AudioMind
Overview
AudioMind is a Python-based solution designed to extract meaningful insights from audio files. By leveraging whisper and LLMs, the platform transcribes and summarizes audio content, making it easier to derive actionable information.
Stack
LLM
- OpenAI
Speech to Text
- Whisper (Openai API) [DEFAULT]
- Whisper (On-Device)
Current Solutions
- Create a journal entry from your voice note.
Goals
- Transcribe audio files to text.
- Summarize the transcribed text.
- Easy to integrate and use.
- Get Insights from any audio file, including podcasts , interviews, lectures, etc.
- Solve actual problems.
Installation
Prerequisites
- Python 3.x
- pip
Use PIP Package
Steps to Install
-
Clone the Repository
git clone https://github.com/onlyoneaman/audiomind.git cd audiomind
-
Create a Virtual Environment
python3 -m venv .venv
Activate the virtual environment:
-
Unix or MacOS
source .venv/bin/activate
-
Windows
.\.venv\Scripts\activate
-
-
Install Dependencies
pip install -r requirements.txt
-
Environment Variables
Copy
.env.templateto.env.cp .env.template .env
Open
.envand provide your OpenAI API key:OPENAI_API_KEY=your_openai_api_key_here DREAMBOAT_API_KEY=your_dreamboat_api_key_here // optional
-
Run the Application
python3 -m audiomind
Usage
Place the audio files in the /exmaples folder and run the audio_to_journal.py script. The script will transcribe the audio and summarize it.
python3 -m audiomind --file examples/1.mp3
You can add some information about yourself in person.txt file.
Audiomind will use this information too while creating the journal entry.
Roadmap
- Transcribe audio files to text.
- Summarize the transcribed text.
- Easy to integrate and use.
- Get Insights from any audio file, including podcasts , interviews, lectures, etc.
- Create a journal entry from your voice note.
- Improve the journal entry.
- Create a summary of a podcast episode.
- Create a summary of a lecture.
- Create a summary of a meeting.
Contributing
Feel free to submit issues and enhancement requests.
License
MIT
Enjoy using AudioMind!
Metadata
Release files for audiomind 0.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| audiomind-0.2.0.tar.gz | 14.0 MB | Without provenance | Details for audiomind-0.2.0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| audiomind-0.2.0-py3-none-any.whl | Python 3 | none | any | Details for audiomind-0.2.0-py3-none-any.whl |
Total release size: 28.0 MB
Release files / audiomind-0.2.0.tar.gz
| Download URL | audiomind-0.2.0.tar.gz |
|---|---|
| Size | 14.0 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.17
|
Release files / audiomind-0.2.0-py3-none-any.whl
| Download URL | audiomind-0.2.0-py3-none-any.whl |
|---|---|
| Size | 14.0 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
b1aebc56295770e31d52d09aec97a134903db07802261c9be000190b18aac1ac
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.17
|
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