"
+ ]
+ },
+ "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",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0ex [00:00, ?ex/s]"
+ ]
+ },
+ "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,
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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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+ ]
+ },
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+ "outputId": "b0a00853-9905-4b31-e528-6b46cdab5f2a"
+ },
+ "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,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/1.18G [00:00, ?B/s]"
+ ]
+ },
+ "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",
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\n",
+ " \n",
+ " 1900 | \n",
+ " 0.290300 | \n",
+ " 1.064526 | \n",
+ " 0.581438 | \n",
+ "
\n",
+ " \n",
+ " 2000 | \n",
+ " 0.281200 | \n",
+ " 1.031865 | \n",
+ " 0.585357 | \n",
+ "
\n",
+ " \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"
+ ]
+ }
+ ],
+ "source": [
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
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+ },
+ "outputs": [],
+ "source": [
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