bengali-ai-wave-2-vec-4-infer.ipynb

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   "source": [
    "- These inference notebook is based on @nischaydnk's [notebook](https://www.kaggle.com/code/nischaydnk/bengali-finetuning-baseline-wav2vec2-inference). If it's helpful to you, please upvote his firstly.\n",
    "- Running this notebook you can score 0.338 on leaderboard.\n",
    "- This model is trained around 8 epochs with 1e-4 learning rate on the data of [Common Voice 13](http://www.kaggle.com/datasets/umongsain/common-voice-13-bengali-normalized) provided by @UMONG SAIN and the competition data 'valid' part."
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    {
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     "output_type": "stream",
     "text": [
      "jiwer/\r\n",
      "jiwer/jiwer-2.3.0-py3-none-any.whl\r\n",
      "jiwer/python-Levenshtein-0.12.2.tar.gz\r\n",
      "jiwer/setuptools-65.3.0-py3-none-any.whl\r\n",
      "Looking in links: ./\r\n",
      "Processing ./jiwer/jiwer-2.3.0-py3-none-any.whl\r\n",
      "INFO: pip is looking at multiple versions of jiwer to determine which version is compatible with other requirements. This could take a while.\r\n",
      "\u001b[31mERROR: Could not find a version that satisfies the requirement python-Levenshtein==0.12.2 (from jiwer) (from versions: none)\u001b[0m\u001b[31m\r\n",
      "\u001b[0m\u001b[31mERROR: No matching distribution found for python-Levenshtein==0.12.2\u001b[0m\u001b[31m\r\n",
      "\u001b[0mnormalizer/\r\n",
      "normalizer/bnunicodenormalizer-0.0.24.tar.gz\r\n",
      "Looking in links: ./\r\n",
      "Processing ./normalizer/bnunicodenormalizer-0.0.24.tar.gz\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hBuilding wheels for collected packages: bnunicodenormalizer\r\n",
      "  Building wheel for bnunicodenormalizer (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for bnunicodenormalizer: filename=bnunicodenormalizer-0.0.24-py3-none-any.whl size=17628 sha256=b7b23bc7fccb5c777e5c5a7481295d3e89b7e9ef513e1228c7b987596461d61b\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/78/d7/75/6986dc3616718f950b80e3bd79a796ef618eaef6cd800e7909\r\n",
      "Successfully built bnunicodenormalizer\r\n",
      "Installing collected packages: bnunicodenormalizer\r\n",
      "Successfully installed bnunicodenormalizer-0.0.24\r\n",
      "pyctcdecode/\r\n",
      "pyctcdecode/hypothesis-6.54.4-py3-none-any.whl\r\n",
      "pyctcdecode/sortedcontainers-2.4.0-py2.py3-none-any.whl\r\n",
      "pyctcdecode/exceptiongroup-1.0.0rc9-py3-none-any.whl\r\n",
      "pyctcdecode/pyctcdecode-0.4.0-py2.py3-none-any.whl\r\n",
      "pyctcdecode/numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl\r\n",
      "pyctcdecode/attrs-22.1.0-py2.py3-none-any.whl\r\n",
      "pyctcdecode/pygtrie-2.5.0.tar.gz\r\n",
      "Looking in links: ./\r\n",
      "Processing ./pyctcdecode/attrs-22.1.0-py2.py3-none-any.whl\r\n",
      "Installing collected packages: attrs\r\n",
      "  Attempting uninstall: attrs\r\n",
      "    Found existing installation: attrs 23.1.0\r\n",
      "    Uninstalling attrs-23.1.0:\r\n",
      "      Successfully uninstalled attrs-23.1.0\r\n",
      "Successfully installed attrs-22.1.0\r\n",
      "Looking in links: ./\r\n",
      "Processing ./pyctcdecode/exceptiongroup-1.0.0rc9-py3-none-any.whl\r\n",
      "Installing collected packages: exceptiongroup\r\n",
      "  Attempting uninstall: exceptiongroup\r\n",
      "    Found existing installation: exceptiongroup 1.1.1\r\n",
      "    Uninstalling exceptiongroup-1.1.1:\r\n",
      "      Successfully uninstalled exceptiongroup-1.1.1\r\n",
      "Successfully installed exceptiongroup-1.0.0rc9\r\n",
      "Looking in links: ./\r\n",
      "Processing ./pyctcdecode/hypothesis-6.54.4-py3-none-any.whl\r\n",
      "Installing collected packages: hypothesis\r\n",
      "Successfully installed hypothesis-6.54.4\r\n",
      "Looking in links: ./\r\n",
      "\u001b[31mERROR: numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl is not a supported wheel on this platform.\u001b[0m\u001b[31m\r\n",
      "\u001b[0mLooking in links: ./\r\n",
      "Processing ./pyctcdecode/pygtrie-2.5.0.tar.gz\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25hBuilding wheels for collected packages: pygtrie\r\n",
      "  Building wheel for pygtrie (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for pygtrie: filename=pygtrie-2.5.0-py3-none-any.whl size=20945 sha256=4dbb60d5ee7c30720faa2b6d1a3d896e238ca901c70df318d28b877f1cbf2eed\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/78/28/09/b62c97a3e77102645c7ecc78c97580ad57090b1eee5438d6ac\r\n",
      "Successfully built pygtrie\r\n",
      "Installing collected packages: pygtrie\r\n",
      "Successfully installed pygtrie-2.5.0\r\n",
      "Looking in links: ./\r\n",
      "Processing ./pyctcdecode/sortedcontainers-2.4.0-py2.py3-none-any.whl\r\n",
      "sortedcontainers is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\r\n",
      "Looking in links: ./\r\n",
      "Processing ./pyctcdecode/pyctcdecode-0.4.0-py2.py3-none-any.whl\r\n",
      "Installing collected packages: pyctcdecode\r\n",
      "Successfully installed pyctcdecode-0.4.0\r\n",
      "pypikenlm/\r\n",
      "pypikenlm/pypi-kenlm-0.1.20220713.tar.gz\r\n",
      "Looking in links: ./\r\n",
      "Processing ./pypikenlm/pypi-kenlm-0.1.20220713.tar.gz\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25hBuilding wheels for collected packages: pypi-kenlm\r\n",
      "  Building wheel for pypi-kenlm (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \bdone\r\n",
      "\u001b[?25h  Created wheel for pypi-kenlm: filename=pypi_kenlm-0.1.20220713-cp310-cp310-linux_x86_64.whl size=333264 sha256=f13a70dfaf1a58824505d0915ce178809f2c88a5ad835a24b7a740e56459c70f\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/1e/7a/db/27645fac296d5d5ba5c461b1af834eebc0ba4643290dbc5476\r\n",
      "Successfully built pypi-kenlm\r\n",
      "Installing collected packages: pypi-kenlm\r\n",
      "Successfully installed pypi-kenlm-0.1.20220713\r\n"
     ]
    }
   ],
   "source": [
    "!cp -r ../input/python-packages2 ./\n",
    "\n",
    "!tar xvfz ./python-packages2/jiwer.tgz\n",
    "!pip install ./jiwer/jiwer-2.3.0-py3-none-any.whl -f ./ --no-index\n",
    "!tar xvfz ./python-packages2/normalizer.tgz\n",
    "!pip install ./normalizer/bnunicodenormalizer-0.0.24.tar.gz -f ./ --no-index\n",
    "!tar xvfz ./python-packages2/pyctcdecode.tgz\n",
    "!pip install ./pyctcdecode/attrs-22.1.0-py2.py3-none-any.whl -f ./ --no-index --no-deps\n",
    "!pip install ./pyctcdecode/exceptiongroup-1.0.0rc9-py3-none-any.whl -f ./ --no-index --no-deps\n",
    "!pip install ./pyctcdecode/hypothesis-6.54.4-py3-none-any.whl -f ./ --no-index --no-deps\n",
    "!pip install ./pyctcdecode/numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl -f ./ --no-index --no-deps\n",
    "!pip install ./pyctcdecode/pygtrie-2.5.0.tar.gz -f ./ --no-index --no-deps\n",
    "!pip install ./pyctcdecode/sortedcontainers-2.4.0-py2.py3-none-any.whl -f ./ --no-index --no-deps\n",
    "!pip install ./pyctcdecode/pyctcdecode-0.4.0-py2.py3-none-any.whl -f ./ --no-index --no-deps\n",
    "\n",
    "!tar xvfz ./python-packages2/pypikenlm.tgz\n",
    "!pip install ./pypikenlm/pypi-kenlm-0.1.20220713.tar.gz -f ./ --no-index --no-deps"
   ]
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   "source": [
    "rm -r python-packages2 jiwer normalizer pyctcdecode pypikenlm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n",
      "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\n",
      "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n",
      "  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n",
      "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\n",
      "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n",
      "  warnings.warn(f\"file system plugins are not loaded: {e}\")\n"
     ]
    }
   ],
   "source": [
    "import typing as tp\n",
    "from pathlib import Path\n",
    "from functools import partial\n",
    "from dataclasses import dataclass, field\n",
    "\n",
    "import pandas as pd\n",
    "import pyctcdecode\n",
    "import numpy as np\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import librosa\n",
    "\n",
    "import pyctcdecode\n",
    "import kenlm\n",
    "import torch\n",
    "from transformers import Wav2Vec2Processor, Wav2Vec2ProcessorWithLM, Wav2Vec2ForCTC\n",
    "from bnunicodenormalizer import Normalizer\n",
    "\n",
    "import cloudpickle as cpkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c095cae2",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "ROOT = Path.cwd().parent\n",
    "INPUT = ROOT / \"input\"\n",
    "DATA = INPUT / \"bengaliai-speech\"\n",
    "TRAIN = DATA / \"train_mp3s\"\n",
    "TEST = DATA / \"test_mp3s\"\n",
    "\n",
    "SAMPLING_RATE = 16_000\n",
    "MODEL_PATH = INPUT / \"bengali-ai-wave-2-vec-4\"\n",
    "LM_PATH = INPUT / \"arijitx-full-model/wav2vec2-xls-r-300m-bengali/language_model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e2292b0",
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    },
    "tags": []
   },
   "source": [
    "### load model, processor, decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "633c4962",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = Wav2Vec2ForCTC.from_pretrained(MODEL_PATH)\n",
    "processor = Wav2Vec2Processor.from_pretrained(MODEL_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "61ccb95a",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "vocab_dict = processor.tokenizer.get_vocab()\n",
    "sorted_vocab_dict = {k: v for k, v in sorted(vocab_dict.items(), key=lambda item: item[1])}\n",
    "\n",
    "decoder = pyctcdecode.build_ctcdecoder(\n",
    "    list(sorted_vocab_dict.keys()),\n",
    "    str(LM_PATH / \"5gram.bin\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "23b89e16",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "processor_with_lm = Wav2Vec2ProcessorWithLM(\n",
    "    feature_extractor=processor.feature_extractor,\n",
    "    tokenizer=processor.tokenizer,\n",
    "    decoder=decoder\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f23841a4",
   "metadata": {
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     "start_time": "2023-10-14T13:49:50.339041",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## prepare dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "50f8c180",
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    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "class BengaliSRTestDataset(torch.utils.data.Dataset):\n",
    "    \n",
    "    def __init__(\n",
    "        self,\n",
    "        audio_paths: list[str],\n",
    "        sampling_rate: int\n",
    "    ):\n",
    "        self.audio_paths = audio_paths\n",
    "        self.sampling_rate = sampling_rate\n",
    "        \n",
    "    def __len__(self,):\n",
    "        return len(self.audio_paths)\n",
    "    \n",
    "    def __getitem__(self, index: int):\n",
    "        audio_path = self.audio_paths[index]\n",
    "        sr = self.sampling_rate\n",
    "        w = librosa.load(audio_path, sr=sr, mono=False)[0]\n",
    "        \n",
    "        return w"
   ]
  },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             id                                           sentence\n",
      "0  0f3dac00655e  এছাড়াও নিউজিল্যান্ড এ ক্রিকেট দলের হয়েও খেলছ...\n",
      "1  a9395e01ad21  এছাড়াও নিউজিল্যান্ড এ ক্রিকেট দলের হয়েও খেলছ...\n",
      "2  bf36ea8b718d  এছাড়াও নিউজিল্যান্ড এ ক্রিকেট দলের হয়েও খেলছ...\n"
     ]
    }
   ],
   "source": [
    "test = pd.read_csv(DATA / \"sample_submission.csv\", dtype={\"id\": str})\n",
    "print(test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f7aabd9a",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_audio_paths = [str(TEST / f\"{aid}.mp3\") for aid in test[\"id\"].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e9c5a46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-14T13:49:50.451458Z",
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "test_dataset = BengaliSRTestDataset(\n",
    "    test_audio_paths, SAMPLING_RATE\n",
    ")\n",
    "\n",
    "collate_func = partial(\n",
    "    processor_with_lm.feature_extractor,\n",
    "    return_tensors=\"pt\", sampling_rate=SAMPLING_RATE,\n",
    "    padding=True,\n",
    ")\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    test_dataset, batch_size=16, shuffle=False,\n",
    "    num_workers=2, collate_fn=collate_func, drop_last=False,\n",
    "    pin_memory=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "534b94a7",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fb6bd93a",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "if not torch.cuda.is_available():\n",
    "    device = torch.device(\"cpu\")\n",
    "else:\n",
    "    device = torch.device(\"cuda\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fcca3d6f",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "model = model.to(device)\n",
    "model = model.eval()\n",
    "model = model.half()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a8169105",
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     "status": "completed"
    },
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   },
   "outputs": [
    {
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       "  0%|          | 0/1 [00:00<?, ?it/s]"
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   ],
   "source": [
    "pred_sentence_list = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for batch in tqdm(test_loader):\n",
    "        x = batch[\"input_values\"]\n",
    "        x = x.to(device, non_blocking=True)\n",
    "        with torch.cuda.amp.autocast(True):\n",
    "            y = model(x).logits\n",
    "        y = y.detach().cpu().numpy()\n",
    "        \n",
    "        for l in y:  \n",
    "            sentence = processor_with_lm.decode(l, beam_width=512).text\n",
    "            pred_sentence_list.append(sentence)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35cd8bdc",
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    "tags": []
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   "source": [
    "## Make Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ccaf8325",
   "metadata": {
    "execution": {
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   "source": [
    "bnorm = Normalizer()\n",
    "\n",
    "def postprocess(sentence):\n",
    "    period_set = set([\".\", \"?\", \"!\", \"।\"])\n",
    "    _words = [bnorm(word)['normalized']  for word in sentence.split()]\n",
    "    sentence = \" \".join([word for word in _words if word is not None])\n",
    "    try:\n",
    "        if sentence[-1] not in period_set:\n",
    "            sentence+=\"।\"\n",
    "    except:\n",
    "        # print(sentence)\n",
    "        sentence = \"।\"\n",
    "    return sentence"
   ]
  },
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    "pp_pred_sentence_list = [postprocess(s) for s in tqdm(pred_sentence_list)]"
   ]
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0f3dac00655e</td>\n",
       "      <td>একটু বয়স হলে একটি বিদেশি।</td>\n",
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       "      <td>এ কারণে সরকার নির্ধারিত হারে পরিবহনজনিত ক্ষতি ...</td>\n",
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       "             id                                           sentence\n",
       "0  0f3dac00655e                          একটু বয়স হলে একটি বিদেশি।\n",
       "1  a9395e01ad21  কী কারণে তুমি এতাবৎ কাল পর্যন্ত এ দারুল দৈব দু...\n",
       "2  bf36ea8b718d  এ কারণে সরকার নির্ধারিত হারে পরিবহনজনিত ক্ষতি ..."
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    "test[\"sentence\"] = pp_pred_sentence_list\n",
    "\n",
    "test.to_csv(\"submission.csv\", index=False)\n",
    "\n",
    "display(test.head())"
   ]
  }
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