"
+ ]
+ },
+ "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": "584be85c-3e58-4f11-8726-a16822254feb"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Target text: ବୁଢୀ ପଚାରିଲା କିଏ ଡାହାଣୀରେ\n",
+ "Input array shape: (56064,)\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": [
+ "da4dd47985ca4ab686672dcda25f8a38",
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+ "d22f8ae4dba74506b862358bf13d5e97",
+ "e31f3ed817b943bfa9478f7334d17150"
+ ]
+ },
+ "id": "A7gT1EQMZsvz",
+ "outputId": "110b660c-9b01-4083-b107-2ac030a61fcf"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "da4dd47985ca4ab686672dcda25f8a38",
+ "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": "6382ef3a17034f57889f401ef8ccee75",
+ "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,
+ "referenced_widgets": [
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+ ]
+ },
+ "id": "T1zgf2ZKZ1et",
+ "outputId": "c05b0c2d-af8c-47af-892b-d3e1d71e99d9"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "6a256ee8367a46b2ab4a37084a705e44",
+ "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/",
+ "height": 192,
+ "referenced_widgets": [
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+ ]
+ },
+ "id": "fEzH8f_lZ5FG",
+ "outputId": "a4c30faa-9463-4aee-a50e-2c80d878e168"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "651a835e842b473293982190a107ec3b",
+ "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": "0fa99e930a6c4a19baf0a1ef91275931",
+ "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: ['quantizer.codevectors', 'quantizer.weight_proj.bias', 'project_q.bias', 'project_hid.bias', 'project_hid.weight', 'project_q.weight', 'quantizer.weight_proj.weight']\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=0.35,\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": "b51628dd-df6f-4ef8-c8e0-f859015f4d6c"
+ },
+ "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=300,\n",
+ " eval_steps=300,\n",
+ " logging_steps=300,\n",
+ " learning_rate=3e-4,\n",
+ " warmup_steps=1000,\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": "27bc2c81-2868-4d29-bbd3-87da3df3b80d"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/content/wav2vec2-large-xls-r-300m-or-d5 is already a clone of https://huggingface.co/DrishtiSharma/wav2vec2-large-xls-r-300m-or-d5. 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": "649c42a4-bdbb-420e-ecc6-f4f1dc625005"
+ },
+ "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 = 753\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 = 4800\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
\n",
+ " [4800/4800 7:39:25, Epoch 200/200]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Step | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ " Wer | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 300 | \n",
+ " 9.295800 | \n",
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+ " 600 | \n",
+ " 3.406500 | \n",
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+ " 0.568122 | \n",
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+ " 3900 | \n",
+ " 0.103500 | \n",
+ " 0.914773 | \n",
+ " 0.549603 | \n",
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\n",
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+ " 4200 | \n",
+ " 0.090100 | \n",
+ " 0.947977 | \n",
+ " 0.548280 | \n",
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+ " 4500 | \n",
+ " 0.081700 | \n",
+ " 0.979875 | \n",
+ " 0.551587 | \n",
+ "
\n",
+ " \n",
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+ " 0.079000 | \n",
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+ "
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+ " \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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-300\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-300/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-300/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-300/preprocessor_config.json\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-600\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-600/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-600/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-600/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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-900\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-900/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-900/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-900/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-1200\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1200/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1200/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1200/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-1500\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1500/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-1800\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1800/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1800/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1800/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-2100\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2100/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2100/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2100/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-2400\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2400/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2400/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2400/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-2700\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2700/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2700/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2700/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-2100] 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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-3000\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3000/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-2400] 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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-3300\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3300/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3300/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3300/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-2700] 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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-3600\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3600/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3600/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3600/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-3000] 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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-3900\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3900/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3900/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3900/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-3300] 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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-4200\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4200/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4200/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4200/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-3600] 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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-4500\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4500/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-3900] 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 = 213\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-4800\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4800/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4800/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4800/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-4200] due to args.save_total_limit\n",
+ "\n",
+ "\n",
+ "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
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+ "text": [
+ "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/config.json\n",
+ "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/preprocessor_config.json\n",
+ "Several commits (2) will be pushed upstream.\n",
+ "The progress bars may be unreliable.\n"
+ ]
+ },
+ {
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+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "68701b89df614445b0e9d59f5b7c3724",
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+ "text/plain": [
+ "Upload file pytorch_model.bin: 0%| | 3.37k/1.18G [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
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+ "model_id": "95f5eefd4647483eb92a71f50234f05d",
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+ "text/plain": [
+ "Upload file runs/Feb04_14-09-11_22a371ddc006/events.out.tfevents.1643983793.22a371ddc006.95.0: 27%|##7 …"
+ ]
+ },
+ "metadata": {}
+ },
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+ "name": "stderr",
+ "text": [
+ "To https://huggingface.co/DrishtiSharma/wav2vec2-large-xls-r-300m-or-d5\n",
+ " 0841d8d..a5ccbdf main -> main\n",
+ "\n",
+ "Dropping the following result as it does not have all the necessary fields:\n",
+ "{'dataset': {'name': 'common_voice', 'type': 'common_voice', 'args': 'or'}}\n",
+ "To https://huggingface.co/DrishtiSharma/wav2vec2-large-xls-r-300m-or-d5\n",
+ " a5ccbdf..0cc372e main -> main\n",
+ "\n"
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+ ]
+ },
+ "metadata": {},
+ "execution_count": 44
+ }
+ ],
+ "source": [
+ "trainer.push_to_hub()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "vMeJybu4aPpa"
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