def generate_diffs(filea, fileb):
wavea = read_wave_file(filea, True)
waveb = read_wave_file(fileb, True)
diffl = normalized_difference(*aligned_sublists(wavea[0], waveb[0]))
diffr = normalized_difference(*aligned_sublists(wavea[1], waveb[1]))
peakl = peak_diff(wavea[0], waveb[0])
peakr = peak_diff(wavea[1], waveb[1])
# for line in aligned_sublists(wavea[0], waveb[0]):
# plt.plot(normalized(line[:10000]))
# plt.show()
# louder_a = wavea[0] if numpy.amax(wavea[0]) \
# > numpy.amax(wavea[1]) else wavea[1]
# louder_b = waveb[0] if numpy.amax(waveb[0]) \
# > numpy.amax(waveb[1]) else waveb[1]
# freqd = freq_diff(normalized(louder_a), normalized(louder_b))
return (
diffl,
diffr,
peakl,
peakr,
0, # freqd,
os.path.split(filea)[-1],
os.path.split(fileb)[-1])
def concat_samples(regions, path, name=None):
if name is None:
output_filename = 'all_samples_%f.flac' % time.time()
else:
output_filename = '%s.flac' % name
concat_filename = 'concat.txt'
with open(concat_filename, 'w') as outfile:
global_offset = 0
for region in regions:
sample = region.attributes['sample']
sample_data = read_wave_file(full_path(path, sample))
sample_length = len(sample_data[0])
region.attributes['offset'] = global_offset
region.attributes['end'] = (global_offset + sample_length -
ANTI_CLICK_OFFSET)
# TODO: make sure endpoint is a zero crossing to prevent clicks
region.attributes['sample'] = output_filename
outfile.write("file '%s'\n" % full_path(path, sample))
global_offset += sample_length
create_flac(concat_filename, full_path(path, output_filename))
os.unlink(concat_filename)
return regions
def normalize_file(filename):
data = read_wave_file(filename, True)
if len(data):
normalized_data = normalized(data) * (2**(bit_depth - 1) - 1)
else:
normalized_data = data
save_to_file(filename, 2, normalized_data)
def process_all(start, stop, *files):
start = int(start)
stop = int(stop)
chunk_offset = ((-1 * start) % CHUNK_SIZE)
for file in files:
stereo = read_wave_file(file)
left = stereo[0]
right = stereo[1]
plt.plot(left[start:stop])
plt.plot(right[start:stop])
plt.show()
def level_volume(regions, dirname):
if len(regions) == 0:
return None
velcurve = {}
velsorted = list(
reversed(sorted(regions, key=lambda x: int(x.attributes['hivel']))))
for high, low in pairwise(velsorted):
try:
diff = (peak_rms(
read_wave_file(full_path(dirname, low.attributes['sample']))) /
peak_rms(
read_wave_file(
full_path(dirname, high.attributes['sample']))))
except ZeroDivisionError:
print "Got ZeroDivisionError with high sample path: %s" % \
high.attributes['sample']
raise
for attr in REMOVE_ATTRS:
if attr in high.attributes:
del high.attributes[attr]
velcurve.update({
('amp_velcurve_%d' % int(high.attributes['hivel'])):
1,
('amp_velcurve_%d' % (int(high.attributes['lovel']) + 1)):
diff,
})
# print the last region that didn't have a lower counterpart
low = velsorted[-1]
for attr in REMOVE_ATTRS:
if attr in low.attributes:
del low.attributes[attr]
velcurve.update({
('amp_velcurve_%d' % int(low.attributes['hivel'])):
1,
('amp_velcurve_%d' % (int(low.attributes['lovel']) + 1)):
0,
})
return Group(velcurve, velsorted)
def freq_shift():
files = ['A1_v111_15.00s.aif', 'A1_v95_15.00s.aif']
wavea, waveb = [
read_wave_file(os.path.join(root_dir, file)) for file in files
]
louder_a = wavea[0] if (
numpy.amax(wavea[0]) > numpy.amax(wavea[1])) else wavea[1]
louder_b = waveb[0] if (
numpy.amax(waveb[0]) > numpy.amax(waveb[1])) else waveb[1]
freqd = freq_diff(normalized(louder_a), normalized(louder_b))
waveb_shifted = [shift_freq(channel, freqd) for channel in waveb]
louder_shifted_b = waveb_shifted[0] if (numpy.amax(waveb_shifted[0]) >
numpy.amax(waveb_shifted[1])) \
else waveb_shifted[1]
shifted_freqd = freq_diff(normalized(louder_a),
normalized(louder_shifted_b))
# lefts_aligned = aligned_sublists(wavea[0], waveb[0])
rights_aligned = aligned_sublists(wavea[1], waveb[1])
# shifted_lefts_aligned = aligned_sublists(wavea[0], waveb_shifted[0])
diffl = normalized_difference(*aligned_sublists(wavea[0], waveb[0]))
diffr = normalized_difference(*aligned_sublists(wavea[1], waveb[1]))
plt.plot(normalized(rights_aligned[0][:10000]))
plt.plot(normalized(rights_aligned[1][:10000]))
plt.plot(
numpy.absolute(
normalized(rights_aligned[0][:10000]) -
normalized(rights_aligned[1][:10000])))
plt.show()
shifted_diffl = normalized_difference(
*aligned_sublists(wavea[0], waveb_shifted[0]))
shifted_diffr = normalized_difference(
*aligned_sublists(wavea[1], waveb_shifted[1]))
print files
print 'diffs\t\t', diffl, diffr
print 'shifted diffs\t', shifted_diffl, shifted_diffr
print 'freqs', freqd
print 'shifted freqs', shifted_freqd
def graph_ffts():
files = ['A1_v111_15.00s.aif', 'A2_v31_15.00s.aif']
for file in files:
stereo = read_wave_file(os.path.join(root_dir, file))
left = stereo[0]
right = stereo[1]
list = left[:100000]
w = numpy.fft.rfft(list)
freqs = numpy.fft.fftfreq(len(w))
# Find the peak in the coefficients
# idx = numpy.argmax(numpy.abs(w[:len(w) / 2]))
idx = numpy.argmax(numpy.abs(w))
freq = freqs[idx]
plt.plot(w)
print freq
print \
fundamental_frequency(normalized(list)), \
fundamental_frequency(normalized(left + right))
def starts_with_click(filename, threshold_samples=default_threshold_samples):
sample_data = read_wave_file(filename)
return (abs(sample_data[0][0]) > threshold_samples
or abs(sample_data[0][1]) > threshold_samples)
def max_amp(filename):
return numpy.amax(read_wave_file(filename)) / (2.**(bit_depth - 1))
def chop(aif):
file = read_wave_file(aif)
start, end = min([start_of(chan) for chan in file]), \
max([end_of(chan) for chan in file])
print aif, start, end, float(end) / len(file[0])
interpolation="none"
)
plt.colorbar()
plt.xlabel("time (s)")
plt.ylabel("frequency (hz)")
plt.xlim([0, timebins - 1])
plt.ylim([0, freqbins])
xlocs = np.float32(np.linspace(0, timebins - 1, 5))
plt.xticks(xlocs, [
"%.02f" % l
for l in (
((xlocs * len(samples) / timebins) + (0.5 * binsize)) / samplerate
)
])
ylocs = np.int16(np.round(np.linspace(0, freqbins - 1, 10)))
plt.yticks(ylocs, ["%.02f" % freq[i] for i in ylocs])
if plotpath:
plt.savefig(plotpath, bbox_inches="tight")
else:
plt.show()
plt.clf()
if __name__ == "__main__":
stereo = read_wave_file(sys.argv[1])
left = stereo[0]
plotstft(48000, left)