File size: 6,815 Bytes
8fae231
 
 
4ad544c
b55d194
4ad544c
 
 
 
 
8fae231
 
4ad544c
 
 
 
bde6df9
 
4ad544c
 
 
 
 
 
 
 
 
5837401
 
4ad544c
 
 
5837401
 
4ad544c
 
5837401
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4ad544c
 
 
 
 
cd490ad
4ad544c
 
 
 
5837401
4ad544c
 
 
 
5837401
4ad544c
5837401
4ad544c
5837401
 
 
 
 
 
 
4ad544c
 
 
 
 
5837401
65c585b
5837401
 
 
 
 
 
65c585b
5837401
 
 
 
 
 
 
 
 
 
 
65c585b
5837401
 
 
65c585b
5837401
 
65c585b
5837401
4ad544c
 
5837401
4ad544c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e1d0c17
4ad544c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
09984c5
4ad544c
886ce38
4ad544c
7af3767
886ce38
 
5a9b440
 
 
 
7af3767
5a9b440
d26af00
5a9b440
4ad544c
 
 
 
 
 
 
 
 
5837401
4ad544c
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
#=======================================================================================
# https://huggingface.co/spaces/asigalov61/Imagen-POP-Music-Medley-Diffusion-Transformer
#=======================================================================================

import os
import time as reqtime
import datetime
from pytz import timezone

import torch
from imagen_pytorch import Unet, Imagen, ImagenTrainer
from imagen_pytorch.data import Dataset

import spaces
import gradio as gr

import numpy as np

import random
import tqdm

from midi_to_colab_audio import midi_to_colab_audio
import TMIDIX
         
# =================================================================================================
                       
@spaces.GPU
def Generate_POP_Medley(input_num_medley_comps):
    
    print('=' * 70)
    print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
    start_time = reqtime.time()
    print('=' * 70)
    
    print('Loading model...')

    DIM = 64
    CHANS = 1
    TSTEPS = 1000
    DEVICE = 'cuda' # 'cpu'

    unet = Unet(
        dim = DIM,
        dim_mults = (1, 2, 4, 8),
        num_resnet_blocks = 1,
        channels=CHANS,
        layer_attns = (False, False, False, True),
        layer_cross_attns = False
    )
    
    imagen = Imagen(
        condition_on_text = False,  # this must be set to False for unconditional Imagen
        unets = unet,
        channels=CHANS,
        image_sizes = 128,
        timesteps = TSTEPS
    )
    
    trainer = ImagenTrainer(
        imagen = imagen,
        split_valid_from_train = True # whether to split the validation dataset from the training
    ).to(DEVICE)

    print('=' * 70)

    print('Loading model checkpoint...')

    trainer.load('Imagen_POP909_64_dim_12638_steps_0.00983_loss.ckpt')

    print('Done!')
    print('=' * 70)

    print('Req number of medley compositions:', input_num_medley_comps)

    print('=' * 70)
    print('Generating...')

    images = trainer.sample(batch_size = input_num_medley_comps, return_pil_images = True)

    threshold = 128
    
    imgs_array = []
    
    for i in images:
      arr = np.array(i)
      farr = np.where(arr < threshold, 0, 1)
      imgs_array.append(farr)

    print('Done!')
    print('=' * 70)
    
    #===============================================================================

    print('Converting images to scores...')
    

    medley_compositions_escores = []

    for i in imgs_array:

        bmatrix = TPLOTS.images_to_binary_matrix([i])
    
        score = TMIDIX.binary_matrix_to_original_escore_notes(bmatrix)

        medley_compositions_escores.append(score)

    print('Done!')
    print('=' * 70)
    print('Creating medley score...')

    medley_labels = ['Composition #' + str(i+1) for i in range(len(medley_compositions_escores))]

    medley_escore = TMIDIX.escore_notes_medley(medley_compositions_escores, medley_labels)  
    
    #===============================================================================
    print('Rendering results...')
    print('=' * 70)
    
    print('Sample INTs', medley_escore[:15])
    print('=' * 70)

    fn1 = "Imagen-POP-Music-Medley-Diffusion-Transformer-Composition"
    
    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
                                                              output_signature = 'Imagen POP Music Medley',
                                                              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'>Imagen POP Music Medley Diffusion Transformer</h1>")
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate unique POP music medleys with Imagen diffusion transformer</h1>")
        gr.Markdown("![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Imagen-POP-Music-Medley-Diffusion-Transformer&style=flat)\n\n"
                    "This is a demo for MIDI Images dataset\n\n"
                    "Please see [MIDI Images](https://huggingface.co/datasets/asigalov61/MIDI-Images) Hugging Face repo for more information\n\n"
                    )        
        
        input_num_medley_comps = gr.Slider(1, 64, value=8, step=1, label="Number of medley compositions")

        run_btn = gr.Button("Generate POP Medley", 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(Generate_POP_Medley, [input_num_medley_comps],
                                  [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])

        app.queue().launch()