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87 lines
2.7 KiB
87 lines
2.7 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 "kaldi/decoder/decodable-itf.h"
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#include "matrix/kaldi-matrix.h"
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#include "nnet/nnet_itf.h"
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#include "nnet/nnet_producer.h"
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namespace ppspeech {
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struct DecodableOpts;
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class Decodable : public kaldi::DecodableInterface {
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public:
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explicit Decodable(const std::shared_ptr<NnetProducer>& nnet_producer,
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kaldi::BaseFloat acoustic_scale = 1.0);
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// nnet logprob output, used by wfst
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virtual kaldi::BaseFloat LogLikelihood(int32 frame, int32 index);
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// nnet output
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virtual bool FrameLikelihood(int32 frame,
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std::vector<kaldi::BaseFloat>* likelihood);
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// forward nnet with feats
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bool AdvanceChunk();
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// forward nnet with feats, and get nnet output
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bool AdvanceChunk(kaldi::Vector<kaldi::BaseFloat>* logprobs,
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int* vocab_dim);
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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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virtual bool IsLastFrame(int32 frame);
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// nnet output dim, e.g. vocab size
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virtual int32 NumIndices() const;
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virtual int32 NumFramesReady() const;
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void Reset();
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bool IsInputFinished() const { return nnet_producer_->IsFinished(); }
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bool EnsureFrameHaveComputed(int32 frame);
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int32 TokenId2NnetId(int32 token_id);
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// for offline test
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void Acceptlikelihood(const kaldi::Matrix<kaldi::BaseFloat>& likelihood);
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private:
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std::shared_ptr<NnetProducer> nnet_producer_;
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// the frame is nnet prob frame rather than audio feature frame
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// nnet frame subsample the feature frame
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// eg: 35 frame features output 8 frame inferences
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int32 frame_offset_;
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int32 frames_ready_;
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// todo: feature frame mismatch with nnet inference frame
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// so use subsampled_frame
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int32 current_log_post_subsampled_offset_;
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int32 num_chunk_computed_;
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std::vector<kaldi::BaseFloat> framelikelihood_;
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kaldi::BaseFloat acoustic_scale_;
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};
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} // namespace ppspeech
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