Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw開源語音增強模型

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Wangyou Zhang Chime4 Enh Train Enh Conv Tasnet Raw

由espnet開發

基於ESPnet框架訓練的語音增強模型，使用chime4數據集進行訓練，適用於單通道語音增強任務。

音頻增強 #語音增強 #Conv-TasNet架構 #多場景降噪

下載量 57

發布時間 : 4/11/2022

模型概述

該模型採用Conv-TasNet架構，專門用於語音增強任務，能夠從含噪聲的混合語音中分離出清晰的語音信號。

模型特點

基於Conv-TasNet架構

採用時間卷積網絡(Temporal Convolutional Network)進行語音分離，具有高效的特徵提取能力。

端到端訓練

直接從原始音頻學習到目標語音的映射，無需複雜的特徵工程。

SI-SNR優化目標

使用尺度不變信噪比(SI-SNR)作為損失函數，優化語音質量。

模型能力

單通道語音增強

噪聲抑制

語音分離

使用案例

語音處理

會議語音增強

在嘈雜的會議環境中提取清晰的語音信號

提高語音識別準確率和可懂度

電話語音增強

改善移動通信中的語音質量

提升通話體驗

🚀 ESPnet2 ENH模型

本模型是基於ESPnet框架的音頻增強模型，利用chime4數據集進行訓練，能夠有效處理音頻到音頻的轉換任務，為音頻處理提供了強大的支持。

🚀 快速開始

模型介紹

espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw 這個模型由王友張使用 espnet 中的chime4配方進行訓練。

演示：在ESPnet2中如何使用

cd espnet

pip install -e .
cd egs2/chime4/enh1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw

📚 詳細文檔

ENH配置

展開

config: conf/tuning/train_enh_conv_tasnet.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/enh_train_enh_conv_tasnet_raw
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 2
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 57680
dist_launcher: null
multiprocessing_distributed: true
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: 4
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - si_snr
    - max
-   - valid
    - loss
    - min
keep_nbest_models: 1
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
unused_parameters: false
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
pretrain_path: null
init_param: []
freeze_param: []
num_iters_per_epoch: null
batch_size: 8
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/enh_stats_16k/train/speech_mix_shape
- exp/enh_stats_16k/train/speech_ref1_shape
valid_shape_file:
- exp/enh_stats_16k/valid/speech_mix_shape
- exp/enh_stats_16k/valid/speech_ref1_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 80000
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 32000
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
-   - dump/raw/tr05_simu_isolated_1ch_track/wav.scp
    - speech_mix
    - sound
-   - dump/raw/tr05_simu_isolated_1ch_track/spk1.scp
    - speech_ref1
    - sound
valid_data_path_and_name_and_type:
-   - dump/raw/dt05_simu_isolated_1ch_track/wav.scp
    - speech_mix
    - sound
-   - dump/raw/dt05_simu_isolated_1ch_track/spk1.scp
    - speech_ref1
    - sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
    lr: 0.001
    eps: 1.0e-08
    weight_decay: 1.0e-05
scheduler: reducelronplateau
scheduler_conf:
    mode: min
    factor: 0.5
    patience: 3
init: xavier_uniform
model_conf:
    loss_type: si_snr
use_preprocessor: false
encoder: conv
encoder_conf:
    channel: 256
    kernel_size: 20
    stride: 10
separator: tcn
separator_conf:
    num_spk: 1
    layer: 8
    stack: 4
    bottleneck_dim: 256
    hidden_dim: 512
    kernel: 3
    causal: false
    norm_type: gLN
    nonlinear: relu
decoder: conv
decoder_conf:
    channel: 256
    kernel_size: 20
    stride: 10
required:
- output_dir
version: 0.9.7
distributed: true

引用ESPnet

如果你在研究中使用了ESPnet，可以按照以下格式進行引用：

@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}

@inproceedings{li2021espnetse,
  title={{ESPnet-SE}: End-to-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration},
  author={Li, Chenda and Shi, Jing and Zhang, Wangyou and Subramanian, Aswin Shanmugam and Chang, Xuankai and Kamo, Naoyuki and Hira, Moto and Hayashi, Tomoki and Boeddeker, Christoph and Chen, Zhuo and Watanabe, Shinji},
  booktitle={Proc. IEEE Spoken Language Technology Workshop (SLT)},
  pages={785--792},
  year={2021},
}

或者引用arXiv版本：

@misc{watanabe2018espnet,
  title={ESPnet: End-to-End Speech Processing Toolkit}, 
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  year={2018},
  eprint={1804.00015},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}

@inproceedings{li2021espnetse,
  title={{ESPnet-SE}: End-to-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration},
  author={Li, Chenda and Shi, Jing and Zhang, Wangyou and Subramanian, Aswin Shanmugam and Chang, Xuankai and Kamo, Naoyuki and Hira, Moto and Hayashi, Tomoki and Boeddeker, Christoph and Chen, Zhuo and Watanabe, Shinji},
  year={2020},
  eprint={2011.03706},
  archivePrefix={arXiv},
  primaryClass={eess.AS}
}