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bambara-asr-large
This model is a fine-tuned version of openai/whisper-large-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3458
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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4668 | 1.0 | 2766 | 0.4860 |
| 0.4199 | 2.0 | 5532 | 0.4270 |
| 0.3137 | 3.0 | 8298 | 0.3943 |
| 0.2657 | 4.0 | 11064 | 0.3682 |
| 0.2449 | 5.0 | 13830 | 0.3479 |
| 0.1897 | 5.9981 | 16590 | 0.3458 |
Framework versions
- PEFT 0.14.1.dev0
- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for djelia/bambara-asr-large
Base model
openai/whisper-large-v2