training 1 species

Dataloader_solo.py

@@ -216,14 +216,14 @@ class Dataset(data.Dataset):
         print("** WARNING ** No data loaded from " + path)
     return dict_classes
   
-  def mixed_audio_augmentation(self, audio, sampling_rate):
+  def get_noise(self):
       classe_noise   = random.randint(0, len(list(self.dict_noises.keys()))-1)
       classe_noise   = list(self.dict_noises.keys())[classe_noise]
       #random natural noise augmentation
       filename_noise = self.dict_noises[classe_noise][random.randint(0, len(self.dict_noises[classe_noise])-1)]
-#      audio_noise, sr = load_file(filename_noise)
-#      coeff = int(np.ceil(np.max(audio)*random.choice([1, 2, 3, 4, 5, 6, 7])))
-#      noisy_audio = audio + (audio_noise)*coeff
+      return filename_noise
+
+  def data_augment(audio):
       #random pitch shifting
 #      step_pitch = random.uniform(-0.001, 0.001)
 #      mod_audio = librosa.effects.pitch_shift(noisy_audio, sampling_rate, n_steps=step_pitch)
@@ -296,10 +296,13 @@ class Dataset(data.Dataset):
        
       'Build mixed mask'
       if self.augmentation:
-#          file_noise = self.mixed_audio_augmentation(audio_mix, sr)
-          n_noise = np.random.normal(loc=0, scale=1, size=(1, max_time*sr))
-          n_noise = librosa.to_mono(n_noise)
-          snr = np.random.randint(-10, 5)  #-10/5 for natural noise, 30/50
+         if random.randint(0, 1) == 1: 
+             n_noise = self.get_noise()
+             snr = np.random.randint(-10, 5)
+         else:
+              n_noise = np.random.normal(loc=0, scale=1, size=(1, max_time*sr))
+              n_noise = librosa.to_mono(n_noise)
+              snr = np.random.randint(30, 50)  #-10/5 for natural noise, 30/50
           audio_mix = _add_noise(audio_mix, n_noise, snr, sr)
           
       mag_mix, phase_mix  = _stft(audio_mix)    

Main_solo.py

@@ -17,7 +17,7 @@ from unet import UNet5
 import torch.nn as nn
 #from tensorboardX import SummaryWriter
 from matplotlib import image as mpimg
-from Dataloader import Dataset
+from Dataloader_solo import Dataset
 import matplotlib
 import numpy as np
 import collections
@@ -143,7 +143,7 @@ if __name__ == '__main__':
     args.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     args.starting_training_time = time.time()
     args.save_per_batchs = 500
-    args.nb_classes = 3 
+    args.nb_classes = 1 
     args.mode = 'train'
     args.lr_sounds = 1e-5   
     args.saved_model = '5_5000'
@@ -165,7 +165,7 @@ if __name__ == '__main__':
         #Dataset loading
         root = './data_sound/trainset/'
         ext  = '.wav'    
-        train_classes = Dataset(root, nb_classes=args.nb_classes, path_background="./data_sound/noises/")
+        train_classes = Dataset(root, nb_classes=args.nb_classes, nb_classes_noise=3, path_background="./data_sound/noises/")
         loader_train  = torch.utils.data.DataLoader(
         train_classes,
         batch_size = args.batch_size,