OmniVoice MLX

OmniVoice MLX

Speaks text with the OmniVoice text-to-speech model through the community MLX backend, for people who want to try it or run it on an Apple silicon Mac.

Made by mlx-community on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: OmniVoice MLX is a text-to-speech demo that runs the OmniVoice model through the community MLX backend. It adapts the official OmniVoice Space to load that backend. The default model is the 4-bit version, chosen to ease memory use. On Hugging Face it runs on a Linux CPU, so it can be slow.

Who it suits: People who want to try OmniVoice speech, and Apple silicon Mac owners who plan to run it locally.

Industry | Who uses it | How they use it | Example
Speech technology | Developers | Try OmniVoice through the MLX backend | Checking how the 4-bit model sounds
Content creation | Creators who need voice-overs | Turn a script into speech | Hearing a short script read aloud
Research | People who compare model sizes | Switch between the listed MLX model versions | Comparing the 4-bit and 8-bit models

If you want fast, interactive speech, run the model on an Apple silicon Mac instead, because the hosted demo can be slow.

Where to use it:

• A web browser, on the Space's page at Hugging Face
• An Apple silicon Mac, where MLX performs best
• Python on Windows, macOS or Linux, for a copy you run yourself

Use case | Who | What they get back
Hear text spoken | Anyone | Generated speech from the text you type
Try another model size | Developers | Output from a different MLX model version
Run it locally | Mac owners | The same demo on their own hardware

How to set it up: Online, nothing needs installing. For a copy on your own computer you need a few things first.

• Python
• The pip tool
• The packages listed in requirements.txt

1. Install the Hugging Face hub package.
2. Download the Space's files into a folder.
3. Move into that folder.
4. Install Gradio at the pinned version.
5. Install the packages from requirements.txt.
6. Start app.py.

pip install -U huggingface_hub
hf download mlx-community/OmniVoice --repo-type space --local-dir OmniVoice
cd OmniVoice
pip install gradio==6.14.0
pip install -r requirements.txt
python app.py

To load a different MLX model, set the OMNIVOICE_MODEL setting to another MLX repository before you start the app.

How to use it: Open the Space's page, or start app.py from your own copy, type the text you want spoken and run it. The source documents no other controls, so the labels on the page are your guide.

python app.py

Example: You open the page, type a short sentence and run it. After a wait, which can be long on the shared CPU, the page returns speech for that sentence. The source shows no sample output, so the voice and quality are not described here.

For newcomers and experts: MLX is Apple's machine learning framework, and it works best on Apple silicon. To start, open the page, type one short sentence and be patient while it runs.

The Space names five model versions: the default, 4-bit, 8-bit, bfloat16 and fp32. The 4-bit one is the default to reduce memory pressure. On Linux the demo uses the MLX CPU backend, which is why it can be slow. For regular use, the author advises installing the model on an Apple silicon Mac.

Open the page in a browser
Type the text to speak
MLX CPU backend runs the 4-bit model
Speech comes back

Good to know:

• Limits: the hosted demo uses a Linux CPU backend and can be slow
• License: Apache 2.0
• Cost to run: none named
• Data it reaches: the text you type, and the MLX model versions named in its metadata
• Not for: fast interactive use on the hosted page, since the author points to a local Apple silicon Mac for that

On Hugging Face, 13 November 2026

• Likes: 6
• Built with: gradio
• License: apache-2.0

Links

• Space: https://huggingface.co/spaces/mlx-community/OmniVoice
• Files: https://huggingface.co/spaces/mlx-community/OmniVoice/tree/main

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.