// 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 "frontend/frontend_itf.h" namespace ppspeech { class FeatureCache : public FrontendInterface { public: explicit FeatureCache( size_t max_size = kint16max, std::unique_ptr base_extractor = NULL); // Feed feats or waves virtual void Accept(const std::vector& inputs); // feats size = num_frames * feat_dim virtual bool Read(std::vector* feats); // feat dim virtual size_t Dim() const { return dim_; } virtual void SetFinished() { std::unique_lock lock(mutex_); LOG(INFO) << "set finished"; // read the last chunk data Compute(); base_extractor_->SetFinished(); LOG(INFO) << "compute last feats done."; } virtual bool IsFinished() const { return base_extractor_->IsFinished(); } void Reset() override { std::queue> empty; std::swap(cache_, empty); nframe_ = 0; base_extractor_->Reset(); VLOG(3) << "feature cache reset: cache size: " << cache_.size(); } private: bool Compute(); int32 dim_; size_t max_size_; // cache capacity std::unique_ptr base_extractor_; std::queue> cache_; // feature cache std::mutex mutex_; int32 nframe_; // num of feature computed DISALLOW_COPY_AND_ASSIGN(FeatureCache); }; } // namespace ppspeech