File size: 13,546 Bytes
3888ab7
 
 
 
 
 
2278710
3888ab7
 
 
 
 
 
6df655f
 
 
 
 
06d58f4
 
6df655f
 
3888ab7
 
6df655f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
cc38963
6df655f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
497670e
 
 
2154fa3
497670e
 
6df655f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
cf3ba84
6df655f
 
7155cdd
6df655f
 
 
3888ab7
 
 
 
 
eec4853
3888ab7
eec4853
1aa6fb6
eec4853
3888ab7
e469266
 
 
 
 
3888ab7
 
6dd35be
8ba6ef1
e30e402
6dd35be
2154fa3
 
3888ab7
2154fa3
 
3888ab7
1aa6fb6
2154fa3
3adc64e
 
c04453c
3adc64e
56e508f
2154fa3
3adc64e
c04453c
6df655f
 
d85cb0d
7dd6e93
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
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
import argparse
import glob
import os.path

import gradio as gr

import pickle
import tqdm
import json

import MIDI
from midi_synthesizer import synthesis

import copy
from collections import Counter
import random
import statistics

import matplotlib.pyplot as plt

#==========================================================================================================

in_space = os.getenv("SYSTEM") == "spaces"

#==========================================================================================================

def match_midi(midi, progress=gr.Progress()):
  
    print('=' * 70)
    print('Loading MIDI file...')
    
    #==================================================
    
    score = MIDI.midi2score(midi)
    
    events_matrix = []
    
    track_count = 0
    
    for s in score:
    
        if track_count > 0:
            track = s
            track.sort(key=lambda x: x[1])
            events_matrix.extend(track)
        else:
            midi_ticks = s
    
        track_count += 1
    
    events_matrix.sort(key=lambda x: x[1])
    
    mult_pitches_counts = []
    
    for i in range(-6, 6):
    
      events_matrix1 = []
    
      for e in events_matrix:
    
        ev = copy.deepcopy(e)
    
        if e[0] == 'note':
            if e[3] == 9:
                ev[4] = ((e[4] % 128) + 128)
            else:
              ev[4] = ((e[4] % 128) + i)
    
            events_matrix1.append(ev)
    
      pitches_counts = [[y[0],y[1]] for y in Counter([y[4] for y in events_matrix1 if y[0] == 'note']).most_common()]
      pitches_counts.sort(key=lambda x: x[0], reverse=True)
    
      mult_pitches_counts.append(pitches_counts)
    
    patches_list = sorted(list(set([y[3] for y in events_matrix if y[0] == 'patch_change'])))
    
    
    #==================================================
    
    ms_score = MIDI.midi2ms_score(midi)
    
    ms_events_matrix = []
    
    itrack1 = 1
    
    while itrack1 < len(ms_score):
        for event in ms_score[itrack1]:
            if event[0] == 'note':
                ms_events_matrix.append(event)
        itrack1 += 1
    
    ms_events_matrix.sort(key=lambda x: x[1])
    
    
    chords = []
    pe = ms_events_matrix[0]
    cho = []
    for e in ms_events_matrix:
        if (e[1] - pe[1]) == 0:
          if e[3] != 9:
            if (e[4] % 12) not in cho:
              cho.append(e[4] % 12)
        else:
          if len(cho) > 0:
            chords.append(sorted(cho))
          cho = []
          if e[3] != 9:
            if (e[4] % 12) not in cho:
              cho.append(e[4] % 12)
    
        pe = e
    
    if len(cho) > 0:
        chords.append(sorted(cho))
    
    ms_chords_counts = sorted([[list(key), val] for key,val in Counter([tuple(c) for c in chords if len(c) > 1]).most_common()], reverse=True, key = lambda x: x[1])
    
    times = []
    pt = ms_events_matrix[0][1]
    start = True
    for e in ms_events_matrix:
        if (e[1]-pt) != 0 or start == True:
            times.append((e[1]-pt))
            start = False
        pt = e[1]
    
    durs = [e[2] for e in ms_events_matrix]
    vels = [e[5] for e in ms_events_matrix]
    
    avg_time = int(sum(times) / len(times))
    avg_dur = int(sum(durs) / len(durs))
    
    mode_time = statistics.mode(times)
    mode_dur = statistics.mode(durs)
    
    median_time = int(statistics.median(times))
    median_dur = int(statistics.median(durs))
    
    #==================================================
    
    print('=' * 70)
    print('Done!')
    print('=' * 70)
    
    #==========================================================================================================

    #@title MIDI Pitches Search

    #@markdown Match ratio control option
    
    maximum_match_ratio_to_search_for = 1 #@param {type:"slider", min:0, max:1, step:0.01}
    
    #@markdown MIDI pitches search options
    
    pitches_counts_cutoff_threshold_ratio = 0 #@param {type:"slider", min:0, max:1, step:0.05}
    search_transposed_pitches = False #@param {type:"boolean"}
    skip_exact_matches = True #@param {type:"boolean"}
    
    #@markdown Additional search options
    
    add_pitches_counts_ratios = False #@param {type:"boolean"}
    add_timings_ratios = False #@param {type:"boolean"}
    add_durations_ratios = False #@param {type:"boolean"}
    
    print('=' * 70)
    print('MIDI Pitches Search')
    print('=' * 70)
    
    final_ratios = []
    
    for d in progress.tqdm(meta_data):
    

        p_counts = d[1][10][1]
        p_counts.sort(reverse = True, key = lambda x: x[1])
        max_p_count = p_counts[0][1]
        trimmed_p_counts = [y for y in p_counts if y[1] >= (max_p_count * pitches_counts_cutoff_threshold_ratio)]
        total_p_counts = sum([y[1] for y in trimmed_p_counts])
    
        if search_transposed_pitches:
          search_pitches = mult_pitches_counts
        else:
          search_pitches = [mult_pitches_counts[6]]
    
        #===================================================
    
        ratios_list = []
    
        #===================================================
    
        atrat = [0]
    
        if add_timings_ratios:
    
          source_times = [avg_time,
                          median_time,
                          mode_time]
    
          match_times = meta_data[0][1][3][1]
    
          times_ratios = []
    
          for i in range(len(source_times)):
            maxtratio = max(source_times[i], match_times[i])
            mintratio = min(source_times[i], match_times[i])
            times_ratios.append(mintratio / maxtratio)
    
          avg_times_ratio = sum(times_ratios) / len(times_ratios)
    
          atrat[0] = avg_times_ratio
    
        #===================================================
    
        adrat = [0]
    
        if add_durations_ratios:
    
          source_durs = [avg_dur,
                          median_dur,
                          mode_dur]
    
          match_durs = meta_data[0][1][4][1]
    
          durs_ratios = []
    
          for i in range(len(source_durs)):
            maxtratio = max(source_durs[i], match_durs[i])
            mintratio = min(source_durs[i], match_durs[i])
            durs_ratios.append(mintratio / maxtratio)
    
          avg_durs_ratio = sum(durs_ratios) / len(durs_ratios)
    
          adrat[0] = avg_durs_ratio
    
        #===================================================
    
        for m in search_pitches:
    
          sprat = []
    
          m.sort(reverse = True, key = lambda x: x[1])
          max_pitches_count = m[0][1]
          trimmed_pitches_counts = [y for y in m if y[1] >= (max_pitches_count * pitches_counts_cutoff_threshold_ratio)]
          total_pitches_counts = sum([y[1] for y in trimmed_pitches_counts])
    
          same_pitches = set([T[0] for T in trimmed_p_counts]) & set([m[0] for m in trimmed_pitches_counts])
          num_same_pitches = len(same_pitches)
    
          if num_same_pitches == len(trimmed_pitches_counts):
            same_pitches_ratio = (num_same_pitches / len(trimmed_p_counts))
          else:
            same_pitches_ratio = (num_same_pitches / max(len(trimmed_p_counts), len(trimmed_pitches_counts)))
    
          if skip_exact_matches:
            if same_pitches_ratio == 1:
              same_pitches_ratio = 0
    
          sprat.append(same_pitches_ratio)
    
          #===================================================
    
          spcrat = [0]
    
          if add_pitches_counts_ratios:
    
            same_trimmed_p_counts = sorted([T for T in trimmed_p_counts if T[0] in same_pitches], reverse = True)
            same_trimmed_pitches_counts = sorted([T for T in trimmed_pitches_counts if T[0] in same_pitches], reverse = True)
    
            same_trimmed_p_counts_ratios = [[s[0], s[1] / total_p_counts] for s in same_trimmed_p_counts]
            same_trimmed_pitches_counts_ratios = [[s[0], s[1] / total_pitches_counts] for s in same_trimmed_pitches_counts]
    
            same_pitches_counts_ratios = []
    
            for i in range(len(same_trimmed_p_counts_ratios)):
              mincratio = min(same_trimmed_p_counts_ratios[i][1], same_trimmed_pitches_counts_ratios[i][1])
              maxcratio = max(same_trimmed_p_counts_ratios[i][1], same_trimmed_pitches_counts_ratios[i][1])
              same_pitches_counts_ratios.append([same_trimmed_p_counts_ratios[i][0], mincratio / maxcratio])
    
            same_counts_ratios = [s[1] for s in same_pitches_counts_ratios]
    
            if len(same_counts_ratios) > 0:
              avg_same_pitches_counts_ratio = sum(same_counts_ratios) / len(same_counts_ratios)
            else:
              avg_same_pitches_counts_ratio = 0
    
            spcrat[0] = avg_same_pitches_counts_ratio
    
          #===================================================
    
          r_list = [sprat[0]]
    
          if add_pitches_counts_ratios:
            r_list.append(spcrat[0])
    
          if add_timings_ratios:
            r_list.append(atrat[0])
    
          if add_durations_ratios:
            r_list.append(adrat[0])
    
          ratios_list.append(r_list)
    
        #===================================================
    
        avg_ratios_list = []
    
        for r in ratios_list:
          avg_ratios_list.append(sum(r) / len(r))
    
        #===================================================
    
        final_ratio = max(avg_ratios_list)
    
        if final_ratio > maximum_match_ratio_to_search_for:
            final_ratio = 0
    
        final_ratios.append(final_ratio)
    
        #===================================================
    
    max_ratio = max(final_ratios)
    max_ratio_index = final_ratios.index(max_ratio)
    
    print('FOUND')
    print('=' * 70)
    print('Match ratio', max_ratio)
    print('MIDI file name', meta_data[max_ratio_index][0])
    print('=' * 70)
    
    fn = meta_data[max_ratio_index][0]

    #==========================================================================================================

    md = meta_data[max_ratio_index]

    mid_seq = md[1][17:-1]
    mid_seq_ticks = md[1][16][1]
    mdata = md[1][:16]

    txt_mdata = ''

    for m in mdata:
        txt_mdata += str(m[0]) + ': ' + str(m[1])
        txt_mdata += chr(10)

    x = []
    y = []
    c = []
    
    colors = ['red', 'yellow', 'green', 'cyan',
            'blue', 'pink', 'orange', 'purple',
            'gray', 'white', 'gold', 'silver',
            'lightgreen', 'indigo', 'maroon', 'turquoise']
    
    for s in [m for m in mid_seq if m[0] == 'note']:
        x.append(s[1])
        y.append(s[4])
        c.append(colors[s[3]])

    plt.close()
    plt.figure(figsize=(14,5))
    ax=plt.axes(title='MIDI Search Plot')
    ax.set_facecolor('black')
    
    plt.scatter(x,y, c=c)
    plt.xlabel("Time")
    plt.ylabel("Pitch")
 
    with open(f"MIDI-Match-Sample.mid", 'wb') as f:
        f.write(MIDI.score2midi([mid_seq_ticks, mid_seq]))
    audio = synthesis(MIDI.score2opus([mid_seq_ticks, mid_seq]), soundfont_path)
    yield txt_mdata, "MIDI-Match-Sample.mid", (44100, audio), plt

#==========================================================================================================

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
    parser.add_argument("--port", type=int, default=7860, help="gradio server port")
    parser.add_argument("--max-gen", type=int, default=1024, help="max")
    
    opt = parser.parse_args()
    
    soundfont_path = "SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2"
    meta_data_path = "meta-data/LAMD_META_10000.pickle"

    print('Loading meta-data...')
    with open(meta_data_path, 'rb') as f:
        meta_data = pickle.load(f)
    print('Done!')
    
    app = gr.Blocks()
    with app:
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>MIDI Match</h1>")
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Upload any MIDI file to find its closest match</h1>")
        
        gr.Markdown("![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.MIDI-Match&style=flat)\n\n"
                    "Los Angeles MIDI Dataset Search and Explore Demo\n\n"
                    "Please see [Los Angeles MIDI Dataset](https://github.com/asigalov61/Los-Angeles-MIDI-Dataset) for more information and features\n\n"
                    "[Open In Colab]"
                    "(https://colab.research.google.com/github/asigalov61/Los-Angeles-MIDI-Dataset/blob/main/Los_Angeles_MIDI_Dataset_Search_and_Explore.ipynb)"
                    " for faster execution"
                    )
        
        input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"], type="binary")

        gr.Markdown("# Match results")
        
        output_audio = gr.Audio(label="Output MIDI match sample audio", format="mp3", elem_id="midi_audio")
        output_plot = gr.Plot(label="Output MIDI match sample plot")
        output_midi = gr.File(label="Output MIDI match sample MIDI", file_types=[".mid"])
        output_midi_seq = gr.Textbox(label="Output MIDI match metadata")
        
        run_event = input_midi.upload(match_midi, [input_midi],
                                                  [output_midi_seq, output_midi, output_audio, output_plot])
        
    app.queue(1).launch(server_port=opt.port, share=opt.share, inbrowser=True)