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app.py

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+
+import os
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+os.environ["TF_FORCE_CPU"] = '1'
+
+
+
+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 datetime
+import os
+
+import keras
+
+from tensorflow.keras.utils import custom_object_scope
+
+import gradio as st
+
+
+
+
+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 main():
+  # AQUI EN EL MAIN DEBERIA INCLUIRSE STREAMLIT
+  description = st.Textbox(label="Description", placeholder="acoustic, guitar, melody, trap, d minor, 90 bpm")
+  melody_audio = st.Audio(label="Melody Audio (optional)", type="filepath")
+  output_path = st.Audio(label="Generated Music", type="filepath")
+    
+  seed = 42
+  tf.random.set_seed(seed)
+  np.random.seed(seed)
+
+  _SAMPLING_RATE = 16000
+  
+  # LEER ARCHIVO
+  sample_file = "Preludes 2 Through Major keys 39.mid"
+  pm = pretty_midi.PrettyMIDI(sample_file)
+  
+  print('Número de instrumentos:', len(pm.instruments))
+  for i in range(len(pm.instruments)):
+    instrument = pm.instruments[i]
+    instrument_name = pretty_midi.program_to_instrument_name(instrument.program)
+    print('Nombre del instrumento:', instrument_name)
+  
+  raw_notes = midi_to_notes(sample_file)
+  
+  key_order = ['pitch', 'step', 'duration']
+  
+  seq_length = 25
+  vocab_size = 128
+  
+  temperature = 2.0
+  #num_predictions = input("Ingrese el número de notas: ")
+  num_predictions = 120#int(num_predictions)
+
+  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]))
+
+  with custom_object_scope({'mse_with_positive_pressure': mse_with_positive_pressure}):
+      model = keras.saving.load_model("mi_modelo_music.h5")
+
+  model.load_weights("mi_pesos_music.h5", skip_mismatch=False, by_name=False, options=None)
+
+  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'))
+  
+  out_file = 'output.mid'
+  
+  out_pm = notes_to_midi(
+    generated_notes, out_file=out_file, instrument_name=instrument_name)
+  
+  # Reproducir audio 
+
+if __name__ == "__main__":
+    main()

mi_modelo03_music.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a456d332ce0b5617e258db48a24ffc56910ef4ba228f66c1642d42c7dc08b5d4
+size 1059128

mi_modelo_music.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e6c7b349a19e94cdf0429f0986b909c9bd120a2311591411def479e824806dbc
+size 1058096

mi_pesos03_music.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d21bf784a6b7ea000de799930bb5990f060a3746943cbead7bf7f7210a783725
+size 357608

mi_pesos_music.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dc1813143fe96939597c7a03ff9c8dcc6aa94e820676dd3cb5c98f5f8c01f8e6
+size 357608

requirements.txt

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+numpy==1.25.2
+pandas==2.0.3
+pretty_midi==0.2.10
+seaborn==0.13.1
+matplotlib==3.7.1
+tensorflow
+keras==2.15.0
+