Upload app.py

app.py

@@ -0,0 +1,486 @@
+#==================================================================================
+# https://huggingface.co/spaces/projectlosangeles/MuseCraft-Piano-Chords-Texturing
+#==================================================================================
+
+print('=' * 70)
+print('MuseCraft Piano Chords Texturing Gradio App')
+
+print('=' * 70)
+print('Loading core MuseCraft Piano Chords Texturing modules...')
+
+import os
+import copy
+
+import time as reqtime
+import datetime
+from pytz import timezone
+
+print('=' * 70)
+print('Loading main MuseCraft Piano Chords Texturing modules...')
+
+os.environ['USE_FLASH_ATTENTION'] = '1'
+
+import torch
+
+torch.set_float32_matmul_precision('medium')
+torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
+torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
+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)
+
+from huggingface_hub import hf_hub_download
+
+import TMIDIX
+
+from midi_to_colab_audio import midi_to_colab_audio
+
+from x_transformer_2_3_1 import *
+
+import random
+
+import tqdm
+
+print('=' * 70)
+print('Loading aux MuseCraft Piano Chords Texturing modules...')
+
+import matplotlib.pyplot as plt
+
+import gradio as gr
+import spaces
+
+print('=' * 70)
+print('PyTorch version:', torch.__version__)
+print('=' * 70)
+print('Done!')
+print('Enjoy! :)')
+print('=' * 70)
+
+#==================================================================================
+
+MODEL_CHECKPOINT = 'Godzilla_Piano_Chords_Texturing_Trained_Model_36457_steps_0.5384_loss_0.8417_acc.pth'
+
+SOUDFONT_PATH = 'SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2'
+
+MAX_MELODY_NOTES = 64
+
+MAX_GEN_TOKS = 3072
+
+#==================================================================================
+
+print('=' * 70)
+print('Loading popular hook melodies dataset...')
+
+popular_hook_melodies_pickle = hf_hub_download(repo_id='projectlosangeles/MuseCraft-Piano-Chords-Texturing', 
+                                               filename='popular_hook_melodies_24_64_CC_BY_NC_SA.pickle'
+                                              )
+
+popular_hook_melodies = TMIDIX.Tegridy_Any_Pickle_File_Reader(popular_hook_melodies_pickle)
+
+print('=' * 70)
+print('Done!')
+print('=' * 70)
+
+#==================================================================================
+
+print('=' * 70)
+print('Instantiating model...')
+
+device_type = 'cuda'
+dtype = 'bfloat16'
+
+ptdtype = {'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
+ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype)
+
+SEQ_LEN = 4096
+PAD_IDX = 1794
+
+model = TransformerWrapper(
+        num_tokens = PAD_IDX+1,
+        max_seq_len = SEQ_LEN,
+        attn_layers = Decoder(dim = 2048,
+                              depth = 4,
+                              heads = 32,
+                              rotary_pos_emb = True,
+                              attn_flash = True
+                              )
+)
+
+model = AutoregressiveWrapper(model, ignore_index=PAD_IDX, pad_value=PAD_IDX)
+
+print('=' * 70)
+print('Loading model checkpoint...')      
+
+model_checkpoint = hf_hub_download(repo_id='projectlosangeles/MuseCraft-Piano-Chords-Texturing', filename=MODEL_CHECKPOINT)
+
+model.load_state_dict(torch.load(model_checkpoint, map_location='cpu', weights_only=True))
+
+model = torch.compile(model, mode='max-autotune')
+
+print('=' * 70)
+print('Done!')
+print('=' * 70)
+print('Model will use', dtype, 'precision...')
+print('=' * 70)
+
+#==================================================================================
+
+def load_midi(input_midi, melody_patch=-1, use_nth_note=1):
+
+    raw_score = TMIDIX.midi2single_track_ms_score(input_midi)
+
+    escore_notes = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0]
+    escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes, timings_divider=32)
+    
+    sp_escore_notes = TMIDIX.solo_piano_escore_notes(escore_notes, keep_drums=False)
+
+    if melody_patch == -1:
+        zscore = TMIDIX.recalculate_score_timings(sp_escore_notes)
+
+    else:
+        mel_score = [e for e in sp_escore_notes if e[6] == melody_patch]
+
+        if mel_score:
+            zscore = TMIDIX.recalculate_score_timings(mel_score)
+
+        else:
+            zscore = TMIDIX.recalculate_score_timings(sp_escore_notes)
+    
+    cscore = TMIDIX.chordify_score([1000, zscore])[:MAX_MELODY_NOTES:use_nth_note]
+    
+    score = []
+    
+    score_list = []
+    
+    pc = cscore[0]
+    
+    for c in cscore:
+        score.append(max(0, min(127, c[0][1]-pc[0][1])))
+    
+        scl = [[max(0, min(127, c[0][1]-pc[0][1]))]]
+    
+        n = c[0]
+        
+        score.extend([max(1, min(127, n[2]))+128, max(1, min(127, n[4]))+256])
+        scl.append([max(1, min(127, n[2]))+128, max(1, min(127, n[4]))+256])
+    
+        score_list.append(scl)
+    
+        pc = c
+    
+    score_list.append(scl)
+
+    return score, score_list
+
+#==================================================================================
+
+@spaces.GPU
+def Generate_Accompaniment(input_midi,
+                           input_melody,
+                           melody_patch,
+                           use_nth_note,
+                           model_temperature,
+                           model_sampling_top_k
+                          ):
+
+    #===============================================================================
+
+    def generate_full_seq(input_seq, 
+                          max_toks=3072, 
+                          temperature=0.9, 
+                          top_k_value=15, 
+                          verbose=True
+                         ):
+    
+        seq_abs_run_time = sum([t for t in input_seq if t < 128])
+    
+        cur_time = 0
+    
+        full_seq = copy.deepcopy(input_seq)
+    
+        toks_counter = 0
+    
+        while cur_time <= seq_abs_run_time+32:
+    
+            if verbose:
+                if toks_counter % 128 == 0:
+                    print('Generated', toks_counter, 'tokens')
+    
+            x = torch.LongTensor(full_seq).cuda()
+    
+            with ctx:
+                out = model.generate(x,
+                                     1,
+                                     filter_logits_fn=top_k,
+                                     filter_kwargs={'k': top_k_value},
+                                     temperature=temperature,
+                                     return_prime=False,
+                                     verbose=False)
+            
+            y = out.tolist()[0][0]
+    
+            if y < 128:
+                cur_time += y
+    
+            full_seq.append(y)
+    
+            toks_counter += 1
+
+            if toks_counter == max_toks:
+                return full_seq
+    
+        return full_seq
+
+    #===============================================================================
+
+    print('=' * 70)
+    print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
+    start_time = reqtime.time()
+    print('=' * 70)
+
+    print('=' * 70)
+    print('Requested settings:')
+    print('=' * 70)
+    if input_midi:
+        fn = os.path.basename(input_midi)
+        fn1 = fn.split('.')[0]
+        print('Input MIDI file name:', fn)
+
+    else:
+        print('Input sample melody:', input_melody)
+    print('Source melody patch:', melody_patch)
+    print('Use nth melody note:', use_nth_note)
+    print('Model temperature:', model_temperature)
+    print('Model top k:', model_sampling_top_k)
+   
+    print('=' * 70)
+
+    #==================================================================
+
+    print('Prepping melody...')
+
+    if input_midi:
+        inp_mel = 'Custom MIDI'
+        score, score_list = load_midi(input_midi.name, melody_patch, use_nth_note)
+
+    else:
+        mel_list = [m[0].lower() for m in popular_hook_melodies]
+
+        inp_mel = random.choice(mel_list).title()
+
+        for m in mel_list:
+            if input_melody.lower().strip() in m:
+                inp_mel = m.title()
+                break
+
+        score = popular_hook_melodies[[m[0] for m in popular_hook_melodies].index(inp_mel)][1]
+        score_list = [[[score[i]], score[i+1:i+3]] for i in range(0, len(score)-3, 3)]
+
+        print('Selected melody:', inp_mel)
+        
+    print('Sample score events', score[:12])
+
+    #==================================================================
+    
+    print('=' * 70)
+    print('Generating...')
+
+    model.to(device_type)
+    model.eval()
+
+    #==================================================================
+
+    start_score_seq = [1792] + score + [1793]
+
+    #==================================================================
+
+    input_seq = generate_full_seq(start_score_seq,
+                                  max_toks=MAX_GEN_TOKS,
+                                  temperature=model_temperature,
+                                  top_k_value=model_sampling_top_k,
+                                 )
+
+    final_song = input_seq[len(start_score_seq):]
+   
+    print('=' * 70)
+    print('Done!')
+    print('=' * 70)
+    
+    #===============================================================================
+    
+    print('Rendering results...')
+    
+    print('=' * 70)
+    print('Sample INTs', final_song[:15])
+    print('=' * 70)
+
+    song_f = []
+    
+    if len(final_song) != 0:
+    
+        time = 0
+        dur = 0
+        vel = 90
+        pitch = 0
+        channel = 0
+        patch = 0
+    
+        channels_map = [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 9, 12, 13, 14, 15]
+        patches_map = [40, 0, 10, 19, 24, 35, 40, 52, 56, 9, 65, 73, 0, 0, 0, 0]
+        velocities_map = [125, 80, 100, 80, 90, 100, 100, 80, 110, 110, 110, 110, 80, 80, 80, 80]
+    
+        for m in final_song:
+    
+            if 0 <= m < 128:
+                time += m * 32
+    
+            elif 128 < m < 256:
+                dur = (m-128) * 32
+    
+            elif 256 < m < 1792:
+                cha = (m-256) // 128
+                pitch = (m-256) % 128
+    
+                channel = channels_map[cha]
+                patch = patches_map[channel]
+                vel = velocities_map[channel]
+    
+                song_f.append(['note', time, dur, channel, pitch, vel, patch])
+
+    fn1 = "MuseCraft-Piano-Chords-Texturing-Composition"
+    
+    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
+                                                              output_signature = 'MuseCraft Piano Chords Texturing',
+                                                              output_file_name = fn1,
+                                                              track_name='Project Los Angeles',
+                                                              list_of_MIDI_patches=patches_map
+                                                              )
+    
+    new_fn = fn1+'.mid'
+            
+    
+    audio = midi_to_colab_audio(new_fn, 
+                        soundfont_path=SOUDFONT_PATH,
+                        sample_rate=16000,
+                        volume_scale=10,
+                        output_for_gradio=True
+                        )
+    
+    print('Done!')
+    print('=' * 70)
+
+    #========================================================
+
+    output_title = str(inp_mel)
+    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 melody title:', output_title)
+    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_title, output_audio, output_plot, output_midi
+    
+#==================================================================================
+
+PDT = timezone('US/Pacific')
+
+print('=' * 70)
+print('App start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
+print('=' * 70)
+
+#==================================================================================
+
+with gr.Blocks() as demo:
+
+    #==================================================================================
+
+    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>MuseCraft Piano Chords Texturing</h1>")
+    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Solo Piano chords texturing model for MuseCraft project</h1>")
+    gr.HTML("""            
+            <p> 
+                <a href="https://huggingface.co/spaces/projectlosangeles/MuseCraft-Piano-Chords-Texturing?duplicate=true">
+                    <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-md.svg" alt="Duplicate in Hugging Face">
+                </a>
+            </p>
+            
+            for faster execution and endless generation!
+            """)
+    
+    #==================================================================================
+    
+    gr.Markdown("## Upload source melody MIDI or enter a search query for a sample melody below")
+    
+    input_midi = gr.File(label="Input MIDI", 
+                         file_types=[".midi", ".mid", ".kar"]
+                        )
+
+    input_melody = gr.Textbox(value="Hotel California", 
+                              label="Popular melodies database search query",
+                              info='If the query is not found, random melody will be selected. Custom MIDI overrides search query'
+                             )
+    
+    gr.Markdown("## Generation options")
+    
+    melody_patch = gr.Slider(-1, 127, value=-1, step=1, label="Source melody MIDI patch")
+    use_nth_note = gr.Slider(1, 8, value=1, step=1, label="Use each nth melody note")
+    model_temperature = gr.Slider(0.1, 1, value=0.9, step=0.01, label="Model temperature")
+    model_sampling_top_k = gr.Slider(1, 100, value=15, step=1, label="Model sampling top k value")
+    
+    generate_btn = gr.Button("Generate", variant="primary")
+
+    gr.Markdown("## Generation results")
+
+    output_title = gr.Textbox(label="MIDI melody title")
+    output_audio = gr.Audio(label="MIDI audio", format="wav", elem_id="midi_audio")
+    output_plot = gr.Plot(label="MIDI score plot")
+    output_midi = gr.File(label="MIDI file", file_types=[".mid"])
+
+    generate_btn.click(Generate_Accompaniment, 
+                       [input_midi,
+                        input_melody,
+                        melody_patch,
+                        use_nth_note,
+                        model_temperature,
+                        model_sampling_top_k
+                       ], 
+                       [output_title,
+                        output_audio,
+                        output_plot,
+                        output_midi                          
+                       ]
+                      )
+
+    gr.Examples(
+                [["Sharing The Night Together.kar", "Custom MIDI", -1, 1, 0.9, 15]
+                ],
+                [input_midi,
+                 input_melody,
+                 melody_patch,
+                 use_nth_note,
+                 model_temperature,
+                 model_sampling_top_k
+                ],
+                [output_title,
+                 output_audio,
+                 output_plot,
+                 output_midi
+                ],
+                Generate_Accompaniment
+    )
+        
+#==================================================================================
+
+demo.launch()
+
+#==================================================================================