Whisper large V3 Urdu ASR Model 🥇

This model is a fine-tuned version of openai/whisper-large-v3 on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0204
  • Wer: 21.4712
  • Cer: 7.1975

Quick Usage

from transformers import pipeline

transcriber = pipeline(
  "automatic-speech-recognition", 
  model="kingabzpro/whisper-large-v3-turbo-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'}

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 8
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Use 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: 100
  • training_steps: 1500

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.0261 0.5089 300 0.0254 30.0224 10.3646
0.0211 1.0170 600 0.0226 25.8588 8.5780
0.0121 1.5259 900 0.0206 24.2158 7.9412
0.0093 2.0339 1200 0.0195 21.3032 7.2018
0.0043 2.5428 1500 0.0204 21.4712 7.1975

Framework versions

  • Transformers 4.52.2
  • Pytorch 2.7.1+cu126
  • Datasets 3.4.1
  • Tokenizers 0.21.2

Evaluation

Urdu ASR Evaluation on Common Voice 17.0 (Test Split).

Metric Value Description
WER 26.019% Word Error Rate (lower is better)
CER 9.426% Character Error Rate
BLEU 59.446% BLEU Score (higher is better)
ChrF 82.902 Character n-gram F-score

👉 Review the testing script: Testing Whisper Large V3 Urdu

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