7_WFST.ipynb

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      "name": "python3",
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      "name": "python"
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    {
      "cell_type": "markdown",
      "source": [
        "<a href=\"https://colab.research.google.com/github/dbstj1231/2023_AI_Academy_ASR/blob/main/7_WFST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ],
      "metadata": {
        "id": "RoGoF1bq8_J0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Nemo+K2 ASR Demo\n",
        "---\n",
        "WFST의 장점\n",
        "- 지정된 unit들의 sequence를 가장 최적화해서 모델링할 수 있음  \n",
        "⁻ (입력, 출력이 정해져 있는 경우에 대해 가장 좋은 모델링 방법)\n",
        "- WFST의 효과  \n",
        "⁻ 빠른 처리 속도 (shortest path가 지정되어 있으므로)  \n",
        "---\n",
        "  \n",
        " 높은 성능을 보이는 음향 모델인 end-to-end 모델에 대하여, WFST로 표현된 word set과 언어 모델을 결합해 성능을 높임\n"
      ],
      "metadata": {
        "id": "PbpP6YXrtR4a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "materials\n",
        "\n",
        "logits, indexed words, indexed tokens and lexicon from Nemo\n",
        "Arpa format from n-gram models"
      ],
      "metadata": {
        "id": "PAttN39GMPjh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## k2-fsa(k2)\n",
        "-  OpenFST를 개량한 python 기반의 WFST 패키지\n",
        "- End-to-end 모델과 WFST를 결합할 목적으로 제작됨\n",
        "- FSA, FST를 tensor 형태로 구현하여 GPU에서 WFST 연산을 가능하게 함\n",
        "- https://github.com/k2-fsa/k2"
      ],
      "metadata": {
        "id": "qRYJNaR-ysEu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## K2 설치 및 기본 환경 세팅\n",
        "\n",
        "### Prerequisities (pip 기준)\n",
        "- PyTorch == 1.13.1 (conda 설치의 경우 >=1.13.1)\n",
        "\n",
        "### PyPI (PIP)를 이용한 설치 방법 (권장, but colab에선 비권장)\n",
        "- pip install k2 (Prerequisities 자동으로 설치됨)\n",
        "\n",
        "### Colab에서 돌아가는 pip 설치 방법\n",
        "- !pip install torch==1.13.1 torchaudio==0.13.1\n",
        "- !pip install k2==1.24.3.dev20230629+cpu.torch1.13.1 -f https://k2-fsa.org/nightly/\n",
        "\n",
        "### Source code (github)을 이용한 설치 방법\n",
        "- git clone https://github.com/k2-fsa/k2.git\n",
        "- cd k2\n",
        "- python3 setup.py install"
      ],
      "metadata": {
        "id": "MLZJsgNz_92N"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "HnDNN-YcaKOH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7156f6dd-d523-431a-8626-cec364a5e23a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting torch==1.13.1\n",
            "  Downloading torch-1.13.1-cp310-cp310-manylinux1_x86_64.whl (887.5 MB)\n",
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            "\u001b[?25hCollecting nvidia-cublas-cu11==11.10.3.66 (from torch==1.13.1)\n",
            "  Downloading nvidia_cublas_cu11-11.10.3.66-py3-none-manylinux1_x86_64.whl (317.1 MB)\n",
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            "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from nvidia-cublas-cu11==11.10.3.66->torch==1.13.1) (67.7.2)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (from nvidia-cublas-cu11==11.10.3.66->torch==1.13.1) (0.40.0)\n",
            "Installing collected packages: nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cublas-cu11, nvidia-cudnn-cu11, torch, torchaudio\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.0.1+cu118\n",
            "    Uninstalling torch-2.0.1+cu118:\n",
            "      Successfully uninstalled torch-2.0.1+cu118\n",
            "  Attempting uninstall: torchaudio\n",
            "    Found existing installation: torchaudio 2.0.2+cu118\n",
            "    Uninstalling torchaudio-2.0.2+cu118:\n",
            "      Successfully uninstalled torchaudio-2.0.2+cu118\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",
            "torchdata 0.6.1 requires torch==2.0.1, but you have torch 1.13.1 which is incompatible.\n",
            "torchtext 0.15.2 requires torch==2.0.1, but you have torch 1.13.1 which is incompatible.\n",
            "torchvision 0.15.2+cu118 requires torch==2.0.1, but you have torch 1.13.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 torch-1.13.1 torchaudio-0.13.1\n",
            "Looking in links: https://k2-fsa.org/nightly/\n",
            "Collecting k2==1.24.3.dev20230629+cpu.torch1.13.1\n",
            "  Downloading https://k2-fsa.org/nightly/whl/k2-1.24.3.dev20230629%2Bcpu.torch1.13.1-cp310-cp310-linux_x86_64.whl (2.8 MB)\n",
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            "\u001b[?25hRequirement already satisfied: torch==1.13.1 in /usr/local/lib/python3.10/dist-packages (from k2==1.24.3.dev20230629+cpu.torch1.13.1) (1.13.1)\n",
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            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==1.13.1->k2==1.24.3.dev20230629+cpu.torch1.13.1) (4.7.1)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /usr/local/lib/python3.10/dist-packages (from torch==1.13.1->k2==1.24.3.dev20230629+cpu.torch1.13.1) (11.7.99)\n",
            "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /usr/local/lib/python3.10/dist-packages (from torch==1.13.1->k2==1.24.3.dev20230629+cpu.torch1.13.1) (8.5.0.96)\n",
            "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /usr/local/lib/python3.10/dist-packages (from torch==1.13.1->k2==1.24.3.dev20230629+cpu.torch1.13.1) (11.10.3.66)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /usr/local/lib/python3.10/dist-packages (from torch==1.13.1->k2==1.24.3.dev20230629+cpu.torch1.13.1) (11.7.99)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from nvidia-cublas-cu11==11.10.3.66->torch==1.13.1->k2==1.24.3.dev20230629+cpu.torch1.13.1) (67.7.2)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (from nvidia-cublas-cu11==11.10.3.66->torch==1.13.1->k2==1.24.3.dev20230629+cpu.torch1.13.1) (0.40.0)\n",
            "Installing collected packages: k2\n",
            "Successfully installed k2-1.24.3.dev20230629+cpu.torch1.13.1\n"
          ]
        }
      ],
      "source": [
        "!pip install torch==1.13.1 torchaudio==0.13.1\n",
        "!pip install k2==1.24.3.dev20230629+cpu.torch1.13.1 -f https://k2-fsa.org/nightly/"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import k2, torch"
      ],
      "metadata": {
        "id": "7ICax8qboPIn"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import k2.version\n",
        "k2.version.version.main()"
      ],
      "metadata": {
        "id": "3S_oVGdQAaWq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c703e87-2b85-4de3-ac2c-0e296490117c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting environment information...\n",
            "\n",
            "k2 version: 1.24.3\n",
            "Build type: Release\n",
            "Git SHA1: 42e92fdd4097adcfe9937b4d2df7736d227b8e85\n",
            "Git date: Wed Jun 28 01:50:36 2023\n",
            "Cuda used to build k2: \n",
            "cuDNN used to build k2: \n",
            "Python version used to build k2: 3.10\n",
            "OS used to build k2: Ubuntu 20.04.6 LTS\n",
            "CMake version: 3.26.4\n",
            "GCC version: 9.4.0\n",
            "CMAKE_CUDA_FLAGS: \n",
            "CMAKE_CXX_FLAGS:  -D_GLIBCXX_USE_CXX11_ABI=0 -Wno-unused-variable  -Wno-strict-overflow \n",
            "PyTorch version used to build k2: 1.13.1+cpu\n",
            "PyTorch is using Cuda: \n",
            "NVTX enabled: False\n",
            "With CUDA: False\n",
            "Disable debug: True\n",
            "Sync kernels : False\n",
            "Disable checks: False\n",
            "Max cpu memory allocate: 214748364800 bytes (or 200.0 GB)\n",
            "k2 abort: False\n",
            "__file__: /usr/local/lib/python3.10/dist-packages/k2/version/version.py\n",
            "_k2.__file__: /usr/local/lib/python3.10/dist-packages/_k2.cpython-310-x86_64-linux-gnu.so\n",
            "    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## K2를 이용한 WFST 구현\n",
        "\n",
        "### Weighted Finite-state Acceptor(WFSA)\n",
        "- StartState \\t EndState \\t Symbol(index) \\t Weight\n",
        "- Symbol은 기본적으로 index로 표현/계산됨\n",
        "- 시각화할 일이 있을 때만 symbol을 표현\n",
        "- Symbol table 정의가 필요함\n",
        "- 일반적으로 k2.Fsa.from_str()를 사용하여 생성함\n",
        "- draw() or to_dot() methods를 이용하여 시각화"
      ],
      "metadata": {
        "id": "41ISeYBjAB4l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## WFSA creation"
      ],
      "metadata": {
        "id": "df18aGu2ALWr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "s = '''\n",
        "0 1 1 1\n",
        "0 1 3 5\n",
        "0 2 1 3\n",
        "0 2 2 4\n",
        "0 2 3 7\n",
        "0 2 4 8\n",
        "0 2 5 9\n",
        "1 3 6 9\n",
        "1 3 5 8\n",
        "2 3 6 12\n",
        "3 4 -1 0\n",
        "4\n",
        "'''\n",
        "a_fsa = k2.Fsa.from_str(s)"
      ],
      "metadata": {
        "id": "QGuY6fNLzEUv"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sym_str = '''\n",
        "  <eps> 0\n",
        " a 1\n",
        " b 2\n",
        " c 3\n",
        " d 4\n",
        " e 5\n",
        " f 6\n",
        "'''\n",
        "a_fsa.symbols = k2.SymbolTable.from_str(sym_str)\n",
        "a_fsa.labels_sym = k2.SymbolTable.from_str(sym_str)\n",
        "\n",
        "a_fsa = k2.arc_sort(a_fsa)\n",
        "a_fsa.draw('a_fsa.svg')"
      ],
      "metadata": {
        "id": "wDp5Y9ZBYhld",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "c4d7ad53-9450-4e9b-9ba9-0325f64a2c4c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
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249.9,-122.73\"/>\n<text text-anchor=\"middle\" x=\"234\" y=\"-123.03\" font-family=\"Times,serif\" font-size=\"14.00\">&#45;1/0</text>\n</g>\n</g>\n</svg>\n",
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              "<graphviz.graphs.Digraph at 0x7df19870b2b0>"
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          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###  Weighted Finite-state Transducer(WFST)\n",
        "- WFST: StartState \\t EndState \\t InputSymbol \\t OutputSymbol\\t Weight\n",
        "- 시각화된 표현은 arc당 InputSymbol:OutputSymbol/weight\n",
        "- CTC, lexicon, language model의 최적화된 표현을 위해\n",
        "- k2.Fsa.from_str의 acceptor=False option을 이용하여 생성"
      ],
      "metadata": {
        "id": "quwc3MPeAOAX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "s = '''\n",
        "0 1 1 1 1\n",
        "0 1 3 3 5\n",
        "0 2 1 1 3\n",
        "0 2 2 2 4\n",
        "0 2 3 3 7\n",
        "0 2 4 4 8\n",
        "0 2 5 5 9\n",
        "1 3 6 6 9\n",
        "1 3 5 5 8\n",
        "2 3 6 6 12\n",
        "3 4 -1 -1 0\n",
        "4\n",
        "'''\n",
        "\n",
        "a_fst = k2.Fsa.from_str(s, acceptor=False)\n",
        "\n",
        "sym_str = '''\n",
        " <eps> 0\n",
        " a 1\n",
        " b 2\n",
        " c 3\n",
        " d 4\n",
        " e 5\n",
        " f 6\n",
        "'''\n",
        "\n",
        "a_fst.symbols = k2.SymbolTable.from_str(sym_str)\n",
        "a_fst.labels_sym = k2.SymbolTable.from_str(sym_str)\n",
        "a_fst.aux_labels_sym = k2.SymbolTable.from_str(sym_str)\n",
        "\n",
        "a_fst = k2.arc_sort(a_fst)\n",
        "a_fst.draw('a_fst.svg')"
      ],
      "metadata": {
        "id": "gFEtAfPtZkxY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "4cebf4f3-da28-4865-eb56-c57d7a507e76"
      },
      "execution_count": 14,
      "outputs": [
        {
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    },
    {
      "cell_type": "code",
      "source": [
        "print(k2.to_dot(a_fsa, 'title'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "brGHHRvsWpmx",
        "outputId": "b4595ac0-6439-4dd9-f7fa-76f30697d9be"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "digraph WFST {\n",
            "\tgraph [center=1 label=title nodesep=0.25 orientation=Portrait rankdir=LR ranksep=0.4 size=\"8.5,11\"]\n",
            "\t0 [label=0 fontsize=14 shape=circle style=bold]\n",
            "\t1 [label=1 fontsize=14 shape=circle style=bold]\n",
            "\t0 -> 1 [label=\"1:1/1\"]\n",
            "\t0 -> 1 [label=\"3:3/5\"]\n",
            "\t2 [label=2 fontsize=14 shape=circle style=bold]\n",
            "\t0 -> 2 [label=\"1:1/3\"]\n",
            "\t0 -> 2 [label=\"2:2/4\"]\n",
            "\t0 -> 2 [label=\"3:3/7\"]\n",
            "\t0 -> 2 [label=\"4:4/8\"]\n",
            "\t0 -> 2 [label=\"5:5/9\"]\n",
            "\t3 [label=3 fontsize=14 shape=circle style=bold]\n",
            "\t1 -> 3 [label=\"6:6/9\"]\n",
            "\t1 -> 3 [label=\"5:5/8\"]\n",
            "\t2 -> 3 [label=\"6:6/12\"]\n",
            "\t4 [label=4 fontsize=14 shape=doublecircle style=bold]\n",
            "\t3 -> 4 [label=\"-1:-1/0\"]\n",
            "}\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Composition & Determinization"
      ],
      "metadata": {
        "id": "o7NetP3FASi7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "s = '''\n",
        "0 1 1 2 0.1\n",
        "1 2 2 2 0.3\n",
        "1 3 2 2 0.4\n",
        "2 3 1 2 0.5\n",
        "3 3 1 1 0.6\n",
        "3 4 -1 -1 0\n",
        "4\n",
        "'''\n",
        "# k2.Fsa.from_str(s, acceptor=False)\n",
        "\n",
        "sym_str = '''\n",
        " <eps> 0\n",
        " a 1\n",
        " b 2\n",
        " c 3\n",
        " d 4\n",
        " e 5\n",
        " f 6\n",
        "'''\n",
        "# a_fsa.symbols = k2.SymbolTable.from_str(sym_str)\n",
        "# symbols, labels_sym, aux_labels_sym\n",
        "\n",
        "\n",
        "# b_fst = k2.arc_sort(b_fst)\n",
        "# b_fst.draw('b_fst.svg')"
      ],
      "metadata": {
        "id": "Ub-YXHfxc46h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "outputId": "d9338d7e-1e90-492b-e0ad-8be6bc326f25"
      },
      "execution_count": 16,
      "outputs": [
        {
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s = '''\n",
        "0 1 2 2 0.1\n",
        "1 1 2 1 0.2\n",
        "1 2 1 2 0.3\n",
        "2 3 2 1 0.5\n",
        "3 4 -1 -1 0.6\n",
        "4\n",
        "'''\n",
        "# k2.Fsa.from_str(s)\n",
        "\n",
        "sym_str = '''\n",
        " <eps> 0\n",
        " a 1\n",
        " b 2\n",
        " c 3\n",
        " d 4\n",
        " e 5\n",
        " f 6\n",
        "'''\n",
        "# a_fsa.symbols = k2.SymbolTable.from_str(sym_str)\n",
        "# symbols, labels_sym, aux_labels_sym\n",
        "\n",
        "\n",
        "# b_fst = k2.arc_sort(b_fst)\n",
        "# b_fst.draw('b_fst.svg')"
      ],
      "metadata": {
        "id": "nOR8Nq8OdSpc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compose\n",
        "bc ="
      ],
      "metadata": {
        "id": "JSKyl2KHdrWM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# determinization\n",
        "a_fsa.draw('a_fsa.svg')"
      ],
      "metadata": {
        "id": "SWK8ApfSecnM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a_deter = k2.determinize(a_fsa)\n",
        "a_deter = k2.arc_sort(a_deter)\n",
        "a_deter.labels_sym = k2.SymbolTable.from_str(sym_str)\n",
        "a_deter.draw('deter.svg')"
      ],
      "metadata": {
        "id": "MSfF0CoueBb5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# WFST\n",
        "⁻ U (utterance) WFSA: End-to-end 모델로부터 생성 (본 실습에서는 CTC 기반의 모델)  \n",
        "⁻ C (CTC) WFST: CTC output을 subword로 collapsing할 수 있는 transducer  \n",
        "⁻ L (Lexicon) WFST: subword를 word로 바꿔주는 transducer  \n",
        "⁻ G (Grammar) WFST: 언어 모델을 사용하여 scoring하는 transducer  "
      ],
      "metadata": {
        "id": "UW6Ml3Y3-9ae"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown --id 1_n-g04-8Kk_Lv4Kw3_6gW2ZO-9d_RAC4 --output logits.pt\n",
        "!gdown --id 1GRvCoPI-Laq33yQIN5ErVbccN80yafwW --output lang/lm/lexicon.txt\n",
        "!gdown --id 1pOoccbovNdm2Wxo-mXazpSWEi7FibcyV --output lang/lm/libri_3_gram.arpa.gz\n",
        "!gdown --id 1s4P_PuwN6NVVVKu68I02oLKpZsnkPqYx --output lang/lm/tokens.txt\n",
        "!gdown --id 1ok0ww50lIBTYELayrgZRaVt0EeV3Eg9U --output lang/lm/words.txt\n",
        "!gdown --id 1KUboQqmJoymk1ptpI0cuotaxvpE33sOu --output lang/lm/word.raw\n",
        "!gdown --id 1qomtuOyUM68c4MPfowggSf7fDVtRKDQw --output utils.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I5LN-ZIBZ7tp",
        "outputId": "9c461179-b765-4645-a279-bcbf992e1090"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1_n-g04-8Kk_Lv4Kw3_6gW2ZO-9d_RAC4\n",
            "To: /content/logits.pt\n",
            "100% 252M/252M [00:03<00:00, 76.5MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1GRvCoPI-Laq33yQIN5ErVbccN80yafwW\n",
            "To: /content/lang/lm/lexicon.txt\n",
            "100% 24.5M/24.5M [00:00<00:00, 70.9MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1pOoccbovNdm2Wxo-mXazpSWEi7FibcyV\n",
            "To: /content/lang/lm/libri_3_gram.arpa.gz\n",
            "100% 34.1M/34.1M [00:00<00:00, 69.4MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1s4P_PuwN6NVVVKu68I02oLKpZsnkPqYx\n",
            "To: /content/lang/lm/tokens.txt\n",
            "100% 975/975 [00:00<00:00, 4.35MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1ok0ww50lIBTYELayrgZRaVt0EeV3Eg9U\n",
            "To: /content/lang/lm/words.txt\n",
            "100% 14.6M/14.6M [00:00<00:00, 44.7MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1KUboQqmJoymk1ptpI0cuotaxvpE33sOu\n",
            "To: /content/lang/lm/word.raw\n",
            "100% 8.39M/8.39M [00:00<00:00, 39.8MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1qomtuOyUM68c4MPfowggSf7fDVtRKDQw\n",
            "To: /content/utils.py\n",
            "100% 21.3k/21.3k [00:00<00:00, 64.4MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gzip -d lang/lm/libri_3_gram.arpa.gz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdlqe3u0dBTm",
        "outputId": "441aade9-5e2d-4edd-f5a7-6c11bd4517e4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gzip: lang/lm/libri_3_gram.arpa already exists; do you wish to overwrite (y or n)? ^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "words_values = k2.SymbolTable.from_file('lang/lm/words.txt')\n",
        "tokens_values = k2.SymbolTable.from_file('lang/lm/tokens.txt')"
      ],
      "metadata": {
        "id": "MIWPiEpwLHp8"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CTC transducer & Lexicon transducer"
      ],
      "metadata": {
        "id": "YpT3fE6lcLmD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- CTC transducer의 경우 k2의 내장 함수를 이용해서 쉽게 구현할 수 있음"
      ],
      "metadata": {
        "id": "oPQQ01TncnXS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "C = k2.ctc_topo(max_token=129, modified=False)"
      ],
      "metadata": {
        "id": "PAPGgAXs-QMR"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# draw ctc transducer\n",
        "C_draw = k2.ctc_topo(max_token=2)\n",
        "C_draw.draw('C_draw.svg')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "id": "8zw3tQlKdGPB",
        "outputId": "1b75bb31-7aa7-41c4-b0ca-8b1de18be827"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
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          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- read_lexicon method를 통한 Lexicon Transduser 불러오기  \n",
        "- Composition을 위한 disambig symbols 추가\n",
        "- lexicon을 fst로 변경"
      ],
      "metadata": {
        "id": "0t-XkPa-dDyF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from utils import read_lexicon\n",
        "\n",
        "lexicon = read_lexicon(\"lang/lm/lexicon.txt\")"
      ],
      "metadata": {
        "id": "_COJkkFkEFGx"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from utils import add_disambig_symbols\n",
        "\n",
        "lexicon_disambig, max_disambig = add_disambig_symbols(lexicon)"
      ],
      "metadata": {
        "id": "v_YskpYoEnHO"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_disambig"
      ],
      "metadata": {
        "id": "qq7uWsUIFNM-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de77ec65-33f7-4e77-89c0-f5ed9e765df0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokens_values.add('#0', 128)\n",
        "tokens_values.add('#1', 129)\n",
        "\n",
        "print(tokens_values.get('#0'), tokens_values.get('#1'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FlH9s71qMdEb",
        "outputId": "bf5a1f21-df30-4feb-b072-181a8c488acb"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "128 129\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from utils import lexicon_to_fst\n",
        "\n",
        "L = lexicon_to_fst(\n",
        "    lexicon_disambig,\n",
        "    token2id=tokens_values._sym2id,\n",
        "    word2id=words_values._sym2id,\n",
        "    need_self_loops=True\n",
        ")"
      ],
      "metadata": {
        "id": "a2nQda-kFOPM"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
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)"
      ],
      "metadata": {
        "id": "hWKyL4o2JHvx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Grammar transducer\n",
        "- Arpa format을 openFST 스타일의 fst로 변환할 수 있는 kaldilm library 설치 및 실행\n",
        "- OpenFST 스타일의 fst.txt를 불러와서 k2스타일로 저장"
      ],
      "metadata": {
        "id": "zXeWxmwmcIvW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kaldilm"
      ],
      "metadata": {
        "id": "bBfsue39K0C0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be9b0a50-a179-4b2d-e445-a8a061cb34d1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting kaldilm\n",
            "  Downloading kaldilm-1.15-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.0 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.5/1.0 MB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: kaldilm\n",
            "Successfully installed kaldilm-1.15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python3 -m kaldilm --read-symbol-table=\"lang/lm/words.txt\" --disambig-symbol='#0' --max-order=3 lang/lm/libri_3_gram.arpa > lang/G.fst.txt"
      ],
      "metadata": {
        "id": "JDp0eJq5VgEe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be145eab-4115-4e94-f621-b5dda0504ea9"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):79\n",
            "[I] Reading \\data\\ section.\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):140\n",
            "[I] Reading \\1-grams: section.\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 9 [-2.752519\t<UNK>\t-0.2779175] skipped: word '<UNK>' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186182 [-6.437047\tUNSELECTED\t-0.04899115] skipped: word 'UNSELECTED' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186183 [-6.091385\tUNSELF\t-0.611021] skipped: word 'UNSELF' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186184 [-6.202638\tUNSELFCONSCIOUS] skipped: word 'UNSELFCONSCIOUS' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186185 [-4.826539\tUNSELFISH\t-0.2318797] skipped: word 'UNSELFISH' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186186 [-5.415152\tUNSELFISHLY\t-0.1414847] skipped: word 'UNSELFISHLY' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186187 [-5.027331\tUNSELFISHNESS\t-0.3369114] skipped: word 'UNSELFISHNESS' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186188 [-6.437047\tUNSENSATIONAL] skipped: word 'UNSENSATIONAL' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186189 [-6.541995\tUNSENSIBLE] skipped: word 'UNSENSIBLE' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186190 [-6.407031\tUNSENSITIVE] skipped: word 'UNSENSITIVE' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186191 [-6.120373\tUNSENT\t-0.1616285] skipped: word 'UNSENT' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186192 [-6.541995\tUNSENTIENT] skipped: word 'UNSENTIENT' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186193 [-5.58687\tUNSENTIMENTAL\t-0.05624036] skipped: word 'UNSENTIMENTAL' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186194 [-6.469291\tUNSENTIMENTALLY\t-0.08938877] skipped: word 'UNSENTIMENTALLY' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186195 [-6.469291\tUNSEPARATED\t-0.08309033] skipped: word 'UNSEPARATED' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186196 [-5.850203\tUNSER\t-0.2518655] skipped: word 'UNSER' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186197 [-6.541995\tUNSERE\t-0.1705734] skipped: word 'UNSERE' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186198 [-6.541995\tUNSERIOUS] skipped: word 'UNSERIOUS' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186199 [-6.68067\tUNSERN\t-0.1719356] skipped: word 'UNSERN' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186200 [-6.327729\tUNSERVED\t-0.1425847] skipped: word 'UNSERVED' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186201 [-5.638036\tUNSERVICEABLE\t-0.259801] skipped: word 'UNSERVICEABLE' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186202 [-5.783227\tUNSET\t-0.1897694] skipped: word 'UNSET' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186203 [-6.378957\tUNSETTING\t-0.173494] skipped: word 'UNSETTING' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186204 [-5.73129\tUNSETTLE\t-0.2754151] skipped: word 'UNSETTLE' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186205 [-4.873146\tUNSETTLED\t-0.2670988] skipped: word 'UNSETTLED' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186206 [-6.68067\tUNSETTLEDNESS\t-0.08956671] skipped: word 'UNSETTLEDNESS' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186207 [-6.184891\tUNSETTLEMENT\t-0.2240927] skipped: word 'UNSETTLEMENT' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186208 [-5.875039\tUNSETTLES\t-0.2147736] skipped: word 'UNSETTLES' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186209 [-5.541101\tUNSETTLING\t-0.2139992] skipped: word 'UNSETTLING' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):213\n",
            "[W] line 186210 [-6.378957\tUNSEVERED\t-0.07243801] skipped: word 'UNSEVERED' not in symbol table\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):140\n",
            "[I] Reading \\2-grams: section.\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):140\n",
            "[I] Reading \\3-grams: section.\n",
            "/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):253\n",
            "[W] Of 739546 parse warnings, 30 were reported. Run program with --max-arpa-warnings=-1 to see all warnings\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "with open(\"lang/G.fst.txt\") as f:\n",
        "  G = k2.Fsa.from_openfst(f.read(), acceptor=False)\n",
        "  torch.save(G.as_dict(), \"lang/G.pt\")\n",
        "\n",
        "print(G.shape)"
      ],
      "metadata": {
        "id": "R-QrVYJEkvc0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13b00bef-4339-4305-eb1b-c436ecc9a94a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2155145, None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Composition & Determinization"
      ],
      "metadata": {
        "id": "tYvx47yA09uC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Composition"
      ],
      "metadata": {
        "id": "qyeP2SKY1HFL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "L = k2.arc_sort(L)\n",
        "G = k2.arc_sort(G)\n",
        "\n",
        "LG = k2.compose(L, G)\n",
        "#L_inv = L.invert()\n",
        "#L_inv = k2.arc_sort(L_inv)\n",
        "#L_inv.rename_tensor_attribute_('aux_labels', 'left_labels')\n",
        "#LG = k2.intersect(L_inv, G, treat_epsilons_specially=True)\n",
        "#LG.rename_tensor_attribute_('left_labels', 'labels')\n",
        "LG = k2.connect(LG)\n",
        "\n",
        "print(LG.shape)"
      ],
      "metadata": {
        "id": "QFrrgXX8E5y0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "044fd5b7-8d5d-48a5-eb03-dda8cb27e71a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10110557, None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Determinization"
      ],
      "metadata": {
        "id": "kxyhd-oZ3SXz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LG = k2.determinize(LG)\n",
        "LG = k2.connect(LG)\n",
        "print(LG.shape)"
      ],
      "metadata": {
        "id": "BlNHrH1TFBfJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f855456-49bb-4132-fdde-93941b615210"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(7508309, None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Epsilon removal"
      ],
      "metadata": {
        "id": "n0GaLACZ3UYj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LG.labels[LG.labels >= tokens_values[\"#0\"]] = 0\n",
        "# See https://github.com/k2-fsa/k2/issues/874\n",
        "# for why we need to set LG.properties to None\n",
        "LG.__dict__[\"_properties\"] = None\n",
        "\n",
        "assert isinstance(LG.aux_labels, k2.RaggedTensor)\n",
        "LG.aux_labels.values[LG.aux_labels.values >= words_values[\"#0\"]] = 0\n",
        "\n",
        "LG = k2.remove_epsilon(LG)\n",
        "#logging.info(f\"LG shape after k2.remove_epsilon: {LG.shape}\")\n",
        "\n",
        "LG = k2.connect(LG)\n",
        "LG.aux_labels = LG.aux_labels.remove_values_eq(0)"
      ],
      "metadata": {
        "id": "Hk5YfnndFDtF"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(LG.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xIcOqgUpFEN2",
        "outputId": "54c783bc-6787-4c65-8245-261b3371a1ba"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4191914, None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CLG composition"
      ],
      "metadata": {
        "id": "N7QNVYtn23e7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(LG.as_dict(), \"lang/LG.pt\")"
      ],
      "metadata": {
        "id": "mWR8k2EOFGL4"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "C = k2.arc_sort(C)\n",
        "LG = k2.arc_sort(LG)"
      ],
      "metadata": {
        "id": "3NkNa8hoFGer"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CLG = k2.compose(C, LG)\n",
        "\n",
        "CLG = k2.connect(CLG)\n",
        "\n",
        "print(CLG.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dEFpKKoLFIK9",
        "outputId": "8c182601-2947-464e-a2d1-5eed53f7974f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(8383944, None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(CLG.as_dict(), 'lang/CLG.pt')"
      ],
      "metadata": {
        "id": "nqs7W_1iFJCb"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import k2, torch\n",
        "CLG = k2.Fsa.from_dict(torch.load('lang/CLG.pt', map_location=\"cpu\"))"
      ],
      "metadata": {
        "id": "t77AW2f0FKiP"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Utterance transducer"
      ],
      "metadata": {
        "id": "HhS8gqdL2_Gc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "nnet_outputs = torch.load('logits.pt')"
      ],
      "metadata": {
        "id": "8ogqCuM8FLqp"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(nnet_outputs))\n",
        "print(nnet_outputs[0].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kqQVNmLzFPdq",
        "outputId": "f661e903-9126-4e26-8afb-f5e021c47d83"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2620\n",
            "torch.Size([487, 129])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rearrange_blksym(nnet_outputs):\n",
        "    nnet_t = nnet_outputs.T\n",
        "    tmp = nnet_t[1:-1]\n",
        "    tmp2 = nnet_t[-1:]\n",
        "    logits = torch.cat([tmp2, tmp])\n",
        "    logits = logits.T\n",
        "    logits = torch.tensor([logits.numpy()])\n",
        "\n",
        "    return logits\n"
      ],
      "metadata": {
        "id": "GGNpcqi_ZYmx"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logits = rearrange_blksym(nnet_outputs[0])"
      ],
      "metadata": {
        "id": "ALuq1VK0ZaNp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "296f4e18-1317-4295-e17d-b08a46a00ed9"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-40-509705e966f4>:7: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:230.)\n",
            "  logits = torch.tensor([logits.numpy()])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logits.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5ot3zRDZbmX",
        "outputId": "d98ebce0-95b1-4462-dba3-e1cced952aaa"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 487, 128])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "supervision_segments = torch.tensor([[0, 0, logits.shape[1]]], dtype=torch.int32)"
      ],
      "metadata": {
        "id": "20wYdjfsZcsn"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dense_fsa_vec = k2.DenseFsaVec(\n",
        "    logits,\n",
        "    supervision_segments)"
      ],
      "metadata": {
        "id": "PygimBFKZd2l"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lattice = k2.intersect_dense_pruned(CLG, dense_fsa_vec, 30.0, 15, 30, 1000000)"
      ],
      "metadata": {
        "id": "zveWY_IgZfMT"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_path = k2.shortest_path(lattice, use_double_scores=True)"
      ],
      "metadata": {
        "id": "BEY6SeKQZgd3"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from utils import get_texts\n",
        "\n",
        "token_ids = get_texts(best_path)\n",
        "hyp = [[words_values[i] for i in ids] for ids in token_ids]\n",
        "print(\" \".join(hyp[0]))"
      ],
      "metadata": {
        "id": "_aO2lD0bZhvs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd551b5e-d50b-4ab3-d3f7-a61f8771a35f"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AT THE CONCLUSION OF THE BANQUET THAT SERVED AT FIVE O'CLOCK THE KING ENTERED HIS CABINET PHERE HIS TAILORS AERE AWAITING HIM FOR THE PURPOSE OF TRYING ON THE CELEBRATED COSTUME REPRESENTING SPRING THIS IS THE RESULT OF SO MUCH IMAGINATION AND HAD CAUSED SO MANY EFFORTS OF THOUGHT TO THE DESIGNERS AND ORNAMENT PORKERS OF THE COURT\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "token_ids"
      ],
      "metadata": {
        "id": "ELB9vrcIZitc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "F3wXZaxYmS6x"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}