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README.md
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## Pipeline description
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This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings.
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### Compute your speaker embeddings
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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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The model was trained with SpeechBrain (aa018540).
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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/VoxCeleb/SpeakerRec
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python train_speaker_embeddings.py hparams/train_ecapa_tdnn.yaml --data_folder=your_data_folder
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```
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You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1-ahC1xeyPinAHp2oAohL-02smNWO41Cc?usp=sharing).
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#### Referencing ECAPA-TDNN
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```
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@inproceedings{DBLP:conf/interspeech/DesplanquesTD20,
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author = {Brecht Desplanques and
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publisher = {{ISCA}},
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year = {2020},
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}
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```
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# **Citing SpeechBrain**
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Please, cite SpeechBrain if you use it for your research or business.
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```bibtex
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@misc{speechbrain,
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title={{SpeechBrain}: A General-Purpose Speech Toolkit},
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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},
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note={arXiv:2106.04624}
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}
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```
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# **About SpeechBrain**
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- Website: https://speechbrain.github.io/
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- Code: https://github.com/speechbrain/speechbrain/
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- HuggingFace: https://huggingface.co/speechbrain/
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## Pipeline description
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This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings.
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You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1-ahC1xeyPinAHp2oAohL-02smNWO41Cc?usp=sharing).
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### Compute your speaker embeddings
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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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#### References
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```
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@inproceedings{DBLP:conf/interspeech/DesplanquesTD20,
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author = {Brecht Desplanques and
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publisher = {{ISCA}},
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year = {2020},
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}
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@misc{speechbrain,
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title={{SpeechBrain}: A General-Purpose Speech Toolkit},
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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},
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note={arXiv:2106.04624}
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}
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```
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