{
 "cells": [
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     "nuid": "edc5629a-00ee-47dc-a271-e42d01e4f94e",
     "showTitle": false,
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   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[43mNote: you may need to restart the kernel using dbutils.library.restartPython() to use updated packages.\u001B[0m\nLooking in indexes: https://aws:****@adi-dp-pypi-232090812308.d.codeartifact.ap-southeast-1.amazonaws.com/pypi/adi-dp-pypi/simple/\nCollecting git+https://github.com/openai/whisper.git\n  Cloning https://github.com/openai/whisper.git to /tmp/pip-req-build-ou0jayhp\n  Running command git clone --filter=blob:none --quiet https://github.com/openai/whisper.git /tmp/pip-req-build-ou0jayhp\n  Resolved https://github.com/openai/whisper.git to commit e8622f9afc4eba139bf796c210f5c01081000472\n  Installing build dependencies: started\n  Installing build dependencies: finished with status 'done'\n  Getting requirements to build wheel: started\n  Getting requirements to build wheel: finished with status 'done'\n  Preparing metadata (pyproject.toml): started\n  Preparing metadata (pyproject.toml): finished with status 'done'\nRequirement already satisfied: numba in /databricks/python3/lib/python3.10/site-packages (from openai-whisper==20230314) (0.55.1)\nRequirement already satisfied: tqdm in /databricks/python3/lib/python3.10/site-packages (from openai-whisper==20230314) (4.64.1)\nRequirement already satisfied: triton==2.0.0 in /local_disk0/.ephemeral_nfs/envs/pythonEnv-92b71cb9-7d4b-4828-a60e-efe79e60c1e5/lib/python3.10/site-packages (from openai-whisper==20230314) (2.0.0)\nRequirement already satisfied: numpy in /databricks/python3/lib/python3.10/site-packages (from openai-whisper==20230314) (1.21.5)\nRequirement already satisfied: more-itertools in /usr/lib/python3/dist-packages (from openai-whisper==20230314) (8.10.0)\nRequirement already satisfied: torch in /databricks/python3/lib/python3.10/site-packages (from openai-whisper==20230314) (1.13.1+cu117)\nRequirement already satisfied: tiktoken==0.3.3 in /databricks/python3/lib/python3.10/site-packages (from openai-whisper==20230314) (0.3.3)\nRequirement already satisfied: requests>=2.26.0 in /databricks/python3/lib/python3.10/site-packages (from tiktoken==0.3.3->openai-whisper==20230314) (2.28.1)\nRequirement already satisfied: regex>=2022.1.18 in /databricks/python3/lib/python3.10/site-packages (from tiktoken==0.3.3->openai-whisper==20230314) (2022.7.9)\nRequirement already satisfied: cmake in /local_disk0/.ephemeral_nfs/envs/pythonEnv-92b71cb9-7d4b-4828-a60e-efe79e60c1e5/lib/python3.10/site-packages (from triton==2.0.0->openai-whisper==20230314) (3.27.2)\nRequirement already satisfied: filelock in /databricks/python3/lib/python3.10/site-packages (from triton==2.0.0->openai-whisper==20230314) (3.6.0)\nRequirement already satisfied: lit in /local_disk0/.ephemeral_nfs/envs/pythonEnv-92b71cb9-7d4b-4828-a60e-efe79e60c1e5/lib/python3.10/site-packages (from triton==2.0.0->openai-whisper==20230314) (16.0.6)\nRequirement already satisfied: setuptools in /databricks/python3/lib/python3.10/site-packages (from numba->openai-whisper==20230314) (63.4.1)\nRequirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in /databricks/python3/lib/python3.10/site-packages (from numba->openai-whisper==20230314) (0.38.0)\nRequirement already satisfied: typing-extensions in /databricks/python3/lib/python3.10/site-packages (from torch->openai-whisper==20230314) (4.3.0)\nRequirement already satisfied: idna<4,>=2.5 in /databricks/python3/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken==0.3.3->openai-whisper==20230314) (3.3)\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /databricks/python3/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken==0.3.3->openai-whisper==20230314) (1.26.11)\nRequirement already satisfied: certifi>=2017.4.17 in /databricks/python3/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken==0.3.3->openai-whisper==20230314) (2022.9.14)\nRequirement already satisfied: charset-normalizer<3,>=2 in /databricks/python3/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken==0.3.3->openai-whisper==20230314) (2.0.4)\n\u001B[43mNote: you may need to restart the kernel using dbutils.library.restartPython() to use updated packages.\u001B[0m\n"
     ]
    }
   ],
   "source": [
    "pip install git+https://github.com/openai/whisper.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
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     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "import whisper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
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    }
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   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "import glob\n",
    "import librosa\n",
    "import librosa.display\n",
    "import IPython.display as ipd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import soundfile\n",
    "from tqdm import tqdm\n",
    "from sklearn.preprocessing import StandardScaler,LabelEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.compose import ColumnTransformer\n",
    "import tensorflow\n",
    "from sklearn.decomposition import PCA \n",
    "import seaborn as sns\n",
    "from PIL import Image\n",
    "from matplotlib import cm\n",
    "from collections import Counter\n",
    "from torchvision import datasets, transforms, models\n",
    "import torch.optim as optim\n",
    "\n",
    "from sklearn.utils.multiclass import unique_labels\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score,balanced_accuracy_score\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# import lightgbm as lgb\n",
    "# import xgboost as xgb\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from keras.models import Model,load_model\n",
    "from keras.layers import Input,Dense,Flatten\n",
    "from keras.layers import Conv2D,MaxPooling2D,Dropout,BatchNormalization,Conv1D,MaxPooling1D\n",
    "from keras.models import load_model\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.datasets import ImageFolder\n",
    "\n",
    "from datasets import load_dataset\n",
    "import os\n",
    "import pandas as pd\n",
    "#pd.set_option('display.max_colwidth', None)\n",
    "import logging\n",
    "import librosa\n",
    "import torch\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "\n",
    "from datasets import DatasetDict, load_dataset, load_metric\n",
    "\n",
    "from transformers import (\n",
    "    HubertForSequenceClassification,\n",
    "    PretrainedConfig,\n",
    "    Trainer,\n",
    "    TrainingArguments,\n",
    "    Wav2Vec2FeatureExtractor,\n",
    ")\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.cuda.amp import GradScaler, autocast\n",
    "from transformers import AdamW\n",
    "from torch.nn.utils.rnn import pad_sequence\n",
    "import torch.nn as nn\n",
    "from transformers import Wav2Vec2Processor,AutoTokenizer\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "device\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
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     },
     "inputWidgets": {},
     "nuid": "06a125f7-a71b-435c-b25a-95bb1b9aca0f",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS/Actor_01/03-01-01-01-01-01-01.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS/Actor_01/03-01-01-01-01-02-01.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS/Actor_01/03-01-01-01-02-01-01.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS/Actor_01/03-01-01-01-02-02-01.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS/Actor_01/03-01-02-01-01-01-01.wav']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directory_path = '/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS/'\n",
    "directory_path_with_forward_slashes = directory_path.replace(\"\\\\\", \"/\")\n",
    "\n",
    "tempwavfiles = sorted(glob.glob(directory_path+'/**/*.wav', recursive=True))\n",
    "tempwavfiles[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
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     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "## Defining a general purpose function\n",
    "def get_file_name(link):\n",
    "    '''\n",
    "    General purpose function to get absolute filename from relative path\n",
    "    Parameters: Filename with relative path\n",
    "    Returns: Filename\n",
    "    '''\n",
    "    newPath = link.replace(os.sep, '/')\n",
    "    filename = newPath.split('/')[::-1][0]\n",
    "    return filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
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     },
     "inputWidgets": {},
     "nuid": "47ff51f4-b07c-49a5-8597-45345fe11a42",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "# Get labels from filename\n",
    "def get_label_from_filename(file_list):\n",
    "    '''\n",
    "    Function to extract label from the filename.\n",
    "    Argument: Full file path.\n",
    "    '''\n",
    "    feeling_list=[]\n",
    "    item_list = []\n",
    "    for fullfilename in file_list:\n",
    "        item = get_file_name(fullfilename)\n",
    "        item_list.append(fullfilename)\n",
    "        #print(f'item is {item}')\n",
    "        if (item[6:-16]=='02' or item[6:-16]=='01'):\n",
    "            feeling_list.append('calm')\n",
    "        elif item[6:-16]=='03':\n",
    "            feeling_list.append('happy')\n",
    "        elif item[6:-16]=='04':\n",
    "            feeling_list.append('sad')\n",
    "        elif item[6:-16]=='05':\n",
    "            feeling_list.append('angry')\n",
    "        elif item[6:-16]=='06':\n",
    "            feeling_list.append('fearful')\n",
    "        elif item[6:-16]=='07':\n",
    "            feeling_list.append('disgust')\n",
    "        elif item[6:-16]=='08':\n",
    "            feeling_list.append('surprised')          \n",
    "    return feeling_list,item_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "30d054bb-ce6e-43ca-afb8-a325400e8871",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'angry', 'calm', 'disgust', 'fearful', 'happy', 'sad', 'surprised'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(get_label_from_filename(tempwavfiles)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "afa454c9-2cb7-4aa9-806d-32cfe24b7c61",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2880"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(get_label_from_filename(tempwavfiles)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
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      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "d091fdee-78c6-48d6-ba94-546e85f81dd3",
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     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2880"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(get_label_from_filename(tempwavfiles)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "5ad4c5c3-2911-4d28-b720-71b431e63acb",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "df_ravdess = pd.DataFrame({'Filenames':get_label_from_filename(tempwavfiles)[1],'Emotions':get_label_from_filename(tempwavfiles)[0],'Dataset':['RAVDESS']*len(get_label_from_filename(tempwavfiles)[0])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
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     },
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     "nuid": "1090a657-b53a-4b55-be6d-5e696b30aa95",
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     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "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>Filenames</th>\n",
       "      <th>Emotions</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>calm</td>\n",
       "      <td>RAVDESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>calm</td>\n",
       "      <td>RAVDESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>calm</td>\n",
       "      <td>RAVDESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>calm</td>\n",
       "      <td>RAVDESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>calm</td>\n",
       "      <td>RAVDESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2875</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>RAVDESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2876</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>RAVDESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2877</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>RAVDESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2878</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>RAVDESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2879</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>RAVDESS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2880 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Filenames   Emotions  Dataset\n",
       "0     /dbfs/FileStore/wav-files/FinalExperiments/RAV...       calm  RAVDESS\n",
       "1     /dbfs/FileStore/wav-files/FinalExperiments/RAV...       calm  RAVDESS\n",
       "2     /dbfs/FileStore/wav-files/FinalExperiments/RAV...       calm  RAVDESS\n",
       "3     /dbfs/FileStore/wav-files/FinalExperiments/RAV...       calm  RAVDESS\n",
       "4     /dbfs/FileStore/wav-files/FinalExperiments/RAV...       calm  RAVDESS\n",
       "...                                                 ...        ...      ...\n",
       "2875  /dbfs/FileStore/wav-files/FinalExperiments/RAV...  surprised  RAVDESS\n",
       "2876  /dbfs/FileStore/wav-files/FinalExperiments/RAV...  surprised  RAVDESS\n",
       "2877  /dbfs/FileStore/wav-files/FinalExperiments/RAV...  surprised  RAVDESS\n",
       "2878  /dbfs/FileStore/wav-files/FinalExperiments/RAV...  surprised  RAVDESS\n",
       "2879  /dbfs/FileStore/wav-files/FinalExperiments/RAV...  surprised  RAVDESS\n",
       "\n",
       "[2880 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ravdess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[43mNote: you may need to restart the kernel using dbutils.library.restartPython() to use updated packages.\u001B[0m\nLooking in indexes: https://aws:****@adi-dp-pypi-232090812308.d.codeartifact.ap-southeast-1.amazonaws.com/pypi/adi-dp-pypi/simple/\nCollecting git+https://github.com/openai/whisper.git\n  Cloning https://github.com/openai/whisper.git to /tmp/pip-req-build-9qdu9iba\n  Running command git clone --filter=blob:none --quiet https://github.com/openai/whisper.git /tmp/pip-req-build-9qdu9iba\n  Resolved https://github.com/openai/whisper.git to commit e8622f9afc4eba139bf796c210f5c01081000472\n  Installing build dependencies: started\n  Installing build dependencies: finished with status 'done'\n  Getting requirements to build wheel: started\n  Getting requirements to build wheel: finished with status 'done'\n  Preparing metadata (pyproject.toml): started\n  Preparing metadata (pyproject.toml): finished with status 'done'\nRequirement already satisfied: more-itertools in /usr/lib/python3/dist-packages (from openai-whisper==20230314) (8.10.0)\nRequirement already satisfied: torch in /databricks/python3/lib/python3.10/site-packages (from openai-whisper==20230314) (1.13.1+cu117)\nCollecting triton==2.0.0\n  Downloading https://adi-dp-pypi-232090812308.d.codeartifact.ap-southeast-1.amazonaws.com/pypi/adi-dp-pypi/simple/triton/2.0.0/triton-2.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (63.3 MB)\n     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 63.3/63.3 MB 15.7 MB/s eta 0:00:00\nRequirement already satisfied: numba in /databricks/python3/lib/python3.10/site-packages (from openai-whisper==20230314) (0.55.1)\nRequirement already satisfied: numpy in /databricks/python3/lib/python3.10/site-packages (from openai-whisper==20230314) (1.21.5)\nRequirement already satisfied: tqdm in /databricks/python3/lib/python3.10/site-packages (from openai-whisper==20230314) (4.64.1)\nRequirement already satisfied: tiktoken==0.3.3 in /databricks/python3/lib/python3.10/site-packages (from openai-whisper==20230314) (0.3.3)\nRequirement already satisfied: requests>=2.26.0 in /databricks/python3/lib/python3.10/site-packages (from tiktoken==0.3.3->openai-whisper==20230314) (2.28.1)\nRequirement already satisfied: regex>=2022.1.18 in /databricks/python3/lib/python3.10/site-packages (from tiktoken==0.3.3->openai-whisper==20230314) (2022.7.9)\nRequirement already satisfied: filelock in /databricks/python3/lib/python3.10/site-packages (from triton==2.0.0->openai-whisper==20230314) (3.6.0)\nCollecting cmake\n  Downloading https://adi-dp-pypi-232090812308.d.codeartifact.ap-southeast-1.amazonaws.com/pypi/adi-dp-pypi/simple/cmake/3.27.2/cmake-3.27.2-py2.py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (26.1 MB)\n     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 26.1/26.1 MB 33.2 MB/s eta 0:00:00\nCollecting lit\n  Downloading https://adi-dp-pypi-232090812308.d.codeartifact.ap-southeast-1.amazonaws.com/pypi/adi-dp-pypi/simple/lit/16.0.6/lit-16.0.6.tar.gz (153 kB)\n     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 153.7/153.7 kB 28.5 MB/s eta 0:00:00\n  Installing build dependencies: started\n  Installing build dependencies: finished with status 'done'\n  Getting requirements to build wheel: started\n  Getting requirements to build wheel: finished with status 'done'\n  Installing backend dependencies: started\n  Installing backend dependencies: finished with status 'done'\n  Preparing metadata (pyproject.toml): started\n  Preparing metadata (pyproject.toml): finished with status 'done'\nRequirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in /databricks/python3/lib/python3.10/site-packages (from numba->openai-whisper==20230314) (0.38.0)\nRequirement already satisfied: setuptools in /databricks/python3/lib/python3.10/site-packages (from numba->openai-whisper==20230314) (63.4.1)\nRequirement already satisfied: typing-extensions in /databricks/python3/lib/python3.10/site-packages (from torch->openai-whisper==20230314) (4.3.0)\nRequirement already satisfied: idna<4,>=2.5 in /databricks/python3/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken==0.3.3->openai-whisper==20230314) (3.3)\nRequirement already satisfied: charset-normalizer<3,>=2 in /databricks/python3/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken==0.3.3->openai-whisper==20230314) (2.0.4)\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /databricks/python3/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken==0.3.3->openai-whisper==20230314) (1.26.11)\nRequirement already satisfied: certifi>=2017.4.17 in /databricks/python3/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken==0.3.3->openai-whisper==20230314) (2022.9.14)\nBuilding wheels for collected packages: openai-whisper, lit\n  Building wheel for openai-whisper (pyproject.toml): started\n  Building wheel for openai-whisper (pyproject.toml): finished with status 'done'\n  Created wheel for openai-whisper: filename=openai_whisper-20230314-py3-none-any.whl size=798395 sha256=0cb784f67ad93c8c9a11770d000a7395190446567eac4267417af48b91103c11\n  Stored in directory: /tmp/pip-ephem-wheel-cache-_k32lioi/wheels/8b/6c/d0/622666868c179f156cf595c8b6f06f88bc5d80c4b31dccaa03\n  Building wheel for lit (pyproject.toml): started\n  Building wheel for lit (pyproject.toml): finished with status 'done'\n  Created wheel for lit: filename=lit-16.0.6-py3-none-any.whl size=93589 sha256=47e926de3cf93bfce2b42fb10fb061d63e6012d0850673629b1bec50278c6585\n  Stored in directory: /root/.cache/pip/wheels/d6/07/d6/1cd2e698633b5d603ab2aa3137ff42349a51edf71da7a50bb5\nSuccessfully built openai-whisper lit\nInstalling collected packages: lit, cmake, triton, openai-whisper\nSuccessfully installed cmake-3.27.2 lit-16.0.6 openai-whisper-20230314 triton-2.0.0\n\u001B[43mNote: you may need to restart the kernel using dbutils.library.restartPython() to use updated packages.\u001B[0m\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
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     "cellMetadata": {
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     },
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     "nuid": "22d5d79d-efa4-4dbc-ad0d-d06610474eed",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|                                               | 0.00/139M [00:00<?, ?iB/s]\r  7%|██▍                                   | 9.01M/139M [00:00<00:01, 94.0MiB/s]\r 14%|█████▌                                 | 19.8M/139M [00:00<00:01, 104MiB/s]\r 21%|████████▎                              | 29.7M/139M [00:00<00:01, 103MiB/s]\r 29%|███████████▏                           | 39.6M/139M [00:00<00:01, 103MiB/s]\r 36%|██████████████                         | 49.8M/139M [00:00<00:00, 104MiB/s]\r 43%|████████████████▉                      | 60.0M/139M [00:00<00:00, 105MiB/s]\r 51%|███████████████████▋                   | 70.1M/139M [00:00<00:00, 103MiB/s]\r 59%|██████████████████████▊                | 81.2M/139M [00:00<00:00, 107MiB/s]\r 67%|██████████████████████████             | 92.5M/139M [00:00<00:00, 111MiB/s]\r 75%|█████████████████████████████▉          | 104M/139M [00:01<00:00, 112MiB/s]\r 83%|█████████████████████████████████       | 114M/139M [00:01<00:00, 112MiB/s]\r 90%|████████████████████████████████████    | 125M/139M [00:01<00:00, 109MiB/s]\r 98%|███████████████████████████████████████▏| 136M/139M [00:01<00:00, 109MiB/s]\r100%|████████████████████████████████████████| 139M/139M [00:01<00:00, 106MiB/s]\n"
     ]
    }
   ],
   "source": [
    "speech_model = whisper.load_model(\"base\")\n",
    "def speechtoText(wavfile):\n",
    "    return speech_model.transcribe(wavfile)['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "arguments": {},
       "data": "",
       "errorSummary": "Cancelled",
       "errorTraceType": "html",
       "metadata": {},
       "type": "ipynbError"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "tqdm.pandas()\n",
    "df_ravdess['Transcription'] = df_ravdess['Filenames'].progress_apply(lambda x: speechtoText(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "3fd0f459-02db-4fc0-99e6-9df1706732f5",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "arguments": {},
       "data": "",
       "errorSummary": "Cancelled",
       "errorTraceType": "html",
       "metadata": {},
       "type": "ipynbError"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "destination_dir = '/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS/files'\n",
    "for f in tempwavfiles:\n",
    "    shutil.copy(f, destination_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "7d1a4fb1-3d28-4d9b-ba8b-2bd542fe3a40",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "arguments": {},
       "data": "",
       "errorSummary": "Cancelled",
       "errorTraceType": "html",
       "metadata": {},
       "type": "ipynbError"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ravdess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "39f86b83-8984-43dc-bfcc-1892d4d929ea",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "arguments": {},
       "data": "",
       "errorSummary": "Cancelled",
       "errorTraceType": "html",
       "metadata": {},
       "type": "ipynbError"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ravdess.to_csv('/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "1f3d687e-1c98-4f80-a3c9-b7844b725878",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "df_ravdess= pd.read_csv('/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
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      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "ef693eb2-8e05-4088-be22-b9f925a3f5f2",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Filenames</th>\n",
       "      <th>Emotions</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Transcription</th>\n",
       "      <th>DataBricksPath</th>\n",
       "      <th>FilenameOnly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>calm</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Kids are talking by the door.</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-01-01-01-01-01.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>calm</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Kids are talking by the door.</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-01-01-01-02-01.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>calm</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Dogs are sitting by the door.</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-01-01-02-01-01.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>calm</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Dogs are sitting by the door.</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-01-01-02-02-01.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>calm</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Kids, talk about it.</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-02-01-01-01-01.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Dogs are sitting by the door.</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-08-01-02-02-24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Kids are talking by the door.</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-08-02-01-01-24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Kids are talking by the door?</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-08-02-01-02-24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Dogs are sitting by the door.</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-08-02-02-01-24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Dogs are sitting by the door.</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-08-02-02-02-24.wav</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1440 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Filenames  ...              FilenameOnly\n",
       "0     /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-01-01-01-01-01.wav\n",
       "1     /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-01-01-01-02-01.wav\n",
       "2     /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-01-01-02-01-01.wav\n",
       "3     /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-01-01-02-02-01.wav\n",
       "4     /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-02-01-01-01-01.wav\n",
       "...                                                 ...  ...                       ...\n",
       "1435  /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-08-01-02-02-24.wav\n",
       "1436  /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-08-02-01-01-24.wav\n",
       "1437  /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-08-02-01-02-24.wav\n",
       "1438  /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-08-02-02-01-24.wav\n",
       "1439  /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-08-02-02-02-24.wav\n",
       "\n",
       "[1440 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ravdess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
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      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "52db22c3-863c-4a57-98cf-0b937253fd9e",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/dbfs/FileStore/wav-files/FinalExperiments/FinalExperiments/Augmented-HubertModel7Epochs\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af814cdd4d644fdf9739414a2a7f779b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)rocessor_config.json:   0%|          | 0.00/213 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's load the augmented Hubert model trained w/o text\n",
    "\n",
    "PROJECT_ROOT='/dbfs/FileStore/wav-files/FinalExperiments/FinalExperiments/'\n",
    "\n",
    "model_id = \"facebook/hubert-base-ls960\"\n",
    "MODEL_DIR='Augmented-HubertModel7Epochs'\n",
    "NUM_LABELS = 7  # Replace with the number of labels used during training\n",
    "model_dir = os.path.join(PROJECT_ROOT, MODEL_DIR)  # Replace with the path to the directory where your model was saved\n",
    "print(model_dir)\n",
    "from transformers import (\n",
    "    HubertForSequenceClassification,\n",
    "    Wav2Vec2FeatureExtractor\n",
    ")\n",
    "\n",
    "# Load the model\n",
    "hubert_model = HubertForSequenceClassification.from_pretrained(\n",
    "model_dir,\n",
    "num_labels=NUM_LABELS,\n",
    "ignore_mismatched_sizes=True,\n",
    ")\n",
    "feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_id)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "b0392e48-c7e6-4b00-b87d-10ac3fce180d",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/databricks/python_shell/dbruntime/huggingface_patches/datasets.py:27: UserWarning: This dataset can not be stored in DBFS because either `cache_dir` or the environment variable `HF_DATASETS_CACHE` is set to a non-DBFS path. If this cluster restarts, all saved dataset information will be lost.\n  warnings.warn(\n/databricks/python_shell/dbruntime/huggingface_patches/datasets.py:13: UserWarning: During large dataset downloads, there could be multiple progress bar widgets that can cause performance issues for your notebook or browser. To avoid these issues, use `datasets.utils.logging.disable_progress_bar()` to turn off the progress bars.\n  warnings.warn(\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset ravdess_benchmarking/clean to /root/.cache/huggingface/datasets/ravdess_benchmarking/clean-9b66cbcbd5068a74/0.0.0/bfca3933991df7bc3c135a088be3c017a07219ed419e89895ae740796e58bb30...\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77b58e2df0eb47c19d850e211ea7df76",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "files is /dbfs/FileStore/wav-files/FinalExperiments\n\naudio dir is /dbfs/FileStore/wav-files/FinalExperiments/RAVDESS/files/\n\nDataset ravdess_benchmarking downloaded and prepared to /root/.cache/huggingface/datasets/ravdess_benchmarking/clean-9b66cbcbd5068a74/0.0.0/bfca3933991df7bc3c135a088be3c017a07219ed419e89895ae740796e58bb30. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4252394518a54c948dcc7fb76c3d820c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating test dataset\n",
    "\n",
    "PROJECT_ROOT='/dbfs/FileStore/wav-files/FinalExperiments'\n",
    "dataset_config = {  \n",
    "  \"LOADING_SCRIPT_FILES\": os.path.join(PROJECT_ROOT, \"RAVDESS_Benchmarking.py\"),\n",
    "  \"CONFIG_NAME\": \"clean\",\n",
    "  \"DATA_DIR\": os.path.join(PROJECT_ROOT),\n",
    " # \"CACHE_DIR\": os.path.join(PROJECT_ROOT, \"cached_emo/\"),\n",
    "}   \n",
    "ds = load_dataset(\n",
    "  dataset_config[\"LOADING_SCRIPT_FILES\"],\n",
    "  dataset_config[\"CONFIG_NAME\"],    \n",
    "  data_dir=dataset_config[\"DATA_DIR\"],\n",
    "#  cache_dir=dataset_config[\"CACHE_DIR\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "aa5b4e9b-9b69-48c6-a6cb-93f434380a29",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n    train: Dataset({\n        features: ['file', 'labels', 'text'],\n        num_rows: 1440\n    })\n})\n"
     ]
    }
   ],
   "source": [
    "print(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "8d4fc8f1-6035-49bb-9644-cf29793ca8e2",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS/Actor_01/03-01-01-01-01-01-01.wav'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df = pd.read_csv('/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS.csv')\n",
    "# df.Filenames.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "57a388cd-f98f-46ac-9bcb-f4d0e80d4489",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "# ## Defining a general purpose function\n",
    "# def get_file_name(link):\n",
    "#     '''\n",
    "#     General purpose function to get absolute filename from relative path\n",
    "#     Parameters: Filename with relative path\n",
    "#     Returns: Filename\n",
    "#     '''\n",
    "#     newPath = link.replace(os.sep, '/')\n",
    "#     filename = newPath.split('\\\\')[::-1][0]\n",
    "\n",
    "#     return '/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS/files/'+filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "038bb32d-c443-4be2-bf82-af83a6bff8ae",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "# ## Defining a general purpose function\n",
    "# def get_file_nameonly(link):\n",
    "#     '''\n",
    "#     General purpose function to get absolute filename from relative path\n",
    "#     Parameters: Filename with relative path\n",
    "#     Returns: Filename\n",
    "#     '''\n",
    "#     newPath = link.replace(os.sep, '/')\n",
    "#     filename = newPath.split('/')[::-1][0]\n",
    "\n",
    "#     return filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "1c0a8ab7-c437-48c8-a682-32bada078196",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/1440 [00:00<?, ?it/s]\r100%|██████████| 1440/1440 [00:00<00:00, 520537.60it/s]\n"
     ]
    }
   ],
   "source": [
    "# tqdm.pandas()\n",
    "# df['FilenameOnly'] = df['Filenames'].progress_apply(lambda x: get_file_nameonly(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "a60640c8-f356-4c22-af26-87f9ea8cfba0",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/1440 [00:00<?, ?it/s]\r100%|██████████| 1440/1440 [00:00<00:00, 528554.98it/s]\n"
     ]
    }
   ],
   "source": [
    "# tqdm.pandas()\n",
    "# df['DataBricksPath'] = df['Filenames'].progress_apply(lambda x: get_file_name(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "597a2b10-510b-4a23-a505-d7aec35c0cb3",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Filenames         False\n",
       "Emotions          False\n",
       "Dataset           False\n",
       "Transcription     False\n",
       "DataBricksPath    False\n",
       "FilenameOnly      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ravdess.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "dd9f0a3a-b2d9-4a92-a1f8-15cf745d8add",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "# df.to_csv('/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS.csv',index=False)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "9a6b241e-84d0-4fbf-859c-b5a924a6d5ab",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "# df = pd.read_csv('/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS.csv')\n",
    "# df.loc[df['Filenames'] == file, 'Emotions'].empty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "e5469679-c1ec-4f06-9e70-3bf340c8185e",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "3ec7442e-ed23-4a31-94fa-77f8561ccbe6",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n    train: Dataset({\n        features: ['file', 'labels', 'text'],\n        num_rows: 1440\n    })\n})\n"
     ]
    }
   ],
   "source": [
    "# print(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "420f443a-f71e-41df-ade4-e87030d2c35c",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "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>Filenames</th>\n",
       "      <th>Emotions</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Transcription</th>\n",
       "      <th>DataBricksPath</th>\n",
       "      <th>FilenameOnly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>fearful</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Kids are talking by the door!</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-06-02-01-01-16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>fearful</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Kids are talking by the door!</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-06-02-01-02-16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>fearful</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Dogs are sitting by the door!</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-06-02-02-01-16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>fearful</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Dogs are sitting by the door!</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-06-02-02-02-16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>disgust</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Kids are talking by the door.</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-07-01-01-01-16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>fearful</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Dogs are sitting by the door!</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-06-01-02-02-17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>fearful</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Kids are talking by the door!</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-06-02-01-01-17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>fearful</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Kids are talking by the door!</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-06-02-01-02-17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>fearful</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Dogs are sitting by the door!</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-06-02-02-01-17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>fearful</td>\n",
       "      <td>RAVDESS</td>\n",
       "      <td>Dogs are sitting by the door.</td>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/RAV...</td>\n",
       "      <td>03-01-06-02-02-02-17.wav</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Filenames  ...              FilenameOnly\n",
       "940   /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-06-02-01-01-16.wav\n",
       "941   /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-06-02-01-02-16.wav\n",
       "942   /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-06-02-02-01-16.wav\n",
       "943   /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-06-02-02-02-16.wav\n",
       "944   /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-07-01-01-01-16.wav\n",
       "...                                                 ...  ...                       ...\n",
       "999   /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-06-01-02-02-17.wav\n",
       "1000  /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-06-02-01-01-17.wav\n",
       "1001  /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-06-02-01-02-17.wav\n",
       "1002  /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-06-02-02-01-17.wav\n",
       "1003  /dbfs/FileStore/wav-files/FinalExperiments/RAV...  ...  03-01-06-02-02-02-17.wav\n",
       "\n",
       "[64 rows x 6 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ravdess.iloc[940:1004]\n",
    "#/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS/files/03-01-06-02-01-02-16.wav\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "533383c3-47aa-4a4b-a973-ef5f93614d58",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([ 1.4403500e-05,  2.7266444e-05,  3.2287273e-05, ...,\n",
       "       -9.8497458e-06,  5.6316446e-07, -1.8365588e-05], dtype=float32)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "librosa.load('/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS/files/03-01-01-01-02-01-01.wav',sr=16000, mono=False)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "b7b0c798-c99e-49eb-ac63-40343569692c",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([-5.6843419e-14, -1.7053026e-13,  0.0000000e+00, ...,\n",
       "       -1.1841621e-09,  9.6542863e-10,  0.0000000e+00], dtype=float32)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "librosa.load('/dbfs/FileStore/wav-files/FinalExperiments/RAVDESS/files/03-01-02-01-01-02-01.wav', sr=16000, mono=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "2143e624-b43b-490a-82a4-5100754e5e34",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a6bec1dd5a24a78a30f8e9a5cd0f066",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1440 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "443e496d894b4d9d91f22bd217546049",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1440 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "76fa89f600dd47d28d33db4a56c7dff8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Casting to class labels:   0%|          | 0/1440 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "# CONVERING RAW AUDIO TO ARRAYS\n",
    "# ds = ds.map(\n",
    "#         lambda x: {\n",
    "#             \"array\": librosa.load(x[\"file\"], sr=16000, mono=False)[0]\n",
    "#         }#,\n",
    "#         #num_proc=12,\n",
    "#     )\n",
    "ds = ds.map(\n",
    "    lambda x: {\n",
    "        \"array\": list(librosa.load(x[\"file\"], sr=16000, mono=True)[0])\n",
    "    }\n",
    ")\n",
    "\n",
    "\n",
    "# PROCESS THE DATASET TO THE FORMAT EXPECTED BY THE MODEL FOR TRAINING\n",
    "\n",
    "INPUT_FIELD = \"input_values\"\n",
    "LABEL_FIELD = \"labels\"\n",
    "\n",
    "def prepare_dataset(batch, feature_extractor):\n",
    "    audio_arr = batch[\"array\"]\n",
    "    input = feature_extractor(\n",
    "        audio_arr, sampling_rate=16000, padding=True, return_tensors=\"pt\"\n",
    "    )\n",
    "\n",
    "    batch[INPUT_FIELD] = input.input_values[0]\n",
    "    batch[LABEL_FIELD] = batch[\"labels\"]  # colname MUST be labels as Trainer will look for it by default\n",
    "\n",
    "    return batch\n",
    "\n",
    "# APPLY THE DATA PREP USING FEATURE EXTRACTOR TO ALL EXAMPLES\n",
    "ds = ds.map(\n",
    "    prepare_dataset,\n",
    "    fn_kwargs={\"feature_extractor\": feature_extractor},\n",
    "    #num_proc=15,\n",
    ")\n",
    "\n",
    "# LABEL TO ID\n",
    "ds = ds.class_encode_column(\"labels\")\n",
    "\n",
    "\n",
    "# INTRODUCE TRAIN TEST VAL SPLITS\n",
    "\n",
    "# # 90% train, 10% test + validation\n",
    "# test = ds[\"test\"].train_test_split(shuffle=True, test_size=0.1)\n",
    "\n",
    "# ds = DatasetDict({\n",
    "#     'train': train_testvalid['train'],\n",
    "#     'val': train_testvalid['test']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "7c7f801f-d071-4649-8c6d-9ff6faab7087",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n    train: Dataset({\n        features: ['file', 'labels', 'text', 'array', 'input_values'],\n        num_rows: 1440\n    })\n})\n"
     ]
    }
   ],
   "source": [
    "print(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "a4b753e0-7110-43dd-967a-e24a162ce59a",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "bert_model_name = \"bert-base-uncased\"\n",
    "\n",
    "def collate_fnTrain(batch):\n",
    "    # Get the inputs and targets from the batch\n",
    "    input_audio = [torch.tensor(item[\"input_values\"]) for item in batch]\n",
    "    input_text = [tokenizer.encode(item[\"text\"], padding='max_length', truncation=True, max_length=512, return_tensors=\"pt\")[0] for item in batch]\n",
    "    targets = [torch.tensor(item[\"labels\"]) for item in batch]\n",
    "\n",
    "    # Pad the audio inputs\n",
    "    input_audio = pad_sequence(input_audio, batch_first=True, padding_value=0.0)\n",
    "    \n",
    "    # Stack the text inputs\n",
    "    input_text = torch.stack(input_text)\n",
    "\n",
    "    # Stack the targets\n",
    "    targets = torch.stack(targets)\n",
    "\n",
    "    # Return the padded inputs and targets\n",
    "    return {\"input_audio\": input_audio, \"input_text\": input_text, \"targets\": targets}\n",
    "test_data_loader = DataLoader(ds[\"train\"], batch_size=2, shuffle=True,collate_fn=collate_fnTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "af5aeb9c-8130-422c-9d63-16dae001368c",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "# Check if GPU (cuda) is available\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device('cuda')\n",
    "else:\n",
    "    device = torch.device('cpu')\n",
    "\n",
    "#Define Labels related info\n",
    "num_labels=7\n",
    "label_mapping = ['angry', 'calm', 'disgust', 'fearful', 'happy', 'sad', 'surprised']\n",
    "\n",
    "#PROJECT_ROOT='/dbfs/FileStore/wav-files/models/'\n",
    "PROJECT_ROOT='/dbfs/FileStore/wav-files/FinalExperiments/FinalExperiments/'\n",
    "\n",
    "#MODEL_DIR='Augmented-HubertModel'\n",
    "\n",
    "model_id = \"facebook/hubert-base-ls960\"\n",
    "bert_model_name = \"bert-base-uncased\"\n",
    "hubert_model_name = os.path.join(PROJECT_ROOT, 'Augmented-HubertModel7Epochs')\n",
    "MultiModalDir = os.path.join(PROJECT_ROOT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "7d0ae5b9-5fe4-478d-b3af-f8d53f470770",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "from transformers import AutoModel, HubertForSequenceClassification\n",
    "\n",
    "# class MultimodalModel(nn.Module):\n",
    "#     '''\n",
    "#     Custom PyTorch model that takes as input both the audio features and the text embeddings, and concatenates the last hidden states from the Hubert and BERT models.\n",
    "#     '''\n",
    "#     def __init__(self, hubert_model_name, bert_model_name, num_labels):\n",
    "#         super().__init__()\n",
    "#         self.hubert = HubertForSequenceClassification.from_pretrained(hubert_model_name, num_labels=num_labels).hubert\n",
    "#         self.bert = AutoModel.from_pretrained(bert_model_name)\n",
    "#         self.classifier = nn.Linear(self.hubert.config.hidden_size + self.bert.config.hidden_size, num_labels)\n",
    "\n",
    "#     def forward(self, input_values, text):\n",
    "#         hubert_output = self.hubert(input_values).last_hidden_state\n",
    "\n",
    "#         bert_output = self.bert(text).last_hidden_state\n",
    "\n",
    "#         # Apply mean pooling along the sequence dimension\n",
    "#         hubert_output = hubert_output.mean(dim=1)\n",
    "#         bert_output = bert_output.mean(dim=1)\n",
    "\n",
    "#         concat_output = torch.cat((hubert_output, bert_output), dim=-1)\n",
    "#         logits = self.classifier(concat_output)\n",
    "#         return logits\n",
    "\n",
    "\n",
    "class MultimodalModel(nn.Module):\n",
    "    '''\n",
    "    Custom PyTorch model that takes as input both the audio features and the text embeddings, and concatenates the last hidden states from the Hubert and BERT models.\n",
    "    '''\n",
    "    def __init__(self, hubert_model_name, bert_model_name, num_labels, dropout_prob=0.1):\n",
    "        super().__init__()\n",
    "        self.hubert = HubertForSequenceClassification.from_pretrained(hubert_model_name, num_labels=num_labels).hubert\n",
    "        self.bert = AutoModel.from_pretrained(bert_model_name)\n",
    "        self.classifier = nn.Linear(self.hubert.config.hidden_size + self.bert.config.hidden_size, num_labels)\n",
    "        self.dropout = nn.Dropout(dropout_prob)\n",
    "\n",
    "    def forward(self, input_values, text):\n",
    "        # print(f'input_values is {input_values.shape}\\n')\n",
    "        # print(f'text is {text.shape}\\n')\n",
    "        hubert_output = self.hubert(input_values).last_hidden_state\n",
    "        # print(f'I am here before\\n')\n",
    "\n",
    "        bert_output = self.bert(text).last_hidden_state\n",
    "        # print(f'I am here\\n')\n",
    "\n",
    "        # Apply mean pooling along the sequence dimension\n",
    "        hubert_output = hubert_output.mean(dim=1)\n",
    "        bert_output = bert_output.mean(dim=1)\n",
    "\n",
    "        # Apply dropout to both Hubert and BERT output\n",
    "        hubert_output = self.dropout(hubert_output)\n",
    "        bert_output = self.dropout(bert_output)\n",
    "\n",
    "        concat_output = torch.cat((hubert_output, bert_output), dim=-1)\n",
    "        logits = self.classifier(concat_output)\n",
    "        return logits\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "4f8e0333-5491-471c-8d07-db8b1aa24e0b",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77bb795b46b146968f2ee5205f1ddc9d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "116b49a0a9724b059ef61ae73d50f803",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading pytorch_model.bin:   0%|          | 0.00/440M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias']\n- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "15763e749e234164baedf78a7d54e326",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d41304da69764d7180d0d8b09b65c215",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b203da21709549859d5be60830991ee4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def load_model():\n",
    "    # Load the model\n",
    "    multiModel = MultimodalModel(hubert_model_name, bert_model_name, num_labels)\n",
    "    multiModel.load_state_dict(torch.load(MultiModalDir + \"/MultiModal_model_state_dict.pth\",map_location=device),strict=False)\n",
    "    tokenizer = AutoTokenizer.from_pretrained(bert_model_name)\n",
    "    return multiModel,tokenizer\n",
    "\n",
    "ser_model,tokenizer = load_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "78478022-4d00-4295-a475-8f8842818b0e",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "720\n"
     ]
    }
   ],
   "source": [
    "print(len(test_data_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "3d1851df-7589-4a37-bc02-3899ebe2a354",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "multiModel = ser_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "3945dfee-ba5f-45b7-9659-215eb58e1237",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
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650\n\nProcessing test batch 651\n\nProcessing test batch 652\n\nProcessing test batch 653\n\nProcessing test batch 654\n\nProcessing test batch 655\n\nProcessing test batch 656\n\nProcessing test batch 657\n\nProcessing test batch 658\n\nProcessing test batch 659\n\nProcessing test batch 660\n\nProcessing test batch 661\n\nProcessing test batch 662\n\nProcessing test batch 663\n\nProcessing test batch 664\n\nProcessing test batch 665\n\nProcessing test batch 666\n\nProcessing test batch 667\n\nProcessing test batch 668\n\nProcessing test batch 669\n\nProcessing test batch 670\n\nProcessing test batch 671\n\nProcessing test batch 672\n\nProcessing test batch 673\n\nProcessing test batch 674\n\nProcessing test batch 675\n\nProcessing test batch 676\n\nProcessing test batch 677\n\nProcessing test batch 678\n\nProcessing test batch 679\n\nProcessing test batch 680\n\nProcessing test batch 681\n\nProcessing test batch 682\n\nProcessing test batch 683\n\nProcessing test batch 684\n\nProcessing test batch 685\n\nProcessing test batch 686\n\nProcessing test batch 687\n\nProcessing test batch 688\n\nProcessing test batch 689\n\nProcessing test batch 690\n\nProcessing test batch 691\n\nProcessing test batch 692\n\nProcessing test batch 693\n\nProcessing test batch 694\n\nProcessing test batch 695\n\nProcessing test batch 696\n\nProcessing test batch 697\n\nProcessing test batch 698\n\nProcessing test batch 699\n\nProcessing test batch 700\n\nProcessing test batch 701\n\nProcessing test batch 702\n\nProcessing test batch 703\n\nProcessing test batch 704\n\nProcessing test batch 705\n\nProcessing test batch 706\n\nProcessing test batch 707\n\nProcessing test batch 708\n\nProcessing test batch 709\n\nProcessing test batch 710\n\nProcessing test batch 711\n\nProcessing test batch 712\n\nProcessing test batch 713\n\nProcessing test batch 714\n\nProcessing test batch 715\n\nProcessing test batch 716\n\nProcessing test batch 717\n\nProcessing test batch 718\n\nProcessing test batch 719\n\nUnweighed Accuracy (UA) : 0.7931\nWeighed Accuracy (WA) : 0.7845\n"
     ]
    }
   ],
   "source": [
    "from torch.cuda.amp import GradScaler, autocast\n",
    "multiModel.to(device)  # Move the model to the device\n",
    "\n",
    "# Define the loss function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Set the model to evaluation mode\n",
    "multiModel.eval()\n",
    "\n",
    "# Initialize lists to store predictions and targets\n",
    "all_logits = []\n",
    "all_targets = []\n",
    "\n",
    "# Initialize variables to keep track of the total test loss and accuracy\n",
    "total_test_loss = 0.0\n",
    "total_test_acc = 0.0\n",
    "num_test_steps = 0\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Iterate over the test data\n",
    "with torch.no_grad():\n",
    "    for idx, batch in enumerate(test_data_loader):\n",
    "        print(f'Processing test batch {idx}\\n')\n",
    "        # Get the inputs and targets from the batch\n",
    "        input_audio = batch[\"input_audio\"].to(device)\n",
    "        input_text = batch[\"input_text\"].to(device)\n",
    "        targets = batch[\"targets\"].to(device)\n",
    "        \n",
    "        # Forward pass with autocasting\n",
    "        with autocast():\n",
    "            logits = multiModel(input_audio, input_text)\n",
    "            test_loss = criterion(logits, targets)\n",
    "        \n",
    "        # Accumulate the test loss and accuracy\n",
    "        total_test_loss += test_loss.item()\n",
    "        total_test_acc += (logits.argmax(dim=1) == targets).float().mean().item()\n",
    "        num_test_steps += 1\n",
    "\n",
    "        logits_np = (logits.argmax(dim=1)).cpu().numpy()\n",
    "\n",
    "        # Convert logits and targets to numpy arrays\n",
    "        #logits_np = logits.cpu().numpy()\n",
    "        targets_np = targets.cpu().numpy()\n",
    "\n",
    "        all_logits.extend(logits_np)\n",
    "        all_targets.extend(targets_np)\n",
    "\n",
    "# Calculate the unbalanced accuracy for each class\n",
    "unbalanced_acc = accuracy_score(all_targets, all_logits)\n",
    "print(f\"Unweighed Accuracy (UA) : {unbalanced_acc:.4f}\")\n",
    "balanced_acc = balanced_accuracy_score(all_targets, all_logits)\n",
    "print(f\"Weighed Accuracy (WA) : {balanced_acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "b551c4ec-e42d-40e5-8435-c0c3faf8ce1b",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/DC_su06.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/DC_su07.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/DC_su08.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/DC_su09.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/DC_su10.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/DC_su11.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/DC_su12.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/DC_su13.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/DC_su14.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/DC_su15.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/JE_a01.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/JE_a02.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/JE_a03.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/JE_a04.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/JE_a05.wav']"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directory_path = '/dbfs/FileStore/wav-files/FinalExperiments/SAVEE'\n",
    "directory_path_with_forward_slashes = directory_path.replace(\"\\\\\", \"/\")\n",
    "\n",
    "tempwavfiles = sorted(glob.glob(directory_path+'/**/*.wav', recursive=True))\n",
    "tempwavfiles[110:125]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "5c4595c1-20dc-422d-9985-4613ba62fa7c",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "# Get labels from filename\n",
    "def get_label_from_filename(file_list):\n",
    "    '''\n",
    "    Function to extract label from the filename.\n",
    "    Argument: Full file path.\n",
    "    '''\n",
    "    \n",
    "    feeling_list=[]\n",
    "    item_list = []\n",
    "    for fullfilename in file_list:\n",
    "        item = get_file_name(fullfilename)\n",
    "        item_list.append(fullfilename)\n",
    "        #print(f'item is {item}')\n",
    "        word = item.split('_')[1][:-6]\n",
    "        feeling_list.append(word)\n",
    "    return feeling_list,item_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "3dcee7fc-baa5-486e-9ab2-7c441673eebd",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "df_SAVEE = pd.DataFrame({'Filenames':get_label_from_filename(tempwavfiles)[1],'Emotions':get_label_from_filename(tempwavfiles)[0],'Dataset':['SAVEE']*len(tempwavfiles)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "e1f52664-4dcc-4b93-94dd-9b95cd29ba53",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "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>Filenames</th>\n",
       "      <th>Emotions</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>a</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>a</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>a</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>a</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>a</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>su</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>su</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>su</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>su</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>su</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Filenames Emotions Dataset\n",
       "0    /dbfs/FileStore/wav-files/FinalExperiments/SAV...        a   SAVEE\n",
       "1    /dbfs/FileStore/wav-files/FinalExperiments/SAV...        a   SAVEE\n",
       "2    /dbfs/FileStore/wav-files/FinalExperiments/SAV...        a   SAVEE\n",
       "3    /dbfs/FileStore/wav-files/FinalExperiments/SAV...        a   SAVEE\n",
       "4    /dbfs/FileStore/wav-files/FinalExperiments/SAV...        a   SAVEE\n",
       "..                                                 ...      ...     ...\n",
       "475  /dbfs/FileStore/wav-files/FinalExperiments/SAV...       su   SAVEE\n",
       "476  /dbfs/FileStore/wav-files/FinalExperiments/SAV...       su   SAVEE\n",
       "477  /dbfs/FileStore/wav-files/FinalExperiments/SAV...       su   SAVEE\n",
       "478  /dbfs/FileStore/wav-files/FinalExperiments/SAV...       su   SAVEE\n",
       "479  /dbfs/FileStore/wav-files/FinalExperiments/SAV...       su   SAVEE\n",
       "\n",
       "[480 rows x 3 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SAVEE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "eabe4277-1023-457e-9b4e-c73a75c75324",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "## Standardizing the label to be consistent across datasets!\n",
    "emo_dict = {\n",
    "    'fru':'angry',\n",
    "    'a':'angry',\n",
    "    'ANG':'angry',\n",
    "    'ANG': 'angry',\n",
    "    'neu':'calm',\n",
    "    'n':'calm',\n",
    "    'NEU':'calm',\n",
    "    'neutral':'calm',\n",
    "    'exc':'happy',\n",
    "    'h':'happy',\n",
    "    'HAP':'happy',\n",
    "    'sad':'sad',\n",
    "    'SAD':'sad',\n",
    "    'sa':'sad',\n",
    "    'ang':'angry',\n",
    "    'hap':'happy',\n",
    "    'sur':'surprised',\n",
    "    'su':'surprised',\n",
    "    'ps':'surprised',\n",
    "    'fea':'fearful',\n",
    "    'fear':'fearful',\n",
    "    'f':'fearful',\n",
    "    'FEA':'fearful',\n",
    "    'dis':'disgust',\n",
    "    'DIS':'disgust',\n",
    "    'd':'disgust'\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "a42d8b7f-bf7b-4f54-aa52-6bf11a7c1d38",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "df_SAVEE['Emotions'] =   df_SAVEE['Emotions'].apply(lambda x: emo_dict[x] if x in emo_dict else x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "7f608ee4-09eb-45b0-859e-13ebe0f29179",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "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>Filenames</th>\n",
       "      <th>Emotions</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Filenames   Emotions Dataset\n",
       "0    /dbfs/FileStore/wav-files/FinalExperiments/SAV...      angry   SAVEE\n",
       "1    /dbfs/FileStore/wav-files/FinalExperiments/SAV...      angry   SAVEE\n",
       "2    /dbfs/FileStore/wav-files/FinalExperiments/SAV...      angry   SAVEE\n",
       "3    /dbfs/FileStore/wav-files/FinalExperiments/SAV...      angry   SAVEE\n",
       "4    /dbfs/FileStore/wav-files/FinalExperiments/SAV...      angry   SAVEE\n",
       "..                                                 ...        ...     ...\n",
       "475  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  surprised   SAVEE\n",
       "476  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  surprised   SAVEE\n",
       "477  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  surprised   SAVEE\n",
       "478  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  surprised   SAVEE\n",
       "479  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  surprised   SAVEE\n",
       "\n",
       "[480 rows x 3 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SAVEE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "677eaa93-b32f-4022-b00c-f00f3c847586",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "df_SAVEE.to_csv('/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/SAVEE_Baseline.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "96844ae9-ffd1-4149-9738-ed44b80da7a2",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'/dbfs/FileStore/wav-files/FinalExperiments/SAVEE/ALL/DC_a01.wav'"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SAVEE.Filenames.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "959edaae-8195-4235-aee1-c5a3eef660d0",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "tqdm.pandas()\n",
    "df_SAVEE['Transcription'] = df_SAVEE['Filenames'].progress_apply(lambda x: speechtoText(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "8d3de44c-0a19-48bd-bdde-58a7d5ac0ed0",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "## Defining a general purpose function\n",
    "def get_file_nameonly(link):\n",
    "    '''\n",
    "    General purpose function to get absolute filename from relative path\n",
    "    Parameters: Filename with relative path\n",
    "    Returns: Filename\n",
    "    '''\n",
    "    newPath = link.replace(os.sep, '/')\n",
    "    filename = newPath.split('/')[::-1][0]\n",
    "\n",
    "    return filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "a244ce37-63c3-4908-855c-46581894aefb",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/480 [00:00<?, ?it/s]\r100%|██████████| 480/480 [00:00<00:00, 371931.63it/s]\n"
     ]
    }
   ],
   "source": [
    "tqdm.pandas()\n",
    "df_SAVEE['FilenameOnly'] = df_SAVEE['Filenames'].progress_apply(lambda x: get_file_nameonly(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "038bee54-f379-48e0-a21e-3d25d002acdc",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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>Filenames</th>\n",
       "      <th>Emotions</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Transcription</th>\n",
       "      <th>FilenameOnly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Shall you dark suit in greasy wash water all ...</td>\n",
       "      <td>DC_a01.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Don't ask me to carry an oily rag like that.</td>\n",
       "      <td>DC_a02.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Will you tell me why?</td>\n",
       "      <td>DC_a03.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Who authorised your limited expense account?</td>\n",
       "      <td>DC_a04.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Destroy every file related to my audits.</td>\n",
       "      <td>DC_a05.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Salvation reconsidered.</td>\n",
       "      <td>KL_su11.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>properly used the prison book as an excellent...</td>\n",
       "      <td>KL_su12.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Lighting windows glowed dual bright through t...</td>\n",
       "      <td>KL_su13.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>But this doesn't distract from its merit as a...</td>\n",
       "      <td>KL_su14.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>He further proposed grant of an unsubesified ...</td>\n",
       "      <td>KL_su15.wav</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Filenames  ... FilenameOnly\n",
       "0    /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...   DC_a01.wav\n",
       "1    /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...   DC_a02.wav\n",
       "2    /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...   DC_a03.wav\n",
       "3    /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...   DC_a04.wav\n",
       "4    /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...   DC_a05.wav\n",
       "..                                                 ...  ...          ...\n",
       "475  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...  KL_su11.wav\n",
       "476  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...  KL_su12.wav\n",
       "477  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...  KL_su13.wav\n",
       "478  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...  KL_su14.wav\n",
       "479  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...  KL_su15.wav\n",
       "\n",
       "[480 rows x 5 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SAVEE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
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      "rowLimit": 10000
     },
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     "nuid": "c45a8b62-25e3-4c8b-ba9b-41c63f6c43ee",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "df_SAVEE.to_csv('/dbfs/FileStore/wav-files/FinalExperiments/SAVEE_Baseline.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
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     },
     "inputWidgets": {},
     "nuid": "4aecb8d1-5bbe-4438-ad03-cb70ca111105",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>FilenameOnly</th>\n",
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       "      <td>Shall you dark suit in greasy wash water all ...</td>\n",
       "      <td>DC_a01.wav</td>\n",
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       "      <th>1</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
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       "      <td>SAVEE</td>\n",
       "      <td>Don't ask me to carry an oily rag like that.</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Will you tell me why?</td>\n",
       "      <td>DC_a03.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Who authorised your limited expense account?</td>\n",
       "      <td>DC_a04.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Destroy every file related to my audits.</td>\n",
       "      <td>DC_a05.wav</td>\n",
       "    </tr>\n",
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       "      <th>475</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Salvation reconsidered.</td>\n",
       "      <td>KL_su11.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>properly used the prison book as an excellent...</td>\n",
       "      <td>KL_su12.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Lighting windows glowed dual bright through t...</td>\n",
       "      <td>KL_su13.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>But this doesn't distract from its merit as a...</td>\n",
       "      <td>KL_su14.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>He further proposed grant of an unsubesified ...</td>\n",
       "      <td>KL_su15.wav</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 5 columns</p>\n",
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      ],
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       "                                             Filenames  ... FilenameOnly\n",
       "0    /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...   DC_a01.wav\n",
       "1    /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...   DC_a02.wav\n",
       "2    /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...   DC_a03.wav\n",
       "3    /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...   DC_a04.wav\n",
       "4    /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...   DC_a05.wav\n",
       "..                                                 ...  ...          ...\n",
       "475  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...  KL_su11.wav\n",
       "476  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...  KL_su12.wav\n",
       "477  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...  KL_su13.wav\n",
       "478  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...  KL_su14.wav\n",
       "479  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...  KL_su15.wav\n",
       "\n",
       "[480 rows x 5 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/dbfs/FileStore/wav-files/FinalExperiments/SAVEE_Baseline.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "2e8793cd-fb7a-4e6f-96b3-b263f298176a",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "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>Filenames</th>\n",
       "      <th>Emotions</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Transcription</th>\n",
       "      <th>FilenameOnly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Shall you dark suit in greasy wash water all ...</td>\n",
       "      <td>DC_a01.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Don't ask me to carry an oily rag like that.</td>\n",
       "      <td>DC_a02.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Will you tell me why?</td>\n",
       "      <td>DC_a03.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Who authorised your limited expense account?</td>\n",
       "      <td>DC_a04.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>angry</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Destroy every file related to my audits.</td>\n",
       "      <td>DC_a05.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Salvation reconsidered.</td>\n",
       "      <td>KL_su11.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>properly used the prison book as an excellent...</td>\n",
       "      <td>KL_su12.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>Lighting windows glowed dual bright through t...</td>\n",
       "      <td>KL_su13.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>But this doesn't distract from its merit as a...</td>\n",
       "      <td>KL_su14.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/SAV...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>SAVEE</td>\n",
       "      <td>He further proposed grant of an unsubesified ...</td>\n",
       "      <td>KL_su15.wav</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Filenames  ... FilenameOnly\n",
       "0    /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...   DC_a01.wav\n",
       "1    /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...   DC_a02.wav\n",
       "2    /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...   DC_a03.wav\n",
       "3    /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...   DC_a04.wav\n",
       "4    /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...   DC_a05.wav\n",
       "..                                                 ...  ...          ...\n",
       "475  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...  KL_su11.wav\n",
       "476  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...  KL_su12.wav\n",
       "477  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...  KL_su13.wav\n",
       "478  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...  KL_su14.wav\n",
       "479  /dbfs/FileStore/wav-files/FinalExperiments/SAV...  ...  KL_su15.wav\n",
       "\n",
       "[480 rows x 5 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SAVEE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "1e4b2eaa-19fc-4497-ade2-b4114fa56288",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Filenames', 'Emotions', 'Dataset', 'Transcription', 'FilenameOnly'], dtype='object')\nDownloading and preparing dataset savee_benchmarking/clean to /dbfs/FileStore/wav-files/FinalExperiments/cached_emo/savee_benchmarking/clean-9b66cbcbd5068a74/0.0.0/0ec8621888fd47f93241a3889ecc6d354cf9cc628e2460507333c36c695eb8c1...\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3b0def8309634df684c0e2514e64d099",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "files is /dbfs/FileStore/wav-files/FinalExperiments\n\naudio dir is /dbfs/FileStore/wav-files/FinalExperiments/SAVEE/\n\nDataset savee_benchmarking downloaded and prepared to /dbfs/FileStore/wav-files/FinalExperiments/cached_emo/savee_benchmarking/clean-9b66cbcbd5068a74/0.0.0/0ec8621888fd47f93241a3889ecc6d354cf9cc628e2460507333c36c695eb8c1. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6340bcbcc704ea9876e952676b7d823",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating test dataset\n",
    "\n",
    "PROJECT_ROOT='/dbfs/FileStore/wav-files/FinalExperiments'\n",
    "dataset_config = {  \n",
    "  \"LOADING_SCRIPT_FILES\": os.path.join(PROJECT_ROOT, \"SAVEE_Benchmarking.py\"),\n",
    "  \"CONFIG_NAME\": \"clean\",\n",
    "  \"DATA_DIR\": os.path.join(PROJECT_ROOT),\n",
    "  \"CACHE_DIR\": os.path.join(PROJECT_ROOT, \"cached_emo/\"),\n",
    "}   \n",
    "ds = load_dataset(\n",
    "  dataset_config[\"LOADING_SCRIPT_FILES\"],\n",
    "  dataset_config[\"CONFIG_NAME\"],    \n",
    "  data_dir=dataset_config[\"DATA_DIR\"],\n",
    "  cache_dir=dataset_config[\"CACHE_DIR\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "35c7d648-382c-4fb3-aac7-b325ea880987",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n    train: Dataset({\n        features: ['file', 'labels', 'text'],\n        num_rows: 480\n    })\n})\n"
     ]
    }
   ],
   "source": [
    "print(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "23d486ee-72b1-49a6-9016-c25d1093eeef",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab1c8bc4bc13426ea171b2a506535306",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/480 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fddd966689a046cbb4a837f76ea1007f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/480 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1543213999054f77904cacc2fec05087",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Casting to class labels:   0%|          | 0/480 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ds = ds.map(\n",
    "    lambda x: {\n",
    "        \"array\": list(librosa.load(x[\"file\"], sr=16000, mono=True)[0])\n",
    "    }\n",
    ")\n",
    "\n",
    "\n",
    "# PROCESS THE DATASET TO THE FORMAT EXPECTED BY THE MODEL FOR TRAINING\n",
    "\n",
    "INPUT_FIELD = \"input_values\"\n",
    "LABEL_FIELD = \"labels\"\n",
    "\n",
    "def prepare_dataset(batch, feature_extractor):\n",
    "    audio_arr = batch[\"array\"]\n",
    "    input = feature_extractor(\n",
    "        audio_arr, sampling_rate=16000, padding=True, return_tensors=\"pt\"\n",
    "    )\n",
    "\n",
    "    batch[INPUT_FIELD] = input.input_values[0]\n",
    "    batch[LABEL_FIELD] = batch[\"labels\"]  # colname MUST be labels as Trainer will look for it by default\n",
    "\n",
    "    return batch\n",
    "\n",
    "# APPLY THE DATA PREP USING FEATURE EXTRACTOR TO ALL EXAMPLES\n",
    "ds = ds.map(\n",
    "    prepare_dataset,\n",
    "    fn_kwargs={\"feature_extractor\": feature_extractor},\n",
    "    #num_proc=15,\n",
    ")\n",
    "\n",
    "# LABEL TO ID\n",
    "ds = ds.class_encode_column(\"labels\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "6d059f8d-8660-4d4e-a8fc-5ac41fa745bb",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n    train: Dataset({\n        features: ['file', 'labels', 'text', 'array', 'input_values'],\n        num_rows: 480\n    })\n})\n"
     ]
    }
   ],
   "source": [
    "print(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "7e41a432-d55b-46ad-8382-e0d7dcad4a11",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "test_data_loader = DataLoader(ds[\"train\"], batch_size=2, shuffle=True,collate_fn=collate_fnTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "bf3e0833-25a8-4ed4-9b7e-2560e48572d5",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing test batch 0\n\nProcessing test batch 1\n\nProcessing test batch 2\n\nProcessing test batch 3\n\nProcessing test batch 4\n\nProcessing test batch 5\n\nProcessing test batch 6\n\nProcessing test batch 7\n\nProcessing test batch 8\n\nProcessing test batch 9\n\nProcessing test batch 10\n\nProcessing test batch 11\n\nProcessing test batch 12\n\nProcessing test batch 13\n\nProcessing test batch 14\n\nProcessing test batch 15\n\nProcessing test batch 16\n\nProcessing test batch 17\n\nProcessing test batch 18\n\nProcessing test batch 19\n\nProcessing test batch 20\n\nProcessing test batch 21\n\nProcessing test batch 22\n\nProcessing test batch 23\n\nProcessing test batch 24\n\nProcessing test batch 25\n\nProcessing test batch 26\n\nProcessing test batch 27\n\nProcessing test batch 28\n\nProcessing test batch 29\n\nProcessing test batch 30\n\nProcessing test batch 31\n\nProcessing test batch 32\n\nProcessing test batch 33\n\nProcessing test batch 34\n\nProcessing test batch 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     ]
    }
   ],
   "source": [
    "from torch.cuda.amp import GradScaler, autocast\n",
    "multiModel.to(device)  # Move the model to the device\n",
    "\n",
    "# Define the loss function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Set the model to evaluation mode\n",
    "multiModel.eval()\n",
    "\n",
    "# Initialize lists to store predictions and targets\n",
    "all_logits = []\n",
    "all_targets = []\n",
    "\n",
    "# Initialize variables to keep track of the total test loss and accuracy\n",
    "total_test_loss = 0.0\n",
    "total_test_acc = 0.0\n",
    "num_test_steps = 0\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Iterate over the test data\n",
    "with torch.no_grad():\n",
    "    for idx, batch in enumerate(test_data_loader):\n",
    "        print(f'Processing test batch {idx}\\n')\n",
    "        # Get the inputs and targets from the batch\n",
    "        input_audio = batch[\"input_audio\"].to(device)\n",
    "        input_text = batch[\"input_text\"].to(device)\n",
    "        targets = batch[\"targets\"].to(device)\n",
    "        \n",
    "        # Forward pass with autocasting\n",
    "        with autocast():\n",
    "            logits = multiModel(input_audio, input_text)\n",
    "            test_loss = criterion(logits, targets)\n",
    "        \n",
    "        # Accumulate the test loss and accuracy\n",
    "        total_test_loss += test_loss.item()\n",
    "        total_test_acc += (logits.argmax(dim=1) == targets).float().mean().item()\n",
    "        num_test_steps += 1\n",
    "\n",
    "        logits_np = (logits.argmax(dim=1)).cpu().numpy()\n",
    "\n",
    "        # Convert logits and targets to numpy arrays\n",
    "        #logits_np = logits.cpu().numpy()\n",
    "        targets_np = targets.cpu().numpy()\n",
    "\n",
    "        all_logits.extend(logits_np)\n",
    "        all_targets.extend(targets_np)\n",
    "\n",
    "# Calculate the unbalanced accuracy for each class\n",
    "unbalanced_acc = accuracy_score(all_targets, all_logits)\n",
    "print(f\"Unweighed Accuracy (UA) : {unbalanced_acc:.4f}\")\n",
    "balanced_acc = balanced_accuracy_score(all_targets, all_logits)\n",
    "print(f\"Weighed Accuracy (WA) : {balanced_acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "4f10d0ff-7fcf-47ea-b3c0-549e86cc33fd",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_phone_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_phone_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_phone_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_phone_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_phone_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_phone_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pick_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pick_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pick_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pick_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pick_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pick_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pick_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pike_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pike_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pike_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pike_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pike_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pike_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pike_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pole_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pole_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pole_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pole_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pole_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pole_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pole_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pool_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pool_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pool_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pool_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pool_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pool_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_pool_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_puff_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_puff_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_puff_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_puff_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_puff_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_puff_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_puff_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rag_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rag_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rag_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rag_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rag_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rag_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rag_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_raid_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_raid_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_raid_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_raid_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_raid_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_raid_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_raid_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rain_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rain_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rain_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rain_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rain_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rain_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rain_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_raise_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_raise_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_raise_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_raise_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_raise_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_raise_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_raise_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rat_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rat_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rat_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rat_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rat_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rat_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rat_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_reach_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_reach_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_reach_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_reach_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_reach_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_reach_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_reach_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_read_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_read_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_read_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_read_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_read_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_read_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_read_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_red_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_red_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_red_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_red_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_red_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_red_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_red_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_ring_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_ring_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_ring_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_ring_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_ring_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_ring_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_ring_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_ripe_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_ripe_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_ripe_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_ripe_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_ripe_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_ripe_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_ripe_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_road_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_road_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_road_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_road_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_road_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_road_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_road_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_room_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_room_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_room_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_room_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_room_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_room_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_room_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rose_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rose_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rose_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rose_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rose_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rose_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rose_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rot_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rot_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rot_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rot_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rot_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rot_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rot_sad.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rough_angry.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rough_disgust.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rough_fear.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rough_happy.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rough_neutral.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rough_ps.wav',\n",
       " '/dbfs/FileStore/wav-files/FinalExperiments/TESS/OAF_rough_sad.wav',\n",
       " ...]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directory_path = '/dbfs/FileStore/wav-files/FinalExperiments/TESS/'\n",
    "directory_path_with_forward_slashes = directory_path.replace(\"\\\\\", \"/\")\n",
    "\n",
    "tempwavfiles = sorted(glob.glob(directory_path+'/**/*.wav', recursive=True))\n",
    "tempwavfiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "879241be-2532-4add-a176-30179bdd2237",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2800"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tempwavfiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "e32e7489-f025-4dbb-a7e0-1205653a2f45",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "# Get labels from filename\n",
    "def get_label_from_filename(file_list):\n",
    "    '''\n",
    "    Function to extract label from the filename.\n",
    "    Argument: Full file path.\n",
    "    '''\n",
    "    \n",
    "    feeling_list=[]\n",
    "    item_list = []\n",
    "    for fullfilename in file_list:\n",
    "        item = get_file_name(fullfilename)\n",
    "        item_list.append(fullfilename)\n",
    "        #print(f'item is {item}')\n",
    "        word = item.split('_')[2].split('.')[0]\n",
    "        feeling_list.append(word)\n",
    "    return feeling_list,item_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "17b9e444-bfb4-4130-ba2d-5f04564a5f9a",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "df_TESS = pd.DataFrame({'Filenames':get_label_from_filename(tempwavfiles)[1],'Emotions':get_label_from_filename(tempwavfiles)[0],'Dataset':['TESS']*len(tempwavfiles)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "9d03a095-0c33-461e-869d-c34e72260071",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "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>Filenames</th>\n",
       "      <th>Emotions</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>angry</td>\n",
       "      <td>TESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>disgust</td>\n",
       "      <td>TESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>fear</td>\n",
       "      <td>TESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>happy</td>\n",
       "      <td>TESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>neutral</td>\n",
       "      <td>TESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2795</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>fear</td>\n",
       "      <td>TESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2796</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>happy</td>\n",
       "      <td>TESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2797</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>neutral</td>\n",
       "      <td>TESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2798</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>ps</td>\n",
       "      <td>TESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2799</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>sad</td>\n",
       "      <td>TESS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2800 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Filenames Emotions Dataset\n",
       "0     /dbfs/FileStore/wav-files/FinalExperiments/TES...    angry    TESS\n",
       "1     /dbfs/FileStore/wav-files/FinalExperiments/TES...  disgust    TESS\n",
       "2     /dbfs/FileStore/wav-files/FinalExperiments/TES...     fear    TESS\n",
       "3     /dbfs/FileStore/wav-files/FinalExperiments/TES...    happy    TESS\n",
       "4     /dbfs/FileStore/wav-files/FinalExperiments/TES...  neutral    TESS\n",
       "...                                                 ...      ...     ...\n",
       "2795  /dbfs/FileStore/wav-files/FinalExperiments/TES...     fear    TESS\n",
       "2796  /dbfs/FileStore/wav-files/FinalExperiments/TES...    happy    TESS\n",
       "2797  /dbfs/FileStore/wav-files/FinalExperiments/TES...  neutral    TESS\n",
       "2798  /dbfs/FileStore/wav-files/FinalExperiments/TES...       ps    TESS\n",
       "2799  /dbfs/FileStore/wav-files/FinalExperiments/TES...      sad    TESS\n",
       "\n",
       "[2800 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "df_TESS"
   ]
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   "source": [
    "## Standardizing the label to be consistent across datasets!\n",
    "emo_dict = {\n",
    "    'fru':'angry',\n",
    "    'a':'angry',\n",
    "    'ANG':'angry',\n",
    "    'ANG': 'angry',\n",
    "    'neu':'calm',\n",
    "    'n':'calm',\n",
    "    'NEU':'calm',\n",
    "    'exc':'happy',\n",
    "    'h':'happy',\n",
    "    'HAP':'happy',\n",
    "    'sad':'sad',\n",
    "    'SAD':'sad',\n",
    "    'sa':'sad',\n",
    "    'ang':'angry',\n",
    "    'hap':'happy',\n",
    "    'sur':'surprised',\n",
    "    'su':'surprised',\n",
    "    'ps':'surprised',\n",
    "    'fea':'fearful',\n",
    "    'fear':'fearful',\n",
    "    'f':'fearful',\n",
    "    'FEA':'fearful',\n",
    "    'dis':'disgust',\n",
    "    'DIS':'disgust',\n",
    "    'd':'disgust'\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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   "outputs": [],
   "source": [
    "df_TESS['Emotions'] =    df_TESS['Emotions'].apply(lambda x: emo_dict[x] if x in emo_dict else x)"
   ]
  },
  {
   "cell_type": "code",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filenames</th>\n",
       "      <th>Emotions</th>\n",
       "      <th>Dataset</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
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       "      <th>1</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>fearful</td>\n",
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       "      <th>3</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
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       "      <td>TESS</td>\n",
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       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
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       "    <tr>\n",
       "      <th>2796</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>happy</td>\n",
       "      <td>TESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2797</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>neutral</td>\n",
       "      <td>TESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2798</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>TESS</td>\n",
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       "    <tr>\n",
       "      <th>2799</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>sad</td>\n",
       "      <td>TESS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2800 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Filenames   Emotions Dataset\n",
       "0     /dbfs/FileStore/wav-files/FinalExperiments/TES...      angry    TESS\n",
       "1     /dbfs/FileStore/wav-files/FinalExperiments/TES...    disgust    TESS\n",
       "2     /dbfs/FileStore/wav-files/FinalExperiments/TES...    fearful    TESS\n",
       "3     /dbfs/FileStore/wav-files/FinalExperiments/TES...      happy    TESS\n",
       "4     /dbfs/FileStore/wav-files/FinalExperiments/TES...    neutral    TESS\n",
       "...                                                 ...        ...     ...\n",
       "2795  /dbfs/FileStore/wav-files/FinalExperiments/TES...    fearful    TESS\n",
       "2796  /dbfs/FileStore/wav-files/FinalExperiments/TES...      happy    TESS\n",
       "2797  /dbfs/FileStore/wav-files/FinalExperiments/TES...    neutral    TESS\n",
       "2798  /dbfs/FileStore/wav-files/FinalExperiments/TES...  surprised    TESS\n",
       "2799  /dbfs/FileStore/wav-files/FinalExperiments/TES...        sad    TESS\n",
       "\n",
       "[2800 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_TESS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Filenames    False\n",
       "Emotions     False\n",
       "Dataset      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_TESS.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
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    }
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   "outputs": [],
   "source": [
    "df_TESS = df_TESS[df_TESS.Emotions!='neutral']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
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    {
     "output_type": "execute_result",
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       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
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       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
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       "      <td>TESS</td>\n",
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       "<p>2400 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Filenames   Emotions Dataset\n",
       "0     /dbfs/FileStore/wav-files/FinalExperiments/TES...      angry    TESS\n",
       "1     /dbfs/FileStore/wav-files/FinalExperiments/TES...    disgust    TESS\n",
       "2     /dbfs/FileStore/wav-files/FinalExperiments/TES...    fearful    TESS\n",
       "3     /dbfs/FileStore/wav-files/FinalExperiments/TES...      happy    TESS\n",
       "5     /dbfs/FileStore/wav-files/FinalExperiments/TES...  surprised    TESS\n",
       "...                                                 ...        ...     ...\n",
       "2794  /dbfs/FileStore/wav-files/FinalExperiments/TES...    disgust    TESS\n",
       "2795  /dbfs/FileStore/wav-files/FinalExperiments/TES...    fearful    TESS\n",
       "2796  /dbfs/FileStore/wav-files/FinalExperiments/TES...      happy    TESS\n",
       "2798  /dbfs/FileStore/wav-files/FinalExperiments/TES...  surprised    TESS\n",
       "2799  /dbfs/FileStore/wav-files/FinalExperiments/TES...        sad    TESS\n",
       "\n",
       "[2400 rows x 3 columns]"
      ]
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     "execution_count": 19,
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    "df_TESS"
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[00:31<16:28,  2.37it/s]\r  2%|▎         | 60/2400 [00:31<16:26,  2.37it/s]\r  3%|▎         | 61/2400 [00:31<16:19,  2.39it/s]\r  3%|▎         | 62/2400 [00:32<16:25,  2.37it/s]\r  3%|▎         | 63/2400 [00:32<17:15,  2.26it/s]\r  3%|▎         | 64/2400 [00:33<16:13,  2.40it/s]\r  3%|▎         | 65/2400 [00:33<16:58,  2.29it/s]\r  3%|▎         | 66/2400 [00:34<16:57,  2.29it/s]\r  3%|▎         | 67/2400 [00:34<16:37,  2.34it/s]\r  3%|▎         | 68/2400 [00:34<17:20,  2.24it/s]\r  3%|▎         | 69/2400 [00:35<17:13,  2.26it/s]\r  3%|▎         | 70/2400 [00:35<15:50,  2.45it/s]\r  3%|▎         | 71/2400 [00:36<16:46,  2.31it/s]\r  3%|▎         | 72/2400 [00:36<16:24,  2.36it/s]\r  3%|▎         | 73/2400 [00:37<15:53,  2.44it/s]\r  3%|▎         | 74/2400 [00:37<16:23,  2.37it/s]\r  3%|▎         | 75/2400 [00:37<16:19,  2.37it/s]\r  3%|▎         | 76/2400 [00:38<16:09,  2.40it/s]\r  3%|▎         | 77/2400 [00:38<17:17,  2.24it/s]\r  3%|▎         | 78/2400 [00:39<17:27,  2.22it/s]\r  3%|▎         | 79/2400 [00:39<17:07,  2.26it/s]\r  3%|▎         | 80/2400 [00:40<18:38,  2.07it/s]\r  3%|▎         | 81/2400 [00:40<17:51,  2.16it/s]\r  3%|▎         | 82/2400 [00:41<16:24,  2.35it/s]\r  3%|▎         | 83/2400 [00:41<18:01,  2.14it/s]\r  4%|▎         | 84/2400 [00:42<17:42,  2.18it/s]\r  4%|▎         | 85/2400 [00:42<16:59,  2.27it/s]\r  4%|▎         | 86/2400 [00:42<17:41,  2.18it/s]\r  4%|▎         | 87/2400 [00:43<17:51,  2.16it/s]\r  4%|▎         | 88/2400 [00:43<17:06,  2.25it/s]\r  4%|▎         | 89/2400 [00:44<18:10,  2.12it/s]\r  4%|▍         | 90/2400 [00:44<17:41,  2.18it/s]\r  4%|▍         | 91/2400 [00:45<17:51,  2.15it/s]\r  4%|▍         | 92/2400 [00:45<18:01,  2.13it/s]\r  4%|▍         | 93/2400 [00:46<17:31,  2.19it/s]\r  4%|▍         | 94/2400 [00:46<17:05,  2.25it/s]\r  4%|▍         | 95/2400 [00:47<17:38,  2.18it/s]\r  4%|▍         | 96/2400 [00:47<17:07,  2.24it/s]\r  4%|▍         | 97/2400 [00:47<16:14,  2.36it/s]\r  4%|▍         | 98/2400 [00:48<16:45,  2.29it/s]\r  4%|▍         | 99/2400 [00:48<16:20,  2.35it/s]\r  4%|▍         | 100/2400 [00:49<15:13,  2.52it/s]\r  4%|▍         | 101/2400 [00:49<16:12,  2.36it/s]\r  4%|▍         | 102/2400 [00:49<15:46,  2.43it/s]\r  4%|▍         | 103/2400 [00:50<16:02,  2.39it/s]\r  4%|▍         | 104/2400 [00:50<17:06,  2.24it/s]\r  4%|▍         | 105/2400 [00:51<16:54,  2.26it/s]\r  4%|▍         | 106/2400 [00:51<16:43,  2.29it/s]\r  4%|▍         | 107/2400 [00:52<17:12,  2.22it/s]\r  4%|▍         | 108/2400 [00:52<16:17,  2.35it/s]\r  5%|▍         | 109/2400 [00:53<16:25,  2.33it/s]\r  5%|▍         | 110/2400 [00:53<15:57,  2.39it/s]\r  5%|▍         | 111/2400 [00:53<15:58,  2.39it/s]\r  5%|▍         | 112/2400 [00:54<15:54,  2.40it/s]\r  5%|▍         | 113/2400 [00:54<16:23,  2.32it/s]\r  5%|▍         | 114/2400 [00:55<15:10,  2.51it/s]\r  5%|▍         | 115/2400 [00:55<15:27,  2.46it/s]\r  5%|▍         | 116/2400 [00:55<15:47,  2.41it/s]\r  5%|▍         | 117/2400 [00:56<16:19, 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 6%|▌         | 137/2400 [01:04<16:00,  2.36it/s]\r  6%|▌         | 138/2400 [01:05<16:17,  2.31it/s]\r  6%|▌         | 139/2400 [01:05<15:56,  2.36it/s]\r  6%|▌         | 140/2400 [01:06<17:01,  2.21it/s]\r  6%|▌         | 141/2400 [01:06<16:33,  2.27it/s]\r  6%|▌         | 142/2400 [01:07<16:37,  2.26it/s]\r  6%|▌         | 143/2400 [01:07<17:19,  2.17it/s]\r  6%|▌         | 144/2400 [01:07<16:25,  2.29it/s]\r  6%|▌         | 145/2400 [01:08<15:52,  2.37it/s]\r  6%|▌         | 146/2400 [01:08<17:01,  2.21it/s]\r  6%|▌         | 147/2400 [01:09<16:17,  2.30it/s]\r  6%|▌         | 148/2400 [01:09<15:35,  2.41it/s]\r  6%|▌         | 149/2400 [01:10<16:12,  2.31it/s]\r  6%|▋         | 150/2400 [01:10<15:26,  2.43it/s]\r  6%|▋         | 151/2400 [01:10<16:20,  2.29it/s]\r  6%|▋         | 152/2400 [01:11<17:18,  2.16it/s]\r  6%|▋         | 153/2400 [01:11<17:20,  2.16it/s]\r  6%|▋         | 154/2400 [01:12<16:41,  2.24it/s]\r  6%|▋         | 155/2400 [01:12<17:08,  2.18it/s]\r  6%|▋        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2.36it/s]\n<command-4131444200676377>:3: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  df_TESS['Transcription'] = df_TESS['Filenames'].progress_apply(lambda x: speechtoText(x))\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "tqdm.pandas()\n",
    "df_TESS['Transcription'] = df_TESS['Filenames'].progress_apply(lambda x: speechtoText(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "beca2840-15b2-441f-8b3d-f0a0a6ec6ad9",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "## Defining a general purpose function\n",
    "def get_file_nameonly(link):\n",
    "    '''\n",
    "    General purpose function to get absolute filename from relative path\n",
    "    Parameters: Filename with relative path\n",
    "    Returns: Filename\n",
    "    '''\n",
    "    newPath = link.replace(os.sep, '/')\n",
    "    filename = newPath.split('/')[::-1][0]\n",
    "\n",
    "    return filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "32a1f382-0df6-4858-8dab-28a37e81029e",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/2400 [00:00<?, ?it/s]\r100%|██████████| 2400/2400 [00:00<00:00, 561017.09it/s]\n<command-4131444200676380>:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  df_TESS['FilenameOnly'] = df_TESS['Filenames'].progress_apply(lambda x: get_file_nameonly(x))\n"
     ]
    }
   ],
   "source": [
    "tqdm.pandas()\n",
    "df_TESS['FilenameOnly'] = df_TESS['Filenames'].progress_apply(lambda x: get_file_nameonly(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "00325445-88a4-4bc4-9b9b-66cfb43d29cd",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "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>Filenames</th>\n",
       "      <th>Emotions</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Transcription</th>\n",
       "      <th>FilenameOnly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>angry</td>\n",
       "      <td>TESS</td>\n",
       "      <td>say the word back.</td>\n",
       "      <td>OAF_back_angry.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>disgust</td>\n",
       "      <td>TESS</td>\n",
       "      <td>Say the word back.</td>\n",
       "      <td>OAF_back_disgust.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>fearful</td>\n",
       "      <td>TESS</td>\n",
       "      <td>Say the word back.</td>\n",
       "      <td>OAF_back_fear.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>happy</td>\n",
       "      <td>TESS</td>\n",
       "      <td>Say the word back.</td>\n",
       "      <td>OAF_back_happy.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>TESS</td>\n",
       "      <td>Say the word back.</td>\n",
       "      <td>OAF_back_ps.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2794</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>disgust</td>\n",
       "      <td>TESS</td>\n",
       "      <td>Say the word youth.</td>\n",
       "      <td>YAF_youth_disgust.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2795</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>fearful</td>\n",
       "      <td>TESS</td>\n",
       "      <td>Say the word youth.</td>\n",
       "      <td>YAF_youth_fear.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2796</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>happy</td>\n",
       "      <td>TESS</td>\n",
       "      <td>Say the word youth.</td>\n",
       "      <td>YAF_youth_happy.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2798</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>surprised</td>\n",
       "      <td>TESS</td>\n",
       "      <td>Say the word youth.</td>\n",
       "      <td>YAF_youth_ps.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2799</th>\n",
       "      <td>/dbfs/FileStore/wav-files/FinalExperiments/TES...</td>\n",
       "      <td>sad</td>\n",
       "      <td>TESS</td>\n",
       "      <td>Say the word youth.</td>\n",
       "      <td>YAF_youth_sad.wav</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Filenames  ...           FilenameOnly\n",
       "0     /dbfs/FileStore/wav-files/FinalExperiments/TES...  ...     OAF_back_angry.wav\n",
       "1     /dbfs/FileStore/wav-files/FinalExperiments/TES...  ...   OAF_back_disgust.wav\n",
       "2     /dbfs/FileStore/wav-files/FinalExperiments/TES...  ...      OAF_back_fear.wav\n",
       "3     /dbfs/FileStore/wav-files/FinalExperiments/TES...  ...     OAF_back_happy.wav\n",
       "5     /dbfs/FileStore/wav-files/FinalExperiments/TES...  ...        OAF_back_ps.wav\n",
       "...                                                 ...  ...                    ...\n",
       "2794  /dbfs/FileStore/wav-files/FinalExperiments/TES...  ...  YAF_youth_disgust.wav\n",
       "2795  /dbfs/FileStore/wav-files/FinalExperiments/TES...  ...     YAF_youth_fear.wav\n",
       "2796  /dbfs/FileStore/wav-files/FinalExperiments/TES...  ...    YAF_youth_happy.wav\n",
       "2798  /dbfs/FileStore/wav-files/FinalExperiments/TES...  ...       YAF_youth_ps.wav\n",
       "2799  /dbfs/FileStore/wav-files/FinalExperiments/TES...  ...      YAF_youth_sad.wav\n",
       "\n",
       "[2400 rows x 5 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_TESS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "c1b18468-21be-433a-9321-3944d7f2b1c1",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "df_TESS.to_csv('/dbfs/FileStore/wav-files/FinalExperiments/TESS_Baseline.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "81bcdcb7-a883-4811-95db-42f32a41c059",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/databricks/python_shell/dbruntime/huggingface_patches/datasets.py:13: UserWarning: During large dataset downloads, there could be multiple progress bar widgets that can cause performance issues for your notebook or browser. To avoid these issues, use `datasets.utils.logging.disable_progress_bar()` to turn off the progress bars.\n  warnings.warn(\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Filenames', 'Emotions', 'Dataset', 'Transcription', 'FilenameOnly'], dtype='object')\nDownloading and preparing dataset tess_benchmarking/clean to /dbfs/FileStore/wav-files/FinalExperiments/cached_emo/tess_benchmarking/clean-9b66cbcbd5068a74/0.0.0/54dd96fa57ef5df2ee9216541170c41066df10b2aa5537b02693986895738a8d...\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7d984674c2d74032b7b39cf0a6547354",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "files is /dbfs/FileStore/wav-files/FinalExperiments\n\naudio dir is /dbfs/FileStore/wav-files/FinalExperiments/TESS/\n\nDataset tess_benchmarking downloaded and prepared to /dbfs/FileStore/wav-files/FinalExperiments/cached_emo/tess_benchmarking/clean-9b66cbcbd5068a74/0.0.0/54dd96fa57ef5df2ee9216541170c41066df10b2aa5537b02693986895738a8d. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6a758a44a7704b7393685a87aee57107",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating test dataset\n",
    "PROJECT_ROOT='/dbfs/FileStore/wav-files/FinalExperiments'\n",
    "dataset_config = {  \n",
    "  \"LOADING_SCRIPT_FILES\": os.path.join(PROJECT_ROOT, \"TESS_Benchmarking.py\"),\n",
    "  \"CONFIG_NAME\": \"clean\",\n",
    "  \"DATA_DIR\": os.path.join(PROJECT_ROOT),\n",
    "  \"CACHE_DIR\": os.path.join(PROJECT_ROOT, \"cached_emo/\"),\n",
    "}   \n",
    "ds = load_dataset(\n",
    "  dataset_config[\"LOADING_SCRIPT_FILES\"],\n",
    "  dataset_config[\"CONFIG_NAME\"],    \n",
    "  data_dir=dataset_config[\"DATA_DIR\"],\n",
    "  cache_dir=dataset_config[\"CACHE_DIR\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "edbe7392-e4f6-4924-aa7a-06e71ea3abf3",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0a8a447c02034e6a964cb8920562c4a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/2400 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d114b0bd2d4e48e89080d6ed63d1abd2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/2400 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca3792e2cc7541f0b18d1104d10a62e9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Casting to class labels:   0%|          | 0/2400 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ds = ds.map(\n",
    "    lambda x: {\n",
    "        \"array\": list(librosa.load(x[\"file\"], sr=16000, mono=True)[0])\n",
    "    }\n",
    ")\n",
    "\n",
    "\n",
    "# PROCESS THE DATASET TO THE FORMAT EXPECTED BY THE MODEL FOR TRAINING\n",
    "\n",
    "INPUT_FIELD = \"input_values\"\n",
    "LABEL_FIELD = \"labels\"\n",
    "\n",
    "def prepare_dataset(batch, feature_extractor):\n",
    "    audio_arr = batch[\"array\"]\n",
    "    input = feature_extractor(\n",
    "        audio_arr, sampling_rate=16000, padding=True, return_tensors=\"pt\"\n",
    "    )\n",
    "\n",
    "    batch[INPUT_FIELD] = input.input_values[0]\n",
    "    batch[LABEL_FIELD] = batch[\"labels\"]  # colname MUST be labels as Trainer will look for it by default\n",
    "\n",
    "    return batch\n",
    "\n",
    "# APPLY THE DATA PREP USING FEATURE EXTRACTOR TO ALL EXAMPLES\n",
    "ds = ds.map(\n",
    "    prepare_dataset,\n",
    "    fn_kwargs={\"feature_extractor\": feature_extractor},\n",
    "    #num_proc=15,\n",
    ")\n",
    "\n",
    "# LABEL TO ID\n",
    "ds = ds.class_encode_column(\"labels\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "a80638c0-37a1-408b-b15a-9c35bae1c185",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n    train: Dataset({\n        features: ['file', 'labels', 'text', 'array', 'input_values'],\n        num_rows: 2400\n    })\n})\n"
     ]
    }
   ],
   "source": [
    "print(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "55e906af-2fb5-4a85-89e8-a0cfff9e2d2b",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "test_data_loader = DataLoader(ds[\"train\"], batch_size=2, shuffle=True,collate_fn=collate_fnTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "e0fee045-06c6-4213-acab-609ed781727e",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n    train: Dataset({\n        features: ['file', 'labels', 'text', 'array', 'input_values'],\n        num_rows: 2400\n    })\n})\n"
     ]
    }
   ],
   "source": [
    "print(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "efc40ee7-4489-46dc-ba52-d002807453b9",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing test batch 0\n\nProcessing test batch 1\n\nProcessing test batch 2\n\nProcessing test batch 3\n\nProcessing test batch 4\n\nProcessing test batch 5\n\nProcessing test batch 6\n\nProcessing test batch 7\n\nProcessing test batch 8\n\nProcessing test batch 9\n\nProcessing test batch 10\n\nProcessing test batch 11\n\nProcessing test batch 12\n\nProcessing test batch 13\n\nProcessing test batch 14\n\nProcessing test batch 15\n\nProcessing test batch 16\n\nProcessing test batch 17\n\nProcessing test batch 18\n\nProcessing test batch 19\n\nProcessing test batch 20\n\nProcessing test batch 21\n\nProcessing test batch 22\n\nProcessing test batch 23\n\nProcessing test batch 24\n\nProcessing test batch 25\n\nProcessing test batch 26\n\nProcessing test batch 27\n\nProcessing test batch 28\n\nProcessing test batch 29\n\nProcessing test batch 30\n\nProcessing test batch 31\n\nProcessing test batch 32\n\nProcessing test batch 33\n\nProcessing test batch 34\n\nProcessing test batch 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1123\n\nProcessing test batch 1124\n\nProcessing test batch 1125\n\nProcessing test batch 1126\n\nProcessing test batch 1127\n\nProcessing test batch 1128\n\nProcessing test batch 1129\n\nProcessing test batch 1130\n\nProcessing test batch 1131\n\nProcessing test batch 1132\n\nProcessing test batch 1133\n\nProcessing test batch 1134\n\nProcessing test batch 1135\n\nProcessing test batch 1136\n\nProcessing test batch 1137\n\nProcessing test batch 1138\n\nProcessing test batch 1139\n\nProcessing test batch 1140\n\nProcessing test batch 1141\n\nProcessing test batch 1142\n\nProcessing test batch 1143\n\nProcessing test batch 1144\n\nProcessing test batch 1145\n\nProcessing test batch 1146\n\nProcessing test batch 1147\n\nProcessing test batch 1148\n\nProcessing test batch 1149\n\nProcessing test batch 1150\n\nProcessing test batch 1151\n\nProcessing test batch 1152\n\nProcessing test batch 1153\n\nProcessing test batch 1154\n\nProcessing test batch 1155\n\nProcessing test batch 1156\n\nProcessing test batch 1157\n\nProcessing test batch 1158\n\nProcessing test batch 1159\n\nProcessing test batch 1160\n\nProcessing test batch 1161\n\nProcessing test batch 1162\n\nProcessing test batch 1163\n\nProcessing test batch 1164\n\nProcessing test batch 1165\n\nProcessing test batch 1166\n\nProcessing test batch 1167\n\nProcessing test batch 1168\n\nProcessing test batch 1169\n\nProcessing test batch 1170\n\nProcessing test batch 1171\n\nProcessing test batch 1172\n\nProcessing test batch 1173\n\nProcessing test batch 1174\n\nProcessing test batch 1175\n\nProcessing test batch 1176\n\nProcessing test batch 1177\n\nProcessing test batch 1178\n\nProcessing test batch 1179\n\nProcessing test batch 1180\n\nProcessing test batch 1181\n\nProcessing test batch 1182\n\nProcessing test batch 1183\n\nProcessing test batch 1184\n\nProcessing test batch 1185\n\nProcessing test batch 1186\n\nProcessing test batch 1187\n\nProcessing test batch 1188\n\nProcessing test batch 1189\n\nProcessing test batch 1190\n\nProcessing test batch 1191\n\nProcessing test batch 1192\n\nProcessing test batch 1193\n\nProcessing test batch 1194\n\nProcessing test batch 1195\n\nProcessing test batch 1196\n\nProcessing test batch 1197\n\nProcessing test batch 1198\n\nProcessing test batch 1199\n\nUnweighed Accuracy (UA) : 0.1658\nWeighed Accuracy (WA) : 0.1658\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/databricks/python/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1999: UserWarning: y_pred contains classes not in y_true\n  warnings.warn(\"y_pred contains classes not in y_true\")\n"
     ]
    }
   ],
   "source": [
    "from torch.cuda.amp import GradScaler, autocast\n",
    "multiModel.to(device)  # Move the model to the device\n",
    "\n",
    "# Define the loss function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Set the model to evaluation mode\n",
    "multiModel.eval()\n",
    "\n",
    "# Initialize lists to store predictions and targets\n",
    "all_logits = []\n",
    "all_targets = []\n",
    "\n",
    "# Initialize variables to keep track of the total test loss and accuracy\n",
    "total_test_loss = 0.0\n",
    "total_test_acc = 0.0\n",
    "num_test_steps = 0\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Iterate over the test data\n",
    "with torch.no_grad():\n",
    "    for idx, batch in enumerate(test_data_loader):\n",
    "        print(f'Processing test batch {idx}\\n')\n",
    "        # Get the inputs and targets from the batch\n",
    "        input_audio = batch[\"input_audio\"].to(device)\n",
    "        input_text = batch[\"input_text\"].to(device)\n",
    "        targets = batch[\"targets\"].to(device)\n",
    "        \n",
    "        # Forward pass with autocasting\n",
    "        with autocast():\n",
    "            logits = multiModel(input_audio, input_text)\n",
    "            test_loss = criterion(logits, targets)\n",
    "        \n",
    "        # Accumulate the test loss and accuracy\n",
    "        total_test_loss += test_loss.item()\n",
    "        total_test_acc += (logits.argmax(dim=1) == targets).float().mean().item()\n",
    "        num_test_steps += 1\n",
    "\n",
    "        logits_np = (logits.argmax(dim=1)).cpu().numpy()\n",
    "\n",
    "        # Convert logits and targets to numpy arrays\n",
    "        #logits_np = logits.cpu().numpy()\n",
    "        targets_np = targets.cpu().numpy()\n",
    "\n",
    "        all_logits.extend(logits_np)\n",
    "        all_targets.extend(targets_np)\n",
    "\n",
    "# Calculate the unbalanced accuracy for each class\n",
    "unbalanced_acc = accuracy_score(all_targets, all_logits)\n",
    "print(f\"Unweighed Accuracy (UA) : {unbalanced_acc:.4f}\")\n",
    "balanced_acc = balanced_accuracy_score(all_targets, all_logits)\n",
    "print(f\"Weighed Accuracy (WA) : {balanced_acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
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     "cellMetadata": {},
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     "nuid": "090fd968-3dde-46ca-b74c-55fadfd99ec4",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "notebookMetadata": {
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