metadata

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

Neural SI-SNR Estimator

This repository provides the SI-SNR estimator model introduced for the REAL-M dataset.

| Release | Test-Set (WHAMR!) average l1 error |

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

| 18-10-21 | 1.7 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

model = separator.from_hparams(source="speechbrain/sepformer-whamr", savedir='pretrained_models/sepformer-whamr2')
est_sources = model.separate_file(path='speechbrain/sepformer-wsj02mix/test_mixture.wav')

snr_est_model = snrest.from_hparams(source="speechbrain/REAL-M-sisnr-estimator-main")

mix, fs = torchaudio.load('test_mixture.wav')
snrhat = snr_est_model.estimate_batch(mix, est_sources)

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/REAL-M/sisnr-estimation

python train.py hparams/pool_sisnrestimator.yaml --data_folder /yourLibri2Mixpath --base_folder_dm /yourLibriSpeechpath --rir_path /yourpathforwhamrRIRs --dynamic_mixing True --use_whamr_train True --whamr_data_folder /yourpath/whamr --base_folder_dm_whamr /yourpath/wsj0-processed/si_tr_s

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/