ArNum-TTS: does the number survive?

The same TTS engine recovers 11 of 15 numbers typed as 2026 and 1 of 15 typed as ٢٠٢٦. Same sentences; only the digit script differs.

هل ينجو الرقم؟ ٤٥ جملة عربية لقياس قدرة أنظمة تحويل النص إلى كلام على نطق الأرقام بثلاث صيغ: أرقام غربية، وأرقام هندية، وأرقام مكتوبة بالحروف.

The question: when an Arabic text-to-speech engine reads a sentence with a number in it, can a listener recover the number? Arabic writes the same year as 2026 or ٢٠٢٦, and both are correct. TTS demos rarely contain numbers, so this failure goes unnoticed until a date, price or phone number is spoken wrong in production.

Design: 15 sentences × 3 numeral forms = 45 utterances. Each sentence is identical across the three forms except for how the number is written. The forms are western (2026), arabic_indic (٢٠٢٦) and spelled (ألفين وستة وعشرين). Categories: year, percentage, decimal, count, currency, time, date, phone, ordinal and range. Scoring: TTS → Whisper (small) → Arabic number parser (arnum.py, 34 tests) → was the expected value recovered?

Results

Computed from the recovered field of each results file. python rescore.py re-derives the same numbers from the stored transcripts.

numeral form	fish s2.1-pro-free	Apple Majed (ar_001)	ArTST MBZUAI/speecht5_tts_clartts_ar
western 2026	11/15 (73%)	13/15 (87%)	0/15 (0%)
arabic_indic ٢٠٢٦	1/15 (7%)	13/15 (87%)	0/15 (0%)
spelled ألفين وستة وعشرين	13/15 (87%)	9/15 (60%)	4/15 (27%)
overall	25/45 (56%)	35/45 (78%)	4/45 (9%)

• Apple handles both digit forms equally (13/15 each). This shows the problem can be solved.
• fish handles Western digits and collapses on Arabic-Indic: 11/15 vs 1/15. The failures are noise, not mispronunciations. For example, ٢٨ in a date came back as تخاناتر.
• ArTST doesn't speak either digit form. ٠-٩ are missing from its 87-token vocabulary, so all 15 Arabic-Indic numerals are cut to <unk> before synthesis (data/artst_tokens.json). Western digits do reach the model (15/15) but produce almost no audio. In a Whisper-free duration control (data/artst_duration_control.json), digits add +0.19 s of audio for ArTST vs +2.44 s for Apple (data/apple_duration_control.json).
• A larger ASR model doesn't change the digit result. Re-scored with whisper-medium (data/artst_asr_medium.jsonl), ArTST gets western 0/15, arabic_indic 0/15 and spelled 6/15.
• Phone numbers fail everywhere: 0/3 on every engine. Full analysis: FINDING_artst_numerals.md.

Practical rule: normalise every numeral before it reaches an Arabic TTS engine. For fish, convert to Western digits; for ArTST, convert to Arabic words. arabic-tts-frontend (pip install arabic-tts-frontend) does this. Its release notes report a re-run on this set.

Usage

from datasets import load_dataset

sentences = load_dataset("syamjithnk/arnum-tts", "sentences", split="test")      # 45 rows: id, category, form, text, expect
fish      = load_dataset("syamjithnk/arnum-tts", "results_fish", split="test")   # + heard, recovered
apple     = load_dataset("syamjithnk/arnum-tts", "results_apple", split="test")
artst     = load_dataset("syamjithnk/arnum-tts", "results_artst", split="test")  # + n_unk, numeral_reached_model, seen_by_model

# score by numeral form
import collections
c = collections.Counter((r["form"], r["recovered"]) for r in fish)

To benchmark your own engine, run python build_set.py, then python run_bench.py --engine <name> (see run_bench.py). Run python rescore.py to re-score the stored transcripts without re-synthesising.

file	what
data/sentences.jsonl	the 45 test utterances
results_{fish,apple,artst}.jsonl	per-utterance transcript and verdict, one file per engine
arnum.py, test_arnum.py	Arabic number-word → value parser, 34 tests
run_bench.py, build_set.py, rescore.py	synthesise → transcribe → score, plus re-scoring
artst_duration_control.py, data/*duration_control.json	Whisper-free audio-duration check
data/artst_tokens.json, data/artst_asr_medium.jsonl	what ArTST's tokenizer saw; whisper-medium re-score

No audio is redistributed. ArTST weights are CC BY-NC 4.0.

Limitations

• Small: 45 utterances, 3 engines, one voice per engine. The comparison between numeral forms is robust (7% vs 87% isn't transcription noise); the absolute percentages are less so.
• Automatic scoring: TTS → Whisper → parser. An error anywhere in that chain counts against the TTS engine. There has been no human listening pass yet.
• ArTST is run off-domain. It was trained on Classical Arabic (ClArTTS), and this set is Modern Standard Arabic news copy. Its numeral-specific effect is the drop from 27% to 0%, not the whole 0%.
• Not yet tested: ElevenLabs, Azure and Google. Don't generalise these results to "Arabic TTS" as a field.
• Clock-time rules depend on Whisper: they only fire when the transcript contains ساعة. A garbled transcript can therefore still score a correct half-past reading as lost.

Corrections

Scorer fixes are re-derived from stored transcripts, with no re-synthesis. rescore.py refuses to write if a digit-form score moves without an explicit reason.

• 2026-08-20: four spelled-form parser gaps were fixed (النصف/الربع, the tanween alef, two-word ordinals, clock grouping). fish spelled went from 11 to 13/15, Apple spelled from 8 to 9/15, and ArTST was unchanged.
• 2026-08-21: بالمئة had been read as 100, and spoken decimals weren't parsed. Apple went from 12 to 13/15 on both digit forms.
• 2026-10-09: the card's Apple overall cell still read 33/45 after the 21 Aug fix. The results files give 35/45. Only the card was corrected; no data changed. (FINDING_artst_numerals.md still shows Apple's pre-21-Aug digit scores, 12/15.)

Part of "Does Arabic survive the pipeline?"

• ArNum-TTS (this dataset): does the number survive speech synthesis?
• ArShape: does Arabic survive text rendering?
• ArPDF: does Arabic survive a PDF round trip?
• Arabic Corpus Integrity Audit: does reshaped Arabic reach public training data? (341 Hub datasets)
• arabic-lint Space: paste Arabic and watch a tokenizer turn corrupted text into [UNK]

Links

• Code: github.com/Syamjith-NK/arnum-tts · fix: arabic-tts-frontend · linter: arabic-lint · also by the author: shotdrift-comfyui
• Write-up: One of these reads ٢٠٢٦ as a year. The other reads it as noise.
• Blog: The Arabic fix everyone recommends is now the bug · Is Arabic training data corrupted?

Citation

@misc{syamjithnk_arnum_2026,
  author       = {Syamjith NK},
  title        = {ArNum-TTS: numeral handling in Arabic speech synthesis},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/syamjithnk/arnum-tts}},
  note         = {Data CC BY 4.0; code MIT}
}

License: data CC BY 4.0, code MIT (see LICENSE). Author: Syamjith NK.