Update app.py

app.py

@@ -52,62 +52,92 @@ def Generate_Chords_Progression(total_song_length_in_chords_chunks,
     print('Pitches Chords Progressions Generator')
     print('=' * 70)
 
+    print('=' * 70)
+    print('Chunk-by-chunk generation')
+    print('=' * 70)
+    print('Generating...')
+    print('=' * 70)
     
-    result_toks = [final_result[0]-512, final_result[1]-512]
-    mc_song_artist = song_artist_tokens_to_song_artist(result_toks)
-    gidx = genre_labels_fnames.index(mc_song_artist)
-    mc_genre = genre_labels[gidx][1]
+    matching_long_chords_chunks = []
     
-    print('Most common classification genre label:', mc_genre)
-    print('Most common classification song-artist label:', mc_song_artist)
-    print('Most common song-artist classification label ratio:' , results.count(final_result) / len(results))
-    print('=' * 70)
+    ridx = random.randint(0, len(all_long_chords_tokens_chunks)-1)
     
-    classification_summary_string += 'Most common classification genre label: ' + str(mc_genre) + '\n'
-    classification_summary_string += 'Most common classification song-artist label: ' + str(mc_song_artist) + '\n'
-    classification_summary_string += 'Most common song-artist classification label ratio: '+ str(results.count(final_result) / len(results)) + '\n'
-    classification_summary_string += '=' * 70
-    classification_summary_string += '\n' 
+    matching_long_chords_chunks.append(ridx)
     
-    print('All classification labels summary:')
-    print('=' * 70)
+    max_song_len = 0
     
-    all_artists_labels = []
+    tries = 0
     
-    for i, res in enumerate(results):
-        result_toks = [res[0]-512, res[1]-512]
-        song_artist = song_artist_tokens_to_song_artist(result_toks)
-        gidx = genre_labels_fnames.index(song_artist)
-        genre = genre_labels[gidx][1]
-        
-        print('Notes', i*(340-samples_overlap), '-', (i*(340-samples_overlap))+340, '===', genre, '---', song_artist)
-        classification_summary_string += 'Notes ' + str(i*samples_overlap) + ' - ' +  str((i*samples_overlap)+340) + ' === ' + str(genre) + ' --- ' + str(song_artist) + '\n'
-        
-        artist_label = str_strip_artist(song_artist.split(' --- ')[1])
-        
-        all_artists_labels.append(artist_label)
-        
-    classification_summary_string += '=' * 70
-    classification_summary_string += '\n'    
-    print('=' * 70)
+    while len(matching_long_chords_chunks) < total_song_length_in_chords_chunks:
+    
+        matching_long_chords_chunks = []
+    
+        ridx = random.randint(0, len(all_long_chords_tokens_chunks)-1)
+    
+        matching_long_chords_chunks.append(ridx)
+        seen = [ridx]
+    
+        for a in range(16):
+    
+          schunk = all_long_chords_tokens_chunks[matching_long_chords_chunks[-1]]
+          trg_long_chunk = np.array(schunk[-chunk_size:])
+          idxs = np.where((src_long_chunks == trg_long_chunk).all(axis=1))[0].tolist()
     
-    mode_artist_label = mode(all_artists_labels)
-    mode_artist_label_count = all_artists_labels.count(mode_artist_label)
+          if len(idxs) > 1:
+            eidx = random.choice(idxs)
+            tr = 0
+            while eidx in seen and tr < 5:
+              eidx = random.choice(idxs)
+              tr += 1
     
-    print('Aggregated artist classification label:', mode_artist_label)
-    print('Aggregated artist classification label ratio:', mode_artist_label_count / len(all_artists_labels))
-    classification_summary_string += 'Aggregated artist classification label: ' + str(mode_artist_label) + '\n'
-    classification_summary_string += 'Aggregated artist classification label ratio: ' + str(mode_artist_label_count / len(all_artists_labels)) + '\n'
-    classification_summary_string += '=' * 70
-    classification_summary_string += '\n'
+            if eidx not in seen:
     
+              matching_long_chords_chunks.append(eidx)
+              seen.append(eidx)
+    
+              if chords_chunks_memory_length > 0:
+                seen = seen[-chords_chunks_memory_length:]
+              elif chords_chunks_memory_length == 0:
+                seen = []
+    
+            else:
+              break
+    
+          else:
+            break
+    
+        if len(matching_long_chords_chunks) > max_song_len:
+          print('Current song length:', len(matching_long_chords_chunks), 'chords chunks')
+          print('=' * 70)
+          final_song = matching_long_chords_chunks
+    
+        max_song_len = max(max_song_len, len(matching_long_chords_chunks))
+    
+        tries += 1
+    
+        if tries % 500 == 0:
+          print('Number of passed tries:', tries)
+          print('=' * 70)
+    
+    if len(matching_long_chords_chunks) > max_song_len:
+      print(len(matching_long_chords_chunks))
+      final_song = matching_long_chords_chunks
+    
+    f_song = []
+    
+    for mat in final_song:
+      f_song.extend(all_long_good_chords_chunks[mat][:-chunk_size])
+    f_song.extend(all_long_good_chords_chunks[mat][-chunk_size:])
+    
+    print('Generated final song after', tries, 'tries with', len(final_song), 'chords chunks and', len(f_song), 'chords')
     print('=' * 70)
+
     print('Done!')
     print('=' * 70)
     
     #===============================================================================
-    print('Rendering results...')
     
+    print('Rendering results...')
     print('=' * 70)
 
     fn1 = "Melody2Song-Seq2Seq-Music-Transformer-Composition"