--- license: cc-by-nc-4.0 task_categories: - text-to-speech - automatic-speech-recognition language: - vi pretty_name: 'ZeroBench-TTS: Vietnamese Zero-Shot TTS & Voice Cloning Benchmark' size_categories: - n<1K tags: - tts - text-to-speech - vietnamese - vietnamese-tts - tieng-viet - zero-shot - voice-cloning - speech-synthesis - code-switching - cross-lingual - benchmark - evaluation - wer - speaker-similarity 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 — 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](#scoring-your-model-in-3-commands). From [ZeroWeight AI](https://huggingface.co/zeroweight-ai). Companion to **[ZeroTTS](https://huggingface.co/zeroweight-ai/ZeroTTS)**, our open Vietnamese zero-shot TTS model — see [current results](#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evaluation: papers and model cards report WER against different ASRs, different reference text, and different (usually unpublished) test sets, so no two numbers can be compared.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. --- ## 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. --- ## 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. ```bash # 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 ... # 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: ```json {"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](#reproducibility--fair-use).) 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 ` — seconds, no GPU. --- ## 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` ```python 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)) ``` --- ## 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. --- ## 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. --- ## Results Four systems, 137/137 items each, scored by `zerobench_eval`: | Model | WER (raw text) | WER (normalized) | SSIM | UTMOS | Excess silence | |---|---|---|---|---|---| | **[ZeroTTS](https://huggingface.co/zeroweight-ai/ZeroTTS)** | **1.03 %** | **0.56 %** | 0.936 | **2.91** | **0.029 s** | | [OmniVoice](https://huggingface.co/k2-fsa/OmniVoice) | 4.13 % | 2.12 % | **0.950** | 2.76 | 0.340 s | | [XTTS-v2-vietnamse](https://huggingface.co/thivux/XTTS-v2-vietnamse) | 16.42 % | 7.27 % | 0.940 | 2.43 | 0.532 s | | [viXTTS](https://huggingface.co/capleaf/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](https://github.com/zeroweight-ai/ZeroTTS/blob/main/docs/BENCHMARKS.md). **Submitting a result:** open a discussion on this dataset with your `summary.json` and a note on how the wavs were produced. --- ## 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](https://huggingface.co/datasets/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. --- ## Provenance & license Vietnamese reference audio is redistributed from [AILAB-VNUHCM/vivos](https://huggingface.co/datasets/AILAB-VNUHCM/vivos), [capleaf/viVoice](https://huggingface.co/datasets/capleaf/viVoice) and [thivux/phoaudiobook](https://huggingface.co/datasets/thivux/phoaudiobook). `cross_lingual` reference audio is redistributed from [amphion/Emilia-Dataset](https://huggingface.co/datasets/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 ```bibtex @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](https://huggingface.co/zeroweight-ai/ZeroTTS)** — open Vietnamese zero-shot TTS model (the reference system for this benchmark) * **[ZeroTTS on GitHub](https://github.com/zeroweight-ai/ZeroTTS)** — 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