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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
+         
+# =================================================================================================
+
+print('=' * 70)
+print('Loading models...')
+print('=' * 70)
+print('Loading chords texturing model...')
+print('=' * 70)
+
+SEQ_LEN = 2048
+PAD_IDX = 721
+DEVICE = 'cuda'
+
+tex_model = TransformerWrapper(
+    num_tokens = PAD_IDX+1,
+    max_seq_len = SEQ_LEN,
+    attn_layers = Decoder(dim = 2048,
+                          depth = 12,
+                          heads = 16,
+                          rotary_pos_emb = True,
+                          attn_flash = True
+                         )
+    )
+
+tex_model = AutoregressiveWrapper(tex_model, ignore_index=PAD_IDX)
+
+tex_model.to(DEVICE)
+print('=' * 70)
+
+print('Loading model checkpoint...')
+
+checkpoint = hf_hub_download(
+    repo_id='asigalov61/Chordified-Piano-Transformer',
+    filename='Chordified_Piano_Transformer_Texturing_Trained_Model_18092_steps_0.7058_loss_0.7977_acc.pth'
+)
+
+tex_model.load_state_dict(torch.load(checkpoint, map_location=DEVICE, weights_only=True))
+
+tex_model.eval()
+
+tex_model = torch.compile(tex_model)
+
+print('=' * 70)
+print('Done!')
+print('=' * 70)
+
+# =================================================================================================
+
+print('Loading chords progressions model...')
+print('=' * 70)
+
+SEQ_LEN = 380
+PAD_IDX = 324
+DEVICE = 'cuda'
+
+prg_model = TransformerWrapper(
+    num_tokens = PAD_IDX+1,
+    max_seq_len = SEQ_LEN,
+    attn_layers = Decoder(dim = 2048,
+                          depth = 6,
+                          heads = 16,
+                          rotary_pos_emb = True,
+                          attn_flash = True
+                         )
+    )
+
+prg_model = AutoregressiveWrapper(prg_model, ignore_index=PAD_IDX)
+
+prg_model.to(DEVICE)
+print('=' * 70)
+
+print('Loading model checkpoint...')
+
+checkpoint = hf_hub_download(
+    repo_id='asigalov61/Chordified-Piano-Transformer',
+    filename='Chordified_Piano_Transformer_Chords_Progressions_Trained_Model_3569_steps_1.8604_loss_0.4727_acc.pth'
+)
+
+prg_model.load_state_dict(torch.load(checkpoint, map_location=DEVICE, weights_only=True))
+
+prg_model.eval()
+
+prg_model = torch.compile(prg_model)
+
+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)
+
+# =================================================================================================
+
+@spaces.GPU
+def generate_chords(chords,
+                    input_temperature,
+                    input_top_p_value
+                   ):
+    
+    print('*' * 70)
+    print('Generating chords progression...')
+
+    chords = [321] + chords + [322]
+
+    x = torch.LongTensor([chords] * 256).to(DEVICE)
+
+    with ctx:
+        out = prg_model.generate(x,
+                                 380-len(chords),
+                                 temperature=input_temperature,
+                                 filter_logits_fn=top_p,
+                                 filter_kwargs={'thres': input_top_p_value},
+                                 eos_token=323,
+                                 return_prime=False,
+                                 verbose=True
+                                )
+                
+        out = out.tolist()
+
+    good_outs = []
+
+    for o in out:
+        if len(set(o)) >= len(chords):
+            good_outs.append(o)
+
+    if len(good_outs) > 0:
+        cho_prg = sorted(good_outs, key=lambda x: -len(set(x)))[0]
+
+    else:
+        cho_prg = sorted(out, key=lambda x: -len(set(x)))[0]
+    
+    cho_prg = [c for c in cho_prg if 0 <= c < 321]
+
+    ncho = [0, 89, 178, 233, 267, 288, 301, 309, 314, 317, 319, 320]
+
+    inp_cho_prg = [c+140 if c not in ncho else ncho.index(c)+128 for c in cho_prg]
+    
+    print('Done!')
+    print('*' * 70)
+    print('Number of good chords progressions:', len(good_outs))
+    print('*' * 70)
+    print('Texturing selected generated chords progression...')
+
+    x = torch.LongTensor([718] + inp_cho_prg + [719]).to(DEVICE)
+
+    with ctx:
+        out = tex_model.generate(x,
+                                 2048-len(cho_prg)+2,
+                                 temperature=input_temperature,
+                                 filter_logits_fn=top_p,
+                                 filter_kwargs={'thres': input_top_p_value},
+                                 eos_token=720,
+                                 return_prime=False,
+                                 verbose=True
+                                )
+                
+        score = out.tolist()
+
+    print('Done!')
+    print('=' * 70)
+    
+    return cho_prg, score
+                       
+# =================================================================================================
+
+def tokens_to_escore_notes(tokens):
+
+    song_f = []
+
+    time = 0
+    dur = 1
+    vel = 90
+    pitch = 60
+    channel = 0
+    patch = 0
+
+    patches = [0] * 16
+
+    for m in tokens:
+    
+        if 0 <= m < 128:
+            time += m
+            
+        elif 461 < m < 589:
+            pitch = (m-461)
+            
+        elif 589 < m < 717:
+            dur = (m-589)
+            song_f.append(['note', time, dur, 0, pitch, max(40, pitch), 0])
+
+    return song_f
+
+# =================================================================================================
+
+def Generate_Chords(input_example,
+                    input_chords,
+                    input_temperature,
+                    input_top_p_value
+                   ):
+    
+    print('=' * 70)
+    print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
+    start_time = reqtime.time()
+
+    print('=' * 70)
+    print('Input example:', input_example)
+    print('Input chords:', input_chords)
+    print('Req model temp:', input_temperature)
+    print('Req top_k value:', input_top_p_value)
+    print('=' * 70)
+
+    if input_chords is not None:
+        chords = []
+        
+        for c in input_chords:
+            cho = [int(t) for t in c.split('-')]
+            chords.append(TMIDIX.ALL_CHORDS_SORTED.index(cho))
+
+    else:
+        if input_example == 'Blue Bird':
+            chords = blue_bird
+
+        elif input_example == 'Come To My Window':
+            chords = come_to_my_window
+
+        else:
+            chords = sharing_the_night_together
+
+    print('There are', len(chords), 'chords')
+    print('Sample chords:', chords[:5])
+    print('=' * 70)
+
+    #===============================================================================
+   
+    print('Sample chords', chords[:2])
+    print('=' * 70)
+    print('Generating...')
+
+    cho_prg, score = generate_chords(chords,
+                                     input_temperature,
+                                     input_top_p_value
+                                    )
+
+    final_chords = [TMIDIX.ALL_CHORDS_SORTED[c] for c in cho_prg]
+    final_score = tokens_to_escore_notes(score)
+
+    final_score = TMIDIX.remove_duplicate_pitches_from_escore_notes(final_score)
+
+    final_score = TMIDIX.fix_escore_notes_durations(final_score, 
+                                               min_notes_gap=0
+                                              )
+
+    final_score = TMIDIX.humanize_velocities_in_escore_notes(final_score)
+    
+    #===============================================================================
+    print('Rendering results...')
+    print('=' * 70)
+
+    now = datetime.datetime.now(PDT)
+    ms4 = now.strftime("%f")[:4]
+
+    fn1 = (
+        'Orpheus-Masked-Pitches-Inpainter-Composition-'
+        + now.strftime(f"%Y-%m-%d-%H-%M-%S-{ms4}")
+    )
+    
+    output_score, patches, overflow_patches = TMIDIX.patch_enhanced_score_notes(final_score)
+    
+    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(output_score,
+                                                              output_signature = 'Orpheus Masked Pitches Inpainter',
+                                                              output_file_name = fn1,
+                                                              track_name='Project Los Angeles',
+                                                              list_of_MIDI_patches=patches,
+                                                              timings_multiplier=32
+                                                              )
+    
+    new_fn = fn1+'.mid'
+            
+    
+    audio = midi_to_colab_audio(new_fn, 
+                                soundfont_path=SOUNDFONT_PATH,
+                                sample_rate=16000,
+                                output_for_gradio=True
+                                )
+    
+    print('Done!')
+    print('=' * 70)
+
+    #========================================================
+
+    output_gen_chords = '\n'.join(str(c) for c in final_chords)
+    output_midi = str(new_fn)
+    output_audio = (16000, audio)
+    
+    output_plot = TMIDIX.plot_ms_SONG(output_score,
+                                      timings_multiplier=32,
+                                      plot_title=output_midi,
+                                      return_plt=True
+                                     )
+
+    print('Output gen chords:', output_gen_chords[:3])
+    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_gen_chords, output_audio, output_plot, output_midi
+        
+# =================================================================================================
+
+chords_labels = []
+
+for c in TMIDIX.ALL_CHORDS_SORTED:
+    
+    cho = '-'.join([str(t) for t in c])
+
+    chords_labels.append(cho)
+
+# =================================================================================================
+
+blue_bird = [89, 301, 314, 317, 114, 280, 110, 318, 194, 221, 320, 187, 270, 191, 303, 162,
+ 298, 178, 181, 308, 267, 283, 94, 104, 96, 272, 215, 288, 296, 105, 102, 144,
+ 91, 284, 273, 227, 316, 164, 189, 147, 281, 268, 179, 186, 213, 159, 165, 92,
+ 188, 150, 218, 112, 309, 285, 302, 217, 290, 306, 148, 1, 310, 4, 289, 0, 307,
+ 119, 212, 117, 233, 254, 34, 3, 180, 319]
+
+come_to_my_window = [16, 0, 13, 216, 309, 178, 194, 301, 192, 317, 320, 1, 191, 89, 319, 314, 288,
+ 267, 195, 282, 280, 183, 18, 14, 181, 179, 215, 303, 184, 213, 306, 37, 272,
+ 310, 34, 228, 212, 312, 227, 97, 308]
+
+sharing_the_night_together = [267, 270, 281, 314, 148, 280, 146, 102, 233, 316, 283, 320, 10, 104, 235, 117,
+ 89, 91, 256, 285, 21, 159, 112, 264, 301, 238, 303, 0, 263, 94, 144, 110, 153,
+ 103, 106, 170, 95, 171, 119, 268, 90, 108, 114, 317, 306, 136, 134, 254, 307,
+ 302, 31, 284, 319, 258, 243, 272]
+
+# =================================================================================================
+  
+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'>Generate and texture unique chords progressions</h1>")
+
+    gr.Markdown("## Select example chords progression or create your own")
+
+    input_example = gr.Dropdown(label="Example chords progressions",
+                                choices=['Blue Bird', 'Come To My Window', 'Sharing The Night Together'],
+                                value='Blue Bird',
+                                info='NOTE: Selecting custom chords below will override example selection'
+                               )
+    
+    input_chords = gr.Dropdown(label="Desired chords to generate",
+                               choices=chords_labels,
+                               value=None,
+                               multiselect=True,
+                               info='NOTE: Selected chords will be introduced into generated chords progression in order of selection'
+                              )
+    input_temperature = gr.Slider(0.1, 1.0, value=0.9, step=0.01, label="Model temperature")
+    input_top_p_value = gr.Slider(0.1, 1.0, value=0.96, step=0.01, label="Model sampling top_p value")
+    
+    run_btn = gr.Button("generate", variant="primary")
+
+    gr.Markdown("## Generation results")
+
+    output_gen_chords = gr.Textbox(label="Generated chords list", lines=7)
+    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(Generate_Chords,
+                              [input_example,
+                               input_chords,
+                               input_temperature,
+                               input_top_p_value
+                              ],
+                              [output_gen_chords,
+                               output_audio,
+                               output_plot,
+                               output_midi
+                              ])
+    
+    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