---
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. No fine-tuning, no per-speaker training.
* ⚡ **Real-time on CPU, streaming** — first audio chunk in ~100 ms, then chunks
ramp up. No GPU required.
* 🇻🇳 **Built for Vietnamese** — tones, code-switched English, and a built-in
normalizer that reads `31/12/2025` and `1.250 tỷ` the way a person would.
* 📊 **Measured, not asserted** — every number below comes from
[ZeroBench-TTS](https://huggingface.co/datasets/zeroweight-ai/ZeroBench-TTS)'s
own public scorer, on 59 held-out voices.
* Code, examples, browser demo: **https://github.com/zeroweight-ai/ZeroTTS**
* Benchmark dataset: **https://huggingface.co/datasets/zeroweight-ai/ZeroBench-TTS**
```python
pip install zerotts
```
```python
from zerotts import ZeroTTS
tts = ZeroTTS.from_pretrained("zeroweight-ai/ZeroTTS")
audio = tts.synthesize("Xin chào các bạn, mình là ZeroTTS.", voice="arya")
tts.save_audio(audio, "out.wav")
```
Streaming, with first audio in roughly 100 ms:
```python
for chunk in tts.synthesize_stream("Một đoạn văn bản dài hơn…", voice="arya"):
play(chunk) # (1, n) float32 at 48 kHz
```
## Benchmarks
Measured on **[ZeroBench-TTS](https://huggingface.co/datasets/zeroweight-ai/ZeroBench-TTS)**
Every system reads **normalized text** — dates, numbers and acronyms already
spoken out, from the benchmark's own curated reading.
| | **ZeroTTS** | OmniVoice | XTTS-v2-vietnamse | viXTTS |
|---|:-:|:-:|:-:|:-:|
| **WER** ↓ | **0.56 %** | 2.12 % | 7.27 % | 8.61 % |
| **Naturalness** (UTMOS) ↑ | **2.91** | 2.75 | 2.49 | 2.34 |
| **Voice similarity** (SSIM) ↑ | 0.938 | **0.951** | 0.941 | 0.935 |
| **Dead air** (excess silence) ↓ | **0.029 s** | 0.386 s | 0.568 s | 0.215 s |
| Size | **81 M**, CPU | 3.1 GB, GPU | 1.9 GB, GPU | 1.9 GB, GPU |
**4× fewer word errors than the next-best system**, ~0.2 MOS more natural, an
order of magnitude less dead air — from a model small enough to run real-time
on a laptop CPU. Median WER is **0.00 %** on all four subsets: the typical
generation is transcribed exactly. (Every figure is from the same
normalized-text runs, so the rows are mutually consistent.)
### WER — normalized text
The headline condition: numbers and dates already spoken out, as the shipped
normalizer produces.
| Subset | what it tests | **ZeroTTS** | OmniVoice | XTTS-v2-vietnamse | viXTTS |
|---|---|:-:|:-:|:-:|:-:|
| `vietnamese` | monolingual Vietnamese | **0.21 %** | 0.50 % | 7.21 % | 7.54 % |
| `code_switch` | Vietnamese + embedded English | 0.95 % | **0.46 %** | 10.14 % | 5.86 % |
| `cross_lingual` | foreign voice prompt → Vietnamese | **0.38 %** | 9.60 % | 4.94 % | 6.61 % |
| `challenging` | acronyms, dates, %, currency | **0.61 %** | 1.56 % | 5.63 % | 13.44 % |
| **overall** | | **0.56 %** | **2.12 %** | **7.27 %** | **8.61 %** |
### WER — raw text
The harder condition: the model is handed `31/12/2025` and `ChatGPT` verbatim
and has to read them itself, with no normalizer in front. This is what a system
with no Vietnamese text frontend faces.
| Subset | what it tests | **ZeroTTS** | OmniVoice | XTTS-v2-vietnamse | viXTTS |
|---|---|:-:|:-:|:-:|:-:|
| `vietnamese` | monolingual Vietnamese | **0.16 %** | 0.50 % | 7.92 % | 9.56 % |
| `code_switch` | Vietnamese + embedded English | 0.97 % | **0.46 %** | 10.94 % | 9.25 % |
| `cross_lingual` | foreign voice prompt → Vietnamese | **1.42 %** | 17.71 % | 21.37 % | 27.27 % |
| `challenging` | acronyms, dates, %, currency | **1.75 %** | 4.46 % | 27.86 % | 31.85 % |
| **overall** | | **1.03 %** | **4.13 %** | **16.42 %** | **18.40 %** |
## 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](https://zeroweight.ai)**
> or get in touch.
Because a voice is just an array, latents obtained that way drop into
`voices//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`.
```bibtex
@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.