Initial commit: FunASR Speech Recognition Toolkit
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Add complete FunASR codebase including models, runtime, and documentation.
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// fstext/context-fst-test.cc
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// Copyright 2009-2011 Microsoft Corporation
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// See ../../COPYING for clarification regarding multiple authors
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
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// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
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// MERCHANTABLITY OR NON-INFRINGEMENT.
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// See the Apache 2 License for the specific language governing permissions and
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// limitations under the License.
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#include "fstext/context-fst.h"
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#include "fstext/fst-test-utils.h"
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#include "tree/context-dep.h"
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#include "util/kaldi-io.h"
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#include "base/kaldi-math.h"
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namespace fst
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{
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using std::vector;
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using std::cout;
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// GenAcceptorFromSequence generates a linear acceptor (identical input+output symbols) that has this
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// sequence of symbols, and
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template<class Arc>
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static VectorFst<Arc> *GenAcceptorFromSequence(const vector<typename Arc::Label> &symbols, float cost) {
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typedef typename Arc::Weight Weight;
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typedef typename Arc::StateId StateId;
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vector<float> split_cost(symbols.size()+1, 0.0); // for #-arcs + end-state.
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{ // compute split_cost. it must sum to "cost".
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std::set<int32> indices;
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size_t num_indices = 1 + (kaldi::Rand() % split_cost.size());
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while (indices.size() < num_indices) indices.insert(kaldi::Rand() % split_cost.size());
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for (std::set<int32>::iterator iter = indices.begin(); iter != indices.end(); ++iter) {
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split_cost[*iter] = cost / num_indices;
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}
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}
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VectorFst<Arc> *fst = new VectorFst<Arc>();
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StateId cur_state = fst->AddState();
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fst->SetStart(cur_state);
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for (size_t i = 0; i < symbols.size(); i++) {
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StateId next_state = fst->AddState();
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Arc arc;
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arc.ilabel = symbols[i];
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arc.olabel = symbols[i];
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arc.nextstate = next_state;
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arc.weight = (Weight) split_cost[i];
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fst->AddArc(cur_state, arc);
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cur_state = next_state;
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}
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fst->SetFinal(cur_state, (Weight)split_cost[symbols.size()]);
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return fst;
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}
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// CheckPhones is used to test the correctness of an FST that is the result of
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// composition with a ContextFst.
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template<class Arc>
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static float CheckPhones(const VectorFst<Arc> &linear_fst,
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const vector<typename Arc::Label> &phone_ids,
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const vector<typename Arc::Label> &disambig_ids,
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const vector<typename Arc::Label> &phone_seq,
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const vector<vector<typename Arc::Label> > &ilabel_info,
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int N, int P) {
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typedef typename Arc::Label Label;
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typedef typename Arc::StateId StateId;
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typedef typename Arc::Weight Weight;
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assert(kaldi::IsSorted(phone_ids)); // so we can do binary_search.
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vector<int32> input_syms;
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vector<int32> output_syms;
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Weight tot_cost;
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bool ans = GetLinearSymbolSequence(linear_fst, &input_syms,
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&output_syms, &tot_cost);
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assert(ans); // should be linear.
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vector<int32> phone_seq_check;
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for (size_t i = 0; i < output_syms.size(); i++)
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if (std::binary_search(phone_ids.begin(), phone_ids.end(), output_syms[i]))
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phone_seq_check.push_back(output_syms[i]);
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assert(phone_seq_check == phone_seq);
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vector<vector<int32> > input_syms_long;
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for (size_t i = 0; i < input_syms.size(); i++) {
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Label isym = input_syms[i];
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if (ilabel_info[isym].size() == 0) continue; // epsilon.
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if ( (ilabel_info[isym].size() == 1 &&
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ilabel_info[isym][0] <= 0) ) continue; // disambig.
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input_syms_long.push_back(ilabel_info[isym]);
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}
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for (size_t i = 0; i < input_syms_long.size(); i++) {
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vector<int32> phone_context_window(N); // phone at pos i will be at pos P in this window.
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int pos = ((int)i) - P; // pos of first phone in window [ may be out of range] .
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for (int j = 0; j < N; j++, pos++) {
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if (static_cast<size_t>(pos) < phone_seq.size()) phone_context_window[j] = phone_seq[pos];
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else phone_context_window[j] = 0; // 0 is a special symbol that context-dep-itf expects to see
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// when no phone is present due to out-of-window. context-fst knows about this too.
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}
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assert(input_syms_long[i] == phone_context_window);
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}
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return tot_cost.Value();
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}
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template<class Arc>
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static VectorFst<Arc> *GenRandPhoneSeq(vector<typename Arc::Label> &phone_syms,
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vector<typename Arc::Label> &disambig_syms,
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typename Arc::Label subsequential_symbol,
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int num_subseq_syms,
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float seq_prob,
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vector<typename Arc::Label> *phoneseq_out) {
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KALDI_ASSERT(phoneseq_out != NULL);
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typedef typename Arc::Label Label;
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// Generate an FST that is a random phone sequence, ending
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// with "num_subseq_syms" subsequential symbols. It will
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// have disambiguation symbols randomly interspersed throughout.
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// The number of phones is random (possibly zero).
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size_t len = (kaldi::Rand() % 4) * (kaldi::Rand() % 3); // up to 3*2=6 phones.
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float disambig_prob = 0.33;
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phoneseq_out->clear();
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vector<Label> syms; // the phones
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for (size_t i = 0; i < len; i++) {
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while (kaldi::RandUniform() < disambig_prob) syms.push_back(disambig_syms[kaldi::Rand() % disambig_syms.size()]);
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Label phone_id = phone_syms[kaldi::Rand() % phone_syms.size()];
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phoneseq_out->push_back(phone_id); // record in output the underlying phone sequence.
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syms.push_back(phone_id);
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}
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for (size_t i = 0; static_cast<int32>(i) < num_subseq_syms; i++) {
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while (kaldi::RandUniform() < disambig_prob) syms.push_back(disambig_syms[kaldi::Rand() % disambig_syms.size()]);
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syms.push_back(subsequential_symbol);
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}
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while (kaldi::RandUniform() < disambig_prob) syms.push_back(disambig_syms[kaldi::Rand() % disambig_syms.size()]);
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// OK, now have the symbols of the FST as a vector.
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return GenAcceptorFromSequence<Arc>(syms, seq_prob);
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}
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// Don't instantiate with log semiring, as RandEquivalent may fail.
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// TestContestFst also test ReadILabelInfo and WriteILabelInfo.
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static void TestContextFst(bool verbose, bool use_matcher) {
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typedef StdArc Arc;
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typedef Arc::Label Label;
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typedef Arc::StateId StateId;
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typedef Arc::Weight Weight;
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// Generate a random set of phones.
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size_t num_phones = 1 + kaldi::Rand() % 10;
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std::set<int32> phones_set;
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while (phones_set.size() < num_phones) phones_set.insert(1 + kaldi::Rand() % (num_phones + 5)); // don't use 0 [== epsilon]
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vector<int32> phones;
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kaldi::CopySetToVector(phones_set, &phones);
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int N = 1 + kaldi::Rand() % 4; // Context size, in range 1..4.
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int P = kaldi::Rand() % N; // 1.. N-1.
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if (verbose) std::cout << "N = "<< N << ", P = "<<P<<'\n';
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Label subsequential_symbol = 1000;
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vector<int32> disambig_syms;
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for (size_t i =0; i < 5; i++) disambig_syms.push_back(500 + i);
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vector<int32> phone_syms;
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for (size_t i = 0; i < phones.size();i++) phone_syms.push_back(phones[i]);
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InverseContextFst inv_cfst(subsequential_symbol,
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phones, disambig_syms,
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N, P);
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/* Now create random phone-sequences and compose them with the context FST.
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*/
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for (size_t p = 0; p < 10; p++) {
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vector<int32> phone_seq;
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int num_subseq = N - P - 1; // zero if P == N-1, i.e. P is last element, i.e. left-context only.
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float tot_cost = 20.0 * kaldi::RandUniform();
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VectorFst<Arc> *f = GenRandPhoneSeq<Arc>(phone_syms, disambig_syms, subsequential_symbol, num_subseq, tot_cost, &phone_seq);
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if (verbose) {
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std::cout << "Sequence FST is:\n";
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{ // Try to print the fst.
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FstPrinter<Arc> fstprinter(*f, NULL, NULL, NULL, false, true, "\t");
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fstprinter.Print(&std::cout, "standard output");
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}
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}
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VectorFst<Arc> fst_composed;
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ComposeDeterministicOnDemandInverse(*f, &inv_cfst, &fst_composed);
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// Testing WriteILabelInfo and ReadILabelInfo.
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{
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bool binary = (kaldi::Rand() % 2 == 0);
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WriteILabelInfo(kaldi::Output("tmpf", binary).Stream(),
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binary, inv_cfst.IlabelInfo());
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bool binary_in;
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vector<vector<int32> > ilabel_info;
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kaldi::Input ki("tmpf", &binary_in);
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ReadILabelInfo(ki.Stream(),
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binary_in, &ilabel_info);
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assert(ilabel_info == inv_cfst.IlabelInfo());
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}
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if (verbose) {
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std::cout << "Composed FST is:\n";
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{ // Try to print the fst.
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FstPrinter<Arc> fstprinter(fst_composed, NULL, NULL, NULL, false, true, "\t");
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fstprinter.Print(&std::cout, "standard output");
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}
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}
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// now check the composed FST.
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float tot_cost_check = CheckPhones<Arc>(fst_composed,
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phone_syms,
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disambig_syms,
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phone_seq,
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inv_cfst.IlabelInfo(),
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N, P);
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kaldi::AssertEqual(tot_cost, tot_cost_check);
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delete f;
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}
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unlink("tmpf");
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}
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} // namespace fst
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int main() {
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for (int i = 0;i < 16;i++) {
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bool verbose = (i < 4);
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bool use_matcher = ( (i/4) % 2 == 0);
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fst::TestContextFst(verbose, use_matcher);
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}
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}
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