| --- |
| library_name: peft |
| language: |
| - fr |
| license: apache-2.0 |
| base_model: openai/whisper-small |
| tags: |
| - base_model:adapter:openai/whisper-small |
| - lora |
| - transformers |
| metrics: |
| - wer |
| model-index: |
| - name: Whisper Small Fr - IMT Atlantique X 52 Hertz Full |
| results: [] |
| --- |
| |
| <!-- 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 Fr - IMT Atlantique X 52 Hertz Full |
|
|
| This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the FullDatabase dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6708 |
| - Wer: 0.3327 |
|
|
| ## 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: 0.001 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 3 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Wer | |
| |:-------------:|:------:|:----:|:---------------:|:------:| |
| | 1.3574 | 0.4762 | 20 | 1.4124 | 0.3671 | |
| | 1.1555 | 0.9524 | 40 | 1.1674 | 0.3728 | |
| | 0.7839 | 1.4286 | 60 | 0.7497 | 0.3231 | |
| | 0.4459 | 1.9048 | 80 | 0.6860 | 0.3614 | |
| | 0.533 | 2.3810 | 100 | 0.6819 | 0.3461 | |
| | 0.2029 | 2.8571 | 120 | 0.6708 | 0.3327 | |
| |
| |
| ### Framework versions |
| |
| - PEFT 0.18.0 |
| - Transformers 4.57.3 |
| - Pytorch 2.9.0+cu126 |
| - Datasets 4.4.1 |
| - Tokenizers 0.22.1 |
