł": 34410, "Ġunemployed": 34411, "Ġfreshly": 34412, "Ġ×ŀ×¢": 34413, "ĠDh": 34414, "Ġtackling": 34415, "Ġogr": 34416, "Ġì´Īë": 34417, "ãĤĪãĤį": 34418, "Ġloft": 34419, "arah": 34420, "ĠAirl": 34421, "ĠDir": 34422, "ĠÐľÐ¾Ð¶Ð½Ð¾": 34423, "Ġbooking": 34424, "ĠCRA": 34425, "Ġhttps": 34426, "Ġchoke": 34427, "Ġgown": 34428, "Ġnoite": 34429, "Ġzac": 34430, "istol": 34431, "Ġsecre": 34432, "Ġresembles": 34433, "Ġcuad": 34434, "ìĤ¬ê°Ģ": 34435, "show": 34436, "Ġblanc": 34437, "Ġagu": 34438, "ĠPrint": 34439, "asted": 34440, "ĠWeather": 34441, "ipl": 34442, "Ġobscure": 34443, "Ġconte": 34444, "oughs": 34445, ");": 34446, "ĠDame": 34447, "ä¸Ģ缴": 34448, "Ġclarification": 34449, "Ġintimacy": 34450, "Ġuphold": 34451, "ĠMirror": 34452, "Ġwagon": 34453, "xide": 34454, "Ġclog": 34455, "apper": 34456, "ĠImmediately": 34457, "úde": 34458, "Ġtouchdown": 34459, "Ġrooft": 34460, "аÑĪа": 34461, "Ġçıkt": 34462, "Ġlaisser": 34463, "ĠUnreal": 34464, "ensitive": 34465, "Ġ123": 34466, "Ġplaster": 34467, "Ġducks": 34468, "Ġetme": 34469, "Ġbishop": 34470, "brevi": 34471, "Ġbic": 34472, "ä¸ĭåİ»": 34473, "Ġruntime": 34474, "Ġambitions": 34475, "маÑĤ": 34476, "ĠWein": 34477, "ĠMari": 34478, "ĠíĬ¸ë": 34479, "Ġresolver": 34480, "ĠngÃły": 34481, "ĠRise": 34482, "ãĤĪãģĨãģ«": 34483, "ĠCrus": 34484, "Ġmerchandise": 34485, "Ġeli": 34486, "Ġstatewide": 34487, "Ġowl": 34488, "éģł": 34489, "æĶ¹": 34490, "Ġtwisting": 34491, "Ġcontaminated": 34492, "ĠCommerce": 34493, "hythm": 34494, "ĠÃĪ": 34495, "Ġìĭ¤ë": 34496, "Ġmusste": 34497, "uir": 34498, "Ġsums": 34499, "ĠSomewhere": 34500, "ãĥİ": 34501, "Ġkami": 34502, "Ġaired": 34503, "ĠANDREW": 34504, "Ġêº": 34505, "Ġviendo": 34506, "Ġantibody": 34507, "Ġabsolument": 34508, "Ġprotesters": 34509, "ĠQuébec": 34510, "stadt": 34511, "Shaun": 34512, "Ġchambers": 34513, "ĠWear": 34514, "ĠEffects": 34515, "Ġhazards": 34516, "Ġnei": 34517, "Ġcorazón": 34518, "Ġá¼": 34519, "ĠSG": 34520, "Ķ©": 34521, "ĠìĹŃìĭľ": 34522, "Ġcomfy": 34523, "ĠCody": 34524, "Ġpensando": 34525, "Ġganska": 34526, "ĠAcross": 34527, "öllig": 34528, "abyte": 34529, "Ġwedge": 34530, "Ġkalian": 34531, "Ġsigue": 34532, "endes": 34533, "ĠGroÃŁ": 34534, "Ġutiliser": 34535, "Ġflown": 34536, "аниÑİ": 34537, "Ġlevar": 34538, "restrial": 34539, "Ġillustrations": 34540, "Ġaslında": 34541, "BLEEP": 34542, "ĠдоÑģÑĤ": 34543, "Ġturret": 34544, "Ġsuitcase": 34545, "ziÄĻki": 34546, "Ġsketches": 34547, "Ġacred": 34548, "ĠRei": 34549, "Ġtsun": 34550, "ĠSag": 34551, "Ġthirds": 34552, "ĠKIRBY": 34553, "rai": 34554, "Ġhumanos": 34555, "Ġrecommends": 34556, "Ġextraordinarily": 34557, "Ġcommencement": 34558, "KN": 34559, "opez": 34560, "Ġ×ijש": 34561, "Ġlethal": 34562, "ĠEstamos": 34563, "Ġinspector": 34564, "ĠSeok": 34565, "eun": 34566, "Ġoffshore": 34567, "Ġgettin": 34568, "years": 34569, "ĠSilence": 34570, "ĠNatur": 34571, "upun": 34572, "Ġtrzy": 34573, "Ġnoget": 34574, "Ġhamburger": 34575, "ĠPraise": 34576, "énd": 34577, "Ġ1971": 34578, "ylie": 34579, "krit": 34580, "ĠìĥĿê°ģìĿ´": 34581, "çļ®": 34582, "Ġmomentos": 34583, "Ġesté": 34584, "Ġdissemin": 34585, "Ġgigs": 34586, "Ġdesaf": 34587, "Ġavis": 34588, "ĠZoo": 34589, "ĠìķĬìĿĢ": 34590, "häng": 34591, "åı¥": 34592, "hake": 34593, "ĠBism": 34594, "Ġrethink": 34595, "ĠMalcolm": 34596, "Ġidentifies": 34597, "lower": 34598, "ixel": 34599, "ĠtvÃ¥": 34600, "ked": 34601, "ierz": 34602, "Ġöffentlich": 34603, "Ġproclaim": 34604, "soon": 34605, "lol": 34606, "Ġloi": 34607, "Ġbitten": 34608, "rollo": 34609, "Ġsermon": 34610, "Ġesqu": 34611, "Ġjackets": 34612, "Ġgráfic": 34613, "ĠпоказÑĭв": 34614, "Ġcabeza": 34615, "chodzi": 34616, "Ġpelvis": 34617, "Ġnostalgia": 34618, "Ġbrew": 34619, "Ġshortcuts": 34620, "ĠAdemás": 34621, "Ġsuperficial": 34622, "åħ©åĢĭ": 34623, "Ġboca": 34624, "ĠæĪijæĺ¯": 34625, "imentos": 34626, "åĽłä¸º": 34627, "Ġsprouts": 34628, "é£Ľ": 34629, "ĠJonas": 34630, "ĠFlorence": 34631, "static": 34632, "daughter": 34633, "*)": 34634, "ÅĤby": 34635, "fashion": 34636, "ĠGinger": 34637, "Ġ매ë": 34638, "Ġhustle": 34639, "utos": 34640, "ĠÑĤÑıж": 34641, "ĠLös": 34642, "ש×Ļ×Ŀ": 34643, "anych": 34644, "tuber": 34645, "Ġtidy": 34646, "Ġfrontal": 34647, "Ġwhiskey": 34648, "Ġhumid": 34649, "ĠÎŁ": 34650, "Ġridge": 34651, "Ġmarin": 34652, "Ġbientôt": 34653, "ĠCarrie": 34654, "chw": 34655, "Ġtahun": 34656, "ĠErgeb": 34657, "FR": 34658, "Ġìłķë¶Ģ": 34659, "ĠSoldier": 34660, "Ġenlightenment": 34661, "Ġexamining": 34662, "ĠNotre": 34663, "Ġeram": 34664, "ĠSunny": 34665, "Ġlayered": 34666, "ĠDazu": 34667, "rades": 34668, "好åIJĥ": 34669, "ĠнаÑĪей": 34670, "Ġtimber": 34671, "Ġmanners": 34672, "ĠBirmingham": 34673, "Ġminiature": 34674, "ometers": 34675, "Ġfiller": 34676, "ĠRip": 34677, "ĠKomb": 34678, "owner": 34679, "ì¿": 34680, "idian": 34681, "Ġdemás": 34682, "ĠÙĪت": 34683, "Ġprecautions": 34684, "Ġgoverno": 34685, "zelf": 34686, "ĠComplete": 34687, "å¸ĥ": 34688, "ĠPhantom": 34689, "ãģ¾ãģļ": 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, "Ġversuchen": 34749, "Ġtransistor": 34750, "ĠÐŁÑĢоÑģÑĤо": 34751, "ĠпонÑĢав": 34752, "Ġrhyme": 34753, "ĠVermont": 34754, "platz": 34755, "è®°": 34756, "ĠÄ°ÅŁte": 34757, "ĠHag": 34758, "ĠÐĺм": 34759, "ĠÑĢаÑģÑģказ": 34760, "Ġmetros": 34761, "ĠInfinity": 34762, "wolf": 34763, "ibal": 34764, "ftig": 34765, "ĠÚĨ": 34766, "Ġíĺ¹ìĭľ": 34767, "Ġoggi": 34768, "Ġdisposit": 34769, "ĠпÑĢил": 34770, "ĠвÑĭпол": 34771, "Ġthôi": 34772, "ĠKENN": 34773, "Ġhanding": 34774, "actus": 34775, "Ġtacos": 34776, "Ġformerly": 34777, "ĠCorinthians": 34778, "ãģ«ãģ¯": 34779, "ÑĨÑĸÑĹ": 34780, "Ġpadre": 34781, "Ġcongregation": 34782, "æij": 34783, "fert": 34784, "Ġsubir": 34785, "aiser": 34786, "qua": 34787, "araoh": 34788, "ĠCurry": 34789, "ĠìķĬëĬĶ": 34790, "елÑİ": 34791, "Ġfuss": 34792, "Ġbooty": 34793, "Ġlows": 34794, "Ġhommes": 34795, "ĠMH": 34796, "ĠDisneyland": 34797, "went": 34798, "Ġresidue": 34799, "Ġbeeping": 34800, "è¼ķ": 34801, "ätta": 34802, "Ġmould": 34803, "ĠProjekt": 34804, "stalk": 34805, "Ġartifact": 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, 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"Ġadapting": 34942, "ШÐIJ": 34943, "ĠScr": 34944, "ändert": 34945, "Ġplaus": 34946, "ä»Ĭ天çļĦ": 34947, "Ġonset": 34948, "Ġassistants": 34949, "Ġvalves": 34950, "Ġscatter": 34951, "ĠRust": 34952, "awia": 34953, "Ġreadiness": 34954, "Ġpais": 34955, "Ġbible": 34956, "Ġambiente": 34957, "ĠамеÑĢик": 34958, "Ġuncond": 34959, "Ġkalk": 34960, "åĬ¨": 34961, "Ġmoc": 34962, "unn": 34963, "Ġactu": 34964, "Ġhumming": 34965, "issimo": 34966, "ĠPatrol": 34967, "gow": 34968, "ãĥ¤": 34969, "ĠTHEY": 34970, "ĠBoden": 34971, "ĠBie": 34972, "Ġreel": 34973, "ĠÑĥÑģлов": 34974, "Ġendeavor": 34975, "ĠPeriod": 34976, "ustomed": 34977, "mals": 34978, "alon": 34979, "Box": 34980, "ĠÏĥαÏĤ": 34981, "Ġomdat": 34982, "Ġaltre": 34983, "ĠHeh": 34984, "kad": 34985, "Ġprotector": 34986, "Ġdominance": 34987, "odynamic": 34988, "Ġcommunicated": 34989, "kö": 34990, "Ġpredecessor": 34991, "ĠLuk": 34992, "ĠFlower": 34993, "Ġãģ©": 34994, "poque": 34995, "ÑĤиÑĢов": 34996, "Ġretrospect": 34997, "Ġdecisive": 34998, "Ġexempel": 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"Ġsalaries": 35057, "Ġinsecurity": 35058, "ÅĤoÅĽci": 35059, "ĠабÑģолÑİÑĤно": 35060, "inken": 35061, "ĠEddy": 35062, "udos": 35063, "Ġdummy": 35064, "Ðļак": 35065, "six": 35066, "Ġinbox": 35067, "ẩ": 35068, "People": 35069, "á»ĵng": 35070, "Ġorganizers": 35071, "find": 35072, "Ġül": 35073, "ĠCOM": 35074, "ża": 35075, "weile": 35076, "Commentary": 35077, "íĬ¸ë¥¼": 35078, "ĠMittel": 35079, "kus": 35080, "èĽĭ": 35081, "न": 35082, "iral": 35083, "Ġgarment": 35084, "ικά": 35085, "Ġstool": 35086, "payers": 35087, "Ġshimmer": 35088, "ĠOllie": 35089, "ĠJeżeli": 35090, "è¿ĺæľī": 35091, "Ġ1977": 35092, "Ġjeux": 35093, "Ġextinct": 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": 35152, "ĠDNS": 35153, "Ġönemli": 35154, "ĠLuo": 35155, "Ŀ¤": 35156, "ĠCounsel": 35157, "ligen": 35158, "анÑĮÑĪе": 35159, "Ġtrumpet": 35160, "Ġdapat": 35161, "ĠJM": 35162, "ĠEVERY": 35163, "Ġå°įä¸įå°į": 35164, "夢": 35165, "ĠLayer": 35166, "Ġcô": 35167, "нал": 35168, "ĠJoo": 35169, "ĠHack": 35170, "Ġsunt": 35171, "ĠLeonard": 35172, "ĠFirebase": 35173, "änger": 35174, "Ġexploding": 35175, "voy": 35176, "Ġì¦IJ": 35177, "ĠÑģеÑĢÑĮ": 35178, "Ġseverity": 35179, "Ġbestimm": 35180, "çµIJæŀľ": 35181, "Ġtiring": 35182, "Ġprocurement": 35183, "Ġdiplomacy": 35184, "Ġdecorative": 35185, "ĠÙĬا": 35186, "Ġpenetration": 35187, "Õ«": 35188, "Ġoutright": 35189, "ENE": 35190, "ĠUni": 35191, "odles": 35192, "Ġzeros": 35193, "Ġdelightful": 35194, "jm": 35195, "Ġdopo": 35196, "没äºĭ": 35197, "Ġpositivity": 35198, "ĠVISTA": 35199, "ĠResource": 35200, "íĥĢë": 35201, "ÑĪие": 35202, "Carl": 35203, "Ġpiping": 35204, "Ġchopping": 35205, "ĠGanze": 35206, "üss": 35207, "ĠAo": 35208, "Ġshattered": 35209, "ĠDetective": 35210, "Ġundoubtedly": 35211, "Ġhalluc": 35212, "Ġench": 35213, "ÑĭÑĩно": 35214, "ÑĥлÑıÑĢ": 35215, "isesti": 35216, "Ġpedals": 35217, "Ġdurum": 35218, "¤íĶ": 35219, "laimer": 35220, "Ġpropre": 35221, "Cu": 35222, "Ġtranslator": 35223, "ĠcaÅĤ": 35224, "Ġ그걸": 35225, "ĠcaÅĤy": 35226, "UA": 35227, "Ġrevised": 35228, "Ġподоб": 35229, "ĠArticle": 35230, "ĠHaiti": 35231, "ĠÃĵ": 35232, "ĠCtrl": 35233, "Ġrozm": 35234, "lait": 35235, "Ġletzte": 35236, "ispering": 35237, "display": 35238, "Ġaluminium": 35239, "Ġpalabras": 35240, "Ġconocer": 35241, "Ġzitten": 35242, "Ġdirig": 35243, "åıªæľī": 35244, "Ġbrainstorm": 35245, "Ġwifi": 35246, "ĠParticip": 35247, "Ġviewpoint": 35248, "ĠQuan": 35249, "Ġhierarch": 35250, "Welcome": 35251, "対": 35252, "Ġoffen": 35253, "ĠRecovery": 35254, "gano": 35255, "Would": 35256, "Ġrepro": 35257, "Ġperceptions": 35258, "Ġdemasi": 35259, "ĠBangladesh": 35260, "ĠIncredible": 35261, "Ġletzt": 35262, "Ġbehaving": 35263, "Ġastonishing": 35264, "ĠâĨ": 35265, "ĠëĤ¨ìŀIJ": 35266, "èµ°äºĨ": 35267, "ãĥĶ": 35268, "ĠGORDON": 35269, "CAR": 35270, "?!\"": 35271, "ĠPrest": 35272, "Ġë§ŀìķĦìļĶ": 35273, "Ġtand": 35274, "Ġlash": 35275, "çĬ": 35276, "ificant": 35277, "Ġintoler": 35278, "ĠгеÑĢо": 35279, "Ġteu": 35280, "aso": 35281, "ĠÑģовеÑĤ": 35282, "Ġtravelers": 35283, "ĠSynd": 35284, "ĠвеÑĢÑģ": 35285, "Fonda": 35286, "adı": 35287, "Ġtranscription": 35288, "Ġtitanium": 35289, "Ġtwists": 35290, "Ġgearbox": 35291, "ensation": 35292, "fat": 35293, "Coll": 35294, "ĠCommonwealth": 35295, "zon": 35296, "ĠPolizei": 35297, "ĠAPPLAUSE": 35298, "fry": 35299, "ĠJuda": 35300, "esteem": 35301, "Ġsock": 35302, "ĠJugend": 35303, "ĠкÑģÑĤаÑĤи": 35304, "ĠDro": 35305, "Ġprochaine": 35306, "ãĥ¼ãĥ«": 35307, "Ġliksom": 35308, "ĠEnergie": 35309, "ĠMarina": 35310, "Ġ230": 35311, "Ġê°ĢìĦľ": 35312, "umping": 35313, "Ġlone": 35314, "ç´ļ": 35315, "Ġfonts": 35316, "Ġbusinessman": 35317, "Ġply": 35318, "Ġdoe": 35319, "grid": 35320, "ĠMilwaukee": 35321, "ĠEden": 35322, "!\".": 35323, "ĠÛĮÛģ": 35324, "ogens": 35325, "Ġteaser": 35326, "Ġquién": 35327, "Ġincentiv": 35328, "govern": 35329, "Ġchildcare": 35330, "Ġsneakers": 35331, "Ġimprisoned": 35332, "®": 35333, "иÑĤеÑģÑĮ": 35334, "anbul": 35335, "Ġregain": 35336, "Ġtranquil": 35337, "Redner": 35338, "鼨": 35339, "IFA": 35340, "Ġideological": 35341, "ĠmayorÃŃa": 35342, "Ġbureau": 35343, "eterm": 35344, "ĠDID": 35345, "ìĬ·": 35346, "Ġwaving": 35347, "Ġbeb": 35348, "Ġár": 35349, "Ġкв": 35350, "Ġenvoy": 35351, "anut": 35352, "икÑĥ": 35353, "ĠEnvironment": 35354, "ĠAssass": 35355, "ãĤĵãģ§": 35356, "ĠBread": 35357, "ĠТÑĥÑĤ": 35358, "Ġstaircase": 35359, "ĠDisease": 35360, "Ġaucun": 35361, "ĠëĭĪ": 35362, "Ġconfrontation": 35363, "Ġ1941": 35364, "Ġirony": 35365, "Ġworsh": 35366, "ãĤĮãĤĭ": 35367, "Ġfick": 35368, "ĠNaomi": 35369, "Ġbackside": 35370, "ieux": 35371, "Kap": 35372, "Ġvedere": 35373, "Ġlengthy": 35374, "Ġbreaker": 35375, "ĠRolle": 35376, "Ġpredator": 35377, "Ġnossos": 35378, "Ġadvertise": 35379, "è³ĩ": 35380, "ÑĢоде": 35381, "Rednerwechsel": 35382, "reten": 35383, "Ġcollectors": 35384, "ıģımız": 35385, "Ġtrig": 35386, "Ġaxes": 35387, "inters": 35388, "Ġpenalties": 35389, "ĠOsman": 35390, "ĠJenna": 35391, "Ġflakes": 35392, "Ġtrainers": 35393, "Ġstunned": 35394, "ĠScroll": 35395, "ĠPip": 35396, "ĠнаÑģÑĤ": 35397, "ĠnhÃł": 35398, "ĠSmack": 35399, "ẫn": 35400, "ratos": 35401, "ĠÑĢабоÑĤÑĭ": 35402, "Ġucz": 35403, "ĠLemon": 35404, "ĠSind": 35405, "Ġpsychic": 35406, "ĠAbg": 35407, "Ġmammals": 35408, "Ġimmersive": 35409, "Ġbots": 35410, "Ġverschiedene": 35411, "Ġgeral": 35412, "Ġfollower": 35413, "Ġä»ĸ": 35414, "Ġseguridad": 35415, "Ġimmersed": 35416, "feito": 35417, "cross": 35418, "Ġöld": 35419, "íĥĦ": 35420, "Ġãģĵãģ®": 35421, "Ġ×Ķ×Ļ×IJ": 35422, "ĠJian": 35423, "Ġbiliyor": 35424, "area": 35425, "Ġkaf": 35426, "Ġgodt": 35427, "çĽ¸ä¿¡": 35428, "Ġë°©ìĨ¡": 35429, "Ġdetriment": 35430, "æ¥ļ": 35431, "Ñĸл": 35432, "ĠÄijâu": 35433, "Ġchloride": 35434, "øre": 35435, "lei": 35436, "Ġmonte": 35437, "Ġdifférentes": 35438, "à¯ģ.": 35439, "Ġcaregivers": 35440, "Ġinadequ": 35441, "Ġfarewell": 35442, "ĠÑĤипа": 35443, "ontec": 35444, "ĠEph": 35445, "HHH": 35446, "ĠTodos": 35447, "ĠСШÐIJ": 35448, "Ġtrov": 35449, "Ġlige": 35450, "Ġcông": 35451, "ĠCiv": 35452, "Ġcapaz": 35453, "ĠVallahi": 35454, "Ġqueste": 35455, "Ġreplica": 35456, "سب": 35457, "zna": 35458, "ĠÑģлÑĥж": 35459, 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36438, "Ġanimate": 36439, "×ķ×IJ×": 36440, "еÑĢв": 36441, "Ġminced": 36442, "Ġkaum": 36443, "ãģĤãģģ": 36444, "ÏĢε": 36445, "лег": 36446, "existing": 36447, "Ġplataform": 36448, "ĠKRIS": 36449, "ìĽł": 36450, "ĠFamilien": 36451, "ĠLibya": 36452, "Ġbiodiversity": 36453, "Ġidiots": 36454, "irdi": 36455, "Ġszyb": 36456, "ĠRolling": 36457, "ücht": 36458, "ĠÑĥдив": 36459, "ÑģÑĥд": 36460, "Ġrealizar": 36461, "Ġcanned": 36462, "ĠÑĢан": 36463, "Ġmetabolic": 36464, "ĠBeef": 36465, "Ġkilka": 36466, "лÑİÑģ": 36467, "Ġregistry": 36468, "моÑĤÑĢиÑĤе": 36469, "Ġvielä": 36470, "Ġodc": 36471, "Ġcondemned": 36472, "æ©ĭ": 36473, "fal": 36474, "ĠDil": 36475, "woÅĽci": 36476, "Aw": 36477, "Ġstatistically": 36478, "Ġsogen": 36479, "ĠBETH": 36480, "Ġshaving": 36481, "幸": 36482, "ocal": 36483, "ĠFunny": 36484, "Ġpeacefully": 36485, "Ġaddictive": 36486, "ĠInsert": 36487, "lauf": 36488, "Ġexperiencia": 36489, "é¦ĸåħĪ": 36490, "иÑĤелÑı": 36491, "ÃŃgen": 36492, "ágina": 36493, "Ġabdomen": 36494, "íķľëĭ¤": 36495, 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36666, "à±": 36667, "Ġstumbled": 36668, "Ġaider": 36669, "Ġsaben": 36670, "xter": 36671, "ĠCities": 36672, "ĠTürk": 36673, "ëĭ¥": 36674, "chine": 36675, "Ġtopping": 36676, "Ġpoisoned": 36677, "ĠRomania": 36678, "×ĵ×Ļ": 36679, "Ģë¡ľ": 36680, "ĠпоÑĢÑıд": 36681, "Ġchirping": 36682, "ĠìĻĦë": 36683, "×ij×¢": 36684, "Ġcuanto": 36685, "Ġdonating": 36686, "ĠRegent": 36687, "ĠBeruf": 36688, "Ġdistracting": 36689, "Ġstamina": 36690, "ĠDarren": 36691, "Ġì¶ķ": 36692, "lists": 36693, "dal": 36694, "chuss": 36695, "Ġeconomist": 36696, "ãģĪãĥ¼": 36697, "orgt": 36698, "Ġistiyorum": 36699, "è¿Ľ": 36700, "ĠSurprise": 36701, "ĠHao": 36702, "Ġìµľê³ł": 36703, "ĠGW": 36704, "ĠInner": 36705, "Ġquieren": 36706, "Ġminded": 36707, "Ġsupercomputer": 36708, "Ġdiagrams": 36709, "íĬľë": 36710, "ê²łìĸ´": 36711, "ĠобÑĬÑıÑģ": 36712, "Ġestaban": 36713, "Ġdestroys": 36714, "ĠBreaking": 36715, "ĠkarÄ±ÅŁ": 36716, "Ġrebuilding": 36717, "ľëĮĢ": 36718, "ливо": 36719, "ĠSauce": 36720, "ĠFusion": 36721, "×ķ×ŀ×": 36722, 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36781, "òng": 36782, "çŁ³": 36783, "«": 36784, "ĠClaudia": 36785, "ĠCharacter": 36786, "ниÑĨ": 36787, "coat": 36788, "Ġprogressed": 36789, "ĠFergus": 36790, "Ġìĺ¤ëĬ": 36791, "Ġoat": 36792, "ordable": 36793, "ĠLey": 36794, "ĠHeraus": 36795, "Ġresultados": 36796, "ĠKayla": 36797, "Ġriff": 36798, "Ġchegou": 36799, "Ġxi": 36800, "Ġspacious": 36801, "Ġrecognised": 36802, "Ġech": 36803, "ĠTie": 36804, "Ġlauncher": 36805, "Jim": 36806, "Ġsuppression": 36807, "ĠImpossible": 36808, "Ġguitars": 36809, "ĠFourier": 36810, "иÑĩеÑģкий": 36811, "ĠTherap": 36812, "ĠKaf": 36813, "centered": 36814, "ĠÑģооÑĤвеÑĤ": 36815, "Ġklim": 36816, "Ġcarbohydrates": 36817, "ignant": 36818, "ĠAstron": 36819, "Ġemple": 36820, "Ġdrastic": 36821, "ĠмиÑĢе": 36822, "вин": 36823, "uw": 36824, "Ġprettier": 36825, "Ġdonuts": 36826, "ĠAthena": 36827, "Ġdissert": 36828, "Ġplante": 36829, "Ġuranium": 36830, "ìĿĮë": 36831, "aré": 36832, "Ġrzecz": 36833, "Ġdisplaying": 36834, "æĪ²": 36835, "Ġsarc": 36836, "rão": 36837, "Ġtampoco": 36838, "Ġphilosophers": 36839, "ĠRecht": 36840, "æĵļ": 36841, "Ġcomentarios": 36842, "yse": 36843, "Ġìľ¤": 36844, "Ġmise": 36845, "ĠGin": 36846, "Ġном": 36847, "ĠFROM": 36848, "liner": 36849, "atif": 36850, "ĠspoÅĤec": 36851, "xa": 36852, "ĠÑĤÑĢÑĥд": 36853, "Ġwag": 36854, "기ìĹIJ": 36855, "ĠMG": 36856, "Ġoffspring": 36857, "ĠUnderstanding": 36858, "åıªæĺ¯": 36859, "ORA": 36860, "Ġwhirring": 36861, "Ġsurrend": 36862, "Ġpoker": 36863, "Ġmonuments": 36864, "ĠâĻ©": 36865, "Ġorganised": 36866, "ĠSozial": 36867, "ĠFactory": 36868, "Ñħа": 36869, "Ġresemble": 36870, "зд": 36871, "Ġexplosions": 36872, "Ġpayroll": 36873, "Ġomn": 36874, "ĠJorge": 36875, "ιÏĥ": 36876, "Ġfracture": 36877, "Ġpersecution": 36878, "Ġdemais": 36879, "ECH": 36880, ",)": 36881, "Ġcriar": 36882, "ĠJOSH": 36883, "Ġdemographics": 36884, "Ġ1600": 36885, "Ġcurrencies": 36886, "ĠTips": 36887, "ĠéĢĻåĢĭ": 36888, "ĠRefer": 36889, "ĠDancing": 36890, "Ġinconsistent": 36891, "Ġdeh": 36892, "Ġimmens": 36893, "Ġmeist": 36894, "Ġimpatient": 36895, "Ġbehaves": 36896, "æĿ¾": 36897, "ĠëĤ´ìļ©": 36898, "Ġbackstory": 36899, "Ġagreeing": 36900, "ĠÅģ": 36901, "ihin": 36902, "Ġtemperatura": 36903, "ĠBackground": 36904, "Ġnutzen": 36905, "Ġëħ¹": 36906, "ĠMänner": 36907, "Ġcollaborations": 36908, "ĠKos": 36909, "éģİåİ»": 36910, "Ġnightmares": 36911, "ëĵ±": 36912, "ĠQueensland": 36913, "Ġassociates": 36914, "ĠKok": 36915, "Ġfactorial": 36916, "ĠHyung": 36917, "Ġê·¸ëĭ¤ìĿĮ": 36918, "Ġfilho": 36919, "Ġelét": 36920, "Ġíĸīë³µ": 36921, "°±": 36922, "Ġgefunden": 36923, "Ġsemicondu": 36924, "Ġcounselors": 36925, "ĠUpper": 36926, "ĠAub": 36927, "ickers": 36928, "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, 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"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": 38993, "ĠÑģим": 38994, "Ġизб": 38995, "ĠPanel": 38996, "Ġinstincts": 38997, "ĠCommunications": 38998, "麻": 38999, "midt": 39000, "Ġë§Įëĵ¤ìĸ´": 39001, "ĠÑģлова": 39002, "ĠGilbert": 39003, "缮åīį": 39004, "Так": 39005, "voorbeeld": 39006, "еÑİÑģÑĮ": 39007, "aryn": 39008, "quez": 39009, "Ġdart": 39010, "ÑĸÑĪ": 39011, "ĠHut": 39012, "Sal": 39013, "Ġsoutheast": 39014, "Ġpesticides": 39015, "Ġhelicopters": 39016, "Ġendured": 39017, "iada": 39018, "Ġbrewing": 39019, "ìŬë": 39020, "ĠÑģвобод": 39021, "ĠSaints": 39022, "ĠFrançais": 39023, "ĠEconomics": 39024, "Ġdisloc": 39025, "ophobia": 39026, "Camer": 39027, "Ġnegotiated": 39028, "ĠÑģÑĤали": 39029, "ìĬ¤íģ": 39030, "ogie": 39031, "Ġtsunami": 39032, "Ġpeeled": 39033, "Ġmotivations": 39034, "è¨Ń": 39035, "ostat": 39036, "flan": 39037, "ĠDAC": 39038, "Ġkav": 39039, "'RE": 39040, "ĠPearson": 39041, "bbe": 39042, "czenie": 39043, "Ġatenção": 39044, "íĨµëł¹": 39045, "ãģ£ãģ¡": 39046, "ĠÑĥдаÑĢ": 39047, "Ġintroductory": 39048, "ĠIci": 39049, "ëĮĢë": 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, "ĠSpit": 39108, "ĠItu": 39109, "Ġstrains": 39110, "Ġstamped": 39111, "Ġplaint": 39112, "Ġpotion": 39113, "Ġconsolidation": 39114, "è©ķ": 39115, "оÑĩкÑĥ": 39116, "Ġvlogging": 39117, "Ġslate": 39118, "ĠAuft": 39119, "ĠIncor": 39120, "ừng": 39121, "§IJ": 39122, "enh": 39123, "ĠheiÃŁ": 39124, "Ġdomest": 39125, "ĠStrom": 39126, "åį³": 39127, "akis": 39128, "Ġfragen": 39129, "Ġfiner": 39130, "ĠSug": 39131, "Ġuphill": 39132, "Ġéén": 39133, "âĢ¦)": 39134, "ĠÑģоп": 39135, "ĠCorey": 39136, "Ġsiebie": 39137, "Ġmuse": 39138, "Ġcloves": 39139, "Ġpous": 39140, "ĠFinanz": 39141, "ĠRoute": 39142, "amat": 39143, "Ġmutually": 39144, "ĠвнÑĥÑĤÑĢи": 39145, "ĠSelena": 39146, "ëĶ": 39147, "ĠGaussian": 39148, "ë¶ĢíĦ°": 39149, "Ġ×ij׼": 39150, "Ġejerc": 39151, "å¾®": 39152, "kea": 39153, "ĠGerry": 39154, "ĠSic": 39155, "大çļĦ": 39156, "Ġ1966": 39157, "iese": 39158, "Ġfossils": 39159, "Ġestad": 39160, "ĠKane": 39161, "ciÄĩ": 39162, "ĠìľłíĬľë": 39163, "Ġпам": 39164, "ĠCruise": 39165, "intérieur": 39166, "Ġbekannt": 39167, "ĠPode": 39168, "Ġdemander": 39169, "Rem": 39170, "Ġinvade": 39171, "Ġdecorating": 39172, "ropic": 39173, "Ġcowboy": 39174, "ĠPhoto": 39175, "opolit": 39176, "Ġì»¬ëŁ¬ë": 39177, "Ġreap": 39178, "Ġhandwriting": 39179, "à¹Ħร": 39180, "Ġëļ": 39181, "Ġبعد": 39182, "ĠMt": 39183, "ÙĢ": 39184, "Ġspaceship": 39185, "Ġnationalism": 39186, "Ġcouncils": 39187, "ĠGriffin": 39188, "ĠAhmed": 39189, 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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": 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"Ġê·¸ëĮĢë¡ľ": 41711, "ìĭ¤í": 41712, "Ġsimulated": 41713, "ĠëĦ¤ê°Ģ": 41714, "Ġbö": 41715, "ĠOtto": 41716, "LING": 41717, "Ġillustrates": 41718, "ĠDestroy": 41719, "Ġ1961": 41720, "ĠTagen": 41721, "Ġmelon": 41722, "ĠPascal": 41723, "QUE": 41724, "ĠполÑĥÑĩиÑĤÑĮ": 41725, "Ġincidence": 41726, "ĠStevens": 41727, "ĠGins": 41728, "rue": 41729, "Ġunreasonable": 41730, "ĠJie": 41731, "ysics": 41732, "Ġ몰ëĿ¼": 41733, "Ġfishes": 41734, "©´ì": 41735, "Ġprecurs": 41736, "ĠmogÄĻ": 41737, "tight": 41738, "eté": 41739, "Ġmundial": 41740, "ìĹĪëĭ¤": 41741, "âĢ¦!": 41742, "BU": 41743, "Ġsociology": 41744, "Ġbrutality": 41745, "Ġpersonaje": 41746, "ĠnÃŃvel": 41747, "Ġfazem": 41748, "Ġessen": 41749, "Ġdwelling": 41750, "Ġcommercially": 41751, "Ġedits": 41752, "Ġdues": 41753, "ĠGSA": 41754, "ìĿ¸ê°Ģ": 41755, "ĠíĹĪíĮĿ": 41756, "ĠYahoo": 41757, "енеÑĢ": 41758, "ìľ¨": 41759, "ÑĥÑĪки": 41760, "left": 41761, "Ġcaptive": 41762, "cipher": 41763, "Ġ×ŀ×ŀ×": 41764, "ĠгÑĢом": 41765, "Ġinnate": 41766, "Ġimpul": 41767, "ĠìŬìŀIJ": 41768, "Ġswallowed": 41769, "ĠTabii": 41770, "ìĿ´ìĭ": 41771, "ĠÑģоÑģÑĤав": 41772, "Ġoyun": 41773, "Ġobrigado": 41774, "ĠAph": 41775, "Katie": 41776, "Ġcena": 41777, "ĠAllÄģh": 41778, "ÙĪس": 41779, "Ġprzyp": 41780, "Ġpept": 41781, "Ġvoluntarily": 41782, "ĠOÄŁlum": 41783, "ĠElo": 41784, "oue": 41785, "Bir": 41786, "burger": 41787, "ĠSBS": 41788, "Ġ6000": 41789, "Ġpromotional": 41790, "ĠHerrn": 41791, "Ġstamping": 41792, "Ġqualifying": 41793, "Ġcosmos": 41794, "Ġafar": 41795, "æ±Ł": 41796, "abus": 41797, "Ġdads": 41798, "ãģŃãģĩ": 41799, "ĠÑįконом": 41800, "incarn": 41801, "Ġìĸ´ëĶ": 41802, "Ġлеж": 41803, "ĠBET": 41804, "Ġнайд": 41805, "onter": 41806, "Ġreusable": 41807, "Ġkomma": 41808, "ĠBij": 41809, "ĠTeraz": 41810, "ĠOlá": 41811, "ĠìķĦ침": 41812, "ĠÑĢазмеÑĢ": 41813, "awan": 41814, "Ġcarta": 41815, "æIJŀ": 41816, "iceless": 41817, "Ġsme": 41818, "ĠTutaj": 41819, "ĠÈĺi": 41820, "Ġprobation": 41821, "Ġadequately": 41822, "ĠPresidential": 41823, "indruck": 41824, "blade": 41825, "Ġveulent": 41826, "Ġcioè": 41827, "åĮħæĭ¬": 41828, "Ġreverb": 41829, "Ġgegenüber": 41830, "ĠEspero": 41831, "Ġbege": 41832, "ĠSTUDENT": 41833, "sound": 41834, "ĠDü": 41835, "Ġoffend": 41836, "Ġ\"..": 41837, "kennt": 41838, "ĠÑģлÑĥÑĪ": 41839, "Ġpurposely": 41840, "ĠLit": 41841, "ĠíĽ¨": 41842, "ucher": 41843, "Ġhina": 41844, "ých": 41845, "ignon": 41846, "THE": 41847, "Ġglide": 41848, "ourcing": 41849, "ĠØ£ÙĨا": 41850, "Ġollut": 41851, "Ġarchety": 41852, "Ġshady": 41853, "Ġsomm": 41854, "Ġepile": 41855, "Keep": 41856, "Ġnajbardziej": 41857, "à¤ķ": 41858, "itutional": 41859, "Ġмай": 41860, "Ġsinful": 41861, "ĠBronx": 41862, "ĠглÑĥб": 41863, "Ġvam": 41864, "Ġpresets": 41865, "ĠDag": 41866, "ĠìĻĦìĦ±": 41867, "Ġcreek": 41868, "itures": 41869, "ĠLords": 41870, "ött": 41871, "UNT": 41872, "Ra": 41873, "Ġinequalities": 41874, "Ġcollateral": 41875, "Ġwrists": 41876, "Ġgrouped": 41877, "ĠобÑĭÑĩно": 41878, "Ġarmored": 41879, "Ġtung": 41880, "Ġconverge": 41881, "Ġbok": 41882, "ĠDodge": 41883, "нÑıÑı": 41884, "Ġfleeing": 41885, "ĠMartinez": 41886, "ĠDreams": 41887, "kek": 41888, "Ġsociale": 41889, "ĠPlaza": 41890, "دة": 41891, "Ġkell": 41892, "ĠStellen": 41893, "felt": 41894, "ĠÑģпаÑģ": 41895, "ĠPv": 41896, "Ġcanción": 41897, "ĠHert": 41898, "ĠBalance": 41899, "Ġselves": 41900, "Ġvandaag": 41901, "Ġpry": 41902, "Ġnajle": 41903, "ĠвидиÑĤе": 41904, "Ġvelvet": 41905, "Ġgroot": 41906, "Ġfout": 41907, "模": 41908, "ĠSchulen": 41909, "ĠMohammed": 41910, "ĠCenters": 41911, "Ġhaver": 41912, "Ġfreuen": 41913, "¤íĬ¸": 41914, "лан": 41915, "POS": 41916, "inki": 41917, "Ġëĭµ": 41918, "Ġparalyzed": 41919, "GLISH": 41920, "Ġcasts": 41921, "ĠVC": 41922, "ìĿ´ìħĺ": 41923, "Ġتھ": 41924, "票": 41925, "Ġì¤ĺ": 41926, "Ġר×ķצ": 41927, "Ġsuced": 41928, "Ġprogresses": 41929, "ĠEÄŁer": 41930, "°ëıĦ": 41931, "Ġinstallations": 41932, "pedo": 41933, "еÑĢб": 41934, "interpret": 41935, "Ġê³łë¯¼": 41936, "ĠAzerbai": 41937, "ividades": 41938, "Ġì£ĦìĨ¡": 41939, "Ġentfer": 41940, "Ġchwil": 41941, "ĠHerbert": 41942, "ĠAlexandria": 41943, "yty": 41944, "Ġsechs": 41945, "Ġcaliber": 41946, "ĠWeise": 41947, "ĠHeck": 41948, "ĠYug": 41949, "ĠاÙĦØ·": 41950, "Ġpesar": 41951, "Ġcigar": 41952, "Ġmél": 41953, "Ġhaird": 41954, "Ġprzypadku": 41955, "Ġconfidently": 41956, "Ġanarch": 41957, "ĠGian": 41958, "Ġdobre": 41959, "cjÄĻ": 41960, "awy": 41961, "ĠRece": 41962, "ĠGobierno": 41963, "Ġcarga": 41964, "umsy": 41965, "Ġnorte": 41966, "Ġhandler": 41967, "Ġrespecting": 41968, "Ġallied": 41969, "ĠPiet": 41970, "ichtlich": 41971, "Ġolds": 41972, "Ġdusty": 41973, "Ġgry": 41974, "Ġ-...": 41975, "GHT": 41976, "Ġneo": 41977, "Ñĩики": 41978, "ежд": 41979, "aide": 41980, "ĠбÑĥло": 41981, "íį¼": 41982, "Ġtemporada": 41983, "Ġdoute": 41984, "âĺĨ": 41985, "ĠìĪł": 41986, "ĠJUSTIN": 41987, "auto": 41988, "Ġrationale": 41989, "prob": 41990, "Ġfishy": 41991, "Ġdoorway": 41992, "Ġemptiness": 41993, "еннаÑı": 41994, "Ġbrag": 41995, "ĠÐĵде": 41996, "çĪ¾": 41997, "Ġtransient": 41998, "Ġmittlerweile": 41999, "ĠBret": 42000, "Ġfij": 42001, "Ġdeposited": 42002, "NS": 42003, "ĠìķŀìĹIJ": 42004, "Ġkimse": 42005, "Ġcharities": 42006, "ĠMillenn": 42007, "dogs": 42008, "Ġmoyen": 42009, "Ġnuevos": 42010, "ĠCookie": 42011, "parable": 42012, "doing": 42013, "ĠSail": 42014, "Ġicy": 42015, "haba": 42016, "Ġqueens": 42017, "Ġchocolates": 42018, "ĠNay": 42019, "ĠÑĦин": 42020, "Ġvec": 42021, "Ġhelmets": 42022, "TM": 42023, "ĠArmed": 42024, "Ġimpairment": 42025, "ĠTus": 42026, "ĠMême": 42027, "omez": 42028, "ĠRequ": 42029, "ĠInvestig": 42030, "íİĺ": 42031, "Ġgolpe": 42032, "ĠRac": 42033, "igraph": 42034, "Ġkwest": 42035, "Ġsailors": 42036, "Ġstatutory": 42037, "Ġmilestones": 42038, "ĠMash": 42039, "ĠGesetzentwurf": 42040, "éĬ": 42041, "Ġcoloured": 42042, "huma": 42043, "Ġyere": 42044, "Ġsubtitles": 42045, "Ġembodied": 42046, "Ġmisschien": 42047, "ĠiPh": 42048, "ützen": 42049, "Ġdetached": 42050, "Ġdescrição": 42051, "ciamo": 42052, "Ġrecoil": 42053, "ĠÐŃÑĤоÑĤ": 42054, "Ġexported": 42055, "ĠAlone": 42056, "antry": 42057, "Ġestan": 42058, "ĠSod": 42059, "Ġlavoro": 42060, "æĬĬå®ĥ": 42061, "ר×ij": 42062, "ĠÄijá»ĭ": 42063, "Ġswag": 42064, "ĠPCB": 42065, "ĠKaiser": 42066, "ĠModer": 42067, "jug": 42068, "Ġtextile": 42069, "Tw": 42070, "Ġnac": 42071, "frei": 42072, "Ġretard": 42073, "iscern": 42074, "Ġtallest": 42075, "ĠLuca": 42076, "Rah": 42077, "Ġpreacher": 42078, "Ġjut": 42079, "ĠRica": 42080, "iciency": 42081, "ĠÄijiá»ģu": 42082, "Ġkaufen": 42083, "Ġnett": 42084, "Ġdiscut": 42085, "Ġdeprived": 42086, "¡Ń": 42087, "Ġspricht": 42088, "Ġenclosed": 42089, "ĠSubst": 42090, "ç§ij": 42091, "ĠRabbit": 42092, "prised": 42093, "Ġbitches": 42094, "ìŁģ": 42095, "çīĪ": 42096, "Ġtapa": 42097, "ĠEssen": 42098, "ĠBao": 42099, "Ġdevient": 42100, "ĠWuhan": 42101, "ĠTipp": 42102, "Ġdisast": 42103, "ÑģÑĤвÑĥ": 42104, "ublique": 42105, "Ġqualité": 42106, "Ġinadequate": 42107, "Ġbargaining": 42108, "ĠGotcha": 42109, "евиÑĩ": 42110, "ievous": 42111, "erton": 42112, "blue": 42113, "ĠìĽĢì§ģ": 42114, "Ġsandbox": 42115, "ĠRein": 42116, "親": 42117, "ĠìĿ´ê²ĥëıĦ": 42118, "Ġsax": 42119, "zogen": 42120, "unächst": 42121, "Ġherkes": 42122, "Ġ-,": 42123, "zeni": 42124, "rising": 42125, "Ġresposta": 42126, "Ġpromotions": 42127, "ĠUnterstüt": 42128, "ĠMAS": 42129, "Nothing": 42130, "otics": 42131, "ĠвÑĭй": 42132, "Ġrotates": 42133, "kien": 42134, "Ġhabla": 42135, "ĠDani": 42136, "union": 42137, "Ġwack": 42138, "Ġarchaeological": 42139, "ĠCurtis": 42140, "ĠHoriz": 42141, "Ġ골ë": 42142, "Ġwaiver": 42143, "åĺ¿": 42144, "Bon": 42145, "Ġrotated": 42146, "Ġpitcher": 42147, "Ġinad": 42148, "Ġhugs": 42149, "ĠNortheast": 42150, "×Ļת×Ļ": 42151, "Ġplea": 42152, "Ġcupcake": 42153, "ĠLY": 42154, "Ġfamili": 42155, "Ġgroo": 42156, "ĠBlair": 42157, "Ġlij": 42158, "Ġhabitats": 42159, "Ġcommunism": 42160, "osium": 42161, "bars": 42162, "ĠFreeman": 42163, "neo": 42164, "Ġdiffuse": 42165, "Ġcylinders": 42166, "ĠDebat": 42167, "íĸĪëĬĶëį°": 42168, "еÑĪе": 42169, "Ġfingerprints": 42170, "Ġamar": 42171, "вид": 42172, "ĠìłķëıĦë¡ľ": 42173, 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43779, "Ġsash": 43780, "ĠLub": 43781, "Ġnotebooks": 43782, "Ġmae": 43783, "Ġconflicting": 43784, "Ġsummertime": 43785, "acas": 43786, "Ġbauen": 43787, "blowing": 43788, "ạo": 43789, "Ġìĸ¸ìłľ": 43790, "ä»ĬæĹ¥ãģ¯": 43791, "ĠSenhor": 43792, "ĠiPhones": 43793, "ĠQuarter": 43794, "ĠìłľëĮĢë¡ľ": 43795, "uÃŁ": 43796, "Ġë§Ī무ë": 43797, "Ġsettlers": 43798, "Ġcrest": 43799, "Ġtransc": 43800, "æĽ¾": 43801, "Ġriots": 43802, "Ġclones": 43803, "ĠOprah": 43804, "ίζ": 43805, "Ġpals": 43806, ".......": 43807, "ãģĶãģĸãģĦãģ¾ãģĻ": 43808, "ĠÑĢоÑģÑģ": 43809, "ĠLaser": 43810, "Ġzaczy": 43811, "Ġsevi": 43812, "Ġregeneration": 43813, "ìĹ¼": 43814, "would": 43815, "Ġüzerine": 43816, "ĠStraÃŁe": 43817, "Ġvengeance": 43818, "Ġrer": 43819, "ĠSafari": 43820, "ĠHEY": 43821, "çķ«": 43822, "Ġsacar": 43823, "Ġimagem": 43824, "ĠBundest": 43825, "mesan": 43826, "ĠPaste": 43827, "Ġsizz": 43828, "ĠпоÑģÑĤÑĥп": 43829, "×Ķ×ķ": 43830, "trad": 43831, "Ġfrançaise": 43832, "ĠBou": 43833, "Ġbarre": 43834, "ĠZhi": 43835, "ĠGeez": 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43894, "Ġrugby": 43895, "Ġmarshmallow": 43896, "Ġfiery": 43897, "Ġaccountant": 43898, "Ġabla": 43899, "ĠMonroe": 43900, "ĠFont": 43901, "ĠBoost": 43902, "ĠBarnes": 43903, "answer": 43904, "ĠBurning": 43905, "Ġä¸įæĺ¯": 43906, "Ġangef": 43907, "ĠWesley": 43908, "lls": 43909, "ìµ": 43910, "ש׾": 43911, "iliÅĽmy": 43912, "×IJף": 43913, "amura": 43914, "ĠFuj": 43915, "Ġpani": 43916, "ĠTrop": 43917, "arbeiten": 43918, "Ġrue": 43919, "ĠRare": 43920, "ängen": 43921, "ĠÑģмоÑĤÑĢеÑĤÑĮ": 43922, "ĠÐļаÑĢ": 43923, "ĠMTV": 43924, "boarding": 43925, "][": 43926, "ĠëłĪë": 43927, "stanbul": 43928, "pielt": 43929, "ĠHardy": 43930, "ĠEngagement": 43931, "ĠDienst": 43932, "Ġwären": 43933, "Ġfuego": 43934, "Ġestruct": 43935, "Ġcalam": 43936, "ĠResponse": 43937, "ĠãĤĦ": 43938, "ĠMohammad": 43939, "Ġresisting": 43940, "Ġdurant": 43941, "èģ¯": 43942, "åĨµ": 43943, "ĠOLED": 43944, "Ġverz": 43945, "män": 43946, "ĠÙĨÛĴ": 43947, "Ġparanoid": 43948, "ĠAware": 43949, "ĠEngineers": 43950, "Ġprocedural": 43951, 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"Ġεκ": 44009, "Ġgerms": 44010, "Ġgelir": 44011, "Ġdul": 44012, ",-": 44013, "Ġìĸ¸ë": 44014, "Ġ×ŀצ": 44015, "ĠÑıÑĢ": 44016, "Ġquotid": 44017, "Ġprzysz": 44018, "Ġhardness": 44019, "Ġaquatic": 44020, "ĠJungle": 44021, "ĠPCR": 44022, "ĠEliot": 44023, "Ġostr": 44024, "Ġmapa": 44025, "essä": 44026, "ĠGIR": 44027, "ĠDriving": 44028, "ĠSami": 44029, "ĠMedien": 44030, "ĠCompanies": 44031, "ĠPharm": 44032, "seits": 44033, "ĠRim": 44034, "ĠοÏĢο": 44035, "Ġweiteren": 44036, "Ġpizzas": 44037, "ĠLydia": 44038, "ĠHeights": 44039, "Ġsincerity": 44040, "Ġnossas": 44041, "ĠdÅĤ": 44042, "Ġalarming": 44043, "ĠCauc": 44044, "ĠÑģмÑĭÑģ": 44045, "facing": 44046, "bags": 44047, "WW": 44048, "ĠØ´ÙĬ": 44049, "Ġcourtroom": 44050, "ĠPhillip": 44051, "Ġê²ĥì²ĺëŁ¼": 44052, "ĠSpieler": 44053, "ãĤıãģĭ": 44054, "Ġkant": 44055, "Ġadmitting": 44056, "ãĥģãĥ£ãĥ³ãĥįãĥ«": 44057, "Ġcontainment": 44058, "å¼ł": 44059, "Ġremovable": 44060, "Ġjumper": 44061, "focused": 44062, "ĠиÑĤоге": 44063, "ĠТем": 44064, "Ġvase": 44065, "ĠUSC": 44066, "ĠMonate": 44067, "ĠJacobs": 44068, "ĠHOL": 44069, "iked": 44070, "erweise": 44071, "Ġgoodies": 44072, "Ġhomage": 44073, "׼ש×Ļ×ķ": 44074, "Ġquais": 44075, "Ġinicial": 44076, "Ġguarding": 44077, "Ġdazz": 44078, "Ġcombos": 44079, "ĠÑĥпÑĢав": 44080, "ĠTalent": 44081, "å¥ĩæĢª": 44082, "Ġór": 44083, "Ġintermittent": 44084, "ĠMcCarthy": 44085, "Ġspans": 44086, "Ġtyre": 44087, "Ġquy": 44088, "èĪĪ": 44089, "jut": 44090, "ĠZent": 44091, "Ġgat": 44092, "大åĵ¥": 44093, "Ġscaffold": 44094, "Ġnecesario": 44095, "ĠZahlen": 44096, "ĠSAND": 44097, "ĠPU": 44098, "Everything": 44099, "----------------": 44100, "ĠвзÑıÑĤÑĮ": 44101, "Ġsparks": 44102, "Ġpendulum": 44103, "×ŀף": 44104, "Ġìĥīê¹": 44105, "Ġmultiplier": 44106, "Ġладно": 44107, "urat": 44108, "Ġupsetting": 44109, "è¡Ģ": 44110, "bak": 44111, "ĠìµľëĮĢ": 44112, "Ġanál": 44113, "ĠJOE": 44114, "Ġkosten": 44115, "ĠPatty": 44116, "ĠGuin": 44117, "cked": 44118, "ĠEgyptians": 44119, "ĠCitizens": 44120, "ר׼": 44121, "ĠÐķÑīе": 44122, "Ġйого": 44123, "Ġsnowfl": 44124, "Ġlekker": 44125, "Ġacost": 44126, "ĠBabe": 44127, "Ġgamble": 44128, "Ġadjective": 44129, "кими": 44130, "oys": 44131, "Ġmontre": 44132, "ĠHyundai": 44133, "Ġmoisturizing": 44134, "Ġmozzarella": 44135, "OOO": 44136, "Ġfacult": 44137, "Ġdoet": 44138, "Ġfearless": 44139, "Ġespresso": 44140, "Ġallora": 44141, "ĠCinc": 44142, "ãĥ¼ãĤ¸": 44143, "Ġconteúdo": 44144, "ĠPelosi": 44145, "Ġminder": 44146, "root": 44147, "Ġíķłë": 44148, "Ġпад": 44149, "ĠCalling": 44150, "ĠConfig": 44151, "ĠConsole": 44152, "insky": 44153, "énergie": 44154, "Ġsolitary": 44155, "оде": 44156, "Ġguarded": 44157, "160": 44158, "ĠпÑģиÑħ": 44159, "ĠShap": 44160, "Ġtitre": 44161, "ologne": 44162, "ĠпаÑĢÑĥ": 44163, "ĠPRE": 44164, "ãĥ¼ãĥī": 44165, "Ġln": 44166, "ĠMitgl": 44167, "ĠCarry": 44168, "Ġspind": 44169, "ĠCanton": 44170, "Ġkingdoms": 44171, "remo": 44172, "Ġraging": 44173, "Ġincapable": 44174, "ĠWR": 44175, "åĨįè§ģ": 44176, "ĠÑģобÑģÑĤвен": 44177, "ĠкакиÑħ": 44178, "ĠSHE": 44179, "ëĭ¹íŀĪ": 44180, "Ġscarcity": 44181, "Ġperde": 44182, "Ġexits": 44183, "ĠSinger": 44184, "Ġsupper": 44185, "Ġmunicipality": 44186, "ĠDiversity": 44187, "Ġtiro": 44188, "iels": 44189, "ĠlÃŃder": 44190, "Ġbluff": 44191, "Ġatra": 44192, "lys": 44193, "Ġmahd": 44194, "Ġcódigo": 44195, "ĠHarlem": 44196, "rule": 44197, "icity": 44198, "Ġsimplistic": 44199, "ĠKonst": 44200, "åģ¥": 44201, "ELLI": 44202, "Ġförsta": 44203, "Ġconstitutes": 44204, "ĠÑģÑĤоÑĢонÑĥ": 44205, "Ġurged": 44206, "ĠPanda": 44207, "ì°¨ë": 44208, "rece": 44209, "Ġpatriot": 44210, "ĠCrush": 44211, "Ġwink": 44212, "ойÑĤи": 44213, "urança": 44214, "Ġseizures": 44215, "Ġelectrod": 44216, "ĠDonkey": 44217, "ĠIU": 44218, "ĠMOS": 44219, "Ġalkal": 44220, "ì´ī": 44221, "besondere": 44222, "Ġparallels": 44223, "Ġbitterness": 44224, "ättre": 44225, "essional": 44226, "Ġsoybean": 44227, "Ġcollab": 44228, "ĠReporting": 44229, "å§Ķ": 44230, "Ġкомпании": 44231, "Ġwszyscy": 44232, "ĠCrunch": 44233, "iseen": 44234, "Ġambassadors": 44235, "ĠChev": 44236, "åįĪ": 44237, "овÑĭе": 44238, "sca": 44239, "ĠÑĢеÑĪил": 44240, "оÑĤо": 44241, "Ġgleichzeitig": 44242, "mern": 44243, "üst": 44244, "ĠHae": 44245, "³´ê²łìĬµëĭĪëĭ¤": 44246, "Ġshores": 44247, "Ġdepress": 44248, "Ġahor": 44249, "ĠSteuer": 44250, "ahh": 44251, "Ġrevise": 44252, "ĠÑģамÑĭе": 44253, "jat": 44254, "Ġherbal": 44255, "Ġcuánt": 44256, "Ġbuna": 44257, "niejsze": 44258, "Finally": 44259, "×ķ×ĸ": 44260, "cje": 44261, "ĠìŀĪê±°ëĵłìļĶ": 44262, "ĠëĤĺëĪ": 44263, "Ġprzest": 44264, "ãĥ¼ãĥł": 44265, "lica": 44266, "ĠDuch": 44267, "å°įå°į": 44268, "ÑĸйÑģÑĮ": 44269, "passen": 44270, "Ġsatisfies": 44271, "ĠAdditional": 44272, "Ġcámara": 44273, "еÑĩение": 44274, "Ġpomp": 44275, "Ġë§IJìĿ´": 44276, "ĠMills": 44277, "евид": 44278, "Ġrespectable": 44279, "Ġfilament": 44280, "Ġvender": 44281, "Ġmattered": 44282, "oure": 44283, "층": 44284, "Korean": 44285, "Ġestudio": 44286, "Ġcactus": 44287, "ĠVive": 44288, "ĠRag": 44289, "Ġcompliqué": 44290, "ĠÙĪÛģ": 44291, "Ġtao": 44292, "¦¿": 44293, "Since": 44294, "Ġjeopard": 44295, "ĠSell": 44296, "åºĶ": 44297, "ĠìĺĽ": 44298, "Ġketo": 44299, "Ġintelig": 44300, "ĠAngeb": 44301, "Ġtiden": 44302, "Ġsocio": 44303, "Ġreminiscent": 44304, "Ġcaregiver": 44305, "Space": 44306, "ĠExercise": 44307, "ĠBecome": 44308, "êts": 44309, "akk": 44310, "!..": 44311, "ĠÑģпÑĢоÑģ": 44312, "ĠαÏĢο": 44313, "Ġshootings": 44314, "Ġape": 44315, "ĠSammy": 44316, "ĠKung": 44317, "Ġcuál": 44318, "ĠLup": 44319, "æĿŁ": 44320, "ä¾ĨåĪ°": 44321, "ĠÑģÑĤÑĥд": 44322, "Ġsweeter": 44323, "Ġcomum": 44324, "ĠAds": 44325, "hyung": 44326, "ĠбÑĥдÑĥÑī": 44327, "Ġwaffle": 44328, "ĠOrb": 44329, "Ġlaut": 44330, "Ġforecasting": 44331, "åª": 44332, "Ġrapping": 44333, "Ġprefers": 44334, "Ġbenz": 44335, "Ġnik": 44336, "ĠBahn": 44337, "Ġsanding": 44338, "Ġimminent": 44339, "ĠпÑĢоблемÑĭ": 44340, "Ġdoivent": 44341, "ола": 44342, "Ġżycia": 44343, "ihu": 44344, "Ġexistem": 44345, "ĠInterior": 44346, "ĠTakes": 44347, "Ġtoddler": 44348, "Ġdictatorship": 44349, "ĠSmithson": 44350, "ĠAllahu": 44351, "ÏİÏģα": 44352, "ìķĺìĬµëĭĪëĭ¤": 44353, "ĠVote": 44354, "ĠSmells": 44355, "одно": 44356, "Ġhindsight": 44357, "VR": 44358, "ĠPatch": 44359, "ĠJahres": 44360, "Ġsouvenir": 44361, "Ġneutron": 44362, "Ġlongtime": 44363, "Ġsayin": 44364, "ä¹IJ": 44365, "asaki": 44366, "ĠоÑģÑĤанов": 44367, "Ġexpelled": 44368, "Ġcryptocurrencies": 44369, "ĠMurder": 44370, "ĠCitizen": 44371, "WAY": 44372, "Ġplu": 44373, "Ġlemonade": 44374, "Ġconveniently": 44375, "ĠHI": 44376, "Ġ2023": 44377, "ש×ķת": 44378, "аÑĨион": 44379, "Ġ뼰": 44380, "ĠÙĦÙĥÙĨ": 44381, "Ġнемножко": 44382, "Ġunused": 44383, "Ġmaioria": 44384, "Ġastrology": 44385, "ĠDownt": 44386, "Nick": 44387, "Ġpreoccup": 44388, "Ġdemain": 44389, "×ŀ×¢": 44390, "ĠводÑĭ": 44391, "ĠSanskrit": 44392, "Ġprêt": 44393, "Ġstranded": 44394, "Ġrefin": 44395, "ĠпÑĢиним": 44396, "ĠповеÑĢÑħ": 44397, "à¯į?": 44398, "Ġzrob": 44399, "Ġintertw": 44400, "ĠDavidson": 44401, "лена": 44402, "ĠпонÑıÑĤÑĮ": 44403, "ĠReno": 44404, "ĠполÑĥÑĩилоÑģÑĮ": 44405, 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45032, "funding": 45033, "Ġzust": 45034, "ĠPens": 45035, "sed": 45036, "ĠíŤ": 45037, "Ġgereki": 45038, "Ġalarms": 45039, "ĠWha": 45040, "ĠMarkus": 45041, "aksi": 45042, "ĠÐIJле": 45043, "klore": 45044, "Ġéner": 45045, "Ġtilde": 45046, "boxing": 45047, "ĠìĦŀ": 45048, "Ġencontramos": 45049, "ĠPhar": 45050, "наком": 45051, "óst": 45052, "ĠÄ°s": 45053, "Ġëĭĺ": 45054, "Ġsquats": 45055, "Ġpretended": 45056, "Ġdez": 45057, "Ġê´ľì°®ìķĦ": 45058, "jach": 45059, "ëĿ¼ê³ł": 45060, "ĠíĻķì§Ħ": 45061, "ĠAnsch": 45062, "imerk": 45063, "Ġconjugate": 45064, "Ġpeninsula": 45065, "Ġgorilla": 45066, "Ġphotographed": 45067, "ĠAunque": 45068, "Ġentren": 45069, "ĠDeutschen": 45070, "ĠAladdin": 45071, "Ġ무ìĦľ": 45072, "ĠStella": 45073, "ĠElection": 45074, "outine": 45075, "Grand": 45076, "ĠWak": 45077, "ĠSergio": 45078, "horse": 45079, "ahon": 45080, "ĠFamilies": 45081, "Ġhating": 45082, "ĠBett": 45083, "à¸Ļะà¸Ħะ": 45084, "Ġcurling": 45085, "ĠIsraelis": 45086, "Ġ׾×IJ×": 45087, "ĠMyers": 45088, "Ġscanned": 45089, "ĠBEC": 45090, "ileri": 45091, "Ġcalle": 45092, "ĠMinh": 45093, "Ġmicron": 45094, "Ġconduc": 45095, "ÃŃv": 45096, "ĠвозÑĮ": 45097, "Ġactionable": 45098, "ĠTrustees": 45099, "Ġtief": 45100, "Ġheaders": 45101, "Ġanimales": 45102, "ìĽĢ": 45103, "лоÑħ": 45104, "unity": 45105, "lya": 45106, "Ġjangan": 45107, "Ġhani": 45108, "Ġcasing": 45109, "Ġjóvenes": 45110, "ĠSplit": 45111, "ĠCarlo": 45112, "ĠBeim": 45113, "å°įä¸įèµ·": 45114, "Ġnuanced": 45115, "Ġteddy": 45116, "ĠClan": 45117, "ächen": 45118, "pier": 45119, "Ġдополн": 45120, "Ġdiaper": 45121, "effective": 45122, "ĠNiagara": 45123, "Ġwart": 45124, "Ġcorro": 45125, "ĠKampf": 45126, "zte": 45127, "Ġdéveloppement": 45128, "Ġattackers": 45129, "ĠSherman": 45130, "Ġ1914": 45131, "Ġmeow": 45132, "ĠPÃ¥": 45133, "ìº": 45134, "cit": 45135, "Ġcoupe": 45136, "Ġê·¸ëĭ¤ìĿĮìĹIJ": 45137, "Ġhumour": 45138, "Ġcole": 45139, "ĠWarning": 45140, "ĠTil": 45141, "calm": 45142, "buat": 45143, "Ġcine": 45144, "kiej": 45145, "Kevin": 45146, "Ġmilligrams": 45147, 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"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, "></": 48986, "ĠespÃŃ": 48987, "ĠCOP": 48988, "åŃ©åŃIJ": 48989, "visible": 48990, "ĠпÑĢеÑģÑĤÑĥп": 48991, "Ġstitched": 48992, "à¯Ī.": 48993, "Ġlatent": 48994, "ĠPrab": 48995, "ĠMcN": 48996, "ĠHealing": 48997, "ĠCuriosity": 48998, "cert": 48999, "Ġ민주": 49000, "Ġpatiently": 49001, "ĠYT": 49002, "foreign": 49003, "Ġvẫn": 49004, "Ġindustri": 49005, "Ġcocktails": 49006, "Ġbrighten": 49007, "Ġconsolidated": 49008, "аÑĢд": 49009, "ltry": 49010, "Ġgrille": 49011, "Ġbona": 49012, "Ġdiligently": 49013, "ĠWrestleMania": 49014, "erkt": 49015, "energy": 49016, "999": 49017, "à®ķவ": 49018, "Ġtote": 49019, "iono": 49020, "DIO": 49021, "Ġschizophrenia": 49022, "Ġpostponed": 49023, "ĠQiu": 49024, "ĠÏĥÏħν": 49025, "ĠzdjÄĻ": 49026, "Ġspannend": 49027, "ĠDIS": 49028, "Rel": 49029, "Ġrhin": 49030, "immune": 49031, "Old": 49032, "Ġplötzlich": 49033, "Ġmound": 49034, "Ġastronomical": 49035, "ĠGuid": 49036, "ĠCul": 49037, "HI": 49038, "ĠÅł": 49039, "Ġrepo": 49040, "ĠMaurice": 49041, "ä¸ĢçĤ¹": 49042, "Ġbandits": 49043, "ĠDesktop": 49044, "äss": 49045, "fta": 49046, "Ġlicence": 49047, "Ġimaginar": 49048, "ĠEntreprene": 49049, "xo": 49050, "Ġ맼ìŀĪëĬĶ": 49051, "Ġ×Ķ×ij": 49052, "Ġpumpkins": 49053, "Ġkanssa": 49054, "ĠjÄĻzy": 49055, "Ġcommunauté": 49056, "bür": 49057, "Ġerhö": 49058, "ĠWolver": 49059, "ĠSharing": 49060, "令": 49061, "Ġpakai": 49062, "Ġinsulted": 49063, "ÐľÑĭ": 49064, "оÑĹ": 49065, "Ġconsiste": 49066, "æĮij": 49067, "Ġyoungsters": 49068, "Ġgleichen": 49069, "weder": 49070, "Ġmote": 49071, "Ġclauses": 49072, "état": 49073, "prus": 49074, "Ġwast": 49075, "ç»ĻæĪij": 49076, "ĠCrisp": 49077, "ĠçĦ¶å¾Į": 49078, "Ġoffenders": 49079, "Ġconvection": 49080, "Ġconfian": 49081, "ollow": 49082, "amet": 49083, "ĠÑĹÑħ": 49084, "第äºĮåĢĭ": 49085, "fficiency": 49086, "Ġunglaub": 49087, "igans": 49088, "Ġmarketed": 49089, "ĠVAN": 49090, "Ġproclaimed": 49091, "Ġcélulas": 49092, "Ġcollide": 49093, "ĠOculus": 49094, "adore": 49095, "Ji": 49096, "Ġsustaining": 49097, "ĠFasc": 49098, "Ġsetzt": 49099, "Ġnosaltres": 49100, "Most": 49101, "ĠвÑĩ": 49102, "Ġnauc": 49103, "ĠBhar": 49104, "çĪ¸çĪ¸": 49105, "æĪijè·Łä½łè¬Ľ": 49106, "Ġyêu": 49107, "Ġtimest": 49108, "Ġpertama": 49109, "irmi": 49110, "Ġzwr": 49111, "Ġverbess": 49112, "Ġvortex": 49113, "ĠSTACK": 49114, "ثر": 49115, "¹Ħë": 49116, "ĶĶìĺ¤": 49117, "Ġlinkage": 49118, "ĠFraser": 49119, "enario": 49120, "ĠëĿ¼ëĬĶ": 49121, "ĠìĦłë°°": 49122, "hthal": 49123, "Ġê¹Į": 49124, "ĠKhông": 49125, "Ãĥ": 49126, "Ġscrambled": 49127, "ĠEink": 49128, "Ġmicroorgan": 49129, "Ġnarcissist": 49130, "ĠKombat": 49131, "Ġ맡": 49132, "ĠAGA": 49133, "Ġperfekt": 49134, "ĠSerie": 49135, "determ": 49136, "-'": 49137, "Ġponytail": 49138, "Ġkoska": 49139, "ìĵ": 49140, "Ġobec": 49141, "Ġchests": 49142, "veer": 49143, "Ġuprising": 49144, "Ġstoked": 49145, "associ": 49146, "Ġprodução": 49147, "ĠShape": 49148, "ìłľê°Ģ": 49149, "ĠëĶ°": 49150, "Ġjon": 49151, "Ġinadvert": 49152, "antas": 49153, "ĠнаконеÑĨ": 49154, "Ġå°įåķĬ": 49155, "ĠArsenal": 49156, "Ġproteg": 49157, "Ġliberté": 49158, "Ġglare": 49159, "åĪļ": 49160, "å·²ç»ı": 49161, "Ġverein": 49162, "Ġinserts": 49163, "ĠJana": 49164, "Ġwydaje": 49165, "ÅĤum": 49166, "Ġ%.": 49167, "origine": 49168, "Ġsynagogue": 49169, "Ġfallait": 49170, "Ġdisobed": 49171, "Ġantic": 49172, "ĠCycl": 49173, "Ġasynchronous": 49174, "Ġë²Įìį¨": 49175, "Ġgesund": 49176, "Ġgagn": 49177, "Ġpea": 49178, "Ġgrin": 49179, "ést": 49180, "Ġsauc": 49181, "ĠMäd": 49182, "íķ´ëıĦ": 49183, "pps": 49184, "ĠεÏĢι": 49185, "Ġpeuple": 49186, "Ġdeben": 49187, "ĠBree": 49188, "ĠÑĢолÑĮ": 49189, "Ġкаким": 49190, "Ġútil": 49191, "Ġdistributor": 49192, "алÑĭ": 49193, "ĠswojÄħ": 49194, "Ġfolklore": 49195, "Ġreceivers": 49196, "ĠMOO": 49197, "bins": 49198, "astre": 49199, "ìķĪë": 49200, "ĠëĦ£ê³ł": 49201, "Ġmultimedia": 49202, "Ġgebaut": 49203, "овÑĭÑħ": 49204, "ãy": 49205, "Ġdane": 49206, "okol": 49207, "emitism": 49208, "ONEY": 49209, "ĠyaÄŁ": 49210, "Ġchauff": 49211, "容æĺĵ": 49212, "Ġesfuer": 49213, "Äĥn": 49214, "ertas": 49215, "Ġfonctionne": 49216, "omina": 49217, "Ġivory": 49218, "ĠYoutuber": 49219, "ĠSkywalker": 49220, "иÑĩеÑģкаÑı": 49221, "toi": 49222, "Ġveya": 49223, "Ġgelernt": 49224, "Ġchancellor": 49225, "ĠStatistics": 49226, "Ġwelded": 49227, "Ġondan": 49228, "ĠSei": 49229, "Ġmedically": 49230, "Ġenergized": 49231, "ĠVia": 49232, "Ġвик": 49233, "Ġuninter": 49234, "Ġhighness": 49235, "ĠíĮĶë": 49236, "Ġamplified": 49237, "ĠSergey": 49238, "ĠMins": 49239, "warm": 49240, "pell": 49241, "ophile": 49242, "Ġhè": 49243, "ĠBelo": 49244, "ĠSketch": 49245, "Ġcharacterization": 49246, "ansen": 49247, "ĠÑĤÑĥÑĢ": 49248, "Ġãħĭãħĭãħĭ": 49249, "Note": 49250, "ĠkoÅŁ": 49251, "Ġciert": 49252, "flu": 49253, "Ġbaht": 49254, "ĠDowntown": 49255, "ĠCRIS": 49256, "odie": 49257, "140": 49258, "Ġlitres": 49259, "Ġgriev": 49260, "æ§ĺ": 49261, "ĠìĶ¨ê°Ģ": 49262, "Ġsucceeds": 49263, "Ġ__": 49264, "enting": 49265, "Ġvimos": 49266, "Ġsì": 49267, "defense": 49268, "ĠMcD": 49269, "ĠMarion": 49270, "ĠDont": 49271, "ĠDDR": 49272, "ĠLazar": 49273, "ĠDAR": 49274, "Ġkuv": 49275, "Kn": 49276, "Ġsembla": 49277, "Ġairborne": 49278, "ĠViolence": 49279, "ëIJIJ": 49280, "Ġrestraint": 49281, "Ġwhistles": 49282, "Ġscolded": 49283, "Ġacceso": 49284, "Ġabsolutamente": 49285, "ĠTyl": 49286, "ĠSap": 49287, "¶Ģë¶Ħ": 49288, "itäten": 49289, "adem": 49290, "Ġý": 49291, "Ġprescribe": 49292, "ĠMage": 49293, "ĠHelena": 49294, "å¾Īæľī": 49295, "亲": 49296, "vt": 49297, "Ġvienen": 49298, "Ġsneez": 49299, "Ġmolé": 49300, "Æ°á»Łng": 49301, "Ġtransporting": 49302, "ĠLean": 49303, "Ġkung": 49304, "ÑĥÑĢа": 49305, "ÏĦÎŃ": 49306, "utches": 49307, "onders": 49308, "liyor": 49309, "Nat": 49310, "Ġzij": 49311, "Ġmammal": 49312, "Ġkäyt": 49313, "ĠJoanna": 49314, "sent": 49315, "Ġस": 49316, "Ġvested": 49317, "ĠErfahrung": 49318, "okee": 49319, "Ġclipping": 49320, "ĠListening": 49321, "Ġ(#": 49322, "fö": 49323, "Ġvidare": 49324, "Ġbrittle": 49325, "ĠSTART": 49326, "ĠDamas": 49327, "ĠYog": 49328, "ãĤĵãģ¨": 49329, "gart": 49330, "Ġverlier": 49331, "Ġheartfelt": 49332, "ĠdoÅĽÄĩ": 49333, "ì¹ĺê°Ģ": 49334, ".»": 49335, "Ġmaximal": 49336, "Ġdistintos": 49337, "ĠìĻľëĥIJíķĺë©´": 49338, "Ġsailed": 49339, "Ġconveyed": 49340, "ĠTinder": 49341, "ĠSUPER": 49342, "ниÑĨÑĥ": 49343, "controlled": 49344, "Ġfunz": 49345, "Ġbastards": 49346, "ĠGinsburg": 49347, "Ġnuovo": 49348, "ĠPere": 49349, "ĠJES": 49350, "ĠDingen": 49351, "ĠBets": 49352, "umba": 49353, "acción": 49354, "ĠìŀĪì§Ģë§Į": 49355, "Ġretra": 49356, "ĠLaurent": 49357, "Ġpozy": 49358, "Ġgrooves": 49359, "Ġmáquina": 49360, "Ġminion": 49361, "Ġdeinen": 49362, "ĠShaun": 49363, "×Ļ×Ļ": 49364, "Ġhonorary": 49365, "osaurus": 49366, "Ġzeit": 49367, "Ġespecie": 49368, "ĠBCE": 49369, "аÑĤе": 49370, "Justin": 49371, "ĠWheels": 49372, "ĠìĿ´íķ´": 49373, "ĠبÙĬÙĨ": 49374, "Ġpropulsion": 49375, "Ġperceber": 49376, "ĠNewman": 49377, "å´": 49378, "culosis": 49379, "Mi": 49380, "ĠаккÑĥ": 49381, "Ġmastering": 49382, "Ġläh": 49383, "Ġfists": 49384, "ä»Ķ": 49385, "Ġmarinade": 49386, "Lilly": 49387, "Ġëħ¸ëł¥": 49388, "ĠYH": 49389, "Ġurgently": 49390, "Ġinformational": 49391, "Ġacordo": 49392, "izzy": 49393, "ãģĦãģı": 49394, "ìĿ´ìĸ´": 49395, "imar": 49396, "ĠëĤĺìĺ¤ë": 49397, "Ġtwenties": 49398, "Ġrasp": 49399, "Ġbumpy": 49400, "بة": 49401, "worker": 49402, "Ġquickest": 49403, "Ġattaches": 49404, "виг": 49405, "ĠëĤĺíĥĢë": 49406, "Ġpuree": 49407, "Ġoversized": 49408, "Ġstirred": 49409, "Ġjakim": 49410, "Ġhomicide": 49411, "ãĤĤãģĹ": 49412, "iscilla": 49413, "Ġì±Ļ": 49414, "Ġspeculative": 49415, "Ġassists": 49416, "main": 49417, "jähr": 49418, "indet": 49419, "ĠÅŁur": 49420, "Ġforecasts": 49421, "Ġdiversion": 49422, "Ġtare": 49423, "Ġogl": 49424, "ĠOrganisation": 49425, "ĠChevy": 49426, "Ġbaja": 49427, "andır": 49428, "ĠÙĪÙĦا": 49429, "Ġradiant": 49430, "Ġliaison": 49431, "Ġdemokrat": 49432, "ĠMARC": 49433, "ÏĢοÏħ": 49434, "Ġrunt": 49435, "Ġprécis": 49436, "Ġgeven": 49437, "Ġvéhic": 49438, "ĠJESS": 49439, "STR": 49440, "Ġìĸĺë": 49441, "Ġvisionary": 49442, "Ġburadan": 49443, "ĠãģĤãĤĬ": 49444, "Ġrebirth": 49445, "Ġexhibited": 49446, "ĠMetall": 49447, "olie": 49448, "elyn": 49449, "Ġflavours": 49450, "Ġescrito": 49451, "ĠDelete": 49452, "ĠìķĮìķĺìĸ´": 49453, "ĠÑĥкÑĢаÑĹн": 49454, "Ġinterrupting": 49455, "Ġidentific": 49456, "ĠSuzuki": 49457, "ĠLanding": 49458, "件äºĭæĥħ": 49459, "andi": 49460, "Ġestran": 49461, "Ġcouleur": 49462, "Ġagrad": 49463, "ĠSny": 49464, "Ġà®ĩல": 49465, "Ġander": 49466, "Ġrua": 49467, "Ġprise": 49468, "Ġlaure": 49469, "ĠíĬĢ": 49470, "Ġmoderation": 49471, "Ġerfahren": 49472, "Ġdeconst": 49473, "ĠReese": 49474, "ĠPK": 49475, "etos": 49476, "ãģĵãĤĮãģ§": 49477, "ĠGravity": 49478, "ĠEren": 49479, "Ġoverboard": 49480, "Ġmüsst": 49481, "ĠEmail": 49482, "еÑĢм": 49483, "ydi": 49484, "iÄĻdzy": 49485, "ĠLOU": 49486, "ĠFuÃŁball": 49487, "ĠRD": 49488, "alts": 49489, "ĠìĬ¤íĬ¸ë": 49490, "ĠÐļÑĢаÑģ": 49491, "Ġtelev": 49492, "ĠÑĢо": 49493, "Ġresignation": 49494, "Ġjingle": 49495, "ĠStudien": 49496, "ĠIX": 49497, "ĠSentinel": 49498, "ĠPang": 49499, "éĦ": 49500, "Jake": 49501, "Ġpersonagem": 49502, "Ġmédia": 49503, "ĠChern": 49504, "antically": 49505, "Ġthá»Ŀi": 49506, "Ġparalysis": 49507, "Ġjapanese": 49508, "Ġconex": 49509, "Ġefic": 49510, "Ġunderside": 49511, "Ġneol": 49512, "Ġfian": 49513, "имоÑģÑĤÑĮ": 49514, "Ġquirky": 49515, "Ġpista": 49516, "ĠClement": 49517, "nothing": 49518, "ĠпоеÑħ": 49519, "Ġhorrend": 49520, "Ġconsolidate": 49521, "ploys": 49522, "emaker": 49523, "Jennifer": 49524, "Ġnuméro": 49525, "Ġfamoso": 49526, "ĠNeptune": 49527, "ĠíĸĪìĸ´": 49528, "ĠпÑĢезид": 49529, "Ġsitcom": 49530, "Ġserio": 49531, "Ġmue": 49532, "Ġglands": 49533, "Ġbörjar": 49534, "ĠYJ": 49535, "ĠRiot": 49536, "paragus": 49537, "Ġsegurança": 49538, "Ġimmature": 49539, "ĠMadonna": 49540, "à¸į": 49541, "Ġlingering": 49542, "Ġacesso": 49543, "ĠOrient": 49544, "ĠRecomm": 49545, "Ġcomplac": 49546, "founded": 49547, "attend": 49548, "Ġcielo": 49549, "ĠZhan": 49550, "naires": 49551, "cco": 49552, "Ġ×IJ׳": 49553, "Ġstata": 49554, "Ġcontradictory": 49555, "ĠSé": 49556, "ĠSAN": 49557, "ĠConnie": 49558, "Ġëĭ¹ìĭľ": 49559, "ĠÑģамой": 49560, "Ġmajestic": 49561, "ĠPenguin": 49562, "ĠCOME": 49563, "ÃŃcios": 49564, "pero": 49565, "Ġmg": 49566, "Ġfauc": 49567, "Ġcorrer": 49568, "ĠGottes": 49569, "ĠAnglo": 49570, "Har": 49571, "á»Ĺi": 49572, "Ġvitesse": 49573, "Ġannouncer": 49574, "ĠOmaha": 49575, "kum": 49576, "Ġspared": 49577, "ĠÑĢаза": 49578, "ĠполÑĥÑĩиÑĤÑģÑı": 49579, "Ġtähän": 49580, "Ġпонад": 49581, "Ġpertaining": 49582, "ĠRate": 49583, "iern": 49584, "Gold": 49585, "Ġteste": 49586, "ĠdeÄŁild": 49587, "Ġdamping": 49588, "ĠPartnership": 49589, "zysta": 49590, "geld": 49591, "Ġsmokes": 49592, "ĠMarriage": 49593, "쪽ìĹIJ": 49594, "èħ³": 49595, "isce": 49596, "Ġtryna": 49597, "ĠDirectory": 49598, "ĠëĤĺìĺ¬": 49599, "Ġshameful": 49600, "Ġmentre": 49601, "Ġassigning": 49602, "æĺ¯éĢĻ樣": 49603, "Ġrepertoire": 49604, "Ġobjetos": 49605, "稱": 49606, "Ġunderworld": 49607, "Ġendeavors": 49608, "Ġignite": 49609, "ĠÙĪج": 49610, "Ġexperient": 49611, "ĠÐĹап": 49612, "ĠзаклÑİÑĩ": 49613, "Ġvoltages": 49614, "Ġniego": 49615, "Ġdeficits": 49616, "Ġbuenos": 49617, "ĠSleeping": 49618, "ĠSalem": 49619, "Ġunlocking": 49620, "Ġinteracted": 49621, "Ġentendeu": 49622, "ĠSuperintendent": 49623, "Ġszczegól": 49624, "Ġquas": 49625, "Ġpaling": 49626, "Ġkho": 49627, "بØŃ": 49628, "Ġcolabor": 49629, "ĠпÑĢигоÑĤов": 49630, "Ġmauv": 49631, "ĠJudas": 49632, "ĠAssist": 49633, "ĠÑĤеÑĢÑĢи": 49634, "ĠнаÑģколÑĮко": 49635, "Ġsubsidy": 49636, "ĠEmbassy": 49637, "Ġdagen": 49638, "ĠSanto": 49639, "èĪ¬": 49640, "ש×ķ×ij": 49641, "Ġabruptly": 49642, "ĠAdapt": 49643, "Ġvaak": 49644, "Ġpostal": 49645, "Ġinvestir": 49646, "Ġfiquei": 49647, "Ġdowntime": 49648, "ĠWebb": 49649, "ĠNCAA": 49650, "ĠEstoy": 49651, "олоÑĤ": 49652, "ĠìĤ¬ê±´": 49653, "Ġnationalist": 49654, "ĠKathryn": 49655, "ĠKop": 49656, "éª": 49657, "Sean": 49658, "ONA": 49659, "ĠBj": 49660, "×¢×Ŀ": 49661, "ÃŃb": 49662, "idamente": 49663, "Ġглаза": 49664, "Ġunnie": 49665, "Ġgemaakt": 49666, "ĠINTERVIEWER": 49667, "ĠHaut": 49668, "ίο": 49669, "geois": 49670, "wydd": 49671, "Ġколи": 49672, "Ġtightened": 49673, "Ġplanners": 49674, "Ġherum": 49675, "Ġgörün": 49676, "Ġelectronically": 49677, "Ġceram": 49678, "Ġëĭ¤ìĸijíķľ": 49679, "Ġepilepsy": 49680, "ĠeÄŁ": 49681, "lins": 49682, "ĠShiny": 49683, "æł¡": 49684, "ĠÑģолн": 49685, "Ġmacaron": 49686, "Ġimpacto": 49687, "ĠVegan": 49688, "zeÅĦ": 49689, "ĠRapha": 49690, "ĠPars": 49691, "ĠLEO": 49692, "ãģĬãģ£": 49693, "cü": 49694, "Ġ׾×Ķ×Ļ×ķת": 49695, "Ġähnlich": 49696, "Ġfloss": 49697, "ĠAZ": 49698, "Ġmöchten": 49699, "Ġgrooming": 49700, "Ġgrasses": 49701, "ranch": 49702, "Ġrecibir": 49703, "Ġbouncy": 49704, "ĠHobby": 49705, "Ġviktig": 49706, "Ġbegitu": 49707, "ĠPicasso": 49708, "ĠKush": 49709, "모": 49710, "Ġobstruction": 49711, "Ġë¶ĦìľĦ": 49712, "Ġmicrob": 49713, "ĠWestminster": 49714, "rops": 49715, "dul": 49716, "Ġdevo": 49717, "ĠLehrer": 49718, "ĠAdvisor": 49719, "ucken": 49720, "ĠбÑĥм": 49721, "Ġflattering": 49722, "ĠTruman": 49723, "ĠSempre": 49724, "ĠMcCain": 49725, "ĠHindus": 49726, "Julia": 49727, "Ġwatershed": 49728, "Ġlush": 49729, "ìłĦë": 49730, "Before": 49731, "ĠÐĴÑĤоÑĢ": 49732, "ĠSaaS": 49733, "Ġsitzt": 49734, "Ġbeetle": 49735, "ĠEssential": 49736, "enko": 49737, "ĠëķĮëıĦ": 49738, "Ġrevving": 49739, "Ġpoorer": 49740, "Ġcoerc": 49741, "Ġidee": 49742, "Ġcoû": 49743, "alet": 49744, "Ġzdrow": 49745, "Ġfender": 49746, "growth": 49747, "DING": 49748, "Ġzde": 49749, "ä¸ĬéĿ¢": 49750, "ENTS": 49751, "Ġfacets": 49752, "éļª": 49753, "ushima": 49754, "ĠÅŁeh": 49755, "Ġparasite": 49756, "Ġlapse": 49757, "ĠMeer": 49758, "ĠKund": 49759, "Ġslog": 49760, "Ġbrunch": 49761, "ĠChart": 49762, "arz": 49763, "ĠMUS": 49764, "Ġoffenses": 49765, "Ġinglés": 49766, "Ġfoliage": 49767, "oplan": 49768, "Aut": 49769, "ĠJacqu": 49770, "tak": 49771, "iembre": 49772, "Ġxen": 49773, "Ġnominees": 49774, "Ġbiomedical": 49775, "ésus": 49776, "Ġestuv": 49777, "ÏĦÏĮ": 49778, "ATHAN": 49779, "Ġíķľëį°": 49780, "Ġheed": 49781, "crosstalk": 49782, "Bill": 49783, "Ġspouses": 49784, "ĠÑģÑİж": 49785, "Ġverso": 49786, "ĠSven": 49787, "ĠCau": 49788, "cuz": 49789, "Ġë³´ìĦ¸ìļĶ": 49790, "ĠÑħозÑı": 49791, "Ġmocking": 49792, "ĠOna": 49793, "ĠDá": 49794, "Ġfruitful": 49795, "Ġbanquet": 49796, "udding": 49797, "inctions": 49798, "dert": 49799, "sud": 49800, "Ġdescon": 49801, "ĠJC": 49802, "Ġ§": 49803, "Ġpubli": 49804, "ëĪĪ": 49805, "éģķãģĨ": 49806, "Ġentschieden": 49807, "ĠROI": 49808, "ãģįãģŁ": 49809, "ĠìĥĿê²¼": 49810, "Ġkäytt": 49811, "yani": 49812, "shaw": 49813, "Ġunleash": 49814, "Ġmanne": 49815, "Ġhistogram": 49816, "æĬ¥": 49817, "à¸Ńะà¹Ħร": 49818, "Ġgn": 49819, "Ġfella": 49820, "Ġeinges": 49821, "ĠBuilt": 49822, "Ġrepresenta": 49823, "Ġpunishing": 49824, "Ġoutsiders": 49825, "нÑĥÑĤÑĮÑģÑı": 49826, "current": 49827, 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\ No newline at end of file
diff --git a/modules/whisper_extractor/audio.py b/modules/whisper_extractor/audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9dbaed4c33b02e29351bb63750c199645b0326e
--- /dev/null
+++ b/modules/whisper_extractor/audio.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.
+
+# 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 functools import lru_cache
+from typing import Union
+
+import ffmpeg
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from .utils import exact_div
+
+# hard-coded audio hyperparameters
+SAMPLE_RATE = 16000
+N_FFT = 400
+N_MELS = 80
+HOP_LENGTH = 160
+CHUNK_LENGTH = 30
+N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE  # 480000: number of samples in a chunk
+N_FRAMES = exact_div(
+    N_SAMPLES, HOP_LENGTH
+)  # 3000: number of frames in a mel spectrogram input
+
+
+def load_audio(file: str, sr: int = SAMPLE_RATE):
+    """
+    Open an audio file and read as mono waveform, resampling as necessary
+
+    Parameters
+    ----------
+    file: str
+        The audio file to open
+
+    sr: int
+        The sample rate to resample the audio if necessary
+
+    Returns
+    -------
+    A NumPy array containing the audio waveform, in float32 dtype.
+    """
+    try:
+        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
+        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
+        out, _ = (
+            ffmpeg.input(file, threads=0)
+            .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
+            .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
+        )
+    except ffmpeg.Error as e:
+        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+
+    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
+
+
+def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
+    """
+    Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
+    """
+    if torch.is_tensor(array):
+        if array.shape[axis] > 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 100755
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</span>",
+    "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 "<number> and a half" with "<number> 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 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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:
+        <code>
+          for p in group["params"]:
+             state = self.state[p]
+             ...
+        </code>
+        you can do:
+        <code>
+          with self.batched_params(group["params"]) as batches:
+             for p, state, p_names in batches:
+                 ...
+        </code>
+
+        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/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/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/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..090a81121c8db19a03e6d3fe668903567a8da0b3
--- /dev/null
+++ b/preprocessors/libritts.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.
+
+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")
+    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 = []
+
+    train_index_count = 0
+    test_index_count = 0
+
+    train_total_duration = 0
+    test_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"])
+
+                    waveform, sample_rate = torchaudio.load(res["Path"])
+                    duration = waveform.size(-1) / sample_rate
+                    res["Duration"] = duration
+
+                    if not "train" in distribution:
+                        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_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..5661d74f0d27f9c3c949d82a41b010e6760b3802
--- /dev/null
+++ b/preprocessors/processor.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 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,
+)
+
+
+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)
+
+
+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/processors/acoustic_extractor.py b/processors/acoustic_extractor.py
index 3c56486336b28a8ae4560d927cf8eb633b7c4513..24d096bd2b651d6cd9ae733daf3c501d0ca28bf3 100644
--- a/processors/acoustic_extractor.py
+++ b/processors/acoustic_extractor.py
@@ -21,7 +21,7 @@ from scipy.interpolate import interp1d
 
 ZERO = 1e-12
 
-            
+
 def extract_utt_acoustic_features_parallel(metadata, dataset_output, cfg, n_workers=1):
     """Extract acoustic features from utterances using muliprocess
 
@@ -215,15 +215,54 @@ def __extract_utt_acoustic_features(dataset_output, cfg, utt):
         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)
-            
+                save_feature(
+                    dataset_output, cfg.preprocess.acoustic_token_dir, uid, codes
+                )
+
 
 def extract_utt_acoustic_features_tts(dataset_output, cfg, utt):
     __extract_utt_acoustic_features(dataset_output, cfg, utt)
 
 
 def extract_utt_acoustic_features_svc(dataset_output, cfg, utt):
-    __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"]
+
+    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:
+            energy = (mel.exp() ** 2).sum(0).sqrt().cpu().numpy()
+            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)
 
 
 def extract_utt_acoustic_features_tta(dataset_output, cfg, utt):
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/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 N D
+ABANDONEDLY	AH0 B AE1 N D AH0 N D L IY0
+ABANDONING  AH0 B AE1 N D AH0 N IH0 NG
+ABANDONMENT  AH0 B AE1 N D AH0 N M AH0 N T
+ABANDONMENTS  AH0 B AE1 N D AH0 N M AH0 N T S
+ABANDONS  AH0 B AE1 N D AH0 N Z
+ABAOUT	AH0 B EY1 AW2 T
+ABARA	AH0 B AA1 R AH0
+ABARAK	AH0 B AA1 R AH0 K
+ABARIA	AA0 B AA1 R IY0 AH0
+ABARIAN	AH0 B EH1 R IY0 AH0 N
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+CONFRATERNITY	K AH0 N F R AH0 T ER1 N AH0 T IY0
+CONFRERE  K AA1 N F R EH0 R
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+CONFUCIANIST	K AH0 N F Y UW1 SH AH0 N IH0 S T
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+CONG  K AO1 NG
+CONGAREE	K AH0 N G ER0 IY1
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+CONGLOMERATED	K AH0 N G L AA1 M ER0 EY2 T IH0 D
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+CONGRATULATED  K AH0 N G R AE1 CH AH0 L EY2 T IH0 D
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+CONGREGATE  K AA1 NG G R AH0 G EY2 T
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+CONGREGATING	K AA1 NG G R AH0 G EY2 T IH0 NG
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+CONGREGATIONAL  K AA2 NG G R AH0 G EY1 SH AH0 N AH0 L
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+CONGREGATIONALIST	K AA1 NG G R AH0 G EY1 SH AH0 N AH0 L IH0 S T
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+CONNEAUT	K AH0 N OW2 T
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+CONOVER'S	K AA1 N AH0 V ER0 Z
+CONQU'RING	K AA1 NG K W R IH0 NG
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+CONQUERABLE	K AA1 NG K ER0 AH0 B AH0 L
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+CONQUETE	K AA1 NG K W AH0 T
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+CONQUISTADOR	K AA1 N K W IH2 S T AH0 D AO2 R
+CONQUISTADORES	K AA1 N K W IH2 S T AH0 D AO1 R Z
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+CONRADINO	K AA1 N R AH0 D IY1 N OW0
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+CONSANGUINEOUS	K AH0 N S AE1 NG G W IY1 N IY0 AH0 S
+CONSANGUINITY	K AH0 N S AA0 NG IY0 N AH0 T IY0
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+CONSCIENTIAE	K AA2 N CH IY0 EH1 N CH IY0 EY2
+CONSCIENTIOUS  K AA2 N SH IY0 EH1 N SH AH0 S
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+CONSCRIPT  K AA1 N S K R IH2 P T
+CONSCRIPT  K AH0 N S K R IH1 P T
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+DILLWYN	D IH1 L W IH0 N
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+DILLY  D IH1 L IY0
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+DILSEY'S	D IH1 L Z IY0 Z
+DILSIE	D IH1 L S IY0
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+DILUTE  D AY0 L UW1 T
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+DILUTED  D AY0 L UW1 T AH0 D
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+DILUTING  D AY0 L UW1 T IH0 NG
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+DILUTIONS	D IH0 L UW1 SH AH0 N Z
+DILUVIAL	D IH0 L UW1 V IY0 AH0 L
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+DILWYN	D IH1 L W IH0 N
+DILYS	D IH0 L IY0 Z
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+DIMENSIONED  D AH0 M EH1 N CH AH0 N D
+DIMENSIONLESS	D IH0 M EH1 N SH AH0 N L AH0 S
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+DIMENSOSCOPE	D IH0 M EH1 N SH AH0 S K OW2 P
+DIMES  D AY1 M Z
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+DIMINISHED  D IH0 M IH1 N IH0 SH T
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+DIMINISHING  D IH0 M IH1 N IH0 SH IH0 NG
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+DIMITIES	D IH1 M IH0 T IY0 Z
+DIMITRI  D IH0 M IY1 T R IY0
+DIMITRI'S	D IH0 M IY1 T R IY0 Z
+DIMITRIEVNA	D IH0 M IY1 T R IY0 AH0 V N AH0
+DIMITRY	D IH0 M IY1 T R IY0
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+DIMMLER	D IH1 M L ER0
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+DIMPLED  D IH1 M P AH0 L D
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+DIMPLING	D IH1 M P AH0 L IH0 NG
+DIMPLY	D IH0 M P L AY1
+DIMS  D IH1 M Z
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+DIMSDELL	D IH1 M Z D AH0 L
+DIMSTED	D IH1 M S T IH0 D
+DIN  D IH1 N
+DIN'D	D AY1 N D
+DIN'S	D IH0 N Z
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+DINA  D IY1 N AH0
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+DINADAN	D IH0 N AA1 D AH0 N
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+DINDE	D IH1 N D
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+DINERS  D AY1 N ER0 Z
+DINES  D AY1 N Z
+DINEWAN	D AY1 N W AA0 N
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+DING  D IH1 NG
+DINGAAN	D IH1 NG G AA2 N
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+DINGAN	D IH1 NG G AH0 N
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+DINGE	D IH1 N JH
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+DINGER  D IH1 NG ER0
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+DINGFIELD	D IH1 NG F IY2 L D
+DINGHAL	D IH1 NG AH0 L
+DINGHIES	D IH1 NG IY0 Z
+DINGHRA	D IH1 NG R AH0
+DINGHY  D IH1 NG IY0
+DINGHY'S	D IH1 NG IY0 Z
+DINGIER	D IH1 N JH IY0 ER0
+DINGIEST	D IH1 NG G IY0 AH0 S T
+DINGILY	D IH1 N JH AH0 L IY0
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+DINGING	D IH1 NG IH0 NG
+DINGLE  D IH1 NG G AH0 L
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+DINGLEFORD	D IH1 NG G AH0 L F ER0 D
+DINGLEHEIMER	D IH1 NG G AH0 L HH AY2 M ER0
+DINGLES	D IH1 NG G AH0 L Z
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+DINGO  D IH1 NG G OW0
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+DINHAM	D IH1 N AH0 M
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+DININ	D AY1 N IH0 N
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+DININGROOM	D AY1 N IH0 NG R UW2 M
+DINIS	D IY1 N IY0 Z
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+DINKA	D IH1 NG K AH0
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+DINKIE'S	D IH1 NG K IY0 Z
+DINKMAN	D IH1 NG K M AH0 N
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+DINMONT	D IH1 N M AA2 N T
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+DINNA	D IH1 N AH0
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+DINNETT	D IH1 N IH0 T
+DINNEY	D IH1 N IY0
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+DINNIS	D IH0 N IH0 S
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+DINNY'S	D IH1 N IY0 Z
+DINO  D IY1 N OW0
+DINO'S	D IY1 N OW0 Z
+DINORAH	D AY1 N AO1 R AH0
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+DINOSAUR  D AY1 N AH0 S AO2 R
+DINOSAURS  D AY1 N AH0 S AO2 R Z
+DINS	D IH1 N Z
+DINSHAW	D IH1 N SH AO2
+DINSHAW'S	D IH1 N SH AO2 Z
+DINSMORE  D IY1 N S M AO0 R
+DINSMORE'S	D IY1 N S M AO0 R Z
+DINSMORES	D IY1 N S M AO0 R Z
+DINT  D IH1 N T
+DINTED	D IH1 N IH0 D
+DINTON	D IH1 N T AH0 N
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+DINVILLE	D IH0 N V IH2 L
+DINWIDDIE  D IH1 N W IH0 D IY0
+DINWIDDIE'S	D IH1 N W IH0 D IY2 Z
+DINY	D AY1 N IY0
+DIO	D IY0 OW0
+DIO'S	D IY0 OW0 Z
+DIOCESAN  D AY0 AA1 S AH0 S AH0 N
+DIOCESE  D AY1 AH0 S AH0 S
+DIOCESE  D AY1 AH0 S IY2 Z
+DIOCESES  D AY1 AH0 S IY2 Z
+DIOCESES  D AY1 AH0 S IY2 Z AH0 Z
+DIOCLES	D IY1 AA0 K AH0 L Z
+DIOCLESIAN	D AY2 AH0 K L IY2 Z IY0 AH0 N
+DIOCLETIAN	D AY2 AH0 K L IY1 SH Y IH0 N
+DIOCLETIAN'S	D AY2 AH0 K L IY1 SH AH0 N Z
+DIODATI  D IY0 OW0 D AA1 T IY0
+DIODOKIS	D IY0 OW0 D OW1 K IY0 Z
+DIODOROS	D AY2 AH0 D AO1 R OW0 Z
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+DIODOTUS	D IY0 OW0 D OW1 T AH0 S
+DIOGENES	D AY0 AA1 JH EH2 N IY0 S
+DIOGENIANUS	D AY2 AH0 JH IY2 N IY0 AE1 N AH0 S
+DIOGO	D IY0 OW0 G OW1
+DIOM	D IY1 AA0 M
+DIOMED	D IY1 AH0 M EH0 D
+DIOMED'S	D IY1 AH0 M EH0 D Z
+DIOMEDE	D AY2 AH0 M IY2 D
+DIOMEDES	D IY0 OW0 M EH1 D EH0 S
+DIOMIDITCH	D AY1 OW2 M IH0 D IH1 CH
+DION  D AY1 AH0 N
+DION  D IY1 AO2 N
+DION'S	D AY2 AH0 N Z
+DIONAEA	D AY2 AH0 N IY1 AH0
+DIONE  D IY1 AA0 N
+DIONEO	D AY2 AH0 N IY0 OW0
+DIONETTA	D IY0 OW0 N EH1 T AH0
+DIONIS	D AY2 AH0 N IH1 S
+DIONISIO  D AY2 AH0 N IH1 S IY0 OW0
+DIONYSE	D AY2 AH0 N IH1 S
+DIONYSIA	D AY2 AH0 N IH1 S IY0 AH0
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+DIONYSIAC	D AY2 AH0 N IH1 S IY0 AE0 K
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+DIONYSODORUS	D AY2 AH0 N IH1 S AH0 D AO1 R AH0 S
+DIONYSOS	D AY2 AH0 N IH1 S OW2 Z
+DIONYSUS	D AY2 AH0 N IH1 S AH0 S
+DIOPHANES	D AY2 AH0 F EY2 N Z
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+DIOPTRIC	D IY0 OW0 P T R IH0 K
+DIORAMA	D AY1 ER0 AE1 M AH0
+DIORES	D IY2 AO1 R Z
+DIORITE  D AY1 ER0 AY2 T
+DIOS  D IY1 OW0 S
+DIOSCORIDES	D IY1 OW0 S K ER0 AY2 D Z
+DIOSCURI	D IY1 OW0 S K UH1 R IY0
+DIOSPOLIS	D IY1 OW0 S P AH0 L IH0 S
+DIOTIMA	D IY0 AA1 T IH0 M AH0
+DIOTISALVI	D IY0 OW1 T IH0 S AA1 L V IY0
+DIOU	D IY1 UW0
+DIOUOL	D AY0 AA1 OW1 L
+DIOXIDE  D AY0 AA1 K S AY2 D
+DIOXYS	D AY0 AA1 K S IY0 Z
+DIP  D IH1 P
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+DIPHTHERITIC	D IH1 F TH ER0 IH1 T IH0 K
+DIPHTHONG  D IH1 F TH AO2 NG
+DIPHTHONGS	D IH1 F TH AO2 NG Z
+DIPLODOCUS	D IH1 P L AH0 D OW1 K AH0 S
+DIPLOMA  D IH0 P L OW1 M AH0
+DIPLOMACIES	D IH0 P L OW1 M AH0 S IY0 Z
+DIPLOMACY  D IH0 P L OW1 M AH0 S IY0
+DIPLOMAS  D IH0 P L OW1 M AH0 Z
+DIPLOMAT  D IH1 P L AH0 M AE2 T
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+DIPLOMATE	D IH1 P L AH0 M EY2 T
+DIPLOMATES	D IH1 P L AH0 M EY2 T S
+DIPLOMATIC  D IH2 P L AH0 M AE1 T IH0 K
+DIPLOMATICALLY  D IH2 P L AH0 M AE1 T IH0 K L IY0
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+DIPLOMATS  D IH1 P L AH0 M AE2 T S
+DIPLOW	D IH0 P L OW0
+DIPP'D	D IH1 P D
+DIPPED  D IH1 P T
+DIPPEL  D IH1 P AH0 L
+DIPPER  D IH1 P ER0
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+DIPT	D IH1 P T
+DIPTERA	D IH1 P T EH1 R AH0
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+DIR	D IH0 R
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+DIRAI	D IH0 R AA1 IY0
+DIRAIT	D IH0 R EY1 T
+DIRANDA	D ER0 AE1 N D AH0
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+DIRCK	D ER1 K
+DIRCK'S	D ER1 K S
+DIRE  D AY1 ER0
+DIRE  D AY1 R
+DIRECKLY	D AY0 R EH1 K L IY0
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+DIRECT  D ER0 EH1 K T
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+DIRECTED  D AY0 R EH1 K T IH0 D
+DIRECTED  D ER0 EH1 K T AH0 D
+DIRECTED  D ER0 EH1 K T IH0 D
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+DIRECTER	D ER0 EH1 K T ER0
+DIRECTEST	D ER0 EH1 K T AH0 S T
+DIRECTETH	D ER0 EH1 K T IH0 TH
+DIRECTEUR	D ER0 EH0 K T UW1 R
+DIRECTIN	D IH0 R EH1 K T AH0 N
+DIRECTING  D AY0 R EH1 K T IH0 NG
+DIRECTING  D ER0 EH1 K T IH0 NG
+DIRECTING  D IH0 R EH1 K T IH0 NG
+DIRECTING  D IY0 R EH1 K T IH0 NG
+DIRECTION  D AY0 R EH1 K SH IH0 N
+DIRECTION  D ER0 EH1 K SH AH0 N
+DIRECTION  D IH0 R EH1 K SH IH0 N
+DIRECTION  D IY0 R EH1 K SH IH0 N
+DIRECTIONAL  D AY0 R EH1 K SH IH0 N AH0 L
+DIRECTIONAL  D ER0 EH1 K SH AH0 N AH0 L
+DIRECTIONAL  D IH0 R EH1 K SH IH0 N AH0 L
+DIRECTIONAL  D IY0 R EH1 K SH IH0 N AH0 L
+DIRECTIONS  D AY0 R EH1 K SH IH0 N Z
+DIRECTIONS  D ER0 EH1 K SH AH0 N Z
+DIRECTIONS  D IH0 R EH1 K SH IH0 N Z
+DIRECTIONS  D IY0 R EH1 K SH IH0 N Z
+DIRECTIVE  D AY0 R EH1 K T IH0 V
+DIRECTIVE  D ER0 EH1 K T IH0 V
+DIRECTIVE  D IH0 R EH1 K T IH0 V
+DIRECTIVE  D IY0 R EH1 K T IH0 V
+DIRECTIVES  D AY0 R EH1 K T IH0 V Z
+DIRECTIVES  D ER0 EH1 K T IH0 V Z
+DIRECTIVES  D IH0 R EH1 K T IH0 V Z
+DIRECTIVES  D IY0 R EH1 K T IH0 V Z
+DIRECTLY  D AY0 R EH1 K L IY0
+DIRECTLY  D ER0 EH1 K T L IY0
+DIRECTLY  D IH0 R EH1 K L IY0
+DIRECTLY  D IY0 R EH1 K L IY0
+DIRECTNESS  D AY0 R EH1 K N AH0 S
+DIRECTNESS  D ER0 EH1 K T N AH0 S
+DIRECTNESS  D IH0 R EH1 K N AH0 S
+DIRECTNESS  D IY0 R EH1 K N AH0 S
+DIRECTOIRE	D ER0 EH1 K T W AA1 R
+DIRECTOR  D AY0 R EH1 K T ER0
+DIRECTOR  D ER0 EH1 K T ER0
+DIRECTOR  D IH0 R EH1 K T ER0
+DIRECTOR  D IY0 R EH1 K T ER0
+DIRECTOR'S  D AY0 R EH1 K T ER0 Z
+DIRECTOR'S  D ER0 EH1 K T ER0 Z
+DIRECTOR'S  D IH0 R EH1 K T ER0 Z
+DIRECTOR'S  D IY0 R EH1 K T ER0 Z
+DIRECTORATE  D AY0 R EH1 K T ER0 AH0 T
+DIRECTORATE  D ER0 EH1 K T ER0 AH0 T
+DIRECTORATE  D IH0 R EH1 K T ER0 AH0 T
+DIRECTORATE  D IY0 R EH1 K T ER0 AH0 T
+DIRECTORATES	D IH0 R EH1 K T ER0 AH0 T S
+DIRECTORCILLO	D IH0 R EH1 K T ER0 CH IH1 L OW0
+DIRECTORIAL  D AY0 R EH0 K T AO1 R IY0 AH0 L
+DIRECTORIAL  D ER0 EH0 K T AO1 R IY0 AH0 L
+DIRECTORIAL  D IH0 R EH0 K T AO1 R IY0 AH0 L
+DIRECTORIAL  D IY0 R EH0 K T AO1 R IY0 AH0 L
+DIRECTORIES  D AY0 R EH1 K T ER0 IY0 Z
+DIRECTORIES  D ER0 EH1 K T ER0 IY0 Z
+DIRECTORIES  D IH0 R EH1 K T ER0 IY0 Z
+DIRECTORIES  D IY0 R EH1 K T ER0 IY0 Z
+DIRECTORIN	D ER0 EH1 K T AH0 R IH0 N
+DIRECTORS  D AY0 R EH1 K T ER0 Z
+DIRECTORS  D ER0 EH1 K T ER0 Z
+DIRECTORS  D IH0 R EH1 K T ER0 Z
+DIRECTORS  D IY0 R EH1 K T ER0 Z
+DIRECTORSHIP  D AY0 R EH1 K T ER0 SH IH2 P
+DIRECTORSHIP  D ER0 EH1 K T ER0 SH IH2 P
+DIRECTORSHIP  D IH0 R EH1 K T ER0 SH IH2 P
+DIRECTORSHIP  D IY0 R EH1 K T ER0 SH IH2 P
+DIRECTORSHIPS  D AY0 R EH1 K T ER0 SH IH2 P S
+DIRECTORSHIPS  D ER0 EH1 K T ER0 SH IH2 P S
+DIRECTORSHIPS  D IH0 R EH1 K T ER0 SH IH2 P S
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+DIRECTORY  D ER0 EH1 K T ER0 IY0
+DIRECTORY  D IH0 R EH1 K T ER0 IY0
+DIRECTORY  D IY0 R EH1 K T ER0 IY0
+DIRECTRESS	D ER0 EH1 K T R AH0 S
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+DIRECTS  D AY0 R EH1 K T S
+DIRECTS  D ER0 EH1 K T S
+DIRECTS  D IH0 R EH1 K T S
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+DIREFUL	D AY0 R F AH0 L
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+DIREGUS	D IY0 R EH0 G AH0 S
+DIREKTOR	D AY0 R EH1 K T ER0
+DIRELY	D AY1 ER0 L IY0
+DIRER	D AY1 ER0 R
+DIREST  D AY1 R AH0 S T
+DIREXIA	D ER0 EH1 K S IY0 AH0
+DIRGE  D ER1 JH
+DIRGES	D ER1 JH IH0 Z
+DIRGING	D ER1 JH IH0 NG
+DIRHAM	D ER1 AH0 M
+DIRHEM	D ER1 AH0 M
+DIRHEMS	D ER1 AH0 M Z
+DIRIGIBLE	D IH0 R AH0 JH AH0 B AH0 L
+DIRIGIBLE'S	D IH0 R AH0 JH AH0 B AH0 L Z
+DIRIGIBLES	D IH0 R AH0 JH AH0 B AH0 L Z
+DIRK  D ER1 K
+DIRK'S	D ER1 K S
+DIRKOVITCH	D ER1 K AH0 V IH0 CH
+DIRKS  D ER1 K S
+DIRL	D ER1 L
+DIROLA	D IH0 R OW1 L AH0
+DIRRIK	D ER1 IH0 K
+DIRRUL	D ER1 AH0 L
+DIRRUL'S	D ER1 AH0 L Z
+DIRT  D ER1 T
+DIRT'S  D ER1 T S
+DIRTHY	D ER1 DH IY0
+DIRTIED	D ER1 T IY0 D
+DIRTIER  D ER1 T IY0 ER0
+DIRTIES	D ER1 T IY0 Z
+DIRTIEST  D ER1 T IY0 AH0 S T
+DIRTILY	D ER1 T IY0 L IY0
+DIRTINESS	D ER1 T IY0 N AH0 S
+DIRTY  D ER1 T IY0
+DIRTYING	D ER1 T IY0 IH0 NG
+DIRZED	D ER1 Z D
+DIRZEE	D ER1 Z IY0
+DIS  D IH1 S
+DISA  D IH1 S AH0
+DISABILITIES  D IH0 S AH0 B IH1 L AH0 T IY0 Z
+DISABILITIES  D IH0 S AH0 B IH1 L IH0 T IY0 Z
+DISABILITY  D IH0 S AH0 B IH1 L IH0 T IY0 Z
+DISABILITY  D IH2 S AH0 B IH1 L IH0 T IY0
+DISABLE  D IH0 S EY1 B AH0 L
+DISABLED  D IH0 S EY1 B AH0 L D
+DISABLEMENT	D IH0 S EY1 B AH0 L M AH0 N T
+DISABLES  D IH0 S EY1 B AH0 L Z
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+DISABLING  D IH2 S EY1 B L IH0 NG
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+DISABUSE  D IH0 S AH0 B Y UW1 Z
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+DISABUSING	D IH0 S AH0 B Y UW1 Z IH0 NG
+DISACCORD	D IH2 S AH0 K AO1 R D
+DISADVANTAGE  D IH2 S AH0 D V AE1 N IH0 JH
+DISADVANTAGE  D IH2 S AH0 D V AE1 N T IH0 JH
+DISADVANTAGED  D IH0 S AH0 D V AE1 N T IH0 JH D
+DISADVANTAGED  D IH2 S AH0 D V AE1 N IH0 JH D
+DISADVANTAGEOUS  D IH2 S AE2 D V AE2 N T EY1 JH AH0 S
+DISADVANTAGEOUSLY	D IH2 S AH0 D V AE1 N IH0 JH AH0 S L IY0
+DISADVANTAGES  D IH2 S AH0 D V AE1 N IH0 JH Z
+DISADVANTAGES  D IH2 S AH0 D V AE1 N T IH0 JH IH0 Z
+DISAFFECTED  D IH2 S AH0 F EH1 K T IH0 D
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+DISAGREABLE	D IH0 S AH0 G R IY1 AH0 B AH0 L
+DISAGREE  D IH0 S AH0 G R IY1
+DISAGREEABLE  D IH2 S AH0 G R IY1 AH0 B AH0 L
+DISAGREEABLENESS	D IH0 S AH0 G R IY1 AH0 B AH0 L N AH0 S
+DISAGREEABLES	D IH0 S AH0 G R IY1 AH0 B AH0 L Z
+DISAGREEABLY	D IH0 S AH0 G R IY1 AH0 B L IY0
+DISAGREED  D IH0 S AH0 G R IY1 D
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+DISAGREEMENTS  D IH2 S AH0 G R IY1 M AH0 N T S
+DISAGREES  D IH0 S AH0 G R IY1 Z
+DISAIT	D IH2 S EY1 T
+DISALLOW  D IH2 S AH0 L AW1
+DISALLOWANCE  D IH2 S AH0 L AW1 AH0 N S
+DISALLOWED  D IH2 S AH0 L AW1 D
+DISALLOWING  D IH0 S AH0 L AW1 IH0 NG
+DISALLOWS	D IH2 S AH0 L AW1 Z
+DISANNUL	D IH0 Z AE1 N AH0 L
+DISANT	D IH0 S AE1 N T
+DISAPOINTMENT	D IH2 S AH0 P OY1 N T M AH0 N T
+DISAPP'INTED	D IH2 S AH0 P IH2 N T IH0 D
+DISAPP'INTMENT	D IH2 S AH0 P IH1 N T M AH0 N T
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+DISAPPEAR  D IH2 S AH0 P IY1 R
+DISAPPEAR'D	D IH2 S AH0 P IY1 R D
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+DISAPPEARANCE  D IH2 S AH0 P IY1 R AH0 N S
+DISAPPEARANCES  D IH2 S AH0 P IH1 R AH0 N S IH0 Z
+DISAPPEARANCES  D IH2 S AH0 P IY1 R AH0 N S IH0 Z
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+DISAPPEARED  D IH2 S AH0 P IY1 R D
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+DISAPPEARING  D IH2 S AH0 P IH1 R IH0 NG
+DISAPPEARING  D IH2 S AH0 P IY1 R IH0 NG
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+DISAPPEARS  D IH2 S AH0 P IY1 R Z
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+DISAPPOINTING  D IH2 S AH0 P OY1 N T IH0 NG
+DISAPPOINTINGLY  D IH0 S AH0 P OY1 N IH0 NG L IY0
+DISAPPOINTINGLY  D IH0 S AH0 P OY1 N T IH0 NG L IY0
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+DISAPPROBATION	D IH2 S AH0 P R AH0 B EY1 SH AH0 N
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+DISARRANGE	D IH2 S ER0 EY1 N JH
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+DISARRANGING	D IH2 S ER0 EY1 N JH IH0 NG
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+DISARRAYED	D IH2 S AH0 R EY1 D
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+DISCARDED  D IH0 S K AA1 R D IH0 D
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+DISCARNATED	D IH0 S K AA1 R N EY2 T IH0 D
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+DISCOURTEOUS	D IH1 S K AO0 R T IY0 IH0 S
+DISCOURTEOUSLY	D IH1 S K AO0 R T IY0 AH0 S L IY0
+DISCOURTESIES	D IH0 S K ER1 T AH0 S IY0 Z
+DISCOURTESY	D IH0 S K ER1 T AH0 S IY0
+DISCOVER  D IH0 S K AH1 V ER0
+DISCOVER'D	D IH0 S K AH1 V R AH0 D
+DISCOVERABLE  D IH0 S K AH1 V ER0 AH0 B AH0 L
+DISCOVERABLE  D IH0 S K AH1 V R AH0 B AH0 L
+DISCOVERED  D IH0 S K AH1 V ER0 D
+DISCOVERER	D IH0 S K AH1 V ER0 ER0
+DISCOVERER'S	D IH0 S K AH1 V ER0 ER0 Z
+DISCOVERERS	D IH0 S K AH1 V ER0 ER0 Z
+DISCOVERETH	D IH0 S K AH1 V R IH0 TH
+DISCOVERIES  D IH0 S K AH1 V ER0 IY0 Z
+DISCOVERIN	D IH0 S K AH1 V ER0 IH0 N
+DISCOVERING  D IH0 S K AH1 V ER0 IH0 NG
+DISCOVERS  D IH0 S K AH1 V ER0 Z
+DISCOVERY  D IH0 S K AH1 V ER0 IY0
+DISCOVERY  D IH0 S K AH1 V R IY0
+DISCOVERY  D IH2 S K AH1 V R IY0
+DISCREDIT  D IH0 S K R EH1 D AH0 T
+DISCREDITABLE	D IH0 S K R EH1 D AH0 T AH0 B AH0 L
+DISCREDITABLY	D IH0 S K R EH1 D AH0 T AH0 B L IY0
+DISCREDITED  D IH0 S K R EH1 D IH0 T IH0 D
+DISCREDITING  D IH0 S K R EH1 D AH0 T IH0 NG
+DISCREDITS  D IH0 S K R EH1 D AH0 T S
+DISCREET  D IH0 S K R IY1 T
+DISCREETER	D IH0 S K R IY1 T ER0
+DISCREETEST	D IH0 S K R IY1 T AH0 S T
+DISCREETLY  D IH0 S K R IY1 T L IY0
+DISCREETNESS	D IH0 S K R IY1 T N AH0 S
+DISCREPANCIES  D IH0 S K R EH1 P AH0 N S IY0 Z
+DISCREPANCY  D IH0 S K R EH1 P AH0 N S IY0
+DISCREPANT	D IH0 S K R EH1 P AH0 N T
+DISCRETE  D IH0 S K R IY1 T
+DISCRETELY	D IH0 S K R IY2 T L IY0
+DISCRETION  D IH0 S K R EH1 SH AH0 N
+DISCRETION'S	D IH0 S K R EH1 SH AH0 N Z
+DISCRETIONARY  D IH0 S K R EH1 SH AH0 N EH2 R IY0
+DISCRETIONS  D IH0 S K R EH1 SH AH0 N Z
+DISCRIBED	D IH0 S K R AY1 B D
+DISCRIMINA	D IH0 S K R IH1 M AH0 N AH0
+DISCRIMINATE  D IH0 S K R IH1 M AH0 N EY2 T
+DISCRIMINATED  D IH0 S K R IH1 M AH0 N EY2 T AH0 D
+DISCRIMINATED  D IH0 S K R IH1 M AH0 N EY2 T IH0 D
+DISCRIMINATELY	D IH0 S K R IH1 M AH0 N AH0 T L IY0
+DISCRIMINATES  D IH0 S K R IH1 M AH0 N EY2 T S
+DISCRIMINATING  D IH0 S K R IH1 M AH0 N EY2 T IH0 NG
+DISCRIMINATINGLY	D IH0 S K R IH1 M AH0 N EY2 T IH0 NG L IY0
+DISCRIMINATION  D IH0 S K R IH2 M AH0 N EY1 SH AH0 N
+DISCRIMINATIONS	D IH0 S K R IH2 M AH0 N EY1 SH AH0 N Z
+DISCRIMINATIVE  D IH0 S K R IH1 M AH0 N AH0 T IH0 V
+DISCRIMINATORY  D IH0 S K R IH1 M AH0 N AH0 T AO2 R IY0
+DISCRIPTION	D IH0 S K R IH1 P SH AH0 N
+DISCROWN	D IH0 S K R OW1 N
+DISCROWNED	D IH0 S K R AW1 N D
+DISCS  D IH1 S K S
+DISCURSIVE  D IH0 S K ER1 S IH0 V
+DISCURSIVELY	D IH0 S K ER1 S IH0 V L IY0
+DISCURSIVENESS	D IH0 S K ER1 S IH0 V N AH0 S
+DISCUS  D IH1 S K AH0 S
+DISCUSS  D IH0 S K AH1 S
+DISCUSS'D	D IH0 S K AH1 SH D
+DISCUSSED  D IH0 S K AH1 S T
+DISCUSSES  D IH0 S K AH1 S AH0 Z
+DISCUSSES  D IH0 S K AH1 S IH0 Z
+DISCUSSIN	D IH0 S K AH1 S IH0 N
+DISCUSSING  D IH0 S K AH1 S IH0 NG
+DISCUSSION  D IH0 S K AH1 SH AH0 N
+DISCUSSIONS  D IH0 S K AH1 SH AH0 N Z
+DISCUST	D IH1 S K AH0 S T
+DISDAIN  D IH0 S D EY1 N
+DISDAIN'D	D IH0 S D EY1 N D
+DISDAINE	D IH0 S D EY1 N
+DISDAINED  D IH0 S D EY1 N D
+DISDAINFUL  D IH0 S D EY1 N F AH0 L
+DISDAINFULLY	D IH0 S D EY1 N F AH0 L IY0
+DISDAINING  D IH0 S D EY1 N IH0 NG
+DISDAINS  D IH0 S D EY1 N Z
+DISE  D AY1 S
+DISE  D AY1 Z
+DISEAS'D	D IH0 Z IY1 Z D
+DISEASE  D IH0 Z IY1 Z
+DISEASED  D IH0 Z IY1 Z D
+DISEASES  D IH0 Z IY1 Z AH0 Z
+DISEASES  D IH0 Z IY1 Z IH0 Z
+DISEMBARK  D IH0 S EH0 M B AA1 R K
+DISEMBARKATION  D IH0 S EH2 M B AA0 R K EY1 SH AH0 N
+DISEMBARKED	D IH0 S EH0 M B AA1 R K T
+DISEMBARKING	D IH0 S EH0 M B AA1 R K IH0 NG
+DISEMBARKS	D IH0 S EH0 M B AA1 R K S
+DISEMBARRASS	D IH0 S IH0 B EH1 R AH0 S
+DISEMBARRASSED	D IH0 S IH0 B EH1 R AH0 S T
+DISEMBARRASSING	D IH0 S IH0 B EH1 R AH0 S IH0 NG
+DISEMBODIED  D IH0 S IH0 B AA1 D IY0 D
+DISEMBODIMENT	D IH0 S IH0 B AA1 D IY0 M AH0 N T
+DISEMBOGUES	D IH0 S IH0 B AA1 G Z
+DISEMBOWEL	D IH0 S IH0 B AW1 AH0 L
+DISEMBOWELED	D IH0 S IH0 B AW1 AH0 L D
+DISEMBOWELING	D IH0 S IH0 B AW1 AH0 L IH0 NG
+DISEMBOWELLED	D IH0 S IH0 B AA1 W EH0 L D
+DISEMBOWELLING	D IH0 S IH0 B AA1 W EH1 L IH0 NG
+DISENCHANT	D IH0 S IH0 N CH AE1 N T
+DISENCHANTED  D IH0 S IH0 N CH AE1 N IH0 D
+DISENCHANTED  D IH0 S IH0 N CH AE1 N T IH0 D
+DISENCHANTING	D IH0 S IH0 N CH AE1 N T IH0 NG
+DISENCHANTMENT  D IH0 S IH0 N CH AE1 N T M AH0 N T
+DISENCHANTMENTS	D IH0 S IH0 N CH AE1 N T M AH0 N T S
+DISENCUMBER	D IH0 S IH0 N K AH1 M B ER0
+DISENCUMBERED	D IH0 S IH0 N K AH1 M B ER0 D
+DISENCUMBERING	D IH0 S IH0 N K AH1 M B ER0 IH0 NG
+DISENDOWMENT	D IH0 S IH0 N D AW1 M AH0 N T
+DISENFRANCHISED  D IH0 S IH0 N F R AE1 N CH AY2 Z D
+DISENGAGE  D IH0 S IH0 N G EY1 JH
+DISENGAGED  D IH0 S IH0 N G EY1 JH D
+DISENGAGEMENT  D IH0 S IH0 N G EY1 JH M AH0 N T
+DISENGAGES	D IH0 S IH0 N G EY1 JH IH0 Z
+DISENGAGING  D IH0 S IH0 N G EY1 JH IH0 NG
+DISENT	D IH0 S IH0 N T
+DISENTANGLE  D IH2 S AH0 N T AE1 NG 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 IH0 S F IH1 G Y ER0 IH0 NG
+DISFRANCHISE	D IH0 S F R AE2 N CH AY0 Z
+DISFRANCHISED	D IH0 S F R AE1 N CH AY2 Z D
+DISFRANCHISEMENT	D IH0 S F R AE1 N CH AY2 Z M AH0 N T
+DISFRANCHISING	D IH0 S F R AE2 N CH AY0 Z IH0 NG
+DISGORGE  D IH0 S G AO1 R JH
+DISGORGED  D IH0 S G AO1 R JH D
+DISGORGES	D IH0 S G AO1 R JH IH0 Z
+DISGORGING	D IH0 S G AO1 R JH IH0 NG
+DISGRAC'D	D IH0 S G R EY1 S D
+DISGRACE  D IH0 S G R EY1 S
+DISGRACED  D IH0 S G R EY1 S T
+DISGRACEFUL  D IH0 S G R EY1 S F AH0 L
+DISGRACEFULLY	D IH0 S G R EY1 S F AH0 L IY0
+DISGRACES	D IH0 S G R EY1 S IH0 Z
+DISGRACIN	D IH0 S G R EY1 S IH0 N
+DISGRACING	D IH0 S G R EY1 S IH0 NG
+DISGRAZIA	D IH0 S G R AA1 Z IY0 AH0
+DISGRUNTLED  D IH0 S G R AH1 N AH0 L D
+DISGRUNTLED  D IH0 S G R AH1 N T AH0 L D
+DISGUIS'D	D IH0 S G AY1 Z D
+DISGUISE  D IH0 S G AY1 Z
+DISGUISED  D IH0 S G AY1 Z D
+DISGUISEMENT	D IH0 S G AY1 Z M AH0 N T
+DISGUISES  D IH0 S G AY1 Z IH0 Z
+DISGUISING  D IH0 S G AY1 Z IH0 NG
+DISGUST  D IH0 S G AH1 S T
+DISGUSTED  D IH0 S G AH1 S T AH0 D
+DISGUSTED  D IH0 S G AH1 S T IH0 D
+DISGUSTEDLY	D IH0 S G AH1 S T IH0 D L IY0
+DISGUSTFUL	D IH0 S G AH1 S T F AH0 L
+DISGUSTIN	D IH0 S G AH1 S T IH0 N
+DISGUSTING  D IH0 S G AH1 S T IH0 NG
+DISGUSTINGLY	D IH0 S G AH1 S T IH0 NG L IY0
+DISGUSTS  D IH0 S G AH1 S T S
+DISH  D IH1 SH
+DISHABILLE	D IH0 SH AH0 B IH2 L
+DISHARMONIES	D IH0 S HH AA1 R M AH0 N IY0 Z
+DISHARMONY  D IH0 S HH AA1 R M AH0 N IY0
+DISHART	D IH0 S HH AA2 R T
+DISHART'S	D IH0 S HH AA1 R T S
+DISHCLOTH	D IH1 SH K L AO2 TH
+DISHCLOTHS	D IH1 SH K L AO2 TH S
+DISHCLOUT	D IH1 SH K L AW1 T
+DISHEARTEN	D IH0 S HH AA1 R T AH0 N
+DISHEARTENED  D IH0 S HH AA1 R T AH0 N D
+DISHEARTENING  D IH0 S HH AA1 R T AH0 N IH0 NG
+DISHEARTENING  D IH2 S HH AA1 R T N IH0 NG
+DISHEARTENINGLY	D IH0 S HH AA1 R T AH0 N IH0 NG L IY0
+DISHEARTENMENT	D IH0 S HH AA1 R T AH0 N M AH0 N T
+DISHEARTENS	D IH0 S HH AA1 R T AH0 N Z
+DISHED  D IH1 SH T
+DISHER  D IH1 SH ER0
+DISHERITED	D IH0 S HH EH1 R AH0 T IH0 D
+DISHES  D IH1 SH AH0 Z
+DISHES  D IH1 SH IH0 Z
+DISHEVEL'D	D IH0 SH EH1 V AH0 L D
+DISHEVELED  D IH0 SH EH1 V AH0 L D
+DISHEVELLED	D IH0 SH EH1 V AH0 L D
+DISHEVELMENT	D IH0 SH EH1 V AH0 L M AH0 N T
+DISHFUL	D IH1 SH F AH0 L
+DISHIN	D IH1 SH IH0 N
+DISHING  D IH1 SH IH0 NG
+DISHON  D IH1 S AH0 N
+DISHONEST  D IH0 S AA1 N AH0 S T
+DISHONESTIES	D IH0 S AA1 N AH0 S T IY0 Z
+DISHONESTLY  D IH0 S AO1 N AH0 S T L IY0
+DISHONESTY  D IH0 S AA1 N AH0 S T IY0
+DISHONOR  D IH0 S AA1 N ER0
+DISHONORABLE  D IH0 S AA1 N ER0 AH0 B AH0 L
+DISHONORABLY	D IH0 S AA1 N ER0 AH0 B L IY0
+DISHONORED  D IH0 S AA1 N ER0 D
+DISHONORING	D IH0 S AA1 N ER0 IH0 NG
+DISHONORS	D IH0 S AA1 N ER0 Z
+DISHONOUR	D IH0 S AA1 N ER0
+DISHONOUR'D	D IH0 S AA1 N AW0 R D
+DISHONOURABLE	D IH0 S AA1 N UH1 R AH0 B AH0 L
+DISHONOURABLY	D IH0 S AA1 N ER0 AH0 B L IY0
+DISHONOURED	D IH0 S AA1 N AW1 ER0 D
+DISHONOURING	D IH0 S AA1 N ER1 IH0 NG
+DISHONOURS	D IH0 S AA1 N AO1 R Z
+DISHPAN	D IH0 S P AH0 N
+DISHPANS	D IH0 S P AE2 N Z
+DISHRAG	D IH0 S R AE1 G
+DISHWASHER  D IH1 SH W AA2 SH ER0
+DISHWASHING  D IH1 SH W AA2 SH IH0 NG
+DISHWATER	D IH1 SH W AA2 T ER0
+DISHYER	D IH0 S HH IY0 ER0
+DISILLUSION  D IH2 S IH0 L UW1 ZH AH0 N
+DISILLUSIONED  D IH2 S IH0 L UW1 ZH AH0 N D
+DISILLUSIONING  D IH2 S IH0 L UW1 ZH AH0 N IH0 NG
+DISILLUSIONISED	D IH2 S IH0 L UW1 ZH AH0 N AH0 S T
+DISILLUSIONIZE	D IH2 S IH0 L UW1 ZH AH0 N AY2 Z
+DISILLUSIONIZED	D IH2 S IH0 L UW1 ZH AH0 N AY2 Z D
+DISILLUSIONIZING	D IH2 S IH0 L UW1 ZH AH0 N AY2 Z IH0 NG
+DISILLUSIONMENT  D IH2 S IH0 L UW1 ZH AH0 N M AH0 N T
+DISILLUSIONMENTS	D IH2 S IH0 L UW1 ZH AH0 N M AH0 N T S
+DISILLUSIONS	D IH2 S IH0 L UW1 ZH AH0 N Z
+DISINCLINATION  D IH0 S IH0 N K L AH0 N EY1 SH AH0 N
+DISINCLINE  D IH2 S IH0 N K L AY1 N
+DISINCLINED  D IH2 S IH0 N K L AY1 N D
+DISINFECT  D IH0 S IH0 N F EH1 K T
+DISINFECTANT  D IH0 S IH0 N F EH1 K T AH0 N T
+DISINFECTANTS  D IH0 S IH0 N F EH1 K T AH0 N T S
+DISINFECTED	D IH2 S IH0 N F EH1 K T IH0 D
+DISINFECTING	D IH2 S IH0 N F EH1 K T IH0 NG
+DISINFECTION  D IH0 S IH0 N F EH1 K SH AH0 N
+DISINGENUITY	D IH0 S IH1 N JH AH0 N UW1 AH0 T IY0
+DISINGENUOUS  D IH0 S IH0 N JH EH1 N Y UW0 AH0 S
+DISINGENUOUSLY	D IH2 S IH0 N JH EH1 N Y UW0 AH0 S L IY0
+DISINGENUOUSNESS  D IH2 S IH0 N JH EH1 N Y UW0 AH0 S N AH0 S
+DISINHERIT	D IH2 S IH0 N HH EH1 R AH0 T
+DISINHERITANCE	D IH2 S IH0 N HH EH1 R AH0 T AH0 N S
+DISINHERITED	D IH2 S IH0 N HH EH1 R AH0 T IH0 D
+DISINHERITING	D IH2 S IH0 N HH EH1 R AH0 T IH0 NG
+DISINHERITS	D IH2 S IH0 N HH EH1 R AH0 T S
+DISINTEGRATE  D IH0 S IH1 N T AH0 G R EY2 T
+DISINTEGRATED  D IH0 S IH1 N T AH0 G R EY2 T IH0 D
+DISINTEGRATES  D IH0 S IH1 N T AH0 G R EY2 T S
+DISINTEGRATING  D IH0 S IH1 N T AH0 G R EY2 T IH0 NG
+DISINTEGRATION  D IH0 S IH2 N AH0 G R EY1 SH AH0 N
+DISINTEGRATION  D IH0 S IH2 N T AH0 G R EY1 SH AH0 N
+DISINTEGRATIONS	D IH0 S IH1 N T AH0 G R EY1 SH AH0 N Z
+DISINTEGRATOR	D IH0 S IH1 N T AH0 G R EY2 T ER0
+DISINTEGRATORS	D IH0 S IH1 N T AH0 G R EY2 T ER0 Z
+DISINTER	D IH0 S IH0 N T ER0
+DISINTEREST  D IH0 S IH1 N T ER0 AH0 S T
+DISINTERESTED  D IH0 S IH1 N R IH0 S T IH0 D
+DISINTERESTED  D IH0 S IH1 N T R IH0 S T IH0 D
+DISINTERESTEDLY	D IH0 S IH1 N T R IH0 S T IH0 D L IY0
+DISINTERESTEDNESS	D IH0 S IH1 N T R IH0 S T IH0 D N AH0 S
+DISINTERMENT	D IH0 S IH0 N T ER0 M AH0 N T
+DISINTERRED	D IH0 S IH0 N T ER1 D
+DISINTERRING	D IH0 S IH0 N T ER1 IH0 NG
+DISJECTA	D IH0 S JH IH0 K T AH0
+DISJOIN	D IH0 S JH OY1 N
+DISJOIN'D	D IH0 S JH OY1 N D
+DISJOINED	D IH0 S JH OY1 N D
+DISJOINT  D IH0 S JH OY1 N T
+DISJOINTED  D IH0 S JH OY1 N T IH0 D
+DISJOINTEDLY	D IH0 S JH OY1 N T IH0 D L IY0
+DISJOINTING	D IH0 S JH OY1 N T IH0 NG
+DISJUNCTION	D IH0 S JH AH1 NG K SH AH0 N
+DISJUNCTIVE	D IH0 S JH AH2 NG K T IH0 V
+DISJUNCTIVELY	D IH0 S JH AH2 NG K T IH0 V L IY0
+DISK  D IH1 S K
+DISKED	D IH1 S K T
+DISKIVER	D IH1 S K AY1 V ER0
+DISKIVERED	D IH1 S K AY1 V ER0 D
+DISKO	D IH1 S K OW0
+DISKO'S	D IH1 S K OW0 Z
+DISKOS	D IH1 S K OW0 Z
+DISKS  D IH1 S K S
+DISLIKE  D IH0 S L AY1 K
+DISLIKED  D IH0 S L AY1 K T
+DISLIKES  D IH0 S L AY1 K S
+DISLIKING  D IH0 S L AY1 K IH0 NG
+DISLIKINGS	D IH0 S L AY1 K IH0 NG Z
+DISLOCATE  D IH1 S L OW0 K EY0 T
+DISLOCATED  D IH1 S L OW0 K EY0 T IH0 D
+DISLOCATES	D IH1 S L OW0 K EY0 T S
+DISLOCATING	D IH1 S L OW0 K EY0 T IH0 NG
+DISLOCATION  D IH0 S L OW1 K EY1 SH AH0 N
+DISLOCATIONS  D IH0 S L OW1 K EY1 SH AH0 N Z
+DISLODGE  D IH0 S L AA1 JH
+DISLODGED  D IH0 S L AA1 JH D
+DISLODGES	D IH0 S L AA1 JH IH0 Z
+DISLODGING  D IH0 S L AA1 JH IH0 NG
+DISLODGMENT	D IH0 S L AA1 JH M AH0 N T
+DISLOYAL  D IH0 S L OY1 AH0 L
+DISLOYALLY	D IH0 S L OY1 AH0 L IY0
+DISLOYALTY  D IH0 S L OY1 AH0 L T IY0
+DISMAL  D IH1 Z M AH0 L
+DISMALEST	D IH0 S M EY1 L AH0 S T
+DISMALLEST	D IH0 S M AE1 L IH0 S T
+DISMALLY  D IH1 Z M AH0 L IY0
+DISMALNESS	D IH1 Z M AH0 L N EH0 S
+DISMALS	D IH1 Z M AH0 L Z
+DISMANTLE  D IH0 S M AE1 N AH0 L
+DISMANTLE  D IH0 S M AE1 N T AH0 L
+DISMANTLED  D IH0 S M AE1 N AH0 L D
+DISMANTLED  D IH0 S M AE1 N T AH0 L D
+DISMANTLING  D IH0 S M AE1 N AH0 L IH0 NG
+DISMANTLING  D IH0 S M AE1 N T AH0 L IH0 NG
+DISMANTLING  D IH2 S M AE1 N T L IH0 NG
+DISMAS	D IH1 Z M AH0 Z
+DISMASTED	D IH0 S M EY1 S T IH0 D
+DISMAY  D IH0 S M EY1
+DISMAY'D	D IH0 S M EY1 D
+DISMAYED  D IH0 S M EY1 D
+DISMAYING  D IH0 S M EY1 IH0 NG
+DISMAYINGLY	D IH0 S M EY1 IH0 NG L IY0
+DISMAYS  D IH0 S M EY1 Z
+DISMEMBER  D IH0 S M EH1 M B ER0
+DISMEMBERED  D IH0 S M EH1 M B ER0 D
+DISMEMBERING  D IH0 S M EH1 M B ER0 IH0 NG
+DISMEMBERMENT  D IH0 S M EH1 M B ER0 M AH0 N T
+DISMISS  D IH0 S M IH1 S
+DISMISS'D	D IH0 S M IH1 SH D
+DISMISSAL  D IH0 S M IH1 S AH0 L
+DISMISSALS  D IH0 S M IH1 S AH0 L Z
+DISMISSED  D IH0 S M IH1 S T
+DISMISSES  D IH0 S M IH1 S IH0 Z
+DISMISSING  D IH0 S M IH1 S IH0 NG
+DISMISSION	D IH0 S M IH1 SH AH0 N
+DISMIST	D IH0 S M AH0 S T
+DISMOUNT	D IH0 S M AW2 N T
+DISMOUNTED	D IH0 S M AW2 N T IH0 D
+DISMOUNTING	D IH0 S M AW1 N T IH0 NG
+DISMOUNTS	D IH0 S M AW2 N T S
+DISMUKE  D IH1 S M Y UW0 K
+DISNA	D IH0 S N AH0
+DISNEY  D IH1 Z N IY0
+DISNEY'S  D IH1 Z N IY0 Z
+DISOBEDIENCE  D IH2 S AH0 B IY1 D IY0 AH0 N S
+DISOBEDIENCE  D IH2 S OW0 B IY1 D IY0 AH0 N S
+DISOBEDIENT  D IH2 S AH0 B IY1 D IY0 AH0 N T
+DISOBEDIENT  D IH2 S OW0 B IY1 D IY0 AH0 N T
+DISOBEDIENTLY	D IH2 S OW0 B IY1 D IY0 AH0 N T L IY0
+DISOBEY  D IH2 S AH0 B EY1
+DISOBEYED  D IH2 S OW0 B EY1 D
+DISOBEYING  D IH2 S OW0 B EY1 IH0 NG
+DISOBEYS	D IH2 S OW0 B EY1 Z
+DISOBLIGE	D IH2 S AH0 B L AY1 JH
+DISOBLIGED	D IH2 S AH0 B L AY1 JH D
+DISOBLIGING	D IH2 S AH0 B L AY1 JH IH0 NG
+DISOCCUPATION	D IH2 S AA2 K Y AH0 P EY1 SH AH0 N
+DISOIT	D IH0 S OY1 T
+DISORDER  D IH0 S AO1 R D ER0
+DISORDER'D	D IH0 S AO1 R D ER0 D
+DISORDERED  D IH0 S AO1 R D ER0 D
+DISORDERING	D IH0 S AO1 R D ER0 IH0 NG
+DISORDERLIES	D IH0 S AO1 R D ER0 L IY0 Z
+DISORDERLINESS	D IH0 S AO1 R D ER0 L IY0 N AH0 S
+DISORDERLY  D IH0 S AO1 R D ER0 L IY0
+DISORDERS  D IH0 S AO1 R D ER0 Z
+DISORGANISATION	D IH0 S AO1 R G AH0 N IH0 Z EY1 SH AH0 N
+DISORGANISE	D IH0 S AO1 R G AH0 N AH0 S
+DISORGANISED	D IH0 S AO1 R G AH0 N IH0 Z D
+DISORGANISING	D IH0 S AO1 R G AH0 N IH1 Z IH0 NG
+DISORGANIZATION  D IH0 S AO2 R G AH0 N AH0 Z EY1 SH AH0 N
+DISORGANIZE  D IH0 S AO1 R G AH0 N AY2 Z
+DISORGANIZED  D IH0 S AO1 R G AH0 N AY2 Z D
+DISORGANIZERS	D IH0 S AO1 R G AH0 N AY2 Z ER0 Z
+DISORGANIZES	D IH0 S AO1 R G AH0 N AY2 Z IH0 Z
+DISORGANIZING	D IH0 S AO1 R G AH0 N AY2 Z IH0 NG
+DISORIENTATION  D IH0 S AO2 R IY0 AH0 N T EY1 SH AH0 N
+DISORIENTED  D IH0 S AO1 R IY0 EH2 N T IH0 D
+DISOWN  D IH0 S OW1 N
+DISOWNED  D IH0 S OW1 N D
+DISOWNING	D IH0 S OW1 N IH0 NG
+DISOWNS	D IH0 S OW1 N Z
+DISP	D IH1 S P
+DISPAIR  D IH0 S P EH1 R
+DISPARAGE  D IH0 S P EH1 R IH0 JH
+DISPARAGED  D IH0 S P EH1 R IH0 JH D
+DISPARAGEMENT	D IH0 S P EH1 R IH0 JH M AH0 N T
+DISPARAGEMENTS	D IH0 S P EH1 R IH0 JH M AH0 N T S
+DISPARAGES  D IH0 S P EH1 R IH0 JH IH0 Z
+DISPARAGING  D IH0 S P EH1 R IH0 JH IH0 NG
+DISPARAGINGLY  D IH0 S P EH1 R IH0 JH IH0 NG L IY0
+DISPARATE  D IH0 S P EH1 R IH0 T
+DISPARATE  D IH1 S P ER0 IH0 T
+DISPARITIES  D IH0 S P EH1 R AH0 T IY0 Z
+DISPARITY  D IH0 S P EH1 R AH0 T IY0
+DISPART	D IH0 S P AA2 R T
+DISPARTED	D IH0 S P AA1 R T IH0 D
+DISPARTING	D IH0 S P AA2 R T IH0 NG
+DISPASSION	D IH0 S P AE1 SH AH0 N
+DISPASSIONATE  D IH0 S P AE1 SH AH0 N AH0 T
+DISPASSIONATELY  D IH0 S P AE1 SH AH0 N AH0 T L IY0
+DISPASSIONATENESS	D IH0 S P AE1 SH AH0 N AH0 T N AH0 S
+DISPATCH  D IH0 S P AE1 CH
+DISPATCH'D	D IH0 S P AE1 CH IH0 D
+DISPATCHED  D IH0 S P AE1 CH T
+DISPATCHER  D IH0 S P AE1 CH ER0
+DISPATCHER  D IH1 S P AE2 CH ER0
+DISPATCHER'S  D IH0 S P AE1 CH ER0 Z
+DISPATCHER'S  D IH1 S P AE2 CH ER0 Z
+DISPATCHES  D IH0 S P AE1 CH IH0 Z
+DISPATCHING  D IH0 S P AE1 CH IH0 NG
+DISPEACE	D IH2 S P IY2 S
+DISPEL  D IH0 S P EH1 L
+DISPELL'D	D IH0 S P EH1 L D
+DISPELLED  D IH0 S P EH1 L D
+DISPELLER	D IH0 S P EH1 L ER0
+DISPELLING  D IH0 S P EH1 L IH0 NG
+DISPELS  D IH0 S P EH1 L Z
+DISPENCE	D IH0 S P EH1 N S
+DISPENSABLE  D IH0 S P EH1 N S AH0 B AH0 L
+DISPENSARIES	D IH1 S P EH0 N S EH2 R IY0 Z
+DISPENSARY  D IH0 S P EH1 N S ER0 IY0
+DISPENSARY  D IH1 S P EH0 N S EH2 R IY0
+DISPENSATION  D IH2 S P AH0 N S EY1 SH AH0 N
+DISPENSATIONAL	D IH2 S P AH0 N S EY1 SH AH0 N AH0 L
+DISPENSATIONS	D IH2 S P AH0 N S EY1 SH AH0 N Z
+DISPENSATOR	D IH2 S P AH0 N S EY2 T ER0
+DISPENSATORY	D IH0 S P EH1 N S AH0 T AO2 R IY0
+DISPENSE  D IH0 S P EH1 N S
+DISPENSED  D IH0 S P EH1 N S T
+DISPENSER  D IH0 S P EH1 N S ER0
+DISPENSERS  D IH0 S P EH1 N S ER0 Z
+DISPENSES  D IH0 S P EH1 N S IH0 Z
+DISPENSING  D IH0 S P EH1 N S IH0 NG
+DISPEOPLED	D IH0 S P IY2 P AH0 L D
+DISPERS'D	D IH0 S P ER1 S AH0 D
+DISPERSAL  D IH0 S P ER1 S AH0 L
+DISPERSE  D IH0 S P ER1 S
+DISPERSED  D IH0 S P ER1 S T
+DISPERSEDLY	D IH0 S P ER1 S AH0 D L IY0
+DISPERSES	D IH0 S P ER1 S AH0 Z
+DISPERSING  D IH0 S P ER1 S IH0 NG
+DISPERSION  D IH0 S P ER1 ZH AH0 N
+DISPERSIONS	D IH0 S P ER1 ZH AH0 N Z
+DISPERSIVE  D IH0 S P ER1 S IH0 V
+DISPIRIT	D IH0 S P IH1 R IH0 T
+DISPIRITED  D IH0 S P IH1 R AH0 T IH0 D
+DISPIRITEDLY	D IH0 S P IH1 R IH0 T AH0 D L IY0
+DISPIRITING  D IH0 S P IH1 R IH0 T IH0 NG
+DISPLACE  D IH0 S P L EY1 S
+DISPLACED  D IH0 S P L EY1 S T
+DISPLACEMENT  D IH0 S P L EY1 S M AH0 N T
+DISPLACEMENTS  D IH0 S P L EY1 S M AH0 N T S
+DISPLACER	D IH0 S P L EY1 S ER0
+DISPLACES  D IH0 S P L EY1 S IH0 Z
+DISPLACING  D IH0 S P L EY1 S IH0 NG
+DISPLAY  D IH0 S P L EY1
+DISPLAY'D	D IH0 S P L EY1 D
+DISPLAYED  D IH0 S P L EY1 D
+DISPLAYIN	D IH0 S P L EY1 IH0 N
+DISPLAYING  D IH0 S P L EY1 IH0 NG
+DISPLAYS  D IH0 S P L EY1 Z
+DISPLEAS'D	D IH0 S P L IY1 Z D
+DISPLEASE  D IH0 S P L IY1 Z
+DISPLEASED  D IH0 S P L IY1 Z D
+DISPLEASES	D IH0 S P L IY1 Z IH0 Z
+DISPLEASING	D IH0 S P L IY1 Z IH0 NG
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+DISPLEASURES	D IH0 S P L EH1 ZH ER0 Z
+DISPOGED	D IH2 S P AH0 JH D
+DISPORT	D IH0 S P AO2 R T
+DISPORTED	D IH0 S P AO1 R T AH0 D
+DISPORTING	D IH0 S P AO1 R T IH0 NG
+DISPORTS	D IH0 S P AO2 R T S
+DISPOS'D	D IH2 S P AH0 D
+DISPOSABLE  D IH0 S P OW1 Z AH0 B AH0 L
+DISPOSAL  D IH0 S P OW1 Z AH0 L
+DISPOSE  D IH0 S P OW1 Z
+DISPOSED  D IH0 S P OW1 Z D
+DISPOSEDLY	D IH0 S P OW1 Z AH0 D L IY0
+DISPOSER  D IH0 S P OW1 Z ER0
+DISPOSES  D IH0 S P OW1 Z IH0 Z
+DISPOSETH	D IH0 S P OW1 Z AH0 TH
+DISPOSICION	D IH0 S P AH0 Z IH1 SH AH0 N
+DISPOSING  D IH0 S P OW1 Z IH0 NG
+DISPOSITION  D IH2 S P AH0 Z IH1 SH AH0 N
+DISPOSITIONED	D IH0 S P AH0 Z IH1 SH AH0 N D
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+DISPOSSESS  D IH2 S P AH0 Z EH1 S
+DISPOSSESSED  D IH2 S P AH0 Z EH1 S T
+DISPOSSESSING	D IH2 S P AH0 Z EH1 S IH0 NG
+DISPOSSESSION	D IH2 S P AH0 Z EH1 SH AH0 N
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+DISPRAISE	D IH0 S P R EY1 Z
+DISPRAISED	D IH0 S P R EY1 Z D
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+DISPROOF	D IH0 S P R UW2 F
+DISPROPORTION	D IH2 S P R AH0 P AO1 R SH AH0 N
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+DISPROPORTIONED	D IH2 S P R AH0 P AO1 R SH AH0 N D
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+DISPROVED  D IH0 S P R UW1 V D
+DISPROVEN  D IH0 S P R UW1 V IH0 N
+DISPROVES  D IH0 S P R UW1 V Z
+DISPROVING	D IH0 S P R UW1 V IH0 NG
+DISPUTA	D IH0 S P Y UW1 T AH0
+DISPUTABLE  D IH0 S P Y UW1 T AH0 B AH0 L
+DISPUTANDUM	D IH0 S P Y UW1 T AH0 N D AH0 M
+DISPUTANT	D IH0 S P Y UW1 T AH0 N T
+DISPUTANTS	D IH0 S P Y UW1 T AH0 N T S
+DISPUTATION  D IH0 S P Y UW1 T EY1 SH AH0 N
+DISPUTATIONS	D IH0 S P Y UW1 T EY1 SH AH0 N Z
+DISPUTATIOUS	D IH0 S P Y UW1 T EY1 SH AH0 S
+DISPUTATIVE	D IH0 S P Y UW1 T EY2 T IH0 V
+DISPUTE  D IH0 S P Y UW1 T
+DISPUTED  D IH0 S P Y UW1 T AH0 D
+DISPUTED  D IH0 S P Y UW1 T IH0 D
+DISPUTER	D IH0 S P Y UW1 T ER0
+DISPUTES  D IH0 S P Y UW1 T S
+DISPUTIN	D IH0 S P Y UW1 T AH0 N
+DISPUTING  D IH0 S P Y UW1 T IH0 NG
+DISPUTINGS	D IH0 S P Y UW1 T IH0 NG Z
+DISQUALIFICATION  D IH0 S K W AA2 L AH0 F AH0 K EY1 SH AH0 N
+DISQUALIFICATIONS	D IH0 S K W AA2 L AH0 F AH0 K EY1 SH AH0 N Z
+DISQUALIFIED  D IH0 S K W AA1 L AH0 F AY2 D
+DISQUALIFIES  D IH0 S K W AA1 L AH0 F AY2 Z
+DISQUALIFY  D IH0 S K W AA1 L AH0 F AY2
+DISQUALIFYING  D IH0 S K W AA1 L AH0 F AY2 IH0 NG
+DISQUIET  D IH0 S K W AY1 AH0 T
+DISQUIETED	D IH0 S K W AY1 AH0 T AH0 D
+DISQUIETETH	D IH0 S K W AY1 AH0 T AH0 TH
+DISQUIETING  D IH0 S K W AY1 AH0 T IH0 NG
+DISQUIETINGLY	D IH0 S K W AY1 AH0 T IH0 NG L IY0
+DISQUIETS	D IH0 S K W AY1 AH0 T S
+DISQUIETUDE	D IH0 S K W AY1 AH0 T UW2 D
+DISQUIETUDES	D IH0 S K W AY1 AH0 T UW2 D Z
+DISQUISITION	D IH0 S K W AH0 Z IH1 SH AH0 N
+DISQUISITIONS	D IH0 S K W AH0 Z IH1 SH AH0 N Z
+DISRAELI  D IH0 Z R EY1 L IY0
+DISRAELI'S	D IH0 Z R EY1 L IY0 Z
+DISRATED	D IH0 S R EY2 T IH0 D
+DISREGARD  D IH2 S R IH0 G AA1 R D
+DISREGARDED  D IH2 S R IH0 G AA1 R D IH0 D
+DISREGARDFUL	D IH2 S R IH0 G AA1 R D F AH0 L
+DISREGARDING  D IH2 S R IH0 G AA1 R D IH0 NG
+DISREGARDS  D IH2 S R IH0 G AA1 R D Z
+DISRELISH	D IH0 S R EH1 L IH0 SH
+DISRELISHED	D IH0 S R EH1 L IH0 SH T
+DISREMEMBER	D IH0 S R IY0 M EH1 M B ER0
+DISREMEMBERED	D IH0 S R IY0 M EH1 M B ER0 D
+DISREMEMBERS	D IH0 S R IY0 M EH1 M B ER0 Z
+DISREPAIR  D IH2 S R IH0 P EH1 R
+DISREPUTABILITY	D IH2 S R IH0 P Y UW1 T AH0 B IH1 L IH0 T IY0
+DISREPUTABLE  D IH0 S R EH1 P Y AH0 T AH0 B AH0 L
+DISREPUTABLES	D IH2 S R IH0 P Y UW1 T AH0 B AH0 L Z
+DISREPUTABLY	D IH2 S R IH0 P Y UW1 T AH0 B L IY0
+DISREPUTATION	D IH0 S R EH1 P Y AH0 T EY1 SH AH0 N
+DISREPUTE  D IH2 S R IH0 P Y UW1 T
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+DISRESPECTABLE	D IH2 S R IH0 S P EH1 K T AH0 B AH0 L
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+DISRUPTING  D IH0 S R AH1 P T IH0 NG
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+DISS  D IH1 S
+DISSA	D IH1 S AH0
+DISSATISFACTION  D IH2 S AE0 T IH0 S F AE1 K SH AH0 N
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+DISSATISFYING	D IH0 S AE1 T AH0 S F AY2 IH0 NG
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+DISSECTION  D AY0 S EH1 K SH AH0 N
+DISSECTION  D AY1 S EH0 K SH AH0 N
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+DISSEMBLE  D IH0 S EH1 M B AH0 L
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+DISSEMBLER	D IH0 S EH1 M B L ER0
+DISSEMBLERS	D IH0 S EH1 M B L ER0 Z
+DISSEMBLES	D IH0 S EH1 M B AH0 L Z
+DISSEMBLING  D IH0 S EH1 M B L IH0 NG
+DISSEMINATE  D IH0 S EH1 M AH0 N EY2 T
+DISSEMINATED  D IH0 S EH1 M AH0 N EY2 T AH0 D
+DISSEMINATES  D IH0 S EH1 M AH0 N EY2 T S
+DISSEMINATING  D IH0 S EH1 M AH0 N EY2 T IH0 NG
+DISSEMINATION  D IH0 S EH2 M AH0 N EY1 SH AH0 N
+DISSEMINATOR	D IH0 S EH1 M AH0 N EY2 T ER0
+DISSEMINATORS	D IH0 S EH1 M AH0 N EY2 T ER0 Z
+DISSENSION  D IH0 S EH1 N SH AH0 N
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+DISSENTED  D IH0 S EH1 N T IH0 D
+DISSENTER  D IH0 S EH1 N T ER0
+DISSENTERS  D IH0 S EH1 N ER0 Z
+DISSENTERS  D IH0 S EH1 N T ER0 Z
+DISSENTIENT	D IH0 S EH1 N SH AH0 N T
+DISSENTIENTS	D IH0 S EH1 N CH AH0 N T S
+DISSENTING  D IH0 S EH1 N IH0 NG
+DISSENTING  D IH0 S EH1 N T IH0 NG
+DISSENTINGLY	D IH0 S EH1 N T IH0 NG L IY0
+DISSENTION	D IH0 S EH1 N CH AH0 N
+DISSENTIONS	D IH0 S EH1 N CH AH0 N Z
+DISSENTS  D IH0 S EH1 N T S
+DISSERTATION  D IH2 S ER0 T EY1 SH AH0 N
+DISSERTATIONS	D IH2 S ER0 T EY1 SH AH0 N Z
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+DISSEVER	D AY1 S EH0 V ER0
+DISSEVERED	D IH2 S AH0 V IH1 R D
+DISSEVERING	D IH0 S EH1 V ER0 IH0 NG
+DISSIDENCE  D IH1 S AH0 D IH0 N S
+DISSIDENT  D IH1 S AH0 D IH0 N T
+DISSIDENTS  D IH1 S AH0 D AH0 N T S
+DISSIMILAR  D IH0 S IH1 M AH0 L ER0
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+DISSIMILARITY  D IH2 S IH2 M AH0 L AE1 R AH0 T IY0
+DISSIMILITUDE	D IH1 S AH0 M IH1 L AH0 T UW2 D
+DISSIMULATE	D IH2 S IH2 M Y AH0 L EY2 T
+DISSIMULATED	D IH2 S IH2 M Y AH0 L EY2 T IH0 D
+DISSIMULATING	D IH2 S IH2 M Y AH0 L EY2 T IH0 NG
+DISSIMULATION	D IH2 S IH2 M Y AH0 L EY1 SH AH0 N
+DISSIMULATIONS	D IH2 S IH2 M Y AH0 L EY1 SH AH0 N Z
+DISSIPATE  D IH1 S AH0 P EY2 T
+DISSIPATED  D IH1 S AH0 P EY2 T IH0 D
+DISSIPATES  D IH1 S AH0 P EY2 T S
+DISSIPATING  D IH1 S AH0 P EY2 T IH0 NG
+DISSIPATION  D IH2 S IH0 P EY1 SH AH0 N
+DISSIPATIONS	D IH2 S IH0 P EY1 SH AH0 N Z
+DISSOCIAL	D IH0 S OW1 SH AH0 L
+DISSOCIATE  D IH0 S OW1 S IY0 EY0 T
+DISSOCIATED	D IH0 S OW1 S IY0 EY2 T AH0 D
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+DISSOCIATING	D IH0 S OW1 S IY0 EY2 T IH0 NG
+DISSOCIATION  D IH0 S OW2 S IY0 EY1 SH AH0 N
+DISSOLUBLE	D IH0 Z AA1 L Y AH0 B AH0 L
+DISSOLUTE	D IH2 S AH0 L UW1 T
+DISSOLUTENESS	D IH2 S AH0 L UW2 T N AH0 S
+DISSOLUTION  D IH2 S AH0 L UW1 SH AH0 N
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+DISSOLVING  D IH0 Z AO1 L V IH0 NG
+DISSONANCE  D IH1 S AH0 N AH0 N S
+DISSONANCES	D IH1 S AH0 N AH0 N S IH0 Z
+DISSONANT  D IH1 S AH0 N AH0 N T
+DISSTON	D IH2 S T AH0 N
+DISSUADE  D IH0 S W EY1 D
+DISSUADED  D IH0 S W EY1 D IH0 D
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+DISSUASIVES	D IH0 S W EY1 S IH0 V Z
+DISSY	D IH1 S IY0
+DISSYLLABIC	D IH2 S IH0 L AE1 B IH0 K
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+DIST	D IH0 S T
+DISTAFF	D IH0 S T AE2 F
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+DISTAINED	D IH0 S T EY1 N D
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+DISTANCES  D IH1 S T AH0 N S AH0 Z
+DISTANCES  D IH1 S T AH0 N S IH0 Z
+DISTANCING  D IH1 S T AH0 N S IH0 NG
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+DISTASTED	D IH0 S T EY1 S T IH0 D
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+DOWNPOURING	D AW1 N P AO1 R IH0 NG
+DOWNPOURS  D AW1 N P AO2 R Z
+DOWNRIGHT  D AW1 N R AY2 T
+DOWNRIGHTLY	D AW1 N R AY2 T L IY0
+DOWNRIGHTNESS	D AW1 N R AY2 T N AH0 S
+DOWNRIVER  D AW2 N R IH1 V ER0
+DOWNS  D AW1 N Z
+DOWNS'S	D AW1 N Z IH0 Z
+DOWNSBURY	D AW1 N S B EH0 R IY0
+DOWNSHIRE	D AW1 N SH AY2 R
+DOWNSIDE  D AW1 N S AY2 D
+DOWNSTAIR	D AW1 N S T EH1 R
+DOWNSTAIRS  D AW1 N S T EH1 R Z
+DOWNSTREAM  D AW1 N S T R IY1 M
+DOWNTON	D AW1 N T AH0 N
+DOWNTOWN  D AW1 N T AW1 N
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+DOWNWARD  D AW1 N W ER0 D
+DOWNWARDS  D AW1 N W ER0 D Z
+DOWNWIND  D AW0 N W IH1 N D
+DOWNY  D AW1 N IY0
+DOWNY'S	D AW1 N IY0 Z
+DOWRIES	D AW1 R IY0 Z
+DOWRY  D AW1 R IY0
+DOWS	D AW1 Z
+DOWSABEL	D AW1 Z AH0 B EH2 L
+DOWSE  D AW1 S
+DOWSED	D AW1 Z D
+DOWSER	D AW1 Z ER0
+DOWSETT	D AW1 S IH0 T
+DOWSING  D AW1 S IH0 NG
+DOWSON	D AW1 S AH0 N
+DOWSON'S	D AW1 S AH0 N Z
+DOWST	D AW1 S T
+DOWTON	D AW1 T AH0 N
+DOX	D AA1 K S
+DOXEY  D AA1 K S IY0
+DOXIES	D AA1 K S IY0 Z
+DOXOLOGIES  D AA0 K S AA1 L AH0 JH IY0 Z
+DOXOLOGY  D AA0 K S AA1 L AH0 JH IY0
+DOXY  D AA1 K S IY0
+DOY	D OY1
+DOYCE	D OY1 S
+DOYEN  D OY1 IH0 N
+DOYENNE	D OY1 AH0 N
+DOYLE  D OY1 L
+DOYLE'S  D OY1 L Z
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+DOYLEY	D OY1 L IY0
+DOYNE	D OY0 N
+DOZ	D OW1 Z
+DOZE  D OW1 Z
+DOZED  D OW1 Z D
+DOZEN  D AH1 Z AH0 N
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+DOZENT	D AH1 Z AH0 N T
+DOZENTH	D AH1 Z AH0 N TH
+DOZES  D OW1 Z IH0 Z
+DOZEY	D OW1 Z IY0
+DOZIA	D OW1 Z IY0 AH0
+DOZIA'S	D OW1 Z IY0 AH0 Z
+DOZIER  D OW1 Z IY0 ER0
+DOZIN	D OW1 Z IH0 N
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+DOZY	D OW1 Z IY0
+DR  D AA1 K T ER0
+DR  D R AY1 V
+DRA	D R AH0
+DRAA	D R AH0
+DRAB  D R AE1 B
+DRABANTS	D R AH0 B AE2 N T S
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+DRABBLED	D R AE1 B AH0 L D
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+DRABDUMP	D R AE1 B D AH1 M P
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+DRABNESS	D R AE1 B N AH0 S
+DRABS  D R AE1 B Z
+DRAC	D R AE1 K
+DRACAENA	D R AE1 K EY1 N AH0
+DRACHART	D R AE1 K ER0 T
+DRACHEN	D R AE1 K AH0 N
+DRACHENFELS	D R AE1 K IH0 N F AH0 L Z
+DRACHENFLIEGER	D R AE1 K AH0 N F L IY1 G ER0
+DRACHENHAUSEN	D R AE1 K AH0 N HH AW2 Z AH0 N
+DRACHENSTOLZ	D R AE1 K AH0 N S T OW1 L Z
+DRACHM	D R AE1 K M
+DRACHMA  D R AE1 K M AH0
+DRACHMAE	D R AE1 K M IY0
+DRACHMAS  D R AA1 K M AH0 Z
+DRACHMS	D R AE1 K M Z
+DRACO  D R AE1 K OW0
+DRACO  D R EY1 K OW0
+DRACONIAN  D R AH0 K OW1 N IY0 AH0 N
+DRACONIAN  D R EY0 K OW1 N IY0 AH0 N
+DRACONIC	D R AH0 K OW1 N IH0 K
+DRACONIDES	D R AH0 K OW1 N AY2 D Z
+DRACONMEYER	D R AH0 K OW1 N M AY0 ER0
+DRACONMEYER'S	D R AH0 K OW1 N M AY2 ER0 Z
+DRACOPHILS	D R AE1 K OW0 F IH1 L Z
+DRACULA  D R AE1 K Y UW0 L AH0
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+DRAF	D R AE1 F
+DRAFF	D R AE1 F
+DRAFT  D R AE1 F T
+DRAFTED  D R AE1 F T IH0 D
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+DRAFTSMAN  D R AE1 F T S M AH0 N
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+DRAG  D R AE1 G
+DRAGA	D R AA1 G AH0
+DRAGE  D R EY1 JH
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+DRAGEN	D R AE1 G AH0 N
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+DRAGGED  D R AE1 G D
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+DRAGGINGLY	D R AE1 G IH0 NG L IY0
+DRAGGLE	D R AE1 G AH0 L
+DRAGGLED	D R AE1 G AH0 L D
+DRAGGLING	D R AE1 G AH0 L IH0 NG
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+DRAGHETTA	D R AE1 G EH1 T AH0
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+DRAGOMAN	D R AA1 G OW0 M AH0 N
+DRAGOMAN'S	D R AE1 G AH0 M AH0 N Z
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+DRAGOMEN	D R AH0 G OW1 M AH0 N
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+DRAGON'S  D R AE1 G AH0 N Z
+DRAGONDEL	D R AE1 G AH0 N D AH0 L
+DRAGONESS	D R AE1 G AH0 N IH0 S
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+DRAGONFLY	D R AE1 G AH0 N F L IY0
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+DRAJ	D R AA1 ZH
+DRAKE  D R EY1 K
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+DRAKES  D R EY1 K S
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+DRAM  D IY1 R AE2 M
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+DRAMATIS	D R AH0 M AE1 T IH0 S
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+DRAMATURGIC	D R AA1 M AH0 T ER1 JH IH0 K
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+DRAPPED	D R AE1 P T
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+G'EASED	JH IY2 OW0 IY2 S T
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+HAPGOOD'S	HH AE1 P G UH2 D Z
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+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
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+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
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+HEC	HH EH1 K
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+HECHNAHOUL	HH EH1 K N AH0 UW1 L
+HECHO	HH EH1 K OW0
+HECHT  HH EH1 K T
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+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
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+HECKLE  HH EH1 K AH0 L
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+HECTIC  HH EH1 K T IH0 K
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+HECTOGRAPH  HH EH1 K T AH0 G R AE2 F
+HECTOR  HH EH1 K T ER0
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+HED	HH EH1 D
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+HEDGEHOG  HH EH1 JH HH AA2 G
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+HEDGING  HH EH1 JH IH0 NG
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+HEGELMANN	HH EH1 G AH0 L M AH0 N
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+HEGNER  HH EH1 G N ER0
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+HEH  HH EH1
+HEHRMANN	HH EH1 R M AH0 N
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+HEI	HH AY1
+HEIAU	HH AY1 AW0
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+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
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+HEIDLEMANN	HH AY1 D AH0 L M AH0 N
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+HEIDREK	HH AY1 D R EH0 K
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+HEIGHT  HH AY1 T
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+HEIGHTENING  HH AY1 T AH0 N IH0 NG
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+HEIGHTH  HH AY1 TH
+HEIGHTS  HH AY1 T S
+HEIGHTY	HH AY1 T IY0
+HEIKE	HH AY1 K
+HEIL  HH AY1 L
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+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
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+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
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+HEINE  HH AY1 N
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+HEINECKE  HH AY1 N IH0 K
+HEINECKEN	HH AY1 N IH0 K AH0 N
+HEINEMAN  HH AY1 N M AH0 N
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+HEINFETTER	HH AY1 N F EH1 T ER0
+HEINHOLD	HH AY1 N HH OW0 L D
+HEINIE	HH AY1 N IY0
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+HEINK	HH AY1 NG K
+HEINOUS  HH EY1 N AH0 S
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+HEINOUSNESS	HH EY1 N AH0 S N AH0 S
+HEINRICH  HH AY1 N R IH0 K
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+HEINTZELMAN  HH AY1 N T Z AH0 L M AH0 N
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+HEINY  HH AY1 N IY0
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+HEINZLEMAN	HH AY1 N Z AH0 L M AH0 N
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+HEIR  EH1 R
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+HELLUP	HH EH1 L AH0 P
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+HELLWARDS	HH EH1 L W ER0 D Z
+HELLWIG  HH EH1 L W IH0 G
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+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
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+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
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+HELON	HH EH1 L AH0 N
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+HELOUAN	HH EH1 L AH0 W AA1 N
+HELOUIN	HH EH1 L W IY1 N
+HELP  HH EH1 L P
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+HELPER  HH EH1 L P ER0
+HELPER'S	HH EH1 L P ER0 Z
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+HELPEST	HH EH1 L P AH0 S T
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+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
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+HELPIT	HH EH1 L P IH0 T
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+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
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+HELSTONE	HH EH1 L S T OW2 N
+HELSTONLEIGH	HH EH1 L S OW2 N L AH0
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+HELTER  HH EH1 L T ER0
+HELTH	HH EH1 L TH
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+HELUVA	HH EH1 L UW1 V AH0
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+HELVE	HH EH1 L V
+HELVELLYN	HH EH1 L V EH1 L IH0 N
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+HENLEY'S  HH EH1 N L IY0 Z
+HENLOPEN	HH EH1 N L OW1 P AH0 N
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+HENN  HH EH1 N
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+HENNE  HH EH1 N
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+HENNEBON	HH EH1 N IH0 B AO0 N
+HENNELEINLEIN	HH EH1 N AH0 L AY1 N L AY2 N
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+HENNEPIN  HH EH1 N IH0 P IH0 N
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+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
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+HENNIE	HH EH1 N IY0
+HENNIFER	HH EH1 N IH0 F ER0
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+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
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+HENRI  AA2 N R IY1
+HENRI  AO2 R IY1
+HENRI  HH EH1 N R IY0
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+HENRIADE	HH EH2 N R IY0 EY2 D
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+HENRIET	HH EH2 N R IY0 EH1 T
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+HENRIETTE  HH EH2 N R IY0 EH1 T
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+HENRIKSEN  HH EH1 N R IH0 K S AH0 N
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+HENRIOT	HH EH2 N R IY0 AH0 T
+HENRIOT'S	HH EH2 N R IY0 AH0 T S
+HENRIQUE  AA0 N R IY1 K
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+HENRIQUES  HH EH0 N R IY1 K EH0 Z
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+HENROOST	HH EH1 N R UW1 S T
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+HENRY  HH EH1 N R IY0
+HENRY'LL	HH EH1 N ER0 IY0 AH0 L
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+HENS  HH EH1 N Z
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+HENSHAWS	HH EH1 N SH AO2 Z
+HENSLAER	HH EH1 N Z L IH1 R
+HENSLER  HH EH1 N S AH0 L ER0
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+HENSLOW	HH EH1 N S L OW2
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+HENSLOWE'S	HH EH1 N S L OW1 Z
+HENSON  HH EH1 N S AH0 N
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+HENSOR	HH EH1 N S ER0
+HENSTEAD	HH EH1 N S T EH2 D
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+HENTZI	HH EH1 N T Z IY0
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+IMAGISM	IH0 M AE1 JH IH2 Z AH0 M
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+IMAGISTS	IH0 M AE1 JH AH0 S T S
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+IMAM  AY1 M AE0 M
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+IMAUM	IH0 M AO1 M
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+IMBALANCE  IH0 M B AE1 L AH0 N S
+IMBAUN	IH0 M B AO1 N
+IMBECILE  IH1 M B AH0 S AH0 L
+IMBECILE  IH1 M B AH0 S AY0 L
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+IMBECILES  IH1 M B AH0 S AY0 L Z
+IMBECILIC	IH1 M B AH0 S AY0 L IH0 K
+IMBECILITIES	IH1 M B AH0 S IH1 L AH0 T IY0 Z
+IMBECILITY	IH1 M B AH0 S IH1 L AH0 T IY0
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+IMBEDDED  IH0 M B EH1 D IH0 D
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+IMBERTS	IH1 M B ER0 T S
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+IMBIBER	IH0 M B AY1 B ER0
+IMBIBES	IH0 M B AY1 B Z
+IMBIBING	IH0 M B AY1 B IH0 NG
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+IME	AY1 M
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+IMITATIVENESS	IH1 M AH0 T EY2 T IH0 V N AH0 S
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+IMMATERIALISM	IH2 M AH0 T IH1 R IY0 AH0 L IH2 Z AH0 M
+IMMATERIALITY	IH2 M AH0 T IH2 R IY0 AE1 L AH0 T IY0
+IMMATURE  IH2 M AH0 T Y UH1 R
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+IMMEASUREABLE	IH0 M EH1 ZH ER0 AH0 B AH0 L
+IMMEDIACY  IH0 M IY1 D IY0 AH0 S IY0
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+IMMEDIATELY  IH0 M IY1 D IY0 AH0 T L IY0
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+IMMEDICABLE	IH0 M EH1 D AH0 K AH0 B AH0 L
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+IMMEMORIAL  IH2 M AH0 M AO1 R IY0 AH0 L
+IMMEMORIALLY	IH2 M AH0 M AO1 R IY0 AH0 L IY0
+IMMENCE	IH0 M EH1 N S
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+IMMENSITY	IH0 M EH1 N S IH0 T IY0
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+IMMIGRANTS  IH1 M AH0 G R AH0 N T S
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+IMMIGRATION  IH2 M AH0 G R EY1 SH AH0 N
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+IMMO	IH2 M OW0
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+IMMODESTY	IH0 M OW1 D AH0 S T IY0
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+IMMOLATED  IH1 M AH0 L EY2 T AH0 D
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+IMMOLATION  IH1 M AH0 L EY2 SH AH0 N
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+IMMORTALITIES	IH2 M AO0 R T AE1 L AH0 T IY0 Z
+IMMORTALITY  IH2 M AO0 R T AE1 L IH0 T IY0
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+IMMORTALIZED  IH0 M AO1 R T AH0 L AY0 Z D
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+IMMORTALIZING  IH0 M AO1 R T AH0 L AY0 Z IH0 NG
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+IMMUNITY  IH0 M Y UW1 N AH0 T IY0
+IMMUNITY  IH0 M Y UW1 N IH0 T IY0
+IMMUNIZATION  IH2 M Y UW0 N AH0 Z EY1 SH AH0 N
+IMMUNIZE  IH1 M Y UW0 N AY2 Z
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+IMMUTABLE  IH0 M Y UW1 T AH0 B AH0 L
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+IMNACHAIR	IH1 M N AH0 CH EH2 R
+IMO  AY1 EH1 M OW1
+IMO  AY1 M OW0
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+IMOGENE  IH1 M AH0 JH IY2 N
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+IMOLA	IH1 M AH0 L AH0
+IMP  IH1 M P
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+IMPACT  IH1 M P AE0 K T
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+IMPARTING  IH0 M P AA1 R T IH0 NG
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+IMPASSABLE  IH0 M P AE1 S AH0 B AH0 L
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+IMPECUNIOUS	IH0 M P EH1 K UW1 N IY0 AH0 S
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+IMPEDIMENTA	IH0 M P EH1 D AH0 M EH1 N T AH0
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+IMPEDING  IH0 M P IY1 D IH0 NG
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+IMPENDENT	IH0 M P EH1 N D AH0 N T
+IMPENDING  IH0 M P EH1 N D IH0 NG
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+IMPENITENT	IH0 M P EH1 N IH0 T IH0 N T
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+IMPERENCE	IH0 M P IH1 R AH0 N S
+IMPERENT	IH0 M P IH1 R AH0 N T
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+IMPERFECTION  IH2 M P ER0 F EH1 K SH AH0 N
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+IMPERMANENT	IH0 M P ER1 M AH0 N AH0 N T
+IMPERMEABLE	IH0 M P ER1 M IY0 AH0 B AH0 L
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+IMPERSONATED  IH0 M P ER1 S AH0 N EY2 T IH0 D
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+IMPERSONATING  IH0 M P ER1 S AH0 N EY2 T IH0 NG
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+IMPERTINENCE	IH0 M P ER1 T AH0 N AH0 N S
+IMPERTINENCES	IH0 M P ER1 T AH0 N AH0 N S AH0 Z
+IMPERTINENCIES	IH0 M P ER1 T AH0 N AH0 N S IY0 Z
+IMPERTINENT  IH0 M P ER1 T AH0 N AH0 N T
+IMPERTINENTLY	IH0 M P ER1 T IH0 N IH0 N T L IY0
+IMPERTINENTS	IH0 M P ER1 T IH0 N IH0 N T S
+IMPERTURBABILITY	IH0 M P ER2 T ER0 B AH0 B IH1 L IH0 T IY0
+IMPERTURBABLE	IH0 M P ER2 T ER0 B EY1 B AH0 L
+IMPERTURBABLY	IH0 M P ER2 T ER0 B AH0 B L IY0
+IMPERTURBED	IH2 M P ER0 T ER1 B D
+IMPERVIOUS  IH0 M P ER1 V IY0 AH0 S
+IMPERVIOUSNESS	IH0 M P ER1 V IY0 AH0 S N AH0 S
+IMPETT	IH1 M P AH0 T
+IMPETUOSITIES	IH0 M P EH1 CH UW0 AA1 S IH0 T IY0 Z
+IMPETUOSITY	IH0 M P EH1 CH UW0 AA1 S IH0 T IY0
+IMPETUOUS  IH0 M P EH1 CH W AH0 S
+IMPETUOUSLY	IH0 M P EH1 CH W AH0 S L IY0
+IMPETUOUSNESS	IH0 M P EH1 CH W AH0 S N AH0 S
+IMPETUS  IH1 M P AH0 T AH0 S
+IMPEY	IH0 M P IY0
+IMPHM	IH0 M P M
+IMPI	IH0 M P IY0
+IMPIDENCE	IH0 M P AH0 D AH0 N S
+IMPIDENT	IH0 M P IH0 D AH0 N T
+IMPIETIES	IH0 M P AY1 AH0 T IY0 Z
+IMPIETY	IH0 M P AY1 AH0 T IY0
+IMPINGE  IH0 M P IH1 N JH
+IMPINGED  IH0 M P IH1 N JH D
+IMPINGEMENT	IH0 M P IH1 N JH M AH0 N T
+IMPINGES  IH0 M P IH1 N JH IH0 Z
+IMPINGING  IH0 M P IH1 N JH IH0 NG
+IMPINGTON	IH1 M P IH0 NG T AH0 N
+IMPIOUS  IH0 M P AY1 AH0 S
+IMPIOUSLY	IH0 M P AY1 AH0 S L IY0
+IMPIS	IH0 M P IH0 S
+IMPISH  IH1 M P IH0 SH
+IMPISHLY	IH0 M P IH0 SH L IY0
+IMPISHNESS	IH0 M P IH1 SH N AH0 S
+IMPLACABILITY	IH0 M P L AE1 K AH0 B IH1 L IH0 T IY0
+IMPLACABLE  IH0 M P L AE1 K AH0 B AH0 L
+IMPLACABLENESS	IH0 M P L AE1 K AH0 B AH2 L N AH0 S
+IMPLACABLY	IH0 M P L AE1 K AH0 B L IY0
+IMPLANT  IH0 M P L AE1 N T
+IMPLANT  IH1 M P L AE2 N T
+IMPLANTATION  IH0 M P L AE0 N T EY1 SH AH0 N
+IMPLANTED  IH0 M P L AE1 N T IH0 D
+IMPLANTING  IH0 M P L AE1 N T IH0 NG
+IMPLANTS  IH0 M P L AE1 N T S
+IMPLANTS  IH1 M P L AE2 N T S
+IMPLEMENT  IH1 M P L AH0 M AH0 N T
+IMPLEMENTATION  IH2 M P L AH0 M EH0 N T EY1 SH AH0 N
+IMPLEMENTED  IH1 M P L AH0 M EH2 N AH0 D
+IMPLEMENTED  IH1 M P L AH0 M EH2 N T AH0 D
+IMPLEMENTING  IH1 M P L AH0 M EH2 N IH0 NG
+IMPLEMENTING  IH1 M P L AH0 M EH2 N T IH0 NG
+IMPLEMENTS  IH1 M P L AH0 M AH0 N T S
+IMPLEX	IH1 M P L EH2 K S
+IMPLICATE  IH1 M P L IH0 K EY2 T
+IMPLICATED  IH1 M P L IH0 K EY2 T IH0 D
+IMPLICATES  IH1 M P L IH0 K EY2 T S
+IMPLICATING  IH1 M P L IH0 K EY2 T IH0 NG
+IMPLICATION  IH2 M P L AH0 K EY1 SH AH0 N
+IMPLICATIONS  IH2 M P L AH0 K EY1 SH AH0 N Z
+IMPLICIT  IH0 M P L IH1 S AH0 T
+IMPLICITLY  IH0 M P L IH1 S AH0 T L IY0
+IMPLICITY	IH0 M P L IH1 S AH0 T IY0
+IMPLIED  IH0 M P L AY1 D
+IMPLIEDLY	IH0 M P L AY1 D L IY0
+IMPLIES  IH0 M P L AY1 Z
+IMPLOR'D	IH0 M P L AO1 R D
+IMPLORA	IH0 M P L AO1 R AH0
+IMPLORATION	IH0 M P L AO0 R EY1 SH AH0 N
+IMPLORE  IH0 M P L AO1 R
+IMPLORED  IH0 M P L AO1 R D
+IMPLORES  IH0 M P L AO1 R Z
+IMPLORING  IH0 M P L AO1 R IH0 NG
+IMPLORINGLY	IH0 M P L AO1 R IH0 NG L IY0
+IMPLORINGS	IH0 M P L AO1 R IH0 NG Z
+IMPLOY	IH0 M P L OY1
+IMPLOYED	IH0 M P L OY1 D
+IMPLOYMENT	IH0 M P L OY1 M AH0 N T
+IMPLUVIUM	IH0 M P L UW1 V IY0 AH0 M
+IMPLY  IH0 M P L AY1
+IMPLYING  IH0 M P L AY1 IH0 NG
+IMPOLICY	IH0 M P AA1 L AH0 S IY0
+IMPOLITE  IH0 M P AH0 L AY2 T
+IMPOLITELY	IH0 M P AH0 L AY1 T L IY0
+IMPOLITENESS	IH0 M P AH0 L AY1 T N AH0 S
+IMPOLITIC  IH0 M P AO1 L IH1 T IH2 K
+IMPONDERABLE  IH0 M P AA1 N D ER0 AH0 B AH0 L
+IMPONDERABLES  IH0 M P AA1 N D ER0 AH0 B AH0 L Z
+IMPORT  IH0 M P AO1 R T
+IMPORT  IH1 M P AO0 R T
+IMPORTA	IH0 M P AO1 R T AH0
+IMPORTANCE  IH0 M P AO1 R T AH0 N S
+IMPORTANCES	IH0 M P AO1 R T AH0 N S IH0 Z
+IMPORTANT  IH0 M P AO1 R T AH0 N T
+IMPORTANTLY  IH0 M P AO1 R T AH0 N T L IY0
+IMPORTANTS	IH0 M P AO1 R T AH0 N T S
+IMPORTATION  IH2 M P AO0 R T EY1 SH AH0 N
+IMPORTATIONS	IH1 M P AO0 R T EY1 SH AH0 N Z
+IMPORTED  IH0 M P AO1 R T IH0 D
+IMPORTER  IH0 M P AO1 R T ER0
+IMPORTERS  IH0 M P AO1 R T ER0 Z
+IMPORTETH	IH1 M P AO0 R T EH1 TH
+IMPORTING  IH0 M P AO1 R T IH0 NG
+IMPORTS  IH0 M P AO1 R T S
+IMPORTS  IH1 M P AO0 R T S
+IMPORTUNATE	IH0 M P AO1 R CH AH0 N AH0 T
+IMPORTUNATELY	IH0 M P AO1 R CH AH0 N AH0 T L IY0
+IMPORTUNE	IH0 M P AO1 R CH AH0 N
+IMPORTUNED	IH0 M P ER0 T UW1 N D
+IMPORTUNES	IH0 M P AO1 R CH AH0 N Z
+IMPORTUNING	IH0 M P ER0 T UW1 N IH0 NG
+IMPORTUNITIES	IH0 M P ER0 T UW1 N AH0 T IY0 Z
+IMPORTUNITY	IH0 M P ER0 T UW1 N AH0 T IY0
+IMPOS'D	IH0 M P AO2 D
+IMPOSE  IH0 M P OW1 Z
+IMPOSED  IH0 M P OW1 Z D
+IMPOSER	IH0 M P OW1 Z ER0
+IMPOSERS	IH0 M P OW1 Z ER0 Z
+IMPOSES  IH0 M P OW1 Z AH0 Z
+IMPOSES  IH0 M P OW1 Z IH0 Z
+IMPOSIN	IH0 M P AA1 Z AH0 N
+IMPOSING  IH0 M P OW1 Z IH0 NG
+IMPOSINGLY	IH0 M P OW1 Z IH0 NG L IY0
+IMPOSITION  IH2 M P AH0 Z IH1 SH AH0 N
+IMPOSITIONS	IH0 M P AH0 Z IH1 SH AH0 N Z
+IMPOSSIBILE	IH0 M P AA1 S AH0 B IH1 L IY0
+IMPOSSIBILITIES	IH0 M P AA1 S AH0 B IH1 L AH0 T IY0 Z
+IMPOSSIBILITY  IH0 M P AO2 S IH0 B IH1 L IH0 T IY0
+IMPOSSIBLE  IH0 M P AA1 S AH0 B AH0 L
+IMPOSSIBLES	IH0 M P AA1 S AH0 B AH0 L Z
+IMPOSSIBLY  IH0 M P AA1 S AH0 B L IY0
+IMPOST	IH0 M P AO1 S T
+IMPOSTER	IH0 M P AO1 S T ER0
+IMPOSTERS	IH0 M P AO1 S T ER0 Z
+IMPOSTHUME	IH0 M P AA1 S CH UH0 M
+IMPOSTHUMES	IH0 M P AA1 S CH UH0 M Z
+IMPOSTOR  IH0 M P AO1 S T ER0
+IMPOSTOR'S	IH0 M P AO1 S T ER0 Z
+IMPOSTORS  IH0 M P AO1 S T ER0 Z
+IMPOSTS	IH0 M P AO1 S T S
+IMPOSTURE	IH0 M P AA1 S CH ER0
+IMPOSTURES	IH0 M P AA1 S CH ER0 Z
+IMPOT	IH1 M P AH0 T
+IMPOTENCE  IH1 M P AH0 T AH0 N S
+IMPOTENCY	IH1 M P AH0 T AH0 N S IY0
+IMPOTENT  IH1 M P AH0 T AH0 N T
+IMPOTENTLY	IH1 M P AH0 T AH0 N T L IY0
+IMPOUND  IH0 M P AW1 N D
+IMPOUNDED  IH0 M P AW1 N D IH0 D
+IMPOUNDING  IH0 M P AW1 N D IH0 NG
+IMPOVERISH  IH0 M P AA1 V R IH0 SH
+IMPOVERISHED  IH0 M P AA1 V R IH0 SH T
+IMPOVERISHES  IH0 M P AA1 V R IH0 SH AH0 Z
+IMPOVERISHING  IH0 M P AA1 V R IH0 SH IH0 NG
+IMPOVERISHMENT  IH0 M P AA1 V R IH0 SH M AH0 N T
+IMPOWERED	IH0 M P AW1 ER0 D
+IMPOWERING	IH0 M P AW1 ER0 IH0 NG
+IMPRACTICABILITY	IH0 M P R AE1 K T AH0 K AH0 B IH1 L IH0 T IY0
+IMPRACTICABLE  IH0 M P R AE1 K T IH0 K AH0 B AH0 L
+IMPRACTICAL  IH0 M P R AE1 K T AH0 K AH0 L
+IMPRACTICAL  IH0 M P R AE1 K T IH0 K AH0 L
+IMPRACTICALITY	IH0 M P R AE1 K T IH0 K AE1 L AH0 T IY0
+IMPRECATE	IH1 M P R AH0 K EY2 T
+IMPRECATED	IH1 M P R AH0 K EY2 T AH0 D
+IMPRECATES	IH1 M P R AH0 K EY2 T S
+IMPRECATING	IH1 M P R AH0 K EY2 T IH0 NG
+IMPRECATION	IH1 M P R AH0 K EY2 SH AH0 N
+IMPRECATIONS	IH1 M P R AH0 K EY2 SH AH0 N Z
+IMPRECATORY	IH0 M P R EH2 K AH0 T AO2 R IY0
+IMPREGNABILITY	IH0 M P R EH1 G N AH0 B IH1 L IH0 T IY0
+IMPREGNABLE  IH0 M P R EH1 G N AH0 B AH0 L
+IMPREGNABLY	IH0 M P R EH1 G N AH0 B L IY0
+IMPREGNATE  IH0 M P R EH1 G N EY2 T
+IMPREGNATED  IH0 M P R EH1 G N EY2 T AH0 D
+IMPREGNATES  IH0 M P R EH1 G N EY2 T S
+IMPREGNATING  IH0 M P R EH1 G N EY2 T IH0 NG
+IMPREGNATION  IH0 M P R EH1 G N EY1 SH AH0 N
+IMPRESARIO  IH2 M P R IH0 S AA1 R IY0 OW2
+IMPRESARIOS	IH2 M P R IH0 S AA1 R IY0 OW2 Z
+IMPRESCRIPTIBLE	IH1 M P R AH0 S K R IH1 P T AH0 B AH0 L
+IMPRESS  IH0 M P R EH1 S
+IMPRESS  IH1 M P R EH2 S
+IMPRESS'D	IH0 M P R EH1 SH D
+IMPRESSARIO	IH0 M P R EH0 S AA1 R IY0 OW2
+IMPRESSED  IH0 M P R EH1 S T
+IMPRESSES  IH0 M P R EH1 S IH0 Z
+IMPRESSIBILITY	IH0 M P R EH1 S AH0 B IH1 L AH0 T IY0
+IMPRESSIBLE	IH0 M P R EH1 S AH0 B AH0 L
+IMPRESSING  IH0 M P R EH1 S IH0 NG
+IMPRESSION  IH0 M P R EH1 SH AH0 N
+IMPRESSIONABILITY	IH0 M P R EH1 SH AH0 N AH0 B IH1 L IH0 T IY0
+IMPRESSIONABLE  IH0 M P R EH1 SH AH0 N AH0 B AH0 L
+IMPRESSIONABLENESS	IH0 M P R EH1 SH AH0 N AH0 B AH2 L N AH0 S
+IMPRESSIONISM  IH0 M P R EH1 SH AH0 N IH2 Z AH0 M
+IMPRESSIONIST  IH0 M P R EH1 SH AH0 N AH0 S T
+IMPRESSIONIST  IH0 M P R EH1 SH AH0 N IH0 S T
+IMPRESSIONISTIC  IH0 M P R EH2 SH AH0 N IH1 S T IH0 K
+IMPRESSIONISTS  IH0 M P R EH1 SH AH0 N IH0 S T S
+IMPRESSIONS  IH0 M P R EH1 SH AH0 N Z
+IMPRESSIVE  IH0 M P R EH1 S IH0 V
+IMPRESSIVELY  IH0 M P R EH1 S IH0 V L IY0
+IMPRESSIVENESS	IH0 M P R EH1 S IH0 V N AH0 S
+IMPRESSMENT  IH0 M P R EH1 S M AH0 N T
+IMPRESSMENTS	IH0 M P R EH1 S M AH0 N T S
+IMPRESSOR	IH0 M P R EH1 S ER0
+IMPREST	IH0 M P R EH1 S T
+IMPRIMATUR  IH2 M P R IH0 M AA1 T ER0
+IMPRIMIS  IH0 M P R IY1 M IH0 S
+IMPRINT  IH0 M P R IH1 N T
+IMPRINT  IH1 M P R IH0 N T
+IMPRINTED  IH0 M P R IH1 N IH0 D
+IMPRINTED  IH0 M P R IH1 N T IH0 D
+IMPRINTING  IH0 M P R IH1 N IH0 NG
+IMPRINTING  IH0 M P R IH1 N T IH0 NG
+IMPRINTS  IH0 M P R IH1 N T S
+IMPRISON  IH0 M P R IH1 Z AH0 N
+IMPRISON'D	IH0 M P R IH1 Z AH0 N D
+IMPRISONED  IH0 M P R IH1 Z AH0 N D
+IMPRISONING  IH0 M P R IH1 Z AH0 N IH0 NG
+IMPRISONMENT  IH0 M P R IH1 Z AH0 N M AH0 N T
+IMPRISONMENTS	IH0 M P R IH1 Z AH0 N M AH0 N T S
+IMPRISONS  IH0 M P R IH1 Z AH0 N Z
+IMPROBABILITIES	IH0 M P R AA1 B AH0 B IH1 L AH0 T IY0 Z
+IMPROBABILITY	IH0 M P R AA1 B AH0 B IH1 L IH0 T IY0
+IMPROBABLE  IH0 M P R AA1 B AH0 B AH0 L
+IMPROBABLY  IH0 M P R AA1 B AH0 B L IY0
+IMPROBITY	IH0 M P R OW1 B AH0 T IY0
+IMPROBUS	IH0 M P R OW1 B IH0 S
+IMPROMPTU  IH0 M P R AA1 M P T UW0
+IMPROMPTUS	IH0 M P R AA1 M P T AH0 S
+IMPROPER  IH0 M P R AA1 P ER0
+IMPROPERLY  IH0 M P R AA1 P ER0 L IY0
+IMPROPRIETIES  IH2 M P R AH0 P R AY1 AH0 T IY0 Z
+IMPROPRIETY  IH2 M P R AH0 P R AY1 AH0 T IY0
+IMPROV'D	IH2 M P R AA0 V D
+IMPROVABLE	IH0 M P R UW1 V AH0 B AH0 L
+IMPROVE  IH0 M P R UW1 V
+IMPROVEABLE	IH0 M P R UW1 V AH0 B AH0 L
+IMPROVED  IH0 M P R UW1 V D
+IMPROVEMENT  IH0 M P R UW1 V M AH0 N T
+IMPROVEMENTS  IH0 M P R UW1 V M AH0 N T S
+IMPROVER	IH0 M P R UW1 V ER0
+IMPROVERS	IH0 M P R UW1 V ER0 Z
+IMPROVES  IH0 M P R UW1 V Z
+IMPROVIDENCE	IH0 M P R AA1 V AH0 D AH0 N S
+IMPROVIDENT	IH0 M P R AA1 V AH0 D AH0 N T
+IMPROVIDENTLY	IH0 M P R AA1 V AH0 D AH0 N T L IY0
+IMPROVIN	IH2 M P R AA0 V IH0 N
+IMPROVING  IH0 M P R UW1 V IH0 NG
+IMPROVISATION  IH2 M P R AA0 V IH0 Z EY1 SH AH0 N
+IMPROVISATIONS  IH2 M P R AA0 V IH0 Z EY1 SH AH0 N Z
+IMPROVISATOR	IH2 M P R AA0 V IH0 Z EY1 T ER0
+IMPROVISATORE	IH2 M P R AA0 V IH0 Z AA0 T AO1 R IY0
+IMPROVISATORI	IH2 M P R AA0 V IH0 Z AH0 T AO1 R IY0
+IMPROVISATRICE	IH2 M P R AA0 V IH0 Z EY1 T R IH0 S
+IMPROVISE  IH1 M P R AH0 V AY2 Z
+IMPROVISE  IH2 M P R AH0 V AY1 Z
+IMPROVISED  IH1 M P R AH0 V AY2 Z D
+IMPROVISER	IH1 M P R AH0 V AY1 Z ER0
+IMPROVISERS	IH1 M P R AH0 V AY2 Z ER0 Z
+IMPROVISES  IH1 M P R AH0 V AY2 Z AH0 Z
+IMPROVISES  IH2 M P R AH0 V AY1 Z AH0 Z
+IMPROVISING  IH1 M P R AH0 V AY2 Z IH0 NG
+IMPROVISO	IH1 M P R AH0 V AY1 Z OW0
+IMPROVVISATORE	IH0 M P R UW1 V IY0 S AA0 T AO1 R IY0
+IMPRUDENCE  IH0 M P R UW1 D AH0 N S
+IMPRUDENCES	IH0 M P R UW1 D AH0 N S IH0 Z
+IMPRUDENT  IH0 M P R UW1 D AH0 N T
+IMPRUDENTLY  IH0 M P R UW1 D AH0 N T L IY0
+IMPS	IH1 M P S
+IMPTY	IH0 M P T IY0
+IMPUDENCE	IH0 M P Y UW1 D AH0 N S
+IMPUDENT	IH0 M P Y UW1 D AH0 N T
+IMPUDENTLY	IH0 M P Y UW1 D AH0 N T L IY0
+IMPUGN  IH0 M P Y UW1 N
+IMPUGNED  IH0 M P Y UW1 N D
+IMPUGNING  IH0 M P Y UW1 N IH0 NG
+IMPUGNS  IH0 M P Y UW1 N Z
+IMPULSE  IH0 M P AH1 L S
+IMPULSE  IH1 M P AH0 L S
+IMPULSES  IH0 M P AH1 L S IH0 Z
+IMPULSES  IH1 M P AH0 L S IH0 Z
+IMPULSION	IH0 M P AH1 L SH AH0 N
+IMPULSIONS	IH0 M P AH1 L SH AH0 N Z
+IMPULSIVE  IH0 M P AH1 L S IH0 V
+IMPULSIVELY  IH0 M P AH1 L S IH0 V L IY0
+IMPULSIVENESS	IH0 M P AH1 L S IH0 V N AH0 S
+IMPUNE	IH0 M P Y UW1 N
+IMPUNITY  IH0 M P Y UW1 N IH0 T IY0
+IMPURE  IH0 M P Y UH1 R
+IMPURITIES  IH0 M P Y UH1 R AH0 T IY0 Z
+IMPURITY  IH0 M P Y UH1 R AH0 T IY0
+IMPURPLED	IH0 M P ER1 P AH0 L D
+IMPUTABLE	IH0 M P Y UW1 T AH0 B AH0 L
+IMPUTANTUR	IH0 M P Y UW1 T AH0 N T ER0
+IMPUTATION  IH2 M P Y AH0 T EY1 SH AH0 N
+IMPUTATIONS	IH2 M P Y AH0 T EY1 SH AH0 N Z
+IMPUTE  IH0 M P Y UW1 T
+IMPUTED  IH0 M P Y UW1 T IH0 D
+IMPUTES	IH0 M P Y UW1 T S
+IMPUTING	IH0 M P Y UW1 T IH0 NG
+IMRAY	IH2 M R EY0
+IMRE  IH1 M R AH0
+IMRERA	IH2 M R EH1 R AH0
+IMRI	IH1 M ER0 IY0
+IMRY	IH1 M R IY0
+IMSEL	IH1 M S AH0 L
+IMSELF	IH0 M S EH1 L F
+IMSEN	IH1 M S AH0 N
+IMSHI	IH1 M SH IY0
+IMSON	IH1 M S AH0 N
+IMTIAZAN	IH1 M SH AH0 Z AE1 N
+IN  IH0 N
+IN  IH1 N
+IN'	IH0 N
+IN'ARDS	IH1 N AA1 R D Z
+IN'S  IH0 N Z
+IN'S  IH1 N Z
+IN'T	IH1 N T
+INA  IY1 N AH0
+INA'S	IH2 N AH0 Z
+INABA	IH0 N AA1 B AH0
+INABILITIES	IH0 N AH0 B IH1 L AH0 T IY0 Z
+INABILITY  IH2 N AH0 B IH1 L IH0 T IY0
+INACCEPTABLE	IH2 N AH0 K S EH1 P T AH0 B AH0 L
+INACCESSABLE	IH2 N AH0 K S EH1 S AH0 B AH0 L
+INACCESSIBILITY  IH2 N AH0 K S EH2 S AH0 B IH1 L AH0 T IY0
+INACCESSIBLE  IH2 N AH0 K S EH1 S AH0 B AH0 L
+INACCESSIBLY	IH2 N AH0 K S EH1 S AH0 B L IY0
+INACCURACIES  IH0 N AE1 K Y ER0 AE2 S IY0 Z
+INACCURACY  IH0 N AE1 K Y ER0 AH0 S IY0
+INACCURATE  IH0 N AE1 K Y ER0 AH0 T
+INACCURATELY  IH0 N AE1 K Y ER0 AH0 T L IY0
+INACHUS	IH0 N AE1 CH AH0 S
+INACTION  IH0 N AE1 K SH AH0 N
+INACTIVE  IH0 N AE1 K T IH0 V
+INACTIVELY	IH0 N AE1 K T IH0 V L IY0
+INACTIVITY  IH0 N AE0 K T IH1 V IH0 T IY0
+INADEQUACIES  IH0 N AE1 D AH0 K W AH0 S IY0 Z
+INADEQUACY  IH0 N AE1 D IH0 K W AH0 S IY0
+INADEQUATE  IH0 N AE1 D AH0 K W AH0 T
+INADEQUATE  IH0 N AE1 D AH0 K W EY2 T
+INADEQUATELY  IH0 N AE1 D AH0 K W AH0 T L IY0
+INADEQUATENESS	IH0 N AE1 D AH0 K W AH0 T N AH0 S
+INADMISSIBLE  IH0 N AE0 D M IH1 S AH0 B AH0 L
+INADMISSIBLE  IH0 N AH0 D M IH1 S AH0 B AH0 L
+INADVERTENCE  IH2 N AE0 D V ER1 T AH0 N S
+INADVERTENCE  IH2 N AH0 D V ER1 T AH0 N S
+INADVERTENCIES	IH2 N AE0 D V ER1 T AH0 N S IY0 Z
+INADVERTENCY	IH2 N AE0 D V ER1 T AH0 N S IY0
+INADVERTENT  IH2 N AE0 D V ER1 T AH0 N T
+INADVERTENT  IH2 N AH0 D V ER1 T AH0 N T
+INADVERTENTLY  IH2 N AE0 D V ER1 T AH0 N T L IY0
+INADVERTENTLY  IH2 N AH0 D V ER1 T AH0 N T L IY0
+INADVISABILITY	IH2 N AE0 D V AY1 Z AH0 B IH1 L IH0 T IY0
+INADVISABLE  IH0 N AE0 D V AY1 Z AH0 B AH0 L
+INADVISABLE  IH0 N AH0 D V AY1 Z AH0 B AH0 L
+INAGAKI	IH2 N AH0 G AA1 K IY0
+INAIYUT	IH0 N EY0 Y UW1 T
+INALIENABLE  IH0 N EY1 L Y AH0 N AH0 B AH0 L
+INALIENABLY	IH0 N EY1 L Y EH0 N AH0 B L IY0
+INALTERABLE	IH0 N AO2 L T ER0 AH0 B AH0 L
+INAMORATA	IH1 N AH0 M AO2 R AA1 T AH0
+INAMORATAS	IH1 N AH0 M AO2 R AA1 T AH0 Z
+INAMORATO	IH0 N AA0 M AO0 R AA1 T OW0
+INANDA'S	IH0 N AE1 N D AH0 Z
+INANE  IH0 N EY1 N
+INANELY	IH0 N EY1 N L IY0
+INANIMATE  IH0 N AE1 N AH0 M AH0 T
+INANIMATION	IH0 N AE1 N AH0 M EY1 SH AH0 N
+INANITIES	IH0 N AE1 N AH0 T IY0 Z
+INANITION	IH0 N AE0 N IH1 SH AH0 N
+INANITY	IH0 N AE1 N AH0 T IY0
+INAPPEASABLE	IH2 N AH0 P IY1 S AH0 B AH0 L
+INAPPLICABILITY	IH0 N AE1 P L AH0 K AH0 B IH1 L IH0 T IY0
+INAPPLICABLE  IH0 N AE1 P L IH0 K AH0 B AH0 L
+INAPPOSITE	IH2 N AH0 P AH0 Z AH0 T
+INAPPRECIABLE	IH2 N AH0 P R IY1 SH AH0 B AH0 L
+INAPPRECIATION	IH2 N AH0 P R IY2 SH IY0 EY1 SH AH0 N
+INAPPRECIATIVE	IH2 N AH0 P R IY1 SH Y AH0 T IH0 V
+INAPPREHENSIBLE	IH2 N AH0 P R IH0 HH EH1 N S AH0 B AH0 L
+INAPPROACHABLE	IH2 N AH0 P R OW1 CH AH0 B AH0 L
+INAPPROPRIATE  IH2 N AH0 P R OW1 P R IY0 IH0 T
+INAPPROPRIATELY  IH0 N AH0 P R AA1 P R IY0 AH0 T L IY0
+INAPPROPRIATENESS	IH2 N AH0 P R OW1 P R IY0 AH0 T N AH0 S
+INAPT	IH0 N AE1 P T
+INAPTITUDE	IH0 N AE1 P T AH0 T UW2 D
+INAPTLY	IH0 N AE1 P T L IY0
+INAPTNESS	IH0 N AE0 P T N AH0 S
+INARI	IH0 N AA1 R IY0
+INARTICULATE  IH0 N AA0 R T IH1 K Y AH0 L AH0 T
+INARTICULATELY	IH0 N AA0 R T IH1 K Y AH0 L AH0 T L IY0
+INARTICULATENESS	IH0 N AA0 R T IH1 K Y AH0 L AH0 T N AH0 S
+INARTIFICIAL	IH0 N AA0 R T AH0 F IH1 SH AH0 L
+INARTISTIC	IH0 N AA0 R T IH1 S T IH0 K
+INARTISTICALLY	IH0 N AA0 R T IH1 S T IH0 K L IY0
+INASMUCH  IH0 N AE1 S M AH0 K
+INATTENTION  IH0 N AH0 T EH1 N CH AH0 N
+INATTENTIONS	IH2 N AH0 T EH1 N SH AH0 N Z
+INATTENTIVE  IH2 N AH0 T EH1 N T IH0 V
+INATTENTIVELY	IH2 N AH0 T EH1 N T IH0 V L IY0
+INATTENTIVENESS	IH2 N AH0 T EH1 N T IH0 V N AH0 S
+INAUDIBILITY	IH0 N AO1 D AH0 B IH1 L AH0 T IY0
+INAUDIBLE  IH0 N AO1 D AH0 B AH0 L
+INAUDIBLY  IH0 N AO1 D AH0 B L IY0
+INAUGURAL  IH0 N AO1 G ER0 AH0 L
+INAUGURAL  IH0 N AO1 G Y ER0 AH0 L
+INAUGURALS	IH0 N AO1 G Y ER0 AH0 L Z
+INAUGURATE  IH0 N AO1 G Y ER0 EY0 T
+INAUGURATE  IH0 N AO1 G Y ER0 IH0 T
+INAUGURATED  IH0 N AO1 G ER0 EY2 T IH0 D
+INAUGURATED  IH0 N AO1 G Y ER0 EY2 T IH0 D
+INAUGURATES  IH0 N AO1 G Y ER0 IH0 T S
+INAUGURATING  IH0 N AO1 G Y ER0 EY2 T IH0 NG
+INAUGURATION  IH0 N AO2 G Y ER0 EY1 SH AH0 N
+INAUGURATIONS  IH0 N AO2 G Y ER0 EY1 SH AH0 N Z
+INAUGURATOR	IH0 N AO1 G Y ER0 EY2 T ER0
+INAUSPICIOUS  IH0 N AW2 S P IH1 SH IH0 S
+INAUSPICIOUSLY	IH0 N AW2 S P IH1 SH AH0 S L IY0
+INAYAT	IH0 N AY1 AE0 T
+INBOARD  IH1 N B AO2 R D
+INBORN  IH1 N B AO2 R N
+INBOUND  IH0 N B AW1 N D
+INBOUND  IH1 N B AW0 N D
+INBREATHING	IH1 N B R IY1 DH IH0 NG
+INBRED  IH1 N B R EH2 D
+INBREEDING  IH2 N B R IY1 D IH0 NG
+INCA  IH1 NG K AH0
+INCA'S	IH1 NG K AH0 Z
+INCALCULABLE  IH0 N K AE1 L K Y AH0 L AH0 B AH0 L
+INCALCULABLY	IH0 N K AE1 L K Y AH0 L AH0 B L IY0
+INCAMPED	IH0 N K AE1 M P T
+INCAN	IH0 N K AE1 N
+INCANDESCENCE	IH2 N K AH0 N D EH1 S AH0 N S
+INCANDESCENT  IH2 N K AH0 N D EH1 S AH0 N T
+INCANDESCENTS	IH2 N K AH0 N D EH1 S AH0 N T S
+INCANTATION  IH0 N K AE1 N T EY1 SH AH0 N
+INCANTATIONS	IH0 N K AE1 N T EY1 SH AH0 N Z
+INCAPABILITY	IH0 N K EY1 P AH0 B IH1 L IH0 T IY0
+INCAPABLE  IH0 N K EY1 P AH0 B AH0 L
+INCAPABLES	IH0 N K EY1 P AH0 B AH0 L Z
+INCAPABLY	IH0 N K EY1 P AH0 B L IY0
+INCAPACITATE  IH0 N K AH0 P AE1 S IH0 T EY2 T
+INCAPACITATED  IH0 N K AH0 P AE1 S IH0 T EY2 T IH0 D
+INCAPACITATES	IH0 N K AH0 P AE1 S IH0 T EY2 T S
+INCAPACITATING  IH0 N K AH0 P AE1 S IH0 T EY2 T IH0 NG
+INCAPACITATION  IH0 N K AH0 P AE2 S IH0 T EY1 SH AH0 N
+INCAPACITIES	IH0 N K AH0 P AE1 S IH0 T IY0 Z
+INCAPACITY  IH0 N K AH0 P AE1 S AH0 T IY0
+INCARCERATE  IH0 N K AA1 R S ER0 EY2 T
+INCARCERATED  IH0 N K AA1 R S ER0 EY2 T IH0 D
+INCARCERATING  IH0 N K AA1 R S ER0 EY2 T IH0 NG
+INCARCERATION  IH0 N K AA2 R S ER0 EY1 SH AH0 N
+INCARNACION	IH0 N K AA0 R N AA0 S IY0 AO1 N
+INCARNADINE	IH0 N K AA0 R N AA0 D IY1 N IY0
+INCARNADINED	IH0 N K AA1 R N AH0 D IY1 N D
+INCARNATE  IH0 N K AA1 R N AH0 T
+INCARNATE  IH0 N K AA1 R N EY2 T
+INCARNATED	IH0 N K AA1 R N EY2 T IH0 D
+INCARNATES	IH0 N K AA1 R N EY2 T S
+INCARNATING	IH0 N K AA1 R N EY2 T IH0 NG
+INCARNATION  IH0 N K AA1 R N EY1 SH AH0 N
+INCARNATIONS  IH0 N K AA0 R N EY1 SH AH0 N Z
+INCAS  IH1 NG K AH0 Z
+INCASE  IH0 N K EY1 S
+INCASED	IH0 N K EY1 S T
+INCASING	IH0 N K EY1 S IH0 NG
+INCAUTION	IH0 N K AO1 SH AH0 N
+INCAUTIOUS	IH0 N K AO1 SH AH0 S
+INCAUTIOUSLY	IH0 N K AO1 SH AH0 S L IY0
+INCAUTIOUSNESS	IH0 N K AO1 SH AH0 S N AH0 S
+INCE  IH1 N S
+INCEKU	IH0 N S EH1 K UW0
+INCENDIARIES	IH0 N S EH1 N D IY0 EH2 R IY0 Z
+INCENDIARISM	IH0 N S EH1 N D IY0 EH0 R IH0 Z AH0 M
+INCENDIARY  IH0 N S EH1 N D IY0 EH0 R IY0
+INCENDIARY'S	IH0 N S EH1 N D IY0 EH0 R IY0 Z
+INCENS'D	IH0 N S EH1 N S AH0 D
+INCENSE  IH0 N S EH1 N S
+INCENSE  IH1 N S EH2 N S
+INCENSED  IH1 N S EH2 N S T
+INCENSES	IH0 N S EH1 N S IH0 Z
+INCENSING	IH0 N S EH1 N S IH0 NG
+INCENTIVE  IH0 N S EH1 N IH0 V
+INCENTIVE  IH0 N S EH1 N T IH0 V
+INCENTIVES  IH0 N S EH1 N T IH0 V Z
+INCENTIVES  IH0 N S IH1 N IH0 V Z
+INCEPTION  IH0 N S EH1 P SH AH0 N
+INCEPTIVE	IH0 N S EH1 P T IH0 V
+INCERTITUDE	IH0 N S ER1 T AH0 T UW2 D
+INCESSANT  IH0 N S EH1 S AH0 N T
+INCESSANTLY  IH0 N S EH1 S AH0 N T L IY0
+INCEST  IH1 N S EH2 S T
+INCESTUOUS  IH0 N S EH1 S CH W AH0 S
+INCH  IH1 N CH
+INCHANTED	IH1 N CH AE1 N T IH0 D
+INCHBALD	IH1 N CH B AO2 L D
+INCHBARE	IH1 N CH B ER0
+INCHBARE'S	IH1 N CH B ER0 Z
+INCHBRAE	IH1 N CH B R EY2
+INCHCALDY	IH1 N CH K AO1 L D IY0
+INCHCAPE  IH1 N CH K EY2 P
+INCHE	IH1 N CH
+INCHED  IH1 N CH T
+INCHERS	IH1 N CH ER0 Z
+INCHES  IH1 N CH AH0 Z
+INCHES  IH1 N CH IH0 Z
+INCHI	IH1 N CH IY0
+INCHICORE	IH1 N CH IH1 K AO2 R
+INCHIE	IH1 N CH IY0
+INCHING  IH1 N CH IH0 NG
+INCHIQUIN	IH1 N CH IH0 K W IY1 N
+INCHKEITH	IH1 N CH K IY1 TH
+INCHKENNETH	IH1 N CH K EH1 N IH0 TH
+INCHLING	IH1 N K L IH0 NG
+INCHMAN	IH1 NG K M AH0 N
+INCHOATE	IH1 N CH OW1 T
+INCHS	IH1 N K S
+INCHY	IH1 N CH IY0
+INCIDENCE  IH1 N S AH0 D AH0 N S
+INCIDENCE  IH1 N S IH0 D AH0 N S
+INCIDENT  IH1 N S AH0 D AH0 N T
+INCIDENTAL  IH2 N S IH0 D EH1 N T AH0 L
+INCIDENTALLY  IH2 N S IH0 D EH1 N T AH0 L IY0
+INCIDENTALLY  IH2 N S IH0 D EH1 N T L IY0
+INCIDENTALS  IH2 N S IH0 D EH1 N T AH0 L Z
+INCIDENTAMBA	IH2 N S IH0 D EH1 N T AH0 M B AH0
+INCIDENTLY	IH1 N S AH0 D AH0 N T L IY0
+INCIDENTS  IH1 N S AH0 D AH0 N T S
+INCINERATED  IH0 N S IH1 N ER0 EY2 T IH0 D
+INCINERATION  IH0 N S IH1 N ER0 EY2 SH AH0 N
+INCINERATOR  IH0 N S IH1 N ER0 EY2 T ER0
+INCIPIENCY	IH0 N S IH1 P IY0 AH0 N S IY0
+INCIPIENT  IH0 N S IH1 P IY0 AH0 N T
+INCIPIT	IH0 N S IH1 P IH0 T
+INCISA	IH0 N S AY1 Z AH0
+INCISED  IH0 N S AY1 Z D
+INCISION  IH0 N S IH1 ZH AH0 N
+INCISIONS  IH0 N S IH1 ZH AH0 N Z
+INCISIVE  IH0 N S AY1 S IH0 V
+INCISIVELY	IH0 N S AY1 S IH0 V L IY0
+INCISIVENESS	IH0 N S AY1 S IH0 V N AH0 S
+INCISOR  IH0 N S AY1 Z ER0
+INCISORS  IH0 N S AY1 Z ER0 Z
+INCITE  IH0 N S AY1 T
+INCITED  IH0 N S AY1 T IH0 D
+INCITEMENT  IH0 N S AY1 T M AH0 N T
+INCITEMENTS  IH0 N S AY1 T M AH0 N T S
+INCITER	IH0 N S AY1 T ER0
+INCITES  IH0 N S AY1 T S
+INCITING  IH0 N S AY1 T IH0 NG
+INCIVILITIES	IH0 N S IH0 V IH1 L IH0 T IY0 Z
+INCIVILITY  IH0 N S IH0 V IH1 L IH0 T IY0
+INCIVISM	IH0 N S IH0 V IH2 Z AH0 M
+INCLE	IH1 NG K AH0 L
+INCLEDON	IH0 N K L D AH0 N
+INCLEMENCIES	IH1 N K L IH0 M AH0 N S IY0 Z
+INCLEMENCY	IH1 N K L IH0 M AH0 N S IY0
+INCLEMENT  IH0 N K L EH1 M AH0 N T
+INCLEMENT  IH1 N K L IH0 M AH0 N T
+INCLIN'D	IH2 N K L AH0 N D
+INCLINABLE	IH0 N K L AY1 N AH0 B AH0 L
+INCLINATION  IH2 N K L AH0 N EY1 SH AH0 N
+INCLINATIONS  IH2 N K L AH0 N EY1 SH AH0 N Z
+INCLINE  IH0 N K L AY1 N
+INCLINE  IH1 N K L AY0 N
+INCLINED  IH0 N K L AY1 N D
+INCLINES  IH0 N K L AY1 N Z
+INCLINES  IH1 N K L AY0 N Z
+INCLINETH	IH0 N K L AY1 N TH
+INCLINING	IH0 N K L AY1 N IH0 NG
+INCLININGS	IH0 N K L AY1 N IH0 NG Z
+INCLOS'D	IH0 N K L OW1 ZH D
+INCLOSE	IH0 N K L OW1 Z
+INCLOSED	IH0 N K L OW1 Z D
+INCLOSES	IH0 N K L OW1 Z IH0 Z
+INCLOSING	IH0 N K L OW1 Z IH0 NG
+INCLOSURE  IH0 N K L OW1 ZH ER0
+INCLOSURES	IH0 N K L OW1 ZH ER0 Z
+INCLUDE  IH0 N K L UW1 D
+INCLUDED  IH0 N K L UW1 D AH0 D
+INCLUDED  IH0 N K L UW1 D IH0 D
+INCLUDES  IH0 N K L UW1 D Z
+INCLUDIN	IH0 N K L UW1 D IH0 N
+INCLUDING  IH0 N K L UW1 D IH0 NG
+INCLUSION  IH0 N K L UW1 ZH AH0 N
+INCLUSIONS  IH0 N K L UW1 ZH AH0 N Z
+INCLUSIVE  IH0 N K L UW1 S IH0 V
+INCLUSIVELY	IH0 N K L UW1 S IH0 V L IY0
+INCLUSIVENESS  IH0 N K L UW1 S IH0 V N EH0 S
+INCLUSIVENESS  IH0 N K L UW1 S IH0 V N IH0 S
+INCOG	IH0 N K AO0 G
+INCOGITATIVE	IH0 NG K AA2 JH IH0 T EY2 T IH0 V
+INCOGNITA	IH0 N K AO0 G N IY1 T AH0
+INCOGNITO  IH0 N K AO0 G N IY1 T OW0
+INCOHERENCE  IH0 N K OW0 HH IH1 R AH0 N S
+INCOHERENCES	IH0 N K OW0 HH IH1 R AH0 N S IH0 Z
+INCOHERENCIES	IH0 N K OW0 HH IH1 R AH0 N S IY0 Z
+INCOHERENCY	IH0 N K OW0 HH IH1 R AH0 N S IY0
+INCOHERENT  IH0 N K OW0 HH IH1 R AH0 N T
+INCOHERENTLY  IH0 N K OW0 HH IH1 R AH0 N T L IY0
+INCOMBUSTIBILITY	IH0 N K AH0 M B AH1 S T AH0 B IH1 L AH0 T IY0
+INCOMBUSTIBLE	IH0 N K AH0 M B AH1 S T AH0 B AH0 L
+INCOME  IH1 N K AH2 M
+INCOME'S	IH1 N K AH2 M Z
+INCOMER	IH1 N K AH2 M ER0
+INCOMERS	IH1 N K AH2 M ER0 Z
+INCOMES  IH1 N K AH2 M Z
+INCOMING  IH1 N K AH2 M IH0 NG
+INCOMINGS	IH1 N K AH2 M IH0 NG Z
+INCOMMENSURABLE	IH0 N K AH0 M EH1 N S ER0 AH0 B AH0 L
+INCOMMENSURATE	IH0 N K AH0 M EH1 N S ER0 AH0 T
+INCOMMODE	IH0 N K AH0 M OW1 D
+INCOMMODED	IH0 N K AH0 M OW1 D IH0 D
+INCOMMODES	IH0 N K AH0 M OW1 D Z
+INCOMMODING	IH0 N K AH0 M AA1 D IH0 NG
+INCOMMODIOUS	IH0 N K AH0 M OW1 D IY0 AH0 S
+INCOMMODITIES	IH0 N K AH0 M AA1 D AH0 T IY0 Z
+INCOMMODITY	IH0 N K AH0 M AA1 D AH0 T IY0
+INCOMMUNICABLE	IH0 N K AH0 M Y UW1 N AH0 K AH0 B AH0 L
+INCOMMUNICABLY	IH0 N K AH0 M Y UW1 N AH0 K AH0 B L IY0
+INCOMMUNICADO  IH2 N K AH0 M Y UW2 N AH0 K AA1 D OW0
+INCOMMUNICATIVE	IH0 N K AH0 M Y UW1 N AH0 K EY2 T IH0 V
+INCOMPARABLE  IH0 N K AA1 M P ER0 AH0 B AH0 L
+INCOMPARABLY  IH0 N K AA1 M P ER0 AH0 B L IY0
+INCOMPATIBILITIES	IH0 NG K AA2 M P AH0 T IH0 B IH1 L IH0 T IY0 Z
+INCOMPATIBILITY  IH0 N K AA2 M P AH0 T IH0 B IH1 L IH0 T IY0
+INCOMPATIBLE  IH0 N K AH0 M P AE1 T AH0 B AH0 L
+INCOMPATIBLES	IH0 NG K AA2 M P AH0 T IH0 B AH0 L Z
+INCOMPETENCE  IH0 N K AA1 M P AH0 T AH0 N S
+INCOMPETENCY  IH0 N K AA1 M P AH0 T AH0 N S IY0
+INCOMPETENT  IH0 N K AA1 M P AH0 T AH0 N T
+INCOMPETENTS  IH0 NG K AA1 M P AH0 T AH0 N T S
+INCOMPLETE  IH0 N K AH0 M P L IY1 T
+INCOMPLETED	IH0 N K AH0 M P L IY1 T IH0 D
+INCOMPLETELY	IH0 N K AH0 M P L IY1 T L IY0
+INCOMPLETENESS	IH0 N K AH0 M P L IY1 T N AH0 S
+INCOMPLETION	IH0 N K AH0 M P L IY1 SH AH0 N
+INCOMPREHENSIBILITIES	IH0 NG K AA2 M P R IY0 HH EH1 N S AH0 B IH1 L AH0 T IY0 Z
+INCOMPREHENSIBILITY	IH0 NG K AA2 M P R IY0 HH EH1 N S IH0 B IH1 L IH0 T IY0
+INCOMPREHENSIBLE  IH0 NG K AA2 M P R AH0 HH EH1 N S IH0 B AH0 L
+INCOMPREHENSIBLY  IH0 NG K AA2 M P R AH0 HH EH1 N S IH0 B L IY0
+INCOMPREHENSION	IH0 NG K AA2 M P R IY0 HH EH1 N SH AH0 N
+INCOMPREHENSIVELY	IH0 NG K AA2 M P R IY0 HH EH1 N S IH0 V L IY0
+INCOMPRESSIBLE  IH0 N K AH0 M P R EH1 S AH0 B AH0 L
+INCOMPRIS	IH0 N K AH0 M P R AY1 Z
+INCOMPRISE	IH0 N K AH0 M P R AY1 Z
+INCONCEIVABILITY	IH2 N K AH0 N S IY1 V AH0 B IH1 L IH0 T IY0
+INCONCEIVABLE  IH2 N K AH0 N S IY1 V AH0 B AH0 L
+INCONCEIVABLY	IH2 N K AH0 N S IY1 V AH0 B L IY0
+INCONCLUSIVE  IH0 N K AH0 N K L UW1 S IH0 V
+INCONCLUSIVELY  IH0 NG K AA1 N K L UW0 S IH0 V L IY0
+INCONCLUSIVENESS	IH2 N K AH0 N K L UW1 S IH0 V N AH0 S
+INCONGRUITIES	IH2 NG K AO0 NG R UW1 IH0 T IY0 Z
+INCONGRUITY  IH2 NG K AO0 NG R UW1 IH0 T IY0
+INCONGRUOUS  IH0 NG K AO1 NG R UW0 AH0 S
+INCONGRUOUSLY  IH0 NG K AO1 NG R UW0 AH0 S L IY0
+INCONGRUOUSNESS	IH0 NG K AO1 NG R UW0 AH0 S N AH0 S
+INCONNU	IH0 N K AH0 N UW0
+INCONNUE	IH0 N K AH0 N UW0
+INCONSCIENT	IH0 N K AA1 N SH AH0 N T
+INCONSECUTIVE	IH2 N K AH0 N S EH1 K Y AH0 T IH0 V
+INCONSECUTIVENESS	IH2 N K AH0 N S EH1 K Y AH0 T IH0 V N AH0 S
+INCONSEQUENCE	IH0 NG K AA1 N S AH0 K W AH0 N S
+INCONSEQUENCES	IH0 NG K AA1 N S AH0 K W AH0 N S IH0 Z
+INCONSEQUENT	IH0 NG K AA1 N S AH0 K W AH0 N T
+INCONSEQUENTIAL  IH0 NG K AA2 N S AH0 K W EH1 N CH AH0 L
+INCONSEQUENTIALITIES	IH0 NG K AA1 N S AH0 K W EH2 N SH IY0 AE1 L AH0 T IY0 Z
+INCONSEQUENTIALITY	IH0 NG K AA1 N S AH0 K W EH2 N SH IY0 AE1 L AH0 T IY0
+INCONSEQUENTIALLY	IH0 NG K AA1 N S AH0 K W EH1 N CH AH0 L IY0
+INCONSEQUENTLY	IH0 NG K AA1 N S AH0 K W AH0 N T L IY0
+INCONSIDERABLE	IH2 N K AH0 N S IH1 D ER0 AH0 B AH0 L
+INCONSIDERABLY	IH2 N K AH0 N S IH1 D ER0 AH0 B L IY0
+INCONSIDERATE	IH2 N K AH0 N S IH1 D ER0 AH0 T
+INCONSIDERATELY	IH2 N K AH0 N S IH1 D ER0 AH0 T L IY0
+INCONSIDERATENESS	IH2 N K AH0 N S IH1 D ER0 AH0 T N AH0 S
+INCONSIDERATION	IH2 N K AH0 N S IH2 D ER0 EY1 SH AH0 N
+INCONSISTENCE	IH2 N K AH0 N S IH1 S T AH0 N S
+INCONSISTENCIES  IH0 NG K AA1 N S IH0 S T EH2 N S IY0 Z
+INCONSISTENCY  IH2 N K AH0 N S IH1 S T AH0 N S IY0
+INCONSISTENT  IH2 N K AH0 N S IH1 S T AH0 N T
+INCONSISTENTLY	IH2 N K AH0 N S IH1 S T AH0 N T L IY0
+INCONSOLABLE	IH2 N K AH0 N S OW1 L AH0 B AH0 L
+INCONSOLABLY	IH2 N K AH0 N S OW1 L AH0 B L IY0
+INCONSPICUOUS  IH0 NG K AA1 N S P IH0 K W AH0 S
+INCONSPICUOUSLY	IH2 N K AH0 N S P IH1 K Y UW0 AH0 S L IY0
+INCONSPICUOUSNESS	IH2 N K AH0 N S P IH1 K Y UW0 AH0 S N AH0 S
+INCONSTANCY  IH0 N K AA1 N S T AH0 N S IY0
+INCONSTANT	IH0 NG K AA1 N S T AH0 N T
+INCONTESTABLE	IH2 N K AH0 N T EH1 S T AH0 B AH0 L
+INCONTESTABLY	IH2 N K AH0 N T EH1 S T AH0 B L IY0
+INCONTESTIBLE	IH2 N K AH0 N T EH1 S T AH0 B AH0 L
+INCONTESTIBLY	IH2 N K AH0 N T EH1 S T AH0 B L IY0
+INCONTINENCE  IH0 N K AA1 N T AH0 N AH0 N S
+INCONTINENCY	IH0 N K AA1 N T AH0 N AH0 N S IY0
+INCONTINENT  IH0 N K AA1 N T AH0 N AH0 N T
+INCONTINENTLY	IH0 N K AA1 N T AH0 N AH0 N T L IY0
+INCONTROLLABLE	IH2 N K AH0 N T R OW1 L AH0 B AH0 L
+INCONTROVERTIBLE  IH0 NG K AA2 N T R OW0 V ER1 T IH0 B AH0 L
+INCONTROVERTIBLY	IH0 N K AA1 N T R OW0 V ER2 T AH0 B L IY0
+INCONVENIENCE  IH2 N K AH0 N V IY1 N Y AH0 N S
+INCONVENIENCED  IH2 N K AH0 N V IY1 N Y AH0 N S T
+INCONVENIENCES  IH2 N K AH0 N V IY1 N Y AH0 N S IH0 Z
+INCONVENIENCIES	IH2 N K AH0 N V IY1 N Y AH0 N S IY0 Z
+INCONVENIENCING	IH2 N K AH0 N V IY1 N Y AH0 N S IH0 NG
+INCONVENIENCY	IH2 N K AH0 N V IY1 N Y AH0 N S IY0
+INCONVENIENT  IH2 N K AH0 N V IY1 N Y AH0 N T
+INCONVENIENTLY	IH2 N K AH0 N V IY1 N Y AH0 N T L IY0
+INCONVERTIBLE	IH2 N K AH0 N V ER1 T AH0 B AH0 L
+INCORPORATE  IH0 N K AO1 R P ER0 EY2 T
+INCORPORATED  IH0 N K AO1 R P ER0 EY2 T AH0 D
+INCORPORATED  IH0 N K AO1 R P ER0 EY2 T IH0 D
+INCORPORATES  IH0 N K AO1 R P ER0 EY2 T S
+INCORPORATING  IH0 N K AO1 R P ER0 EY2 T IH0 NG
+INCORPORATION  IH0 N K AO2 R P ER0 EY1 SH AH0 N
+INCORPORATIONS  IH0 N K AO2 R P ER0 EY1 SH AH0 N Z
+INCORPORATORS	IH0 N K AO1 R P ER0 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 IH0 NG
+INCREASINGLY  IH0 N K R IY1 S IH0 NG G L IY0
+INCREASINGLY  IH0 N K R IY1 S IH0 NG L IY0
+INCREATE	IH1 N K R IY0 EY1 T
+INCREDIBILITIES	IH0 N K R EH1 D AH0 B IH1 L AH0 T IY0 Z
+INCREDIBILITY	IH0 N K R EH1 D AH0 B IH1 L AH0 T IY0
+INCREDIBLE  IH0 N K R EH1 D AH0 B AH0 L
+INCREDIBLY  IH0 N K R EH1 D AH0 B L IY0
+INCREDULITIES	IH2 N K R AH0 D UW1 L IH0 T IY0 Z
+INCREDULITY  IH2 N K R AH0 D UW1 L IH0 T IY0
+INCREDULOUS  IH0 N K R EH1 JH AH0 L AH0 S
+INCREDULOUSLY	IH0 N K R EH1 JH AH0 L AH0 S L IY0
+INCREDULOUSNESS	IH0 N K R EH1 JH AH0 L AH0 S N AH0 S
+INCREMENT  IH1 N K R AH0 M AH0 N T
+INCREMENTS  IH1 NG K R AH0 M AH0 N T S
+INCRIMINATE  IH0 N K R IH1 M AH0 N EY2 T
+INCRIMINATED	IH0 N K R IH1 M AH0 N EY2 T AH0 D
+INCRIMINATES	IH0 N K R IH1 M AH0 N EY2 T S
+INCRIMINATING  IH0 N K R IH1 M AH0 N EY2 T IH0 NG
+INCRIMINATION  IH0 N K R IH2 M AH0 N EY1 SH AH0 N
+INCRIMINATORY	IH0 N K R IH1 M AH0 N AH0 T AO2 R IY0
+INCROYABLE	IH0 N K R OY1 AH0 B AH0 L
+INCROYABLES	IH0 N K R OY1 AH0 B AH0 L Z
+INCRUSTATION  IH2 N K R AH0 S T EY1 SH AH0 N
+INCRUSTATIONS	IH2 N K R AH0 S T EY1 SH AH0 N Z
+INCRUSTED	IH0 N K R AH1 S T IH0 D
+INCUBACY	IH1 N K Y AH0 B AH0 S IY0
+INCUBATE  IH1 N K Y AH0 B EY2 T
+INCUBATED	IH1 N K Y AH0 B EY2 T IH0 D
+INCUBATING  IH1 N K Y AH0 B EY2 T IH0 NG
+INCUBATION  IH2 NG K Y UW0 B EY1 SH AH0 N
+INCUBATOR  IH1 NG K Y AH0 B EY2 T ER0
+INCUBATORS  IH1 NG K Y UW0 B EY2 T ER0 Z
+INCUBI	IH1 N K Y AH0 B AY2
+INCUBU	IH1 N K Y AH0 B UW0
+INCUBUS	IH1 N K Y AH0 B IH0 S
+INCULCATE  IH1 NG K AH0 L K EY2 T
+INCULCATED  IH1 NG K AH0 L K EY2 T AH0 D
+INCULCATES  IH1 NG K AH0 L K EY2 T S
+INCULCATING	IH1 NG K AH0 L K EY2 T IH0 NG
+INCULCATION	IH1 NG K AH0 L K EY2 SH AH0 N
+INCULCATIONS	IH1 NG K AH0 L K EY2 SH AH0 N Z
+INCULPATE	IH0 N K AH1 L P EY0 T
+INCULPATED	IH0 N K AH1 L P EY0 T AH0 D
+INCULPATING	IH0 N K AH1 L P EY0 T IH0 NG
+INCULPATION	IH0 N K AH1 L P EY0 SH AH0 N
+INCUMBENCY  IH0 N K AH1 M B AH0 N S IY0
+INCUMBENT  IH0 N K AH1 M B AH0 N T
+INCUMBENT'S  IH0 N K AH1 M B AH0 N T S
+INCUMBENTS  IH0 N K AH1 M B AH0 N T S
+INCUMBER	IH0 N K AH1 M B ER0
+INCUMBERED	IH0 N K AH1 M B ER0 D
+INCUMBRANCE	IH0 N K AH1 M B R AH0 N S
+INCUMBRANCES	IH0 N K AH1 M B R AH0 N S IH0 Z
+INCUNABULA	IH1 NG K AH0 N AA0 B UW1 L AH0
+INCUR  IH0 N K ER1
+INCURABLE  IH0 N K Y UH1 R AH0 B AH0 L
+INCURABLES	IH0 N K Y UH1 R AH0 B AH0 L Z
+INCURABLY	IH1 N K Y ER0 AH0 B L IY0
+INCURIOSITY	IH0 N K Y UH2 R IY0 AA1 S AH0 T IY0
+INCURIOUS	IH0 N K Y UH1 R IY0 AH0 S
+INCURIOUSLY	IH0 N K Y UH1 R IY0 AH0 S L IY0
+INCURR'D	IH0 N K ER1 D
+INCURRED  IH0 N K ER1 D
+INCURRING  IH0 N K ER1 IH0 NG
+INCURS  IH0 N K ER1 Z
+INCURSION  IH0 N K ER1 ZH AH0 N
+INCURSIONS  IH0 N K ER1 ZH AH0 N Z
+INCURVE	IH0 N K ER1 V
+INCURVED	IH0 N K ER1 V D
+INCURVING	IH0 N K ER1 V IH0 NG
+IND	IH0 N D
+INDABA  IH0 N D AA1 B AH0
+INDABEZIMBI	IH0 N D AH0 B IH0 Z IH1 M B IY0
+INDADE	IH0 N D EY1 D
+INDARA	IH0 N D AA1 R AH0
+INDE	IH0 N D
+INDEANS	IH0 N D IY1 AH0 N Z
+INDEAVOUR	IH0 N D EH1 V ER0
+INDEAVOURED	IH0 N D EH1 V ER0 D
+INDEBTED  IH0 N D EH1 T AH0 D
+INDEBTED  IH0 N D EH1 T IH0 D
+INDEBTEDNESS  IH0 N D EH1 T IH0 D N IH0 S
+INDECENCIES	IH0 N D IY1 S AH0 N S IY0 Z
+INDECENCY  IH0 N D IY1 S AH0 N S IY0
+INDECENT  IH0 N D IY1 S AH0 N T
+INDECENTLY	IH0 N D IY1 S AH0 N T L IY0
+INDECIPHERABLE	IH2 N D IH0 S AY1 F ER0 AH0 B AH0 L
+INDECISION  IH0 N D IH0 S IH1 ZH AH0 N
+INDECISIONS	IH2 N D IH0 S IH1 ZH AH0 N Z
+INDECISIVE  IH2 N D IH0 S AY1 S IH0 V
+INDECISIVELY	IH2 N D IH0 S AY1 S IH0 V L IY0
+INDECISIVENESS  IH0 N D EH1 S IH0 S IH0 V N AH0 S
+INDECOROUS	IH0 N D EH1 K ER0 AH0 S
+INDECOROUSLY	IH0 N D EH1 K ER0 AH0 S L IY0
+INDECORUM	IH2 N D IH0 K AO1 R AH0 M
+INDECORUMS	IH2 N D IH0 K AO1 R AH0 M Z
+INDEED  IH0 N D IY1 D
+INDEEDE	IH0 N D IY1 D
+INDEEDY	IH0 N D IY1 D IY0
+INDEET	IH0 N D IY1 T
+INDEFATIGABILITY	IH2 N D IH0 F AE1 T IH0 G AH0 B IH1 L IH0 T IY0
+INDEFATIGABLE  IH2 N D IH0 F AE1 T IH0 G AH0 B AH0 L
+INDEFATIGABLY	IH2 N D IH0 F AE1 T IH0 G AH0 B L IY0
+INDEFEASIBLE	IH2 N D IH0 F IY1 Z AH0 B AH0 L
+INDEFEASIBLY	IH2 N D IH0 F IY1 Z AH0 B L IY0
+INDEFECTIBLE	IH2 N D IH0 F EH1 K T IH0 B AH0 L
+INDEFENSIBLE  IH0 N D IH0 F EH1 N S AH0 B AH0 L
+INDEFENSIBLY	IH2 N D IH0 F EH1 N S AH0 B L IY0
+INDEFER	IH2 N D IH0 F ER0
+INDEFINABLE  IH0 N D IH0 F AY1 N AH0 B AH0 L
+INDEFINABLY	IH2 N D IH0 F AY1 N AH0 B L IY0
+INDEFINITE  IH0 N D EH1 F AH0 N AH0 T
+INDEFINITELY  IH0 N D EH1 F AH0 N AH0 T L IY0
+INDEFINITENESS	IH0 N D EH1 F AH0 N AH0 T N AH0 S
+INDEFINITUM	IH0 N D EH1 F AH0 N AH0 T AH0 M
+INDELIBLE  IH0 N D EH1 L IH0 B AH0 L
+INDELIBLY  IH0 N D EH1 L AH0 B L IY0
+INDELICACIES	IH0 N D EH1 L IH0 K AH0 S IY0 Z
+INDELICACY	IH0 N D EH1 L IH0 K AH0 S IY0
+INDELICATE  IH0 N D EH1 L IH0 K AH0 T
+INDELICATELY	IH0 N D EH1 L AH0 K AH0 T L IY0
+INDEMNIFICATION  IH0 N D EH2 M N AH0 F IH0 K EY1 SH AH0 N
+INDEMNIFIED  IH0 N D EH1 M N AH0 F AY2 D
+INDEMNIFIES	IH0 N D EH1 M N AH0 F AY2 Z
+INDEMNIFY  IH0 N D EH1 M N AH0 F AY2
+INDEMNIFYING  IH0 N D EH2 M N IH0 F AY1 IH0 NG
+INDEMNITIES  IH0 N D EH1 M N IH0 T IY0 Z
+INDEMNITY  IH0 N D EH1 M N AH0 T IY0
+INDEMNITY  IH0 N D EH1 M N IH0 T IY0
+INDENT  IH0 N D EH1 N T
+INDENTATION  IH0 N D EH2 N T EY1 SH AH0 N
+INDENTATIONS	IH0 N D EH2 N T EY1 SH AH0 N Z
+INDENTED	IH0 N D EH2 N T IH0 D
+INDENTING	IH0 N D EH1 N T IH0 NG
+INDENTS	IH2 N D AH0 N T S
+INDENTURE  IH0 N D EH1 N CH ER0
+INDENTURED  IH0 N D EH1 N CH ER0 D
+INDENTURES  IH0 N D EH1 N CH ER0 Z
+INDEPENDANCE	IH2 N D IH0 P EH1 N D AH0 N S
+INDEPENDANT	IH2 N D IH0 P EH1 N D AH0 N T
+INDEPENDENCE  IH2 N D IH0 P EH1 N D AH0 N S
+INDEPENDENCIA	IH2 N D IH0 P EH1 N D AH0 N S IY0 AH0
+INDEPENDENCY	IH2 N D IH0 P EH1 N D AH0 N S IY0
+INDEPENDENT  IH2 N D IH0 P EH1 N D AH0 N T
+INDEPENDENTLY  IH2 N D IH0 P EH1 N D AH0 N T L IY0
+INDEPENDENTS  IH2 N D IH0 P EH1 N D AH0 N T S
+INDES	IH1 N D EH0 S
+INDESCRIBABLE  IH2 N D IH0 S K R AY1 B AH0 B AH0 L
+INDESCRIBABLES	IH2 N D IH0 S K R AY1 B AH0 B AH0 L Z
+INDESCRIBABLY	IH2 N D IH0 S K R AY1 B AH0 B L IY0
+INDESTRUCTIBILITY  IH2 N D AH0 S T R AH2 K T IH0 B IH1 L IH0 T IY0
+INDESTRUCTIBLE  IH2 N D AH0 S T R AH1 K T IH0 B AH0 L
+INDESTRUCTIBLY	IH2 N D IH0 S T R AH1 K T IH0 B L IY0
+INDETERMINABLE	IH2 N D IH0 T ER1 M AH0 N AH0 B AH0 L
+INDETERMINATE  IH2 N D IH0 T ER1 M IH0 N IH0 T
+INDETERMINATELY	IH2 N D IH0 T ER1 M AH0 N AH0 T L IY0
+INDETERMINATENESS	IH2 N D IH0 T ER1 M AH0 N AH0 T N AH0 S
+INDETERMINATION	IH2 N D IH0 T ER2 M AH0 N EY1 SH AH0 N
+INDEX  IH1 N D EH0 K S
+INDEXED  IH1 N D EH0 K S T
+INDEXES  IH1 N D EH0 K S IH0 Z
+INDEXING  IH1 N D EH0 K S IH0 NG
+INDEXOGRAPH	IH1 N D EH0 K S AH0 G R AE2 F
+INDHLOVU	IH0 N D L OW1 V UW1
+INDI	IH1 N D IY0
+INDIA  IH1 N D IY0 AH0
+INDIA'S  IH1 N D IY0 AH0 Z
+INDIAMAN	IH0 N D AY1 AH0 M AH0 N
+INDIAMAN'S	IH0 N D AY1 AH0 M AH0 N Z
+INDIAMEN	IH1 N D IY0 EY1 M AH0 N
+INDIAN  IH1 N D IY0 AH0 N
+INDIAN'S  IH1 N D IY0 AH0 N Z
+INDIANA  IH2 N D IY0 AE1 N AH0
+INDIANA'S  IH2 N D IY0 AE1 N AH0 Z
+INDIANAPOLIS  IH2 N D IY0 AH0 N AE1 P AH0 L IH0 S
+INDIANIAN  IH2 N D IY0 AE1 N IY0 AH0 N
+INDIANIANS  IH2 N D IY0 AE1 N IY0 AH0 N Z
+INDIANIZED	IH1 N D IY0 AH0 N AY2 Z D
+INDIANNA	IH2 N D IY0 AE1 N AH0
+INDIANOLA  IH1 N D IY0 AH0 N OW1 L AH0
+INDIANS  IH1 N D IY0 AH0 N Z
+INDIANTOWN	IH1 N D IY0 AH0 N T AW2 N
+INDIANY	IH1 N D IY0 AH0 N IY0
+INDIARUBBER	IH0 N D IY0 ER0 AH1 B ER0
+INDIAS	IH1 N D IY0 AH0 Z
+INDICA  IH1 D AH0 K AH0
+INDICATE  IH1 N D AH0 K EY2 T
+INDICATED  IH1 N D AH0 K EY2 T AH0 D
+INDICATED  IH1 N D AH0 K EY2 T IH0 D
+INDICATES  IH1 N D IH0 K EY2 T S
+INDICATIN	IH0 N D IH1 K AH0 T IH0 N
+INDICATING  IH1 N D AH0 K EY2 T IH0 NG
+INDICATION  IH2 N D AH0 K EY1 SH AH0 N
+INDICATIONS  IH2 N D AH0 K EY1 SH AH0 N Z
+INDICATIVE  IH0 N D IH1 K AH0 T IH0 V
+INDICATIVELY	IH0 N D IH1 K AH0 T IH0 V L IY0
+INDICATOR  IH1 N D AH0 K EY2 T ER0
+INDICATORS  IH1 N D AH0 K EY2 T ER0 Z
+INDICATORY	IH0 N D IH1 K AH0 T AO2 R IY0
+INDICES  IH1 N D AH0 S IH0 Z
+INDICES  IH1 N D IH0 S IY2 Z
+INDICIA  IH0 N D IH1 S Y AH0
+INDICIUM	IH0 N D IH1 S Y AH0 M
+INDICO	IH1 N D AH0 K OW0
+INDICT  IH0 N D AY1 T
+INDICTABLE	IH0 N D IH1 K T AH0 B AH0 L
+INDICTED  IH0 N D AY1 T AH0 D
+INDICTED  IH0 N D AY1 T IH0 D
+INDICTING  IH0 N D AY1 T IH0 NG
+INDICTMENT  IH0 N D AY1 T M AH0 N T
+INDICTMENTS  IH0 N D AY1 T M AH0 N T S
+INDICUS	IH1 N D IH0 K AH0 S
+INDIENNE	IH1 N D IY0 EH2 N
+INDIES  IH1 N D IY0 Z
+INDIFFERENCE  IH0 N D IH1 F ER0 AH0 N S
+INDIFFERENCE  IH0 N D IH1 F R AH0 N S
+INDIFFERENCES	IH0 N D IH1 F R AH0 N S IH0 Z
+INDIFFERENCY	IH0 N D IH1 F R AH0 N S IY0
+INDIFFERENT  IH0 N D IH1 F ER0 AH0 N T
+INDIFFERENT  IH0 N D IH1 F R AH0 N T
+INDIFFERENTISM	IH0 N D IH1 F R AH0 N T IH0 Z AH0 M
+INDIFFERENTIST	IH0 N D IH1 F R AH0 N T IH0 S T
+INDIFFERENTLY	IH0 N D IH1 F R AH0 N T L IY0
+INDIFFERENTS	IH0 N D IH1 F R AH0 N T S
+INDIGENCE	IH0 N D IH1 JH AH0 N S
+INDIGENE	IH1 N D IH0 JH IY2 N
+INDIGENES	IH0 N D AY0 JH IY1 N Z
+INDIGENOUS  IH0 N D IH1 JH AH0 N AH0 S
+INDIGENT  IH1 N D IH0 JH AH0 N T
+INDIGESTED	IH0 N D AY0 JH EH1 S T AH0 D
+INDIGESTIBLE	IH0 N D AY0 JH EH1 S T AH0 B AH0 L
+INDIGESTION  IH2 N D AY0 JH EH1 S CH AH0 N
+INDIGESTIONS	IH0 N D AY0 JH EH1 S CH AH0 N Z
+INDIGNANT  IH0 N D IH1 G N AH0 N T
+INDIGNANTLY  IH0 N D IH1 G N AH0 N T L IY0
+INDIGNATIO	IH1 N D IH0 G N EY1 SH IY0 OW0
+INDIGNATION  IH2 N D IH0 G N EY1 SH AH0 N
+INDIGNATIONS	IH1 N D IH0 G N EY1 SH AH0 N Z
+INDIGNE	IH0 N D IH1 G N IY0
+INDIGNITIES  IH0 N D IH1 G N AH0 T IY0 Z
+INDIGNITY  IH0 N D IH1 G N AH0 T IY0
+INDIGO  IH1 N D AH0 G OW2
+INDIGO  IH1 N D IH0 G OW2
+INDIGOES	IH1 N D AH0 G OW2 Z
+INDIO	IH1 N D IY0 OW0
+INDIOS	IH1 N D IY0 OW1 S
+INDIRECT  IH0 N D ER0 EH1 K T
+INDIRECTION	IH0 N D ER0 EH1 K SH AH0 N
+INDIRECTIONS	IH0 N D ER0 EH1 K SH AH0 N Z
+INDIRECTLY  IH0 N D ER0 EH1 K L IY0
+INDIRECTLY  IH0 N D ER0 EH1 K T L IY0
+INDIRECTNESS	IH0 N D ER0 EH1 K N AH0 S
+INDISCERNIBLE	IH1 N D IH0 S ER1 N AH0 B AH0 L
+INDISCIPLINE	IH0 N D IH1 S AH0 P L AH0 N
+INDISCOVERABLE	IH2 N D IH0 S K AH1 V ER0 AH0 B AH0 L
+INDISCREET  IH0 N D IH0 S K R IY1 T
+INDISCREETLY	IH2 N D IH0 S K R IY1 T L IY0
+INDISCRETION  IH2 N D IH0 S K R EH1 SH AH0 N
+INDISCRETIONS  IH2 N D IH0 S K R EH1 SH AH0 N Z
+INDISCRIMINATE  IH0 N D IH0 S K R IH1 M AH0 N AH0 T
+INDISCRIMINATELY  IH2 N D IH0 S K R IH1 M AH0 N AH0 T L IY0
+INDISCRIMINATING	IH2 N D IH0 S K R IH1 M AH0 N EY2 T IH0 NG
+INDISCRIMINATION	IH2 N D IH0 S K R IH2 M AH0 N EY1 SH AH0 N
+INDISPENSABILITY	IH2 N D IH0 S P EH1 N S AH0 B IH1 L IH0 T IY0
+INDISPENSABLE  IH2 N D IH0 S P EH1 N S AH0 B AH0 L
+INDISPENSABLENESS	IH2 N D IH0 S P EH1 N S AH0 B AH0 L N AH0 S
+INDISPENSABLES	IH2 N D IH0 S P EH1 N S AH0 B AH0 L Z
+INDISPENSABLY	IH2 N D IH0 S P EH1 N S AH0 B L IY0
+INDISPENSIBLE  IH2 N D IH0 S P EH1 N S IH0 B AH0 L
+INDISPENSIBLY	IH2 N D IH0 S P EH1 N S AH0 B L IY0
+INDISPOSE	IH2 N D IH0 S P OW1 Z
+INDISPOSED	IH2 N D IH0 S P OW1 Z D
+INDISPOSES	IH2 N D IH0 S P OW1 Z IH0 Z
+INDISPOSITION	IH2 N D IH0 S P AH0 Z IH1 SH AH0 N
+INDISPOSITIONS	IH2 N D IH0 S P AH0 Z IH1 SH AH0 N Z
+INDISPUTABLE  IH2 N D IH0 S P Y UW1 T AH0 B AH0 L
+INDISPUTABLY  IH0 N D IH1 S P Y UW0 T AE2 B L IY0
+INDISSOLUBILITY	IH1 N D IH0 Z AA1 L Y AH0 B IH1 L IH0 T IY0
+INDISSOLUBLE	IH1 N D IH0 Z AA1 L Y AH0 B AH0 L
+INDISSOLUBLY	IH1 N D IH0 Z AA1 L Y AH0 B AH0 L IY0
+INDISTINCT  IH0 N D IH0 S T IH1 NG K T
+INDISTINCTION	IH2 N D IH0 S T IH1 NG K SH AH0 N
+INDISTINCTIVE	IH2 N D IH0 S T IH1 NG K T IH0 V
+INDISTINCTLY	IH2 N D IH0 S T IH1 NG K T L IY0
+INDISTINCTNESS	IH2 N D IH0 S T IH1 NG K T N AH0 S
+INDISTINGUISHABLE  IH0 N D IH0 S T IH1 NG G W IH0 SH AH0 B AH0 L
+INDISTINGUISHABLY	IH2 N D IH0 S T IH1 NG G W IH0 SH AH0 B L IY0
+INDITE  IH0 N D AY1 T
+INDITED  IH0 N D AY1 T IH0 D
+INDITES	IH0 N D AY1 T S
+INDITING	IH0 N D AY1 T IH0 NG
+INDIVIDUAL  IH2 N D AH0 V IH1 JH AH0 W AH0 L
+INDIVIDUAL'S  IH2 N D AH0 V IH1 JH AH0 W AH0 L Z
+INDIVIDUALISATION	IH2 N D AH0 V IH1 JH AH0 W AH0 L IH0 Z EY1 SH AH0 N
+INDIVIDUALISE	IH2 N D IH0 V IH1 JH AH0 L AY0 Z
+INDIVIDUALISED	IH2 N D IH0 V IH1 JH AH0 L AY0 Z D
+INDIVIDUALISING	IH2 N D IH0 V IH1 JH AH0 L AY0 Z IH0 NG
+INDIVIDUALISM  IH2 N D IH0 V IH0 D UW1 AH0 L IH2 Z AH0 M
+INDIVIDUALIST  IH2 N D IH0 V IH0 D UW1 AH0 L IH0 S T
+INDIVIDUALISTIC  IH2 N D IH0 V IH2 JH UW0 AH0 L IH1 S T IH0 K
+INDIVIDUALISTS  IH2 N D AH0 V IH1 JH UW0 AH0 L IH0 S T S
+INDIVIDUALITIES	IH0 N D IH2 V IH0 JH UW0 AE1 L IH0 T IY0 Z
+INDIVIDUALITY  IH0 N D IH2 V IH0 JH UW0 AE1 L IH0 T IY0
+INDIVIDUALIZATION	IH2 N D IH0 V IH1 JH UW0 AH0 L IH0 Z EY1 SH AH0 N
+INDIVIDUALIZE  IH2 N D IH0 V IH1 JH AH0 L AY0 Z
+INDIVIDUALIZE  IH2 N D IH0 V IH1 JH UW0 AH0 L AY0 Z
+INDIVIDUALIZED  IH2 N D IH0 V IH1 JH AH0 L AY0 Z D
+INDIVIDUALIZED  IH2 N D IH0 V IH1 JH UW0 AH0 L AY0 Z D
+INDIVIDUALIZES	IH2 N D IH0 V IH1 JH AH0 L AY0 Z IH0 Z
+INDIVIDUALIZING	IH2 N D IH0 V IH1 JH AH0 L AY0 Z IH0 NG
+INDIVIDUALLY  IH2 N D IH0 V IH1 JH AH0 L IY0
+INDIVIDUALLY  IH2 N D IH0 V IH1 JH UW0 AH0 L IY0
+INDIVIDUALS  IH2 N D AH0 V IH1 JH AH0 W AH0 L Z
+INDIVIDUATION	IH2 N D AH0 V IH1 JH AH0 W EY1 SH AH0 N
+INDIVIDUUM	IH2 N D IH0 V IH1 JH UW0 AH0 M
+INDIVISIBILITY	IH2 N D IH0 V IH1 S IH0 B IH1 L IH0 T IY0
+INDIVISIBLE  IH2 N D IH0 V IH1 S IH0 B AH0 L
+INDIVISIBLY	IH2 N D IH0 V IH1 Z AH0 B L IY0
+INDO  IH1 N D OW0
+INDOCILE	IH0 N D AA1 S AH0 L
+INDOCILITY	IH0 N D AA0 S IH1 L AH0 T IY0
+INDOCTRINATE	IH0 N D AA2 K T R AH0 N EY2 T
+INDOCTRINATED  IH0 N D AA1 K T R AH0 N EY2 T IH0 D
+INDOCTRINATING	IH0 N D AA2 K T R AH0 N EY2 T IH0 NG
+INDOCTRINATION  IH0 N D AA2 K T R AH0 N EY1 SH AH0 N
+INDOLENCE	IH1 N D AH0 L AH0 N S
+INDOLENT  IH1 N D AH0 L AH0 N T
+INDOLENTLY	IH1 N D AH0 L AH0 N T L IY0
+INDOMITABILITY	IH0 N D AA1 M AH0 T AH0 B IH1 L IH0 T IY0
+INDOMITABLE  IH0 N D AA1 M AH0 T AH0 B AH0 L
+INDOMITABLENESS	IH0 N D AA1 M AH0 T AH0 B AH0 L N AH0 S
+INDOMITABLY	IH0 N D AA1 M AH0 T AH0 B L IY0
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+INTERDEPENDENCE  IH2 N T ER0 D AH0 P EH1 N D AH0 N S
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+INTERDICT  IH1 N T ER0 D IH2 K T
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+INTEREA	IH2 N ER0 IY1 AH0
+INTERESSANT	IH2 N T ER0 EH1 S AH0 N T
+INTEREST  IH1 N T ER0 AH0 S T
+INTEREST  IH1 N T ER0 IH0 S T
+INTEREST  IH1 N T R AH0 S T
+INTEREST  IH1 N T R IH0 S T
+INTEREST'S	IH1 N T R AH0 S T S
+INTERESTED  IH1 N T ER0 AH0 S T AH0 D
+INTERESTED  IH1 N T ER0 IH0 S T IH0 D
+INTERESTED  IH1 N T R AH0 S T AH0 D
+INTERESTED  IH1 N T R IH0 S T IH0 D
+INTERESTEDLY	IH1 N T R IH0 S T IH0 D L IY0
+INTERESTEDNESS	IH1 N T R IH0 S T IH0 D N AH0 S
+INTERESTIN	IH1 N T R IH0 S T IH0 N
+INTERESTING  IH1 N T ER0 AH0 S T IH0 NG
+INTERESTING  IH1 N T ER0 IH0 S T IH0 NG
+INTERESTING  IH1 N T R AH0 S T IH0 NG
+INTERESTING  IH1 N T R IH0 S T IH0 NG
+INTERESTINGLY  IH1 N T ER0 EH2 S T IH0 NG L IY0
+INTERESTINGNESS	IH1 N T R AH0 S T IH0 NG N IH0 S
+INTERESTS  IH1 N T ER0 AH0 S T S
+INTERESTS  IH1 N T ER0 IH0 S T S
+INTERESTS  IH1 N T R AH0 S T S
+INTERESTS  IH1 N T R IH0 S T S
+INTERET	IH2 N T ER0 IH0 T
+INTERETS	IH2 N T ER0 IH0 T S
+INTERFERE  IH2 N ER0 F IH1 R
+INTERFERE  IH2 N T ER0 F IH1 R
+INTERFERED  IH2 N ER0 F IH1 R D
+INTERFERED  IH2 N T ER0 F IH1 R D
+INTERFERENCE  IH2 N ER0 F IH1 R AH0 N S
+INTERFERENCE  IH2 N T ER0 F IH1 R AH0 N S
+INTERFERENCES	IH2 N T ER0 F IH1 R AH0 N S IH0 Z
+INTERFERER	IH2 N T ER0 F IH1 R ER0
+INTERFERERS	IH2 N T ER0 F IH1 R ER0 Z
+INTERFERES  IH2 N ER0 F IH1 R Z
+INTERFERES  IH2 N T ER0 F IH1 R Z
+INTERFERIN	IH2 N T ER0 F IH1 R IH0 N
+INTERFERING  IH2 N ER0 F IH1 R IH0 NG
+INTERFERING  IH2 N T ER0 F IH1 R IH0 NG
+INTERFUSE	IH2 N T ER0 F Y UW1 Z
+INTERFUSED	IH2 N T ER0 F Y UW1 Z D
+INTERFUSION	IH2 N T ER0 F Y UW1 ZH AH0 N
+INTERGALACTIC	IH2 N T ER0 G AH0 L AE1 K T IH0 K
+INTERGLACIAL	IH2 N T ER0 G L EY1 SH AH0 L
+INTERIEUR	IH0 N T IH1 R IY0 ER0
+INTERIEURE	IH0 N T EH1 R IY0 UW2 R
+INTERIM  IH1 N ER0 AH0 M
+INTERIM  IH1 N T ER0 AH0 M
+INTERIM  IH1 N T R AH0 M
+INTERIMS	IH1 N T R AH0 M Z
+INTERIOR  IH0 N T IH1 R IY0 ER0
+INTERIORLY	IH0 N T IH1 R IY0 ER0 L IY0
+INTERIORS  IH0 N T IH1 R IY0 ER0 Z
+INTERJACENT	IH2 N T ER0 JH EY1 S AH0 N T
+INTERJECT  IH2 N T ER0 JH EH1 K T
+INTERJECTED  IH2 N T ER0 JH EH1 K T IH0 D
+INTERJECTING	IH2 N T ER0 JH EH1 K T IH0 NG
+INTERJECTION  IH2 N T ER0 JH EH1 K SH AH0 N
+INTERJECTIONAL	IH2 N T ER0 JH EH1 K SH AH0 N AH0 L
+INTERJECTIONS  IH2 N T ER0 JH EH1 K SH AH0 N Z
+INTERJECTORY	IH2 N T ER0 JH EH1 K T ER0 IY0
+INTERJECTS  IH2 N T ER0 JH EH1 K T S
+INTERLACE  IH2 N T ER0 L EY1 S
+INTERLACED  IH2 N T ER0 L EY1 S T
+INTERLACEMENT	IH2 N T ER0 L EY1 S M AH0 N T
+INTERLACHEN	IH2 N T ER0 L AE1 K AH0 N
+INTERLACING	IH2 N T ER0 L EY1 S IH0 NG
+INTERLACINGS	IH2 N T ER0 L EY1 S IH0 NG Z
+INTERLAKEN	IH2 N T ER0 L EY1 K AH0 N
+INTERLARD	IH2 N T ER0 L ER0 D
+INTERLARDED	IH2 N T ER0 L AA1 R D IH0 D
+INTERLARDING	IH2 N T ER0 L AA1 R D IH0 NG
+INTERLARDS	IH2 N T ER0 L ER0 D Z
+INTERLEAVED  IH2 N T ER0 L IY1 V D
+INTERLINEAR	IH2 N T ER0 L IH1 N IY0 ER0
+INTERLINEATION	IH2 N T ER0 L IH2 N IY0 EY1 SH AH0 N
+INTERLINEATIONS	IH2 N T ER0 L IH2 N IY0 EY1 SH AH0 N Z
+INTERLINED	IH2 N T ER0 L AY2 N D
+INTERLINING	IH2 N T ER0 L AY2 N IH0 NG
+INTERLINKED  IH2 N T ER0 L IH1 NG K T
+INTERLINKING	IH2 N T ER0 L IH1 NG K IH0 NG
+INTERLOCK  IH2 N T ER0 L AA1 K
+INTERLOCKED  IH2 N T ER0 L AA1 K D
+INTERLOCKING  IH1 N T ER0 L AA2 K IH0 NG
+INTERLOCUTOR  IH2 N T ER0 L AA1 K Y AH0 T ER0
+INTERLOCUTOR'S	IH2 N T ER0 L AA1 K Y AH0 T ER0 Z
+INTERLOCUTORS  IH2 N T ER0 L AA1 K Y AH0 T ER0 Z
+INTERLOCUTORY	IH2 N T ER0 L AA1 K Y AH0 T ER0 IY0
+INTERLOCUTRESS	IH2 N T ER0 L AA1 K Y AH0 T R IH0 S
+INTERLOPER  IH1 N T ER0 L OW2 P ER0
+INTERLOPER'S	IH2 N T ER0 L OW2 P ER0 Z
+INTERLOPERS  IH1 N T ER0 L OW2 P ER0 Z
+INTERLOPING	IH2 N T ER0 L AH0 P IH0 NG
+INTERLUDE  IH1 N T ER0 L UW2 D
+INTERLUDES  IH1 N T ER0 L UW2 D Z
+INTERMARRIAGE  IH2 N T ER0 M EH1 R IH0 JH
+INTERMARRIAGES	IH2 N T ER0 M EH1 R IH0 JH IH0 Z
+INTERMARRIED  IH2 N T ER0 M AE1 R IY0 D
+INTERMARRY  IH2 N T ER0 M AE1 R IY0
+INTERMARRYING	IH2 N T ER0 M EH1 R IY0 IH0 NG
+INTERMEDDLE	IH2 N T ER0 M EH1 D AH0 L
+INTERMEDDLED	IH2 N T ER0 M EH1 D AH0 L D
+INTERMEDDLER	IH2 N T ER0 M EH1 D L ER0
+INTERMEDDLERS	IH2 N T ER0 M EH1 D AH0 L ER0 Z
+INTERMEDDLETH	IH2 N ER0 M IY1 D AH0 L EH2 TH
+INTERMEDDLING	IH2 N T ER0 M EH1 D AH0 L IH0 NG
+INTERMEDIARIES  IH2 N ER0 M IY1 D IY0 EH2 R IY0 Z
+INTERMEDIARIES  IH2 N T ER0 M IY1 D IY0 EH2 R IY0 Z
+INTERMEDIARY  IH2 N ER0 M IY1 D IY0 EH0 R IY0
+INTERMEDIARY  IH2 N T ER0 M IY1 D IY0 EH0 R IY0
+INTERMEDIATE  IH2 N ER0 M IY1 D IY0 IH0 T
+INTERMEDIATE  IH2 N T ER0 M IY1 D IY0 IH0 T
+INTERMEDIATELY	IH2 N ER0 M IY1 D IY0 AH0 T L IY0
+INTERMEDIATES  IH2 N ER0 M IY1 D IY0 AH0 T S
+INTERMEDIATES  IH2 N ER0 M IY1 D IY0 EY0 T S
+INTERMEDIATES  IH2 N T ER0 M IY1 D IY0 AH0 T S
+INTERMEDIATES  IH2 N T ER0 M IY1 D IY0 EY0 T S
+INTERMEDIATION	IH2 N ER0 M IY1 D IY0 EY1 SH AH0 N
+INTERMENT	IH2 N T ER0 M AH0 N T
+INTERMENTS	IH2 N T ER0 M AH0 N T S
+INTERMEZZO	IH2 N T ER0 M EH2 Z OW0
+INTERMINABLE  IH0 N T ER1 M AH0 N AH0 B AH0 L
+INTERMINABLY  IH1 N T ER0 M IH0 N AE2 B L IY0
+INTERMINABLY  IH2 N T ER1 M IH0 N AH0 B L IY0
+INTERMINGLE  IH2 N T ER0 M IH1 NG G AH0 L
+INTERMINGLED  IH2 N T ER0 M IH1 NG G AH0 L D
+INTERMINGLES	IH2 N T ER0 M IH1 NG G AH0 L Z
+INTERMINGLING  IH2 N T ER0 M IH1 NG G AH0 L IH0 NG
+INTERMINGLING  IH2 N T ER0 M IH1 NG G L IH0 NG
+INTERMISSION  IH2 N T ER0 M IH1 SH AH0 N
+INTERMISSIONS  IH2 N T ER0 M IH1 SH AH0 N Z
+INTERMIT	IH2 N T ER0 M IH1 T
+INTERMITION	IH2 N T ER0 M IH1 SH AH0 N
+INTERMITS	IH0 N T ER1 M AH0 T S
+INTERMITTED	IH2 N T ER0 M IH1 T IH0 D
+INTERMITTENCE	IH2 N T ER0 M IH1 T AH0 N S
+INTERMITTENT  IH2 N T ER0 M IH1 T AH0 N T
+INTERMITTENTLY  IH2 N T ER0 M IH1 T AH0 N T L IY0
+INTERMITTING	IH2 N T ER0 M IH1 T IH0 NG
+INTERMIX  IH2 N T ER0 M IH1 K S
+INTERMIXED  IH2 N T ER0 M IH1 K S T
+INTERMIXING  IH1 N T ER0 M IH1 K S IH0 NG
+INTERMIXT	IH2 N T ER0 M IH1 K S T
+INTERMIXTURE	IH2 N T ER0 M IH1 K S CH ER0
+INTERMIXTURES	IH2 N T ER0 M IH1 K S CH ER0 Z
+INTERMOUNTAIN  IH0 N T ER0 M AW1 N T IH0 N
+INTERN  IH1 N T ER0 N
+INTERNAL  IH0 N T ER1 N AH0 L
+INTERNALLY  IH0 N T ER1 N AH0 L IY0
+INTERNALS	IH0 N T ER1 N AH0 L Z
+INTERNATIONAL  IH2 N ER0 N AE1 SH AH0 N AH0 L
+INTERNATIONAL  IH2 N T ER0 N AE1 SH AH0 N AH0 L
+INTERNATIONALE  IH0 N ER0 N AE2 SH AH0 N AA1 L IY0
+INTERNATIONALE  IH0 N T ER0 N AE2 SH AH0 N AA1 L IY0
+INTERNATIONALISM  IH0 N ER0 N AE1 SH AH0 N AH0 L IH2 Z AH0 M
+INTERNATIONALISM  IH0 N T ER0 N AE1 SH AH0 N AH0 L IH2 Z AH0 M
+INTERNATIONALIST  IH0 N ER0 N AE1 SH AH0 N AH0 L IH0 S T
+INTERNATIONALIST  IH0 N T ER0 N AE1 SH AH0 N AH0 L IH0 S T
+INTERNATIONALISTS  IH2 N ER0 N AE1 SH AH0 N AH0 L IH0 S
+INTERNATIONALISTS  IH2 N ER0 N AE1 SH AH0 N AH0 L IH0 S T S
+INTERNATIONALISTS  IH2 N T ER0 N AE1 SH AH0 N AH0 L IH0 S
+INTERNATIONALISTS  IH2 N T ER0 N AE1 SH AH0 N AH0 L IH0 S T S
+INTERNATIONALLY  IH2 N ER0 N AE1 SH AH0 N AH0 L IY0
+INTERNATIONALLY  IH2 N ER0 N AE1 SH N AH0 L IY0
+INTERNATIONALLY  IH2 N T ER0 N AE1 SH AH0 N AH0 L IY0
+INTERNATIONALLY  IH2 N T ER0 N AE1 SH N AH0 L IY0
+INTERNATIONALS  IH2 N T ER0 N AE1 SH AH0 N AH0 L Z
+INTERNE	IH0 N T ER1 N
+INTERNECINE  IH0 N T ER1 N AH0 S IY2 N
+INTERNED  IH1 N T ER2 N D
+INTERNES	IH0 N T ER1 N Z
+INTERNET  IH1 N T ER0 N EH2 T
+INTERNING	IH0 N T ER1 N IH0 NG
+INTERNMENT  IH0 N T ER1 N M AH0 N T
+INTERNS  IH1 N T ER0 N Z
+INTEROCEANIC	IH2 N ER0 OW2 SH IY2 AE1 N IH0 K
+INTERPELLATION	IH0 N T ER1 P AH0 L EY1 SH AH0 N
+INTERPELLATIONS	IH0 N T ER1 P AH0 L EY1 SH AH0 N Z
+INTERPENETRATE	IH0 N T ER1 P EH1 N AH0 T R EY2 T
+INTERPENETRATED	IH0 N T ER1 P EH1 N AH0 T R EY2 T IH0 D
+INTERPENETRATES	IH0 N T ER1 P EH1 N AH0 T R EY2 T S
+INTERPENETRATING	IH0 N T ER1 P EH1 N AH0 T R EY2 T IH0 NG
+INTERPENETRATION	IH0 N T ER1 P EH1 N AH0 T R EY1 SH AH0 N
+INTERPETER	IH0 N T ER1 P AH0 T ER0
+INTERPETERS	IH0 N T ER1 P AH0 T ER0 Z
+INTERPHONE	IH2 N ER0 F OW1 N
+INTERPHONES	IH2 N ER0 F OW2 N Z
+INTERPLANET	IH2 N T ER0 P L AE1 N AH0 T
+INTERPLANETARIAN	IH2 N T ER0 P L AE2 N AH0 T EH1 R IY0 AH0 N
+INTERPLANETARY  IH2 N T ER0 P L AE1 N AH0 T EH2 R IY0
+INTERPLAY  IH1 N T ER0 P L EY2
+INTERPOLATE  IH2 T ER1 P AH0 L EY2 T
+INTERPOLATED  IH2 T ER1 P AH0 L EY2 T IH0 D
+INTERPOLATES	IH0 N T ER1 P AH0 L EY2 T S
+INTERPOLATING	IH0 N T ER1 P AH0 L EY2 T IH0 NG
+INTERPOLATION	IH0 N T ER1 P AH0 L EY1 SH AH0 N
+INTERPOLATIONS	IH0 N T ER1 P AH0 L EY1 SH AH0 N Z
+INTERPOS'D	IH0 N T ER1 P AH0 D
+INTERPOSE  IH2 N T ER0 P OW1 Z
+INTERPOSED	IH2 N T ER0 P OW1 Z D
+INTERPOSES	IH2 N T ER0 P OW1 Z IH0 Z
+INTERPOSING	IH2 N T ER0 P OW1 Z IH0 NG
+INTERPOSITION	IH0 N T ER1 P AH0 Z IH1 SH AH0 N
+INTERPOSITIONS	IH0 N T ER1 P AH0 Z IH1 SH AH0 N Z
+INTERPRET  IH0 N T ER1 P R AH0 T
+INTERPRETATION  IH0 N T ER2 P R IH0 T EY1 SH AH0 N
+INTERPRETATIONS  IH0 N T ER2 P R IH0 T EY1 SH AH0 N Z
+INTERPRETATIVE	IH0 N T ER1 P R AH0 T EY1 T IH0 V
+INTERPRETE	IH0 N T ER1 P R AH0 T
+INTERPRETED  IH0 N T ER1 P R AH0 T AH0 D
+INTERPRETER  IH0 N T ER1 P R AH0 T ER0
+INTERPRETER'S	IH0 N T ER1 P R AH0 T ER0 Z
+INTERPRETERS  IH0 N T ER1 P R AH0 T ER0 Z
+INTERPRETING  IH1 N T ER0 P R EH2 T IH0 NG
+INTERPRETIVE  IH0 N T ER1 P R AH0 T IH0 V
+INTERPRETS  IH0 N T ER1 P R AH0 T S
+INTERPROVINCIAL  IH2 N T ER0 P R OW0 V IH1 N SH AH0 L
+INTERR'D	IH2 N T ER0 D
+INTERRED  IH2 N T ER1 D
+INTERREGNUM  IH2 N T ER0 R EH1 G N AH0 M
+INTERRELATED  IH2 N T ER0 R IH0 L EY1 T IH0 D
+INTERRELATED  IH2 N T ER0 R IY0 L EY1 T IH0 D
+INTERRELATION	IH2 N T ER0 R IY0 L EY1 SH AH0 N
+INTERRELATIONS	IH2 N T ER0 R IY0 L EY1 SH AH0 N Z
+INTERRELATIONSHIP  IH1 N T ER0 R IY0 L EY1 SH AH0 N SH IH0 P
+INTERREX	IH2 N ER0 R EH0 K S
+INTERRING	IH0 N T ER1 IH0 NG
+INTERROGATE  IH0 N T EH1 R AH0 G EY2 T
+INTERROGATED  IH0 N T EH1 R AH0 G EY2 T IH0 D
+INTERROGATES	IH0 N T EH2 R AH0 G EY2 T S
+INTERROGATING  IH0 N T EH1 R AH0 G EY2 T IH0 NG
+INTERROGATINGLY	IH0 N T EH2 R AH0 G EY2 T IH0 NG L IY0
+INTERROGATION  IH0 N T EH2 R AH0 G EY1 SH AH0 N
+INTERROGATIONS  IH0 N T EH2 R AH0 G EY1 SH AH0 N Z
+INTERROGATIVE	IH0 N T EH0 R AA1 G AH0 T IH0 V
+INTERROGATIVELY	IH0 N T EH0 R AA1 G AH0 T IH0 V L IY0
+INTERROGATIVES	IH0 N T EH0 R AA1 G AH0 T IH0 V Z
+INTERROGATOR  IH2 N T EH1 R AH0 G EY2 T ER0
+INTERROGATOR'S	IH0 N T EH2 R AH0 G EY2 T ER0 Z
+INTERROGATORIES  IH0 N T EH0 R AA1 G AH0 T AO2 R IY0 Z
+INTERROGATORS  IH2 N T EH1 R AH0 G EY2 T ER0 Z
+INTERROGATORY  IH0 N T EH0 R AA1 G AH0 T AO2 R IY0
+INTERRUP	IH2 N ER0 AH1 P
+INTERRUPT  IH2 N ER0 AH1 P T
+INTERRUPT  IH2 N T ER0 AH1 P T
+INTERRUPTED  IH2 N ER0 AH1 P T IH0 D
+INTERRUPTED  IH2 N T ER0 AH1 P T IH0 D
+INTERRUPTEDLY	IH2 N ER0 AH1 P T IH0 D L IY0
+INTERRUPTER	IH2 N ER0 AH1 P T ER0
+INTERRUPTERS	IH2 N ER0 AH1 P T ER0 Z
+INTERRUPTIN	IH2 N ER0 AH1 P T IH0 N
+INTERRUPTING  IH2 N ER0 AH1 P T IH0 NG
+INTERRUPTING  IH2 N T ER0 AH1 P T IH0 NG
+INTERRUPTION  IH2 N ER0 AH1 P SH AH0 N
+INTERRUPTION  IH2 N T ER0 AH1 P SH AH0 N
+INTERRUPTIONS  IH2 N ER0 AH1 P SH AH0 N Z
+INTERRUPTIONS  IH2 N T ER0 AH1 P SH AH0 N Z
+INTERRUPTS  IH2 N ER0 AH1 P T S
+INTERRUPTS  IH2 N T ER0 AH1 P T S
+INTERSCHOLASTIC  IH2 N T ER0 S K AH0 L AE1 S T IH0 K
+INTERSECT  IH2 N ER0 S EH1 K T
+INTERSECT  IH2 N T ER0 S EH1 K T
+INTERSECTED  IH2 N ER0 S EH1 K T IH0 D
+INTERSECTED  IH2 N T ER0 S EH1 K T IH0 D
+INTERSECTING  IH2 N ER0 S EH1 K T IH0 NG
+INTERSECTING  IH2 N T ER0 S EH1 K T IH0 NG
+INTERSECTION  IH2 N ER0 S EH1 K SH AH0 N
+INTERSECTION  IH2 N T ER0 S EH1 K SH AH0 N
+INTERSECTIONS  IH2 N ER0 S EH1 K SH AH0 N Z
+INTERSECTIONS  IH2 N T ER0 S EH1 K SH AH0 N Z
+INTERSECTS  IH2 N ER0 S EH1 K S
+INTERSECTS  IH2 N ER0 S EH1 K T S
+INTERSECTS  IH2 N T ER0 S EH1 K S
+INTERSECTS  IH2 N T ER0 S EH1 K T S
+INTERSPACE	IH2 N T ER0 S P EY2 S
+INTERSPACED	IH2 N T ER0 S P EY2 S T
+INTERSPACES	IH2 N T ER0 S P EY2 S IH0 Z
+INTERSPERSE  IH2 N T ER0 S P ER1 S
+INTERSPERSED  IH2 N T ER0 S P ER1 S T
+INTERSPERSES  IH2 N T ER0 S P ER1 S AH0 Z
+INTERSPERSING	IH2 N T ER0 S P ER1 S IH0 NG
+INTERSTATE  IH2 N ER0 S T EY1 T
+INTERSTATE  IH2 N T ER0 S T EY1 T
+INTERSTELLAR  IH2 N T ER0 S T EH1 L ER0
+INTERSTICE	IH2 N T ER0 S T IH0 S
+INTERSTICES	IH2 N T ER0 S T IH0 S IH0 Z
+INTERSTITIA	IH2 N T ER0 S T IH1 SH AH0
+INTERSTITIAL	IH2 N T ER0 S T IH1 SH AH0 L
+INTERTANGLED	IH2 N T ER0 T AE2 NG G AH0 L D
+INTERTEXTURE	IH2 N ER0 T EH1 K S CH ER0
+INTERTRIBAL  IH2 N T ER0 T R AY1 B AH0 L
+INTERTROPICAL	IH2 N T ER0 T R AA1 P IH0 K AH0 L
+INTERTWINE  IH0 N T ER0 T W AY1 N
+INTERTWINED  IH0 N T ER0 T W AY1 N D
+INTERTWINING  IH1 N T ER0 T W AY2 N IH0 NG
+INTERTWISTED	IH2 N T ER0 T W IH1 S T IH0 D
+INTERTWISTING	IH2 N T ER0 T W IH1 S T IH0 NG
+INTERURBAN  IH2 N T ER0 ER1 B AH0 N
+INTERVAL  IH1 N ER0 V AH0 L
+INTERVAL  IH1 N T ER0 V AH0 L
+INTERVALE	IH2 N T ER0 V EY2 L
+INTERVALES	IH2 N ER0 V AH0 L Z
+INTERVALLO	IH2 N T ER0 V AA1 L OW0
+INTERVALS  IH1 N ER0 V AH0 L Z
+INTERVALS  IH1 N T ER0 V AH0 L Z
+INTERVENE  IH2 N ER0 V IY1 N
+INTERVENE  IH2 N T ER0 V IY1 N
+INTERVENED  IH2 N ER0 V IY1 N D
+INTERVENED  IH2 N T ER0 V IY1 N D
+INTERVENER	IH2 N ER0 V IY1 N ER0
+INTERVENES  IH2 N ER0 V IY1 N Z
+INTERVENES  IH2 N T ER0 V IY1 N Z
+INTERVENING  IH2 N ER0 V IY1 N IH0 NG
+INTERVENING  IH2 N T ER0 V IY1 N IH0 NG
+INTERVENTION  IH2 N ER0 V EH1 N CH AH0 N
+INTERVENTION  IH2 N T ER0 V EH1 N CH AH0 N
+INTERVENTIONS  IH2 N ER0 V EH1 N CH AH0 N Z
+INTERVENTIONS  IH2 N T ER0 V EH1 N CH AH0 N Z
+INTERVIEW  IH1 N ER0 V Y UW2
+INTERVIEW  IH1 N T ER0 V Y UW2
+INTERVIEWED  IH1 N ER0 V Y UW2 D
+INTERVIEWED  IH1 N T ER0 V Y UW2 D
+INTERVIEWER  IH1 N ER0 V Y UW2 ER0
+INTERVIEWER  IH1 N T ER0 V Y UW2 ER0
+INTERVIEWER'S	IH2 N ER0 V Y UW2 ER0 Z
+INTERVIEWERS  IH1 N ER0 V Y UW2 ER0 Z
+INTERVIEWERS  IH1 N T ER0 V Y UW2 ER0 Z
+INTERVIEWING  IH1 N ER0 V Y UW2 IH0 NG
+INTERVIEWING  IH1 N T ER0 V Y UW2 IH0 NG
+INTERVIEWS  IH1 N ER0 V Y UW2 Z
+INTERVIEWS  IH1 N T ER0 V Y UW2 Z
+INTERVOLVED	IH2 N T ER0 V AA1 L V D
+INTERWEAVE  IH2 N T ER0 W IY1 V
+INTERWEAVES	IH2 N T ER0 W IY1 V Z
+INTERWEAVING  IH2 N T ER0 W IY1 V IH0 NG
+INTERWORLD	IH2 N T ER0 W ER2 L D
+INTERWOVE	IH2 N T ER0 W OW1 V
+INTERWOVEN  IH2 N T ER0 W OW1 V AH0 N
+INTESTACY	IH0 N T EH1 S T AH0 S IY0
+INTESTATE	IH0 N T EH1 S T EY2 T
+INTESTINAL  IH0 N T EH1 S T AH0 N AH0 L
+INTESTINE  IH0 N T EH1 S T AH0 N
+INTESTINES  IH0 N T EH1 S T AH0 N Z
+INTEW	IH2 N T UW1
+INTHEREST	IH0 N TH ER0 AH0 S T
+INTHRALLED	IH1 N TH R AO1 L D
+INTIALO	IH1 N T IY0 AE1 L OW0
+INTICE	IH1 N T AH0 S
+INTIL	IH1 N T AH0 L
+INTIL'T	IH1 N T AH0 L T
+INTILL	IH0 N T IH1 L
+INTIMACIES	IH1 N T AH0 M AH0 S IY0 Z
+INTIMACY  IH1 N T AH0 M AH0 S IY0
+INTIMATE  IH1 N AH0 M AH0 T
+INTIMATE  IH1 N T AH0 M AH0 T
+INTIMATE  IH1 N T AH0 M EY2 T
+INTIMATED  IH1 N T AH0 M EY2 T IH0 D
+INTIMATELY  IH1 N T AH0 M AH0 T L IY0
+INTIMATES  IH1 N T AH0 M AH0 T S
+INTIMATING	IH1 N T AH0 M EY2 T IH0 NG
+INTIMATION  IH2 N T AH0 M EY1 SH AH0 N
+INTIMATIONS  IH2 N T AH0 M EY1 SH AH0 N Z
+INTIME	IH0 N T AY2 M
+INTIMES	IH0 N T AY2 M Z
+INTIMIDATE  IH0 N T IH1 M IH0 D EY2 T
+INTIMIDATED  IH0 N T IH1 M IH0 D EY2 T IH0 D
+INTIMIDATES  IH0 N T IH1 M IH0 D EY2 T S
+INTIMIDATING  IH0 N T IH1 M IH0 D EY2 T IH0 NG
+INTIMIDATION  IH0 N T IH2 M IH0 D EY1 SH AH0 N
+INTIMIDATIONS	IH0 N T IH1 M IH0 D EY1 SH AH0 N Z
+INTIND	IH1 N T IH0 N D
+INTINDED	IH1 N T AH0 N D IH0 D
+INTINTION	IH1 N T AH0 N SH AH0 N
+INTIRE	IH2 N T AY2 R
+INTIRELY	IH0 N T AY1 ER0 L IY0
+INTITLED	IH0 N T AY1 T AH0 L D
+INTITULED	IH1 N T AH0 T Y UW2 L D
+INTO  IH0 N T AH0
+INTO  IH0 N T UW1
+INTO  IH1 N T UW0
+INTOIRELY	IH0 N T W AA1 R L IY0
+INTOLERABLE  IH0 N T AA1 L ER0 AH0 B AH0 L
+INTOLERABLY  IH0 N T AA1 L ER0 AH0 B L IY0
+INTOLERANCE  IH0 N T AA1 L ER0 AH0 N S
+INTOLERANT  IH0 N T AA1 L ER0 AH0 N T
+INTOLERANTLY	IH0 N T AA1 L ER0 AH0 N T L IY0
+INTOMBI	IH1 N T AH0 M B IY0
+INTONATED	IH1 N T AH0 N EY2 T IH0 D
+INTONATION  IH0 N T AH0 N EY1 SH AH0 N
+INTONATIONS  IH0 N T AH0 N EY1 SH AH0 N Z
+INTONE  IH0 N T OW1 N
+INTONED  IH0 N T OW1 N D
+INTONES  IH0 N T OW1 N Z
+INTONING  IH0 N T OW1 N IH0 NG
+INTORNO	IH0 N T AO1 R N OW0
+INTOSH	IH0 N T AA2 SH
+INTOSH'S	IH0 N T AA2 SH IH0 Z
+INTOURIST  IH0 N T UH1 R IH0 S T
+INTOXICANT	IH0 N T AA1 K S IH0 K AH0 N T
+INTOXICANTS	IH0 N T AA1 K S IH0 K AH0 N T S
+INTOXICATE  IH0 N T AA1 K S AH0 K EY2 T
+INTOXICATED  IH0 N T AA1 K S AH0 K EY2 T AH0 D
+INTOXICATED  IH0 N T AA1 K S IH0 K EY2 T IH0 D
+INTOXICATES  IH0 N T AA1 K S AH0 K EY2 T S
+INTOXICATING  IH0 N T AA1 K S IH0 K EY2 T IH0 NG
+INTOXICATINGLY	IH0 N T AA1 K S AH0 K EY2 T IH0 NG L IY0
+INTOXICATION  IH0 N T AA2 K S AH0 K EY1 SH AH0 N
+INTOXICATIONS	IH0 N T AA1 K S AH0 K EY1 SH AH0 N Z
+INTRA  IH1 N T R AH0
+INTRACTABILITY	IH0 N T R AE1 K T AH0 B IH1 L IH0 T IY0
+INTRACTABLE  IH0 N T R AE1 K T AH0 B AH0 L
+INTRAMURAL  IH2 N T R AH0 M Y UH1 R AH0 L
+INTRANSIGEANT	IH0 N T R AE1 N S AH0 JH AH0 N T
+INTRANSIGENT  IH0 N T R AE1 N S AH0 JH AH0 N T
+INTRANSIGENT  IH0 N T R AE1 N Z AH0 JH AH0 N T
+INTRANSITIVE	IH0 N T R AE1 N Z IH0 T IH0 V
+INTRAVENOUS  IH2 N T R AH0 V IY1 N AH0 S
+INTRAVISCERAL	IH2 N T R AH0 V IH1 S ER0 AH0 L
+INTREAT	IH0 N T R IY1 T
+INTREATED	IH0 N T R IY1 T IH0 D
+INTREATIES	IH0 N T R IY1 T IY0 Z
+INTREATING	IH0 N T R IY1 T IH0 NG
+INTREATY	IH0 N T R IY1 T IY0
+INTRENCH	IH0 N T R EH1 N CH
+INTRENCHED	IH0 N T R EH1 N CH T
+INTRENCHING	IH0 N T R EH1 N CH IH0 NG
+INTRENCHMENT	IH0 N T R EH1 N CH M AH0 N T
+INTRENCHMENTS	IH0 N T R EH1 N CH M AH0 N T S
+INTREPID  IH0 N T R EH1 P AH0 D
+INTREPID'S	IH0 N T R EH1 P AH0 D Z
+INTREPIDITY	IH0 N T R EH1 P AH0 D AH0 T IY0
+INTREPIDLY  IH0 N T R EH1 P AH0 D L IY0
+INTRICACIES  IH1 N T R AH0 K AH0 S IY0 Z
+INTRICACY  IH1 N T R AH0 K AH0 S IY0
+INTRICATE  IH1 N T R AH0 K AH0 T
+INTRICATELY  IH1 N T R AH0 K AH0 T L IY0
+INTRIGANT	IH0 N T R IY1 G AH0 N T
+INTRIGANTE	IH2 N T R IY0 G AA1 N T IY0
+INTRIGUANT	IH0 N T R IY1 G AH0 N T
+INTRIGUANTE	IH0 N T R IY1 G AA1 N T IY0
+INTRIGUE  IH0 N T R IY1 G
+INTRIGUE  IH1 N T R IY0 G
+INTRIGUED  IH0 N T R IY1 G D
+INTRIGUED  IH1 N T R IY0 G D
+INTRIGUER	IH0 N T R IY1 G ER0
+INTRIGUERS	IH0 N T R IY1 G ER0 Z
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+INTRIGUES  IH1 N T R IY0 G Z
+INTRIGUING  IH0 N T R IY1 G IH0 NG
+INTRINSIC  IH0 N T R IH1 N S IH0 K
+INTRINSICALLY  IH0 N T R IH1 N S IH0 K AH0 L IY0
+INTRINSICALLY  IH0 N T R IH1 N S IH0 K L IY0
+INTRINSICK	IH0 N T R IH1 N S IH0 K
+INTRO  IH1 N T R OW0
+INTROD	IH2 N T R AH0 D
+INTRODUC'D	IH2 N T R AH0 D UW1 S D
+INTRODUCE  IH2 N T R AH0 D UW1 S
+INTRODUCE  IH2 N T R OW0 D UW1 S
+INTRODUCED  IH2 N T R AH0 D UW1 S T
+INTRODUCED  IH2 N T R OW0 D UW1 S T
+INTRODUCER	IH2 N T R AH0 D UW1 S ER0
+INTRODUCERS	IH2 N T R AH0 D UW1 S ER0 Z
+INTRODUCES  IH2 N T R AH0 D UW1 S IH0 Z
+INTRODUCES  IH2 N T R OW0 D UW1 S IH0 Z
+INTRODUCETH	IH2 N T R AH0 D UW1 S IH0 TH
+INTRODUCIN	IH2 N T R AH0 D UW1 S IH0 N
+INTRODUCING  IH2 N T R AH0 D UW1 S IH0 NG
+INTRODUCING  IH2 N T R OW0 D UW1 S IH0 NG
+INTRODUCTION  IH2 N T R AH0 D AH1 K SH AH0 N
+INTRODUCTION  IH2 N T R OW0 D AH1 K SH AH0 N
+INTRODUCTIONS  IH2 N T R AH0 D AH1 K SH AH0 N Z
+INTRODUCTIONS  IH2 N T R OW0 D AH1 K SH AH0 N Z
+INTRODUCTORY  IH2 N T R AH0 D AH1 K T ER0 IY0
+INTRODUCTORY  IH2 N T R OW0 D AH1 K T ER0 IY0
+INTROIT	IH0 N T R OY1 T
+INTROMISSION	IH2 N T R OW0 M IH1 SH AH0 N
+INTROMISSIONS	IH2 N T R OW0 M IH1 SH AH0 N Z
+INTROSPECTION  IH2 N T R AH0 S P EH1 K SH AH0 N
+INTROSPECTION  IH2 N T R OW0 S P EH1 K SH AH0 N
+INTROSPECTIONS	IH2 N T R AH0 S P EH1 K SH AH0 N Z
+INTROSPECTIVE  IH2 N T R AH0 S P EH1 K T IH0 V
+INTROSPECTIVE  IH2 N T R OW0 S P EH1 K T IH0 V
+INTROSPECTIVELY	IH2 N T R AH0 S P EH1 K T IH0 V L IY0
+INTROVERSION	IH2 N T R AH0 V ER1 ZH AH0 N
+INTROVERT  IH1 N T R OW0 V ER2 T
+INTROVERTED  IH1 N T R OW0 V ER2 T IH0 D
+INTRUDE  IH0 N T R UW1 D
+INTRUDED  IH0 N T R UW1 D AH0 D
+INTRUDER  IH0 N T R UW1 D ER0
+INTRUDER'S	IH0 N T R UW1 D ER0 Z
+INTRUDERS  IH0 N T R UW1 D ER0 Z
+INTRUDES  IH0 N T R UW1 D Z
+INTRUDIN	IH0 N T R UW1 D IH0 N
+INTRUDING  IH0 N T R UW1 D IH0 NG
+INTRUSION  IH0 N T R UW1 ZH AH0 N
+INTRUSIONS  IH0 N T R UW1 ZH AH0 N Z
+INTRUSIVE  IH0 N T R UW1 S IH0 V
+INTRUSIVELY	IH0 N T R UW1 S IH0 V L IY0
+INTRUSIVENESS  IH0 N T R UW1 S IH0 V N EH0 S
+INTRUST	IH0 N T R AH2 S T
+INTRUSTED	IH0 N T R AH1 S T IH0 D
+INTRUSTING	IH0 N T R AH1 S T IH0 NG
+INTRUSTS	IH0 N T R AH1 S T S
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+INTUITIONAL	IH2 N T UW0 IH1 SH AH0 N AH0 L
+INTUITIONS	IH2 N T UW0 IH1 SH AH0 N Z
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+INTUITIVELY  IH0 N T UW1 IH0 T IH0 V L IY0
+INTUITIVENESS	IH0 N T UW1 AH0 T IH0 V N AH0 S
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+INTYRE	IH0 N T AY2 R
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+INUENDO	IH0 N Y UW0 EH1 N D OW0
+INUENDOES	IH0 N Y UW0 EH1 N D OW0 Z
+INUENDOS	IH0 N Y UW0 EH1 N D OW0 Z
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+INUNDATING  IH1 N AH0 N D EY2 T IH0 NG
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+INURE  IH0 N Y UH1 R
+INURED  IH0 N Y UH1 R D
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+INURING	IH0 N Y UH1 R IH0 NG
+INURNED	IH0 N Y UH1 R N D
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+INUTILITY	IH0 N Y UW0 T IH1 L AH0 T IY0
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+INVADED  IH0 N V EY1 D IH0 D
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+INVADES  IH0 N V EY1 D Z
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+INVALID  IH1 N V AH0 L AH0 D
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+INVALID'S	IH0 N V AE1 L IH0 D Z
+INVALIDATE  IH0 N V AE1 L IH0 D EY2 T
+INVALIDATED  IH0 N V AE1 L AH0 D EY2 T AH0 D
+INVALIDATED  IH0 N V AE1 L IH0 D EY2 T IH0 D
+INVALIDATES	IH0 N V AE1 L AH0 D EY0 T S
+INVALIDATING  IH0 N V AE1 L AH0 D EY2 T IH0 NG
+INVALIDATION  IH0 N V AE2 L AH0 D EY1 SH AH0 N
+INVALIDED	IH0 N V AE1 L AH0 D IH0 D
+INVALIDES	IH0 N V AE1 L AH0 D Z
+INVALIDISH	IH0 N V AE1 L IH0 D IH0 SH
+INVALIDISM	IH0 N V AE1 L AH0 D IH2 Z AH0 M
+INVALIDITY	IH1 N V AH0 L IH1 D AH0 T IY0
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+INVAR	IH0 N V ER0
+INVAR'S	IH0 N V EH1 R Z
+INVARIABILITY	IH0 N V EH1 R IY0 AH0 B IH1 L IH0 T IY0
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+INVARIABLY  IH0 N V EH1 R IY0 AH0 B L IY0
+INVASION  IH0 N V EY1 ZH AH0 N
+INVASIONS  IH0 N V EY1 ZH AH0 N Z
+INVASIVE  IH0 N V EY1 S IH0 V
+INVECTIVE  IH0 N V EH1 K T IH0 V
+INVECTIVES	IH0 N V EH1 K T IH0 V Z
+INVEIGH	IH0 N V EY1
+INVEIGHED	IH0 N V EY1 D
+INVEIGHING	IH0 N V EY1 IH0 NG
+INVEIGHS	IH0 N V EY1 Z
+INVEIGLE	IH0 N V IY1 G AH0 L
+INVEIGLED	IH0 N V IY1 G AH0 L D
+INVEIGLES	IH0 N V IY1 G AH0 L Z
+INVEIGLING	IH0 N V IY1 G AH0 L IH0 NG
+INVENIT	IH0 N V EH1 N IH0 T
+INVENT  IH0 N V EH1 N T
+INVENTED  IH0 N V EH1 N T AH0 D
+INVENTED  IH0 N V EH1 N T IH0 D
+INVENTER	IH0 N V EH1 N T ER0
+INVENTIN	IH0 N V EH1 N T IH0 N
+INVENTING  IH0 N V EH1 N T IH0 NG
+INVENTION  IH0 N V EH1 N SH AH0 N
+INVENTIONS  IH0 N V EH1 N SH AH0 N Z
+INVENTIVE  IH0 N V EH1 N T IH0 V
+INVENTIVENESS  IH0 N V EH1 N T IH0 V N AH0 S
+INVENTOR  IH0 N V EH1 N T ER0
+INVENTOR'S	IH2 N V AH0 N T AO2 R Z
+INVENTORIED  IH1 N V AH0 N T AO2 R IY0 D
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+INVENTORS  IH0 N V EH1 N T ER0 Z
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+INVENTRESS	IH0 N V EH1 N T R AH0 S
+INVENTS  IH0 N V EH1 N T S
+INVENTUS	IH0 N V EH1 N T AH0 S
+INVERARAY	IH0 N V ER1 AH0 R EY0
+INVERARITY	IH0 N V EH1 R EH1 R IH0 T IY0
+INVERARY	IH1 N V ER0 EH2 R IY0
+INVERASHIEL	IH0 N V ER1 AH0 SH IY2 L
+INVERAWE	IH0 N V EH1 R AO1
+INVERELL	IH0 N V EH0 R EY1 L
+INVERESK	IH1 N V ER0 EH1 S K
+INVEREY	IH0 N V EH0 R EY1
+INVERFORTH	IH0 N V ER1 F ER0 TH
+INVERLEITH	IH0 N V ER1 L IY1 TH
+INVERLOCHY	IH1 N V ER0 L OW2 CH IY0
+INVERMALLUCH	IH1 N V ER0 M AE1 L AH0 K
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+INVERSE  IH0 N V ER1 S
+INVERSELY  IH0 N V ER1 S L IY0
+INVERSION  IH0 N V ER1 ZH AH0 N
+INVERSIONS	IH0 N V ER1 ZH AH0 N Z
+INVERSNAID	IH0 N V ER1 S N EY1 D
+INVERT  IH0 N V ER1 T
+INVERTEBRATA	IH0 N V ER1 T AH0 B R EY2 T AH0
+INVERTEBRATE  IH2 N V ER1 T AH0 B R AH0 T
+INVERTEBRATE  IH2 N V ER1 T AH0 B R EY2 T
+INVERTEBRATES  IH0 N V ER1 T AH0 B R AH0 T S
+INVERTEBRATES  IH2 N V ER1 T AH0 B R EY2 T S
+INVERTED  IH0 N V ER1 T IH0 D
+INVERTING	IH0 N V ER1 T IH0 NG
+INVERTS	IH0 N V ER1 T S
+INVEST  IH0 N V EH1 S T
+INVESTED  IH0 N V EH1 S T AH0 D
+INVESTED  IH0 N V EH1 S T IH0 D
+INVESTIGATE  IH0 N V EH1 S T AH0 G EY2 T
+INVESTIGATED  IH0 N V EH1 S T AH0 G EY2 T AH0 D
+INVESTIGATED  IH0 N V EH1 S T AH0 G EY2 T IH0 D
+INVESTIGATES  IH0 N V EH1 S T AH0 G EY2 T S
+INVESTIGATIN	IH0 N V EH1 S T AH0 G EY2 T IH0 N
+INVESTIGATING  IH0 N V EH1 S T AH0 G EY2 T IH0 NG
+INVESTIGATION  IH0 N V EH2 S T AH0 G EY1 SH AH0 N
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+INVESTIGATOR  IH0 N V EH1 S T AH0 G EY2 T ER0
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+INVESTIGATORY  IH0 N V EH1 S T AH0 G AH0 T AO2 R IY0
+INVESTIN	IH0 N V EH1 S T AH0 N
+INVESTING  IH0 N V EH1 S T IH0 NG
+INVESTITURE  IH0 N V EH1 S T AH0 CH ER0
+INVESTITURES	IH0 N V EH1 S T IH0 CH ER0 Z
+INVESTMENT  IH0 N V EH1 S M AH0 N T
+INVESTMENT  IH0 N V EH1 S T M AH0 N T
+INVESTMENTS  IH0 N V EH1 S M AH0 N T S
+INVESTMENTS  IH0 N V EH1 S T M AH0 N T S
+INVESTOR  IH0 N V EH1 S T ER0
+INVESTORS  IH0 N V EH1 S T ER0 Z
+INVESTS  IH0 N V EH1 S T S
+INVETERACY	IH0 N V EH1 T ER0 AH0 S IY0
+INVETERATE  IH0 N V EH1 T ER0 AH0 T
+INVETERATELY	IH0 N V EH1 T ER0 AH0 T L IY0
+INVICTUS	IH0 N V IH1 K T AH0 S
+INVIDIA	IH0 N V IH1 D IY0 AH0
+INVIDIOUS  IH0 N V IH1 D IY0 AH0 S
+INVIDIOUSLY	IH0 N V IH1 D IY0 AH0 S L IY0
+INVIDIOUSNESS	IH0 N V IH1 D IY0 AH0 S N AH0 S
+INVIGORATE  IH0 N V IH1 G ER0 IH0 T
+INVIGORATED  IH0 N V IH1 G ER0 EY2 T IH0 D
+INVIGORATES	IH0 N V IH1 G ER0 EY2 T S
+INVIGORATING  IH0 N V IH1 G ER0 EY2 T IH0 NG
+INVIGORATION	IH0 N V IH2 G ER0 EY1 SH AH0 N
+INVIGORATOR	IH0 N V IH1 G ER0 EY2 T ER0
+INVINCIBILITY  IH0 N V IH2 N S AH0 B IH1 L IH0 T IY0
+INVINCIBLE  IH0 N V IH1 N S AH0 B AH0 L
+INVINCIBLES	IH0 N V IH2 N S AH0 B AH0 L Z
+INVINCIBLY	IH0 N V IH2 N S AH0 B L IY0
+INVINTED	IH0 N V IH2 N T IH0 D
+INVIOLABILITY	IH0 N V AY1 AH0 L AH0 B IH1 L IH0 T IY0
+INVIOLABLE  IH0 N V AY1 AH0 L AH0 B AH0 L
+INVIOLABLY	IH0 N V AY1 AH0 L AH0 B L IY0
+INVIOLATE  IH0 N V AY1 AH0 L IH0 T
+INVIS'BLE	IH0 N V IH1 Z B AH0 L
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+INVISIBLE  IH0 N V IH1 Z AH0 B AH0 L
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+INVISIBLY	IH0 N V IH1 Z AH0 B L IY0
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+INVITATION  IH2 N V IH0 T EY1 SH AH0 N
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+INVITE  IH0 N V AY1 T
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+INVITED  IH0 N V AY1 T IH0 D
+INVITER	IH0 N V AY1 T ER0
+INVITES  IH0 N V AY1 T S
+INVITETH	IH0 N V AY1 T IH0 TH
+INVITIN	IH0 N V AY1 T IH0 N
+INVITING  IH0 N V AY1 T IH0 NG
+INVITINGLY	IH0 N V AY1 T IH0 NG L IY0
+INVOCATION  IH2 N V AH0 K EY1 SH AH0 N
+INVOCATIONS	IH0 N V AH0 K EY1 SH AH0 N Z
+INVOICE  IH1 N V OY0 S
+INVOICED	IH1 N V OY0 S T
+INVOICES  IH1 N V OY0 S IH0 Z
+INVOK'D	IH0 N V OW1 K D
+INVOKE  IH0 N V OW1 K
+INVOKED  IH0 N V OW1 K T
+INVOKER	IH0 N V OW1 K ER0
+INVOKES  IH0 N V OW1 K S
+INVOKING  IH0 N V OW1 K IH0 NG
+INVOLUNTARILY  IH2 N V AA2 L AH0 N T ER1 AH0 L IY0
+INVOLUNTARILY  IH2 N V OW0 L AH1 N T ER0 IH2 L IY0
+INVOLUNTARY  IH0 N V AA1 L AH0 N T EH2 R IY0
+INVOLUTED	IH0 N V AH0 L UW2 T AH0 D
+INVOLUTION	IH2 N V OW0 L UW1 SH AH0 N
+INVOLUTIONS	IH2 N V OW0 L UW1 SH AH0 N Z
+INVOLV'D	IH0 N V AA1 L V D
+INVOLVE  IH0 N V AA1 L V
+INVOLVED  IH0 N V AA1 L V D
+INVOLVEMENT  IH0 N V AA1 L V M AH0 N T
+INVOLVEMENTS  IH0 N V AA1 L V M AH0 N T S
+INVOLVES  IH0 N V AA1 L V Z
+INVOLVING  IH0 N V AA1 L V IH0 NG
+INVULNERABILITY  IH0 N V AH2 L N ER0 AH0 B IH1 L IH0 T IY0
+INVULNERABLE  IH0 N V AH1 L N ER0 AH0 B AH0 L
+INVULNERABLES	IH0 N V AH2 L N ER0 AH0 B AH0 L Z
+INWALD	IH1 N W AO2 L D
+INWARD  IH1 N W ER0 D
+INWARDLY  IH1 N W ER0 D L IY0
+INWARDNESS	IH1 N W ER0 D N AH0 S
+INWARDS	IH1 N W ER0 D Z
+INWOLD	IH1 N W OW2 L D
+INWOOD  IH1 N W UH2 D
+INWOVE	IH1 N W OW1 V
+INWOVEN	IH1 N W OW1 V AH0 N
+INWRAP	IH0 N R AE1 P
+INWROUGHT	IH0 N R AO1 T
+INYANGA	IH0 N Y AA1 NG G AH0
+INYATI	IH0 N Y AA1 T IY0
+INYOKA	IH0 N Y OW1 K AH0
+INYONI	IH0 N Y OW1 N IY0
+INYOVU	IH0 N Y OW1 V UW1
+INZA	IH1 N Z AH0
+INZA'S	IH1 N Z AH0 Z
+INZANA	IH1 N Z AE1 N AH0
+INZARILLO	IH1 N Z ER0 IH1 L OW0
+IO  AY1 OW0
+IO'S	IY0 OW0 Z
+IOAS	AY0 OW1 AH0 Z
+IOBATES	AY0 OW1 B EY2 T S
+IOCO	AY0 OW1 K OW0
+IODIDE  AY1 AH0 D AY2 D
+IODINE  AY1 AH0 D AY2 N
+IODOFORM	AY1 AH0 D AO1 F AO2 R M
+IOHANAN	AY0 AA1 HH AH0 N AE0 N
+IOHN	AY0 AA1 N
+IOLA  AY0 OW1 L AH0
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+IOLAUS	IY0 AH0 L AW0 Z
+IOLCHOS	AY0 OW1 L CH OW0 Z
+IOLCOS	IY0 AH0 L K OW2 Z
+IOLE  IY0 OW1 L IY0
+ION  AY1 AA2 N
+ION  AY1 AH0 N
+IONA  AY0 OW1 N AH0
+IONA'S	AY0 OW1 N AH0 Z
+IONE  AY0 OW1 N IY0
+IONE'S	AY0 OW1 N IY0 Z
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+MISBEHAVIOUR	M IH2 S B IY0 HH EY1 V Y ER0
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+MISBELIEVERS	M IH2 S B AH0 L IY1 V ER0 Z
+MISBELIEVING	M IH2 S B AH0 L IY1 V IH0 NG
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+MISCALCULATED  M IH0 S K AE1 L K Y AH0 L EY2 T IH0 D
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+MISCALCULATION  M IH0 S K AE1 L K Y AH0 L EY1 SH AH0 N
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+MISCALL	M IH0 S K AO2 L
+MISCALLED	M IH0 S K AA1 L D
+MISCALLING	M IH0 S K AE1 L IH0 NG
+MISCARRIAGE  M IH0 S K EH1 R AH0 JH
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+MISCARRIED	M IH0 S K EH1 R IY0 D
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+MISCAST  M IH0 S K AE1 S T
+MISCEGENATION	M IH0 S CH EH1 G AH0 N EY1 SH AH0 N
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+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
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+MISDEMEANOUR	M IH2 S D AH0 M IY1 N ER0
+MISDEMEANOURS	M IH2 S D AH0 M IY1 N AO1 R Z
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+MISDIRECTED  M IH0 S D IH0 R EH1 K T IH0 D
+MISDIRECTING	M IH0 S D IH0 R EH1 K T IH0 NG
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+MISER  M AY1 Z ER0
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+MISERABLE  M IH1 Z ER0 AH0 B AH0 L
+MISERABLE  M IH1 Z R AH0 B AH0 L
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+MISERABLES  M IH1 Z R AH0 B AH0 L Z
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+MISERABLY  M IH1 Z R AH0 B L IY0
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+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
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+MISFORMED	M IH0 S F AO1 R M D
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+MISGOVERN	M IH0 S G AH1 V ER0 N
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+MISINFORMATION  M IH2 S IH0 N F ER0 M EY1 SH AH0 N
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+MISJUDGMENT  M IH0 S JH AH1 JH M AH0 N T
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+MISKA  M IH1 S K AH0
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+MISLAYING	M IH0 S L EY1 IH0 NG
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+MISLEADS  M IH0 S L IY1 D Z
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+MISLIKE	M IH0 S L AY1 K
+MISLIKED	M IH0 S L AY1 K T
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+MISLIKING	M IH0 S L AY1 K IH0 NG
+MISMANAGE  M IH0 S M AE1 N IH0 JH
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+MISMANAGING  M IH0 S M AE1 N IH0 JH IH0 NG
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+MISMATED	M IH0 S M AE1 D
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+MISNOMERS	M IH0 S N OW1 M ER0 Z
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+MISOGYNISTIC	M IH1 Z AH0 JH IH0 N IH0 S T IH0 K
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+MISOGYNISTS  M IH1 Z AH0 JH IH0 N IH0 S T S
+MISOGYNY  M IH1 Z AH0 JH IH0 N IY0
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+MISPLACE  M IH0 S P L EY1 S
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+MISPRONOUNCED  M IH0 S P R AH0 N AW1 N S T
+MISPRONOUNCING  M IH0 S P R AH0 N AW1 N S IH0 NG
+MISPRONUNCIATION	M IH0 S P R AH0 N AH2 N S IY0 EY1 SH AH0 N
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+MISQUOTATION	M IH0 S K W OW0 T EY1 SH AH0 N
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+MISREPRESENTATION  M IH0 S R EH2 P R IH0 Z EH0 N T EY1 SH AH0 N
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+MISREPRESENTED  M IH2 S R EH0 P R IH0 Z EH1 N IH0 D
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+MISSISQUOI	M IH2 S IH0 K W OY0
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+MISSOURI  M AH0 Z UH1 R IY0
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+MISSOURI'S  M AH0 Z UH1 R IY0 Z
+MISSOURIAN  M AH0 Z UH1 R IY0 AH0 N
+MISSOURIAN'S	M AH0 Z UH1 R IY0 AH0 N Z
+MISSOURIANS  M AH0 Z UH1 R IY0 AH0 N Z
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+MISSOURIES	M AH0 Z UH1 R IY0 Z
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+OUTCROPS	AW1 T K R AA1 P S
+OUTCRY  AW1 T K R AY2
+OUTCURVE	AW1 T K ER1 V
+OUTDATED  AW1 T D EY2 T IH0 D
+OUTDID  AW1 T D IH2 D
+OUTDISTANCE  AW1 T D IH1 S T AH0 N S
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+OUTDO  AW2 T D UW1
+OUTDOES  AW2 T D AH1 Z
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+OUTE	AW1 T
+OUTED  AW1 T AH0 D
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+OUTFACE	AW1 T F EY2 S
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+OUTFALL	AW1 T F AO2 L
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+OUTFIELDERS  AW1 T F IY2 L D ER0 Z
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+OUTFIT  AW1 T F IH2 T
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+OUTFITTING  AW1 T F IH2 T IH0 NG
+OUTFLANK  AW1 T F L AE2 NG K
+OUTFLANKED  AW0 T F L AE1 NG K T
+OUTFLANKING  AW1 T F L AE2 NG K IH0 NG
+OUTFLASH	AW1 T F L AE2 SH
+OUTFLASHING	AW0 T F L AE1 SH IH0 NG
+OUTFLOW  AW1 T F L OW2
+OUTFLOWING	AW1 T F L OW2 IH0 NG
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+OUTFOUGHT	AW0 T F AA1 T
+OUTGAME	AW1 T G EY1 M
+OUTGATE	AW1 T G EY2 T
+OUTGENERAL	AW1 T G EH1 N ER0 AH0 L
+OUTGENERALED	AW1 T JH EH2 N ER0 AE1 L D
+OUTGENERALLED	AW1 T JH EH1 N R AH0 L D
+OUTGO  AW1 T G OW2
+OUTGOES	AW1 T G OW2 Z
+OUTGOING  AW1 T G OW2 IH0 NG
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+OUTGONE	AW1 T G OW2 N
+OUTGREW  AW2 T G R UW1
+OUTGROW  AW2 T G R OW1
+OUTGROWING  AW1 T G R OW2 IH0 NG
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+OUTGROWS  AW2 T G R OW1 Z
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+OUTGROWTHS	AW2 T G R OW1 TH S
+OUTGUESS  AW1 T G EH2 S
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+OUTGUSH	AW1 T G AH2 SH
+OUTGUSHING	AW1 T G AH2 SH IH0 NG
+OUTHELD	AW1 T HH EH1 L D
+OUTHER	AH1 DH ER0
+OUTHOUSE  AW1 T HH AW2 S
+OUTHOUSES  AW1 T HH AW2 S IH0 Z
+OUTHWAITE	AW1 TH W EY2 T
+OUTINA	AW0 T IY1 N AH0
+OUTING  AW1 T IH0 NG
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+OUTJUMP	AW1 T JH AH1 M P
+OUTJUTTING	AW1 T JH AH1 T IH0 NG
+OUTLAND  AW1 T L AE2 N D
+OUTLAND  AW1 T L AH0 N D
+OUTLANDER	AW1 T L AH0 N D ER0
+OUTLANDERS	AW1 T L AH0 N D ER0 Z
+OUTLANDISH  AW0 T L AE1 N D IH0 SH
+OUTLANDISHLY	AW0 T L AE1 N D IH0 SH L IY0
+OUTLANDISHNESS	AW0 T L AE1 N D IH0 SH N AH0 S
+OUTLANDS	AW1 T L AE2 N D Z
+OUTLAST  AW0 T L AE1 S T
+OUTLAST  AW1 T L AE2 S T
+OUTLASTED  AW0 T L AE1 S T IH0 D
+OUTLASTED  AW1 T L AE2 S T IH0 D
+OUTLASTING  AW0 T L AE1 S T IH0 NG
+OUTLASTING  AW1 T L AE2 S T IH0 NG
+OUTLASTS  AW0 T L AE1 S
+OUTLASTS  AW0 T L AE1 S T S
+OUTLASTS  AW1 T L AE2 S
+OUTLASTS  AW1 T L AE2 S T S
+OUTLAW  AW1 T L AO2
+OUTLAW'D	AW1 T L AO2 D
+OUTLAW'S	AW1 T L AO2 Z
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+OUTLAWING  AW1 T L AO2 IH0 NG
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+OUTLAWS  AW1 T L AO2 Z
+OUTLAY  AW1 T L EY2
+OUTLAYS  AW1 T L EY2 Z
+OUTLEAP	AW1 T L IY1 P
+OUTLEDGE	AW1 T L IH0 JH
+OUTLET  AW1 T L EH2 T
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+OUTLIER  AW1 T L AY0 ER0
+OUTLIERS  AW1 T L AY0 ER0 Z
+OUTLINE  AW1 T L AY2 N
+OUTLINED  AW1 T L AY2 N D
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+OUTLINING  AW1 T L AY2 N IH0 NG
+OUTLIVE  AW2 T L IH1 V
+OUTLIVED  AW2 T L IH1 V D
+OUTLIVES  AW2 T L IH1 V Z
+OUTLIVING  AW2 T L IH1 V IH0 NG
+OUTLOOK  AW1 T L UH2 K
+OUTLOOKING	AW1 T L UH2 K IH0 NG
+OUTLOOKS  AW1 T L UH2 K S
+OUTLYING  AW1 T L AY2 IH0 NG
+OUTMANEUVER  AW0 T M AH0 N UW1 V ER0
+OUTMANEUVERED  AW0 T M AH0 N UW1 V ER0 D
+OUTMANOEUVRED	AW2 T M AE1 N ER1 V D
+OUTMARCH	AW1 T M AA2 R K
+OUTMARCHED	AW2 T M AA1 R CH T
+OUTMATCH	AW2 T M AE1 CH
+OUTMATCHED	AW2 T M AE1 CH T
+OUTMODED  AW2 T M OW1 D AH0 D
+OUTMODED  AW2 T M OW1 D IH0 D
+OUTMOST	AW1 T M OW2 S T
+OUTNESS	AW1 T N AH0 S
+OUTNUMBER  AW0 T N AH1 M B ER0
+OUTNUMBERED  AW2 T N AH1 M B ER0 D
+OUTNUMBERING  AW1 T N AH2 M B ER0 IH0 NG
+OUTNUMBERS  AW0 T N AH1 M B ER0 Z
+OUTOFWORK	AW1 T AH0 F W ER2 K
+OUTPACE  AW1 T P EY2 S
+OUTPACED  AW1 T P EY2 S T
+OUTPLAY	AW1 T P L EY2
+OUTPLAYED	AW1 T P L EY2 D
+OUTPOINTED	AW1 T P OY1 N T IH0 D
+OUTPORT	AW1 T P AO2 R T
+OUTPORTS	AW1 T P AO2 R T S
+OUTPOST  AW1 T P OW2 S T
+OUTPOSTS  AW1 T P OW2 S T S
+OUTPOUR  AW2 T P AO1 R
+OUTPOURED	AW2 T P AO1 R D
+OUTPOURING  AW2 T P AO1 R IH0 NG
+OUTPOURINGS	AW2 T P AO1 R IH0 NG Z
+OUTPOURS  AW2 T P AO1 R Z
+OUTPUT  AW1 T P UH2 T
+OUTRACE	AW0 T R EY1 S
+OUTRACED	AW0 T R EY1 S T
+OUTRAGE  AW1 T R EY2 JH
+OUTRAGED  AW1 T R EY2 JH D
+OUTRAGEOUS  AW0 T R EY1 JH AH0 S
+OUTRAGEOUSLY  AW2 T R EY1 JH AH0 S L IY0
+OUTRAGEOUSNESS  AW0 T R EY1 JH AH0 S N AH0 S
+OUTRAGES  AW1 T R EY2 JH IH0 Z
+OUTRAGING  AW1 T R EY2 JH IH0 NG
+OUTRAGIOUS	AW1 T R EY2 JH AH0 S
+OUTRAM	AW1 T R AE2 M
+OUTRAM'S	AW1 T R AE2 M Z
+OUTRAN  AW0 T R AE1 N
+OUTRANCE	AW0 T R AE1 N S
+OUTRANGED	AW0 T R EY1 N JH D
+OUTRANK	AW0 T R AE1 NG K
+OUTRANKED	AW0 T R AE1 NG K T
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+OUTRE	AW1 T ER0
+OUTREACH  AW1 T R IY2 CH
+OUTREACHED	AW1 T R IY2 CH T
+OUTREACHING	AW1 T R IY2 CH IH0 NG
+OUTRIDDEN	AW1 T R IH2 D AH0 N
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+OUTRIDER  AW1 T R AY2 D ER0
+OUTRIDERS  AW1 T R AY2 D ER0 Z
+OUTRIDING	AW1 T R AY2 D IH0 NG
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+OUTRIGGERS  AW1 T R IH2 G ER0 Z
+OUTRIGHT  AW1 T R AY1 T
+OUTRIVAL	AW1 T R AY1 V AH0 L
+OUTRIVALLED	AW1 T R AY2 V AH0 L D
+OUTRIVALS	AW1 T R AY1 V AH0 L Z
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+OUTRODE	AW1 T R OW1 D
+OUTRUN  AW0 T R AH1 N
+OUTRUNNERS	AW0 T R AH1 N ER0 Z
+OUTRUNNING  AW0 T R AH1 N IH0 NG
+OUTRUNS  AW0 T R AH1 N Z
+OUTRUSH	AW0 T R AH1 SH
+OUTRUSHING	AW0 T R AH1 SH IH0 NG
+OUTS  AW1 T S
+OUTSAIL	AW0 T S EY2 L
+OUTSAILED	AW0 T S EY1 L D
+OUTSAILING	AW0 T S EY1 L IH0 NG
+OUTSET  AW1 T S EH2 T
+OUTSETTING	AW1 T S EH2 T IH0 NG
+OUTSHINE  AW1 CH AY2 N
+OUTSHINES  AW1 CH AY2 N Z
+OUTSHINING  AW1 CH AY2 N IH0 NG
+OUTSHONE  AW0 T SH OW1 N
+OUTSHOOT	AW0 T SH UW2 T
+OUTSHORE	AW0 T SH AO2 R
+OUTSHOT	AW0 T SH AA2 T
+OUTSHOUT	AW1 CH AW2 T
+OUTSIDE  AW1 T S AY1 D
+OUTSIDER  AW0 T S AY1 D ER0
+OUTSIDER'S  AW0 T S AY1 D ER0 Z
+OUTSIDERS  AW0 T S AY1 D ER0 Z
+OUTSIDES  AW1 T S AY1 D Z
+OUTSING	AW0 T S IH0 NG
+OUTSIZE  AW1 T S AY2 Z
+OUTSKIRT  AW1 T S K ER2 T
+OUTSKIRTS  AW1 T S K ER2 T S
+OUTSMARTED  AW1 T S M AA2 R T AH0 D
+OUTSOARED	AW2 T S AO1 R D
+OUTSOARS	AW2 T S AO1 R Z
+OUTSPAKE	AW0 T S P EY1 K
+OUTSPAN	AW0 T S P AE2 N
+OUTSPANNED	AW0 T S P AE1 N D
+OUTSPANNING	AW0 T S P AE1 N IH0 NG
+OUTSPANS	AW0 T S P AE2 N Z
+OUTSPEAKING	AW0 T S P IY1 K IH0 NG
+OUTSPED	AW0 T S P T
+OUTSPEED	AW0 T S P IY2 D
+OUTSPEEDING	AW0 T S P IY1 D IH0 NG
+OUTSPOKE	AW0 T S P OW1 K
+OUTSPOKEN  AW1 T S P OW1 K AH0 N
+OUTSPOKENLY	AW0 T S P OW1 K AH0 N L IY0
+OUTSPOKENNESS  AW0 T S P OW1 K AH0 N AH0 S
+OUTSPREAD	AW0 T S P R EH1 D
+OUTSPREADING	AW0 T S P R EH1 D IH0 NG
+OUTSPREADS	AW0 T S P R EH1 D Z
+OUTSTANDING  AW2 T S T AE1 N D IH0 NG
+OUTSTARE	AW0 T S T EH1 R
+OUTSTARED	AW0 T S T EH1 R D
+OUTSTART	AW0 T S T AA2 R T
+OUTSTAY	AW0 T S T EY1
+OUTSTAYED	AW0 T S T EY1 D
+OUTSTAYING	AW0 T S T EY1 IH0 NG
+OUTSTEP	AW0 T S T EH2 P
+OUTSTEPPED	AW0 T S T EH2 P T
+OUTSTRETCH  AW0 T S T R EH1 CH
+OUTSTRETCH'D	AW0 T S T R EH1 CH D
+OUTSTRETCHED  AW0 T S T R EH1 CH T
+OUTSTRETCHING	AW0 T S T R EH1 CH IH0 NG
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+OUTSTRIPPED  AW0 T S T R IH1 P T
+OUTSTRIPPING  AW0 T S T R IH1 P IH0 NG
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+OUTTA  AW1 T AH0
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+OUTTHRUST	AW1 T TH R AH1 S T
+OUTTIME	AW0 T T AY2 M
+OUTVALUES	AW0 T V AE2 L Y UW0 Z
+OUTVIE	AW0 T V IY0
+OUTVIED	AW0 T V IY0 D
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+OUTVOTE  AW0 T V OW1 T
+OUTVOTED  AW0 T V OW1 T AH0 D
+OUTVYING	AW0 T V IY0 IH0 NG
+OUTWALK	AW1 T W AO2 K
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+OUTWARDNESS	AW1 T W ER0 D N AH0 S
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+OUTWEIGHED  AW0 T W EY1 D
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+OUTWORN	AW1 T W AO1 R N
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+OUZEL	AW1 Z AH0 L
+OUZELFORD	AW1 Z EH1 L F ER0 D
+OUZELS	AW1 Z AH0 L Z
+OV	AA0 V
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+OVARIAN  OW0 V EH1 R IY0 AH0 N
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+OVATE  OW1 V EY0 T
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+OVEH	AH1 V AH0
+OVEN  AH1 V AH0 N
+OVEN'S	AH1 V AH0 N Z
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+OVERABUNDANCE  OW1 V ER0 AH0 B AH1 N D AH0 N S
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+OVERACTING	OW1 V ER0 AE2 K T IH0 NG
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+OVERBALANCE	OW1 V ER0 B AE2 L AH0 N S
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+OVERBALANCES	OW1 V ER0 B AE2 L AH0 N S IH0 Z
+OVERBALANCING	OW1 V ER0 B AE2 L AH0 N S IH0 NG
+OVERBEAR	OW1 V ER0 B EH2 R
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+OVERBORNE	OW1 V ER0 B ER0 N
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+OVERBURDEN  OW1 V ER0 B ER1 D AH0 N
+OVERBURDENED  OW1 V ER0 B ER1 D AH0 N D
+OVERBURDENING	OW1 V ER0 B ER1 D AH0 N IH0 NG
+OVERBURTHENED	OW1 V ER0 B ER1 TH AH0 N D
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+OVERCAME  OW1 V ER0 K EY1 M
+OVERCAREFUL	OW1 V ER0 K EH1 R F AH0 L
+OVERCAST  OW1 V ER0 K AE2 S T
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+OVERCAUTIOUS	OW1 V ER0 K AO1 SH AH0 S
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+OVERCLEAN	OW1 V ER0 K L EY1 N
+OVERCLOUD	OW1 V ER0 K L AW1 D
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+OVERCOMBE	OW1 V ER0 K AH0 M
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+OVERCONFIDENCE  OW2 V ER0 K AA1 N F IH0 D AH0 N S
+OVERCONFIDENT  OW2 V ER0 K AA1 N F IH0 D AH0 N T
+OVERCROWD  OW2 V ER0 K R AW1 D
+OVERCROWDED  OW1 V ER0 K R AW2 D IH0 D
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+OVERDEVELOPED	OW1 V ER0 D IH0 V EH1 L AH0 P T
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+OVERDOES  OW2 V ER0 D AH1 Z
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+OVERDRAFT  OW1 V ER0 D R AE2 F T
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+OVERDRAW  OW1 V ER0 D R AO2
+OVERDRAWING	OW1 V ER0 D R AO1 IH0 NG
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+OVERDRESS  OW1 V ER0 D R EH2 S
+OVERDRESSED	OW1 V ER0 D R EH2 S T
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+OVERDRIVE  OW1 V ER0 D R AY2 V
+OVERDRIVEN	OW1 V ER0 D R IH1 V AH0 N
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+OVEREAT  OW1 V ER0 IY2 T
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+OVEREATING  OW1 V ER0 IY1 T IH0 NG
+OVEREMPHASIS	OW2 V ER0 EH1 M F AH0 S AH0 S
+OVEREMPHASIZED  OW1 V ER0 EH1 M F AH0 S AY2 Z D
+OVEREND	OW2 V ER0 EH1 N D
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+OVERESTIMATES  OW2 V ER0 EH1 S T AH0 M EY2 T S
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+OVEREXCITED  OW2 V ER0 EH0 K S AY1 T IH0 D
+OVEREXERTION	OW2 V ER0 IH0 G Z ER1 SH AH0 N
+OVERFALLS	OW1 V ER0 F AO2 L Z
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+OVERFEEDING	OW1 V ER0 F IY1 D IH0 NG
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+OVERFLOW  OW2 V ER0 F L OW1
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+OVERFLOWING  OW1 V ER0 F L OW2 IH0 NG
+OVERFLOWINGS	OW1 V ER0 F L OW2 IH0 NG Z
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+OVERFLOWS  OW1 V ER0 F L OW2 Z
+OVERFOND	OW1 V ER0 F AA1 N D
+OVERFULL	OW1 V ER0 F AH0 L
+OVERGENEROUS  OW1 V ER0 JH EH2 N ER0 AH0 S
+OVERGONE	OW1 V ER0 G AO1 N
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+OVERGROUND	OW1 V ER0 G R AW2 N D
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+OVERHADDON	OW1 V ER0 HH AE1 D AH0 N
+OVERHAND	OW1 V ER0 HH AE2 N D
+OVERHANG  OW1 V ER0 HH AE2 NG
+OVERHANGING  OW1 V ER0 HH AE2 NG IH0 NG
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+OVERHASTE	OW1 V ER0 HH EY2 S T
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+OVERHAUL  OW1 V ER0 HH AO2 L
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+OVERHEATED  OW1 V ER0 HH IY2 T IH0 D
+OVERHEATING  OW1 V ER0 HH IY2 T IH0 NG
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+OVERINDULGED	OW1 V ER0 IH0 N D AH1 L JH D
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+OVERISH	OW1 V ER0 IH0 SH
+OVERJOYED  OW2 V ER0 JH OY1 D
+OVERKIND	OW1 V ER0 K IH2 N D
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+OVERLAP  OW1 V ER0 L AE2 P
+OVERLAPPED  OW1 V ER0 L AE2 P T
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+OVERLAY  OW1 V ER0 L EY2
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+OVERMASTER	OW1 V ER0 M AE2 S T ER0
+OVERMASTERED	OW1 V ER0 M AE2 S T ER0 D
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+OVERPLAY  OW1 V ER0 P L EY1
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+OVERPLEASED	OW1 V ER0 P L IY1 Z D
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+OVERSCORED	OW1 V ER0 S K AO1 R D
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+OVERSEA  OW2 V ER0 S IY1
+OVERSEAS  OW1 V ER0 S IY1 Z
+OVERSEE  OW1 V ER0 S IY2
+OVERSEEING  OW1 V ER0 S IY2 IH0 NG
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+OVERSEER'S	OW1 V ER0 S IY1 ER0 Z
+OVERSEERS  OW2 V ER0 S IY1 ER0 Z
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+OVERSET	OW1 V ER0 S EH2 T
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+OVERSETTING	OW1 V ER0 S EH2 T IH0 NG
+OVERSEXED	OW1 V ER0 S EH2 K S T
+OVERSHADED	OW1 V ER0 SH EY1 D IH0 D
+OVERSHADOW  OW1 V ER0 SH AE1 D OW0
+OVERSHADOWED  OW2 V ER0 SH AE1 D OW0 D
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+OVERSHADOWS  OW1 V ER0 SH AE1 D OW0 Z
+OVERSHIRT	OW1 V ER0 SH ER2 T
+OVERSHOE	OW1 V ER0 SH UW2
+OVERSHOES	OW1 V ER0 SH UW2 Z
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+OVERSHUTE	OW1 V ER0 SH UW1 T
+OVERSIDE	OW1 V ER0 S AY2 D
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+OVERSKIRT	OW1 V ER0 S K ER2 T
+OVERSLAUGH	OW1 V ER0 S L AO2
+OVERSLEEP  OW2 V ER0 S L IY1 P
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+OVERSOUL	OW1 V ER0 S OW1 L
+OVERSPENT  OW1 V ER0 S P EH1 N T
+OVERSPREAD	OW1 V ER0 S P R EH1 D
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+OVERSTATE  OW1 V ER0 S T EY2 T
+OVERSTATED  OW1 V ER0 S T EY2 T IH0 D
+OVERSTATEMENT  OW1 V ER0 S T EY2 T M AH0 N T
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+OVERSTAY  OW2 V ER0 S T EY1
+OVERSTAYED  OW2 V ER0 S T EY1 D
+OVERSTAYING	OW1 V ER0 S T EY1 IH0 NG
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+OVERSTEPPED  OW1 V ER0 S T EH2 P T
+OVERSTEPPING  OW1 V ER0 S T EH2 P IH0 NG
+OVERSTEPS	OW1 V ER0 S T EH2 P S
+OVERSTIMULATED	OW1 V ER0 S T IH1 M Y AH0 L EY2 T IH0 D
+OVERSTIMULATION	OW1 V ER0 S T IH1 M Y AH0 L EY1 SH AH0 N
+OVERSTOCK  OW1 V ER0 S T AA1 K
+OVERSTOCKED  OW1 V ER0 S T AA1 K T
+OVERSTOCKING	OW1 V ER0 S T AA1 K IH0 NG
+OVERSTONE	OW1 V ER0 S T OW2 N
+OVERSTOW	OW1 V ER0 S T OW2
+OVERSTRAIN	OW1 V ER0 S T R EY1 N
+OVERSTRAINED	OW1 V ER0 S T R EY1 N D
+OVERSTRAINING	OW1 V ER0 S T R EY1 N IH0 NG
+OVERSTRAND	OW1 V ER0 S T R AH0 N D
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+OVERSTRETCHED	OW1 V ER0 S T R EH1 CH T
+OVERSTRONG	OW1 V ER0 S T R AO1 NG
+OVERSTRUNG	OW1 V ER0 S T R AH2 NG
+OVERSTUDY	OW1 V ER0 S T AH2 D IY0
+OVERSTUFFED  OW1 V ER0 S T AH2 F T
+OVERSUPPLY  OW2 V ER0 S AH0 P L AY1
+OVERSWEPT	OW1 V ER0 S W EH2 P T
+OVERT  OW0 V ER1 T
+OVERT  OW1 V ER0 T
+OVERTAKE  OW1 V ER0 T EY2 K
+OVERTAKEN  OW1 V ER0 T EY2 K AH0 N
+OVERTAKES  OW1 V ER0 T EY2 K S
+OVERTAKETH	OW1 V ER0 T EY2 K AH0 TH
+OVERTAKING  OW1 V ER0 T EY2 K IH0 NG
+OVERTASK	OW1 V ER0 T AE2 S K
+OVERTASKED	OW1 V ER0 T AE1 S K T
+OVERTASKING	OW1 V ER0 T AE2 S K IH0 NG
+OVERTAX  OW1 V ER0 T AE2 K S
+OVERTAXED  OW2 V ER0 T AE1 K S T
+OVERTAXING	OW1 V ER0 T AE1 K S IH0 NG
+OVERTHREW  OW2 V ER0 TH R UW1
+OVERTHROW  OW1 V ER0 TH R OW2
+OVERTHROWETH	OW1 V ER0 TH R OW2 EH1 TH
+OVERTHROWING  OW1 V ER0 TH R OW2 IH0 NG
+OVERTHROWN  OW2 V ER0 TH R OW1 N
+OVERTHROWS  OW1 V ER0 TH R OW2 Z
+OVERTHRUST	OW1 V ER0 TH R AH1 S T
+OVERTHWART	OW1 V ER0 TH W AO1 R T
+OVERTIME  OW1 V ER0 T AY2 M
+OVERTIRE	OW1 V ER0 T AY2 R
+OVERTIRED	OW1 V ER0 T AY1 ER0 D
+OVERTLY  OW0 V ER1 T L IY0
+OVERTON  OW1 V ER0 T AH0 N
+OVERTON'S	OW1 V ER0 T AH0 N Z
+OVERTONE  OW1 V ER0 T OW2 N
+OVERTONES  OW1 V ER0 T OW2 N Z
+OVERTOOK  OW2 V ER0 T UH1 K
+OVERTOP	OW1 V ER0 T AA2 P
+OVERTOP'S	OW1 V ER0 T AA2 P S
+OVERTOPPED	OW1 V ER0 T AA1 P T
+OVERTOPPING	OW1 V ER0 T AA1 P IH0 NG
+OVERTOPS	OW1 V ER0 T AA2 P S
+OVERTOWERING	OW1 V ER0 T AW1 ER0 IH0 NG
+OVERTRAINED	OW1 V ER0 T R EY1 N D
+OVERTURE  OW1 V ER0 CH ER0
+OVERTURES  OW1 V ER0 CH UH2 R Z
+OVERTURN  OW1 V ER0 T ER2 N
+OVERTURN'D	OW1 V ER0 T ER2 N D
+OVERTURNED  OW1 V ER0 T ER2 N D
+OVERTURNING  OW1 V ER0 T ER2 N IH0 NG
+OVERTURNINGS	OW1 V ER0 T ER2 N IH0 NG Z
+OVERTURNS  OW1 V ER0 T ER2 N Z
+OVERVALUE  OW1 V ER0 V AE2 L Y UW0
+OVERVALUED  OW1 V ER0 V AE2 L Y UW0 D
+OVERVALUING	OW1 V ER0 V AE2 L Y UW0 IH0 NG
+OVERWEARIED	OW2 V ER0 W IH1 R IY0 D
+OVERWEARY	OW2 V ER0 W EH2 R IY0
+OVERWEENING	OW2 V ER0 W IY1 N IH0 NG
+OVERWEENINGLY	OW2 V ER0 W IY1 N IH0 NG L IY0
+OVERWEG	OW2 V ER0 W EH2 G
+OVERWEIGH	OW2 V ER0 W EY2
+OVERWEIGHED	OW2 V ER0 W EY1 D
+OVERWEIGHT  OW2 V ER0 W EY1 T
+OVERWEIGHTED  OW2 V ER0 W EY1 T IH0 D
+OVERWELL	OW2 V ER0 W EH2 L
+OVERWHELM  OW2 V ER0 HH W EH1 L M
+OVERWHELM  OW2 V ER0 W EH1 L M
+OVERWHELM'D	OW2 V ER0 W EH1 L M D
+OVERWHELMED  OW2 V ER0 HH W EH1 L M D
+OVERWHELMED  OW2 V ER0 W EH1 L M D
+OVERWHELMING  OW2 V ER0 HH W EH1 L M IH0 NG
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+OVERWHELMINGLY  OW2 V ER0 W EH1 L M IH0 NG L IY0
+OVERWHELMS  OW2 V ER0 HH W EH1 L M Z
+OVERWHELMS  OW2 V ER0 W EH1 L M Z
+OVERWISE	OW2 V ER0 W AY2 Z
+OVERWORK  OW2 V ER0 W ER1 K
+OVERWORKED  OW2 V ER0 W ER1 K T
+OVERWORKING  OW2 V ER0 W ER1 K IH0 NG
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+OVERWORN	OW2 V ER0 W ER0 N
+OVERWROUGHT  OW2 V ER0 R AO1 T
+OVERYSSEL	OW1 V ER0 IH1 S AH0 L
+OVERZEALOUS  OW2 V ER0 Z EH1 L AH0 S
+OVES	OW1 V Z
+OVID	OW1 V IH0 D
+OVID'S	OW0 V IH1 D Z
+OVIDE	OW0 V AY1 D
+OVIDIAN	OW0 V IH1 D IY0 AH0 N
+OVIDIO	OW0 V IY1 D IY0 OW0
+OVIDIUS	OW0 V IH1 D IY0 AH0 S
+OVIDUCT	OW0 V IH1 D AH0 K T
+OVIDUCTS	OW0 V IH1 D AH0 K T S
+OVIEDO  OW0 V IY0 EY1 D OW0
+OVINE	OW0 V IY1 N IY0
+OVINGTON	OW1 V IH0 NG T AH0 N
+OVINGTON'S	OW1 V IH0 NG T AH0 N Z
+OVIPAROUS  OW0 V IH1 P ER0 AH0 S
+OVIPOSITOR	OW0 V IH1 P AA1 Z IH0 T ER0
+OVIS	OW1 V IH0 S
+OVO	OW1 V OW0
+OVOID  OW1 V OY2 D
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+OVREBO	OW1 V R IY1 B OW0
+OVULATION  OW2 V Y AH0 L EY1 SH AH0 N
+OVULE  OW1 V Y UW0 L
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+OVUM  OW1 V AH0 M
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+OWER	OW1 ER0
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+OWING  OW1 IH0 NG
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+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
+RETRENCHED  R IY0 T R EH1 N CH T
+RETRENCHING  R IY0 T R EH1 N CH IH0 NG
+RETRENCHMENT  R IY0 T R EH1 N CH M AH0 N T
+RETRENCHMENTS  R IY0 T R EH1 N CH M AH0 N T S
+RETRIAL  R IY0 T R AY1 AH0 L
+RETRIBUTION  R EH2 T R AH0 B Y UW1 SH AH0 N
+RETRIBUTIONS	R EH2 T R AH0 B Y UW1 SH AH0 N Z
+RETRIBUTIVE	R IY0 T R IH1 B Y AH0 T IH0 V
+RETRIBUTORY	R IY0 T R IH1 B Y AH0 T AO2 R IY0
+RETRIEVAL  R IH0 T R IY1 V AH0 L
+RETRIEVAL  R IY0 T R IY1 V AH0 L
+RETRIEVE  R IH0 T R IY1 V
+RETRIEVE  R IY0 T R IY1 V
+RETRIEVED  R IY0 T R IY1 V D
+RETRIEVER  R IY0 T R IY1 V ER0
+RETRIEVER'S	R IY0 T R IY1 V ER0 Z
+RETRIEVERS  R IY0 T R IY1 V ER0 Z
+RETRIEVES  R IH0 T R IY1 V Z
+RETRIEVES  R IY0 T R IY1 V Z
+RETRIEVING  R IY0 T R IY1 V IH0 NG
+RETRIMMED	R IY0 T R IH1 M D
+RETRIMMING	R IY0 T R IH1 M IH0 NG
+RETRO  R EH1 T R OW0
+RETROACTIVE  R EH2 T R OW0 AE1 K T IH0 V
+RETROCESSION  R EH2 T R OW0 S EH1 SH AH0 N
+RETROD	R EH2 T R AH0 D
+RETROGRADATION	R EH2 T R AH0 G R EY0 D EY1 SH AH0 N
+RETROGRADE  R EH1 T R AH0 G R EY2 D
+RETROGRADED	R EH2 T R AH0 G R EY2 D IH0 D
+RETROGRADES	R EH2 T R AH0 G R EY2 D Z
+RETROGRADING	R EH2 T R AH0 G R EY2 D IH0 NG
+RETROGRESSION	R EH2 T R AH0 G R EH1 SH AH0 N
+RETROGRESSIONS	R EH2 T R AH0 G R EH1 SH AH0 N Z
+RETROGRESSIVE	R EH2 T R AH0 G R EH1 S IH0 V
+RETRORSUM	R IY0 T R AO0 R S AH0 M
+RETROSPECT  R EH1 T R AH0 S P EH2 K T
+RETROSPECTION	R EH2 T R AH0 S P EH1 K SH AH0 N
+RETROSPECTIONS	R EH2 T R AH0 S P EH1 K SH AH0 N Z
+RETROSPECTIVE  R EH2 T R AH0 S P EH1 K T IH0 V
+RETROSPECTIVELY  R EH2 T R OW0 S P EH1 K T IH0 V L IY0
+RETROSPECTS	R EH2 T R AH0 S P EH1 K S
+RETROUSSE	R IY0 T R AW1 S
+RETROUVE	R IY0 T R UW2 V
+RETTA	R EH1 T AH0
+RETTEL	R EH1 T AH0 L
+RETTIMO	R EH1 T IH0 M OW2
+RETTO	R EH1 T OW0
+RETTY	R EH1 T IY0
+RETURN  R IH0 T ER1 N
+RETURN  R IY0 T ER1 N
+RETURN'D	R IH0 T ER0 N D
+RETURNABLE  R IY0 T ER1 N AH0 B AH0 L
+RETURND	R IY0 T ER1 N T
+RETURNE	R IH0 T ER0 N
+RETURNED  R IH0 T ER1 N D
+RETURNED  R IY0 T ER1 N D
+RETURNERS	R IY0 T ER1 N ER0 Z
+RETURNEST	R IY0 T ER1 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
+REUTLINGEN	R OY1 T L IH0 NG AH0 N
+REUTON	R OY1 T AH0 N
+REUTTER  R OY1 T ER0
+REV  R EH1 V
+REV'RENCE	R EH2 V R AH0 N S
+REV'REND	R EH2 V R EH0 N D
+REVA  R EY1 V AH0
+REVAILLOUD	R EH1 V EY2 L UW1 D
+REVAL	R EH1 V AH0 L
+REVALUATION  R IY0 V AE1 L Y UW0 EY1 SH AH0 N
+REVAMP  R IY0 V AE1 M P
+REVAMPED  R IY0 V AE1 M P T
+REVANCHE	R EH0 V AA1 N K IY0
+REVARD  R IH0 V AA1 R D
+REVARNISHED	R IH0 V AA1 R N IH0 SH T
+REVE	R IH0 V
+REVEAL  R IH0 V IY1 L
+REVEAL  R IY0 V IY1 L
+REVEAL'D	R IY0 V IY1 L D
+REVEALED  R IH0 V IY1 L D
+REVEALED  R IY0 V IY1 L D
+REVEALER	R IY0 V IY1 L ER0
+REVEALERS	R IY0 V IY1 L ER0 Z
+REVEALETH	R IY0 V IY1 L EH2 TH
+REVEALING  R IH0 V IY1 L IH0 NG
+REVEALING  R IY0 V IY1 L IH0 NG
+REVEALINGLY	R IY0 V IY1 L IH0 NG L IY0
+REVEALINGS	R IY0 V IY1 L IH0 NG Z
+REVEALMENT	R IY0 V IY1 L M AH0 N T
+REVEALS  R IH0 V IY1 L Z
+REVEALS  R IY0 V IY1 L Z
+REVECCE	R IH0 V EH1 CH IY0
+REVEIL	R EY0 V EY1 L
+REVEILLE	R EH1 V AY0 L
+REVEILLEE	R EH1 V AY0 L IY0
+REVEILLERE	R EH1 V AY0 L ER0
+REVEILLON	R EY0 V EY1 L AH0 N
+REVEL  R EH1 V AH0 L
+REVEL'S	R EH1 V AH0 L Z
+REVELATION  R EH2 V AH0 L EY1 SH AH0 N
+REVELATIONS  R EH2 V AH0 L EY1 SH AH0 N Z
+REVELATOR	R EH2 V AH0 L EY1 T ER0
+REVELATORY  R IH0 V EH1 L AH0 T AO2 R IY0
+REVELED  R EH1 V AH0 L D
+REVELER  R EH1 V AH0 L ER0
+REVELERS  R EH1 V AH0 L ER0 Z
+REVELEY	R EH1 V L IY0
+REVELING  R EH1 V AH0 L IH0 NG
+REVELINGS	R EH1 V AH0 L IH0 NG Z
+REVELL  R EY0 V EY1 L
+REVELL'D	R EH1 V AH0 L D
+REVELLED	R EH1 V AH0 L D
+REVELLER  R EH1 V AH0 L ER0
+REVELLER'S	R EH1 V AH0 L ER0 Z
+REVELLERS  R EH1 V AH0 L ER0 Z
+REVELLING	R EH1 V AH0 L IH0 NG
+REVELLINGS	R EH1 V AH0 L IH0 NG Z
+REVELLY	R EH1 V AH0 L IY0
+REVELRIES	R EH1 V AH0 L R IY0 Z
+REVELRY  R EH1 V AH0 L R IY0
+REVELS  R EH1 V AH0 L Z
+REVELSTOKE	R EH1 V AH0 L S T OW1 K
+REVENANT	R EH1 V AH0 N AH0 N T
+REVENANTS	R EH1 V AH0 N AH0 N T S
+REVENEZ	R EH1 V AH0 N EH1 Z
+REVENG'D	R IH0 V IH1 NG AH0 D
+REVENGE  R IY0 V EH1 N JH
+REVENGE'S	R IY0 V EH1 N JH IH0 Z
+REVENGED	R IY0 V EH1 N JH D
+REVENGEFUL	R IY0 V EH1 N JH F AH0 L
+REVENGEFULLY	R IY0 V EH1 N JH F AH0 L IY0
+REVENGEFULNESS	R IY0 V EH1 N JH F AH0 L N AH0 S
+REVENGEMENT	R IY0 V EH1 N JH M AH0 N T
+REVENGER	R IY0 V EH1 N JH ER0
+REVENGES	R EH1 V AH0 N JH AH0 Z
+REVENGING	R IY0 V EH1 N JH IH0 NG
+REVENIR	R EH1 V AH0 N IH1 R
+REVENITA	R EH1 V AH0 N IY1 T AH0
+REVENONS	R EH1 V AH0 N AH0 N Z
+REVENTLOW	R IH0 V EH1 N T L OW0
+REVENUE  R EH1 V AH0 N UW2
+REVENUE  R EH1 V AH0 N Y UW2
+REVENUER  R EH1 V AH0 N UW2 ER0
+REVENUER  R EH1 V AH0 N Y UW2 ER0
+REVENUERS  R EH1 V AH0 N UW2 ER0 Z
+REVENUERS  R EH1 V AH0 N Y UW2 ER0 Z
+REVENUES  R EH1 V AH0 N UW2 Z
+REVENUES  R EH1 V AH0 N Y UW2 Z
+REVER	R EH1 V ER0
+REVER'D	R IH0 V ER1 D
+REVERBERANT	R IY0 V ER1 B ER0 AH0 N T
+REVERBERATE  R IH0 V ER1 B ER0 AH0 T
+REVERBERATE  R IH0 V ER1 B ER0 EY0 T
+REVERBERATED  R IH0 V ER1 B ER0 EY2 T IH0 D
+REVERBERATED  R IY0 V ER1 B ER0 EY2 T AH0 D
+REVERBERATED  R IY0 V ER1 B ER0 EY2 T IH0 D
+REVERBERATES  R IH0 V ER1 B ER0 EY2 T S
+REVERBERATING  R IH0 V ER1 B ER0 EY2 T IH0 NG
+REVERBERATING  R IY0 V ER1 B ER0 EY2 T IH0 NG
+REVERBERATION  R IY0 V ER2 B ER0 EY1 SH AH0 N
+REVERBERATIONS  R IH0 V ER2 B ER0 EY1 SH AH0 N Z
+REVERBERATOR	R IY0 V ER1 B ER0 EY2 T ER0
+REVERDY	R IH0 V ER1 D IY0
+REVERE  R IH0 V IH1 R
+REVERE'S	R IH0 V IH1 R Z
+REVERED  R IH0 V IH1 R D
+REVERENCE  R EH1 V ER0 AH0 N S
+REVERENCE'S	R EH1 V ER0 AH0 N S IH0 Z
+REVERENCED	R EH1 V ER0 AH0 N S T
+REVERENCES	R EH1 V ER0 AH0 N S IH0 Z
+REVERENCING	R EH1 V ER0 AH0 N S IH0 NG
+REVEREND  R EH1 V ER0 AH0 N D
+REVEREND  R EH1 V R AH0 N D
+REVEREND'S	R EH1 V ER0 AH0 N D Z
+REVERENDO	R EH1 V ER0 AH0 N D OW0
+REVERENDS	R EH1 V ER0 AH0 N D Z
+REVERENT  R EH1 V ER0 AH0 N T
+REVERENTIA	R EH2 V ER0 EH1 N SH AH0
+REVERENTIAL  R EH2 V ER0 EH1 N CH AH0 L
+REVERENTIAL  R EH2 V ER0 EH1 N SH AH0 L
+REVERENTIALLY	R EH2 V ER0 EH1 N CH AH0 L IY0
+REVERENTLY  R EH1 V ER0 AH0 N T L IY0
+REVERES  R IH0 V IH1 R Z
+REVERIE  R EH1 V ER0 IY0
+REVERIES  R EH1 V ER0 IY0 Z
+REVERING  R IH0 V IH1 R IH0 NG
+REVERS	R EH1 V ER0 Z
+REVERS'D	R IH0 V ER1 S AH0 D
+REVERSAL  R IH0 V ER1 S AH0 L
+REVERSAL  R IY0 V ER1 S AH0 L
+REVERSALS  R IH0 V ER1 S AH0 L Z
+REVERSALS  R IY0 V ER1 S AH0 L Z
+REVERSE  R IH0 V ER1 S
+REVERSE  R IY0 V ER1 S
+REVERSED  R IH0 V ER1 S T
+REVERSED  R IY0 V ER1 S T
+REVERSELY	R IH0 V ER1 S L IY0
+REVERSES  R IH0 V ER1 S IH0 Z
+REVERSES  R IY0 V ER1 S AH0 Z
+REVERSES  R IY0 V ER1 S IH0 Z
+REVERSIBLE  R IH0 V ER1 S AH0 B AH0 L
+REVERSIBLE  R IY0 V ER1 S AH0 B AH0 L
+REVERSING  R IH0 V ER1 S IH0 NG
+REVERSING  R IY0 V ER1 S IH0 NG
+REVERSION  R IH0 V ER1 ZH AH0 N
+REVERSIONARY	R IH0 V ER1 ZH AH0 N EH2 R IY0
+REVERSIONS	R IH0 V ER1 ZH AH0 N Z
+REVERT  R IH0 V ER1 T
+REVERT  R IY0 V ER1 T
+REVERTED  R IH0 V ER1 T IH0 D
+REVERTED  R IY0 V ER1 T IH0 D
+REVERTING  R IH0 V ER1 T IH0 NG
+REVERTS  R IH0 V ER1 T S
+REVERY	R EH1 V R IY0
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+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
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+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
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+STRIDENTLY  S T R AY1 D AH0 N T L IY0
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+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
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+STRIK'ST	S T R AY1 K S T
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+STRIKE'S  S T R AY1 K S
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+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
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+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
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+STRIPED  S T R AY1 P T
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+STRIPLING  S T R IH1 P L IH0 NG
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+STRIPLINGS	S T R IH1 P L IH0 NG Z
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+STRIPPED  S T R IH1 P T
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+STRIPPING  S T R IH1 P IH0 NG
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+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
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+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
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+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
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+STRODE  S T R OW1 D
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+STROGANOFFS	S T R OW1 G AH0 N AO2 F S
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+STROGUE	S T R OW1 G
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+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
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+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
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+STROMBERG'S	S T R AA1 M B ER0 G Z
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+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
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+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
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+STROOD	S T R UW1 D
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+STROPS	S T R AA1 P S
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+STROUD  S T R AW1 D
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+STROUSS	S T R AW1 S
+STROUT  S T R AW1 T
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+STROW  S T R OW1
+STROW'D	S T R AW1 D
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+STROZZI	S T R AA1 Z IY0
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+STRUBLE  S T R UW1 B AH0 L
+STRUCK  S T R AH1 K
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+TRANSCRIBER  T R AE0 N S K R AY1 B ER0
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+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
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+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
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+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
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+TRAVELED  T R AE1 V AH0 L D
+TRAVELER  T R AE1 V AH0 L ER0
+TRAVELER  T R AE1 V L ER0
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+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
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+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
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+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
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+TRAVINO	T R AA0 V IY1 N OW0
+TRAVIS  T R AE1 V IH0 S
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+TRAVNIK  T R AE1 V N IH0 K
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+TREAS	T R IY1 Z
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+TREASONABLY	T R IY1 Z AH0 N AH0 B L IY0
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+TREASURER'S  T R EH1 ZH ER0 ER0 Z
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+TREASURING	T R EH1 ZH ER0 IH0 NG
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+TREAT  T R IY1 T
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+TREATMENT  T R IY1 T M AH0 N T
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+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
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+UNCOMPLICATED  AH0 N K AA1 M P L AH0 K EY2 T IH0 D
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+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	AH2 N K AA2 M P R IH0 HH EH1 N D IH0 NG L IY0
+UNCOMPROMISED	AH0 N K AA1 M P R AH0 M AY2 Z D
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+UNCONCEALABLE	AH2 N K AH0 N S IY1 L AH0 B AH0 L
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+UNCONDEMNED	AH2 N K AH0 N D EH1 M D
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+UNCONDITIONALLY  AH2 N K AH0 N D IH1 SH N AH0 L IY0
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+UNCONGENIAL	AH2 N K AH0 N JH IY1 N Y AH0 L
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+UNCONJECTURED	AH2 N K AH0 N JH EH1 K CH ER0 D
+UNCONNECTED  AH2 N K AH0 N EH1 K T IH0 D
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+UNCONQUER'D	AH0 N K AA1 NG K ER0 D
+UNCONQUERABLE	AH0 N K AA1 NG K ER0 AH0 B AH0 L
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+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
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+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
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+UNCONSTITUTIONALLY  AH2 N K AA2 N S T AH0 T UW1 SH AH0 N AH0 L IY0
+UNCONSTITUTIONALLY  AH2 N K AA2 N S T AH0 T UW1 SH N AH0 L IY0
+UNCONSTRAINED  AH2 N K AH0 N S T R EY1 N D
+UNCONSTRAINEDLY	AH2 N K AH0 N S T R EY1 N D L IY0
+UNCONSTRAINT	AH2 N K AH0 N S T R EY1 N T
+UNCONSULTED	AH2 N K AH0 N S AH1 L T IH0 D
+UNCONSUMED	AH2 N K AH0 N S UW1 M D
+UNCONSUMING	AH2 N K AH0 N S UW1 M IH0 NG
+UNCONTAMINATED  AH2 N K AH0 N T AE1 M AH0 N EY2 T AH0 D
+UNCONTENT	AH2 N K AH0 N T EH1 N T
+UNCONTESTED  AH2 N K AH0 N T EH1 S T IH0 D
+UNCONTRADICTED  AH2 N K AO0 N T R AH0 D IH1 K T IH0 D
+UNCONTROLABLE	AH2 N K AH0 N T R OW2 L AH0 B AH0 L
+UNCONTROLL'D	AH2 N K AH0 N T R OW1 L D
+UNCONTROLLABLE  AH2 N K AH0 N T R OW1 L AH0 B AH0 L
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+UNCONTROLLED  AH2 N K AH0 N T R OW1 L D
+UNCONTROULABLE	AH2 N K AH0 N T R UW1 L AH0 B AH0 L
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+UNREALITIES	AH2 N R IY0 AE1 L AH0 T IY0 Z
+UNREALITY  AH2 N R IY0 AE1 L AH0 T IY0
+UNREALIZABLE	AH0 N R IY2 AH0 L AY1 Z AH0 B AH0 L
+UNREALIZED  AH0 N R IY1 AH0 L AY2 Z D
+UNREALIZING	AH0 N R IY1 L AY2 Z IH0 NG
+UNREAPED	AH0 N R IY1 P T
+UNREASON	AH0 N R IY1 Z AH0 N
+UNREASONABLE  AH0 N R IY1 Z N AH0 B AH0 L
+UNREASONABLENESS	AH0 N R IY1 Z N AH0 B AH2 L N AH0 S
+UNREASONABLY  AH0 N R IY1 Z AH0 N AH0 B L IY0
+UNREASONED	AH0 N R IY1 Z AH0 N D
+UNREASONING  AH0 N R IY1 Z N IH0 NG
+UNREASONINGLY	AH0 N R IY1 Z AH0 N IH0 NG L IY0
+UNREBUKED	AH2 N R IY0 B Y UW1 K T
+UNRECEPTIVE  AH0 N R IH0 S EH1 P T IH0 V
+UNRECIPROCATED	AH0 N R IH0 S IH1 P R AH0 K EY2 T IH0 D
+UNRECKING	AH0 N R EH1 K IH0 NG
+UNRECKONABLE	AH0 N R EH1 K AH0 N AH0 B AH0 L
+UNRECKONED	AH0 N R EH1 K AH0 N D
+UNRECKONING	AH0 N R EH1 K AH0 N IH0 NG
+UNRECLAIMED	AH2 N R IY0 K L EY1 M D
+UNRECOGNISABLE	AH0 N R EH1 K AH0 G N AY2 Z AH0 B AH0 L
+UNRECOGNISED	AH0 N R EH1 K AH0 G N AY1 Z D
+UNRECOGNISING	AH0 N R EH1 K AH0 G N AY2 Z IH0 NG
+UNRECOGNITION	AH0 N R EH1 K AH0 G N IH1 SH AH0 N
+UNRECOGNIZABLE  AH0 N R EH2 K AH0 G N AY1 Z AH0 B AH0 L
+UNRECOGNIZABLY	AH0 N R EH1 K AH0 G N AY2 Z AH0 B L IY0
+UNRECOGNIZED  AH0 N R EH1 K AH0 G N AY2 Z D
+UNRECOGNIZING	AH0 N R EH1 K AH0 G N AY2 Z IH0 NG
+UNRECOGNIZINGLY	AH0 N R EH1 K AH0 G N AY2 Z IH0 NG L IY0
+UNRECOMPENSED	AH0 N R EH1 K AH0 M P EH1 N S T
+UNRECONCILED  AH0 N R EH1 K AH0 N S AY2 L D
+UNRECONSTRUCTED  AH2 N R IY0 K AH0 N S T R AH1 K T IH0 D
+UNRECORDED  AH2 N R IH0 K AO1 R D IH0 D
+UNRECOVERABLE	AH2 N R IH0 K AH1 V ER0 AH0 B AH0 L
+UNRECOVERED	AH2 N R IH0 K AH1 V ER0 D
+UNREDEEMABLE	AH2 N R IY0 D IY1 M AH0 B AH0 L
+UNREDEEMED  AH2 N R IY0 D IY1 M D
+UNREDRESSED	AH2 N R IY0 D R EH1 S T
+UNREELED	AH0 N R IY1 L D
+UNREELING	AH0 N R IY1 L IH0 NG
+UNREEVE	AH2 N R IY0 IH0 V
+UNREFINED  AH2 N R IY0 F AY1 N D
+UNREFLECTED	AH2 N R IH0 F L EH1 K T IH0 D
+UNREFLECTING	AH2 N R IH0 F L EH1 K T IH0 NG
+UNREFLECTINGLY	AH2 N R IH0 F L EH1 K T IH0 NG L IY0
+UNREFLECTIVE	AH2 N R IH0 F L EH1 K T IH0 V
+UNREFORMED	AH2 N R IH0 F AO1 R M D
+UNREFRESHED	AH2 N R IY0 F R EH1 SH T
+UNREFRESHING	AH2 N R IY0 F R EH1 SH IH0 NG
+UNREFUTED  AH2 N R IY0 F Y UW1 T IH0 D
+UNREGARDED	AH2 N R IH0 G AA1 R D IH0 D
+UNREGARDFUL	AH2 N R IH0 G AA2 R D F AH0 L
+UNREGARDING	AH2 N R IH0 G AA1 R D IH0 NG
+UNREGENERATE	AH2 N R IY0 JH EH1 N ER0 EY2 T
+UNREGENERATED	AH2 N R IY0 JH EH1 N ER0 EY2 T IH0 D
+UNREGISTERED  AH0 N R EH1 JH IH0 S T ER0 D
+UNREGRETFUL	AH2 N R IH0 G R EH1 T F AH0 L
+UNREGRETTED	AH2 N R IH0 G R EH1 T IH0 D
+UNREGULATED  AH0 N R EH1 G Y AH0 L EY2 T IH0 D
+UNREHEARSED  AH0 N R IY0 HH ER1 S T
+UNREINED	AH0 N R AY0 N D
+UNRELATED  AH2 N R IH0 L EY1 T IH0 D
+UNRELATED  AH2 N R IY0 L EY1 T IH0 D
+UNRELAXED	AH2 N R IY0 L AE1 K S T
+UNRELAXING	AH2 N R IY0 L AE1 K S IH0 NG
+UNRELEASED  AH0 N R IH0 L IY1 S T
+UNRELENTING  AH2 N R IY0 L EH1 N T IH0 NG
+UNRELENTINGLY	AH2 N R IY0 L EH1 N T IH0 NG L IY0
+UNRELIABILITY  AH2 N R IY0 L AY2 AH0 B IH1 L IH0 T IY0
+UNRELIABLE  AH2 N R IH0 L AY1 AH0 B AH0 L
+UNRELIABLE  AH2 N R IY0 L AY1 AH0 B AH0 L
+UNRELIEVED  AH2 N R IY0 L IY1 V D
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+UNRELUCTANT	AH2 N R IY0 L AH1 K T AH0 N T
+UNRELUCTANTLY	AH2 N R IY0 L AH1 K T AH0 N T L IY0
+UNREMARKABLE  AH0 N R IH0 M AA1 R K AH0 B AH0 L
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+UNREMEMBERED	AH0 N R IH0 M EH1 M B ER0 D
+UNREMEMBERING	AH0 N R IH0 M EH1 M B ER0 IH0 NG
+UNREMITTED  AH0 N R IH0 M IH1 T IH0 D
+UNREMITTING  AH2 N R IH0 M IH1 T IH0 NG
+UNREMITTING  AH2 N R IY0 M IH1 T IH0 NG
+UNREMITTINGLY	AH0 N R IH0 M IH1 T IH0 NG L IY0
+UNREMOVED	AH2 N R IY0 M UW1 V D
+UNREMUNERATED	AH0 N R IH0 M Y UW2 N ER0 EY2 T IH0 D
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+UNRENEWED	AH2 N R IY0 N UW1 D
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+UNREPAIRED	AH2 N R IH0 P EH1 R D
+UNREPEALED	AH2 N R IY0 P IY1 L D
+UNREPEATABLE	AH2 N R IY0 P IY1 T AH0 B AH0 L
+UNREPELLED	AH2 N R IH0 P EH1 L D
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+UNREPENTANT  AH2 N R IY0 P EH1 N T AH0 N T
+UNREPENTED	AH2 N R IH0 P EH1 N T IH0 D
+UNREPENTING	AH2 N R IY0 P EH1 N IH0 NG
+UNREPINING	AH2 N R IY0 P AY1 N IH0 NG
+UNREPININGLY	AH2 N R IY0 P AY1 N IH0 NG L IY0
+UNREPORTED  AH2 N R IY0 P AO1 R T IH0 D
+UNREPRESENTATIVE  AH0 N R EH2 P R IH0 Z EH1 N T AH0 T IH0 V
+UNREPRESENTED  AH0 N R EH2 P R IH0 Z EH1 N T IH0 D
+UNREPRESSED	AH2 N R IY0 P R EH1 S T
+UNREPROACHED	AH2 N R IY0 P R OW1 CH T
+UNREPROACHFUL	AH2 N R IY0 P R OW1 CH F AH0 L
+UNREPROACHING	AH2 N R IY0 P R OW1 CH IH0 NG
+UNREPROVED	AH0 N R EH2 P R UW1 V D
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+UNREQUIRED	AH2 N R IY0 K W AY1 R D
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+UNRESENTED	AH2 N R IY0 Z EH1 N T IH0 D
+UNRESENTFUL	AH2 N R IY0 Z EH1 N T F AH0 L
+UNRESENTFULLY	AH2 N R IY0 Z EH1 N T F AH0 L IY0
+UNRESERVE	AH2 N R IY0 Z ER1 V
+UNRESERVED	AH2 N R IY0 Z ER1 V D
+UNRESERVEDLY	AH2 N R IY0 Z ER1 V AH0 D L IY0
+UNRESERVEDNESS	AH2 N R IY0 Z ER1 V AH0 D N AH0 S
+UNRESIGNED	AH2 N R IY0 S AY1 N D
+UNRESISTED	AH2 N R IY0 Z IH1 S T IH0 D
+UNRESISTING	AH2 N R IY0 Z IH1 S T IH0 NG
+UNRESISTINGLY	AH2 N R IY0 Z IH1 S T IH0 NG L IY0
+UNRESOLVED  AH0 N R IH0 Z AA1 L V D
+UNRESPECTABLE	AH2 N R IY0 S P EH1 K T AH0 B AH0 L
+UNRESPECTED	AH2 N R IY0 S P EH1 K T IH0 D
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+UNRESPONSIVENESS	AH2 N R IY0 S P AA1 N S IH0 V N AH0 S
+UNREST  AH0 N R EH1 S T
+UNRESTED	AH0 N R EH1 S T IH0 D
+UNRESTFUL	AH0 N R EH1 S T F AH0 L
+UNRESTING	AH0 N R EH1 S T IH0 NG
+UNRESTINGLY	AH0 N R EH1 S T IH0 NG L IY0
+UNRESTORED	AH2 N R IH0 S T AO1 R D
+UNRESTRAINABLE	AH2 N R IY0 S T R EY1 N AH0 B AH0 L
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+UNRESTRAINEDLY	AH2 N R IY0 S T R EY1 N D L IY0
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+UNRESTRICTED  AH2 N R IY0 S T R IH1 K T IH0 D
+UNRESTRICTEDLY	AH2 N R IY0 S T R IH1 K T IH0 D L IY0
+UNRETURNED	AH2 N R IY0 T ER1 N D
+UNRETURNING	AH2 N R IY0 T ER1 N IH0 NG
+UNREVEALED	AH0 N R IY0 V IY1 L D
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+UNRIVALLED	AH0 N R AY1 V AH0 L D
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+UNSADDLE	AH0 N S AE1 D AH0 L
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+UNSALTED	AH0 N S EY1 L T IH0 D
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+VAILELE	V EY1 L EY1 L IY0
+VAILIMA	V EY1 L IY1 M AH0
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+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
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+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
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+VAISRAVANA	V EY1 Z R AH0 V AE1 N AH0
+VAISSEAU	V EY1 S OW1
+VAISSEAUX	V EY1 S OW1
+VAISYA	V EY1 Z IY0 AH0
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+VAIT	V EY1 T
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+VAIZEY	V EY1 Z IY0
+VAJDAR	V AY1 D AA2 R
+VAKA	V AA1 K AH0
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+VAKEEL	V AE1 K IY0 L
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+VALANCOURT	V AE1 L AH0 N K AO2 R T
+VALANCOURT'S	V AE1 L AH0 N K AO2 R T S
+VALANDIER	V AA0 L AA1 N D IY0 ER0
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+VALAPEE	V AE2 L AH0 P IY1
+VALASCO	V AH0 L AA1 S K OW0
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+VALBUE	V AA0 L B Y UW0
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+VALCARTIER	V AA0 L K AA1 R T IY0 ER0
+VALCHITA	V AA0 L CH IY1 T AH0
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+VALCOURS	V AH0 L K AO1 R Z
+VALDARNO	V AA0 L D AA1 R N OW0
+VALDE	V AA0 L D EY0
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+VALDEMAR'S	V AA0 L D EY0 M AA1 R Z
+VALDEN	V AE2 L D AH0 N
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+VALDERS	V AE2 L D ER0 Z
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+VALDESCHI	V AE2 L D EH1 S K IY0
+VALDEZ  V AE0 L D EH1 Z
+VALDEZ'S	V AE2 L D EH0 Z IH0 Z
+VALDIERI	V AE2 L D IH1 R IY0
+VALDIVIA  V AA0 L D IY1 V IY0 AH0
+VALDIVIAN	V AA0 L D IY1 V IY0 AH0 N
+VALDO	V AA0 L D OW0
+VALDOREME	V AE2 L D AO1 R IY1 M
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+VALE'S	V EY1 L Z
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+VALECOURT	V AE2 L AH0 K AO2 R T
+VALEDICTION	V AE2 L AH0 D IH1 K SH AH0 N
+VALEDICTORIAN  V AE2 L AH0 D IH2 K T AO1 R IY0 AH0 N
+VALEDICTORY  V AE2 L AH0 D IH1 K T ER0 IY0
+VALEDOLMO	V AE2 L AH0 D OW1 L M OW0
+VALEER	V AE1 L IH1 R
+VALEHAMPTON	V AA1 L EH0 HH AE1 M P T AH0 N
+VALENCAY	V AH0 L EH1 N K EY1
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+VALENCIA  V AH0 L EH1 N S IY0 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
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+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
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+VALERA  V AH0 L EH1 R AH0
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+VALERIA  V AH0 L IY1 R IY0 AH0
+VALERIA'S	V AH0 L IY1 R IY0 AH0 Z
+VALERIAN  V AH0 L IH1 R IY0 AH0 N
+VALERIE  V AE1 L ER0 IY0
+VALERIE'S	V AH0 L IH1 R IY0 Z
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+VALERIO  V AH0 L IY1 R IY0 OW0
+VALERIUS	V AH0 L IY1 R IY0 IH0 S
+VALERO  V AH0 L EH1 R OW0
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+VALESCURE	V AE2 L AH0 S K Y UH1 R
+VALESKI	V AH0 L EH1 S K IY0
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+VALESKY'S	V AA0 L EY1 S K IY0 Z
+VALET  V AE0 L EY1
+VALET'S	V AE0 L EY1 Z
+VALETE	V AH0 L IY1 T
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+VALETTA'S	V AH0 L EH1 T AH0 Z
+VALETTE	V AH0 L EH1 T
+VALETUDINARIAN	V AE2 L AH0 T UW1 D AH0 N EH1 R IY0 AH0 N
+VALETUDINARIANS	V AE2 L AH0 T UW1 D AH0 N EH1 R IY0 AH0 N Z
+VALEUR	V AE1 L ER1
+VALEVSKA	V AA0 L EH1 V S K AH0
+VALEYON	V AA0 L IY0 AA0 N
+VALFEUILLU	V AE1 L F IY0 UW1 L Y UW0
+VALFREYNE	V AE2 L F R EY1 N
+VALGAME	V AA0 L G AA1 M IY0
+VALGRAND	V AE1 L G R AE0 N D
+VALGUANERA	V AA0 L G W AH0 N EH1 R AH0
+VALHAL	V AE2 L HH AE1 L
+VALHALLA  V AE2 L HH AE1 L AH0
+VALHALLA'S	V AE2 L HH AE1 L AH0 Z
+VALHURST	V AE1 L HH ER0 S T
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+VALIANCE	V AE1 L Y AH0 N S
+VALIANCY	V AE1 L Y AH0 N S IY0
+VALIANT  V AE1 L Y AH0 N T
+VALIANTLY  V AE1 L Y AH0 N T L IY0
+VALIANTS	V AE1 L Y AH0 N T S
+VALID  V AE1 L AH0 D
+VALID  V AE1 L IH0 D
+VALIDA  V AA0 L IY1 D AH0
+VALIDATE  V AE1 L AH0 D EY0 T
+VALIDATED  V AE1 L AH0 D EY0 T AH0 D
+VALIDE	V AE1 L IH0 D
+VALIDEH	V AE1 L AH0 D EH2
+VALIDITY  V AH0 L IH1 D AH0 T IY0
+VALIDITY  V AH0 L IH1 D IH0 T IY0
+VALIDLY  V AE1 L IH0 D L IY0
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+VALIENTE  V AA0 L IY1 N T IY0
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+VALINGFORD	V AE1 L IH0 NG F ER0 D
+VALIQUET'S	V AE1 L IH0 K EY1 Z
+VALISE	V AA0 L IY1 S
+VALISES	V AE1 L IH0 S IH0 Z
+VALJEAN	V AA0 L JH IY1 N
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+VALKAN	V AE1 L K AH0 N
+VALKANHAYN	V AE1 L K EY2 N EY1 N
+VALKANHAYN'S	V AE1 L K EY2 N EY1 N Z
+VALKED	V AO1 K T
+VALKENBURG	V AO1 K AH0 N B ER0 G
+VALKENBURGH	V AO1 K AH0 N B ER0 G
+VALKIA	V AE1 L K IY0 AH0
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+VALKYRIA	V AO1 K IH1 R IY0 AH0
+VALKYRIE	V AO1 K AY1 R IY0
+VALKYRIES	V AO1 K IY0 R AY2 Z
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+VALL	V AA0 L
+VALL'S	V AO1 L Z
+VALLA  V AE1 L AH0
+VALLABH	V AE1 L AH0 B
+VALLADOLID	V AA0 L AA0 D AO1 L AH0 D
+VALLAIS	V EY1 L IY0 AH0 S
+VALLANCE  V AE1 L AH0 N S
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+VALLANCEY	V AE1 L AH0 N S IY0
+VALLANDIGHAM	V AE0 L AH1 N D IH1 G AH0 M
+VALLANZA	V AA0 L AA1 N Z AH0
+VALLAT	V AE1 L AH0 T
+VALLAURIS	V AA0 L AA1 R IY0 Z
+VALLAVA	V AA0 L AA1 V AH0
+VALLE  V EY1 L
+VALLEE  V AE1 L IY0
+VALLEIX	V AA0 L EY1 IH0 K S
+VALLEJO  V AA0 L EY1 Y OW0
+VALLENCE	V AA0 L EH1 N S
+VALLERY  V AE1 L ER0 IY0
+VALLES  V EY1 L Z
+VALLET	V AA0 L EH1 T
+VALLETORT	V AE1 L IY0 T AO1 R T
+VALLETORT'S	V AE1 L IY0 T AO1 R T S
+VALLETTA	V AE2 L EH1 T AH0
+VALLETTE  V AE2 L EH1 T
+VALLEY  V AE1 L IY0
+VALLEY'S  V AE1 L IY0 Z
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+VALLEYVIEW	V AE1 L EY2 V Y UW2
+VALLI  V AE1 L IY0
+VALLIE  V AO1 L IY0
+VALLIER  V AE1 L IY0 ER0
+VALLIERE  V AE1 L IY0 EH0 R
+VALLIERE'S	V AE1 L IY0 EH0 R Z
+VALLIES	V AE1 L IY0 Z
+VALLINGTON	V AO1 L IH0 NG T AH0 N
+VALLIS  V AE1 L IH0 S
+VALLISNERIA	V AE1 L IH0 S N EH1 R IY0 AH0
+VALLOIS	V AA0 L W AA1
+VALLOMBREUSE	V AA0 L OW1 M B R UW1 Z
+VALLOMBROSA	V AE1 L AH0 M B R OW1 S AH0
+VALLON	V AE1 L AH0 N
+VALLON'S	V AE1 L AH0 N Z
+VALLS  V AO1 L Z
+VALLUM	V AE1 L AH0 M
+VALLY	V AE1 L IY0
+VALLYBLE	V AA0 L IH1 B AH0 L
+VALMA	V AA1 L M AH0
+VALMERAS	V AE1 L M R AH0 Z
+VALMIKI	V AE1 L M IY1 K IY0
+VALMOND	V AE1 L M AH0 N D
+VALMOND'S	V AE1 L M AH0 N D Z
+VALMONDE	V AE1 L M AH0 N D
+VALMONT  V AO1 L M AA2 N T
+VALMONT'S	V AO1 L M AA2 N T S
+VALMORE	V AE1 L M AO0 R
+VALMY	V AO1 M IY0
+VALNY	V AE1 L N IY0
+VALOGNES	V AH0 L OW1 N Z
+VALOIR	V AE0 L W AA1 R
+VALOIS  V AE0 L W AA1
+VALOISE	V AE0 L W AA1 Z
+VALOMBROSA	V AE1 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
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+ZUYDER	Z AY1 D ER0
+ZUYLEN	Z UW1 L AH0 N
+ZVANTZEV	Z V AA1 N T Z AH0 V
+ZVERKOV	Z V ER0 K AO2 V
+ZWAAN	Z W AA2 N
+ZWANZIGER	Z W AA1 N Z IH0 G ER0
+ZWART  Z W AO1 R T
+ZWEETE	Z W IY1 T
+ZWEI	Z W AY1
+ZWEIBRUCKEN	Z W AY1 B R AH1 K AH0 N
+ZWEIG  Z W AY1 G
+ZWEISIMMEN	Z W AY1 Z IY0 M AH0 N
+ZWEMER	Z W IY1 M ER0
+ZWENGLER	Z W IH1 NG AH0 L ER0
+ZWENKEN	Z W EH1 NG K AH0 N
+ZWENO	Z W IY1 N OW0
+ZWICKAU	Z W IH1 K AW0
+ZWIDE	Z W AY2 D
+ZWIEBACK	Z W IY1 B AE0 K
+ZWILK	Z W IH1 L K
+ZWILLIEVIC	Z W IH1 L Y AH0 V IH0 K
+ZWINGER	Z W IH1 NG ER0
+ZWINGLI	Z W IH1 NG G L IY0
+ZWINGLI'S	Z W IH1 NG G L IY0 Z
+ZWINGLIAN	Z W IH1 NG G L IY0 AH0 N
+ZWIRINA	Z W IH0 R IY1 N AH0
+ZWISCHEN	Z W IH1 SH AH0 N
+ZWOLLE	Z W AA1 L
+ZY	Z IY0
+ZYARULLA	Z Y AA0 R UW1 L AH0
+ZYCH  Z AY1 CH
+ZYGFRIED	Z AY1 G F R IY2 D
+ZYGFRIED'S	Z AY1 G F R IY2 D Z
+ZYGOMATIC	Z AY1 G OW0 M AE1 T IH0 K
+ZYL	Z IH1 L
+ZYL'S	Z IH1 L Z
+ZYMOTIC	Z AY1 M AA1 T IH0 K
+ZYNDRAM	Z IH1 N D R AE2 M
+ZYNOOL	Z IH1 N UW1 L
+ZYNOOL'S	Z IH1 N UW1 L Z
+ZYOBOR	Z AY1 OW0 B 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
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+an3 an3
+an4 an4
+an5 an5
+ang1 ang1
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+ang3 ang3
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+ang5 ang5
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+ao2 ao2
+ao3 ao3
+ao4 ao4
+ao5 ao5
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+beng5 b eng5
+bi1 b i1
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+bi3 b i3
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+bi5 b i5
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+bian5 b ian5
+biao1 b iao1
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+bu1 b u1
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+bu4 b u4
+bu5 b u5
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+cai5 c ai5
+can1 c an1
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+chu1 ch u1
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+chuai1 ch uai1
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+ci1 c ii1
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+cong1 c ong1
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+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 = '<eps>'
+    '''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, '<eps>')
+
+        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::
+
+            <eps> 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::
+
+            <eps> 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 = "<pad>",
+        bos_symbol: str = "<bos>",
+        eos_symbol: str = "<eos>",
+    ):
+        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