--- title: "OmniVoice MLX" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/24cf0ac2-b1c8-4df5-ad3b-e18a10a2037e" updated: "2026-11-12" --- # OmniVoice MLX > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. OmniVoice MLX is a kit in the Productivity 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 mlx-community ## 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. ```sh 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. ```sh 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh 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. ```sh 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. ```flow 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. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [mlx-community/OmniVoice on Hugging Face](https://huggingface.co/spaces/mlx-community/OmniVoice) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | OmniVoice MLX (this page) | 6 | gradio | apache-2.0 | 2026-05-30 | | [MusicGen](https://mindket.com/p/07af2531-69de-4005-a4f6-8c16dd10048d) | 5,116 | gradio | cc-by-nc-4.0 | 2026-05-27 | | [Kokoro TTS](https://mindket.com/p/aa996ded-5768-48f4-b403-33520c1b1bdf) | 3,556 | gradio | apache-2.0 | 2025-04-09 | | [Background Removal](https://mindket.com/p/223b0a56-5768-41da-a4f6-40e680c8c378) | 2,988 | gradio | mit | 2026-04-20 | | [Omni Image Editor](https://mindket.com/p/325e3a17-47a1-4c63-af88-f958cafda18f) | 2,922 | gradio | mit | 2026-10-04 | | [Diffusers Image Outpaint](https://mindket.com/p/17629f6c-cc47-4be0-bc83-ff22dfa7389a) | 2,565 | gradio | apache-2.0 | 2026-05-13 | Numbers as Mindket last read them, on 2026-11-12. ## 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: 2026-05-30 - First published: 2026-05-15 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open mlx-community/OmniVoice on Hugging Face](https://huggingface.co/spaces/mlx-community/OmniVoice) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/24cf0ac2-b1c8-4df5-ad3b-e18a10a2037e) - [Its page on Mindket](https://mindket.com/p/24cf0ac2-b1c8-4df5-ad3b-e18a10a2037e)