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117 lines
3.9 KiB
117 lines
3.9 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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#include "frontend/audio/feature_cache.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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FeatureCache::FeatureCache(FeatureCacheOptions opts,
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unique_ptr<FrontendInterface> base_extractor) {
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max_size_ = opts.max_size;
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frame_chunk_stride_ = opts.frame_chunk_stride;
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frame_chunk_size_ = opts.frame_chunk_size;
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timeout_ = opts.timeout; // ms
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base_extractor_ = std::move(base_extractor);
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dim_ = base_extractor_->Dim();
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}
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void FeatureCache::Accept(const kaldi::VectorBase<kaldi::BaseFloat>& inputs) {
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// read inputs
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base_extractor_->Accept(inputs);
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// feed current data
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bool result = false;
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do {
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result = Compute();
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} while (result);
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}
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// pop feature chunk
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bool FeatureCache::Read(kaldi::Vector<kaldi::BaseFloat>* feats) {
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kaldi::Timer timer;
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std::unique_lock<std::mutex> lock(mutex_);
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while (cache_.empty() && base_extractor_->IsFinished() == false) {
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// todo refactor: wait
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// ready_read_condition_.wait(lock);
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int32 elapsed = static_cast<int32>(timer.Elapsed() * 1000); // ms
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if (elapsed > timeout_) {
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return false;
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}
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usleep(100); // sleep 0.1 ms
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}
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if (cache_.empty()) return false;
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// read from cache
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feats->Resize(cache_.front().Dim());
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feats->CopyFromVec(cache_.front());
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cache_.pop();
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ready_feed_condition_.notify_one();
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return true;
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}
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// read all data from base_feature_extractor_ into cache_
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bool FeatureCache::Compute() {
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// compute and feed
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Vector<BaseFloat> feature;
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bool result = base_extractor_->Read(&feature);
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if (result == false || feature.Dim() == 0) return false;
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// join with remained
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int32 joint_len = feature.Dim() + remained_feature_.Dim();
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Vector<BaseFloat> joint_feature(joint_len);
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joint_feature.Range(0, remained_feature_.Dim())
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.CopyFromVec(remained_feature_);
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joint_feature.Range(remained_feature_.Dim(), feature.Dim())
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.CopyFromVec(feature);
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// one by one, or stride with window
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// controlled by frame_chunk_stride_ and frame_chunk_size_
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int32 num_chunk =
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((joint_len / dim_) - frame_chunk_size_) / frame_chunk_stride_ + 1;
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for (int chunk_idx = 0; chunk_idx < num_chunk; ++chunk_idx) {
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int32 start = chunk_idx * frame_chunk_stride_ * dim_;
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Vector<BaseFloat> feature_chunk(frame_chunk_size_ * dim_);
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SubVector<BaseFloat> tmp(joint_feature.Data() + start,
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frame_chunk_size_ * dim_);
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feature_chunk.CopyFromVec(tmp);
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std::unique_lock<std::mutex> lock(mutex_);
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while (cache_.size() >= max_size_) {
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// cache full, wait
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ready_feed_condition_.wait(lock);
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}
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// feed cache
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cache_.push(feature_chunk);
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ready_read_condition_.notify_one();
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}
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// cache remained feats
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int32 remained_feature_len =
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joint_len - num_chunk * frame_chunk_stride_ * dim_;
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remained_feature_.Resize(remained_feature_len);
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remained_feature_.CopyFromVec(joint_feature.Range(
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frame_chunk_stride_ * num_chunk * dim_, remained_feature_len));
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return result;
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}
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} // namespace ppspeech
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