class DeezerComplete(Provider):
"""Deezer Plugin representing complete tracks on Deezer's infrastructure"""
implements(IProvider)
@staticmethod
@interfacedoc
def id():
return 'deezer_complete'
@staticmethod
@interfacedoc
def name():
return "Deezer Complete"
@staticmethod
@interfacedoc
def ressource_access():
return False
@interfacedoc
def get_source_from_id(self, external_id, path, download=False):
return ''
@interfacedoc
def get_source_from_url(self, url, path, download=False):
return ''
@interfacedoc
def get_id_from_url(self, url):
return url.split("/")[-1:][0]
class WaveformContourWhite(WaveformContourBlack):
"""an white amplitude contour wavform.
"""
implements(IGrapher)
@interfacedoc
def __init__(self,
width=1024,
height=256,
bg_color=(255, 255, 255),
color_scheme='default'):
super(WaveformContourWhite, self).__init__(width, height, bg_color,
color_scheme)
self.color_offset = 60
@staticmethod
@interfacedoc
def id():
return "waveform_contour_white"
@staticmethod
@interfacedoc
def name():
return "Contour white"
class NewGrapher(cls):
_id = grapher_id
implements(IGrapher)
@interfacedoc
def __init__(self,
width=1024,
height=256,
bg_color=(0, 0, 0),
color_scheme='default'):
super(NewGrapher, self).__init__(width, height, bg_color,
color_scheme)
self.parents.append(analyzer)
self._result_id = result_id # TODO : make it generic when analyzer will be "atomize"
@staticmethod
@interfacedoc
def id():
return grapher_id
@staticmethod
@interfacedoc
def name():
return grapher_name
__doc__ = """Builds a PIL image representing """ + grapher_name
class WavEncoder(GstEncoder):
"""WAV encoder based on Gstreamer"""
implements(IEncoder)
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(WavEncoder, self).setup(channels, samplerate, blocksize,
totalframes)
self.pipe = ''' appsrc name=src
! audioconvert
! wavenc
'''
if self.filename and self.streaming:
self.pipe += ''' ! tee name=t
! queue ! filesink location=%s
t. ! queue ! appsink name=app sync=False
''' % self.filename
elif self.filename:
self.pipe += '! filesink location=%s async=False sync=False ' % self.filename
else:
self.pipe += '! queue ! appsink name=app sync=False '
self.start_pipeline(channels, samplerate)
@staticmethod
@interfacedoc
def id():
return "wav_encoder"
@staticmethod
@interfacedoc
def format():
return "WAV"
@staticmethod
@interfacedoc
def version():
return "1.0"
@staticmethod
@interfacedoc
def file_extension():
return "gst.wav"
@staticmethod
@interfacedoc
def mime_type():
return "audio/x-wav"
@interfacedoc
def set_metadata(self, metadata):
# TODO
pass
class IRITDiverg(Analyzer):
implements(IAnalyzer)
'''
'''
def __init__(self, blocksize=1024, stepsize=None):
super(IRITDiverg, self).__init__()
self.parents['waveform'] = Waveform()
self.ordre = 2
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(IRITDiverg, self).setup(channels, samplerate, blocksize,
totalframes)
@staticmethod
@interfacedoc
def id():
return "irit_diverg"
@staticmethod
@interfacedoc
def name():
return "IRIT Forward/Backward Divergence Segmentation"
@staticmethod
@interfacedoc
def unit():
return ""
def __str__(self):
return "Stationnary Segments"
def process(self, frames, eod=False):
return frames, eod
def post_process(self):
audio_data = self.parents['waveform'].results['waveform_analyzer'].data
if audio_data.shape[1] > 1:
data = list(audio_data.mean(axis=1))
else:
data = list(audio_data)
frontieres = segment(data, self.samplerate(), self.ordre)
segs = self.new_result(data_mode='label', time_mode='event')
segs.id_metadata.id += '.' + 'segments'
segs.id_metadata.name += ' ' + 'Segments'
label = {0: 'Instable', 1: 'Forward', -1: 'Backward'}
segs.data_object.label_metadata.label = label
segs.data_object.label = [s[1] for s in frontieres]
segs.data_object.time = [(float(s[0]) / self.samplerate())
for s in frontieres]
self.add_result(segs)
return
class WaveformContourWhite(WaveformContourBlack):
""" Builds a PIL image representing an amplitude coutour (envelop) of the audio stream.
"""
implements(IGrapher)
@interfacedoc
def __init__(self,
width=1024,
height=256,
bg_color=(255, 255, 255),
color_scheme='default'):
super(WaveformContourWhite, self).__init__(width, height, bg_color,
color_scheme)
self.color_offset = 60
@staticmethod
@interfacedoc
def id():
return "waveform_contour_white"
@staticmethod
@interfacedoc
def name():
return "Contour white"
class Gain(Processor):
"""Gain effect processor"""
implements(IEffect)
@interfacedoc
def __init__(self, gain=1.0):
super(Gain, self).__init__()
self.gain = gain
@staticmethod
@interfacedoc
def id():
return "fx_gain"
@staticmethod
@interfacedoc
def version():
return "1.0"
@staticmethod
@interfacedoc
def name():
return "Gain effect"
def process(self, frames, eod=False):
return numpy.multiply(frames, self.gain), eod
class Waveform(Grapher):
"""Simple monochrome waveform image.
"""
implements(IGrapher)
@interfacedoc
def __init__(self,
width=1024,
height=256,
bg_color=(255, 255, 255),
color_scheme='default'):
super(Waveform, self).__init__(width, height, bg_color, color_scheme)
self.line_color = (0, 0, 0)
@staticmethod
@interfacedoc
def id():
return "waveform_simple"
@staticmethod
@interfacedoc
def name():
return "Waveform simple"
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(Waveform, self).setup(channels, samplerate, blocksize,
totalframes)
@interfacedoc
def process(self, frames, eod=False):
if len(frames) != 1:
if len(frames.shape) > 1:
buffer = frames[:, 0]
else:
buffer = frames
buffer.shape = (len(buffer), 1)
for samples, end in self.pixels_adapter.process(buffer, eod):
if self.pixel_cursor < self.image_width - 1:
self.draw_peaks(self.pixel_cursor, peaks(samples),
self.line_color)
self.pixel_cursor += 1
if self.pixel_cursor == self.image_width - 1:
self.draw_peaks(self.pixel_cursor, peaks(samples),
self.line_color)
self.pixel_cursor += 1
return frames, eod
@interfacedoc
def post_process(self, output=None):
a = 1
for x in range(self.image_width):
self.pixel[x, self.image_height / 2] = tuple(
map(lambda p: p + a, self.pixel[x, self.image_height / 2]))
class VorbisEncoder(GstEncoder):
""" gstreamer-based OGG Vorbis encoder """
implements(IEncoder)
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(VorbisEncoder, self).setup(channels, samplerate, blocksize,
totalframes)
self.pipe = ''' appsrc name=src
! audioconvert ! audioresample
! vorbisenc quality=0.9
! oggmux
'''
if self.filename and self.streaming:
self.pipe += ''' ! tee name=t
! queue ! filesink location=%s
t. ! queue ! appsink name=app sync=False
''' % self.filename
elif self.filename:
self.pipe += '! filesink location=%s async=False sync=False ' % self.filename
else:
self.pipe += '! queue ! appsink name=app sync=False '
self.start_pipeline(channels, samplerate)
@staticmethod
@interfacedoc
def id():
return "gst_vorbis_enc"
@staticmethod
@interfacedoc
def description():
return "Vorbis GStreamer based encoder"
@staticmethod
@interfacedoc
def format():
return "OGG"
@staticmethod
@interfacedoc
def file_extension():
return "ogg"
@staticmethod
@interfacedoc
def mime_type():
return "application/ogg"
@interfacedoc
def set_metadata(self, metadata):
self.metadata = metadata
class AubioEncoder(Processor):
implements(IEncoder)
abstract()
type = 'encoder'
def __init__(self, output, streaming=False, overwrite=False):
super (AubioEncoder, self).__init__()
if isinstance(output, str):
import os.path
if os.path.isdir(output):
raise IOError("Encoder output must be a file, not a directory")
elif os.path.isfile(output) and not overwrite:
raise IOError(
"Encoder output %s exists, but overwrite set to False")
self.filename = output
else:
self.filename = None
self.streaming = streaming
if not self.filename and not self.streaming:
raise Exception('Must give an output')
self.eod = False
self.metadata = None
self.num_samples = 0
@interfacedoc
def setup(self, channels=None, samplerate=None, blocksize=None,
totalframes=None):
super(AubioEncoder, self).setup(
channels, samplerate, blocksize, totalframes)
print ('setting up aubio with with', samplerate, blocksize)
self.sink = aubio.sink(self.filename, samplerate, channels)
@interfacedoc
def release(self):
if hasattr (self, 'sink'):
self.sink.close()
delattr(self, 'sink')
@interfacedoc
def set_metadata(self, metadata):
self.metadata = metadata
@interfacedoc
def process(self, frames, eod=False):
self.eod = eod
max_write = 4096
indices = range(max_write, frames.shape[0], max_write)
for f_slice in np.array_split(frames, indices):
write_frames = f_slice.shape[0]
self.sink.do_multi(f_slice.T.copy(), write_frames)
self.num_samples += write_frames
return frames, eod
class DeezerPreview(Provider):
"""Deezer Plugin to retrieve deezer's 30 seconds tracks preview"""
implements(IProvider)
@staticmethod
@interfacedoc
def id():
return 'deezer_preview'
@staticmethod
@interfacedoc
def name():
return "Deezer Preview"
@staticmethod
@interfacedoc
def ressource_access():
return True
@interfacedoc
def get_source_from_id(self, external_id, path, download=False):
request_url = 'https://api.deezer.com/track/' + external_id
try:
request = get(request_url)
assert request.status_code == 200
except AssertionError:
raise ProviderError('deezer_preview', external_id=external_id)
request_dict = json.loads(request.content)
source_uri = request_dict['preview']
if download:
file_name = request_dict['artist']['name'] + '-' + request_dict[
'title_short'] + '-' + external_id
file_name = slugify(file_name) + '.mp3'
file_path = os.path.join(path, file_name)
r = get(source_uri)
if not os.path.exists(path):
os.makedirs(path)
with open(file_path, 'wb') as f:
f.write(r.content)
f.close()
return file_path
else:
return source_uri
@interfacedoc
def get_source_from_url(self, url, path, download=False):
deezer_track_id = self.get_id_from_url(url)
try:
return self.get_source_from_id(deezer_track_id, path, download)
except ProviderError:
raise ProviderError('deezer_preview', external_uri=url)
@interfacedoc
def get_id_from_url(self, url):
return url.split("/")[-1:][0]
class AubioMfcc(Analyzer):
"""Aubio MFCC analyzer"""
implements(IAnalyzer)
def __init__(self):
super(AubioMfcc, self).__init__()
self.input_blocksize = 1024
self.input_stepsize = self.input_blocksize / 4
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(AubioMfcc, self).setup(channels, samplerate, blocksize,
totalframes)
self.n_filters = 40
self.n_coeffs = 13
self.pvoc = pvoc(self.input_blocksize, self.input_stepsize)
self.mfcc = mfcc(self.input_blocksize, self.n_filters, self.n_coeffs,
samplerate)
self.block_read = 0
self.mfcc_results = numpy.zeros([
self.n_coeffs,
])
@staticmethod
@interfacedoc
def id():
return "aubio_mfcc"
@staticmethod
@interfacedoc
def name():
return "MFCC (aubio)"
@staticmethod
@interfacedoc
def unit():
return ""
@downmix_to_mono
@frames_adapter
def process(self, frames, eod=False):
fftgrain = self.pvoc(frames)
coeffs = self.mfcc(fftgrain)
self.mfcc_results = numpy.vstack((self.mfcc_results, coeffs))
self.block_read += 1
return frames, eod
def post_process(self):
mfcc = self.new_result(data_mode='value', time_mode='framewise')
mfcc.parameters = dict(n_filters=self.n_filters,
n_coeffs=self.n_coeffs)
mfcc.data_object.value = self.mfcc_results
self.process_pipe.results.add(mfcc)
class WaveformCentroid(Waveform):
"""Waveform where peaks are colored relatively to the spectral centroids of each frame buffer.
"""
implements(IGrapher)
@interfacedoc
def __init__(self,
width=1024,
height=256,
bg_color=(0, 0, 0),
color_scheme='default'):
super(WaveformCentroid, self).__init__(width, height, bg_color,
color_scheme)
self.lower_freq = 200
colors = default_color_schemes[color_scheme]['waveform']
self.color_lookup = interpolate_colors(colors)
@staticmethod
@interfacedoc
def id():
return "waveform_centroid"
@staticmethod
@interfacedoc
def name():
return "Waveform spectral"
@staticmethod
@interfacedoc
def version():
return "1.0"
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(WaveformCentroid, self).setup(channels, samplerate, blocksize,
totalframes)
@interfacedoc
def process(self, frames, eod=False):
if len(frames) != 1:
buffer = frames[:, 0].copy()
buffer.shape = (len(buffer), 1)
for samples, end in self.pixels_adapter.process(buffer, eod):
if self.pixel_cursor < self.image_width:
(spectral_centroid,
db_spectrum) = self.spectrum.process(samples, True)
line_color = self.color_lookup[int(spectral_centroid *
255.0)]
self.draw_peaks(self.pixel_cursor, peaks(samples),
line_color)
self.pixel_cursor += 1
return frames, eod
class OpusEncoder(GstEncoder):
"""Opus encoder based on Gstreamer"""
implements(IEncoder)
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(OpusEncoder, self).setup(channels, samplerate, blocksize,
totalframes)
self.pipe = '''appsrc name=src
! audioconvert ! audioresample
! opusenc audio=true bitrate=128000
! oggmux
'''
if self.filename and self.streaming:
self.pipe += ''' ! tee name=t
! queue ! filesink location=%s
t. ! queue! appsink name=app sync=False
''' % self.filename
elif self.filename:
self.pipe += '! filesink location=%s async=False sync=False ' % self.filename
else:
self.pipe += '! queue ! appsink name=app sync=False'
self.start_pipeline(channels, samplerate)
@staticmethod
@interfacedoc
def id():
return "opus_encoder"
@staticmethod
@interfacedoc
def format():
return "Opus"
@staticmethod
@interfacedoc
def file_extension():
return "opus"
@staticmethod
@interfacedoc
def mime_type():
return "audio/ogg"
@interfacedoc
def set_metadata(self, metadata):
self.metadata = metadata
class WaveformContourBlack(Waveform):
"""Black amplitude contour waveform.
"""
implements(IGrapher)
@interfacedoc
def __init__(self,
width=1024,
height=256,
bg_color=(0, 0, 0),
color_scheme='default'):
super(WaveformContourBlack, self).__init__(width, height, bg_color,
color_scheme)
self.contour = numpy.zeros(self.image_width)
self.ndiv = 4
self.x = numpy.r_[0:self.image_width - 1:1]
self.symetry = True
self.color_offset = 160
@staticmethod
@interfacedoc
def id():
return "waveform_contour_black"
@staticmethod
@interfacedoc
def name():
return "Contour black"
@staticmethod
@interfacedoc
def version():
return "1.0"
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(WaveformContourBlack, self).setup(channels, samplerate,
blocksize, totalframes)
@interfacedoc
def process(self, frames, eod=False):
if len(frames) != 1:
buffer = frames[:, 0].copy()
buffer.shape = (len(buffer), 1)
for samples, end in self.pixels_adapter.process(buffer, eod):
if self.pixel_cursor < self.image_width:
self.contour[self.pixel_cursor] = numpy.max(peaks(samples))
self.pixel_cursor += 1
if eod:
self.draw_peaks_contour()
return frames, eod
class AubioMelEnergy(Analyzer):
"""Aubio Mel Energy analyzer"""
implements(IAnalyzer)
def __init__(self):
super(AubioMelEnergy, self).__init__()
self.input_blocksize = 1024
self.input_stepsize = self.input_blocksize / 4
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(AubioMelEnergy, self).setup(channels, samplerate, blocksize,
totalframes)
self.n_filters = 40
self.n_coeffs = 13
self.pvoc = pvoc(self.input_blocksize, self.input_stepsize)
self.melenergy = filterbank(self.n_filters, self.input_blocksize)
self.melenergy.set_mel_coeffs_slaney(samplerate)
self.block_read = 0
self.melenergy_results = []
@staticmethod
@interfacedoc
def id():
return "aubio_melenergy"
@staticmethod
@interfacedoc
def name():
return "Mel Energy (aubio)"
@staticmethod
@interfacedoc
def unit():
return ""
@downmix_to_mono
@frames_adapter
def process(self, frames, eod=False):
fftgrain = self.pvoc(frames)
self.melenergy_results.append(self.melenergy(fftgrain))
self.block_read += 1
return frames, eod
def post_process(self):
melenergy = self.new_result(data_mode='value', time_mode='framewise')
melenergy.parameters = dict(n_filters=self.n_filters,
n_coeffs=self.n_coeffs)
melenergy.data_object.value = self.melenergy_results
self.process_pipe.results.add(melenergy)
class WaveformTransparent(Waveform):
"""Transparent waveform.
"""
implements(IGrapher)
@interfacedoc
def __init__(self,
width=1024,
height=256,
bg_color=None,
color_scheme='default'):
super(WaveformTransparent, self).__init__(width, height, bg_color,
color_scheme)
self.line_color = (255, 255, 255)
@staticmethod
@interfacedoc
def id():
return "waveform_transparent"
@staticmethod
@interfacedoc
def name():
return "Waveform transparent"
@staticmethod
@interfacedoc
def version():
return "1.0"
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(WaveformTransparent, self).setup(channels, samplerate, blocksize,
totalframes)
@interfacedoc
def process(self, frames, eod=False):
if len(frames) != 1:
buffer = frames[:, 0]
buffer.shape = (len(buffer), 1)
for samples, end in self.pixels_adapter.process(buffer, eod):
if self.pixel_cursor < self.image_width - 1:
self.draw_peaks_inverted(self.pixel_cursor, peaks(samples),
self.line_color)
self.pixel_cursor += 1
if self.pixel_cursor == self.image_width - 1:
self.draw_peaks_inverted(self.pixel_cursor, peaks(samples),
self.line_color)
self.pixel_cursor += 1
return frames, eod
class NewGrapher(cls):
_id = grapher_id
_staging = staging
_from_analyzer = True
_analyzer = analyzer
_analyzer_parameters = analyzer_parameters
_result_id = result_id
_grapher_name = grapher_name
implements(IGrapher)
@interfacedoc
def __init__(self, width=1024, height=256, bg_color=(0, 0, 0),
color_scheme='default'):
super(NewGrapher, self).__init__(width, height, bg_color,
color_scheme)
# Add a parent waveform analyzer
if background == 'waveform':
self._background = True
bg_analyzer = timeside.plugins.analyzer.waveform.Waveform()
self._bg_id = bg_analyzer.id()
self.parents['bg_analyzer'] = bg_analyzer
elif background == 'spectrogram':
self._background = True
bg_analyzer = timeside.plugins.analyzer.spectrogram.Spectrogram()
self._bg_id = bg_analyzer.id()
self.parents['bg_analyzer'] = bg_analyzer
else:
self._background = None
parent_analyzer = analyzer(**analyzer_parameters)
self.parents['analyzer'] = parent_analyzer
self._result_id = result_id
@staticmethod
@interfacedoc
def id():
return grapher_id
@staticmethod
@interfacedoc
def version():
return grapher_version
@staticmethod
@interfacedoc
def name():
return grapher_name
__doc__ = """Image representing """ + grapher_name
class VampTempo(VampAnalyzer):
"""Tempo from QMUL vamp plugins"""
implements(IValueAnalyzer)
_schema = {
'$schema': 'http://json-schema.org/schema#',
'properties': {},
'type': 'object'
}
@store_parameters
def __init__(self):
super(VampTempo, self).__init__()
# Define Vamp plugin key and output
self.plugin_key = 'qm-vamp-plugins:qm-tempotracker'
self.plugin_output = 'tempo'
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(VampTempo, self).setup(channels, samplerate, blocksize,
totalframes)
@staticmethod
@interfacedoc
def id():
return "vamp_tempo"
@staticmethod
@interfacedoc
def name():
return "Tempo"
@staticmethod
@interfacedoc
def version():
return "1.1.0"
@staticmethod
@interfacedoc
def unit():
return "bpm"
def post_process(self):
super(VampTempo, self).post_process()
tempo = self.new_result(data_mode='value', time_mode='global')
if self.vamp_results['list']:
tempo.data_object.value = self.vamp_results['list'][0]['values']
self.add_result(tempo)
class FlacEncoder(GstEncoder):
"""FLAC encoder based on Gstreamer"""
implements(IEncoder)
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(FlacEncoder, self).setup(channels, samplerate, blocksize,
totalframes)
self.pipe = ''' appsrc name=src ! audioconvert
! flacenc '''
if self.filename and self.streaming:
self.pipe += ''' ! tee name=t
! queue ! filesink location=%s
t. ! queue ! appsink name=app sync=False
''' % self.filename
elif self.filename:
self.pipe += '! filesink location=%s async=False sync=False ' % self.filename
else:
self.pipe += '! queue ! appsink name=app sync=False '
self.start_pipeline(channels, samplerate)
@staticmethod
@interfacedoc
def id():
return "flac_encoder"
@staticmethod
@interfacedoc
def format():
return "FLAC"
@staticmethod
@interfacedoc
def file_extension():
return "flac"
@staticmethod
@interfacedoc
def mime_type():
return 'audio/x-flac'
@interfacedoc
def set_metadata(self, metadata):
self.metadata = metadata
class Spectrogram(Analyzer):
"""Spectrogram analyzer"""
implements(IAnalyzer)
def __init__(self, blocksize=2048, stepsize=None, fft_size=None):
super(Spectrogram, self).__init__()
self.input_blocksize = blocksize
if stepsize:
self.input_stepsize = stepsize
else:
self.input_stepsize = blocksize / 2
if not fft_size:
self.FFT_SIZE = blocksize
else:
self.FFT_SIZE = fft_size
self.values = []
@interfacedoc
def setup(self, channels=None, samplerate=None,
blocksize=None, totalframes=None):
super(Spectrogram, self).setup(channels, samplerate,
blocksize, totalframes)
@staticmethod
@interfacedoc
def id():
return "spectrogram_analyzer"
@staticmethod
@interfacedoc
def name():
return "Spectrogram Analyzer"
@staticmethod
@interfacedoc
def unit():
return ""
@downmix_to_mono
@frames_adapter
def process(self, frames, eod=False):
self.values.append(np.abs(np.fft.rfft(frames, self.FFT_SIZE)))
return frames, eod
def post_process(self):
spectrogram = self.new_result(data_mode='value', time_mode='framewise')
spectrogram.parameters = {'FFT_SIZE': self.FFT_SIZE}
spectrogram.data_object.value = self.values
self.process_pipe.results.add(spectrogram)
class VampTuning(VampAnalyzer):
"""Tuning from NNLS Chroma vamp plugins"""
implements(IValueAnalyzer)
_schema = {
'$schema': 'http://json-schema.org/schema#',
'properties': {},
'type': 'object'
}
@store_parameters
def __init__(self):
super(VampTuning, self).__init__()
# Define Vamp plugin key and output
self.plugin_key = 'nnls-chroma:tuning'
self.plugin_output = 'tuning'
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(VampTuning, self).setup(channels, samplerate, blocksize,
totalframes)
@staticmethod
@interfacedoc
def id():
return "vamp_tuning"
@staticmethod
@interfacedoc
def name():
return "Tuning"
@staticmethod
@interfacedoc
def version():
return "1.1.0"
@staticmethod
@interfacedoc
def unit():
return "Hz"
def post_process(self):
super(VampTuning, self).post_process()
tuning = self.new_result(data_mode='value', time_mode='global')
tuning.data_object.value = self.vamp_results['list'][0]['values']
self.add_result(tuning)
class Essentia_Dissonance_Value(Analyzer):
"""Mean Dissonance Value from Essentia"""
implements(IValueAnalyzer)
_schema = {'$schema': 'http://json-schema.org/schema#',
'properties': {
},
'type': 'object'}
@store_parameters
def __init__(self):
super(Essentia_Dissonance_Value, self).__init__()
self.parents['dissonance'] = Essentia_Dissonance()
@interfacedoc
def setup(self, channels=None, samplerate=None,
blocksize=None, totalframes=None):
super(Essentia_Dissonance_Value, self).setup(
channels, samplerate, blocksize, totalframes)
@staticmethod
@interfacedoc
def id():
return "essentia_dissonance_value"
@staticmethod
@interfacedoc
def name():
return "Dissonance mean value from Essentia"
@staticmethod
@interfacedoc
def unit():
return ""
@downmix_to_mono
@frames_adapter
def process(self, frames, eod=False):
return frames, eod
def post_process(self):
dissonance = self.parents['dissonance'].results['essentia_dissonance'].data
if dissonance.any():
disson_mean = dissonance[dissonance > 0].mean()
else:
disson_mean = 0
dissonance_value = self.new_result(data_mode='value', time_mode='global')
dissonance_value.data_object.value = disson_mean
self.add_result(dissonance_value)
class Waveform(Analyzer):
"""Waveform analyzer"""
implements(IAnalyzer) # TODO check if needed with inheritance
def __init__(self):
super(Waveform, self).__init__()
# self.input_blocksize = 2048
# self.input_stepsize = self.input_blocksize / 2
@interfacedoc
def setup(self, channels=None, samplerate=None,
blocksize=None, totalframes=None):
super(Waveform, self).setup(channels, samplerate,
blocksize, totalframes)
self.values = []
self.result_blocksize = 1
self.result_stepsize = 1
@staticmethod
@interfacedoc
def id():
return "waveform_analyzer"
@staticmethod
@interfacedoc
def name():
return "Waveform Analyzer"
@staticmethod
@interfacedoc
def version():
return "1.0"
@staticmethod
@interfacedoc
def unit():
return ""
# @downmix_to_mono
# @frames_adapter
def process(self, frames, eod=False):
self.values.append(frames)
return frames, eod
def post_process(self):
waveform = self.new_result(data_mode='value', time_mode='framewise')
waveform.data_object.value = np.vstack(self.values)
self.add_result(waveform)
class SpectrogramLinear(SpectrogramLog):
"""Linear scaled spectrogram (level vs. frequency vs. time).
Adds pixels iteratively thanks to the adapter providing fixed size frame buffers."""
implements(IGrapher)
@interfacedoc
def __init__(self,
width=1024,
height=256,
bg_color=(0, 0, 0),
color_scheme='default'):
super(SpectrogramLinear, self).__init__(width, height, bg_color,
color_scheme)
@staticmethod
@interfacedoc
def id():
return "spectrogram_lin"
@staticmethod
@interfacedoc
def name():
return "Spectrogram Lin"
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(SpectrogramLinear, self).setup(channels, samplerate, blocksize,
totalframes)
def set_scale(self):
"""generate the lookup which translates y-coordinate to fft-bin"""
f_min = float(self.lower_freq)
f_max = float(self.higher_freq)
y_min = f_min
y_max = f_max
for y in range(self.image_height):
freq = y_min + y / (self.image_height - 1.0) * (y_max - y_min)
fft_bin = freq / f_max * (self.fft_size / 2 + 1)
if fft_bin < self.fft_size / 2:
alpha = fft_bin - int(fft_bin)
self.y_to_bin.append((int(fft_bin), alpha * 255))
class DummyAnalyzer(Analyzer):
"""A dummy analyzer returning random samples from audio frames"""
implements(IAnalyzer)
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(DummyAnalyzer, self).setup(channels, samplerate, blocksize,
totalframes)
self.values = numpy.array([0])
@staticmethod
@interfacedoc
def id():
return "dummy"
@staticmethod
@interfacedoc
def version():
return "0.1"
@staticmethod
@interfacedoc
def name():
return "Dummy analyzer"
@staticmethod
@interfacedoc
def unit():
return "None"
def process(self, frames, eod=False):
size = frames.size
if size:
index = numpy.random.randint(0, size, 1)
self.values = numpy.append(self.values, frames[index])
return frames, eod
def post_process(self):
result = self.new_result(data_mode='value', time_mode='global')
result.data_object.value = self.values
self.add_result(result)
class MeanDCShift(Analyzer):
"""Mean DC shift analyzer"""
implements(IValueAnalyzer)
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(MeanDCShift, self).setup(channels, samplerate, blocksize,
totalframes)
self.values = numpy.array([0])
@staticmethod
@interfacedoc
def id():
return "mean_dc_shift"
@staticmethod
@interfacedoc
def name():
return "Mean DC shift Analyzer"
@staticmethod
@interfacedoc
def version():
return "1.0"
@staticmethod
@interfacedoc
def unit():
return "%"
def process(self, frames, eod=False):
if frames.size:
self.values = numpy.append(self.values, numpy.mean(frames))
return frames, eod
def post_process(self):
dc_result = self.new_result(data_mode='value', time_mode='global')
dc_result.data_object.value = numpy.round(
numpy.mean(100 * self.values), 3)
self.add_result(dc_result)
class Gain(Processor):
implements(IEffect)
@interfacedoc
def __init__(self, gain=1.0):
self.gain = gain
@staticmethod
@interfacedoc
def id():
return "test_gain"
@staticmethod
@interfacedoc
def name():
return "Gain test effect"
def process(self, frames, eod=False):
return numpy.multiply(frames, self.gain), eod
class VorbisEncoder(AubioEncoder):
"""OGG Vorbis encoder based on aubio"""
implements(IEncoder)
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(VorbisEncoder, self).setup(channels, samplerate, blocksize,
totalframes)
@staticmethod
@interfacedoc
def id():
return "vorbis_aubio_encoder"
@staticmethod
@interfacedoc
def format():
return "OGG"
@staticmethod
@interfacedoc
def version():
return "1.0"
@staticmethod
@interfacedoc
def file_extension():
return "ogg"
@staticmethod
@interfacedoc
def mime_type():
return 'audio/ogg'
class WavEncoder(AubioEncoder):
"""Wav encoder based on aubio"""
implements(IEncoder)
@interfacedoc
def setup(self,
channels=None,
samplerate=None,
blocksize=None,
totalframes=None):
super(WavEncoder, self).setup(channels, samplerate, blocksize,
totalframes)
@staticmethod
@interfacedoc
def id():
return "wav_aubio_encoder"
@staticmethod
@interfacedoc
def format():
return "WAV"
@staticmethod
@interfacedoc
def version():
return "1.0"
@staticmethod
@interfacedoc
def file_extension():
return "wav"
@staticmethod
@interfacedoc
def mime_type():
return 'audio/x-wav'