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2.py
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2.py
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import numpy, wave, scipy.io.wavfile, math, wavefile, sys, json, string
from matplotlib import pyplot, mlab, ticker
from collections import defaultdict
main_filename = "1.wav"
SAMPLE_RATE = 44100
WINDOW_SIZE = 2048
WINDOW_STEP = 512
#################################################################################
types = {
1: numpy.int8,
2: numpy.int16,
4: numpy.int32
}
wav = wave.open(main_filename, mode="r")
(nchannels, sampwidth, framerate, nframes, comptype, compname) = wav.getparams()
duration = nframes / framerate
w, h = 800, 300
k = nframes/w/32
DPI = 72
peak = 256 ** sampwidth / 2
content = wav.readframes(nframes)
samples = numpy.fromstring(content, dtype=types[sampwidth])
pyplot.figure(1, figsize=(float(w)/DPI, float(h)/DPI), dpi=DPI)
pyplot.subplots_adjust(wspace=0, hspace=0)
def format_time(x, pos=None):
global duration, nframes, k
progress = int(x / float(nframes) * duration * k)
mins, secs = divmod(progress, 60)
hours, mins = divmod(mins, 60)
out = "%d:%02d" % (mins, secs)
if hours > 0:
out = "%d:" % hours
return out
def format_db(x, pos=None):
if pos == 0:
return ""
global peak
if x == 0:
return "-inf"
db = 20 * math.log10(abs(x) / float(peak))
return int(db)
#################################################################################
def get_wave_data(filename):
sample_rate, wave_data = scipy.io.wavfile.read(filename)
assert sample_rate == SAMPLE_RATE, sample_rate
if isinstance(wave_data[0], numpy.ndarray):
wave_data = wave_data.mean(1)
return wave_data
def show_specgram(wave_data):
fig = pyplot.figure()
ax = fig.add_axes((0.1, 0.1, 0.8, 0.8))
ax.specgram(wave_data,
NFFT=WINDOW_SIZE, noverlap=WINDOW_SIZE - WINDOW_STEP, Fs=SAMPLE_RATE)
#pyplot.show()
print("WAVE DATA: ")
print(wave_data[0])
#specToJSON(wave_data)
fig.savefig("test.png")
print("[+] specgram showing -- done!")
def getWaveDuration(filename):
w = wave.open(filename, 'r')
time = (1.0 * w.getnframes ()) / w.getframerate ()
return time
def getPerFrame(filename, time):
w = wave.open(filename, 'r')
res = float(time)/float(w.getnframes())
res = format(res, '.8f')
return res
def omniCut(filename):
start = 0
end = 0
for n in range(nchannels):
channel = samples[n::nchannels]
channel = channel[0::k]
if nchannels == 1:
channel = channel - peak
framesCount = len(channel)
print("FramesCount(channel length): " + str(framesCount))
print("Full Frames Count: " + str(wav.getnframes()))
dif = round(float(wav.getnframes()) / float(framesCount))
for i in range(0, framesCount):
if channel[i] > 80:
start = i
break
i = framesCount - 1
while i > 0:
if channel[i] > 80:
end = i
break
i -=1
#start = (float(start) / float(SAMPLE_RATE)) - 0.085
#end = (float(end) / float(SAMPLE_RATE)) + 0.085
res = []
res.append(start)
res.append(end)
res.append(dif)
return res
def analyzeMute(filename):
w = wave.open(filename, 'r')
da = numpy.fromstring(w.readframes(44100), dtype=numpy.int16)
print(str(len(da)) + " is len, one value: " + str(da[30000]))
rate, data = scipy.io.wavfile.read(filename)
t = numpy.arange(len(data[:,0]))*1.0/rate
print(t[1])
for i in range(w.getnframes()):
frame = w.readframes(1)
all_zero = True
for j in range(len(frame)):
if ord(frame[j]) > 0:
all_zero = False
break
if all_zero:
pass
'''print 'silence found at frame %s' % w.tell()
print 'silence found at second %s' % (w.tell()/w.getframerate())'''
print("[+] mute analyzing -- done!")
def cutWav(filename, start, end, dif):
new_name = 'new_' + filename
win= wave.open(filename, 'rb')
wout= wave.open(new_name, 'wb')
print("New CUT WAV: " + new_name)
print(str(start) + " -(start/end)- " + str(end))
print("FrameRate: " + str(win.getframerate()))
print("nFrames: " + str(win.getnframes()))
#start, end = (start*dif) - (start/6), (end*dif) + (end/6)
start, end = (start*dif) - 1000, (end*dif) + 1000
print(dif)
print("New: " + str(start) + " -(start/end)- " + str(end))
s0, s1= int(start), int(end) #40000, 60000 # interval
win.readframes(s0) # discard
frames= win.readframes(s1-s0)
wout.setparams(win.getparams())
wout.writeframes(frames)
win.close()
wout.close()
print("[+] audiofile cutting -- done!")
def getAmplitudeGram(filename):
for n in range(nchannels):
channel = samples[n::nchannels]
channel = channel[0::wav.getnframes()/800/32]
if nchannels == 1:
channel = channel - peak
axes = pyplot.subplot(2, 1, n+1, axisbg="k")
for i in range(0, len(channel)):
if channel[i] > 100:
pass
#print(i)
#print("Channel vertical line: " + str(i))
axes.plot(channel, "g")
#print(channel[10000])
axes.yaxis.set_major_formatter(ticker.FuncFormatter(format_db))
pyplot.grid(True, color="w")
axes.xaxis.set_major_formatter(ticker.NullFormatter())
axes.xaxis.set_major_formatter(ticker.FuncFormatter(format_time))
pyplot.savefig("wave", dpi=DPI)
#pyplot.show()
def getAmplGram(filename):
wr = wave.open(filename, 'r')
sz = 44100 # Read and process 1 second at a time.
da = numpy.fromstring(wr.readframes(sz), dtype=numpy.int16)
wr.close()
left, right = da[0::2], da[1::2]
print(left)
print(right)
print(da)
print("AMPL DONE!")
def getMinMaxAmpl(filename):
w = wavefile.load(filename)
signal = w[1][0]
frames = str(len(signal))+" frames"
minAmpl = str(min(abs(signal))*100)
maxAmpl = str(max(abs(signal))*100)
res = []
res.append(minAmpl)
res.append(maxAmpl)
return res
def specToJSON(arr_data):
spec_arr = []
with open('data.json') as data_file:
spec_arr = json.loads(data_file.read())
i = 0
while i < len(arr_data):
spec_arr['spec'].append(arr_data[i])
i += 1
file = open("data.json","w")
json.dump(spec_arr ,file, indent=4)
def ExpendArr(out, out_len, source, source_len):
k = float((float(out_len) - 1.0) / float(source_len - 1.0))
i = 1
while i < out_len - 1:
i1 = int(i/k)
frac = float(float(i)/float(k) - float(i1))
out[i] = float(float(source[i1]) * (1.0 - frac) + float(source[i1+1]) * frac)
i += 1
out[0] = source[0]
out[out_len - 1] = source[source_len - 1]
return out
def ExpendWaveData(filename):
src = get_wave_data(filename)
out = [None] * 5000
out = ExpendArr(out, len(out), src, len(src))
print(len(src))
print(len(out))
filename = filename[:5]
f = open(filename + '.txt', 'w')
#f = open(filename + '.txt', 'a')
for i in range(0, len(out)):
f.write(str(out[i]) + '\n')
return out
def doGeneral():
global main_filename
arr = list(range(1, 6))
for i in range(0, len(arr)):
main_filename = str(arr[i]) + '.wav'
print(main_filename)
Main(str(arr[i]))
def Main(letter):
wdata = get_wave_data(main_filename)
print("Min&Max Amplitude: " + str(getMinMaxAmpl(main_filename)))
print("Per frame: " + getPerFrame(main_filename, getWaveDuration(main_filename)))
cutting = omniCut(letter + '.wav')
cutWav(main_filename, cutting[0], cutting[1], cutting[2])
analyzeMute(main_filename)
getWaveDuration(main_filename)
wdata = get_wave_data("new_" + main_filename)
show_specgram(wdata)
getAmplitudeGram(main_filename)
getAmplGram("new_" + main_filename)
ExpendWaveData("new_" + main_filename)
doGeneral()