abmami / Multilingual-Video-Transcription-using-Whisper

A Python tool for transcribing videos using Whisper

Multilingual Video Transcription using Whisper

A Python command-line tool for downloading and/or transcribing videos using OpenAI's Whisper open-source model. It supports YouTube video URLs, playlists, videos already downloaded locally, or YouTube URLs from a JSON file. It can download videos in different resolutions, and the resulting transcriptions are saved in a JSON file.

Setup

1. Clone the repository:

git clone https://github.com/abmami/Multilingual-Video-Transcription-using-Whisper.git
cd Multilingual-Video-Transcription-using-Whisper

2. Create and activate a virtual environment:

python3 -m venv venv
source venv/bin/activate  # On Linux
venv\Scripts\activate.bat  # On Windows

3. Install the required Python packages:

pip install -r requirements.txt

4. Install FFmpeg:
   • On Ubuntu:

   sudo apt-get install ffmpeg

   • On Windows:
     • Download the latest static build of FFmpeg from the official website: https://ffmpeg.org/download.html#build-windows
     • Extract the downloaded ZIP file to a folder on your system.
     • Add the path to the bin folder of the extracted FFmpeg to your system's PATH environment variable

Usage

• Transcribe videos from the urls JSON file in data folder using the following command:

python transcribe.py

• Transcribe videos that have already been downloaded locally and stored in the folder data/videos using the following command:

python transcribe.py --locally

• Transcribe a Youtube playlist using the following command:

python transcribe.py --playlist YT_PLAYLIST_URL

• Transcribe a single Youtube Video using the following command:

python transcribe.py --url YT_VIDEO_URL

Additional Options

• --res: The resolution of the video(s) to download (default: 360).
• --no-save: Add this to delete the video(s) after transcription.

Configuration

The tool uses the following paths:

• input_path: The path to the input file (default: data/urls.json).
• videos_path: The path to the folder where the videos are saved (default: data/videos).
• output_path: The path to the output file (default: data/output.json).

The tool also uses the Whisper's small model. The size of the small model is ~461M. You can change it in the code to use the base or another model.

• model_name: The name of the Whisper model to use (default: small).