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audio-ai-hub
The hub for audio AI research: papers, open models, benchmarks & datasets across audio LLMs, speech recognition, TTS, music & audio generation.
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View guide →Self-hosted easy install: next-gen ai language tutor utilizing a novel trace-cag.
LexiLingo is an open-source easy install project by InfinityZero3000 (InfinityZero3000/LexiLingo on GitHub). It has 47 GitHub stars and 7 forks, is released under the MIT license, and was last updated on GitHub on 28 September 2026.
Fetch the source code onto your machine.
Set up the Python environment the project needs.
Copy the sample config or env file and adjust it.
Start the app from the source directory.
Built around ai tutor for a focused workflow.
Built around cag for a focused workflow.
Built around graphcag for a focused workflow.
Built around knowledge graph for a focused workflow.
Inspect, fork, and modify every line of the code.
Run it on your own machine — no account lock-in.
Get the source on your machine.
git clone https://github.com/InfinityZero3000/LexiLingo.gitSet up Python dependencies.
cd LexiLingo && pip install -r requirements.txtLaunch the application.
cd LexiLingo && python main.pyIt next-gen AI language tutor utilizing a novel Trace-CAG pipeline for zero-hallucination pedagogical feedback, giving you a working starting point you can run and adapt.
Yes — the code is open-source, so you can use and modify it without a license fee.
Clone the GitHub repository and follow the README; the typical flow is install dependencies, configure, and run.
It is primarily written in Python, so you will need that toolchain to build or extend it.
The GitHub repository is the hub: open issues for bugs and check the README and discussions for guidance.
The hub for audio AI research: papers, open models, benchmarks & datasets across audio LLMs, speech recognition, TTS, music & audio generation.
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