sound_eval.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### generating then ranking audio\n",
    "- load the CNN model (or some other ranker) from feature_extraction \n",
    "- genereate parameters and its corresponding audio \"randomly\"\n",
    "- rank the parameters using the ranker\n",
    "- save the parameters that made the sound along with the rankings to some dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torchvision\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "# generation imports\n",
    "from pippi.soundbuffer import SoundBuffer\n",
    "from pippi import dsp,fx\n",
    "import param_generation as pg\n",
    "import _pickle as pickle\n",
    "from IPython.display import Audio\n",
    "from feature_extraction.mir_utils import *\n",
    "###\n",
    "import torch.utils.data as utils\n",
    "import torchvision.transforms as transforms\n",
    "from PIL import Image\n",
    "###\n",
    "import scipy.stats as ss\n",
    "import common_vars as comv\n",
    "import imp\n",
    "import librosa\n",
    "import librosa.display\n",
    "import sys\n",
    "import csv\n",
    "sys.path.append(\"..\")\n",
    "\n",
    "import helpers\n",
    "imp.reload(helpers)\n",
    "imp.reload(comv)\n",
    "imp.reload(pg)\n",
    "from feature_extraction import pytorch_models as tm\n",
    "imp.reload(tm)\n",
    "from helpers import *\n",
    "\n",
    "# from common_vars import SR\n",
    "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "# save_path=\"./generated_sample_pack/\"\n",
    "# device = \"cpu\"\n",
    "# stack_size=3\n",
    "# BATCH_SIZE=1\n",
    "# NUM_BINS=100\n",
    "\n",
    "# classes=comv.classes\n",
    "# classes_ranked=comv.classes_ranked\n",
    "# cDict={v:i for i,v in enumerate(classes)}\n",
    "\n",
    "# drum_groups=['tom_low','snare','hihat_closed','rim','synth_noise','clap','kick','hihat_open','tom_mid']\n",
    "\n",
    "\n",
    "# drum_df=pd.DataFrame(columns=[\"name\",\"fc\",\"cnnlstm\",\"env+freq\",\"consensus\",\"stack_size\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import feature_extraction.pytorch_models as pm\n",
    "import imp\n",
    "\n",
    "imp.reload(pm)\n",
    "\n",
    "input_dim = 120\n",
    "seq_dim=100\n",
    "output_size = 5\n",
    "hidden_dim = 1000\n",
    "n_layers = 1\n",
    "\n",
    "    \n",
    "#########DVN#########\n",
    "#env model stuff\n",
    "env_model_dvn = tm.env_Model(D_in=10,H1=10,H2=5,H3=10,H4=2,H5=10,device=device)\n",
    "state='./feature_extraction/models/fc/env_dvn_95_35.pt'\n",
    "env_model_dvn.load_state_dict(torch.load(state))\n",
    "env_trans=tm.envTrans()\n",
    "env_model_dvn.to(device)\n",
    "\n",
    "\n",
    "# pitch model stuff\n",
    "fc_spec_model_dvn=tm.getFCSpecModel(D_in=20*20,H1=20,H2=10,H3=4,D_out=2)\n",
    "state='./feature_extraction/models/fc/spec_fc_dvn_96.pt'\n",
    "fc_spec_model_dvn.load_state_dict(torch.load(state))\n",
    "fc_spec_trans_dvn=tm.specTrans(20)\n",
    "fc_spec_model_dvn.to(device)\n",
    "\n",
    "#cnn lstm\n",
    "cnn_model_dvn=pm.CNNLSTM_dvn()\n",
    "state='./feature_extraction/models/cnn/clst_dvn_96_279.pt'\n",
    "cnn_model_dvn.load_state_dict(torch.load(state))\n",
    "cnn_model_dvn.to(device)\n",
    "cnn_model_dvn.to(device)\n",
    "#########DVD#########\n",
    "#fc dvd\n",
    "dvd_fc=pm.getFCSpecModel(D_in=20*20,H1=100,H2=50,H3=100,D_out=len(drum_groups))\n",
    "state='./feature_extraction/models/fc/spec_fc_dvd_81.pt'\n",
    "dvd_fc.load_state_dict(torch.load(state))\n",
    "dvd_fc.to(device)\n",
    "\n",
    "#cnn_dvd\n",
    "dvd_cnn=pm.CNNLSTM_dvd(len_out=len(drum_groups))\n",
    "state='./feature_extraction/models/cnn/clst_dvd_90_170.pt'\n",
    "dvd_cnn.load_state_dict(torch.load(state))\n",
    "dvd_cnn.to(device)\n",
    "\n",
    "#freq+env model\n",
    "freq_env_model_dvd = tm.env_freq_Model(D_in=60,H1=30,H2=10,H3=10,D_out=len(drum_groups))\n",
    "state='./feature_extraction/models/fc/freq_env_dvd_86_351.pt'\n",
    "freq_env_model_dvd.load_state_dict(torch.load(state))\n",
    "freq_env_trans=tm.freq_and_env_Trans(feat_mels=50,env_mels=1)\n",
    "freq_env_model_dvd.to(device)\n",
    "\n",
    "\n",
    "\n",
    "from IPython.display import Audio\n",
    "import librosa.display\n",
    "\n",
    "for g in drum_groups:\n",
    "    Path(save_path+\"/\"+g).mkdir(parents=True, exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/asalimi/miniconda3/lib/python3.7/site-packages/torch/nn/modules/container.py:117: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  input = module(input)\n",
      "/home/asalimi/mir/Synths_Stacks_Search/feature_extraction/pytorch_models.py:189: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  out=self.lsm(out)\n",
      "/home/asalimi/mir/Synths_Stacks_Search/feature_extraction/pytorch_models.py:167: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  out=self.lsm(out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env score 0.7013065218925476\n",
      "fc_spec 0.005326379556208849\n",
      "o_cnn_spec 0.00022549997083842754\n",
      "fc category synth_noise \n",
      "cnn category synth_noise \n",
      "env+freq snare\n",
      "total consensus synth_noise\n",
      "it took 26 iteration, stack size 2\n"
     ]
    },
    {
     "data": {
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       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x216 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def findDrum(stack_size=1,iteration=0):\n",
    "    out,params= stackMaker(stack_size)\n",
    "    a= memToAud(out)\n",
    "    try:\n",
    "        a=librosa.util.normalize(a)\n",
    "    except:\n",
    "        pass\n",
    "    transform_input= {\"signal\":torch.tensor(a,dtype=torch.float),\"label\":torch.tensor(0)}\n",
    "    env_feats=env_trans.call(transform_input)[\"feats\"].to(device)\n",
    "    freq_feats=freq_env_trans.call(transform_input)[\"feats\"].to(device)\n",
    "    pitch_feats=fc_spec_trans_dvn.call(transform_input)[\"feats\"].to(device)\n",
    "    spec_flat=torch.flatten(pitch_feats, start_dim=1)\n",
    "\n",
    "    (params,a,\n",
    "    outputEnvFreq,\n",
    "    outputCNNDVD,\n",
    "    outputFCDVD,\n",
    "    outputE,\n",
    "    outputSPECFC,\n",
    "    outputCNN,\n",
    "    env_feats,\n",
    "    freq_feats,pitch_feats)=(params,a,\n",
    "            freq_env_model_dvd(freq_feats),\n",
    "            dvd_cnn(pitch_feats.reshape(-1,1,20,20)),\n",
    "            dvd_fc(spec_flat),\n",
    "            env_model_dvn(env_feats),\n",
    "            fc_spec_model_dvn(spec_flat),\n",
    "            cnn_model_dvn(pitch_feats.reshape(-1,1,20,20)),\n",
    "            env_feats,\n",
    "            freq_feats,\n",
    "            pitch_feats)\n",
    "    \n",
    "    o_e,o_fc_spec,o_cnn_spec=outputE[0].item(), outputSPECFC[0][0].item() ,outputCNN[0][0].item()\n",
    "    tote=outputE[0].item()+outputSPECFC[0][0].item()\n",
    "\n",
    "    gfc=drum_groups[torch.argmax(outputFCDVD).item()]\n",
    "    gcnn=drum_groups[torch.argmax(outputCNNDVD).item()]\n",
    "    genvfreq=drum_groups[torch.argmax(outputEnvFreq).item()]\n",
    "    cat_consensus=drum_groups[torch.argmax(outputFCDVD+outputCNNDVD+outputEnvFreq)]\n",
    "    \n",
    "    spec_score=(o_fc_spec+o_cnn_spec)/2\n",
    "    if (o_e>=0.5 and o_fc_spec>=0.) or (o_e>0.5 and o_cnn_spec>0.7):\n",
    "        print(\"env score\",o_e)\n",
    "        print(\"fc_spec\",o_fc_spec)\n",
    "        print(\"o_cnn_spec\",o_cnn_spec)\n",
    "        print(\"fc category\",gfc,\"\\ncnn category\",gcnn,\"\\nenv+freq\",genvfreq)\n",
    "        print(\"total consensus\",cat_consensus)\n",
    "        \n",
    "#         drum_name=(\"%d-%s.wav\"%(stack_size,string_generator()))\n",
    "#         out.write(\"./generated_sample_pack/survey/%s\"%(drum_name,))\n",
    "        \n",
    "#         row=[drum_name,spec_score,gfc,gcnn,genvfreq,cat_consensus,stack_size,iteration]\n",
    "#         print(row)\n",
    "\n",
    "#         with open(save_path+\"/model_predictions.csv\", \"a\", newline='') as fp:\n",
    "#             wr = csv.writer(fp, dialect='excel')\n",
    "#             wr.writerow(row)\n",
    "            \n",
    "        return(a,env_feats,freq_feats,pitch_feats,stack_size,\"found\")\n",
    "    return (a,env_feats,freq_feats,pitch_feats,stack_size,\"not_found\")\n",
    "\n",
    "i=0\n",
    "j=0\n",
    "while i<10000000:\n",
    "    i=i+1\n",
    "    j=j+1\n",
    "    stack_size=np.random.choice([2])\n",
    "    (a,env_feats,freq_feats,pitch_feats,stack_size,results)=findDrum(stack_size,iteration=i)\n",
    "    if results!=\"not_found\":\n",
    "        print(\"it took %d iteration, stack size %d\"%(j,stack_size))\n",
    "        j=0\n",
    "        break\n",
    "\n",
    "fig = plt.figure(figsize=(12,3))\n",
    "fig = plt.figure(figsize=(20,4))\n",
    "ax = plt.subplot(1, 3,1)\n",
    "\n",
    "plt.plot(env_feats)\n",
    "ax = plt.subplot(1, 3,2)\n",
    "plt.plot(freq_feats[0:50])\n",
    "ax=plt.subplot(1,3,3)\n",
    "ft=pitch_feats\n",
    "ft=ft.numpy()\n",
    "librosa.display.specshow(ft[0])\n",
    "Audio(a,rate=sr, autoplay=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/asalimi/miniconda3/lib/python3.7/site-packages/torch/nn/modules/container.py:100: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  input = module(input)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'tom_low': 1.8427476788929198e-06,\n",
       " 'snare': 0.12403315305709839,\n",
       " 'hihat_closed': 5.930256286035274e-12,\n",
       " 'rim': 1.3170044610433251e-07,\n",
       " 'synth_noise': 0.3502587080001831,\n",
       " 'clap': 0.5189052224159241,\n",
       " 'kick': 6.689430706821955e-18,\n",
       " 'hihat_open': 0.006772371008992195,\n",
       " 'tom_mid': 2.8531714633572847e-05}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#used by GA for classsification\n",
    "class drum_classifier():\n",
    "    def __init__(self,device=\"cpu\"):\n",
    "        self.device=device\n",
    "    \n",
    "    def transformInput(self,a):\n",
    "        self.transform_input= {\"signal\":torch.tensor(a,dtype=torch.float),\"label\":torch.tensor(0)}\n",
    "    \n",
    "    def getEnvDVN(self):\n",
    "        #return prob of being drum\n",
    "        self.env_feats=env_trans.call(self.transform_input)[\"feats\"].to(self.device)\n",
    "        self.outputE=env_model_dvn(self.env_feats)\n",
    "        return self.outputE[0].item()\n",
    "        \n",
    "    def getEnvFreqDVD(self):\n",
    "        self.freq_feats=freq_env_trans.call(self.transform_input)[\"feats\"].to(self.device)\n",
    "        self.outputEnvFreq=freq_env_model_dvd(self.freq_feats)\n",
    "        self.envfreqDVD=drum_groups[torch.argmax(self.outputEnvFreq).item()]\n",
    "        \n",
    "        return dict(zip(drum_groups,self.outputEnvFreq.tolist()))\n",
    "    \n",
    "    def drumTypeEstimation(self,predictions):\n",
    "        #predictions is a array of prediction values, which are mapped to drum types\n",
    "        #returns most likely and least likely\n",
    "        likely_drum_index=torch.argmax(predictions).item()\n",
    "        unlikely_drum_index=torch.argmin(predictions).item()\n",
    "        return {\"likely\":[predictions[likely_drum_index],drum_groups[likely_drum_index]],\n",
    "                \"unlike\":[predictions[unlikely_drum_index],drum_groups[unlikely_drum_index]]}\n",
    "\n",
    "    def setAllFeats():\n",
    "        \n",
    "        self.pitch_feats=fc_spec_trans_dvn.call(self.transform_input)[\"feats\"].to(self.device)\n",
    "        self.spec_flat=torch.flatten(self.pitch_feats, start_dim=1)\n",
    "\n",
    "    def setAllOutputs():    \n",
    "        self.outputCNNDVD=dvd_cnn(self.pitch_feats.reshape(-1,1,20,20))\n",
    "        self.outputCNN= cnn_model_dvn(self.pitch_feats.reshape(-1,1,20,20))\n",
    "\n",
    "        self.outputFCDVD=dvd_fc(self.spec_flat)\n",
    "        self.outputSPECFC=fc_spec_model_dvn(self.spec_flat)\n",
    "\n",
    "        o_fc_spec,o_cnn_spec=outputSPECFC[0][0].item() ,outputCNN[0][0].item()\n",
    "        tote=outputE[0].item()+outputSPECFC[0][0].item()\n",
    "\n",
    "        gfc=drum_groups[torch.argmax(outputFCDVD).item()]\n",
    "        gcnn=drum_groups[torch.argmax(outputCNNDVD).item()]\n",
    "        cat_consensus=drum_groups[torch.argmax(outputFCDVD+outputCNNDVD+self.outputEnvFreq)]\n",
    "\n",
    "dc = drum_classifier()\n",
    "dc.transformInput(a)\n",
    "\n",
    "dc.getEnvDVN()\n",
    "dc.getEnvFreqDVD()"
   ]
  }
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