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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 GenerateAccompaniment(input_midi, input_num_tokens, input_conditioning_type, input_strip_notes):
+    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 = 707 # 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 = 2048, depth = 4, heads = 16, 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('Chords_Progressions_Transformer_Small_2048_Trained_Model_12947_steps_0.9316_loss_0.7386_acc.pth',
+                   map_location=DEVICE))
+    print('=' * 70)
+
+    model.eval()
+
+    if DEVICE == 'cpu':
+        dtype = torch.bfloat16
+    else:
+        dtype = torch.float16
+
+    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_tokens = max(4, min(128, input_num_tokens))
+
+    print('-' * 70)
+    print('Input file name:', fn)
+    print('Req num toks:', input_num_tokens)
+    print('Conditioning type:', input_conditioning_type)
+    print('Strip notes:', input_strip_notes)
+    print('-' * 70)
+
+    #===============================================================================
+    raw_score = TMIDIX.midi2single_track_ms_score(input_midi.name)
+    
+    #===============================================================================
+    # Enhanced score notes
+    
+    escore_notes = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0]
+    
+    no_drums_escore_notes = [e for e in escore_notes if e[6] < 80]
+    
+    if len(no_drums_escore_notes) > 0:
+    
+        #=======================================================
+        # PRE-PROCESSING
+        
+        #===============================================================================
+        # Augmented enhanced score notes
+        
+        no_drums_escore_notes = TMIDIX.augment_enhanced_score_notes(no_drums_escore_notes)
+        
+        cscore = TMIDIX.chordify_score([1000, no_drums_escore_notes])
+        
+        clean_cscore = []
+        
+        for c in cscore:
+            pitches = []
+            cho = []
+            for cc in c:
+                if cc[4] not in pitches:
+                    cho.append(cc)
+                    pitches.append(cc[4])
+        
+            clean_cscore.append(cho)
+        
+        #=======================================================
+        # FINAL PROCESSING
+        
+        melody_chords = []
+        chords = []
+        times = [0]
+        durs = []
+        
+        #=======================================================
+        # MAIN PROCESSING CYCLE
+        #=======================================================
+        
+        pe = clean_cscore[0][0]
+        
+        first_chord = True
+        
+        for c in clean_cscore:
+        
+            # Chords
+        
+            c.sort(key=lambda x: x[4], reverse=True)
+        
+            tones_chord = sorted(set([cc[4] % 12 for cc in c]))
+        
+            try:
+                chord_token = TMIDIX.ALL_CHORDS_SORTED.index(tones_chord)
+            except:
+                checked_tones_chord = TMIDIX.check_and_fix_tones_chord(tones_chord)
+                chord_token = TMIDIX.ALL_CHORDS_SORTED.index(checked_tones_chord)
+        
+            melody_chords.extend([chord_token+384])
+        
+            if input_strip_notes:
+              if len(tones_chord) > 1:
+                chords.extend([chord_token+384])
+        
+            else:
+              chords.extend([chord_token+384])
+        
+            if first_chord:
+                    melody_chords.extend([0])
+                    first_chord = False
+        
+            for e in c:
+        
+                #=======================================================
+                # Timings...
+        
+                time = e[1]-pe[1]
+        
+                dur = e[2]
+        
+                if time != 0 and time % 2 != 0:
+                    time += 1
+                if dur % 2 != 0:
+                    dur += 1
+        
+                delta_time = int(max(0, min(255, time)) / 2)
+        
+                # Durations
+        
+                dur = int(max(0, min(255, dur)) / 2)
+        
+                # Pitches
+        
+                ptc = max(1, min(127, e[4]))
+        
+                #=======================================================
+                # FINAL NOTE SEQ
+        
+                # Writing final note asynchronously
+        
+                if delta_time != 0:
+                    melody_chords.extend([delta_time, dur+128, ptc+256])
+                    if input_strip_notes:
+                      if len(c) > 1:
+                        times.append(delta_time)
+                        durs.append(dur+128)
+                    else:
+                        times.append(delta_time)
+                        durs.append(dur+128)
+                else:
+                    melody_chords.extend([dur+128, ptc+256])
+        
+                pe = e
+        
+    #==================================================================
+
+    print('=' * 70)
+    
+    print('Sample output events', melody_chords[:5])
+    print('=' * 70)
+    print('Generating...')
+
+    output = []
+    
+    max_chords_limit = 8
+    temperature=0.9
+    num_memory_tokens=4096
+
+    output = []
+    
+    idx = 0
+    
+    for c in chords[:input_num_tokens]:
+     
+        output.append(c)
+    
+        if input_conditioning_type == 'Chords-Times' or input_conditioning_type == 'Chords-Times-Durations':
+          output.append(times[idx])
+    
+        if input_conditioning_type == 'Chords-Times-Durations':
+          output.append(durs[idx])
+
+        x = torch.tensor([output] * 1, dtype=torch.long, device='cuda')
+        
+        o = 0
+        
+        ncount = 0
+        
+        while o < 384 and ncount < max_chords_limit:
+          with ctx:
+            out = model.generate(x[-num_memory_tokens:],
+                                1,
+                                temperature=temperature,
+                                return_prime=False,
+                                verbose=False)
+        
+          o = out.tolist()[0][0]
+        
+          if 256 <= o < 384:
+            ncount += 1
+        
+          if o < 384:
+            x = torch.cat((x, out), 1)
+        
+        outy =  x.tolist()[0][len(output):]
+          
+        output.extend(outy)
+    
+        idx += 1
+
+        if idx == len(chords[:input_num_tokens])-1:
+            break
+
+    print('=' * 70)
+    print('Done!')
+    print('=' * 70)
+    
+    #===============================================================================
+    print('Rendering results...')
+    
+    print('=' * 70)
+    print('Sample INTs', output[:12])
+    print('=' * 70)
+    
+    out1 = output
+
+    if len(out1) != 0:
+    
+        song = out1
+        song_f = []
+    
+        time = 0
+        dur = 0
+        vel = 90
+        pitch = 0
+        channel = 0
+        
+        patches = [0] * 16
+        
+        channel = 0
+        
+        for ss in song:
+        
+          if 0 <= ss < 128:
+        
+              time += ss * 32
+        
+          if 128 <= ss < 256:
+        
+              dur = (ss-128) * 32
+        
+          if 256 <= ss < 384:
+        
+              pitch = (ss-256)
+        
+              vel = max(40, pitch)
+        
+              song_f.append(['note', time, dur, channel, pitch, vel, 0])
+
+    fn1 = "Chords-Progressions-Transformer-Composition"
+    
+    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
+                                                              output_signature = 'Chords Progressions 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:', '')
+    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'>Chords Progressions Transformer</h1>")
+        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Chords-conditioned music transformer</h1>")
+        gr.Markdown(
+            "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Chords-Progressions-Transformer&style=flat)\n\n"
+            "Generate music based on chords progressions\n\n"
+            "Check out [Chords Progressions Transformer](https://github.com/asigalov61/Chords-Progressions-Transformer) on GitHub!\n\n"
+            "[Open In Colab]"
+            "(https://colab.research.google.com/github/asigalov61/Chords-Progressions-Transformer/blob/main/Chords_Progressions_Transformer.ipynb)"
+            " for 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_tokens = gr.Slider(4, 128, value=32, step=1, label="Number of composition chords to generate progression for")
+        input_conditioning_type = gr.Radio(["Chords", "Chords-Times", "Chords-Times-Durations"], label="Conditioning type")
+        input_strip_notes = gr.Checkbox(label="Strip notes from the composition")
+        
+        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(GenerateAccompaniment, [input_midi, input_num_tokens, input_conditioning_type, input_strip_notes],
+                                  [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])
+
+        gr.Examples(
+            [["Chords-Progressions-Transformer-Piano-Seed-1.mid", 128, "Chords", False], 
+             ["Chords-Progressions-Transformer-Piano-Seed-2.mid", 128, "Chords-Times", False], 
+             ["Chords-Progressions-Transformer-Piano-Seed-3.mid", 128, "Chords-Times-Durations", False],
+             ["Chords-Progressions-Transformer-Piano-Seed-4.mid", 128, "Chords", False],
+             ["Chords-Progressions-Transformer-Piano-Seed-5.mid", 128, "Chords-Times", False],
+             ["Chords-Progressions-Transformer-Piano-Seed-6.mid", 128, "Chords-Times-Durations", False],
+             ["Chords-Progressions-Transformer-MI-Seed-1.mid", 128, "Chords", False],
+             ["Chords-Progressions-Transformer-MI-Seed-2.mid", 128, "Chords-Times", False],
+             ["Chords-Progressions-Transformer-MI-Seed-3.mid", 128, "Chords-Times-Durations", False],
+             ["Chords-Progressions-Transformer-MI-Seed-4.mid", 128, "Chords-Times", False],
+             ["Chords-Progressions-Transformer-MI-Seed-5.mid", 128, "Chords", False],
+             ["Chords-Progressions-Transformer-MI-Seed-6.mid", 128, "Chords-Times-Durations", False]
+            ],
+            [input_midi, input_num_tokens, input_conditioning_type, input_strip_notes],
+            [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot],
+            GenerateAccompaniment,
+            cache_examples=True,
+        )
+        
+        app.queue().launch()

packages.txt

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

requirements.txt

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