w_large
This model is a fine-tuned version of openai/whisper-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7156
- Wer: 66.4973
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.7439 | 0.4548 | 1000 | 0.7228 | 107.9816 |
| 0.6638 | 0.9095 | 2000 | 0.6496 | 82.4336 |
| 0.413 | 1.3643 | 3000 | 0.6292 | 76.3384 |
| 0.4303 | 1.8190 | 4000 | 0.6144 | 69.9421 |
| 0.3339 | 2.2738 | 5000 | 0.6557 | 71.5521 |
| 0.3224 | 2.7285 | 6000 | 0.6553 | 63.5360 |
| 0.1991 | 3.1833 | 7000 | 0.7058 | 64.2753 |
| 0.1752 | 3.6380 | 8000 | 0.7156 | 66.4973 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu118
- Datasets 3.0.0
- Tokenizers 0.19.1
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Base model
openai/whisper-large