Whisper Medium Basque
This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_17_0 eu dataset. It achieves the following results on the evaluation set:
- Loss: 0.1787
- Wer: 8.8021
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: 6.25e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
0.3171 | 0.0625 | 500 | 0.3369 | 25.5304 |
0.1852 | 0.125 | 1000 | 0.2409 | 17.3110 |
0.2353 | 0.1875 | 1500 | 0.2050 | 14.2228 |
0.1569 | 1.037 | 2000 | 0.1815 | 12.2861 |
0.125 | 1.0995 | 2500 | 0.1692 | 11.1144 |
0.12 | 1.162 | 3000 | 0.1600 | 10.6975 |
0.069 | 2.0115 | 3500 | 0.1540 | 9.7649 |
0.0606 | 2.074 | 4000 | 0.1550 | 9.8199 |
0.0434 | 2.1365 | 4500 | 0.1580 | 9.4571 |
0.0455 | 2.199 | 5000 | 0.1533 | 9.1410 |
0.0216 | 3.0485 | 5500 | 0.1620 | 9.0842 |
0.017 | 3.111 | 6000 | 0.1704 | 9.0980 |
0.0174 | 3.1735 | 6500 | 0.1681 | 9.0723 |
0.0098 | 4.023 | 7000 | 0.1725 | 8.8625 |
0.0076 | 4.0855 | 7500 | 0.1765 | 8.8351 |
0.007 | 4.148 | 8000 | 0.1787 | 8.8021 |
Framework versions
- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.2.dev0
- Tokenizers 0.20.0
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Model tree for xezpeleta/whisper-medium-eu
Base model
openai/whisper-mediumDataset used to train xezpeleta/whisper-medium-eu
Evaluation results
- Wer on mozilla-foundation/common_voice_17_0 eutest set self-reported8.802