AppleHolic

import mxnet as mx
import numpy as np

# make and return data iterator
def get_data_iter(data, tags, labels, shuffle=False, batch_size=64):
    nditer = mx.io.NDArrayIter(data={'data' : data, 'tags' : tags},  label={'labels': labels}, batch_size=batch_size, shuffle=shuffle)
    return nditer

# origins (precision recall functions)
'''

AppleHolic

x = 80 # spectrogram scale
output = 24000  # waveform scale
max_f = 50
max_s = 50
N = 2

# calculate function
calc_conv_dim = lambda x, f, s, p: (x - f + p) // s + 1
calc_deconv_dim = lambda x, f, s, p: int((x - 1) * s - p + f)

AppleHolic

export default class {
  constructor (options) {
    this.bufferSize = options.bufferSize || 4096
    this.sampleRate = options.sampleRate
    this.samples    = options.samples
  }

  getData () {
    this._joinSamples()