README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - text-to-speech
language:
  - vi
pretty_name: ZeroBench-TTS
size_categories:
  - n<1K
tags:
  - tts
  - zero-shot
  - voice-cloning
  - code-switching
  - cross-lingual
  - vietnamese
configs:
  - config_name: challenging
    data_files:
      - split: test
        path: data/challenging.parquet
  - config_name: code_switch
    data_files:
      - split: test
        path: data/code_switch.parquet
  - config_name: cross_lingual
    data_files:
      - split: test
        path: data/cross_lingual.parquet
  - config_name: vietnamese
    data_files:
      - split: test
        path: data/vietnamese.parquet

ZeroBench-TTS

From ZeroWeight AI — the benchmark counterpart to ZeroTTS. A small, fully specified benchmark for zero-shot Vietnamese TTS / voice cloning: 39 held-out Vietnamese reference voices × 3 subsets (vietnamese, code_switch, challenging), plus 20 non-Vietnamese reference voices × 1 cross_lingual subset = 137 test items. Every item pairs one reference clip (with its ground-truth transcript) with one Vietnamese target sentence to synthesize — cross_lingual tests whether a system can speak Vietnamese given a foreign-language voice prompt, which is the direction that matters for a Vietnamese TTS system (not the reverse).

It is deliberately small enough to run every checkpoint on, and every item is scored the same way — WER, SSIM, UTMOS — so numbers are comparable across systems.

Subsets

config	n	language of text	what it stresses
challenging	39	vi	repeated words, acronyms (ChatGPT, ASEAN, WHO), times, dates, percentages, dense punctuation
code_switch	39	vi	natural Vietnamese with embedded English (names, brands, workplace loanwords)
cross_lingual	20	vi	non-Vietnamese reference voice (Emilia: zh/de/fr/ja/ko) reading Vietnamese text — many languages -> Vietnamese zero-shot cloning
vietnamese	39	vi	monolingual Vietnamese — the baseline case

Within every subset the target sentences are split evenly into short / medium / long thirds (length_bucket), so length is covered across the board rather than confounded with subset. cross_lingual's target texts are drawn from the other three subsets' pools (mixed, not a pool of its own) — see text_origin_subset.

Fields

field	type	description
id	string	{subset}/{voice_id}
subset	string	one of the configs above
text	string	the text to synthesize, verbatim — always Vietnamese
text_normalized	string	spoken-out form of text (equal to text when nothing needs expanding)
has_normalization	bool	whether text_normalized actually differs
lang	string	target language of text (always vi)
text_origin_subset	string	which pool text came from (vietnamese/code_switch/challenging) — trivially equal to subset except for cross_lingual rows
length_bucket	string	short / medium / long
n_chars, n_words	int	length of text
ref_audio	audio	reference voice to clone, mono 24000 Hz
ref_text	string	ground-truth transcript of ref_audio, in voice_lang — for systems that need an in-context audio+text prompt. Speaker-encoder models can ignore it.
ref_duration	float	seconds of ref_audio
voice_id	string	stable voice identifier; the same voice appears once per subset
voice_source	string	vivos / viVoice / phoaudiobook / emilia
source_speaker, source_utterance	string	provenance in the source corpus
voice_lang	string	language of the reference speaker (vi for the first three subsets; zh/de/fr/ja/ko for cross_lingual)
cross_lingual	bool	lang != voice_lang — always False except for the cross_lingual subset

Usage

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

ds = load_dataset("zeroweight-ai/ZeroBench-TTS", "challenging", split="test")

# Reading the raw wav bytes works on every `datasets` version; asking for a
# decoded array needs a codec backend (`pip install torchcodec`) on datasets>=5.
ds = ds.cast_column("ref_audio", Audio(decode=False))
row = ds[0]
wav, sr = sf.read(io.BytesIO(row["ref_audio"]["bytes"]))

audio = my_tts(text=row["text"], reference=(wav, sr))   # clone and speak

Metrics

The reference implementation is scripts/eval_tts.py in the ZeroTTS repo.

• WER — vinai/PhoWhisper-large (a Vietnamese-specialized Whisper finetune — NOT raw openai/whisper-large-v3) transcribes the generated audio; WER is computed against the target text after lowercasing, punctuation stripping and whitespace collapsing. scripts/eval_tts.py --whisper_model can swap in any other Whisper-family checkpoint if you want a different ASR.
• SSIM — cosine similarity between microsoft/wavlm-base-plus-sv x-vectors of the generated clip and ref_audio.
• UTMOS — UTMOSv2 predicted naturalness MOS of the generated clip.
• Excess silence — seconds of unwanted lead-in/tail/mid-utterance pause, specifically to catch weird long silences (stalls, dead air, a stuck decoder) that WER/SSIM/UTMOS don't penalize on their own — none of the three metrics above notice a generation that's otherwise correct but padded with several extra seconds of dead air.

Scoring the challenging subset: take min(WER(hyp, text), WER(hyp, text_normalized)). Whisper may transcribe 3,2% either as digits or as ba phẩy hai phần trăm, and that choice belongs to the ASR, not to the TTS system under test — scoring against a single form would measure Whisper's formatting policy instead of intelligibility.

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 over 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.

All Vietnamese reference clips are 4–12 s, mono, 24000 Hz.

• 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).

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.