def add_replay_gain(cls, filenames, progress=None):
"""Adds ReplayGain values to a list of filename strings.
All the filenames must be of this AudioFile type.
Raises ValueError if some problem occurs during ReplayGain application.
"""
tracks = [track for track in open_files(filenames) if
isinstance(track, cls)]
if (len(tracks) > 0):
for (track,
track_gain,
track_peak,
album_gain,
album_peak) in calculate_replay_gain(tracks, progress):
metadata = track.get_metadata()
if (metadata is None):
metadata = WavPackAPEv2([])
metadata["replaygain_track_gain"] = ApeTagItem.string(
"replaygain_track_gain",
u"%+1.2f dB" % (track_gain))
metadata["replaygain_track_peak"] = ApeTagItem.string(
"replaygain_track_peak",
u"%1.6f" % (track_peak))
metadata["replaygain_album_gain"] = ApeTagItem.string(
"replaygain_album_gain",
u"%+1.2f dB" % (album_gain))
metadata["replaygain_album_peak"] = ApeTagItem.string(
"replaygain_album_peak",
u"%1.6f" % (album_peak))
track.set_metadata(metadata)
def add_replay_gain(cls, filenames):
"""Adds ReplayGain values to a list of filename strings.
All the filenames must be of this AudioFile type.
Raises ValueError if some problem occurs during ReplayGain application.
"""
tracks = [
track for track in open_files(filenames) if isinstance(track, cls)
]
if (len(tracks) > 0):
for (track, track_gain, track_peak, album_gain,
album_peak) in calculate_replay_gain(tracks):
metadata = track.get_metadata()
if (metadata is None):
metadata = WavPackAPEv2([])
metadata["replaygain_track_gain"] = ApeTagItem.string(
"replaygain_track_gain", u"%+1.2f dB" % (track_gain))
metadata["replaygain_track_peak"] = ApeTagItem.string(
"replaygain_track_peak", u"%1.6f" % (track_peak))
metadata["replaygain_album_gain"] = ApeTagItem.string(
"replaygain_album_gain", u"%+1.2f dB" % (album_gain))
metadata["replaygain_album_peak"] = ApeTagItem.string(
"replaygain_album_peak", u"%1.6f" % (album_peak))
track.set_metadata(metadata)
def compute_gain(self, fnames: Iterable[str], album: bool = True) -> Dict[str, Dict[str, float]]:
fnames = list(fnames)
audio_files = audiotools.open_files(fnames)
if len(audio_files) != len(fnames):
raise Exception("Could not load some files")
rginfo = {}
tag_order = (
"replaygain_track_gain",
"replaygain_track_peak",
"replaygain_album_gain",
"replaygain_album_peak",
)
for rg in audiotools.calculate_replay_gain(audio_files):
rginfo[rg[0].filename] = dict(zip(tag_order, rg[1:]))
return rginfo
def compute_gain(self,
fnames: Iterable[str],
album: bool = True) -> Dict[str, Dict[str, float]]:
fnames = list(fnames)
audio_files = audiotools.open_files(fnames)
if len(audio_files) != len(fnames):
raise Exception("Could not load some files")
rginfo = {}
tag_order = (
"replaygain_track_gain",
"replaygain_track_peak",
"replaygain_album_gain",
"replaygain_album_peak",
)
for rg in audiotools.calculate_replay_gain(audio_files):
rginfo[rg[0].filename] = dict(zip(tag_order, rg[1:]))
return rginfo
def get_redirect_url(self, *args, **kwargs):
album = get_object_or_404(Album, pk=kwargs['pk'])
filenames = []
album_tracks = album.track_set.all()
for track in album_tracks:
filenames.append(audiotools.Filename.from_unicode(track.get_fullpath()))
at_files = audiotools.open_files(filenames, False)
rg_list = audiotools.calculate_replay_gain(at_files)
for index, rg in enumerate(rg_list):
track = album_tracks[index]
track.rg_gain = rg[1]
track.rg_peak = rg[2]
track.save()
if index == 0:
album.rg_gain = rg[3]
album.rg_peak = rg[4]
album.save()
return super(AlbumRecalculateRGView, self).get_redirect_url(kwargs['pk'])
def mixFilesInSpeakerPath(spInd, folder):
speakerPath = os.path.join(rootPathCleanCorpus, folder)
wavFileList = glob.glob(os.path.join(speakerPath, '*.wav'))
print 'Starting speaker %s...' % (folder)
for (ind,wavFile) in enumerate(wavFileList):
try:
(fs, samples) = scipy.io.wavfile.read(wavFile)
#samples = samples.astype(np.float64)/65536.0 # 64 Bit wav files
samples = samples.astype(np.float64)/16384.0 # 16 Bit wav files
#print 'Speech snippet %s read.' % (wavFile)
tmp_file = os.path.dirname(wavFile)
root_dir_name = os.path.basename(tmp_file)
root_filename = str(os.path.splitext(os.path.basename(wavFile))[0])+'_out'+str('.wav')
print '*** Root Name file: '+str(root_filename)
#read annotation
#with open(wavFile.replace("wav", "ano")) as f:
# anoList = [int(line.rstrip()) for line in list(f)]
#if len(anoList) != len(samples):
# print 'Mismatch in size between annotation and track!'
#get replaygain stats of current file
file_rplgain = list(audiotools.calculate_replay_gain([ \
audiotools.open(wavFile) \
]))[0][1]
#calculate gain to ref file and normalize accordingly
gain = file_rplgain - ref_rplgain
normSignal = samples * (10**(gain/20.0))
if (random.random() < probOfNoise):
#mix with noise of same size
noise = getRandomFadedNoise(len(normSignal))
#calculate the random SNR
randomSNR = snrMin + (snrMax-snrMin) * random.random()
#amplify signal by reducing noise
noise /= 10**(randomSNR/20) #normSignal *= 10**(randomSNR/20);
normSignal += noise
# CONVOLVING NOISE MIXED SPEECH SIGNALS WITH THE IMPSULSE RESPONSE SIGNALS
irTrain1 = random.choice(impResps)
irTrain2 = np.asarray(irTrain1)
irTrain = irTrain2.flatten()
#print "irTrain Type: "+str(type(irTrain))
#print "irTrain Value: "+str(irTrain)
#print "irTrain Length: "+str(len(irTrain))
#print "irTrain Shape: "+str(irTrain.shape)
#print "normSignal Length: "+str(len(normSignal))
convolvedSignal1 = scipy.signal.fftconvolve(normSignal, irTrain)[:len(normSignal)]
outputDir = os.path.join(outputPath1, root_dir_name)
#print '*** Output DIR: '+str(outputDir)
#print '*** Root Name file: '+str(root_filename)
outputFile1 = os.path.join(outputDir, root_filename)
#print '*** Output File Name: '+str(outputFile1)
if not os.path.exists(outputDir):
os.makedirs(outputDir)
#shutil.copyfile(wavFile.replace("wav", "ano"), outputFile1.replace("wav", "ano"))
#print 'Writing %s.' % (outputFile)
scipy.io.wavfile.write(outputFile1, wantedFs, convolvedSignal1)
#f = open(outputFile1.replace("wav", "ano"),'w')
#for (ind,line) in enumerate(anoList):
# if ind == (len(anoList) - 1):
# #no \n at end of file
# f.write("%i" % (line))
# else:
# f.write("%i\n" % (line))
#f.close()
except ValueError:
print "Value Error"
print 'Speaker %s done' % (folder)
# f.write("%i" % (line))
# else:
# f.write("%i\n" % (line))
#f.close()
except ValueError:
print "Value Error"
print 'Speaker %s done' % (folder)
if __name__ == '__main__':
cacheNoiseFiles()
cacheImpulseResponses()
#replaygain val of reference file
ref_rplgain = list(audiotools.calculate_replay_gain([ \
audiotools.open(replay_gain_ref_path) \
]))[0][1]
#get folder names (folders = speakers)
all_speaker_names = os.walk(rootPathCleanCorpus).next()[1]
print '%d speakers detected.' % (len(all_speaker_names))
#USING SINGLE PROCESS
for (ind,speaker) in enumerate(all_speaker_names):
mixFilesInSpeakerPath(ind,speaker)
# UTILIZING MULTIPLE PROCESSES via joblib.
#results = Parallel(n_jobs=numJobs)(delayed(mixFilesInSpeakerPath)(ind,speaker) \
# for (ind,speaker) in enumerate(all_speaker_names))
print 'All done.'
def main():
# look for command line arguments
args = sys.argv[1:]
if '-h' in args or '--help' in args or '-?' in args:
helptext()
sys.exit(0)
if '-l' in args:
logging = True
logfilename = args[args.index('-l') + 1]
trimlogfile(logfilename)
logfile = open(logfilename, 'a')
oldstdout = sys.stdout
oldstderr = sys.stderr
logsplit = LogFileSplitter(sys.stdout, logfile)
sys.stdout = logsplit
sys.stderr = logsplit
print('Logging started.')
if '-c' in args:
configfile = args[args.index('-c') + 1]
if not os.path.isfile(configfile):
print(configfile + ' does not exist, create it? (Y/N):')
if not raw_input().lower() == 'y':
print('OK, config file will not be created')
if logging == True:
sys.stdout = oldstdout
sys.stderr = oldstderr
logfile.close()
sys.exit(0)
else:
configfile = '/etc/ssp_sermon_podcast.xml'
if ('--config' in args) or not os.path.isfile(configfile):
edit_config_file(configfile)
print('Config file created and will be used on next run.')
if logging:
sys.stdout = oldstdout
sys.stderr = oldstderr
logfile.close()
sys.exit(0)
#load the config file
if os.path.isfile(configfile):
try:
configET = ET.parse(configfile)
except:
print('Can\'t parse config file ' + configfile)
sys.exit(1)
config = configET.getroot()
if not ((config.tag == 'config') and
(config.attrib['description']
== 'Seriously Simple Podcasting Sermon Podcast settings')):
print(configfile + ' is not a SSP Sermon Podcast config file.')
if logging:
sys.stdout = oldstdout
sys.stderr = oldstderr
logfile.close()
sys.exit(1)
#get the settings from the config
settings = config.find('settings')
#open the wordpress object
wordpress_url = settings.find('wordpress_url').text
if wordpress_url.endswith('/'):
xmlrpc_url = wordpress_url + 'xmlrpc.php'
else:
xmlrpc_url = wordpress_url + '/xmlrpc.php'
wp = Client(xmlrpc_url,
settings.find('wordpress_user').text,
decryptpassword(settings.find('wordpress_pass').text))
#get a list of podcast objects
allpodcasts = []
interval = 20
offset = 0
while True:
podcastbatch = wp.call(
GetPosts({
'post_type': 'podcast',
'number': interval,
'offset': offset
}))
if len(podcastbatch) == 0:
break
allpodcasts.extend(podcastbatch)
offset += interval
print('Retrieved ' + str(len(allpodcasts)) +
' podcasts from WordPress site.')
#get the series settings from the config and find out which series will be podcast
allseriesconfigs = config.findall('series_config')
termids_to_podcast = []
for seriesconfig in allseriesconfigs:
termids_to_podcast.append(seriesconfig.attrib['term_id'])
#get a list of series from the blog
listofseriesterms = []
interval = 20
offset = 0
while True:
termsbatch = wp.call(
GetTerms('series', {
'number': interval,
'offset': offset
}))
if len(termsbatch) == 0:
break
listofseriesterms.extend(termsbatch)
offset += interval
print('Found ' + str(len(listofseriesterms)) +
' podcast series on the WordPress site.')
#find out the hierarchy of the series so we can do the lowest children first
termpriority = {}
term_parents = {}
for term in listofseriesterms:
term_parents[term.id] = term.parent
order = 0
parentid = term.parent
while parentid != '0':
order += 1
for parentterm in listofseriesterms:
if parentid == parentterm.id:
parentid = parentterm.parent
break
termpriority[term.id] = order
#so the order to approach term.ids is
termid_order = []
for termid, order in sorted(termpriority.iteritems(),
key=lambda x: x[1],
reverse=True):
termid_order.append(termid)
print('This is the order the series terms will be published:')
print(', '.join(termid_order))
#find which series config the posts should be published with (if any)
podcasts_to_do = {}
extension = extension_dot(settings.findtext('source_audio_type'))
for termid in termid_order:
if termid in termids_to_podcast:
for podcast in allpodcasts:
#check whether the podcast is flagged to be published and has a date:
date_recorded = get_post_custom_field(podcast, 'date_recorded')
if get_post_custom_field(podcast,
'publish_now') and date_recorded:
podcast_termids = ['0']
for podcastterm in podcast.terms:
podcast_termids.append(podcastterm.id)
for podcast_termid in podcast_termids:
if podcast_termid in term_parents:
podcast_termids.append(
term_parents[podcast_termid])
if termid in podcast_termids and not podcast.id in podcasts_to_do:
#work out what the start of the source file name will be:
termid_seriesconfig = seriescfg_from_term_id(
allseriesconfigs, termid)
source_date_format = termid_seriesconfig.find(
'source_date_format').text
date_recorded_format = settings.find(
'date_recorded_format').text
sourcefile_name_start = getdatetime(
date_recorded, user_format=date_recorded_format
).strftime(
source_date_format) + termid_seriesconfig.findtext(
'source_file_code', default='')
sourcepath = termid_seriesconfig.findtext(
'source_path')
#and does it exist?
directorylist = []
if os.path.exists(sourcepath):
#this seems to timeout sometimes, so will loop if need be:
retrycount = 3
while retrycount:
try:
directorylist = os.listdir(sourcepath)
retrycount = 0
except OSError as errmsg:
print(errmsg)
retrycount -= 1
if retrycount:
print('Retrying directory list...')
for filename in directorylist:
if filename[:len(sourcefile_name_start
)] == sourcefile_name_start:
if extension:
extposn = -len(extension)
if filename[
extposn:] == extension or extension == None:
ordered_podcast_termids = []
for termid_again in termid_order:
if termid_again in podcast_termids:
ordered_podcast_termids.append(
termid_again)
ordered_podcast_termids.append('0')
podcasts_to_do[podcast.id] = [
podcast, termid_seriesconfig,
os.path.abspath(
os.path.join(sourcepath,
filename)),
ordered_podcast_termids
]
print('There are ' + str(len(podcasts_to_do)) +
' podcasts to process in this pass.')
if len(podcasts_to_do) != 0:
listofposttags = []
interval = 20
offset = 0
while True:
termsbatch = wp.call(
GetTerms('post_tag', {
'number': interval,
'offset': offset
}))
if len(termsbatch) == 0:
break
listofposttags.extend(termsbatch)
offset += interval
posttagsdict = {}
for posttag in listofposttags:
posttagsdict[posttag.name.lower()] = posttag
print('Retrieved ' + str(len(posttagsdict)) +
' post tags from WordPress site.')
#iterate over the podcasts
for podcast_id, podcast_and_config in podcasts_to_do.iteritems():
#open the audio file
print('\n')
print('Now processing file ' + podcast_and_config[2])
backuppodcast = copy.deepcopy(podcast_and_config[0])
try:
sourceaudio = audiotools.open(podcast_and_config[2])
sourcepcm = sourceaudio.to_pcm()
#calculate its loudness
loudness = audiotools.calculate_replay_gain([sourceaudio])
for loudnesstuple in loudness:
gain = loudnesstuple[1]
peak = loudnesstuple[2]
if peak == 0:
print('This audio file is silent, ignoring it.')
continue
#mix it to the specified number of channels
gaincorrection = 0
if settings.findtext('audiochannels') == '1':
print('Converting to mono.')
sourcepcm_mixed = audiotools.pcmconverter.Averager(sourcepcm)
elif settings.findtext('audiochannels') == '2':
print('Converting to stereo.')
sourcepcm_mixed = audiotools.pcmconverter.Downmixer(sourcepcm)
if sourceaudio.channels() == 1:
gaincorrection = 6.0
else:
sourcepcm_mixed = sourcepcm
#adjust the gain to the users' preference instead of replaygain's target -20
target_loudness = float(
settings.findtext('target_loudness', default='-24'))
newgain = gain + (target_loudness + 20.0) + gaincorrection
newpeak = 1.0 / (10.0**(newgain / 20.0))
if (peak / (10.0**(gaincorrection / 20.0))) > newpeak:
newpeak = peak / (10.0**(gaincorrection / 20.0))
print(
'Normalising for gain: ' + str(round(newgain, 2)) +
'dB, peak = ' + str(
round(
(20.0 * log10(peak /
(10.0**(gaincorrection / 20.0)))), 2)) +
'dBFS.')
#normalise the audio to the target loudness
sourcepcm_normalised = audiotools.replaygain.ReplayGainReader(
sourcepcm_mixed, newgain, newpeak)
try:
bitspersample = int(settings.findtext('bitspersample'))
except:
bitspersample = None
if bitspersample:
print('Quantising to ' + str(bitspersample) + '-bit.')
sourcepcm_resampled = audiotools.pcmconverter.BPSConverter(
sourcepcm_normalised, bitspersample)
#make some tempfiles:
process_tempfile = tempfile.mkstemp(
suffix='.wav', prefix='sermon_process_tempfile')
processed_tempfile = tempfile.mkstemp(
suffix='.wav', prefix='sermon_processed_tempfile')
encoded_tempfile = tempfile.mkstemp(
suffix=extension_dot(settings.findtext('encoded_audio_type')),
prefix='sermon_encoded_tempfile')
print('tempfiles: ' + process_tempfile[1] + ', ' +
processed_tempfile[1] + ', ' + encoded_tempfile[1])
#write the audio back out to a wave file for processing
audiotools.WaveAudio.from_pcm(process_tempfile[1],
sourcepcm_resampled)
sourcepcm_normalised.close()
sourcepcm_mixed.close()
sourcepcm.close()
sourceaudio = None
audioparams = getaudioparams(sourcepcm_resampled)
sourcepcm_resampled.close()
subprocess_args = [settings.findtext('processing_utility')]
for argsubelement in settings.findall('processing_utility_arg'):
subprocess_args.append(
Template(argsubelement.text).substitute(audioparams))
tempstring = settings.findtext('processing_utility_infile')
if tempstring:
subprocess_args.append(tempstring)
subprocess_args.append(process_tempfile[1])
tempstring = settings.findtext('processing_utility_outfile')
if tempstring:
subprocess_args.append(tempstring)
subprocess_args.append(processed_tempfile[1])
print('Now processing audio ...')
print(
subprocess.Popen(subprocess_args,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
universal_newlines=True).communicate()[0])
os.remove(process_tempfile[1])
processedfile = audiotools.open(processed_tempfile[1])
audioparams = getaudioparams(processedfile.to_pcm())
subprocess_args = [settings.findtext('encoding_utility')]
for argsubelement in settings.findall('encoding_utility_arg'):
subprocess_args.append(
Template(argsubelement.text).substitute(audioparams))
tempstring = settings.findtext('encoding_utility_infile')
if tempstring:
subprocess_args.append(tempstring)
subprocess_args.append(processed_tempfile[1])
tempstring = settings.findtext('encoding_utility_outfile')
if tempstring:
subprocess_args.append(tempstring)
subprocess_args.append(encoded_tempfile[1])
print('Now encoding audio ...')
print(
subprocess.Popen(subprocess_args,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
universal_newlines=True).communicate()[0])
os.remove(processed_tempfile[1])
wp_details = make_podcast_dict(podcast_and_config, wp, settings)
wp_details['post_status'] = 'publish'
wp_details['publish_now'] = ''
updated_podcast = publish_post(podcast_and_config, wp_details, wp,
settings)
podcast_and_config[0] = updated_podcast
updated_details = make_podcast_dict(podcast_and_config,
wp,
settings,
final_pass=True)
try:
imageurl = urllib2.urlopen(updated_details['image'])
podcastimage = imageurl.read()
except:
podcastimage = False
try:
audioimage = [
audiotools.Image.new(podcastimage, u'Artwork', 0)
]
except:
audioimage = []
outputmetadata = audiotools.MetaData(
track_name=updated_details['title'],
track_number=int(updated_details['episode_number']),
album_name=updated_details['series'],
artist_name=updated_details['preacher'],
copyright=updated_details['copyright'],
publisher=updated_details['publisher'],
year=updated_details['date'].strftime('%Y'),
date=updated_details['date_recorded'],
comment=updated_details['content'],
images=audioimage)
outputfile = audiotools.open(encoded_tempfile[1])
outputfile.set_metadata(outputmetadata)
outputfile_seconds = int(outputfile.seconds_length())
outputfile_name = updated_details[
'output_file_template'] + extension_dot(
settings.findtext('encoded_audio_type'))
outputfile_size = ftp_encodedfile(encoded_tempfile[1],
outputfile_name,
podcast_and_config[1])
if outputfile_size == None:
raise Exception('FTP appears not to have worked.')
print('\n')
print('Output file size = ' + str(outputfile_size))
print('Output file duration = ' + str(outputfile_seconds))
print('\n')
os.remove(encoded_tempfile[1])
urlpath = podcast_and_config[1].findtext('download_path')
if not urlpath[-1] == '/':
urlpath = urlpath + '/'
updated_details['audio_file'] = urlpath + outputfile_name
updated_details['filesize_raw'] = str(outputfile_size)
mins = str(outputfile_seconds / 60)
secs = str(outputfile_seconds % 60)
if len(secs) == 1:
secs = '0' + secs
updated_details['duration'] = mins + ':' + secs
#put the preacher in as a tag:
updated_details['tags'] = []
if updated_details['preacher'].lower() in posttagsdict:
updated_details['tags'].append(
posttagsdict[updated_details['preacher'].lower()])
else:
tag = WordPressTerm()
tag.taxonomy = 'post_tag'
tag.name = updated_details['preacher']
tag.id = wp.call(NewTerm(tag))
updated_details['tags'].append(tag)
posttagsdict[tag.name.lower()] = tag
#put the book(s) of the bible in as tags:
#This bit is really messy and I should try to write my own scripture regular expressions, but in the interest of speed:
listofpassages = scriptures.extract(
updated_details['bible_passage'])
if 'song of songs' in updated_details['bible_passage'].lower():
listofpassages.append(('Song of Songs', 1, 1, 1, 1))
for passage in listofpassages:
book = passage[0]
if book[:4] == 'III ':
bookname = '3 ' + book[4:]
elif book[:3] == 'II ':
bookname = '2 ' + book[3:]
elif book[:2] == 'I ':
bookname = '1 ' + book[2:]
elif book == 'Song of Solomon':
bookname = 'Song of Songs'
else:
bookname = book
if bookname.lower() in posttagsdict:
updated_details['tags'].append(
posttagsdict[bookname.lower()])
else:
tag = WordPressTerm()
tag.taxonomy = 'post_tag'
tag.name = bookname
tag.id = wp.call(NewTerm(tag))
updated_details['tags'].append(tag)
posttagsdict[tag.name.lower()] = tag
finalpost = publish_post(podcast_and_config, updated_details, wp,
settings)
print('Final Post details are as follows:\n')
for field, contents in finalpost.struct.iteritems():
try:
if type(contents) == types.StringType:
print(field + ' : ' + contents)
elif type(contents) == types.ListType:
for subcontents in contents:
print(field + ' : ' + str(subcontents))
elif type(contents) == types.DictType:
for subfield, subcontents in contents.iteritems():
print(field + ' : ' + subfield + ' : ' +
str(subcontents))
elif type(contents) == types.UnicodeType:
print(field + ' : ' +
contents.encode('ascii', 'ignore'))
else:
print(field + ' : ' + str(contents))
except:
print('Can\'t print field')
except Exception as message:
print('ERROR: Exception raised while processing that podcast:')
print(message)
print(
'Attempting to restore original post prior to modification...')
try:
if wp.call(EditPost(backuppodcast.id, backuppodcast)):
print('Post restored.')
else:
print('Unable to restore original post.')
except Exception as message:
print('Unable to restore original post: ')
print(message)
try:
os.remove(encoded_tempfile[1])
except:
pass
try:
os.remove(processed_tempfile[1])
except:
pass
try:
os.remove(process_tempfile[1])
except:
pass
logsplit.write('Completed with normal exit\n\n\n')
if logging:
sys.stdout = oldstdout
sys.stderr = oldstderr
logfile.close()
def audioeval(speechFile, referenceFile,noiseFile, root_dir_name, output_root_directory,ir_noise_file):
"This function evaluates a single audio file."
print 'Noise File Path: '+str(noiseFile)
print 'IR-Noise File Path: '+str(ir_noise_file)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# ReplayGain calculation of reference
ref = audiotools.open(referenceFile)
ref_replay_gain = audiotools.calculate_replay_gain([ref])
ref_track_gain = list(list(ref_replay_gain)[0])[1]
#print ref_track_gain
# ReplayGain calculation of example speech file
speech = audiotools.open(speechFile)
speech_replay_gain = audiotools.calculate_replay_gain([speech])
speech_track_gain = list(list(speech_replay_gain)[0])[1]
#print speech_track_gain
# Normalization of example speech file
(rate_speech, data_speech) = wav.read(speechFile)
gain = ref_track_gain-speech_track_gain
data_normalized = numpy.asarray(data_speech*math.pow(10, (-(gain)/20)), dtype=numpy.int16)
normalizedFile = "speech_normalized.wav"
wav.write(normalizedFile , rate_speech, data_normalized)
# Loudness test of normalized example speech
test = audiotools.open(normalizedFile)
test_replay_gain = audiotools.calculate_replay_gain([test])
test_track_gain = list(list(test_replay_gain)[0])[1]
#print test_track_gain
# Randomly choosing one noise file from the pool
# here I just fix one waiting for implementation later
# Using pydub API to calculate the length of normalized speech file and the noise file
speech_normalized = pydub.AudioSegment.from_wav(normalizedFile)
#We have converted all the noise files to 16 bit int format and then passed the directoyr location to randomly choose noise files, which is different for each speech file.
noise = pydub.AudioSegment.from_wav(noiseFile)
speech_normalized_length = speech_normalized.duration_seconds
noise_length = noise.duration_seconds
# Selecting a randow start point of the noise file to get a segment of the required length
start = random.randrange(0,int(noise_length-speech_normalized_length)*1000)
# pydub does things in milliseconds
noise_segmented = noise[start:int(start+speech_normalized_length*1000)]
noise_segmented.export("noise_segmented.wav",format="wav")
# Linear fading of sharply segmented noised segment
# 1 sec fade in, 1 sec fade out
noise_faded = noise_segmented.fade_in(1000).fade_out(1000)
noise_faded.export("noise_faded.wav",format="wav")
# how long is good? 1 sec?
# Picking a random signal to noise ratio (SNR)
SNR_ratio = random.randint(-2, 20)
#print "SNR_ratio: " + str(SNR_ratio)
# loudness in dBFS (Decibels relative to full scale)
# (all peak measurements will be negative numbers)
speech_dB = speech_normalized.dBFS
noise_dB = noise_segmented.dBFS
#print "loudness of speech: " + str(speech_dB)
#print "loudness of noise: " + str(noise_dB)
# Change the amplitude (generally, loudness) of the speech by SNR ratio from noise.
# Gain is specified in dB.
gain = SNR_ratio-(speech_dB-noise_dB)
#print "gain: " + str(gain)
speech_SNRed = speech_normalized.apply_gain(gain)
#print "loudness of adjusted speech: " + str(speech_SNRed.dBFS)
# check SNR
#print "check SNR: " + str(speech_SNRed.dBFS - noise_dB)
# mix the two tracks by adding the respective samples
# (If the overlaid AudioSegment is longer than this one, the result will be truncated)
noisy_speech = speech_SNRed.overlay(noise_segmented)
noisy_speech.export("noisy_speech.wav",format="wav")
# Since the sample values have increased through the summation, it is possible that they exceed the maximum imposed by the data type. How this API deals with this problem?
# draw an impulse response from the pool
# ...waiting to implement
# peak-normalize it to 0dB (=1) by dividing the IR vector through its maximum value.
(rate_IR, data_IR) = wav.read(ir_noise_file)
# data_IR.dtype is int16, change it into float64
data_IR = data_IR.astype(numpy.float64)/65536.0
data_IR = data_IR / data_IR.max()
# convolve speech with the normalized IR
(rate_noisy_speech, data_noisy_speech) = wav.read("noisy_speech.wav")
speech_convolved = numpy.convolve(data_IR, data_noisy_speech)
#print "Root Directory Name: "+str(root_dir_name)
output_directory = os.path.join(output_root_directory, root_dir_name)
#print output_directory
if not os.path.exists(output_directory):
os.makedirs(output_directory)
#speech_convolved_file = output_directory+'/'+str(os.path.splitext(speechFile)[0])+"_convolved.wav"
speech_convolved_file_name = os.path.basename(speechFile)
#print "Speech File Name: "+str(speech_convolved_file_name)
speech_convolved_file = os.path.join(output_directory, speech_convolved_file_name)
print "Final output file path: "+str(speech_convolved_file)
# cut the convolved track to its original length if prolonged and store the resulting track
wav.write(speech_convolved_file, rate_noisy_speech, speech_convolved[:data_noisy_speech.size])
# MFCC CODE *********** COMMENTED OUT **************
# MFCC Feature extraction
# Do the default parameters (frame size etc.) work for you?
#(rate,sig) = wav.read(speech_convolved_file)
#mfcc_feat = mfcc(sig,rate)
#print "MFCC Shape:"
#print mfcc_feat.shape
#print mfcc_feat
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Cleaup code which deletes the intermediate files which get generated.
return;
def mixFilesInSpeakerPath(spInd, folder):
speakerPath = os.path.join(rootPathCleanCorpus, folder)
wavFileList = glob.glob(os.path.join(speakerPath, '*.wav'))
print 'Starting speaker %s...' % (folder)
for (ind, wavFile) in enumerate(wavFileList):
try:
(fs, samples) = scipy.io.wavfile.read(wavFile)
#samples = samples.astype(np.float64)/65536.0 # 64 Bit wav files
samples = samples.astype(np.float64) / 16384.0 # 16 Bit wav files
#print 'Speech snippet %s read.' % (wavFile)
tmp_file = os.path.dirname(wavFile)
root_dir_name = os.path.basename(tmp_file)
root_filename = str(
os.path.splitext(
os.path.basename(wavFile))[0]) + '_out' + str('.wav')
print '*** Root Name file: ' + str(root_filename)
#read annotation
#with open(wavFile.replace("wav", "ano")) as f:
# anoList = [int(line.rstrip()) for line in list(f)]
#if len(anoList) != len(samples):
# print 'Mismatch in size between annotation and track!'
#get replaygain stats of current file
file_rplgain = list(audiotools.calculate_replay_gain([ \
audiotools.open(wavFile) \
]))[0][1]
#calculate gain to ref file and normalize accordingly
gain = file_rplgain - ref_rplgain
normSignal = samples * (10**(gain / 20.0))
if (random.random() < probOfNoise):
#mix with noise of same size
noise = getRandomFadedNoise(len(normSignal))
#calculate the random SNR
randomSNR = snrMin + (snrMax - snrMin) * random.random()
#amplify signal by reducing noise
noise /= 10**(randomSNR / 20
) #normSignal *= 10**(randomSNR/20);
normSignal += noise
# CONVOLVING NOISE MIXED SPEECH SIGNALS WITH THE IMPSULSE RESPONSE SIGNALS
irTrain1 = random.choice(impResps)
irTrain2 = np.asarray(irTrain1)
irTrain = irTrain2.flatten()
#print "irTrain Type: "+str(type(irTrain))
#print "irTrain Value: "+str(irTrain)
#print "irTrain Length: "+str(len(irTrain))
#print "irTrain Shape: "+str(irTrain.shape)
#print "normSignal Length: "+str(len(normSignal))
convolvedSignal1 = scipy.signal.fftconvolve(
normSignal, irTrain)[:len(normSignal)]
outputDir = os.path.join(outputPath1, root_dir_name)
#print '*** Output DIR: '+str(outputDir)
#print '*** Root Name file: '+str(root_filename)
outputFile1 = os.path.join(outputDir, root_filename)
#print '*** Output File Name: '+str(outputFile1)
if not os.path.exists(outputDir):
os.makedirs(outputDir)
#shutil.copyfile(wavFile.replace("wav", "ano"), outputFile1.replace("wav", "ano"))
#print 'Writing %s.' % (outputFile)
scipy.io.wavfile.write(outputFile1, wantedFs, convolvedSignal1)
#f = open(outputFile1.replace("wav", "ano"),'w')
#for (ind,line) in enumerate(anoList):
# if ind == (len(anoList) - 1):
# #no \n at end of file
# f.write("%i" % (line))
# else:
# f.write("%i\n" % (line))
#f.close()
except ValueError:
print "Value Error"
print 'Speaker %s done' % (folder)
# if ind == (len(anoList) - 1):
# #no \n at end of file
# f.write("%i" % (line))
# else:
# f.write("%i\n" % (line))
#f.close()
except ValueError:
print "Value Error"
print 'Speaker %s done' % (folder)
if __name__ == '__main__':
cacheNoiseFiles()
cacheImpulseResponses()
#replaygain val of reference file
ref_rplgain = list(audiotools.calculate_replay_gain([ \
audiotools.open(replay_gain_ref_path) \
]))[0][1]
#get folder names (folders = speakers)
all_speaker_names = os.walk(rootPathCleanCorpus).next()[1]
print '%d speakers detected.' % (len(all_speaker_names))
#USING SINGLE PROCESS
for (ind, speaker) in enumerate(all_speaker_names):
mixFilesInSpeakerPath(ind, speaker)
# UTILIZING MULTIPLE PROCESSES via joblib.
#results = Parallel(n_jobs=numJobs)(delayed(mixFilesInSpeakerPath)(ind,speaker) \
# for (ind,speaker) in enumerate(all_speaker_names))
print 'All done.'
def audioeval(speechFile, referenceFile, noiseFile, root_dir_name,
output_root_directory, ir_noise_file):
"This function evaluates a single audio file."
print 'Noise File Path: ' + str(noiseFile)
print 'IR-Noise File Path: ' + str(ir_noise_file)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# ReplayGain calculation of reference
ref = audiotools.open(referenceFile)
ref_replay_gain = audiotools.calculate_replay_gain([ref])
ref_track_gain = list(list(ref_replay_gain)[0])[1]
#print ref_track_gain
# ReplayGain calculation of example speech file
speech = audiotools.open(speechFile)
speech_replay_gain = audiotools.calculate_replay_gain([speech])
speech_track_gain = list(list(speech_replay_gain)[0])[1]
#print speech_track_gain
# Normalization of example speech file
(rate_speech, data_speech) = wav.read(speechFile)
gain = ref_track_gain - speech_track_gain
data_normalized = numpy.asarray(data_speech * math.pow(10, (-(gain) / 20)),
dtype=numpy.int16)
normalizedFile = "speech_normalized.wav"
wav.write(normalizedFile, rate_speech, data_normalized)
# Loudness test of normalized example speech
test = audiotools.open(normalizedFile)
test_replay_gain = audiotools.calculate_replay_gain([test])
test_track_gain = list(list(test_replay_gain)[0])[1]
#print test_track_gain
# Randomly choosing one noise file from the pool
# here I just fix one waiting for implementation later
# Using pydub API to calculate the length of normalized speech file and the noise file
speech_normalized = pydub.AudioSegment.from_wav(normalizedFile)
#We have converted all the noise files to 16 bit int format and then passed the directoyr location to randomly choose noise files, which is different for each speech file.
noise = pydub.AudioSegment.from_wav(noiseFile)
speech_normalized_length = speech_normalized.duration_seconds
noise_length = noise.duration_seconds
# Selecting a randow start point of the noise file to get a segment of the required length
start = random.randrange(
0,
int(noise_length - speech_normalized_length) * 1000)
# pydub does things in milliseconds
noise_segmented = noise[start:int(start + speech_normalized_length * 1000)]
noise_segmented.export("noise_segmented.wav", format="wav")
# Linear fading of sharply segmented noised segment
# 1 sec fade in, 1 sec fade out
noise_faded = noise_segmented.fade_in(1000).fade_out(1000)
noise_faded.export("noise_faded.wav", format="wav")
# how long is good? 1 sec?
# Picking a random signal to noise ratio (SNR)
SNR_ratio = random.randint(-2, 20)
#print "SNR_ratio: " + str(SNR_ratio)
# loudness in dBFS (Decibels relative to full scale)
# (all peak measurements will be negative numbers)
speech_dB = speech_normalized.dBFS
noise_dB = noise_segmented.dBFS
#print "loudness of speech: " + str(speech_dB)
#print "loudness of noise: " + str(noise_dB)
# Change the amplitude (generally, loudness) of the speech by SNR ratio from noise.
# Gain is specified in dB.
gain = SNR_ratio - (speech_dB - noise_dB)
#print "gain: " + str(gain)
speech_SNRed = speech_normalized.apply_gain(gain)
#print "loudness of adjusted speech: " + str(speech_SNRed.dBFS)
# check SNR
#print "check SNR: " + str(speech_SNRed.dBFS - noise_dB)
# mix the two tracks by adding the respective samples
# (If the overlaid AudioSegment is longer than this one, the result will be truncated)
noisy_speech = speech_SNRed.overlay(noise_segmented)
noisy_speech.export("noisy_speech.wav", format="wav")
# Since the sample values have increased through the summation, it is possible that they exceed the maximum imposed by the data type. How this API deals with this problem?
# draw an impulse response from the pool
# ...waiting to implement
# peak-normalize it to 0dB (=1) by dividing the IR vector through its maximum value.
(rate_IR, data_IR) = wav.read(ir_noise_file)
# data_IR.dtype is int16, change it into float64
data_IR = data_IR.astype(numpy.float64) / 65536.0
data_IR = data_IR / data_IR.max()
# convolve speech with the normalized IR
(rate_noisy_speech, data_noisy_speech) = wav.read("noisy_speech.wav")
speech_convolved = numpy.convolve(data_IR, data_noisy_speech)
#print "Root Directory Name: "+str(root_dir_name)
output_directory = os.path.join(output_root_directory, root_dir_name)
#print output_directory
if not os.path.exists(output_directory):
os.makedirs(output_directory)
#speech_convolved_file = output_directory+'/'+str(os.path.splitext(speechFile)[0])+"_convolved.wav"
speech_convolved_file_name = os.path.basename(speechFile)
#print "Speech File Name: "+str(speech_convolved_file_name)
speech_convolved_file = os.path.join(output_directory,
speech_convolved_file_name)
print "Final output file path: " + str(speech_convolved_file)
# cut the convolved track to its original length if prolonged and store the resulting track
wav.write(speech_convolved_file, rate_noisy_speech,
speech_convolved[:data_noisy_speech.size])
# MFCC CODE *********** COMMENTED OUT **************
# MFCC Feature extraction
# Do the default parameters (frame size etc.) work for you?
#(rate,sig) = wav.read(speech_convolved_file)
#mfcc_feat = mfcc(sig,rate)
#print "MFCC Shape:"
#print mfcc_feat.shape
#print mfcc_feat
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Cleaup code which deletes the intermediate files which get generated.
return
