explore.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load packages\n",
    "import IPython.display as ipd\n",
    "import os\n",
    "import json\n",
    "import random\n",
    "import pydub\n",
    "import numpy as np\n",
    "import torch\n",
    "import copy\n",
    "from torch.utils.data import DataLoader\n",
    "from models import Wav2Letter\n",
    "torch.cuda.set_device(1)\n",
    "# Import \n",
    "from utils import AudioDataset, PostProcess\n",
    "root_dir = '../../ssd/audio'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of paths\n",
    "kore_word_dir = os.path.join(root_dir,'kore_words')\n",
    "\n",
    "from glob import glob\n",
    "audio_dir = os.path.join(kore_word_dir,'audio')\n",
    "glob_pattern = os.path.join(audio_dir, '*')\n",
    "audio_list = sorted(glob(glob_pattern), key=os.path.getctime)\n",
    "\n",
    "target_dir = os.path.join(kore_word_dir,'targets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "セッチ\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source 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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": [
    "n_audio_max = 80000\n",
    "n_target_max = 9\n",
    "dataset = AudioDataset(audio_list,target_dir,n_audio_max,n_target_max)\n",
    "lookup_dict = json.load(open('./lookup.json'))\n",
    "postprocessor = PostProcess(lookup_dict)\n",
    "random_integer = random.randint(0,len(audio_list))\n",
    "original_word_audio_dir =  os.path.join(kore_word_dir,'audio')\n",
    "\n",
    "random_audio, random_target, _ = dataset[random_integer]\n",
    "random_audio_ID = os.path.split(audio_list[random_integer])[-1][:-4]\n",
    "original_audio_path = os.path.join(original_word_audio_dir,random_audio_ID) + '.mp3'\n",
    "\n",
    "rs = int(pydub.utils.mediainfo(original_audio_path)['sample_rate'])\n",
    "\n",
    "print(postprocessor.target2kana(random_target.cpu().numpy()))\n",
    "ipd.display(ipd.Audio(random_audio.cpu().numpy()*2**15, rate=rs,autoplay=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_dataset = len(audio_list)\n",
    "train_proportion = .9\n",
    "n_train = int(.9*n_dataset)\n",
    "inds = np.arange(n_dataset)\n",
    "np.random.shuffle(inds)\n",
    "inds_train = inds[:n_train]\n",
    "inds_val = inds[n_train:]\n",
    "n_val = n_dataset - n_train\n",
    "\n",
    "audio_list_train = np.array(audio_list)[inds_train].tolist()\n",
    "audio_list_val = np.array(audio_list)[inds_val].tolist()\n",
    "\n",
    "target_list_train = np.array(target_list)[inds_train].tolist()\n",
    "target_list_val = np.array(target_list)[inds_val].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I know that it possible to use the Subset class to separate the train and validation loaders,\n",
    "# however I don't know how to make the validation dataset not randomly subsampled in that case\n",
    "\n",
    "dataset_train = AudioDataset(audio_list_train,target_dir,n_audio_max,n_target_max)\n",
    "dataset_val = AudioDataset(audio_list_val,target_dir,n_audio_max,n_target_max)\n",
    "\n",
    "batch_size_train = 32\n",
    "batch_size_val = 64\n",
    "\n",
    "train_loader = DataLoader(dataset_train, batch_size=batch_size_train,shuffle=True)\n",
    "val_loader = DataLoader(dataset_val, batch_size=batch_size_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model,train_loader,val_loader,optimizer,criterion,n_epoch) :\n",
    "    \n",
    "    optimal_model = copy.copy(model)\n",
    "    av_lev_dist_old = 1e6\n",
    "    \n",
    "    for e in range(n_epoch) :\n",
    "    \n",
    "        total_training_loss = 0   \n",
    "        model.train()\n",
    "        for batch in train_loader :\n",
    "\n",
    "            optimizer.zero_grad()   \n",
    "\n",
    "            audio = batch[0]\n",
    "            targets = batch[1]\n",
    "            target_lengths = batch[2]        \n",
    "            current_batch_size = audio.size()[0]\n",
    "            output = model(audio)\n",
    "\n",
    "            # this basically a tensor vector of the length the size of the current\n",
    "            # batch size, each entry being the length of the predictions (determined in the model)\n",
    "            input_lengths = torch.full(size=(current_batch_size,), fill_value=output.size()[-1], dtype=torch.long)\n",
    "\n",
    "            # loss = ctc_loss(input, target, input_lengths, target_lengths)\n",
    "            loss = criterion(output.transpose(1, 2).transpose(0, 1),targets,input_lengths,target_lengths)        \n",
    "            total_training_loss += float(loss.cpu())\n",
    "\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "        total_val_loss = 0  \n",
    "        total_lev_dist = 0\n",
    "        model.eval()      \n",
    "        for batch in val_loader :\n",
    "\n",
    "            audio = batch[0]\n",
    "            targets = batch[1]\n",
    "            target_lengths = batch[2]        \n",
    "            current_batch_size = audio.size()[0]\n",
    "            output = model(audio)        \n",
    "\n",
    "            input_lengths = torch.full(size=(current_batch_size,), fill_value=output.size()[-1], dtype=torch.long)\n",
    "            loss = criterion(output.transpose(1, 2).transpose(0, 1),targets,input_lengths,target_lengths)\n",
    "\n",
    "            total_val_loss += float(loss.cpu())\n",
    "\n",
    "            targets = targets.cpu().numpy().astype('int')     \n",
    "            outmax = torch.argmax(output,dim=1).cpu().numpy()\n",
    "            for i, vec in enumerate(outmax):\n",
    "\n",
    "                original = postprocessor.target2kana(targets[i]) \n",
    "                predicted = postprocessor.target2kana(vec,refine = True)\n",
    "                lev_dist = postprocessor.levenshtein(original,predicted)\n",
    "                total_lev_dist += lev_dist/len(original)\n",
    "\n",
    "        av_lev_dist = total_lev_dist/n_val\n",
    "\n",
    "        if av_lev_dist < av_lev_dist_old :\n",
    "            optimal_model = copy.deepcopy(model)\n",
    "            av_lev_dist_old = copy.copy(av_lev_dist)\n",
    "\n",
    "        print(e,total_training_loss/n_train,total_val_loss/n_val,av_lev_dist)\n",
    "        \n",
    "    return optimal_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 56.53797395101063 35.49236479744887 0.9880554893075765\n",
      "1 17.758373162830473 15.537959689488993 1.0\n",
      "2 15.110529739294954 14.935969230129643 1.0\n",
      "3 13.657690435349389 12.230540891720576 1.0\n",
      "4 10.37464885534736 9.123771552848497 0.8422171873757863\n",
      "5 7.2925479244876215 7.337885224560465 0.6500934096510068\n",
      "6 5.2562935399212956 5.1130653088399285 0.4491473885046507\n",
      "7 3.931375539634577 3.959322080787315 0.3298096033070994\n",
      "8 3.244817306292079 4.012112319767972 0.32222752206057725\n",
      "9 2.6548094393813324 3.590493764224554 0.29549844979728124\n",
      "10 2.3279921820079683 3.36490088512185 0.284790126401145\n",
      "11 2.001450323837365 2.5386652492720616 0.223330550918197\n",
      "12 1.8641546507713305 2.836307799477808 0.21898998330550934\n",
      "13 1.6103112831186497 4.070242256075392 0.3042054217346372\n",
      "14 1.462457661460636 2.6174999549114246 0.20836910724222926\n",
      "15 1.2935534592240994 2.446475759770516 0.19792312584466173\n",
      "16 1.18836284418053 2.7754965194676675 0.22974203036807395\n",
      "17 1.1720571162306974 2.6507234652969793 0.20289768662055813\n",
      "18 0.9616342280923988 2.3352918927378963 0.1776890054853327\n",
      "19 0.9931750426708215 2.2561668243153465 0.1780189204229271\n",
      "20 0.783494182331885 2.222192251622577 0.1811272756180936\n",
      "21 0.6984817933947788 3.07853337002915 0.21641227442563002\n",
      "22 0.7962700256390121 2.505650400916404 0.1813498688290008\n",
      "23 0.8171871404700908 2.783356929263209 0.19800659829875186\n",
      "24 0.6581155546963325 2.2022138891713645 0.1741434136258844\n",
      "25 0.5795539602056726 2.5841984820485315 0.21052150409412507\n",
      "26 0.6379384476974855 3.203876418940014 0.2093131409492011\n",
      "27 0.49088458952965675 2.31982605286154 0.16976905954368393\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-22-4db3447cc820>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mcriterion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCTCLoss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mn_epoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtrained_model1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mval_loader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn_epoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-21-ed0a1263553e>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, train_loader, val_loader, optimizer, criterion, n_epoch)\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mtotal_training_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain_loader\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     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m             \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pt/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    344\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    345\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 346\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    347\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pt/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\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     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pt/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\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     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/talisol/wav2kana/utils.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m     44\u001b[0m             \u001b[0maudio_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maudio_tensor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiff_pad\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdiff_pad\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiff_pad\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'constant'\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[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0mfileID\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maudio_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m         \u001b[0mtarget_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfileID\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'.npy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0mtarget_npy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pt/lib/python3.7/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mpad\u001b[0;34m(input, pad, mode, value)\u001b[0m\n\u001b[1;32m   2846\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Padding length too large'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2847\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'constant'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2848\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_VF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstant_pad_nd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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   2849\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2850\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Padding mode \"{}\"\" doesn\\'t take in value argument'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "n_class = 79\n",
    "model = Wav2Letter(n_class)\n",
    "model = model.cuda()\n",
    "optimizer = torch.optim.Adam(model.parameters(),lr=1e-4)\n",
    "criterion = torch.nn.CTCLoss(reduction='sum')\n",
    "n_epoch = 200\n",
    "trained_model1 = train(model,train_loader,val_loader,optimizer,criterion,n_epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def infer(model,val_loader) :\n",
    "    model.eval()   \n",
    "    total_lev_dist = 0\n",
    "    n_val = len(val_loader.dataset)\n",
    "    for data in val_loader :\n",
    "        \n",
    "        audio = data[0]\n",
    "        targets = data[1].cpu().numpy().astype('int')     \n",
    "        output = model(audio)\n",
    "        outmax = torch.argmax(output,dim=1).cpu().numpy()\n",
    "        \n",
    "        for i, vec in enumerate(outmax):\n",
    "            \n",
    "            original = postprocessor.target2kana(targets[i]) \n",
    "            predicted = postprocessor.target2kana(vec,refine = True)\n",
    "            lev_dist = postprocessor.levenshtein(original,predicted)\n",
    "            total_lev_dist += lev_dist/len(original)\n",
    "            \n",
    "            print(original, predicted,lev_dist,round(lev_dist/len(original),2))\n",
    "            \n",
    "        av_lev = total_lev_dist/n_val\n",
    "        \n",
    "    print('average Levenshtein distance',av_lev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "オネエサン オメイサ 3 0.6\n",
      "ヤワラカ ヤワラカ 0 0.0\n",
      "キレイ キレイ 0 0.0\n",
      "テンサイ テンサイ 0 0.0\n",
      "クミタテル クミカテル 1 0.2\n",
      "タトエバ タトイバ 1 0.25\n",
      "ジンブンカガク ジンブンクラク 2 0.29\n",
      "トッサニ トッサニ 0 0.0\n",
      "サツジン サツジン 0 0.0\n",
      "ドーイ ドイ 1 0.33\n",
      "オカス オカス 0 0.0\n",
      "タハタ タハタ 0 0.0\n",
      "ニシ ミシ 1 0.5\n",
      "ガンジツ ガンジツ 0 0.0\n",
      "カマウ カナン 2 0.67\n",
      "ヒル ヒル 0 0.0\n",
      "ハイセン ハイセン 0 0.0\n",
      "ブンカ ブンカ 0 0.0\n",
      "エンピツ エンキツ 1 0.25\n",
      "ナメラカ ナメラカ 0 0.0\n",
      "ホントーニ ホントーニ 0 0.0\n",
      "ハンドル ハンドル 0 0.0\n",
      "イダイ イダイ 0 0.0\n",
      "レクリエーション レクリエーション 0 0.0\n",
      "ブンルイ ブンルイ 0 0.0\n",
      "ワイン ワイン 0 0.0\n",
      "レストラン レストラン 0 0.0\n",
      "オイシイ オイシイ 0 0.0\n",
      "シンコー シンコー 0 0.0\n",
      "ミズ ミズ 0 0.0\n",
      "ドクトク ドクトク 0 0.0\n",
      "センメン センネン 1 0.25\n",
      "テツズキ テツズキ 0 0.0\n",
      "ハッテン ハッテン 0 0.0\n",
      "ニンズー ミンズン 2 0.5\n",
      "セイジツ セイジツ 0 0.0\n",
      "ウェーター ウェイター 1 0.2\n",
      "イジ イジ 0 0.0\n",
      "ギロン ギロン 0 0.0\n",
      "キエル キエル 0 0.0\n",
      "ハンイ ハンイ 0 0.0\n",
      "ヨユー ヨユー 0 0.0\n",
      "ミワタス ミワカス 1 0.25\n",
      "トル トル 0 0.0\n",
      "ハチ ハチ 0 0.0\n",
      "スイジュン スイジュン 0 0.0\n",
      "デザート エザート 1 0.25\n",
      "ゴールデンウィーク ーレク 7 0.78\n",
      "ベタベタ デタ 3 0.75\n",
      "タクサン パクサン 1 0.25\n",
      "ハダ ハダ 0 0.0\n",
      "イソグ イソン 1 0.33\n",
      "タンスー タンスー 0 0.0\n",
      "ハツゲン ハツベン 1 0.25\n",
      "センゾ センゾ 0 0.0\n",
      "ミハラシ ミハラシ 0 0.0\n",
      "センゼン センゼン 0 0.0\n",
      "ミブリ ミブリ 0 0.0\n",
      "ジシュー ジシュー 0 0.0\n",
      "ボンチ ボンチ 0 0.0\n",
      "ゲ ゲ 0 0.0\n",
      "ゼンシン ゼンシン 0 0.0\n",
      "ガッカリ タッカイ 2 0.5\n",
      "テスト テスト 0 0.0\n",
      "カタミチ カタミチ 0 0.0\n",
      "ヨキン ヨキン 0 0.0\n",
      "ソードー ソードー 0 0.0\n",
      "チュー チュー 0 0.0\n",
      "ジシン ジシン 0 0.0\n",
      "カメラマン カメラマン 0 0.0\n",
      "ガカ ガカ 0 0.0\n",
      "ヒキアゲル ヒキアゲル 0 0.0\n",
      "セイメイ セイネイ 1 0.25\n",
      "コレマデ コレガデ 1 0.25\n",
      "チラカル チダカル 1 0.25\n",
      "アキラメル アキラメル 0 0.0\n",
      "ジツヨー ジツヨー 0 0.0\n",
      "ゾーカ ジゾーカ 1 0.33\n",
      "ワリアテル ワリアテル 0 0.0\n",
      "チャンス チョンス 1 0.25\n",
      "ジドー ジドー 0 0.0\n",
      "オイツク オイツク 0 0.0\n",
      "ナントナク ナントマク 1 0.2\n",
      "デンシャ デンシャ 0 0.0\n",
      "モダン モダン 0 0.0\n",
      "フッカツ フッタツ 1 0.25\n",
      "ハナミ ハナニ 1 0.33\n",
      "フショー フショー 0 0.0\n",
      "シュニン シュニン 0 0.0\n",
      "マメ マメ 0 0.0\n",
      "イキナリ イキナリ 0 0.0\n",
      "キリストキョー キリストキョー 0 0.0\n",
      "キチ キチ 0 0.0\n",
      "ウナズク ウマズク 1 0.25\n",
      "フビョウドー フブードー 3 0.5\n",
      "アイスル アイスリ 1 0.25\n",
      "コームイン コーンイン 1 0.2\n",
      "セワ セワ 0 0.0\n",
      "イッソー イッソー 0 0.0\n",
      "タッセイ タッセイ 0 0.0\n",
      "ショキュー ショキュー 0 0.0\n",
      "キツエン キツエン 0 0.0\n",
      "オル オル 0 0.0\n",
      "イワイ イワイ 0 0.0\n",
      "ゲキ ゲキ 0 0.0\n",
      "ミゾ ミゾ 0 0.0\n",
      "ジッセキ ジッセキ 0 0.0\n",
      "カセットテープ タセットテープ 1 0.14\n",
      "バイオリン バイヨリン 1 0.2\n",
      "ザラザラ ザラザラ 0 0.0\n",
      "オレ オレ 0 0.0\n",
      "トク トク 0 0.0\n",
      "ニチ ニチ 0 0.0\n",
      "ミコミ ミコミ 0 0.0\n",
      "コシカケル コシカケル 0 0.0\n",
      "シンジツ シンジツ 0 0.0\n",
      "サケル サケル 0 0.0\n",
      "ザッシ ザッシ 0 0.0\n",
      "カミソリ カソリ 1 0.25\n",
      "ツマラナイ ツマラナイ 0 0.0\n",
      "ハツデン ハツデン 0 0.0\n",
      "チジミ チジニ 1 0.33\n",
      "トー トー 0 0.0\n",
      "タショー タショー 0 0.0\n",
      "ハヤイ ハヤイ 0 0.0\n",
      "オク オク 0 0.0\n",
      "ガイジン ガイジン 0 0.0\n",
      "ヒロガル ヒロガエ 1 0.25\n",
      "ヨロコビ ヨロクビ 1 0.25\n",
      "メシ ネシ 1 0.5\n",
      "ケンコー ケンコー 0 0.0\n",
      "テラス テラス 0 0.0\n",
      "ヒョージョー ヒョージョー 0 0.0\n",
      "スイミン スイミン 0 0.0\n",
      "トマル トマル 0 0.0\n",
      "アニ アミ 1 0.5\n",
      "トリシマル トリシマル 0 0.0\n",
      "アタル アタル 0 0.0\n",
      "スイデン スイデン 0 0.0\n",
      "カミサマ カンサマ 1 0.25\n",
      "セイフク セイフク 0 0.0\n",
      "コーエン コーン 1 0.25\n",
      "ハンエイ ハンエイ 0 0.0\n",
      "アイズ アイズ 0 0.0\n",
      "センモン センモン 0 0.0\n",
      "ネラウ メラウ 1 0.33\n",
      "カオク カーク 1 0.33\n",
      "ウカブ ウカブ 0 0.0\n",
      "チキン チキン 0 0.0\n",
      "タリョー タリョー 0 0.0\n",
      "テアライ テアライ 0 0.0\n",
      "セイセキ セイセキ 0 0.0\n",
      "ガサガサ ガサガサ 0 0.0\n",
      "ヌケル ムケル 1 0.33\n",
      "チカク チカク 0 0.0\n",
      "フエ フエ 0 0.0\n",
      "ブヒン ブヒン 0 0.0\n",
      "モーケル モーケル 0 0.0\n",
      "トー トー 0 0.0\n",
      "コーカ コーカ 0 0.0\n",
      "ヨケル ヨケル 0 0.0\n",
      "オー オー 0 0.0\n",
      "ガラガラ ガラガラ 0 0.0\n",
      "トリカエス トリカエス 0 0.0\n",
      "ナツヤスミ ナツヤスニ 1 0.2\n",
      "イゼン イゼン 0 0.0\n",
      "ホノオ ホノー 1 0.33\n",
      "モノオト モノート 1 0.25\n",
      "クミ クミ 0 0.0\n",
      "シンシツ シンシツ 0 0.0\n",
      "タカサ タカサ 0 0.0\n",
      "オイダス オイダス 0 0.0\n",
      "ヘンコー ヘンコー 0 0.0\n",
      "レイゾーコ レイゾーコ 0 0.0\n",
      "セイベツ セイベツ 0 0.0\n",
      "イカス イカス 0 0.0\n",
      "テキスト テキスト 0 0.0\n",
      "アエテ アイテ 1 0.33\n",
      "マエウリケン マーリテン 3 0.5\n",
      "オオハバ オーハバ 1 0.25\n",
      "ヨーイン ヨイン 1 0.25\n",
      "アツリョク アツリョク 0 0.0\n",
      "ハクブツカン ハクブツカン 0 0.0\n",
      "オセジ オセジ 0 0.0\n",
      "ギョーセキ ギョーセキ 0 0.0\n",
      "アヤシイ アヤシイ 0 0.0\n",
      "ケッセキ テッセキ 1 0.25\n",
      "キョク キョク 0 0.0\n",
      "コトバ コトバ 0 0.0\n",
      "カクド カクロ 1 0.33\n",
      "キモチ キモチ 0 0.0\n",
      "ヒトガラ ヒトガラ 0 0.0\n",
      "キョードー キョードー 0 0.0\n",
      "カイガン カイヤン 1 0.25\n",
      "ヨチ ヨチ 0 0.0\n",
      "ケツエキ ケツイキ 1 0.25\n",
      "キレ キレ 0 0.0\n",
      "シメッポイ シメッパイ 1 0.2\n",
      "オトズレル オトズレル 0 0.0\n",
      "ツリアウ ツリアウ 0 0.0\n",
      "サイコー サイコー 0 0.0\n",
      "クーコー クーコー 0 0.0\n",
      "ラン ラン 0 0.0\n",
      "ムセン ムセン 0 0.0\n",
      "マザル マザル 0 0.0\n",
      "ウッタエル ウッタエル 0 0.0\n",
      "オーボ オーボ 0 0.0\n",
      "アコガレ アコガレ 0 0.0\n",
      "カミノケ カミノケ 0 0.0\n",
      "タビタビ タリビタビ 1 0.25\n",
      "エレベーター エレデイター 2 0.33\n",
      "ムカエ ムカエ 0 0.0\n",
      "イチョー イチョー 0 0.0\n",
      "デザイン デザイン 0 0.0\n",
      "ショッキ ショッキ 0 0.0\n",
      "オロス オロス 0 0.0\n",
      "ツクリアゲル ツクリアゲル 0 0.0\n",
      "ケッカン テッカン 1 0.25\n",
      "テン テン 0 0.0\n",
      "トイアワセル トイアオセル 1 0.17\n",
      "ツツミガミ ツミガニ 2 0.4\n",
      "ジンルイ ジンイ 1 0.25\n",
      "ソト ソト 0 0.0\n",
      "ワリビク ワリク 1 0.25\n",
      "ガケ ガケ 0 0.0\n",
      "タツ タツ 0 0.0\n",
      "クルー クルー 0 0.0\n",
      "ヒモ ヒモ 0 0.0\n",
      "サダメル サバメル 1 0.25\n",
      "ザンギョー ザンギョー 0 0.0\n",
      "ジョシュ ジョシュ 0 0.0\n",
      "ソー ソル 1 0.5\n",
      "ニッコー ニッコー 0 0.0\n",
      "カイカク カイカク 0 0.0\n",
      "ヒトシイ ヒトシイ 0 0.0\n",
      "コーオン コーオン 0 0.0\n",
      "ヤクワリ ヤクワリ 0 0.0\n",
      "チョーシ チョーシ 0 0.0\n",
      "ボンオドリ ボンモドリ 1 0.2\n",
      "オット オット 0 0.0\n",
      "ナガイキ ナガイキ 0 0.0\n",
      "ハンドバッグ ハンドバップ 1 0.17\n",
      "ハジメ ハジメ 0 0.0\n",
      "コッチ コッチ 0 0.0\n",
      "ナンド ナンド 0 0.0\n",
      "コーテイ コーテイ 0 0.0\n",
      "ゴートー ゴートー 0 0.0\n",
      "ドーグ ドーム 1 0.33\n",
      "イマ イナ 1 0.5\n",
      "スイゾクカン スイゾクカン 0 0.0\n",
      "カケル カケル 0 0.0\n",
      "キカイ キカイ 0 0.0\n",
      "テツダウ テツダウ 0 0.0\n",
      "フアン フワン 1 0.33\n",
      "ナキゴエ ナキゴエ 0 0.0\n",
      "サムイ サマムイ 1 0.33\n",
      "ニドト ミドト 1 0.33\n",
      "シチョー シチョー 0 0.0\n",
      "ムガイ ムガイ 0 0.0\n",
      "ゲンカイ ゲンカイ 0 0.0\n",
      "シュー シュー 0 0.0\n",
      "シュンカン シュンカン 0 0.0\n",
      "モデル モベル 1 0.33\n",
      "テモト テマト 1 0.33\n",
      "アラタ アパ 2 0.67\n",
      "ライゲツ ライゲツ 0 0.0\n",
      "ムレ ムレ 0 0.0\n",
      "カタログ カタロ 1 0.25\n",
      "ワクワク ワクアク 1 0.25\n",
      "トーゼン トーゼン 0 0.0\n",
      "カワル カワブ 1 0.33\n",
      "キカン キカン 0 0.0\n",
      "セイヒン セイヒン 0 0.0\n",
      "センザイ センザイ 0 0.0\n",
      "クッツケル クッツケル 0 0.0\n",
      "クダモノ クダモノ 0 0.0\n",
      "シンケン シンケン 0 0.0\n",
      "キューソク キューソク 0 0.0\n",
      "チキュー チキュー 0 0.0\n",
      "シンセツ シンセツ 0 0.0\n",
      "シューショク シューショク 0 0.0\n",
      "ネンレイ メンレイ 1 0.25\n",
      "フタン フタン 0 0.0\n",
      "セイザ セイザ 0 0.0\n",
      "ナゲル ナデル 1 0.33\n",
      "ハライモドス ハライモドス 0 0.0\n",
      "シュッサン シュッサン 0 0.0\n",
      "クカク クカク 0 0.0\n",
      "サク サク 0 0.0\n",
      "ページ テイジ 2 0.67\n",
      "チューシャ チューシャ 0 0.0\n",
      "アチコチ アチコチ 0 0.0\n",
      "モッタイナイ モッタインナイ 1 0.17\n",
      "タントー タントー 0 0.0\n",
      "ナマケル ナマケル 0 0.0\n",
      "サクバン サクバン 0 0.0\n",
      "ヨワキ ヨワキ 0 0.0\n",
      "キンシ キンシ 0 0.0\n",
      "タイスル タイスル 0 0.0\n",
      "ドージ ドージ 0 0.0\n",
      "ハンコ ハンコ 0 0.0\n",
      "ムリ ムリ 0 0.0\n",
      "ヒジョーニ ヒジョーニ 0 0.0\n",
      "ナカナカ ナカナカ 0 0.0\n",
      "カット カット 0 0.0\n",
      "ハメル ハメル 0 0.0\n",
      "チョッケイ チョッケイ 0 0.0\n",
      "キセキ セキ 1 0.33\n",
      "オッシャル オッシャル 0 0.0\n",
      "トチ トチ 0 0.0\n",
      "キョーレツ キョーレツ 0 0.0\n",
      "アルミニウム アルミニング 2 0.33\n",
      "コレカラ コレカラ 0 0.0\n",
      "ナガバナシ ナガバナシ 0 0.0\n",
      "オコナウ オコガウ 1 0.25\n",
      "カイサイ カイサイ 0 0.0\n",
      "ヒコージョー ヒコージゾー 1 0.17\n",
      "オイカケル オイカケル 0 0.0\n",
      "ミノマワリ ミノマワリ 0 0.0\n",
      "タイイン タイン 1 0.25\n",
      "チチ シチ 1 0.5\n",
      "モチイル モチル 1 0.25\n",
      "タ タ 0 0.0\n",
      "オク オク 0 0.0\n",
      "ケンカイ テンカイ 1 0.25\n",
      "ウゴキ ウモキ 1 0.33\n",
      "スクナイ ステナイ 1 0.25\n",
      "ツキアイ ツキアイ 0 0.0\n",
      "カワ カワ 0 0.0\n",
      "カチ カチ 0 0.0\n",
      "ハナタバ ハマタバ 1 0.25\n",
      "ルスバン ブスバン 1 0.25\n",
      "ジショ ジショ 0 0.0\n",
      "アシタ アシタ 0 0.0\n",
      "ホオ ホー 1 0.5\n",
      "ヒロイ ヒロイ 0 0.0\n",
      "ヨボー ヨボー 0 0.0\n",
      "ソーコ ソーコ 0 0.0\n",
      "クサイ クシャイ 2 0.67\n",
      "アンテイ アンテイ 0 0.0\n",
      "マシテ マシテ 0 0.0\n",
      "サンコー サンコー 0 0.0\n",
      "ミダレル ミダレル 0 0.0\n",
      "プール プール 0 0.0\n",
      "ツケル ツケル 0 0.0\n",
      "カンセツ カンセツ 0 0.0\n",
      "ハレル ハレル 0 0.0\n",
      "ヒリツ ヒリツ 0 0.0\n",
      "ツク ツク 0 0.0\n",
      "イショクジュー イショクジー 1 0.14\n",
      "ショーヒン ショーヒン 0 0.0\n",
      "ヤル ヤル 0 0.0\n",
      "ヨコガオ ヨコガオ 0 0.0\n",
      "タイカク タイカク 0 0.0\n",
      "サカヤ サカヤ 0 0.0\n",
      "イス イス 0 0.0\n",
      "ユゲ ユゲ 0 0.0\n",
      "ベッド デンド 2 0.67\n",
      "アキチ アキチ 0 0.0\n",
      "エンダカ エンダカ 0 0.0\n",
      "ダイスキ ダイスキ 0 0.0\n",
      "マエ マイ 1 0.5\n",
      "カコム カコム 0 0.0\n",
      "マンゾク マンゾク 0 0.0\n",
      "クシ フシ 1 0.5\n",
      "モテル モテル 0 0.0\n",
      "オオイソギ オーイソム 2 0.4\n",
      "アケガタ アケガタ 0 0.0\n",
      "ガッカ ガッカ 0 0.0\n",
      "ショーガイ ショーガイ 0 0.0\n",
      "メンゼイ ネンゼイ 1 0.25\n",
      "ケイカイ ケイカイ 0 0.0\n",
      "タイフー タイフー 0 0.0\n",
      "ヒショ ショ 1 0.33\n",
      "コガラシ コガラシ 0 0.0\n",
      "サクヒン サクジン 1 0.25\n",
      "オサエル オサエル 0 0.0\n",
      "トーゴー トゴー 1 0.25\n",
      "タイキン タイキン 0 0.0\n",
      "ジューギョーイン ジューギヨイン 2 0.25\n",
      "ソロー ソロー 0 0.0\n",
      "フツー フツー 0 0.0\n",
      "ジンミン ジンギン 1 0.25\n",
      "セイケン セイケン 0 0.0\n",
      "ウツワ ウツワ 0 0.0\n",
      "ワガクニ ワガクニ 0 0.0\n",
      "ニッソ ミッソ 1 0.33\n",
      "セット セット 0 0.0\n",
      "コーカン コーカン 0 0.0\n",
      "ムスコ ムスコ 0 0.0\n",
      "ムイカ ウィカ 2 0.67\n",
      "ジナン ジナン 0 0.0\n",
      "ウスグライ ウスグライ 0 0.0\n",
      "ホーシャノー ホーショノ 2 0.33\n",
      "チュー チュー 0 0.0\n",
      "ボン ボン 0 0.0\n",
      "シツ シツ 0 0.0\n",
      "カイフク カイフク 0 0.0\n",
      "ゴガク ゴマク 1 0.33\n",
      "ノーカ ノーカ 0 0.0\n",
      "コクテツ コクテツ 0 0.0\n",
      "カイホー カヤホー 1 0.25\n",
      "ワシキ ワシキ 0 0.0\n",
      "ミュージック ニュージック 1 0.17\n",
      "ノーリツ ノオリツ 1 0.25\n",
      "ニブイ ニブイ 0 0.0\n",
      "テツヤ テツヤ 0 0.0\n",
      "ミチビク ミチビク 0 0.0\n",
      "デマエ デマエ 0 0.0\n",
      "ネエサン イサン 2 0.5\n",
      "ムセイブツ ムセーブツ 1 0.2\n",
      "ムカンケイ ムカンケイ 0 0.0\n",
      "チョーショ チョーシャ 1 0.2\n",
      "ツケル スケル 1 0.33\n",
      "イライ イライ 0 0.0\n",
      "ミエル ニエル 1 0.33\n",
      "ワゴ ワゴ 0 0.0\n",
      "クサル クサル 0 0.0\n",
      "ツキアタル ツキアタイ 1 0.2\n",
      "ウカガウ ウカガウ 0 0.0\n",
      "チツジョ チツジョ 0 0.0\n",
      "ソレ ソレ 0 0.0\n",
      "サシアゲル サシアゲル 0 0.0\n",
      "ノボリ モボリ 1 0.33\n",
      "カリニ カリ 1 0.33\n",
      "キノドク キノドク 0 0.0\n",
      "ハチ ハチ 0 0.0\n",
      "ゲンイン ゲイン 1 0.25\n",
      "オオク オーク 1 0.33\n",
      "タンキ タンキ 0 0.0\n",
      "メシアガル ネシヤガル 2 0.4\n",
      "クンレン クンデン 1 0.25\n",
      "ツージョー スーゾー 3 0.6\n",
      "ト ト 0 0.0\n",
      "ショーテン ショーテン 0 0.0\n",
      "ミチ ミチ 0 0.0\n",
      "ショーチョー ショーチョー 0 0.0\n",
      "ショー ショー 0 0.0\n",
      "キズツケル キズツケル 0 0.0\n",
      "ザシキ ザシキ 0 0.0\n",
      "ツイ ツイ 0 0.0\n",
      "クタビレル クタビレル 0 0.0\n",
      "キョーリョク キョーリョク 0 0.0\n",
      "ソコ ソコ 0 0.0\n",
      "イト イト 0 0.0\n",
      "コクサイカ コクサイカ 0 0.0\n",
      "ハカセ ハカセ 0 0.0\n",
      "キャベツ キョデツ 2 0.5\n",
      "スクイ スクイ 0 0.0\n",
      "カミ カミ 0 0.0\n",
      "アマエル アマエル 0 0.0\n",
      "ゲンショー ゲンショー 0 0.0\n",
      "ショゾク ショゾク 0 0.0\n",
      "ウンチン ウンチン 0 0.0\n",
      "カンジル カンジル 0 0.0\n",
      "ミミ ミギ 1 0.5\n",
      "ボーネンカイ ボーメンカイ 1 0.17\n",
      "ゲリ ゲリ 0 0.0\n",
      "カミ カミ 0 0.0\n",
      "ハイキング ハイキンム 1 0.2\n",
      "イモート イモート 0 0.0\n",
      "ウナギ ウナニ 1 0.33\n",
      "ハッケン ハッケン 0 0.0\n",
      "ワフー ワフー 0 0.0\n",
      "ネカス ネカス 0 0.0\n",
      "クルマイス クルガイス 1 0.2\n",
      "コセキ トセキ 1 0.33\n",
      "ナゴヤカ ナモヤカ 1 0.25\n",
      "フスマ フスマ 0 0.0\n",
      "トータツ トーカツ 1 0.25\n",
      "アドバイス アドバイス 0 0.0\n",
      "ハ ハ 0 0.0\n",
      "ナニカ ナニカ 0 0.0\n",
      "ケシゴム ケシボグン 3 0.75\n",
      "コレラ コレラ 0 0.0\n",
      "トシツキ トシツキ 0 0.0\n",
      "ツル ツル 0 0.0\n",
      "シンゴー シゴー 1 0.25\n",
      "シゲキ シニキ 1 0.33\n",
      "バター バター 0 0.0\n",
      "ヒジョー ヒジョー 0 0.0\n",
      "リクエスト リクエスト 0 0.0\n",
      "ロンブン ロンブン 0 0.0\n",
      "ホンブン ホンブン 0 0.0\n",
      "ヨク ヨク 0 0.0\n",
      "シロイ シドイ 1 0.33\n",
      "ジュータク ジュータク 0 0.0\n",
      "シュツエン シツエン 1 0.2\n",
      "オソレル オソレル 0 0.0\n",
      "シダイニ シダイニ 0 0.0\n",
      "インテリ インテリ 0 0.0\n",
      "リョーイキ リョーイキ 0 0.0\n",
      "コージョー ホージョー 1 0.2\n",
      "タマタマ タマタマ 0 0.0\n",
      "パパ パタ 1 0.5\n",
      "タイワ タイワ 0 0.0\n",
      "マゼル マゼル 0 0.0\n",
      "ザイセイ ザイセイ 0 0.0\n",
      "シキ シキ 0 0.0\n",
      "チャント チャント 0 0.0\n",
      "ツモリ ツモリ 0 0.0\n",
      "コトシ コトシ 0 0.0\n",
      "ナント ナント 0 0.0\n",
      "ショーセツ ショーセツ 0 0.0\n",
      "マックロ マックラ 1 0.25\n",
      "ホシュ ホシュ 0 0.0\n",
      "キカク キカク 0 0.0\n",
      "ガクネン ガクネン 0 0.0\n",
      "コクレン コクレン 0 0.0\n",
      "アワセル アラセル 1 0.25\n",
      "ムケル ムケル 0 0.0\n",
      "ジューミン ジューミニン 1 0.2\n",
      "テンジョー テンジョー 0 0.0\n",
      "サス サス 0 0.0\n",
      "カタチ カタチ 0 0.0\n",
      "シツド シツド 0 0.0\n",
      "アメ アメ 0 0.0\n",
      "コーヨー コオヨー 1 0.25\n",
      "ユトリ ユトリ 0 0.0\n",
      "カイギ カニ 2 0.67\n",
      "トリクム トリクム 0 0.0\n",
      "サット サット 0 0.0\n",
      "コトニ コトン 1 0.33\n",
      "カケル カケル 0 0.0\n",
      "ムスー ムスー 0 0.0\n",
      "シメル シメル 0 0.0\n",
      "オキル オキル 0 0.0\n",
      "エイズ エイズ 0 0.0\n",
      "グーゼン グーゼン 0 0.0\n",
      "ミダス ミダス 0 0.0\n",
      "アサ アサ 0 0.0\n",
      "ヘイホー ヘイホー 0 0.0\n",
      "ビーチ リイチ 2 0.67\n",
      "コトワザ コトワザ 0 0.0\n",
      "ハンノー ハンノー 0 0.0\n",
      "シンコー シンコー 0 0.0\n",
      "モル モル 0 0.0\n",
      "シリアイ シリアイ 0 0.0\n",
      "コーシュー コーシュー 0 0.0\n",
      "ヒレイ ヒレイ 0 0.0\n",
      "ツクリバナシ スクリバナシ 1 0.17\n",
      "キキテ キテ 1 0.33\n",
      "アキル アキル 0 0.0\n",
      "ムラ ムラ 0 0.0\n",
      "ウケトメル ウケトメル 0 0.0\n",
      "ドレス ドレス 0 0.0\n",
      "ホシ ホシ 0 0.0\n",
      "ハンバイ ハンバイ 0 0.0\n",
      "アウ アウ 0 0.0\n",
      "ワリアイニ ワリアギ 2 0.4\n",
      "ツイシン ツイシン 0 0.0\n",
      "オバ オバ 0 0.0\n",
      "コドク コドク 0 0.0\n",
      "ヨッツ ヨッツ 0 0.0\n",
      "ネンゲツ ネンニツ 1 0.25\n",
      "チイサイ チイサイ 0 0.0\n",
      "ロンジル ロンジル 0 0.0\n",
      "モノゴト モノモト 1 0.25\n",
      "ビンカン ビンカン 0 0.0\n",
      "ブンガクシャ ブンガシャ 1 0.17\n",
      "テープ テイプ 1 0.33\n",
      "カンチョー カンチョー 0 0.0\n",
      "オーダン オーダン 0 0.0\n",
      "コッカイ コッカイ 0 0.0\n",
      "ケイ ケイ 0 0.0\n",
      "リョーシン リョーシン 0 0.0\n",
      "ブラブラ ブラグラ 1 0.25\n",
      "トニカク トミニカク 1 0.25\n",
      "トリイレル トリレル 1 0.2\n",
      "アルバイト アバイト 1 0.2\n",
      "キンキュー キンキュー 0 0.0\n",
      "セイシュン セイシュン 0 0.0\n",
      "マンテン マンテン 0 0.0\n",
      "ソダテル ソバテル 1 0.25\n",
      "ハジマリ ハジマリ 0 0.0\n",
      "ヨーフク ヨーフク 0 0.0\n",
      "フロク フロク 0 0.0\n",
      "キンエン キンゲン 1 0.25\n",
      "サル サル 0 0.0\n",
      "スキトオル スキトル 1 0.2\n",
      "モーフ モーフ 0 0.0\n",
      "シュノー シュノー 0 0.0\n",
      "イケバナ イケバ 1 0.25\n",
      "ケイヒ ケイヒ 0 0.0\n",
      "ゲスイ ゲスイ 0 0.0\n",
      "ロクオン ロクオン 0 0.0\n",
      "カワルガワル カワルガワル 0 0.0\n",
      "ヒノデ ヒノデ 0 0.0\n",
      "フボ フボ 0 0.0\n",
      "フー フー 0 0.0\n",
      "オボエル オボエル 0 0.0\n",
      "ドージョー ドージョー 0 0.0\n",
      "キッチリ キッチリ 0 0.0\n",
      "チューキュー チューキュー 0 0.0\n",
      "アラワス アラワス 0 0.0\n",
      "シューカク シューカク 0 0.0\n",
      "スナハマ スナハマ 0 0.0\n",
      "ヨム モン 2 1.0\n",
      "ミョーニチ ミョーミチ 1 0.2\n",
      "average Levenshtein distance 0.0906775843336779\n"
     ]
    }
   ],
   "source": [
    "infer(trained_model1,val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_aug = Wav2Letter(n_class)\n",
    "model_aug = model_aug.cuda()\n",
    "optimizer = torch.optim.Adam(model_aug.parameters(),lr=1e-4)\n",
    "criterion = torch.nn.CTCLoss(reduction='sum')\n",
    "\n",
    "dataset_train_aug = AudioDataset(audio_list_train,target_list_train,n_audio_max,n_target_max,random_pad = True,change_speed=True)\n",
    "dataset_val_aug = AudioDataset(audio_list_val,target_list_val,n_audio_max,n_target_max)\n",
    "\n",
    "train_loader_aug = DataLoader(dataset_train_aug, batch_size=batch_size_train,shuffle=True)\n",
    "val_loader_aug = DataLoader(dataset_val_aug, batch_size=batch_size_val)\n",
    "\n",
    "n_epoch = 200\n",
    "trained_model2 = train(model_aug,train_loader_aug,val_loader_aug,optimizer,criterion,n_epoch)\n",
    "infer(trained_model2,val_loader_aug)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "infer(trained_model2,val_loader_aug)"
   ]
  }
 ],
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