ZeroTTS / README.md

Correct the normalized-headline framing

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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

Ultra-natural Vietnamese speech, cloned from seconds of audio — streaming, real-time on a CPU

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'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

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="arya")
tts.save_audio(audio, "out.wav")

Streaming, with first audio in roughly 100 ms:

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 — 137 items, 59 held-out reference voices × 4 subsets — against the two public Vietnamese XTTS-v2 finetunes. 137/137 scored, 0 empty generations.

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) — which reaches the benchmark's reading on 27 of the 35 items that need normalization; the remaining gaps are date separators and alphanumeric codes. Neither baseline ships a Vietnamese frontend at all, which is why the raw-text table below is so much harsher on them.

	ZeroTTS	XTTS-v2-vietnamse	viXTTS
WER ↓	0.56 %	7.27 %	8.61 %
Naturalness (UTMOS) ↑	2.91	2.43	2.35
Voice similarity (SSIM) ↑	0.936	0.940	0.935
Dead air (excess silence) ↓	0.029 s	0.532 s	0.233 s

13× fewer word errors, ~0.5 MOS more natural, an order of magnitude less dead air. Median WER is 0.00 % on all four subsets — the typical generation is transcribed exactly.

WER — normalized text

The headline condition: numbers and dates already spoken out, as the shipped normalizer produces.

Subset	what it tests	ZeroTTS	XTTS-v2-vietnamse	viXTTS
`vietnamese`	monolingual Vietnamese	0.21 %	7.21 %	7.54 %
`code_switch`	Vietnamese + embedded English	0.95 %	10.14 %	5.86 %
`cross_lingual`	foreign voice prompt → Vietnamese	0.38 %	4.94 %	6.61 %
`challenging`	acronyms, dates, %, currency	0.61 %	5.63 %	13.44 %
overall		0.56 %	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	XTTS-v2-vietnamse	viXTTS
`vietnamese`	monolingual Vietnamese	0.16 %	7.92 %	9.56 %
`code_switch`	Vietnamese + embedded English	0.97 %	10.94 %	9.25 %
`cross_lingual`	foreign voice prompt → Vietnamese	1.42 %	21.37 %	27.27 %
`challenging`	acronyms, dates, %, currency	1.75 %	27.86 %	31.85 %
overall		1.03 %	16.42 %	18.40 %

Reading these fairly:

Normalization is where the baselines gain most, and we still win. Their tokenizers genuinely 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. The gap narrows from 16× to 13× and stops there, because 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.
Voice similarity is a tie, not a win. 0.936 / 0.940 / 0.935 is within noise. On cross_lingual ZeroTTS is genuinely behind (0.911 vs ~0.935): it carries a foreign speaker's timbre into Vietnamese slightly less faithfully, while winning that subset's WER by 15×.
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. Its test suite pins the policy in both directions: format artifacts must score 0, real defects must still cost.
Our remaining errors are published, not hidden. Every item scoring above 0.00 is audited in docs/BENCHMARKS.md — mostly voiced leading zeros in dates and W/H acronym letter names.

Reproduce, or score your own system:

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:

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 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.

@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.