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{
"cells": [
{
"cell_type": "markdown",
"source": [
"# marker pdf conversion\n",
"\n",
"\n",
"- https://github.com/VikParuchuri/marker/tree/master\n",
"\n",
"\n",
"---"
],
"metadata": {
"id": "PzogeOQ6rbvL"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lIYdn1woOS1n"
},
"outputs": [],
"source": [
"!git clone https://github.com/VikParuchuri/marker.git\n",
"%cd marker"
]
},
{
"cell_type": "code",
"source": [
"!pip install -U -q pip ninja poetry"
],
"metadata": {
"id": "YkbBuXMudX5g"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"%%bash\n",
"set -e\n",
"sudo apt-get update\n",
"sudo apt-get install -y software-properties-common\n",
"sudo apt-get install -y build-essential python3 python3-pip git\n",
"sudo apt-get install -y python3-dev libssl-dev libc6-dev && sudo apt install zip unzip tmux nano p7zip-full git -y\n",
"clear\n",
"echo \"done\""
],
"metadata": {
"id": "JzW38yGYhH3W"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# %%bash\n",
"!sudo apt update && sudo apt upgrade -y > /dev/null\n",
"!bash scripts/install/tesseract_5_install.sh\n",
"# !bash scripts/install/ghostscript_install.sh && clear\n",
"!sudo apt install ghostscript && clear\n",
"!cat scripts/install/apt-requirements.txt | xargs sudo apt install -y --allow-unauthenticated > LOG_installs_apt.log"
],
"metadata": {
"id": "cDcjkP3hd_Kh"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"%%writefile convert_pdf_dir.sh\n",
"#@title convert_pdf_dir.sh\n",
"#!/bin/bash\n",
"\n",
"# Default source directory is the current directory if not specified\n",
"SOURCE_DIR=${1:-$(pwd)}\n",
"# Default target directory is parent of source directory + marker-pdf2text\n",
"TARGET_DIR=${2:-$(dirname \"$(realpath $SOURCE_DIR)\")/marker-pdf2text}\n",
"\n",
"# Function to convert a single PDF file\n",
"convert_pdf() {\n",
" local source_pdf=$1\n",
" local target_md=$2\n",
" mkdir -p \"$(dirname \"$target_md\")\" # Ensure the target directory exists\n",
" poetry run python convert_single.py \"$source_pdf\" \"$target_md\"\n",
"}\n",
"\n",
"export -f convert_pdf\n",
"\n",
"# Find all PDF files and convert them\n",
"find \"$SOURCE_DIR\" -type f -name '*.pdf' -exec bash -c 'convert_pdf \"$0\" \"${1/pdf/md}\"' {} \"$TARGET_DIR/$(realpath --relative-to=\"$SOURCE_DIR\" \"{}\")\" \\;\n",
"\n",
"echo \"Conversion complete. All markdown files have been saved to $TARGET_DIR.\""
],
"metadata": {
"cellView": "form",
"id": "aRi6CBR_ekIw"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!find / -name tessdata\n",
"!echo \"TESSDATA_PREFIX=/usr/share/tesseract-ocr/5/tessdata\" > local.env\n",
"!sudo rm *.lock\n",
"!poetry install -q"
],
"metadata": {
"id": "TlQ6wV_Lfawh",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "299eca48-79ba-41a3-95c1-7b5ffc657b14"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1A\u001b[0J \u001b[34;1m-\u001b[39;22m \u001b[39mInstalling \u001b[39m\u001b[36mtorch\u001b[39m\u001b[39m (\u001b[39m\u001b[39;1m2.3.0\u001b[39;22m\u001b[39m)\u001b[39m: \u001b[34mInstalling...\u001b[39m\n",
"\u001b[1A\u001b[0J \u001b[32;1m-\u001b[39;22m \u001b[39mInstalling \u001b[39m\u001b[36mtorch\u001b[39m\u001b[39m (\u001b[39m\u001b[32m2.3.0\u001b[39m\u001b[39m)\u001b[39m\n",
"\n",
"\u001b[34mWriting lock file\u001b[39m\n",
"\n",
"\u001b[39;1mInstalling\u001b[39;22m the current project: \u001b[36mmarker-pdf\u001b[39m (\u001b[39;1m0.1.3\u001b[39;22m)\u001b[1G\u001b[2K\u001b[39;1mInstalling\u001b[39;22m the current project: \u001b[36mmarker-pdf\u001b[39m (\u001b[32m0.1.3\u001b[39m)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"---"
],
"metadata": {
"id": "jNOFjXVJSz2L"
}
},
{
"cell_type": "markdown",
"source": [
"## from dropbox\n"
],
"metadata": {
"id": "yn0qVN93V0v6"
}
},
{
"cell_type": "code",
"source": [
"URL = \"https://www.dropbox.com/scl/fo/kbbliz7or5ptwc1c7vgb9/AF0b76tmbY3BuxHDnMzNO1E?rlkey=x5q3r7o9wdob85xjazjanloe3&dl=1\" # @param {type:\"string\"}\n",
"!wget -q -O data.zip $URL\n",
"!unzip data.zip -d /content/pdf-textbooks\n",
"!rm data.zip && clear"
],
"metadata": {
"id": "YwnblF4_iZuu"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# !bash convert_pdf_dir.sh /content/pdf-textbooks"
],
"metadata": {
"id": "MmkB5ix-imgH"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!DEFAULT_LANG=\"en\" TORCH_DEVICE=cuda poetry run python convert.py\\\n",
" /content/pdf-textbooks/python-book \\\n",
" /content/marker-md2text/python-book \\\n",
" --workers 6 --min_length 100"
],
"metadata": {
"id": "GWZURJALmDtV"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"out_file=\"/content/archived-all-marker.7z\"\n",
"!find /content/marker-md2text -type f -name \"*.json\" -print -delete # fuck off\n",
"!7z a $out_file /content/marker-md2text"
],
"metadata": {
"id": "c5xndco3mJdq"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## napierone"
],
"metadata": {
"id": "qybBz9Oqp2pH"
}
},
{
"cell_type": "code",
"source": [
"target_dir = '/content/napierone-epub'\n",
"!wget -q http://napierone.com.s3-eu-north-1.amazonaws.com/NapierOne/Data/EPUB/EPUB-total.zip\n",
"!unzip -o -q EPUB-total.zip -d $target_dir\n",
"!ls $target_dir | wc -l\n",
"!rm *.zip"
],
"metadata": {
"id": "jhB5lOA6n8f5"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!DEFAULT_LANG=\"en\" TORCH_DEVICE=cuda poetry run python convert.py\\\n",
" $target_dir \\\n",
" /content/marker-napierone-file2text/EPUB \\\n",
" --workers 6 --min_length 300"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Poi65wYSpryu",
"outputId": "8a20c15e-ae37-4be3-85e8-be3dea88afb8"
},
"execution_count": null,
"outputs": [
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"2024-05-08 22:37:05,427\tINFO worker.py:1749 -- Started a local Ray instance.\n",
"Loaded texify model to cuda with torch.float16 dtype\n",
"Converting 5000 pdfs in chunk 1/1 with 6 processes, and storing in /content/marker-napierone-file2text/EPUB\n",
"100% 4999/5000 [14:01:52<00:08, 8.66s/it]"
]
}
]
},
{
"cell_type": "code",
"source": [
"out_file=\"/content/archived-marker-napierone-epub.7z\"\n",
"!find /content/marker-napierone-file2text -type f -name \"*.json\" -print -delete # fuck off\n",
"!7z a $out_file /content/marker-napierone-file2text"
],
"metadata": {
"id": "sU8QpnNStjFn"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from google.colab import files\n",
"files.download(out_file)"
],
"metadata": {
"id": "iyzMPMmtqAzI"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "CiYoo9ZEtrEz"
},
"execution_count": null,
"outputs": []
}
],
"metadata": {
"colab": {
"provenance": [],
"gpuType": "L4",
"machine_shape": "hm"
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"accelerator": "GPU"
},
"nbformat": 4,
"nbformat_minor": 0
} |