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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 | |
} |