auto_song.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import file_utils\n",
    "import yamnet_classifier\n",
    "import librosa\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen = file_utils.iterate_dataset_map(\"complete_map.json\")\n",
    "audioset_mapper = yamnet_classifier.AudiosetClasses(\"./misc/class_labels_indices.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "available keys for ['tom'] track: ['path', 'keywords', 'numsamps', 'intervals', 'audioset']\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for -: 'list' and 'int'",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-81aab5dae1db>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"available keys for {track['keywords']} track: {list(track.keys())}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0minterval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrack\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'intervals'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrack\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'intervals'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mstart_secs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minterval\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m44100\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'list' and 'int'"
     ]
    }
   ],
   "source": [
    "track = next(gen)\n",
    "print(f\"available keys for {track['keywords']} track: {list(track.keys())}\")\n",
    "\n",
    "interval = track['intervals'][np.random.randint(0,len(track['intervals']-1))]\n",
    "\n",
    "start_secs = interval[0] / 44100\n",
    "end_secs = interval[1] / 44100\n",
    "\n",
    "print(f'start {start_secs} end {end_secs}')\n",
    "\n",
    "audio = librosa.load(track[\"path\"], sr=44100, res_type=\"kaiser_fast\", offset=start_secs, duration=end_secs-start_secs)[0]\n",
    "\n",
    "print(audio.shape)\n",
    "print(track[\"audioset\"][0])\n",
    "print(audioset_mapper.get_class_str(track[\"audioset\"][0]))\n",
    "plt.plot(audio)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}