🦜 VieNeu-TTS

September 4, 2026 · View on GitHub

VieNeu-TTS is the next generation of on-device Vietnamese TTS, featuring 10,000+ hours of bilingual training, instant voice cloning, and a dedicated Podcast/Conversation mode.

Important

🦜 VieNeu-TTS v4 — available on vieneu.io

VieNeu-TTS v4 delivers near-original voice cloning fidelity, allowing a short reference clip to be reproduced with very high speaker similarity.

Due to the strength of its voice-cloning capabilities and the potential for misuse, v4 is proprietary and will not be open-sourced. It is available exclusively through the VieNeu API / vieneu.io.

VieNeu-TTS v3 Turbo remains the latest open-source version available in this repository. Future open-source releases, including potential v3.x updates, may also be published here.

Note

🦜 VieNeu-TTS v3 Turbo is officially released! A brand-new architecture designed and trained from scratch by Phạm Nguyễn Ngọc Bảo (codec: MOSS-Audio-Tokenizer-Nano; phonemizer: sea-g2p):

48 kHz high-fidelity audio (up from 24 kHz).
Built-in default voices — stable and consistent, no reference clip needed.
Natural reading style everywhere — the style follows the reference voice (the style argument is deprecated and ignored).
Emotion / non-verbal cues (experimental): drop [cười], [thở dài], [hắng giọng] straight into the text.
Batched generation (batch size up to 32), including a multi-speaker Conversation mode that batches the whole script regardless of speaker.
Instant voice cloning from a 3–8s clip, with automatic reference denoising.

Try it in the Web UI (backbone "VieNeu-TTS-v3-Turbo") or the SDK (Vieneu(mode="v3turbo"), the default).

🦜 1. Installation & Web UI

Setup with `uv` (Recommended)

uv is the fastest way to manage dependencies.

# Windows:
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# Linux/macOS:
curl -LsSf https://astral.sh/uv/install.sh | sh

Clone the Repo:

git clone https://github.com/pnnbao97/VieNeu-TTS.git
cd VieNeu-TTS

Install Dependencies:
- Option 1: CPU & macOS (minimal, torch-free) — recommended for maximum speed — runs v3 Turbo via ONNX
  
  💡 No GPU required. Installs only the lightweight ONNX stack; v3 Turbo runs on CPU (48 kHz) with default voices, voice cloning and emotion cues. PyTorch is never installed.
  
  ⚡ For the fastest CPU inference, install with uv sync — not pip install. uv sync reproduces the locked environment that pins the optimized ONNX Runtime build, so you get maximum speed out of the box.
  
  🍎 macOS users: use this option too. For v3 Turbo the torch-free ONNX path on the CPU is faster than the MPS/PyTorch build (--group gpu), so prefer uv sync for top speed on Apple Silicon.
```
uv sync
```
- Option 2: GPU — v3 Turbo on GPU (PyTorch)
  
  💡 Requires a CUDA NVIDIA GPU (CUDA ≥ 12.8) or Apple Silicon MPS. NVIDIA Toolkit recommended. Adds the PyTorch stack so v3 Turbo runs on GPU — inference is batched automatically on CUDA (same API, no code change).
```
uv sync --group gpu
```
Start the Web UI:
```
uv run vieneu-web
```
Access the UI at http://127.0.0.1:7860.

📦 2. Using the Python SDK (vieneu)

The vieneu SDK defaults to VieNeu-TTS v3 Turbo (48 kHz). The minimal install is torch-free: on CPU everything runs on ONNX Runtime (PyTorch is never imported), and on a CUDA machine it auto-switches to the PyTorch engine — where inference is batched automatically (same API, no code change).

Quick Start

CPU (default) — torch-free, runs v3 Turbo via ONNX Runtime. Most users want this:

⚡On CPU the backbone runs fp32 by default (maximum fidelity). Need more speed? Pass Vieneu(precision="int8") — ~1.6× faster and ~4× smaller, but it requires a CPU with VNNI (AVX-512 VNNI / AVX-VNNI); on older CPUs int8 can produce garbled audio. precision only affects the CPU/ONNX path; on GPU it's ignored (PyTorch).

🪶 Still too slow, or deploying on a phone / ARM board? Try VieNeu-TTS v3 Nano (preview) — Vieneu(mode="v3nano"), ~3× faster than Turbo fp32 on CPU (RTF 0.11–0.22 on a desktop CPU), but noticeably lower quality (especially English / bilingual), 24 kHz, 6 preset voices only. Details and caveats in the v3 Nano section below.

pip install vieneu

GPU (CUDA) — only if you have an NVIDIA GPU.

ℹ️ When is GPU actually worth it? The GPU win comes from batching, so it only pays off on long text (many chunks generated together in one forward — long-form or bulk synthesis). For short text the torch-free CPU/ONNX path is usually faster (there's no batch to fill, and no kernel-launch overhead). Use CPU for short, interactive calls; reach for GPU for long-form or high-throughput work.

pip install torch==2.8.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu128
pip install "transformers==4.57.6"   # pinned — most stable transformers for the GPU SDK
pip install vieneu

import time
from vieneu import Vieneu

# Default = v3 Turbo (48 kHz). GPU → PyTorch (auto-detected).
vieneu = Vieneu() # On a GPU machine you can still switch to ONNX/CPU if you prefer: Vieneu(backend="onnx")

# 1. Built-in voice by name — no reference clip needed
print("🔊 Generating speech...")

start_time = time.time()
audio = vieneu.infer("[cười] Trời ơi, cái giọng nó tự nhiên mà nó mượt mà dã man, nghe không khác gì người thật luôn. Giờ thì tha hồ mà quẩy content với cả kho giọng nói đa dạng, đủ mọi sắc thái biểu cảm. Mọi người bật loa lên rồi cùng trải nghiệm thử với mình nhé!", voice="Phạm Tuyên")
elapsed_time = time.time() - start_time

vieneu.save(audio, "output.wav")
print("✅ Saved to output.wav")

# Tính RTF (Real-Time Factor)
sample_rate = 48000
audio_duration = len(audio) / sample_rate
rtf = elapsed_time / audio_duration

print(f"\n⏱️  Thời gian xử lý: {elapsed_time:.3f}s")
print(f"🎵 Thời lượng audio: {audio_duration:.3f}s")
print(f"📊 RTF: {rtf:.4f}  ({'nhanh hơn' if rtf < 1 else 'chậm hơn'} real-time {1/rtf:.2f}x)" if rtf > 0 else "")

# List the built-in voices
voices = vieneu.list_preset_voices()
print(f"\n🎙️  {len(voices)} built-in voices available:")
for label, voice_id in voices:
    print(f"  - {label} ({voice_id})")

# 2. ⚡ Batch on GPU: infer_batch() runs many texts in ONE batched forward — same API.
#    On a CUDA GPU the chunks from every text share each forward step (big throughput
#    win). On CPU it still WORKS (no error) — just sequentially, so there's no batch
#    gain. Batch caps at max_batch_size (default 32; tune via Vieneu(max_batch_size=64)
#    or infer_batch(..., batch_size=64), or batch_size=1 to disable). A single long
#    infer() also auto-batches its own chunks. Uncomment to try (GPU recommended):
#
# import time
# texts = [
#     "Chào cả nhà, hôm nay mình sẽ hướng dẫn các bạn cách cài đặt và sử dụng bộ giọng đọc mới.",
#     "Giọng nghe cực kỳ tự nhiên và truyền cảm, lại có thể chuyển đổi biểu cảm một cách linh hoạt.",
#     "Nếu thấy hữu ích, các bạn nhớ để lại một lượt thích và chia sẻ video này cho mọi người nhé!",
# ] * 10   # 30 texts — enough to fill the batch and really show the GPU throughput win
# t0 = time.time()
# audios = vieneu.infer_batch(texts, voice="Adam")
# elapsed = time.time() - t0
# total_audio = sum(len(a) for a in audios) / 48_000
# print(f"⚡ {len(texts)} texts | audio {total_audio:.1f}s | wall {elapsed:.1f}s | RTF {elapsed/total_audio:.3f}")
# for i, a in enumerate(audios):
#     vieneu.save(a, f"batch_{i}.wav")

Streaming (real-time) 🔊

v3 Turbo supports frame-level streaming: audio starts in ~300 ms and generation stays ahead of playback (RTF < 1 on CPU — ~2–3× on a laptop, ~7× on Apple Silicon), so it's ideal for realtime / interactive apps. Streaming runs on the > ONNX/CPU engine — low first-audio latency, frame-by-frame; the GPU/PyTorch engine is built for batch throughput, not streaming, so pin backend="onnx" for realtime. Just iterate infer_stream:

from vieneu import Vieneu
vieneu = Vieneu(backend="onnx")                      # force ONNX/CPU — the streaming path (int8)
for chunk in vieneu.infer_stream("Xin chào các bạn!", voice="Adam"):
    play(chunk)                                   # np.float32 @ 48 kHz — play/write as it arrives

A complete FastAPI web streaming demo is in apps/web_stream.py:

uv run python -m apps.web_stream                  # → http://127.0.0.1:8001

Available Voices

The v3 Turbo engine includes 20 curated preset voices covering 3 regions (North, Central, South) with diverse genders and speaking characters:

Northern (Bắc): e.g. Minh Đức, Phạm Tuyên, Trúc Ly, Mai Anh, Quỳnh Anh
Central (Trung): Quang Sơn, Ngọc Trân
Southern (Nam): e.g. Adam (default), Xuân Vĩnh, Thái Sơn, Thùy Dung, Mỹ Duyên

Reading style — deprecated ⚠️

Warning

style is deprecated on v3 Turbo and has no effect. The reading style is already baked into the reference itself (the speaker embedding + reference codes of the preset voice or of your cloned clip), so every generation follows the reference and comes out in its natural reading style.

The style argument is still accepted by infer, infer_stream, infer_batch and add_voice so existing code keeps running — whatever you pass ("tin_tuc", "doc_truyen", …) is simply ignored. New code should just omit it.

# Old code — still runs, but `style` is ignored
audio = vieneu.infer("Bản tin sáng nay.", voice="Adam", style="tin_tuc")

# New code — pick the reading character through the voice / reference clip instead
audio = vieneu.infer("Bản tin sáng nay.", voice="Adam")

Emotion cues (experimental)

Inline tags are supported anywhere in the text: [cười] (chuckle), [thở dài] (sigh), [hắng giọng] (clear throat).

audio = vieneu.infer("Nghe hay quá đi [cười]. Để mình nói tiếp [hắng giọng].", voice="Adam")

Voice cloning

Clone any voice from a short reference clip. The clip is cleaned up automatically (background noise removed, and trimmed to ≤ 8 seconds) before cloning — keep denoise=True unless your clip is already clean.

audio = vieneu.infer(
    "Đây là giọng được nhân bản tức thì.",
    ref_audio="my_voice.wav",   # a 3–8s reference clip
    denoise=True,               # default; set False if the clip is already clean
)
vieneu.save(audio, "cloned.wav")

Save & reuse a cloned voice

# Enroll a voice (denoises + extracts the speaker profile once)
vieneu.add_voice("Giọng của tôi", "my_voice.wav")

# Now reuse it anywhere, including the conversation mode
audio = vieneu.infer("Câu này dùng giọng đã lưu.", voice="Giọng của tôi")

# Persist your voices so they load next session
vieneu.save_voices()                 # writes to the default voices file
# vieneu.remove_voice("Giọng của tôi")

# Add a voice you already cleaned yourself → skip denoising
vieneu.add_voice("Giọng sạch", "already_clean.wav", denoise=False)

Clean up a clip on its own

Get the denoised audio without synthesizing anything (e.g. to inspect or store it):

wav, sr = vieneu.denoise("noisy.wav", out_path="clean.wav")   # 44.1 kHz mono

Note: denoise, add_voice, and voice cloning work on every backend — the torch-free CPU/ONNX install included (the whole cloning pipeline runs on onnxruntime + soxr + kaldi-native-fbank). The one exception is v3 Nano below (preset voices only).

v3 Nano (preview) — for edge devices / weak CPUs only 🪶

Warning

v3 Turbo remains the default and the recommended model. Use v3 Nano only when Turbo is too slow on your hardware (old laptops, mini PCs, ARM boards, CPUs without AVX-512/VNNI where the int8 Turbo build produces garbled audio). Nano is a 48M-parameter flow-matching model (ONNX, CPU, torch-free) and it trades quality for speed:

Lower quality than v3 Turbo — most noticeably on English and code-switched (En-Vi) text. Vietnamese is close; English words come out with a Vietnamese accent and are less stable.
24 kHz output (Turbo: 48 kHz).
6 preset voices only, no voice cloning (ref_audio, add_voice, encode_reference raise).
No frame-level streaming — infer_stream yields one finished chunk at a time.

Measured on the same desktop CPU (12th-gen Intel i7, 6 ONNX Runtime threads, ~9 s of speech):

Engine	RTF ↓	Sample rate	Load time
v3 Turbo ONNX fp32 (default on CPU)	0.62	48 kHz	~19 s
v3 Turbo ONNX int8	0.37	48 kHz	~14 s
v3 Nano, 16 steps, cfg 3 (default)	0.22	24 kHz	~3 s
v3 Nano, 8 steps, sway −1	0.11	24 kHz	~3 s

RTF = compute time ÷ audio duration (lower is faster; 0.22 = 4.5× faster than real time). The ratio carries over to slower machines: expect Nano to be roughly 1.7× faster than Turbo int8 and ~3× faster than Turbo fp32, with a 282 MB download instead of Turbo's.

from vieneu import Vieneu

tts = Vieneu(mode="v3nano")                      # ONNX, CPU, torch-free
audio = tts.infer("Xin chào, mình là giọng đọc của VieNeu Nano.", voice="Adam")
tts.save(audio, "nano.wav")                      # 24 kHz

tts.list_preset_voices()                         # Adam, Ái Hân, Mỹ Duyên, Đức Trí, Hữu Quân, Xuân Tiên
audio = tts.infer("Bản nhanh cho máy rất yếu.", voice="Ái Hân", steps=8, sway=-1)   # ~2× faster

Knobs: steps (Euler steps, 16 default; 8 ≈ 2× faster, slightly rougher — pair with sway=-1), cfg (classifier-free guidance, 3.0 default; cfg=0 halves compute but hurts intelligibility), speed, seed, threads. Emotion cues [cười] [thở dài] [hắng giọng] work as on Turbo.

🐳 3. High-Quality Server (Standard Mode)

Deploy VieNeu-TTS as a high-performance API Server (powered by LMDeploy) with a single command.

1. Run with Docker (Recommended)

Requirement: NVIDIA Container Toolkit is required for GPU support.

Start the Server with a Public Tunnel (No port forwarding needed):

docker run --gpus all -p 23333:23333 -v huggingface_cache:/root/.cache/huggingface pnnbao/vieneu-tts:latest --tunnel

Default: The server loads the VieNeu-TTS-v2 model for maximum quality.
Tunneling: The Docker image includes a built-in bore tunnel. Check the container logs to find your public address (e.g., bore.pub:31631).

2. Using the SDK (Remote Mode)

Once the server is running, you can connect from anywhere (Colab, Web Apps, etc.) without loading heavy models locally.

Installation:

pip install "vieneu[legacy]"

Usage:

from vieneu import Vieneu
import os

# Configuration
REMOTE_API_BASE = 'http://your-server-ip:23333/v1'  # Or bore tunnel URL
REMOTE_MODEL_ID = "pnnbao-ump/VieNeu-TTS-v2"

# Initialization (LIGHTWEIGHT - only loads small codec locally)
# Default emotion is "natural" (conversational) - set emotion="storytelling" for storytelling mode
vieneu = Vieneu(mode='remote', api_base=REMOTE_API_BASE, model_name=REMOTE_MODEL_ID, emotion="natural")
os.makedirs("outputs", exist_ok=True)

# List remote voices
available_voices = vieneu.list_preset_voices()
for desc, name in available_voices:
    print(f"   - {desc} (ID: {name})")

# Use specific voice (dynamically select second voice)
if available_voices:
    _, my_voice_id = available_voices[1]
    voice_data = vieneu.get_preset_voice(my_voice_id)
    audio_spec = vieneu.infer(text="Chào bạn, tôi đang nói bằng giọng của bác sĩ Tuyên.", voice=voice_data)
    vieneu.save(audio_spec, f"outputs/remote_{my_voice_id}.wav")
    print(f"💾 Saved synthesis to: outputs/remote_{my_voice_id}.wav")

# Standard synthesis (uses default voice)
text_input = "Chế độ remote giúp tích hợp VieNeu vào ứng dụng Web hoặc App cực nhanh mà không cần GPU tại máy khách."
audio = vieneu.infer(text=text_input)
vieneu.save(audio, "outputs/remote_output.wav")
print("💾 Saved remote synthesis to: outputs/remote_output.wav")

# Zero-shot voice cloning (encodes audio locally, sends codes to server)
if os.path.exists("examples/audio_ref/example_ngoc_huyen.wav"):
    cloned_audio = vieneu.infer(
        text="Đây là giọng nói được clone và xử lý thông qua VieNeu Server.",
        ref_audio="examples/audio_ref/example_ngoc_huyen.wav",
        ref_text="Tác phẩm dự thi bảo đảm tính khoa học, tính đảng, tính chiến đấu, tính định hướng."
    )
    vieneu.save(cloned_audio, "outputs/remote_cloned_output.wav")
    print("💾 Saved remote cloned voice to: outputs/remote_cloned_output.wav")

For full implementation details, see: examples/main_remote.py

Voice Preset Specification (v1.0)

VieNeu-TTS uses the official vieneu.voice.presets specification to define reusable voice assets. Only voices.json files following this spec are guaranteed to be compatible with VieNeu-TTS SDK ≥ v1.x.

3. Advanced Configuration

Customize the server to run specific versions or your own fine-tuned models.

Run the 0.3B Model (Faster):

docker run --gpus all pnnbao/vieneu-tts:serve --model pnnbao-ump/VieNeu-TTS-0.3B --tunnel

Serve a Local Fine-tuned Model: If you have merged a LoRA adapter, mount your output directory to the container:

# Linux / macOS
docker run --gpus all \
  -v $(pwd)/finetune/output:/workspace/models \
  pnnbao/vieneu-tts:serve \
  --model /workspace/models/merged_model --tunnel

🔬 4. Model Overview

Model	Format	Device	Bilingual	Features	Speed
VieNeu-TTS-v3-Turbo (default)	PyTorch/ONNX	GPU/CPU	✅	48 kHz, Default voices, Cloning, Emotion cues, Conversation	Fast (batched)
VieNeu-TTS-v3-Nano (preview)	ONNX	weak CPU / edge	⚠️ weak	24 kHz, 6 preset voices, emotion cues — no cloning, lower quality (esp. English / En-Vi)	Fastest on CPU (RTF 0.11–0.22 desktop)
VieNeu-TTS-v2	PyTorch	GPU	✅	Podcast, En-Vi CS	Fast (LMDeploy)
VieNeu-v2-CPU	GGUF/ONNX	CPU/Edge	✅	Podcast, En-Vi CS	Extreme Speed
VieNeu-v2-Turbo	GGUF/ONNX	CPU/Edge	✅	Lightweight En-Vi	Ultra Fast
VieNeu-TTS (v1)	PyTorch	GPU/CPU	❌	Stable (Vi only)	Standard

🚀 5. Roadmap

VieNeu-TTS-v2: Full high-fidelity bilingual architecture with Podcast Mode and Voice Cloning.
VieNeu-Codec: Optimized neural codec for Vietnamese (ONNX).
Turbo Voice Cloning: Bringing instant cloning to the lightweight Turbo engine.
VieNeu-TTS v3 Turbo: New from-scratch 48 kHz architecture — built-in default voices (speaker tokens), experimental emotion cues, batched generation & multi-speaker conversation.
VieNeu-TTS v3 (full release): Complete v3 with finalized quality, stable emotion control, more default voices & streaming server.
Mobile SDK: Official support for Android/iOS deployment.

🤝 6. Support & Contact

Hugging Face: pnnbao-ump
Discord: Join our community
Facebook: Pham Nguyen Ngoc Bao
License: Apache 2.0 (Free to use).

📑 7. Citation

@misc{vieneutts2026,
  title        = {VieNeu-TTS-v2: Advanced Vietnamese Text-to-Speech with Podcast and Code-Switching Support},
  author       = {Pham Nguyen Ngoc Bao},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/pnnbao-ump/VieNeu-TTS}}
}