| /* | |
| * one.c -- Lua core, libraries, and interpreter in a single file (Lua 5.3) | |
| */ | |
| /* default is to build the full interpreter */ | |
| #ifndef MAKE_LIB | |
| #ifndef MAKE_LUAC | |
| #ifndef MAKE_LUA | |
| #define MAKE_LUA | |
| #endif |
Minimal example: transcode from MP3 to WMA:
ffmpeg -i input.mp3 output.wma
You can get the list of supported formats with:
ffmpeg -formats
Convert WAV to MP3, mix down to mono (use 1 audio channel), set bit rate to 64 kbps and sample rate to 22050 Hz:
| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
| """ | |
| This is a batched LSTM forward and backward pass | |
| """ | |
| import numpy as np | |
| import code | |
| class LSTM: | |
| @staticmethod | |
| def init(input_size, hidden_size, fancy_forget_bias_init = 3): |
| #include <cstring> | |
| #include <numeric> | |
| #include <functional> | |
| /** | |
| * Hackers Delight: http://www.hackersdelight.org/HDcode/nlz.c.txt | |
| */ | |
| int pop(unsigned x) { | |
| x = x - ((x >> 1) & 0x55555555); | |
| x = (x & 0x33333333) + ((x >> 2) & 0x33333333); |