{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "id": "9MRhjbpm6IK2" }, "outputs": [], "source": [ "%%capture\n", "!pip install datasets --upgrade --user\n", "!pip install transformers --upgrade --user\n", "#!pip install torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html\n", "!pip install librosa\n", "!pip install jiwer\n", "#!pip install kaggle\n", "!pip install huggingface_hub==0.1" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "wwJJ57AI6IK3", "outputId": "425ea36a-e43a-485d-bf75-ffedb422ac6f" }, "outputs": [ { "data": { "text/plain": [ "'4.17.0.dev0'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import transformers\n", "transformers.__version__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "RimCN6er6IK3", "outputId": "76ed5db9-1e1f-4419-a688-898fe28277b5" }, "outputs": [ { "data": { "text/plain": [ "'0.10.2+cu102'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torchaudio\n", "torchaudio.__version__" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fZ7KBQ2H6IK3", "outputId": "5ff14194-90b3-47b9-fb21-830940d436c3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.18.3\n" ] } ], "source": [ "import datasets\n", "from datasets import load_dataset, load_metric, Audio\n", "datasets.set_caching_enabled(False)\n", "print(datasets.__version__)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "aE6HSloZ6IK4" }, "outputs": [], "source": [ "# Environment settings: \n", "import pandas as pd\n", "pd.set_option('display.max_column', None)\n", "pd.set_option('display.max_rows', None)\n", "pd.set_option('display.max_seq_items', None)\n", "pd.set_option('display.max_colwidth', 500)\n", "pd.set_option('expand_frame_repr', True)\n", "\n", "from datasets import concatenate_datasets, load_dataset, Audio" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 239, "referenced_widgets": [ "788cdcbedc8a4991a3b534419a0ccd6a", "7bab74822a7b44d8ada58e748d77fb2d", "4d2580b0ba464e0b84da40f315eeda49", "a89d970858d64e8f83877877297cac95", "1e7bd26d7af84d579ce27b134b5c0ad4", "3b0c46e5462a42f6bc6165a42d2764ae", "9d95fb669a084746a1d0a2d1501592d4", "392df3b560104792b77c93a6eedf803b", "89227add654e4d519c7b3064765cb629", "9d2413cd1db84267becfa867a1a38e60", "c6462f9ceb234ec5a0704d6a134060a7", "6190087ca74b412e84aa4e2412430e4a", "3361dea70d5644fe898a3f95e6ad02d0", "f8b4af22c7ca446998791962c80171c4", "0c07d443d4fa4843bb1a70f28a29fa08", "afa4f545b4974675ba5ac137a10b6adb" ] }, "id": "yiK2-jAZ6IK4", "outputId": "6b901e8f-37b4-45a9-d27e-3691cd868f3a" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5c42c8c793a742709b1aa95fbf103f54", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
\\n\n", " \n", " \n", " \n", " sentence\n", " \n", " \n", " \n", " \n", " 0\n", " Keeruliste aegade Euroopa vajab liidreid, kes julgevad kaitsta väärtusi, millele Euroopa-idee toetub.\n", " \n", " \n", " 1\n", " Paralleelselt baltisakslaste lahkumisega algas NSV Liidu valitsuse tõsisel survel Punaarmee baaside rajamine kõikidesse Balti riikidesse.\n", " \n", " \n", " 2\n", " Kui aga ärksamad ajakirjanikud ja poliitikavaatlejad kutsuvad meid konservatismi mõiste kaasaegsemale ja selgemale sisustamisele, siis sellega tuleb muidugi nõustuda.\n", " \n", " \n", " 3\n", " Kui Infortar müüb laevafirma Tallink ära, on loomulik, et varem või hiljem tuleb selle nime kasutamisest loobuda ka teistes ärides.\n", " \n", " \n", " 4\n", " Tegu on mõne aasta taguse videoga, kuid ometi ei suuda kommenteerijad ära imestada, et kusagil on korraga koos nii palju kenasid neide, vahendab brightside.me.\n", " \n", " \n", " 5\n", " See annab kindlasti enesekindlust, ausalt öelda ei keskendu praegu sellele.\n", " \n", " \n", " 6\n", " See kurb sündmus on suurepärane näide erinevate kuulduste ja rahvajuttude omapärasest põimumisest üheks väga segaseks looks.\n", " \n", " \n", " 7\n", " Ma ei saa ju teibaga maha lüüa valitsust ja selle valijaid.\n", " \n", " \n", " 8\n", " Taxify kirjeldas oma teates, et omandas Londonis litsenseeritud operaatori City Drive Services, mis kasutaks Taxify trehnoloogilist platvormi.\n", " \n", " \n", " 9\n", " Unusta tavaliste trennide-dieetide veri, higi ja pisarad, rõkkab GQ.\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\", \"audio\"]), num_examples=10)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "NdLHxhRd6IK7" }, "outputs": [], "source": [ "import re\n", "CHARS_TO_IGNORE = [\",\", \"?\", \"¿\", \".\", \"!\", \"¡\", \";\", \"；\", \":\", '\"\"', \"%\", '\"', \"�\", \"ʿ\", \"·\", \"჻\", \"~\", \"՞\",\n", " \"؟\", \"،\", \"।\", \"॥\", \"«\", \"»\", \"„\", \"“\", \"”\", \"「\", \"」\", \"‘\", \"’\", \"《\", \"》\", \"(\", \")\", \"[\", \"]\",\n", " \"{\", \"}\", \"=\", \"`\", \"_\", \"+\", \"<\", \">\", \"…\", \"–\", \"°\", \"´\", \"ʾ\", \"‹\", \"›\", \"©\", \"®\", \"—\", \"→\", \"。\",\n", " \"、\", \"﹂\", \"﹁\", \"‧\", \"～\", \"﹏\", \"，\", \"｛\", \"｝\", \"（\", \"）\", \"［\", \"］\", \"【\", \"】\", \"‥\", \"〽\",\n", " \"『\", \"』\", \"〝\", \"〟\", \"⟨\", \"⟩\", \"〜\", \"：\", \"！\", \"？\", \"♪\", \"؛\", \"/\", \"\\\\\", \"º\", \"−\", \"^\", \"ʻ\", \"ˆ\"]\n", "\n", "\n", "chars_to_remove_regex = f\"[{re.escape(''.join(CHARS_TO_IGNORE))}]\"\n", "\n", "def remove_special_characters(batch):\n", " batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"]).lower()\n", " batch[\"sentence\"] = re.sub('[-]', ' ', batch[\"sentence\"]).lower()\n", " return batch" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "e3f4dd7cb7814691b99a98e00e42b413", "07356cbb5c644aed8eb9de437e8e4691", "24f1bfd43ca34ef9933d19e2b2ee93d6", "d0ea9b81b1254e00a3f407d4632ebe50", "92089a2dfb794fbe8a6a4b553f281d85", "add1cdf7f5904571ba09b696819e6724", "b083339d05a645718b9c697c0d3ce2a0", "a5ec641870f44cbe9d5c4f2a6caf9e8b", "8e3c6d7df70d47c5bf1ccfc5ce6b490c", "699c71d9472b4a64a993e19bbe6dd735", "2aaa74b624de44a78a55cea202430754", "37c68e11a6d748018e1478f583a03051", "990fd8d131e640e6ba0f6ed3613cfc8b", "156189bcb95b4a2893d58e4dae6335d7", "a4c60066b2ce4c78acb09269cf3e2894", "515e18bdede14b8f86c3d2b41e4cef00", "b8af84200dd44930970a304cc8fbeb8c", "b269c11828d8431198f55a0f1825a58c", "ba794f1f60eb4bd99c5a22a20acce667", "60493e52db344e60afc883249d71c170", "17e6b65ba76f4b2eb83148730734bddf", "430b2d8471f74285b910f96e44f62924" ] }, "id": "q7YFJgON6IK7", "outputId": "f9c0c89c-08bb-4e06-e29d-40cf7da492c3" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9368a8d5d58d4e60870f24c505606c99", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e33f059a0467483b9722b969127a1ab9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {}, "output_type": "display_data" } ], "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": 17, "metadata": { "id": "NkGbORLu6IK8" }, "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": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "6825d36977104f0facdc9216b62ffeb7", "85b98111a8f4486d865d5e9213f28ebf", "0f745c9c99b44616a7dc0b41d63d800c", "ed001b4d74c24bda8a75e05f1c4cebff", "c854815e32334e88b18b3fc380b9dd01", "c8c624a55eeb4398aeb78978f1534073", "3da34632f24e40f18fc0d78691e58ba6", "d3a824f5974941beb35aa9d718db3476", "1cb79ffb0385495ba396e69add87a5fc", "15ed138efd55476da386e8f5ed48d583", "a1dfee9c96ab446cb1807d6dafadfab5", "a12d06dcf0a64868a18634f1f6b6bb54", "79a24cbc91f64305bc63e438f3b6be2a", "d378de92cbe24153b561cb416219d3ff", "51ae1caebc9a40b99e3889f79c01db9a", "ad09e4d8e90c4537a592a51f55af3858", "313a4ec6c0c247279c0520aaf1536f47", "cb22cfc078494aebab308a9daef8729b", "f9ae0de86e4244dd88d97fe64df30d3c", "3fbdad25464044029ebddaa7c4503cb6", "f63362b3cbad406891125013c655ab92", "954014a8b8a84c80a365609dfc2181b7" ] }, "id": "Ju5Q4mA-6IK8", "outputId": "80cdeca0-05ed-414d-f779-78e05671cbc4" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c3a86f71d73f44cda2945f2bef2609b1", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1 [00:00 main\n", "\n" ] }, { "data": { "text/plain": [ "'https://huggingface.co/RASMUS/wav2vec2-xlsr-1b-et/commit/69fdc6958a35d1f5d8af7690b6a219722f1278d5'" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenizer.push_to_hub(repo_name)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "id": "4GDqmpau6IK9" }, "outputs": [], "source": [ "from transformers import Wav2Vec2FeatureExtractor\n", "\n", "feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "id": "3ydgeSof6IK-" }, "outputs": [], "source": [ "from transformers import Wav2Vec2Processor\n", "\n", "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "id": "6RGWMIrk6IK-" }, "outputs": [], "source": [ "import torchaudio" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "id": "2oXxiiMx6IK-" }, "outputs": [], "source": [ "common_voice_train = common_voice_train.cast_column(\"audio\", Audio(sampling_rate=16_000))\n", "common_voice_test = common_voice_test.cast_column(\"audio\", Audio(sampling_rate=16_000))" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ljOF6qF86IK-", "outputId": "531347dd-66dd-4e28-9ff4-ee52b7222398" }, "outputs": [ { "data": { "text/plain": [ "{'path': 'cv-corpus-8.0-2022-01-19/et/clips/common_voice_et_18039906.mp3',\n", " 'array': array([0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 2.8755874e-05,\n", " 7.3133713e-05, 4.6529221e-05], dtype=float32),\n", " 'sampling_rate': 16000}" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "common_voice_train[0][\"audio\"]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "id": "2Lt4U5wL6IK-" }, "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": 37, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 98, "referenced_widgets": [ "5ce68963158f43c6afd697112ae91b86", "9e3f3a604e6a455e96d0b8c2c1ad437b", "fd832e95e5e545d6ba00375c311f4d6d", "ca3ab0df68934372874e745484f452c8", "8afadf17db944fd8aa3160963d9ce1d3", "55ad365db37744b8aeba007e99ec719e", "9bc1101beda048109d123fb002d5400c", "d90cdb5e20d643cd939b195f67c6480b", "fe4b7a51a8454b8c97b66cc5f8a96040", "ddb0349a1010496d9ef755fdc0b7960e", "61368172bb5a431d990ef898bc23d9cc", "e1ded32ac98d45b98a0ea4f96f5f472a", "47974c9e252d43c6b516db6853a0d78c", "86f48fe824a4424691ac207bdb9be81c", "7051513a70fd45e292b742f8b0e1c24a", "a757021c8c2240e788df46ec84a67587", "b8531ca6621a479796636b6f5460240c", "f1acc768df0c4d50bdb0c1c008aad15e", "1b433b3ae9c84d47bd5fb1d3978f4f3a", "f82689a91e3e4962935dd07e20c13426", "7a1499b32a28488087c951ef8af473a0", "3ac86f6958ba46f4a53ffa937e66427c" ] }, "id": "ldXFrogk6IK-", "outputId": "839b1d78-4017-4b1e-c259-c1fec5641de0" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "29057dc672f94ff9ac14970da2113e38", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "done train\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ebbf56cd919d46f2a24022c64cc1b74b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names)\n", "print(\"done train\")\n", "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "id": "JP1OWPs36IK-" }, "outputs": [], "source": [ "max_input_length = 20.0 * feature_extractor.sampling_rate\n", "min_input_length = 0.0 * feature_extractor.sampling_rate" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "id": "1gHoa7lL6IK-" }, "outputs": [], "source": [ "def is_audio_in_length_range(length):\n", " return length > min_input_length and length < max_input_length" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "b9e3a30cafbf4a7ca7bbdf3fe9a636b4", "433af8fbc3a444f38a7e018a20cb6fd1", "b64a8d5d660a4faf838c5f03becaf176", "9ecf7f11f0d342939ee906c4b2beb3e6", "435be14c703a41148dff1bd6be5a16bf", "dbecceb62ccf4ebd94c5b96c4a2aebd7", "26e1e39eaaa241f2b427be2195f3a6c3", "0dc42365459344bb94b495a103153b4d", "8a4049e6a66f4fe99968b8f4550bc8a3", "ae384809c29d4a2b996d0733fd45ea35", "8d14f0d27388487aa0f605df23bcb466" ] }, "id": "TjEkpBpJ6IK_", "outputId": "0b74024a-66b5-444c-a230-9f119eab3824" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8a5254a972c142b185fbd59df9c26f0b", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/7 [00:00=\n", " 7.5 (Volta).\n", " \"\"\"\n", "\n", " processor: AutoProcessor\n", " padding: Union[bool, str] = \"longest\"\n", " pad_to_multiple_of: Optional[int] = None\n", " pad_to_multiple_of_labels: Optional[int] = None\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", " pad_to_multiple_of=self.pad_to_multiple_of,\n", " return_tensors=\"pt\",\n", " )\n", "\n", " with self.processor.as_target_processor():\n", " labels_batch = self.processor.pad(\n", " label_features,\n", " padding=self.padding,\n", " pad_to_multiple_of=self.pad_to_multiple_of_labels,\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": 42, "metadata": { "id": "WcQgSGyj6IK_" }, "outputs": [], "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "6cc16ec4852a4d4582eaf33d1cff826c", "2f30c5df319c4a48821ec755ee5da56a", "de951c72061b4336bf75d740af794ca0", "a79cfde642384d619ce5c63f39ecc663", "e5dbc60918ca45da95c182a1dee60b11", "d32b18b2918d4c93bea93f36576e3126", "2322e918a1234201baea4d53d759971c", "6389d412e77e4ec09f349a917d00dc46", "f6a6332f550741f1813bab57254c631c", "19aa057986cd46a1bdea803c140a2ac0", "e210bd8662c04b0b96f8736b71463994" ] }, "id": "wSSJacE46IK_", "outputId": "3bbde635-9e7b-458d-c89c-ef3986c8f5f2" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "26f2239d38184a389c96417e23fd2d64", 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"/workspace/wav2vec2-xlsr-1b-et is already a clone of https://huggingface.co/RASMUS/wav2vec2-xlsr-1b-et. Make sure you pull the latest changes with `repo.git_pull()`.\n", "Using amp half precision backend\n" ] } ], "source": [ "import bitsandbytes as bnb\n", "from transformers import Trainer\n", "from transformers.trainer_pt_utils import get_parameter_names\n", "\n", "decay_parameters = get_parameter_names(model, [torch.nn.LayerNorm])\n", "decay_parameters = [name for name in decay_parameters if \"bias\" not in name]\n", "optimizer_grouped_parameters = [\n", " {\n", " \"params\": [p for n, p in model.named_parameters() if n in decay_parameters],\n", " \"weight_decay\": training_args.weight_decay,\n", " },\n", " {\n", " \"params\": [p for n, p in model.named_parameters() if n not in decay_parameters],\n", " \"weight_decay\": 0.0,\n", " },\n", "]\n", "optimizer = bnb.optim.Adam8bit(\n", " params=optimizer_grouped_parameters,\n", " lr=training_args.learning_rate,\n", " betas=(training_args.adam_beta1, training_args.adam_beta2),\n", " eps=training_args.adam_epsilon,\n", ")\n", "\n", "optimizers = (optimizer, None)\n", "\n", "# Initialize Trainer\n", "trainer = Trainer(\n", " model=model,\n", " data_collator=data_collator,\n", " args=training_args,\n", " compute_metrics=compute_metrics,\n", " train_dataset=common_voice_train,\n", " eval_dataset=common_voice_test,\n", " tokenizer=processor.feature_extractor,\n", " optimizers=optimizers,\n", ")" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "id": "955VTrNd6ILB" }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 529 }, "id": "tMtJagfc6ILB", "outputId": "9022f51c-29ca-4f66-8f46-8eeeba41bc60" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running training *****\n", " Num examples = 6521\n", " Num Epochs = 10\n", " Instantaneous batch size per device = 32\n", " Total train batch size (w. parallel, distributed & accumulation) = 32\n", " Gradient Accumulation steps = 1\n", " Total optimization steps = 2040\n" ] }, { "data": { "text/html": [ "\n", "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "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 = 2613\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-et/checkpoint-500\n", "Configuration saved in wav2vec2-xlsr-1b-et/checkpoint-500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-et/checkpoint-500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-et/checkpoint-500/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-et/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 = 2613\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-et/checkpoint-1000\n", "Configuration saved in wav2vec2-xlsr-1b-et/checkpoint-1000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-et/checkpoint-1000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-et/checkpoint-1000/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-et/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 = 2613\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-et/checkpoint-1500\n", "Configuration saved in wav2vec2-xlsr-1b-et/checkpoint-1500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-et/checkpoint-1500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-et/checkpoint-1500/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-et/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 = 2613\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-et/checkpoint-2000\n", "Configuration saved in wav2vec2-xlsr-1b-et/checkpoint-2000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-et/checkpoint-2000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-et/checkpoint-2000/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-et/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-et/checkpoint-500] due to args.save_total_limit\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n", "Loading best model from wav2vec2-xlsr-1b-et/checkpoint-1500 (score: 0.20082018927444795).\n" ] }, { "data": { "text/plain": [ "TrainOutput(global_step=2040, training_loss=0.7346986307817347, metrics={'train_runtime': 10075.7398, 'train_samples_per_second': 6.472, 'train_steps_per_second': 0.202, 'total_flos': 4.170633739317263e+19, 'train_loss': 0.7346986307817347, 'epoch': 10.0})" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WpLhQDv26ILB" }, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "name": "train_xlsr_et_common_voice_8.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, 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