--- language: "en" thumbnail: tags: - Source Separation - Speech Separation - Audio Source Separation - WHAM! - SepFormer - Transformer license: "apache-2.0" datasets: - WHAMR! metrics: - SI-SNRi - SDRi ---

# SepFormer trained on WHAM! This repository provides all the necessary tools to perform audio source separation with a [SepFormer](https://arxiv.org/abs/2010.13154v2) model, implemented with SpeechBrain, and pretrained on [WHAMR!](http://wham.whisper.ai/) 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](https://speechbrain.github.io). 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](https://speechbrain.github.io). ### Perform source separation on your own audio file ```python 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. #### Referencing SpeechBrain ``` @misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\url{https://github.com/speechbrain/speechbrain}}, } ``` #### 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 SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain