import numpy as np
import sys
-wav_file = sys.argv[-1]
-#wav_file = '/home/thomas/code/timeside/voix.wav'
+#wav_file = sys.argv[-1]
+wav_file = '/home/thomas/code/timeside/voix.wav'
# normal
d = timeside.decoder.FileDecoder(wav_file)
# Get the vamp plugin result and plot it
-vamp.results.keys()
+for key in vamp.results.keys():
+ print vamp.results[key].data
res_vamp = vamp.results['vamp_simple_host.percussiononsets.detectionfunction']
self.block_read += 1
return frames, eod
- def release(self):
+ def post_process(self):
melenergy = self.new_result(data_mode='value', time_mode='framewise')
self.block_read += 1
return frames, eod
- def release(self):
+ def post_process(self):
# MFCC
mfcc = self.new_result(data_mode='value', time_mode='framewise')
self.block_read += 1
return frames, eod
- def release(self):
+ def post_process(self):
# set Result
pitch = self.new_result(data_mode='value', time_mode='framewise')
self.specdesc[method](fftgrain)[0]]
return frames, eod
- def release(self):
+ def post_process(self):
# For each method store results in container
for method in self.methods:
self.block_read += 1
return frames, eod
- def release(self):
+ def post_process(self):
#---------------------------------
# Onsets
self.values = numpy.append(self.values, numpy.mean(frames))
return frames, eod
- def release(self):
+ def post_process(self):
dc_result = self.new_result(data_mode='value', time_mode='global')
# Set Data
return frames, eod
- def release(self):
+ def post_process(self):
'''
'''
self.entropyValue.append(entropy(frames))
return frames, eod
- def release(self):
+ def post_process(self):
entropyValue = array(self.entropyValue)
w = self.modulLen * self.samplerate() / self.blocksize()
np.mean(np.square(frames)))
return frames, eod
- def release(self):
+ def post_process(self):
# Max level
max_level = self.new_result(data_mode='value', time_mode='global')
return frames, eod
- def release(self):
+ def post_process(self):
# set Result
spectrogram = self.new_result(data_mode='value', time_mode='framewise')
pass
return frames, eod
- def release(self):
+ def post_process(self):
#plugin = 'vamp-example-plugins:amplitudefollower:amplitude'
wavfile = self.mediainfo()['uri'].split('file://')[-1]
return frames, eod
- def release(self):
+ def post_process(self):
# set Result
waveform = self.new_result(data_mode='value', time_mode='framewise')
return frames, eod
- def release(self):
+ def post_process(self):
# Get feature extraction results from yaafe
featNames = self.yaafe_engine.getOutputs().keys()
if len(featNames) == 0:
Warning: it is required to call setup() before this method."""
+ def post_process(self):
+ '''
+ Post-Process data after processign the input frames with process()
+
+ Processors such as analyzers will produce Results during the Post-Process
+ '''
+
def release(self):
"""Release resources owned by this processor. The processor cannot
be used anymore after calling this method."""
def process(self, frames, eod):
return frames, eod
+ @interfacedoc
+ def post_process(self):
+ pass
+
@interfacedoc
def release(self):
pass
for item in items:
frames, eod = item.process(frames, eod)
+ for item in items:
+ item.post_process()
+
for item in items:
item.release()