Remove deprecated syntax and add workflow

.github/workflows/python-package.yml

@@ -0,0 +1,36 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: Python package
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    branches: [ master ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.7, 3.8, 3.9]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install flake8 pytest
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics

pyAudioAnalysis/ShortTermFeatures.py

@@ -6,6 +6,7 @@
 import matplotlib.pyplot as plt
 from scipy.signal import lfilter
 from scipy.fftpack.realtransforms import dct
+from scikits.talkbox import lpc
 from tqdm import tqdm
 
 eps = sys.float_info.epsilon
@@ -604,6 +605,7 @@ def feature_extraction(signal, sampling_rate, window, step, deltas=True):
         feature_names = feature_names_2
 
     features = []
+    feature_vector_prev = None
     # for each short-term window to end of signal
     while current_position + window - 1 < number_of_samples:
         count_fr += 1
@@ -669,7 +671,7 @@ def feature_extraction(signal, sampling_rate, window, step, deltas=True):
             features.append(feature_vector)
         else:
             # delta features
-            if count_fr > 1:
+            if count_fr > 1 and feature_vector_prev:
                 delta = feature_vector - feature_vector_prev
                 feature_vector_2 = np.concatenate((feature_vector, delta))
             else:

pyAudioAnalysis/data/recordRadio.py

@@ -110,9 +110,9 @@ def recordStation(stationName, outputName, sleepTime = -1, Listen = False):
 	r.listen = Listen
 	r.start()
 
-	print r.bus()
+	print(r.bus())
 	if sleepTime<=0:
-		raw_input('Press [Enter] to stop')
+		input('Press [Enter] to stop')
 	else:
 		time.sleep(sleepTime)
 	r.stop()

pyAudioAnalysis/data/testComputational.py

@@ -16,31 +16,35 @@ def main(argv):
 			t1 = time.time()
 			F = MidTermFeatures.short_term_feature_extraction(x, Fs, 0.050 * Fs, 0.050 * Fs);
 			t2 = time.time()
-			perTime1 =  duration / (t2-t1); print "short-term feature extraction: {0:.1f} x realtime".format(perTime1)
+			perTime1 =  duration / (t2-t1)
+			print("short-term feature extraction: {0:.1f} x realtime".format(perTime1))
 	elif argv[1] == "-classifyFile":
 		for i in range(nExp):
 			[Fs, x] = audioBasicIO.read_audio_file("diarizationExample.wav");
 			duration = x.shape[0] / float(Fs)		
 			t1 = time.time()
 			aT.file_classification("diarizationExample.wav", "svmSM", "svm")
 			t2 = time.time()
-			perTime1 =  duration / (t2-t1); print "Mid-term feature extraction + classification \t {0:.1f} x realtime".format(perTime1)
+			perTime1 =  duration / (t2-t1)
+			print("Mid-term feature extraction + classification \t {0:.1f} x realtime".format(perTime1))
 	elif argv[1] == "-mtClassify":
 		for i in range(nExp):
 			[Fs, x] = audioBasicIO.read_audio_file("diarizationExample.wav");
 			duration = x.shape[0] / float(Fs)		
 			t1 = time.time()
 			[flagsInd, classesAll, acc] = aS.mid_term_file_classification("diarizationExample.wav", "svmSM", "svm", False, '')
 			t2 = time.time()
-			perTime1 =  duration / (t2-t1); print "Fix-sized classification - segmentation \t {0:.1f} x realtime".format(perTime1)
+			perTime1 =  duration / (t2-t1)
+			print("Fix-sized classification - segmentation \t {0:.1f} x realtime".format(perTime1))
 	elif argv[1] == "-hmmSegmentation":
 		for i in range(nExp):
 			[Fs, x] = audioBasicIO.read_audio_file("diarizationExample.wav");
 			duration = x.shape[0] / float(Fs)		
 			t1 = time.time()
 			aS.hmm_segmentation('diarizationExample.wav', 'hmmRadioSM', False, '')             
 			t2 = time.time()
-			perTime1 =  duration / (t2-t1); print "HMM-based classification - segmentation \t {0:.1f} x realtime".format(perTime1)
+			perTime1 =  duration / (t2-t1)
+			print("HMM-based classification - segmentation \t {0:.1f} x realtime".format(perTime1))
 	elif argv[1] == "-silenceRemoval":
 		for i in range(nExp):
 			[Fs, x] = audioBasicIO.read_audio_file("diarizationExample.wav");
@@ -49,31 +53,35 @@ def main(argv):
 			[Fs, x] = audioBasicIO.read_audio_file("diarizationExample.wav");
 			segments = aS.silence_removal(x, Fs, 0.050, 0.050, smooth_window= 1.0, Weight = 0.3, plot = False)
 			t2 = time.time()
-			perTime1 =  duration / (t2-t1); print "Silence removal \t {0:.1f} x realtime".format(perTime1)
+			perTime1 =  duration / (t2-t1)
+			print("Silence removal \t {0:.1f} x realtime".format(perTime1))
 	elif argv[1] == "-thumbnailing":
 		for i in range(nExp):
 			[Fs1, x1] = audioBasicIO.read_audio_file("scottish.wav")
 			duration1 = x1.shape[0] / float(Fs1)		
 			t1 = time.time()
 			[A1, A2, B1, B2, Smatrix] = aS.music_thumbnailing(x1, Fs1, 1.0, 1.0, 15.0)	# find thumbnail endpoints
 			t2 = time.time()
-			perTime1 =  duration1 / (t2-t1); print "Thumbnail \t {0:.1f} x realtime".format(perTime1)
+			perTime1 =  duration1 / (t2-t1)
+			print("Thumbnail \t {0:.1f} x realtime".format(perTime1))
 	elif argv[1] == "-diarization-noLDA":
 		for i in range(nExp):
 			[Fs1, x1] = audioBasicIO.read_audio_file("diarizationExample.wav")
 			duration1 = x1.shape[0] / float(Fs1)		
 			t1 = time.time()		
 			aS.speaker_diarization("diarizationExample.wav", 4, LDAdim = 0, PLOT = False)
 			t2 = time.time()
-			perTime1 =  duration1 / (t2-t1); print "Diarization \t {0:.1f} x realtime".format(perTime1)
+			perTime1 =  duration1 / (t2-t1)
+			print("Diarization \t {0:.1f} x realtime".format(perTime1))
 	elif argv[1] == "-diarization-LDA":
 		for i in range(nExp):
 			[Fs1, x1] = audioBasicIO.read_audio_file("diarizationExample.wav")
 			duration1 = x1.shape[0] / float(Fs1)		
 			t1 = time.time()		
 			aS.speaker_diarization("diarizationExample.wav", 4, PLOT = False)
 			t2 = time.time()
-			perTime1 =  duration1 / (t2-t1); print "Diarization \t {0:.1f} x realtime".format(perTime1)
+			perTime1 =  duration1 / (t2-t1)
+			print("Diarization \t {0:.1f} x realtime".format(perTime1))
 
 if __name__ == '__main__':
 	main(sys.argv)