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