Whisper Large v3 Lingala fine-tune
This model is a fine-tuned version of openai/whisper-large-v3 on the Google FLEURS dataset. It achieves the following results on the evaluation set:
- Loss: 0.4297
- Wer: 14.8266
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
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0012 | 8.9686 | 2000 | 0.4297 | 14.8266 |
Framework versions
- Transformers 4.56.2
- Pytorch 2.8.0+cu126
- Datasets 3.6.0
- Tokenizers 0.22.1