# https://huggingface.co/spaces/asigalov61/Ultimate-MIDI-Classifier
import os
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
from statistics import mode
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 ClassifyMIDI(input_midi):
print('=' * 70)
print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
start_time = reqtime.time()
print('=' * 70)
fn = os.path.basename(input_midi.name)
fn1 = fn.split('.')[0]
print('=' * 70)
print('Ultimate MIDI Classifier')
print('=' * 70)
print('Input MIDI file name:', fn)
print('=' * 70)
print('Loading MIDI file...')
midi_name = fn
raw_score = TMIDIX.midi2single_track_ms_score(open(input_midi.name, 'rb').read())
escore_notes = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0]
#===============================================================================
# Augmented enhanced score notes
escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes, timings_divider=32)
escore_notes = [e for e in escore_notes if e[6] < 80 or e[6] == 128]
#=======================================================
# Augmentation
#=======================================================
# FINAL PROCESSING
melody_chords = []
#=======================================================
# MAIN PROCESSING CYCLE
#=======================================================
pe = escore_notes[0]
pitches = []
notes_counter = 0
for e in escore_notes:
#=======================================================
# Timings...
delta_time = max(0, min(127, e[1]-pe[1]))
if delta_time != 0:
pitches = []
# Durations and channels
dur = max(1, min(127, e[2]))
# Patches
pat = max(0, min(128, e[6]))
# Pitches
if pat == 128:
ptc = max(1, min(127, e[4]))+128
else:
ptc = max(1, min(127, e[4]))
#=======================================================
# FINAL NOTE SEQ
# Writing final note synchronously
if ptc not in pitches:
melody_chords.extend([delta_time, dur+128, ptc+256])
pitches.append(ptc)
notes_counter += 1
pe = e
#==============================================================
print('Done!')
print('=' * 70)
print('Sampling score...')
chunk_size = 1020
score = melody_chords
input_data = []
for i in range(0, len(score)-chunk_size, chunk_size // classification_sampling_resolution):
schunk = score[i:i+chunk_size]
if len(schunk) == chunk_size:
td = [937]
td.extend(schunk)
td.extend([938])
input_data.append(td)
print('Done!')
print('=' * 70)
#==============================================================
classification_summary_string = '=' * 70
classification_summary_string += '\n'
samples_overlap = 340 - chunk_size // classification_sampling_resolution // 3
print('Composition has', notes_counter, 'notes')
print('=' * 70)
print('Composition was split into' , len(input_data), 'samples', 'of 340 notes each with', samples_overlap, 'notes overlap')
print('=' * 70)
print('Number of notes in all composition samples:', len(input_data) * 340)
print('=' * 70)
classification_summary_string += 'Composition has ' + str(notes_counter) + ' notes\n'
classification_summary_string += '=' * 70
classification_summary_string += '\n'
classification_summary_string += 'Composition was split into ' + 'samples of 340 notes each with ' + str(samples_overlap) + ' notes overlap\n'
classification_summary_string += 'Number of notes in all composition samples: ' + str(len(input_data) * 340) + '\n'
classification_summary_string += '=' * 70
classification_summary_string += '\n'
print('Loading model...')
SEQ_LEN = 1026
PAD_IDX = 940
DEVICE = 'cuda' # 'cuda'
# instantiate the model
model = TransformerWrapper(
num_tokens = PAD_IDX+1,
max_seq_len = SEQ_LEN,
attn_layers = Decoder(dim = 1024, depth = 24, heads = 32, attn_flash = True)
)
model = AutoregressiveWrapper(model, ignore_index=PAD_IDX, pad_value=PAD_IDX)
model = torch.nn.DataParallel(model)
model.to(DEVICE)
print('=' * 70)
print('Loading model checkpoint...')
model.load_state_dict(
torch.load('Ultimate_MIDI_Classifier_Trained_Model_29886_steps_0.556_loss_0.8339_acc.pth',
map_location=DEVICE))
print('=' * 70)
if DEVICE == 'cpu':
dtype = torch.bfloat16
else:
dtype = torch.bfloat16
ctx = torch.amp.autocast(device_type=DEVICE, dtype=dtype)
print('Done!')
print('=' * 70)
#==================================================================
print('=' * 70)
print('Ultimate MIDI Classifier')
print('=' * 70)
print('Classifying...')
torch.cuda.empty_cache()
model.eval()
artist_results = []
song_results = []
results = []
for input in input_data:
x = torch.tensor(input[:1022], dtype=torch.long, device='cuda')
with ctx:
out = model.module.generate(x,
2,
filter_logits_fn=top_k,
filter_kwargs={'k': 1},
temperature=0.9,
return_prime=False,
verbose=False)
result = tuple(out[0].tolist())
results.append(result)
final_result = mode(results)
print('=' * 70)
print('Done!')
print('=' * 70)
result_toks = [final_result[0]-512, final_result[1]-512]
mc_song_artist = song_artist_tokens_to_song_artist(result_toks)
gidx = genre_labels_fnames.index(mc_song_artist)
mc_genre = genre_labels[gidx][1]
print('Most common classification genre label:', mc_genre)
print('Most common classification song-artist label:', mc_song_artist)
print('Most common song-artist classification label ratio:' , results.count(final_result) / len(results))
print('=' * 70)
classification_summary_string += 'Most common classification genre label: ' + str(mc_genre) + '\n'
classification_summary_string += 'Most common classification song-artist label: ' + str(mc_song_artist) + '\n'
classification_summary_string += 'Most common song-artist classification label ratio: '+ str(results.count(final_result) / len(results)) + '\n'
classification_summary_string += '=' * 70
classification_summary_string += '\n'
print('All classification labels summary:')
print('=' * 70)
all_artists_labels = []
for i, res in enumerate(results):
result_toks = [res[0]-512, res[1]-512]
song_artist = song_artist_tokens_to_song_artist(result_toks)
gidx = genre_labels_fnames.index(song_artist)
genre = genre_labels[gidx][1]
print('Notes', i*samples_overlap, '-', (i*samples_overlap)+340, '===', genre, '---', song_artist)
classification_summary_string += 'Notes ' + str(i*samples_overlap) + ' - ' + str((i*samples_overlap)+340) + ' === ' + str(genre) + ' --- ' + str(song_artist)) + '\n'
artist_label = str_strip_artist(song_artist.split(' --- ')[1])
all_artists_labels.append(artist_label)
classification_summary_string += '=' * 70
classification_summary_string += '\n'
print('=' * 70)
mode_artist_label = mode(all_artists_labels)
mode_artist_label_count = all_artists_labels.count(mode_artist_label)
print('Aggregated artist classification label:', mode_artist_label)
print('Aggregated artist classification label ratio:', mode_artist_label_count / len(all_artists_labels))
classification_summary_string += 'Aggregated artist classification label: ' + str(mode_artist_label) + '\n'
classification_summary_string += 'Aggregated artist classification label ratio: ' + str(mode_artist_label_count / len(all_artists_labels)) + '\n'
classification_summary_string += '=' * 70
classification_summary_string += '\n'
print('=' * 70)
print('Done!')
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 classification_summary_string
# =================================================================================================
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"
#===============================================================================
# Helper functions
#===============================================================================
def str_strip_song(string):
if string is not None:
string = string.replace('-', ' ').replace('_', ' ').replace('=', ' ')
str1 = re.compile('[^a-zA-Z ]').sub('', string)
return re.sub(' +', ' ', str1).strip().title()
else:
return ''
def str_strip_artist(string):
if string is not None:
string = string.replace('-', ' ').replace('_', ' ').replace('=', ' ')
str1 = re.compile('[^0-9a-zA-Z ]').sub('', string)
return re.sub(' +', ' ', str1).strip().title()
else:
return ''
def song_artist_to_song_artist_tokens(file_name):
idx = classifier_labels.index(file_name)
tok1 = idx // 424
tok2 = idx % 424
return [tok1, tok2]
def song_artist_tokens_to_song_artist(file_name_tokens):
tok1 = file_name_tokens[0]
tok2 = file_name_tokens[1]
idx = (tok1 * 424) + tok2
return classifier_labels[idx]
#===============================================================================
print('=' * 70)
print('Loading Ultimate MIDI Classifier labels...')
print('=' * 70)
classifier_labels = TMIDIX.Tegridy_Any_Pickle_File_Reader('Ultimate-MIDI-Classifier/Data/Ultimate_MIDI_Classifier_Song_Artist_Labels')
print('=' * 70)
genre_labels = TMIDIX.Tegridy_Any_Pickle_File_Reader('Ultimate-MIDI-Classifier/Data/Ultimate_MIDI_Classifier_Music_Genre_Labels')
genre_labels_fnames = [f[0] for f in genre_labels]
print('=' * 70)
print('Done!')
print('=' * 70)
app = gr.Blocks()
with app:
gr.Markdown("Ultimate MIDI Classifier
")
gr.Markdown("Classify absolutely any MIDI by genre, song and artist
")
gr.Markdown(
"\n\n"
"This is a demo for Ultimate MIDI Classifier\n\n"
"Check out [Ultimate MIDI Classifier](https://github.com/asigalov61/Ultimate-MIDI-Classifier) on GitHub!\n\n"
)
gr.Markdown("## Upload any MIDI to classify")
input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"])
run_btn = gr.Button("classify", variant="primary")
gr.Markdown("## Classification results")
output_midi_cls_summary = gr.Textbox(label="MIDI classification results")
run_event = run_btn.click(ClassifyMIDI, [input_midi],
[output_midi_cls_summary])
app.queue().launch()