Datasets:

wayu-ai
/

thai-tts-keyword-bench

license: cc-by-4.0
task_categories:
  - text-to-speech
language:
  - th
pretty_name: Thai TTS Hard-Keyword Benchmark
size_categories:
  - 1K<n<10K
tags:
  - speech
  - thai
  - tts
  - benchmark
  - code-switching
  - intelligibility
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test.parquet

Thai TTS Hard-Keyword Benchmark

Does a Thai TTS actually say the hard word — the brand, the name, the rare compound, the chat spelling — or does it fluently say something else? One wrong word in a fluent 150-character sentence costs ~1% CER and reads as "fine", which is why sentence CER cannot answer this.

1,531 Thai sentences, each with exactly one keyword under test. Synthesize, transcribe with one pinned Thai ASR, check the keyword is there. It scores audio against text and prescribes nothing about how the audio was made.

{"id": "cs-0001", "category": "code_switch",
 "text": "เมื่อวานเรียก Grab ไปทำงาน รถมาเร็วมาก",   ← synthesize this verbatim
 "keyword": "Grab",
 "expected": "แกร็บ",                                ← what the scoring ASR writes when it is spoken right
 "alternates": ["Grab", "แกร๊บ", "แกรบ"]}            ← other accepted ASR spellings

Correct = the transcript contains expected or an alternates entry, as an exact substring after Thai canonicalization. No fuzzy matching.

category	n	probes
`code_switch`	391	Latin spans in Thai: brands, acronyms, tickers
`rare_words`	410	low-frequency, technical and literary vocabulary
`names`	310	Thai personal/place names — many-to-one orthography
`informal_spelling`	210	chat orthography outside standard dictionaries
`long_sentences`	210	does the keyword survive a long clause

Each item also carries text_mono (Latin respelled in Thai) and text_bilingual. The gold is the same for all three — pick one contract, use it for every system you compare, and say which. meta holds construction metadata only: the category stratum (cs_class, name_type, kind, freq_band, tnc_freq, keyword_position, length_bucket, phenomenon), the canonical spelling of an informal item (canonical) and whether a long-sentence target is reused from another category (reused_keyword).

Use

The harness is one file, eval_keyword.py, in wayu-research/thai-tts-eval. It pulls the items itself.

python3 eval_keyword.py texts --out texts.tsv        # {id}<TAB>{text} to synthesize
python3 eval_keyword.py transcribe --audio-dir audio/mysystem \
    --out runs/mysystem/transcripts.json
python3 eval_keyword.py score --transcripts runs/mysystem/transcripts.json \
    --out runs/mysystem

Audio goes in as {id}.wav. --transcripts also takes output from your own ASR stack. compare runs a paired A/B with an exact McNemar test — use it when two systems are close.

Before you quote a number

A perfect system cannot score 100%. Thai orthography is many-to-one, so the scoring ASR sometimes writes an unlisted homophone of a correctly-spoken word. Rankings survive this, so compare systems freely; just never read a score as a share of 100%.
The scoring ASR is fixed. expected/alternates are authored against it, so swapping it fails correct renders on spelling alone.
Binary per item, and not a pronunciation judge — it asks only whether one ASR wrote one string.
Central Thai only. Sentences were LLM-assisted in authoring, so contamination is not formally excluded.

Baselines

Three systems on all 1,531 items. Gemini and OmniVoice were fed text. Wayu-Paxa-TTS-Edge was fed text_bilingual — the paper's LLM-verbalized form of text, which the paper counts as part of that system's frontend; the released wayu-tts package has no LLM verbalizer, so feeding it text scores slightly lower.

system	overall	code_switch	names	rare_words	informal	long_sent.
Gemini 3.1 Flash TTS	79.8%	83.1%	62.9%	85.4%	87.1%	80.0%
OmniVoice (zero-shot teacher)	72.8%	69.8%	54.8%	85.4%	78.6%	74.3%
Wayu-Paxa-TTS-Edge (82M student)	68.2%	62.7%	51.9%	79.8%	75.7%	72.4%

Companion: wayu-ai/thai-tts-pause-bench scores phrase-break placement on the 210 long_sentences items, same ids — one render set covers both.

Citation

If you use this benchmark, cite the paper and the harness it builds on.

@misc{pipatanakul2026buildingevaluatingfixedvoicethai,
      title={Building and Evaluating Fixed-Voice Thai TTS from Synthetic Speech}, 
      author={Kunat Pipatanakul and Potsawee Manakul and Warit Sirichotedumrong and Sittipong Sripaisarnmongkol and Pakorn Nathong and Phatrasek Jirabovonvisut},
      year={2026},
      eprint={2609.03502},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2609.03502}, 
}

The item schema and the matcher derive from Potsawee Manakul's th01-tts-eval (thai-tts-keyword-eval), whose pilot set seeded this one:

@misc{manakul2026th01ttseval,
  title        = {thai-tts-keyword-eval: Keyword accuracy for Thai TTS},
  author       = {Manakul, Potsawee},
  year         = {2026},
  howpublished = {\url{https://github.com/potsawee/th01-tts-eval}}
}

License

Research artifact, released for reproducibility and further research — not a certification or a product, and provided without warranty or liability of any kind. See the repository README for the full disclaimer.

CC-BY-4.0. No audio is redistributed.

Kunat Pipatanakul — research@wayuresearch.org. Gold corrections are welcome — open an issue or a pull request.