- Compared various Speech to text API and selected Google Cloud Speech to Text API whose Precision and recall is higher compared to all other APIs.
- Used NLTK Toolkit for Tokenization, Stemming and Synsets of words for Preprocessing of sentences for summarization and used various predefined factors such as TF-ISF algorithm to find score of each sentence for summarization.
- Achieved accuracy of 70% compared to other summarization algorithm.
Install the following dependencies: pip install nltk, pip install wordnet, pip install youtube-dl, pip install google-cloud, pip install ffmpeg.
Run the following commands python test.py --q "Motivational Speech", python automate.py "# Text-Summarization"