Update app.py

app.py

@@ -317,7 +317,7 @@ def tokens_to_MIDI(tokens, MIDI_name):
     print('Done!')
     print('=' * 70)
 
-    return audio, new_fn
+    return new_fn, audio
 
 # =================================================================================================
                        
@@ -381,27 +381,71 @@ def CompareMIDIs(input_src_midi, input_trg_midi, input_sampling_resolution, inpu
 
     #===============================================================================
 
-    toekns, notes = read_MIDI(input_midi.name)
-    
+    print('Loading MIDIs...')
 
+    src_tokens, src_notes = read_MIDI(input_src_midi.name)
+    trg_tokens, trg_notes = read_MIDI(input_trg_midi.name)
+    
     #==================================================================
 
     print('=' * 70)
-    print('Number of tokens:', len(toekns))
-    print('Number of notes:', len(notes))
-    print('Sample output events', toekns[:5])
-    print('=' * 70)
-    print('Generating...')
+    print('Number of src tokens:', len(src_tokens))
+    print('Number of src notes:', len(src_notes))
+    print('Number of trg tokens:', len(trg_tokens))
+    print('Number of trg notes:', len(trg_notes))
 
-    temperature = 0.85
+    #==========================================================================
     
+    print('=' * 70)
+    print('Comparing...')    
     print('=' * 70)
     print('Giant Music Transformer MIDI Comparator')
     print('=' * 70)
 
-    #==========================================================================
-
-
+    input_src_tokens = src_tokens
+    input_trg_tokens = trg_tokens
+    
+    sampling_resolution = input_sampling_overlap * 3
+    sampling_overlap = input_sampling_overlap * 3
+    
+    comp_length = (min(len(input_src_tokens), len(input_trg_tokens)) // sampling_resolution) * sampling_resolution
+    
+    comp_cos_sims = []
+    
+    for i in range(0, comp_length, sampling_resolution-sampling_overlap):
+    
+      torch.cuda.empty_cache()
+    
+      inp = [input_src_tokens[i:i+sampling_resolution]]
+    
+      inp = torch.LongTensor(inp).cuda()
+    
+      with ctx:
+        out = model(inp)
+        cache = out[2]
+        src_embedings = cache.layer_hiddens[-1]
+    
+      torch.cuda.empty_cache()
+    
+      inp = [input_trg_tokens[i:i+sampling_resolution]]
+    
+      inp = torch.LongTensor(inp).cuda()
+    
+      with ctx:
+        out = model(inp)
+        cache = out[2]
+        trg_embedings = cache.layer_hiddens[-1]
+    
+    
+      cos_sim = pairwise.cosine_similarity([src_embedings.cpu().detach().numpy()[0].flatten()],
+                                          [trg_embedings.cpu().detach().numpy()[0].flatten()]
+                                          ).tolist()[0][0]
+    
+      comp_cos_sims.append(cos_sim)
+    
+    min_cos_sim = min(comp_cos_sims)
+    avg_cos_sim = sum(comp_cos_sims) / len(comp_cos_sims)
+    max_cos_sim = max(comp_cos_sims)
 
     print('=' * 70)
     print('Done!')