/**
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* Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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* MIT License (https://opensource.org/licenses/MIT)
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*/
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#pragma once
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#define C10_USE_GLOG
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#include <torch/serialize.h>
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#include <torch/script.h>
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#include <torch/torch.h>
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#include <torch/csrc/jit/passes/tensorexpr_fuser.h>
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#include "precomp.h"
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#include "fst/fstlib.h"
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#include "fst/symbol-table.h"
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#include "bias-lm.h"
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#include "phone-set.h"
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namespace funasr {
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class ParaformerTorch : public Model {
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/**
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* Author: Speech Lab of DAMO Academy, Alibaba Group
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* Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
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* https://arxiv.org/pdf/2206.08317.pdf
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*/
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private:
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Vocab* vocab = nullptr;
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Vocab* lm_vocab = nullptr;
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SegDict* seg_dict = nullptr;
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PhoneSet* phone_set_ = nullptr;
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//const float scale = 22.6274169979695;
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const float scale = 1.0;
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void LoadConfigFromYaml(const char* filename);
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void LoadCmvn(const char *filename);
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void LfrCmvn(std::vector<std::vector<float>> &asr_feats);
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using TorchModule = torch::jit::script::Module;
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std::shared_ptr<TorchModule> model_ = nullptr;
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std::vector<torch::Tensor> encoder_outs_;
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bool use_hotword;
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public:
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ParaformerTorch();
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~ParaformerTorch();
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void InitAsr(const std::string &am_model, const std::string &am_cmvn, const std::string &am_config, int thread_num);
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void InitHwCompiler(const std::string &hw_model, int thread_num);
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void InitSegDict(const std::string &seg_dict_model);
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std::vector<std::vector<float>> CompileHotwordEmbedding(std::string &hotwords);
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void Reset();
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void FbankKaldi(float sample_rate, const float* waves, int len, std::vector<std::vector<float>> &asr_feats);
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std::vector<std::string> Forward(float** din, int* len, bool input_finished=true, const std::vector<std::vector<float>> &hw_emb={{0.0}}, void* wfst_decoder=nullptr, int batch_in=1);
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string GreedySearch( float* in, int n_len, int64_t token_nums,
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bool is_stamp=false, std::vector<float> us_alphas={0}, std::vector<float> us_cif_peak={0});
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string Rescoring();
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string GetLang(){return language;};
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int GetAsrSampleRate() { return asr_sample_rate; };
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void SetBatchSize(int batch_size) {batch_size_ = batch_size;};
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int GetBatchSize() {return batch_size_;};
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void StartUtterance();
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void EndUtterance();
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void InitLm(const std::string &lm_file, const std::string &lm_cfg_file, const std::string &lex_file);
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string BeamSearch(WfstDecoder* &wfst_decoder, float* in, int n_len, int64_t token_nums);
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string FinalizeDecode(WfstDecoder* &wfst_decoder,
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bool is_stamp=false, std::vector<float> us_alphas={0}, std::vector<float> us_cif_peak={0});
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Vocab* GetVocab();
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Vocab* GetLmVocab();
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PhoneSet* GetPhoneSet();
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knf::FbankOptions fbank_opts_;
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vector<float> means_list_;
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vector<float> vars_list_;
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int lfr_m = PARA_LFR_M;
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int lfr_n = PARA_LFR_N;
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// paraformer-offline
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std::string language="zh-cn";
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// lm
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std::shared_ptr<fst::Fst<fst::StdArc>> lm_ = nullptr;
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string window_type = "hamming";
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int frame_length = 25;
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int frame_shift = 10;
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int n_mels = 80;
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int encoder_size = 512;
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int fsmn_layers = 16;
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int fsmn_lorder = 10;
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int fsmn_dims = 512;
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float cif_threshold = 1.0;
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float tail_alphas = 0.45;
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int asr_sample_rate = MODEL_SAMPLE_RATE;
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int batch_size_ = 1;
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};
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} // namespace funasr
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