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Add minor progression.
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"""
Find the inspiration for this project as well as the pretrained model
we used here: https://github.com/bearpelican/musicautobot
"""
import gradio as gr
from musicautobot.utils.setup_musescore import play_wav
from music21.midi.translate import midiFileToStream
from pathlib import Path
from midi2audio import FluidSynth
# from musicautobot.numpy_encode import *
from musicautobot.config import default_config
from musicautobot.music_transformer import *
from musicautobot.utils.midifile import *
# from musicautobot.utils.file_processing import process_all
import pickle
import subprocess
import os
print(os.getcwd())
# Load the stored data. This is needed to generate the vocab.
print('Loading data to build vocabulary.')
data_dir = Path('.')
data = load_data(data_dir, 'data.pkl')
from huggingface_hub import hf_hub_download
print('Downloading model.')
model_cache_path = hf_hub_download(repo_id="psistolar/musicautobot-fine1", filename="model.pth")
# Default config options
config = default_config()
config['encode_position'] = True
print("Building model.")
# Load our fine-tuned model
learner = music_model_learner(
data,
config=config.copy(),
pretrained_path=model_cache_path
)
print("Ready to use.")
musical_letters = 'abcdefg'
from music21 import note
def sonify_text(text, sentiment):
name = Path('C Major Scale.midi')
item = MusicItem.from_file(name, data.vocab)
note_names = [f"{letter.upper()}4" for letter in text.lower() if letter in musical_letters]
p = music21.stream.Part()
if sentiment == 'NEGATIVE':
# If negative, use TODO
p.append(music21.chord.Chord('A3 C4 E4', type='half')) # i
p.append(music21.chord.Chord('F3 A4 C4', type='half')) # VI
p.append(music21.chord.Chord('C3 E3 G3', type='half')) # III
p.append(music21.chord.Chord('G3 B3 D4', type='half')) # VII
else:
# If positive, use a partial progression I-V-vi in C Major.
p.append(music21.chord.Chord('C4 E4 G4', type='half')) # I
p.append(music21.chord.Chord('G3 B3 D4', type='half')) # V
p.append(music21.chord.Chord('A3 C4 E4', type='half')) # vi
notes = []
for note_name in note_names:
note_obj = note.Note(note_name)
note_obj.duration.type = "quarter"
p.append(note_obj)
s = music21.stream.Score([p])
musical_seed = MusicItem.from_stream(s, data.vocab)
return musical_seed
def process_midi(MIDI_File, Text_to_Sonify, Randomness, Amount_of_Music_to_Add):
if MIDI_File is not None:
name = Path(MIDI_File.name)
else:
name = Path('C Major Scale.midi')
sonification = False
if MIDI_File is None and Text_to_Sonify is not None:
sonification = True
# create the model input object
if sonification:
sentiment = 'NEGATIVE'
item = sonify_text(Text_to_Sonify, sentiment)
else:
item = MusicItem.from_file(name, data.vocab)
# full is the prediction appended to the input
temp = Randomness / 100
pred, full = learner.predict(
item,
n_words=Amount_of_Music_to_Add,
temperatures=(temp, temp)
)
# convert to stream and then MIDI file
if sonification:
# do not replay the musical seed if sonifying
stream = pred.to_stream()
else:
stream = full.to_stream()
out = music21.midi.translate.streamToMidiFile(stream)
# save MIDI file
out.open('result.midi', 'wb')
out.write()
out.close()
# use fluidsynth to convert MIDI to WAV so the user can hear the output
sound_font = "/usr/share/sounds/sf2/FluidR3_GM.sf2"
FluidSynth(sound_font).midi_to_audio('result.midi', 'result.wav')
return 'result.wav'
midi_file_desc = """Upload your own MIDI file here.
If you do not have a MIDI file, add some text and we will turn it into music!
"""
iface = gr.Interface(
fn=process_midi,
inputs=[
gr.inputs.File(optional=True, label=midi_file_desc),
"text",
gr.inputs.Slider(0, 250, default=100, step=50),
gr.inputs.Radio([100, 200, 500], type="value", default=100)
],
outputs="audio",
# examples=['C major scale.midi']
)
iface.launch()