---
library_name: transformers
language:
- fr
license: mit
base_model: bofenghuang/whisper-large-v3-french
tags:
- generated_from_trainer
datasets:
- PraxySante/PxCorpus-PxSLU
metrics:
- wer
model-index:
- name: Whisper Large v3 French PxCorpus - Fine-tuning test
  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: 4.112554112554113
---

<!-- 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 Large v3 French PxCorpus - Fine-tuning test

This model is a fine-tuned version of [bofenghuang/whisper-large-v3-french](https://huggingface.co/bofenghuang/whisper-large-v3-french) on the PxCorpus PxSLU dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1903
- Wer: 4.1126

## 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: 16
- eval_batch_size: 8
- 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.0016        | 8.1967  | 1000 | 0.1864          | 5.1948 |
| 0.0003        | 16.3934 | 2000 | 0.1773          | 5.1948 |
| 0.0001        | 24.5902 | 3000 | 0.1860          | 4.1126 |
| 0.0           | 32.7869 | 4000 | 0.1903          | 4.1126 |


### Framework versions

- Transformers 4.44.1
- Pytorch 2.4.0+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1