def get_data(current_set,
set_size,
wsz=2049,
N=4096,
hop=384,
T=100,
L=20,
B=16):
"""
Method to acquire training data. The STFT analysis is included.
Args:
current_set : (int) An integer denoting the current training set.
set_size : (int) The amount of files a set has.
wsz : (int) Window size in samples.
N : (int) The FFT size.
hop : (int) Hop size in samples.
T : (int) Length of the time-sequence.
L : (int) Number of context frames from the time-sequence.
B : (int) Batch size.
Returns:
ms_train : (3D Array) Mixture magnitude training data, for the current set.
vs_train : (3D Array) Singing voice magnitude training data, for the current set.
"""
# Generate full paths for dev and test
dev_mixtures_list = sorted(os.listdir(mixtures_path + foldersList[0]))
dev_mixtures_list = [
mixtures_path + foldersList[0] + '/' + i for i in dev_mixtures_list
]
dev_sources_list = sorted(os.listdir(sources_path + foldersList[0]))
dev_sources_list = [
sources_path + foldersList[0] + '/' + i for i in dev_sources_list
]
# Current lists for training
c_train_slist = dev_sources_list[(current_set - 1) * set_size:current_set *
set_size]
c_train_mlist = dev_mixtures_list[(current_set - 1) *
set_size:current_set * set_size]
for index in range(len(c_train_mlist)):
# print('Reading:' + c_train_mlist[index])
# Reading
vox, _ = Io.wavRead(os.path.join(c_train_slist[index], keywords[3]),
mono=False)
mix, _ = Io.wavRead(os.path.join(c_train_mlist[index], keywords[4]),
mono=False)
# STFT Analysing
ms_seg, _ = tf.TimeFrequencyDecomposition.STFT(
0.5 * np.sum(mix, axis=-1), tf.hamming(wsz, True), N, hop)
vs_seg, _ = tf.TimeFrequencyDecomposition.STFT(
0.5 * np.sum(vox, axis=-1), tf.hamming(wsz, True), N, hop)
# Remove null frames
ms_seg = ms_seg[3:-3, :]
vs_seg = vs_seg[3:-3, :]
# Stack some spectrograms and fit
if index == 0:
ms_train = ms_seg
vs_train = vs_seg
else:
ms_train = np.vstack((ms_train, ms_seg))
vs_train = np.vstack((vs_train, vs_seg))
# Data preprocessing
# Freeing up some memory
ms_seg = None
vs_seg = None
# Learning the filtering process
mask = Fm(ms_train, vs_train, ms_train, [], [], alpha=1., method='IRM')
vs_train = mask()
vs_train *= 2.
vs_train = np.clip(vs_train, a_min=0., a_max=1.)
ms_train = np.clip(ms_train, a_min=0., a_max=1.)
mask = None
ms_train, vs_train, _ = prepare_overlap_sequences(ms_train, vs_train,
ms_train, T, L * 2, B)
return ms_train, vs_train
def test_eval(nnet, B, T, N, L, wsz, hop):
"""
Method to test the model on the test data. Writes the outcomes in ".wav" format and.
stores them under the defined results path. Optionally, it performs BSS-Eval using
MIREval python toolbox (Used only for comparison to BSSEval Matlab implementation).
The evaluation results are stored under the defined save path.
Args:
nnet : (List) A list containing the Pytorch modules of the skip-filtering model.
B : (int) Batch size.
T : (int) Length of the time-sequence.
N : (int) The FFT size.
L : (int) Number of context frames from the time-sequence.
wsz : (int) Window size in samples.
hop : (int) Hop size in samples.
"""
nnet[0].eval()
nnet[1].eval()
nnet[2].eval()
nnet[3].eval()
def my_res(mx, vx, L, wsz):
"""
A helper function to reshape data according
to the context frame.
"""
mx = np.ascontiguousarray(mx[:, L:-L, :], dtype=np.float32)
mx.shape = (mx.shape[0] * mx.shape[1], wsz)
vx = np.ascontiguousarray(vx[:, L:-L, :], dtype=np.float32)
vx.shape = (vx.shape[0] * vx.shape[1], wsz)
return mx, vx
# Paths for loading and storing the test-set
# Generate full paths for test
test_sources_list = sorted(os.listdir(sources_path + foldersList[1]))
test_sources_list = [
sources_path + foldersList[1] + '/' + i for i in test_sources_list
]
# Initializing the containers of the metrics
sdr = []
sir = []
sar = []
for indx in xrange(len(test_sources_list)):
print('Reading:' + test_sources_list[indx])
# Reading
bass, _ = Io.wavRead(os.path.join(test_sources_list[indx],
keywords[0]),
mono=False)
drums, _ = Io.wavRead(os.path.join(test_sources_list[indx],
keywords[1]),
mono=False)
oth, _ = Io.wavRead(os.path.join(test_sources_list[indx], keywords[2]),
mono=False)
vox, _ = Io.wavRead(os.path.join(test_sources_list[indx], keywords[3]),
mono=False)
bk_true = np.sum(bass + drums + oth, axis=-1) * 0.5
mix = np.sum(bass + drums + oth + vox, axis=-1) * 0.5
sv_true = np.sum(vox, axis=-1) * 0.5
# STFT Analysing
mx, px = tf.TimeFrequencyDecomposition.STFT(mix, tf.hamming(wsz, True),
N, hop)
# Data reshaping (magnitude and phase)
mx, px, _ = prepare_overlap_sequences(mx, px, px, T, 2 * L, B)
# The actual "denoising" part
vx_hat = np.zeros((mx.shape[0], T - L * 2, wsz), dtype=np.float32)
for batch in xrange(mx.shape[0] / B):
H_enc = nnet[0](mx[batch * B:(batch + 1) * B, :, :])
H_j_dec = it_infer.iterative_recurrent_inference(nnet[1],
H_enc,
criterion=None,
tol=1e-3,
max_iter=10)
vs_hat, mask = nnet[2](H_j_dec,
mx[batch * B:(batch + 1) * B, :, :])
y_out = nnet[3](vs_hat)
vx_hat[batch * B:(batch + 1) * B, :, :] = y_out.data.cpu().numpy()
# Final reshaping
vx_hat.shape = (vx_hat.shape[0] * vx_hat.shape[1], wsz)
mx, px = my_res(mx, px, L, wsz)
# Time-domain recovery
# Iterative G-L algorithm
for GLiter in range(10):
sv_hat = tf.TimeFrequencyDecomposition.iSTFT(
vx_hat, px, wsz, hop, True)
_, px = tf.TimeFrequencyDecomposition.STFT(sv_hat,
tf.hamming(wsz, True),
N, hop)
# Removing the samples that no estimation exists
mix = mix[L * hop:]
sv_true = sv_true[L * hop:]
bk_true = bk_true[L * hop:]
# Background music estimation
if len(sv_true) > len(sv_hat):
bk_hat = mix[:len(sv_hat)] - sv_hat
else:
bk_hat = mix - sv_hat[:len(mix)]
# Disk writing for external BSS_eval using DSD100-tools (used in our paper)
Io.wavWrite(
sv_true, 44100, 16,
os.path.join(save_path, 'tf_true_sv_' + str(indx) + '.wav'))
Io.wavWrite(
bk_true, 44100, 16,
os.path.join(save_path, 'tf_true_bk_' + str(indx) + '.wav'))
Io.wavWrite(sv_hat, 44100, 16,
os.path.join(save_path, 'tf_hat_sv_' + str(indx) + '.wav'))
Io.wavWrite(bk_hat, 44100, 16,
os.path.join(save_path, 'tf_hat_bk_' + str(indx) + '.wav'))
Io.wavWrite(mix, 44100, 16,
os.path.join(save_path, 'tf_mix_' + str(indx) + '.wav'))
# Internal BSSEval using librosa (just for comparison)
if len(sv_true) > len(sv_hat):
c_sdr, _, c_sir, c_sar, _ = bss_eval.bss_eval_images_framewise(
[sv_true[:len(sv_hat)], bk_true[:len(sv_hat)]],
[sv_hat, bk_hat])
else:
c_sdr, _, c_sir, c_sar, _ = bss_eval.bss_eval_images_framewise(
[sv_true, bk_true],
[sv_hat[:len(sv_true)], bk_hat[:len(sv_true)]])
sdr.append(c_sdr)
sir.append(c_sir)
sar.append(c_sar)
# Storing the results iteratively
pickle.dump(sdr, open(os.path.join(save_path, 'SDR.p'), 'wb'))
pickle.dump(sir, open(os.path.join(save_path, 'SIR.p'), 'wb'))
pickle.dump(sar, open(os.path.join(save_path, 'SAR.p'), 'wb'))
return None
def test_nnet(nnet, seqlen=100, olap=40, wsz=2049, N=4096, hop=384, B=16):
"""
Method to test the model on some data. Writes the outcomes in ".wav" format and.
stores them under the defined results path.
Args:
nnet : (List) A list containing the Pytorch modules of the skip-filtering model.
seqlen : (int) Length of the time-sequence.
olap : (int) Overlap between spectrogram time-sequences
(to recover the missing information from the context information).
wsz : (int) Window size in samples.
N : (int) The FFT size.
hop : (int) Hop size in samples.
B : (int) Batch size.
"""
nnet[0].eval()
nnet[1].eval()
nnet[2].eval()
nnet[3].eval()
L = olap / 2
w = tf.hamming(wsz, True)
x, fs = Io.wavRead('results/test_files/test.wav', mono=True)
mx, px = tf.TimeFrequencyDecomposition.STFT(x, w, N, hop)
mx, px, _ = prepare_overlap_sequences(mx, px, mx, seqlen, olap, B)
vs_out = np.zeros((mx.shape[0], seqlen - olap, wsz), dtype=np.float32)
for batch in xrange(mx.shape[0] / B):
# Mixture to Singing voice
H_enc = nnet[0](mx[batch * B:(batch + 1) * B, :, :])
H_j_dec = it_infer.iterative_recurrent_inference(nnet[1],
H_enc,
criterion=None,
tol=1e-3,
max_iter=10)
vs_hat, mask = nnet[2](H_j_dec, mx[batch * B:(batch + 1) * B, :, :])
y_out = nnet[3](vs_hat)
vs_out[batch * B:(batch + 1) * B, :, :] = y_out.data.cpu().numpy()
vs_out.shape = (vs_out.shape[0] * vs_out.shape[1], wsz)
if olap == 1:
mx = np.ascontiguousarray(mx, dtype=np.float32)
px = np.ascontiguousarray(px, dtype=np.float32)
else:
mx = np.ascontiguousarray(mx[:, olap / 2:-olap / 2, :],
dtype=np.float32)
px = np.ascontiguousarray(px[:, olap / 2:-olap / 2, :],
dtype=np.float32)
mx.shape = (mx.shape[0] * mx.shape[1], wsz)
px.shape = (px.shape[0] * px.shape[1], wsz)
# Approximated sources
# Iterative G-L algorithm
for GLiter in range(10):
y_recb = tf.TimeFrequencyDecomposition.iSTFT(vs_out, px, wsz, hop,
True)
_, px = tf.TimeFrequencyDecomposition.STFT(y_recb,
tf.hamming(wsz,
True), N, hop)
x = x[olap / 2 * hop:]
Io.wavWrite(y_recb, 44100, 16, 'results/test_files/test_sv.wav')
Io.wavWrite(x[:len(y_recb)], 44100, 16, 'results/test_files/test_mix.wav')
return None
def get_data_shunit(current_set,
set_size,
wsz=2049,
N=4096,
hop=384,
T=100,
L=20,
B=16):
"""
Method to acquire training data. The STFT analysis is included.
Args:
current_set : (int) An integer denoting the current training set.
set_size : (int) The amount of files a set has.
wsz : (int) Window size in samples.
N : (int) The FFT size.
hop : (int) Hop size in samples.
T : (int) Length of the time-sequence.
L : (int) Number of context frames from the time-sequence.
B : (int) Batch size.
Returns:
ms_train : (3D Array) Mixture magnitude training data, for the current set.
vs_train : (3D Array) Singing Voice Activity Detector training data, for the current set.
"""
# Generate full paths for dev and test
dev_mixtures_list = sorted(os.listdir(mixtures_path + foldersList[0]))
dev_mixtures_list = [
mixtures_path + foldersList[0] + '/' + i for i in dev_mixtures_list
]
dev_sources_list = sorted(os.listdir(sources_path + foldersList[0]))
dev_sources_list = [
sources_path + foldersList[0] + '/' + i for i in dev_sources_list
]
# Current lists for training
c_train_slist = dev_sources_list[(current_set - 1) * set_size:current_set *
set_size]
c_train_mlist = dev_mixtures_list[(current_set - 1) *
set_size:current_set * set_size]
for index in range(len(c_train_mlist)):
# print('Reading:' + c_train_mlist[index])
# Reading
vox, _ = Io.wavRead(os.path.join(c_train_slist[index], keywords[3]),
mono=False)
mix, _ = Io.wavRead(os.path.join(c_train_mlist[index], keywords[4]),
mono=False)
# STFT Analysing
ms_seg, _ = tf.TimeFrequencyDecomposition.STFT(
0.5 * np.sum(mix, axis=-1), tf.hamming(wsz, True), N, hop)
vs_seg, _ = tf.TimeFrequencyDecomposition.STFT(
0.5 * np.sum(vox, axis=-1), tf.hamming(wsz, True), N, hop)
# Remove null frames
ms_seg = ms_seg[3:-3, :]
vs_seg = vs_seg[3:-3, :]
vs_mean = vs_seg.mean()
vs_median = np.median(vs_seg)
vs_threshold = 0.8 * vs_mean + 0.2 * vs_median
vs_seg[vs_seg > vs_threshold] = 1
vs_seg[vs_seg <= vs_threshold] = 0
# plt.hist(vs_seg.flatten(), bins=100)
# import matplotlib.pyplot as plt
# plt.imshow(vs_seg.T, cmap='hot', interpolation='nearest')
# plt.show()
# exit()
# Stack some spectrograms and fit
if index == 0:
ms_train = ms_seg
vs_train = vs_seg
else:
ms_train = np.vstack((ms_train, ms_seg))
vs_train = np.vstack((vs_train, vs_seg))
# Data preprocessing
# Freeing up some memory
ms_seg = None
vs_seg = None
# Learning the filtering process
mask = Fm(ms_train, vs_train, ms_train, [], [], alpha=1., method='IRM')
vs_train = mask()
vs_train *= 2.
vs_train = np.clip(vs_train, a_min=0., a_max=1.)
ms_train = np.clip(ms_train, a_min=0., a_max=1.)
mask = None
ms_train, vs_train, _ = prepare_overlap_sequences(ms_train, vs_train,
ms_train, T, L * 2, B)
# # vs_train[vs_train != 0] = 1
# print(vs_train.shape)
#
# # exit()
#
# # print(vs_train.mean())
# # print(np.median(vs_train))
# import matplotlib.pyplot as plt
# # vs_train = vs_train.sum(axis=2)
# print(vs_train.shape)
#
# # plt.hist(vs_train.flatten(), bins=100)
# plt.imshow(vs_train[0, :, :], cmap='hot', interpolation='nearest')
# plt.show()
#
# # print(np.amax(vs_train))
# # print(np.amin(vs_train))
#
#
# # print('shunit')
# # vs_train = np.ceil(vs_train)
# # print(type(vs_train))
# # print(vs_train.mean())
# # print(np.unique(vs_train))
# exit()
return ms_train, vs_train