A Montreal Forced Aligner acoustic model purpose-built for
Quranic recitation
in the Hafs riwaya
, with a pronunciation dictionary derived from a
rule-verified phonetic script. Built for phone-level tajweed measurement:
madd durations, ghunna, qalqala, not just word timestamps.
What makes it different
Generic Arabic aligners fail on recitation: multi-second madd vowels, ghunna
nasals, melismatic (mujawwad) style, and mosque reverb are far outside normal
speech. This model was trained and evaluated specifically against those:
Phone set
: derived from the Quran Phonetic Script via
quran-transcript
, re-encoded so
every articulation is ONE phone (
ا+
= long vowel of any prescribed length,
vs. the source convention of repeating characters, which is degenerate for
HMM alignment). 67 symbols; tajweed-bearing segments are single intervals.
Pronunciation dictionary
: whole-ayah contextual phonetization (wasl
forms, cross-word idgham/ikhfa/iqlab), never isolated-word forms, which
measure 20.5% phone error vs. 1.6% for contextual.
Training data (~110 h, style-balanced)
: human-word-count-validated ayah
clips from QUL reciters, a targeted lazem/mottasel oversample, and 40 h of
mujawwad cut from a large verified crawl. Balance matters: a 5x larger
unbalanced corpus
degraded
mujawwad coverage in our ablations.
Training config
: silence_probability 0.15, boost_silence 1.0 (defaults
subsidize the silence model, which then swallows sustained vowels), beams
40/160 for multi-second phones.
Measured performance
Metric
Result
Phone-boundary jitter vs. signal landmarks (geminate stop releases, n=533, 4 reciters incl. mujawwad)
10-20 ms median
Speech uncovered by any phone (murattal, held-out reciters)
0.0-0.4%
Speech uncovered (mujawwad, Abdul Basit held-out)
5.0%
Madd duration vs. prescription (tempo-normalized, anchored): normal (2) / monfasel (4)
2.1 / 4.8
Word boundary on a human-labelled mujawwad elongation (58:20)
within ~100 ms
Notes for measurement use: word-onset comparisons against QUL word timings are
biased: QUL starts are ~245 ms early vs. physical burst landmarks (playback
convention). Trust obstruent-anchored spans; treat boundaries
inside
sonorant runs as untrustworthy and measure rule segments between obstruent
anchors. Pre-pause madds benefit from an energy/voicing end-trim (breath and
room decay otherwise attach to the final phone).
phone symbol table (67 + blank) for CTC integrations
rule_index.jsonl
per-ayah tajweed rule annotations: 93,430 positions with the governing rule and its prescribed length (
golden_len
); join with alignments to measure tajweed
Corpus: one wav (16 kHz mono) + one
.lab
per ayah clip,
.lab
containing
the
Uthmani
ayah text (whole words; the dictionary handles phonetization).
Keep all paths ASCII (OpenFST on Windows fails on non-ASCII paths).
Limitations
Hafs only.
The phonetizer supports no other riwaya; aligning Warsh/Qalun
audio with this model would systematically mislabel exactly the features
tajweed cares about.
Mid-ayah waqf is unmodelled (the Uthmani text carries no waqf marks); a
reciter pausing mid-ayah takes pausal forms the dictionary does not offer.
Mujawwad residual: ~5% of sung speech (mostly pre-breath decrescendos) is
attributed to silence; use signal-side end-trims for duration work there.
One model, deliberately: a mujawwad-specialist ablation
underperformed
this balanced model, and MFA performs per-speaker adaptation at align time.
Provenance
Built by the Quran-Lab effort on a 59,000-hour multi-site crawl of public
recitation audio (verified per-ayah against canonical text before any
training; label source is always the canonical Uthmani text, never ASR
output). Phone representation and rule index from the companion
quran-phones
package.
Runs of Quran-Lab mfa-quran-hafs on huggingface.co
62
Total runs
0
24-hour runs
9
3-day runs
13
7-day runs
-97
30-day runs
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