U-CTC

U-CTC is an Urdu automatic speech recognition (ASR) model based on the Parakeet-CTC-0.6B architecture. It has been fine-tuned on ~21 hours of Urdu speech data using the NVIDIA NeMo framework. The model is optimized for CTC-based transcription of spoken Urdu.

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Model Summary

• Model Name: U-CTC
• Base Architecture: Parakeet-CTC-0.6B
• Framework: NVIDIA NeMo
• Language: Urdu
• Model Type: Conformer Encoder + CTC Decoder
• Loss Function: CTC Loss
• Hardware: Trained on NVIDIA RTX 3090

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Training Configuration

Setting	Value
Epochs	69
Max Steps	14,800
Optimizer	AdamW
Learning Rate	0.001
Betas	(0.9, 0.98)
Weight Decay	0.001
Scheduler	CosineAnnealing
Warmup Steps	15,000
Min LR	0.0001

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Dataset

The model was trained and evaluated on a manually curated Urdu speech dataset:

Split	Files	Duration
Train	9,425	10.87 h
Validation	4,056	5.22 h
Test	4,056	5.22 h

• Total audio hours: ~21.3 hours
• Samples skipped due to CTC alignment failure: ~2.57%
• Average AM sequence length: 50.39
• Average target sequence length: 30.51
• AM-to-target length ratio: ~1.83

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