def generate_single_midi():
file_obj = request.files['midi_object']
script_dir = os.path.dirname(__file__)
# Save input as input.mid in data directory
rel_path = "data/input.mid"
abs_path = os.path.join(script_dir, rel_path)
data = file_obj.read()
with open(abs_path, 'wb') as out_file:
out_file.write(data)
# Get notes from midi file
# directory only consists of one midi file (simply to reuse code)
notes = Tools.get_notes("data/*.mid")
print("length of notes dictionary: " + str(len(notes)))
# Get the number of pitch names
n_vocab = len(set(notes))
# Load dictionaries
note_to_int = Tools.load_obj(f'{data_set}_note2int')
int_to_note = Tools.load_obj(f'{data_set}_int2note')
# convert notes into numerical input
network_input = Tools.prepare_sequences_single(notes, note_to_int, n_vocab)
network_input = network_input.tolist()
# Use the model to generate a new midi
model = torch.load(f'lstm_pretrained_{data_set}.pt')
model.eval()
prediction_output = Tools.generate_notes_single(model, int_to_note,
network_input,
len(set(notes)))
Tools.create_midi(prediction_output, f'{data_set}_midi_g')
bucket = s3.Bucket(bucket_name)
bucket.upload_file(f'{data_set}_midi_g.mid', f'{data_set}_midi_g.mid')
print("New midi file has been generated and uploaded to database")
return f'/getfile/{data_set}_midi_g.mid'
import sys
import torch
from tools import Tools
""" Generate the music from a random midi in training set"""
# Get notes from midi files
dataset_name = sys.argv[1]
notes = Tools.load_obj(f'{dataset_name}_notes')
# Get the number of pitch names
n_vocab = len(set(notes))
# Convert notes into numerical input
network_input, network_output = Tools.prepare_sequences(notes, n_vocab)
network_input = network_input.tolist()
# Use the model to generate a midi
model = torch.load(f'lstm_pretrained_{dataset_name}.pt')
model.eval()
prediction_output = Tools.generate_notes(model, notes, network_input,
len(set(notes)))
Tools.create_midi(prediction_output, f'{dataset_name}_midi_g')
