get_avg_mels.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import tgt\n",
    "from scipy.stats import mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "phoneme_list = ['AA0', 'AA1', 'AA2', 'AE0', 'AE1', 'AE2', \n",
    "                'AH0', 'AH1', 'AH2', 'AO0', 'AO1', 'AO2', 'AW0', \n",
    "                'AW1', 'AW2', 'AY0', 'AY1', 'AY2', 'B', 'CH', 'D', 'DH', \n",
    "                'EH0', 'EH1', 'EH2', 'ER0', 'ER1', 'ER2', \n",
    "                'EY0', 'EY1', 'EY2', 'F', 'G', 'HH', 'IH0', 'IH1', 'IH2', \n",
    "                'IY0', 'IY1', 'IY2', 'JH', 'K', 'L', 'M', 'N', 'NG', \n",
    "                'OW0', 'OW1', 'OW2', 'OY0', 'OY1', 'OY2', 'P', \n",
    "                'R', 'S', 'SH', 'T', 'TH', 'UH0', 'UH1', 'UH2', \n",
    "                'UW0', 'UW1', 'UW2', 'V', 'W', 'Y', 'Z', 'ZH', 'sil', 'sp', 'spn']\n",
    "phoneme_dict = dict()\n",
    "for j, p in enumerate(phoneme_list):\n",
    "    phoneme_dict[p] = j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Speaker 1150: 3738\n"
     ]
    }
   ],
   "source": [
    "data_dir = 'LibriTTS'\n",
    "mels_mode_dict = dict()\n",
    "lens_dict = dict()\n",
    "for p in phoneme_list:\n",
    "    mels_mode_dict[p] = []\n",
    "    lens_dict[p] = []\n",
    "speakers = os.listdir(os.path.join(data_dir, 'mels'))\n",
    "for s, speaker in enumerate(speakers):\n",
    "    print('Speaker %d: %s' % (s + 1, speaker))\n",
    "    textgrids = os.listdir(os.path.join(data_dir, 'textgrids', speaker))\n",
    "    for textgrid in textgrids:\n",
    "        t = tgt.io.read_textgrid(os.path.join(data_dir, 'textgrids', speaker, textgrid))\n",
    "        m = np.load(os.path.join(data_dir, 'mels', speaker, textgrid.replace('.TextGrid', '_mel.npy')))\n",
    "        t = t.get_tier_by_name('phones')\n",
    "        for i in range(len(t)):\n",
    "            phoneme = t[i].text\n",
    "            start_frame = int(t[i].start_time * 22050.0) // 256\n",
    "            end_frame = int(t[i].end_time * 22050.0) // 256 + 1\n",
    "            mels_mode_dict[phoneme] += [np.round(np.median(m[:, start_frame:end_frame], 1), 1)]\n",
    "            lens_dict[phoneme] += [end_frame - start_frame]\n",
    "\n",
    "mels_mode = dict()\n",
    "lens = dict()\n",
    "for p in phoneme_list:\n",
    "    mels_mode[p] = mode(np.asarray(mels_mode_dict[p]), 0).mode[0]\n",
    "    lens[p] = np.mean(np.asarray(lens_dict[p]))\n",
    "del mels_mode_dict\n",
    "del lens_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Speaker 1129: 1974\n",
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      "Speaker 1131: 8388\n",
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      "Speaker 1138: 3876\n",
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      "Speaker 1140: 103\n",
      "Speaker 1141: 64\n",
      "Speaker 1142: 7800\n",
      "Speaker 1143: 1447\n",
      "Speaker 1144: 154\n",
      "Speaker 1145: 460\n",
      "Speaker 1146: 2514\n",
      "Speaker 1147: 2816\n",
      "Speaker 1148: 27\n",
      "Speaker 1149: 242\n",
      "Speaker 1150: 3738\n"
     ]
    }
   ],
   "source": [
    "for s, speaker in enumerate(speakers):\n",
    "    print('Speaker %d: %s' % (s + 1, speaker))\n",
    "    os.mkdir(os.path.join(data_dir, 'mels_mode', speaker))\n",
    "    textgrids = os.listdir(os.path.join(data_dir, 'textgrids', speaker))\n",
    "    for textgrid in textgrids:\n",
    "        t = tgt.io.read_textgrid(os.path.join(data_dir, 'textgrids', speaker, textgrid))\n",
    "        m = np.load(os.path.join(data_dir, 'mels', speaker, textgrid.replace('.TextGrid', '_mel.npy')))\n",
    "        m_mode = np.copy(m)\n",
    "        t = t.get_tier_by_name('phones')\n",
    "        for i in range(len(t)):\n",
    "            phoneme = t[i].text\n",
    "            start_frame = int(t[i].start_time * 22050.0) // 256\n",
    "            end_frame = int(t[i].end_time * 22050.0) // 256 + 1\n",
    "            m_mode[:, start_frame:end_frame] = np.repeat(np.expand_dims(mels_mode[phoneme], 1), end_frame - start_frame, 1)\n",
    "        np.save(os.path.join(data_dir, 'mels_mode', speaker, textgrid.replace('.TextGrid', '_avgmel.npy')), m_mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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