{ "data": { "a": { "slug": "ming-3-0-tts-tiny", "name": "Ming-3.0-tts-tiny", "vendor": "inclusionAI (Ant Group)", "vendorUrl": "https://www.inclusion-ai.org", "kind": "weights", "category": "text-to-speech", "summary": "Ming-3.0-tts-tiny is inclusionAI's open-weight text-to-speech model, a Ling V3 mixture of experts with 7.9B backbone parameters and about 1.3B active, for voice cloning, voice design and controlled speech in seven languages, under MIT.", "url": "https://www.anchorterminal.com/tools/ming-3-0-tts-tiny", "markdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-tts-tiny.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-tts-tiny.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ming-3-0-tts-tiny.json", "repo": "https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "inclusionAI/Ming-3.0-tts-tiny" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). Inference needs the publisher's GitHub repository, which is public.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. inclusionAI sells no per-call access to these weights. Running them costs the hardware, about 17.7 GB of BF16 safetensors, on an NVIDIA H800 as the publisher recommends (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://github.com/inclusionAI/Ming-3.0-tts", "capabilities": [ "speech.tts", "speech.voices", "speech.languages", "voice.clone", "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "text-to-speech", "voice-cloning", "multilingual", "mit", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-20", "graded": true, "anchor": { "graded": true, "score": 52.2, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 15, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 44 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "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.", "bestFor": "Agents on a 24 GB NVIDIA GPU that need Chinese or English narration, voice cloning or multi-speaker audio under MIT", "strengths": [ "Plain MIT licence, no gate and no account", "BF16 files of 18.0 GB fit a 24 GB GPU", "Ten task modes documented with the inputs each accepts, from cloning to voice design and podcast scripts", "An OpenAI-compatible /v1/audio/speech endpoint through SGLang-Omni, with the command on the card", "Safetensors with a SHA-256 for every large file, and active upkeep in its first week" ], "weaknesses": [ "No runtime among llama.cpp, Ollama, vLLM, SGLang, LM Studio or Transformers runs it from its own code", "Loading runs the publisher's Python code, pinned to transformers 4.46.3, torch 2.7.1 and CUDA 11.8", "No quantised build from inclusionAI and no hosted provider", "No training data or safety evaluation, and no security route of its own", "One task per call, no streaming and no timestamps", "The card's generate() temperature disagrees with the config files" ], "agentNotes": [ "Install from the GitHub repository, `git clone https://github.com/inclusionAI/Ming-3.0-tts.git \u0026\u0026 cd Ming-3.0-tts \u0026\u0026 pip install -r requirements-tiny.txt`, in its own Python 3.10.15 environment, since the 0.5B model pins different versions", "Quick start is `bash examples/one_click/run_seed.sh`, which clones from the bundled reference clip", "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", "Pick one task per call (seed, multilingual, paralinguistic, podcast, basic, emotion, dialect, freeform, voice_design, ip) and pass only the inputs that task accepts", "Expect one complete audio body, since there is no streaming, and no timestamps", "Pass temperature yourself, since the reference generate() uses 0 and SGLang-Omni takes 1.5 from the config", "Run `python tools/preflight_tiny.py --model_path \u003cdir\u003e` on the downloaded checkpoint before first use", "Label output as synthetic speech where it could pass for a real recording, as the card asks" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.2 } ], "editorialScores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 44 }, "provenanceScore": 57, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "git clone https://github.com/inclusionAI/Ming-3.0-tts.git \u0026\u0026 cd Ming-3.0-tts\nconda create -n ming-tiny python=3.10.15 -y \u0026\u0026 conda activate ming-tiny\npip install -r requirements-tiny.txt\n# zero-shot cloning from the bundled reference clip (writes outputs/seed.wav)\nbash examples/one_click/run_seed.sh", "http": "# after: sgl-omni serve --model-path inclusionAI/Ming-3.0-tts-tiny\ncurl -X POST http://127.0.0.1:8000/v1/audio/speech \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"inclusionAI/Ming-3.0-tts-tiny\", \"input\": \"Get the trust fund to the bank early.\", \"response_format\": \"wav\", \"seed\": 123}' \\\n --output speech.wav" }, "letme": { "capability": "https://letme.dev/speech.tts", "tool": "https://letme.dev/ming-3-0-tts-tiny" }, "sameCompany": [ "ming-3-0-asr-tiny", "ming-3-0-audio-flash", "ming-3-0-audio-tiny", "ling-3-0-tiny" ], "weights": { "trainedBy": "inclusionAI (Ant Group)", "builtOn": "Ling V3", "params": "7.9B", "active": "1.3B", "goodFor": "Text to speech with voice cloning, voice design and controlled speech in seven languages, on a mixture-of-experts backbone.", "runtimes": [], "builds": [ { "name": "Ming-3.0-tts-tiny", "url": "https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny", "who": "publisher", "precision": "bf16", "sizeGB": 18, "memoryGB": 24, "format": "safetensors", "note": "Runs with inclusionAI's code or SGLang-Omni, none of the runtimes listed for the machine" } ], "run": "git clone https://github.com/inclusionAI/Ming-3.0-tts.git \u0026\u0026 cd Ming-3.0-tts \u0026\u0026 pip install -r requirements-tiny.txt \u0026\u0026 bash examples/one_click/run_seed.sh", "runBuild": "Ming-3.0-tts-tiny", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "inclusionAI (Ant Group)", "family": "Ming 3.0", "url": "https://www.anchorterminal.com/open-weights/#inclusionai", "others": [ { "name": "Ming-3.0-tts-0.5B", "url": "https://huggingface.co/inclusionAI/Ming-3.0-tts-0.5B", "params": "0.5B (dense)", "listing": "https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b" }, { "name": "Ming-3.0-audio-flash", "url": "https://huggingface.co/inclusionAI/Ming-3.0-audio-flash", "params": "126B total, 6.5B active", "listing": "https://www.anchorterminal.com/tools/ming-3-0-audio-flash" }, { "name": "Ming-3.0-audio-tiny", "url": "https://huggingface.co/inclusionAI/Ming-3.0-audio-tiny", "params": "9.3B total, 2.7B active", "listing": "https://www.anchorterminal.com/tools/ming-3-0-audio-tiny" }, { "name": "Ming-3.0-asr-tiny", "url": "https://huggingface.co/inclusionAI/Ming-3.0-asr-tiny", "params": "not stated on the card (Hub metadata counts 8,865,800,992 in the files)", "listing": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny" } ], "olderCount": 54 }, "alsoIn": [ "voice-cloning" ], "area": "voice", "provenance": { "legalEntity": "inclusionAI, Ant Group (named in the site footer of inclusion-ai.org; the LICENSE copyright line reads inclusionAI)", "domain": "inclusion-ai.org", "domainRegistered": "2025-04-03", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI, and the GitHub organisation's profile describes inclusionAI as Ant Group's open-source projects. The footer reads \"© 2026 inclusionAI, Ant Group\". No registered company name was found.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the Hugging Face commit history, whose 24 messages say what changed (a corrected parameter description, updated metrics, the SGLang-Omni instructions).", "inclusion-ai.org/.well-known/security.txt answered 404 on 21 October 2026. Domain registration date from RDAP (rdap.org) on 21 October 2026.", "Ant Group runs a company-wide security response centre, AntSRC (https://security.alipay.com/, read on 21 October 2026). Nothing on inclusionAI's card or sites links it." ], "score": 57 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ming-3-0-tts-tiny.json" }, "answer": "OmniVoice scores 56.9 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 1 of 4 scored categories. Ming-3.0-tts-tiny leads on reach, risk and agent interface.", "b": { "slug": "omnivoice", "name": "OmniVoice", "vendor": "k2-fsa (Next-gen Kaldi)", "vendorUrl": "https://k2-fsa.org", "kind": "weights", "category": "text-to-speech", "summary": "OmniVoice is k2-fsa's open-weight zero-shot text-to-speech model for over 600 languages, initialised from Qwen3-0.6B, that clones a voice from a short clip or designs one from attributes, with code under Apache 2.0 and weights under CC-BY-NC by the card.", "url": "https://www.anchorterminal.com/tools/omnivoice", "markdownUrl": "https://www.anchorterminal.com/tools/omnivoice.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/omnivoice.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/omnivoice.json", "repo": "https://huggingface.co/k2-fsa/OmniVoice", "license": "CC-BY-NC (weights, per the card); Apache-2.0 (code)", "transports": [], "packages": [ { "registry": "huggingface", "name": "k2-fsa/OmniVoice" }, { "registry": "pypi", "name": "omnivoice" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). The omnivoice package and its source are public on PyPI and GitHub.", "pricing": "free", "pricingNotes": "Free to download. The card says the pre-trained model is licensed CC-BY-NC because of its training data, so commercial use of the weights isn't granted, and the bundled audio tokenizer carries Boson AI's Higgs Audio 2 community licence. The code is Apache 2.0. Running it costs the hardware, 3.3 GB of F32 safetensors with the tokenizer, on an NVIDIA GPU, Apple Silicon or an Intel Arc GPU per the README (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://github.com/k2-fsa/OmniVoice", "capabilities": [ "speech.tts", "speech.languages", "voice.clone", "model.open-weights" ], "tags": [ "open-weights", "model", "text-to-speech", "voice-cloning", "voice-design", "multilingual", "diffusion", "non-commercial", "safetensors", "self-hosted" ], "lastRelease": "2026-07-03", "graded": true, "anchor": { "graded": true, "score": 56.9, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 13, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 86, "interface": 50, "reach": 40, "risk": 35, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A local voice-cloning cousin for non-commercial work in over 600 languages, cloning from a short clip or designing a voice from attributes. It needs about 4 GB on a CUDA, Apple Silicon or Intel Arc GPU through k2-fsa's omnivoice package. The main risk is the licence, changed from Apache 2.0 to non-commercial in July 2026 without a note.", "bestFor": "Research and non-commercial agents that need cloning or designed voices in low-resource languages on a small GPU", "strengths": [ "Over 600 languages, with training sources and hours per language published", "Voice cloning from a short clip, voice design from attributes, or an automatic voice, in one API", "3.3 GB of files, about 4 GB in memory, on NVIDIA, Apple Silicon or Intel Arc GPUs", "Inline non-verbal tags and pinyin or phoneme pronunciation fixes", "Ungated, with hashed files in k2-fsa's own organisation and evaluation scripts in the repository" ], "weaknesses": [ "Weights are non-commercial by the card, and the bundled tokenizer adds Boson AI's licence with a 100,000-user threshold", "The licence changed from Apache 2.0 on 3 July 2026 with no dated note, and recent licence questions have no reply", "Not in any released version of Transformers, vLLM, SGLang, llama.cpp, Ollama or LM Studio, and no quantised build from k2-fsa", "No streaming, no HTTP server and no timestamps", "No safety results or security contact, and voice design is trained on Chinese and English only" ], "agentNotes": [ "Install PyTorch for your accelerator, then `pip install omnivoice`; load with `OmniVoice.from_pretrained('k2-fsa/OmniVoice', device_map='cuda:0', dtype=torch.float16)` (or mps, or xpu)", "Clone with `model.generate(text=..., ref_audio='ref.wav', ref_text='...')`; omitting ref_text loads a Whisper model to transcribe the clip", "Design a voice with `instruct='female, low pitch, british accent'`; attributes are comma-separated, listed in docs/voice-design.md", "Output is a list of numpy arrays at 24 kHz; write with `sf.write('out.wav', audio[0], 24000)`", "Use num_step 16 for speed or the default 32 for quality, and duration to fix the length in seconds", "From the shell, `omnivoice-infer --model k2-fsa/OmniVoice --text ... --output out.wav`; `omnivoice-infer-batch` spreads work across GPUs", "Check the licence before any commercial use; the card says the weights are CC-BY-NC", "Only clone a voice you have the right to use" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.9 } ], "editorialScores": { "capability": 86, "interface": 50, "reach": 40, "risk": 35, "upkeep": 48 }, "provenanceScore": 68, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install torch==2.8.0+cu128 torchaudio==2.8.0+cu128 --extra-index-url https://download.pytorch.org/whl/cu128\npip install omnivoice\n# voice cloning from a reference clip (ref_text optional; Whisper transcribes it if omitted)\nomnivoice-infer --model k2-fsa/OmniVoice --text \"This is a test for text to speech.\" --ref_audio ref.wav --ref_text \"Transcription of the reference audio.\" --output hello.wav" }, "letme": { "capability": "https://letme.dev/speech.tts", "tool": "https://letme.dev/omnivoice" }, "weights": { "trainedBy": "k2-fsa (Next-gen Kaldi)", "builtOn": "Qwen/Qwen3-0.6B (initialised from it as the language-model backbone; Apache 2.0)", "params": "0.61B (main checkpoint) plus the Higgs Audio v2 tokenizer", "goodFor": "Zero-shot voice cloning and attribute-designed voices in over 600 languages, for non-commercial use.", "runtimes": [], "builds": [ { "name": "OmniVoice", "url": "https://huggingface.co/k2-fsa/OmniVoice", "who": "publisher", "precision": "fp32", "sizeGB": 3.27, "memoryGB": 8, "format": "safetensors", "note": "Includes the 0.81 GB Higgs Audio v2 tokenizer under Boson AI's licence. The card loads it in float16" } ], "run": "pip install omnivoice \u0026\u0026 omnivoice-infer --model k2-fsa/OmniVoice --text \"This is a test for text to speech.\" --instruct \"female, low pitch, british accent\" --output hello.wav", "runBuild": "OmniVoice", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "k2-fsa (Next-gen Kaldi)", "family": "OmniVoice", "url": "https://www.anchorterminal.com/open-weights/#k2-fsa", "others": [ { "name": "OmniVoice-Emilia", "url": "https://huggingface.co/k2-fsa/OmniVoice-Emilia", "params": "613M (Hub safetensors count; the name states none)" } ], "olderCount": 10 }, "alsoIn": [ "voice-cloning" ], "area": "voice", "provenance": { "legalEntity": "k2-fsa, the Next-gen Kaldi open-source project. The paper's authors are at Xiaomi Corp.; no company is named as the licensor", "domain": "k2-fsa.org", "domainRegistered": "2021-02-08", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/k2-fsa/OmniVoice/commits/master", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card links github.com/k2-fsa/OmniVoice, and the GitHub README links back to huggingface.co/k2-fsa/OmniVoice. The k2-fsa organisation on Hugging Face isn't checked for verification here.", "Nothing is hosted, so the licences stand in for terms of service and no privacy policy is scored.", "The Hugging Face history is 18 commits from 30 March to 3 July 2026. The weights haven't changed since 30 March; later commits are card edits and the tokenizer's LICENSE. The GitHub repository has tags 0.1.2 to 0.2.1 (4 April to 16 July 2026) and its last commit on 24 August 2026.", "k2-fsa.org/.well-known/security.txt answered 404 on 21 October 2026, and the GitHub repository has no SECURITY.md. RDAP gives the domain's registration as 8 February 2021." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/omnivoice.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "inclusionAI (Ant Group)", "b": "k2-fsa (Next-gen Kaldi)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "CC-BY-NC (weights, per the card); Apache-2.0 (code)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-20", "b": "2026-07-03", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.5/5 (4)", "b": "2.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "OmniVoice scores 56.9 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 1 of 4 scored categories. Ming-3.0-tts-tiny leads on reach, risk and agent interface.", "question": "Which is better for AI agents, Ming-3.0-tts-tiny or OmniVoice?" } ], "goodFor": [ { "aheadOn": [ "Reach, 50 against 40", "Risk, 55 against 35", "Agent interface, 60 against 50" ], "also": null, "goodFor": "Agents on a 24 GB NVIDIA GPU that need Chinese or English narration, voice cloning or multi-speaker audio under MIT", "slug": "ming-3-0-tts-tiny", "watchFor": "No runtime among llama.cpp, Ollama, vLLM, SGLang, LM Studio or Transformers runs it from its own code" }, { "aheadOn": null, "also": null, "goodFor": "Research and non-commercial agents that need cloning or designed voices in low-resource languages on a small GPU", "slug": "omnivoice", "watchFor": "Weights are non-commercial by the card, and the bundled tokenizer adds Boson AI's licence with a 100,000-user threshold" } ], "job": { "capability": "speech.tts", "name": "Text-to-speech" }, "others": [ { "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-ming-3-0-tts-tiny.json", "title": "Ming-3.0-tts-0.5B vs Ming-3.0-tts-tiny", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-ming-3-0-tts-tiny" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice.json", "title": "Ming-3.0-tts-0.5B vs OmniVoice", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-qwen3-tts-1-7b-customvoice.json", "title": "Ming-3.0-tts-tiny vs Qwen3-TTS 12Hz 1.7B CustomVoice", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-qwen3-tts-1-7b-customvoice" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-vieneu-tts-v3-turbo.json", "title": "Ming-3.0-tts-tiny vs VieNeu-TTS v3 Turbo", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-vieneu-tts-v3-turbo" }, { "json": "https://www.anchorterminal.com/compare/omnivoice-vs-qwen3-tts-1-7b-customvoice.json", "title": "OmniVoice vs Qwen3-TTS 12Hz 1.7B CustomVoice", "url": "https://www.anchorterminal.com/compare/omnivoice-vs-qwen3-tts-1-7b-customvoice" }, { "json": "https://www.anchorterminal.com/compare/omnivoice-vs-vieneu-tts-v3-turbo.json", "title": "OmniVoice vs VieNeu-TTS v3 Turbo", "url": "https://www.anchorterminal.com/compare/omnivoice-vs-vieneu-tts-v3-turbo" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "ming-3-0-tts-tiny", "key": "reach", "ming-3-0-tts-tiny": 50, "name": "Reach", "omnivoice": 40, "weight": 25 }, { "by": 20, "edge": "ming-3-0-tts-tiny", "key": "risk", "ming-3-0-tts-tiny": 55, "name": "Risk", "omnivoice": 35, "weight": 20 }, { "by": 10, "edge": "ming-3-0-tts-tiny", "key": "interface", "ming-3-0-tts-tiny": 60, "name": "Agent interface", "omnivoice": 50, "weight": 10 }, { "by": 4, "edge": "omnivoice", "key": "upkeep", "ming-3-0-tts-tiny": 44, "name": "Documentation and upkeep", "omnivoice": 48, "weight": 10 } ], "summary": "OmniVoice scores 56.9 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 1 of 4 scored categories. Ming-3.0-tts-tiny leads on reach, risk and agent interface. Both do text-to-speech.", "tutorials": [], "verdicts": { "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.", "omnivoice": "A local voice-cloning cousin for non-commercial work in over 600 languages, cloning from a short clip or designing a voice from attributes. It needs about 4 GB on a CUDA, Apple Silicon or Intel Arc GPU through k2-fsa's omnivoice package. The main risk is the licence, changed from Apache 2.0 to non-commercial in July 2026 without a note." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice", "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice.min.md" }, "markdown": "OmniVoice scores 56.9 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 1 of 4 scored categories. Ming-3.0-tts-tiny leads on reach, risk and agent interface. Both do text-to-speech.\n\n- Ming-3.0-tts-tiny: grade D, 52.2/100, rank #88 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ming-3-0-tts-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ming-3-0-tts-tiny.json\n- OmniVoice: grade C, 56.9/100, rank #72 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/omnivoice.md · JSON https://www.anchorterminal.com/api/v1/tools/omnivoice.json\n- Best text-to-speech APIs for AI agents: https://www.anchorterminal.com/best/text-to-speech/index.md\n- All 76 tts comparisons: https://www.anchorterminal.com/compare/text-to-speech/index.md\n- Best voice cloning and custom voice APIs for AI agents: https://www.anchorterminal.com/best/voice-cloning/index.md\n- All 76 cloning comparisons: https://www.anchorterminal.com/compare/voice-cloning/index.md\n\n## Which one, for what\n\n### Ming-3.0-tts-tiny (D)\n\nGood for: Agents on a 24 GB NVIDIA GPU that need Chinese or English narration, voice cloning or multi-speaker audio under MIT\n\nAhead on:\n- Reach, 50 against 40\n- Risk, 55 against 35\n- Agent interface, 60 against 50\n\nWatch for: No runtime among llama.cpp, Ollama, vLLM, SGLang, LM Studio or Transformers runs it from its own code\n\n### OmniVoice (C)\n\nGood for: Research and non-commercial agents that need cloning or designed voices in low-resource languages on a small GPU\n\nWatch for: Weights are non-commercial by the card, and the bundled tokenizer adds Boson AI's licence with a 100,000-user threshold\n\n\n## Score by category\n\n| Category | Weight | Ming-3.0-tts-tiny | OmniVoice | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 40 | Ming-3.0-tts-tiny +10 |\n| Risk | 20% (20 this run) | 55 | 35 | Ming-3.0-tts-tiny +20 |\n| Agent interface | 10% (10 this run) | 60 | 50 | Ming-3.0-tts-tiny +10 |\n| Documentation and upkeep | 10% (10 this run) | 44 | 48 | OmniVoice +4 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **52.2 · D** | **56.9 · C** | |\n\n## Facts side by side\n\n| Fact | Ming-3.0-tts-tiny | OmniVoice |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | k2-fsa (Next-gen Kaldi) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | CC-BY-NC (weights, per the card); Apache-2.0 (code) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-07-03 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.5/5 (4) | 2.3/5 (4) |\n\n## Verdicts\n\n**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.\n\n**OmniVoice.** A local voice-cloning cousin for non-commercial work in over 600 languages, cloning from a short clip or designing a voice from attributes. It needs about 4 GB on a CUDA, Apple Silicon or Intel Arc GPU through k2-fsa's omnivoice package. The main risk is the licence, changed from Apache 2.0 to non-commercial in July 2026 without a note.\n\n## Before you call either\n\n### Ming-3.0-tts-tiny\n\n1. Install from the GitHub repository, `git clone https://github.com/inclusionAI/Ming-3.0-tts.git \u0026\u0026 cd Ming-3.0-tts \u0026\u0026 pip install -r requirements-tiny.txt`, in its own Python 3.10.15 environment, since the 0.5B model pins different versions\n2. Quick start is `bash examples/one_click/run_seed.sh`, which clones from the bundled reference clip\n3. 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\n4. Pick one task per call (seed, multilingual, paralinguistic, podcast, basic, emotion, dialect, freeform, voice_design, ip) and pass only the inputs that task accepts\n5. Expect one complete audio body, since there is no streaming, and no timestamps\n6. Pass temperature yourself, since the reference generate() uses 0 and SGLang-Omni takes 1.5 from the config\n7. Run `python tools/preflight_tiny.py --model_path \u003cdir\u003e` on the downloaded checkpoint before first use\n8. Label output as synthetic speech where it could pass for a real recording, as the card asks\n\n### OmniVoice\n\n1. Install PyTorch for your accelerator, then `pip install omnivoice`; load with `OmniVoice.from_pretrained('k2-fsa/OmniVoice', device_map='cuda:0', dtype=torch.float16)` (or mps, or xpu)\n2. Clone with `model.generate(text=..., ref_audio='ref.wav', ref_text='...')`; omitting ref_text loads a Whisper model to transcribe the clip\n3. Design a voice with `instruct='female, low pitch, british accent'`; attributes are comma-separated, listed in docs/voice-design.md\n4. Output is a list of numpy arrays at 24 kHz; write with `sf.write('out.wav', audio[0], 24000)`\n5. Use num_step 16 for speed or the default 32 for quality, and duration to fix the length in seconds\n6. From the shell, `omnivoice-infer --model k2-fsa/OmniVoice --text ... --output out.wav`; `omnivoice-infer-batch` spreads work across GPUs\n7. Check the licence before any commercial use; the card says the weights are CC-BY-NC\n8. Only clone a voice you have the right to use\n\n## Questions\n\n### Which is better for AI agents, Ming-3.0-tts-tiny or OmniVoice?\n\nOmniVoice scores 56.9 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 1 of 4 scored categories. Ming-3.0-tts-tiny leads on reach, risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ming-3-0-tts-tiny\", \"b\": \"omnivoice\"}`. From a terminal: `anchor compare ming-3-0-tts-tiny omnivoice`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ming-3-0-tts-tiny.json and https://www.anchorterminal.com/api/v1/tools/omnivoice.json\n\n## Other comparisons with Ming-3.0-tts-tiny or OmniVoice\n\n- [Ming-3.0-tts-0.5B vs Ming-3.0-tts-tiny](https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-ming-3-0-tts-tiny.md)\n- [Ming-3.0-tts-0.5B vs OmniVoice](https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice.md)\n- [Ming-3.0-tts-tiny vs Qwen3-TTS 12Hz 1.7B CustomVoice](https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-qwen3-tts-1-7b-customvoice.md)\n- [Ming-3.0-tts-tiny vs VieNeu-TTS v3 Turbo](https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-vieneu-tts-v3-turbo.md)\n- [OmniVoice vs Qwen3-TTS 12Hz 1.7B CustomVoice](https://www.anchorterminal.com/compare/omnivoice-vs-qwen3-tts-1-7b-customvoice.md)\n- [OmniVoice vs VieNeu-TTS v3 Turbo](https://www.anchorterminal.com/compare/omnivoice-vs-vieneu-tts-v3-turbo.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Ming-3.0-tts-tiny vs OmniVoice", "url": "" } ], "description": "OmniVoice scores 56.9 (C) to Ming-3.0-tts-tiny's 52.2 (D) for text-to-speech. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Ming-3.0-tts-tiny D 52.2", "OmniVoice C 56.9", "scores" ], "h1": "Ming-3.0-tts-tiny vs OmniVoice", "image": "https://www.anchorterminal.com/assets/og/compare-ming-3-0-tts-tiny-vs-omnivoice.png", "path": "/compare/ming-3-0-tts-tiny-vs-omnivoice", "published": "2026-10-01", "section": "tools", "title": "Ming-3.0-tts-tiny vs OmniVoice for AI agents (2026) | Anchor Terminal", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice" }, "tokens": { "markdown": 2100, "slim": 430 }, "version": 1 }