metadata

language: en
thumbnail: null
tags:
  - audio-to-audio
  - audio-source-separation
  - Source Separation
  - Speech Separation
  - WHAM!
  - SepFormer
  - Transformer
  - pytorch
  - speechbrain
license: apache-2.0
datasets:
  - WHAMR!
metrics:
  - SI-SNRi
  - SDRi

SepFormer trained on WHAMR! (16k sampling frequency)

This repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WHAMR! dataset with 16k sampling frequency, which is basically a version of WSJ0-Mix dataset with environmental noise and reverberation in 16k. For a better experience we encourage you to learn more about SpeechBrain. The given model performance is 13.5 dB SI-SNRi on the test set of WHAMR! dataset.

| Release | Test-Set SI-SNRi | Test-Set SDRi |

|:-------------:|:--------------:|:--------------:|

| 30-03-21 | 13.5 dB | 13.0 dB |

Install SpeechBrain

First of all, please install SpeechBrain with the following command:


pip install speechbrain

Please notice that we encourage you to read our tutorials and learn more about SpeechBrain.

Perform source separation on your own audio file


from speechbrain.pretrained import SepformerSeparation as separator

import torchaudio

model = separator.from_hparams(source="speechbrain/sepformer-whamr16k", savedir='pretrained_models/sepformer-whamr16k')

# for custom file, change path

est_sources = model.separate_file(path='speechbrain/sepformer-whamr16k/test_mixture16k.wav') 

torchaudio.save("source1hat.wav", est_sources[:, :, 0].detach().cpu(), 16000)

torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 16000)

Inference on GPU

To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.

Training

The model was trained with SpeechBrain (fc2eabb7).

To train it from scratch follows these steps:

Clone SpeechBrain:


git clone https://github.com/speechbrain/speechbrain/

Install it:


cd speechbrain

pip install -r requirements.txt

pip install -e .

Run Training:


cd  recipes/WHAMandWHAMR/separation/

python train.py hparams/sepformer-whamr.yaml --data_folder=your_data_folder --sample_rate=16000

You can find our training results (models, logs, etc) here.

Limitations

The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

Referencing 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}

}

Referencing 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}

}

About SpeechBrain

Website: https://speechbrain.github.io/
Code: https://github.com/speechbrain/speechbrain/
HuggingFace: https://huggingface.co/speechbrain/