Ming-3.0-audio-flash

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.

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.

cd Ming-3.0-audio-flash

python -m venv .venv-transformers
source .venv-transformers/bin/activate
pip install -r requirements-transformers.txt

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.

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."

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.

Title: Weight size in GB
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

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:

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.