{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b10d042",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "88f6b162",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'six.moves.collections_abc'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_3162562/2495421161.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"WANDB_CONSOLE\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"off\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"WAND_DISABLE\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"true\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mwandb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mwandb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"service\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venvs/ml/lib/python3.8/site-packages/wandb/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mwandb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterm\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtermsetup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtermlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtermerror\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtermwarn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mwandb\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msdk\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mwandb_sdk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwandb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venvs/ml/lib/python3.8/site-packages/wandb/sdk/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \"\"\"\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwandb_helper\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mhelper\u001b[0m  \u001b[0;31m# noqa: F401\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mwandb_alerts\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAlertLevel\u001b[0m  \u001b[0;31m# noqa: F401\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mwandb_artifacts\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mArtifact\u001b[0m  \u001b[0;31m# noqa: F401\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venvs/ml/lib/python3.8/site-packages/wandb/sdk/wandb_helper.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mwandb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mUsageError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconfig_util\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venvs/ml/lib/python3.8/site-packages/wandb/sdk/lib/config_util.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mwandb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mwandb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_yaml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0myaml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venvs/ml/lib/python3.8/site-packages/wandb/util.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmoves\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mqueue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msys\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgetsizeof\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmoves\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollections_abc\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMapping\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mimportlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mimport_module\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msentry_sdk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'six.moves.collections_abc'"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
    "os.environ[\"WANDB_CONSOLE\"] = \"off\"\n",
    "os.environ[\"WAND_DISABLE\"] = \"true\"\n",
    "import wandb\n",
    "wandb.require(\"service\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5828eb77",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d902d4be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "af5018ac",
   "metadata": {},
   "source": [
    "### Prep pretrained checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd58b921",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import torch\n",
    "# from speechbrain.pretrained import EncoderClassifier\n",
    "# pretrained_model = EncoderClassifier.from_hparams(\n",
    "#     \"speechbrain/lang-id-commonlanguage_ecapa\", \n",
    "# #    savedir=\"tmp_model\"\n",
    "# )\n",
    "# pretrained_state_dict = pretrained_model.mods.state_dict()\n",
    "# del pretrained_state_dict['classifier.weight']\n",
    "# torch.save(pretrained_state_dict, \"pretrained_voxlingua_model/pretrained.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7960c5ba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b6943b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b0838caa",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac3b6b8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ~12mins/epoch for 500k (1s-5s) samples @2 gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90b0f537",
   "metadata": {},
   "outputs": [],
   "source": [
    "CUDA_VISIBLE_DEVICES=2,3 python \\\n",
    "    \"/home/georg/notebooks/tasks/foreign_language/speechbrain/custom_params/train_ecapa.py\" \\\n",
    "    \"/home/georg/notebooks/tasks/foreign_language/speechbrain/custom_params/train_ecapa.yaml\" \\\n",
    "    --data_parallel_backend \\\n",
    "    --shards_url=\"/mnt/data-ssd-1/data/custom/lang_id-en_tl/shards\" \\\n",
    "    --data_folder=\"/mnt/data-ssd-1/data/sb_rir\" \\\n",
    "    --wandb_yaml_filepath=\"/home/georg/notebooks/tasks/foreign_language/speechbrain/custom_params/train_ecapa.yaml\" \\\n",
    "    --pretrained_filepath=\"/home/georg/notebooks/tasks/foreign_language/speechbrain/pretrained_voxlingua_model/pretrained.pt\" \\\n",
    "    --number_of_epochs=50 \\\n",
    "    --lr=0.0001 \\\n",
    "    --lr_final=0.00001 \\\n",
    "    --wandb_name=\"new_data\" \\\n",
    "    --output_folder=\"/mnt/data-ssd-1/checkpoints/sb_lang_id\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "461a25ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c093cd47",
   "metadata": {},
   "source": [
    "### test checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "71948edf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dir = \"/mnt/data-ssd-1/checkpoints/sb_lang_id/d2039732-25bf-44ef-8d9f-2022728c5052/\"\n",
    "# override_checkpoint = \"CKPT+2022-06-08+17-11-01+00\"\n",
    "override_checkpoint = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2480652e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using checkpoint: CKPT+2022-06-12+14-30-21+00\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from suno_utils.utils.notebook import Audio\n",
    "from suno_utils.audio.conversion import _collapse_to_numpy_array\n",
    "from speechbrain.pretrained import EncoderClassifier\n",
    "from dateutil import parser\n",
    "\n",
    "def _get_newest_checkpoint_filename(model_dir):\n",
    "    checkpoint_paths = [\n",
    "        (fn, fn[5:15] + \" \" + fn[16:].replace(\"-\", \":\"))\n",
    "        for fn in os.listdir(os.path.join(model_dir, \"save\")) \n",
    "        if fn.startswith(\"CKPT\")\n",
    "    ]\n",
    "    newest_checkpoint_name = sorted(checkpoint_paths, key=lambda x: x[-1], reverse=True)[0][0]\n",
    "    return newest_checkpoint_name\n",
    "\n",
    "def _get_model(model_dir, override_checkpoint=None):\n",
    "\n",
    "    if override_checkpoint is None:\n",
    "        checkpoint_name = _get_newest_checkpoint_filename(model_dir)\n",
    "    else:\n",
    "        checkpoint_name = override_checkpoint\n",
    "    print(\"using checkpoint:\", checkpoint_name)\n",
    "    checkpoint_dir = os.path.join(model_dir, \"save\", checkpoint_name)\n",
    "    if checkpoint_dir[-1] != \"/\":\n",
    "        checkpoint_dir += \"/\"\n",
    "    model = EncoderClassifier.from_hparams(\n",
    "        source=checkpoint_dir, \n",
    "        hparams_file=\"/home/georg/notebooks/tasks/foreign_language/speechbrain/custom_params/escapa_inference.yaml\",\n",
    "        overrides={\n",
    "            \"model_dir\": model_dir,\n",
    "            \"checkpoint_dir\": checkpoint_dir,\n",
    "            \"pretrained_path\": checkpoint_dir,\n",
    "        }\n",
    "    )\n",
    "    model.eval();\n",
    "    return model\n",
    "\n",
    "model = _get_model(model_dir, override_checkpoint=override_checkpoint)\n",
    "\n",
    "def pred_lang(audio_data):\n",
    "    if isinstance(audio_data, str):\n",
    "        audio_arr = torch.Tensor(Audio.from_file(audio_data).convert(16_000, 2, 1).array_float)\n",
    "    else:\n",
    "        audio_arr = torch.Tensor(audio_data.convert(16_000, 2, 1).array_float)\n",
    "    logits, _, _, label = model.classify_batch(audio_arr)\n",
    "    logits = _collapse_to_numpy_array(logits)\n",
    "    probs = np.exp(logits)\n",
    "    return probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2d6271c",
   "metadata": {},
   "outputs": [],
   "source": [
    "VOXLINGUA_DIR = \"/mnt/data-ssd-1/data/voxlingua107/\"\n",
    "\n",
    "sample_en_1 = VOXLINGUA_DIR + \"dev/en/3-7IY1VNKuA__U__S59---0418.980-0424.640.wav\"\n",
    "sample_tl_1 = VOXLINGUA_DIR + \"dev/tl/22hLDSxp5C8__U__S0---0048.230-0058.780.wav\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b8cd84d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.001 0.986 0.013]\n",
      "[0.001 0.032 0.966]\n"
     ]
    }
   ],
   "source": [
    "print(pred_lang(sample_en_1).round(3))\n",
    "print(pred_lang(sample_tl_1).round(3))\n",
    "# [0.998 0.002]\n",
    "# [0. 1.]\n",
    "\n",
    "# [0.001 0.986 0.013]\n",
    "# [0.001 0.032 0.966]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7154438",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd11b5b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7574eebf",
   "metadata": {},
   "source": [
    "### test on custom data 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c462d5ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import tqdm\n",
    "from suno_utils.utils.numbers import safe_round\n",
    "\n",
    "def load_custom_annotations():\n",
    "    with open(\"/home/georg/notebooks/tasks/foreign_language/data/segment_1_annotation.json\") as f:\n",
    "        annotations_raw = json.load(f)\n",
    "    segments = [\n",
    "        ((safe_round(e[\"start\"]), safe_round(e[\"end\"])), e[\"labels\"][0]) \n",
    "        for e in annotations_raw[0][\"label\"]\n",
    "    ]\n",
    "    segments = sorted(segments, key=lambda x: x[0][0])\n",
    "    return segments\n",
    "\n",
    "custom_segments = load_custom_annotations()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "59cdea38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0. English  (01s) -- p_en:  89.4%, p_tl:  10.6%\n",
      " 1. Filipino (01s) -- p_en:  25.7%, p_tl:  74.3%\n",
      " 2. English  (02s) -- p_en:  88.3%, p_tl:  11.7%\n",
      " 3. Filipino (01s) -- p_en:  12.4%, p_tl:  87.6%\n",
      " 4. English  (01s) -- p_en:  43.2%, p_tl:  56.8%\n",
      " 5. Filipino (07s) -- p_en:   6.0%, p_tl:  93.8%\n",
      " 6. English  (03s) -- p_en:  50.2%, p_tl:  49.7%\n",
      " 7. Filipino (01s) -- p_en:   3.7%, p_tl:  96.3%\n",
      " 8. English  (03s) -- p_en:  89.3%, p_tl:  10.7%\n",
      " 9. English  (05s) -- p_en:  75.0%, p_tl:  25.0%\n",
      "10. Filipino (01s) -- p_en:  57.3%, p_tl:  42.7%\n",
      "11. English  (03s) -- p_en:  91.8%, p_tl:   8.2%\n",
      "12. Filipino (02s) -- p_en:  50.1%, p_tl:  49.9%\n",
      "13. English  (06s) -- p_en:  94.0%, p_tl:   6.0%\n"
     ]
    }
   ],
   "source": [
    "audio = Audio.from_file(\"/home/georg/notebooks/tasks/foreign_language/data/segment_1.mp3\").convert(16_000, 2, 1)\n",
    "for n, ((start_s, end_s), lang_label) in enumerate(custom_segments):\n",
    "    a = audio.get_slice(start_s, end_s)\n",
    "#     a.play()\n",
    "    si_p, en_p, tl_p = pred_lang(a)\n",
    "    en_p = str(round(en_p * 100, 1)).rjust(5)\n",
    "    tl_p = str(round(tl_p * 100, 1)).rjust(5)\n",
    "    duration_s = int(round(end_s - start_s))\n",
    "    print(\"{}. {} ({}s) -- p_en: {}%, p_tl: {}%\".format(\n",
    "        str(n).rjust(2), lang_label.ljust(8), str(duration_s).zfill(2), en_p, tl_p\n",
    "    ))\n",
    "#     print(\"-\"*10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "9913ed10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# best custom\n",
    "#  0. English  (01s) -- p_en:  78.5%, p_tl:  21.5%\n",
    "#  1. Filipino (01s) -- p_en:  21.6%, p_tl:  78.4%\n",
    "#  2. English  (02s) -- p_en:  90.0%, p_tl:  10.0%\n",
    "#  3. Filipino (01s) -- p_en:  38.1%, p_tl:  61.9%\n",
    "#  4. English  (01s) -- p_en:  37.9%, p_tl:  62.1%\n",
    "#  5. Filipino (07s) -- p_en:   5.1%, p_tl:  94.9%\n",
    "#  6. English  (03s) -- p_en:  85.6%, p_tl:  14.4%\n",
    "#  7. Filipino (01s) -- p_en:  52.5%, p_tl:  47.5%\n",
    "#  8. English  (03s) -- p_en:  86.9%, p_tl:  13.1%\n",
    "#  9. English  (05s) -- p_en:  85.7%, p_tl:  14.3%\n",
    "# 10. Filipino (01s) -- p_en:  47.3%, p_tl:  52.7%\n",
    "# 11. English  (03s) -- p_en:  92.5%, p_tl:   7.5%\n",
    "# 12. Filipino (02s) -- p_en:  62.2%, p_tl:  37.8%\n",
    "# 13. English  (06s) -- p_en:  83.4%, p_tl:  16.6%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7d60139f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████| 151/151 [00:04<00:00, 30.45it/s]\n"
     ]
    }
   ],
   "source": [
    "window_s = 0.5\n",
    "shift_s = 0.25\n",
    "\n",
    "audio = Audio.from_file(\n",
    "    \"/home/georg/notebooks/tasks/foreign_language/data/segment_1.mp3\"\n",
    ").convert(16_000, 2, 1)\n",
    "assert(int(window_s / shift_s) == window_s / shift_s)\n",
    "n_avg = int(window_s / shift_s)\n",
    "start_times = np.arange(0, audio.duration_s - window_s, shift_s)\n",
    "results = np.zeros((len(start_times), len(start_times) + n_avg - 1)) * np.nan\n",
    "for n, start_s in tqdm.tqdm(enumerate(start_times), total=len(start_times)):\n",
    "    end_s = start_s + window_s\n",
    "    a = audio.get_slice(start_s, end_s)\n",
    "    sil_p, en_p, tl_p = pred_lang(a)\n",
    "    en_p = en_p + en_p / (en_p + tl_p) * sil_p\n",
    "    results[n, n:n+n_avg] = en_p\n",
    "x_values_s =  np.arange(0, audio.duration_s - shift_s, shift_s)\n",
    "assert(len(x_values_s) == np.nanmean(results, axis=0).shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1e1fa39e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# s = pd.Series(s).rolling(5, center=True).mean().values\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot([x_values_s[0], x_values_s[-1]], [0.5, 0.5], \"--k\", alpha=1.0, linewidth=0.5)\n",
    "plt.plot(x_values_s, np.nanmean(results, axis=0), \".-\")\n",
    "for (start_s, end_s), lang_label in custom_segments:\n",
    "    lab = lang_label[:2]\n",
    "    y_val = 1 if lab == \"En\" else 0\n",
    "    plt.plot([start_s, end_s], [y_val, y_val], \"--\", alpha=1.0, linewidth=1, color=\"orange\")\n",
    "#     plt.text((start_s+end_s)/2-0.3, 0.05, lab)\n",
    "plt.xlabel(\"time (s)\")\n",
    "plt.title(\"classify english vs not english\")\n",
    "plt.legend([\"cutoff\", \"model\", \"true\"], loc=\"lower right\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b08974e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # use out-of-the-box model\n",
    "# from speechbrain.pretrained import EncoderClassifier\n",
    "# language_id = EncoderClassifier.from_hparams(source=\"speechbrain/lang-id-voxlingua107-ecapa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa20bef3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1441412f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18b49b73",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d835c710",
   "metadata": {},
   "source": [
    "### test on custom data 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8f7421c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import tqdm\n",
    "from suno_utils.utils.numbers import safe_round\n",
    "\n",
    "def load_custom_annotations_2():\n",
    "    with open(\"/home/georg/notebooks/tasks/foreign_language/data/segment_2_annotation.json\") as f:\n",
    "        annotations_raw = json.load(f)\n",
    "    segments = [\n",
    "        ((safe_round(e[0][0]), safe_round(e[0][1])), e[1]) \n",
    "        for e in annotations_raw\n",
    "    ]\n",
    "    segments = sorted(segments, key=lambda x: x[0][0])\n",
    "    return segments\n",
    "\n",
    "custom_segments = load_custom_annotations_2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bfec46c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0. English  (21s) -- p_en:  79.9%, p_tl:  20.1%\n",
      " 1. Tagalog  (10s) -- p_en:   8.7%, p_tl:  91.3%\n",
      " 2. English  (11s) -- p_en:  82.4%, p_tl:  17.6%\n",
      " 3. Tagalog  (18s) -- p_en:   4.6%, p_tl:  94.4%\n"
     ]
    }
   ],
   "source": [
    "audio = Audio.from_file(\"/home/georg/notebooks/tasks/foreign_language/data/segment_2.mp3\").convert(16_000, 2, 1)\n",
    "for n, ((start_s, end_s), lang_label) in enumerate(custom_segments):\n",
    "    a = audio.get_slice(start_s, end_s)\n",
    "#     a.play()\n",
    "    sil_p, en_p, tl_p = pred_lang(a)\n",
    "    en_p = str(round(en_p * 100, 1)).rjust(5)\n",
    "    tl_p = str(round(tl_p * 100, 1)).rjust(5)\n",
    "    duration_s = int(round(end_s - start_s))\n",
    "    print(\"{}. {} ({}s) -- p_en: {}%, p_tl: {}%\".format(\n",
    "        str(n).rjust(2), lang_label.ljust(8), str(duration_s).zfill(2), en_p, tl_p\n",
    "    ))\n",
    "#     print(\"-\"*10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f53fd929",
   "metadata": {},
   "outputs": [],
   "source": [
    "# best custom\n",
    "#  0. English  (21s) -- p_en:  87.6%, p_tl:  12.4%\n",
    "#  1. Tagalog  (10s) -- p_en:   6.5%, p_tl:  93.5%\n",
    "#  2. English  (11s) -- p_en:  83.4%, p_tl:  16.6%\n",
    "#  3. Tagalog  (18s) -- p_en:   1.6%, p_tl:  98.4%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "91eb7609",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████| 237/237 [00:08<00:00, 27.25it/s]\n"
     ]
    }
   ],
   "source": [
    "window_s = 1.0\n",
    "shift_s = 0.25\n",
    "\n",
    "audio = Audio.from_file(\n",
    "    \"/home/georg/notebooks/tasks/foreign_language/data/segment_2.mp3\"\n",
    ").convert(16_000, 2, 1)\n",
    "assert(int(window_s / shift_s) == window_s / shift_s)\n",
    "n_avg = int(window_s / shift_s)\n",
    "start_times = np.arange(0, audio.duration_s - window_s, shift_s)\n",
    "results = np.zeros((len(start_times), len(start_times) + n_avg - 1)) * np.nan\n",
    "for n, start_s in tqdm.tqdm(enumerate(start_times), total=len(start_times)):\n",
    "    end_s = start_s + window_s\n",
    "    a = audio.get_slice(start_s, end_s)\n",
    "    sil_p, en_p, tl_p = pred_lang(a)\n",
    "    en_p = en_p + en_p / (en_p + tl_p) * sil_p\n",
    "    results[n, n:n+n_avg] = en_p\n",
    "x_values_s =  np.arange(0, audio.duration_s - shift_s, shift_s)\n",
    "assert(len(x_values_s) == np.nanmean(results, axis=0).shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3eb53649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# s = pd.Series(s).rolling(5, center=True).mean().values\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot([x_values_s[0], x_values_s[-1]], [0.5, 0.5], \"--k\", alpha=1.0, linewidth=0.5)\n",
    "plt.plot(x_values_s, np.nanmean(results, axis=0), \".-\")\n",
    "for (start_s, end_s), lang_label in custom_segments:\n",
    "    lab = lang_label[:2]\n",
    "    y_val = 1 if lab == \"En\" else 0\n",
    "    plt.plot([start_s, end_s], [y_val, y_val], \"--\", alpha=1.0, linewidth=1, color=\"orange\")\n",
    "#     plt.text((start_s+end_s)/2-0.3, 0.05, lab)\n",
    "plt.xlabel(\"time (s)\")\n",
    "plt.title(\"classify english vs tagalog (filipino)\")\n",
    "plt.legend([\"cutoff\", \"model\", \"true\"], loc=\"upper right\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "164c21c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# s = pd.Series(s).rolling(5, center=True).mean().values\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot([x_values_s[0], x_values_s[-1]], [0.5, 0.5], \"--k\", alpha=1.0, linewidth=0.5)\n",
    "plt.plot(x_values_s, np.nanmean(results, axis=0), \".-\")\n",
    "for (start_s, end_s), lang_label in custom_segments:\n",
    "    lab = lang_label[:2]\n",
    "    y_val = 1 if lab == \"En\" else 0\n",
    "    plt.plot([start_s, end_s], [y_val, y_val], \"--\", alpha=1.0, linewidth=1, color=\"orange\")\n",
    "#     plt.text((start_s+end_s)/2-0.3, 0.05, lab)\n",
    "plt.xlabel(\"time (s)\")\n",
    "plt.title(\"classify english vs tagalog (filipino)\")\n",
    "plt.legend([\"cutoff\", \"model\", \"true\"], loc=\"upper right\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8b4d17de",
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_segments_2 = [((0.0, 21.0), 'English'),\n",
    " ((21.0, 25), 'Tagalog'),\n",
    "((27, 30.5), 'Tagalog'),\n",
    " ((30.5, 41.8), 'English'),\n",
    " ((41.8, 60.0), 'Tagalog')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "22beca31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# s = pd.Series(s).rolling(5, center=True).mean().values\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot([x_values_s[0], x_values_s[-1]], [0.5, 0.5], \"--k\", alpha=1.0, linewidth=0.5)\n",
    "plt.plot(x_values_s, np.nanmean(results, axis=0), \".-\")\n",
    "for (start_s, end_s), lang_label in custom_segments_2:\n",
    "    lab = lang_label[:2]\n",
    "    y_val = 1 if lab == \"En\" else 0\n",
    "    plt.plot([start_s, end_s], [y_val, y_val], \"--\", alpha=1.0, linewidth=1, color=\"orange\")\n",
    "#     plt.text((start_s+end_s)/2-0.3, 0.05, lab)\n",
    "plt.xlabel(\"time (s)\")\n",
    "plt.title(\"classify english vs tagalog (filipino)\")\n",
    "plt.legend([\"cutoff\", \"model\", \"true\"], loc=\"upper right\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "10ded33c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NeMo W 2022-06-10 16:31:54 nemo_logging:349] /home/georg/venvs/ml/lib/python3.8/site-packages/huggingface_hub/utils/_deprecation.py:39: FutureWarning: Pass library_name=False as keyword args. From version 0.8 passing these as positional arguments will result in an error\n",
      "      warnings.warn(\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "# use out-of-the-box model\n",
    "from speechbrain.pretrained import EncoderClassifier\n",
    "language_id = EncoderClassifier.from_hparams(source=\"speechbrain/lang-id-voxlingua107-ecapa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "71440448",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred_lang_public(audio_data):\n",
    "    if isinstance(audio_data, str):\n",
    "        audio_arr = torch.Tensor(Audio.from_file(audio_data).convert(16_000, 2, 1).array_float)\n",
    "    else:\n",
    "        audio_arr = torch.Tensor(audio_data.convert(16_000, 2, 1).array_float)\n",
    "    logits, _, _, label = language_id.classify_batch(audio_arr)\n",
    "    logits = _collapse_to_numpy_array(logits)\n",
    "    probs = np.exp(logits)\n",
    "    return probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4d85a176",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████| 117/117 [00:03<00:00, 29.98it/s]\n"
     ]
    }
   ],
   "source": [
    "window_s = 2.0\n",
    "shift_s = 0.5\n",
    "\n",
    "audio = Audio.from_file(\n",
    "    \"/home/georg/notebooks/tasks/foreign_language/data/segment_2.mp3\"\n",
    ").convert(16_000, 2, 1)\n",
    "assert(int(window_s / shift_s) == window_s / shift_s)\n",
    "n_avg = int(window_s / shift_s)\n",
    "start_times = np.arange(0, audio.duration_s - window_s, shift_s)\n",
    "results_2 = np.zeros((len(start_times), len(start_times) + n_avg - 1)) * np.nan\n",
    "for n, start_s in tqdm.tqdm(enumerate(start_times), total=len(start_times)):\n",
    "    end_s = start_s + window_s\n",
    "    a = audio.get_slice(start_s, end_s)\n",
    "    lang_probs = pred_lang_public(a)\n",
    "    en_p = lang_probs[20]\n",
    "    tl_p = lang_probs[96]\n",
    "    offs_p = (1 - en_p - tl_p) / 2\n",
    "    en_p += offs_p\n",
    "    tl_p += offs_p\n",
    "    results_2[n, n:n+n_avg] = en_p\n",
    "x_values_s =  np.arange(0, audio.duration_s - shift_s, shift_s)\n",
    "assert(len(x_values_s) == np.nanmean(results_2, axis=0).shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e349a6a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# s = pd.Series(s).rolling(5, center=True).mean().values\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot([x_values_s[0], x_values_s[-1]], [0.5, 0.5], \"--k\", alpha=1.0, linewidth=0.3, label='_nolegend_')\n",
    "plt.plot(x_values_s, np.nanmean(results_2, axis=0), \".-\")\n",
    "plt.plot(x_values_s, np.nanmean(results, axis=0), \".-\", alpha=0.7)\n",
    "for (start_s, end_s), lang_label in custom_segments:\n",
    "    lab = lang_label[:2]\n",
    "    y_val = 1.01 if lab == \"En\" else -0.01\n",
    "    c = \"purple\" if lab == \"En\" else \"green\"\n",
    "    plt.plot([start_s, end_s], [y_val, y_val], \"--\", alpha=1.0, linewidth=3, color=c)\n",
    "#     plt.text((start_s+end_s)/2-0.3, 0.05, lab)\n",
    "plt.xlabel(\"time (s)\")\n",
    "plt.title(\"Detect English vs Tagalog (\\\"Filipino\\\")\")\n",
    "plt.legend([\"SOTA (Speechbrain)\", \"Suno\", \"English\", \"Tagalog\"], loc=\"upper right\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "430e55e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cc5bebe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
