openai/whisper-medium
This model is a fine-tuned version of openai/whisper-medium on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3069
- Wer: 19.8399
Model description
The Model is fine-tuned for 1000 steps/updates on CV11 Hungarian train+valiation data.
- Zero-shot - 26.9 (CV9 test data, even on CV11 the WER is closer a bit higher than this)
- Fine-tuned - 19.83 (CV11 test data)
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: 64
- 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: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0031 | 7.46 | 1000 | 0.3069 | 19.8399 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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Dataset used to train sgangireddy/whisper-medium-hu
Evaluation results
- Wer on mozilla-foundation/common_voice_11_0test set self-reported19.840