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Whisper Large Swedish
This model is a fine-tuned version of openai/whisper-large-v2 trained on NST Swedish ASR and evaluated on Common Voice 11 testset. It achieves the following results on the evaluation set
- Loss: 0.2337
- Wer: 9.2206
Model description
openai/whisper-large-v2 had a WER of 10.6 on Common Voice 9 testset.
Intended uses & limitations
More information needed
Training and evaluation data
The training dataset contains 276 000 examples and with a batch size of 64 and training 5000 it is 1.14 epochs. More training data or more epochs would probably improve the result even further.
Training procedure
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: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|---|---|---|---|---|
| 0.0695 | 0.2 | 1000 | 0.2695 | 12.4671 |
| 0.0524 | 0.4 | 2000 | 0.2659 | 11.6367 |
| 0.046 | 0.6 | 3000 | 0.2402 | 10.6557 |
| 0.0342 | 0.8 | 4000 | 0.2339 | 10.1774 |
| 0.0224 | 1.14 | 5000 | 0.2337 | 9.2206 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
