chore(analyzer): add traits support for all analyzer

Also add a test to chack if analyzer __init__ arguments are also defined as traits parameters.

diff --git a/timeside/analyzer/irit_noise_startSilences.py b/timeside/analyzer/irit_noise_startSilences.py
index 7b77400e1e108f30a41d7890d3ea94a175eafdba..60f55cbc1b6fb4e319ffdf37ea4d1ec40c4df8f8 100644 (file)
--- a/timeside/analyzer/irit_noise_startSilences.py
+++ b/timeside/analyzer/irit_noise_startSilences.py
@@ -42,11 +42,9 @@ class IRITStartSeg(Analyzer):
     Properties:
     '''
     @interfacedoc
-    def __init__(self, save_lab=False):
+    def __init__(self):
         super(IRITStartSeg, self).__init__()
 
-        self._save_lab = save_lab
-
         self._buffer = BufferTable()
 
         # self.energy = []
@@ -54,6 +52,7 @@ class IRITStartSeg(Analyzer):
         self.max_energy = 0.002*2
         self.min_overlap = 20
         self.threshold = 0.12
+
     @interfacedoc
     def setup(self, channels=None, samplerate=None,
               blocksize=None, totalframes=None):
@@ -164,19 +163,6 @@ class IRITStartSeg(Analyzer):
 
         label = {0: 'Start', 1: 'Session'}
 
-        if self._save_lab:
-            with open('out.lab', 'w') as f:
-                for s in selected_segs:
-                    f.write(
-                        '%.2f\t%.2f\t%s\n' %
-                        (s[0] * step, s[1] * step, label[s[2]]))
-
-            with open('cand.lab', 'w') as f:
-                for s in candidates:
-                    f.write('%.2f\t%.2f\t%f\n' % (s[0] * step,
-                                                  s[1] * step,
-                                                  s[2]))
-
         segs = self.new_result(data_mode='label', time_mode='segment')
         segs.id_metadata.id += '.' + 'segments'
         segs.id_metadata.name += ' ' + 'Segments'

diff --git a/timeside/analyzer/irit_speech_4hz.py b/timeside/analyzer/irit_speech_4hz.py
index a64c4ff2d7ad222dd99ac21946b3959148e72cb2..385f10ed54bb3ac37c3962c8fa0b0fd9fbf2e916 100644 (file)
--- a/timeside/analyzer/irit_speech_4hz.py
+++ b/timeside/analyzer/irit_speech_4hz.py
@@ -28,6 +28,8 @@ from numpy import array, hamming, dot, mean, float, mod
 from numpy.fft import rfft
 from scipy.signal import firwin, lfilter
 
+from ..tools.parameters import Float, HasTraits
+
 
 class IRITSpeech4Hz(Analyzer):
 
@@ -48,6 +50,10 @@ class IRITSpeech4Hz(Analyzer):
 
     implements(IAnalyzer)
 
+    # Define Parameters
+    class _Param(HasTraits):
+        medfilt_duration = Float()
+
     @interfacedoc
     def __init__(self, medfilt_duration=5):
         super(IRITSpeech4Hz, self).__init__()

diff --git a/timeside/analyzer/limsi_sad.py b/timeside/analyzer/limsi_sad.py
index ba18b83dfba5f1e52e06a127930adc060a894b04..2d0af7bc1b3ad94a0b67214e84284a4abbaf8fea 100644 (file)
--- a/timeside/analyzer/limsi_sad.py
+++ b/timeside/analyzer/limsi_sad.py
@@ -24,6 +24,8 @@ from timeside.analyzer.core import Analyzer
 from timeside.api import IAnalyzer
 import timeside
 
+from ..tools.parameters import Enum, HasTraits
+
 import yaafelib
 import numpy as np
 import pickle
@@ -62,6 +64,10 @@ class LimsiSad(Analyzer):
     """
     implements(IAnalyzer)
 
+   # Define Parameters
+    class _Param(HasTraits):
+      sad_model = Enum('etape', 'maya')
+
     def __init__(self, sad_model='etape'):
         """
         Parameters:
@@ -74,16 +80,13 @@ class LimsiSad(Analyzer):
         super(LimsiSad, self).__init__()
 
         # feature extraction defition
-        spec = yaafelib.FeaturePlan(sample_rate=16000)
-        spec.addFeature(
-            'mfcc: MFCC CepsIgnoreFirstCoeff=0 blockSize=1024 stepSize=256')
-        spec.addFeature(
-            'mfccd1: MFCC CepsIgnoreFirstCoeff=0 blockSize=1024 stepSize=256 > Derivate DOrder=1')
-        spec.addFeature(
-            'mfccd2: MFCC CepsIgnoreFirstCoeff=0 blockSize=1024 stepSize=256 > Derivate DOrder=2')
-        spec.addFeature('zcr: ZCR blockSize=1024 stepSize=256')
-        parent_analyzer = get_processor('yaafe')(spec)
-        self.parents['yaafe'] = parent_analyzer
+        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=16000)
 
         # informative parameters
         # these are not really taken into account by the system
@@ -95,6 +98,7 @@ class LimsiSad(Analyzer):
         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
         picfname = os.path.join(
             timeside.__path__[0], 'analyzer', 'trained_models', 'limsi_sad_%s.pkl' % sad_model)
         self.gmms = pickle.load(open(picfname, 'rb'))