Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw开源语音增强模型

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Wangyou Zhang Chime4 Enh Train Enh Conv Tasnet Raw

由 espnet 开发

基于ESPnet框架训练的语音增强模型，使用chime4数据集进行训练，适用于单通道语音增强任务。

音频增强 #语音增强 #Conv-TasNet架构 #多场景降噪

下载量 57

发布时间 : 4/11/2022

模型简介

该模型采用Conv-TasNet架构，专门用于语音增强任务，能够从含噪声的混合语音中分离出清晰的语音信号。

模型特点

基于Conv-TasNet架构

采用时间卷积网络(Temporal Convolutional Network)进行语音分离，具有高效的特征提取能力。

端到端训练

直接从原始音频学习到目标语音的映射，无需复杂的特征工程。

SI-SNR优化目标

使用尺度不变信噪比(SI-SNR)作为损失函数，优化语音质量。

模型能力

单通道语音增强

噪声抑制

语音分离

使用案例

语音处理

会议语音增强

在嘈杂的会议环境中提取清晰的语音信号

提高语音识别准确率和可懂度

电话语音增强

改善移动通信中的语音质量

提升通话体验

🚀 ESPnet2 ENH模型

本模型是基于ESPnet框架的音频增强模型，利用chime4数据集进行训练，能够有效处理音频到音频的转换任务，为音频处理提供了强大的支持。

🚀 快速开始

模型介绍

espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw 这个模型由王友张使用 espnet 中的chime4配方进行训练。

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

📚 详细文档

ENH配置

展开

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，可以按照以下格式进行引用：

@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}
}