def main():
filename = sys.argv[1]
wave_reader = wave.open(filename)
voice_classifier,keyboard_classifier = pickle.load(open("classifier.pickle"))
try:
wave_reader.setpos(int(current_end_of_file(filename)*48000)+1)
except:
print "file done"
return
print "bees"
print current_time(wave_reader)
start_time, end_time = find_endpoints(wave_reader)
print "bees2"
while start_time < current_end_of_file(filename):
assert False
start_time, end_time = find_endpoints(wave_reader)
print start_time
for i in range(0,50):
print start_time, end_time
if numpy.sum(vadutils.read_region(filename, start_time, end_time-start_time)) == 0:
print "silence detected!"
voice = False
keyboard = False
else:
play_region(filename, start_time, end_time)
vectors = []
vadutils.load_vectors(filename, vectors,start_time, end_time)
print numpy.average(voice_classifier.predict_proba(vectors), axis=0)
voice = raw_input("was that voice? [y/n] ") == "y"
keyboard = raw_input("was that keyboard? [y/n] ") == "y"
insert_sample(filename, start_time, end_time, voice, keyboard)
start_time, end_time = find_endpoints(wave_reader)
if abs(current_time(wave_reader)-sample_end_time(wave_reader)) <= 0.5:
break
def main():
filename = sys.argv[1]
wave_reader = wave.open(filename)
voice_classifier, keyboard_classifier = pickle.load(
open("classifier.pickle"))
try:
wave_reader.setpos(int(current_end_of_file(filename) * 48000) + 1)
except:
print "file done"
return
print "bees"
print current_time(wave_reader)
start_time, end_time = find_endpoints(wave_reader)
print "bees2"
while start_time < current_end_of_file(filename):
assert False
start_time, end_time = find_endpoints(wave_reader)
print start_time
for i in range(0, 50):
print start_time, end_time
if numpy.sum(
vadutils.read_region(filename, start_time,
end_time - start_time)) == 0:
print "silence detected!"
voice = False
keyboard = False
else:
play_region(filename, start_time, end_time)
vectors = []
vadutils.load_vectors(filename, vectors, start_time, end_time)
print numpy.average(voice_classifier.predict_proba(vectors),
axis=0)
voice = raw_input("was that voice? [y/n] ") == "y"
keyboard = raw_input("was that keyboard? [y/n] ") == "y"
insert_sample(filename, start_time, end_time, voice, keyboard)
start_time, end_time = find_endpoints(wave_reader)
if abs(current_time(wave_reader) -
sample_end_time(wave_reader)) <= 0.5:
break
import vadutils
from sklearn.ensemble import RandomForestClassifier
import numpy
import cPickle as pickle
import wave
if __name__ == "__main__":
s = vadutils.load_vectors("sample.wav", 0, 8)
c = pickle.loads(open("final_classifier").read())
c.verbose = 3
headings = sorted(s[0].headings())
vectors = numpy.array([x.to_vector(headings) for x in s])
predictions = c.predict(vectors)
f = wave.open("sample.wav")
writer = wave.open("sample-filtered.wav", "w")
writer.setnchannels(1)
writer.setsampwidth(f.getsampwidth())
writer.setframerate(f.getframerate())
for pred in predictions:
x = f.readframes(768)
if pred == 1:
writer.writeframes(x)
else:
writer.writeframes("".join(["\x00" for value in x]))
writer.close()
import vadutils
from sklearn.ensemble import RandomForestClassifier
import numpy
import cPickle as pickle
import wave
if __name__ == "__main__":
s = vadutils.load_vectors("sample.wav",0,8)
c = pickle.loads(open("final_classifier").read())
c.verbose = 3
headings = sorted(s[0].headings())
vectors = numpy.array([x.to_vector(headings) for x in s])
predictions = c.predict(vectors)
f = wave.open("sample.wav")
writer = wave.open("sample-filtered.wav", "w")
writer.setnchannels(1)
writer.setsampwidth(f.getsampwidth())
writer.setframerate(f.getframerate())
for pred in predictions:
x = f.readframes(768)
if pred == 1:
writer.writeframes(x)
else: