{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "id": "RyOvLnqjdP2v" }, "outputs": [], "source": [ "#Wav2vec2 2.0 \n", "#Install modules\n", "%%capture\n", "!pip install datasets \n", "!pip install transformers #HuggingFace library used for creating the model\n", "!pip install torchaudio #for loading the audio files\n", "!pip install librosa #for loading the audio files\n", "!pip install jiwer #for model evaluation WER\n", "!pip install huggingface_hub #if someone wants to push the model directly to hugging face hub\n" ] }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/gdrive/')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gmFWqikLMN6L", "outputId": "d48c70d7-342a-4096-c143-f0badae1ddcb" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/gdrive/\n" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NlvLQLvcgWUj", "colab": { "base_uri": "https://localhost:8080/", "height": 359, "referenced_widgets": [ "47ac8f6f569f49d9980c7de7a6f7b15a", "84587050838441d68407efbe32c1744a", "e4fe12cb754744929f6a081c52f298df", "8f2fe9f5f8bc4e9f854c69bef8438e04", "57085bf34d8e4bc7858091bd64fb3217", "f468bb97c4a74aeb88d9352f678b94c1", "b0b3b20698264c498bf9fcd9cd5da5d8", "08ff9d6fe7e44ddfa0b4b2f2aa8cb3af", "98033032db1a47d6a4a8d69f78a8455f", "60c33149b33648a0a213cd18d0f9660b", "d33339edbbd0467ab8fc1bce583ec171", "34401fb0badf40edb5bce0ea3170b096", "47da9d0ef1ee49cb978e1c2a9878b715", "730caa834b63474caebc131f15573424", "ae666d961e924076ad40bb862788eb2d", "ab9ea1b3404c4df7ba873cd27edca409", "eaeb731ac0cb415fa2bc531423d0c085" ] }, "outputId": "9053a02d-a32a-4bb0-af36-b100d0ff0651" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Login successful\n", "Your token has been saved to /root/.huggingface/token\n", "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n", "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n", "\n", "git config --global credential.helper store\u001b[0m\n" ] } ], "source": [ "#logging to Hugging-Face\n", "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Xh3Mzh5ysDkU" }, "outputs": [], "source": [ "%%capture\n", "!apt install git-lfs" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "X_WOWptkhi-h", "colab": { "base_uri": "https://localhost:8080/", "height": 185, "referenced_widgets": [ "417f58611d284451bbf393fda2753d0b", "855852f23f2145178b3137be808ade3e", "391083a679eb42ef9670735017bc304e", "130eb279f08541c2bf1c06ec614f6e65", "b1d8bbde806d46dab0f8da467ad77cd7", "058af92b4f214d82aa1b9f5090ace9db", "a227440c9d9440d4b8c9e69e0ec7ccb1", "ec88f234a9bd4d98807e87b3ba66c5c3", "208e9d83d0d84d849f5ba971c5a30963", "f609a93232684798bc716d1893c5703f", 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"88bd7051db0a4a61bafaf358fb0f0778", "9950fe8b5dff4676ade57937b947732c", "d3d58b461772472d8418a53f0c9cf31e", "0f375161d90f42d3883eb144c06850e4", "302a96cca6fb45fca84e42cf0cd93a07", "a252b3e551f54fc588d8057ac421d665", "30e4b1223cf84598ab6de155c0963204", "f08a4876542c4754b485a85eae5c8cd7", "164d54baf3ff48a8a8a0f7c87d2d8002", "59c9dcddea4e4d0e94e0476a57a6e1e5", "dd72ce18e9a0425ca7c7702e5a16c4b0", "f4b20f1e8709423c9965d55cab5117e5", "99db9b2686ec4702bad74527c64f1f7e", "1c431170c5314838882ff67594fe2436", "86dcaf64e6ea43e2bca67a0dc21d77ec", "1422806bf33b4a97ba76cf3d0631fd3a", "5af9a8bbbe52413293ff61b72970e60d", "d93310e5558048d29a7072e854589b81", "eee88c06498b41a0b97232b104ff6542", "5ac926175349417cbcf594f9de9f7672", "6bc62146709744e1bef9e6c18bef4b8e", "d2ca39b697cf4c36848889ed7addd005", "06252bfe961b4506a3320b8f2f29e3ea", "05ef9108c93a4e18878afbfd928e0e7a", "9f20f63c7211497fbf932ba0ec90e9db", "215c4d391af44edfb0b26d9591785f37", "2f30c51f7ed54d0895d2ef348ed0f476", "96d882fec32848a6a24a96cfe6dba28a", "73a6f501cb0d4e339cba2eda6502c080", "db43759bd2924d618d56ecdf81496fd0", "138d2c569f864ee7b4abc62a44f5486f", "0b4b82d5fc984374aeea9709fd2e7b96", "512c9fddd2034664a8946b20e32e9b64", "205231fb3198480881ca8d0364bf9c52", "23e9d7716d2a4464a9b1063190ba80d0", "0bf3bf2814284384a0e6cf34dd8e771c", "0cde47a2f51742dfa4d641179032a807", "24051130f31f48be9fe37b61cecc5b5a", "b2757c54b06142ef9fae1026973f60e0", "6113930d58564ab7962920f268b4c19d", "2d6f5172396d4a999ad94c70e890de96", "9df753ad1d704a428a0dfe61f54536e4", "c12888d55d224222a169eccc97a1fe91", "e92a8ac4a96a45cb8482ee3736910962", "7d5685e2ace04938b56a67239d68f555", "bd3a6d75d4034f27abc21608482f9e3f", "ac7fcd264d6a47b8b5166fa0b0797534", "51f2d6432aeb4f5fb0e2f6d77d4fcb55", "3f344ff857c24e6d9f53c513608772fb", "c40c18db96c84c0da306e56c3760c471", "12a4824099fb458392c4bd0a5f53ef07", "9f483733f8554ab8b1b08fbf175ae205", "7446f51e94214c749f286ef3f374388f", "6f4efbce50ca4003a183fb29254181fb", "fe116393cba34220a6e6749e44698839", "392c3e69886a4bba87125dad39730216", "534d0df580ba4452bd25cfc20056dddc", "b3a6c8eb75034b5a805b60ff81ef9fa0", "c0a5b30f9e4f4971a34a0719fdc0d76a", "06e39fea39b84fc78962fe9ad2f4dc0e", "91bb9ed32f024dff9ad3f2b7dcf35adb", "19f78768d2d343b799406a8e341f9ef1" ] }, "outputId": "73664946-cd02-45e6-a5a1-432a35061f80" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading builder script: 0%| | 0.00/5.21k [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#Common voice Greek dataset comes in with many collumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m#Removing unwanted df collumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mcommon_voice_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcommon_voice_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove_columns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"accent\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"age\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"client_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"down_votes\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"gender\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"locale\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"segment\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"up_votes\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mcommon_voice_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcommon_voice_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove_columns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"accent\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"age\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"client_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"down_votes\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"gender\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"locale\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"segment\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"up_votes\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'common_voice_train' is not defined" ] } ], "source": [ "#Common voice Greek dataset comes in with many collumns\n", "#Removing unwanted df collumns\n", "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": "b7MYsCEIiiMm" }, "outputs": [], "source": [ "from datasets import ClassLabel\n", "import random\n", "import pandas as pd\n", "from IPython.display import display, HTML\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 h 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": { "id": "-bxdrBnenuxH", "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "outputId": "8eb0dc00-9234-4785-fcf1-c5822913b67a" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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sentence
0πώς σε αυτή τη χώρα των όμορφων γυμνασμένων κοριτσιών
1Αν είναι διασκεδαστικά, πες τα αμέσως, ειδεμή άστα για αργότερα.
2που να του γυρέψει βοήθεια ή συμβουλή.
3Και τώρα, είπε, εμπρός!
4Σε μια στιγμή, ο καβαλάρης ξεμπερδεύτηκε από τις πατήτρες και σηκώθηκε.
5Τι να κάνουμε;
6Όχι, αλλά για τα καστανά μάτια του γιου του.
7Τώρα έχω ό,τι μου χρειάζεται για το γιαχνί μου
8\"Πήγαν στο μαγειριό ν' αφήσουν το φορτίο τους\"
9όπως είδα να ψήνει το κρέας η κυρα-Φρόνηση.
" ] }, "metadata": {} } ], "source": [ "#Exploring dataset\n", "show_random_elements(common_voice_train.remove_columns([\"path\", \"audio\"]), num_examples=10)" ] }, { "cell_type": "markdown", "source": [ "## Data Preprocess is very essential. \n", "Speech chunks that do not correspond to any characteristic sound, just make the classification part of the model more difficult. \n", "So it is important to remove any unwanted characters such special characters or converting 'ς' to 'σ'. Also important, all letters should be lower case, as the sound of a speech unit does not rely on letter being capital or lower case." ], "metadata": { "id": "1TXu6tAvA5in" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xUQB9l4lnx6e" }, "outputs": [], "source": [ "import re\n", "chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�]'\n", "normalize_greek_letters = {\"ς\": \"σ\"}\n", "normalize_greek_letters = {\"ς\": \"σ\", \"ώ\": \"ό\", \"ω\" : \"ο\", \"η\" : \"ι\", 'ή' : 'ί'}\n", "remove_chars_greek = {\"a\": \"\", \"h\": \"\", \"n\": \"\", \"g\": \"\", \"o\": \"\", \"v\": \"\", \"e\": \"\", \"r\": \"\", \"t\": \"\", \"«\": \"\", \"»\": \"\", \"m\": \"\", '́': '', \"·\": \"\", \"’\": \"\", '´': \"\"}\n", "replacements = {**normalize_greek_letters, **remove_chars_greek}\n", "\n", "def remove_special_characters(batch):\n", " batch[\"sentence\"] = re.sub(chars_to_ignore_regex, '', batch[\"sentence\"]).lower() + \" \"\n", " batch[\"sentence\"] = re.sub(chars_to_ignore_regex, '', batch[\"sentence\"]).lower()\n", " for key, value in replacements.items():\n", " batch[\"sentence\"] = batch[\"sentence\"].replace(key, value)\n", " return batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DdriQiW6n-EB", "colab": { "base_uri": "https://localhost:8080/", "height": 200 }, "outputId": "84783652-ce3b-4179-e64a-d73fede81fee" }, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#Removing Special characters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcommon_voice_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcommon_voice_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremove_special_characters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mcommon_voice_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcommon_voice_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremove_special_characters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'common_voice_train' is not defined" ] } ], "source": [ "#Removing Special characters\n", "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": { "id": "gtnz13kIoNod", "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "outputId": "60541c73-7298-4a15-dde5-4a1ee0e5e0fd" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "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
0και σαν τι να κάμο εγό
1ενό σιοπιλά ξέκοβε ο κυρ κατρακυλάκοσ
2ένασ άνθροποσ με το ποτίρι στο χέρι
3ύστερα πίρε τιν απόφασι του
4άπλοσε επιφυλακτικά τιν παλάμι του και τισ χάιδεψε το χέρι
5και ξαναγύρισε στι δουλειά τισ
6γιατί τα γράμματα κάθονται σούζα όταν τα κοιτάσ
7είπε ο δάσκαλοσ διακόβοντασ τιν κουβέντα
8δεν είδα όμοσ κανέναν άνθροπο
9ο αρχικαγκελάριοσ έπιασε τιν καμπούρα
" ] }, "metadata": {} } ], "source": [ "#Check\n", "#No special characters, all letters to lowercase\n", "show_random_elements(common_voice_train.remove_columns([\"path\",\"audio\"]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Huz4_pjkoXLd" }, "outputs": [], "source": [ "#Creating the vocab\n", "#Transforming speech chunks to letters\n", "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": { "id": "mgloaRQ2o86Y", "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "e127964e1e484358a99968cbd195db87", "dc3d209b9d2c48d8a29a3a90a01523b6", "a3830e94d0fa4cf8b0a56bd14a074c32", "3b1a68ed129f4641b65b0b88fb157ae3", "6d7964871c0f4f0895336671c6d41a53", "b3c869c75ea04d04ae7c9e8d0d4bfde7", "d2754522c445477384106ffbecdffcb8", "aa02fa2d41cf4a2f9444e9bd4ce3ff98", "f3278c68e33541f58f0d1a79de5b7b31", "21824fafc9eb4960ba676a4fcfcb92a7", "6a100d8a5db64a6dad98fa5487d353d8", "792af2d61ae84521b9561973f9b7607c", "1514e3d6a68841f3aab1a2e55acad1cb", "a1b86c42d0034965bf0a8a483aaaa3c2", "8f306c9f666a4a7293e5b8d7356f6cde", "334a2801c02e4cfb8895a398cf6444bb", "c728d130145e468dbb38022ba6ba1fcf", "f9ddf51aa64547998682f7cada36f4df", "5630eccd227b4662b2796358d2d320a7", "d339ca00598d465d94bb6d468ae0bfce", "56418310052d4bddb741f392b7cb4954", "4632ce9986d843eabe8944a7ef0e7dba" ] }, "outputId": "d5d73ca4-15ba-402d-bf4b-a624e42a8ccd" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/1 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mwith\u001b[0m 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Wav2Vec2CTCTokenizer(\"./vocab.json\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ndQlYQacwYgD" }, "outputs": [], "source": [ "#Creating Feature extractor \n", "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": "pFumAK2Tw26-" }, "outputs": [], "source": [ "from transformers import Wav2Vec2Processor\n", "\n", "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)" ] }, { "cell_type": "code", "source": [ "processor.save_pretrained(\"wav2vec-Model_2/\")" ], "metadata": { "id": "Rq-BcDCcuji2" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Myp7YFV-xnIM", "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "outputId": "adef2142-1938-4bc0-a144-4f5cf12effcb" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'/root/.cache/huggingface/datasets/downloads/extracted/efb95333555cde6432f679057bba200e150897c731a761366f2ab444d4049b7f/cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20430274.mp3'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 23 } ], "source": [ "common_voice_train[0][\"path\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vY4XsJzYxrVk", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "9a30d449-e400-4ee7-ef37-885b1187374f" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'array': array([ 0. , 0. , 0. , ..., -0.00034624,\n", " -0.00026977, -0.00026107], dtype=float32),\n", " 'path': '/root/.cache/huggingface/datasets/downloads/extracted/efb95333555cde6432f679057bba200e150897c731a761366f2ab444d4049b7f/cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20430274.mp3',\n", " 'sampling_rate': 48000}" ] }, "metadata": {}, "execution_count": 24 } ], "source": [ "common_voice_train[0][\"audio\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cUtpPLiWxvzR" }, "outputs": [], "source": [ "#wav2vec models are pretrained with datasets in 16kHz, meaning that the input should be of the same sampling rate\n", "#This step is only essential if the input is different than that of 16kHz\n", "#Changing 48kHz to 16kHz\n", "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": { "id": "SiIVNG03x284", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "86477ab0-b4c9-4bc5-b707-5d7d82c14d73" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'array': array([ 0. , 0. , 0. , ..., -0.00054888,\n", " -0.00040615, -0.00034593], dtype=float32),\n", " 'path': '/root/.cache/huggingface/datasets/downloads/extracted/efb95333555cde6432f679057bba200e150897c731a761366f2ab444d4049b7f/cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20430274.mp3',\n", " 'sampling_rate': 16000}" ] }, "metadata": {}, "execution_count": 26 } ], "source": [ "common_voice_train[0][\"audio\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6tL5nCdLx5t2", "colab": { "base_uri": "https://localhost:8080/", "height": 75 }, "outputId": "6956c0be-87ec-4120-a508-4910acad1eb8" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {}, "execution_count": 27 } ], "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", "ipd.Audio(data=common_voice_train[rand_int][\"audio\"][\"array\"], autoplay=True, rate=16000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xacVpRA4x-JU", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "f8520c63-c228-45b4-8fcf-548494802b53" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Target text: πόσ ρότισε αποθαρρυμένοσ ο κακομοιρίδισ \n", "Input array shape: (83328,)\n", "Sampling rate: 16000\n" ] } ], "source": [ "#Random example\n", "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": "6Mhf7TKVyDeT" }, "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": "piZQLlWjyIOP", "colab": { "base_uri": "https://localhost:8080/", "height": 325, "referenced_widgets": [ "9fa52d7bbdd440e2bf1a8f2c08e57536", "a6d99498597f408a9c75a18e844691b8", "c9ba3f5076de47faa9bbe003564c3294", "5eadcf8e3c0d4de68174206d16bcd8eb", "4969ef4a8d7c407ea385ba50d7350375", "3fbff9e590c6419bbd97922cb52ec53c", "f154575af5164fdfa7463ad044a6014f", "f779f611a575425cac1148b5fdeff2a4", "80ad9ac300994153a4d3edc61687923a", "d1c76cd933f0424c8f9ae7db80332380", "c339f15da7e44dcaa502bdf7a4185ab9", "3bf6e7a7c24f4e51a3ce6d212f394a47", "80822c3d058a4029ba6e697641977ae4", "d04d708993ba4fd8a1cb69a588ffd77d", "e082e6b2e8e24df7851d7108032a1bcd", "768e4296788d417b873b2833938aed2d", "81084223d2394d6a9c1eb24b7eeb9888", "1e4b73894763489fb20efb50a8a0419c", "5a0216f6d84d4cbd96fb16b1b593193d", "4ad234ce37394915ab39b54832a0dd06", "8255c684f5c044e09f9b78901c1ef00d", "1f82a4b57bc74bea8759e1884e3de56f", "bf6e8ba4316244a885e21d2b7e19d194", "1bbf3e616e1e4e2e9304cd6712cbbce7", "b9d38b4dc47748e6831ec2121b691aa9", "423a8ccd1797476c8a859cdea50b8f3f", "c7236e62051d45c6b37e0a92c85b8bce", "febba83d83634803ba96edaefcc28870", "39b2a36d99fa49f9996a7d44c71d4bd0", "ce9e57f6491f4c6b99cd1caaf935bc7f", "f48ee048f4024aa381eb124aaa7f09f4", "f97bfad65b5747eeb7887627d47c3e9c", "8b2de92f9dec40a68c199494dafa36eb", "266264bab4da4a81ae3c7f08ce85f530", "5c0dd88412114fc2a91cb54b7d0115f1", "34e9ac865ba743099061a111f233b82e", "23130c60411c4be6bbf8110cd92b53df", "868823b490f2433182e2aef8780da715", "7e32c6467f154ec39b70abeea742702e", "2103590a002b42dcb4b98fbbc90739e4", "d87f709246c14693bda347c2e5a063a9", "6f7d1e7b7b004597ae0bb35232b86e25", "e376de1977184650b50a6951c1b60a2a", "4887b20191ab4755b77323743dc1aaed", "dd7c4678db2645ca9aeb6d4bd9223a6b", "dafdd16d22234a50aaaf851eccbe3774", "1a7dd409c57242ee8241e057d204590e", "09f35b8d1e984144b1a48e1fef244086", "6b99968aba4e4487811c24746032e484", "f9cc7c3b2b314c45b7003da825591dbb", "ba2864e0251445968ea3da1002e0b03b", "1b8e309eed5e40eb9873a55a2460aa5b", "33d8ce9554454011a40d7e2399e5f491", "34716bf919cc40e5af90ae3b3b9b9a48", "7f025b373a024d9784a918a8f39082fb", "1b10d1428694450b889da9d29bdb3d93", "15f93c623d1e497a90b24d40b296fe24", "d7c639315a384c8599992f6fed08336a", "acfb1744290540f3b01542bab7da8ed8", "4d5e4e95110d4190933542144db48b18", "af8bb42786d945ff83fdebb902ac43b0", "9a5a98da25374eb682e92e3fbe477942", "06c07a5b2f2c49b78c701e75fc39bb34", "511b7bef98ea473c9f7d36c1cd292559", "c62b249471434fe38e4def29caf1c11b", "6bfc290c929044158d6abc4a8b4764fb", "2a1282ab405346229c676a3f3900a660", "f860566e82954a20899d1c7fe46b7fc4", "f33d3e4f20c04ab795729968be6a3f46", "e47976c14ec743de886cdfa3665bd649", "07407623b19544ce9b53380bbb9170b3", "2b2caa7351384488baefe3d8af6474d9", "75b97dba33b1434a8c5d0567db0700b0", "d9292a7901484b17b77a08b3f4d02eda", "dd81a25a28404e80bef1e0f3c9bbb6a4", "a45c42743ee44f068d86c904d2954035", "63b74e7b0a3841f6ad3857c6875912b0", "0d08b66ec0434b4189f65b0c6535eb95", "cb68c81e1436470aaae53b9f773009f3", "c0946558bb344108b82f587e4afa41f8", "0c2ee67ad3ed4cda9cec32b467822e72", "ae4ac424de7d489fbe5d0925234e22a4", "02b99b36d8c84eae917e1eb1946f5c18", "2888dcf7ec3a4f28b49ece7ba00c76ae", "4e887e4bc92b4730a8b652a7a561374b", "ef68ed7f06764fa99840529377e0d8e7", "2275d904a74742ce993c59145908ae76", "b0531025e5cd4043844859279856b60b" ] }, "outputId": "fe6478b2-7d4a-4d05-ad29-cc567f749df3" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " " ] }, { "output_type": "display_data", "data": { "text/plain": [ "#0: 0%| | 0/930 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;34m@\u001b[0m\u001b[0mdataclass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mDataCollatorCTCWithPadding\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \"\"\"\n", "\u001b[0;32m\u001b[0m in \u001b[0;36mDataCollatorCTCWithPadding\u001b[0;34m()\u001b[0m\n\u001b[1;32m 30\u001b[0m \"\"\"\n\u001b[1;32m 31\u001b[0m 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Union\n", "\n", "@dataclass\n", "class DataCollatorCTCWithPadding:\n", " \"\"\"\n", " Data collator that will dynamically pad the inputs received.\n", " Args:\n", " processor (:class:`~transformers.Wav2Vec2Processor`)\n", " The processor used for proccessing the data.\n", " padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n", " Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n", " among:\n", " * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n", " sequence if provided).\n", " * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n", " maximum acceptable input length for the model if that argument is not provided.\n", " * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n", " different lengths).\n", " max_length (:obj:`int`, `optional`):\n", " Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).\n", " max_length_labels (:obj:`int`, `optional`):\n", " Maximum length of the ``labels`` returned list and optionally padding length (see above).\n", " pad_to_multiple_of (:obj:`int`, `optional`):\n", " If set will pad the sequence to a multiple of the provided value.\n", " This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=\n", " 7.5 (Volta).\n", " \"\"\"\n", "\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", " # 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", " 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", " # 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": null, "metadata": { "id": "q5U77HHP0auZ" }, "outputs": [], "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Y3g1RyrR0dAt", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "e2222bf6b7db499b95b4891ed870ab9e", "1d3107520ce84a49a5cfc75bb4b15766", "ce4eb7d443214e6aaa35ac31601b47f9", "2810bcc54f3f4da5a7e51aee47d7607d", "ca6fb350f31c427e8cb7bb83afbcf944", "b0d19fde1a1440948bcf2bf2e65f5a0e", "72d60dcfbace45b78984d578cbb11f63", "50a3d8f5890b4063bbf2372a1feb73b4", "5ecdcd4c49964e8b9207dcc3d1fef506", "b793f18988524fb29ee9356f490c5ee2", "57ee7dd6cacc46e4a7dbe970c22bf78a" ] }, "outputId": "35f45756-5b61-4ee2-d3cb-b2bc16a7021f" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading builder script: 0%| | 0.00/1.90k [00:00" ], "text/html": [ "\n", "
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StepTraining LossValidation LossWer
1003.1239003.0593121.002826
2000.9688000.9548440.790552
3000.3964000.5677590.607046
4000.3744000.5504970.604320
5000.3476000.5535590.604623
6000.2889000.5836560.600081
7000.2332000.6378930.613808
8000.2079000.5938660.591198
9000.2128000.5813810.600384
10000.2182000.5650510.573433
11000.1794000.5288730.562128
12000.1553000.5504570.560917
13000.1973000.5820140.581306
14000.1952000.5748570.574644
15000.2345000.5372950.565963
16000.2509000.5918360.569799
17000.3033000.5599900.576057
18000.3360000.4807910.554356
19000.2147000.5578420.562632
20000.2286000.5961730.579388
21000.2244000.5236820.546482
22000.1976000.5357940.544262
23000.1859000.5073810.533764
24000.1787000.5338680.532956
25000.1669000.5590900.538104
26000.1386000.5149880.542647
27000.1780000.5365580.559503
28000.1525000.5451090.529121
29000.1397000.5797650.563743
30000.1547000.5681370.550520
31000.1767000.6139480.553750
32000.2083000.6552590.569093
33000.2615000.5969980.538205
34000.2352000.6249840.550419
35000.1395000.5855800.538508
36000.1574000.5758590.535278
37000.1485000.5905380.534370
38000.1075000.5716560.523468
39000.1207000.5493760.522459
40000.1358000.5534120.537297
41000.1186000.5655700.537398
42000.1314000.5915060.527001
43000.1308000.5797720.520541
44000.1314000.5795680.518825
45000.1508000.5510110.520036
46000.1285000.5595440.526900
47000.2506000.6359800.547593
48000.1863000.5496200.519128

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "Saving model checkpoint to /content/gdrive/MyDrive/Kotro_2/checkpoint-500\n", "Configuration saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-500/config.json\n", "Model weights saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-500/pytorch_model.bin\n", "Feature extractor saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-500/preprocessor_config.json\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "Saving model checkpoint to /content/gdrive/MyDrive/Kotro_2/checkpoint-1000\n", "Configuration saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-1000/config.json\n", "Model weights saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-1000/pytorch_model.bin\n", "Feature extractor saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-1000/preprocessor_config.json\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "Saving model checkpoint to /content/gdrive/MyDrive/Kotro_2/checkpoint-1500\n", "Configuration saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-1500/config.json\n", "Model weights saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-1500/pytorch_model.bin\n", "Feature extractor saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-1500/preprocessor_config.json\n", "Deleting older checkpoint [/content/gdrive/MyDrive/Kotro_2/checkpoint-500] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "Saving model checkpoint to /content/gdrive/MyDrive/Kotro_2/checkpoint-2000\n", "Configuration saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-2000/config.json\n", "Model weights saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-2000/pytorch_model.bin\n", "Feature extractor saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-2000/preprocessor_config.json\n", "Deleting older checkpoint [/content/gdrive/MyDrive/Kotro_2/checkpoint-1000] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "Saving model checkpoint to /content/gdrive/MyDrive/Kotro_2/checkpoint-2500\n", "Configuration saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-2500/config.json\n", "Model weights saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-2500/pytorch_model.bin\n", "Feature extractor saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-2500/preprocessor_config.json\n", "Deleting older checkpoint [/content/gdrive/MyDrive/Kotro_2/checkpoint-1500] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "Saving model checkpoint to /content/gdrive/MyDrive/Kotro_2/checkpoint-3000\n", "Configuration saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-3000/config.json\n", "Model weights saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-3000/pytorch_model.bin\n", "Feature extractor saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-3000/preprocessor_config.json\n", "Deleting older checkpoint [/content/gdrive/MyDrive/Kotro_2/checkpoint-2000] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "Saving model checkpoint to /content/gdrive/MyDrive/Kotro_2/checkpoint-3500\n", "Configuration saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-3500/config.json\n", "Model weights saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-3500/pytorch_model.bin\n", "Feature extractor saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-3500/preprocessor_config.json\n", "Deleting older checkpoint [/content/gdrive/MyDrive/Kotro_2/checkpoint-2500] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "Saving model checkpoint to /content/gdrive/MyDrive/Kotro_2/checkpoint-4000\n", "Configuration saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-4000/config.json\n", "Model weights saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-4000/pytorch_model.bin\n", "Feature extractor saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-4000/preprocessor_config.json\n", "Deleting older checkpoint [/content/gdrive/MyDrive/Kotro_2/checkpoint-3000] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "Saving model checkpoint to /content/gdrive/MyDrive/Kotro_2/checkpoint-4500\n", "Configuration saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-4500/config.json\n", "Model weights saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-4500/pytorch_model.bin\n", "Feature extractor saved in /content/gdrive/MyDrive/Kotro_2/checkpoint-4500/preprocessor_config.json\n", "Deleting older checkpoint [/content/gdrive/MyDrive/Kotro_2/checkpoint-3500] due to args.save_total_limit\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n", "***** Running Evaluation *****\n", " Num examples = 1522\n", " Batch size = 8\n" ] } ], "source": [ "import numpy as np\n", "trainer.train() #Start training" ] }, { "cell_type": "markdown", "source": [ "### Model Evaluation" ], "metadata": { "id": "8EQr2lZwSiAe" } }, { "cell_type": "code", "source": [ "test_dataset = load_dataset(\"common_voice\", \"el\", split=\"test[:2%]\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sL4gUg9GuFCp", "outputId": "1a8de3c8-42d7-4bff-c2b8-b24ce150436f" }, "execution_count": 24, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Reusing dataset common_voice (/root/.cache/huggingface/datasets/common_voice/el/6.1.0/a1dc74461f6c839bfe1e8cf1262fd4cf24297e3fbd4087a711bd090779023a5e)\n" ] } ] }, { "cell_type": "code", "source": [ "import torch\n", "import torchaudio\n", "from datasets import load_dataset\n", "from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor\n", "\n", "#processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)\n", "processor = Wav2Vec2Processor.from_pretrained(\"aggtamv/Wav2vec2Askisi\")\n", "model = Wav2Vec2ForCTC.from_pretrained(\"aggtamv/Wav2vec2Askisi\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dZkIJYVnuVFZ", "outputId": "306dabc4-01a1-4069-cc6a-2c5300e8dad5" }, "execution_count": 25, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" ] } ] }, { "cell_type": "code", "source": [ "resampler = torchaudio.transforms.Resample(48_000, 16_000)\n", "# Preprocessing the datasets.\n", "# We need to read the aduio files as arrays\n", "def speech_file_to_array_fn(batch):\n", " speech_array, sampling_rate = torchaudio.load(batch[\"path\"])\n", " batch[\"speech\"] = resampler(speech_array).squeeze().numpy()\n", " return batch\n", "test_dataset = test_dataset.map(speech_file_to_array_fn)\n", "inputs = processor(test_dataset[\"speech\"][:2], sampling_rate=16_000, return_tensors=\"pt\", padding=True)\n", "with torch.no_grad():\n", " logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits\n", "predicted_ids = torch.argmax(logits, dim=-1)\n", "print(\"Prediction:\", processor.batch_decode(predicted_ids))\n", "print(\"Reference:\", test_dataset[\"sentence\"][:2])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 104, "referenced_widgets": [ "88d1026c6d47425db96e5e84ca011f0c", "564357edb302496681b3c7a753c34248", "c1305858a0ff41e7ba4c43566d1869c7", "b930fceddf644dd583f0e24053b9f670", "09d0e4d125324c72bd769d706c80d3cb", "c71d047a14ef4d46a5000758a5186238", "c02c0b6208294ab78d7829f83081a327", "5878a95eceb743e3950e0744c025d5e7", "5d7c7275e98648cab9a41d9a09385a74", "ca0a79fde7034a72bfe270b9d810554a", "897e15f51802413ba384fa2251e9ca1b" ] }, "id": "fyf-ttcDu2RB", "outputId": "dc18fcc0-fff8-4c87-dc14-ba2072ef926e" }, "execution_count": 26, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/30 [00:00‘ιπτ οθθοαεθτ[UNK]χθτ‘θλχφϋπττ', 'θοα[UNK]τλιτ οθ[UNK]βγπτχθτ‘ι βτ θνωαλθνλξγλτρδξθτχν[UNK] ιτ']\n", "Reference: ['και προχωρεί προς το ποτάμι.', 'χωρίς να προσέχει στο κακό που γίνουνταν γύρω της.']\n" ] } ] }, { "cell_type": "code", "source": [ "test_dataset = test_dataset.map(speech_file_to_array_fn)\n", "inputs = processor(test_dataset[\"speech\"][:2], sampling_rate=16_000, return_tensors=\"pt\", padding=True)\n", "with torch.no_grad():\n", " logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits\n", "predicted_ids = torch.argmax(logits, dim=-1)\n", "print(\"Prediction:\", processor.batch_decode(predicted_ids))\n", "print(\"Reference:\", test_dataset[\"sentence\"][:2])" ], "metadata": { "id": "y0PZHxkdwR8p", "colab": { "base_uri": "https://localhost:8080/", "height": 104, "referenced_widgets": [ "f6e742246c374eb4aa86b3d38b867869", "8492a0ca6de345fcb3536abb457f54b0", "8345fe306f904b74b2ddd87b916947e9", "cceec1b6406f45e1a4fcc5eaff5ca2c1", "b33e126700da4ada8b2167ecb53ccaa9", "fc30511c2b554ca89f90f6029732adbb", "d75277ba840345e7b6ba2615821d6eb2", "b73c5fa400dc4e0bbd622a5b0db90d8c", "80453cfa33a344d6ad05d21f224a04b2", "a707a35335c04d0f8ed80dc26c1f9a90", "d9e16cf71f3a4b7ea2a45b22ff9b5206" ] }, "outputId": "58cfd777-eb31-4fe6-da8e-8290cce18060" }, "execution_count": 27, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/30 [00:00‘ιπτ οθθοαεθτ[UNK]χθτ‘θλχφϋπττ', 'θοα[UNK]τλιτ οθ[UNK]βγπτχθτ‘ι βτ θνωαλθνλξγλτρδξθτχν[UNK] ιτ']\n", "Reference: ['και προχωρεί προς το ποτάμι.', 'χωρίς να προσέχει στο κακό που γίνουνταν γύρω της.']\n" ] } ] }, { "cell_type": "code", "source": [ "import re\n", "from datasets import load_dataset, load_metric\n", "wer = load_metric(\"wer\")\n", "test_dataset = load_dataset(\"common_voice\", \"el\", split=\"test\") #replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.\n", "wer = load_metric(\"wer\")\n", "processor = Wav2Vec2Processor.from_pretrained(\"aggtamv/Wav2vec2Askisi\") #replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`\n", "model = Wav2Vec2ForCTC.from_pretrained(\"aggtamv/Wav2vec2Askisi\") #replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`\n", "model.to(\"cuda\")\n", "chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“]' \n", "normalize_greek_letters = {\"ς\": \"σ\"}\n", "normalize_greek_letters = {\"ά\": \"α\", \"έ\": \"ε\", \"ί\": \"ι\", 'ϊ': \"ι\", \"ύ\": \"υ\", \"ς\": \"σ\", \"ΐ\": \"ι\", 'ϋ': \"υ\", \"ή\": \"η\", \"ώ\": \"ω\", 'ό': \"ο\"}\n", "remove_chars_greek = {\"a\": \"\", \"h\": \"\", \"n\": \"\", \"g\": \"\", \"o\": \"\", \"v\": \"\", \"e\": \"\", \"r\": \"\", \"t\": \"\", \"«\": \"\", \"»\": \"\", \"m\": \"\", '́': '', \"·\": \"\", \"’\": \"\", '´': \"\"}\n", "replacements = {**normalize_greek_letters, **remove_chars_greek}\n", "resampler = {\n", " 48_000: torchaudio.transforms.Resample(48_000, 16_000),\n", " 44100: torchaudio.transforms.Resample(44100, 16_000),\n", " 32000: torchaudio.transforms.Resample(32000, 16_000)\n", "}" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cIQC4UCQR1Ta", "outputId": "1d74cf39-03a3-4a00-c6b5-02a67817ff80" }, "execution_count": 28, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Reusing dataset common_voice (/root/.cache/huggingface/datasets/common_voice/el/6.1.0/a1dc74461f6c839bfe1e8cf1262fd4cf24297e3fbd4087a711bd090779023a5e)\n", "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" ] } ] }, { "cell_type": "code", "source": [ "def speech_file_to_array_fn(batch):\n", " batch[\"sentence\"] = re.sub(chars_to_ignore_regex, '', batch[\"sentence\"]).lower()\n", " for key, value in replacements.items():\n", " batch[\"sentence\"] = batch[\"sentence\"].replace(key, value)\n", " speech_array, sampling_rate = torchaudio.load(batch[\"path\"])\n", " batch[\"speech\"] = resampler[sampling_rate](speech_array).squeeze().numpy()\n", " return batch\n", "\n", "\n", "test_dataset = test_dataset.map(speech_file_to_array_fn)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "df2bde1daf364f30ad772cc0d3ba582f", "9a5304b4935d4f75b6e806ca95ee3c2a", "39a7146b276c477f992352536dfe7a28", "fa4ee181e93d422cade85d1bcebda0a3", "f9388f2a9db847698279b85ef19532a3", "8dd92eb4073049979401a843b41ec645", "c02fb100ece14d87ad7cb94577486e65", "deeb14839e824a028d1d6533060c34df", "17bcc8fbdb634a178179261b3af56788", "988829fd1ef5425dba87109e73afb5f2", "125041a5d80c43c1ac645ebe4140d124" ] }, "id": "FKw12Ky3SD2Y", "outputId": "530c36b5-98b2-4676-db7f-0088ed3c8e4c" }, "execution_count": 29, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/1522 [00:00
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