/**
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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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#include "precomp.h"
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namespace funasr {
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CTTransformer::CTTransformer()
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:env_(ORT_LOGGING_LEVEL_ERROR, ""),session_options{}
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{
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}
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void CTTransformer::InitPunc(const std::string &punc_model, const std::string &punc_config, const std::string &token_file, int thread_num){
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session_options.SetIntraOpNumThreads(thread_num);
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session_options.SetGraphOptimizationLevel(ORT_ENABLE_ALL);
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session_options.DisableCpuMemArena();
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try{
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m_session = std::make_unique<Ort::Session>(env_, ORTSTRING(punc_model).c_str(), session_options);
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LOG(INFO) << "Successfully load model from " << punc_model;
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}
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catch (std::exception const &e) {
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LOG(ERROR) << "Error when load punc onnx model: " << e.what();
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exit(-1);
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}
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// read inputnames outputnames
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GetInputNames(m_session.get(), m_strInputNames, m_szInputNames);
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GetOutputNames(m_session.get(), m_strOutputNames, m_szOutputNames);
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m_tokenizer.OpenYaml(punc_config.c_str(), token_file.c_str());
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m_tokenizer.JiebaInit(punc_config);
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}
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CTTransformer::~CTTransformer()
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{
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}
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string CTTransformer::AddPunc(const char* sz_input, std::string language)
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{
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string strResult;
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vector<string> strOut;
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vector<int> InputData;
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m_tokenizer.Tokenize(sz_input, strOut, InputData);
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int nTotalBatch = ceil((float)InputData.size() / TOKEN_LEN);
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int nCurBatch = -1;
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int nSentEnd = -1, nLastCommaIndex = -1;
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vector<int32_t> RemainIDs; //
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vector<string> RemainStr; //
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vector<int> NewPunctuation; //
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vector<string> NewString; //
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vector<string> NewSentenceOut;
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vector<int> NewPuncOut;
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int nDiff = 0;
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for (size_t i = 0; i < InputData.size(); i += TOKEN_LEN)
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{
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nDiff = (i + TOKEN_LEN) < InputData.size() ? (0) : (i + TOKEN_LEN - InputData.size());
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vector<int32_t> InputIDs(InputData.begin() + i, InputData.begin() + i + (TOKEN_LEN - nDiff));
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vector<string> InputStr(strOut.begin() + i, strOut.begin() + i + (TOKEN_LEN - nDiff));
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InputIDs.insert(InputIDs.begin(), RemainIDs.begin(), RemainIDs.end()); // RemainIDs+InputIDs;
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InputStr.insert(InputStr.begin(), RemainStr.begin(), RemainStr.end()); // RemainStr+InputStr;
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auto Punction = Infer(InputIDs);
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nCurBatch = i / TOKEN_LEN;
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if (nCurBatch < nTotalBatch - 1) // not the last minisetence
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{
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nSentEnd = -1;
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nLastCommaIndex = -1;
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for (int nIndex = Punction.size() - 2; nIndex > 0; nIndex--)
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{
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if (m_tokenizer.Id2Punc(Punction[nIndex]) == m_tokenizer.Id2Punc(PERIOD_INDEX) || m_tokenizer.Id2Punc(Punction[nIndex]) == m_tokenizer.Id2Punc(QUESTION_INDEX))
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{
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nSentEnd = nIndex;
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break;
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}
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if (nLastCommaIndex < 0 && m_tokenizer.Id2Punc(Punction[nIndex]) == m_tokenizer.Id2Punc(COMMA_INDEX))
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{
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nLastCommaIndex = nIndex;
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}
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}
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if (nSentEnd < 0 && InputStr.size() > CACHE_POP_TRIGGER_LIMIT && nLastCommaIndex > 0)
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{
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nSentEnd = nLastCommaIndex;
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Punction[nSentEnd] = PERIOD_INDEX;
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}
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RemainStr.assign(InputStr.begin() + (nSentEnd + 1), InputStr.end());
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RemainIDs.assign(InputIDs.begin() + (nSentEnd + 1), InputIDs.end());
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InputStr.assign(InputStr.begin(), InputStr.begin() + (nSentEnd + 1)); // minit_sentence
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Punction.assign(Punction.begin(), Punction.begin() + (nSentEnd + 1));
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}
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NewPunctuation.insert(NewPunctuation.end(), Punction.begin(), Punction.end());
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vector<string> WordWithPunc;
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for (int i = 0; i < InputStr.size(); i++)
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{
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// if (i > 0 && !(InputStr[i][0] & 0x80) && (i + 1) <InputStr.size() && !(InputStr[i+1][0] & 0x80))// �м��Ӣ�ģ�
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if (i > 0 && !(InputStr[i-1][0] & 0x80) && !(InputStr[i][0] & 0x80))
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{
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InputStr[i] = " " + InputStr[i];
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}
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WordWithPunc.push_back(InputStr[i]);
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if (Punction[i] != NOTPUNC_INDEX) // �»���
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{
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WordWithPunc.push_back(m_tokenizer.Id2Punc(Punction[i]));
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}
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}
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NewString.insert(NewString.end(), WordWithPunc.begin(), WordWithPunc.end()); // new_mini_sentence += "".join(words_with_punc)
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NewSentenceOut = NewString;
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NewPuncOut = NewPunctuation;
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// last mini sentence
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if(nCurBatch == nTotalBatch - 1)
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{
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if (NewString[NewString.size() - 1] == m_tokenizer.Id2Punc(COMMA_INDEX) || NewString[NewString.size() - 1] == m_tokenizer.Id2Punc(DUN_INDEX))
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{
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NewSentenceOut.assign(NewString.begin(), NewString.end() - 1);
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NewSentenceOut.push_back(m_tokenizer.Id2Punc(PERIOD_INDEX));
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NewPuncOut.assign(NewPunctuation.begin(), NewPunctuation.end() - 1);
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NewPuncOut.push_back(PERIOD_INDEX);
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}
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else if (NewString[NewString.size() - 1] != m_tokenizer.Id2Punc(PERIOD_INDEX) && NewString[NewString.size() - 1] != m_tokenizer.Id2Punc(QUESTION_INDEX))
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{
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NewSentenceOut = NewString;
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NewSentenceOut.push_back(m_tokenizer.Id2Punc(PERIOD_INDEX));
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NewPuncOut = NewPunctuation;
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NewPuncOut.push_back(PERIOD_INDEX);
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}
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}
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}
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for (auto& item : NewSentenceOut){
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strResult += item;
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}
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if(language == "en-bpe"){
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std::vector<std::string> chineseSymbols;
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chineseSymbols.push_back("，");
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chineseSymbols.push_back("。");
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chineseSymbols.push_back("、");
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chineseSymbols.push_back("？");
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std::string englishSymbols = ",.,?";
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for (size_t i = 0; i < chineseSymbols.size(); i++) {
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size_t pos = 0;
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while ((pos = strResult.find(chineseSymbols[i], pos)) != std::string::npos) {
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strResult.replace(pos, 3, 1, englishSymbols[i]);
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pos++;
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}
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}
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}
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return strResult;
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}
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vector<int> CTTransformer::Infer(vector<int32_t> input_data)
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{
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Ort::MemoryInfo m_memoryInfo = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
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vector<int> punction;
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std::array<int64_t, 2> input_shape_{ 1, (int64_t)input_data.size()};
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Ort::Value onnx_input = Ort::Value::CreateTensor<int32_t>(
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m_memoryInfo,
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input_data.data(),
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input_data.size(),
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input_shape_.data(),
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input_shape_.size());
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std::array<int32_t,1> text_lengths{ (int32_t)input_data.size() };
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std::array<int64_t,1> text_lengths_dim{ 1 };
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Ort::Value onnx_text_lengths = Ort::Value::CreateTensor(
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m_memoryInfo,
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text_lengths.data(),
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text_lengths.size() * sizeof(int32_t),
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text_lengths_dim.data(),
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text_lengths_dim.size(), ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32);
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std::vector<Ort::Value> input_onnx;
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input_onnx.emplace_back(std::move(onnx_input));
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input_onnx.emplace_back(std::move(onnx_text_lengths));
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try {
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auto outputTensor = m_session->Run(Ort::RunOptions{nullptr}, m_szInputNames.data(), input_onnx.data(), m_szInputNames.size(), m_szOutputNames.data(), m_szOutputNames.size());
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std::vector<int64_t> outputShape = outputTensor[0].GetTensorTypeAndShapeInfo().GetShape();
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int64_t outputCount = std::accumulate(outputShape.begin(), outputShape.end(), 1, std::multiplies<int64_t>());
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float * floatData = outputTensor[0].GetTensorMutableData<float>();
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for (int i = 0; i < outputCount; i += CANDIDATE_NUM)
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{
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int index = Argmax(floatData + i, floatData + i + CANDIDATE_NUM-1);
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punction.push_back(index);
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}
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}
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catch (std::exception const &e)
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{
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LOG(ERROR) << "Error when run punc onnx forword: " << (e.what());
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}
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return punction;
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}
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} // namespace funasr
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