Update app.py

app.py

@@ -1,4 +1,4 @@
-# https://huggingface.co/spaces/asigalov61/Inpaint-Music-Transformer
+# https://huggingface.co/spaces/asigalov61/Intelligent-MIDI-Comparator
 
 import os.path
 
@@ -21,64 +21,11 @@ import TMIDIX
 import matplotlib.pyplot as plt
     
 # =================================================================================================
-                       
-@spaces.GPU
-def InpaintPitches(input_midi, input_num_of_notes, input_patch_number):
-    print('=' * 70)
-    print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
-    start_time = reqtime.time()
-
-    print('Loading model...')
-
-    SEQ_LEN = 8192 # Models seq len
-    PAD_IDX = 19463 # Models pad index
-    DEVICE = 'cuda' # 'cuda'
-
-    # instantiate the model
-
-    model = TransformerWrapper(
-        num_tokens = PAD_IDX+1,
-        max_seq_len = SEQ_LEN,
-        attn_layers = Decoder(dim = 1024, depth = 32, heads = 32, attn_flash = True)
-        )
-    
-    model = AutoregressiveWrapper(model, ignore_index = PAD_IDX)
-
-    model.to(DEVICE)
-    print('=' * 70)
-
-    print('Loading model checkpoint...')
-
-    model.load_state_dict(
-        torch.load('Giant_Music_Transformer_Large_Trained_Model_36074_steps_0.3067_loss_0.927_acc.pth',
-                   map_location=DEVICE))
-    print('=' * 70)
-
-    model.eval()
-
-    if DEVICE == 'cpu':
-        dtype = torch.bfloat16
-    else:
-        dtype = torch.bfloat16
 
-    ctx = torch.amp.autocast(device_type=DEVICE, dtype=dtype)
-
-    print('Done!')
-    print('=' * 70)
-
-    fn = os.path.basename(input_midi.name)
-    fn1 = fn.split('.')[0]
-
-    input_num_of_notes = max(8, min(2048, input_num_of_notes))
-
-    print('-' * 70)
-    print('Input file name:', fn)
-    print('Req num of notes:', input_num_of_notes)
-    print('Req patch number:', input_patch_number)
-    print('-' * 70)
+def read_MIDI(input_midi)
 
     #===============================================================================
-    raw_score = TMIDIX.midi2single_track_ms_score(input_midi.name)
+    raw_score = TMIDIX.midi2single_track_ms_score(input_midi)
     
     #===============================================================================
     # Enhanced score notes
@@ -191,35 +138,85 @@ def InpaintPitches(input_midi, input_num_of_notes, input_patch_number):
           melody_chords2.append([delta_time, dur_vel+256, pat_ptc+2304])
     
           pe = e
+
+    return melody_chords, melody_chords2
+
+# =================================================================================================
+
+                       
+@spaces.GPU
+def InpaintPitches(input_midi, input_num_of_notes, input_patch_number):
+    print('=' * 70)
+    print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
+    start_time = reqtime.time()
+
+    print('Loading model...')
+
+    SEQ_LEN = 8192 # Models seq len
+    PAD_IDX = 19463 # Models pad index
+    DEVICE = 'cuda' # 'cuda'
+
+    # instantiate the model
+
+    model = TransformerWrapper(
+        num_tokens = PAD_IDX+1,
+        max_seq_len = SEQ_LEN,
+        attn_layers = Decoder(dim = 1024, depth = 32, heads = 32, attn_flash = True)
+        )
     
-        
+    model = AutoregressiveWrapper(model, ignore_index = PAD_IDX)
+
+    model.to(DEVICE)
+    print('=' * 70)
+
+    print('Loading model checkpoint...')
+
+    model.load_state_dict(
+        torch.load('Giant_Music_Transformer_Large_Trained_Model_36074_steps_0.3067_loss_0.927_acc.pth',
+                   map_location=DEVICE))
+    print('=' * 70)
+
+    model.eval()
+
+    if DEVICE == 'cpu':
+        dtype = torch.bfloat16
+    else:
+        dtype = torch.bfloat16
+
+    ctx = torch.amp.autocast(device_type=DEVICE, dtype=dtype)
+
+    print('Done!')
+    print('=' * 70)
+
+    fn = os.path.basename(input_midi.name)
+    fn1 = fn.split('.')[0]
+
+    input_num_of_notes = max(8, min(2048, input_num_of_notes))
+
+    print('-' * 70)
+    print('Input file name:', fn)
+    print('Req num of notes:', input_num_of_notes)
+    print('Req patch number:', input_patch_number)
+    print('-' * 70)
+
+    #===============================================================================
+
+    toekns, notes = read_MIDI(input_midi.name)
+    
+
     #==================================================================
 
     print('=' * 70)
-    print('Number of tokens:', len(melody_chords))
-    print('Number of notes:', len(melody_chords2))
-    print('Sample output events', melody_chords[:5])
+    print('Number of tokens:', len(toekns))
+    print('Number of notes:', len(notes))
+    print('Sample output events', toekns[:5])
     print('=' * 70)
     print('Generating...')
 
-    #@title Pitches/Instruments Inpainting
-    
-    #@markdown You can stop the inpainting at any time to render partial results
-    
-    #@markdown Inpainting settings
-    
-    #@markdown Select MIDI patch present in the composition to inpaint
-    
-    inpaint_MIDI_patch = input_patch_number
-    
-    #@markdown Generation settings
-    number_of_prime_notes = 24
-    number_of_memory_tokens = 1024 # @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}
     temperature = 0.85
     
     print('=' * 70)
-    print('Giant Music Transformer Inpainting Model Generator')
+    print('Giant Music Transformer MIDI Comparator')
     print('=' * 70)
 
     #==========================================================================