ZeroBench-TTS — a Vietnamese zero-shot TTS & voice-cloning benchmark

Bộ benchmark đánh giá text-to-speech tiếng Việt (zero-shot voice cloning).

137 test items · 59 held-out reference voices · 4 subsets · one command to score your model · official scorer included.

From ZeroWeight AI. Companion to ZeroTTS, our open Vietnamese zero-shot TTS model — see current results below.

Vietnamese TTS has had no shared evaluation: papers and model cards report WER against different ASRs, different reference text, and different (usually unpublished) test sets, so no two numbers can be compared. ZeroBench-TTS is an attempt at a fixed target — public items, a published scorer, and a WER definition that measures the synthesizer rather than the ASR's spelling habits.

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Why another benchmark

Most Vietnamese TTS evaluations quietly measure the wrong thing. Three failure modes we hit ourselves, and what this benchmark does about them:

1. A single ASR cannot judge Vietnamese TTS. vinai/PhoWhisper-large handles Vietnamese phonology well but cannot emit Latin script — it transcribes Slack as "sờ lếch" and backup as "bắt cấp", charging WER to audio that is perfectly intelligible. openai/whisper-large-v3 writes the Latin spelling but is weaker on Vietnamese tone. Their failure modes are close to disjoint. → ZeroBench runs both and takes the minimum. A clip is only charged when neither ASR can recover the target.

2. One reference sentence cannot cover correct readings. 31/12/2025 is correctly read as "ba mươi mốt tháng mười hai năm hai nghìn không trăm hai mươi lăm" or "ba mốt tháng mười hai hai ngàn hai mươi lăm", and correctly transcribed as 31 tháng 12, 2025. With k independent formatting decisions in a sentence there are 2^k correct transcripts; an ASR routinely produces a hybrid — acronym spelled out, numbers left as digits — that matches neither of two hand-written references. → References are expanded per surface span and WER is the minimum over the whole set. ChatGPT/"chat GPT", nghìn/ngàn, thứ Sáu/thứ 6, bốn/tư are all free.

3. A permissive reference set is just as wrong. Widening references until everything scores 0 measures nothing. → Tone-only variants are never admitted. Vietnamese tone is phonemic, so khuyến mãi → khuyến mại and sảnh → sành stay errors — they are the most common way a TTS model actually gets a Vietnamese word wrong. Neither are wrong-but-plausible readings: a voiced leading zero (18/04 → "tháng không tư") or a collapsed magnitude (92.000.000 → "chín mươi hai nghìn nghìn") costs WER, as it should.

Both directions are enforced by a test suite, not by good intentions: zerobench_eval/test_references.py asserts that format artifacts score 0.00 and that real defects still cost. Any change to the reference policy that breaks either direction fails CI.

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Scoring your model in 3 commands

The scorer never loads a TTS model. It reads finished wavs and reports metrics — bring your own synthesis, in any framework, any language.

# 0. get the benchmark + scorer
huggingface-cli download zeroweight-ai/ZeroBench-TTS --repo-type dataset \
    --local-dir ZeroBench-TTS
cd ZeroBench-TTS && pip install -r zerobench_eval/requirements.txt

# 1. what to synthesize (137 rows: text, reference clip, output path)
python -m zerobench_eval manifest --out manifest.jsonl

# 2. ... your own synthesis, writing each clip to <wav_dir>/<subset>/<voice_id>.wav ...

# 3. score
python -m zerobench_eval score --wav_dir my_wavs/ --name MyModel

Output: a printed table plus per_sample.csv (every metric, both ASR transcripts, and which reference matched — so any number is auditable), summary.json, and report.txt.

Each manifest.jsonl row:

{"id": "challenging/vivos-VIVOSDEV08", "subset": "challenging",
 "text": "Tôi dùng ChatGPT mỗi ngày.", "lang": "vi",
 "ref_audio": "/abs/path/audio/vivos-VIVOSDEV08.wav",
 "ref_text": "...", "output_wav": "challenging/vivos-VIVOSDEV08.wav"}

Synthesize text using ref_audio as the voice prompt. ref_text is there for systems that need an audio+text in-context prompt; speaker-encoder models can ignore it. Never feed text_normalized to the model for a headline number — reading raw orthography is the task. (It is available as a deliberate ablation; see Reproducibility.)

Useful flags: --skip_utmos (UTMOSv2 is an optional dependency), --subsets challenging, --allow_missing (partial submission, flagged in the summary), --device cpu.

Changed the reference policy and want to re-score without re-running the ASRs? python -m zerobench_eval rescore <run_dir> — seconds, no GPU.

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What's in it

config	n	what it stresses
vietnamese	39	monolingual Vietnamese — the baseline case
code_switch	39	natural Vietnamese with embedded English (brands, workplace loanwords)
challenging	39	acronyms (ChatGPT, ASEAN, WHO), dates, times, percentages, currency, repeated words, dense punctuation
cross_lingual	20	non-Vietnamese reference voice (zh/de/fr/ja/ko) reading Vietnamese text

The target sentence is Vietnamese in every subset. cross_lingual varies the reference voice, not the language of the text — it asks whether a foreign speaker's timbre can carry Vietnamese, which is the direction that matters for a Vietnamese TTS system.

Within every subset the sentences split evenly into short / medium / long thirds (length_bucket), so length is covered rather than confounded with subset. cross_lingual target texts are drawn from the other three pools (see text_origin_subset).

It is deliberately small enough to run on every checkpoint.

Fields

field	type	description
id	string	{subset}/{voice_id}
text	string	the text to synthesize, verbatim — always Vietnamese
text_normalized	string	spoken-out form, a scoring reference only (equals text when nothing needs expanding)
has_normalization	bool	whether text_normalized differs
lang	string	language of text (always vi)
length_bucket	string	short / medium / long
n_chars, n_words	int	length of text
ref_audio	audio	reference voice to clone, mono 24 kHz, 4–12 s
ref_text	string	ground-truth transcript of ref_audio
ref_duration	float	seconds
voice_id	string	stable voice id; the same voice appears once per subset
voice_source	string	vivos / viVoice / phoaudiobook / emilia
voice_lang	string	vi, or zh/de/fr/ja/ko for cross_lingual
cross_lingual	bool	lang != voice_lang
text_origin_subset	string	which pool text came from
source_speaker, source_utterance	string	provenance in the source corpus

Loading with datasets

import io, soundfile as sf
from datasets import load_dataset, Audio

ds = load_dataset("zeroweight-ai/ZeroBench-TTS", "challenging", split="test")
ds = ds.cast_column("ref_audio", Audio(decode=False))   # raw bytes: works on every version
row = ds[0]
wav, sr = sf.read(io.BytesIO(row["ref_audio"]["bytes"]))

audio = my_tts(text=row["text"], reference=(wav, sr))

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Metrics

metric	definition	direction
WER	min over {whisper-large-v3, PhoWhisper-large} × all acceptable references	lower better
SSIM	cosine similarity of microsoft/wavlm-base-plus-sv x-vectors, generated vs ref_audio	higher better
UTMOS	UTMOSv2 predicted naturalness MOS (seeded — see below)	higher better
Excess silence	seconds of unwanted lead-in / tail / mid-utterance pause	lower better

Three WER numbers are reported on every run:

policy	references	use
strict	the written text, verbatim	what a naive pipeline measures
norm	text + curated text_normalized	the usual two-reference scheme
robust	+ every acceptable reading, expanded per span	the headline number

Reporting all three is the point: the gap between them tells you how much of a WER figure is scoring policy rather than synthesis.

Excess silence exists because nothing else catches dead air — an ASR happily transcribes a clip that opens with 1.5 s of nothing, the x-vector is unaffected, and UTMOS rates the audio quality of silence as fine. A stalled or padded decoder is invisible to WER/SSIM/UTMOS.

UTMOS is seeded. UTMOSv2 ensembles over randomly sampled crops, so unseeded it returns 3.05 / 3.03 / 2.96 for the same clip. The scorer resets the RNG before every clip, making UTMOS a deterministic function of the audio — without that, the column is not reproducible between runs.

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Reproducibility & fair use

• Report robust WER as the headline, and don't change --asr — the ASR pair is part of the benchmark definition.
• Synthesize from text, not text_normalized. Feeding the spoken-out form simulates a perfect Vietnamese text-normalization frontend; it is a legitimate and interesting ablation (it isolates grapheme-to-phoneme errors from acoustic ones), but it is not the benchmark task and must be labelled if reported.
• Report all 137 items. Partial runs are marked complete: false in summary.json.
• per_sample.csv keeps both ASR transcripts and the matched reference for every item, so any surprising number can be audited rather than trusted.

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Results

Four systems, 137/137 items each, scored by zerobench_eval:

Model	WER (raw text)	WER (normalized)	SSIM	UTMOS	Excess silence
ZeroTTS	1.03 %	0.56 %	0.936	2.91	0.029 s
OmniVoice	4.13 %	2.12 %	0.950	2.76	0.340 s
XTTS-v2-vietnamse	16.42 %	7.27 %	0.940	2.43	0.532 s
viXTTS	18.40 %	8.61 %	0.935	2.35	0.233 s

raw text = the model reads 31/12/2025 and ChatGPT as written, the benchmark task. normalized = every model is handed the spoken-out form instead, simulating a perfect Vietnamese text frontend. Scoring references are identical in both, so the columns are comparable.

Per subset, raw text:

Subset	ZeroTTS	OmniVoice	XTTS-v2-vietnamse	viXTTS
vietnamese	0.16 %	0.50 %	7.92 %	9.56 %
code_switch	0.97 %	0.46 %	10.94 %	9.25 %
cross_lingual	1.42 %	17.71 %	21.37 %	27.27 %
challenging	1.75 %	4.46 %	27.86 %	31.85 %

Worth reading the subsets, not just the total. OmniVoice leads on code_switch and on speaker similarity, and its overall figure is dominated by cross_lingual — foreign voice prompt, Vietnamese text — where it degrades by reference language (German 0.00 %, Korean 0.13 %, Japanese 0.41 %). The two XTTS finetunes gain the most from normalization because their tokenizers have no Vietnamese number expansion at all.

OmniVoice was given its optional language="vi" hint, which its model card recommends; without it, it scores 5.15 % / 3.87 % instead.

Full per-subset tables, per-item audits and reproduction commands live in the ZeroTTS repository.

Submitting a result: open a discussion on this dataset with your summary.json and a note on how the wavs were produced.

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Voice selection

• VIVOS — every speaker of the VIVOS test split (VIVOSDEV01–VIVOSDEV19), one clip each, chosen closest to 6 s.
• viVoice / phoaudiobook — 10 speakers each. These corpora carry no usable speaker labels, so clips were selected geometrically: k-means over WeSpeaker x-vectors, keeping each cluster's medoid. That yields mutually distinct voices spread across the corpus's voice space, while medoids stay typical of their region rather than being recording outliers. source_speaker is therefore a synthetic cluster id, not an upstream label.
• Emilia (cross_lingual only) — 4 speakers each from 5 non-Vietnamese languages (de, fr, ja, ko, zh), sampled from the gated amphion/Emilia-Dataset. Candidates are filtered by duration, DNSMOS quality, and a per-language text sanity check (script range for zh/ja/ko, function-word check for de/fr) — Emilia's own per-clip language tag is not fully reliable (English text was found mislabeled ko in the first Korean shard during a spot check).

All Vietnamese reference clips are 4–12 s, mono, 24 kHz. Every voice is held out: none appears in ZeroTTS's training data.

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Provenance & license

Vietnamese reference audio is redistributed from AILAB-VNUHCM/vivos, capleaf/viVoice and thivux/phoaudiobook. cross_lingual reference audio is redistributed from amphion/Emilia-Dataset. Please honour each upstream corpus's own license and cite them alongside this benchmark. The target sentences are original, written for this benchmark. Released for research and evaluation use.

Citation

@misc{zerobench_tts_2026,
  title  = {ZeroBench-TTS: A Vietnamese Zero-Shot Text-to-Speech and
            Voice Cloning Benchmark},
  author = {ZeroWeight AI},
  year   = {2026},
  url    = {https://huggingface.co/datasets/zeroweight-ai/ZeroBench-TTS}
}

See also

• ZeroTTS — open Vietnamese zero-shot TTS model (the reference system for this benchmark)
• ZeroTTS on GitHub — training, inference and the full evaluation write-up

_{Keywords: Vietnamese TTS benchmark · vietnamese text to speech evaluation · zero-shot voice cloning Vietnamese · đánh giá TTS tiếng Việt · benchmark giọng nói tiếng Việt · Vietnamese speech synthesis WER · code-switching Vietnamese English TTS · cross-lingual voice cloning · PhoWhisper WER evaluation · tổng hợp tiếng nói tiếng Việt}