newidea.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DL based Onsets & Offsets Transcription of Piano\n",
    "___\n",
    "by WYQY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Using matplotlib backend: Qt5Agg\n"
     ]
    }
   ],
   "source": [
    "# import modules\n",
    "import os\n",
    "import time\n",
    "import random\n",
    "import importlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import tensorflow_addons as tfa\n",
    "import matplotlib.pyplot as plt\n",
    "# setup magic function\n",
    "%load_ext tensorboard\n",
    "%matplotlib auto\n",
    "# %config InlineBackend.figure_format = 'svg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import projects\n",
    "import src.component.initial as initial\n",
    "import src.component.util as util\n",
    "import src.component.pre_spec_transform as spec_transform\n",
    "import src.component.pre_sheet_transform as sheet_transform\n",
    "import src.component.model_preprocessing as model_preprocessing\n",
    "# import CNN models\n",
    "import src.component.model_cnn_common as model_cnn_common\n",
    "import src.component.model_cnn_build as model_cnn_build\n",
    "import src.component.model_cnn_train as model_cnn_train\n",
    "import src.component.model_cnn_inference as model_cnn_inference\n",
    "import src.component.model_cnn_eval as model_cnn_eval\n",
    "# import RNN models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "2.4.1\n2.4.0\n0.12.1\nGPU [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    }
   ],
   "source": [
    "# test\n",
    "# 输出Tensorflow版本信息, 检查GPU\n",
    "# 在Tensorflow2.4.0 (GPU@CUDA11.0@cuDNN8.0.4) Tensroflow_Addons0.12.1版本上检查通过\n",
    "# 若GPU无法找到, 请至配置文件中自定义xfa路径并重新初始化\n",
    "print(tf.__version__)\n",
    "print(tf.keras.__version__)\n",
    "print(tfa.__version__)\n",
    "print('GPU', tf.config.list_physical_devices('GPU'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'src.component.model_cnn_inference' from 'd:\\\\Model\\\\WYQY_Model\\\\src\\\\component\\\\model_cnn_inference.py'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (备用) 重载入模块\n",
    "# base\n",
    "importlib.reload(initial)\n",
    "importlib.reload(util)\n",
    "importlib.reload(spec_transform)\n",
    "importlib.reload(sheet_transform)\n",
    "importlib.reload(model_preprocessing)\n",
    "# CNN model\n",
    "importlib.reload(model_cnn_common)\n",
    "importlib.reload(model_cnn_build)\n",
    "importlib.reload(model_cnn_train)\n",
    "importlib.reload(model_cnn_inference)\n",
    "# RNN model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data Initialization & Spectrum Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1) Data Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainsets percentage: 100.0 [210/210]\n",
      "testests percentage: 100.0 [60/60]\n"
     ]
    }
   ],
   "source": [
    "# (可选) 预处理频谱数据删除\n",
    "model_preprocessing.delete_spec_preprocess()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainsets percentage: 100.0 [210/210]\n",
      "testests percentage: 100.0 [60/60]\n"
     ]
    }
   ],
   "source": [
    "# (可选) 预处理曲谱数据删除\n",
    "model_preprocessing.delete_sheet_preprocess()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainsets percentage: 100.0 [210/210]\n",
      "testests percentage: 100.0 [60/60]\n"
     ]
    }
   ],
   "source": [
    "# 频谱数据预处理\n",
    "model_preprocessing.spec_preprocess()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainsets percentage: 100.0 [210/210]\n",
      "testests percentage: 100.0 [60/60]\n"
     ]
    }
   ],
   "source": [
    "# 曲谱数据预处理\n",
    "model_preprocessing.sheet_preprocess()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2) Spectrogram & PianoRoll Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### logMel Spectrum\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\Program-Files\\Anaconda\\envs\\tensorflow\\lib\\site-packages\\librosa\\util\\utils.py:198: UserWarning: librosa.util.frame called with axis=-1 on a non-contiguous input. This will result in a copy.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.649548768997192\n",
      "(48163, 229, 2)\n"
     ]
    }
   ],
   "source": [
    "# 测试指定位置的wav文件logMel频谱, 并画图\n",
    "spec_logmel = spec_transform.rs_spec_logMel(sr=initial.config['spec.logmel.sampling'],\n",
    "                                            lower_edge_hertz=initial.config['spec.logmel.lower.edge'],\n",
    "                                            upper_edge_hertz=initial.config['spec.logmel.upper.edge'],\n",
    "                                            hop_length=initial.config['spec.logmel.hop_length'],\n",
    "                                            frame_length=initial.config['spec.logmel.frame_length'],\n",
    "                                            n_mel=initial.config['spec.logmel.n_mel'],\n",
    "                                            fft_length=initial.config['spec.logmel.fft_length'],\n",
    "                                            window=initial.config['spec.logmel.window'])\n",
    "time_start = time.time()\n",
    "out = spec_logmel(\n",
    "    'data\\\\MAPS_MUS-bk_xmas1_ENSTDkAm.wav')\n",
    "time_end = time.time()\n",
    "print(time_end - time_start)\n",
    "print(out.shape)\n",
    "# 画图\n",
    "util.plot_single_transform(out[0:1024, :, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CQT Spectrum\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "23.031189441680908\n(33195, 356, 2)\n"
     ]
    }
   ],
   "source": [
    "# 测试指定位置的wav文件CQT频谱, 并画图\n",
    "spec_cqt = spec_transform.rs_spec_cqt(n_bins=initial.config['spec.cqt.n_bins'],\n",
    "                                      bins_per_octave=initial.config['spec.cqt.bins_per_octave'],\n",
    "                                      fmin=initial.config['spec.cqt.fmin'],\n",
    "                                      frame_length=initial.config['spec.cqt.frame_length'],\n",
    "                                      hop_length=initial.config['spec.cqt.hop_length'],\n",
    "                                      window=initial.config['spec.cqt.window'])\n",
    "time_start = time.time()\n",
    "out = spec_cqt(\n",
    "    'data\\\\MAPS_MUS-bk_xmas1_ENSTDkAm.wav')\n",
    "time_end = time.time()\n",
    "print(time_end - time_start)\n",
    "print(out.shape)\n",
    "# 画图\n",
    "util.plot_single_transform(out[-1024:, :, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Piano-Roll Test\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ms_sheet = sheet_transform.midi_trans_start(\n",
    "        'data\\MAPS_MUS-bk_xmas1_ENSTDkAm.mid', 'data\\MAPS_MUS-bk_xmas1_ENSTDkAm.wav')\n",
    "cs_sheet = sheet_transform.midi_trans_common(\n",
    "        'data\\MAPS_MUS-bk_xmas1_ENSTDkAm.mid', 'data\\MAPS_MUS-bk_xmas1_ENSTDkAm.wav')\n",
    "md_sheet = sheet_transform.midi_trans_duration(\n",
    "        'data\\MAPS_MUS-bk_xmas1_ENSTDkAm.mid', 'data\\MAPS_MUS-bk_xmas1_ENSTDkAm.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "util.plot_single_label(md_sheet[0:512, :], 'gray_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(array([0, 0, 0, 0, 1], dtype=int64), array([35, 40, 44, 64, 35], dtype=int64))"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "np.where(ms_sheet[300:320, :]>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "ms_sheet[299, 64]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alignment Test\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试预处理后的频谱和曲谱对齐\n",
    "gen = model_cnn_common.Train_CNN_DataGenerator(io='test', output='multitone_duration')\n",
    "g = gen()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(2048, 9, 356, 2) (2048, 88)\n0.0 0.9986632 0.52806664 0.14473954\n"
     ]
    }
   ],
   "source": [
    "# 可反复测试\n",
    "y = next(g)\n",
    "print(y[0].shape, y[1].shape)\n",
    "print(np.min(y[0][:, :, :, 0]), np.max(y[0][:, :, :, 0]),\n",
    "      np.mean(y[0][:, :, :, 0]), np.std(y[0][:, :, :, 0]))\n",
    "# (1024, 9, 356, 2) (1024, 88)\n",
    "util.plot_dual_transform(y[0][:, 0, :, 1], y[1][:, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "offest = 0\n",
    "piece = 4\n",
    "util.plot_dual_transform(y[0][offest:offest+64, piece, :, 1], y[1][offest:offest+64, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "offest = 40\n",
    "util.plot_single_transform(y[0][offest, :, :, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. CNN Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1) Multi-tone Starting Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Build\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"multitone_model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "spec_input (InputLayer)      [(None, 9, 356, 2)]       0         \n",
      "_________________________________________________________________\n",
      "conv_1 (Conv2D)              (None, 8, 341, 10)        650       \n",
      "_________________________________________________________________\n",
      "bn_1 (BatchNormalization)    (None, 8, 341, 10)        40        \n",
      "_________________________________________________________________\n",
      "relu_1 (LeakyReLU)           (None, 8, 341, 10)        0         \n",
      "_________________________________________________________________\n",
      "pool_1 (MaxPooling2D)        (None, 8, 171, 10)        0         \n",
      "_________________________________________________________________\n",
      "conv_2 (Conv2D)              (None, 6, 160, 20)        7220      \n",
      "_________________________________________________________________\n",
      "bn_2 (BatchNormalization)    (None, 6, 160, 20)        80        \n",
      "_________________________________________________________________\n",
      "relu_2 (LeakyReLU)           (None, 6, 160, 20)        0         \n",
      "_________________________________________________________________\n",
      "pool_2 (MaxPooling2D)        (None, 6, 80, 20)         0         \n",
      "_________________________________________________________________\n",
      "conv_3 (Conv2D)              (None, 4, 72, 40)         21640     \n",
      "_________________________________________________________________\n",
      "bn_3 (BatchNormalization)    (None, 4, 72, 40)         160       \n",
      "_________________________________________________________________\n",
      "relu_3 (LeakyReLU)           (None, 4, 72, 40)         0         \n",
      "_________________________________________________________________\n",
      "pool_3 (MaxPooling2D)        (None, 2, 36, 40)         0         \n",
      "_________________________________________________________________\n",
      "conv_4 (Conv2D)              (None, 1, 32, 80)         32080     \n",
      "_________________________________________________________________\n",
      "bn_4 (BatchNormalization)    (None, 1, 32, 80)         320       \n",
      "_________________________________________________________________\n",
      "relu_4 (LeakyReLU)           (None, 1, 32, 80)         0         \n",
      "_________________________________________________________________\n",
      "reshape_5 (Flatten)          (None, 2560)              0         \n",
      "_________________________________________________________________\n",
      "fc_5 (Dense)                 (None, 256)               655616    \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "relu_5 (LeakyReLU)           (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "fc_6 (Dense)                 (None, 88)                22616     \n",
      "=================================================================\n",
      "Total params: 740,422\n",
      "Trainable params: 740,122\n",
      "Non-trainable params: 300\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "start_tone_model = model_cnn_build.detect_multitone_model_cnn_build(needPrint=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Train & Test\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "4162/4162 [==============================] - 328s 78ms/step - loss: 0.6250 - macro_binary_fb_evaluation: 0.1038\n",
      "Epoch 2/30\n",
      "4162/4162 [==============================] - 312s 75ms/step - loss: 0.2792 - macro_binary_fb_evaluation: 0.2726\n",
      "Epoch 3/30\n",
      "4162/4162 [==============================] - 311s 75ms/step - loss: 0.2174 - macro_binary_fb_evaluation: 0.3660\n",
      "Epoch 4/30\n",
      "4162/4162 [==============================] - 311s 75ms/step - loss: 0.2153 - macro_binary_fb_evaluation: 0.3703\n",
      "Epoch 5/30\n",
      "4162/4162 [==============================] - 311s 75ms/step - loss: 0.2122 - macro_binary_fb_evaluation: 0.3746\n",
      "\n",
      "Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
      "Epoch 6/30\n",
      "4162/4162 [==============================] - 311s 75ms/step - loss: 0.1577 - macro_binary_fb_evaluation: 0.3680\n",
      "Epoch 7/30\n",
      "4162/4162 [==============================] - 311s 75ms/step - loss: 0.1400 - macro_binary_fb_evaluation: 0.4135\n",
      "Epoch 8/30\n",
      "4162/4162 [==============================] - 314s 75ms/step - loss: 0.1349 - macro_binary_fb_evaluation: 0.4283\n",
      "Epoch 9/30\n",
      "4162/4162 [==============================] - 314s 75ms/step - loss: 0.1319 - macro_binary_fb_evaluation: 0.4382\n",
      "Epoch 10/30\n",
      "4162/4162 [==============================] - 308s 74ms/step - loss: 0.1302 - macro_binary_fb_evaluation: 0.4431\n",
      "Epoch 11/30\n",
      "4162/4162 [==============================] - 306s 74ms/step - loss: 0.1288 - macro_binary_fb_evaluation: 0.4458\n",
      "\n",
      "Epoch 00011: ReduceLROnPlateau reducing learning rate to 9.999999310821295e-05.\n",
      "Epoch 12/30\n",
      "4162/4162 [==============================] - 306s 74ms/step - loss: 0.1242 - macro_binary_fb_evaluation: 0.4066\n",
      "Epoch 13/30\n",
      "4162/4162 [==============================] - 310s 74ms/step - loss: 0.1169 - macro_binary_fb_evaluation: 0.4271\n",
      "Epoch 14/30\n",
      "4162/4162 [==============================] - 313s 75ms/step - loss: 0.1155 - macro_binary_fb_evaluation: 0.4337\n",
      "Epoch 15/30\n",
      "4162/4162 [==============================] - 313s 75ms/step - loss: 0.1146 - macro_binary_fb_evaluation: 0.4372\n",
      "Epoch 16/30\n",
      "4162/4162 [==============================] - 314s 75ms/step - loss: 0.1141 - macro_binary_fb_evaluation: 0.4384\n",
      "Epoch 17/30\n",
      "4162/4162 [==============================] - 307s 74ms/step - loss: 0.1134 - macro_binary_fb_evaluation: 0.4414\n",
      "\n",
      "Epoch 00017: ReduceLROnPlateau reducing learning rate to 9.999999019782991e-06.\n",
      "Epoch 18/30\n",
      "4162/4162 [==============================] - 307s 74ms/step - loss: 0.1154 - macro_binary_fb_evaluation: 0.4210\n",
      "Epoch 19/30\n",
      "4162/4162 [==============================] - 306s 73ms/step - loss: 0.1130 - macro_binary_fb_evaluation: 0.4349\n",
      "Epoch 20/30\n",
      "4162/4162 [==============================] - 306s 74ms/step - loss: 0.1126 - macro_binary_fb_evaluation: 0.4364\n",
      "Epoch 21/30\n",
      "4162/4162 [==============================] - 307s 74ms/step - loss: 0.1123 - macro_binary_fb_evaluation: 0.4373\n",
      "Epoch 22/30\n",
      "4162/4162 [==============================] - 307s 74ms/step - loss: 0.1121 - macro_binary_fb_evaluation: 0.4378\n",
      "Epoch 23/30\n",
      "4162/4162 [==============================] - 306s 74ms/step - loss: 0.1120 - macro_binary_fb_evaluation: 0.4385\n",
      "\n",
      "Epoch 00023: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
      "Epoch 24/30\n",
      "4162/4162 [==============================] - 308s 74ms/step - loss: 0.1125 - macro_binary_fb_evaluation: 0.4340\n",
      "Epoch 25/30\n",
      "4162/4162 [==============================] - 313s 75ms/step - loss: 0.1118 - macro_binary_fb_evaluation: 0.4391\n",
      "Epoch 26/30\n",
      "4162/4162 [==============================] - 314s 76ms/step - loss: 0.1118 - macro_binary_fb_evaluation: 0.4393\n",
      "Epoch 27/30\n",
      "4162/4162 [==============================] - 313s 75ms/step - loss: 0.1117 - macro_binary_fb_evaluation: 0.4397\n",
      "Epoch 28/30\n",
      "4162/4162 [==============================] - 312s 75ms/step - loss: 0.1117 - macro_binary_fb_evaluation: 0.4398\n",
      "Epoch 29/30\n",
      "4162/4162 [==============================] - 312s 75ms/step - loss: 0.1116 - macro_binary_fb_evaluation: 0.4402\n",
      "Epoch 30/30\n",
      "4162/4162 [==============================] - 312s 75ms/step - loss: 0.1116 - macro_binary_fb_evaluation: 0.4405\n",
      "INFO:tensorflow:Assets written to: models\\save\\multitone_start\\saved_2021-05-11-17_33_22\\assets\n",
      "1317/1317 [==============================] - 44s 33ms/step - loss: 0.2149 - macro_binary_fb_evaluation: 0.2620\n"
     ]
    }
   ],
   "source": [
    "# %tensorboard --logdir \"models\\tensorboard\\multitone_start\"\n",
    "# for vscode: ctrl+shift+p, Python: Launch Tensorboard\n",
    "# 如果需要接续训练, 请在此写入checkpoint路径\n",
    "# models\\checkpoint\\multitone_start\\chckpt_???.hdf5\n",
    "ckpt_path = ''\n",
    "start_tone_train = model_cnn_train.train_model(model=start_tone_model,\n",
    "                                               type='multitone_start',\n",
    "                                               resume=False,\n",
    "                                               ckpt_path=ckpt_path)\n",
    "mts_history, mts_loss = start_tone_train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2) Common Starting Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Build\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"start_common_model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "spec_input (InputLayer)      [(None, 9, 356, 2)]       0         \n",
      "_________________________________________________________________\n",
      "conv_1 (Conv2D)              (None, 5, 341, 10)        1610      \n",
      "_________________________________________________________________\n",
      "bn_1 (BatchNormalization)    (None, 5, 341, 10)        40        \n",
      "_________________________________________________________________\n",
      "relu_1 (LeakyReLU)           (None, 5, 341, 10)        0         \n",
      "_________________________________________________________________\n",
      "pool_1 (MaxPooling2D)        (None, 5, 171, 10)        0         \n",
      "_________________________________________________________________\n",
      "conv_2 (Conv2D)              (None, 3, 160, 20)        7220      \n",
      "_________________________________________________________________\n",
      "bn_2 (BatchNormalization)    (None, 3, 160, 20)        80        \n",
      "_________________________________________________________________\n",
      "relu_2 (LeakyReLU)           (None, 3, 160, 20)        0         \n",
      "_________________________________________________________________\n",
      "pool_2 (MaxPooling2D)        (None, 3, 80, 20)         0         \n",
      "_________________________________________________________________\n",
      "reshape_3 (Flatten)          (None, 4800)              0         \n",
      "_________________________________________________________________\n",
      "fc_3 (Dense)                 (None, 256)               1229056   \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "relu_3 (LeakyReLU)           (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "fc_4 (Dense)                 (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 1,238,263\n",
      "Trainable params: 1,238,203\n",
      "Non-trainable params: 60\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "start_common_model = model_cnn_build.detect_start_common_model_cnn_build(needPrint=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Train & Test\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "4162/4162 [==============================] - 224s 53ms/step - loss: 1.3133 - macro_binary_fb_evaluation: 0.4024\n",
      "Epoch 2/30\n",
      "4162/4162 [==============================] - 176s 42ms/step - loss: 0.3678 - macro_binary_fb_evaluation: 0.5249\n",
      "Epoch 3/30\n",
      "4162/4162 [==============================] - 176s 42ms/step - loss: 0.3671 - macro_binary_fb_evaluation: 0.5332\n",
      "Epoch 4/30\n",
      "4162/4162 [==============================] - 175s 42ms/step - loss: 0.3633 - macro_binary_fb_evaluation: 0.5352\n",
      "Epoch 5/30\n",
      "4162/4162 [==============================] - 171s 41ms/step - loss: 0.3682 - macro_binary_fb_evaluation: 0.5316\n",
      "\n",
      "Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
      "Epoch 6/30\n",
      "4162/4162 [==============================] - 176s 42ms/step - loss: 0.3329 - macro_binary_fb_evaluation: 0.5388\n",
      "Epoch 7/30\n",
      "4162/4162 [==============================] - 177s 42ms/step - loss: 0.3282 - macro_binary_fb_evaluation: 0.5465\n",
      "Epoch 8/30\n",
      "4162/4162 [==============================] - 177s 42ms/step - loss: 0.3277 - macro_binary_fb_evaluation: 0.5463\n",
      "Epoch 9/30\n",
      "4162/4162 [==============================] - 176s 42ms/step - loss: 0.3270 - macro_binary_fb_evaluation: 0.5461\n",
      "Epoch 10/30\n",
      "4162/4162 [==============================] - 177s 42ms/step - loss: 0.3269 - macro_binary_fb_evaluation: 0.5465\n",
      "Epoch 11/30\n",
      "4162/4162 [==============================] - 177s 42ms/step - loss: 0.3262 - macro_binary_fb_evaluation: 0.5481\n",
      "\n",
      "Epoch 00011: ReduceLROnPlateau reducing learning rate to 9.999999310821295e-05.\n",
      "Epoch 12/30\n",
      "4162/4162 [==============================] - 177s 42ms/step - loss: 0.3245 - macro_binary_fb_evaluation: 0.5420\n",
      "Epoch 13/30\n",
      "4162/4162 [==============================] - 177s 42ms/step - loss: 0.3208 - macro_binary_fb_evaluation: 0.5528\n",
      "Epoch 14/30\n",
      "4162/4162 [==============================] - 177s 42ms/step - loss: 0.3196 - macro_binary_fb_evaluation: 0.5565\n",
      "Epoch 15/30\n",
      "4162/4162 [==============================] - 177s 42ms/step - loss: 0.3185 - macro_binary_fb_evaluation: 0.5570\n",
      "Epoch 16/30\n",
      "4162/4162 [==============================] - 177s 42ms/step - loss: 0.3178 - macro_binary_fb_evaluation: 0.5577\n",
      "Epoch 17/30\n",
      "4162/4162 [==============================] - 177s 42ms/step - loss: 0.3168 - macro_binary_fb_evaluation: 0.5581\n",
      "\n",
      "Epoch 00017: ReduceLROnPlateau reducing learning rate to 9.999999019782991e-06.\n",
      "Epoch 18/30\n",
      "4162/4162 [==============================] - 177s 42ms/step - loss: 0.3180 - macro_binary_fb_evaluation: 0.5533\n",
      "Epoch 19/30\n",
      "4162/4162 [==============================] - 177s 42ms/step - loss: 0.3163 - macro_binary_fb_evaluation: 0.5558\n",
      "Epoch 20/30\n",
      "4162/4162 [==============================] - 177s 43ms/step - loss: 0.3158 - macro_binary_fb_evaluation: 0.5581\n",
      "Epoch 21/30\n",
      "4162/4162 [==============================] - 177s 43ms/step - loss: 0.3155 - macro_binary_fb_evaluation: 0.5606\n",
      "Epoch 22/30\n",
      "4162/4162 [==============================] - 177s 43ms/step - loss: 0.3150 - macro_binary_fb_evaluation: 0.5613\n",
      "Epoch 23/30\n",
      "4162/4162 [==============================] - 177s 43ms/step - loss: 0.3148 - macro_binary_fb_evaluation: 0.5635\n",
      "\n",
      "Epoch 00023: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
      "Epoch 24/30\n",
      "4162/4162 [==============================] - 177s 43ms/step - loss: 0.3150 - macro_binary_fb_evaluation: 0.5632\n",
      "Epoch 25/30\n",
      "4162/4162 [==============================] - 177s 43ms/step - loss: 0.3147 - macro_binary_fb_evaluation: 0.5633\n",
      "Epoch 26/30\n",
      "4162/4162 [==============================] - 177s 43ms/step - loss: 0.3145 - macro_binary_fb_evaluation: 0.5633\n",
      "Epoch 27/30\n",
      "4162/4162 [==============================] - 177s 43ms/step - loss: 0.3145 - macro_binary_fb_evaluation: 0.5628\n",
      "Epoch 28/30\n",
      "4162/4162 [==============================] - 177s 43ms/step - loss: 0.3145 - macro_binary_fb_evaluation: 0.5629\n",
      "Epoch 29/30\n",
      "4162/4162 [==============================] - 177s 43ms/step - loss: 0.3144 - macro_binary_fb_evaluation: 0.5638\n",
      "Epoch 30/30\n",
      "4162/4162 [==============================] - 177s 43ms/step - loss: 0.3144 - macro_binary_fb_evaluation: 0.5641\n",
      "INFO:tensorflow:Assets written to: models\\save\\common_start\\saved_2021-05-11-14_56_40\\assets\n",
      "1317/1317 [==============================] - 48s 36ms/step - loss: 0.5535 - macro_binary_fb_evaluation: 0.3573\n"
     ]
    }
   ],
   "source": [
    "# %tensorboard --logdir \"models\\tensorboard\\common_start\"\n",
    "# for vscode: ctrl+shift+p, Python: Launch Tensorboard\n",
    "# 如果需要接续训练, 请在此写入checkpoint路径\n",
    "# models\\checkpoint\\common_start\\chckpt_???.hdf5\n",
    "ckpt_path = ''\n",
    "start_common_train = model_cnn_train.train_model(model=start_common_model,\n",
    "                                                 type='common_start',\n",
    "                                                 resume=False,\n",
    "                                                 ckpt_path=ckpt_path)\n",
    "cs_history, cs_loss = start_common_train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3) Multi-tone Duration Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Build\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"multitone_model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "spec_input (InputLayer)      [(None, 9, 356, 2)]       0         \n",
      "_________________________________________________________________\n",
      "conv_1 (Conv2D)              (None, 8, 341, 10)        650       \n",
      "_________________________________________________________________\n",
      "bn_1 (BatchNormalization)    (None, 8, 341, 10)        40        \n",
      "_________________________________________________________________\n",
      "relu_1 (LeakyReLU)           (None, 8, 341, 10)        0         \n",
      "_________________________________________________________________\n",
      "pool_1 (MaxPooling2D)        (None, 8, 171, 10)        0         \n",
      "_________________________________________________________________\n",
      "conv_2 (Conv2D)              (None, 6, 160, 20)        7220      \n",
      "_________________________________________________________________\n",
      "bn_2 (BatchNormalization)    (None, 6, 160, 20)        80        \n",
      "_________________________________________________________________\n",
      "relu_2 (LeakyReLU)           (None, 6, 160, 20)        0         \n",
      "_________________________________________________________________\n",
      "pool_2 (MaxPooling2D)        (None, 6, 80, 20)         0         \n",
      "_________________________________________________________________\n",
      "conv_3 (Conv2D)              (None, 4, 72, 40)         21640     \n",
      "_________________________________________________________________\n",
      "bn_3 (BatchNormalization)    (None, 4, 72, 40)         160       \n",
      "_________________________________________________________________\n",
      "relu_3 (LeakyReLU)           (None, 4, 72, 40)         0         \n",
      "_________________________________________________________________\n",
      "pool_3 (MaxPooling2D)        (None, 2, 36, 40)         0         \n",
      "_________________________________________________________________\n",
      "conv_4 (Conv2D)              (None, 1, 32, 80)         32080     \n",
      "_________________________________________________________________\n",
      "bn_4 (BatchNormalization)    (None, 1, 32, 80)         320       \n",
      "_________________________________________________________________\n",
      "relu_4 (LeakyReLU)           (None, 1, 32, 80)         0         \n",
      "_________________________________________________________________\n",
      "reshape_5 (Flatten)          (None, 2560)              0         \n",
      "_________________________________________________________________\n",
      "fc_5 (Dense)                 (None, 256)               655616    \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "relu_5 (LeakyReLU)           (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "fc_6 (Dense)                 (None, 88)                22616     \n",
      "=================================================================\n",
      "Total params: 740,422\n",
      "Trainable params: 740,122\n",
      "Non-trainable params: 300\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "duration_tone_model = model_cnn_build.detect_multitone_model_cnn_build(needPrint=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Train & Test\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "4162/4162 [==============================] - 422s 100ms/step - loss: 3.3985 - macro_binary_fb_evaluation: 0.2383\n",
      "Epoch 2/30\n",
      "4162/4162 [==============================] - 313s 75ms/step - loss: 1.3592 - macro_binary_fb_evaluation: 0.6037\n",
      "Epoch 3/30\n",
      "4162/4162 [==============================] - 312s 75ms/step - loss: 1.2907 - macro_binary_fb_evaluation: 0.6456\n",
      "Epoch 4/30\n",
      "4162/4162 [==============================] - 312s 75ms/step - loss: 1.2858 - macro_binary_fb_evaluation: 0.6522\n",
      "Epoch 5/30\n",
      "4162/4162 [==============================] - 312s 75ms/step - loss: 1.2719 - macro_binary_fb_evaluation: 0.6534\n",
      "\n",
      "Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
      "Epoch 6/30\n",
      "4162/4162 [==============================] - 313s 75ms/step - loss: 1.0968 - macro_binary_fb_evaluation: 0.5859\n",
      "Epoch 7/30\n",
      "4162/4162 [==============================] - 312s 75ms/step - loss: 0.9445 - macro_binary_fb_evaluation: 0.6414\n",
      "Epoch 8/30\n",
      "4162/4162 [==============================] - 311s 75ms/step - loss: 0.9098 - macro_binary_fb_evaluation: 0.6568\n",
      "Epoch 9/30\n",
      "4162/4162 [==============================] - 312s 75ms/step - loss: 0.8864 - macro_binary_fb_evaluation: 0.6689\n",
      "Epoch 10/30\n",
      "4162/4162 [==============================] - 312s 75ms/step - loss: 0.8732 - macro_binary_fb_evaluation: 0.6761\n",
      "Epoch 11/30\n",
      "4162/4162 [==============================] - 312s 75ms/step - loss: 0.8668 - macro_binary_fb_evaluation: 0.6787\n",
      "\n",
      "Epoch 00011: ReduceLROnPlateau reducing learning rate to 9.999999310821295e-05.\n",
      "Epoch 12/30\n",
      "4162/4162 [==============================] - 312s 75ms/step - loss: 0.9134 - macro_binary_fb_evaluation: 0.5825\n",
      "Epoch 13/30\n",
      "4162/4162 [==============================] - 312s 75ms/step - loss: 0.8193 - macro_binary_fb_evaluation: 0.6245\n",
      "Epoch 14/30\n",
      "4162/4162 [==============================] - 308s 74ms/step - loss: 0.8015 - macro_binary_fb_evaluation: 0.6331\n",
      "Epoch 15/30\n",
      "4162/4162 [==============================] - 304s 73ms/step - loss: 0.7912 - macro_binary_fb_evaluation: 0.6378\n",
      "Epoch 16/30\n",
      "4162/4162 [==============================] - 305s 73ms/step - loss: 0.7833 - macro_binary_fb_evaluation: 0.6413\n",
      "Epoch 17/30\n",
      "4162/4162 [==============================] - 305s 73ms/step - loss: 0.7771 - macro_binary_fb_evaluation: 0.6445\n",
      "\n",
      "Epoch 00017: ReduceLROnPlateau reducing learning rate to 9.999999019782991e-06.\n",
      "Epoch 18/30\n",
      "4162/4162 [==============================] - 305s 73ms/step - loss: 0.8393 - macro_binary_fb_evaluation: 0.5897\n",
      "Epoch 19/30\n",
      "4162/4162 [==============================] - 304s 73ms/step - loss: 0.7844 - macro_binary_fb_evaluation: 0.6190\n",
      "Epoch 20/30\n",
      "4162/4162 [==============================] - 304s 73ms/step - loss: 0.7723 - macro_binary_fb_evaluation: 0.6255\n",
      "Epoch 21/30\n",
      "4162/4162 [==============================] - 304s 73ms/step - loss: 0.7662 - macro_binary_fb_evaluation: 0.6289\n",
      "Epoch 22/30\n",
      "4162/4162 [==============================] - 304s 73ms/step - loss: 0.7620 - macro_binary_fb_evaluation: 0.6314\n",
      "Epoch 23/30\n",
      "4162/4162 [==============================] - 304s 73ms/step - loss: 0.7584 - macro_binary_fb_evaluation: 0.6335\n",
      "\n",
      "Epoch 00023: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
      "Epoch 24/30\n",
      "4162/4162 [==============================] - 304s 73ms/step - loss: 0.7694 - macro_binary_fb_evaluation: 0.6198\n",
      "Epoch 25/30\n",
      "4162/4162 [==============================] - 304s 73ms/step - loss: 0.7615 - macro_binary_fb_evaluation: 0.6267\n",
      "Epoch 26/30\n",
      "4162/4162 [==============================] - 303s 73ms/step - loss: 0.7582 - macro_binary_fb_evaluation: 0.6289\n",
      "Epoch 27/30\n",
      "4162/4162 [==============================] - 304s 73ms/step - loss: 0.7563 - macro_binary_fb_evaluation: 0.6303\n",
      "Epoch 28/30\n",
      "4162/4162 [==============================] - 304s 73ms/step - loss: 0.7546 - macro_binary_fb_evaluation: 0.6310\n",
      "Epoch 29/30\n",
      "4162/4162 [==============================] - 304s 73ms/step - loss: 0.7533 - macro_binary_fb_evaluation: 0.6315\n",
      "Epoch 30/30\n",
      "4162/4162 [==============================] - 305s 73ms/step - loss: 0.7520 - macro_binary_fb_evaluation: 0.6325\n",
      "INFO:tensorflow:Assets written to: models\\save\\multitone_duration\\saved_2021-05-11-13_25_53\\assets\n",
      "1317/1317 [==============================] - 55s 42ms/step - loss: 0.8645 - macro_binary_fb_evaluation: 0.5800\n"
     ]
    }
   ],
   "source": [
    "# %tensorboard --logdir \"models\\tensorboard\\multitone_duration\"\n",
    "# for vscode: ctrl+shift+p, Python: Launch Tensorboard\n",
    "# 如果需要接续训练, 请在此写入checkpoint路径\n",
    "# models\\checkpoint\\multitone_duration\\chckpt_???.hdf5\n",
    "ckpt_path = ''\n",
    "duration_tone_train = model_cnn_train.train_model(model=duration_tone_model,\n",
    "                                               type='multitone_duration',\n",
    "                                               resume=False,\n",
    "                                               ckpt_path=ckpt_path)\n",
    "mtd_history, mtd_loss = duration_tone_train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Evaluate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1) Accurency Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-tone Starting Model\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "665/665 [==============================] - 62s 89ms/step - loss: 0.1993 - macro_binary_fb_evaluation: 0.2545\n"
     ]
    }
   ],
   "source": [
    "start_tone_model = model_cnn_build.detect_multitone_model_cnn_build()\n",
    "# models\\checkpoint\\multitone_start\\chckpt_???.hdf5\n",
    "weight_path = initial.config['detect.model.multistart.weight']\n",
    "start_tone_test = model_cnn_eval.test_model(model=start_tone_model,\n",
    "                                            type='multitone_start',\n",
    "                                            weight_path_start=weight_path)\n",
    "mts_loss = start_tone_test()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Common Starting Model\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "665/665 [==============================] - 50s 74ms/step - loss: 0.5176 - macro_binary_fb_evaluation: 0.3412\n"
     ]
    }
   ],
   "source": [
    "start_common_model = model_cnn_build.detect_start_common_model_cnn_build()\n",
    "# models\\checkpoint\\common_start\\chckpt_???.hdf5\n",
    "weight_path = initial.config['detect.model.commonstart.weight']\n",
    "start_common_test = model_cnn_eval.test_model(model=start_common_model,\n",
    "                                              type='common_start',\n",
    "                                              weight_path_start=weight_path)\n",
    "cs_loss = start_common_test()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-tone Duration Model\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "665/665 [==============================] - 39s 58ms/step - loss: 0.8382 - macro_binary_fb_evaluation: 0.5678\n"
     ]
    }
   ],
   "source": [
    "duration_tone_model = model_cnn_build.detect_multitone_model_cnn_build()\n",
    "# models\\checkpoint\\multitone_duration\\chckpt_???.hdf5\n",
    "weight_path = initial.config['detect.model.multiduration.weight']\n",
    "duration_tone_test = model_cnn_eval.test_model(model=duration_tone_model,\n",
    "                                               type='multitone_duration',\n",
    "                                               weight_path_start=weight_path)\n",
    "mtd_loss = duration_tone_test()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2) Diagram Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "diagram_test = model_cnn_inference.Transcription_Model(\n",
    "    multitone_start_path = initial.config['detect.model.multistart.weight'],\n",
    "    common_start_path = initial.config['detect.model.commonstart.weight'],\n",
    "    multitone_duration_path = initial.config['detect.model.multiduration.weight'])\n",
    "diagrams = diagram_test.sheet_plot('data\\MAPS_MUS-bk_xmas1_ENSTDkAm.wav')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-tone Starting Model\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "   </g>\r\n",
       "   <g id=\"patch_3\">\r\n",
       "    <path d=\"M 45.442188 353.037188 \r\n",
       "L 45.442188 26.877187 \r\n",
       "\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_4\">\r\n",
       "    <path d=\"M 715.042188 353.037188 \r\n",
       "L 715.042188 26.877187 \r\n",
       "\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_5\">\r\n",
       "    <path d=\"M 45.442188 353.037188 \r\n",
       "L 715.042187 353.037188 \r\n",
       "\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_6\">\r\n",
       "    <path d=\"M 45.442188 26.877187 \r\n",
       "L 715.042187 26.877187 \r\n",
       "\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_18\">\r\n",
       "    <!-- Transform -->\r\n",
       "    <g transform=\"translate(335.48125 20.877187)scale(0.18 -0.18)\">\r\n",
       "     <use xlink:href=\"#DejaVuSans-84\"/>\r\n",
       "     <use x=\"46.333984\" xlink:href=\"#DejaVuSans-114\"/>\r\n",
       "     <use x=\"87.447266\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "     <use x=\"148.726562\" xlink:href=\"#DejaVuSans-110\"/>\r\n",
       "     <use x=\"212.105469\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "     <use x=\"264.205078\" xlink:href=\"#DejaVuSans-102\"/>\r\n",
       "     <use x=\"299.410156\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "     <use x=\"360.591797\" xlink:href=\"#DejaVuSans-114\"/>\r\n",
       "     <use x=\"399.955078\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "  </g>\r\n",
       " </g>\r\n",
       " <defs>\r\n",
       "  <clipPath id=\"p04bc2709f7\">\r\n",
       "   <rect height=\"326.16\" width=\"669.6\" x=\"45.442188\" y=\"26.877187\"/>\r\n",
       "  </clipPath>\r\n",
       " </defs>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diagram = np.where(diagrams[0]>0.5, 1.0, 0.0)\n",
    "util.plot_single_transform(diagram[0:1024, :], cmap='gray_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Common Starting Model\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "     <use x=\"212.105469\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "     <use x=\"264.205078\" xlink:href=\"#DejaVuSans-102\"/>\r\n",
       "     <use x=\"299.410156\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "     <use x=\"360.591797\" xlink:href=\"#DejaVuSans-114\"/>\r\n",
       "     <use x=\"399.955078\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "  </g>\r\n",
       " </g>\r\n",
       " <defs>\r\n",
       "  <clipPath id=\"p5ffff76e89\">\r\n",
       "   <rect height=\"326.16\" width=\"669.6\" x=\"57\" y=\"26.877187\"/>\r\n",
       "  </clipPath>\r\n",
       " </defs>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diagram = np.where(diagrams[1]>0.5, 1.0, 0.0)\n",
    "util.plot_single_transform(diagram[0:1024, :], cmap='gray_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Multi-tone Duration Model\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      ],
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diagram = np.where(diagrams[2]>0.5, 1.0, 0.0)\n",
    "util.plot_single_transform(diagram[0:1024, :], cmap='gray_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3) Predict Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_test = model_cnn_inference.Transcription_Model(\n",
    "    multitone_start_path = initial.config['detect.model.multistart.weight'],\n",
    "    common_start_path = initial.config['detect.model.commonstart.weight'],\n",
    "    multitone_duration_path = initial.config['detect.model.multiduration.weight'])\n",
    "piano_roll, notes = predict_test.notes_plot('data\\MAPS_MUS-bk_xmas1_ENSTDkAm.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "metadata": {
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   ],
   "source": [
    "util.plot_single_transform(piano_roll[0:1024, :], cmap='gray_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4) mir_eval Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluation percentage: 100.0 [60/60]\n"
     ]
    }
   ],
   "source": [
    "all_pitch_eval_result = model_cnn_eval.eval_all_pitch(\n",
    "    multitone_start_path = initial.config['detect.model.multistart.weight'],\n",
    "    common_start_path = initial.config['detect.model.commonstart.weight'],\n",
    "    multitone_duration_path = initial.config['detect.model.multiduration.weight']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th>Precision</th>\n",
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       "      <th>1</th>\n",
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       "      <td>0.892125</td>\n",
       "      <td>0.824526</td>\n",
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       "      <td>0.732470</td>\n",
       "      <td>0.676969</td>\n",
       "      <td>0.703627</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.218636</td>\n",
       "      <td>0.258646</td>\n",
       "      <td>0.867777</td>\n",
       "      <td>0.754850</td>\n",
       "      <td>0.521315</td>\n",
       "      <td>0.616715</td>\n",
       "      <td>0.642699</td>\n",
       "      <td>0.805996</td>\n",
       "      <td>0.556638</td>\n",
       "      <td>0.658501</td>\n",
       "      <td>0.618166</td>\n",
       "      <td>0.426918</td>\n",
       "      <td>0.505043</td>\n",
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       "      <td>0.892838</td>\n",
       "      <td>0.907145</td>\n",
       "      <td>0.871125</td>\n",
       "      <td>0.888770</td>\n",
       "      <td>0.789887</td>\n",
       "      <td>0.920203</td>\n",
       "      <td>0.883664</td>\n",
       "      <td>0.901564</td>\n",
       "      <td>0.767864</td>\n",
       "      <td>0.737374</td>\n",
       "      <td>0.752310</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.413598</td>\n",
       "      <td>0.356387</td>\n",
       "      <td>0.382867</td>\n",
       "      <td>0.851020</td>\n",
       "      <td>0.769594</td>\n",
       "      <td>0.663141</td>\n",
       "      <td>0.712413</td>\n",
       "      <td>0.651351</td>\n",
       "      <td>0.830973</td>\n",
       "      <td>0.716029</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.725212</td>\n",
       "      <td>0.624898</td>\n",
       "      <td>0.671329</td>\n",
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       "      <th>5</th>\n",
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       "      <td>0.901623</td>\n",
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       "      <td>0.681879</td>\n",
       "      <td>0.759059</td>\n",
       "      <td>0.734414</td>\n",
       "      <td>0.882056</td>\n",
       "      <td>0.702685</td>\n",
       "      <td>0.782219</td>\n",
       "      <td>0.770008</td>\n",
       "      <td>0.613423</td>\n",
       "      <td>0.682854</td>\n",
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       "      <th>6</th>\n",
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       "      <td>0.276067</td>\n",
       "      <td>0.759022</td>\n",
       "      <td>0.841847</td>\n",
       "      <td>0.465761</td>\n",
       "      <td>0.599720</td>\n",
       "      <td>0.560284</td>\n",
       "      <td>0.911100</td>\n",
       "      <td>0.504076</td>\n",
       "      <td>0.649055</td>\n",
       "      <td>0.720530</td>\n",
       "      <td>0.398641</td>\n",
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       "      <td>0.846154</td>\n",
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       "      <td>0.733865</td>\n",
       "      <td>0.723265</td>\n",
       "      <td>0.455674</td>\n",
       "      <td>0.559101</td>\n",
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       "      <th>8</th>\n",
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       "      <td>0.415563</td>\n",
       "      <td>0.453888</td>\n",
       "      <td>0.861849</td>\n",
       "      <td>0.657371</td>\n",
       "      <td>0.546358</td>\n",
       "      <td>0.596745</td>\n",
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       "      <td>0.695219</td>\n",
       "      <td>0.577815</td>\n",
       "      <td>0.631103</td>\n",
       "      <td>0.898406</td>\n",
       "      <td>0.746689</td>\n",
       "      <td>0.815552</td>\n",
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       "      <th>9</th>\n",
       "      <td>0.466345</td>\n",
       "      <td>0.309761</td>\n",
       "      <td>0.372257</td>\n",
       "      <td>0.842426</td>\n",
       "      <td>0.902387</td>\n",
       "      <td>0.599394</td>\n",
       "      <td>0.720325</td>\n",
       "      <td>0.654000</td>\n",
       "      <td>0.957629</td>\n",
       "      <td>0.636088</td>\n",
       "      <td>0.764423</td>\n",
       "      <td>0.735318</td>\n",
       "      <td>0.488422</td>\n",
       "      <td>0.586963</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.415194</td>\n",
       "      <td>0.393635</td>\n",
       "      <td>0.404127</td>\n",
       "      <td>0.916933</td>\n",
       "      <td>0.818021</td>\n",
       "      <td>0.775544</td>\n",
       "      <td>0.796217</td>\n",
       "      <td>0.715342</td>\n",
       "      <td>0.839223</td>\n",
       "      <td>0.795645</td>\n",
       "      <td>0.816853</td>\n",
       "      <td>0.738516</td>\n",
       "      <td>0.700167</td>\n",
       "      <td>0.718831</td>\n",
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       "      <th>11</th>\n",
       "      <td>0.409509</td>\n",
       "      <td>0.276255</td>\n",
       "      <td>0.329935</td>\n",
       "      <td>0.842540</td>\n",
       "      <td>0.862730</td>\n",
       "      <td>0.581997</td>\n",
       "      <td>0.695088</td>\n",
       "      <td>0.627798</td>\n",
       "      <td>0.929448</td>\n",
       "      <td>0.627005</td>\n",
       "      <td>0.748842</td>\n",
       "      <td>0.775307</td>\n",
       "      <td>0.523021</td>\n",
       "      <td>0.624652</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.583679</td>\n",
       "      <td>0.509047</td>\n",
       "      <td>0.543814</td>\n",
       "      <td>0.928759</td>\n",
       "      <td>0.872407</td>\n",
       "      <td>0.760856</td>\n",
       "      <td>0.812822</td>\n",
       "      <td>0.767405</td>\n",
       "      <td>0.926349</td>\n",
       "      <td>0.807901</td>\n",
       "      <td>0.863080</td>\n",
       "      <td>0.820885</td>\n",
       "      <td>0.715923</td>\n",
       "      <td>0.764820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.317029</td>\n",
       "      <td>0.233957</td>\n",
       "      <td>0.269231</td>\n",
       "      <td>0.753238</td>\n",
       "      <td>0.715580</td>\n",
       "      <td>0.528075</td>\n",
       "      <td>0.607692</td>\n",
       "      <td>0.640229</td>\n",
       "      <td>0.769928</td>\n",
       "      <td>0.568182</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>0.621377</td>\n",
       "      <td>0.458556</td>\n",
       "      <td>0.527692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.621212</td>\n",
       "      <td>0.484325</td>\n",
       "      <td>0.544294</td>\n",
       "      <td>0.928005</td>\n",
       "      <td>0.937063</td>\n",
       "      <td>0.730577</td>\n",
       "      <td>0.821037</td>\n",
       "      <td>0.780137</td>\n",
       "      <td>0.971445</td>\n",
       "      <td>0.757383</td>\n",
       "      <td>0.851162</td>\n",
       "      <td>0.847319</td>\n",
       "      <td>0.660609</td>\n",
       "      <td>0.742405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.401201</td>\n",
       "      <td>0.341339</td>\n",
       "      <td>0.368857</td>\n",
       "      <td>0.873003</td>\n",
       "      <td>0.796396</td>\n",
       "      <td>0.677568</td>\n",
       "      <td>0.732192</td>\n",
       "      <td>0.676232</td>\n",
       "      <td>0.861862</td>\n",
       "      <td>0.733265</td>\n",
       "      <td>0.792380</td>\n",
       "      <td>0.703303</td>\n",
       "      <td>0.598365</td>\n",
       "      <td>0.646604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.476625</td>\n",
       "      <td>0.416750</td>\n",
       "      <td>0.444681</td>\n",
       "      <td>0.860799</td>\n",
       "      <td>0.799316</td>\n",
       "      <td>0.698903</td>\n",
       "      <td>0.745745</td>\n",
       "      <td>0.723876</td>\n",
       "      <td>0.819840</td>\n",
       "      <td>0.716849</td>\n",
       "      <td>0.764894</td>\n",
       "      <td>0.701254</td>\n",
       "      <td>0.613160</td>\n",
       "      <td>0.654255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.306613</td>\n",
       "      <td>0.256281</td>\n",
       "      <td>0.279197</td>\n",
       "      <td>0.877533</td>\n",
       "      <td>0.719439</td>\n",
       "      <td>0.601340</td>\n",
       "      <td>0.655109</td>\n",
       "      <td>0.640933</td>\n",
       "      <td>0.731463</td>\n",
       "      <td>0.611390</td>\n",
       "      <td>0.666058</td>\n",
       "      <td>0.725451</td>\n",
       "      <td>0.606365</td>\n",
       "      <td>0.660584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.442332</td>\n",
       "      <td>0.247429</td>\n",
       "      <td>0.317344</td>\n",
       "      <td>0.827449</td>\n",
       "      <td>0.881112</td>\n",
       "      <td>0.492871</td>\n",
       "      <td>0.632139</td>\n",
       "      <td>0.636326</td>\n",
       "      <td>0.963644</td>\n",
       "      <td>0.539037</td>\n",
       "      <td>0.691351</td>\n",
       "      <td>0.691183</td>\n",
       "      <td>0.386629</td>\n",
       "      <td>0.495878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.404440</td>\n",
       "      <td>0.234537</td>\n",
       "      <td>0.296900</td>\n",
       "      <td>0.801105</td>\n",
       "      <td>0.868589</td>\n",
       "      <td>0.503699</td>\n",
       "      <td>0.637632</td>\n",
       "      <td>0.581364</td>\n",
       "      <td>0.936463</td>\n",
       "      <td>0.543060</td>\n",
       "      <td>0.687459</td>\n",
       "      <td>0.717785</td>\n",
       "      <td>0.416247</td>\n",
       "      <td>0.526927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.622951</td>\n",
       "      <td>0.500659</td>\n",
       "      <td>0.555150</td>\n",
       "      <td>0.894707</td>\n",
       "      <td>0.871311</td>\n",
       "      <td>0.700264</td>\n",
       "      <td>0.776479</td>\n",
       "      <td>0.787761</td>\n",
       "      <td>0.930328</td>\n",
       "      <td>0.747694</td>\n",
       "      <td>0.829072</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.683136</td>\n",
       "      <td>0.757487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.411360</td>\n",
       "      <td>0.357250</td>\n",
       "      <td>0.382400</td>\n",
       "      <td>0.908402</td>\n",
       "      <td>0.648881</td>\n",
       "      <td>0.563528</td>\n",
       "      <td>0.603200</td>\n",
       "      <td>0.759364</td>\n",
       "      <td>0.693632</td>\n",
       "      <td>0.602392</td>\n",
       "      <td>0.644800</td>\n",
       "      <td>0.759036</td>\n",
       "      <td>0.659193</td>\n",
       "      <td>0.705600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.500714</td>\n",
       "      <td>0.352776</td>\n",
       "      <td>0.413923</td>\n",
       "      <td>0.857956</td>\n",
       "      <td>0.842135</td>\n",
       "      <td>0.593323</td>\n",
       "      <td>0.696165</td>\n",
       "      <td>0.706735</td>\n",
       "      <td>0.916928</td>\n",
       "      <td>0.646018</td>\n",
       "      <td>0.757994</td>\n",
       "      <td>0.751356</td>\n",
       "      <td>0.529364</td>\n",
       "      <td>0.621121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.675754</td>\n",
       "      <td>0.609538</td>\n",
       "      <td>0.640940</td>\n",
       "      <td>0.877371</td>\n",
       "      <td>0.927716</td>\n",
       "      <td>0.836811</td>\n",
       "      <td>0.879922</td>\n",
       "      <td>0.786560</td>\n",
       "      <td>0.941347</td>\n",
       "      <td>0.849106</td>\n",
       "      <td>0.892850</td>\n",
       "      <td>0.802148</td>\n",
       "      <td>0.723547</td>\n",
       "      <td>0.760823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.304805</td>\n",
       "      <td>0.208848</td>\n",
       "      <td>0.247863</td>\n",
       "      <td>0.894748</td>\n",
       "      <td>0.642643</td>\n",
       "      <td>0.440329</td>\n",
       "      <td>0.522589</td>\n",
       "      <td>0.656524</td>\n",
       "      <td>0.675676</td>\n",
       "      <td>0.462963</td>\n",
       "      <td>0.549451</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.494856</td>\n",
       "      <td>0.587302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.521064</td>\n",
       "      <td>0.440405</td>\n",
       "      <td>0.477351</td>\n",
       "      <td>0.906241</td>\n",
       "      <td>0.832816</td>\n",
       "      <td>0.703898</td>\n",
       "      <td>0.762949</td>\n",
       "      <td>0.742550</td>\n",
       "      <td>0.873171</td>\n",
       "      <td>0.738006</td>\n",
       "      <td>0.799919</td>\n",
       "      <td>0.798670</td>\n",
       "      <td>0.675038</td>\n",
       "      <td>0.731668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.562746</td>\n",
       "      <td>0.537496</td>\n",
       "      <td>0.549832</td>\n",
       "      <td>0.863538</td>\n",
       "      <td>0.931340</td>\n",
       "      <td>0.889551</td>\n",
       "      <td>0.909966</td>\n",
       "      <td>0.732125</td>\n",
       "      <td>0.947438</td>\n",
       "      <td>0.904926</td>\n",
       "      <td>0.925694</td>\n",
       "      <td>0.719120</td>\n",
       "      <td>0.686853</td>\n",
       "      <td>0.702616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.464000</td>\n",
       "      <td>0.433483</td>\n",
       "      <td>0.448223</td>\n",
       "      <td>0.916990</td>\n",
       "      <td>0.694400</td>\n",
       "      <td>0.648729</td>\n",
       "      <td>0.670788</td>\n",
       "      <td>0.760140</td>\n",
       "      <td>0.745600</td>\n",
       "      <td>0.696562</td>\n",
       "      <td>0.720247</td>\n",
       "      <td>0.752000</td>\n",
       "      <td>0.702541</td>\n",
       "      <td>0.726430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.378317</td>\n",
       "      <td>0.253308</td>\n",
       "      <td>0.303442</td>\n",
       "      <td>0.792981</td>\n",
       "      <td>0.719368</td>\n",
       "      <td>0.481664</td>\n",
       "      <td>0.576993</td>\n",
       "      <td>0.629801</td>\n",
       "      <td>0.804630</td>\n",
       "      <td>0.538752</td>\n",
       "      <td>0.645380</td>\n",
       "      <td>0.692264</td>\n",
       "      <td>0.463516</td>\n",
       "      <td>0.555254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.521701</td>\n",
       "      <td>0.362923</td>\n",
       "      <td>0.428063</td>\n",
       "      <td>0.859644</td>\n",
       "      <td>0.872396</td>\n",
       "      <td>0.606884</td>\n",
       "      <td>0.715812</td>\n",
       "      <td>0.699117</td>\n",
       "      <td>0.965278</td>\n",
       "      <td>0.671498</td>\n",
       "      <td>0.792023</td>\n",
       "      <td>0.764757</td>\n",
       "      <td>0.532005</td>\n",
       "      <td>0.627493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.509197</td>\n",
       "      <td>0.236033</td>\n",
       "      <td>0.322551</td>\n",
       "      <td>0.799612</td>\n",
       "      <td>0.911907</td>\n",
       "      <td>0.422706</td>\n",
       "      <td>0.577648</td>\n",
       "      <td>0.660710</td>\n",
       "      <td>0.969506</td>\n",
       "      <td>0.449405</td>\n",
       "      <td>0.614135</td>\n",
       "      <td>0.805905</td>\n",
       "      <td>0.373570</td>\n",
       "      <td>0.510501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.539339</td>\n",
       "      <td>0.492196</td>\n",
       "      <td>0.514690</td>\n",
       "      <td>0.844442</td>\n",
       "      <td>0.936146</td>\n",
       "      <td>0.854318</td>\n",
       "      <td>0.893362</td>\n",
       "      <td>0.699440</td>\n",
       "      <td>0.950969</td>\n",
       "      <td>0.867846</td>\n",
       "      <td>0.907508</td>\n",
       "      <td>0.693273</td>\n",
       "      <td>0.632674</td>\n",
       "      <td>0.661589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.208411</td>\n",
       "      <td>0.153793</td>\n",
       "      <td>0.176984</td>\n",
       "      <td>0.541314</td>\n",
       "      <td>0.864486</td>\n",
       "      <td>0.637931</td>\n",
       "      <td>0.734127</td>\n",
       "      <td>0.337867</td>\n",
       "      <td>0.951402</td>\n",
       "      <td>0.702069</td>\n",
       "      <td>0.807936</td>\n",
       "      <td>0.716822</td>\n",
       "      <td>0.528966</td>\n",
       "      <td>0.608730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.514655</td>\n",
       "      <td>0.424156</td>\n",
       "      <td>0.465044</td>\n",
       "      <td>0.778179</td>\n",
       "      <td>0.884914</td>\n",
       "      <td>0.729307</td>\n",
       "      <td>0.799610</td>\n",
       "      <td>0.637668</td>\n",
       "      <td>0.931466</td>\n",
       "      <td>0.767673</td>\n",
       "      <td>0.841675</td>\n",
       "      <td>0.721121</td>\n",
       "      <td>0.594316</td>\n",
       "      <td>0.651607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.435188</td>\n",
       "      <td>0.210117</td>\n",
       "      <td>0.283402</td>\n",
       "      <td>0.819210</td>\n",
       "      <td>0.877718</td>\n",
       "      <td>0.423780</td>\n",
       "      <td>0.571586</td>\n",
       "      <td>0.596648</td>\n",
       "      <td>0.955945</td>\n",
       "      <td>0.461549</td>\n",
       "      <td>0.622529</td>\n",
       "      <td>0.787913</td>\n",
       "      <td>0.380420</td>\n",
       "      <td>0.513103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.402071</td>\n",
       "      <td>0.348541</td>\n",
       "      <td>0.373397</td>\n",
       "      <td>0.913045</td>\n",
       "      <td>0.811044</td>\n",
       "      <td>0.703067</td>\n",
       "      <td>0.753205</td>\n",
       "      <td>0.680442</td>\n",
       "      <td>0.870578</td>\n",
       "      <td>0.754675</td>\n",
       "      <td>0.808494</td>\n",
       "      <td>0.759275</td>\n",
       "      <td>0.658190</td>\n",
       "      <td>0.705128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.243464</td>\n",
       "      <td>0.198402</td>\n",
       "      <td>0.218635</td>\n",
       "      <td>0.624568</td>\n",
       "      <td>0.861111</td>\n",
       "      <td>0.701731</td>\n",
       "      <td>0.773294</td>\n",
       "      <td>0.427060</td>\n",
       "      <td>0.935458</td>\n",
       "      <td>0.762317</td>\n",
       "      <td>0.840059</td>\n",
       "      <td>0.668301</td>\n",
       "      <td>0.544607</td>\n",
       "      <td>0.600147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.256694</td>\n",
       "      <td>0.232150</td>\n",
       "      <td>0.243806</td>\n",
       "      <td>0.731615</td>\n",
       "      <td>0.867498</td>\n",
       "      <td>0.784551</td>\n",
       "      <td>0.823942</td>\n",
       "      <td>0.527741</td>\n",
       "      <td>0.897045</td>\n",
       "      <td>0.811274</td>\n",
       "      <td>0.852006</td>\n",
       "      <td>0.623730</td>\n",
       "      <td>0.564092</td>\n",
       "      <td>0.592414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.335470</td>\n",
       "      <td>0.283394</td>\n",
       "      <td>0.307241</td>\n",
       "      <td>0.772493</td>\n",
       "      <td>0.844017</td>\n",
       "      <td>0.712996</td>\n",
       "      <td>0.772994</td>\n",
       "      <td>0.645396</td>\n",
       "      <td>0.897436</td>\n",
       "      <td>0.758123</td>\n",
       "      <td>0.821918</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.487365</td>\n",
       "      <td>0.528376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.469568</td>\n",
       "      <td>0.268260</td>\n",
       "      <td>0.341451</td>\n",
       "      <td>0.865537</td>\n",
       "      <td>0.810269</td>\n",
       "      <td>0.462898</td>\n",
       "      <td>0.589195</td>\n",
       "      <td>0.673837</td>\n",
       "      <td>0.872832</td>\n",
       "      <td>0.498640</td>\n",
       "      <td>0.634689</td>\n",
       "      <td>0.756205</td>\n",
       "      <td>0.432012</td>\n",
       "      <td>0.549883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.496381</td>\n",
       "      <td>0.415945</td>\n",
       "      <td>0.452617</td>\n",
       "      <td>0.862273</td>\n",
       "      <td>0.884695</td>\n",
       "      <td>0.741334</td>\n",
       "      <td>0.806695</td>\n",
       "      <td>0.718642</td>\n",
       "      <td>0.940538</td>\n",
       "      <td>0.788128</td>\n",
       "      <td>0.857614</td>\n",
       "      <td>0.739400</td>\n",
       "      <td>0.619584</td>\n",
       "      <td>0.674210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.425610</td>\n",
       "      <td>0.303349</td>\n",
       "      <td>0.354227</td>\n",
       "      <td>0.850191</td>\n",
       "      <td>0.811608</td>\n",
       "      <td>0.578464</td>\n",
       "      <td>0.675484</td>\n",
       "      <td>0.656009</td>\n",
       "      <td>0.864579</td>\n",
       "      <td>0.616218</td>\n",
       "      <td>0.719571</td>\n",
       "      <td>0.831414</td>\n",
       "      <td>0.592580</td>\n",
       "      <td>0.691969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.463722</td>\n",
       "      <td>0.355072</td>\n",
       "      <td>0.402189</td>\n",
       "      <td>0.913604</td>\n",
       "      <td>0.735016</td>\n",
       "      <td>0.562802</td>\n",
       "      <td>0.637483</td>\n",
       "      <td>0.758043</td>\n",
       "      <td>0.813880</td>\n",
       "      <td>0.623188</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.760252</td>\n",
       "      <td>0.582126</td>\n",
       "      <td>0.659371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.510836</td>\n",
       "      <td>0.380184</td>\n",
       "      <td>0.435931</td>\n",
       "      <td>0.844411</td>\n",
       "      <td>0.919505</td>\n",
       "      <td>0.684332</td>\n",
       "      <td>0.784676</td>\n",
       "      <td>0.695207</td>\n",
       "      <td>0.959752</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.819022</td>\n",
       "      <td>0.773994</td>\n",
       "      <td>0.576037</td>\n",
       "      <td>0.660502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.492326</td>\n",
       "      <td>0.368700</td>\n",
       "      <td>0.421638</td>\n",
       "      <td>0.843029</td>\n",
       "      <td>0.872885</td>\n",
       "      <td>0.653699</td>\n",
       "      <td>0.747556</td>\n",
       "      <td>0.696656</td>\n",
       "      <td>0.922078</td>\n",
       "      <td>0.690539</td>\n",
       "      <td>0.789687</td>\n",
       "      <td>0.708776</td>\n",
       "      <td>0.530799</td>\n",
       "      <td>0.607010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.668275</td>\n",
       "      <td>0.591249</td>\n",
       "      <td>0.627407</td>\n",
       "      <td>0.874071</td>\n",
       "      <td>0.928830</td>\n",
       "      <td>0.821772</td>\n",
       "      <td>0.872027</td>\n",
       "      <td>0.771576</td>\n",
       "      <td>0.946924</td>\n",
       "      <td>0.837780</td>\n",
       "      <td>0.889015</td>\n",
       "      <td>0.798552</td>\n",
       "      <td>0.706510</td>\n",
       "      <td>0.749717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.390455</td>\n",
       "      <td>0.295581</td>\n",
       "      <td>0.336458</td>\n",
       "      <td>0.829984</td>\n",
       "      <td>0.822420</td>\n",
       "      <td>0.622584</td>\n",
       "      <td>0.708684</td>\n",
       "      <td>0.646974</td>\n",
       "      <td>0.919423</td>\n",
       "      <td>0.696018</td>\n",
       "      <td>0.792272</td>\n",
       "      <td>0.743618</td>\n",
       "      <td>0.562931</td>\n",
       "      <td>0.640780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.597603</td>\n",
       "      <td>0.504335</td>\n",
       "      <td>0.547022</td>\n",
       "      <td>0.901934</td>\n",
       "      <td>0.872432</td>\n",
       "      <td>0.736272</td>\n",
       "      <td>0.798589</td>\n",
       "      <td>0.766421</td>\n",
       "      <td>0.880137</td>\n",
       "      <td>0.742775</td>\n",
       "      <td>0.805643</td>\n",
       "      <td>0.803938</td>\n",
       "      <td>0.678468</td>\n",
       "      <td>0.735893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.565511</td>\n",
       "      <td>0.450505</td>\n",
       "      <td>0.501499</td>\n",
       "      <td>0.794220</td>\n",
       "      <td>0.903635</td>\n",
       "      <td>0.719865</td>\n",
       "      <td>0.801349</td>\n",
       "      <td>0.685050</td>\n",
       "      <td>0.930685</td>\n",
       "      <td>0.741414</td>\n",
       "      <td>0.825337</td>\n",
       "      <td>0.762468</td>\n",
       "      <td>0.607407</td>\n",
       "      <td>0.676162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.493382</td>\n",
       "      <td>0.405172</td>\n",
       "      <td>0.444948</td>\n",
       "      <td>0.895546</td>\n",
       "      <td>0.855317</td>\n",
       "      <td>0.702399</td>\n",
       "      <td>0.771352</td>\n",
       "      <td>0.707344</td>\n",
       "      <td>0.903241</td>\n",
       "      <td>0.741754</td>\n",
       "      <td>0.814571</td>\n",
       "      <td>0.794614</td>\n",
       "      <td>0.652549</td>\n",
       "      <td>0.716608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.486529</td>\n",
       "      <td>0.410428</td>\n",
       "      <td>0.445250</td>\n",
       "      <td>0.827268</td>\n",
       "      <td>0.833597</td>\n",
       "      <td>0.703209</td>\n",
       "      <td>0.762872</td>\n",
       "      <td>0.716302</td>\n",
       "      <td>0.885895</td>\n",
       "      <td>0.747326</td>\n",
       "      <td>0.810732</td>\n",
       "      <td>0.687797</td>\n",
       "      <td>0.580214</td>\n",
       "      <td>0.629442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.382209</td>\n",
       "      <td>0.209911</td>\n",
       "      <td>0.270992</td>\n",
       "      <td>0.785702</td>\n",
       "      <td>0.850819</td>\n",
       "      <td>0.467274</td>\n",
       "      <td>0.603244</td>\n",
       "      <td>0.573832</td>\n",
       "      <td>0.942115</td>\n",
       "      <td>0.517415</td>\n",
       "      <td>0.667974</td>\n",
       "      <td>0.676953</td>\n",
       "      <td>0.371786</td>\n",
       "      <td>0.479970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.401002</td>\n",
       "      <td>0.275862</td>\n",
       "      <td>0.326864</td>\n",
       "      <td>0.833939</td>\n",
       "      <td>0.844612</td>\n",
       "      <td>0.581034</td>\n",
       "      <td>0.688458</td>\n",
       "      <td>0.622452</td>\n",
       "      <td>0.964912</td>\n",
       "      <td>0.663793</td>\n",
       "      <td>0.786517</td>\n",
       "      <td>0.661654</td>\n",
       "      <td>0.455172</td>\n",
       "      <td>0.539326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.443970</td>\n",
       "      <td>0.477338</td>\n",
       "      <td>0.821185</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.727855</td>\n",
       "      <td>0.782559</td>\n",
       "      <td>0.679352</td>\n",
       "      <td>0.853598</td>\n",
       "      <td>0.734258</td>\n",
       "      <td>0.789443</td>\n",
       "      <td>0.781638</td>\n",
       "      <td>0.672359</td>\n",
       "      <td>0.722892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.371322</td>\n",
       "      <td>0.266449</td>\n",
       "      <td>0.310263</td>\n",
       "      <td>0.749115</td>\n",
       "      <td>0.850431</td>\n",
       "      <td>0.610243</td>\n",
       "      <td>0.710589</td>\n",
       "      <td>0.562454</td>\n",
       "      <td>0.910421</td>\n",
       "      <td>0.653290</td>\n",
       "      <td>0.760715</td>\n",
       "      <td>0.750122</td>\n",
       "      <td>0.538264</td>\n",
       "      <td>0.626774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.393711</td>\n",
       "      <td>0.253032</td>\n",
       "      <td>0.308071</td>\n",
       "      <td>0.845190</td>\n",
       "      <td>0.871698</td>\n",
       "      <td>0.560226</td>\n",
       "      <td>0.682087</td>\n",
       "      <td>0.654089</td>\n",
       "      <td>0.962264</td>\n",
       "      <td>0.618432</td>\n",
       "      <td>0.752953</td>\n",
       "      <td>0.647799</td>\n",
       "      <td>0.416330</td>\n",
       "      <td>0.506890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.459486</td>\n",
       "      <td>0.277208</td>\n",
       "      <td>0.345797</td>\n",
       "      <td>0.835440</td>\n",
       "      <td>0.884295</td>\n",
       "      <td>0.533494</td>\n",
       "      <td>0.665496</td>\n",
       "      <td>0.662857</td>\n",
       "      <td>0.959653</td>\n",
       "      <td>0.578958</td>\n",
       "      <td>0.722208</td>\n",
       "      <td>0.753251</td>\n",
       "      <td>0.454436</td>\n",
       "      <td>0.566876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.514286</td>\n",
       "      <td>0.499397</td>\n",
       "      <td>0.506732</td>\n",
       "      <td>0.908118</td>\n",
       "      <td>0.847205</td>\n",
       "      <td>0.822678</td>\n",
       "      <td>0.834761</td>\n",
       "      <td>0.758227</td>\n",
       "      <td>0.878261</td>\n",
       "      <td>0.852835</td>\n",
       "      <td>0.865361</td>\n",
       "      <td>0.772671</td>\n",
       "      <td>0.750302</td>\n",
       "      <td>0.761322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.501134</td>\n",
       "      <td>0.483589</td>\n",
       "      <td>0.492205</td>\n",
       "      <td>0.929004</td>\n",
       "      <td>0.786848</td>\n",
       "      <td>0.759300</td>\n",
       "      <td>0.772829</td>\n",
       "      <td>0.770514</td>\n",
       "      <td>0.849584</td>\n",
       "      <td>0.819840</td>\n",
       "      <td>0.834447</td>\n",
       "      <td>0.712774</td>\n",
       "      <td>0.687819</td>\n",
       "      <td>0.700074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.585795</td>\n",
       "      <td>0.477616</td>\n",
       "      <td>0.526203</td>\n",
       "      <td>0.842093</td>\n",
       "      <td>0.910773</td>\n",
       "      <td>0.742579</td>\n",
       "      <td>0.818121</td>\n",
       "      <td>0.718195</td>\n",
       "      <td>0.923008</td>\n",
       "      <td>0.752555</td>\n",
       "      <td>0.829111</td>\n",
       "      <td>0.768726</td>\n",
       "      <td>0.626764</td>\n",
       "      <td>0.690524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Precision    Recall  F-measure  Average_Overlap_Ratio  \\\n",
       "0    0.382213  0.303996   0.338647               0.778806   \n",
       "1    0.552319  0.510469   0.530570               0.908422   \n",
       "2    0.316578  0.218636   0.258646               0.867777   \n",
       "3    0.622416  0.597701   0.609808               0.892838   \n",
       "4    0.413598  0.356387   0.382867               0.851020   \n",
       "5    0.509688  0.406040   0.451999               0.901623   \n",
       "6    0.387525  0.214402   0.276067               0.759022   \n",
       "7    0.409006  0.257683   0.316171               0.836731   \n",
       "8    0.500000  0.415563   0.453888               0.861849   \n",
       "9    0.466345  0.309761   0.372257               0.842426   \n",
       "10   0.415194  0.393635   0.404127               0.916933   \n",
       "11   0.409509  0.276255   0.329935               0.842540   \n",
       "12   0.583679  0.509047   0.543814               0.928759   \n",
       "13   0.317029  0.233957   0.269231               0.753238   \n",
       "14   0.621212  0.484325   0.544294               0.928005   \n",
       "15   0.401201  0.341339   0.368857               0.873003   \n",
       "16   0.476625  0.416750   0.444681               0.860799   \n",
       "17   0.306613  0.256281   0.279197               0.877533   \n",
       "18   0.442332  0.247429   0.317344               0.827449   \n",
       "19   0.404440  0.234537   0.296900               0.801105   \n",
       "20   0.622951  0.500659   0.555150               0.894707   \n",
       "21   0.411360  0.357250   0.382400               0.908402   \n",
       "22   0.500714  0.352776   0.413923               0.857956   \n",
       "23   0.675754  0.609538   0.640940               0.877371   \n",
       "24   0.304805  0.208848   0.247863               0.894748   \n",
       "25   0.521064  0.440405   0.477351               0.906241   \n",
       "26   0.562746  0.537496   0.549832               0.863538   \n",
       "27   0.464000  0.433483   0.448223               0.916990   \n",
       "28   0.378317  0.253308   0.303442               0.792981   \n",
       "29   0.521701  0.362923   0.428063               0.859644   \n",
       "30   0.509197  0.236033   0.322551               0.799612   \n",
       "31   0.539339  0.492196   0.514690               0.844442   \n",
       "32   0.208411  0.153793   0.176984               0.541314   \n",
       "33   0.514655  0.424156   0.465044               0.778179   \n",
       "34   0.435188  0.210117   0.283402               0.819210   \n",
       "35   0.402071  0.348541   0.373397               0.913045   \n",
       "36   0.243464  0.198402   0.218635               0.624568   \n",
       "37   0.256694  0.232150   0.243806               0.731615   \n",
       "38   0.335470  0.283394   0.307241               0.772493   \n",
       "39   0.469568  0.268260   0.341451               0.865537   \n",
       "40   0.496381  0.415945   0.452617               0.862273   \n",
       "41   0.425610  0.303349   0.354227               0.850191   \n",
       "42   0.463722  0.355072   0.402189               0.913604   \n",
       "43   0.510836  0.380184   0.435931               0.844411   \n",
       "44   0.492326  0.368700   0.421638               0.843029   \n",
       "45   0.668275  0.591249   0.627407               0.874071   \n",
       "46   0.390455  0.295581   0.336458               0.829984   \n",
       "47   0.597603  0.504335   0.547022               0.901934   \n",
       "48   0.565511  0.450505   0.501499               0.794220   \n",
       "49   0.493382  0.405172   0.444948               0.895546   \n",
       "50   0.486529  0.410428   0.445250               0.827268   \n",
       "51   0.382209  0.209911   0.270992               0.785702   \n",
       "52   0.401002  0.275862   0.326864               0.833939   \n",
       "53   0.516129  0.443970   0.477338               0.821185   \n",
       "54   0.371322  0.266449   0.310263               0.749115   \n",
       "55   0.393711  0.253032   0.308071               0.845190   \n",
       "56   0.459486  0.277208   0.345797               0.835440   \n",
       "57   0.514286  0.499397   0.506732               0.908118   \n",
       "58   0.501134  0.483589   0.492205               0.929004   \n",
       "59   0.585795  0.477616   0.526203               0.842093   \n",
       "\n",
       "    Precision_no_offset  Recall_no_offset  F-measure_no_offset  \\\n",
       "0              0.867617          0.690065             0.768722   \n",
       "1              0.856526          0.791625             0.822798   \n",
       "2              0.754850          0.521315             0.616715   \n",
       "3              0.907145          0.871125             0.888770   \n",
       "4              0.769594          0.663141             0.712413   \n",
       "5              0.855939          0.681879             0.759059   \n",
       "6              0.841847          0.465761             0.599720   \n",
       "7              0.846154          0.533097             0.654097   \n",
       "8              0.657371          0.546358             0.596745   \n",
       "9              0.902387          0.599394             0.720325   \n",
       "10             0.818021          0.775544             0.796217   \n",
       "11             0.862730          0.581997             0.695088   \n",
       "12             0.872407          0.760856             0.812822   \n",
       "13             0.715580          0.528075             0.607692   \n",
       "14             0.937063          0.730577             0.821037   \n",
       "15             0.796396          0.677568             0.732192   \n",
       "16             0.799316          0.698903             0.745745   \n",
       "17             0.719439          0.601340             0.655109   \n",
       "18             0.881112          0.492871             0.632139   \n",
       "19             0.868589          0.503699             0.637632   \n",
       "20             0.871311          0.700264             0.776479   \n",
       "21             0.648881          0.563528             0.603200   \n",
       "22             0.842135          0.593323             0.696165   \n",
       "23             0.927716          0.836811             0.879922   \n",
       "24             0.642643          0.440329             0.522589   \n",
       "25             0.832816          0.703898             0.762949   \n",
       "26             0.931340          0.889551             0.909966   \n",
       "27             0.694400          0.648729             0.670788   \n",
       "28             0.719368          0.481664             0.576993   \n",
       "29             0.872396          0.606884             0.715812   \n",
       "30             0.911907          0.422706             0.577648   \n",
       "31             0.936146          0.854318             0.893362   \n",
       "32             0.864486          0.637931             0.734127   \n",
       "33             0.884914          0.729307             0.799610   \n",
       "34             0.877718          0.423780             0.571586   \n",
       "35             0.811044          0.703067             0.753205   \n",
       "36             0.861111          0.701731             0.773294   \n",
       "37             0.867498          0.784551             0.823942   \n",
       "38             0.844017          0.712996             0.772994   \n",
       "39             0.810269          0.462898             0.589195   \n",
       "40             0.884695          0.741334             0.806695   \n",
       "41             0.811608          0.578464             0.675484   \n",
       "42             0.735016          0.562802             0.637483   \n",
       "43             0.919505          0.684332             0.784676   \n",
       "44             0.872885          0.653699             0.747556   \n",
       "45             0.928830          0.821772             0.872027   \n",
       "46             0.822420          0.622584             0.708684   \n",
       "47             0.872432          0.736272             0.798589   \n",
       "48             0.903635          0.719865             0.801349   \n",
       "49             0.855317          0.702399             0.771352   \n",
       "50             0.833597          0.703209             0.762872   \n",
       "51             0.850819          0.467274             0.603244   \n",
       "52             0.844612          0.581034             0.688458   \n",
       "53             0.846154          0.727855             0.782559   \n",
       "54             0.850431          0.610243             0.710589   \n",
       "55             0.871698          0.560226             0.682087   \n",
       "56             0.884295          0.533494             0.665496   \n",
       "57             0.847205          0.822678             0.834761   \n",
       "58             0.786848          0.759300             0.772829   \n",
       "59             0.910773          0.742579             0.818121   \n",
       "\n",
       "    Average_Overlap_Ratio_no_offset  Onset_Precision  Onset_Recall  \\\n",
       "0                          0.571815         0.895451      0.712203   \n",
       "1                          0.772099         0.892125      0.824526   \n",
       "2                          0.642699         0.805996      0.556638   \n",
       "3                          0.789887         0.920203      0.883664   \n",
       "4                          0.651351         0.830973      0.716029   \n",
       "5                          0.734414         0.882056      0.702685   \n",
       "6                          0.560284         0.911100      0.504076   \n",
       "7                          0.642206         0.949343      0.598109   \n",
       "8                          0.759652         0.695219      0.577815   \n",
       "9                          0.654000         0.957629      0.636088   \n",
       "10                         0.715342         0.839223      0.795645   \n",
       "11                         0.627798         0.929448      0.627005   \n",
       "12                         0.767405         0.926349      0.807901   \n",
       "13                         0.640229         0.769928      0.568182   \n",
       "14                         0.780137         0.971445      0.757383   \n",
       "15                         0.676232         0.861862      0.733265   \n",
       "16                         0.723876         0.819840      0.716849   \n",
       "17                         0.640933         0.731463      0.611390   \n",
       "18                         0.636326         0.963644      0.539037   \n",
       "19                         0.581364         0.936463      0.543060   \n",
       "20                         0.787761         0.930328      0.747694   \n",
       "21                         0.759364         0.693632      0.602392   \n",
       "22                         0.706735         0.916928      0.646018   \n",
       "23                         0.786560         0.941347      0.849106   \n",
       "24                         0.656524         0.675676      0.462963   \n",
       "25                         0.742550         0.873171      0.738006   \n",
       "26                         0.732125         0.947438      0.904926   \n",
       "27                         0.760140         0.745600      0.696562   \n",
       "28                         0.629801         0.804630      0.538752   \n",
       "29                         0.699117         0.965278      0.671498   \n",
       "30                         0.660710         0.969506      0.449405   \n",
       "31                         0.699440         0.950969      0.867846   \n",
       "32                         0.337867         0.951402      0.702069   \n",
       "33                         0.637668         0.931466      0.767673   \n",
       "34                         0.596648         0.955945      0.461549   \n",
       "35                         0.680442         0.870578      0.754675   \n",
       "36                         0.427060         0.935458      0.762317   \n",
       "37                         0.527741         0.897045      0.811274   \n",
       "38                         0.645396         0.897436      0.758123   \n",
       "39                         0.673837         0.872832      0.498640   \n",
       "40                         0.718642         0.940538      0.788128   \n",
       "41                         0.656009         0.864579      0.616218   \n",
       "42                         0.758043         0.813880      0.623188   \n",
       "43                         0.695207         0.959752      0.714286   \n",
       "44                         0.696656         0.922078      0.690539   \n",
       "45                         0.771576         0.946924      0.837780   \n",
       "46                         0.646974         0.919423      0.696018   \n",
       "47                         0.766421         0.880137      0.742775   \n",
       "48                         0.685050         0.930685      0.741414   \n",
       "49                         0.707344         0.903241      0.741754   \n",
       "50                         0.716302         0.885895      0.747326   \n",
       "51                         0.573832         0.942115      0.517415   \n",
       "52                         0.622452         0.964912      0.663793   \n",
       "53                         0.679352         0.853598      0.734258   \n",
       "54                         0.562454         0.910421      0.653290   \n",
       "55                         0.654089         0.962264      0.618432   \n",
       "56                         0.662857         0.959653      0.578958   \n",
       "57                         0.758227         0.878261      0.852835   \n",
       "58                         0.770514         0.849584      0.819840   \n",
       "59                         0.718195         0.923008      0.752555   \n",
       "\n",
       "    Onset_F-measure  Offset_Precision  Offset_Recall  Offset_F-measure  \n",
       "0          0.793383          0.691107       0.549676          0.612331  \n",
       "1          0.856995          0.732470       0.676969          0.703627  \n",
       "2          0.658501          0.618166       0.426918          0.505043  \n",
       "3          0.901564          0.767864       0.737374          0.752310  \n",
       "4          0.769231          0.725212       0.624898          0.671329  \n",
       "5          0.782219          0.770008       0.613423          0.682854  \n",
       "6          0.649055          0.720530       0.398641          0.513296  \n",
       "7          0.733865          0.723265       0.455674          0.559101  \n",
       "8          0.631103          0.898406       0.746689          0.815552  \n",
       "9          0.764423          0.735318       0.488422          0.586963  \n",
       "10         0.816853          0.738516       0.700167          0.718831  \n",
       "11         0.748842          0.775307       0.523021          0.624652  \n",
       "12         0.863080          0.820885       0.715923          0.764820  \n",
       "13         0.653846          0.621377       0.458556          0.527692  \n",
       "14         0.851162          0.847319       0.660609          0.742405  \n",
       "15         0.792380          0.703303       0.598365          0.646604  \n",
       "16         0.764894          0.701254       0.613160          0.654255  \n",
       "17         0.666058          0.725451       0.606365          0.660584  \n",
       "18         0.691351          0.691183       0.386629          0.495878  \n",
       "19         0.687459          0.717785       0.416247          0.526927  \n",
       "20         0.829072          0.850000       0.683136          0.757487  \n",
       "21         0.644800          0.759036       0.659193          0.705600  \n",
       "22         0.757994          0.751356       0.529364          0.621121  \n",
       "23         0.892850          0.802148       0.723547          0.760823  \n",
       "24         0.549451          0.722222       0.494856          0.587302  \n",
       "25         0.799919          0.798670       0.675038          0.731668  \n",
       "26         0.925694          0.719120       0.686853          0.702616  \n",
       "27         0.720247          0.752000       0.702541          0.726430  \n",
       "28         0.645380          0.692264       0.463516          0.555254  \n",
       "29         0.792023          0.764757       0.532005          0.627493  \n",
       "30         0.614135          0.805905       0.373570          0.510501  \n",
       "31         0.907508          0.693273       0.632674          0.661589  \n",
       "32         0.807936          0.716822       0.528966          0.608730  \n",
       "33         0.841675          0.721121       0.594316          0.651607  \n",
       "34         0.622529          0.787913       0.380420          0.513103  \n",
       "35         0.808494          0.759275       0.658190          0.705128  \n",
       "36         0.840059          0.668301       0.544607          0.600147  \n",
       "37         0.852006          0.623730       0.564092          0.592414  \n",
       "38         0.821918          0.576923       0.487365          0.528376  \n",
       "39         0.634689          0.756205       0.432012          0.549883  \n",
       "40         0.857614          0.739400       0.619584          0.674210  \n",
       "41         0.719571          0.831414       0.592580          0.691969  \n",
       "42         0.705882          0.760252       0.582126          0.659371  \n",
       "43         0.819022          0.773994       0.576037          0.660502  \n",
       "44         0.789687          0.708776       0.530799          0.607010  \n",
       "45         0.889015          0.798552       0.706510          0.749717  \n",
       "46         0.792272          0.743618       0.562931          0.640780  \n",
       "47         0.805643          0.803938       0.678468          0.735893  \n",
       "48         0.825337          0.762468       0.607407          0.676162  \n",
       "49         0.814571          0.794614       0.652549          0.716608  \n",
       "50         0.810732          0.687797       0.580214          0.629442  \n",
       "51         0.667974          0.676953       0.371786          0.479970  \n",
       "52         0.786517          0.661654       0.455172          0.539326  \n",
       "53         0.789443          0.781638       0.672359          0.722892  \n",
       "54         0.760715          0.750122       0.538264          0.626774  \n",
       "55         0.752953          0.647799       0.416330          0.506890  \n",
       "56         0.722208          0.753251       0.454436          0.566876  \n",
       "57         0.865361          0.772671       0.750302          0.761322  \n",
       "58         0.834447          0.712774       0.687819          0.700074  \n",
       "59         0.829111          0.768726       0.626764          0.690524  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_pitch_eval_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "each_pitch_eval_result = model_cnn_eval.eval_each_pitch(\n",
    "    multitone_start_path = initial.config['detect.model.multistart.weight'],\n",
    "    common_start_path = initial.config['detect.model.commonstart.weight'],\n",
    "    multitone_duration_path = initial.config['detect.model.multiduration.weight']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "each_pitch_eval_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "est_pitch_eval_result = model_cnn_eval.est_each_pitch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    count\n",
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       "[88 rows x 1 columns]"
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    "est_pitch_eval_result"
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