RGAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import time\n",
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.autograd import Variable\n",
    "import torch.nn.utils.rnn as rnn\n",
    "from torch.utils.data import Dataset, DataLoader, BatchSampler, RandomSampler\n",
    "import matplotlib.pyplot as plt\n",
    "import librosa\n",
    "import librosa.display\n",
    "import IPython.display as ipd\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "DEVICE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "male = np.load(\"male_spect.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "282"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male.shape\n",
    "min([mal.shape[0] for mal in male])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(348, 129)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TrainDataset(Dataset):\n",
    "    def __init__(self, train):\n",
    "        self.train = train\n",
    "        lens = [a.shape[0] for a in train]\n",
    "        self.max_len = np.max(lens)\n",
    "    \n",
    "    def __len__(self):\n",
    "        return self.train.shape[0]\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        diff = self.max_len - self.train[idx].shape[0]\n",
    "        train = np.pad(self.train[idx], [(0, diff),(0,0)], \"constant\", constant_values=0)\n",
    "        train = train[:1000,:]\n",
    "        return train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = TrainDataset(male)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 129)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset[5].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 20\n",
    "train_loader = DataLoader(train_dataset,\n",
    "                        batch_sampler=BatchSampler(RandomSampler(train_dataset), batch_size, False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([20, 1000, 129])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iter(train_loader).next().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "     def __init__(self, hidden_size=129):\n",
    "        super(Generator, self).__init__()\n",
    "        self.rnn = nn.LSTM(200, hidden_size, num_layers=3, batch_first=True)\n",
    "     \n",
    "     def forward(self, z):\n",
    "        x,_ = self.rnn(z)\n",
    "        return x\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "    def __init__(self, hidden_size=50):\n",
    "        super(Discriminator, self).__init__()\n",
    "        self.rnn = nn.LSTM(129, hidden_size, num_layers=3, batch_first=True)\n",
    "        self.linear = nn.Linear(hidden_size, 2)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x,_ = self.rnn(x)\n",
    "        x = self.linear(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_epoch(gen, disc, optimizer1, optimizer2, train_loader, epoch):\n",
    "    batch_size = 20\n",
    "    criterion = nn.CrossEntropyLoss() \n",
    "    criterion = criterion.to(DEVICE)\n",
    "    false_label = torch.zeros((batch_size * 1000)).long()\n",
    "    true_label = torch.ones((batch_size * 1000)).long()\n",
    "    false_label = false_label.to(DEVICE)\n",
    "    true_label = true_label.to(DEVICE)\n",
    "    batch_id=0\n",
    "    losses = []\n",
    "    gen.train()\n",
    "    disc.train()\n",
    "    for inputs in train_loader: # lists, presorted, preloaded on GPU\n",
    "        batch_id+=1\n",
    "        #discriminator\n",
    "        inputs = inputs.to(DEVICE)\n",
    "        output = disc(inputs)\n",
    "        output = output.view(-1,2)\n",
    "    #   print(output.shape)\n",
    "        d_loss = criterion(output, true_label)\n",
    "        z = torch.randn((batch_size,1000, 200))\n",
    "        z = z.to(DEVICE)\n",
    "    #   print(z.shape)\n",
    "        output = gen(z)\n",
    "        output = disc(output)\n",
    "        output = output.view(-1,2)\n",
    "    #   print(output.shape)\n",
    "        d_loss = d_loss + criterion(output, false_label)\n",
    "        optimizer2.zero_grad()\n",
    "        d_loss.backward()\n",
    "        optimizer2.step()\n",
    "        \n",
    "        # generator\n",
    "        z = torch.randn((batch_size,1000, 200))\n",
    "        z = z.to(DEVICE)\n",
    "    #   print(z.shape)\n",
    "        output = gen(z)\n",
    "        output = disc(output)\n",
    "        output = output.view(-1,2)\n",
    "    #   print(output.shape)\n",
    "        g_loss = criterion(output, true_label)\n",
    "        optimizer1.zero_grad()\n",
    "        g_loss.backward()\n",
    "        optimizer1.step()\n",
    "        \n",
    "        # generator\n",
    "        z = torch.randn((batch_size,1000, 200))\n",
    "        z = z.to(DEVICE)\n",
    "    #   print(z.shape)\n",
    "        output = gen(z)\n",
    "        output = disc(output)\n",
    "        output = output.view(-1,2)\n",
    "    #   print(output.shape)\n",
    "        g_loss = criterion(output, true_label)\n",
    "        optimizer1.zero_grad()\n",
    "        g_loss.backward()\n",
    "        optimizer1.step()\n",
    "        print(\"discriminator loss\", d_loss.item())\n",
    "        print(\"generator loss\", g_loss.item())\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/jet/var/python/lib/python3.6/site-packages/torch/backends/cudnn/__init__.py:89: UserWarning: PyTorch was compiled without cuDNN support. To use cuDNN, rebuild PyTorch making sure the library is visible to the build system.\n",
      "  \"PyTorch was compiled without cuDNN support. To use cuDNN, rebuild \"\n"
     ]
    }
   ],
   "source": [
    "gen = Generator()\n",
    "gen = gen.to(DEVICE)\n",
    "disc = Discriminator()\n",
    "disc = disc.to(DEVICE)\n",
    "optimizer1 = torch.optim.Adam(gen.parameters(),lr=0.001)\n",
    "optimizer2 = torch.optim.Adam(disc.parameters(),lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/jet/var/python/lib/python3.6/site-packages/torch/backends/cudnn/__init__.py:89: UserWarning: PyTorch was compiled without cuDNN support. To use cuDNN, rebuild PyTorch making sure the library is visible to the build system.\n",
      "  \"PyTorch was compiled without cuDNN support. To use cuDNN, rebuild \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "generator loss 3.7108280658721924\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-94-32518739ffbc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mtrain_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-92-030741d80f2f>\u001b[0m in \u001b[0;36mtrain_epoch\u001b[0;34m(gen, disc, optimizer1, optimizer2, train_loader, epoch)\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0;31m# generator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m         \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m         \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;31m#   print(z.shape)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    train_epoch(gen, disc, optimizer1, optimizer2, train_loader, i+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/jet/var/python/lib/python3.6/site-packages/torch/backends/cudnn/__init__.py:89: UserWarning: PyTorch was compiled without cuDNN support. To use cuDNN, rebuild PyTorch making sure the library is visible to the build system.\n",
      "  \"PyTorch was compiled without cuDNN support. To use cuDNN, rebuild \"\n"
     ]
    }
   ],
   "source": [
    "z = torch.randn((batch_size,1000, 200))\n",
    "z = z.to(DEVICE)\n",
    "output = gen(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 1000, 129)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = output.detach().cpu().numpy()\n",
    "output.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dOX0Xl5vlJq9+Mosiz9bl15LkPNby+gb7zql4PQT87/alLH/PlaW+iJkVWsNS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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "librosa.display.specshow(librosa.power_to_db(output[0], ref=np.max),y_axis='mel', fmax=8000, x_axis='time')\n",
    "plt.colorbar(format='%+2.0f dB')\n",
    "plt.title('Mel spectrogram')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 16)\n",
      "(16, 129)\n"
     ]
    }
   ],
   "source": [
    "# ipd.Audio(output[4].reshape(-1), rate= 22050)\n",
    "comps, acts = librosa.decompose.decompose(np.abs(output[0]), n_components=16, sort=True)\n",
    "print(comps.shape)\n",
    "print(acts.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" >\n",
       "                    <source src=\"data:audio/wav;base64,UklGRiYBAABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQIBAAAHAP9/9WZuAGUKSAUEAUsIjARtAy8AwgARACQEhwYlBNABmgqgCQ5GEgEyAagAhgA7AMoATAAhAIgEMwEjAK0AbgIDAFgA7wNEAQkAgwAVAEgADwC4ADUAcQFtAAgAgQAIAAoBWwVlAHIACwABAAIADQAUABUAKwAgAB4AHgCsBIUALAATAAgAAwBPAVIAIAI5ADoAAwBjAA4AFgBCAAsABQABABcAGAAHAH4ABAACAAkAAAAAAAEAAAAKAAAADwAGAAIAAQAbAAAABgAAAAAAAAAEAAsABwAFAAYAAQANAAUAAQADAAAAAwAAAAAAAQABAAAAAQABAAAAAAAAAAEAAwA=\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ipd.display(ipd.Audio(male[0], rate=16000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "write_wav() missing 1 required positional argument: 'sr'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-148-6d5d410a1a45>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_wav\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"male.wav\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmale\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'male.wav'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: write_wav() missing 1 required positional argument: 'sr'"
     ]
    }
   ],
   "source": [
    "librosa.output.write_wav(\"male.wav\", male[0], 22050)\n",
    "x, sr = librosa.load('male.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=float32)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ipd.Audio(x, rate= 22050)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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