Ming-3.0-tts-0.5B vs VieNeu-TTS v3 Turbo

Ming-3.0-tts-0.5B

A small MIT-licensed speech cousin for offline narration, voice cloning and voice design in seven languages and two dialects. It needs about 4 GB but runs only through inclusionAI's own Python scripts on CUDA, with no streaming. The main risk is a loading path that runs the publisher's code, with no safety evaluation or security contact.

VieNeu-TTS v3 Turbo

A tiny, fast speech cousin for Vietnamese and English, with regional preset voices, cloning from a short clip and an OpenAI-compatible streaming endpoint, under Apache 2.0. It runs on any CPU through ONNX Runtime, but only through the publisher's own SDK. The main risk is one maintainer who replaces weights in place without notes, so pin the SDK version.

Ming-3.0-tts-0.5B

1. Clone github.com/inclusionAI/Ming-3.0-tts and pip install -r requirements.txt (torch 2.6.0, transformers 4.46.3), since the Hugging Face repository alone won't load
2. Start with python infer.py --model_path inclusionAI/Ming-3.0-tts-0.5B --task IP --text "The garden will remain open until sunset." --ip US_James --use_zero_spk_emb --no_text_normalization --output output/tts.wav
3. Pass --no_text_normalization unless pynini 2.1.6 is installed, since text normalisation imports it
4. Pick one task per call and pass only that task's controls, since controls from another task are rejected
5. Run python tools/preflight.py --model_path <dir> to check the checkpoint before the first call
6. inclusionAI tested only NVIDIA H800 and H20 with CUDA 12.4, and no doc covers PyTorch on ROCm, so the AMD reference machine is untested
7. On Apple silicon, mlx-audio 0.6.1 or later loads mlx-community's conversion, which isn't inclusionAI's
8. Clone only voices you have consent for and label the audio as synthetic, as the card asks

VieNeu-TTS v3 Turbo

1. On CPU, pip install vieneu, then from vieneu import Vieneu; tts = Vieneu(); tts.save(tts.infer(text, voice='Mai Anh'), 'out.wav'); Vieneu(precision='int8') is about twice as fast on a CPU with VNNI
2. On GPU, install torch 2.8 for CUDA 12.8 and transformers 4.57.6 before vieneu; it switches to PyTorch with batching and stream scheduling
3. Serve with uv run python -m apps.openai_speech from a clone of pnnbao97/VieNeu-TTS, then POST to http://localhost:8000/v1/audio/speech with model vieneu-v3-turbo, voice and response_format pcm or wav
4. Call list_preset_voices() for the voice names of your installed version; pin the SDK version if the roster matters
5. Clone with ref_audio='voice.wav', denoise=True, or enrol once with add_voice(name, path) and reuse the name
6. Keep temperature near 0.8; the style argument is ignored on v3 Turbo
7. Only clone a voice you have the right to use