// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #pragma once #include "base/common.h" #include "base/safe_queue.h" #include "frontend/frontend_itf.h" #include "nnet/nnet_itf.h" namespace ppspeech { class NnetProducer { public: explicit NnetProducer(std::shared_ptr nnet, std::shared_ptr frontend, float blank_threshold); // Feed feats or waves void Accept(const std::vector& inputs); void Acceptlikelihood(const kaldi::Matrix& likelihood); // nnet bool Read(std::vector* nnet_prob); bool Empty() const { return cache_.empty(); } void SetInputFinished() { LOG(INFO) << "set finished"; frontend_->SetFinished(); } // the compute thread exit bool IsFinished() const { return (frontend_->IsFinished() && finished_); } ~NnetProducer() {} void Reset() { if (frontend_ != NULL) frontend_->Reset(); if (nnet_ != NULL) nnet_->Reset(); cache_.clear(); finished_ = false; } void AttentionRescoring(const std::vector>& hyps, float reverse_weight, std::vector* rescoring_score); bool Compute(); private: std::shared_ptr frontend_; std::shared_ptr nnet_; SafeQueue> cache_; std::vector last_frame_logprob_; bool is_last_frame_skip_ = false; int last_max_elem_ = -1; float blank_threshold_ = 0.0; bool finished_; DISALLOW_COPY_AND_ASSIGN(NnetProducer); }; } // namespace ppspeech