Whisper-tiny-finetuned-minds-en-navodit
This model is a fine-tuned version of openai/whisper-tiny on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.7334
- eval_wer_ortho: 0.4466
- eval_wer: 0.3300
- eval_runtime: 36.2075
- eval_samples_per_second: 3.121
- eval_steps_per_second: 0.221
- epoch: 17.8571
- step: 500
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: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0
- Datasets 3.2.0
- Tokenizers 0.21.0
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Base model
openai/whisper-tiny