license: mit
language:
- vi
library_name: onnx
pipeline_tag: text-to-speech
tags:
- text-to-speech
- tts
- vietnamese
- onnx
- onnxruntime
- zero-shot
- speech-synthesis
- voice-cloning
- vietnamese-tts
- tieng-viet
metrics:
- wer
model-index:
- name: ZeroTTS
results:
- task:
type: text-to-speech
name: Zero-Shot Text-to-Speech
dataset:
type: zeroweight-ai/ZeroBench-TTS
name: ZeroBench-TTS
split: test
metrics:
- type: wer
value: 0.56
name: WER (%) — normalized text
- type: utmos
value: 2.91
name: UTMOSv2 naturalness MOS
- type: speaker_similarity
value: 0.936
name: Speaker similarity (WavLM-SV cosine)
- type: excess_silence
value: 0.029
name: Excess silence (s)
- task:
type: text-to-speech
name: Zero-Shot TTS — monolingual Vietnamese
dataset:
type: zeroweight-ai/ZeroBench-TTS
name: ZeroBench-TTS (vietnamese)
config: vietnamese
split: test
metrics:
- type: wer
value: 0.21
name: WER (%) — normalized text
- task:
type: text-to-speech
name: Zero-Shot TTS — Vietnamese/English code-switching
dataset:
type: zeroweight-ai/ZeroBench-TTS
name: ZeroBench-TTS (code_switch)
config: code_switch
split: test
metrics:
- type: wer
value: 0.95
name: WER (%) — normalized text
- task:
type: text-to-speech
name: Zero-Shot TTS — cross-lingual voice prompt
dataset:
type: zeroweight-ai/ZeroBench-TTS
name: ZeroBench-TTS (cross_lingual)
config: cross_lingual
split: test
metrics:
- type: wer
value: 0.38
name: WER (%) — normalized text
- task:
type: text-to-speech
name: Zero-Shot TTS — acronyms, dates, numbers
dataset:
type: zeroweight-ai/ZeroBench-TTS
name: ZeroBench-TTS (challenging)
config: challenging
split: test
metrics:
- type: wer
value: 0.61
name: WER (%) — normalized text
ZeroTTS
Vietnamese Zero-Shot Text-to-Speech (TTS) with real-time streaming and voice cloning from seconds of audio. Fast, natural, and optimised for CPU inference.
The most accurate open Vietnamese TTS we know of — 13× fewer word errors than the next open model, and it runs faster than real time on a laptop CPU.
🎯 Ultra-natural — 2.91 UTMOS, ~0.5 MOS above every other open Vietnamese system, with near-zero dead air (0.029 s vs 0.23–0.53 s).
🗣️ Zero-shot voice cloning — a voice is a small latent array; drop it in and the model speaks in it, cloned from as little as 3 seconds of reference audio (up to 30 seconds). No fine-tuning, no per-speaker training.
⚡ Real-time on CPU, streaming — ~2× faster than real time (RTF 0.5×), first audio chunk in ~70 ms. No GPU required.
🇻🇳 Built for Vietnamese — tones, code-switched English, and a built-in normalizer that reads
31/12/2025and1.250 tỷthe way a person would.Code, examples, browser demo: https://github.com/zeroweight-ai/ZeroTTS
Benchmark dataset: https://huggingface.co/datasets/zeroweight-ai/ZeroBench-TTS
pip install zerotts
from zerotts import ZeroTTS
tts = ZeroTTS.from_pretrained("zeroweight-ai/ZeroTTS")
audio = tts.synthesize("Xin chào các bạn, mình là ZeroTTS.", voice="maichi")
tts.save_audio(audio, "out.wav")
Streaming, with first audio in roughly 70 ms:
for chunk in tts.synthesize_stream("Một đoạn văn bản dài hơn…", voice="maichi"):
play(chunk) # (1, n) float32 at 48 kHz
Benchmarks
Measured on ZeroBench-TTS against OmniVoice, XTTS-v2-vietnamse and viXTTS, on normalized text (dates, numbers and acronyms already spoken out): 0.56 % WER (4× fewer errors than the next-best system, median 0.00 % on all four subsets), 2.91 UTMOS, and 0.029 s of dead air. On CPU it runs ~2× faster than real time (RTF 0.5×) with first audio in ~70 ms of streaming.
Full comparison tables, per-subset breakdowns, and CPU speed methodology: docs/BENCHMARKS.md on GitHub.
Voices, and voice cloning
A voice is a small array of speaker latents, (1, n_voice_queries, d_model),
shipped as a .npz under voices/. That array is the entire speaker
conditioning — no reference transcript, no audio prompt.
Voice cloning is not available in this release. Those latents come from a voice encoder that reads a reference clip, and that encoder is not published. This repository ships ready-to-use voices; it cannot create new ones from audio.
To get latents for your own speaker, see zeroweight.ai or get in touch.
Because a voice is just an array, latents obtained that way drop into
voices/<name>/voice.npz and work with no code change.
Intended use and limitations
Built for Vietnamese. It handles English words embedded in Vietnamese text
(code_switch), but it is not an English TTS system and is not evaluated as one.
Do not use it to impersonate a real person, to generate speech attributed to someone without their consent, or to produce audio intended to deceive. The shipped voices are for evaluation and demos.
Synthetic speech should be disclosed as synthetic wherever a listener might reasonably assume otherwise.
Credits
Speech codec: MOSS-Audio-Tokenizer-Nano by the OpenMOSS team, Apache-2.0.
Its ONNX decoder graphs are redistributed under onnx/codec/ so ZeroTTS has
no external runtime dependency; the encoder is not included. See
onnx/codec/LICENSE-Apache-2.0.txt.
@misc{gong2026mossaudiotokenizerscalingaudiotokenizers,
title={MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models},
author={Yitian Gong and Kuangwei Chen and Zhaoye Fei and Xiaogui Yang and Ke Chen
and Yang Wang and Kexin Huang and Mingshu Chen and Ruixiao Li
and Qingyuan Cheng and Shimin Li and Xipeng Qiu},
year={2026}, eprint={2602.10934}, archivePrefix={arXiv}, primaryClass={cs.SD}
}
License
ZeroTTS weights and code: MIT.
The ZeroBench-TTS dataset is CC-BY-NC-4.0 because it redistributes reference audio from VIVOS, viVoice, phoaudiobook and Emilia. That license applies to the benchmark dataset only — not to these weights.