README.md

metadata

library_name: transformers
language:
  - fr
license: mit
base_model: bofenghuang/whisper-large-v3-french
tags:
  - generated_from_trainer
datasets:
  - PraxySante/PxCorpus-PxSLU
  - PraxySante/MediaSpeech
  - BrunoHays/multilingual-TEDX-fr
  - mozilla-foundation/common_voice_17_0
metrics:
  - wer
model-index:
  - name: Whisper Large v3 French PraxySante - Fine-tuned
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: PxCorpus PxSLU
          type: PraxySante/PxCorpus-PxSLU
          args: 'config: fr, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 27.715877437325904
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: MediaSpeech
          type: PraxySante/MediaSpeech
        metrics:
          - name: Wer
            type: wer
            value: 27.715877437325904
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Multilingual TedX Fr
          type: BrunoHays/multilingual-TEDX-fr
        metrics:
          - name: Wer
            type: wer
            value: 27.715877437325904
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 17
          type: mozilla-foundation/common_voice_17_0
        metrics:
          - name: Wer
            type: wer
            value: 27.715877437325904

Whisper Large v3 French PraxySante - Fine-tuned

This model is a fine-tuned version of bofenghuang/whisper-large-v3-french on the PxCorpus PxSLU, the MediaSpeech, the Multilingual TedX Fr and the Common Voice 17 datasets. It achieves the following results on the evaluation set:

• Loss: 0.6630
• Wer: 27.7159

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: 8
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 2
• total_train_batch_size: 128
• total_eval_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 3.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.5569	1.6129	25	0.6630	27.7159

Framework versions

• Transformers 4.44.2
• Pytorch 2.3.0
• Datasets 2.21.0
• Tokenizers 0.19.1