Whisper Small Esperanto
This model is a fine-tuned version of openai/whisper-small on the Common Voice 6.1 dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.2157
- eval_wer: 16.8844
- eval_runtime: 444.7648
- eval_samples_per_second: 3.373
- eval_steps_per_second: 0.423
- epoch: 0.9798
- step: 1750
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use 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: 4000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 2.21.0
- Tokenizers 0.21.1
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