Finetune Whisper on Frisian and English
Collection
Assessing Knowledge-Distillation Based Compression of Whisper Model for Frisian ASR
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DistilFT-English-10h
This model is a fine-tuned version of distil-small.en on the librispeech dataset. It achieves the following results on the evaluation set:
- Loss: 0.2318
- Wer: 4.4905
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: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.651 | 0.5556 | 100 | 0.9641 | 3.4754 |
| 0.5006 | 1.1111 | 200 | 0.7651 | 3.5039 |
| 0.3531 | 1.6667 | 300 | 0.5188 | 3.5121 |
| 0.2176 | 2.2222 | 400 | 0.3514 | 4.0258 |
| 0.1834 | 2.7778 | 500 | 0.2878 | 4.3132 |
| 0.1587 | 3.3333 | 600 | 0.2589 | 4.4049 |
| 0.1553 | 3.8889 | 700 | 0.2447 | 4.5007 |
| 0.1566 | 4.4444 | 800 | 0.2370 | 4.5007 |
| 0.1226 | 5.0 | 900 | 0.2332 | 4.5048 |
| 0.1533 | 5.5556 | 1000 | 0.2318 | 4.4905 |
Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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