Ming 3.0 TTS 0.5B GGUF

Ming 3.0 TTS 0.5B GGUF

GGUF packages of Ming 3.0 TTS 0.5B for the audio.cpp runtime: speech from text, voice cloning and voice design in seven languages, with mono 44.1 kHz output and no Python runtime. For developers.

Made by audio-cpp 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 TTS 0.5B is a text-to-speech model, and this repository offers it as GGUF files built for the audio.cpp runtime. Each package bundles everything the model needs, including a Qwen-based backbone, a flow-matching head, an audio autoencoder and a speaker encoder. Output is mono at 44.1 kHz, and no separate codec or Python runtime is needed.

Who it suits: It suits developers who want speech synthesis from a command line, in a small package, in Chinese, English, Japanese, Korean, German, French or Spanish.

Industry | Who uses it | How they use it | Example
Software | Application developers | Call the command line tool to add spoken output to an app | Reading app messages aloud
Media | Content creators | Turn scripts into narration with a designed voice | Narrating a short script
Localization | Language teams | Speak the same text in several of the supported languages | Producing French and Spanish versions
Research | Speech researchers | Compare the runtime's speed and memory use with the Python code | Checking real-time factor on their own GPU

If you want a ready-made app with buttons, you can skip it, since this is a command line tool and a model file.

Where to use it: You run it from a terminal with the audio.cpp command line program.

• A terminal on Windows, macOS or Linux with the audiocpp_cli program
• A machine with a CUDA GPU, since the card's examples use that backend
• A Python install, only for the Hugging Face download command

Use case | Who | What they get back
Plain text to speech | Developers | A wav file of the text spoken
Voice cloning from a reference clip | Creators | A wav file in the reference voice
Voice design from a description | Creators | A wav file in a voice you describe in words

How to set it up: You need the audio.cpp program, which comes from its GitHub project, and one of the two model files. The card gives no build steps for audio.cpp.

• The audiocpp_cli program from audio.cpp
• A model file, either the BF16 package or the Q8_0 package
• A CUDA GPU, for the commands shown below

1. Get audio.cpp and build or install audiocpp_cli by its own instructions.
2. Download this repository's model file.
3. Run audiocpp_cli with the ming3_tts family and your chosen model file.

pip install -U huggingface_hub
hf download audio-cpp/Ming-3.0-TTS-0.5B-GGUF

How to use it: You run audiocpp_cli with a task name, the model file, a language option and your text. Three tasks are shown, and each writes a wav file.

Tool | What it does
tts task | Speaks the text you give it
clon task | Speaks the text in the voice of a reference wav file
vdes task | Speaks the text in a voice you describe in an instruct option
reference_text option | Adds the words spoken in the reference when cloning

audiocpp_cli --family ming3_tts --task tts --backend cuda \
  --model ming-3.0-tts-0.5b-bf16.gguf \
  --request-option language=en \
  --text "Good evening. The harbor is quiet tonight." --out speech.wav --log

audiocpp_cli --family ming3_tts --task clon --backend cuda \
  --model ming-3.0-tts-0.5b-bf16.gguf --voice-ref reference.wav \
  --request-option language=en \
  --text "Please leave the lamp beside the window." --out clone.wav --log

audiocpp_cli --family ming3_tts --task vdes --backend cuda \
  --model ming-3.0-tts-0.5b-bf16.gguf \
  --request-option language=en \
  --request-option instruct="A calm adult man with a clear, warm voice." \
  --text "Good evening. The harbor is quiet tonight." --out designed.wav --log

Cloning without a transcript uses speaker conditioning. To include the reference's acoustic prefix as well, also pass the reference_text option with the words spoken in the clip.

Example: You run the tts task with language set to en and the text of a short delivery notice. The program writes speech.wav, which holds that notice read aloud in the model's default voice.

For newcomers and experts: Text to speech turns written words into audio. Start with the plain tts command and one sentence, play the speech.wav it writes, then try the vdes task with a short voice description.

For experts, the whole stack runs in C++ through audio.cpp, with no codec or Python runtime. The card's tests on an RTX 5090 give a warm real-time factor near 0.17 for audio.cpp against about 0.65 to 0.68 for the official Python code. The speed setting steers the model as a category it was conditioned on, and does not stretch the finished audio.

The text goes in with a language option
The Qwen backbone and flow-matching head run
The audio autoencoder builds the waveform
A speaker encoder shapes the voice
A mono 44.1 kHz wav file is written

Good to know: The speed figures are synthesis speed measurements, not a claim that the sampled audio matches the Python output. Options let you steer the voice, the emotion, the dialect and how it speaks, and the project guide lists the values.

Title: Peak GPU memory in the card's test
Official Python, BF16: 3660 MiB
audio.cpp, BF16: 2768 MiB
audio.cpp, Q8_0: 1924 MiB

• License: MIT, and the weights keep their upstream MIT license
• Cost to run: none named
• Data it reaches: the text and any reference wav you give it, plus the model files you download
• Not for: cloning a voice you have no right to use, so use only voices you are allowed to clone

On Hugging Face, 13 November 2026

• Task: text to speech
• Formats: GGUF
• Downloads in the last 30 days: 70,871
• Likes: 7
• License: mit
• Languages: zh, en, ja, ko, de, fr, es
• Based on: inclusionAI/Ming-3.0-tts-0.5B
• Tags: gguf, audio.cpp, text-to-speech

Links

• Model: https://huggingface.co/audio-cpp/Ming-3.0-TTS-0.5B-GGUF
• Files: https://huggingface.co/audio-cpp/Ming-3.0-TTS-0.5B-GGUF/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download audio-cpp/Ming-3.0-TTS-0.5B-GGUF

Then follow the usage in its model card. Nothing in this kit runs by itself.