Audify

Description

Audify is an application designed to enhance audio quality and provide transcription services. By leveraging the power of DeepFilterNet and OpenAI Whisper, Audify ensures a streamlined experience for users looking to improve their audio files and obtain accurate transcriptions.

Features

Audio Enhancement: Utilizes DeepFilterNet to significantly enhance the audio quality. Transcription: Leverages OpenAI Whisper to provide accurate transcriptions of audio files.

Integrated Technologies

DeepFilterNet: A deep learning model for audio enhancement.

OpenAI Whisper: An automatic speech recognition (ASR) system by OpenAI.

Author

Corey Tarter

Version

1.0

Installation

1. Clone the repository:

   git clone https://github.com/ctarter/audify.git
   
   

2. Navigate to the cloned repository:

   cd audify
   
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

   Need a Pytorch-specific alternative with GPU support? Check out torch-audiomentations!

Usage

python audify.py

Example

Audio Source:

1. File
2. Directory
3. Link
4. URL List
5. YouTube

Enter your choice: 1

Select File: /path/to/audio/file.wav

Denoise? (Y/N): Y

Export as: 
1. WAV
2. MP3
3. FLAC
Enter your choice: 2

Choose Folder: /path/to/output/folder

Transcribe? (Y/N): Y

Choose a transcription model:
1. tiny.en
2. base.en
3. small.en
4. medium.en

Enter your choice: 2

Transcribe with timestamps? (Y/N): Y

Choose Folder: /path/to/transcription/folder

Generate Keywords from transcription file? (Y/N): Y

How many keywords to generate? 10

Choose Folder: /path/to/keywords/folder

License

Audify is free and open source! All code in this repository is dual-licensed under either:

• MIT License (LICENSE-MIT or http://opensource.org/licenses/MIT)
• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)

at your option. This means you can select the license you prefer!

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.