Whisper Small Hi - Sanchit Gandhi
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5304
- Wer: 30.4673
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 |
|---|---|---|---|---|
| 0.3345 | 1.7778 | 500 | 0.4103 | 43.8302 |
| 0.1379 | 3.5556 | 1000 | 0.4276 | 37.6756 |
| 0.0612 | 5.3333 | 1500 | 0.4672 | 32.8039 |
| 0.0234 | 7.1111 | 2000 | 0.4806 | 32.9948 |
| 0.0167 | 8.8889 | 2500 | 0.4829 | 31.0247 |
| 0.0074 | 10.6667 | 3000 | 0.5092 | 34.4762 |
| 0.0023 | 12.4444 | 3500 | 0.5247 | 29.9786 |
| 0.0008 | 14.2222 | 4000 | 0.5304 | 30.4673 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
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Model tree for galelka/whisper-large-optimized
Base model
openai/whisper-small