| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: openai/whisper-small |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - wer |
| | model-index: |
| | - name: whisper-small-akan |
| | 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-akan |
| |
|
| | This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.2208 |
| | - Wer: 9.3196 |
| |
|
| | ## 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: 0.0001 |
| | - 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: 200 |
| | - training_steps: 2000 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Wer | |
| | |:-------------:|:-----:|:----:|:---------------:|:-------:| |
| | | 0.0726 | 10.0 | 250 | 0.8217 | 48.0395 | |
| | | 0.0246 | 20.0 | 500 | 0.9650 | 44.0903 | |
| | | 0.011 | 30.0 | 750 | 0.9165 | 40.6770 | |
| | | 0.0022 | 40.0 | 1000 | 0.9419 | 39.9436 | |
| | | 0.0009 | 50.0 | 1250 | 0.2120 | 9.7770 | |
| | | 0.0002 | 60.0 | 1500 | 0.2179 | 9.0909 | |
| | | 0.0001 | 70.0 | 1750 | 0.2201 | 9.3196 | |
| | | 0.0001 | 80.0 | 2000 | 0.2208 | 9.3196 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.44.2 |
| | - Pytorch 2.4.0+cu121 |
| | - Datasets 2.21.0 |
| | - Tokenizers 0.19.1 |
| | |
