def task_transcode(source,
media,
encoder_id,
item_public_id,
mime_type,
metadata=None):
# Get or Set transcoding status flag
item = MediaItem.objects.get(public_id=item_public_id)
transcoded_flag = MediaItemTranscodingFlag.objects.get(item=item,
mime_type=mime_type)
progress_flag, c = MediaItemTranscodingFlag.objects.get_or_create(
item=item, mime_type=mime_type + '/transcoding')
progress_flag.value = False
progress_flag.save()
# Transcode
decoder = get_processor('file_decoder')(source)
encoder = get_processor(encoder_id)(media, streaming=False, overwrite=True)
if metadata:
encoder.set_metadata(metadata)
pipe = decoder | encoder
progress_flag.value = True
progress_flag.save()
pipe.run()
transcoded_flag.value = True
transcoded_flag.save()
progress_flag.delete()
def __init__(self):
"""
Parameters:
----------
"""
super(LabriSing, self).__init__()
# feature extraction defition
feature_plan = [
'mfcc: MFCC blockSize=480 stepSize=160 MelMinFreq=20 MelMaxFreq=5000 MelNbFilters=22 CepsNbCoeffs=12',
'e: Energy blockSize=480 stepSize=160',
'mfcc_d1: MFCC blockSize=480 stepSize=160 MelMinFreq=20 MelMaxFreq=5000 MelNbFilters=22 CepsNbCoeffs=12 > Derivate DOrder=1',
'e_d1: Energy blockSize=480 stepSize=160 > Derivate DOrder=1',
'mfcc_d2: MFCC blockSize=480 stepSize=160 MelMinFreq=20 MelMaxFreq=5000 MelNbFilters=22 CepsNbCoeffs=12 > Derivate DOrder=2',
'e_d2: Energy blockSize=480 stepSize=160 > Derivate DOrder=2'
]
self.parents['yaafe'] = get_processor('yaafe')(
feature_plan=feature_plan, input_samplerate=self.force_samplerate)
self.parents['aubio_temporal'] = get_processor('aubio_temporal')(
) # TF: ici on rajoute AubioTemporal() comme parent
# these are not really taken into account by the system
# these are bypassed by yaafe feature plan
# BUT they are important for aubio (onset detection)
self.input_blocksize = 1024
self.input_stepsize = self.input_blocksize // 2
self.input_samplerate = self.force_samplerate
def task_transcode(source, media, encoder_id,
item_public_id, mime_type,
metadata=None):
# Get or Set transcoding status flag
item = MediaItem.objects.get(public_id=item_public_id)
transcoded_flag = MediaItemTranscodingFlag.objects.get(
item=item,
mime_type=mime_type)
progress_flag, c = MediaItemTranscodingFlag.objects.get_or_create(
item=item,
mime_type=mime_type + '/transcoding')
progress_flag.value = False
progress_flag.save()
# Transcode
decoder = get_processor('file_decoder')(source)
encoder = get_processor(encoder_id)(media,
streaming=False,
overwrite=True)
if metadata:
encoder.set_metadata(metadata)
pipe = decoder | encoder
progress_flag.value = True
progress_flag.save()
pipe.run()
transcoded_flag.value = True
transcoded_flag.save()
progress_flag.delete()
def __init__(self):
"""
Parameters:
----------
"""
super(LabriSing, self).__init__()
# feature extraction defition
feature_plan = ['mfcc: MFCC blockSize=480 stepSize=160 MelMinFreq=20 MelMaxFreq=5000 MelNbFilters=22 CepsNbCoeffs=12',
'e: Energy blockSize=480 stepSize=160',
'mfcc_d1: MFCC blockSize=480 stepSize=160 MelMinFreq=20 MelMaxFreq=5000 MelNbFilters=22 CepsNbCoeffs=12 > Derivate DOrder=1',
'e_d1: Energy blockSize=480 stepSize=160 > Derivate DOrder=1',
'mfcc_d2: MFCC blockSize=480 stepSize=160 MelMinFreq=20 MelMaxFreq=5000 MelNbFilters=22 CepsNbCoeffs=12 > Derivate DOrder=2',
'e_d2: Energy blockSize=480 stepSize=160 > Derivate DOrder=2']
self.parents['yaafe'] = get_processor('yaafe')(feature_plan=feature_plan,
input_samplerate=self.force_samplerate)
self.parents['aubio_temporal'] =get_processor('aubio_temporal')() # TF: ici on rajoute AubioTemporal() comme parent
# these are not really taken into account by the system
# these are bypassed by yaafe feature plan
# BUT they are important for aubio (onset detection)
self.input_blocksize = 1024
self.input_stepsize = self.input_blocksize // 2
self.input_samplerate = self.force_samplerate
def testOnLevelAnaylyzer(self):
from timeside.core import get_processor
from timeside.core.tools.test_samples import samples
wav_file = samples["C4_scale.wav"]
decoder = get_processor("file_decoder")(uri=wav_file)
analyzer = get_processor("level")()
pipe = decoder | analyzer
pipe.run()
self.result = analyzer.results
def testOnLevelAnaylyzer(self):
from timeside.core import get_processor
from timeside.core.tools.test_samples import samples
wav_file = samples['C4_scale.wav']
decoder = get_processor('file_decoder')(uri=wav_file)
analyzer = get_processor('level')()
pipe = (decoder | analyzer)
pipe.run()
self.result = analyzer.results
def tearDown(self):
decoder = FileDecoder(self.source)
encoder_cls = get_processor(self.encoder_id)
file_extension = '.' + encoder_cls.file_extension()
self.target = tmp_file_sink(prefix=self.__class__.__name__,
suffix=file_extension)
encoder = encoder_cls(self.target)
(decoder | encoder).run()
decoder_encoded = FileDecoder(self.target)
pipe = ProcessPipe(decoder_encoded)
pipe.run()
os.unlink(self.target)
#print decoder.channels(), decoder.samplerate(), written_frames
#print media_channels
if self.test_channels:
self.assertEqual(decoder.channels(), decoder_encoded.channels())
else:
self.assertEqual(2, decoder_encoded.channels()) # voaacenc bug ?
self.assertEqual(decoder.samplerate(),
decoder_encoded.samplerate())
if self.test_duration:
self.assertAlmostEqual(decoder.input_duration,
decoder_encoded.input_duration,
delta=0.2)
def _benchmark_analyzer(self, analyzer_id, benchmark_type='time'):
analyzer = get_processor(analyzer_id)()
pipe = (self.decoder | analyzer)
pipe.run()
print pipe.results.keys()
if benchmark_type == 'mem':
return pipe
def analyze_speech(nbuffs=1000, use_vad=False):
decoder = get_processor('live_decoder')(num_buffers=nbuffs, vad=use_vad)
decoder.output_blocksize = 4096
decoder.output_samplerate = 8000
decoder.output_channels = 1
# apply Hamming with numpy before processing
featplan = ['lpc: LPC LPCNbCoeffs=12 blockSize=256 stepSize=64',
'loud: Loudness FFTWindow=Hamming LMode=Total blockSize=256 stepSize=128',
'autopeaks: Energy blockSize=256 stepSize=128 > AutoCorrelationPeaksIntegrator ACPNbPeaks=3 ACPInterPeakMinDist=3 NbFrames=32 StepNbFrames=8']
# 'magspec: MagnitudeSpectrum blockSize=512 stepSize=128 '
level = get_processor('level')()
aubio = get_processor('aubio_pitch')(blocksize_s=256, stepsize_s=64)
yaaf = get_processor('yaafe')(featplan, 8000)
# irit = get_processor('irit_speech_4hz')()
# iritgraph = get_processor('grapher_irit_speech_4hz_segments')
# odf = get_processor('onset_detection_function')()
# odfgraph = get_processor('grapher_onset_detection_function')()
pipe = (decoder | aubio | yaaf | level) # | odf | irit)
pipe.run()
# print 'Pauses: %s' % str(decoder.pauses)
print
loud_mean, loud_std = yaaf.loud_mean(), yaaf.loud_std()
level_rms, level_max = level.results['level.rms'].data[0], level.results['level.max'].data[0]
pitch_mean, pitch_std = mean(aubio.results['aubio_pitch.pitch'].data), std(aubio.results['aubio_pitch.pitch'].data)
syllrate_mean, syllrate_std = yaaf.syll_rate_mean(), yaaf.syll_rate_std()
fps = yaaf.fp_count
print 'Average Loudness = %.1f sons\t(level = %.1f dBFS)' % (yaaf.loud_mean(),
level.results['level.rms'].data[0])
print 'Average articulation rate = %.2f syllables/sec' % (yaaf.syll_rate_mean())
print 'Total filled pauses: %d' % fps
return pipe
def process_wav(wavfile):
# if not os.path.isdir(OUTPUT_DIR):
# os.makedirs(OUTPUT_DIR)
# TODO: set bit rate for mp3 file to 320kbps
# TODO: calculate BPM
decoder = get_processor('file_decoder')(wavfile)
result_dir = os.path.join(OUTPUT_DIR, decoder.sha1)
if not os.path.isdir(result_dir):
os.makedirs(result_dir)
grapher = get_processor('waveform_simple')()
analyzer_aubio_temporal = get_processor('aubio_temporal')()
analyzer_aubio_mfcc = get_processor('aubio_mfcc')()
encoder = get_processor('mp3_encoder')(os.path.join(result_dir, 'encoded.mp3'))
pipe = decoder | grapher | analyzer_aubio_temporal | analyzer_aubio_mfcc | encoder
pipe.run(samplerate=44100)
grapher.render(os.path.join(result_dir, 'waveform_simple.png'))
for res_uuid, result in pipe.results.items():
result_path = os.path.join(result_dir, res_uuid + '.yaml')
result.to_yaml(result_path)
def tearDown(self):
decoder = FileDecoder(self.source)
encoder_cls = get_processor(self.encoder_id)
file_extension = '.' + encoder_cls.file_extension()
self.target_filesink = tmp_file_sink(prefix=self.__class__.__name__,
suffix=file_extension)
self.target_appsink = tmp_file_sink(prefix=self.__class__.__name__,
suffix=file_extension)
encoder = encoder_cls(self.target_filesink, streaming=True)
pipe = (decoder | encoder)
with open(self.target_appsink, 'w') as f:
for chunk in pipe.stream():
f.write(chunk)
decoder_encoded = FileDecoder(self.target_filesink)
pipe2 = ProcessPipe(decoder_encoded)
pipe2.run()
import os
filesink_size = os.path.getsize(self.target_filesink)
appsink_size = os.path.getsize(self.target_appsink)
os.unlink(self.target_filesink)
os.unlink(self.target_appsink)
#print decoder.channels(), decoder.samplerate(), written_frames
#print media_channels
if self.test_channels:
self.assertEqual(decoder.channels(), decoder_encoded.channels())
else:
self.assertEqual(2, decoder_encoded.channels()) # voaacenc bug ?
if not self.expected_sample_rate:
self.expected_sample_rate = decoder.samplerate()
self.assertEqual(self.expected_sample_rate,
decoder_encoded.samplerate())
if self.test_duration:
self.assertAlmostEqual(decoder.input_duration,
decoder_encoded.input_duration,
delta=0.2)
self.assertAlmostEqual(filesink_size, appsink_size,
delta=self.filesize_delta)
def tearDown(self):
decoder = FileDecoder(self.source)
encoder_cls = get_processor(self.encoder_id)
file_extension = '.' + encoder_cls.file_extension()
self.target_filesink = tmp_file_sink(prefix=self.__class__.__name__,
suffix=file_extension)
self.target_appsink = tmp_file_sink(prefix=self.__class__.__name__,
suffix=file_extension)
encoder = encoder_cls(self.target_filesink, streaming=True)
pipe = (decoder | encoder)
with open(self.target_appsink, 'w') as f:
for chunk in pipe.stream():
f.write(chunk)
decoder_encoded = FileDecoder(self.target_filesink)
pipe2 = ProcessPipe(decoder_encoded)
pipe2.run()
import os
filesink_size = os.path.getsize(self.target_filesink)
appsink_size = os.path.getsize(self.target_appsink)
os.unlink(self.target_filesink)
os.unlink(self.target_appsink)
#print decoder.channels(), decoder.samplerate(), written_frames
#print media_channels
if self.test_channels:
self.assertEqual(decoder.channels(), decoder_encoded.channels())
else:
self.assertEqual(2, decoder_encoded.channels()) # voaacenc bug ?
if not self.expected_sample_rate:
self.expected_sample_rate = decoder.samplerate()
self.assertEqual(self.expected_sample_rate,
decoder_encoded.samplerate())
if self.test_duration:
self.assertAlmostEqual(decoder.input_duration,
decoder_encoded.input_duration,
delta=0.2)
self.assertAlmostEqual(filesink_size, appsink_size,
delta=self.filesize_delta)
def process_wav(wavfile):
# if not os.path.isdir(OUTPUT_DIR):
# os.makedirs(OUTPUT_DIR)
# TODO: set bit rate for mp3 file to 320kbps
# TODO: calculate BPM
decoder = get_processor('file_decoder')(wavfile)
result_dir = os.path.join(OUTPUT_DIR, decoder.sha1)
if not os.path.isdir(result_dir):
os.makedirs(result_dir)
grapher = get_processor('waveform_simple')()
analyzer_aubio_temporal = get_processor('aubio_temporal')()
analyzer_aubio_mfcc = get_processor('aubio_mfcc')()
encoder = get_processor('mp3_encoder')(os.path.join(
result_dir, 'encoded.mp3'))
pipe = decoder | grapher | analyzer_aubio_temporal | analyzer_aubio_mfcc | encoder
pipe.run(samplerate=44100)
grapher.render(os.path.join(result_dir, 'waveform_simple.png'))
for res_uuid, result in pipe.results.items():
result_path = os.path.join(result_dir, res_uuid + '.yaml')
result.to_yaml(result_path)
#! /usr/bin/env python
import unittest
from unit_timeside import TestRunner
from timeside.core.tools.test_samples import samples
from timeside.core import get_processor
FileDecoder = get_processor('file_decoder')
try:
AubioSpecdesc = get_processor('aubio_specdesc')
except:
AubioSpecdesc = None
@unittest.skipIf(not AubioSpecdesc, 'Aubio library is not available')
class TestAubioSpecdesc(unittest.TestCase):
def setUp(self):
self.analyzer = AubioSpecdesc()
def testOnSweep(self):
"runs on sweep"
self.source = samples["sweep.wav"]
def testOnC4Scale(self):
"runs on C4 scale"
self.source = samples["C4_scale.wav"]
def tearDown(self):
decoder = FileDecoder(self.source)
(decoder | self.analyzer).run()
results = self.analyzer.results
def setUp(self):
self.analyzer = get_processor('aubio_temporal')()
def setUp(self):
self.analyzer_dissonance = get_processor('essentia_dissonance')()
self.analyzer_dissonance_value = get_processor(
'essentia_dissonance_value')()
import timeside
from timeside.core import get_processor
# Define some processors:
file_decoder = get_processor('gst_dec')('sweep.wav')
analyzer = get_processor('level')()
grapher = get_processor('spectrogram_log')()
encoder = get_processor('gst_vorbis_enc')('sweep.ogg')
# Then, the magic pipeline:
(file_decoder | analyzer | grapher | encoder).run()
# Get the results:
grapher.render(output='image.png')
for key in analyzer.results.keys():
print '%s in %s : %s' % (analyzer.results[key].name,
analyzer.results[key].unit,
analyzer.results[key].data)
#! /usr/bin/env python
import unittest
from unit_timeside import TestRunner
from timeside.core.tools.test_samples import samples
from timeside.core import get_processor
FileDecoder = get_processor('file_decoder')
try:
AubioMfcc = get_processor('aubio_mfcc')
except:
AubioMfcc = None
@unittest.skipIf(not AubioMfcc, 'Aubio library is not available')
class TestAubioMfcc(unittest.TestCase):
def setUp(self):
self.analyzer = AubioMfcc()
def testOnSweep(self):
"runs on sweep"
self.source = samples["sweep.wav"]
def testOnScale(self):
"runs on C4 scale"
self.source = samples["C4_scale.wav"]
def tearDown(self):
decoder = FileDecoder(self.source)
(decoder | self.analyzer).run()
results = self.analyzer.results
def setUp(self):
self.analyzer = get_processor('aubio_melenergy')()
def setUp(self):
self.analyzer = get_processor('loudness_itu')()
def setUp(self):
self.analyzer = get_processor('aubio_pitch')()
import timeside
from timeside.core import get_processor
# Define some processors:
file_decoder = get_processor('gst_dec')('sweep.wav')
analyzer = get_processor('level')()
grapher = get_processor('spectrogram_log')()
encoder = get_processor('gst_vorbis_enc')('sweep.ogg')
# Then, the magic pipeline:
(file_decoder | analyzer | grapher | encoder).run()
# Get the results:
grapher.render(output='image.png')
for key in analyzer.results.keys():
print '%s in %s : %s'% (analyzer.results[key].name,
analyzer.results[key].unit,
analyzer.results[key].data)
def setUp(self):
self.analyzer = get_processor('vamp_tempo')()
def setUp(self):
self.analyzer = get_processor('vamp_simple_host')()
def setUp(self):
self.analyzer = get_processor("aubio_mfcc")()
from __future__ import division
import matplotlib.pylab as pylab
pylab.rcParams['figure.figsize'] = 16, 12 # that's default image size for this interactive session
import timeside
from timeside.core import get_processor
import matplotlib.pyplot as plt
import numpy as np
import sys, os
audiofile = sys.argv[1]
analysis_def = sys.argv[2]
# normal
file_decoder = get_processor('file_decoder')(audiofile, start=10, duration=20)
#e = timeside.encoder.VorbisEncoder('output.ogg', overwrite = True)
aubio_pitch = get_processor('aubio_pitch')()
aubio_temporal = get_processor('aubio_temporal')()
level = get_processor('level')()
specgram_ = get_processor('spectrogram_analyzer')()
waveform = get_processor('waveform_analyzer')()
#g = timeside.grapher.Spectrogram()
speech_decoder = get_processor('file_decoder')(audiofile, start=10, duration=20)
speech_decoder.output_blocksize=2048
irit4hz = get_processor('irit_speech_4hz')()
iritEntropy = get_processor('irit_speech_entropy')()
spspecgram_ = get_processor('spectrogram_analyzer')()
#print 'Pitch (etc.) results:\n'
def setUp(self):
self.analyzer = get_processor('vamp_tempo')()
def setUp(self):
self.analyzer = get_processor('vamp_tuning')()
#! /usr/bin/env python
import unittest
from unit_timeside import TestRunner
from timeside.core.tools.test_samples import samples
from timeside.core import get_processor
FileDecoder = get_processor('file_decoder')
try:
AubioPitch = get_processor('aubio_pitch')
except:
AubioPitch = None
@unittest.skipIf(not AubioPitch, 'Aubio library is not available')
class TestAubioPitch(unittest.TestCase):
def setUp(self):
self.analyzer = AubioPitch()
def testOnSweep(self):
"runs on sweep"
self.source = samples["sweep.wav"]
def testOnC4Scale(self):
"runs on C4 scale"
self.source = samples["C4_scale.wav"]
def tearDown(self):
decoder = FileDecoder(self.source)
(decoder | self.analyzer).run()
results = self.analyzer.results
#! /usr/bin/env python
import unittest
from unit_timeside import TestRunner
from timeside.core.tools.test_samples import samples
from timeside.core import get_processor
FileDecoder = get_processor('file_decoder')
try:
Yaafe = get_processor('yaafe')
except Exception:
Yaafe = None
@unittest.skipIf(not Yaafe, 'Yaafe library is not available')
class TestYaafe(unittest.TestCase):
def setUp(self):
self.samplerate = 16000
def testOnSweepWithFeaturePlan(self):
"runs on sweep"
self.source = samples["sweep.wav"]
# Setup Yaafe Analyzer
# Define Yaafe Feature Plan
fp = ['mfcc: MFCC blockSize=512 stepSize=256',
'mfcc_d1: MFCC blockSize=512 stepSize=256 > Derivate DOrder=1',
'mfcc_d2: MFCC blockSize=512 stepSize=256 > Derivate DOrder=2']
# Setup a new Yaafe TimeSide analyzer
# from FeaturePlan
def setUp(self):
self.analyzer = get_processor('vamp_constantq')()
#! /usr/bin/env python
import unittest
from unit_timeside import TestRunner
from timeside.core import get_processor
from timeside.core.tools.test_samples import samples
FileDecoder = get_processor('file_decoder')
try:
AubioMelEnergy = get_processor('aubio_melenergy')
except:
AubioMelEnergy = None
@unittest.skipIf(not AubioMelEnergy, 'Aubio library is not available')
class TestAubioMelEnergy(unittest.TestCase):
def setUp(self):
self.analyzer = AubioMelEnergy()
def testOnSweep(self):
"runs on sweep"
self.source = samples["sweep.wav"]
def testOnGuitar(self):
"runs on C4_scale"
self.source = samples["C4_scale.wav"]
def tearDown(self):
decoder = FileDecoder(self.source)
(decoder | self.analyzer).run()
results = self.analyzer.results
def setUp(self):
self.analyzer = get_processor('aubio_mfcc')()
def setUp(self):
self.analyzer = get_processor('vamp_tuning')()
def setUp(self):
self.analyzer = get_processor('aubio_melenergy')()
def setUp(self):
self.gain = 2
self.fx = get_processor('fx_gain')(gain=self.gain)
self.level = get_processor('level')()
def __init__(self,
sad_model='etape',
dews=0.2,
speech_threshold=1.,
dllh_min=-10.,
dllh_max=10.,
adapt=False,
exclude=0.01,
keep=0.20):
"""
Parameters:
----------
sad_model : string bellowing to ['etape', 'maya']
Allows the selection of trained speech activity detection models.
* 'etape' models were trained on data distributed in the framework of the
ETAPE campaign (http://www.afcp-parole.org/etape.html)
These models are suited for radionews material (0.974 AUC on Etape data)
* 'maya' models were obtained on data collected by EREA – Centre
Enseignement et Recherche en Ethnologie Amerindienne
These models are suited to speech obtained in noisy environments
(0.915 AUC on Maya data)
dews: dilatation and erosion window size (seconds)
This value correspond to the size in seconds of the sliding window
used to perform a dilation followed by an erosion procedure
these procedures consist to output the max (respectively the min) of the
speech detection estimate. The order of these procedures is aimed at removing
non-speech frames corresponding to fricatives or short pauses
The size of the windows correspond to the minimal size of the resulting
speech/non speech segments
speech_threshold: threshold used for speech/non speech decision
based on the log likelihood difference
dllh_min, dllh_max: raw log likelihood difference estimates will be bound
according this (min_llh_difference, max_llh_difference) tuple
Usefull for plotting log likelihood differences
if set to None, no bounding will be done
adapt: perform unsupervised adaptation of models (bool)
exclude, keep: ratio of higher/lower LLR-frames to keep for retraining
speech/non-speech models
"""
super(LimsiSad, self).__init__()
# feature extraction defition
feature_plan = [
'mfcc: MFCC CepsIgnoreFirstCoeff=0 blockSize=1024 stepSize=256',
'mfccd1: MFCC CepsIgnoreFirstCoeff=0 blockSize=1024 stepSize=256 > Derivate DOrder=1',
'mfccd2: MFCC CepsIgnoreFirstCoeff=0 blockSize=1024 stepSize=256 > Derivate DOrder=2',
'zcr: ZCR blockSize=1024 stepSize=256'
]
yaafe_analyzer = get_processor('yaafe')
self.parents['yaafe'] = yaafe_analyzer(
feature_plan=feature_plan, input_samplerate=self.force_samplerate)
# informative parameters
# these are not really taken into account by the system
# these are bypassed by yaafe feature plan
self.input_blocksize = 1024
self.input_stepsize = 256
# load gmm model
if sad_model not in ['etape', 'maya']:
raise ValueError(
"argument sad_model %s not supported. Supported values are 'etape' or 'maya'"
% sad_model)
self.sad_model = sad_model
path = os.path.split(__file__)[0]
models_dir = os.path.join(path, 'trained_models')
picfname = os.path.join(models_dir, 'limsi_sad_%s.pkl' % sad_model)
self.gmms = pickle.load(open(picfname, 'rb'))
self.dews = dews
self.speech_threshold = speech_threshold
self.dllh_min = dllh_min
self.dllh_max = dllh_max
self.adapt = adapt
self.exclude = exclude
self.keep = keep
def setUp(self):
self.analyzer = get_processor('aubio_specdesc')()
def setUp(self):
self.gain = 2
self.fx = get_processor('fx_gain')(gain=self.gain)
self.level = get_processor('level')()
def setUp(self):
self.analyzer = get_processor('vamp_simple_host')()
#! /usr/bin/env python
import unittest
from unit_timeside import TestRunner
from timeside.core.tools.test_samples import samples
from timeside.core import get_processor
FileDecoder = get_processor('file_decoder')
try:
AubioTemporal = get_processor('aubio_temporal')
except:
AubioTemporal = None
@unittest.skipIf(not AubioTemporal, 'Aubio library is not available')
class TestAubioTemporal(unittest.TestCase):
def setUp(self):
self.analyzer = AubioTemporal()
def testOnSweep(self):
"runs on sweep"
self.source = samples["sweep.wav"]
def testOnC4Scale(self):
"runs on C4 scale"
self.source = samples["C4_scale.wav"]
def tearDown(self):
decoder = FileDecoder(self.source)
(decoder | self.analyzer).run()
results = self.analyzer.results
def setUp(self):
self.analyzer = get_processor('aubio_specdesc')()
def __init__(self, sad_model='etape', dews=0.2, speech_threshold=1.,
dllh_min = -10., dllh_max = 10., adapt = False, exclude = 0.01, keep = 0.20):
"""
Parameters:
----------
sad_model : string bellowing to ['etape', 'maya']
Allows the selection of trained speech activity detection models.
* 'etape' models were trained on data distributed in the framework of the
ETAPE campaign (http://www.afcp-parole.org/etape.html)
These models are suited for radionews material (0.974 AUC on Etape data)
* 'maya' models were obtained on data collected by EREA – Centre
Enseignement et Recherche en Ethnologie Amerindienne
These models are suited to speech obtained in noisy environments
(0.915 AUC on Maya data)
dews: dilatation and erosion window size (seconds)
This value correspond to the size in seconds of the sliding window
used to perform a dilation followed by an erosion procedure
these procedures consist to output the max (respectively the min) of the
speech detection estimate. The order of these procedures is aimed at removing
non-speech frames corresponding to fricatives or short pauses
The size of the windows correspond to the minimal size of the resulting
speech/non speech segments
speech_threshold: threshold used for speech/non speech decision
based on the log likelihood difference
dllh_min, dllh_max: raw log likelihood difference estimates will be bound
according this (min_llh_difference, max_llh_difference) tuple
Usefull for plotting log likelihood differences
if set to None, no bounding will be done
adapt: perform unsupervised adaptation of models (bool)
exclude, keep: ratio of higher/lower LLR-frames to keep for retraining
speech/non-speech models
"""
super(LimsiSad, self).__init__()
# feature extraction defition
feature_plan = ['mfcc: MFCC CepsIgnoreFirstCoeff=0 blockSize=1024 stepSize=256',
'mfccd1: MFCC CepsIgnoreFirstCoeff=0 blockSize=1024 stepSize=256 > Derivate DOrder=1',
'mfccd2: MFCC CepsIgnoreFirstCoeff=0 blockSize=1024 stepSize=256 > Derivate DOrder=2',
'zcr: ZCR blockSize=1024 stepSize=256']
yaafe_analyzer = get_processor('yaafe')
self.parents['yaafe'] = yaafe_analyzer(feature_plan=feature_plan,
input_samplerate=self.force_samplerate)
# informative parameters
# these are not really taken into account by the system
# these are bypassed by yaafe feature plan
self.input_blocksize = 1024
self.input_stepsize = 256
# load gmm model
if sad_model not in ['etape', 'maya']:
raise ValueError(
"argument sad_model %s not supported. Supported values are 'etape' or 'maya'" % sad_model)
self.sad_model = sad_model
path = os.path.split(__file__)[0]
models_dir = os.path.join(path, 'trained_models')
picfname = os.path.join(models_dir, 'limsi_sad_%s.pkl' % sad_model)
self.gmms = pickle.load(open(picfname, 'rb'))
self.dews = dews
self.speech_threshold = speech_threshold
self.dllh_min = dllh_min
self.dllh_max = dllh_max
self.adapt = adapt
self.exclude = exclude
self.keep = keep
def setUp(self):
self.analyzer = get_processor('vamp_constantq')()
def setUp(self):
self.analyzer = get_processor('aubio_pitch')()
