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import os
import math
import mido
import pumpp
import numpy as np
from scipy.ndimage import gaussian_filter1d
from cq2m_models import mask_voas_cnn_model, late_deep_cnn_model
############################################################
def downsample_bins(voice):
voice_0 = np.array(voice.T[0::5]).T
voice_1 = np.array(voice.T[1::5]).T
voice_2 = np.array(voice.T[2::5]).T
voice_3 = np.array(voice.T[3::5]).T
voice_4 = np.array(voice.T[4::5]).T
voice_0 = voice_0.T[1:70].T
voice_1 = voice_1.T[1:70].T
voice_2 = voice_2.T[1:70].T
voice_3 = voice_3.T[0:69].T
voice_4 = voice_4.T[0:69].T
voice_sums = voice_0 + voice_1 + voice_2 + voice_3 + voice_4
voice_argmax = np.argmax(voice_sums, axis=1)
threshold = np.zeros(voice_sums.shape)
threshold[np.arange(voice_argmax.size), voice_argmax] = 1
threshold[:, 0] = 0
voice_sums = threshold
return voice_sums
############################################################
def create_midi(pr, write_path='./midi_track.mid', ticks_per_beat=58,
tempo=90, save_to_file=True, program=53, channel=0):
def pr_to_list(pr):
# List event = (pitch, velocity, time)
T, N = pr.shape
t_last = 0
pr_tm1 = np.zeros(N)
list_event = []
for t in range(T):
pr_t = pr[t]
mask = (pr_t != pr_tm1)
if(N == 360): range_step = 5
else: range_step = 1
if (mask).any():
for n in range(0, N):
if mask[n]:
if(N <= 72):
pitch = 25 + n
else:
pitch = 24 + round(n/5)
if int(pr_t[n] * 127) >= 50:
velocity = 127
else:
velocity = 0
# Time is incremented since last event
t_event = t - t_last
t_last = t
list_event.append((pitch, velocity, t_event))
pr_tm1 = pr_t
list_event.append((0, 0, T - t_last))
return list_event
# Tempo
microseconds_per_beat = mido.bpm2tempo(tempo)
# Write a pianoroll in a midi file
mid = mido.MidiFile()
mid.ticks_per_beat = ticks_per_beat
# Add a new track with the instrument name to the midi file
track = mid.add_track("Voice Aah")
# transform the matrix in a list of (pitch, velocity, time)
events = pr_to_list(pr)
#print(events)
# Tempo
track.append(mido.MetaMessage('set_tempo', tempo=microseconds_per_beat))
track.append(mido.MetaMessage('channel_prefix', channel=channel))
# Add the program_change
#Choir Aahs = 53, Voice Oohs (or Doos) = 54, Synch Choir = 55
track.append(mido.Message('program_change', program=program, channel=channel))
# This list is required to shut down
# notes that are on, intensity modified, then off only 1 time
# Example :
# (60,20,0)
# (60,40,10)
# (60,0,15)
notes_on_list = []
# Write events in the midi file
for event in events:
pitch, velocity, time = event
if velocity == 0:
# Get the channel
track.append(mido.Message('note_off', note=pitch, velocity=0, time=time, channel=channel))
if(pitch in notes_on_list):
notes_on_list.remove(pitch)
else:
if pitch in notes_on_list:
track.append(mido.Message('note_off', note=pitch, velocity=0, time=time, channel=channel))
notes_on_list.remove(pitch)
time = 0
track.append(mido.Message('note_on', note=pitch, velocity=velocity, time=time, channel=channel))
notes_on_list.append(pitch)
if save_to_file:
mid.save(write_path)
return mid
############################################################
def song_to_midi(sop, alto, ten, bass):
savepath = './output.mid'
down_sop = downsample_bins(sop.T)
down_alto = downsample_bins(alto.T)
down_ten = downsample_bins(ten.T)
down_bass = downsample_bins(bass.T)
mid_sop = create_midi(down_sop, save_to_file=False, program=52, channel=0)
mid_alto = create_midi(down_alto, save_to_file=False, program=53, channel=1)
mid_ten = create_midi(down_ten, save_to_file=False, program=49, channel=2)
mid_bass = create_midi(down_bass, save_to_file=False, program=50, channel=3)
mid_mix = mido.MidiFile()
mid_mix.ticks_per_beat = mid_sop.ticks_per_beat
mid_mix.tracks = mid_sop.tracks + mid_alto.tracks + mid_ten.tracks + mid_bass.tracks
mid_mix.save(savepath)
return savepath
############################################################
def prediction_postproc(input_array, argmax_and_threshold=True, gaussian_blur=True):
prediction = np.moveaxis(input_array, 0, 1).reshape(360, -1)
if(argmax_and_threshold):
prediction = np.argmax(prediction, axis=0)
prediction = np.array([i if i <= 357 else 0 for i in prediction])
threshold = np.zeros((360, prediction.shape[0]))
threshold[prediction, np.arange(prediction.size)] = 1
prediction = threshold
if(gaussian_blur):
prediction = np.array(gaussian_filter1d(prediction, 1, axis=0, mode='wrap'))
prediction = (prediction - np.min(prediction))/(np.max(prediction)-np.min(prediction))
return prediction
############################################################
def get_hcqt_params():
bins_per_octave = 60
n_octaves = 6
over_sample = 5
harmonics = [1, 2, 3, 4, 5]
sr = 22050
fmin = 32.7
hop_length = 256
return bins_per_octave, n_octaves, harmonics, sr, fmin, hop_length, over_sample
############################################################
def create_pump_object():
(bins_per_octave, n_octaves, harmonics,
sr, f_min, hop_length, over_sample) = get_hcqt_params()
p_phdif = pumpp.feature.HCQTPhaseDiff(name='dphase', sr=sr, hop_length=hop_length,
fmin=f_min, n_octaves=n_octaves, over_sample=over_sample, harmonics=harmonics, log=True)
pump = pumpp.Pump(p_phdif)
return pump
############################################################
def compute_pump_features(pump, audio_fpath):
data = pump(audio_f=audio_fpath)
return data
############################################################
def get_mpe_prediction(model, audio_file=None):
"""Generate output from a model given an input numpy file.
Part of this function is part of deepsalience
"""
split_value = 2500
if audio_file is not None:
pump = create_pump_object()
features = compute_pump_features(pump, audio_file)
input_hcqt = features['dphase/mag'][0]
input_dphase = features['dphase/dphase'][0]
else:
raise ValueError("One audio_file must be specified")
input_hcqt = input_hcqt.transpose(1, 2, 0)[np.newaxis, :, :, :]
input_dphase = input_dphase.transpose(1, 2, 0)[np.newaxis, :, :, :]
n_t = input_hcqt.shape[3]
t_slices = list(np.arange(0, n_t, split_value))
output_list = []
for t in t_slices:
p = model.predict([np.transpose(input_hcqt[:, :, :, t:t+split_value], (0, 1, 3, 2)),
np.transpose(input_dphase[:, :, :, t:t+split_value], (0, 1, 3, 2))]
)[0, :, :]
output_list.append(p)
predicted_output = np.hstack(output_list).astype(np.float32)
return predicted_output
############################################################
def get_va_prediction(model, f0_matrix):
splits = f0_matrix.shape[1]//256
splits_diff = 256 - (f0_matrix.shape[1] - splits * 256)
fill = np.zeros((360, splits_diff))
mix_filled = np.concatenate((np.copy(f0_matrix), fill), axis=1)
mix_filled = np.reshape(mix_filled, (360, -1, 256, 1)).transpose((1, 0, 2, 3))
batches = math.ceil(mix_filled.shape[0]/24)
s_pred_result = np.zeros((0, 360, 256))
a_pred_result = np.zeros((0, 360, 256))
t_pred_result = np.zeros((0, 360, 256))
b_pred_result = np.zeros((0, 360, 256))
for i in range(batches):
s_pred, a_pred, t_pred, b_pred = model.predict(mix_filled[i*24:(i+1)*24])
s_pred_result = np.append(s_pred_result, s_pred, axis=0)
a_pred_result = np.append(a_pred_result, a_pred, axis=0)
t_pred_result = np.append(t_pred_result, t_pred, axis=0)
b_pred_result = np.append(b_pred_result, b_pred, axis=0)
s_pred_result = prediction_postproc(s_pred_result)[:, :f0_matrix.shape[1]]
a_pred_result = prediction_postproc(a_pred_result)[:, :f0_matrix.shape[1]]
t_pred_result = prediction_postproc(t_pred_result)[:, :f0_matrix.shape[1]]
b_pred_result = prediction_postproc(b_pred_result)[:, :f0_matrix.shape[1]]
return s_pred_result, a_pred_result, t_pred_result, b_pred_result
############################################################
def cq2m(audiofile, mpe=late_deep_cnn_model(), va=mask_voas_cnn_model()):
mpe_pred = get_mpe_prediction(mpe, audiofile)
s_pred, a_pred, t_pred, b_pred = get_va_prediction(va, mpe_pred)
midi = song_to_midi(s_pred, a_pred, t_pred, b_pred)
return midi
############################################################
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