vadimbelsky/qwen3-asr-arabic-uae

Qwen3-ASR Arabic — UAE Emirati Dialect

Fine-tuned Qwen/Qwen3-ASR-1.7B for UAE Emirati Arabic dialect speech recognition.

Results

Metric	Zero-shot (base)	Fine-tuned	Improvement
WER	13.53%	9.98%	-26%
CER	3.33%	2.55%	-23%

Evaluated on 2,497 UAE Arabic validation samples.

What improved

• Matches informal Emirati dialect style (شي vs شيء, الاماكن vs الأماكن)
• Removes spurious punctuation that the base model adds
• Better handling of dialect-specific words and expressions

Training Details

• Base model: Qwen/Qwen3-ASR-1.7B (2B params, audio encoder + 1.7B LLM decoder)
• Training data: ~22,500 UAE Emirati Arabic dialect samples from vadimbelsky/UAE_Arabic_English_Bilingual_Dataset_40k
• Strategy: Audio encoder frozen, only LLM decoder fine-tuned (84.4% of params)
• Precision: bfloat16
• Epochs: 3
• Effective batch size: 32 (batch 2 × gradient accumulation 16)
• Learning rate: 2e-5 with linear schedule
• Gradient checkpointing: enabled
• Text normalization: Diacritics removed, alef/teh marbuta normalized, punctuation stripped

Usage

from qwen_asr import Qwen3ASRModel

model = Qwen3ASRModel.from_pretrained("vadimbelsky/qwen3-asr-arabic-uae")
result = model.transcribe("audio.wav", language="Arabic")
print(result)

Or with transformers directly:

from transformers import AutoModelForCausalLM, AutoProcessor

model = AutoModelForCausalLM.from_pretrained("vadimbelsky/qwen3-asr-arabic-uae")
processor = AutoProcessor.from_pretrained("vadimbelsky/qwen3-asr-arabic-uae")

Limitations

• Trained on synthetic/generated Arabic speech data
• Optimized for UAE Emirati dialect — may not generalize to other Arabic dialects
• Short utterances only (training data mostly < 20s)

License

Apache 2.0 (same as base model)