connect_all.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/chaecheche/24w_audio/blob/main/connect_all.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bddWDwA95dd"
      },
      "source": [
        "## Prerequisties"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FumT6ipIOoKW",
        "outputId": "d986e531-5f30-48f2-e3dc-919e33b38aa7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "CSqQQcW7nfrs",
        "outputId": "5ced6e30-516f-4ae1-ea91-eee1cfb75a7c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting hydra-core\n",
            "  Downloading hydra_core-1.3.2-py3-none-any.whl (154 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.5/154.5 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting omegaconf<2.4,>=2.2 (from hydra-core)\n",
            "  Downloading omegaconf-2.3.0-py3-none-any.whl (79 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.5/79.5 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting antlr4-python3-runtime==4.9.* (from hydra-core)\n",
            "  Downloading antlr4-python3-runtime-4.9.3.tar.gz (117 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.0/117.0 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from hydra-core) (24.0)\n",
            "Requirement already satisfied: PyYAML>=5.1.0 in /usr/local/lib/python3.10/dist-packages (from omegaconf<2.4,>=2.2->hydra-core) (6.0.1)\n",
            "Building wheels for collected packages: antlr4-python3-runtime\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=31b2480bb8ef4686ed2f343aac0fba9c3c65c9f38bd0afdf6f8d5922ac4e832c\n",
            "  Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n",
            "Successfully built antlr4-python3-runtime\n",
            "Installing collected packages: antlr4-python3-runtime, omegaconf, hydra-core\n",
            "Successfully installed antlr4-python3-runtime-4.9.3 hydra-core-1.3.2 omegaconf-2.3.0\n"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "id": "df374a7e8c374da7807efc1486f771e6",
              "pip_warning": {
                "packages": [
                  "pydevd_plugins"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"! wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh\\n! chmod +x Miniconda3-py37_4.9.2-Linux-x86_64.sh\\n! bash ./Miniconda3-py37_4.9.2-Linux-x86_64.sh -b -f -p /usr/local\\nimport sys\\nsys.path.append('/usr/local/lib/python3.7/site-packages/')\\n!which conda  # should return /usr/local/bin/conda\\n!conda --version\""
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#!pip install warp_rnnt\n",
        "#!pip install torchaudio==0.6.0\n",
        "#!pip install hydra-core --upgrade\n",
        "\n",
        "################################################################################\n",
        "# INSTALL CONDA ON GOOGLE COLAB\n",
        "################################################################################\n",
        "'''! wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh\n",
        "! chmod +x Miniconda3-py37_4.9.2-Linux-x86_64.sh\n",
        "! bash ./Miniconda3-py37_4.9.2-Linux-x86_64.sh -b -f -p /usr/local\n",
        "import sys\n",
        "sys.path.append('/usr/local/lib/python3.7/site-packages/')\n",
        "!which conda  # should return /usr/local/bin/conda\n",
        "!conda --version'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "jXFyn4lVKu2U",
        "outputId": "155c9854-7dcd-442b-ff74-913663538b95"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content'"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pwd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TlziuQ2b2cJ_",
        "outputId": "f694f2e1-eb20-42c5-d111-5f3c22914308"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from -r /content/drive/MyDrive/kospeech/dataset/kspon/requirements.txt (line 1)) (4.66.2)\n",
            "Collecting gluonnlp (from -r /content/drive/MyDrive/kospeech/dataset/kspon/requirements.txt (line 2))\n",
            "  Downloading gluonnlp-0.10.0.tar.gz (344 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m344.5/344.5 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from -r /content/drive/MyDrive/kospeech/dataset/kspon/requirements.txt (line 3)) (0.1.99)\n",
            "Collecting hgtk (from -r /content/drive/MyDrive/kospeech/dataset/kspon/requirements.txt (line 4))\n",
            "  Downloading hgtk-0.2.1-py2.py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from -r /content/drive/MyDrive/kospeech/dataset/kspon/requirements.txt (line 5)) (1.5.3)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from gluonnlp->-r /content/drive/MyDrive/kospeech/dataset/kspon/requirements.txt (line 2)) (1.25.2)\n",
            "Requirement already satisfied: cython in /usr/local/lib/python3.10/dist-packages (from gluonnlp->-r /content/drive/MyDrive/kospeech/dataset/kspon/requirements.txt (line 2)) (3.0.9)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from gluonnlp->-r /content/drive/MyDrive/kospeech/dataset/kspon/requirements.txt (line 2)) (24.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->-r /content/drive/MyDrive/kospeech/dataset/kspon/requirements.txt (line 5)) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->-r /content/drive/MyDrive/kospeech/dataset/kspon/requirements.txt (line 5)) (2023.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->-r /content/drive/MyDrive/kospeech/dataset/kspon/requirements.txt (line 5)) (1.16.0)\n",
            "Building wheels for collected packages: gluonnlp\n",
            "  Building wheel for gluonnlp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gluonnlp: filename=gluonnlp-0.10.0-cp310-cp310-linux_x86_64.whl size=661762 sha256=1d8d6e19ec3f23aa343491eeba52810e5a84fbc72c43b8a18088d5b01c918fbf\n",
            "  Stored in directory: /root/.cache/pip/wheels/1a/1e/0d/99f55911d90f2b95b9f7c176d5813ef3622894a4b30fde6bd3\n",
            "Successfully built gluonnlp\n",
            "Installing collected packages: hgtk, gluonnlp\n",
            "Successfully installed gluonnlp-0.10.0 hgtk-0.2.1\n"
          ]
        }
      ],
      "source": [
        "!pip install -r /content/drive/MyDrive/kospeech/dataset/kspon/requirements.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CDy0Ya68-LRu"
      },
      "source": [
        "## Deep Speech2를 이용한 Ko-SPEECH 학습"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oAu0nnIwK6ki"
      },
      "source": [
        "### 학습 준비\n",
        "- 전처리 및 단어 사전 준비\n",
        "- 전사 자료의 벡터화\n",
        "-> 결론적으로 학습을 위해서 transscript.txt(대본같은 역할)를 생성해야함\n",
        "\n",
        "필요한 데이터는 크게 음성 데이터(wav, pcm 등)와 해당 음성 파일의 전사 데이터가 필요"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NarwcAj7YZPP"
      },
      "outputs": [],
      "source": [
        "# 압축해제\n",
        "#!unzip -qq \"/content/drive/MyDrive/한국어_방언_발화(경상도)/Training/[라벨]경상도_학습데이터_1.zip\" -d \"/content/drive/MyDrive/한국어_방언_발화(경상도)/unzip\"\n",
        "\n",
        "!unzip -qq \"/content/drive/MyDrive/kospeechdata/KsponSpeechall/KsponSpeech_01.zip\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OKrSULJmd-r7",
        "outputId": "96a4a1ae-dc23-4484-e736-8b9ee111c8f1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "요거\n"
          ]
        }
      ],
      "source": [
        "import re\n",
        "\n",
        "def rule(x):\n",
        "    # 괄호\n",
        "    a = re.compile(r'\\([^)]*\\)')\n",
        "    # 문장 부호\n",
        "    b = re.compile('[^가-힣 ]')\n",
        "    x = re.sub(pattern=a, repl='', string= x)\n",
        "    x = re.sub(pattern=b, repl='', string= x)\n",
        "    return x\n",
        "\n",
        "# 예제 문장\n",
        "x = \"&company-name2&요거.\"\n",
        "\n",
        "# 처리 결과 출력\n",
        "result = rule(x)\n",
        "print(result)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "63J3AbG6h-HA"
      },
      "source": [
        "## Preprocessing!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9LLJ6VDdeGms"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZGadJgXhkcVx",
        "outputId": "cad534f7-c42a-4830-b8a3-b7d7190233eb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of subfolders: 1018\n"
          ]
        }
      ],
      "source": [
        "# 데이터 갯수(큰 폴더 개수) 구하는 코드\n",
        "import os\n",
        "\n",
        "def count_subfolders(directory):\n",
        "    if not os.path.isdir(directory):\n",
        "        return \"Invalid directory path\"\n",
        "\n",
        "    subfolders = [f for f in os.listdir(directory) if os.path.isdir(os.path.join(directory, f))]\n",
        "    return len(subfolders)\n",
        "\n",
        "# Example usage:\n",
        "directory_path = '/content/drive/MyDrive/DKSR__data_share/Training/train_extracted'\n",
        "print(\"Number of subfolders:\", count_subfolders(directory_path))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bi7tiiQOy1cO",
        "outputId": "0a4986c0-f28b-4b1a-b6a5-8d4f43c553e6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The file has 262106 lines.\n"
          ]
        }
      ],
      "source": [
        "#데이터 개수 구하는 코드\n",
        "def count_lines_in_file(file_path):\n",
        "    with open(file_path, 'r', encoding='utf-8') as file:\n",
        "        lines = file.readlines()\n",
        "        return len(lines)\n",
        "\n",
        "file_path = '/content/drive/MyDrive/kospeech/test/transcripts.txt'  # 여기에 파일 경로를 입력하세요.\n",
        "line_count = count_lines_in_file(file_path)\n",
        "print(f\"The file has {line_count} lines.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5y3hUaLGzdKH"
      },
      "source": [
        "## 잠깐 보류"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ARbZ0jlTG1Jo"
      },
      "outputs": [],
      "source": [
        "# permission denied 뜨는 거 없애기\n",
        "\n",
        "!chmod +x /content/drive/MyDrive/kospeech/dataset/kspon/preprocess.sh"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q8XlmT8ibwiH",
        "outputId": "32f14a53-3ddd-468a-98e4-d2b9baa9b94d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Pre-process Our Dataset..\n",
            "Dataset Path : /content/drive/MyDrive/validation_made\n",
            "Vocab Destination : /content/drive/MyDrive/kospeech/dataset\n",
            "Save Path : ./data\n",
            "Output-Unit : character\n",
            "Preprocess Mode : phonetic\n",
            "preprocess started..\n",
            "/content/drive/MyDrive/validation_made/DKSR20002370/DKSR20002370_1_1_120.txt\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/MyDrive/kospeech/dataset/kspon/main.py\", line 80, in <module>\n",
            "    main()\n",
            "  File \"/content/drive/MyDrive/kospeech/dataset/kspon/main.py\", line 62, in main\n",
            "    audio_paths, transcripts = preprocess(opt.dataset_path, opt.preprocess_mode)\n",
            "  File \"/content/drive/MyDrive/kospeech/dataset/kspon/preprocess/preprocess.py\", line 112, in preprocess\n",
            "    for jdx, file in enumerate(os.listdir(path)):\n",
            "NotADirectoryError: [Errno 20] Not a directory: '/content/drive/MyDrive/validation_made/DKSR20002370/DKSR20002370_1_1_120.txt'\n"
          ]
        }
      ],
      "source": [
        "# 초기에 한번 실행했으면 이제 안돌려도 됨\n",
        "! /content/drive/MyDrive/kospeech/dataset/kspon/preprocess.sh"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lrhtyowasnIb"
      },
      "source": [
        "3시간 30분 넘게 걸림...."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B8uNCQ_SYrWK"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zh4JAERbTbdH"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "56SgJHfZvzsq"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HxXfRM5PODcm"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7BdPBZPYiXq4"
      },
      "source": [
        "## Training(학습)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ur7MW80lUuDd"
      },
      "outputs": [],
      "source": [
        "#하하하하하하하22"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "I6ZqmWKiY7lu",
        "outputId": "969dd786-0da9-42d1-af3a-5b026659e9a0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting hydra-core\n",
            "  Downloading hydra_core-1.3.2-py3-none-any.whl (154 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.5/154.5 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting omegaconf<2.4,>=2.2 (from hydra-core)\n",
            "  Downloading omegaconf-2.3.0-py3-none-any.whl (79 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.5/79.5 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting antlr4-python3-runtime==4.9.* (from hydra-core)\n",
            "  Downloading antlr4-python3-runtime-4.9.3.tar.gz (117 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.0/117.0 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from hydra-core) (24.0)\n",
            "Requirement already satisfied: PyYAML>=5.1.0 in /usr/local/lib/python3.10/dist-packages (from omegaconf<2.4,>=2.2->hydra-core) (6.0.1)\n",
            "Building wheels for collected packages: antlr4-python3-runtime\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=b42b2469c67703c9f90e5cb1977efdcd2120f4e36a53b6466c6e3f4af159be4d\n",
            "  Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n",
            "Successfully built antlr4-python3-runtime\n",
            "Installing collected packages: antlr4-python3-runtime, omegaconf, hydra-core\n",
            "Successfully installed antlr4-python3-runtime-4.9.3 hydra-core-1.3.2 omegaconf-2.3.0\n"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "id": "93a3e5c8ee0c432bbc5b93af58b80121",
              "pip_warning": {
                "packages": [
                  "pydevd_plugins"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting Levenshtein\n",
            "  Downloading Levenshtein-0.25.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (177 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.4/177.4 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting rapidfuzz<4.0.0,>=3.1.0 (from Levenshtein)\n",
            "  Downloading rapidfuzz-3.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: rapidfuzz, Levenshtein\n",
            "Successfully installed Levenshtein-0.25.0 rapidfuzz-3.7.0\n"
          ]
        }
      ],
      "source": [
        "!pip install hydra-core --upgrade\n",
        "!pip install Levenshtein"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D6dBRd5_jISj"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S9fxddM73TTz"
      },
      "outputs": [],
      "source": [
        "!python /content/drive/MyDrive/kospeech/bin/main.py model=ds2 train=ds2_train train.dataset_path=\"/content/drive/MyDrive/DKSR__data_share/Training\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5_0TKLhzvPPn"
      },
      "source": [
        "## Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uBcro8NhyWeM"
      },
      "outputs": [],
      "source": [
        "# outputs경로를 옮기는 코드\n",
        "#!cp -rf outputs '/content/drive/MyDrive/'\n",
        "#trainer.train(resume_from_checkpoint=\"/content/drive/MyDrive/Colab Notebooks/outputs/checkpoint-1970\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1G6yFJuwAOoe",
        "outputId": "90a9f037-bd50-49fd-83b5-95085a373e2d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: omegaconf in /usr/local/lib/python3.10/dist-packages (2.3.0)\n",
            "Requirement already satisfied: antlr4-python3-runtime==4.9.* in /usr/local/lib/python3.10/dist-packages (from omegaconf) (4.9.3)\n",
            "Requirement already satisfied: PyYAML>=5.1.0 in /usr/local/lib/python3.10/dist-packages (from omegaconf) (6.0.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install omegaconf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JTDPidmeBz1P",
        "outputId": "efa9465d-0945-4999-ec3a-180c6713466f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cpu\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AzbxUdN4Ljfm",
        "outputId": "3a8dd0b6-c396-4072-c9d8-89e6d87dceef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "그쎄 어렸을 때는진 짜렸을 때는 진짜 택도 없는 꿈들이 많다이가뭐눈에 모이면 다하고 싶고 이것도보고 싶고 저것도 하고 싶고 막\n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "\n",
        "!python3 /content/drive/MyDrive/kospeech/bin/inference.py --model_path '/content/drive/MyDrive/kspon_checkpoint_models/outputs/2024_04_01/06-59-01/model.pt' --audio_path '/content/drive/MyDrive/DKSR__data_share/Training/train_extracted/DKSR20002401/DKSR20002401_1_1_2.wav' --device \"cpu\"\n",
        "\n",
        "#Output :음성인식 결과 문장이 나옵니다"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nNYzl4Kub1KO"
      },
      "source": [
        "## WER(Word Error Rate) 측정"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uWP8Hikxb0CH"
      },
      "outputs": [],
      "source": [
        "import Levenshtein as Lev\n",
        "\n",
        "def wer(ref, hyp ,debug=False):\n",
        "    r = ref.split()\n",
        "    h = hyp.split()\n",
        "    #costs will holds the costs, like in the Levenshtein distance algorithm\n",
        "    costs = [[0 for inner in range(len(h)+1)] for outer in range(len(r)+1)]\n",
        "    # backtrace will hold the operations we've done.\n",
        "    # so we could later backtrace, like the WER algorithm requires us to.\n",
        "    backtrace = [[0 for inner in range(len(h)+1)] for outer in range(len(r)+1)]\n",
        "\n",
        "    OP_OK = 0\n",
        "    OP_SUB = 1\n",
        "    OP_INS = 2\n",
        "    OP_DEL = 3\n",
        "\n",
        "    DEL_PENALTY=1 # Tact\n",
        "    INS_PENALTY=1 # Tact\n",
        "    SUB_PENALTY=1 # Tact\n",
        "    # First column represents the case where we achieve zero\n",
        "    # hypothesis words by deleting all reference words.\n",
        "    for i in range(1, len(r)+1):\n",
        "        costs[i][0] = DEL_PENALTY*i\n",
        "        backtrace[i][0] = OP_DEL\n",
        "\n",
        "    # First row represents the case where we achieve the hypothesis\n",
        "    # by inserting all hypothesis words into a zero-length reference.\n",
        "    for j in range(1, len(h) + 1):\n",
        "        costs[0][j] = INS_PENALTY * j\n",
        "        backtrace[0][j] = OP_INS\n",
        "\n",
        "    # computation\n",
        "    for i in range(1, len(r)+1):\n",
        "        for j in range(1, len(h)+1):\n",
        "            if r[i-1] == h[j-1]:\n",
        "                costs[i][j] = costs[i-1][j-1]\n",
        "                backtrace[i][j] = OP_OK\n",
        "            else:\n",
        "                substitutionCost = costs[i-1][j-1] + SUB_PENALTY # penalty is always 1\n",
        "                insertionCost    = costs[i][j-1] + INS_PENALTY   # penalty is always 1\n",
        "                deletionCost     = costs[i-1][j] + DEL_PENALTY   # penalty is always 1\n",
        "\n",
        "                costs[i][j] = min(substitutionCost, insertionCost, deletionCost)\n",
        "                if costs[i][j] == substitutionCost:\n",
        "                    backtrace[i][j] = OP_SUB\n",
        "                elif costs[i][j] == insertionCost:\n",
        "                    backtrace[i][j] = OP_INS\n",
        "                else:\n",
        "                    backtrace[i][j] = OP_DEL\n",
        "\n",
        "    # back trace though the best route:\n",
        "    i = len(r)\n",
        "    j = len(h)\n",
        "    numSub = 0\n",
        "    numDel = 0\n",
        "    numIns = 0\n",
        "    numCor = 0\n",
        "    if debug:\n",
        "        print(\"OP\\tREF\\tHYP\")\n",
        "        lines = []\n",
        "    while i > 0 or j > 0:\n",
        "        if backtrace[i][j] == OP_OK:\n",
        "            numCor += 1\n",
        "            i-=1\n",
        "            j-=1\n",
        "            if debug:\n",
        "                lines.append(\"OK\\t\" + r[i]+\"\\t\"+h[j])\n",
        "        elif backtrace[i][j] == OP_SUB:\n",
        "            numSub +=1\n",
        "            i-=1\n",
        "            j-=1\n",
        "            if debug:\n",
        "                lines.append(\"SUB\\t\" + r[i]+\"\\t\"+h[j])\n",
        "        elif backtrace[i][j] == OP_INS:\n",
        "            numIns += 1\n",
        "            j-=1\n",
        "            if debug:\n",
        "                lines.append(\"INS\\t\" + \"****\" + \"\\t\" + h[j])\n",
        "        elif backtrace[i][j] == OP_DEL:\n",
        "            numDel += 1\n",
        "            i-=1\n",
        "            if debug:\n",
        "                lines.append(\"DEL\\t\" + r[i]+\"\\t\"+\"****\")\n",
        "    if debug:\n",
        "        lines = reversed(lines)\n",
        "        for line in lines:\n",
        "            print(line)\n",
        "        print(\"Ncor \" + str(numCor))\n",
        "        print(\"Nsub \" + str(numSub))\n",
        "        print(\"Ndel \" + str(numDel))\n",
        "        print(\"Nins \" + str(numIns))\n",
        "    return numCor, numSub, numDel, numIns, (numSub + numDel + numIns) / (float) (len(r))\n",
        "\n",
        "\n",
        "def cer(ref, hyp):\n",
        "    ref = ref.replace(' ', '')\n",
        "    hyp = hyp.replace(' ', '')\n",
        "    dist = Lev.distance(hyp, ref)\n",
        "    length = len(ref)\n",
        "    return dist, length, dist/length"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HkdYDImVnY4H"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import glob\n",
        "import shutil\n",
        "# 검색할 디렉토리 경로 설정\n",
        "source_directory = '/content/drive/MyDrive/validation_made/DKSR20002370'\n",
        "\n",
        "# 새 폴더(이동할 디렉토리) 설정\n",
        "destination_directory =  '/content/drive/MyDrive/validation_made/DKSR20002370_only_wave'\n",
        "\n",
        "# 새 디렉토리 생성, 이미 존재하면 넘어감\n",
        "if not os.path.exists(destination_directory):\n",
        "    os.makedirs(destination_directory)\n",
        "\n",
        "# 해당 디렉토리와 모든 하위 디렉토리에서 .wav 파일 검색\n",
        "txt_files = glob.glob(f'{source_directory}/**/*.wav', recursive=True)\n",
        "\n",
        "# 각 .txt 파일 이동\n",
        "for file_path in txt_files:\n",
        "    try:\n",
        "        # 이동할 새 경로 생성\n",
        "        new_path = os.path.join(destination_directory, os.path.basename(file_path))\n",
        "\n",
        "        # 파일 이동\n",
        "        shutil.move(file_path, new_path)\n",
        "        print(f'Moved: {file_path} to {new_path}')\n",
        "    except OSError as e:\n",
        "        print(f\"Error moving {file_path} to {new_path}: {e.strerror}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zv0312fPdKRo",
        "outputId": "384e8383-124e-4dde-ce5c-57f1fa65fa79"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "299 files test starts...\n",
            "CER/WER caculation: True\n",
            "-> transcript: /content/drive/MyDrive/kospeech/data/transcripts.txt\n",
            "Output destination: /content/outputs\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/MyDrive/kospeech/bin/inference_wer.py\", line 87, in <module>\n",
            "    shutil.rmtree(dst_path)\n",
            "  File \"/usr/lib/python3.10/shutil.py\", line 725, in rmtree\n",
            "    _rmtree_safe_fd(fd, path, onerror)\n",
            "  File \"/usr/lib/python3.10/shutil.py\", line 633, in _rmtree_safe_fd\n",
            "    onerror(os.scandir, path, sys.exc_info())\n",
            "  File \"/usr/lib/python3.10/shutil.py\", line 629, in _rmtree_safe_fd\n",
            "    with os.scandir(topfd) as scandir_it:\n",
            "NotADirectoryError: [Errno 20] Not a directory: '/content/outputs/wer_results_2024_04_01_06-59-01.txt'\n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "!python3 /content/drive/MyDrive/kospeech/bin/inference_wer.py --model_path '/content/drive/MyDrive/kspon_checkpoint_models/outputs/2024_04_01/06-59-01/model.pt' --audio_path '/content/drive/MyDrive/validation_made/DKSR20002370' --transcript_path '/content/drive/MyDrive/kospeech/data/transcripts.txt' --device \"cpu\"\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOv2h-RHrwtZ"
      },
      "source": [
        "## Checkpoints\n",
        "Checkpoints are organized by experiments and timestamps as shown in the following file structure.\n",
        "\n",
        "#### outputs\n",
        "##### +-- YYYY_mm_dd\n",
        "##### |  +-- HH_MM_SS\n",
        "#####   |  +-- trainer_states.pt\n",
        "#####   |  +-- model.pt\n",
        "You can resume and load from checkpoints."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-6kHRbf4-5J"
      },
      "source": [
        "# KO_BART"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "zUKWH3Ws00mT",
        "outputId": "c0af2bdd-194d-42cb-d296-5cd12a7b0a7a"
      },
      "outputs": [
        {
          "ename": "OSError",
          "evalue": "Incorrect path_or_model_id: '/content/drive/MyDrive/saved_model'. Please provide either the path to a local folder or the repo_id of a model on the Hub.",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHFValidationError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m    397\u001b[0m         \u001b[0;31m# Load from URL or cache if already cached\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m         resolved_file = hf_hub_download(\n\u001b[0m\u001b[1;32m    399\u001b[0m             \u001b[0mpath_or_repo_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    109\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0marg_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"repo_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"from_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"to_id\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 110\u001b[0;31m                 \u001b[0mvalidate_repo_id\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36mvalidate_repo_id\u001b[0;34m(repo_id)\u001b[0m\n\u001b[1;32m    157\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mrepo_id\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 158\u001b[0;31m         raise HFValidationError(\n\u001b[0m\u001b[1;32m    159\u001b[0m             \u001b[0;34m\"Repo id must be in the form 'repo_name' or 'namespace/repo_name':\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHFValidationError\u001b[0m: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/content/drive/MyDrive/saved_model'. Use `repo_type` argument if needed.",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-def2403194f6>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mnlg_pipeline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'text2text-generation'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/__init__.py\u001b[0m in \u001b[0;36mpipeline\u001b[0;34m(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[1;32m    777\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPretrainedConfig\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mpretrained_model_name_or_path\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    778\u001b[0m             \u001b[0;31m# We make a call to the config file first (which may be absent) to get the commit hash as soon as possible\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m             resolved_config_file = cached_file(\n\u001b[0m\u001b[1;32m    780\u001b[0m                 \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m                 \u001b[0mCONFIG_NAME\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m    460\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mEnvironmentError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"There was a specific connection error when trying to load {path_or_repo_id}:\\n{err}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mHFValidationError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m         raise EnvironmentError(\n\u001b[0m\u001b[1;32m    463\u001b[0m             \u001b[0;34mf\"Incorrect path_or_model_id: '{path_or_repo_id}'. Please provide either the path to a local folder or the repo_id of a model on the Hub.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m         ) from e\n",
            "\u001b[0;31mOSError\u001b[0m: Incorrect path_or_model_id: '/content/drive/MyDrive/saved_model'. Please provide either the path to a local folder or the repo_id of a model on the Hub."
          ]
        }
      ],
      "source": [
        "model_path= '/content/drive/MyDrive/saved_model'\n",
        "\n",
        "model_name = \"gogamza/kobart-base-v2\"\n",
        "\n",
        "from transformers import pipeline\n",
        "\n",
        "nlg_pipeline=pipeline('text2text-generation',model=model_path,tokenizer=model_name)\n",
        "\n",
        "\n",
        "def generate_text(pipe, text, num_return_sequences, max_length):\n",
        "    target_style_name = \"표준어\"\n",
        "    text = f\"{target_style_name} 말투로 변환:{text}\"\n",
        "    out = pipe(text, num_return_sequences=num_return_sequences, max_length=max_length)\n",
        "    #num_return_sequences의 값에 따라서 반환되는 텍스트의 개수가 바뀐다. 만약 3으로 지정했다면 길이가 3인 리스트에 담겨서 값이 반환될 것임!\n",
        "    return [x['generated_text'] for x in out]\n",
        "\n",
        "\n",
        "print(\"Write 'q' to exit\")\n",
        "while True:\n",
        "    src_text=input(\"Dialect to translate(입력받을 사투리) : \")\n",
        "    if src_text == 'q':\n",
        "        break\n",
        "    target_text_ko=generate_text(nlg_pipeline,src_text,num_return_sequences=1,max_length=64)[0]\n",
        "    print(f\"Translated Standard (표준어로 출력): {target_text_ko}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-AzQTSA06ep9"
      },
      "source": [
        "# 최종 연결!!(Ko-Speech & Ko-BART& Tensorflow-TTS)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dFBFOZk4AWHs"
      },
      "source": [
        "## Ko=BART 불러오기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214,
          "referenced_widgets": [
            "fc84f725ccaf47159b753f920297ed78",
            "fb1f1673791c419baef117c6cd78ab1b",
            "958865bf83b148cf9cce8b0bd8455d1e",
            "131771e2fc3648368e47ac965383855a",
            "ab1926eeb72e49f5bd8b910d7b723ca0",
            "7c60633c2c514f4399286e18abaf6809",
            "644766a37f0e402eb148df882ffe455b",
            "260907e734184c91bbc347bcb381c3af",
            "3bfef3f6fc0349ca9c8633ec5945b149",
            "d5225758716a461aa57c97597a32119e",
            "f269cdc733c444689c6de1cfae313f03",
            "09323d50c7214ddb9d57c984a989510e",
            "57d6a63a2a884aa5a577718da1500258",
            "5e327436eaf54a30ac7b69deddd654e8",
            "7f2da2b9c25240a7974504d10bc0144e",
            "ff2f1e2a8194426a8c866e0ecb4a2b6e",
            "d87c0820fc914762aa5b9d72639a49a1",
            "74d1d7897099412e931d2204ca85ff8f",
            "705b9e17349446da8912f9b1a1cfd3c6",
            "52ffea01edb340aca07381d8b016830e",
            "b28ad146494243cab47a390a081e9cff",
            "d8664b8b2639464d8a03228d1335e626",
            "6c3f7353566942df88f789eb5ca8b3d0",
            "da2d78fc9bc84c8ab7f039260e49e1bd",
            "1f5beb929d864cdc812adfc3eb07228e",
            "63b2bd66f9b448eeabbd072a55dcc24e",
            "f741b59d3e5e43c5ad18d133488e6dc4",
            "fbefaf0136a843959d2e62ab68a6bdd4",
            "f4e5e00bc7c543ca91165aef4d74096a",
            "ad8ba2c776134257ba3f8b71540a31ce",
            "3a5cf1731ad14d499743071230ff3a25",
            "a0ee7cc9e3cd4f9393725686ae41ae21",
            "4a062acff0174ddab28d2d248970b5ce",
            "c04923995c9c464b8a63847bec0e671b",
            "0ff09628ce1a41049c1914547135e093",
            "e9089deb0ab349528ea9ca8115ad05ee",
            "a08fe9c2317b41b6988208b04e010aeb",
            "359c5eb62ae24ea19ed2fb775f6a2f31",
            "9fe63afc035f4a33bef3616bce2c7e75",
            "d1aa512e3cd743ae95afe43a834ffc01",
            "03f900028fd24ea5a2df49e098e89752",
            "51ddbf1a6e6443a89029478bc944d3c4",
            "aa022d8e76a54166b11964f1df241793",
            "67076cf3fbd04b3b9f3dfb89f5bda783"
          ]
        },
        "id": "2rvJCs41929P",
        "outputId": "39fbc0fc-9335-441f-f30d-63636bf39e6d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You passed along `num_labels=3` with an incompatible id to label map: {'0': 'NEGATIVE', '1': 'POSITIVE'}. The number of labels wil be overwritten to 2.\n",
            "You passed along `num_labels=3` with an incompatible id to label map: {'0': 'NEGATIVE', '1': 'POSITIVE'}. The number of labels wil be overwritten to 2.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fc84f725ccaf47159b753f920297ed78",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/1.36k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You passed along `num_labels=3` with an incompatible id to label map: {'0': 'NEGATIVE', '1': 'POSITIVE'}. The number of labels wil be overwritten to 2.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "09323d50c7214ddb9d57c984a989510e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/682k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6c3f7353566942df88f789eb5ca8b3d0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/4.00 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c04923995c9c464b8a63847bec0e671b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You passed along `num_labels=3` with an incompatible id to label map: {'0': 'NEGATIVE', '1': 'POSITIVE'}. The number of labels wil be overwritten to 2.\n"
          ]
        }
      ],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from transformers import pipeline\n",
        "\n",
        "model_path= '/content/drive/MyDrive/saved_model'\n",
        "model_name = \"gogamza/kobart-base-v2\"\n",
        "\n",
        "\n",
        "nlg_pipeline=pipeline('text2text-generation',model=model_path,tokenizer=model_name)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JiDroyq_AR2t"
      },
      "source": [
        "### TensorflowTTS(Fast-Speech2) 불러오기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 984
        },
        "id": "t6r_e7m1-7PS",
        "outputId": "89649c44-4e7b-49b1-891d-cb00a1fc4793"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive\n",
            "Processing /content/drive/MyDrive/TensorFlowTTS\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "INFO: pip is looking at multiple versions of tensorflowtts to determine which version is compatible with other requirements. This could take a while.\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement tensorflow-gpu==2.7.0 (from tensorflowtts) (from versions: 2.8.0rc0, 2.8.0rc1, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0rc0, 2.9.0rc1, 2.9.0rc2, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0rc0, 2.10.0rc1, 2.10.0rc2, 2.10.0rc3, 2.10.0, 2.10.1, 2.11.0rc0, 2.11.0rc1, 2.11.0rc2, 2.11.0, 2.12.0)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for tensorflow-gpu==2.7.0\u001b[0m\u001b[31m\n",
            "\u001b[0mProcessing /content/drive/MyDrive/TensorFlowTTS\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "INFO: pip is looking at multiple versions of tensorflowtts to determine which version is compatible with other requirements. This could take a while.\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement tensorflow-gpu==2.7.0 (from tensorflowtts) (from versions: 2.8.0rc0, 2.8.0rc1, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0rc0, 2.9.0rc1, 2.9.0rc2, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0rc0, 2.10.0rc1, 2.10.0rc2, 2.10.0rc3, 2.10.0, 2.10.1, 2.11.0rc0, 2.11.0rc1, 2.11.0rc2, 2.11.0, 2.12.0)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for tensorflow-gpu==2.7.0\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        },
        {
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'jamo'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-29d6a1b9c76f>\u001b[0m in \u001b[0;36m<cell line: 32>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/drive/MyDrive/TensorFlowTTS/\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_tts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAutoConfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_tts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTFAutoModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_tts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAutoProcessor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/drive/MyDrive/TensorFlowTTS/tensorflow_tts/inference/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_tts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_model\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTFAutoModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_tts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_config\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAutoConfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_tts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_processor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAutoProcessor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/drive/MyDrive/TensorFlowTTS/tensorflow_tts/inference/auto_model.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mOrderedDict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m from tensorflow_tts.configs import (\n\u001b[0m\u001b[1;32m     25\u001b[0m     \u001b[0mFastSpeechConfig\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0mFastSpeech2Config\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/drive/MyDrive/TensorFlowTTS/tensorflow_tts/configs/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_tts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfigs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_config\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBaseConfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_tts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfigs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfastspeech\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFastSpeechConfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_tts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfigs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfastspeech2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFastSpeech2Config\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m from tensorflow_tts.configs.melgan import (\n\u001b[1;32m      5\u001b[0m     \u001b[0mMelGANDiscriminatorConfig\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/drive/MyDrive/TensorFlowTTS/tensorflow_tts/configs/base_config.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_tts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCONFIG_FILE_NAME\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/drive/MyDrive/TensorFlowTTS/tensorflow_tts/utils/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m from tensorflow_tts.utils.cleaners import (\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mbasic_cleaners\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mcollapse_whitespace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mconvert_to_ascii\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0menglish_cleaners\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/drive/MyDrive/TensorFlowTTS/tensorflow_tts/utils/cleaners.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_tts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkorean\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtokenize\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mko_tokenize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_tts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber_norm\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnormalize_numbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0munidecode\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0munidecode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/drive/MyDrive/TensorFlowTTS/tensorflow_tts/utils/korean.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mjamo\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mh2j\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhangul_to_jamo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj2h\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjamo_to_hcj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m etc_dictionary = {\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'jamo'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "#!pip install h5py==2.10\n",
        "#!pip install git+https://github.com/repodiac/german_transliterate\n",
        "%cd '/content/drive/MyDrive'\n",
        "\n",
        "print(\"Downloading FastSpeech2 model...\")\n",
        "!gdown --id {\"1adb_hA9q0Qg959bl70WiRpm5a9Hdc2Ym\"} -O fastspeech2-200k.h5\n",
        "\n",
        "\n",
        "\n",
        "import os\n",
        "!git clone https://github.com/TensorSpeech/TensorFlowTTS.git\n",
        "os.chdir(\"TensorFlowTTS\")\n",
        "!pip install .\n",
        "os.chdir(\"..\")\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"TensorFlowTTS/\")\n",
        "import tensorflow as tf\n",
        "import os\n",
        "import yaml\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import IPython.display as ipd\n",
        "\n",
        "os.chdir(\"/content/drive/MyDrive/TensorFlowTTS\")\n",
        "!pip install .\n",
        "os.chdir(\"..\")\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"/content/drive/MyDrive/TensorFlowTTS/\")\n",
        "\n",
        "from tensorflow_tts.inference import AutoConfig\n",
        "from tensorflow_tts.inference import TFAutoModel\n",
        "from tensorflow_tts.inference import AutoProcessor\n",
        "\n",
        "fastspeech2_config = AutoConfig.from_pretrained('/content/drive/MyDrive/TensorFlowTTS/examples/fastspeech2/conf/fastspeech2.kss.v1.yaml')\n",
        "fastspeech2 = TFAutoModel.from_pretrained(\n",
        "    config=fastspeech2_config,\n",
        "    pretrained_path=\"fastspeech2-200k.h5\",\n",
        "    name=\"fastspeech2\"\n",
        ")\n",
        "\n",
        "# Vocoder model (mel spectrogram -> audio)\n",
        "# 멜로디 생성 모델을 사용합니다.\n",
        "mb_melgan_config = AutoConfig.from_pretrained('TensorFlowTTS/examples/multiband_melgan/conf/multiband_melgan.v1.yaml')\n",
        "mb_melgan = TFAutoModel.from_pretrained(\n",
        "    config=mb_melgan_config,\n",
        "    pretrained_path=\"mb.melgan-1000k.h5\",\n",
        "    name=\"mb_melgan\"\n",
        ")\n",
        " # input text를 inference시 숫자로 이뤄진 sequence로 변환할 때 필요합니다.\n",
        "processor = AutoProcessor.from_pretrained(pretrained_path=\"./kss_mapper.json\")\n",
        "\n",
        "# 직접 음성합성을 진행하는 함수입니다.\n",
        "# text -> mel spectrogram model, vocoder model을 인자로 선택합니다.\n",
        "def do_synthesis(input_text, text2mel_model, vocoder_model, text2mel_name, vocoder_name):\n",
        "  input_ids = processor.text_to_sequence(input_text)\n",
        "\n",
        "  # text2mel part\n",
        "  if text2mel_name == \"TACOTRON\":\n",
        "    _, mel_outputs, stop_token_prediction, alignment_history = text2mel_model.inference(\n",
        "        tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),\n",
        "        tf.convert_to_tensor([len(input_ids)], tf.int32),\n",
        "        tf.convert_to_tensor([0], dtype=tf.int32)\n",
        "    )\n",
        "  elif text2mel_name == \"FASTSPEECH2\":\n",
        "    mel_before, mel_outputs, duration_outputs, _, _ = text2mel_model.inference(\n",
        "        tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),\n",
        "        speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),\n",
        "        speed_ratios=tf.convert_to_tensor([1.2], dtype=tf.float32),\n",
        "        f0_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),\n",
        "        energy_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),\n",
        "    )\n",
        "  else:\n",
        "    raise ValueError(\"Only TACOTRON, FASTSPEECH2 are supported on text2mel_name\")\n",
        "\n",
        "  # vocoder part\n",
        "  if vocoder_name == \"MB-MELGAN\":\n",
        "    audio = vocoder_model.inference(mel_outputs)[0, :, 0]\n",
        "  else:\n",
        "    raise ValueError(\"Only MB_MELGAN are supported on vocoder_name\")\n",
        "\n",
        "  # tacotron2면 attention graph도 함께 반환합니다.\n",
        "  if text2mel_name == \"TACOTRON\":\n",
        "    return mel_outputs.numpy(), alignment_history.numpy(), audio.numpy()\n",
        "  else:\n",
        "    return mel_outputs.numpy(), audio.numpy()\n",
        "# attention을 시각화해서 보여주는 함수입니다.\n",
        "# tacotron2의 encoder와 decoder가 어떠한 mapping을 형성하는지 보여줍니다.\n",
        "def visualize_attention(alignment_history):\n",
        "  import matplotlib.pyplot as plt\n",
        "\n",
        "  fig = plt.figure(figsize=(8, 6))\n",
        "  ax = fig.add_subplot(111)\n",
        "  ax.set_title(f'Alignment steps')\n",
        "  im = ax.imshow(\n",
        "      alignment_history,\n",
        "      aspect='auto',\n",
        "      origin='lower',\n",
        "      interpolation='none')\n",
        "  fig.colorbar(im, ax=ax)\n",
        "  xlabel = 'Encoder timestep'\n",
        "  plt.xlabel(xlabel)\n",
        "  plt.ylabel('Decoder timestep')\n",
        "  plt.tight_layout()\n",
        "  plt.show()\n",
        "  plt.close()\n",
        "\n",
        "def visualize_mel_spectrogram(mels):\n",
        "  mels = tf.reshape(mels, [-1, 80]).numpy()\n",
        "  fig = plt.figure(figsize=(10, 8))\n",
        "  ax1 = fig.add_subplot(311)\n",
        "  ax1.set_title(f'Predicted Mel-after-Spectrogram')\n",
        "  im = ax1.imshow(np.rot90(mels), aspect='auto', interpolation='none')\n",
        "  fig.colorbar(mappable=im, shrink=0.65, orientation='horizontal', ax=ax1)\n",
        "  plt.show()\n",
        "  plt.close()\n",
        "\n",
        "\n",
        "def visualize_mel(alignment_history) :\n",
        "  fig = plt.figure(figsize=(8, 6))\n",
        "  ax = fig.add_subplot(111)\n",
        "  ax.set_title(f'Alignment steps')\n",
        "  im = ax.imshow(\n",
        "      alignment_history,\n",
        "      aspect='auto',\n",
        "      origin='lower',\n",
        "      interpolation='none')\n",
        "  fig.colorbar(im, ax=ax)\n",
        "  xlabel = 'Decoder timestep'\n",
        "  plt.xlabel(xlabel)\n",
        "  plt.ylabel('Encoder timestep')\n",
        "  plt.tight_layout()\n",
        "  plt.show()\n",
        "  plt.close()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vd1VvGknnNP5"
      },
      "source": [
        "## Ko-Speech version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "mkTDbnZK5qPM",
        "outputId": "433b6839-e13f-4c35-f5f4-3ee6f4d12f9c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Ko-Speech 결과(음성인식) :  그런 어떤 쫌 들어?\n",
            "Translated Standard (표준어로 출력): 그런 어떤 조금 들어?\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "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",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def generate_text(pipe, text, num_return_sequences, max_length):\n",
        "    target_style_name = \"표준어\"\n",
        "    text = f\"{target_style_name} 말투로 변환:{text}\"\n",
        "    out = pipe(text, num_return_sequences=num_return_sequences, max_length=max_length)\n",
        "    #num_return_sequences의 값에 따라서 반환되는 텍스트의 개수가 바뀐다. 만약 3으로 지정했다면 길이가 3인 리스트에 담겨서 값이 반환될 것임!\n",
        "    return [x['generated_text'] for x in out]\n",
        "\n",
        "\n",
        "src_text = !python3 /content/drive/MyDrive/kospeech/bin/inference.py --model_path '/content/drive/MyDrive/kospeechdata/models/30000/model.pt' --audio_path '/content/drive/MyDrive/DKSR__data_share/Training/train_extracted/DKSR20002401/DKSR20002401_1_1_21.wav' --device \"cpu\"\n",
        "src_text = str(src_text)[2:-2]\n",
        "print(\"Ko-Speech 결과(음성인식) : \", src_text)\n",
        "\n",
        "target_text_ko = generate_text(nlg_pipeline,src_text,num_return_sequences=1,max_length=64)[0]\n",
        "print(f\"Translated Standard (표준어로 출력): {target_text_ko}\")\n",
        "\n",
        "\n",
        "mels, audios = do_synthesis(target_text_ko, fastspeech2, mb_melgan, \"FASTSPEECH2\", \"MB-MELGAN\")\n",
        "visualize_mel_spectrogram(mels[0])\n",
        "ipd.Audio(audios, rate=22050)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lLl0K6IAmmHC"
      },
      "source": [
        "## Whisper ver"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-ncO2d1XmnsB"
      },
      "outputs": [],
      "source": [
        "# whisper 다운로드\n",
        "!pip install git+https://github.com/openai/whisper.git\n",
        "!sudo apt update && sudo apt install ffmpeg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rvvpCOG-mqjY"
      },
      "outputs": [],
      "source": [
        "# 파이프라인 연결\n",
        "import re\n",
        "start_time  = time.time()\n",
        "\n",
        "def generate_text(pipe, text, num_return_sequences, max_length):\n",
        "    target_style_name = \"표준어\"\n",
        "    text = f\"{target_style_name} 말투로 변환:{text}\"\n",
        "    out = pipe(text, num_return_sequences=num_return_sequences, max_length=max_length)\n",
        "    return [x['generated_text'] for x in out]\n",
        "\n",
        "src_text = !whisper \"/content/drive/MyDrive/24w_deepdaiv/drama.wav\" --model medium --language Korean\n",
        "src_text = [re.sub(r'\\[.*?\\]\\s*', '', text) for text in src_text]\n",
        "src_text = rule(str(src_text))\n",
        "print(\"Ko-Speech Result (음성 인식) : \", src_text)\n",
        "\n",
        "target_text_ko = generate_text(nlg_pipeline,src_text,num_return_sequences=1,max_length=64)[0]\n",
        "print(f\"Translated Standard Result (표준어체로 출력): {target_text_ko}\")\n",
        "\n",
        "mels, audios = do_synthesis(target_text_ko, fastspeech2, mb_melgan, \"FASTSPEECH2\", \"MB-MELGAN\")\n",
        "print(\"최종 출력 표준어 발화 : \")\n",
        "visualize_mel_spectrogram(mels[0])\n",
        "output_wav = ipd.Audio(audios, rate=22050)\n",
        "output_wav"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "V100",
      "provenance": [],
      "include_colab_link": true
    },
    "kaggle": {
      "accelerator": "nvidiaTeslaT4",
      "dataSources": [
        {
          "datasetId": 4665607,
          "sourceId": 7936609,
          "sourceType": "datasetVersion"
        }
      ],
      "dockerImageVersionId": 30674,
      "isGpuEnabled": true,
      "isInternetEnabled": true,
      "language": "python",
      "sourceType": "notebook"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
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