README.md

---
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
# NOTE deliberately NO `datasets:` field. It is the only thing that populates
# the Hub's cross-link, but the Hub renders it as "Models trained or fine-tuned
# on <dataset>" — which for our own held-out benchmark reads as train/test
# contamination. ZeroBench-TTS is EVALUATION data; every voice in it is held
# out of training. The `model-index` block below states that correctly, and the
# body links the benchmark in prose.
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
---

<img src="banner.png" alt="ZeroTTS — Vietnamese zero-shot text-to-speech" width="100%">

# 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)** —
137 items, 59 held-out reference voices × 4 subsets — against
[OmniVoice](https://huggingface.co/k2-fsa/OmniVoice) and the two public
Vietnamese XTTS-v2 finetunes. 137/137 scored for every system, 0 empty
generations. OmniVoice is given its optional `language="vi"` hint, which its
model card recommends and which measurably helps it.

**Scored by the benchmark, not by us.** ZeroTTS synthesizes the clips and hands
them to `zerobench_eval`, the official scorer published inside the benchmark
dataset repo. Nothing in this repo computes a metric.

### Headline

Every system reads **normalized text** — dates, numbers and acronyms already
spoken out, from the benchmark's own curated reading. Every system gets exactly
the same input, so the comparison is like-for-like.

This is the condition a Vietnamese TTS system meets in production, where a text
frontend runs ahead of the model. ZeroTTS ships one — `normalize_vi_text`,
applied by default (see the [GitHub README](https://github.com/zeroweight-ai/ZeroTTS#usage)) — which reproduces the benchmark's
reading on 34 of the 35 items that need normalization. Neither baseline ships a
Vietnamese frontend at all, which is why the raw-text table below is so much
harsher on them.

| | **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 %** |

**Reading these fairly:**

* **OmniVoice beats us on two things, and they are worth naming.** Its speaker
  similarity is the best of the four (0.951 vs our 0.938), and on `code_switch`
  it is roughly half our error rate (0.46 % vs 0.95 %). If cloning fidelity or
  English-in-Vietnamese is your priority, it is a genuinely strong option — at
  3.1 GB on a GPU.
* **OmniVoice's overall figure is dominated by one subset.** `cross_lingual`
  (foreign voice prompt, Vietnamese text) costs it 17.71 % raw against our
  1.42 %, and it is language-dependent — German 0.00 %, Korean 0.13 %, Japanese
  0.41 %. Excluding that subset it lands near 1.7 % raw. Both ASRs agree the
  audio genuinely degrades there, so it is the model, not the scorer.
* **Normalization is where the weakest systems gain most, and the order does not
  change.** The XTTS tokenizers have no Vietnamese number expansion, so raw text
  punishes them hard (`challenging` 27.86 %) and the normalized column is the
  fairest comparison available — it improves XTTS 2.3× and viXTTS 2.1×, against
  1.8× for us. What remains is the acoustic model.
* **`vietnamese` barely moves for anyone** (0.16 % → 0.21 % for ZeroTTS). It has
  no digits or acronyms, so there is nothing to normalize — which is the control
  showing the other subsets' gains are real and not a scoring artifact.
* **On `cross_lingual` our voice similarity is the weak spot** (0.911 vs
  ~0.935 for the others): ZeroTTS carries a foreign speaker's timbre into
  Vietnamese slightly less faithfully, while winning that subset's WER by 12×.
* **The WER definition matters more than the WER.** ZeroBench scores every clip
  with **two ASRs** (`whisper-large-v3` + `PhoWhisper-large`, min taken — neither
  can judge Vietnamese code-switch TTS alone) against **every acceptable
  reading** of the target text, so a system is never charged for an ASR's
  choice between "31/12/2025" and "ba mươi mốt tháng mười hai". Its test suite
  pins that in both directions: format differences must score 0, real
  mispronunciations must still cost.
* **Our remaining errors are published, not hidden.** Every item scoring above
  0.00 is audited in [docs/BENCHMARKS.md](https://github.com/zeroweight-ai/ZeroTTS/blob/main/docs/BENCHMARKS.md). The two recurring
  ones: a leading zero read aloud (`18/04` → "tháng **không** tư"), and the
  letters `W` and `H` coming out wrong when an acronym has to be spelled —
  `WHO` should be spelled out letter by letter, and instead comes out as
  something like "Hall".

Reproduce, or score your own system:

```bash
pip install "zerotts[eval]"
SYNTH_FROM=text_normalized OUT_DIR=./eval/norm ./evaluation/run_benchmark.sh
./evaluation/run_benchmark.sh                                   # raw text
```

Not using ZeroTTS? The scorer stands alone — bring wavs from any system:

```bash
huggingface-cli download zeroweight-ai/ZeroBench-TTS --repo-type dataset --local-dir ZeroBench-TTS
cd ZeroBench-TTS && pip install -r zerobench_eval/requirements.txt
python -m zerobench_eval manifest --out manifest.jsonl    # what to synthesize
python -m zerobench_eval score --wav_dir my_wavs/ --name MyModel
```

## 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/<name>/voice.npz` and work with no code change.

## Repository layout

```
config.json                          runtime config
tokenizer.json                       BPE tokenizer
null_voice_emb.npy                   learned unconditional voice prefix
onnx/text_encoder.onnx               text → encoder states (once per utterance)
onnx/prefix_step.onnx                global transformer step (once per frame)
onnx/local_frame_decode.onnx         frame decode + sampling (once per frame)
onnx/codec/                          MOSS-Audio-Tokenizer-Nano decoder (Apache-2.0)
voices/<name>/voice.npz              speaker latents
```

fp32, not quantized: ~900 MB total. Two ONNX Runtime calls per audio frame;
frames are produced at 12.5 Hz and decoded to 48 kHz.

The model architecture, training code, and the ONNX export script are not
published, and the voice encoder is not included.

## 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**.
Bundled MOSS codec decoder: **Apache-2.0**.

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.