dirname = os.path.normpath(sys.argv[1])
seq = sequencer.Sequencer() #create a sequencer to handle midi input
tracks = glob.glob(os.path.join(dirname, '*.mid')) #match any midi file
for t in tracks:
track = os.path.splitext(t)[0]
# sequence MIDI to saw + sine melody
print "Sequencing %s..." %track
wavData = seq.parseMidiFile("%s.mid" %track)
sawData = wavData[0]
melodyData = wavData[1]
print "Saving %s... wav and melody" %track
wave_gen.saveAudioBuffer("%s.wav" %track, sawData)
wave_gen.saveAudioBuffer("%smelody.wav" %track, melodyData)
print "Generating feature data..."
data = wave_reader.wavToFeatures("%s.wav" %track)
melody = wave_reader.wavToFeatures("%smelody.wav" %track)
labels = melody.argmax(axis=1) #axis=1, max frequency across a sample
numData = min(data.shape[0],labels.shape[0])
data = data[0:numData,]
labels = labels[0:numData]
matrix = numpy.concatenate([data, numpy.array([labels]).T], axis=1) #horizontal concatenate
saveFile('%sdata.csv' %track, matrix)
seq.resetSequencer()
import numpy
from pybrain.tools.customxml import NetworkWriter, NetworkReader
import sys
import os
import wave_gen
import wave_reader
import midi_util
# Script to test a trained neural net on a wav file (000106b_.wav)
#produces a result wav so you can listen to the prediction
if __name__ == '__main__':
dirname = os.path.normpath(sys.argv[1])
#track = os.path.join(dirname, '000106b_.wav')
print "Reading wav into input data..."
data = wave_reader.wavToFeatures('000106b_.wav')
numData = data.shape[0]
labels = numpy.zeros(numData)
print "Reloading neural network..."
net = NetworkReader.readFrom(os.path.basename(dirname) + 'designnet')
print "Activating neural network..."
for i in range(numData):
labels[i] = net.activate(data[i])
print "Generating result wav..."
cdata = numpy.array([])
for label in labels:
#if(freq > 0):
# freq = midi_util.frequencyToNoteFrequency(label)