Author: Fernando Pérez.
A demonstration of how to use Python, Julia, Fortran and R cooperatively to analyze data, in the same process.
This is supported by the IPython kernel and a few extensions that take advantage of IPython's magic system to provide low-level integration between Python and other languages.
See the companion notebook for data preparation and setup.
| """Performs automatic speed edits to audio books. | |
| Example usage: | |
| Assuming you have an audiobook book.aax on your Desktop: | |
| 1. Convert it to wav: | |
| ffmpeg -i ~/Desktop/book.aax ~/Desktop/book.wav | |
| 2. Adjust the speed: |
| from functools import wraps | |
| from itertools import islice, tee, zip_longest, chain, product | |
| from collections import deque | |
| from pandas import DataFrame | |
| nwise = lambda g, *, n=2: zip(*(islice(g, i, None) for i, g in enumerate(tee(g, n)))) | |
| nwise_longest = lambda g, *, n=2, fv=object(): zip_longest(*(islice(g, i, None) for i, g in enumerate(tee(g, n))), fillvalue=fv) | |
| first = lambda g, *, n=1: zip(chain(repeat(True, n), repeat(False)), g) | |
| last = lambda g, *, m=1, s=object(): ((y[-1] is s, x) for x, *y in nwise_longest(g, n=m+1, fv=s)) |
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
