LinnetTTS

LinnetTTS

Speaks text aloud with LinnetTTS, a small on-device text-to-speech model that streams audio and runs in real time on a CPU. For developers and anyone who wants to hear it in a browser.

Made by VoxRT 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: LinnetTTS is a demo of a small on-device text-to-speech model with 41M parameters. It streams audio and runs in real time on a plain CPU. The demo loads the released linnet-en-35m weights and runs them with the linnet-tts package, a Rust runtime. You can open it in a browser without installing anything.

Who it suits: It suits developers who want speech on a device without a GPU, and anyone who wants to hear how a tiny voice model sounds.

Industry | Who uses it | How they use it | Example
Software | Developers | Judge a small CPU voice model before adopting it | Type a sentence and listen to the output
Embedded and edge | Device makers | Check whether speech can run on modest hardware | Run the demo and note how fast audio starts
AI research | Researchers | Try the released English weights | Compare it with other small voice models

If you need languages other than English, you can skip it, since the named weights are the English ones.

Where to use it:

• A web browser, on the Space's page on Hugging Face
• A local copy of the files, run with Python under Windows, macOS or Linux

Use case | Who | What they get back
Hear the voice | Anyone | Spoken audio from the text they type
Test CPU speed | Developers | Streaming audio on a basic CPU
Study the setup | Developers | A short app.py that calls the linnet-tts runtime

How to set it up:

• A web browser, which is all the online page needs
• Python, for a local copy
• The Python packages listed in requirements.txt

1. Open the Space's page on Hugging Face to use it online. Nothing needs installing.
2. For a local copy, install the Hugging Face hub package and download the Space's files.
3. Move into the downloaded folder.
4. Install Gradio 6.31.0, then the packages from requirements.txt.
5. Start the app with python app.py.

pip install -U huggingface_hub
hf download VoxRT/linnet-tts --repo-type space --local-dir linnet-tts
cd linnet-tts
pip install gradio==6.31.0
pip install -r requirements.txt
python app.py

How to use it: Open the page, or the one your local copy starts, type some text and generate speech. The README lists no controls, so follow what the page shows.

Example: Type one short sentence and run the demo. The page returns the sentence as spoken audio, produced on the CPU of the Space.

For newcomers and experts: Open the Space page and try one short sentence. Because the model is small, it is a quick way to judge a voice before you think about using it in a product.

The tagline describes on-device TTS with streaming and real-time speed on a CPU. The Space runs the linnet-tts wheel, a Rust runtime, on basic CPU hardware, and the repository holds only app.py, requirements.txt and a README. VoxRT, which licenses the weights, operates the demo.

You type some text
app.py passes it to the linnet-tts runtime
The Rust runtime runs the weights on the CPU
Streaming audio is played in the page

Good to know: The code and the weights have different licenses, so check the weights' terms before you use them elsewhere.

• License: Apache 2.0 for the code, and the Linnet Weights License for the weights
• Cost to run: none named, and the online copy runs on basic CPU hardware
• Data it reaches: the text you type and the VoxRT/linnet-en-35m weights

On Hugging Face, 13 November 2026

• Likes: 5
• Built with: gradio
• License: apache-2.0

Links

• Space: https://huggingface.co/spaces/VoxRT/linnet-tts
• Files: https://huggingface.co/spaces/VoxRT/linnet-tts/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.