generate_audioset_training_data.ipynb

{
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   "cell_type": "markdown",
   "id": "6d6b358d-cec9-44db-a75d-b9d79117eaf3",
   "metadata": {},
   "source": [
    "## Download training data (class labels and MPCC features extracted from raw audio)\n",
    "#### Only need to run this code once\n",
    "The unbalanced_train_segments is probably the most important since the balanced_train_segments and eval_segments only have >59 examples of each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d90fe37d-474e-43db-bb3b-374b02993991",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !wget http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/eval_segments.csv\n",
    "# !wget http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/balanced_train_segments.csv\n",
    "# !wget http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/unbalanced_train_segments.csv\n",
    "# !wget http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/class_labels_indices.csv\n",
    "# !wget http://storage.googleapis.com/us_audioset/youtube_corpus/v1/features/features.tar.gz\n",
    "# !tar xvzf features.tar.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b604802d-44ca-4e9a-92f4-479dd7cc647e",
   "metadata": {},
   "source": [
    "### Figuring out what I just downloaded\n",
    "\n",
    "Hypothesis: Each TFRecord contains data for a set of videos. \n",
    "\n",
    "Example: nE.tfrecord contains data for all the youtube videos whose IDs start with \"nE\"\n",
    "\n",
    "For each of those videos, there is a sequence example which contains a 128 length feature vector for each second of data (so there are 128x10=1280 features). I also think that for each video there is a set of labels that describe the whole video (i.e. this 10-second clip contains laughter and crying) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "46cbb626-f9cb-4618-9038-bc813f80fef8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-30 03:00:19.812048: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-06-30 03:00:19.812891: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-06-30 03:00:19.815367: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-06-30 03:00:19.822661: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-06-30 03:00:19.841702: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-06-30 03:00:19.841736: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-06-30 03:00:19.851154: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-06-30 03:00:20.866086: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<TFRecordDatasetV2 element_spec=TensorSpec(shape=(), dtype=tf.string, name=None)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "filename = 'audioset_v1_embeddings/unbal_train/nE.tfrecord'\n",
    "filenames = [filename]\n",
    "raw_dataset = tf.data.TFRecordDataset(filenames)\n",
    "raw_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a1554d7-fc01-4c48-9f9b-c53a7e572a78",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "context {\n",
      "  feature {\n",
      "    key: \"video_id\"\n",
      "    value {\n",
      "      bytes_list {\n",
      "        value: \"nE-qCFeZaCw\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"start_time_seconds\"\n",
      "    value {\n",
      "      float_list {\n",
      "        value: 0\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"labels\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 0\n",
      "        value: 300\n",
      "        value: 316\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"end_time_seconds\"\n",
      "    value {\n",
      "      float_list {\n",
      "        value: 6\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "feature_lists {\n",
      "  feature_list {\n",
      "    key: \"audio_embedding\"\n",
      "    value {\n",
      "      feature {\n",
      "        bytes_list {\n",
      "          value: \"2jt\\013\\342a\\217z\\226\\231\\223\\356\\247\\205\\346\\233/\\210Z\\243\\250\\204\\214\\207\\242\\224\\260P\\230@u}|\\377\\233.\\265\\\\K\\277\\246\\225\\320uZ*\\005e\\366Nd+{\\334NJ8\\212\\377t\\316A\\310\\231i\\2279\\254s\\247\\235U\\301\\026?\\313<^\\207{\\0107\\312eNa\\216}\\210N$\\227\\264\\260\\000 j\\307\\264j\\302\\327\\301@n\\210\\264k{\\255\\377\\000M\\264\\215\\207\\000\\377\\377d_4\\377p)\\377\\205\\341\"\n",
      "        }\n",
      "      }\n",
      "      feature {\n",
      "        bytes_list {\n",
      "          value: \"5sz_\\373U~l\\275\\225y\\314\\316@\\324\\227\\023\\250w\\304\\\\\\212\\233\\235\\306l\\230\\000\\213?\\241\\254r\\377\\222Z\\235R\\000\\231$\\324\\243\\226{<\\000V\\3204X\\000\\303\\020\\216W\\000\\335\\345L\\377r\\324Z\\213nAf\\236A\\255\\214\\333r\\225R4G\\224\\242\\312\\000~\\377hLFh\\355=Y\\231\\200\\236\\271\\025Zv\\276\\000\\377S\\377\\032\\217\\313O\\324\\225\\232\\377\\000\\257\\000\\\\\\231\\000\\227\\377@\\000\\256R\\344\\250cxB\"\n",
      "        }\n",
      "      }\n",
      "      feature {\n",
      "        bytes_list {\n",
      "          value: \"\\036\\215\\223O\\377\\217\\327u\\244\\253N\\253\\372\\\"X\\345I\\213\\216\\227S\\307h;\\310W\\207B!O\\231?6\\3774N|{Ay}\\310\\377l\\0108+\\260\\234\\273\\351Y\\352\\000\\303\\263\\203*\\224\\247\\245.Uc[\\267\\265\\377{\\303\\322dV\\201\\264?\\241\\000\\032\\371\\370JA\\377K\\005~\\024\\226q\\234\\2430\\327\\210\\000\\377\\377\\023c\\202\\000\\377\\252\\021\\312.\\317|\\254\\377\\274\\000o\\241\\352B\\331\\377\\302GS\\000\\000\\260|\\242\\000\"\n",
      "        }\n",
      "      }\n",
      "      feature {\n",
      "        bytes_list {\n",
      "          value: \",x|r\\377x\\303O\\234\\305j\\236\\360H\\212utkD\\327Z\\352\\267\\225\\346,\\356\\r\\007Q\\307\\242}\\361\\220X\\204_4du\\266l\\337pr\\000\\266Q6}1\\274\\030\\345kwO\\3012\\230\\000\\254\\255g\\205x{\\231\\260\\301V\\224\\325Q\\234n9\\000\\271\\317\\202\\253\\377[y2\\000\\324Z\\234\\2306Z\\223M\\210\\343L\\002\\213#\\377\\022\\231\\3303\\206\\252u\\317\\210\\036X\\311\\243\\247\\333\\377\\246\\331\\235\\021Z\\310\\240\\204\\000\"\n",
      "        }\n",
      "      }\n",
      "      feature {\n",
      "        bytes_list {\n",
      "          value: \"\\000\\275X<\\377\\314\\331q0{A\\253\\377\\000\\033\\377\\210\\235A\\336\\225\\352\\207\\033\\377\\000E\\000\\016\\\"q\\231\\000\\377\\335\\243\\2531\\023J\\313g\\362u-\\000D\\222\\277=\\217\\206\\312\\000[\\371\\266{Us]\\241a3U\\306\\204j\\377X~7\\r]\\240L\\234t\\000\\000\\263\\221\\270\\261G\\273DD\\341\\000|\\304_\\000\\224\\340\\204Kg\\000R\\212\\347\\373\\006\\220\\000\\350g#\\377b]N\\204C\\377\\000\\251\\377\\237\\000 U\\377\\216\\000\\000\"\n",
      "        }\n",
      "      }\n",
      "      feature {\n",
      "        bytes_list {\n",
      "          value: \")\\177\\177r\\344\\205\\273,\\177$|\\262\\3155\\222\\214\\000qt\\377\\211\\236\\302t\\377:\\305\\000@1\\241J\\000\\377\\336\\257\\245H\\000\\274V\\332\\334[\\224]\\000\\230\\037eLM\\367\\na\\033JYq4\\251\\000\\225wd\\327:z\\377\\262Jo\\r\\377\\000\\206\\245\\203\\000X\\377Q\\270\\312D\\254k\\241W\\026\\003\\327\\000\\210kj\\367g_[\\230a\\377\\244\\027\\214\\000\\272\\214\\216\\333\\222\\206i\\000\\311\\317\\000\\260\\3043w\\032z\\035n\\254p\"\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# for raw_record in raw_dataset.take(1):\n",
    "for raw_record in raw_dataset.take(100):\n",
    "    example = tf.train.SequenceExample()\n",
    "    example.ParseFromString(raw_record.numpy())\n",
    "\n",
    "    # print(example)\n",
    "    # for i in range(10):\n",
    "    #     print(f\"{i}: {np.frombuffer(example.feature_lists.feature_list['audio_embedding'].feature[i].bytes_list.value[0],np.uint8).astype(np.float32)}\")\n",
    "    # context = [example.context.feature[i] for i in range(4)]\n",
    "    # print(len(example.feature_lists.feature_list['audio_embedding']))\n",
    "    if len(example.feature_lists.feature_list['audio_embedding'].feature) != 10:\n",
    "        print(example)\n",
    "        break\n",
    "    # print(len(example.feature_lists.feature_list['audio_embedding'].feature))\n",
    "    # print(example.context)\n",
    "    # print(example.context.feature[\"video_id\"].bytes_list.value[0])\n",
    "    # print(example.context.feature[\"start_time_seconds\"].float_list.value[0])\n",
    "    # print(example.context.feature[\"labels\"].int64_list.value)\n",
    "    # print(example.context.feature[\"end_time_seconds\"].float_list.value[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dba418bf-5a26-4078-b1d4-ed4783f8c8d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=int64, numpy=-2>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_dataset.cardinality()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f8978954-06fb-4986-bb88-4ef16da9adee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_feature_data(raw_dataset):\n",
    "    \"\"\"\n",
    "    This function takes the raw dataset and produces a table that contains the raw 128x10 features and a column that is the presence of laughter (0 or 1)\n",
    "\n",
    "    Note: we will skip videos that aren't 10 seconds long\n",
    "    \"\"\"\n",
    "    laugh_labels = [16, 17, 18, 19, 20, 21]\n",
    "\n",
    "    extracted_table = []\n",
    "    \n",
    "    for raw_record in raw_dataset:\n",
    "        example = tf.train.SequenceExample()\n",
    "        example.ParseFromString(raw_record.numpy())\n",
    "\n",
    "        if len(example.feature_lists.feature_list['audio_embedding'].feature) != 10:\n",
    "            continue\n",
    "\n",
    "        audio_features_10s = [np.frombuffer(example.feature_lists.feature_list['audio_embedding'].feature[i].bytes_list.value[0],np.uint8).astype(np.float32) for i in range(10)]\n",
    "        # audio_features = np.frombuffer(example.feature_lists.feature_list['audio_embedding'].feature[0].bytes_list.value[0],np.uint8).astype(np.float32)\n",
    "        # video_id = example.context.feature[\"video_id\"].bytes_list.value[0]\n",
    "        # start_time_secs = example.context.feature[\"start_time_seconds\"].float_list.value[0]\n",
    "        labels = example.context.feature[\"labels\"].int64_list.value\n",
    "        # end_time_secs = example.context.feature[\"end_time_seconds\"].float_list.value[0]\n",
    "\n",
    "        has_laughter = any([label in laugh_labels for label in labels])\n",
    "\n",
    "        flat_audio_features = [j for sub in audio_features_10s for j in sub]\n",
    "\n",
    "        extracted_data = flat_audio_features + [1 if has_laughter else 0]\n",
    "        extracted_table.append(extracted_data)\n",
    "\n",
    "    return extracted_table\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6edb5564-c68f-40e8-99fa-7b48b1596f14",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-30 03:00:22.253740: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
     ]
    }
   ],
   "source": [
    "extracted_table_nE = parse_feature_data(raw_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7506145d-342d-4b6e-bf4b-422fad2e1606",
   "metadata": {},
   "outputs": [],
   "source": [
    "np_table_nE = np.array(extracted_table_nE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7fdbf018-bcc0-4d59-a166-910e95a1f5f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(381, 1281)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_table_nE.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e105de09-de35-451a-8feb-9268b4c3fd61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_table_nE[:, 1280].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78008993-5b5b-4f29-b7d8-825e87cea89f",
   "metadata": {},
   "source": [
    "The above cell shows that in the nE.tfrecord (all videos starting with \"nE\") there are only *two* instances of laughter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5f045bf-0666-4f23-955a-2bb7dd4c022a",
   "metadata": {},
   "source": [
    "### I've successfully extracted a table of data from the nE.tfrecord (shape: 381x1281). Now all I have to do is repeat this process for all of the TFRecords and I will have my whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bf695690-e8dd-4afe-9608-a5d12dd8491f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "home_path = Path('/home/vedrau/work/laugh_track')\n",
    "unbal_audio_features_path = home_path / 'audioset_v1_embeddings' / 'unbal_train'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c9e00b35-4936-4019-8637-bee0f23b3c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "unbal_filenames = [str(e) for e in unbal_audio_features_path.iterdir()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d5d94d3-4528-4227-ac64-08c6e510bcea",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/lO.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Zj.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Yk.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Xg.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/I-.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/1T.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Br.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Ig.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/fU.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Ma.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/kT.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/GL.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/0F.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/cz.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/_I.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/8T.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Ze.tfrecord',\n",
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       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/k3.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/5i.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/rj.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Gy.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/cl.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/rG.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/3O.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/mZ.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/az.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/xO.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/5B.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/tz.tfrecord',\n",
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       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/nz.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/C5.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/J7.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/IH.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/d7.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/jn.tfrecord',\n",
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       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Sr.tfrecord',\n",
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       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/5F.tfrecord',\n",
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       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/dV.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/aq.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/S6.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/G3.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/By.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/i_.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/UK.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/O9.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Zr.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/o-.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/gv.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/04.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/dt.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/LA.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/ba.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Je.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/LL.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/5r.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Cz.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/yT.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Wt.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/cj.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/uy.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/iS.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/D3.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/j4.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Yc.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/O_.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/lf.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/oD.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/mD.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/d9.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/iT.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/IU.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/ci.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/WV.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/oS.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/NA.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/IF.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Fg.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/dI.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/qW.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/pE.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/OI.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/KH.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/RW.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/m8.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/zR.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/q_.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/fa.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/3-.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Hm.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/TL.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/-0.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/6e.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/q5.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/BL.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/9g.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/0l.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/K3.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/tl.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/om.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/ez.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/rI.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/id.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/O-.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/0S.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/0B.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/TD.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/i1.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/LO.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/dq.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/cx.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/wH.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/ly.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/rE.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/3e.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/TE.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/2q.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/QD.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/bk.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Yb.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Pp.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/KU.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/9s.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Zq.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/SC.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/gm.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/n6.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/uY.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Qb.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/ep.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/vn.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/lY.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Vj.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Dx.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/gI.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/k5.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Vo.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/aj.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/KA.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/2k.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Ho.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/uB.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Ub.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Ib.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Vg.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/XK.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/9h.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/_q.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/nk.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/uK.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Te.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Uf.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/pw.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/S4.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/1Z.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/JS.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/GS.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Od.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/4e.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Qy.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/b9.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/LE.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/CX.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/iO.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/pM.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/3K.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/l0.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Vv.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Bs.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Hp.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/zr.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/UI.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Lo.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/iw.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/nd.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/ZC.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/5_.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/iK.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Xy.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/h2.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/7v.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/AA.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Zu.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/so.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/3b.tfrecord',\n",
       " '/home/vedrau/work/laugh_track/audioset_v1_embeddings/unbal_train/Gx.tfrecord',\n",
       " ...]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unbal_filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "17dbd447-bd49-40ab-8ee2-8fa8fc280482",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset = tf.data.TFRecordDataset(unbal_filenames[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "df7d7087-3d99-4dbe-8022-a66c955ea1a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TFRecordDatasetV2 element_spec=TensorSpec(shape=(), dtype=tf.string, name=None)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e9188699-ea7e-4ca9-bad2-40d12817574a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-30 03:00:22.690566: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
     ]
    }
   ],
   "source": [
    "test_extracted_table = parse_feature_data(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "51fd2c75-f447-4416-b9d0-85bd625a1d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "np_test_table = np.array(test_extracted_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f48fd48e-73dc-4ea2-858c-fa80c9ddd951",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(838, 1281)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_test_table.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f4cc2fd3-7823-4563-a452-0d207d9f7850",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_test_table[:,1280].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "445a0402-696b-4bcd-9106-fb5a996c6bcf",
   "metadata": {},
   "source": [
    "## Creating the final data product using all of the TFRecords (starting with bal_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "12e14452-360d-4c46-b6da-5f278dbd2124",
   "metadata": {},
   "outputs": [],
   "source": [
    "bal_audio_features_path = home_path / 'audioset_v1_embeddings' / 'bal_train'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4741fcfd-0687-4160-949e-17bec5e21f23",
   "metadata": {},
   "outputs": [],
   "source": [
    "bal_filenames = [str(e) for e in bal_audio_features_path.iterdir()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "988eca02-f6f3-4043-95dc-2cb998fae0f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_dataset = tf.data.TFRecordDataset(filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2a042a98-061c-4244-9c79-d841af4bb378",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-30 03:00:23.021522: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
     ]
    }
   ],
   "source": [
    "all_extracted_table = parse_feature_data(all_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a6ca1bd1-d69b-4ae4-b0ba-4b1c3923e869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4070, 4096)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bal_filenames), len(unbal_filenames)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14cc0e1-e5c3-4916-94bb-c7e854faf523",
   "metadata": {},
   "source": [
    "The way to load only the balanced train data is to load the balanced_train_segments.csv and only read the videos whose IDs are in the CSV. That data is guaranteed to include at least 59 examples of laughter (although I think more like 59*2.5 examples since there are 5 laugh labels)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8debaa70-6fe3-4cbc-abd8-90c87136bc45",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "bal_train_segs = pd.read_csv('balanced_train_segments.csv',header=2, quotechar=r'\"',skipinitialspace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8b6e0439-26eb-4695-9988-d0c7fa99cfb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># YTID</th>\n",
       "      <th>start_seconds</th>\n",
       "      <th>end_seconds</th>\n",
       "      <th>positive_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>--PJHxphWEs</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>/m/09x0r,/t/dd00088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>--ZhevVpy1s</td>\n",
       "      <td>50.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>/m/012xff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>--aE2O5G5WE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>/m/03fwl,/m/04rlf,/m/09x0r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>--aO5cdqSAg</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>/t/dd00003,/t/dd00005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>--aaILOrkII</td>\n",
       "      <td>200.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>/m/032s66,/m/073cg4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        # YTID  start_seconds  end_seconds             positive_labels\n",
       "0  --PJHxphWEs           30.0         40.0         /m/09x0r,/t/dd00088\n",
       "1  --ZhevVpy1s           50.0         60.0                   /m/012xff\n",
       "2  --aE2O5G5WE            0.0         10.0  /m/03fwl,/m/04rlf,/m/09x0r\n",
       "3  --aO5cdqSAg           30.0         40.0       /t/dd00003,/t/dd00005\n",
       "4  --aaILOrkII          200.0        210.0         /m/032s66,/m/073cg4"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bal_train_segs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "25483ea2-8856-4ff0-b7af-f7810ff3db3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_laughter_feature_data(dataset):\n",
    "    \"\"\"\n",
    "    This function takes the dataset and produces a table of the laughter data (the raw 128x10 features and a column that is the presence of laughter)\n",
    "\n",
    "    Note: we will skip videos that aren't 10 seconds long\n",
    "    \"\"\"\n",
    "    laugh_labels = [16, 17, 18, 19, 20, 21]\n",
    "\n",
    "    extracted_table = []\n",
    "    \n",
    "    for record in dataset:\n",
    "        example = tf.train.SequenceExample()\n",
    "        example.ParseFromString(record.numpy())\n",
    "\n",
    "        if len(example.feature_lists.feature_list['audio_embedding'].feature) != 10:\n",
    "            continue\n",
    "\n",
    "        audio_features_10s = [np.frombuffer(example.feature_lists.feature_list['audio_embedding'].feature[i].bytes_list.value[0],np.uint8).astype(np.float32) for i in range(10)]\n",
    "        # audio_features = np.frombuffer(example.feature_lists.feature_list['audio_embedding'].feature[0].bytes_list.value[0],np.uint8).astype(np.float32)\n",
    "        # video_id = example.context.feature[\"video_id\"].bytes_list.value[0]\n",
    "        # start_time_secs = example.context.feature[\"start_time_seconds\"].float_list.value[0]\n",
    "        labels = example.context.feature[\"labels\"].int64_list.value\n",
    "        # end_time_secs = example.context.feature[\"end_time_seconds\"].float_list.value[0]\n",
    "\n",
    "        flat_audio_features = [j for sub in audio_features_10s for j in sub]\n",
    "        \n",
    "        has_laughter: bool = any([label in laugh_labels for label in labels])\n",
    "        if has_laughter:\n",
    "            # this should always be appending ones\n",
    "            extracted_data = flat_audio_features + [1 if has_laughter else 0]\n",
    "            extracted_table.append(extracted_data)\n",
    "\n",
    "    return extracted_table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2697e2b0-a614-405c-bb70-d5dcdf7df25b",
   "metadata": {},
   "outputs": [],
   "source": [
    "unbal_dataset = tf.data.TFRecordDataset(unbal_filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c5cd2323-5a48-4007-9df3-53359f1183d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-30 03:07:25.371927: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
     ]
    }
   ],
   "source": [
    "laughter_features = parse_laughter_feature_data(unbal_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "947143ca-354f-4c75-a9de-d684c8ec5c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "np_laughter_features = np.array(laughter_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9209025d-4abc-4f99-ba2c-23ff3d8db7d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9215, 1281)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_laughter_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3dc2210a-7827-4fab-8325-e667f23e75c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('unbal_laughter_features.npy', np_laughter_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3ab498e-f717-4546-802f-9f0653cbacd9",
   "metadata": {},
   "source": [
    "### Downsample the total laughter features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "80790b58-e86e-4ffc-b137-996e4c721b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_rows = 100\n",
    "idxs = np.random.choice(list(range(9215)), size=num_rows, replace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7dce6d66-0cb1-426b-bbc1-bb47114ec347",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 1281)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_laughter_features[idxs].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "372fc3fb-30a2-4f9e-8faf-cf8f60daa77a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_non_laughter_feature_data(dataset, num_rows):\n",
    "    \"\"\"\n",
    "    This function takes the dataset and produces a table of the non-laughter data (the raw 128x10 features and a column that is the presence of laughter)\n",
    "\n",
    "    Note: we will skip videos that aren't 10 seconds long\n",
    "    \"\"\"\n",
    "    laugh_labels = [16, 17, 18, 19, 20, 21]\n",
    "\n",
    "    extracted_table = []\n",
    "    \n",
    "    for record in dataset:\n",
    "        # we only want to extract num_rows rows\n",
    "        if len(extracted_table) >= num_rows:\n",
    "            break\n",
    "            \n",
    "        example = tf.train.SequenceExample()\n",
    "        example.ParseFromString(record.numpy())\n",
    "\n",
    "        if len(example.feature_lists.feature_list['audio_embedding'].feature) != 10:\n",
    "            continue\n",
    "\n",
    "        labels = example.context.feature[\"labels\"].int64_list.value\n",
    "        has_laughter: bool = any([label in laugh_labels for label in labels])\n",
    "        if not has_laughter:\n",
    "            audio_features_10s = [np.frombuffer(example.feature_lists.feature_list['audio_embedding'].feature[i].bytes_list.value[0],np.uint8).astype(np.float32) for i in range(10)]\n",
    "            # audio_features = np.frombuffer(example.feature_lists.feature_list['audio_embedding'].feature[0].bytes_list.value[0],np.uint8).astype(np.float32)\n",
    "            # video_id = example.context.feature[\"video_id\"].bytes_list.value[0]\n",
    "            # start_time_secs = example.context.feature[\"start_time_seconds\"].float_list.value[0]\n",
    "            \n",
    "            # end_time_secs = example.context.feature[\"end_time_seconds\"].float_list.value[0]\n",
    "    \n",
    "            flat_audio_features = [j for sub in audio_features_10s for j in sub]\n",
    "\n",
    "            # this should always be appending zeroes\n",
    "            extracted_data = flat_audio_features + [1 if has_laughter else 0]\n",
    "            extracted_table.append(extracted_data)\n",
    "\n",
    "\n",
    "    return extracted_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "458786a2-d8e6-40ee-aaa8-60393091a8f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "non_laughter_features = parse_non_laughter_feature_data(unbal_dataset, num_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "27a3c379-f045-4b4d-a3e4-b001dd1bd72d",
   "metadata": {},
   "outputs": [],
   "source": [
    "np_non_laughter_features = np.array(non_laughter_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fad9b417-74e8-4bf7-975e-3824258e4188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 1281)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_non_laughter_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2bf33f78-e82e-4bdc-806d-23a1159ccb9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('non_laughter_features_100.npy', np_non_laughter_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5418ef2a-981f-45c5-8927-6904f2b607f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "np_laughter_features_100 = np_laughter_features[idxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "364340b0-ee8c-49ea-86d8-0d5db36799c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('laughter_features_100.npy', np_laughter_features_100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ef8a24-e727-4c74-84c1-b396eae0342a",
   "metadata": {},
   "source": [
    "### Create a CSV of the laughter and non-laughter data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a2771343-a4c1-4078-9476-132d842f9c58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 1281)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_laughter_features_100.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "407ed099-1310-479c-b6fd-c1dd3ab05a95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 1281)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_non_laughter_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "25a2adca-6638-4932-875f-f4bc762724f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_features = np.concatenate((np_laughter_features_100, np_non_laughter_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3e2e3987-5aa1-4983-917f-b11bfab7252b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I have a 200 x 1281 numpy array. I want to get a CSV with 200 rows and 1281 columns\n",
    "data_dict = {}\n",
    "for i in range(1280):\n",
    "    data_dict[f\"x_{i}\"] = list(all_features[:, i])\n",
    "data_dict[\"y\"] = list(all_features[:, 1280])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bb74faf0-9704-40af-a756-a0b4e695d115",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame.from_dict(data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "68726f17-19b9-44f2-8a63-f4dbb9fc0b42",
   "metadata": {},
   "outputs": [
    {
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       "      <th>x_0</th>\n",
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       "      <td>180.0</td>\n",
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       "      <td>87.0</td>\n",
       "      <td>149.0</td>\n",
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       "      <td>71.0</td>\n",
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       "      <td>187.0</td>\n",
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       "      <td>117.0</td>\n",
       "      <td>113.0</td>\n",
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       "      <td>200.0</td>\n",
       "      <td>72.0</td>\n",
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       "      <td>29.0</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>...</td>\n",
       "      <td>143.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>...</td>\n",
       "      <td>215.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>119.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1281 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     x_0    x_1    x_2    x_3    x_4    x_5    x_6    x_7    x_8    x_9  ...  \\\n",
       "0  135.0  124.0  180.0  116.0  238.0   62.0  109.0   31.0  155.0  149.0  ...   \n",
       "1  135.0  103.0  255.0  187.0  137.0  156.0  113.0   30.0  117.0  113.0  ...   \n",
       "2    0.0  133.0  193.0    8.0   69.0  255.0   57.0  141.0  117.0  110.0  ...   \n",
       "3   43.0   63.0  160.0   15.0   77.0  159.0    4.0   91.0  206.0  146.0  ...   \n",
       "4  119.0   78.0  166.0   95.0  175.0   91.0   63.0   61.0  227.0   89.0  ...   \n",
       "\n",
       "   x_1271  x_1272  x_1273  x_1274  x_1275  x_1276  x_1277  x_1278  x_1279    y  \n",
       "0    69.0   166.0    87.0   149.0    25.0   255.0    71.0   101.0    42.0  1.0  \n",
       "1   255.0   200.0    72.0   176.0    99.0   225.0    29.0     0.0   148.0  1.0  \n",
       "2   143.0    91.0   125.0    62.0   177.0   172.0     0.0   243.0     0.0  1.0  \n",
       "3   215.0   129.0   199.0   255.0   113.0    36.0    43.0     0.0   197.0  1.0  \n",
       "4    63.0   195.0    61.0   197.0    70.0    80.0     0.0     0.0   220.0  1.0  \n",
       "\n",
       "[5 rows x 1281 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a91ed975-414b-43f7-9c3e-e68aee7f37ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('tabular_data_100_features.csv', index=False)"
   ]
  }
 ],
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