{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "LBSYoWbi-45k" }, "source": [ "# **Fine-tuning Multi-Lingual Speech Model with 🤗 Transformers**" ] }, { "cell_type": "markdown", "metadata": { "id": "nT_QrfWtsxIz" }, "source": [ "This notebook shows how to fine-tune multi-lingual pretrained speech models for Automatic Speech Recognition." ] }, { "cell_type": "markdown", "metadata": { "id": "OK7AOAkwVOrx" }, "source": [ "This notebook is built to run on the [Common Voice dataset](https://huggingface.co/datasets/common_voice) with any multi-lingual speech model checkpoint from the [Model Hub](https://huggingface.co/models?language=multilingual&pipeline_tag=automatic-speech-recognition&sort=downloads) as long as that model has a version with a Connectionist Temporal Classification (CTC) head. Depending on the model and the GPU you are using, you might need to adjust the batch size to avoid out-of-memory errors. Set those two parameters, then the rest of the notebook should run smoothly:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fw_GGnvwVjOl" }, "outputs": [], "source": [ "model_checkpoint = \"facebook/wav2vec2-large-xlsr-53\"\n", "batch_size = 16" ] }, { "cell_type": "markdown", "metadata": { "id": "ZUUqzyKnVpvc" }, "source": [ "For a more in-detail explanation of how multi-lingual pretrained speech models function, please take a look at the [🤗 Blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2)." ] }, { "cell_type": "markdown", "metadata": { "id": "e335hPmdtASZ" }, "source": [ "Before we start, let's install both `datasets` and `transformers` from master. Also, we need the `torchaudio` and `librosa` package to load audio files and the `jiwer` to evaluate our fine-tuned model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric ${}^1$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "c8eh87Hoee5d" }, "outputs": [], "source": [ "%%capture\n", "!pip install datasets==1.14\n", "!pip install transformers==4.11.3\n", "!pip install torchaudio\n", "!pip install librosa\n", "!pip install jiwer" ] }, { "cell_type": "markdown", "metadata": { "id": "0xxt_LwxDQlO" }, "source": [ "Next we strongly suggest to upload your training checkpoints directly to the [🤗 Hub](https://huggingface.co/) while training. The [🤗 Hub](https://huggingface.co/) has integrated version control so you can be sure that no model checkpoint is getting lost during training. \n", "\n", "To do so you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 239, "referenced_widgets": [ "4597fce67897473a8e0dc6604002d3ae", "b449fbbb6b1b471a8d50eb9d6c26af09", "70619a1016d347bd9191267dcd005b56", "c18dfdb314284e2a9d9c07bb4dcf1d49", "09de7a45bdcf43019fac8faf21d37f4d", "68dd5b7aa43e4156b72db64761cf66e4", "32234e5488dc4e958a7b4edf284085b1", "604598e08be24d8daee5193119855567", "0cc8170d04624602b6fcb913046065b9", "d493bfc3e89147c4b423d9001519d62f", "4338db0dd26a4c11828326ca20ee4fde", "17252e60e7c8408588fc6f81ba8d262e", "aac4d40371604e1599d7748e57ecf464", "b18eb056b44e419692f3c08b34a7cc81", "d537ad0980dc41889484f227eba49b51", "cdb1234dfc50408eadeef7bbfb1b388b" ] }, "id": "mlMSH3T3EazV", "outputId": "5fcd694f-80a4-4fd5-e18b-3d23ab8a77f8" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4597fce67897473a8e0dc6604002d3ae", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"
\\n\n", " \n", " \n", " \n", " sentence\n", " \n", " \n", " \n", " \n", " 0\n", " Birkaç saat sonra, Belçika da aynı yolu izledi.\n", " \n", " \n", " 1\n", " Veliler öfkeliydi.\n", " \n", " \n", " 2\n", " Şimdi Ankara yeniden denemeye hazır.\n", " \n", " \n", " 3\n", " Görüşmelerde Kosova konusuna da değinildi.\n", " \n", " \n", " 4\n", " \"Arnavutluk'ta ihracat zayıf.\"\n", " \n", " \n", " 5\n", " Kredilerin büyük bölümü özel şirketlere verildi.\n", " \n", " \n", " 6\n", " Ancak iki taraf da konuşma yapmadı.\n", " \n", " \n", " 7\n", " Yeni anayasanın onaylamasına çalışılacak.\n", " \n", " \n", " 8\n", " Uyulmadığı takdirde ülke cezalandırılacak.\n", " \n", " \n", " 9\n", " İlk dersler Eylül ayında başlayacak.\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\", \"audio\"]), num_examples=10)" ] }, { "cell_type": "markdown", "metadata": { "id": "fowcOllGNNju" }, "source": [ "Alright! The transcriptions look fairly clean. Having translated the transcribed sentences, it seems that the language corresponds more to written-out text than noisy dialogue. This makes sense considering that [Common Voice](https://huggingface.co/datasets/common_voice) is a crowd-sourced read speech corpus." ] }, { "cell_type": "markdown", "metadata": { "id": "vq7OR50LN49m" }, "source": [ "We can see that the transcriptions contain some special characters, such as `,.?!;:`. Without a language model, it is much harder to classify speech chunks to such special characters because they don't really correspond to a characteristic sound unit. *E.g.*, the letter `\"s\"` has a more or less clear sound, whereas the special character `\".\"` does not.\n", "Also in order to understand the meaning of a speech signal, it is usually not necessary to include special characters in the transcription.\n", "\n", "In addition, we normalize the text to only have lower case letters and append a word separator token at the end." ] }, { "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": [ "e3a3083b43784898acb579cea2fc0c9b", "d22042dd9c614f249d4177cd162edb9d", "ce11a521ee08472580ec9873f6f2c8ff", "0945eebd6624416a82b94c11d1f46e75", "f6d66815620f44e98305e0c3eb5a10b4", "980fc9d1dad044568f2cf09c5aabe851", "7716e1bdb2fa48caa0e5b747d8417ed9", "05af76b0c4fc410685102102cf39c2af", "6c0b90a363434052999ec37f4590f355", "3ccfaa3ba3ad4baaa68b4b2ca3f396a7", "28e0bf7007664d528ab282d9dce4ca08", "3f1d57e62b8c45bf83f2107e5803c430", "a52c9d7c77154b558535321558a99f82", "719906e201484ded9b781b9dad38f934", "204723b0f09d4bf9ab57fd77c764eb18", "98b226199dc54316b771f143c3009cc9", "d258296f72f541c291db35ea264527de", "a0f19ef147eb458b9e8b31e080acf150", "9658824e0065469f8cf73d8c4d0fa93b", "6a86593225b145f4b8116df9e6b0888e", "562b1a7721654e5190079e152a25f900", "0c219edc78ea47ee86d100109107f9e6" ] }, "id": "XIHocAuTQbBR", "outputId": "ba4bbeb2-7adf-452b-8b43-a1ce60654529" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e3a3083b43784898acb579cea2fc0c9b", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/3478 [00:00\n", " \n", " \n", " \n", " sentence\n", " \n", " \n", " \n", " \n", " 0\n", " diğer partiler gereken baraja ulaşamadılar\n", " \n", " \n", " 1\n", " yatırımlar üç virgül iki milyon avroyu buldu\n", " \n", " \n", " 2\n", " muhalefet bu sava inanmıyor\n", " \n", " \n", " 3\n", " bende mücadele edeceğime bu yolu seçtim\n", " \n", " \n", " 4\n", " ancak jurciç teklifi reddetmişti\n", " \n", " \n", " 5\n", " operasyon olaysız geçti\n", " \n", " \n", " 6\n", " birkaç günde öğrenmek için çok fazla şey vardı\n", " \n", " \n", " 7\n", " bu kış ziyaretine gittim\n", " \n", " \n", " 8\n", " ancak yetkililer nuh deyip peygamber demiyorlar\n", " \n", " \n", " 9\n", " ve tabii ki banyoda kaçak var\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\",\"audio\"]))" ] }, { "cell_type": "markdown", "metadata": { "id": "jwfaptH5RJwA" }, "source": [ "Good! This looks better. We have removed most special characters from transcriptions and normalized them to lower-case only.\n", "\n", "In CTC, it is common to classify speech chunks into letters, so we will do the same here. \n", "Let's extract all distinct letters of the training and test data and build our vocabulary from this set of letters.\n", "\n", "We write a mapping function that concatenates all transcriptions into one long transcription and then transforms the string into a set of chars. \n", "It is important to pass the argument `batched=True` to the `map(...)` function so that the mapping function has access to all transcriptions at once." ] }, { "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": [ "bb7cca1dbee0473f9ffa601f26de2bff", "fcb420f307994c76bf477fe2007a2cfc", "ec0643d015f0466a8f6178a191d78978", "cd98ac18cda14f1191d8be051b0b99f9", "866bdbdda70f4377a1d41de89e02e685", "90ba1f42d4f14030978d5cb801ed7aee", "29a58c0cbf4d4dc3943bbd22c4b74881", "44f30bfe15914a0991c82652cb7ba884", "b115da2b43d0491fb33da0fea4d749e4", "ee9d0fc762d94aa4b2725b9c2826034b", "0d17008b61f043adba012a654eab9c73", "b25d7d91d7a6424b95cf52c88c331853", "f2479f0a421e43aca3f0507a195623ef", "209efc5b56eb4acda1a59bdc50040da6", "fe928771f52d4e0aaa69cd59a8d32f06", "d0cbd8d8f5c44ce18fe3c56898032531", "66dbc3cb68e549788aca9b885916ac21", "d77d2d03adff4193a1b48194341221b3", "c03c80a730eb4e77926d202999355433", "2ab34edf1481420f9dd350b1d8b8767c", "34f5c9847be647c2bee711dd612d4e48", "8fd31916b9a34145844878916488ac3e" ] }, "id": "_m6uUjjcfbjH", "outputId": "666f9fde-05d6-4687-c1e7-da68bdd1edcc" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bb7cca1dbee0473f9ffa601f26de2bff", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1 [00:00 to the vocabulary\n", "Adding to the vocabulary\n", "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" ] } ], "source": [ "from transformers import AutoTokenizer\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\n", " \"./\",\n", " config=config,\n", " tokenizer_type=tokenizer_type,\n", " unk_token=\"[UNK]\",\n", " pad_token=\"[PAD]\",\n", " word_delimiter_token=\"|\",\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "KvL12DrNV4cx" }, "source": [ "If one wants to re-use the just created tokenizer with the fine-tuned model of this notebook, it is strongly advised to upload the `tokenizer` to the [🤗 Hub](https://huggingface.co/). Let's call the repo to which we will upload the files\n", "`\"wav2vec2-large-xlsr-turkish-demo-colab\"`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "A1XApZBAF2zr" }, "outputs": [], "source": [ "model_checkpoint_name = model_checkpoint.split(\"/\")[-1]\n", "repo_name = f\"{model_checkpoint_name}-demo-colab\"" ] }, { "cell_type": "markdown", "metadata": { "id": "B1BiezWZF16d" }, "source": [ "and upload the tokenizer to the [🤗 Hub](https://huggingface.co/)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 161 }, "id": "zytE1175GAKM", "outputId": "38618d69-21f2-46b2-d4ac-4d54f51a4068" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Cloning https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-demo-colab into local empty directory.\n", "tokenizer config file saved in wav2vec2-large-xlsr-53-demo-colab/tokenizer_config.json\n", "Special tokens file saved in wav2vec2-large-xlsr-53-demo-colab/special_tokens_map.json\n", "added tokens file saved in wav2vec2-large-xlsr-53-demo-colab/added_tokens.json\n", "To https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-demo-colab\n", " d42ec75..6c70130 main -> main\n", "\n" ] }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-demo-colab/commit/6c70130c37a0bcfe24913105c3e6264e2f5c368d'" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenizer.push_to_hub(repo_name)" ] }, { "cell_type": "markdown", "metadata": { "id": "SwQM8lH_GGuP" }, "source": [ "Great, you can see the just created repository under `https://huggingface.co//wav2vec2-large-xlsr-turkish-demo-colab` ." ] }, { "cell_type": "markdown", "metadata": { "id": "YFmShnl7RE35" }, "source": [ "### Preprocess Data\n", "\n", "So far, we have not looked at the actual values of the speech signal but just the transcription. In addition to `sentence`, our datasets include two more column names `path` and `audio`. `path` states the absolute path of the audio file. Let's take a look.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "TTCS7W6XJ9BG", "outputId": "ddafaa49-7f03-421e-e6a5-334b41f3287e" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3'" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "common_voice_train[0][\"path\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "T6ndIjHGFp0W" }, "source": [ "`XLSR-Wav2Vec2` expects the input in the format of a 1-dimensional array of 16 kHz. This means that the audio file has to be loaded and resampled.\n", "\n", " Thankfully, `datasets` does this automatically by calling the other column `audio`. Let try it out. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qj_z5Zc3GAs9", "outputId": "a910e54e-b2a6-4101-e01a-2c26eb7fe2bb" }, "outputs": [ { "data": { "text/plain": [ "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", " -8.8930130e-05, -3.8027763e-05, -2.9146671e-05], dtype=float32),\n", " 'path': '/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3',\n", " 'sampling_rate': 48000}" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "common_voice_train[0][\"audio\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "WUUTgI1bGHW-" }, "source": [ "Great, we can see that the audio file has automatically been loaded. This is thanks to the new [`\"Audio\"` feature](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=audio#datasets.Audio) introduced in `datasets == 1.13.3`, which loads and resamples audio files on-the-fly upon calling.\n", "\n", "In the example above we can see that the audio data is loaded with a sampling rate of 48kHz whereas 16kHz are expected by the model. We can set the audio feature to the correct sampling rate by making use of [`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column):" ] }, { "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": "markdown", "metadata": { "id": "PcnO4x-NGBEi" }, "source": [ "Let's take a look at `\"audio\"` again." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aKtkc1o_HWHC", "outputId": "7b565ed2-d842-4096-ac59-2fb68ae38664" }, "outputs": [ { "data": { "text/plain": [ "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", " -7.4556941e-05, -1.4621433e-05, -5.7861507e-05], dtype=float32),\n", " 'path': '/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3',\n", " 'sampling_rate': 16000}" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "common_voice_train[0][\"audio\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "SOckzFd4Mbzq" }, "source": [ "This seemed to have worked! Let's listen to a couple of audio files to better understand the dataset and verify that the audio was correctly loaded. \n", "\n", "**Note**: *You can click the following cell a couple of times to listen to different speech samples.*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 92 }, "id": "dueM6U7Ev0OA", "outputId": "0af491c1-f19e-4419-92c6-7f3fc43ee28b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "seçimlerde önemli bir olay bildirilmedi \n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 72, "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": "markdown", "metadata": { "id": "gY8m3vARHYTa" }, "source": [ "It seems like the data is now correctly loaded and resampled. " ] }, { "cell_type": "markdown", "metadata": { "id": "1MaL9J2dNVtG" }, "source": [ "It can be heard, that the speakers change along with their speaking rate, accent, and background environment, etc. Overall, the recordings sound acceptably clear though, which is to be expected from a crowd-sourced read speech corpus.\n", "\n", "Let's do a final check that the data is correctly prepared, by printing the shape of the speech input, its transcription, and the corresponding sampling rate.\n", "\n", "**Note**: *You can click the following cell a couple of times to verify multiple samples.*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1Po2g7YPuRTx", "outputId": "df268899-0558-4f55-d9ce-68a2e6148186" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Target text: çok güzel \n", "Input array shape: (34560,)\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": "markdown", "metadata": { "id": "M9teZcSwOBJ4" }, "source": [ "Good! Everything looks fine - the data is a 1-dimensional array, the sampling rate always corresponds to 16kHz, and the target text is normalized.\n", "\n", "Next, we should process the data with the model's feature extractor. Let's load the feature extractor" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "f5ed78d4c93d4ad8a3c36e9c6be42954", "5923ee1f3d31466c949f9abb7f98ee48", "4201952adf02450bb106bc90b65b21a8", "136e3698e7ae4f37b2b6eaabd74fff37", "bb2ce51969174648805e61c6fe5bbc26", "1eb0e9573a374f36a9c23ff9be46eec5", "92b6d40d90b74f1ab3085fd39ae7fb8a", "20da06bc68034ad889ee2d243c1871a6", "b37445039fca441aac2eba6153b52e41", "b74262bbe4ba4798b737eb687c77493f", "10d392f730e24029a1d0cf27de2f757b" ] }, "id": "UuA-9bgBYT4x", "outputId": "3c13fd12-3982-42ce-db39-b42d75e77b83" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "loading configuration file https://huggingface.co/facebook/wav2vec2-large-xlsr-53/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/8508c73cd595eb416a1d517b90762416c0bc6cfbef529578079aeae4d8c14336.7581ed2ee0c677f1e933180df51bd1a668c4a2b6d5fd1297d32069373dac097c\n", "Model config Wav2Vec2Config {\n", " \"activation_dropout\": 0.0,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForPreTraining\"\n", " ],\n", " \"attention_dropout\": 0.1,\n", " \"bos_token_id\": 1,\n", " \"classifier_proj_size\": 256,\n", " \"codevector_dim\": 768,\n", " \"contrastive_logits_temperature\": 0.1,\n", " \"conv_bias\": true,\n", " \"conv_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512\n", " ],\n", " \"conv_kernel\": [\n", " 10,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 2,\n", " 2\n", " ],\n", " \"conv_stride\": [\n", " 5,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2\n", " ],\n", " \"ctc_loss_reduction\": \"sum\",\n", " \"ctc_zero_infinity\": false,\n", " \"diversity_loss_weight\": 0.1,\n", " \"do_stable_layer_norm\": true,\n", " \"eos_token_id\": 2,\n", " \"feat_extract_activation\": \"gelu\",\n", " \"feat_extract_dropout\": 0.0,\n", " \"feat_extract_norm\": \"layer\",\n", " \"feat_proj_dropout\": 0.1,\n", " \"feat_quantizer_dropout\": 0.0,\n", " \"final_dropout\": 0.0,\n", " \"gradient_checkpointing\": false,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout\": 0.1,\n", " \"hidden_size\": 1024,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 4096,\n", " \"layer_norm_eps\": 1e-05,\n", " \"layerdrop\": 0.1,\n", " \"mask_channel_length\": 10,\n", " \"mask_channel_min_space\": 1,\n", " \"mask_channel_other\": 0.0,\n", " \"mask_channel_prob\": 0.0,\n", " \"mask_channel_selection\": \"static\",\n", " \"mask_feature_length\": 10,\n", " \"mask_feature_prob\": 0.0,\n", " \"mask_time_length\": 10,\n", " \"mask_time_min_space\": 1,\n", " \"mask_time_other\": 0.0,\n", " \"mask_time_prob\": 0.075,\n", " \"mask_time_selection\": \"static\",\n", " \"model_type\": \"wav2vec2\",\n", " \"num_attention_heads\": 16,\n", " \"num_codevector_groups\": 2,\n", " \"num_codevectors_per_group\": 320,\n", " \"num_conv_pos_embedding_groups\": 16,\n", " \"num_conv_pos_embeddings\": 128,\n", " \"num_feat_extract_layers\": 7,\n", " \"num_hidden_layers\": 24,\n", " \"num_negatives\": 100,\n", " \"pad_token_id\": 0,\n", " \"proj_codevector_dim\": 768,\n", " \"transformers_version\": \"4.12.0.dev0\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 32\n", "}\n", "\n", "https://huggingface.co/facebook/wav2vec2-large-xlsr-53/resolve/main/preprocessor_config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpupzebk0d\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f5ed78d4c93d4ad8a3c36e9c6be42954", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/212 [00:00=\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": "lbQf5GuZyQ4_" }, "outputs": [], "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "xO-Zdj-5cxXp" }, "source": [ "Next, the evaluation metric is defined. As mentioned earlier, the \n", "predominant metric in ASR is the word error rate (WER), hence we will use it in this notebook as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9Xsux2gmyXso" }, "outputs": [], "source": [ "wer_metric = load_metric(\"wer\")" ] }, { "cell_type": "markdown", "metadata": { "id": "E1qZU5p-deqB" }, "source": [ "The model will return a sequence of logit vectors:\n", "$\\mathbf{y}_1, \\ldots, \\mathbf{y}_m$ with $\\mathbf{y}_1 = f_{\\theta}(x_1, \\ldots, x_n)[0]$ and $n >> m$.\n", "\n", "A logit vector $\\mathbf{y}_1$ contains the log-odds for each word in the vocabulary we defined earlier, thus $\\text{len}(\\mathbf{y}_i) =$ `config.vocab_size`. We are interested in the most likely prediction of the model and thus take the `argmax(...)` of the logits. Also, we transform the encoded labels back to the original string by replacing `-100` with the `pad_token_id` and decoding the ids while making sure that consecutive tokens are **not** grouped to the same token in CTC style ${}^1$." ] }, { "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": "markdown", "metadata": { "id": "Xmgrx4bRwLIH" }, "source": [ "Now, we can load the pretrained speech checkpoint. The tokenizer's `pad_token_id` must be to define the model's `pad_token_id` or in the case of a CTC speech model also CTC's *blank token* ${}^2$.\n", "\n", "Because the dataset is quite small (~6h of training data) and because Common Voice is quite noisy, fine-tuning might require some hyper-parameter tuning, which is why a couple of hyperparameters are set in the following.\n", "\n", "**Note**: When using this notebook to train speech models on another language of Common Voice those hyper-parameter settings might not work very well. Feel free to adapt those depending on your use case. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "4fe9e026e1e74cf4ba5554e7a0460c2f", "521e2c26f96a4de79782855121ea0766", "db21a864f02e438dafefbf2ecf4192ae", "fbd3b32a954c45c788eb8967103dec9d", "f6c41bd10465450287aa50dd569a96a9", "5799f1ce85ec4722a48d45a666aee28b", "762da8b71d714f3c9898d54c26ded701", "34cf654ccffa445c92307c28e74496db", "218f3651b2f84f15b4b3c7bb2c05e260", "36bd4de2f3974c39ba1c8becb10001aa", "db84a3fcf62e41318e13d4f36efee8c9" ] }, "id": "e7cqAWIayn6w", "outputId": "a8bb67d5-2b38-4170-82b5-c76ceb53483e" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "loading configuration file https://huggingface.co/facebook/wav2vec2-large-xlsr-53/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/8508c73cd595eb416a1d517b90762416c0bc6cfbef529578079aeae4d8c14336.7581ed2ee0c677f1e933180df51bd1a668c4a2b6d5fd1297d32069373dac097c\n", "Model config Wav2Vec2Config {\n", " \"activation_dropout\": 0.0,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForPreTraining\"\n", " ],\n", " \"attention_dropout\": 0.1,\n", " \"bos_token_id\": 1,\n", " \"classifier_proj_size\": 256,\n", " \"codevector_dim\": 768,\n", " \"contrastive_logits_temperature\": 0.1,\n", " \"conv_bias\": true,\n", " \"conv_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512\n", " ],\n", " \"conv_kernel\": [\n", " 10,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 2,\n", " 2\n", " ],\n", " \"conv_stride\": [\n", " 5,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2\n", " ],\n", " \"ctc_loss_reduction\": \"mean\",\n", " \"ctc_zero_infinity\": false,\n", " \"diversity_loss_weight\": 0.1,\n", " \"do_stable_layer_norm\": true,\n", " \"eos_token_id\": 2,\n", " \"feat_extract_activation\": \"gelu\",\n", " \"feat_extract_dropout\": 0.0,\n", " \"feat_extract_norm\": \"layer\",\n", " \"feat_proj_dropout\": 0.0,\n", " \"feat_quantizer_dropout\": 0.0,\n", " \"final_dropout\": 0.0,\n", " \"gradient_checkpointing\": false,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout\": 0.1,\n", " \"hidden_size\": 1024,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 4096,\n", " \"layer_norm_eps\": 1e-05,\n", " \"layerdrop\": 0.1,\n", " \"mask_channel_length\": 10,\n", " \"mask_channel_min_space\": 1,\n", " \"mask_channel_other\": 0.0,\n", " \"mask_channel_prob\": 0.0,\n", " \"mask_channel_selection\": \"static\",\n", " \"mask_feature_length\": 10,\n", " \"mask_feature_prob\": 0.0,\n", " \"mask_time_length\": 10,\n", " \"mask_time_min_space\": 1,\n", " \"mask_time_other\": 0.0,\n", " \"mask_time_prob\": 0.05,\n", " \"mask_time_selection\": \"static\",\n", " \"model_type\": \"wav2vec2\",\n", " \"num_attention_heads\": 16,\n", " \"num_codevector_groups\": 2,\n", " \"num_codevectors_per_group\": 320,\n", " \"num_conv_pos_embedding_groups\": 16,\n", " \"num_conv_pos_embeddings\": 128,\n", " \"num_feat_extract_layers\": 7,\n", " \"num_hidden_layers\": 24,\n", " \"num_negatives\": 100,\n", " \"pad_token_id\": 39,\n", " \"proj_codevector_dim\": 768,\n", " \"transformers_version\": \"4.12.0.dev0\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 42\n", "}\n", "\n", "https://huggingface.co/facebook/wav2vec2-large-xlsr-53/resolve/main/pytorch_model.bin not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmppd8nibgp\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4fe9e026e1e74cf4ba5554e7a0460c2f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/1.18G [00:00\n", " \n", " \n", " [2850/2850 1:44:25, Epoch 30/30]\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", " \n", " \n", " \n", " \n", " \n", " \n", "
StepTraining LossValidation LossWer
4005.0179001.4935411.024921
8000.7075000.4546090.607088
12000.3072000.3946860.540088
16000.2145000.4049220.519355
20000.1647000.4199370.500255
24000.1338000.4144010.485854
28000.1160000.4055310.480033

" ], "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 = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-400\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-400/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-400/preprocessor_config.json\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/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 = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-800\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-800/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-800/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 = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1200\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1200/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-400] 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 = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1600\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1600/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-800] 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 = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2000\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2000/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1200] 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 = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2400\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2400/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-1600] 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 = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2800\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2800/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xlsr-turkish-demo-colab/checkpoint-2000] 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=2850, training_loss=0.9370169477295457, metrics={'train_runtime': 6267.5878, 'train_samples_per_second': 14.489, 'train_steps_per_second': 0.455, 'total_flos': 1.0303791368179046e+19, 'train_loss': 0.9370169477295457, 'epoch': 30.0})" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.train()" ] }, { "cell_type": "markdown", "metadata": { "id": "kPNxoqifM7R3" }, "source": [ "You can now upload the final result of the training to the Hub. Just execute this instruction:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 351, "referenced_widgets": [ "4014d89119054c25ac0d1938ac4e2f8a", "c8db6791b1e64c62b6df2f098738eaa4", "fa08a1c6fc6f49038a878076ae2809c5", "f8e74bf4449f4d55bfc52d765ba44882", "115ae9ce4fcd4ff5b5c18648e034c446", "953293c28fa14e71a8e354ea1b844b75", "e84826abaf754217b98672dfd41d35fc", "d506cfa68fef41b6933a71d6d00b7b1e", "946ad0cbe0eb4ee3af73d2b1de3ea95a", "7a16d09a448d47938a4c016a54522ae3", "5738d171b6744ae2a991751a6bc2d850", "5ac1a0aaf868461ca207a09079ca6c53", "5e9e02a9b0bd473081cafbd5bf128507", "2ea0e8ecacb74670b9f9a3455e496eda", "a89587d630fa44bab17fed5a58ebb68b", "7ca6e27713b54a03b5bd52e626f33fc7", "222eef3cbb084b3d8a5ffe8068060340", "cb4652f2b0974680b9f224ef1ef97ddb", "678ac926ffa64e4482f0461aecbf4484", "b46ceb3a5c0d4f4eb7d222f1639c88f5", "2896391751074c528768de57d29d807e", "de70b5deb8894bf8ab9f75a79a228f8d" ] }, "id": "FKgqc1FhAxAP", "outputId": "cac62ad7-d950-43b6-e6cb-d5d76a721f38" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Saving model checkpoint to wav2vec2-large-xlsr-turkish-demo-colab\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/config.json\n", "Model weights saved in wav2vec2-large-xlsr-turkish-demo-colab/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xlsr-turkish-demo-colab/preprocessor_config.json\n", "Several commits (2) will be pushed upstream.\n", "The progress bars may be unreliable.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4014d89119054c25ac0d1938ac4e2f8a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Upload file pytorch_model.bin: 0%| | 3.36k/1.18G [00:00 main\n", "\n", "Dropping the following result as it does not have all the necessary field:\n", "{'dataset': {'name': 'common_voice', 'type': 'common_voice', 'args': 'tr'}}\n", "To https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab\n", " f642252..38e6fca main -> main\n", "\n" ] }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab/commit/f64225259534e44179de0f715b96ada1a621551f'" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.push_to_hub()" ] }, { "cell_type": "markdown", "metadata": { "id": "gKSOruDqNJxO" }, "source": [ "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier \"your-username/the-name-you-picked\" so for instance:" ] }, { "cell_type": "markdown", "metadata": { "id": "2x-GybJDNQoH" }, "source": [ "```python\n", "from transformers import AutoModelForCTC, Wav2Vec2Processor\n", "\n", "model = AutoModelForCTC.from_pretrained(\"patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab\")\n", "processor = Wav2Vec2Processor.from_pretrained(\"patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab\")\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "RHIVc44_fY2N" }, "source": [ "To fine-tune larger models on larger datasets using CTC loss, one should take a look at the official speech-recognition examples [here](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition#connectionist-temporal-classification-without-language-model-ctc-wo-lm) 🤗." ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "include_colab_link": true, "machine_shape": "hm", "name": "Fine-Tune Multi-Lingual Speech Recognition Model with 🤗 Transformers using CTC.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": 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