def __weighted_union(self, left, right, left_prob, right_prob):
'''
Union the FSTs left, right with a weight.
'''
# left hand side part.
left_w = -math.log(left_prob)
lhs = fst.Fst()
lhs.set_input_symbols(left.input_symbols())
lhs.set_output_symbols(left.output_symbols())
lhs.add_state()
lhs.set_start(0)
lhs.add_state()
lhs.add_arc(0, fst.Arc(0, 0, left_w, 1))
lhs.set_final(1)
lhs.concat(left)
# prefix part.
right_w = -math.log(right_prob)
rhs = fst.Fst()
rhs.set_input_symbols(right.input_symbols())
rhs.set_output_symbols(right.output_symbols())
rhs.add_state()
rhs.set_start(0)
rhs.add_state()
rhs.add_arc(0, fst.Arc(0, 0, right_w, 1))
rhs.set_final(1)
rhs.concat(right)
lhs.union(rhs)
return lhs
def spellout_machine(wrdfname, ltr2wrdfst):
lm = fst.Fst.read(ltr2wrdfst)
s_in = lm.output_symbols()
s_out = lm.input_symbols()
letter = fst.Fst()
letter.set_input_symbols(s_in)
letter.set_output_symbols(s_out)
letter.add_state()
for word in open(wrdfname, "r").readlines():
word = word.strip()
orig = copy.copy(word)
# word = list(word)
word += "#"
#word = dig2word(word)
nletter = fst.Fst()
nletter.set_input_symbols(s_in)
nletter.set_output_symbols(s_out)
nletter.add_state()
for i, ltr in enumerate(word):
nletter.add_state()
code2 = s_out.find(ltr)
if i == 0:
nletter.set_start(0)
code1 = s_in.find(orig)
nletter.add_arc(i, fst.Arc(code1, code2, None, i + 1))
else:
code1 = s_in.find("<epsilon>")
nletter.add_arc(i, fst.Arc(code1, code2, None, i + 1))
nletter.set_final(i + 1)
letter.union(nletter)
letter.rmepsilon()
letter.write("spellout.fst")
def make_lexicon_fst(input_words, words):
compiler = fst.Compiler()
lexicon_fst = fst.Fst()
start = lexicon_fst.add_state()
lexicon_fst.set_start(start)
last = lexicon_fst.add_state()
# this line projects space to epsilon
lexicon_fst.add_arc(
start, fst.Arc(32, 0, fst.Weight.One(lexicon_fst.weight_type()), last))
lexicon_fst.set_final(last)
for i, w in enumerate(words):
w = w.strip()
index = i + 1
last = lexicon_fst.add_state()
lexicon_fst.add_arc(
start,
fst.Arc(ord(w[0]), index,
fst.Weight.One(lexicon_fst.weight_type()), last))
for c in w[1:]:
this = lexicon_fst.add_state()
lexicon_fst.add_arc(
last,
fst.Arc(ord(c), 0, fst.Weight.One(lexicon_fst.weight_type()),
this))
last = this
lexicon_fst.set_final(last, 0)
lexicon_fst = fst.determinize(lexicon_fst).minimize().closure()
with open('words.syms', 'w') as f:
f.write('<eps> 0\n')
for i, w in enumerate(words + input_words): # we put word symbol here
f.write('{} {}'.format(w, str(i + 1)))
f.write('\n')
f.write('<SPACE> {}\n'.format(str(32)))
epsilon_fst = fst.Fst()
start = epsilon_fst.add_state()
end = epsilon_fst.add_state()
for i, w in enumerate(words):
index = i + 1
epsilon_fst.add_arc(
start,
fst.Arc(0, index, fst.Weight.One(epsilon_fst.weight_type()), end))
epsilon_fst.add_arc(
start, fst.Arc(0, 32, fst.Weight.One(epsilon_fst.weight_type()), end))
epsilon_fst.set_final(end, 0)
epsilon_fst.set_start(start)
epsilon_fst = epsilon_fst.closure()
return lexicon_fst, epsilon_fst
def clear(self):
"""
Clears all internal data.
"""
self.syms = fst.SymbolTable()
self.E = fst.Fst()
self.Ig = fst.Fst()
self.Ip = fst.Fst()
self.Ip_r = re.compile(u"")
self.status = 0
def decompound(self, word):
tree = Tree(word)
self._split(word, tree)
#print(tree)
nleafnodes = tree.nleafnodes()
#print("Number of leaf nodes:", nleafnodes)
symtablel = sorted(tree.getsyms())
symtable = dict([(s, i) for i, s in enumerate(symtablel)])
#print("Symbols:")
#print(symtable)
fst = wfst.Fst()
[fst.add_state() for i in range(nleafnodes + 1)]
fst.set_final(nleafnodes, wfst.Weight.One(fst.weight_type()))
fst.set_start(0)
tree.makelattice(fst, 0, symtable, self.wordcost, firstword=True)
#output fst for debugging
# fstsymtable = wfst.SymbolTable(b"default")
# for i, sym in enumerate(symtablel):
# fstsymtable.add_symbol(sym.encode("utf-8"), i)
# fst.set_input_symbols(fstsymtable)
# fst.set_output_symbols(fstsymtable)
# fst.write("/tmp/debug.fst")
best = wfst.shortestpath(fst, nshortest=1)
wordseq = label_seq(best, symtablel)
return wordseq
def normalize(self, anfst):
'''
produce a normalized fst
'''
# possibly there's a shorter way
# that keeps all in fst land
dist = []
labels = []
syms = anfst.input_symbols()
state = anfst.start()
for arc in anfst.arcs(state):
label = syms.find(arc.ilabel)
pr = float(arc.weight)
dist.append(BitWeight(pr)) # ebitweight gets -log(pr) only
labels.append(label)
sum_value = sum(dist, BitWeight(1e6)) # will sum in log domain (log-add)
norm_dist = [(prob/sum_value).loge() for prob in dist]
del anfst
# construct a norm fst
output = fst.Fst()
output.set_input_symbols(syms)
output.set_output_symbols(syms)
output.add_state()
output.add_state()
for (pr, label) in zip(norm_dist,labels):
code = syms.find(label)
output.add_arc(0, fst.Arc(code, code, pr, 1))
output.set_start(0)
output.set_final(1)
return output
def enterRuleBody(self, ctx):
super().enterRuleBody(ctx)
# Create new FST for rule
self.fst = fst.Fst()
self.start_state = self.fst.add_state()
self.fst.set_start(self.start_state)
self.last_states[self.rule_name] = self.start_state
self.weight_one = fst.Weight.One(self.fst.weight_type())
if self.is_public:
# Check if this is the main rule of the grammar
grammar_rule = self.grammar_name + "." + self.grammar_name
if self.rule_name == grammar_rule:
self.grammar_fst = self.fst
# Cache FST
self.fsts[self.rule_name] = self.fst
# Reset state
self.group_depth = 0
self.opt_states = {}
self.alt_states = {}
self.tag_states = {}
self.exp_states = {}
self.alt_ends = {}
# Save anchor state
self.alt_states[self.group_depth] = self.last_states[self.rule_name]
def build_chain_fst(labels, arc_type='log', vocab=None):
"""
Build an acceptor for string given by elements of labels.
Args:
labels - a sequence of labels in the range 1..S
arc_type - fst arc type (standard or log)
Returns:
FST consuming symbols in the range 1..S.
Notes:
Elements of labels are assumed to be greater than zero
(which maps to blank)!
"""
C = fst.Fst(arc_type=arc_type)
weight_one = fst.Weight.One(C.weight_type())
s = C.add_state()
C.set_start(s)
for l in labels:
s_next = C.add_state()
C.add_arc(s, fst.Arc(l, l, weight_one, s_next))
s = s_next
C.set_final(s)
C.arcsort('ilabel')
return C
def _build_transliterator():
td = fst.Fst()
initial_state = td.add_state()
td.set_start(initial_state)
td.set_final(initial_state)
long_vowel_possibilities = {
'u': 'ウ',
'i': 'イ',
'e': 'エ',
'o': ['オ', 'ウ'],
'a': 'ア'
}
long_vowel_states = {
k: _long_vowel_mark_state(td, initial_state, v)
for k, v in long_vowel_possibilities.items()
}
end_states = {
'n': initial_state,
'y': _build_small_y_state(td, long_vowel_states),
**long_vowel_states
}
_build_sjsh(td, initial_state, end_states)
_build_vowels(td, initial_state, end_states)
_build_tdch(td, initial_state, end_states)
_build_big_y(td, initial_state, end_states)
_build_hpb(td, initial_state, end_states)
_build_kg(td, initial_state, end_states)
_build_r(td, initial_state, end_states)
_build_m(td, initial_state, end_states)
_build_n(td, initial_state, end_states)
_build_w(td, initial_state, end_states)
return td
def __call__(self, x):
x, xs = transform_output(x)
# Normalize log-posterior matrices, if necessary
if self._normalize:
x = log_softmax(x, dim=2)
x = x.permute(1, 0, 2).cpu()
self._output = []
D = x.size(2)
for logpost, length in zip(x, xs):
f = fst.Fst()
f.set_start(f.add_state())
for t in range(length):
f.add_state()
for j in range(D):
weight = fst.Weight(f.weight_type(), float(-logpost[t, j]))
f.add_arc(
t,
fst.Arc(
j + 1, # input label
j + 1, # output label
weight, # -logpost[t, j]
t + 1, # nextstate
),
)
f.set_final(length, fst.Weight.One(f.weight_type()))
f.verify()
self._output.append(f)
return self._output
def enterRuleBody(self, ctx):
self.in_rule = True
if self.is_public:
# Use main start state
self.last_states[self.rule_name] = self.start_state
else:
# Create new FST
self.fst = fst.Fst()
self.start_state = self.fst.add_state()
self.fst.set_start(self.start_state)
self.last_states[self.rule_name] = self.start_state
self.fsts[self.rule_name] = self.fst
# Reset
self.group_depth = 0
self.opt_states = {}
self.alt_states = {}
self.tag_states = {}
self.exp_states = {}
self.alt_ends = {}
# Save anchor state
self.alt_states[self.group_depth] = self.last_states[self.rule_name]
def genStrFst(ilabels, olabels=None):
if olabels is None:
olabels = ilabels
fst = pywrapfst.Fst()
initFst(fst)
addArcLinear(fst, 0, ilabels, olabels, is_loop=False)
return fst
def _make_slot_fst(state: int, intent_fst: fst.Fst,
slot_to_fst: Dict[str, fst.Fst]):
out_symbols = intent_fst.output_symbols()
one_weight = fst.Weight.One(intent_fst.weight_type())
for arc in intent_fst.arcs(state):
label = out_symbols.find(arc.olabel).decode()
if label.startswith("__begin__"):
slot_name = label[9:]
# Big assumption here that each instance of a slot (e.g., location)
# will produce the same FST, and therefore doesn't need to be
# processed again.
if slot_name in slot_to_fst:
continue # skip duplicate slots
end_label = f"__end__{slot_name}"
# Create new FST
slot_fst = fst.Fst()
slot_fst.set_input_symbols(intent_fst.input_symbols())
slot_fst.set_output_symbols(intent_fst.output_symbols())
start_state = slot_fst.add_state()
slot_fst.set_start(start_state)
q = [arc.nextstate]
state_map = {arc.nextstate: start_state}
# Copy states/arcs from intent FST until __end__ is found
while len(q) > 0:
q_state = q.pop()
for q_arc in intent_fst.arcs(q_state):
slot_arc_label = out_symbols.find(q_arc.olabel).decode()
if slot_arc_label != end_label:
if not q_arc.nextstate in state_map:
state_map[q_arc.nextstate] = slot_fst.add_state()
# Create arc
slot_fst.add_arc(
state_map[q_state],
fst.Arc(
q_arc.ilabel,
q_arc.olabel,
one_weight,
state_map[q_arc.nextstate],
),
)
# Continue copy
q.append(q_arc.nextstate)
else:
# Mark previous state as final
slot_fst.set_final(state_map[q_state])
slot_to_fst[slot_name] = minimize_fst(slot_fst)
# Recurse
_make_slot_fst(arc.nextstate, intent_fst, slot_to_fst)
def acceptor_for_strings(strings: List[str], weights: List[float]) -> fst.Fst:
"""Create an acceptor for strings with weights"""
strings, weights = zip(*sorted(zip(strings, weights)))
td = fst.Fst()
start_state = td.add_state()
td.set_start(start_state)
_build_acceptor_recursive(td, strings, weights, start_state, 0, 0,
len(strings))
return td
def create_empty_fst(self, input_sym, output_sym):
'''
Create an empty fst (only one state being final).
'''
f = fst.Fst()
f.set_input_symbols(input_sym)
f.set_output_symbols(output_sym)
f.add_state()
f.set_start(0)
f.set_final(0)
return f
def make_input(chars, stoi):
fst = wfst.Fst()
s0 = fst.add_state()
fst.set_start(s0)
cs = s0
for c in chars:
ns = fst.add_state()
fst.add_arc(cs, wfst.Arc(stoi[c], stoi[c], wfst.Weight.One(fst.weight_type()), ns))
cs = ns
fst.set_final(cs, wfst.Weight.One(fst.weight_type()))
return fst
def make_kleeneplus(s, graphs, stoi):
"""one-or-more-graphs"""
fst = wfst.Fst()
start = fst.add_state()
end = fst.add_state()
fst.set_start(start)
fst.set_final(end, wfst.Weight.One(fst.weight_type()))
for g in graphs:
fst.add_arc(start, wfst.Arc(stoi[g], stoi[g], wfst.Weight.One(fst.weight_type()), end))
fst.add_arc(end, wfst.Arc(stoi[g], stoi[g], wfst.Weight.One(fst.weight_type()), end))
return fst
def __init__(self):
'''
Constructor
'''
self.fst = pywrapfst.Fst()
self.STATE_START = self.fst.add_state()
self.fst.set_start(self.STATE_START)
self.fst.set_input_symbols(pywrapfst.SymbolTable())
self.fst.set_output_symbols(pywrapfst.SymbolTable())
self.fst.mutable_input_symbols().add_symbol(LAB_EPS, key=SYM_EPS)
self.fst.mutable_output_symbols().add_symbol(LAB_EPS, key=SYM_EPS)
def make_compounder(syms, word_ids):
c = fst.Fst()
start_state = c.add_state()
assert (start_state == 0)
c.set_start(start_state)
space_id = syms["<space>"]
c.add_arc(0, fst.Arc(space_id, syms["<eps>"], 1, 0))
c.add_arc(0, fst.Arc(space_id, syms["+C+"], 1, 0))
c.add_arc(0, fst.Arc(space_id, syms["+D+"], 1, 0))
for word_id in word_ids:
c.add_arc(0, fst.Arc(word_id, word_id, 1, 0))
c.set_final(0, 1)
return c
def append_eeg_evidence(self, ch_dist):
new_ch = fst.Fst()
new_ch.set_input_symbols(self.ch_syms)
new_ch.set_output_symbols(self.ch_syms)
new_ch.add_state()
new_ch.set_start(0)
new_ch.add_state()
new_ch.set_final(1)
for ch, pr in ch_dist:
code = self.ch_syms.find(ch)
new_ch.add_arc(0, fst.Arc(code, code, pr, 1))
new_ch.arcsort(sort_type="olabel")
self.history_fst.concat(new_ch).rmepsilon()
def get_trivial_fst(word_index):
trivial_word_fst = fst.Fst()
start = trivial_word_fst.add_state()
end = trivial_word_fst.add_state()
trivial_word_fst.set_start(start)
trivial_word_fst.set_final(end, 0)
trivial_word_fst.add_arc(
start,
fst.Arc(word_index, 0, fst.Weight.One(trivial_word_fst.weight_type()),
end))
return trivial_word_fst
def update(self, ch_dist):
'''
Update the history with the new likelihood array in the correct scale
(nagative log space) to the history.
'''
new_ch = fst.Fst()
new_ch.set_input_symbols(self.ch_syms)
new_ch.set_output_symbols(self.ch_syms)
new_ch.add_state()
new_ch.set_start(0)
new_ch.add_state()
new_ch.set_final(1)
space_code = -1
space_pr = 0.
for ch, pr in ch_dist:
code = self.ch_syms.find(ch)
if ch == '#': # Adds space after we finish updating trailing chars.
space_code = code
space_pr = pr
continue
new_ch.add_arc(0, fst.Arc(code, code, pr, 1))
new_ch.arcsort(sort_type="olabel")
# Adds the trailing characters to existing binned history.
for words_bin in self.prefix_words:
if words_bin[2] >= 10: # We discard the whole trail in this case (TODO)
continue
# Unless we are testing a straight line machine, this normally
# doesn't happen in practice.
if new_ch.num_arcs(0) == 0:
continue
words_bin[1].concat(new_ch).rmepsilon()
words_bin[2] += 1
# Continues updating the history and adds back the space if necessary.
if space_code >= 0:
new_ch.add_arc(0, fst.Arc(space_code, space_code, space_pr, 1))
self.history_fst.concat(new_ch).rmepsilon()
# Respectively update the binned history
if space_code >= 0: # If there is a space
# Finishes the prefix words in current position
word_lattice = fst.compose(self.history_fst, self.ltr2wrd)
word_lattice.project(project_output=True).rmepsilon()
word_lattice = fst.determinize(word_lattice)
word_lattice.minimize()
if word_lattice.num_states() == 0:
word_lattice = self.create_empty_fst(self.wd_syms, self.wd_syms)
trailing_chars = self.create_empty_fst(self.ch_syms, self.ch_syms)
self.prefix_words.append([word_lattice, trailing_chars, 0])
def make_termfst(s, paths, stoi):
fst = wfst.Fst()
start = fst.add_state()
fst.set_start(start)
for path in paths:
a = start
for g in path:
b = fst.add_state()
fst.add_arc(a, wfst.Arc(stoi[g], stoi[g], wfst.Weight.One(fst.weight_type()), b))
a = b
fst.set_final(b, wfst.Weight.One(fst.weight_type()))
fst = wfst.determinize(fst)
fst.minimize()
return fst
def make_intent_fst(grammar_fsts: Dict[str, fst.Fst],
eps: str = "<eps>") -> fst.Fst:
"""Merges grammar FSTs created with grammar_to_fsts into a single acceptor FST."""
input_symbols = fst.SymbolTable()
output_symbols = fst.SymbolTable()
in_eps: int = input_symbols.add_symbol(eps)
out_eps: int = output_symbols.add_symbol(eps)
intent_fst = fst.Fst()
weight_one = fst.Weight.One(intent_fst.weight_type())
# Create start/final states
start_state = intent_fst.add_state()
intent_fst.set_start(start_state)
final_state = intent_fst.add_state()
intent_fst.set_final(final_state)
replacements: Dict[int, fst.Fst] = {}
for intent, grammar_fst in grammar_fsts.items():
intent_label = f"__label__{intent}"
out_label = output_symbols.add_symbol(intent_label)
# --[__label__INTENT]-->
intent_start = intent_fst.add_state()
intent_fst.add_arc(
start_state, fst.Arc(in_eps, out_label, weight_one, intent_start))
# --[__replace__INTENT]-->
intent_end = intent_fst.add_state()
replace_symbol = f"__replace__{intent}"
out_replace = output_symbols.add_symbol(replace_symbol)
intent_fst.add_arc(
intent_start, fst.Arc(in_eps, out_replace, weight_one, intent_end))
# --[eps]-->
intent_fst.add_arc(intent_end,
fst.Arc(in_eps, out_eps, weight_one, final_state))
replacements[out_replace] = grammar_fst
# Fix symbol tables
intent_fst.set_input_symbols(input_symbols)
intent_fst.set_output_symbols(output_symbols)
# Do replacements
return _replace_fsts(intent_fst, replacements, eps=eps)
def longest_path(the_fst: fst.Fst, eps: str = "<eps>") -> fst.Fst:
output_symbols = the_fst.output_symbols()
out_eps = output_symbols.find(eps)
visited_states: Set[int] = set()
best_path: List[int] = []
state_queue: Deque[Tuple[int, List[int]]] = deque()
state_queue.append((the_fst.start(), []))
# Determine longest path
while len(state_queue) > 0:
state, path = state_queue.popleft()
if state in visited_states:
continue
visited_states.add(state)
if len(path) > len(best_path):
best_path = path
for arc in the_fst.arcs(state):
next_path = list(path)
next_path.append(arc.olabel)
state_queue.append((arc.nextstate, next_path))
# Create FST with longest path
path_fst = fst.Fst()
input_symbols = fst.SymbolTable()
input_symbols.add_symbol(eps)
path_fst.set_output_symbols(output_symbols)
weight_one = fst.Weight.One(path_fst.weight_type())
state = path_fst.add_state()
path_fst.set_start(state)
for olabel in best_path:
osym = output_symbols.find(olabel).decode()
next_state = path_fst.add_state()
path_fst.add_arc(
state,
fst.Arc(input_symbols.add_symbol(osym), olabel, weight_one,
next_state),
)
state = next_state
path_fst.set_final(state)
path_fst.set_input_symbols(input_symbols)
return path_fst
def _make_input_fst(string):
# Input is passed as acceptors that accept a single string
td = fst.Fst()
curr = td.add_state()
td.set_start(curr)
for c in string:
nxt = td.add_state()
try:
_char_arc(td, curr, c, c, nxt)
except KeyError:
raise ValueError(
'Character {} not in input symbol table'.format(c))
curr = nxt
td.set_final(curr)
return td
def build_ctc_mono_decoding_fst(S, arc_type='log', add_syms=False):
"""
Build a monophone CTC decoding fst.
Args:
S - number of monophones
arc_type - log or standard. Gives the interpretation of the FST.
Returns:
an FST that accepts all sequences over [1,..,S]^* and returns
shorter ones with duplicates and blanks removed.
The input labels are shifted by one, so that there are no epsilon
transitions.
The output labels are not (blank is zero), allowing one to read out
the label sequence easily.
"""
CTC = fst.Fst(arc_type=arc_type)
weight_one = fst.Weight.One(CTC.weight_type())
for s in range(S):
s1 = CTC.add_state()
assert s == s1
CTC.set_final(s1)
CTC.set_start(0)
for s in range(S):
# transitions out of symbol s
# self-loop, don't emit
CTC.add_arc(s, fst.Arc(s + 1, 0, weight_one, s))
for s_next in range(S):
if s_next == s:
continue
# transition to next symbol
CTC.add_arc(s, fst.Arc(s_next + 1, s_next, weight_one, s_next))
CTC.arcsort('olabel')
if add_syms:
in_syms = fst.SymbolTable()
in_syms.add_symbol('<eps>', 0)
in_syms.add_symbol('B', 1)
for s in range(1, S):
in_syms.add_symbol(chr(ord('a') + s - 1), s + 1)
out_syms = fst.SymbolTable()
out_syms.add_symbol('<eps>', 0)
for s in range(1, S):
out_syms.add_symbol(chr(ord('a') + s - 1), s)
CTC.set_input_symbols(in_syms)
CTC.set_output_symbols(out_syms)
return CTC
def make_intent_fst(grammar_fsts: Dict[str, fst.Fst], eps=0) -> fst.Fst:
"""Merges grammar FSTs created with jsgf2fst into a single acceptor FST."""
intent_fst = fst.Fst()
all_in_symbols = fst.SymbolTable()
all_out_symbols = fst.SymbolTable()
all_in_symbols.add_symbol("<eps>", eps)
all_out_symbols.add_symbol("<eps>", eps)
# Merge symbols from all FSTs
for grammar_fst in grammar_fsts.values():
in_symbols = grammar_fst.input_symbols()
for i in range(in_symbols.num_symbols()):
all_in_symbols.add_symbol(in_symbols.find(i).decode())
out_symbols = grammar_fst.output_symbols()
for i in range(out_symbols.num_symbols()):
all_out_symbols.add_symbol(out_symbols.find(i).decode())
# Add __label__ for each intent
for intent_name in grammar_fsts.keys():
all_out_symbols.add_symbol(f"__label__{intent_name}")
intent_fst.set_input_symbols(all_in_symbols)
intent_fst.set_output_symbols(all_out_symbols)
# Create start/final states
start_state = intent_fst.add_state()
intent_fst.set_start(start_state)
final_state = intent_fst.add_state()
intent_fst.set_final(final_state)
# Merge FSTs in
for intent_name, grammar_fst in grammar_fsts.items():
label_sym = all_out_symbols.find(f"__label__{intent_name}")
replace_and_patch(intent_fst,
start_state,
final_state,
grammar_fst,
label_sym,
eps=eps)
# BUG: Fst.minimize does not pass allow_nondet through, so we have to call out to the command-line
minimize_cmd = ["fstminimize", "--allow_nondet"]
return fst.Fst.read_from_string(
subprocess.check_output(minimize_cmd,
input=intent_fst.write_to_string()))
def toFst(self):
"""Convert the HMM graph to an OpenFst object.
You need to have installed the OpenFst python extension to use
this method.
Returns
-------
graph : pywrapfst.Fst
The FST representation of the HMM graph. An super initial
state and a super final state will be added though they are
not present in the HMM.
"""
import pywrapfst as fst
f = fst.Fst('log')
start_state = f.add_state()
f.set_start(start_state)
end_state = f.add_state()
f.set_final(end_state)
state_fstid = {}
for state in self.states:
fstid = f.add_state()
state_fstid[state.state_id] = fstid
for state in self.states:
for next_state_id, weight in state.next_states.items():
fstid = state_fstid[state.state_id]
next_fstid = state_fstid[next_state_id]
arc = fst.Arc(0, 0, fst.Weight('log', -weight), next_fstid)
f.add_arc(fstid, arc)
for state in self.init_states:
fstid = state_fstid[state.state_id]
arc = fst.Arc(0, 0, fst.Weight.One('log'), fstid)
f.add_arc(start_state, arc)
for state in self.final_states:
fstid = state_fstid[state.state_id]
arc = fst.Arc(0, 0, fst.Weight.One('log'), end_state)
f.add_arc(fstid, arc)
return f
def __align_fst(self, g, p):
'''
Creates an alignment of a grapheme and phoneme sequence pair encoded as fst.
'''
t3 = self.segment(g)
t3.project(project_output=True)
t4 = self.expand(p)
t4.project(project_output=True)
if t4.start() == -1 or t4.num_arcs(t4.start()) == 0:
return fst.Fst()
t5 = fst.compose(t3, self.E)
t6 = fst.compose(t5, t4)
return t6
