model_analysis_v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = \"0\"\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn.functional import interpolate \n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "import time\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",
    "from PIL import Image\n",
    "###\n",
    "import imp\n",
    "import librosa\n",
    "import librosa.display\n",
    "import sys\n",
    "import csv\n",
    "from scipy.io.wavfile import write\n",
    "import threading\n",
    "from IPython.display import clear_output\n",
    "sys.path.append(\"..\")\n",
    "\n",
    "from feature_extraction import resnet\n",
    "\n",
    "import helpers\n",
    "imp.reload(resnet)\n",
    "imp.reload(helpers)\n",
    "imp.reload(pg)\n",
    "from feature_extraction import pytorch_models as tm\n",
    "\n",
    "from helpers import *\n",
    "# from common_vars import SR\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "drum_types = ['hat', 'kick', 'shake', 'snare']\n",
    "\n",
    "# save_path=\"./generated_sample_pack/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# good models\n",
    "# cnet_dvn = tm.Transformer_DVN(attention_dropout=0.3,d_model=100,heads=20,encoding_layers=12,pool_dim=2,pct_start=0.05,max_lr=1e-4,max_momentum=0.95,epochs=50)\n",
    "# cnet_dvn = cnet_dvn.load_from_checkpoint(\n",
    "#     \"models/transformer/dvn:epoch=10-val_auc=0.9987-val_accuracy=0.988-val_loss=0.039.ckpt\")\n",
    "# cnet_dvd = tm.Transformer_DVD(attention_dropout=0.3,d_model=100,heads=20,encoding_layers=12,pool_dim=2,pct_start=0.05,max_lr=1e-4,max_momentum=0.95,epochs=50)\n",
    "# cnet_dvd = cnet_dvd.load_from_checkpoint(\n",
    "#     \"models/transformer/dvd:epoch=41-val_loss=0.247-val_auc=0.9712-val_accuracy=0.8948.ckpt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp.reload(tm)\n",
    "cnet_dvn = tm.Transformer_DVN(attention_dropout=0.3,d_model=120,heads=30,encoding_layers=12,pool_dim=2,)\n",
    "cnet_dvn = cnet_dvn.load_from_checkpoint(\n",
    "    \"models/transformer/DVNepoch=41-val_accuracy=0.991-val_loss=0.048.ckpt\")\n",
    "\n",
    "cnet_dvd = tm.Transformer_DVD(attention_dropout=0.3,d_model=100,heads=20,encoding_layers=12,pool_dim=1,pct_start=0.05,max_lr=1e-4,max_momentum=0.95,epochs=50)\n",
    "cnet_dvd = cnet_dvd.load_from_checkpoint(\n",
    "    \"models/transformer/DVDepoch=59-val_accuracy=0.917-val_auc=0.9779-val_loss=0.327.ckpt\")\n",
    "cnet_dvn.to(device)\n",
    "cnet_dvd.to(device)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hat tensor([[-6.9360]])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SR = 44100\n",
    "# make sound and evaluate\n",
    "def make_and_eval(stack_size = 1):\n",
    "    out,params = StackMaker(stack_size)\n",
    "    a = memToAud(out)\n",
    "    try:\n",
    "        signal = torch.tensor(librosa.resample(a.reshape(-1),SR,SR//2))[0:SR//4]\n",
    "    except Exception as e:\n",
    "        signal = torch.tensor(np.zeros([1,1,SR//4])).float()\n",
    "    with torch.no_grad():\n",
    "        cnet_dvn.eval()\n",
    "        ps_dvn = cnet_dvn(signal.reshape(1,-1).to(device))\n",
    "        ps_dvd = cnet_dvd(signal.reshape(1,-1).to(device))\n",
    "        \n",
    "#         print(dict(zip(drum_types,ps_dvd[0].numpy())))\n",
    "    return a,signal,ps_dvn.cpu(),ps_dvd.cpu()\n",
    "\n",
    "synth_out,a,ps,ps_dvd = make_and_eval()\n",
    "print(drum_types[np.argmax(ps_dvd)],ps)\n",
    "\n",
    "a = a.reshape([-1])\n",
    "\n",
    "# print(len(a))\n",
    "Audio(a,rate=SR//2,autoplay=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 41 attempts\n",
      "found a  snare with probability: 2.747054\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source 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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loop until found\n",
    "\n",
    "biggest_p = []\n",
    "best_sounds = {}\n",
    "for i in range(250):\n",
    "    synth_out,a,ps,ps_dvd = make_and_eval(1)\n",
    "    print(\"number of attempts:\",i,end=\"\\r\")\n",
    "    if ps[0].item() > 1 and a.sum():\n",
    "        clear_output()\n",
    "        print(\"after %d attempts\"%i)\n",
    "        print(\"found a \",drum_types[np.argmax(ps_dvd)],\"with probability: %f\"%ps[0].item())\n",
    "        break\n",
    "    biggest_p.append(ps[0])\n",
    "a = a.reshape([-1]).cpu().numpy()[0:SR//4]\n",
    "anorm =  librosa.util.normalize(a)\n",
    "Audio(anorm[0:SR//6].reshape([-1]),rate=SR//2,autoplay=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7504149079322815 2542   \n",
      " type: hat\n",
      "6.190188884735107 2608    \n",
      " type: snare\n",
      "2.3530147075653076 2923  2 \n",
      " type: snare\n",
      "6.190117359161377 2935 4 \n",
      " type: kick\n",
      "4.690552711486816 3001    \n",
      " type: snare\n",
      "1.1748387813568115 3022  \n",
      " type: hat\n",
      "6.189702033996582 3078    \n",
      " type: hat\n",
      "2.9681973457336426 3152  \n",
      " type: kick\n",
      "1.134226679801941 3218    \n",
      " type: kick\n",
      "6.1899614334106445 3275  \n",
      " type: hat\n",
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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loop until found\n",
    "\n",
    "biggest_p = []\n",
    "best_sounds = {}\n",
    "for i in range(5000):\n",
    "    synth_out,a,ps,ps_dvd = make_and_eval(np.random.choice([1,3,5]))\n",
    "    print(ps[0].item(),i,end=\" \\r\")\n",
    "#     print(drum_types[np.argmax(ps_dvd)])\n",
    "    if ps[0] > 0.5 and a.sum():\n",
    "        write('loops/kits/%.4f:%s.wav'%(ps[0],drum_types[np.argmax(ps_dvd)]), \n",
    "                                      44100, synth_out[0:SR//2])\n",
    "        print(\"\\n type:\",drum_types[np.argmax(ps_dvd)])\n",
    "    biggest_p.append(ps[0])\n",
    "# a = synth_out.reshape([-1])[0:SR//4]\n",
    "a = a.reshape([-1]).cpu().numpy()[0:SR//4]\n",
    "anorm =  librosa.util.normalize(a)\n",
    "Audio(anorm[0:SR//6].reshape([-1]),rate=SR//2,autoplay=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source 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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Audio(anorm[0:SR//4].reshape([-1]),rate=SR//2,autoplay=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# getting the best sound out of n tries\n",
    "\n",
    "best_sounds = {}\n",
    "best_sounds = dict.fromkeys(drum_types)\n",
    "for k in drum_types:\n",
    "    best_sounds[k] = (0,[0])\n",
    "def f():\n",
    "    for i in range(100):\n",
    "        print(i,end=\"\\r\")\n",
    "        synth_out,a,ps,ps_dvd = make_and_eval(4)\n",
    "        if ps[0]>-10:\n",
    "            t,p = drum_types[np.argmax(ps_dvd)],ps_dvd[0][np.argmax(ps_dvd)]\n",
    "            if best_sounds[t][0]<p:\n",
    "                best_sounds[t] = (p,a)\n",
    "    return True\n",
    "x = f()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# # playing a loop while drum kit is updated in seperate thread\n",
    "drum_kit = {}\n",
    "drum_kit = dict.fromkeys(drum_types)\n",
    "counts = dict.fromkeys(drum_types)\n",
    "for k in counts:\n",
    "    counts[k] = 0\n",
    "    \n",
    "bpm = 120\n",
    "num_beats = 8\n",
    "\n",
    "def make_beat(drum_kit):\n",
    "    loop = np.zeros(num_beats*30*SR//bpm ) # init silent loop\n",
    "    loop = add_to_loop(loop,\"kick\",0,drum_kit)\n",
    "    loop = add_to_loop(loop,\"hat\",1,drum_kit)\n",
    "    loop = add_to_loop(loop,\"snare\",2,drum_kit)\n",
    "    loop = add_to_loop(loop,\"kick\",3,drum_kit)\n",
    "    loop = add_to_loop(loop,\"hat\",4,drum_kit)\n",
    "    loop = add_to_loop(loop,\"snare\",5,drum_kit)\n",
    "    loop = add_to_loop(loop,\"kick\",6,drum_kit)\n",
    "    loop = add_to_loop(loop,\"shake\",7,drum_kit)\n",
    "    return loop\n",
    "\n",
    "# bpm = 300\n",
    "# num_beats = 7\n",
    "# def make_beat(drum_kit):\n",
    "#     loop = np.zeros(num_beats*60*SR//bpm ) # init silent loop\n",
    "#     loop = add_to_loop(loop,\"kick\",0,drum_kit)\n",
    "#     loop = add_to_loop(loop,\"hat\",1,drum_kit)\n",
    "#     loop = add_to_loop(loop,\"kick\",2,drum_kit)\n",
    "#     loop = add_to_loop(loop,\"snare\",4,drum_kit)\n",
    "#     loop = add_to_loop(loop,\"shake\",5,drum_kit)\n",
    "#     return loop\n",
    "\n",
    "def add_to_loop(loop,drum,beat,drum_kit):\n",
    "        # given a loop, it puts a drum at beat n\n",
    "        beat_position = int(SR*beat*30//bpm)\n",
    "        try:\n",
    "            drum_sound =  drum_kit[drum][1][0:SR//4]\n",
    "        except:\n",
    "            drum_sound = np.zeros(SR)\n",
    "        start,stop = beat_position,min(beat_position+len(drum_sound),len(loop))\n",
    "        loop[start:stop] += drum_sound[0:stop-start]\n",
    "        return loop\n",
    "\n",
    "def play_loop(loop,sr=SR):\n",
    "        ipd.set_matplotlib_close(close=True)\n",
    "        ipd.display(ipd.Audio(loop, rate=sr,autoplay=True))\n",
    "        loop_length = len(loop)/sr\n",
    "        time.sleep(loop_length)\n",
    "        clear_output()\n",
    "\n",
    "\n",
    "def kit_updater(drum_kit,counts,tries=50,):\n",
    "    # updates a drum kit for n tries\n",
    "    loops_made = 0\n",
    "    for i in range(tries):\n",
    "        synth_out,a,ps,ps_dvd =  make_and_eval(4)\n",
    "        if ps[0] > 0 :\n",
    "            t,p = drum_types[np.argmax(ps_dvd)],ps_dvd[0][np.argmax(ps_dvd)]\n",
    "#             a = mix_down(a,t)\n",
    "            drum_kit[t] = (p,synth_out)\n",
    "            counts[t] += 1\n",
    "            loop = make_beat(drum_kit)\n",
    "#             write('loops/mantra/loop:%d-attempt:%d.wav'%(loops_made,i), 44100, loop)\n",
    "            loops_made+=1\n",
    "        if loops_made>100:\n",
    "            break\n",
    "\n",
    "def morph_kit():\n",
    "    kit_updater_thread = threading.Thread(target=kit_updater,args=(drum_kit,counts,1000))\n",
    "    kit_updater_thread.start()\n",
    "    while kit_updater_thread.is_alive():\n",
    "        print(drum_kit)\n",
    "        loop = make_beat(drum_kit)\n",
    "        ipd.set_matplotlib_close(close=True)\n",
    "        ipd.display(ipd.Audio(loop, rate=SR,autoplay=True))\n",
    "        loop_length = len(loop)/SR\n",
    "        time.sleep(loop_length)\n",
    "        clear_output()\n",
    "# loop = make_beat()\n",
    "# play_loop(loop)\n",
    "morph_kit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kit_updater(drum_kit,tries=10,):\n",
    "    for i in range(tries):\n",
    "        a,a_ds,ps,pss,pss_dvd= make_and_eval(1)\n",
    "        if pss[0]>pss[1]:\n",
    "            t,p = drum_types[np.argmax(pss_dvd)],pss_dvd[np.argmax(pss_dvd)]\n",
    "            drum_kit[t] = (p,a)\n",
    "        \n",
    "def check_kit():\n",
    "    kit_updater_thread = threading.Thread(target=kit_updater,args=(drum_kit,500))\n",
    "    kit_updater_thread.start()\n",
    "    while kit_updater_thread.is_alive():\n",
    "        time.sleep(1)\n",
    "check_kit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Concatenation operation is not implemented for NumPy arrays, use np.concatenate() instead. Please do not rely on this error; it may not be given on all Python implementations.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-4d4e3c2a7e20>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mloop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_to_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"shake\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdrum_kit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mloop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mloop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_beat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_sounds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mipd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mipd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAudio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mSR\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mautoplay\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-12-4d4e3c2a7e20>\u001b[0m in \u001b[0;36mmake_beat\u001b[0;34m(drum_kit)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_beat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrum_kit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mloop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_beats\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mSR\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0mbpm\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;31m# init silent loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mloop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_to_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"kick\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdrum_kit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mloop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_to_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"hat\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdrum_kit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mloop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_to_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"kick\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdrum_kit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-11-55cd43a7f548>\u001b[0m in \u001b[0;36madd_to_loop\u001b[0;34m(loop, drum, beat, drum_kit)\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0mdrum_sound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbeat_position\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeat_position\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrum_sound\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m         \u001b[0mloop\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mdrum_sound\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mloop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Concatenation operation is not implemented for NumPy arrays, use np.concatenate() instead. Please do not rely on this error; it may not be given on all Python implementations."
     ]
    }
   ],
   "source": [
    "bpm = 300\n",
    "num_beats = 7\n",
    "def make_beat(drum_kit):\n",
    "    loop = np.zeros(num_beats*60*SR//bpm ) # init silent loop\n",
    "    loop = add_to_loop(loop,\"kick\",0,drum_kit)\n",
    "    loop = add_to_loop(loop,\"hat\",1,drum_kit)\n",
    "    loop = add_to_loop(loop,\"kick\",2,drum_kit)\n",
    "    loop = add_to_loop(loop,\"snare\",4,drum_kit)\n",
    "    loop = add_to_loop(loop,\"shake\",5,drum_kit)\n",
    "    return loop\n",
    "loop = make_beat(best_sounds)\n",
    "ipd.display(ipd.Audio(loop, rate=SR,autoplay=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "likes = pd.read_csv(\"csvs/drum_track_abram.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54, 55)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "likes.columns=[\"loop\",\"bad\",\"c1\",\"c2\",\"c3\"]\n",
    "likes[0:99][\"bad\"].sum(),likes[99:][\"bad\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mix_down(a,cat):\n",
    "    a = librosa.util.normalize(a)\n",
    "    if cat==\"kick\":\n",
    "        a = helpers.butter_bandpass_filter(a,30,8000,48000,order=2)\n",
    "        a = librosa.util.normalize(a)\n",
    "    if cat == \"shake\":\n",
    "        a = helpers.butter_bandpass_filter(a,10000,15000,48000,order=2)\n",
    "        a = librosa.util.normalize(a)\n",
    "        a = a * 0.6\n",
    "    if cat == \"hat\":\n",
    "        a = helpers.butter_bandpass_filter(a,8000,15000,48000,order=2)\n",
    "        a = librosa.util.normalize(a)\n",
    "        a = a * 0.9\n",
    "    if cat == \"snare\":\n",
    "        a = helpers.butter_bandpass_filter(a,300,12000,48000,order=2)\n",
    "        a = librosa.util.normalize(a)\n",
    "        a = a * 0.9\n",
    "        a = a.reshape([-1])\n",
    "    return a"
   ]
  }
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