vectorDb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import os\n",
    "import configparser\n",
    "from random import randrange\n",
    "import uuid\n",
    "\n",
    "\n",
    "\n",
    "# TODO document how to set up the needed folders and paths in configuration.txt\n",
    "# TODO document how to use recreateDb\n",
    "# TODO indices\n",
    "configParser = configparser.RawConfigParser()   \n",
    "configFilePath = r'configuration.txt'\n",
    "configParser.read(configFilePath)\n",
    "\n",
    "datasetPathVideo =  configParser.get('COMMON', 'test_datasetPathVideo')\n",
    "datasetPathDatabase =  configParser.get('COMMON', 'test_datasetPathDatabase') + '/dataset.db'\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lancedb\n",
    "uri = datasetPathDatabase\n",
    "db = lancedb.connect(uri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "files_to_insert = []\n",
    "\n",
    "\n",
    "for root, dirs, files in os.walk(datasetPathVideo):\n",
    "    #tasks = [insertIntoDb(file,root) for file in files]\n",
    "    # TODO split into chunks of say 4 files\n",
    "    #await asyncio.wait(tasks)\n",
    "    #await asyncio.gather(*tasks)\n",
    "    for file in files:\n",
    "        files_to_insert.append(file)\n",
    "        print(root + file)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.DataFrame()\n",
    "i = 0\n",
    "for file in files_to_insert:\n",
    "    df = df.append({'id': i, 'video_path': file, 'face_path': '', \n",
    "        'blurred_face_path': '', 'features_path': '', 'vector' : np.random.rand(128),'stage': ''}, ignore_index=True)\n",
    "    i = i +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "db.drop_table(\"video\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "db.create_table(\"video\", df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(db.table_names())\n",
    "tbl = db.open_table(\"video\")\n",
    "tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tbl.search(np.random.rand(128)).limit(2).to_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.iloc[0]['video_path']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('INSERTED ' + str(len(df)) + ' VIDEOS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import os\n",
    "p_small = pathlib.Path(os.path.dirname('/media/gamal/Passport/Datasets/VoxCeleb2Test/Voxceleb2TestVideo/mp4/id00061/GtTmoAoWxMc/a.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_big = p_small.parent.absolute()\n",
    "Action: race: 100%|██████████| 3/3 [00:01<00:00,  2.31it/s]  \n",
    "age :--------------43\n",
    "gender :--------------Woman\n",
    "ethnicity :--------------white\n",
    "/home/gamal/anaconda3/envs/ds2f_pre/lib/python3.8/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\n",
    "Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.)\n",
    "  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]\n",
    "0    English\n",
    "Name: caption_l, dtype: object\n",
    "[NeMo W 2023-09-24 12:27:16 optimizers:54] Apex was not found. Using the lamb or fused_adam optimizer will error out.\n",
    "[NeMo W 2023-09-24 12:27:16 experimental:27] Module <class 'nemo.collections.asr.modules.audio_modules.SpectrogramToMultichannelFeatures'> is experimental, not ready for production and is not fully supported. Use at your own risk.\n",
    "Lightning automatically upgraded your loaded checkpoint from v1.2.7 to v1.9.4. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file ../../.cache/torch/pyannote/models--pyannote--embedding/snapshots/ef54f1b59c9fbef89ea2bab0e27b4a5dd9cfce36/pytorch_model.bin`\n",
    "Lightning automatically upgraded your loaded checkpoint from v1.2.7 to v1.9.4. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file ../../.cache/torch/pyannote/models--pyannote--embedding/snapshots/ef54f1b59c9fbef89ea2bab0e27b4a5dd9cfce36/pytorch_model.bin`\n",
    "Model was trained with pyannote.audio 0.0.1, yours is 2.1.1. Bad things might happen unless you revert pyannote.audio to 0.x.\n",
    "Model was trained with torch 1.8.1+cu102, yours is 2.0.1+cu117. Bad things might happen unless you revert torch to 1.x.\n",
    "Model was trained with pyannote.audio 0.0.1, yours is 2.1.1. Bad things might happen unless you revert pyannote.audio to 0.x.\n",
    "Model was trained with torch 1.8.1+cu102, yours is 2.0.1+cu117. Bad things might happen unless you revert torch to 1.x.\n",
    "[NeMo W 2023-09-24 12:27:18 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\n",
    "    Train config : \n",
    "    manifest_filepath: /manifests/combined_fisher_swbd_voxceleb12_librispeech/train.json\n",
    "    sample_rate: 16000\n",
    "    labels: null\n",
    "    batch_size: 64\n",
    "    shuffle: true\n",
    "    is_tarred: false\n",
    "    tarred_audio_filepaths: null\n",
    "    tarred_shard_strategy: scatter\n",
    "    augmentor:\n",
    "      noise:\n",
    "        manifest_path: /manifests/noise/rir_noise_manifest.json\n",
    "        prob: 0.5\n",
    "        min_snr_db: 0\n",
    "        max_snr_db: 15\n",
    "      speed:\n",
    "        prob: 0.5\n",
    "        sr: 16000\n",
    "        resample_type: kaiser_fast\n",
    "        min_speed_rate: 0.95\n",
    "        max_speed_rate: 1.05\n",
    "    num_workers: 15\n",
    "    pin_memory: true\n",
    "    \n",
    "...\n",
    "    shuffle: false\n",
    "    num_workers: 15\n",
    "    pin_memory: true\n",
    "    \n",
    "Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...\n",
    "[NeMo I 2023-09-24 12:27:18 features:287] PADDING: 16\n",
    "[NeMo I 2023-09-24 12:27:19 save_restore_connector:249] Model EncDecSpeakerLabelModel was successfully restored from /home/gamal/.cache/huggingface/hub/models--nvidia--speakerverification_en_titanet_large/snapshots/4e0b2d387a805da7c208b13d5898ee09de8ec1e9/speakerverification_en_titanet_large.nemo.\n",
    "[NeMo I 2023-09-24 12:27:19 cloud:58] Found existing object /home/gamal/.cache/torch/NeMo/NeMo_1.18.1/speakerverification_speakernet/a8330fa516557b963a89ccbf0fcbe2f2/speakerverification_speakernet.nemo.\n",
    "[NeMo I 2023-09-24 12:27:19 cloud:64] Re-using file from: /home/gamal/.cache/torch/NeMo/NeMo_1.18.1/speakerverification_speakernet/a8330fa516557b963a89ccbf0fcbe2f2/speakerverification_speakernet.nemo\n",
    "[NeMo I 2023-09-24 12:27:19 common:913] Instantiating model from pre-trained checkpoint\n",
    "[NeMo W 2023-09-24 12:27:19 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\n",
    "    Train config : \n",
    "    manifest_filepath: /ws/manifests/raid/combined/train_manifest.json\n",
    "    sample_rate: 16000\n",
    "    labels: null\n",
    "    batch_size: 64\n",
    "    shuffle: true\n",
    "    time_length: 8\n",
    "    augmentor:\n",
    "      noise:\n",
    "        manifest_path: /ws/manifests/raid/musan/musan_music_noise_manifest_dur8.json\n",
    "        prob: 0.2\n",
    "        min_snr_db: 5\n",
    "        max_snr_db: 15\n",
    "    num_workers: 4\n",
    "    \n",
    "[NeMo W 2023-09-24 12:27:19 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s). \n",
    "    Validation config : \n",
    "    manifest_filepath: /ws/manifests/raid/voxceleb/small_manifest.json\n",
    "    sample_rate: 16000\n",
    "    labels: null\n",
    "    batch_size: 64\n",
    "    shuffle: false\n",
    "    time_length: 8\n",
    "    num_workers: 1\n",
    "    \n",
    "[NeMo I 2023-09-24 12:27:19 features:287] PADDING: 16\n",
    "[NeMo I 2023-09-24 12:27:19 save_restore_connector:249] Model EncDecSpeakerLabelModel was successfully restored from /home/gamal/.cache/torch/NeMo/NeMo_1.18.1/speakerverification_speakernet/a8330fa516557b963a89ccbf0fcbe2f2/speakerverification_speakernet.nemo.\n",
    "Action: race: 100%|██████████| 3/3 [00:01<00:00,  2.45it/s]  \n",
    "age :--------------32\n",
    "gender :--------------Woman\n",
    "ethnicity :--------------white\n",
    "/home/gamal/anaconda3/envs/ds2f_pre/lib/python3.8/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\n",
    "Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.)\n",
    "  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]\n",
    "0    English\n",
    "Name: caption_l, dtype: object\n",
    "[NeMo W 2023-09-24 12:28:04 optimizers:54] Apex was not found. Using the lamb or fused_adam optimizer will error out.\n",
    "[NeMo W 2023-09-24 12:28:04 experimental:27] Module <class 'nemo.collections.asr.modules.audio_modules.SpectrogramToMultichannelFeatures'> is experimental, not ready for production and is not fully supported. Use at your own risk.\n",
    "Lightning automatically upgraded your loaded checkpoint from v1.2.7 to v1.9.4. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file ../../.cache/torch/pyannote/models--pyannote--embedding/snapshots/ef54f1b59c9fbef89ea2bab0e27b4a5dd9cfce36/pytorch_model.bin`\n",
    "Lightning automatically upgraded your loaded checkpoint from v1.2.7 to v1.9.4. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file ../../.cache/torch/pyannote/models--pyannote--embedding/snapshots/ef54f1b59c9fbef89ea2bab0e27b4a5dd9cfce36/pytorch_model.bin`\n",
    "Model was trained with pyannote.audio 0.0.1, yours is 2.1.1. Bad things might happen unless you revert pyannote.audio to 0.x.\n",
    "Model was trained with torch 1.8.1+cu102, yours is 2.0.1+cu117. Bad things might happen unless you revert torch to 1.x.\n",
    "Model was trained with pyannote.audio 0.0.1, yours is 2.1.1. Bad things might happen unless you revert pyannote.audio to 0.x.\n",
    "Model was trained with torch 1.8.1+cu102, yours is 2.0.1+cu117. Bad things might happen unless you revert torch to 1.x.\n",
    "[NeMo W 2023-09-24 12:28:06 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\n",
    "    Train config : \n",
    "    manifest_filepath: /manifests/combined_fisher_swbd_voxceleb12_librispeech/train.json\n",
    "    sample_rate: 16000\n",
    "    labels: null\n",
    "    batch_size: 64\n",
    "    shuffle: true\n",
    "    is_tarred: false\n",
    "    tarred_audio_filepaths: null\n",
    "    tarred_shard_strategy: scatter\n",
    "    augmentor:\n",
    "      noise:\n",
    "        manifest_path: /manifests/noise/rir_noise_manifest.json\n",
    "        prob: 0.5\n",
    "        min_snr_db: 0\n",
    "        max_snr_db: 15\n",
    "      speed:\n",
    "        prob: 0.5\n",
    "        sr: 16000\n",
    "        resample_type: kaiser_fast\n",
    "        min_speed_rate: 0.95\n",
    "        max_speed_rate: 1.05\n",
    "    num_workers: 15\n",
    "    pin_memory: true\n",
    "    \n",
    "...\n",
    "    shuffle: false\n",
    "    num_workers: 15\n",
    "    pin_memory: true\n",
    "    \n",
    "Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...\n",
    "[NeMo I 2023-09-24 12:28:06 features:287] PADDING: 16\n",
    "[NeMo I 2023-09-24 12:28:07 save_restore_connector:249] Model EncDecSpeakerLabelModel was successfully restored from /home/gamal/.cache/huggingface/hub/models--nvidia--speakerverification_en_titanet_large/snapshots/4e0b2d387a805da7c208b13d5898ee09de8ec1e9/speakerverification_en_titanet_large.nemo.\n",
    "[NeMo I 2023-09-24 12:28:07 cloud:58] Found existing object /home/gamal/.cache/torch/NeMo/NeMo_1.18.1/speakerverification_speakernet/a8330fa516557b963a89ccbf0fcbe2f2/speakerverification_speakernet.nemo.\n",
    "[NeMo I 2023-09-24 12:28:07 cloud:64] Re-using file from: /home/gamal/.cache/torch/NeMo/NeMo_1.18.1/speakerverification_speakernet/a8330fa516557b963a89ccbf0fcbe2f2/speakerverification_speakernet.nemo\n",
    "[NeMo I 2023-09-24 12:28:07 common:913] Instantiating model from pre-trained checkpoint\n",
    "[NeMo W 2023-09-24 12:28:07 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\n",
    "    Train config : \n",
    "    manifest_filepath: /ws/manifests/raid/combined/train_manifest.json\n",
    "    sample_rate: 16000\n",
    "    labels: null\n",
    "    batch_size: 64\n",
    "    shuffle: true\n",
    "    time_length: 8\n",
    "    augmentor:\n",
    "      noise:\n",
    "        manifest_path: /ws/manifests/raid/musan/musan_music_noise_manifest_dur8.json\n",
    "        prob: 0.2\n",
    "        min_snr_db: 5\n",
    "        max_snr_db: 15\n",
    "    num_workers: 4\n",
    "    \n",
    "[NeMo W 2023-09-24 12:28:07 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s). \n",
    "    Validation config : \n",
    "    manifest_filepath: /ws/manifests/raid/voxceleb/small_manifest.json\n",
    "    sample_rate: 16000\n",
    "    labels: null\n",
    "    batch_size: 64\n",
    "    shuffle: false\n",
    "    time_length: 8\n",
    "    num_workers: 1\n",
    "    \n",
    "[NeMo I 2023-09-24 12:28:07 features:287] PADDING: 16\n",
    "[NeMo I 2023-09-24 12:28:07 save_restore_connector:249] Model EncDecSpeakerLabelModel was successfully restored from /home/gamal/.cache/torch/NeMo/NeMo_1.18.1/speakerverification_speakernet/a8330fa516557b963a89ccbf0fcbe2f2/speakerverification_speakernet.nemo.\n",
    "id\tvideo_path\tface_path\tuser\tblurred_face_path\tfeatures_path\tvector\tstage\t_distance\n",
    "0\t1\t/media/gamal/Passport/Datasets/VoxCeleb2Test/V...\t/media/gamal/Passport/Datasets/VoxCeleb2Test/V...\tid02181\t/media/gamal/Passport/Datasets/VoxCeleb2Test/V...\t/media/gamal/Passport/Datasets/VoxCeleb2Test/V...\t[0.005858798, 0.0042796144, 0.0035715802, 0.01...\t1\t860.733032\n",
    "1\t0\t/media/gamal/Passport/Datasets/VoxCeleb2Test/V...\t/media/gamal/Passport/Datasets/VoxCeleb2Test/V...\tid02181\t/media/gamal/Passport/Datasets/VoxCeleb2Test/V...\t/media/gamal/Passport/Datasets/VoxCeleb2Test/V...\t[0.0015495215, 0.0004572298, 0.0035203435, 0.0...\t1\t860.944"
   ]
  }
 ],
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