VieNeu-TTS-0.3B-q4-gguf

[](https://github.com/pnnbao97/VieNeu-TTS) [](https://huggingface.co/pnnbao-ump/VieNeu-TTS-0.3B) [](https://discord.gg/yJt8kzjzWZ) **VieNeu-TTS-0.3B-Q4-0-GGUF** is a **Q4_0** quantized version of [VieNeu-TTS-0.3B](https://huggingface.co/pnnbao-ump/VieNeu-TTS-0.3B). This model is specifically optimized to run directly on the **CPU**, providing extremely fast speech synthesis without the need for a dedicated GPU. Author: **Phạm Nguyễn Ngọc Bảo** Training high-quality TTS models requires significant GPU resources. If you find this model useful, please consider supporting the development: [](https://buymeacoffee.com/pnnbao) eSpeak NG is **mandatory** for phonemization. - **Windows:** Download `.msi` from [eSpeak NG Releases](https://github.com/espeak-ng/espeak-ng/releases). - **macOS:** `brew install espeak` - **Linux:** `sudo apt install espeak-ng` Use the source code from GitHub for the best experience with full text preprocessing support: git clone https://github.com/pnnbao97/VieNeu-TTS.git cd VieNeu-TTS uv sync uv run gradio_app.py In the UI, select **Backbone**: `VieNeu-TTS-0.3B-q4-gguf` and **Device**: `CPU`. Install the SDK to integrate VieNeu-TTS-0.3B into your research or applications: pip install vieneu --extra-index-url https://pnnbao97.github.io/llama-cpp-python-v0.3.16/cpu/ pip install vieneu from vieneu import Vieneu import os tts = Vieneu() # Default: 0.3B-Q4 GGUF for CPU os.makedirs("outputs", exist_ok=True) available_voices = tts.list_preset_voices() for desc, name in available_voices: print(f" - {desc} (ID: {name})") if available_voices: _, my_voice_id = available_voices[1] if len(available_voices) > 1 else available_voices[0] voice_data = tts.get_preset_voice(my_voice_id) audio_spec = tts.infer(text="Chào bạn, tôi đang nói bằng giọng của bác sĩ Tuyên.", voice=voice_data) tts.save(audio_spec, f"outputs/standard_{my_voice_id}.wav") print(f"💾 Saved synthesis to: outputs/standard_{my_voice_id}.wav") text = "Xin chào, tôi là VieNeu. Tôi có thể giúp bạn đọc sách, làm chatbot thời gian thực, hoặc thậm chí clone giọng nói của bạn." audio = tts.infer(text=text) tts.save(audio, "outputs/standard_output.wav") print("💾 Saved synthesis to: outputs/standard_output.wav") if os.path.exists("examples/audio_ref/example_ngoc_huyen.wav"): cloned_audio = tts.infer( text="Đây là giọng nói đã được clone thành công từ file mẫu.", 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." ) tts.save(cloned_audio, "outputs/standard_cloned_output.wav") print("💾 Saved cloned voice to: outputs/standard_cloned_output.wav") tts.close() - **Format**: GGUF (Q4_0) - **Size**: ~200 MB - **Standard Devices**: Office laptops, Mini PCs, and computers without GPUs. - **Performance**: Achieves real-time speed even on standard consumer CPUs. This model is released under the **CC BY-NC 4.0** license. - ✅ **Free:** For students, researchers, and non-profit purposes. - ⚠️ **Commercial/Enterprise:** Use for businesses or commercial products is strictly prohibited without prior authorization. - **Commercial Licensing:** Please contact the author (**Phạm Nguyễn Ngọc Bảo**) for licensing terms (Estimated: **5,000 USD/year** - negotiable). @misc{vieneutts03bgguf2026, title = {VieNeu-TTS-0.3B-GGUF: CPU-Optimized Vietnamese Text-to-Speech}, author = {Pham Nguyen Ngoc Bao}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/pnnbao-ump/VieNeu-TTS-0.3B-q4-gguf}} } **Made with ❤️ for the Vietnamese TTS community**