Handle the case where Essentia won't analyze silent sounds

analyze.py

@@ -74,7 +74,7 @@ def analyze_multiple(self, sound_files, standardize=True):
         self.add_derivative_series(sound_files)
 
         if standardize:
-            self.add_standardized_series(sound_files)
+            self.add_standardized_series([sf for sf in sound_files if not sf.is_silent])
 
     def ensure_equal_lengths(self, sounds, series_key='series'):
         # Check if series length is equal for all series, and if not, try to fix it with padding

essentia_analyzer.py

@@ -91,6 +91,15 @@ def analyze_multiple(self, sound_files):
 
             for j in range(len(processes)):
                 processes[j].wait()
+                stdout = processes[j].communicate()[0]
+                if settings.VERBOSE:
+                    print(stdout)
+                if 'completely silent file' in stdout:
+                    if settings.VERBOSE:
+                        print('Discarding completely silent file')
+                    sound_files[i + j].is_silent = True
+                    continue
+
                 self.parse_output(sound_files[i + j])
                 self.post_process(sound_files[i + j])
                 self.clean_up(sound_files[i + j])

neuroevolution.py

@@ -432,8 +432,12 @@ def evaluate_fitness(self, individuals):
             ]
         self.analyzer.analyze_multiple(sound_files)
 
+        for ind in individuals:
+            if ind.output_sound.is_silent:
+                ind.set_fitness(0.0)
+
         self.fitness_evaluator_class.evaluate_multiple(
-            individuals,
+            [ind for ind in individuals if not ind.output_sound.is_silent],
             self.target_sound
         )
 

sound_file.py

@@ -28,6 +28,7 @@ def __init__(self, filename, is_input=True, check_if_file_exists=False):
             'ksmps': settings.HOP_SIZE,
             'series': {}
         }
+        self.is_silent = False
 
     def compute_duration(self):
         with contextlib.closing(wave.open(self.file_path, 'r')) as f: