def test_recompute_hash(self):
token1 = LatticeDecoder.Token(history=[1, 12, 203, 3004, 23455])
token2 = LatticeDecoder.Token(history=[2, 12, 203, 3004, 23455])
token1.recompute_hash(None)
token2.recompute_hash(None)
self.assertNotEqual(token1.recombination_hash,
token2.recombination_hash)
token1.recompute_hash(5)
token2.recompute_hash(5)
self.assertNotEqual(token1.recombination_hash,
token2.recombination_hash)
token1.recompute_hash(4)
token2.recompute_hash(4)
self.assertEqual(token1.recombination_hash, token2.recombination_hash)
def test_recompute_total(self):
token = LatticeDecoder.Token(history=[1, 2],
ac_logprob=math.log(0.1),
lat_lm_logprob=math.log(0.2),
nn_lm_logprob=math.log(0.3))
token.recompute_total(0.25, 1.0, 0.0, True)
assert_almost_equal(token.lm_logprob,
math.log(0.25 * 0.3 + 0.75 * 0.2))
assert_almost_equal(token.total_logprob,
math.log(0.1 * (0.25 * 0.3 + 0.75 * 0.2)))
token.recompute_total(0.25, 1.0, 0.0, False)
assert_almost_equal(token.lm_logprob,
0.25 * math.log(0.3) + 0.75 * math.log(0.2))
assert_almost_equal(
token.total_logprob,
math.log(0.1) + 0.25 * math.log(0.3) + 0.75 * math.log(0.2))
token.recompute_total(0.25, 10.0, 0.0, True)
assert_almost_equal(token.lm_logprob,
math.log(0.25 * 0.3 + 0.75 * 0.2))
assert_almost_equal(
token.total_logprob,
math.log(0.1) + math.log(0.25 * 0.3 + 0.75 * 0.2) * 10.0)
token.recompute_total(0.25, 10.0, 0.0, False)
assert_almost_equal(token.lm_logprob,
0.25 * math.log(0.3) + 0.75 * math.log(0.2))
assert_almost_equal(
token.total_logprob,
math.log(0.1) +
(0.25 * math.log(0.3) + 0.75 * math.log(0.2)) * 10.0)
token.recompute_total(0.25, 10.0, -20.0, True)
assert_almost_equal(token.lm_logprob,
math.log(0.25 * 0.3 + 0.75 * 0.2))
assert_almost_equal(
token.total_logprob,
math.log(0.1) + math.log(0.25 * 0.3 + 0.75 * 0.2) * 10.0 - 40.0)
token.recompute_total(0.25, 10.0, -20.0, False)
assert_almost_equal(token.lm_logprob,
0.25 * math.log(0.3) + 0.75 * math.log(0.2))
assert_almost_equal(
token.total_logprob,
math.log(0.1) +
(0.25 * math.log(0.3) + 0.75 * math.log(0.2)) * 10.0 - 40.0)
token = LatticeDecoder.Token(history=[1, 2],
ac_logprob=-1000,
lat_lm_logprob=-1001,
nn_lm_logprob=-1002)
token.recompute_total(0.75, 1.0, 0.0, True)
# ln(exp(-1000) * (0.75 * exp(-1002) + 0.25 * exp(-1001)))
assert_almost_equal(token.total_logprob, -2001.64263, decimal=4)
def test_append_word(self):
decoding_options = {
'nnlm_weight': 1.0,
'lm_scale': 1.0,
'wi_penalty': 0.0,
'ignore_unk': False,
'unk_penalty': 0.0,
'linear_interpolation': False,
'max_tokens_per_node': 10,
'beam': None,
'recombination_order': None
}
initial_state = RecurrentState(self.network.recurrent_state_size)
token1 = LatticeDecoder.Token(history=[self.sos_id], state=initial_state)
token2 = LatticeDecoder.Token(history=[self.sos_id, self.yksi_id], state=initial_state)
decoder = LatticeDecoder(self.network, decoding_options)
self.assertSequenceEqual(token1.history, [self.sos_id])
self.assertSequenceEqual(token2.history, [self.sos_id, self.yksi_id])
assert_equal(token1.state.get(0), numpy.zeros(shape=(1,1,3)).astype(theano.config.floatX))
assert_equal(token2.state.get(0), numpy.zeros(shape=(1,1,3)).astype(theano.config.floatX))
self.assertEqual(token1.nn_lm_logprob, 0.0)
self.assertEqual(token2.nn_lm_logprob, 0.0)
decoder._append_word([token1, token2], self.kaksi_id)
self.assertSequenceEqual(token1.history, [self.sos_id, self.kaksi_id])
self.assertSequenceEqual(token2.history, [self.sos_id, self.yksi_id, self.kaksi_id])
assert_equal(token1.state.get(0), numpy.ones(shape=(1,1,3)).astype(theano.config.floatX))
assert_equal(token2.state.get(0), numpy.ones(shape=(1,1,3)).astype(theano.config.floatX))
token1_nn_lm_logprob = math.log(self.sos_prob + self.kaksi_prob)
token2_nn_lm_logprob = math.log(self.yksi_prob + self.kaksi_prob)
self.assertAlmostEqual(token1.nn_lm_logprob, token1_nn_lm_logprob)
self.assertAlmostEqual(token2.nn_lm_logprob, token2_nn_lm_logprob)
decoder._append_word([token1, token2], self.eos_id)
self.assertSequenceEqual(token1.history, [self.sos_id, self.kaksi_id, self.eos_id])
self.assertSequenceEqual(token2.history, [self.sos_id, self.yksi_id, self.kaksi_id, self.eos_id])
assert_equal(token1.state.get(0), numpy.ones(shape=(1,1,3)).astype(theano.config.floatX) * 2)
assert_equal(token2.state.get(0), numpy.ones(shape=(1,1,3)).astype(theano.config.floatX) * 2)
token1_nn_lm_logprob += math.log(self.kaksi_prob + self.eos_prob)
token2_nn_lm_logprob += math.log(self.kaksi_prob + self.eos_prob)
self.assertAlmostEqual(token1.nn_lm_logprob, token1_nn_lm_logprob)
self.assertAlmostEqual(token2.nn_lm_logprob, token2_nn_lm_logprob)
lm_scale = 2.0
token1.recompute_total(1.0, lm_scale, -0.01)
token2.recompute_total(1.0, lm_scale, -0.01)
self.assertAlmostEqual(token1.total_logprob, token1_nn_lm_logprob * lm_scale - 0.03)
self.assertAlmostEqual(token2.total_logprob, token2_nn_lm_logprob * lm_scale - 0.04)
def test_copy_token(self):
history = [1, 2, 3]
token1 = LatticeDecoder.Token(history)
token2 = LatticeDecoder.Token.copy(token1)
token2.history.append(4)
self.assertSequenceEqual(token1.history, [1, 2, 3])
self.assertSequenceEqual(token2.history, [1, 2, 3, 4])
def test_copy_token(self):
history = (1, 2, 3)
token1 = LatticeDecoder.Token(history)
token2 = LatticeDecoder.Token.copy(token1)
token2.history = token2.history + (4, )
self.assertSequenceEqual(token1.history, (1, 2, 3))
self.assertSequenceEqual(token2.history, (1, 2, 3, 4))
def __init__(self):
self._sorted_nodes = [Lattice.Node(id) for id in range(5)]
self._sorted_nodes[0].time = 0.0
self._sorted_nodes[1].time = 1.0
self._sorted_nodes[2].time = 1.0
self._sorted_nodes[3].time = None
self._sorted_nodes[4].time = 3.0
self._tokens = [[LatticeDecoder.Token()], [LatticeDecoder.Token()],
[
LatticeDecoder.Token(),
LatticeDecoder.Token(),
LatticeDecoder.Token()
], [LatticeDecoder.Token()], []]
self._tokens[0][0].total_logprob = -10.0
self._tokens[0][0].recombination_hash = 1
self._sorted_nodes[0].best_logprob = -10.0
self._tokens[1][0].total_logprob = -20.0
self._tokens[1][0].recombination_hash = 1
self._sorted_nodes[1].best_logprob = -20.0
self._tokens[2][0].total_logprob = -30.0
self._tokens[2][0].recombination_hash = 1
self._tokens[2][1].total_logprob = -50.0
self._tokens[2][1].recombination_hash = 2
self._tokens[2][2].total_logprob = -70.0
self._tokens[2][2].recombination_hash = 3
self._sorted_nodes[2].best_logprob = -30.0
self._tokens[3][0].total_logprob = -100.0
self._tokens[3][0].recombination_hash = 1
self._sorted_nodes[3].best_logprob = -100.0
def decode(args):
log_file = args.log_file
log_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(log_level, int):
print("Invalid logging level requested:", args.log_level)
sys.exit(1)
log_format = '%(asctime)s %(funcName)s: %(message)s'
if args.log_file == '-':
logging.basicConfig(stream=sys.stdout, format=log_format, level=log_level)
else:
logging.basicConfig(filename=log_file, format=log_format, level=log_level)
if args.debug:
theano.config.compute_test_value = 'warn'
else:
theano.config.compute_test_value = 'off'
theano.config.profile = args.profile
theano.config.profile_memory = args.profile
with h5py.File(args.model_path, 'r') as state:
print("Reading vocabulary from network state.")
sys.stdout.flush()
vocabulary = Vocabulary.from_state(state)
print("Number of words in vocabulary:", vocabulary.num_words())
print("Number of word classes:", vocabulary.num_classes())
print("Building neural network.")
sys.stdout.flush()
architecture = Architecture.from_state(state)
network = Network(architecture, vocabulary,
mode=Network.Mode(minibatch=False))
print("Restoring neural network state.")
sys.stdout.flush()
network.set_state(state)
log_scale = 1.0 if args.log_base is None else numpy.log(args.log_base)
if args.wi_penalty is None:
wi_penalty = None
else:
wi_penalty = args.wi_penalty * log_scale
if args.unk_penalty is None:
ignore_unk = False
unk_penalty = None
elif args.unk_penalty == 0:
ignore_unk = True
unk_penalty = None
else:
ignore_unk = False
unk_penalty = args.unk_penalty
decoding_options = {
'nnlm_weight': args.nnlm_weight,
'lm_scale': args.lm_scale,
'wi_penalty': wi_penalty,
'ignore_unk': ignore_unk,
'unk_penalty': unk_penalty,
'linear_interpolation': args.linear_interpolation,
'max_tokens_per_node': args.max_tokens_per_node,
'beam': args.beam,
'recombination_order': args.recombination_order
}
logging.debug("DECODING OPTIONS")
for option_name, option_value in decoding_options.items():
logging.debug("%s: %s", option_name, str(option_value))
print("Building word lattice decoder.")
sys.stdout.flush()
decoder = LatticeDecoder(network, decoding_options)
# Combine paths from command line and lattice list.
lattices = args.lattices
lattices.extend(args.lattice_list.readlines())
lattices = [path.strip() for path in lattices]
# Ignore empty lines in the lattice list.
lattices = list(filter(None, lattices))
# Pick every Ith lattice, if --num-jobs is specified and > 1.
if args.num_jobs < 1:
print("Invalid number of jobs specified:", args.num_jobs)
sys.exit(1)
if (args.job < 0) or (args.job > args.num_jobs - 1):
print("Invalid job specified:", args.job)
sys.exit(1)
lattices = lattices[args.job::args.num_jobs]
file_type = TextFileType('r')
for index, path in enumerate(lattices):
logging.info("Reading word lattice: %s", path)
lattice_file = file_type(path)
lattice = SLFLattice(lattice_file)
if not lattice.utterance_id is None:
utterance_id = lattice.utterance_id
else:
utterance_id = os.path.basename(lattice_file.name)
logging.info("Utterance `%s' -- %d/%d of job %d",
utterance_id,
index + 1,
len(lattices),
args.job)
tokens = decoder.decode(lattice)
for index in range(min(args.n_best, len(tokens))):
line = format_token(tokens[index],
utterance_id,
vocabulary,
log_scale,
args.output)
args.output_file.write(line + "\n")
def test_decode(self):
vocabulary = Vocabulary.from_word_counts({
'TO': 1,
'AND': 1,
'IT': 1,
'BUT': 1,
'A.': 1,
'IN': 1,
'A': 1,
'AT': 1,
'THE': 1,
'E.': 1,
"DIDN'T": 1,
'ELABORATE': 1
})
projection_vector = tensor.ones(shape=(vocabulary.num_words(), ),
dtype=theano.config.floatX)
projection_vector *= 0.05
network = DummyNetwork(vocabulary, projection_vector)
decoding_options = {
'nnlm_weight': 0.0,
'lm_scale': None,
'wi_penalty': None,
'ignore_unk': False,
'unk_penalty': None,
'linear_interpolation': True,
'max_tokens_per_node': None,
'beam': None,
'recombination_order': None
}
decoder = LatticeDecoder(network, decoding_options)
tokens = decoder.decode(self.lattice)
# Compare tokens to n-best list given by SRILM lattice-tool.
log_scale = math.log(10)
print()
for token in tokens:
print(token.ac_logprob / log_scale,
token.lat_lm_logprob / log_scale,
token.total_logprob / log_scale,
' '.join(vocabulary.id_to_word[token.history]))
all_paths = [
"<s> IT DIDN'T ELABORATE </s>", "<s> BUT IT DIDN'T ELABORATE </s>",
"<s> THE DIDN'T ELABORATE </s>",
"<s> AND IT DIDN'T ELABORATE </s>", "<s> E. DIDN'T ELABORATE </s>",
"<s> IN IT DIDN'T ELABORATE </s>", "<s> A DIDN'T ELABORATE </s>",
"<s> AT IT DIDN'T ELABORATE </s>",
"<s> IT IT DIDN'T ELABORATE </s>",
"<s> TO IT DIDN'T ELABORATE </s>",
"<s> A. IT DIDN'T ELABORATE </s>", "<s> A IT DIDN'T ELABORATE </s>"
]
paths = [
' '.join(vocabulary.id_to_word[token.history]) for token in tokens
]
self.assertListEqual(paths, all_paths)
token = tokens[0]
history = ' '.join(vocabulary.id_to_word[token.history])
self.assertAlmostEqual(token.ac_logprob / log_scale,
-8686.28,
places=2)
self.assertAlmostEqual(token.lat_lm_logprob / log_scale,
-94.3896,
places=2)
self.assertAlmostEqual(token.nn_lm_logprob, math.log(0.1) * 4)
token = tokens[1]
self.assertAlmostEqual(token.ac_logprob / log_scale,
-8743.96,
places=2)
self.assertAlmostEqual(token.lat_lm_logprob / log_scale,
-111.488,
places=2)
self.assertAlmostEqual(token.nn_lm_logprob, math.log(0.1) * 5)
token = tokens[-1]
self.assertAlmostEqual(token.ac_logprob / log_scale,
-8696.26,
places=2)
self.assertAlmostEqual(token.lat_lm_logprob / log_scale,
-178.00,
places=2)
self.assertAlmostEqual(token.nn_lm_logprob, math.log(0.1) * 5)
def test_append_word(self):
decoding_options = {
'nnlm_weight': 1.0,
'lm_scale': 1.0,
'wi_penalty': 0.0,
'ignore_unk': False,
'unk_penalty': 0.0,
'linear_interpolation': False,
'max_tokens_per_node': 10,
'beam': None,
'recombination_order': None
}
initial_state = RecurrentState(self.network.recurrent_state_size)
token1 = LatticeDecoder.Token(history=[self.sos_id],
state=initial_state)
token2 = LatticeDecoder.Token(history=[self.sos_id, self.yksi_id],
state=initial_state)
decoder = LatticeDecoder(self.network, decoding_options)
self.assertSequenceEqual(token1.history, [self.sos_id])
self.assertSequenceEqual(token2.history, [self.sos_id, self.yksi_id])
assert_equal(token1.state.get(0),
numpy.zeros(shape=(1, 1, 3)).astype(theano.config.floatX))
assert_equal(token2.state.get(0),
numpy.zeros(shape=(1, 1, 3)).astype(theano.config.floatX))
self.assertEqual(token1.nn_lm_logprob, 0.0)
self.assertEqual(token2.nn_lm_logprob, 0.0)
decoder._append_word([token1, token2], self.kaksi_id)
self.assertSequenceEqual(token1.history, [self.sos_id, self.kaksi_id])
self.assertSequenceEqual(token2.history,
[self.sos_id, self.yksi_id, self.kaksi_id])
assert_equal(token1.state.get(0),
numpy.ones(shape=(1, 1, 3)).astype(theano.config.floatX))
assert_equal(token2.state.get(0),
numpy.ones(shape=(1, 1, 3)).astype(theano.config.floatX))
token1_nn_lm_logprob = math.log(self.sos_prob + self.kaksi_prob)
token2_nn_lm_logprob = math.log(self.yksi_prob + self.kaksi_prob)
self.assertAlmostEqual(token1.nn_lm_logprob, token1_nn_lm_logprob)
self.assertAlmostEqual(token2.nn_lm_logprob, token2_nn_lm_logprob)
decoder._append_word([token1, token2], self.eos_id)
self.assertSequenceEqual(token1.history,
[self.sos_id, self.kaksi_id, self.eos_id])
self.assertSequenceEqual(
token2.history,
[self.sos_id, self.yksi_id, self.kaksi_id, self.eos_id])
assert_equal(
token1.state.get(0),
numpy.ones(shape=(1, 1, 3)).astype(theano.config.floatX) * 2)
assert_equal(
token2.state.get(0),
numpy.ones(shape=(1, 1, 3)).astype(theano.config.floatX) * 2)
token1_nn_lm_logprob += math.log(self.kaksi_prob + self.eos_prob)
token2_nn_lm_logprob += math.log(self.kaksi_prob + self.eos_prob)
self.assertAlmostEqual(token1.nn_lm_logprob, token1_nn_lm_logprob)
self.assertAlmostEqual(token2.nn_lm_logprob, token2_nn_lm_logprob)
lm_scale = 2.0
token1.recompute_total(1.0, lm_scale, -0.01)
token2.recompute_total(1.0, lm_scale, -0.01)
self.assertAlmostEqual(token1.total_logprob,
token1_nn_lm_logprob * lm_scale - 0.03)
self.assertAlmostEqual(token2.total_logprob,
token2_nn_lm_logprob * lm_scale - 0.04)
def decode(args):
"""A function that performs the "theanolm decode" command.
:type args: argparse.Namespace
:param args: a collection of command line arguments
"""
log_file = args.log_file
log_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(log_level, int):
print("Invalid logging level requested:", args.log_level)
sys.exit(1)
log_format = '%(asctime)s %(funcName)s: %(message)s'
if args.log_file == '-':
logging.basicConfig(stream=sys.stdout,
format=log_format,
level=log_level)
else:
logging.basicConfig(filename=log_file,
format=log_format,
level=log_level)
if args.debug:
theano.config.compute_test_value = 'warn'
else:
theano.config.compute_test_value = 'off'
theano.config.profile = args.profile
theano.config.profile_memory = args.profile
network = Network.from_file(args.model_path,
mode=Network.Mode(minibatch=False))
log_scale = 1.0 if args.log_base is None else numpy.log(args.log_base)
if args.wi_penalty is None:
wi_penalty = None
else:
wi_penalty = args.wi_penalty * log_scale
if args.unk_penalty is None:
ignore_unk = False
unk_penalty = None
elif args.unk_penalty == 0:
ignore_unk = True
unk_penalty = None
else:
ignore_unk = False
unk_penalty = args.unk_penalty
decoding_options = {
'nnlm_weight': args.nnlm_weight,
'lm_scale': args.lm_scale,
'wi_penalty': wi_penalty,
'ignore_unk': ignore_unk,
'unk_penalty': unk_penalty,
'linear_interpolation': args.linear_interpolation,
'max_tokens_per_node': args.max_tokens_per_node,
'beam': args.beam,
'recombination_order': args.recombination_order
}
logging.debug("DECODING OPTIONS")
for option_name, option_value in decoding_options.items():
logging.debug("%s: %s", option_name, str(option_value))
print("Building word lattice decoder.")
sys.stdout.flush()
decoder = LatticeDecoder(network, decoding_options)
# Combine paths from command line and lattice list.
lattices = args.lattices
if args.lattice_list is not None:
lattices.extend(args.lattice_list.readlines())
lattices = [path.strip() for path in lattices]
# Ignore empty lines in the lattice list.
lattices = [x for x in lattices if x]
# Pick every Ith lattice, if --num-jobs is specified and > 1.
if args.num_jobs < 1:
print("Invalid number of jobs specified:", args.num_jobs)
sys.exit(1)
if (args.job < 0) or (args.job > args.num_jobs - 1):
print("Invalid job specified:", args.job)
sys.exit(1)
lattices = lattices[args.job::args.num_jobs]
file_type = TextFileType('r')
for index, path in enumerate(lattices):
logging.info("Reading word lattice: %s", path)
lattice_file = file_type(path)
lattice = SLFLattice(lattice_file)
if lattice.utterance_id is not None:
utterance_id = lattice.utterance_id
else:
utterance_id = os.path.basename(lattice_file.name)
logging.info("Utterance `%s' -- %d/%d of job %d", utterance_id,
index + 1, len(lattices), args.job)
tokens = decoder.decode(lattice)
for index in range(min(args.n_best, len(tokens))):
line = format_token(tokens[index], utterance_id,
network.vocabulary, log_scale, args.output)
args.output_file.write(line + "\n")
def test_decode(self):
vocabulary = Vocabulary.from_word_counts({
'TO': 1,
'AND': 1,
'IT': 1,
'BUT': 1,
'A.': 1,
'IN': 1,
'A': 1,
'AT': 1,
'THE': 1,
'E.': 1,
"DIDN'T": 1,
'ELABORATE': 1})
projection_vector = tensor.ones(shape=(vocabulary.num_words(),),
dtype=theano.config.floatX)
projection_vector *= 0.05
network = DummyNetwork(vocabulary, projection_vector)
decoding_options = {
'nnlm_weight': 0.0,
'lm_scale': None,
'wi_penalty': None,
'ignore_unk': False,
'unk_penalty': None,
'linear_interpolation': True,
'max_tokens_per_node': None,
'beam': None,
'recombination_order': None
}
decoder = LatticeDecoder(network, decoding_options)
tokens = decoder.decode(self.lattice)
# Compare tokens to n-best list given by SRILM lattice-tool.
log_scale = math.log(10)
print()
for token in tokens:
print(token.ac_logprob / log_scale,
token.lat_lm_logprob / log_scale,
token.total_logprob / log_scale,
' '.join(vocabulary.id_to_word[token.history]))
all_paths = ["<s> IT DIDN'T ELABORATE </s>",
"<s> BUT IT DIDN'T ELABORATE </s>",
"<s> THE DIDN'T ELABORATE </s>",
"<s> AND IT DIDN'T ELABORATE </s>",
"<s> E. DIDN'T ELABORATE </s>",
"<s> IN IT DIDN'T ELABORATE </s>",
"<s> A DIDN'T ELABORATE </s>",
"<s> AT IT DIDN'T ELABORATE </s>",
"<s> IT IT DIDN'T ELABORATE </s>",
"<s> TO IT DIDN'T ELABORATE </s>",
"<s> A. IT DIDN'T ELABORATE </s>",
"<s> A IT DIDN'T ELABORATE </s>"]
paths = [' '.join(vocabulary.id_to_word[token.history])
for token in tokens]
self.assertListEqual(paths, all_paths)
token = tokens[0]
history = ' '.join(vocabulary.id_to_word[token.history])
self.assertAlmostEqual(token.ac_logprob / log_scale, -8686.28, places=2)
self.assertAlmostEqual(token.lat_lm_logprob / log_scale, -94.3896, places=2)
self.assertAlmostEqual(token.nn_lm_logprob, math.log(0.1) * 4)
token = tokens[1]
self.assertAlmostEqual(token.ac_logprob / log_scale, -8743.96, places=2)
self.assertAlmostEqual(token.lat_lm_logprob / log_scale, -111.488, places=2)
self.assertAlmostEqual(token.nn_lm_logprob, math.log(0.1) * 5)
token = tokens[-1]
self.assertAlmostEqual(token.ac_logprob / log_scale, -8696.26, places=2)
self.assertAlmostEqual(token.lat_lm_logprob / log_scale, -178.00, places=2)
self.assertAlmostEqual(token.nn_lm_logprob, math.log(0.1) * 5)
def test_decode(self):
vocabulary = Vocabulary.from_word_counts({
'to': 1,
'and': 1,
'it': 1,
'but': 1,
'a.': 1,
'in': 1,
'a': 1,
'at': 1,
'the': 1,
"didn't": 1,
'elaborate': 1
})
projection_vector = tensor.ones(
shape=(vocabulary.num_shortlist_words(), ),
dtype=theano.config.floatX)
projection_vector *= 0.05
network = DummyNetwork(vocabulary, projection_vector)
decoding_options = {
'nnlm_weight': 0.0,
'lm_scale': None,
'wi_penalty': None,
'unk_penalty': None,
'use_shortlist': False,
'unk_from_lattice': False,
'linear_interpolation': True,
'max_tokens_per_node': None,
'beam': None,
'recombination_order': 20
}
decoder = LatticeDecoder(network, decoding_options)
tokens = decoder.decode(self.lattice)[0]
# Compare tokens to n-best list given by SRILM lattice-tool.
log_scale = math.log(10)
print()
for token in tokens:
print(token.ac_logprob / log_scale,
token.lat_lm_logprob / log_scale,
token.total_logprob / log_scale,
' '.join(token.history_words(vocabulary)))
all_paths = [
"<s> it didn't elaborate </s>", "<s> but it didn't elaborate </s>",
"<s> the didn't elaborate </s>",
"<s> and it didn't elaborate </s>", "<s> e. didn't elaborate </s>",
"<s> in it didn't elaborate </s>", "<s> a didn't elaborate </s>",
"<s> at it didn't elaborate </s>",
"<s> it it didn't elaborate </s>",
"<s> to it didn't elaborate </s>",
"<s> a. it didn't elaborate </s>", "<s> a it didn't elaborate </s>"
]
paths = [' '.join(token.history_words(vocabulary)) for token in tokens]
self.assertListEqual(paths, all_paths)
token = tokens[0]
history = ' '.join(token.history_words(vocabulary))
self.assertAlmostEqual(token.ac_logprob / log_scale,
-8686.28,
places=2)
self.assertAlmostEqual(token.lat_lm_logprob / log_scale,
-94.3896,
places=2)
self.assertAlmostEqual(token.nn_lm_logprob, math.log(0.1) * 4)
token = tokens[1]
self.assertAlmostEqual(token.ac_logprob / log_scale,
-8743.96,
places=2)
self.assertAlmostEqual(token.lat_lm_logprob / log_scale,
-111.488,
places=2)
self.assertAlmostEqual(token.nn_lm_logprob, math.log(0.1) * 5)
token = tokens[-1]
self.assertAlmostEqual(token.ac_logprob / log_scale,
-8696.26,
places=2)
self.assertAlmostEqual(token.lat_lm_logprob / log_scale,
-178.00,
places=2)
self.assertAlmostEqual(token.nn_lm_logprob, math.log(0.1) * 5)
def decode(args):
"""A function that performs the "theanolm decode" command.
:type args: argparse.Namespace
:param args: a collection of command line arguments
"""
log_file = args.log_file
log_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(log_level, int):
print("Invalid logging level requested:", args.log_level,
file=sys.stderr)
sys.exit(1)
log_format = '%(asctime)s %(funcName)s: %(message)s'
if args.log_file == '-':
logging.basicConfig(stream=sys.stdout, format=log_format, level=log_level)
else:
logging.basicConfig(filename=log_file, format=log_format, level=log_level)
if args.debug:
theano.config.compute_test_value = 'warn'
else:
theano.config.compute_test_value = 'off'
theano.config.profile = args.profile
theano.config.profile_memory = args.profile
if (args.lattice_format == 'kaldi') or (args.output == 'kaldi'):
if args.kaldi_vocabulary is None:
print("Kaldi lattice vocabulary is not given.", file=sys.stderr)
sys.exit(1)
default_device = get_default_device(args.default_device)
network = Network.from_file(args.model_path,
mode=Network.Mode(minibatch=False),
default_device=default_device)
log_scale = 1.0 if args.log_base is None else numpy.log(args.log_base)
if (args.log_base is not None) and (args.lattice_format == 'kaldi'):
logging.info("Warning: Kaldi lattice reader doesn't support logarithm "
"base conversion.")
if args.wi_penalty is None:
wi_penalty = None
else:
wi_penalty = args.wi_penalty * log_scale
decoding_options = {
'nnlm_weight': args.nnlm_weight,
'lm_scale': args.lm_scale,
'wi_penalty': wi_penalty,
'unk_penalty': args.unk_penalty,
'use_shortlist': args.shortlist,
'unk_from_lattice': args.unk_from_lattice,
'linear_interpolation': args.linear_interpolation,
'max_tokens_per_node': args.max_tokens_per_node,
'beam': args.beam,
'recombination_order': args.recombination_order,
'prune_relative': args.prune_relative,
'abs_min_max_tokens': args.abs_min_max_tokens,
'abs_min_beam': args.abs_min_beam
}
logging.debug("DECODING OPTIONS")
for option_name, option_value in decoding_options.items():
logging.debug("%s: %s", option_name, str(option_value))
logging.info("Building word lattice decoder.")
decoder = LatticeDecoder(network, decoding_options)
batch = LatticeBatch(args.lattices, args.lattice_list, args.lattice_format,
args.kaldi_vocabulary, args.num_jobs, args.job)
for lattice_number, lattice in enumerate(batch):
if lattice.utterance_id is None:
lattice.utterance_id = str(lattice_number)
logging.info("Utterance `%s´ -- %d of job %d",
lattice.utterance_id,
lattice_number + 1,
args.job)
log_free_mem()
final_tokens, recomb_tokens = decoder.decode(lattice)
if (args.output == "slf") or (args.output == "kaldi"):
rescored_lattice = RescoredLattice(lattice,
final_tokens,
recomb_tokens,
network.vocabulary)
rescored_lattice.lm_scale = args.lm_scale
rescored_lattice.wi_penalty = args.wi_penalty
if args.output == "slf":
rescored_lattice.write_slf(args.output_file)
else:
assert args.output == "kaldi"
rescored_lattice.write_kaldi(args.output_file,
batch.kaldi_word_to_id)
else:
for token in final_tokens[:min(args.n_best, len(final_tokens))]:
line = format_token(token,
lattice.utterance_id,
network.vocabulary,
log_scale,
args.output)
args.output_file.write(line + "\n")
gc.collect()
