15.2 Index Local MP3s.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from tinytag import TinyTag, TinyTagException\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from collections import defaultdict\n",
    "from keras.models import load_model\n",
    "import librosa\n",
    "from collections import Counter\n",
    "import multiprocessing\n",
    "from tqdm import tqdm\n",
    "from keras.models import Model\n",
    "import numpy as np\n",
    "import sounddevice as sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7458"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MUSIC_ROOT = '/Users/douwe/Songs/'\n",
    "mp3s = []\n",
    "for root, subdirs, files in os.walk(MUSIC_ROOT):\n",
    "    for fn in files:\n",
    "        if fn.endswith('.mp3'):\n",
    "            mp3s.append(os.path.join(root, fn))\n",
    "len(mp3s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
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    },
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     ]
    }
   ],
   "source": [
    "TO_SKIP = {'Podcast', 'Books & Spoken'}\n",
    "\n",
    "def process_mp3(path):\n",
    "    try:\n",
    "        tag = TinyTag.get(path)\n",
    "        if tag.genre in TO_SKIP:\n",
    "            return None\n",
    "    except TinyTagException:\n",
    "        print('error')\n",
    "        return None\n",
    "    signal, sr = librosa.load(path, res_type='kaiser_fast', offset=30, duration=30)\n",
    "    try:\n",
    "        melspec = librosa.feature.melspectrogram(signal, sr=sr).T[:1280,]\n",
    "        if len(melspec) != 1280:\n",
    "            return None\n",
    "    except ValueError:\n",
    "        return None\n",
    "    return {'path': path,\n",
    "            'melspecs': np.asarray(np.split(melspec, 10)),\n",
    "            'tag': tag}\n",
    "\n",
    "songs = [process_mp3(path) for path in tqdm(mp3s[:1000])]\n",
    "songs = [song for song in songs if song]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9050, 128, 128)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs = []\n",
    "for song in songs:\n",
    "    inputs.extend(song['melspecs'])\n",
    "inputs = np.array(inputs)\n",
    "inputs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "/Users/douwe/Dropbox/Douwe/Proj/notebooks/venv3/lib/python3.5/site-packages/keras/models.py:288: UserWarning: Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n",
      "  warnings.warn('Error in loading the saved optimizer '\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(9050, 256)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn_model = load_model('zoo/15/song_classify.h5')\n",
    "vectorize_model = Model(inputs=cnn_model.input, outputs=cnn_model.layers[-4].output)\n",
    "vectors = vectorize_model.predict(inputs)\n",
    "vectors.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbrs = NearestNeighbors(n_neighbors=10, algorithm='ball_tree').fit(vectors)\n",
    "\n",
    "def most_similar_songs(song_idx):\n",
    "    distances, indices = nbrs.kneighbors(vectors[song_idx * 10: song_idx * 10 + 10])\n",
    "    c = Counter()\n",
    "    for row in indices:\n",
    "        for idx in row[1:]:\n",
    "            c[idx // 10] += 1\n",
    "    return c.most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/douwe/Songs/00 shocking blue - Venus (yes the.mp3\n",
      "---\n",
      "/Users/douwe/Songs/00 shocking blue - Venus (yes the.mp3 20\n",
      "/Users/douwe/Songs/The Shocking Blue/Have A Nice Day_ Vol 1/00 Venus.mp3 12\n",
      "/Users/douwe/Songs/The Byrds/00 Eve of Destruction.mp3 12\n",
      "/Users/douwe/Songs/Goldfinger _ Weezer _ NoFx _ L/00 AWESOME.mp3 6\n",
      "/Users/douwe/Songs/Boudewijn de Groot/Wonderkind Aan Het Strand (dis/03 Prikkebeen.mp3 3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "song_idx = 7\n",
    "print(songs[song_idx]['path'])\n",
    "\n",
    "print('---')\n",
    "for idx, score in most_similar_songs(song_idx)[:5]:\n",
    "    print(songs[idx]['path'], score)\n",
    "print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "duration = 30  # seconds\n",
    "fs = 22050\n",
    "myrecording = sd.rec(int(duration * fs), samplerate=fs, channels=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(661500, 1)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myrecording.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd.play(myrecording, samplerate=fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.40555808"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myrecording.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 128, 128)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal, sr = librosa.load('/Users/douwe/Dropbox/Apps/Hi-Q Recordings/recording-20180219-162112.mp3', res_type='kaiser_fast', offset=0, duration=30)\n",
    "\n",
    "melspec = librosa.feature.melspectrogram(signal, sr=sr).T[:1280,]\n",
    "melspecs = np.asarray(np.split(melspec, 10))\n",
    "melspecs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "recorded_vectors = vectorize_model.predict(melspecs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/douwe/Songs/Alan Parsons Project/Anthology/15 La Sagrada Familia.mp3\n",
      "/Users/douwe/Songs/Tom Petty & The Heartbreakers/Greatest hits/13 Runnin' down a dream.mp3\n",
      "/Users/douwe/Songs/Miklós Szenthelyi_ György Gy/Zeit für Romantik - Romantisc/02 Beethoven_ Violin Romance #2 I.mp3\n",
      "/Users/douwe/Songs/Mozart/_notag_/08 Eine Kleine Nachtmusik.mp3\n",
      "/Users/douwe/Songs/00 Cafe Del Mar - Ibiza - 01 - Jo.mp3\n",
      "/Users/douwe/Songs/Golden Earring/The Naked Truth/04 Mad Love's Comin'.mp3\n"
     ]
    }
   ],
   "source": [
    "distances, indices = nbrs.kneighbors(recorded_vectors)\n",
    "c = Counter()\n",
    "for row in indices:\n",
    "    for idx in row[1:]:\n",
    "        c[idx // 10] += 1\n",
    "for idx, _ in c.most_common():\n",
    "    print(songs[idx]['path'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "signal, sr = librosa.load('/Users/douwe/Dropbox/Apps/Hi-Q Recordings/recording-20180219-162112.mp3', res_type='kaiser_fast', offset=0, duration=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd.play(signal.flatten(), samplerate=sr)"
   ]
  },
  {
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