Audio8-TTS-Realistic 0.6B vs Ming-3.0-tts-0.5B

Audio8-TTS-Realistic 0.6B

A small local voice-cloning cousin for 23 languages under Apache 2.0, cloning from one clip and its exact transcript. It needs about 3 GB on a CUDA GPU through Transformers with Edge0's remote code, or Edge0's SGLang Omni plugin for a speech endpoint. The main risks are remote code and a pickled codec, and no independent benchmark yet.

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.

Audio8-TTS-Realistic 0.6B

1. Pin transformers>=4.57.0,<5; Transformers 5 can produce all-zero codes with this custom code
2. Load with AutoProcessor.from_pretrained('Edge0/Audio8-TTS-Realistic-0.6b', trust_remote_code=True) and AutoModel.from_pretrained(..., trust_remote_code=True, dtype=torch.bfloat16); review the four .py files first
3. Clone with processor(text=[...], reference_audio=['ref.wav'], reference_text=['exact transcript'], return_tensors='pt'); a transcript that doesn't match the clip lowers stability
4. Decode with model.decode_audio(output.codes) and write at model.config.codec_sample_rate (44,100 Hz)
5. Split long text into short utterances; reference, transcript and text share 2,048 positions
6. For an HTTP endpoint, install Edge0's SGLang Omni adapter at the pinned SGLang Omni 0.1.0 and call /v1/audio/speech; start the server with AUDIO8_TTS_STREAM_ENABLED=1 for streaming
7. Only clone a voice you have permission to use, and disclose synthetic audio

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