Update app.py

app.py

@@ -269,6 +269,31 @@ def check_seq(seq, mel_len, mel_ptcs):
 #==================================================================================
 
 @spaces.GPU
+def generate_full_seq(score_seq,
+                      score_len, 
+                      temperature=0.9, 
+                      top_k_value=15,
+                      num_batches=64,
+                      verbose=True
+                     ):
+
+    x = torch.LongTensor([score_seq] * num_batches).cuda()
+    
+    with ctx:
+        out = model.generate(x,
+                             32*score_len,
+                             filter_logits_fn=top_k,
+                             filter_kwargs={'k': top_k_value},
+                             temperature=temperature,
+                             return_prime=False,
+                             verbose=verbose)
+    
+    output = out.tolist()
+
+    return output
+
+#==================================================================================
+
 def Generate_Accompaniment(input_midi,
                            input_melody,
                            melody_patch,
@@ -278,54 +303,6 @@ def Generate_Accompaniment(input_midi,
 
     #===============================================================================
 
-    def generate_full_seq(input_seq, 
-                          max_toks=3072, 
-                          temperature=0.9, 
-                          top_k_value=15, 
-                          verbose=True
-                         ):
-    
-        seq_abs_run_time = sum([t for t in input_seq if t < 128])
-    
-        cur_time = 0
-    
-        full_seq = copy.deepcopy(input_seq)
-    
-        toks_counter = 0
-    
-        while cur_time <= seq_abs_run_time+32:
-    
-            if verbose:
-                if toks_counter % 128 == 0:
-                    print('Generated', toks_counter, 'tokens')
-    
-            x = torch.LongTensor(full_seq).cuda()
-    
-            with ctx:
-                out = model.generate(x,
-                                     1,
-                                     filter_logits_fn=top_k,
-                                     filter_kwargs={'k': top_k_value},
-                                     temperature=temperature,
-                                     return_prime=False,
-                                     verbose=False)
-            
-            y = out.tolist()[0][0]
-    
-            if y < 128:
-                cur_time += y
-    
-            full_seq.append(y)
-    
-            toks_counter += 1
-
-            if toks_counter == max_toks:
-                return full_seq
-    
-        return full_seq
-
-    #===============================================================================
-
     print('=' * 70)
     print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
     start_time = reqtime.time()
@@ -353,7 +330,7 @@ def Generate_Accompaniment(input_midi,
 
     if input_midi:
         inp_mel = 'Custom MIDI'
-        score, score_list = load_midi(input_midi.name, melody_patch, use_nth_note)
+        score, score_len, score_ptcs = load_midi(input_midi.name, melody_patch, use_nth_note)
 
     else:
         mel_list = [m[0].lower() for m in popular_hook_melodies]
@@ -366,8 +343,9 @@ def Generate_Accompaniment(input_midi,
                 break
 
         score = popular_hook_melodies[[m[0] for m in popular_hook_melodies].index(inp_mel)][1]
-        score_list = [[[score[i]], score[i+1:i+3]] for i in range(0, len(score)-3, 3)]
-
+        score_len = len(score) // 3
+        score_ptcs = [t-256 for t in score if t > 256]
+        
         print('Selected melody:', inp_mel)
         
     print('Sample score events', score[:12])
@@ -382,12 +360,12 @@ def Generate_Accompaniment(input_midi,
 
     #==================================================================
 
-    start_score_seq = [1792] + score + [1793]
+    score_seq = [1792] + score + [1793]
 
     #==================================================================
 
-    input_seq = generate_full_seq(start_score_seq,
-                                  max_toks=MAX_GEN_TOKS,
+    input_seq = generate_full_seq(score_seq,
+                                  score_len,
                                   temperature=model_temperature,
                                   top_k_value=model_sampling_top_k,
                                  )