methods.py

import numpy as np
import pretty_midi
from pandas import DataFrame
from pandas import concat

def piano_roll_to_pretty_midi(piano_roll, fs=100, program=0):
    '''Convert a Piano Roll array into a PrettyMidi object
     with a single instrument.
    Parameters
    ----------
    piano_roll : np.ndarray, shape=(128,frames), dtype=int
        Piano roll of one instrument
    fs : int
        Sampling frequency of the columns, i.e. each column is spaced apart
        by ``1./fs`` seconds.
    program : int
        The program number of the instrument.
    Returns
    -------
    midi_object : pretty_midi.PrettyMIDI
        A pretty_midi.PrettyMIDI class instance describing
        the piano roll.
    '''
    notes, frames = piano_roll.shape
    pm = pretty_midi.PrettyMIDI()
    instrument = pretty_midi.Instrument(program=program)

    # pad 1 column of zeros so we can acknowledge inital and ending events
    piano_roll = np.pad(piano_roll, [(0, 0), (1, 1)], 'constant')

    # use changes in velocities to find note on / note off events
    velocity_changes = np.nonzero(np.diff(piano_roll).T)

    # keep track on velocities and note on times
    prev_velocities = np.zeros(notes, dtype=int)
    note_on_time = np.zeros(notes)

    for time, note in zip(*velocity_changes):
        # use time + 1 because of padding above
        velocity = piano_roll[note, time + 1]
        time = time / fs
        if velocity > 0:
            if prev_velocities[note] == 0:
                note_on_time[note] = time
                prev_velocities[note] = velocity
        else:
            pm_note = pretty_midi.Note(
                velocity=prev_velocities[note],
                pitch=note,
                start=note_on_time[note],
                end=time)
            instrument.notes.append(pm_note)
            prev_velocities[note] = 0
    pm.instruments.append(instrument)
    return pm

# convert series to supervised learning
import keras.backend.tensorflow_backend as tfb
import tensorflow as tf

POS_WEIGHT = 10  # multiplier for positive targets, needs to be tuned


def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
	n_vars = 1 if type(data) is list else data.shape[1]
	df = DataFrame(data)
	cols, names = list(), list()
	# input sequence (t-n, ... t-1)
	for i in range(n_in, 0, -1):
		cols.append(df.shift(i))
		names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
	# forecast sequence (t, t+1, ... t+n)
	for i in range(0, n_out):
		cols.append(df.shift(-i))
		if i == 0:
			names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
		else:
			names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
	# put it all together
	agg = concat(cols, axis=1)
	agg.columns = names
	# drop rows with NaN values
	if dropnan:
		agg.dropna(inplace=True)
	return agg


def convert(path):
    timeres = 20
    midi_data = pretty_midi.PrettyMIDI(path)
    roll = midi_data.get_piano_roll(fs=timeres)
    roll= np.where(roll>0 ,1,0)
    while(np.all(roll[:,0] == 0)): #drop leading "0" columns
        roll = np.delete(roll,0,1)
    return np.transpose(roll[40:100]) #pitch LtR, time UtD


def convert_back(roll,path):
    midi_out_path = path.split('.')[0] + "-enriched.midi"
    timeres = 20
    roll = np.transpose(roll)
    roll= np.where(roll>0.5 ,127,0)
    leading_zeros=np.zeros([40,roll.shape[1]])
    roll = np.vstack((leading_zeros, roll))
    bck = piano_roll_to_pretty_midi(roll, fs = timeres)
    bck.write(midi_out_path)

def weighted_binary_crossentropy(target, output):
    """
    Weighted binary crossentropy between an output tensor
    and a target tensor. POS_WEIGHT is used as a multiplier
    for the positive targets.

    Combination of the following functions:
    * keras.losses.binary_crossentropy
    * keras.backend.tensorflow_backend.binary_crossentropy
    * tf.nn.weighted_cross_entropy_with_logits
    """
    # transform back to logits
    _epsilon = tfb._to_tensor(tfb.epsilon(), output.dtype.base_dtype)
    output = tf.clip_by_value(output, _epsilon, 1 - _epsilon)
    output = tf.log(output / (1 - output))
    # compute weighted loss
    loss = tf.nn.weighted_cross_entropy_with_logits(targets=target,
                                                    logits=output,
                                                    pos_weight=POS_WEIGHT)
    return tf.reduce_mean(loss, axis=-1)