SER.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2711227f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Collecting opendatasets\n",
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      "\u001b[?25hBuilding wheels for collected packages: kaggle\n",
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      "\u001b[?25h  Created wheel for kaggle: filename=kaggle-1.6.8-py3-none-any.whl size=111967 sha256=439896fb9fcdcff8d5c4c31e33d61c928c26b59a4a419b176d257b7efa021c71\n",
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      "\u001b[33m  WARNING: The script kaggle is installed in '/usr4/ugrad/jhua2/.local/bin' which is not on PATH.\n",
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      "cuda\n"
     ]
    }
   ],
   "source": [
    "# Import libraries\n",
    "import torch\n",
    "import torchaudio\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import IPython\n",
    "import matplotlib.pyplot as plt\n",
    "from torchaudio.utils import download_asset\n",
    "import numpy as np\n",
    "\n",
    "!pip install librosa\n",
    "import librosa\n",
    "\n",
    "!pip install opendatasets\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bf14d497",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
      "Your Kaggle username: jaylinlily\n",
      "Your Kaggle Key: ········\n",
      "Downloading berlin-database-of-emotional-speech-emodb.zip to ./datasets/berlin-database-of-emotional-speech-emodb\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 38.0M/38.0M [00:00<00:00, 69.9MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
      "Your Kaggle username: jaylinlily\n",
      "Your Kaggle Key: ········\n",
      "Downloading toronto-emotional-speech-set-tess.zip to ./datasets/toronto-emotional-speech-set-tess\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 428M/428M [00:05<00:00, 81.7MB/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
      "Your Kaggle username: jaylinlily\n",
      "Your Kaggle Key: ········\n",
      "Downloading cremad.zip to ./datasets/cremad\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 451M/451M [00:07<00:00, 65.4MB/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
      "Your Kaggle username: jaylinlily\n",
      "Your Kaggle Key: ········\n",
      "Downloading ravdess-emotional-speech-audio.zip to ./datasets/ravdess-emotional-speech-audio\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 429M/429M [00:03<00:00, 114MB/s]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
      "Your Kaggle username: jaylinlily\n",
      "Your Kaggle Key: ········\n",
      "Downloading savee-database.zip to ./datasets/savee-database\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 215M/215M [00:03<00:00, 65.4MB/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
      "Your Kaggle username: jaylinlily\n",
      "Your Kaggle Key: ········\n",
      "Downloading shemo-persian-speech-emotion-detection-database.zip to ./datasets/shemo-persian-speech-emotion-detection-database\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 829M/829M [00:12<00:00, 71.1MB/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "tensor([ 9.1553e-05, -3.0518e-04, -7.9346e-04,  ..., -1.2207e-03,\n",
      "        -1.4343e-03, -1.5259e-03])\n",
      "anxiety\n",
      "                                                filename speaker_n intensity  \\\n",
      "0      ./datasets/berlin-database-of-emotional-speech...        16        NA   \n",
      "1      ./datasets/berlin-database-of-emotional-speech...        10        NA   \n",
      "2      ./datasets/berlin-database-of-emotional-speech...        16        NA   \n",
      "3      ./datasets/berlin-database-of-emotional-speech...        16        NA   \n",
      "4      ./datasets/berlin-database-of-emotional-speech...        14        NA   \n",
      "...                                                  ...       ...       ...   \n",
      "15692  ./datasets/shemo-persian-speech-emotion-detect...        56        NA   \n",
      "15693  ./datasets/shemo-persian-speech-emotion-detect...        12        NA   \n",
      "15694  ./datasets/shemo-persian-speech-emotion-detect...        12        NA   \n",
      "15695  ./datasets/shemo-persian-speech-emotion-detect...        04        NA   \n",
      "15696  ./datasets/shemo-persian-speech-emotion-detect...        12        NA   \n",
      "\n",
      "       emotion version language database  \n",
      "0      anxiety       a   german    emodb  \n",
      "1        bored       b   german    emodb  \n",
      "2        bored       a   german    emodb  \n",
      "3      neutral       b   german    emodb  \n",
      "4        bored       a   german    emodb  \n",
      "...        ...     ...      ...      ...  \n",
      "15692  neutral       5  persian    shemo  \n",
      "15693  neutral      70  persian    shemo  \n",
      "15694    anger       9  persian    shemo  \n",
      "15695  neutral      44  persian    shemo  \n",
      "15696    anger      68  persian    shemo  \n",
      "\n",
      "[15697 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from dataset import download_datasets, SpeechEmotionDataset, get_dataset_info\n",
    "\n",
    "\n",
    "\n",
    "# Specify the directory you want the datasets to be contained in\n",
    "!mkdir -p datasets\n",
    "dataset_dir = \"./datasets\"\n",
    "\n",
    "\n",
    "# Only needed to run this code once\n",
    "'''\n",
    "# Download a single dataset\n",
    "download_datasets(dataset_dir, dname=\"emodb\")\n",
    "\n",
    "# Download the rest of the datasets available\n",
    "download_datasets(dataset_dir)\n",
    "'''\n",
    "\n",
    "# Acquire info on datasets (those that have functions to get data for)\n",
    "df = get_dataset_info(dataset_dir)\n",
    "\n",
    "# Make into a Dataset object that a pytorch optimizer can use\n",
    "# Can optionally specify a sampling rate for all audio files to be in\n",
    "trainset = SpeechEmotionDataset(df, fs=16000)\n",
    "\n",
    "# Check it works\n",
    "trainset = SpeechEmotionDataset(df, fs=16000)\n",
    "dataiter = iter(trainset)\n",
    "data, label = next(dataiter)\n",
    "print(data)\n",
    "print(label)\n",
    "print(df) # columns are: filename, speaker_n, intensity, emotion, version, language, database \n",
    "\n",
    "# Put into a dataloader\n",
    "trainloader = DataLoader(trainset, batch_size=4, shuffle=True, num_workers=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "48199cbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample rate:  16000\n",
      "Labels:  ('-', '|', 'E', 'T', 'A', 'O', 'N', 'I', 'H', 'S', 'R', 'D', 'L', 'U', 'M', 'W', 'C', 'F', 'G', 'Y', 'P', 'B', 'V', 'K', \"'\", 'X', 'J', 'Q', 'Z')\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Feature Extraction\n",
    "1) Wav2vec2 model\n",
    "'''\n",
    "bundle = torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H # can choose another wav2vec2 model\n",
    "print(\"Sample rate: \", bundle.sample_rate)\n",
    "print(\"Labels: \", bundle.get_labels())\n",
    "model = bundle.get_model().to(device)\n",
    "\n",
    "# Example audio\n",
    "SPEECH_FILE = download_asset(\"tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav\")\n",
    "IPython.display.Audio(SPEECH_FILE)\n",
    "waveform, sample_rate = torchaudio.load(SPEECH_FILE)\n",
    "waveform = waveform.to(device)\n",
    "# Resample example audio if its sample rate doesn't match the pipeline's sample rate\n",
    "if sample_rate != bundle.sample_rate:\n",
    "    print(\"Audio vs Bundle sample rate: \", sample_rate, bundle.sample_rate)\n",
    "    waveform = torchaudio.functional.resample(waveform, sample_rate, bundle.sample_rate)\n",
    "    \n",
    "# Extract features\n",
    "with torch.inference_mode():\n",
    "    features, _ = model.extract_features(waveform)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7d89a9cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Feature Extraction\n",
    "2) Hand-crafted features: ZCR, RMSE, MFCC\n",
    "'''\n",
    "X, sample_rate = librosa.load('./datasets/berlin-database-of-emotional-speech-emodb/wav/03a01Fa.wav')\n",
    "mfccs = np.mean(librosa.feature.mfcc(y=X, n_mfcc=25,), axis = 0) # calculating mean?\n",
    "rms = librosa.feature.rms(y=X) # root mean square value for each frame of audio sample\n",
    "zcr = librosa.feature.zero_crossing_rate(y=X) # zero crossing rate of audio time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7f85300f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<librosa.display.AdaptiveWaveplot at 0x15412202c280>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's visualize what we are doing!\n",
    "# some of this code taken from https://github.com/MiteshPuthran/Speech-Emotion-Analyzer/blob/master/final_results_gender_test.ipynb\n",
    "import librosa.display\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "librosa.display.waveshow(X, sr=sample_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aae3d827",
   "metadata": {},
   "outputs": [],
   "source": [
    "# putting features into dataframes\n",
    "import os\n",
    "\n",
    "# needs \"/\" at end of filepath\n",
    "data_filepath = \"./datasets/berlin-database-of-emotional-speech-emodb/wav/\"\n",
    "\n",
    "database_name = \"berlin-database-of-emotional-speech-emodb\"\n",
    "filenames_list = os.listdir(data_filepath) # filenames without full filepath\n",
    "full_filenames_list = [data_filepath + filename for filename in filenames_list] # adding full filepath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "17112979",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        filename                                              mfccs  \\\n",
      "0    16b01Aa.wav  [-14.780913, -14.325403, -13.693046, -13.05944...   \n",
      "1    10b09Lb.wav  [-11.474661, -9.416283, -9.904519, -10.41776, ...   \n",
      "2    16a05La.wav  [-14.149419, -11.2464285, -8.616501, -8.848203...   \n",
      "3    16a02Nb.wav  [-12.976416, -12.096367, -8.972505, -7.3359556...   \n",
      "4    14a02La.wav  [-11.86352, -10.168923, -10.589436, -11.380253...   \n",
      "..           ...                                                ...   \n",
      "530  08b10La.wav  [-10.3835335, -7.8500013, -6.9223866, -3.99789...   \n",
      "531  16b03La.wav  [-15.408475, -12.201984, -11.546626, -11.74224...   \n",
      "532  12b02Fb.wav  [-18.119234, -7.136423, -3.4530761, -4.165739,...   \n",
      "533  13a05Ea.wav  [-7.609747, -6.5680695, -5.8979325, -6.145536,...   \n",
      "534  13b09Wa.wav  [-16.652184, -16.380753, -15.454414, -12.80498...   \n",
      "\n",
      "                                                   rms  \\\n",
      "0    [[0.0009390469, 0.001251537, 0.0014829956, 0.0...   \n",
      "1    [[0.002468433, 0.0026285178, 0.0029226877, 0.0...   \n",
      "2    [[0.001259113, 0.0069509973, 0.028159384, 0.06...   \n",
      "3    [[0.0015351741, 0.008724172, 0.04540591, 0.086...   \n",
      "4    [[0.0030758574, 0.0031758535, 0.003902222, 0.0...   \n",
      "..                                                 ...   \n",
      "530  [[0.0039746906, 0.004723523, 0.013538511, 0.03...   \n",
      "531  [[0.0010636328, 0.0013595184, 0.0016803497, 0....   \n",
      "532  [[0.004595412, 0.012777808, 0.032219317, 0.055...   \n",
      "533  [[0.010170098, 0.012149153, 0.013322965, 0.017...   \n",
      "534  [[0.00037988424, 0.000489194, 0.0012323102, 0....   \n",
      "\n",
      "                                                   zcr  emotion  \n",
      "0    [[0.0380859375, 0.0498046875, 0.05615234375, 0...  anxiety  \n",
      "1    [[0.01220703125, 0.0224609375, 0.0263671875, 0...    bored  \n",
      "2    [[0.0068359375, 0.0283203125, 0.044921875, 0.0...    bored  \n",
      "3    [[0.0439453125, 0.099609375, 0.1142578125, 0.1...  neutral  \n",
      "4    [[0.0263671875, 0.03173828125, 0.03173828125, ...    bored  \n",
      "..                                                 ...      ...  \n",
      "530  [[0.0146484375, 0.02197265625, 0.080078125, 0....    bored  \n",
      "531  [[0.0849609375, 0.11572265625, 0.1669921875, 0...    bored  \n",
      "532  [[0.1533203125, 0.22216796875, 0.31201171875, ...    happy  \n",
      "533  [[0.01025390625, 0.0146484375, 0.01708984375, ...  disgust  \n",
      "534  [[0.02783203125, 0.04150390625, 0.0673828125, ...    anger  \n",
      "\n",
      "[535 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# extract features of all audio files into a dataframe\n",
    "import pandas as pd\n",
    "\n",
    "features_df = pd.DataFrame(columns=['filename', 'mfccs', 'rms', 'zcr', 'emotion'])\n",
    "\n",
    "# works for a single folder of audio files at once, all from the same dataset\n",
    "for i, filename in enumerate(full_filenames_list):\n",
    "    X, sample_rate = librosa.load(filename) # load waveform\n",
    "    mfccs = np.mean(librosa.feature.mfcc(y=X, n_mfcc=25,), axis = 0) # calculate feature values\n",
    "    rms = librosa.feature.rms(y=X)\n",
    "    zcr = librosa.feature.zero_crossing_rate(y=X)\n",
    "    features_df.at[i, 'mfccs'] = mfccs # save X features to dataframe\n",
    "    features_df.at[i, 'rms'] = np.array(rms)\n",
    "    features_df.at[i, 'zcr'] = np.array(zcr)\n",
    "    \n",
    "    row_index = df.loc[df['filename'] == filename].index[0] # finding the correct emotion for the filename\n",
    "    features_df.at[i, 'emotion'] = df.at[row_index,'emotion'] # selects correct emotion from Noah's df\n",
    "    \n",
    "    split_name = filename.split('/') # get the correct filename\n",
    "    features_df.at[i, 'filename'] = split_name[-1]\n",
    "    \n",
    "    #features_df.at[i,'emotion'] = df.loc['im','emotion']\n",
    "\n",
    "#features_df = pd.concat([features_df, labels_df], axis=1)\n",
    "print(features_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7aadf0e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        filename                                              mfccs  \\\n",
      "284  03b02Aa.wav  [-14.079135, -12.323546, -11.474248, -9.09061,...   \n",
      "347  12b03Ta.wav  [-9.775077, -7.466317, -6.8036146, -6.629843, ...   \n",
      "440  13b02Nb.wav  [-9.2703085, -7.783403, -7.8381305, -6.5176373...   \n",
      "421  14b10Lb.wav  [-14.253737, -13.220742, -10.550857, -8.464507...   \n",
      "149  11b01Fc.wav  [-16.935766, -15.290205, -14.847381, -13.48610...   \n",
      "..           ...                                                ...   \n",
      "71   13a04Wc.wav  [-17.091368, -14.799666, -13.599548, -13.60320...   \n",
      "106  16b03Ad.wav  [-15.301025, -13.604454, -14.486785, -15.71678...   \n",
      "270  09b03Nb.wav  [-9.124634, -8.144319, -8.019176, -7.788812, -...   \n",
      "435  14a04Tc.wav  [-10.157893, -7.504263, -6.495217, -6.782695, ...   \n",
      "102  08b03Fe.wav  [-14.783207, -14.364163, -13.856281, -13.53113...   \n",
      "\n",
      "                                                   rms  \\\n",
      "284  [[0.0013622892, 0.0019244545, 0.0062129647, 0....   \n",
      "347  [[0.007245047, 0.011252102, 0.013342347, 0.017...   \n",
      "440  [[0.005388466, 0.007304628, 0.008596973, 0.014...   \n",
      "421  [[0.0009580753, 0.0021076025, 0.0031811625, 0....   \n",
      "149  [[0.00056565506, 0.0007321395, 0.0012822378, 0...   \n",
      "..                                                 ...   \n",
      "71   [[0.00072822947, 0.00081294, 0.0010067298, 0.0...   \n",
      "106  [[0.000528787, 0.0006213201, 0.001090527, 0.01...   \n",
      "270  [[0.004337546, 0.0060529415, 0.0071090045, 0.0...   \n",
      "435  [[0.01571129, 0.017804034, 0.018841198, 0.0197...   \n",
      "102  [[0.001352824, 0.0015487866, 0.0016961781, 0.0...   \n",
      "\n",
      "                                                   zcr  emotion  \n",
      "284  [[0.03466796875, 0.06787109375, 0.1435546875, ...  anxiety  \n",
      "347  [[0.01220703125, 0.01220703125, 0.0244140625, ...  sadness  \n",
      "440  [[0.013671875, 0.013671875, 0.01708984375, 0.1...  neutral  \n",
      "421  [[0.00927734375, 0.0126953125, 0.02587890625, ...    bored  \n",
      "149  [[0.0498046875, 0.06591796875, 0.13037109375, ...    happy  \n",
      "..                                                 ...      ...  \n",
      "71   [[0.06884765625, 0.076171875, 0.09765625, 0.10...    anger  \n",
      "106  [[0.02783203125, 0.0576171875, 0.09033203125, ...  anxiety  \n",
      "270  [[0.00341796875, 0.0068359375, 0.01025390625, ...  neutral  \n",
      "435  [[0.0, 0.001953125, 0.00244140625, 0.002441406...  sadness  \n",
      "102  [[0.00341796875, 0.005859375, 0.00830078125, 0...    happy  \n",
      "\n",
      "[428 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# Now let's shuffle these boiz\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train, test = train_test_split(features_df, test_size=0.2, random_state=42, shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f713cc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import torchvision.transforms as transforms\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from torchvision import datasets\n",
    "from sklearn.metrics import f1_score, accuracy_score, recall_score, precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b2e988ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42\n",
    "torch.manual_seed(seed)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b65a6f9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Building Network architecture based on the paper:\n",
    "Human–Computer Interaction with a Real-Time Speech\n",
    "Emotion Recognition with Ensembling Techniques 1D\n",
    "Convolution Neural Network and Attention\n",
    "(https://doi.org/10.3390/s23031386)\n",
    "\n",
    "We are taking the output of CNN as the input of LSTM.\n",
    "CNN captures local patterns in audio features, and\n",
    "LSTM learns temporal dependencies before making final prediction. This supports\n",
    "robust sequence prediction.\n",
    "\"\"\"\n",
    "\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class CNN_LSTM(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNN_LSTM, self).__init__()\n",
    "        # bn = batch normalization\n",
    "        ####################\n",
    "        # Convolution blocks: 1dconv, batch norm, ReLU, max pooling\n",
    "        # Conv block 1\n",
    "        self.conv1 = nn.Conv1d(in_channels = 3, out_channels = 8, kernel_size = 3, padding = 1)\n",
    "        self.bn1 = nn.BatchNorm1d(8)\n",
    "\n",
    "        # Conv block 2\n",
    "        self.conv2 = nn.Conv1d(in_channels = 8, out_channels = 16, kernel_size = 3, padding = 1)\n",
    "        self.bn2 = nn.BatchNorm1d(16)\n",
    "\n",
    "        # Conv block 3\n",
    "        self.conv3 = nn.Conv1d(in_channels = 16, out_channels = 32, kernel_size = 3, padding = 1)\n",
    "        self.bn3 = nn.BatchNorm1d(32)\n",
    "\n",
    "        # Conv block 4\n",
    "        self.conv4 = nn.Conv1d(in_channels = 32, out_channels = 64, kernel_size = 3, padding = 1)\n",
    "        self.bn4 = nn.BatchNorm1d(64)\n",
    "\n",
    "        # Conv block 5\n",
    "        self.conv5 = nn.Conv1d(in_channels = 64, out_channels = 128, kernel_size = 3, padding = 1)\n",
    "        self.bn5 = nn.BatchNorm1d(128)\n",
    "\n",
    "        self.relu = nn.ReLU()\n",
    "        self.pool = nn.MaxPool1d(kernel_size = 2, stride = 2)\n",
    "        ####################\n",
    "\n",
    "        ####################\n",
    "        # LSTM + attention block\n",
    "        self.lstm1 = nn.LSTM(input_size = 128, hidden_size = 64, num_layers = 1, bias = True)\n",
    "\n",
    "        self.attention = nn.Linear(64, 1)\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "\n",
    "        self.lstm2 = nn.LSTM(input_size = 64, hidden_size = 64, num_layers = 1, bias = True)\n",
    "        ####################\n",
    "\n",
    "\n",
    "        self.fc1 = nn.Linear(64, 32) # May need to increase 32 to capture more complex data (?)\n",
    "        self.bn6 = nn.BatchNorm1d(32)\n",
    "        self.fc2 = nn.Linear(32, 16)\n",
    "\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Conv block 1\n",
    "        x = self.conv1(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.pool(x)\n",
    "\n",
    "        # Conv block 2\n",
    "        x = self.conv2(x)\n",
    "        x = self.bn2(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.pool(x)\n",
    "\n",
    "        # Conv block 3\n",
    "        x = self.conv3(x)\n",
    "        x = self.bn3(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.pool(x)\n",
    "\n",
    "        # Conv block 4\n",
    "        x = self.conv4(x)\n",
    "        x = self.bn4(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.pool(x)\n",
    "\n",
    "        # Conv block 5\n",
    "        x = self.conv5(x)\n",
    "        x = self.bn5(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.pool(x)\n",
    "\n",
    "        # LSTM + attention block\n",
    "        # output_tensor, hiddenstate = self.lstm()\n",
    "        lstm1_out, _ = self.lstm1(x)\n",
    "\n",
    "        attention_weights = self.softmax(self.attention(lstm1_out))\n",
    "        context = torch.sum(attention_weights * lstm1_out, dim=1)\n",
    "\n",
    "        lstm2_out, _ = self.lstm2(context.unsqueeze(-1))\n",
    "\n",
    "        # fully connected layers and softmax\n",
    "        x = self.fc1(lstm2_out.squeeze(1))\n",
    "        x = self.bn6(x)\n",
    "        x = self.fc2(x)\n",
    "\n",
    "        # Softmax for prediction\n",
    "        # F.softmax is used instead of self.softmax because it is not associated\n",
    "        # with any parameters\n",
    "        x = F.softmax(x, dim=1)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5d571cc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "net =  CNN_LSTM().to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer =  optim.Adam(net.parameters(), lr=1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2ea1b57e",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'tuple' object has no attribute 'to'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[36], line 31\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m net\n\u001b[1;32m     30\u001b[0m trainloader \u001b[38;5;241m=\u001b[39m DataLoader(trainset, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, num_workers\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m---> 31\u001b[0m net \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_on_features\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrainloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[36], line 14\u001b[0m, in \u001b[0;36mtrain_on_features\u001b[0;34m(net, optimizer, device, trainloader, critrerion, epochs)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, data \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(trainloader):\n\u001b[1;32m     13\u001b[0m     inputs, labels \u001b[38;5;241m=\u001b[39m data\n\u001b[0;32m---> 14\u001b[0m     inputs, labels \u001b[38;5;241m=\u001b[39m inputs\u001b[38;5;241m.\u001b[39mto(device), \u001b[43mlabels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m(device)\n\u001b[1;32m     16\u001b[0m     optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m     18\u001b[0m     pred \u001b[38;5;241m=\u001b[39m net(inputs)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'to'"
     ]
    }
   ],
   "source": [
    "# Training loop\n",
    "root_dir = './runs'\n",
    "os.makedirs(root_dir, exist_ok=True)\n",
    "\n",
    "def train_on_features(net, optimizer, device, trainloader, critrerion, epochs=1):\n",
    "    if torch.cuda.is_available():\n",
    "        net.cuda()\n",
    "    net.train()\n",
    "\n",
    "    loss_min = float('inf')\n",
    "    for epoch in range(epochs):\n",
    "        for i, data in enumerate(trainloader):\n",
    "            inputs, labels = data\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            pred = net(inputs)\n",
    "            loss = criterion(pred, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            if loss < loss_min:\n",
    "                torch.save(net.state_dict(), os.path.join(root_dir, \"best_ser_model.pth\"))\n",
    "\n",
    "\n",
    "    print('Finished Training')\n",
    "    return net\n",
    "\n",
    "net = train_on_features(net, optimizer, device, trainloader, criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "724f9822",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(tensor([ 9.1553e-05, -3.0518e-04, -7.9346e-04,  ..., -1.2207e-03,\n",
      "        -1.4343e-03, -1.5259e-03]), 'anxiety')\n"
     ]
    }
   ],
   "source": [
    "#net = train_on_features(net, optimizer, device, trainloader, criterion)\n",
    "# trainloader = DataLoader(trainset, batch_size=1, shuffle=True, num_workers=1)\n",
    "# for i, data in enumerate(trainloader):\n",
    "#     print(data)\n",
    "print(trainset[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80d64990",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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