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asr-bambara
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: 0.2985
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 |
|---|---|---|---|
| 1.646 | 1.0 | 1708 | 0.4390 |
| 1.4284 | 2.0 | 3416 | 0.3806 |
| 1.287 | 3.0 | 5124 | 0.3378 |
| 1.0537 | 4.0 | 6832 | 0.3140 |
| 0.7434 | 5.0 | 8540 | 0.2950 |
| 0.6049 | 5.9969 | 10242 | 0.2985 |
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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Base model
openai/whisper-large-v2