{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "epSR1CyPu0-M" }, "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", "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" ], "metadata": { "id": "FLe_Nq02u5jO" }, "execution_count": 1, "outputs": [] }, { "cell_type": "code", "execution_count": 2, "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": "GquuHi8Ju0-Q" }, "source": [ "## Print main infos" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5P6I-W9ts-kR", "outputId": "7a8adbe6-dc88-42ae-93b2-3e670f79030c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\n", "CPU cores: 2\n", "Python version: 3.7.12\n", "PyTorch version: 1.10.0+cu111\n", "GPU is visible: True\n", "Transformers version: 4.17.0.dev0\n", "Datasets version: 1.18.1\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": "e9YhHNfbu0-S" }, "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": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YT7fRnKctggU", "outputId": "be0e697f-fd8f-4bbf-f929-4a1d48637b38" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Fri Jan 28 03:31:46 2022 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 495.46 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 35C P8 9W / 70W | 3MiB / 15109MiB | 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": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 241, "referenced_widgets": [ "506d22accd9a4738931752c6c743b8c5", "35b07861c5f64921b37457ef8de2d46c", "fa587ebcaaa54f558d1c13b1b0113fc8", "7c28275ea90644a38706dbae1f4f8d2f", "088411fa5833470383fcdc476c99bf6c", "3bb166a55b2f492ba63aa674406fb167", "9efdb892757f4825b5182a3551099bc1", "630d58f2ba1f4c95b2252e98c0c3d680", "a09dd8ff85d64404b5f93e2bdf9f79e3", "1da443f51b794ff1b92ffe5f13275d8b", "4aeff351b65145488713937e9fd15df1", "6f085e4087b0436db43827be093ee6c8", "ff17c34db37c440d8ce83c3ec6563d1d", "27517fc2492047b09e9cef3b99afc47c", "d35feca8b13e4f14bd56add4ba6690bc", "d5e8ea08318b4604810d3ed78f260d72" ] }, "id": "JXfFtOjZu0-T", "outputId": "691803de-597f-4fa3-c25d-180288d41cbe" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Login successful\n", "Your token has been saved to /root/.huggingface/token\n", "\u001b[1m\u001b[31mAuthenticated through git-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", "source": [ "%%capture\n", "!apt install git-lfs" ], "metadata": { "id": "W4Yue2q1u-Np" }, "execution_count": 6, "outputs": [] }, { "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": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fevoJD15u4Ss", "outputId": "e9fc7c6f-4c84-4163-c23b-7ab6653747e9" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2022-01-28 03:34:16-- https://raw.githubusercontent.com/huggingface/transformers/master/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py\n", "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n", "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 30347 (30K) [text/plain]\n", "Saving to: ‘run_speech_recognition_ctc.py’\n", "\n", "\r run_speec 0%[ ] 0 --.-KB/s \rrun_speech_recognit 100%[===================>] 29.64K --.-KB/s in 0.001s \n", "\n", "2022-01-28 03:34:16 (24.7 MB/s) - ‘run_speech_recognition_ctc.py’ saved [30347/30347]\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" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "QPffzfr2u0-V" }, "outputs": [], "source": [ "# \t--learning_rate=\"7.5e-5\" \\\n", "# 84.5" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Mz4bubhxxsad", "outputId": "80a0e604-70d1-4348-c030-bb515fedd655", "collapsed": true }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "01/28/2022 03:36:33 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True\n", "01/28/2022 03:36:33 - 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=0.0003,\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-greek/runs/Jan28_03-36-32_1be27c3b03ee,\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-greek,\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-greek,\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 03:36:34 - WARNING - datasets.builder - Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/el/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n", "01/28/2022 03:36:36 - WARNING - datasets.builder - Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/el/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n", "01/28/2022 03:36:36 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/el/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/cache-c88de5610ac14b24.arrow\n", "01/28/2022 03:36:36 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/el/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/cache-29b6a8b15b8c03d6.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, 6.58ba/s]\n", "100% 1/1 [00:00<00:00, 19.09ba/s]\n", "Didn't find file ./wav2vec2-large-xls-r-300m-greek/tokenizer.json. We won't load it.\n", "loading file ./wav2vec2-large-xls-r-300m-greek/vocab.json\n", "loading file ./wav2vec2-large-xls-r-300m-greek/tokenizer_config.json\n", "loading file ./wav2vec2-large-xls-r-300m-greek/added_tokens.json\n", "loading file ./wav2vec2-large-xls-r-300m-greek/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", "https://huggingface.co/facebook/wav2vec2-xls-r-300m/resolve/main/preprocessor_config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpfk9jtajv\n", "Downloading: 100% 212/212 [00:00<00:00, 241kB/s]\n", "storing https://huggingface.co/facebook/wav2vec2-xls-r-300m/resolve/main/preprocessor_config.json in cache at /root/.cache/huggingface/transformers/6fb028b95b394059e7d3b367bbca2382b576c66aebe896f04d2cd34e1b575f5b.d4484dc1c81456a2461485e7168b04347a7b9a4e3b1ef3aba723323b33e12326\n", "creating metadata file for /root/.cache/huggingface/transformers/6fb028b95b394059e7d3b367bbca2382b576c66aebe896f04d2cd34e1b575f5b.d4484dc1c81456a2461485e7168b04347a7b9a4e3b1ef3aba723323b33e12326\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", "https://huggingface.co/facebook/wav2vec2-xls-r-300m/resolve/main/pytorch_model.bin not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmp10cxxmf9\n", "Downloading: 100% 1.18G/1.18G [00:58<00:00, 21.6MB/s]\n", "storing https://huggingface.co/facebook/wav2vec2-xls-r-300m/resolve/main/pytorch_model.bin in cache at /root/.cache/huggingface/transformers/1e6a6507f3b689035cd4b247e2a37c154e27f39143f31357a49b4e38baeccc36.1edb32803799e27ed554eb7dd935f6745b1a0b17b0ea256442fe24db6eb546cd\n", "creating metadata file for /root/.cache/huggingface/transformers/1e6a6507f3b689035cd4b247e2a37c154e27f39143f31357a49b4e38baeccc36.1edb32803799e27ed554eb7dd935f6745b1a0b17b0ea256442fe24db6eb546cd\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.codevectors', 'project_hid.weight', 'project_hid.bias', 'quantizer.weight_proj.weight', 'project_q.weight', 'project_q.bias', '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: 3601ex [00:47, 76.50ex/s] \n", "preprocess datasets: 1703ex [00:15, 110.56ex/s]\n", "100% 4/4 [00:00<00:00, 362.84ba/s]\n", "100% 2/2 [00:00<00:00, 645.67ba/s]\n", "Downloading: 4.49kB [00:00, 3.53MB/s] \n", "Configuration saved in ./wav2vec2-large-xls-r-300m-greek/preprocessor_config.json\n", "tokenizer config file saved in ./wav2vec2-large-xls-r-300m-greek/tokenizer_config.json\n", "Special tokens file saved in ./wav2vec2-large-xls-r-300m-greek/special_tokens_map.json\n", "added tokens file saved in ./wav2vec2-large-xls-r-300m-greek/added_tokens.json\n", "Configuration saved in ./wav2vec2-large-xls-r-300m-greek/config.json\n", "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-greek/preprocessor_config.json\n", "loading configuration file ./wav2vec2-large-xls-r-300m-greek/config.json\n", "Model config Wav2Vec2Config {\n", " \"_name_or_path\": \"./wav2vec2-large-xls-r-300m-greek\",\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\": 49,\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\": 52,\n", " \"xvector_output_dim\": 512\n", "}\n", "\n", "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-greek/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-greek/tokenizer.json. We won't load it.\n", "loading file ./wav2vec2-large-xls-r-300m-greek/vocab.json\n", "loading file ./wav2vec2-large-xls-r-300m-greek/tokenizer_config.json\n", "loading file ./wav2vec2-large-xls-r-300m-greek/added_tokens.json\n", "loading file ./wav2vec2-large-xls-r-300m-greek/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", "/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py:718: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n", " FutureWarning,\n", "Cloning https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-greek into local empty directory.\n", "01/28/2022 03:39:02 - WARNING - huggingface_hub.repository - Cloning https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-greek into local empty directory.\n", "Download file pytorch_model.bin: 0% 3.47k/1.18G [00:00\n", " main()\n", " File \"run_speech_recognition_ctc.py\", line 688, in main\n", " train_result = trainer.train(resume_from_checkpoint=checkpoint)\n", " File \"/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\", line 1448, in train\n", " self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)\n", " File \"/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\", line 1573, in _maybe_log_save_evaluate\n", " metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)\n", " File \"/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\", line 2223, in evaluate\n", " metric_key_prefix=metric_key_prefix,\n", " File \"/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\", line 2390, in evaluation_loop\n", " loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)\n", " File \"/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\", line 2598, in prediction_step\n", " loss, outputs = self.compute_loss(model, inputs, return_outputs=True)\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 1.96 GiB (GPU 0; 14.76 GiB total capacity; 9.64 GiB already allocated; 785.75 MiB free; 12.51 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", " 4% 500/11300 [25:01<9:00:32, 3.00s/it]\n", "\n", " \u001b[A" ] } ], "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=\"el\" \\\n", "\t--output_dir=\"./wav2vec2-large-xls-r-300m-greek\" \\\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=\"3e-4\" \\\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": { "collapsed": true, "id": "mT5MWR3Xu0-X", "outputId": "9d2aafa9-8795-487b-8d47-531f5534d2df" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "01/28/2022 02:46:18 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True\n", "01/28/2022 02:46:18 - 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=400,\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-guarani/runs/Jan28_02-46-18_job-8be8b741-e32e-4579-bbec-1e00d9824b4f,\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=200.0,\n", "optim=OptimizerNames.ADAMW_HF,\n", "output_dir=./wav2vec2-large-xls-r-300m-guarani,\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=[],\n", "resume_from_checkpoint=None,\n", "run_name=./wav2vec2-large-xls-r-300m-guarani,\n", "save_on_each_node=False,\n", "save_steps=400,\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=400,\n", "weight_decay=0.0,\n", "xpu_backend=None,\n", ")\n", "Downloading and preparing dataset common_voice/gn to /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/gn/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b...\n", "Downloading: 100%|█████████████████████████| 33.2M/33.2M [00:04<00:00, 7.55MB/s]\n", "Dataset common_voice downloaded and prepared to /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/gn/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b. Subsequent calls will reuse this data.\n", "01/28/2022 02:46:32 - WARNING - datasets.builder - Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/gn/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n", "remove special characters from datasets: 100%|█| 298/298 [00:00<00:00, 7125.08ex\n", "remove special characters from datasets: 100%|█| 134/134 [00:00<00:00, 7627.77ex\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, 91.49ba/s]\n", "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 238.62ba/s]\n", "Didn't find file ./wav2vec2-large-xls-r-300m-guarani/tokenizer_config.json. We won't load it.\n", "Didn't find file ./wav2vec2-large-xls-r-300m-guarani/added_tokens.json. We won't load it.\n", "Didn't find file ./wav2vec2-large-xls-r-300m-guarani/special_tokens_map.json. We won't load it.\n", "Didn't find file ./wav2vec2-large-xls-r-300m-guarani/tokenizer.json. We won't load it.\n", "loading file ./wav2vec2-large-xls-r-300m-guarani/vocab.json\n", "loading file None\n", "loading file None\n", "loading file None\n", "loading file None\n", "file ./wav2vec2-large-xls-r-300m-guarani/config.json not found\n", "Adding to the vocabulary\n", "Adding to the vocabulary\n", "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", "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: ['quantizer.codevectors', 'project_q.bias', 'quantizer.weight_proj.weight', 'project_q.weight', 'quantizer.weight_proj.bias', 'project_hid.weight', 'project_hid.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.bias', 'lm_head.weight']\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%|███████████████████| 298/298 [00:01<00:00, 153.75ex/s]\n", "preprocess datasets: 100%|███████████████████| 134/134 [00:00<00:00, 141.84ex/s]\n", "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 607.25ba/s]\n", "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 877.47ba/s]\n", "Configuration saved in ./wav2vec2-large-xls-r-300m-guarani/preprocessor_config.json\n", "tokenizer config file saved in ./wav2vec2-large-xls-r-300m-guarani/tokenizer_config.json\n", "Special tokens file saved in ./wav2vec2-large-xls-r-300m-guarani/special_tokens_map.json\n", "added tokens file saved in ./wav2vec2-large-xls-r-300m-guarani/added_tokens.json\n", "Configuration saved in ./wav2vec2-large-xls-r-300m-guarani/config.json\n", "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-guarani/preprocessor_config.json\n", "loading configuration file ./wav2vec2-large-xls-r-300m-guarani/config.json\n", "Model config Wav2Vec2Config {\n", " \"_name_or_path\": \"./wav2vec2-large-xls-r-300m-guarani\",\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\": 41,\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\": 44,\n", " \"xvector_output_dim\": 512\n", "}\n", "\n", "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-guarani/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-guarani/tokenizer.json. We won't load it.\n", "loading file ./wav2vec2-large-xls-r-300m-guarani/vocab.json\n", "loading file ./wav2vec2-large-xls-r-300m-guarani/tokenizer_config.json\n", "loading file ./wav2vec2-large-xls-r-300m-guarani/added_tokens.json\n", "loading file ./wav2vec2-large-xls-r-300m-guarani/special_tokens_map.json\n", "loading file None\n", "Adding to the vocabulary\n", "Adding to the vocabulary\n", "Cloning https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-guarani into local empty directory.\n", "01/28/2022 02:46:52 - WARNING - huggingface_hub.repository - Cloning https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-guarani into local empty directory.\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 = 298\n", " Num Epochs = 200\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 = 2000\n", "{'loss': 16.6077, 'learning_rate': 1.6975e-05, 'epoch': 10.0} \n", "{'loss': 5.2501, 'learning_rate': 3.4475e-05, 'epoch': 20.0} \n", "{'loss': 3.6008, 'learning_rate': 5.1974999999999996e-05, 'epoch': 30.0} \n", "{'loss': 3.2047, 'learning_rate': 6.9475e-05, 'epoch': 40.0} \n", " 20%|████████ | 400/2000 [10:28<35:31, 1.33s/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 = 134\n", " Batch size = 32\n", "\n", " 0%| | 0/5 [00:00\n", " \n", " \n", " \n", " sentence\n", " \n", " \n", " \n", " \n", " 0\n", " του Δήμου Καρύστου Ευβοίας\n", " \n", " \n", " 1\n", " Ο Κακομοιρίδης, ακούοντας τις φωνές, βγήκε και αυτός\n", " \n", " \n", " 2\n", " και αργά, αλλά με βήμα κανονικό, ανέβηκε την ακροποταμιά\n", " \n", " \n", " 3\n", " Πήδηξε το Βασιλόπουλο πίσω του\n", " \n", " \n", " 4\n", " Μεγαλέμπορος στα ξένα, Αφέντη μου, αποκρίθηκε ο πρωτοβεστιάριος.\n", " \n", " \n", " 5\n", " που γύρευε να μας ζημιώσει.\n", " \n", " \n", " 6\n", " «Πολυτρανότατε Βασιλιά και ανεψιέ.\n", " \n", " \n", " 7\n", " Ποιος κλαίει;\n", " \n", " \n", " 8\n", " Ποιος είναι; ρώτησε από μέσα μια γυναικεία φωνή.\n", " \n", " \n", " 9\n", " Οι κύριοι που περίμεναν τη σειρά τους τον κοίταξαν αδιάφορα\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {} } ], "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\", \"audio\"]), num_examples=10)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "id": "p0mRPmRNu0-c" }, "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": 36, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "5501334e48a84b1eaf85753b0f36e230", "720b283a227d4df1914f9bed388a09d5", "2213ae0d586a4a339804dd8ad5a756cd", "6a9b9b9f0f924a9ea298620c8f4e2fb6", "1f8fc630966b4b3db90054f368261e5b", "1022bb78bb994434886adadc86cd0c7c", "90c51e69e0024bd0b3e37cd55186ef34", "b6912421467f4ff3b2d9314c68ab5842", "4af9c3299d6c441c97a77fa2222781b3", "85b60caf17534a39a19184c7deea87c2", "d4a54cfae3c048c28a212ff6ce939968", "412700f967bb432d9d7d45236bfd412f", "de104f53e608447db0db2e7af1b74182", "c04e73e7b6c847bfa4dbe162205b7b8e", "7bc37befa4a849719e7f07425fb9589d", "a588fe64992648558253593fa5b3d0d9", "440753103ee94491bc0ff99344ffab87", "6a18a930146c46ff9989534822e17725", "baf91a8779134a87a48c34c60e039c29", "96ec10bc1e1e4cfcb3ea4e6038c628c0", "dd067e6e637246708d1022067b3d2113", "fce10bab4a2d4361b15eee46e9f6f8af" ] }, "id": "agqzGAIMu0-c", "outputId": "7b425866-f479-4a19-9e35-3a4d437f45b1" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5501334e48a84b1eaf85753b0f36e230", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "412700f967bb432d9d7d45236bfd412f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {} } ], "source": [ "common_voice_train = common_voice_train.map(remove_special_characters)\n", "common_voice_test = common_voice_test.map(remove_special_characters)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "id": "53AlYBMDu0-d" }, "outputs": [], "source": [ "# start_with_ar = common_voice_train.filter(lambda example: \"'\" in example['sentence'])\n", "# start_with_ar[0]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "id": "rD0gtEmou0-d" }, "outputs": [], "source": [ "# start_with_ar" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "id": "xJRULzrSu0-d" }, "outputs": [], "source": [ "def replace_hatted_characters(batch):\n", "# batch[\"sentence\"] = re.sub('[â]', 'a', batch[\"sentence\"])\n", "# batch[\"sentence\"] = re.sub('[î]', 'i', batch[\"sentence\"])\n", "# batch[\"sentence\"] = re.sub('[ô]', 'o', batch[\"sentence\"])\n", "# batch[\"sentence\"] = re.sub('[û]', 'u', batch[\"sentence\"])\n", " return batch" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "192a7f0c4eee4c419bcc5ccadd759e7c", "3ccf0207ee774c11847443efe3e086d8", "d38c1a4492034bc4bd63d6bc48372e35", "d4a2e4b0b6204cfa80ca0cb5c950245e", "07f91d6c45ec4d359cef8e1c1df556cc", "24c89216eb204bc69d48cabd2284c1fd", "92d0f05094a24170b920f0c7340dada2", "ae49baef87d74c48980d6538128e953f", "ba15bda03d254464999ba9b8444ea4f4", "7f9a33dfc82041ee91cd02bdb431048f", "5ecf664e916f4815af551f040a5214d1", "b1b8456429134365bddc7d4455659707", "46f54bdb935141a589e5f5a2de7b13b6", "c0898ca05eb8445db76c62a55586a1d7", "ec9baec03d2844169fb1ea07aa3ef4df", "ec747a88d462468092adc85543104e3a", "08440681ebf0422094cf3cc47c876deb", "521c210abf8549f1b0b029c85128bd5b", "456d873c4b0d43ce89240d273e1e61cb", "8abf03d2ee6d4186a4c40c898e51816e", "fa32fe18968d4d62b57f5e89eddcea74", "26e0bb7815d44fc7b6a61de967526f2c" ] }, "id": "hLAh7RO5u0-e", "outputId": "ada190d8-067f-4dc7-bd4b-6620b98df276" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "192a7f0c4eee4c419bcc5ccadd759e7c", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b1b8456429134365bddc7d4455659707", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {} } ], "source": [ "common_voice_train = common_voice_train.map(replace_hatted_characters)\n", "common_voice_test = common_voice_test.map(replace_hatted_characters)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "id": "nXVCeDwmu0-f" }, "outputs": [], "source": [ "def extract_all_chars(batch):\n", " all_text = \" \".join(batch[\"sentence\"])\n", " vocab = list(set(all_text))\n", " return {\"vocab\": [vocab], \"all_text\": [all_text]}" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "bfaf547bb5de47ecb0f61f49ee54ba81", "8e4d4c249a1445fabbd4371ed4d87353", "23a8cd7810c245ac9949628c39818df2", "df77a14794c24a22b1676a83fa676e90", "0b4b0815009f4b27b1ad87a902a72387", "d12acd64b76f4da8a559e4f57946c7ff", "6a36c1cbd1544ebf8887fff2e2db6d4f", "18fdeeac144b470d8b1c7b503a45f830", "40e6e444f6214db2929978e36912edbb", "c47439d6d97e4c3abfaa18b4cc0922a9", "f176dfc2a36e4df8ab6b6e889cb865c5", "167a5c9f5adf4c8da1770dc385aee84b", "03d81a2e990f4169880221312bed0f01", "3c8265c24c0f44e7813e9c56116c30c5", "121bd902f39e443d853bb49502f18b0c", "09e39c613b3b4cefb65dea8717a9d15b", "f5c7426f75554834b5fa9a28533ab4f9", "902adb6401e34ee1810a83e608f1d155", "5342c532e5ea47ceae57516244618554", "ae3b191a8c75407a8ce5be8a907a8ee4", "0d717e4fa2f940c4a2c11d7eaa86d0e8", "3c9c69a1823349baa8b931cb9e71c982" ] }, "id": "esAt_StOu0-f", "outputId": "2c8aae9a-3d3b-4e4c-fd16-dd63e1d3923b" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bfaf547bb5de47ecb0f61f49ee54ba81", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00] 4.63K --.-KB/s in 0s \n", "\n", "2022-01-27 16:52:27 (21.9 MB/s) - ‘eval.py’ saved [4738/4738]\n", "\n", "total 1232584\n", "-rw-r--r-- 1 ovh ovh 399 Jan 27 12:09 vocab.json\n", "-rw-r--r-- 1 ovh ovh 294 Jan 27 12:09 tokenizer_config.json\n", "-rw-r--r-- 1 ovh ovh 695 Jan 27 12:09 special_tokens_map.json\n", "-rw-r--r-- 1 ovh ovh 23 Jan 27 12:09 added_tokens.json\n", "drwxr-xr-x 2 ovh ovh 4096 Jan 27 16:15 checkpoint-7500\n", "drwxr-xr-x 2 ovh ovh 4096 Jan 27 16:31 checkpoint-8000\n", "-rw-r--r-- 1 ovh ovh 197 Jan 27 16:43 train_results.json\n", "-rw-r--r-- 1 ovh ovh 14654 Jan 27 16:43 trainer_state.json\n", "-rw-r--r-- 1 ovh ovh 225 Jan 27 16:44 eval_results.json\n", "-rw-r--r-- 1 ovh ovh 2033 Jan 27 16:44 config.json\n", "-rw-r--r-- 1 ovh ovh 400 Jan 27 16:44 all_results.json\n", "-rw-r--r-- 1 ovh ovh 1262083569 Jan 27 16:44 pytorch_model.bin\n", "-rw-r--r-- 1 ovh ovh 3055 Jan 27 16:44 training_args.bin\n", "-rw-r--r-- 1 ovh ovh 212 Jan 27 16:44 preprocessor_config.json\n", "-rw-r--r-- 1 ovh ovh 2484 Jan 27 16:48 README.md\n", "-rw-r--r-- 1 ovh ovh 4738 Jan 27 16:52 eval.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-georgian\n", "!ls -ltr wav2vec2-large-xls-r-300m-georgian" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Qa9QSlvhu0-h", "outputId": "948d088f-0f7e-4c2f-d3c8-226f0040b71a" }, "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-georgian; python eval.py \\\n", " --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config ka --split test --log_outputs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lBxt3YqJu0-h" }, "outputs": [], "source": [ "!cd wav2vec2-large-xls-r-300m-georgian; python eval.py \\\n", " --model_id infinitejoy/wav2vec2-large-xls-r-300m-georgian --dataset speech-recognition-community-v2/dev_data \\\n", " --config ka --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": "jnJFuiwou0-i", "outputId": "92fcb6b6-59aa-465d-e38a-f38d2db6adfb" }, "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": null, "metadata": { "colab": { "referenced_widgets": [ "42ac3a57a96b4987b0c62aa41aa13702", "29dbfe94c1e9436ea3feefd9a7ba5d34", "acd0b1dccad24943bf760273d89aced3" ] }, "id": "m6t_dfe4u0-i", "outputId": "a81aeb22-0f83-4f95-cd2d-23972ce23da9" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/el/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "42ac3a57a96b4987b0c62aa41aa13702", "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
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