Whisper Small Sr Fleurs- Sagicc
This model is a fine-tuned version of openai/whisper-small on the Google Fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.4134
- Wer Ortho: 28.9292
- Wer: 25.6021
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.0649 | 2.49 | 500 | 0.3685 | 30.6352 | 27.1489 |
| 0.0181 | 4.98 | 1000 | 0.4134 | 28.9292 | 25.6021 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for Sagicc/whisper-small-sr-fleurs
Base model
openai/whisper-smallDataset used to train Sagicc/whisper-small-sr-fleurs
Evaluation results
- Wer on Google Fleurstest set self-reported25.602