whisper-small-uyghur-common-voice

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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.

This model achieves the following results on the evaluation set:

• Loss: 1.5920
• Wer Ortho: 42.9701
• Wer: 28.2995
• Cer: 10.8968

Training and evaluation data

The training was done using the combined train and dev set of common_voice_15_0 (11215 recordings, ~20hrs of audio).

The testing was done using the test set of THUYG20 as the standard benchmark for Uyghur speech models.

Training procedure

Finetuning code avaiblable in https://github.com/ixxan/ug-speech

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: 300
• training_steps: 4000
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.46.2
• Pytorch 2.5.1+cu121
• Datasets 3.1.0
• Tokenizers 0.20.3