/**
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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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CTTransformerOnline::CTTransformerOnline()
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:env_(ORT_LOGGING_LEVEL_ERROR, ""),session_options{}
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{
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}
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void CTTransformerOnline::InitPunc(const std::string &punc_model, const std::string &punc_config, 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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string strName;
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GetInputName(m_session.get(), strName);
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m_strInputNames.push_back(strName.c_str());
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GetInputName(m_session.get(), strName, 1);
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m_strInputNames.push_back(strName);
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GetInputName(m_session.get(), strName, 2);
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m_strInputNames.push_back(strName);
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GetInputName(m_session.get(), strName, 3);
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m_strInputNames.push_back(strName);
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GetOutputName(m_session.get(), strName);
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m_strOutputNames.push_back(strName);
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for (auto& item : m_strInputNames)
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m_szInputNames.push_back(item.c_str());
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for (auto& item : m_strOutputNames)
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m_szOutputNames.push_back(item.c_str());
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m_tokenizer.OpenYaml(punc_config.c_str());
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}
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CTTransformerOnline::~CTTransformerOnline()
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{
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}
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string CTTransformerOnline::AddPunc(const char* sz_input, vector<string> &arr_cache, 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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string strText; //full_text
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strText = accumulate(arr_cache.begin(), arr_cache.end(), strText);
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strText += sz_input; // full_text = precache + text
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m_tokenizer.Tokenize(strText.c_str(), 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> new_mini_sentence_punc; // sentence_punc_list = []
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vector<string> sentenceOut; // sentenceOut
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vector<string> sentence_punc_list,sentence_words_list,sentence_punc_list_out; // sentence_words_list = []
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int nSkipNum = 0;
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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, arr_cache.size());
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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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for (auto& item : Punction)
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{
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sentence_punc_list.push_back(m_tokenizer.Id2Punc(item));
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}
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sentence_words_list.insert(sentence_words_list.end(), InputStr.begin(), InputStr.end());
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new_mini_sentence_punc.insert(new_mini_sentence_punc.end(), Punction.begin(), Punction.end());
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}
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vector<string> WordWithPunc;
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for (int i = 0; i < sentence_words_list.size(); i++) // for i in range(0, len(sentence_words_list)):
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{
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if (!(sentence_words_list[i][0] & 0x80) && (i + 1) < sentence_words_list.size() && !(sentence_words_list[i + 1][0] & 0x80))
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{
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sentence_words_list[i] = " " + sentence_words_list[i];
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}
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if (nSkipNum < arr_cache.size()) // if skip_num < len(cache):
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nSkipNum++;
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else
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WordWithPunc.push_back(sentence_words_list[i]);
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if (nSkipNum >= arr_cache.size())
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{
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sentence_punc_list_out.push_back(sentence_punc_list[i]);
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if (sentence_punc_list[i] != NOTPUNC)
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{
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WordWithPunc.push_back(sentence_punc_list[i]);
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}
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}
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}
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sentenceOut.insert(sentenceOut.end(), WordWithPunc.begin(), WordWithPunc.end()); //
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nSentEnd = -1;
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for (int i = sentence_punc_list.size() - 2; i > 0; i--)
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{
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if (new_mini_sentence_punc[i] == PERIOD_INDEX || new_mini_sentence_punc[i] == QUESTION_INDEX)
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{
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nSentEnd = i;
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break;
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}
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}
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arr_cache.assign(sentence_words_list.begin() + (nSentEnd + 1), sentence_words_list.end());
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if (sentenceOut.size() > 0 && m_tokenizer.IsPunc(sentenceOut[sentenceOut.size() - 1]))
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{
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sentenceOut.assign(sentenceOut.begin(), sentenceOut.end() - 1);
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sentence_punc_list_out[sentence_punc_list_out.size() - 1] = m_tokenizer.Id2Punc(NOTPUNC_INDEX);
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}
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return accumulate(sentenceOut.begin(), sentenceOut.end(), string(""));
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}
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vector<int> CTTransformerOnline::Infer(vector<int32_t> input_data, int nCacheSize)
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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(
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m_memoryInfo,
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input_data.data(),
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input_data.size() * sizeof(int32_t),
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input_shape_.data(),
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input_shape_.size(), ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32);
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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<int32_t>(
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m_memoryInfo,
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text_lengths.data(),
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text_lengths.size(),
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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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//vad_mask
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// vector<float> arVadMask,arSubMask;
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vector<float> arVadMask;
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int nTextLength = input_data.size();
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VadMask(nTextLength, nCacheSize, arVadMask);
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// Triangle(nTextLength, arSubMask);
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std::array<int64_t, 4> VadMask_Dim{ 1,1, nTextLength ,nTextLength };
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Ort::Value onnx_vad_mask = Ort::Value::CreateTensor<float>(
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m_memoryInfo,
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arVadMask.data(),
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arVadMask.size(), // * sizeof(float),
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VadMask_Dim.data(),
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VadMask_Dim.size()); // , ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT);
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//sub_masks
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std::array<int64_t, 4> SubMask_Dim{ 1,1, nTextLength ,nTextLength };
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Ort::Value onnx_sub_mask = Ort::Value::CreateTensor<float>(
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m_memoryInfo,
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arVadMask.data(),
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arVadMask.size(),
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SubMask_Dim.data(),
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SubMask_Dim.size()); // , ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT);
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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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input_onnx.emplace_back(std::move(onnx_vad_mask));
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input_onnx.emplace_back(std::move(onnx_sub_mask));
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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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void CTTransformerOnline::VadMask(int nSize, int vad_pos, vector<float>& Result)
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{
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Result.resize(0);
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Result.assign(nSize * nSize, 1);
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if (vad_pos <= 0 || vad_pos >= nSize)
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{
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return;
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}
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for (int i = 0; i < vad_pos-1; i++)
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{
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for (int j = vad_pos; j < nSize; j++)
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{
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Result[i * nSize + j] = 0.0f;
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}
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}
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}
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void CTTransformerOnline::Triangle(int text_length, vector<float>& Result)
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{
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Result.resize(0);
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Result.assign(text_length * text_length,1); // generate a zeros: text_length x text_length
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for (int i = 0; i < text_length; i++) // rows
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{
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for (int j = i+1; j<text_length; j++) //cols
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{
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Result[i * text_length + j] = 0.0f;
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}
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}
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//Transport(Result, text_length, text_length);
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}
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void CTTransformerOnline::Transport(vector<float>& In,int nRows, int nCols)
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{
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vector<float> Out;
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Out.resize(nRows * nCols);
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int i = 0;
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for (int j = 0; j < nCols; j++) {
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for (; i < nRows * nCols; i++) {
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Out[i] = In[j + nCols * (i % nRows)];
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if ((i + 1) % nRows == 0) {
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i++;
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break;
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}
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}
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}
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In = Out;
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}
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} // namespace funasr
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