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app.py

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+#=================================================================================
+# https://huggingface.co/spaces/projectlosangeles/Orpheus-Masked-Pitches-Inpainter
+#=================================================================================
+
+print('=' * 70)
+print('Orpheus Masked Pitches Inpainter Gradio App')
+print('=' * 70)
+
+import os
+
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+os.environ['USE_FLASH_ATTENTION'] = '1'
+
+import time as reqtime
+from pytz import timezone
+
+import torch
+
+torch.set_float32_matmul_precision('high')
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+torch.backends.cuda.enable_mem_efficient_sdp(True)
+torch.backends.cuda.enable_math_sdp(True)
+torch.backends.cuda.enable_flash_sdp(True)
+torch.backends.cuda.enable_cudnn_sdp(True)
+
+import spaces
+import gradio as gr
+
+from x_transformer_2_3_1 import *
+
+import datetime
+import random
+import tqdm
+
+from midi_to_colab_audio import midi_to_colab_audio
+import TMIDIX
+
+import matplotlib.pyplot as plt
+
+from huggingface_hub import hf_hub_download
+         
+# =================================================================================================
+
+OUTPUT_MIDIS_DIR = 'output_midis'
+
+# =================================================================================================
+
+print('=' * 70)
+print('Loading models...')
+print('=' * 70)
+print('Loading Orpheus masked encoder model...')
+print('=' * 70)
+
+SEQ_LEN = 2048
+PAD_IDX = 18820
+DEVICE = 'cuda'
+
+model = TransformerWrapper(
+    num_tokens = PAD_IDX+1,
+    max_seq_len = SEQ_LEN,
+    attn_layers = Encoder(dim = 2048,
+                          depth = 12,
+                          heads = 16,
+                          rotary_pos_emb = True,
+                          attn_flash = True
+                         )
+    )
+
+model.to(DEVICE)
+
+print('=' * 70)
+print('Loading model checkpoint...')
+
+checkpoint = hf_hub_download(
+    repo_id='asigalov61/Orpheus-Music-Transformer',
+    filename='Orpheus_Music_Transformer_Masked_Encoder_Trained_Model_23000_steps_0.6548_loss_0.8132_acc.pth'
+)
+
+model.load_state_dict(torch.load(checkpoint, map_location=DEVICE, weights_only=True))
+
+model.eval()
+
+# model = torch.compile(model)
+
+print('=' * 70)
+print('Done!')
+print('=' * 70)
+
+# =================================================================================================
+
+dtype = torch.bfloat16
+
+ctx = torch.amp.autocast(device_type=DEVICE, dtype=dtype)
+
+print('Done!')
+print('=' * 70)
+
+# =================================================================================================
+
+print('Loading SoundFont...')
+
+SOUNDFONT_PATH = hf_hub_download(repo_id='projectlosangeles/soundfonts4u',
+                                 repo_type='dataset',
+                                 filename='SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2'
+                                )
+
+print('Done!')
+print('=' * 70)
+
+# =================================================================================================
+
+def load_midi(input_midi):
+    
+    """Process the input MIDI file and create a token sequence."""
+
+    raw_score = TMIDIX.midi2single_track_ms_score(input_midi.name, do_not_check_MIDI_signature=True)
+    
+    escore_notes = TMIDIX.advanced_score_processor(raw_score, 
+                                                   return_enhanced_score_notes=True, 
+                                                   apply_sustain=True
+                                                  )
+
+    if escore_notes and escore_notes[0]:
+    
+        escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes[0], 
+                                                           sort_drums_last=True
+                                                          )
+
+        escore_notes = TMIDIX.remove_duplicate_pitches_from_escore_notes(escore_notes)
+
+        escore_notes = TMIDIX.fix_escore_notes_durations(escore_notes, 
+                                                         min_notes_gap=0
+                                                        )
+        
+        dscore = TMIDIX.delta_score_notes(escore_notes)
+        
+        dcscore = TMIDIX.chordify_score([d[1:] for d in dscore])
+        
+        melody_chords = [18816]
+        
+        #=======================================================
+        # MAIN PROCESSING CYCLE
+        #=======================================================
+        
+        for i, c in enumerate(dcscore):
+        
+            delta_time = c[0][0]
+        
+            melody_chords.append(delta_time)
+        
+            for e in c:
+            
+                #=======================================================
+                
+                # Durations
+                dur = max(1, min(255, e[1]))
+        
+                # Patches
+                pat = max(0, min(128, e[5]))
+                
+                # Pitches
+                ptc = max(1, min(127, e[3]))
+                
+                # Velocities
+                # Calculating octo-velocity
+                vel = max(8, min(127, e[4]))
+                velocity = round(vel / 15)-1
+                
+                #=======================================================
+                # FINAL NOTE SEQ
+                #=======================================================
+                
+                # Writing final note
+                pat_ptc = (128 * pat) + ptc 
+                dur_vel = (8 * dur) + velocity
+        
+                melody_chords.extend([pat_ptc+256, dur_vel+16768])
+            
+        return melody_chords
+
+    else:
+        return [18816]
+
+# =================================================================================================
+
+def save_midi(tokens):
+    
+    """Convert token sequence back to a MIDI score and write it using TMIDIX.
+    """
+
+    time = 0
+    dur = 1
+    vel = 90
+    pitch = 60
+    channel = 0
+    patch = 0
+
+    patches = [-1] * 16
+
+    channels = [0] * 16
+    channels[9] = 1
+
+    song_f = []
+
+    for ss in tokens:
+
+        if 0 <= ss < 256:
+
+            time += ss * 16
+
+        if 256 <= ss < 16768:
+
+            patch = (ss-256) // 128
+
+            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-256) % 128
+
+
+        if 16768 <= ss < 18816:
+
+            dur = ((ss-16768) // 8) * 16
+            vel = (((ss-16768) % 8)+1) * 15
+
+            song_f.append(['note', time, dur, channel, pitch, vel, patch])
+
+    if song_f is not None and song_f:
+
+        song_f = TMIDIX.remove_duplicate_pitches_from_escore_notes(song_f)
+    
+        song_f = TMIDIX.fix_escore_notes_durations(song_f, 
+                                                   min_notes_gap=0
+                                                  )
+    
+        output_score, patches, overflow_patches = TMIDIX.patch_enhanced_score_notes(song_f)
+    
+        now = datetime.datetime.now(PDT)
+        ms4 = now.strftime("%f")[:4]  # first four digits of microseconds
+        
+        fname = (
+            "Orpheus-Masked-Pitches-Inpainter-Composition-"
+            + now.strftime(f"%Y-%m-%d-%H-%M-%S-{ms4}")
+        )
+
+        os.makedirs(OUTPUT_MIDIS_DIR, exist_ok=True)
+
+        output_fname = os.path.join(OUTPUT_MIDIS_DIR, fname)
+        
+        TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(
+            output_score,
+            output_signature='Orpheus Masked Pitches Inpainter',
+            output_file_name=output_fname,
+            track_name='Project Los Angeles',
+            list_of_MIDI_patches=patches,
+            verbose=False
+        )
+        return output_fname, output_score
+
+    else:
+        return None, None
+
+# =================================================================================================
+
+@spaces.GPU
+def inpaint_pitches(inp_seq,
+                    input_patch,
+                    input_inpaint_ratio,
+                    input_num_prime_notes
+                   ):
+    
+    print('*' * 70)
+    print('Inpainting pitches...')
+
+    inp_seq = inp_seq[:SEQ_LEN]
+
+    m_pos = [i for i in range(SEQ_LEN) if (128*input_patch)+256 < inp_seq[i] < (128*(input_patch+1))+256]
+
+    m_pos = m_pos[min(len(m_pos), input_num_prime_notes):]
+
+    if input_inpaint_ratio < 1:
+        m_pos = sorted(random.sample(m_pos, k=int(round(len(m_pos) * input_inpaint_ratio))))
+
+    results = predict_masked_tokens(model, inp_seq, mask_positions=m_pos, topk=1)
+
+    output_seq = results['predicted_ids']
+
+    print('Done!')
+    print('=' * 70)
+    
+    return output_seq
+                       
+# =================================================================================================
+
+def Inpaint_Pitches(input_midi,
+                    input_patch,
+                    input_inpaint_ratio,
+                    input_num_prime_notes
+                   ):
+
+    if input_midi is not None:
+    
+        print('=' * 70)
+        print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
+        start_time = reqtime.time()
+
+        print('=' * 70)
+        fn = os.path.basename(input_midi.name)
+        fn1 = fn.split('.')[0]
+        print('Input file name:', fn)
+        print('Input patch:', input_patch)
+        print('Input inpaint ratio:', input_inpaint_ratio)
+        print('Input number of prime notes:', input_num_prime_notes)
+        print('=' * 70)
+        print('Loading MIDI...')
+        inp_seq = load_midi(input_midi)
+    
+        print('Composition has', len(inp_seq), 'tokens')
+        print('Sample composition tokens:', inp_seq[:5])
+        print('=' * 70)
+    
+        #===============================================================================
+    
+        output_seq = inpaint_pitches(inp_seq,
+                                     input_patch,
+                                     input_inpaint_ratio,
+                                     input_num_prime_notes
+                                    )
+    
+        #===============================================================================
+    
+        print('Saving MIDI...')
+        print('=' * 70)
+    
+        output_fname, output_score = save_midi(output_seq)
+        
+        #===============================================================================
+        
+        print('Rendering results...')
+        print('=' * 70)            
+        
+        audio = midi_to_colab_audio(output_fname+'.mid', 
+                                    soundfont_path=SOUNDFONT_PATH,
+                                    sample_rate=16000,
+                                    output_for_gradio=True
+                                    )
+    
+        #========================================================
+    
+        output_audio = (16000, audio)
+        
+        output_plot = TMIDIX.plot_ms_SONG(output_score,
+                                          plot_title=os.path.basename(output_fname)+'.mid',
+                                          return_plt=True
+                                         )
+    
+        print('Done!')
+        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_audio, output_plot, output_fname+'.mid'
+
+    return None, None, None
+
+# =================================================================================================
+  
+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: left; margin-bottom: 1rem'>Orpheus Masked Pitches Inpainter</h1>")
+    gr.Markdown("<h1 style='text-align: left; margin-bottom: 1rem'>Instantly inpaint pitches in any MIDI with Orpheus masked encoder</h1>")
+
+    with gr.Row(elem_classes="duplicate-row"):
+        gr.DuplicateButton(
+            value="🤗 Duplicate 🤗",
+            variant="huggingface",
+            size="md",
+            link="https://huggingface.co/spaces/projectlosangeles/Orpheus-Masked-Pitches-Inpainter?duplicate=true",
+            link_target="_blank"
+        )
+        
+        gr.Button(
+            value="❤️ Models ❤️",
+            variant="huggingface",
+            size="md",
+            link="https://huggingface.co/asigalov61/Orpheus-Music-Transformer",
+            link_target="_blank"
+        )
+
+        gr.Button(
+            value="🚀 Spaces 🚀",
+            variant="huggingface",
+            size="md",
+            link="https://huggingface.co/collections/asigalov61/orpheus-music-transformer",
+            link_target="_blank"
+        )
+        
+        gr.Button(
+            value="🦖 Dataset 🦖",
+            variant="huggingface",
+            size="md",
+            link="https://huggingface.co/datasets/projectlosangeles/Godzilla-MIDI-Dataset",
+            link_target="_blank"
+        )
+
+    gr.Markdown("## Upload your MIDI or select an example MIDI at the bottom of the page")
+
+    input_midi = gr.File(label="Input MIDI", file_types=["midi", ".mid", ".kar"])
+
+    input_patch = gr.Slider(0, 128, value=40, step=1, label="Patch number to inpaint")
+    input_num_prime_notes = gr.Slider(0, 64, value=16, step=1, label="Number of prime notes")
+    input_inpaint_ratio = gr.Slider(0.01, 1.0, value=1, step=0.01, label="Pitches inpaint ratio")
+
+    run_btn = gr.Button("Inpaint Pitches", variant="primary")
+
+    gr.Markdown("## Generation results")
+
+    output_audio = gr.Audio(label="Output MIDI audio", format="mp3", 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(Inpaint_Pitches,
+                              [input_midi,
+                               input_patch,
+                               input_inpaint_ratio,
+                               input_num_prime_notes
+                              ],
+                              [output_audio,
+                               output_plot,
+                               output_midi
+                              ])
+
+    gr.Examples(
+            [["Gang Stop.mid", 40, 1, 16],
+             ["Soli.mid", 40, 1, 16]
+            ],
+            [input_midi,
+             input_patch,
+             input_inpaint_ratio,
+             input_num_prime_notes
+            ],
+            [output_audio,
+             output_plot,
+             output_midi
+            ],
+            Inpaint_Pitches
+    )
+    
+    app.launch(mcp_server=True)

packages.txt

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

requirements.txt

@@ -0,0 +1,10 @@
+tqdm
+numpy
+scikit-learn
+matplotlib
+gradio
+hf-transfer
+huggingface_hub
+torch
+einops
+einx