Got an hour-long video and not really into manually creating subtitles? not plans to put it on YouTube for their automated transcription services? then – try Google Cloud Speech-to-Text! In this post I’ll share some scripts for automating the process and creating an .str file to go along your video for displaying the subtitles.
Tag: python
For a class I’m teaching (on deep learning and art) I had to create a machine that auto starts a jupyter notebook with tensorflow and GPU support. Just create an instance and presto – Jupyter notebook with TF and GPU!
How awesome is that?
Well… building it wasn’t that simple.
So for your enjoyment – here’s my recipe:
As part of the computer vision class I’m teaching at SBU I asked students to implement a segmentation method based on SLIC superpixels. Here is my boilerplate implementation.
This follows the work I’ve done a very long time ago (2010) on the same subject.
For graph-cut I’ve used PyMaxflow: https://github.com/pmneila/PyMaxflow, which is very easily installed by just pip install PyMaxflow
The method is simple:
- Calculate SLIC superpixels (the SKImage implementation)
- Use markings to determine the foreground and background color histograms (from the superpixels under the markings)
- Setup a graph with a straightforward energy model: Smoothness term = K-L-Div between superpix histogram and neighbor superpix histogram, and Match term = inf if marked as BG or FG, or K-L-Div between SuperPix histogram and FG and BG.
- To find neighbors I’ve used Delaunay tessellation (from scipy.spatial), for simplicity. But a full neighbor finding could be implemented by looking at all the neighbors on the superpix’s boundary.
- Color histograms are 2D over H-S (from the HSV)
Result
¡Hola mis amigos!
I’m learning spanish, but I’m also annoyed with collaborating on LaTeX papers. That’s why I’ve created the GDoc-LaTeXifier so the syntax will be clear when I collaborate on a paper with a remote friend.
But now we both want to compile a PDF on our machines. So I’ve created the tiny shell script that downloads the paper and runs PDFLaTeX.
The problem is that this opens a new terminal window and runs the script. I’ve been able to sort it out so that it closes the terminal window when it’s done, by on my friend’s mac it doesn’t, so he ends up with a ton of open windows.
Enter – the GDoc/LaTeX compiler GUI.
Hi,
Just sharing a snippet of code. Part of a project I’m doing, I need to analyse the links in the Wikipedia corpus. While using the API is one solution, it doesn’t retain the order of where links appear in the page. It also returns links that are not part of the main text, which makes the linkage DB very cluttered.
So, I set out to parse the raw MediaWiki format all Wikipedia articles are written in, to get only the relevant links and in order. I call them contextual because they live inside the text and have context.
Initially I used string matching, and other complex string scraping parsing methods. It was a bust. There are too many end-cases to deal with. That is when I discovered PyParsing, the excellent parsing library. It did the job, and here are the results.
Hi
I believe that every builder-hacker should have their own little Swiss-army-knife server that just does everything they need, but as a webservice. You can basically do anything as a service nowadays: image/audio/video manipulation, mock-cloud data storage, offload heavy computation, and so on.
Tornado, the lightweight Python webserver is perfect for this, and since so many of the projects these days have Python binding (see python-tesseract), it should be a breeze to integrate them with minimal work.
Let’s see how it’s done