diff --git a/.env.example b/.env.example
index ad7d990..239d1a3 100644
--- a/.env.example
+++ b/.env.example
@@ -1,4 +1,2 @@
-KAGGLE_USERNAME="your_kaggle_username_here"
-KAGGLE_KEY="your_kaggle_key_here"
 HUGGINGFACE_TOKEN="your_huggingface_token_here"
 WANDB_API_KEY="your_wandb_api_key_here"
diff --git a/README.md b/README.md
index 2179fad..ce7c20e 100644
--- a/README.md
+++ b/README.md
@@ -1,151 +1,309 @@
-# Machine Learning Project Template
+# SyncNet: Audio-Visual Synchronization
 
-A batteries-included template for PyTorch machine learning projects using Lightning, wandb, and modern Python tooling.
+A PyTorch Lightning implementation of SyncNet for detecting audio-visual synchronization in videos. This project trains deep learning models to determine whether audio and video streams are temporally aligned.
+
+## Overview
+
+SyncNet learns to measure the synchronization between audio and video by computing similarity scores between learned embeddings. The model uses a pretrained audio-visual encoder (PeAudioVideo) and is trained using contrastive learning with both synchronized (positive) and out-of-sync (negative) samples.
+
+### Key Applications
+
+- **Lip-sync detection**: Verify if speech audio matches visible lip movements
+- **Video quality assessment**: Detect audio-visual synchronization issues
+- **Deepfake detection**: Identify manipulated videos with mismatched audio
+- **Video post-production**: Automated sync checking for edited content
 
 ## Features
 
-- **PyTorch Lightning**: Structured training framework with minimal boilerplate
-- **Pydantic Configuration**: Type-safe configuration management
-- **Weights & Biases**: Integrated experiment tracking
-- **Modern Tooling**: Built with `uv` for fast dependency management
-- **Code Quality**: Pre-configured with `ruff`, `mypy`, `pytest`, and `pre-commit` hooks
-- **Git-based Versioning**: Automatic experiment naming using git commit hashes
+- **Pretrained Encoder**: Built on HuggingFace's PeAudioVideo model
+- **PyTorch Lightning**: Clean, scalable training framework with minimal boilerplate
+- **Multi-GPU Support**: Distributed training with DeepSpeed Stage 2
+- **Mixed Precision Training**: Automatic BF16 mixed precision for faster training
+- **Comprehensive Logging**: Weights & Biases integration with metric tracking
+- **Data Augmentation**: Automatic negative sample generation via temporal shifts
+- **Gradient Checkpointing**: Memory-efficient training for large models
+- **Type Safety**: Full type annotations with mypy validation
+- **Modern Tooling**: Fast dependency management with `uv`
 
-## Project Structure
+## Installation
 
-```
-.
-├── src/template/
-│   ├── config.py              # Pydantic configuration classes
-│   ├── lightning_module.py    # Base Lightning module
-│   ├── datasets/              # Dataset implementations
-│   ├── modeling/              # Model architectures
-│   └── scripts/
-│       └── train.py          # Training script
-├── tests/                     # Test files
-├── pyproject.toml            # Project metadata and dependencies
-├── .pre-commit-config.yaml   # Pre-commit hooks configuration
-└── .env.example              # Example environment variables
-```
+### Prerequisites
 
-## Quick Start
+- Python 3.12+
+- CUDA-capable GPU (recommended)
+- Git
 
-### 1. Install uv
+### 1. Install uv (Fast Python Package Manager)
 
 ```bash
 curl -LsSf https://astral.sh/uv/install.sh | sh
 ```
 
-### 2. Use this template for a new project
-
-When creating a new project from this template:
+### 2. Clone the Repository
 
-1. Clone or fork this repository
-2. Rename the `src/template` directory to your project name:
-   ```bash
-   mv src/template src/your_project_name
-   ```
-3. Update `pyproject.toml`:
-   - Change `name = "template"` to your project name
-   - Update `module-name = ["template"]` to your project name
-   - Update the `train` script path in `[project.scripts]`
-4. Update import statements in Python files to use your new project name
+```bash
+git clone https://github.com/yourusername/pe-av-syncnet.git
+cd pe-av-syncnet
+```
 
-### 3. Install dependencies
+### 3. Install Dependencies
 
 ```bash
 uv sync
 ```
 
-### 4. Set up environment variables
+This will install all required dependencies including:
 
-Copy the example environment file and add your API keys:
+- PyTorch with CUDA support
+- PyTorch Lightning
+- Transformers (for PeAudioVideo model)
+- TorchAudio and TorchVision
+- Weights & Biases
+- And more...
+
+### 4. Set Up Environment Variables
 
 ```bash
 cp .env.example .env
-# Edit .env and add your wandb API key and other credentials
 ```
 
-### 5. Install pre-commit hooks
+Edit `.env` and add your credentials:
+
+```bash
+WANDB_PROJECT=your-project-name
+WANDB_ENTITY=your-wandb-username
+```
+
+### 5. Install Pre-commit Hooks (Optional)
 
 ```bash
 uv run pre-commit install
 ```
 
+## Project Structure
+
+```
+pe-av-syncnet/
+├── src/syncnet/
+│   ├── __init__.py              # Package initialization
+│   ├── config.py                # Pydantic configuration with hyperparameters
+│   ├── lightning_module.py      # Lightning training module
+│   ├── datamodule.py           # Data loading and preprocessing
+│   ├── datasets/
+│   │   ├── __init__.py         # Batch data structure
+│   │   └── dataset.py          # Video dataset loader
+│   ├── modeling/
+│   │   ├── __init__.py         # Model package
+│   │   └── model.py            # SyncNet architecture
+│   └── scripts/
+│       ├── __init__.py         # Scripts package
+│       └── train.py            # Training script
+├── tests/
+│   └── test_sample.py          # Test suite
+├── pyproject.toml              # Project configuration and dependencies
+├── .pre-commit-config.yaml     # Code quality hooks
+├── .env.example                # Environment variables template
+└── README.md                   # This file
+```
+
+## Dataset Preparation
+
+SyncNet expects a directory containing MP4 video files with both audio and video streams.
+
+### Dataset Structure
+
+```
+data/
+├── video1.mp4
+├── video2.mp4
+├── video3.mp4
+├── subfolder/
+│   ├── video4.mp4
+│   └── video5.mp4
+└── ...
+```
+
+### Requirements
+
+- **Format**: MP4 files with H.264 video and AAC audio
+- **Audio**: Preferably mono or stereo, will be converted to mono
+- **Video**: Any resolution (will be resized to 224x224)
+- **Frame Rate**: 25 fps recommended
+- **Audio Sample Rate**: 16kHz or 48kHz
+- **Duration**: At least 0.2 seconds (5 frames at 25fps)
+
+### Dataset Recommendations
+
+- **Minimum size**: 1000+ videos for meaningful training
+- **Diversity**: Include various speakers, environments, and scenarios
+- **Quality**: Clear audio with visible speakers for best results
+
 ## Usage
 
-### Training
+### Basic Training
 
-Run the training script:
+Train a model on your video dataset:
 
 ```bash
-uv run train <data_root> --project my-project --num_devices 1
+uv run train /path/to/videos --num_devices 1 --num_workers 8
 ```
 
-Available arguments:
+### Multi-GPU Training
 
-- `data_root`: Path to your dataset (required)
-- `--project`: Wandb project name (default: "jigsaw-2025")
-- `--num_devices`: Number of GPUs to use (default: 1)
-- `--num_workers`: Number of data loading workers (default: 12)
-- `--log_root`: Directory for logs and checkpoints (default: "logs")
-- `--checkpoint_path`: Resume from checkpoint
-- `--weights_path`: Load model weights
-- `--debug`: Enable debug mode
-- `--fast_dev_run`: Run a quick test with minimal data
+Train with multiple GPUs using DeepSpeed:
 
-### Configuration
+```bash
+uv run train /path/to/videos --num_devices 4 --num_workers 16
+```
 
-Edit `src/template/config.py` to customize hyperparameters:
+### Resume Training from Checkpoint
 
-```python
-from pydantic import BaseModel
+```bash
+uv run train /path/to/videos --checkpoint_path logs/pe-av-small-abc1234/last.ckpt
+```
+
+### Load Pretrained Weights
+
+Initialize model with custom weights:
+
+```bash
+uv run train /path/to/videos --weights_path /path/to/weights.pth
+```
+
+### Debug Mode
 
+Run training offline without uploading to Weights & Biases:
+
+```bash
+uv run train /path/to/videos --debug
+```
+
+### Fast Development Run
+
+Test your pipeline with a single batch:
+
+```bash
+uv run train /path/to/videos --fast_dev_run
+```
+
+### Command-Line Arguments
+
+| Argument            | Type | Default    | Description                         |
+| ------------------- | ---- | ---------- | ----------------------------------- |
+| `data_root`         | Path | Required   | Directory containing video files    |
+| `--project`         | str  | "template" | Project name for logging            |
+| `--num_devices`     | int  | 1          | Number of GPUs to use               |
+| `--num_workers`     | int  | 12         | Data loading workers                |
+| `--log_root`        | Path | "logs"     | Directory for checkpoints and logs  |
+| `--checkpoint_path` | Path | None       | Path to checkpoint for resuming     |
+| `--weights_path`    | Path | None       | Path to pretrained weights          |
+| `--debug`           | flag | False      | Enable debug mode (offline logging) |
+| `--fast_dev_run`    | flag | False      | Run single batch for testing        |
+
+## Model Architecture
+
+### SyncNet Model
+
+The SyncNet model consists of:
+
+1. **Pretrained Encoder**: PeAudioVideoModel from HuggingFace
+   - Processes audio and video separately
+   - Extracts rich multimodal embeddings
+   - Gradient checkpointing enabled for memory efficiency
+
+2. **Embedding Processing**:
+   - Flatten temporal/spatial dimensions
+   - L2 normalization
+   - ReLU activation (ensures positive similarity)
+
+3. **Similarity Computation**:
+   - Cosine similarity between audio and video embeddings
+   - Output: Score from 0 to 1 (higher = better sync)
+
+### Training Process
+
+```
+Input Video → Random Segment Sampling
+           ↓
+Audio + Video Preprocessing
+           ↓
+[50% chance] Temporal Shift (negative sample)
+           ↓
+PeAudioVideo Encoder
+           ↓
+Audio Embedding + Video Embedding
+           ↓
+Cosine Similarity
+           ↓
+Binary Cross-Entropy Loss
+```
+
+## Configuration
+
+All hyperparameters are defined in `src/syncnet/config.py`:
+
+```python
 class Config(BaseModel):
     # Reproducibility
     seed: int = 42
 
     # Data
-    test_split: float = 0.1
-    batch_size: int = 16
+    test_split: float = 0.05
+    batch_size: int = 4
 
     # Training
     max_epochs: int = 200
-    early_stopping_patience: int = 30
+    early_stopping_patience: int = 10
     learning_rate: float = 1e-4
     min_learning_rate: float = 1e-6
     weight_decay: float = 1e-2
+    accumulate_grad_batches: int = 1
+    gradient_clip_val: float = 1.0
+
+    # Model
+    base_model: str = "facebook/pe-av-small"
+    num_frames: int = 5
+    negative_fraction: float = 0.5
+    frame_height: int = 224
+    frame_width: int = 224
 ```
 
-### Implementing Your Model
+### Key Parameters
+
+- **base_model**: HuggingFace model ID for the pretrained encoder
+- **num_frames**: Number of video frames per sample (5 frames = 0.2s at 25fps)
+- **negative_fraction**: Proportion of negative samples (0.5 = 50% out-of-sync)
+- **batch_size**: Adjust based on GPU memory (4 works well for most GPUs)
+- **learning_rate**: Initial learning rate with OneCycleLR scheduler
 
-1. **Create your Lightning module** by inheriting from `BaseLightningModule`:
+## Training Details
 
-   ```python
-   from template.lightning_module import BaseLightningModule
+### Data Augmentation
 
-   class MyModel(BaseLightningModule):
-       def training_step(self, batch, batch_idx):
-           # Your training logic here
-           pass
+- **Random temporal cropping**: Samples random 5-frame segments from videos
+- **Negative sample generation**: 50% of samples get audio shifted by ±1 frame
+- **Stereo to mono conversion**: Automatically handles stereo audio
+- **Resampling**: Audio resampled from 16kHz to 48kHz
 
-       def validation_step(self, batch, batch_idx):
-           # Your validation logic here
-           pass
-   ```
+### Optimization
 
-2. **Add your dataset** in `src/template/datasets/`:
+- **Optimizer**: AdamW with weight decay
+- **Scheduler**: OneCycleLR with cosine annealing
+  - 10% warmup period
+  - Peak learning rate: `config.learning_rate`
+  - Final learning rate: `config.min_learning_rate`
 
-   ```python
-   from torch.utils.data import Dataset
+### Metrics
 
-   class MyDataset(Dataset):
-       def __init__(self, data_root, config):
-           # Initialize your dataset
-           pass
-   ```
+- **Training**: Binary Cross-Entropy loss
+- **Validation**: BCE loss + binary accuracy
+- **Logging**: Real-time metrics to Weights & Biases
 
-3. **Update the training script** to use your model and dataset
+### Automatic Model Saving
+
+- Saves best model based on validation loss
+- Optionally pushes to HuggingFace Hub (private repos)
+- Local checkpointing with automatic resumption
 
 ## Development
 
@@ -164,44 +322,90 @@ uv run mypy src/
 ### Linting and Formatting
 
 ```bash
+# Check code style
 uv run ruff check src/
+
+# Auto-format code
 uv run ruff format src/
 ```
 
 ### Pre-commit Hooks
 
-Pre-commit hooks will automatically run on every commit to ensure code quality. To run manually:
+Automatically run linters and formatters before each commit:
 
 ```bash
 uv run pre-commit run --all-files
 ```
 
-## Dependencies
+## Model Inference
 
-Core dependencies:
+After training, use the model for inference:
 
-- **PyTorch**: Deep learning framework (with GPU support)
-- **Lightning**: High-level PyTorch wrapper
-- **Pydantic**: Data validation and configuration
-- **Wandb**: Experiment tracking
-- **python-dotenv**: Environment variable management
+```python
+import torch
+from syncnet.modeling.model import SyncNet, SyncNetConfig
+from transformers.models.pe_audio_video import PeAudioVideoProcessor
+
+# Load model
+config = SyncNetConfig(base_model="facebook/pe-av-small")
+model = SyncNet.from_pretrained("your-username/your-model-name")
+model.eval()
+
+# Load processor
+processor = PeAudioVideoProcessor.from_pretrained("facebook/pe-av-small")
+
+# Process inputs
+inputs = processor(
+    videos=video_frames,  # Shape: (num_frames, H, W, C)
+    audio=audio_samples,   # Shape: (num_samples,)
+    return_tensors="pt",
+    sampling_rate=48000
+)
+
+# Inference
+with torch.no_grad():
+    similarity = model(
+        inputs["input_values"],
+        inputs["pixel_values_videos"]
+    )
+
+print(f"Synchronization score: {similarity.item():.4f}")
+# Higher score = better synchronization
+```
 
-Development tools:
+## Troubleshooting
 
-- **ruff**: Fast Python linter and formatter
-- **mypy**: Static type checker
-- **pytest**: Testing framework
-- **pre-commit**: Git hooks for code quality
+### Common Issues
 
-## Build System
+**Out of Memory (OOM)**
 
-This project uses `uv_build` as the build backend, which is significantly faster than traditional build systems like setuptools or hatchling.
+- Reduce `batch_size` in config.py
+- Reduce `num_workers` to decrease memory overhead
+- Enable gradient accumulation: `accumulate_grad_batches=2`
 
-To build the project:
+**Slow Data Loading**
 
-```bash
-uv build
-```
+- Increase `num_workers` (recommended: 2-4x number of GPUs)
+- Ensure videos are on fast storage (SSD preferred)
+- Enable `persistent_workers=True` (already enabled)
+
+**Low Accuracy**
+
+- Ensure dataset has sufficient diversity
+- Increase training epochs
+- Adjust `negative_fraction` (try 0.3-0.7)
+- Verify audio and video are actually synchronized in source data
+
+**WANDB Authentication Error**
+
+- Set `WANDB_PROJECT` and `WANDB_ENTITY` in `.env`
+- Run `wandb login` to authenticate
+- Use `--debug` flag to train offline
+
+### Related Work
+
+- **SyncNet**: [Out of time: automated lip sync in the wild](https://www.robots.ox.ac.uk/~vgg/software/lipsync/)
+- **PeAudioVideo**: HuggingFace Transformers multimodal encoder
 
 ## License
 
@@ -209,14 +413,29 @@ See [LICENSE](LICENSE) file for details.
 
 ## Contributing
 
-1. Create a new branch for your feature
-2. Make your changes
-3. Ensure all tests pass and pre-commit hooks succeed
-4. Submit a pull request
+Contributions are welcome! Please:
+
+1. Fork the repository
+2. Create a feature branch (`git checkout -b feature/amazing-feature`)
+3. Make your changes with tests
+4. Ensure all tests and pre-commit hooks pass
+5. Submit a pull request
+
+## Acknowledgments
+
+- Built with [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/)
+- Uses [HuggingFace Transformers](https://huggingface.co/docs/transformers/)
+- Dependency management by [uv](https://github.com/astral-sh/uv)
+- Experiment tracking with [Weights & Biases](https://wandb.ai/)
+
+## Support
+
+For questions or issues:
+
+- Open an issue on GitHub
+- Check existing issues for solutions
+- Refer to documentation in docstrings
 
-## Tips
+---
 
-- Experiment names automatically include the git commit hash for reproducibility
-- Use `.env` for sensitive information (API keys, credentials)
-- The config system uses Pydantic for type safety and validation
-- Lightning automatically handles distributed training, gradient accumulation, and mixed precision
+**Note**: This is a research/educational implementation. For production use, additional validation and optimization may be required.
diff --git a/pyproject.toml b/pyproject.toml
index 4879bdb..eb6a175 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,23 +1,29 @@
 [project]
-name = "template"
+name = "syncnet"
 version = "0.1.0"
-description = "A general purpose machine learning project template."
+description = "SyncNet is a model that scores the similarity between lip movements and audio speech."
 readme = "README.md"
 authors = [{ name = "Will Rice", email = "wrice20@gmail.com" }]
 requires-python = ">=3.12"
 dependencies = [
+  "av>=16.0.1",
   "git-python>=1.0.3",
   "lightning>=2.5.5",
+  "mediapipe>=0.10.9",
   "mypy>=1.15.0",
+  "opencv-python>=4.9.0",
   "pre-commit>=4.1.0",
   "pydantic>=2.12.2",
   "pydocstyle>=6.3.0",
   "pytest>=8.3.4",
   "python-dotenv>=1.1.1",
   "ruff>=0.9.7",
+  "timm>=1.0.22",
   "torch>=2.8.0",
   "torchaudio>=2.8.0",
+  "torchcodec>=0.9.1",
   "torchvision>=0.23.0",
+  "transformers>=4.57.3",
   "wandb>=0.22.2",
 ]
 
@@ -26,10 +32,13 @@ requires = ["uv_build>=0.9.2"]
 build-backend = "uv_build"
 
 [tool.uv.build-backend]
-module-name = ["template"]
+module-name = ["syncnet"]
+
+[tool.uv.sources]
+transformers = { git = "https://github.com/huggingface/transformers.git" }
 
 [tool.ruff.lint.isort]
-known-first-party = ["template"]
+known-first-party = ["syncnet"]
 
 [tool.ruff.lint]
 select = ["C", "E", "F", "I", "W", "D", "N", "B", "PTH", "ANN"]
@@ -48,4 +57,5 @@ follow_imports_for_stubs = true
 exclude = [".venv"]
 
 [project.scripts]
-train = "template.scripts.train:main"
+train = "syncnet.scripts.train:main"
+compile = "syncnet.scripts.compile:main"
diff --git a/src/syncnet/__init__.py b/src/syncnet/__init__.py
new file mode 100644
index 0000000..7073aac
--- /dev/null
+++ b/src/syncnet/__init__.py
@@ -0,0 +1,19 @@
+"""SyncNet: Audio-Visual Synchronization Package.
+
+This package provides tools for training and evaluating SyncNet models,
+which determine whether audio and video streams are synchronized.
+
+The package includes:
+- Configuration management for model training
+- PyTorch Lightning modules for training
+- Data loading and preprocessing utilities
+- Pre-trained model architectures
+
+Example:
+    Basic usage for training a SyncNet model:
+
+    >>> from syncnet.config import Config
+    >>> from syncnet.lightning_module import SyncNetLightningModule
+    >>> config = Config()
+    >>> model = SyncNetLightningModule(config)
+"""
diff --git a/src/syncnet/config.py b/src/syncnet/config.py
new file mode 100644
index 0000000..895f095
--- /dev/null
+++ b/src/syncnet/config.py
@@ -0,0 +1,76 @@
+"""Configuration module for SyncNet training and model parameters.
+
+This module defines the main configuration class that controls all aspects
+of the SyncNet model training process, including data loading, model
+architecture, training hyperparameters, and reproducibility settings.
+"""
+
+from pydantic import BaseModel, Field
+
+
+class Config(BaseModel):
+    """Configuration class for SyncNet model training and evaluation.
+
+    This class uses Pydantic for configuration management with automatic
+    validation and default values. It organizes settings into logical groups:
+    reproducibility, data processing, training hyperparameters, and model
+    architecture.
+
+    Attributes:
+        seed: Random seed for reproducibility across runs.
+        test_split: Fraction of the dataset to use for validation/testing.
+        batch_size: Number of samples per batch during training.
+        max_epochs: Maximum number of training epochs.
+        early_stopping_patience: Number of epochs to wait before early stopping.
+        learning_rate: Initial learning rate for the optimizer.
+        min_learning_rate: Minimum learning rate for the scheduler.
+        weight_decay: L2 regularization coefficient.
+        accumulate_grad_batches: Number of batches to accumulate gradients.
+        gradient_clip_val: Maximum gradient norm for clipping.
+        base_model: HuggingFace model identifier or path to pretrained model.
+        num_frames: Number of video frames to process per sample.
+        negative_fraction: Fraction of negative (out-of-sync) samples.
+        frame_height: Target height for input video frames in pixels.
+        frame_width: Target width for input video frames in pixels.
+
+    Example:
+        >>> config = Config(batch_size=8, learning_rate=1e-3)
+        >>> print(config.seed)
+        42
+    """
+
+    # Reproducibility
+    seed: int = Field(default=42, description="Random seed for reproducibility.")
+
+    # Data
+    test_split: float = Field(
+        default=0.05, description="Proportion of data for val/testing."
+    )
+    batch_size: int = Field(default=4, description="Batch size.")
+
+    # Training
+    max_epochs: int = Field(default=200, description="Maximum number of epochs.")
+    early_stopping_patience: int = Field(
+        default=10, description="Early stopping patience."
+    )
+    learning_rate: float = Field(default=1e-4, description="Initial learning rate.")
+    min_learning_rate: float = Field(default=1e-6, description="Minimum learning rate.")
+    weight_decay: float = Field(default=1e-2, description="Weight decay for optimizer.")
+    accumulate_grad_batches: int = Field(
+        default=1, description="Number of gradient accumulation."
+    )
+    gradient_clip_val: float = Field(
+        default=1.0, description="Gradient clipping value."
+    )
+
+    # Model
+    base_model: str = Field(
+        default="facebook/pe-av-small", description="Base model name or path."
+    )
+
+    num_frames: int = Field(default=5, description="Number of video frames to process.")
+    negative_fraction: float = Field(
+        default=0.5, description="Fraction of negative samples."
+    )
+    frame_height: int = Field(default=224, description="Height of input video frames.")
+    frame_width: int = Field(default=224, description="Width of input video frames.")
diff --git a/src/syncnet/datamodule.py b/src/syncnet/datamodule.py
new file mode 100644
index 0000000..833805d
--- /dev/null
+++ b/src/syncnet/datamodule.py
@@ -0,0 +1,238 @@
+"""PyTorch Lightning DataModule for SyncNet training.
+
+This module implements the data loading and preprocessing pipeline for training
+SyncNet models using PyTorch Lightning. It handles dataset splitting, batch
+collation, audio-visual preprocessing, and data augmentation.
+"""
+
+import random
+
+import torch
+import torchaudio
+import torchvision
+from lightning.pytorch import LightningDataModule
+from torch.utils.data import DataLoader, Dataset
+from transformers.models.pe_audio_video import PeAudioVideoProcessor
+
+from syncnet.config import Config
+from syncnet.datasets import Batch
+
+
+class SyncNetDataModule(LightningDataModule):
+    """PyTorch Lightning DataModule for SyncNet training and evaluation.
+
+    This DataModule handles all aspects of data loading, preprocessing, and
+    augmentation for training audio-visual synchronization models. It manages
+    dataset splitting, batch collation with variable-length sequences, audio
+    resampling, video resizing, and negative sample generation.
+
+    The module uses a HuggingFace processor for preprocessing and applies
+    random temporal shifts to create negative (out-of-sync) samples for
+    contrastive learning.
+
+    Attributes:
+        dataset: The underlying dataset containing video files.
+        config: Configuration object with training parameters.
+        num_workers: Number of worker processes for parallel data loading.
+        processor: HuggingFace processor for audio-visual preprocessing.
+        resample: Audio resampling transform (16kHz -> 48kHz).
+        resize: Video frame resizing transform.
+        train_dataset: Training split of the dataset.
+        val_dataset: Validation split of the dataset.
+
+    Args:
+        dataset: PyTorch Dataset containing audio-visual samples.
+        config: Configuration object with model and training settings.
+        num_workers: Number of parallel workers for data loading. Default: 4.
+
+    Example:
+        >>> dataset = SyncNetDataset(Path("/data/videos"))
+        >>> config = Config(batch_size=8)
+        >>> datamodule = SyncNetDataModule(dataset, config, num_workers=8)
+        >>> datamodule.setup("fit")
+        >>> train_loader = datamodule.train_dataloader()
+    """
+
+    train_dataset: Dataset
+    val_dataset: Dataset
+
+    def __init__(self, dataset: Dataset, config: Config, num_workers: int = 4) -> None:
+        super().__init__()
+        self.dataset = dataset
+        self.config = config
+        self.num_workers = num_workers
+        self.processor = PeAudioVideoProcessor.from_pretrained(config.base_model)
+        self.resample = torchaudio.transforms.Resample(16000, 48000)
+        self.resize = torchvision.transforms.Resize(
+            (config.frame_height, config.frame_width)
+        )
+
+    def setup(self, stage: str | None = None) -> None:
+        """Set up datasets for different training stages.
+
+        Splits the dataset into training and validation sets using an 80/20 split.
+        This method is called automatically by PyTorch Lightning before training.
+
+        Args:
+            stage: Current stage ('fit', 'validate', 'test', or None). If None
+                or 'fit', datasets are prepared for training and validation.
+        """
+        if stage == "fit" or stage is None:
+            dataset_size = len(self.dataset)  # type: ignore
+            train_size = int(0.8 * dataset_size)
+            val_size = dataset_size - train_size
+            self.train_dataset, self.val_dataset = torch.utils.data.random_split(
+                self.dataset, [train_size, val_size]
+            )
+
+    def train_dataloader(self) -> DataLoader:
+        """Create and return the training data loader.
+
+        Returns:
+            DataLoader configured for training with shuffling, multiple workers,
+            custom collation, and prefetching enabled.
+        """
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.config.batch_size,
+            shuffle=True,
+            num_workers=self.num_workers,
+            pin_memory=True,
+            collate_fn=self.pad_collate_fn,
+            persistent_workers=self.num_workers > 0,
+            prefetch_factor=2 if self.num_workers > 0 else None,
+        )
+
+    def val_dataloader(self) -> DataLoader:
+        """Create and return the validation data loader.
+
+        Returns:
+            DataLoader configured for validation without shuffling but with
+            multiple workers and custom collation.
+        """
+        return DataLoader(
+            self.val_dataset,
+            batch_size=self.config.batch_size,
+            shuffle=False,
+            num_workers=self.num_workers,
+            pin_memory=True,
+            collate_fn=self.pad_collate_fn,
+            persistent_workers=self.num_workers > 0,
+            prefetch_factor=2 if self.num_workers > 0 else None,
+        )
+
+    def pad_collate_fn(
+        self, samples: list[tuple[torch.Tensor, torch.Tensor, dict]]
+    ) -> Batch:
+        """Custom collate function for batching variable-length audio-visual samples.
+
+        This function processes a list of raw video samples and creates a training
+        batch with the following steps:
+        1. Extract random temporal segments from each video
+        2. Convert stereo audio to mono
+        3. Resize video frames to target dimensions
+        4. Resample audio to target sample rate
+        5. Randomly create negative samples by shifting audio
+        6. Preprocess using HuggingFace processor
+        7. Stack samples into batch tensors
+
+        Args:
+            samples: List of tuples, each containing (video, audio, metadata)
+                from the dataset. Video shape: (T, H, W, C), Audio shape: (C, N).
+
+        Returns:
+            Batch object containing stacked audio tensors, video tensors, and
+            binary labels indicating synchronization (1.0) or not (0.0).
+
+        Note:
+            - Samples with insufficient audio length (< 3200 samples) are skipped
+            - Approximately 50% of samples are converted to negative examples
+            - Errors during processing are caught and logged, skipping bad samples
+        """
+        video_segments, audio_segments, labels = [], [], []
+        for sample in samples:
+            try:
+                video, audio, metadata = sample
+                audio = audio.mean(dim=0, keepdim=True)  # Convert to mono if stereo
+                video_segment, audio_segment = self.sample_random_segment(
+                    video,
+                    audio,
+                    num_frames=self.config.num_frames,
+                    fps=25,
+                    sample_rate=16000,
+                )
+                if audio_segment.shape[1] < 3200:
+                    continue
+                video_segment = self.resize(video_segment.permute(0, 3, 1, 2))
+                audio_segment = self.resample(audio_segment)
+                if random.random() > 0.5:
+                    audio_segment = audio_segment.roll(
+                        1 if random.random() > 0.5 else -1, dims=-1
+                    )
+                    label = torch.tensor(0.0)
+                else:
+                    label = torch.tensor(1.0)
+
+                input_values = self.processor(
+                    videos=video_segment,
+                    audio=audio_segment.squeeze(0),
+                    return_tensors="pt",
+                    padding=False,
+                    sampling_rate=48000,
+                )
+                video_segments.append(input_values["pixel_values_videos"][0])
+                audio_segments.append(input_values["input_values"][0])
+                labels.append(label)
+            except Exception as e:
+                print(f"Error processing sample: {e}")
+                continue
+
+        return Batch(
+            audio=torch.stack(audio_segments),
+            video=torch.stack(video_segments),
+            labels=torch.stack(labels),
+        )
+
+    @staticmethod
+    def sample_random_segment(
+        video: torch.Tensor,
+        audio: torch.Tensor,
+        num_frames: int,
+        fps: int = 25,
+        sample_rate: int = 16000,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Sample a random temporal segment from video with corresponding audio.
+
+        Randomly selects a contiguous segment of video frames and extracts the
+        temporally-aligned audio segment. The audio segment duration matches
+        the video segment duration based on the fps and sample rate.
+
+        Args:
+            video: Video tensor of shape (T, H, W, C) where T is total frames,
+                H and W are frame dimensions, and C is number of channels.
+            audio: Audio tensor of shape (C, N) where C is number of channels
+                and N is total audio samples.
+            num_frames: Number of consecutive video frames to extract.
+            fps: Frames per second of the video. Default: 25.
+            sample_rate: Audio sample rate in Hz. Default: 16000.
+
+        Returns:
+            Tuple containing:
+                - video_segment: Tensor of shape (num_frames, H, W, C)
+                - audio_segment: Tensor of shape (C, num_samples) where
+                  num_samples = num_frames / fps * sample_rate
+
+        Raises:
+            ValueError: If num_frames exceeds the video length.
+        """
+        start_frame = random.randint(0, video.shape[0] - num_frames)
+        video_segment = video[start_frame : start_frame + num_frames]
+
+        # Calculate corresponding audio segment
+        audio_start_frame = int(start_frame / fps * sample_rate)
+        num_audio_samples = int(num_frames / fps * sample_rate)
+        audio_segment = audio[
+            :, audio_start_frame : audio_start_frame + num_audio_samples
+        ]
+
+        return video_segment, audio_segment
diff --git a/src/syncnet/datasets/__init__.py b/src/syncnet/datasets/__init__.py
new file mode 100644
index 0000000..18ac2ad
--- /dev/null
+++ b/src/syncnet/datasets/__init__.py
@@ -0,0 +1,40 @@
+"""Dataset utilities and data structures for SyncNet.
+
+This module provides data structures and utilities for handling audio-visual
+data in the SyncNet training pipeline. It defines the batch structure used
+throughout the training process.
+"""
+
+from typing import NamedTuple
+
+import torch
+
+
+class Batch(NamedTuple):
+    """A batch of audio-visual training data with labels.
+
+    This named tuple represents a single training batch containing preprocessed
+    audio and video tensors along with synchronization labels.
+
+    Attributes:
+        audio: Preprocessed audio tensor of shape (batch_size, audio_features).
+            Contains the audio embeddings or spectrograms for each sample.
+        video: Preprocessed video tensor of shape
+        (batch_size, num_frames, channels, height, width).
+            Contains the video frames for each sample.
+        labels: Binary labels of shape (batch_size,) indicating whether audio
+            and video are synchronized (1.0) or not (0.0).
+
+    Example:
+        >>> batch = Batch(
+        ...     audio=torch.randn(4, 1024),
+        ...     video=torch.randn(4, 5, 3, 224, 224),
+        ...     labels=torch.tensor([1.0, 0.0, 1.0, 0.0])
+        ... )
+        >>> print(batch.audio.shape)
+        torch.Size([4, 1024])
+    """
+
+    audio: torch.Tensor
+    video: torch.Tensor
+    labels: torch.Tensor
diff --git a/src/syncnet/datasets/dataset.py b/src/syncnet/datasets/dataset.py
new file mode 100644
index 0000000..8dc2165
--- /dev/null
+++ b/src/syncnet/datasets/dataset.py
@@ -0,0 +1,84 @@
+"""Dataset class for loading audio-visual data for SyncNet training.
+
+This module provides a PyTorch Dataset implementation for loading video files
+containing both audio and visual streams. It recursively searches for MP4 files
+in a given directory and loads them for training.
+"""
+
+import os
+from pathlib import Path
+
+from torch.utils.data import Dataset
+from torchvision.io import read_video
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+class SyncNetDataset(Dataset):
+    """PyTorch Dataset for loading audio-visual synchronization training data.
+
+    This dataset loads video files (MP4 format) from a directory tree and
+    extracts both video frames and audio waveforms. It's designed for training
+    audio-visual synchronization models.
+
+    The dataset recursively searches for all .mp4 files in the provided root
+    directory and its subdirectories. Each sample contains the video frames,
+    audio waveform, and metadata from the video file.
+
+    Attributes:
+        data_root: Path to the root directory containing video files.
+        sample_paths: List of paths to all MP4 files found in the directory tree.
+
+    Args:
+        root: Root directory path containing the video files. The dataset will
+            recursively search this directory for all .mp4 files.
+
+    Example:
+        >>> dataset = SyncNetDataset(Path("/path/to/videos"))
+        >>> print(len(dataset))
+        1000
+        >>> video, audio, metadata = dataset[0]
+        >>> print(video.shape, audio.shape)
+        torch.Size([100, 224, 224, 3]) torch.Size([2, 48000])
+
+    Note:
+        This class sets TOKENIZERS_PARALLELISM=false to avoid warnings when
+        using HuggingFace tokenizers in multiprocessing data loaders.
+    """
+
+    def __init__(self, root: Path) -> None:
+        """Initialize the dataset with the root directory path.
+
+        Args:
+            root: Path to the root directory containing video files.
+        """
+        super().__init__()
+        self.data_root = root
+        self.sample_paths = list(root.rglob("*.mp4"))
+
+    def __len__(self) -> int:
+        """Return the number of samples in the dataset.
+
+        Returns:
+            The total number of video files found in the dataset.
+        """
+        return len(self.sample_paths)
+
+    def __getitem__(self, idx: int) -> tuple:
+        """Load and return the video, audio, and metadata for a sample.
+
+        Args:
+            idx: Index of the sample to load (0 to len(dataset)-1).
+
+        Returns:
+            A tuple containing:
+                - video: Tensor of shape (T, H, W, C) with video frames.
+                - audio: Tensor of shape (C, N) with audio samples.
+                - metadata: Dictionary with video metadata (fps, duration, etc).
+
+        Raises:
+            IndexError: If idx is out of bounds.
+            RuntimeError: If the video file cannot be read or is corrupted.
+        """
+        video, audio, metadata = read_video(str(self.sample_paths[idx]), pts_unit="sec")
+        return video, audio, metadata
diff --git a/src/syncnet/lightning_module.py b/src/syncnet/lightning_module.py
new file mode 100644
index 0000000..a5a71cb
--- /dev/null
+++ b/src/syncnet/lightning_module.py
@@ -0,0 +1,197 @@
+"""PyTorch Lightning Module for SyncNet model training.
+
+This module implements the training loop, validation, optimization, and model
+checkpointing for the SyncNet audio-visual synchronization model using PyTorch
+Lightning framework.
+"""
+
+from pathlib import Path
+
+import torch
+from lightning.pytorch import LightningModule
+from lightning.pytorch.utilities.memory import garbage_collection_cuda
+from torch import nn
+from torchmetrics import Accuracy, MeanMetric, MetricCollection
+from transformers.models.pe_audio_video import PeAudioVideoProcessor
+
+from syncnet.config import Config
+from syncnet.datasets import Batch
+from syncnet.modeling.model import SyncNet, SyncNetConfig
+
+
+class SyncNetLightningModule(LightningModule):
+    """PyTorch Lightning Module for training SyncNet models.
+
+    This module encapsulates the full training pipeline including model
+    initialization, training/validation steps, metric tracking, optimizer
+    configuration, and optional model uploading to HuggingFace Hub.
+
+    The module uses Binary Cross-Entropy loss to train the model to distinguish
+    between synchronized and out-of-sync audio-visual pairs. It tracks training
+    loss, validation loss, and validation accuracy, and automatically saves the
+    best model based on validation loss.
+
+    Attributes:
+        config: Configuration object with training hyperparameters.
+        model: SyncNet model for audio-visual synchronization.
+        push_to_hub: Whether to push model checkpoints to HuggingFace Hub.
+        sync_dist: Whether to synchronize metrics across distributed processes.
+        loss_fn: Binary cross-entropy loss function.
+        train_loss: Metric for tracking mean training loss.
+        val_loss: Metric for tracking mean validation loss.
+        val_metrics: Collection of validation metrics including accuracy.
+        processor: HuggingFace processor for preprocessing.
+        lowest_val_loss: Best validation loss achieved during training.
+
+    Args:
+        config: Configuration object with model and training settings.
+        push_to_hub: If True, automatically push model to HuggingFace Hub when
+            validation loss improves. Default: False.
+        sync_dist: If True, synchronize metrics across distributed processes
+            during multi-GPU training. Default: False.
+
+    Example:
+        >>> config = Config(learning_rate=1e-4, max_epochs=100)
+        >>> module = SyncNetLightningModule(config, push_to_hub=True)
+        >>> trainer = Trainer(max_epochs=100)
+        >>> trainer.fit(module, datamodule)
+    """
+
+    def __init__(
+        self, config: Config, push_to_hub: bool = False, sync_dist: bool = False
+    ) -> None:
+        """Initialize the Lightning Module with config and training options.
+
+        Args:
+            config: Configuration object with model and training settings.
+            push_to_hub: Whether to push to HuggingFace Hub on improvement.
+            sync_dist: Whether to sync metrics across distributed processes.
+        """
+        super().__init__()
+        self.save_hyperparameters(config.model_dump())
+        self.config = config
+        self.model = SyncNet(SyncNetConfig(**config.model_dump()))
+        self.push_to_hub = push_to_hub
+        self.sync_dist = sync_dist
+        self.loss_fn = nn.BCELoss()
+        self.train_loss = MeanMetric()
+        self.val_loss = MeanMetric()
+        self.val_metrics = MetricCollection({"val_accuracy": Accuracy(task="binary")})
+        self.processor = PeAudioVideoProcessor.from_pretrained(config.base_model)
+        self.lowest_val_loss = float("inf")
+
+    def training_step(self, batch: Batch, batch_idx: int) -> None:
+        """Execute a single training step.
+
+        Performs forward pass through the model, computes loss, and logs metrics.
+        The loss is computed using Binary Cross-Entropy between predicted
+        similarity scores and ground truth labels.
+
+        Args:
+            batch: Batch object containing audio, video, and labels.
+            batch_idx: Index of the current batch (unused but required by Lightning).
+
+        Returns:
+            Loss tensor for backpropagation.
+        """
+        similarity = self.model(batch.audio, batch.video)
+        with torch.autocast(device_type=self.device.type, enabled=False):
+            loss = self.loss_fn(similarity.squeeze(-1), batch.labels.squeeze(-1))
+        self.log("train_loss", self.train_loss(loss), prog_bar=True)
+        return loss
+
+    def validation_step(self, batch: Batch, batch_idx: int) -> None:
+        """Execute a single validation step.
+
+        Performs forward pass and computes validation metrics without gradient
+        computation. Updates running metrics for loss and accuracy.
+
+        Args:
+            batch: Batch object containing audio, video, and labels.
+            batch_idx: Index of the current batch (unused but required by Lightning).
+
+        Returns:
+            Loss tensor for the validation batch.
+        """
+        similarity = self.model(batch.audio, batch.video)
+        with torch.autocast(device_type=self.device.type, enabled=False):
+            loss = self.loss_fn(similarity.squeeze(-1), batch.labels.squeeze(-1))
+        self.val_loss.update(loss)
+        self.val_metrics.update(similarity.squeeze(-1), batch.labels.squeeze(-1))
+        return loss
+
+    def on_validation_epoch_end(self) -> None:
+        """Process metrics and save models at the end of validation epoch.
+
+        This method:
+        1. Computes and logs validation metrics (loss and accuracy)
+        2. Checks if current validation loss is the best so far
+        3. If best, optionally pushes model and processor to HuggingFace Hub
+        4. Resets metrics for next epoch
+        5. Performs CUDA garbage collection to free memory
+
+        Note:
+            Model uploading to Hub requires proper authentication and will
+            create a private repository named after the log directory.
+        """
+        self.log_dict(self.val_metrics.compute(), sync_dist=self.sync_dist)
+        val_loss = self.val_loss.compute().item()
+        self.log("val_loss", val_loss, prog_bar=True, sync_dist=self.sync_dist)
+        if val_loss < self.lowest_val_loss:
+            self.lowest_val_loss = val_loss
+
+            log_path = Path(self.trainer.default_root_dir)
+            if self.push_to_hub:
+                try:
+                    # Push main model
+                    self.model.push_to_hub(  # type: ignore[call-arg]
+                        repo_id=log_path.name,
+                        commit_message="Add model checkpoint",
+                        token=True,
+                        private=True,
+                    )
+
+                    self.processor.push_to_hub(
+                        repo_id=log_path.name,
+                        commit_message="Add tokenizer",
+                        token=True,
+                        private=True,
+                    )
+                except Exception as e:
+                    print(f"Failed to push to hub: {e}")
+
+        self.val_metrics.reset()
+        self.val_loss.reset()
+        garbage_collection_cuda()
+
+    def configure_optimizers(self) -> tuple[list[torch.optim.Optimizer], list]:
+        """Configure optimizers and learning rate schedulers.
+
+        Sets up AdamW optimizer with weight decay and OneCycleLR scheduler
+        for cosine annealing learning rate schedule with warmup.
+
+        Returns:
+            Tuple containing:
+                - List with single AdamW optimizer
+                - List with single OneCycleLR scheduler
+
+        Note:
+            The scheduler uses:
+            - 10% of training for warmup (pct_start=0.1)
+            - Cosine annealing strategy
+            - Final learning rate of config.min_learning_rate
+        """
+        optimizer = torch.optim.AdamW(
+            self.model.parameters(),
+            lr=self.config.learning_rate,
+            weight_decay=self.config.weight_decay,
+        )
+        lr_scheduler = torch.optim.lr_scheduler.OneCycleLR(
+            optimizer,
+            max_lr=self.config.learning_rate,
+            total_steps=self.config.max_epochs,
+            pct_start=0.1,
+            anneal_strategy="cos",
+            final_div_factor=self.config.learning_rate / self.config.min_learning_rate,
+        )
+        return [optimizer], [lr_scheduler]
diff --git a/src/syncnet/modeling/__init__.py b/src/syncnet/modeling/__init__.py
new file mode 100644
index 0000000..fbc2fbb
--- /dev/null
+++ b/src/syncnet/modeling/__init__.py
@@ -0,0 +1,5 @@
+"""Neural network models for audio-visual synchronization.
+
+This package contains the model architectures and configurations used for
+training SyncNet models to determine audio-visual synchronization.
+"""
diff --git a/src/syncnet/modeling/model.py b/src/syncnet/modeling/model.py
new file mode 100644
index 0000000..96b5d4e
--- /dev/null
+++ b/src/syncnet/modeling/model.py
@@ -0,0 +1,125 @@
+"""SyncNet model architecture for audio-visual synchronization.
+
+This module implements the SyncNet model that learns to determine whether
+audio and video streams are temporally synchronized. The model uses a
+pretrained audio-visual encoder and computes cosine similarity between
+audio and video embeddings.
+"""
+
+import torch
+import torch.nn as nn
+from transformers import PreTrainedConfig, PreTrainedModel
+from transformers.models.pe_audio_video import PeAudioVideoModel
+
+
+class SyncNetConfig(PreTrainedConfig):
+    """Configuration class for SyncNet model.
+
+    Extends HuggingFace's PreTrainedConfig to enable model serialization
+    and integration with the Transformers ecosystem. All configuration
+    parameters from the main Config class can be passed to this.
+
+    Attributes:
+        model_type: Identifier for this model architecture ("syncnet").
+
+    Args:
+        **kwargs: Arbitrary configuration parameters passed to parent class.
+            Typically includes base_model, learning_rate, etc. from Config.
+
+    Example:
+        >>> config = SyncNetConfig(base_model="facebook/pe-av-small")
+        >>> model = SyncNet(config)
+    """
+
+    model_type = "syncnet"
+
+    def __init__(self, **kwargs: object) -> None:
+        """Initialize configuration with arbitrary parameters.
+
+        Args:
+            **kwargs: Configuration parameters to store.
+        """
+        super().__init__(**kwargs)  # type: ignore
+
+
+class SyncNet(PreTrainedModel):
+    """Audio-Visual Synchronization Model using pretrained encoder.
+
+    This model determines whether audio and video streams are synchronized
+    by computing the similarity between learned embeddings. It uses a
+    pretrained audio-visual encoder (PeAudioVideo) to extract separate
+    embeddings for audio and video, then computes their cosine similarity.
+
+    The model applies:
+    1. Feature extraction using pretrained encoder
+    2. Flattening of temporal/spatial dimensions
+    3. L2 normalization of embeddings
+    4. ReLU activation for non-negative similarities
+    5. Cosine similarity computation
+
+    Attributes:
+        encoder: Pretrained PeAudioVideoModel for feature extraction.
+        similarity_fn: Cosine similarity function for comparing embeddings.
+
+    Args:
+        config: SyncNetConfig containing model configuration including
+            the pretrained model name/path.
+
+    Example:
+        >>> config = SyncNetConfig(base_model="facebook/pe-av-small")
+        >>> model = SyncNet(config)
+        >>> audio = torch.randn(4, 1024)
+        >>> video = torch.randn(4, 5, 3, 224, 224)
+        >>> similarity = model(audio, video)
+        >>> print(similarity.shape)
+        torch.Size([4])
+    """
+
+    def __init__(self, config: SyncNetConfig) -> None:
+        """Initialize SyncNet with pretrained encoder.
+
+        Args:
+            config: Configuration object containing model parameters.
+        """
+        super().__init__(config=config)
+        self.encoder = PeAudioVideoModel.from_pretrained(config.base_model)
+        self.encoder.gradient_checkpointing = True
+        self.similarity_fn = nn.CosineSimilarity(dim=-1)
+
+    def forward(
+        self, input_values: torch.Tensor, pixel_values: torch.Tensor
+    ) -> torch.Tensor:
+        """Compute synchronization similarity between audio and video.
+
+        Processes audio and video inputs through the encoder, normalizes
+        the resulting embeddings, and computes cosine similarity to determine
+        if they are synchronized.
+
+        Args:
+            input_values: Preprocessed audio tensor from the processor.
+                Shape: (batch_size, audio_features).
+            pixel_values: Preprocessed video frames from the processor.
+                Shape: (batch_size, num_frames, channels, height, width).
+
+        Returns:
+            Similarity scores between 0 and 1, where higher values indicate
+            better synchronization. Shape: (batch_size,).
+
+        Note:
+            The embeddings are flattened, L2-normalized, and ReLU-activated
+            before similarity computation to ensure positive, bounded scores.
+        """
+        outputs = self.encoder(
+            input_values=input_values, pixel_values_videos=pixel_values
+        )
+        audio_emb = outputs.audio_embeds
+        face_emb = outputs.video_embeds
+
+        audio_emb = audio_emb.view(audio_emb.size(0), -1)
+        face_emb = face_emb.view(face_emb.size(0), -1)
+
+        audio_emb = nn.functional.normalize(audio_emb, p=2.0, dim=1)
+        face_emb = nn.functional.normalize(face_emb, p=2.0, dim=1)
+
+        similarity = self.similarity_fn(audio_emb.relu(), face_emb.relu()).squeeze(-1)
+        return similarity
diff --git a/src/syncnet/scripts/__init__.py b/src/syncnet/scripts/__init__.py
new file mode 100644
index 0000000..aac0ea4
--- /dev/null
+++ b/src/syncnet/scripts/__init__.py
@@ -0,0 +1,5 @@
+"""Command-line scripts for training and evaluation.
+
+This package contains executable scripts for training SyncNet models,
+running experiments, and evaluating model performance.
+"""
diff --git a/src/syncnet/scripts/train.py b/src/syncnet/scripts/train.py
new file mode 100644
index 0000000..c71ffb6
--- /dev/null
+++ b/src/syncnet/scripts/train.py
@@ -0,0 +1,159 @@
+"""Train script."""
+
+import logging
+import os
+from argparse import ArgumentParser
+from pathlib import Path
+
+import torch
+from dotenv import load_dotenv
+from git import Repo
+from lightning import Trainer, seed_everything
+from lightning.pytorch import callbacks, loggers
+
+from syncnet.config import Config
+from syncnet.datamodule import SyncNetDataModule
+from syncnet.datasets.dataset import SyncNetDataset
+from syncnet.lightning_module import SyncNetLightningModule
+
+
+def main() -> None:
+    r"""Execute the training pipeline for SyncNet models.
+
+    This function orchestrates the entire training process:
+    1. Parses command-line arguments
+    2. Loads environment variables and validates configuration
+    3. Initializes dataset, datamodule, and model
+    4. Configures logging, callbacks, and trainer
+    5. Executes training with automatic checkpointing
+
+    The training uses PyTorch Lightning for training orchestration,
+    Weights & Biases for experiment tracking, and supports distributed
+    training across multiple GPUs with DeepSpeed.
+
+    Command-line Arguments:
+        data_root: Path to directory containing video files
+        --project: Project name for logging (default: "template")
+        --num_devices: Number of GPUs to use (default: 1)
+        --num_workers: Number of data loading workers (default: 12)
+        --log_root: Directory for saving logs and checkpoints (default: "logs")
+        --checkpoint_path: Path to checkpoint for resuming training
+        --weights_path: Path to pretrained weights to initialize model
+        --debug: Enable debug mode with offline logging
+        --fast_dev_run: Run single batch for testing
+
+    Environment Variables:
+        WANDB_PROJECT: Required. Weights & Biases project name
+        WANDB_ENTITY: Required. Weights & Biases entity/username
+
+    Raises:
+        ValueError: If required environment variables are not set.
+
+    Example:
+        >>> # In command line:
+        >>> python -m syncnet.scripts.train /data/videos \\
+        ...     --num_devices 4 --num_workers 16 --project my_experiment
+    """
+    parser = ArgumentParser(description="Train script.")
+    parser.add_argument("data_root", type=Path)
+    parser.add_argument("--project", default="template", type=str)
+    parser.add_argument("--num_devices", default=1, type=int)
+    parser.add_argument("--num_workers", default=12, type=int)
+    parser.add_argument("--log_root", default="logs", type=Path)
+    parser.add_argument("--checkpoint_path", default=None, type=Path)
+    parser.add_argument("--weights_path", type=Path, default=None)
+    parser.add_argument("--debug", action="store_true")
+    parser.add_argument("--fast_dev_run", action="store_true")
+    args = parser.parse_args()
+    load_dotenv()
+
+    logging.basicConfig(level=logging.INFO)
+    logger = logging.getLogger(__name__)
+
+    # Validate required environment variables
+    wandb_project = os.environ.get("WANDB_PROJECT")
+    wandb_entity = os.environ.get("WANDB_ENTITY")
+
+    if not wandb_project or not wandb_entity:
+        raise ValueError(
+            "WANDB_PROJECT and WANDB_ENTITY must be set in environment variables. "
+            "Copy .env.example to .env and set these values."
+        )
+
+    config = Config()
+
+    seed_everything(config.seed, workers=True)
+
+    git_repo = Repo()
+    git_hash = git_repo.head.object.hexsha[:7]
+    model_name = config.base_model.split("/")[-1]
+    log_path = args.log_root / f"{model_name}-{git_hash}"
+    log_path.mkdir(exist_ok=True, parents=True)
+
+    dataset = SyncNetDataset(args.data_root)
+    datamodule = SyncNetDataModule(dataset, config=config, num_workers=args.num_workers)
+    lightning_module = SyncNetLightningModule(
+        config=config,
+        sync_dist=args.num_devices > 1,
+        push_to_hub=not (args.fast_dev_run or args.debug),
+    )
+    # Load weights if specified
+    if args.weights_path:
+        logger.info(f"Loading weights from {args.weights_path}")
+        lightning_module.model.load_state_dict(torch.load(args.weights_path))
+
+    # Setup logger
+    experiment_logger = loggers.WandbLogger(
+        project=wandb_project,
+        name=log_path.name,
+        offline=args.debug,
+        entity=wandb_entity,
+    )
+
+    # Setup callbacks
+    lr_monitor = callbacks.LearningRateMonitor(logging_interval="step")
+    early_stopping = callbacks.EarlyStopping(
+        monitor="val_loss", patience=config.early_stopping_patience, mode="max"
+    )
+    model_checkpoint = callbacks.ModelCheckpoint(
+        dirpath=log_path,
+        filename="best-{epoch}-{val_loss:.4f}",
+        monitor="val_loss",
+        mode="min",
+        save_top_k=1,
+        save_last=True,
+    )
+
+    callbacks_list = [lr_monitor, early_stopping, model_checkpoint]
+
+    # Setup trainer
+    last_checkpoint = log_path / "last.ckpt"
+    if args.checkpoint_path:
+        last_checkpoint = args.checkpoint_path
+
+    trainer = Trainer(
+        default_root_dir=log_path,
+        max_epochs=config.max_epochs,
+        accelerator="gpu" if torch.cuda.is_available() else "cpu",
+        devices=args.num_devices,
+        logger=experiment_logger,
+        precision="bf16-mixed" if torch.cuda.is_available() else 32,
+        accumulate_grad_batches=config.accumulate_grad_batches,
+        gradient_clip_val=config.gradient_clip_val,
+        callbacks=callbacks_list,
+        enable_checkpointing=True,
+        val_check_interval=1000,
+        limit_val_batches=100,
+        fast_dev_run=args.fast_dev_run,
+        strategy="deepspeed_stage_2" if args.num_devices > 1 else "auto",
+    )
+
+    trainer.fit(
+        lightning_module,
+        datamodule=datamodule,
+        ckpt_path=last_checkpoint if last_checkpoint.exists() else None,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/template/__init__.py b/src/template/__init__.py
deleted file mode 100644
index 9421b91..0000000
--- a/src/template/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Template module."""
diff --git a/src/template/config.py b/src/template/config.py
deleted file mode 100644
index f826643..0000000
--- a/src/template/config.py
+++ /dev/null
@@ -1,24 +0,0 @@
-"""Main config class."""
-
-from pydantic import BaseModel
-
-
-class Config(BaseModel):
-    """Main config class."""
-
-    # Reproducibility
-    seed: int = 42
-
-    # Data
-    test_split: float = 0.1
-    batch_size: int = 16
-
-    # Training
-    max_epochs: int = 200
-    early_stopping_patience: int = 30
-    learning_rate: float = 1e-4
-    min_learning_rate: float = 1e-6
-    weight_decay: float = 1e-2
-
-    # Model
-    base_model: str = "some_pretrained_model"
diff --git a/src/template/datasets/__init__.py b/src/template/datasets/__init__.py
deleted file mode 100644
index 98d0f52..0000000
--- a/src/template/datasets/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""Datasets."""
-
-from typing import NamedTuple
-
-
-class Batch(NamedTuple):
-    """Batch of data."""
diff --git a/src/template/lightning_module.py b/src/template/lightning_module.py
deleted file mode 100644
index 710a84b..0000000
--- a/src/template/lightning_module.py
+++ /dev/null
@@ -1,37 +0,0 @@
-"""Lightning Module."""
-
-import torch
-from lightning.pytorch import LightningModule
-
-from template.config import Config
-from template.datasets import Batch
-
-
-class BaseLightningModule(LightningModule):
-    """Base Lightning Module."""
-
-    def __init__(
-        self, config: Config, push_to_hub: bool = False, sync_dist: bool = False
-    ) -> None:
-        super().__init__()
-        self.save_hyperparameters(config.model_dump())
-        self.config = config
-        self.push_to_hub = push_to_hub
-        self.sync_dist = sync_dist
-
-    def training_step(self, batch: Batch, batch_idx: int) -> None:
-        """Train step."""
-        raise NotImplementedError("Training step not implemented.")
-
-    def validation_step(self, batch: Batch, batch_idx: int) -> None:
-        """Validate step."""
-        raise NotImplementedError("Validation step not implemented.")
-
-    def configure_optimizers(self) -> tuple[list[torch.optim.Optimizer], list]:
-        """Configure optimizers."""
-        optimizer = torch.optim.AdamW(
-            self.parameters(),
-            lr=self.config.learning_rate,
-            weight_decay=self.config.weight_decay,
-        )
-        return [optimizer], []
diff --git a/src/template/modeling/__init__.py b/src/template/modeling/__init__.py
deleted file mode 100644
index 67f0447..0000000
--- a/src/template/modeling/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Models."""
diff --git a/src/template/scripts/__init__.py b/src/template/scripts/__init__.py
deleted file mode 100644
index a9a18d7..0000000
--- a/src/template/scripts/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Scripts."""
diff --git a/src/template/scripts/train.py b/src/template/scripts/train.py
deleted file mode 100644
index b9ea5ce..0000000
--- a/src/template/scripts/train.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""Train script."""
-
-from argparse import ArgumentParser
-from pathlib import Path
-
-from dotenv import load_dotenv
-from git import Repo
-from lightning import seed_everything
-
-from template.config import Config
-
-
-def main() -> None:
-    """Train script."""
-    parser = ArgumentParser(description="Train script.")
-    parser.add_argument("data_root", type=Path)
-    parser.add_argument("--project", default="template", type=str)
-    parser.add_argument("--num_devices", default=1, type=int)
-    parser.add_argument("--num_workers", default=12, type=int)
-    parser.add_argument("--log_root", default="logs", type=Path)
-    parser.add_argument("--checkpoint_path", default=None, type=Path)
-    parser.add_argument("--weights_path", type=Path, default=None)
-    parser.add_argument("--debug", action="store_true")
-    parser.add_argument("--fast_dev_run", action="store_true")
-    args = parser.parse_args()
-    load_dotenv()
-
-    config = Config()
-
-    seed_everything(config.seed, workers=True)
-
-    git_repo = Repo()
-    git_hash = git_repo.head.object.hexsha[:7]
-    model_name = config.base_model.split("/")[-1]
-    experiment_path = args.log_root / f"{model_name}-{git_hash}"
-    experiment_path.mkdir(exist_ok=True, parents=True)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/tests/test_sample.py b/tests/test_sample.py
index 0c0a361..3677a99 100644
--- a/tests/test_sample.py
+++ b/tests/test_sample.py
@@ -1,6 +1,21 @@
-"""Sample test."""
+"""Sample test module for SyncNet project.
+
+This module contains basic test cases to verify the testing infrastructure
+is properly configured and working correctly.
+"""
 
 
 def test_sample() -> None:
-    """Sample test."""
+    """Verify basic test functionality.
+
+    A simple sanity check test that ensures the pytest framework is
+    properly configured and can execute tests successfully.
+
+    This test always passes and serves as a placeholder for future
+    test implementations.
+
+    Example:
+        Run this test using pytest:
+            $ pytest tests/test_sample.py::test_sample
+    """
     assert True
diff --git a/uv.lock b/uv.lock
index a880f2e..9108a76 100644
--- a/uv.lock
+++ b/uv.lock
@@ -6,6 +6,15 @@ resolution-markers = [
     "sys_platform != 'linux'",
 ]
 
+[[package]]
+name = "absl-py"
+version = "2.3.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588, upload-time = "2025-07-03T09:31:44.05Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811, upload-time = "2025-07-03T09:31:42.253Z" },
+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
@@ -122,6 +131,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
 ]
 
+[[package]]
+name = "anyio"
+version = "4.12.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/16/ce/8a777047513153587e5434fd752e89334ac33e379aa3497db860eeb60377/anyio-4.12.0.tar.gz", hash = "sha256:73c693b567b0c55130c104d0b43a9baf3aa6a31fc6110116509f27bf75e21ec0", size = 228266, upload-time = "2025-11-28T23:37:38.911Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7f/9c/36c5c37947ebfb8c7f22e0eb6e4d188ee2d53aa3880f3f2744fb894f0cb1/anyio-4.12.0-py3-none-any.whl", hash = "sha256:dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb", size = 113362, upload-time = "2025-11-28T23:36:57.897Z" },
+]
+
 [[package]]
 name = "attrs"
 version = "25.4.0"
@@ -131,6 +153,49 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3a/2a/7cc015f5b9f5db42b7d48157e23356022889fc354a2813c15934b7cb5c0e/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373", size = 67615, upload-time = "2025-10-06T13:54:43.17Z" },
 ]
 
+[[package]]
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