| --- |
| license: apache-2.0 |
| base_model: openai/whisper-medium |
| tags: |
| - generated_from_trainer |
| metrics: |
| - bleu |
| model-index: |
| - name: whisper-medium-english-2-wolof |
| 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-medium-english-2-wolof |
|
|
| This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.1668 |
| - Bleu: 34.6061 |
|
|
| ## 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: 20000 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Bleu | |
| |:-------------:|:------:|:-----:|:---------------:|:-------:| |
| | 0.9771 | 0.8941 | 2000 | 0.9736 | 22.8506 | |
| | 0.6832 | 1.7881 | 4000 | 0.8379 | 30.0113 | |
| | 0.4568 | 2.6822 | 6000 | 0.8083 | 33.4759 | |
| | 0.2623 | 3.5762 | 8000 | 0.8506 | 33.4723 | |
| | 0.1608 | 4.4703 | 10000 | 0.9128 | 33.6342 | |
| | 0.0758 | 5.3643 | 12000 | 0.9808 | 33.7770 | |
| | 0.0315 | 6.2584 | 14000 | 1.0546 | 34.0842 | |
| | 0.0133 | 7.1524 | 16000 | 1.1085 | 34.2531 | |
| | 0.0057 | 8.0465 | 18000 | 1.1455 | 34.5325 | |
| | 0.0046 | 8.9405 | 20000 | 1.1668 | 34.6061 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.41.2 |
| - Pytorch 2.4.0+cu121 |
| - Datasets 3.2.0 |
| - Tokenizers 0.19.1 |
| |
