diff --git "a/Boosting_Wav2Vec2_with_n_grams_in_🤗_Transformers_(1).ipynb" "b/Boosting_Wav2Vec2_with_n_grams_in_🤗_Transformers_(1).ipynb"
new file mode 100644--- /dev/null
+++ "b/Boosting_Wav2Vec2_with_n_grams_in_🤗_Transformers_(1).ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1LrJXl6HY3xO"
+ },
+ "source": [
+ "# **Boosting Wav2Vec2 with n-grams in 🤗 Transformers**\n",
+ "\n",
+ "**Wav2Vec2** is a popular pre-trained model for speech recognition. Released in [September 2020](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) by Meta AI Research, the novel architecture catalyzed progress in self-supervised pretraining for speech recognition, *e.g.* [*G. Ng et al.*, 2021](https://arxiv.org/pdf/2104.03416.pdf), [*Chen et al*, 2021](https://arxiv.org/abs/2110.13900), [*Hsu et al.*, 2021](https://arxiv.org/abs/2106.07447) and [*Babu et al.*, 2021](https://arxiv.org/abs/2111.09296). On the Hugging Face Hub, Wav2Vec2's most popular pre-trained checkpoint currently amounts to over [**250,000** monthly downloads](https://huggingface.co./facebook/wav2vec2-base-960h).\n",
+ "\n",
+ "Using Connectionist Temporal Classification (CTC), pre-trained Wav2Vec2-like checkpoints are extremely easy to fine-tune on downstream speech recognition tasks.\n",
+ "In a nutshell, fine-tuning pre-trained Wav2Vec2 checkpoints works as follows: \n",
+ "\n",
+ "A single randomly initialized linear layer is stacked on top of the pre-trained checkpoint and trained to classify raw audio input to a sequence of letters. It does so by:\n",
+ "\n",
+ "1. extracting audio representations from the raw audio (using CNN layers),\n",
+ "2. processing the sequence of audio representations with a stack of transformer layers, and,\n",
+ "3. classifying the processed audio representations into a sequence of output letters.\n",
+ "\n",
+ "Previously audio classification models required an additional language model (LM) and a dictionary to transform the sequence of classified audio frames to a coherent transcription.\n",
+ "Wav2Vec2's architecture is based on transformer layers, thus giving each processed audio representation context \n",
+ "from all other audio representations. In addition, \n",
+ "Wav2Vec2 leverages the [CTC algorithm](https://distill.pub/2017/ctc/) for fine-tuning, which solves the problem of alignment between a varying \"input audio length\"-to-\"output text length\" ratio.\n",
+ "\n",
+ "Having contextualized audio classifications and no alignment problems, Wav2Vec2 does not require \n",
+ "an external language model or dictionary to yield acceptable audio transcriptions.\n",
+ "\n",
+ "As can be seen in Appendix C of the [official paper](https://arxiv.org/abs/2006.11477), Wav2Vec2 gives impressive downstream performances on [LibriSpeech]((https://huggingface.co./datasets/librispeech_asr)) without using a language model at all. However, from the appendix, it also becomes clear that using Wav2Vec2 in combination with a language model can yield a significant improvement, especially when the model was trained on only 10 minutes of transcribed audio.\n",
+ "\n",
+ "Until recently, the 🤗 Transformers library did not offer a simple user interface to decode audio files with a fine-tuned Wav2Vec2 **and** a language model. This has thankfully changed. 🤗 Transformers now offers an easy-to-use integration with *Kensho Technologies'* [pyctcdecode library](https://github.com/kensho-technologies/pyctcdecode). This blog post is a step-by-step **technical** guide to explain how one can create an **n-gram** language model and combine it with an existing fine-tuned Wav2Vec2 checkpoint using 🤗 Datasets and 🤗 Transformers.\n",
+ "\n",
+ "We start by:\n",
+ "\n",
+ "1. How does decoding audio with an LM differ from decoding audio without an LM?\n",
+ "2. How to get suitable data for a language model?\n",
+ "3. How to build an *n-gram* with KenLM?\n",
+ "4. How to combine the *n-gram* with a fine-tuned Wav2Vec2 checkpoint?\n",
+ "\n",
+ "For a deep dive into how Wav2Vec2 functions - which is not necessary for this blog post - the reader is advised to consult the following material:\n",
+ "\n",
+ "- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477)\n",
+ "- [Fine-Tune Wav2Vec2 for English ASR with 🤗 Transformers](https://huggingface.co./blog/fine-tune-wav2vec2-english)\n",
+ "- [An Illustrated Tour of Wav2vec 2.0](https://jonathanbgn.com/2021/09/30/illustrated-wav2vec-2.html)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nu5oeVSvprSp"
+ },
+ "source": [
+ "## **1. Decoding audio data with Wav2Vec2 and a language model**\n",
+ "\n",
+ "As shown in 🤗 Transformers [exemple docs of Wav2Vec2](https://huggingface.co./docs/transformers/master/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC), audio can be transcribed as follows.\n",
+ "\n",
+ "First, we install `datasets` and `transformers`.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "OWGc_zfyq5_T",
+ "outputId": "4cc791f5-6a7c-4c21-c880-bd47df479744"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: datasets in /opt/conda/lib/python3.8/site-packages (1.18.2.dev0)\n",
+ "Collecting datasets\n",
+ " Downloading datasets-1.18.3-py3-none-any.whl (311 kB)\n",
+ " |████████████████████████████████| 311 kB 24.2 MB/s \n",
+ "\u001b[?25hRequirement already satisfied: transformers in /opt/conda/lib/python3.8/site-packages (4.17.0.dev0)\n",
+ "Requirement already satisfied: aiohttp in /opt/conda/lib/python3.8/site-packages (from datasets) (3.8.1)\n",
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+ "Requirement already satisfied: dill in /opt/conda/lib/python3.8/site-packages (from datasets) (0.3.4)\n",
+ "Requirement already satisfied: fsspec[http]>=2021.05.0 in /opt/conda/lib/python3.8/site-packages (from datasets) (2022.1.0)\n",
+ "Requirement already satisfied: pyarrow!=4.0.0,>=3.0.0 in /opt/conda/lib/python3.8/site-packages (from datasets) (6.0.1)\n",
+ "Requirement already satisfied: pandas in /opt/conda/lib/python3.8/site-packages (from datasets) (1.4.0)\n",
+ "Requirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in /opt/conda/lib/python3.8/site-packages (from datasets) (0.4.0)\n",
+ "Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.8/site-packages (from datasets) (1.19.2)\n",
+ "Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from datasets) (21.3)\n",
+ "Requirement already satisfied: tqdm>=4.62.1 in /opt/conda/lib/python3.8/site-packages (from datasets) (4.62.3)\n",
+ "Requirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.8/site-packages (from datasets) (2.24.0)\n",
+ "Requirement already satisfied: tokenizers!=0.11.3,>=0.10.1 in /opt/conda/lib/python3.8/site-packages (from transformers) (0.11.4)\n",
+ "Requirement already satisfied: sacremoses in /opt/conda/lib/python3.8/site-packages (from transformers) (0.0.47)\n",
+ "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.8/site-packages (from transformers) (2022.1.18)\n",
+ "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.8/site-packages (from transformers) (5.4.1)\n",
+ "Requirement already satisfied: filelock in /opt/conda/lib/python3.8/site-packages (from transformers) (3.0.12)\n",
+ "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.8/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (4.0.1)\n",
+ "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging->datasets) (3.0.7)\n",
+ "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (1.25.11)\n",
+ "Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (2.10)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (2020.12.5)\n",
+ "Requirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (3.0.4)\n",
+ "Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.8/site-packages (from aiohttp->datasets) (1.3.0)\n",
+ "Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.8/site-packages (from aiohttp->datasets) (1.2.0)\n",
+ "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /opt/conda/lib/python3.8/site-packages (from aiohttp->datasets) (4.0.2)\n",
+ "Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.8/site-packages (from aiohttp->datasets) (21.4.0)\n",
+ "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.8/site-packages (from aiohttp->datasets) (6.0.2)\n",
+ "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.8/site-packages (from aiohttp->datasets) (1.7.2)\n",
+ "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /opt/conda/lib/python3.8/site-packages (from aiohttp->datasets) (2.0.10)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.8/site-packages (from pandas->datasets) (2021.1)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/lib/python3.8/site-packages (from pandas->datasets) (2.8.2)\n",
+ "Requirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from sacremoses->transformers) (1.15.0)\n",
+ "Requirement already satisfied: joblib in /opt/conda/lib/python3.8/site-packages (from sacremoses->transformers) (1.1.0)\n",
+ "Requirement already satisfied: click in /opt/conda/lib/python3.8/site-packages (from sacremoses->transformers) (8.0.3)\n",
+ "Installing collected packages: datasets\n",
+ " Attempting uninstall: datasets\n",
+ " Found existing installation: datasets 1.18.2.dev0\n",
+ " Uninstalling datasets-1.18.2.dev0:\n",
+ "\u001b[31mERROR: Could not install packages due to an OSError: [Errno 13] Permission denied: 'WHEEL'\n",
+ "Consider using the `--user` option or check the permissions.\n",
+ "\u001b[0m\n",
+ "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 is available.\n",
+ "You should consider upgrading via the '/opt/conda/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install -U datasets transformers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZzcM5yC5rICZ"
+ },
+ "source": [
+ "Let's load a small excerpt of the [Librispeech dataset](https://huggingface.co./datasets/librispeech_asr) to demonstrate Wav2Vec2's speech transcription capabilities."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "dAerOhydrNFR",
+ "outputId": "4ba5ed61-f6ef-40ae-828d-7e4f5c0f4f87"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Reusing dataset librispeech_asr (/root/.cache/huggingface/datasets/hf-internal-testing___librispeech_asr/clean/2.1.0/f2c70a4d03ab4410954901bde48c54b85ca1b7f9bf7d616e7e2a72b5ee6ddbfc)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Dataset({\n",
+ " features: ['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'],\n",
+ " num_rows: 73\n",
+ "})"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from datasets import load_dataset\n",
+ "\n",
+ "dataset = load_dataset(\"hf-internal-testing/librispeech_asr_demo\", \"clean\", split=\"validation\")\n",
+ "dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-AkdbAlyrszm"
+ },
+ "source": [
+ "We can pick one of the 73 audio samples and listen to it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 93
+ },
+ "id": "pSomT7k_r1QX",
+ "outputId": "8475f442-56e6-4c51-bd11-edf5d212ec48"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "he tells us that at this festive season of the year with christmas and roast beef looming before us similes drawn from eating and its results occur most readily to the mind\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import IPython.display as ipd\n",
+ "\n",
+ "audio_sample = dataset[2]\n",
+ "print(audio_sample[\"text\"].lower())\n",
+ "ipd.Audio(data=audio_sample[\"audio\"][\"array\"], autoplay=True, rate=audio_sample[\"audio\"][\"sampling_rate\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZbBGOqQusdO6"
+ },
+ "source": [
+ "Having chosen a data sample, we now load the fine-tuned model and processor."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5FUPvu0crmsY"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC\n",
+ "\n",
+ "processor = Wav2Vec2Processor.from_pretrained(\"facebook/wav2vec2-base-100h\")\n",
+ "model = Wav2Vec2ForCTC.from_pretrained(\"facebook/wav2vec2-base-100h\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7L8Th_yTslta"
+ },
+ "source": [
+ "Next, we process the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "6NoMX8qfssuw"
+ },
+ "outputs": [],
+ "source": [
+ "inputs = processor(audio_sample[\"audio\"][\"array\"], sampling_rate=16_000, return_tensors=\"pt\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xyM2MqwEs1p7"
+ },
+ "source": [
+ "forward it to the model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "4KWDRrG0s27p"
+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "\n",
+ "with torch.no_grad():\n",
+ " logits = model(**inputs).logits"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pEHhf_1os4rZ"
+ },
+ "source": [
+ "and decode it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "id": "aJREUIdqs5ak",
+ "outputId": "40de5ef4-5afd-4518-e3bc-c81cb976b46e"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'he tells us that at this festive season of the year with christmaus and rose beef looming before us simalyis drawn from eating and its results occur most readily to the mind'"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "predicted_ids = torch.argmax(logits, dim=-1)\n",
+ "transcription = processor.batch_decode(predicted_ids)\n",
+ "\n",
+ "transcription[0].lower()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ifBW-tM0yWhS"
+ },
+ "source": [
+ "Comparing the transcription to the target transcription above, we can see that some words *sound* correct, but are not *spelled* correctly, *e.g.*:\n",
+ "\n",
+ "- *christmaus* vs. *christmas*\n",
+ "- *rose* vs. *roast*\n",
+ "- *simalyis* vs. *similes*\n",
+ "\n",
+ "Let's see whether combining Wav2Vec2 with an ***n-gram*** lnguage model can help here."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JC1FrBDnzTJ5"
+ },
+ "source": [
+ "First, we need to install `pyctcdecode` and `kenlm`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "TvDJ7CYpzSJQ",
+ "outputId": "cb8e254d-7e9e-4549-ad29-55eded55c172"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
+ "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
+ "Collecting https://github.com/kpu/kenlm/archive/master.zip\n",
+ " Downloading https://github.com/kpu/kenlm/archive/master.zip\n",
+ " \\ 541 kB 1.8 MB/s\n",
+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25hCollecting pyctcdecode\n",
+ " Downloading pyctcdecode-0.3.0-py2.py3-none-any.whl (43 kB)\n",
+ " |████████████████████████████████| 43 kB 7.0 MB/s \n",
+ "\u001b[?25hRequirement already satisfied: numpy<2.0.0,>=1.15.0 in /opt/conda/lib/python3.8/site-packages (from pyctcdecode) (1.19.2)\n",
+ "Collecting hypothesis<7,>=6.14\n",
+ " Downloading hypothesis-6.36.1-py3-none-any.whl (376 kB)\n",
+ " |████████████████████████████████| 376 kB 55.0 MB/s \n",
+ "\u001b[?25hCollecting pygtrie<3.0,>=2.1\n",
+ " Downloading pygtrie-2.4.2.tar.gz (35 kB)\n",
+ " Preparing metadata (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25hRequirement already satisfied: attrs>=19.2.0 in /opt/conda/lib/python3.8/site-packages (from hypothesis<7,>=6.14->pyctcdecode) (21.4.0)\n",
+ "Collecting sortedcontainers<3.0.0,>=2.1.0\n",
+ " Downloading sortedcontainers-2.4.0-py2.py3-none-any.whl (29 kB)\n",
+ "Building wheels for collected packages: kenlm, pygtrie\n",
+ " Building wheel for kenlm (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for kenlm: filename=kenlm-0.0.0-cp38-cp38-linux_x86_64.whl size=2345337 sha256=dc0d81c6f059a5f0841cf0f6a4ca235cae715d4f9f52a9311e67989f64782ae3\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-7xk1d9qa/wheels/ff/08/4e/a3ddc0e786e0f3c1fcd2e7a82c4324c02fc3ae2638471406d2\n",
+ " Building wheel for pygtrie (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for pygtrie: filename=pygtrie-2.4.2-py3-none-any.whl size=19063 sha256=ea48bfe279335b62a3582df18a9f3b64abb89e71bb06753eea974f57f03f97e7\n",
+ " Stored in directory: /workspace/.cache/pip/wheels/31/03/7b/f685b394a937bc97d2d40908d45aa31f3d9473bca6e9019153\n",
+ "Successfully built kenlm pygtrie\n",
+ "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
+ "Installing collected packages: sortedcontainers, pygtrie, hypothesis, pyctcdecode, kenlm\n",
+ "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
+ "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
+ "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
+ "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
+ "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
+ "Successfully installed hypothesis-6.36.1 kenlm-0.0.0 pyctcdecode-0.3.0 pygtrie-2.4.2 sortedcontainers-2.4.0\n",
+ "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
+ "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
+ "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
+ "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 is available.\n",
+ "You should consider upgrading via the '/opt/conda/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ak_X8SHqzjOY"
+ },
+ "source": [
+ "For demonstration purposes, we have prepared a new model repository [patrickvonplaten/wav2vec2-base-100h-with-lm](https://huggingface.co./patrickvonplaten/wav2vec2-base-100h-with-lm) which contains the same Wav2Vec2 checkpoint but has an additional **4-gram** language model for English.\n",
+ "\n",
+ "Instead of using `Wav2Vec2Processor`, this time we use `Wav2Vec2ProcessorWithLM` to load the **4-gram** model in addition to the feature extractor and tokenizer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "UydQ00uI0OEG"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import Wav2Vec2ProcessorWithLM\n",
+ "\n",
+ "processor = Wav2Vec2ProcessorWithLM.from_pretrained(\"patrickvonplaten/wav2vec2-base-100h-with-lm\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cYZrzLQ02U4y"
+ },
+ "source": [
+ "In constrast to decoding the audio without language model, the processor now directly receives the model's output `logits` instead of the `argmax(logits)` (called `predicted_ids`) above. The reason is that when decoding with a language model, at each time step, the processor takes the probabilities of all possible output characters into account. Let's take a look at the dimension of the `logits` output."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "WJvldi0C5OaW",
+ "outputId": "ef6210a9-9652-4fc0-dc61-007dc85de5f5"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([1, 624, 32])"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "logits.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ySU0i3oZ5Vdm"
+ },
+ "source": [
+ "We can see that the `logits` correspond to a sequence of 624 vectors each having 32 entries. Each of the 32 entries thereby stands for the logit probability of one of the 32 possible output characters of the model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "id": "rRz0_vm95i6E",
+ "outputId": "0b076acf-c839-4a66-8059-e7261e9f0024"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "\"' A B C D E F G H I J K L M N O P Q R S T U V W X Y Z |\""
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\" \".join(sorted(processor.tokenizer.get_vocab()))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "u60UcdWL5ub-"
+ },
+ "source": [
+ "Intuitively, one can understand the decoding process of `Wav2Vec2ProcessorWithLM` as applying beam search through a matrix of size 624 $\\times$ 32 probabilities while leveraging the probabilities of the next letters as given by the *n-gram* language model.\n",
+ "\n",
+ "OK, let's run the decoding step again. `pyctcdecode` language model decoder does not automatically convert `torch` tensors to `numpy` so we'll have to convert them ourselves before."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "id": "DFRgWyuUAZI4",
+ "outputId": "45b35f41-bc95-4297-e8a7-30d7c3980ac9"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'he tells us that at this festive season of the year with christmas and rose beef looming before us similes drawn from eating and its results occur most readily to the mind'"
+ ]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "transcription = processor.batch_decode(logits.numpy()).text\n",
+ "transcription[0].lower()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "E4CiVIftDEMd"
+ },
+ "source": [
+ "Cool! Recalling the words `facebook/wav2vec2-base-100h` without a language model transcribed incorrectly previously, *e.g.*,\n",
+ "\n",
+ "> - *christmaus* vs. *christmas*\n",
+ "- *rose* vs. *roast*\n",
+ "- *simalyis* vs. *similes*\n",
+ "\n",
+ "we can take another look at the transcription of `facebook/wav2vec2-base-100h` **with** a 4-gram language model. 2 out of 3 errors are corrected; *christmas* and *similes* have been correctly transcribed.\n",
+ "\n",
+ "Interestingly, the incorrect transcription of *rose* persists. However, this should not surprise us very much. Decoding audio without a language model is much more prone to yield spelling mistakes, such as *christmaus* or *similes* (those words don't exist in the English language as far as I know). This is because the speech recognition system almost solely bases its prediction on the acoustic input it was given and not really on the language modeling context of previous and successive predicted letters ${}^1$. \n",
+ "If on the other hand, we add a language model, we can be fairly sure that the speech recognition system will heavily reduce spelling errors since a well-trained *n-gram* model will surely not predict a word that has spelling errors. But the word *rose* is a valid English word and therefore the 4-gram will predict this word with a probability that is not insignificant. \n",
+ "\n",
+ "The language model on its own most likely does favor the correct word *roast* since the word sequence *roast beef* is much more common in English than *rose beef*. Because the final transcription is derived from a weighted combination of `facebook/wav2vec2-base-100h` output probabilities and those of the *n-gram* language model, it is quite common to see incorrectly transcribed words such as *rose*.\n",
+ "\n",
+ "For more information on how you can tweak different parameters when decoding with `Wav2Vec2ProcessorWithLM`, please take a look at the official documentation [here](https://huggingface.co./docs/transformers/master/en/model_doc/wav2vec2#transformers.Wav2Vec2ProcessorWithLM.batch_decode).\n",
+ "\n",
+ "---\n",
+ "${}^1$ Some research shows that a model such as `facebook/wav2vec2-base-100h` - when sufficiently large and trained on enough data - can learn language modeling dependencies between intermediate audio representations similar to a language model.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9FKW9cqzKMMo"
+ },
+ "source": [
+ "Great, now that you have seen the advantages adding an *n-gram* language model can bring, let's dive into how to create an *n-gram* and `Wav2Vec2ProcessorWithLM` from scratch."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "QEz599PfmPeG"
+ },
+ "source": [
+ "## **2. Getting data for your language model**\n",
+ "\n",
+ "A language model that is useful for a speech recognition system should support the acoustic model, *e.g.* Wav2Vec2, in predicting the next word (or token, letter) and therefore model the following distribution:\n",
+ "\n",
+ "$\\mathbf{P}(w_n | \\mathbf{w}_0^{t-1})$ with $w_n$ being the next word and $\\mathbf{w}_0^{t-1}$ being the sequence of all previous words since the beginning of the utterance. Simply said, the language model should be good at predicting the next word given all previously transcribed words regardless of the audio input given to the speech recognition system.\n",
+ "\n",
+ "As always a language model is only as good as the data it is trained on. In the case of speech recognition, we should therefore ask ourselves for what kind of data, the speech recognition will be used for: *conversations*, *audiobooks*, *movies*, *speeches*, *, etc*, ...?\n",
+ "\n",
+ "The language model should be good at modeling language that corresponds to the \n",
+ "target transcriptions of the speech recognition system. \n",
+ "For demonstration purposes, we assume here that we have fine-tuned a pre-trained [`facebook/wav2vec2-xls-r-300m`](https://huggingface.co./facebook/wav2vec2-xls-r-300m) on [Common Voice 7](https://huggingface.co./datasets/mozilla-foundation/common_voice_7_0) in Swedish. The fine-tuned checkpoint can \n",
+ "be found [here](https://huggingface.co./hf-test/xls-r-300m-sv).\n",
+ "Common Voice 7 is a relatively crowd-sourced read-out audio dataset and we will evaluate the model on its test data.\n",
+ "\n",
+ "Let's now look for suitable text data on the Hugging Face Hub. We search all datasets for those [that contain Swedish data](https://huggingface.co./datasets?languages=languages:sv&sort=downloads). \n",
+ "Browsing a bit through the datasets, we are looking for a dataset that is similar to Common Voice's read-out audio data. The obvious choices of [oscar](https://huggingface.co./datasets/oscar) and [mc4](https://huggingface.co./datasets/mc4) might not be the most suitable here because they:\n",
+ "\n",
+ "- are generated from crawling the web, which might not be very clean and correspond well to spoken language\n",
+ "- require a lot of pre-processing\n",
+ "- are very large which is not ideal for demonstration purposes here 😉\n",
+ "\n",
+ "A dataset that seems sensible here and which is relatively clean and easy to pre-process is [europarl_bilingual](https://huggingface.co./datasets/europarl_bilingual) as it's a dataset that is based on discussions and talks of the European parliament. It should therefore be relatively clean and correspond well to read-out audio data. The dataset is originally designed for machine translation and can therefore only be accessed in translation pairs. We will only extract the text of the target language, Swedish (`sv`), from the *English-to-Swedish* translations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "chZw03lUVAnr"
+ },
+ "outputs": [],
+ "source": [
+ "target_lang=\"en\" # change to your target lang"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cOLruPJsVS98"
+ },
+ "source": [
+ "Let's download the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 255,
+ "referenced_widgets": [
+ "c5eb721fe1b841168a49b5bc22435791",
+ "b9861ef1c3534e90b472be9ed8862f17",
+ "77db336868d84805937445c51ea72df4",
+ "fb4d2faef4da4dc0ae1203f9376053af",
+ "edbdbc0a82854bde92e5abf6c8f87534",
+ "eb4ed31888734fe39dd96f9642abd38b",
+ "d050daf64b5944fdad48b963ad482f7c",
+ "88e5417fad3d442585c799cfd9d2f8f4",
+ "5a0353a8a1be47a1a42f1e05eb72eb68",
+ "40a6127b87464cddba87f6c97c308594",
+ "c1931c4022d84d28815d460f72ef908b",
+ "880b6bbcaad54eb8ba377377b1858ffd",
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+ "850edc7115a34dde9ddf6e36b5f1e8dd",
+ "3f7f23c3d40a4a629c26115bad8b19c6",
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+ "3752bd8127d140829a62045098151b37",
+ "d73548881b594fd88f074b947b07c408",
+ "1963287764bb48fa86c7437eb2dad75b",
+ "95d736dd67b849ebae4433bff689cd06",
+ "8584eaf09efb49c995f5a2b1c25fe089",
+ "a377ec0ebad0485987b93ae5212ab19f",
+ "00b92ed648e94e8b975f4c66cc329c19",
+ "5f132d8f475345f7acef6a4996a026cf",
+ "90ec52eebee44eeea4cdead5bf665b67",
+ "16a8075896df4ca181a70973a59d1fd7",
+ "4e562a18f1a0471ea0ebd4007a2d674e",
+ "e3dafae56ffb4a5cbc84d80bc7f30c87",
+ "9681133982cc42048815e2c30e1b4436",
+ "be4fd6ba2c024224a10571a3a2c44637",
+ "73e64b85117b4bc69d013fad313da9c6",
+ "678d73e72aa941b2b1f3d1e85ed0d952",
+ "5bb04fe5c73b4bc09f1aa05952244413",
+ "d4adf3cee88e4482b20f9f0d3b87a36a",
+ "0f21362b91214406a29a82fcffc34bf7",
+ "f692fa3e8bad4f359baaff49a70d5cd5",
+ "3717640fa6e740c6a73e061fd9cd59e8",
+ "6433d64ddce540f3acacb9990c6f8b20",
+ "1b4a50a4aeea429795ff12ccf7e25ecc",
+ "d8d482db758d4052afe8019ba59d816a",
+ "a0ec12647b784b2d94a298db506e1ca4"
+ ]
+ },
+ "id": "IrAzjWc3Ok2l",
+ "outputId": "591b75dd-b38f-4129-a748-21fefa0cf7b6"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c5eb721fe1b841168a49b5bc22435791",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/2.08k [00:00\\n\t0\n",
+ "0\t\t-0.09341879\n",
+ "-4.2426977\tالذهب\t-0.2197705\n",
+ "-5.265963\tبعشرة\t-0.1773004\n",
+ "-4.3284874\tآلاف\t-0.29791862\n",
+ "-4.0686555\tدولار\t-0.33736664\n",
+ "-4.603849\tسان\t-0.4746134\n",
+ "-5.4108515\tفرانسيسكو\t-0.11966315\n",
+ "-2.7832785\tلم\t-0.62132126\n",
+ "-4.8193336\tيكن\t-0.12888719\n",
+ "-1.9863459\tمن\t-0.8163693\n",
+ "-4.8497424\tالسهل\t-0.14798915\n"
+ ]
+ }
+ ],
+ "source": [
+ "!head -20 5gram.arpa"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "l3jfwr2RTKPn"
+ },
+ "source": [
+ "There is a small problem that 🤗 Transformers will not be happy about later on.\n",
+ "The *5-gram* correctly includes a \"Unknown\" or ``, as well as a *begin-of-sentence*, `` token, but no *end-of-sentence*, ` ` token.\n",
+ "This sadly has to be corrected currently after the build.\n",
+ "\n",
+ "We can simply add the *end-of-sentence* token by adding the line `0 -0.11831701` below the *begin-of-sentence* token and increasing the `ngram 1` count by 1. Because the file has roughly 100 million lines, this command will take *ca.* 2 minutes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "id": "_7u7dVPkvyRZ"
+ },
+ "outputs": [],
+ "source": [
+ "with open(\"5gram.arpa\", \"r\") as read_file, open(\"5gram_correct.arpa\", \"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": "markdown",
+ "metadata": {
+ "id": "u9Y8uC3VW5vc"
+ },
+ "source": [
+ "Let's now inspect the corrected *5-gram*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "YF1RSm-Pxst5",
+ "outputId": "cc06a4bd-d8cf-4ebc-9381-a1751c56b9df"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\\data\\\n",
+ "ngram 1=154929\n",
+ "ngram 2=1951043\n",
+ "ngram 3=3808342\n",
+ "ngram 4=4594632\n",
+ "ngram 5=4848900\n",
+ "\n",
+ "\\1-grams:\n",
+ "-6.2970614\t\t0\n",
+ "0\t\t-0.09341879\n",
+ "0\t \t-0.09341879\n",
+ "-4.2426977\tالذهب\t-0.2197705\n",
+ "-5.265963\tبعشرة\t-0.1773004\n",
+ "-4.3284874\tآلاف\t-0.29791862\n",
+ "-4.0686555\tدولار\t-0.33736664\n",
+ "-4.603849\tسان\t-0.4746134\n",
+ "-5.4108515\tفرانسيسكو\t-0.11966315\n",
+ "-2.7832785\tلم\t-0.62132126\n",
+ "-4.8193336\tيكن\t-0.12888719\n",
+ "-1.9863459\tمن\t-0.8163693\n"
+ ]
+ }
+ ],
+ "source": [
+ "!head -20 5gram_correct.arpa"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "m7NfKtyjXCiE"
+ },
+ "source": [
+ "Great, this looks better! We're done at this point and all that is left to do is to correctly integrate the `\"ngram\"` with [`pyctcdecode`](https://github.com/kensho-technologies/pyctcdecode) and 🤗 Transformers."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kPImHXzDG8aH"
+ },
+ "source": [
+ "## **4. Combine an *n-gram* with Wav2Vec2**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kfZ7qYvMXfSV"
+ },
+ "source": [
+ "In a final step, we want to wrap the *5-gram* into a `Wav2Vec2ProcessorWithLM` object to make the *5-gram* boosted decoding as seamless as shown in Section 1.\n",
+ "We start by downloading the currently \"LM-less\" processor of [`xls-r-300m-sv`](https://huggingface.co./hf-test/xls-r-300m-sv)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 177,
+ "referenced_widgets": [
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+ "be05ffe206b943b0adc5747b4b0bf5f7",
+ "4f5b99cea30146caad68f453156b2ecb",
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+ ]
+ },
+ "id": "paV71gdAtkDC",
+ "outputId": "6d1b5a03-8f2f-4c99-f12f-99fa6b5eaa6a"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "698d0b728b4746e7b4a1bec8d9b475ba",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/214 [00:00 1 in alphabet. This is unusual unless style is BPE, but the alphabet was not recognized as BPE type. Is this correct?\n",
+ "Unigrams and labels don't seem to agree.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from pyctcdecode import build_ctcdecoder\n",
+ "\n",
+ "decoder = build_ctcdecoder(\n",
+ " labels=list(sorted_vocab_dict.keys()),\n",
+ " kenlm_model_path=\"5gram_correct.arpa\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IzJYQsSdZQ4-"
+ },
+ "source": [
+ "We can safely ignore the warning and all that is left to do now is to wrap the just created `decoder`, together with the processor's `tokenizer` and `feature_extractor` into a `Wav2Vec2ProcessorWithLM` class."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "id": "VBVf50EzZgAQ"
+ },
+ "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": "markdown",
+ "metadata": {
+ "id": "enhS2VRNZ79c"
+ },
+ "source": [
+ "We want to directly upload the LM-boosted processor into \n",
+ "the model folder of [`xls-r-300m-sv`](https://huggingface.co./hf-test/xls-r-300m-sv) to have all relevant files in one place.\n",
+ "\n",
+ "Let's clone the repo, add the new decoder files and upload them afterward.\n",
+ "First, we need to install `git-lfs`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "BZZm3ECc5TMP",
+ "outputId": "3cb8115b-f9d0-455c-dfee-03d624530c6a"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Reading package lists... Done\n",
+ "Building dependency tree \n",
+ "Reading state information... Done\n",
+ "The following packages were automatically installed and are no longer required:\n",
+ " cuda-command-line-tools-10-0 cuda-command-line-tools-10-1\n",
+ " cuda-command-line-tools-11-0 cuda-compiler-10-0 cuda-compiler-10-1\n",
+ " cuda-compiler-11-0 cuda-cuobjdump-10-0 cuda-cuobjdump-10-1\n",
+ " cuda-cuobjdump-11-0 cuda-cupti-10-0 cuda-cupti-10-1 cuda-cupti-11-0\n",
+ " cuda-cupti-dev-11-0 cuda-documentation-10-0 cuda-documentation-10-1\n",
+ " cuda-documentation-11-0 cuda-documentation-11-1 cuda-gdb-10-0 cuda-gdb-10-1\n",
+ " cuda-gdb-11-0 cuda-gpu-library-advisor-10-0 cuda-gpu-library-advisor-10-1\n",
+ " cuda-libraries-10-0 cuda-libraries-10-1 cuda-libraries-11-0\n",
+ " cuda-memcheck-10-0 cuda-memcheck-10-1 cuda-memcheck-11-0 cuda-nsight-10-0\n",
+ " cuda-nsight-10-1 cuda-nsight-11-0 cuda-nsight-11-1 cuda-nsight-compute-10-0\n",
+ " cuda-nsight-compute-10-1 cuda-nsight-compute-11-0 cuda-nsight-compute-11-1\n",
+ " cuda-nsight-systems-10-1 cuda-nsight-systems-11-0 cuda-nsight-systems-11-1\n",
+ " cuda-nvcc-10-0 cuda-nvcc-10-1 cuda-nvcc-11-0 cuda-nvdisasm-10-0\n",
+ " cuda-nvdisasm-10-1 cuda-nvdisasm-11-0 cuda-nvml-dev-10-0 cuda-nvml-dev-10-1\n",
+ " cuda-nvml-dev-11-0 cuda-nvprof-10-0 cuda-nvprof-10-1 cuda-nvprof-11-0\n",
+ " cuda-nvprune-10-0 cuda-nvprune-10-1 cuda-nvprune-11-0 cuda-nvtx-10-0\n",
+ " cuda-nvtx-10-1 cuda-nvtx-11-0 cuda-nvvp-10-0 cuda-nvvp-10-1 cuda-nvvp-11-0\n",
+ " cuda-nvvp-11-1 cuda-samples-10-0 cuda-samples-10-1 cuda-samples-11-0\n",
+ " cuda-samples-11-1 cuda-sanitizer-11-0 cuda-sanitizer-api-10-1\n",
+ " cuda-toolkit-10-0 cuda-toolkit-10-1 cuda-toolkit-11-0 cuda-toolkit-11-1\n",
+ " cuda-tools-10-0 cuda-tools-10-1 cuda-tools-11-0 cuda-tools-11-1\n",
+ " cuda-visual-tools-10-0 cuda-visual-tools-10-1 cuda-visual-tools-11-0\n",
+ " cuda-visual-tools-11-1 default-jre dkms freeglut3 freeglut3-dev\n",
+ " keyboard-configuration libargon2-0 libcap2 libcryptsetup12\n",
+ " libdevmapper1.02.1 libfontenc1 libidn11 libip4tc0 libjansson4\n",
+ " libnvidia-cfg1-510 libnvidia-common-460 libnvidia-common-510\n",
+ " libnvidia-extra-510 libnvidia-fbc1-510 libnvidia-gl-510 libpam-systemd\n",
+ " libpolkit-agent-1-0 libpolkit-backend-1-0 libpolkit-gobject-1-0 libxfont2\n",
+ " libxi-dev libxkbfile1 libxmu-dev libxmu-headers libxnvctrl0 libxtst6\n",
+ " nsight-compute-2020.2.1 nsight-compute-2022.1.0 nsight-systems-2020.3.2\n",
+ " nsight-systems-2020.3.4 nsight-systems-2021.5.2 nvidia-dkms-510\n",
+ " nvidia-kernel-common-510 nvidia-kernel-source-510 nvidia-modprobe\n",
+ " nvidia-settings openjdk-11-jre policykit-1 policykit-1-gnome python3-xkit\n",
+ " screen-resolution-extra systemd systemd-sysv udev x11-xkb-utils\n",
+ " xserver-common xserver-xorg-core-hwe-18.04 xserver-xorg-video-nvidia-510\n",
+ "Use 'sudo apt autoremove' to remove them.\n",
+ "The following NEW packages will be installed:\n",
+ " git-lfs tree\n",
+ "0 upgraded, 2 newly installed, 0 to remove and 39 not upgraded.\n",
+ "Need to get 2,169 kB of archives.\n",
+ "After this operation, 7,767 kB of additional disk space will be used.\n",
+ "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 git-lfs amd64 2.3.4-1 [2,129 kB]\n",
+ "Get:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tree amd64 1.7.0-5 [40.7 kB]\n",
+ "Fetched 2,169 kB in 1s (1,526 kB/s)\n",
+ "debconf: unable to initialize frontend: Dialog\n",
+ "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 2.)\n",
+ "debconf: falling back to frontend: Readline\n",
+ "debconf: unable to initialize frontend: Readline\n",
+ "debconf: (This frontend requires a controlling tty.)\n",
+ "debconf: falling back to frontend: Teletype\n",
+ "dpkg-preconfigure: unable to re-open stdin: \n",
+ "Selecting previously unselected package git-lfs.\n",
+ "(Reading database ... 155672 files and directories currently installed.)\n",
+ "Preparing to unpack .../git-lfs_2.3.4-1_amd64.deb ...\n",
+ "Unpacking git-lfs (2.3.4-1) ...\n",
+ "Selecting previously unselected package tree.\n",
+ "Preparing to unpack .../tree_1.7.0-5_amd64.deb ...\n",
+ "Unpacking tree (1.7.0-5) ...\n",
+ "Setting up tree (1.7.0-5) ...\n",
+ "Setting up git-lfs (2.3.4-1) ...\n",
+ "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"
+ ]
+ }
+ ],
+ "source": [
+ "!sudo apt-get install git-lfs tree"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bvo2rUbfa367"
+ },
+ "source": [
+ "Cloning and uploading of modeling files can be done conveniently with the `huggingface_hub`'s `Repository` class. \n",
+ "\n",
+ "More information on how to use the `huggingface_hub` to upload any files, please take a look at the [official docs](https://huggingface.co./docs/hub/how-to-upstream)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 162,
+ "referenced_widgets": [
+ "49f7e6f289254977a2aafa2f1ee142db",
+ "8ee054a3be1a4c03a529940b1643f0d1",
+ "a0b28f4e2ad544f0b69dce9518c81b50",
+ "2a424bddd9174d0b80c3903dbe775bfc",
+ "7ae3d834443145d495f1656371ae9d6a",
+ "397128fbc4af4e9a90e3c63b706d5ed0",
+ "c8f2d3eaeeb3423dab3cdbb38e0d798e",
+ "ce944a90a4224ceb9961dc57c3d657f6",
+ "0b06f2f2710d43eea0426d1737c2a64b",
+ "fdeac6256f4b43468c00a4b251df955f",
+ "1e0d61dc183b4f07adbfdfeb6fb202f6",
+ "8eca6b521e024c80b98e1fc55683ce1b",
+ "1fcb7caae2b241d58338e5e527cf614b",
+ "45530b8c2e434dba8ef7c0b606097ddb",
+ "e1b77a2002554785b0ce7773feea287f",
+ "def607cd03ff47978cb0e4113e0fbb5d",
+ "fe77f2e7f6bc471b8ca41a655cda3f19",
+ "68746a06857e481b96d093b2839f8d5b",
+ "5c64944d7f67438eba35a49ba21cd3a2",
+ "bb60a920af7a429695df2971d9259ec6",
+ "526dec5c46e64ffbb2a5de18d89f7f0d",
+ "cdc9a0d590614f98afac8eacaa6d3160",
+ "21afae65d1604439a5942e6eddc25cda",
+ "79a64935d39d41aebea8a9ca07de4311",
+ "ab38d6da79ef41c49ee49264ca3fb0a1",
+ "a96ce339661e4e9eba96fea631035256",
+ "c49c137fc01d44f38ac770609ff9f71c",
+ "1b32fdbb446f4476ad1f81a49f6196c6",
+ "1517ca3338324e2baf15f6d4313b5bbe",
+ "17dbd042241d4532b34d1b81e331363d",
+ "23836dc9596848cbb3bff90ccf12778c",
+ "3ce1b50621bb4d0dbdfc3e9105df998b",
+ "2e0bf48c91454784a25304feb36dc026",
+ "3472b8bd64a040e388701c060eef23b7",
+ "027e2a80d66f48029d8b2b62d711c1c8",
+ "1784b7fff3a24535b9ebd9bfdefec3d5",
+ "80bdf3a46f844e59a4fc1a8d757928d8",
+ "ff3f3b40641e456caaa66eb280b61db1",
+ "e8fa47058b44474cbdca965fb8b1c522",
+ "f01093cd46814afb9e7448b2c21e7d67",
+ "d5e3a11b22a54e84a0be282fb55f70f6",
+ "02eefb0e19c74cf896c8718e255d4a65",
+ "9448492ea7b844d5ac3fcc4131defd06",
+ "d9a20f62b4604975802f84046fd79b3c"
+ ]
+ },
+ "id": "fIfcunhF4YM6",
+ "outputId": "6f691851-9d17-4b11-b0f0-c8419259cd99"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Cloning https://huggingface.co./kingabzpro/wav2vec2-large-xlsr-300-arabic into local empty directory.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "42e499dd86dc450abf57174382b707a7",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Download file pytorch_model.bin: 0%| | 1.58k/1.18G [00:00\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mrepo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpush_to_hub\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcommit_message\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mEval-results\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/opt/conda/lib/python3.8/site-packages/huggingface_hub/repository.py:1249\u001b[0m, in \u001b[0;36mRepository.push_to_hub\u001b[0;34m(self, commit_message, blocking, clean_ok, auto_lfs_prune)\u001b[0m\n\u001b[1;32m 1247\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRepo currently clean. Ignoring push_to_hub\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1248\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1249\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgit_add\u001b[49m\u001b[43m(\u001b[49m\u001b[43mauto_lfs_track\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 1250\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgit_commit(commit_message)\n\u001b[1;32m 1251\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgit_push(\n\u001b[1;32m 1252\u001b[0m upstream\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morigin \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcurrent_branch\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 1253\u001b[0m blocking\u001b[38;5;241m=\u001b[39mblocking,\n\u001b[1;32m 1254\u001b[0m auto_lfs_prune\u001b[38;5;241m=\u001b[39mauto_lfs_prune,\n\u001b[1;32m 1255\u001b[0m )\n",
+ "File \u001b[0;32m/opt/conda/lib/python3.8/site-packages/huggingface_hub/repository.py:921\u001b[0m, in \u001b[0;36mRepository.git_add\u001b[0;34m(self, pattern, auto_lfs_track)\u001b[0m\n\u001b[1;32m 914\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 915\u001b[0m \u001b[38;5;124;03mgit add\u001b[39;00m\n\u001b[1;32m 916\u001b[0m \n\u001b[1;32m 917\u001b[0m \u001b[38;5;124;03mSetting the `auto_lfs_track` parameter to `True` will automatically track files that are larger\u001b[39;00m\n\u001b[1;32m 918\u001b[0m \u001b[38;5;124;03mthan 10MB with `git-lfs`.\u001b[39;00m\n\u001b[1;32m 919\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 920\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m auto_lfs_track:\n\u001b[0;32m--> 921\u001b[0m tracked_files \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mauto_track_large_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpattern\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 922\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tracked_files:\n\u001b[1;32m 923\u001b[0m logger\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m 924\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAdding files tracked by Git LFS: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtracked_files\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. This may take a bit of time if the files are large.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 925\u001b[0m )\n",
+ "File \u001b[0;32m/opt/conda/lib/python3.8/site-packages/huggingface_hub/repository.py:846\u001b[0m, in \u001b[0;36mRepository.auto_track_large_files\u001b[0;34m(self, pattern)\u001b[0m\n\u001b[1;32m 843\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m 845\u001b[0m path_to_file \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(os\u001b[38;5;241m.\u001b[39mgetcwd(), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlocal_dir, filename)\n\u001b[0;32m--> 846\u001b[0m size_in_mb \u001b[38;5;241m=\u001b[39m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetsize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_to_file\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1024\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1024\u001b[39m)\n\u001b[1;32m 848\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 849\u001b[0m size_in_mb \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10\u001b[39m\n\u001b[1;32m 850\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_tracked_with_lfs(path_to_file)\n\u001b[1;32m 851\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_git_ignored(path_to_file)\n\u001b[1;32m 852\u001b[0m ):\n\u001b[1;32m 853\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlfs_track(filename)\n",
+ "File \u001b[0;32m/opt/conda/lib/python3.8/genericpath.py:50\u001b[0m, in \u001b[0;36mgetsize\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgetsize\u001b[39m(filename):\n\u001b[1;32m 49\u001b[0m \u001b[38;5;124;03m\"\"\"Return the size of a file, reported by os.stat().\"\"\"\u001b[39;00m\n\u001b[0;32m---> 50\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mst_size\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/workspace/wav2vec2-large-xlsr-300-arabic/\".ipynb_checkpoints/Boosting_Wav2Vec2_with_n_grams_in_\\\\360\\\\237\\\\244\\\\227_Transformers_(1)-checkpoint.ipynb\"'"
+ ]
+ }
+ ],
+ "source": [
+ "repo.push_to_hub(commit_message=\"Eval-results\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "x_rVwrJQVBN5",
+ "outputId": "09179a83-557b-4e5c-ba3c-b0281f147c2e"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading: 100%|█████████████████████████| 9.88k/9.88k [00:00<00:00, 5.92MB/s]\n",
+ "Downloading: 100%|█████████████████████████| 2.98k/2.98k [00:00<00:00, 1.83MB/s]\n",
+ "Downloading: 100%|█████████████████████████| 49.7k/49.7k [00:00<00:00, 1.72MB/s]\n",
+ "Downloading and preparing dataset common_voice/ar to /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/ar/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b...\n",
+ "Downloading: 100%|█████████████████████████| 2.95G/2.95G [02:10<00:00, 22.5MB/s]\n",
+ "Dataset common_voice downloaded and prepared to /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/ar/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b. Subsequent calls will reuse this data.\n",
+ "Downloading: 100%|██████████████████████████| 2.02k/2.02k [00:00<00:00, 887kB/s]\n",
+ "Downloading: 100%|██████████████████████████████| 262/262 [00:00<00:00, 166kB/s]\n",
+ "Downloading: 100%|█████████████████████████| 1.18G/1.18G [00:18<00:00, 69.7MB/s]\n",
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+ "Downloading: 100%|███████████████████████████| 85.0/85.0 [00:00<00:00, 53.8kB/s]\n",
+ "Downloading: 100%|██████████████████████████████| 533/533 [00:00<00:00, 303kB/s]\n",
+ "Downloading: 100%|███████████████████████████| 342M/342M [00:07<00:00, 48.5MB/s]\n",
+ "Downloading: 100%|███████████████████████████| 78.0/78.0 [00:00<00:00, 53.8kB/s]\n",
+ "Downloading: 100%|█████████████████████████| 2.14M/2.14M [00:00<00:00, 16.9MB/s]\n",
+ "8ex [00:00, 19.71ex/s]/opt/conda/lib/python3.8/site-packages/transformers/pipelines/base.py:976: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
+ " warnings.warn(\n",
+ "10284ex [07:27, 22.99ex/s]\n",
+ "Downloading: 4.48kB [00:00, 3.06MB/s] \n",
+ "Downloading: 5.59kB [00:00, 2.87MB/s] \n",
+ "WER: 0.3882512836001208\n",
+ "CER: 0.15335327074758484\n",
+ "10284ex [00:00, 19585.20ex/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "!python eval.py --model_id kingabzpro/wav2vec2-large-xlsr-300-arabic --dataset mozilla-foundation/common_voice_7_0 --config ar --split test\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gUPAx3_MdyQv"
+ },
+ "source": [
+ "That's it. Now you should be able to use the *5gram* for LM-boosted decoding as shown in Section 1.\n",
+ "\n",
+ "As can be seen on [`xls-r-300m-sv`'s model card](https://huggingface.co./hf-test/xls-r-300m-sv#inference-with-lm) our *5gram* LM-boosted decoder yields a WER of 18.85% on Common Voice's 7 test set which is a relative performance of *ca.* 30% 🔥."
+ ]
+ }
+ ],
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