Hui Zhang
3 years ago
parent
7dc9cba3be
commit
86eb718908
@ -1,41 +0,0 @@
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// Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
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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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// 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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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include "base/common.h"
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namespace ppspeech {
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struct WordPiece {
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std::string word;
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int start = -1;
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int end = -1;
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WordPiece(std::string word, int start, int end)
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: word(std::move(word)), start(start), end(end) {}
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};
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struct DecodeResult {
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float score = -kBaseFloatMax;
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std::string sentence;
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std::vector<WordPiece> word_pieces;
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static bool CompareFunc(const DecodeResult& a, const DecodeResult& b) {
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return a.score > b.score;
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}
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};
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} // namespace ppspeech
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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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// 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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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "decoder/u2_recognizer.h"
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#include "nnet/u2_nnet.h"
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namespace ppspeech {
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using kaldi::Vector;
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using kaldi::VectorBase;
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using kaldi::BaseFloat;
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using std::vector;
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using kaldi::SubVector;
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using std::unique_ptr;
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U2Recognizer::U2Recognizer(const U2RecognizerResource& resource): opts_(resource) {
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const FeaturePipelineOptions& feature_opts = resource.feature_pipeline_opts;
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feature_pipeline_.reset(new FeaturePipeline(feature_opts));
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std::shared_ptr<NnetInterface> nnet(new U2Nnet(resource.model_opts));
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BaseFloat am_scale = resource.acoustic_scale;
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decodable_.reset(new Decodable(nnet, feature_pipeline_, am_scale));
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decoder_.reset(new CTCPrefixBeamSearch(resource.vocab_path, resource.decoder_opts.ctc_prefix_search_opts));
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unit_table_ = decoder_->VocabTable();
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symbol_table_ = unit_table_;
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input_finished_ = false;
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}
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void U2Recognizer::Reset() {
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global_frame_offset_ = 0;
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num_frames_ = 0;
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result_.clear();
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feature_pipeline_->Reset();
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decodable_->Reset();
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decoder_->Reset();
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}
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void U2Recognizer::ResetContinuousDecoding() {
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global_frame_offset_ = num_frames_;
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num_frames_ = 0;
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result_.clear();
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feature_pipeline_->Reset();
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decodable_->Reset();
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decoder_->Reset();
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}
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void U2Recognizer::Accept(const VectorBase<BaseFloat>& waves) {
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feature_pipeline_->Accept(waves);
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}
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void U2Recognizer::Decode() {
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decoder_->AdvanceDecode(decodable_);
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}
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void U2Recognizer::Rescoring() {
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// Do attention Rescoring
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kaldi::Timer timer;
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AttentionRescoring();
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VLOG(1) << "Rescoring cost latency: " << timer.Elapsed() << " sec.";
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}
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void U2Recognizer::UpdateResult(bool finish) {
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const auto& hypotheses = decoder_->Outputs();
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const auto& inputs = decoder_->Inputs();
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const auto& likelihood = decoder_->Likelihood();
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const auto& times = decoder_->Times();
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result_.clear();
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CHECK_EQ(hypotheses.size(), likelihood.size());
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for (size_t i = 0; i < hypotheses.size(); i++) {
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const std::vector<int>& hypothesis = hypotheses[i];
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DecodeResult path;
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path.score = likelihood[i];
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for (size_t j = 0; j < hypothesis.size(); j++) {
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std::string word = symbol_table_->Find(hypothesis[j]);
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// A detailed explanation of this if-else branch can be found in
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// https://github.com/wenet-e2e/wenet/issues/583#issuecomment-907994058
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if (decoder_->Type() == kWfstBeamSearch) {
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path.sentence += (" " + word);
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} else {
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path.sentence += (word);
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}
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}
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// TimeStamp is only supported in final result
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// TimeStamp of the output of CtcWfstBeamSearch may be inaccurate due to
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// various FST operations when building the decoding graph. So here we use
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// time stamp of the input(e2e model unit), which is more accurate, and it
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// requires the symbol table of the e2e model used in training.
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if (unit_table_ != nullptr && finish) {
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int offset = global_frame_offset_ * FrameShiftInMs();
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const std::vector<int>& input = inputs[i];
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const std::vector<int> time_stamp = times[i];
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CHECK_EQ(input.size(), time_stamp.size());
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for (size_t j = 0; j < input.size(); j++) {
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std::string word = unit_table_->Find(input[j]);
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int start = time_stamp[j] * FrameShiftInMs() - time_stamp_gap_ > 0
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? time_stamp[j] * FrameShiftInMs() - time_stamp_gap_
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: 0;
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if (j > 0) {
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start = (time_stamp[j] - time_stamp[j - 1]) * FrameShiftInMs() <
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time_stamp_gap_
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? (time_stamp[j - 1] + time_stamp[j]) / 2 *
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FrameShiftInMs()
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: start;
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}
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int end = time_stamp[j] * FrameShiftInMs();
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if (j < input.size() - 1) {
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end = (time_stamp[j + 1] - time_stamp[j]) * FrameShiftInMs() <
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time_stamp_gap_
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? (time_stamp[j + 1] + time_stamp[j]) / 2 *
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FrameShiftInMs()
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: end;
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}
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WordPiece word_piece(word, offset + start, offset + end);
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path.word_pieces.emplace_back(word_piece);
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}
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}
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// if (post_processor_ != nullptr) {
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// path.sentence = post_processor_->Process(path.sentence, finish);
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// }
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result_.emplace_back(path);
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}
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if (DecodedSomething()) {
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VLOG(1) << "Partial CTC result " << result_[0].sentence;
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}
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}
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void U2Recognizer::AttentionRescoring() {
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decoder_->FinalizeSearch();
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UpdateResult(true);
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// No need to do rescoring
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if (0.0 == opts_.decoder_opts.rescoring_weight) {
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LOG_EVERY_N(WARNING, 3) << "Not do AttentionRescoring!";
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return;
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}
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LOG_EVERY_N(WARNING, 3) << "Do AttentionRescoring!";
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// Inputs() returns N-best input ids, which is the basic unit for rescoring
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// In CtcPrefixBeamSearch, inputs are the same to outputs
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const auto& hypotheses = decoder_->Inputs();
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int num_hyps = hypotheses.size();
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if (num_hyps <= 0) {
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return;
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}
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kaldi::Timer timer;
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std::vector<float> rescoring_score;
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decodable_->AttentionRescoring(
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hypotheses, opts_.decoder_opts.reverse_weight, &rescoring_score);
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VLOG(1) << "Attention Rescoring takes " << timer.Elapsed() << " sec.";
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// combine ctc score and rescoring score
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for (size_t i = 0; i < num_hyps; i++) {
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VLOG(1) << "hyp " << i << " rescoring_score: " << rescoring_score[i]
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<< " ctc_score: " << result_[i].score;
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result_[i].score = opts_.decoder_opts.rescoring_weight * rescoring_score[i] +
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opts_.decoder_opts.ctc_weight * result_[i].score;
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}
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std::sort(result_.begin(), result_.end(), DecodeResult::CompareFunc);
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VLOG(1) << "result: " << result_[0].sentence
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<< " score: " << result_[0].score;
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}
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std::string U2Recognizer::GetFinalResult() {
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return result_[0].sentence;
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}
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std::string U2Recognizer::GetPartialResult() {
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return result_[0].sentence;
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}
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void U2Recognizer::SetFinished() {
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feature_pipeline_->SetFinished();
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input_finished_ = true;
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}
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} // namespace ppspeech
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@ -0,0 +1,164 @@
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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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// 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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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include "decoder/common.h"
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#include "decoder/ctc_beam_search_opt.h"
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#include "decoder/ctc_prefix_beam_search_decoder.h"
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#include "decoder/decoder_itf.h"
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#include "frontend/audio/feature_pipeline.h"
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#include "nnet/decodable.h"
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#include "fst/fstlib.h"
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#include "fst/symbol-table.h"
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namespace ppspeech {
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struct DecodeOptions {
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// chunk_size is the frame number of one chunk after subsampling.
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// e.g. if subsample rate is 4 and chunk_size = 16, the frames in
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// one chunk are 67=16*4 + 3, stride is 64=16*4
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int chunk_size;
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int num_left_chunks;
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// final_score = rescoring_weight * rescoring_score + ctc_weight *
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// ctc_score;
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// rescoring_score = left_to_right_score * (1 - reverse_weight) +
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// right_to_left_score * reverse_weight
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// Please note the concept of ctc_scores
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// in the following two search methods are different. For
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// CtcPrefixBeamSerch,
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// it's a sum(prefix) score + context score For CtcWfstBeamSerch, it's a
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// max(viterbi) path score + context score So we should carefully set
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// ctc_weight accroding to the search methods.
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float ctc_weight;
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float rescoring_weight;
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float reverse_weight;
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// CtcEndpointConfig ctc_endpoint_opts;
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CTCBeamSearchOptions ctc_prefix_search_opts;
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DecodeOptions()
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: chunk_size(16),
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num_left_chunks(-1),
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ctc_weight(0.5),
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rescoring_weight(1.0),
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reverse_weight(0.0) {}
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void Register(kaldi::OptionsItf* opts) {
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std::string module = "DecoderConfig: ";
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opts->Register(
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"chunk-size",
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&chunk_size,
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module + "the frame number of one chunk after subsampling.");
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opts->Register("num-left-chunks",
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&num_left_chunks,
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module + "the left history chunks number.");
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opts->Register("ctc-weight",
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&ctc_weight,
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module +
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"ctc weight for rescore. final_score = "
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"rescoring_weight * rescoring_score + ctc_weight * "
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"ctc_score.");
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opts->Register("rescoring-weight",
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&rescoring_weight,
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module +
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"attention score weight for rescore. final_score = "
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"rescoring_weight * rescoring_score + ctc_weight * "
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"ctc_score.");
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opts->Register("reverse-weight",
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&reverse_weight,
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module +
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"reverse decoder weight. rescoring_score = "
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"left_to_right_score * (1 - reverse_weight) + "
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"right_to_left_score * reverse_weight.");
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}
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};
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struct U2RecognizerResource {
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FeaturePipelineOptions feature_pipeline_opts{};
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ModelOptions model_opts{};
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DecodeOptions decoder_opts{};
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// CTCBeamSearchOptions beam_search_opts;
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kaldi::BaseFloat acoustic_scale{1.0};
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std::string vocab_path{};
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};
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class U2Recognizer {
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public:
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explicit U2Recognizer(const U2RecognizerResource& resouce);
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void Reset();
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void ResetContinuousDecoding();
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void Accept(const kaldi::VectorBase<kaldi::BaseFloat>& waves);
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void Decode();
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void Rescoring();
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std::string GetFinalResult();
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std::string GetPartialResult();
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void SetFinished();
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bool IsFinished() { return input_finished_; }
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bool DecodedSomething() const {
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return !result_.empty() && !result_[0].sentence.empty();
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}
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int FrameShiftInMs() const {
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// one decoder frame length in ms
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return decodable_->Nnet()->SubsamplingRate() *
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feature_pipeline_->FrameShift();
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}
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const std::vector<DecodeResult>& Result() const { return result_; }
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private:
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void AttentionRescoring();
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void UpdateResult(bool finish = false);
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private:
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U2RecognizerResource opts_;
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// std::shared_ptr<U2RecognizerResource> resource_;
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// U2RecognizerResource resource_;
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std::shared_ptr<FeaturePipeline> feature_pipeline_;
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std::shared_ptr<Decodable> decodable_;
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std::unique_ptr<CTCPrefixBeamSearch> decoder_;
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// e2e unit symbol table
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std::shared_ptr<fst::SymbolTable> unit_table_ = nullptr;
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std::shared_ptr<fst::SymbolTable> symbol_table_ = nullptr;
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std::vector<DecodeResult> result_;
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// global decoded frame offset
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int global_frame_offset_;
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// cur decoded frame num
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int num_frames_;
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// timestamp gap between words in a sentence
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const int time_stamp_gap_ = 100;
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bool input_finished_;
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};
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} // namespace ppspeech
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@ -0,0 +1,137 @@
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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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// 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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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "decoder/u2_recognizer.h"
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#include "decoder/param.h"
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#include "kaldi/feat/wave-reader.h"
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#include "kaldi/util/table-types.h"
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DEFINE_string(wav_rspecifier, "", "test feature rspecifier");
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DEFINE_string(result_wspecifier, "", "test result wspecifier");
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DEFINE_double(streaming_chunk, 0.36, "streaming feature chunk size");
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DEFINE_int32(sample_rate, 16000, "sample rate");
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ppspeech::U2RecognizerResource InitOpts() {
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ppspeech::U2RecognizerResource resource;
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resource.acoustic_scale = FLAGS_acoustic_scale;
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resource.feature_pipeline_opts = ppspeech::InitFeaturePipelineOptions();
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ppspeech::ModelOptions model_opts;
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model_opts.model_path = FLAGS_model_path;
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resource.model_opts = model_opts;
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ppspeech::DecodeOptions decoder_opts;
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decoder_opts.chunk_size=16;
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decoder_opts.num_left_chunks = -1;
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decoder_opts.ctc_weight = 0.5;
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decoder_opts.rescoring_weight = 1.0;
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decoder_opts.reverse_weight = 0.3;
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decoder_opts.ctc_prefix_search_opts.blank = 0;
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decoder_opts.ctc_prefix_search_opts.first_beam_size = 10;
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decoder_opts.ctc_prefix_search_opts.second_beam_size = 10;
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resource.decoder_opts = decoder_opts;
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return resource;
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}
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int main(int argc, char* argv[]) {
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gflags::SetUsageMessage("Usage:");
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gflags::ParseCommandLineFlags(&argc, &argv, false);
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google::InitGoogleLogging(argv[0]);
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google::InstallFailureSignalHandler();
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FLAGS_logtostderr = 1;
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int32 num_done = 0, num_err = 0;
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double tot_wav_duration = 0.0;
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ppspeech::U2RecognizerResource resource = InitOpts();
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ppspeech::U2Recognizer recognizer(resource);
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kaldi::SequentialTableReader<kaldi::WaveHolder> wav_reader(
|
||||
FLAGS_wav_rspecifier);
|
||||
kaldi::TokenWriter result_writer(FLAGS_result_wspecifier);
|
||||
|
||||
int sample_rate = FLAGS_sample_rate;
|
||||
float streaming_chunk = FLAGS_streaming_chunk;
|
||||
int chunk_sample_size = streaming_chunk * sample_rate;
|
||||
LOG(INFO) << "sr: " << sample_rate;
|
||||
LOG(INFO) << "chunk size (s): " << streaming_chunk;
|
||||
LOG(INFO) << "chunk size (sample): " << chunk_sample_size;
|
||||
|
||||
kaldi::Timer timer;
|
||||
|
||||
for (; !wav_reader.Done(); wav_reader.Next()) {
|
||||
std::string utt = wav_reader.Key();
|
||||
const kaldi::WaveData& wave_data = wav_reader.Value();
|
||||
LOG(INFO) << "utt: " << utt;
|
||||
LOG(INFO) << "wav dur: " << wave_data.Duration() << " sec.";
|
||||
tot_wav_duration += wave_data.Duration();
|
||||
|
||||
int32 this_channel = 0;
|
||||
kaldi::SubVector<kaldi::BaseFloat> waveform(wave_data.Data(),
|
||||
this_channel);
|
||||
int tot_samples = waveform.Dim();
|
||||
LOG(INFO) << "wav len (sample): " << tot_samples;
|
||||
|
||||
int sample_offset = 0;
|
||||
while (sample_offset < tot_samples) {
|
||||
int cur_chunk_size =
|
||||
std::min(chunk_sample_size, tot_samples - sample_offset);
|
||||
|
||||
kaldi::Vector<kaldi::BaseFloat> wav_chunk(cur_chunk_size);
|
||||
for (int i = 0; i < cur_chunk_size; ++i) {
|
||||
wav_chunk(i) = waveform(sample_offset + i);
|
||||
}
|
||||
// wav_chunk = waveform.Range(sample_offset + i, cur_chunk_size);
|
||||
|
||||
recognizer.Accept(wav_chunk);
|
||||
if (cur_chunk_size < chunk_sample_size) {
|
||||
recognizer.SetFinished();
|
||||
}
|
||||
recognizer.Decode();
|
||||
LOG(INFO) << "Pratial result: " << recognizer.GetPartialResult();
|
||||
|
||||
// no overlap
|
||||
sample_offset += cur_chunk_size;
|
||||
}
|
||||
// second pass decoding
|
||||
recognizer.Rescoring();
|
||||
|
||||
std::string result = recognizer.GetFinalResult();
|
||||
|
||||
recognizer.Reset();
|
||||
|
||||
if (result.empty()) {
|
||||
// the TokenWriter can not write empty string.
|
||||
++num_err;
|
||||
LOG(INFO) << " the result of " << utt << " is empty";
|
||||
continue;
|
||||
}
|
||||
|
||||
LOG(INFO) << " the result of " << utt << " is " << result;
|
||||
|
||||
result_writer.Write(utt, result);
|
||||
|
||||
++num_done;
|
||||
}
|
||||
|
||||
double elapsed = timer.Elapsed();
|
||||
|
||||
LOG(INFO) << "Done " << num_done << " out of " << (num_err + num_done);
|
||||
LOG(INFO) << "cost:" << elapsed << " sec";
|
||||
LOG(INFO) << "total wav duration is: " << tot_wav_duration << " sec";
|
||||
LOG(INFO) << "the RTF is: " << elapsed / tot_wav_duration;
|
||||
}
|
||||
Loading…
Reference in new issue