experiments/fma/train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "geAK_iYTsfmX"
   },
   "source": [
    "# Wav2Rec\n",
    "\n",
    "Operating System: Ubuntu 18.04.5 LTS. <br>\n",
    "Python Version: v3.7.11\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Fetch the FMA Datasets (Small & Medium) + Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hlnOZUdYZlmW"
   },
   "outputs": [],
   "source": [
    "!apt-get install p7zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cPCTD1dZZchg"
   },
   "outputs": [],
   "source": [
    "%%shell\n",
    "\n",
    "if [ ! -f /content/fma_metadata.zip ];\n",
    "then\n",
    "  echo \"Getting Metadata...\"\n",
    "  wget https://os.unil.cloud.switch.ch/fma/fma_metadata.zip -q --show-progress\n",
    "  echo \"Unzipping...\"\n",
    "  7z x /content/fma_metadata.zip\n",
    "  echo \"Unzipping Complete\"\n",
    "  rm /content/fma_metadata.zip\n",
    "else\n",
    "  echo \"Metadata ready.\"\n",
    "fi\n",
    "\n",
    "if [ ! -f /content/fma_small.zip ];\n",
    "then\n",
    "  echo \"Getting Small FMA Audio Data...\"\n",
    "  wget https://os.unil.cloud.switch.ch/fma/fma_small.zip -q --show-progress\n",
    "  echo \"Unzipping...\"\n",
    "  7z x /content/fma_small.zip\n",
    "  echo \"Unzipping Complete\"\n",
    "  rm /content/fma_small.zip\n",
    "else\n",
    "  echo \"Small FMA Audio Data Ready.\"\n",
    "fi\n",
    "\n",
    "if [ ! -f /content/fma_medium.zip ];\n",
    "then\n",
    "  echo \"Getting Medium FMA Audio Data...\"\n",
    "  wget https://os.unil.cloud.switch.ch/fma/fma_medium.zip -q --show-progress\n",
    "  echo \"Unzipping...\"\n",
    "  7z x /content/fma_medium.zip\n",
    "  echo \"Unzipping Complete\"\n",
    "  rm /content/fma_medium.zip\n",
    "else\n",
    "  echo \"Medium FMA Audio Data Ready.\"\n",
    "fi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6l43fBuNZQmj"
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "teghw1DUG_oo"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import pytorch_lightning as pl\n",
    "from pytorch_lightning.loggers import TensorBoardLogger\n",
    "from pytorch_lightning.callbacks.early_stopping import EarlyStopping\n",
    "from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint\n",
    "\n",
    "from datetime import datetime\n",
    "from pathlib import Path\n",
    "from multiprocessing import cpu_count\n",
    "from torch.utils.data import DataLoader, Dataset, random_split\n",
    "from typing import Any, Tuple\n",
    "\n",
    "from experiments.fma.data.dataset import FmaDataset\n",
    "from wav2rec.nn.lightening import Wav2RecNet\n",
    "from wav2rec.nn.audionets import AudioResnet50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dfJ0leuej9qE"
   },
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: in order to import `experiments` and `wav2rec` you must either (a) run this notebook from the root of the repository or (b) set the working directory to the root of the repository."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Globals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UyVTYpDeZchf"
   },
   "outputs": [],
   "source": [
    "EFFECTIVE_CPU_COUNT = max(1, cpu_count() - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KaqP-dPYLmvT"
   },
   "outputs": [],
   "source": [
    "METADATA_PATH = Path(\"/content/fma_metadata\").absolute()\n",
    "AUDIO_PATH = Path(\"/content\").absolute()\n",
    "\n",
    "MODEL_CHECKPOINT_PATH = Path(\"/content/checkpoints\").absolute()\n",
    "MODEL_CHECKPOINT_PATH.mkdir(exist_ok=True, parents=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 100,
     "referenced_widgets": [
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    "id": "Udtmbd7HQIQw",
    "outputId": "19b028f0-f2b0-4af5-9e90-53b3fb09c7f1"
   },
   "outputs": [],
   "source": [
    "fma_dataset = FmaDataset(\n",
    "    audio_path=AUDIO_PATH,\n",
    "    metadata_path=METADATA_PATH,\n",
    "    duration=10,\n",
    "    min_listens=0,\n",
    ").scan()\n",
    "\n",
    "print(f\"\\n\\nCollected {len(fma_dataset.files)} files\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: by default, FmaDataset() only loads audio with a Public Domain license. See `experiments.fma.data.meta.FmaMetadata`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fOGOANw8jhW4",
    "outputId": "13641c20-21a7-4825-ade4-68dd0042c286"
   },
   "outputs": [],
   "source": [
    "fma_dataset.cache_all(lazy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4TiuhQFTjfT_",
    "outputId": "c5cb46fd-2f11-4b00-bed7-7890580f01e1"
   },
   "outputs": [],
   "source": [
    "assert len(fma_dataset) > 0\n",
    "\n",
    "print(f\"Files in cache: {len(fma_dataset)}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JpjM4EEKZchi"
   },
   "outputs": [],
   "source": [
    "def split_dataset(\n",
    "    dataset: Dataset,\n",
    "    train_prop: float = 0.95,\n",
    "    seed: int = 42,\n",
    ") -> Tuple[Dataset, Dataset]:\n",
    "    train_size = int(len(dataset) * train_prop)\n",
    "\n",
    "    train_dataset, val_dataset = random_split(\n",
    "        dataset,\n",
    "        lengths=[train_size, len(dataset) - train_size],\n",
    "        generator=torch.Generator().manual_seed(seed),\n",
    "    )\n",
    "    return train_dataset, val_dataset\n",
    "\n",
    "\n",
    "def dataset2loader(\n",
    "    dataset: Dataset,\n",
    "    batch_size: int = 16,\n",
    "    shuffle: bool = False,\n",
    "    **kwargs: Any,\n",
    ") -> DataLoader:\n",
    "    return DataLoader(\n",
    "        dataset=dataset,\n",
    "        shuffle=shuffle,\n",
    "        batch_size=batch_size,\n",
    "        num_workers=kwargs.pop(\"num_workers\", EFFECTIVE_CPU_COUNT),\n",
    "        **kwargs,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hk-_FCE3Zchi"
   },
   "source": [
    "#### Datasets & Dataloaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "R9tlrIahScTS"
   },
   "outputs": [],
   "source": [
    "# Split into train & test\n",
    "train_dataset, val_dataset = split_dataset(fma_dataset, train_prop=0.95)\n",
    "\n",
    "# Build dataloaders\n",
    "train_dataloader = dataset2loader(train_dataset, batch_size=32, shuffle=True)\n",
    "val_dataloader = dataset2loader(val_dataset, batch_size=32, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1GcnSx0FZchj"
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VynsMAGEPeWQ"
   },
   "source": [
    "## Train\n",
    "\n",
    "Now we can finally train a model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ptPMlqhwwI8-"
   },
   "outputs": [],
   "source": [
    "def do_training(wav2rec_model: torch.nn.Module, max_epochs: int = 10) -> Tuple[Wav2RecNet, datetime]:\n",
    "    start_time = str(datetime.utcnow().isoformat())\n",
    "    print(\"Start time:\", start_time)\n",
    "\n",
    "    checkpoint = ModelCheckpoint(\n",
    "      dirpath=str(MODEL_CHECKPOINT_PATH.joinpath(f'wav2rec/{start_time}')),\n",
    "      monitor='loss',\n",
    "      verbose=True,\n",
    "      save_top_k=-1,\n",
    "      filename='wav2rec-{epoch:02d}-{loss:.5f}',\n",
    "      save_last=True,\n",
    "    )\n",
    "    early_stopping = EarlyStopping(monitor='val_loss')\n",
    "\n",
    "    trainer = pl.Trainer(\n",
    "        gpus=1, \n",
    "        max_epochs=max_epochs,\n",
    "        callbacks=[checkpoint, early_stopping],\n",
    "        stochastic_weight_avg=True,\n",
    "        logger=TensorBoardLogger(\"logs\", name=f\"wav2rec\"),\n",
    "    )\n",
    "    trainer.fit(wav2rec_model, train_dataloader, val_dataloader)\n",
    "    return wav2rec_model, start_time"
   ]
  },
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    "id": "-XD2G97OS-yv",
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   "source": [
    "model, start_time = do_training(\n",
    "    Wav2RecNet(encoder=AudioResnet50()),\n",
    "    max_epochs=25\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: try experimenting with other encoders such as `wav2rec.nn.audionets.AudioVit`, or building your own using the `wav2rec.nn.audionets.AudioImageNetwork` base model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Collect Projections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the model trained, we can go ahead at get some predictions from the model. <br>\n",
    "\n",
    "Specifically, the code below will use the model trained above to compute projections for all songs in the training and validation datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _to_dataframe(all_track_ids: List[int], arrays: List[np.ndarray]) -> pd.DataFrame:\n",
    "    df = pd.DataFrame(np.concatenate(arrays, axis=0)).assign(track_id=all_track_ids)\n",
    "    df.columns = [f\"feature_{i}\" if isinstance(i, int) else i for i in df.columns]\n",
    "    return df\n",
    "\n",
    "\n",
    "def _get_projection(x: torch.Tensor, net: Wav2RecNet) -> np.ndarray:\n",
    "    with torch.no_grad():\n",
    "        proj = net.learner.encoder(x.unsqueeze(0).cuda())\n",
    "    return proj.detach().cpu().numpy()\n",
    "\n",
    "\n",
    "def _get_projections_from_dataset(\n",
    "    dataset: torch.utils.data.Dataset,\n",
    "    net: Wav2RecNet,\n",
    ") -> pd.DataFrame:\n",
    "    all_track_ids = list()\n",
    "    all_projections = list()\n",
    "    for file, x in tqdm(dataset):  # Note: could be optimized by iterating over the dataloader instead of dataset.\n",
    "        all_track_ids.append(Path(file).stem)\n",
    "        all_projections.append(_get_projection(x, net=net))\n",
    "    return _to_dataframe(all_track_ids, arrays=all_projections)\n",
    "\n",
    "\n",
    "def get_all_projections(\n",
    "    net: Wav2RecNet,\n",
    "    train_dataset: Dataset, \n",
    "    val_dataset: Dataset,\n",
    "    verbose: bool = True,\n",
    ") -> pd.DataFrame:\n",
    "    all_frames = list()\n",
    "    for stage, dataset in ((\"val\", val_dataset), (\"train\", train_dataset)):\n",
    "        if verbose:\n",
    "            print(f\"Working on stage '{stage}'...\")\n",
    "        frame = _get_projections_from_dataset(dataset, net=net).assign(stage=stage)\n",
    "        all_frames.append(frame)\n",
    "    return pd.concat(all_frames, ignore_index=True)"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "proj_df = get_all_projections(\n",
    "    net=model.cuda().eval(),\n",
    "    train_dataset=train_dataset,\n",
    "    val_dataset=val_dataset,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "proj_df.to_csv('wav2rec_projections.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are interested in seeing some example of how the projections computed above can be used, check out [inference.ipynb](inference.ipynb)."
   ]
  }
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