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whisper-base-full-data-v4
This model is a fine-tuned version of openai/whisper-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2221
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
- distributed_type: tpu
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- training_steps: 63840
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.2569 | 1.57 | 5000 | 0.4138 |
| 0.1774 | 3.13 | 10000 | 0.3295 |
| 0.1485 | 4.7 | 15000 | 0.2936 |
| 0.1316 | 6.27 | 20000 | 0.2745 |
| 0.1187 | 7.83 | 25000 | 0.2599 |
| 0.1037 | 9.4 | 30000 | 0.2496 |
| 0.1011 | 10.97 | 35000 | 0.2407 |
| 0.0905 | 12.53 | 40000 | 0.2356 |
| 0.0827 | 14.1 | 45000 | 0.2316 |
| 0.0789 | 15.67 | 50000 | 0.2278 |
| 0.0741 | 17.23 | 55000 | 0.2248 |
| 0.0714 | 18.8 | 60000 | 0.2228 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.1.0a0+gitcc01568
- Datasets 2.13.1
- Tokenizers 0.13.3
