{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "ZG_P29nKcSeI" }, "source": [ "# HuggingFace challenge - Debugger notebook\n", "Run this notebook to verify your libraries versions, check GPU config and run a quick training" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "YacvHugMc1Ka" }, "outputs": [], "source": [ "# %%capture\n", "# !pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode\n", "# !pip install datasets==1.18.1\n", "# !pip install git+https://github.com/huggingface/transformers.git\n", "# !pip install huggingface_hub==0.1\n", "# !pip install torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html\n", "# !pip install jiwer\n", "# !pip install -U git+https://github.com/huggingface/transformers.git" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vy63SoiZbnB5", "outputId": "17391c60-b894-4571-b8a4-d46b18cb42e2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting git+https://github.com/huggingface/transformers.git\n", " Cloning https://github.com/huggingface/transformers.git to /tmp/pip-req-build-i45amciw\n", " Running command git clone -q https://github.com/huggingface/transformers.git /tmp/pip-req-build-i45amciw\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n", "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (0.1.0)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (3.4.2)\n", "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (4.10.1)\n", "Requirement already satisfied: tokenizers!=0.11.3,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (0.11.4)\n", "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (0.0.47)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (1.19.5)\n", "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (4.62.3)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (2.23.0)\n", "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (6.0)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (21.3)\n", "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (2019.12.20)\n", "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers==4.17.0.dev0) (3.10.0.2)\n", "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers==4.17.0.dev0) (3.0.7)\n", "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers==4.17.0.dev0) (3.7.0)\n", "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.17.0.dev0) (2.10)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.17.0.dev0) (2021.10.8)\n", "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.17.0.dev0) (3.0.4)\n", "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.17.0.dev0) (1.24.3)\n", "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.17.0.dev0) (1.15.0)\n", "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.17.0.dev0) (1.1.0)\n", "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.17.0.dev0) (7.1.2)\n" ] } ], "source": [ "# !pip install -U git+https://github.com/huggingface/transformers.git" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "T2utsYSKszvv" }, "outputs": [], "source": [ "import platform\n", "import multiprocessing\n", "\n", "import torch\n", "import transformers\n", "import datasets\n", "\n", "import soundfile" ] }, { "cell_type": "markdown", "metadata": { "id": "ejKNEyJEcSeO" }, "source": [ "## Print main infos" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5P6I-W9ts-kR", "outputId": "bd0c00d8-91c9-4b1a-8f2c-24182c2b227f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Platform: Linux-5.11.0-37-generic-x86_64-with-glibc2.10\n", "CPU cores: 60\n", "Python version: 3.8.8\n", "PyTorch version: 1.10.1+cu102\n", "GPU is visible: True\n", "Transformers version: 4.16.0.dev0\n", "Datasets version: 1.17.1.dev0\n", "soundfile version: 0.10.3\n" ] } ], "source": [ "print(f\"Platform: {platform.platform()}\")\n", "print(f\"CPU cores: {multiprocessing.cpu_count()}\")\n", "\n", "print(f\"Python version: {platform.python_version()}\")\n", "\n", "print(f\"PyTorch version: {torch.__version__}\")\n", "print(f\"GPU is visible: {torch.cuda.is_available()}\")\n", "\n", "print(f\"Transformers version: {transformers.__version__}\")\n", "print(f\"Datasets version: {datasets.__version__}\")\n", "\n", "print(f\"soundfile version: {soundfile.__version__}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "_VUKw21PcSeQ" }, "source": [ "## Check your GPU informations (if any)\n", "If you launched an AI Training job with GPU resources, they should be listed below (Tesla V100s 32GB).\n", "Driver and CUDA version " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YT7fRnKctggU", "outputId": "1fb2c851-11c3-4fcd-ad23-9032f25d7f8d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sat Jan 29 03:27:00 2022 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 470.57.02 Driver Version: 470.57.02 CUDA Version: 11.4 |\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 V100S-PCI... Off | 00000000:00:06.0 Off | 0 |\n", "| N/A 35C P0 26W / 250W | 4MiB / 32510MiB | 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": [ "!nvidia-smi" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 241, "referenced_widgets": [ "50a1252082d942b09bfc620a9fa9d1d0", "e270b7c82f784ebbbba4b17fb07c310d", "32eb83bb6fd34c56bb345368e47e8f6f", "34417f648cd54ed5b6d91f53af3e2713", "7518572223ac480b89af2ab71f38b2ed", "ce8bb7d0fb744e7b9ce2ff35cfdbc679", "aa47a09bf444413ba95322d979c1908c", "0b83a8775ea1441980d8ba945be752fe", "127389ec566e423ab9a8f60a9d61caaa", "4e4bc5550505497ba35f6bd7dde2893f", "e5124c5171e04625b70795e4b7a18819", "e410e7aecf23433f880a0f7169a8ce97", "0f6b3cf1d33f46f594934874170bcd83", "e549178ba75f4939aba6ae1cf743722a", "9c28978adf974326a21259ae56f47fe9", "7d3231a0b7794b11af662170b352d9e0" ] }, "id": "3Wj2W4tWcSeR", "outputId": "ad4eb63f-d643-45bd-b8d7-6adfefd9f773" }, "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-crendential store but this isn't the helper defined on your machine.\n", "You will have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal to set it 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": 8, "metadata": { "id": "wHpUxFQPeWE2" }, "outputs": [], "source": [ "%%capture\n", "!apt install git-lfs" ] }, { "cell_type": "markdown", "metadata": { "id": "TorMtpwPv6RQ" }, "source": [ "## Quick training run with a dummy model and data\n", "more information on https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fevoJD15u4Ss", "outputId": "64745ecf-65b0-494d-a88d-52826eaae0f8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2022-01-28 09:12:30-- https://raw.githubusercontent.com/huggingface/transformers/master/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py\n", "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n", "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 31209 (30K) [text/plain]\n", "Saving to: ‘run_speech_recognition_ctc.py’\n", "\n", "run_speech_recognit 100%[===================>] 30.48K --.-KB/s in 0.001s \n", "\n", "2022-01-28 09:12:30 (21.4 MB/s) - ‘run_speech_recognition_ctc.py’ saved [31209/31209]\n", "\n" ] } ], "source": [ "!wget -O run_speech_recognition_ctc.py https://raw.githubusercontent.com/huggingface/transformers/master/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py\n", "# !wget -O run_speech_recognition_ctc.py https://raw.githubusercontent.com/huggingface/transformers/master/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "XJRA51HjcSeT" }, "outputs": [], "source": [ "# \t--learning_rate=\"7.5e-5\" \\\n", "# 84.5" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hZOB6ZAnsvDX", "outputId": "7b6a85b5-950c-46a1-c005-b885f8a9bd17" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nvcc: NVIDIA (R) Cuda compiler driver\n", "Copyright (c) 2005-2020 NVIDIA Corporation\n", "Built on Mon_Oct_12_20:09:46_PDT_2020\n", "Cuda compilation tools, release 11.1, V11.1.105\n", "Build cuda_11.1.TC455_06.29190527_0\n" ] } ], "source": [ "!nvcc --version" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NKlgW0E-sldT", "outputId": "b925521a-29d2-4787-dd5b-6520dda688e4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting bitsandbytes-cuda111\n", " Downloading bitsandbytes_cuda111-0.26.0-py3-none-any.whl (4.0 MB)\n", "\u001b[K |████████████████████████████████| 4.0 MB 4.3 MB/s \n", "\u001b[?25hInstalling collected packages: bitsandbytes-cuda111\n", "Successfully installed bitsandbytes-cuda111-0.26.0\n" ] } ], "source": [ "!pip install bitsandbytes-cuda111" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "remove special characters from datasets: 100%|█| 3526/3526 [00:00<00:00, 4381.43\n", "remove special characters from datasets: 100%|█| 1646/1646 [00:00<00:00, 5621.35\n", "loading configuration file https://huggingface.co./facebook/wav2vec2-xls-r-300m/resolve/main/config.json from cache at /workspace/.cache/huggingface/transformers/dabc27df63e37bd2a7a221c7774e35f36a280fbdf917cf54cadfc7df8c786f6f.a3e4c3c967d9985881e0ae550a5f6f668f897db5ab2e0802f9b97973b15970e6\n", "Model config Wav2Vec2Config {\n", " \"_name_or_path\": \"facebook/wav2vec2-xls-r-300m\",\n", " \"activation_dropout\": 0.0,\n", " \"adapter_kernel_size\": 3,\n", " \"adapter_stride\": 2,\n", " \"add_adapter\": false,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForPreTraining\"\n", " ],\n", " \"attention_dropout\": 0.1,\n", " \"bos_token_id\": 1,\n", " \"classifier_proj_size\": 256,\n", " \"codevector_dim\": 768,\n", " \"contrastive_logits_temperature\": 0.1,\n", " \"conv_bias\": true,\n", " \"conv_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512\n", " ],\n", " \"conv_kernel\": [\n", " 10,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 2,\n", " 2\n", " ],\n", " \"conv_stride\": [\n", " 5,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2\n", " ],\n", " \"ctc_loss_reduction\": \"sum\",\n", " \"ctc_zero_infinity\": false,\n", " \"diversity_loss_weight\": 0.1,\n", " \"do_stable_layer_norm\": true,\n", " \"eos_token_id\": 2,\n", " \"feat_extract_activation\": \"gelu\",\n", " \"feat_extract_dropout\": 0.0,\n", " \"feat_extract_norm\": \"layer\",\n", " \"feat_proj_dropout\": 0.1,\n", " \"feat_quantizer_dropout\": 0.0,\n", " \"final_dropout\": 0.0,\n", " \"gradient_checkpointing\": false,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout\": 0.1,\n", " \"hidden_size\": 1024,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 4096,\n", " \"layer_norm_eps\": 1e-05,\n", " \"layerdrop\": 0.1,\n", " \"mask_feature_length\": 10,\n", " \"mask_feature_min_masks\": 0,\n", " \"mask_feature_prob\": 0.0,\n", " \"mask_time_length\": 10,\n", " \"mask_time_min_masks\": 2,\n", " \"mask_time_prob\": 0.075,\n", " \"model_type\": \"wav2vec2\",\n", " \"num_adapter_layers\": 3,\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", " \"output_hidden_size\": 1024,\n", " \"pad_token_id\": 0,\n", " \"proj_codevector_dim\": 768,\n", " \"tdnn_dilation\": [\n", " 1,\n", " 2,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"tdnn_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 1500\n", " ],\n", " \"tdnn_kernel\": [\n", " 5,\n", " 3,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.16.0.dev0\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 32,\n", " \"xvector_output_dim\": 512\n", "}\n", "\n", "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00, 5.13ba/s]\n", "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00, 11.87ba/s]\n", "Didn't find file ./wav2vec2-large-xls-r-300m-maltese/tokenizer.json. We won't load it.\n", "loading file ./wav2vec2-large-xls-r-300m-maltese/vocab.json\n", "loading file ./wav2vec2-large-xls-r-300m-maltese/tokenizer_config.json\n", "loading file ./wav2vec2-large-xls-r-300m-maltese/added_tokens.json\n", "loading file ./wav2vec2-large-xls-r-300m-maltese/special_tokens_map.json\n", "loading file None\n", "Adding to the vocabulary\n", "Adding to the vocabulary\n", "loading configuration file https://huggingface.co./facebook/wav2vec2-xls-r-300m/resolve/main/config.json from cache at /workspace/.cache/huggingface/transformers/dabc27df63e37bd2a7a221c7774e35f36a280fbdf917cf54cadfc7df8c786f6f.a3e4c3c967d9985881e0ae550a5f6f668f897db5ab2e0802f9b97973b15970e6\n", "Model config Wav2Vec2Config {\n", " \"_name_or_path\": \"facebook/wav2vec2-xls-r-300m\",\n", " \"activation_dropout\": 0.0,\n", " \"adapter_kernel_size\": 3,\n", " \"adapter_stride\": 2,\n", " \"add_adapter\": false,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForPreTraining\"\n", " ],\n", " \"attention_dropout\": 0.1,\n", " \"bos_token_id\": 1,\n", " \"classifier_proj_size\": 256,\n", " \"codevector_dim\": 768,\n", " \"contrastive_logits_temperature\": 0.1,\n", " \"conv_bias\": true,\n", " \"conv_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512\n", " ],\n", " \"conv_kernel\": [\n", " 10,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 2,\n", " 2\n", " ],\n", " \"conv_stride\": [\n", " 5,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2\n", " ],\n", " \"ctc_loss_reduction\": \"sum\",\n", " \"ctc_zero_infinity\": false,\n", " \"diversity_loss_weight\": 0.1,\n", " \"do_stable_layer_norm\": true,\n", " \"eos_token_id\": 2,\n", " \"feat_extract_activation\": \"gelu\",\n", " \"feat_extract_dropout\": 0.0,\n", " \"feat_extract_norm\": \"layer\",\n", " \"feat_proj_dropout\": 0.1,\n", " \"feat_quantizer_dropout\": 0.0,\n", " \"final_dropout\": 0.0,\n", " \"gradient_checkpointing\": false,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout\": 0.1,\n", " \"hidden_size\": 1024,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 4096,\n", " \"layer_norm_eps\": 1e-05,\n", " \"layerdrop\": 0.1,\n", " \"mask_feature_length\": 10,\n", " \"mask_feature_min_masks\": 0,\n", " \"mask_feature_prob\": 0.0,\n", " \"mask_time_length\": 10,\n", " \"mask_time_min_masks\": 2,\n", " \"mask_time_prob\": 0.075,\n", " \"model_type\": \"wav2vec2\",\n", " \"num_adapter_layers\": 3,\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", " \"output_hidden_size\": 1024,\n", " \"pad_token_id\": 0,\n", " \"proj_codevector_dim\": 768,\n", " \"tdnn_dilation\": [\n", " 1,\n", " 2,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"tdnn_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 1500\n", " ],\n", " \"tdnn_kernel\": [\n", " 5,\n", " 3,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.16.0.dev0\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 32,\n", " \"xvector_output_dim\": 512\n", "}\n", "\n", "loading feature extractor configuration file https://huggingface.co./facebook/wav2vec2-xls-r-300m/resolve/main/preprocessor_config.json from cache at /workspace/.cache/huggingface/transformers/6fb028b95b394059e7d3b367bbca2382b576c66aebe896f04d2cd34e1b575f5b.d4484dc1c81456a2461485e7168b04347a7b9a4e3b1ef3aba723323b33e12326\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,\n", " \"return_attention_mask\": true,\n", " \"sampling_rate\": 16000\n", "}\n", "\n", "loading weights file https://huggingface.co./facebook/wav2vec2-xls-r-300m/resolve/main/pytorch_model.bin from cache at /workspace/.cache/huggingface/transformers/1e6a6507f3b689035cd4b247e2a37c154e27f39143f31357a49b4e38baeccc36.1edb32803799e27ed554eb7dd935f6745b1a0b17b0ea256442fe24db6eb546cd\n", "Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['project_q.weight', 'quantizer.weight_proj.weight', 'project_hid.bias', 'quantizer.codevectors', 'project_q.bias', 'project_hid.weight', 'quantizer.weight_proj.bias']\n", "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-300m and are newly initialized: ['lm_head.weight', 'lm_head.bias']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n", "preprocess datasets: 100%|█████████████████| 3526/3526 [00:32<00:00, 110.12ex/s]\n", "preprocess datasets: 100%|██████████████████| 1646/1646 [00:16<00:00, 99.44ex/s]\n", "100%|████████████████████████████████████████████| 4/4 [00:00<00:00, 803.20ba/s]\n", "100%|████████████████████████████████████████████| 2/2 [00:00<00:00, 846.39ba/s]\n", "Configuration saved in ./wav2vec2-large-xls-r-300m-maltese/preprocessor_config.json\n", "tokenizer config file saved in ./wav2vec2-large-xls-r-300m-maltese/tokenizer_config.json\n", "Special tokens file saved in ./wav2vec2-large-xls-r-300m-maltese/special_tokens_map.json\n", "added tokens file saved in ./wav2vec2-large-xls-r-300m-maltese/added_tokens.json\n", "Configuration saved in ./wav2vec2-large-xls-r-300m-maltese/config.json\n", "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-maltese/preprocessor_config.json\n", "loading configuration file ./wav2vec2-large-xls-r-300m-maltese/config.json\n", "Model config Wav2Vec2Config {\n", " \"_name_or_path\": \"./wav2vec2-large-xls-r-300m-maltese\",\n", " \"activation_dropout\": 0.1,\n", " \"adapter_kernel_size\": 3,\n", " \"adapter_stride\": 2,\n", " \"add_adapter\": false,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForPreTraining\"\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", " \"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\": 64,\n", " \"mask_feature_min_masks\": 0,\n", " \"mask_feature_prob\": 0.25,\n", " \"mask_time_length\": 10,\n", " \"mask_time_min_masks\": 2,\n", " \"mask_time_prob\": 0.75,\n", " \"model_type\": \"wav2vec2\",\n", " \"num_adapter_layers\": 3,\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", " \"output_hidden_size\": 1024,\n", " \"pad_token_id\": 40,\n", " \"proj_codevector_dim\": 768,\n", " \"tdnn_dilation\": [\n", " 1,\n", " 2,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"tdnn_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 1500\n", " ],\n", " \"tdnn_kernel\": [\n", " 5,\n", " 3,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.16.0.dev0\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 43,\n", " \"xvector_output_dim\": 512\n", "}\n", "\n", "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-maltese/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,\n", " \"return_attention_mask\": true,\n", " \"sampling_rate\": 16000\n", "}\n", "\n", "Didn't find file ./wav2vec2-large-xls-r-300m-maltese/tokenizer.json. We won't load it.\n", "loading file ./wav2vec2-large-xls-r-300m-maltese/vocab.json\n", "loading file ./wav2vec2-large-xls-r-300m-maltese/tokenizer_config.json\n", "loading file ./wav2vec2-large-xls-r-300m-maltese/added_tokens.json\n", "loading file ./wav2vec2-large-xls-r-300m-maltese/special_tokens_map.json\n", "loading file None\n", "Adding to the vocabulary\n", "Adding to the vocabulary\n", "/workspace/votic_training/./wav2vec2-large-xls-r-300m-maltese is already a clone of https://huggingface.co./infinitejoy/wav2vec2-large-xls-r-300m-maltese. Make sure you pull the latest changes with `repo.git_pull()`.\n", "Using amp half precision backend\n", "The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "/opt/conda/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use thePyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", " warnings.warn(\n", "***** Running training *****\n", " Num examples = 3526\n", " Num Epochs = 100\n", " Instantaneous batch size per device = 32\n", " Total train batch size (w. parallel, distributed & accumulation) = 32\n", " Gradient Accumulation steps = 1\n", " Total optimization steps = 11100\n", " 18%|██████▍ | 2000/11100 [52:36<4:46:07, 1.89s/it]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 = 1646\n", " Batch size = 1\n", "\n", " 0%| | 0/1646 [00:00 out.log" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "Mz4bubhxxsad", "jupyter": { "outputs_hidden": true }, "outputId": "487b7c2e-cba3-49a5-c317-169898151f66" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "01/28/2022 09:21:58 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True\n", "01/28/2022 09:21:58 - INFO - __main__ - Training/evaluation parameters TrainingArguments(\n", "_n_gpu=1,\n", "adafactor=False,\n", "adam_beta1=0.9,\n", "adam_beta2=0.999,\n", "adam_epsilon=1e-08,\n", "bf16=False,\n", "bf16_full_eval=False,\n", "dataloader_drop_last=False,\n", "dataloader_num_workers=0,\n", "dataloader_pin_memory=True,\n", "ddp_bucket_cap_mb=None,\n", "ddp_find_unused_parameters=None,\n", "debug=[],\n", "deepspeed=None,\n", "disable_tqdm=False,\n", "do_eval=True,\n", "do_predict=False,\n", "do_train=True,\n", "eval_accumulation_steps=None,\n", "eval_steps=500,\n", "evaluation_strategy=IntervalStrategy.STEPS,\n", "fp16=True,\n", "fp16_backend=auto,\n", "fp16_full_eval=False,\n", "fp16_opt_level=O1,\n", "gradient_accumulation_steps=1,\n", "gradient_checkpointing=True,\n", "greater_is_better=None,\n", "group_by_length=True,\n", "half_precision_backend=auto,\n", "hub_model_id=None,\n", "hub_strategy=HubStrategy.EVERY_SAVE,\n", "hub_token=,\n", "ignore_data_skip=False,\n", "label_names=None,\n", "label_smoothing_factor=0.0,\n", "learning_rate=7e-05,\n", "length_column_name=input_length,\n", "load_best_model_at_end=False,\n", "local_rank=-1,\n", "log_level=-1,\n", "log_level_replica=-1,\n", "log_on_each_node=True,\n", "logging_dir=./wav2vec2-large-xls-r-300m-hausa/runs/Jan28_09-21-57_54bb55ed0d99,\n", "logging_first_step=False,\n", "logging_nan_inf_filter=True,\n", "logging_steps=100,\n", "logging_strategy=IntervalStrategy.STEPS,\n", "lr_scheduler_type=SchedulerType.LINEAR,\n", "max_grad_norm=1.0,\n", "max_steps=-1,\n", "metric_for_best_model=None,\n", "mp_parameters=,\n", "no_cuda=False,\n", "num_train_epochs=100.0,\n", "optim=OptimizerNames.ADAMW_HF,\n", "output_dir=./wav2vec2-large-xls-r-300m-hausa,\n", "overwrite_output_dir=True,\n", "past_index=-1,\n", "per_device_eval_batch_size=32,\n", "per_device_train_batch_size=32,\n", "prediction_loss_only=False,\n", "push_to_hub=True,\n", "push_to_hub_model_id=None,\n", "push_to_hub_organization=None,\n", "push_to_hub_token=,\n", "remove_unused_columns=True,\n", "report_to=['tensorboard'],\n", "resume_from_checkpoint=None,\n", "run_name=./wav2vec2-large-xls-r-300m-hausa,\n", "save_on_each_node=False,\n", "save_steps=500,\n", "save_strategy=IntervalStrategy.STEPS,\n", "save_total_limit=2,\n", "seed=42,\n", "sharded_ddp=[],\n", "skip_memory_metrics=True,\n", "tf32=None,\n", "tpu_metrics_debug=False,\n", "tpu_num_cores=None,\n", "use_legacy_prediction_loop=False,\n", "warmup_ratio=0.0,\n", "warmup_steps=500,\n", "weight_decay=0.0,\n", "xpu_backend=None,\n", ")\n", "01/28/2022 09:22:02 - WARNING - datasets.builder - Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/ha/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n", "01/28/2022 09:22:07 - WARNING - datasets.builder - Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/ha/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n", "01/28/2022 09:22:07 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/ha/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/cache-598fed4651a7fb25.arrow\n", "01/28/2022 09:22:07 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/ha/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/cache-b62e36164d4b7009.arrow\n", "loading configuration file https://huggingface.co./facebook/wav2vec2-xls-r-300m/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/dabc27df63e37bd2a7a221c7774e35f36a280fbdf917cf54cadfc7df8c786f6f.a3e4c3c967d9985881e0ae550a5f6f668f897db5ab2e0802f9b97973b15970e6\n", "Model config Wav2Vec2Config {\n", " \"_name_or_path\": \"facebook/wav2vec2-xls-r-300m\",\n", " \"activation_dropout\": 0.0,\n", " \"adapter_kernel_size\": 3,\n", " \"adapter_stride\": 2,\n", " \"add_adapter\": false,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForPreTraining\"\n", " ],\n", " \"attention_dropout\": 0.1,\n", " \"bos_token_id\": 1,\n", " \"classifier_proj_size\": 256,\n", " \"codevector_dim\": 768,\n", " \"contrastive_logits_temperature\": 0.1,\n", " \"conv_bias\": true,\n", " \"conv_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512\n", " ],\n", " \"conv_kernel\": [\n", " 10,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 2,\n", " 2\n", " ],\n", " \"conv_stride\": [\n", " 5,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2\n", " ],\n", " \"ctc_loss_reduction\": \"sum\",\n", " \"ctc_zero_infinity\": false,\n", " \"diversity_loss_weight\": 0.1,\n", " \"do_stable_layer_norm\": true,\n", " \"eos_token_id\": 2,\n", " \"feat_extract_activation\": \"gelu\",\n", " \"feat_extract_dropout\": 0.0,\n", " \"feat_extract_norm\": \"layer\",\n", " \"feat_proj_dropout\": 0.1,\n", " \"feat_quantizer_dropout\": 0.0,\n", " \"final_dropout\": 0.0,\n", " \"gradient_checkpointing\": false,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout\": 0.1,\n", " \"hidden_size\": 1024,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 4096,\n", " \"layer_norm_eps\": 1e-05,\n", " \"layerdrop\": 0.1,\n", " \"mask_feature_length\": 10,\n", " \"mask_feature_min_masks\": 0,\n", " \"mask_feature_prob\": 0.0,\n", " \"mask_time_length\": 10,\n", " \"mask_time_min_masks\": 2,\n", " \"mask_time_prob\": 0.075,\n", " \"model_type\": \"wav2vec2\",\n", " \"num_adapter_layers\": 3,\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", " \"output_hidden_size\": 1024,\n", " \"pad_token_id\": 0,\n", " \"proj_codevector_dim\": 768,\n", " \"tdnn_dilation\": [\n", " 1,\n", " 2,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"tdnn_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 1500\n", " ],\n", " \"tdnn_kernel\": [\n", " 5,\n", " 3,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.17.0.dev0\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 32,\n", " \"xvector_output_dim\": 512\n", "}\n", "\n", "100% 1/1 [00:00<00:00, 15.11ba/s]\n", "100% 1/1 [00:00<00:00, 168.77ba/s]\n", "Didn't find file ./wav2vec2-large-xls-r-300m-hausa/tokenizer.json. We won't load it.\n", "loading file ./wav2vec2-large-xls-r-300m-hausa/vocab.json\n", "loading file ./wav2vec2-large-xls-r-300m-hausa/tokenizer_config.json\n", "loading file ./wav2vec2-large-xls-r-300m-hausa/added_tokens.json\n", "loading file ./wav2vec2-large-xls-r-300m-hausa/special_tokens_map.json\n", "loading file None\n", "Adding to the vocabulary\n", "Adding to the vocabulary\n", "loading configuration file https://huggingface.co./facebook/wav2vec2-xls-r-300m/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/dabc27df63e37bd2a7a221c7774e35f36a280fbdf917cf54cadfc7df8c786f6f.a3e4c3c967d9985881e0ae550a5f6f668f897db5ab2e0802f9b97973b15970e6\n", "Model config Wav2Vec2Config {\n", " \"_name_or_path\": \"facebook/wav2vec2-xls-r-300m\",\n", " \"activation_dropout\": 0.0,\n", " \"adapter_kernel_size\": 3,\n", " \"adapter_stride\": 2,\n", " \"add_adapter\": false,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForPreTraining\"\n", " ],\n", " \"attention_dropout\": 0.1,\n", " \"bos_token_id\": 1,\n", " \"classifier_proj_size\": 256,\n", " \"codevector_dim\": 768,\n", " \"contrastive_logits_temperature\": 0.1,\n", " \"conv_bias\": true,\n", " \"conv_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 512\n", " ],\n", " \"conv_kernel\": [\n", " 10,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 2,\n", " 2\n", " ],\n", " \"conv_stride\": [\n", " 5,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2,\n", " 2\n", " ],\n", " \"ctc_loss_reduction\": \"sum\",\n", " \"ctc_zero_infinity\": false,\n", " \"diversity_loss_weight\": 0.1,\n", " \"do_stable_layer_norm\": true,\n", " \"eos_token_id\": 2,\n", " \"feat_extract_activation\": \"gelu\",\n", " \"feat_extract_dropout\": 0.0,\n", " \"feat_extract_norm\": \"layer\",\n", " \"feat_proj_dropout\": 0.1,\n", " \"feat_quantizer_dropout\": 0.0,\n", " \"final_dropout\": 0.0,\n", " \"gradient_checkpointing\": false,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout\": 0.1,\n", " \"hidden_size\": 1024,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 4096,\n", " \"layer_norm_eps\": 1e-05,\n", " \"layerdrop\": 0.1,\n", " \"mask_feature_length\": 10,\n", " \"mask_feature_min_masks\": 0,\n", " \"mask_feature_prob\": 0.0,\n", " \"mask_time_length\": 10,\n", " \"mask_time_min_masks\": 2,\n", " \"mask_time_prob\": 0.075,\n", " \"model_type\": \"wav2vec2\",\n", " \"num_adapter_layers\": 3,\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", " \"output_hidden_size\": 1024,\n", " \"pad_token_id\": 0,\n", " \"proj_codevector_dim\": 768,\n", " \"tdnn_dilation\": [\n", " 1,\n", " 2,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"tdnn_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 1500\n", " ],\n", " \"tdnn_kernel\": [\n", " 5,\n", " 3,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.17.0.dev0\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 32,\n", " \"xvector_output_dim\": 512\n", "}\n", "\n", "loading feature extractor configuration file https://huggingface.co./facebook/wav2vec2-xls-r-300m/resolve/main/preprocessor_config.json from cache at /root/.cache/huggingface/transformers/6fb028b95b394059e7d3b367bbca2382b576c66aebe896f04d2cd34e1b575f5b.d4484dc1c81456a2461485e7168b04347a7b9a4e3b1ef3aba723323b33e12326\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,\n", " \"return_attention_mask\": true,\n", " \"sampling_rate\": 16000\n", "}\n", "\n", "loading weights file https://huggingface.co./facebook/wav2vec2-xls-r-300m/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/1e6a6507f3b689035cd4b247e2a37c154e27f39143f31357a49b4e38baeccc36.1edb32803799e27ed554eb7dd935f6745b1a0b17b0ea256442fe24db6eb546cd\n", "Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['quantizer.weight_proj.bias', 'project_q.weight', 'project_hid.weight', 'quantizer.codevectors', 'quantizer.weight_proj.weight', 'project_hid.bias', 'project_q.bias']\n", "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-300m and are newly initialized: ['lm_head.weight', 'lm_head.bias']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n", "preprocess datasets: 1386ex [00:24, 55.91ex/s] \n", "preprocess datasets: 149ex [00:01, 101.75ex/s]\n", "100% 2/2 [00:00<00:00, 283.67ba/s]\n", "100% 1/1 [00:00<00:00, 381.89ba/s]\n", "Configuration saved in ./wav2vec2-large-xls-r-300m-hausa/preprocessor_config.json\n", "tokenizer config file saved in ./wav2vec2-large-xls-r-300m-hausa/tokenizer_config.json\n", "Special tokens file saved in ./wav2vec2-large-xls-r-300m-hausa/special_tokens_map.json\n", "added tokens file saved in ./wav2vec2-large-xls-r-300m-hausa/added_tokens.json\n", "Configuration saved in ./wav2vec2-large-xls-r-300m-hausa/config.json\n", "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-hausa/preprocessor_config.json\n", "loading configuration file ./wav2vec2-large-xls-r-300m-hausa/config.json\n", "Model config Wav2Vec2Config {\n", " \"_name_or_path\": \"./wav2vec2-large-xls-r-300m-hausa\",\n", " \"activation_dropout\": 0.1,\n", " \"adapter_kernel_size\": 3,\n", " \"adapter_stride\": 2,\n", " \"add_adapter\": false,\n", " \"apply_spec_augment\": true,\n", " \"architectures\": [\n", " \"Wav2Vec2ForPreTraining\"\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", " \"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\": 64,\n", " \"mask_feature_min_masks\": 0,\n", " \"mask_feature_prob\": 0.25,\n", " \"mask_time_length\": 10,\n", " \"mask_time_min_masks\": 2,\n", " \"mask_time_prob\": 0.75,\n", " \"model_type\": \"wav2vec2\",\n", " \"num_adapter_layers\": 3,\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", " \"output_hidden_size\": 1024,\n", " \"pad_token_id\": 34,\n", " \"proj_codevector_dim\": 768,\n", " \"tdnn_dilation\": [\n", " 1,\n", " 2,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"tdnn_dim\": [\n", " 512,\n", " 512,\n", " 512,\n", " 512,\n", " 1500\n", " ],\n", " \"tdnn_kernel\": [\n", " 5,\n", " 3,\n", " 3,\n", " 1,\n", " 1\n", " ],\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.17.0.dev0\",\n", " \"use_weighted_layer_sum\": false,\n", " \"vocab_size\": 37,\n", " \"xvector_output_dim\": 512\n", "}\n", "\n", "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-hausa/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,\n", " \"return_attention_mask\": true,\n", " \"sampling_rate\": 16000\n", "}\n", "\n", "Didn't find file ./wav2vec2-large-xls-r-300m-hausa/tokenizer.json. We won't load it.\n", "loading file ./wav2vec2-large-xls-r-300m-hausa/vocab.json\n", "loading file ./wav2vec2-large-xls-r-300m-hausa/tokenizer_config.json\n", "loading file ./wav2vec2-large-xls-r-300m-hausa/added_tokens.json\n", "loading file ./wav2vec2-large-xls-r-300m-hausa/special_tokens_map.json\n", "loading file None\n", "Adding to the vocabulary\n", "Adding to the vocabulary\n", "/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", "/content/./wav2vec2-large-xls-r-300m-hausa is already a clone of https://huggingface.co./infinitejoy/wav2vec2-large-xls-r-300m-hausa. Make sure you pull the latest changes with `repo.git_pull()`.\n", "01/28/2022 09:22:51 - WARNING - huggingface_hub.repository - /content/./wav2vec2-large-xls-r-300m-hausa is already a clone of https://huggingface.co./infinitejoy/wav2vec2-large-xls-r-300m-hausa. Make sure you pull the latest changes with `repo.git_pull()`.\n", "Using amp half precision backend\n", "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 = 1386\n", " Num Epochs = 100\n", " Instantaneous batch size per device = 32\n", " Total train batch size (w. parallel, distributed & accumulation) = 32\n", " Gradient Accumulation steps = 1\n", " Total optimization steps = 4400\n", " 0% 10/4400 [04:09<23:26:32, 19.22s/it]Traceback (most recent call last):\n", " File \"run_speech_recognition_ctc.py\", line 760, in \n", " main()\n", " File \"run_speech_recognition_ctc.py\", line 711, in main\n", " train_result = trainer.train(resume_from_checkpoint=checkpoint)\n", " File \"/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\", line 1373, in train\n", " tr_loss_step = self.training_step(model, inputs)\n", " File \"/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\", line 1948, in training_step\n", " loss = self.compute_loss(model, inputs)\n", " File \"/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\", line 1980, in compute_loss\n", " outputs = model(**inputs)\n", " File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 1102, in _call_impl\n", " return forward_call(*input, **kwargs)\n", " File \"/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py\", line 1747, in forward\n", " return_dict=return_dict,\n", " File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 1102, in _call_impl\n", " return forward_call(*input, **kwargs)\n", " File \"/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py\", line 1332, in forward\n", " extract_features = self.feature_extractor(input_values)\n", " File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 1102, in _call_impl\n", " return forward_call(*input, **kwargs)\n", " File \"/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py\", line 500, in forward\n", " hidden_states = conv_layer(hidden_states)\n", " File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 1102, in _call_impl\n", " return forward_call(*input, **kwargs)\n", " File \"/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py\", line 375, in forward\n", " hidden_states = self.layer_norm(hidden_states)\n", " File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 1102, in _call_impl\n", " return forward_call(*input, **kwargs)\n", " File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/normalization.py\", line 190, in forward\n", " input, self.normalized_shape, self.weight, self.bias, self.eps)\n", " File \"/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py\", line 2347, in layer_norm\n", " return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)\n", "RuntimeError: CUDA out of memory. Tried to allocate 1.70 GiB (GPU 0; 11.17 GiB total capacity; 7.24 GiB already allocated; 1.50 GiB free; 9.05 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF\n", " 0% 10/4400 [04:11<30:40:15, 25.15s/it]\n" ] } ], "source": [ "# !python run_speech_recognition_ctc.py \\\n", "# \t--dataset_name=\"mozilla-foundation/common_voice_7_0\" \\\n", "# \t--model_name_or_path=\"facebook/wav2vec2-xls-r-300m\" \\\n", "# \t--dataset_config_name=\"ha\" \\\n", "# \t--max_duration_in_seconds=\"10\" \\\n", "# \t--output_dir=\"./wav2vec2-large-xls-r-300m-hausa\" \\\n", "# \t--overwrite_output_dir \\\n", "# \t--num_train_epochs=\"100\" \\\n", "# \t--per_device_train_batch_size=\"32\" \\\n", "# \t--per_device_eval_batch_size=\"32\" \\\n", "# \t--gradient_accumulation_steps=\"1\" \\\n", "# \t--learning_rate=\"7e-5\" \\\n", "# \t--warmup_steps=\"500\" \\\n", "# \t--length_column_name=\"input_length\" \\\n", "# \t--evaluation_strategy=\"steps\" \\\n", "# \t--text_column_name=\"sentence\" \\\n", "# \t--chars_to_ignore , ? . ! \\- \\; \\: \\\" “ % ‘ ” � — ’ … – \\\n", "# \t--save_steps=\"500\" \\\n", "# \t--eval_steps=\"500\" \\\n", "# \t--logging_steps=\"100\" \\\n", "# \t--layerdrop=\"0.0\" \\\n", "# \t--activation_dropout=\"0.1\" \\\n", "# \t--save_total_limit=\"2\" \\\n", "# \t--freeze_feature_encoder \\\n", "# \t--feat_proj_dropout=\"0.0\" \\\n", "# \t--mask_time_prob=\"0.75\" \\\n", "# \t--mask_time_length=\"10\" \\\n", "# \t--mask_feature_prob=\"0.25\" \\\n", "# \t--mask_feature_length=\"64\" \\\n", "# \t--gradient_checkpointing \\\n", "# \t--use_auth_token \\\n", "# \t--fp16 \\\n", "# \t--group_by_length \\\n", "# \t--do_train --do_eval \\\n", "# --push_to_hub" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0zBb4QMVcSeV" }, "outputs": [], "source": [ "# !rm -rf wav2vec2-large-xls-r-300m-bashkir" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jxvhTTQ2cSeV" }, "outputs": [], "source": [ "!ls -ltr" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "okCO9-XTcSeV", "outputId": "a47bb25e-904a-4c1e-8871-d996a16b6bcc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filesystem Size Used Avail Use% Mounted on\n", "overlay 3.5T 1.2T 2.2T 34% /\n", "tmpfs 64M 0 64M 0% /dev\n", "tmpfs 87G 0 87G 0% /sys/fs/cgroup\n", "tmpfs 87G 0 87G 0% /dev/shm\n", "/dev/md0 3.5T 1.2T 2.2T 34% /etc/group\n", "tmpfs 87G 12K 87G 1% /proc/driver/nvidia\n", "/dev/vda1 49G 6.5G 42G 14% /usr/bin/nvidia-smi\n", "udev 87G 0 87G 0% /dev/nvidia0\n", "tmpfs 87G 0 87G 0% /proc/acpi\n", "tmpfs 87G 0 87G 0% /proc/scsi\n", "tmpfs 87G 0 87G 0% /sys/firmware\n" ] } ], "source": [ "!df -h" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "axSDvjOMdkxW" }, "outputs": [], "source": [ "# !pip install -U datasets" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 238, "referenced_widgets": [ "7c34d36b28e54989b0c509eae1bd9a0f", "eba629a92467433c92840e4450e7a937", "cf1afb1025d24c1cbbb1eefd26535a26", "f347c0838adf462d886a4ae36a3a6b41", "37bdb17bf4734fd4b92759c874a4d4b8", "4685ef4f82764fada48035b4de9af9e2", "aab799184cf8453e9cf026a32abff619", "1795d07714684311b1ccea7514f298e4", "7fa8f65c508e4e629b1a2212aaa64ebc", "c139ed75ff4d47d593f8cb5f3fa4c105", "776dc15d8836456281084dc154d769e4", "f3a862eb1219484b8d9381fb0d16b063", "da3f94cc1140466cbcbdb3e03cbea8c2", "2fedf1edcc184d9b8c67712511f8bfef", "25142b9649ef403c8b37cdb7f9a8de4b", "8f5cd0e3111241b8a61914dac82acf73", "7340567ea42d42709f8099a249f6b5dd", "7365cf85ddff4b26a27c9b797c573949", "2fbc062ac19f4eb7a8adff2a5118bea4", "ae5b0f9f37e44e8e965f7e20dfdf3bfa", "24aeaf260d2240d08466c5e3a01d95cb", "06ec543be0a34943959c3140119c4d6e", "311cbd6bf6df4c35b7819e49fb55a562", "3bc2760daaa346b2b20d76d6cf4ed336", "c4b226675ad84ff29f62847767065469", "0be3f91b1071464d979c0c59baff32f4", "7c4a653d81474818b084b71657f71e0f", "cb10ec01c16a4c50bf8e4c8aec491aa2", "ec67f65de50b4038ac3b01496ef56f98", "4b2562825d8e4c5484008cd054e01216", "209d975f5d4e4300bf01bb6b2472d493", "690f71c3c232421c8cd92a28b5435b55", "4f4d422bdd49486c940713c19e754479", "e5d1a213afc04270926da41e12b30362", "30afb513746845b481227b3191df4c90", "c7017ddc94104c27b42658f27f275908", "155de8f44ddf4021a5d1d4d4968934db", "cb3b32862a12486f8625d667bb45c368", "832b4fcaf152402e84bfdaf9833d061f", "8af6a305cc8a4a038f74f39e6ea8f040", "4c316c3eddd64af1b4d892516e1ced03", "efd0fc9b3766457484533a6eb59f2cd4", "27d72d36fe604e5d96d6a979ed6d50ee", "f90669ec059249ca81a0e2c5891834db", "67d3fcb0869a4485b24846d3b1e34fca", "3db73d64f4e54cad8f8cd0f5facc33c0", "d434124da4654ada92573070353dbce1", "3c36f662c44e453ca935753e6dc18060", "0d0ab06d275d49f5b1ac57b28c53c158", "61771b0bdfe543b88fc8673a510a986c", "63d4b794d9df49c6ab6f77f10a76861d", "42bb543380e14d859f42e966b3c54bc2", "00a1878e3cda42e1982093e185935937", "9cce7704e9e74588aa7aa3b9ddf9672f", "a27c1dd0b5c447058bf8abde274d7085", "1ee70ac9891d4104ad801f75b4081c9f", "eda7343054624f4d8a2e2b981b4fab41", "f56579df97b94a5a8b3a0fbf32905687", "aee17658cd4b4fe49a759ad6c9d5a576", "3a6e34083c8f4066a6718c957958cfa6", "8148f4330d0f441998d9a3ca4942bc22", "9ea974dfe1184fe3897a7d9d031c7624", "a968de55d2e148f88084ac96444c17ee", "c0aeab2086de4ca7ad8b5f0bbcde009c", "05d04f345a3148dd9053a5d524592333", "7a68ba6f90a24162a973ba5146c2f546", "a4411af1dda24dec9b863793ccd22390", "f085643a56b94b74bb7e883598170f01", "ee8a677f68a147e5b10a35518616e264", "315ae5446f264660bbe6119e8261495d", "64b970adf3af40268fb60e38140157e2", "2ac4df7918404aed92611750471cd85f", "7bf164fec94c40858cf5280937f8e00a", "0e1672eeb5244df9bf0cbd095625d68a", "ee80362b77ef4375bb931af34bc16d07", "fed5fdea500f46618789c44aef2bff3b", "f49c5c9c58ee482a8264e422d4610a8a", "6a9e0e280ef7493eb4557429d6f53685", "c51fb67419ed47f98c5ed4ad4e33aeff", "2de6d3927c534397ab122a9cf6332a33", "f3891dcc62b74ccd8d5a61b0ca761b2a", "9958cd546fbe477092527a14bb3bfe21", "639f180d5e02425dba7d4c4bca07c59b", "4da0d9054bd74fb2a77bb40371c99a7b", "3f8a5e226fbf4175b4fa7f39a2a9d290", "41515b22976648aabe660b8df3506c4c", "b2a72b0caf104aee8dd95bff01cc52a4", "6b8769a26838449e9d7d45fc5cc7a6f6", "50862512d9c14dbd92f8cc3d795d4cd2", "352fc0a527024af8a284c53f4d521fec", "67653ac95966464994b1e0a889cfc5d9", "778d0a9a7de243eba8dd1c0caf3aa82e", "14eb779636914797867b7315f347839d", "25a5802292874e49bb42a1489ff54b31", "89a05d4149534d78935e169c6623f458", "49f46100f43346d2bdb402e2fd1a1951", "5e2e7ad6aa8f4f51adf7f6376b84f618", "2e918f153be0489dbf0ad64bc45c563c", "c319fa946f3e4380864aed6d3fbb77e7" ] }, "id": "82uZWUF_cSeW", "outputId": "e78215f2-d452-4d92-a94c-0a469f8760d4" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/mt/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n", "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/mt/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "3526\n" ] } ], "source": [ "from datasets import load_dataset, load_metric, Audio\n", "\n", "common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"mt\", use_auth_token=True, split=\"train+validation\")\n", "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"mt\", use_auth_token=True, split=\"test\")\n", "\n", "print(len(common_voice_train))" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1Qa9wKa4cSeW", "outputId": "da721286-89ac-421c-a269-e779449488c6" }, "outputs": [ { "data": { "text/plain": [ "Dataset({\n", " features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],\n", " num_rows: 3526\n", "})" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "common_voice_train" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "H_KRIMbEcSeX", "outputId": "90601843-d465-4cd3-dff0-9d2302e02699" }, "outputs": [ { "data": { "text/plain": [ "11018.75" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(common_voice_train) * 100 / 32" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "id": "ZUc_UAMbcSeX" }, "outputs": [], "source": [ "common_voice_train = common_voice_train.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])\n", "common_voice_test = common_voice_test.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "id": "OKxWKzjMcSeX" }, "outputs": [], "source": [ "from datasets import ClassLabel\n", "import random\n", "import pandas as pd\n", "from IPython.display import display, HTML\n", "\n", "def show_random_elements(dataset, num_examples=10):\n", " assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n", " picks = []\n", " for _ in range(num_examples):\n", " pick = random.randint(0, len(dataset)-1)\n", " while pick in picks:\n", " pick = random.randint(0, len(dataset)-1)\n", " picks.append(pick)\n", " \n", " df = pd.DataFrame(dataset[picks])\n", " display(HTML(df.to_html()))" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "uR3e--0AcSeY", "outputId": "efb84606-2717-4040-ca02-86975a2f4824" }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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sentence
0Heathrow u Gatwick infurmaw lill-passiġġiera li kienu qed joperaw bħas-soltu.
1Se jinġabru wkoll donazzjonijiet mill-ġenituri u l-ħbieb li jmorru jarawhom jilagħbu.
2\"Però taf x'ġara?\"
3\"Patri Patrick Magro nħatar bħala l-Provinċjal il-ġdid tal-Ġiżwiti f'Malta.\"
4Kien hemm min għandu diesel engines, u min għandu fuel oil.
5Ir-raġel ġie arrestat mill-pulizija.
6Nafu l-istruttura tal-Qorti kif inhi llum il-ġurnata.
7U minn hemm imbagħad nimxi fuq id-direzzjoni li jagħtina l-iSpeaker.
8Mhux nassumi, Direttur kien.
9Imma dawn mhumiex servizzi għal matul il-ġurnata.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\", \"audio\"]), num_examples=10)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "id": "x_zfqqoVcSeY" }, "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": 41, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "d8426e73abce4cbaa58a89aef1fce8b7", "ae51183c24fe42809d080fd15c298f92", "21c9c4302a76449784f314c15ca59bea", "fcc23e29fde64cde92f2ae57d7cabd78", "61ac7115c9b24ebb855343cc01b1d3f4", "90b3e47068e747c7be958d22fb56fe4f", "820d84c1afc7416e9368a246ab8d5ce9", "07447e6083b04bfeb04e5a601fe475bd", "822d95bb43c44a4394441d92e25120d7", "138580d9724141448ff8a5e11ef415ce", "1a03059af7bb40da924ecf3e709d7e0d", "b9d888877a7e4a24b07f4fb91ceda179", "36db5c636fcf46518685b91a168d9c11", "4407f3810d5d4820acf8db794ce305e6", "72fee1a44b5343a7add71c9649139317", "9b22b13729bf4f20b8b96da540cfaa3f", "90bde27c6e564ca285a65d6b594d6865", "256669df6862481cbd0bbcee229e2efe", "07b40214652e48adbae525787288795d", "57e054662b5d497b8e1f3d99fb72034f", "81a7889575ed4e0293f7ce56032e6edb", "00a619827a094be4ae891726e44ddd97" ] }, "id": "BQfNU564cSeZ", "outputId": "6ca3ff91-8acb-4096-d746-aed7edb4055a" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "85d918933f434036a59949397c6bec1a", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/3526 [00:00 main\n", "\n" ] }, { "data": { "text/plain": [ "'https://huggingface.co./infinitejoy/wav2vec2-large-xls-r-300m-maltese/commit/bf3a24bbb9d89bd987f1ea9b093029ce076c7f91'" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vocab_dict[\"|\"] = vocab_dict[\" \"]\n", "del vocab_dict[\" \"]\n", "\n", "vocab_dict[\"[UNK]\"] = len(vocab_dict)\n", "vocab_dict[\"[PAD]\"] = len(vocab_dict)\n", "print(len(vocab_dict))\n", "\n", "import json\n", "with open('./vocab.json', 'w') as vocab_file:\n", " json.dump(vocab_dict, vocab_file)\n", " \n", "from transformers import Wav2Vec2CTCTokenizer\n", "\n", "tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(\"./\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")\n", "\n", "repo_name = \"wav2vec2-large-xls-r-300m-maltese\"\n", "\n", "# tokenizer.save_pretrained(repo_name)\n", "\n", "tokenizer.push_to_hub(repo_name)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1XVJcIykcSeb", "outputId": "67c53812-24ce-4dee-bb95-608971d61338" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2022-01-30 07:10:29-- https://raw.githubusercontent.com/huggingface/transformers/master/examples/research_projects/robust-speech-event/eval.py\n", "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 4738 (4.6K) [text/plain]\n", "Saving to: ‘eval.py’\n", "\n", "eval.py 100%[===================>] 4.63K --.-KB/s in 0s \n", "\n", "2022-01-30 07:10:29 (16.6 MB/s) - ‘eval.py’ saved [4738/4738]\n", "\n", "total 1232584\n", "-rw-r--r-- 1 ovh ovh 300 Jan 30 02:51 vocab.json\n", "-rw-r--r-- 1 ovh ovh 260 Jan 30 02:51 tokenizer_config.json\n", "-rw-r--r-- 1 ovh ovh 309 Jan 30 02:51 special_tokens_map.json\n", "-rw-r--r-- 1 ovh ovh 23 Jan 30 02:51 added_tokens.json\n", "drwxr-xr-x 2 ovh ovh 4096 Jan 30 04:36 checkpoint-500\n", "drwxr-xr-x 2 ovh ovh 4096 Jan 30 06:22 checkpoint-1000\n", "-rw-r--r-- 1 ovh ovh 2521 Jan 30 07:06 trainer_state.json\n", "-rw-r--r-- 1 ovh ovh 197 Jan 30 07:06 train_results.json\n", "-rw-r--r-- 1 ovh ovh 224 Jan 30 07:06 eval_results.json\n", "-rw-r--r-- 1 ovh ovh 2033 Jan 30 07:06 config.json\n", "-rw-r--r-- 1 ovh ovh 398 Jan 30 07:06 all_results.json\n", "-rw-r--r-- 1 ovh ovh 1262063089 Jan 30 07:06 pytorch_model.bin\n", "-rw-r--r-- 1 ovh ovh 212 Jan 30 07:06 preprocessor_config.json\n", "-rw-r--r-- 1 ovh ovh 3055 Jan 30 07:06 training_args.bin\n", "-rw-r--r-- 1 ovh ovh 1709 Jan 30 07:08 README.md\n", "-rw-r--r-- 1 ovh ovh 4738 Jan 30 07:10 eval.py\n", "-rw-r--r-- 1 ovh ovh 30348 Jan 30 07:10 run_speech_recognition_ctc.py\n" ] } ], "source": [ "!wget -O eval.py https://raw.githubusercontent.com/huggingface/transformers/master/examples/research_projects/robust-speech-event/eval.py\n", "!cp eval.py wav2vec2-large-xls-r-300m-irish\n", "!cp run_speech_recognition_ctc.py wav2vec2-large-xls-r-300m-irish\n", "!ls -ltr wav2vec2-large-xls-r-300m-irish" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "id": "OLB-MXricSec", "outputId": "784016e5-2c0a-4235-b432-96bf126b33ba" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Error: mkl-service + Intel(R) MKL: MKL_THREADING_LAYER=INTEL is incompatible with libgomp-a34b3233.so.1 library.\n", "\tTry to import numpy first or set the threading layer accordingly. Set MKL_SERVICE_FORCE_INTEL to force it.\n" ] } ], "source": [ "!cd wav2vec2-large-xls-r-300m-i;python eval.py \\\n", " --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config kmr --split test --log_outputs" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "id": "aoMHnv5ocSec" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Error: mkl-service + Intel(R) MKL: MKL_THREADING_LAYER=INTEL is incompatible with libgomp-a34b3233.so.1 library.\n", "\tTry to import numpy first or set the threading layer accordingly. Set MKL_SERVICE_FORCE_INTEL to force it.\n" ] } ], "source": [ "!cd wav2vec2-large-xls-r-300m-irish; python eval.py \\\n", " --model_id ./ --dataset speech-recognition-community-v2/dev_data \\\n", " --config kmr --split validation --chunk_length_s 10 --stride_length_s 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "referenced_widgets": [ "24592b0be30e4eafb1949cf09d1c4fb4", "f9bf2ab0d2fa4d3f9235cc6d1ab772f1", "b0791474a34043da8057e06741472ade", "1ccbd582d616458b87c76ac8dc5b6b36" ] }, "id": "5vvo9g7HcSec", "outputId": "c2cab8b0-2b67-4039-b8ca-9ab13c9629c3" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "24592b0be30e4eafb1949cf09d1c4fb4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/260 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mlogits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlogits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAssertionError\u001b[0m: 55" ] } ], "source": [ "# from transformers import AutoModelForCTC, AutoProcessor\n", "# from datasets import load_dataset\n", "\n", "# model = AutoModelForCTC.from_pretrained(\"infinitejoy/wav2vec2-large-xls-r-300m-bashkir\")\n", "# processor = AutoProcessor.from_pretrained(\"infinitejoy/wav2vec2-large-xls-r-300m-bashkir\")\n", "\n", "# input_values = processor(common_voice_test[0][\"audio\"][\"array\"], return_tensors=\"pt\", sampling_rate=16_000).input_values\n", "# # input_values = input_values.to(\"cuda\")\n", "\n", "# logits = model(input_values).logits\n", "\n", "# assert logits.shape[-1] == 32, logits.shape[-1]" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "referenced_widgets": [ "42ac3a57a96b4987b0c62aa41aa13702", "29dbfe94c1e9436ea3feefd9a7ba5d34", "acd0b1dccad24943bf760273d89aced3" ] }, "id": "z26_Ce-kcSed", "outputId": "fb46fc7b-5450-4d76-e2cb-3b6417133d44" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/lv/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "81d295b921134614b692ce51d86e1fda", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/1.99k [00:00\nHugging Face\n
\nThe AI community building the future\n
\nImmediately click login after typing your password or it might be stored in plain text in this notebook file.\n" } }, "33fffaf4bc4a405187a2dd4eaa7ffc67": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "34417f648cd54ed5b6d91f53af3e2713": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", 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