readme: ---
  language: fr
  license: apache-2.0
  tags:
  - generated_from_trainer
  - whisper-event
  datasets:
  - mozilla-foundation/common_voice_11_0
  metrics:
  - wer
  - wer_norm
  model-index:
  - name: openai/whisper-medium
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: mozilla-foundation/common_voice_11_0
        type: mozilla-foundation/common_voice_11_0
        config: fr
        split: test
        args: fr
      metrics:
      - name: Wer
        type: wer
        value: 11.1406
      - name: Wer (without normalization)
        type: wer_without_norm
        value: 15.89689189275029
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # French Medium Whisper
  
  This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the common_voice_11_0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2664
  - Wer (without normalization): 15.8969
  - Wer (with normalization): **11.1406**
  
  ## Blog post
  
  All information about this model in this blog post: [Speech-to-Text & IA | Transcreva qualquer áudio para o português com o Whisper (OpenAI)... sem nenhum custo!](https://medium.com/@pierre_guillou/speech-to-text-ia-transcreva-qualquer-%C3%A1udio-para-o-portugu%C3%AAs-com-o-whisper-openai-sem-ad0c17384681).
  
  ## New SOTA
  
  The Normalized WER in the [OpenAI Whisper article](https://cdn.openai.com/papers/whisper.pdf) with the [Common Voice 9.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0) test dataset is 16.0. 
  
  As this test dataset is similar to the [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) test dataset used to evaluate our model (WER and WER Norm), it means that **our French Medium Whisper is better than the [Medium Whisper](https://huggingface.co/openai/whisper-medium) model at transcribing audios French in text**.
  
  ![OpenAI results with Whisper Medium and Test dataset of Commons Voice 9.0](https://huggingface.co/pierreguillou/whisper-medium-french/resolve/main/whisper_medium_french_wer_commonvoice9.png)
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-05
  - train_batch_size: 32
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 5000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     | Wer Norm |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:--------:|
  | 0.2695        | 0.2   | 1000 | 0.3080          | 17.8083 | 12.9791  |
  | 0.2099        | 0.4   | 2000 | 0.2981          | 17.4792 | 12.4242  |
  | 0.1978        | 0.6   | 3000 | 0.2864          | 16.7767 | 12.0913  |
  | 0.1455        | 0.8   | 4000 | 0.2752          | 16.4597 | 11.8966  |
  | 0.1712        | 1.0   | 5000 | 0.2664          | 15.8969 | 11.1406  |
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2