| --- |
| license: apache-2.0 |
| base_model: openai/whisper-tiny |
| tags: |
| - generated_from_trainer |
| metrics: |
| - wer |
| model-index: |
| - name: whisper-tiny-basque |
| 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-tiny-basque |
|
|
| This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4296 |
| - Wer: 28.7020 |
|
|
| ## 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: 256 |
| - eval_batch_size: 128 |
| - 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: 10000 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Wer | |
| |:-------------:|:-----:|:-----:|:---------------:|:-------:| |
| | 0.7245 | 0.21 | 500 | 0.9431 | 64.8287 | |
| | 0.4752 | 0.42 | 1000 | 0.6923 | 48.8196 | |
| | 0.4115 | 0.63 | 1500 | 0.6084 | 40.8976 | |
| | 0.3696 | 0.84 | 2000 | 0.5597 | 37.1048 | |
| | 0.3336 | 1.05 | 2500 | 0.5289 | 36.0029 | |
| | 0.3189 | 1.26 | 3000 | 0.5082 | 33.5204 | |
| | 0.3007 | 1.47 | 3500 | 0.4903 | 32.4969 | |
| | 0.2935 | 1.68 | 4000 | 0.4802 | 31.6632 | |
| | 0.2839 | 1.89 | 4500 | 0.4695 | 30.7243 | |
| | 0.2594 | 2.1 | 5000 | 0.4615 | 30.2538 | |
| | 0.2507 | 2.31 | 5500 | 0.4547 | 29.4160 | |
| | 0.2574 | 2.52 | 6000 | 0.4487 | 29.5852 | |
| | 0.2523 | 2.73 | 6500 | 0.4429 | 28.5473 | |
| | 0.2471 | 2.94 | 7000 | 0.4393 | 29.3562 | |
| | 0.2329 | 3.15 | 7500 | 0.4373 | 29.2592 | |
| | 0.2346 | 3.36 | 8000 | 0.4343 | 28.5947 | |
| | 0.2322 | 3.57 | 8500 | 0.4332 | 29.0136 | |
| | 0.2304 | 3.78 | 9000 | 0.4305 | 28.1387 | |
| | 0.2299 | 3.99 | 9500 | 0.4300 | 28.0768 | |
| | 0.236 | 4.2 | 10000 | 0.4296 | 28.7020 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.36.0 |
| - Pytorch 2.1.1+cu121 |
| - Datasets 2.8.0 |
| - Tokenizers 0.15.2 |
| |
