Whisper Tiny PT
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6077
- Wer: 29.9844
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: 64
- 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
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4143 | 1.04 | 500 | 0.5325 | 32.7399 |
| 0.2693 | 3.03 | 1000 | 0.4718 | 29.4867 |
| 0.1724 | 5.01 | 1500 | 0.4758 | 28.7218 |
| 0.0849 | 7.0 | 2000 | 0.5070 | 29.2211 |
| 0.0659 | 8.04 | 2500 | 0.5223 | 29.3169 |
| 0.0539 | 10.03 | 3000 | 0.5402 | 30.1458 |
| 0.0376 | 12.02 | 3500 | 0.5755 | 29.9995 |
| 0.0217 | 14.0 | 4000 | 0.6067 | 29.6565 |
| 0.0168 | 15.04 | 4500 | 0.6082 | 29.8162 |
| 0.0205 | 17.03 | 5000 | 0.6077 | 29.9844 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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Dataset used to train dominguesm/whisper-tiny-pt
Evaluation results
- WER on Common Voice 11.0test set self-reported29.110
- WER on google/fleurstest set self-reported26.360
- WER on mozilla-foundation/common_voice_9_0test set self-reported28.680