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--- |
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language: "en" |
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thumbnail: |
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tags: |
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- embeddings |
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- Speaker |
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- Verification |
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- Identification |
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- pytorch |
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- xvectors |
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- TDNN |
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license: "apache-2.0" |
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datasets: |
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- voxceleb |
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metrics: |
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- EER |
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- min\\\\_dct |
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--- |
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<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> |
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<br/><br/> |
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# Speaker Verification with xvector embeddings on Voxceleb |
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This repository provides all the necessary tools to extract speaker embeddings with a pretrained TDNN model using SpeechBrain. |
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The system is trained on Voxceleb 1+ Voxceleb2 training data. |
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For a better experience, we encourage you to learn more about |
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[SpeechBrain](https://speechbrain.github.io). The given ASR model performance on Voxceleb1-test set are: |
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| Release | EER(%) |
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|:-------------:|:--------------:| |
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| 05-03-21 | 3.2 | |
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## Pipeline description |
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This system is composed of a TDNN model coupled with statistical pooling. The system is trained with Categorical Cross-Entropy Loss. |
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## Install SpeechBrain |
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First of all, please install SpeechBrain with the following command: |
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``` |
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pip install speechbrain |
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``` |
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Please notice that we encourage you to read our tutorials and learn more about |
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[SpeechBrain](https://speechbrain.github.io). |
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### Compute your speaker embeddings |
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```python |
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import torchaudio |
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from speechbrain.pretrained import EncoderClassifier |
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classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-xvect-voxceleb", savedir="pretrained_models/spkrec-xvect-voxceleb") |
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signal, fs =torchaudio.load('samples/audio_samples/example1.wav') |
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embeddings = classifier.encode_batch(signal) |
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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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#### Referencing xvectors |
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```@inproceedings{DBLP:conf/odyssey/SnyderGMSPK18, |
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author = {David Snyder and |
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Daniel Garcia{-}Romero and |
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Alan McCree and |
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Gregory Sell and |
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Daniel Povey and |
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Sanjeev Khudanpur}, |
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title = {Spoken Language Recognition using X-vectors}, |
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booktitle = {Odyssey 2018}, |
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pages = {105--111}, |
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year = {2018}, |
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} |
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``` |
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#### Referencing SpeechBrain |
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``` |
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@misc{SB2021, |
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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 }, |
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title = {SpeechBrain}, |
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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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#### About SpeechBrain |
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SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. |
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Website: https://speechbrain.github.io/ |
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GitHub: https://github.com/speechbrain/speechbrain |
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