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Update (June 2026): weights upgraded in place - real-phone word-error roughly halved (~20% → ~10%) with the same clean-studio accuracy, and the pronunciation/tajweed head retrained to match. Same file layout as before. Live low-latency streaming model: fastconformer-quran-streaming.

FastConformer-Quran

State-of-the-art automatic speech recognition for Quranic recitation, with a multi-signal mispronunciation detector built on top.

🚀 #1 on a leakage-free Quran benchmark (Hafs riwayah): 4.13% overall WER on held-out audio, ahead of nvidia FastConformer (8.14%, current public #1) and Tarteel Whisper (21.31%) on identical clips. On held-out reciters never seen in training: 0.93% WER. 🎯 ~80% sensitivity at ~9% FPR on a 39K-token held-out mispronunciation benchmark (verified on the current weights). 📱 iOS-ready via the companion CoreML repos (offline · streaming).

Headline numbers

Honesty note (June 2026). Earlier versions of this card quoted a 0.029% WER on the EveryAyah test split. That split overlaps our training data: the public EveryAyah test shard shares ~88% of its clips with reciters we trained on, so that number measured memorization, not generalization. The numbers below come from a leakage-free held-out benchmark - every clip is verified absent from training - and are the ones you should trust. The model is still SOTA on this stricter test; it just reports an honest ~0.9% on unseen reciters instead of a leaked 0.029%.

📊 Live, interactive board (this model vs nvidia, whisper, seamless, mms, omniASR, cohere, Tarteel): Quranic ASR Leaderboard.

Leakage-free Quran benchmark (600 clips, same decoding/normalization for every model)

Three held-out sources, 200 clips each: EveryAyah reciters with zero training samples (clean studio), a QUL reciter (Al-Nufais) we never trained on, and held-out real phone-mic recitation (tlog). Ranked by overall WER (lower = better); WER/CER are over diacritic-normalized text.

⭐ Tarteel (official, realtime) is Tarteel's current production ASR (a streaming FastConformer), not their old open Whisper model. We ran all 600 benchmark clips through it at voice-v2.tarteel.io and scored them with the same scorer. It is by far the strongest external system here (10.99 overall), far ahead of their old public whisper-base-ar-quran (21.31).

This model is #1 overall, ahead of the current public leaderboard #1 (nvidia FastConformer) on the same held-out clips. Against Tarteel's official production model (⭐, their realtime streaming FastConformer at voice-v2.tarteel.io), this model leads overall 4.13 vs 10.99.

One honest caveat on that head-to-head: this repo is the offline (full-utterance) model, and Tarteel's production model is streaming (realtime), so it is not a like-for-like comparison - full context is an advantage. The fair streaming-vs-streaming number is our separate streaming model at 11.96 overall, which is competitive with Tarteel's 10.99 (within the benchmark's noise on a 600-clip set). So: our offline model is clearly ahead of every external system including Tarteel official; our streaming model is roughly on par with Tarteel's streaming production system. General Arabic models (whisper-large-v3, seamless, mms) are strong on broadcast Arabic but degrade on diacritized Quranic recitation - which is the point of a Quran-specific model. Full interactive board: Quranic ASR Leaderboard Space.

The held-out phone-mic column is the hard, real-world case. tlog also carries substantial label noise (filename↔ayah mismatches, user-added basmala, partial recitations), so a portion of that 8.88% is the model correctly transcribing what was actually said against wrong metadata.

Two further leaderboard families were attempted but not scored here: cohere-transcribe-03-2026 (gated, requires license access) and omniASR-LLM-7B (fairseq2 7B - loads but its CUDA inference path segfaults on our hardware). They can be folded into the benchmark on a machine where they run.

Demo audio

Alafasy reciting Q 1:4:

Predicted: مَالِكِ يَوْمِ الدِّينِ ✓

Abdullah Basfar reciting Q 112:1:

Predicted: قُلْ هُوَ اللَّهُ أَحَدٌ ✓

Alafasy reciting Q 78:2:

Predicted: قُلْ هُوَ نَبَأٌ عَظِيمٌ ✓

Try the live demo: Space.

Mispronunciation detection

We also ship a multi-signal pronunciation scorer combining three orthogonal signals on the same CTC architecture:

A token is flagged by the consensus rule when at least 2 of 3 signals agree (default thresholds: head < 0.5, anchor > 0.20, GOP < -3.0).

Held-out evaluation

On 39,173 tokens from 996 tlog clips that were never seen by the pronunciation head during training, with per-token consensus labels from our ASR and ElevenLabs Scribe v2:

The combined detector catches ~80% of real mispronunciations at a ~9% false-positive rate. This is the deployable operating point. (Re-verified June 2026 on the current shipped weights; the head and encoder are the same vintage.)

What this model is and isn't

Is:

• The best published ASR for Quranic recitation in Hafs riwayah.
• A frame-level CTC model with 512-dim encoder features exposed for downstream scoring.
• Production-ready ONNX (fp32 437 MB, fp16 219 MB), running at RTF ~0.001 on an RTX 4090.
• Diacritic-aware: outputs fully harakat-marked Arabic text.

Isn't:

• A general Arabic ASR. Trained only on Quranic audio with a 1,024-token BPE tokenizer. Performance on dialectal, news, or conversational Arabic will be poor by design.
• A streaming model itself. This repo is the offline (full-utterance) model. For true token-by-token low-latency streaming there is now a separately-trained cache-aware variant: fastconformer-quran-streaming (+ CoreML/ANE). On the leakage-free benchmark the streaming model trades some accuracy for latency (11.96% overall WER vs 4.13% offline), as expected.
• Trained for other qira'at. Hafs riwayah only.

How it was built

Stage 1: base training on EveryAyah. Fine-tuned NVIDIA's stt_ar_fastconformer_hybrid_large_pcd_v1.0 (Arabic-pretrained, ~1100 h) on 22 K EveryAyah clips. Reached 0.0757% WER on the held-out test split.

Stage 2: pronunciation scoring stack. Built a per-token head on top of frozen encoder features (512-dim pooled + 64-dim token embedding + 16-dim Quran-phonology features into an MLP). Initial training on weak labels (CTC-vs-expected disagreement + GOP scores) plus master qari anchors (Husary, Abdul Basit, Alafasy clean recitations).

Stage 3: phone-audio fine-tune. Three rounds of low-LR continuation on EveryAyah and tlog: 22,585 clean clips, 6,869 high-quality tlog clips (full weight), 20,589 borderline tlog clips (half weight). LR schedule 1e-5, 5e-6, 2.5e-6 over six epochs total. Trend was monotonic improvement on a held-out tlog slice through round three, then saturated.

Stage 4: dual-ASR consensus labels. Ran ElevenLabs Scribe v2 over the 6,168 highest-quality tlog clips. Aligned Scribe transcripts vs. expected ayah text at the character level (diacritic-insensitive Levenshtein), then aligned vs. our ASR output. Asymmetric-trust consensus rule:

A token is labeled CORRECT if EITHER ASR or Scribe says correct. It is labeled WRONG only when BOTH agree wrong.

Result: 144,664 per-token labels at ~98.0% positive rate, with 6,789 ASR-vs-Scribe disagreements flagged as high-information tokens. Cost: ~$11 in ElevenLabs Creator-plan credits.

Stage 5: final pronunciation head. Retrained on encoder features extracted with the final ASR (consistent features for both consensus labels and master qari anchors).

Stage 6: tajweed rule engine. Pure-Python rule engine that takes the expected diacritized text, audio, and per-token alignment, producing per-letter tajweed feedback across 27 dispatched rules (noon sakinah, meem sakinah, madd typology, qalqalah sughra/kubra, ra tafkheem/tarqeeq, Allah lafdh, lam shamsiyyah, hamzat wasl, idgham types, leen letters, more).

Architecture

• Backbone: NVIDIA FastConformer Large encoder + CTC head (114.6 M params)
• Tokenizer: SentencePiece BPE, 1,024 vocab + 1 blank = 1,025 output classes
• Audio: 16 kHz, log-mel features (80 channels)
• Decoder: CTC greedy_batched (frame-independent, noise-robust, deterministic)
• Output: token log-probabilities and 512-dim encoder features per output frame

CTC is the right decoder for this task: frame-level alignment for the pronunciation pipeline, no auto-correction that would mask user mispronunciations, faster inference, and (per the decoder ablation) sub-1% WER on held-out studio reciters - so there's no quality reason to add decoder complexity that would also hide mispronunciations.

Quick start (CTC, ONNX)

import numpy as np, soundfile as sf, onnxruntime as ort, sentencepiece as spm

session = ort.InferenceSession("onnx/model_with_encoder.onnx",
 providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
sp = spm.SentencePieceProcessor(model_file="tokenizer.model")

wav, sr = sf.read("clip.wav") # 16 kHz mono float32
features = log_mel(wav) # see tajweed/aligner.py for the pipeline
features = features[None, ...] # (B=1, 80, T_in)
length = np.array([features.shape[2]], dtype=np.int64)

logprobs, encoder_features = session.run(
 ["logprobs", "encoder_output"],
 {"audio_signal": features, "length": length},
)
# encoder_features: (1, 512, T_out). Feed to the pronunciation head.
# logprobs: (1, T_out, 1025). Argmax + CTC collapse to get tokens.

The full multi-signal scorer (CTC + head + anchor + GOP + tajweed) is in tajweed/full_scorer.py.

Files

For iOS / CoreML deployment, see Muno459/fastconformer-quran-coreml.

Datasets

The current (June 2026) weights were trained on a fully canonical-imlaei labeled mix of:

• Tarteel EveryAyah (tarteel-ai/everyayah): CC-BY 4.0. Multi-reciter Hafs studio recitation, the clean backbone.
• Tarteel tlog (tarteel-ai/tlog): gated. Real user phone recordings (the real-world / robustness signal).
• Muaalem (obadx/muaalem-annotated-v3): additional clean Hafs recitation (~12 K clips), relabeled from its diacritized text to our consistent imlaei orthography.

All audio is Hafs riwayah. Labels are each clip's canonical ayah text in one imlaei orthography.

License

Apache 2.0, matching the upstream FastConformer-Hybrid license.

Citation

@misc{fastconformer-quran-2026,
 title = {FastConformer-Quran: Quranic ASR and unsupervised mispronunciation scoring},
 author = {Anon},
 year = {2026},
 url = {https://huggingface.co/Muno459/fastconformer-quran},
}

Base model