diff --git "a/hindi_training_script.ipynb" "b/hindi_training_script.ipynb"
new file mode 100644--- /dev/null
+++ "b/hindi_training_script.ipynb"
@@ -0,0 +1,4674 @@
+{
+ "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 17:23:04 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 0.002s \n",
+ "\n",
+ "2022-01-21 13:32:51 (18.2 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": 34,
+ "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 17:27:56 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True\n",
+ "01/21/2022 17:27:56 - 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=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-hindi/runs/Jan21_17-27-56_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=100.0,\n",
+ "optim=OptimizerNames.ADAMW_HF,\n",
+ "output_dir=./wav2vec2-large-xls-r-300m-hindi,\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-hindi,\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=2000,\n",
+ "weight_decay=0.0,\n",
+ "xpu_backend=None,\n",
+ ")\n",
+ "01/21/2022 17:27:58 - WARNING - datasets.builder - Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/hi/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n",
+ "01/21/2022 17:28:01 - WARNING - datasets.builder - Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/hi/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n",
+ "01/21/2022 17:28:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/hi/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba/cache-5429990fadba3bab.arrow\n",
+ "01/21/2022 17:28:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/hi/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba/cache-7555c0f50082da96.arrow\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, 4.66ba/s]\n",
+ "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00, 17.16ba/s]\n",
+ "Didn't find file ./wav2vec2-large-xls-r-300m-hindi/tokenizer_config.json. We won't load it.\n",
+ "Didn't find file ./wav2vec2-large-xls-r-300m-hindi/added_tokens.json. We won't load it.\n",
+ "Didn't find file ./wav2vec2-large-xls-r-300m-hindi/special_tokens_map.json. We won't load it.\n",
+ "Didn't find file ./wav2vec2-large-xls-r-300m-hindi/tokenizer.json. We won't load it.\n",
+ "loading file ./wav2vec2-large-xls-r-300m-hindi/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-hindi/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: ['project_hid.weight', 'project_hid.bias', 'quantizer.weight_proj.bias', 'project_q.weight', 'project_q.bias', 'quantizer.weight_proj.weight', 'quantizer.codevectors']\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%|█████████████████| 4711/4711 [00:30<00:00, 153.38ex/s]\n",
+ "preprocess datasets: 100%|█████████████████| 2095/2095 [00:16<00:00, 128.84ex/s]\n",
+ "100%|████████████████████████████████████████████| 5/5 [00:00<00:00, 729.80ba/s]\n",
+ "100%|████████████████████████████████████████████| 3/3 [00:00<00:00, 446.54ba/s]\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/preprocessor_config.json\n",
+ "tokenizer config file saved in ./wav2vec2-large-xls-r-300m-hindi/tokenizer_config.json\n",
+ "Special tokens file saved in ./wav2vec2-large-xls-r-300m-hindi/special_tokens_map.json\n",
+ "added tokens file saved in ./wav2vec2-large-xls-r-300m-hindi/added_tokens.json\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/config.json\n",
+ "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-hindi/preprocessor_config.json\n",
+ "loading configuration file ./wav2vec2-large-xls-r-300m-hindi/config.json\n",
+ "Model config Wav2Vec2Config {\n",
+ " \"_name_or_path\": \"./wav2vec2-large-xls-r-300m-hindi\",\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\": 68,\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\": 70,\n",
+ " \"xvector_output_dim\": 512\n",
+ "}\n",
+ "\n",
+ "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-hindi/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-hindi/tokenizer.json. We won't load it.\n",
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+ "loading file ./wav2vec2-large-xls-r-300m-hindi/tokenizer_config.json\n",
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+ "loading file ./wav2vec2-large-xls-r-300m-hindi/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-hindi into local empty directory.\n",
+ "01/21/2022 17:29:04 - WARNING - huggingface_hub.repository - Cloning https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-hindi into local empty directory.\n",
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+ "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 = 4711\n",
+ " Num Epochs = 100\n",
+ " Instantaneous batch size per device = 32\n",
+ " Total train batch size (w. parallel, distributed & accumulation) = 32\n",
+ " Gradient Accumulation steps = 1\n",
+ " Total optimization steps = 14800\n",
+ "{'loss': 19.2806, 'learning_rate': 3.6375e-06, 'epoch': 0.68} \n",
+ "{'loss': 10.959, 'learning_rate': 7.3875e-06, 'epoch': 1.35} \n",
+ "{'loss': 6.9106, 'learning_rate': 1.1137499999999998e-05, 'epoch': 2.03} \n",
+ "{'loss': 5.624, 'learning_rate': 1.48875e-05, 'epoch': 2.7} \n",
+ "{'loss': 4.6095, 'learning_rate': 1.86375e-05, 'epoch': 3.38} \n",
+ " 3%|█▎ | 500/14800 [11:43<5:24:26, 1.36s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
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+ " \u001b[A\n",
+ "\u001b[A{'eval_loss': 4.5881452560424805, 'eval_wer': 0.9999453103636861, 'eval_runtime': 92.8763, 'eval_samples_per_second': 22.557, 'eval_steps_per_second': 0.711, 'epoch': 3.38}\n",
+ " 3%|█▎ | 500/14800 [13:16<5:24:26, 1.36s/it]\n",
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+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-500\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-500/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-500/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-500/preprocessor_config.json\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/preprocessor_config.json\n",
+ "{'loss': 3.9225, 'learning_rate': 2.23875e-05, 'epoch': 4.05} \n",
+ "{'loss': 3.5503, 'learning_rate': 2.6137499999999995e-05, 'epoch': 4.73} \n",
+ "{'loss': 3.4262, 'learning_rate': 2.9887499999999998e-05, 'epoch': 5.41} \n",
+ "{'loss': 3.391, 'learning_rate': 3.36375e-05, 'epoch': 6.08} \n",
+ "{'loss': 3.3396, 'learning_rate': 3.7387499999999994e-05, 'epoch': 6.76} \n",
+ " 7%|██▍ | 1000/14800 [25:18<7:28:32, 1.95s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "{'eval_loss': 3.3301022052764893, 'eval_wer': 1.0000546896363138, 'eval_runtime': 93.143, 'eval_samples_per_second': 22.492, 'eval_steps_per_second': 0.709, 'epoch': 6.76}\n",
+ "\n",
+ " 7%|██▍ | 1000/14800 [26:51<7:28:32, 1.95s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-1000\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-1000/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-1000/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-1000/preprocessor_config.json\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/preprocessor_config.json\n",
+ "{'loss': 3.2895, 'learning_rate': 4.11375e-05, 'epoch': 7.43} \n",
+ "{'loss': 3.228, 'learning_rate': 4.48875e-05, 'epoch': 8.11} \n",
+ "{'loss': 2.987, 'learning_rate': 4.8637499999999996e-05, 'epoch': 8.78} \n",
+ "{'loss': 2.4352, 'learning_rate': 5.23875e-05, 'epoch': 9.46} \n",
+ "{'loss': 2.0061, 'learning_rate': 5.61375e-05, 'epoch': 10.14} \n",
+ " 10%|███▋ | 1500/14800 [39:58<4:56:08, 1.34s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "{'eval_loss': 1.20964777469635, 'eval_wer': 1.0063439978124145, 'eval_runtime': 93.1759, 'eval_samples_per_second': 22.484, 'eval_steps_per_second': 0.708, 'epoch': 10.14}\n",
+ "\n",
+ " 10%|███▋ | 1500/14800 [41:31<4:56:08, 1.34s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-1500\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-1500/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-1500/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-1500/preprocessor_config.json\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/preprocessor_config.json\n",
+ "{'loss': 1.7983, 'learning_rate': 5.988749999999999e-05, 'epoch': 10.81} \n",
+ "{'loss': 1.6684, 'learning_rate': 6.36375e-05, 'epoch': 11.49} \n",
+ "{'loss': 1.6307, 'learning_rate': 6.738749999999999e-05, 'epoch': 12.16} \n",
+ "{'loss': 1.5654, 'learning_rate': 7.11375e-05, 'epoch': 12.84} \n",
+ "{'loss': 1.523, 'learning_rate': 7.48875e-05, 'epoch': 13.51} \n",
+ " 14%|████▊ | 2000/14800 [54:30<6:56:38, 1.95s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "{'eval_loss': 0.7836298942565918, 'eval_wer': 1.0051408258135084, 'eval_runtime': 93.1376, 'eval_samples_per_second': 22.494, 'eval_steps_per_second': 0.709, 'epoch': 13.51}\n",
+ "\n",
+ " 14%|████▊ | 2000/14800 [56:03<6:56:38, 1.95s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-2000\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-2000/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-2000/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-2000/preprocessor_config.json\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-500] due to args.save_total_limit\n",
+ "{'loss': 1.4937, 'learning_rate': 7.4431640625e-05, 'epoch': 14.19} \n",
+ "{'loss': 1.4538, 'learning_rate': 7.384570312499999e-05, 'epoch': 14.86} \n",
+ "{'loss': 1.4016, 'learning_rate': 7.325976562499999e-05, 'epoch': 15.54} \n",
+ "{'loss': 1.403, 'learning_rate': 7.2673828125e-05, 'epoch': 16.22} \n",
+ "{'loss': 1.3868, 'learning_rate': 7.2087890625e-05, 'epoch': 16.89} \n",
+ " 17%|█████▋ | 2500/14800 [1:09:08<4:01:28, 1.18s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "100%|███████████████████████████████████████████| 66/66 [01:28<00:00, 1.08s/it]\u001b[A\n",
+ "{'eval_loss': 0.6836622953414917, 'eval_wer': 1.007984686901832, 'eval_runtime': 92.9915, 'eval_samples_per_second': 22.529, 'eval_steps_per_second': 0.71, 'epoch': 16.89}\n",
+ "\n",
+ " 17%|█████▋ | 2500/14800 [1:10:40<4:01:28, 1.18s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-2500\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-2500/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-2500/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-2500/preprocessor_config.json\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-1000] due to args.save_total_limit\n",
+ "{'loss': 1.3313, 'learning_rate': 7.150195312499999e-05, 'epoch': 17.57} \n",
+ "{'loss': 1.2931, 'learning_rate': 7.091601562499999e-05, 'epoch': 18.24} \n",
+ "{'loss': 1.3319, 'learning_rate': 7.0330078125e-05, 'epoch': 18.92} \n",
+ "{'loss': 1.3133, 'learning_rate': 6.9744140625e-05, 'epoch': 19.59} \n",
+ "{'loss': 1.2807, 'learning_rate': 6.915820312499999e-05, 'epoch': 20.27} \n",
+ " 20%|██████▉ | 3000/14800 [1:23:41<6:18:55, 1.93s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "{'eval_loss': 0.656791090965271, 'eval_wer': 1.0112113754443532, 'eval_runtime': 93.0318, 'eval_samples_per_second': 22.519, 'eval_steps_per_second': 0.709, 'epoch': 20.27}\n",
+ "\n",
+ " 20%|██████▉ | 3000/14800 [1:25:14<6:18:55, 1.93s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-3000\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-3000/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-3000/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-3000/preprocessor_config.json\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/preprocessor_config.json\n",
+ "{'loss': 1.2626, 'learning_rate': 6.857226562499999e-05, 'epoch': 20.95} \n",
+ "{'loss': 1.2535, 'learning_rate': 6.7986328125e-05, 'epoch': 21.62} \n",
+ "{'loss': 1.2297, 'learning_rate': 6.7400390625e-05, 'epoch': 22.3} \n",
+ "{'loss': 1.2074, 'learning_rate': 6.681445312499999e-05, 'epoch': 22.97} \n",
+ "{'loss': 1.231, 'learning_rate': 6.6234375e-05, 'epoch': 23.65} \n",
+ " 24%|████████ | 3500/14800 [1:38:14<3:47:50, 1.21s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ " 98%|██████████████████████████████████████████▎| 65/66 [01:28<00:01, 1.30s/it]\u001b[A\n",
+ " \u001b[A\n",
+ "\u001b[A{'eval_loss': 0.612037718296051, 'eval_wer': 1.0105004101722723, 'eval_runtime': 93.2672, 'eval_samples_per_second': 22.462, 'eval_steps_per_second': 0.708, 'epoch': 23.65}\n",
+ " 24%|████████ | 3500/14800 [1:39:48<3:47:50, 1.21s/it]\n",
+ "100%|███████████████████████████████████████████| 66/66 [01:31<00:00, 1.08s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-3500\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-3500/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-3500/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-3500/preprocessor_config.json\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-2000] due to args.save_total_limit\n",
+ "{'loss': 1.2092, 'learning_rate': 6.564843749999999e-05, 'epoch': 24.32} \n",
+ "{'loss': 1.1775, 'learning_rate': 6.50625e-05, 'epoch': 25.0} \n",
+ "{'loss': 1.1916, 'learning_rate': 6.44765625e-05, 'epoch': 25.68} \n",
+ "{'loss': 1.1798, 'learning_rate': 6.3890625e-05, 'epoch': 26.35} \n",
+ "{'loss': 1.1673, 'learning_rate': 6.330468749999999e-05, 'epoch': 27.03} \n",
+ " 27%|█████████▏ | 4000/14800 [1:52:47<5:54:31, 1.97s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ " 98%|██████████████████████████████████████████▎| 65/66 [01:28<00:01, 1.31s/it]\u001b[A\n",
+ " \u001b[A\n",
+ "\u001b[A{'eval_loss': 0.5971782207489014, 'eval_wer': 1.0089144107191688, 'eval_runtime': 93.4057, 'eval_samples_per_second': 22.429, 'eval_steps_per_second': 0.707, 'epoch': 27.03}\n",
+ " 27%|█████████▏ | 4000/14800 [1:54:20<5:54:31, 1.97s/it]\n",
+ "100%|███████████████████████████████████████████| 66/66 [01:31<00:00, 1.09s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-4000\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-4000/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-4000/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-4000/preprocessor_config.json\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-2500] due to args.save_total_limit\n",
+ "{'loss': 1.1626, 'learning_rate': 6.271875e-05, 'epoch': 27.7} \n",
+ "{'loss': 1.162, 'learning_rate': 6.21328125e-05, 'epoch': 28.38} \n",
+ "{'loss': 1.1471, 'learning_rate': 6.1546875e-05, 'epoch': 29.05} \n",
+ "{'loss': 1.1203, 'learning_rate': 6.09609375e-05, 'epoch': 29.73} \n",
+ "{'loss': 1.1416, 'learning_rate': 6.0375e-05, 'epoch': 30.41} \n",
+ " 30%|██████████▎ | 4500/14800 [2:07:21<3:25:21, 1.20s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ " \u001b[A\n",
+ "\u001b[A {'eval_loss': 0.5780259966850281, 'eval_wer': 1.0131802023516543, 'eval_runtime': 92.6708, 'eval_samples_per_second': 22.607, 'eval_steps_per_second': 0.712, 'epoch': 30.41}\n",
+ " 30%|██████████▎ | 4500/14800 [2:08:54<3:25:21, 1.20s/it]\n",
+ "100%|███████████████████████████████████████████| 66/66 [01:30<00:00, 1.07s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-4500\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-4500/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-4500/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-4500/preprocessor_config.json\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-3000] due to args.save_total_limit\n",
+ "{'loss': 1.1175, 'learning_rate': 5.97890625e-05, 'epoch': 31.08} \n",
+ "{'loss': 1.1053, 'learning_rate': 5.9203125e-05, 'epoch': 31.76} \n",
+ "{'loss': 1.107, 'learning_rate': 5.86171875e-05, 'epoch': 32.43} \n",
+ "{'loss': 1.115, 'learning_rate': 5.803125e-05, 'epoch': 33.11} \n",
+ "{'loss': 1.0738, 'learning_rate': 5.74453125e-05, 'epoch': 33.78} \n",
+ " 34%|███████████▍ | 5000/14800 [2:21:57<5:10:57, 1.90s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ " 98%|██████████████████████████████████████████▎| 65/66 [01:30<00:01, 1.33s/it]\u001b[A\n",
+ " \u001b[A\n",
+ "\u001b[A{'eval_loss': 0.580643892288208, 'eval_wer': 1.0123051681706317, 'eval_runtime': 95.0733, 'eval_samples_per_second': 22.036, 'eval_steps_per_second': 0.694, 'epoch': 33.78}\n",
+ " 34%|███████████▍ | 5000/14800 [2:23:32<5:10:57, 1.90s/it]\n",
+ "100%|███████████████████████████████████████████| 66/66 [01:32<00:00, 1.09s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-5000\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-5000/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-5000/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-5000/preprocessor_config.json\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-3500] due to args.save_total_limit\n",
+ "{'loss': 1.0778, 'learning_rate': 5.686523437499999e-05, 'epoch': 34.46} \n",
+ "{'loss': 1.0948, 'learning_rate': 5.6279296874999994e-05, 'epoch': 35.14} \n",
+ "{'loss': 1.062, 'learning_rate': 5.569335937499999e-05, 'epoch': 35.81} \n",
+ "{'loss': 1.0503, 'learning_rate': 5.5107421874999994e-05, 'epoch': 36.49} \n",
+ "{'loss': 1.0771, 'learning_rate': 5.452148437499999e-05, 'epoch': 37.16} \n",
+ " 37%|████████████▋ | 5500/14800 [2:36:32<3:08:03, 1.21s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ " \u001b[A\n",
+ "\u001b[A{'eval_loss': 0.5585715174674988, 'eval_wer': 1.0066721356302981, 'eval_runtime': 92.7751, 'eval_samples_per_second': 22.581, 'eval_steps_per_second': 0.711, 'epoch': 37.16}\n",
+ " 37%|████████████▋ | 5500/14800 [2:38:05<3:08:03, 1.21s/it]\n",
+ "100%|███████████████████████████████████████████| 66/66 [01:30<00:00, 1.08s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-5500\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-5500/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-5500/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-5500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-4000] due to args.save_total_limit\n",
+ "{'loss': 1.0368, 'learning_rate': 5.3935546874999995e-05, 'epoch': 37.84} \n",
+ "{'loss': 1.0329, 'learning_rate': 5.334960937499999e-05, 'epoch': 38.51} \n",
+ "{'loss': 1.0402, 'learning_rate': 5.2763671874999995e-05, 'epoch': 39.19} \n",
+ "{'loss': 1.0346, 'learning_rate': 5.217773437499999e-05, 'epoch': 39.86} \n",
+ "{'loss': 1.0287, 'learning_rate': 5.1591796874999995e-05, 'epoch': 40.54} \n",
+ " 41%|█████████████▊ | 6000/14800 [2:49:44<4:40:11, 1.91s/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 = 2095\n",
+ " Batch size = 32\n",
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+ " \u001b[A\n",
+ "\u001b[A{'eval_loss': 0.5463963747024536, 'eval_wer': 1.0057971014492753, 'eval_runtime': 92.708, 'eval_samples_per_second': 22.598, 'eval_steps_per_second': 0.712, 'epoch': 40.54}\n",
+ " 41%|█████████████▊ | 6000/14800 [2:51:17<4:40:11, 1.91s/it]\n",
+ "100%|███████████████████████████████████████████| 66/66 [01:30<00:00, 1.08s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-6000\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-6000/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-6000/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-6000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-4500] due to args.save_total_limit\n",
+ "{'loss': 1.0196, 'learning_rate': 5.100585937499999e-05, 'epoch': 41.22} \n",
+ "{'loss': 1.0198, 'learning_rate': 5.0419921874999995e-05, 'epoch': 41.89} \n",
+ "{'loss': 0.9864, 'learning_rate': 4.983398437499999e-05, 'epoch': 42.57} \n",
+ "{'loss': 0.999, 'learning_rate': 4.9248046874999996e-05, 'epoch': 43.24} \n",
+ "{'loss': 1.0106, 'learning_rate': 4.866210937499999e-05, 'epoch': 43.92} \n",
+ " 44%|██████████████▉ | 6500/14800 [3:02:55<2:28:25, 1.07s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "{'eval_loss': 0.5407418608665466, 'eval_wer': 1.0061799289034727, 'eval_runtime': 92.9267, 'eval_samples_per_second': 22.545, 'eval_steps_per_second': 0.71, 'epoch': 43.92}\n",
+ "\n",
+ " 44%|██████████████▉ | 6500/14800 [3:04:28<2:28:25, 1.07s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-6500\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-6500/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-6500/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-6500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-5000] due to args.save_total_limit\n",
+ "{'loss': 0.9816, 'learning_rate': 4.8076171874999996e-05, 'epoch': 44.59} \n",
+ "{'loss': 0.976, 'learning_rate': 4.749023437499999e-05, 'epoch': 45.27} \n",
+ "{'loss': 0.9833, 'learning_rate': 4.6904296874999996e-05, 'epoch': 45.95} \n",
+ "{'loss': 0.9905, 'learning_rate': 4.631835937499999e-05, 'epoch': 46.62} \n",
+ "{'loss': 0.9538, 'learning_rate': 4.5732421875e-05, 'epoch': 47.3} \n",
+ " 47%|████████████████ | 7000/14800 [3:16:07<4:01:49, 1.86s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ " \u001b[A\n",
+ "\u001b[A{'eval_loss': 0.5333988070487976, 'eval_wer': 1.0089144107191688, 'eval_runtime': 92.3771, 'eval_samples_per_second': 22.679, 'eval_steps_per_second': 0.714, 'epoch': 47.3}\n",
+ " 47%|████████████████ | 7000/14800 [3:17:39<4:01:49, 1.86s/it]\n",
+ "100%|███████████████████████████████████████████| 66/66 [01:30<00:00, 1.08s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-7000\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-7000/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-7000/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-7000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-5500] due to args.save_total_limit\n",
+ "{'loss': 0.9578, 'learning_rate': 4.5146484374999993e-05, 'epoch': 47.97} \n",
+ "{'loss': 0.9607, 'learning_rate': 4.4560546875e-05, 'epoch': 48.65} \n",
+ "{'loss': 0.9451, 'learning_rate': 4.3974609374999994e-05, 'epoch': 49.32} \n",
+ "{'loss': 0.9453, 'learning_rate': 4.3388671875e-05, 'epoch': 50.0} \n",
+ "{'loss': 0.9607, 'learning_rate': 4.2802734374999994e-05, 'epoch': 50.68} \n",
+ " 51%|█████████████████▏ | 7500/14800 [3:29:15<2:05:41, 1.03s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "{'eval_loss': 0.5395269989967346, 'eval_wer': 1.0110473065354115, 'eval_runtime': 92.6715, 'eval_samples_per_second': 22.607, 'eval_steps_per_second': 0.712, 'epoch': 50.68}\n",
+ "\n",
+ " 51%|█████████████████▏ | 7500/14800 [3:30:48<2:05:41, 1.03s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-7500\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-7500/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-7500/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-7500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-6000] due to args.save_total_limit\n",
+ "{'loss': 0.9445, 'learning_rate': 4.2216796875e-05, 'epoch': 51.35} \n",
+ "{'loss': 0.9314, 'learning_rate': 4.1630859374999994e-05, 'epoch': 52.03} \n",
+ "{'loss': 0.9166, 'learning_rate': 4.1044921875e-05, 'epoch': 52.7} \n",
+ "{'loss': 0.9264, 'learning_rate': 4.0458984374999995e-05, 'epoch': 53.38} \n",
+ "{'loss': 0.9108, 'learning_rate': 3.987304687499999e-05, 'epoch': 54.05} \n",
+ " 54%|██████████████████▍ | 8000/14800 [3:42:30<3:41:51, 1.96s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ " \u001b[A\n",
+ "\u001b[A{'eval_loss': 0.5501919388771057, 'eval_wer': 1.0137270987147935, 'eval_runtime': 92.8418, 'eval_samples_per_second': 22.565, 'eval_steps_per_second': 0.711, 'epoch': 54.05}\n",
+ " 54%|██████████████████▍ | 8000/14800 [3:44:03<3:41:51, 1.96s/it]\n",
+ "100%|███████████████████████████████████████████| 66/66 [01:30<00:00, 1.07s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-8000\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-8000/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-8000/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-8000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-6500] due to args.save_total_limit\n",
+ "{'loss': 0.9215, 'learning_rate': 3.9287109374999995e-05, 'epoch': 54.73} \n",
+ "{'loss': 0.9326, 'learning_rate': 3.870117187499999e-05, 'epoch': 55.41} \n",
+ "{'loss': 0.9199, 'learning_rate': 3.8115234374999995e-05, 'epoch': 56.08} \n",
+ "{'loss': 0.9043, 'learning_rate': 3.752929687499999e-05, 'epoch': 56.76} \n",
+ "{'loss': 0.9252, 'learning_rate': 3.6943359374999996e-05, 'epoch': 57.43} \n",
+ " 57%|███████████████████▌ | 8500/14800 [3:55:40<1:52:35, 1.07s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "{'eval_loss': 0.5498473644256592, 'eval_wer': 1.0062346185397868, 'eval_runtime': 92.009, 'eval_samples_per_second': 22.77, 'eval_steps_per_second': 0.717, 'epoch': 57.43}\n",
+ "\n",
+ " 57%|███████████████████▌ | 8500/14800 [3:57:12<1:52:35, 1.07s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-8500\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-8500/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-8500/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-8500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-7000] due to args.save_total_limit\n",
+ "{'loss': 0.9041, 'learning_rate': 3.635742187499999e-05, 'epoch': 58.11} \n",
+ "{'loss': 0.8963, 'learning_rate': 3.5771484374999996e-05, 'epoch': 58.78} \n",
+ "{'loss': 0.901, 'learning_rate': 3.518554687499999e-05, 'epoch': 59.46} \n",
+ "{'loss': 0.8947, 'learning_rate': 3.4599609374999996e-05, 'epoch': 60.14} \n",
+ "{'loss': 0.8943, 'learning_rate': 3.401367187499999e-05, 'epoch': 60.81} \n",
+ " 61%|████████████████████▋ | 9000/14800 [4:08:54<2:38:12, 1.64s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "{'eval_loss': 0.5447660684585571, 'eval_wer': 1.0158053048947224, 'eval_runtime': 92.1905, 'eval_samples_per_second': 22.725, 'eval_steps_per_second': 0.716, 'epoch': 60.81}\n",
+ "\n",
+ " 61%|████████████████████▋ | 9000/14800 [4:10:26<2:38:12, 1.64s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-9000\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-9000/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-9000/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-9000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-7500] due to args.save_total_limit\n",
+ "{'loss': 0.875, 'learning_rate': 3.3427734375e-05, 'epoch': 61.49} \n",
+ "{'loss': 0.8961, 'learning_rate': 3.284765625e-05, 'epoch': 62.16} \n",
+ "{'loss': 0.8848, 'learning_rate': 3.2261718749999996e-05, 'epoch': 62.84} \n",
+ "{'loss': 0.8573, 'learning_rate': 3.167578125e-05, 'epoch': 63.51} \n",
+ "{'loss': 0.8728, 'learning_rate': 3.1089843749999996e-05, 'epoch': 64.19} \n",
+ " 64%|█████████████████████▊ | 9500/14800 [4:22:01<1:34:24, 1.07s/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 = 2095\n",
+ " Batch size = 32\n",
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+ "{'eval_loss': 0.5256930589675903, 'eval_wer': 1.0113207547169811, 'eval_runtime': 92.3173, 'eval_samples_per_second': 22.693, 'eval_steps_per_second': 0.715, 'epoch': 64.19}\n",
+ "\n",
+ " 64%|█████████████████████▊ | 9500/14800 [4:23:33<1:34:24, 1.07s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-9500\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-9500/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-9500/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-9500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-8000] due to args.save_total_limit\n",
+ "{'loss': 0.8819, 'learning_rate': 3.050390625e-05, 'epoch': 64.86} \n",
+ "{'loss': 0.8324, 'learning_rate': 2.991796875e-05, 'epoch': 65.54} \n",
+ "{'loss': 0.8461, 'learning_rate': 2.933203125e-05, 'epoch': 66.22} \n",
+ "{'loss': 0.8512, 'learning_rate': 2.874609375e-05, 'epoch': 66.89} \n",
+ "{'loss': 0.8577, 'learning_rate': 2.816015625e-05, 'epoch': 67.57} \n",
+ " 68%|██████████████████████▎ | 10000/14800 [4:35:20<2:15:47, 1.70s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ " \u001b[A\n",
+ "\u001b[A{'eval_loss': 0.554978609085083, 'eval_wer': 1.0177741318020235, 'eval_runtime': 92.3777, 'eval_samples_per_second': 22.679, 'eval_steps_per_second': 0.714, 'epoch': 67.57}\n",
+ " 68%|██████████████████████▎ | 10000/14800 [4:36:52<2:15:47, 1.70s/it]\n",
+ "100%|███████████████████████████████████████████| 66/66 [01:30<00:00, 1.08s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-10000\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-10000/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-10000/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-10000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-8500] due to args.save_total_limit\n",
+ "{'loss': 0.845, 'learning_rate': 2.7574218749999997e-05, 'epoch': 68.24} \n",
+ "{'loss': 0.8622, 'learning_rate': 2.6988281249999997e-05, 'epoch': 68.92} \n",
+ "{'loss': 0.8534, 'learning_rate': 2.6402343749999998e-05, 'epoch': 69.59} \n",
+ "{'loss': 0.8328, 'learning_rate': 2.5816406249999998e-05, 'epoch': 70.27} \n",
+ "{'loss': 0.8332, 'learning_rate': 2.5230468749999998e-05, 'epoch': 70.95} \n",
+ " 71%|███████████████████████▍ | 10500/14800 [4:48:28<1:07:59, 1.05it/s]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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "{'eval_loss': 0.5607455968856812, 'eval_wer': 1.0165709598031174, 'eval_runtime': 91.9364, 'eval_samples_per_second': 22.787, 'eval_steps_per_second': 0.718, 'epoch': 70.95}\n",
+ "\n",
+ " 71%|███████████████████████▍ | 10500/14800 [4:50:00<1:07:59, 1.05it/s]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-10500\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-10500/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-10500/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-10500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-9000] due to args.save_total_limit\n",
+ "{'loss': 0.8389, 'learning_rate': 2.4644531249999998e-05, 'epoch': 71.62} \n",
+ "{'loss': 0.8225, 'learning_rate': 2.4058593749999998e-05, 'epoch': 72.3} \n",
+ "{'loss': 0.8141, 'learning_rate': 2.347265625e-05, 'epoch': 72.97} \n",
+ "{'loss': 0.8215, 'learning_rate': 2.288671875e-05, 'epoch': 73.65} \n",
+ "{'loss': 0.8174, 'learning_rate': 2.230078125e-05, 'epoch': 74.32} \n",
+ " 74%|████████████████████████▌ | 11000/14800 [5:01:49<1:45:06, 1.66s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ " \u001b[A\n",
+ "\u001b[A{'eval_loss': 0.5428524613380432, 'eval_wer': 1.0145474432595023, 'eval_runtime': 94.2641, 'eval_samples_per_second': 22.225, 'eval_steps_per_second': 0.7, 'epoch': 74.32}\n",
+ " 74%|████████████████████████▌ | 11000/14800 [5:03:24<1:45:06, 1.66s/it]\n",
+ "100%|███████████████████████████████████████████| 66/66 [01:32<00:00, 1.10s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-11000\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-11000/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-11000/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-11000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-9500] due to args.save_total_limit\n",
+ "{'loss': 0.7876, 'learning_rate': 2.171484375e-05, 'epoch': 75.0} \n",
+ "{'loss': 0.8226, 'learning_rate': 2.112890625e-05, 'epoch': 75.68} \n",
+ "{'loss': 0.8177, 'learning_rate': 2.054296875e-05, 'epoch': 76.35} \n",
+ "{'loss': 0.7941, 'learning_rate': 1.995703125e-05, 'epoch': 77.03} \n",
+ "{'loss': 0.8168, 'learning_rate': 1.937109375e-05, 'epoch': 77.7} \n",
+ " 78%|███████████████████████████▏ | 11500/14800 [5:15:04<50:34, 1.09it/s]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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "{'eval_loss': 0.5561283230781555, 'eval_wer': 1.0116488925348646, 'eval_runtime': 94.9459, 'eval_samples_per_second': 22.065, 'eval_steps_per_second': 0.695, 'epoch': 77.7}\n",
+ "\n",
+ " 78%|███████████████████████████▏ | 11500/14800 [5:16:39<50:34, 1.09it/s]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-11500\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-11500/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-11500/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-11500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-10000] due to args.save_total_limit\n",
+ "{'loss': 0.8095, 'learning_rate': 1.878515625e-05, 'epoch': 78.38} \n",
+ "{'loss': 0.791, 'learning_rate': 1.8199218749999996e-05, 'epoch': 79.05} \n",
+ "{'loss': 0.7812, 'learning_rate': 1.7613281249999997e-05, 'epoch': 79.73} \n",
+ "{'loss': 0.8103, 'learning_rate': 1.7033203125e-05, 'epoch': 80.41} \n",
+ "{'loss': 0.7872, 'learning_rate': 1.6447265625e-05, 'epoch': 81.08} \n",
+ " 81%|██████████████████████████▊ | 12000/14800 [5:28:27<1:19:20, 1.70s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "100%|███████████████████████████████████████████| 66/66 [01:30<00:00, 1.10s/it]\u001b[A\n",
+ "{'eval_loss': 0.5477647185325623, 'eval_wer': 1.0163522012578616, 'eval_runtime': 95.0024, 'eval_samples_per_second': 22.052, 'eval_steps_per_second': 0.695, 'epoch': 81.08}\n",
+ "\n",
+ " 81%|██████████████████████████▊ | 12000/14800 [5:30:02<1:19:20, 1.70s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-12000\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-12000/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-12000/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-12000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-10500] due to args.save_total_limit\n",
+ "{'loss': 0.7658, 'learning_rate': 1.5861328125e-05, 'epoch': 81.76} \n",
+ "{'loss': 0.7891, 'learning_rate': 1.5275390625e-05, 'epoch': 82.43} \n",
+ "{'loss': 0.7723, 'learning_rate': 1.4689453124999998e-05, 'epoch': 83.11} \n",
+ "{'loss': 0.7773, 'learning_rate': 1.4103515624999998e-05, 'epoch': 83.78} \n",
+ "{'loss': 0.7707, 'learning_rate': 1.3517578124999998e-05, 'epoch': 84.46} \n",
+ " 84%|█████████████████████████████▌ | 12500/14800 [5:41:43<36:52, 1.04it/s]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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "100%|███████████████████████████████████████████| 66/66 [01:30<00:00, 1.10s/it]\u001b[A\n",
+ "{'eval_loss': 0.5412248373031616, 'eval_wer': 1.021602406343998, 'eval_runtime': 95.413, 'eval_samples_per_second': 21.957, 'eval_steps_per_second': 0.692, 'epoch': 84.46}\n",
+ "\n",
+ " 84%|█████████████████████████████▌ | 12500/14800 [5:43:19<36:52, 1.04it/s]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-12500\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-12500/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-12500/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-12500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-11000] due to args.save_total_limit\n",
+ "{'loss': 0.7876, 'learning_rate': 1.2931640624999999e-05, 'epoch': 85.14} \n",
+ "{'loss': 0.7707, 'learning_rate': 1.2345703124999999e-05, 'epoch': 85.81} \n",
+ "{'loss': 0.7654, 'learning_rate': 1.1759765624999999e-05, 'epoch': 86.49} \n",
+ "{'loss': 0.77, 'learning_rate': 1.1173828124999999e-05, 'epoch': 87.16} \n",
+ "{'loss': 0.7742, 'learning_rate': 1.0587890625e-05, 'epoch': 87.84} \n",
+ " 88%|██████████████████████████████▋ | 13000/14800 [5:55:08<46:23, 1.55s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "{'eval_loss': 0.5391495227813721, 'eval_wer': 1.0206726825266612, 'eval_runtime': 95.0784, 'eval_samples_per_second': 22.034, 'eval_steps_per_second': 0.694, 'epoch': 87.84}\n",
+ "\n",
+ " 88%|██████████████████████████████▋ | 13000/14800 [5:56:43<46:23, 1.55s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-13000\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-13000/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-13000/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-13000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-11500] due to args.save_total_limit\n",
+ "{'loss': 0.7765, 'learning_rate': 9.421874999999999e-06, 'epoch': 89.19} \n",
+ "{'loss': 0.7598, 'learning_rate': 8.835937499999999e-06, 'epoch': 89.86} \n",
+ "{'loss': 0.7564, 'learning_rate': 8.249999999999999e-06, 'epoch': 90.54} \n",
+ "{'loss': 0.7594, 'learning_rate': 7.664062499999999e-06, 'epoch': 91.22} \n",
+ " 91%|███████████████████████████████▉ | 13500/14800 [6:08:21<20:25, 1.06it/s]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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ " \u001b[A\n",
+ "\u001b[A{'eval_loss': 0.5379434823989868, 'eval_wer': 1.020836751435603, 'eval_runtime': 95.2181, 'eval_samples_per_second': 22.002, 'eval_steps_per_second': 0.693, 'epoch': 91.22}\n",
+ " 91%|███████████████████████████████▉ | 13500/14800 [6:09:56<20:25, 1.06it/s]\n",
+ "100%|███████████████████████████████████████████| 66/66 [01:32<00:00, 1.10s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-13500\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-13500/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-13500/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-13500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-12000] due to args.save_total_limit\n",
+ "{'loss': 0.7739, 'learning_rate': 7.078124999999999e-06, 'epoch': 91.89} \n",
+ "{'loss': 0.7646, 'learning_rate': 6.492187499999999e-06, 'epoch': 92.57} \n",
+ "{'loss': 0.7321, 'learning_rate': 5.9062499999999996e-06, 'epoch': 93.24} \n",
+ "{'loss': 0.7754, 'learning_rate': 5.3203125e-06, 'epoch': 93.92} \n",
+ "{'loss': 0.7678, 'learning_rate': 4.734375e-06, 'epoch': 94.59} \n",
+ " 95%|█████████████████████████████████ | 14000/14800 [6:21:46<19:40, 1.48s/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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "{'eval_loss': 0.541484534740448, 'eval_wer': 1.0197976483456386, 'eval_runtime': 92.5358, 'eval_samples_per_second': 22.64, 'eval_steps_per_second': 0.713, 'epoch': 94.59}\n",
+ "\n",
+ " 95%|█████████████████████████████████ | 14000/14800 [6:23:18<19:40, 1.48s/it]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-14000\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-14000/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-14000/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-14000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-12500] due to args.save_total_limit\n",
+ "{'loss': 0.7499, 'learning_rate': 4.1484375e-06, 'epoch': 95.27} \n",
+ "{'loss': 0.7511, 'learning_rate': 3.5624999999999998e-06, 'epoch': 95.95} \n",
+ "{'loss': 0.7652, 'learning_rate': 2.9765625e-06, 'epoch': 96.62} \n",
+ "{'loss': 0.7596, 'learning_rate': 2.3906249999999997e-06, 'epoch': 97.3} \n",
+ "{'loss': 0.7502, 'learning_rate': 1.8046874999999998e-06, 'epoch': 97.97} \n",
+ " 98%|██████████████████████████████████▎| 14500/14800 [6:34:59<03:43, 1.34it/s]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 = 2095\n",
+ " Batch size = 32\n",
+ "\n",
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+ "{'eval_loss': 0.5409459471702576, 'eval_wer': 1.0191413727098715, 'eval_runtime': 94.0294, 'eval_samples_per_second': 22.28, 'eval_steps_per_second': 0.702, 'epoch': 97.97}\n",
+ "\n",
+ " 98%|██████████████████████████████████▎| 14500/14800 [6:36:33<03:43, 1.34it/s]\u001b[A\n",
+ " \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi/checkpoint-14500\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-14500/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-14500/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/checkpoint-14500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-hindi/checkpoint-13000] due to args.save_total_limit\n",
+ "{'loss': 0.7587, 'learning_rate': 1.21875e-06, 'epoch': 98.65} \n",
+ "{'loss': 0.7614, 'learning_rate': 6.328125e-07, 'epoch': 99.32} \n",
+ "{'loss': 0.7354, 'learning_rate': 4.6874999999999995e-08, 'epoch': 100.0} \n",
+ "100%|███████████████████████████████████| 14800/14800 [6:43:38<00:00, 1.06it/s]\n",
+ "\n",
+ "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
+ "\n",
+ "\n",
+ "{'train_runtime': 24218.0743, 'train_samples_per_second': 19.452, 'train_steps_per_second': 0.611, 'train_loss': 1.4159005551724821, 'epoch': 100.0}\n",
+ "100%|███████████████████████████████████| 14800/14800 [6:43:38<00:00, 1.64s/it]\n",
+ "Saving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/preprocessor_config.json\n",
+ "***** train metrics *****\n",
+ " epoch = 100.0\n",
+ " train_loss = 1.4159\n",
+ " train_runtime = 6:43:38.07\n",
+ " train_samples = 4711\n",
+ " train_samples_per_second = 19.452\n",
+ " train_steps_per_second = 0.611\n",
+ "01/22/2022 00:13:48 - INFO - __main__ - *** Evaluate ***\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 2095\n",
+ " Batch size = 32\n",
+ "100%|███████████████████████████████████████████| 66/66 [01:31<00:00, 1.39s/it]\n",
+ "***** eval metrics *****\n",
+ " epoch = 100.0\n",
+ " eval_loss = 0.5414\n",
+ " eval_runtime = 0:01:33.91\n",
+ " eval_samples = 2095\n",
+ " eval_samples_per_second = 22.308\n",
+ " eval_steps_per_second = 0.703\n",
+ " eval_wer = 1.0194\n",
+ "Saving model checkpoint to ./wav2vec2-large-xls-r-300m-hindi\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/config.json\n",
+ "Model weights saved in ./wav2vec2-large-xls-r-300m-hindi/pytorch_model.bin\n",
+ "Configuration saved in ./wav2vec2-large-xls-r-300m-hindi/preprocessor_config.json\n",
+ "Several commits (2) will be pushed upstream.\n",
+ "01/22/2022 00:16:37 - WARNING - huggingface_hub.repository - Several commits (2) will be pushed upstream.\n",
+ "The progress bars may be unreliable.\n",
+ "01/22/2022 00:16:37 - WARNING - huggingface_hub.repository - The progress bars may be unreliable.\n",
+ "Upload file pytorch_model.bin: 97%|██████▊| 1.14G/1.18G [00:52<00:01, 28.5MB/s]To https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-hindi\n",
+ " 13be5b6..aac3ebd main -> main\n",
+ "\n",
+ "01/22/2022 00:17:33 - WARNING - huggingface_hub.repository - To https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-hindi\n",
+ " 13be5b6..aac3ebd main -> main\n",
+ "\n",
+ "Upload file pytorch_model.bin: 100%|███████| 1.18G/1.18G [00:53<00:00, 23.6MB/s]\n",
+ "Dropping the following result as it does not have all the necessary fields:\n",
+ "{'dataset': {'name': 'MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI', 'type': 'common_voice', 'args': 'Config: hi, Training split: train+validation, Eval split: test'}}\n",
+ "To https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-hindi\n",
+ " aac3ebd..ae07d7b main -> main\n",
+ "\n",
+ "01/22/2022 00:17:40 - WARNING - huggingface_hub.repository - To https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-hindi\n",
+ " aac3ebd..ae07d7b main -> main\n",
+ "\n",
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+ "Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "01/22/2022 02:06:06 - ERROR - huggingface_hub.repository - Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "01/22/2022 02:09:35 - ERROR - huggingface_hub.repository - Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "01/22/2022 02:13:04 - ERROR - huggingface_hub.repository - Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "01/22/2022 02:16:32 - ERROR - huggingface_hub.repository - Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "01/22/2022 02:20:01 - ERROR - huggingface_hub.repository - Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "01/22/2022 02:26:58 - ERROR - huggingface_hub.repository - Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "01/22/2022 02:30:27 - ERROR - huggingface_hub.repository - Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "01/22/2022 02:33:56 - ERROR - huggingface_hub.repository - Waiting for the following commands to finish before shutting down: [[push command, status code: running, in progress. PID: 776656]].\n",
+ "^C\n",
+ "Error in atexit._run_exitfuncs:\n",
+ "Traceback (most recent call last):\n",
+ " File \"/opt/conda/lib/python3.8/site-packages/huggingface_hub/repository.py\", line 878, in lfs_prune\n",
+ " logger.info(result.stdout)\n",
+ " File \"/opt/conda/lib/python3.8/contextlib.py\", line 120, in __exit__\n",
+ " next(self.gen)\n",
+ " File \"/opt/conda/lib/python3.8/site-packages/huggingface_hub/repository.py\", line 343, in lfs_log_progress\n",
+ " x.join()\n",
+ " File \"/opt/conda/lib/python3.8/threading.py\", line 1011, in join\n",
+ " self._wait_for_tstate_lock()\n",
+ " File \"/opt/conda/lib/python3.8/threading.py\", line 1027, in _wait_for_tstate_lock\n",
+ " elif lock.acquire(block, timeout):\n",
+ "KeyboardInterrupt\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=\"hi\" \\\n",
+ "\t--output_dir=\"./wav2vec2-large-xls-r-300m-hindi\" \\\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=\"7.5e-5\" \\\n",
+ "\t--warmup_steps=\"2000\" \\\n",
+ "\t--length_column_name=\"input_length\" \\\n",
+ "\t--evaluation_strategy=\"steps\" \\\n",
+ "\t--text_column_name=\"sentence\" \\\n",
+ "\t--chars_to_ignore , ? . ! \\- \\; \\: \\\" “ % ‘ ” � — \\’ … \\– \\' \\’ \\– � \\' \\’ \\– \\& a-z A-Z \\। \\\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": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/hi/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n",
+ "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/hi/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\", \"hi\", use_auth_token=True, split=\"train+validation\")\n",
+ "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"hi\", use_auth_token=True, split=\"test\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "7360.9375"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(common_voice_train) * 50 / 32"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "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": 8,
+ "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": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " | \n",
+ " sentence | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " रोज़ेटा यूरोपीय अंतरिक्ष एजेंसी ने बनाया था | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " आप थोड़ा धीरे बोल सकते हैं क्या | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " पाक में बाढ़ की स्थिति का फायदा उठा सकते हैं चरमपंथीअमेरिका | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " दिल्ली पुलिस के हत्थे चढ़ा माओवादियों का कारतूस सप्लायर | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " यूपी में बंदर बना ड्राइवर दो गाड़ियों में मारी टक्कर | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " उत्तराखंड सरकारी कार्यालयों में सिंगल यूज प्लास्टिक पर लगेगा प्रतिबंध | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " वही तो | \n",
+ "
\n",
+ " \n",
+ " 7 | \n",
+ " उसेन बोल्ट का गेंदबाजी एक्शन देखकर हैरान रह गयाः हरभज��� सिंह | \n",
+ "
\n",
+ " \n",
+ " 8 | \n",
+ " इटली एक बहुत सुंदर देश है | \n",
+ "
\n",
+ " \n",
+ " 9 | \n",
+ " तुम्हारी बंदूक कहाँ है | \n",
+ "
\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "show_random_elements(common_voice_train.remove_columns([\"path\", \"audio\"]), num_examples=10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import re\n",
+ "chars_to_remove_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�\\'\\’\\–\\&a-zA-Z\\।]'\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": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "3910ff24d244410b96941c17b57935c2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/4711 [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2e0588d82cf64da6891eb33583b084d5",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/2095 [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "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": 20,
+ "metadata": {},
+ "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": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "23341f319e4e4b39a8210d2ef15c1dd6",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/4711 [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "229fadd88cc34b6ea067dc9d0090f4c6",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/2095 [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "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": 22,
+ "metadata": {},
+ "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": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "82fea9d203994830b4169e565c146c76",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/1 [00:00, ?ba/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "d19518b1fa1641e79772be2cccdaaa67",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/1 [00:00, ?ba/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)\n",
+ "vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vocab_list = list(set(vocab_train[\"vocab\"][0]) | set(vocab_test[\"vocab\"][0]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{' ': 0,\n",
+ " '|': 1,\n",
+ " 'ँ': 2,\n",
+ " 'ं': 3,\n",
+ " 'ः': 4,\n",
+ " 'अ': 5,\n",
+ " 'आ': 6,\n",
+ " 'इ': 7,\n",
+ " 'ई': 8,\n",
+ " 'उ': 9,\n",
+ " 'ऊ': 10,\n",
+ " 'ऋ': 11,\n",
+ " 'ए': 12,\n",
+ " 'ऐ': 13,\n",
+ " 'ऑ': 14,\n",
+ " 'ओ': 15,\n",
+ " 'औ': 16,\n",
+ " 'क': 17,\n",
+ " 'ख': 18,\n",
+ " 'ग': 19,\n",
+ " 'घ': 20,\n",
+ " 'च': 21,\n",
+ " 'छ': 22,\n",
+ " 'ज': 23,\n",
+ " 'झ': 24,\n",
+ " 'ञ': 25,\n",
+ " 'ट': 26,\n",
+ " 'ठ': 27,\n",
+ " 'ड': 28,\n",
+ " 'ढ': 29,\n",
+ " 'ण': 30,\n",
+ " 'त': 31,\n",
+ " 'थ': 32,\n",
+ " 'द': 33,\n",
+ " 'ध': 34,\n",
+ " 'न': 35,\n",
+ " 'प': 36,\n",
+ " 'फ': 37,\n",
+ " 'ब': 38,\n",
+ " 'भ': 39,\n",
+ " 'म': 40,\n",
+ " 'य': 41,\n",
+ " 'र': 42,\n",
+ " 'ल': 43,\n",
+ " 'व': 44,\n",
+ " 'श': 45,\n",
+ " 'ष': 46,\n",
+ " 'स': 47,\n",
+ " 'ह': 48,\n",
+ " '़': 49,\n",
+ " 'ा': 50,\n",
+ " 'ि': 51,\n",
+ " 'ी': 52,\n",
+ " 'ु': 53,\n",
+ " 'ू': 54,\n",
+ " 'ृ': 55,\n",
+ " 'ॅ': 56,\n",
+ " 'े': 57,\n",
+ " 'ै': 58,\n",
+ " 'ॉ': 59,\n",
+ " 'ो': 60,\n",
+ " 'ौ': 61,\n",
+ " '्': 62,\n",
+ " 'क़': 63,\n",
+ " 'ग़': 64,\n",
+ " 'ज़': 65,\n",
+ " 'ड़': 66,\n",
+ " 'ढ़': 67}"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vocab_dict = {v: k for k, v in enumerate(sorted(vocab_list))}\n",
+ "vocab_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2022-01-22 02:39:02-- https://raw.githubusercontent.com/huggingface/transformers/master/examples/research_projects/robust-speech-event/eval.py\n",
+ "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.111.133, ...\n",
+ "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 4419 (4.3K) [text/plain]\n",
+ "Saving to: ‘eval.py’\n",
+ "\n",
+ "eval.py 100%[===================>] 4.32K --.-KB/s in 0s \n",
+ "\n",
+ "2022-01-22 02:39:02 (11.4 MB/s) - ‘eval.py’ saved [4419/4419]\n",
+ "\n",
+ "total 1232728\n",
+ "drwxr-xr-x 5 ovh ovh 4096 Jan 21 17:29 runs\n",
+ "-rw-r--r-- 1 ovh ovh 23 Jan 21 17:29 added_tokens.json\n",
+ "-rw-r--r-- 1 ovh ovh 771 Jan 21 17:29 vocab.json\n",
+ "-rw-r--r-- 1 ovh ovh 260 Jan 21 17:29 tokenizer_config.json\n",
+ "-rw-r--r-- 1 ovh ovh 309 Jan 21 17:29 special_tokens_map.json\n",
+ "drwxr-xr-x 2 ovh ovh 4096 Jan 21 23:40 checkpoint-13500\n",
+ "drwxr-xr-x 2 ovh ovh 4096 Jan 21 23:53 checkpoint-14000\n",
+ "drwxr-xr-x 2 ovh ovh 4096 Jan 22 00:06 checkpoint-14500\n",
+ "-rw-r--r-- 1 ovh ovh 197 Jan 22 00:13 train_results.json\n",
+ "-rw-r--r-- 1 ovh ovh 25203 Jan 22 00:13 trainer_state.json\n",
+ "-rw-r--r-- 1 ovh ovh 225 Jan 22 00:15 eval_results.json\n",
+ "-rw-r--r-- 1 ovh ovh 2033 Jan 22 00:15 config.json\n",
+ "-rw-r--r-- 1 ovh ovh 400 Jan 22 00:15 all_results.json\n",
+ "-rw-r--r-- 1 ovh ovh 1262210673 Jan 22 00:15 pytorch_model.bin\n",
+ "-rw-r--r-- 1 ovh ovh 3055 Jan 22 00:15 training_args.bin\n",
+ "-rw-r--r-- 1 ovh ovh 212 Jan 22 00:15 preprocessor_config.json\n",
+ "-rw-r--r-- 1 ovh ovh 3291 Jan 22 00:17 README.md\n",
+ "-rw-r--r-- 1 ovh ovh 4419 Jan 22 02:39 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-hindi\n",
+ "!ls -ltr wav2vec2-large-xls-r-300m-hindi"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/hi/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n",
+ "100%|███████████████████████████████████████████| 10/10 [00:05<00:00, 1.99ex/s]\n",
+ "WER: 1.0166666666666666\n",
+ "CER: 1.0327272727272727\n",
+ "100%|████████████████████████████████████████| 10/10 [00:00<00:00, 12905.55ex/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cd wav2vec2-large-xls-r-300m-hindi; python eval.py --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config hi --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
+}