audio-merge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "from glob import glob\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "wav_files = sorted(glob('./data/background/YD/*.wav'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(wav_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "librosa.core.get_duration(filename=wav_files[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiStreamLoader():\n",
    "    \n",
    "    def __init__(self, files, fs=22050):\n",
    "        self.files = files\n",
    "        self.fs = fs\n",
    "        self.current_tracks = self.files.copy()\n",
    "        random.shuffle(self.current_tracks)\n",
    "        self.buffer_length = int(fs * 0.1)\n",
    "        self.stream = librosa.core.stream(self.current_tracks[0], 1,\n",
    "                                          self.buffer_length, self.buffer_length)\n",
    "        \n",
    "    def _update_stream(self):\n",
    "        self.current_tracks.pop(0)\n",
    "        \n",
    "        if len(self.current_tracks) == 0:\n",
    "            self.current_tracks = self.files.copy()\n",
    "            random.shuffle(self.current_tracks)\n",
    "        \n",
    "        self.stream = librosa.core.stream(self.current_tracks[0], 1,\n",
    "                                          self.buffer_length, self.buffer_length)\n",
    "        \n",
    "    def _get_stream(self):\n",
    "        try:\n",
    "            return next(self.stream)\n",
    "        except StopIteration:\n",
    "            self._update_stream()\n",
    "            return next(self.stream)\n",
    "        \n",
    "    def get(self, sec):\n",
    "        buffer = self._get_stream()\n",
    "        while len(buffer) < sec * self.fs:\n",
    "            buffer = np.concatenate((buffer, self._get_stream()), axis=0)\n",
    "        return buffer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# librosa.core.stream(mp3_files[0], block_length, frame_length, hop_length)\n",
    "# #                     dtype=<class 'numpy.float32'>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "background_stream_loader = MultiStreamLoader(wav_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1024.0\n",
      "256.0\n",
      "\n",
      "1411.2\n",
      "352.8\n",
      "\n",
      "15.96371882086168\n",
      "64.03628117913831\n",
      "\n",
      "23.219954648526077\n",
      "92.87981859410431\n",
      "\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Pre-processing. \n",
    "The original raw waveforms were first downsampled from 48kHz to 16kHz.\n",
    "For the actual model input, complex-valued spectrograms were obtained \n",
    "from the downsampled waveforms via STFT with a 64ms sized Hann window and 16ms hop length.\n",
    "'''\n",
    "print(16000 * 64 / 1000)\n",
    "print(16000 * 16 / 1000)\n",
    "print()\n",
    "\n",
    "print(22050 * 64 / 1000)\n",
    "print(22050 * 16 / 1000)\n",
    "print()\n",
    "\n",
    "print(352 / 22050 * 1000)\n",
    "print(1412 / 22050 * 1000)\n",
    "print()\n",
    "\n",
    "print(512 / 22050 * 1000)\n",
    "print(2048 / 22050 * 1000)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in range(1):\n",
    "\n",
    "    x = background_stream_loader.get(10)\n",
    "#     print(background_stream_loader.current_tracks[0])\n",
    "#     print(len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "speech_files = sorted(glob('./data/speech/KSS/*/*.wav'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "speech_file_lengths = [(f, librosa.core.get_duration(filename=f)) for f in speech_files]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [s[1] for s in speech_file_lengths]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.hist(l, bins=30) \n",
    "plt.axvline(x=np.mean(l), c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(sorted(l))\n",
    "plt.axhline(y=6.0, c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "mu_graph = list()\n",
    "sig_graph = list()\n",
    "\n",
    "for i in np.arange(5, 7, 0.05):\n",
    "\n",
    "#     plt.figure()\n",
    "    l_array = np.array(sorted(l))\n",
    "    summing_array = l_array[l_array <= i]\n",
    "    left_array = l_array[l_array > i]\n",
    "    sum_array = summing_array + np.flip(summing_array)\n",
    "    result = np.concatenate([sum_array, left_array])\n",
    "#     plt.plot(result)\n",
    "#     plt.show()\n",
    "    \n",
    "    mu = np.mean(result)\n",
    "    sig = np.std(result)\n",
    "    \n",
    "#     print(mu)\n",
    "#     print(sig)\n",
    "    \n",
    "    mu_graph.append(i)\n",
    "    sig_graph.append(sig)\n",
    "    \n",
    "#     plt.figure()\n",
    "#     plt.hist(result, bins=30) \n",
    "#     plt.axvline(x=mu, c='r')\n",
    "#     plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n",
      "6.049999999999996\n"
     ]
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(mu_graph, sig_graph)\n",
    "plt.show()\n",
    "\n",
    "idx = np.argmin(sig_graph)\n",
    "print(idx)\n",
    "print(mu_graph[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "speech_file_lengths.sort(key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "idx = np.argmin([abs(l[1] - 6.05) for l in speech_file_lengths])\n",
    "if idx % 2 != 1:\n",
    "    idx +=1 \n",
    "    \n",
    "folding_files = speech_file_lengths[:idx]\n",
    "left_files = speech_file_lengths[idx:]\n",
    "\n",
    "def fold(input_list):\n",
    "    center = int((len(input_list) - 1) / 2)\n",
    "    x = input_list[:center]\n",
    "    y = input_list[center:][::-1]\n",
    "    \n",
    "    return [((a[0], b[0]), (a[1], b[1])) for a, b in zip(x, y)]\n",
    "\n",
    "folded_files = fold(folding_files)\n",
    "\n",
    "merge_files = folded_files + left_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6531"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merge_files)"
   ]
  },
  {
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}