| | --- |
| | library_name: transformers |
| | language: |
| | - en |
| | license: apache-2.0 |
| | base_model: openai/whisper-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - wer |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: ./whisper-base-ea_8.5hr |
| | 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-base-ea_8.5hr |
| | |
| | This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Afrispeech-200 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.7306 |
| | - Wer Ortho: 0.2818 |
| | - Wer: 0.2261 |
| | - Cer: 0.1004 |
| | - Precision: 0.8516 |
| | - Recall: 0.8552 |
| | - F1: 0.8527 |
| | |
| | ## 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: 8 |
| | - seed: 42 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: constant_with_warmup |
| | - lr_scheduler_warmup_steps: 50 |
| | - training_steps: 500 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | Cer | Precision | Recall | F1 | |
| | |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:---------:|:------:|:------:| |
| | | 1.0157 | 0.1825 | 100 | 0.9950 | 0.3122 | 0.2683 | 0.1220 | 0.8325 | 0.8364 | 0.8338 | |
| | | 0.8347 | 0.3650 | 200 | 0.8313 | 0.3056 | 0.2562 | 0.1182 | 0.8429 | 0.8450 | 0.8433 | |
| | | 0.6958 | 0.5474 | 300 | 0.7851 | 0.2905 | 0.2393 | 0.1060 | 0.8478 | 0.8503 | 0.8484 | |
| | | 0.8021 | 0.7299 | 400 | 0.7532 | 0.2848 | 0.2286 | 0.1014 | 0.8541 | 0.8563 | 0.8546 | |
| | | 0.8634 | 0.9124 | 500 | 0.7306 | 0.2818 | 0.2261 | 0.1004 | 0.8516 | 0.8552 | 0.8527 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.51.3 |
| | - Pytorch 2.6.0+cu124 |
| | - Datasets 3.6.0 |
| | - Tokenizers 0.21.1 |
| |
|
