wsj0_2mix_skim_small_causal開源語音增強模型 - 免費部署實現混合語音信號分離

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Wsj0 2mix Skim Small Causal

由lichenda開發

這是一個基於ESPnet框架訓練的語音增強模型，專門用於處理wsj0_2mix數據集中的混合語音信號分離任務。

音頻增強英語#即時語音增強 #因果性模型 #低延遲處理

下載量 26

發布時間 : 5/17/2023

模型概述

該模型採用skim架構，具有因果性處理能力，適用於即時語音增強場景，能夠有效分離混合語音中的不同說話人信號。

模型特點

因果處理能力

模型採用因果結構設計，適合即時語音處理應用場景

輕量級架構

小型skim架構設計，在保持性能的同時降低計算複雜度

多說話人分離

能夠有效分離混合語音中的兩個說話人信號

模型能力

語音增強

說話人分離

即時語音處理

使用案例

語音通信

會議語音增強

在多人會議場景中分離不同說話人的聲音

STOI指標達到94.20，SDR指標14.33

語音識別預處理

ASR前端處理

為語音識別系統提供更乾淨的輸入信號

可提升語音識別系統在嘈雜環境中的準確率

🚀 ESPnet2 ENH模型

本模型是一個音頻增強模型，基於ESPnet框架訓練，使用wsj0_2mix數據集，可用於音頻到音頻的處理任務。

🚀 快速開始

安裝ESPnet

若你還未安裝ESPnet，請按照ESPnet安裝說明進行操作。

運行示例

cd espnet
git checkout 3897ed8380bfb526d5c5dd2197eccbffbba7d8f8
pip install -e .
cd egs2/wsj0_2mix/enh1
./run.sh --skip_data_prep false --skip_train true --download_model lichenda/wsj0_2mix_skim_small_causal

📚 詳細文檔

模型訓練信息

此模型由李辰達（Chenda Li）使用espnet中的wsj0_2mix配方進行訓練。

實驗結果

環境信息

日期: Wed May 10 20:30:26 CST 2023
Python版本: 3.9.16 (main, Mar 8 2023, 14:00:05) [GCC 11.2.0]
ESPnet版本: espnet 202304
PyTorch版本: pytorch 2.0.1
Git哈希值: 3897ed8380bfb526d5c5dd2197eccbffbba7d8f8
- 提交日期: Tue May 9 13:27:37 2023 +0800

評估指標

數據集	STOI	SAR	SDR	SIR	SI_SNR
enhanced_cv_min_8k	93.41	15.54	14.92	24.87	14.51
enhanced_tt_min_8k	94.20	15.00	14.33	24.18	13.92

增強配置

展開

config: conf/tuning/train_enh_skim_causal_small.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/enh_train_enh_skim_causal_small_raw
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 150
patience: 50
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - si_snr_loss
    - min
-   - valid
    - loss
    - min
keep_nbest_models: 1
nbest_averaging_interval: 0
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
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 16
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/enh_stats_8k/train/speech_mix_shape
- exp/enh_stats_8k/train/speech_ref1_shape
- exp/enh_stats_8k/train/speech_ref2_shape
valid_shape_file:
- exp/enh_stats_8k/valid/speech_mix_shape
- exp/enh_stats_8k/valid/speech_ref1_shape
- exp/enh_stats_8k/valid/speech_ref2_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 80000
- 80000
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 32000,16000,8000
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
train_data_path_and_name_and_type:
-   - dump/raw/tr_min_8k/wav.scp
    - speech_mix
    - sound
-   - dump/raw/tr_min_8k/spk1.scp
    - speech_ref1
    - sound
-   - dump/raw/tr_min_8k/spk2.scp
    - speech_ref2
    - sound
valid_data_path_and_name_and_type:
-   - dump/raw/cv_min_8k/wav.scp
    - speech_mix
    - sound
-   - dump/raw/cv_min_8k/spk1.scp
    - speech_ref1
    - sound
-   - dump/raw/cv_min_8k/spk2.scp
    - speech_ref2
    - sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
    lr: 0.001
    eps: 1.0e-08
    weight_decay: 0
scheduler: steplr
scheduler_conf:
    step_size: 2
    gamma: 0.97
init: xavier_uniform
model_conf:
    stft_consistency: false
    loss_type: mask_mse
    mask_type: null
criterions:
-   name: si_snr
    conf: {}
    wrapper: pit
    wrapper_conf:
        weight: 1.0
        independent_perm: true
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
use_reverberant_ref: false
num_spk: 1
num_noise_type: 1
sample_rate: 8000
force_single_channel: false
dynamic_mixing: false
utt2spk: null
dynamic_mixing_gain_db: 0.0
encoder: conv
encoder_conf:
    channel: 128
    kernel_size: 8
    stride: 4
separator: skim
separator_conf:
    causal: true
    num_spk: 2
    layer: 3
    nonlinear: relu
    unit: 384
    segment_size: 50
    dropout: 0.0
    mem_type: hc
    seg_overlap: false
decoder: conv
decoder_conf:
    channel: 128
    kernel_size: 8
    stride: 4
mask_module: multi_mask
mask_module_conf: {}
preprocessor: null
preprocessor_conf: {}
required:
- output_dir
version: '202304'
distributed: false

引用ESPnet

如果你在研究中使用了ESPnet，請引用以下文獻：

@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{ESPnet-SE,
  author = {Chenda Li and Jing Shi and Wangyou Zhang and Aswin Shanmugam Subramanian and Xuankai Chang and
  Naoyuki Kamo and Moto Hira and Tomoki Hayashi and Christoph B{"{o}}ddeker and Zhuo Chen and Shinji Watanabe},
  title = {ESPnet-SE: End-To-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration},
  booktitle = {{IEEE} Spoken Language Technology Workshop, {SLT} 2021, Shenzhen, China, January 19-22, 2021},
  pages = {785--792},
  publisher = {{IEEE}},
  year = {2021},
  url = {https://doi.org/10.1109/SLT48900.2021.9383615},
  doi = {10.1109/SLT48900.2021.9383615},
  timestamp = {Mon, 12 Apr 2021 17:08:59 +0200},
  biburl = {https://dblp.org/rec/conf/slt/Li0ZSCKHHBC021.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@inproceedings{liSkimSkippingMemory2022,
  title = {Skim: {{Skipping Memory Lstm}} for {{Low-Latency Real-Time Continuous Speech Separation}}},
  shorttitle = {Skim},
  booktitle = {{{ICASSP}} 2022 - 2022 {{IEEE International Conference}} on {{Acoustics}}, {{Speech}} and {{Signal Processing}} ({{ICASSP}})},
  author = {Li, Chenda and Yang, Lei and Wang, Weiqin and Qian, Yanmin},
  year = {2022},
  month = may,
  pages = {681--685},
  issn = {2379-190X},
  doi = {10.1109/ICASSP43922.2022.9746372},
}

或者引用arXiv上的文獻：

@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}
}