Muno459 / zipformer_p-arabic

Broad-Arabic Phoneme Zipformer (streaming, encoder + CTC head)

A streaming phoneme recognizer for general Arabic , both MSA and dialect . It maps audio to a stream of phonemes (consonant + ḥaraka units) that reflect what was actually pronounced , including dialectal forms. It does not normalise speech back to MSA. This is the broad-Arabic pre-training model that the Qur'an phoneme zipformer is fine-tuned from, published on its own for phonetic transcription, pronunciation/dialect analysis, and as a pre-training checkpoint.

• Architecture: Zipformer2 (icefall/k2), causal streaming, 65.5M params , CTC over a 250-symbol phoneme inventory.
• Output: phoneme units only. No text decoder is included , this repo is intentionally phonemes-only.
• Training: about 1700 h of Arabic speech (MASC, SADA, MGB2, CommonVoice, ArVoice, FLEURS), 5 epochs, phoneme-CTC.

Phoneme labels: LLM-phonemized, source-aware, dialect-preserving

The broad-Arabic phoneme targets were produced by an LLM phonemizer with source-aware prompting (a prompt tailored to each corpus / register, since spontaneous dialect, broadcast MSA and read MSA phonemize differently). A deliberate goal, and one we spot-checked , was to retain dialectal information : the labels (and therefore the model) capture the actual dialectal pronunciation rather than collapsing it to Modern Standard Arabic. Because it is LLM-generated, the labelling carries some noise (a minority of clips show vowel-length artifacts). The Qur'an fine-tune is sharper because Qur'an phonemes are deterministic (rule-based).

What it does (spot-check)

Predicted phonemes vs reference text (the model output is the PHON line).

Dialect (Moroccan Darija): it keeps the dialect, e.g. the negation circumfix ما…ش and dialectal lexemes.

ref  متخافيش يوجع غير شوية          phon  مَتَخَاافِۦۦش يُوعفِۦۦ ل شُوَييَت     ("ma-txafi-š ... šwayya")
ref  ماتعاونينيش يزوجوني بسيف        phon  مَتَءَوِيَنِۦۦش يَزَوجُۥۥنِۦۦ بَسيَف   (keeps the "-niš" suffix)

MSA (read): faithful phonetics. Sun-letter assimilation, gemination, and long vowels are all there.

ref  يتمُّ دعم التعلُّم التفاعليّ      phon  يَتِمم دَاامُ ل تتَعَللُم ل تتَفَااعُلِي   (gemination "taʿallum")
ref  تشكلت في المحيط الأطلسي         phon  تَشَككَت فِۦۦ ل مَمُحِۦۦط ِل ءَطلَااصِي

It is a good, honest phonetic transcriber : the words and their pronunciation come through clearly in both registers, with occasional minor slips (a dropped or merged consonant here and there).

Good for

• Phonetic / dialect transcription: what was actually pronounced, at the phoneme level.
• Pronunciation-assessment front-end: compare predicted phonemes to a reference.
• Pre-training: it carries broad Arabic acoustic knowledge. Fine-tuning on a deterministically-labelled domain (Qur'an) yields a sharp model, which is exactly how the Qur'an model was built.

Note on text / WER

This repo is the encoder only: no seq2seq, no text decoder . Phonemes to text is a separate, hard problem for open-vocabulary Arabic, so this model should not be judged by word-level text WER , which measures a decoder rather than this encoder. If you need Arabic text , use a text ASR. If you need Arabic phonemes / pronunciation (and dialect-faithful at that), use this.

Files

Usage

icefall Zipformer2 , 80-bin kaldi fbank (povey) at 16 kHz, CTC-greedy decode to phoneme units. Same inference path as the Qur'an model: see quran_per_eval.py in that repo for a runnable example (swap the checkpoint). For the 1000 ms look-ahead profile use chunk_size=(24,) , left_context_frames=(256,) .

License

Apache-2.0.

Related benchmark

This broad encoder is the pre-training base for zipformer_p-quran , which ranks #1 among streaming models on the leakage-free Quranic ASR Leaderboard (dataset: Quran-Lab/quranic-asr-benchmark ). This phoneme encoder itself is for general Arabic and is not on that Qur'an board.