speaker_id.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import logging\n",
    "\n",
    "os.environ[\"HF_HOME\"] = \"../models/\"\n",
    "os.environ[\"TORCH_HOME\"] = \"../models/\"\n",
    "os.environ[\"TRANSFORMERS_OFFLINE\"] = \"1\"\n",
    "\n",
    "# logging.basicConfig(level=logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NeMo W 2023-06-20 19:22:17 optimizers:54] Apex was not found. Using the lamb or fused_adam optimizer will error out.\n",
      "[NeMo W 2023-06-20 19:22:18 experimental:27] Module <class 'nemo.collections.asr.modules.audio_modules.SpectrogramToMultichannelFeatures'> is experimental, not ready for production and is not fully supported. Use at your own risk.\n"
     ]
    }
   ],
   "source": [
    "from til_23_finals.services.speaker import NeMoSpeakerIDService\n",
    "from til_23_finals.utils import load_audio_from_dir\n",
    "import librosa\n",
    "import numpy as np\n",
    "import itertools\n",
    "from pathlib import Path\n",
    "from time import sleep\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NeMo W 2023-06-20 19:22:21 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\n",
      "    Train config : \n",
      "    manifest_filepath: /manifests/combined_fisher_swbd_voxceleb12_librispeech/train.json\n",
      "    sample_rate: 16000\n",
      "    labels: null\n",
      "    batch_size: 64\n",
      "    shuffle: true\n",
      "    is_tarred: false\n",
      "    tarred_audio_filepaths: null\n",
      "    tarred_shard_strategy: scatter\n",
      "    augmentor:\n",
      "      noise:\n",
      "        manifest_path: /manifests/noise/rir_noise_manifest.json\n",
      "        prob: 0.5\n",
      "        min_snr_db: 0\n",
      "        max_snr_db: 15\n",
      "      speed:\n",
      "        prob: 0.5\n",
      "        sr: 16000\n",
      "        resample_type: kaiser_fast\n",
      "        min_speed_rate: 0.95\n",
      "        max_speed_rate: 1.05\n",
      "    num_workers: 15\n",
      "    pin_memory: true\n",
      "    \n",
      "[NeMo W 2023-06-20 19:22:21 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s). \n",
      "    Validation config : \n",
      "    manifest_filepath: /manifests/combined_fisher_swbd_voxceleb12_librispeech/dev.json\n",
      "    sample_rate: 16000\n",
      "    labels: null\n",
      "    batch_size: 128\n",
      "    shuffle: false\n",
      "    num_workers: 15\n",
      "    pin_memory: true\n",
      "    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NeMo I 2023-06-20 19:22:21 features:287] PADDING: 16\n",
      "[NeMo I 2023-06-20 19:22:22 save_restore_connector:249] Model EncDecSpeakerLabelModel was successfully restored from /home/jh/code/til/til-23-finals/models/speakerverification_en_titanet_large.nemo.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "fatal: not a git repository (or any of the parent directories): .git\n"
     ]
    }
   ],
   "source": [
    "speaker_service = NeMoSpeakerIDService(\n",
    "    \"models/speakerverification_en_titanet_large.nemo\",\n",
    "    \"models/DeepFilterNet3\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "speaker_dir = \"data/audio/train\"\n",
    "val_dir = \"data/audio/val\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with speaker_service:\n",
    "    speaker_service.clear_speakers()\n",
    "    speaker_audio = load_audio_from_dir(speaker_dir)\n",
    "    for name, (wav, sr) in speaker_audio.items():\n",
    "        # if all(n.upper() not in name.upper() for n in [us_team, them_team]):\n",
    "        #     continue\n",
    "\n",
    "        team, member = name.split(\"_\")[:2]\n",
    "        speaker_service.enroll_speaker(wav, sr, team_id=team, member_id=member)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(wav, sr, team_a, team_b, correct):\n",
    "    d_raw_a, d_clean_a = speaker_service.identify_speaker(wav, sr, team_id=team_a, return_all=True)\n",
    "    d_raw_b, d_clean_b = speaker_service.identify_speaker(wav, sr, team_id=team_b, return_all=True)\n",
    "    d_raw = d_raw_a\n",
    "    d_clean = d_clean_a\n",
    "    d_raw = {**d_raw_a, **d_raw_b}\n",
    "    d_clean = {**d_clean_a, **d_clean_b}\n",
    "\n",
    "    raw = np.array(list(d_raw.values()))\n",
    "    clean = np.array(list(d_clean.values()))\n",
    "    raw_sd = (raw.max() - raw.mean()) / raw.std()\n",
    "    clean_sd = (clean.max() - clean.mean()) / clean.std()\n",
    "    raw_ans = \"_\".join(max(d_raw, key=d_raw.get))\n",
    "    clean_ans = \"_\".join(max(d_clean, key=d_clean.get))\n",
    "    raw_sorted = np.sort(raw)\n",
    "    clean_sorted = np.sort(clean)\n",
    "    raw_diff = raw_sorted[-1] - raw_sorted[-2]\n",
    "    clean_diff = clean_sorted[-1] - clean_sorted[-2]\n",
    "    \n",
    "    if True or raw_diff < 0.2 and raw_ans == correct:\n",
    "        print(f\"{team_a} vs {team_b}, correct: {correct}\")\n",
    "        # print(clean)\n",
    "        # print(clean_diff - raw_diff)\n",
    "        print(f\"[{'✓' if raw_ans == correct else 'X'} Raw] {raw_ans} {raw.max()*100:.1f}% {raw_sd:.2f}σ {raw_diff*100:.1f}%\")\n",
    "        print(f\"[{'✓' if clean_ans == correct else 'X'} Clean] {clean_ans} {clean.max()*100:.1f}% {clean_sd:.2f}σ {clean_diff*100:.1f}%\")\n",
    "        print()\n",
    "    return raw_ans != correct, raw_ans, raw_sd, clean_ans != correct, clean_ans, clean_sd, clean_diff - raw_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals = list(Path(val_dir).glob(\"*.wav\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMAGINELOSIN vs PALMTREE, correct: IMAGINELOSIN_memberB\n",
      "[✓ Raw] IMAGINELOSIN_memberB 77.6% 1.93σ 26.0%\n",
      "[✓ Clean] IMAGINELOSIN_memberB 76.4% 1.93σ 24.0%\n",
      "\n",
      "IMAGINELOSIN vs PALMTREE, correct: PALMTREE_memberD\n",
      "[✓ Raw] PALMTREE_memberD 77.0% 1.21σ 0.3%\n",
      "[✓ Clean] PALMTREE_memberD 82.1% 1.28σ 3.7%\n",
      "\n",
      "IMAGINELOSIN vs ANYTHING, correct: IMAGINELOSIN_memberB\n",
      "[✓ Raw] IMAGINELOSIN_memberB 77.6% 1.85σ 26.0%\n",
      "[✓ Clean] IMAGINELOSIN_memberB 76.4% 1.89σ 24.0%\n",
      "\n",
      "IMAGINELOSIN vs ANYTHING, correct: ANYTHING_memberC\n",
      "[✓ Raw] ANYTHING_memberC 89.5% 2.45σ 49.5%\n",
      "[✓ Clean] ANYTHING_memberC 90.4% 2.51σ 53.9%\n",
      "\n",
      "IMAGINELOSIN vs ACESOFSPADES, correct: IMAGINELOSIN_memberB\n",
      "[✓ Raw] IMAGINELOSIN_memberB 77.6% 2.42σ 26.0%\n",
      "[✓ Clean] IMAGINELOSIN_memberB 76.4% 2.39σ 24.0%\n",
      "\n",
      "IMAGINELOSIN vs ACESOFSPADES, correct: ACESOFSPADES_memberC\n",
      "[✓ Raw] ACESOFSPADES_memberC 73.9% 2.05σ 10.0%\n",
      "[✓ Clean] ACESOFSPADES_memberC 76.8% 1.97σ 9.8%\n",
      "\n",
      "IMAGINELOSIN vs 10000SGDMRT, correct: IMAGINELOSIN_memberB\n",
      "[✓ Raw] IMAGINELOSIN_memberB 77.6% 2.25σ 26.0%\n",
      "[✓ Clean] IMAGINELOSIN_memberB 76.4% 2.23σ 24.0%\n",
      "\n",
      "IMAGINELOSIN vs 10000SGDMRT, correct: 10000SGDMRT_memberB\n",
      "[✓ Raw] 10000SGDMRT_memberB 73.5% 2.47σ 31.6%\n",
      "[✓ Clean] 10000SGDMRT_memberB 75.6% 2.42σ 33.1%\n",
      "\n",
      "PALMTREE vs IMAGINELOSIN, correct: PALMTREE_memberD\n",
      "[✓ Raw] PALMTREE_memberD 77.0% 1.21σ 0.3%\n",
      "[✓ Clean] PALMTREE_memberD 82.1% 1.28σ 3.7%\n",
      "\n",
      "PALMTREE vs IMAGINELOSIN, correct: IMAGINELOSIN_memberB\n",
      "[✓ Raw] IMAGINELOSIN_memberB 77.6% 1.93σ 26.0%\n",
      "[✓ Clean] IMAGINELOSIN_memberB 76.4% 1.93σ 24.0%\n",
      "\n",
      "PALMTREE vs ANYTHING, correct: PALMTREE_memberD\n",
      "[✓ Raw] PALMTREE_memberD 77.0% 1.20σ 0.3%\n",
      "[✓ Clean] PALMTREE_memberD 82.1% 1.27σ 3.7%\n",
      "\n",
      "PALMTREE vs ANYTHING, correct: ANYTHING_memberC\n",
      "[✓ Raw] ANYTHING_memberC 89.5% 2.43σ 49.5%\n",
      "[✓ Clean] ANYTHING_memberC 90.4% 2.48σ 53.9%\n",
      "\n",
      "PALMTREE vs ACESOFSPADES, correct: PALMTREE_memberD\n",
      "[✓ Raw] PALMTREE_memberD 77.0% 1.22σ 0.3%\n",
      "[✓ Clean] PALMTREE_memberD 82.1% 1.29σ 3.7%\n",
      "\n",
      "PALMTREE vs ACESOFSPADES, correct: ACESOFSPADES_memberC\n",
      "[✓ Raw] ACESOFSPADES_memberC 73.9% 1.58σ 10.0%\n",
      "[✓ Clean] ACESOFSPADES_memberC 76.8% 1.56σ 9.8%\n",
      "\n",
      "PALMTREE vs 10000SGDMRT, correct: PALMTREE_memberD\n",
      "[✓ Raw] PALMTREE_memberD 77.0% 1.19σ 0.3%\n",
      "[✓ Clean] PALMTREE_memberD 82.1% 1.26σ 3.7%\n",
      "\n",
      "PALMTREE vs 10000SGDMRT, correct: 10000SGDMRT_memberB\n",
      "[✓ Raw] 10000SGDMRT_memberB 73.5% 2.21σ 43.4%\n",
      "[✓ Clean] 10000SGDMRT_memberB 75.6% 2.29σ 43.4%\n",
      "\n",
      "ANYTHING vs IMAGINELOSIN, correct: ANYTHING_memberC\n",
      "[✓ Raw] ANYTHING_memberC 89.5% 2.45σ 49.5%\n",
      "[✓ Clean] ANYTHING_memberC 90.4% 2.51σ 53.9%\n",
      "\n",
      "ANYTHING vs IMAGINELOSIN, correct: IMAGINELOSIN_memberB\n",
      "[✓ Raw] IMAGINELOSIN_memberB 77.6% 1.85σ 26.0%\n",
      "[✓ Clean] IMAGINELOSIN_memberB 76.4% 1.89σ 24.0%\n",
      "\n",
      "ANYTHING vs PALMTREE, correct: ANYTHING_memberC\n",
      "[✓ Raw] ANYTHING_memberC 89.5% 2.43σ 49.5%\n",
      "[✓ Clean] ANYTHING_memberC 90.4% 2.48σ 53.9%\n",
      "\n",
      "ANYTHING vs PALMTREE, correct: PALMTREE_memberD\n",
      "[✓ Raw] PALMTREE_memberD 77.0% 1.20σ 0.3%\n",
      "[✓ Clean] PALMTREE_memberD 82.1% 1.27σ 3.7%\n",
      "\n",
      "ANYTHING vs ACESOFSPADES, correct: ANYTHING_memberC\n",
      "[✓ Raw] ANYTHING_memberC 89.5% 2.51σ 49.5%\n",
      "[✓ Clean] ANYTHING_memberC 90.4% 2.56σ 51.0%\n",
      "\n",
      "ANYTHING vs ACESOFSPADES, correct: ACESOFSPADES_memberC\n",
      "[✓ Raw] ACESOFSPADES_memberC 73.9% 1.81σ 10.0%\n",
      "[✓ Clean] ACESOFSPADES_memberC 76.8% 1.82σ 9.8%\n",
      "\n",
      "ANYTHING vs 10000SGDMRT, correct: ANYTHING_memberC\n",
      "[✓ Raw] ANYTHING_memberC 89.5% 2.35σ 43.1%\n",
      "[✓ Clean] ANYTHING_memberC 90.4% 2.40σ 43.5%\n",
      "\n",
      "ANYTHING vs 10000SGDMRT, correct: 10000SGDMRT_memberB\n",
      "[✓ Raw] 10000SGDMRT_memberB 73.5% 2.24σ 25.2%\n",
      "[✓ Clean] 10000SGDMRT_memberB 75.6% 2.25σ 23.5%\n",
      "\n",
      "ACESOFSPADES vs IMAGINELOSIN, correct: ACESOFSPADES_memberC\n",
      "[✓ Raw] ACESOFSPADES_memberC 73.9% 2.05σ 10.0%\n",
      "[✓ Clean] ACESOFSPADES_memberC 76.8% 1.97σ 9.8%\n",
      "\n",
      "ACESOFSPADES vs IMAGINELOSIN, correct: IMAGINELOSIN_memberB\n",
      "[✓ Raw] IMAGINELOSIN_memberB 77.6% 2.42σ 26.0%\n",
      "[✓ Clean] IMAGINELOSIN_memberB 76.4% 2.39σ 24.0%\n",
      "\n",
      "ACESOFSPADES vs PALMTREE, correct: ACESOFSPADES_memberC\n",
      "[✓ Raw] ACESOFSPADES_memberC 73.9% 1.58σ 10.0%\n",
      "[✓ Clean] ACESOFSPADES_memberC 76.8% 1.56σ 9.8%\n",
      "\n",
      "ACESOFSPADES vs PALMTREE, correct: PALMTREE_memberD\n",
      "[✓ Raw] PALMTREE_memberD 77.0% 1.22σ 0.3%\n",
      "[✓ Clean] PALMTREE_memberD 82.1% 1.29σ 3.7%\n",
      "\n",
      "ACESOFSPADES vs ANYTHING, correct: ACESOFSPADES_memberC\n",
      "[✓ Raw] ACESOFSPADES_memberC 73.9% 1.81σ 10.0%\n",
      "[✓ Clean] ACESOFSPADES_memberC 76.8% 1.82σ 9.8%\n",
      "\n",
      "ACESOFSPADES vs ANYTHING, correct: ANYTHING_memberC\n",
      "[✓ Raw] ANYTHING_memberC 89.5% 2.51σ 49.5%\n",
      "[✓ Clean] ANYTHING_memberC 90.4% 2.56σ 51.0%\n",
      "\n",
      "ACESOFSPADES vs 10000SGDMRT, correct: ACESOFSPADES_memberC\n",
      "[✓ Raw] ACESOFSPADES_memberC 73.9% 1.74σ 10.0%\n",
      "[✓ Clean] ACESOFSPADES_memberC 76.8% 1.77σ 9.8%\n",
      "\n",
      "ACESOFSPADES vs 10000SGDMRT, correct: 10000SGDMRT_memberB\n",
      "[✓ Raw] 10000SGDMRT_memberB 73.5% 2.49σ 33.0%\n",
      "[✓ Clean] 10000SGDMRT_memberB 75.6% 2.41σ 31.3%\n",
      "\n",
      "10000SGDMRT vs IMAGINELOSIN, correct: 10000SGDMRT_memberB\n",
      "[✓ Raw] 10000SGDMRT_memberB 73.5% 2.47σ 31.6%\n",
      "[✓ Clean] 10000SGDMRT_memberB 75.6% 2.42σ 33.1%\n",
      "\n",
      "10000SGDMRT vs IMAGINELOSIN, correct: IMAGINELOSIN_memberB\n",
      "[✓ Raw] IMAGINELOSIN_memberB 77.6% 2.25σ 26.0%\n",
      "[✓ Clean] IMAGINELOSIN_memberB 76.4% 2.23σ 24.0%\n",
      "\n",
      "10000SGDMRT vs PALMTREE, correct: 10000SGDMRT_memberB\n",
      "[✓ Raw] 10000SGDMRT_memberB 73.5% 2.21σ 43.4%\n",
      "[✓ Clean] 10000SGDMRT_memberB 75.6% 2.29σ 43.4%\n",
      "\n",
      "10000SGDMRT vs PALMTREE, correct: PALMTREE_memberD\n",
      "[✓ Raw] PALMTREE_memberD 77.0% 1.19σ 0.3%\n",
      "[✓ Clean] PALMTREE_memberD 82.1% 1.26σ 3.7%\n",
      "\n",
      "10000SGDMRT vs ANYTHING, correct: 10000SGDMRT_memberB\n",
      "[✓ Raw] 10000SGDMRT_memberB 73.5% 2.24σ 25.2%\n",
      "[✓ Clean] 10000SGDMRT_memberB 75.6% 2.25σ 23.5%\n",
      "\n",
      "10000SGDMRT vs ANYTHING, correct: ANYTHING_memberC\n",
      "[✓ Raw] ANYTHING_memberC 89.5% 2.35σ 43.1%\n",
      "[✓ Clean] ANYTHING_memberC 90.4% 2.40σ 43.5%\n",
      "\n",
      "10000SGDMRT vs ACESOFSPADES, correct: 10000SGDMRT_memberB\n",
      "[✓ Raw] 10000SGDMRT_memberB 73.5% 2.49σ 33.0%\n",
      "[✓ Clean] 10000SGDMRT_memberB 75.6% 2.41σ 31.3%\n",
      "\n",
      "10000SGDMRT vs ACESOFSPADES, correct: ACESOFSPADES_memberC\n",
      "[✓ Raw] ACESOFSPADES_memberC 73.9% 1.74σ 10.0%\n",
      "[✓ Clean] ACESOFSPADES_memberC 76.8% 1.77σ 9.8%\n",
      "\n",
      "Raw False: 0/40 (0.00%)\n",
      "Raw Mean Sigma: 1.98\n",
      "Raw Wrong Answers: []\n",
      "Clean False: 0/40 (0.00%)\n",
      "Clean Mean Sigma: 2.00\n",
      "Clean Wrong Answers: []\n",
      "IMAGINELOSIN -0.019601643\n",
      "PALMTREE 0.03430611\n",
      "ANYTHING 0.02645304\n",
      "ACESOFSPADES -0.002206564\n",
      "10000SGDMRT -0.0049088895\n"
     ]
    }
   ],
   "source": [
    "total = 0\n",
    "raw_false = 0\n",
    "raw_wrong_ans = []\n",
    "clean_false = 0\n",
    "clean_wrong_ans = []\n",
    "raw_sds = []\n",
    "clean_sds = []\n",
    "diff = defaultdict(list)\n",
    "with speaker_service:\n",
    "    for audio1, audio2 in itertools.permutations(vals, 2):\n",
    "        total += 2\n",
    "        team_1, member1 = audio1.stem.split(\"_\")[:2]\n",
    "        team_2, member2 = audio2.stem.split(\"_\")[:2]\n",
    "        wav1, sr1 = librosa.load(audio1, sr=None)\n",
    "        wav2, sr2 = librosa.load(audio2, sr=None)\n",
    "\n",
    "        raw_wrong, raw_ans, raw_sd, clean_wrong, clean_ans, clean_sd, d = evaluate(wav1, sr1, team_1, team_2, f\"{team_1}_{member1}\")\n",
    "        if raw_wrong:\n",
    "            raw_false += 1\n",
    "            raw_wrong_ans.append(raw_ans)\n",
    "        else:\n",
    "            raw_sds.append(raw_sd)\n",
    "        if clean_wrong:\n",
    "            clean_false += 1\n",
    "            clean_wrong_ans.append(clean_ans)\n",
    "        else:\n",
    "            clean_sds.append(clean_sd)\n",
    "            diff[team_1].append(d)\n",
    "\n",
    "        raw_wrong, raw_ans, raw_sd, clean_wrong, clean_ans, clean_sd, d = evaluate(wav2, sr2, team_1, team_2, f\"{team_2}_{member2}\")\n",
    "        if raw_wrong:\n",
    "            raw_false += 1\n",
    "            raw_wrong_ans.append(raw_ans)\n",
    "        else:\n",
    "            raw_sds.append(raw_sd)\n",
    "        if clean_wrong:\n",
    "            clean_false += 1\n",
    "            clean_wrong_ans.append(clean_ans)\n",
    "        else:\n",
    "            clean_sds.append(clean_sd)\n",
    "            diff[team_2].append(d)\n",
    "\n",
    "print(f\"Raw False: {raw_false}/{total} ({raw_false/total*100:.2f}%)\")\n",
    "print(f\"Raw Mean Sigma: {np.mean(raw_sds):.2f}\")\n",
    "print(f\"Raw Wrong Answers: {raw_wrong_ans}\")\n",
    "print(f\"Clean False: {clean_false}/{total} ({clean_false/total*100:.2f}%)\")\n",
    "print(f\"Clean Mean Sigma: {np.mean(clean_sds):.2f}\")\n",
    "print(f\"Clean Wrong Answers: {clean_wrong_ans}\")\n",
    "for k, v in diff.items():\n",
    "    print(k, np.mean(v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from til_23_finals.services.digit import FasterWhisperDigitDetectionService\n",
    "import librosa\n",
    "service = FasterWhisperDigitDetectionService(\"models/faster-whisper-large-v2\", \"models/DeepFilterNet3\")\n",
    "wav, sr = librosa.load(\"data/simulator/orgteam1_vs_orgteam2/valid_checkpoint_2/digits/digits2.wav\", sr=16000)\n",
    "service.activate()\n",
    "print(service.transcribe_audio_to_digits(wav, 16000))"
   ]
  }
 ],
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