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73 lines
2.1 KiB
73 lines
2.1 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 "frontend/audio/frontend_itf.h"
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namespace ppspeech {
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struct AssemblerOptions {
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// refer:https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/paddlespeech/s2t/exps/deepspeech2/model.py
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// the nnet batch forward
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int32 receptive_filed_length;
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int32 subsampling_rate;
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int32 nnet_decoder_chunk;
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AssemblerOptions()
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: receptive_filed_length(1),
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subsampling_rate(1),
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nnet_decoder_chunk(1) {}
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};
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class Assembler : public FrontendInterface {
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public:
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explicit Assembler(
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AssemblerOptions opts,
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std::unique_ptr<FrontendInterface> base_extractor = NULL);
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// Feed feats or waves
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virtual void Accept(const kaldi::VectorBase<kaldi::BaseFloat>& inputs);
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// feats size = num_frames * feat_dim
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virtual bool Read(kaldi::Vector<kaldi::BaseFloat>* feats);
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// feat dim
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virtual size_t Dim() const { return dim_; }
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virtual void SetFinished() {
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base_extractor_->SetFinished();
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}
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virtual bool IsFinished() const { return base_extractor_->IsFinished(); }
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virtual void Reset() {
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base_extractor_->Reset();
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}
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private:
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bool Compute(kaldi::Vector<kaldi::BaseFloat>* feats);
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int32 dim_;
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int32 frame_chunk_size_; // window
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int32 frame_chunk_stride_; // stride
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int32 receptive_filed_length_;
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std::queue<kaldi::Vector<kaldi::BaseFloat>> feature_cache_;
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std::unique_ptr<FrontendInterface> base_extractor_;
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DISALLOW_COPY_AND_ASSIGN(Assembler);
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};
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} // namespace ppspeech
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