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# Machine learning, flow and data
import tensorflow as tf
import numpy as np
import pandas as pd
# Audio
import pretty_midi
# Displaying
from IPython import display
# Extras
import collections
_CAP = 3501 # Cap for the number of notes
_SAMPLING_RATE = 16000 # Parameter to pass continuous signal to a discrete one
_INSTRUMENT_NAME = "Acoustic Grand Piano" # MIDI instrument used
_SCALING_FACTORS = pd.Series(
{"pitch": 64.024558, "step": 0.101410, "duration": 0.199386}
) # Factors used to normalize song maps
def midi_to_notes(midi_file: str) -> pd.DataFrame:
# Convert midi file to "song map" (dataframe where each note is broken
# into its components)
pm = pretty_midi.PrettyMIDI(midi_file)
instrument = pm.instruments[0]
notes = collections.defaultdict(list)
# Sort the notes by start time
sorted_notes = sorted(instrument.notes, key=lambda note: note.start)
prev_start = sorted_notes[0].start
# Separate each individual note in pitch, step and duration
for note in sorted_notes:
start = note.start
end = note.end
notes['pitch'].append(note.pitch)
notes['step'].append(start - prev_start)
notes['duration'].append(end - start)
prev_start = start
# Put notes in a dataframe
notes_df = pd.DataFrame({name: np.array(value) for name, value in notes.items()})
notes_df = notes_df[:_CAP] # Cap the song to match the model's architecture
return notes_df / _SCALING_FACTORS # Scale
def display_audio(pm: pretty_midi.PrettyMIDI, seconds=120):
waveform = pm.fluidsynth(fs=_SAMPLING_RATE)
# Take a sample of the generated waveform to mitigate kernel resets
waveform_short = waveform[:seconds*_SAMPLING_RATE]
return display.Audio(waveform_short, rate=_SAMPLING_RATE)
# Define function to convert song map to wav
def map_to_wav(song_map: pd.DataFrame, out_file: str, velocity: int=100):
# Convert "song map" to midi file (reverse process with respect to midi_to_notes)
contracted_map = tf.squeeze(song_map)
song_map_T = contracted_map.numpy().T
notes = pd.DataFrame(song_map_T, columns=["pitch", "step", "duration"]).mul(_SCALING_FACTORS, axis=1)
notes["pitch"] = notes["pitch"].astype('int32').clip(1, 127)
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 generate_and_display(out_file, model, z_sample=None, velocity=100, seconds=120):
song_map = model.generate(z_sample)
wav = map_to_wav(song_map, out_file, velocity)
return display_audio(wav, seconds) |