Fish Audio S2 Pro vs Ming-3.0-tts-tiny

Fish Audio S2 Pro

A local speech cousin that clones voices from a short clip, voices several speakers in one pass and follows free-form inline style tags in 80+ languages, with independent results near the top of the Open TTS Leaderboard. It needs Fish Audio's own code and about a 24 GB GPU. The main limit is the licence, research and non-commercial use only.

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.

Fish Audio S2 Pro

1. Install fish-speech from GitHub (uv sync --python 3.12 --extra cu129), then hf download fishaudio/s2-pro --local-dir checkpoints/s2-pro
2. Start the server with python tools/api_server.py --listen 127.0.0.1:8080 --llama-checkpoint-path checkpoints/s2-pro --decoder-checkpoint-path checkpoints/s2-pro/codec.pth --decoder-config-name modded_dac_vq; check GET /v1/health
3. POST /v1/tts takes msgpack, not JSON; use tools/api_client.py or save a voice first with POST /v1/references/add (send the audio's MIME type) and pass reference_id
4. Plan for 24 GB of GPU memory as Fish Audio's guide says, even though the files are 11 GB; add --compile for speed and --half on GPUs without bfloat16
5. Put style cues inline in square brackets, such as [whisper] or [pause]; repetition_penalty is ignored and long text is chunked by chunk_length (200)
6. Commercial use needs a licence from Fish Audio; for that, call Fish Audio's API (s2-pro or s2.1-pro) instead

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