def from_openfst(cls, s: str, acceptor: bool = True) -> 'Fsa':
'''Create an Fsa from a string in OpenFST format.
The given string `s` consists of lines with the following format:
(1) When it represents an acceptor:
src_state dest_state label score
(2) When it represents a transducer:
src_state dest_state label aux_label score
The line for the final state consists of two fields:
final_state score
Note:
Fields are separated by space(s), tab(s) or both. The `score`
field is a float, while other fields are integers.
There might be multiple final states. Also, OpenFst may omit the score
if it is 0.0.
Args:
s:
The input string. Refer to the above comment for its format.
acceptor:
Optional. If true, interpret the input string as an acceptor;
otherwise, interpret it as a transducer.
'''
arcs, aux_labels = _fsa_from_str(s, acceptor, True)
return Fsa(arcs, aux_labels=aux_labels)
def from_str(cls, s: str) -> 'Fsa':
'''Create an Fsa from a string in the k2 format.
(See also from_openfst).
The given string `s` consists of lines with the following format:
(1) When it represents an acceptor:
src_state dest_state label score
(2) When it represents a transducer:
src_state dest_state label aux_label score
The line for the final state consists of only one field:
final_state
Note:
Fields are separated by space(s), tab(s) or both. The `score`
field is a float, while other fields are integers.
Caution:
The first column has to be non-decreasing.
Caution:
The final state has the largest state number. There is only
one final state. All arcs that are connected to the final state
have label -1.
Args:
s:
The input string. Refer to the above comment for its format.
'''
# Figure out acceptor/transducer for k2 fsa.
acceptor = True
line = s.strip().split('\n', 1)[0]
fields = line.strip().split()
assert len(fields) == 4 or len(fields) == 5
if len(fields) == 5:
acceptor = False
ans = cls.__new__(cls)
super(Fsa, ans).__init__()
ans._init_internal()
arcs, aux_labels = _fsa_from_str(s, acceptor, False)
ans.arcs = arcs
ans._init_properties()
ans._tensor_attr['scores'] = _as_float(ans.arcs.values()[:, -1])
if aux_labels is not None:
ans.aux_labels = aux_labels.to(torch.int32)
return ans
def from_str(cls, s: str):
'''Create an Fsa from a string.
The given string `s` consists of lines with the following format:
(1) When it represents an acceptor:
src_state dest_state label score
(2) When it represents a transducer:
src_state dest_state label aux_label score
The line for the final state consists of only one field:
final_state
Note:
Fields are separated by space(s), tab(s) or both. The `score`
field is a float, while other fields are integers.
Caution:
The first column has to be non-decreasing.
Caution:
The final state has the largest state number. There is only
one final state. All arcs that are connected to the final state
have label -1.
Args:
s:
The input string. Refer to the above comment for its format.
'''
# Figure out acceptor/transducer for k2 fsa.
acceptor = True
line = s.strip().split('\n', 1)[0]
fields = line.strip().split()
assert len(fields) == 4 or len(fields) == 5
if len(fields) == 5:
acceptor = False
ans = cls.__new__(cls)
super(Fsa, ans).__init__()
fsa, aux_labels = _fsa_from_str(s, acceptor, False)
ans._fsa = fsa
ans._aux_labels = aux_labels
return ans
def __init__(self, s: str, negate_scores: bool = False):
'''Create an Fsa from a string.
The given string `s` consists of lines with the following format:
(1) When it represents an acceptor:
src_state dest_state label score
(2) When it represents a transducer:
src_state dest_state label aux_label score
The line for the final state consists of only one field:
final_state
Note:
Fields are separated by space(s), tab(s) or both. The `score`
field is a float, while other fields are integers.
Caution:
The first column has to be non-decreasing.
Caution:
The final state has the largest state number. There is only
one final state. All arcs that are connected to the final state
have label -1.
Args:
s:
The input string. Refer to the above comment for its format.
negate_scores:
Optional. If true, the string form has the weights as costs,
not scores, so we negate as we read.
'''
fsa: _Fsa
aux_labels: Optional[torch.Tensor]
fsa, aux_labels = _fsa_from_str(s, negate_scores)
self._fsa = fsa
self._aux_labels = aux_labels
def from_openfst(cls, s: str, acceptor: bool = True) -> 'Fsa':
'''Create an Fsa from a string in OpenFST format.
The given string `s` consists of lines with the following format:
(1) When it represents an acceptor:
src_state dest_state label score
(2) When it represents a transducer:
src_state dest_state label aux_label score
The line for the final state consists of two fields:
final_state score
Note:
Fields are separated by space(s), tab(s) or both. The `score`
field is a float, while other fields are integers.
There might be multiple final states. Also, OpenFst may omit the score
if it is 0.0.
Args:
s:
The input string. Refer to the above comment for its format.
acceptor:
Optional. If true, interpret the input string as an acceptor;
otherwise, interpret it as a transducer.
'''
ans = cls.__new__(cls)
super(Fsa, ans).__init__()
ans._init_internal()
arcs, aux_labels = _fsa_from_str(s, acceptor, True)
ans.arcs = arcs
ans._init_properties()
ans._tensor_attr['scores'] = _as_float(ans.arcs.values()[:, -1])
if aux_labels is not None:
ans.aux_labels = aux_labels.to(torch.int32)
return ans
