Separate song pickle files

cnn/model.py

@@ -118,7 +118,7 @@ class convNet(nn.Module):
             song.answer = np.zeros((song.feats.shape[2]))
 
            
-            song.major_note_index = np.rint(timing[np.where(syllable != "")] * song.samplerate/512).astype(np.int32)
+            song.major_note_index = np.rint(timing[np.where(syllable != 0)] * song.samplerate/512).astype(np.int32)
             
             song.major_note_index = np.delete(song.major_note_index, np.where(song.major_note_index >= song.feats.shape[2]))
 

cnn/music_processor.py

@@ -73,9 +73,9 @@ class Audio:
         for line in LINES_KARA.findall(CONTENT):
             lastTime = dateToTime(line[0])
             for couple in RGX_TAGS.findall(line[2]):
-                self.timestamp.append((lastTime/100, couple[1]))
+                self.timestamp.append((lastTime/100, 1 if len(couple[1]) > 0 else 0))
                 lastTime += int(couple[0])
-        self.timestamp = np.array(self.timestamp, dtype='float, object')
+        self.timestamp = np.array(self.timestamp, dtype='float, int')
 
 
 def make_frame(data, nhop, nfft):
@@ -189,19 +189,19 @@ def music_for_train(serv, deletemusic=True, verbose=False, nhop=512, nffts=[1024
         if verbose:
             print(songplace)
         
+        songname = songplace.split("/")[-1]
+        
         song = Audio(glob(songplace+"/*.ogg")[0])
         song.import_ass(glob(songplace+"/*.ass")[-1])
         song.data = (song.data[:, 0]+song.data[:, 1])/2
-        songs.append(song)
 
-    multi_fft_and_melscale(songs, nhop, nffts, mel_nband, mel_freqlo, mel_freqhi, include_zero_cross=include_zero_cross)
+        song.feats = fft_and_melscale(song, nhop, nffts, mel_nband, mel_freqlo, mel_freqhi)
 
-    if deletemusic:
-        for song in songs:
+        if deletemusic:
             song.data = None
     
-    with open('./data/pickles/train_data.pickle', mode='wb') as f:
-        pickle.dump(songs, f)
+        with open(f'./data/pickles/{songname:s}.pickle', mode='wb') as f:
+            pickle.dump(song, f)
 
 
 def music_for_test(serv, deletemusic=True, verbose=False):

cnn_train.py

 from cnn.model import *
 from cnn.music_processor import *
+from glob import glob
 
 
 device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
 net = convNet()
 net = net.to(device)
-        
-with open('./data/pickles/train_data.pickle', mode='rb') as f:
-    songs = pickle.load(f)
+
+songplaces = glob('./data/pickles/*.pickle')     
+songs = []   
+
+for songplace in songplaces:
+    with open(songplace, mode='rb') as f:
+        song = pickle.load(f)
+        songs.append(song)
 
 minibatch = 128
 soundlen = 15