Here you will find examples of how to work with YouTube videos using Python and various APIs.
Part 1: youtube-demos.ipynb
In this notebook I demonstrate:
- A Jupyter notebook that provides a minimum viable product for a YouTube video downloader application.
- How to quickly setup and use the notebook, including how to run it with zero install effort, in Google Colab.
- Three different ways to download YouTube videos and extract audio to mp3.
- Using the Python Speech Recognition library, along with the Google Speech Recognition API, to transcribe mp3 audio into text.
- Extracting pre-existing transcripts from YouTube videos, and how to translate such transcripts.
- Using the Google Video Intelligence API to provide more reliable and more accurate trancription.
- Using Google Gemini Generative AI to transcribe, translate and summarise video content.
- How to build your Jupyter notebook so it can run locally, in Google Colab, or in Google Vertex AI Workbench.
Part 3: video-intelligence-streamlit
- Provide a UI in the form of a Streamlit application
- Containerise the application using Docker
- Host the application on Google Cloud Run
If you don't know much about Jupyter notebooks, then I suggest you start with my article here, which covers:
- The value and point of Jupyter notebooks.
- Good use cases for Jupyter notebooks.
- Several ways to run the notebooks
- How to run your own - or someone else's notebooks (like the ones in this repo) - quickly and easily, for free in Google Colab.
Here we create a Python virtual environment, install Jupyter notebook to the environment, and then run our notebooks from there.
py -m pip install --upgrade pip
# Create virtual env, if you haven't already
py -m venv .venv
# Activate the venv
./.venv/Scripts/activate
# Install requirements - i.e. notebook
py -m pip install -r requirements.txtNow you can use your venv as your Jupyter kernel.