Whisper-Small-TF-TIMIT
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7104
- Wer: 98.0856
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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
0.3408 | 3.45 | 500 | 0.3994 | 83.6838 |
0.2057 | 6.9 | 1000 | 0.4079 | 92.3470 |
0.0616 | 10.34 | 1500 | 0.5076 | 94.2053 |
0.023 | 13.79 | 2000 | 0.5998 | 95.3184 |
0.0043 | 17.24 | 2500 | 0.6825 | 97.1284 |
0.0023 | 20.69 | 3000 | 0.7104 | 98.0856 |
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
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