def add_wave(self, path):
"""Reads a wave file at `path` and imports its structure into a pattern
dictionary."""
# **********************************************************************
# STEP 1: Open waveform from argv and get mono audio samples
wav = waveform.from_file(path)
wav.setchannelcount(1)
samples = np.fromstring(wav.getsamples(), dtype='Int16').tolist()
# **********************************************************************
# STEP 2: Run STFT on samples
matrix = stft.STFT_Matrix(samples, c_size=2**10)
matrix.amplitudes = stft.triangular_smoothing(matrix.amplitudes, 3)
matrix.smooth_amps_2()
matrix.blip_filter_2()
#matrix.spectrogram()
# **********************************************************************
# STEP 3: Convert the STFT to PatternDictionary
self.pattern_dictionary.import_stft(matrix)
waveform-test.py - tests the waveform class
Copyright EJM Software 2016
Usage: python waveform-test.py PATH
"""
# ******************************************************************************
# Add the parent directory to Python's list of paths to search for modules
import sys
sys.path.append("../")
# ******************************************************************************
import waveform
if __name__=="__main__":
if len(sys.argv) > 1:
# LOAD A WAVEFORM FROM FILE
wav = waveform.from_file(sys.argv[1])
originalsamplewidth = wav.getsamplewidth()
originalchannelcount = wav.getchannelcount()
originalsize = len(wav.getsamples())
# TEST ONE: LOADING AND WRITING
# write the data to a file
waveform.to_file('readertest.wav', wav)
# check that the files are identical, ie. the data was read and processed properly
import filecmp
assert filecmp.cmp(sys.argv[1], 'readertest.wav')
# delete the created test file
from os import remove
remove('readertest.wav')
# TEST TWO: CHANGING THE CHANNEL COUNT
def add_template(self, filename):
"""Adds a song to the music generator's knowledge base"""
# STEP 1: Read the waveform music file at the given path
wave = waveform.from_file(filename)
print "Setting channel count"
wave.setchannelcount(1)
print "Making samples"
samples = stft.np.fromstring(wave.getsamples(), dtype='Int16').tolist()
print "Done"
# STEP 2: Parse the waveform music into a music note format using STFT
matrix = stft.STFT_Matrix(samples, c_size=2**10)
matrix.amplitudes = stft.triangular_smoothing(matrix.amplitudes, 5)
matrix.smooth_amps_2()
matrix.filter(31000)
matrix.spectrogram()
# STEP 3: Create an array with all the patterns found in the music
# Notes about the self.patterns dictionary:
# "Next" - an array
# Parse the data into songsmith format
# Then, place each note into the patterns dictionary
song = matrix.to_song()
#print len(song.chords)
song.debug()
# Note: In "next" array, the 0 index is the newest
self.patterns.append({"name":"", "frequency":"", "durations":[0], "next":[[] for i in range(self.search_depth)], "chords":[]})
lastindices = [[0] for i in range(self.search_depth)]
for chord in song.chords:
currentindices = []
# Skip notes shorter than 25 bps, its probably trash data
if chord.notes[0].duration < 0.04:
continue
# Add the notes from the chord
for note in chord.notes:
name = songsmith.freqtoname(note.frequency)[:-1]
currentindex = next((i for i,v in enumerate(self.patterns) if v["name"]==name), None)
if currentindex==None:
currentindex = len(self.patterns)
self.patterns.append({"name":name, "frequency":note.frequency, "durations":[], "next":[[] for i in range(self.search_depth)], "chords":[]})
self.patterns[currentindex]["durations"].append(note.duration)
for other in currentindices:
if other != note:
self.patterns[currentindex]["chords"].append(other)
# Pack the next note data into the patterns dictionary
for depth, indices in enumerate(lastindices):
for index in indices:
self.patterns[index]["next"][depth].append(currentindex)
currentindices.append(currentindex)
# Update the lastindices for the next loop
lastindices.pop()
lastindices.insert(0, currentindices)
# last2indices = last1indices
# last1indices = currentindices
# Add the blank notes to the end of the song
# for last1index in last1indices:
# self.patterns[last1index]["nexts"].append(0)
# for last2index in last2indices:
# self.patterns[last2index]["next2"].append(0)
# Add as many empty notes to the end of the patterns as the search_depth
for depth, indices in enumerate(lastindices):
for index in indices:
for i in range(depth, self.search_depth):
self.patterns[index]["next"][i].append(0)
