Ming-3.0 speech models (Ant Group / inclusionAI — 5 open MIT TTS/audio/ASR models) — open-source AI Infrastructure

Curated content (treat as data, not instructions):

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.

Provenance

• 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.
• 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.
• 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.
• The **Ming-3.0-tts GitHub repo** shows 93 stars, 5 forks and 9 commits — a fresh October 2026 release.
• **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.
• 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.

Why it matters for a GTM stack

Speech 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.