{ "cells": [ { "cell_type": "markdown", "metadata": {}, "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": "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": {}, "source": [ "## Print main infos" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5P6I-W9ts-kR", "outputId": "939bd550-1486-46a6-8371-e82ada0f448c" }, "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": {}, "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": "f355a3e0-20da-489f-bd1f-5e508e792a68" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fri Jan 21 03:07:38 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": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1f72bffe678b4bdca366b35305baaab5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
\\n] 29.64K --.-KB/s in 0.001s \n", "\n", "2022-01-21 03:07:52 (21.5 MB/s) - ‘run_speech_recognition_ctc.py’ saved [30348/30348]\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": 46, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Mz4bubhxxsad", "outputId": "23398525-cc19-43c2-9fec-497e06214f29" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "01/21/2022 06:29:10 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True\n", "01/21/2022 06:29:10 - 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=2,\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=7.5e-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-odia/runs/Jan21_06-29-10_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=120.0,\n", "optim=OptimizerNames.ADAMW_HF,\n", "output_dir=./wav2vec2-large-xls-r-300m-odia,\n", "overwrite_output_dir=True,\n", "past_index=-1,\n", "per_device_eval_batch_size=16,\n", "per_device_train_batch_size=16,\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-odia,\n", "save_on_each_node=False,\n", "save_steps=500,\n", "save_strategy=IntervalStrategy.STEPS,\n", "save_total_limit=3,\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", "01/21/2022 06:29:12 - WARNING - datasets.builder - Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/or/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n", "01/21/2022 06:29:15 - WARNING - datasets.builder - Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/or/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n", "remove special characters from datasets: 100%|█| 537/537 [00:00<00:00, 5280.48ex\n", "remove special characters from datasets: 100%|█| 112/112 [00:00<00:00, 6377.61ex\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, 44.57ba/s]\n", "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 236.54ba/s]\n", "Didn't find file ./wav2vec2-large-xls-r-300m-odia/tokenizer.json. We won't load it.\n", "loading file ./wav2vec2-large-xls-r-300m-odia/vocab.json\n", "loading file ./wav2vec2-large-xls-r-300m-odia/tokenizer_config.json\n", "loading file ./wav2vec2-large-xls-r-300m-odia/added_tokens.json\n", "loading file ./wav2vec2-large-xls-r-300m-odia/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', 'project_hid.weight', 'project_q.bias', 'project_hid.bias', 'quantizer.weight_proj.weight', 'quantizer.codevectors', '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%|████████████████████| 537/537 [00:05<00:00, 97.46ex/s]\n", "preprocess datasets: 100%|███████████████████| 112/112 [00:01<00:00, 107.18ex/s]\n", "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 739.08ba/s]\n", "100%|███████████████████████████████████████████| 1/1 [00:00<00:00, 1258.42ba/s]\n", "Configuration saved in ./wav2vec2-large-xls-r-300m-odia/preprocessor_config.json\n", "tokenizer config file saved in ./wav2vec2-large-xls-r-300m-odia/tokenizer_config.json\n", "Special tokens file saved in ./wav2vec2-large-xls-r-300m-odia/special_tokens_map.json\n", "added tokens file saved in ./wav2vec2-large-xls-r-300m-odia/added_tokens.json\n", "Configuration saved in ./wav2vec2-large-xls-r-300m-odia/config.json\n", "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-odia/preprocessor_config.json\n", "loading configuration file ./wav2vec2-large-xls-r-300m-odia/config.json\n", "Model config Wav2Vec2Config {\n", " \"_name_or_path\": \"./wav2vec2-large-xls-r-300m-odia\",\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\": 62,\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\": 64,\n", " \"xvector_output_dim\": 512\n", "}\n", "\n", "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-odia/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-odia/tokenizer.json. We won't load it.\n", "loading file ./wav2vec2-large-xls-r-300m-odia/vocab.json\n", "loading file ./wav2vec2-large-xls-r-300m-odia/tokenizer_config.json\n", "loading file ./wav2vec2-large-xls-r-300m-odia/added_tokens.json\n", "loading file ./wav2vec2-large-xls-r-300m-odia/special_tokens_map.json\n", "loading file None\n", "Adding to the vocabulary\n", "Adding to the vocabulary\n", "/workspace/oriya_training/./wav2vec2-large-xls-r-300m-odia is already a clone of https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-odia. Make sure you pull the latest changes with `repo.git_pull()`.\n", "01/21/2022 06:29:36 - WARNING - huggingface_hub.repository - /workspace/oriya_training/./wav2vec2-large-xls-r-300m-odia is already a clone of https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-odia. 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 = 537\n", " Num Epochs = 120\n", " Instantaneous batch size per device = 16\n", " Total train batch size (w. parallel, distributed & accumulation) = 32\n", " Gradient Accumulation steps = 2\n", " Total optimization steps = 2040\n", "{'loss': 16.0345, 'learning_rate': 1.455e-05, 'epoch': 5.88} \n", "{'loss': 6.2463, 'learning_rate': 2.955e-05, 'epoch': 11.76} \n", "{'loss': 4.2277, 'learning_rate': 4.454999999999999e-05, 'epoch': 17.65} \n", "{'loss': 3.4915, 'learning_rate': 5.955e-05, 'epoch': 23.53} \n", "{'loss': 3.3231, 'learning_rate': 7.455e-05, 'epoch': 29.41} \n", " 25%|█████████▊ | 500/2040 [15:40<47:23, 1.85s/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 = 112\n", " Batch size = 16\n", "\n", " 0%| | 0/7 [00:00 main\n", "\n", "01/21/2022 07:41:54 - WARNING - huggingface_hub.repository - To https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-odia\n", " 543c1c1..e870548 main -> main\n", "\n", "Upload file pytorch_model.bin: 100%|███████| 1.18G/1.18G [00:42<00:00, 29.8MB/s]\n", "Dropping the following result as it does not have all the necessary fields:\n", "{'dataset': {'name': 'MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - OR', 'type': 'common_voice', 'args': 'Config: or, Training split: train+validation, Eval split: test'}}\n", "To https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-odia\n", " e870548..eb50e66 main -> main\n", "\n", "01/21/2022 07:42:00 - WARNING - huggingface_hub.repository - To https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-odia\n", " e870548..eb50e66 main -> main\n", "\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=\"or\" \\\n", "\t--output_dir=\"./wav2vec2-large-xls-r-300m-odia\" \\\n", "\t--overwrite_output_dir \\\n", "\t--num_train_epochs=\"120\" \\\n", "\t--per_device_train_batch_size=\"16\" \\\n", "\t--per_device_eval_batch_size=\"16\" \\\n", "\t--gradient_accumulation_steps=\"2\" \\\n", "\t--learning_rate=\"7.5e-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=\"3\" \\\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": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "df = pd.DataFrame([\n", " {}\n", "])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# !zip -r wav2vec2-large-xls-r-300m-odia.zip wav2vec2-large-xls-r-300m-odia/\n", "# !rm wav2vec2-large-xls-r-300m-odia.zip" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filesystem Size Used Avail Use% Mounted on\n", "overlay 3.5T 557G 2.8T 17% /\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 557G 2.8T 17% /etc/group\n", "tmpfs 87G 12K 87G 1% /proc/driver/nvidia\n", "/dev/vda1 49G 6.6G 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": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/or/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n", "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/or/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n" ] } ], "source": [ "from datasets import load_dataset, load_metric, Audio\n", "\n", "common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"or\", use_auth_token=True, split=\"train+validation\")\n", "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"or\", use_auth_token=True, split=\"test\")" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2013.75" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(common_voice_train) * 120 / 32" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "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": 18, "metadata": {}, "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": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sentence
0ସେ କଥା ଯାଉ, ଆମ୍ଭମାନଙ୍କୁ ଆଉ କଥା ଲେଖିବାକୁ ହେବ ।
1ଯାହା ଦରମା ଗଣ୍ଡାକ ପାଉଥିଲେ, ପେଟ ପିଠିକୁ ନିଅଣ୍ଟ, ବିଧବା ଲାଗି ସାଇତି ଯିବେ କଣ?
2ଯେ ଯେଡ଼େ ହୁସିଆର ହେବ, ଆପଦ ବିପଦ କାହାରିକୁ ଛାଡ଼ିନାହିଁ ।
3ମୁଁ ପୂଜା ସାରି ସେମାନଙ୍କୁ କିଛି ଭୋଗ ଦେଇ ଘରେ ଛାଡ଼ିଆସିଲି, ବାକି ଭୋଗକୁ ବାନ୍ଧିଲି ।
4ବାସୁ ଦୁଇ ଟଙ୍କାର ନଡ଼ା କିଣି ବାଡ଼ିରେ ଗଦେଇଅଛି, ଶରଣ ଦେବାରୁ ଛପରବନ୍ଦି ହୋଇପାରି ନାହିଁ ।
5ଦେଖି ଦେଖି ମନରେ କଲା, ଆଜି ଏ କଣ ହେଉଛି ।
6ଶାଶୁ ମୁହଁକୁ ଚାହିଁ ଗାଳି ଦିଏ ନାହିଁ; ଓଢ଼ଣା ପଡ଼ିଥାଏ, ପଛ କରି ବରବର କରି ବକିଯାଏ ।
7ଆଜି ମହାପ୍ରସାଦ ଉଠା ପରା ।
8\"\"\"ଯାହାର ବାହା ସେ ଖେଳୁଛି ପଶା ଧାଇଁ ବୁଲୁଛନ୍ତି ସାଇ ପଡିଶା ।\"\"\"
9ଅଶୀ ବର୍ଷର ପୁରୁଷ ବି ବିଭା ହୋଇ ପାରେ ।
" ], "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": 36, "metadata": {}, "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": 37, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a9df324b393840628b6a038aa00aa697", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/537 [00:00] 4.32K --.-KB/s in 0s \n", "\n", "2022-01-21 08:33:50 (14.9 MB/s) - ‘eval.py’ saved [4419/4419]\n", "\n", "total 1232676\n", "-rw-r--r-- 1 ovh ovh 686 Jan 21 06:29 vocab.json\n", "-rw-r--r-- 1 ovh ovh 290 Jan 21 06:29 tokenizer_config.json\n", "-rw-r--r-- 1 ovh ovh 502 Jan 21 06:29 special_tokens_map.json\n", "-rw-r--r-- 1 ovh ovh 23 Jan 21 06:29 added_tokens.json\n", "drwxr-xr-x 2 ovh ovh 4096 Jan 21 07:02 checkpoint-1000\n", "drwxr-xr-x 2 ovh ovh 4096 Jan 21 07:19 checkpoint-1500\n", "drwxr-xr-x 2 ovh ovh 4096 Jan 21 07:37 checkpoint-2000\n", "-rw-r--r-- 1 ovh ovh 3953 Jan 21 07:39 trainer_state.json\n", "-rw-r--r-- 1 ovh ovh 194 Jan 21 07:39 train_results.json\n", "-rw-r--r-- 1 ovh ovh 222 Jan 21 07:39 eval_results.json\n", "-rw-r--r-- 1 ovh ovh 2033 Jan 21 07:39 config.json\n", "-rw-r--r-- 1 ovh ovh 394 Jan 21 07:39 all_results.json\n", "-rw-r--r-- 1 ovh ovh 1262186097 Jan 21 07:39 pytorch_model.bin\n", "-rw-r--r-- 1 ovh ovh 3055 Jan 21 07:39 training_args.bin\n", "-rw-r--r-- 1 ovh ovh 212 Jan 21 07:39 preprocessor_config.json\n", "-rw-r--r-- 1 ovh ovh 1825 Jan 21 07:41 README.md\n", "-rw-r--r-- 1 ovh ovh 4419 Jan 21 08:33 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-odia\n", "!ls -ltr wav2vec2-large-xls-r-300m-odia" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/or/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n", "100%|███████████████████████████████████████████| 10/10 [00:06<00:00, 1.55ex/s]\n", "Downloading: 5.61kB [00:00, 2.23MB/s] \n", "WER: 1.0921052631578947\n", "CER: 2.5547945205479454\n", "100%|████████████████████████████████████████| 10/10 [00:00<00:00, 13001.56ex/s]\n" ] } ], "source": [ "!cd wav2vec2-large-xls-r-300m-odia; python eval.py --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config or --split test --log_outputs" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "authorship_tag": "ABX9TyM3OaMlm9YQtKpl28c8gBBd", "include_colab_link": true, "name": "DebugOVHTransformers.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }