---
language:
- fr
license: apache-2.0
tags:
- whisper
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_15_0
- BrunoHays/multilingual-tedx-fr
- PolyAI/minds14
- facebook/multilingual_librispeech
- facebook/voxpopuli
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper tiny French
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset1:
      name: mozilla-foundation/common_voice_15_0 fr
      type: mozilla-foundation/common_voice_15_0
      config: fr
      split: test
      args: fr
    metrics:
      - name: Wer
        type: wer
        value: 40.0
    dataset2:
      name: facebook/multilingual_librispeech fr
      type: facebook/multilingual_librispeech
      config: fr
      split: test
      args: fr
      wer : 26.1
    dataset3:
      name: facebook/voxpopuli fr
      type: facebook/voxpopuli
      config: fr
      split: test
      args: fr
      wer : 29.4
    dataset4:
      name: google/fleurs fr
      type: google/fleurs
      config: fr
      split: test
      args: fr
      wer : 33.7
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper tiny fr - JaepaX
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the fr datasets.

## WER Result
It achieves the following results on the evaluation sets
- Mulit-Libri : "26.1", 
- common : "40.0"
- voxpopuli : "29.4"
- fleurs : "33.7"