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

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-tiny

A text-to-speech cousin for voice cloning, voice design and multi-speaker podcast audio in seven languages, under MIT. It needs the publisher's own code or SGLang-Omni and about 22 GB of GPU memory, with CUDA 11.8 pinned. The main risks are that loading runs repository code, no safety work is published and it can clone a voice from a short clip.

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-tiny

1. Install from the GitHub repository, git clone https://github.com/inclusionAI/Ming-3.0-tts.git && cd Ming-3.0-tts && pip install -r requirements-tiny.txt, in its own Python 3.10.15 environment, since the 0.5B model pins different versions
2. Quick start is bash examples/one_click/run_seed.sh, which clones from the bundled reference clip
3. To serve, run sgl-omni serve --model-path inclusionAI/Ming-3.0-tts-tiny from SGLang-Omni's main branch and POST to /v1/audio/speech with input, response_format and, for cloning, ref_audio plus ref_text
4. Pick one task per call (seed, multilingual, paralinguistic, podcast, basic, emotion, dialect, freeform, voice_design, ip) and pass only the inputs that task accepts
5. Expect one complete audio body, since there is no streaming, and no timestamps
6. Pass temperature yourself, since the reference generate() uses 0 and SGLang-Omni takes 1.5 from the config
7. Run python tools/preflight_tiny.py --model_path <dir> on the downloaded checkpoint before first use
8. Label output as synthetic speech where it could pass for a real recording, as the card asks