def load_labels(filelist, predictions, fps, datadir):
labels = []
for fn in filelist:
ffn = os.path.join(datadir, 'labels', fn.rsplit('.', 1)[0] + '.lab')
with io.open(ffn) 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(predictions[fn])) / float(fps)
labels.append(create_aligned_targets(segments, timestamps, np.bool))
return labels
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():
# 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 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 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'))