whisper-small-ar-v2
This model is for Arabic automatic speech recognition (ASR). It is a fine-tuned version of openai/whisper-small on the Arabic portion of the mozilla-foundation/common_voice_16_1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4007
- Wer: 47.7264
Model description
Whisper model fine-tuned on Arabic data, following the official tutorial.
Intended uses & limitations
It is recommended to fine-tune and evaluate on your data before using it.
Training and evaluation data
Training Data: CommonVoice (v16.1) Arabic train + validation splits
Validation Data: CommonVoice (v16.1) Arabic test split
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: 8000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2742 | 0.82 | 1000 | 0.3790 | 275.2463 |
| 0.1625 | 1.65 | 2000 | 0.3353 | 228.5252 |
| 0.1002 | 2.47 | 3000 | 0.3311 | 238.8858 |
| 0.0751 | 3.3 | 4000 | 0.3354 | 158.1532 |
| 0.0601 | 4.12 | 5000 | 0.3576 | 48.9285 |
| 0.0612 | 4.95 | 6000 | 0.3575 | 47.8937 |
| 0.0383 | 5.77 | 7000 | 0.3819 | 46.9085 |
| 0.0234 | 6.6 | 8000 | 0.4007 | 47.7264 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu118
- Datasets 2.17.1
- Tokenizers 0.15.2