def time_stretch_hpss(audio, f):
if f == 1.0:
return audio
stft = core.stft(audio)
# Perform HPSS
stft_harm, stft_perc = decompose.hpss(
stft, kernel_size=31) # original kernel size 31
# OLA the percussive part
# make sure the signals properly overlap
y_perc = librosa.util.fix_length(core.istft(stft_perc, dtype=audio.dtype),
len(audio))
y_perc = time_stretch_sola(y_perc, f, wsola=True)
#~ # Phase-vocode the harmonic part
stft_stretch = core.phase_vocoder(stft_harm, 1.0 / f)
#~ # Inverse STFT of harmonic
y_harm = librosa.util.fix_length(
core.istft(stft_stretch, dtype=y_perc.dtype), len(y_perc))
# y_harm = librosa.util.fix_length(core.istft(stft_harm, dtype=audio.dtype), len(audio))
# y_harm = librosa.util.fix_length(time_stretch_sola(core.istft(stft_harm, dtype=audio.dtype), f, wsola = True), len(y_perc))
# Add them together
return y_harm + y_perc
def get_data(train=True):
batch_out = []
interval = int(self.clip_sec * self.samplerate) * 2
for batch_idx in range(self.batch_size):
if train:
rec_idx = np.random.randint(len(train_data_wav))
crop_idx = np.random.randint(len(train_data_wav[rec_idx][0]) - interval)
sources = [i[crop_idx:crop_idx + interval] for i in train_data_wav[rec_idx]]
else:
rec_idx = np.random.randint(len(test_data_wav))
crop_idx = np.random.randint(len(test_data_wav[rec_idx][0]) - interval)
sources = [i[crop_idx:crop_idx + interval] for i in test_data_wav[rec_idx]]
if config.pitch_aug and train:
n_steps = pitch_shift_list[np.random.randint(len(pitch_shift_list))]
if not n_steps==0:
sources = [pitch_shift(i, self.samplerate, n_steps=n_steps) for i in sources]
sources = [from_polar(to_stft(i, self.nfft)) for i in sources]
if config.bpm_aug and train:
rate = stretch_rate_list[np.random.randint(len(stretch_rate_list))]
if not rate==1.0:
for i in range(len(sources)):
augmented = phase_vocoder(sources[i][:, :, 0] + 1j * sources[i][:, :, 1], rate=rate)
sources[i] = np.array([np.real(augmented), np.imag(augmented)]).transpose(1, 2, 0)
if config.amp_aug and train:
sources = [i * (0.75 + (np.random.random() * 0.5)) for i in sources]
sources = random_crop(sources, self.ydim)
batch_out.append(sources)
batch_out = np.array(batch_out).transpose(1, 0, 2, 3, 4)
if train and true_wp(config.shuffle_sources_aug_prob) == 1.0:
for source_i in range(self.num_sources):
np.random.shuffle(batch_out[source_i])
return batch_out
def eval(net1, net2, speech_file_loc, melody_file_loc):
# Evaluates the result of net1, net2 on a given speech file and melody file
# speech_file_loc, melody_file_loc are strings that specify the location of the respective audio files
network1, network2 = net1.eval(), net2.eval()
# Read input audio
orig_speech = core.load(speech_file_loc, sr)[0]
inp_speech = DL.remove_silent_frames(orig_speech)
#inp_speech = 1.0 * orig_speech
stft_inp = core.stft(inp_speech,
n_fft=nfft,
hop_length=hop,
win_length=wlen)
# Extract melody and create its image
melody = utils.MelodyExt.melody_extraction(melody_file_loc,
'runtime_folder/ref_melody')[0]
ref_pc = melody[:, 1]
ref_time = melody[:, 0]
const = hop * 1.0 / sr
new_sampling_time = np.arange(const, ref_time[-1], const)
interp_melody = np.interp(new_sampling_time, ref_time, ref_pc)
n_frames = new_sampling_time.shape[0]
idx1 = (1.0 * interp_melody * nfft / sr).astype(int)
idx2 = np.array(range(n_frames))
pc = np.zeros([1 + nfft / 2, n_frames])
pc[idx1, idx2] = 1
pc[-1] = 1 * pc[0]
pc[0] = 0 * pc[0]
# Complete input preprocessing
rate = stft_inp.shape[1] * 1.0 / n_frames
stft_inp = core.phase_vocoder(stft_inp, rate,
hop) # Stretch input speech to target length
n_frames += 8 - n_frames % 8
# Append zeros to make it suitable for network
stft_inp = np.concatenate([
stft_inp,
np.zeros([stft_inp.shape[0], n_frames - stft_inp.shape[1]])
],
axis=1)
pc = np.concatenate(
[pc, np.zeros([pc.shape[0], n_frames - pc.shape[1]])], axis=1)
stft_inp = np.log(1 + np.abs(stft_inp))
stft_inp, pc = torch.from_numpy(stft_inp).float().unsqueeze(
0), torch.from_numpy(pc).float().unsqueeze(0) # Make tensors
# Extract output
encode2 = network2(Variable(pc.to(device)))
pred, encode1 = network1(Variable(stft_inp.to(device)), encode2)
pred = pred[0].cpu().data.numpy()
pred[pred < 0] = 0
pred = np.exp(pred) - 1
time_pred = 3.0 * utils.gl_rec(pred, hop, wlen, core.istft(
pred, hop, wlen)) # Adding a multiplier to increase loudness
return time_pred
def change_speed(input_signal, rate):
"""Change the playback speed of an audio signal
Parameters
----------
input_signal : numpy.array
Input array, must have numerical type.
rate : numeric
Desired rate of change to the speed.
To increase the speed, pass in a value greater than 1.0.
To decrease the speed, pass in a value between 0.0 and 1.0.
Returns
-------
numpy.array representing the audio signal with changed speed.
"""
if input_signal.dtype.kind not in 'iu' and input_signal.dtype.kind != 'f':
raise TypeError(
"'input_signal' must be an array of integers or floats")
if rate <= 0:
raise Exception('rate must be a positive number')
# Convert input signal to a -1.0 to 1.0 float if it's an integer type
if input_signal.dtype.kind in 'iu':
i = np.iinfo('float32')
abs_max = 2**(i.bits - 1)
offset = i.min + abs_max
input_signal = (input_signal.astype('float32') - offset) / abs_max
# Transform signal to frequency domain
frequency_domain_signal = core.stft(input_signal)
# Change speed with the phase vocoding method
fds_changed_speed = core.phase_vocoder(frequency_domain_signal, rate)
# Transform frequency domain signal back to time domain
output_signal = core.istft(fds_changed_speed, dtype=input_signal.dtype)
return output_signal
def __getitem__(self, samp_info):
usr = samp_info[0] # Which user
snum = samp_info[1] # Which song of the user
inp_start = float(
samp_info[4]) * self.sr # Start index of the time-domian signal
inp_end = float(
samp_info[5]) * self.sr # End index of the time-domain signal
lines_read = samp_info[6]
lines_sung = samp_info[7]
inp_audio = np.array([])
file_path = self.root_dir + str(usr) + '/read/' + str(snum) + '.wav'
inp_audio = self.all_audio[file_path][int(inp_start):int(inp_end)]
inp_audio = remove_silent_frames(inp_audio)
rps = np.random.uniform(-1.0, 1.0)
inp_rps = librosa.effects.pitch_shift(inp_audio, self.sr, n_steps=rps)
stft_inp = core.stft(inp_audio,
n_fft=self.nfft,
hop_length=self.hop,
win_length=self.wlen)
stft_rps = core.stft(inp_rps,
n_fft=self.nfft,
hop_length=self.hop,
win_length=self.wlen)
out_start = float(
samp_info[2]) * self.sr # Start index of the time signal
out_end = float(samp_info[3]) * self.sr # End index of the time signal
file_path = self.root_dir + str(usr) + '/sing/' + str(snum) + '.wav'
#out_audio = core.load(file_path, self.sr)[0][int(out_start):int(out_end)]
out_audio = self.all_audio[file_path][int(out_start):int(out_end)]
out_rps = librosa.effects.pitch_shift(out_audio, self.sr, n_steps=rps)
stft_out = core.stft(out_audio,
n_fft=self.nfft,
hop_length=self.hop,
win_length=self.wlen)
stft_rps_out = core.stft(out_rps,
n_fft=self.nfft,
hop_length=self.hop,
win_length=self.wlen)
rate = stft_inp.shape[1] * 1.0 / stft_out.shape[1]
stft_inp_orig = 1 * stft_inp
stft_inp = core.phase_vocoder(stft_inp, rate, self.hop)
stft_inp = stft_inp[:, :stft_out.shape[1]]
stft_rps = core.phase_vocoder(stft_rps, rate, self.hop)
stft_rps = stft_rps[:, :stft_out.shape[1]]
#phn_matrix = np.zeros([len(cmu_phn), stft_out.shape[1]])
phn_matrix = np.zeros(stft_out.shape[1]).astype(int)
hop_time = (self.hop * 1.0 / self.sr)
for idx in range(0, len(lines_read)):
phn_start, phn_end = extract_time(lines_sung[idx])
if (lines_sung[idx][-3] == ' '):
cur_phn = lines_sung[idx][-2:-1]
elif (lines_sung[idx][-4] == ' '):
cur_phn = lines_sung[idx][-3:-1]
if (cur_phn[-1] == ' '):
cur_phn = cur_phn[0]
start_time = float(samp_info[2])
end_time = float(samp_info[3])
if (
phn_end - phn_start > 0.005
): # Just a check that the phone should sustain for more than a few ms
start_idx = int((phn_start - start_time) / hop_time)
end_idx = int((phn_end - start_time) / hop_time)
#print start_idx, end_idx, cur_phn, phn_matrix.shape
#phn_matrix[:, start_idx:end_idx] = np.tile(phn_dict[cur_phn], (end_idx-start_idx, 1)).transpose()
phn_matrix[start_idx:end_idx] = int(phn_dict[cur_phn])
return [
np.abs(stft_inp),
np.abs(stft_out),
pitch_max(np.abs(stft_inp)),
pitch_pyin(int(out_start), usr, snum, stft_out.shape), rate,
stft_inp_orig, stft_inp, stft_out,
np.abs(stft_rps),
pitch_max(np.abs(stft_rps)),
np.abs(stft_rps_out),
self.fld.index(usr), phn_matrix
]
def __getitem__(self, samp_info):
#print samp_info[0], samp_info[1], samp_info[2], samp_info[3]
usr = samp_info[0] # Which user
snum = samp_info[1] # Which song out of user's 4 songs
inp_start = float(
samp_info[4]) * self.sr # Start index of the time-domian signal
inp_end = float(
samp_info[5]) * self.sr # End index of the time-domain signal
lines_read = samp_info[6]
lines_sung = samp_info[7]
inp_audio = np.array([])
file_path = self.root_dir + str(usr) + '/read/' + str(snum) + '.wav'
#'''
inp_full = self.all_audio[file_path]
for idx in range(0, len(lines_read)):
r_start, r_end = extract_time(lines_read[idx])
s_start, s_end = extract_time(lines_sung[idx])
stretch_rate = (r_end - r_start) / (1e-3 + s_end - s_start)
#print r_end, r_start, s_end, s_start
inp_phn = inp_full[int(r_start * self.sr):int(r_end * self.sr)]
inp_phn_stretch = librosa.effects.time_stretch(
inp_phn, stretch_rate)
inp_audio = np.append(inp_audio, inp_phn_stretch)
rps = np.random.uniform(-1.0, 1.0)
#rps = 3.0
inp_rps = librosa.effects.pitch_shift(inp_audio, self.sr, n_steps=rps)
stft_inp = core.stft(inp_audio,
n_fft=self.nfft,
hop_length=self.hop,
win_length=self.wlen)
stft_rps = core.stft(inp_rps,
n_fft=self.nfft,
hop_length=self.hop,
win_length=self.wlen)
out_start = float(
samp_info[2]) * self.sr # Start index of the time signal
out_end = float(samp_info[3]) * self.sr # End index of the time signal
file_path = self.root_dir + str(usr) + '/sing/' + str(snum) + '.wav'
#out_audio = core.load(file_path, self.sr)[0][int(out_start):int(out_end)]
out_audio = self.all_audio[file_path][int(out_start):int(out_end)]
out_rps = librosa.effects.pitch_shift(out_audio, self.sr, n_steps=rps)
stft_out = core.stft(out_audio,
n_fft=self.nfft,
hop_length=self.hop,
win_length=self.wlen)
stft_rps_out = core.stft(out_rps,
n_fft=self.nfft,
hop_length=self.hop,
win_length=self.wlen)
# Making input also length of 3 seconds (if not wanted comment next 2 lines)
rate = stft_inp.shape[1] * 1.0 / stft_out.shape[1]
stft_inp_orig = 1 * stft_inp
stft_inp = core.phase_vocoder(stft_inp, rate, self.hop)
stft_inp = stft_inp[:, :stft_out.shape[1]]
stft_rps = core.phase_vocoder(stft_rps, rate, self.hop)
stft_rps = stft_rps[:, :stft_out.shape[1]]
#phn_matrix = np.zeros([len(cmu_phn), stft_out.shape[1]])
phn_matrix = np.zeros(stft_out.shape[1]).astype(int)
hop_time = (hop * 1.0 / sr)
for idx in range(0, len(lines_read)):
phn_start, phn_end = extract_time(lines_sung[idx])
if (lines_sung[idx][-3] == ' '):
cur_phn = lines_sung[idx][-2:-1]
elif (lines_sung[idx][-4] == ' '):
cur_phn = lines_sung[idx][-3:-1]
if (cur_phn[-1] == ' '):
cur_phn = cur_phn[0]
start_time = float(samp_info[2])
end_time = float(samp_info[3])
if (phn_end - phn_start > 0.005):
start_idx = int((phn_start - start_time) / hop_time)
end_idx = int((phn_end - start_time) / hop_time)
#print start_idx, end_idx, cur_phn, phn_matrix.shape
#phn_matrix[:, start_idx:end_idx] = np.tile(phn_dict[cur_phn], (end_idx-start_idx, 1)).transpose()
phn_matrix[start_idx:end_idx] = int(phn_dict[cur_phn])
return [
np.abs(stft_inp),
np.abs(stft_out),
pitch_max(np.abs(stft_inp)),
pitch_pyin(int(out_start), usr, snum, stft_out.shape), rate,
stft_inp_orig, stft_inp, stft_out,
np.abs(stft_rps),
pitch_max(np.abs(stft_rps)),
np.abs(stft_rps_out),
self.fld.index(usr), phn_matrix
] # Input, Output, Original Input-Output length ratio
def __getitem__(self, index):
audio_list = []
tf = self.id_list[index]
read_file = self.get_read(tf)
read_audio = f'{read_file[:-4]}.wav'
song_audio = f'{tf[:-4]}.wav'
with open(tf, 'rb') as f:
song_txt = f.read().splitlines()
with open(read_file, 'rb') as f:
read_txt = f.read().splitlines()
melody_name = "_".join([tf.split('/')[-3], tf.split('/')[-1]])[:-4]
melody = np.load(f'../sp2si-code/melody_contour/{melody_name}.npy')
index_begin = randint(0, len(read_txt) - 40) + 1
index_end = index_begin + 10
song_begin = float(song_txt[index_begin].split()[0])
song_end = float(song_txt[index_end].split()[1])
song_dur = song_end - song_begin
while song_dur < 5 and index_end < len(read_txt) - 2:
index_end += 1
song_end = float(song_txt[index_end].split()[1])
song_dur = song_end - song_begin
while song_dur > 8:
index_end -= 1
song_end = float(song_txt[index_end].split()[1])
song_dur = song_end - song_begin
read_begin = float(read_txt[index_begin].split()[0])
read_end = float(read_txt[index_end].split()[1])
read_dur = read_end - read_begin
read_audio = core.load(read_audio,
sr=self.sr,
mono=True,
offset=read_begin,
duration=read_dur)[0]
song_audio = core.load(song_audio,
sr=self.sr,
mono=True,
offset=song_begin,
duration=song_dur)[0]
read_stft = core.stft(read_audio,
n_fft=self.nfft,
hop_length=self.hop,
win_length=self.wlen)
song_stft = core.stft(song_audio,
n_fft=self.nfft,
hop_length=self.hop,
win_length=self.wlen)
song_stft = song_stft[..., :-1]
melody = torch.from_numpy(melody).unsqueeze(0).unsqueeze(0)
melody = F.interpolate(melody,
scale_factor=(22050 / 16000 / 2),
mode='nearest')
melody = torch.squeeze(melody)
pitch = melody[int(song_begin * self.sr /
self.hop):int(song_begin * self.sr / self.hop) +
song_stft.shape[1]]
pitch = pitch.cpu().numpy()
pitch = librosa.core.hz_to_midi(pitch)
rate = read_stft.shape[1] / song_stft.shape[1]
read_stft = core.phase_vocoder(read_stft, rate, self.hop)
read_stft = read_stft[:, :song_stft.shape[1]]
read_stft = abs(read_stft)
song_stft = abs(song_stft)
if args.feat_type == "mel":
read_stft = np.matmul(mel_basis, read_stft)
song_stft = np.matmul(mel_basis, song_stft)
read_mag = np.log10(np.clip((read_stft), a_min=1e-5, a_max=100000))
song_mag = np.log10(np.clip((song_stft), a_min=1e-5, a_max=100000))
return song_mag, read_mag, pitch, read_audio
def __getitem__(self, index):
audio_list = []
tf = self.id_list[index]
read_file = self.get_read(tf)
read_audio = f'{read_file[:-4]}.wav'
song_audio = f'{tf[:-4]}.wav'
with open(tf, 'rb') as f:
song_txt = f.read().splitlines()
with open(read_file, 'rb') as f:
read_txt = f.read().splitlines()
melody_name = "_".join([tf.split('/')[-3],
tf.split('/')[-1]])[:-4]
melody = np.load(f'../sp2si-code/melody_contour/{melody_name}.npy')
index_begin = randint(0,len(read_txt) - 40) + 1
index_end = index_begin + 30
song_begin = float(song_txt[index_begin].split()[0])
song_end = float(song_txt[index_end].split()[1])
song_dur = song_end - song_begin
while song_dur < 7 and index_end < len(read_txt) - 2:
index_end += 1
song_end = float(song_txt[index_end].split()[1])
song_dur = song_end - song_begin
while song_dur > 10 :
index_end -= 1
song_end = float(song_txt[index_end].split()[1])
song_dur = song_end - song_begin
read_begin = float(read_txt[index_begin].split()[0])
read_end = float(read_txt[index_end].split()[1])
read_dur = read_end - read_begin
read_audio = core.load(read_audio, sr=self.sr, mono=True, offset=read_begin, duration=read_dur)[0]
song_audio = core.load(song_audio, sr=self.sr, mono=True, offset=song_begin, duration=song_dur)[0]
index_sing_dur = []
index_read_dur = []
t = self.sr
rsi = float(read_txt[index_begin].split()[0])
ssi = float(song_txt[index_begin].split()[0])
i = index_begin
while i < index_end+1:
s_begin, s_end, _ = song_txt[i].split()
r_begin, r_end, _ = read_txt[i].split()
s_begin, s_end, r_begin, r_end = float(s_begin), float(s_end), float(r_begin), float(r_end)
while (s_end - s_begin< 0.2 or r_end - r_begin < 0.2) and i<index_end + 1:
i = i + 1
_, s_end, _ = song_txt[i].split()
_, r_end, _ = read_txt[i].split()
s_end , r_end = float(s_end), float(r_end)
index_read_dur += [((r_begin-rsi)*t , (r_end-rsi)*t) ]
index_sing_dur += [((s_begin-ssi)*t , (s_end-ssi)*t)]
i = i + 1
read_audio_list = []
for i in range(len(index_read_dur)):
r_begin, r_end = index_read_dur[i]
s_begin, s_end = index_sing_dur[i]
if r_end - r_begin ==0 or s_end - s_begin == 0:
read_audio_list += [read_audio[int(r_begin):int(r_end)]]
continue
rate = (s_end - s_begin)/(r_end - r_begin)
read_audio_slice = librosa.effects.time_stretch(read_audio[int(r_begin):int(r_end)], 1/rate)
read_audio_list += [read_audio_slice]
read_audio = np.concatenate(read_audio_list, axis=0)
read_stft = core.stft(read_audio, n_fft=self.nfft, hop_length=self.hop, win_length=self.wlen)
song_stft = core.stft(song_audio, n_fft=self.nfft, hop_length=self.hop, win_length=self.wlen)
song_stft = song_stft[...,:-1]
rate = read_stft.shape[1] / song_stft.shape[1]
read_stft = core.phase_vocoder(read_stft, rate, self.hop)
read_stft = read_stft[:, :song_stft.shape[1]]
read_stft = abs(read_stft)
song_stft = abs(song_stft)
if args.feat_type == "mel":
read_stft = np.matmul(mel_basis, read_stft)
song_stft = np.matmul(mel_basis, song_stft)
read_mag = np.log10(np.clip((read_stft), a_min=1e-5, a_max=100000))
song_mag = np.log10(np.clip((song_stft), a_min=1e-5, a_max=100000))
return song_mag, read_mag, read_audio
