{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchaudio\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import glob\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_spectral_centroid(audio_tensor, sample_rate, n_fft=4096, hop_size=2048):\n",
    "    # Split into frames\n",
    "    frame_length = n_fft\n",
    "    hop_length = hop_size\n",
    "    frames = audio_tensor.unfold(1, frame_length, hop_length)\n",
    "    # Compute FFT for each frame\n",
    "    spectrum = torch.fft.rfft(frames.squeeze(0))  # [num_frames, n_fft//2 + 1]\n",
    "    freqs = torch.fft.rfftfreq(n_fft, d=1/sample_rate)  # [n_fft//2 + 1]\n",
    "    \n",
    "    # Compute magnitudes for each frame\n",
    "    magnitudes =torch.abs(spectrum)  # [num_frames, n_fft//2 + 1]\n",
    "    \n",
    "    # Compute centroid for each frame\n",
    "    numerator = torch.sum(freqs.view(1, -1) * magnitudes, dim=1)  # Sum over frequencies for each frame\n",
    "    denominator = torch.sum(magnitudes, dim=1)\n",
    "\n",
    "\n",
    "    # Compute mean centroid across all frames\n",
    "    centroid = numerator / (denominator + 1e-8)\n",
    "\n",
    "    return centroid\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# global config\n",
    "sr = 48000\n",
    "n_fft = int(sr * 1)\n",
    "hop_size = n_fft // 2\n",
    "num_samples = 500\n",
    "target_length = 300\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n"
     ]
    }
   ],
   "source": [
    "# first load reference audio and compure centroids\n",
    "reference_filepaths = glob.glob(\"/app/suno/data/audio_2ch_48khz_lg/val/genius_hq/*.wav\")\n",
    "print(len(reference_filepaths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [01:38<00:00,  5.07it/s]\n"
     ]
    }
   ],
   "source": [
    "reference_centroids = []\n",
    "reference_loudness_db = []\n",
    "\n",
    "import pyloudnorm as pyln   \n",
    "meter = pyln.Meter(48000)\n",
    "\n",
    "for reference_filepath in tqdm(reference_filepaths[:num_samples]):\n",
    "    x, sr = torchaudio.load(reference_filepath)    \n",
    "    x = x.mean(dim=0, keepdim=True)\n",
    "    loudness_db = meter.integrated_loudness(x.permute(1, 0).numpy())\n",
    "    # check for nan\n",
    "    if np.isnan(loudness_db):\n",
    "        loudness_db = -80.0\n",
    "    reference_loudness_db.append(loudness_db)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        centroid = compute_spectral_centroid(x, sr, n_fft=n_fft, hop_size=hop_size)\n",
    "    \n",
    "    #centroid = torch.nn.functional.interpolate(centroid.unsqueeze(0).unsqueeze(0), size=points, mode='linear')\n",
    "    reference_centroids.append(centroid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "207.0\n",
      "torch.Size([482, 150])\n",
      "torch.Size([500])\n"
     ]
    }
   ],
   "source": [
    "# check the median length of the centroids\n",
    "centroid_lengths = [len(centroid) for centroid in reference_centroids]\n",
    "print(np.median(centroid_lengths))\n",
    "\n",
    "target_length = 150\n",
    "\n",
    "cropped_reference_centroids = []\n",
    "for centroid in reference_centroids:\n",
    "    centroid = centroid[:target_length]\n",
    "    # interpolate the centroid to the same length\n",
    "    if len(centroid) < target_length:\n",
    "        continue\n",
    "\n",
    "    cropped_reference_centroids.append(centroid)\n",
    "\n",
    "cropped_reference_centroids = torch.stack(cropped_reference_centroids).squeeze()\n",
    "print(cropped_reference_centroids.shape)\n",
    "reference_loudness_db = torch.tensor(reference_loudness_db)\n",
    "print(reference_loudness_db.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_name\n",
      "chirp-v4-h-s-32          582\n",
      "chirp-v4-h-s-32-u-4-7    582\n",
      "Name: count, dtype: int64\n",
      "model_name\n",
      "chirp-v4-h-s-32          582\n",
      "chirp-v4-h-s-32-u-4-7    582\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# compare two models\n",
    "\n",
    "import pandas as pd\n",
    "#pkl_filepath_0 = \"/home/tony/Data/Preference/up_v3/interesting_clips_c_9_exp_20250123_full.pkl\"#\n",
    "pkl_filepath_1 = \"/home/tony/Data/Preference/up_v1/interesting_clips_up_u_1_20241205_full_slice.pkl\"\n",
    "pkl_filepath_2 = \"/home/tony/Data/Preference/up_v2/interesting_clips_up_u_2_20241212_full.pkl\"\n",
    "pkl_filepath_3 = \"/home/tony/Data/Preference/13b_v32/interesting_clips_v4_h_s_32_20250123_full.pkl\"\n",
    "pkl_filepath_4 = \"/home/tony/Data/Preference/up_v3/interesting_clips_v1_t22_exp_20250124_full.pkl\"\n",
    "pkl_filepath_5 = \"/home/tony/Data/Preference/up_v3/interesting_clips_v1_t24_exp_20250124_full.pkl\"\n",
    "pkl_filepath_6 = \"/home/tony/Data/Preference/up_v3/interesting_clips_v3_t10_exp_20250125_full.pkl\"\n",
    "pkl_filepath_7 = \"/home/tony/Data/Preference/up_v3/interesting_clips_v1_t25_exp_20250125_full.pkl\"\n",
    "pkl_filepath_8 = \"/home/tony/Data/Preference/up_v3/interesting_clips_v1_t25_exp_20250125_full.pkl\"\n",
    "pkl_filepath_9 = \"/home/tony/Data/Preference/up_v3/interesting_clips_v3_c10_t25_gens_exp_20250126_full.pkl\"\n",
    "pkl_filepath_10 = \"/home/tony/Data/Preference/up_v3/interesting_clips_v3_c10_t25_exp_20250126_full.pkl\"\n",
    "pkl_filepath_11 = \"/home/tony/Data/Preference/up_v3/interesting_clips_cfg_exp_20250128.pkl\"\n",
    "pkl_filepath_12 = \"/home/tony/Data/Preference/13b_v32/interesting_clips_exp_20250129_full.pkl\"\n",
    "pkl_filepath_13 = \"/home/tony/Data/Preference/13b_v32/interesting_clips_exp_20250220_full.pkl\"\n",
    "\n",
    "\n",
    "df = pd.read_pickle(pkl_filepath_13)\n",
    "df[\"id_x\"] = df[\"id\"]\n",
    "print(df[\"model_name\"].value_counts())\n",
    "\n",
    "#concatenate the two dataframes\n",
    "#df = pd.concat([df_1, df_2, df_3, df_4, df_5, df_6])\n",
    "# reset the index\n",
    "df = df.reset_index(drop=True)\n",
    "print(df[\"model_name\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Cannot take a larger sample than population when 'replace=False'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# lets sample 1000 rows max from each of the model_name\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m df_subset \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mreset_index(drop\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(df_subset[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_name\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue_counts())\n",
      "File \u001b[0;32m~/miniconda3/envs/suno_env/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:1353\u001b[0m, in \u001b[0;36mGroupBy.apply\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1351\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m   1352\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1353\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_python_apply_general\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_selected_obj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1354\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   1355\u001b[0m         \u001b[38;5;66;03m# gh-20949\u001b[39;00m\n\u001b[1;32m   1356\u001b[0m         \u001b[38;5;66;03m# try again, with .apply acting as a filtering\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1360\u001b[0m         \u001b[38;5;66;03m# fails on *some* columns, e.g. a numeric operation\u001b[39;00m\n\u001b[1;32m   1361\u001b[0m         \u001b[38;5;66;03m# on a string grouper column\u001b[39;00m\n\u001b[1;32m   1363\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_python_apply_general(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_obj_with_exclusions)\n",
      "File \u001b[0;32m~/miniconda3/envs/suno_env/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:1402\u001b[0m, in \u001b[0;36mGroupBy._python_apply_general\u001b[0;34m(self, f, data, not_indexed_same, is_transform, is_agg)\u001b[0m\n\u001b[1;32m   1367\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m   1368\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_python_apply_general\u001b[39m(\n\u001b[1;32m   1369\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1374\u001b[0m     is_agg: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m   1375\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NDFrameT:\n\u001b[1;32m   1376\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1377\u001b[0m \u001b[38;5;124;03m    Apply function f in python space\u001b[39;00m\n\u001b[1;32m   1378\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1400\u001b[0m \u001b[38;5;124;03m        data after applying f\u001b[39;00m\n\u001b[1;32m   1401\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1402\u001b[0m     values, mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1403\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m not_indexed_same \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1404\u001b[0m         not_indexed_same \u001b[38;5;241m=\u001b[39m mutated\n",
      "File \u001b[0;32m~/miniconda3/envs/suno_env/lib/python3.10/site-packages/pandas/core/groupby/ops.py:767\u001b[0m, in \u001b[0;36mBaseGrouper.apply\u001b[0;34m(self, f, data, axis)\u001b[0m\n\u001b[1;32m    765\u001b[0m \u001b[38;5;66;03m# group might be modified\u001b[39;00m\n\u001b[1;32m    766\u001b[0m group_axes \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39maxes\n\u001b[0;32m--> 767\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    768\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mutated \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_indexed_like(res, group_axes, axis):\n\u001b[1;32m    769\u001b[0m     mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[5], line 2\u001b[0m, in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# lets sample 1000 rows max from each of the model_name\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m df_subset \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_name\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m)\u001b[49m)\u001b[38;5;241m.\u001b[39mreset_index(drop\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(df_subset[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_name\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue_counts())\n",
      "File \u001b[0;32m~/miniconda3/envs/suno_env/lib/python3.10/site-packages/pandas/core/generic.py:5858\u001b[0m, in \u001b[0;36mNDFrame.sample\u001b[0;34m(self, n, frac, replace, weights, random_state, axis, ignore_index)\u001b[0m\n\u001b[1;32m   5855\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weights \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   5856\u001b[0m     weights \u001b[38;5;241m=\u001b[39m sample\u001b[38;5;241m.\u001b[39mpreprocess_weights(\u001b[38;5;28mself\u001b[39m, weights, axis)\n\u001b[0;32m-> 5858\u001b[0m sampled_indices \u001b[38;5;241m=\u001b[39m \u001b[43msample\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   5859\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(sampled_indices, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[1;32m   5861\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ignore_index:\n",
      "File \u001b[0;32m~/miniconda3/envs/suno_env/lib/python3.10/site-packages/pandas/core/sample.py:151\u001b[0m, in \u001b[0;36msample\u001b[0;34m(obj_len, size, replace, weights, random_state)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    149\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid weights: weights sum to zero\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 151\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchoice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mastype(\n\u001b[1;32m    152\u001b[0m     np\u001b[38;5;241m.\u001b[39mintp, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    153\u001b[0m )\n",
      "File \u001b[0;32mnumpy/random/mtrand.pyx:1001\u001b[0m, in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Cannot take a larger sample than population when 'replace=False'"
     ]
    }
   ],
   "source": [
    "# lets sample 1000 rows max from each of the model_name\n",
    "#df_subset = df.groupby(\"model_name\").apply(lambda x: x.sample(1000)).reset_index(drop=True)\n",
    "#print(df_subset[\"model_name\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1164\n"
     ]
    }
   ],
   "source": [
    "df_subset = df\n",
    "print(len(df_subset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first download audios in parallel to local from s3\n",
    "base_dir = \"/app/suno/christian/data\"\n",
    "out_dir = \"interesting_clips_exp_20250220_full\"\n",
    "os.makedirs(os.path.join(base_dir, out_dir), exist_ok=True)\n",
    "\n",
    "\n",
    "def download_audio(song_id):\n",
    "    mp3_filepath = f\"s3://suno-data-uploads/studio/uploads/{song_id}.mp3\"\n",
    "    out_filepath = os.path.join(base_dir, out_dir, f\"{song_id}.mp3\")\n",
    "    # surpress output\n",
    "    if not os.path.exists(out_filepath):\n",
    "        os.system(f\"aws s3 cp {mp3_filepath} {out_filepath} > /dev/null 2>&1\")\n",
    "\n",
    "def download_audio_pair(request_id, song_id_a, song_id_b, model_name_a, model_name_b):\n",
    "    mp3_filepath_a = f\"s3://suno-data-uploads/studio/uploads/{song_id_a}.mp3\"\n",
    "    mp3_filepath_b = f\"s3://suno-data-uploads/studio/uploads/{song_id_b}.mp3\"\n",
    "    out_filepath_a = os.path.join(base_dir, out_dir, f\"{request_id}_{model_name_a}_{song_id_a}.mp3\")\n",
    "    out_filepath_b = os.path.join(base_dir, out_dir, f\"{request_id}_{model_name_b}_{song_id_b}.mp3\")\n",
    "    # surpress output\n",
    "    if not os.path.exists(out_filepath_a):\n",
    "        os.system(f\"aws s3 cp {mp3_filepath_a} {out_filepath_a} > /dev/null 2>&1\")\n",
    "    if not os.path.exists(out_filepath_b):\n",
    "        os.system(f\"aws s3 cp {mp3_filepath_b} {out_filepath_b} > /dev/null 2>&1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1164\n"
     ]
    }
   ],
   "source": [
    "\n",
    "s3_ids = df_subset[\"id_x\"].tolist()\n",
    "#s3_ids = df_subset[df_subset[\"model_name\"] == \"chirp-v4-up-u-1-c-24\"][\"id_x\"].tolist()\n",
    "print(len(s3_ids))\n",
    "\n",
    "import multiprocessing\n",
    "with multiprocessing.Pool(processes=32) as pool:\n",
    "    pool.map(download_audio, s3_ids)\n",
    "\n",
    "# now load the audio and compute the centroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "24436dee-3914-4af1-944d-ff2a6f5de263 1cb2fb4c-bba9-4f47-99e3-aee5feedb91e\n",
      "chirp-v4-up-u-3-c-10-25-1 chirp-v4-up-u-3\n",
      "eb9190df-055e-4149-aa80-73425d511aa0 4163eee3-4dde-4051-890e-532ecfde5651\n",
      "chirp-v4-up-u-3-c-10-25-1 chirp-v4-up-u-3\n",
      "dd703279-8274-4584-a224-92ac890b4a8f 3cc17d6f-d464-4dd8-9ae0-53fd1e16e37d\n",
      "chirp-v4-up-u-3-c-10-25-1 chirp-v4-up-u-3\n",
      "cf30ca10-c440-456b-a134-a9ee7947d9f8 066c1299-d539-48d6-8b0f-19e9a43a26ff\n"
     ]
    }
   ],
   "source": [
    "# download audio pairs and add model name to the filename for comparision\n",
    "# iterate over the dataframe rows by even number index\n",
    "for idx in range(0, len(df_subset), 2):\n",
    "    row_a = df_subset.iloc[idx]\n",
    "    row_b = df_subset.iloc[idx + 1]\n",
    "    model_name_a = row_a[\"model_name\"]\n",
    "    model_name_b = row_b[\"model_name\"]\n",
    "    s3_id_a = row_a[\"id_x\"]\n",
    "    s3_id_b = row_b[\"id_x\"]\n",
    "    download_audio_pair(idx, s3_id_a, s3_id_b, model_name_a, model_name_b)\n",
    "    print(model_name_a, model_name_b)\n",
    "    print(s3_id_a, s3_id_b)\n",
    "\n",
    "    if idx > 100:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model_df = df[df[\"model_name\"] == \"chirp-v4-up-u-3-c-9\"]\n",
    "s3_ids = base_model_df[\"id_x\"].to_list()\n",
    "\n",
    "for s3_id in s3_ids[:10]:\n",
    "    mp3_filepath = f\"s3://suno-data-uploads/studio/uploads/{s3_id}.mp3\"\n",
    "    out_filepath = os.path.join(base_dir, out_dir, f\"{s3_id}.mp3\")\n",
    "    if not os.path.exists(out_filepath):\n",
    "        os.system(f\"aws s3 cp {mp3_filepath} {out_filepath} > /dev/null 2>&1\")\n",
    "    \n",
    "    print(out_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "/home/christian/miniconda3/envs/suno_env/lib/python3.10/site-packages/torch/utils/_pytree.py:185: FutureWarning: optree is installed but the version is too old to support PyTorch Dynamo in C++ pytree. C++ pytree support is disabled. Please consider upgrading optree using `python3 -m pip install --upgrade 'optree>=0.13.0'`.\n",
      "  warnings.warn(\n",
      "100%|██████████| 1164/1164 [00:24<00:00, 47.69it/s]\n"
     ]
    }
   ],
   "source": [
    "# first compute the centroid frames for each item in the dataframe \n",
    "# we should be able to add this into the dataframe\n",
    "import pyloudnorm as pyln\n",
    "\n",
    "\n",
    "meter = pyln.Meter(48000)\n",
    "\n",
    "# Define the processing function\n",
    "def process_audio(row_tuple):\n",
    "    idx, row = row_tuple\n",
    "    song_id = row[\"id_x\"]\n",
    "    out_filepath = os.path.join(base_dir, out_dir, f\"{song_id}.mp3\")\n",
    "    try:\n",
    "        y, sr = torchaudio.load(out_filepath)\n",
    "        y = y.mean(dim=0, keepdim=True)\n",
    "        with torch.no_grad():\n",
    "            centroid = compute_spectral_centroid(y, sr, n_fft=n_fft, hop_size=hop_size)\n",
    "        \n",
    "\n",
    "        loudness_db = meter.integrated_loudness(y.permute(1, 0).numpy())\n",
    "        # check for nan\n",
    "        if np.isnan(loudness_db):\n",
    "            loudness_db = -80.0\n",
    "\n",
    "        # Convert to numpy array, flatten and store as list\n",
    "        centroid_list = centroid.numpy().flatten().tolist()\n",
    "        return idx, centroid_list, loudness_db\n",
    "    except Exception as e:\n",
    "        print(f\"Error processing {out_filepath}: {str(e)}\")\n",
    "        return idx, None, None\n",
    "\n",
    "# Initialize the centroid column first\n",
    "df_subset[\"centroid\"] = None\n",
    "df_subset[\"loudness_db\"] = None\n",
    "# Use joblib instead of multiprocessing - it works better in notebooks\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "# Create list of (idx, row) tuples to process\n",
    "items = list(df_subset.iterrows())\n",
    "\n",
    "# Process items in parallel with progress bar\n",
    "results = Parallel(n_jobs=32, backend=\"loky\")(\n",
    "    delayed(process_audio)(item) for item in tqdm(items)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_subset[\"centroid\"] = None\n",
    "df_subset[\"loudness_db\"] = None\n",
    "\n",
    "#  Update dataframe with results\n",
    "for idx, centroid_list, loudness_db in results:\n",
    "    if centroid_list is not None:\n",
    "        df_subset.at[idx, \"centroid\"] = centroid_list\n",
    "    if loudness_db is not None:\n",
    "        df_subset.at[idx, \"loudness_db\"] = loudness_db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model_name\n",
       "chirp-v4-h-s-32          582\n",
       "chirp-v4-h-s-32-u-4-7    582\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset[\"model_name\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "374.0\n"
     ]
    }
   ],
   "source": [
    "# count median centroid length\n",
    "print(df_subset[\"centroid\"].apply(len).median())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chirp-v4-h-s-32\n",
      "torch.Size([410, 300])\n",
      "chirp-v4-h-s-32-u-4-7\n",
      "torch.Size([411, 300])\n"
     ]
    }
   ],
   "source": [
    "centroids_interp = {\n",
    "    \"chirp-v4-h-s-32\": [],\n",
    "    #\"chirp-v4-h-s-32-u-3\": [],\n",
    "    #\"chirp-v4-h-s-32_n_tag_3\": [],\n",
    "    #\"chirp-v4-h-s-32_step_10\": [],\n",
    "    #\"chirp-v4-h-s-32_step_8\": [],\n",
    "    #\"chirp-v4-h-s-32_temp_s_70\": [],\n",
    "    #\"chirp-v4-h-s-32_temp_s_80\": [],\n",
    "    \"chirp-v4-h-s-32-u-4-7\": [],\n",
    "}\n",
    "\n",
    "loudness_interp = {}\n",
    "\n",
    "colors = [\"tab:red\", \"tab:green\", \"tab:orange\", \"tab:purple\", \"tab:brown\", \"tab:pink\", \"tab:olive\", \"tab:cyan\", \"tab:gray\", \"tab:blue\"]\n",
    "assert len(colors) >= len(centroids_interp.keys())\n",
    "\n",
    "\n",
    "for model_name in centroids_interp.keys():\n",
    "    print(model_name)\n",
    "    a_centroids = df_subset[df_subset[\"model_name\"] == model_name][\"centroid\"].values.tolist()\n",
    "    a_loudness_db = df_subset[df_subset[\"model_name\"] == model_name][\"loudness_db\"].values.tolist()\n",
    "    a_centroids_interp = []\n",
    "    a_loudness_db_interp = []\n",
    "    for centroid, loudness_db in zip(a_centroids, a_loudness_db):\n",
    "        if centroid is not None:\n",
    "            if len(centroid) < 300:   \n",
    "                continue\n",
    "            centroid = torch.tensor(centroid)[:300]\n",
    "            #centroid = torch.nn.functional.interpolate(centroid.unsqueeze(0).unsqueeze(0), size=points, mode='linear')\n",
    "            a_centroids_interp.append(centroid)\n",
    "            a_loudness_db_interp.append(loudness_db)\n",
    "\n",
    "    a_centroids_interp = torch.stack(a_centroids_interp).squeeze()\n",
    "    print(a_centroids_interp.shape)\n",
    "    centroids_interp[model_name] = a_centroids_interp\n",
    "    loudness_interp[model_name] = a_loudness_db_interp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n"
     ]
    }
   ],
   "source": [
    "#df_subset_base = df_subset[df_subset[\"model_name\"] == \"chirp-v4-up-u-1\"]\n",
    "#print(len(df_subset_base))\n",
    "\n",
    "# filter based on model_name to exclude chirp-v4-h-s-32-u-3\n",
    "df_subset_base = df_subset[df_subset[\"model_name\"] == \"chirp-v4-h-s-32\"]\n",
    "\n",
    "print(len(df_subset_base))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "torch.Size([386, 300])\n",
      "1\n",
      "torch.Size([354, 300])\n"
     ]
    }
   ],
   "source": [
    "centroids_interp = {\n",
    "    0: [],\n",
    "    1: []\n",
    "}\n",
    "\n",
    "loudness_interp = {\n",
    "    0: [],\n",
    "    1: []\n",
    "}\n",
    "\n",
    "target_length = 300\n",
    "\n",
    "for model_name in centroids_interp.keys():\n",
    "    print(model_name)\n",
    "    a_centroids = df_subset_base[df_subset_base[\"preference\"] == model_name][\"centroid\"].values.tolist()\n",
    "    a_loudness_db = df_subset_base[df_subset_base[\"preference\"] == model_name][\"loudness_db\"].values.tolist()\n",
    "    a_centroids_interp = []\n",
    "    a_loudness_db_interp = []\n",
    "    for centroid, loudness_db in zip(a_centroids, a_loudness_db):\n",
    "        if centroid is not None:\n",
    "            if len(centroid) < target_length:\n",
    "                continue\n",
    "            centroid = torch.tensor(centroid)[:target_length]\n",
    "            a_centroids_interp.append(centroid)\n",
    "            a_loudness_db_interp.append(loudness_db)\n",
    "    a_centroids_interp = torch.stack(a_centroids_interp).squeeze()\n",
    "    print(a_centroids_interp.shape)\n",
    "    centroids_interp[model_name] = a_centroids_interp\n",
    "    loudness_interp[model_name] = a_loudness_db_interp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1658088/906507657.py:20: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "  axs[0].legend()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# loudness histogram\n",
    "print(len(loudness_interp.keys()))\n",
    "print(len(colors))\n",
    "\n",
    "# make subplot for each model\n",
    "fig, axs = plt.subplots(len(loudness_interp.keys()) + 1, 1, figsize=(4, 6), sharex=True, sharey=True)\n",
    "\n",
    "# add reference to the loudness_interp\n",
    "min_loudness = -25\n",
    "max_loudness = -10\n",
    "num_bins = 100\n",
    "reference_color = \"tab:blue\"\n",
    "density = True\n",
    "#mean_loudness = np.mean(reference_loudness_db.numpy())\n",
    "#axs[0].hist(reference_loudness_db.numpy(), bins=np.linspace(min_loudness, max_loudness, num_bins), label=f\"Reference ({mean_loudness:.2f} dB)\", alpha=0.5, color=reference_color, zorder=11, density=density)\n",
    "axs[0].grid(True, c=\"lightgray\", zorder=0)\n",
    "axs[0].set_xlabel('Loudness (dB)')\n",
    "axs[0].set_ylabel('Frequency')\n",
    "axs[0].set_xlim(min_loudness, max_loudness)\n",
    "axs[0].legend()\n",
    "\n",
    "for i, model_name in enumerate(loudness_interp.keys()):\n",
    "    mean_loudness = np.mean(loudness_interp[model_name])\n",
    "    axs[i + 1].hist(loudness_interp[model_name], bins=np.linspace(min_loudness, max_loudness, num_bins), label=f\"{model_name} ({mean_loudness:.2f} dB)\", alpha=0.5, color=colors[i], zorder=11, density=density)\n",
    "    axs[i + 1].grid(True, c=\"lightgray\", zorder=0)\n",
    "    axs[i + 1].set_xlabel('Loudness (dB)')\n",
    "    axs[i + 1].set_ylabel('Frequency')\n",
    "    axs[i + 1].set_xlim(min_loudness, max_loudness)\n",
    "    axs[i + 1].legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"/home/christian/code/christian/notebooks/plots/loudness_histogram_chirp_v4_h_s_32_u_4_7.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.signal as signal\n",
    "\n",
    "start_frame = 0\n",
    "end_frame = 300\n",
    "\n",
    "frame_indices = torch.arange(0, end_frame - start_frame)\n",
    "\n",
    "reference_color = \"tab:blue\"\n",
    "a_color = \"tab:red\"\n",
    "b_color = \"tab:green\"\n",
    "\n",
    "#reference_mean = reference_centroids[:, start_frame:end_frame].mean(dim=0)\n",
    "#reference_std = reference_centroids[:, start_frame:end_frame].std(dim=0)\n",
    "for i, model_name in enumerate(centroids_interp.keys()):\n",
    "    a_mean = centroids_interp[model_name][:, start_frame:end_frame].mean(dim=0)\n",
    "    plt.plot(times[:len(a_mean)], a_mean, label=f\"{model_name} ({len(centroids_interp[model_name])})\", color=colors[i], zorder=11, alpha=0.7)\n",
    "\n",
    "times = frame_indices * (hop_size / sr)\n",
    "\n",
    "# moving average smoothing with savgol filter\n",
    "#plt.plot(frame_indices, reference_mean, label=f\"Reference\", color=reference_color, zorder=11)\n",
    "times = frame_indices * (hop_size / sr)\n",
    "\n",
    "\n",
    "ticks = np.arange(0, times[-1], 5)  # Add 30 to include last tick\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('Spectral Centroid')\n",
    "plt.title(f'Mean Diffusion Decay')\n",
    "plt.grid(True, c=\"lightgray\", zorder=0)\n",
    "plt.legend()\n",
    "#plt.show()\n",
    "plt.savefig(f\"/home/christian/code/christian/notebooks/plots/mean_diffusion_decay_chirp_v4_h_s_32_u_4_7.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_frame = 0\n",
    "end_frame = 300\n",
    "\n",
    "frame_indices = torch.arange(start_frame, end_frame)\n",
    "# convert frame indices to seconds\n",
    "frame_indices = frame_indices * (hop_size / sr)\n",
    "\n",
    "reference_color = \"tab:blue\"\n",
    "\n",
    "reference_mean = cropped_reference_centroids[:, start_frame:end_frame].mean(dim=0)\n",
    "reference_std = cropped_reference_centroids[:, start_frame:end_frame].std(dim=0)\n",
    "plt.plot(frame_indices[:len(reference_mean)], reference_mean, label=f\"Reference ({len(cropped_reference_centroids)})\", color=reference_color, zorder=11, alpha=0.7)\n",
    "\n",
    "for i, model_name in enumerate(centroids_interp.keys()):\n",
    "    a_mean = centroids_interp[model_name][:, start_frame:end_frame].mean(dim=0)\n",
    "    a_std = centroids_interp[model_name][:, start_frame:end_frame].std(dim=0)\n",
    "    plt.plot(frame_indices[:len(a_mean)], a_mean, label=f\"{model_name} ({len(centroids_interp[model_name])})\", color=colors[i], zorder=11, alpha=0.7)\n",
    "\n",
    "times = frame_indices * (hop_size / sr)\n",
    "\n",
    "# Set ticks every 30 seconds\n",
    "#max_time = times[-1]\n",
    "#ticks = np.arange(0, max_time + 30, 30)  # Add 30 to include last tick\n",
    "#plt.xticks(ticks)\n",
    "# set ticks to be very 30s\n",
    "ticks = np.arange(0, frame_indices[-1], 30)\n",
    "plt.xticks(ticks)\n",
    "plt.xlim(0, 180)\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('Spectral Centroid')\n",
    "plt.title(f'Mean Diffusion Decay')\n",
    "plt.grid(True, c=\"lightgray\", zorder=0)\n",
    "plt.legend()\n",
    "plt.ylim(3000, 4000)\n",
    "plt.tight_layout()\n",
    "#plt.show()\n",
    "plt.savefig(f\"/home/christian/code/christian/notebooks/plots/diffusion_decay_chirp_v4_up_u_3_c_9_vs_chirp_v4_up_u_3_vs_chirp_v4_up_u_1_preference.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now look at delta from start to end, take the first 100 and the last 100\n",
    "delta_reference = (reference_centroids[:, -300:-200] - reference_centroids[:, 100:200]).mean(dim=0)\n",
    "delta_a = (centroids_interp[\"chirp-v4-up-u-3-c-9\"][:, -300:-200] - centroids_interp[\"chirp-v4-up-u-3-c-9\"][:, 100:200]).mean(dim=0)\n",
    "delta_b = (centroids_interp[\"chirp-v4-up-u-3\"][:, -300:-200] - centroids_interp[\"chirp-v4-up-u-3\"][:, 100:200]).mean(dim=0)\n",
    "delta_c = (centroids_interp[\"chirp-v4-up-u-1\"][:, -300:-200] - centroids_interp[\"chirp-v4-up-u-1\"][:, 100:200]).mean(dim=0)\n",
    "delta_d = (centroids_interp[\"chirp-v4-up-u-2\"][:, -300:-200] - centroids_interp[\"chirp-v4-up-u-2\"][:, 100:200]).mean(dim=0)\n",
    "\n",
    "bins = np.linspace(-1500, 1500, 100)\n",
    "# make a histogram of the deltas\n",
    "plt.hist(delta_reference.numpy(), bins=bins, label=\"Reference\", color=reference_color, alpha=0.66)\n",
    "plt.hist(delta_a.numpy(), bins=bins, label=\"chirp-v4-up-u-3-c-9\", color=a_color, alpha=0.66)\n",
    "plt.hist(delta_b.numpy(), bins=bins, label=\"chirp-v4-up-u-3\", color=b_color, alpha=0.66)\n",
    "plt.hist(delta_c.numpy(), bins=bins, label=\"chirp-v4-up-u-1\", color=c_color, alpha=0.66)\n",
    "plt.hist(delta_d.numpy(), bins=bins, label=\"chirp-v4-up-u-2\", color=d_color, alpha=0.66)\n",
    "plt.xlabel('Spectral Centroid Delta')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title(f'Histogram of Diffusion Decay (N={num_samples})')\n",
    "#plt.grid(True, c=\"lightgray\", zorder=0)\n",
    "plt.legend()\n",
    "plt.savefig(f\"/home/christian/code/christian/notebooks/plots/diffusion_decay_chirp_v4_up_u_3_c_9_vs_chirp_v4_up_u_3_delta.png\")\n",
    "#plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "suno_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}