File size: 9,066 Bytes
810e364 3564777 810e364 1187db6 915be1c 810e364 8fcfbac 810e364 1a8538a f105af5 8fcfbac fc80115 2a9431a f105af5 66580cd f105af5 1a8538a f105af5 1a8538a 3564777 0eefd91 1a8538a 8fcfbac 3eaac00 810e364 3eaac00 f105af5 8fcfbac fc80115 8fcfbac 2a9431a 8fcfbac 810e364 3564777 0eefd91 3564777 810e364 |
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 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 |
import collections
#import datetime
#import glob
import numpy as np
#import pathlib
import pandas as pd
import pretty_midi
import seaborn as sns
from matplotlib import pyplot as plt
from typing import Optional
import tensorflow as tf
import keras
from tensorflow.keras.utils import custom_object_scope
import streamlit as st
from midi2audio import FluidSynth
import tempfile
import os
import base64
def midi_to_notes(midi_file: str) -> pd.DataFrame:
pm = pretty_midi.PrettyMIDI(midi_file)
instrument = pm.instruments[0]
notes = collections.defaultdict(list)
sorted_notes = sorted(instrument.notes, key=lambda note: note.start)
prev_start = sorted_notes[0].start
for note in sorted_notes:
start = note.start
end = note.end
notes['pitch'].append(note.pitch)
notes['start'].append(start)
notes['end'].append(end)
notes['step'].append(start - prev_start)
notes['duration'].append(end - start)
prev_start = start
return pd.DataFrame({name: np.array(value) for name, value in notes.items()})
def notes_to_midi(
notes: pd.DataFrame,
out_file: str,
instrument_name: str,
velocity: int = 100,
) -> pretty_midi.PrettyMIDI:
pm = pretty_midi.PrettyMIDI()
instrument = pretty_midi.Instrument(
program=pretty_midi.instrument_name_to_program(
instrument_name))
prev_start = 0
for i, note in notes.iterrows():
start = float(prev_start + note['step'])
end = float(start + note['duration'])
note = pretty_midi.Note(
velocity=velocity,
pitch=int(note['pitch']),
start=start,
end=end,
)
instrument.notes.append(note)
prev_start = start
pm.instruments.append(instrument)
pm.write(out_file)
return pm
def plot_roll(notes: pd.DataFrame, count: Optional[int] = None):
if count:
title = f'First {count} notes'
else:
title = f'Whole track'
count = len(notes['pitch'])
plt.figure(figsize=(20, 4))
plot_pitch = np.stack([notes['pitch'], notes['pitch']], axis=0)
plot_start_stop = np.stack([notes['start'], notes['end']], axis=0)
plt.plot(
plot_start_stop[:, :count], plot_pitch[:, :count], color="b", marker=".")
plt.xlabel('Time [s]')
plt.ylabel('Pitch')
_ = plt.title(title)
def plot_distributions(notes: pd.DataFrame, drop_percentile=2.5):
plt.figure(figsize=[15, 5])
plt.subplot(1, 3, 1)
sns.histplot(notes, x="pitch", bins=20)
plt.subplot(1, 3, 2)
max_step = np.percentile(notes['step'], 100 - drop_percentile)
sns.histplot(notes, x="step", bins=np.linspace(0, max_step, 21))
def predict_next_note(
notes: np.ndarray,
model: tf.keras.Model,
temperature: float = 1.0) -> tuple[int, float, float]:
assert temperature > 0
inputs = tf.expand_dims(notes, 0)
predictions = model.predict(inputs)
pitch_logits = predictions['pitch']
step = predictions['step']
duration = predictions['duration']
pitch_logits /= temperature
pitch = tf.random.categorical(pitch_logits, num_samples=1)
pitch = tf.squeeze(pitch, axis=-1)
duration = tf.squeeze(duration, axis=-1)
step = tf.squeeze(step, axis=-1)
step = tf.maximum(0, step)
duration = tf.maximum(0, duration)
return int(pitch), float(step), float(duration)
def mse_with_positive_pressure(y_true: tf.Tensor, y_pred: tf.Tensor):
mse = (y_true - y_pred) ** 2
positive_pressure = 10 * tf.maximum(-y_pred, 0.0)
return tf.reduce_mean(mse + positive_pressure)
def calcular_duracion_midi(archivo_midi):
midi = pretty_midi.PrettyMIDI(archivo_midi)
return midi.get_end_time()
def main():
seed = 42
tf.random.set_seed(seed)
np.random.seed(seed)
st.title('GENERADOR DE MELODIAS CON RNN')
# Rutas de archivos
#sample_file = 'Preludes 2 Through Major keys 39.mid'
out_file = 'output.mid'
uploaded_file = st.file_uploader("Sube un archivo MIDI")
model=''
pesos=''
option = st.selectbox(
"Elige con que modelo entrenar",
("Maestro", "Lakh"))
option_musica = st.selectbox(
"Elige instrumento a generar las melodias",
("Piano", "Chromatic Percussion", "Organ", "Guitar", "Bass", "Strings", "Ensemble", "Brass",
"Reed", "Pipe", "Synth Lead", "Synth Pad", "Synth Effects", "Ethnic", "Percussive", "Sound Effects"))
num_predictions = st.number_input("Ingrese el n煤mero de notas:", min_value=100, max_value=150, value=120, step=1)
if uploaded_file and option is not None:
if option=="Maestro":
model="mi_modelo_music.h5"
pesos="mi_pesos_music.h5"
else:
model="mi_modelo03_music.h5"
pesos="mi_pesos03_music.h5"
st.subheader("Archivo cargado:")
st.write(uploaded_file.name)
# Guardar el archivo en una ubicaci贸n temporal
with open(uploaded_file.name, 'wb') as f:
f.write(uploaded_file.getbuffer())
sample_file=uploaded_file.name
# Duracion del MIDI
duracion = calcular_duracion_midi(sample_file)
minutos, segundos = divmod(duracion, 60)
st.write(f"La duraci贸n del archivo MIDI subido es: {int(minutos)}:{int(segundos)}")
st.subheader("Modelo elegido:")
st.write(option)
# Cargar modelo y pesos
with custom_object_scope({'mse_with_positive_pressure': mse_with_positive_pressure}):
model = keras.models.load_model(model)
model.load_weights(pesos, skip_mismatch=False, by_name=False, options=None)
# Convertir MIDI generado por el modelo a archivo WAV
pm = pretty_midi.PrettyMIDI(sample_file)
instrument_name = ""
if option_musica is not None:
if option_musica=="Piano":
instrument_name="Acoustic Grand Piano"
elif option_musica=="Chromatic Percussion":
instrument_name="Celesta"
elif option_musica=="Organ":
instrument_name="Hammond Organ"
elif option_musica=="Guitar":
instrument_name="Acoustic Guitar (nylon)"
elif option_musica=="Bass":
instrument_name="Acoustic Bass"
elif option_musica=="Strings":
instrument_name="Violin"
elif option_musica=="Ensemble":
instrument_name="String Ensemble 1"
elif option_musica=="Brass":
instrument_name="Trumpet"
elif option_musica=="Reed":
instrument_name="Soprano Sax"
elif option_musica=="Pipe":
instrument_name="Piccolo"
elif option_musica=="Synth Lead":
instrument_name="Lead 2 (sawtooth)"
elif option_musica=="Synth Pad":
instrument_name="Pad 2 (warm)"
elif option_musica=="Synth Effects":
instrument_name="FX 2 (soundtrack)"
elif option_musica=="Ethnic":
instrument_name="Banjo"
elif option_musica=="Percussive":
instrument_name="Melodic Tom"
elif option_musica=="Sound Effects":
instrument_name="Guitar Fret Noise"
else:
instrument_name=pretty_midi.program_to_instrument_name(pm.instruments[0].program)
raw_notes = midi_to_notes(sample_file)
key_order = ['pitch', 'step', 'duration']
seq_length = 25
vocab_size = 128
temperature = 2.0
sample_notes = np.stack([raw_notes[key] for key in key_order], axis=1)
input_notes = (sample_notes[:seq_length] / np.array([vocab_size, 1, 1]))
generated_notes = []
prev_start = 0
for _ in range(num_predictions):
pitch, step, duration = predict_next_note(input_notes, model, temperature)
start = prev_start + step
end = start + duration
input_note = (pitch, step, duration)
generated_notes.append((*input_note, start, end))
input_notes = np.delete(input_notes, 0, axis=0)
input_notes = np.append(input_notes, np.expand_dims(input_note, 0), axis=0)
prev_start = start
generated_notes = pd.DataFrame(
generated_notes, columns=(*key_order, 'start', 'end'))
notes_to_midi(
generated_notes, out_file=out_file, instrument_name=instrument_name)
# Interfaz de Streamlit
st.title("Generador de notas musicales")
archivo_midi = open(out_file, 'rb').read()
st.download_button(
label="Descargar MIDI",
data=archivo_midi,
file_name=out_file, # Nombre del archivo que se descargar谩
mime='audio/midi'
)
# Duracion del MIDI resultante
duracion_f = calcular_duracion_midi(sample_file)
minutos_f, segundos_f = divmod(duracion_f, 60)
st.write(f"La duraci贸n del archivo MIDI resultante es: {int(minutos_f)}:{int(segundos_f)}")
if __name__ == "__main__":
main() |