README.md

---
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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![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.*