Audio-to-Audio
speechbrain
English
Source Separation
Speech Separation
Audio Source Separation
WSJ02Mix
SepFormer
Transformer
audio-source-separation
Instructions to use speechbrain/sepformer-wsj02mix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- speechbrain
How to use speechbrain/sepformer-wsj02mix with speechbrain:
from speechbrain.pretrained import SepformerSeparation model = SepformerSeparation.from_hparams( "speechbrain/sepformer-wsj02mix" ) model.separate_file("file.wav") - Notebooks
- Google Colab
- Kaggle
Commit ·
c1f4fa3
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Parent(s): 5dd18c0
Update README.md
Browse files
README.md
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@@ -60,6 +60,29 @@ torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 8000)
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### Inference on GPU
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To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
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### Limitations
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The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
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year = {2021},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/speechbrain/speechbrain}},
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}
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```
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### Inference on GPU
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To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
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### Training
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The model was trained with SpeechBrain (d0accc8).
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To train it from scratch follows these steps:
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1. Clone SpeechBrain:
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```bash
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git clone https://github.com/speechbrain/speechbrain/
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```
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2. Install it:
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```
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cd speechbrain
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pip install -r requirements.txt
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pip install -e .
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```
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3. Run Training:
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```
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cd recipes/WSJ0Mix/separation
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python train.py hparams/sepformer.yaml --data_folder=your_data_folder
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https://drive.google.com/drive/folders/1fcVP52gHgoMX9diNN1JxX_My5KaRNZWs?usp=sharing
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You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1suvbKScf3VbkxRjZlpi1Q4hKU9yTdBVM?usp=sharing)
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### Limitations
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The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
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year = {2021},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\\url{https://github.com/speechbrain/speechbrain}},
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}
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```
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