dataset_txt_creater.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "absence_boats_folder = '/storage/Imagine_UC6/data_new_ais/data50_extra'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/storage/Imagine_UC6/data_new_ais/data50_extra [] []\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Processing files: 0it [00:00, ?it/s]\u001b[A\n"
     ]
    }
   ],
   "source": [
    "file_paths = []\n",
    "for root, dirs, files in os.walk(absence_boats_folder):\n",
    "    print(root,dirs,files)\n",
    "    for file in tqdm(files, desc=\"Processing files\",position=1, leave=True):\n",
    "        file_path = os.path.join(root, file)\n",
    "        relative_path = os.path.relpath(file_path,absence_boats_folder)\n",
    "        relative_path=relative_path.replace(\" \", \"_\")\n",
    "        try:\n",
    "            AudioSegment.from_file(file_path) \n",
    "            file_paths.append(relative_path)\n",
    "        except:\n",
    "            print(\"skipping\")\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Function to create the text files\n",
    "def create_txt_files(absence_boats_folder, train_txt_file, test_txt_file, val_txt_file,class_txt_file):\n",
    "    file_paths = []\n",
    "    for root, dirs, files in tqdm(os.walk(absence_boats_folder), desc=\"Searching files\"):\n",
    "        for file in tqdm(files, desc=\"Processing files\"):\n",
    "            file_path = os.path.join(root, file)\n",
    "            relative_path = os.path.relpath(file_path, absence_boats_folder)\n",
    "            file_paths.append(relative_path)\n",
    "\n",
    "    # Get a list of folder names within the \"absence_boats\" directory\n",
    "    folder_names = next(os.walk(absence_boats_folder))[1]\n",
    "\n",
    "    # Assign numbers based on the location of each folder in the list\n",
    "    folder_numbers = {folder_names[i]: i for i in range(len(folder_names)) if folder_names[i] != \".ipynb_checkpoints\"}\n",
    "\n",
    "    folder_numbers = {}\n",
    "    index_counter = 0\n",
    "\n",
    "    for i in range(len(folder_names)):\n",
    "        if folder_names[i] != \".ipynb_checkpoints\":\n",
    "            folder_numbers[folder_names[i]] = index_counter\n",
    "            index_counter += 1\n",
    "\n",
    "    # Split the boat files into training, testing, and validation sets\n",
    "    random.shuffle(file_paths)\n",
    "    num_samples = len(file_paths)\n",
    "    train_cutoff = int(num_samples * train_ratio)\n",
    "    test_cutoff = train_cutoff + int(num_samples * test_ratio)\n",
    "\n",
    "    train_files = file_paths[:train_cutoff]\n",
    "    test_files = file_paths[train_cutoff:test_cutoff]\n",
    "    val_files = file_paths[test_cutoff:]\n",
    "#     return train_files, folder_numbers\n",
    "    # Create the training text file\n",
    "    with open(train_txt_file, 'w') as f_train:\n",
    "        for file in tqdm(train_files, desc=\"Writing training file\"):\n",
    "            f_train.write(file + ' ' + str(folder_numbers[file.split(\"/\")[0]]) + '\\n')\n",
    "\n",
    "    # Create the testing text file\n",
    "    with open(test_txt_file, 'w') as f_test:\n",
    "        for file in tqdm(test_files, desc=\"Writing testing file\"):\n",
    "            f_test.write(file + ' ' + str(folder_numbers[file.split(\"/\")[0]]) + '\\n')\n",
    "\n",
    "    # Create the validation text file\n",
    "    with open(val_txt_file, 'w') as f_val:\n",
    "        for file in tqdm(val_files, desc=\"Writing validation file\"):\n",
    "            f_val.write(file + ' ' + str(folder_numbers[file.split(\"/\")[0]]) + '\\n')\n",
    "    # Create the validation text file\n",
    "    with open(class_txt_file, 'w') as f_class:\n",
    "        for label in tqdm(folder_numbers, desc=\"Writing classes file\"):\n",
    "            f_class.write(str(label) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Searching files: 0it [00:00, ?it/s]\n",
      "Processing files: 0it [00:00, ?it/s]\u001b[A\n",
      "\n",
      "Processing files: 100%|██████████| 3153/3153 [00:00<00:00, 39492.81it/s]\n",
      "\n",
      "Processing files: 0it [00:00, ?it/s]\u001b[A\n",
      "Searching files: 3it [00:00, 29.18it/s]\n",
      "Processing files:   0%|          | 0/13588 [00:00<?, ?it/s]\u001b[A\n",
      "Processing files:  30%|██▉       | 4032/13588 [00:00<00:00, 40317.35it/s]\u001b[A\n",
      "Processing files:  59%|█████▊    | 7980/13588 [00:00<00:00, 40061.78it/s]\u001b[A\n",
      "Processing files: 100%|██████████| 13588/13588 [00:00<00:00, 39714.94it/s]\u001b[A\n",
      "Searching files: 4it [00:00,  8.41it/s]\n",
      "Writing training file: 100%|██████████| 13392/13392 [00:00<00:00, 475896.97it/s]\n",
      "Writing testing file: 100%|██████████| 1674/1674 [00:00<00:00, 425016.03it/s]\n",
      "Writing validation file: 100%|██████████| 1675/1675 [00:00<00:00, 370697.51it/s]\n",
      "Writing classes file: 100%|██████████| 2/2 [00:00<00:00, 5105.67it/s]\n"
     ]
    }
   ],
   "source": [
    "# Set the train, test, and validation ratios\n",
    "train_ratio = 0.8\n",
    "test_ratio = 0.1\n",
    "val_ratio = 0.1\n",
    "\n",
    "absence_boats_folder = r'/srv/audio-classification-tf/data/audios'\n",
    "data_set_folder = r'/srv/audio-classification-tf/data/dataset_files'\n",
    "train_txt_file=os.path.join(data_set_folder,'train.txt')\n",
    "test_txt_file =os.path.join(data_set_folder,'test.txt')\n",
    "val_txt_file=os.path.join(data_set_folder,'val.txt')\n",
    "class_txt_file=os.path.join(data_set_folder,'classes.txt')\n",
    "# Get the relative file paths of all files within the \"boats\" and \"no_boats\" subfolders\n",
    "\n",
    "\n",
    "# Create the text files\n",
    "create_txt_files(absence_boats_folder,train_txt_file, test_txt_file, val_txt_file,class_txt_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(folder_numbers[file.split(\"/\")[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['no_boats/Belwind_20220623_370.wav_540_550.wav']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file.split(\"\\\\\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boats': 0, 'no_boats': 1}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folder_numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Writing training file:   0%|          | 0/13392 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'no_boats/Belwind_20220623_370.wav_540_550.wav'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-877bda2418c8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_txt_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf_train\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_files\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Writing training file\"\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----> 3\u001b[0;31m         \u001b[0mf_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfolder_numbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\\\\"\u001b[0m\u001b[0;34m)\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[0;34m+\u001b[0m \u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 'no_boats/Belwind_20220623_370.wav_540_550.wav'"
     ]
    }
   ],
   "source": [
    "    with open(train_txt_file, 'w') as f_train:\n",
    "        for file in tqdm(train_files, desc=\"Writing training file\"):\n",
    "            f_train.write(file + ' ' + str(folder_numbers[file.split(\"\\\\\")[0]]) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       " ...]"
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     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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