Update app.py

app.py

@@ -208,50 +208,65 @@ def InpaintPitches(input_midi, input_num_of_notes, input_patch_number):
     temperature=0.9
     num_memory_tokens=4096
 
-    output = []
+    #@title Pitches/Instruments Inpainting
     
-    idx = 0
+    #@markdown You can stop the inpainting at any time to render partial results
     
-    for c in chords[:input_num_tokens]:
-     
-        output.append(c)
+    #@markdown Inpainting settings
     
-        if input_conditioning_type == 'Chords-Times' or input_conditioning_type == 'Chords-Times-Durations':
-          output.append(times[idx])
+    #@markdown Select MIDI patch present in the composition to inpaint
     
-        if input_conditioning_type == 'Chords-Times-Durations':
-          output.append(durs[idx])
-
-        x = torch.tensor([output] * 1, dtype=torch.long, device=DEVICE)
-        
-        o = 0
-        
-        ncount = 0
-        
-        while o < 384 and ncount < max_chords_limit:
-          with ctx:
-            out = model.generate(x[-num_memory_tokens:],
-                                1,
-                                temperature=temperature,
-                                return_prime=False,
-                                verbose=False)
-        
-          o = out.tolist()[0][0]
-        
-          if 256 <= o < 384:
-            ncount += 1
-        
-          if o < 384:
-            x = torch.cat((x, out), 1)
-        
-        outy =  x.tolist()[0][len(output):]
-          
-        output.extend(outy)
+    inpaint_MIDI_patch = input_patch_number
     
-        idx += 1
-
-        if idx == len(chords[:input_num_tokens])-1:
-            break
+    #@markdown Generation settings
+    
+    number_of_prime_tokens = 90 # @param {type:"slider", min:3, max:8190, step:3}
+    number_of_memory_tokens = 2048 # @param {type:"slider", min:3, max:8190, step:3}
+    number_of_samples_per_inpainted_note = 1 #@param {type:"slider", min:1, max:16, step:1}
+    
+    print('=' * 70)
+    print('Giant Music Transformer Inpainting Model Generator')
+    print('=' * 70)
+    
+    out2 = []
+    
+    for m in melody_chords[:number_of_prime_tokens]:
+      out2.append(m)
+    
+    for i in range(number_of_prime_tokens, len(melody_chords)):
+    
+        cpatch = (melody_chords[i]-2304) // 129
+    
+        if 2304 <= melody_chords[i] < 18945 and (cpatch) == inpaint_MIDI_patch:
+    
+            samples = []
+    
+            for j in range(number_of_samples_per_inpainted_note):
+    
+              inp = torch.LongTensor(out2[-number_of_memory_tokens:]).cuda()
+    
+              with ctx:
+                out1 = model.generate(inp,
+                                      1,
+                                      temperature=temperature,
+                                      return_prime=True,
+                                      verbose=False)
+    
+                with torch.no_grad():
+                  test_loss, test_acc = model(out1)
+    
+              samples.append([out1.tolist()[0][-1], test_acc.tolist()])
+    
+            accs = [y[1] for y in samples]
+            max_acc = max(accs)
+            max_acc_sample = samples[accs.index(max_acc)][0]
+    
+            cpitch = (max_acc_sample-2304) % 129
+    
+            out2.extend([((cpatch * 129) + cpitch)+2304])
+    
+        else:
+            out2.append(melody_chords[i])
 
     print('=' * 70)
     print('Done!')
@@ -261,14 +276,12 @@ def InpaintPitches(input_midi, input_num_of_notes, input_patch_number):
     print('Rendering results...')
     
     print('=' * 70)
-    print('Sample INTs', output[:12])
+    print('Sample INTs', out2[:12])
     print('=' * 70)
     
-    out1 = output
-
-    if len(out1) != 0:
+    if len(out2) != 0:
     
-        song = out1
+        song = out2
         song_f = []
     
         time = 0
@@ -276,33 +289,52 @@ def InpaintPitches(input_midi, input_num_of_notes, input_patch_number):
         vel = 90
         pitch = 0
         channel = 0
-        
-        patches = [0] * 16
-        
-        channel = 0
-        
+    
+        patches = [-1] * 16
+    
+        channels = [0] * 16
+        channels[9] = 1
+    
         for ss in song:
-        
-          if 0 <= ss < 128:
-        
-              time += ss * 32
-        
-          if 128 <= ss < 256:
-        
-              dur = (ss-128) * 32
-        
-          if 256 <= ss < 384:
-        
-              pitch = (ss-256)
-        
-              vel = max(40, pitch)
-        
-              song_f.append(['note', time, dur, channel, pitch, vel, 0])
-
-    fn1 = "Chords-Progressions-Transformer-Composition"
     
+            if 0 <= ss < 256:
+    
+                time += ss * 16
+    
+            if 256 <= ss < 2304:
+    
+                dur = ((ss-256) // 8) * 16
+                vel = (((ss-256) % 8)+1) * 15
+    
+            if 2304 <= ss < 18945:
+    
+                patch = (ss-2304) // 129
+    
+                if patch < 128:
+    
+                    if patch not in patches:
+                      if 0 in channels:
+                          cha = channels.index(0)
+                          channels[cha] = 1
+                      else:
+                          cha = 15
+    
+                      patches[cha] = patch
+                      channel = patches.index(patch)
+                    else:
+                      channel = patches.index(patch)
+    
+                if patch == 128:
+                    channel = 9
+    
+                pitch = (ss-2304) % 129
+    
+                song_f.append(['note', time, dur, channel, pitch, vel, patch ])
+
+    patches = [0 if x==-1 else x for x in patches]
+   
     detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
-                                                              output_signature = 'Chords Progressions Transformer',
+                                                              output_signature = 'Giant Music Transformer',
                                                               output_file_name = fn1,
                                                               track_name='Project Los Angeles',
                                                               list_of_MIDI_patches=patches
@@ -359,15 +391,15 @@ if __name__ == "__main__":
    
     app = gr.Blocks()
     with app:
-        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Chords Progressions Transformer</h1>")
-        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Chords-conditioned music transformer</h1>")
+        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Inpaint Music Transformer</h1>")
+        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Inpaint pitches in any MIDI</h1>")
         gr.Markdown(
-            "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Chords-Progressions-Transformer&style=flat)\n\n"
-            "Generate music based on chords progressions\n\n"
-            "Check out [Chords Progressions Transformer](https://github.com/asigalov61/Chords-Progressions-Transformer) on GitHub!\n\n"
+            "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Inpaint-Music-Transformer&style=flat)\n\n"
+            "This is a demo of the Giant Music Transformer pitches inpainting feature\n\n"
+            "Check out [Giant Music Transformer](https://github.com/asigalov61/Giant-Music-Transformer) on GitHub!\n\n"
             "[Open In Colab]"
-            "(https://colab.research.google.com/github/asigalov61/Chords-Progressions-Transformer/blob/main/Chords_Progressions_Transformer.ipynb)"
-            " for faster execution and endless generation"
+            "(https://colab.research.google.com/github/asigalov61/Giant-Music-Transformer/blob/main/Giant_Music_Transformer.ipynb)"
+            " for all features, faster execution and endless generation"
         )
         gr.Markdown("## Upload your MIDI or select a sample example MIDI")