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import argparse |
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import glob |
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import os.path |
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import gradio as gr |
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import pickle |
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import tqdm |
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import json |
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import MIDI |
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from midi_synthesizer import synthesis |
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from fuzzywuzzy import process |
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import copy |
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from collections import Counter |
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import random |
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import statistics |
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import matplotlib.pyplot as plt |
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in_space = os.getenv("SYSTEM") == "spaces" |
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def find_midi(title, artist): |
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print('=' * 70) |
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print('Loading MIDI file...') |
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print('Searching titles...Please wait...') |
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random.shuffle(AUX_DATA) |
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titles_index = [] |
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for A in AUX_DATA: |
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titles_index.append(A[0]) |
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search_string = '' |
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if enter_desired_song_title != '' and enter_desired_artist != '': |
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search_string = enter_desired_song_title + ' --- ' + enter_desired_artist |
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else: |
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search_string = enter_desired_song_title + enter_desired_artist |
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search_match = process.extract(query=search_string, choices=titles_index, limit=1) |
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search_index = titles_index.index(search_match[0][0]) |
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print('Done!') |
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print('=' * 70) |
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print('Selected title:', AUX_DATA[search_index][0]) |
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print('=' * 70) |
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outy = AUX_DATA[search_index][1] |
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print('Sample INTs', outy[:12]) |
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print('=' * 70) |
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if len(outy) != 0: |
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song = outy |
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song_f = [] |
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time = 0 |
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dur = 0 |
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vel = 90 |
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pitch = 0 |
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channel = 0 |
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patches = [-1] * 16 |
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channels = [0] * 16 |
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channels[9] = 1 |
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for ss in song: |
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if 0 <= ss < 256: |
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time += ss * 16 |
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if 256 <= ss < 2304: |
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dur = ((ss-256) // 8) * 16 |
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vel = (((ss-256) % 8)+1) * 15 |
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if 2304 <= ss < 18945: |
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patch = (ss-2304) // 129 |
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if patch < 128: |
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if patch not in patches: |
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if 0 in channels: |
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cha = channels.index(0) |
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channels[cha] = 1 |
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else: |
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cha = 15 |
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patches[cha] = patch |
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channel = patches.index(patch) |
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else: |
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channel = patches.index(patch) |
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if patch == 128: |
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channel = 9 |
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pitch = (ss-2304) % 129 |
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song_f.append(['note', time, dur, channel, pitch, vel, patch ]) |
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patches = [0 if x==-1 else x for x in patches] |
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detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f, |
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output_signature = 'Giant Music Transformer', |
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output_file_name = '/content/Giant-Music-Transformer-Music-Composition_'+str(i), |
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track_name='Project Los Angeles', |
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list_of_MIDI_patches=patches |
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) |
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x = [] |
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y = [] |
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c = [] |
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colors = ['red', 'yellow', 'green', 'cyan', |
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'blue', 'pink', 'orange', 'purple', |
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'gray', 'white', 'gold', 'silver', |
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'lightgreen', 'indigo', 'maroon', 'turquoise'] |
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for s in [m for m in mid_seq if m[0] == 'note']: |
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x.append(s[1]) |
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y.append(s[4]) |
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c.append(colors[s[3]]) |
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plt.close() |
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plt.figure(figsize=(14,5)) |
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ax=plt.axes(title='MIDI Match Plot') |
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ax.set_facecolor('black') |
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plt.scatter(x,y, c=c) |
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plt.xlabel("Time in MIDI ticks") |
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plt.ylabel("MIDI Pitch") |
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with open(f"MIDI-Match-Sample.mid", 'wb') as f: |
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f.write(MIDI.score2midi([mid_seq_ticks, mid_seq])) |
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audio = synthesis(MIDI.score2opus([mid_seq_ticks, mid_seq]), soundfont_path) |
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yield txt_mdata, "MIDI-Match-Sample.mid", (44100, audio), plt |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--share", action="store_true", default=False, help="share gradio app") |
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parser.add_argument("--port", type=int, default=7860, help="gradio server port") |
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parser.add_argument("--max-gen", type=int, default=1024, help="max") |
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opt = parser.parse_args() |
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soundfont_path = "SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2" |
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meta_data_path = "Giant_Music_Transformer_Aux_Data.pickle" |
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print('Loading meta-data...') |
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with open(meta_data_path, 'rb') as f: |
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AUX_DATA = pickle.load(f) |
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print('Done!') |
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app = gr.Blocks() |
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with app: |
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gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>MIDI Search</h1>") |
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gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Upload any MIDI file to find its closest match</h1>") |
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gr.Markdown("\n\n" |
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"Los Angeles MIDI Dataset Search and Explore Demo\n\n" |
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"Please see [Los Angeles MIDI Dataset](https://github.com/asigalov61/Los-Angeles-MIDI-Dataset) for more information and features\n\n" |
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"[Open In Colab]" |
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"(https://colab.research.google.com/github/asigalov61/Los-Angeles-MIDI-Dataset/blob/main/Los_Angeles_MIDI_Dataset_Search_and_Explore.ipynb)" |
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" for faster execution" |
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) |
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gr.Markdown("# Upload MIDI") |
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artist = gr.Textbox() |
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title = gr.Textbox() |
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gr.Markdown("# Match results") |
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output_audio = gr.Audio(label="Output MIDI match sample audio", format="mp3", elem_id="midi_audio") |
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output_plot = gr.Plot(label="Output MIDI match sample plot") |
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output_midi = gr.File(label="Output MIDI match sample MIDI", file_types=[".mid"]) |
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output_midi_seq = gr.Textbox(label="Output MIDI match metadata") |
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run_event = input_midi.upload(match_midi, [input_midi, maximum_match_ratio], |
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[output_midi_seq, output_midi, output_audio, output_plot]) |
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app.queue(1).launch(server_port=opt.port, share=opt.share, inbrowser=True) |
