def VAD(folder,new_folder):
for filename in os.listdir(folder):
audio_regions = split(folder+'/'+filename)
for region in audio_regions:
region.play(progress_bar=True)
newname = region.save(new_folder+'/'+filename)
print("region saved as: {}".format(newname))
def get_regions(url, dirpath, min_duration, max_duration):
yt = YouTube(url)
for stream in yt.streams.filter(mime_type="audio/webm"):
stream.download(dirpath, TEMP_FILENAME)
regions = split(
os.path.join(dirpath, TEMP_FILENAME + ".webm"),
min_duration,
max_duration,
)
return {"regions": list(regions)}
return ("No avaiable webm audios", 404)
def get_noise_duration(filename: str):
audio_regions = auditok.split(
filename,
min_dur=0.2, # minimum duration of a valid audio event in seconds
max_dur=4, # maximum duration of an event
max_silence=
0.3, # maximum duration of tolerated continuous silence within an event
energy_threshold=55 # threshold of detection
)
noise_duration = 0.0
for i, r in enumerate(audio_regions):
noise_duration += (r.meta.end - r.meta.start)
return noise_duration
def find_regions(fname,
min_dur=.2,
max_dur=2,
max_silence=.3,
energy_threshold=55,
analysis_window=.01,
drop_trailing_silence=True):
'''
Uses empirical values to calculate regions of when the participant started speaking and when he/she finished.
Input:
------
fname (string): full path to .wav recording
Output:
-------
result (list of 3 element tuples): 0 - region id, 1 - region start, 2 - region end
*.jpg (image): saves an image with the region identified in the same folder with the .wav recording
picName (string): the name of the image (same as fname with .jpg extension)
'''
kwargs = {
'min_dur': min_dur,
'max_dur': max_dur,
'max_silence': max_silence,
'energy_threshold': energy_threshold,
'analysis_window': analysis_window,
'drop_trailing_silence': drop_trailing_silence
}
audio_regions = auditok.split(fname, **kwargs)
result = [(i, r.meta.start, r.meta.end)
for i, r in enumerate(audio_regions)]
# Plot
region = auditok.load(fname)
picName = fname.rstrip(
'.wav') + '.jpg' # Change '.wav' if another format is used
regions = region.splitp(show=False, save_as=picName, **kwargs)
# Prints a RuntimeWarning: More than ... figures have been opened
# Haven't found a way to bypass it
plt.close('all')
del regions
return result, picName
def on_data_received(self, data):
self._global_data += audioop.ulaw2lin(RTP(data).load,
2) # data.decode()
audio_regions = auditok.split(
self._global_data,
audio_format='bytes',
sampling_rate=8000,
sample_width=2,
channels=1,
min_dur=0.3, # minimum duration of a valid audio event in seconds
max_dur=6, # maximum duration of an event
max_silence=
0.3, # maximum duration of tolerated continuous silence within an event
energy_threshold=50 # threshold of detection
)
if len(list(audio_regions)) > 1:
self.queue_audio.put(self._global_data)
self._global_data = b''
def check_test_regions(search_uuid: str):
"""
Check whether non-silence regions have been derived for a given file.
If no such regions exist, then derive them and insert the corresponding
time spans into the test_regions table.
"""
unregioned_files = crud.get_unregioned_files(db, search_uuid)
for file_uuid in unregioned_files:
tmp_path = f"/tmp/{file_uuid}.wav"
# Fetch file from s3 bucket if not already in /tmp folder
if not os.path.isfile(tmp_path):
s3.fget_object("audio-wav", f"{file_uuid}.wav", tmp_path)
test_regions = auditok.split(tmp_path, energy_threshold=40)
for r in test_regions:
crud.create_test_region(db,
UUID(file_uuid), round(r.meta.start, 3),
round(r.meta.end, 3))
if len(unregioned_files) == 0:
logger.info(
"Audio activitiy regions already derived for all test file(s).")
else:
derived_regions = ", ".join(unregioned_files)
logger.info(
f"Audio activitiy regions derived for test file(s): {derived_regions}"
)
# Pass search_uuid to next task in chain
return search_uuid