Update app.py

app.py

@@ -1,4 +1,6 @@
+# =================================================================================================
 # https://huggingface.co/spaces/asigalov61/Chords-Progressions-Generator
+# =================================================================================================
 
 import os
 import time as reqtime
@@ -7,15 +9,19 @@ from pytz import timezone
 
 import gradio as gr
 
+import numpy as np
+
+import os
 import random
+from collections import Counter
 
 import TMIDIX
 
-import numpy as np
-    
+from midi_to_colab_audio import midi_to_colab_audio
+
 # =================================================================================================
 
-def ClassifyMIDI(input_midi, input_sampling_resolution):
+def Generate_Chords_Progression(input_midi, input_sampling_resolution):
 
     print('=' * 70)
     print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
@@ -33,105 +39,9 @@ def ClassifyMIDI(input_midi, input_sampling_resolution):
     print('Input MIDI file name:', fn)
 
     print('=' * 70)
-    print('Loading MIDI file...')
-    
-    midi_name = fn
-    
-    raw_score = TMIDIX.midi2single_track_ms_score(open(input_midi.name, 'rb').read())
-    
-    escore_notes = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0]
-    
-    #===============================================================================
-    # Augmented enhanced score notes
-
-    escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes, timings_divider=32)
-
-    escore_notes = [e for e in escore_notes if e[6] < 80 or e[6] == 128]
-
-    #=======================================================
-    # Augmentation
-
-    #=======================================================
-    # FINAL PROCESSING
-
-    melody_chords = []
-
-    #=======================================================
-    # MAIN PROCESSING CYCLE
-    #=======================================================
-
-    pe = escore_notes[0]
-
-    pitches = []
-
-    notes_counter = 0
-
-    for e in escore_notes:
-
-        #=======================================================
-        # Timings...
-
-        delta_time = max(0, min(127, e[1]-pe[1]))
-
-        if delta_time != 0:
-            pitches = []
-
-        # Durations and channels
-
-        dur = max(1, min(127, e[2]))
-
-        # Patches
-        pat = max(0, min(128, e[6]))
-
-        # Pitches
-
-        if pat == 128:
-            ptc = max(1, min(127, e[4]))+128
-        else:
-            ptc = max(1, min(127, e[4]))
-
-        #=======================================================
-        # FINAL NOTE SEQ
-
-        # Writing final note synchronously
-
-        if ptc not in pitches:
-            melody_chords.extend([delta_time, dur+128, ptc+256])
-            pitches.append(ptc)
-            notes_counter += 1
-
-        pe = e
-
-    #==============================================================
-    
-    print('Done!')
-    print('=' * 70)
-    
-    print('Sampling score...') 
-    
-    chunk_size = 1020
-    
-    score = melody_chords
-    
-    input_data = []
-    
-    for i in range(0, len(score)-chunk_size, chunk_size // input_sampling_resolution):
-        schunk = score[i:i+chunk_size]
-    
-        if len(schunk) == chunk_size:
-    
-            td = [937]
-    
-            td.extend(schunk)
     
-            td.extend([938])
-    
-            input_data.append(td)
-            
     print('Done!')
     print('=' * 70)
-    
-    #==============================================================
 
     classification_summary_string = '=' * 70
     classification_summary_string += '\n'
@@ -153,79 +63,11 @@ def ClassifyMIDI(input_midi, input_sampling_resolution):
     classification_summary_string += '=' * 70
     classification_summary_string += '\n'
 
-    print('Loading model...')
-    
-    SEQ_LEN = 1026
-    PAD_IDX = 940
-    DEVICE = 'cuda' # 'cuda'
-    
-    # instantiate the model
-    
-    model = TransformerWrapper(
-        num_tokens = PAD_IDX+1,
-        max_seq_len = SEQ_LEN,
-        attn_layers = Decoder(dim = 1024, depth = 24, heads = 32, attn_flash = True)
-    )
-    
-    model = AutoregressiveWrapper(model, ignore_index=PAD_IDX, pad_value=PAD_IDX)
-    
-    model = torch.nn.DataParallel(model)
-    
-    model.to(DEVICE)
-    
-    print('=' * 70)
-    
-    print('Loading model checkpoint...')
-    
-    model.load_state_dict(
-        torch.load('Ultimate_MIDI_Classifier_Trained_Model_29886_steps_0.556_loss_0.8339_acc.pth',
-                   map_location=DEVICE))
-    print('=' * 70)
-    
-    if DEVICE == 'cpu':
-        dtype = torch.bfloat16
-    else:
-        dtype = torch.bfloat16
-    
-    ctx = torch.amp.autocast(device_type=DEVICE, dtype=dtype)
-
-    print('Done!')
-    print('=' * 70)
-
     #==================================================================
 
     print('=' * 70)
     print('Ultimate MIDI Classifier')
     print('=' * 70)
-    print('Classifying...')
-    
-    torch.cuda.empty_cache()
-    
-    model.eval()
-
-    artist_results = []
-    song_results = []
-    
-    results = []
-    
-    for input in input_data:
-    
-        x = torch.tensor(input[:1022], dtype=torch.long, device='cuda')
-    
-        with ctx:
-          out = model.module.generate(x,
-                                      2,
-                                      filter_logits_fn=top_k,
-                                      filter_kwargs={'k': 1},
-                                      temperature=0.9,
-                                      return_prime=False,
-                                      verbose=False)
-    
-        result = tuple(out[0].tolist())
-    
-        results.append(result)
-    
-    final_result = mode(results)
     
     print('=' * 70)
     print('Done!')
@@ -302,93 +144,39 @@ if __name__ == "__main__":
     print('App start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
     print('=' * 70)
 
-    #===============================================================================
-    # Helper functions
-    #===============================================================================
-    
-    def str_strip_song(string):
-      if string is not None:
-        string = string.replace('-', ' ').replace('_', ' ').replace('=', ' ')
-        str1 = re.compile('[^a-zA-Z ]').sub('', string)
-        return re.sub(' +', ' ', str1).strip().title()
-      else:
-        return ''
-    
-    def str_strip_artist(string):
-      if string is not None:
-        string = string.replace('-', ' ').replace('_', ' ').replace('=', ' ')
-        str1 = re.compile('[^0-9a-zA-Z ]').sub('', string)
-        return re.sub(' +', ' ', str1).strip().title()
-      else:
-        return ''
-    
-    def song_artist_to_song_artist_tokens(file_name):
-        idx = classifier_labels.index(file_name)
-    
-        tok1 = idx // 424
-        tok2 = idx % 424
-    
-        return [tok1, tok2]
-    
-    def song_artist_tokens_to_song_artist(file_name_tokens):
-    
-        tok1 = file_name_tokens[0]
-        tok2 = file_name_tokens[1]
-    
-        idx = (tok1 * 424) + tok2
-    
-        return classifier_labels[idx]
-    
-    #===============================================================================
-    
     print('=' * 70)
     print('Loading Ultimate MIDI Classifier labels...')
     print('=' * 70)
-    classifier_labels = TMIDIX.Tegridy_Any_Pickle_File_Reader('Ultimate_MIDI_Classifier_Song_Artist_Labels')
-    print('=' * 70)
-    genre_labels = TMIDIX.Tegridy_Any_Pickle_File_Reader('Ultimate_MIDI_Classifier_Music_Genre_Labels')
-    genre_labels_fnames = [f[0] for f in genre_labels]
+    good_chords_chunks = TMIDIX.Tegridy_Any_Pickle_File_Reader('pitches_chords_progressions_5_3_15')
+
     print('=' * 70)
     print('Done!')
     print('=' * 70)
 
     app = gr.Blocks()
     with app:
-        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Ultimate MIDI Classifier</h1>")
-        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Classify absolutely any MIDI by genre, song and artist</h1>")
+        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Chords Progressions Generator</h1>")
+        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate unique chords progressions</h1>")
         gr.Markdown(
-            "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Ultimate-MIDI-Classifier&style=flat)\n\n"
-            "This is a demo for Ultimate MIDI Classifier\n\n"
-            "Check out [Ultimate MIDI Classifier](https://github.com/asigalov61/Ultimate-MIDI-Classifier) on GitHub!\n\n"
+            "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Chords-Progressions-Generator&style=flat)\n\n"
+            "This is a demo for Tegridy MIDI Dataset\n\n"
+            "Check out [Tegridy MIDI Dataset](https://github.com/asigalov61/Tegridy-MIDI-Dataset) on GitHub!\n\n"
             "[Open In Colab]"
-            "(https://colab.research.google.com/github/asigalov61/Ultimate-MIDI-Classifier/blob/main/Ultimate_MIDI_Classifier.ipynb)"
-            " for all options, faster execution and endless classification"
+            "(https://colab.research.google.com/github/asigalov61/Tegridy-MIDI-Dataset/blob/master/Chords-Progressions/Pitches_Chords_Progressions_Generator.ipynb)"
+            " for all options, faster execution and endless generation"
         )
 
-        gr.Markdown("## Upload any MIDI to classify")
-        gr.Markdown("### Please note that the MIDI file must have at least 340 notes for this demo to work")
-        
-        input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"])
+        gr.Markdown("## Select generation options")
+
         input_sampling_resolution = gr.Slider(1, 5, value=2, step=1, label="Classification sampling resolution")
 
         run_btn = gr.Button("classify", variant="primary")
 
-        gr.Markdown("## Classification results")
+        gr.Markdown("## Generation results")
 
         output_midi_cls_summary = gr.Textbox(label="MIDI classification results")
 
         run_event = run_btn.click(ClassifyMIDI, [input_midi, input_sampling_resolution],
                                   [output_midi_cls_summary])
-        gr.Examples(
-            [["Honesty.kar", 2], 
-             ["House Of The Rising Sun.mid", 2], 
-             ["Nothing Else Matters.kar", 2],
-             ["Sharing The Night Together.kar", 2]
-            ],
-            [input_midi, input_sampling_resolution],
-            [output_midi_cls_summary],
-            ClassifyMIDI,
-            cache_examples=True,
-        )
-        
+   
         app.queue().launch()