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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+16.5mg_gm_gs_mt32_v2.51_bank.sf2 filter=lfs diff=lfs merge=lfs -text

16.5mg_gm_gs_mt32_v2.51_bank.sf2

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c5971c61f49f749837b0bcf7e2b5d17d5bd6d2b60df19f98da320315a029813f
+size 16944892

app.py

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+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 main():
+    seed = 42
+    tf.random.set_seed(seed)
+    np.random.seed(seed)
+
+    # Rutas de archivos
+    sample_file = '732628744bb532074f4993acfa07c718.mid'
+    out_file = 'output.mid'
+
+    # Cargar modelo y pesos
+    with custom_object_scope({'mse_with_positive_pressure': mse_with_positive_pressure}):
+        model = keras.models.load_model("mi_modelo_music.h5")
+
+    model.load_weights("mi_pesos_music.h5", 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 = 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
+    num_predictions = 120
+    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 descargará
+        mime='audio/midi'  
+    )
+
+if __name__ == "__main__":
+    main()

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==2.15.0
+keras==2.15.0
+
+midi2audio