whisper-large-english-TG
This model is a fine-tuned version of openai/whisper-large on the common_voice dataset. It achieves the following results on the evaluation set:
- Loss: 0.4494
- Wer: 18.0005
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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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 |
|---|---|---|---|---|
| 0.0452 | 2.6350 | 1000 | 0.3455 | 19.6915 |
| 0.0034 | 5.2701 | 2000 | 0.3999 | 17.8823 |
| 0.0005 | 7.9051 | 3000 | 0.4770 | 18.1438 |
| 0.0001 | 10.5402 | 4000 | 0.4494 | 18.0005 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
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Base model
openai/whisper-large