UG Speech Data ASR - Ewe nornmaliser
This model is a fine-tuned version of openai/whisper-small on the ugspeechdata-ewe dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.4720
- eval_wer_ortho: 45.4249
- eval_wer: 37.4757
- eval_cer: 12.9706
- eval_runtime: 1781.9648
- eval_samples_per_second: 2.159
- eval_steps_per_second: 0.135
- epoch: 2.8708
- step: 2400
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: 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: 4000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Tokenizers 0.21.1
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Base model
openai/whisper-small