{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:04:28.850645Z",
     "iopub.status.busy": "2025-09-16T13:04:28.850339Z",
     "iopub.status.idle": "2025-09-16T13:04:28.863856Z",
     "shell.execute_reply": "2025-09-16T13:04:28.863450Z",
     "shell.execute_reply.started": "2025-09-16T13:04:28.850630Z"
    }
   },
   "outputs": [],
   "source": [
    "# setup autoload\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:21.040680Z",
     "start_time": "2024-05-16T13:58:19.777010Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-16T13:04:28.864587Z",
     "iopub.status.busy": "2025-09-16T13:04:28.864333Z",
     "iopub.status.idle": "2025-09-16T13:04:32.263233Z",
     "shell.execute_reply": "2025-09-16T13:04:32.262686Z",
     "shell.execute_reply.started": "2025-09-16T13:04:28.864573Z"
    }
   },
   "outputs": [],
   "source": [
    "import ast\n",
    "import os\n",
    "import shutil\n",
    "import sys\n",
    "from collections import defaultdict\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from preference_data_preparation_diff import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "from suno_utils.utils.s3 import download_s3_files\n",
    "from suno_utils.utils.text import read_json, read_jsonl, write_json, write_jsonl\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from suno_utils.audio import Audio\n",
    "from suno_analytics.preference_helper import get_preference_counts\n",
    "\n",
    "pd.set_option(\"display.max_rows\", 500)\n",
    "pd.set_option(\"display.max_columns\", 500)\n",
    "pd.set_option(\"display.width\", 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:21.082172Z",
     "start_time": "2024-05-16T13:58:21.041926Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-16T13:04:32.264049Z",
     "iopub.status.busy": "2025-09-16T13:04:32.263815Z",
     "iopub.status.idle": "2025-09-16T13:04:34.980072Z",
     "shell.execute_reply": "2025-09-16T13:04:34.979508Z",
     "shell.execute_reply.started": "2025-09-16T13:04:32.264031Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output dir is /app2/suno/data/dpo/diff3_carp_t1_v3/\n",
      "Total pair quality scores: 20535\n",
      "Total hoot cer scores: 19542\n"
     ]
    }
   ],
   "source": [
    "OUT_DATA_DIR = \"/app2/suno/data/dpo/diff3_carp_t1_v3/\"\n",
    "os.makedirs(OUT_DATA_DIR, exist_ok=True)\n",
    "print(\"Output dir is\", OUT_DATA_DIR)\n",
    "NPZ_DIR = \"/app2/suno/data/dpo/carp_t1\"\n",
    "\n",
    "with open(\"/home/tony/Data/Preference/carp_t1/full_pair_quality.json\", \"r\") as file:\n",
    "    full_pair_quality = json.load(file)\n",
    "print(\"Total pair quality scores:\", len(full_pair_quality))\n",
    "\n",
    "with open(\"/home/tony/Data/Preference/carp_t1/hoot_cer.json\", \"r\") as file:\n",
    "    clip_id_to_cer = json.load(file)\n",
    "print(\"Total hoot cer scores:\", len(clip_id_to_cer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:53.962528Z",
     "start_time": "2024-05-16T13:58:21.105919Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-16T13:04:34.980773Z",
     "iopub.status.busy": "2025-09-16T13:04:34.980622Z",
     "iopub.status.idle": "2025-09-16T13:04:35.319753Z",
     "shell.execute_reply": "2025-09-16T13:04:35.319186Z",
     "shell.execute_reply.started": "2025-09-16T13:04:34.980758Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preference data shape (37444, 91)\n",
      "unique users 13530\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle(\n",
    "    \"/home/tony/Data/Preference/carp_t1/interesting_clips_carp_t1_20250916.pkl\"\n",
    ")  # , engine='python')\n",
    "print(\"Preference data shape\", df.shape)\n",
    "print(\"unique users\", df[\"user_id\"].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:04:35.320468Z",
     "iopub.status.busy": "2025-09-16T13:04:35.320316Z",
     "iopub.status.idle": "2025-09-16T13:05:55.782615Z",
     "shell.execute_reply": "2025-09-16T13:05:55.782081Z",
     "shell.execute_reply.started": "2025-09-16T13:04:35.320452Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "37444it [01:20, 465.53it/s]\n"
     ]
    }
   ],
   "source": [
    "# find all the hoot jsons in the json dir\n",
    "# clip_id_to_cer = {}\n",
    "JSON_DIR = \"/app2/suno/data/dpo/carp_t1_json/\"\n",
    "for _, row in tqdm(df.iterrows()):\n",
    "    clip_id = row[\"s3_id\"]\n",
    "    if clip_id in clip_id_to_cer:\n",
    "        continue\n",
    "    hoot_json_path = os.path.join(JSON_DIR, f\"{clip_id}_hoot.json\")\n",
    "    if not os.path.exists(hoot_json_path):\n",
    "        clip_id_to_cer[clip_id] = 1.0\n",
    "        continue\n",
    "    with open(os.path.join(JSON_DIR, f\"{clip_id}_hoot.json\"), \"r\") as f:\n",
    "        data = json.load(f)\n",
    "    for data_dict in data:\n",
    "        if \"hoot_cer\" in data_dict:\n",
    "            clip_id_to_cer[clip_id] = data_dict[\"hoot_cer\"]\n",
    "            break\n",
    "\n",
    "# add the cer to the df\n",
    "df[\"cer\"] = df[\"s3_id\"].map(clip_id_to_cer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:05:55.783341Z",
     "iopub.status.busy": "2025-09-16T13:05:55.783192Z",
     "iopub.status.idle": "2025-09-16T13:05:56.869231Z",
     "shell.execute_reply": "2025-09-16T13:05:56.868681Z",
     "shell.execute_reply.started": "2025-09-16T13:05:55.783326Z"
    }
   },
   "outputs": [],
   "source": [
    "# update the hoot cer cache\n",
    "with open(\"/home/tony/Data/Preference/carp_t1/hoot_cer.json\", \"w\") as file:\n",
    "    json.dump(clip_id_to_cer, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:05:56.869984Z",
     "iopub.status.busy": "2025-09-16T13:05:56.869831Z",
     "iopub.status.idle": "2025-09-16T13:05:58.075527Z",
     "shell.execute_reply": "2025-09-16T13:05:58.075035Z",
     "shell.execute_reply.started": "2025-09-16T13:05:56.869969Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentile 5: -0.038\n",
      "Percentile 10: -0.022\n",
      "Percentile 15: -0.015\n",
      "Percentile 85: 0.015\n",
      "Percentile 90: 0.021\n",
      "Percentile 95: 0.034\n",
      "\n",
      "Debug info:\n",
      "Total positive preference samples: 18722\n",
      "cer_diff range: -0.832 to 0.894\n",
      "cer_diff mean: -0.001\n",
      "cer_diff median: 0.000\n",
      "cer_diff std: 0.052\n",
      "Positive cer_diff samples: 7798 (41.7%)\n",
      "Negative cer_diff samples: 7741 (41.3%)\n",
      "Zero cer_diff samples: 3183 (17.0%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.fillna({\"cer\": 1})\n",
    "df[\"cer_diff\"] = df[\"cer\"].diff()\n",
    "df = df.fillna({\"cer_diff\": 0})\n",
    "# plot the positive preference and negative preference cer\n",
    "positive_pref = df[df[\"preference\"]]\n",
    "negative_pref = df[~df[\"preference\"]]\n",
    "\n",
    "# Create figure with 2 subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n",
    "\n",
    "# First subplot: CER distribution\n",
    "ax1.hist(\n",
    "    positive_pref[\"cer\"], bins=50, alpha=0.2, color=\"green\", label=\"Positive Preference\"\n",
    ")\n",
    "ax1.hist(\n",
    "    negative_pref[\"cer\"], bins=50, alpha=0.2, color=\"blue\", label=\"Negative Preference\"\n",
    ")\n",
    "ax1.set_xlabel(\"CER\")\n",
    "ax1.set_ylabel(\"Frequency\")\n",
    "ax1.set_title(\"CER Distribution - Positive vs Negative Preference\")\n",
    "ax1.legend()\n",
    "\n",
    "# Second subplot: CER diff for positive preference only\n",
    "ax2.hist(\n",
    "    positive_pref[\"cer_diff\"],\n",
    "    bins=50,\n",
    "    alpha=0.7,\n",
    "    color=\"green\",\n",
    "    label=\"Positive Preference\",\n",
    "    range=(-0.75, 0.75),\n",
    ")\n",
    "ax2.set_yscale(\"log\")\n",
    "ax2.set_xlabel(\"CER Diff\")\n",
    "ax2.set_ylabel(\"Frequency\")\n",
    "ax2.set_title(\"CER Diff Distribution - Positive Preference Only\")\n",
    "\n",
    "# Add percentile lines BEFORE the legend\n",
    "percentiles = [0.05, 0.10, 0.15, 0.85, 0.90, 0.95]\n",
    "colors = [\"red\", \"orange\", \"blue\", \"blue\", \"orange\", \"red\"]\n",
    "# Fix: Use sorted() instead of .sort() which returns None\n",
    "sorted_positive_cer_diff = sorted(positive_pref[\"cer_diff\"].values.tolist())\n",
    "for i, (p, color) in enumerate(zip(percentiles, colors)):\n",
    "    percentile_value = np.percentile(\n",
    "        sorted_positive_cer_diff, p * 100\n",
    "    )  # Fix: multiply by 100 for np.percentile\n",
    "    print(f\"Percentile {p*100:.0f}: {percentile_value:.3f}\")\n",
    "    ax2.axvline(\n",
    "        x=percentile_value,\n",
    "        color=color,\n",
    "        linestyle=\"--\",\n",
    "        alpha=0.8,\n",
    "        linewidth=2,\n",
    "        label=f\"{p*100:.0f}th percentile: {percentile_value:.3f}\",\n",
    "    )\n",
    "\n",
    "# Add legend AFTER the percentile lines\n",
    "ax2.legend()\n",
    "plt.tight_layout()\n",
    "\n",
    "# Debug: Check the actual distribution of cer_diff\n",
    "print(\"\\nDebug info:\")\n",
    "print(f\"Total positive preference samples: {len(positive_pref)}\")\n",
    "print(\n",
    "    f\"cer_diff range: {positive_pref['cer_diff'].min():.3f} to {positive_pref['cer_diff'].max():.3f}\"\n",
    ")\n",
    "print(f\"cer_diff mean: {positive_pref['cer_diff'].mean():.3f}\")\n",
    "print(f\"cer_diff median: {positive_pref['cer_diff'].median():.3f}\")\n",
    "print(f\"cer_diff std: {positive_pref['cer_diff'].std():.3f}\")\n",
    "\n",
    "# Check if there are any positive values\n",
    "positive_cer_diff = positive_pref[positive_pref[\"cer_diff\"] > 0]\n",
    "negative_cer_diff = positive_pref[positive_pref[\"cer_diff\"] < 0]\n",
    "zero_cer_diff = positive_pref[positive_pref[\"cer_diff\"] == 0]\n",
    "\n",
    "print(\n",
    "    f\"Positive cer_diff samples: {len(positive_cer_diff)} ({len(positive_cer_diff)/len(positive_pref)*100:.1f}%)\"\n",
    ")\n",
    "print(\n",
    "    f\"Negative cer_diff samples: {len(negative_cer_diff)} ({len(negative_cer_diff)/len(positive_pref)*100:.1f}%)\"\n",
    ")\n",
    "print(\n",
    "    f\"Zero cer_diff samples: {len(zero_cer_diff)} ({len(zero_cer_diff)/len(positive_pref)*100:.1f}%)\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:05:58.076244Z",
     "iopub.status.busy": "2025-09-16T13:05:58.076090Z",
     "iopub.status.idle": "2025-09-16T13:05:58.093755Z",
     "shell.execute_reply": "2025-09-16T13:05:58.093277Z",
     "shell.execute_reply.started": "2025-09-16T13:05:58.076228Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is_public\n",
      "False    35600\n",
      "True      1844\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"is_public\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.199480Z",
     "start_time": "2024-05-16T13:58:53.963687Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-16T13:05:58.094483Z",
     "iopub.status.busy": "2025-09-16T13:05:58.094346Z",
     "iopub.status.idle": "2025-09-16T13:05:58.296511Z",
     "shell.execute_reply": "2025-09-16T13:05:58.295855Z",
     "shell.execute_reply.started": "2025-09-16T13:05:58.094468Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61682\n",
      "20438\n",
      "41244\n",
      "pre-downloaded df (37444, 94)\n",
      "downloaded df (37438, 94)\n",
      "vae downloaded df (37438, 94)\n"
     ]
    }
   ],
   "source": [
    "df[\"upsample_clip_id\"] = df[\"metadata\"].apply(lambda x: x.get(\"upsample_clip_id\", \"\"))\n",
    "all_converted_paths = os.listdir(NPZ_DIR)\n",
    "print(len(all_converted_paths))\n",
    "\n",
    "converted_paths = set(\n",
    "    [f.replace(\".npz\", \"\") for f in all_converted_paths if \"vae\" not in f]\n",
    ")\n",
    "print(len(converted_paths))\n",
    "vae_converted_paths = set(\n",
    "    [f.replace(\"_vae.npz\", \"\") for f in all_converted_paths if \"vae\" in f]\n",
    ")\n",
    "print(len(vae_converted_paths))\n",
    "\n",
    "print(\"pre-downloaded df\", df.shape)\n",
    "df[df[\"upsample_clip_id\"].isin(converted_paths)].shape\n",
    "df = df[df[\"upsample_clip_id\"].isin(converted_paths)].copy()\n",
    "print(\"downloaded df\", df.shape)\n",
    "df[df[\"s3_id\"].isin(vae_converted_paths)].shape\n",
    "df = df[df[\"s3_id\"].isin(vae_converted_paths)].copy()\n",
    "print(\"vae downloaded df\", df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.467253Z",
     "start_time": "2024-05-16T13:58:56.207647Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-16T13:05:58.297235Z",
     "iopub.status.busy": "2025-09-16T13:05:58.297085Z",
     "iopub.status.idle": "2025-09-16T13:05:58.322103Z",
     "shell.execute_reply": "2025-09-16T13:05:58.321648Z",
     "shell.execute_reply.started": "2025-09-16T13:05:58.297220Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_up\n",
       "True    37438\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"is_up\"] = df[\"model_name\"].str.contains(\"up\")\n",
    "df[\"is_up\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LET's do the data prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.592883Z",
     "start_time": "2024-05-16T13:58:56.470781Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-16T13:05:58.322760Z",
     "iopub.status.busy": "2025-09-16T13:05:58.322614Z",
     "iopub.status.idle": "2025-09-16T13:05:58.343235Z",
     "shell.execute_reply": "2025-09-16T13:05:58.342749Z",
     "shell.execute_reply.started": "2025-09-16T13:05:58.322746Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preference  model_name       \n",
      "False       chirp-bass-up-c-1    18719\n",
      "True        chirp-bass-up-c-1    18719\n",
      "Name: count, dtype: int64\n",
      "(37438, 95)\n",
      "(37438, 95)\n"
     ]
    }
   ],
   "source": [
    "## for 13b this is easy for now\n",
    "print(df.groupby([\"preference\"])[\"model_name\"].value_counts())\n",
    "print(df.shape)\n",
    "# df = df[df[\"model_name\"].isin([\"chirp-v4-up-u-d-2-2\"])]\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:05:58.343867Z",
     "iopub.status.busy": "2025-09-16T13:05:58.343729Z",
     "iopub.status.idle": "2025-09-16T13:05:58.385717Z",
     "shell.execute_reply": "2025-09-16T13:05:58.385202Z",
     "shell.execute_reply.started": "2025-09-16T13:05:58.343853Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "task\n",
      "upsample    37438\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"task\"].value_counts())\n",
    "df = df[df[\"task\"] != \"fixed_infill\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:58:56.909539Z",
     "start_time": "2024-05-16T13:58:56.595736Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-16T13:05:58.386376Z",
     "iopub.status.busy": "2025-09-16T13:05:58.386236Z",
     "iopub.status.idle": "2025-09-16T13:05:58.432944Z",
     "shell.execute_reply": "2025-09-16T13:05:58.432425Z",
     "shell.execute_reply.started": "2025-09-16T13:05:58.386363Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(37438, 95)\n",
      "(37438, 95)\n",
      "preference  model_name       \n",
      "False       chirp-bass-up-c-1    18719\n",
      "True        chirp-bass-up-c-1    18719\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df = df[\n",
    "    df[\"request_id\"].isin(\n",
    "        df[\"request_id\"].value_counts().index[df[\"request_id\"].value_counts() == 2]\n",
    "    )\n",
    "]\n",
    "print(df.shape)\n",
    "print(df.groupby([\"preference\"])[\"model_name\"].value_counts())\n",
    "assert df.shape[0] == df[\"request_id\"].nunique() * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:05:58.433609Z",
     "iopub.status.busy": "2025-09-16T13:05:58.433470Z",
     "iopub.status.idle": "2025-09-16T13:05:58.509292Z",
     "shell.execute_reply": "2025-09-16T13:05:58.508780Z",
     "shell.execute_reply.started": "2025-09-16T13:05:58.433596Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total unpacked pair quality scores: 265992\n"
     ]
    }
   ],
   "source": [
    "unpacked_pair_quality = {}\n",
    "for request_id, pairs_of_qualities in full_pair_quality.items():\n",
    "    for clip_id, pair_quality in pairs_of_qualities.items():\n",
    "        unpacked_pair_quality[clip_id] = pair_quality\n",
    "print(\"Total unpacked pair quality scores:\", len(unpacked_pair_quality))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:05:58.509971Z",
     "iopub.status.busy": "2025-09-16T13:05:58.509824Z",
     "iopub.status.idle": "2025-09-16T13:06:00.950535Z",
     "shell.execute_reply": "2025-09-16T13:06:00.949975Z",
     "shell.execute_reply.started": "2025-09-16T13:05:58.509957Z"
    }
   },
   "outputs": [],
   "source": [
    "# Initialize lists to store metrics\n",
    "clip_diffs = []\n",
    "clip_ratios = []\n",
    "loudness_diff = []\n",
    "spec_decay_diff = []\n",
    "last_spec_decay_diff = []\n",
    "pos_spec_decay_values = []  # New list for positive decay values\n",
    "neg_spec_decay_values = []  # New list for negative decay values\n",
    "clip_id_to_mean_ear_score = {}\n",
    "clip_id_to_mean_shimmer_score = {}\n",
    "total_clip_ratios = []\n",
    "\n",
    "for request_id, pairs_of_qualities in full_pair_quality.items():\n",
    "    # Initialize lists for current request\n",
    "    mean_neg_scores = []\n",
    "    mean_pos_scores = []\n",
    "    pos_scores = []\n",
    "    neg_scores = []\n",
    "    neg_loudness = []\n",
    "    pos_loudness = []\n",
    "    neg_spec_decay = []\n",
    "    pos_spec_decay = []\n",
    "    neg_shimmer = []\n",
    "    pos_shimmer = []\n",
    "    neg_clip_id = None\n",
    "    pos_clip_id = None\n",
    "\n",
    "    # Process each clip pair\n",
    "    for i, (clip_id, pair_quality) in enumerate(pairs_of_qualities.items()):\n",
    "        if pair_quality is None:\n",
    "            continue\n",
    "\n",
    "        if i % 2 == 0:  # Negative clip\n",
    "            mean_neg_scores.append(np.mean(pair_quality[\"ear_v2_quality_scores\"]))\n",
    "            neg_scores.extend(pair_quality[\"ear_v2_quality_scores\"])\n",
    "            neg_loudness.append(pair_quality[\"abs_loudness_factor\"])\n",
    "            neg_spec_decay.append(pair_quality[\"spectrum_decay\"])\n",
    "            neg_shimmer.append(pair_quality[\"shimmer_score\"])\n",
    "            if neg_clip_id is None:\n",
    "                neg_clip_id = clip_id\n",
    "        else:  # Positive clip\n",
    "            mean_pos_scores.append(np.mean(pair_quality[\"ear_v2_quality_scores\"]))\n",
    "            pos_scores.extend(pair_quality[\"ear_v2_quality_scores\"])\n",
    "            pos_loudness.append(pair_quality[\"abs_loudness_factor\"])\n",
    "            pos_spec_decay.append(pair_quality[\"spectrum_decay\"])\n",
    "            pos_shimmer.append(pair_quality[\"shimmer_score\"])\n",
    "            if pos_clip_id is None:\n",
    "                pos_clip_id = clip_id\n",
    "\n",
    "    # Skip if we don't have both positive and negative samples\n",
    "    if not (mean_pos_scores and mean_neg_scores):\n",
    "        continue\n",
    "\n",
    "    # Calculate ratios and differences\n",
    "    ratios = [\n",
    "        (pos - neg) / (pos + 0.0001)\n",
    "        for pos, neg in zip(mean_pos_scores, mean_neg_scores)\n",
    "    ]\n",
    "    pos_diffs = [\n",
    "        (pos - prev_pos) / (prev_pos + 0.0001)\n",
    "        for prev_pos, pos in zip(mean_pos_scores, mean_pos_scores[1:])\n",
    "    ]\n",
    "    neg_diffs = [\n",
    "        (neg - prev_neg) / (prev_neg + 0.0001)\n",
    "        for prev_neg, neg in zip(mean_neg_scores, mean_neg_scores[1:])\n",
    "    ]\n",
    "\n",
    "    # Calculate loudness and spectrum decay differences\n",
    "    loudness_diff.extend(\n",
    "        [\n",
    "            (pos_l - neg_l) / (pos_l + neg_l + 0.0001)\n",
    "            for pos_l, neg_l in zip(pos_loudness, neg_loudness)\n",
    "        ]\n",
    "    )\n",
    "    spec_decay_diff.extend(\n",
    "        [\n",
    "            (pos_s - neg_s) / (pos_s + neg_s + 0.0001)\n",
    "            for pos_s, neg_s in zip(pos_spec_decay, neg_spec_decay)\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    # Store individual decay values\n",
    "    pos_spec_decay_values.extend(pos_spec_decay)\n",
    "    neg_spec_decay_values.extend(neg_spec_decay)\n",
    "\n",
    "    # Calculate last spectrum decay difference only if we have values\n",
    "    if pos_spec_decay and neg_spec_decay:\n",
    "        last_spec_decay_diff.append(\n",
    "            (pos_spec_decay[-1] - neg_spec_decay[-1])\n",
    "            / (pos_spec_decay[-1] + neg_spec_decay[-1] + 0.0001)\n",
    "        )\n",
    "\n",
    "    # Store clip ratios and differences\n",
    "    if len(ratios) > 1:\n",
    "        for i in range(1, len(ratios)):\n",
    "            clip_ratios.append(ratios[i])\n",
    "            clip_diffs.append(pos_diffs[i - 1] - neg_diffs[i - 1])\n",
    "\n",
    "    # Store mean scores\n",
    "    if pos_clip_id is not None and neg_clip_id is not None:\n",
    "        clip_id_to_mean_ear_score[neg_clip_id] = np.mean(neg_scores)\n",
    "        clip_id_to_mean_ear_score[pos_clip_id] = np.mean(pos_scores)\n",
    "        clip_id_to_mean_shimmer_score[neg_clip_id] = np.mean(neg_shimmer)\n",
    "        clip_id_to_mean_shimmer_score[pos_clip_id] = np.mean(pos_shimmer)\n",
    "\n",
    "        # Calculate total clip ratio\n",
    "        total_clip_ratios.append(\n",
    "            (np.mean(pos_scores) - np.mean(neg_scores)) / (np.mean(pos_scores) + 0.001)\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:00.951283Z",
     "iopub.status.busy": "2025-09-16T13:06:00.951133Z",
     "iopub.status.idle": "2025-09-16T13:06:02.521878Z",
     "shell.execute_reply": "2025-09-16T13:06:02.521371Z",
     "shell.execute_reply.started": "2025-09-16T13:06:00.951268Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Spectrum Decay Difference Percentiles:\n",
      "5th percentile: -0.2481\n",
      "10th percentile: -0.1749\n",
      "15th percentile: -0.1290\n",
      "85th percentile: 0.1310\n",
      "90th percentile: 0.1763\n",
      "95th percentile: 0.2502\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Create subplots\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 15))\n",
    "\n",
    "# Calculate means for legend\n",
    "mean_spec_decay_diff = np.mean(spec_decay_diff)\n",
    "mean_last_spec_decay_diff = np.mean(last_spec_decay_diff)\n",
    "mean_pos_decay = np.mean(pos_spec_decay_values)\n",
    "mean_neg_decay = np.mean(neg_spec_decay_values)\n",
    "mean_diff = np.mean(np.array(pos_spec_decay_values) - np.array(neg_spec_decay_values))\n",
    "\n",
    "# Calculate percentiles for spec_decay_diff\n",
    "percentiles = [0.05, 0.1, 0.15, 0.85, 0.9, 0.95]\n",
    "percentile_values = np.percentile(spec_decay_diff, [p * 100 for p in percentiles])\n",
    "print(\"\\nSpectrum Decay Difference Percentiles:\")\n",
    "for p, v in zip(percentiles, percentile_values):\n",
    "    print(f\"{p*100:.0f}th percentile: {v:.4f}\")\n",
    "\n",
    "# Plot histograms of differences\n",
    "ax1.hist(\n",
    "    spec_decay_diff,\n",
    "    bins=100,\n",
    "    range=(-0.5, 0.5),\n",
    "    alpha=0.3,\n",
    "    label=f\"All Spectrum Decay Differences (mean={mean_spec_decay_diff:.3f})\",\n",
    "    color=\"blue\",\n",
    ")\n",
    "ax1.hist(\n",
    "    last_spec_decay_diff,\n",
    "    bins=100,\n",
    "    range=(-0.5, 0.5),\n",
    "    alpha=0.3,\n",
    "    label=f\"Last Spectrum Decay Differences (mean={mean_last_spec_decay_diff:.3f})\",\n",
    "    color=\"red\",\n",
    ")\n",
    "\n",
    "# Add labels and title for differences plot\n",
    "ax1.set_xlabel(\"Normalized Spectrum Decay Difference\", fontsize=12)\n",
    "ax1.set_ylabel(\"Count\", fontsize=12)\n",
    "ax1.set_title(\n",
    "    \"Distribution of Spectrum Decay Differences\\nBetween Positive and Negative Samples\",\n",
    "    fontsize=14,\n",
    ")\n",
    "ax1.legend(fontsize=10)\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot histograms of raw values\n",
    "ax2.hist(\n",
    "    pos_spec_decay_values,\n",
    "    bins=100,\n",
    "    range=(-100000, 100000),\n",
    "    alpha=0.3,\n",
    "    label=f\"Positive Sample Decay (mean={mean_pos_decay:.1f})\",\n",
    "    color=\"green\",\n",
    ")\n",
    "ax2.hist(\n",
    "    neg_spec_decay_values,\n",
    "    bins=100,\n",
    "    range=(-100000, 100000),\n",
    "    alpha=0.3,\n",
    "    label=f\"Negative Sample Decay (mean={mean_neg_decay:.1f})\",\n",
    "    color=\"orange\",\n",
    ")\n",
    "\n",
    "# Add labels and title for raw values plot\n",
    "ax2.set_xlabel(\"Spectrum Decay Value\", fontsize=12)\n",
    "ax2.set_ylabel(\"Count\", fontsize=12)\n",
    "ax2.set_title(\n",
    "    \"Distribution of Raw Spectrum Decay Values\\nFor Positive and Negative Samples\",\n",
    "    fontsize=14,\n",
    ")\n",
    "ax2.legend(fontsize=10)\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# Calculate and plot the difference between positive and negative values\n",
    "diff_values = np.array(pos_spec_decay_values) - np.array(neg_spec_decay_values)\n",
    "ax3.hist(\n",
    "    diff_values,\n",
    "    bins=100,\n",
    "    range=(-100000, 100000),\n",
    "    alpha=0.7,\n",
    "    label=f\"Positive - Negative Difference (mean={mean_diff:.1f})\",\n",
    "    color=\"purple\",\n",
    ")\n",
    "\n",
    "# Add labels and title for difference plot\n",
    "ax3.set_xlabel(\"Difference Value (Positive - Negative)\", fontsize=12)\n",
    "ax3.set_ylabel(\"Count\", fontsize=12)\n",
    "ax3.set_title(\n",
    "    \"Distribution of Differences Between\\nPositive and Negative Spectrum Decay Values\",\n",
    "    fontsize=14,\n",
    ")\n",
    "ax3.legend(fontsize=10)\n",
    "ax3.grid(True, alpha=0.3)\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:02.522602Z",
     "iopub.status.busy": "2025-09-16T13:06:02.522450Z",
     "iopub.status.idle": "2025-09-16T13:06:03.179807Z",
     "shell.execute_reply": "2025-09-16T13:06:03.179312Z",
     "shell.execute_reply.started": "2025-09-16T13:06:02.522587Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = clip_ratios\n",
    "y = clip_diffs\n",
    "# Create the 2D histogram (heatmap)\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# Create a 2D histogram\n",
    "bin_edges = np.linspace(-0.5, 0.5, 101)  # 30 bins from -1 to 1\n",
    "hist, x_edges, y_edges = np.histogram2d(\n",
    "    x,\n",
    "    y,\n",
    "    bins=[bin_edges, bin_edges],  # Same bins for both x and y\n",
    "    range=[[-0.5, 0.5], [-0.5, 0.5]],  # Ensure range is from -1 to 1 for both axes\n",
    ")\n",
    "\n",
    "# Create a heatmap using pcolormesh for better control\n",
    "X, Y = np.meshgrid(x_edges[:-1], y_edges[:-1])\n",
    "plt.pcolormesh(X, Y, hist.T, cmap=\"viridis\", shading=\"auto\")\n",
    "\n",
    "# Add a color bar\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label(\"Counts\", rotation=270, labelpad=20, fontsize=12)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel(\"Clip quality diff ratios\", fontsize=12)\n",
    "plt.ylabel(\"Clip quality diff ratio difference with prev\", fontsize=12)\n",
    "plt.title(\"2D Histogram (Heatmap) of Correlated Data\", fontsize=14)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"2d_histogram.png\", dpi=300)  # Save to file (optional)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:03.180541Z",
     "iopub.status.busy": "2025-09-16T13:06:03.180392Z",
     "iopub.status.idle": "2025-09-16T13:06:03.433567Z",
     "shell.execute_reply": "2025-09-16T13:06:03.433081Z",
     "shell.execute_reply.started": "2025-09-16T13:06:03.180526Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentile  |  Quantile Value\n",
      "-------------------------------\n",
      "  5th      |   -0.095\n",
      " 10th      |   -0.071\n",
      " 20th      |   -0.046\n",
      " 50th      |    0.000\n",
      " 80th      |    0.043\n",
      " 90th      |    0.067\n",
      " 95th      |    0.088\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "# Plot histogram (PDF)\n",
    "counts, bins, patches = plt.hist(\n",
    "    total_clip_ratios,\n",
    "    bins=np.linspace(-0.5, 0.5, 100),\n",
    "    color=\"skyblue\",\n",
    "    edgecolor=\"black\",\n",
    "    alpha=0.7,\n",
    "    label=\"Histogram (PDF)\",\n",
    ")\n",
    "\n",
    "# Plot CDF on the same axis\n",
    "sorted_ratios = np.sort(total_clip_ratios)\n",
    "cdf = np.arange(1, len(sorted_ratios) + 1) / len(sorted_ratios)\n",
    "plt.plot(\n",
    "    sorted_ratios, cdf * counts.max(), color=\"red\", linewidth=2, label=\"CDF (scaled)\"\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Clip quality diff ratios\", fontsize=12)\n",
    "plt.ylabel(\"Count\", fontsize=12)\n",
    "plt.title(\"Distribution of Clip Quality Difference Ratios\", fontsize=14)\n",
    "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.5)\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "percentages = [0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95]\n",
    "print(\"Percentile  |  Quantile Value\")\n",
    "print(\"-------------------------------\")\n",
    "for idx, percentage in enumerate(percentages):\n",
    "    quantile_value = np.quantile(sorted_ratios, percentage)\n",
    "    quantile_value_rounded = round(quantile_value, 3)\n",
    "    print(f\"{int(percentage*100):>3d}th      |  {quantile_value_rounded:>7.3f}\")\n",
    "    # Draw the vertical line\n",
    "    plt.axvline(\n",
    "        quantile_value,\n",
    "        color=\"k\",\n",
    "        linestyle=\"dotted\",\n",
    "        linewidth=1,\n",
    "        alpha=0.8,\n",
    "        label=f\"{int(percentage*100)}th percentile\"\n",
    "        if idx == 0\n",
    "        else None,  # Only label first to avoid duplicate legend\n",
    "    )\n",
    "\n",
    "plt.xlim(-0.5, 0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:03.434478Z",
     "iopub.status.busy": "2025-09-16T13:06:03.434122Z",
     "iopub.status.idle": "2025-09-16T13:06:03.751032Z",
     "shell.execute_reply": "2025-09-16T13:06:03.750495Z",
     "shell.execute_reply.started": "2025-09-16T13:06:03.434462Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_audio_quality_measures(s3_id):\n",
    "    audio_quality = unpacked_pair_quality.get(s3_id, [])\n",
    "    if not audio_quality:\n",
    "        return [None for _ in range(11)]\n",
    "    return [\n",
    "        np.mean(\n",
    "            audio_quality[\"ear_v2_quality_scores\"]\n",
    "        ),  # float(audio_quality[\"ear_v2_quality_scores\"]),\n",
    "        float(audio_quality[\"shimmer_score\"]),\n",
    "        float(audio_quality[\"loudness_factor\"]),\n",
    "        audio_quality[\"spectral_character\"],\n",
    "        float(audio_quality[\"spectral_centroid\"]),\n",
    "        float(audio_quality[\"bass_ratio\"]),\n",
    "        float(audio_quality[\"mid_ratio\"]),\n",
    "        float(audio_quality[\"high_ratio\"]),\n",
    "        float(audio_quality[\"stereo_width\"]),\n",
    "        int(audio_quality[\"total_clips\"]),\n",
    "        float(audio_quality[\"clips_per_second\"]),\n",
    "        float(audio_quality[\"abs_loudness_factor\"]),\n",
    "        float(audio_quality[\"spectrum_decay\"]),\n",
    "    ]\n",
    "\n",
    "\n",
    "df[\n",
    "    [\n",
    "        \"pair_quality\",\n",
    "        \"total_shimmer_score\",\n",
    "        \"loudness_factor\",\n",
    "        \"spectral_character\",\n",
    "        \"spectral_centroid\",\n",
    "        \"bass_ratio\",\n",
    "        \"mid_ratio\",\n",
    "        \"high_ratio\",\n",
    "        \"stereo_width\",\n",
    "        \"total_clips\",\n",
    "        \"clips_per_second\",\n",
    "        \"loudness_abs\",\n",
    "        \"spectrum_decay\",\n",
    "    ]\n",
    "] = pd.DataFrame(df[\"s3_id\"].apply(get_audio_quality_measures).tolist(), index=df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:03.751733Z",
     "iopub.status.busy": "2025-09-16T13:06:03.751589Z",
     "iopub.status.idle": "2025-09-16T13:06:03.844516Z",
     "shell.execute_reply": "2025-09-16T13:06:03.843937Z",
     "shell.execute_reply.started": "2025-09-16T13:06:03.751719Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(37438, 108)\n",
      "(37430, 108)\n",
      "(37430, 108)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df = df.dropna(\n",
    "    subset=[\n",
    "        \"pair_quality\",\n",
    "        \"total_shimmer_score\",\n",
    "        \"loudness_factor\",\n",
    "        \"spectral_character\",\n",
    "        \"spectral_centroid\",\n",
    "        \"bass_ratio\",\n",
    "        \"mid_ratio\",\n",
    "        \"high_ratio\",\n",
    "        \"stereo_width\",\n",
    "        \"total_clips\",\n",
    "        \"clips_per_second\",\n",
    "        \"loudness_abs\",\n",
    "        \"spectrum_decay\",\n",
    "    ]\n",
    ")\n",
    "print(df.shape)\n",
    "df = df[\n",
    "    df[\"request_id\"].isin(\n",
    "        df[\"request_id\"].value_counts().index[df[\"request_id\"].value_counts() == 2]\n",
    "    )\n",
    "]\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:36.043975Z",
     "start_time": "2024-05-16T13:58:56.910958Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:03.845233Z",
     "iopub.status.busy": "2025-09-16T13:06:03.845084Z",
     "iopub.status.idle": "2025-09-16T13:06:03.865797Z",
     "shell.execute_reply": "2025-09-16T13:06:03.865302Z",
     "shell.execute_reply.started": "2025-09-16T13:06:03.845219Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique_requests 18715\n"
     ]
    }
   ],
   "source": [
    "# Let's use the old selection for now -- for quality assurance\n",
    "# expand the metadata columns -- this takes forever...~ 6 mins\n",
    "# test_slice = df[\"metadata\"].apply(lambda x: ast.literal_eval(str(x)))\n",
    "# test_slice = df[\"metadata\"]  # .apply(lambda x: custom_parse(x))\n",
    "# test_slice_series = test_slice.apply(pd.Series)\n",
    "# df = pd.concat([df, test_slice_series], axis=1, join=\"inner\")\n",
    "print(\"unique_requests\", df[\"request_id\"].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:40.799375Z",
     "start_time": "2024-05-16T13:59:36.394236Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:03.866464Z",
     "iopub.status.busy": "2025-09-16T13:06:03.866322Z",
     "iopub.status.idle": "2025-09-16T13:06:03.963412Z",
     "shell.execute_reply": "2025-09-16T13:06:03.962844Z",
     "shell.execute_reply.started": "2025-09-16T13:06:03.866450Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    37430\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    18715\n",
      "True     18715\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-bass-up-c-1    37430\n",
      "Name: count, dtype: int64 preference  model_name       \n",
      "False       chirp-bass-up-c-1    18715\n",
      "True        chirp-bass-up-c-1    18715\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    37430\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = df.loc[:, ~df.columns.duplicated()].copy()\n",
    "# get the original duration of the clips, if they are concacted\n",
    "df[\"original_duration_s\"] = df[\"total_start_s\"] + df[\"duration\"]\n",
    "# classify the continue at behavoirs by the duration choice\n",
    "audio_prompt_id_to_continue_at = {}\n",
    "for _, row in df[~df[\"continued_parent\"].isna()].iterrows():\n",
    "    audio_prompt_id = row[\"continued_parent\"]\n",
    "    if audio_prompt_id not in audio_prompt_id_to_continue_at:\n",
    "        audio_prompt_id_to_continue_at[audio_prompt_id] = row.get(\n",
    "            \"continue_at\", row[\"duration\"]\n",
    "        )\n",
    "    else:\n",
    "        # pick the max\n",
    "        audio_prompt_id = max(\n",
    "            audio_prompt_id_to_continue_at[audio_prompt_id],\n",
    "            row.get(\"continue_at\", row[\"duration\"]),\n",
    "        )\n",
    "print(len(audio_prompt_id_to_continue_at))\n",
    "df[\"has_continue_and_start_continue_at\"] = df[\"s3_id\"].apply(\n",
    "    lambda x: audio_prompt_id_to_continue_at.get(x)\n",
    ")\n",
    "# we want continue at to be at most of the clip...\n",
    "df[\"good_continue_at\"] = (\n",
    "    (df[\"has_continue_and_start_continue_at\"] / df[\"duration\"]) > 0.9\n",
    ") | df[\"has_continue_and_start_continue_at\"].isna()\n",
    "print(df[\"good_continue_at\"].value_counts())\n",
    "\n",
    "\n",
    "print(\n",
    "    \"\\n Check some basics... \\n\",\n",
    "    df[\"preference\"].value_counts(),\n",
    "    df[\"model_name\"].value_counts(),\n",
    "    df.groupby([\"preference\"])[\"model_name\"].value_counts(),\n",
    ")\n",
    "\n",
    "df = df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "df[\"duration_rel_diff\"] = df[\"duration\"].diff()\n",
    "df[\"play_rel_diff\"] = df[\"reaction_play_count\"].diff()\n",
    "print(df[\"task\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:03.968175Z",
     "iopub.status.busy": "2025-09-16T13:06:03.967856Z",
     "iopub.status.idle": "2025-09-16T13:06:04.022112Z",
     "shell.execute_reply": "2025-09-16T13:06:04.021624Z",
     "shell.execute_reply.started": "2025-09-16T13:06:03.968159Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pos_diff_preference\n",
       "1.0    13614\n",
       "2.0     5101\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values(by=[\"request_id\", \"preference\", \"diff_preference\"])\n",
    "df[\"pos_diff_preference\"] = df[\"diff_preference\"].diff()\n",
    "df[df[\"preference\"]][\"pos_diff_preference\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:04.022791Z",
     "iopub.status.busy": "2025-09-16T13:06:04.022647Z",
     "iopub.status.idle": "2025-09-16T13:06:04.111742Z",
     "shell.execute_reply": "2025-09-16T13:06:04.111194Z",
     "shell.execute_reply.started": "2025-09-16T13:06:04.022777Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    37430.000000\n",
      "mean        23.321426\n",
      "std          2.320646\n",
      "min         10.527700\n",
      "25%         22.188575\n",
      "50%         23.660797\n",
      "75%         24.876119\n",
      "max         31.184998\n",
      "Name: mean_ear_score, dtype: float64\n",
      "count    18715.000000\n",
      "mean         0.004152\n",
      "std          1.280919\n",
      "min         -7.640421\n",
      "25%         -0.820897\n",
      "50%          0.005867\n",
      "75%          0.818327\n",
      "max          6.406567\n",
      "Name: mean_ear_score_diff, dtype: float64\n",
      "count    18715.000000\n",
      "mean        -0.001313\n",
      "std          0.055987\n",
      "min         -0.457290\n",
      "25%         -0.035874\n",
      "50%          0.000230\n",
      "75%          0.034154\n",
      "max          0.276713\n",
      "Name: mean_ear_score_diff_ratio, dtype: float64\n",
      "count    37430.000000\n",
      "mean         0.886098\n",
      "std          1.359154\n",
      "min          0.000000\n",
      "25%          0.133333\n",
      "50%          0.440370\n",
      "75%          1.100000\n",
      "max         40.090411\n",
      "Name: mean_shimmer_score, dtype: float64\n",
      "count    18715.000000\n",
      "mean         0.005474\n",
      "std          0.985906\n",
      "min        -10.957143\n",
      "25%         -0.222222\n",
      "50%          0.000000\n",
      "75%          0.219048\n",
      "max         29.753333\n",
      "Name: mean_shimmer_score_diff, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df[\"mean_ear_score\"] = df[\"s3_id\"].map(clip_id_to_mean_ear_score)\n",
    "print(df[\"mean_ear_score\"].describe())\n",
    "\n",
    "df[\"mean_ear_score_diff\"] = df[\"mean_ear_score\"].diff()\n",
    "print(df[df[\"preference\"]][\"mean_ear_score_diff\"].describe())\n",
    "\n",
    "df[\"mean_ear_score_diff_ratio\"] = df[\"mean_ear_score\"].diff() / (\n",
    "    df[\"mean_ear_score\"] + 0.1\n",
    ")\n",
    "print(df[df[\"preference\"]][\"mean_ear_score_diff_ratio\"].describe())\n",
    "\n",
    "df[\"mean_shimmer_score\"] = df[\"s3_id\"].map(clip_id_to_mean_shimmer_score)\n",
    "print(df[\"mean_shimmer_score\"].describe())\n",
    "\n",
    "df[\"mean_shimmer_score_diff\"] = df[\"mean_shimmer_score\"].diff()\n",
    "print(df[df[\"preference\"]][\"mean_shimmer_score_diff\"].describe())\n",
    "\n",
    "df[\"mean_shimmer_score_diff_ratio\"] = df[\"mean_shimmer_score\"].diff() / (\n",
    "    df[\"mean_shimmer_score\"] + 0.1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:04.112419Z",
     "iopub.status.busy": "2025-09-16T13:06:04.112275Z",
     "iopub.status.idle": "2025-09-16T13:06:04.457700Z",
     "shell.execute_reply": "2025-09-16T13:06:04.457207Z",
     "shell.execute_reply.started": "2025-09-16T13:06:04.112404Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"loudness_abs\"],\n",
    "    label=\"pos\",\n",
    "    bins=np.linspace(-20, -5, 100),\n",
    "    alpha=0.25,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"loudness_abs\"],\n",
    "    label=\"neg\",\n",
    "    bins=np.linspace(-20, -5, 100),\n",
    "    alpha=0.25,\n",
    ")\n",
    "plt.xlabel(\"Loudness (dB)\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"Distribution of Loudness (abs) for Positive and Negative Preferences\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.25)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:04.458386Z",
     "iopub.status.busy": "2025-09-16T13:06:04.458240Z",
     "iopub.status.idle": "2025-09-16T13:06:04.971635Z",
     "shell.execute_reply": "2025-09-16T13:06:04.971144Z",
     "shell.execute_reply.started": "2025-09-16T13:06:04.458371Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tony/anaconda3/envs/suno_env_dev/lib/python3.10/site-packages/numpy/lib/histograms.py:906: RuntimeWarning: invalid value encountered in divide\n",
      "  return n/db/n.sum(), bin_edges\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean loudness (web): -13.653604648677533\n",
      "Mean loudness (mobile): nan\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare data\n",
    "web_loudness = df[(df[\"preference\"]) & (df[\"source\"] == \"web\")][\"loudness_abs\"]\n",
    "mobile_loudness = df[(df[\"preference\"]) & (df[\"source\"] != \"web\")][\"loudness_abs\"]\n",
    "bins = list(np.linspace(-20, -5, 100))\n",
    "\n",
    "# Plot histogram (PDF)\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(\n",
    "    web_loudness,\n",
    "    label=\"web\",\n",
    "    bins=bins,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    ")\n",
    "plt.hist(\n",
    "    mobile_loudness,\n",
    "    label=\"mobile\",\n",
    "    bins=bins,\n",
    "    alpha=0.5,\n",
    "    density=True,\n",
    ")\n",
    "plt.xlabel(\"Loudness (dB)\")\n",
    "plt.ylabel(\"Density\")\n",
    "plt.title(\"PDF: Loudness (abs) for Positive Preferences by Source\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.25)\n",
    "\n",
    "# Plot CDF\n",
    "plt.subplot(1, 2, 2)\n",
    "web_sorted = np.sort(web_loudness)\n",
    "web_cdf = np.arange(1, len(web_sorted) + 1) / len(web_sorted)\n",
    "plt.plot(web_sorted, web_cdf, label=\"web\")\n",
    "\n",
    "mobile_sorted = np.sort(mobile_loudness)\n",
    "mobile_cdf = np.arange(1, len(mobile_sorted) + 1) / len(mobile_sorted)\n",
    "plt.plot(mobile_sorted, mobile_cdf, label=\"mobile\")\n",
    "\n",
    "plt.xlabel(\"Loudness (dB)\")\n",
    "plt.ylabel(\"CDF\")\n",
    "plt.title(\"CDF: Loudness (abs) for Positive Preferences by Source\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.25)\n",
    "\n",
    "plt.tight_layout()\n",
    "print(\"Mean loudness (web):\", web_loudness.mean())\n",
    "print(\"Mean loudness (mobile):\", mobile_loudness.mean())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:04.972348Z",
     "iopub.status.busy": "2025-09-16T13:06:04.972192Z",
     "iopub.status.idle": "2025-09-16T13:06:05.207413Z",
     "shell.execute_reply": "2025-09-16T13:06:05.206914Z",
     "shell.execute_reply.started": "2025-09-16T13:06:04.972333Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "df[\"loudness_diff\"] = df[\"loudness_abs\"].diff() / df[\"loudness_abs\"]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"loudness_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"loudness_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['loudness_diff']):.2f}\",\n",
    "    bins=np.linspace(-0.5, 0.5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(f\"Loudness difference --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:05.208096Z",
     "iopub.status.busy": "2025-09-16T13:06:05.207952Z",
     "iopub.status.idle": "2025-09-16T13:06:05.705127Z",
     "shell.execute_reply": "2025-09-16T13:06:05.704627Z",
     "shell.execute_reply.started": "2025-09-16T13:06:05.208081Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "\n",
    "# Calculate mean_shimmer_score_diff and its ratio\n",
    "df[\"mean_shimmer_score_diff\"] = df[\"mean_shimmer_score\"].diff()\n",
    "df[\"mean_shimmer_score_diff_ratio\"] = (\n",
    "    df[\"mean_shimmer_score\"].diff() / df[\"mean_shimmer_score\"]\n",
    ")\n",
    "\n",
    "# Prepare percentiles for both diff and diff_ratio\n",
    "diff_data = df[df[\"preference\"]][\"mean_shimmer_score_diff\"].dropna()\n",
    "diff_ratio_data = df[df[\"preference\"]][\"mean_shimmer_score_diff_ratio\"].dropna()\n",
    "diff_percentiles = np.percentile(diff_data, lookup_percentiles)\n",
    "diff_ratio_percentiles = np.percentile(diff_ratio_data, lookup_percentiles)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "# Left plot: mean_shimmer_score_diff\n",
    "hist_range = (-1, 1)\n",
    "axes[0].hist(\n",
    "    diff_data,\n",
    "    label=f\"pos, mean: {np.mean(diff_data):.2f}\",\n",
    "    bins=np.linspace(*hist_range, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "diff_textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(diff_percentiles)\n",
    "    ]\n",
    ")\n",
    "axes[0].text(\n",
    "    0.05,\n",
    "    0.98,\n",
    "    diff_textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    transform=axes[0].transAxes,\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "# Only plot lines within the histogram range\n",
    "for percentile in diff_percentiles:\n",
    "    if hist_range[0] <= percentile <= hist_range[1]:\n",
    "        axes[0].axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "axes[0].set_xlim(hist_range)\n",
    "axes[0].set_title(\n",
    "    f\"Mean shimmer score diff\\n{lookup_percentiles[-1]}th: {diff_percentiles[-1]:.2f}\"\n",
    ")\n",
    "axes[0].legend()\n",
    "\n",
    "# Right plot: mean_shimmer_score_diff_ratio\n",
    "hist_ratio_range = (-2.5, 2.5)\n",
    "axes[1].hist(\n",
    "    diff_ratio_data,\n",
    "    label=f\"pos, mean: {np.mean(diff_ratio_data):.2f}\",\n",
    "    bins=np.linspace(*hist_ratio_range, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "diff_ratio_textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(diff_ratio_percentiles)\n",
    "    ]\n",
    ")\n",
    "axes[1].text(\n",
    "    0.05,\n",
    "    0.98,\n",
    "    diff_ratio_textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    transform=axes[1].transAxes,\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "# Only plot lines within the histogram range\n",
    "for percentile in diff_ratio_percentiles:\n",
    "    if hist_ratio_range[0] <= percentile <= hist_ratio_range[1]:\n",
    "        axes[1].axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "axes[1].set_xlim(hist_ratio_range)\n",
    "axes[1].set_title(\n",
    "    f\"Mean shimmer score diff ratio\\n{lookup_percentiles[-1]}th: {diff_ratio_percentiles[-1]:.2f}\"\n",
    ")\n",
    "axes[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:05.705817Z",
     "iopub.status.busy": "2025-09-16T13:06:05.705672Z",
     "iopub.status.idle": "2025-09-16T13:06:06.073934Z",
     "shell.execute_reply": "2025-09-16T13:06:06.073423Z",
     "shell.execute_reply.started": "2025-09-16T13:06:05.705803Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"pair_quality\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"pair_quality\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['pair_quality']):.2f}\",\n",
    "    bins=np.linspace(10, 30, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"pair_quality\"],\n",
    "    label=f\"neg, mean: {np.mean(df[df['preference']]['pair_quality']):.2f}\",\n",
    "    bins=np.linspace(10, 30, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(f\"Pair quality --> {lookup_percentiles[0]}th, {percentiles[0]:.2f}\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:06.074622Z",
     "iopub.status.busy": "2025-09-16T13:06:06.074477Z",
     "iopub.status.idle": "2025-09-16T13:06:06.091493Z",
     "shell.execute_reply": "2025-09-16T13:06:06.091038Z",
     "shell.execute_reply.started": "2025-09-16T13:06:06.074607Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"pair_quality_diff\"] = df[\"pair_quality\"].diff() / df[\"pair_quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:06.092111Z",
     "iopub.status.busy": "2025-09-16T13:06:06.091974Z",
     "iopub.status.idle": "2025-09-16T13:06:06.323619Z",
     "shell.execute_reply": "2025-09-16T13:06:06.323146Z",
     "shell.execute_reply.started": "2025-09-16T13:06:06.092098Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"pair_quality_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"pair_quality_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['pair_quality_diff']):.2f}\",\n",
    "    bins=np.linspace(-0.5, 0.5, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(f\"Pair quality diff --> {lookup_percentiles[0]}th, {percentiles[0]:.2f}\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:06.324287Z",
     "iopub.status.busy": "2025-09-16T13:06:06.324141Z",
     "iopub.status.idle": "2025-09-16T13:06:06.350436Z",
     "shell.execute_reply": "2025-09-16T13:06:06.350012Z",
     "shell.execute_reply.started": "2025-09-16T13:06:06.324273Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>request_id</th>\n",
       "      <th>pair_quality</th>\n",
       "      <th>preference</th>\n",
       "      <th>loudness_abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>d3aadfe3-8793-4673-8ec6-3bcea7419395</td>\n",
       "      <td>0088a93b-ad47-49d9-975b-759555d577d2</td>\n",
       "      <td>21.215900</td>\n",
       "      <td>False</td>\n",
       "      <td>-15.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>6fad74b1-f74b-454d-a01d-0615b9a68482</td>\n",
       "      <td>0088a93b-ad47-49d9-975b-759555d577d2</td>\n",
       "      <td>26.933483</td>\n",
       "      <td>True</td>\n",
       "      <td>-15.446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>3d3bcb6f-31f4-47c9-ab73-f3fbc76ad66a</td>\n",
       "      <td>01428d83-f019-4395-8796-cb8a71114c11</td>\n",
       "      <td>14.912433</td>\n",
       "      <td>False</td>\n",
       "      <td>-12.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>586487be-c711-4d45-a73f-983ed5fb33a1</td>\n",
       "      <td>01428d83-f019-4395-8796-cb8a71114c11</td>\n",
       "      <td>21.146933</td>\n",
       "      <td>True</td>\n",
       "      <td>-14.559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8530</th>\n",
       "      <td>50197954-a9d5-4d52-8b13-9ec81d4cfbd7</td>\n",
       "      <td>2359f3aa-25fb-4000-b42f-683e793492f8</td>\n",
       "      <td>16.645800</td>\n",
       "      <td>False</td>\n",
       "      <td>-14.392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8531</th>\n",
       "      <td>04ada686-54ad-4669-9383-f7266dcdcc28</td>\n",
       "      <td>2359f3aa-25fb-4000-b42f-683e793492f8</td>\n",
       "      <td>23.052367</td>\n",
       "      <td>True</td>\n",
       "      <td>-14.352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id                            request_id  pair_quality  preference  loudness_abs\n",
       "150   d3aadfe3-8793-4673-8ec6-3bcea7419395  0088a93b-ad47-49d9-975b-759555d577d2     21.215900       False       -15.905\n",
       "151   6fad74b1-f74b-454d-a01d-0615b9a68482  0088a93b-ad47-49d9-975b-759555d577d2     26.933483        True       -15.446\n",
       "344   3d3bcb6f-31f4-47c9-ab73-f3fbc76ad66a  01428d83-f019-4395-8796-cb8a71114c11     14.912433       False       -12.944\n",
       "345   586487be-c711-4d45-a73f-983ed5fb33a1  01428d83-f019-4395-8796-cb8a71114c11     21.146933        True       -14.559\n",
       "8530  50197954-a9d5-4d52-8b13-9ec81d4cfbd7  2359f3aa-25fb-4000-b42f-683e793492f8     16.645800       False       -14.392\n",
       "8531  04ada686-54ad-4669-9383-f7266dcdcc28  2359f3aa-25fb-4000-b42f-683e793492f8     23.052367        True       -14.352"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_requests = df[(df[\"pair_quality_diff\"] > 0.2) & (df[\"preference\"])][\n",
    "    \"request_id\"\n",
    "].unique()\n",
    "df[df[\"request_id\"].isin(subset_requests)][\n",
    "    [\"id\", \"request_id\", \"pair_quality\", \"preference\", \"loudness_abs\"]\n",
    "].head(n=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:06.351144Z",
     "iopub.status.busy": "2025-09-16T13:06:06.351006Z",
     "iopub.status.idle": "2025-09-16T13:06:07.486245Z",
     "shell.execute_reply": "2025-09-16T13:06:07.485751Z",
     "shell.execute_reply.started": "2025-09-16T13:06:06.351130Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CS said no tails is good\n",
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"spectral_centroid\"].dropna(), lookup_percentiles\n",
    ")\n",
    "percentiles = np.percentile(\n",
    "    df[~df[\"preference\"]][\"spectral_centroid\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"spectral_centroid\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['spectral_centroid']):.2f}\",\n",
    "    bins=np.linspace(0, 8000, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df[~df[\"preference\"]][\"spectral_centroid\"],\n",
    "    label=f\"neg, mean: {np.mean(df[~df['preference']]['spectral_centroid']):.2f}\",\n",
    "    bins=np.linspace(0, 8000, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\"Spectral Centroid\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:07.487156Z",
     "iopub.status.busy": "2025-09-16T13:06:07.486788Z",
     "iopub.status.idle": "2025-09-16T13:06:07.936753Z",
     "shell.execute_reply": "2025-09-16T13:06:07.936251Z",
     "shell.execute_reply.started": "2025-09-16T13:06:07.487139Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CS said no tails is good\n",
    "# take the relative centroid diff\n",
    "df[\"spectral_centroid_diff\"] = df[\"spectral_centroid\"].diff() / df[\"spectral_centroid\"]\n",
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95, 98]\n",
    "percentiles = np.percentile(\n",
    "    df[df[\"preference\"]][\"spectral_centroid_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df[df[\"preference\"]][\"spectral_centroid_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df[df['preference']]['spectral_centroid_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\n",
    "    f\"Spectral Centroid Difference ratio --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\"\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:07.937442Z",
     "iopub.status.busy": "2025-09-16T13:06:07.937298Z",
     "iopub.status.idle": "2025-09-16T13:06:08.049306Z",
     "shell.execute_reply": "2025-09-16T13:06:08.048737Z",
     "shell.execute_reply.started": "2025-09-16T13:06:07.937427Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "good_continue_at\n",
      "True    37430\n",
      "Name: count, dtype: int64\n",
      "\n",
      " Check some basics... \n",
      " preference\n",
      "False    18715\n",
      "True     18715\n",
      "Name: count, dtype: int64 model_name\n",
      "chirp-bass-up-c-1    37430\n",
      "Name: count, dtype: int64 preference  model_name       \n",
      "False       chirp-bass-up-c-1    18715\n",
      "True        chirp-bass-up-c-1    18715\n",
      "Name: count, dtype: int64\n",
      "task\n",
      "upsample    37430\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = df.loc[:, ~df.columns.duplicated()].copy()\n",
    "# get the original duration of the clips, if they are concacted\n",
    "df[\"original_duration_s\"] = df[\"total_start_s\"] + df[\"duration\"]\n",
    "# classify the continue at behavoirs by the duration choice\n",
    "audio_prompt_id_to_continue_at = {}\n",
    "for _, row in df[~df[\"continued_parent\"].isna()].iterrows():\n",
    "    audio_prompt_id = row[\"continued_parent\"]\n",
    "    if audio_prompt_id not in audio_prompt_id_to_continue_at:\n",
    "        audio_prompt_id_to_continue_at[audio_prompt_id] = row[\"continue_at\"]\n",
    "    else:\n",
    "        # pick the max\n",
    "        audio_prompt_id = max(\n",
    "            audio_prompt_id_to_continue_at[audio_prompt_id], row[\"continue_at\"]\n",
    "        )\n",
    "print(len(audio_prompt_id_to_continue_at))\n",
    "df[\"has_continue_and_start_continue_at\"] = df[\"s3_id\"].apply(\n",
    "    lambda x: audio_prompt_id_to_continue_at.get(x)\n",
    ")\n",
    "# we want continue at to be at most of the clip...\n",
    "df[\"good_continue_at\"] = (\n",
    "    (df[\"has_continue_and_start_continue_at\"] / df[\"duration\"]) > 0.9\n",
    ") | df[\"has_continue_and_start_continue_at\"].isna()\n",
    "print(df[\"good_continue_at\"].value_counts())\n",
    "\n",
    "\n",
    "print(\n",
    "    \"\\n Check some basics... \\n\",\n",
    "    df[\"preference\"].value_counts(),\n",
    "    df[\"model_name\"].value_counts(),\n",
    "    df.groupby([\"preference\"])[\"model_name\"].value_counts(),\n",
    ")\n",
    "\n",
    "df = df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "df[\"duration_rel_diff\"] = df[\"duration\"].diff()\n",
    "df[\"play_rel_diff\"] = df[\"reaction_play_count\"].diff()\n",
    "print(df[\"task\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:08.050037Z",
     "iopub.status.busy": "2025-09-16T13:06:08.049887Z",
     "iopub.status.idle": "2025-09-16T13:06:08.869125Z",
     "shell.execute_reply": "2025-09-16T13:06:08.868571Z",
     "shell.execute_reply.started": "2025-09-16T13:06:08.050023Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2670 duplicated prompts 1335 unique requests\n",
      "Found 669 request_ids with duplicate prompts but not highest play counts in their group\n",
      "['1513823f-300c-4c71-897d-9d206a7cf65d', '53bb881a-2a0a-4f7a-9909-4155bf2a0c81', 'ccc0cf8c-dbc5-4338-8559-9c744287f122', '37a5891f-4655-4230-b767-02b0494073c1', '5c5a9880-14de-493b-8dca-cef1e4f1a0c6', 'e7730267-2178-4982-bcc7-de6601e51b4e', 'b3aa0d8b-f8fb-433d-8b90-e069fb8ccd04', 'cfd9daa9-e40a-4f21-acfe-260bb8b3ac25', '816caa81-e611-4857-91fd-1c7d5a4c956d', 'becd6e94-d62e-4a96-b096-2771c7b403aa']\n",
      "Before dedup user gen requests 37430\n",
      "After dedup user gen requests 36092\n"
     ]
    }
   ],
   "source": [
    "# Find duplicated prompts with count > 2\n",
    "df[\"tags\"] = df[\"metadata\"].apply(lambda x: x.get(\"tags\", \"\"))\n",
    "duplicate_entries = df.groupby([\"user_id\", \"prompt_text\", \"tags\"]).filter(\n",
    "    lambda x: len(x) > 2\n",
    ")\n",
    "print(\n",
    "    \"Found\",\n",
    "    len(duplicate_entries),\n",
    "    \"duplicated prompts\",\n",
    "    len(duplicate_entries[\"request_id\"].unique()),\n",
    "    \"unique requests\",\n",
    ")\n",
    "\n",
    "# Group by user_id, prompt_text, and tags to find duplicate prompt groups\n",
    "prompt_groups = duplicate_entries.groupby([\"user_id\", \"prompt_text\", \"tags\"])\n",
    "\n",
    "# For each prompt group, find the request_id with the highest total reaction_play_count\n",
    "low_play_count_request_ids = []\n",
    "for prompt_key, prompt_group in prompt_groups:\n",
    "    # Get the sum of reaction_play_count for each request_id in this group\n",
    "    request_play_counts = prompt_group.groupby(\"request_id\")[\n",
    "        \"reaction_play_count\"\n",
    "    ].sum()\n",
    "\n",
    "    # Find the max play count in this group\n",
    "    max_play_count = request_play_counts.max()\n",
    "\n",
    "    # Add request_ids that don't have the max play count to our filter list\n",
    "    lower_play_count_request_ids = request_play_counts[\n",
    "        request_play_counts < max_play_count\n",
    "    ].index.tolist()\n",
    "    low_play_count_request_ids.extend(lower_play_count_request_ids)\n",
    "\n",
    "# Display the filtered request IDs\n",
    "print(\n",
    "    f\"Found {len(low_play_count_request_ids)} request_ids with duplicate prompts but not highest play counts in their group\"\n",
    ")\n",
    "print(\n",
    "    low_play_count_request_ids[:10]\n",
    "    if len(low_play_count_request_ids) > 10\n",
    "    else low_play_count_request_ids\n",
    ")\n",
    "print(\"Before dedup user gen requests\", df.shape[0])\n",
    "df = df[~df[\"request_id\"].isin(low_play_count_request_ids)]\n",
    "print(\"After dedup user gen requests\", df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:08.869825Z",
     "iopub.status.busy": "2025-09-16T13:06:08.869679Z",
     "iopub.status.idle": "2025-09-16T13:06:08.894934Z",
     "shell.execute_reply": "2025-09-16T13:06:08.894446Z",
     "shell.execute_reply.started": "2025-09-16T13:06:08.869810Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    36092.000000\n",
      "mean       632.705087\n",
      "std        846.451909\n",
      "min         20.000000\n",
      "25%        161.000000\n",
      "50%        375.000000\n",
      "75%        742.000000\n",
      "max      17460.000000\n",
      "Name: user_n_clips, dtype: float64\n",
      "count    36092.000000\n",
      "mean         3.861243\n",
      "std          3.562980\n",
      "min          2.000000\n",
      "25%          2.000000\n",
      "50%          2.000000\n",
      "75%          4.000000\n",
      "max         42.000000\n",
      "Name: user_n_clips, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"user_n_clips\"].describe())\n",
    "df[\"user_n_clips\"] = df.groupby(\"user_id\")[\"s3_id\"].transform(\"count\")\n",
    "print(df[\"user_n_clips\"].describe())\n",
    "# BREAK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.035167Z",
     "start_time": "2024-05-16T13:59:40.801098Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:08.895604Z",
     "iopub.status.busy": "2025-09-16T13:06:08.895455Z",
     "iopub.status.idle": "2025-09-16T13:06:08.951835Z",
     "shell.execute_reply": "2025-09-16T13:06:08.951318Z",
     "shell.execute_reply.started": "2025-09-16T13:06:08.895589Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative 16745 positive 10250\n",
      "total pair requests 18046 selected pair requests 9414 frac 0.522\n"
     ]
    }
   ],
   "source": [
    "normal_pos_play_count = 3\n",
    "# this is lower, cause a concat is probably already ensuring that it is good\n",
    "concat_pos_play_count = 2\n",
    "# this is a filter on the concated clip\n",
    "concat_total_play_count = 3\n",
    "\n",
    "neg_filter_selection_mask = (\n",
    "    (~df[\"preference\"])  # get basics aligned\n",
    "    & (df[\"reaction_play_count\"] >= 1)  # has to be played once\n",
    "    # & (df[\"play_count\"] <= 3)  # if it is actually bad, shouldn't be listened often\n",
    "    & (df[\"duration\"] >= 30)  # can't be too short, otherwise it is obvious\n",
    "    # & (df[\"duration\"] <= 60)  # can't be badly long\n",
    "    & (df[\"has_continue_and_start_continue_at\"].isna())  # won't have any continues\n",
    "    & (df[\"norm_play_frac\"] <= 2.1)\n",
    "    # & (df[\"sum_total_play_duration_5\"] >= 31)  # this cut doesn't matter as much tbh\n",
    "    # & (df[\"dislike_count\"] >= 1) # this is kinda strict\n",
    "    #     & (\n",
    "    #         (df_slice[\"is_in_playlist\"] == False)\n",
    "    #         & (df_slice[\"concat_in_playlist\"] == False)\n",
    "    #     )  # can't be part of a playlist -- otherwise there are some like signal in it?\n",
    ")\n",
    "pos_filter_selectin_mask = (\n",
    "    (df[\"preference\"])  # get basics aligned\n",
    "    & (\n",
    "        df[\"good_continue_at\"]\n",
    "    )  # if continue, needs to continue off a certain percentage\n",
    "    & (df[\"reaction_play_count\"] >= 1)\n",
    "    & (df[\"play_rel_diff\"] >= 0)  # this is more like quality assurance\n",
    "    & (df[\"duration\"] >= 30)  # can't be too short, otherwise it is obvious\n",
    "    & (df[\"dislike_count\"] == 0)  # can't have dislikes\n",
    "    & (df[\"flag_count\"] == 0)  # can't have issues\n",
    "    & (\n",
    "        (\n",
    "            (df[\"part_of_concat\"])\n",
    "            & (df[\"reaction_play_count\"] >= concat_pos_play_count)\n",
    "            & (df[\"concat_play_counts\"] >= concat_total_play_count)\n",
    "        )\n",
    "        | (\n",
    "            (~df[\"part_of_concat\"])\n",
    "            & (df[\"reaction_play_count\"] >= normal_pos_play_count)\n",
    "            & (\n",
    "                df[\"norm_play_frac\"] >= 2.1\n",
    "            )  # this is a bit of a luxury cut...not for now...\n",
    "            & (df[\"sum_total_play_duration_5\"] >= 31)\n",
    "        )\n",
    "    )\n",
    "    # & (abs(df[\"mean_ear_score_diff\"]) >= 1)\n",
    "    # & (abs(df[\"mean_ear_score_diff_ratio\"]) >= 0.05)\n",
    "    & (df[\"source\"] == \"web\")\n",
    "    & (df[\"cer_diff\"] < 0.05)\n",
    "    # & (df[\"mean_shimmer_score_diff\"] < 1.0)\n",
    "    # & (df[\"norm_play_frac\"] >= 1.9)\n",
    "    & (df[\"user_n_clips\"] >= 2)  # user needs to have genereated at least 20\n",
    "    # & (df[\"duration_rel_diff\"] < 10) # positive isn't just longer\n",
    "    # & ((df[\"task\"] == \"\") | (df[\"task\"] == \"extend\"))\n",
    "    # & (\n",
    "    #     (df[\"upvote_count\"] >= 1)\n",
    "    #     | (df[\"reaction_play_count\"] >= 5)\n",
    "    #     | (df[\"concat_play_counts\"] >= 5)\n",
    "    # )\n",
    "    # & (df[\"pos_diff_preference\"] == 2)\n",
    "    # & ((0 < df[\"similarity\"]) &  (df[\"similarity\"] <= 0.99))\n",
    "    # & (\n",
    "    #     (df[\"cer_diff_preference\"] < 0.25) & (df[\"cer\"] < 0.8)\n",
    "    # )  # cut on hoot cer difference and abs cer\n",
    "    # & (df[\"pair_quality\"] > 0.31)  # bottom 5%\n",
    "    # & ((df[\"total_shimmer_score\"] < 1) | (df[\"shimmer_score_diff\"] < 0.4))\n",
    "    # & (df[\"stereo_width_diff\"] > -0.2)  # cut off bottom 5%\n",
    "    # & (df[\"spectral_centroid_diff\"] < 0.25)  # crop off the top 5%\n",
    ")\n",
    "print(\n",
    "    \"negative\",\n",
    "    sum(neg_filter_selection_mask),\n",
    "    \"positive\",\n",
    "    sum(pos_filter_selectin_mask),\n",
    ")\n",
    "\n",
    "neg_filter_requests = df[neg_filter_selection_mask][\"request_id\"].unique()\n",
    "pos_filter_requests = df[pos_filter_selectin_mask][\"request_id\"].unique()\n",
    "# looking for very strong signal here:\n",
    "# listen to the positive/negative more than once\n",
    "# disliked one of the clips\n",
    "unique_requests = set(pos_filter_requests).intersection(neg_filter_requests)\n",
    "print(\n",
    "    \"total pair requests\",\n",
    "    df[\"request_id\"].nunique(),\n",
    "    \"selected pair requests\",\n",
    "    len(unique_requests),\n",
    "    f\"frac {len(unique_requests) / df['request_id'].nunique():.3f}\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.250737Z",
     "start_time": "2024-05-16T13:59:41.036434Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:08.952604Z",
     "iopub.status.busy": "2025-09-16T13:06:08.952459Z",
     "iopub.status.idle": "2025-09-16T13:06:09.007593Z",
     "shell.execute_reply": "2025-09-16T13:06:09.007040Z",
     "shell.execute_reply.started": "2025-09-16T13:06:08.952589Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " requests 9414 clips 18828 total khrs 1.098; N gpus for 1000 iters 1.177; 4 gpus for x iters 294.188; n unique users 7592 n pro users 7399\n"
     ]
    }
   ],
   "source": [
    "df_slice = df[df[\"request_id\"].isin(set(unique_requests))].copy()\n",
    "print(\n",
    "    f\"{os.path.basename(OUT_DATA_DIR)} requests\",\n",
    "    df_slice[\"request_id\"].nunique(),\n",
    "    \"clips\",\n",
    "    df_slice.shape[0],\n",
    "    f\"total khrs {sum(df_slice['duration'] / 3600 / 1000):.3f};\",\n",
    "    f\"N gpus for 1000 iters {df_slice.shape[0] / 8 / 2 / 1000:.3f};\",\n",
    "    f\"4 gpus for x iters {df_slice.shape[0] / 8 / 2 / 4:.3f};\",\n",
    "    f\"n unique users {df_slice['user_id'].nunique()}\",\n",
    "    f\"n pro users {df_slice[df_slice['is_pro_user']]['user_id'].nunique()}\",\n",
    ")\n",
    "# up t7 requests 37735 clips 75470 total khrs 3.964; N gpus for 1000 iters 4.717; 4 gpus for x iters 1179.219; n unique users 19093 n pro users 16217\n",
    "# up t17 requests 49262 clips 98524 total khrs 5.187; N gpus for 1000 iters 6.158; 4 gpus for x iters 1539.438; n unique users 24093 n pro users 20326\n",
    "# up v2 t2 requests 10772 clips 21544 total khrs 1.154; N gpus for 1000 iters 1.347; 4 gpus for x iters 336.625; n unique users 6527 n pro users 6027\n",
    "# up v3 t10 requests 27102 clips 54204 total khrs 2.938; N gpus for 1000 iters 3.388; 4 gpus for x iters 846.938; n unique users 13932 n pro users 12187\n",
    "# up v4 t1  requests 3201 clips 6402 total khrs 0.343; N gpus for 1000 iters 0.400; 4 gpus for x iters 100.031; n unique users 2363 n pro users 2321\n",
    "# up v4 t7  requests 31797 clips 63594 total khrs 3.384; N gpus for 1000 iters 3.975; 4 gpus for x iters 993.656; n unique users 16398 n pro users 15325\n",
    "# up v5 t2  requests 20030 clips 40060 total khrs 2.108; N gpus for 1000 iters 2.504; 4 gpus for x iters 625.938; n unique users 12395 n pro users 9156\n",
    "# up v6 t11  requests 43842 clips 87684 total khrs 4.588; N gpus for 1000 iters 5.480; 4 gpus for x iters 1370.062; n unique users 24777 n pro users 16668\n",
    "# v2 v1 t0  requests 3688 clips 7376 total khrs 0.381; N gpus for 1000 iters 0.461; 4 gpus for x iters 115.250; n unique users 3261 n pro users 2141\n",
    "# v2 v1 t1-5  requests 5406 clips 10812 total khrs 0.565; N gpus for 1000 iters 0.676; 4 gpus for x iters 168.938; n unique users 4655 n pro users 3180\n",
    "# v2 v1 t1-6   requests 9134 clips 18268 total khrs 0.952; N gpus for 1000 iters 1.142; 4 gpus for x iters 285.438; n unique users 7658 n pro users 5242\n",
    "# v2 v1 t1-7  requests 12274 clips 24548 total khrs 1.282; N gpus for 1000 iters 1.534; 4 gpus for x iters 383.562; n unique users 10036 n pro users 6818\n",
    "# v2 v1 t1-17  requests 14235 clips 28470 total khrs 1.497; N gpus for 1000 iters 1.779; 4 gpus for x iters 444.844; n unique users 11018 n pro users 8181\n",
    "# v2 v1 t1-18  requests 10007 clips 20014 total khrs 1.044; N gpus for 1000 iters 1.251; 4 gpus for x iters 312.719; n unique users 7998 n pro users 5831\n",
    "# v2 v3 t1   requests 13157 clips 26314 total khrs 1.430; N gpus for 1000 iters 1.645; 4 gpus for x iters 411.156; n unique users 8409 n pro users 8193\n",
    "# v2 v3 t2   requests 22601 clips 45202 total khrs 2.465; N gpus for 1000 iters 2.825; 4 gpus for x iters 706.281; n unique users 13991 n pro users 13639\n",
    "# v2 v3 t3  requests 48158 clips 96316 total khrs 5.253; N gpus for 1000 iters 6.020; 4 gpus for x iters 1504.938; n unique users 26195 n pro users 25433\n",
    "# v2 v3 t4  requests 62132 clips 124264 total khrs 6.772; N gpus for 1000 iters 7.766; 4 gpus for x iters 1941.625; n unique users 32164 n pro users 31090\n",
    "# v2 v3 t8  requests 65469 clips 130938 total khrs 7.135; N gpus for 1000 iters 8.184; 4 gpus for x iters 2045.906; n unique users 33619 n pro users 32454\n",
    "# v2 v3 t9 requests 33031 clips 66062 total khrs 3.666; N gpus for 1000 iters 4.129; 4 gpus for x iters 1032.219; n unique users 21173 n pro users 20503\n",
    "# v2 v3 t10  requests 36607 clips 73214 total khrs 4.066; N gpus for 1000 iters 4.576; 4 gpus for x iters 1143.969; n unique users 23179 n pro users 22385\n",
    "# v2 t3 t11  requests 43078 clips 86156 total khrs 4.788; N gpus for 1000 iters 5.385; 4 gpus for x iters 1346.188; n unique users 26646 n pro users 25611\n",
    "# v2 t3 t14  requests 45156 clips 90312 total khrs 5.018; N gpus for 1000 iters 5.644; 4 gpus for x iters 1411.125; n unique users 27724 n pro users 26626\n",
    "# v2 t3 t20  requests 48822 clips 97644 total khrs 5.425; N gpus for 1000 iters 6.103; 4 gpus for x iters 1525.688; n unique users 29583 n pro users 28314\n",
    "# v2 t3 t22  requests 173478 clips 346956 total khrs 18.892; N gpus for 1000 iters 21.685; 4 gpus for x iters 5421.188; n unique users 67818 n pro users 64118\n",
    "# v2 t4 t1   requests 5563 clips 11126 total khrs 0.611; N gpus for 1000 iters 0.695; 4 gpus for x iters 173.844; n unique users 4514 n pro users 4478\n",
    "# v2 t4 t3  requests 4667 clips 9334 total khrs 0.513; N gpus for 1000 iters 0.583; 4 gpus for x iters 145.844; n unique users 3884 n pro users 3851\n",
    "# v2 t4 t4  requests 5563 clips 11126 total khrs 0.611; N gpus for 1000 iters 0.695; 4 gpus for x iters 173.844; n unique users 4514 n pro users 4478\n",
    "# v2 t4 t5   requests 6589 clips 13178 total khrs 0.718; N gpus for 1000 iters 0.824; 4 gpus for x iters 205.906; n unique users 5204 n pro users 5160\n",
    "# v2 t4 t9  requests 4836 clips 9672 total khrs 0.534; N gpus for 1000 iters 0.605; 4 gpus for x iters 151.125; n unique users 3827 n pro users 3799\n",
    "# v2 t4 t16  requests 11003 clips 22006 total khrs 1.217; N gpus for 1000 iters 1.375; 4 gpus for x iters 343.844; n unique users 7822 n pro users 7724\n",
    "# v2 t4 t17 requests 15605 clips 31210 total khrs 1.724; N gpus for 1000 iters 1.951; 4 gpus for x iters 487.656; n unique users 10557 n pro users 10374\n",
    "# v2 t4 t18  requests 21479 clips 42958 total khrs 2.385; N gpus for 1000 iters 2.685; 4 gpus for x iters 671.219; n unique users 13858 n pro users 13589\n",
    "# v2 t4 t19  requests 19816 clips 39632 total khrs 2.206; N gpus for 1000 iters 2.477; 4 gpus for x iters 619.250; n unique users 13045 n pro users 12793\n",
    "# v2 t4 t20  requests 17780 clips 35560 total khrs 1.979; N gpus for 1000 iters 2.223; 4 gpus for x iters 555.625; n unique users 12041 n pro users 11810\n",
    "# v2 t4 t21  requests 69490 clips 138980 total khrs 7.583; N gpus for 1000 iters 8.686; 4 gpus for x iters 2171.562; n unique users 33013 n pro users 32231\n",
    "# v2 t4 t23  requests 20702 clips 41404 total khrs 2.273; N gpus for 1000 iters 2.588; 4 gpus for x iters 646.938; n unique users 10816 n pro users 10610\n",
    "# v2 t4 t24  requests 9801 clips 19602 total khrs 1.079; N gpus for 1000 iters 1.225; 4 gpus for x iters 306.281; n unique users 2464 n pro users 2442\n",
    "# v2 t4 t25   requests 14973 clips 29946 total khrs 1.649; N gpus for 1000 iters 1.872; 4 gpus for x iters 467.906; n unique users 5062 n pro users 4978\n",
    "# v2 t4 t26   requests 20952 clips 41904 total khrs 2.274; N gpus for 1000 iters 2.619; 4 gpus for x iters 654.750; n unique users 4129 n pro users 4071\n",
    "# v2 t4 t27  requests 7953 clips 15906 total khrs 0.870; N gpus for 1000 iters 0.994; 4 gpus for x iters 248.531; n unique users 1940 n pro users 1919\n",
    "# v2 t4 t28   requests 7937 clips 15874 total khrs 0.890; N gpus for 1000 iters 0.992; 4 gpus for x iters 248.031; n unique users 2152 n pro users 2135\n",
    "# v2 t4 t39  requests 12626 clips 25252 total khrs 1.418; N gpus for 1000 iters 1.578; 4 gpus for x iters 394.562; n unique users 3352 n pro users 328\n",
    "# carp t1 v1  requests 944 clips 1888 total khrs 0.109; N gpus for 1000 iters 0.118; 4 gpus for x iters 29.500; n unique users 872 n pro users 867\n",
    "# carp t1 v2  requests 5046 clips 10092 total khrs 0.585; N gpus for 1000 iters 0.631; 4 gpus for x iters 157.688; n unique users 4309 n pro users 4255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.277006Z",
     "start_time": "2024-05-16T13:59:41.252105Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:09.008268Z",
     "iopub.status.busy": "2025-09-16T13:06:09.008117Z",
     "iopub.status.idle": "2025-09-16T13:06:09.027170Z",
     "shell.execute_reply": "2025-09-16T13:06:09.026684Z",
     "shell.execute_reply.started": "2025-09-16T13:06:09.008253Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive in playlist (2197, 124)\n"
     ]
    }
   ],
   "source": [
    "test_mask = (df_slice[\"preference\"]) & (\n",
    "    (df_slice[\"is_in_playlist\"]) | (df_slice[\"concat_in_playlist\"])\n",
    ")\n",
    "print(\"positive in playlist\", df_slice[test_mask].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:09.027804Z",
     "iopub.status.busy": "2025-09-16T13:06:09.027668Z",
     "iopub.status.idle": "2025-09-16T13:06:09.740898Z",
     "shell.execute_reply": "2025-09-16T13:06:09.740377Z",
     "shell.execute_reply.started": "2025-09-16T13:06:09.027790Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CS said -1 means stero to mono; 1 means mono to stereo\n",
    "# only cut off the left side\n",
    "df_slice[\"stereo_width_diff\"] = df_slice[\n",
    "    \"stereo_width\"\n",
    "].diff()  # / df_slice[\"stereo_width\"]\n",
    "# df_slice[\"stereo_width_diff\"] = df_slice[\"stereo_width\"]\n",
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "percentiles = np.percentile(\n",
    "    df_slice[df_slice[\"preference\"]][\"stereo_width_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df_slice[df_slice[\"preference\"]][\"stereo_width_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df_slice[df_slice['preference']]['stereo_width_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df_slice[(df_slice[\"preference\"]) & (df_slice[\"source\"] == \"web\")][\n",
    "        \"stereo_width_diff\"\n",
    "    ],\n",
    "    label=f\"pos, mean: {np.mean(df_slice[(df_slice['preference']) & (df_slice['source'] == 'web')]['stereo_width_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 400),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(\n",
    "    f\"Stereo Width Difference --> {lookup_percentiles[0]}th, {percentiles[0]:.2f}\"\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:09.741595Z",
     "iopub.status.busy": "2025-09-16T13:06:09.741449Z",
     "iopub.status.idle": "2025-09-16T13:06:10.563773Z",
     "shell.execute_reply": "2025-09-16T13:06:10.563277Z",
     "shell.execute_reply.started": "2025-09-16T13:06:09.741579Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13333333333333333\n",
      "0.8\n",
      "2.3333333333333335\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    df_slice[df_slice[\"preference\"]][\"total_shimmer_score\"],\n",
    "    label=f\"pos, mean: {np.mean(df_slice[df_slice['preference']]['total_shimmer_score']):.2f}\",\n",
    "    bins=np.linspace(0, 10, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "plt.hist(\n",
    "    df_slice[~df_slice[\"preference\"]][\"total_shimmer_score\"],\n",
    "    label=f\"neg, mean: {np.mean(df_slice[~df_slice['preference']]['total_shimmer_score']):.2f}\",\n",
    "    bins=np.linspace(0, 10, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "percentiles = np.percentile(\n",
    "    df_slice[df_slice[\"preference\"]][\"total_shimmer_score\"].dropna(), [50, 75, 90]\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    print(percentile)\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.yscale(\"log\")\n",
    "plt.title(f\"Shimmer score\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:10.564457Z",
     "iopub.status.busy": "2025-09-16T13:06:10.564313Z",
     "iopub.status.idle": "2025-09-16T13:06:10.799435Z",
     "shell.execute_reply": "2025-09-16T13:06:10.798967Z",
     "shell.execute_reply.started": "2025-09-16T13:06:10.564443Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lookup_percentiles = [5, 10, 20, 50, 80, 90, 95]\n",
    "df_slice[\"loudness_diff\"] = df_slice[\"loudness_abs\"].diff() / df_slice[\"loudness_abs\"]\n",
    "percentiles = np.percentile(\n",
    "    df_slice[df_slice[\"preference\"]][\"loudness_diff\"].dropna(), lookup_percentiles\n",
    ")\n",
    "plt.hist(\n",
    "    df_slice[df_slice[\"preference\"]][\"loudness_diff\"],\n",
    "    label=f\"pos, mean: {np.mean(df_slice[df_slice['preference']]['loudness_diff']):.2f}\",\n",
    "    bins=np.linspace(-1, 1, 100),\n",
    "    alpha=0.5,\n",
    ")\n",
    "textstr = \"\\n\".join(\n",
    "    [\n",
    "        f\"{lookup_percentiles[i]}th: {percentile:.2f}\"\n",
    "        for i, percentile in enumerate(percentiles)\n",
    "    ]\n",
    ")\n",
    "plt.gcf().text(\n",
    "    0.15,\n",
    "    0.98,\n",
    "    textstr,\n",
    "    fontsize=10,\n",
    "    verticalalignment=\"top\",\n",
    "    horizontalalignment=\"left\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.5),\n",
    ")\n",
    "for percentile in percentiles:\n",
    "    plt.axvline(x=percentile, color=\"r\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.title(f\"Loudness difference --> {lookup_percentiles[-1]}th, {percentiles[-1]:.2f}\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:10.800092Z",
     "iopub.status.busy": "2025-09-16T13:06:10.799949Z",
     "iopub.status.idle": "2025-09-16T13:06:10.815052Z",
     "shell.execute_reply": "2025-09-16T13:06:10.814620Z",
     "shell.execute_reply.started": "2025-09-16T13:06:10.800077Z"
    }
   },
   "outputs": [],
   "source": [
    "# tr_metas_t1_v7 = read_jsonl(os.path.join(\"/app2/suno/data/dpo/diff2_v2_d3_v10\", f\"metas_tr.jsonl\"))\n",
    "# known_train_ids = set()\n",
    "# for prev_tr_meta in tr_metas_t1_v7:\n",
    "#     known_train_ids.add(prev_tr_meta[\"id_x\"])\n",
    "# print(len(known_train_ids))\n",
    "# print(df_slice.shape)\n",
    "# df_slice = df_slice[~df_slice[\"id\"].isin(known_train_ids)].copy()\n",
    "# print(df_slice.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:10.815627Z",
     "iopub.status.busy": "2025-09-16T13:06:10.815498Z",
     "iopub.status.idle": "2025-09-16T13:06:10.828663Z",
     "shell.execute_reply": "2025-09-16T13:06:10.828236Z",
     "shell.execute_reply.started": "2025-09-16T13:06:10.815614Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice[\"created_at\"] = pd.to_datetime(df_slice[\"created_at\"], utc=True)\n",
    "# cutoff_date = pd.to_datetime(\"2025-05-12\", utc=True)\n",
    "# print(df_slice.shape, df_slice[df_slice[\"created_at\"] >= cutoff_date].shape)\n",
    "# df_slice = df_slice[(df_slice[\"created_at\"] >= cutoff_date)].copy()\n",
    "# print(\"after date cut\", df_slice.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:10.829317Z",
     "iopub.status.busy": "2025-09-16T13:06:10.829181Z",
     "iopub.status.idle": "2025-09-16T13:06:10.845309Z",
     "shell.execute_reply": "2025-09-16T13:06:10.844845Z",
     "shell.execute_reply.started": "2025-09-16T13:06:10.829304Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source\n",
      "web    18828\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_slice[\"source\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:10.845892Z",
     "iopub.status.busy": "2025-09-16T13:06:10.845755Z",
     "iopub.status.idle": "2025-09-16T13:06:14.022320Z",
     "shell.execute_reply": "2025-09-16T13:06:14.021622Z",
     "shell.execute_reply.started": "2025-09-16T13:06:10.845879Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROD\n"
     ]
    }
   ],
   "source": [
    "# fetch lyrics\n",
    "home_dir = os.path.expanduser(\"~\")\n",
    "snow_password_path = os.path.join(home_dir, \".aws\", \"snow_pw.txt\")\n",
    "if os.path.exists(snow_password_path):\n",
    "    # !pip install snowflake\n",
    "    from snowflake.core import Root\n",
    "    from snowflake.snowpark import Session\n",
    "\n",
    "    with open(snow_password_path, \"r\") as fp:\n",
    "        fp_lines = fp.readlines()\n",
    "        snow_password = fp_lines[0].strip()\n",
    "        snow_username = fp_lines[1].strip()\n",
    "\n",
    "    CONNECTION_PARAMETERS = {\n",
    "        \"account\": \"fu90569.us-east-2.aws\",\n",
    "        \"user\": snow_username,\n",
    "        \"private_key_file\": \"/home/tony/.aws/rsa_key.p8\",\n",
    "        \"role\": \"ACCOUNTADMIN\",\n",
    "        \"database\": \"SUNO_PROD\",\n",
    "        \"warehouse\": \"SUNO_PROD_LARGE\",\n",
    "        \"schema\": \"PROD\",\n",
    "    }\n",
    "\n",
    "if not os.path.exists(snow_password_path):\n",
    "    raise Exception(\"you are not authorized to access snowflake -- please setup\")\n",
    "\n",
    "snow_session = Session.builder.configs(CONNECTION_PARAMETERS).create()\n",
    "\n",
    "snow_root = Root(snow_session)\n",
    "snow_schema = snow_root.databases[\"SUNO_PROD\"].schemas[\"PROD\"]\n",
    "print(snow_schema.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:14.023540Z",
     "iopub.status.busy": "2025-09-16T13:06:14.023072Z",
     "iopub.status.idle": "2025-09-16T13:06:58.732485Z",
     "shell.execute_reply": "2025-09-16T13:06:58.731832Z",
     "shell.execute_reply.started": "2025-09-16T13:06:14.023520Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                                                           | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clip IDs in this chunk: 18828\n",
      "Length of the ID query string: 734291\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:44<00:00, 44.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "Shape of df_snow_test:\n",
      "Rows: 18828\n",
      "Columns: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False    17420\n",
       "True      1408\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v4_clip_ids = list(str(s) for s in df_slice[\"id\"].unique())\n",
    "snow_batch_size = 100_000\n",
    "snow_results = []\n",
    "\n",
    "for clip_ids_chunk in tqdm(\n",
    "    [\n",
    "        v4_clip_ids[i : i + snow_batch_size]\n",
    "        for i in range(0, len(v4_clip_ids), snow_batch_size)\n",
    "    ]\n",
    "):\n",
    "    id_query_str = \",\".join(\"'\" + x + \"'\" for x in clip_ids_chunk)\n",
    "    print(f\"Number of clip IDs in this chunk: {len(clip_ids_chunk)}\")\n",
    "    print(f\"Length of the ID query string: {len(id_query_str)}\")\n",
    "\n",
    "    session_query = snow_session.sql(\n",
    "        f\"\"\"select ID, PROMPT_TEXT\n",
    "        from DDB_CLIP_META_HEAVY\n",
    "        where ID in ({id_query_str})\n",
    "        order by p_hour desc;\"\"\"\n",
    "    )\n",
    "    temp_df_snow_test = pd.DataFrame(session_query.collect())\n",
    "    snow_results.append(temp_df_snow_test)\n",
    "print(len(snow_results))\n",
    "df_snow_test = pd.concat(snow_results)\n",
    "df_snow_test = df_snow_test.rename(columns=lambda x: x.lower())\n",
    "# df_snow_test = df_snow_test.rename(columns={\"song_id\": \"str_id\"})\n",
    "print(\"Shape of df_snow_test:\")\n",
    "print(f\"Rows: {df_snow_test.shape[0]}\")\n",
    "print(f\"Columns: {df_snow_test.shape[1]}\")\n",
    "df_slice[\"id\"] = df_slice[\"id\"].astype(str)\n",
    "df_slice = df_slice.rename(columns={\"prompt_text\": \"prompt_text_old\"})\n",
    "df_slice = df_slice.merge(df_snow_test, on=\"id\", how=\"left\")\n",
    "(df_slice[\"prompt_text\"] == df_slice[\"prompt_text_old\"]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T14:00:20.866354Z",
     "start_time": "2024-05-16T14:00:12.443344Z"
    },
    "execution": {
     "iopub.execute_input": "2025-09-16T13:06:58.733372Z",
     "iopub.status.busy": "2025-09-16T13:06:58.733202Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_slice.to_pickle(\n",
    "#     f\"/home/tony/Data/Preference/up_v2_d3/interesting_clips_ahi_d3_20250519_slice.pkl\",\n",
    "# )\n",
    "# print(df_slice.shape)\n",
    "BREAK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Need to kick out the ones has gpt prompt -- these are pairs with different text inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.932966Z",
     "start_time": "2024-05-16T13:59:41.932957Z"
    }
   },
   "outputs": [],
   "source": [
    "final_filtered_requests = df_slice[\"request_id\"].unique()\n",
    "print(len(final_filtered_requests))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934277Z",
     "start_time": "2024-05-16T13:59:41.934268Z"
    }
   },
   "outputs": [],
   "source": [
    "train_requests, val_requests = train_test_split(\n",
    "    sorted(list(final_filtered_requests)), test_size=0.01, random_state=42\n",
    ")\n",
    "print(len(train_requests), len(val_requests))\n",
    "\n",
    "train_df = df_slice[df_slice[\"request_id\"].isin(set(train_requests))].copy()\n",
    "val_df = df_slice[df_slice[\"request_id\"].isin(set(val_requests))].copy()\n",
    "train_df = train_df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "train_df = train_df  # .reset_index()\n",
    "val_df = val_df.sort_values(by=[\"request_id\", \"preference\"])\n",
    "val_df = val_df  # .reset_index()\n",
    "\n",
    "print(train_df.shape, val_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# BREAK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Actually make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.934954Z",
     "start_time": "2024-05-16T13:59:41.934946Z"
    }
   },
   "outputs": [],
   "source": [
    "# val_df[[\"request_id\", \"metadata\", \"updated_at\", \"user_id\", \"preference\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.935620Z",
     "start_time": "2024-05-16T13:59:41.935613Z"
    }
   },
   "outputs": [],
   "source": [
    "total_duration = 0\n",
    "for i, row in tqdm(train_df.iterrows(), total=len(train_df)):\n",
    "    # we need to alternate between preference: neg, pos\n",
    "    # print(i, row)\n",
    "    try:\n",
    "        assert row[\"preference\"] == (i % 2 == 1)\n",
    "    except:\n",
    "        print(i, row)\n",
    "    total_duration += row[\"duration\"]\n",
    "print(\n",
    "    f\"{round(total_duration / 60 / 60):,} hours of {train_df.shape[0]} clips, {train_df.shape[0] / 8 / 2 / 1000} nodes, {train_df.shape[0] / 8 / 2 / 4} steps\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936268Z",
     "start_time": "2024-05-16T13:59:41.936260Z"
    }
   },
   "outputs": [],
   "source": [
    "make_dataset(\n",
    "    val_df,\n",
    "    OUT_DATA_DIR,\n",
    "    is_val=True,\n",
    "    npz_dir=NPZ_DIR,\n",
    "    do_extend_chunks=True,\n",
    "    clip_id_to_quality_scores=unpacked_pair_quality,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_npz = np.load(\"/app/suno/data/dpo/diff2_v2/506a8426-551f-45d9-9638-b1fa673cc031.npz\")\n",
    "# for key in test_npz.keys():\n",
    "#     print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.936964Z",
     "start_time": "2024-05-16T13:59:41.936957Z"
    }
   },
   "outputs": [],
   "source": [
    "make_dataset(\n",
    "    train_df,\n",
    "    OUT_DATA_DIR,\n",
    "    is_val=False,\n",
    "    npz_dir=NPZ_DIR,\n",
    "    do_extend_chunks=True,\n",
    "    clip_id_to_quality_scores=unpacked_pair_quality,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import time\n",
    "# time.sleep(3600 * 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-29T19:46:47.549860Z",
     "start_time": "2024-01-29T19:46:47.548015Z"
    }
   },
   "source": [
    "# Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.937879Z",
     "start_time": "2024-05-16T13:59:41.937870Z"
    }
   },
   "outputs": [],
   "source": [
    "# verify\n",
    "metas_val = read_jsonl(os.path.join(OUT_DATA_DIR, \"metas_val.jsonl\"))\n",
    "print(len(metas_val) / 2)\n",
    "mm_semantic_val = np.memmap(\n",
    "    os.path.join(OUT_DATA_DIR, \"data_semantic_val.bin\"), dtype=np.uint16, mode=\"r\"\n",
    ")\n",
    "mm_vae_val = np.memmap(\n",
    "    os.path.join(OUT_DATA_DIR, \"data_vae_val.bin\"), dtype=np.float16, mode=\"r\"\n",
    ")\n",
    "\n",
    "\n",
    "mm_vae_val = mm_vae_val.reshape(-1, VAE_MEMMAP_SIZE, VAE_DIM)\n",
    "print(mm_vae_val.shape)\n",
    "\n",
    "mm_semantic_val = mm_semantic_val.reshape(-1, SEMANTIC_MEMMAP_SIZE)\n",
    "print(mm_semantic_val.shape)\n",
    "\n",
    "assert len(metas_val) == mm_vae_val.shape[0] == mm_semantic_val.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # load codec for decoding\n",
    "# from suno_utils.tasks.dac_vae_100hz_peaq import (  # NOTE: works for 25hz as well\n",
    "#     preload_models as preload_codec_models,\n",
    "#     decode as codec_decode,\n",
    "#     encode as codec_encode,\n",
    "#     get_embedding_rate,\n",
    "#     load_model as load_codec_model,\n",
    "# )\n",
    "\n",
    "# CODEC_FILEPATH = \"s3://suno-data/christian/25hz_vae_peaq_kl_0.005.pth\"\n",
    "# preload_codec_models(CODEC_FILEPATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # decode some audio\n",
    "idx = 108\n",
    "# # ensure even index\n",
    "assert idx % 2 == 0\n",
    "# print(metas_val[idx])\n",
    "# print(\"negative\")\n",
    "# audio = codec_decode(mm_vae_val[idx])\n",
    "# audio.normalize_volume().play()\n",
    "\n",
    "# print(metas_val[idx + 1])\n",
    "# print(\"positive\")\n",
    "# audio = codec_decode(mm_vae_val[idx + 1])\n",
    "# audio.normalize_volume().play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.equal(torch.tensor(mm_semantic_val[idx]), torch.tensor(mm_semantic_val[idx + 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941167Z",
     "start_time": "2024-05-16T13:59:41.941159Z"
    }
   },
   "outputs": [],
   "source": [
    "# original_npz_path = f\"/app/suno/data/dpo/7b_npz/{test_metas[idx]['id']}.npz\"\n",
    "# original_npz_path = \"/app/suno/data/dpo/7b_npz/729c3011-f672-4ccd-8d82-1cbf2b52ff69.npz\"\n",
    "# original_arr = np.load(original_npz_path)[\"v2_raw\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.941801Z",
     "start_time": "2024-05-16T13:59:41.941793Z"
    }
   },
   "outputs": [],
   "source": [
    "def validation_on_metas(input_metas):\n",
    "    total_bad = 0\n",
    "    total_good = 0\n",
    "    for idx in range(len(input_metas)):\n",
    "        if idx % 2 == 0:\n",
    "            pos_idx = idx + 1\n",
    "            if input_metas[idx].get(\"tags\") != input_metas[pos_idx].get(\"tags\"):\n",
    "                # print(test_metas[idx].get(\"text\") == test_metas[pos_idx].get(\"text\"), test_metas[idx].get(\"tags\"), test_metas[pos_idx].get(\"tags\"))\n",
    "                total_bad += 1\n",
    "            else:\n",
    "                total_good += 1\n",
    "    print(total_good, total_bad)\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metas_tr = read_jsonl(os.path.join(OUT_DATA_DIR, \"metas_tr.jsonl\"))\n",
    "validation_on_metas(metas_tr)\n",
    "print(len(metas_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum(len(meta[\"tags\"][0]) == 0 for meta in metas_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import time\n",
    "# time.sleep(3600 * 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-16T13:59:41.945972Z",
     "start_time": "2024-05-16T13:59:41.945964Z"
    }
   },
   "outputs": [],
   "source": [
    "# !cd /home/tony/Work/tony/slurm/diffusion && sbatch run_diffusion_infill.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil\n",
    "\n",
    "# Basic file copy\n",
    "shutil.copy(\n",
    "    \"/home/tony/Work/tony/Preference/make_dataset_diff_carp_v1.ipynb\",\n",
    "    os.path.join(OUT_DATA_DIR, \"make_dataset.ipynb\"),\n",
    ")\n",
    "shutil.copy(\n",
    "    \"/home/tony/Work/tony/Preference/preference_data_preparation_diff.py\",\n",
    "    os.path.join(OUT_DATA_DIR, \"preference_data_preparation_diff.py\"),\n",
    ")\n",
    "print(\"Cache kept!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspections "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[df[\"preference\"] & (df[\"shimmer_score_diff\"] > 3)][\n",
    "#     [\n",
    "#         \"index\",\n",
    "#         \"s3_id\",\n",
    "#         \"total_shimmer_score\",\n",
    "#         \"shimmer_score_diff\",\n",
    "#         \"request_id\",\n",
    "#         \"preference\",\n",
    "#     ]\n",
    "# ].tail()\n",
    "\n",
    "# df[df[\"preference\"] & (df[\"pair_quality\"] < 0.1)][\n",
    "#     [\n",
    "#         \"index\",\n",
    "#         \"s3_id\",\n",
    "#         \"total_shimmer_score\",\n",
    "#         \"shimmer_score_diff\",\n",
    "#         \"pair_quality\",\n",
    "#         \"request_id\",\n",
    "#         \"preference\",\n",
    "#     ]\n",
    "# ].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_pair_df = df[df[\"request_id\"] == \"621c8b02-a905-48f1-a2d5-a4e8423d1505\"]\n",
    "# print(\n",
    "#     test_pair_df[\n",
    "#         [\n",
    "#             \"s3_id\",\n",
    "#             \"total_shimmer_score\",\n",
    "#             \"pair_quality\",\n",
    "#             \"request_id\",\n",
    "#             \"preference\",\n",
    "#             \"prompt_text\",\n",
    "#         ]\n",
    "#     ]\n",
    "# )\n",
    "# negative_audio = Audio.from_s3(\n",
    "#     f\"s3://suno-data-uploads/studio/uploads/{test_pair_df['s3_id'].values[0]}.mp3\",\n",
    "#     n_channels=2,\n",
    "# )\n",
    "# print(\"negative\")\n",
    "# negative_audio.get_segment(0, 30).play()\n",
    "# positive_audio = Audio.from_s3(\n",
    "#     f\"s3://suno-data-uploads/studio/uploads/{test_pair_df['s3_id'].values[1]}.mp3\",\n",
    "#     n_channels=2,\n",
    "# )\n",
    "# print(\"positive\")\n",
    "# positive_audio.get_segment(0, 30).play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# total_dict = {}\n",
    "# total_dict.update(pair_quality_dict)\n",
    "# total_dict.update(pair_quality_1_dict)\n",
    "# total_dict.update(pair_quality_2_dict)\n",
    "# total_dict.update(pair_quality_3_dict)\n",
    "# len(total_dict)\n",
    "# with open(\n",
    "#     os.path.join(\"/home/tony/Data/Preference/up_v1\", \"pair_quality.json\"), \"w\"\n",
    "# ) as fp:\n",
    "#     json.dump(total_dict, fp, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_arr = np.load(\"/home/tony/Data/test_npz/diffusion_input_tensor([ 18, 182]).npy\")\n",
    "# test_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mm_vae_val = np.memmap(\n",
    "#     os.path.join(OUT_DATA_DIR, \"data_vae_val.bin\"), dtype=np.float16, mode=\"r\"\n",
    "# )\n",
    "\n",
    "# mm_vae_val = mm_vae_val.reshape(-1, VAE_MEMMAP_SIZE, VAE_DIM)\n",
    "# print(mm_vae_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"negative\")\n",
    "# audio = codec_decode(test_arr[0].T / 2.5)\n",
    "# audio.normalize_volume().play()\n",
    "\n",
    "# print(\"positive\")\n",
    "# audio = codec_decode(test_arr[1].T / 2.5)\n",
    "# audio.normalize_volume().play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"negative\")\n",
    "# audio = codec_decode(mm_vae_val[18])\n",
    "# audio.normalize_volume().play()\n",
    "\n",
    "# print(\"positive\")\n",
    "# audio = codec_decode(mm_vae_val[19])\n",
    "# audio.normalize_volume().play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "rng = torch.quasirandom.SobolEngine(1, scramble=True, seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = rng.draw(4)[:, 0].to(torch.bfloat16)\n",
    "print(t)\n",
    "# Replace 1% of t with ones to ensure training on terminal SNR\n",
    "t = torch.where(torch.rand_like(t) < 0.5, torch.ones_like(t), t)\n",
    "print(t)\n",
    "t = torch.repeat_interleave(t, repeats=2, dim=0)\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(t * 32).to(int) / 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t[0] = 0.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.round(t * 32) / 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge jsons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # import json\n",
    "# with open(f\"/home/tony/Data/Preference/carp_t1/full_pair_quality.json\", \"r\") as f:\n",
    "#    result = json.load(f)\n",
    "# # result = {}\n",
    "# print(len(result))\n",
    "# for job_idx in range(8):\n",
    "#     with open(f\"/home/tony/Data/Preference/carp_t1/full_pair_quality_{job_idx}.json\", \"r\") as fp:\n",
    "#         current_result = json.load(fp)\n",
    "#         result.update(current_result)\n",
    "# print(len(result))\n",
    "\n",
    "# with open(f\"/home/tony/Data/Preference/carp_t1/full_pair_quality.json\", \"w\") as f:\n",
    "#     json.dump(result, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import json\n",
    "# with open(f\"/home/tony/Data/Preference/up_v6/full_pair_quality.json\", \"r\") as f:\n",
    "#    result = json.load(f)\n",
    "# print(len(result))\n",
    "# print(len(result_loundess))\n",
    "# new_result = {}\n",
    "# for k, v in result.items():\n",
    "#     new_v = v.copy()\n",
    "#     for clip_id, contents in v.items():\n",
    "#         if contents and \"abs_loudness_factor\" not in contents:\n",
    "#             # print(contents, result_loundess[k][clip_id])\n",
    "#             try:\n",
    "#                 new_v[clip_id][\"abs_loudness_factor\"] =  result_loundess[k][clip_id][\"abs_loudness_factor\"]\n",
    "#             except:\n",
    "#                 # print(clip_id)\n",
    "#                 new_v[clip_id][\"abs_loudness_factor\"] = 0.0\n",
    "#             # break\n",
    "#     # print(k, v)\n",
    "#     # break\n",
    "#     new_result[k] = new_v\n",
    "# print(len(new_result))\n",
    "# with open(f\"/home/tony/Data/Preference/up_v6/full_pair_quality.json\", \"w\") as f:\n",
    "#     json.dump(new_result, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import torch\n",
    "# semantic_codes_chunk = torch.ones((1, 100))\n",
    "# semantic_skip_phase = 0\n",
    "# semantic_skip_factor = 4\n",
    "# mask = torch.ones_like(semantic_codes_chunk, dtype=torch.bool)\n",
    "# indices = (\n",
    "#     torch.arange(semantic_codes_chunk.size(1)) + semantic_skip_phase\n",
    "# ) % semantic_skip_factor == 0\n",
    "# mask[:, indices] = False\n",
    "# mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_slice.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_slice[(df_slice[\"preference\"]) & (df_slice[\"total_shimmer_score\"] < 0.5)][[\"s3_id\", \"total_shimmer_score\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_slice[(df_slice[\"preference\"]) & (df_slice[\"total_shimmer_score\"] > 2)][[\"s3_id\", \"total_shimmer_score\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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