---
language:
- fr
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
base_model: openai/whisper-small
model-index:
- name: Whisper Small French
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0 fr
      type: mozilla-foundation/common_voice_11_0
      config: fr
      split: test
      args: fr
    metrics:
    - type: wer
      value: 15.38
      name: Wer
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: facebook/voxpopuli fr
      type: facebook/voxpopuli
      config: fr
      split: test
      args: fr
    metrics:
    - type: wer
      value: 16.29
      name: Wer
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: google/fleurs fr_fr
      type: google/fleurs
      config: fr_fr
      split: test
      args: fr_fr
    metrics:
    - type: wer
      value: 13.98
      name: Wer
---

<!-- 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. -->

# Whisper Small French

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 fr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.00
- WER on `mozilla-foundation/common_voice_11_0` FR (with normalization): 15.38 %
- WER on `facebook/voxpopuli` FR (with normalization): 16.29 %
- WER on `google/fleurs` fr_fr (with normalization): 13.98 %

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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

### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2