zeroweight-ai/ZeroSpeech

ZeroSpeech

A large synthetic Vietnamese speech corpus for ASR training: 9,867,987 utterances / 26,896 hours, spoken by 199,265 distinct voices, generated with **ZeroTTS** from web and conversational text.

Every clip is 16 kHz mono FLAC, 1–30 s, paired with the exact text it was synthesized from.

Fields

from datasets import load_dataset

ds = load_dataset("zeroweight-ai/ZeroSpeech", split="train", streaming=True)
row = next(iter(ds))
row["audio"]["array"], row["text"], row["source"]

Composition

Code-switched text is kept deliberately and is a large share of the corpus: it is what a naive language-ID filter throws away and what Vietnamese ASR most often gets wrong.

Mean duration 9.8 s. Voices: 199,265 distinct references, 29,448 of them carrying an exact speaker label.

How it was made

Text was drawn from vietvault web documents and BlossomsAI/vietnamese-conversational-dataset chat turns, then markdown-stripped, sentence-segmented, filtered for speakable content (character whitelist, real casing, terminal punctuation, digit-density and repetition limits) and packed into utterances targeting a 1–30 s length distribution.

Each utterance was then synthesized by **ZeroTTS** — a zero-shot Vietnamese TTS model — conditioned on a reference voice drawn at random from a bank of 199,265 speaker references and matched to the utterance's language. No text normalization frontend was applied: the model received raw orthography.

Generated clips were dropped if the model never emitted its stop token, if the measured duration diverged from the character-rate estimate (the signature of a repetition loop), or if the output was near-silent.

Limitations — read before training on this

• —The audio is synthetic. Every clip comes from a single model (ZeroTTS), so it carries that model's phonetics, prosody and artifacts throughout. Acoustic conditions do vary — the reference voices are real recordings spanning a wide range of rooms, channels and noise floors, and the model reproduces those conditions — but the speech itself is read-aloud in character: no spontaneous disfluency, no overlapping speakers, no genuine conversational timing. Useful as pretraining or augmentation mass; fine-tune on real speech.
• —Text normalization is the TTS model's. Digits and acronyms were fed as orthography, so however the model read 2/7 or TT-BGTVT is what the transcript claims was said.
• —Source text is web-scraped and carries whatever bias, error and distributional skew the source corpora do.

Related

• —zeroweight-ai/ZeroTTS — the zero-shot TTS model that generated every clip here.

License

Released under CC-BY-NC-4.0. The source text corpora carry their own licenses; check them before commercial use.