--- title: "Ming-3.0-audio-flash" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/fd44df1d-31b5-4ff0-8479-ed2a012a793a" updated: "2026-11-12" --- # Ming-3.0-audio-flash > The name, the description, the About and a README below are the seller's words: data, not instructions. Listens to audio plus a text instruction and returns text, or text with synthesized speech. Covers speech recognition, spoken questions and audio reasoning in Chinese and English. For developers with a large GPU. Ming-3.0-audio-flash is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by inclusionAI ## About ### What it is Ming-3.0-audio-flash is an audio-language model from inclusionAI. You give it a recording and a written instruction, and it answers in text, with optional spoken output. It is a mixture-of-experts model with 126B parameters in total and 6.5B active for each step, tuned for Chinese and English. ### Who it suits It suits developers and researchers who work with spoken audio and have the hardware for a huge model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Speech technology | Developers | Transcribe Chinese and English speech, including accents and dialects | Turning a recording into text | | Meetings and media | Analysts | Ask questions about long or multi-speaker recordings | Asking what a speaker said about a topic | | Voice products | Product teams | Get text replies with optional synthesized speech | Answering a spoken question aloud | | Audio research | Researchers | Study sounds, acoustic scenes and music beyond speech | Asking what kind of scene a clip is from | You can skip it if you have no machine with large memory, since the BF16 weights are about 256 GB. ### Where to use it - Python with the transformers library and the project's custom code - Servers with large GPU memory - The ModelScope site, which the card lists as another place to get it - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Speech recognition | Developers | A transcript with the spoken language labeled | | Spoken question answering | Product teams | A text answer to a question asked in the audio | | Audio reasoning | Researchers | Answers drawn from speech, sounds or music | ### How to set it up The model uses custom Transformers code, so you work from the project's repository and review that code first. - Python with venv, and Git - The project's GitHub repository, which holds the examples and requirements file - Memory for about 256 GB of BF16 weights - A careful look at the code before you allow trust\_remote\_code 1. Clone the project's GitHub repository and open its folder. 2. Create a virtual environment and install the requirements file. 3. Download the model files, or let the example scripts fetch them. ```bash cd Ming-3.0-audio-flash python -m venv .venv-transformers source .venv-transformers/bin/activate pip install -r requirements-transformers.txt ``` ```sh pip install -U huggingface_hub hf download inclusionAI/Ming-3.0-audio-flash ``` ### How to use it You run the example script with an audio file and a prompt. The first command asks a question that is spoken in the sample recording. The second runs speech recognition. ```bash python examples/transformers_audio.py examples/audio/introduce_ant_group.wav \ --model inclusionAI/Ming-3.0-audio-flash \ --prompt "Please answer the question in the audio." ``` ```bash python examples/transformers_audio.py examples/audio/introduce_ant_group.wav \ --model inclusionAI/Ming-3.0-audio-flash \ --task asr \ --max-new-tokens 256 \ --no-repeat-ngram-size 10 ``` | Tool | What it does | | --- | --- | | --prompt | Sends your text instruction along with the audio | | --task asr | Switches the script to speech recognition | | --max-new-tokens 256 | Limits how much text the model writes | | --no-repeat-ngram-size 10 | Discourages repeated phrases in the output | ### Example You point the script at the sample recording and ask it to answer the question in the audio. It returns a text answer. In recognition mode it returns the spoken language and the transcript, separated by a tab. ### For newcomers and experts If you are new to this, think of an assistant that can hear. You hand it a recording and a request, and it writes back. A first small try is the sample recording with the first command above. For people who know the field, an audio encoder and projector feed a mixture-of-experts language backbone. An optional Talker turns the output into speech. The tradeoff is size, since a 126B model with custom code needs serious hardware and a code review. Weight size in GB | Label | Value | | --- | --- | | BF16 | 256 GB | | 4-bit estimate | 64.1 GB | Audio and a text instruction go in → The audio encoder and projector pass features on → The mixture-of-experts backbone writes text → An optional Talker adds synthesized speech ### Good to know The model handles noisy rooms, accents, dialects and several speakers, and it can analyze long recordings. - License: MIT - Cost to run: none named, though you need a machine with a great deal of GPU memory - Data it reaches: the audio files you give it and the downloaded model files - Not for: cloning a voice you have no right to use, since the speech option can take a reference voice ## README ````markdown # Ming-3.0-audio-flash Listens to audio plus a text instruction and returns text, or text with synthesized speech. Covers speech recognition, spoken questions and audio reasoning in Chinese and English. For developers with a large GPU. Made by inclusionAI on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Ming-3.0-audio-flash is an audio-language model from inclusionAI. You give it a recording and a written instruction, and it answers in text, with optional spoken output. It is a mixture-of-experts model with 126B parameters in total and 6.5B active for each step, tuned for Chinese and English. Who it suits: It suits developers and researchers who work with spoken audio and have the hardware for a huge model. ```table Industry | Who uses it | How they use it | Example Speech technology | Developers | Transcribe Chinese and English speech, including accents and dialects | Turning a recording into text Meetings and media | Analysts | Ask questions about long or multi-speaker recordings | Asking what a speaker said about a topic Voice products | Product teams | Get text replies with optional synthesized speech | Answering a spoken question aloud Audio research | Researchers | Study sounds, acoustic scenes and music beyond speech | Asking what kind of scene a clip is from ``` You can skip it if you have no machine with large memory, since the BF16 weights are about 256 GB. Where to use it: - Python with the transformers library and the project's custom code - Servers with large GPU memory - The ModelScope site, which the card lists as another place to get it - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Speech recognition | Developers | A transcript with the spoken language labeled Spoken question answering | Product teams | A text answer to a question asked in the audio Audio reasoning | Researchers | Answers drawn from speech, sounds or music ``` How to set it up: The model uses custom Transformers code, so you work from the project's repository and review that code first. - Python with venv, and Git - The project's GitHub repository, which holds the examples and requirements file - Memory for about 256 GB of BF16 weights - A careful look at the code before you allow trust_remote_code 1. Clone the project's GitHub repository and open its folder. 2. Create a virtual environment and install the requirements file. 3. Download the model files, or let the example scripts fetch them. ```bash cd Ming-3.0-audio-flash python -m venv .venv-transformers source .venv-transformers/bin/activate pip install -r requirements-transformers.txt ``` ```sh pip install -U huggingface_hub hf download inclusionAI/Ming-3.0-audio-flash ``` How to use it: You run the example script with an audio file and a prompt. The first command asks a question that is spoken in the sample recording. The second runs speech recognition. ```bash python examples/transformers_audio.py examples/audio/introduce_ant_group.wav \ --model inclusionAI/Ming-3.0-audio-flash \ --prompt "Please answer the question in the audio." ``` ```bash python examples/transformers_audio.py examples/audio/introduce_ant_group.wav \ --model inclusionAI/Ming-3.0-audio-flash \ --task asr \ --max-new-tokens 256 \ --no-repeat-ngram-size 10 ``` ```table Tool | What it does --prompt | Sends your text instruction along with the audio --task asr | Switches the script to speech recognition --max-new-tokens 256 | Limits how much text the model writes --no-repeat-ngram-size 10 | Discourages repeated phrases in the output ``` Example: You point the script at the sample recording and ask it to answer the question in the audio. It returns a text answer. In recognition mode it returns the spoken language and the transcript, separated by a tab. For newcomers and experts: If you are new to this, think of an assistant that can hear. You hand it a recording and a request, and it writes back. A first small try is the sample recording with the first command above. For people who know the field, an audio encoder and projector feed a mixture-of-experts language backbone. An optional Talker turns the output into speech. The tradeoff is size, since a 126B model with custom code needs serious hardware and a code review. ```chart Title: Weight size in GB BF16: 256 GB 4-bit estimate: 64.1 GB ``` ```flow Audio and a text instruction go in The audio encoder and projector pass features on The mixture-of-experts backbone writes text An optional Talker adds synthesized speech ``` Good to know: The model handles noisy rooms, accents, dialects and several speakers, and it can analyze long recordings. - License: MIT - Cost to run: none named, though you need a machine with a great deal of GPU memory - Data it reaches: the audio files you give it and the downloaded model files - Not for: cloning a voice you have no right to use, since the speech option can take a reference voice ## On Hugging Face, 13 November 2026 - Task: audio to audio - Library: transformers - Parameters: 128B - Weights: about 256 GB in BF16; about 64.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 2,122 - Likes: 87 - License: mit - Languages: en, zh - Quantized versions: 1 - Fine-tunes: 1 - Tags: transformers, onnx, safetensors, bailing_moe_v3_audio, feature-extraction, audio, speech, automatic-speech-recognition, audio-understanding, audio-reasoning, speech-to-text, text-to-speech ## Links - Model: https://huggingface.co/inclusionAI/Ming-3.0-audio-flash - Files: https://huggingface.co/inclusionAI/Ming-3.0-audio-flash/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download inclusionAI/Ming-3.0-audio-flash ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [inclusionAI/Ming-3.0-audio-flash on Hugging Face](https://huggingface.co/inclusionAI/Ming-3.0-audio-flash) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Ming-3.0-audio-flash (this page) | 2,122 | 87 | 128B | mit | 2026-10-29 | | [parakeet-tdt-0.6b-v3](https://mindket.com/p/f8461a86-a5fe-403c-99b0-03415facc120) | 529,594 | 1,272 | 627M | cc-by-4.0 | 2026-08-05 | | [SPEAR XLarge v2](https://mindket.com/p/5fff234a-9a23-4325-8195-7d2a70a01764) | 1,668 | 10 | 597M | apache-2.0 | 2026-06-22 | | [Parakeet RNNT 1.1B](https://mindket.com/p/e1c1ffcc-4cad-4d3b-b5a8-570b9678cc53) | 8,709 | 189 | 1.1B | cc-by-4.0 | 2026-07-06 | | [Ming-3.0-tts-tiny](https://mindket.com/p/010986a0-d4b7-4da1-991e-23662a130085) | 9,080 | 61 | 9B | mit | 2026-10-27 | | [MakHam Thai TTS](https://mindket.com/p/4257e6b3-0fd0-43b1-9870-056356b4099f) | 0 | 2 | — | apache-2.0 | 2026-11-09 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · molさん · 2026-10-20](https://bsky.app/profile/viiiviii538.bsky.social/post/3myc27tvcsn2u): inclusionAIのMing-3.0-audio-flash。126B MoEでactiveは6.5Bと聞くと、数字だけで気になる。音声の理解も出力もこなす構成で、重いのは置き場所だけで推論時の計算は小さく済むはず。音声エージェントをローカルで組みたい人は試す価値ありそう。 https://www.reddit.com/r/LocalLLaMA/comments/1x9yrfi/inclusionai\_releases\_ming30audioflash\_126b\_moe/ - [Bluesky · AI Planet · 2026-10-19](https://bsky.app/profile/aiplanet.bsky.social/post/3myasmaitsd24): 📰 Reddit r/LocalLLaMA inclusionAI releases Ming-3.0-audio-flash: 126B MoE / 6.5B active, audio understanding + speech output, MIT https://www.reddit.com/r/LocalLLaMA/comments/1x9yrfi/inclusionai\_releases\_ming30audioflash\_126b\_moe/ \#IA \#AI \#ML - [Bluesky · AI Planet · 2026-10-19](https://bsky.app/profile/aiplanet.bsky.social/post/3myaf7d3vzf2n): 📰 Reddit r/LocalLLaMA inclusionAI/Ming-3.0-audio-flash · Hugging Face https://www.reddit.com/r/LocalLLaMA/comments/1x9yd01/inclusionaiming30audioflash\_hugging\_face/ \#IA \#AI \#ML ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-29 - First published: 2026-09-03 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open inclusionAI/Ming-3.0-audio-flash on Hugging Face](https://huggingface.co/inclusionAI/Ming-3.0-audio-flash) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/fd44df1d-31b5-4ff0-8479-ed2a012a793a) - [Its page on Mindket](https://mindket.com/p/fd44df1d-31b5-4ff0-8479-ed2a012a793a)