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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2 filter=lfs diff=lfs merge=lfs -text

SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cd41a4639c9e7a96413b4b22540d48e6741e24bcdabcb2eff22cd65929df3cfa
+size 553961496

app.py

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+import os.path
+
+import time as reqtime
+import datetime
+from pytz import timezone
+
+import torch
+
+import spaces
+import gradio as gr
+
+from x_transformer_1_23_2 import *
+import random
+import tqdm
+
+from midi_to_colab_audio import midi_to_colab_audio
+import TMIDIX
+
+import matplotlib.pyplot as plt
+
+in_space = os.getenv("SYSTEM") == "spaces"
+         
+# =================================================================================================
+                       
+@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)
+
+    #===============================================================================
+    raw_score = TMIDIX.midi2single_track_ms_score(input_midi.name)
+    
+    #===============================================================================
+    # Enhanced score notes
+    
+    events_matrix1 = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0]
+    
+    #=======================================================
+    # PRE-PROCESSING
+    
+    # checking number of instruments in a composition
+    instruments_list_without_drums = list(set([y[3] for y in events_matrix1 if y[3] != 9]))
+    instruments_list = list(set([y[3] for y in events_matrix1]))
+    
+    if len(events_matrix1) > 0 and len(instruments_list_without_drums) > 0:
+    
+      #======================================
+    
+      events_matrix2 = []
+    
+      # Recalculating timings
+      for e in events_matrix1:
+    
+          # Original timings
+          e[1] = int(e[1] / 16)
+          e[2] = int(e[2] / 16)
+    
+      #===================================
+      # ORIGINAL COMPOSITION
+      #===================================
+    
+      # Sorting by patch, pitch, then by start-time
+    
+      events_matrix1.sort(key=lambda x: x[6])
+      events_matrix1.sort(key=lambda x: x[4], reverse=True)
+      events_matrix1.sort(key=lambda x: x[1])
+    
+      #=======================================================
+      # FINAL PROCESSING
+    
+      melody_chords = []
+      melody_chords2 = []
+    
+      # Break between compositions / Intro seq
+    
+      if 9 in instruments_list:
+          drums_present = 19331 # Yes
+      else:
+          drums_present = 19330 # No
+    
+      if events_matrix1[0][3] != 9:
+          pat = events_matrix1[0][6]
+      else:
+          pat = 128
+    
+      melody_chords.extend([19461, drums_present, 19332+pat]) # Intro seq
+    
+      #=======================================================
+      # MAIN PROCESSING CYCLE
+      #=======================================================
+    
+      abs_time = 0
+    
+      pbar_time = 0
+    
+      pe = events_matrix1[0]
+    
+      chords_counter = 1
+    
+      comp_chords_len = len(list(set([y[1] for y in events_matrix1])))
+    
+      for e in events_matrix1:
+    
+          #=======================================================
+          # Timings...
+    
+          # Cliping all values...
+          delta_time = max(0, min(255, e[1]-pe[1]))
+    
+          # Durations and channels
+    
+          dur = max(0, min(255, e[2]))
+          cha = max(0, min(15, e[3]))
+    
+          # Patches
+          if cha == 9: # Drums patch will be == 128
+              pat = 128
+    
+          else:
+              pat = e[6]
+    
+          # Pitches
+    
+          ptc = max(1, min(127, e[4]))
+    
+          # Velocities
+    
+          # Calculating octo-velocity
+          vel = max(8, min(127, e[5]))
+          velocity = round(vel / 15)-1
+    
+          #=======================================================
+          # FINAL NOTE SEQ
+    
+          # Writing final note asynchronously
+    
+          dur_vel = (8 * dur) + velocity
+          pat_ptc = (129 * pat) + ptc
+    
+          melody_chords.extend([delta_time, dur_vel+256, pat_ptc+2304])
+          melody_chords2.append([delta_time, dur_vel+256, pat_ptc+2304])
+    
+          pe = e
+    
+        
+    #==================================================================
+
+    print('=' * 70)
+    print('Number of tokens:', len(melody_chords))
+    print('Number of notes:', len(melody_chords2))
+    print('Sample output events', melody_chords[: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_tokens = 90 # @param {type:"slider", min:3, max:8190, step:3}
+    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('=' * 70)
+
+    nidx = 0
+    
+    for i, m in enumerate(melody_chords):
+        
+        cpatch = (melody_chords[i]-2304) // 129
+        
+        if 2304 <= melody_chords[i] < 18945 and (cpatch) == inpaint_MIDI_patch:
+            nidx += 1
+
+        if nidx == input_num_of_notes+(number_of_prime_tokens // 3):
+            break
+            
+    nidx = i
+    
+    out2 = []
+    
+    for m in melody_chords[:number_of_prime_tokens]:
+      out2.append(m)
+    
+    for i in range(number_of_prime_tokens, len(melody_chords[:nidx])):
+    
+        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!')
+    print('=' * 70)
+    
+    #===============================================================================
+    print('Rendering results...')
+    
+    print('=' * 70)
+    print('Sample INTs', out2[:12])
+    print('=' * 70)
+    
+    if len(out2) != 0:
+    
+        song = out2
+        song_f = []
+    
+        time = 0
+        dur = 0
+        vel = 90
+        pitch = 0
+        channel = 0
+    
+        patches = [-1] * 16
+    
+        channels = [0] * 16
+        channels[9] = 1
+    
+        for ss in song:
+    
+            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 = 'Giant Music Transformer',
+                                                              output_file_name = fn1,
+                                                              track_name='Project Los Angeles',
+                                                              list_of_MIDI_patches=patches
+                                                              )
+    
+    new_fn = fn1+'.mid'
+            
+    
+    audio = midi_to_colab_audio(new_fn, 
+                        soundfont_path=soundfont,
+                        sample_rate=16000,
+                        volume_scale=10,
+                        output_for_gradio=True
+                        )
+    
+    print('Done!')
+    print('=' * 70)
+
+    #========================================================
+
+    output_midi_title = str(fn1)
+    output_midi_summary = str(song_f[:3])
+    output_midi = str(new_fn)
+    output_audio = (16000, audio)
+    
+    output_plot = TMIDIX.plot_ms_SONG(song_f, plot_title=output_midi, return_plt=True)
+
+    print('Output MIDI file name:', output_midi)
+    print('Output MIDI title:', output_midi_title)
+    print('Output MIDI summary:', output_midi_summary)
+    print('=' * 70) 
+    
+
+    #========================================================
+    
+    print('-' * 70)
+    print('Req end time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
+    print('-' * 70)
+    print('Req execution time:', (reqtime.time() - start_time), 'sec')
+
+    return output_midi_title, output_midi_summary, output_midi, output_audio, output_plot
+
+# =================================================================================================
+
+if __name__ == "__main__":
+    
+    PDT = timezone('US/Pacific')
+    
+    print('=' * 70)
+    print('App start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
+    print('=' * 70)
+
+    soundfont = "SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2"
+   
+    app = gr.Blocks()
+    with app:
+        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.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/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")
+        
+        input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"])
+        input_num_of_notes = gr.Slider(8, 2048, value=128, step=8, label="Number of composition notes to inpaint")
+        input_patch_number = gr.Slider(0, 127, value=0, step=1, label="Composition MIDI patch to inpaint")
+        
+        run_btn = gr.Button("generate", variant="primary")
+
+        gr.Markdown("## Generation results")
+
+        output_midi_title = gr.Textbox(label="Output MIDI title")
+        output_midi_summary = gr.Textbox(label="Output MIDI summary")
+        output_audio = gr.Audio(label="Output MIDI audio", format="wav", elem_id="midi_audio")
+        output_plot = gr.Plot(label="Output MIDI score plot")
+        output_midi = gr.File(label="Output MIDI file", file_types=[".mid"])
+
+
+        run_event = run_btn.click(InpaintPitches, [input_midi, input_num_of_notes, input_patch_number],
+                                  [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])
+
+        gr.Examples(
+            [["Giant-Music-Transformer-Piano-Seed-1.mid", 128, 0], 
+             ["Giant-Music-Transformer-Piano-Seed-2.mid", 128, 0], 
+             ["Giant-Music-Transformer-Piano-Seed-3.mid", 128, 0],
+             ["Giant-Music-Transformer-Piano-Seed-4.mid", 128, 0],
+             ["Giant-Music-Transformer-Piano-Seed-5.mid", 128, 0],
+             ["Giant-Music-Transformer-Piano-Seed-6.mid", 128, 0],
+             ["Giant-Music-Transformer-MI-Seed-1.mid", 128, 71],
+             ["Giant-Music-Transformer-MI-Seed-2.mid", 128, 40],
+             ["Giant-Music-Transformer-MI-Seed-3.mid", 128, 40],
+             ["Giant-Music-Transformer-MI-Seed-4.mid", 128, 40],
+             ["Giant-Music-Transformer-MI-Seed-5.mid", 128, 40],
+             ["Giant-Music-Transformer-MI-Seed-6.mid", 128, 0]
+            ],
+            [input_midi, input_num_of_notes, input_patch_number],
+            [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot],
+            InpaintPitches,
+            cache_examples=True,
+        )
+        
+        app.queue().launch()

packages.txt

@@ -0,0 +1 @@
+fluidsynth

requirements.txt

@@ -0,0 +1,3 @@
+torch
+gradio
+einops