openai/whisper-large-v2
This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7993
- Wer: 21.2788
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: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- training_steps: 800
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.007 | 8.33 | 100 | 0.5728 | 21.4885 |
| 0.0007 | 16.67 | 200 | 0.7017 | 22.1174 |
| 0.0003 | 25.0 | 300 | 0.7358 | 21.5933 |
| 0.0002 | 33.33 | 400 | 0.7598 | 21.5933 |
| 0.0002 | 41.67 | 500 | 0.7793 | 22.0126 |
| 0.0001 | 50.0 | 600 | 0.7896 | 22.0126 |
| 0.0001 | 58.33 | 700 | 0.7969 | 21.2788 |
| 0.0001 | 66.67 | 800 | 0.7993 | 21.2788 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2