Whisper Small Fine-tuned with Uyghur Common Voice

This model is a fine-tuned version of openai/whisper-small on the Uyghur Common Voice dataset.

As a proof-of-concept, only 3264 recordings (~5.5 hrs of audio) were used for training, and 937 recordings (~1.5 hrs of audio) were used for validation. You may find the full dataset for Uyghur and other languages here: https://commonvoice.mozilla.org/en/datasets.

This model achieves the following results on the evaluation set:

  • Loss: 0.5105
  • Wer Ortho: 41.6377
  • Wer: 34.9961

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: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 50
  • training_steps: 500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
0.0574 2.4510 500 0.5105 41.6377 34.9961

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.5.0+cu121
  • Datasets 3.1.0
  • Tokenizers 0.19.1
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Evaluation results