def test_optim2(self):
elbo_fn = beer.EvidenceLowerBound(len(self.data))
for i, model in enumerate([self.ppca, self.plda]):
with self.subTest(i=i):
optim = beer.BayesianModelCoordinateAscentOptimizer(
*model.grouped_parameters, lrate=1.)
previous = -float('inf')
for _ in range(100):
optim.zero_grad()
elbo = elbo_fn(model, self.data)
elbo.natural_backward()
optim.step()
elbo = round(float(elbo) / (len(self.data) * self.dim), 3)
self.assertGreaterEqual(elbo - previous, -TOLERANCE)
previous = elbo
def test_optim(self):
for i, model in enumerate(self.models):
with self.subTest(model=self.conf_files[i]):
optim = beer.BayesianModelCoordinateAscentOptimizer(
model.mean_field_groups, lrate=1.)
previous = -float('inf')
for _ in range(N_ITER):
self.seed(1)
optim.zero_grad()
elbo = beer.evidence_lower_bound(model, self.data)
elbo.natural_backward()
optim.step()
elbo = round(float(elbo) / (len(self.data) * self.dim), 3)
self.assertGreaterEqual(elbo - previous, -TOLERANCE)
previous = elbo
def main():
parser = argparse.ArgumentParser()
parser.add_argument('lm', help='unigram language model to train')
parser.add_argument('data', help='data')
parser.add_argument('outlm', help='output model')
args = parser.parse_args()
# Load the model.
with open(args.lm, 'rb') as fh:
model = pickle.load(fh)
# Load the data for the training.
data = np.load(args.data)
# Count the number of in the training data.
tot_counts = 0
for utt in data:
tot_counts += len(data[utt])
# Prepare the optimizer for the training.
params = model.mean_field_factorization()
optimizer = beer.BayesianModelCoordinateAscentOptimizer(params, lrate=1.)
optimizer.zero_grad()
# Initialize the objective function.
elbo = beer.evidence_lower_bound(datasize=tot_counts)
# Re-estimate the LM.
for utt in data:
ft = torch.from_numpy(data[utt])
elbo += beer.evidence_lower_bound(model, ft, datasize=tot_counts)
elbo.backward()
optimizer.step()
# Save the model.
with open(args.outlm, 'wb') as fh:
model = pickle.dump(model, fh)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--batch-size',
type=int,
default=-1,
help='utterance number in each batch')
parser.add_argument('--epochs',
type=int,
default=1,
help='number of epochs to train')
parser.add_argument('--fast-eval', action='store_true')
parser.add_argument('--kl-weight',
type=float,
default=1.,
help='weighting of KL div. of the ELBO')
parser.add_argument('--lrate-nnet',
type=float,
default=1e-3,
help='learning rate for the nnet components')
parser.add_argument('--lrate',
type=float,
default=1.,
help='learning rate')
parser.add_argument('--nnet-optim-state',
help='file where to load/save state of the nnet '
'optimizer')
parser.add_argument('--use-gpu', action='store_true')
parser.add_argument('--verbose', action='store_true')
parser.add_argument('model', help='model to train')
parser.add_argument('alis', help='alignments')
parser.add_argument('feats', help='Feature file')
parser.add_argument('feat_stats', help='data statistics')
parser.add_argument('out', help='output model')
args = parser.parse_args()
if args.verbose:
logging.getLogger().setLevel(logging.DEBUG)
# Load the data.
alis = np.load(args.alis)
feats = np.load(args.feats)
stats = np.load(args.feat_stats)
# Load the model and move it to the chosen device (CPU/GPU)
with open(args.model, 'rb') as fh:
model = pickle.load(fh)
if args.use_gpu:
device = torch.device('cuda')
else:
device = torch.device('cpu')
model = model.to(device)
# NNET optimizer.
nnet_optim = torch.optim.Adam(model.modules_parameters(),
lr=args.lrate_nnet,
eps=1e-3,
amsgrad=False,
weight_decay=1e-2)
if args.nnet_optim_state and os.path.isfile(args.nnet_optim_state):
logging.debug('load nnet optimizer state: {}'.format(
args.nnet_optim_state))
optim_state = torch.load(args.nnet_optim_state)
nnet_optim.load_state_dict(optim_state)
# Prepare the optimizer for the training.
params = model.mean_field_factorization()
optimizer = beer.BayesianModelCoordinateAscentOptimizer(
params, lrate=args.lrate, std_optim=nnet_optim)
# If no batch_size is specified, use the whole data.
batch_size = len(feats.files)
if args.batch_size > 0:
batch_size = args.batch_size
tot_counts = int(stats['nframes'])
for epoch in range(1, args.epochs + 1):
# Shuffle the order of the utterance.
keys = list(feats.keys())
random.shuffle(keys)
batches = [
keys[i:i + batch_size] for i in range(0, len(keys), batch_size)
]
logging.debug('Data shuffled into {} batches'.format(len(batches)))
for batch_no, batch_keys in enumerate(batches, start=1):
# Reset the gradients.
optimizer.zero_grad()
# Load the batch data.
ft, labels = load_batch(feats, alis, batch_keys)
ft, labels = ft.to(device), labels.to(device)
# Compute the objective function.
elbo = beer.evidence_lower_bound(model,
ft,
state_path=labels,
kl_weight=args.kl_weight,
datasize=tot_counts,
fast_eval=args.fast_eval)
# Compute the gradient of the model.
#elbo.natural_backward()
elbo.backward()
# Clip the gradient to make avoid explosion.
torch.nn.utils.clip_grad_norm_(model.modules_parameters(), 100.0)
# Update the parameters.
optimizer.step()
elbo_value = float(elbo) / tot_counts
log_msg = 'epoch={}/{} batch={}/{} elbo={}'
logging.info(
log_msg.format(epoch, args.epochs, batch_no, len(batches),
round(elbo_value, 3)))
if args.nnet_optim_state:
torch.save(nnet_optim.state_dict(), args.nnet_optim_state)
with open(args.out, 'wb') as fh:
pickle.dump(model.to(torch.device('cpu')), fh)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--batch-size', type=int, default=-1,
help='utterance number in each batch')
parser.add_argument('--epochs', type=int, default=1,
help='number of epochs')
parser.add_argument('--fast-eval', action='store_true')
parser.add_argument('--lrate', type=float, default=1.,
help='learning rate')
parser.add_argument('--use-gpu', action='store_true')
parser.add_argument('--verbose', action='store_true')
parser.add_argument('hmm', help='hmm model to train')
parser.add_argument('alis', help='alignments')
parser.add_argument('feats', help='Feature file')
parser.add_argument('feat_stats', help='data statistics')
parser.add_argument('out', help='output model')
args = parser.parse_args()
if args.verbose:
logging.getLogger().setLevel(logging.DEBUG)
# Load the data.
alis = np.load(args.alis)
feats = np.load(args.feats)
stats = np.load(args.feat_stats)
# Load the model and move it to the chosen device (CPU/GPU)
with open(args.hmm, 'rb') as fh:
model = pickle.load(fh)
if args.use_gpu:
device = torch.device('cuda')
else:
device = torch.device('cpu')
model = model.to(device)
# Prepare the optimizer for the training.
params = model.mean_field_groups
optimizer = beer.BayesianModelCoordinateAscentOptimizer(params,
lrate=args.lrate)
# If no batch_size is specified, use the whole data.
batch_size = len(feats.files)
if args.batch_size > 0:
batch_size = args.batch_size
tot_counts = int(stats['nframes'])
for epoch in range(1, args.epochs + 1):
# Shuffle the order of the utterance.
keys = list(feats.keys())
random.shuffle(keys)
batches = [keys[i: i + batch_size]
for i in range(0, len(keys), batch_size)]
logging.debug('Data shuffled into {} batches'.format(len(batches)))
for batch_no, batch_keys in enumerate(batches, start=1):
# Reset the gradients.
optimizer.zero_grad()
elbo = beer.evidence_lower_bound(datasize=tot_counts)
for uttid in batch_keys:
# Load the batch data.
ft = torch.from_numpy(feats[uttid]).float()
ali = torch.from_numpy(alis[uttid]).long()
ft, ali = ft.to(device), ali.to(device)
# Compute the objective function.
elbo += beer.evidence_lower_bound(model, ft, state_path=ali,
datasize=tot_counts,
fast_eval=args.fast_eval)
# Compute the gradient of the model.
elbo.natural_backward()
# Update the parameters.
optimizer.step()
elbo_value = float(elbo) / tot_counts
log_msg = 'epoch={}/{} batch={}/{} elbo={}'
logging.info(log_msg.format(
epoch, args.epochs,
batch_no, len(batches),
round(elbo_value, 3))
)
del ft, ali
with open(args.out, 'wb') as fh:
pickle.dump(model.to(torch.device('cpu')), fh)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--alignments', help='utterance alignemnts')
parser.add_argument('--batch-size',
type=int,
help='utterance number in each batch')
parser.add_argument('--epochs', type=int)
parser.add_argument('--fast-eval', action='store_true')
parser.add_argument('--infer-type',
default='viterbi',
choices=['baum_welch', 'viterbi'],
help='how to compute the state posteriors')
parser.add_argument('--lrate', type=float, help='learning rate')
parser.add_argument('--tmpdir', help='directory to store intermediary ' \
'models')
parser.add_argument('--use-gpu', action='store_true')
parser.add_argument('hmm', help='hmm model to train')
parser.add_argument('feats', help='Feature file')
parser.add_argument('feat_stats', help='data statistics')
parser.add_argument('out', help='output model')
args = parser.parse_args()
# Load the data for the training.
feats = np.load(args.feats)
ali = None
if args.alignments:
ali = np.load(args.alignments)
stats = np.load(args.feat_stats)
with open(args.hmm, 'rb') as fh:
model = pickle.load(fh)
if args.use_gpu:
device = torch.device('cuda')
else:
device = torch.device('cpu')
model = model.to(device)
# Prepare the optimizer for the training.
params = model.mean_field_groups
optimizer = beer.BayesianModelCoordinateAscentOptimizer(params,
lrate=args.lrate)
tot_counts = int(stats['nframes'])
for epoch in range(1, args.epochs + 1):
# Shuffle the order of the utterance.
keys = list(feats.keys())
random.shuffle(keys)
batches = [
keys[i:i + args.batch_size]
for i in range(0, len(keys), args.batch_size)
]
logging.debug('Data shuffled into {} batches'.format(len(batches)))
# One mini-batch update.
for batch_no, batch_keys in enumerate(batches, start=1):
# Reset the gradients.
optimizer.zero_grad()
# Initialize the ELBO.
elbo = beer.evidence_lower_bound(datasize=tot_counts)
for utt in batch_keys:
ft = torch.from_numpy(feats[utt]).float().to(device)
# Get the alignment graph if provided.
graph = None
if ali is not None:
graph = ali[utt][0].to(device)
elbo += beer.evidence_lower_bound(
model,
ft,
datasize=tot_counts,
fast_eval=args.fast_eval,
inference_graph=graph,
inference_type=args.infer_type)
# Compute the gradient of the model.
elbo.natural_backward()
# Update the parameters.
optimizer.step()
elbo_value = float(elbo) / (tot_counts * len(batch_keys))
log_msg = 'epoch={}/{} batch={}/{} ELBO={}'
logging.info(
log_msg.format(epoch, args.epochs, batch_no, len(batches),
round(elbo_value, 3)))
if args.tmpdir:
path = os.path.join(args.tmpdir, str(epoch) + '.mdl')
with open(path, 'wb') as fh:
pickle.dump(model.to(torch.device('cpu')), fh)
with open(args.out, 'wb') as fh:
pickle.dump(model.to(torch.device('cpu')), fh)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--epochs',
type=int,
default=1,
help='number of epochs to train')
parser.add_argument('--fast-eval', action='store_true')
parser.add_argument('--lrate',
type=float,
default=1.,
help='learning rate')
parser.add_argument('--use-gpu', action='store_true')
parser.add_argument('--verbose', action='store_true')
parser.add_argument('model', help='model to train')
parser.add_argument('batches', help='list of batches file')
parser.add_argument('feat_stats', help='data statistics')
parser.add_argument('out', help='output model')
args = parser.parse_args()
if args.verbose:
logging.getLogger().setLevel(logging.DEBUG)
# Load the data.
stats = np.load(args.feat_stats)
# Load the batches.
batches_list = []
with open(args.batches, 'r') as f:
for line in f:
batches_list.append(line.strip())
# Load the model and move it to the chosen device (CPU/GPU)
with open(args.model, 'rb') as fh:
model = pickle.load(fh)
if args.use_gpu:
device = torch.device('cuda')
else:
device = torch.device('cpu')
model = model.to(device)
# Prepare the optimizer for the training.
params = model.mean_field_groups
optimizer = beer.BayesianModelCoordinateAscentOptimizer(params,
lrate=args.lrate)
tot_counts = int(stats['nframes'])
for epoch in range(1, args.epochs + 1):
# Shuffle the order of the utterance.
random.shuffle(batches_list)
for batch_no, path in enumerate(batches_list, start=1):
# Reset the gradients.
optimizer.zero_grad()
# Load the batch data.
batch = np.load(path)
ft = torch.from_numpy(batch['features']).float()
ft = ft.to(device)
# Compute the objective function.
elbo = beer.evidence_lower_bound(model,
ft,
datasize=tot_counts,
fast_eval=args.fast_eval)
# Compute the gradient of the model.
elbo.natural_backward()
# Update the parameters.
optimizer.step()
elbo_value = float(elbo) / tot_counts
log_msg = 'epoch={}/{} batch={}/{} elbo={}'
logging.info(
log_msg.format(epoch, args.epochs, batch_no, len(batches_list),
round(elbo_value, 3)))
with open(args.out, 'wb') as fh:
pickle.dump(model.to(torch.device('cpu')), fh)
