{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9db7bd27",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "72b076a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "391c8ebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import torch\n",
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0f0b166a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataset import *\n",
    "from cnn import CNNetwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b690f559",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device cpu\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "        device = \"cuda\"\n",
    "else:\n",
    "        device = \"cpu\"\n",
    "print(f\"Using device {device}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "5b4cac66",
   "metadata": {},
   "outputs": [],
   "source": [
    "mel_spectrogram = torchaudio.transforms.MelSpectrogram(\n",
    "        sample_rate=16000,\n",
    "        n_fft=1024,\n",
    "        hop_length=512,\n",
    "        n_mels=64\n",
    "    )\n",
    "dataset = VoiceDataset('../data/train', mel_spectrogram, 16000, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "55928782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5717"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "296fc1d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[[0.2647, 0.0247, 0.0324,  ..., 0.0230, 0.1026, 0.5454],\n",
       "          [0.0812, 0.0178, 0.0890,  ..., 0.2376, 0.5061, 0.5292],\n",
       "          [0.0052, 0.0212, 0.1341,  ..., 0.9336, 0.2778, 0.1372],\n",
       "          ...,\n",
       "          [0.5154, 0.3950, 0.4497,  ..., 0.4916, 0.4505, 0.7709],\n",
       "          [0.1919, 0.4804, 0.5144,  ..., 0.5931, 0.4466, 0.4706],\n",
       "          [0.1208, 0.4357, 0.4016,  ..., 0.5168, 0.7007, 0.3696]]]),\n",
       " 0)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b921ef42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 64, 157])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "83671781",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn = CNNetwork()\n",
    "# summary(cnn, (1, 64, 44))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "5a12b59f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.tensor(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "4845de38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'aman': 0, 'imran': 1, 'labib': 2}"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.label_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "51c03aaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[[0.2647, 0.0247, 0.0324,  ..., 0.0230, 0.1026, 0.5454],\n",
       "          [0.0812, 0.0178, 0.0890,  ..., 0.2376, 0.5061, 0.5292],\n",
       "          [0.0052, 0.0212, 0.1341,  ..., 0.9336, 0.2778, 0.1372],\n",
       "          ...,\n",
       "          [0.5154, 0.3950, 0.4497,  ..., 0.4916, 0.4505, 0.7709],\n",
       "          [0.1919, 0.4804, 0.5144,  ..., 0.5931, 0.4466, 0.4706],\n",
       "          [0.1208, 0.4357, 0.4016,  ..., 0.5168, 0.7007, 0.3696]]]),\n",
       " 0)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ba6b88ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "now = datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "a6046ccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20230512_222912'"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now.strftime(\"%Y%m%d_%H%M%S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "d7789a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn.load_state_dict(torch.load(\"../models/void_20230512_225714.pth\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "a6030b42",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = dataset[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "78352b6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = dataset._labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "b8cc2162",
   "metadata": {},
   "outputs": [],
   "source": [
    "input = x.unsqueeze_(0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "845ecea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(model, input, target, class_mapping):\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        predictions = model(input)\n",
    "        predicted_index = predictions[0].argmax(0)\n",
    "        predicted = class_mapping[predicted_index]\n",
    "        expected = class_mapping[target]\n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "eb8d1e55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1.0000e+00, 1.3728e-20, 2.8026e-44]])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('aman', 'aman')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(cnn, input, y, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "5d58683e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[[0.0259, 0.1384, 0.0784,  ..., 0.0000, 0.0000, 0.0000],\n",
       "          [0.0334, 0.1320, 0.0701,  ..., 0.0000, 0.0000, 0.0000],\n",
       "          [0.0481, 0.0324, 0.0545,  ..., 0.0000, 0.0000, 0.0000],\n",
       "          ...,\n",
       "          [0.2665, 0.3647, 0.3147,  ..., 0.0000, 0.0000, 0.0000],\n",
       "          [0.2710, 0.3796, 0.2160,  ..., 0.0000, 0.0000, 0.0000],\n",
       "          [0.1950, 0.2607, 0.1905,  ..., 0.0000, 0.0000, 0.0000]]]])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "b0af5b69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 1, 64, 157])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "28c0768a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 1, 64, 157])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "c5817d01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data/aman/aman_1'"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.join('data', 'aman', 'aman_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "03bb835e",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset =  VoiceDataset('../data/test', mel_spectrogram, 16000, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "151f8cb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('aman_test2.m4a', 'aman'),\n",
       " ('aman_6.m4a', 'aman'),\n",
       " ('aman_4.m4a', 'aman'),\n",
       " ('aman_5.m4a', 'aman'),\n",
       " ('aman_1.m4a', 'aman'),\n",
       " ('aman_2.m4a', 'aman'),\n",
       " ('aman_3.m4a', 'aman'),\n",
       " ('aman_test.m4a', 'aman'),\n",
       " ('Imran 6.m4a', 'imran'),\n",
       " ('Imran 5.m4a', 'imran'),\n",
       " ('Imran 4.m4a', 'imran'),\n",
       " ('Imran 1.m4a', 'imran'),\n",
       " ('Imran 3.m4a', 'imran'),\n",
       " ('Imran 2.m4a', 'imran'),\n",
       " ('labib_6.m4a', 'labib'),\n",
       " ('labib_4.m4a', 'labib'),\n",
       " ('labib_5.m4a', 'labib'),\n",
       " ('labib_1.m4a', 'labib'),\n",
       " ('labib_2.m4a', 'labib'),\n",
       " ('labib_3.m4a', 'labib')]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dataset.audio_files_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "4c09f2c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 64, 157])"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_input, test_output = test_dataset[0]\n",
    "test_input.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "1f4254aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_input = test_input.unsqueeze_(0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "f1559f5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 1, 64, 157])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_input.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "5e683c2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 0., 0.]])\n"
     ]
    }
   ],
   "source": [
    "output = predict(cnn, test_input, test_output, test_dataset._labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "581911ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = torch.argmax(output, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "135eecde",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
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