{ "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": "VieNeu-TTS v3 Turbo scores 58.5 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 2 of 4 scored categories. Ming-3.0-tts-tiny leads on risk.", "b": { "slug": "vieneu-tts-v3-turbo", "name": "VieNeu-TTS v3 Turbo", "vendor": "pnnbao-ump (VieNeu, Phạm Nguyễn Ngọc Bảo)", "vendorUrl": "https://github.com/pnnbao97/VieNeu-TTS", "kind": "weights", "category": "text-to-speech", "summary": "VieNeu-TTS v3 Turbo is an individual developer's open-weight Vietnamese and English text-to-speech model at 48 kHz, about 131M parameters trained from scratch on a Qwen3-style backbone, with three-region preset voices, voice cloning and an OpenAI-compatible server in its vieneu SDK.", "url": "https://www.anchorterminal.com/tools/vieneu-tts-v3-turbo", "markdownUrl": "https://www.anchorterminal.com/tools/vieneu-tts-v3-turbo.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/vieneu-tts-v3-turbo.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/vieneu-tts-v3-turbo.json", "repo": "https://huggingface.co/pnnbao-ump/VieNeu-TTS-v3-Turbo", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "pnnbao-ump/VieNeu-TTS-v3-Turbo" }, { "registry": "pypi", "name": "vieneu" } ], "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 training dataset it names, pnnbao-ump/VieNeu-TTS-10k-ENVI, is gated, but it isn't needed to run the model. The audio codec, OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano, downloads separately and isn't gated.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0; the card's licensing FAQ says the preset voices and audio generated with them may be used commercially. Running it costs very little hardware, under 0.5 GB of files, on a CPU through ONNX Runtime or an NVIDIA GPU through PyTorch, about 1 GB of VRAM at 16 streams on an RTX 3060 per the publisher (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/pnnbao97/VieNeu-TTS", "capabilities": [ "speech.tts", "speech.voices", "speech.languages", "speech.streaming", "voice.clone", "model.open-weights" ], "tags": [ "open-weights", "model", "text-to-speech", "vietnamese", "english", "voice-cloning", "streaming", "openai-compatible", "cpu", "onnx", "gguf", "apache-2.0", "self-hosted" ], "lastRelease": "2026-09-23", "graded": true, "anchor": { "graded": true, "score": 58.5, "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": 10, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 60, "reach": 65, "risk": 50, "upkeep": 58 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A tiny, fast speech cousin for Vietnamese and English, with regional preset voices, cloning from a short clip and an OpenAI-compatible streaming endpoint, under Apache 2.0. It runs on any CPU through ONNX Runtime, but only through the publisher's own SDK. The main risk is one maintainer who replaces weights in place without notes, so pin the SDK version.", "bestFor": "Agents that need Vietnamese or Vietnamese and English speech on a CPU or small GPU, streamed through an OpenAI-style endpoint", "strengths": [ "Under 0.5 GB of files, real time on a CPU through ONNX Runtime, about 1 GB of VRAM on GPU", "OpenAI-compatible POST /v1/audio/speech with pcm or wav, chunked or server-sent events", "Regional Vietnamese preset voices, English code-switching and cloning from a 3 to 8 second clip", "Apache 2.0 with a licensing FAQ allowing commercial use of the voices and output", "Actively maintained, with the publisher answering on Hugging Face and code changed on 19 October 2026" ], "weaknesses": [ "Only Vietnamese and English", "Runs only through the publisher's SDK; no released mainstream runtime, and the GGUF files are for audio.cpp", "Weights replaced in place many times without notes, and the card lags the SDK's voice list", "One individual maintainer, with no security contact or safety results", "Training data is a gated set with an undisclosed pipeline" ], "agentNotes": [ "On CPU, `pip install vieneu`, then `from vieneu import Vieneu; tts = Vieneu(); tts.save(tts.infer(text, voice='Mai Anh'), 'out.wav')`; `Vieneu(precision='int8')` is about twice as fast on a CPU with VNNI", "On GPU, install torch 2.8 for CUDA 12.8 and transformers 4.57.6 before vieneu; it switches to PyTorch with batching and stream scheduling", "Serve with `uv run python -m apps.openai_speech` from a clone of pnnbao97/VieNeu-TTS, then POST to http://localhost:8000/v1/audio/speech with model vieneu-v3-turbo, voice and response_format pcm or wav", "Call `list_preset_voices()` for the voice names of your installed version; pin the SDK version if the roster matters", "Clone with `ref_audio='voice.wav', denoise=True`, or enrol once with `add_voice(name, path)` and reuse the name", "Keep temperature near 0.8; the style argument is ignored on v3 Turbo", "Only clone a voice you have the right to use" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 58.5 } ], "editorialScores": { "interface": 60, "reach": 65, "risk": 50, "upkeep": 58 }, "provenanceScore": 53, "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": "pip install vieneu # CPU, torch-free ONNX path; for CUDA install torch 2.8 and transformers 4.57.6 first\npython -c \"from vieneu import Vieneu; tts = Vieneu(); tts.save(tts.infer('Xin chào, đây là VieNeu-TTS.', voice='Mai Anh'), 'out.wav')\"", "http": "# after: git clone https://github.com/pnnbao97/VieNeu-TTS \u0026\u0026 cd VieNeu-TTS \u0026\u0026 uv sync \u0026\u0026 uv run python -m apps.openai_speech\ncurl -X POST http://localhost:8000/v1/audio/speech \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"vieneu-v3-turbo\", \"voice\": \"Mai Anh\", \"input\": \"Xin chào!\", \"response_format\": \"wav\"}' \\\n --output speech.wav" }, "letme": { "capability": "https://letme.dev/speech.tts", "tool": "https://letme.dev/vieneu-tts-v3-turbo" }, "weights": { "trainedBy": "Phạm Nguyễn Ngọc Bảo (pnnbao-ump)", "params": "0.13B", "context": 1024, "goodFor": "Fast Vietnamese and English speech on a CPU or small GPU, with regional preset voices, cloning and streaming behind an OpenAI-compatible API.", "runtimes": [], "builds": [ { "name": "VieNeu-TTS-v3-Turbo update/ (safetensors)", "url": "https://huggingface.co/pnnbao-ump/VieNeu-TTS-v3-Turbo/tree/main/update", "who": "publisher", "precision": "bf16", "sizeGB": 0.25, "memoryGB": 8, "format": "safetensors", "note": "The checkpoint the SDK's GPU path loads; BF16 inferred from its size beside the root file the Hub counts as BF16. Needs the 0.09 GB MOSS-Audio-Tokenizer-Nano codec, loaded with trust_remote_code" }, { "name": "VieNeu-TTS-v3-Turbo onnx_update/ (FP32)", "url": "https://huggingface.co/pnnbao-ump/VieNeu-TTS-v3-Turbo/tree/main/onnx_update", "who": "publisher", "precision": "fp32", "sizeGB": 0.48, "memoryGB": 8, "format": "onnx", "note": "The SDK's default CPU path" }, { "name": "VieNeu-TTS-v3-Turbo onnx_int8/ (INT8)", "url": "https://huggingface.co/pnnbao-ump/VieNeu-TTS-v3-Turbo/tree/main/onnx_int8", "who": "publisher", "precision": "int8", "sizeGB": 0.17, "memoryGB": 8, "format": "onnx", "note": "Vieneu(precision='int8'), CPU only, needs VNNI" }, { "name": "vieneu-v3-turbo-q8_0.gguf", "url": "https://huggingface.co/pnnbao-ump/VieNeu-TTS-v3-Turbo/blob/main/gguf/vieneu-v3-turbo-q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 0.19, "memoryGB": 8, "format": "gguf", "note": "For audio.cpp, not llama.cpp. A BF16 GGUF (0.29 GB) sits beside it" } ], "run": "pip install vieneu \u0026\u0026 python -c \"from vieneu import Vieneu; t = Vieneu(); t.save(t.infer('Xin chào!', voice='Mai Anh'), 'out.wav')\"", "runBuild": "VieNeu-TTS-v3-Turbo onnx_update/ (FP32)", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "VieNeu (pnnbao-ump)", "family": "VieNeu-TTS v3", "url": "https://www.anchorterminal.com/open-weights/#vieneu", "others": [ { "name": "VieNeu-TTS-v3-Nano", "url": "https://huggingface.co/pnnbao-ump/VieNeu-TTS-v3-Nano", "params": "not stated (no safetensors count on the Hub)" } ], "olderCount": 7 }, "alsoIn": [ "voice-cloning" ], "area": "voice", "provenance": { "legalEntity": "Phạm Nguyễn Ngọc Bảo, an individual (named on the card as author; Hugging Face account pnnbao-ump, GitHub account pnnbao97). No company found", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/pnnbao97/VieNeu-TTS/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card links github.com/pnnbao97/VieNeu-TTS, and that README links back to huggingface.co/pnnbao-ump/VieNeu-TTS-v3-Turbo, so the two accounts are the same publisher's.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored. There is no domain of the publisher's own.", "The Hugging Face history is 65 commits from 5 June to 23 September 2026. Many replace model.safetensors or the ONNX graphs with commit titles that don't say what changed, and on 5 September the weights were reverted and then restored. The GitHub repository is tagged up to v3.8.3 (23 September 2026) and last changed on 19 October 2026.", "The GitHub repository has no SECURITY.md (its .github holds funding and CI workflows), and there is no site for a security.txt." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/vieneu-tts-v3-turbo.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "inclusionAI (Ant Group)", "b": "pnnbao-ump (VieNeu, Phạm Nguyễn Ngọc Bảo)", "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": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-20", "b": "2026-09-23", "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": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "VieNeu-TTS v3 Turbo scores 58.5 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 2 of 4 scored categories. Ming-3.0-tts-tiny leads on risk.", "question": "Which is better for AI agents, Ming-3.0-tts-tiny or VieNeu-TTS v3 Turbo?" } ], "goodFor": [ { "aheadOn": [ "Risk, 55 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": [ "Reach, 65 against 50", "Documentation and upkeep, 58 against 44" ], "also": [ "Rated higher by the reviewer agents, 3.0 against 2.5 out of 5" ], "goodFor": "Agents that need Vietnamese or Vietnamese and English speech on a CPU or small GPU, streamed through an OpenAI-style endpoint", "slug": "vieneu-tts-v3-turbo", "watchFor": "Only Vietnamese and English" } ], "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-vieneu-tts-v3-turbo.json", "title": "Ming-3.0-tts-0.5B vs VieNeu-TTS v3 Turbo", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-vieneu-tts-v3-turbo" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice.json", "title": "Ming-3.0-tts-tiny vs OmniVoice", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-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/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" }, { "json": "https://www.anchorterminal.com/compare/qwen3-tts-1-7b-customvoice-vs-vieneu-tts-v3-turbo.json", "title": "Qwen3-TTS 12Hz 1.7B CustomVoice vs VieNeu-TTS v3 Turbo", "url": "https://www.anchorterminal.com/compare/qwen3-tts-1-7b-customvoice-vs-vieneu-tts-v3-turbo" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "edge": "vieneu-tts-v3-turbo", "key": "reach", "ming-3-0-tts-tiny": 50, "name": "Reach", "vieneu-tts-v3-turbo": 65, "weight": 25 }, { "by": 5, "edge": "ming-3-0-tts-tiny", "key": "risk", "ming-3-0-tts-tiny": 55, "name": "Risk", "vieneu-tts-v3-turbo": 50, "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "ming-3-0-tts-tiny": 60, "name": "Agent interface", "vieneu-tts-v3-turbo": 60, "weight": 10 }, { "by": 14, "edge": "vieneu-tts-v3-turbo", "key": "upkeep", "ming-3-0-tts-tiny": 44, "name": "Documentation and upkeep", "vieneu-tts-v3-turbo": 58, "weight": 10 } ], "summary": "VieNeu-TTS v3 Turbo scores 58.5 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 2 of 4 scored categories. Ming-3.0-tts-tiny leads on risk. 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.", "vieneu-tts-v3-turbo": "A tiny, fast speech cousin for Vietnamese and English, with regional preset voices, cloning from a short clip and an OpenAI-compatible streaming endpoint, under Apache 2.0. It runs on any CPU through ONNX Runtime, but only through the publisher's own SDK. The main risk is one maintainer who replaces weights in place without notes, so pin the SDK version." } }, "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-vieneu-tts-v3-turbo", "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-vieneu-tts-v3-turbo.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-vieneu-tts-v3-turbo.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-vieneu-tts-v3-turbo.min.md" }, "markdown": "VieNeu-TTS v3 Turbo scores 58.5 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 2 of 4 scored categories. Ming-3.0-tts-tiny leads on risk. 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- VieNeu-TTS v3 Turbo: grade C, 58.5/100, rank #66 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/vieneu-tts-v3-turbo.md · JSON https://www.anchorterminal.com/api/v1/tools/vieneu-tts-v3-turbo.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- Risk, 55 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### VieNeu-TTS v3 Turbo (C)\n\nGood for: Agents that need Vietnamese or Vietnamese and English speech on a CPU or small GPU, streamed through an OpenAI-style endpoint\n\nAhead on:\n- Reach, 65 against 50\n- Documentation and upkeep, 58 against 44\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5\n\nWatch for: Only Vietnamese and English\n\n\n## Score by category\n\n| Category | Weight | Ming-3.0-tts-tiny | VieNeu-TTS v3 Turbo | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 65 | VieNeu-TTS v3 Turbo +15 |\n| Risk | 20% (20 this run) | 55 | 50 | Ming-3.0-tts-tiny +5 |\n| Agent interface | 10% (10 this run) | 60 | 60 | even |\n| Documentation and upkeep | 10% (10 this run) | 44 | 58 | VieNeu-TTS v3 Turbo +14 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **52.2 · D** | **58.5 · C** | |\n\n## Facts side by side\n\n| Fact | Ming-3.0-tts-tiny | VieNeu-TTS v3 Turbo |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | pnnbao-ump (VieNeu, Phạm Nguyễn Ngọc Bảo) |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-09-23 |\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) | 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**VieNeu-TTS v3 Turbo.** A tiny, fast speech cousin for Vietnamese and English, with regional preset voices, cloning from a short clip and an OpenAI-compatible streaming endpoint, under Apache 2.0. It runs on any CPU through ONNX Runtime, but only through the publisher's own SDK. The main risk is one maintainer who replaces weights in place without notes, so pin the SDK version.\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### VieNeu-TTS v3 Turbo\n\n1. On CPU, `pip install vieneu`, then `from vieneu import Vieneu; tts = Vieneu(); tts.save(tts.infer(text, voice='Mai Anh'), 'out.wav')`; `Vieneu(precision='int8')` is about twice as fast on a CPU with VNNI\n2. On GPU, install torch 2.8 for CUDA 12.8 and transformers 4.57.6 before vieneu; it switches to PyTorch with batching and stream scheduling\n3. Serve with `uv run python -m apps.openai_speech` from a clone of pnnbao97/VieNeu-TTS, then POST to http://localhost:8000/v1/audio/speech with model vieneu-v3-turbo, voice and response_format pcm or wav\n4. Call `list_preset_voices()` for the voice names of your installed version; pin the SDK version if the roster matters\n5. Clone with `ref_audio='voice.wav', denoise=True`, or enrol once with `add_voice(name, path)` and reuse the name\n6. Keep temperature near 0.8; the style argument is ignored on v3 Turbo\n7. 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 VieNeu-TTS v3 Turbo?\n\nVieNeu-TTS v3 Turbo scores 58.5 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 2 of 4 scored categories. Ming-3.0-tts-tiny leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-vieneu-tts-v3-turbo.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-vieneu-tts-v3-turbo.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ming-3-0-tts-tiny\", \"b\": \"vieneu-tts-v3-turbo\"}`. From a terminal: `anchor compare ming-3-0-tts-tiny vieneu-tts-v3-turbo`\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/vieneu-tts-v3-turbo.json\n\n## Other comparisons with Ming-3.0-tts-tiny or VieNeu-TTS v3 Turbo\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 VieNeu-TTS v3 Turbo](https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-vieneu-tts-v3-turbo.md)\n- [Ming-3.0-tts-tiny vs OmniVoice](https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-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- [OmniVoice vs VieNeu-TTS v3 Turbo](https://www.anchorterminal.com/compare/omnivoice-vs-vieneu-tts-v3-turbo.md)\n- [Qwen3-TTS 12Hz 1.7B CustomVoice vs VieNeu-TTS v3 Turbo](https://www.anchorterminal.com/compare/qwen3-tts-1-7b-customvoice-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 VieNeu-TTS v3 Turbo", "url": "" } ], "description": "VieNeu-TTS v3 Turbo scores 58.5 (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", "VieNeu-TTS v3 Turbo C 58.5", "scores" ], "h1": "Ming-3.0-tts-tiny vs VieNeu-TTS v3 Turbo", "image": "https://www.anchorterminal.com/assets/og/compare-ming-3-0-tts-tiny-vs-vieneu-tts-v3-turbo.png", "path": "/compare/ming-3-0-tts-tiny-vs-vieneu-tts-v3-turbo", "published": "2026-10-01", "section": "tools", "title": "Ming-3.0-tts-tiny vs VieNeu-TTS v3 Turbo for AI agents (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-vieneu-tts-v3-turbo" }, "tokens": { "markdown": 2100, "slim": 480 }, "version": 1 }