Create README.md

README.md

@@ -0,0 +1,76 @@
+---
+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-whamr-16k", savedir='pretrained_models/sepformer-whamr')
+
+# for custom file, change path
+est_sources = model.separate_file(path='speechbrain/sepformer-wsj02mix/test_mixture.wav') 
+
+torchaudio.save("source1hat.wav", est_sources[:, :, 0].detach().cpu(), 16000)
+torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 16000)
+
+
+```
+
+#### 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}
+}
+```