def maybe_create_ad_db():
if os.path.exists(ads_db_path):
return
print 'Create fingerprint db...'
item_paths = []
for item in os.listdir(AD_EXAMPLES_DIR_PATH):
item_path = os.path.join(AD_EXAMPLES_DIR_PATH, item)
try:
audio.get_audio_length(item_path)
except:
print "maybe_create_ad_db: skip item: %s" % item_path
continue
item_paths.append(item_path)
txt_paths_file_path = os.path.join(TMP_DIR_PATH, 'ad_examples_paths.txt')
txt = open(txt_paths_file_path, 'w')
txt.write('\n'.join(item_paths))
txt.close()
# create db
p = subprocess.Popen(
"venv/bin/python "
+ audfprint_script_path
+ " new"
+ " --dbase "+ads_db_path
+ " --density 150"
+ " --shifts 1"
+ " --samplerate 11025"
+ " --list "+txt_paths_file_path,
shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
out, err = p.communicate()
print out
if p.returncode != 0:
raise Exception("failed ads db creating: "+err)
def get_regions_to_cut(wav_path):
fngpt_regions = audio_fingerprint_finder.find_audio_from_db(wav_path)
intrmttnt_regions = audio_intermittent_segmentation.get_commertial_intervals(wav_path)
intrmttnt_regions = [r for r in intrmttnt_regions if r[2] == 1]
print "Fingerprint regions:"
print_regions(fngpt_regions)
print "Audio_intermittent regions:"
print_regions(intrmttnt_regions)
print "---------"
regions_to_cut = []
used_fngpt_regions = []
for ir in intrmttnt_regions:
print "for region %s" % region_str(ir)
# find close to end intro
#print "for time %s" % str(datetime.timedelta(seconds=ir[1]))
start = ir[0]
end = ir[1]
intro_r = find_region_near_time(end, fngpt_regions, 30)
if intro_r:
print "found fngpt region %s" % region_str(intro_r)
end = intro_r[1]
if intro_r[0] < start:
start = intro_r[0]
used_fngpt_regions.append(intro_r)
regions_to_cut.append((start, end, 'commertial'))
else:
# didnt find intro finishing commertial
# accept it only if its in the end
# and cut till the end
total_duration = audio.get_audio_length(wav_path)
if abs(end - total_duration) < 20:
end = total_duration
regions_to_cut.append((start, end, 'commertial'))
print "======="
# also add unused intermittent regions to cut just intro
not_used_fngpt_regions = [r for r in fngpt_regions if not any([x[0] == r[0] for x in used_fngpt_regions])]
regions_to_cut.extend(not_used_fngpt_regions)
if len(not_used_fngpt_regions) > 0:
print('also added %i not used fingerprint regions' % len(not_used_fngpt_regions))
print "commertial regions: "
print_regions(regions_to_cut)
return regions_to_cut
def inject_noise_sample(self, data, noise_path, noise_level):
noise_len = get_audio_length(noise_path)
data_len = len(data) / self.sample_rate
noise_start = np.random.rand() * (noise_len - data_len)
noise_end = noise_start + data_len
noise_dst = audio_with_sox(
noise_path, self.sample_rate, noise_start, noise_end)
assert len(data) == len(noise_dst)
noise_energy = np.sqrt(noise_dst.dot(noise_dst) / noise_dst.size)
data_energy = np.sqrt(data.dot(data) / data.size)
data += noise_level * noise_dst * data_energy / noise_energy
return data
def get_regions_to_save(input_file):
total_duration = audio.get_audio_length(input_file)
regions_to_cut = get_regions_to_cut(input_file)
save_regions = []
for i, region in enumerate(regions_to_cut):
region_start = 0 if i == 0 else regions_to_cut[i-1][1]
region_end = region[0]
region_duration = region_end - region_start
if region_duration < 5:
continue
save_regions.append((region_start, region_end, 'save'))
# add last region
if len(regions_to_cut) > 0:
region_start = regions_to_cut[-1][1]
region_end = total_duration
region_duration = region_end - region_start
if not(region_duration < 5):
save_regions.append((region_start, region_end, 'save'))
else:
save_regions.append((0, total_duration, 'save'))
print '%i save regions: %s' % (len(save_regions), str(save_regions))
return save_regions
def find_audio_from_db(input_path):
maybe_create_ad_db()
precomputed_path = precompute(input_path)
print 'Finding audio from fingerprint db in %s...' % input_path
print 'using db %s' % ads_db_path
matches_file_path = os.path.join(TMP_DIR_PATH, os.path.basename(input_path)+'_matches.txt')
if not os.path.exists(matches_file_path):
print 'Matching...'
p = subprocess.Popen(
"venv/bin/python "
+ audfprint_script_path
+ " match"
+ ' --find-time-range'
+ ' --max-matches 25'
+ " --dbase " + ads_db_path
+ " --ncores 1"
+ ' --sortbytime'
+ ' ' + precomputed_path,
#+ ' --opfile ' + map_file_path,
shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
out, err = p.communicate()
print out
if p.returncode != 0:
raise Exception("failed matching: "+err)
# write to file
txt = open(matches_file_path, 'w')
txt.write(out)
txt.close()
print 'Wrote matches from cache %s' % matches_file_path
# read cached
print 'Read matches from cache %s' % matches_file_path
txt = open(matches_file_path, 'r')
out = txt.read()
txt.close()
# match results
regexp = re.compile(r'Matched[\s]*(?P<matched_duration>[0-9\.]+).*starting at[\s]*(?P<start_target>[0-9\.]*).*to time[\s]*(?P<start_piece>[0-9\.]*).*in.*\/(?P<ad_type>.*)\..*with[\s]*(?P<hashes_matched>[0-9]*).*of[\s]*(?P<hashes_total>[0-9]*)')
groups = [m.groupdict() for m in regexp.finditer(out)]
# collect regions
regions = []
pointer = 0
total_duration = audio.get_audio_length(input_path)
while pointer < len(groups):
m = groups[pointer]
start = float(m['start_target'])
end = float(m['start_target']) + float(m['matched_duration'])
if 'intro' in m['ad_type']:
if len(regions) == 0 and start < 2*60:
# if first and not so far from beginning - cut from 0 to ad end
# (case where there are ads in beginning and then headpiece)
regions.append((
0,
end+1, # + 1 cause specific intro sound
m['ad_type']
))
else:
# else - just cut matched ad
regions.append((
start,
end+1, # + 1 cause specific intro sound
m['ad_type']
))
pointer += 1
else:
raise Exception('WARNING: undefined ad type "%s"' % ad['type'])
print 'fingerptited audio regions: %s' % str(regions)
return regions