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| typedef int64_t int64; | |
| namespace Shirakana { | |
| struct WavHead { | |
| char RIFF[4]; | |
| long int size0; | |
| char WAVE[4]; | |
| char FMT[4]; | |
| long int size1; | |
| short int fmttag; | |
| short int channel; | |
| long int samplespersec; | |
| long int bytepersec; | |
| short int blockalign; | |
| short int bitpersamples; | |
| char DATA[4]; | |
| long int size2; | |
| }; | |
| int conArr2Wav(int64 size, int16_t* input, const char* filename) { | |
| WavHead head = { {'R','I','F','F'},0,{'W','A','V','E'},{'f','m','t',' '},16, | |
| 1,1,22050,22050 * 2,2,16,{'d','a','t','a'}, | |
| 0 }; | |
| head.size0 = size * 2 + 36; | |
| head.size2 = size * 2; | |
| std::ofstream ocout; | |
| char* outputData = (char*)input; | |
| ocout.open(filename, std::ios::out | std::ios::binary); | |
| ocout.write((char*)&head, 44); | |
| ocout.write(outputData, (int32_t)(size * 2)); | |
| ocout.close(); | |
| return 0; | |
| } | |
| inline std::wstring to_wide_string(const std::string& input) | |
| { | |
| std::wstring_convert<std::codecvt_utf8<wchar_t>> converter; | |
| return converter.from_bytes(input); | |
| } | |
| inline std::string to_byte_string(const std::wstring& input) | |
| { | |
| std::wstring_convert<std::codecvt_utf8<wchar_t>> converter; | |
| return converter.to_bytes(input); | |
| } | |
| } | |
| int main() | |
| { | |
| torch::jit::Module Vits; | |
| std::string buffer; | |
| std::vector<int64> text; | |
| std::vector<int16_t> data; | |
| while(true) | |
| { | |
| while (true) | |
| { | |
| std::cin >> buffer; | |
| if (buffer == "end") | |
| return 0; | |
| if(buffer == "model") | |
| { | |
| std::cin >> buffer; | |
| Vits = torch::jit::load(buffer); | |
| continue; | |
| } | |
| if (buffer == "endinfer") | |
| { | |
| Shirakana::conArr2Wav(data.size(), data.data(), "temp\\tmp.wav"); | |
| data.clear(); | |
| std::cout << "endofinfe"; | |
| continue; | |
| } | |
| if (buffer == "line") | |
| { | |
| std::cin >> buffer; | |
| while (buffer.find("endline")==std::string::npos) | |
| { | |
| text.push_back(std::atoi(buffer.c_str())); | |
| std::cin >> buffer; | |
| } | |
| val InputTensor = torch::from_blob(text.data(), { 1,static_cast<int64>(text.size()) }, torch::kInt64); | |
| std::array<int64, 1> TextLength{ static_cast<int64>(text.size()) }; | |
| val InputTensor_length = torch::from_blob(TextLength.data(), { 1 }, torch::kInt64); | |
| std::vector<torch::IValue> inputs; | |
| inputs.push_back(InputTensor); | |
| inputs.push_back(InputTensor_length); | |
| if (buffer.length() > 7) | |
| { | |
| std::array<int64, 1> speakerIndex{ (int64)atoi(buffer.substr(7).c_str()) }; | |
| inputs.push_back(torch::from_blob(speakerIndex.data(), { 1 }, torch::kLong)); | |
| } | |
| val output = Vits.forward(inputs).toTuple()->elements()[0].toTensor().multiply(32276.0F); | |
| val outputSize = output.sizes().at(2); | |
| val floatOutput = output.data_ptr<float>(); | |
| int16_t* outputTmp = (int16_t*)malloc(sizeof(float) * outputSize); | |
| if (outputTmp == nullptr) { | |
| throw std::exception("内存不足"); | |
| } | |
| for (int i = 0; i < outputSize; i++) { | |
| *(outputTmp + i) = (int16_t) * (floatOutput + i); | |
| } | |
| data.insert(data.end(), outputTmp, outputTmp+outputSize); | |
| free(outputTmp); | |
| text.clear(); | |
| std::cout << "endofline"; | |
| } | |
| } | |
| } | |
| //model S:\VSGIT\ShirakanaTTSUI\build\x64\Release\Mods\AtriVITS\AtriVITS_LJS.pt | |
| } |