TubeLearns is a Python script designed for extracting and cleaning YouTube video transcripts for preprocessing in machine learning. This versatile tool streamlines the process of acquiring high-quality text data from YouTube videos, making it ideal for various natural language processing tasks, sentiment analysis, speech recognition, and more.
- Extracts video transcripts from YouTube videos.
- Saves cleaned transcripts into separate text files.
- Supports individual video URLs, batch processing from a list of URLs, and entire playlists.
- Streamlines the dataset collection process for machine learning applications.
- New Feature: Tokenization and Punctuation Removal for text preprocessing and cleaning.
You can install TubeLearns using pip:
pip install tubelearnsfrom tubelearns import Acquisition
# Initialize the Acquisition class
model = Acquisition()
# Grab transcripts from a YouTube playlist
playlist_url = 'https://www.youtube.com/your_playlist_url'
model.PlaylistGrab(playlist_url, name="raw_data")# Extract video links from a YouTube playlist
playlist_url = 'https://www.youtube.com/your_playlist_url'
model.Play2Text(playlist_url)from tubelearns.tokenizers import Tokenization, Cleaning
# Initialize the Tokenization class
tokenizer = Tokenization()
cleaner = Cleaning()
# Tokenize text data
text_data = "Your input text here."
tokenized_data = tokenizer.TokenizeRaw(text_data)
cleaned_data = tokenizer.PunctList(tokenized_data)Refer to the TubeLearns documentation for detailed usage instructions and examples.
If you'd like to contribute to TubeLearns or report issues, please check out the GitHub repository.
This project is licensed under the MIT License - see the LICENSE.md file for details.
Enjoy using TubeLearns! If you have any questions or encounter issues, please don't hesitate to get in touch.