llm-audio is a small C++ library that helps convert speech to text. It uses large language model (LLM) APIs to turn audio into written words. The library works with Whisper and other similar services. It does not require any extra software or tools to run, making it easy to use.
Although llm-audio is meant as a developer tool, this guide will help you set it up on Windows, so you can run it without technical knowledge.
To run llm-audio on your Windows PC, you will need the following:
- Windows 10 or later: 64-bit editions work best.
- At least 4 GB RAM: To handle audio processing smoothly.
- 100 MB free disk space: For the application and temporary files.
- Internet connection: The software connects to external LLM APIs to transcribe audio.
- Basic sound files: Audio in formats like WAV or MP3 to transcribe.
No additional software or drivers are needed before running llm-audio.
- Easy setup with a single file.
- No need to install other programs or libraries.
- Supports OpenAI Whisper and other compatible speech-to-text APIs.
- Works with common audio formats.
- Fast transcription using modern language models.
- Small size with no dependencies.
- Designed for reliability and low system impact.
Click the large green button below or this link to the official GitHub page to visit the download page:
Because this link leads to the GitHub repository, you will need to download the files from there.
On the GitHub page:
- Look for the Releases section in the right sidebar or under the "Code" tab.
- Click the latest release version.
- Inside, find the download package (usually a ZIP file).
- Click the file to start downloading.
After downloading the ZIP file:
- Locate the file in your "Downloads" folder.
- Right-click the file and select Extract All.
- Choose a destination folder you can easily find (for example, Desktop\llm-audio).
- Click Extract.
Depending on what is included, you might have a ready-to-run file or a set of source files.
If you receive an executable file:
- Double-click the .exe file to start transcription.
If you only see code files (.h and .cpp):
- This library needs to be used within other programs by developers.
- You can give audio files to someone who knows programming to run them with llm-audio.
Once running, you can choose audio files from your PC to transcribe into text. The program will:
- Read the sound data.
- Connect automatically to the speech-to-text API.
- Show the transcription on screen.
llm-audio can handle audio in common file types such as:
- WAV (.wav)
- MP3 (.mp3)
- FLAC (.flac)
- OGG (.ogg)
Ensure your files are clear and recorded in a quiet space for best results. Background noise may reduce transcription accuracy.
If you run into issues, try the following:
- Check your internet connection.
- Make sure your audio files are not corrupted.
- Confirm you have extracted all files if using the ZIP package.
- Restart your computer and run the program again.
- If a prompt asks for permission, allow the app to connect to the internet.
llm-audio sends audio data to external services to perform transcription. This means your audio is processed outside your PC. Avoid uploading sensitive or private information if you have privacy concerns.
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Official GitHub page to download: https://github.com/pro6692abou/llm-audio
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Learn about Whisper and compatible APIs on OpenAI’s website (for reference).
No. llm-audio relies on online large language models. You need an active internet connection for transcription.
Basics of the library require programming. However, the provided files may include ready-to-use tools you can run directly on Windows.
Yes. However, processing time may increase with longer files.
Developers can include llm-audio in their own programs to automate transcription.
llm-audio is open-source. For technical help or to contribute, visit the GitHub repository.
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Visit the download page at:
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Download the latest release ZIP file.
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Extract files to a folder.
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Run the executable if available.
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Load audio files to begin transcription.
This process brings simple speech-to-text to your Windows PC with minimal hassle.