readme: ---
  license: apache-2.0
  language: fr
  library_name: transformers
  thumbnail: null
  tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - robust-speech-event
  - CTC
  - Wav2vec2
  datasets:
  - common_voice
  - mozilla-foundation/common_voice_11_0
  - facebook/multilingual_librispeech
  - polinaeterna/voxpopuli
  - gigant/african_accented_french
  metrics:
  - wer
  model-index:
  - name: Fine-tuned Wav2Vec2 XLS-R 1B model for ASR in French
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 11.0
        type: mozilla-foundation/common_voice_11_0
        args: fr
      metrics:
      - name: Test WER
        type: wer
        value: 14.80
      - name: Test WER (+LM)
        type: wer
        value: 12.61
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Multilingual LibriSpeech (MLS)
        type: facebook/multilingual_librispeech
        args: french
      metrics:
      - name: Test WER
        type: wer
        value: 9.39
      - name: Test WER (+LM)
        type: wer
        value: 8.06
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: VoxPopuli
        type: polinaeterna/voxpopuli
        args: fr
      metrics:
      - name: Test WER
        type: wer
        value: 11.80
      - name: Test WER (+LM)
        type: wer
        value: 9.94
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: African Accented French
        type: gigant/african_accented_french
        args: fr
      metrics:
      - name: Test WER
        type: wer
        value: 22.98
      - name: Test WER (+LM)
        type: wer
        value: 20.73
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: fr
      metrics:
      - name: Test WER
        type: wer
        value: 17.88
      - name: Test WER (+LM)
        type: wer
        value: 14.01
  ---
  
  # Fine-tuned Wav2Vec2 XLS-R 1B model for ASR in French
  
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  img {
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  ![Model architecture](https://img.shields.io/badge/Model_Architecture-Wav2Vec2--CTC-lightgrey)
  ![Model size](https://img.shields.io/badge/Params-962M-lightgrey)
  ![Language](https://img.shields.io/badge/Language-French-lightgrey)
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on French using the train and validation splits of [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli), [Multilingual TEDx](http://www.openslr.org/100), [MediaSpeech](https://www.openslr.org/108), and [African Accented French](https://huggingface.co/datasets/gigant/african_accented_french) on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
  
  *Genrally we advise to use [bofenghuang/asr-wav2vec2-ctc-french](https://huggingface.co/bofenghuang/asr-wav2vec2-ctc-french) because it has the smaller model size and the better performance.*
  
  