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# RE-SepFormer trained on WSJ0-2Mix

This repository provides all the necessary tools to perform audio source separation with a [RE-SepFormer](https://arxiv.org/abs/2206.09507) 
model, implemented with SpeechBrain, and pretrained on WSJ0-2Mix dataset. For a better experience we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io). The model performance is 18.6 dB on the test set of WSJ0-2Mix dataset.

| Release | Test-Set SI-SNRi | Test-Set SDRi |
|:-------------:|:--------------:|:--------------:|
| 19-06-22 | 18.6dB | 18.9dB |


## 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/resepformer-wsj02mix", savedir='pretrained_models/resepformer-wsj02mix')

# 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 (fc2eabb7).
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/WSJ0Mix/separation
python train.py hparams/sepformer.yaml --data_folder=your_data_folder
```

You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1cON-eqtKv_NYnJhaE9VjLT_e2ybn-O7u?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 RE-SepFormer
```bibtex
@misc{subakan2022resourceefficient,
      title={Resource-Efficient Separation Transformer}, 
      author={Cem Subakan and Mirco Ravanelli and Samuele Cornell and Frédéric Lepoutre and François Grondin},
      year={2022},
      eprint={2206.09507},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}
```

# **About SpeechBrain**
- Website: https://speechbrain.github.io/
- Code: https://github.com/speechbrain/speechbrain/
- HuggingFace: https://huggingface.co/speechbrain/

{"language": "en", "license": "apache-2.0", "tags": ["Source Separation", "Speech Separation", "Audio Source Separation", "WSJ02Mix", "SepFormer", "Transformer", "audio-to-audio", "audio-source-separation", "speechbrain"], "datasets": ["WSJ0-2Mix"], "metrics": ["SI-SNRi", "SDRi"]}
speechbrain Source Separation Speech Separation Audio Source Separation WSJ02Mix SepFormer Transformer audio-to-audio audio-source-separation en dataset:WSJ0-2Mix arxiv:2206.09507 arxiv:2106.04624 license:apache-2.0 region:us
TAGS
#speechbrain #Source Separation #Speech Separation #Audio Source Separation #WSJ02Mix #SepFormer #Transformer #audio-to-audio #audio-source-separation #en #dataset-WSJ0-2Mix #arxiv-2206.09507 #arxiv-2106.04624 #license-apache-2.0 #region-us 


  
  

RE-SepFormer trained on WSJ0-2Mix
=================================


This repository provides all the necessary tools to perform audio source separation with a RE-SepFormer
model, implemented with SpeechBrain, and pretrained on WSJ0-2Mix dataset. For a better experience we encourage you to learn more about
SpeechBrain. The model performance is 18.6 dB on the test set of WSJ0-2Mix dataset.



Install SpeechBrain
-------------------


First of all, please install SpeechBrain with the following command:


Please notice that we encourage you to read our tutorials and learn more about
SpeechBrain.


### Perform source separation on your own audio file


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 (fc2eabb7).
To train it from scratch follows these steps:


1. Clone SpeechBrain:
2. Install it:
3. Run Training:


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


#### Referencing RE-SepFormer


About SpeechBrain
=================


* Website: URL
* Code: URL
* HuggingFace: URL



### Perform source separation on your own audio file


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 (fc2eabb7).
To train it from scratch follows these steps:


1. Clone SpeechBrain:
2. Install it:
3. Run Training:


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 #### Referencing RE-SepFormer


About SpeechBrain
=================


* Website: URL
* Code: URL
* HuggingFace: URL
TAGS
#speechbrain #Source Separation #Speech Separation #Audio Source Separation #WSJ02Mix #SepFormer #Transformer #audio-to-audio #audio-source-separation #en #dataset-WSJ0-2Mix #arxiv-2206.09507 #arxiv-2106.04624 #license-apache-2.0 #region-us 
 ### Perform source separation on your own audio file


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 (fc2eabb7).
To train it from scratch follows these steps:


1. Clone SpeechBrain:
2. Install it:
3. Run Training:


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 #### Referencing RE-SepFormer


About SpeechBrain
=================


* Website: URL
* Code: URL
* HuggingFace: URL
passage: TAGS
#speechbrain #Source Separation #Speech Separation #Audio Source Separation #WSJ02Mix #SepFormer #Transformer #audio-to-audio #audio-source-separation #en #dataset-WSJ0-2Mix #arxiv-2206.09507 #arxiv-2106.04624 #license-apache-2.0 #region-us 
### Perform source separation on your own audio file


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 (fc2eabb7).
To train it from scratch follows these steps:


1. Clone SpeechBrain:
2. Install it:
3. Run Training:


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#### Referencing RE-SepFormer


About SpeechBrain
=================


* Website: URL
* Code: URL
* HuggingFace: URL