endolith

from __future__ import division
from numpy.fft import rfft
from numpy import argmax, mean, diff, log, nonzero
from scipy.signal import blackmanharris, correlate
from time import time
import sys
try:
    import soundfile as sf
except ImportError:
    from scikits.audiolab import flacread

kastnerkyle

# (C) Kyle Kastner, June 2014
# License: BSD 3 clause

import scipy.stats as st
import numpy as np

class gmmhmm:
    #This class converted with modifications from https://code.google.com/p/hmm-speech-recognition/source/browse/Word.m
    def __init__(self, n_states):
        self.n_states = n_states

tlhunter

#!/usr/bin/env node

var amqp = require('amqplib');
var exchange_name = 'pubsub';

amqp.connect('amqp://localhost').then(function(conn) {

  process.once('SIGINT', function() { conn.close(); });

  return conn.createChannel().then(function(channel) {

josephspurrier

/*
********************************************************************************
Golang - Asterisk and Ampersand Cheatsheet
********************************************************************************

Also available at: https://play.golang.org/p/lNpnS9j1ma

Allowed:
--------
p := Person{"Steve", 28} 	stores the value

karpathy

"""
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)

tjanczuk

// How to connect 5 publishers with 5 subscribers 
// over TCP using ZeroMQ's XPUB/XSUB proxy.

// sub (connect) 
//   <-8701-> 
// (bind) xpub <---> xsub (bind) 
//   <-8700-> 
// (connect) pub

var zmq = require('zmq');

srli

#!/usr/bin/env python

from pocketsphinx.pocketsphinx import *
from sphinxbase.sphinxbase import *

import os
import pyaudio
import wave
import audioop
from collections import deque

mbollmann

class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one:

tam17aki

# -*- coding: utf-8 -*-

# Copyright (C) 2016 by Akira TAMAMORI

# This program is free software; you can redistribute it and/or modify it under
# the terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT

arxdsilva

package main

// https://play.golang.org/p/5VsRVVtyo-J

import (
  "crypto/rand"
  "fmt"
)

func tokenGenerator() string {