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--- |
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license: apache-2.0 |
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datasets: |
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- librispeech_asr |
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language: |
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- en |
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library_name: sklearn |
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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=medium" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> |
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<br/><br/> |
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# K-means (Quantization) |
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This folder contains pre-trained K-means models for the LibriSpeech Dataset. |
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The model serves to quantize self-supervised representations into discrete representation. Thus representations can be used as a discrete audio input for various tasks including classification, ASR and speech gneration. |
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It supports kmeans models using the features from HuBERT, WAVLM or Wav2Vec. |
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### Training |
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The model was trained with SpeechBrain. |
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To train it from scratch follow these steps: |
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1. Clone SpeechBrain: |
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```bash |
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git clone --branch unstable-v0.6 https://github.com/speechbrain/speechbrain/ |
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``` |
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2. Install it: |
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```bash |
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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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```bash |
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cd recipes/LibriSpeech/quantization/ |
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pip install -r rextra-requirements.txt |
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python train.py hparams/train_with_[ssl_model].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://huggingface.co/speechbrain/SSL_Quantization). |
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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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#### Referencing SpeechBrain |
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|
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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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|
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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 |
