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

python

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)

vadimbelsky/arabic-parakeet-tdt-uae

Arabic Parakeet TDT — UAE Dialect

Model Description

Training Details

Current Results

Usage

Limitations

Next Steps

License

vadimbelsky/
arabic-parakeet-tdt-uae