training.ipynb

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          "text": [
            "Sun Dec 18 11:41:12 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\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 T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   52C    P0    28W /  70W |      0MiB / 15109MiB |      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": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Not connected to a GPU')\n",
        "else:\n",
        "  print(gpu_info)"
      ]
    },
    {
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        "trusted": true
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Token is valid.\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets==1.18.3\n",
        "!pip install transformers==4.24.0\n",
        "!pip install tokenizers==0.13.2\n",
        "!pip install huggingface_hub==0.11\n",
        "!pip install torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html\n",
        "!pip install jiwer\n",
        "!apt install git-lfs\n",
        "!pip install wandb\n",
        "!pip install pandas\n",
        "\n",
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2022-12-15T11:04:03.999087Z",
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        },
        "id": "iZymJVBmmNI6",
        "outputId": "11e14295-e024-4a77-dac0-3e17e459dad3",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "henilp105\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        }
      ],
      "source": [
        "from huggingface_hub.hf_api import HfFolder\n",
        "\n",
        "HfFolder.save_token('HF_TOKEN')\n",
        "\n",
        "!huggingface-cli whoami\n",
        "!wandb login --cloud HF_TOKEN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FepsN4gAmNI7",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# load IIITH ASR dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "ePvDoi9mmNI7",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import json\n",
        "import csv\n",
        "import shutil\n",
        "import torchaudio\n",
        "import librosa\n",
        "import numpy as np\n",
        "from datasets import load_dataset, load_metric, Audio\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "import os\n",
        "import re\n",
        "import json\n",
        "from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor ,Wav2Vec2Processor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "1dRDJIL0mNI8",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "SEED = 13\n",
        "chars_to_remove_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�\\']'\n",
        "\n",
        "def dataset_folder(foldername,setname):\n",
        "  a=[]\n",
        "  with open(f'{foldername}/data.json') as f:\n",
        "        data = json.load(f)\n",
        "        for i in data:\n",
        "          if setname in i['audioFilename']:\n",
        "            a.append(i)\n",
        "\n",
        "  with open(f'{foldername}/data_{setname}.json', 'w') as fp:\n",
        "      json.dump(a, fp)\n",
        "\n",
        "  df = pd.read_json(f\"{foldername}/data_{setname}.json\")\n",
        "  df['sentence'] = df['text']\n",
        "  print(\"dataset length:\",len(df))\n",
        "  train_df, test_df = train_test_split(df, test_size=0.3, random_state = SEED)\n",
        "  !rm -rf test train\n",
        "  !mkdir test train\n",
        "  print(\"train dataset: \",len(train_df))\n",
        "  print(\"test dataset: \",len(test_df))\n",
        "  train_df['path'] = \"train/\"+train_df.audioFilename.str.slice(12)\n",
        "  test_df['path'] = \"test/\"+test_df.audioFilename.str.slice(12)\n",
        "  train_df.to_csv(\"train/metadata.csv\")\n",
        "  !cp -r {foldername}/audio/{setname}/*.wav train/\n",
        "  !cp -r {foldername}/audio/{setname}/*.wav test/\n",
        "  test_df.to_csv(\"test/metadata.csv\", index = False)\n",
        "\n",
        "def remove_special_characters(batch):\n",
        "  text = re.sub(chars_to_remove_regex, '', batch[\"sentence\"]).lower() + \" \"\n",
        "  text = text.replace(\"\\\\n\",\"\\n\")\n",
        "  text = ' '.join(text.split())\n",
        "  text = re.sub(r'''%''',\" శాతం \", text)\n",
        "  text = re.sub(r'''(/|-|_)''',\" \", text)\n",
        "  text = re.sub(\"ై\",\"ై\", text)\n",
        "  text = text.strip()\n",
        "  batch['sentence'] = text\n",
        "  return batch\n",
        "\n",
        "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]}\n",
        "\n",
        "def prepare_dataset(batch):\n",
        "    audio = batch[\"audio\"]\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\n",
        "\n",
        "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}\n",
        "\n",
        "def speech_to_array(batch):\n",
        "    file_path = batch['path']\n",
        "    speech_array, _ = torchaudio.load(file_path)\n",
        "    speech_array = speech_array[0].numpy()\n",
        "    sampling_rate = 16_000\n",
        "    a = dict()\n",
        "    a['path'] = file_path\n",
        "    a['array'] = speech_array\n",
        "    a['sampling_rate'] = sampling_rate\n",
        "    batch['audio'] = a\n",
        "    return batch\n"
      ]
    },
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        },
        "id": "OmqSp6ZHmNI-",
        "outputId": "16dab5a2-348b-481f-ff88-7b479a010f3b",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dataset length: 2000\n",
            "train dataset:  1400\n",
            "test dataset:  600\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration train-b6a694798e802f8e\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset csv/train to /root/.cache/huggingface/datasets/csv/train-b6a694798e802f8e/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e...\n"
          ]
        },
        {
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              "model_id": "bf9872833fe9413eb0fa8b67fcb80b07",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/train-b6a694798e802f8e/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e. Subsequent calls will reuse this data.\n"
          ]
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          "text": [
            "WARNING:datasets.builder:Using custom data configuration test-a27ec00413b8a746\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset csv/test to /root/.cache/huggingface/datasets/csv/test-a27ec00413b8a746/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e...\n"
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/test-a27ec00413b8a746/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e. Subsequent calls will reuse this data.\n"
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      ],
      "source": [
        "dataset_folder(\"f\",\"set_2\")\n",
        "common_voice_train = load_dataset(\"train\", data_dir=\"train\")['train']\n",
        "common_voice_train = common_voice_train.map(remove_special_characters)\n",
        "common_voice_train = common_voice_train.map(speech_to_array)\n",
        "common_voice_train = common_voice_train.remove_columns([\"channel\", \"age\",\"audioFilename\",\"text\"])\n",
        "\n",
        "common_voice_test = load_dataset(\"test\", data_dir=\"test\")['train']\n",
        "common_voice_test = common_voice_test.map(remove_special_characters)\n",
        "common_voice_test = common_voice_test.map(speech_to_array)\n",
        "common_voice_test = common_voice_test.remove_columns([\"channel\", \"age\",\"audioFilename\",\"text\"])"
      ]
    },
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        {
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "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)\n",
        "\n",
        "vocab_list = list(set(vocab_train[\"vocab\"][0]) | set(vocab_test[\"vocab\"][0]))\n",
        "vocab_dict = {v: k for k, v in enumerate(sorted(vocab_list))}\n",
        "vocab_dict[\"|\"] = vocab_dict[\" \"]\n",
        "del vocab_dict[\" \"]\n",
        "vocab_dict[\"[UNK]\"] = len(vocab_dict)\n",
        "vocab_dict[\"[PAD]\"] = len(vocab_dict)\n",
        "\n",
        "with open('vocab.json', 'w') as vocab_file:\n",
        "    json.dump(vocab_dict, vocab_file)\n",
        "    \n",
        "tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(\"./\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")\n",
        "repo_name = \"wav2vec2-large-xls-r-300m-telugu-asr\"\n",
        "tokenizer.push_to_hub(repo_name)\n",
        "feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True)\n",
        "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "-yz52HAimNI_",
        "trusted": true
      },
      "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"
      ]
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      "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)\n",
        "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)\n",
        "wer_metric = load_metric(\"wer\")"
      ]
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          "text": [
            "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at henilp105/wav2vec2-large-xls-r-300m-telugu-asr and are newly initialized because the shapes did not match:\n",
            "- lm_head.weight: found shape torch.Size([64, 1024]) in the checkpoint and torch.Size([68, 1024]) in the model instantiated\n",
            "- lm_head.bias: found shape torch.Size([64]) in the checkpoint and torch.Size([68]) in the model instantiated\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.8/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1635: 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",
            "  warnings.warn(\n",
            "Cloning https://huggingface.co/henilp105/wav2vec2-large-xls-r-300m-telugu-asr into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/henilp105/wav2vec2-large-xls-r-300m-telugu-asr into local empty directory.\n"
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        {
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          "text": [
            "Using cuda_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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "***** Running training *****\n",
            "  Num examples = 1400\n",
            "  Num Epochs = 30\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 2\n",
            "  Total optimization steps = 2610\n",
            "  Number of trainable parameters = 311298244\n",
            "Automatic Weights & Biases logging enabled, to disable set os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
            "ERROR:wandb.jupyter:Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mhenilp105\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
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            "/usr/local/lib/python3.8/dist-packages/transformers/models/wav2vec2/processing_wav2vec2.py:154: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.\n",
            "  warnings.warn(\n"
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              "      [ 979/2610 42:21 < 1:10:42, 0.38 it/s, Epoch 11.24/30]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Wer</th>\n",
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          "text": [
            "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 600\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 600\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 600\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 600\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-large-xls-r-300m-telugu-asr/checkpoint-800\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-telugu-asr/checkpoint-800/config.json\n",
            "Model weights saved in wav2vec2-large-xls-r-300m-telugu-asr/checkpoint-800/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-large-xls-r-300m-telugu-asr/checkpoint-800/preprocessor_config.json\n",
            "Feature extractor saved in wav2vec2-large-xls-r-300m-telugu-asr/preprocessor_config.json\n",
            "/usr/local/lib/python3.8/dist-packages/transformers/models/wav2vec2/processing_wav2vec2.py:154: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.\n",
            "  warnings.warn(\n"
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              "      [2331/2610 1:42:55 < 12:19, 0.38 it/s, Epoch 26.78/30]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
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        {
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          "text": [
            "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 600\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 600\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 600\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 600\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-large-xls-r-300m-telugu-asr/checkpoint-1600\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-telugu-asr/checkpoint-1600/config.json\n",
            "Model weights saved in wav2vec2-large-xls-r-300m-telugu-asr/checkpoint-1600/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-large-xls-r-300m-telugu-asr/checkpoint-1600/preprocessor_config.json\n",
            "/usr/local/lib/python3.8/dist-packages/transformers/models/wav2vec2/processing_wav2vec2.py:154: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.\n",
            "  warnings.warn(\n",
            "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 600\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 600\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 600\n",
            "  Batch size = 8\n"
          ]
        }
      ],
      "source": [
        "from transformers import Wav2Vec2ForCTC, TrainingArguments, Trainer\n",
        "# from transformers.trainer_utils import get_last_checkpoint\n",
        "# last_checkpoint = get_last_checkpoint(\"wav2vec2-large-xls-r-300m-te\")\n",
        "\n",
        "model = Wav2Vec2ForCTC.from_pretrained(\n",
        "    \"henilp105/wav2vec2-large-xls-r-300m-telugu-asr\",\n",
        "    attention_dropout=0.0,\n",
        "    ignore_mismatched_sizes=True,\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",
        ")\n",
        "\n",
        "model.freeze_feature_extractor()\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "  output_dir=repo_name,\n",
        "  group_by_length=True,\n",
        "  per_device_train_batch_size=8,  \n",
        "  gradient_accumulation_steps=2,  # 2 \n",
        "  evaluation_strategy=\"steps\",\n",
        "  num_train_epochs=30,  # 30 max    \n",
        "  gradient_checkpointing=True,\n",
        "  fp16=True,\n",
        "  save_steps=800,   # save 800 eval 400 epochs 30   \n",
        "  eval_steps=200,\n",
        "  logging_steps=200,\n",
        "  learning_rate=3e-4,\n",
        "  warmup_steps=500,\n",
        "  save_total_limit=2,\n",
        "  push_to_hub=True,\n",
        ")\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",
        ")\n",
        "\n",
        "train_result = trainer.train()\n",
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V3B7t48ymNJC",
        "trusted": true
      },
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      "source": [
        "metrics = train_result.metrics\n",
        "max_train_samples = len(common_voice_train)\n",
        "metrics[\"train_samples\"] = min(max_train_samples, len(common_voice_train))\n",
        "\n",
        "# trainer.save_model()\n",
        "\n",
        "# trainer.log_metrics(\"train\", metrics)\n",
        "# trainer.save_metrics(\"train\", metrics)\n",
        "# trainer.save_state()\n",
        "# trainer.push_to_hub()\n",
        "# from transformers.trainer_utils import get_last_checkpoint\n",
        "# last_checkpoint = get_last_checkpoint(\"wav2vec2-large-xls-r-300m-te\")\n",
        "# print(last_checkpoint)\n",
        "# train_result = trainer.train(resume_from_checkpoint=last_checkpoint)"
      ]
    }
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