| --- |
| 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](https://huggingface.co/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 |
|
|
| ```python |
| 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`](https://huggingface.co/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](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). |
| |
| ## 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. |
| |
