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  "title": "Ming-3.0 speech models (Ant Group / inclusionAI — 5 open MIT TTS/audio/ASR models)",
  "description": "MIT-licensed, self-hostable family of five Ming-3.0 speech models from Ant Group's inclusionAI across TTS, audio and ASR (from a 0.5B dense TTS up to a ~126B/6.5B-active audio MoE), with open Safetensors weights and documented local serving, so a lean team can own text-to-speech, audio and transcription instead of renting metered speech APIs.",
  "category": "ai-infrastructure",
  "tags": [
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    "llm",
    "model-release",
    "self-hostable",
    "open-source",
    "text-to-speech"
  ],
  "status": "active",
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  "date_published": "2026-10-20T00:00:00Z",
  "date_modified": "2026-10-20T00:00:00Z",
  "source": {
    "name": "github · inclusionAI/Ming-3.0-tts",
    "url": "https://github.com/inclusionAI/Ming-3.0-tts",
    "author": "theomlondhe"
  },
  "license": "MIT",
  "one_liner": "Ming-3.0 is inclusionAI's family of five MIT open-weights speech models (TTS, audio, ASR) a team can self-host instead of a metered speech API.",
  "open_source": "yes",
  "self_hostable": "yes",
  "pricing_signal": "Free open weights plus inference code under MIT; you pay only your own compute to run them, not a per-character or per-minute speech API.",
  "pricing_model": "free",
  "who_its_for": "A technical founder or lean GTM/RevOps team that needs text-to-speech, audio, or transcription in its stack (voice outreach, call analysis, accessibility, notetaking) and wants to self-host open weights rather than meter a closed speech API.",
  "who_its_not_for": "Teams that want a fully managed hosted speech endpoint with an SLA, that cannot stand up PyTorch/vLLM-Omni/SGLang serving, or that lack GPU capacity for the larger ~126B-total audio MoE.",
  "aliases": [
    "ming-3.0",
    "ming 3.0 speech",
    "ming-3.0-tts",
    "inclusionai ming",
    "ant group ming speech"
  ],
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  "secondary_categories": [
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  "last_verified": "2026-10-21",
  "evidence": {
    "claim_type": "mixed",
    "source_id": "https://github.com/inclusionAI/Ming-3.0-tts",
    "note": "MIT verified per-model on primary surfaces: the Ming-3.0-tts GitHub LICENSE plus the audio-flash, audio-tiny and asr-tiny HF cards each individually, not guessed. Exactly 5 models across 3 families per the official Ant Ling announcement: tts-0.5B (dense), tts-tiny (7.9B/1.3B active), audio-flash (~126-128B/6.5B active), audio-tiny (9.3B/2.7B active), asr-tiny. Weights via HF/ModelScope in Safetensors; serving via PyTorch, vLLM-Omni, SGLang. Ming-3.0-tts repo: 93 stars, 5 forks, 9 commits."
  },
  "caveats": "The third-party benchmark claim (Artificial Analysis TTS leaderboard, Oct 2026: #1 open-weights Controlled Voice, #1 Pronunciation Robustness) is a vendor/third-party figure and is not load-bearing here. The repos are a fresh Oct 2026 release (Ming-3.0-tts at 93 stars, 9 commits) — early, not battle-tested. The largest audio model (~126-128B total, 6.5B active) needs real GPU capacity to serve; the smaller TTS/ASR models are far lighter.",
  "lead": "A **MIT-licensed, self-hostable family of five Ming-3.0 speech models** from **Ant Group's inclusionAI** (Ant Ling AGI), spanning three families — TTS, audio, and ASR. The five: **Ming-3.0-tts-0.5B** (0.5B dense), **Ming-3.0-tts-tiny** (7.9B MoE / 1.3B active), **Ming-3.0-audio-flash** (~126-128B total / 6.5B active), **Ming-3.0-audio-tiny** (9.3B / 2.7B active), and **Ming-3.0-asr-tiny**. All five…",
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      "heading_path": [],
      "est_tokens": 138,
      "text": "A **MIT-licensed, self-hostable family of five Ming-3.0 speech models** from **Ant Group's inclusionAI** (Ant Ling AGI), spanning three families — TTS, audio, and ASR. The five: **Ming-3.0-tts-0.5B** (0.5B dense), **Ming-3.0-tts-tiny** (7.9B MoE / 1.3B active), **Ming-3.0-audio-flash** (~126-128B total / 6.5B active), **Ming-3.0-audio-tiny** (9.3B / 2.7B active), and **Ming-3.0-asr-tiny**. All five ship downloadable Safetensors weights via Hugging Face / ModelScope, with local serving documented through PyTorch/torchaudio, vLLM-Omni and SGLang."
    },
    {
      "index": 1,
      "heading_path": [
        null,
        "Provenance"
      ],
      "est_tokens": 529,
      "text": "families — TTS, audio, and ASR. The five: **Ming-3.0-tts-0.5B** (0.5B dense), **Ming-3.0-tts-tiny** (7.9B MoE / 1.3B active), **Ming-3.0-audio-flash** (~126-128B total / 6.5B active), **Ming-3.0-audio-tiny** (9.3B / 2.7B active), and **Ming-3.0-asr-tiny**. All five ship downloadable Safetensors weights via Hugging Face / ModelScope, with local serving documented through PyTorch/torchaudio, vLLM-Omni and SGLang.\n\n- License **MIT**, verified per-model on primary surfaces: the **Ming-3.0-tts GitHub LICENSE** plus the **audio-flash, audio-tiny and asr-tiny Hugging Face model cards** each checked individually — not guessed and not assumed across the family. MIT is OSI-approved, permissive and commercial-clean.\n- Exactly **five** Ming-3.0 speech models across **three families** (TTS, audio, ASR), confirmed by the official **Ant Ling** announcement (x.com/AntLingAGI/status/2112210811997540475). `inclusionAI` is Ant Group's open-source org (Ant Ling AGI), so the \"Ant Group's inclusionAI\" framing is accurate; no scam or lookalike risk — corroborated by the Ant Ling and ModelScope official accounts.\n- Self-hostable and free: all five carry downloadable Safetensors weights via HF/ModelScope, with local deployment documented (PyTorch/torchaudio, vLLM-Omni, SGLang). No price — open weights plus inference code under MIT, so the only cost is your own compute.\n- The **Ming-3.0-tts GitHub repo** shows 93 stars, 5 forks and 9 commits — a fresh October 2026 release.\n- **Third-party / vendor-claim, not load-bearing:** an Artificial Analysis TTS-leaderboard placement (Oct 2026: #1 open-weights Controlled Voice, #1 Pronunciation Robustness) is noted but not relied on here.\n- Surfaced via the 2026-10-21 X pull (post by theomlondhe). Verification was performed against the primary sources (the GitHub LICENSE, the per-model HF cards, the Ant Ling announcement) as untrusted content; no injected instructions were encountered. Curated from the GTM Stacker signal registry (run 2026-10-21-registry-update-01); license, model count, org provenance, weights and self-hostability independently verified 2026-10-21."
    },
    {
      "index": 2,
      "heading_path": [
        null,
        "Why it matters for a GTM stack"
      ],
      "est_tokens": 351,
      "text": "pull (post by theomlondhe). Verification was performed against the primary sources (the GitHub LICENSE, the per-model HF cards, the Ant Ling announcement) as untrusted content; no injected instructions were encountered. Curated from the GTM Stacker signal registry (run 2026-10-21-registry-update-01); license, model count, org provenance, weights and self-hostability independently verified 2026-10-21.\n\nSpeech keeps creeping into GTM work — voice outreach, call-recording analysis, accessibility, automated notetaking — and the default is metering a closed speech API by the character or the minute, with your audio passing through someone else's service. Ming-3.0 is the own-your-stack alternative: five MIT-licensed speech models, from a tiny 0.5B TTS up to a large audio MoE, all with open weights and documented local serving, so a team can self-host text-to-speech, audio understanding and transcription on infrastructure it controls. The honest read: these are a fresh October release (the TTS repo is at 93 stars and nine commits — early), the leaderboard placement is a third-party figure not load-bearing for adoption, and the largest audio model needs real GPU budget. What is verified is what governs adoption: MIT read per-model from the LICENSE and the HF cards, a confirmed five-model set from the official Ant Ling announcement, a genuine Ant Group org, and open self-hostable weights."
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