{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pwqX3mdhXwx5", "outputId": "a0ea338d-c156-4bee-bdc4-bfa367356cf3" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Tue Feb 1 16:35:38 2022 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 495.46 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 36C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ] } ], "source": [ "!nvidia-smi" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5SUbafEAX0CJ" }, "outputs": [], "source": [ "%%capture\n", "!pip install datasets\n", "!pip install transformers\n", "!pip install huggingface_hub\n", "!pip install torchaudio\n", "!pip install jiwer" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 388, "referenced_widgets": [ "4a95dc7e61d041e8ac296a96a9753416", "5bf791226f414adeaaeeb6bfe3e53945", "a762bba1eca64289b6256fe8e8e0018c", "c594f7c283f84981a104a3032783f1e6", "6a5195a35d6b4c51884b88ee2bc557e1", "e9035d83d7e84b97b01f4d4725329b97", "afd0fa012e014033bfd6156b9158ac1d", "2ab06fdae1054b429655a34bcc34a724", "60a3e734c7de4937a8cf4977f49210d2", "5cfa7e1cefca43988624c89b152a7541", "53d5d6f17f234b62b93543bd54821aeb", "21681cd55ab44ed7b107592a252acd57", "0904e2d4dcd249f9a0ff64132481eb15", "8fe84ed5c5404c629759f9b708df60e1", "78e53f04f6d14e4989ef93b35d169ef8", "d40913ca1f4d4c6eb098e2d354d0581d", "240eaf779fa14146a3b8a958b8330eb1" ] }, "id": "rWmcNVb2X7Hd", "outputId": "fe3eecfc-2b58-46af-dfd6-4078dc8fb3ae" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Login successful\n", "Your token has been saved to /root/.huggingface/token\n", "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n", "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n", "\n", "git config --global credential.helper store\u001b[0m\n" ] } ], "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3X_6k7imX9L8" }, "outputs": [], "source": [ "%%capture\n", "!apt install git-lfs" ] }, { "cell_type": "markdown", "metadata": { "id": "pP37N8UPYJ9K" }, "source": [ "#Create Wav2Vec2CTCTokenizer" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 220, "referenced_widgets": [ "70792b6320e7426d9b44f8d43d8c494c", "4c6a076dcf2f4bdbbfbfae468f15f669", "f36755104d3143f8972ee5915f4fce8a", "93e0094d4c52443b8341f4f3f481ca4c", "49b031d25efd45678dfed4bb5c7c1975", "3f81be1b2b0b437caa50f3bd90c5ed85", "fbc8f17272e9493a8d03cb5a5d6f03b1", "5ffd34479bcd4f93be25cc3a26d478d5", "2e5af03bc5a24bdd8d95e7cf199618b0", "11027cd60e7942ca916d8b6570a89b8b", "283bc35351ec4ebe8c29ba8e44ad106b", "4d5ea0b19d3b49cca010f8741134efd3", "eee8e378263849a6bf2683a51c1dacd6", "54b871c25c8d40cd99cdc30f1fadd46f", "9a1cdf6b74c448d3982b20ec78461909", "217c3ec98eea43d7bd6df65d1cb53bb5", "b1afdca4f6324a1288238a2eab82f890", "43d8df52c9fb476184a66ebd1578bff0", "f28bf416bb2e49a2af7a83900154ddc8", "d68fef83d7c34ce7b31ad6ef847620ab", "69859ad3f8e24038bde0ddf766ea409d", "8ab9af6ab59046afa4542fe1469e3cd0", "88a925d024754a14a0a24c4f427dbc2f", "3c8a3aedcf66465d9a51f8c03facc208", "068169825cca4435b91af85b84579d0a", "45459335d23f416c9603f0aa1abe872d", "f73483595112415abba98424a2ddcf19", "f361daab4d734e53812b97372ebb3d08", "285ad0334d174e5db484b7ed5abd0403", "db08e121380e40ea97e8353234083c7d", "717e5953d25444d58f4969ee54788f46", "22e32347cb8640d9b7f5784401bd1fd9", "ac1e63b6b2f54da38abc632fe2204b4e", "770a4f1e980f43c0a2ae704f37207358", "24d8f9b9a90d47feab4aa669e4897475", 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"e81e038dfb864af38253213725e083b4", "2249e9a51d1a4a1f8a409ad23469dc72", "7cdcc597147644208a0b9e6ea72d5070", "0e0f93b0daab42e3bae3c6156076de5c", "78fe7adf756648648b3a6142b9594fc9", "96d5294de2704dbd973f2433e3da8f7b", "6944678fcd954c9fbf0c85afaa5484d1", "0db34ce01c8e42bb9e806d163fc3723b", "7005b11984c0410bbacd5521230a27ef", "577bb4b84edb426cb73a8e3331532343", "721f2bfc5552494084ebb1c57d1dd298", "80fab726f9474e7cbf06347bf1e013c9", "dda7c4badade4626bbcf8fd13a3a909e", "cc8458af63b34b59a89837f7b12d18c7", "7e8a9f8125ba4a23852b0129a1486611", "72bce4ca28404f418c8b626cf975117c", "ade3e44686f642f2aa76165ab1477a4a", "49c2d057c67f47d0800f637682c483fc", "ec3a4d511e754a419d2b7a153a92afd2", "011bdb01e21b499686136b9b99742fcd", "bee4c82e1e7a413a8c9488d96c40eab1", "34667ff59990492a8fcbe6b02919942a", "07237610374947139e17e9af3dd01b87", "fa2281b8ebde4310a6b6ed0016bdc405", "28ae0fb07bf145e984e7aeabf806550f", "351ac9e44b754029a145aa95232a8cc9", "82cc88b453484e3eb49aeb168dc5b885", "17915738ffd44d70831690c845ef0fce", "b0ab7879f67f417692fc753007029d3c", "4ef4db865b0d47e4ba7e56bd04c8894a", "6dbdb0c5165541ba8e53076c8dc49af1", "447a5287cb15475c8f179e761d04270a", "c33ba2e2bbb74f9cb5b7fed1391ef016", "17256986208c4409b542085156f4ebad", "39dc8e04e6434c27bf33c4bb12e7f83f", "b34f216d7c964c0e8e5352a169a0048f", "6bd965584567403b9a8608ef76416c52" ] }, "id": "O2xxPBqwYH0Z", "outputId": "d2a64975-252e-4254-db7a-dee36d8526dd" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "70792b6320e7426d9b44f8d43d8c494c", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/10.1k [00:00\n", " \n", " \n", " \n", " sentence\n", " \n", " \n", " \n", " \n", " 0\n", " Још увек не видим компромис.\n", " \n", " \n", " 1\n", " Пусти ме, пусти!\n", " \n", " \n", " 2\n", " Он је слушао.\n", " \n", " \n", " 3\n", " И, где си?\n", " \n", " \n", " 4\n", " А у мене?\n", " \n", " \n", " 5\n", " Извештај објављен у четвртак пружио је комплекснију слику.\n", " \n", " \n", " 6\n", " Нисам ни покушала.\n", " \n", " \n", " 7\n", " Коштуница је својевремено протест беретки окарактерисао као легитиман.\n", " \n", " \n", " 8\n", " То су велике.\n", " \n", " \n", " 9\n", " То је био?\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {} } ], "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\", \"audio\"]), num_examples=10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NRDcDNteYabw" }, "outputs": [], "source": [ "import re\n", "chars_to_remove_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�\\'\\।\\&\\|\\’]'\n", "\n", "def remove_special_characters(batch):\n", " batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"]).lower()\n", " return batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "37f06a549fc244aaa1ca2ae5070a81de", "0f6c158bc89146c38f4f2c8c01d57a32", "0dec9be4ff034e57a35f86ad2e425cb6", "ed53eaea5f7d4efdb25d4f348a8cf0bb", "db35f7c21192417cae840a2147cb677d", "db9f54502ff44961b678691246272545", "fdd1fffbc3ae4e31882c10e305c28b1c", "e7a06956b0d74a08ab9b7cabce1e14a2", "7ae74d427a4d48ccb90989bcdb144c92", "d764b068b1914a6daf41bea522d72c48", "a0efd69e132b45c8ac07a9a22959fc00", "7939038b8e9749c9bc3ec5cf185ad989", "da152712972940f1834727ab1641f26c", "7146df3e6c6e4f75bcd4bfd2a47428ac", "267a02e9ce3f45048f06b32f72bd7dbe", "48dc2b52c8a24e0fb23efcc1a8079289", "bde9b941c1e94ed48f44467be30753ee", "118ff740b9bb48ac8ea02b438f090288", "889066c86aa84808b900c4138e8d2806", "5e17bb04938846349a04104a1de79fd7", "10c2e296b67c4d45a02f032b06b3a1e9", "253a7e172bee4d5b9da743f0db268b0a" ] }, "id": "SyWGPpzNYciF", "outputId": "ff36f976-08d4-4aad-83f9-06df34700787" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "37f06a549fc244aaa1ca2ae5070a81de", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7939038b8e9749c9bc3ec5cf185ad989", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {} } ], "source": [ "common_voice_train = common_voice_train.map(remove_special_characters)\n", "common_voice_test = common_voice_test.map(remove_special_characters)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "5XHPLLvAYeMY", "outputId": "818370b2-0b01-4cb3-90ac-020b9e6729e0" }, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sentence
0зашто би се
1о али има
2па не мислим
3у свом стану
4седи молим те
5који је ко
6а ја сам
7и плакала сам
8ма шта је теби
9шта да радим
" ], "text/plain": [ "" ] }, "metadata": {} } ], "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\",\"audio\"]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "z0UilCejYk66" }, "outputs": [], "source": [ "def extract_all_chars(batch):\n", " all_text = \" \".join(batch[\"sentence\"])\n", " vocab = list(set(all_text))\n", " return {\"vocab\": [vocab], \"all_text\": [all_text]}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "17ea898bbd6a471c92bb6823cd811ba6", "319e13c1a0f6448199a6ca3584e5107d", "97613a28b80746738d3c2f6acd29fd5d", "d3a9d8d564c74fc389d9e505e9e16bf1", "7cae8be7d6e34b34b64207f8abd7783e", "c9d0dea2b615403682050b0fb313928c", "eb790d1d48b348a7a84c3c7ae27f236e", "0cf0902d6c524e06810ea6b4b33eac66", "138ff59319f64ba0af7aff3ee359170b", "040c769494f547c38da9f449b0518e8f", "7af7dc0ea5284a938341325cfb6b9d87", "9c868c0244e64405bdff123dc998a64d", "fe35c8ae88dd44f78c2a33569c8186b7", "bad5cc06c2a142739b7b206914ce92ba", "427ba5de338b472b83d2f2feee95cbec", "35ad4813c37e470c8485298dbdce43f8", "9dc6a16556cc48f7a71961b0384abfd6", "e4538568f8a04afd8d985406b97091d4", "32226f28bf0e490fb12464f3a02061f0", "fb617157611e460ea9bd934d5359279b", "db37b146dcbf4c09a0888f4568e63162", "56b730b43f47422baa3b7551502fe1b6" ] }, "id": "GbPSiLAXYmvK", "outputId": "b527bdcb-bd87-4ecc-8d61-9b3c54f8cf31" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "17ea898bbd6a471c92bb6823cd811ba6", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00\n", " \n", " Your browser does not support the audio element.\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "execution_count": 31 } ], "source": [ "import IPython.display as ipd\n", "import numpy as np\n", "import random\n", "\n", "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(common_voice_train[rand_int][\"sentence\"])\n", "ipd.Audio(data=common_voice_train[rand_int][\"audio\"][\"array\"], autoplay=True, rate=16000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EE5jG9PVZmWU", "outputId": "c31bcbb4-5c94-4e28-ef52-4b378be7a6b8" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Target text: о да бар\n", "Input array shape: (31680,)\n", "Sampling rate: 16000\n" ] } ], "source": [ "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(\"Target text:\", common_voice_train[rand_int][\"sentence\"])\n", "print(\"Input array shape:\", common_voice_train[rand_int][\"audio\"][\"array\"].shape)\n", "print(\"Sampling rate:\", common_voice_train[rand_int][\"audio\"][\"sampling_rate\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XobzwvyNZpxv" }, "outputs": [], "source": [ "def prepare_dataset(batch):\n", " audio = batch[\"audio\"]\n", "\n", " # batched output is \"un-batched\"\n", " batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n", " batch[\"input_length\"] = len(batch[\"input_values\"])\n", " \n", " with processor.as_target_processor():\n", " batch[\"labels\"] = processor(batch[\"sentence\"]).input_ids\n", " return batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "d9697c7cd7ed469bbe321962722fec89", "bd56fe0b42c44d2f85eb6e1bd8ec5ae1", "caacfdc6da8b4f0893e735581469b529", "bc1e57f971c147d4aa1d5cda626a82cd", "321d91a3924248a5ba2ceb32182d617d", "c39061c1a55b407794a29bd22129d6c9", "5f98a023f2674a7a809b235614efac9a", "67c80607a8e245f4ab4a0b1d92a69232", "cfa82407ae214bcf860549c91b16d369", "ca47241cbd964b3d835714b9bede29ed", "7b7335d0954349668b2495376d8b6123", "a2f3ccbf1f7947e191df7b9fc00daaaa", "a5297b40e6e348aaa0bbfb3ea99da482", "86d27b29224c4b09848e916abcd1d0ab", "746729bd72e14e3f94cdf82ae421aa95", "4debc8e787094ceb9a628f3cc531d7a0", "d84801f047bd4096a19144a2ec04030b", "ae15fcec028a4e88890567f709581239", "1d2f2c7f9f1e4bae8c564b5dbec95601", "82923c3464114131b6344ba94e154a97", "de5cd0f4c4224ece80d8518c478f67ba", "05cd3f91db104c599960ab5e74dcd82b" ] }, "id": "A7gT1EQMZsvz", "outputId": "af6297ba-e410-4b39-8611-45d6e5806fb6" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d9697c7cd7ed469bbe321962722fec89", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a2f3ccbf1f7947e191df7b9fc00daaaa", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {} } ], "source": [ "common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names)\n", "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names)" ] }, { "cell_type": "markdown", "metadata": { "id": "Hu8imi4QZx3f" }, "source": [ "Training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "rAYCcrJFZubm" }, "outputs": [], "source": [ "import torch\n", "\n", "from dataclasses import dataclass, field\n", "from typing import Any, Dict, List, Optional, Union\n", "\n", "@dataclass\n", "class DataCollatorCTCWithPadding:\n", " \"\"\"\n", " Data collator that will dynamically pad the inputs received.\n", " Args:\n", " processor (:class:`~transformers.Wav2Vec2Processor`)\n", " The processor used for proccessing the data.\n", " padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n", " Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n", " among:\n", " * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n", " sequence if provided).\n", " * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n", " maximum acceptable input length for the model if that argument is not provided.\n", " * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n", " different lengths).\n", " \"\"\"\n", "\n", " processor: Wav2Vec2Processor\n", " padding: Union[bool, str] = True\n", "\n", " def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n", " # split inputs and labels since they have to be of different lenghts and need\n", " # different padding methods\n", " input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n", " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n", "\n", " batch = self.processor.pad(\n", " input_features,\n", " padding=self.padding,\n", " return_tensors=\"pt\",\n", " )\n", " with self.processor.as_target_processor():\n", " labels_batch = self.processor.pad(\n", " label_features,\n", " padding=self.padding,\n", " return_tensors=\"pt\",\n", " )\n", "\n", " # replace padding with -100 to ignore loss correctly\n", " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n", "\n", " batch[\"labels\"] = labels\n", "\n", " return batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "k05sM3rsZzGS" }, "outputs": [], "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "d9fe467af4d8469bae893f726adb0c42", "a369316dc8954a75b8bbca79a68609c7", "f4913cb060fb4f9485dd542e7e0f7785", "8807fc1dc04544de93c6c49b552cfe52", "7c35a88013654a8882d7e39f2950ef8e", "1bd9e6db42b8490fabb89166cbbf483d", "dafb6ab0f6a948cd82086632de08ef2d", "4e4b9b70fa6a4114998b1df63dbc0b2b", "79854f7e9390474d816f604bf3097b29", "bfe15a915f724b54983f64a008ef375a", "b6f57ea5f3b44d88a4d310c0dc26af6c" ] }, "id": "T1zgf2ZKZ1et", "outputId": "005f4c5d-fabb-4927-e654-50c53d7d7b53" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d9fe467af4d8469bae893f726adb0c42", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/1.91k [00:00\n", " \n", " \n", " [5590/8000 3:21:54 < 1:27:04, 0.46 it/s, Epoch 139.72/200]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StepTraining LossValidation LossWer
3008.2934002.9776670.999533
6001.5049000.5035750.480635
9000.3263000.5822120.405506
12000.2008000.5608830.403173
15000.1543000.5203010.370975
18000.1158000.6458150.398507
21000.0997000.6226980.401307
24000.0834000.6047530.383574
27000.0665000.6196750.368642
30000.0602000.5418460.345310
33000.0524000.5310050.348577
36000.0445000.5599100.337378
39000.0406000.5957640.332711
42000.0358000.6017270.326178
45000.0302000.5612860.324778
48000.0285000.5658940.346244
51000.0213000.5568020.320579
54000.0215000.6524360.347177

" ], "text/plain": [ "" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-300\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-300/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-300/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-300/preprocessor_config.json\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/preprocessor_config.json\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-600\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-600/preprocessor_config.json\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-900\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-900/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-900/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-900/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-300] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-1200\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-1200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-1200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-1200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-600] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-1500\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-1500/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-1500/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-1500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-900] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-1800\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-1800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-1800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-1800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-1200] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-2100\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-2100/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-2100/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-2100/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-1500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-2400\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-2400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-2400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-2400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-1800] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-2700\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-2700/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-2700/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-2700/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-2100] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3000\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-2400] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3300\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3300/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3300/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3300/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-2700] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3600\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3900\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3900/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3900/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3900/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3300] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-4200\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-4200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-4200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-4200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3600] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-4500\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-4500/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-4500/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-4500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-3900] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-4800\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-4800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-4800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-4800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-4200] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-5100\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-5100/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-5100/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-5100/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-4500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 598\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-sr-v4/checkpoint-5400\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-5400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-5400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-sr-v4/checkpoint-5400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-sr-v4/checkpoint-4800] due to args.save_total_limit\n" ] } ], "source": [ "trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qKKeV9gEaLHr" }, "outputs": [], "source": [ "trainer.push_to_hub()" ] }, { "cell_type": "code", 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