Spaces:
Runtime error
Runtime error
File size: 1,633 Bytes
7cbc42d ff647b0 1de73a8 789718b ff647b0 84a8d96 e49932c 84a8d96 e49932c 3f41985 a973402 3f41985 a973402 f19aeec e49932c 84a8d96 e49932c 776a79b e49932c 50efdde e49932c a1f6dfc a9764fc ff647b0 1de73a8 e49932c c7cb9a5 ff647b0 c7cb9a5 7cbc42d |
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 |
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 *
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.
data_dir = Path('.')
data = load_data(data_dir, 'data.pkl')
# Default config options
config = default_config()
config['encode_position'] = True
# Load our fine-tuned model
learner = music_model_learner(
data,
config=config.copy(),
pretrained_path='model.pth'
)
def process_midi(midi_file):
name = Path(midi_file.name)
# create the model input object
item = MusicItem.from_file(name, data.vocab);
# full is the prediction appended to the input
pred, full = learn.predict(item, n_words=100)
# convert to stream and then MIDI file
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'
iface = gr.Interface(
fn=process_midi,
inputs="file",
outputs="audio"
)
iface.launch() |