readme: ---
  license: apache-2.0
  language: fr
  library_name: transformers
  thumbnail: null
  tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - whisper-event
  datasets:
  - mozilla-foundation/common_voice_11_0
  metrics:
  - wer
  model-index:
  - name: Fine-tuned whisper-small 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
        config: fr
        split: test
        args: fr
      metrics:
      - name: WER (Greedy)
        type: wer
        value: 11.76
      - name: WER (Beam 5)
        type: wer
        value: 10.99
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Multilingual LibriSpeech (MLS)
        type: facebook/multilingual_librispeech
        config: french
        split: test
        args: french
      metrics:
      - name: WER (Greedy)
        type: wer
        value: 9.65
      - name: WER (Beam 5)
        type: wer
        value: 8.91
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: VoxPopuli
        type: facebook/voxpopuli
        config: fr
        split: test
        args: fr
      metrics:
      - name: WER (Greedy)
        type: wer
        value: 14.45
      - name: WER (Beam 5)
        type: wer
        value: 13.66
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Fleurs
        type: google/fleurs
        config: fr_fr
        split: test
        args: fr_fr
      metrics:
      - name: WER (Greedy)
        type: wer
        value: 10.76
      - name: WER (Beam 5)
        type: wer
        value: 9.83
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: African Accented French
        type: gigant/african_accented_french
        config: fr
        split: test
        args: fr
      metrics:
      - name: WER (Greedy)
        type: wer
        value: 10.81
      - name: WER (Beam 5)
        type: wer
        value: 9.26
  ---
  
  <style>
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  ![Model architecture](https://img.shields.io/badge/Model_Architecture-seq2seq-lightgrey)
  ![Model size](https://img.shields.io/badge/Params-244M-lightgrey)
  ![Language](https://img.shields.io/badge/Language-French-lightgrey)
  
  # Fine-tuned whisper-small model for ASR in French
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small), trained on the mozilla-foundation/common_voice_11_0 fr dataset. When using the model make sure that your speech input is also sampled at 16Khz. **This model also predicts casing and punctuation.**
  
  ## Performance
  
  *Below are the WERs of the pre-trained models on the [Common Voice 9.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli) and [Fleurs](https://huggingface.co/datasets/google/fleurs). These results are reported in the original [paper](https://cdn.openai.com/papers/whisper.pdf).*
  
  | Model | Common Voice 9.0 | MLS | VoxPopuli | Fleurs |
  | --- | :---: | :---: | :---: | :---: |
  | [openai/whisper-small](https://huggingface.co/openai/whisper-small) | 22.7 | 16.2 | 15.7 | 15.0 |
  | [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) | 16.0 | 8.9 | 12.2 | 8.7 |
  | [openai/whisper-large](https://huggingface.co/openai/whisper-large) | 14.7 | 8.9 | **11.0** | **7.7** |
  | [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) | **13.9** | **7.3** | 11.4 | 8.3 |
  
  *Below are the WERs of the fine-tuned models on the [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), and [Fleurs](https://huggingface.co/datasets/google/fleurs). Note that these evaluation datasets have been filtered and preprocessed to only contain French alphabet characters and are removed of punctuation outside of apostrophe. The results in the table are reported as `WER (greedy search) / WER (beam search with beam width 5)`.*
  
  | Model | Common Voice 11.0 | MLS | VoxPopuli | Fleurs |
  | --- | :---: | :---: | :---: | :---: |
  | [bofenghuang/whisper-small-cv11-french](https://huggingface.co/bofenghuang/whisper-small-cv11-french) | 11.76 / 10.99 | 9.65 / 8.91 | 14.45 / 13.66 | 10.76 / 9.83 |
  | [bofenghuang/whisper-medium-cv11-french](https://huggingface.co/bofenghuang/whisper-medium-cv11-french) | 9.03 / 8.54 | 6.34 / 5.86 | 11.64 / 11.35 | 7.13 / 6.85 |
  | [bofenghuang/whisper-medium-french](https://huggingface.co/bofenghuang/whisper-medium-french) | 9.03 / 8.73 | 4.60 / 4.44 | 9.53 / 9.46 | 6.33 / 5.94 |
  | [bofenghuang/whisper-large-v2-cv11-french](https://huggingface.co/bofengh