| --- |
| 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 |
| --- |
| |
| <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 — 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, ~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 `31/12/2025` and `ZeroTTS` the way a person would. |
|
|
| * 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="maichi") |
| tts.save_audio(audio, "out.wav") |
| ``` |
|
|
| Streaming, with first audio in roughly 70 ms: |
|
|
| ```python |
| 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](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 | |
| | **RTF, CPU** ↓ | **0.50×** | 6.12× | 0.71× | 0.73× | |
| | **Time to first audio, CPU** ↓ | **~70 ms** | ~34 s | ~6.1 s | ~5.1 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**, and the only one of the four |
| that's actually fast on CPU — the others are sized and tuned for a GPU, and it |
| shows. |
|
|
| Full comparison tables, per-subset breakdowns, and CPU speed methodology: |
| **[docs/BENCHMARKS.md](https://github.com/zeroweight-ai/ZeroTTS/blob/main/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** | OmniVoice | XTTS-v2-vietnamse | viXTTS | |
| |---|:-:|:-:|:-:|:-:| |
| | RTF — short | **0.51×** | 10.87× | 0.70× | 0.71× | |
| | RTF — medium | **0.47×** | 4.82× | 0.70× | 0.70× | |
| | RTF — long | **0.53×** | 2.67× | 0.71× | 0.78× | |
| | TTFA — short | **53 ms** | 21.7 s | 4.02 s | 2.45 s | |
| | TTFA — medium | **66 ms** | 28.9 s | 4.02 s | 3.72 s | |
| | TTFA — long | **89 ms** | 52.3 s | 10.3 s | 9.22 s | |
|
|
| 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](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. |
|
|
| ## 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. |
|
|
