| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - wer |
| model-index: |
| - name: large_config |
| 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. --> |
|
|
| # large_config |
| |
| This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2441 |
| - Wer: 17.8570 |
| - Cer: 8.0073 |
| |
| ## 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: 16 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 32 |
| - 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 | Cer | |
| |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| |
| | 0.5155 | 0.1 | 1000 | 0.4045 | 27.7845 | 12.1308 | |
| | 0.5436 | 1.02 | 2000 | 0.3784 | 27.0983 | 11.9181 | |
| | 0.265 | 1.12 | 3000 | 0.3227 | 22.8815 | 10.0236 | |
| | 0.4765 | 2.04 | 4000 | 0.3021 | 21.9744 | 9.6005 | |
| | 0.1818 | 2.14 | 5000 | 0.3002 | 21.0336 | 9.1830 | |
| | 0.4031 | 3.05 | 6000 | 0.2496 | 18.0914 | 7.9181 | |
| | 0.1991 | 3.15 | 7000 | 0.2971 | 21.7029 | 9.9984 | |
| | 0.3023 | 4.07 | 8000 | 0.2445 | 17.7946 | 7.9248 | |
| | 0.2185 | 4.17 | 9000 | 0.2842 | 20.3760 | 9.2891 | |
| | 0.2114 | 5.09 | 10000 | 0.2441 | 17.8570 | 8.0073 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.26.1 |
| - Pytorch 1.13.1+cu117 |
| - Datasets 2.9.0 |
| - Tokenizers 0.13.2 |
| |
