🦜 VieNeu-TTS v2 Turbo — GGUF
Ultra-fast Vietnamese & English TTS — runs entirely on CPU, no GPU required.
📖 Model Description
VieNeu-TTS v2 Turbo is the lightweight, CPU-optimized edition of the VieNeu-TTS family — a state-of-the-art Vietnamese Text-to-Speech system. Quantized to GGUF format and paired with an ONNX neural codec, this model delivers near-real-time speech synthesis on commodity hardware: laptops, edge devices, and even Raspberry Pi class machines.
This repository hosts the GGUF quantized weights intended for use with llama-cpp-python as the inference backend, alongside the companion ONNX codec for waveform generation.
What makes it special?
- 🇻🇳🇺🇸 Bilingual (Code-switching): Naturally handles mixed Vietnamese–English sentences, powered by sea-g2p. No need to pre-label language boundaries.
- ⚡ Extreme Speed: Optimized GGUF quantization achieves real-time or faster inference on a standard CPU.
- 💻 Zero GPU Dependency: Runs fully offline on any x86_64 / ARM64 machine with sufficient RAM.
- 🔇 AI Watermarking: Audio output embeds an imperceptible identifier for responsible AI content tracing.
- 🔊 24 kHz Audio: High-fidelity waveform output suitable for production applications.
🗂️ Repository Contents
| File | Description |
|---|
vieneu-v2-turbo-*.gguf | GGUF quantized LLM backbone (multiple quant levels) |
🚀 Quickstart
Option 1 — Install via vieneu SDK (Recommended)
1# Minimal installation (Turbo/CPU Only)
2pip install vieneu
3
4# Optional: Pre-built llama-cpp-python for CPU (if building fails)
5pip install vieneu --extra-index-url https://pnnbao97.github.io/llama-cpp-python-v0.3.16/cpu/
6
7# Optional: macOS Metal acceleration
8pip install vieneu --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal/
1from vieneu import Vieneu
2
3# Initialize in Turbo mode (Default - Minimal dependencies)
4tts = Vieneu()
5
6# 1. Simple synthesis (uses default Southern Male voice 'Xuân Vĩnh')
7text = "Hệ thống điện chủ yếu sử dụng alternating current because it is more efficient."
8audio = tts.infer(text=text)
9
10# Save to file
11tts.save(audio, "output_Xuân Vĩnh.wav")
12print("💾 Saved to output_Xuân Vĩnh.wav")
13
14# 2. Using a specific Preset Voice
15voices = tts.list_preset_voices()
16for desc, voice_id in voices:
17 print(f"Voice: {desc} (ID: {voice_id})")
18
19my_voice_id = voices[1][1] if len(voices) > 1 else voices[0][1] # Giọng Phạm Tuyên
20voice_data = tts.get_preset_voice(my_voice_id)
21
22audio_custom = tts.infer(text="Tôi đang nói bằng giọng của Bác sĩ Tuyên.", voice=voice_data)
23
24# 3. Save to file
25tts.save(audio_custom, "output_Phạm Tuyên.wav")
26print("💾 Saved to output_Phạm Tuyên.wav")
🦜 Zero-shot Voice Cloning (SDK)
Clone any voice with only 3-5 seconds of audio using the local Turbo engine:
1from vieneu import Vieneu
2
3tts = Vieneu() # Defaults to Turbo mode
4
5# 1. Encode the reference audio (extracts speaker embedding)
6# Supported formats: .wav, .mp3, .flac
7my_voice = tts.encode_reference("examples/audio_ref/example.wav")
8
9# 2. Synthesize with the cloned voice
10# No reference text required for Turbo v2!
11audio = tts.infer(
12 text="Đây là giọng nói được clone trực tiếp bằng SDK của VieNeu-TTS.",
13 voice=my_voice
14)
15
16tts.save(audio, "cloned_voice.wav")
Option 2 — Web UI (Full repo)
1git clone https://github.com/pnnbao97/VieNeu-TTS.git
2cd VieNeu-TTS
3uv sync # minimal install (Turbo/CPU)
4uv run vieneu-web
5# → Open http://127.0.0.1:7860
🔬 Model Architecture
VieNeu-TTS v2 Turbo is a two-stage TTS system:
- LLM Backbone (GGUF): A transformer language model conditioned on text tokens and speaker embeddings. It predicts discrete audio codec tokens autoregressively.
- Neural Codec (ONNX): A VQ-VAE-based neural codec (VieNeu-Codec) decodes the predicted token sequence into a 24 kHz waveform.
The bilingual capability is enabled by sea-g2p, which converts mixed-language graphemes to phonemes before the LLM backbone processes them.
📊 Training Data
The model was trained on over 20,000 hours of combined Vietnamese and English speech data, covering a wide range of speakers, accents, recording conditions, and speaking styles.
| Dataset | Language | Description |
|---|
pnnbao-ump/VieNeu-TTS-1000h | Vietnamese | Curated studio-quality Vietnamese speech corpus |
pnnbao-ump/vietnamese-audio-corpus | Vietnamese | Diverse multi-speaker Vietnamese audio |
amphion/Emilia-Dataset | Multilingual | Large-scale multilingual speech dataset |
facebook/multilingual_librispeech | English + others | Multilingual read speech |
🗺️ Roadmap
🤝 Related Resources
📄 License
This model is released under the Apache License 2.0 — free for personal and commercial use.
Made with ❤️ for the Vietnamese TTS community by @pnnbao97 and contributors. 
