colab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "65CU-n-JHhbY"
      },
      "source": [
        "# Clone repository"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_0vZ-OjHVNu",
        "outputId": "3f4a500c-8728-4d9b-e014-615339461f3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'vits-mandarin-biaobei'...\n",
            "remote: Enumerating objects: 75, done.\u001b[K\n",
            "remote: Counting objects: 100% (75/75), done.\u001b[K\n",
            "remote: Compressing objects: 100% (73/73), done.\u001b[K\n",
            "remote: Total 75 (delta 24), reused 30 (delta 2), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (75/75), done.\n",
            "/content/vits-mandarin-biaobei\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/AlexandaJerry/vits-mandarin-biaobei\n",
        "%cd vits-mandarin-biaobei"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!/opt/bin/nvidia-smi"
      ],
      "metadata": {
        "id": "smGF80ufrZxu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1dccd34-a9c1-4265-893d-005b87c619a0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Aug 12 08:55:19 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   37C    P0    25W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "6vOAlBTavYsc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "13ed039d-51a5-4dea-f54d-9230e2a46e66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting Cython==0.29.21\n",
            "  Downloading Cython-0.29.21-cp37-cp37m-manylinux1_x86_64.whl (2.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.0 MB 5.0 MB/s \n",
            "\u001b[?25hCollecting librosa==0.8.0\n",
            "  Downloading librosa-0.8.0.tar.gz (183 kB)\n",
            "\u001b[K     |████████████████████████████████| 183 kB 54.8 MB/s \n",
            "\u001b[?25hCollecting matplotlib==3.3.1\n",
            "  Downloading matplotlib-3.3.1-cp37-cp37m-manylinux1_x86_64.whl (11.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 11.6 MB 30.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy==1.21.6 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (1.21.6)\n",
            "Collecting phonemizer==2.2.1\n",
            "  Downloading phonemizer-2.2.1-py3-none-any.whl (49 kB)\n",
            "\u001b[K     |████████████████████████████████| 49 kB 5.4 MB/s \n",
            "\u001b[?25hCollecting scipy==1.5.2\n",
            "  Downloading scipy-1.5.2-cp37-cp37m-manylinux1_x86_64.whl (25.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 25.9 MB 1.3 MB/s \n",
            "\u001b[?25hCollecting Unidecode==1.1.1\n",
            "  Downloading Unidecode-1.1.1-py2.py3-none-any.whl (238 kB)\n",
            "\u001b[K     |████████████████████████████████| 238 kB 61.5 MB/s \n",
            "\u001b[?25hCollecting pypinyin==0.47.0\n",
            "  Downloading pypinyin-0.47.0-py2.py3-none-any.whl (1.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.4 MB 65.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (2.1.9)\n",
            "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (1.0.2)\n",
            "Requirement already satisfied: joblib>=0.14 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (1.1.0)\n",
            "Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (4.4.2)\n",
            "Requirement already satisfied: resampy>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (0.3.1)\n",
            "Requirement already satisfied: numba>=0.43.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (0.56.0)\n",
            "Requirement already satisfied: soundfile>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (0.10.3.post1)\n",
            "Requirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.8.0->-r requirements.txt (line 2)) (1.6.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.1->-r requirements.txt (line 3)) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.1->-r requirements.txt (line 3)) (2.8.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.1->-r requirements.txt (line 3)) (7.1.2)\n",
            "Requirement already satisfied: certifi>=2020.06.20 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.1->-r requirements.txt (line 3)) (2022.6.15)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.1->-r requirements.txt (line 3)) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.1->-r requirements.txt (line 3)) (1.4.4)\n",
            "Collecting segments\n",
            "  Downloading segments-2.2.1-py2.py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: attrs>=18.1 in /usr/local/lib/python3.7/dist-packages (from phonemizer==2.2.1->-r requirements.txt (line 5)) (22.1.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib==3.3.1->-r requirements.txt (line 3)) (4.1.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.43.0->librosa==0.8.0->-r requirements.txt (line 2)) (57.4.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from numba>=0.43.0->librosa==0.8.0->-r requirements.txt (line 2)) (4.12.0)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.43.0->librosa==0.8.0->-r requirements.txt (line 2)) (0.39.0)\n",
            "Requirement already satisfied: appdirs>=1.3.0 in /usr/local/lib/python3.7/dist-packages (from pooch>=1.0->librosa==0.8.0->-r requirements.txt (line 2)) (1.4.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from pooch>=1.0->librosa==0.8.0->-r requirements.txt (line 2)) (21.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from pooch>=1.0->librosa==0.8.0->-r requirements.txt (line 2)) (2.23.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib==3.3.1->-r requirements.txt (line 3)) (1.15.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa==0.8.0->-r requirements.txt (line 2)) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa==0.8.0->-r requirements.txt (line 2)) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa==0.8.0->-r requirements.txt (line 2)) (1.24.3)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn!=0.19.0,>=0.14.0->librosa==0.8.0->-r requirements.txt (line 2)) (3.1.0)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.7/dist-packages (from soundfile>=0.9.0->librosa==0.8.0->-r requirements.txt (line 2)) (1.15.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.0->soundfile>=0.9.0->librosa==0.8.0->-r requirements.txt (line 2)) (2.21)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.43.0->librosa==0.8.0->-r requirements.txt (line 2)) (3.8.1)\n",
            "Collecting csvw>=1.5.6\n",
            "  Downloading csvw-3.1.1-py2.py3-none-any.whl (56 kB)\n",
            "\u001b[K     |████████████████████████████████| 56 kB 4.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (2022.6.2)\n",
            "Collecting clldutils>=1.7.3\n",
            "  Downloading clldutils-3.12.0-py2.py3-none-any.whl (197 kB)\n",
            "\u001b[K     |████████████████████████████████| 197 kB 55.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.7/dist-packages (from clldutils>=1.7.3->segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (0.8.10)\n",
            "Collecting colorlog\n",
            "  Downloading colorlog-6.6.0-py2.py3-none-any.whl (11 kB)\n",
            "Collecting isodate\n",
            "  Downloading isodate-0.6.1-py2.py3-none-any.whl (41 kB)\n",
            "\u001b[K     |████████████████████████████████| 41 kB 599 kB/s \n",
            "\u001b[?25hCollecting rdflib\n",
            "  Downloading rdflib-6.2.0-py3-none-any.whl (500 kB)\n",
            "\u001b[K     |████████████████████████████████| 500 kB 54.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: babel in /usr/local/lib/python3.7/dist-packages (from csvw>=1.5.6->segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (2.10.3)\n",
            "Requirement already satisfied: jsonschema in /usr/local/lib/python3.7/dist-packages (from csvw>=1.5.6->segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (4.3.3)\n",
            "Collecting colorama\n",
            "  Downloading colorama-0.4.5-py2.py3-none-any.whl (16 kB)\n",
            "Collecting language-tags\n",
            "  Downloading language_tags-1.1.0-py2.py3-none-any.whl (210 kB)\n",
            "\u001b[K     |████████████████████████████████| 210 kB 60.4 MB/s \n",
            "\u001b[?25hCollecting rfc3986<2\n",
            "  Downloading rfc3986-1.5.0-py2.py3-none-any.whl (31 kB)\n",
            "Requirement already satisfied: uritemplate>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from csvw>=1.5.6->segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (3.0.1)\n",
            "Requirement already satisfied: pytz>=2015.7 in /usr/local/lib/python3.7/dist-packages (from babel->csvw>=1.5.6->segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (2022.1)\n",
            "Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema->csvw>=1.5.6->segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (5.9.0)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema->csvw>=1.5.6->segments->phonemizer==2.2.1->-r requirements.txt (line 5)) (0.18.1)\n",
            "Building wheels for collected packages: librosa\n",
            "  Building wheel for librosa (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for librosa: filename=librosa-0.8.0-py3-none-any.whl size=201396 sha256=a6a20a8eb919c964b683a14e24e8db9bb8964e3f5662c97cc415e19e09c19045\n",
            "  Stored in directory: /root/.cache/pip/wheels/de/1e/aa/d91797ae7e1ce11853ee100bee9d1781ae9d750e7458c95afb\n",
            "Successfully built librosa\n",
            "Installing collected packages: isodate, rfc3986, rdflib, language-tags, colorama, csvw, colorlog, scipy, clldutils, segments, Unidecode, pypinyin, phonemizer, matplotlib, librosa, Cython\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.7.3\n",
            "    Uninstalling scipy-1.7.3:\n",
            "      Successfully uninstalled scipy-1.7.3\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.2.2\n",
            "    Uninstalling matplotlib-3.2.2:\n",
            "      Successfully uninstalled matplotlib-3.2.2\n",
            "  Attempting uninstall: librosa\n",
            "    Found existing installation: librosa 0.8.1\n",
            "    Uninstalling librosa-0.8.1:\n",
            "      Successfully uninstalled librosa-0.8.1\n",
            "  Attempting uninstall: Cython\n",
            "    Found existing installation: Cython 0.29.32\n",
            "    Uninstalling Cython-0.29.32:\n",
            "      Successfully uninstalled Cython-0.29.32\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "pymc3 3.11.5 requires scipy<1.8.0,>=1.7.3, but you have scipy 1.5.2 which is incompatible.\u001b[0m\n",
            "Successfully installed Cython-0.29.21 Unidecode-1.1.1 clldutils-3.12.0 colorama-0.4.5 colorlog-6.6.0 csvw-3.1.1 isodate-0.6.1 language-tags-1.1.0 librosa-0.8.0 matplotlib-3.3.1 phonemizer-2.2.1 pypinyin-0.47.0 rdflib-6.2.0 rfc3986-1.5.0 scipy-1.5.2 segments-2.2.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  espeak-data libespeak1 libportaudio2 libsonic0\n",
            "The following NEW packages will be installed:\n",
            "  espeak espeak-data libespeak1 libportaudio2 libsonic0\n",
            "0 upgraded, 5 newly installed, 0 to remove and 19 not upgraded.\n",
            "Need to get 1,219 kB of archives.\n",
            "After this operation, 3,031 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libportaudio2 amd64 19.6.0-1 [64.6 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic/main amd64 libsonic0 amd64 0.2.0-6 [13.4 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 espeak-data amd64 1.48.04+dfsg-5 [934 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libespeak1 amd64 1.48.04+dfsg-5 [145 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu bionic/universe amd64 espeak amd64 1.48.04+dfsg-5 [61.6 kB]\n",
            "Fetched 1,219 kB in 1s (1,365 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 5.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package libportaudio2:amd64.\n",
            "(Reading database ... 155680 files and directories currently installed.)\n",
            "Preparing to unpack .../libportaudio2_19.6.0-1_amd64.deb ...\n",
            "Unpacking libportaudio2:amd64 (19.6.0-1) ...\n",
            "Selecting previously unselected package libsonic0:amd64.\n",
            "Preparing to unpack .../libsonic0_0.2.0-6_amd64.deb ...\n",
            "Unpacking libsonic0:amd64 (0.2.0-6) ...\n",
            "Selecting previously unselected package espeak-data:amd64.\n",
            "Preparing to unpack .../espeak-data_1.48.04+dfsg-5_amd64.deb ...\n",
            "Unpacking espeak-data:amd64 (1.48.04+dfsg-5) ...\n",
            "Selecting previously unselected package libespeak1:amd64.\n",
            "Preparing to unpack .../libespeak1_1.48.04+dfsg-5_amd64.deb ...\n",
            "Unpacking libespeak1:amd64 (1.48.04+dfsg-5) ...\n",
            "Selecting previously unselected package espeak.\n",
            "Preparing to unpack .../espeak_1.48.04+dfsg-5_amd64.deb ...\n",
            "Unpacking espeak (1.48.04+dfsg-5) ...\n",
            "Setting up libportaudio2:amd64 (19.6.0-1) ...\n",
            "Setting up espeak-data:amd64 (1.48.04+dfsg-5) ...\n",
            "Setting up libsonic0:amd64 (0.2.0-6) ...\n",
            "Setting up libespeak1:amd64 (1.48.04+dfsg-5) ...\n",
            "Setting up espeak (1.48.04+dfsg-5) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.5) ...\n"
          ]
        }
      ],
      "source": [
        "!pip install -r requirements.txt\n",
        "!sudo apt-get install espeak -y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G0iLn2JxKYhl"
      },
      "source": [
        "# Mount Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZOgjdsQgKTfD",
        "outputId": "b02c6486-634d-4e3a-e5ef-b864a6680ad0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UZ9maSoUmHaS"
      },
      "source": [
        "# Unpack dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "N3a-FsHghwXS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60bc1795-1aef-4a8d-9b5c-172e09f65bda"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyunpack\n",
            "  Downloading pyunpack-0.3-py2.py3-none-any.whl (4.1 kB)\n",
            "Collecting easyprocess\n",
            "  Downloading EasyProcess-1.1-py3-none-any.whl (8.7 kB)\n",
            "Collecting entrypoint2\n",
            "  Downloading entrypoint2-1.1-py2.py3-none-any.whl (9.9 kB)\n",
            "Installing collected packages: entrypoint2, easyprocess, pyunpack\n",
            "Successfully installed easyprocess-1.1 entrypoint2-1.1 pyunpack-0.3\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting patool\n",
            "  Downloading patool-1.12-py2.py3-none-any.whl (77 kB)\n",
            "\u001b[K     |████████████████████████████████| 77 kB 3.1 MB/s \n",
            "\u001b[?25hInstalling collected packages: patool\n",
            "Successfully installed patool-1.12\n",
            "[Errno 2] No such file or directory: 'vits-mandarin-biaobei'\n",
            "/content/vits-mandarin-biaobei\n"
          ]
        }
      ],
      "source": [
        "!pip install pyunpack\n",
        "!pip install patool\n",
        "from pyunpack import Archive\n",
        "%cd vits-mandarin-biaobei\n",
        "Archive('/content/drive/MyDrive/biaobei.rar').extractall('/content/vits-mandarin-biaobei')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get install sox"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gB6Bz3KoWYra",
        "outputId": "ccf53ea4-dd9b-4217-caee-219d7d7c100c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  libmagic-mgc libmagic1 libopencore-amrnb0 libopencore-amrwb0 libsox-fmt-alsa\n",
            "  libsox-fmt-base libsox3\n",
            "Suggested packages:\n",
            "  file libsox-fmt-all\n",
            "The following NEW packages will be installed:\n",
            "  libmagic-mgc libmagic1 libopencore-amrnb0 libopencore-amrwb0 libsox-fmt-alsa\n",
            "  libsox-fmt-base libsox3 sox\n",
            "0 upgraded, 8 newly installed, 0 to remove and 19 not upgraded.\n",
            "Need to get 760 kB of archives.\n",
            "After this operation, 6,717 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libopencore-amrnb0 amd64 0.1.3-2.1 [92.0 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libopencore-amrwb0 amd64 0.1.3-2.1 [45.8 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libmagic-mgc amd64 1:5.32-2ubuntu0.4 [184 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libmagic1 amd64 1:5.32-2ubuntu0.4 [68.6 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libsox3 amd64 14.4.2-3ubuntu0.18.04.1 [226 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libsox-fmt-alsa amd64 14.4.2-3ubuntu0.18.04.1 [10.6 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libsox-fmt-base amd64 14.4.2-3ubuntu0.18.04.1 [32.1 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 sox amd64 14.4.2-3ubuntu0.18.04.1 [101 kB]\n",
            "Fetched 760 kB in 1s (868 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 8.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package libopencore-amrnb0:amd64.\n",
            "(Reading database ... 156005 files and directories currently installed.)\n",
            "Preparing to unpack .../0-libopencore-amrnb0_0.1.3-2.1_amd64.deb ...\n",
            "Unpacking libopencore-amrnb0:amd64 (0.1.3-2.1) ...\n",
            "Selecting previously unselected package libopencore-amrwb0:amd64.\n",
            "Preparing to unpack .../1-libopencore-amrwb0_0.1.3-2.1_amd64.deb ...\n",
            "Unpacking libopencore-amrwb0:amd64 (0.1.3-2.1) ...\n",
            "Selecting previously unselected package libmagic-mgc.\n",
            "Preparing to unpack .../2-libmagic-mgc_1%3a5.32-2ubuntu0.4_amd64.deb ...\n",
            "Unpacking libmagic-mgc (1:5.32-2ubuntu0.4) ...\n",
            "Selecting previously unselected package libmagic1:amd64.\n",
            "Preparing to unpack .../3-libmagic1_1%3a5.32-2ubuntu0.4_amd64.deb ...\n",
            "Unpacking libmagic1:amd64 (1:5.32-2ubuntu0.4) ...\n",
            "Selecting previously unselected package libsox3:amd64.\n",
            "Preparing to unpack .../4-libsox3_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking libsox3:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Selecting previously unselected package libsox-fmt-alsa:amd64.\n",
            "Preparing to unpack .../5-libsox-fmt-alsa_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking libsox-fmt-alsa:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Selecting previously unselected package libsox-fmt-base:amd64.\n",
            "Preparing to unpack .../6-libsox-fmt-base_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking libsox-fmt-base:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Selecting previously unselected package sox.\n",
            "Preparing to unpack .../7-sox_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking sox (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Setting up libmagic-mgc (1:5.32-2ubuntu0.4) ...\n",
            "Setting up libmagic1:amd64 (1:5.32-2ubuntu0.4) ...\n",
            "Setting up libopencore-amrnb0:amd64 (0.1.3-2.1) ...\n",
            "Setting up libopencore-amrwb0:amd64 (0.1.3-2.1) ...\n",
            "Setting up libsox3:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Setting up libsox-fmt-base:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Setting up libsox-fmt-alsa:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Setting up sox (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.5) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Processing triggers for mime-support (3.60ubuntu1) ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/vits-mandarin-biaobei/biaobei"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQT4d7zqcaF5",
        "outputId": "d314f364-f619-4f08-d81c-099eaa3c9e33"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/vits-mandarin-biaobei/biaobei\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "for x in ./*.wav\n",
        "do \n",
        "  b=${x##*/}\n",
        "  sox $b -r 22050 tmp_$b\n",
        "  rm -rf $b\n",
        "  mv tmp_$b $b\n",
        "done"
      ],
      "metadata": {
        "id": "5E0ENKZiduQ-"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LY9d2hgjmYUF"
      },
      "source": [
        "# Alignment"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "path = \"/content/vits-mandarin-biaobei\"\n",
        "os.chdir(path)\n",
        "print(os.getcwd())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L-pAq7ppSp-Z",
        "outputId": "e02edc31-4328-48eb-c7c6-47d61753c8ac"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/vits-mandarin-biaobei\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LOsV22D8IUTS",
        "outputId": "6c049fb4-4e1e-4d4d-f32d-e373bcd1607e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/vits-mandarin-biaobei/monotonic_align\n",
            "Compiling core.pyx because it changed.\n",
            "[1/1] Cythonizing core.pyx\n",
            "/usr/local/lib/python3.7/dist-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /content/vits-mandarin-biaobei/monotonic_align/core.pyx\n",
            "  tree = Parsing.p_module(s, pxd, full_module_name)\n",
            "running build_ext\n",
            "building 'monotonic_align.core' extension\n",
            "creating build\n",
            "creating build/temp.linux-x86_64-3.7\n",
            "x86_64-linux-gnu-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.7/dist-packages/numpy/core/include -I/usr/include/python3.7m -c core.c -o build/temp.linux-x86_64-3.7/core.o\n",
            "creating /content/vits-mandarin-biaobei/monotonic_align/monotonic_align\n",
            "x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -g -fwrapv -O2 -Wl,-Bsymbolic-functions -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.7/core.o -o /content/vits-mandarin-biaobei/monotonic_align/monotonic_align/core.cpython-37m-x86_64-linux-gnu.so\n",
            "/content/vits-mandarin-biaobei\n"
          ]
        }
      ],
      "source": [
        "%cd monotonic_align\n",
        "!python setup.py build_ext --inplace\n",
        "%cd .."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5feIfJWdRNRB",
        "outputId": "f28f9e51-9a5a-49e5-93d6-91a97b8f59c9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/vits-mandarin-biaobei\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "path = \"/content/vits-mandarin-biaobei\"\n",
        "os.chdir(path)\n",
        "print(os.getcwd())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JXYOrkJmRuSh",
        "outputId": "9cbff4b0-58d3-498c-96d4-57131dc5512d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "START: /content/vits-mandarin-biaobei/filelists/train_filelist.txt\n",
            "START: /content/vits-mandarin-biaobei/filelists/val_filelist.txt\n"
          ]
        }
      ],
      "source": [
        "!python preprocess.py --text_index 1 --text_cleaners chinese_cleaners1 --filelists /content/vits-mandarin-biaobei/filelists/train_filelist.txt /content/vits-mandarin-biaobei/filelists/val_filelist.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gjIAR_UsmPEz"
      },
      "source": [
        "# Train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MrV5GulH3us_",
        "outputId": "6220e7c7-bb5d-4218-b807-a5b6bfea49c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] {'train': {'log_interval': 200, 'eval_interval': 1000, 'seed': 1234, 'epochs': 200, 'learning_rate': 0.0002, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 24, 'fp16_run': True, 'lr_decay': 0.999875, 'segment_size': 8192, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0}, 'data': {'training_files': 'filelists/train_filelist.txt.cleaned', 'validation_files': 'filelists/val_filelist.txt.cleaned', 'text_cleaners': ['chinese_cleaners1'], 'max_wav_value': 32768.0, 'sampling_rate': 22050, 'filter_length': 1024, 'hop_length': 256, 'win_length': 1024, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': None, 'add_blank': True, 'n_speakers': 0, 'cleaned_text': True}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [8, 8, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'n_layers_q': 3, 'use_spectral_norm': False}, 'model_dir': './logs/biaobei_base'}\n",
            "./logs/biaobei_base/G_0.pth\n",
            "[INFO] Loaded checkpoint './logs/biaobei_base/G_0.pth' (iteration 1)\n",
            "./logs/biaobei_base/D_0.pth\n",
            "[INFO] Loaded checkpoint './logs/biaobei_base/D_0.pth' (iteration 1)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at  ../aten/src/ATen/EmptyTensor.cpp:31.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/autograd/__init__.py:175: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed.  This is not an error, but may impair performance.\n",
            "grad.sizes() = [1, 9, 96], strides() = [46944, 96, 1]\n",
            "bucket_view.sizes() = [1, 9, 96], strides() = [864, 96, 1] (Triggered internally at  ../torch/csrc/distributed/c10d/reducer.cpp:312.)\n",
            "  allow_unreachable=True, accumulate_grad=True)  # Calls into the C++ engine to run the backward pass\n",
            "[INFO] Train Epoch: 1 [0%]\n",
            "[INFO] [6.084116458892822, 6.0839314460754395, 0.29333028197288513, 89.30412292480469, 1.4348154067993164, 156.98046875, 0, 0.0002]\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] Saving model and optimizer state at iteration 1 to ./logs/biaobei_base/G_0.pth\n",
            "[INFO] Saving model and optimizer state at iteration 1 to ./logs/biaobei_base/D_0.pth\n",
            "[INFO] Train Epoch: 1 [50%]\n",
            "[INFO] [2.4417457580566406, 2.3982975482940674, 2.748124837875366, 43.192771911621094, 1.5272194147109985, 1.9969310760498047, 200, 0.0002]\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:607: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at  ../aten/src/ATen/native/SpectralOps.cpp:800.)\n",
            "  normalized, onesided, return_complex)\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 1\n",
            "[INFO] Train Epoch: 2 [1%]\n",
            "[INFO] [1.4412362575531006, 3.7378997802734375, 6.991755485534668, 37.147499084472656, 1.432862639427185, 1.5886093378067017, 400, 0.000199975]\n",
            "[INFO] Train Epoch: 2 [51%]\n",
            "[INFO] [2.536994457244873, 2.6759510040283203, 3.281309127807617, 33.389408111572266, 1.4817153215408325, 1.5160833597183228, 600, 0.000199975]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 2\n",
            "[INFO] Train Epoch: 3 [1%]\n",
            "[INFO] [2.409064292907715, 2.3073856830596924, 3.3189518451690674, 35.795204162597656, 1.4083104133605957, 1.9691193103790283, 800, 0.000199950003125]\n",
            "[INFO] Train Epoch: 3 [51%]\n",
            "[INFO] [2.746138095855713, 1.787191390991211, 2.5603747367858887, 33.32743453979492, 1.3997528553009033, 1.5535820722579956, 1000, 0.000199950003125]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] Saving model and optimizer state at iteration 3 to ./logs/biaobei_base/G_1000.pth\n",
            "[INFO] Saving model and optimizer state at iteration 3 to ./logs/biaobei_base/D_1000.pth\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 3\n",
            "[INFO] Train Epoch: 4 [2%]\n",
            "[INFO] [2.419834852218628, 2.0565052032470703, 2.996807336807251, 33.93955612182617, 1.4203672409057617, 1.6924939155578613, 1200, 0.00019992500937460937]\n",
            "[INFO] Train Epoch: 4 [52%]\n",
            "[INFO] [2.9197604656219482, 1.9120299816131592, 2.43605375289917, 30.982423782348633, 1.4912872314453125, 1.4987608194351196, 1400, 0.00019992500937460937]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 4\n",
            "[INFO] Train Epoch: 5 [2%]\n",
            "[INFO] [2.4966511726379395, 2.3101582527160645, 3.0514235496520996, 29.65100860595703, 1.5399270057678223, 2.0126428604125977, 1600, 0.00019990001874843754]\n",
            "[INFO] Train Epoch: 5 [52%]\n",
            "[INFO] [2.5708014965057373, 1.7448824644088745, 2.274507522583008, 26.66349983215332, 1.4470289945602417, 1.6776108741760254, 1800, 0.00019990001874843754]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 5\n",
            "[INFO] Train Epoch: 6 [3%]\n",
            "[INFO] [2.825239419937134, 2.100724220275879, 2.29148006439209, 27.370080947875977, 1.4402334690093994, 1.7556214332580566, 2000, 0.00019987503124609398]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] Saving model and optimizer state at iteration 6 to ./logs/biaobei_base/G_2000.pth\n",
            "[INFO] Saving model and optimizer state at iteration 6 to ./logs/biaobei_base/D_2000.pth\n",
            "[INFO] Train Epoch: 6 [53%]\n",
            "[INFO] [2.5783028602600098, 1.7449078559875488, 2.479212999343872, 25.641738891601562, 1.511744737625122, 1.7865135669708252, 2200, 0.00019987503124609398]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 6\n",
            "[INFO] Train Epoch: 7 [3%]\n",
            "[INFO] [2.640836477279663, 1.7572307586669922, 2.705371618270874, 26.70126724243164, 1.527987003326416, 1.9366952180862427, 2400, 0.0001998500468671882]\n",
            "[INFO] Train Epoch: 7 [53%]\n",
            "[INFO] [2.7672274112701416, 1.818725824356079, 1.8327858448028564, 24.210783004760742, 1.4015765190124512, 1.8849365711212158, 2600, 0.0001998500468671882]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 7\n",
            "[INFO] Train Epoch: 8 [4%]\n",
            "[INFO] [2.664569616317749, 2.078366994857788, 2.618999481201172, 25.72434425354004, 1.4494619369506836, 1.6781935691833496, 2800, 0.00019982506561132978]\n",
            "[INFO] Train Epoch: 8 [54%]\n",
            "[INFO] [2.844681978225708, 1.9013457298278809, 2.220146894454956, 24.32044792175293, 1.3842315673828125, 1.7413629293441772, 3000, 0.00019982506561132978]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] Saving model and optimizer state at iteration 8 to ./logs/biaobei_base/G_3000.pth\n",
            "[INFO] Saving model and optimizer state at iteration 8 to ./logs/biaobei_base/D_3000.pth\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 8\n",
            "[INFO] Train Epoch: 9 [4%]\n",
            "[INFO] [2.700615406036377, 2.292174816131592, 2.384277105331421, 25.27933692932129, 1.3030858039855957, 1.6341955661773682, 3200, 0.00019980008747812837]\n",
            "[INFO] Train Epoch: 9 [54%]\n",
            "[INFO] [2.743894338607788, 1.9604068994522095, 2.3496131896972656, 23.112289428710938, 1.2711584568023682, 1.7833446264266968, 3400, 0.00019980008747812837]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 9\n",
            "[INFO] Train Epoch: 10 [5%]\n",
            "[INFO] [2.6869912147521973, 1.9352359771728516, 2.5188097953796387, 23.4274845123291, 1.3380227088928223, 1.7740949392318726, 3600, 0.0001997751124671936]\n",
            "[INFO] Train Epoch: 10 [55%]\n",
            "[INFO] [2.7764949798583984, 1.6710231304168701, 2.007585048675537, 22.923805236816406, 1.327287197113037, 1.7803704738616943, 3800, 0.0001997751124671936]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 10\n",
            "[INFO] Train Epoch: 11 [5%]\n",
            "[INFO] [2.8530619144439697, 1.9538522958755493, 2.505608081817627, 22.968854904174805, 1.324030876159668, 1.7356280088424683, 4000, 0.00019975014057813518]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] Saving model and optimizer state at iteration 11 to ./logs/biaobei_base/G_4000.pth\n",
            "[INFO] Saving model and optimizer state at iteration 11 to ./logs/biaobei_base/D_4000.pth\n",
            "[INFO] Train Epoch: 11 [55%]\n",
            "[INFO] [2.7253758907318115, 1.9672753810882568, 2.2072982788085938, 21.55106544494629, 1.3643673658370972, 1.5139884948730469, 4200, 0.00019975014057813518]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 11\n",
            "[INFO] Train Epoch: 12 [6%]\n",
            "[INFO] [2.710725784301758, 1.7867790460586548, 2.1654531955718994, 23.21925926208496, 1.2693941593170166, 1.6313729286193848, 4400, 0.00019972517181056292]\n",
            "[INFO] Train Epoch: 12 [56%]\n",
            "[INFO] [2.6724307537078857, 1.7789229154586792, 2.391763210296631, 23.39394760131836, 1.2505900859832764, 1.7837204933166504, 4600, 0.00019972517181056292]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 12\n",
            "[INFO] Train Epoch: 13 [6%]\n",
            "[INFO] [2.737412214279175, 1.8569896221160889, 2.2427494525909424, 22.143415451049805, 1.2883596420288086, 1.5434919595718384, 4800, 0.0001997002061640866]\n",
            "[INFO] Train Epoch: 13 [56%]\n",
            "[INFO] [2.7240333557128906, 1.6929125785827637, 2.2408742904663086, 22.582965850830078, 1.2616945505142212, 1.6578495502471924, 5000, 0.0001997002061640866]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] Saving model and optimizer state at iteration 13 to ./logs/biaobei_base/G_5000.pth\n",
            "[INFO] Saving model and optimizer state at iteration 13 to ./logs/biaobei_base/D_5000.pth\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 13\n",
            "[INFO] Train Epoch: 14 [7%]\n",
            "[INFO] [2.795337200164795, 1.6596165895462036, 2.3774819374084473, 21.676546096801758, 1.2574995756149292, 1.6747722625732422, 5200, 0.00019967524363831608]\n",
            "[INFO] Train Epoch: 14 [57%]\n",
            "[INFO] [2.8059263229370117, 1.859124779701233, 2.100916862487793, 20.741226196289062, 1.2215628623962402, 1.4731595516204834, 5400, 0.00019967524363831608]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 14\n",
            "[INFO] Train Epoch: 15 [7%]\n",
            "[INFO] [2.774426221847534, 1.9955708980560303, 2.383021354675293, 21.531986236572266, 1.3024400472640991, 1.789406657218933, 5600, 0.0001996502842328613]\n",
            "[INFO] Train Epoch: 15 [57%]\n",
            "[INFO] [2.802056312561035, 1.8395187854766846, 2.1710376739501953, 21.044178009033203, 1.250455617904663, 1.8578144311904907, 5800, 0.0001996502842328613]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 15\n",
            "[INFO] Train Epoch: 16 [8%]\n",
            "[INFO] [2.6887869834899902, 1.9888904094696045, 2.2489898204803467, 21.914621353149414, 1.2303470373153687, 1.6883699893951416, 6000, 0.00019962532794733217]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] Saving model and optimizer state at iteration 16 to ./logs/biaobei_base/G_6000.pth\n",
            "[INFO] Saving model and optimizer state at iteration 16 to ./logs/biaobei_base/D_6000.pth\n",
            "[INFO] Train Epoch: 16 [58%]\n",
            "[INFO] [2.734205484390259, 1.6239826679229736, 2.353121519088745, 21.414894104003906, 1.2701444625854492, 1.7555757761001587, 6200, 0.00019962532794733217]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 16\n",
            "[INFO] Train Epoch: 17 [8%]\n",
            "[INFO] [2.7895848751068115, 1.9784533977508545, 2.0921571254730225, 21.534574508666992, 1.2579307556152344, 1.75993013381958, 6400, 0.00019960037478133875]\n",
            "[INFO] Train Epoch: 17 [58%]\n",
            "[INFO] [2.924771785736084, 1.8922921419143677, 2.0394113063812256, 21.05266571044922, 1.2261463403701782, 1.6322624683380127, 6600, 0.00019960037478133875]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 17\n",
            "[INFO] Train Epoch: 18 [9%]\n",
            "[INFO] [2.8778138160705566, 1.7331665754318237, 2.13059663772583, 20.085569381713867, 1.2867584228515625, 1.8014692068099976, 6800, 0.00019957542473449108]\n",
            "[INFO] Train Epoch: 18 [59%]\n",
            "[INFO] [2.9135074615478516, 1.844761610031128, 2.3517770767211914, 22.37508201599121, 1.2295209169387817, 1.6714726686477661, 7000, 0.00019957542473449108]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] Saving model and optimizer state at iteration 18 to ./logs/biaobei_base/G_7000.pth\n",
            "[INFO] Saving model and optimizer state at iteration 18 to ./logs/biaobei_base/D_7000.pth\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 18\n",
            "[INFO] Train Epoch: 19 [9%]\n",
            "[INFO] [2.743447780609131, 1.7904363870620728, 2.642989158630371, 22.002586364746094, 1.2774255275726318, 1.89876127243042, 7200, 0.00019955047780639926]\n",
            "[INFO] Train Epoch: 19 [59%]\n",
            "[INFO] [2.7805516719818115, 1.9519226551055908, 2.4283533096313477, 20.12021255493164, 1.234122633934021, 1.6406763792037964, 7400, 0.00019955047780639926]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 19\n",
            "[INFO] Train Epoch: 20 [10%]\n",
            "[INFO] [2.7074551582336426, 1.802383542060852, 2.4140470027923584, 21.853384017944336, 1.2304021120071411, 1.7317063808441162, 7600, 0.00019952553399667344]\n",
            "[INFO] Train Epoch: 20 [60%]\n",
            "[INFO] [2.8339757919311523, 1.8614946603775024, 2.1415278911590576, 20.868751525878906, 1.253136396408081, 1.5782139301300049, 7800, 0.00019952553399667344]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 20\n",
            "[INFO] Train Epoch: 21 [10%]\n",
            "[INFO] [2.698446750640869, 1.8610132932662964, 2.6437385082244873, 19.71364974975586, 1.1622449159622192, 1.463873267173767, 8000, 0.00019950059330492385]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] Saving model and optimizer state at iteration 21 to ./logs/biaobei_base/G_8000.pth\n",
            "[INFO] Saving model and optimizer state at iteration 21 to ./logs/biaobei_base/D_8000.pth\n",
            "[INFO] Train Epoch: 21 [60%]\n",
            "[INFO] [2.6374335289001465, 2.05804705619812, 2.536536455154419, 22.417245864868164, 1.24099862575531, 1.7765955924987793, 8200, 0.00019950059330492385]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 21\n",
            "[INFO] Train Epoch: 22 [11%]\n",
            "[INFO] [2.9157843589782715, 1.7263212203979492, 2.1869561672210693, 20.874919891357422, 1.189208984375, 1.5717899799346924, 8400, 0.00019947565573076072]\n",
            "[INFO] Train Epoch: 22 [61%]\n",
            "[INFO] [2.711268424987793, 1.8381141424179077, 2.5105741024017334, 22.661685943603516, 1.2553364038467407, 1.701624870300293, 8600, 0.00019947565573076072]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 22\n",
            "[INFO] Train Epoch: 23 [11%]\n",
            "[INFO] [2.7569165229797363, 1.788022518157959, 2.6186747550964355, 20.19710922241211, 1.3225374221801758, 1.6863317489624023, 8800, 0.00019945072127379438]\n",
            "[INFO] Train Epoch: 23 [61%]\n",
            "[INFO] [2.7659268379211426, 1.8211963176727295, 2.6121342182159424, 20.748703002929688, 1.3593143224716187, 1.6192176342010498, 9000, 0.00019945072127379438]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] Saving model and optimizer state at iteration 23 to ./logs/biaobei_base/G_9000.pth\n",
            "[INFO] Saving model and optimizer state at iteration 23 to ./logs/biaobei_base/D_9000.pth\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 23\n",
            "[INFO] Train Epoch: 24 [12%]\n",
            "[INFO] [2.7761423587799072, 1.7905502319335938, 2.4130306243896484, 20.3702392578125, 1.1943910121917725, 1.9142565727233887, 9200, 0.00019942578993363514]\n",
            "[INFO] Train Epoch: 24 [62%]\n",
            "[INFO] [2.840864658355713, 1.7432241439819336, 2.559861660003662, 20.3363037109375, 1.2255221605300903, 1.8656281232833862, 9400, 0.00019942578993363514]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 24\n",
            "[INFO] Train Epoch: 25 [12%]\n",
            "[INFO] [2.756883144378662, 1.8775752782821655, 2.4978978633880615, 20.03139305114746, 1.2121849060058594, 1.643437147140503, 9600, 0.00019940086170989343]\n",
            "[INFO] Train Epoch: 25 [62%]\n",
            "[INFO] [2.7853052616119385, 1.9367132186889648, 2.5860867500305176, 20.668922424316406, 1.2606821060180664, 1.7472211122512817, 9800, 0.00019940086170989343]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] ====> Epoch: 25\n",
            "[INFO] Train Epoch: 26 [13%]\n",
            "[INFO] [2.784014940261841, 1.9044796228408813, 2.601097345352173, 20.373104095458984, 1.2959768772125244, 1.7463055849075317, 10000, 0.0001993759366021797]\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m[INFO] Saving model and optimizer state at iteration 26 to ./logs/biaobei_base/G_10000.pth\n",
            "[INFO] Saving model and optimizer state at iteration 26 to ./logs/biaobei_base/D_10000.pth\n",
            "[INFO] Train Epoch: 26 [63%]\n",
            "[INFO] [2.8207149505615234, 1.8360484838485718, 2.4782183170318604, 20.326980590820312, 1.2672089338302612, 1.595682144165039, 10200, 0.0001993759366021797]\n",
            "Traceback (most recent call last):\n",
            "  File \"train.py\", line 295, in <module>\n",
            "    main()\n",
            "  File \"train.py\", line 55, in main\n",
            "    mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/multiprocessing/spawn.py\", line 240, in spawn\n",
            "\u001b[0m\u001b[0m  File \"/usr/local/lib/python3.7/dist-packages/torch/multiprocessing/spawn.py\", line 198, in start_processes\n",
            "    while not context.join():\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/multiprocessing/spawn.py\", line 111, in join\n",
            "    timeout=timeout,\n",
            "  File \"/usr/lib/python3.7/multiprocessing/connection.py\", line 921, in wait\n",
            "    ready = selector.select(timeout)\n",
            "  File \"/usr/lib/python3.7/selectors.py\", line 415, in select\n",
            "    fd_event_list = self._selector.poll(timeout)\n",
            "KeyboardInterrupt\n",
            "\u001b[0m\u001b[0m\u001b[0m\u001b[0m"
          ]
        }
      ],
      "source": [
        "!python train.py -c configs/biaobei_base.json -m biaobei_base"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8gpGR7HUx8w",
        "outputId": "017ee1ae-1af5-4dc3-831a-d5427c44c710"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/vits-mandarin-biaobei\n"
          ]
        }
      ],
      "source": [
        "#在\"代码执行程序\"下拉菜单选择\"重新启动代码程序\"\n",
        "#再从该代码框开始，进行推断和输出语音\n",
        "import os\n",
        "path = \"/content/vits-mandarin-biaobei\"\n",
        "os.chdir(path)\n",
        "print(os.getcwd())\n",
        "\n",
        "%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",
      "source": [
        "hps = utils.get_hparams_from_file(\"/content/vits-mandarin-biaobei/configs/biaobei_base.json\")"
      ],
      "metadata": {
        "id": "o3OCVb09sM3D"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "O3K5fYBnJSfT"
      },
      "outputs": [],
      "source": [
        "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(\"/content/vits-mandarin-biaobei/logs/biaobei_base/G_10000.pth\", net_g, None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "Q1MxZNQudiPa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "406b6e81-9a24-43be-f983-b8911c38fe89"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "stn_tst = get_text(\"你好，别在这里发癫。\", 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",
        "    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))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#把checkpoint存入google drive\n",
        "!cp -r /content/vits-mandarin-biaobei/logs/biaobei_base /content/drive/MyDrive/"
      ],
      "metadata": {
        "id": "5Qx691T7K2W8"
      },
      "execution_count": 9,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "“vits.ipynb”的副本",
      "provenance": [],
      "machine_shape": "hm"
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}