{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "LBSYoWbi-45k" }, "source": [ "# **Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers**\n", "\n", "***New (11/2021)***: *This blog post has been updated to feature XLSR's successor, called [XLS-R](https://huggingface.co/models?other=xls_r)*." ] }, { "cell_type": "markdown", "metadata": { "id": "V7YOT2mnUiea" }, "source": [ "**Wav2Vec2** is a pretrained model for Automatic Speech Recognition (ASR) and was released in [September 2020](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) by *Alexei Baevski, Michael Auli, and Alex Conneau*. Soon after the superior performance of Wav2Vec2 was demonstrated on one of the most popular English datasets for ASR, called [LibriSpeech](https://huggingface.co/datasets/librispeech_asr), *Facebook AI* presented a multi-lingual version of Wav2Vec2, called [XLSR](https://arxiv.org/abs/2006.13979). XLSR stands for *cross-lingual speech representations* and refers to model's ability to learn speech representations that are useful across multiple languages.\n", "\n", "XLSR's successor, simply called **XLS-R** (refering to the [*''XLM-R*](https://ai.facebook.com/blog/-xlm-r-state-of-the-art-cross-lingual-understanding-through-self-supervision/) *for Speech''*), was released in [November 2021](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) by *Arun Babu, Changhan Wang, Andros Tjandra, et al.* XLS-R used almost **half a million** hours of audio data in 128 languages for self-supervised pre-training and comes in sizes ranging from 300 milion up to **two billion** parameters. You can find the pretrained checkpoints on the 🤗 Hub:\n", "\n", "- [**Wav2Vec2-XLS-R-300M**](https://huggingface.co/facebook/wav2vec2-xls-r-300m)\n", "- [**Wav2Vec2-XLS-R-1B**](https://huggingface.co/facebook/wav2vec2-xls-r-1b)\n", "- [**Wav2Vec2-XLS-R-2B**](https://huggingface.co/facebook/wav2vec2-xls-r-2b)\n", "\n", "Similar to [BERT's masked language modeling objective](http://jalammar.github.io/illustrated-bert/), XLS-R learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network during self-supervised pre-training (*i.e.* diagram on the left below). \n", "\n", "For fine-tuning, a single linear layer is added on top of the pre-trained network to train the model on labeled data of audio downstream tasks such as speech recognition, speech translation and audio classification (*i.e.* diagram on the right below).\n", "\n", "![wav2vec2_structure](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xls_r.png)\n", "\n", "XLS-R shows impressive improvements over previous state-of-the-art results on both speech recognition, speech translation and speaker/language identification, *cf.* with Table 3-6, Table 7-10, and Table 11-12 respectively of the official [paper](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages)." ] }, { "cell_type": "markdown", "metadata": { "id": "nT_QrfWtsxIz" }, "source": [ "## Notebook Setup" ] }, { "cell_type": "markdown", "metadata": { "id": "kruqixOYHaIo" }, "source": [ "\n", "In this notebook, we will give an in-detail explanation of how XLS-R - more specifically the pre-trained checkpoint [**Wav2Vec2-XLS-R-300M**](https://huggingface.co/facebook/wav2vec2-xls-r-300m) - can be fine-tuned for ASR. \n", "\n", "For demonstration purposes, we fine-tune the model on the low resource ASR dataset of [Common Voice](https://huggingface.co/datasets/common_voice) that contains only *ca.* 4h of validated training data." ] }, { "cell_type": "markdown", "metadata": { "id": "Gx9OdDYrCtQ1" }, "source": [ "XLS-R is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems, such as ASR and handwriting recognition. \n", "\n", "I highly recommend reading the well-written blog post [*Sequence Modeling with CTC (2017)*](https://distill.pub/2017/ctc/) by Awni Hannun." ] }, { "cell_type": "markdown", "metadata": { "id": "wcHuXIaWyHZU" }, "source": [ "First, let's try to get a good GPU in our colab! With Google Colab's free version it's sadly becoming much harder to get access to a good GPU. With Google Colab Pro, however, one should easily get either a V100 or P100 GPU." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YELVqGxMxnbG", "outputId": "3924b4dc-e7e5-4b0e-dd8e-e7a21171b026" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mon Feb 7 03:40:45 2022 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 41C P8 26W / 149W | 0MiB / 11441MiB | 0% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ] } ], "source": [ "gpu_info = !nvidia-smi\n", "gpu_info = '\\n'.join(gpu_info)\n", "if gpu_info.find('failed') >= 0:\n", " print('Not connected to a GPU')\n", "else:\n", " print(gpu_info)" ] }, { "cell_type": "markdown", "metadata": { "id": "e335hPmdtASZ" }, "source": [ "Before we start, let's install `datasets` and `transformers`. Also, we need the `torchaudio` to load audio files and `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": "rHufFdcERXqR" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "H4coY6imSBHg", "outputId": "3873e965-1519-4aa7-c139-833931ed0fa4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mounted at /content/drive\n", "/content/drive/MyDrive\n", "/content/drive/MyDrive/xls-r-ngram\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')\n", "\n", "%cd /content/drive/MyDrive/\n", "!mkdir -p xls-r-ngram\n", "%cd xls-r-ngram/" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "GlowzSgpV63_" }, "outputs": [], "source": [ "%%capture\n", "!pip install huggingface_hub" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359, "referenced_widgets": [ "cdbfda8da02f4840bba819749c1a8b8a", "63fcb51cec034a008a809ef5530f47cf", "ab2cb08387934cf29e8fe3312bd2c5e1", "8382ccc807da4304be196b92325c5034", "b0f1736a26da4f68853860c7154f6eaf", "ca60f70530de4f259833f839baedad16", "e2835674ae7e4bd495f9f0ea31d8bb46", "fa7bc4e547d84c76b9c69cb291fa5ca3", "466ea64bc8c7408289e272bb47219dbf", "23c12c3c08a64192a769e52df7e857c1", "40e5876fb61e41318f2339c328e70f63", "b72fcb9a096141c89a6d2e7922799796", "416b4bb226754a8390b72bf35115334c", "10239b285e4a4ebbb437dc1385fc143e", "c978841915524174b21b49cba579a7eb", "2c1abc4179614d99be8071ce262f7a16", "0ca23f89ca5c43eb823fc72ec849dfe1" ] }, "id": "x_shSQfsWuA4", "outputId": "5e414cc1-6e86-4455-a21f-1439ab5a5900" }, "outputs": [ { "name": "stdout", "output_type": "stream", "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": "code", "execution_count": 4, "metadata": { "id": "ecgrzk9fatm7" }, "outputs": [], "source": [ "!git config --global credential.helper store" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "ZHDfOgD9X7XZ" }, "outputs": [], "source": [ "%%capture\n", "!apt install git-lfs" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "8aa997f3b5c844aab87c76a39c9646c1", "93cba765d778423d8c8305a1ff9bf175", "7f73584f85854308a3eb8a6a9495ea05", "dcf0700c9d244d58b495edba803dcc26", "4032973b5bec43b492b15d75c6f77004", "c16eb0d3720f4d0bbd155db5732499ec", "c60617b05f124ee49d3c36cf052235fd", 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"e7a616154dff43a29145c906d93e75a9" ] }, "id": "JYlw3ZtlhXGa", "outputId": "70ff38bc-b9f8-40a2-bf1b-dc56d6c91446" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Cloning https://huggingface.co/lucio/xls-r-uzbek-cv8 into local empty directory.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8aa997f3b5c844aab87c76a39c9646c1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Download file pytorch_model.bin: 0%| | 1.58k/1.18G [00:00\n", " \n", " \n", " \n", " sentence\n", " \n", " \n", " \n", " \n", " 0\n", " Etkinliğe dünyadan otuzdan fazla sanatçı katıldı.\n", " \n", " \n", " 1\n", " İnşaat iki bin dokuz yılında başlayacak.\n", " \n", " \n", " 2\n", " Lükse yer kalmıyor.\n", " \n", " \n", " 3\n", " Gümrük idaresi beş yüz yetmiş iki virgül iki milyon avro topladı.\n", " \n", " \n", " 4\n", " Pahor bu hareketi eleştirdi.\n", " \n", " \n", " 5\n", " İki tane vardı.\n", " \n", " \n", " 6\n", " Mali sorunlara rağmen, bunu tekrar başardı.\n", " \n", " \n", " 7\n", " Ancak yakında bu durum değişebilir.\n", " \n", " \n", " 8\n", " Bu teklifleri hangi koşullarda kabul edersiniz?\n", " \n", " \n", " 9\n", " Fakat bu yaz, bunların bir kısmı adadan ayrıldı.\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", "Let's simply remove all characters that don't contribute to the meaning of a word and cannot really be represented by an acoustic sound and normalize the text." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "svKzVJ_hQGK6" }, "outputs": [], "source": [ "import re\n", "chars_to_remove_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�\\']'\n", "\n", "def remove_special_characters(batch):\n", " batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"]).lower()\n", " return batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XIHocAuTQbBR", "outputId": "32b09453-6f60-42c4-e417-4e87edad58dd" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading cached processed dataset at /root/.cache/huggingface/datasets/common_voice/tr/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9/cache-2963c9a6418469ef.arrow\n", "Loading cached processed dataset at /root/.cache/huggingface/datasets/common_voice/tr/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9/cache-a3aa7587e4d2fd25.arrow\n" ] } ], "source": [ "common_voice_train = common_voice_train.map(remove_special_characters)\n", "common_voice_test = common_voice_test.map(remove_special_characters)" ] }, { "cell_type": "markdown", "metadata": { "id": "TxnVS9gIhIma" }, "source": [ "Let's look at the processed text labels again." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359 }, "id": "RBDRAAYxRE6n", "outputId": "5bbca4d5-57b3-48ca-e591-b5feeab0db3d" }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sentence
0ama sonra pazarlık başlıyordu
1diğerleri de hep birlikte işi bırakabilir
2tüm bölge liderleri başsağlığı dilediler
3onurun gözlerindeki büyü
4i̇lkiyse bin dokuz yüz elli dokuz yılında meydana gelmişti
5selin kontrol edilememesi ihtimali hakkındaki fikri sorulan cumhurbaşkanı allah korusun dedi
6bunlar iyi işlemesi gereken ufak çarklar
7halkın yaklaşık yüzde otuz üçü oy kullanmayacak
8güzel alternatif hedefler aramadığını belirtti
9kamuoyu denktaşa karşı
" ], "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", "Before finalizing the pre-processing, it is always advantageous to consult a native speaker of the target language to see whether the text can be further simplified. \n", "For this blog post, [Merve](https://twitter.com/mervenoyann) was kind enough to take a quick look and noted that \"hatted\" characters - like `â` - aren't really used anymore in Turkish and can be replaced by their \"un-hatted\" equivalent, *e.g.* `a`. \n", "\n", "This means that we should replace a sentence like `\"yargı sistemi hâlâ sağlıksız\"` to `\"yargı sistemi hala sağlıksız\"`.\n", "\n", "Let's write another short mapping function to further simplify the text labels. Remember - the simler the text labels, the easier it is for the model to learn to predict those labels.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aZcrz6z7lgGm" }, "outputs": [], "source": [ "def replace_hatted_characters(batch):\n", " batch[\"sentence\"] = re.sub('[â]', 'a', batch[\"sentence\"])\n", " batch[\"sentence\"] = re.sub('[î]', 'i', batch[\"sentence\"])\n", " batch[\"sentence\"] = re.sub('[ô]', 'o', batch[\"sentence\"])\n", " batch[\"sentence\"] = re.sub('[û]', 'u', batch[\"sentence\"])\n", " return batch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ieGhhND5mSwI", "outputId": "2f5cee33-a98a-45ae-a8b4-0f10d97f4d15" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading cached processed dataset at /root/.cache/huggingface/datasets/common_voice/tr/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9/cache-42d8bb33221ffcb6.arrow\n", "Loading cached processed dataset at /root/.cache/huggingface/datasets/common_voice/tr/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9/cache-7feed192f520d0a3.arrow\n" ] } ], "source": [ "common_voice_train = common_voice_train.map(replace_hatted_characters)\n", "common_voice_test = common_voice_test.map(replace_hatted_characters)" ] }, { "cell_type": "markdown", "metadata": { "id": "3ORHDb2Th2TW" }, "source": [ "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": [ "e78238c96d6a4152bf40f5d7a81e5495", "75b26f4a95d24e9d8939d1f7b376eb51", "f58af0561fc242238cfd0163f0ad5e5f", "a1419cc920324f62be58d6e48a82e275", "6ec3c80319fb45f3b6f7715049c9bce0", "02519344818c40d0b961c5ea210a5c62", "e575e46791b24f449b8afcb773e46dcc", "5cff6b2dd6944c59b8c2f205db5f49bf", "5ba2a38c71e64253a4fd39cf7d3b3326", "bc11f833cc9c441b9a58d69ea792fa9c", "4f81e460906a428f9f74a0aa469ba63f", "093927de0d86406290365759e88fd4e7", "f6e777f3a1684a7da3efc63281553b5e", "609781a3f55d4d3dbf0b3640972755fa", "8783792878a64402981a416989924348", "92e295e353624110880cff6835b9e119", "db7a9c27eabb4ba391bfb9d819d7a36e", "fd2ba2bf4b2946078a0761b773626b64", "a5777207c788498fb4ac4900af91d7cf", "890ff54dc35e460b9108e2690966fafe", "cc70741031664fa89bfe8e8e672c5b2b", "46c8681bd36f419495d33b06c96311ed" ] }, "id": "_m6uUjjcfbjH", "outputId": "67fa96dd-2df9-4393-f551-4d599c42efff" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e78238c96d6a4152bf40f5d7a81e5495", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1 [00:00 main\n", "\n" ] }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-turkish-colab/commit/56ebe744b5acf5853233e4425bd914b938c4f013'" ] }, "execution_count": 53, "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-xls-r-300m-tr-colab`" ] }, { "cell_type": "markdown", "metadata": { "id": "mYcIiR2FQ96i" }, "source": [ "### Create `Wav2Vec2FeatureExtractor`" ] }, { "cell_type": "markdown", "metadata": { "id": "Y6mDEyW719rx" }, "source": [ "Speech is a continuous signal and to be treated by computers, it first has to be discretized, which is usually called **sampling**. The sampling rate hereby plays an important role in that it defines how many data points of the speech signal are measured per second. Therefore, sampling with a higher sampling rate results in a better approximation of the *real* speech signal but also necessitates more values per second.\n", "\n", "A pretrained checkpoint expects its input data to have been sampled more or less from the same distribution as the data it was trained on. The same speech signals sampled at two different rates have a very different distribution, *e.g.*, doubling the sampling rate results in data points being twice as long. Thus, \n", "before fine-tuning a pretrained checkpoint of an ASR model, it is crucial to verify that the sampling rate of the data that was used to pretrain the model matches the sampling rate of the dataset used to fine-tune the model.\n", "\n", "XLS-R was pretrained on audio data of [Babel](http://www.reading.ac.uk/AcaDepts/ll/speechlab/babel/r), \n", "[Multilingual LibriSpeech (MLS)](https://huggingface.co/datasets/multilingual_librispeech), [Common Voice](https://huggingface.co/datasets/common_voice), [VoxPopuli](https://arxiv.org/abs/2101.00390), and [VoxLingua107](https://arxiv.org/abs/2011.12998) at a sampling rate of 16kHz. Common Voice, in its original form, has a sampling rate of 48kHz, thus we will have to downsample the fine-tuning data to 16kHz in the following.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "KuUbPW7oV-B5" }, "source": [ "A `Wav2Vec2FeatureExtractor` object requires the following parameters to be instantiated:\n", "\n", "- `feature_size`: Speech models take a sequence of feature vectors as an input. While the length of this sequence obviously varies, the feature size should not. In the case of Wav2Vec2, the feature size is 1 because the model was trained on the raw speech signal ${}^2$.\n", "- `sampling_rate`: The sampling rate at which the model is trained on.\n", "- `padding_value`: For batched inference, shorter inputs need to be padded with a specific value\n", "- `do_normalize`: Whether the input should be *zero-mean-unit-variance* normalized or not. Usually, speech models perform better when normalizing the input\n", "- `return_attention_mask`: Whether the model should make use of an `attention_mask` for batched inference. In general, XLS-R models checkpoints should **always** use the `attention_mask`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kAR0-2KLkopp" }, "outputs": [], "source": [ "from transformers import Wav2Vec2FeatureExtractor\n", "\n", "feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "qUETetgqYC3W" }, "source": [ "Great, XLS-R's feature extraction pipeline is thereby fully defined!\n", "\n", "For improved user-friendliness, the feature extractor and tokenizer are *wrapped* into a single `Wav2Vec2Processor` class so that one only needs a `model` and `processor` object." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KYZtoW-tlZgl" }, "outputs": [], "source": [ "from transformers import Wav2Vec2Processor\n", "\n", "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)" ] }, { "cell_type": "markdown", "metadata": { "id": "DrKnYuvDIoOO" }, "source": [ "Next, we can prepare the dataset." ] }, { "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": 53 }, "id": "TTCS7W6XJ9BG", "outputId": "d4b375f8-a536-4be9-b995-578fd321acfe" }, "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": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "common_voice_train[0][\"path\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "T6ndIjHGFp0W" }, "source": [ "XLS-R 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": "6b0dca9c-aadc-493f-f754-b01d0f35fe28" }, "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": 57, "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 == 4.13.3`, 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": "c2c1375e-4812-4112-d843-9e3da6dd327d" }, "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": 59, "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": "1a771d0a-085d-441b-cc1b-f05438425ce4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "yapabileceklerine inanıyorum\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 60, "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": "63478ae9-2927-4ec1-c13c-41fb3754e18e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Target text: ülke olimpiyatları yirmi ikinci sırada tamamladı\n", "Input array shape: (74880,)\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." ] }, { "cell_type": "markdown", "metadata": { "id": "k3Pbn5WvOYZF" }, "source": [ "Finally, we can leverage `Wav2Vec2Processor` to process the data to the format expected by `Wav2Vec2ForCTC` for training. To do so let's make use of Dataset's [`map(...)`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=map#datasets.DatasetDict.map) function.\n", "\n", "First, we load and resample the audio data, simply by calling `batch[\"audio\"]`.\n", "Second, we extract the `input_values` from the loaded audio file. In our case, the `Wav2Vec2Processor` only normalizes the data. For other speech models, however, this step can include more complex feature extraction, such as [Log-Mel feature extraction](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum). \n", "Third, we encode the transcriptions to label ids.\n", "\n", "**Note**: This mapping function is a good example of how the `Wav2Vec2Processor` class should be used. In \"normal\" context, calling `processor(...)` is redirected to `Wav2Vec2FeatureExtractor`'s call method. When wrapping the processor into the `as_target_processor` context, however, the same method is redirected to `Wav2Vec2CTCTokenizer`'s call method.\n", "For more information please check the [docs](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#transformers.Wav2Vec2Processor.__call__)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eJY7I0XAwe9p" }, "outputs": [], "source": [ "def prepare_dataset(batch):\n", " audio = batch[\"audio\"]\n", "\n", " # batched output is \"un-batched\"\n", " batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n", " batch[\"input_length\"] = len(batch[\"input_values\"])\n", " \n", " with processor.as_target_processor():\n", " batch[\"labels\"] = processor(batch[\"sentence\"]).input_ids\n", " return batch" ] }, { "cell_type": "markdown", "metadata": { "id": "q6Pg_WR3OGAP" }, "source": [ "Let's apply the data preparation function to all examples." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "012b09b271a842d0a7f1cd1260654c82", "6a40faf77bf445308534e7acada6bf0d", "a62ff1ee03dc47c9834b43720df991ea", "03d00b443bfd445d9286f64fd8556031", "557063bf861648f7aefacf71aa53ea9b", "21af0f39c0ce40a5846b980f98481940", "f6e0ea791e5a44abb5d5891bb3e254ad", "6323e35581be4b338a904266a5e85005", "6da7072e7f634a3aa2927fca1bcf38c0", "716dc4ab4fe74abab49f3c0f009357f5", "8a669878b8354867944e577860910001", "3aa317d2b42e4674b38dafa87b5599c5", "0a7c2124936345febe50288a476a12a6", "bdcf9380af5d4601b9b2de84b06ecb40", "b9c5532ffabb44078de8a6f76a4b8be6", "7732bb1317f940faa455f992f4fd2b26", "f28c64f34f9540b6b6bdc765c84e2307", "02016dcc44994c15b69e7f2c4dd0d9c4", "bbfc45e0cf5644f6bc24118fa9c6c098", "eca458b185ae4570b1ec1e77389c871e", "fe156fa95ee9410daff8f7c9a8b0d3a3", "a5e2e1fe3ab94cc6ae1a73cc1ab6f9a8" ] }, "id": "-np9xYK-wl8q", "outputId": "00d6940a-a7bf-4128-896b-76bc289e5b7f" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "012b09b271a842d0a7f1cd1260654c82", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/3478 [00:00 Dict[str, torch.Tensor]:\n", " # split inputs and labels since they have to be of different lenghts and need\n", " # different padding methods\n", " input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n", " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n", "\n", " batch = self.processor.pad(\n", " input_features,\n", " padding=self.padding,\n", " return_tensors=\"pt\",\n", " )\n", " with self.processor.as_target_processor():\n", " labels_batch = self.processor.pad(\n", " label_features,\n", " padding=self.padding,\n", " return_tensors=\"pt\",\n", " )\n", "\n", " # replace padding with -100 to ignore loss correctly\n", " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n", "\n", " batch[\"labels\"] = labels\n", "\n", " return batch" ] }, { "cell_type": "code", "execution_count": 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": [ "7c81059d35534b799623afe872095cac", "379128a308ab43ee9e785e234bb94049", "ff8e746cf8ef4f988617b8d4c0dd3c9c", "0204a7bc668746a68f6e5e5632753c1c", "84361b363998428a87b9e50b6625cca9", "c0919f3c8d594911815ce63273f7fe51", "69f2a9e473534148b96b2e9bd639a087", "5cd8c74ba2e845d498afdc1c25009767", "3f0c587f7b764d96837c8e16254aab12", "d3b5f5a0d35642e0b606ac63c38f88fa", "7ce84a97d72848c0967f732734afdd68" ] }, "id": "9Xsux2gmyXso", "outputId": "18ceeb9e-1a0d-4ee8-f511-a12ad3608bf1" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7c81059d35534b799623afe872095cac", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/1.95k [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 checkpoint of [Wav2Vec2-XLS-R-300M](https://huggingface.co/facebook/wav2vec2-xls-r-300m). The tokenizer's `pad_token_id` must be to define the model's `pad_token_id` or in the case of `Wav2Vec2ForCTC` also CTC's *blank token* ${}^2$. To save GPU memory, we enable PyTorch's [gradient checkpointing](https://pytorch.org/docs/stable/checkpoint.html) and also set the loss reduction to \"*mean*\".\n", "\n", "Because the dataset is quite small (~6h of training data) and because Common Voice is quite noisy, fine-tuning Facebook's [wav2vec2-xls-r-300m checkpoint](https://huggingface.co/facebook/wav2vec2-xls-r-300m) seems to require some hyper-parameter tuning. Therefore, I had to play around a bit with different values for dropout, [SpecAugment](https://arxiv.org/abs/1904.08779)'s masking dropout rate, layer dropout, and the learning rate until training seemed to be stable enough. \n", "\n", "**Note**: When using this notebook to train XLS-R 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": 191, "referenced_widgets": [ "1ca82f9af3ae423096661a1ffd8a165f", "ce429683f48349c083e5fc3253a897d7", "acc35dfdfca542828d1bb02cbde15dfe", "aa92ff120d9c4dd68ae941d7a1f33a81", "731a283141f642b9ab5bf9f7053381d2", "4246da00789f44e2abce952906bde6f1", "059588e34a88476eb6bbc1860ddc27ef", "6ae3b3139e6641fc9e62ae5744349add", "92c30a7c6102443cad2f6f03211c32e0", "ad95ae421231492e814238bde2ecb551", "136f973d9623421eb38e09c73c6b6810", "367c85905a024248bce6c620b822b2ee", "7ea310732a56432da1a095a8d30edc1c", "2d2b8b8d5774494aa73d872e9a4c10d9", "f4af6a0756e241e49e5d325f1bdb18b4", "2b7e52a9be904663ae74226691de6ebd", "771530ea22364f1ea8963e2d542fcf49", "1cdac9cda666466ca1e09f32661d40ed", "3ec9917890f4452883bcaeac444d056c", "e9531fcd48b74929bfdb45cd3f3315e7", "349c9e8b2a8846c1bff5a03d2a28066a", "de1e5c31eacf4fa59cfa51926354acca" ] }, "id": "e7cqAWIayn6w", "outputId": "3e6cebea-78ef-45df-87b0-f63b78ba9644" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1ca82f9af3ae423096661a1ffd8a165f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/1.53k [00:00 inspect -> Console tab and insert code*)." ] }, { "cell_type": "markdown", "metadata": { "id": "VYYAvgkW4P0m" }, "source": [ "```javascript\n", "function ConnectButton(){\n", " console.log(\"Connect pushed\"); \n", " document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n", "}\n", "setInterval(ConnectButton,60000);\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "7bGgLV2r0yvZ" }, "source": [ "Depending on what GPU was allocated to your google colab it might be possible that you are seeing an `\"out-of-memory\"` error here. In this case, it's probably best to reduce `per_device_train_batch_size` to 8 or even less and increase [`gradient_accumulation`](https://huggingface.co/transformers/master/main_classes/trainer.html#trainingarguments)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true, "base_uri": "https://localhost:8080/", "height": 311 }, "id": "9fRr9TG5pGBl", "outputId": "122ea040-7b24-452a-c7d4-7e72e1c46973" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running training *****\n", " Num examples = 3478\n", " Num Epochs = 30\n", " Instantaneous batch size per device = 16\n", " Total train batch size (w. parallel, distributed & accumulation) = 32\n", " Gradient Accumulation steps = 2\n", " Total optimization steps = 3270\n", "/usr/local/lib/python3.7/dist-packages/transformers/feature_extraction_utils.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:201.)\n", " tensor = as_tensor(value)\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" ] }, { "data": { "text/html": [ "\n", "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-400\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-400/preprocessor_config.json\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/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 = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-800\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-800/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 = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1200\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-400] 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 = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1600\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-800] 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 = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2000\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1200] 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 = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2400\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-1600] 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 = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2800\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/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 = 1647\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-3200\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-3200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-3200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-3200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-turkish-colab/checkpoint-2400] 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", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n" ] }, { "data": { "text/plain": [ "TrainOutput(global_step=3270, training_loss=0.5767540113641582, metrics={'train_runtime': 16918.4457, 'train_samples_per_second': 6.167, 'train_steps_per_second': 0.193, 'total_flos': 1.2873093995838829e+19, 'train_loss': 0.5767540113641582, 'epoch': 30.0})" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.train()" ] }, { "cell_type": "markdown", "metadata": { "id": "a9q4mgMZplr_" }, "source": [ "The training loss and validation WER go down nicely." ] }, { "cell_type": "markdown", "metadata": { "id": "4Ya7WEy0pd13" }, "source": [ "You can now upload the result of the training to the 🤗 Hub, just execute this instruction:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true, "referenced_widgets": [ "2d2f71d2c70d466cb9a0d3317fb3095e", "2e894a5b95cb489db8b27c6617fd9533" ] }, "id": "ArG1Thf6NBWm", "outputId": "62ef1c3d-786c-4e25-f9c5-4020e71aa298" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Saving model checkpoint to wav2vec2-large-xls-r-300m-turkish-colab\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-colab/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-turkish-colab/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-turkish-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": "2d2f71d2c70d466cb9a0d3317fb3095e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Upload file pytorch_model.bin: 0%| | 3.35k/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-xls-r-300m-turkish-colab\n", " fe76946..5f0d67b main -> main\n", "\n" ] }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-turkish-colab/commit/fe769461e4e2fb9534740e6c278a0cfabf268474'" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.push_to_hub()" ] }, { "cell_type": "markdown", "metadata": { "id": "RHIVc44_fY2N" }, "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": "5lWWIKyBpx1h" }, "source": [ "```python\n", "from transformers import AutoModelForCTC, Wav2Vec2Processor\n", "\n", "model = AutoModelForCTC.from_pretrained(\"patrickvonplaten/wav2vec2-large-xls-r-300m-tr-colab\")\n", "processor = Wav2Vec2Processor.from_pretrained(\"patrickvonplaten/wav2vec2-large-xls-r-300m-tr-colab\")\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "pmi1cX0fRBit" }, "source": [ "For more examples of how XLS-R can be fine-tuned, please take a look at the [official speech recognition examples](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition#examples)." ] }, { "cell_type": "markdown", "metadata": { "id": "L8b8Qkoy3KyS" }, "source": [ "### Evaluation\n", "\n", "As a final check, let's load the model and verify that it indeed has learned to transcribe Turkish speech.\n", "\n", "Let's first load the pretrained checkpoint." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true }, "id": "R351I9IQp_9D", "outputId": "f2a2ee99-7db6-4962-e140-0107054102d3" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "loading configuration file wav2vec2-large-xls-r-300m-turkish-colab/config.json\n", "Model config Wav2Vec2Config {\n", " \"_name_or_path\": \"facebook/wav2vec2-xls-r-300m\",\n", " \"activation_dropout\": 0.0,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForCTC\"\n", " ],\n", " \"attention_dropout\": 0.0,\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\": 0.0,\n", " \"hidden_size\": 1024,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 4096,\n", " \"layer_norm_eps\": 1e-05,\n", " \"layerdrop\": 0.0,\n", " \"mask_feature_length\": 10,\n", " \"mask_feature_prob\": 0.0,\n", " \"mask_time_length\": 10,\n", " \"mask_time_prob\": 0.05,\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\": 36,\n", " \"proj_codevector_dim\": 768,\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.11.3\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 39\n", "}\n", "\n", "loading weights file wav2vec2-large-xls-r-300m-turkish-colab/pytorch_model.bin\n", "All model checkpoint weights were used when initializing Wav2Vec2ForCTC.\n", "\n", "All the weights of Wav2Vec2ForCTC were initialized from the model checkpoint at wav2vec2-large-xls-r-300m-turkish-colab.\n", "If your task is similar to the task the model of the checkpoint was trained on, you can already use Wav2Vec2ForCTC for predictions without further training.\n", "loading feature extractor configuration file wav2vec2-large-xls-r-300m-turkish-colab/preprocessor_config.json\n", "Feature extractor Wav2Vec2FeatureExtractor {\n", " \"do_normalize\": true,\n", " \"feature_extractor_type\": \"Wav2Vec2FeatureExtractor\",\n", " \"feature_size\": 1,\n", " \"padding_side\": \"right\",\n", " \"padding_value\": 0.0,\n", " \"return_attention_mask\": true,\n", " \"sampling_rate\": 16000\n", "}\n", "\n", "Didn't find file wav2vec2-large-xls-r-300m-turkish-colab/tokenizer.json. We won't load it.\n", "loading file wav2vec2-large-xls-r-300m-turkish-colab/vocab.json\n", "loading file wav2vec2-large-xls-r-300m-turkish-colab/tokenizer_config.json\n", "loading file wav2vec2-large-xls-r-300m-turkish-colab/added_tokens.json\n", "loading file wav2vec2-large-xls-r-300m-turkish-colab/special_tokens_map.json\n", "loading file None\n", "Adding to the vocabulary\n", "Adding to the vocabulary\n" ] } ], "source": [ "model = Wav2Vec2ForCTC.from_pretrained(repo_name).to(\"cuda\")\n", "processor = Wav2Vec2Processor.from_pretrained(repo_name)" ] }, { "cell_type": "markdown", "metadata": { "id": "jD7TZ1YS3S_K" }, "source": [ "\n", "Now, we will just take the first example of the test set, run it through the model and take the `argmax(...)` of the logits to retrieve the predicted token ids." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true }, "id": "pax07TnL3WZn", "outputId": "867787ff-0cb7-41e9-f926-96f7b53e7134" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "It is strongly recommended to pass the ``sampling_rate`` argument to this function.Failing to do so can result in silent errors that might be hard to debug.\n" ] } ], "source": [ "input_dict = processor(common_voice_test[0][\"input_values\"], return_tensors=\"pt\", padding=True)\n", "\n", "logits = model(input_dict.input_values.to(\"cuda\")).logits\n", "\n", "pred_ids = torch.argmax(logits, dim=-1)[0]" ] }, { "cell_type": "markdown", "metadata": { "id": "7nkzSQu53Zs2" }, "source": [ "We adapted `common_voice_test` quite a bit so that the dataset instance does not contain the original sentence label anymore. Thus, we re-use the original dataset to get the label of the first example." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true, "referenced_widgets": [ "54097a6c744849128d7cc3da8aad6609", "b8827ecbec9e44f09216f62c1ee12840", "8728bb32478240b7abe576375b01e640", "19b8a530352a423794395979e6415bea", "5fc8ec04870e4bb7b63b20844c355ab6" ] }, "id": "fe2AE-2xqKHx", "outputId": "1d8321b3-4f41-4d71-e74e-f33f32a7b261" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using custom data configuration tr-ad9f7b76efa9f3a0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading and preparing dataset common_voice/tr (download: 592.09 MiB, generated: 2.89 MiB, post-processed: Unknown size, total: 594.98 MiB) to /root/.cache/huggingface/datasets/common_voice/tr-ad9f7b76efa9f3a0/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "54097a6c744849128d7cc3da8aad6609", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b8827ecbec9e44f09216f62c1ee12840", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8728bb32478240b7abe576375b01e640", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "19b8a530352a423794395979e6415bea", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5fc8ec04870e4bb7b63b20844c355ab6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Dataset common_voice downloaded and prepared to /root/.cache/huggingface/datasets/common_voice/tr-ad9f7b76efa9f3a0/6.1.0/f7a9d973839b7706e9e281c19b7e512f31badf3c0fdbd21c671f3c4bf9acf3b9. Subsequent calls will reuse this data.\n" ] } ], "source": [ "common_voice_test_transcription = load_dataset(\"common_voice\", \"tr\", data_dir=\"./cv-corpus-6.1-2020-12-11\", split=\"test\")" ] }, { "cell_type": "markdown", "metadata": { "id": "epu8kCQZ3h70" }, "source": [ "\n", "Finally, we can decode the example." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true }, "id": "K4xWqmk_qMn0", "outputId": "d9e40b3c-f02a-48a1-d081-6d7e8b37dcaf" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prediction:\n", "ha ta küçük şeyleri için bir büyük biş şeylir koğoluyor ve yeneküçük şeyler için bir birmizi incilkiyoruz\n", "\n", "Reference:\n", "hayatta küçük şeyleri kovalıyor ve yine küçük şeyler için birbirimizi incitiyoruz.\n" ] } ], "source": [ "print(\"Prediction:\")\n", "print(processor.decode(pred_ids))\n", "\n", "print(\"\\nReference:\")\n", "print(common_voice_test_transcription[0][\"sentence\"].lower())" ] }, { "cell_type": "markdown", "metadata": { "id": "HwhyoMml3oOT" }, "source": [ "Alright! The transcription can definitely be recognized from our prediction, but it is not perfect yet. Training the model a bit longer, spending more time on the data preprocessing, and especially using a language model for decoding would certainly improve the model's overall performance.\n", "\n", "For a demonstration model on a low-resource language, the results are quite acceptable however 🤗." ] }, { "cell_type": "markdown", "metadata": { "id": "uA4FF5fTZCK_" }, "source": [ "## Decoding with an n-gram language model" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "W2jMmdD6-54j", "outputId": "42155c88-e3fc-4baf-9a43-d18ee288e476" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/content/drive/MyDrive/xls-r-ngram\n" ] } ], "source": [ "%cd /content/drive/MyDrive/xls-r-ngram/" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RZZmQONlQQAV", "outputId": "ce7c8c35-b567-4b5d-be65-503b0075325c" }, "outputs": [ { "name": 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lm/CMakeFiles/kenlm_benchmark.dir/kenlm_benchmark_main.cc.o\u001b[0m\n", "[ 76%] \u001b[32mBuilding CXX object lm/CMakeFiles/query.dir/query_main.cc.o\u001b[0m\n", "[ 77%] \u001b[32m\u001b[1mLinking CXX executable ../bin/query\u001b[0m\n", "[ 77%] Built target query\n", "\u001b[35m\u001b[1mScanning dependencies of target kenlm_builder\u001b[0m\n", "[ 78%] \u001b[32mBuilding CXX object lm/builder/CMakeFiles/kenlm_builder.dir/adjust_counts.cc.o\u001b[0m\n", "[ 80%] \u001b[32mBuilding CXX object lm/builder/CMakeFiles/kenlm_builder.dir/corpus_count.cc.o\u001b[0m\n", "[ 81%] \u001b[32mBuilding CXX object lm/builder/CMakeFiles/kenlm_builder.dir/initial_probabilities.cc.o\u001b[0m\n", "[ 82%] \u001b[32mBuilding CXX object lm/builder/CMakeFiles/kenlm_builder.dir/interpolate.cc.o\u001b[0m\n", "[ 83%] \u001b[32m\u001b[1mLinking CXX executable ../bin/kenlm_benchmark\u001b[0m\n", "[ 83%] Built target kenlm_benchmark\n", "\u001b[35m\u001b[1mScanning dependencies of target phrase_table_vocab\u001b[0m\n", "[ 85%] \u001b[32mBuilding CXX object lm/filter/CMakeFiles/phrase_table_vocab.dir/phrase_table_vocab_main.cc.o\u001b[0m\n", "[ 86%] \u001b[32mBuilding CXX object lm/builder/CMakeFiles/kenlm_builder.dir/output.cc.o\u001b[0m\n", "[ 87%] \u001b[32m\u001b[1mLinking CXX executable ../../bin/phrase_table_vocab\u001b[0m\n", "[ 87%] Built target phrase_table_vocab\n", "\u001b[35m\u001b[1mScanning dependencies of target filter\u001b[0m\n", "[ 88%] \u001b[32mBuilding CXX object lm/filter/CMakeFiles/filter.dir/filter_main.cc.o\u001b[0m\n", "[ 90%] \u001b[32mBuilding CXX object lm/builder/CMakeFiles/kenlm_builder.dir/pipeline.cc.o\u001b[0m\n", "[ 91%] \u001b[32m\u001b[1mLinking CXX static library ../../lib/libkenlm_builder.a\u001b[0m\n", "[ 91%] Built target kenlm_builder\n", "\u001b[35m\u001b[1mScanning dependencies of target count_ngrams\u001b[0m\n", "[ 92%] \u001b[32m\u001b[1mLinking CXX executable ../../bin/filter\u001b[0m\n", "[ 93%] \u001b[32mBuilding CXX object lm/builder/CMakeFiles/count_ngrams.dir/count_ngrams_main.cc.o\u001b[0m\n", "[ 93%] Built target filter\n", "\u001b[35m\u001b[1mScanning dependencies of target lmplz\u001b[0m\n", "[ 95%] \u001b[32mBuilding CXX object lm/builder/CMakeFiles/lmplz.dir/lmplz_main.cc.o\u001b[0m\n", "[ 96%] \u001b[32m\u001b[1mLinking CXX executable ../../bin/lmplz\u001b[0m\n", "[ 97%] \u001b[32m\u001b[1mLinking CXX executable ../../bin/count_ngrams\u001b[0m\n", "[ 97%] Built target lmplz\n", "[ 97%] Built target count_ngrams\n" ] } ], "source": [ "\n", "!wget -O - https://kheafield.com/code/kenlm.tar.gz | tar xz\n", "!mkdir kenlm/build && cd kenlm/build && cmake .. && make -j2\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uME_zJUaQrlw", "outputId": "e2d23914-2260-47cf-902a-cabd80f3f734" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "build_binary filter\tkenlm_benchmark phrase_table_vocab\t query\n", "count_ngrams fragment\tlmplz\t\t probing_hash_table_benchmark\n" ] } ], "source": [ "!ls kenlm/build/bin\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "npRCvP_sQyjF" }, "outputs": [], "source": [ "from transformers import AutoFeatureExtractor, AutoTokenizer, pipeline\n", "from datasets import Audio, Dataset, DatasetDict, load_dataset, load_metric\n", "\n", "import re\n", "import string\n", "import unidecode" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 614, "referenced_widgets": [ "00476100c826427eb1f7263479c5016c", "726cc87d1f98485780bc4587a46b965b", "29b52705884b4db2adb6847d4442695b", "6426470292d54a48bf01afb45a7de5bd", "c14dcaf4cd4e497f9535ac4e254a5266", "9de26e256a8d4160a5706541717e9cbf", "22f20ec1dcae469fae45e064466d6cce", "5da9c8844a774726a6d3cca1726adaf9", "c614cd6411874ba1981e899a2378e489", "25147eb1ad6b41e1bb14adba58fb75c1", "5713f10a05354997992831927b2a7df2", "81bbbc0424d44dd28ad7e8db15551f0c", "37c0008e833945a0a6b8bb923c8388b1", "e53283cdb36e4b0baadc6d229f1213ff", "01697ee216504717859012fa30a830a2", "829066d0d2be4b8399a0eebd3dc189bc", "994eec70261b4875ab35ba8b3ef1b302", "ca22def6f49a40c187e8a32f719ae9c4", "05025a12f01844589b7c7e487a480d7c", "796d167f620744d0b26c89151a65b650", "7e4a3e9f0cb349018ea3283f37c4e68c", "ccc261d30e784dc9bd821080a78089ef", "ac1b20485eb743ed9ec1b2ed03e5ef1b", "dfa4963bdbab4467852c3ec41a140f24", "f2e6e6ca0ed74e3b941e725066af2c99", "8992c9d2db984e21bb11297c2c604226", "833af7094b2945518b1ec4a2d786eeb6", "b1b672592b234d84aa5d4c0b6c689249", "f2d76e600d944fbe9158641127e0018f", "64b2e004758e4053b85ec781d96ab81e", "7a3d80c4c8f84af789423414033aee8c", "660a2c647ecd4eb5a7f904bf7dcbd0eb", "cc1eaaa0bec245abb61b7c1e71ffbfa1", "3a641a06a54d4b26832b7379abd3a7ce", "b5791f2528dc44c99aba57ca0992ae53", "469f6cd817314eab945223af2d04982f", "b37ef48f06ed49e1aee66e686d04fd33", "920886e2397c4c21bdbc0aef47ada862", "df74123905f34c48818215fe9346e9cf", "42b991b0bd3c4b8c924c49e2be5aaa0e", "279bf256d1254af48c18ceedac598546", "01e90d1463ae4f878909371cad10f537", "18af11ce35e740fe8f8d0464329f2fe0", "fa151be49bc04c7c93de6b79cdba4f95", "f4e127b42b9f49f885cd0343f2e126f8", "f3bbdfe60af84e51b64948f844cdb38c", "1f23c54c46824343bdd1984cf76e7ad2", "29e6714961b34fe08206274d879aaa83", "4db8f422bbee415fa866a104a43efcc4", "4c1e6fc1d4b24ab586bcc4fbfd062e67", "18487b4cc6b940a0bda5b707b13a5730", "792810e60b044a1cb7e5c843e7ca1cab", "55c1b1180400477d8cd5c508739e574f", "15c336710c19406b84351ac69adcfcaa", "45d7c4d3bf7949139944d1a56cea87d9", "63bf33cb3ad242589fd99fa1c93f516f", "25db4aa59b8b481386cbc848b913bd6b", "6f9ca8c750b74a2fbed097c337f33927", "8f8306cf062043f1849550835d5cc5d1", "cee69d595e404aa487740548cc6cc447", "ec34cf9b05214ab88885ee9a51554075", "01c760a27fe5478eb28786ce14ca79b9", "c1af7adeef4c4203986f8d457676de80", "77ce11abade8448bbb9db69ba2110f42", "5fdaae7ef2b94aa8a1149195c76a4b39", "bf12149c6bda49d3bd3537c336c551d3", "5f37174308264acfa0f512aa0918d912", "1caa07af07d54b3b887a4e26db228a87", "d15ec24df1094e5588f88f840ebd1f4b", "02f20474872c4b91929c5526928d493c", "f750b2cb9e5b4f0fbcc0fea9a6f989b4", "ec28e9a7699a4e6cae6f82fe0452e312", "a49e6915ca56455780097ffc43c0f4aa", "0c45b3a3dd234482ad09a6649127bbe3", "a5db72bd2fbd4b5b87a15b099ff5f5f9", "b0b3485ff312478f871d3ce0fd27eecc", "0b5888109c5e4fddbdc4cba4b85794a3", "3134f767901b48b5a241d0ee9249241c", "acfae183ac14477cacf6d4646f6a14f5", "cb6d49f4d0634ddc86f0068e2a4bdea7", "d8f8781216a4472a9a2051f008f5b0d5", "06b325f08a6e4998931a00ef3dcb9d05", "d3922913583348a18d213432ddd6c362", "2bfc7e79dc704b29a548b4c0d9241c40", "245245ed97b64050a6734b09dab5b480", "f1d8af549afa4bb7b24d1de284460b81", "e26769ea23414403b41a2de5fd9574b4", "10273a7d4aae4c05a36b7ed9a8a2ba4c", "66bc73d5cab943d38870ad5e797d4755", "56ec604d97864db9bf88d075184b50c4", "2387b655b23c49788309cdac13146839", "0e08c58a3bc44c489967a502a18f02f6", "fa2fdca1923146c38968020f2dcd31af", "5d1529a6bb1c489492e10fb9cdc45cc3", "eb7701cf79274057b198cae0a2e1a345", "ce454b556ca34befa512e6878324367a", "bb62bc622b3d427cafa2c2c7180ab56a", "65c4c0af846644d89d8f8b318f40aa0a", "249f1ff433db46b68a7e72fc8ee5f43f", "3bfbc26c9f8d41f9a638a093ed6ef10c", "421e7c39be7849f586caea0edc750d3d", "7018f0deca5d4b878b93bef5eb69d865", "c004c6e6d15c4c93af70bb6c60eae484", "8616fe21e3e0430d9e654eb499ccd5e9", "26d566914b2b40e99c3d29797e1bbb86", "00cd2e770d1e43048e7f41bcf6a5f961", "2aba7b884caa4963baa0931fc64a9ab5", "3e5a97b65b3741cb924af45b6628ca6d", "377b283f33434ccaa5e64926c6bff6e3", "f93d52a5e3be4c9a87f3b8e29cfaa4bc" ] }, "id": "M1YRPnTZaFHW", "outputId": "414a50f9-7183-4537-a600-b2d57b361299" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "00476100c826427eb1f7263479c5016c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/10.1k [00:00‘\", # after o/g indicate modified char\n", " unidecode.unidecode(batch[\"sentence\"]).lower()\n", " )\n", " )\n", " ) + \" \"\n", " return batch" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "a4f265ea99fa432a879b949520d37b3f", "5d84de8fa34d4636b1801669a179dba5", "208e960fe7fd4f269b1c6ede3c3f09f3", "3f219581737d4148982607d28672bc90", "a184146a75c74e2c85ef500b10da05d7", "aaa27c8a6db34928aa54c43fb0cb2c3f", "1d66f0adcfc64857ba64bf0eead85e1a", "35acd5d105754454a989c49e3cf54ab3", "4f306a5c4e034fa5b7469da0ae8888a4", "9552e2c379a5427fa821e71eecd02495", "445bbdb812d545adb779ed1933f8278d" ] }, "id": "4qTL2Fc0cbMq", "outputId": "ab387ea4-5cd7-4f32-b9e6-cdb12de40d8c" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a4f265ea99fa432a879b949520d37b3f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataset_train = dataset_dict[\"train\"].map(remove_special_characters, remove_columns=dataset_dict[\"train\"].column_names)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "bd4bae3547204defbf78cfa2945b5e22", "112242cfa09b440182720e34e65195f5", "b6248483c38b42e4addcaea467adbc04", "cc3eb3f99cc84814a46e2997022ab1fc", "306d398e3247444fbcbc039b1507b964", "e5221d40d2ba4b1cb44c3278bccd4e5b", "ffb2b52590f446aca3b9a721405dde4b", "2e3522d2bd084ee6991c38ca338d08ea", "a34dcc92c9874068852812dc5060b91d", "3642769698e7435ebd25d3e4ff1498ac", "ed2a911a02ad40bcab9b947e5302a45a" ] }, "id": "-NrwpeLz-SF7", "outputId": "6a307267-d7b3-4bb9-e7b6-dad3bfe163bc" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bd4bae3547204defbf78cfa2945b5e22", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataset_other = dataset_dict[\"other\"].map(remove_special_characters, remove_columns=dataset_dict[\"other\"].column_names)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sX1xsY8ccWOv", "outputId": "8968734b-d4d5-4b22-e157-fff1ad1933ef" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0 1013740 8446974 xls-r-uzbek-cv8/uz_cv8_text.txt\n" ] } ], "source": [ "text_data = \"xls-r-uzbek-cv8/uz_cv8_text.txt\"\n", "with open(text_data, \"w\") as fs:\n", " fs.write(\" \".join(dataset_train[\"text\"]) + \" \")\n", "\n", "with open(text_data, \"a\") as fs:\n", " fs.write(\" \".join(dataset_other[\"text\"]))\n", "\n", "\n", "!wc $text_data" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "be17oucOcp8_", "outputId": "588be248-fed1-4031-a19b-31edb7804137" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== 1/5 Counting and sorting n-grams ===\n", "Reading /content/drive/MyDrive/xls-r-ngram/xls-r-uzbek-cv8/uz_cv8_text.txt\n", "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n", "tcmalloc: large alloc 3854925824 bytes == 0x5625c9612000 @ 0x7f1e3a0b41e7 0x5625c7fe17a2 0x5625c7f7c51e 0x5625c7f5b2eb 0x5625c7f47066 0x7f1e3824dbf7 0x5625c7f48baa\n", "tcmalloc: large alloc 17989623808 bytes == 0x5626af26a000 @ 0x7f1e3a0b41e7 0x5625c7fe17a2 0x5625c7fd07ca 0x5625c7fd1208 0x5625c7f5b308 0x5625c7f47066 0x7f1e3824dbf7 0x5625c7f48baa\n", "****************************************************************************************************\n", "Unigram tokens 1013740 types 98005\n", "=== 2/5 Calculating and sorting adjusted counts ===\n", "Chain sizes: 1:1176060 2:2133515264 3:4000341248 4:6400545792 5:9334129664\n", "tcmalloc: large alloc 9334136832 bytes == 0x5625c9612000 @ 0x7f1e3a0b41e7 0x5625c7fe17a2 0x5625c7fd07ca 0x5625c7fd1208 0x5625c7f5b8d7 0x5625c7f47066 0x7f1e3824dbf7 0x5625c7f48baa\n", "tcmalloc: large alloc 2133516288 bytes == 0x5627f5cf4000 @ 0x7f1e3a0b41e7 0x5625c7fe17a2 0x5625c7fd07ca 0x5625c7fd1208 0x5625c7f5bcdd 0x5625c7f47066 0x7f1e3824dbf7 0x5625c7f48baa\n", "tcmalloc: large alloc 4000342016 bytes == 0x562874fa2000 @ 0x7f1e3a0b41e7 0x5625c7fe17a2 0x5625c7fd07ca 0x5625c7fd1208 0x5625c7f5bcdd 0x5625c7f47066 0x7f1e3824dbf7 0x5625c7f48baa\n", "tcmalloc: large alloc 6400548864 bytes == 0x562ae0750000 @ 0x7f1e3a0b41e7 0x5625c7fe17a2 0x5625c7fd07ca 0x5625c7fd1208 0x5625c7f5bcdd 0x5625c7f47066 0x7f1e3824dbf7 0x5625c7f48baa\n", "Statistics:\n", "1 98004 D1=0.62768 D2=1.18989 D3+=1.46871\n", "2 539023 D1=0.794198 D2=1.50474 D3+=1.7039\n", "3 719386 D1=0.858088 D2=1.76379 D3+=2.1921\n", "4 793206 D1=0.885229 D2=1.81888 D3+=2.47486\n", "5 850589 D1=0.711001 D2=1.87963 D3+=2.58459\n", "Memory estimate for binary LM:\n", "type MB\n", "probing 63 assuming -p 1.5\n", "probing 76 assuming -r models -p 1.5\n", "trie 31 without quantization\n", "trie 17 assuming -q 8 -b 8 quantization \n", "trie 28 assuming -a 22 array pointer compression\n", "trie 14 assuming -a 22 -q 8 -b 8 array pointer compression and quantization\n", "=== 3/5 Calculating and sorting initial probabilities ===\n", "Chain sizes: 1:1176048 2:8624368 3:14387720 4:19036944 5:23816492\n", "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n", "####################################################################################################\n", "=== 4/5 Calculating and writing order-interpolated probabilities ===\n", "Chain sizes: 1:1176048 2:8624368 3:14387720 4:19036944 5:23816492\n", "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n", "####################################################################################################\n", "=== 5/5 Writing ARPA model ===\n", "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n", "****************************************************************************************************\n", "Name:lmplz\tVmPeak:28049656 kB\tVmRSS:3944028 kB\tRSSMax:3944184 kB\tuser:3.28185\tsys:3.47537\tCPU:6.75725\treal:8.10671\n" ] } ], "source": [ "ngram_data = \"xls-r-uzbek-cv8/5gram.arpa\"\n", "!kenlm/build/bin/lmplz -o 5 < $text_data > $ngram_data " ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "po4zWZRjdOGc", "outputId": "8b93829b-d871-4fde-d2a3-5c602313e5a6" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\\data\\\n", "ngram 1=98004\n", "ngram 2=539023\n", "ngram 3=719386\n", "ngram 4=793206\n", "ngram 5=850589\n", "\n", "\\1-grams:\n", "-5.7507653\t\t0\n", "0\t\t-0.10007139\n", "-5.608028\tshartmidi\t-0.10007139\n", "-3.97965\tdeyman\t-0.12756462\n", "-3.4230683\tkeyingi\t-0.20062704\n", "-4.135141\tvoqealar\t-0.17204677\n", "-2.7541177\tbiz\t-0.29683134\n", "-3.5234666\tshunchaki\t-0.12191775\n", "-3.4540253\tqo‘l\t-0.18548343\n", "-3.53991\tberib\t-0.12553446\n", "-4.464714\tketadigan\t-0.10406738\n", "-2.817535\temas\t-0.23384908\n" ] } ], "source": [ "!head -20 $ngram_data" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "55U0j6JCdbT8" }, "outputs": [], "source": [ "corrected_data = \"xls-r-uzbek-cv8/5gram_correct.arpa\"\n", "with open(ngram_data, \"r\") as read_file, open(corrected_data, \"w\") as write_file:\n", " has_added_eos = False\n", " for line in read_file:\n", " if not has_added_eos and \"ngram 1=\" in line:\n", " count=line.strip().split(\"=\")[-1]\n", " write_file.write(line.replace(f\"{count}\", f\"{int(count)+1}\"))\n", " elif not has_added_eos and \"\" in line:\n", " write_file.write(line)\n", " write_file.write(line.replace(\"\", \"\"))\n", " has_added_eos = True\n", " else:\n", " write_file.write(line)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gVUKO_KDearL", "outputId": "914eae8a-ae79-468e-d378-f9d929cc1ebc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\\data\\\n", "ngram 1=98005\n", "ngram 2=539023\n", "ngram 3=719386\n", "ngram 4=793206\n", "ngram 5=850589\n", "\n", "\\1-grams:\n", "-5.7507653\t\t0\n", "0\t\t-0.10007139\n", "0\t\t-0.10007139\n", "-5.608028\tshartmidi\t-0.10007139\n", "-3.97965\tdeyman\t-0.12756462\n", "-3.4230683\tkeyingi\t-0.20062704\n", "-4.135141\tvoqealar\t-0.17204677\n", "-2.7541177\tbiz\t-0.29683134\n", "-3.5234666\tshunchaki\t-0.12191775\n", "-3.4540253\tqo‘l\t-0.18548343\n", "-3.53991\tberib\t-0.12553446\n", "-4.464714\tketadigan\t-0.10406738\n" ] } ], "source": [ "!head -20 $corrected_data" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "id": "sSpNeQ_bel36" }, "outputs": [], "source": [ "from transformers import AutoProcessor\n", "\n", "processor = AutoProcessor.from_pretrained(\"./xls-r-uzbek-cv8/\")" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ms020VOvlmYr", "outputId": "d1a35aee-83ed-4acf-ca39-0af66f22168d" }, "outputs": [ { "data": { "text/plain": [ "['|',\n", " 'a',\n", " 'b',\n", " 'c',\n", " 'd',\n", " 'e',\n", " 'f',\n", " 'g',\n", " 'h',\n", " 'i',\n", " 'j',\n", " 'k',\n", " 'l',\n", " 'm',\n", " 'n',\n", " 'o',\n", " 'p',\n", " 'q',\n", " 'r',\n", " 's',\n", " 't',\n", " 'u',\n", " 'v',\n", " 'w',\n", " 'x',\n", " 'y',\n", " 'z',\n", " '‘',\n", " '’',\n", " '[UNK]',\n", " '[PAD]',\n", " '',\n", " '']" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vocab_dict =processor.tokenizer.get_vocab()\n", "labels = [k for k, v in sorted(vocab_dict.items(), key=lambda x: x[1])]\n", "labels" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zUZ8MQ2rmFT6", "outputId": "190b0f71-e1ac-40c5-c797-fd60764b3acc" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Found entries of length > 1 in alphabet. This is unusual unless style is BPE, but the alphabet was not recognized as BPE type. Is this correct?\n" ] } ], "source": [ "from pyctcdecode import build_ctcdecoder\n", "\n", "decoder = build_ctcdecoder(labels=labels, kenlm_model_path=corrected_data)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "id": "PTNuGmy9nFRr" }, "outputs": [], "source": [ "from transformers import Wav2Vec2ProcessorWithLM\n", "\n", "processor_with_lm = Wav2Vec2ProcessorWithLM(\n", " feature_extractor=processor.feature_extractor,\n", " tokenizer=processor.tokenizer,\n", " decoder = decoder,\n", ")" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "id": "2GuhuK11nlBe" }, "outputs": [], "source": [ "processor_with_lm.save_pretrained(\"xls-r-uzbek-cv8\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jrV4hXy3oEJI", "outputId": "c886e32b-6712-4c9f-8d7a-1a4aaccda2bd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 144M\n", "-rw------- 1 root root 143M Feb 7 23:59 5gram_correct.arpa\n", "-rw------- 1 root root 78 Feb 8 00:01 attrs.json\n", "-rw------- 1 root root 1.1M Feb 8 00:01 unigrams.txt\n" ] } ], "source": [ "#!rm -r xls-r-uzbek-cv8/language_model/\n", "!ls -alh xls-r-uzbek-cv8/language_model/" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GxIaO9JLAk8A", "outputId": "7a0104e7-0864-4014-a602-69a98d28f417" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading xls-r-uzbek-cv8/language_model/5gram_correct.arpa\n", "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n", "****************************************************************************************************\n", "SUCCESS\n", "total 67M\n", "-rw------- 1 root root 66M Feb 8 00:01 5gram.bin\n", "-rw------- 1 root root 78 Feb 8 00:01 attrs.json\n", "-rw------- 1 root root 1.1M Feb 8 00:01 unigrams.txt\n" ] } ], "source": [ "!kenlm/build/bin/build_binary xls-r-uzbek-cv8/language_model/5gram_correct.arpa xls-r-uzbek-cv8/language_model/5gram.bin\n", "!rm xls-r-uzbek-cv8/language_model/5gram_correct.arpa\n", "!ls -alh xls-r-uzbek-cv8/language_model/" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WigwTkyWoJgg", "outputId": "9f72faed-3c5c-49c7-a02c-90e1e3f9c433" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/uz/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8)\n", "Traceback (most recent call last):\n", " File \"./eval.py\", line 229, in \n", " main(args)\n", " File \"./eval.py\", line 161, in main\n", " asr = pipeline(\"automatic-speech-recognition\", model=args.model_id)\n", " File \"/usr/local/lib/python3.7/dist-packages/transformers/pipelines/__init__.py\", line 628, in pipeline\n", " decoder = BeamSearchDecoderCTC.load_from_hf_hub(model_name, allow_regex=allow_regex)\n", " File \"/usr/local/lib/python3.7/dist-packages/pyctcdecode/decoder.py\", line 757, in load_from_hf_hub\n", " cached_directory = snapshot_download(model_id, cache_dir=cache_dir, **kwargs)\n", " File \"/usr/local/lib/python3.7/dist-packages/huggingface_hub/snapshot_download.py\", line 144, in snapshot_download\n", " model_info = _api.model_info(repo_id=repo_id, revision=revision, token=token)\n", " File \"/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py\", line 868, in model_info\n", " r.raise_for_status()\n", " File \"/usr/local/lib/python3.7/dist-packages/requests/models.py\", line 941, in raise_for_status\n", " raise HTTPError(http_error_msg, response=self)\n", "requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/api/models/./revision/main\n" ] } ], "source": [ "!python ./eval.py --model_id . --dataset mozilla-foundation/common_voice_8_0 --config uz --split \"test\"" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "29B3FYPhyHST", "outputId": "5650c5b0-8295-47df-87fb-6e80e57841b0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/content/drive/MyDrive/xls-r-ngram/xls-r-uzbek-cv8\n" ] } ], "source": [ "%cd xls-r-uzbek-cv8/" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "W8DN9SZ-Errc" }, "outputs": [], "source": [ "!git add eval.py preprocessor_config.json tokenizer_config.json language_model/" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "sLIBafqmEyq6" }, "outputs": [], "source": [ "!git add alphabet.json " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WnQl8yyuFg8u", "outputId": "df213bf0-8839-4e23-845e-8117ed4cf2e3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fatal: cannot exec '.git/hooks/post-commit': Permission denied\n", "[with_ngram_lm 9c389f7] add ngram LM from train+other\n", " 7 files changed, 98037 insertions(+), 7 deletions(-)\n", " create mode 100644 alphabet.json\n", " mode change 100755 => 100644 eval.py\n", " create mode 100644 language_model/5gram.bin\n", " create mode 100644 language_model/attrs.json\n", " create mode 100644 language_model/unigrams.txt\n" ] } ], "source": [ "!git commit -m \"add ngram LM from train+other\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "igL2FAM7F4gL" }, "outputs": [], "source": [ "!git config --global user.email \"lucien@discurs.us\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "R5x8nIucGJK3" }, "outputs": [], "source": [ "!git config --global user.name \"Lucio\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YBb6acjCGMSt", "outputId": "a2bae291-ecd2-424b-fad5-1a4be282a5c5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fatal: cannot exec '.git/hooks/pre-push': Permission denied\n", "^C\n" ] } ], "source": [ "!git push origin with_ngram_lm" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "2ad17af72da04ec4ab695dfb147eda87", "01de6c74611743d0b4801d54a38853a1", "88f09b7ed0a746809d5a4045d571434d", "8b61d9255e5c43848428c93bffd191ff", "e856d22763a04dee9c170a99a88025a6", "eb91c6bd94214119bcc16896fc73d8e2", "a337a59ad08449028d962fd248107b8e", "1ef399f1a5c2471ea285a9b61b49a60b", "602ce704893b4e6f9fd62b6e6667d684", "926e307def7848ffa46f36420288119c", "5d3bf35b7f0e49589e255be80b64cfde", "2321870be69c4f0795370293e47fe521", "2b9f0bf91f264709b71def3214574440", "3dfd4abd484c4b7c942950375eebcfc3", "f5c12d322e5740a9a17766a1c88a621f", "1483f656caf94f27a90a74d1c7bd2fbe", "b2497a113d7848d0aa62f3accd7cf641", "6e73acd1ae24406492a2ee466e06bfa2", "32fffdcc49c74027be8c7ef74abab432", "8e356d865ad3416eb09efde4b362d181", "f06a3bc660a047d8b48defb94eb6e740", "5e435c4eba4c4d15aa50dc7625ed7ac6", "0f6dd549e171494a91748ed3d5b5b7f1", "04032b1512a242fc831cd1c160bf2f07", "6e081a444059400094c3c00e839a062e", "2e6bb308dbca442797012eff31f94ce6", "674dbff511ed49f18253131450c3fcb3", "63435251198a448ea420511fea0a35b2", "f15671db4816438998e4f9cb9e3ddb67", "ae4b05ce5ea947f78032070a5f61b1e0", "e62236730d644dce971e0359ccbd6d44", "264e59dfc47643e59601da78e2a1ad98", "9413d65a8f314f2d8914159bcda94f15", "a346886cde3b4e828d4018c5f1e890b6", "6ce9d4f7d489464d961c6f0fb9731222", "be5498ee95e14e498b5424594c2f8832", "bb6a8b69668b455badc2d62e63b06300", "d748b8b8231f428789e20742d84efb50", "476f592d9c4c49c7a282a0549690724b", "4a696f51cd514adfb91af1ef1baecd62", "a8ee913b34df46c597a40f9c830edb53", "4ef20c84cf394097b0eba6bed5613a1f", "9621e777569641ee900dc2cc22ebd709", "277da190e09d4bc6993b41870f6d87c7", "8d56ec67e37742ddb30f250c46ce7ec7", "846ce682d611402ba65963895419d931", "51cc2aef029c432995f1805ff033fe68", "61b91214eb5f441fb8eb5aaaac357526", "dfc70651984242f19d3981fa377d2094", "fae3c0cd01514c61b80e820988a0c3c6", "9357f6fe70e74a06a0d4115024af9ac2", "11836dbb8129439bacee852ed14148ee", "782172e2a30f4625aeede72d6f8caf95", "65fdb35eb6464e7d9381fcbb7d74c2b3", "0354b5f2631d41e28d78f901d0897467", "7ace3cb4117e4766b1e4ef4a16c19e73", "436acd9e5d21426ab948a5b9b9c3baee", "d3d242a4a62d49569f9d9e6c2150afdd", "6f62a46a9bbc43cf99b00ec44aa2bcf4", "c67937c11e0e43a38086aabd32a10d91", "5329fbcbb63e4664b5f40c376c779f33", "eafd88bac7e440d8abf8339557bc262f", "19e3d418d09046378cd123a9a54b3240", "3f8c95cf925b4b00a02b7c94e193e72c", "1ee8d8ac76494304b387fd4caa6d0651", "32a212c6b78d4876b26ce7c95026fd86", "19842b45675846af8d94a30c74dfe875", "fd37b4f4cd5b47a89e9027d662dbd1b3", "ead25350f2c5469fb398a32ceb6a335d", "502422363056412db37a0b8b395c54a1", "41a908a3ccc9475d800b0ff76a3db6f7", "dbbe4dd2f80e42afb825ff15a0230202", "7e6a482dcfe9452ab44605cb033c53df", "dbe20b367e2b42ee94d662dc894497a5", "618de1bc6e5940db81e7913e7da03483", "27c057b600e24e21ac021e5b73a5d024", "d01034f0156349809fdf71cd4449a715", "d39603dfa36e4e77a193402d58a0bb5f", "6f2f03c6e2ff4a408918116203b5cbba", "705e7d9f767a45358489e9c24f62ca24", "12f2f97c152549498adbcb0df4099343", "4a8ceee86cab4be7a9e52e9b7a54cad1", "983cdb515319438a8ab50317471547d8", "a3bd5b42e333407cb5223b5085573336", "da2992535a62496bb42d72037e8d25f3", "3a1eedac78ed4ef6880cec825f4202fc", "fe03467630ef4479bfc57b2e4c12c9b6", "14ddfeb640b04b769561e7d96d6616f6", "9cbefa15f9854c1b96577d720769c936", "e1bb50964e1d4198b1245569f6c96f96", "dd2173c360b743fa94c7dcca0dac9ec8", "7fca4e095d58494380bc9df24bf14fba", "5d46835bf3c14303b04271ec7e559513", "2b995d4b6e2041be94dc953c1d4c3620", "837a12c0a15946f9879a5fbd2fed1295", "11620944b261431182b2e8e6a3fd5d42", "78b5afa50c834cf7bbe82383c372c6c5", 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"stream", "text": [ "/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py:718: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n", " FutureWarning,\n", "Cloning https://huggingface.co/lucio/xls-r-uzbek-cv8 into local empty directory.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2ad17af72da04ec4ab695dfb147eda87", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Download file pytorch_model.bin: 0%| | 16.0k/1.18G [00:00...\" to update what will be committed)\n", " (use \"git checkout -- ...\" to discard changes in working directory)\n", "\n", "\t\u001b[31mmodified: .ipynb_checkpoints/eval-checkpoint.py\u001b[m\n", "\t\u001b[31mmodified: .ipynb_checkpoints/run_speech_recognition_ctc-checkpoint.py\u001b[m\n", "\t\u001b[31mmodified: eval.py\u001b[m\n", "\t\u001b[31mmodified: kenlm/clean_query_only.sh\u001b[m\n", "\t\u001b[31mmodified: kenlm/compile_query_only.sh\u001b[m\n", "\t\u001b[31mmodified: kenlm/lm/common/test_data/generate.sh\u001b[m\n", "\t\u001b[31mmodified: kenlm/python/example.py\u001b[m\n", "\t\u001b[31mmodified: 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