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: 0.3622
- Wer: 10.6091
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 |
---|---|---|---|---|
0.5314 | 0.12 | 500 | 0.2762 | 13.2758 |
0.1619 | 1.09 | 1000 | 0.2541 | 11.6909 |
0.069 | 2.05 | 1500 | 0.2768 | 10.2892 |
0.0756 | 3.02 | 2000 | 0.2756 | 11.6142 |
0.0324 | 3.14 | 2500 | 0.2961 | 11.1800 |
0.0171 | 4.11 | 3000 | 0.3322 | 11.1689 |
0.0046 | 5.07 | 3500 | 0.3653 | 10.5858 |
0.0091 | 6.03 | 4000 | 0.3622 | 10.6091 |
Framework versions
- Transformers 4.29.0
- Pytorch 1.14.0a0+44dac51
- Datasets 2.12.0
- Tokenizers 0.13.3
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Evaluation results
- WER on rishabhjain16/infer_mysttest set self-reported11.960
- WER on rishabhjain16/infer_pfstest set self-reported3.120
- WER on rishabhjain16/infer_cmutest set self-reported8.920
- WER on rishabhjain16/libritts_dev_cleantest set self-reported5.390
- WER on rishabhjain16/infer_pf_swedishtest set self-reported7.740
- WER on rishabhjain16/infer_pf_germantest set self-reported36.210
- WER on rishabhjain16/infer_pf_italiantest set self-reported4.160
- WER on rishabhjain16/infer_so_chinesetest set self-reported14.400