# https://huggingface.co/spaces/asigalov61/MIDI-Search
import os
import time as reqtime
import datetime
from pytz import timezone
from sentence_transformers import SentenceTransformer
from sentence_transformers import util
import numpy as np
import gradio as gr
import copy
import random
import pickle
import zlib
from midi_to_colab_audio import midi_to_colab_audio
import TMIDIX
import matplotlib.pyplot as plt
#==========================================================================================================
def find_midi(title, artist):
print('=' * 70)
print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
start_time = reqtime.time()
print('-' * 70)
print('Req title:', title)
print('Req artist:', artist)
print('-' * 70)
input_text = ''
if title != '':
input_text += title
if artist != '':
input_text += ' by ' + artist
print('Searching...')
query_embedding = model.encode([input_text])
# Compute cosine similarity between query and each sentence in the corpus
similarities = util.cos_sim(query_embedding, corpus_embeddings)
top_ten_matches_idxs = np.argsort(-similarities)[0][:10].tolist()
# Find the index of the most similar sentence
closest_index = np.argmax(similarities)
closest_index_match_ratio = max(similarities[0]).tolist()
best_corpus_match = all_MIDI_files_names[closest_index]
top_ten_matches = ''
for t in top_ten_matches_idxs:
top_ten_matches += str(all_MIDI_files_names[t][0]).title() + '\n'
print('Done!')
print('=' * 70)
print('Match corpus index', closest_index)
print('Match corpus ratio', closest_index_match_ratio)
print('=' * 70)
print('Done!')
print('=' * 70)
song_artist = best_corpus_match[0]
song_artist_title = str(song_artist).title()
zlib_file_name = best_corpus_match[1]
print('Fetching MIDI score...')
with open(zlib_file_name, 'rb') as f:
compressed_data = f.read()
# Decompress the data
decompressed_data = zlib.decompress(compressed_data)
# Convert the bytes back to a list using pickle
scores_data = pickle.loads(decompressed_data)
fnames = [f[0] for f in scores_data]
fnameidx = fnames.index(song_artist)
MIDI_score_data = scores_data[fnameidx][1]
print('Rendering results...')
print('=' * 70)
print('MIDi Title:', song_artist_title)
print('Sample INTs', MIDI_score_data[:12])
print('=' * 70)
if len(MIDI_score_data) != 0:
song = MIDI_score_data
song_f = []
time = 0
dur = 0
vel = 90
pitch = 0
channel = 0
patches = [-1] * 16
channels = [0] * 16
channels[9] = 1
for ss in song:
if 0 <= ss < 256:
time += ss * 16
if 256 <= ss < 512:
dur = (ss-256) * 16
if 512 <= ss <= 640:
patch = (ss-512)
if patch < 128:
if patch not in patches:
if 0 in channels:
cha = channels.index(0)
channels[cha] = 1
else:
cha = 15
patches[cha] = patch
channel = patches.index(patch)
else:
channel = patches.index(patch)
if patch == 128:
channel = 9
if 640 < ss < 768:
ptc = (ss-640)
if 768 < ss < 896:
vel = (ss - 768)
song_f.append(['note', time, dur, channel, ptc, vel, patch ])
patches = [0 if x==-1 else x for x in patches]
print('=' * 70)
#===============================================================================
output_score, patches, overflow_patches = TMIDIX.patch_enhanced_score_notes(song_f)
detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(output_score,
output_signature = 'Los Angeles MIDI Dataset Search',
output_file_name = song_artist_title,
track_name='Project Los Angeles',
list_of_MIDI_patches=patches
)
new_fn = song_artist_title + '.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(song_artist_title)
output_midi_summary = str(top_ten_matches)
output_midi = str(new_fn)
output_audio = (16000, audio)
output_plot = TMIDIX.plot_ms_SONG(output_score, plot_title=output_midi_title, 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"
print('Loading files list...')
all_MIDI_files_names = TMIDIX.Tegridy_Any_Pickle_File_Reader('all_MIDI_files_names')
print('=' * 70)
print('Loading MIDI corpus embeddings...')
corpus_embeddings = np.load('MIDI_corpus_embeddings_all-mpnet-base-v2.npz')['data']
print('Done!')
print('=' * 70)
print('Loading Sentence Transformer model...')
model = SentenceTransformer('all-mpnet-base-v2')
print('Done!')
print('=' * 70)
app = gr.Blocks()
with app:
gr.Markdown("Advanced MIDI Search
")
gr.Markdown("Search and explore 179k+ MIDI titles with sentence transformer
")
gr.Markdown("\n\n")
gr.Markdown("# Enter any desired title, artist or both\n\n")
title = gr.Textbox(label="Song Title", value="Family Guy")
artist = gr.Textbox(label="Song Artist", value="TV Themes")
submit = gr.Button(value='Search')
gr.ClearButton(components=[title, artist])
gr.Markdown("# Search results")
output_midi_title = gr.Textbox(label="Output MIDI title")
output_midi_summary = gr.Textbox(label="Top ten MIDI matches")
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 = submit.click(find_midi, [title, artist],
[output_midi_title, output_midi_summary, output_midi, output_audio, output_plot ])
app.launch()