whisper-base-urdu-full

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

Whisper Base Urdu ASR Model

This model is a fine-tuned version of openai/whisper-base on the common_voice_17_0 dataset.

Usage

from transformers import pipeline

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

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

transcription = transcriber("audio2.mp3")
print(transcription)

{'text': 'دیکھیے پانی کپ تک بہتا اور مچھلی کپ تک تیرتی ہے'}

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• 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: 1500
• mixed_precision_training: Native AMP

Training results

Framework versions

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

Evaluation

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

👉 Review the testing script: Testing Whisper Base Urdu Full

---

Summary:
The high Word Error Rate (WER) of 39.12% is a significant weakness, indicating that nearly two out of every five words are transcribed incorrectly. However, the model is much more effective at the character level. The moderate Character Error Rate (CER) of 14.78% and the strong ChrF score of 69.62 show that the system is good at predicting the correct sequence of characters, even if it struggles to form the complete, correct words.