readme: ---
  language:
  - fr
  thumbnail: null
  pipeline_tag: automatic-speech-recognition
  tags:
  - CTC
  - pytorch
  - speechbrain
  - Transformer
  - hf-asr-leaderboard
  license: apache-2.0
  datasets:
  - commonvoice
  metrics:
  - wer
  - cer
  model-index:
  - name: asr-wav2vec2-commonvoice-fr
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: CommonVoice 6.1 (French)
        type: mozilla-foundation/common_voice_6_1
        config: fr
        split: test
        args:
          language: fr
      metrics:
      - name: Test WER
        type: wer
        value: '9.96'
  ---
  
  <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 with CTC/Attention trained on CommonVoice French (No LM)
  
  This repository provides all the necessary tools to perform automatic speech
  recognition from an end-to-end system pretrained on CommonVoice (French Language) within
  SpeechBrain. For a better experience, we encourage you to learn more about
  [SpeechBrain](https://speechbrain.github.io).
  
  The performance of the model is the following:
  
  | Release | Test CER | Test WER | GPUs |
  |:-------------:|:--------------:|:--------------:| :--------:|
  | 24-08-21 | 3.19 | 9.96 | 2xV100 32GB |
  
  ## Pipeline description
  
  This ASR system is composed of 2 different but linked blocks:
  - Tokenizer (unigram) that transforms words into subword units and trained with
  the train transcriptions (train.tsv) of CommonVoice (FR).
  - Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([LeBenchmark/wav2vec2-FR-7K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-large)) is combined with two DNN layers and finetuned on CommonVoice FR.
  The obtained final acoustic representation is given to the CTC greedy decoder.
  
  The system is trained with recordings sampled at 16kHz (single channel).
  The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.
  
  ## Install SpeechBrain
  
  First of all, please install tranformers and SpeechBrain with the following command:
  
  ```
  pip install speechbrain transformers
  ```
  
  Please notice that we encourage you to read our tutorials and learn more about
  [SpeechBrain](https://speechbrain.github.io).
  
  ### Transcribing your own audio files (in French)
  
  ```python
  from speechbrain.pretrained import EncoderASR
  
  asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-fr", savedir="pretrained_models/asr-wav2vec2-commonvoice-fr")
  asr_model.transcribe_file('speechbrain/asr-wav2vec2-commonvoice-fr/example-fr.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/CommonVoice/ASR/CTC/
  python train_with_wav2vec.py hparams/train_fr_with_wav2vec.yaml --data_folder=your_data_folder
  ```
  
  You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1T9DfdZwcNI9CURxhLCi8GA5JVz8adiY8?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
  