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 — 4× fewer word errors than the next best model, and it runs faster than real time on a laptop CPU.

• 🎯 Ultra-natural — 2.91 UTMOS above every other open Vietnamese system, with near-zero dead air (0.029 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 reads 31/12/2025 and ZeroTTS without text normalizer.

• Code, examples, browser demo: https://github.com/zeroweight-ai/ZeroTTS

• Benchmark dataset: https://huggingface.co/datasets/zeroweight-ai/ZeroBench-TTS

• Blogpost: https://zeroweight.ai/blog/zero-tts

Samples

Two-speaker conversation

Long-form narration

News read, code-switched English

Cross-lingual

Reference audio (Vietnamese)

Output (English)

Usage

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:

import queue

import numpy as np
import sounddevice as sd   # pip install sounddevice

TEXT = ("Đây là chế độ phát trực tuyến. Âm thanh được tạo ra và phát ngay lập tức, "
        "không cần chờ toàn bộ đoạn văn hoàn thành. Nhờ vậy, người nghe chỉ mất "
        "khoảng 70 mili giây là đã nghe thấy câu đầu tiên, ngay cả khi mô "
        "hình đang chạy trên CPU của một chiếc laptop bình thường.")

pending, tail = queue.Queue(), np.zeros(0, dtype="float32")

def feed(outdata, frames, _time, _status):
    global tail
    while len(tail) < frames and not pending.empty():
        tail = np.concatenate([tail, pending.get_nowait()])
    n = min(frames, len(tail))
    outdata[:n, 0] = tail[:n]
    outdata[n:] = 0
    tail = tail[n:]

with sd.OutputStream(samplerate=tts.sample_rate, channels=1,
                     dtype="float32", callback=feed):
    for chunk in tts.synthesize_stream(TEXT, voice="maichi"):
        pending.put(chunk.reshape(-1))   # chunk is (1, n) float32 at 48 kHz
    while not pending.empty() or len(tail):
        sd.sleep(50)                     # let the buffer drain before closing

Benchmarks

Measured on ZeroBench-TTS

Every system reads raw text — dates, numbers and acronyms verbatim, exactly as they appear in the wild, with no text frontend in front of the model.

4× fewer word errors than the next-best system, and the fastest of the four on CPU. The gap is much wider in latency than in throughput: the two XTTS fine-tunes also beat real time (0.71×) but need seconds to emit their first sample, while OmniVoice is 6× slower than real time. All three are sized and tuned for a GPU, and it shows.

Full comparison tables, per-subset breakdowns, and CPU speed methodology: docs/BENCHMARKS.md

Speed — CPU

RTF (realtime factor, wall-clock synthesis time ÷ output audio duration — lower is faster; below 1× is faster than real time) and time-to-first-audio, all measured on CPU, single request, 8 inference threads pinned to a dedicated core pool (no other synthesis running concurrently). Three Vietnamese samples — short (26 chars), medium (77 chars), long (227 chars) — each run 6 times with the first 2 (cold-cache) discarded; figures below are the mean of the remaining 4.

ZeroTTS's time-to-first-audio comes from its real streaming path — first audio frame, not first full utterance. The three baselines have no working CPU streaming path, so their TTFA is the time to the complete utterance.

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.