readme: ---
  language: "en"
  thumbnail:
  tags:
  - pretraining
  - CTC
  - pytorch
  - speechbrain
  - speech
  license: "apache-2.0"
  datasets:
  - commonvoice
  ---
  
  <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>
  <br/><br/>
  
  # wav2vec 2.0 base model pretrained on librispeech 960h
  
  This HuggingFace repository provides all the necessary tools to extract wav2vec2
  embeddings from a pretrained model. For a better experience, we encourage you to learn more about
  [SpeechBrain](https://speechbrain.github.io). The wav2vec2 model has entirely been
  pretrained with SpeechBrain (not with fairseq or HuggingFace).
  
  The performance of the model is the following:
  
  | Release | Test WER | GPUs | 
  |:-------------:|:--------------:| :--------:| 
  | 22-09-22 | 7.X | 1xV100 32GB |
  
  ## Pipeline description
  
  This w2v2 system is composed of 2 different but linked blocks:
  - A convolutional backend to extract features from the raw waveform.
  - A latent encoder made of a transformer network.
  The obtained embeddings are the output of the transformer after going through each
  block.
  
  ## 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).
  
  ### Extracting embeddings for your own audio files
  
  ```python
  from speechbrain.pretrained import WaveformEncoder
  
  ssl_model = WaveformEncoder.from_hparams(source="speechbrain/ssl-wav2vec2-base-librispeech", savedir="speechbrain/ssl-wav2vec2-base-librispeech")
  ssl_model.encode_file("mywavfile.wav")
  
  ```
  ### 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.
  To train it from scratch follow these steps:
  1. Clone SpeechBrain:
  ```bash
  git clone https://github.com/speechbrain/speechbrain/
  ```
  2. Install it:
  ```bash
  cd speechbrain
  pip install -r requirements.txt
  pip install -e .
  ```
  
  3. Run Training:
  ```bash
  cd recipes/LibriSpeech/self-supervised-learning/wav2vec2
  python train_sb_wav2vec2.py hparams/wav2vec2_base.yaml --data_folder=your_data_folder
  ```
  
  You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1eXA6HQtiKfgrPejvvoKvRRfTEvOI3BQt?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
  
  ```
  @misc{SB2021,
      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 },
      title = {SpeechBrain},
      year = {2021},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
    }
  ```
  
  #### About SpeechBrain
  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.
  
  Website: https://speechbrain.github.io/
  
  GitHub: https://github.com/speechbrain/speechbrain
  