
Explain that the moviepy library has a new version 2.x, and provide two files per topic, one for 1.x and one for 2.x, named _2x.py.
Install python on windows 11, add to path, and verify with a hello world test. Install numpy, scipy, matplotlib, cvxopt, sympy, and moviepy for MPC, LQR, and sound processing workflows.
Learn how to install Python 3.8.7 on macOS, set up pip, install NumPy and other libraries from the terminal, and verify the setup with simple tests.
Learn to import and alias key python libraries for sound processing, plot audio graphs with matplotlib, and explore moviepy audio clip tools for stereo left-right sound manipulation.
Convert non-silent sample intervals to seconds using the order array and differences, identify silences over 40000 samples, and cut audio with left and right margins at 44100 samples per second.
Learn to handle video processing exceptions by applying conditional statements to adjust cuts for first and left margins, duration, and silence using samples per second.
Expand your Python NumPy workflow to process multiple videos in a for loop, importing, extracting audio, cutting and merging, exporting each video, while separating plotting from processing and tuning margins.
Explore an alternative approach to building an order array for audio processing in Python NumPy, using a preallocated zero array and a moving index to store non-silence sample positions.
Compare three Python NumPy implementations to generate an order array of non-silent samples from two-column boolean arrays, confirming identical results across versions and highlighting performance considerations.
Explore NumPy vectorization and the where function to create a boolean order array of non-silent samples, speeding up audio processing and silence cutting.
Apply cutting and merging operations on non-silent samples with a vectorized A−B method in numpy, using order and order_position arrays to efficiently determine cut intervals and improve performance.
Refactor video clip concatenation by building a new clips list and appending each temp clip. Concatenate once at the end to speed up processing with moviepy, especially for long files.
Explore digital images stored as pixel matrices, convert color images to grayscale and then to binary using traditional loops and vectorized approaches in Python with Pillow, and compare performance.
Import color images, convert them to grayscale, and threshold to binary for export. Learn that grayscale is a pixel matrix 0–255 and thresholding yields black or white.
Process images with a python numpy workflow to create binarized results, choosing between loop-based and factorization routes, convert to grayscale, and export binary and grayscale outputs.
Programming is one of the most flexible fields I know of. You can create a program that achieves a certain task in so many ways. However, that does not mean that all ways are equal. Some are better than others.
That is especially visible when your program has to work with big data. Working with big data means working with gigantic arrays and matrices.
You can create a program that achieves the same task like the other one, but it does so 1000 times faster. It all depends on how you code and which coding practices you use.
And this is what you will learn here. You will learn the good and the bad coding practices, so that you would learn to code the right way when dealing with big data.
In this 100% project based course, we will use Python, the Numpy and the Moviepy library to create a fully functional sound processing program.
This program will import your videos in sequence, extract their audio, automatically identify the silent intervals in that audio, and then cut them out while still keeping some silence on the edges to preserve a bit of pause in between sentences.
Sound processing naturally deals with millions and millions array elements and so it really matters how we write that program. We will do it in a bad way and in a good way, because I want you to see both sides of the coin.
In the end, you will see that the last version of your Python Numpy code will be more than 1000 times faster than the first version, and so, you will see how to code and how definitely not to code.
Finally, I really want you to see that this knowledge is universal and can be applied in other fields as well, not only audio processing. And therefore, in the last section, there will be an assignment in computer vision.
Digital images are in fact, gigantic matrices, and so, it really matters how you handle them in the code. We will build a small program that can binarize these images and we will also do it in a good and in a bad way.
We will use the Python image processing library called Pillow to process all this big data inside the image matrices.
After this course, you will know how to approach programming in the right way from the beginning. Take a look at some of my free preview videos and if you like what you see, then, ENROLL NOW and let's get started! I'll see you inside.