diff --git "a/train_notebook.ipynb" "b/train_notebook.ipynb" new file mode 100644--- /dev/null +++ "b/train_notebook.ipynb" @@ -0,0 +1,10533 @@ +{ + "cells": [ + { + "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": "i69NoS4Kvh_f" + }, + "outputs": [], + "source": [ + "\n", + "model_checkpoint = \"facebook/wav2vec2-xls-r-300m\"\n", + "batch_size = 16" + ] + }, + { + "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==1.18.3\n", + "#common_voice 7 below\n", + "#!pip install datasets==1.14\n", + "!pip install transformers==4.11.3\n", + "\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": 359, + "referenced_widgets": [ + "f806b26119884e688585835e33bd9cda", + "62281d26fca2464b891e4e8dced07110", + "e8ed03cfa00c4941a2351dde3c2e4cb7", + "5f40b4c187664350ad4f7bce35518d47", + "400d6ff9e84d476cadeaddfc57ba7f31", + "68ab1ecb6d724fc29777d7216081a667", + "4cc4957451364dfab93f760d0f8cd0b6", + "32c43163ae214a73871c7952f91c1b79", + "343e824ec8014081aa0dc4656e5c1247", + "168bb48911bd4b398e1bef1e57282a4a", + "589efd9025484b199b2a6fe6f6b06027", + "8dcd01c2904745a3a5f9cfbe2339c344", + "888e4ce603eb413dbe1affc067555376", + "7f838aab3d2c4d98a50652fb30493b10", + "958d9ceb018941378ce9e62cafcc2930", + "40a18d78a96f450a96d127a2504c18cf", + "6a3e10c948c94bf5b4f35496f22feb4d" + ] + }, + "id": "mlMSH3T3EazV", + "outputId": "ad0ddaaf-b3c8-4a55-b5f2-c655b3a32ba0" + }, + "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": [ + "from huggingface_hub import notebook_login\n", + "\n", + "notebook_login()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ujdZ2TxhElk6" + }, + "source": [ + "\n", + "Then you need to install Git-LFS to upload your model checkpoints:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WcR-d83OEkqb" + }, + "outputs": [], + "source": [ + "%%capture\n", + "!apt install git-lfs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Mn9swf6EQ9Vd" + }, + "source": [ + "\n", + "\n", + "\n", + "---\n", + "\n", + "${}^1$ In the [paper](https://arxiv.org/pdf/2006.13979.pdf), the model was evaluated using the phoneme error rate (PER), but by far the most common metric in ASR is the word error rate (WER). To keep this notebook as general as possible we decided to evaluate the model using WER." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0mW-C1Nt-j7k" + }, + "source": [ + "## Prepare Data, Tokenizer, Feature Extractor" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BeBosnY9BH3e" + }, + "source": [ + "ASR models transcribe speech to text, which means that we both need a feature extractor that processes the speech signal to the model's input format, *e.g.* a feature vector, and a tokenizer that processes the model's output format to text. \n", + "\n", + "In 🤗 Transformers, speech recognition models are thus accompanied by both a tokenizer, and a feature extractor.\n", + "\n", + "Let's start by creating the tokenizer responsible for decoding the model's predictions." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sEXEWEJGQPqD" + }, + "source": [ + "### Create Tokenizer for Speech Recognition" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "idBczw8mWzgt" + }, + "source": [ + "First, let's go to [Common Voice](https://commonvoice.mozilla.org/en/datasets) and pick a language to fine-tune XLSR-Wav2Vec2 on. For this notebook, we will use Turkish. \n", + "\n", + "For each language-specific dataset, you can find a language code corresponding to your chosen language. On [Common Voice](https://commonvoice.mozilla.org/en/datasets), look for the field \"Version\". The language code then corresponds to the prefix before the underscore. For Turkish, *e.g.* the language code is `\"tr\"`.\n", + "\n", + "Great, now we can use 🤗 Datasets' simple API to download the data. The dataset name will be `\"common_voice\"`, the config name corresponds to the language code - `\"tr\"` in our case." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bee4g9rpLxll" + }, + "source": [ + "Common Voice has many different splits including `invalidated`, which refers to data that was not rated as \"clean enough\" to be considered useful. In this notebook, we will only make use of the splits `\"train\"`, `\"validation\"` and `\"test\"`. \n", + "\n", + "Because the Turkish dataset is so small, we will merge both the validation and training data into a training dataset and simply use the test data for validation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Swi42bp-hrvF", + "outputId": "2de050e3-97f1-447b-c807-c4c98b88e198" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1.18.3\n" + ] + } + ], + "source": [ + "import datasets\n", + "print(datasets.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 217, + "referenced_widgets": [ + "23bdf3807fe34d63a600fcea26520f78", + "2f73de7bd34b4377a14b90ccf962d327", + "cdcd548b670e4fdbb061a7a38f305fcd", + "bbc251ada7504759b531d9b846b06944", + 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[00:00\n", + " \n", + " \n", + " \n", + " sentence\n", + " \n", + " \n", + " \n", + " \n", + " 0\n", + " El terazi, göz mizan.\n", + " \n", + " \n", + " 1\n", + " Daha yüz yirmi getireceksin!\n", + " \n", + " \n", + " 2\n", + " \"Adalet'in gerdanı açıktı.\"\n", + " \n", + " \n", + " 3\n", + " Haydi, al şu yirmi beşi de, bu hesabı kapayalım…\n", + " \n", + " \n", + " 4\n", + " Evrakı ve raporları savcılık kaleminde duruyor, takip eden olmadığı için sıra bekliyordu.\n", + " \n", + " \n", + " 5\n", + " Yandık!\n", + " \n", + " \n", + " 6\n", + " Anlat bakalım.\n", + " \n", + " \n", + " 7\n", + " Ak akçe kara gün içindir.\n", + " \n", + " \n", + " 8\n", + " Bindim vapura geldim. Hemen bara yerleştim. Beş on kuruş kazandım.\n", + " \n", + " \n", + " 9\n", + " Ahlak sohbetleri.\n", + " \n", + " \n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {} + } + ], + "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": [ + "da1051f673f747e4955f863369212aeb", + "d4d7845f9b8a49f592b61cd51e439e6d", + "9ec68324ba8f427eab819c06ea2e6c5f", + "ca95007c72854a118eb2826170a7e5ee", + "62c200f505f64e939ceaaea4cf5c1f1f", + "3df6c9b2730e4b10aaa46d59ef6db333", + "76c4fe0608734983a22e47799cb1c383", + "e951dfe6a3b845069aca8a81c7248232", + "4f35d24404484227801de58669037020", + "a1aee963e2764fbca86cf242647022e0", + "caa9274766ed4102988e69986c44aa0a", + "19ebe7b3d430420cb28da897a88c092c", + "d889e7cfdf9b4c758eae00b2e303a021", + "612da199c8dc4ef48d26b26e4fcfcd55", + "11cde7ee4f5b4a6796391b53bfb2b7da", + "d0487450690a41da805bc8b48c72271b", + "69d46dd2eea14bd88faf5a1ca4cfed7c", + "6b00d315427a4c9cb833605dc07801f6", + "ebdf3801c865463a9294893f62fd62f3", + "728ea53bb2954d4ba98c0e3898f381e5", + "682910d92af240b5942e46fbdf134421", + "4ad9488f342c4ff8bcb71def50f081ef" + ] + }, + "id": "XIHocAuTQbBR", + "outputId": "2df8c280-45fd-4941-8f03-4d098e86e1a7" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "da1051f673f747e4955f863369212aeb", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "0ex [00:00, ?ex/s]" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "19ebe7b3d430420cb28da897a88c092c", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "0ex [00:00, ?ex/s]" + ] + }, + "metadata": {} + } + ], + "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": 363 + }, + "id": "RBDRAAYxRE6n", + "outputId": "ff1fdea5-bde6-48ff-b4f2-775cce206ccf" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
sentence
0koca adama bir şeyler oluyor
1güney istikametinde gidiyordum
2sorma
3boş ver onu
4sana ihtiyacımız var
5sonradan gelen devlet devlet değildir
6bana öyle gelmiyor
7bize de üsküdar'da toptaşı'na yakın ahşap bir ev bıraktı
8akıllıca
9diğerleri…
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {} + } + ], + "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": [ + "04805b7e6dbd4e878543b035c6ac9ffd", + "e30ada8532d34e279a5c917187030a35", + "88ba207d0a65433ca0e5ea1298055795", + "573756adc4da43ebbff370e6c5a6cee1", + "f13c45348bc346f8b0acdaa00b4fb75b", + "f223dcd00b224f229ffa30efb18a21bb", + "444a0eabada2472a8a0293d89d3056d4", + "f1909a70344a49849911d5221fe2e09c", + "5d497cd406e6459797fc16df24d06f39", + "24f87018abdd4a05ae26a0b123b81d4a", + "37d657c09da14715826ea3da6a3c8bf1", + "80d72bcc95bb4a2aaf27d16bc81c013f", + "ab6646153a4a49e6b468b2f2f1711605", + "c921133532a7448f90c9add21fb9098f", + "2154a5d07421448aaf6fe53c5c9568a4", + "7ef4dfd22751446fb255f9102f3419f9", + "e68c242c463e4e6895dad01ca33c9326", + "f6129bdabec34a01af416293eeb47ad7", + "b1909d011ebd4129b4992347797ef0a4", + "36228aad1ec24e5c97fc0d046e00561e", + "f0b1028065cd4765b310b7af3ae26b4b", + "7d5c696360e347cd8e00ef545f08ef43" + ] + }, + "id": "_m6uUjjcfbjH", + "outputId": "77777471-db25-402e-d0a6-0eb95544d515" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "04805b7e6dbd4e878543b035c6ac9ffd", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + " 0%| | 0/1 [00:00 main\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'https://huggingface.co./emre/wav2vec2-xls-r-300m-tr-CV8-v1/commit/b9dfb7ea3acf138a98773a2e1cea53ec73cbf18b'" + ] + }, + "metadata": {}, + "execution_count": 24 + } + ], + "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": "64e75171-4e98-429a-b6fe-42ee19686281" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'cv-corpus-8.0-2022-01-19/tr/clips/common_voice_tr_17528071.mp3'" + ] + }, + "metadata": {}, + "execution_count": 25 + } + ], + "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": "ad749bc4-29e6-40fe-e529-26eefdd4f09a" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", + " -2.9087067e-05, -2.5093555e-05, -5.6624413e-06], dtype=float32),\n", + " 'path': 'cv-corpus-8.0-2022-01-19/tr/clips/common_voice_tr_17528071.mp3',\n", + " 'sampling_rate': 48000}" + ] + }, + "metadata": {}, + "execution_count": 26 + } + ], + "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": "0db16df8-99c0-4bef-eb9d-bf49ee04c676" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", + " -1.4103199e-05, 4.2269539e-06, -2.5639036e-05], dtype=float32),\n", + " 'path': 'cv-corpus-8.0-2022-01-19/tr/clips/common_voice_tr_17528071.mp3',\n", + " 'sampling_rate': 16000}" + ] + }, + "metadata": {}, + "execution_count": 28 + } + ], + "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": 93 + }, + "id": "dueM6U7Ev0OA", + "outputId": "6cc8d624-0884-454f-fe7b-d1181e395026" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "haşan yolunu kesmiş emine demiş bu dünyada gönlüne karşı gelen babayiğit çıkmamış \n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 29 + } + ], + "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": "3a09dd08-01a8-4977-fe03-7e50a8c8c020" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Target text: o hemen yerinden fırladı yeleğinin cebinden kibritini çıkardı \n", + "Input array shape: (89280,)\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": 49, + "referenced_widgets": [ + "2a2cbbf93c8c4ad69052b1b0478acfea", + "a4c449e803d942d7a4bdc9f5c923107d", + "475e841d2f254301a9c4a5da9be07bfe", + "189dcc43f91946669a77445e3acb1143", + "75c01c6a6b094b13995c8c061749e127", + "f1d9f253969b446faeaca0167b84afb8", + "210d7c933d564b769964e68668491128", + "feccd14aa429488fb5194cf7f18ddc05", + "06ecae394d624765baf9bdbaddde248e", + "da191a839e85403c8e0f9e27144b7a69", + "9b45662a7c774bf8913b547705021a5f" + ] + }, + "id": "UuA-9bgBYT4x", + "outputId": "420cad05-e704-4253-a8d4-8a3d728df53a" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2a2cbbf93c8c4ad69052b1b0478acfea", + "version_minor": 0, + "version_major": 2 + }, + "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": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "3584a24565254be4a665568c91261a40", + "ecc615a58d1a45cbadde4079b14b2a8c", + "608329d74f56402fae1bd3ba3318a0ff", + "3ddeca68918e44eaa65a975acba73081", + "71f27170a8064045826d41aa63fbf29e", + "0fc84d9b108c4cb38c0f18b4d1a3d27a", + "542355f122e248bfb7f6a8d902459e16", + "bf1febb1eb594bbd90c9cb94363536a1", + "01fa773649da4e3fa955110d97a57793", + "10073907af5a4e9b9fd03f41221c328c", + "98cbd815a3f4437bbf80f67e0964d2bb" + ] + }, + "id": "9Xsux2gmyXso", + "outputId": "e9c74e6e-b2ea-4a53-8d46-ea9748ea3dc3" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3584a24565254be4a665568c91261a40", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "Downloading: 0%| | 0.00/1.90k [00:00> 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": 156, + "referenced_widgets": [ + "e204b2ca0ef444779bf10e8d37943284", + "46ad1ddaa7814ff38877f14a4f3167fc", + "114d8e5de5a94e1d8a61fad25bfce8f9", + "ce0f4b3a12f74e9d9dbcfdf07de58c83", + "c9d344680b9f428a8398c4c492fe60d8", + "50123cd248334539882bc450f0c76315", + "bc3f2ee267264173ba9bc54d1ebf5d0b", + "f58a3b6fc7d54398a502b620e9180751", + "62853e9a99f3490b9b5b268d52c2102d", + "98d1ddd134964990b74392bfc94f6bb6", + "c5ad073383524924be6e8ce7c1d9a1d5" + ] + }, + "id": "e7cqAWIayn6w", + "outputId": "ce06bac2-ff48-409b-c34f-216531b9cffe" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e204b2ca0ef444779bf10e8d37943284", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "Downloading: 0%| | 0.00/1.18G [00:00\n", + " \n", + " \n", + " [14501/19620 19:34:03 < 6:54:30, 0.21 it/s, Epoch 22.17/30]\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", + " \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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
StepTraining LossValidation LossWer
5006.6114002.8527300.999977
10001.4395000.5008430.712646
15000.8105000.3884320.645293
20000.6855000.3430370.610986
25000.6387000.3067370.584678
30000.5938000.3042340.580553
35000.5667000.2988750.563640
40000.5348000.2906690.556330
45000.5250000.2984710.551884
50000.5012000.2927870.552090
55000.4835000.2795110.541755
60000.4751000.2773530.540884
65000.4596000.2652670.529631
70000.4435000.2676040.530434
75000.4289000.2641860.520006
80000.4319000.2703820.518517
85000.4169000.2604750.519250
90000.4038000.2555640.513200
95000.4013000.2671530.504217
100000.3879000.2495670.502933
105000.3807000.2471310.496815
110000.3754000.2472300.496792
115000.3722000.2414580.495348
120000.3571000.2440720.495325
125000.3521000.2357270.493240
130000.3432000.2315850.487579
135000.3428000.2355340.483271
140000.3282000.2401420.481208

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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-500\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-500/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-500/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-500/preprocessor_config.json\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/preprocessor_config.json\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1000\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1000/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1000/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1000/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-500] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1500\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1500/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1500/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1500/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1000] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2000\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2000/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2000/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2000/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1500] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2500\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2500/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2500/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2500/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2000] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3000\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3000/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3000/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3000/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2500] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3500\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3500/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3500/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3500/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3000] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4000\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4000/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4000/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4000/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3500] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4500\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4500/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4500/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4500/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4000] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5000\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5000/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5000/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5000/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4500] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5500\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5500/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5500/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5500/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5000] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6000\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6000/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6000/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6000/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5500] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6500\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6500/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6500/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6500/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6000] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7000\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7000/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7000/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7000/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6500] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7500\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7500/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7500/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7500/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7000] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8000\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8000/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8000/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8000/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7500] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8500\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8500/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8500/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8500/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8000] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9000\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9000/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9000/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9000/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8500] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9500\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9500/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9500/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9500/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9000] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10000\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10000/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10000/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10000/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9500] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10500\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10500/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10500/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10500/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10000] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11000\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11000/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11000/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11000/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10500] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11500\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11500/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11500/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11500/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11000] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12000\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12000/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12000/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12000/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11500] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12500\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12500/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12500/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12500/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12000] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13000\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13000/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13000/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13000/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12500] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13500\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13500/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13500/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13500/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13000] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n", + "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-14000\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-14000/config.json\n", + "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-14000/pytorch_model.bin\n", + "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-14000/preprocessor_config.json\n", + "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13500] due to args.save_total_limit\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", + " return (input_length - kernel_size) // stride + 1\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 = 8339\n", + " Batch size = 8\n" + ] + } + ], + "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": { + "id": "FKgqc1FhAxAP", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 165 + }, + "outputId": "8b001341-0116-4451-b5fc-92bfc3beb4e2" + }, + "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[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\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[0;31mNameError\u001b[0m: name 'trainer' is not defined" + ] + } + ], + "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) 🤗." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8Yj_7_SS2XMb" + }, + "outputs": [], + "source": [ + "import argparse\n", + "import re\n", + "from typing import Dict\n", + "\n", + "import torch\n", + "from datasets import Audio, Dataset, load_dataset, load_metric\n", + "\n", + "from transformers import AutoFeatureExtractor, pipeline\n", + "\n", + "\n", + "def log_results(result: Dataset, args: Dict[str, str]):\n", + " \"\"\"DO NOT CHANGE. This function computes and logs the result metrics.\"\"\"\n", + "\n", + " log_outputs = args.log_outputs\n", + " dataset_id = \"_\".join(args.dataset.split(\"/\") + [args.config, args.split])\n", + "\n", + " # load metric\n", + " wer = load_metric(\"wer\")\n", + " cer = load_metric(\"cer\")\n", + "\n", + " # compute metrics\n", + " wer_result = wer.compute(references=result[\"target\"], predictions=result[\"prediction\"])\n", + " cer_result = cer.compute(references=result[\"target\"], predictions=result[\"prediction\"])\n", + "\n", + " # print & log results\n", + " result_str = f\"WER: {wer_result}\\n\" f\"CER: {cer_result}\"\n", + " print(result_str)\n", + "\n", + " with open(f\"{dataset_id}_eval_results.txt\", \"w\") as f:\n", + " f.write(result_str)\n", + "\n", + " # log all results in text file. Possibly interesting for analysis\n", + " if log_outputs is not None:\n", + " pred_file = f\"log_{dataset_id}_predictions.txt\"\n", + " target_file = f\"log_{dataset_id}_targets.txt\"\n", + "\n", + " with open(pred_file, \"w\") as p, open(target_file, \"w\") as t:\n", + "\n", + " # mapping function to write output\n", + " def write_to_file(batch, i):\n", + " p.write(f\"{i}\" + \"\\n\")\n", + " p.write(batch[\"prediction\"] + \"\\n\")\n", + " t.write(f\"{i}\" + \"\\n\")\n", + " t.write(batch[\"target\"] + \"\\n\")\n", + "\n", + " result.map(write_to_file, with_indices=True)\n", + "\n", + "\n", + "def normalize_text(text: str) -> str:\n", + " \"\"\"DO ADAPT FOR YOUR USE CASE. this function normalizes the target text.\"\"\"\n", + "\n", + " chars_to_ignore_regex = '[,?.!\\-\\;\\:\"“%‘”�—’…–]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training\n", + "\n", + " text = re.sub(chars_to_ignore_regex, \"\", text.lower())\n", + "\n", + " # In addition, we can normalize the target text, e.g. removing new lines characters etc...\n", + " # note that order is important here!\n", + " token_sequences_to_ignore = [\"\\n\\n\", \"\\n\", \" \", \" \"]\n", + "\n", + " for t in token_sequences_to_ignore:\n", + " text = \" \".join(text.split(t))\n", + "\n", + " return text\n", + "\n", + "\n", + "def main(args):\n", + " # load dataset\n", + " dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)\n", + "\n", + " # for testing: only process the first two examples as a test\n", + " # dataset = dataset.select(range(10))\n", + "\n", + " # load processor\n", + " feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)\n", + " sampling_rate = feature_extractor.sampling_rate\n", + "\n", + " # resample audio\n", + " dataset = dataset.cast_column(\"audio\", Audio(sampling_rate=sampling_rate))\n", + "\n", + " # load eval pipeline\n", + " if args.device is None:\n", + " args.device = 0 if torch.cuda.is_available() else -1\n", + " asr = pipeline(\"automatic-speech-recognition\", model=args.model_id, device=args.device)\n", + "\n", + " # map function to decode audio\n", + " def map_to_pred(batch):\n", + " prediction = asr(\n", + " batch[\"audio\"][\"array\"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s\n", + " )\n", + "\n", + " batch[\"prediction\"] = prediction[\"text\"]\n", + " batch[\"target\"] = normalize_text(batch[\"sentence\"])\n", + " return batch\n", + "\n", + " # run inference on all examples\n", + " result = dataset.map(map_to_pred, remove_columns=dataset.column_names)\n", + "\n", + " # compute and log_results\n", + " # do not change function below\n", + " log_results(result, args)\n", + "\n", + "'''\n", + "if __name__ == \"__main__\":\n", + " parser = argparse.ArgumentParser()\n", + "\n", + " parser.add_argument(\n", + " \"--model_id\", type=str, required=True, help=\"Model identifier. Should be loadable with 🤗 Transformers\"\n", + " )\n", + " parser.add_argument(\n", + " \"--dataset\",\n", + " type=str,\n", + " required=True,\n", + " help=\"Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets\",\n", + " )\n", + " parser.add_argument(\n", + " \"--config\", type=str, required=True, help=\"Config of the dataset. *E.g.* `'en'` for Common Voice\"\n", + " )\n", + " parser.add_argument(\"--split\", type=str, required=True, help=\"Split of the dataset. *E.g.* `'test'`\")\n", + " parser.add_argument(\n", + " \"--chunk_length_s\", type=float, default=None, help=\"Chunk length in seconds. Defaults to 5 seconds.\"\n", + " )\n", + " parser.add_argument(\n", + " \"--stride_length_s\", type=float, default=None, help=\"Stride of the audio chunks. Defaults to 1 second.\"\n", + " )\n", + " parser.add_argument(\n", + " \"--log_outputs\", action=\"store_true\", help=\"If defined, write outputs to log file for analysis.\"\n", + " )\n", + " parser.add_argument(\n", + " \"--device\",\n", + " type=int,\n", + " default=None,\n", + " help=\"The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.\",\n", + " )\n", + " args = parser.parse_args()\n", + "\n", + " main(args)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "machine_shape": "hm", + "name": "Emre-Turkish-Fine-Tune Multi-Lingual Speech Recognition Model with 🤗 Transformers using CTC.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "f806b26119884e688585835e33bd9cda": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "VBoxView", + "_dom_classes": [], + "_model_name": "VBoxModel", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.5.0", + "box_style": "", + "layout": "IPY_MODEL_62281d26fca2464b891e4e8dced07110", + "_model_module": "@jupyter-widgets/controls", + "children": [ + "IPY_MODEL_e8ed03cfa00c4941a2351dde3c2e4cb7", + "IPY_MODEL_5f40b4c187664350ad4f7bce35518d47", + "IPY_MODEL_400d6ff9e84d476cadeaddfc57ba7f31", + "IPY_MODEL_68ab1ecb6d724fc29777d7216081a667", + "IPY_MODEL_4cc4957451364dfab93f760d0f8cd0b6" + ] + } + }, + "62281d26fca2464b891e4e8dced07110": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": "column", + "width": "50%", + "min_width": null, + "border": null, + "align_items": "center", + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": "flex", + "left": null + } + }, + "e8ed03cfa00c4941a2351dde3c2e4cb7": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HTMLView", + "style": "IPY_MODEL_32c43163ae214a73871c7952f91c1b79", + "_dom_classes": [], + "description": "", + "_model_name": "HTMLModel", + "placeholder": "​", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": "
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