{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YELVqGxMxnbG", "outputId": "a82687bb-f883-4e7b-a0c2-efb25ec5501b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sat Jan 29 17:52:45 2022 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 470.57.02 Driver Version: 470.57.02 CUDA Version: 11.4 |\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 V100S-PCI... Off | 00000000:00:08.0 Off | 0 |\n", "| N/A 36C P0 54W / 250W | 0MiB / 32510MiB | 23% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ] } ], "source": [ "gpu_info = !nvidia-smi\n", "gpu_info = '\\n'.join(gpu_info)\n", "if gpu_info.find('failed') >= 0:\n", " print('Not connected to a GPU')\n", "else:\n", " print(gpu_info)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 123, "referenced_widgets": [ "ce7c0523fe0945259eff9020e573a218", "2928d41f4f264ca0b9a31118462c0602", "2710004a535a4300b89f9f588c0e33e7", "95c3dfbff82641efbc32cdf38c3af315", "e75a354f58414a2aba65ac5f96f794d5", "498e0d2721e94804981d2caf9a14e998", "a8acab811f1a4c4480d2fbedecb3ef2d", "3e21794aba48497ab840c5076b766779", "15dce6e6c76b4b338dc8aa6fa70a68fb", "9d8cf219056e480f90b37b862a884f7f", "efc1548fb11249f1b6abf558feb70fdf", "139cb79e652741fb9800938f513a64ee", "132bece034d7436a92131a2d7c5447dd", "f09ffab604c84d10bc8f8a276fc56db8", "789237e5c6f844aaa461002dd4ff81a7", "bc7e8da097354af69a9eb31184b10cd9", "766028d8d74845c29e061423adafcc2f", "0b503fa95f214ca9a4a4f52ca6cf39ea", "01940c92e2124694a353fbe9daa3f519", "906ac30b1a53494292d5464776d4185d", "27192257c291482c9f45352a099fd293", "778e27b2713440a7a0f57b48ce98cac5", "8a02fd0543a04de680b0570df49c4ec7", "7aca3591dbd148e1bf684305f5898e9f", "37786f2415c54cf1a3feb36f51b1a9a1", "32decdfe9bce4359bee27513e12caae4", "af0431f7b5bb4c959c298136a2666c94", "14fb1d361cf04ceb82dc916d3ac62e58", "d0e7bdb7003845f18c57fa102c3d39ff", "dd05c4ec056448eebbd698715e4f206f", "45b52b113a404da9b395b816309cd48e", "99f1c52fc3294096a6614ed8cb0f8678", "3f0a938c876244dab220a0692240c68c", "df0a58bd514d4adb96eefa9c2e348e68", "5f9c66c0890c4732a01e25a3617d677f", "f83bdd44d071406490e036d64af71f86", "b7825d5c508e406da79df3c8d5bbf644", "6eefb11b33e144a8ba2776b450584d67", "910b0c36ffac45a39690e7da4b7bdd30", "f5b86244d5cd4651ab2c7849c3ef40b7", "fffa8820a0b3493cab82c939169016c7", "c01ad658495d499fb1f77f1f6e88091e", "6416eed0c2a24d11a54f99e217b1246b", "ddb765d3e944448ead769abd536452d3", "74222e06c53f46a9a4454214e7fc515b", "24da28b982fe4de5b11997146dc8ef5d", "f1a004bbcfe84b2da8bb9ab33d397de5", "0320e7bb34ab4273ae088c82df4fee0f", "9000f234d4c14beb87d4e82879b858df", "100cee605ec44cf5993d404b9be1ff61", "3b02e2b26f6446fb8ffb8357ff785b65", "c5991408225c4b3cb1f8dd069e947057", "5743625f77ca43779b8f971952e00f5d", "da885d5a5ef84747b9629690f35ecdc9", "10fa9c68afa245a4950e41df9e8c1578", "3bb6ab80915c46e09b42dcb87342959d", "169dcfbe96b544f0a4cf818249174fcb", "3d9b6280f5a249408192664ef7d81c59", "f4be19b71d7442199eda374c4e538009", "3995d68b7cb144549384cdf303f39f64", "be80480c4af1425299ad90f3bf699e60", "0d02bb1653904c8fb4f6a64c8676d721", "7a49ab8162374715ae5c639e9809835d", "49d7f826750f4a66b7bbe03a4afd5de0", "35c17be43018490c9667e8742a5538a5", "8f47611f826b493f9c8da960d5dca80f" ] }, "id": "2MMXcWFFgCXU", "outputId": "f12100b6-e3b1-4b8d-e4c5-85c8343f1e8a" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/cs/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8)\n", "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/cs/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8)\n" ] } ], "source": [ "from datasets import load_dataset, load_metric, Audio\n", "\n", "common_voice_train = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"cs\", split=\"train+validation\", use_auth_token=True)\n", "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"cs\", split=\"test\", use_auth_token=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bGF9rR_9oX71", "outputId": "363b62cd-c825-49d1-d1cd-f6fa20742dba" }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "common_voice_train.cleanup_cache_files()\n", "common_voice_test.cleanup_cache_files()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "kbyq6lDgQc2a" }, "outputs": [], "source": [ "common_voice_train = common_voice_train.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])\n", "common_voice_test = common_voice_test.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "72737oog2F6U" }, "outputs": [], "source": [ "from datasets import ClassLabel\n", "import random\n", "import pandas as pd\n", "from IPython.display import display, HTML\n", "\n", "def show_random_elements(dataset, num_examples=10):\n", " assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n", " picks = []\n", " for _ in range(num_examples):\n", " pick = random.randint(0, len(dataset)-1)\n", " while pick in picks:\n", " pick = random.randint(0, len(dataset)-1)\n", " picks.append(pick)\n", " \n", " df = pd.DataFrame(dataset[picks])\n", " display(HTML(df.to_html()))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "K_JUmf3G3b9S", "outputId": "1c220991-320f-470b-b334-992ac78c411e" }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sentence
0Žila v obci Říčky.
1Frontmanem skupiny byl Steve Harley.
2Zaměřují se především na zachování základních technologických metod jemné ruční práce.
3Infekce u nich probíhá nejčastěji bez příznaků.
4Nebezpečí násilných konfliktů bohužel nebylo úplně zažehnáno.
5Někteří ze spolupacientů se vyléčí nebo uchlastají.
6Rozvoj ekologie člověka podpořil další využívání ekologie při navrhování a plánování měst.
7Jaké má takové nepochopení důsledky?
8Na druhé straně zápis i velikých čísel by mohl být poměrně krátký.
9Ze svých rozhodnutí bychom se měli zodpovídat svým spoluobčanům.
" ], "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": 7, "metadata": { "id": "ZcVsD0ETElrR" }, "outputs": [], "source": [ "import re\n", "chars_to_remove_regex = '[\\,\\?\\.\\!\\-\\;\\:\\/\\\"\\“\\„\\%\\”\\�\\–\\'\\`\\«\\»\\—\\’\\…]'\n", "\n", "\n", "CHARS = {\n", " 'ü': 'ue',\n", " 'ö': 'oe',\n", " 'ï': 'i',\n", " 'ë': 'e',\n", " 'ä': 'ae',\n", " 'ã': 'a',\n", " 'à': 'á',\n", " 'ø': 'o',\n", " 'è': 'é',\n", " 'ê': 'é',\n", " 'å': 'ó',\n", " 'î': 'i',\n", " 'ñ': 'ň',\n", " 'ç': 's',\n", " 'ľ': 'l',\n", " 'ż': 'ž',\n", " 'ł': 'w',\n", " 'ć': 'č',\n", " 'þ': 't',\n", " 'ß': 'ss',\n", " 'ę': 'en',\n", " 'ą': 'an',\n", " 'æ': 'ae',\n", " }\n", "\n", "def replace_chars(sentence):\n", " result = ''\n", " for ch in sentence:\n", " new = CHARS[ch] if ch in CHARS else ch\n", " result += new\n", "\n", " return result\n", "\n", "\n", "def remove_special_characters(batch):\n", " batch[\"sentence\"] = re.sub(chars_to_remove_regex, ' ', batch[\"sentence\"]).lower()\n", " batch[\"sentence\"] = replace_chars(batch['sentence'])\n", " batch[\"sentence\"] = \" \".join([char for char in batch[\"sentence\"].split(\" \") if char != \"\"])\n", " return batch\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NUiPgNRzoX76", "outputId": "dba17591-1d37-489b-e5a9-ec55bed467bc" }, "outputs": [ { "data": { "text/plain": [ "{'sentence': 'jak se vede bjoerne'}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "batch = {\"sentence\": \"Jak se vede, /Björne?\"}\n", "remove_special_characters(batch)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 395, "referenced_widgets": [ "f9a0f686f75749f6b403472ed4e23c41", "4a2bf06b19f4463187d55018d95816e1", "e9f7b6f84f1444719a09b4b8596ae0f4", "443b7b937b124b8fb76299c95ae0427c", "c2ad79e0a9ea4c9386103888d5babdfa", "963f4075f46540a9870dc2806216af9e", "7db6c36db0d5499290b09a0f01451f2c", "8f0c1ea28c274345a5748af60f4d55f1", "b071746dc76d4673a47b298a8bc54562", "f9490b9aa92e4d909605d93209a8f746", "81b75ff4e800472c8e3fa5516dbdda01" ] }, "id": "6falIJSBED65", "outputId": "17bdc176-6574-4c95-8ee8-028171eb91a6" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fc7f83b6e3ae43a190e55197573c04ad", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/20200 [00:00\n", " \n", " \n", " \n", " sentence\n", " \n", " \n", " \n", " \n", " 0\n", " jezevec obývá mírné pásmo evropy a asie\n", " \n", " \n", " 1\n", " oslavy kulatého výročí uspořádal spolek českých učitelek v umělecké besedě\n", " \n", " \n", " 2\n", " ocenění zlatá deska album získalo i v německu a maďarsku\n", " \n", " \n", " 3\n", " v současnosti bydlí s rodinou ve stockholmu\n", " \n", " \n", " 4\n", " po porážce nacistického německa celé slezsko připadlo opět polsku\n", " \n", " \n", " 5\n", " rovné zacházení znamená taky rovnost před zákonem\n", " \n", " \n", " 6\n", " po návratu na zemi na sobě začal pozorovat různé změny\n", " \n", " \n", " 7\n", " dalším bodem jsou jednominutové projevy k politickým otázkám\n", " \n", " \n", " 8\n", " vlastnil největší sbírku jeho děl v berlíně\n", " \n", " \n", " 9\n", " je mi jasné kdo tam nosí kalhoty\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_random_elements(common_voice_train.map(remove_special_characters).remove_columns([\"path\", \"audio\"]), num_examples=10)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 87, "referenced_widgets": [ "0e458554a0bc49989a29d142143c3d75", "037bba1874d949e78e5c119de5c49346", "456b8b329b3146b6ab5c62fa95b09bca", "b475c78857b94d1682ac8a05f721d9e1", "652001e3c17747019f9c586a33ac5f51", "f9514e38321144ceae29a5d66055c3eb", "85a8171857e54c9eab29e6ba66a6f573", "d7174f55c32b48c4be4ef195745ffcce", "75020f745e3448b588edba867ff5f80d", "a5370b4c03e644b780cc95f27c131677", "3015c569defd4355a7a41cd196178c30" ] }, "id": "XIHocAuTQbBR", "outputId": "a12a767f-c745-483a-e34f-c166cbc449b5" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading cached processed dataset at /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/cs/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8/cache-1a4fd589a52b871e.arrow\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8b53a349cba64996afde5ac4fc866131", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/7267 [00:00\n", " \n", " \n", " \n", " sentence\n", " \n", " \n", " \n", " \n", " 0\n", " rovněž je nutné zasazovat vztahy s tureckem do širšího kontextu\n", " \n", " \n", " 1\n", " narodil se ve vídeňském novém městě v rakousku\n", " \n", " \n", " 2\n", " studoval práva na univerzitě ve štýrském hradci\n", " \n", " \n", " 3\n", " ano je to bohužel tak\n", " \n", " \n", " 4\n", " po odchodu z armády pracoval jako úředník\n", " \n", " \n", " 5\n", " pohřeb se stal masovou demonstrací\n", " \n", " \n", " 6\n", " souhlasím s doporučeními pana ozdemira a hlasuji pro zprávu\n", " \n", " \n", " 7\n", " námětem většiny jejích povídek jsou skutečné příběhy a často i její vlastní zážitky\n", " \n", " \n", " 8\n", " musíme však prokázat solidaritu ačkoli tato solidarita musí být dobrovolná\n", " \n", " \n", " 9\n", " pracoval také jako redaktor nakladatelství albatros\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\",\"audio\"]))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "LwCshNbbeRZR" }, "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": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "f5973eb0a46649d687b6b39f9eb6ffa8", "aa8f567a963e45a6aec3c45b115237b0", "475d142e1b534033a1eccd883071eead", "3eb9f71f91e64ef9aa52f09b653dbf1d", "acf1dc77638041d5ae9c44f8bfc6f4bd", "ae7e4f8ea7924fea87e5992f7f432b06", "e2bae0a5e3064e30a38577e7eb42c80d", "8f8867a2bae648d49250492abf91bfdc", "a595885e78b84450b737b75f30240241", "627328b576ba467abe48eb9e560eaefe", "f59165b748264c289de5a31fd12d4346", "547084e427ac480eb4df3d9e2b74c2a7", "73168e226d4740a4b8cebb119b262f73", "d105bf5fa4b941ae8e04c38094d46b30", "26fc7d20bc344ee4b73feb32be518235", "5a570f67cfeb4a079c1c7061a1be2090", "aaa5b759b4264a079b7f29168e4e6154", "8cc5dce835864f12bf07206068c76ed9", "68b2db3dd6b8448385258e8491e3764b", "e325fed7d2e64a3ca96ac7a7660786d5", "6e55def79a344b62a7f5bcbf3be3a816", "5c5ea9c8e4974b4db55eee20726f9aa6" ] }, "id": "_m6uUjjcfbjH", "outputId": "edb40365-d4aa-4ef1-c8d8-870e6a5d9e6e" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7c568e511f544c48bf26e174953e656d", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1 [00:00\n", " \n", " Your browser does not support the audio element.\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import IPython.display as ipd\n", "import numpy as np\n", "import random\n", "\n", "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(common_voice_train[rand_int][\"sentence\"])\n", "ipd.Audio(data=common_voice_train[rand_int][\"audio\"][\"array\"], autoplay=True, rate=16000)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1Po2g7YPuRTx", "outputId": "97d86ad2-b5a1-444f-8c7d-f5d807529f90" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Target text: všeobecně vzato dnes demokracie upadá\n", "Input array shape: (62592,)\n", "Sampling rate: 16000\n" ] } ], "source": [ "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(\"Target text:\", common_voice_train[rand_int][\"sentence\"])\n", "print(\"Input array shape:\", common_voice_train[rand_int][\"audio\"][\"array\"].shape)\n", "print(\"Sampling rate:\", common_voice_train[rand_int][\"audio\"][\"sampling_rate\"])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "id": "eJY7I0XAwe9p" }, "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": 31, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "c47ea368dd08403aa09b2bafdbb4b580", "e77cf973d5824ae7b89bafd814805c2a", "071b7647e1fe49609a48e4281a9efd0f", "c97c00fcf2e64f18b637337f9244d748", "9ca82fa27d1043e9ac9f10301e0b33bc", "cc6c7e9931c140db8ba7a977c4461ce5", "d207784bda7e4dd8858170f470ae2833", "0800fef7de6e45d380873f974882d67e", "926440595aa44c698588e02b86eb8c4c", "ea2806c776384f1a90e36b72c2c17a44", "6b72385c07134782995fcd76e675da7c", "3653b92c9f2a408eac253e1d5153daf4", "73ffd9b8166c4ec78ff2b62d17690327", "6b133a1e11e44f68846ff931446559cf", "7c98818547c84af7ba9284bc20101691", "41b501a16b2a4f709197af5cdd5227cb", "3b4fbe2916894e48b8f93ca63e203aca", "c002386685c0413d8181b054d3f9d49f", "cfb70829b5e1461abcb01872b74a194c", "ed943db2b5274022a606ce4103d54425", "cfb242eb549c4e66afcedefb575b4e38", "a0313055d29f4a60837e59ac4d8a3870" ] }, "id": "-np9xYK-wl8q", "outputId": "573f6f67-e5b2-4977-a564-3919e7903592" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ccb68662f1684c749e2be68dc1c31425", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/20200 [00:00 Dict[str, torch.Tensor]:\n", " # split inputs and labels since they have to be of different lenghts and need\n", " # different padding methods\n", " input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n", " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n", "\n", " batch = self.processor.pad(\n", " input_features,\n", " padding=self.padding,\n", " return_tensors=\"pt\",\n", " )\n", " with self.processor.as_target_processor():\n", " labels_batch = self.processor.pad(\n", " label_features,\n", " padding=self.padding,\n", " return_tensors=\"pt\",\n", " )\n", "\n", " # replace padding with -100 to ignore loss correctly\n", " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n", "\n", " batch[\"labels\"] = labels\n", "\n", " return batch" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "id": "lbQf5GuZyQ4_" }, "outputs": [], "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "id": "9Xsux2gmyXso" }, "outputs": [], "source": [ "wer_metric = load_metric(\"wer\")\n", "cer_metric = load_metric(\"cer\")\n", "metrics = [wer_metric, cer_metric]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "id": "1XZ-kjweyTy_" }, "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", " cer = cer_metric.compute(predictions=pred_str, references=label_str)\n", "\n", " return {\"wer\": wer, \"cer\": cer}" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e7cqAWIayn6w", "outputId": "7a7ef020-bc8f-41e2-846c-645be598312e" }, "outputs": [], "source": [ "from transformers import Wav2Vec2ForCTC\n", "\n", "model = Wav2Vec2ForCTC.from_pretrained(\n", " \"comodoro/wav2vec2-xls-r-300m-cs-cv8\", \n", " #\"facebook/wav2vec2-xls-r-300m\", \n", " attention_dropout=0.1,\n", " hidden_dropout=0.1,\n", " feat_proj_dropout=0.0,\n", " mask_time_prob=0.05,\n", " layerdrop=0.1,\n", " ctc_loss_reduction=\"mean\", \n", " pad_token_id=processor.tokenizer.pad_token_id,\n", " vocab_size=len(processor.tokenizer),\n", ")" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "id": "oGI8zObtZ3V0" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1680: 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": 39, "metadata": { "id": "KbeKSV7uzGPP" }, "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=20,\n", " eval_accumulation_steps=20,\n", " evaluation_strategy=\"steps\",\n", " num_train_epochs=50,\n", " gradient_checkpointing=True,\n", " fp16=True,\n", " save_steps=250,\n", " eval_steps=250,\n", " logging_steps=250,\n", " learning_rate=1e-3,\n", " warmup_steps=500,\n", " save_total_limit=2,\n", " report_to=\"tensorboard\"\n", ")" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rY7vBmFCPFgC", "outputId": "a180bf3f-f798-4947-ff58-207d7aaab695" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using amp half precision backend\n" ] } ], "source": [ "from transformers import Trainer\n", "\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", ")" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 312 }, "id": "9fRr9TG5pGBl", "outputId": "8bdf1d11-bca1-46af-db67-518f85586f7a" }, "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", "/opt/conda/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use thePyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", " warnings.warn(\n", "***** Running training *****\n", " Num examples = 20200\n", " Num Epochs = 50\n", " Instantaneous batch size per device = 32\n", " Total train batch size (w. parallel, distributed & accumulation) = 640\n", " Gradient Accumulation steps = 20\n", " Total optimization steps = 1550\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 = 7267\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xls-r-300m-cs-cv8/checkpoint-250\n", "Configuration saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-250/config.json\n", "Model weights saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-250/pytorch_model.bin\n", "Configuration saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-250/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 = 7267\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xls-r-300m-cs-cv8/checkpoint-500\n", "Configuration saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-500/config.json\n", "Model weights saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-500/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 = 7267\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xls-r-300m-cs-cv8/checkpoint-750\n", "Configuration saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-750/config.json\n", "Model weights saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-750/pytorch_model.bin\n", "Configuration saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-750/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-cv8/checkpoint-250] 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 = 7267\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xls-r-300m-cs-cv8/checkpoint-1000\n", "Configuration saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-1000/config.json\n", "Model weights saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-1000/pytorch_model.bin\n", "Configuration saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-1000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-cv8/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 = 7267\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xls-r-300m-cs-cv8/checkpoint-1250\n", "Configuration saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-1250/config.json\n", "Model weights saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-1250/pytorch_model.bin\n", "Configuration saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-1250/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-cv8/checkpoint-750] 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 = 7267\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-xls-r-300m-cs-cv8/checkpoint-1500\n", "Configuration saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-1500/config.json\n", "Model weights saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-1500/pytorch_model.bin\n", "Configuration saved in wav2vec2-xls-r-300m-cs-cv8/checkpoint-1500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-xls-r-300m-cs-cv8/checkpoint-1000] 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" ] }, { "data": { "text/plain": [ "TrainOutput(global_step=1550, training_loss=0.23060073791011687, metrics={'train_runtime': 45370.893, 'train_samples_per_second': 22.261, 'train_steps_per_second': 0.034, 'total_flos': 1.2848155332115138e+20, 'train_loss': 0.23060073791011687, 'epoch': 49.98})" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.train()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Dropping the following result as it does not have all the necessary fields:\n", "{'dataset': {'name': 'common_voice', 'type': 'common_voice', 'args': 'cs'}}\n" ] } ], "source": [ "trainer.create_model_card()" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "machine_shape": "hm", "name": "Kopie sešitu \"Fine-Tune XLS-R on Common Voice.ipynb\"", 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