No project description provided
Project description
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.template
to.env
.cp .env.template .env
Open
.env
and 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!
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file audiomind-0.2.0.tar.gz
.
File metadata
- Download URL: audiomind-0.2.0.tar.gz
- Upload date:
- Size: 14.0 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.17
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | cd7ebabb1a3b3edf4a85b2120ed9228239365fb323ec9d044a8578e7fe27d077 |
|
MD5 | 5b4206f0db78cb14b1cdf1cd5539aad5 |
|
BLAKE2b-256 | b1d75ba4e3d4e41c5f7024fd6d33c5a8820633d4ffbb2f6cad747d72e0f33260 |
File details
Details for the file audiomind-0.2.0-py3-none-any.whl
.
File metadata
- Download URL: audiomind-0.2.0-py3-none-any.whl
- Upload date:
- Size: 14.0 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.17
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 27f2d43f0abfa023fe2b6128d1f47da5900b199297026ff10e4f76b836f18838 |
|
MD5 | 051a6e263c84515d3af6a5ed94f6494d |
|
BLAKE2b-256 | b1aebc56295770e31d52d09aec97a134903db07802261c9be000190b18aac1ac |