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@ -1,3 +1,5 @@
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// Copyright (c) 2020 Mobvoi Inc (Binbin Zhang, Di Wu)
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// 2022 Binbin Zhang (binbzha@qq.com)
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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@ -13,11 +15,12 @@
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// limitations under the License.
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#include "decoder/ctc_prefix_beam_search_decoder.h"
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#include "base/common.h"
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#include "decoder/ctc_beam_search_opt.h"
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#include "decoder/ctc_prefix_beam_search_score.h"
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#include "decoder/ctc_prefix_beam_search_decoder.h"
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#include "utils/math.h"
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#include "absl/strings/str_join.h"
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#ifdef USE_PROFILING
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#include "paddle/fluid/platform/profiler.h"
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@ -29,85 +32,47 @@ namespace ppspeech {
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CTCPrefixBeamSearch::CTCPrefixBeamSearch(const CTCBeamSearchOptions& opts)
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: opts_(opts) {
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InitDecoder();
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Reset();
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}
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void CTCPrefixBeamSearch::InitDecoder() {
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void CTCPrefixBeamSearch::Reset() {
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num_frame_decoded_ = 0;
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cur_hyps_.clear();
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hypotheses_.clear();
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likelihood_.clear();
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viterbi_likelihood_.clear();
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times_.clear();
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outputs_.clear();
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hypotheses_.clear();
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likelihood_.clear();
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viterbi_likelihood_.clear();
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times_.clear();
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outputs_.clear();
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abs_time_step_ = 0;
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// empty hyp with Score
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std::vector<int> empty;
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PrefixScore prefix_score;
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prefix_score.b = 0.0f; // log(1)
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prefix_score.nb = -kBaseFloatMax; // log(0)
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prefix_score.v_b = 0.0f; // log(1)
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prefix_score.v_nb = 0.0f; // log(1)
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cur_hyps_[empty] = prefix_score;
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// empty hyp with Score
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std::vector<int> empty;
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PrefixScore prefix_score;
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prefix_score.b = 0.0f; // log(1)
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prefix_score.nb = -kBaseFloatMax; // log(0)
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prefix_score.v_b = 0.0f; // log(1)
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prefix_score.v_nb = 0.0f; // log(1)
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cur_hyps_[empty] = prefix_score;
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outputs_.emplace_back(empty);
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hypotheses_.emplace_back(empty);
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likelihood_.emplace_back(prefix_score.TotalScore());
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times_.emplace_back(empty);
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}
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outputs_.emplace_back(empty);
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hypotheses_.emplace_back(empty);
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likelihood_.emplace_back(prefix_score.TotalScore());
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times_.emplace_back(empty);
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}
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void CTCPrefixBeamSearch::InitDecoder() { Reset(); }
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void CTCPrefixBeamSearch::Reset() {
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InitDecoder();
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}
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void CTCPrefixBeamSearch::Decode(
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std::shared_ptr<kaldi::DecodableInterface> decodable) {
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return;
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}
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int32 CTCPrefixBeamSearch::NumFrameDecoded() { return num_frame_decoded_ + 1; }
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void CTCPrefixBeamSearch::UpdateOutputs(
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const std::pair<std::vector<int>, PrefixScore>& prefix) {
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const std::vector<int>& input = prefix.first;
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// const std::vector<int>& start_boundaries = prefix.second.start_boundaries;
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// const std::vector<int>& end_boundaries = prefix.second.end_boundaries;
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std::vector<int> output;
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int s = 0;
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int e = 0;
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for (int i = 0; i < input.size(); ++i) {
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// if (s < start_boundaries.size() && i == start_boundaries[s]){
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// // <context>
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// output.emplace_back(context_graph_->start_tag_id());
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// ++s;
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// }
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output.emplace_back(input[i]);
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// if (e < end_boundaries.size() && i == end_boundaries[e]){
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// // </context>
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// output.emplace_back(context_graph_->end_tag_id());
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// ++e;
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// }
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}
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outputs_.emplace_back(output);
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}
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void CTCPrefixBeamSearch::AdvanceDecode(
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const std::shared_ptr<kaldi::DecodableInterface>& decodable) {
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while (1) {
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// forward frame by frame
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std::vector<kaldi::BaseFloat> frame_prob;
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bool flag = decodable->FrameLikelihood(num_frame_decoded_, &frame_prob);
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if (flag == false) break;
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std::vector<std::vector<kaldi::BaseFloat>> likelihood;
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likelihood.push_back(frame_prob);
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AdvanceDecoding(likelihood);
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@ -117,201 +82,279 @@ void CTCPrefixBeamSearch::AdvanceDecode(
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static bool PrefixScoreCompare(
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const std::pair<std::vector<int>, PrefixScore>& a,
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const std::pair<std::vector<int>, PrefixScore>& b) {
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// log domain
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return a.second.TotalScore() > b.second.TotalScore();
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// log domain
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return a.second.TotalScore() > b.second.TotalScore();
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}
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void CTCPrefixBeamSearch::AdvanceDecoding(const std::vector<std::vector<float>>& logp) {
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void CTCPrefixBeamSearch::AdvanceDecoding(
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const std::vector<std::vector<kaldi::BaseFloat>>& logp) {
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#ifdef USE_PROFILING
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RecordEvent event(
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"CtcPrefixBeamSearch::AdvanceDecoding", TracerEventType::UserDefined, 1);
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RecordEvent event("CtcPrefixBeamSearch::AdvanceDecoding",
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TracerEventType::UserDefined,
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1);
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#endif
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if (logp.size() == 0) return;
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int first_beam_size =
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std::min(static_cast<int>(logp[0].size()), opts_.first_beam_size);
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for (int t = 0; t < logp.size(); ++t, ++abs_time_step_) {
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const std::vector<float>& logp_t = logp[t];
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std::unordered_map<std::vector<int>, PrefixScore, PrefixScoreHash> next_hyps;
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// 1. first beam prune, only select topk candidates
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std::vector<float> topk_score;
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std::vector<int32_t> topk_index;
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TopK(logp_t, first_beam_size, &topk_score, &topk_index);
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// 2. token passing
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for (int i = 0; i < topk_index.size(); ++i) {
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int id = topk_index[i];
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auto prob = topk_score[i];
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for (const auto& it : cur_hyps_) {
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const std::vector<int>& prefix = it.first;
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const PrefixScore& prefix_score = it.second;
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// If prefix doesn't exist in next_hyps, next_hyps[prefix] will insert
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// PrefixScore(-inf, -inf) by default, since the default constructor
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// of PrefixScore will set fields b(blank ending Score) and
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// nb(none blank ending Score) to -inf, respectively.
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if (id == opts_.blank) {
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// case 0: *a + <blank> => *a, *a<blank> + <blank> => *a, prefix not
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// change
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PrefixScore& next_score = next_hyps[prefix];
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next_score.b = LogSumExp(next_score.b, prefix_score.Score() + prob);
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// timestamp, blank is slince, not effact timestamp
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next_score.v_b = prefix_score.ViterbiScore() + prob;
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next_score.times_b = prefix_score.Times();
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// Prefix not changed, copy the context from pefix
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if (context_graph_ && !next_score.has_context) {
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next_score.CopyContext(prefix_score);
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next_score.has_context = true;
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}
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} else if (!prefix.empty() && id == prefix.back()) {
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// case 1: *a + a => *a, prefix not changed
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PrefixScore& next_score1 = next_hyps[prefix];
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next_score1.nb = LogSumExp(next_score1.nb, prefix_score.nb + prob);
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// timestamp, non-blank symbol effact timestamp
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if (next_score1.v_nb < prefix_score.v_nb + prob) {
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// compute viterbi Score
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next_score1.v_nb = prefix_score.v_nb + prob;
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if (next_score1.cur_token_prob < prob) {
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// store max token prob
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next_score1.cur_token_prob = prob;
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// update this timestamp as token appeared here.
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next_score1.times_nb = prefix_score.times_nb;
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assert(next_score1.times_nb.size() > 0);
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next_score1.times_nb.back() = abs_time_step_;
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}
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}
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// Prefix not changed, copy the context from pefix
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if (context_graph_ && !next_score1.has_context) {
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next_score1.CopyContext(prefix_score);
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next_score1.has_context = true;
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}
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// case 2: *a<blank> + a => *aa, prefix changed.
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std::vector<int> new_prefix(prefix);
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new_prefix.emplace_back(id);
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PrefixScore& next_score2 = next_hyps[new_prefix];
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next_score2.nb = LogSumExp(next_score2.nb, prefix_score.b + prob);
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// timestamp, non-blank symbol effact timestamp
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if (next_score2.v_nb < prefix_score.v_b + prob) {
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// compute viterbi Score
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next_score2.v_nb = prefix_score.v_b + prob;
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// new token added
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next_score2.cur_token_prob = prob;
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next_score2.times_nb = prefix_score.times_b;
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next_score2.times_nb.emplace_back(abs_time_step_);
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}
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// Prefix changed, calculate the context Score.
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if (context_graph_ && !next_score2.has_context) {
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next_score2.UpdateContext(
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context_graph_, prefix_score, id, prefix.size());
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next_score2.has_context = true;
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}
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} else {
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// id != prefix.back()
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// case 3: *a + b => *ab, *a<blank> +b => *ab
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std::vector<int> new_prefix(prefix);
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new_prefix.emplace_back(id);
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PrefixScore& next_score = next_hyps[new_prefix];
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next_score.nb = LogSumExp(next_score.nb, prefix_score.Score() + prob);
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// timetamp, non-blank symbol effact timestamp
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if (next_score.v_nb < prefix_score.ViterbiScore() + prob) {
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next_score.v_nb = prefix_score.ViterbiScore() + prob;
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next_score.cur_token_prob = prob;
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next_score.times_nb = prefix_score.Times();
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next_score.times_nb.emplace_back(abs_time_step_);
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}
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// Prefix changed, calculate the context Score.
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if (context_graph_ && !next_score.has_context) {
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next_score.UpdateContext(
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context_graph_, prefix_score, id, prefix.size());
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next_score.has_context = true;
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}
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}
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} // end for (const auto& it : cur_hyps_)
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} // end for (int i = 0; i < topk_index.size(); ++i)
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// 3. second beam prune, only keep top n best paths
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std::vector<std::pair<std::vector<int>, PrefixScore>> arr(next_hyps.begin(),
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next_hyps.end());
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int second_beam_size =
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std::min(static_cast<int>(arr.size()), opts_.second_beam_size);
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std::nth_element(arr.begin(),
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arr.begin() + second_beam_size,
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arr.end(),
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PrefixScoreCompare);
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arr.resize(second_beam_size);
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std::sort(arr.begin(), arr.end(), PrefixScoreCompare);
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// 4. update cur_hyps by next_hyps, and get new result
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UpdateHypotheses(arr);
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num_frame_decoded_++;
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} // end for (int t = 0; t < logp.size(); ++t, ++abs_time_step_)
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if (logp.size() == 0) return;
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int first_beam_size =
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std::min(static_cast<int>(logp[0].size()), opts_.first_beam_size);
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for (int t = 0; t < logp.size(); ++t, ++num_frame_decoded_) {
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const std::vector<float>& logp_t = logp[t];
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std::unordered_map<std::vector<int>, PrefixScore, PrefixScoreHash>
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next_hyps;
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// 1. first beam prune, only select topk candidates
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std::vector<float> topk_score;
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std::vector<int32_t> topk_index;
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TopK(logp_t, first_beam_size, &topk_score, &topk_index);
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// 2. token passing
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for (int i = 0; i < topk_index.size(); ++i) {
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int id = topk_index[i];
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auto prob = topk_score[i];
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for (const auto& it : cur_hyps_) {
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const std::vector<int>& prefix = it.first;
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const PrefixScore& prefix_score = it.second;
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// If prefix doesn't exist in next_hyps, next_hyps[prefix] will
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// insert
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// PrefixScore(-inf, -inf) by default, since the default
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// constructor
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// of PrefixScore will set fields b(blank ending Score) and
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// nb(none blank ending Score) to -inf, respectively.
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if (id == opts_.blank) {
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// case 0: *a + <blank> => *a, *a<blank> + <blank> => *a,
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// prefix not
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// change
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PrefixScore& next_score = next_hyps[prefix];
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next_score.b =
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LogSumExp(next_score.b, prefix_score.Score() + prob);
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// timestamp, blank is slince, not effact timestamp
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next_score.v_b = prefix_score.ViterbiScore() + prob;
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next_score.times_b = prefix_score.Times();
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// Prefix not changed, copy the context from pefix
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if (context_graph_ && !next_score.has_context) {
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next_score.CopyContext(prefix_score);
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next_score.has_context = true;
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}
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} else if (!prefix.empty() && id == prefix.back()) {
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// case 1: *a + a => *a, prefix not changed
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PrefixScore& next_score1 = next_hyps[prefix];
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next_score1.nb =
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LogSumExp(next_score1.nb, prefix_score.nb + prob);
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// timestamp, non-blank symbol effact timestamp
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if (next_score1.v_nb < prefix_score.v_nb + prob) {
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// compute viterbi Score
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next_score1.v_nb = prefix_score.v_nb + prob;
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if (next_score1.cur_token_prob < prob) {
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// store max token prob
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next_score1.cur_token_prob = prob;
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// update this timestamp as token appeared here.
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next_score1.times_nb = prefix_score.times_nb;
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assert(next_score1.times_nb.size() > 0);
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next_score1.times_nb.back() = num_frame_decoded_;
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}
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}
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// Prefix not changed, copy the context from pefix
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if (context_graph_ && !next_score1.has_context) {
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next_score1.CopyContext(prefix_score);
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next_score1.has_context = true;
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}
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// case 2: *a<blank> + a => *aa, prefix changed.
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std::vector<int> new_prefix(prefix);
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new_prefix.emplace_back(id);
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PrefixScore& next_score2 = next_hyps[new_prefix];
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next_score2.nb =
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LogSumExp(next_score2.nb, prefix_score.b + prob);
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// timestamp, non-blank symbol effact timestamp
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if (next_score2.v_nb < prefix_score.v_b + prob) {
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// compute viterbi Score
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next_score2.v_nb = prefix_score.v_b + prob;
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// new token added
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next_score2.cur_token_prob = prob;
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next_score2.times_nb = prefix_score.times_b;
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next_score2.times_nb.emplace_back(num_frame_decoded_);
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}
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// Prefix changed, calculate the context Score.
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if (context_graph_ && !next_score2.has_context) {
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next_score2.UpdateContext(
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context_graph_, prefix_score, id, prefix.size());
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next_score2.has_context = true;
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}
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} else {
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// id != prefix.back()
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// case 3: *a + b => *ab, *a<blank> +b => *ab
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std::vector<int> new_prefix(prefix);
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new_prefix.emplace_back(id);
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PrefixScore& next_score = next_hyps[new_prefix];
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next_score.nb =
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LogSumExp(next_score.nb, prefix_score.Score() + prob);
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// timetamp, non-blank symbol effact timestamp
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if (next_score.v_nb < prefix_score.ViterbiScore() + prob) {
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next_score.v_nb = prefix_score.ViterbiScore() + prob;
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next_score.cur_token_prob = prob;
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next_score.times_nb = prefix_score.Times();
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next_score.times_nb.emplace_back(num_frame_decoded_);
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}
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// Prefix changed, calculate the context Score.
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if (context_graph_ && !next_score.has_context) {
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next_score.UpdateContext(
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context_graph_, prefix_score, id, prefix.size());
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next_score.has_context = true;
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}
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}
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} // end for (const auto& it : cur_hyps_)
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} // end for (int i = 0; i < topk_index.size(); ++i)
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// 3. second beam prune, only keep top n best paths
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std::vector<std::pair<std::vector<int>, PrefixScore>> arr(
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next_hyps.begin(), next_hyps.end());
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int second_beam_size =
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std::min(static_cast<int>(arr.size()), opts_.second_beam_size);
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std::nth_element(arr.begin(),
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arr.begin() + second_beam_size,
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arr.end(),
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PrefixScoreCompare);
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arr.resize(second_beam_size);
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std::sort(arr.begin(), arr.end(), PrefixScoreCompare);
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// 4. update cur_hyps by next_hyps, and get new result
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UpdateHypotheses(arr);
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} // end for (int t = 0; t < logp.size(); ++t, ++num_frame_decoded_)
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}
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void CTCPrefixBeamSearch::UpdateHypotheses(
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const std::vector<std::pair<std::vector<int>, PrefixScore>>& hyps) {
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cur_hyps_.clear();
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outputs_.clear();
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hypotheses_.clear();
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likelihood_.clear();
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viterbi_likelihood_.clear();
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times_.clear();
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for (auto& item : hyps) {
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cur_hyps_[item.first] = item.second;
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UpdateOutputs(item);
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hypotheses_.emplace_back(std::move(item.first));
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likelihood_.emplace_back(item.second.TotalScore());
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viterbi_likelihood_.emplace_back(item.second.ViterbiScore());
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times_.emplace_back(item.second.Times());
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}
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cur_hyps_.clear();
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outputs_.clear();
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hypotheses_.clear();
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likelihood_.clear();
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viterbi_likelihood_.clear();
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times_.clear();
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for (auto& item : hyps) {
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cur_hyps_[item.first] = item.second;
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UpdateOutputs(item);
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hypotheses_.emplace_back(std::move(item.first));
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likelihood_.emplace_back(item.second.TotalScore());
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viterbi_likelihood_.emplace_back(item.second.ViterbiScore());
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times_.emplace_back(item.second.Times());
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}
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}
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void CTCPrefixBeamSearch::FinalizeSearch() { UpdateFinalContext(); }
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void CTCPrefixBeamSearch::UpdateOutputs(
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const std::pair<std::vector<int>, PrefixScore>& prefix) {
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const std::vector<int>& input = prefix.first;
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const std::vector<int>& start_boundaries = prefix.second.start_boundaries;
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const std::vector<int>& end_boundaries = prefix.second.end_boundaries;
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// add <context> </context> tag
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std::vector<int> output;
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int s = 0;
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int e = 0;
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for (int i = 0; i < input.size(); ++i) {
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// if (s < start_boundaries.size() && i == start_boundaries[s]){
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// // <context>
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// output.emplace_back(context_graph_->start_tag_id());
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// ++s;
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// }
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output.emplace_back(input[i]);
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// if (e < end_boundaries.size() && i == end_boundaries[e]){
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// // </context>
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// output.emplace_back(context_graph_->end_tag_id());
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// ++e;
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// }
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}
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outputs_.emplace_back(output);
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}
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void CTCPrefixBeamSearch::FinalizeSearch() {
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UpdateFinalContext();
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}
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void CTCPrefixBeamSearch::UpdateFinalContext() {
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if (context_graph_ == nullptr) return;
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assert(hypotheses_.size() == cur_hyps_.size());
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assert(hypotheses_.size() == likelihood_.size());
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// We should backoff the context Score/state when the context is
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// not fully matched at the last time.
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for (const auto& prefix : hypotheses_) {
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PrefixScore& prefix_score = cur_hyps_[prefix];
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if (prefix_score.context_score != 0) {
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// prefix_score.UpdateContext(context_graph_, prefix_score, 0,
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// prefix.size());
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if (context_graph_ == nullptr) return;
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CHECK(hypotheses_.size() == cur_hyps_.size());
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CHECK(hypotheses_.size() == likelihood_.size());
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// We should backoff the context Score/state when the context is
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// not fully matched at the last time.
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for (const auto& prefix : hypotheses_) {
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PrefixScore& prefix_score = cur_hyps_[prefix];
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if (prefix_score.context_score != 0) {
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prefix_score.UpdateContext(context_graph_, prefix_score, 0,
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prefix.size());
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}
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}
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std::vector<std::pair<std::vector<int>, PrefixScore>> arr(cur_hyps_.begin(),
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cur_hyps_.end());
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std::sort(arr.begin(), arr.end(), PrefixScoreCompare);
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// Update cur_hyps_ and get new result
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UpdateHypotheses(arr);
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}
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std::string CTCPrefixBeamSearch::GetBestPath(int index) {
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int n_hyps = Outputs().size();
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CHECK(n_hyps > 0);
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CHECK(index < n_hyps);
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std::vector<int> one = Outputs()[index];
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return std::string(absl::StrJoin(one, kSpaceSymbol));
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}
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std::string CTCPrefixBeamSearch::GetBestPath() {
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return GetBestPath(0);
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}
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std::vector<std::pair<double, std::string>> CTCPrefixBeamSearch::GetNBestPath(int n) {
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int hyps_size = hypotheses_.size();
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CHECK(hyps_size > 0);
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int min_n = n == -1 ? hypotheses_.size() : std::min(n, hyps_size);
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std::vector<std::pair<double, std::string>> n_best;
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n_best.reserve(min_n);
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for (int i = 0; i < min_n; i++){
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n_best.emplace_back(Likelihood()[i], GetBestPath(i) );
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}
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|
return n_best;
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}
|
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std::vector<std::pair<double, std::string>> CTCPrefixBeamSearch::GetNBestPath() {
|
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|
|
return GetNBestPath(-1);
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|
}
|
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|
|
std::vector<std::pair<std::vector<int>, PrefixScore>> arr(cur_hyps_.begin(),
|
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|
|
cur_hyps_.end());
|
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|
|
std::sort(arr.begin(), arr.end(), PrefixScoreCompare);
|
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|
|
|
|
|
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|
|
// Update cur_hyps_ and get new result
|
|
|
|
|
UpdateHypotheses(arr);
|
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|
|
|
std::string CTCPrefixBeamSearch::GetFinalBestPath() {
|
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|
|
return GetBestPath();
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|
|
}
|
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|
|
std::string CTCPrefixBeamSearch::GetPartialResult() {
|
|
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|
|
return GetBestPath();
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|
|
}
|
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|
|
} // namespace ppspeech
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|
|
} // namespace ppspeech
|
Hui Zhang
2 years ago