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78 lines
2.2 KiB
78 lines
2.2 KiB
// 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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#include "base/safe_queue.h"
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#include "frontend/frontend_itf.h"
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#include "nnet/nnet_itf.h"
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namespace ppspeech {
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class NnetProducer {
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public:
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explicit NnetProducer(std::shared_ptr<NnetBase> nnet,
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std::shared_ptr<FrontendInterface> frontend,
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float blank_threshold);
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// Feed feats or waves
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void Accept(const std::vector<kaldi::BaseFloat>& inputs);
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void Acceptlikelihood(const kaldi::Matrix<BaseFloat>& likelihood);
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// nnet
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bool Read(std::vector<kaldi::BaseFloat>* nnet_prob);
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bool Empty() const { return cache_.empty(); }
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void SetInputFinished() {
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LOG(INFO) << "set finished";
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frontend_->SetFinished();
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}
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// the compute thread exit
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bool IsFinished() const {
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return (frontend_->IsFinished() && finished_);
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}
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~NnetProducer() {}
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void Reset() {
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if (frontend_ != NULL) frontend_->Reset();
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if (nnet_ != NULL) nnet_->Reset();
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cache_.clear();
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finished_ = false;
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}
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void AttentionRescoring(const std::vector<std::vector<int>>& hyps,
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float reverse_weight,
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std::vector<float>* rescoring_score);
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bool Compute();
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private:
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std::shared_ptr<FrontendInterface> frontend_;
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std::shared_ptr<NnetBase> nnet_;
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SafeQueue<std::vector<kaldi::BaseFloat>> cache_;
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std::vector<BaseFloat> last_frame_logprob_;
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bool is_last_frame_skip_ = false;
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int last_max_elem_ = -1;
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float blank_threshold_ = 0.0;
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bool finished_;
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DISALLOW_COPY_AND_ASSIGN(NnetProducer);
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};
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} // namespace ppspeech
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