Whisper Tiny Kn - Bharat Ramanathan
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: 0.3057
- Wer: 46.3937
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: 150
- eval_batch_size: 64
- 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 |
|---|---|---|---|---|
| 1.4091 | 0.1 | 300 | 1.4915 | 101.5026 |
| 1.1294 | 0.2 | 600 | 1.2845 | 94.7408 |
| 0.5426 | 0.3 | 900 | 0.4621 | 64.2374 |
| 0.4128 | 1.02 | 1200 | 0.3695 | 54.6582 |
| 0.3629 | 1.12 | 1500 | 0.3414 | 52.9677 |
| 0.3321 | 1.22 | 1800 | 0.3249 | 50.3005 |
| 0.3066 | 1.32 | 2100 | 0.3181 | 48.9106 |
| 0.2958 | 2.03 | 2400 | 0.3136 | 47.7836 |
| 0.2883 | 2.13 | 2700 | 0.3055 | 46.6191 |
| 0.2857 | 2.23 | 3000 | 0.3057 | 46.3937 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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