whisper-medium-ph
This model is a fine-tuned version of openai/whisper-medium on the rbcurzon/ph_dialect_asr all dataset. It achieves the following results on the evaluation set:
- Loss: 0.2901
- Wer: 0.1147
Model description
More information needed
Intended uses & limitations
This model is primarily designed for transcribing Tagalog, Bisaya, Ilocano, Waray, Kapampangan, Pangasinense, and Bikol voice notes and performing batch automatic speech recognition (ASR) for the same languages. It is also suitable for fine-tuning or domain adaptation for these specific speech tasks.
The model has several key limitations:
- It performs poorly in noisy or multi-speaker environments, leading to transcription errors.
- Accuracy is significantly reduced for noisy, accented, or dialectal speech.
- It is not optimized for real-time streaming.
- Like other Whisper-type models, it can produce plausible but incorrect words (hallucinations).
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: 2
- total_train_batch_size: 16
- 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: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1822 | 1.4818 | 1000 | 0.2656 | 0.1445 |
| 0.0706 | 2.9637 | 2000 | 0.2491 | 0.1270 |
| 0.0072 | 4.4448 | 3000 | 0.2729 | 0.1191 |
| 0.005 | 5.9266 | 4000 | 0.2810 | 0.1157 |
| 0.0009 | 7.4077 | 5000 | 0.2901 | 0.1147 |
Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4