{ "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": "e104ff0fceb249b7b91b384bb2655a18", "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", " Позвольте мне добавить несколько слов в своем национальном качестве.\n", " \n", " \n", " 1\n", " «Но подумай хорошенько, – прибавила она, – со стороны твоих родных не будет ли препятствия?»\n", " \n", " \n", " 2\n", " Надеюсь, вы, в отличие от него, не злоупотребили радушием моей жены в своих целях.\n", " \n", " \n", " 3\n", " Одновременно угрозы безопасности в информационно-компьютерной области создают серьезный вызов международному сообществу.\n", " \n", " \n", " 4\n", " Слово имеет посол Швейцарии Фазель.\n", " \n", " \n", " 5\n", " Давайте же мы единодушно потребуем, чтобы он прекратил свои злодеяния.\n", " \n", " \n", " 6\n", " Только начав, мы можем определить, как далеко мы можем продвинуться.\n", " \n", " \n", " 7\n", " Поэтому мы не обязаны выполнять ее решения.\n", " \n", " \n", " 8\n", " И как я полагаю, этот запрос должен оставаться на рассмотрении в рамках председательской шестерки.\n", " \n", " \n", " 9\n", " Обедать, барышни!\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": 16, "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": 17, "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": "812eae255443470aa0440b7fb100656a", "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": "03f9d0c65a5c44fdaecda41030ab8c6f", "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": 18, "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": 19, "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": "d58fa712ef794540b45a3d18ed93acb9", "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-ru/commit/2f7425d996ea36bb7c47215cea4cecc882eaadaf'" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenizer.push_to_hub(repo_name)" ] }, { "cell_type": "code", "execution_count": 32, "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": 33, "metadata": { "id": "3ydgeSof6IK-" }, "outputs": [], "source": [ "from transformers import Wav2Vec2Processor\n", "\n", "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "id": "6RGWMIrk6IK-" }, "outputs": [], "source": [ "import torchaudio" ] }, { "cell_type": "code", "execution_count": 35, "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": 36, "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/ru/clips/common_voice_ru_18849051.mp3',\n", " 'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", " 5.1862571e-05, -7.1976043e-05, -7.0710674e-05], dtype=float32),\n", " 'sampling_rate': 16000}" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "common_voice_train[0][\"audio\"]" ] }, { "cell_type": "code", "execution_count": 37, "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": 38, "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": "4b538c91f6f9471c99421a2ff3f7de02", "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": "1d22be2b5e0541de91f9f87a4cdd891e", "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": 39, "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": 40, "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": 41, "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": "4a27d4c29eaa41b38e525c0122bf1bb3", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/47 [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": 43, "metadata": { "id": "WcQgSGyj6IK_" }, "outputs": [], "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ] }, { "cell_type": "code", "execution_count": 44, "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": [], "source": [ "wer_metric = load_metric(\"wer\")" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "id": "AWh2K1ia6IK_" }, "outputs": [], "source": [ "def compute_metrics(pred):\n", " pred_logits = pred.predictions\n", " pred_ids = np.argmax(pred_logits, axis=-1)\n", "\n", " pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n", "\n", " pred_str = processor.batch_decode(pred_ids)\n", " # we do not want to group tokens when computing the metrics\n", " label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n", "\n", " wer = wer_metric.compute(predictions=pred_str, references=label_str)\n", "\n", " return {\"wer\": wer}" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "4eb1abff92784022addb0bf5b31ae373", "606b15306d5a44e4900407fc381cd166", "20f1708cf53247578a6116a293058037", "e5460d64f66b45e6b2b53c383ce848ce", "09b4b107deac41b7995165510b626466", "99704c7a217f47db8112915355c5cb16", "ff043090146b48de83ce764b5d526df9", "535fbef4f8784d2fbd3e78a8a22fdcca", "c45f7b4056374db59e1214bd853b1156", "e07665eaa8534a2b8650c798a2d38855", "241d1dad276a432dadfcb1db92eb3f5d", "0588c69428e64537a561b079368142b6", "05020a0096864fc58e5946755c857eef", "8d7c7c66485842169988ce66dc064c48", "1f78fb65661945318c678d503eaf687a", "b2007b3777434c39a46b83586061c464", "82cf273e4c7144f9bcd962d883f39ac3", "c78a45f3e3e346f085fd6988a7ee0d26", "4edf587f189445cf93b5b8f66b3160d8", "da3acb63b71b46d6989a71db41480a8f", "d2f297bc8be84b64adf64455026b8772", "62a3c3d1336f4228acef6f2e5be17b13" ] }, "collapsed": true, "id": "XCnGR0uB6ILA", "jupyter": { "outputs_hidden": true }, "outputId": "e77ff43a-9f8d-4a31-8700-bdc7eddb8b23" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Some weights of the model checkpoint at facebook/wav2vec2-xls-r-1b were not used when initializing Wav2Vec2ForCTC: ['project_hid.weight', 'quantizer.codevectors', 'project_q.bias', 'quantizer.weight_proj.weight', 'quantizer.weight_proj.bias', 'project_q.weight', 'project_hid.bias']\n", "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-1b and are newly initialized: ['lm_head.weight', 'lm_head.bias']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] }, { "data": { "text/plain": [ "Wav2Vec2ForCTC(\n", " (wav2vec2): Wav2Vec2Model(\n", " (feature_extractor): Wav2Vec2FeatureEncoder(\n", " (conv_layers): ModuleList(\n", " (0): Wav2Vec2LayerNormConvLayer(\n", " (conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,))\n", " (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (1): Wav2Vec2LayerNormConvLayer(\n", " (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n", " (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (2): Wav2Vec2LayerNormConvLayer(\n", " (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n", " (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (3): Wav2Vec2LayerNormConvLayer(\n", " (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n", " (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (4): Wav2Vec2LayerNormConvLayer(\n", " (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n", " (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (5): Wav2Vec2LayerNormConvLayer(\n", " (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))\n", " (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (6): Wav2Vec2LayerNormConvLayer(\n", " (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))\n", " (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", " )\n", " )\n", " )\n", " (feature_projection): Wav2Vec2FeatureProjection(\n", " (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", " (projection): Linear(in_features=512, out_features=1280, bias=True)\n", " (dropout): Dropout(p=0.04, inplace=False)\n", " )\n", " (encoder): Wav2Vec2EncoderStableLayerNorm(\n", " (pos_conv_embed): Wav2Vec2PositionalConvEmbedding(\n", " (conv): Conv1d(1280, 1280, kernel_size=(128,), stride=(1,), padding=(64,), groups=16)\n", " (padding): Wav2Vec2SamePadLayer()\n", " )\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layers): ModuleList(\n", " (0): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (1): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (2): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (3): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (4): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): 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LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (6): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (7): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (8): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (9): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (10): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (11): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (12): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (13): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): 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Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (37): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (38): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (39): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (40): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (41): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (42): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (43): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (44): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (45): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (46): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (47): Wav2Vec2EncoderLayerStableLayerNorm(\n", " (attention): Wav2Vec2Attention(\n", " (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n", " )\n", " (dropout): Dropout(p=0.047, inplace=False)\n", " (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " (feed_forward): Wav2Vec2FeedForward(\n", " (intermediate_dropout): Dropout(p=0.055, inplace=False)\n", " (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n", " (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n", " (output_dropout): Dropout(p=0.047, inplace=False)\n", " )\n", " (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n", " )\n", " )\n", " )\n", " )\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " (lm_head): Linear(in_features=1280, out_features=59, bias=True)\n", ")" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from transformers import Wav2Vec2ForCTC\n", "\n", "model = Wav2Vec2ForCTC.from_pretrained(\n", " \"facebook/wav2vec2-xls-r-1b\", \n", " attention_dropout=0.094,\n", " hidden_dropout=0.047,\n", " feat_proj_dropout=0.04,\n", " mask_time_prob=0.082,\n", " layerdrop=0.041,\n", " activation_dropout=0.055,\n", " ctc_loss_reduction=\"mean\", \n", " pad_token_id=processor.tokenizer.pad_token_id,\n", " vocab_size=len(processor.tokenizer),\n", ")\n", "model.to('cuda')" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vJ4YmBLK6ILA", "outputId": "5ff744c2-0a83-41fa-8b5b-aa0e430ff1e9" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1700: FutureWarning: The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5.Please use the equivalent `freeze_feature_encoder` method instead.\n", " warnings.warn(\n" ] } ], "source": [ "model.freeze_feature_extractor()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "id": "Wu3bwi8i6ILA" }, "outputs": [], "source": [ "import os\n", "os.environ[\"WANDB_DISABLED\"] = \"true\"" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "id": "0NXLfMI56ILA" }, "outputs": [], "source": [ "from transformers import TrainingArguments\n", "\n", "training_args = TrainingArguments(\n", " output_dir=repo_name,\n", " group_by_length=True,\n", " per_device_train_batch_size=32,\n", " gradient_accumulation_steps=1,\n", " evaluation_strategy=\"steps\",\n", " num_train_epochs=10,\n", " gradient_checkpointing=True,\n", " fp16=True,\n", " save_steps=500,\n", " eval_steps=500,\n", " logging_steps=50,\n", " learning_rate=5e-5,\n", " warmup_steps=500,\n", " save_total_limit=3,\n", " push_to_hub=True,\n", " load_best_model_at_end=True,\n", " greater_is_better=False,\n", " report_to=\"tensorboard\",\n", " metric_for_best_model='eval_wer',\n", ")" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MTdz79SyGz0y", "outputId": "867f5a41-e82e-44be-aaf0-3fc6c0698a7e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sat Feb 5 08:58:36 2022 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 460.32.03 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 K80 Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 73C P0 72W / 149W | 1806MiB / 11441MiB | 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", "+-----------------------------------------------------------------------------+\n" ] } ], "source": [ "!nvidia-smi" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "eTNIJiY66ILA", "jupyter": { "outputs_hidden": true }, "outputId": "4c18731e-6766-439b-de44-5bb70c3040ac" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "Collecting bitsandbytes-cuda111\n", " Downloading bitsandbytes_cuda111-0.26.0-py3-none-any.whl (4.0 MB)\n", " |████████████████████████████████| 4.0 MB 24.2 MB/s \n", "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "\u001b[?25hInstalling collected packages: bitsandbytes-cuda111\n", "Successfully installed bitsandbytes-cuda111-0.26.0\n", "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub 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"\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n", "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 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 tensorboard --user" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 732, "referenced_widgets": [ "dcc0badbab394367a5639c05dac0d33d", "16d1947231f340d89822b4b71b1928e8", "aff7e29683e14cb5b1b76ddbf80e528c", "805c9080eeb64e26859a19405b0529c3", "496ae52b33aa4fbda8bde8bcb3791c9c", "40271b49aaa541e286c5d59432fedff5", "25de7fb2d8884be0944822ca28e54363", "432129ee22174ab0935a48e1be94f759", "526c32e64c3745688d02299848392d80", "a643eaa07fdb480d937fde7e0b3d0d1a", "035281da59bb41d38b55ad86109d5312", "d09f1802735f47c7848aef24b12ba105", "4df809cb50b9497cb739c43b6d4fa7ac", "85c29a546643497b89b17888f89611fe", "b6b9aadad0c84134820285e9b99a2d40", "6961daf34b9a493ca2d71c9480321189", "08502ae7c7f947d381e7342119ed0c85", "4d37fe247d7f47f6b68650646a859f96", "3bcfcf297b604930b8324492d41a9217", "68fe3fd6ec244ad78bcab645a45c0ff1", "5b11d4b1bdc743ff9b2b384930999db7", "248e8404d28140c9900ca32d45ea8e5a", "f91ff8ae2cb14451b2a56a5268863624", "7dcc2490f1424ed5af5f6736361ac66e", "7c5907acf85943b9b430dcea234faf9c", "e0d773078b5f418a82f90f49b982332d", "883742d152ef4a0ead4f096a2a7c07f6", "3e6358f2058943f89c665a6abfaae182", "c21f3bdbe18f4602b9373a7d8e77d295", "c980918aa1e24514a97786f320687a9f", "4558b9a1cfd74888a90987e410a674ec", "f0d829273d4c4af58281ad9d4a4b8448", "6ab9b55b55c94c1c8b8a976d193106aa", "0a0af685fef64be29d99e3f9b8d6bc44", "f72456fb1e3f447e8fbb1bf90fe6446a", "b6f2de905fe4418ea3f1bb836ed76a53", "6c6d262586f84df193b421ef80fb520b", "981d875ba2ca41409ff84b9ad20c66c1", "7c276cf6fb6742f8b090172d1ea4503a", "9116024606b949029dd9f65f8bbc8f2f", "63549d0653e34a19a9c8bf2fd5d480e0", "2b21a4ca692d4fbcbf3842b3d8a008ae", "679b9ffa836b436c948d480865eb9b9c", "b3521a13ed3f4cdeacc9e47e211c1259", "7348ae91f0af4245853d12599942f6f3", "faf529ce4601419c88a0c40cd26ff2e9", "2bc9cbecbb5d4c3bbd543393098ff01a", "8f7dcf3ccd49483695bea0aaeb9c17b7", "695406f681da405bbef0b96a8bd10f99", "793495e0382245bab20edb5d5bae4d73", "6505382f04d640379aa6d7ba551c3c8a", "46ad55fad84c4de4967a36a59a154799", "77aaf9fde36a4461be486d9931e6b651", "9cacd728ad8e4a20b7288c7ec07a29a4", "06358b58b575411f8926c9f055279a46", "c55f7cda5f7a40d18d2782eb77cec5c2", "cc51fd8680ce49b9becae846838ce9a2", "d422f7c49886416d8fd77c3a30bbcd31", "ee69114c112d48a78022de3026e72b4a", "6b2510387830423f806b960cc6622a9f", "1510b8ce0fe446b0be63b1bc36314c65", "1d55e2f3a462485096bd41e5c0ea13b6", "4600ff781e2844cbb08ccc8b3026bb7b", "d933f707550f493c9593e71b027ed25e", "9f0159dfbd4e4ca39495988466a86585", "77ae695ea05240ebaff701dfff5f0d79", "af8d14fecd7545c7978292f333c6b7e1", "271a0f0891644b81b5a684c6756660c9", "bd50420066ea41238c26fa4494303355", "8edad5ac1d974f43b41f25893d517db1", "6cbb7289891a41d9a99f64ab73b663e0", "c6c6c11852b84383a89c7af1583842ed", "c60e7c6ab29149b8988d871df59e493b", "f7fed0712ada4e90bf6f77739d7f36fd", "d68cce18010c44058305c29cbf428598", "165523a9439e48f692ce7f8dff6d2009", "497bde87ca7a415791e24c569f13d046", "019d55028f7245f7a1af1dcaf8ce9efa", "f8ffedca892a481e8b79b92ccf98e497", "aabd57a7b86948fe95f40db66e04a338", "2830259df5184d19809a6af0381804f0", "9a01e7f579a747afb5cc7d0b6a6e119f", "1cd1ed174b4f4612af2b994e248473e4", "235e7ecfc401446c9318394f701b344f", "c999d260928848d08dabff30e9354437", "75c3c64306df4185aa4d9a32b0a024c1", "bac5d5752af5498284b138dbbf69681d", "fb7a264328674db992df5311f41fba41" ] }, "id": "9jie-V0n6ILA", "outputId": "831edb46-1b75-4c69-9694-14be0dadfd00" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/workspace/wav2vec2-xlsr-1b-ru is already a clone of https://huggingface.co/RASMUS/wav2vec2-xlsr-1b-ru. 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": 51, "metadata": { "id": "955VTrNd6ILB" }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 52, "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 = 46329\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 = 14480\n" ] }, { "data": { "text/html": [ "\n", "
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StepTraining LossValidation LossWer
5000.5462000.4027210.357467
10000.4980000.2588130.251255
15000.4279000.2264910.220422
20000.4099000.2189460.197877
25000.4688000.2100090.191966
30000.2241000.1979740.176719
35000.2056000.2020310.168287
40000.3423000.1861680.160555
45000.2478000.1786700.156309
50000.3079000.1758570.155537
55000.2477000.1712860.142268
60000.1718000.1695150.139095
65000.1675000.1676830.137201
70000.1631000.1651510.133293
75000.1429000.1604730.130832
80000.1505000.1611660.124511
85000.1385000.1487410.122497
90000.1285000.1525990.120072
95000.1153000.1463720.117177
100000.1159000.1504620.114270
105000.1061000.1443780.110567
110000.1016000.1426750.107491
115000.1125000.1385530.104511
120000.0937000.1403420.102183
125000.1059000.1406150.102220
130000.0857000.1371720.099240
135000.0901000.1379900.097672
140000.0913000.1351580.097129

" ], "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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-500\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-500/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-1000\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-1000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-1000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-1000/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-1500\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-1500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-1500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-1500/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-2000\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-2000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-2000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-2000/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-500] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-2500\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-2500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-2500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-2500/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-1000] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-3000\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-3000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-3000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-3000/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-3500\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-3500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-3500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-3500/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-2000] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-4000\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-4000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-4000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-4000/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-2500] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-4500\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-4500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-4500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-4500/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-5000\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-5000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-5000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-5000/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-3500] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-5500\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-5500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-5500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-5500/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-4000] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-6000\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-6000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-6000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-6000/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-6500\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-6500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-6500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-6500/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-5000] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-7000\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-7000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-7000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-7000/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-5500] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-7500\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-7500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-7500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-7500/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-6000] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-8000\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-8000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-8000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-8000/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-6500] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-8500\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-8500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-8500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-8500/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-7000] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-9000\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-9000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-9000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-9000/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-7500] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-9500\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-9500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-9500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-9500/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-8000] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-10000\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-10000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-10000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-10000/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-8500] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-10500\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-10500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-10500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-10500/preprocessor_config.json\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-9000] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-11000\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-11000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-11000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-11000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-9500] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-11500\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-11500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-11500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-11500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-10000] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-12000\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-12000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-12000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-12000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-10500] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-12500\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-12500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-12500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-12500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-11000] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-13000\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-13000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-13000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-13000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-11500] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-13500\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-13500/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-13500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-13500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-12000] 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 = 9419\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-14000\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-14000/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-14000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-14000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-12500] 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-ru/checkpoint-14000 (score: 0.09712907117008444).\n" ] }, { "data": { "text/plain": [ "TrainOutput(global_step=14480, training_loss=0.30223861218157394, metrics={'train_runtime': 66440.375, 'train_samples_per_second': 6.973, 'train_steps_per_second': 0.218, 'total_flos': 2.2389266516763502e+20, 'train_loss': 0.30223861218157394, 'epoch': 10.0})" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.train()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "id": "WpLhQDv26ILB" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Saving model checkpoint to wav2vec2-xlsr-1b-ru\n", "Configuration saved in wav2vec2-xlsr-1b-ru/config.json\n", "Model weights saved in wav2vec2-xlsr-1b-ru/pytorch_model.bin\n", "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4a5a3a310e364ff7a951fca976ba2487", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Upload file pytorch_model.bin: 0%| | 3.38k/3.59G [00:00 main\n", "\n", "Dropping the following result as it does not have all the necessary fields:\n", "{'dataset': {'name': 'common_voice', 'type': 'common_voice', 'args': 'ru'}}\n", "To https://huggingface.co/RASMUS/wav2vec2-xlsr-1b-ru\n", " 2be1f44..dacc045 main -> main\n", "\n" ] }, { "data": { "text/plain": [ "'https://huggingface.co/RASMUS/wav2vec2-xlsr-1b-ru/commit/2be1f447609b8e54c6d7d60460897541ca6fc5f8'" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.push_to_hub()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "name": "train_xlsr_et_common_voice_8.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3 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