def test_no_repeated(self):
# standard ctc topo and modified ctc topo
# should be equivalent if there are no
# repeated neighboring symbols in the transcript
max_token = 3
standard = k2.ctc_topo(max_token, modified=False)
modified = k2.ctc_topo(max_token, modified=True)
transcript = k2.linear_fsa([1, 2, 3])
standard_graph = k2.compose(standard, transcript)
modified_graph = k2.compose(modified, transcript)
input1 = k2.linear_fsa([1, 1, 1, 0, 0, 2, 2, 3, 3])
input2 = k2.linear_fsa([1, 1, 0, 0, 2, 2, 0, 3, 3])
inputs = [input1, input2]
for i in inputs:
lattice1 = k2.intersect(standard_graph,
i,
treat_epsilons_specially=False)
lattice2 = k2.intersect(modified_graph,
i,
treat_epsilons_specially=False)
lattice1 = k2.connect(lattice1)
lattice2 = k2.connect(lattice2)
aux_labels1 = lattice1.aux_labels[lattice1.aux_labels != 0]
aux_labels2 = lattice2.aux_labels[lattice2.aux_labels != 0]
aux_labels1 = aux_labels1[:-1] # remove -1
aux_labels2 = aux_labels2[:-1]
assert torch.all(torch.eq(aux_labels1, aux_labels2))
assert torch.all(torch.eq(aux_labels2, torch.tensor([1, 2, 3])))
def test(self):
# for the symbol table
# <eps> 0
# a 0
# b 1
# c 2
# an FSA that recognizes a+(b|c)
s = '''
0 1 1 0.1
1 1 1 0.2
1 2 2 0.3
1 3 3 0.4
2 4 -1 0.5
3 4 -1 0.6
5
'''
a_fsa = k2.Fsa.from_str(s)
a_fsa.requires_grad_(True)
# an FSA that recognizes ab
s = '''
0 1 1 10
1 2 2 20
2 3 -1 30
3
'''
b_fsa = k2.Fsa.from_str(s)
b_fsa.requires_grad_(True)
fsa = k2.intersect(a_fsa, b_fsa)
assert len(fsa.shape) == 2
actual_str = k2.to_str(fsa)
expected_str = '\n'.join(
['0 1 1 10.1', '1 2 2 20.3', '2 3 -1 30.5', '3'])
assert actual_str.strip() == expected_str
loss = fsa.scores.sum()
loss.backward()
# arc 0, 2, and 4 of a_fsa are kept in the final intersected FSA
assert torch.allclose(
a_fsa.scores.grad,
torch.tensor([1, 0, 1, 0, 1, 0], dtype=torch.float32))
assert torch.allclose(b_fsa.scores.grad,
torch.tensor([1, 1, 1], dtype=torch.float32))
# if any of the input FSA is an FsaVec,
# the outupt FSA is also an FsaVec.
a_fsa.scores.grad = None
b_fsa.scores.grad = None
a_fsa = k2.create_fsa_vec([a_fsa])
fsa = k2.intersect(a_fsa, b_fsa)
assert len(fsa.shape) == 3
def test_treat_epsilon_specially_false(self):
devices = [torch.device('cpu')]
if torch.cuda.is_available() and k2.with_cuda:
devices.append(torch.device('cuda'))
for device in devices:
# a_fsa recognizes `(0|1)2*`
s1 = '''
0 1 0 0.1
0 1 1 0.2
1 1 2 0.3
1 2 -1 0.4
2
'''
a_fsa = k2.Fsa.from_str(s1).to(device)
a_fsa.requires_grad_(True)
# b_fsa recognizes `1|2`
s2 = '''
0 1 1 1
0 1 2 2
1 2 -1 3
2
'''
b_fsa = k2.Fsa.from_str(s2).to(device)
b_fsa.requires_grad_(True)
# fsa recognizes `1`
fsa = k2.intersect(a_fsa, b_fsa, treat_epsilons_specially=False)
assert len(fsa.shape) == 2
actual_str = k2.to_str_simple(fsa)
expected_str = '\n'.join(['0 1 1 1.2', '1 2 -1 3.4', '2'])
assert actual_str.strip() == expected_str
loss = fsa.scores.sum()
(-loss).backward()
# arc 1 and 3 of a_fsa are kept in the final intersected FSA
assert torch.allclose(a_fsa.grad,
torch.tensor([0, -1, 0, -1]).to(a_fsa.grad))
# arc 0 and 2 of b_fsa are kept in the final intersected FSA
assert torch.allclose(b_fsa.grad,
torch.tensor([-1, 0, -1]).to(b_fsa.grad))
# if any of the input FSA is an FsaVec,
# the outupt FSA is also an FsaVec.
a_fsa.scores.grad = None
b_fsa.scores.grad = None
a_fsa = k2.create_fsa_vec([a_fsa])
fsa = k2.intersect(a_fsa, b_fsa, treat_epsilons_specially=False)
assert len(fsa.shape) == 3
def test_treat_epsilon_specially_true(self):
# this version works only on CPU and requires
# arc-sorted inputs
# a_fsa recognizes `(1|3)?2*`
s1 = '''
0 1 3 0.0
0 1 1 0.2
0 1 0 0.1
1 1 2 0.3
1 2 -1 0.4
2
'''
a_fsa = k2.Fsa.from_str(s1)
a_fsa.requires_grad_(True)
# b_fsa recognizes `1|2|5`
s2 = '''
0 1 5 0
0 1 1 1
0 1 2 2
1 2 -1 3
2
'''
b_fsa = k2.Fsa.from_str(s2)
b_fsa.requires_grad_(True)
# fsa recognizes 1|2
fsa = k2.intersect(k2.arc_sort(a_fsa), k2.arc_sort(b_fsa))
assert len(fsa.shape) == 2
actual_str = k2.to_str_simple(fsa)
expected_str = '\n'.join(
['0 1 0 0.1', '0 2 1 1.2', '1 2 2 2.3', '2 3 -1 3.4', '3'])
assert actual_str.strip() == expected_str
loss = fsa.scores.sum()
(-loss).backward()
# arc 1, 2, 3, and 4 of a_fsa are kept in the final intersected FSA
assert torch.allclose(a_fsa.grad,
torch.tensor([0, -1, -1, -1, -1]).to(a_fsa.grad))
# arc 1, 2, and 3 of b_fsa are kept in the final intersected FSA
assert torch.allclose(b_fsa.grad,
torch.tensor([0, -1, -1, -1]).to(b_fsa.grad))
# if any of the input FSA is an FsaVec,
# the outupt FSA is also an FsaVec.
a_fsa.scores.grad = None
b_fsa.scores.grad = None
a_fsa = k2.create_fsa_vec([a_fsa])
fsa = k2.intersect(k2.arc_sort(a_fsa), k2.arc_sort(b_fsa))
assert len(fsa.shape) == 3
def test_with_repeated(self):
max_token = 2
standard = k2.ctc_topo(max_token, modified=False)
modified = k2.ctc_topo(max_token, modified=True)
transcript = k2.linear_fsa([1, 2, 2])
standard_graph = k2.compose(standard, transcript)
modified_graph = k2.compose(modified, transcript)
# There is a blank separating 2 in the input
# so standard and modified ctc topo should be equivalent
input = k2.linear_fsa([1, 1, 2, 2, 0, 2, 2, 0, 0])
lattice1 = k2.intersect(standard_graph,
input,
treat_epsilons_specially=False)
lattice2 = k2.intersect(modified_graph,
input,
treat_epsilons_specially=False)
lattice1 = k2.connect(lattice1)
lattice2 = k2.connect(lattice2)
aux_labels1 = lattice1.aux_labels[lattice1.aux_labels != 0]
aux_labels2 = lattice2.aux_labels[lattice2.aux_labels != 0]
aux_labels1 = aux_labels1[:-1] # remove -1
aux_labels2 = aux_labels2[:-1]
assert torch.all(torch.eq(aux_labels1, aux_labels2))
assert torch.all(torch.eq(aux_labels1, torch.tensor([1, 2, 2])))
# There are no blanks separating 2 in the input.
# The standard ctc topo requires that there must be a blank
# separating 2, so lattice1 in the following is empty
input = k2.linear_fsa([1, 1, 2, 2, 0, 0])
lattice1 = k2.intersect(standard_graph,
input,
treat_epsilons_specially=False)
lattice2 = k2.intersect(modified_graph,
input,
treat_epsilons_specially=False)
lattice1 = k2.connect(lattice1)
lattice2 = k2.connect(lattice2)
assert lattice1.num_arcs == 0
# Since there are two 2s in the input and there are also two 2s
# in the transcript, the final output contains only one path.
# If there were more than two 2s in the input, the output
# would contain more than one path
aux_labels2 = lattice2.aux_labels[lattice2.aux_labels != 0]
aux_labels2 = aux_labels2[:-1]
assert torch.all(torch.eq(aux_labels1, torch.tensor([1, 2, 2])))
def compile(self, texts: Iterable[str],
P: k2.Fsa) -> Tuple[k2.Fsa, k2.Fsa]:
'''Create numerator and denominator graphs from transcripts
and the bigram phone LM.
Args:
texts:
A list of transcripts. Within a transcript, words are
separated by spaces.
P:
The bigram phone LM created by :func:`create_bigram_phone_lm`.
Returns:
A tuple (num_graph, den_graph), where
- `num_graph` is the numerator graph. It is an FsaVec with
shape `(len(texts), None, None)`.
- `den_graph` is the denominator graph. It is an FsaVec with the same
shape of the `num_graph`.
'''
assert P.is_cpu()
ctc_topo_P = k2.intersect(self.ctc_topo, P).invert_()
ctc_topo_P = k2.connect(ctc_topo_P)
num_graphs = k2.create_fsa_vec(
[self.compile_one_and_cache(text) for text in texts])
num = k2.compose(ctc_topo_P, num_graphs)
num = k2.connect(num)
num = k2.arc_sort(num)
den = k2.create_fsa_vec([ctc_topo_P.detach()] * len(texts))
return num, den
def test_case1(self):
devices = [torch.device('cpu')]
if torch.cuda.is_available():
devices.append(torch.device('cuda'))
for device in devices:
# suppose we have four symbols: <blk>, a, b, c, d
torch_activation = torch.tensor([0.2, 0.2, 0.2, 0.2,
0.2]).to(device)
k2_activation = torch_activation.detach().clone()
# (T, N, C)
torch_activation = torch_activation.reshape(
1, 1, -1).requires_grad_(True)
# (N, T, C)
k2_activation = k2_activation.reshape(1, 1,
-1).requires_grad_(True)
torch_log_probs = torch.nn.functional.log_softmax(
torch_activation, dim=-1) # (T, N, C)
# we have only one sequence and its label is `a`
targets = torch.tensor([1]).to(device)
input_lengths = torch.tensor([1]).to(device)
target_lengths = torch.tensor([1]).to(device)
torch_loss = torch.nn.functional.ctc_loss(
log_probs=torch_log_probs,
targets=targets,
input_lengths=input_lengths,
target_lengths=target_lengths,
reduction='none')
assert torch.allclose(torch_loss,
torch.tensor([1.6094379425049]).to(device))
# (N, T, C)
k2_log_probs = torch.nn.functional.log_softmax(k2_activation,
dim=-1)
supervision_segments = torch.tensor([[0, 0, 1]], dtype=torch.int32)
dense_fsa_vec = k2.DenseFsaVec(k2_log_probs,
supervision_segments).to(device)
ctc_topo_inv = k2.arc_sort(
build_ctc_topo([0, 1, 2, 3, 4]).invert_())
linear_fsa = k2.linear_fsa([1])
decoding_graph = k2.intersect(ctc_topo_inv, linear_fsa)
decoding_graph = k2.connect(decoding_graph).invert_().to(device)
target_graph = k2.intersect_dense(decoding_graph, dense_fsa_vec,
100.0)
k2_scores = target_graph.get_tot_scores(log_semiring=True,
use_double_scores=False)
assert torch.allclose(torch_loss, -1 * k2_scores)
torch_loss.backward()
(-k2_scores).backward()
assert torch.allclose(torch_activation.grad, k2_activation.grad)
def build_num_graphs(self, texts: List[str]) -> k2.Fsa:
'''Convert transcript to an Fsa with the help of lexicon
and word symbol table.
Args:
texts:
Each element is a transcript containing words separated by spaces.
For instance, it may be 'HELLO SNOWFALL', which contains
two words.
Returns:
Return an FST (FsaVec) corresponding to the transcript. Its `labels` are
phone IDs and `aux_labels` are word IDs.
'''
word_ids_list = []
for text in texts:
word_ids = []
for word in text.split(' '):
if word in self.words:
word_ids.append(self.words[word])
else:
word_ids.append(self.oov_id)
word_ids_list.append(word_ids)
fsa = k2.linear_fsa(word_ids_list, self.device)
fsa = k2.add_epsilon_self_loops(fsa)
num_graphs = k2.intersect(self.L_inv,
fsa,
treat_epsilons_specially=False).invert_()
num_graphs = k2.arc_sort(num_graphs)
return num_graphs
def get_hierarchical_targets(ys: List[List[int]],
lexicon: k2.Fsa) -> List[Tensor]:
"""Get hierarchical transcripts (i.e., phone level transcripts) from transcripts (i.e., word level transcripts).
Args:
ys: Word level transcripts.
lexicon: Its labels are words, while its aux_labels are phones.
Returns:
List[Tensor]: Phone level transcripts.
"""
if lexicon is None:
return ys
else:
L_inv = lexicon
n_batch = len(ys)
indices = torch.tensor(range(n_batch))
transcripts = k2.create_fsa_vec([k2.linear_fsa(x) for x in ys])
transcripts_lexicon = k2.intersect(transcripts, L_inv)
transcripts_lexicon = k2.arc_sort(k2.connect(transcripts_lexicon))
transcripts_lexicon = k2.remove_epsilon(transcripts_lexicon)
transcripts_lexicon = k2.shortest_path(transcripts_lexicon,
use_double_scores=True)
ys = get_texts(transcripts_lexicon, indices)
ys = [torch.tensor(y) for y in ys]
return ys
def compile_one_and_cache(self, text: str) -> Fsa:
tokens = (token if token in self.vocab._sym2id else self.oov
for token in text.split(' '))
word_ids = [self.vocab.get(token) for token in tokens]
fsa = k2.linear_fsa(word_ids)
decoding_graph = k2.connect(k2.intersect(fsa, self.L_inv)).invert_()
decoding_graph = k2.add_epsilon_self_loops(decoding_graph)
return decoding_graph
def compile(self,
texts: Iterable[str],
P: k2.Fsa,
replicate_den: bool = True) -> Tuple[k2.Fsa, k2.Fsa]:
'''Create numerator and denominator graphs from transcripts
and the bigram phone LM.
Args:
texts:
A list of transcripts. Within a transcript, words are
separated by spaces.
P:
The bigram phone LM created by :func:`create_bigram_phone_lm`.
replicate_den:
If True, the returned den_graph is replicated to match the number
of FSAs in the returned num_graph; if False, the returned den_graph
contains only a single FSA
Returns:
A tuple (num_graph, den_graph), where
- `num_graph` is the numerator graph. It is an FsaVec with
shape `(len(texts), None, None)`.
- `den_graph` is the denominator graph. It is an FsaVec with the same
shape of the `num_graph` if replicate_den is True; otherwise, it
is an FsaVec containing only a single FSA.
'''
assert P.device == self.device
P_with_self_loops = k2.add_epsilon_self_loops(P)
ctc_topo_P = k2.intersect(self.ctc_topo_inv,
P_with_self_loops,
treat_epsilons_specially=False).invert()
ctc_topo_P = k2.arc_sort(ctc_topo_P)
num_graphs = self.build_num_graphs(texts)
num_graphs_with_self_loops = k2.remove_epsilon_and_add_self_loops(
num_graphs)
num_graphs_with_self_loops = k2.arc_sort(num_graphs_with_self_loops)
num = k2.compose(ctc_topo_P,
num_graphs_with_self_loops,
treat_epsilons_specially=False)
num = k2.arc_sort(num)
ctc_topo_P_vec = k2.create_fsa_vec([ctc_topo_P.detach()])
if replicate_den:
indexes = torch.zeros(len(texts),
dtype=torch.int32,
device=self.device)
den = k2.index_fsa(ctc_topo_P_vec, indexes)
else:
den = ctc_topo_P_vec
return num, den
def intersect_with_self_loops(base_graph: 'k2.Fsa', aux_graph: 'k2.Fsa') -> 'k2.Fsa':
"""Intersection helper function.
"""
assert hasattr(base_graph, "aux_labels")
assert not hasattr(aux_graph, "aux_labels")
aux_graph_with_self_loops = k2.arc_sort(k2.add_epsilon_self_loops(aux_graph)).to(base_graph.device)
result = k2.intersect(k2.arc_sort(base_graph), aux_graph_with_self_loops, treat_epsilons_specially=False,)
setattr(result, "phones", result.labels)
return result
def test_case3(self):
devices = [torch.device('cpu')]
if torch.cuda.is_available():
devices.append(torch.device('cuda'))
for device in devices:
# (T, N, C)
torch_activation = torch.tensor([[
[-5, -4, -3, -2, -1],
[-10, -9, -8, -7, -6],
[-15, -14, -13, -12, -11.],
]]).permute(1, 0, 2).to(device).requires_grad_(True)
torch_activation = torch_activation.to(torch.float32)
torch_activation.requires_grad_(True)
k2_activation = torch_activation.detach().clone().requires_grad_(
True)
torch_log_probs = torch.nn.functional.log_softmax(
torch_activation, dim=-1) # (T, N, C)
# we have only one sequence and its labels are `b,c`
targets = torch.tensor([2, 3]).to(device)
input_lengths = torch.tensor([3]).to(device)
target_lengths = torch.tensor([2]).to(device)
torch_loss = torch.nn.functional.ctc_loss(
log_probs=torch_log_probs,
targets=targets,
input_lengths=input_lengths,
target_lengths=target_lengths,
reduction='none')
act = k2_activation.permute(1, 0, 2) # (T, N, C) -> (N, T, C)
k2_log_probs = torch.nn.functional.log_softmax(act, dim=-1)
supervision_segments = torch.tensor([[0, 0, 3]], dtype=torch.int32)
dense_fsa_vec = k2.DenseFsaVec(k2_log_probs,
supervision_segments).to(device)
ctc_topo_inv = k2.arc_sort(
build_ctc_topo([0, 1, 2, 3, 4]).invert_())
linear_fsa = k2.linear_fsa([2, 3])
decoding_graph = k2.intersect(ctc_topo_inv, linear_fsa)
decoding_graph = k2.connect(decoding_graph).invert_().to(device)
target_graph = k2.intersect_dense(decoding_graph, dense_fsa_vec,
100.0)
k2_scores = target_graph.get_tot_scores(log_semiring=True,
use_double_scores=False)
assert torch.allclose(torch_loss, -1 * k2_scores)
assert torch.allclose(torch_loss,
torch.tensor([4.938850402832]).to(device))
torch_loss.backward()
(-k2_scores).backward()
assert torch.allclose(torch_activation.grad, k2_activation.grad)
def compile_one_and_cache(self, text: str) -> k2.Fsa:
tokens = (token if token in self.words else self.oov
for token in text.split(' '))
word_ids = [self.words[token] for token in tokens]
fsa = k2.linear_fsa(word_ids)
decoding_graph = k2.connect(k2.intersect(fsa, self.L_inv)).invert_()
decoding_graph = k2.arc_sort(decoding_graph)
decoding_graph = k2.compose(self.ctc_topo, decoding_graph)
decoding_graph = k2.connect(decoding_graph)
return decoding_graph
def test_random_case1(self):
# 1 sequence
devices = [torch.device('cpu')]
if torch.cuda.is_available():
devices.append(torch.device('cuda', 0))
for device in devices:
T = torch.randint(10, 100, (1,)).item()
C = torch.randint(20, 30, (1,)).item()
torch_activation = torch.rand((1, T + 10, C),
dtype=torch.float32,
device=device).requires_grad_(True)
k2_activation = torch_activation.detach().clone().requires_grad_(
True)
# [N, T, C] -> [T, N, C]
torch_log_probs = torch.nn.functional.log_softmax(
torch_activation.permute(1, 0, 2), dim=-1)
input_lengths = torch.tensor([T]).to(device)
target_lengths = torch.randint(1, T, (1,)).to(device)
targets = torch.randint(1, C - 1,
(target_lengths.item(),)).to(device)
torch_loss = torch.nn.functional.ctc_loss(
log_probs=torch_log_probs,
targets=targets,
input_lengths=input_lengths,
target_lengths=target_lengths,
reduction='none')
k2_log_probs = torch.nn.functional.log_softmax(k2_activation,
dim=-1)
supervision_segments = torch.tensor([[0, 0, T]], dtype=torch.int32)
dense_fsa_vec = k2.DenseFsaVec(k2_log_probs,
supervision_segments).to(device)
ctc_topo_inv = k2.arc_sort(
build_ctc_topo(list(range(C))).invert_())
linear_fsa = k2.linear_fsa([targets.tolist()])
decoding_graph = k2.intersect(ctc_topo_inv, linear_fsa)
decoding_graph = k2.connect(decoding_graph).invert_().to(device)
target_graph = k2.intersect_dense(decoding_graph, dense_fsa_vec,
100.0)
k2_scores = target_graph.get_tot_scores(log_semiring=True,
use_double_scores=False)
assert torch.allclose(torch_loss, -1 * k2_scores)
scale = torch.rand_like(torch_loss) * 100
(torch_loss * scale).sum().backward()
(-k2_scores * scale).sum().backward()
assert torch.allclose(torch_activation.grad,
k2_activation.grad,
atol=1e-2)
def create_decoding_graph(texts, L, symbols):
word_ids_list = []
for text in texts:
filter_text = [
i if i in symbols._sym2id else '<UNK>' for i in text.split(' ')
]
word_ids = [symbols.get(i) for i in filter_text]
word_ids_list.append(word_ids)
fsa = k2.linear_fsa(word_ids_list)
decoding_graph = k2.intersect(fsa, L).invert_()
decoding_graph = k2.add_epsilon_self_loops(decoding_graph)
return decoding_graph
def compile(self, texts: Iterable[str],
P: k2.Fsa) -> Tuple[k2.Fsa, k2.Fsa, k2.Fsa]:
'''Create numerator and denominator graphs from transcripts
and the bigram phone LM.
Args:
texts:
A list of transcripts. Within a transcript, words are
separated by spaces.
P:
The bigram phone LM created by :func:`create_bigram_phone_lm`.
Returns:
A tuple (num_graph, den_graph, decoding_graph), where
- `num_graph` is the numerator graph. It is an FsaVec with
shape `(len(texts), None, None)`.
It is the result of compose(ctc_topo, P, L, transcript)
- `den_graph` is the denominator graph. It is an FsaVec with the same
shape of the `num_graph`.
It is the result of compose(ctc_topo, P).
- decoding_graph: It is the result of compose(ctc_topo, L_disambig, G)
Note that it is a single Fsa, not an FsaVec.
'''
assert P.device == self.device
P_with_self_loops = k2.add_epsilon_self_loops(P)
ctc_topo_P = k2.intersect(self.ctc_topo_inv,
P_with_self_loops,
treat_epsilons_specially=False).invert()
ctc_topo_P = k2.arc_sort(ctc_topo_P)
num_graphs = self.build_num_graphs(texts)
num_graphs_with_self_loops = k2.remove_epsilon_and_add_self_loops(
num_graphs)
num_graphs_with_self_loops = k2.arc_sort(num_graphs_with_self_loops)
num = k2.compose(ctc_topo_P,
num_graphs_with_self_loops,
treat_epsilons_specially=False,
inner_labels='phones')
num = k2.arc_sort(num)
ctc_topo_P_vec = k2.create_fsa_vec([ctc_topo_P.detach()])
indexes = torch.zeros(len(texts),
dtype=torch.int32,
device=self.device)
den = k2.index_fsa(ctc_topo_P_vec, indexes)
return num, den, self.decoding_graph
def create_decoding_graph(texts, graph, symbols):
fsas = []
for text in texts:
filter_text = [
i if i in symbols._sym2id else '<UNK>' for i in text.split(' ')
]
word_ids = [symbols.get(i) for i in filter_text]
fsa = k2.linear_fsa(word_ids)
fsa = k2.arc_sort(fsa)
decoding_graph = k2.intersect(fsa, graph).invert_()
decoding_graph = k2.add_epsilon_self_loops(decoding_graph)
fsas.append(decoding_graph)
return k2.create_fsa_vec(fsas)
def __init__(self,
lexicon: Lexicon,
P: k2.Fsa,
device: torch.device,
oov: str = '<UNK>'):
'''
Args:
L_inv:
Its labels are words, while its aux_labels are phones.
P:
A phone bigram LM if the pronunciations in the lexicon are in phones;
a word piece bigram if the pronunciations in the lexicon are word pieces.
phones:
The phone symbol table.
words:
The word symbol table.
oov:
Out of vocabulary word.
'''
self.lexicon = lexicon
L_inv = self.lexicon.L_inv.to(device)
P = P.to(device)
if L_inv.properties & k2.fsa_properties.ARC_SORTED != 0:
L_inv = k2.arc_sort(L_inv)
assert L_inv.requires_grad is False
assert oov in self.lexicon.words
self.L_inv = L_inv
self.oov_id = self.lexicon.words[oov]
self.oov = oov
self.device = device
phone_symbols = get_phone_symbols(self.lexicon.phones)
phone_symbols_with_blank = [0] + phone_symbols
ctc_topo = build_ctc_topo(phone_symbols_with_blank).to(device)
assert ctc_topo.requires_grad is False
ctc_topo_inv = k2.arc_sort(ctc_topo.invert_())
P_with_self_loops = k2.add_epsilon_self_loops(P)
ctc_topo_P = k2.intersect(ctc_topo_inv,
P_with_self_loops,
treat_epsilons_specially=False).invert()
self.ctc_topo_P = k2.arc_sort(ctc_topo_P)
def test_case2(self):
for device in self.devices:
# (T, N, C)
torch_activation = torch.arange(1, 16).reshape(1, 3, 5).permute(
1, 0, 2).to(device)
torch_activation = torch_activation.to(torch.float32)
torch_activation.requires_grad_(True)
k2_activation = torch_activation.detach().clone().requires_grad_(
True)
torch_log_probs = torch.nn.functional.log_softmax(
torch_activation, dim=-1) # (T, N, C)
# we have only one sequence and its labels are `c,c`
targets = torch.tensor([3, 3]).to(device)
input_lengths = torch.tensor([3]).to(device)
target_lengths = torch.tensor([2]).to(device)
torch_loss = torch.nn.functional.ctc_loss(
log_probs=torch_log_probs,
targets=targets,
input_lengths=input_lengths,
target_lengths=target_lengths,
reduction='none')
act = k2_activation.permute(1, 0, 2) # (T, N, C) -> (N, T, C)
k2_log_probs = torch.nn.functional.log_softmax(act, dim=-1)
supervision_segments = torch.tensor([[0, 0, 3]], dtype=torch.int32)
dense_fsa_vec = k2.DenseFsaVec(k2_log_probs,
supervision_segments).to(device)
ctc_topo_inv = k2.arc_sort(
build_ctc_topo([0, 1, 2, 3, 4]).invert_())
linear_fsa = k2.linear_fsa([3, 3])
decoding_graph = k2.intersect(ctc_topo_inv, linear_fsa)
decoding_graph = k2.connect(decoding_graph).invert_().to(device)
target_graph = k2.intersect_dense(decoding_graph, dense_fsa_vec,
100.0)
k2_scores = target_graph.get_tot_scores(log_semiring=True,
use_double_scores=False)
assert torch.allclose(torch_loss, -1 * k2_scores)
assert torch.allclose(torch_loss,
torch.tensor([7.355742931366]).to(device))
torch_loss.backward()
(-k2_scores).backward()
assert torch.allclose(torch_activation.grad, k2_activation.grad)
def compile_LG(L: Fsa, G: Fsa, labels_disambig_id_start: int,
aux_labels_disambig_id_start: int) -> Fsa:
"""
Creates a decoding graph using a lexicon fst ``L`` and language model fsa ``G``.
Involves arc sorting, intersection, determinization, removal of disambiguation symbols
and adding epsilon self-loops.
Args:
L:
An ``Fsa`` that represents the lexicon (L), i.e. has phones as ``symbols``
and words as ``aux_symbols``.
G:
An ``Fsa`` that represents the language model (G), i.e. it's an acceptor
with words as ``symbols``.
labels_disambig_id_start:
An integer ID corresponding to the first disambiguation symbol in the
phonetic alphabet.
aux_labels_disambig_id_start:
An integer ID corresponding to the first disambiguation symbol in the
words vocabulary.
:return:
"""
L_inv = k2.arc_sort(L.invert_())
G = k2.arc_sort(G)
logging.debug("Intersecting L and G")
LG = k2.intersect(L_inv, G)
logging.debug(f'LG shape = {LG.shape}')
logging.debug("Connecting L*G")
LG = k2.connect(LG).invert_()
logging.debug(f'LG shape = {LG.shape}')
logging.debug("Determinizing L*G")
LG = k2.determinize(LG)
logging.debug(f'LG shape = {LG.shape}')
logging.debug("Connecting det(L*G)")
LG = k2.connect(LG)
logging.debug(f'LG shape = {LG.shape}')
logging.debug("Removing disambiguation symbols on L*G")
LG.labels[LG.labels >= labels_disambig_id_start] = 0
LG.aux_labels[LG.aux_labels >= aux_labels_disambig_id_start] = 0
LG = k2.add_epsilon_self_loops(LG)
LG = k2.arc_sort(LG)
logging.debug(
f'LG is arc sorted: {(LG.properties & k2.fsa_properties.ARC_SORTED) != 0}'
)
return LG
def compile_one_and_cache(self, text: str) -> k2.Fsa:
'''Convert transcript to an Fsa with the help of lexicon
and word symbol table.
Args:
text:
The transcript containing words separated by spaces.
Returns:
Return an FST corresponding to the transcript. Its `labels` are
phone IDs and `aux_labels` are word IDs.
'''
tokens = (token if token in self.words else self.oov
for token in text.split(' '))
word_ids = [self.words[token] for token in tokens]
fsa = k2.linear_fsa(word_ids)
num_graph = k2.connect(k2.intersect(fsa, self.L_inv)).invert_()
num_graph = k2.arc_sort(num_graph)
return num_graph
def test_composition_equivalence(self):
index = _generate_fsa_vec()
index = k2.arc_sort(k2.connect(k2.remove_epsilon(index)))
src = _generate_fsa_vec()
replace = k2.replace_fsa(src, index, 1)
replace = k2.top_sort(replace)
f_fsa = _construct_f(src)
f_fsa = k2.arc_sort(f_fsa)
intersect = k2.intersect(index, f_fsa, treat_epsilons_specially=True)
intersect = k2.invert(intersect)
intersect = k2.top_sort(intersect)
delattr(intersect, 'aux_labels')
assert k2.is_rand_equivalent(replace,
intersect,
log_semiring=True,
delta=1e-3)
def create_decoding_graph(texts, L, symbols):
fsas = []
for text in texts:
filter_text = [
i if i in symbols._sym2id else '<UNK>' for i in text.split(' ')
]
word_ids = [symbols.get(i) for i in filter_text]
fsa = k2.linear_fsa(word_ids)
print("linear fsa is ", fsa)
fsa = k2.arc_sort(fsa)
print("linear fsa, arc-sorted, is ", fsa)
print("begin")
print(k2.is_arc_sorted(k2.get_properties(fsa)))
decoding_graph = k2.intersect(fsa, L).invert_()
print("linear fsa, composed, is ", fsa)
print("decoding graph is ", decoding_graph)
decoding_graph = k2.add_epsilon_self_loops(decoding_graph)
print("decoding graph with self-loops is ", decoding_graph)
fsas.append(decoding_graph)
return k2.create_fsa_vec(fsas)
def get_hierarchical_targets(ys: List[List[int]],
lexicon: k2.Fsa) -> List[Tensor]:
"""Get hierarchical transcripts (i.e., phone level transcripts) from transcripts (i.e., word level transcripts).
Args:
ys: Word level transcripts.
lexicon: Its labels are words, while its aux_labels are phones.
Returns:
List[Tensor]: Phone level transcripts.
"""
if lexicon is None:
return ys
else:
L_inv = lexicon
n_batch = len(ys)
indices = torch.tensor(range(n_batch))
device = L_inv.device
transcripts = k2.create_fsa_vec(
[k2.linear_fsa(x, device=device) for x in ys])
transcripts_with_self_loops = k2.add_epsilon_self_loops(transcripts)
transcripts_lexicon = k2.intersect(L_inv,
transcripts_with_self_loops,
treat_epsilons_specially=False)
# Don't call invert_() above because we want to return phone IDs,
# which is the `aux_labels` of transcripts_lexicon
transcripts_lexicon = k2.remove_epsilon(transcripts_lexicon)
transcripts_lexicon = k2.top_sort(transcripts_lexicon)
transcripts_lexicon = k2.shortest_path(transcripts_lexicon,
use_double_scores=True)
ys = get_texts(transcripts_lexicon, indices)
ys = [torch.tensor(y) for y in ys]
return ys
def compile_LG(L: Fsa, G: Fsa, ctc_topo_inv: Fsa,
labels_disambig_id_start: int,
aux_labels_disambig_id_start: int) -> Fsa:
"""
Creates a decoding graph using a lexicon fst ``L`` and language model fsa ``G``.
Involves arc sorting, intersection, determinization, removal of disambiguation symbols
and adding epsilon self-loops.
Args:
L:
An ``Fsa`` that represents the lexicon (L), i.e. has phones as ``symbols``
and words as ``aux_symbols``.
G:
An ``Fsa`` that represents the language model (G), i.e. it's an acceptor
with words as ``symbols``.
ctc_topo_inv: Epsilons are in `aux_labels` and `labels` contain phone IDs.
labels_disambig_id_start:
An integer ID corresponding to the first disambiguation symbol in the
phonetic alphabet.
aux_labels_disambig_id_start:
An integer ID corresponding to the first disambiguation symbol in the
words vocabulary.
:return:
"""
L_inv = k2.arc_sort(L.invert_())
G = k2.arc_sort(G)
logging.debug("Intersecting L and G")
LG = k2.intersect(L_inv, G)
logging.debug(f'LG shape = {LG.shape}')
logging.debug("Connecting L*G")
LG = k2.connect(LG).invert_()
logging.debug(f'LG shape = {LG.shape}')
logging.debug("Determinizing L*G")
LG = k2.determinize(LG)
logging.debug(f'LG shape = {LG.shape}')
logging.debug("Connecting det(L*G)")
LG = k2.connect(LG)
logging.debug(f'LG shape = {LG.shape}')
logging.debug("Removing disambiguation symbols on L*G")
LG.labels[LG.labels >= labels_disambig_id_start] = 0
if isinstance(LG.aux_labels, torch.Tensor):
LG.aux_labels[LG.aux_labels >= aux_labels_disambig_id_start] = 0
else:
LG.aux_labels.values()[
LG.aux_labels.values() >= aux_labels_disambig_id_start] = 0
logging.debug("Removing epsilons")
LG = k2.remove_epsilons_iterative_tropical(LG)
logging.debug(f'LG shape = {LG.shape}')
logging.debug("Connecting rm-eps(det(L*G))")
LG = k2.connect(LG)
logging.debug(f'LG shape = {LG.shape}')
LG.aux_labels = k2.ragged.remove_values_eq(LG.aux_labels, 0)
logging.debug("Arc sorting")
LG = k2.arc_sort(LG)
logging.debug("Composing")
LG = k2.compose(ctc_topo_inv, LG)
logging.debug("Connecting")
LG = k2.connect(LG)
logging.debug("Arc sorting")
LG = k2.arc_sort(LG)
logging.debug(
f'LG is arc sorted: {(LG.properties & k2.fsa_properties.ARC_SORTED) != 0}'
)
return LG
def visualize_ctc_topo():
'''This function shows how to visualize
standard/modified ctc topologies. It's for
demonstration only, not for testing.
'''
max_token = 2
labels_sym = k2.SymbolTable.from_str('''
<blk> 0
z 1
o 2
''')
aux_labels_sym = k2.SymbolTable.from_str('''
z 1
o 2
''')
word_sym = k2.SymbolTable.from_str('''
zoo 1
''')
standard = k2.ctc_topo(max_token, modified=False)
modified = k2.ctc_topo(max_token, modified=True)
standard.labels_sym = labels_sym
standard.aux_labels_sym = aux_labels_sym
modified.labels_sym = labels_sym
modified.aux_labels_sym = aux_labels_sym
standard.draw('standard_topo.svg', title='standard CTC topo')
modified.draw('modified_topo.svg', title='modified CTC topo')
fsa = k2.linear_fst([1, 2, 2], [1, 0, 0])
fsa.labels_sym = labels_sym
fsa.aux_labels_sym = word_sym
fsa.draw('transcript.svg', title='transcript')
standard_graph = k2.compose(standard, fsa)
modified_graph = k2.compose(modified, fsa)
standard_graph.draw('standard_graph.svg', title='standard graph')
modified_graph.draw('modified_graph.svg', title='modified graph')
# z z <blk> <blk> o o <blk> o <blk>
inputs = k2.linear_fsa([1, 1, 0, 0, 2, 2, 0, 2, 0])
inputs.labels_sym = labels_sym
inputs.draw('inputs.svg', title='inputs')
standard_lattice = k2.intersect(standard_graph,
inputs,
treat_epsilons_specially=False)
standard_lattice.draw('standard_lattice.svg', title='standard lattice')
modified_lattice = k2.intersect(modified_graph,
inputs,
treat_epsilons_specially=False)
modified_lattice = k2.connect(modified_lattice)
modified_lattice.draw('modified_lattice.svg', title='modified lattice')
# z z <blk> <blk> o o o <blk>
inputs2 = k2.linear_fsa([1, 1, 0, 0, 2, 2, 2, 0])
inputs2.labels_sym = labels_sym
inputs2.draw('inputs2.svg', title='inputs2')
standard_lattice2 = k2.intersect(standard_graph,
inputs2,
treat_epsilons_specially=False)
standard_lattice2 = k2.connect(standard_lattice2)
# It's empty since the topo requires that there must be a blank
# between the two o's in zoo
assert standard_lattice2.num_arcs == 0
standard_lattice2.draw('standard_lattice2.svg',
title='standard lattice2')
modified_lattice2 = k2.intersect(modified_graph,
inputs2,
treat_epsilons_specially=False)
modified_lattice2 = k2.connect(modified_lattice2)
modified_lattice2.draw('modified_lattice2.svg',
title='modified lattice2')
def test_case4(self):
for device in self.devices:
# put case3, case2 and case1 into a batch
torch_activation_1 = torch.tensor(
[[0., 0., 0., 0., 0.]]).to(device).requires_grad_(True)
torch_activation_2 = torch.arange(1, 16).reshape(3, 5).to(
torch.float32).to(device).requires_grad_(True)
torch_activation_3 = torch.tensor([
[-5, -4, -3, -2, -1],
[-10, -9, -8, -7, -6],
[-15, -14, -13, -12, -11.],
]).to(device).requires_grad_(True)
k2_activation_1 = torch_activation_1.detach().clone(
).requires_grad_(True)
k2_activation_2 = torch_activation_2.detach().clone(
).requires_grad_(True)
k2_activation_3 = torch_activation_3.detach().clone(
).requires_grad_(True)
# [T, N, C]
torch_activations = torch.nn.utils.rnn.pad_sequence(
[torch_activation_3, torch_activation_2, torch_activation_1],
batch_first=False,
padding_value=0)
# [N, T, C]
k2_activations = torch.nn.utils.rnn.pad_sequence(
[k2_activation_3, k2_activation_2, k2_activation_1],
batch_first=True,
padding_value=0)
# [[b,c], [c,c], [a]]
targets = torch.tensor([2, 3, 3, 3, 1]).to(device)
input_lengths = torch.tensor([3, 3, 1]).to(device)
target_lengths = torch.tensor([2, 2, 1]).to(device)
torch_log_probs = torch.nn.functional.log_softmax(
torch_activations, dim=-1) # (T, N, C)
torch_loss = torch.nn.functional.ctc_loss(
log_probs=torch_log_probs,
targets=targets,
input_lengths=input_lengths,
target_lengths=target_lengths,
reduction='none')
assert torch.allclose(
torch_loss,
torch.tensor([4.938850402832, 7.355742931366,
1.6094379425049]).to(device))
k2_log_probs = torch.nn.functional.log_softmax(k2_activations,
dim=-1)
supervision_segments = torch.tensor(
[[0, 0, 3], [1, 0, 3], [2, 0, 1]], dtype=torch.int32)
dense_fsa_vec = k2.DenseFsaVec(k2_log_probs,
supervision_segments).to(device)
ctc_topo_inv = k2.arc_sort(
build_ctc_topo([0, 1, 2, 3, 4]).invert_())
# [ [b, c], [c, c], [a]]
linear_fsa = k2.linear_fsa([[2, 3], [3, 3], [1]])
decoding_graph = k2.intersect(ctc_topo_inv, linear_fsa)
decoding_graph = k2.connect(decoding_graph).invert_().to(device)
target_graph = k2.intersect_dense(decoding_graph, dense_fsa_vec,
100.0)
k2_scores = target_graph.get_tot_scores(log_semiring=True,
use_double_scores=False)
assert torch.allclose(torch_loss, -1 * k2_scores)
scale = torch.tensor([1., -2, 3.5]).to(device)
(torch_loss * scale).sum().backward()
(-k2_scores * scale).sum().backward()
assert torch.allclose(torch_activation_1.grad,
k2_activation_1.grad)
assert torch.allclose(torch_activation_2.grad,
k2_activation_2.grad)
assert torch.allclose(torch_activation_3.grad,
k2_activation_3.grad)
def main():
# load L, G, symbol_table
lang_dir = 'data/lang_nosp'
with open(lang_dir + '/L.fst.txt') as f:
L = k2.Fsa.from_openfst(f.read(), acceptor=False)
with open(lang_dir + '/G.fsa.txt') as f:
G = k2.Fsa.from_openfst(f.read(), acceptor=True)
with open(lang_dir + '/words.txt') as f:
symbol_table = k2.SymbolTable.from_str(f.read())
L = k2.arc_sort(L.invert_())
G = k2.arc_sort(G)
graph = k2.intersect(L, G)
graph = k2.arc_sort(graph)
# load dataset
feature_dir = 'exp/data1'
cuts_train = CutSet.from_json(feature_dir +
'/cuts_train-clean-100.json.gz')
cuts_dev = CutSet.from_json(feature_dir + '/cuts_dev-clean.json.gz')
train = K2SpeechRecognitionIterableDataset(cuts_train, shuffle=True)
validate = K2SpeechRecognitionIterableDataset(cuts_dev, shuffle=False)
train_dl = torch.utils.data.DataLoader(train,
batch_size=None,
num_workers=1)
valid_dl = torch.utils.data.DataLoader(validate,
batch_size=None,
num_workers=1)
dir = 'exp'
setup_logger('{}/log/log-train'.format(dir))
if not torch.cuda.is_available():
logging.error('No GPU detected!')
sys.exit(-1)
device_id = 0
device = torch.device('cuda', device_id)
model = Wav2Letter(num_classes=364, input_type='mfcc', num_features=40)
model.to(device)
learning_rate = 0.001
start_epoch = 0
num_epochs = 10
best_objf = 100000
best_epoch = start_epoch
best_model_path = os.path.join(dir, 'best_model.pt')
best_epoch_info_filename = os.path.join(dir, 'best-epoch-info')
optimizer = optim.Adam(model.parameters(),
lr=learning_rate,
weight_decay=5e-4)
# optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9)
for epoch in range(start_epoch, num_epochs):
curr_learning_rate = learning_rate * pow(0.4, epoch)
for param_group in optimizer.param_groups:
param_group['lr'] = curr_learning_rate
logging.info('epoch {}, learning rate {}'.format(
epoch, curr_learning_rate))
objf = train_one_epoch(dataloader=train_dl,
valid_dataloader=valid_dl,
model=model,
device=device,
graph=graph,
symbols=symbol_table,
optimizer=optimizer,
current_epoch=epoch,
num_epochs=num_epochs)
if objf < best_objf:
best_objf = objf
best_epoch = epoch
save_checkpoint(filename=best_model_path,
model=model,
epoch=epoch,
learning_rate=curr_learning_rate,
objf=objf)
save_training_info(filename=best_epoch_info_filename,
model_path=best_model_path,
current_epoch=epoch,
learning_rate=curr_learning_rate,
objf=best_objf,
best_objf=best_objf,
best_epoch=best_epoch)
# we always save the model for every epoch
model_path = os.path.join(dir, 'epoch-{}.pt'.format(epoch))
save_checkpoint(filename=model_path,
model=model,
epoch=epoch,
learning_rate=curr_learning_rate,
objf=objf)
epoch_info_filename = os.path.join(dir, 'epoch-{}-info'.format(epoch))
save_training_info(filename=epoch_info_filename,
model_path=model_path,
current_epoch=epoch,
learning_rate=curr_learning_rate,
objf=objf,
best_objf=best_objf,
best_epoch=best_epoch)
logging.warning('Done')
def test_random_case2(self):
# 2 sequences
for device in self.devices:
T1 = torch.randint(10, 200, (1, )).item()
T2 = torch.randint(9, 100, (1, )).item()
C = torch.randint(20, 30, (1, )).item()
if T1 < T2:
T1, T2 = T2, T1
torch_activation_1 = torch.rand((T1, C),
dtype=torch.float32,
device=device).requires_grad_(True)
torch_activation_2 = torch.rand((T2, C),
dtype=torch.float32,
device=device).requires_grad_(True)
k2_activation_1 = torch_activation_1.detach().clone(
).requires_grad_(True)
k2_activation_2 = torch_activation_2.detach().clone(
).requires_grad_(True)
# [T, N, C]
torch_activations = torch.nn.utils.rnn.pad_sequence(
[torch_activation_1, torch_activation_2],
batch_first=False,
padding_value=0)
# [N, T, C]
k2_activations = torch.nn.utils.rnn.pad_sequence(
[k2_activation_1, k2_activation_2],
batch_first=True,
padding_value=0)
target_length1 = torch.randint(1, T1, (1, )).item()
target_length2 = torch.randint(1, T2, (1, )).item()
target_lengths = torch.tensor([target_length1,
target_length2]).to(device)
targets = torch.randint(1, C - 1,
(target_lengths.sum(), )).to(device)
# [T, N, C]
torch_log_probs = torch.nn.functional.log_softmax(
torch_activations, dim=-1)
input_lengths = torch.tensor([T1, T2]).to(device)
torch_loss = torch.nn.functional.ctc_loss(
log_probs=torch_log_probs,
targets=targets,
input_lengths=input_lengths,
target_lengths=target_lengths,
reduction='none')
assert T1 >= T2
supervision_segments = torch.tensor([[0, 0, T1], [1, 0, T2]],
dtype=torch.int32)
k2_log_probs = torch.nn.functional.log_softmax(k2_activations,
dim=-1)
dense_fsa_vec = k2.DenseFsaVec(k2_log_probs,
supervision_segments).to(device)
ctc_topo_inv = k2.arc_sort(
build_ctc_topo(list(range(C))).invert_())
linear_fsa = k2.linear_fsa([
targets[:target_length1].tolist(),
targets[target_length1:].tolist()
])
decoding_graph = k2.intersect(ctc_topo_inv, linear_fsa)
decoding_graph = k2.connect(decoding_graph).invert_().to(device)
target_graph = k2.intersect_dense(decoding_graph, dense_fsa_vec,
100.0)
k2_scores = target_graph.get_tot_scores(log_semiring=True,
use_double_scores=False)
assert torch.allclose(torch_loss, -1 * k2_scores)
scale = torch.rand_like(torch_loss) * 100
(torch_loss * scale).sum().backward()
(-k2_scores * scale).sum().backward()
assert torch.allclose(torch_activation_1.grad,
k2_activation_1.grad,
atol=1e-2)
assert torch.allclose(torch_activation_2.grad,
k2_activation_2.grad,
atol=1e-2)
