extractAudioFeatures.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'> <class 'int'>\n"
     ]
    }
   ],
   "source": [
    "import librosa\n",
    "audio_path = 'old/v.mp3'\n",
    "x3 , sr3 = librosa.load(audio_path)\n",
    "x = x3\n",
    "sr = sr3\n",
    "print(type(x), type(sr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98784,)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyAudioAnalysis import audioBasicIO\n",
    "from pyAudioAnalysis import ShortTermFeatures\n",
    "import matplotlib.pyplot as plt\n",
    "[Fs, x] = audioBasicIO.read_audio_file(audio_path)\n",
    "F, f_names = ShortTermFeatures.feature_extraction(x, Fs, int(0.1*Fs), int(0.1*Fs))\n",
    "plt.subplot(2,1,1); plt.plot(F[0,:]); plt.xlabel('Frame no'); plt.ylabel(f_names[0]) \n",
    "plt.subplot(2,1,2); plt.plot(F[1,:]); plt.xlabel('Frame no'); plt.ylabel(f_names[1]); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(68, 44)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-22.32241710868048"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1856.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.116*Fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.5351473922902495e-05"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/22050"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8000.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.5*Fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 62)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lff = librosa.feature.melspectrogram(y=x3, sr=sr3,n_fft=int(0.1*Fs),hop_length=int(0.1*Fs))\n",
    "lff.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(68, 44)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 25)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lff.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/home/gamal/vsc/DiffusionSpeech2Face/extractAudioFeatures.ipynb Cell 13\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/gamal/vsc/DiffusionSpeech2Face/extractAudioFeatures.ipynb#X15sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m brick \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros((\u001b[39m128\u001b[39m\u001b[39m-\u001b[39m\u001b[39m68\u001b[39m,\u001b[39m96\u001b[39m))\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "brick = np.zeros((128-68,96))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 96)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brick.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc = np.vstack((brick,F))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 96)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(324, 352)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "emb = np.vstack((F,lff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "F.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "F.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lff.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lff.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 96)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 133)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lff.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "emb = np.hstack((cc,lff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 229)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d= np.rot90(emb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(229, 128)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7.53844231e-02,  7.70096262e-02,  1.18889861e-01, ...,\n",
       "         8.92611576e-02,  7.87598450e-02,  1.27890986e-01],\n",
       "       [ 5.62096789e-02,  6.18985294e-02,  3.67719749e-02, ...,\n",
       "         4.68803028e-02,  4.64632458e-02,  2.96203499e-02],\n",
       "       [ 2.87671854e+00,  2.89041271e+00,  2.03962190e+00, ...,\n",
       "         2.55026905e+00,  2.91230492e+00,  2.49292326e+00],\n",
       "       ...,\n",
       "       [ 0.00000000e+00,  3.34836703e-03,  1.39219881e-03, ...,\n",
       "         1.69697868e-03,  2.76364650e-02,  3.09143952e-03],\n",
       "       [ 0.00000000e+00,  5.88710359e-04, -7.03623667e-03, ...,\n",
       "        -3.66143414e-02,  2.48077342e-02, -1.09841304e-02],\n",
       "       [ 0.00000000e+00,  7.05549515e-04,  1.33107148e-03, ...,\n",
       "        -5.53365469e-03,  4.80238339e-03, -8.94567533e-04]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a2D = np.array([[1, 2], [3, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 25)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lff.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.905496 , 10.5623045, 20.472239 , 14.192144 ,  9.0893   ,\n",
       "       13.51244  , 14.0014925, 25.119795 , 52.26613  ,  4.8581166,\n",
       "        3.2871306,  6.3394814,  8.594288 ,  8.636654 , 10.212639 ,\n",
       "        6.556416 , 14.991695 , 13.222608 ,  5.028929 ,  8.142223 ,\n",
       "       12.235821 , 27.563248 , 14.720246 , 16.87833  , 36.956398 ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lff[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 128)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d= np.transpose(lff).shape\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "z = np.ones((190,128))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z1 = np.zeros((190,768-128))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gamal/anaconda3/envs/ds2f_pre/lib/python3.8/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\n",
      "Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.)\n",
      "  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]\n"
     ]
    }
   ],
   "source": [
    "import speechbrain as sb\n",
    "import torchaudio\n",
    "from speechbrain.pretrained import EncoderClassifier\n",
    "classifier = EncoderClassifier.from_hparams(source=\"speechbrain/spkrec-ecapa-voxceleb\")\n",
    "classifierLang = EncoderClassifier.from_hparams(source=\"speechbrain/lang-id-commonlanguage_ecapa\", savedir=\"pretrained_models/lang-id-commonlanguage_ecapa\")\n",
    "signal, fs = torchaudio.load('old/v.mp3')\n",
    "embeddings = classifier.encode_batch(signal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 1, 192])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings.shape"
   ]
  }
 ],
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