Numpy_file_creator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tqdm.auto import tqdm\n",
    "import os\n",
    "import glob\n",
    "import shutil\n",
    "\n",
    "import librosa\n",
    "import torchaudio as ta\n",
    "import torch\n",
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "55d6d4a918e94ab69d12126a3a3738dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=1427394.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "clips_path = \"./dataset/cv-corpus-5.1-2020-06-22/en/clips/\"\n",
    "file_nums = []\n",
    "for file in tqdm(os.listdir(clips_path)):\n",
    "    num = file.split(\"_\")[3]\n",
    "    num = int(num.split(\".\")[0])\n",
    "    file_nums.append(num)\n",
    "file_nums = np.asarray(file_nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(file_nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"./dataset_loader_files/signal_paths_nums_save.npy\", file_nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_dir = \"./dataset/UrbanSound8K/audio/\"\n",
    "file_num = 0\n",
    "for root, dirs, files in os.walk(noise_dir):\n",
    "    for name in files:\n",
    "        if name.endswith(\".wav\"):\n",
    "            os.rename(os.path.join(root, name), os.path.join(root, str(file_num)+\".wav\"))\n",
    "        file_num += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy_dir = \"./dataset/UrbanSound8K/all_files/\"\n",
    "for root, dirs, files in os.walk(noise_dir):\n",
    "    for name in files:\n",
    "        if name.endswith(\".wav\"):\n",
    "            shutil.move(os.path.join(root, name), os.path.join(copy_dir, name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_paths = []\n",
    "noise_dir = \"./dataset/UrbanSound8K-Resampled//audio/\"\n",
    "for root, dirs, files in os.walk(noise_dir):\n",
    "    for name in files:\n",
    "        if name.endswith(\".wav\"):\n",
    "            noise_paths.append(os.path.join(root, name))\n",
    "noise_paths_arr = np.asarray(noise_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"dataset_loader_files/noise_paths_resampled_save.npy\", noise_paths_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "version_minor": 0
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       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=8732.0), HTML(value='')))"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "file_nums = []\n",
    "for file in tqdm(os.listdir(copy_dir)):\n",
    "    num = int(file.split(\".\")[0])\n",
    "    file_nums.append(num)\n",
    "file_nums = np.asarray(file_nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8732"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(file_nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./dataset/UrbanSound8K/metadata/UrbanSound8K.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"slice_file_name\", \"fold\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "d03571bf13f34b73badfa867030d5e6d",
       "version_major": 2,
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       "HBox(children=(HTML(value=''), FloatProgress(value=1.0, bar_style='info', layout=Layout(width='20px'), max=1.0…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './dataset/UrbanSound8K/all_files/100032-3-0-0.wav'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-a3697d7b3028>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mnew_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[0mbase_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"fold{fold}\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mold_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[0mcurrent_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mold_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_path\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      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/shutil.py\u001b[0m in \u001b[0;36mcopy2\u001b[0;34m(src, dst, follow_symlinks)\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst\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    265\u001b[0m         \u001b[0mdst\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[0mdst\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[0mbasename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\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--> 266\u001b[0;31m     \u001b[0mcopyfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfollow_symlinks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfollow_symlinks\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    267\u001b[0m     \u001b[0mcopystat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfollow_symlinks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfollow_symlinks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/shutil.py\u001b[0m in \u001b[0;36mcopyfile\u001b[0;34m(src, dst, follow_symlinks)\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymlink\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlink\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\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[0;32m--> 120\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfsrc\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    121\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfdst\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m                 \u001b[0mcopyfileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfsrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './dataset/UrbanSound8K/all_files/100032-3-0-0.wav'"
     ]
    }
   ],
   "source": [
    "base_dir = \"./dataset/UrbanSound8K/audio/\"\n",
    "current_dir = \"./dataset/UrbanSound8K/all_files/\"\n",
    "for index, row in tqdm(df.iterrows()):\n",
    "    file_name = row[\"slice_file_name\"]\n",
    "    fold = row[\"fold\"]\n",
    "    new_path = os.path.join(base_dir, f\"fold{fold}\", file_name)\n",
    "    old_path = os.path.join(current_dir, file_name)\n",
    "    shutil.copy2(old_path, new_path)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ta.set_audio_backend(\"sox_io\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=1427394.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "clips_path = \"./dataset/cv-corpus-5.1-2020-06-22-Resampled/en/clips/\"\n",
    "file_nums = []\n",
    "\n",
    "batch_len = 12\n",
    "default_sr = 16000\n",
    "resampler =  ta.transforms.Resample\n",
    "lst_dir_tqdm = tqdm(enumerate(os.listdir(clips_path)), total=1427394)\n",
    "for index, file in lst_dir_tqdm:\n",
    "    \n",
    "    aud_data, sr = ta.load(os.path.join(clips_path, file))\n",
    "    if not sr==16000:\n",
    "        aud_data = aud_data.to(\"cpu\")\n",
    "        resampler =  ta.transforms.Resample(orig_freq=sr, new_freq=default_sr)\n",
    "        aud_data = resampler(aud_data)\n",
    "        ta.save(os.path.join(clips_path, file), aud_data.to(\"cpu\"), sample_rate=default_sr)\n",
    "    if index/1000 == 0:\n",
    "        gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
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     "output_type": "display_data"
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      "\n"
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      "\n"
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      "\n"
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     "text": [
      "\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clips_path = \"./dataset/UrbanSound8K-Resampled/audio/*\"\n",
    "\n",
    "batch_len = 12\n",
    "default_sr = 16000\n",
    "resampler =  ta.transforms.Resample\n",
    "lst_dir_tqdm = tqdm(enumerate(glob.glob(clips_path)))\n",
    "for index, folder in lst_dir_tqdm:\n",
    "    removed = 0\n",
    "    aud_files = glob.glob(os.path.join(folder, \"*.wav\"))\n",
    "    aud_files_tqdm = tqdm(aud_files)\n",
    "    for file in aud_files_tqdm:\n",
    "        try:\n",
    "            aud_data, sr = ta.load_wav(file)\n",
    "        except RuntimeError:\n",
    "            os.remove(file)\n",
    "            removed += 1\n",
    "            aud_files_tqdm.set_postfix(removed = removed)\n",
    "        if not sr==16000:\n",
    "            aud_data = aud_data.to(\"cpu\")\n",
    "            resampler =  ta.transforms.Resample(orig_freq=sr, new_freq=default_sr)\n",
    "            aud_data = resampler(aud_data)\n",
    "            ta.save(file, aud_data.to(\"cpu\"), sample_rate=default_sr)\n",
    "#         if index/1000 == 0:\n",
    "#             gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./dataset/UrbanSound8K-Resampled/audio/fold5/172460-9-0-24.wav'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = glob.glob(\"./dataset/UrbanSound8K-Resampled/audio/*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./dataset/UrbanSound8K-Resampled/audio/fold5',\n",
       " './dataset/UrbanSound8K-Resampled/audio/fold1',\n",
       " './dataset/UrbanSound8K-Resampled/audio/fold10',\n",
       " './dataset/UrbanSound8K-Resampled/audio/fold4',\n",
       " './dataset/UrbanSound8K-Resampled/audio/fold7',\n",
       " './dataset/UrbanSound8K-Resampled/audio/fold2',\n",
       " './dataset/UrbanSound8K-Resampled/audio/fold3',\n",
       " './dataset/UrbanSound8K-Resampled/audio/fold6',\n",
       " './dataset/UrbanSound8K-Resampled/audio/fold8',\n",
       " './dataset/UrbanSound8K-Resampled/audio/fold9',\n",
       " './dataset/UrbanSound8K-Resampled/audio/UrbanSound8K.csv']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "trace() missing 1 required positional argument: 'example_inputs'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-31d894844ca6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m@\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mresampled_signal_writer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maud_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault_sr\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      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0maud_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maud_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cuda\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m#     print(aud_data.shape)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: trace() missing 1 required positional argument: 'example_inputs'"
     ]
    }
   ],
   "source": [
    "@torch.jit.trace\n",
    "def resampled_signal_writer(aud_data, sr, default_sr):\n",
    "\n",
    "    aud_data = aud_data.to(\"cuda\")\n",
    "    #     print(aud_data.shape)\n",
    "    resampler =  ta.transforms.Resample(orig_freq=torch.tensor(sr), new_freq=torch.tensor(default_sr))\n",
    "    aud_data = resampler(aud_data)\n",
    "    return aud_data\n",
    "    \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clips_path = \"./dataset/cv-corpus-5.1-2020-06-22-Resampled/en/clips/\"\n",
    "lst_dir_tqdm = tqdm(enumerate(os.listdir(clips_path)), total=1427394)\n",
    "\n",
    "for index, file in lst_dir_tqdm:\n",
    "    read_path = os.path.join(clips_path, file)\n",
    "    aud_data, sr = ta.load(path)\n",
    "    aud_data = resampled_signal_writer(aud_data, sr, default_sr)\n",
    "    ta.save(path, aud_data.to(\"cpu\"), sample_rate=default_sr)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clips_path = \"./dataset/cv-corpus-5.1-2020-06-22-Resampled/en/clips/\"\n",
    "file_nums = []\n",
    "\n",
    "workers = 8\n",
    "default_sr = 16000\n",
    "multi_pool = torch.multiprocessing.Pool(processes=workers)\n",
    "\n",
    "def resampler(aud_data_sr):\n",
    "    global default_sr\n",
    "    aud_data, sr = aud_data_sr\n",
    "    resampler =  ta.transforms.Resample(orig_freq=sr, new_freq=default_sr)\n",
    "    aud_data = resampler(aud_data)\n",
    "    \n",
    "\n",
    "lst_dir_tqdm = tqdm(enumerate(os.listdir(clips_path)), total=1427394)\n",
    "for index, file in lst_dir_tqdm:\n",
    "    aud_stack = []\n",
    "    for i in range(workers):\n",
    "        \n",
    "        aud_data, sr = ta.load(os.path.join(clips_path, file))\n",
    "    #     print(aud_data.shape)\n",
    "        resampler =  ta.transforms.Resample(orig_freq=sr, new_freq=default_sr)\n",
    "        aud_data = resampler(aud_data)\n",
    "        ta.save(os.path.join(clips_path, file), aud_data, sample_rate=default_sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1427394"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(clips_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([76032])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aud_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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