{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Train n-gram language model with KenLM on Colab" ] }, { "cell_type": "markdown", "metadata": { "id": "PtkgQE7--Ufg" }, "source": [ "See https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Boosting_Wav2Vec2_with_n_grams_in_Transformers.ipynb#scrollTo=X9qg4FPt2zi8 for detailed explanation on how to use KenLM to boost wav2vec2 fine-tuned models on πŸ€—" ] }, { "cell_type": "markdown", "metadata": { "id": "VBCqCboC6Soc" }, "source": [ "Install KenLM" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-CKLr9bI6GPE", "outputId": "0c6d917e-4896-4e35-c92f-4b085f77c893" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The operation couldn’t be completed. Unable to locate a Java Runtime that supports apt.\r\n", "Please visit http://www.java.com for information on installing Java.\r\n", "\r\n" ] } ], "source": [ "!sudo apt install build-essential cmake libboost-system-dev libboost-thread-dev libboost-program-options-dev libboost-test-dev libeigen3-dev zlib1g-dev libbz2-dev liblzma-dev" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TIlrFi3M6XO4", "outputId": "7c986a6f-f84c-4d29-b3f8-941cb85e6e8d" }, "outputs": [], "source": [ "!wget -O - https://kheafield.com/code/kenlm.tar.gz | tar xz" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KGwSg6Bl6a8Y", "outputId": "025562ab-679d-4986-9474-334dc8bd834e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-- The C compiler identification is GNU 7.5.0\n", "-- The CXX compiler identification is GNU 7.5.0\n", "-- Check for working C compiler: /usr/bin/cc\n", "-- Check for working C compiler: /usr/bin/cc -- works\n", "-- Detecting C compiler ABI info\n", "-- Detecting C compiler ABI info - done\n", "-- Detecting C compile features\n", "-- Detecting C compile features - done\n", "-- Check for working CXX compiler: /usr/bin/c++\n", "-- Check for working CXX compiler: /usr/bin/c++ -- works\n", "-- Detecting CXX compiler ABI info\n", "-- Detecting CXX compiler ABI info - done\n", "-- Detecting CXX compile features\n", "-- Detecting CXX compile features - done\n", "-- Looking for pthread.h\n", "-- Looking for pthread.h - found\n", "-- Looking for pthread_create\n", "-- Looking for pthread_create - not found\n", "-- Looking for pthread_create in pthreads\n", "-- Looking for pthread_create in pthreads - not found\n", "-- Looking for pthread_create in pthread\n", "-- Looking for pthread_create in pthread - found\n", "-- Found Threads: TRUE \n", "-- Boost version: 1.65.1\n", "-- Found the following Boost libraries:\n", "-- program_options\n", "-- system\n", "-- thread\n", "-- unit_test_framework\n", "-- chrono\n", "-- date_time\n", "-- atomic\n", "-- Check if compiler accepts -pthread\n", "-- Check if compiler accepts -pthread - yes\n", "-- Found ZLIB: /usr/lib/x86_64-linux-gnu/libz.so (found version \"1.2.11\") \n", "-- Found BZip2: /usr/lib/x86_64-linux-gnu/libbz2.so (found version \"1.0.6\") \n", "-- Looking for BZ2_bzCompressInit\n", "-- Looking for BZ2_bzCompressInit - found\n", "-- Looking for lzma_auto_decoder in /usr/lib/x86_64-linux-gnu/liblzma.so\n", "-- Looking for lzma_auto_decoder in /usr/lib/x86_64-linux-gnu/liblzma.so - found\n", "-- Looking for lzma_easy_encoder in /usr/lib/x86_64-linux-gnu/liblzma.so\n", "-- Looking for lzma_easy_encoder in /usr/lib/x86_64-linux-gnu/liblzma.so - found\n", "-- Looking for lzma_lzma_preset in /usr/lib/x86_64-linux-gnu/liblzma.so\n", "-- Looking for lzma_lzma_preset in /usr/lib/x86_64-linux-gnu/liblzma.so - found\n", "-- Found LibLZMA: 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\u001b[32m\u001b[1mLinking CXX executable ../../bin/streaming_example\u001b[0m\n", "[100%] Built target streaming_example\n", "build_binary fragment\t lmplz\t\t\t query\n", "count_ngrams interpolate phrase_table_vocab\t streaming_example\n", "filter\t kenlm_benchmark probing_hash_table_benchmark\n" ] } ], "source": [ "!mkdir kenlm/build && cd kenlm/build && cmake .. && make -j2\n", "!ls kenlm/build/bin" ] }, { "cell_type": "markdown", "metadata": { "id": "rUUGXbDy6x7r" }, "source": [ "Install πŸ€— dependencies" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Gs8LAZKr6wF8", "outputId": "2a1785bb-f254-487a-ef4c-e496f037145a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting datasets\n", " Downloading datasets-1.18.0-py3-none-any.whl (311 kB)\n", "\u001b[K |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 311 kB 5.3 MB/s \n", 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MB)\n", "\u001b[K |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 3.3 MB 46.1 MB/s \n", "\u001b[?25hCollecting sacremoses\n", " Downloading sacremoses-0.0.47-py2.py3-none-any.whl (895 kB)\n", "\u001b[K |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 895 kB 51.6 MB/s \n", "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n", "Collecting frozenlist>=1.1.1\n", " Downloading frozenlist-1.3.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (144 kB)\n", "\u001b[K |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 144 kB 51.5 MB/s \n", "\u001b[?25hCollecting yarl<2.0,>=1.0\n", " Downloading yarl-1.7.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (271 kB)\n", "\u001b[K |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 271 kB 56.7 MB/s \n", "\u001b[?25hCollecting asynctest==0.13.0\n", " Downloading asynctest-0.13.0-py3-none-any.whl (26 kB)\n", "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (2.0.10)\n", "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (21.4.0)\n", "Collecting multidict<7.0,>=4.5\n", " Downloading multidict-6.0.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (94 kB)\n", "\u001b[K |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 94 kB 3.0 MB/s \n", "\u001b[?25hCollecting aiosignal>=1.1.2\n", " Downloading aiosignal-1.2.0-py3-none-any.whl (8.2 kB)\n", "Collecting async-timeout<5.0,>=4.0.0a3\n", " Downloading async_timeout-4.0.2-py3-none-any.whl (5.8 kB)\n", "Requirement already 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Found existing installation: PyYAML 3.13\n", " Uninstalling PyYAML-3.13:\n", " Successfully uninstalled PyYAML-3.13\n", "Successfully installed aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 datasets-1.18.0 frozenlist-1.3.0 fsspec-2022.1.0 huggingface-hub-0.4.0 multidict-6.0.2 pyyaml-6.0 sacremoses-0.0.47 tokenizers-0.10.3 transformers-4.15.0 xxhash-2.0.2 yarl-1.7.2\n" ] } ], "source": [ "!pip install datasets transformers" ] }, { "cell_type": "markdown", "metadata": { "id": "6RoHBmOz66fz" }, "source": [ "Load preprocessed dataset from πŸ€— and write it to file as required by KenLM" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 216, "referenced_widgets": [ "ad5d7b0bc9ad4e228b3bc76bc975cc47", "5925567ffea2436691c4ed3b7b147c17", "799acae3451445f0a3616b8932f2e3f3", "1714ea91694842339756f26b2fa9c725", "b5d6b069468246abbb3207f3df6f9dde", "5a9c9d4b60e54a3bb64c576707bd9736", "789d5845a82e48fe9c629af743b5b1f0", "e3414cc0456241eca109f4e9e115d16a", "d3e6acd54d024d6791aab76232557721", "6b43ea2d93c04965a4539b3ef839893b", "4958b4c72d0c48af9a77974fc4ed449c", "d722bbfffeaf4ea7a1060d10dc3a06db", "e81b0bf92adc4aadaafce4ee7d36421e", "a4b5b93b88f549e8a4f37f3d48834ca9", "82692c41501c487fad27c6b19836f46f", "af8f433ef2f540c9bd70d14421904d83", "5e469744bf6a4813983ae8ee727c1c5e", "34c5f87238cb4f13a03b207aa7dc1d18", "c7955974289a4f448b422d7e4640131a", "db7ee45589e04749b80376e25ee377bb", "34d9460b112c419885bbff5211674cb3", "033cb43d32314d279a7b9e1e86bbccdc", "6a7e3547dc4141e7b5937f2baff58cbf", "921a3c1f50a24979838fd560c2cea9e0", "e33033ecda374ed4966ae5fccf6efe37", "52a852c0f98c49aa9e5edfdd4f91e4ca", "d1ff84cb5591449abcc7dd3e37f9a2df", "f479a9629c414cb495a97b0741b0fe4b", "a41cf7f5121a4068842bb5c7d2bc4d62", "31b2d7d8d9054c8fb47bf1b58043aee1", "23c9da8dd7bf4be9a23357806ebfc036", "e084d47529ca4131b233ea3514a6344f", "a6e3c5ce0a3c49ffb3d7cbf92568fe47", "a1eca879a11f414f8173b0c2c260f4c3", "75130a60f93b49c8bee0986665121d02", "328cea1a2aac4fb58bceeaf126b99371", "662d61fdd89d434785e74a7038427fbc", "670d4f16a7e44144afc0ac70eea59325", "7f92331b29fd49a68815b6d7389c1005", "c710ba94fd65486cbcbe1d402919e27f", "5951d1bafdd548b6b835b28cf9960533", "adcffda7f78c4a1c8bdc6010c8704292", "2c37aaee1f524837b477dc584209733a", "0bee4735e017471fa8679ad984b88633" ] }, "id": "0bDpNg9c6mUu", "outputId": "677d294f-2e37-48d5-bab0-6e21d1b4fe30" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ad5d7b0bc9ad4e228b3bc76bc975cc47", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/1.16k [00:00 \"5gram.arpa\"" ] }, { "cell_type": "markdown", "metadata": { "id": "ZJ5OKh358nwR" }, "source": [ "Check head of file" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pv93ZCR68s4m", "outputId": "9489b8a8-789d-4779-85f4-f4aa4e0b3392" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\\data\\\n", "ngram 1=360208\n", "ngram 2=5476741\n", "ngram 3=18177681\n", "ngram 4=30374983\n", "ngram 5=37231651\n", "\n", "\\1-grams:\n", "-6.770219\t\t0\n", "0\t\t-0.11831701\n", "-4.6095004\tΓ₯terupptagande\t-1.2174699\n", "-2.2361007\tav\t-0.79668784\n", "-4.8163533\tsessionen\t-0.37327805\n", "-2.2251768\tjag\t-1.4205662\n", "-4.181505\tfΓΆrklarar\t-0.56261665\n", "-3.5790775\teuropaparlamentets\t-0.63611007\n", "-4.771945\tsession\t-0.3647111\n", "-5.8043895\tΓ₯terupptagen\t-0.3058712\n", "-2.8580177\tefter\t-0.7557702\n", "-5.199537\tavbrottet\t-0.43322718\n" ] } ], "source": [ "!head -20 5gram.arpa" ] }, { "cell_type": "markdown", "metadata": { "id": "FEcPijF77mPY" }, "source": [ "Add end-of-sentence token \"\\\" " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "Sktd-U5a7yZL" }, "outputs": [], "source": [ "with open(\"5gram.arpa\", \"r\") as read_file, open(\"5gram_sv_lm.arpa\", \"w\") as write_file:\n", " has_added_eos = False\n", " for line in read_file:\n", " if not has_added_eos and \"ngram 1=\" in line:\n", " count=line.strip().split(\"=\")[-1]\n", " write_file.write(line.replace(f\"{count}\", f\"{int(count)+1}\"))\n", " elif not has_added_eos and \"\" in line:\n", " write_file.write(line)\n", " write_file.write(line.replace(\"\", \"\"))\n", " has_added_eos = True\n", " else:\n", " write_file.write(line)" ] }, { "cell_type": "markdown", "metadata": { "id": "hqXHYY-K760Q" }, "source": [ "Check head of file" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0QuHk3AY8Hax", "outputId": "090d065f-95c7-48e5-bc0c-01069f69c619" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\\data\\\n", "ngram 1=360209\n", "ngram 2=5476741\n", "ngram 3=18177681\n", "ngram 4=30374983\n", "ngram 5=37231651\n", "\n", "\\1-grams:\n", "-6.770219\t\t0\n", "0\t\t-0.11831701\n", "0\t\t-0.11831701\n", "-4.6095004\tΓ₯terupptagande\t-1.2174699\n", "-2.2361007\tav\t-0.79668784\n", "-4.8163533\tsessionen\t-0.37327805\n", "-2.2251768\tjag\t-1.4205662\n", "-4.181505\tfΓΆrklarar\t-0.56261665\n", "-3.5790775\teuropaparlamentets\t-0.63611007\n", "-4.771945\tsession\t-0.3647111\n", "-5.8043895\tΓ₯terupptagen\t-0.3058712\n", "-2.8580177\tefter\t-0.7557702\n" ] } ], "source": [ "!head -20 5gram_sv_lm.arpa" ] }, { "cell_type": "markdown", "metadata": { "id": "kTvRntrZ9-uq" }, "source": [ "Compress arpa file by converting it to bin" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DnmOlNZ5-ClT", "outputId": "c380c05a-e335-4e9d-98b2-c015645a2d40" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading 5gram_sv_lm.arpa\n", "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n", "****************************************************************************************************\n", "SUCCESS\n" ] } ], "source": [ "!kenlm/build/bin/build_binary 5gram_sv_lm.arpa 5gram_sv_lm.bin" ] }, { "cell_type": "markdown", "metadata": { "id": "Xra-pM-M8MZj" }, "source": [ "Download file to local machine (use Chrome if it fails on another browser)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "M7b5x8Hr8Yuo", "outputId": "5fbedff6-4a41-47c5-903c-2ad3b59983e1" }, "outputs": [ { "data": { "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, 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