vadimbelsky/arabic-parakeet-tdt-uae

Arabic Parakeet TDT — UAE Dialect

🚧 Work in Progress — This model is under active development. Results will improve.

Model Description

Fine-tuned nvidia/parakeet-tdt-1.1b (English-only FastConformer + TDT) for Arabic UAE dialect speech recognition via cross-lingual transfer learning.

Training Details

• Base model: nvidia/parakeet-tdt-1.1b (1.1B params, FastConformer encoder + TDT decoder)
• Training data: 22k Arabic UAE dialect samples (39 hours)
• Tokenizer: SentencePiece Unigram (1024 vocab) trained on Arabic text
• Strategy: Encoder frozen for 10 epochs, then unfrozen with differential LR (encoder 1e-5, decoder 3e-4)
• Text normalization: Diacritics removed, alef/teh marbuta normalized, punctuation stripped
• Epochs: 50
• Best val WER: 0.641

Current Results

Metric	Value
Val WER	0.641

Usage

import nemo.collections.asr as nemo_asr

model = nemo_asr.models.ASRModel.restore_from("arabic-parakeet-tdt-uae.nemo")
transcriptions = model.transcribe(["audio.wav"])
print(transcriptions)

Limitations

• WER is still high (~64%) — cross-lingual transfer from English to Arabic is challenging with limited data
• Repetition artifacts in longer utterances (common RNNT issue)
• Trained on synthetic/generated Arabic speech data
• Not suitable for production use yet

Next Steps

• Pre-train on large Arabic dataset (MGB-2, 1200 hours) before dialect fine-tuning
• Address decoder repetition issues
• Evaluate on more diverse test sets

License

Apache 2.0 (same as base model)