"
]
},
"metadata": {},
"execution_count": 31
}
],
"source": [
"import IPython.display as ipd\n",
"import numpy as np\n",
"import random\n",
"\n",
"rand_int = random.randint(0, len(common_voice_train)-1)\n",
"\n",
"print(common_voice_train[rand_int][\"sentence\"])\n",
"ipd.Audio(data=common_voice_train[rand_int][\"audio\"][\"array\"], autoplay=True, rate=16000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EE5jG9PVZmWU",
"outputId": "fb86bfb5-c22e-4fb0-c4d8-f31510220b5f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Target text: mat eo an holl\n",
"Input array shape: (29184,)\n",
"Sampling rate: 16000\n"
]
}
],
"source": [
"rand_int = random.randint(0, len(common_voice_train)-1)\n",
"\n",
"print(\"Target text:\", common_voice_train[rand_int][\"sentence\"])\n",
"print(\"Input array shape:\", common_voice_train[rand_int][\"audio\"][\"array\"].shape)\n",
"print(\"Sampling rate:\", common_voice_train[rand_int][\"audio\"][\"sampling_rate\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XobzwvyNZpxv"
},
"outputs": [],
"source": [
"def prepare_dataset(batch):\n",
" audio = batch[\"audio\"]\n",
"\n",
" # batched output is \"un-batched\"\n",
" batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n",
" batch[\"input_length\"] = len(batch[\"input_values\"])\n",
" \n",
" with processor.as_target_processor():\n",
" batch[\"labels\"] = processor(batch[\"sentence\"]).input_ids\n",
" return batch"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
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},
"id": "A7gT1EQMZsvz",
"outputId": "c839172b-4ba3-461a-ba41-267ec849577c"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fa0cb6eb3e0047a6b521c8ebc85ebf0a",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"0ex [00:00, ?ex/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a019444372314e1c9ca2752a5f90b6d8",
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"version_major": 2
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"0ex [00:00, ?ex/s]"
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},
"metadata": {}
}
],
"source": [
"common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names)\n",
"common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Hu8imi4QZx3f"
},
"source": [
"Training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rAYCcrJFZubm"
},
"outputs": [],
"source": [
"import torch\n",
"\n",
"from dataclasses import dataclass, field\n",
"from typing import Any, Dict, List, Optional, Union\n",
"\n",
"@dataclass\n",
"class DataCollatorCTCWithPadding:\n",
" \"\"\"\n",
" Data collator that will dynamically pad the inputs received.\n",
" Args:\n",
" processor (:class:`~transformers.Wav2Vec2Processor`)\n",
" The processor used for proccessing the data.\n",
" padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n",
" Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n",
" among:\n",
" * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n",
" sequence if provided).\n",
" * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n",
" maximum acceptable input length for the model if that argument is not provided.\n",
" * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n",
" different lengths).\n",
" \"\"\"\n",
"\n",
" processor: Wav2Vec2Processor\n",
" padding: Union[bool, str] = True\n",
"\n",
" def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
" # split inputs and labels since they have to be of different lenghts and need\n",
" # different padding methods\n",
" input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n",
" label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
"\n",
" batch = self.processor.pad(\n",
" input_features,\n",
" padding=self.padding,\n",
" return_tensors=\"pt\",\n",
" )\n",
" with self.processor.as_target_processor():\n",
" labels_batch = self.processor.pad(\n",
" label_features,\n",
" padding=self.padding,\n",
" return_tensors=\"pt\",\n",
" )\n",
"\n",
" # replace padding with -100 to ignore loss correctly\n",
" labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
"\n",
" batch[\"labels\"] = labels\n",
"\n",
" return batch"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "k05sM3rsZzGS"
},
"outputs": [],
"source": [
"data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
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]
},
"id": "T1zgf2ZKZ1et",
"outputId": "29adbbf5-bb17-45b0-b1d2-893d6b51f4c3"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b1f9950cc1014614a4bc26999488c80b",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/1.90k [00:00, ?B/s]"
]
},
"metadata": {}
}
],
"source": [
"wer_metric = load_metric(\"wer\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2J_feilIZ3Jf"
},
"outputs": [],
"source": [
"def compute_metrics(pred):\n",
" pred_logits = pred.predictions\n",
" pred_ids = np.argmax(pred_logits, axis=-1)\n",
"\n",
" pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n",
"\n",
" pred_str = processor.batch_decode(pred_ids)\n",
" # we do not want to group tokens when computing the metrics\n",
" label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n",
"\n",
" wer = wer_metric.compute(predictions=pred_str, references=label_str)\n",
"\n",
" return {\"wer\": wer}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
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},
"id": "fEzH8f_lZ5FG",
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},
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2f3ea05c95a84474bb9a6656c6db37fa",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/1.53k [00:00, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9ff07907623d4213b528d06e6b39712a",
"version_minor": 0,
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},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['project_q.bias', 'quantizer.weight_proj.weight', 'project_q.weight', 'quantizer.weight_proj.bias', 'project_hid.weight', 'quantizer.codevectors', 'project_hid.bias']\n",
"- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-300m and are newly initialized: ['lm_head.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"
]
}
],
"source": [
"from transformers import Wav2Vec2ForCTC\n",
"\n",
"model = Wav2Vec2ForCTC.from_pretrained(\n",
" \"facebook/wav2vec2-xls-r-300m\", \n",
" attention_dropout=0.0,\n",
" hidden_dropout=0.0,\n",
" feat_proj_dropout=0.0,\n",
" mask_time_prob=0.05,\n",
" layerdrop=0.0,\n",
" ctc_loss_reduction=\"mean\", \n",
" pad_token_id=processor.tokenizer.pad_token_id,\n",
" vocab_size=len(processor.tokenizer),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "h7MYlVI1Z-w8",
"outputId": "8cb47d99-5fc2-46a9-931b-90cec5c36be3"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1703: FutureWarning: The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5.Please use the equivalent `freeze_feature_encoder` method instead.\n",
" FutureWarning,\n"
]
}
],
"source": [
"model.freeze_feature_extractor()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DapYDcW4Z_Z0"
},
"outputs": [],
"source": [
"from transformers import TrainingArguments\n",
"\n",
"training_args = TrainingArguments(\n",
" output_dir=repo_name,\n",
" group_by_length=True,\n",
" per_device_train_batch_size=16,\n",
" gradient_accumulation_steps=2,\n",
" evaluation_strategy=\"steps\",\n",
" num_train_epochs=200,\n",
" gradient_checkpointing=True,\n",
" fp16=True,\n",
" save_steps=100,\n",
" eval_steps=100,\n",
" logging_steps=100,\n",
" learning_rate=0.00034,\n",
" warmup_steps=750,\n",
" save_total_limit=2,\n",
" push_to_hub=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jHiXuohDaD_5",
"outputId": "5d824b14-25f3-4dbb-e987-a0df195ffd9a"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/content/wav2vec2-large-xls-r-300m-br-d2 is already a clone of https://huggingface.co/DrishtiSharma/wav2vec2-large-xls-r-300m-br-d2. Make sure you pull the latest changes with `repo.git_pull()`.\n",
"Using amp half precision backend\n"
]
}
],
"source": [
"from transformers import Trainer\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" data_collator=data_collator,\n",
" args=training_args,\n",
" compute_metrics=compute_metrics,\n",
" train_dataset=common_voice_train,\n",
" eval_dataset=common_voice_test,\n",
" tokenizer=processor.feature_extractor,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "27TZ79d_aIgO",
"outputId": "22dc02fa-0c60-4762-994a-ea636cc7cfb5"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
"/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: 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",
" FutureWarning,\n",
"***** Running training *****\n",
" Num examples = 4725\n",
" Num Epochs = 200\n",
" Instantaneous batch size per device = 16\n",
" Total train batch size (w. parallel, distributed & accumulation) = 32\n",
" Gradient Accumulation steps = 2\n",
" Total optimization steps = 29600\n"
]
},
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" \n",
" \n",
"
\n",
" [ 2276/29600 2:50:44 < 34:11:38, 0.22 it/s, Epoch 15.37/200]\n",
"
\n",
" \n",
" \n",
" \n",
" Step | \n",
" Training Loss | \n",
" Validation Loss | \n",
" Wer | \n",
"
\n",
" \n",
" \n",
" \n",
" 100 | \n",
" 13.257900 | \n",
" 5.425055 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" 200 | \n",
" 3.834100 | \n",
" 3.241349 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" 300 | \n",
" 3.065900 | \n",
" 3.015289 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" 400 | \n",
" 2.597600 | \n",
" 1.664336 | \n",
" 0.965399 | \n",
"
\n",
" \n",
" 500 | \n",
" 1.320900 | \n",
" 1.199632 | \n",
" 0.830530 | \n",
"
\n",
" \n",
" 600 | \n",
" 1.051400 | \n",
" 1.058605 | \n",
" 0.774517 | \n",
"
\n",
" \n",
" 700 | \n",
" 0.845200 | \n",
" 1.055370 | \n",
" 0.763812 | \n",
"
\n",
" \n",
" 800 | \n",
" 0.743400 | \n",
" 1.039097 | \n",
" 0.730071 | \n",
"
\n",
" \n",
" 900 | \n",
" 0.698300 | \n",
" 0.941623 | \n",
" 0.662302 | \n",
"
\n",
" \n",
" 1000 | \n",
" 0.589000 | \n",
" 0.936797 | \n",
" 0.654464 | \n",
"
\n",
" \n",
" 1100 | \n",
" 0.511300 | \n",
" 0.978924 | \n",
" 0.643567 | \n",
"
\n",
" \n",
" 1200 | \n",
" 0.501400 | \n",
" 0.971463 | \n",
" 0.617282 | \n",
"
\n",
" \n",
" 1300 | \n",
" 0.427200 | \n",
" 0.944981 | \n",
" 0.616517 | \n",
"
\n",
" \n",
" 1400 | \n",
" 0.376200 | \n",
" 0.944748 | \n",
" 0.630185 | \n",
"
\n",
" \n",
" 1500 | \n",
" 0.379300 | \n",
" 1.006578 | \n",
" 0.598738 | \n",
"
\n",
" \n",
" 1600 | \n",
" 0.368300 | \n",
" 0.923694 | \n",
" 0.600746 | \n",
"
\n",
" \n",
" 1700 | \n",
" 0.319700 | \n",
" 1.051132 | \n",
" 0.622252 | \n",
"
\n",
" \n",
" 1800 | \n",
" 0.318100 | \n",
" 1.026835 | \n",
" 0.605429 | \n",
"
\n",
" \n",
" 1900 | \n",
" 0.290300 | \n",
" 1.064526 | \n",
" 0.581438 | \n",
"
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" \n",
" 2000 | \n",
" 0.281200 | \n",
" 1.031865 | \n",
" 0.585357 | \n",
"
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" \n",
" 2100 | \n",
" 0.280500 | \n",
" 1.035907 | \n",
" 0.588415 | \n",
"
\n",
" \n",
" 2200 | \n",
" 0.250300 | \n",
" 1.032733 | \n",
" 0.572357 | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
""
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-100\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-100/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-100/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-100/preprocessor_config.json\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/preprocessor_config.json\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-200\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-200/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-200/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-200/preprocessor_config.json\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-300\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-300/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-300/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-300/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-100] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-400\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-400/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-400/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-400/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-200] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-500\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-500/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-500/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-500/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-300] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-600\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-600/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-600/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-600/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-400] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-700\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-700/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-700/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-700/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-500] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-800\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-800/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-800/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-800/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-600] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-900\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-900/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-900/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-900/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-700] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1000\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1000/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1000/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1000/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-800] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1100\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1100/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1100/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1100/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-900] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1200\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1200/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1200/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1200/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1000] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1300\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1300/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1300/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1300/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1100] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1400\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1400/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1400/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1400/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1200] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1500\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1500/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1500/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1500/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1300] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1600\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1600/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1600/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1600/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1400] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1700\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1700/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1700/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1700/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1500] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1800\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1800/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1800/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1800/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1600] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1900\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1900/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1900/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1900/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1700] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-2000\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2000/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2000/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2000/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1800] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-2100\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2100/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2100/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2100/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1900] due to args.save_total_limit\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 = 2147\n",
" Batch size = 8\n",
"Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-2200\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2200/config.json\n",
"Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2200/pytorch_model.bin\n",
"Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2200/preprocessor_config.json\n",
"Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-2000] due to args.save_total_limit\n"
]
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