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from pybrain.datasets import SupervisedDataSet
from pybrain.tools.shortcuts import buildNetwork
from pybrain.structure import SigmoidLayer
from pybrain.supervised.trainers import BackpropTrainer
#from pybrain.supervised.trainers.evolino import EvolinoTrainer

import Nsound

################################################################################

fileName = '/home/dam-lo/Musique/Nanowar/2003 - Triumph Of True Metal Of Steel/06.wav'

nbSample = 5


################################################################################

audio = Nsound.AudioStream(fileName)

audio.speedUp(100000)

sr = audio.getSampleRate()
ch = audio.getNChannels()
sp = audio.getLength()
duration = audio.getDuration()

data = []

for c in xrange(ch):
    data.append(audio.get_at_index(c).toList())

nb_input = nbSample*ch
nb_output = ch

ds = SupervisedDataSet(nb_input, nb_output)

for i in xrange(sp-nbSample-1):
       inputs = []
       outputs = []
       for c in xrange(ch):
          inputs += data[c][i:i+nbSample]
          outputs.append( data[c][i+nbSample])
       ds.addSample(inputs, outputs)

del audio
del data


net = buildNetwork(nb_input, 10, 13, nb_output, fast=True)

print "trainer start"
trainer = BackpropTrainer(net, ds)

while True:
    print trainer.train()