diff --git "a/train_xlsr_ru_common_voice_8.ipynb" "b/train_xlsr_ru_common_voice_8.ipynb" new file mode 100644--- /dev/null +++ "b/train_xlsr_ru_common_voice_8.ipynb" @@ -0,0 +1,12860 @@ +{ + "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": [ + { + 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tensorboard-plugin-wit-1.8.1 werkzeug-2.0.2\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 (/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", + "\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": 53, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 732, + "referenced_widgets": [ + "dcc0badbab394367a5639c05dac0d33d", + "16d1947231f340d89822b4b71b1928e8", + "aff7e29683e14cb5b1b76ddbf80e528c", + 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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" + }, + 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\nThe AI community building the future\n
\nImmediately click login after typing your password or it might be stored in plain text in this notebook file.\n
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