模型:
speechbrain/sepformer-whamr-enhancement
该存储库提供了使用SpeechBrain实施语音增强(降噪+去混响)的所有必要工具,使用 SepFormer 模型,预先训练的数据集为 WHAMR! 数据集,采样频率为8k,该数据集基本上是WSJ0-Mix数据集的8k版本,并具有环境噪声和混响。为了更好的体验,我们鼓励您更多地了解 SpeechBrain 。该模型在WHAMR!数据集的测试集上的性能为10.59 dB SI-SNR。
Release | Test-Set SI-SNR | Test-Set PESQ |
---|---|---|
01-12-21 | 10.59 | 2.84 |
首先,请使用以下命令安装SpeechBrain:
pip install speechbrain
请注意,我们鼓励您阅读我们的教程并了解更多关于 SpeechBrain 的内容。
from speechbrain.pretrained import SepformerSeparation as separator import torchaudio model = separator.from_hparams(source="speechbrain/sepformer-whamr-enhancement", savedir='pretrained_models/sepformer-whamr-enhancement') # for custom file, change path est_sources = model.separate_file(path='speechbrain/sepformer-whamr-enhancement/example_whamr.wav') torchaudio.save("enhanced_whamr.wav", est_sources[:, :, 0].detach().cpu(), 8000)
要在GPU上进行推理,请在调用from_hparams方法时添加run_opts={"device":"cuda"}
训练脚本目前正在进行中的拉取请求上进行工作。
一旦PR合并,我们将更新模型卡片。
您可以在此处找到我们的训练结果(模型、日志等) here 。
SpeechBrain团队不对在其他数据集上使用此模型时的性能提供任何保证。
引用SpeechBrain@misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, year={2021}, eprint={2106.04624}, archivePrefix={arXiv}, primaryClass={eess.AS}, note={arXiv:2106.04624} }引用SepFormer
@inproceedings{subakan2021attention, title={Attention is All You Need in Speech Separation}, author={Cem Subakan and Mirco Ravanelli and Samuele Cornell and Mirko Bronzi and Jianyuan Zhong}, year={2021}, booktitle={ICASSP 2021} }