Quran-Lab/quranic-asr-cloud-rawdata

Quranic ASR Provider Benchmark Results

Professional benchmark artifacts for comparing commercial and official ASR providers on the Quranic ASR benchmark hosted at Quran-Lab/quranic-asr-benchmark.

This repository contains metadata, normalized result tables, raw provider responses, unchanged run scripts, scoring outputs, Tarteel streaming probes, and reports. It does not duplicate the source audio.

What Is Included

Area	Path	Purpose
Benchmark split	data/benchmark/test.jsonl	600 clip IDs, references, source labels, and source-audio pointers
Hypotheses split	data/hypotheses/test.jsonl	Final transcription outputs for each provider/model/clip
Scores split	data/scores/test.jsonl	Official WER/CER metrics by provider and source
Raw outputs	artifacts/outputs/raw/	Raw API/websocket responses captured during runs
Provider scripts	artifacts/scripts/	Run harnesses copied byte-for-byte from the benchmark run
Official scorer	artifacts/source_benchmark/score.py	Source benchmark scorer used for all reported metrics
Reports	artifacts/reports/	Consolidated result notes and Tarteel behavior report
Checksums	MANIFEST.sha256	SHA-256 checksums for all uploaded files

What Is Not Included

Audio files are intentionally not included in this repository.

The audio_path column points to the corresponding files in Quran-Lab/quranic-asr-benchmark. Use that dataset for audio access, licensing, and upstream provenance.

No provider API keys, OAuth tokens, account passwords, local credential files, or auth tokens are included.

Dataset Configs

This repository exposes three clean Hugging Face dataset configs.

Config	Split	Rows	Description
benchmark	test	600	Benchmark metadata and references
hypotheses	test	3,600	Six provider/model outputs for each clip
scores	test	24	Per-provider metrics for each source plus overall

Example loading code:

from datasets import load_dataset

benchmark = load_dataset("Quran-Lab/quranic-asr-cloud-rawdata", "benchmark")
hypotheses = load_dataset("Quran-Lab/quranic-asr-cloud-rawdata", "hypotheses")
scores = load_dataset("Quran-Lab/quranic-asr-cloud-rawdata", "scores")

Schema

benchmark

Column	Type	Description
id	string	Stable clip ID from the source benchmark
source	string	One of everyayah_heldout, qul_alnufais, tlog_holdout
reference_text	string	Quranic reference text used by the official scorer
audio_dataset	string	Source dataset containing the audio
audio_path	string	Relative audio path inside the source dataset

hypotheses

Column	Type	Description
id	string	Stable clip ID
source	string	Benchmark source subset
provider	string	ASR provider or official service
model	string	Provider model name
run_type	string	API mode used for the run
hypothesis_text	string	Final ASR transcript emitted by the provider
has_hypothesis	bool	Whether the provider returned non-empty text for the clip
reference_text	string	Reference text for convenience
audio_dataset	string	Source dataset containing the audio
audio_path	string	Relative audio path inside the source dataset

scores

Column	Type	Description
provider	string	ASR provider or official service
model	string	Provider model name
run_type	string	API mode used for the run
source	string	Source subset or ALL
clips	int	Number of clips scored
wer	float	Word error rate from the official scorer
cer	float	Character error rate from the official scorer
wer_alef	float	Alef-insensitive WER from the official scorer

Benchmark Setup

• Source benchmark: Quran-Lab/quranic-asr-benchmark
• Clips: 600 total, 200 per source
• Duration: 7,701.439 seconds, about 2.14 hours
• Sources: everyayah_heldout, qul_alnufais, tlog_holdout
• Audio format in source dataset: 16 kHz mono WAV
• Scorer: artifacts/source_benchmark/score.py
• Metrics: WER, CER, and alef-insensitive WER/CER

Overall Results

Rank	Provider / model	Run type	WER	CER	WER(alef)
1	Tarteel official	realtime websocket	10.99	7.14	9.72
2	Google Chirp 3	sync recognize	11.92	7.87	10.55
3	Google Chirp 3	realtime streaming recognize	13.56	8.67	12.22
4	ElevenLabs Scribe v2	file API	14.05	7.21	12.75
5	Deepgram nova-3	file API	15.79	9.19	14.43
6	Speechmatics enhanced	batch job	21.06	9.73	20.29

For per-source metrics, use the scores config or see artifacts/reports/provider_benchmark_results.md.

Provider Runs

Provider	Model	Language hint	Mode	Notes
Tarteel	official	ar-SA	websocket realtime	Official app protocol, 16 kHz PCM streaming
Google	Chirp 3	ar-XA	Speech-to-Text v2 sync recognize	Location us; sync API has a 60-second input limit
Google	Chirp 3	ar-XA	Speech-to-Text v2 StreamingRecognize	Realtime audio pacing; no blanks in clean hypotheses
ElevenLabs	Scribe v2	ar	speech-to-text file API	Full 600-clip run
Deepgram	nova-3	ar	listen file API	Full 600-clip run
Speechmatics	enhanced	ar	batch job API	Full 600-clip run

No Quran-specific phrase boosting, target ayah hints, or contextual biasing were used.

Re-Scoring

The included scorer does not require audio. It scores hypothesis files against benchmark.jsonl references.

cd artifacts/source_benchmark
python3 score.py --hyps ../outputs/hypotheses/hyps.jsonl

Primary hypothesis files:

Provider / model	File
Tarteel official	artifacts/outputs/hypotheses/hyps.jsonl
Google Chirp 3 sync	artifacts/outputs/hypotheses/google_chirp3_hyps.clean.jsonl
Google Chirp 3 realtime	artifacts/outputs/hypotheses/google_chirp3_realtime_hyps.clean.jsonl
ElevenLabs Scribe v2	artifacts/outputs/hypotheses/elevenlabs_scribe_v2_hyps.jsonl
Deepgram nova-3	artifacts/outputs/hypotheses/deepgram_nova3_hyps.jsonl
Speechmatics enhanced	artifacts/outputs/hypotheses/speechmatics_enhanced_hyps.jsonl

The normalized hypotheses config keeps exactly 600 rows per provider/model run. Empty provider outputs are retained as rows with has_hypothesis=false.

Tarteel Streaming Probe Notes

The Tarteel websocket exposed a fixed observable 400 ms server processing/update grid during these runs. Active probes found stable audioProcessedMs deltas of 400 ms across tested client packet sizes. This is an observable server update cadence, not proof of the internal model architecture or attention chunk.

See artifacts/reports/limit_test_report.md, artifacts/outputs/probes/, and artifacts/outputs/scores/stream_timing.txt for details.

Reproducibility Notes

The scripts in artifacts/scripts/ are copied byte-for-byte from the run directory. They are intentionally not reformatted so that the uploaded harnesses remain identical to the files used for the benchmark.

To rerun provider benchmarks, obtain source audio from Quran-Lab/quranic-asr-benchmark and provide your own provider credentials. Credential files are intentionally excluded.

Citation

If you use this dataset, cite this repository and the source benchmark:

@dataset{quran_lab_quranic_asr_provider_benchmark,
  title = {Quranic ASR Provider Benchmark Results},
  author = {Quran Lab},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/Quran-Lab/quranic-asr-cloud-rawdata}
}