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--- |
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language: "en" |
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thumbnail: |
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tags: |
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- pretraining |
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- CTC |
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- pytorch |
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- speechbrain |
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- speech |
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license: "apache-2.0" |
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datasets: |
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- commonvoice |
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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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# wav2vec 2.0 base model pretrained on librispeech 960h |
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This HuggingFace repository provides all the necessary tools to extract wav2vec2 |
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embeddings from a pretrained model. For a better experience, we encourage you to learn more about |
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[SpeechBrain](https://speechbrain.github.io). The wav2vec2 model has entirely been |
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pretrained with SpeechBrain (not with fairseq or HuggingFace). |
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The performance of the model is the following: |
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| Release | Test WER | GPUs | |
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|:-------------:|:--------------:| :--------:| |
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| 22-09-22 | 7.X | 1xV100 32GB | |
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## Pipeline description |
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This w2v2 system is composed of 2 different but linked blocks: |
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- A convolutional backend to extract features from the raw waveform. |
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- A latent encoder made of a transformer network. |
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The obtained embeddings are the output of the transformer after going through each |
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block. |
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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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### Extracting embeddings for your own audio files |
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```python |
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from speechbrain.inference.encoders import WaveformEncoder |
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ssl_model = WaveformEncoder.from_hparams(source="speechbrain/ssl-wav2vec2-base-librispeech", savedir="speechbrain/ssl-wav2vec2-base-librispeech") |
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ssl_model.encode_file("mywavfile.wav") |
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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. |
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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 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/self-supervised-learning/wav2vec2 |
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python train_sb_wav2vec2.py hparams/wav2vec2_base.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/1eXA6HQtiKfgrPejvvoKvRRfTEvOI3BQt?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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#### 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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#### 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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