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README.md

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+---
+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.

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