readme: ---
  language: "en"
  thumbnail:
  tags:
  - speechbrain
  - Source Separation
  - Speech Separation
  - Audio Source Separation
  - WHAM!
  - SepFormer
  - Transformer
  - audio-to-audio 
  - audio-source-separation
  license: "apache-2.0"
  datasets:
  - WHAMR!
  metrics:
  - SI-SNRi
  - SDRi
  
  ---
  
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  <br/><br/>
  
  # SepFormer trained on WHAMR!
  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, which is basically a version of WSJ0-Mix dataset with environmental noise and reverberation. For a better experience we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The  model performance is 13.7 dB SI-SNRi on the test set of WHAMR! dataset.
  
  | Release | Test-Set SI-SNRi | Test-Set SDRi |
  |:-------------:|:--------------:|:--------------:|
  | 30-03-21 | 13.7 dB | 12.7 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](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", 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(), 8000)
  torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 8000)
  ```
  The system expects input recordings sampled at 8kHz (single channel).
  If your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface.
  
  ### 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 (e375cd13).
  To train it from scratch follows these steps:
  1. Clone SpeechBrain:
  ```bash
  git clone https://github.com/speechbrain/speechbrain/
  ```
  2. Install it:
  ```
  cd speechbrain
  pip install -r requirements.txt
  pip install -e .
  ```
  
  3. Run Training:
  ```
  cd  recipes/WHAMandWHAMR/separation
  python train.py hparams/sepformer-whamr.yaml --data_folder=YOUR_DATA_FOLDER --rir_path=YOUR_ROOM_IMPULSE_SAVE_PATH 
  ```
  
  You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1m1xfx2ojf7qgOyscJVVCQFRY0VRl0rdi?usp=sharing).
  
  ### Limitations
  The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
  
  #### Referencing SpeechBrain
  
  ```bibtex
  @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
  ```bibtex
  @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/
  
  
  