metadata

language: en
thumbnail: null
tags:
  - Source Separation
  - Speech Separation
  - Audio Source Separation
  - SepFormer
  - speechbrain
license: apache-2.0
datasets:
  - WSJ0-2Mix
metrics:
  - SI-SNRi
  - SDRi

SI-SNR Estimator introduced for the REAL-M dataset

This repository provides all the necessary tools to perform blind SI-SNR estimation on the REAL-M dataset, from mixture signal + estimated sources. While this SI-SNR estimator is introduced with the REAL-M dataset, it can also be used on any speech separation dataset.

The SI-SNR estimator is trained with a recordings from the WHAMR! and LibriMix datasets. We used a mixture of 9 separators to create the training data on the fly.

This model is released together with the REAL-M dataset for source separation on in-the-wild speech mixtures.

The class necessary to perform inference has not yet been yet to the SpeechBrain, but will be merged to the develop branch soon!

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.

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/WSJ0Mix/separation
python train.py hparams/sepformer.yaml --data_folder=your_data_folder

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/