las_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LAS Demo\n",
    "\n",
    "This is a demonstration of how `run_exp.py` works and the result of example config file.\n",
    "May NOT be executable due to modification of model code in latest version.\n",
    "\n",
    "## Notes :\n",
    "\n",
    "    1. The goal is simply to reconstructed LAS and see if it works on TIMIT, all paremeters are NOT fine tuned.\n",
    "\n",
    "    2. I run the experiment once with unassigned random seed and get the result.\n",
    "    \n",
    "    3. Run with smaller batch may help stablize testing performance. (but slowing down training process)\n",
    "    \n",
    "    4. The model trained on example config can be find in `checkpoint/`\n",
    "    \n",
    "\n",
    "\n",
    "## Package & Config setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "from util.timit_dataset import load_dataset,create_dataloader\n",
    "from util.functions import log_parser,batch_iterator, collapse_phn\n",
    "from model.las_model import Listener,Speller\n",
    "import numpy as np\n",
    "from torch.autograd import Variable\n",
    "import torch\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "\n",
    "# Load example config file for experiment\n",
    "config_path = 'config/las_example_config.yaml'\n",
    "conf = yaml.load(open(config_path,'r'))\n",
    "\n",
    "# Parameters loading\n",
    "num_epochs = conf['training_parameter']['num_epochs']\n",
    "training_msg = 'epoch_{:2d}_step_{:3d}_TrLoss_{:.4f}_TrWER_{:.2f}'\n",
    "epoch_end_msg = 'epoch_{:2d}_TrLoss_{:.4f}_TrWER_{:.2f}_ValLoss_{:.4f}_ValWER_{:.2f}_time_{:.2f}'\n",
    "listener_model_path = conf['meta_variable']['checkpoint_dir']+conf['meta_variable']['experiment_name']+'.listener'\n",
    "speller_model_path = conf['meta_variable']['checkpoint_dir']+conf['meta_variable']['experiment_name']+'.speller'\n",
    "verbose_step = conf['training_parameter']['verbose_step']\n",
    "tf_rate_upperbound = conf['training_parameter']['tf_rate_upperbound']\n",
    "tf_rate_lowerbound = conf['training_parameter']['tf_rate_lowerbound']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load preprocessed TIMIT Dataset\n",
    "# X : Padding to shape [num of sample, max_timestep, feature_dim]\n",
    "# Y : Squeeze repeated label and apply one-hot encoding (preserve 0 for <sos> and 1 for <eos>)\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = load_dataset(**conf['meta_variable'])\n",
    "train_set = create_dataloader(X_train, y_train, **conf['model_parameter'], **conf['training_parameter'], shuffle=True)\n",
    "valid_set = create_dataloader(X_val, y_val, **conf['model_parameter'], **conf['training_parameter'], shuffle=False)\n",
    "test_set = create_dataloader(X_test, y_test, **conf['model_parameter'], **conf['training_parameter'], shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construct Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "listener = Listener(**conf['model_parameter'])\n",
    "speller = Speller(**conf['model_parameter'])\n",
    "optimizer = torch.optim.Adam([{'params':listener.parameters()}, {'params':speller.parameters()}], \n",
    "                              lr=conf['training_parameter']['learning_rate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Progress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_ler = 1.0\n",
    "traing_log = open(conf['meta_variable']['training_log_dir']+conf['meta_variable']['experiment_name']+'.log','w')\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    epoch_head = time.time()\n",
    "    tr_loss = 0.0\n",
    "    tr_ler = []\n",
    "    val_loss = 0.0\n",
    "    val_ler = []\n",
    "\n",
    "    # Teacher forcing rate linearly decay\n",
    "    tf_rate = tf_rate_upperbound - (tf_rate_upperbound-tf_rate_lowerbound)*(epoch/num_epochs)\n",
    "    \n",
    "    # Training\n",
    "    for batch_index,(batch_data,batch_label) in enumerate(train_set):\n",
    "        batch_loss, batch_ler = batch_iterator(batch_data, batch_label, listener, speller, optimizer, \n",
    "                                               tf_rate, is_training=True, **conf['model_parameter'])\n",
    "        tr_loss += batch_loss\n",
    "        tr_ler.extend(batch_ler)\n",
    "        if (batch_index+1) % verbose_step == 0:\n",
    "            print(training_msg.format(epoch+1,batch_index+1,tr_loss[0]/(batch_index+1),sum(tr_ler)/len(tr_ler)),end='\\r',flush=True)\n",
    "    training_time = float(time.time()-epoch_head)\n",
    "    \n",
    "    # Validation\n",
    "    for _,(batch_data,batch_label) in enumerate(valid_set):\n",
    "        batch_loss, batch_ler = batch_iterator(batch_data, batch_label, listener, speller, optimizer, \n",
    "                                               tf_rate, is_training=False, **conf['model_parameter'])\n",
    "        val_loss += batch_loss\n",
    "        val_ler.extend(batch_ler)\n",
    "\n",
    "    # Logger\n",
    "    print(epoch_end_msg.format(epoch+1,tr_loss[0]/(batch_index+1),sum(tr_ler)/len(tr_ler),\n",
    "                               val_loss[0]/len(valid_set),sum(val_ler)/len(val_ler),training_time),flush=True)\n",
    "    print(epoch_end_msg.format(epoch+1,tr_loss[0]/(batch_index+1),sum(tr_ler)/len(tr_ler),\n",
    "                               val_loss[0]/len(valid_set),sum(val_ler)/len(val_ler),training_time),flush=True,file=traing_log)\n",
    "\n",
    "    # Checkpoint\n",
    "    if best_ler >= sum(val_ler)/len(val_ler):\n",
    "        best_ler = sum(val_ler)/len(val_ler)\n",
    "        torch.save(listener, listener_model_path)\n",
    "        torch.save(speller, speller_model_path)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result Visualization\n",
    "\n",
    "## Learning Curve & Performance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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MmzfpMU98K2yTrvx/qL3orGO3cbc9nXXsXXncTZ2bO5/UNwzDMAwjWrwJXOFlNT4KKHTO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kJlz3ASF2Xj7KJ0lm+txNVHqnFjiyfN2F8BqCgAaaouCIIgCGOMtPgoUuMi+fO7e1j64Ie8s6Mq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\n9uobnDPq0HNOe/aoYMklEzhp4DEtbbMnnMi/rn6Gl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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f53a707aa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2,figsize=(16,5))\n",
    "tr_loss, tt_loss, tr_ler, tt_ler = log_parser('log/las_example.log')\n",
    "\n",
    "axs[0].plot(tr_loss,label='Training');axs[0].plot(tt_loss,label='Validation');\n",
    "axs[0].legend();axs[0].set_xlabel('training epochs');axs[0].set_ylabel('Loss');\n",
    "axs[0].set_title('Learning Curve of LAS',size=15);axs[0].grid(True);\n",
    "axs[1].plot(tr_ler,label='Training');axs[1].plot(tt_ler,label='Validation');axs[1].grid(True);\n",
    "axs[1].legend();axs[1].set_xlabel('training epochs');axs[1].set_ylabel('Phoneme Error Rate');\n",
    "axs[1].set_ylim(0,1)\n",
    "axs[1].set_title('Performance of LAS',size=15)\n",
    "plt.savefig('log/result.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attention Visualization & Recognition Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kevin1kevin1k/ADLxMLDS2017/virt/lib/python3.5/site-packages/torch/serialization.py:286: SourceChangeWarning: source code of class 'torch.nn.modules.rnn.LSTM' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
      "  warnings.warn(msg, SourceChangeWarning)\n",
      "/home/kevin1kevin1k/ADLxMLDS2017/virt/lib/python3.5/site-packages/torch/serialization.py:286: SourceChangeWarning: source code of class 'torch.nn.modules.activation.ReLU' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
      "  warnings.warn(msg, SourceChangeWarning)\n",
      "/home/kevin1kevin1k/ADLxMLDS2017/virt/lib/python3.5/site-packages/torch/serialization.py:286: SourceChangeWarning: source code of class 'torch.nn.modules.activation.Softmax' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
      "  warnings.warn(msg, SourceChangeWarning)\n",
      "/home/kevin1kevin1k/ADLxMLDS2017/virt/lib/python3.5/site-packages/torch/serialization.py:286: SourceChangeWarning: source code of class 'torch.nn.modules.linear.Linear' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
      "  warnings.warn(msg, SourceChangeWarning)\n",
      "/home/kevin1kevin1k/ADLxMLDS2017/virt/lib/python3.5/site-packages/torch/serialization.py:286: SourceChangeWarning: source code of class 'torch.nn.modules.activation.LogSoftmax' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
      "  warnings.warn(msg, SourceChangeWarning)\n",
      "/home/kevin1kevin1k/Listen-Attend-and-Spell-Pytorch/model/las_model.py:35: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greately increasing memory usage. To compact weights again call flatten_parameters().\n",
      "  output,hidden = self.BLSTM(input_x)\n",
      "/home/kevin1kevin1k/Listen-Attend-and-Spell-Pytorch/model/las_model.py:78: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greately increasing memory usage. To compact weights again call flatten_parameters().\n",
      "  rnn_output, hidden_state = self.rnn_layer(input_word,last_hidden_state)\n"
     ]
    }
   ],
   "source": [
    "target = 0\n",
    "\n",
    "listener = torch.load(listener_model_path)\n",
    "speller = torch.load(speller_model_path)\n",
    "for batch_index,(batch_data,batch_label) in enumerate(test_set):\n",
    "    test_sample = batch_data[target:target+1]\n",
    "    test_label = batch_label[target]\n",
    "    break\n",
    "feature = listener(Variable(test_sample.float()).cuda())\n",
    "pred_seq, attention_score = speller(feature)\n",
    "\n",
    "encoded_feature_length = int(sum(sum(list(np.sum(test_sample.numpy(),axis=-1)!=0)))/8)\n",
    "\n",
    "pred = []\n",
    "att_map = []\n",
    "truth = []\n",
    "\n",
    "for pred_phn,att_score,true_phn in zip(pred_seq, attention_score,test_label):\n",
    "    ground_truth = np.argmax(true_phn.numpy())\n",
    "    pred.append(np.argmax(pred_phn.cpu().data.numpy()))\n",
    "    att_map.append(att_score.cpu().data.numpy()[0,:encoded_feature_length])\n",
    "    truth.append(ground_truth)\n",
    "    if ground_truth == 1:\n",
    "        break\n",
    "    \n",
    "for idx,p in enumerate(pred):\n",
    "    if p == 1:\n",
    "        break\n",
    "att_map = np.array(att_map)  \n",
    "pred = collapse_phn(pred[:idx],return_phn=True,drop_q=False)\n",
    "pred.extend(['<eos>' for i in range(len(truth)-len(pred))])\n",
    "truth = collapse_phn(truth[:-1],return_phn=True,drop_q=False)\n",
    "truth.append('<eos>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Hrsf1927P2vrrGS/B7T4PO5/HG/t1wR9ivj6PO1B6/8BMb6VBRikzjmk7vyQtUnbFl9RL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f53a707a320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,figsize=(15,13))\n",
    "ax2 = ax.twinx()\n",
    "plt.title('Attention Score During Decoding',size=30)\n",
    "ax.set_xlabel('(Encoded) Input Sequence',size=20)\n",
    "ax2.set_xlabel('(Encoded) Input Sequence',size=20)\n",
    "\n",
    "ax.imshow(att_map, cmap='hot')\n",
    "ax2.imshow(att_map, cmap='hot')\n",
    "\n",
    "ax.set_yticks(range(len(truth)))\n",
    "ax.set_yticklabels(pred,size=15)\n",
    "ax.set_ylabel('Decoding Step',size=20)\n",
    "\n",
    "ax2.set_yticks(range(len(truth)))\n",
    "ax2.set_yticklabels(truth,size=15)\n",
    "ax2.set_ylabel('Ground Truth',size=20)\n",
    "\n",
    "plt.savefig('log/attention.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kevin1kevin1k/Listen-Attend-and-Spell-Pytorch/model/las_model.py:35: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greately increasing memory usage. To compact weights again call flatten_parameters().\n",
      "  output,hidden = self.BLSTM(input_x)\n",
      "/home/kevin1kevin1k/Listen-Attend-and-Spell-Pytorch/model/las_model.py:78: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greately increasing memory usage. To compact weights again call flatten_parameters().\n",
      "  rnn_output, hidden_state = self.rnn_layer(input_word,last_hidden_state)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Phoneme Error Rate 0.34177474215145387\n"
     ]
    }
   ],
   "source": [
    "from util.functions import LetterErrorRate\n",
    "\n",
    "test_ler = []\n",
    "for batch_index,(batch_data,batch_label) in enumerate(test_set):\n",
    "    feature = listener(Variable(batch_data.float()).cuda())\n",
    "    pred_seq, _ = speller(feature)\n",
    "    \n",
    "    batch_ler = LetterErrorRate(np.argmax(np.array([p.data.cpu().numpy() for p in pred_seq]).transpose(1,0,2),axis=-1),\n",
    "                                np.argmax(batch_label.numpy(),axis=-1))\n",
    "    test_ler.extend(batch_ler)\n",
    "\n",
    "print('Testing Phoneme Error Rate',sum(test_ler)/len(test_ler))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
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}