whisper-tiny-urdu

This model is a fine-tuned version of openai/whisper-tiny on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7225
  • Wer: 47.8529

Quick Usage

from transformers import pipeline

transcriber = pipeline(
  "automatic-speech-recognition", 
  model="kingabzpro/whisper-tiny-urdu"
)

transcriber.model.generation_config.forced_decoder_ids = None
transcriber.model.generation_config.language = "ur"

transcription = transcriber("audio2.mp3")
print(transcription)
{'text': 'دیکھیے پانی کب تک بہتا اور مچھلی کب تک تیرتی ہے'}

Evaluation

Dataset WER (%) CER (%) BLEU ChrF
Common Voice 17.0 (Urdu) 46.908 18.543 32.631 63.988
HowMannyMore/urdu-audiodataset 51.405 21.830 31.475 64.204

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • 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: cosine
  • lr_scheduler_warmup_steps: 200
  • training_steps: 2500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.6808 1.6949 500 0.7403 52.6699
0.3948 3.3898 1000 0.6850 47.1247
0.2873 5.0847 1500 0.6994 48.1516
0.2024 6.7797 2000 0.7169 46.7326
0.183 8.4746 2500 0.7225 47.8529

Framework versions

  • Transformers 4.51.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.6.0
  • Tokenizers 0.21.1
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