def test(filename=None):
import random, os
import matplotlib.pyplot as plt
from sys import argv
#signal, params = read_signal(sound,WINSIZE)
scenario = None
if filename != None:
scene = os.path.basename(filename)[0]
else:
filename = random.choice([
x for x in os.listdir("tmp/") if os.path.splitext(x)[1] == ".flac"
])
scene = filename[0]
filename = "tmp/" + filename
print(filename)
truths = vad.load_truths()
signal, rate = speech.read_soundfile(filename)
seconds = float(len(signal)) / rate
winsize = librosa.time_to_samples(float(WINMS) / 1000, rate)[0]
window = sp.hanning(winsize)
ltsd = LTSD(winsize, window, 5)
res, threshold, nstart, nend = ltsd.compute(signal)
segments = ltsd.segments(res, threshold)
#print(float(len(signal))/rate, librosa.core.frames_to_time(len(res), 8000, winsize/2))
segments = librosa.core.frames_to_time(segments, rate, winsize / 2)
fig = plt.figure()
ax = fig.add_subplot(111)
#ax.plot((signal/np.max(signal))*np.mean(res)+np.mean(res))
ax.plot(np.linspace(0, seconds, len(res)), res)
ax.plot([0, seconds], [threshold, threshold])
vad.plot_segments(truths[scene]['combined'], segments, ax)
n1 = float(nstart) / rate
n2 = float(nend) / rate
ax.vlines([n1, n2], -20, 20)
plt.show()
def test(filename=None):
import random, os
import matplotlib.pyplot as plt
from sys import argv
#signal, params = read_signal(sound,WINSIZE)
scenario=None
if filename != None:
scene = os.path.basename(filename)[0]
else:
filename = random.choice([x for x in os.listdir("tmp/") if os.path.splitext(x)[1] == ".flac"])
scene = filename[0]
filename = "tmp/"+filename
print(filename)
truths = vad.load_truths()
signal,rate = speech.read_soundfile(filename)
seconds = float(len(signal))/rate
winsize = librosa.time_to_samples(float(WINMS)/1000, rate)[0]
window = sp.hanning(winsize)
ltsd = LTSD(winsize,window,5)
res, threshold,nstart,nend = ltsd.compute(signal)
segments = ltsd.segments(res, threshold)
#print(float(len(signal))/rate, librosa.core.frames_to_time(len(res), 8000, winsize/2))
segments = librosa.core.frames_to_time(segments, rate, winsize/2)
fig = plt.figure()
ax = fig.add_subplot(111)
#ax.plot((signal/np.max(signal))*np.mean(res)+np.mean(res))
ax.plot(np.linspace(0,seconds, len(res)), res)
ax.plot([0, seconds], [threshold, threshold])
vad.plot_segments(truths[scene]['combined'], segments, ax)
n1 = float(nstart)/rate
n2 = float(nend)/rate
ax.vlines([n1,n2], -20,20)
plt.show()
print(filename)
if argv[1] != 'batch':
sig, rate, frames, fsize, naccs, acvars, ltacs, more = pipeline(
filename)
predictions = []
seconds = float(len(sig)) / rate
if argv[1] == 'sig':
plt.plot(
sigutil.deframesig(frames.T, len(sig), fsize, fsize / 2,
signal.hanning))
plt.show()
elif argv[1] == 'ac-spec':
librosa.display.specshow(naccs)
plt.show()
elif argv[1] == 'var':
vad.plot_segments(truths[scene]['combined'], [], plt)
plt.plot(np.linspace(0, seconds, len(acvars)), acvars)
plt.show()
elif argv[1] == 'ltac':
vad.plot_segments(truths[scene]['combined'], [], plt)
plt.plot(np.linspace(0, seconds, len(ltacs)), ltacs)
plt.show()
elif argv[1] == 'ac-feature':
vad.plot_segments(truths[scene][scene + 'i'], 'ti', plt)
vad.plot_segments(truths[scene][scene + 'j'], 'tj', plt)
alpha = 0.05
beta = 2
seconds = float(len(sig)) / rate
average = moving_average(ltacs)
lmin, smoothmin = local_min_array(ltacs)
threshold = alpha * lmin + (1 - alpha) * average + beta
filename = random.choice([x for x in os.listdir("tmp/") if os.path.splitext(x)[1] == ".flac"])
scene = filename[0]
filename = "tmp/"+filename
print(filename)
if argv[1] != 'batch':
sig, rate, frames, fsize, naccs, acvars, ltacs, more = pipeline(filename)
predictions = []
seconds = float(len(sig))/rate
if argv[1] == 'sig':
plt.plot(sigutil.deframesig(frames.T,len(sig),fsize,fsize/2,signal.hanning))
plt.show()
elif argv[1] == 'ac-spec':
librosa.display.specshow(naccs)
plt.show()
elif argv[1] == 'var':
vad.plot_segments(truths[scene]['combined'], [], plt)
plt.plot(np.linspace(0,seconds, len(acvars)), acvars)
plt.show()
elif argv[1] == 'ltac':
vad.plot_segments(truths[scene]['combined'], [], plt)
plt.plot(np.linspace(0,seconds, len(ltacs)), ltacs)
plt.show()
elif argv[1] == 'ac-feature':
vad.plot_segments(truths[scene][scene+'i'], 'ti', plt)
vad.plot_segments(truths[scene][scene+'j'], 'tj', plt)
lmin, smoothmin = local_min_array(ltacs)
lmin = lmin+7
vad.plot_segments(predict(ltacs, lmin), 'p', plt)
#plt.plot(np.linspace(0,seconds, len(acvars)), acvars)
plt.plot(np.linspace(0,seconds, len(ltacs)), ltacs)
plt.plot(np.linspace(0,seconds, len(lmin)), lmin+2)
filename = random.choice([x for x in os.listdir("tmp/") if os.path.splitext(x)[1] == ".flac"])
scene = filename[0]
filename = "tmp/"+filename
truths = vad.load_truths()
print(filename)
sig, rate, frames, fsize, nacc, acvars, ltacs = pipeline(filename)
seconds = float(len(sig))/rate
if argv >= 2 and argv[1] is not 'batch':
if argv[1] == 'sig':
plt.plot(sigutil.deframesig(frames.T,len(sig),fsize,fsize/2,signal.hanning))
plt.show()
if argv[1] == 'ac':
librosa.display.specshow(nacc)
plt.show()
elif argv[1] == 'var':
vad.plot_segments(truths[scene][scene+'i'], 'ti', plt)
vad.plot_segments(truths[scene][scene+'j'], 'tj', plt)
plt.plot(np.linspace(0,seconds, len(acvars)), acvars)
plt.show()
elif argv[1] == 'ltac':
vad.plot_segments(truths[scene][scene+'i'], 'ti', plt)
vad.plot_segments(truths[scene][scene+'j'], 'tj', plt)
plt.plot(np.linspace(0,seconds, len(ltacs)), ltacs)
plt.show()
elif argv[1] == 'test':
print(len(ltacs))
segments,thresholds = predict(ltacs)
vad.plot_segments(truths[scene][scene+'i'], 'ti', plt)
vad.plot_segments(truths[scene][scene+'j'], 'tj', plt)
vad.plot_segments(segments, 'p', plt)
plt.plot(np.linspace(0,seconds, len(ltacs)), ltacs)
res_name = argv[3]+"/sosens_"+os.path.basename(os.path.splitext(f)[0])+".txt"
frame_ms = 64
tasks.append([filename, res_name, frame_ms])
#predictions = predict(rms, H, rms_t, H_t)
#predictions = librosa.core.frames_to_time(predictions, rate, fsize).tolist()
#write_results(predictions, res_name, seconds)
r = pool.map_async(compute_vad, tasks)
r.wait()
pool.terminate()
pool.join()
else:
print(filename)
frame_ms = 64
sig, rate, frames, fsize, rms, H, p = pipeline(filename, frame_ms)
seconds = float(len(sig))/rate
vad.plot_segments(truths[scene][scene+'i'], 'ti', plt)
vad.plot_segments(truths[scene][scene+'j'], 'tj', plt)
rms_t, rms_t_smooth = local_min_array(rms)
H_a = average(H, 20)
H_min, H_min_smooth = local_min_array(H, 30)
rms_t += 0.012
H_t = H_min+(H_a*0.2)
predictions = predict(rms, H, rms_t, H_t)
predictions = librosa.core.frames_to_time(predictions, rate, fsize).tolist()
vad.plot_segments(predictions, 'p', plt)
plt.plot(np.linspace(0,seconds, len(rms)), rms)
plt.plot(np.linspace(0,seconds, len(H)), H)
plt.plot(np.linspace(0,seconds, len(rms_t)), rms_t)
plt.plot(np.linspace(0,seconds, len(H_t)), H_t)
plt.show()
filename = "tmp/" + filename
truths = vad.load_truths()
print(filename)
sig, rate, frames, fsize, nacc, acvars, ltacs = pipeline(filename)
seconds = float(len(sig)) / rate
if argv >= 2 and argv[1] is not 'batch':
if argv[1] == 'sig':
plt.plot(
sigutil.deframesig(frames.T, len(sig), fsize, fsize / 2,
signal.hanning))
plt.show()
if argv[1] == 'ac':
librosa.display.specshow(nacc)
plt.show()
elif argv[1] == 'var':
vad.plot_segments(truths[scene][scene + 'i'], 'ti', plt)
vad.plot_segments(truths[scene][scene + 'j'], 'tj', plt)
plt.plot(np.linspace(0, seconds, len(acvars)), acvars)
plt.show()
elif argv[1] == 'ltac':
vad.plot_segments(truths[scene][scene + 'i'], 'ti', plt)
vad.plot_segments(truths[scene][scene + 'j'], 'tj', plt)
plt.plot(np.linspace(0, seconds, len(ltacs)), ltacs)
plt.show()
elif argv[1] == 'test':
print(len(ltacs))
segments, thresholds = predict(ltacs)
vad.plot_segments(truths[scene][scene + 'i'], 'ti', plt)
vad.plot_segments(truths[scene][scene + 'j'], 'tj', plt)
vad.plot_segments(segments, 'p', plt)
plt.plot(np.linspace(0, seconds, len(ltacs)), ltacs)
