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Whisper finetuned for ceb
This model is a fine-tuned version of openai/whisper-tiny on the Fleurs Ceb subset dataset. It achieves the following results on the evaluation set:
- eval_loss: 1.6985
- eval_wer: 82.1091
- eval_wer_un_norm: 84.0742
- eval_runtime: 767.3857
- eval_samples_per_second: 1.985
- eval_steps_per_second: 0.249
- epoch: 6.92
- step: 5300
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: reduce_lr_on_plateau
- lr_scheduler_warmup_steps: 2000
- training_steps: 20000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1