Whisper Small PL
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3739
- Wer: 8.5898
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: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0474 | 1.1 | 1000 | 0.2561 | 9.4612 |
| 0.0119 | 3.09 | 2000 | 0.2901 | 8.9726 |
| 0.0045 | 5.08 | 3000 | 0.3151 | 8.8870 |
| 0.0007 | 7.07 | 4000 | 0.4218 | 8.6032 |
| 0.0005 | 9.07 | 5000 | 0.3739 | 8.5898 |
Evaluation results
When tested on diffrent polish ASR datasets (splits: test), this model achieves the following results:
| Dataset | WER | WER unnormalized | CER | MER |
|---|---|---|---|---|
| common_voice_11_0 | 8.85 | 21.75 | 2.63 | 8.76 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2