Delete Libtorch C++ Infer

Libtorch C++ Infer/VITS-LibTorch.cpp

@@ -1,121 +0,0 @@
-#include <iostream>
-#include <torch/torch.h>
-#include <torch/script.h>
-#include <string>
-#include <vector>
-#include <locale>
-#include <codecvt>
-#include <direct.h>
-#include <fstream>
-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);
-    }
-}
-
-#define val const auto
-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
-}

Libtorch C++ Infer/toLibTorch.ipynb

@@ -1,142 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "import IPython.display as ipd\n",
-    "\n",
-    "import os\n",
-    "import json\n",
-    "import math\n",
-    "import torch\n",
-    "from torch import nn\n",
-    "from torch.nn import functional as F\n",
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "import ../commons\n",
-    "import ../utils\n",
-    "from ../data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate\n",
-    "from ../models import SynthesizerTrn\n",
-    "from ../text.symbols import symbols\n",
-    "from ../text import text_to_sequence\n",
-    "\n",
-    "from scipy.io.wavfile import write\n",
-    "\n",
-    "\n",
-    "def get_text(text, hps):\n",
-    "    text_norm = text_to_sequence(text, hps.data.text_cleaners)\n",
-    "    if hps.data.add_blank:\n",
-    "        text_norm = commons.intersperse(text_norm, 0)\n",
-    "    text_norm = torch.LongTensor(text_norm)\n",
-    "    return text_norm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#############################################################\n",
-    "#                                                           #\n",
-    "#                      Single Speakers                      #\n",
-    "#                                                           #\n",
-    "#############################################################"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hps = utils.get_hparams_from_file(\"configs/XXX.json\")  #将\"\"内的内容修改为你的模型路径与config路径\n",
-    "net_g = SynthesizerTrn(\n",
-    "    len(symbols),\n",
-    "    hps.data.filter_length // 2 + 1,\n",
-    "    hps.train.segment_size // hps.data.hop_length,\n",
-    "    **hps.model).cuda()\n",
-    "_ = net_g.eval()\n",
-    "\n",
-    "_ = utils.load_checkpoint(\"/path/to/model.pth\", net_g, None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stn_tst = get_text(\"こんにちは\", hps)\n",
-    "with torch.no_grad():\n",
-    "    x_tst = stn_tst.cuda().unsqueeze(0)\n",
-    "    x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda()\n",
-    "    traced_mod = torch.jit.trace(net_g,(x_tst, x_tst_lengths,sid))\n",
-    "    torch.jit.save(traced_mod,\"OUTPUTLIBTORCHMODEL.pt\")\n",
-    "    audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()\n",
-    "ipd.display(ipd.Audio(audio, rate=hps.data.sampling_rate, normalize=False))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#############################################################\n",
-    "#                                                           #\n",
-    "#                     Multiple Speakers                     #\n",
-    "#                                                           #\n",
-    "#############################################################"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hps = utils.get_hparams_from_file(\"./configs/XXX.json\")  #将\"\"内的内容修改为你的模型路径与config路径\n",
-    "net_g = SynthesizerTrn(\n",
-    "    len(symbols),\n",
-    "    hps.data.filter_length // 2 + 1,\n",
-    "    hps.train.segment_size // hps.data.hop_length,\n",
-    "    n_speakers=hps.data.n_speakers,\n",
-    "    **hps.model).cuda()\n",
-    "_ = net_g.eval()\n",
-    "\n",
-    "_ = utils.load_checkpoint(\"/path/to/model.pth\", net_g, None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stn_tst = get_text(\"こんにちは\", hps)\n",
-    "with torch.no_grad():\n",
-    "    x_tst = stn_tst.cuda().unsqueeze(0)\n",
-    "    x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda()\n",
-    "    sid = torch.LongTensor([4]).cuda()\n",
-    "    traced_mod = torch.jit.trace(net_g,(x_tst, x_tst_lengths,sid))\n",
-    "    torch.jit.save(traced_mod,\"OUTPUTLIBTORCHMODEL.pt\")\n",
-    "    audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()\n",
-    "ipd.display(ipd.Audio(audio, rate=hps.data.sampling_rate, normalize=False))"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}