{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "0mW-C1Nt-j7k" }, "source": [ "## Prepare Data, Tokenizer, Feature Extractor" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 187, "referenced_widgets": [ "ff427572296c4f0cb7ecf132b3b328d0", "bdeb8617f3a3430086bce2e6800a6d1f", "71c9a60955bd43afbce1b5cbf650983a", "6625650047ad432bbd3e00c156ec6d53", "7c0d44c9c02f44598c1b40e98cb11fa2", "eac571e0f18041049e24a795222ea281", "4dd188e7a95e43caa220ceeed7e3dd84", "56ff7e6788b34e0e8e1f5f9a80138d47", "32969fbb1c4d460fa51f447648564e24", "89496fe85e1242178c47c7d2bb6c3fdf", "1a402131429441f5afdb7cb5e5c1b5b3", "a2594d257b464b079ad2ace9417c8cd5", "b34ea734eaf34aa0ab5bed7003aa5bd0", "ed6fa346d630492684ec90f0641efa79", "8e3939e4dfd049b689b678686888d008", "c68bb05ceefd4681906e181422e6c53c", "9528f059b480402eabf0aa96c2bd55f4", "cadc0747ecae441e8e8204119796d770", "5d40503b6d28484bb19b9c8a4c898992", "8190e713c0c444f5962fe05884f2c83f", "e1e3f9342aa8486085b55ae68caec5d2", "60a22f6e6ff4423d841fac1339c14734", "ebc644518d2f41c498f32294e6b22f7c", "6d2b3f02e64b4956b2903d696aad4d9a", "df39620f438e4e0d8e9d392927a0e843", "ca0d44f8fad74fd6b944ef5a637dd60f", "b4588e66e85e4fafa7d7ae844ce25dc4", "8458dc3acedb4197aad0deada58cfd25", "d8eefbfbe4764744a002bbb1682cbc88", "61f4330bf7284d73bb12d1100051c8e5", "e0ef591bcd7643a3818d7d5bf2432ae0", "93ab612c7b554e97bf2a522fb783d49c", "9f20df6b18d246339840c37853938c99", "eefa50e77b184b33802135f7480dd2f2", "06ad49ffacf741e2b519af87b755154d", "f5b256d9939e4c769efdd1c9e04d4b54", "f27521d5489d4b3093cd61f2286ca9b2", "5b53fb1bff7940cf9358d9291970d838", "1e6dc5a87c574b478c1e28df7eecadc2", "b91af4f88ec04658908782651f24e730", "7e2db9e4089e4635b24fceaf1724746c", "7630a6d7835e4a7490fb63f29d906429", "8d479f1afbcd4aa6aa3a816f6b4c42c3", "1e720c6683054082a4e17960f19534bd", "6b07c506e16643b1999a3d226b05cf9c", "b6e49bc1deaf4d28816eebbdb109d3bb", "df26bfe066094d9190f14574b34fd996", "3f5f1e14b56f44fa82530418fa593c9c", "7069ed63eafa485eb01492683bb866a9", "24540f306f60473fa28d2fc1d4e9bea9", "a59a855daf914a1b8512ed00cd9c52aa", "d0d70e6bd574436d83ebc6137ce410ba", "dc78b909ec6e4bcd9a79f2f49b3813a5", "6bdb560651fc444885625e8c95aca8a9", "485200f20fbe4184bd71c94fa42ae14e", "965ece753c1f44b58556b550bd210e82", "cd4b8ce147ba417999b894259218bd28", "07a09089af6f4010a5e2ce7ad22e2b06", "76e4bab6217a40edbad61fc031d1bf47", "ae0ba46be2ad485db2d9d7505e1c3f60", "07a27546c3634ff08e02d2cbdd651973", "9648b7ef6a0341dda4ce47a7b60cada0", "79aa1fcdd7b74c8b848a3a30d478ee46", "9e42e0cac1b24a52a8ecd4bbe125cd6e", "362fbd00a94042ec86af1f9305e34b3b", "da7bc14c770443f6ba6e2b816f87305e", "d8b043ef46ec4f909c9964352357e8d9", "356251b037154bb68a1f9e25c4ea6d24", "a26a78afc0504dc9b0194d6c4944e779", "d78788c2e47443ac95d4c246e5aabe63", "7019d14a7d344c958218150060747a6d", "5d02eb5db6814c8b9f0b7684216f805d", "672a41068a1b40289f10008d19f3bed2", "3389ed8a1e054719b70f992a1066a281", "31d72e7602c34353a612f467dc13981b", "0faefadcd55a4a6ea87f22762463c114", "29bb142cd6e443cf86f89caeaeb1d8dd", "dc40046e47b64d6d9ea4616ad5b71923", "46ebcdffe80241fd905eefe7cd08fa06", "4ac2d5f578304b86b9ae5e7946522079", "6e282338983f44f98db6c158a0760211", "9c19e508fec44f6a8cfd4a4891189316", "f9d499bb16b449238148e09ac122c0d2", "257e16813da44cc9a11947d1e37d8dcb", "daa560dfbd1240f5909796222fb14318", "2b7c85a597434e268f9934660a07c699", "178b7aeb7a074c559ff102088fc7a7bc", "01b2476817374433bcd8dcbd224afeac" ] }, "id": "2MMXcWFFgCXU", "outputId": "2db75f10-d2f3-4fb9-b793-621c325df384" }, "outputs": [], "source": [ "from datasets import load_dataset, load_metric, Audio\n", "\n", "common_voice_train = load_dataset(\"common_voice\", \"zh-CN\", split=\"train+validation\")\n", "common_voice_test = load_dataset(\"common_voice\", \"zh-CN\", split=\"test\")" ] }, { "cell_type": "code", "execution_count": null, "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": null, "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), \"empty\"\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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359 }, "id": "K_JUmf3G3b9S", "outputId": "082ccd7f-3c82-4c0f-d385-4eda406fe23c" }, "outputs": [], "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\", \"audio\"]), num_examples=10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "svKzVJ_hQGK6" }, "outputs": [], "source": [ "import re\n", "chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�]'\n", "\n", "def remove_special_characters(batch):\n", " batch[\"sentence\"] = re.sub(chars_to_ignore_regex, '', batch[\"sentence\"]).lower() + \" \"\n", " return batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "a0f7ed4f7bda499e858c3a8b96d192a0", "78f6784d8eab4c3e984b99111576d1d7", "928ff839ec5046cba19d0ebc40da3b57", "2e95594f957c4ac8858a7a9f787f5ea5", "2bcd25eabbed41619641be3b29886d3b", "a82fb3080da4462880e75c800b06ab09", "65b6a575f675428196297acfde14a5a5", "bf7acf0d4ef141c8943ea0fb05bb4f8d", "8fbd5e817554465ea8506b79f5b7ef85", "78b073f8f13243de9ef794f83322935c", "74caa9c93e4b4c7cb559eb6ee89b6470", "d988fac0b5054ec5b64f0070ea0d37cd", "ea8d9171170247d39da281e3d655c896", "1b1d3e58c8e14ac988f7cec9eb9d799a", "9c89ef746c5740248a332d204320a223", "592b37febf02417cb48aea3334406622", "3767f46b20684691b306ba0924aa1816", "2dcf0ff59fe94487acfa828c77e50ff4", "69214b1d8c494a588d40ae8b74fb2762", "ec5f93ed7ec249f5a87ad99cb1faabe9", "024ca8b44da84b2bbd15b169f8edb895", "ea9da49dbceb446698479023e436badd" ] }, "id": "XIHocAuTQbBR", "outputId": "15623228-c648-41e6-c498-ef08840d9fd8" }, "outputs": [], "source": [ "common_voice_train = common_voice_train.map(remove_special_characters)\n", "common_voice_test = common_voice_test.map(remove_special_characters)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359 }, "id": "RBDRAAYxRE6n", "outputId": "4a17c57c-2cab-47bc-c6c8-52ba60537945" }, "outputs": [], "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\",\"audio\"]))" ] }, { "cell_type": "code", "execution_count": null, "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "c8aea32c58df4a3d9ca4fd45f6095811", "e69e0b954a3246658453c263eabceca8", "69541581fafc4ef487adfc0b6bd0bd10", "40108feb76854d4484e83cf337f19a7a", "f2fd2acebb7c4f13b207b09a2013238e", "a38a3445076940ccb54d568edd59fe3a", "dd3ee56120ed475c85df62f5e2d187e6", "bab2d3c1e603423290ffca4531805378", "db4f2261fe1a49528a9b93191ea32865", "e38fa996f996440fb8344a29af696e09", "711f9962574b418f85ed408c9c96b460", "10afc812df7e404dbb5f71e40146c33f", "e635a84d73d34650b0f62b16036cc514", "30d6443f0d2f42b48179030e0602a473", "91f99428ab524bb08f72116a8be72554", "37cd3369ce1c478c92d169519c6bae96", "03faf57e7cdd41658dd0fd949ab44ab4", "5bbc649fda0540ca9332907f866819e2", "9eef8d8ac2f440ce953bef054799962f", "4eba4feaf5324e0996cec7727dd5e089", "39b929d0554f46258816215ab02cdbc2", "a961777137074d0b9f676cfcb28eaaa2" ] }, "id": "_m6uUjjcfbjH", "outputId": "5318bb8b-71fa-4217-dd23-4b2004a65d1e" }, "outputs": [], "source": [ "vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)\n", "vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aQfneNsmlJI0" }, "outputs": [], "source": [ "vocab_list = list(set(vocab_train[\"vocab\"][0]) | set(vocab_test[\"vocab\"][0]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_0kRndSvqaKk", "outputId": "672a1256-740c-4b44-b215-6cd64b3e7e2c" }, "outputs": [], "source": [ "vocab_dict = {v: k for k, v in enumerate(vocab_list)}\n", "vocab_dict" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "npbIbBoLgaFX" }, "outputs": [], "source": [ "vocab_dict[\"|\"] = vocab_dict[\" \"]\n", "del vocab_dict[\" \"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "znF0bNunsjbl", "outputId": "41c5a61f-90e5-4806-8c61-79be23ff38e8" }, "outputs": [], "source": [ "vocab_dict[\"[UNK]\"] = len(vocab_dict)\n", "vocab_dict[\"[PAD]\"] = len(vocab_dict)\n", "len(vocab_dict)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ehyUoh9vk191" }, "outputs": [], "source": [ "import json\n", "with open('vocab.json', 'w') as vocab_file:\n", " json.dump(vocab_dict, vocab_file)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xriFGEWQkO4M" }, "outputs": [], "source": [ "from transformers import Wav2Vec2CTCTokenizer\n", "\n", "tokenizer = Wav2Vec2CTCTokenizer(\"./vocab.json\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kAR0-2KLkopp" }, "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": null, "metadata": { "id": "KYZtoW-tlZgl" }, "outputs": [], "source": [ "from transformers import Wav2Vec2Processor\n", "\n", "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "rrv65aj7G95i" }, "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1Po2g7YPuRTx", "outputId": "ed01e899-1a15-4f4e-a435-c196071f4c82" }, "outputs": [], "source": [ "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(\"Target text:\", common_voice_train[rand_int][\"sentence\"])\n", "print(\"Input array shape:\", common_voice_train[rand_int][\"audio\"][\"array\"].shape)\n", "print(\"Sampling rate:\", common_voice_train[rand_int][\"audio\"][\"sampling_rate\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "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", " \n", " with processor.as_target_processor():\n", " batch[\"labels\"] = processor(batch[\"sentence\"]).input_ids\n", " return batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-np9xYK-wl8q" }, "outputs": [], "source": [ "common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names, num_proc=4)\n", "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names, num_proc=4)" ] }, { "cell_type": "markdown", "metadata": { "id": "Slk403unUS91" }, "source": [ "### Set-up Trainer Class" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "tborvC9hx88e" }, "outputs": [], "source": [ "import torch\n", "\n", "from dataclasses import dataclass, field\n", "from typing import Any, Dict, List, Optional, Union\n", "\n", "@dataclass\n", "class DataCollatorCTCWithPadding:\n", " processor: Wav2Vec2Processor\n", " padding: Union[bool, str] = True\n", " max_length: Optional[int] = None\n", " max_length_labels: Optional[int] = None\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", " 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", " max_length=self.max_length,\n", " pad_to_multiple_of=self.pad_to_multiple_of,\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", " max_length=self.max_length_labels,\n", " pad_to_multiple_of=self.pad_to_multiple_of_labels,\n", " return_tensors=\"pt\",\n", " )\n", "\n", " \n", " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n", "\n", " batch[\"labels\"] = labels\n", "\n", " return batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lbQf5GuZyQ4_" }, "outputs": [], "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "91b9939e3da0414ca210bb60f0649741", "86df11b90a2c41bcb013e48be3aa6ce1", "77eb2d2ed73947d29b96ac194f84cd72", "9934081e12ae48da8340661a95c63f32", "90e16472877749e4b2e1c9f9b998483f", "f933157cee4b4333bc1bd54f91d31ffe", "930a797df100485fad9752132bf0e3c7", "839531fbecb94e23a9e0ec7f1b045c69", "71946fe0f704479aa3feee1da6938bbf", "7f97be3a313b45229e25fbae81d28b95", "1a05d11b10434baba0aa9b042fc3d20d" ] }, "id": "9Xsux2gmyXso", "outputId": "1d1caa4e-f73b-431b-bb1c-227bca1e631d" }, "outputs": [], "source": [ "wer_metric = load_metric(\"wer\")" ] }, { "cell_type": "code", "execution_count": null, "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", "\n", " return {\"wer\": wer}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 191, "referenced_widgets": [ "eaab09a8d1344ef495857150772774d0", "4b3c69af59024d12a226f96ecf4c1160", "e2e22b49f414495e9d65071aa212cf52", "2e1a49e41a2d47768b6d6ad590e99a3e", "657bb2b45ac04c27bbab0be67463ed9c", "fbf7b312dc094c1a81665b180bfa7bc1", "d694f70b57ad419ca7735357299a1c28", "f64233294f004b97b306995e0b1e09d5", "451266e4e47d4f15bb94ebb2ff8ffb89", "605f57cda2214d2fabf09310d07e22e2", "746d4e57cd50455a90770c6e28d8cf23", "edc14234a3a04fc99b264a80d6f07da8", "dea77ede594540009e2f266cb00a4c06", "101840d8f4b94eca9fbf6fd9971aa56c", "d8190a73f9274c62957e0b8ff684294a", "89c8e6eea2424d4ca1c302017f927f67", "97e8f3359f994b85b2773a4d72478856", "7eb9cdff79434d35a46270b7ea0b86ce", "49f24f5f9b5340b499572ebd55b1a058", "985844f3740a4333b9d1b08aa9f95265", "01cb8d98df0d44dda0f1aaf3deca8e85", "7bdcba7e382e4517a0641b4025e69b2f" ] }, "id": "e7cqAWIayn6w", "outputId": "7805bbf0-3796-49a7-8295-15445641c5f8" }, "outputs": [], "source": [ "from transformers import Wav2Vec2ForCTC\n", "\n", "model = Wav2Vec2ForCTC.from_pretrained(\n", " \"facebook/wav2vec2-large-xlsr-53\", \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": null, "metadata": { "id": "oGI8zObtZ3V0" }, "outputs": [], "source": [ "model.freeze_feature_extractor()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JE8uF0dFWeSw" }, "outputs": [], "source": [ "model.gradient_checkpointing_enable()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KbeKSV7uzGPP" }, "outputs": [], "source": [ "from transformers import TrainingArguments\n", "\n", "training_args = TrainingArguments(\n", " # output_dir=\"/content/gdrive/MyDrive/wav2vec2-large-xlsr-turkish-demo\",\n", " output_dir=\"./wav2vec2-large-xlsr-turkish-demo\",\n", " group_by_length=True,\n", " per_device_train_batch_size=16,\n", " gradient_accumulation_steps=2,\n", " evaluation_strategy=\"steps\",\n", " num_train_epochs=30,\n", " fp16=True,\n", " save_steps=100,\n", " eval_steps=100,\n", " logging_steps=10,\n", " learning_rate=3e-4,\n", " warmup_steps=500,\n", " save_total_limit=2,\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rY7vBmFCPFgC", "outputId": "9c2a7176-c426-47f1-ac9f-7ee86adbd4e9" }, "outputs": [], "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": "markdown", "metadata": { "id": "rpvZHM1xReIW" }, 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