openai/whisper-medium
This model is a fine-tuned version of openai/whisper-medium on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3896
- Wer: 200.1910
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: 32
- 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: 4000
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
0.2328 | 0.12 | 500 | 0.2655 | 301.5949 |
0.1838 | 1.11 | 1000 | 0.2496 | 286.1977 |
0.1757 | 2.1 | 1500 | 0.2563 | 118.9213 |
0.0254 | 3.09 | 2000 | 0.2992 | 237.0841 |
0.0282 | 4.07 | 2500 | 0.3342 | 125.1999 |
0.0229 | 5.06 | 3000 | 0.3502 | 268.7414 |
0.0027 | 6.05 | 3500 | 0.3918 | 107.5536 |
0.003 | 7.03 | 4000 | 0.3896 | 200.1910 |
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
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Evaluation results
- WER on rishabhjain16/infer_mysttest set self-reported12.220
- WER on rishabhjain16/infer_pfstest set self-reported2.980
- WER on rishabhjain16/infer_cmu_9htest set self-reported16.050
- WER on rishabhjain16/libritts_dev_cleantest set self-reported5.400
- WER on rishabhjain16/infer_pf_italiantest set self-reported14.080
- WER on rishabhjain16/infer_pf_germantest set self-reported51.530
- WER on rishabhjain16/infer_pf_swedishtest set self-reported16.520
- WER on rishabhjain16/infer_so_chinesetest set self-reported22.800