Resampling Benchmarks

resample_benchmark.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The options for resampling we'll explore in this notebook:\n",
    "\n",
    "1. librosa\n",
    "2. scipy.signal\n",
    "3. resampy\n",
    "4. Kaldi via torchaudio\n",
    "\n",
    "Run this notebook with the `test/data` folder from pyannote-audio-v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install librosa scipy julius resampy -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/harry/miniconda3/lib/python3.8/site-packages/torchaudio/backend/utils.py:53: UserWarning: \"sox\" backend is being deprecated. The default backend will be changed to \"sox_io\" backend in 0.8.0 and \"sox\" backend will be removed in 0.9.0. Please migrate to \"sox_io\" backend. Please refer to https://github.com/pytorch/audio/issues/903 for the detail.\n",
      "  warnings.warn(\n",
      "/home/harry/miniconda3/lib/python3.8/site-packages/torchaudio/backend/utils.py:63: UserWarning: The interface of \"soundfile\" backend is planned to change in 0.8.0 to match that of \"sox_io\" backend and the current interface will be removed in 0.9.0. To use the new interface, do `torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE = False` before setting the backend to \"soundfile\". Please refer to https://github.com/pytorch/audio/issues/903 for the detail.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import librosa\n",
    "librosa.resample\n",
    "from scipy.signal import resample_poly, resample as resample_fft\n",
    "import scipy\n",
    "from IPython.display import Audio\n",
    "import torchaudio\n",
    "from time import time\n",
    "import numpy as np\n",
    "import torch\n",
    "from functools import wraps\n",
    "from torchaudio.transforms import Resample\n",
    "from functools import partial\n",
    "from glob import glob\n",
    "\n",
    "# This is the fastest backend\n",
    "torchaudio.set_audio_backend('soundfile')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "# Nicer looking graphs\n",
    "plt.style.use('ggplot')\n",
    "from julius.resample import ResampleFrac"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Alongside quality of the resample, we're also interested in the performance of the resample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def benchmark(func, num_calls:int=1, name=None):\n",
    "    \"\"\"\n",
    "    This decorator will call the given function \n",
    "    and return statistics about how long it ran for\n",
    "    for each invokation\n",
    "    \"\"\"\n",
    "    \n",
    "    name = func.__name__ if name == None else name\n",
    "\n",
    "    @wraps(func)\n",
    "    def _inner():\n",
    "        call_times = []\n",
    "        for i in range(num_calls):\n",
    "            start = time()\n",
    "            res = func()\n",
    "            end = time() - start\n",
    "            call_times.append(end)\n",
    "        arr = np.array(call_times)\n",
    "        return res, arr\n",
    "    return _inner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We're going to use the data from pyannote audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !rm -rf .git \n",
    "# !git init\n",
    "# !git remote add pyannote https://github.com/pyannote/pyannote-data.git\n",
    "# !git fetch pyannote\n",
    "# !git checkout pyannote/master -- audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./audio/trn02.wav',\n",
       " './audio/trn08.wav',\n",
       " './audio/trn04.wav',\n",
       " './audio/trn03.wav',\n",
       " './audio/trn07.wav',\n",
       " './audio/trn09.wav',\n",
       " './audio/tst00.wav',\n",
       " './audio/tst01.wav',\n",
       " './audio/trn05.wav',\n",
       " './audio/trn00.wav',\n",
       " './audio/dev01.wav',\n",
       " './audio/trn01.wav',\n",
       " './audio/empty.wav',\n",
       " './audio/trn06.wav',\n",
       " './audio/dev00.wav']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA_DIR = './audio/'\n",
    "audio_files = glob(DATA_DIR+'/*.wav')\n",
    "audio_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sig, orig_sr = torchaudio.load(audio_files[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(device(type='cpu'), torch.Size([1, 480001]), 16000)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sig.device, sig.shape, orig_sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 0.4626, -0.1568, -0.5582,  ...,  1.1780, -0.5298,  0.0486]],\n",
       "\n",
       "        [[ 0.0756,  0.0455, -0.6542,  ...,  0.3619, -0.5121, -0.9500]],\n",
       "\n",
       "        [[ 0.0666, -0.5500,  0.1884,  ..., -0.0576, -0.5898,  0.0940]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 0.5070,  1.2152, -0.2362,  ..., -0.6233, -0.9902,  0.4114]],\n",
       "\n",
       "        [[-0.1709, -1.0836, -0.6853,  ..., -0.5138, -0.3581, -0.3333]],\n",
       "\n",
       "        [[ 0.1098, -0.5796, -0.1565,  ..., -0.1964, -0.3195, -0.6369]]],\n",
       "       device='cuda:0')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = ResampleFrac(16000, 8000).cuda()\n",
    "res(torch.randn(1000,1,16000).cuda())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Librosa res_types to try\n",
    "types = ['kaiser_best', 'kaiser_fast', 'fft', 'scipy', 'polyphase']\n",
    "\n",
    "num_of_calls = 10\n",
    "results = {}\n",
    "\n",
    "srs = [10000, 9000, 8000, 6000, 4000]\n",
    "for sr in srs:\n",
    "    rsr = results[sr] = {}\n",
    "\n",
    "    # Librosa \n",
    "    for i in types:\n",
    "        func = partial(librosa.resample, sig.numpy()[0], 16000, sr, res_type=i)\n",
    "        func = benchmark(func, num_of_calls, i)\n",
    "        rsr[i] = func()\n",
    "    \n",
    "    # Kaldi\n",
    "    res_func = Resample(16000, sr)\n",
    "    rsr['kaldi'] = benchmark(partial(res_func, sig), num_of_calls, 'kaldi')()\n",
    "    \n",
    "    # Julius \n",
    "    res_func = ResampleFrac(16000, sr)\n",
    "    rsr['julius'] = benchmark(partial(res_func, sig), num_of_calls, 'julius')()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def play_results(results):\n",
    "    \"\"\"\n",
    "    Use this function to hear the difference\n",
    "    between different methods\n",
    "    \"\"\"\n",
    "    for sr in results:\n",
    "        print(sr, \"============\")\n",
    "        for k in results[sr]:\n",
    "            item = results[sr][k]\n",
    "            print(k)\n",
    "            display(Audio(item[0], rate=int(sr)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "types = [] \n",
    "for t in results[srs[0]]:\n",
    "    types.append(t)\n",
    "\n",
    "for sr in results:\n",
    "    times = []\n",
    "    vars = []\n",
    "    for k in results[sr]:\n",
    "        times.append(results[sr][k][1].mean())\n",
    "        vars.append(results[sr][k][1].std())\n",
    "    plt.barh(types, times, xerr=vars)\n",
    "    plt.title(\"Sample Rate: \" + str(sr))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batches\n",
    "If we did these operations in batches then here is the speeds we would expect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch = torch.randn(10,1, sig.shape[1]).cuda()\n",
    "target_sr = 8000\n",
    "res_func = Resample(16000, target_sr).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "types = ['kaiser_best', 'kaiser_fast', 'fft', 'scipy', 'polyphase']\n",
    "times = []\n",
    "vars = []\n",
    "for t in types:\n",
    "    def resample_batch(batch):\n",
    "        nb = []\n",
    "        for sig in batch:\n",
    "            resampled = librosa.resample(sig.cpu().numpy()[0], 16000, target_sr, res_type=t)\n",
    "            nb.append(resampled)\n",
    "        return torch.tensor(nb).cuda().shape\n",
    "    res = benchmark(partial(resample_batch, batch), 5, t)()[1]\n",
    "    times.append(res.mean())\n",
    "    vars.append(res.std())\n",
    "\n",
    "res = benchmark(partial(res_func, batch), 5, 'kaldi')()[1]\n",
    "types.append(\"kaldi\")\n",
    "times.append(res.mean())\n",
    "vars.append(res.std())\n",
    "\n",
    "res_func = ResampleFrac(16000, target_sr).cuda()\n",
    "res = benchmark(partial(res_func, batch), 5, 'julius')()[1]\n",
    "types.append(\"julius\")\n",
    "times.append(res.mean())\n",
    "vars.append(res.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['kaiser_best', 'kaiser_fast', 'fft', 'scipy', 'polyphase', 'kaldi', 'julius']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAD4CAYAAACqnDJ3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAeHklEQVR4nO3df1RUdeL/8ScyIOFvnGgdiH7gD4paiwzSTNuccldb18y13S1329Tq4I+1BIXSTNcfpNGqK9aWppsdXcvVk7auFafIZdM2o2TF1ERNE3FCKghCYOZ+/+gT3yVQYfjxnsHX4y9n5r7vfd17PPM67ztvZgIsy7IQERFpZe1MBxARkQuTCkhERIxQAYmIiBEqIBERMUIFJCIiRqiARETECJvpAL6uoKDAdASv2O12ioqKTMfwij9nB//Or+xm+HN2qJvf4XA0aJxmQCIiYoQKSEREjFABiYiIESogERExQgUkIiJGqIBERMQIFZCIiBihAhIRESNUQCIiYoQKSEREjFABiYiIESogERExQgUkIiJGqIBERMQIFZCIiBihAhIRESNUQCIiYoRPF9DMmTPP+fqTTz5Jfn4+AAsXLqSsrKw1YomISDPw6Z/knjdvXoO3TU1NbcEkIiLS3Hx6BjR27Fjy8vJIS0ureW7VqlVkZWXV2XbixImUlJTgcrmYNm1azfNbtmzhlVdeAWDbtm088sgjJCUlsWTJkpaOLyIi5+DTM6Dm9tprr7F8+XKCgoLOersuMzOTzMxMANLS0rDb7a0ZsdnYbDZlN8Sf8yu7Gf6cHbzPf0EVUFRUFMuWLePGG28kPj6+3m2cTidOp7PmcVFRUWvFa1Z2u13ZDfHn/Mpuhj9nh7r5HQ5Hg8b59C04gMDAQCzLqnlcVVV13u09Hk+926empjJ06FCOHDlCamoqbre7+QOLiEiD+HwB2e12Pv/8c6qqqigrK+O///3vObfv0qULJSUllJaWUlVVRU5ODgAej4eioiKuueYa7r33XsrLy6moqGiNUxARkXr49C24gIAA7HY7/fv3Z9q0aYSHh3PFFVecc4zNZuPuu+/mscceIywsrGYq6PF4+POf/0x5eTkAP/vZz+jQoUOLn4OIiNQvwPrf+1s+pLS0lBkzZrBixQqjOQoKCowe31v+fE/Zn7ODf+dXdjP8OTu0sc+AiouLmTlzJj//+c9NRxERkRbik7fgwsLCWLp0qekYIiLSgnxyBiQiIm2fCkhERIxQAYmIiBEqIBERMUIFJCIiRqiARETECBWQiIgYoQISEREjVEAiImKECkhERIxQAYmIiBEqIBERMUIFJCIiRqiARETECBWQiIgYoQISEREjVEAiImKECkhERIzwiwJyuVxMmzatQdvm5eWRlpZW72sTJ06kpKQEgJkzZzZbPhERaTy/KKCWMG/ePNMRREQuaDbTARrr1KlTpKenM2HCBNasWUNVVRXBwcEkJibicDhqbVtaWsrSpUspLi6md+/eWJZV89rYsWNZu3Zta8cXEZH/41cFVFBQwJIlS0hMTCQ8PJy5c+cSGBhIbm4u69atIykpqdb2r776KjExMYwePZqcnBzefvvt8x4jMzOTzMxMANLS0rDb7S1yLi3NZrMpuyH+nF/ZzfDn7OB9fr8poJKSEhYtWkRSUhKRkZEUFRWRkZFBYWEhAG63u86YTz75pKaU4uLi6NChw3mP43Q6cTqdNY+Lioqa6Qxal91uV3ZD/Dm/spvhz9mhbv4f3o06G7/5DCg0NBS73c7+/fsB2LBhA7GxsaSnpzNjxgyqqqoMJxQRkcbwmwKy2WwkJSXx7rvvkp2dTXl5OWFhYQBkZWXVO+aqq64iOzsbgI8++oiysrLWiisiIufhNwUEEBISQkpKCv/4xz+4/PLLWb9+PdOnT8fj8dS7/S9/+Us++eQTHn30Ud5//32/vscqItLWBFj/uzRM6igoKDAdwSv+fE/Zn7ODf+dXdjP8OTtcAJ8BiYhI26ICEhERI1RAIiJihApIRESMUAGJiIgRKiARETFCBSQiIkaogERExAgVkIiIGKECEhERI1RAIiJihN/8HpAp7gkjjB4/8IUtRo8vItJSNAMSEREjVEAiImKECkhERIxQAYmIiBEqIBERMUIFJCIiRqiARETECBWQiIgY0WoF9OSTT5Kfn+/V2IkTJ1JSUtLMiURExCTNgERExAivv4rH5XKxYMECrrzySo4cOUJkZCSTJk3i4MGDrF27FrfbTXR0NBMmTCAoKKhm3Ntvv82xY8e4//77AcjMzOTzzz9n2LBh9e6vffv2AGzfvp0PP/yQ6upqHn30USIiIjh06BCrV6+mqqqK4OBgEhMTcTgcHD9+nBUrVlBdXY1lWUybNo0ePXqwY8cO/vnPf1JdXU2vXr0YP3487dr5bgeP2XWAgNGj2bhxo+koIiLNrknfBVdQUMDDDz9MTEwMK1as4PXXXyczM5NZs2bhcDhYvnw5b775JsOHD68ZM2DAADZv3sx9992HzWYjKyuLBx98sN79vfHGG4wY8d13sXXq1ImnnnqKN954g61bt/Lwww/jcDiYO3cugYGB5Obmsm7dOpKSknjrrbcYNmwYt9xyC9XV1Xg8Hj7//HPee+89/vjHP2Kz2Vi5ciX/+te/GDx4cK1zyszMJDMzE4C0tLSmXJ5mERQUhN1ub/Q4m83m1Thf4M/Zwb/zK7sZ/pwdvM/fpALq3r07MTExAAwaNIi///3vhIeH43A4ABg8eDBvvPFGrQIKCQkhNjaWnJwcIiIicLvdREVF4XK56uxv27ZtNQWUkJAAwJVXXsl//vMfAMrLy8nIyKCwsBAAt9sNQO/evdm0aROnT58mISGBHj16sHfvXo4cOUJqaioAlZWVdO7cuc45OZ1OnE5nUy5Ls3nlpj4EvrCeoqKiRo+12+1ejfMF/pwd/Du/spvhz9mhbv7vO+B8mlRAAQEBtR6HhobyzTffnHfckCFD2Lx5Mw6Hg1tvvfWs+/vfxzbbd1HbtWtXUzQbNmwgNjaW5ORkXC4Xc+bMAWDgwIH07NmTnJwcFi5cyIMPPohlWQwePJjf/OY3Xp2riIg0ryZ9AFJUVMTBgwcByM7OJjo6GpfLVTMj2bFjB1dffXWdcb169eL06dP8+9//5uabbz7r/r6fDZ1NeXk5YWFhAGRlZdU8f+rUKS655BKGDRtGv379+Oyzz7j22mvZtWsXX3/9NQDffPMNX3zxhfcnLyIiTdKkGZDD4WD79u08++yzRERE8Pvf/55evXrxzDPP1CxCuP322+sd279/f44ePUrHjh3Pur877rjjnMf/xS9+QUZGBps2bSIuLq7m+Z07d7Jjxw4CAwPp2rUro0aNomPHjvzqV79i3rx5WJZFYGAg48aN4+KLL27KJRARES8FWJZleTPQ5XLx1FNPkZ6e7tWB09LSGD58ONdee22z7K+lHB/ez+jxvf1BOn++p+zP2cG/8yu7Gf6cHbz/DKjV1yCXlZXxhz/8geDg4JryERGRC4/Xt+DCw8O9mq106NCBpUuXNtv+RETEP/nuX2GKiEibpgISEREjVEAiImKECkhERIxo0t8BXQi8XQYtIiLnphmQiIgYoQISEREjVEAiImKECkhERIzQIoTzcE8YYTqCV061wjG0QENEmkIzIBERMUIFJCIiRqiARETECBWQiIgYoQISEREjVEAiImKECkhERIxQAYmIiBFtooDy8/N58cUXTccQEZFGaBPfhBAdHU10dLTpGCIi0gg+XUAVFRX86U9/ori4GI/Hw9133014eDhr1qzhzJkz2Gw2nnjiCQ4fPszWrVtJSUnhlVde4dSpUxQWFlJaWsqIESNwOp0sX76c+Ph44uPjAVi2bBn9+/fnxhtvNHyW/mnMrgP83XQIEfFrPl1AH3/8Md26dSM1NRWA8vJypk+fztSpU+nZsyfl5eUEBwfXGXfs2DHmz59PRUUFM2bMIC4ujttuu43XX3+d+Ph4ysvLOXDgABMnTqwzNjMzk8zMTADS0tJa9gT9nN1ub5H92my2Ftt3a/Dn/Mpuhj9nB+/z+3QBRUVFsXbtWl5++WVuuOEGOnToQLdu3ejZsycAoaGh9Y7r168fwcHBBAcHExsby6FDh4iPj2flypWUlJSwa9cuEhISCAwMrDPW6XTidDpb9Lzagldu6kNRUVGL7Ntut7fYvluDP+dXdjP8OTvUze9wOBo0zqcXITgcDp566imioqL429/+xvvvv9+gcQEBAfU+HjRoEDt27CArK4vbbrut2fOKiEjD+XQBFRcXExwczKBBgxgxYgSHDh3iyy+/5NChQwB8++23uN3uOuM++OADKisrKS0tJS8vr2aBwq233sq2bdsAiIyMbL0TERGROnz6FtyxY8d4+eWXCQgIwGazMX78eCzLYvXq1VRWVhIcHMysWbPqjLvsssuYM2cOpaWl3H333YSFhQHQtWtXIiIitPBARMQH+HQBXXfddVx33XV1np8/f36tx7GxscTGxtY8joqKYtKkSXXGnTlzhsLCQgYOHNjsWUVEpHF8uoCaU25uLs899xzDhw8/6+IFERFpPW2ugMaMGVPv8z/+8Y9ZsWJFK6cREZGz8elFCCIi0napgERExAgVkIiIGKECEhERI9rcIoTmFvjCFtMRvOLvX+0hIm2fZkAiImKECkhERIxQAYmIiBEqIBERMUIFJCIiRmgV3Hm4J4wwHcErp1rhGP66QlBEfINmQCIiYoQKSEREjFABiYiIESogERExQgUkIiJGqIBERMQIFZCIiBjRZv8OaNu2bbz11ltcdtlllJaWUlJSwl133UVhYSGjRo0yHU9E5ILXZmdAb775JjNnzmTYsGEALF68mAEDBrB582bDyUREBNroDOj555/n1KlTzJs3j4KCAkJCQkhOTqZHjx5UVlaSnJzMpZdeypQpU0xHFRG5YAVYlmWZDtESJk6cyMKFCzl+/Dhbt24lJSUFgLFjx7J27dqzjsvMzCQzMxOAtLQ0jg/v1yp5/dElm99rkf3abDaqq6tbZN+twZ/zK7sZ/pwd6uYPDg5u2LiWCuSvnE4nTqfTdAy/0FK/uOrvv+bqz/mV3Qx/zg518zscjgaNa7OfAYmIiG+74ArI36e6IiJtxQVXQEOGDCE5OZlly5aZjiIickFrs58BZWRkABAbG0tsbGzN8/fddx/33XefqVgiIvJ/LrgZkIiI+AYVkIiIGKECEhERI1RAIiJihApIRESMUAGJiIgRbXYZdnMJfGGL6Qhe8fev9hCRtk8zIBERMUIFJCIiRqiARETECBWQiIgYoQISEREjtAruPNwTRpiO4JVTpgN4wV9XHIqIdzQDEhERI1RAIiJihApIRESMUAGJiIgRKiARETFCBSQiIkaogERExAgVkIiIGNGoAnK5XEybNq1B2+bn5/Piiy96Faohdu7cySOPPMKcOXMaPXbTpk0tkEhERBqjxWZA0dHRPPDAA03ah2VZeDyeel97++23eeihh5g9e3aj97t58+Ym5ZKWMXr0aNMRRKQVef1VPKdOnSI9PZ0JEyawZs0aqqqqCA4OJjExEYfDQV5eHlu3biUlJYV9+/axevVqAAICApgzZw4XXXQRW7ZsYefOnVRVVREfH8+YMWNwuVzMnz+fXr16cfjwYVJTU7n44otrHXvjxo3s37+fZ599ln79+jF06FCWL1/OmTNnAHjggQfo06cPX375JUuWLKG8vByPx8P48ePJycmhsrKS5ORkLr30UqZMmVJr35mZmWRmZgKQlpbm7eURLwQFBWG327HZbNjtdtNxvObP+ZXdDH/ODt7n96qACgoKWLJkCYmJiYSHhzN37lwCAwPJzc1l3bp1JCUl1dp+y5YtjBs3jpiYGCoqKggKCmLPnj2cPHmSBQsWYFkWixYtYt++fdjtdgoLC5k4cSK9e/eu9/ijR49m7969jB07lujoaM6cOcPMmTMJDg7m5MmTLF26lLS0NLKzs+nbty+jRo3C4/Fw5swZrrrqKrZv387ixYvr3bfT6cTpdHpzWaSJ1q9fT1FRkd//mqs/51d2M/w5O9TN73A4GjSu0QVUUlLCokWLSEpKIjIykqKiIjIyMigsLATA7XbXGRMTE8NLL73EwIEDSUhIoHv37uzZs4fc3FymT58OQEVFBYWFhdjtdux2+1nLpz5ut5tVq1Zx9OhR2rVrx8mTJ4HvbgM+++yzVFdXEx8fz+WXX97Y0xURkRbS6AIKDQ3Fbrezf/9+IiMj2bBhA7GxsSQnJ+NyuepdFDBy5Eji4uLIyclh1qxZPP744zXP33777bW2dblchISENCrT66+/TpcuXVi8eDGWZXHvvfcCcPXVVzNnzhxycnLIyMjgzjvvZPDgwY09ZRERaQGNXoRgs9lISkri3XffJTs7m/LycsLCwgDIysqqd0xhYSFRUVGMHDmS6OhoTpw4Qd++fXnnnXeoqKgAoLi4mK+//tqrkygvL6dbt260a9eOHTt21Cxc+OKLL+jatStOp5MhQ4Zw5MiRmnOorq726lgiItI8vPoMKCQkhJSUFObNm0dcXBzr169n06ZNxMXF1bv9tm3byMvLIyAggMjISK6//nqCgoI4ceJEzWwoJCSEyZMn065d4xfmDR06lPT0dHbs2EHfvn1p3749QM1CiMDAQEJCQpg0aRIAQ4YMITk5mSuuuKLOIgQREWkdAZZlWaZD+LLjw/uZjnDB+P4H6draB7L+RNnN8Ofs4P0iBH0TgoiIGOHzP8n92GOPUVVVVeu5yZMnExUVZSiRiIg0B58voAULFpiOICIiLUC34ERExAgVkIiIGOHzt+BM+35llr/x91U1ItL2aQYkIiJGqIBERMQIFZCIiBihAhIRESNUQCIiYoRWwZ2He8II0xG8csp0gCbw5+zg3/mV3QxfyG5ixa9mQCIiYoQKSEREjFABiYiIESogERExQgUkIiJGqIBERMQIFZCIiBihAhIRESPOW0Aul4tp06Y1aGf5+fm8+OKLTQ51NmPHjm3yPo4ePUpOTk4zpBERkaZo1m9CiI6OJjo6ukn7sCwLy7Jo165lJmdHjx4lPz+fuLi4Ftm/iIg/GbPrAAABo0fXPLdx48ZWOXajCujUqVOkp6czYcIE1qxZQ1VVFcHBwSQmJuJwOMjLy2Pr1q2kpKSwb98+Vq9eDUBAQABz5szhoosuYsuWLezcuZOqqiri4+MZM2YMLpeL+fPn06tXLw4fPkxqaioXX3xxvRnWrFlDbm4uXbt2ZerUqXTu3JnCwkJWrVpFSUkJ7du356GHHiIiIoKdO3eyceNG2rVrR2hoKLNmzWLDhg1UVlayf/9+7rrrLgYMGFBr/5mZmWRmZgKQlpbmzTUVEfE7QUFBNf+22+2NGmuz2Ro9BhpRQAUFBSxZsoTExETCw8OZO3cugYGB5Obmsm7dOpKSkmptv2XLFsaNG0dMTAwVFRUEBQWxZ88eTp48yYIFC7Asi0WLFrFv3z7sdjuFhYVMnDiR3r17nzXDmTNniI6O5v7772fjxo28+uqrjBs3jueff54JEybQo0cPPv30U1auXMns2bPZuHEjjz/+OGFhYZSVlWGz2bjnnnvIz89n3Lhx9R7D6XTidDobellERPzaKzf1ASDwhfU1zzX215R/+AvMDoejQeMaVEAlJSUsWrSIpKQkIiMjKSoqIiMjg8LCQgDcbnedMTExMbz00ksMHDiQhIQEunfvzp49e8jNzWX69OkAVFRUUFhYiN1ux263n7N84LuZ1PczlltuuYWnn36aiooKDhw4wDPPPFOzXXV1NQB9+vQhIyOD/v37k5CQ0JBTFRGRVtKgAgoNDcVut7N//34iIyPZsGEDsbGxJCcn43K5mDNnTp0xI0eOJC4ujpycHGbNmsXjjz9e8/ztt99ea1uXy0VISEijwwcEBODxeOjQoQOLFy+u8/qDDz7Ip59+Sk5ODikpKbqlJiLiQxr0Sb/NZiMpKYl3332X7OxsysvLCQsLAyArK6veMYWFhURFRTFy5Eiio6M5ceIEffv25Z133qGiogKA4uJivv766waHtSyLXbt2AZCdnU1MTAyhoaGEh4ezc+fOmm2OHj1ak6FXr17cc889dO7cmdOnTxMSEsK3337b4GOKiEjLaPBnQCEhIaSkpDBv3jzi4uJYv349mzZtOutqsm3btpGXl0dAQACRkZFcf/31BAUFceLEiZrZUEhICJMnT27wirf27dtz6NAhNm3aROfOnXnkkUcAmDJlCi+88AKbNm2iurqam2++mcsvv5yXX36ZkydPAnDNNddw2WWXYbfbee2110hOTq53EYKIiLSOAMuyLNMhfNnx4f1MRxARaXFN+UE6bxch6JsQRETECJ/8Se7HHnuMqqqqWs9NnjyZqKgoQ4lERKS5+WQBLViwwHQEERFpYboFJyIiRqiARETECJ+8BedLmrIyxKQfrkrxJ/6cHfw7v7Kb4c/Zm0IzIBERMUIFJCIiRqiARETECBWQiIgYoQISEREjVEAiImKECkhERIxQAYmIiBEqIBERMUK/ByQiIkZoBnQOKSkppiN4TdnN8ef8ym6GP2cH7/OrgERExAgVkIiIGKECOgen02k6gteU3Rx/zq/sZvhzdvA+vxYhiIiIEZoBiYiIESogERExQr+ICnz88cesXr0aj8fDkCFDGDlyZK3Xq6qqWL58OYcPH6ZTp05MnTqV8PBwM2F/4HzZs7KyWLt2LWFhYQD89Kc/ZciQIQaS1rVixQpycnLo0qUL6enpdV63LIvVq1fz0Ucf0b59exITE7nyyisNJK3rfNnz8vJYtGhRzf+ThIQERo8e3dox61VUVERGRgZfffUVAQEBOJ1Ohg0bVmsbX732Dcnuq9e+srKS2bNnU11djdvt5qabbmLMmDG1tvHl95qG5G/0+411gXO73dakSZOswsJCq6qqykpKSrKOHz9ea5vt27dbf/nLXyzLsqzs7GzrmWeeMRG1joZkf+edd6yVK1caSnhueXl5Vn5+vvXoo4/W+/qHH35ozZ8/3/J4PNaBAwes1NTUVk54dufLvnfvXmvhwoWtnKphiouLrfz8fMuyLKu8vNyaMmVKnf83vnrtG5LdV6+9x+Oxvv32W8uyLKuqqspKTU21Dhw4UGsbX32vsayG5W/s+80Ffwvu0KFD/OhHP+KSSy7BZrMxYMAAPvjgg1rb7N69m1tvvRWAm266ib1792L5wNqNhmT3ZVdffTUdO3Y86+u7d+9m0KBBBAQE0Lt3b8rKyvjyyy9bMeHZnS+7L+vWrVvNbOaiiy4iIiKC4uLiWtv46rVvSHZfFRAQQEhICAButxu3201AQECtbXz1vQYalr+xLvhbcMXFxXTv3r3mcffu3fn000/Puk1gYCChoaGUlpbSuXPnVs36Qw3JDvD+++/zySef0KNHD373u99ht9tbM6bXiouLa2Xt3r07xcXFdOvWzWCqhjt48CDJycl069aNsWPHcumll5qOVIfL5eLIkSP07Nmz1vP+cO3Plh1899p7PB5mzJhBYWEhQ4cOpVevXrVe99X3mu+dLz807v3mgi+gtu6GG27g5ptvJigoiLfeeouMjAxmz55tOlabd8UVV7BixQpCQkLIyclh8eLFLFu2zHSsWioqKkhPT+f+++8nNDTUdJxGOVd2X7727dq1Y/HixZSVlfH0009z7NgxoqKiTMdqsPPlb+z7zQV/Cy4sLIzTp0/XPD59+nTNB2j1beN2uykvL6dTp06tmrM+DcneqVMngoKCABgyZAiHDx9u1YxNERYWRlFRUc3j+s7PV4WGhtbcroiLi8PtdlNSUmI41f9XXV1Neno6t9xyCwkJCXVe9+Vrf77svn7tATp06EBsbCwff/xxred99b3mh86Wv7HvNxd8AUVHR3Py5ElcLhfV1dW899579OvXr9Y2N9xwA1lZWQDs2rWL2NjYJt/7bA4Nyf6/9+13795NZGRka8f0Wr9+/dixYweWZXHw4EFCQ0N96hbQuXz11Vc19+4PHTqEx+PxmTcSy7J47rnniIiI4M4776x3G1+99g3J7qvXvqSkhLKyMuC7FWW5ublERETU2sZX32ugYfkb+36jb0IAcnJy+Otf/4rH4+EnP/kJo0aNYsOGDURHR9OvXz8qKytZvnw5R44coWPHjkydOpVLLrnEdGzg/NnXrVvH7t27CQwMpGPHjowfP77OfxpTlixZwr59+ygtLaVLly6MGTOG6upqAO644w4sy2LVqlXs2bOH4OBgEhMTiY6ONpz6O+fLvn37dt58800CAwMJDg7mt7/9LX369DGc+jv79+/niSeeICoqqubN7de//nXNjMeXr31Dsvvqtf/ss8/IyMjA4/FgWRb9+/dn9OjRfvNe05D8jX2/UQGJiIgRF/wtOBERMUMFJCIiRqiARETECBWQiIgYoQISEREjVEAiImKECkhERIz4f3r0KoKUzUWAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(types, times, xerr=vars)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(types[-2:], times[-2:], xerr=vars[-2:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quality\n",
    "\n",
    "The compare the quality of resample methods, a search for a dataset where different types of audio had been recorded through the same device at different sample rates. No luck with this so instead a comparison of the trusted methods from kaiser_best was made to see at what costs to quality are incurred from these speeds. \n",
    "\n",
    "We'll use the following metrics to compare the resampling techniques:\n",
    "\n",
    "### Signal to Noise Ratio\n",
    "\n",
    "This compares how much of the true signal is in the resulting signal after resample with the added noise in the signal. Higher SNR values mean that the audio is clearer.\n",
    "\n",
    "### Log Spectral Distance\n",
    "\n",
    "The Log Spectral Distance shows the restoration of higher frequencies. Lower being better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def signal_to_noise(truth, prediction):\n",
    "    return 10.*np.log10(np.sqrt(np.sum(truth**2))/np.sqrt(\n",
    "        np.sum((truth - prediction)**2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_spectral_distance(truth, prediction):\n",
    "    s1 = librosa.stft(truth)\n",
    "    s2 = librosa.stft(prediction)\n",
    "    true_X = np.log10(np.abs(s1)**2)\n",
    "    ds_X = np.log10(np.abs(s2)**2)\n",
    "    ds_X_diff_squared = (true_X - ds_X)**2\n",
    "    ds_lsd = np.mean(np.sqrt(np.mean(ds_X_diff_squared, axis=0)))\n",
    "    return ds_lsd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.60845244"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resfunc = Resample(16000, 8000)\n",
    "best = librosa.resample(sig.numpy()[0], 16000, 8000)\n",
    "pred = resfunc(sig)\n",
    "\n",
    "signal_to_noise(best, pred.numpy())\n",
    "log_spectral_distance(best, pred.numpy()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(300001,) torch.Size([1, 300001])\n",
      "(300001,) torch.Size([1, 300001])\n",
      "(270001,) torch.Size([1, 270001])\n",
      "(270001,) torch.Size([1, 270001])\n",
      "(240001,) torch.Size([1, 240001])\n",
      "(240001,) torch.Size([1, 240001])\n",
      "(180001,) torch.Size([1, 180001])\n",
      "(180001,) torch.Size([1, 180001])\n",
      "(120001,) torch.Size([1, 120001])\n",
      "(120001,) torch.Size([1, 120001])\n"
     ]
    }
   ],
   "source": [
    "types = ['kaiser_fast', 'fft', 'scipy', 'polyphase']\n",
    "\n",
    "results = {}\n",
    "\n",
    "srs = [10000, 9000, 8000, 6000, 4000]\n",
    "for sr in srs:\n",
    "    rsr = results[sr] = {}\n",
    "    best = librosa.resample(sig.numpy()[0], 16000, sr)\n",
    "    \n",
    "    for i in types:\n",
    "        rsr[i] = {}\n",
    "        func = partial(librosa.resample, sig.numpy()[0], 16000, sr, res_type=i)\n",
    "        res = func()\n",
    "        rsr[i]['SNR'] = signal_to_noise(best, res)\n",
    "        rsr[i]['LSD'] = log_spectral_distance(best,res)\n",
    "    \n",
    "    # Kaldi\n",
    "    res_func = Resample(16000, sr)\n",
    "    res = res_func(sig)\n",
    "    print(best.shape, res.shape)\n",
    "    kaldi_results = rsr['kaldi'] = {}\n",
    "    kaldi_results['SNR'] = signal_to_noise(best, res.numpy())\n",
    "    kaldi_results['LSD'] = log_spectral_distance(best, res.numpy()[0])\n",
    "    \n",
    "    # Julius \n",
    "    res_func = ResampleFrac(16000, sr)\n",
    "    res = res_func(sig)\n",
    "    # Had to do this to make the sample length 300001 instead of 30000\n",
    "    res = torch.cat((res, torch.tensor([[0]])), dim=1)\n",
    "    print(best.shape, res.shape)\n",
    "    julius_res = rsr['julius'] = {}\n",
    "    julius_res['SNR'] = signal_to_noise(best, res.numpy())\n",
    "    julius_res['LSD'] = log_spectral_distance(best, res.numpy()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "types += [\"kaldi\", \"julius\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oKSlBSkoKAECv1yM3NxdPP/00RowYgcLCQhQVFdVGC0RE9AeTO3WmUqmg1WrRs2dPhISEoFmzZmjbtm2V+2g0Grzyyit4//334eDgYJjC6fV6LFmyBIWFhQCAv/3tb2jQoIH0HoiI6P8zqa9yLigoQHh4OJYvX65oHfwqZyKqa+rEVznn5eUhMjISgwYNUroUIiKqQSZz6szBwQGLFi1SugwiIqphJjOjISIi88SgISIiqRg0REQkFYOGiIikYtAQEZFUDBoiIpLKpN6waSquXLmidAm1SqvV/ukP1HuS1LV+AfZcFyjd7xPxhk0iIjJPDBoiIpKKQUNERFIxaIiISCoGDRERSWUyH6ppSsoC/ZUuoVZlK11ALatr/QLm2/Of/Wh7qh2c0RARkVQMGiIikopBQ0REUjFoiIhIKgYNERFJxaAhIiKpGDRERCQVg4aIiKRi0BARkVQMGiIikspkgyYnJwchISHVGpueno7o6OgKtwUHByM/Px8AEBkZWWP1ERFR9Zhs0MgQFRWldAlERHXOE/GhmtnZ2YiJiUFgYCDWrl2LkpISWFpaIigoqNzXhxYUFGDRokXIy8tD+/btcf83VY8cORKxsbG1XT4RUZ1m8kFz5coVLFy4EEFBQWjWrBlmzpwJtVqNtLQ0fPXVVwgNDTUa/80338Dd3R1Dhw5FSkoK9uzZ89DHiI+PR3x8PABUegqOiEyPVqutdJtGo6lyu7kx5X5NOmjy8/Mxb948hIaGomXLlsjNzcWyZcuQlZUFACgrKyu3z8mTJw3h4+HhgQYNGjz0cXQ6HXQ6Xc0WT0TS5ebmVrpNq9VWud3cKN3vg2eX7mfS12hsbGyg1Wpx6tQpAMD69evRuXNnxMTEIDw8HCUlJQpXSERED2PSQaPRaBAaGop9+/YhKSkJhYWFcHBwAAAkJCRUuE/Hjh2RlJQEAEhNTcXt27drq1wiIqqASQcNAFhbW2PKlCn473//izZt2uDrr7/G5MmTodfrKxz/97//HSdPnsSkSZNw6NAhkz1nSURUV6jE/cuyCACQOdBT6RKIqBqq+ipnpa9Z1Dal+31ir9EQEdGTj0FDRERSMWiIiEgqBg0REUnFoCEiIqkYNEREJBWDhoiIpGLQEBGRVCb9oZpKqepNYOZI6Td61ba61i9QN3sm08EZDRERScWgISIiqRg0REQkFYOGiIikYtAQEZFUXHVWgbJAf6VLqFXZShdQy+pav0Dd6bmurRh9UnBGQ0REUjFoiIhIKgYNERFJxaAhIiKpGDRERCQVg4aIiKRi0BARkVQMGiIikopBQ0REUjFoiIhIKqlBM336dGRkZDzWvsHBwcjPz6/hioiIqLZxRkNERFI90odq5uTkYPbs2WjXrh3Onz+Pli1bYvz48Thz5gxiY2NRVlYGFxcXBAYGol69eob99uzZg0uXLmHUqFEAgPj4eFy+fBkDBgyo8HhWVlYAgO3bt+Po0aMoLS3FpEmT0KJFC5w9exZr1qxBSUkJLC0tERQUBCcnJ2RmZmL58uUoLS2FEAIhISFwdHREYmIifvjhB5SWlsLNzQ1jx46FhQXzlYiotjzypzdfuXIF48aNg7u7O5YvX46tW7ciPj4e06ZNg5OTE5YuXYqdO3di4MCBhn28vb0RFxeH1157DRqNBgkJCXjzzTcrPN6OHTvg7/+/T09u2LAh5s6dix07dmDLli0YN24cnJycMHPmTKjVaqSlpeGrr75CaGgodu3ahQEDBuCvf/0rSktLodfrcfnyZezfvx8fffQRNBoNPv30U/z444/w9fU16ik+Ph7x8fEAgOjo6Mf+ZRKRsrRareHfGo3G6La5M+V+HzlomjRpAnd3dwBA79698d1336FZs2ZwcnICAPj6+mLHjh1GQWNtbY3OnTsjJSUFLVq0QFlZGZydnZGTk1PueNu2bTMETY8ePQAA7dq1w+HDhwEAhYWFWLZsGbKysgAAZWVlAID27dtj48aNuH79Onr06AFHR0f88ssvOH/+PCIiIgAAxcXFaNSoUbmedDoddDrdo/4qiMjE5ObmGv6t1WqNbps7pfu9lwEVeeSgUalURrdtbGxw69ath+7n5+eHuLg4ODk5oU+fPpUe7/7bGs3/yrOwsDAEyvr169G5c2eEhYUhJycHM2bMAAD4+PjA1dUVKSkpmDNnDt58800IIeDr64t//vOfj9omERHVkEe+WJGbm4szZ84AAJKSkuDi4oKcnBzDDCMxMRGdOnUqt5+bmxuuX7+On376Cb169ar0ePdmN5UpLCyEg4MDACAhIcFwf3Z2Npo3b44BAwbA09MTFy9eRJcuXXDw4EHcvHkTAHDr1i1cu3btUVsmIqI/4ZFnNE5OTti+fTtWrFiBFi1a4F//+hfc3Nzw8ccfGxYD9O3bt8J9e/bsiQsXLsDW1rbS4/Xr16/Kxx88eDCWLVuGjRs3wsPDw3D/gQMHkJiYCLVaDXt7ewQEBMDW1havvvoqoqKiIISAWq3GmDFj0LRp00dtm4iIHpNKCCGqOzgnJwdz585FTEzMYz1YdHQ0Bg4ciC5dutTI8WTJHOipdAlE9Bju/ypnpa9Z1Dal+63qGk2trPO9ffs23nnnHVhaWhpChoiI6oZHmtHUFZzRED2ZOKOpwzMaIiKquxg0REQkFYOGiIikYtAQEZFUDBoiIpKKQUNERFI98icD1AX3L5GsC5ReFlnb6lq/QN3smUwHZzRERCQVg4aIiKRi0BARkVQMGiIikopBQ0REUnHVWQXKAv2VLqFWZStdgAR1beUgkSnjjIaIiKRi0BARkVQMGiIikopBQ0REUjFoiIhIKgYNERFJxaAhIiKpGDRERCQVg4aIiKRi0BARkVRPbNBkZGTgs88+U7oMIiJ6iCf2s85cXFzg4uKidBlERPQQJhc0RUVFWLBgAfLy8qDX6/HKK6+gWbNmWLt2Le7evQuNRoMPPvgA586dw5YtWzBlyhRs2LAB2dnZyMrKQkFBAfz9/aHT6bB06VJ4eXnBy8sLALB48WL07NkT3bt3V7hLIqK6w+SC5tixY2jcuDEiIiIAAIWFhZg8eTLeffdduLq6orCwEJaWluX2u3TpEmbNmoWioiKEh4fDw8MDL7zwArZu3QovLy8UFhbi9OnTCA4OLrdvfHw84uPjAQDR0dFyG6RaodVqK92m0Wiq3G6O2LP5M+V+TS5onJ2dERsbiy+//BLPPfccGjRogMaNG8PV1RUAYGNjU+F+np6esLS0hKWlJTp37oyzZ8/Cy8sLn376KfLz83Hw4EH06NEDarW63L46nQ46nU5qX1S7cnNzK92m1Wqr3G6O2LP5U7pfJyenSreZ3GIAJycnzJ07F87OzvjPf/6DQ4cOVWs/lUpV4e3evXsjMTERCQkJeOGFF2q8XiIiqprJBU1eXh4sLS3Ru3dv+Pv74+zZs7hx4wbOnj0LALhz5w7KysrK7ZecnIzi4mIUFBQgPT3dsFCgT58+2LZtGwCgZcuWtdcIEREBMMFTZ5cuXcKXX34JlUoFjUaDsWPHQgiBNWvWoLi4GJaWlpg2bVq5/Vq3bo0ZM2agoKAAr7zyChwcHAAA9vb2aNGiBRcAEBEpxOSCplu3bujWrVu5+2fNmmV0u3PnzujcubPhtrOzM8aPH19uv7t37yIrKws+Pj41XisRET2cyQVNTUpLS8PKlSsxcODAShcREBGRXGYRNMOGDavw/meeeQbLly+v5WqIiOh+JrcYgIiIzAuDhoiIpGLQEBGRVAwaIiKSikFDRERSMWiIiEgqs1jeXNPU/7dZ6RJqldIfxkdE5o0zGiIikopBQ0REUjFoiIhIKgYNERFJxaAhIiKpGDRERCQVlzdXoCzQX+kSalW20gVIUNeWqBOZMs5oiIhIKgYNERFJxaAhIiKpGDRERCQVg4aIiKRi0BARkVQMGiIikopBQ0REUjFoiIhIKrP6ZIBt27Zh165daN26NQoKCpCfn4+XX34ZWVlZCAgIULo8IqI6yaxmNDt37kRkZCQGDBgAAJg/fz68vb0RFxencGVERHWX2cxoPvnkE2RnZyMqKgpXrlyBtbU1wsLC4OjoiOLiYoSFhaFVq1aYOHGi0qUSEdUpKiGEULqImhIcHIw5c+YgMzMTW7ZswZQpUwAAI0eORGxsbKX7xcfHIz4+HgAQHR2NzIGetVIvydM8bn+l2zQaDUpLS2uxGuWxZ/OndL+WlpaVbjObGc2fodPpoNPplC6DalBubm6l27RabZXbzRF7Nn9K9+vk5FTpNrO6RkNERKanTgSN0lNKIqK6rE4EjZ+fH8LCwrB48WKlSyEiqnPMajFATeFigCdfVd+wqfS5bCWwZ/OndL+8RkNERIph0BARkVQMGiIikopBQ0REUjFoiIhIKgYNERFJxaAhIiKpGDRERCQVP1SzAlW92c8cKf1GLyIyb5zREBGRVAwaIiKSikFDRERSMWiIiEgqBg0REUnFoCEiIqm4vLkCZYH+SpdQq7KVLqCW1bV+AXk917W3AtDj4YyGiIikYtAQEZFUDBoiIpKKQUNERFIxaIiISCoGDRERScWgISIiqRg0REQkFYOGiIikYtAQEZFUDw2anJwchISEVOtgGRkZ+Oyzz/50UZU5cOAA3nvvPcyYMeOR9924caOEioiI6GFq9LPOXFxc4OLi8qeOIYSAEAIWFuUzcM+ePXjrrbfg7u7+yMeNi4tDQEDAn6qNiIge3SMFTXZ2NmJiYhAYGIi1a9eipKQElpaWCAoKgpOTE9LT07FlyxZMmTIFJ06cwJo1awAAKpUKM2bMQP369bF582YcOHAAJSUl8PLywrBhw5CTk4NZs2bBzc0N586dQ0REBJo2bWr02N9++y1OnTqFFStWwNPTE/3798fSpUtx9+5dAMDo0aPRoUMH3LhxAwsXLkRhYSH0ej3Gjh2LlJQUFBcXIywsDK1atcLEiRONjh0fH4/4+HgAQHR09GP/MonqGq1Wq3QJldJoNCZdX00z5X6rHTRXrlzBwoULERQUhGbNmmHmzJlQq9VIS0vDV199hdDQUKPxmzdvxpgxY+Du7o6ioiLUq1cPx48fx9WrVzF79mwIITBv3jycOHECWq0WWVlZCA4ORvv27St8/KFDh+KXX37ByJEj4eLigrt37yIyMhKWlpa4evUqFi1ahOjoaCQlJaFr164ICAiAXq/H3bt30bFjR2zfvh3z58+v8Ng6nQ46ne4Rfm1EBAC5ublKl1AprVZr0vXVNKX7dXJyqnRbtYImPz8f8+bNQ2hoKFq2bInc3FwsW7YMWVlZAICysrJy+7i7u+OLL76Aj48PevTogSZNmuD48eNIS0vD5MmTAQBFRUXIysqCVquFVqutNGQqUlZWhtWrV+PChQuwsLDA1atXAfzv9N2KFStQWloKLy8vtGnTptrHJCKimletoLGxsYFWq8WpU6fQsmVLrF+/Hp07d0ZYWBhycnIqvDg/ZMgQeHh4ICUlBdOmTcPUqVMN9/ft29dobE5ODqytrR+p8K1bt8LOzg7z58+HEAIjRowAAHTq1AkzZsxASkoKli1bhpdeegm+vr6PdGwiIqo51VrerNFoEBoain379iEpKQmFhYVwcHAAACQkJFS4T1ZWFpydnTFkyBC4uLjgt99+Q9euXbF3714UFRUBAPLy8nDz5s3HKrywsBCNGzeGhYUFEhMTodfrAQDXrl2Dvb09dDod/Pz8cP78eUMPpaWlj/VYRET0+Kp9jcba2hpTpkxBVFQUPDw88PXXX2Pjxo3w8PCocPy2bduQnp4OlUqFli1b4tlnn0W9evXw22+/GWY31tbWmDBhQoUrzB6mf//+iImJQWJiIrp27QorKysAMCxIUKvVsLa2xvjx4wEAfn5+CAsLQ9u2bcstBiAiInlUQgihdBGmJnOgp9IlED0RTPmrnJW+OF7blO63qsUA/GQAIiKSqkbfsFlT3n//fZSUlBjdN2HCBDg7OytUERERPS6TDJrZs2crXQIREdUQnjojIiKpGDRERCQVg4aIiKRi0BARkVQmuRhAaab83gAZlF5/X9vqWr9A3eyZTAdnNEREJBWDhoiIpGLQEBGRVAwaIiKSikFDRERSMWiIiEgqBg0REUnFoCEiIqkYNEREJBW/YZOIiKTijOYBU6ZMUbqEWlfXeq5r/QLsuS4w5X4ZNEREJBWDhoiIpGLQPECn0yldQq2raz3XtX4B9lwXmHK/XAxARERScUZDRERSMWiIiEgqfsPmfY4dO4Y1a9ZAr9fDz88PQ4YMUbokqYKDg2FtbQ0LCwuo1WpER0crXVKNW758OVJSUmBnZ4eYmBgAwK1bt7BgwQJcu3YNTZs2xXvvvQdbW1uFK605FfW8YcMG7N69G40aNQIADB8+HB4eHkqWWWNyc3OxbNky/P7771CpVNDpdBgwYIBZP8+V9Wyyz7MgIYQQZWVlYvz48SIrK0uUlJSI0NBQkZmZqXRZUgUFBYmbN28qXYZU6enpIiMjQ0yaNMlwX2xsrIiLixNCCBEXFydiY2MVqk6Oinpev3692LRpk4JVyZOXlycyMjKEEEIUFhaKiRMniszMTLN+nivr2VSfZ546+8PZs2fx1FNPoXnz5tBoNPD29kZycrLSZdGf1KlTp3J/xSYnJ8PX1xcA4Ovra3bPc0U9m7PGjRujXbt2AID69eujRYsWyMvLM+vnubKeTRVPnf0hLy8PTZo0Mdxu0qQJfv31VwUrqh2zZs0CAPTt29ekl0fWpJs3b6Jx48YAAHt7e9y8eVPhimrHjh07kJiYiHbt2uH11183yzDKycnB+fPn4erqWmee5/t7PnXqlEk+zwyaOuyjjz6Cg4MDbt68iaioKDg5OaFTp05Kl1WrVCoVVCqV0mVI169fPwwdOhQAsH79enzxxRcICgpSuKqaVVRUhJiYGIwaNQo2NjZG28z1eX6wZ1N9nnnq7A8ODg64fv264fb169fh4OCgYEXy3evPzs4O3bt3x9mzZxWuqHbY2dnhxo0bAIAbN24YLpyaM3t7e1hYWMDCwgJ+fn7IyMhQuqQaVVpaipiYGPz1r39Fjx49AJj/81xRz6b6PDNo/uDi4oKrV68iJycHpaWl2L9/Pzw9PZUuS5qioiLcuXPH8O+0tDQ4OzsrXFXt8PT0xL59+wAA+/btQ/fu3RWuSL57/+ECwOHDh9GqVSsFq6lZQgisXLkSLVq0wEsvvWS435yf58p6NtXnmZ8McJ+UlBR8/vnn0Ov1eP755xEQEKB0SdJkZ2fj3//+NwCgrKwMPj4+ZtnvwoULceLECRQUFMDOzg7Dhg1D9+7dsWDBAuTm5prdsleg4p7T09Nx4cIFqFQqNG3aFG+++abh+sWT7tSpU/jggw/g7OxsOD02fPhwuLm5me3zXFnPP/30k0k+zwwaIiKSiqfOiIhIKgYNERFJxaAhIiKpGDRERCQVg4aIiKRi0BARkVQMGiIikur/ATxi8M7KyQDjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "types\n",
    "\n",
    "for sr in results:\n",
    "    SNR = []\n",
    "    LSD = []\n",
    "    for k in results[sr]:\n",
    "        SNR.append(results[sr][k]['SNR'])\n",
    "        LSD.append(results[sr][k]['LSD'])\n",
    "    plt.barh(types, SNR)\n",
    "    plt.title(f\"Sample Rate: {sr} SNR\")\n",
    "    plt.show()\n",
    "    \n",
    "    plt.barh(types, LSD)\n",
    "    plt.title(f\"Sample Rate: {sr} LSD\")\n",
    "    plt.show()"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}