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PaddleSpeech/runtime/engine/kaldi/fstext/determinize-lattice.h

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// fstext/determinize-lattice.h
// Copyright 2009-2011 Microsoft Corporation
// See ../../COPYING for clarification regarding multiple authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#ifndef KALDI_FSTEXT_DETERMINIZE_LATTICE_H_
#define KALDI_FSTEXT_DETERMINIZE_LATTICE_H_
#include <fst/fst-decl.h>
#include <fst/fstlib.h>
#include <algorithm>
#include <map>
#include <set>
#include <vector>
#include "fstext/lattice-weight.h"
namespace fst {
/// \addtogroup fst_extensions
/// @{
// For example of usage, see test-determinize-lattice.cc
/*
DeterminizeLattice implements a special form of determinization
with epsilon removal, optimized for a phase of lattice generation.
Its input is an FST with weight-type BaseWeightType (usually a pair of
floats, with a lexicographical type of order, such as
LatticeWeightTpl<float>). Typically this would be a state-level lattice, with
input symbols equal to words, and output-symbols equal to p.d.f's (so like
the inverse of HCLG). Imagine representing this as an acceptor of type
CompactLatticeWeightTpl<float>, in which the input/output symbols are words,
and the weights contain the original weights together with strings (with zero
or one symbol in them) containing the original output labels (the p.d.f.'s).
We determinize this using acceptor determinization with epsilon removal.
Remember (from lattice-weight.h) that CompactLatticeWeightTpl has a special
kind of semiring where we always take the string corresponding to the best
cost (of type BaseWeightType), and discard the other. This corresponds to
taking the best output-label sequence (of p.d.f.'s) for each input-label
sequence (of words). We couldn't use the Gallic weight for this, or it would
die as soon as it detected that the input FST was non-functional. In our
case, any acyclic FST (and many cyclic ones) can be determinized. We assume
that there is a function Compare(const BaseWeightType &a, const
BaseWeightType &b) that returns (-1, 0, 1) according to whether (a < b, a ==
b, a > b) in the total order on the BaseWeightType... this information should
be the same as NaturalLess would give, but it's more efficient to do it this
way. You can define this for things like TropicalWeight if you need to
instantiate this class for that weight type.
We implement this determinization in a special way to make it efficient for
the types of FSTs that we will apply it to. One issue is that if we
explicitly represent the strings (in CompactLatticeWeightTpl) as vectors of
type vector<IntType>, the algorithm takes time quadratic in the length of
words (in states), because propagating each arc involves copying a whole
vector (of integers representing p.d.f.'s). Instead we use a hash structure
where each string is a pointer (Entry*), and uses a hash from (Entry*,
IntType), to the successor string (and a way to get the latest IntType and
the ancestor Entry*). [this is the class LatticeStringRepository].
Another issue is that rather than representing a determinized-state as a
collection of (state, weight), we represent it in a couple of reduced forms.
Suppose a determinized-state is a collection of (state, weight) pairs; call
this the "canonical representation". Note: these collections are always
normalized to remove any common weight and string part. Define end-states as
the subset of states that have an arc out of them with a label on, or are
final. If we represent a determinized-state a the set of just its
(end-state, weight) pairs, this will be a valid and more compact
representation, and will lead to a smaller set of determinized states (like
early minimization). Call this collection of (end-state, weight) pairs the
"minimal representation". As a mechanism to reduce compute, we can also
consider another representation. In the determinization algorithm, we start
off with a set of (begin-state, weight) pairs (where the "begin-states" are
initial or have a label on the transition into them), and the "canonical
representation" consists of the epsilon-closure of this set (i.e. follow
epsilons). Call this set of (begin-state, weight) pairs, appropriately
normalized, the "initial representation". If two initial representations are
the same, the "canonical representation" and hence the "minimal
representation" will be the same. We can use this to reduce compute. Note
that if two initial representations are different, this does not preclude the
other representations from being the same.
*/
struct DeterminizeLatticeOptions {
float delta; // A small offset used to measure equality of weights.
int max_mem; // If >0, determinization will fail and return false
// when the algorithm's (approximate) memory consumption crosses this
// threshold.
int max_loop; // If >0, can be used to detect non-determinizable input
// (a case that wouldn't be caught by max_mem).
DeterminizeLatticeOptions() : delta(kDelta), max_mem(-1), max_loop(-1) {}
};
/**
This function implements the normal version of DeterminizeLattice, in which
the output strings are represented using sequences of arcs, where all but
the first one has an epsilon on the input side. The debug_ptr argument is
an optional pointer to a bool that, if it becomes true while the algorithm
is executing, the algorithm will print a traceback and terminate (used in
fstdeterminizestar.cc debug non-terminating determinization). More
efficient if ifst is arc-sorted on input label. If the number of arcs gets
more than max_states, it will throw std::runtime_error (otherwise this code
does not use exceptions). This is mainly useful for debug. */
template <class Weight, class IntType>
bool DeterminizeLattice(
const Fst<ArcTpl<Weight> > &ifst, MutableFst<ArcTpl<Weight> > *ofst,
DeterminizeLatticeOptions opts = DeterminizeLatticeOptions(),
bool *debug_ptr = NULL);
/* This is a version of DeterminizeLattice with a slightly more "natural"
output format, where the output sequences are encoded using the
CompactLatticeArcTpl template (i.e. the sequences of output symbols are
represented directly as strings) More efficient if ifst is arc-sorted on
input label. If the #arcs gets more than max_arcs, it will throw
std::runtime_error (otherwise this code does not use exceptions). This is
mainly useful for debug.
*/
template <class Weight, class IntType>
bool DeterminizeLattice(
const Fst<ArcTpl<Weight> > &ifst,
MutableFst<ArcTpl<CompactLatticeWeightTpl<Weight, IntType> > > *ofst,
DeterminizeLatticeOptions opts = DeterminizeLatticeOptions(),
bool *debug_ptr = NULL);
/// @} end "addtogroup fst_extensions"
} // end namespace fst
#include "fstext/determinize-lattice-inl.h"
#endif // KALDI_FSTEXT_DETERMINIZE_LATTICE_H_