{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "id": "YP3vVkqYUpLx" }, "outputs": [], "source": [ "import os\n", "import shutil" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AWly9SmkgSwE", "outputId": "8af190ed-5037-4e3b-b91b-b5286d8e0888" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/bin/bash: sudo: command not found\n" ] } ], "source": [ "!sudo apt-get install git-lfs tree" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OWGc_zfyq5_T", "outputId": "35ea3459-6f2d-449c-e717-74e7a27c41bf" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "Requirement already satisfied: datasets in /workspace/.local/lib/python3.8/site-packages (2.2.2)\n", "Requirement already satisfied: transformers in /opt/conda/lib/python3.8/site-packages (4.17.0.dev0)\n", "Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from datasets) (21.3)\n", "Requirement already satisfied: responses<0.19 in /opt/conda/lib/python3.8/site-packages (from datasets) (0.18.0)\n", "Requirement already satisfied: fsspec[http]>=2021.05.0 in /opt/conda/lib/python3.8/site-packages (from datasets) (2022.1.0)\n", "Requirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.8/site-packages (from datasets) (2.24.0)\n", "Requirement already satisfied: pyarrow>=6.0.0 in /opt/conda/lib/python3.8/site-packages (from datasets) (6.0.1)\n", "Requirement already satisfied: tqdm>=4.62.1 in /opt/conda/lib/python3.8/site-packages (from datasets) (4.62.3)\n", "Requirement already satisfied: pandas in /opt/conda/lib/python3.8/site-packages 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"\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.1.1 is available.\n", "You should consider upgrading via the '/opt/conda/bin/python -m pip install --upgrade pip' command.\u001b[0m\n" ] } ], "source": [ "!pip install datasets transformers" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TvDJ7CYpzSJQ", "outputId": "f58b6c87-1c32-4aa6-9945-8fe3b1eb4a66" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "Collecting https://github.com/kpu/kenlm/archive/master.zip\n", " Downloading https://github.com/kpu/kenlm/archive/master.zip (542 kB)\n", " |████████████████████████████████| 542 kB 3.8 MB/s \n", "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\n", "\u001b[?25hRequirement already satisfied: pyctcdecode in /opt/conda/lib/python3.8/site-packages (0.3.0)\n", "Requirement already satisfied: hypothesis<7,>=6.14 in /opt/conda/lib/python3.8/site-packages (from pyctcdecode) (6.46.9)\n", "Requirement already satisfied: numpy<2.0.0,>=1.15.0 in /opt/conda/lib/python3.8/site-packages (from pyctcdecode) (1.19.2)\n", "Requirement already satisfied: pygtrie<3.0,>=2.1 in /opt/conda/lib/python3.8/site-packages (from pyctcdecode) (2.4.2)\n", "Requirement already satisfied: attrs>=19.2.0 in /opt/conda/lib/python3.8/site-packages (from hypothesis<7,>=6.14->pyctcdecode) (21.4.0)\n", "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /opt/conda/lib/python3.8/site-packages (from hypothesis<7,>=6.14->pyctcdecode) (2.4.0)\n", "Building wheels for collected packages: kenlm\n", " Building wheel for kenlm (setup.py) ... \u001b[?25ldone\n", "\u001b[?25h Created wheel for kenlm: filename=kenlm-0.0.0-cp38-cp38-linux_x86_64.whl size=2341844 sha256=7389c3819998781002180209fa8ff1711b65630ca5dc282cff4b128a9db2c0bd\n", " Stored in directory: /tmp/pip-ephem-wheel-cache-yk63c6mt/wheels/ff/08/4e/a3ddc0e786e0f3c1fcd2e7a82c4324c02fc3ae2638471406d2\n", "Successfully built kenlm\n", "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "Installing collected packages: kenlm\n", "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "Successfully installed kenlm-0.0.0\n", "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.1.1 is available.\n", "You should consider upgrading via the '/opt/conda/bin/python -m pip install --upgrade pip' command.\u001b[0m\n" ] } ], "source": [ "!pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 415, "referenced_widgets": [ "edc436f12376423798af31da019eb50b", "0705b2e7a85c4b4aaebc6cc3494af44b", "8a978848e55a481a94a96b36c30a5076", "acafad3a99b0403486ceac05b768bbbc", "5e07b1951a8d4478b3e38f1e81020d92", "858ba1c848f24b8491d022b277914e84", "3d8e921c0e854bdebb17108b4cabc9f6", "4743028b12014476a634153931a26702", "ef61af4612034388ac9a97125375c2b2", "a45572c8a5714f358bdf6733e7754be3", "0ad2427a4ecc4636a10f295d9178d5c2", "7740c0f39b704684bf0c51a0f2f437af", "48ad04d04f6e4adaa33d635b577a8018", "fd3e054fad4047daae45266d480bcf6a", "ab27dee9582a4f3d9b4010a31558d5fb", "a2a21e4c9deb4a34bffd835a7cb3495b", "244473d30bdb4aa7a08a22e752763da6" ] }, "id": "JHTeonOGXiGq", "outputId": "e4a93331-f896-4d7f-db9a-6f537ee9ad34" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c3eef7b5d70d46feaa5d3d7f1281eb82", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
\\n] 480.36K 799KB/s in 0.6s \n", "\n", "2022-05-26 11:49:57 (799 KB/s) - written to stdout [491888/491888]\n", "\n" ] } ], "source": [ "!wget -O - https://kheafield.com/code/kenlm.tar.gz | tar xz" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MS4mqMyZqVAI", "outputId": "864b730f-b466-412d-9c0a-756baa86aed3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/bin/bash: cmake: command not found\n", "ls: cannot access 'kenlm/build/bin': No such file or directory\n" ] } ], "source": [ "!mkdir kenlm/build && cd kenlm/build && cmake .. && make -j2\n", "!ls kenlm/build/bin" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 511, "referenced_widgets": [ "0f3aff0652324ea5b89f541c5c39daa6", "00f0c4978f6348099f4cba64f24e1c9a", "349bcaf0b7f4457b89ecb16191db9234", "b527c0593094406dad02d7cf141c8bfd", 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"3a2ebc38769841b3be19f1f7fe76d2db", "83642a321c01457b9f79bec70374156a", "0c7d05c1d85a405398955809a64f8632", "443db78ba97e43bb9b9a897422d5b286", "48e0594bdbab4cd4b63dd7915431ee59", "b94672da40004c9b964366399f57bc59", "ee8f070bc16343768f5ea8d1e60f8a3e", "db47a4e65d60434caef4fbd71f7345f8", "2cb9eb0e53cc475bab2be62317c9ec85", "e85f02c4fdbe4f3184718bd5cd70368a", "b751aaa08b0d4b549d4de4ff195b61e1", "e30eb95348b242b49d07d9cf306dcefb", "3cf312fbe7c4422e8835f237047577e1", "fc6b3bcf7e0e413498e82dbc8c4d698b", "76aeff7ad9904177b5f8251294ace117", "de3046217cbe49ec86654912cff1a1d7", "1fcf00519be940ceafc996a0017d8cc8", "66059d30290847b0bb3f71ef5648353c", "da98619043d74be7bf9562fab0018c42", "b01c3f73bdc64f10bf85fd97cc9e7c95", "6dfb3914a5204bc09d75ca33d2e7f43c", "b3b212b349364661bdf21eab8501b888", "15d2d0c9ccf44efbbfa4e11893584283", "d954213b8dd6481dafd36db4e3b559fb", "9e62417501ec49e88ebc60272616376e", "0543f47de00148ee946e96b840bdafe1", "6b1c12698f2b4c1ab4404054c2516fb7", "de0aa54d94224e4e9d1ee80f77cea75c", "5aae03905e4340a595ab896037b5299a", "62b5fba40c8341048349bda753dfb71f", "b89ad50e8eef49f0b1173846a3247ed2", "d0f4bf6852bb473a91b30f69da0a70ce", "60c0e36233cb42d99678e40febcdadf7", "d681d19dea4c4cceaa5ef6e02f587590", "efb9a3abed904825bb98e7943580e95a", "ed24f4e2a6d6460fa05181a9376d8ba6", "581cc95d2e0b42b2b2526f2687921e39", "25e0688b672a4f91b650ed812f605efd", "e0ed17a8eb4c4fab87de270c2435c996", "42254eeebfd04178a53bb3b55344d6a8", "8f4db50fc6ab4a54bd21da5b13f137bf", "826934902f664a128c66665d740b3648", "220e9e73629c46a99390c7fcc7e544f1", "35a73ec2e0e5445491abd9623def886a", "516bc6c09c1f46f1bab30749b636d265", "94ee2770e4244a01ae4a734de8484998", "cf946d76df0342a98a3456acd54feb5a", "4a74660ffcff462da0c9d4751264a63c", "3a7b26fe1c1f4e038406270372e4ed1b", "3f3fe3861cab498ea2fafb29dd0503cb", "a713b22897414cf9998eb89569d0a0cd", "438616f0e3a54373acccc9e6be007b4c", "5f76fb7575ac4b3a88ef6ddffdcc6354", "8382d60ed9424bbd9190954d596f0694", "586352aeff7548d9a23c86bbafc3bb12", "0745b82548bd4c0cae4ac36263390b70", "4470f2821deb484cb93d6870b5f983b8", "443b418341c54b01b9b62f2639d739b1", "b526739e634641bb85076835b1450b2c", "ee2c5de2f528446789396f684779e0b5", "e5391eca718d4a59be8da689961b1397", "8eaf0d4e391d4362ae5c02934c7294db", "727045473d9f4cc1b954b3da7e935f51", "f6b47e7055414df5995693400494aa99", "2ec1314def9a4200b6d0eb720c44c5df", "3c18754adb5e47ad8b0dbedea3330722", "c86fb3b60ae04244b2343b0ced99e5eb", "20edddef884649e8ad70bd12ee89823d", "5e42173732ec402b88b8d074127fc916", "e530b4b9c6d8451fa99c063448937127", "59ea75bc40e34962913f544fafd1df67", "d11b22db44ce4c26b96ca085ee21e5d7", "475d9554fad54c99bb75599556d7d5cc", "6b837e75fa6e42e58f3f59b8a3b32391", "49edd6e9969e4553b737d2754009d143", "8d5d6fdafbd744778e8abf04a9187a17", "639dd666191a4604b1a7684fc4b922e8", "d948507d185346dd8617044f88983d7e", "896211a1e23e423cb5b05e4052b6d232", "724bbaf09c5641b7963b0f1abdd59405", "ba9c7903e94c46dd927d9869d33647e4", "0dfaab15cea64862847e6df08f64eacd", "bdd063d9937b471c95e3738c19c45eeb", "915306da0e46475eb1e4bf82d4b34585", "3b1f7b81454f4e86a3c74b9f4d4e33ba", "577de8d610374fca943450c7d87871df", "c3b04723eec94e7b9750ff316d634307", "b79f8c8b81b141f4ba28dcce7a88afa1", "0072550786c44969a48e81dc6f156ace", "489d06b9d2dd4db7a785b68586f7b75e", "962d48469bf04e97b5af13ecdba2f0c8", "5f092f839d7e40c68c6252f338b99180", "da9f8456e08046758276c8f08b9866a5", "bca9be36ed354e31bd13c5d8220f8ce0", "d617e7d92ca547aab65ecb5178514377", "05d1ae830f75487b8da76df724172e8b", "af5bea550f644e57ae2261bc25d2166f" ] }, "id": "VIgErMqApENm", "outputId": "a1ff4e21-4b06-48f0-be96-9464f732dbe5" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c57c8653f4214621b67e3587c228e22f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/1.16k [00:00 \"5gram.arpa\"" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TRnV8Miusl--", "outputId": "ae772021-8cf5-43ba-fb8b-471cafd1a3d8" }, "outputs": [], "source": [ "!head -20 5gram.arpa" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "_7u7dVPkvyRZ" }, "outputs": [], "source": [ "with open(\"5gram.arpa\", \"r\") as read_file, open(\"5gram_correct.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": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YF1RSm-Pxst5", "outputId": "45046169-0a2e-4170-82b5-ad96a7876d75" }, "outputs": [], "source": [ "!head -20 5gram_correct.arpa" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 209, "referenced_widgets": [ "dab8e1cd48754aea83815750f0119dec", "1adf04d6095b4b5887073d04db00192f", "30c668969b81489e99b0d34a960a65a4", "fa4c7c4270cb4749b1e3f7b0a38f1eca", "b2080813b5614ebeb63e5ef0dfb0b44b", "8bb2fc07bba14bdca3619303011e67bc", "ad5479dd09b84fcaa7dc2348df416e7c", "f667679f6a324d499403a31db46dfea0", "bef17396fad74eb988231e656dd4e493", "6dc5ae19629e42c293854afaaa2055ad", "4a7542e0f0164e7495623a5fa75fb406", "8b2ee6d728ab4542bcaa97461450d875", "8a6ba4a3c589445fbfbeefd297268771", "a73abbd336e14fbcb194b872a01b4aa1", "f7f98fbcc5594135a56f03b4715c32be", "f1d638c78b1541a1be61acbc8cbcabdd", "0b990c79216447e08580625b7fd63cc3", "7ac6b8d40f3f41dc80e6a9047d8d7b52", "ee3d9e1149c0413ca308d3e74a105b59", "c9904fd4ae014aa0a5c3f4d55f1fd8b2", "cab4a119943c496697c6aa4f01442f4c", "c132bc216b83491b88299594791b2b17", "746475c8ccc24046b917ce625e2c128a", "fce32c38a2e948eebb91f0abe750312a", "7bf34d2e0d9642d694e9bf30e402e40d", "a82b240b4e5f49fa96f721c4332f1e66", "2c9e51f430b84543a3bb97acea10d466", "a8f67a4b068148bb802eb1afa42c5631", "562710133bfd4a0e8849f5bdcb771feb", "a11c354dfbbd4026970d16b73daf4ba3", "03f9cfb0618b47619eb7a1afd32a2cb4", "82220f3058464cb8a4a92e8b3e18c311", "49b9d55510384560b5ce7b003476c1d8", "956061f8a9b8480abc1d29f296089f9d", "46055e902f5d48cb8861e081e144a71f", "7db6b17590f3449fbc5c56d8b18fe83a", "fcf6dbfb8ac04474869c36966c628d3c", "236ebc62d230481a90a3dda4ebe06f19", "1b3bded013a04771a0d41b6ef4b56f47", "837d5669c7ac422c8b916330a63c16c1", "0974f73b93b54d8dbf48da8c27e19325", "3a3d8353972b4548b2d5faa19f4b4bbb", "5b26d76fc291465682360755be754b21", "3f3af4e3e78a47c481ef875c27aa56c8", "5f39d3ab4ab7453b981b1b97d4be44d1", "04ed991137ac4593b38bad1390c91a22", "d0f091dd357743a8bf90b9b3b912947d", "baafdf30c74848a2b79ca7045f83549f", "37e9cc813dba41419190c1032354fc45", "e1ba9daf8b714b0182763ac468c739ed", "8044c34651b043f0bf29f95b415eb01c", "02287f8cb6c2405eb33413dac4e7e24c", "697fa201afb0449d94af9553e05ac158", "3204e60c25d347bdb9194ae07d281e99", "455696448fb04f2d9a8011b39fdb9dd7", "7340797b8a164cfc8181c88bd90099e9", "74c35002b1954d198e4c4cf63594579f", "545e8bc98cfe44269887feaf650fffa9", "b5244dec25c940c9a6ccff8ae7ab07b9", "d2841a93cffa4045b843f28369a1c832", "0c2f35471dea48f4bab90b4f7b2a89ae", "7bd67bdd551b411691f2c4e7a66d2710", "abafa7c4f03e49279e82f0547fe385b6", "114b395bde7744b09317d2dc6742cd2f", "5d61354efde946c399bb7334921c853f", "9660c58b0f534aebad5f9e2e2df6423d" ] }, "id": "paV71gdAtkDC", "outputId": "c2df6859-db57-4d4a-92b0-41b54a4215bf" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8f7c10edbad644688af3cd4e4674eac7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/260 [00:00 1 in alphabet. This is unusual unless style is BPE, but the alphabet was not recognized as BPE type. Is this correct?\n", "Unigrams and labels don't seem to agree.\n" ] } ], "source": [ "from pyctcdecode import build_ctcdecoder\n", "\n", "decoder = build_ctcdecoder(\n", " labels=list(sorted_vocab_dict.keys()),\n", " kenlm_model_path=\"5gram_correct.arpa\",\n", " alpha=0.5,\n", " beta=1.5,\n", ")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "id": "VBVf50EzZgAQ" }, "outputs": [], "source": [ "from transformers import Wav2Vec2ProcessorWithLM\n", "\n", "processor_with_lm = Wav2Vec2ProcessorWithLM(\n", " feature_extractor=processor.feature_extractor,\n", " tokenizer=processor.tokenizer,\n", " decoder=decoder\n", ")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "UZ1sWfPH2oce" }, "outputs": [], "source": [ "lm_dir = \"xls-r-300m-sv-robust/language_model\"\n", "if os.path.exists(lm_dir):\n", " shutil.rmtree(lm_dir)\n", "processor_with_lm.save_pretrained(\"xls-r-300m-sv-robust\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ClyENOYFcC_C", "outputId": "f70b816e-f308-4630-8863-d7c72f9ee667" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "xls-r-300m-sv-robust/\n", "├── [ 23] added_tokens.json\n", "├── [ 404] all_results.json\n", "├── [ 223] alphabet.json\n", "├── [2.2K] config.json\n", "├── [ 38K] eda.ipynb\n", "├── [4.6K] eval.py\n", "├── [ 228] eval_results.json\n", "├── [ 38K] join_datasets_asr_ctc.py\n", "├── [1.2K] join_datasets_asr_ctc_run.sh\n", "├── [4.0K] language_model\n", "│   ├── [4.5G] 5gram_correct.arpa\n", "│   ├── [ 79] attrs.json\n", "│   └── [5.8M] unigrams.txt\n", "├── [221K] log_mozilla-foundation_common_voice_8_0_sv-SE_test_predictions.txt\n", "├── [222K] log_mozilla-foundation_common_voice_8_0_sv-SE_test_targets.txt\n", "├── [ 12K] log_speech-recognition-community-v2_dev_data_sv_validation_predictions.txt\n", "├── [ 12K] log_speech-recognition-community-v2_dev_data_sv_validation_targets.txt\n", "├── [ 50] mozilla-foundation_common_voice_8_0_sv-SE_test_eval_results.txt\n", "├── [ 285] prepare_dataset_lm.py\n", "├── [ 260] preprocessor_config.json\n", "├── [1.2G] pytorch_model.bin\n", "├── [3.1K] README.md\n", "├── [1.6K] README_TEMPLATE.md\n", "├── [ 74] requirements.txt\n", "├── [ 207] run_eval_cv.sh\n", "├── [ 217] run_eval_real_world.sh\n", "├── [1.1K] run.sh\n", "├── [ 38K] run_speech_recognition_ctc.py\n", "├── [5.2K] special_tokens_map.json\n", "├── [ 48] speech-recognition-community-v2_dev_data_sv_validation_eval_results.txt\n", "├── [ 335] tokenizer_config.json\n", "├── [ 27K] trainer_state.json\n", "├── [3.0K] training_args.bin\n", "├── [7.4K] train_n_gram_lm_with_KenLM.ipynb\n", "├── [ 198] train_results.json\n", "└── [ 289] vocab.json\n", "\n", "1 directory, 35 files\n" ] } ], "source": [ "!tree -h xls-r-300m-sv-robust/" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X9qg4FPt2zi8", "outputId": "2f6e6de5-e47b-4e4f-bdaa-e57ae5b04368" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading xls-r-300m-sv-robust/language_model/5gram_correct.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 xls-r-300m-sv-robust/language_model/5gram_correct.arpa xls-r-300m-sv-robust/language_model/5gram.bin" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Zn4J-4OZdMPc", "outputId": "e983f1ac-08ed-4989-e7c7-6e084d6b118a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "xls-r-300m-sv-robust/\n", "├── [ 23] added_tokens.json\n", "├── [ 404] all_results.json\n", "├── [ 223] alphabet.json\n", "├── [2.2K] config.json\n", "├── [ 38K] eda.ipynb\n", "├── [4.6K] eval.py\n", "├── [ 228] eval_results.json\n", "├── [ 38K] join_datasets_asr_ctc.py\n", "├── [1.2K] join_datasets_asr_ctc_run.sh\n", "├── [4.0K] language_model\n", "│   ├── [2.0G] 5gram.bin\n", "│   ├── [ 79] attrs.json\n", "│   └── [5.8M] unigrams.txt\n", "├── [221K] log_mozilla-foundation_common_voice_8_0_sv-SE_test_predictions.txt\n", "├── [222K] log_mozilla-foundation_common_voice_8_0_sv-SE_test_targets.txt\n", "├── [ 12K] log_speech-recognition-community-v2_dev_data_sv_validation_predictions.txt\n", "├── [ 12K] log_speech-recognition-community-v2_dev_data_sv_validation_targets.txt\n", "├── [ 50] mozilla-foundation_common_voice_8_0_sv-SE_test_eval_results.txt\n", "├── [ 285] prepare_dataset_lm.py\n", "├── [ 260] preprocessor_config.json\n", "├── [1.2G] pytorch_model.bin\n", "├── [3.1K] README.md\n", "├── [1.6K] README_TEMPLATE.md\n", "├── [ 74] requirements.txt\n", "├── [ 207] run_eval_cv.sh\n", "├── [ 217] run_eval_real_world.sh\n", "├── [1.1K] run.sh\n", "├── [ 38K] run_speech_recognition_ctc.py\n", "├── [5.2K] special_tokens_map.json\n", "├── [ 48] speech-recognition-community-v2_dev_data_sv_validation_eval_results.txt\n", "├── [ 335] tokenizer_config.json\n", "├── [ 27K] trainer_state.json\n", "├── [3.0K] training_args.bin\n", "├── [7.4K] train_n_gram_lm_with_KenLM.ipynb\n", "├── [ 198] train_results.json\n", "└── [ 289] vocab.json\n", "\n", "1 directory, 35 files\n" ] } ], "source": [ "!rm xls-r-300m-sv-robust/language_model/5gram_correct.arpa && tree -h xls-r-300m-sv-robust/" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 87 }, "id": "WEV1sx6ee3aT", "outputId": "b0e8e2e6-ad6c-44b6-bb6a-efb4a3fa1dcb" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "To https://huggingface.co/marinone94/xls-r-300m-sv-robust\n", " fc97a76..7f44855 main -> main\n", "\n" ] }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/marinone94/xls-r-300m-sv-robust/commit/7f448551468d2896a8605cc133b844d92149b0d9'" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "!git config --global user.email \"marinone94@gmail.com\"\n", "repo.push_to_hub(commit_message=\"Upload 5-gram lm-boosted decoder\")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cHv8hehT1wEM", "outputId": "145492ea-77e1-4785-f2c1-9faf3778cdff" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/content/xls-r-300m-sv-robust\n" ] } ], "source": [ "cd xls-r-300m-sv-robust/" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GwzABM0l10Qs", "outputId": "7db0453f-021a-44e2-d847-0c71f794b452" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "On branch main\n", "Your branch is up to date 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