02_predict_with_pretrained_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict with Pre-trained Model\n",
    "  \n",
    "This notebook shows how to load a pre-trained model and to use this for predicting multi-pitch estimates of an unknown audio file.\n",
    "\n",
    "&copy; Christof Weiss and Geoffroy Peeters, Télécom Paris 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "basepath = os.path.abspath(os.path.dirname(os.path.dirname('.')))\n",
    "sys.path.append(basepath)\n",
    "import numpy as np, os, scipy, scipy.spatial, matplotlib.pyplot as plt, IPython.display as ipd\n",
    "from numba import jit\n",
    "import librosa\n",
    "import libfmp.b, libfmp.c3, libfmp.c5\n",
    "import pandas as pd, pickle, re\n",
    "from numba import jit\n",
    "import torch.utils.data\n",
    "import torch.nn as nn\n",
    "import libdl.data_preprocessing\n",
    "from libdl.data_loaders import dataset_context, dataset_context_segm\n",
    "from libdl.nn_models import basic_cnn_segm_sigmoid, basic_cnn_segm_logsoftmax, basic_cnn_segm_blank_logsoftmax\n",
    "from libdl.data_preprocessing import compute_hopsize_cqt, compute_hcqt, compute_efficient_hcqt, compute_annotation_array_nooverlap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Specify and load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "basic_cnn_segm_blank_logsoftmax(\n",
       "  (layernorm): LayerNorm((6, 216), eps=1e-05, elementwise_affine=True)\n",
       "  (conv1): Sequential(\n",
       "    (0): Conv2d(6, 20, kernel_size=(15, 15), stride=(1, 1), padding=(7, 7))\n",
       "    (1): LeakyReLU(negative_slope=0.3)\n",
       "    (2): MaxPool2d(kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), dilation=1, ceil_mode=False)\n",
       "    (3): Dropout(p=0.2, inplace=False)\n",
       "  )\n",
       "  (conv2): Sequential(\n",
       "    (0): Conv2d(20, 20, kernel_size=(3, 3), stride=(1, 3), padding=(1, 0))\n",
       "    (1): LeakyReLU(negative_slope=0.3)\n",
       "    (2): MaxPool2d(kernel_size=(13, 1), stride=(1, 1), padding=(6, 0), dilation=1, ceil_mode=False)\n",
       "    (3): Dropout(p=0.2, inplace=False)\n",
       "  )\n",
       "  (conv3): Sequential(\n",
       "    (0): Conv2d(20, 10, kernel_size=(75, 1), stride=(1, 1))\n",
       "    (1): LeakyReLU(negative_slope=0.3)\n",
       "    (2): Dropout(p=0.2, inplace=False)\n",
       "  )\n",
       "  (conv4): Sequential(\n",
       "    (0): Conv2d(10, 1, kernel_size=(1, 1), stride=(1, 1))\n",
       "    (1): LeakyReLU(negative_slope=0.3)\n",
       "    (2): Dropout(p=0.2, inplace=False)\n",
       "  )\n",
       "  (conv5a): Conv2d(1, 2, kernel_size=(1, 1), stride=(1, 1))\n",
       "  (conv5b): Conv2d(1, 2, kernel_size=(1, 72), stride=(1, 1))\n",
       "  (logsoftmax7): LogSoftmax(dim=1)\n",
       ")"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir_models = os.path.join(basepath, 'models_pretrained')\n",
    "\n",
    "# Set model parameters\n",
    "num_octaves_inp = 6\n",
    "num_output_bins, min_pitch = 72, 24\n",
    "model_params = {'n_chan_input': 6,\n",
    "                'n_chan_layers': [20,20,10,1],\n",
    "                'n_ch_out': 2,\n",
    "                'n_bins_in': num_octaves_inp*12*3,\n",
    "                'n_bins_out': num_output_bins,\n",
    "                'a_lrelu': 0.3,\n",
    "                'p_dropout': 0.2\n",
    "                }\n",
    "mp = model_params\n",
    "\n",
    "\n",
    "### Select model according to paper's experiments (uncomment as desired) ##############################\n",
    "\n",
    "\n",
    "# Experiment 1 (Table 2) - Loss and Model variants ####################################################\n",
    "\n",
    "# Strongly-aligned training (BCE loss):\n",
    "# fn_model, model = 'exp112aS_traintest_schubert_aligned_pitch_nooverlap_segmmodel.pt', basic_cnn_segm_sigmoid(n_chan_input=mp['n_chan_input'], n_chan_layers=mp['n_chan_layers'], n_bins_in=mp['n_bins_in'], n_bins_out=mp['n_bins_out'], a_lrelu=mp['a_lrelu'], p_dropout=mp['p_dropout'])\n",
    "\n",
    "# Separable CTC (SCTC) loss:\n",
    "# fn_model, model = 'exp118b_traintest_schubert_sctcthreecomp_pitch.pt', basic_cnn_segm_logsoftmax(n_chan_input=mp['n_chan_input'], n_chan_layers=mp['n_chan_layers'], n_ch_out=3, n_bins_in=mp['n_bins_in'], n_bins_out=mp['n_bins_out'], a_lrelu=mp['a_lrelu'], p_dropout=mp['p_dropout'])\n",
    "\n",
    "# Non-Epsilon MCTC (MCTC:NE) loss:\n",
    "# fn_model, model = 'exp118d_traintest_schubert_mctcnethreecomp_pitch.pt', basic_cnn_segm_logsoftmax(n_chan_input=mp['n_chan_input'], n_chan_layers=mp['n_chan_layers'], n_ch_out=3, n_bins_in=mp['n_bins_in'], n_bins_out=mp['n_bins_out'], a_lrelu=mp['a_lrelu'], p_dropout=mp['p_dropout'])\n",
    "\n",
    "# MCTC with epsilon (MCTC:WE) loss:\n",
    "# fn_model, model = 'exp118e_traintest_schubert_mctcwe_pitch.pt', basic_cnn_segm_blank_logsoftmax(n_chan_input=mp['n_chan_input'], n_chan_layers=mp['n_chan_layers'], n_ch_out=mp['n_ch_out'], n_bins_in=mp['n_bins_in'], n_bins_out=mp['n_bins_out'], a_lrelu=mp['a_lrelu'], p_dropout=mp['p_dropout'])\n",
    "\n",
    "\n",
    "\n",
    "# Experiment 2 (Section 3.2) - Train/test on common datasets ##########################################\n",
    "\n",
    "# Train/test MusicNet with strongly-aligned training:\n",
    "# fn_model, model = 'exp121a_traintest_musicnet_mctcwe_pitch_basiccnn.pt', basic_cnn_segm_blank_logsoftmax(n_chan_input=mp['n_chan_input'], n_chan_layers=mp['n_chan_layers'], n_ch_out=mp['n_ch_out'], n_bins_in=mp['n_bins_in'], n_bins_out=mp['n_bins_out'], a_lrelu=mp['a_lrelu'], p_dropout=mp['p_dropout'])\n",
    "\n",
    "# Train/test MusicNet with MCTC loss:\n",
    "# fn_model, model = 'exp121cS_traintest_musicnet_aligned_pitch_basiccnn_segmmodel.pt', basic_cnn_segm_sigmoid(n_chan_input=mp['n_chan_input'], n_chan_layers=mp['n_chan_layers'],     n_bins_in=mp['n_bins_in'], n_bins_out=mp['n_bins_out'], a_lrelu=mp['a_lrelu'], p_dropout=mp['p_dropout'])\n",
    "\n",
    "# Train/test MAESTRO with strongly-aligned training:\n",
    "# fn_model, model = 'exp122a_traintest_maestro_mctcwe_pitch_basiccnn.pt', basic_cnn_segm_blank_logsoftmax(n_chan_input=mp['n_chan_input'], n_chan_layers=mp['n_chan_layers'], n_ch_out=mp['n_ch_out'], n_bins_in=mp['n_bins_in'], n_bins_out=mp['n_bins_out'], a_lrelu=mp['a_lrelu'], p_dropout=mp['p_dropout'])\n",
    "\n",
    "# Train/test MAESTRO with MCTC loss:\n",
    "# fn_model, model = 'exp122cS_traintest_maestro_aligned_pitch_basiccnn_segmmodel.pt', basic_cnn_segm_sigmoid(n_chan_input=mp['n_chan_input'], n_chan_layers=mp['n_chan_layers'],     n_bins_in=mp['n_bins_in'], n_bins_out=mp['n_bins_out'], a_lrelu=mp['a_lrelu'], p_dropout=mp['p_dropout'])\n",
    "\n",
    "\n",
    "\n",
    "# Experiment 3 (Figure 2) - Cross-dataset experiment ###################################################\n",
    "\n",
    "# Train MusicNet & MAESTRO, test others (aligned / MCTC):\n",
    "fn_model, model = 'exp123a_trainmaestromunet_testmix_mctcwe_pitch_basiccnn.pt', basic_cnn_segm_blank_logsoftmax(n_chan_input=mp['n_chan_input'], n_chan_layers=mp['n_chan_layers'], n_ch_out=mp['n_ch_out'], n_bins_in=mp['n_bins_in'], n_bins_out=mp['n_bins_out'], a_lrelu=mp['a_lrelu'], p_dropout=mp['p_dropout'])\n",
    "# fn_model, model = 'exp123cS_trainmaestromunet_testmix_aligned_pitch_basiccnn_segmmodel.pt', basic_cnn_segm_sigmoid(n_chan_input=mp['n_chan_input'], n_chan_layers=mp['n_chan_layers'],     n_bins_in=mp['n_bins_in'], n_bins_out=mp['n_bins_out'], a_lrelu=mp['a_lrelu'], p_dropout=mp['p_dropout'])\n",
    "\n",
    "# Test MusicNet, train others (aligned / MCTC):\n",
    "# fn_model, model = 'exp124a_trainmix_testmusicnet_mctcwe_pitch_basiccnn.pt', basic_cnn_segm_blank_logsoftmax(n_chan_input=mp['n_chan_input'], n_chan_layers=mp['n_chan_layers'], n_ch_out=mp['n_ch_out'], n_bins_in=mp['n_bins_in'], n_bins_out=mp['n_bins_out'], a_lrelu=mp['a_lrelu'], p_dropout=mp['p_dropout'])\n",
    "# fn_model, model = 'exp124cS_trainmix_testmusicnet_aligned_pitch_basiccnn_segmmodel.pt', basic_cnn_segm_sigmoid(n_chan_input=mp['n_chan_input'], n_chan_layers=mp['n_chan_layers'],     n_bins_in=mp['n_bins_in'], n_bins_out=mp['n_bins_out'], a_lrelu=mp['a_lrelu'], p_dropout=mp['p_dropout'])\n",
    "\n",
    "# Test MAESTRO, train others (aligned / MCTC):\n",
    "# fn_model, model = 'exp125a_trainmix_testmaestro_mctcwe_pitch_basiccnn.pt', basic_cnn_segm_blank_logsoftmax(n_chan_input=mp['n_chan_input'], n_chan_layers=mp['n_chan_layers'], n_ch_out=mp['n_ch_out'], n_bins_in=mp['n_bins_in'], n_bins_out=mp['n_bins_out'], a_lrelu=mp['a_lrelu'], p_dropout=mp['p_dropout'])\n",
    "# fn_model, model = 'exp125cS_trainmix_testmaestro_aligned_pitch_basiccnn_segmmodel.pt', basic_cnn_segm_sigmoid(n_chan_input=mp['n_chan_input'], n_chan_layers=mp['n_chan_layers'],     n_bins_in=mp['n_bins_in'], n_bins_out=mp['n_bins_out'], a_lrelu=mp['a_lrelu'], p_dropout=mp['p_dropout'])\n",
    "\n",
    "\n",
    "########################################################################################################\n",
    "\n",
    "path_trained_model = os.path.join(dir_models, fn_model)\n",
    "\n",
    "model.load_state_dict(torch.load(path_trained_model, map_location=torch.device('cpu')))\n",
    "\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Load Test Audio and compute HCQT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = 22050\n",
    "audio_folder = os.path.join(basepath, 'data', 'Schubert_Winterreise', 'audio_wav')\n",
    "\n",
    "# fn_audio = 'Schubert_D911-02_HU33.wav'\n",
    "# fn_audio = 'Schubert_D911-02_SC06.wav'\n",
    "# fn_audio = 'Schubert_D911-23_HU33.wav'\n",
    "fn_audio = 'Schubert_D911-23_SC06.wav'\n",
    "\n",
    "path_audio = os.path.join(audio_folder, fn_audio)\n",
    "f_audio, fs_load = librosa.load(path_audio, sr=fs)\n",
    "\n",
    "bins_per_semitone = 3\n",
    "num_octaves = 6\n",
    "n_bins = bins_per_semitone*12*num_octaves\n",
    "num_harmonics = 5\n",
    "num_subharmonics = 1\n",
    "center_bins=True\n",
    "\n",
    "f_hcqt, fs_hcqt, hopsize_cqt = compute_efficient_hcqt(f_audio, fs=22050, fmin=librosa.note_to_hz('C1'), fs_hcqt_target=50, \\\n",
    "                                                    bins_per_octave=bins_per_semitone*12, num_octaves=num_octaves, \\\n",
    "                                                    num_harmonics=num_harmonics, num_subharmonics=num_subharmonics, center_bins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Predict Pitches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7680, 72)\n"
     ]
    }
   ],
   "source": [
    "# Set test parameters\n",
    "test_params = {'batch_size': 1,\n",
    "              'shuffle': False,\n",
    "              'num_workers': 1\n",
    "              }\n",
    "device = 'cpu'\n",
    "\n",
    "test_dataset_params = {'context': 75,\n",
    "                       'seglength': 10,\n",
    "                       'stride': 10,\n",
    "                       'compression': 10\n",
    "                      }\n",
    "half_context = test_dataset_params['context']//2\n",
    "\n",
    "inputs = np.transpose(f_hcqt, (2, 1, 0))\n",
    "targets = np.zeros(inputs.shape[1:]) # need dummy targets to use dataset object\n",
    "\n",
    "inputs_context = torch.from_numpy(np.pad(inputs, ((0, 0), (half_context, half_context+1), (0, 0))))\n",
    "targets_context = torch.from_numpy(np.pad(targets, ((half_context, half_context+1), (0, 0))))\n",
    "\n",
    "test_set = dataset_context_segm(inputs_context, targets_context, test_dataset_params)\n",
    "test_generator = torch.utils.data.DataLoader(test_set, **test_params)\n",
    "\n",
    "pred_tot = np.zeros((0, num_output_bins))\n",
    "\n",
    "\n",
    "### Use this variant for MCTC loss ###############################################################\n",
    "for test_batch, test_labels in test_generator:\n",
    "    # Model computations\n",
    "    y_pred = model(test_batch)\n",
    "    pred_log = torch.squeeze(y_pred.to('cpu')).detach().numpy()\n",
    "    pred_tot = np.append(pred_tot, pred_log[1, :, 1:], axis=0)\n",
    "predictions = np.exp(pred_tot)\n",
    "\n",
    "\n",
    "### Use this variant for aligned training (BCE loss) ############################################    \n",
    "# for test_batch, test_labels in test_generator:\n",
    "#     # Model computations\n",
    "#     y_pred = model(test_batch)\n",
    "#     # pred_log = torch.squeeze(torch.squeeze(y_pred.to('cpu'),2),1).detach().numpy()\n",
    "#     pred_log = torch.squeeze(y_pred.to('cpu')).detach().numpy()\n",
    "#     pred_tot = np.append(pred_tot, pred_log, axis=0)\n",
    "# predictions = pred_tot\n",
    "\n",
    "print(predictions.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Plot Predictions and Annotations (Ground truth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_sec = 25\n",
    "show_sec = 50\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, gridspec_kw={'width_ratios': [1, 0.05]}, figsize=(10, 3.5))\n",
    "im = libfmp.b.plot_matrix(predictions.T[:, int(start_sec*fs_hcqt):int(show_sec*fs_hcqt)], Fs=fs_hcqt, ax=ax, cmap='gray_r', ylabel='MIDI pitch')\n",
    "ax[0].set_yticks(np.arange(0, 73, 12))\n",
    "ax[0].set_yticklabels([str(24+12*octave) for octave in range(0, num_octaves+1)])\n",
    "ax[0].set_title('Multi-pitch predictions')\n",
    "ax[0].set_xticklabels(np.arange(start_sec-5, show_sec+5, 5))\n",
    "ax[1].set_ylim([0, 1])\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "# annot_folder = os.path.join(basepath, 'data', 'Schubert_Winterreise', 'ann_audio_note')\n",
    "# fn_annot = os.path.join(annot_folder, fn_audio[:-4]+'.csv')\n",
    "\n",
    "# df = pd.read_csv(fn_annot, sep=';', skiprows=1, header=None)\n",
    "# note_events = df.to_numpy()[:, :3]\n",
    "        \n",
    "# f_annot_pitch = compute_annotation_array_nooverlap(note_events.copy(), f_hcqt, fs_hcqt, annot_type='pitch', shorten=1.0)\n",
    "\n",
    "# fig, ax = plt.subplots(1, 2, gridspec_kw={'width_ratios': [1, 0.05]}, figsize=(10, 3.5))\n",
    "# cfig, cax, cim = libfmp.b.plot_matrix(f_annot_pitch[24:97, int(start_sec*fs_hcqt):int(show_sec*fs_hcqt)], ax=ax, Fs=fs_hcqt, cmap='gray_r', ylabel='MIDI pitch')\n",
    "# plt.ylim([0, 73])\n",
    "# ax[0].set_yticks(np.arange(0, 73, 12))\n",
    "# ax[0].set_yticklabels([str(24+12*octave) for octave in range(0, num_octaves+1)])\n",
    "# ax[0].set_title('Multi-pitch annotations (piano roll)')\n",
    "# ax[0].set_xticklabels(np.arange(start_sec-5, show_sec+5, 5))\n",
    "# ax[1].set_ylim([0, 1])\n",
    "# plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Compare with pre-computed predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7600, 72)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_pred = os.path.join('predictions', 'exp123a_trainmaestromunet_testmix_mctcwe_pitch_basiccnn', 'Schubert_D911-23_SC06.npy')\n",
    "\n",
    "pred_precomputed = np.load(path_pred)\n",
    "print(pred_precomputed.shape)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, gridspec_kw={'width_ratios': [1, 0.05]}, figsize=(10, 3.5))\n",
    "im = libfmp.b.plot_matrix(pred_precomputed.T[:, int(start_sec*fs_hcqt):int(show_sec*fs_hcqt)], Fs=fs_hcqt, ax=ax, cmap='gray_r', ylabel='MIDI pitch')\n",
    "ax[0].set_yticks(np.arange(0, 73, 12))\n",
    "ax[0].set_yticklabels([str(24+12*octave) for octave in range(0, num_octaves+1)])\n",
    "ax[0].set_title('Multi-pitch predictions')\n",
    "ax[0].set_xticklabels(np.arange(start_sec-5, show_sec+5, 5))\n",
    "ax[1].set_ylim([0, 1])\n",
    "plt.tight_layout()"
   ]
  }
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