Whisper Large V2 - Cantonese - Augmented
This model is a fine-tuned version of openai/whisper-large-v2 on the mozilla-foundation/common_voice_11_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1828
- Cer: 6.2133
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
Training:
- mozilla-foundation/common_voice_11_0 (train+validation)
Evaluation:
Training procedure
Datasets were augmented on-the-fly using audiomentations via PitchShift and TimeStretch transformations at p=0.3.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- 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: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.1126 | 1.21 | 200 | 0.1666 | 7.3103 |
| 0.0467 | 2.42 | 400 | 0.1610 | 6.9419 |
| 0.0217 | 3.63 | 600 | 0.1621 | 6.3874 |
| 0.008 | 4.85 | 800 | 0.1699 | 6.3064 |
| 0.0023 | 6.06 | 1000 | 0.1828 | 6.2133 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
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Dataset used to train Scrya/whisper-large-v2-cantonese
Evaluation results
- CER on mozilla-foundation/common_voice_11_0test set self-reported6.213