YouTube Audio Transcriber with Whisper AI

This Streamlit application allows users to transcribe YouTube videos using Whisper AI. The transcription results in .srt, .txt, and .tsv files, which can be viewed and downloaded directly from the app.

Features

• Transcribe YouTube videos with url.
• Support for various Whisper AI models.
• View and download transcription files in .srt, .txt, and .tsv formats.

Installation

1. Clone the repository:

   git clone https://github.com/rishabh11336/Youtube-Subtitle.git
   cd your-repo-name

2. Install the required Python packages:

   pip install streamlit pytube pydub openai-whisper

Usage

1. Ensure requirements.txt is installed and accessible from the command line:

   pip install -r requirements.txt

You may need rust installed as well, in case tiktoken does not provide a pre-built wheel for your platform. If you see installation errors during the pip install command above, please follow the Getting started page to install Rust development environment. Additionally, you may need to configure the PATH environment variable, e.g. export PATH="$HOME/.cargo/bin:$PATH". If the installation fails with No module named 'setuptools_rust', you need to install setuptools_rust, e.g. by running:

pip install setuptools-rust

2. Run the Streamlit app:

   streamlit run app.py

3. Open your web browser and go to http://localhost:8501 to access the app.

How It Works

1. Enter the YouTube URL: Input the URL of the YouTube video you want to download the audio from.
2. Enter the desired filename: Specify the filename (without extension) for the downloaded audio file.
3. Select the Whisper AI model: Choose the desired Whisper AI model for transcription from the dropdown menu.
4. Download and Transcribe: Click the "Download and Transcribe" button to start the process.
5. View and Download Transcription Files: Once the transcription is complete, you can view and download the .srt, .txt, and .tsv files.

There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and inference speed relative to the large model; actual speed may vary depending on many factors including the available hardware.

Size	Parameters	English-only model	Multilingual model	Required VRAM	Relative speed
tiny	39 M	tiny.en	tiny	~1 GB	~32x
base	74 M	base.en	base	~1 GB	~16x
small	244 M	small.en	small	~2 GB	~6x
medium	769 M	medium.en	medium	~5 GB	~2x
large	1550 M	N/A	large	~10 GB	1x

The .en models for English-only applications tend to perform better, especially for the tiny.en and base.en models. We observed that the difference becomes less significant for the small.en and medium.en models.

Project Structure

Youtube-Subtitle/
├── app.py
├── README.md
└── requirements.txt

• app.py: Main application file containing the Streamlit app code.
• README.md: This README file.
• requirements.txt: List of required Python packages.

Example

1. Enter the YouTube URL.
2. Select the Whisper AI model.
3. Click "Download and Transcribe".
4. View and download the transcription files.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes.

License

This project is licensed under the MIT License. See the LICENSE file for details.