Fish Audio Drama 3 vs Ming-3.0-tts-tiny

Fish Audio Drama 3

A local voice cousin for directed speech, multi-character scenes, voice cloning and word-level edits in about 80 languages. It needs an NVIDIA GPU, about 29 GB for the int4 build or at least 48 GB for FP8, through SGLang-Omni or fish-speech. The main risk is the licence, revocable, capped at USD 500,000 revenue for commercial use and barring hosted services.

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 Drama 3

1. Install SGLang-Omni from source with uv pip install --prerelease=allow -e ".[fishaudio]", then sgl-omni serve --model-path fishaudio/drama-3-int4 --port 8000 (or fishaudio/drama-3 for FP8 on 48 GB or more)
2. POST JSON to /v1/audio/speech with input, optional references (audio_path and its transcript) and previous_takes; set response_format and stream for the output
3. Start each line with <|speaker:N|> (N from 0 to 15) for several voices, and separate lines with <|audio_separator|>
4. Direct with point tags like [sighs], scoped tags like <whisper>...</whisper>, a bracketed style prefix, or Direction: and Line: for a director's note
5. The int4 build needs compute capability 8.0 or newer; only the triton attention backend is accepted
6. Keep each reference clip under 60 seconds and the prompt under 8,192 rows, about 21.5 rows per second of reference audio
7. Check the licence before commercial use or exposing it to other users, and only clone voices with consent
8. Treat text from untrusted sources as a script that can carry directions and speaker tags

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