def prepare_loss_grad_batches(self, loss_grad_path, mel_spects, labels,
mdl_fn, criterion, blocklen, batchsize,
device):
def loss_grad(model_fn, x, label, criterion):
x = x.clone().detach().requires_grad_(True)
loss = criterion(model_fn(x), label)
grad, = torch.autograd.grad(loss, [x])
return grad.cpu().detach().numpy()
def compute_grads(spect, label, mdl_fn, criterion, blocklen, batchsize,
device):
num_excerpts = len(spect) - blocklen + 1
excerpts = np.lib.stride_tricks.as_strided(
spect,
shape=(num_excerpts, blocklen, spect.shape[1]),
strides=(spect.strides[0], spect.strides[0], spect.strides[1]))
total_grads = []
for pos in range(0, num_excerpts, batchsize):
input_data = np.transpose(
excerpts[pos:pos + batchsize, :, :, np.newaxis],
(0, 3, 1, 2))
if (pos + batchsize > num_excerpts):
label_batch = label[pos:num_excerpts,
np.newaxis].astype(np.float32)
else:
label_batch = label[pos:pos + batchsize,
np.newaxis].astype(np.float32)
input_data_loss = input_data
grads = loss_grad(
mdl_fn,
torch.from_numpy(input_data_loss).to(
device).requires_grad_(True),
torch.from_numpy(label_batch).to(device), criterion)
total_grads.append(np.squeeze(grads))
return total_grads
grad = []
for spect, label, fn in zip(mel_spects, labels, self.filelist):
print(fn)
cache_fn = (loss_grad_path
and os.path.join(loss_grad_path, fn + '.npy'))
grad.append(
cached(cache_fn, compute_grads, spect, label, mdl_fn,
criterion, blocklen, 1, device))
#input(grad[0][0][0,:])
return grad
def prepare_audio_batches(self,
sample_rate,
frame_len,
fps,
blocklen,
batchsize,
batch_data=True):
spects = []
for fn in progress(self.filelist, 'File'):
cache_fn = (self.featuredir
and os.path.join(self.featuredir, fn + '.npy'))
spects.append(
cached(cache_fn, audio.read_ffmpeg,
os.path.join(self.datadir, 'audio', fn), sample_rate))
# - load and convert corresponding labels
print("Loading labels...")
labels = []
for fn, spect in zip(self.filelist, spects):
fn = os.path.join(self.datadir, 'labels',
fn.rsplit('.', 1)[0] + '.lab')
with io.open(fn) as f:
segments = [l.rstrip().split() for l in f if l.rstrip()]
segments = [(float(start), float(end), label == 'sing')
for start, end, label in segments]
timestamps = np.arange(len(spect)) / float(sample_rate)
labels.append(create_aligned_targets(segments, timestamps,
np.bool))
if (batch_data):
batches = augment.grab_random_audio_excerpts(
spects, labels, batchsize, sample_rate, frame_len, fps,
blocklen)
return batches
else:
return spects, labels
def prepare_batches(self,
sample_rate,
frame_len,
fps,
mel_bands,
mel_min,
mel_max,
blocklen,
batchsize,
batch_data=True):
bin_nyquist = frame_len // 2 + 1
bin_mel_max = bin_nyquist * 2 * mel_max // sample_rate
spects = []
for fn in progress(self.filelist, 'File'):
cache_fn = (self.featuredir
and os.path.join(self.featuredir, fn + '.npy'))
spects.append(
cached(cache_fn, audio.extract_spect,
os.path.join(self.datadir, 'audio', fn), sample_rate,
frame_len, fps))
# - load and convert corresponding labels
print("Loading labels...")
labels = []
for fn, spect in zip(self.filelist, spects):
fn = os.path.join(self.datadir, 'labels',
fn.rsplit('.', 1)[0] + '.lab')
with io.open(fn) as f:
segments = [l.rstrip().split() for l in f if l.rstrip()]
segments = [(float(start), float(end), label == 'sing')
for start, end, label in segments]
timestamps = np.arange(len(spect)) / float(fps)
labels.append(create_aligned_targets(segments, timestamps,
np.bool))
if (self.input_type == 'stft'):
print('Create dataset with stft output')
if (batch_data):
batches = augment.grab_random_excerpts(spects, labels,
batchsize, blocklen)
return batches
else:
return spects, labels
if (self.input_type == 'mel_spects'
or self.input_type == 'mel_spects_norm'):
# - prepare mel filterbank
filterbank = audio.create_mel_filterbank(sample_rate, frame_len,
mel_bands, mel_min,
mel_max)
filterbank = filterbank[:bin_mel_max].astype(floatX)
# - precompute mel spectra, if needed, otherwise just define a generator
mel_spects = (np.log(
np.maximum(np.dot(spect[:, :bin_mel_max], filterbank), 1e-7))
for spect in spects)
if not self.augment:
mel_spects = list(mel_spects)
del spects
# - load mean/std or compute it, if not computed yet
meanstd_file = os.path.join(os.path.dirname(__file__),
'%s_meanstd.npz' % self.dataset)
try:
with np.load(meanstd_file) as f:
mean = f['mean']
std = f['std']
except (IOError, KeyError):
print("Computing mean and standard deviation...")
mean, std = znorm.compute_mean_std(mel_spects)
np.savez(meanstd_file, mean=mean, std=std)
mean = mean.astype(floatX)
istd = np.reciprocal(std).astype(floatX)
#print(meanstd_file,mean, istd)
#input('wait')
#print(znorm.compute_mean_std(mel_spects))
#input('wait')
# - prepare training data generator
print("Preparing training data feed...")
if not self.augment:
# Without augmentation, we just precompute the normalized mel spectra
# and create a generator that returns mini-batches of random excerpts
if (self.input_type == 'mel_spects'):
print('Creating batches of mel spects without znorm')
if (batch_data):
batches = augment.grab_random_excerpts(
mel_spects, labels, batchsize, blocklen)
else:
pad = np.tile((np.log(1e-7).astype(floatX)),
(blocklen // 2, 80))
mel_spects = (np.concatenate((pad, spect, pad), axis=0)
for spect in mel_spects)
mel_spects = list(mel_spects)
return mel_spects, labels
elif (self.input_type == 'mel_spects_norm'):
print('Creating batches of mel spects with znorm')
#mel_spects = [(spect - mean) * istd for spect in mel_spects]
if (batch_data):
mel_spects = [(spect - mean) * istd
for spect in mel_spects]
batches = augment.grab_random_excerpts(
mel_spects, labels, batchsize, blocklen)
else:
#pad = np.tile((np.log(1e-7)-mean)*istd, (blocklen//2, 1))
#input(pad)
pad = np.tile((np.log(1e-7).astype(floatX)),
(blocklen // 2, 80))
mel_spects = (np.concatenate((pad, spect, pad), axis=0)
for spect in mel_spects)
mel_spects = [(spect - mean) * istd
for spect in mel_spects]
#input(mean.shape)
#mel_spects = [(spect - np.zeros(80)) * np.ones(80) for spect in mel_spects]
mel_spects = list(mel_spects)
return mel_spects, labels
else:
print('Creating batches of stfts')
batches = augment.grab_random_excerpts(spects, labels,
batchsize, blocklen)
else:
# For time stretching and pitch shifting, it pays off to preapply the
# spline filter to each input spectrogram, so it does not need to be
# applied to each mini-batch later.
spline_order = cfg['spline_order']
if spline_order > 1:
from scipy.ndimage import spline_filter
spects = [
spline_filter(spect, spline_order).astype(floatX)
for spect in spects
]
# We define a function to create the mini-batch generator. This allows
# us to easily create multiple generators for multithreading if needed.
def create_datafeed(spects, labels):
# With augmentation, as we want to apply random time-stretching,
# we request longer excerpts than we finally need to return.
max_stretch = cfg['max_stretch']
batches = augment.grab_random_excerpts(
spects,
labels,
batchsize=batchsize,
frames=int(blocklen / (1 - max_stretch)))
# We wrap the generator in another one that applies random time
# stretching and pitch shifting, keeping a given number of frames
# and bins only.
max_shift = cfg['max_shift']
batches = augment.apply_random_stretch_shift(
batches,
max_stretch,
max_shift,
keep_frames=blocklen,
keep_bins=bin_mel_max,
order=spline_order,
prefiltered=True)
# We transform the excerpts to mel frequency and log magnitude.
batches = augment.apply_filterbank(batches, filterbank)
batches = augment.apply_logarithm(batches)
# We apply random frequency filters
max_db = cfg['max_db']
batches = augment.apply_random_filters(batches,
filterbank,
mel_max,
max_db=max_db)
# We apply normalization
batches = augment.apply_znorm(batches, mean, istd)
return batches
# We start the mini-batch generator and augmenter in one or more
# background threads or processes (unless disabled).
bg_threads = cfg['bg_threads']
bg_processes = cfg['bg_processes']
if not bg_threads and not bg_processes:
# no background processing: just create a single generator
batches = create_datafeed(spects, labels)
elif bg_threads:
# multithreading: create a separate generator per thread
batches = augment.generate_in_background(
[
create_datafeed(spects, labels)
for _ in range(bg_threads)
],
num_cached=bg_threads * 5)
elif bg_processes:
# multiprocessing: single generator is forked along with processes
batches = augment.generate_in_background(
[create_datafeed(spects, labels)] * bg_processes,
num_cached=bg_processes * 25,
in_processes=True)
return batches
def main():
print(torch.cuda.is_available())
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# parse command line
parser = opts_parser()
options = parser.parse_args()
modelfile = options.modelfile
cfg = {}
for fn in options.vars:
cfg.update(config.parse_config_file(fn))
cfg.update(config.parse_variable_assignments(options.var))
outfile = options.outfile
sample_rate = cfg['sample_rate']
frame_len = cfg['frame_len']
fps = cfg['fps']
mel_bands = cfg['mel_bands']
mel_min = cfg['mel_min']
mel_max = cfg['mel_max']
blocklen = cfg['blocklen']
batchsize = cfg['batchsize']
bin_nyquist = frame_len // 2 + 1
bin_mel_max = bin_nyquist * 2 * mel_max // sample_rate
# prepare dataset
print("Preparing data reading...")
datadir = os.path.join(os.path.dirname(__file__), os.path.pardir,
'datasets', options.dataset)
# - load filelist
with io.open(os.path.join(datadir, 'filelists', 'valid')) as f:
filelist = [l.rstrip() for l in f if l.rstrip()]
with io.open(os.path.join(datadir, 'filelists', 'test')) as f:
filelist += [l.rstrip() for l in f if l.rstrip()]
# - create generator for spectra
spects = (cached(
options.cache_spectra
and os.path.join(options.cache_spectra, fn + '.npy'),
audio.extract_spect, os.path.join(datadir, 'audio',
fn), sample_rate, frame_len, fps)
for fn in filelist)
# - pitch-shift if needed
if options.pitchshift:
import scipy.ndimage
spline_order = 2
spects = (scipy.ndimage.affine_transform(
spect, (1, 1 / (1 + options.pitchshift / 100.)),
output_shape=(len(spect), mel_max),
order=spline_order) for spect in spects)
# - prepare mel filterbank
filterbank = audio.create_mel_filterbank(sample_rate, frame_len, mel_bands,
mel_min, mel_max)
filterbank = filterbank[:bin_mel_max].astype(floatX)
# - define generator for mel spectra
spects = (np.log(
np.maximum(np.dot(spect[:, :bin_mel_max], filterbank), 1e-7))
for spect in spects)
# - load mean/std
meanstd_file = os.path.join(os.path.dirname(__file__),
'%s_meanstd.npz' % options.dataset)
with np.load(meanstd_file) as f:
mean = f['mean']
std = f['std']
mean = mean.astype(floatX)
istd = np.reciprocal(std).astype(floatX)
# - define generator for Z-scoring
spects = ((spect - mean) * istd for spect in spects)
# - define generator for silence-padding
pad = np.tile((np.log(1e-7) - mean) * istd, (blocklen // 2, 1))
spects = (np.concatenate((pad, spect, pad), axis=0) for spect in spects)
# - we start the generator in a background thread (not required)
spects = augment.generate_in_background([spects], num_cached=1)
mdl = model.CNNModel()
mdl.load_state_dict(torch.load(modelfile))
mdl.to(device)
mdl.eval()
# run prediction loop
print("Predicting:")
predictions = []
for spect in progress(spects, total=len(filelist), desc='File '):
# naive way: pass excerpts of the size used during training
# - view spectrogram memory as a 3-tensor of overlapping excerpts
num_excerpts = len(spect) - blocklen + 1
excerpts = np.lib.stride_tricks.as_strided(
spect,
shape=(num_excerpts, blocklen, spect.shape[1]),
strides=(spect.strides[0], spect.strides[0], spect.strides[1]))
# - pass mini-batches through the network and concatenate results
preds = np.vstack(
mdl(
torch.from_numpy(
np.transpose(
excerpts[pos:pos + batchsize, :, :, np.newaxis], (
0, 3, 1, 2))).to(device)).cpu().detach().numpy()
for pos in range(0, num_excerpts, batchsize))
predictions.append(preds)
# save predictions
print("Saving predictions")
np.savez(outfile, **{fn: pred for fn, pred in zip(filelist, predictions)})
def main():
# parse command line
parser = opts_parser()
options = parser.parse_args()
modelfile = options.modelfile
sample_rate = 22050
frame_len = 1024
fps = 70
mel_bands = 80
mel_min = 27.5
mel_max = 8000
blocklen = 115
batchsize = 32
bin_nyquist = frame_len // 2 + 1
bin_mel_max = bin_nyquist * 2 * mel_max // sample_rate
# prepare dataset
datadir = os.path.join(os.path.dirname(__file__),
os.path.pardir, 'datasets', options.dataset)
# - load filelist
with io.open(os.path.join(datadir, 'filelists', 'train')) as f:
filelist = [l.rstrip() for l in f if l.rstrip()]
# - compute spectra
print("Computing%s spectra..." %
(" or loading" if options.cache_spectra else ""))
spects = []
for fn in progress(filelist, 'File '):
cache_fn = (options.cache_spectra and
os.path.join(options.cache_spectra, fn + '.npy'))
spects.append(cached(cache_fn,
audio.extract_spect,
os.path.join(datadir, 'audio', fn),
sample_rate, frame_len, fps))
# - load and convert corresponding labels
print("Loading labels...")
labels = []
for fn, spect in zip(filelist, spects):
fn = os.path.join(datadir, 'labels', fn.rsplit('.', 1)[0] + '.lab')
with io.open(fn) as f:
segments = [l.rstrip().split() for l in f if l.rstrip()]
segments = [(float(start), float(end), label == 'sing')
for start, end, label in segments]
timestamps = np.arange(len(spect)) / float(fps)
labels.append(create_aligned_targets(segments, timestamps, np.bool))
# - prepare mel filterbank
filterbank = audio.create_mel_filterbank(sample_rate, frame_len, mel_bands,
mel_min, mel_max)
filterbank = filterbank[:bin_mel_max].astype(floatX)
# - precompute mel spectra, if needed, otherwise just define a generator
mel_spects = (np.log(np.maximum(np.dot(spect[:, :bin_mel_max], filterbank),
1e-7))
for spect in spects)
if not options.augment:
mel_spects = list(mel_spects)
del spects
# - load mean/std or compute it, if not computed yet
meanstd_file = os.path.join(os.path.dirname(__file__),
'%s_meanstd.npz' % options.dataset)
try:
with np.load(meanstd_file) as f:
mean = f['mean']
std = f['std']
except (IOError, KeyError):
print("Computing mean and standard deviation...")
mean, std = znorm.compute_mean_std(mel_spects)
np.savez(meanstd_file, mean=mean, std=std)
mean = mean.astype(floatX)
istd = np.reciprocal(std).astype(floatX)
# - prepare training data generator
print("Preparing training data feed...")
if not options.augment:
# Without augmentation, we just precompute the normalized mel spectra
# and create a generator that returns mini-batches of random excerpts
mel_spects = [(spect - mean) * istd for spect in mel_spects]
batches = augment.grab_random_excerpts(
mel_spects, labels, batchsize, blocklen)
else:
# For time stretching and pitch shifting, it pays off to preapply the
# spline filter to each input spectrogram, so it does not need to be
# applied to each mini-batch later.
spline_order = 2
if spline_order > 1:
from scipy.ndimage import spline_filter
spects = [spline_filter(spect, spline_order).astype(floatX)
for spect in spects]
# We define a function to create the mini-batch generator. This allows
# us to easily create multiple generators for multithreading if needed.
def create_datafeed(spects, labels):
# With augmentation, as we want to apply random time-stretching,
# we request longer excerpts than we finally need to return.
max_stretch = .3
batches = augment.grab_random_excerpts(
spects, labels, batchsize=batchsize,
frames=int(blocklen / (1 - max_stretch)))
# We wrap the generator in another one that applies random time
# stretching and pitch shifting, keeping a given number of frames
# and bins only.
max_shift = .3
batches = augment.apply_random_stretch_shift(
batches, max_stretch, max_shift,
keep_frames=blocklen, keep_bins=bin_mel_max,
order=spline_order, prefiltered=True)
# We transform the excerpts to mel frequency and log magnitude.
batches = augment.apply_filterbank(batches, filterbank)
batches = augment.apply_logarithm(batches)
# We apply random frequency filters
batches = augment.apply_random_filters(batches, filterbank,
mel_max, max_db=10)
# We apply normalization
batches = augment.apply_znorm(batches, mean, istd)
return batches
# We start the mini-batch generator and augmenter in one or more
# background threads or processes (unless disabled).
bg_threads = 3
bg_processes = 0
if not bg_threads and not bg_processes:
# no background processing: just create a single generator
batches = create_datafeed(spects, labels)
elif bg_threads:
# multithreading: create a separate generator per thread
batches = augment.generate_in_background(
[create_datafeed(spects, labels)
for _ in range(bg_threads)],
num_cached=bg_threads * 5)
elif bg_processes:
# multiprocessing: single generator is forked along with processes
batches = augment.generate_in_background(
[create_datafeed(spects, labels)] * bg_processes,
num_cached=bg_processes * 25,
in_processes=True)
print("Preparing training function...")
# instantiate neural network
input_var = T.tensor3('input')
inputs = input_var.dimshuffle(0, 'x', 1, 2) # insert "channels" dimension
network = model.architecture(inputs, (None, 1, blocklen, mel_bands))
# create cost expression
target_var = T.vector('targets')
targets = (0.02 + 0.96 * target_var) # map 0 -> 0.02, 1 -> 0.98
targets = targets.dimshuffle(0, 'x') # turn into column vector
outputs = lasagne.layers.get_output(network, deterministic=False)
cost = T.mean(lasagne.objectives.binary_crossentropy(outputs, targets))
# prepare and compile training function
params = lasagne.layers.get_all_params(network, trainable=True)
initial_eta = 0.01
eta_decay = 0.85
momentum = 0.95
eta = theano.shared(lasagne.utils.floatX(initial_eta))
updates = lasagne.updates.nesterov_momentum(cost, params, eta, momentum)
print("Compiling training function...")
train_fn = theano.function([input_var, target_var], cost, updates=updates)
# run training loop
print("Training:")
epochs = 20
epochsize = 2000
batches = iter(batches)
for epoch in range(epochs):
err = 0
for batch in progress(
range(epochsize), min_delay=.5,
desc='Epoch %d/%d: Batch ' % (epoch + 1, epochs)):
err += train_fn(*next(batches))
if not np.isfinite(err):
print("\nEncountered NaN loss in training. Aborting.")
sys.exit(1)
print("Train loss: %.3f" % (err / epochsize))
eta.set_value(eta.get_value() * lasagne.utils.floatX(eta_decay))
# save final network
print("Saving final model")
np.savez(modelfile, **{'param%d' % i: p for i, p in enumerate(
lasagne.layers.get_all_param_values(network))})
def main():
# parse the command line arguments
parser = utils.argument_parser()
args = parser.parse_args()
print("-------------------------------")
print("classifier:%s" % args.classifier)
print("inverter:%s" % args.inverter)
print("dataset_path:%s" % args.dataset_path)
print("dataset name:%s" % args.dataset)
print("results path:%s" % args.results_dir)
print("inverting from: %s" % args.layer)
print("-------------------------------")
# default parameters
sample_rate = 22050
frame_len = 1024
fps = 70
mel_bands = 80
mel_min = 27.5
mel_max = 8000
blocklen = 115
batchsize = 32
start_offset = 10 # secs
end_offset = 20 # secs
bin_nyquist = frame_len // 2 + 1
bin_mel_max = bin_nyquist * 2 * mel_max // sample_rate
# prepare dataset
datadir = os.path.join(os.path.dirname(__file__), args.dataset_path,
'datasets', args.dataset)
# load filelist
with io.open(os.path.join(datadir, 'filelists', 'test')) as f:
filelist = [l.rstrip() for l in f if l.rstrip()]
# compute spectra
print("Computing%s spectra..." %
(" or loading" if args.cache_spectra else ""))
spects = [
] # list of tuples, where each tuple has magnitude and phase information for one audio file
for fn in progress(filelist, 'File '):
cache_fn = (args.cache_spectra
and os.path.join(args.cache_spectra, fn + '.npy'))
spects.append(
cached(cache_fn, audio.extract_spect,
os.path.join(datadir, 'audio', fn), sample_rate, frame_len,
fps))
# prepare mel filterbank
filterbank = audio.create_mel_filterbank(sample_rate, frame_len, mel_bands,
mel_min, mel_max)
filterbank = filterbank[:bin_mel_max].astype(floatX)
# precompute mel spectra, if needed, otherwise just define a generator
mel_spects = (np.log(
np.maximum(np.dot(spect[:, :bin_mel_max], filterbank), 1e-7))
for spect in spects)
# load mean/std or compute it, if not computed yet
meanstd_file = os.path.join(os.path.dirname(__file__),
'%s_meanstd.npz' % args.dataset)
with np.load(meanstd_file) as f:
mean = f['mean']
std = f['std']
mean = mean.astype(floatX)
istd = np.reciprocal(std).astype(floatX)
print("Preparing training data feed...")
# normalised mel spects, without data augmentation
mel_spects = [(spect - mean) * istd for spect in mel_spects]
# we create two theano functions
# the first one uses pre-trained classifier to generate features and predictions
# the second one uses pre-trained inverter to generate mel spectrograms from input features
# classifier (discriminator) model
input_var = T.tensor3('input')
inputs = input_var.dimshuffle(
0, 'x', 1, 2
) # insert "channels" dimension, changes a 32 x 115 x 80 input to 32 x 1 x 115 x 80 input which is fed to the CNN
network = model.architecture(inputs, (None, 1, blocklen, mel_bands))
# load saved weights
with np.load(args.classifier) as f:
lasagne.layers.set_all_param_values(
network['fc9'], [f['param%d' % i] for i in range(len(f.files))])
# create output expression
outputs_score = lasagne.layers.get_output(network[args.layer],
deterministic=True)
outputs_pred = lasagne.layers.get_output(network['fc9'],
deterministic=True)
# prepare and compile prediction function
print("Compiling classifier function...")
pred_fn_score = theano.function([input_var],
outputs_score,
allow_input_downcast=True)
pred_fn = theano.function([input_var],
outputs_pred,
allow_input_downcast=True)
# inverter (generator) model
if (args.layer == 'fc8') or (args.layer == 'fc7'):
input_var_deconv = T.matrix('input_var_deconv')
else:
input_var_deconv = T.tensor4('input_var_deconv')
# inverter (generator) model
if (args.layer == 'fc8'):
gen_network = upconv.architecture_upconv_fc8(
input_var_deconv,
(batchsize, lasagne.layers.get_output_shape(
network[args.layer])[1]))
elif args.layer == 'fc7':
gen_network = upconv.architecture_upconv_fc7(
input_var_deconv,
(batchsize, lasagne.layers.get_output_shape(
network[args.layer])[1]))
elif args.layer == 'mp6':
gen_network = upconv.architecture_upconv_mp6(
input_var_deconv,
(batchsize, lasagne.layers.get_output_shape(
network[args.layer])[1],
lasagne.layers.get_output_shape(network[args.layer])[2],
lasagne.layers.get_output_shape(network[args.layer])[3]),
args.n_conv_layers, args.n_conv_filters)
elif args.layer == 'conv5':
gen_network = upconv.architecture_upconv_conv5(
input_var_deconv,
(batchsize, lasagne.layers.get_output_shape(
network[args.layer])[1],
lasagne.layers.get_output_shape(network[args.layer])[2],
lasagne.layers.get_output_shape(network[args.layer])[3]),
args.n_conv_layers, args.n_conv_filters)
elif args.layer == 'conv4':
gen_network = upconv.architecture_upconv_conv4(
input_var_deconv,
(batchsize, lasagne.layers.get_output_shape(
network[args.layer])[1],
lasagne.layers.get_output_shape(network[args.layer])[2],
lasagne.layers.get_output_shape(network[args.layer])[3]),
args.n_conv_layers, args.n_conv_filters)
elif args.layer == 'mp3':
gen_network = upconv.architecture_upconv_mp3(
input_var_deconv,
(batchsize, lasagne.layers.get_output_shape(
network[args.layer])[1],
lasagne.layers.get_output_shape(network[args.layer])[2],
lasagne.layers.get_output_shape(network[args.layer])[3]),
args.n_conv_layers, args.n_conv_filters)
elif args.layer == 'conv2':
gen_network = upconv.architecture_upconv_conv2(
input_var_deconv,
(batchsize, lasagne.layers.get_output_shape(
network[args.layer])[1],
lasagne.layers.get_output_shape(network[args.layer])[2],
lasagne.layers.get_output_shape(network[args.layer])[3]),
args.n_conv_layers, args.n_conv_filters)
else:
gen_network = upconv.architecture_upconv_conv1(
input_var_deconv,
(batchsize, lasagne.layers.get_output_shape(
network[args.layer])[1],
lasagne.layers.get_output_shape(network[args.layer])[2],
lasagne.layers.get_output_shape(network[args.layer])[3]),
args.n_conv_layers, args.n_conv_filters)
# load saved weights
with np.load(args.inverter) as f:
lasagne.layers.set_all_param_values(
gen_network, [f['param%d' % i] for i in range(len(f.files))])
# create cost expression
outputs = lasagne.layers.get_output(gen_network, deterministic=True)
print("Compiling inverter function...")
test_fn = theano.function([input_var_deconv],
outputs,
allow_input_downcast=True)
# instance-based feature inversion
# (1) pick a file from a dataset (e.g., dataset: Jamendo test) (2) select a time index to read the instance
file_idx = np.arange(0, len(filelist))
hop_size = sample_rate / fps # samples
for file_instance in file_idx:
print("<<<<Analysis for the file: %d>>>>" % (file_instance + 1))
time_idx = np.random.randint(
start_offset, end_offset, 1
)[0] # provides a random integer start position between start and end offsets
# generate excerpts for the selected file_idx
# excerpts is a 3-d array of shape: num_excerpts x blocklen x mel_spects_dimensions
num_excerpts = len(mel_spects[file_instance]) - blocklen + 1
print("Number of excerpts in the file :%d" % num_excerpts)
excerpts = np.lib.stride_tricks.as_strided(
mel_spects[file_instance],
shape=(num_excerpts, blocklen, mel_spects[file_instance].shape[1]),
strides=(mel_spects[file_instance].strides[0],
mel_spects[file_instance].strides[0],
mel_spects[file_instance].strides[1]))
# convert the time_idx to the excerpt index
excerpt_idx = int(np.round((time_idx * sample_rate) / (hop_size)))
print("Time_idx: %f secs, Excerpt_idx: %d" % (time_idx, excerpt_idx))
if ((excerpt_idx + batchsize) > num_excerpts):
print(
"------------------Number of excerpts are less for file: %d--------------------"
% (file_instance + 1))
break
# generating feature representations for the select excerpt.
# CAUTION: Need to feed mini-batch to pre-trained model, so (mini_batch-1) following excerpts are also fed, but are not analysed
# classifier can have less than minibatch data, but the inverter needs a batch of data to make prediction (comes from how the inverter was trained)
scores = pred_fn_score(excerpts[excerpt_idx:excerpt_idx + batchsize])
#print("Feature"),
#print(scores[file_idx])
predictions = pred_fn(excerpts[excerpt_idx:excerpt_idx + batchsize])
#print("Prediction:%f" %(predictions[0][0]))
mel_predictions = np.squeeze(
test_fn(scores), axis=1
) # mel_predictions is a 3-d array of shape batch_size x blocklen x n_mels
# saves plots for the input Mel spectrogram and its inverted representation
# all plots are normalised in [0, 1] range
plots.plot_figures(utils.normalise(excerpts[excerpt_idx]),
utils.normalise(mel_predictions[0]),
predictions[0][0], file_instance, excerpt_idx,
args.results_dir, args.layer)
def main():
# parse command line
parser = opts_parser()
options = parser.parse_args()
modelfile = options.modelfile
outfile = options.outfile
sample_rate = 22050
frame_len = 1024
fps = 70
mel_bands = 80
mel_min = 27.5
mel_max = 8000
blocklen = 115
batchsize = 32
bin_nyquist = frame_len // 2 + 1
bin_mel_max = bin_nyquist * 2 * mel_max // sample_rate
# prepare dataset
print("Preparing data reading...")
datadir = os.path.join(os.path.dirname(__file__),
os.path.pardir, 'datasets', options.dataset)
# - load filelist
with io.open(os.path.join(datadir, 'filelists', 'valid')) as f:
filelist = [l.rstrip() for l in f if l.rstrip()]
with io.open(os.path.join(datadir, 'filelists', 'test')) as f:
filelist += [l.rstrip() for l in f if l.rstrip()]
# - create generator for spectra
spects = (cached(options.cache_spectra and
os.path.join(options.cache_spectra, fn + '.npy'),
audio.extract_spect,
os.path.join(datadir, 'audio', fn),
sample_rate, frame_len, fps)
for fn in filelist)
# - pitch-shift if needed
if options.pitchshift:
import scipy.ndimage
spline_order = 2
spects = (scipy.ndimage.affine_transform(
spect, (1, 1 / (1 + options.pitchshift / 100.)),
output_shape=(len(spect), mel_max),
order=spline_order)
for spect in spects)
# - prepare mel filterbank
filterbank = audio.create_mel_filterbank(sample_rate, frame_len, mel_bands,
mel_min, mel_max)
filterbank = filterbank[:bin_mel_max].astype(floatX)
# - define generator for mel spectra
spects = (np.log(np.maximum(np.dot(spect[:, :bin_mel_max], filterbank),
1e-7))
for spect in spects)
# - load mean/std
meanstd_file = os.path.join(os.path.dirname(__file__),
'%s_meanstd.npz' % options.dataset)
with np.load(meanstd_file) as f:
mean = f['mean']
std = f['std']
mean = mean.astype(floatX)
istd = np.reciprocal(std).astype(floatX)
# - define generator for Z-scoring
spects = ((spect - mean) * istd for spect in spects)
# - define generator for silence-padding
pad = np.tile((np.log(1e-7) - mean) * istd, (blocklen // 2, 1))
spects = (np.concatenate((pad, spect, pad), axis=0) for spect in spects)
# - we start the generator in a background thread (not required)
spects = augment.generate_in_background([spects], num_cached=1)
print("Preparing prediction function...")
# instantiate neural network
input_var = T.tensor3('input')
inputs = input_var.dimshuffle(0, 'x', 1, 2) # insert "channels" dimension
network = model.architecture(inputs, (None, 1, blocklen, mel_bands))
# load saved weights
with np.load(modelfile) as f:
lasagne.layers.set_all_param_values(
network, [f['param%d' % i] for i in range(len(f.files))])
# performant way: convert to fully-convolutional network
if not options.mem_use == 'low':
import model_to_fcn
network = model_to_fcn.model_to_fcn(network, allow_unlink=True)
# create output expression
outputs = lasagne.layers.get_output(network, deterministic=True)
# prepare and compile prediction function
print("Compiling prediction function...")
test_fn = theano.function([input_var], outputs)
# run prediction loop
print("Predicting:")
predictions = []
for spect in progress(spects, total=len(filelist), desc='File '):
if options.mem_use == 'high':
# fastest way: pass full spectrogram through network at once
preds = test_fn(spect[np.newaxis]) # insert batch dimension
elif options.mem_use == 'mid':
# performant way: pass spectrogram in equal chunks of up to one
# minute, taking care to overlap by `blocklen // 2` frames and to
# not pass a chunk shorter than `blocklen` frames
chunks = np.ceil(len(spect) / (fps * 60.))
hopsize = int(np.ceil(len(spect) / chunks))
chunksize = hopsize + blocklen - 1
preds = np.vstack(test_fn(spect[np.newaxis, pos:pos + chunksize])
for pos in range(0, len(spect), hopsize))
else:
# naive way: pass excerpts of the size used during training
# - view spectrogram memory as a 3-tensor of overlapping excerpts
num_excerpts = len(spect) - blocklen + 1
excerpts = np.lib.stride_tricks.as_strided(
spect, shape=(num_excerpts, blocklen, spect.shape[1]),
strides=(spect.strides[0], spect.strides[0], spect.strides[1]))
# - pass mini-batches through the network and concatenate results
preds = np.vstack(test_fn(excerpts[pos:pos + batchsize])
for pos in range(0, num_excerpts, batchsize))
predictions.append(preds)
if options.plot:
if spect.ndim == 3:
spect = spect[0] # remove channel axis
spect = spect[blocklen//2:-blocklen//2] # remove zero padding
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
ax1.imshow(spect.T[::-1], vmin=-3, cmap='hot', aspect='auto',
interpolation='nearest')
ax2.plot(preds)
ax2.set_ylim(0, 1.1)
plt.show()
# save predictions
print("Saving predictions")
np.savez(outfile, **{fn: pred for fn, pred in zip(filelist, predictions)})
def prepare_loss_grad_batches(self, loss_grad_path, mel_spects, labels,
mdl_fn, criterion, blocklen, batchsize,
device):
def loss_grad(model_fn, x, label, criterion):
x = x.clone().detach().requires_grad_(True)
loss = criterion(model_fn(x), label)
grad, = torch.autograd.grad(loss, [x])
return grad.cpu().detach().numpy()
def compute_grads(spect, label, mdl_fn, criterion, blocklen, batchsize,
device):
num_excerpts = len(spect) - blocklen + 1
excerpts = np.lib.stride_tricks.as_strided(
spect,
shape=(num_excerpts, blocklen, spect.shape[1]),
strides=(spect.strides[0], spect.strides[0], spect.strides[1]))
total_grads = []
for pos in range(0, num_excerpts, batchsize):
input_data = np.transpose(
excerpts[pos:pos + batchsize, :, :, np.newaxis],
(0, 3, 1, 2))
if (pos + batchsize > num_excerpts):
label_batch = label[pos:num_excerpts,
np.newaxis].astype(np.float32)
else:
label_batch = label[pos:pos + batchsize,
np.newaxis].astype(np.float32)
input_data_loss = input_data
grads = loss_grad(
mdl_fn,
torch.from_numpy(input_data_loss).to(
device).requires_grad_(True),
torch.from_numpy(label_batch).to(device), criterion)
total_grads.append(np.squeeze(grads))
return total_grads
grad = []
for spect, label, fn in zip(mel_spects, labels, self.filelist):
print(fn)
cache_fn = (loss_grad_path
and os.path.join(loss_grad_path, fn + '.npy'))
grad.append(
cached(cache_fn, compute_grads, spect, label, mdl_fn,
criterion, blocklen, 1, device))
#for spect, label, g in zip(mel_spects, labels, grad):
# num_excerpts = len(spect) - blocklen + 1
# print(num_excerpts)
# input(len(g))
# excerpts = np.lib.stride_tricks.as_strided(
# spect, shape=(num_excerpts, blocklen, spect.shape[1]),
# strides=(spect.strides[0], spect.strides[0], spect.strides[1]))
# total_grads = []
# for pos in range(0, num_excerpts, batchsize):
# input_data = np.transpose(excerpts[pos:pos + batchsize,:,:,np.newaxis],(0,3,1,2))
# if (pos+batchsize>num_excerpts):
# label_batch = label[blocklen//2+pos:blocklen//2+num_excerpts,
# np.newaxis].astype(np.float32)
# else:
# label_batch = label[blocklen//2+pos:blocklen//2+pos+batchsize,
# np.newaxis].astype(np.float32)
# input_data_loss = input_data
# grads = loss_grad(mdl_fn, torch.from_numpy(input_data_loss).to(device).requires_grad_(True), torch.from_numpy(label_batch).to(device), criterion)
# for i in range(batchsize):
# #print('not random')
# v1 = np.argsort(np.abs(grads[i]), axis=None)[-20:]
#
# v2 = np.argsort(np.abs(g[i+pos]),axis=None)[-20:]
# print(v2-v1)
#
# input('wait')
return grad
def main():
# parse command line
parser = opts_parser()
options = parser.parse_args()
modelfile = options.modelfile
outfile = options.outfile
# read configuration files and immediate settings
cfg = {}
if os.path.exists(modelfile + '.vars'):
options.vars.insert(1, modelfile + '.vars')
for fn in options.vars:
cfg.update(config.parse_config_file(fn))
cfg.update(config.parse_variable_assignments(options.var))
# read some settings into local variables
sample_rate = cfg['sample_rate']
frame_len = cfg['frame_len']
fps = cfg['fps']
mel_bands = cfg['mel_bands']
mel_min = cfg['mel_min']
mel_max = cfg['mel_max']
blocklen = cfg['blocklen']
batchsize = cfg['batchsize']
bin_nyquist = frame_len // 2 + 1
bin_mel_max = bin_nyquist * 2 * mel_max // sample_rate
# prepare dataset
print("Preparing data reading...")
datadir = os.path.join(os.path.dirname(__file__), os.path.pardir,
'datasets', options.dataset)
# - load filelist
filelist = []
for d in options.filelists.split(','):
with io.open(os.path.join(datadir, 'filelists', d)) as f:
filelist.extend(l.rstrip() for l in f if l.rstrip())
# - create generator for spectra
spects = (cached(
options.cache_spectra
and os.path.join(options.cache_spectra, fn + '.npy'),
audio.extract_spect, os.path.join(datadir, 'audio',
fn), sample_rate, frame_len, fps)
for fn in filelist)
# - pitch-shift if needed
if options.pitchshift:
import scipy.ndimage
spline_order = 2
spects = (scipy.ndimage.affine_transform(
spect, (1, 1 / (1 + options.pitchshift / 100.)),
output_shape=(len(spect), mel_max),
order=spline_order) for spect in spects)
# - define generator for cropped spectra
spects = (spect[:, :bin_mel_max] for spect in spects)
# - adjust loudness if needed
if options.loudness:
spects = (spect * float(10.**(options.loudness / 10.))
for spect in spects)
# - define generator for silence-padding
pad = np.zeros((blocklen // 2, bin_mel_max), dtype=floatX)
spects = (np.concatenate((pad, spect, pad), axis=0) for spect in spects)
# - we start the generator in a background thread (not required)
spects = augment.generate_in_background([spects], num_cached=1)
print("Preparing prediction function...")
# instantiate neural network
input_var = T.tensor3('input')
inputs = input_var.dimshuffle(0, 'x', 1, 2) # insert "channels" dimension
network = model.architecture(inputs, (None, 1, blocklen, bin_mel_max), cfg)
# load saved weights
with np.load(modelfile) as f:
lasagne.layers.set_all_param_values(
network, [f['param%d' % i] for i in range(len(f.files))])
# performant way: convert to fully-convolutional network
if not options.mem_use == 'low':
import model_to_fcn
network = model_to_fcn.model_to_fcn(network, allow_unlink=True)
# create output expression
outputs = lasagne.layers.get_output(network, deterministic=True)
# prepare and compile prediction function
print("Compiling prediction function...")
test_fn = theano.function([input_var], outputs)
# run prediction loop
print("Predicting:")
predictions = []
for spect in progress(spects, total=len(filelist), desc='File '):
if options.mem_use == 'high':
# fastest way: pass full spectrogram through network at once
preds = test_fn(spect[np.newaxis]) # insert batch dimension
elif options.mem_use == 'mid':
# performant way: pass spectrogram in equal chunks of up to one
# minute, taking care to overlap by `blocklen // 2` frames and to
# not pass a chunk shorter than `blocklen` frames
chunks = np.ceil(len(spect) / (fps * 60.))
hopsize = int(np.ceil(len(spect) / chunks))
chunksize = hopsize + blocklen - 1
preds = np.vstack(
test_fn(spect[np.newaxis, pos:pos + chunksize])
for pos in range(0, len(spect), hopsize))
else:
# naive way: pass excerpts of the size used during training
# - view spectrogram memory as a 3-tensor of overlapping excerpts
num_excerpts = len(spect) - blocklen + 1
excerpts = np.lib.stride_tricks.as_strided(
spect,
shape=(num_excerpts, blocklen, spect.shape[1]),
strides=(spect.strides[0], spect.strides[0], spect.strides[1]))
# - pass mini-batches through the network and concatenate results
preds = np.vstack(
test_fn(excerpts[pos:pos + batchsize])
for pos in range(0, num_excerpts, batchsize))
predictions.append(preds)
if options.plot:
if spect.ndim == 3:
spect = spect[0] # remove channel axis
spect = spect[blocklen // 2:-blocklen // 2] # remove zero padding
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
ax1.imshow(spect.T[::-1],
vmin=-3,
cmap='hot',
aspect='auto',
interpolation='nearest')
ax2.plot(preds)
ax2.set_ylim(0, 1.1)
plt.show()
# save predictions
print("Saving predictions")
data = dict(zip(filelist, predictions))
if outfile.endswith('.pkl'):
try:
import cPickle as pickle
except ImportError:
import pickle
with io.open(outfile, 'wb') as f:
pickle.dump(data, f, protocol=-1)
else:
np.savez(outfile, **data)
def prepare_audio(mean, istd, options):
"""
Reads input audio and creates Mel-spectrogram excerpts
of size 115 x 80 needed by the neural network model
"""
# default parameters from ISMIR 2015: Jan et. al.
sample_rate = 22050
frame_len = 1024
fps = 70
mel_bands = 80
mel_min = 27.5
mel_max = 8000
blocklen = 115
bin_nyquist = frame_len // 2 + 1
bin_mel_max = bin_nyquist * 2 * mel_max // sample_rate
# prepare dataset
print("Preparing data reading...")
datadir = os.path.join(os.path.dirname(__file__), 'dataset')
# - load filelist
with io.open(os.path.join(datadir, 'filelists', 'valid')) as f:
filelist = [l.rstrip() for l in f if l.rstrip()]
with io.open(os.path.join(datadir, 'filelists', 'test')) as f:
filelist += [l.rstrip() for l in f if l.rstrip()]
if not options.partial:
#duration and offset arguments have not use in the part of the code.
# - create generator for spectra
spects = (simplecache.cached(
options.cache_spectra
and os.path.join(options.cache_spectra, fn + '.npy'),
audio.extract_spect, os.path.join(datadir, 'audio',
fn), sample_rate, frame_len, fps)
for fn in filelist)
else:
# - create generator for spectra
spects = (simplecache.cached(
options.cache_spectra
and os.path.join(options.cache_spectra, fn + '.npy'),
audio.extract_spect_partial, os.path.join(datadir, 'audio', fn),
options.save_input, options.dump_path, sample_rate, frame_len, fps,
options.offset, options.duration) for fn in filelist)
if (options.transform == 'mel'):
# - prepare mel filterbank
filterbank = audio.create_mel_filterbank(sample_rate, frame_len,
mel_bands, mel_min, mel_max)
filterbank = filterbank[:bin_mel_max].astype(floatX)
# calculating and saving the pinv (80*bin_mel_max) for later use.
filterbank_pinv = linalg.pinv(
filterbank) # pseudo inv will automatically be of shape: 80 x 372
#filterbank_pinv = filterbank.T # 80 x 372
spects = (np.log(
np.maximum(np.dot(spect[:, :bin_mel_max], filterbank), 1e-7))
for spect in spects)
else:
spects = (np.log(np.maximum(spect, 1e-7)) for spect in spects)
filterbank_pinv = np.ones(
(mel_bands, bin_mel_max)
) # dummy of no use in this case. need to do as same code is used to return
# - define generator for Z-scoring
spects = ((spect - mean) * istd for spect in spects)
# - define generator for silence-padding
pad = np.tile((np.log(1e-7) - mean) * istd, (blocklen // 2, 1))
spects = (np.concatenate((pad, spect, pad), axis=0) for spect in spects)
# - we start the generator in a background thread (not required)
spects = augment.generate_in_background([spects], num_cached=1)
spectrum = [
] # list of 3d arrays.each 3d array for one audio file No. of excerpts x 115 x 80
# run prediction loop
print("Generating excerpts:")
for spect in progress.progress(spects, total=len(filelist), desc='File '):
# - view spectrogram memory as a 3-tensor of overlapping excerpts
num_excerpts = len(spect) - blocklen + 1
excerpts = np.lib.stride_tricks.as_strided(
spect,
shape=(num_excerpts, blocklen, spect.shape[1]),
strides=(spect.strides[0], spect.strides[0], spect.strides[1]))
spectrum.append(excerpts)
return spectrum, filterbank_pinv
def main():
# parse command line
parser = opts_parser()
options = parser.parse_args()
outdir = options.outdir
if options.load_spectra != 'memory' and not options.cache_spectra:
parser.error('option --load-spectra=%s requires --cache-spectra' %
options.load_spectra)
# read configuration files and immediate settings
cfg = {}
for fn in options.vars:
cfg.update(config.parse_config_file(fn))
cfg.update(config.parse_variable_assignments(options.var))
# read some settings into local variables
sample_rate = cfg['sample_rate']
frame_len = cfg['frame_len']
fps = cfg['fps']
mel_bands = cfg['mel_bands']
mel_min = cfg['mel_min']
mel_max = cfg['mel_max']
# prepare dataset
datadir = os.path.join(os.path.dirname(__file__), os.path.pardir,
'datasets', options.dataset)
# - load filelist
filelist = []
ranges = {}
for part in 'train', 'valid', 'test':
a = len(filelist)
with io.open(
os.path.join(datadir, 'filelists',
cfg.get('filelist.%s' % part, part))) as f:
filelist.extend(l.rstrip() for l in f if l.rstrip())
ranges[part] = slice(a, len(filelist))
# - compute spectra
print("Computing%s spectra..." %
(" or loading" if options.cache_spectra else ""))
spects = []
for fn in progress(filelist, 'File '):
cache_fn = (options.cache_spectra
and os.path.join(options.cache_spectra, fn + '.npy'))
spects.append(
cached(cache_fn,
audio.extract_spect,
os.path.join(datadir, 'audio', fn),
sample_rate,
frame_len,
fps,
loading_mode=options.load_spectra))
# - load and convert corresponding labels
print("Loading labels...")
labels = []
for fn, spect in zip(filelist, spects):
fn = os.path.join(datadir, 'labels', fn.rsplit('.', 1)[0] + '.lab')
with io.open(fn) as f:
segments = [l.rstrip().split() for l in f if l.rstrip()]
segments = [(float(start), float(end), label == 'sing')
for start, end, label in segments]
timestamps = np.arange(len(spect)) / float(fps)
labels.append(create_aligned_targets(segments, timestamps, np.bool))
# compute and save different variants of summarized magnitudes
print("Saving files...")
# - ground truth
outfile = os.path.join(outdir, '%s_gt.pkl' % options.dataset)
print(outfile)
with io.open(outfile, 'wb') as f:
pickle.dump({'labels': labels, 'splits': ranges}, f, protocol=-1)
# - summarized spectra
save_spectral_sums(
os.path.join(outdir, '%s_spect_sum.pkl' % options.dataset), spects)
# - summarized mel spectra
bank = audio.create_mel_filterbank(sample_rate, frame_len, mel_bands,
mel_min, mel_max).astype(np.float32)
spects = [np.dot(spect[:, ], bank) for spect in spects]
save_spectral_sums(
os.path.join(outdir, '%s_spect_mel_sum.pkl' % options.dataset), spects)
# - summarized log-mel spectra
spects = [np.log(np.maximum(1e-7, spect)) for spect in spects]
save_spectral_sums(
os.path.join(outdir, '%s_spect_mel_log_sum.pkl' % options.dataset),
spects)
# - summarized standardized log-mel spectra
m, s = znorm.compute_mean_std(spects[ranges['train']], axis=0)
spects = [((spect - m) / s).astype(np.float32) for spect in spects]
save_spectral_sums(
os.path.join(outdir, '%s_spect_mel_log_std_sum.pkl' % options.dataset),
spects)
def main():
# parse the command line arguments
parser = utils.argument_parser()
args = parser.parse_args()
print("-------------------------------")
print("classifier:%s" % args.classifier)
print("inverter:%s" % args.inverter)
print("dataset_path:%s" % args.dataset_path)
print("dataset name:%s" % args.dataset)
print("results path:%s" % args.results_dir)
print("quantitative analysis:%s" % args.quant_analysis)
print("mask inversion flag: %r" % args.mask_inv_flag)
print("plot quant results case: %r" % args.plot_quant_res)
print("-------------------------------")
# just plots the quantitative analysis results and exits
if args.plot_quant_res:
# jamendo results
exp_loss_jamendo_case1 = [
0, 6.48, 10.87, 13.33, 15.51, 19.15, 25.94, 37.56, 49.11, 56.85,
57.77
] #, 57.77]
exp_loss_jamendo_case2 = [
57.77, 59.19, 58.11, 51.81, 43.1, 31.87, 22.84, 15.51, 11.03, 5.86,
0.03
] #, 0]
rel_area_jamendo_case1 = [100, 96, 87, 77, 65, 53, 39, 26, 14, 4,
0] #, 0]
rel_area_jamendo_case2 = [0, 4, 13, 23, 35, 47, 61, 74, 86, 96,
100] #, 100]
exp_losses_jamendo = [exp_loss_jamendo_case1, exp_loss_jamendo_case2]
rel_areas_jamendo = [rel_area_jamendo_case1, rel_area_jamendo_case2]
# rwc results
exp_loss_rwc_case1 = [
0, 6.52, 10.9, 13.39, 15.87, 21.28, 30.92, 43.22, 53.41, 60.85,
63.66
] #, 63.66]
exp_loss_rwc_case2 = [
63.66, 64.5, 61.01, 52.55, 39.39, 26.27, 16.13, 9.55, 5.05, 2.26,
0.03
] #, 0]
rel_area_rwc_case1 = [100, 96, 87, 75, 61, 47, 33, 20, 10, 3, 0] #, 0]
rel_area_rwc_case2 = [0, 4, 13, 25, 39, 53, 67, 80, 90, 97,
100] #, 100]
exp_losses_rwc = [exp_loss_rwc_case1, exp_loss_rwc_case2]
rel_areas_rwc = [rel_area_rwc_case1, rel_area_rwc_case2]
plots.quant_eval(exp_losses_jamendo, rel_areas_jamendo, exp_losses_rwc,
rel_areas_rwc, args.results_dir)
exit(0)
# default parameters
sample_rate = 22050
frame_len = 1024
fps = 70
mel_bands = 80
mel_min = 27.5
mel_max = 8000
blocklen = 115
batchsize = 32
# single instance inversion/quantitative analysis parameters
preds_before = []
if not args.quant_analysis:
time_index = 10
masking_threshold = [0.7]
duration = 0 # no use
increment = 0.5
else:
preds_after = []
area_per_instance = []
result = []
start_offset = 5
end_offset = 20
duration = 200
increment = 0.5
masking_threshold = np.arange(0.0, 1.2, 0.1)
class_threshold = 0.66 # Calculated over Jamendo validation dataset
# printing and plotting parameters
df = True
#inp = []
#expns =[]
bin_nyquist = frame_len // 2 + 1
bin_mel_max = bin_nyquist * 2 * mel_max // sample_rate
# prepare dataset
datadir = os.path.join(os.path.dirname(__file__), args.dataset_path,
'datasets', args.dataset)
# load filelist
with io.open(os.path.join(datadir, 'filelists', 'test')) as f:
filelist = [l.rstrip() for l in f if l.rstrip()]
# compute spectra
print("Computing%s spectra..." %
(" or loading" if args.cache_spectra else ""))
spects = [
] # list of tuples, where each tuple has magnitude and phase information for one audio file
for fn in progress(filelist, 'File '):
cache_fn = (args.cache_spectra
and os.path.join(args.cache_spectra, fn + '.npy'))
spects.append(
cached(cache_fn, audio.extract_spect,
os.path.join(datadir, 'audio', fn), sample_rate, frame_len,
fps))
# prepare mel filterbank
filterbank = audio.create_mel_filterbank(sample_rate, frame_len, mel_bands,
mel_min, mel_max)
filterbank = filterbank[:bin_mel_max].astype(floatX)
# precompute mel spectra, if needed, otherwise just define a generator
mel_spects = (np.log(
np.maximum(np.dot(spect[:, :bin_mel_max], filterbank), 1e-7))
for spect in spects)
# load mean/std or compute it, if not computed yet
meanstd_file = os.path.join(os.path.dirname(__file__),
'%s_meanstd.npz' % args.dataset)
with np.load(meanstd_file) as f:
mean = f['mean']
std = f['std']
mean = mean.astype(floatX)
istd = np.reciprocal(std).astype(floatX)
print("Preparing training data feed...")
# normalised mel spects, without data augmentation
mel_spects = [(spect - mean) * istd for spect in mel_spects]
# we create two theano functions
# the first one uses pre-trained classifier to generate features and predictions
# the second one uses pre-trained inverter to generate mel spectrograms from input features
# classifier (discriminator) model
input_var = T.tensor3('input')
inputs = input_var.dimshuffle(
0, 'x', 1, 2
) # insert "channels" dimension, changes a 32 x 115 x 80 input to 32 x 1 x 115 x 80 input which is fed to the CNN
network = model.architecture(inputs, (None, 1, blocklen, mel_bands))
# load saved weights
with np.load(args.classifier) as f:
lasagne.layers.set_all_param_values(
network['fc9'], [f['param%d' % i] for i in range(len(f.files))])
# create output expression
outputs_score = lasagne.layers.get_output(network['fc8'],
deterministic=True)
outputs_pred = lasagne.layers.get_output(network['fc9'],
deterministic=True)
# prepare and compile prediction function
print("Compiling classifier function...")
pred_fn_score = theano.function([input_var],
outputs_score,
allow_input_downcast=True)
pred_fn = theano.function([input_var],
outputs_pred,
allow_input_downcast=True)
# inverter (generator) model
input_var_deconv = T.matrix('input_var_deconv')
# inverter (generator) model
gen_network = upconv.architecture_upconv_fc8(
input_var_deconv,
(batchsize, lasagne.layers.get_output_shape(network['fc8'])[1]))
# load saved weights
with np.load(args.inverter) as f:
lasagne.layers.set_all_param_values(
gen_network, [f['param%d' % i] for i in range(len(f.files))])
# create cost expression
outputs = lasagne.layers.get_output(gen_network, deterministic=True)
print("Compiling inverter function...")
test_fn = theano.function([input_var_deconv],
outputs,
allow_input_downcast=True)
# instance-based feature inversion
# (1) pick a file from a dataset (e.g., dataset: Jamendo test) (2) select a time index to read the instance
file_idx = np.arange(0, len(filelist))
hop_size = sample_rate / fps # samples
for mt in masking_threshold:
np.random.seed(0)
print("\n ++++++ Masking threshold: %f +++++\n " % (mt))
for file_instance in file_idx:
print("<<<<Analysis for the file: %d>>>>" % (file_instance + 1))
if args.quant_analysis:
time_idx = np.random.randint(
start_offset, end_offset, 1
)[0] # provides a random integer start position between start and end offsets
else:
time_idx = time_index
td = time_idx # no use for the single instance inversion case.
# generate excerpts for the selected file_idx
# excerpts is a 3-d array of shape: num_excerpts x blocklen x mel_spects_dimensions
num_excerpts = len(mel_spects[file_instance]) - blocklen + 1
print("Number of excerpts in the file :%d" % num_excerpts)
excerpts = np.lib.stride_tricks.as_strided(
mel_spects[file_instance],
shape=(num_excerpts, blocklen,
mel_spects[file_instance].shape[1]),
strides=(mel_spects[file_instance].strides[0],
mel_spects[file_instance].strides[0],
mel_spects[file_instance].strides[1]))
while (time_idx <= td + duration):
# convert the time_idx to the excerpt index
excerpt_idx = int(
np.round((time_idx * sample_rate) / (hop_size)))
print("Time_idx: %.2f secs, Excerpt_idx: %d" %
(time_idx, excerpt_idx))
if ((excerpt_idx + batchsize) > num_excerpts):
print(
"------------------Number of excerpts are less for file: %d--------------------"
% (file_instance + 1))
break
# generating feature representations for the select excerpt.
# CAUTION: Need to feed mini-batch to the pre-trained model, so (mini_batch-1) following excerpts are also fed, but are not analysed
# classifier can have less than minibatch data, but the inverter needs a batch of data to make prediction (comes from how the inverter was trained)
scores = pred_fn_score(excerpts[excerpt_idx:excerpt_idx +
batchsize])
#print("Feature"),
#print(scores[file_idx])
predictions = pred_fn(excerpts[excerpt_idx:excerpt_idx +
batchsize])
print("Prediction score for the excerpt without masking:%f" %
(predictions[0][0]))
preds_before.append(predictions[0][0])
mel_predictions = np.squeeze(
test_fn(scores), axis=1
) # mel_predictions is a 3-d array of shape batch_size x blocklen x n_mels
# normalising the inverted mel to create a map, and use the map to cut the section in the input mel
norm_inv = utils.normalise(mel_predictions[0])
norm_inv[norm_inv < mt] = 0 # Binary mask-----
norm_inv[norm_inv >= mt] = 1
if args.quant_analysis:
# calculate area
area = utils.area_calculation(norm_inv, debug_flag=df)
# reversing the mask to keep the portions that seem not useful for the current instance prediction
norm_inv, area = utils.invert_mask(
mask=norm_inv,
mask_inv_flag=args.mask_inv_flag,
area_mask=area,
debug_flag=df)
# masking out the input based on the mask created above
masked_input = np.zeros((batchsize, blocklen, mel_bands))
masked_input[0] = norm_inv * excerpts[excerpt_idx]
if args.quant_analysis:
# save the area enabled
area_per_instance.append(area)
# feed the updated input to regenerate prediction
# just changing the first input.
predictions = pred_fn(masked_input)
print(
"Predictions score for the excerpt after masking:%f\n"
% (predictions[0][0]))
preds_after.append(predictions[0][0])
if not args.quant_analysis: # save results
# saves plots for the input Mel spectrogram and its inverted representation
# all plots are normalised in [0, 1] range
plots.single_inst_inv(
utils.normalise(excerpts[excerpt_idx]),
utils.normalise(mel_predictions[0]), norm_inv,
utils.normalise(masked_input[0]), preds_before[0],
file_instance, excerpt_idx, args.results_dir, 'FC8')
time_idx += increment
# plotting figure 6.4 in thesis
#plots.input_mels()
# plotting figure 6.6 in thesis
'''inp.append(excerpts[excerpt_idx])
expns.append(masked_input[0])
preds_before.append(predictions[0][0])
plots.special_cases(utils.normalise(inp[0]), utils.normalise(expns[0]), utils.normalise(inp[1]), utils.normalise(expns[1]), preds_before[0], preds_before[1], file_instance, excerpt_idx, args.results_dir)'''
if args.quant_analysis:
res_tuple = utils.quant_result_analysis(preds_before,
preds_after,
area_per_instance,
mt,
class_threshold,
debug_flag=df)
result.append(res_tuple) # one result per threshold value
# clearing the lists for the next iteration
preds_before = []
preds_after = []
area_per_instance = []
if args.quant_analysis:
# save the quantitative analysis results
quant_result_columns = [
'threshold', 'total instances', 'total fails',
'explanation loss [%]', 'average area'
]
with open(args.results_dir + '/' + 'quant_analysis_result.csv',
'w') as fp:
results_writer = csv.writer(fp, delimiter=',')
results_writer.writerow(quant_result_columns)
for result_th in result:
results_writer.writerow(result_th)
def main():
# parse command line
parser = opts_parser()
options = parser.parse_args()
modelfile = options.modelfile
if options.load_spectra != 'memory' and not options.cache_spectra:
parser.error('option --load-spectra=%s requires --cache-spectra' %
options.load_spectra)
# read configuration files and immediate settings
cfg = {}
for fn in options.vars:
cfg.update(config.parse_config_file(fn))
cfg.update(config.parse_variable_assignments(options.var))
# read some settings into local variables
sample_rate = cfg['sample_rate']
frame_len = cfg['frame_len']
fps = cfg['fps']
mel_bands = cfg['mel_bands']
mel_min = cfg['mel_min']
mel_max = cfg['mel_max']
blocklen = cfg['blocklen']
batchsize = cfg['batchsize']
bin_nyquist = frame_len // 2 + 1
if cfg['filterbank'] == 'mel_learn':
bin_mel_max = bin_nyquist
else:
bin_mel_max = bin_nyquist * 2 * mel_max // sample_rate
# prepare dataset
datadir = os.path.join(os.path.dirname(__file__), os.path.pardir,
'datasets', options.dataset)
# - load filelist
with io.open(
os.path.join(datadir, 'filelists',
cfg.get('filelist.train', 'train'))) as f:
filelist = [l.rstrip() for l in f if l.rstrip()]
if options.validate:
with io.open(
os.path.join(datadir, 'filelists',
cfg.get('filelist.valid', 'valid'))) as f:
filelist_val = [l.rstrip() for l in f if l.rstrip()]
filelist.extend(filelist_val)
else:
filelist_val = []
# - compute spectra
print("Computing%s spectra..." %
(" or loading" if options.cache_spectra else ""))
spects = []
for fn in progress(filelist, 'File '):
cache_fn = (options.cache_spectra
and os.path.join(options.cache_spectra, fn + '.npy'))
spects.append(
cached(cache_fn,
audio.extract_spect,
os.path.join(datadir, 'audio', fn),
sample_rate,
frame_len,
fps,
loading_mode=options.load_spectra))
# - load and convert corresponding labels
print("Loading labels...")
labels = []
for fn, spect in zip(filelist, spects):
fn = os.path.join(datadir, 'labels', fn.rsplit('.', 1)[0] + '.lab')
with io.open(fn) as f:
segments = [l.rstrip().split() for l in f if l.rstrip()]
segments = [(float(start), float(end), label == 'sing')
for start, end, label in segments]
timestamps = np.arange(len(spect)) / float(fps)
labels.append(create_aligned_targets(segments, timestamps, np.bool))
# - split off validation data, if needed
if options.validate:
spects_val = spects[-len(filelist_val):]
spects = spects[:-len(filelist_val)]
labels_val = labels[-len(filelist_val):]
labels = labels[:-len(filelist_val)]
# - prepare training data generator
print("Preparing training data feed...")
if not options.augment:
# Without augmentation, we just create a generator that returns
# mini-batches of random excerpts
batches = augment.grab_random_excerpts(spects, labels, batchsize,
blocklen, bin_mel_max)
batches = augment.generate_in_background([batches], num_cached=15)
else:
# For time stretching and pitch shifting, it pays off to preapply the
# spline filter to each input spectrogram, so it does not need to be
# applied to each mini-batch later.
spline_order = cfg['spline_order']
if spline_order > 1 and options.load_spectra == 'memory':
from scipy.ndimage import spline_filter
spects = [
spline_filter(spect, spline_order).astype(floatX)
for spect in spects
]
prefiltered = True
else:
prefiltered = False
# We define a function to create the mini-batch generator. This allows
# us to easily create multiple generators for multithreading if needed.
def create_datafeed(spects, labels):
# With augmentation, as we want to apply random time-stretching,
# we request longer excerpts than we finally need to return.
max_stretch = cfg['max_stretch']
batches = augment.grab_random_excerpts(
spects,
labels,
batchsize=batchsize,
frames=int(blocklen / (1 - max_stretch)))
# We wrap the generator in another one that applies random time
# stretching and pitch shifting, keeping a given number of frames
# and bins only.
max_shift = cfg['max_shift']
batches = augment.apply_random_stretch_shift(
batches,
max_stretch,
max_shift,
keep_frames=blocklen,
keep_bins=bin_mel_max,
order=spline_order,
prefiltered=prefiltered)
# We apply random frequency filters
max_db = cfg['max_db']
batches = augment.apply_random_filters(batches, mel_max, max_db)
# We apply random loudness changes
max_loudness = cfg['max_loudness']
if max_loudness:
batches = augment.apply_random_loudness(batches, max_loudness)
return batches
# We start the mini-batch generator and augmenter in one or more
# background threads or processes (unless disabled).
bg_threads = cfg['bg_threads']
bg_processes = cfg['bg_processes']
if not bg_threads and not bg_processes:
# no background processing: just create a single generator
batches = create_datafeed(spects, labels)
elif bg_threads:
# multithreading: create a separate generator per thread
batches = augment.generate_in_background(
[create_datafeed(spects, labels) for _ in range(bg_threads)],
num_cached=bg_threads * 5)
elif bg_processes:
# multiprocessing: single generator is forked along with processes
batches = augment.generate_in_background(
[create_datafeed(spects, labels)] * bg_processes,
num_cached=bg_processes * 25,
in_processes=True)
print("Preparing training function...")
# instantiate neural network
input_var = T.tensor3('input')
inputs = input_var.dimshuffle(0, 'x', 1, 2) # insert "channels" dimension
network = model.architecture(inputs, (None, 1, blocklen, bin_mel_max), cfg)
print(
"- %d layers (%d with weights), %f mio params" %
(len(lasagne.layers.get_all_layers(network)),
sum(hasattr(l, 'W') for l in lasagne.layers.get_all_layers(network)),
lasagne.layers.count_params(network, trainable=True) / 1e6))
print("- weight shapes: %r" % [
l.W.get_value().shape for l in lasagne.layers.get_all_layers(network)
if hasattr(l, 'W') and hasattr(l.W, 'get_value')
])
# create cost expression
target_var = T.vector('targets')
targets = (0.02 + 0.96 * target_var) # map 0 -> 0.02, 1 -> 0.98
targets = targets.dimshuffle(0, 'x') # turn into column vector
outputs = lasagne.layers.get_output(network, deterministic=False)
cost = T.mean(lasagne.objectives.binary_crossentropy(outputs, targets))
if cfg.get('l2_decay', 0):
cost_l2 = lasagne.regularization.regularize_network_params(
network, lasagne.regularization.l2) * cfg['l2_decay']
else:
cost_l2 = 0
# prepare and compile training function
params = lasagne.layers.get_all_params(network, trainable=True)
initial_eta = cfg['initial_eta']
eta_decay = cfg['eta_decay']
eta_decay_every = cfg.get('eta_decay_every', 1)
patience = cfg.get('patience', 0)
trials_of_patience = cfg.get('trials_of_patience', 1)
patience_criterion = cfg.get(
'patience_criterion',
'valid_loss' if options.validate else 'train_loss')
momentum = cfg['momentum']
first_params = params[:cfg['first_params']]
first_params_eta_scale = cfg['first_params_eta_scale']
if cfg['learn_scheme'] == 'nesterov':
learn_scheme = lasagne.updates.nesterov_momentum
elif cfg['learn_scheme'] == 'momentum':
learn_scheme = lasagne.update.momentum
elif cfg['learn_scheme'] == 'adam':
learn_scheme = lasagne.updates.adam
else:
raise ValueError('Unknown learn_scheme=%s' % cfg['learn_scheme'])
eta = theano.shared(lasagne.utils.floatX(initial_eta))
if not first_params or first_params_eta_scale == 1:
updates = learn_scheme(cost + cost_l2, params, eta, momentum)
else:
grads = theano.grad(cost + cost_l2, params)
updates = learn_scheme(grads[len(first_params):],
params[len(first_params):], eta, momentum)
if first_params_eta_scale > 0:
updates.update(
learn_scheme(grads[:len(first_params)], first_params,
eta * first_params_eta_scale, momentum))
print("Compiling training function...")
train_fn = theano.function([input_var, target_var], cost, updates=updates)
# prepare and compile validation function, if requested
if options.validate:
print("Compiling validation function...")
import model_to_fcn
network_test = model_to_fcn.model_to_fcn(network, allow_unlink=False)
outputs_test = lasagne.layers.get_output(network_test,
deterministic=True)
cost_test = T.mean(
lasagne.objectives.binary_crossentropy(outputs_test, targets))
val_fn = theano.function([input_var, target_var],
[cost_test, outputs_test])
# run training loop
print("Training:")
epochs = cfg['epochs']
epochsize = cfg['epochsize']
batches = iter(batches)
if options.save_errors:
errors = []
if first_params and cfg['first_params_log']:
first_params_hist = []
if patience > 0:
best_error = np.inf
best_state = get_state(network, updates)
for epoch in range(epochs):
# actual training
err = 0
for batch in progress(range(epochsize),
min_delay=.5,
desc='Epoch %d/%d: Batch ' %
(epoch + 1, epochs)):
err += train_fn(*next(batches))
if not np.isfinite(err):
print("\nEncountered NaN loss in training. Aborting.")
sys.exit(1)
if first_params and cfg['first_params_log'] and (
batch % cfg['first_params_log'] == 0):
first_params_hist.append(
tuple(param.get_value() for param in first_params))
np.savez(
modelfile[:-4] + '.hist.npz', **{
'param%d' % i: param
for i, param in enumerate(zip(*first_params_hist))
})
# report training loss
print("Train loss: %.3f" % (err / epochsize))
if options.save_errors:
errors.append(err / epochsize)
# compute and report validation loss, if requested
if options.validate:
val_err = 0
preds = []
max_len = int(fps * cfg.get('val.max_len', 30))
for spect, label in zip(spects_val, labels_val):
# pick excerpt of val.max_len seconds in center of file
excerpt = slice(max(0, (len(spect) - max_len) // 2),
(len(spect) + max_len) // 2)
# crop to maximum length and required spectral bins
spect = spect[None, excerpt, :bin_mel_max]
# crop to maximum length and remove edges lost in the network
label = label[excerpt][blocklen // 2:-(blocklen // 2)]
e, pred = val_fn(spect, label)
val_err += e
preds.append((pred[:, 0], label))
print("Validation loss: %.3f" % (val_err / len(filelist_val)))
from eval import evaluate
_, results = evaluate(*zip(*preds))
print("Validation error: %.3f" % (1 - results['accuracy']))
if options.save_errors:
errors.append(val_err / len(filelist_val))
errors.append(1 - results['accuracy'])
# update learning rate and/or apply early stopping, if needed
if patience > 0:
if patience_criterion == 'train_loss':
cur_error = err / epochsize
elif patience_criterion == 'valid_loss':
cur_error = val_err / len(filelist_val)
elif patience_criterion == 'valid_error':
cur_error = 1 - results['accuracy']
if cur_error <= best_error:
best_error = cur_error
best_state = get_state(network, updates)
patience = cfg['patience']
else:
patience -= 1
if patience == 0:
if eta_decay_every == 'trial_of_patience' and eta_decay != 1:
eta.set_value(eta.get_value() *
lasagne.utils.floatX(eta_decay))
restore_state(network, updates, best_state)
patience = cfg['patience']
trials_of_patience -= 1
print("Lost patience (%d remaining trials)." %
trials_of_patience)
if trials_of_patience == 0:
break
if eta_decay_every != 'trial_of_patience' and eta_decay != 1 and \
(epoch + 1) % eta_decay_every == 0:
eta.set_value(eta.get_value() * lasagne.utils.floatX(eta_decay))
# save final network
print("Saving final model")
np.savez(
modelfile, **{
'param%d' % i: p
for i, p in enumerate(lasagne.layers.get_all_param_values(network))
})
with io.open(modelfile + '.vars', 'wb') as f:
f.writelines('%s=%s\n' % kv for kv in cfg.items())
if options.save_errors:
np.savez(modelfile[:-len('.npz')] + '.err.npz',
np.asarray(errors).reshape(epoch + 1, -1))
def main():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# parse command line
parser = opts_parser()
options = parser.parse_args()
modelfile = options.modelfile
cfg = {}
for fn in options.vars:
cfg.update(config.parse_config_file(fn))
cfg.update(config.parse_variable_assignments(options.var))
sample_rate = cfg['sample_rate']
frame_len = cfg['frame_len']
fps = cfg['fps']
mel_bands = cfg['mel_bands']
mel_min = cfg['mel_min']
mel_max = cfg['mel_max']
blocklen = cfg['blocklen']
batchsize = cfg['batchsize']
bin_nyquist = frame_len // 2 + 1
bin_mel_max = bin_nyquist * 2 * mel_max // sample_rate
# prepare dataset
datadir = os.path.join(os.path.dirname(__file__), os.path.pardir,
'datasets', options.dataset)
# - load filelist
with io.open(os.path.join(datadir, 'filelists', 'train')) as f:
filelist = [l.rstrip() for l in f if l.rstrip()]
if options.validate:
with io.open(os.path.join(datadir, 'filelists', 'valid')) as f:
filelist_val = [l.strip() for l in f if l.strip()]
filelist.extend(filelist_val)
else:
filelist_val = []
# - compute spectra
print("Computing%s spectra..." %
(" or loading" if options.cache_spectra else ""))
spects = []
for fn in progress(filelist, 'File '):
cache_fn = (options.cache_spectra
and os.path.join(options.cache_spectra, fn + '.npy'))
spects.append(
cached(cache_fn, audio.extract_spect,
os.path.join(datadir, 'audio', fn), sample_rate, frame_len,
fps))
# - load and convert corresponding labels
print("Loading labels...")
labels = []
for fn, spect in zip(filelist, spects):
fn = os.path.join(datadir, 'labels', fn.rsplit('.', 1)[0] + '.lab')
with io.open(fn) as f:
segments = [l.rstrip().split() for l in f if l.rstrip()]
segments = [(float(start), float(end), label == 'sing')
for start, end, label in segments]
timestamps = np.arange(len(spect)) / float(fps)
labels.append(create_aligned_targets(segments, timestamps, np.bool))
# - prepare mel filterbank
filterbank = audio.create_mel_filterbank(sample_rate, frame_len, mel_bands,
mel_min, mel_max)
filterbank = filterbank[:bin_mel_max].astype(floatX)
if options.validate:
spects_val = spects[-len(filelist_val):]
spects = spects[:-len(filelist_val)]
labels_val = labels[-len(filelist_val):]
labels = labels[:-len(filelist_val)]
# - precompute mel spectra, if needed, otherwise just define a generator
mel_spects = (np.log(
np.maximum(np.dot(spect[:, :bin_mel_max], filterbank), 1e-7))
for spect in spects)
if not options.augment:
mel_spects = list(mel_spects)
del spects
# - load mean/std or compute it, if not computed yet
meanstd_file = os.path.join(os.path.dirname(__file__),
'%s_meanstd.npz' % options.dataset)
try:
with np.load(meanstd_file) as f:
mean = f['mean']
std = f['std']
except (IOError, KeyError):
print("Computing mean and standard deviation...")
mean, std = znorm.compute_mean_std(mel_spects)
np.savez(meanstd_file, mean=mean, std=std)
mean = mean.astype(floatX)
istd = np.reciprocal(std).astype(floatX)
# - prepare training data generator
print("Preparing training data feed...")
if not options.augment:
# Without augmentation, we just precompute the normalized mel spectra
# and create a generator that returns mini-batches of random excerpts
mel_spects = [(spect - mean) * istd for spect in mel_spects]
batches = augment.grab_random_excerpts(mel_spects, labels, batchsize,
blocklen)
else:
# For time stretching and pitch shifting, it pays off to preapply the
# spline filter to each input spectrogram, so it does not need to be
# applied to each mini-batch later.
spline_order = cfg['spline_order']
if spline_order > 1:
from scipy.ndimage import spline_filter
spects = [
spline_filter(spect, spline_order).astype(floatX)
for spect in spects
]
# We define a function to create the mini-batch generator. This allows
# us to easily create multiple generators for multithreading if needed.
def create_datafeed(spects, labels):
# With augmentation, as we want to apply random time-stretching,
# we request longer excerpts than we finally need to return.
max_stretch = cfg['max_stretch']
batches = augment.grab_random_excerpts(
spects,
labels,
batchsize=batchsize,
frames=int(blocklen / (1 - max_stretch)))
# We wrap the generator in another one that applies random time
# stretching and pitch shifting, keeping a given number of frames
# and bins only.
max_shift = cfg['max_shift']
batches = augment.apply_random_stretch_shift(batches,
max_stretch,
max_shift,
keep_frames=blocklen,
keep_bins=bin_mel_max,
order=spline_order,
prefiltered=True)
# We transform the excerpts to mel frequency and log magnitude.
batches = augment.apply_filterbank(batches, filterbank)
batches = augment.apply_logarithm(batches)
# We apply random frequency filters
max_db = cfg['max_db']
batches = augment.apply_random_filters(batches,
filterbank,
mel_max,
max_db=max_db)
# We apply normalization
batches = augment.apply_znorm(batches, mean, istd)
return batches
# We start the mini-batch generator and augmenter in one or more
# background threads or processes (unless disabled).
bg_threads = cfg['bg_threads']
bg_processes = cfg['bg_processes']
if not bg_threads and not bg_processes:
# no background processing: just create a single generator
batches = create_datafeed(spects, labels)
elif bg_threads:
# multithreading: create a separate generator per thread
batches = augment.generate_in_background(
[create_datafeed(spects, labels) for _ in range(bg_threads)],
num_cached=bg_threads * 5)
elif bg_processes:
# multiprocessing: single generator is forked along with processes
batches = augment.generate_in_background(
[create_datafeed(spects, labels)] * bg_processes,
num_cached=bg_processes * 25,
in_processes=True)
###########################################################################
#-----------Main changes to code to make it work with pytorch-------------#
###########################################################################
print("preparing training function...")
mdl = model.CNNModel()
mdl = mdl.to(device)
#Setting up learning rate and learning rate parameters
initial_eta = cfg['initial_eta']
eta_decay = cfg['eta_decay']
momentum = cfg['momentum']
eta_decay_every = cfg.get('eta_decay_every', 1)
eta = initial_eta
#set up loss
criterion = torch.nn.BCELoss()
#set up optimizer
optimizer = torch.optim.SGD(mdl.parameters(),
lr=eta,
momentum=momentum,
nesterov=True)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=eta_decay_every,
gamma=eta_decay)
#set up optimizer
writer = SummaryWriter(os.path.join(modelfile, 'runs'))
epochs = cfg['epochs']
epochsize = cfg['epochsize']
batches = iter(batches)
#conditions to save model
best_val_loss = 100000.
best_val_error = 1.
for epoch in range(epochs):
# - Initialize certain parameters that are used to monitor training
err = 0
total_norm = 0
loss_accum = 0
mdl.train(True)
# - Compute the L-2 norm of the gradients
for p in mdl.parameters():
if p.grad is not None:
param_norm = p.grad.data.norm(2)
total_norm += param_norm.item()**2
total_norm = total_norm**(1. / 2)
# - Start the training for this epoch
for batch in progress(range(epochsize),
min_delay=0.5,
desc='Epoch %d/%d: Batch ' %
(epoch + 1, epochs)):
data = next(batches)
input_data = np.transpose(data[0][:, :, :, np.newaxis],
(0, 3, 1, 2))
labels = data[1][:, np.newaxis].astype(np.float32)
#map labels to make them softer
labels = (0.02 + 0.96 * labels)
optimizer.zero_grad()
outputs = mdl(torch.from_numpy(input_data).to(device))
loss = criterion(outputs, torch.from_numpy(labels).to(device))
loss.backward()
optimizer.step()
loss_accum += loss.item()
# - Compute validation loss and error if desired
if options.validate:
from eval import evaluate
mdl.train(False)
val_loss = 0
preds = []
labs = []
max_len = fps
mel_spects_val = (np.log(
np.maximum(np.dot(spect[:, :bin_mel_max], filterbank), 1e-7))
for spect in spects_val)
mel_spects_val = [(spect - mean) * istd
for spect in mel_spects_val]
num_iter = 0
for spect, label in zip(mel_spects_val, labels_val):
num_excerpts = len(spect) - blocklen + 1
excerpts = np.lib.stride_tricks.as_strided(
spect,
shape=(num_excerpts, blocklen, spect.shape[1]),
strides=(spect.strides[0], spect.strides[0],
spect.strides[1]))
# - Pass mini-batches through the network and concatenate results
for pos in range(0, num_excerpts, batchsize):
input_data = np.transpose(
excerpts[pos:pos + batchsize, :, :, np.newaxis],
(0, 3, 1, 2))
if (pos + batchsize > num_excerpts):
label_batch = label[blocklen // 2 + pos:blocklen // 2 +
num_excerpts,
np.newaxis].astype(np.float32)
else:
label_batch = label[blocklen // 2 + pos:blocklen // 2 +
pos + batchsize,
np.newaxis].astype(np.float32)
pred = mdl(torch.from_numpy(input_data).to(device))
e = criterion(pred,
torch.from_numpy(label_batch).to(device))
preds = np.append(preds, pred[:, 0].cpu().detach().numpy())
labs = np.append(labs, label_batch)
val_loss += e.item()
num_iter += 1
print("Validation loss: %.3f" % (val_loss / num_iter))
_, results = evaluate(preds, labs)
print("Validation error: %.3f" % (1 - results['accuracy']))
if (val_loss / num_iter < best_val_loss
and (1 - results['accuracy']) < best_val_error):
torch.save(mdl.state_dict(),
os.path.join(modelfile, 'model.pth'))
best_val_loss = val_loss / num_iter
best_val_error = 1 - results['accuracy']
print('New saved model', best_val_loss, best_val_error)
#Update the learning rate
scheduler.step()
print('Training Loss per epoch', loss_accum / epochsize)
# - Save parameters for examining
writer.add_scalar('Training Loss', loss_accum / epochsize, epoch)
writer.add_scalar('Validation loss', val_loss / num_iter, epoch)
writer.add_scalar('Gradient norm', total_norm, epoch)
writer.add_scalar('Validation error', 1 - results['accuracy'])
for param_group in optimizer.param_groups:
print(param_group['lr'])
if not options.validate:
torch.save(mdl.state_dict(), os.path.join(modelfile, 'model.pth'))
