diff --git a/BEST-RQ-2.safetensors b/BEST-RQ-2.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..a0d572b6256e185237e5fe05abecdfe919420c65
--- /dev/null
+++ b/BEST-RQ-2.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7111465e6c868e3d0b55c5fe9a23dc5069ac80beba8444c5ee9db4691d796899
+size 483870856
diff --git a/BEST-RQ-2_encoder.py b/BEST-RQ-2_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f00d969c953d60e8336fc73de0ace4b22f2a66a
--- /dev/null
+++ b/BEST-RQ-2_encoder.py
@@ -0,0 +1,233 @@
+import glob
+import os
+import sys
+
+import torch
+import torch.nn as nn
+from omegaconf import OmegaConf
+from safetensors.torch import load_file
+
+# Add audio-embeddings to path dynamically
+# We assume audio-embeddings is a sibling directory to xares-llm or provided via env var
+# Prioritize absolute path if known, otherwise relative
+POSSIBLE_PATHS = [
+    # "/media/ltuncay/Shared-4TB/dev/audio-embeddings",
+    os.path.abspath(os.path.join(os.path.dirname(__file__), "audio-embeddings")),
+    # os.path.abspath(os.path.join(os.getcwd(), "../audio-embeddings")),
+]
+
+AUDIO_EMBEDDINGS_PATH = None
+for p in POSSIBLE_PATHS:
+    if os.path.exists(p):
+        AUDIO_EMBEDDINGS_PATH = p
+        break
+
+if AUDIO_EMBEDDINGS_PATH:
+    if AUDIO_EMBEDDINGS_PATH not in sys.path:
+        sys.path.append(AUDIO_EMBEDDINGS_PATH)
+        print(f"Added {AUDIO_EMBEDDINGS_PATH} to sys.path")
+else:
+    print(
+        "Warning: audio-embeddings path not found. Imports may fail if not installed in environment."
+    )
+
+try:
+    from src.models.best_rq2_module import BestRQ2Module
+except ImportError as e:
+    raise ImportError(
+        f"Could not import src.models.best_rq2_module. Ensure audio-embeddings is correctly located or installed. Error: {e}"
+    )
+
+
+class BestRQ2Encoder(nn.Module):
+    def __init__(self, checkpoint_path=None, model_config_path=None, **kwargs):
+        super().__init__()
+
+        base_path = os.path.dirname(__file__)
+        model_config_path = os.path.join(base_path, "config.yaml")
+        checkpoint_path = os.path.join(base_path, "BEST-RQ-2.safetensors")
+
+        if not os.path.exists(model_config_path):
+            raise FileNotFoundError(f"Config not found at {model_config_path}")
+
+        if not checkpoint_path or not os.path.exists(checkpoint_path):
+            raise FileNotFoundError(f"Checkpoint not found at {checkpoint_path}")
+
+        print(f"Loading BestRQ2 config from {model_config_path}")
+        cfg = OmegaConf.load(model_config_path)
+
+        print(f"Loading BestRQ2 checkpoint from {checkpoint_path}")
+
+        # Reconstruct model args from config
+        model_cfg = cfg.model
+        net_cfg = model_cfg.net
+
+        # Instantiate model
+        # Note: BestRQ2Module inherits from LightningModule
+        self.module = BestRQ2Module(
+            optimizer=None,  # Not needed for inference
+            net=net_cfg,
+            warmup_pct=model_cfg.get("warmup_pct", 0.1),
+            final_lr_ratio=model_cfg.get("final_lr_ratio", 0.001),
+            spectrogram_adjustment_mode=model_cfg.get(
+                "spectrogram_adjustment_mode", "pad"
+            ),
+            codebook_dim=model_cfg.get("codebook_dim", 16),
+            vocab_size=model_cfg.get("vocab_size", 8192),
+            criterion=None,
+        )
+
+        # Load weights
+        try:
+            state_dict = load_file(checkpoint_path)
+        except Exception as e:
+            print(f"Error loading safetensors: {e}. Trying torch.load...")
+            state_dict = torch.load(checkpoint_path, map_location="cpu")
+            if "state_dict" in state_dict:
+                state_dict = state_dict["state_dict"]
+
+        # Handle 'module.' prefix if present in checkpoint vs model
+        # Usually LightningModules save with state_dict keys matching model attributes.
+        # But sometimes they might be wrapped.
+        # We will try loading strict=False and inspect.
+
+        missing, unexpected = self.module.load_state_dict(state_dict, strict=False)
+        if missing:
+            # Check if prefixes match
+            # If all missing keys start with something common, or if state_dict has prefixes
+            print(f"Warning: {len(missing)} keys missing during loading.")
+            # print(missing[:5])
+        if unexpected:
+            print(f"Warning: {len(unexpected)} keys unexpected during loading.")
+
+        self.module.eval()
+        self.output_dim = net_cfg.encoder.embed_dim
+
+        # Extract dynamic parameters for length handling
+        try:
+            # 1. Sample Rate & Hop Length (from Spectrogram)
+            # BestRQ2Module -> Spectrogram -> MelSpectrogram -> hop_length
+            self.sample_rate = self.module.spectrogram.mel_spec.sample_rate
+            self.hop_length = self.module.spectrogram.mel_spec.hop_length
+
+            # 2. Patch Size (Time dimension)
+            # BestRQ2Module -> PatchEmbed -> patch_size (H, W) -> W is time
+            self.patch_size_time = self.module.patch_embed.patch_size[1]
+
+            # 3. Max Input Frames (Time dimension)
+            # BestRQ2Module -> PatchEmbed -> img_size (H, W) -> W is time frames
+            self.max_frames = self.module.patch_embed.img_size[1]
+
+            # Calculations
+            # Minimum samples required to get at least 1 patch width in spectrogram
+            # We need T_spec >= patch_size_time
+            # T_spec = T_samples // hop_length (roughly)
+            # So T_samples >= patch_size_time * hop_length
+            self.min_samples = self.patch_size_time * self.hop_length
+
+            # Chunk size: The maximum audio length the model's positional embeddings can handle
+            # T_samples_max = max_frames * hop_length
+            self.chunk_samples = self.max_frames * self.hop_length
+
+            print(
+                f"BestRQ2Encoder constraints: Min Samples={self.min_samples}, Chunk Samples={self.chunk_samples}"
+            )
+
+        except Exception as e:
+            print(f"Warning: Could not extract dynamic length constraints: {e}")
+            print("Falling back to safe defaults (1s min, 10s chunk)")
+            self.min_samples = 16000
+            self.chunk_samples = 16000 * 10
+
+    def _forward_chunk(self, audio_chunk: torch.Tensor) -> torch.Tensor:
+        """Helper to process a single time-chunk of audio."""
+        # Determine target device from the spectrogram window (safest for STFT)
+        try:
+            target_device = self.module.spectrogram.mel_spec.spectrogram.window.device
+        except AttributeError:
+            if hasattr(self.module.spectrogram.mel_spec, "window"):
+                target_device = self.module.spectrogram.mel_spec.window.device
+            else:
+                target_device = self.module.device
+
+        if audio_chunk.device != target_device:
+            audio_chunk = audio_chunk.to(target_device)
+
+        # BestRQ2Module expects [B, C, T]
+        if audio_chunk.ndim == 2:
+            audio_chunk = audio_chunk.unsqueeze(1)  # [B, 1, T]
+
+        # _process_audio returns (patches, grid_size)
+        patches, grid_size = self.module._process_audio(audio_chunk)
+
+        # Create Dummy Mask (all False = keep all)
+        B, N, D = patches.shape
+        mask = torch.zeros((B, N), dtype=torch.bool, device=patches.device)
+
+        # Compute encoder
+        encoder_out = self.module.compute_encoder(patches, mask, grid_size)
+        return encoder_out
+
+    def forward(
+        self, audio: torch.Tensor, audio_attention_mask=None
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        # audio: [B, T]
+        if audio.ndim == 1:
+            audio = audio.unsqueeze(0)
+
+        B, T = audio.shape
+
+        # 1. Handle Short Audio (Whole Batch)
+        if T < self.min_samples:
+            pad_amt = self.min_samples - T
+            audio = torch.nn.functional.pad(audio, (0, pad_amt))
+            T = self.min_samples  # Update T
+
+        # 2. Sequential Chunking
+        if T <= self.chunk_samples:
+            # Single chunk processing
+            return self._forward_chunk(audio), None
+        else:
+            # Split into chunks of max length
+            chunks = torch.split(audio, self.chunk_samples, dim=1)
+            outputs = []
+
+            for chunk in chunks:
+                # Handle potentially short last chunk
+                chunk_len = chunk.shape[1]
+
+                if chunk_len < self.min_samples:
+                    pad_amt = self.min_samples - chunk_len
+                    chunk = torch.nn.functional.pad(chunk, (0, pad_amt))
+
+                # Process
+                out_chunk = self._forward_chunk(chunk)
+
+                # If we padded the last chunk solely to meet min_samples,
+                # should we slice? BestRQ2 output is patches.
+                # 1 patch covers `min_samples`.
+                # If original was < 1 patch, we produced 1 patch.
+                # We can't slice sub-patch. We just return the 1 patch.
+
+                outputs.append(out_chunk)
+
+            # Concatenate along sequence dimension (dim=1)
+            final_output = torch.cat(outputs, dim=1)
+
+            return final_output, None
+
+
+if __name__ == "__main__":
+    try:
+        mdl = BestRQ2Encoder()
+        print("Model initialized successfully")
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        mdl.module.to(device)
+        x = torch.randn(1, 160000).to(device)
+        y, _ = mdl(x)
+        print(f"Output shape: {y.shape}")
+    except Exception as e:
+        print(f"Error testing model: {e}")
+        import traceback
+
+        traceback.print_exc()
diff --git a/README.md b/README.md
index 7be5fc7f47d5db027d120b8024982df93db95b74..7090a7cbbd4612e5607edf1057e2091e171dcfa1 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,35 @@
----
-license: mit
----
+# BEST-RQ-2 (xares-llm encoder)
+
+This folder contains the BEST-RQ-2 audio encoder integration for `xares-llm`.
+
+Benchmark repository: [xiaomi-research/xares-llm](https://github.com/xiaomi-research/xares-llm)
+
+## Setup
+
+1. Make sure the environment to run `xares-llm` is set up properly (virtual environment initialized and the `xares-llm` package downloaded/installed).
+
+2. Add the `BEST-RQ-2` folder to the `xares-llm` (current) directory so it is available at `./BEST-RQ-2`.
+
+3. Before running a `xares-llm` evaluation, you must install the required packages for BEST-RQ-2:
+
+```bash
+uv pip install -r BEST-RQ-2/audio-embeddings/pyproject.toml
+```
+
+## Run an evaluation
+
+Single task (e.g. to test that everything works):
+
+```bash
+uv run -m xares_llm.run BEST-RQ-2.BEST-RQ-2_encoder.BestRQ2Encoder <task> <task> # e.g. replace <task> with esc-50, score should be around 0.48
+```
+
+All tasks:
+
+```bash
+uv run -m xares_llm.run BEST-RQ-2.BEST-RQ-2_encoder.BestRQ2Encoder all
+```
+
+## Help
+
+If you encounter any problems, contact: [ludovic.tuncay@irit.fr](mailto:ludovic.tuncay@irit.fr)
diff --git a/audio-embeddings/.githooks/post-checkout b/audio-embeddings/.githooks/post-checkout
new file mode 100644
index 0000000000000000000000000000000000000000..e2fbf57c95e7e1151bdb0ef4d4a433b05a2e377d
--- /dev/null
+++ b/audio-embeddings/.githooks/post-checkout
@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+repo_root="$(git rev-parse --show-toplevel)"
+cd "$repo_root"
+
+if ! command -v uv >/dev/null 2>&1; then
+  echo "[post-checkout] uv not found; skipping uv sync" >&2
+  exit 0
+fi
+
+echo "[post-checkout] Running uv sync..."
+uv sync
diff --git a/audio-embeddings/.githooks/post-merge b/audio-embeddings/.githooks/post-merge
new file mode 100644
index 0000000000000000000000000000000000000000..8b62f52bf4ed453f3f6f80f41071c5c6fac5c040
--- /dev/null
+++ b/audio-embeddings/.githooks/post-merge
@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+repo_root="$(git rev-parse --show-toplevel)"
+cd "$repo_root"
+
+if ! command -v uv >/dev/null 2>&1; then
+  echo "[post-merge] uv not found; skipping uv sync" >&2
+  exit 0
+fi
+
+echo "[post-merge] Running uv sync..."
+uv sync
diff --git a/audio-embeddings/.githooks/post-rewrite b/audio-embeddings/.githooks/post-rewrite
new file mode 100644
index 0000000000000000000000000000000000000000..5566be65d5ca4322b11ecaacb0d852d685d30e7e
--- /dev/null
+++ b/audio-embeddings/.githooks/post-rewrite
@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+repo_root="$(git rev-parse --show-toplevel)"
+cd "$repo_root"
+
+if ! command -v uv >/dev/null 2>&1; then
+  echo "[post-rewrite] uv not found; skipping uv sync" >&2
+  exit 0
+fi
+
+echo "[post-rewrite] Running uv sync..."
+uv sync
diff --git a/audio-embeddings/.gitignore b/audio-embeddings/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..31f5a752083809bf1dbfb60db8ac6359b1805b87
--- /dev/null
+++ b/audio-embeddings/.gitignore
@@ -0,0 +1,29 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
+
+# Project logs
+logs/
+
+# Large data files
+*.h5
+
+# Data
+data/
+
+# Jupyter
+.ipynb_checkpoints/
+
+# macOS Finder metadata
+.DS_Store
+
+# Explicitly untracked large docs
+documents/LeJEPA.pdf
+documents/Audio-LeJEPA.pdf
diff --git a/audio-embeddings/.pre-commit-config.yaml b/audio-embeddings/.pre-commit-config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2630562924c43e80fe411490daa19ad17e1251df
--- /dev/null
+++ b/audio-embeddings/.pre-commit-config.yaml
@@ -0,0 +1,39 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      # Trim whitespace at end of lines.
+      - id: trailing-whitespace
+      # Ensure files end with a single newline.
+      - id: end-of-file-fixer
+      # Catch unresolved merge conflict markers.
+      - id: check-merge-conflict
+      # Validate YAML syntax (Hydra configs, etc.).
+      - id: check-yaml
+      # Validate TOML syntax (e.g., pyproject.toml).
+      - id: check-toml
+      # Detect mixed CRLF/LF endings without auto-rewriting.
+      - id: mixed-line-ending
+        args: ["--fix=no"]
+      # Catch accidental debug leftovers (breakpoint/pdb).
+      - id: debug-statements
+      # Block accidental private key commits.
+      - id: detect-private-key
+      # Prevent case-colliding paths across filesystems.
+      - id: check-case-conflict
+      # Block newly added large files unless explicitly allowlisted below.
+      - id: check-added-large-files
+        args: ["--maxkb=5000"]
+        exclude: ^(documents/LeJEPA\.pdf|documents/Audio-LeJEPA\.pdf)$
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    # Ruff version.
+    rev: v0.15.0
+    hooks:
+      # Run the linter.
+      - id: ruff-check
+        types_or: [python, pyi]
+        args: [--fix]
+      # Run the formatter.
+      - id: ruff-format
+        types_or: [python, pyi]
diff --git a/audio-embeddings/.project-root b/audio-embeddings/.project-root
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/audio-embeddings/.python-version b/audio-embeddings/.python-version
new file mode 100644
index 0000000000000000000000000000000000000000..e4fba2183587225f216eeada4c78dfab6b2e65f5
--- /dev/null
+++ b/audio-embeddings/.python-version
@@ -0,0 +1 @@
+3.12
diff --git a/audio-embeddings/AGENTS.md b/audio-embeddings/AGENTS.md
new file mode 100644
index 0000000000000000000000000000000000000000..9aefc00b07620345c3ec6eb5aed36bb1e3debec7
--- /dev/null
+++ b/audio-embeddings/AGENTS.md
@@ -0,0 +1,150 @@
+# AGENTS Guide - audio-embeddings
+
+This file is for coding agents working in this repository.
+Follow these repo-specific rules over generic defaults.
+
+## 1) Environment Snapshot
+- Python: `>=3.12` (from `pyproject.toml`).
+- Dependency manager: `uv`.
+- Main stack: PyTorch, PyTorch Lightning, Hydra, OmegaConf.
+- Project root marker: `.project-root`.
+- Main entrypoint: `src/train.py`.
+
+## 2) Cursor / Copilot Rule Files
+- Checked `.cursor/rules/`: not present.
+- Checked `.cursorrules`: not present.
+- Checked `.github/copilot-instructions.md`: not present.
+- Therefore, no additional Cursor/Copilot rule files are currently enforced.
+
+## 3) Install / Setup Commands
+```bash
+uv sync
+uv run <command>
+uv add <package>
+```
+
+## 4) Build / Train / Eval Commands
+There is no separate "build" step (this is a training codebase).
+Use quick-run training as the integration sanity check.
+```bash
+uv run src/train.py
+uv run src/train.py trainer.fast_dev_run=True
+uv run src/train.py trainer=cpu trainer.fast_dev_run=True
+uv run src/train.py experiment=local/audio_jepa
+uv run src/train.py trainer.max_epochs=10 data.batch_size=32 model.optimizer.lr=1e-4
+```
+Cluster-style execution (existing project pattern):
+```bash
+srun .venv/bin/python -u -O src/train.py experiment=cluster_jepa_audioset_rope +trainer.max_time="00:19:50:00"
+```
+
+## 5) Lint / Formatting / Static Checks
+Use the commands below as pragmatic checks:
+```bash
+uv run pre-commit run --all-files
+uv run pre-commit run ruff --all-files
+uv run pre-commit run ruff-format --all-files
+uv run python -m compileall src
+```
+Ruff is configured via `.pre-commit-config.yaml` and runs both lint fixes and formatting.
+
+## 6) Test Commands (Including Single Test)
+Primary validation in this repo is script-based verification under `tests/`.
+Run test files directly as native Python files:
+```bash
+uv run tests/verify_rope.py
+uv run tests/verify_custom_rope.py
+uv run tests/verify_data.py
+```
+Useful single-file checks (native execution):
+```bash
+uv run src/train.py trainer.fast_dev_run=True
+uv run src/train.py trainer=cpu trainer.fast_dev_run=True
+uv run scripts/verify_shapes.py
+uv run scripts/verify_scheduler.py
+```
+Notes:
+- `tests/test_*.py` are pytest-style and are not part of the default native-file workflow.
+- Prefer `tests/verify_*.py` and `scripts/verify_*.py` for lightweight checks.
+
+## 7) Repository Architecture Expectations
+- `configs/`: Hydra composition (trainer/data/model/logger/callbacks/experiment).
+- `src/train.py`: orchestration only (instantiate and run).
+- `src/models/`: LightningModules (high-level training logic).
+- `src/models/components/`: reusable `nn.Module` building blocks.
+- `src/data/`: DataModules/Datasets and collate logic.
+- `src/utils/`: logging, instantiation, wrappers, scheduler helpers.
+When possible, prefer config changes over hardcoded Python changes.
+
+## 8) Code Style Guidelines
+### Imports
+- Group imports as: standard library -> third-party -> local `src.*`.
+- Keep one import per line unless importing multiple names from same module.
+- Avoid wildcard imports.
+- Prefer absolute imports from `src...`.
+
+### Formatting
+- Use 4-space indentation and readable line lengths.
+- Keep functions small; extract helpers for complex logic.
+- Do not introduce unrelated reformatting in touched files.
+- Keep comments for non-obvious intent, not obvious mechanics.
+
+### Typing
+- Type hints are expected for function arguments and return values.
+- Use concrete tensor/container types when practical.
+- Use `Optional[T]` / `T | None` consistently within a file.
+- For dict-like configs, type as `DictConfig` when passing Hydra config objects.
+
+### Naming
+- `snake_case`: functions, variables, module filenames.
+- `PascalCase`: classes (`AudioJEPAModule`, `AudioSetDataModule`).
+- `UPPER_SNAKE_CASE`: constants.
+- Prefer descriptive names (`mask_indices`) over short names (`m2`) except local math temporaries.
+
+### PyTorch / Lightning / Hydra Conventions
+- Keep heavy compute out of `__init__` where possible.
+- `forward()` for inference logic; training behavior in `training_step()`.
+- Use `self.log(...)` with explicit flags (`on_step`, `on_epoch`, `prog_bar`, `batch_size`).
+- Instantiate components through Hydra (`hydra.utils.instantiate`).
+- Expose tunable parameters in config files, not hardcoded literals.
+
+### Error Handling and Validation
+- Raise informative `ValueError` / `RuntimeError` for invalid config/state.
+- Validate critical tensor assumptions with assertions or explicit checks.
+- Prefer logger/warnings over bare `print()` in new code.
+- For file I/O, prefer `pathlib.Path` and existence checks.
+
+### Data and Paths
+- Do not hardcode absolute machine paths.
+- Use `rootutils.setup_root(..., indicator=".project-root", pythonpath=True)` in entrypoints/scripts when needed.
+- Respect `cfg.paths.*` outputs for logs/checkpoints/artifacts.
+
+## 9) Agent Workflow Rules
+- Reuse existing components before adding new abstractions.
+- Keep `src/train.py` generic; place model/data logic in dedicated modules.
+- Prefer minimal, focused diffs.
+- Update configs and docs when behavior changes.
+- Validate with the smallest meaningful command first (`fast_dev_run`, single test), then broader checks.
+
+## 10) Git / Change Hygiene
+- Do not revert unrelated local changes.
+- Keep commits scoped to one concern.
+- Write clear commit messages describing intent.
+- Prefer Conventional Commit-like format: `type(scope): intent`.
+- Common types in this repo: `feat`, `fix`, `conf`, `build`, `docs`, `style`, `chore`.
+- Never commit secrets, credentials, or environment-specific absolute paths.
+
+## 11) Practical Agent Defaults
+- Prefer reusing existing modules over creating new abstractions.
+- Keep edits local to the requested change; avoid drive-by refactors.
+- Run the smallest useful verification command after changes.
+- If you touch training logic, run at least one fast training sanity check.
+- If you touch model components, run relevant verify script(s) in `tests/`.
+- If you touch Hydra config wiring, run a config-backed entry command via `uv run src/train.py ...`.
+
+## 12) Common Pitfalls
+- Avoid hardcoding data paths; use config (`cfg.paths`, data config fields).
+- Avoid printing in new code paths; use ranked loggers/warnings.
+- Avoid putting heavy tensor compute in constructors.
+- Avoid bypassing Hydra by manually instantiating configurable components.
+- Avoid changing unrelated formatting in files you touch.
diff --git a/audio-embeddings/README.md b/audio-embeddings/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3abbe32cb4c437cc99ba619042bbdfa40c8215f4
--- /dev/null
+++ b/audio-embeddings/README.md
@@ -0,0 +1,142 @@
+# Audio Embeddings with Lightning & Hydra
+
+This project is a clean, modular, and scalable implementation of audio embedding models using **PyTorch Lightning** and **Hydra**. It is designed to be easily extensible and runnable on local or cluster environments. It is based on the [Audio-JEPA](https://github.com/LudovicTuncay/Audio-JEPA) implementation and therefore implements the Audio-JEPA architecture. Other architecture can and will be added in the future.
+
+## 🎯 Goal
+
+The goal of this project is to provide a robust codebase for training and experimenting with audio embedding models. Key features include:
+- **Modular Architecture**: Components like Spectrogram, Masking, and ViT are decoupled.
+- **Configurable Positional Embeddings**: Support for **RoPE** (2D Rotary Embeddings), **SinCos** (2D Sinusoidal), and **Learnable** embeddings.
+- **Hydra Configuration**: flexible experiment management via hierarchical config files.
+- **Lightning Trainer**: Simplified training loop, logging, and checkpointing.
+- **Modern Tooling**: Uses `uv` for fast and reliable dependency management.
+
+## 🚀 Installation
+
+This project uses [`uv`](https://github.com/astral-sh/uv) for dependency management.
+
+1.  **Install `uv`** (if not already installed):
+    ```bash
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+    ```
+
+2.  **Clone the repository**:
+    ```bash
+    git clone <repository_url>
+    cd audio-embeddings
+    ```
+
+3.  **Install dependencies**:
+    ```bash
+    uv sync
+    ```
+
+4.  **Enable shared git hooks** (runs `uv sync` after merge/checkout/rewrite):
+    ```bash
+    git config core.hooksPath .githooks
+    ```
+
+## 🏃 Usage
+
+### Basic Training
+To start training with the default configuration:
+```bash
+uv run src/train.py
+```
+
+### Common Commands
+Run on GPU with Weights & Biases logging:
+```bash
+uv run src/train.py trainer=gpu logger=wandb
+```
+
+Override hyperparameters on the command line:
+```bash
+uv run src/train.py data.batch_size=64 trainer.max_epochs=50
+```
+
+### Configurable Positional Embeddings
+You can switch between different positional embedding strategies easily:
+
+**RoPE**:
+```bash
+uv run src/train.py model.net.encoder.pos_embed_type=rope
+```
+
+### Offline WandB Logging with Model Checkpoints
+To run training offline but still have model checkpoints staged for upload (which standard WandB restricts):
+
+```bash
+uv run src/train.py \
+    logger=wandb \
+    logger.wandb.offline=True \
+    logger.wandb.log_model=False \
+    +callbacks.wandb_offline_checkpoint._target_=src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback \
+    trainer=gpu trainer.devices=1 \
+    data.batch_size=128 trainer.max_epochs=100
+```
+These checkpoints will be uploaded when you run `wandb sync`.
+
+
+**2D SinCos**:
+```bash
+uv run src/train.py ++model.net.encoder.pos_embed_type=sincos ++model.net.predictor.pos_embed_type=sincos
+```
+
+**Learnable**:
+```bash
+uv run src/train.py ++model.net.encoder.pos_embed_type=learnable ++model.net.predictor.pos_embed_type=learnable
+```
+
+## 📂 Project Structure
+
+```text
+├── configs/                 # Hydra configuration files
+│   ├── callbacks/           # Callback configs (checkpoints, early stopping)
+│   ├── data/                # Data configs (AudioSet, etc.)
+│   ├── logger/              # Logger configs (WandB, Tensorboard)
+│   ├── model/               # Model configs (AudioJEPA parameters)
+│   ├── trainer/             # Trainer configs (CPU, GPU, strategies)
+│   └── train.yaml           # Main configuration entry point
+├── src/
+│   ├── data/                # Data loading logic
+│   │   └── audioset_datamodule.py  # AudioSet DataModule & Dataset
+│   ├── models/              # Model architectures
+│   │   ├── components/      # Reusable blocks
+│   │   │   ├── masking.py   # Masking generators
+│   │   │   ├── patch_embed.py # Patchification
+│   │   │   ├── rope.py      # 2D Rotary Embeddings
+│   │   │   ├── spectrogram.py # Audio preprocessing
+│   │   │   └── vit.py       # Vision Transformer (Student/Teacher/Predictor)
+│   │   └── audio_jepa_module.py # Main LightningModule
+│   ├── utils/               # Utility functions
+│   └── train.py             # Training entry point
+├── scripts/                 # Helper scripts
+├── tests/                   # Verification tests
+├── pyproject.toml           # Project dependencies
+└── README.md                # This file
+```
+
+## 🛠️ Extensibility
+
+### Adding a New Model
+1.  Create your model components in `src/models/components/`.
+2.  Create a new LightningModule in `src/models/` (or update `AudioJEPAModule`).
+3.  Create a new config file in `configs/model/my_new_model.yaml`.
+4.  Run with `uv run src/train.py model=my_new_model`.
+
+### Adding a New Dataset
+1.  Create a new DataModule in `src/data/`.
+2.  Create a new config file in `configs/data/my_dataset.yaml`.
+3.  Run with `uv run src/train.py data=my_dataset`.
+
+### Adding Functionalities
+-   **Callbacks**: Add custom callbacks in `src/callbacks/` (if needed) or use existing Lightning callbacks, and configure them in `configs/callbacks/`.
+-   **Metrics**: Add metrics logging in `training_step` or `validation_step` inside `src/models/audio_jepa_module.py`.
+
+## 🧪 Testing
+Run verification scripts to ensure components are working:
+```bash
+uv run tests/verify_rope.py
+uv run tests/verify_custom_rope.py
+```
diff --git a/audio-embeddings/THIRD_PARTY_LICENSES.md b/audio-embeddings/THIRD_PARTY_LICENSES.md
new file mode 100644
index 0000000000000000000000000000000000000000..4fc58fe3549c7c9363fb355cf4e2930d13181155
--- /dev/null
+++ b/audio-embeddings/THIRD_PARTY_LICENSES.md
@@ -0,0 +1,13 @@
+# Third-Party Licenses
+
+This project vendors third-party source code listed below.
+
+## Lightning-AI / pytorch-lightning
+
+- Component: `src/callbacks/lightning_weight_averaging.py`
+- Source repository: `https://github.com/Lightning-AI/pytorch-lightning`
+- Source file: `src/lightning/pytorch/callbacks/weight_averaging.py`
+- Pinned commit: `9bcba1c1e82b45e10f948dc28fc12f4cf04ab736`
+- License: Apache License 2.0
+
+See `licenses/APACHE-2.0-LIGHTNING.txt` for the full license text.
diff --git a/audio-embeddings/configs/__init__.py b/audio-embeddings/configs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..56bf7f4aa4906bc0f997132708cc0826c198e4aa
--- /dev/null
+++ b/audio-embeddings/configs/__init__.py
@@ -0,0 +1 @@
+# this file is needed here to include configs when building project as a package
diff --git a/audio-embeddings/configs/callbacks/default.yaml b/audio-embeddings/configs/callbacks/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..db5a48727026795478b5b39484d4a72914976b4c
--- /dev/null
+++ b/audio-embeddings/configs/callbacks/default.yaml
@@ -0,0 +1,34 @@
+defaults:
+  - model_checkpoint
+  - model_summary
+  - rich_progress_bar
+  - _self_
+
+model_checkpoint:
+  dirpath: ${paths.output_dir}/checkpoints
+  filename: "best-step-{step:06d}"
+  monitor: "val/loss"
+  mode: "min"
+  save_last: True
+  save_weights_only: False
+  save_top_k: 0
+  auto_insert_metric_name: False
+
+safetensors:
+  _target_: src.callbacks.safetensors_callback.SafetensorsCallback
+  cleanup_orphan_safetensors: True
+
+# early_stopping:
+#   monitor: "val/loss"
+#   patience: 100
+#   mode: "min"
+
+model_summary:
+  max_depth: 1
+
+device_stats:
+  _target_: lightning.pytorch.callbacks.DeviceStatsMonitor
+
+visualization:
+  _target_: src.callbacks.visualization_callback.VisualizationCallback
+  num_samples: 4
diff --git a/audio-embeddings/configs/callbacks/early_stopping.yaml b/audio-embeddings/configs/callbacks/early_stopping.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c826c8d58651a5e2c7cca0e99948a9b6ccabccf3
--- /dev/null
+++ b/audio-embeddings/configs/callbacks/early_stopping.yaml
@@ -0,0 +1,15 @@
+# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.EarlyStopping.html
+
+early_stopping:
+  _target_: lightning.pytorch.callbacks.EarlyStopping
+  monitor: ??? # quantity to be monitored, must be specified !!!
+  min_delta: 0. # minimum change in the monitored quantity to qualify as an improvement
+  patience: 3 # number of checks with no improvement after which training will be stopped
+  verbose: False # verbosity mode
+  mode: "min" # "max" means higher metric value is better, can be also "min"
+  strict: True # whether to crash the training if monitor is not found in the validation metrics
+  check_finite: True # when set True, stops training when the monitor becomes NaN or infinite
+  stopping_threshold: null # stop training immediately once the monitored quantity reaches this threshold
+  divergence_threshold: null # stop training as soon as the monitored quantity becomes worse than this threshold
+  check_on_train_epoch_end: null # whether to run early stopping at the end of the training epoch
+  # log_rank_zero_only: False  # this keyword argument isn't available in stable version
diff --git a/audio-embeddings/configs/callbacks/ema_weight_averaging.yaml b/audio-embeddings/configs/callbacks/ema_weight_averaging.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3716103201ecfc5a833e9e6ee721fa8b5f38a398
--- /dev/null
+++ b/audio-embeddings/configs/callbacks/ema_weight_averaging.yaml
@@ -0,0 +1,9 @@
+ema_weight_averaging:
+  _target_: src.callbacks.ema_weight_averaging.WarmupEMAWeightAveraging
+  warmup_pct: ${model.warmup_pct}
+  enabled: ${model.ema.enabled}
+  decay: ${model.ema.decay} # decay rate is 1 - decay_numerator / ( total_steps - warmup_steps )
+  decay_numerator: ${model.ema.decay_numerator} # decay rate is 1 - decay_numerator / ( total_steps - warmup_steps )
+  update_every_n_steps: ${model.ema.update_every_n_steps}
+  use_buffers: ${model.ema.use_buffers}
+  update_starting_at_step: null
diff --git a/audio-embeddings/configs/callbacks/model_checkpoint.yaml b/audio-embeddings/configs/callbacks/model_checkpoint.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..bf946e88b1ecfaf96efa91428e4f38e17267b25f
--- /dev/null
+++ b/audio-embeddings/configs/callbacks/model_checkpoint.yaml
@@ -0,0 +1,17 @@
+# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html
+
+model_checkpoint:
+  _target_: lightning.pytorch.callbacks.ModelCheckpoint
+  dirpath: null # directory to save the model file
+  filename: null # checkpoint filename
+  monitor: null # name of the logged metric which determines when model is improving
+  verbose: False # verbosity mode
+  save_last: null # additionally always save an exact copy of the last checkpoint to a file last.ckpt
+  save_top_k: 1 # save k best models (determined by above metric)
+  mode: "min" # "max" means higher metric value is better, can be also "min"
+  auto_insert_metric_name: True # when True, the checkpoints filenames will contain the metric name
+  save_weights_only: False # if True, then only the model’s weights will be saved
+  every_n_train_steps: null # number of training steps between checkpoints
+  train_time_interval: null # checkpoints are monitored at the specified time interval
+  every_n_epochs: null # number of epochs between checkpoints
+  save_on_train_epoch_end: null # whether to run checkpointing at the end of the training epoch or the end of validation
diff --git a/audio-embeddings/configs/callbacks/model_summary.yaml b/audio-embeddings/configs/callbacks/model_summary.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b75981d8cd5d73f61088d80495dc540274bca3d1
--- /dev/null
+++ b/audio-embeddings/configs/callbacks/model_summary.yaml
@@ -0,0 +1,5 @@
+# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.RichModelSummary.html
+
+model_summary:
+  _target_: lightning.pytorch.callbacks.RichModelSummary
+  max_depth: 1 # the maximum depth of layer nesting that the summary will include
diff --git a/audio-embeddings/configs/callbacks/none.yaml b/audio-embeddings/configs/callbacks/none.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/audio-embeddings/configs/callbacks/rich_progress_bar.yaml b/audio-embeddings/configs/callbacks/rich_progress_bar.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..de6f1ccb11205a4db93645fb6f297e50205de172
--- /dev/null
+++ b/audio-embeddings/configs/callbacks/rich_progress_bar.yaml
@@ -0,0 +1,4 @@
+# https://lightning.ai/docs/pytorch/latest/api/lightning.pytorch.callbacks.RichProgressBar.html
+
+rich_progress_bar:
+  _target_: lightning.pytorch.callbacks.RichProgressBar
diff --git a/audio-embeddings/configs/callbacks/wandb_offline.yaml b/audio-embeddings/configs/callbacks/wandb_offline.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9a0174e6201e5cde2d2c3d32b2d7b0b9a2bebf21
--- /dev/null
+++ b/audio-embeddings/configs/callbacks/wandb_offline.yaml
@@ -0,0 +1,2 @@
+wandb_offline_checkpoint:
+  _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
diff --git a/audio-embeddings/configs/data/audioset.yaml b/audio-embeddings/configs/data/audioset.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..66e25a284489420ef34372ec3da64a44fa565514
--- /dev/null
+++ b/audio-embeddings/configs/data/audioset.yaml
@@ -0,0 +1,12 @@
+_target_: src.data.audioset_datamodule.AudioSetDataModule
+data_dir: ${paths.data_dir}/AudioSet
+batch_size: 64
+num_workers: 4
+pin_memory: True
+train_h5: full_unbal_bal_train_wav.h5
+train_csv: silent_files_full_unbal_bal_train_wav.csv
+val_h5: eval_soxrhq.h5
+val_csv: silent_files_eval_soxrhq.csv
+max_audio_length_sec: 10.0 # 10 seconds
+target_sample_rate: 16000
+collate_mode: pad
diff --git a/audio-embeddings/configs/data/mock_audioset.yaml b/audio-embeddings/configs/data/mock_audioset.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f9ef4453229a09db93a14badbcfd77b22f055ec2
--- /dev/null
+++ b/audio-embeddings/configs/data/mock_audioset.yaml
@@ -0,0 +1,7 @@
+_target_: src.data.mock_audioset_datamodule.MockAudioSetDataModule
+batch_size: 16
+num_workers: 0  # 0 is often better for simple debugging/validation on local Mac
+pin_memory: True
+max_audio_length_sec: 10.0
+target_sample_rate: 16000
+collate_mode: pad
diff --git a/audio-embeddings/configs/data/yt1b.yaml b/audio-embeddings/configs/data/yt1b.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b6fad10e488de9839772424e58cf399881bcd77d
--- /dev/null
+++ b/audio-embeddings/configs/data/yt1b.yaml
@@ -0,0 +1,13 @@
+_target_: src.data.yt1b_datamodule.YT1BDataModule
+data_dir: ${paths.data_dir}/YT-Temporal-1B
+batch_size: 64
+num_workers: 4
+pin_memory: True
+train_parquet: train_metadata.parquet
+val_parquet: val_metadata.parquet
+test_parquet: val_metadata.parquet
+max_audio_length_sec: 10.0
+min_duration_sec: 10.0
+target_sample_rate: 16000
+collate_mode: pad
+decode_window_sec: null
diff --git a/audio-embeddings/configs/experiment/audio_jepa/baseline.yaml b/audio-embeddings/configs/experiment/audio_jepa/baseline.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f66d3ccec02c12b27954766f3f6c328ea01e12a0
--- /dev/null
+++ b/audio-embeddings/configs/experiment/audio_jepa/baseline.yaml
@@ -0,0 +1,34 @@
+# @package _global_
+
+defaults:
+  - override /data: audioset
+  - override /model: audio_jepa
+  - override /trainer: gpu
+  - override /logger: wandb
+  - override /callbacks: default
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["audioset", "jepa", "baseline", "cluster"]
+
+trainer:
+  max_steps: 200000
+
+data:
+  data_dir: ${paths.data_dir}
+  train_h5: AudioSet/full_unbal_bal_train_wav.h5
+  val_h5: AudioSet/eval_soxrhq.h5
+  batch_size: 256
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
diff --git a/audio-embeddings/configs/experiment/audio_jepa/large.yaml b/audio-embeddings/configs/experiment/audio_jepa/large.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0db0499dfd4576391c238eeae03ec462d01795e7
--- /dev/null
+++ b/audio-embeddings/configs/experiment/audio_jepa/large.yaml
@@ -0,0 +1,44 @@
+# @package _global_
+
+defaults:
+  - override /data: audioset
+  - override /model: audio_jepa
+  - override /trainer: gpu
+  - override /logger: wandb
+  - override /callbacks: default
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["audioset", "jepa", "large", "cluster", "1GPU"]
+
+trainer:
+  max_steps: 200000
+
+data:
+  data_dir: ${paths.data_dir}
+  train_h5: AudioSet/full_unbal_bal_train_wav.h5
+  val_h5: AudioSet/eval_soxrhq.h5
+  batch_size: 256
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
+
+# ViT Large Configuration
+model:
+  net:
+    patch_embed:
+      embed_dim: 1024
+    encoder:
+      embed_dim: 1024
+      depth: 24
+      num_heads: 16
diff --git a/audio-embeddings/configs/experiment/audio_jepa/rope.yaml b/audio-embeddings/configs/experiment/audio_jepa/rope.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ca1b5c89e4f5ab9be5dd7c23e6fc0e90cc45394c
--- /dev/null
+++ b/audio-embeddings/configs/experiment/audio_jepa/rope.yaml
@@ -0,0 +1,41 @@
+# @package _global_
+
+defaults:
+  - override /data: audioset
+  - override /model: audio_jepa
+  - override /trainer: gpu
+  - override /logger: wandb
+  - override /callbacks: default
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["audioset", "jepa", "RoPE", "cluster"]
+
+trainer:
+  max_steps: 200000
+
+data:
+  data_dir: ${paths.data_dir}
+  train_h5: AudioSet/full_unbal_bal_train_wav.h5
+  val_h5: AudioSet/eval_soxrhq.h5
+  batch_size: 256
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
+
+model:
+  net:
+    encoder:
+      pos_embed_type: rope
+    predictor:
+      pos_embed_type: rope
diff --git a/audio-embeddings/configs/experiment/audio_jepa/time_res2x.yaml b/audio-embeddings/configs/experiment/audio_jepa/time_res2x.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e38911b7a0b090c3532a8d3e45d2985cf252114f
--- /dev/null
+++ b/audio-embeddings/configs/experiment/audio_jepa/time_res2x.yaml
@@ -0,0 +1,54 @@
+# @package _global_
+
+defaults:
+  - override /data: audioset
+  - override /model: audio_jepa
+  - override /trainer: gpu
+  - override /logger: wandb
+  - override /callbacks: default
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["audioset", "jepa", "time_res2x", "cluster"]
+
+trainer:
+  max_steps: 200000
+
+data:
+  data_dir: ${paths.data_dir}
+  train_h5: AudioSet/full_unbal_bal_train_wav.h5
+  val_h5: AudioSet/eval_soxrhq.h5
+  batch_size: 256
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
+
+model:
+  net:
+    spectrogram:
+      win_length_ms: 64 # halved
+      hop_length_ms: 19.53125 # halved
+
+    patch_embed:
+      img_size: [128, 512] # width doubled because hop is halved
+
+    masking:
+      input_size: [128, 512]
+
+    encoder:
+      num_patches: 256 # (128/16) * (512/16) = 8 * 32 = 256
+      img_size: [128, 512] # Explicitly set img_size for ViT to match patch_embed
+
+    predictor:
+      num_patches: 256
+      img_size: [128, 512]
diff --git a/audio-embeddings/configs/experiment/audio_jepa/time_res4x.yaml b/audio-embeddings/configs/experiment/audio_jepa/time_res4x.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3fec6b6d636214f871181d2bda78d0956fce4d89
--- /dev/null
+++ b/audio-embeddings/configs/experiment/audio_jepa/time_res4x.yaml
@@ -0,0 +1,54 @@
+# @package _global_
+
+defaults:
+  - override /data: audioset
+  - override /model: audio_jepa
+  - override /trainer: gpu
+  - override /logger: wandb
+  - override /callbacks: default
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["audioset", "jepa", "time_res4x", "cluster"]
+
+trainer:
+  max_steps: 200000
+
+data:
+  data_dir: ${paths.data_dir}
+  train_h5: AudioSet/full_unbal_bal_train_wav.h5
+  val_h5: AudioSet/eval_soxrhq.h5
+  batch_size: 256
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
+
+model:
+  net:
+    spectrogram:
+      win_length_ms: 32 # quartered
+      hop_length_ms: 9.765625 # quartered
+
+    patch_embed:
+      img_size: [128, 1024] # width quadrupled because hop is quartered
+
+    masking:
+      input_size: [128, 1024]
+
+    encoder:
+      num_patches: 512 # (128/16) * (1024/16) = 8 * 64 = 512
+      img_size: [128, 1024]
+
+    predictor:
+      num_patches: 512
+      img_size: [128, 1024]
diff --git a/audio-embeddings/configs/experiment/best_rq/audioset.yaml b/audio-embeddings/configs/experiment/best_rq/audioset.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..64c2cd9bfc852e999c5c14c8862eb284767299ed
--- /dev/null
+++ b/audio-embeddings/configs/experiment/best_rq/audioset.yaml
@@ -0,0 +1,33 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=best_rq/audioset
+
+defaults:
+  - override /data: audioset
+  - override /model: best_rq
+  - override /trainer: gpu
+  - override /logger: wandb
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["audioset", "best-rq", "baseline", "cluster GPU"]
+
+trainer:
+  max_steps: 200000
+
+data:
+  batch_size: 256
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
diff --git a/audio-embeddings/configs/experiment/best_rq/yt1b.yaml b/audio-embeddings/configs/experiment/best_rq/yt1b.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..043f77cfb3835e3580616f17b694506e877217ef
--- /dev/null
+++ b/audio-embeddings/configs/experiment/best_rq/yt1b.yaml
@@ -0,0 +1,31 @@
+# @package _global_
+
+defaults:
+  - override /data: yt1b
+  - override /model: best_rq
+  - override /trainer: gpu
+  - override /logger: wandb
+  - override /callbacks: default
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["yt1b", "best-rq", "baseline", "cluster GPU"]
+
+trainer:
+  max_steps: 200000
+
+data:
+  batch_size: 256
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
diff --git a/audio-embeddings/configs/experiment/best_rq_2/audioset.yaml b/audio-embeddings/configs/experiment/best_rq_2/audioset.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4cdb6c4e802bb06f583d0af0dbfb382019dac569
--- /dev/null
+++ b/audio-embeddings/configs/experiment/best_rq_2/audioset.yaml
@@ -0,0 +1,33 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=best_rq_2/audioset
+
+defaults:
+  - override /data: audioset
+  - override /model: best_rq2
+  - override /trainer: gpu
+  - override /logger: wandb
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["audioset", "best-rq-2", "baseline", "cluster GPU"]
+
+trainer:
+  max_steps: 200000
+
+data:
+  batch_size: 256
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
diff --git a/audio-embeddings/configs/experiment/best_rq_2/audioset_100k_512bs.yaml b/audio-embeddings/configs/experiment/best_rq_2/audioset_100k_512bs.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2142116a91223ab4f4e0861e10bf6b0064cf0881
--- /dev/null
+++ b/audio-embeddings/configs/experiment/best_rq_2/audioset_100k_512bs.yaml
@@ -0,0 +1,30 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=best_rq_2/audioset_100k_512bs
+
+defaults:
+  - override /data: audioset
+  - override /model: best_rq2
+  - override /trainer: gpu
+  - override /logger: wandb
+
+tags: ["audioset", "best-rq-2", "100k", "512bs", "cluster GPU"]
+
+trainer:
+  max_steps: 100000
+
+data:
+  batch_size: 512
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
diff --git a/audio-embeddings/configs/experiment/best_rq_2/audioset_1m_128bs_4gpu.yaml b/audio-embeddings/configs/experiment/best_rq_2/audioset_1m_128bs_4gpu.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0110eebb9a6dca57d9f0ade3115665c676d5a4b7
--- /dev/null
+++ b/audio-embeddings/configs/experiment/best_rq_2/audioset_1m_128bs_4gpu.yaml
@@ -0,0 +1,30 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=best_rq_2/audioset_1m_128bs_4gpu
+
+defaults:
+  - override /data: audioset
+  - override /model: best_rq2
+  - override /trainer: ddp
+  - override /logger: wandb
+
+tags: ["audioset", "best-rq-2", "1m", "128bs", "4GPU", "cluster GPU"]
+
+trainer:
+  max_steps: 1000000
+
+data:
+  batch_size: 128
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
diff --git a/audio-embeddings/configs/experiment/best_rq_2/audioset_200k_256bs_4gpu.yaml b/audio-embeddings/configs/experiment/best_rq_2/audioset_200k_256bs_4gpu.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d4315d479d3dc0730bd1a1e5603e6fb9666434ac
--- /dev/null
+++ b/audio-embeddings/configs/experiment/best_rq_2/audioset_200k_256bs_4gpu.yaml
@@ -0,0 +1,30 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=best_rq_2/audioset_200k_256bs_4gpu
+
+defaults:
+  - override /data: audioset
+  - override /model: best_rq2
+  - override /trainer: ddp
+  - override /logger: wandb
+
+tags: ["audioset", "best-rq-2", "200k", "256bs", "4GPU", "cluster GPU"]
+
+trainer:
+  max_steps: 200000
+
+data:
+  batch_size: 256
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
diff --git a/audio-embeddings/configs/experiment/best_rq_2/audioset_400k_128bs.yaml b/audio-embeddings/configs/experiment/best_rq_2/audioset_400k_128bs.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7cf2ee4ef23fff500dfa54dda690ab31cf77313b
--- /dev/null
+++ b/audio-embeddings/configs/experiment/best_rq_2/audioset_400k_128bs.yaml
@@ -0,0 +1,30 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=best_rq_2/audioset_400k_128bs
+
+defaults:
+  - override /data: audioset
+  - override /model: best_rq2
+  - override /trainer: gpu
+  - override /logger: wandb
+
+tags: ["audioset", "best-rq-2", "400k", "128bs", "cluster GPU"]
+
+trainer:
+  max_steps: 400000
+
+data:
+  batch_size: 128
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
diff --git a/audio-embeddings/configs/experiment/best_rq_2/audioset_400k_128bs_4gpu.yaml b/audio-embeddings/configs/experiment/best_rq_2/audioset_400k_128bs_4gpu.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..071f104b81f9ff2062464d2d26b0bd4e9a872b04
--- /dev/null
+++ b/audio-embeddings/configs/experiment/best_rq_2/audioset_400k_128bs_4gpu.yaml
@@ -0,0 +1,30 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=best_rq_2/audioset_400k_128bs_4gpu
+
+defaults:
+  - override /data: audioset
+  - override /model: best_rq2
+  - override /trainer: ddp
+  - override /logger: wandb
+
+tags: ["audioset", "best-rq-2", "400k", "128bs", "4GPU", "cluster GPU"]
+
+trainer:
+  max_steps: 400000
+
+data:
+  batch_size: 128
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
diff --git a/audio-embeddings/configs/experiment/best_rq_2/audioset_800k_64bs_4gpu.yaml b/audio-embeddings/configs/experiment/best_rq_2/audioset_800k_64bs_4gpu.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ebc9e3307bde62bfad27be01cab0b1a974f3d82a
--- /dev/null
+++ b/audio-embeddings/configs/experiment/best_rq_2/audioset_800k_64bs_4gpu.yaml
@@ -0,0 +1,30 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=best_rq_2/audioset_800k_64bs_4gpu
+
+defaults:
+  - override /data: audioset
+  - override /model: best_rq2
+  - override /trainer: ddp
+  - override /logger: wandb
+
+tags: ["audioset", "best-rq-2", "800k", "64bs", "4GPU", "cluster GPU"]
+
+trainer:
+  max_steps: 800000
+
+data:
+  batch_size: 64
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
diff --git a/audio-embeddings/configs/experiment/best_rq_2/audioset_ema.yaml b/audio-embeddings/configs/experiment/best_rq_2/audioset_ema.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a1277473f1a381656f2c04d251621346801cc813
--- /dev/null
+++ b/audio-embeddings/configs/experiment/best_rq_2/audioset_ema.yaml
@@ -0,0 +1,35 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=best_rq_2/audioset_ema
+
+defaults:
+  - /callbacks/ema_weight_averaging
+  - override /data: audioset
+  - override /model: best_rq2
+  - override /trainer: gpu
+  - override /logger: wandb
+
+tags: ["audioset", "best-rq-2", "ema", "cluster GPU"]
+
+trainer:
+  max_steps: 200000
+
+data:
+  batch_size: 256
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
+
+model:
+  ema:
+    enabled: true
diff --git a/audio-embeddings/configs/experiment/best_rq_2/audioset_ema_600k.yaml b/audio-embeddings/configs/experiment/best_rq_2/audioset_ema_600k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7bf514778d990a9071b9ef9b4b35d5fcf77ba238
--- /dev/null
+++ b/audio-embeddings/configs/experiment/best_rq_2/audioset_ema_600k.yaml
@@ -0,0 +1,36 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=best_rq_2/audioset_ema_600k
+
+defaults:
+  - /callbacks/ema_weight_averaging
+  - override /data: audioset
+  - override /model: best_rq2
+  - override /trainer: gpu
+  - override /logger: wandb
+
+tags: ["audioset", "best-rq-2", "ema", "600k", "cluster GPU"]
+
+trainer:
+  max_steps: 600000
+
+data:
+  batch_size: 256
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
+
+model:
+  ema:
+    enabled: true
+    decay_numerator: 40.0
diff --git a/audio-embeddings/configs/experiment/best_rq_2/yt1b_ema.yaml b/audio-embeddings/configs/experiment/best_rq_2/yt1b_ema.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..48174e71c556bab736f3ecbd382ccde2c6f83e37
--- /dev/null
+++ b/audio-embeddings/configs/experiment/best_rq_2/yt1b_ema.yaml
@@ -0,0 +1,37 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=best_rq_2/yt1b_ema
+
+defaults:
+  - /callbacks/ema_weight_averaging
+  - override /data: yt1b
+  - override /model: best_rq2
+  - override /trainer: gpu
+  - override /logger: wandb
+
+tags: ["yt1b", "best-rq-2", "ema", "cluster GPU"]
+
+trainer:
+  max_steps: 600000
+
+data:
+  batch_size: 256
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
+
+model:
+  warmup_pct: 0.03
+  ema:
+    enabled: true
+    decay_numerator: 40.0
diff --git a/audio-embeddings/configs/experiment/local/audio_jepa.yaml b/audio-embeddings/configs/experiment/local/audio_jepa.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7e011616320762feaccdcc493b3c3af7b181ea06
--- /dev/null
+++ b/audio-embeddings/configs/experiment/local/audio_jepa.yaml
@@ -0,0 +1,29 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=audioset_baseline
+
+defaults:
+  - override /data: audioset
+  - override /model: audio_jepa
+  - override /trainer: gpu
+  - override /logger: wandb
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["audioset", "jepa", "baseline", "local GPU"]
+
+trainer:
+  devices: 1
+  max_steps: 200000
+
+data:
+  batch_size: 128
+  num_workers: 16
+
+logger:
+  wandb:
+    name: "local-jepa-audioset-baseline-200k-128x1bs"
+    offline: False
+    log_model: True
diff --git a/audio-embeddings/configs/experiment/local/audio_jepa_rope.yaml b/audio-embeddings/configs/experiment/local/audio_jepa_rope.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..dc97d4a6373c3110831ab79737e1a1d414732cca
--- /dev/null
+++ b/audio-embeddings/configs/experiment/local/audio_jepa_rope.yaml
@@ -0,0 +1,36 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=audioset_rope
+
+defaults:
+  - override /data: audioset
+  - override /model: audio_jepa
+  - override /trainer: gpu
+  - override /logger: wandb
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["audioset", "jepa", "rope", "local GPU"]
+
+trainer:
+  devices: 1
+  max_steps: 200000
+
+data:
+  batch_size: 128
+  num_workers: 16
+
+logger:
+  wandb:
+    name: "local-jepa-audioset-rope-200k-128x1bs"
+    offline: False
+    log_model: True
+
+model:
+  net:
+    encoder:
+      pos_embed_type: rope
+    predictor:
+      pos_embed_type: rope
diff --git a/audio-embeddings/configs/experiment/local/best_rq.yaml b/audio-embeddings/configs/experiment/local/best_rq.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..836c40102a96b22b3d1c32f883dd14df05c12227
--- /dev/null
+++ b/audio-embeddings/configs/experiment/local/best_rq.yaml
@@ -0,0 +1,29 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=local/best_rq
+
+defaults:
+  - override /data: audioset
+  - override /model: best_rq
+  - override /trainer: gpu
+  - override /logger: wandb
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["audioset", "best-rq", "baseline", "local GPU"]
+
+trainer:
+  devices: 1
+  max_steps: 200000
+
+data:
+  batch_size: 128
+  num_workers: 16
+
+logger:
+  wandb:
+    name: "local-best-rq-vit-audioset-200k-128x1bs"
+    offline: False
+    log_model: True
diff --git a/audio-embeddings/configs/experiment/local/best_rq2.yaml b/audio-embeddings/configs/experiment/local/best_rq2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c507177655c2778bb8ba4799a67b3d2500b21dea
--- /dev/null
+++ b/audio-embeddings/configs/experiment/local/best_rq2.yaml
@@ -0,0 +1,38 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=local/best_rq2
+
+defaults:
+  - override /data: audioset
+  - override /model: best_rq2
+  - override /trainer: gpu
+  - override /logger: wandb
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["audioset", "best-rq-2", "baseline", "local GPU"]
+
+trainer:
+  devices: 1
+  max_steps: 100000
+
+data:
+  batch_size: 128
+  num_workers: 16
+
+logger:
+  wandb:
+    name: "cluster-best-rq-2-audioset-100k-128bs"
+    offline: True
+    log_model: False
+
+callbacks:
+  # model_checkpoint:
+  #   save_weights_only: True
+
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
diff --git a/audio-embeddings/configs/experiment/local/m4_mock_jepa.yaml b/audio-embeddings/configs/experiment/local/m4_mock_jepa.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9e62b634269dfff033995aa683dd9b2b2346c2c0
--- /dev/null
+++ b/audio-embeddings/configs/experiment/local/m4_mock_jepa.yaml
@@ -0,0 +1,37 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=local/m4_mock_jepa
+
+defaults:
+  - override /data: mock_audioset
+  - override /model: audio_jepa
+  - override /trainer: mps
+  - override /logger: wandb
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["mock", "jepa", "local", "mps"]
+
+trainer:
+  # devices: 1 # set in trainer/mps.yaml
+  max_steps: 100
+  log_every_n_steps: 1
+  val_check_interval: 50
+  limit_val_batches: 5
+
+data:
+  batch_size: 8
+
+logger:
+  wandb:
+    name: "local-m4-mock-jepa"
+    offline: True
+    log_model: False
+
+callbacks:
+  model_checkpoint:
+    save_weights_only: True
+    monitor: null # verify we can save without monitoring
+  device_stats: null
diff --git a/audio-embeddings/configs/experiment/local/rqa_jepa.yaml b/audio-embeddings/configs/experiment/local/rqa_jepa.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..de4c24d8caf5c5dc5092697b0037a91feeb9007c
--- /dev/null
+++ b/audio-embeddings/configs/experiment/local/rqa_jepa.yaml
@@ -0,0 +1,29 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=local_rqa_jepa_audioset
+
+defaults:
+  - override /data: audioset
+  - override /model: rqa_jepa
+  - override /trainer: gpu
+  - override /logger: wandb
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["audioset", "rqa-jepa", "baseline", "local GPU"]
+
+trainer:
+  devices: 1
+  max_steps: 200000
+
+data:
+  batch_size: 128
+  num_workers: 16
+
+logger:
+  wandb:
+    name: "local-rqa-jepa-audioset-200k-128x1bs"
+    offline: False
+    log_model: True
diff --git a/audio-embeddings/configs/experiment/rqa_jepa/audioset.yaml b/audio-embeddings/configs/experiment/rqa_jepa/audioset.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9b51dd5d3366280e51abc1df5959b3ad9d202e34
--- /dev/null
+++ b/audio-embeddings/configs/experiment/rqa_jepa/audioset.yaml
@@ -0,0 +1,33 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=cluster_rqa_jepa_audioset
+
+defaults:
+  - override /data: audioset
+  - override /model: rqa_jepa
+  - override /trainer: gpu
+  - override /logger: wandb
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["audioset", "rqa-jepa", "baseline", "cluster GPU"]
+
+trainer:
+  max_steps: 200000
+
+data:
+  batch_size: 256
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
diff --git a/audio-embeddings/configs/experiment/rqa_jepa/yt1b.yaml b/audio-embeddings/configs/experiment/rqa_jepa/yt1b.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b0944076c9a74b5035090b784483d4a27588b77a
--- /dev/null
+++ b/audio-embeddings/configs/experiment/rqa_jepa/yt1b.yaml
@@ -0,0 +1,31 @@
+# @package _global_
+
+defaults:
+  - override /data: yt1b
+  - override /model: rqa_jepa
+  - override /trainer: gpu
+  - override /logger: wandb
+  - override /callbacks: default
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["yt1b", "rqa-jepa", "baseline", "cluster GPU"]
+
+trainer:
+  max_steps: 200000
+
+data:
+  batch_size: 256
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+
+logger:
+  wandb:
+    offline: True
+    log_model: False
+
+callbacks:
+  rich_progress_bar: null
+
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
diff --git a/audio-embeddings/configs/extras/default.yaml b/audio-embeddings/configs/extras/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b9c6b622283a647fbc513166fc14f016cc3ed8a0
--- /dev/null
+++ b/audio-embeddings/configs/extras/default.yaml
@@ -0,0 +1,8 @@
+# disable python warnings if they annoy you
+ignore_warnings: False
+
+# ask user for tags if none are provided in the config
+enforce_tags: True
+
+# pretty print config tree at the start of the run using Rich library
+print_config: True
diff --git a/audio-embeddings/configs/hydra/default.yaml b/audio-embeddings/configs/hydra/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ccb230658e7d62faeeefc2176d5de37560027ad4
--- /dev/null
+++ b/audio-embeddings/configs/hydra/default.yaml
@@ -0,0 +1,19 @@
+# https://hydra.cc/docs/configure_hydra/intro/
+
+# enable color logging
+defaults:
+  - override hydra_logging: colorlog
+  - override job_logging: colorlog
+
+# output directory, generated dynamically on each run
+run:
+  dir: ${paths.log_dir}/${task_name}/runs/${now:%Y-%m-%d}_${now:%H-%M-%S}_${oc.env:SLURM_JOB_ID,${now:%f}}
+sweep:
+  dir: ${paths.log_dir}/${task_name}/multiruns/${now:%Y-%m-%d}_${now:%H-%M-%S}_${oc.env:SLURM_JOB_ID,${now:%f}}
+  subdir: ${hydra.job.num}
+
+job_logging:
+  handlers:
+    file:
+      # Incorporates fix from https://github.com/facebookresearch/hydra/pull/2242
+      filename: ${hydra.runtime.output_dir}/${task_name}.log
diff --git a/audio-embeddings/configs/logger/csv.yaml b/audio-embeddings/configs/logger/csv.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fa028e9c146430c319101ffdfce466514338591c
--- /dev/null
+++ b/audio-embeddings/configs/logger/csv.yaml
@@ -0,0 +1,7 @@
+# csv logger built in lightning
+
+csv:
+  _target_: lightning.pytorch.loggers.csv_logs.CSVLogger
+  save_dir: "${paths.output_dir}"
+  name: "csv/"
+  prefix: ""
diff --git a/audio-embeddings/configs/logger/many_loggers.yaml b/audio-embeddings/configs/logger/many_loggers.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..dd586800bdccb4e8f4b0236a181b7ddd756ba9ab
--- /dev/null
+++ b/audio-embeddings/configs/logger/many_loggers.yaml
@@ -0,0 +1,9 @@
+# train with many loggers at once
+
+defaults:
+  # - comet
+  - csv
+  # - mlflow
+  # - neptune
+  - tensorboard
+  - wandb
diff --git a/audio-embeddings/configs/logger/wandb.yaml b/audio-embeddings/configs/logger/wandb.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..16d3ad743ffd876a625371c67ae0274e731afcb7
--- /dev/null
+++ b/audio-embeddings/configs/logger/wandb.yaml
@@ -0,0 +1,16 @@
+# https://wandb.ai
+
+wandb:
+  _target_: lightning.pytorch.loggers.wandb.WandbLogger
+  # name: "" # name of the run (normally generated by wandb)
+  save_dir: "${paths.output_dir}"
+  offline: False
+  id: null # pass correct id to resume experiment!
+  anonymous: null # enable anonymous logging
+  project: "audio embeddings"
+  log_model: True # upload lightning ckpts
+  prefix: "" # a string to put at the beginning of metric keys
+  # entity: "" # set to name of your wandb team
+  group: ""
+  tags: []
+  job_type: ""
diff --git a/audio-embeddings/configs/model/audio_jepa.yaml b/audio-embeddings/configs/model/audio_jepa.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..448dd36a7db34bba553619257967cfe448de9d94
--- /dev/null
+++ b/audio-embeddings/configs/model/audio_jepa.yaml
@@ -0,0 +1,68 @@
+_target_: src.models.audio_jepa_module.AudioJEPAModule
+
+optimizer:
+  _target_: torch.optim.AdamW
+  _partial_: true
+  lr: 1e-4
+  weight_decay: 0.05
+
+warmup_pct: 0.05
+
+ema_decay: 0.996
+ema_end_decay: 1.0
+ema_anneal_end_step: null # computed automatically
+
+spectrogram_adjustment_mode: truncate
+
+criterion:
+  _target_: torch.nn.MSELoss
+  _partial_: true
+  reduction: mean
+
+net:
+  spectrogram:
+    sample_rate: ${data.target_sample_rate}
+    n_fft: 2048
+    # win_length: 2048
+    # hop_length: 625
+    win_length_ms: 128 # 2048 / 16000 * 1000 = 128ms # half is 64ms and a quarter is 32ms
+    hop_length_ms: 39.0625 # 625 / 16000 * 1000 = 39.0625ms # half is 19.53125ms and a quarter is 9.765625ms
+    n_mels: 128
+    f_min: 0
+    f_max: 8000
+    power: 2.0
+
+  patch_embed:
+    img_size: [128, 256]
+    patch_size: [16, 16]
+    in_chans: 1
+    embed_dim: 768
+
+  masking:
+    input_size: [128, 256]
+    patch_size: [16, 16]
+    mask_ratio: [0.4, 0.6]
+
+  encoder:
+    embed_dim: 768
+    depth: 12
+    num_heads: 12
+    mlp_ratio: 4.0
+    qkv_bias: true
+    drop_rate: 0.0
+    attn_drop_rate: 0.0
+    drop_path_rate: 0.1
+    num_patches: 128 # (128/16) * (256/16) = 8 * 16 = 128
+    pos_embed_type: "sincos" # "rope", "sincos", "learnable"
+
+  predictor:
+    embed_dim: 768
+    depth: 4 # Usually smaller than encoder
+    num_heads: 12
+    mlp_ratio: 4.0
+    qkv_bias: true
+    drop_rate: 0.0
+    attn_drop_rate: 0.0
+    drop_path_rate: 0.0
+    num_patches: 128
+    pos_embed_type: "sincos"
diff --git a/audio-embeddings/configs/model/best_rq.yaml b/audio-embeddings/configs/model/best_rq.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..791c4dfef2e6cfc70682a10c35cee9318964131b
--- /dev/null
+++ b/audio-embeddings/configs/model/best_rq.yaml
@@ -0,0 +1,48 @@
+_target_: src.models.best_rq_module.BestRQModule
+
+optimizer:
+  _target_: torch.optim.AdamW
+  _partial_: true
+  lr: 1e-4
+  weight_decay: 0.05
+
+warmup_pct: 0.05
+
+spectrogram_adjustment_mode: truncate
+
+codebook_dim: 16
+vocab_size: 8192
+
+net:
+  spectrogram:
+    sample_rate: ${data.target_sample_rate}
+    n_fft: 2048
+    win_length_ms: 128
+    hop_length_ms: 39.0625
+    n_mels: 128
+    f_min: 0
+    f_max: 8000
+    power: 2.0
+
+  patch_embed:
+    img_size: [128, 256]
+    patch_size: [16, 16]
+    in_chans: 1
+    embed_dim: 768
+
+  masking:
+    input_size: [128, 256]
+    patch_size: [16, 16]
+    mask_ratio: [0.4, 0.6]
+
+  encoder:
+    embed_dim: 768
+    depth: 12
+    num_heads: 12
+    mlp_ratio: 4.0
+    qkv_bias: true
+    drop_rate: 0.0
+    attn_drop_rate: 0.0
+    drop_path_rate: 0.1
+    num_patches: 128
+    pos_embed_type: "sincos"
diff --git a/audio-embeddings/configs/model/best_rq2.yaml b/audio-embeddings/configs/model/best_rq2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1dcc7d5dc74802fe1e56cf09ca7c963b7a9c28f6
--- /dev/null
+++ b/audio-embeddings/configs/model/best_rq2.yaml
@@ -0,0 +1,72 @@
+_target_: src.models.best_rq2_module.BestRQ2Module
+
+optimizer:
+  _target_: torch.optim.AdamW
+  _partial_: true
+  lr: 1e-4
+  weight_decay: 0.05
+
+warmup_pct: 0.05
+
+ema:
+  enabled: false
+  decay: null
+  decay_numerator: 20.0 # decay rate is 1 - decay_numerator / ( total_steps - warmup_steps )
+  update_every_n_steps: 1
+  use_buffers: true
+
+spectrogram_adjustment_mode: truncate
+
+criterion:
+  _target_: torch.nn.CrossEntropyLoss
+  _partial_: true
+  reduction: mean
+
+codebook_dim: 16
+vocab_size: 8192
+
+net:
+  spectrogram:
+    sample_rate: ${data.target_sample_rate}
+    n_fft: 2048
+    win_length_ms: 128
+    hop_length_ms: 39.0625
+    n_mels: 128
+    f_min: 0
+    f_max: 8000
+    power: 2.0
+
+  patch_embed:
+    img_size: [128, 256]
+    patch_size: [16, 16]
+    in_chans: 1
+    embed_dim: 768
+
+  masking:
+    input_size: [128, 256]
+    patch_size: [16, 16]
+    mask_ratio: [0.4, 0.6]
+
+  encoder:
+    embed_dim: 768
+    depth: 12
+    num_heads: 12
+    mlp_ratio: 4.0
+    qkv_bias: true
+    drop_rate: 0.0
+    attn_drop_rate: 0.0
+    drop_path_rate: 0.1
+    num_patches: 128
+    pos_embed_type: "sincos"
+
+  predictor:
+    embed_dim: 768
+    depth: 4
+    num_heads: 12
+    mlp_ratio: 4.0
+    qkv_bias: true
+    drop_rate: 0.0
+    attn_drop_rate: 0.0
+    drop_path_rate: 0.0
+    num_patches: 128
+    pos_embed_type: "sincos"
diff --git a/audio-embeddings/configs/model/rqa_jepa.yaml b/audio-embeddings/configs/model/rqa_jepa.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..58dad126d3ab510964d6f34816271cd89113be00
--- /dev/null
+++ b/audio-embeddings/configs/model/rqa_jepa.yaml
@@ -0,0 +1,76 @@
+_target_: src.models.rqa_jepa_module.RQAJEPAModule
+
+optimizer:
+  _target_: torch.optim.AdamW
+  _partial_: true
+  lr: 1e-4
+  weight_decay: 0.05
+
+warmup_pct: 0.05
+
+ema_decay: 0.996
+ema_end_decay: 1.0
+ema_anneal_end_step: null
+
+spectrogram_adjustment_mode: truncate
+
+jepa_criterion:
+  _target_: torch.nn.MSELoss
+  _partial_: true
+  reduction: mean
+
+rq_criterion:
+  _target_: torch.nn.CrossEntropyLoss
+  _partial_: true
+  reduction: mean
+
+rq_lambda: 0.5
+rq_input_type: teacher
+codebook_dim: 16
+vocab_size: 8192
+
+net:
+  spectrogram:
+    sample_rate: ${data.target_sample_rate}
+    n_fft: 2048
+    win_length_ms: 128
+    hop_length_ms: 39.0625
+    n_mels: 128
+    f_min: 0
+    f_max: 8000
+    power: 2.0
+
+  patch_embed:
+    img_size: [128, 256]
+    patch_size: [16, 16]
+    in_chans: 1
+    embed_dim: 768
+
+  masking:
+    input_size: [128, 256]
+    patch_size: [16, 16]
+    mask_ratio: [0.4, 0.6]
+
+  encoder:
+    embed_dim: 768
+    depth: 12
+    num_heads: 12
+    mlp_ratio: 4.0
+    qkv_bias: true
+    drop_rate: 0.0
+    attn_drop_rate: 0.0
+    drop_path_rate: 0.1
+    num_patches: 128
+    pos_embed_type: "sincos"
+
+  predictor:
+    embed_dim: 768
+    depth: 4
+    num_heads: 12
+    mlp_ratio: 4.0
+    qkv_bias: true
+    drop_rate: 0.0
+    attn_drop_rate: 0.0
+    drop_path_rate: 0.0
+    num_patches: 128
+    pos_embed_type: "sincos"
diff --git a/audio-embeddings/configs/paths/default.yaml b/audio-embeddings/configs/paths/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ec81db2d34712909a79be3e42e65efe08c35ecee
--- /dev/null
+++ b/audio-embeddings/configs/paths/default.yaml
@@ -0,0 +1,18 @@
+# path to root directory
+# this requires PROJECT_ROOT environment variable to exist
+# you can replace it with "." if you want the root to be the current working directory
+root_dir: ${oc.env:PROJECT_ROOT}
+
+# path to data directory
+data_dir: ${paths.root_dir}/data/
+
+# path to logging directory
+log_dir: ${paths.root_dir}/logs/
+
+# path to output directory, created dynamically by hydra
+# path generation pattern is specified in `configs/hydra/default.yaml`
+# use it to store all files generated during the run, like ckpts and metrics
+output_dir: ${hydra:runtime.output_dir}
+
+# path to working directory
+work_dir: ${hydra:runtime.cwd}
diff --git a/audio-embeddings/configs/train.yaml b/audio-embeddings/configs/train.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c841e1358f6292b56c7c9809d4965adc5e21de94
--- /dev/null
+++ b/audio-embeddings/configs/train.yaml
@@ -0,0 +1,49 @@
+# @package _global_
+
+# specify here default configuration
+# order of defaults determines the order in which configs override each other
+defaults:
+  - _self_
+  - data: audioset
+  - model: audio_jepa
+  - callbacks: default
+  - logger: null # set logger here or use command line (e.g. `python train.py logger=tensorboard`)
+  - trainer: default
+  - paths: default
+  - extras: default
+  - hydra: default
+
+  # experiment configs allow for version control of specific hyperparameters
+  # e.g. best hyperparameters for given model and datamodule
+  - experiment: null
+
+  # config for hyperparameter optimization
+  - hparams_search: null
+
+  # optional local config for machine/user specific settings
+  # it's optional since it doesn't need to exist and is excluded from version control
+  - optional local: default
+
+  # debugging config (enable through command line, e.g. `python train.py debug=default)
+  - debug: null
+
+# task name, determines output directory path
+task_name: "train"
+
+# tags to help you identify your experiments
+# you can overwrite this in experiment configs
+# overwrite from command line with `python train.py tags="[first_tag, second_tag]"`
+tags: ["dev"]
+
+# set False to skip model training
+train: True
+
+# evaluate on test set, using best model weights achieved during training
+# lightning chooses best weights based on the metric specified in checkpoint callback
+test: True
+
+# simply provide checkpoint path to resume training
+ckpt_path: null
+
+# seed for random number generators in pytorch, numpy and python.random
+seed: 21072023
diff --git a/audio-embeddings/configs/trainer/cpu.yaml b/audio-embeddings/configs/trainer/cpu.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b7d6767e60c956567555980654f15e7bb673a41f
--- /dev/null
+++ b/audio-embeddings/configs/trainer/cpu.yaml
@@ -0,0 +1,5 @@
+defaults:
+  - default
+
+accelerator: cpu
+devices: 1
diff --git a/audio-embeddings/configs/trainer/ddp.yaml b/audio-embeddings/configs/trainer/ddp.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ab8f89004c399a33440f014fa27e040d4e952bc2
--- /dev/null
+++ b/audio-embeddings/configs/trainer/ddp.yaml
@@ -0,0 +1,9 @@
+defaults:
+  - default
+
+strategy: ddp
+
+accelerator: gpu
+devices: 4
+num_nodes: 1
+sync_batchnorm: True
diff --git a/audio-embeddings/configs/trainer/ddp_sim.yaml b/audio-embeddings/configs/trainer/ddp_sim.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8404419e5c295654967d0dfb73a7366e75be2f1f
--- /dev/null
+++ b/audio-embeddings/configs/trainer/ddp_sim.yaml
@@ -0,0 +1,7 @@
+defaults:
+  - default
+
+# simulate DDP on CPU, useful for debugging
+accelerator: cpu
+devices: 2
+strategy: ddp_spawn
diff --git a/audio-embeddings/configs/trainer/default.yaml b/audio-embeddings/configs/trainer/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8a46151391e00dfbb12d56d28db15d93fe2246c9
--- /dev/null
+++ b/audio-embeddings/configs/trainer/default.yaml
@@ -0,0 +1,16 @@
+_target_: lightning.pytorch.trainer.Trainer
+
+default_root_dir: ${paths.output_dir}
+
+accelerator: cpu
+devices: 1
+
+# mixed precision for extra speed-up
+# precision: 16
+
+# perform a validation loop every N training epochs
+check_val_every_n_epoch: 1
+
+# set True to to ensure deterministic results
+# makes training slower but gives more reproducibility than just setting seeds
+deterministic: False
diff --git a/audio-embeddings/configs/trainer/gpu.yaml b/audio-embeddings/configs/trainer/gpu.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b2389510a90f5f0161cff6ccfcb4a96097ddf9a1
--- /dev/null
+++ b/audio-embeddings/configs/trainer/gpu.yaml
@@ -0,0 +1,5 @@
+defaults:
+  - default
+
+accelerator: gpu
+devices: 1
diff --git a/audio-embeddings/configs/trainer/mps.yaml b/audio-embeddings/configs/trainer/mps.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1ecf6d5cc3a34ca127c5510f4a18e989561e38e4
--- /dev/null
+++ b/audio-embeddings/configs/trainer/mps.yaml
@@ -0,0 +1,5 @@
+defaults:
+  - default
+
+accelerator: mps
+devices: 1
diff --git a/audio-embeddings/inspect_ckpt.py b/audio-embeddings/inspect_ckpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..71da47c1f5431f88c0f7483f95b8ae68c33fbab2
--- /dev/null
+++ b/audio-embeddings/inspect_ckpt.py
@@ -0,0 +1,26 @@
+import torch
+import sys
+
+
+def inspect_checkpoint(path):
+    print(f"Inspecting {path}")
+    try:
+        ckpt = torch.load(path, weights_only=False)
+        print("Keys in checkpoint:", ckpt.keys())
+        if "optimizer_states" in ckpt:
+            print("Optimizer states found: YES")
+        else:
+            print("Optimizer states found: NO")
+
+        if "state_dict" in ckpt:
+            print(f"Model keys: {len(ckpt['state_dict'])} items")
+
+    except Exception as e:
+        print(f"Error loading checkpoint: {e}")
+
+
+if __name__ == "__main__":
+    if len(sys.argv) > 1:
+        inspect_checkpoint(sys.argv[1])
+    else:
+        print("Usage: python inspect_ckpt.py <path_to_ckpt>")
diff --git a/audio-embeddings/licenses/APACHE-2.0-LIGHTNING.txt b/audio-embeddings/licenses/APACHE-2.0-LIGHTNING.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e23e9dd09a86c9b404a9216d7a5351352c44630a
--- /dev/null
+++ b/audio-embeddings/licenses/APACHE-2.0-LIGHTNING.txt
@@ -0,0 +1,176 @@
+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1. Definitions.
+
+"License" shall mean the terms and conditions for use, reproduction,
+and distribution as defined by Sections 1 through 9 of this document.
+
+"Licensor" shall mean the copyright owner or entity authorized by
+the copyright owner that is granting the License.
+
+"Legal Entity" shall mean the union of the acting entity and all
+other entities that control, are controlled by, or are under common
+control with that entity. For the purposes of this definition,
+"control" means (i) the power, direct or indirect, to cause the
+direction or management of such entity, whether by contract or
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+
+"You" (or "Your") shall mean an individual or Legal Entity
+exercising permissions granted by this License.
+
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+including but not limited to software source code, documentation
+source, and configuration files.
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+risks associated with Your exercise of permissions under this License.
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+8. Limitation of Liability. In no event and under no legal theory,
+whether in tort (including negligence), contract, or otherwise,
+unless required by applicable law (such as deliberate and grossly
+negligent acts) or agreed to in writing, shall any Contributor be
+liable to You for damages, including any direct, indirect, special,
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+END OF TERMS AND CONDITIONS
diff --git a/audio-embeddings/pyproject.toml b/audio-embeddings/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..58688c4dd0b3cd249f5eb749a5d3379c6b13bc35
--- /dev/null
+++ b/audio-embeddings/pyproject.toml
@@ -0,0 +1,27 @@
+[project]
+name = "audio-embeddings"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "hydra-colorlog>=1.2.0",
+    "hydra-core>=1.3.2",
+    "hydra-optuna-sweeper>=1.2.0",
+    "lightning>=2.5.6",
+    "pre-commit>=4.4.0",
+    "rich>=14.2.0",
+    "rootutils>=1.0.7",
+    "torch>=2.9.1",
+    "torchaudio>=2.9.1",
+    "torchmetrics>=1.8.2",
+    "wandb>=0.23.0",
+    "h5py>=3.10.0",
+    "pandas>=2.2.0",
+    "einops>=0.7.0",
+    "timm>=0.9.0",
+    "matplotlib>=3.10.7",
+    "soundfile>=0.13.1",
+    "pyarrow>=23.0.0",
+    "torchcodec>=0.10",
+]
diff --git a/audio-embeddings/scripts/check_rope.py b/audio-embeddings/scripts/check_rope.py
new file mode 100644
index 0000000000000000000000000000000000000000..8647edc9727ace59d781894d5871e1a0bb42e420
--- /dev/null
+++ b/audio-embeddings/scripts/check_rope.py
@@ -0,0 +1,8 @@
+import inspect
+import timm.layers
+from timm.models.vision_transformer import Attention
+
+print("Attention init:", inspect.signature(Attention.__init__))
+print("Attention forward:", inspect.signature(Attention.forward))
+
+print("timm.layers members:", dir(timm.layers))
diff --git a/audio-embeddings/scripts/clean_yt1b_corrupted_audio.py b/audio-embeddings/scripts/clean_yt1b_corrupted_audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..71d5f472aed790c8ec8aab6e019fb5490c280e97
--- /dev/null
+++ b/audio-embeddings/scripts/clean_yt1b_corrupted_audio.py
@@ -0,0 +1,534 @@
+import argparse
+from collections import defaultdict
+from collections.abc import Iterable
+from dataclasses import dataclass
+import math
+from pathlib import Path
+import statistics
+import time
+
+import pandas as pd
+from rich.progress import BarColumn
+from rich.progress import MofNCompleteColumn
+from rich.progress import Progress
+from rich.progress import TaskProgressColumn
+from rich.progress import TextColumn
+from rich.progress import TimeElapsedColumn
+from rich.progress import TimeRemainingColumn
+from torch.utils.data import DataLoader
+
+from src.data.yt1b_datamodule import YT1BDataModule
+from src.data.yt1b_datamodule import YT1BDataset
+
+
+def identity_collate(batch: list[dict]) -> list[dict]:
+    return batch
+
+
+@dataclass
+class SplitScanStats:
+    processed_samples: int
+    error_samples: int
+    unique_bad_paths: int
+    num_batches: int
+    elapsed_sec: float
+    mean_batch_sec: float
+    p50_batch_sec: float
+    p90_batch_sec: float
+    p99_batch_sec: float
+
+    @property
+    def samples_per_sec(self) -> float:
+        if self.elapsed_sec <= 0:
+            return 0.0
+        return self.processed_samples / self.elapsed_sec
+
+    @property
+    def error_rate(self) -> float:
+        if self.processed_samples == 0:
+            return 0.0
+        return self.error_samples / self.processed_samples
+
+
+def percentile(values: list[float], q: float) -> float:
+    if not values:
+        return 0.0
+
+    sorted_vals = sorted(values)
+    if len(sorted_vals) == 1:
+        return sorted_vals[0]
+
+    q_clamped = max(0.0, min(1.0, q))
+    idx = q_clamped * (len(sorted_vals) - 1)
+    low = int(idx)
+    high = min(low + 1, len(sorted_vals) - 1)
+    weight = idx - low
+    return sorted_vals[low] * (1.0 - weight) + sorted_vals[high] * weight
+
+
+def scan_split_for_failures(
+    split_name: str,
+    dataset: YT1BDataset,
+    batch_size: int,
+    num_workers: int,
+    pin_memory: bool,
+) -> tuple[set[str], SplitScanStats, list[tuple[float, float]]]:
+    dataloader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=num_workers,
+        pin_memory=pin_memory,
+        persistent_workers=num_workers > 0,
+        collate_fn=identity_collate,
+    )
+
+    bad_paths: set[str] = set()
+    batch_latencies: list[float] = []
+    batch_points: list[tuple[float, float]] = []
+    processed_samples = 0
+    error_samples = 0
+    num_batches = 0
+    start_time = time.perf_counter()
+
+    with Progress(
+        TextColumn("[bold cyan]{task.description}"),
+        BarColumn(),
+        MofNCompleteColumn(),
+        TaskProgressColumn(),
+        TimeRemainingColumn(),
+        TimeElapsedColumn(),
+    ) as progress:
+        task_id = progress.add_task(f"Scanning {split_name}", total=len(dataset))
+
+        dataloader_iter = iter(dataloader)
+        while True:
+            batch_start = time.perf_counter()
+            try:
+                batch = next(dataloader_iter)
+            except StopIteration:
+                break
+
+            fetch_and_process_sec = time.perf_counter() - batch_start
+            batch_total_audio_sec = 0.0
+            for sample in batch:
+                processed_samples += 1
+                sample_index = int(sample["index"])
+                sample_duration_sec = float(dataset.durations_sec[sample_index])
+                if not math.isfinite(sample_duration_sec) or sample_duration_sec < 0.0:
+                    sample_duration_sec = 0.0
+                batch_total_audio_sec += sample_duration_sec
+
+                if sample.get("error", False):
+                    error_samples += 1
+                    bad_paths.add(dataset.paths[sample_index])
+            num_batches += 1
+            batch_latencies.append(fetch_and_process_sec)
+            batch_points.append((batch_total_audio_sec, fetch_and_process_sec))
+            progress.advance(task_id, len(batch))
+
+    elapsed_sec = time.perf_counter() - start_time
+    if batch_latencies:
+        mean_batch_sec = statistics.fmean(batch_latencies)
+        p50_batch_sec = percentile(batch_latencies, 0.50)
+        p90_batch_sec = percentile(batch_latencies, 0.90)
+        p99_batch_sec = percentile(batch_latencies, 0.99)
+    else:
+        mean_batch_sec = 0.0
+        p50_batch_sec = 0.0
+        p90_batch_sec = 0.0
+        p99_batch_sec = 0.0
+
+    stats = SplitScanStats(
+        processed_samples=processed_samples,
+        error_samples=error_samples,
+        unique_bad_paths=len(bad_paths),
+        num_batches=num_batches,
+        elapsed_sec=elapsed_sec,
+        mean_batch_sec=mean_batch_sec,
+        p50_batch_sec=p50_batch_sec,
+        p90_batch_sec=p90_batch_sec,
+        p99_batch_sec=p99_batch_sec,
+    )
+
+    return bad_paths, stats, batch_points
+
+
+def plot_batch_latency_vs_audio_time(
+    points_by_split: dict[str, list[tuple[float, float]]],
+    output_path: str,
+) -> None:
+    if not output_path:
+        return
+
+    all_points = sum((len(points) for points in points_by_split.values()))
+    if all_points == 0:
+        print("Skipping latency plot: no batch points available.")
+        return
+
+    try:
+        import matplotlib.pyplot as plt
+    except ImportError:
+        print(
+            "Skipping latency plot: matplotlib is not installed. "
+            "Install it with `uv add matplotlib`."
+        )
+        return
+
+    colors = {
+        "train": "#1f77b4",
+        "val": "#2ca02c",
+        "test": "#ff7f0e",
+    }
+
+    fig, ax = plt.subplots(figsize=(12.5, 7.5), dpi=180)
+    fig.patch.set_facecolor("#f8fafc")
+    ax.set_facecolor("#ffffff")
+    x_values: list[float] = []
+    y_values: list[float] = []
+
+    for split_name in ["train", "val", "test"]:
+        points = points_by_split.get(split_name, [])
+        if not points:
+            continue
+
+        split_points = [
+            point
+            for point in points
+            if math.isfinite(point[0])
+            and math.isfinite(point[1])
+            and point[0] > 0.0
+            and point[1] > 0.0
+        ]
+        if not split_points:
+            continue
+
+        split_x = [point[0] for point in split_points]
+        split_y = [point[1] for point in split_points]
+        x_values.extend(split_x)
+        y_values.extend(split_y)
+        color = colors.get(split_name, "#4c78a8")
+
+        ax.scatter(
+            split_x,
+            split_y,
+            s=16,
+            alpha=0.12,
+            color=color,
+            edgecolors="none",
+            label=f"{split_name} ({len(split_points):,} batches)",
+        )
+
+        unique_audio_lengths = len(set(split_x))
+        num_bins = min(40, unique_audio_lengths, len(split_points))
+        if num_bins >= 2:
+            sorted_points = sorted(split_points, key=lambda point: point[0])
+            bin_size = max(1, len(sorted_points) // num_bins)
+            trend_x: list[float] = []
+            trend_y: list[float] = []
+            for start_idx in range(0, len(sorted_points), bin_size):
+                group = sorted_points[start_idx : start_idx + bin_size]
+                if not group:
+                    continue
+                group_x = [point[0] for point in group]
+                group_y = [point[1] for point in group]
+                trend_x.append(statistics.median(group_x))
+                trend_y.append(statistics.median(group_y))
+
+            ax.plot(
+                trend_x,
+                trend_y,
+                color=color,
+                linewidth=2.6,
+                alpha=0.95,
+            )
+
+    if not x_values or not y_values:
+        print("Skipping latency plot: no valid positive points for log-scale plot.")
+        plt.close(fig)
+        return
+
+    x_min = min(x_values)
+    x_max = max(x_values)
+    y_min = min(y_values)
+    y_max = max(y_values)
+
+    ax.set_xscale("log")
+    ax.set_yscale("log")
+    ax.set_xlim(x_min / 1.08, x_max * 1.08)
+    ax.set_ylim(y_min / 1.08, y_max * 1.08)
+
+    ax.set_title(
+        "Batch Processing Time vs. Total Audio Duration (log-log)",
+        fontsize=16,
+        fontweight="bold",
+        color="#0f172a",
+        pad=14,
+    )
+    ax.set_xlabel("Total batch audio duration (seconds)", fontsize=12, color="#1e293b")
+    ax.set_ylabel("Time to process batch (seconds)", fontsize=12, color="#1e293b")
+
+    ax.grid(True, which="major", color="#e2e8f0", linewidth=0.9)
+    ax.grid(True, which="minor", color="#f1f5f9", linewidth=0.6)
+    ax.minorticks_on()
+    for spine in ax.spines.values():
+        spine.set_color("#cbd5e1")
+    ax.tick_params(colors="#334155", labelsize=10)
+
+    legend = ax.legend(
+        loc="upper left",
+        frameon=True,
+        fancybox=True,
+        framealpha=0.95,
+        borderpad=0.7,
+    )
+    legend.get_frame().set_facecolor("#ffffff")
+    legend.get_frame().set_edgecolor("#cbd5e1")
+
+    fig.tight_layout()
+
+    output_file = Path(output_path)
+    output_file.parent.mkdir(parents=True, exist_ok=True)
+    fig.savefig(output_file, dpi=220, bbox_inches="tight")
+    plt.close(fig)
+    print(f"Saved latency plot to {output_file}")
+
+
+def clean_parquet_file(
+    parquet_path: str, bad_paths: Iterable[str], dry_run: bool
+) -> int:
+    bad_paths_set = set(bad_paths)
+    if not bad_paths_set:
+        return 0
+
+    df = pd.read_parquet(parquet_path)
+    if "file_path" not in df.columns:
+        raise ValueError(
+            f"Parquet file must contain 'file_path' column: {parquet_path}"
+        )
+
+    bad_mask = df["file_path"].isin(list(bad_paths_set))
+    removed = int(bad_mask.sum())
+
+    if removed > 0 and not dry_run:
+        cleaned_df = df.loc[~bad_mask].reset_index(drop=True)
+        cleaned_df.to_parquet(parquet_path, index=False)
+
+    return removed
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Scan YT-Temporal-1B train/val/test splits with the existing dataloader, "
+            "detect decode failures, and remove failing files from parquet metadata."
+        )
+    )
+    parser.add_argument(
+        "--data-dir",
+        type=str,
+        default="/lustre/fswork/projects/rech/ojz/umz91bs/audio-embeddings/data/YT-Temporal-1B/",
+        help="Root directory containing the parquet metadata files.",
+    )
+    parser.add_argument(
+        "--train-parquet",
+        type=str,
+        default="train_metadata.parquet",
+        help="Train parquet filename under --data-dir.",
+    )
+    parser.add_argument(
+        "--val-parquet",
+        type=str,
+        default="val_metadata.parquet",
+        help="Validation parquet filename under --data-dir.",
+    )
+    parser.add_argument(
+        "--test-parquet",
+        type=str,
+        default="val_metadata.parquet",
+        help="Test parquet filename under --data-dir.",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=64,
+        help="Batch size for scanning.",
+    )
+    parser.add_argument(
+        "--num-workers",
+        type=int,
+        default=24,
+        help="Number of dataloader workers (CPU cores).",
+    )
+    parser.add_argument(
+        "--pin-memory",
+        action="store_true",
+        help="Enable pin_memory for dataloaders.",
+    )
+    parser.add_argument(
+        "--max-audio-length-sec",
+        type=float,
+        default=10.0,
+        help="Maximum waveform duration in seconds while scanning.",
+    )
+    parser.add_argument(
+        "--min-duration-sec",
+        type=float,
+        default=None,
+        help="Optional minimum duration filter (same as datamodule).",
+    )
+    parser.add_argument(
+        "--max-duration-sec",
+        type=float,
+        default=30.0,
+        help="Optional maximum duration filter (same as datamodule).",
+    )
+    parser.add_argument(
+        "--target-sample-rate",
+        type=int,
+        default=16000,
+        help="Target sampling rate used by the dataset resampler.",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Only report removals without modifying parquet files.",
+    )
+    parser.add_argument(
+        "--profile",
+        action="store_true",
+        help="Print detailed throughput and latency metrics per split.",
+    )
+    parser.add_argument(
+        "--batch-latency-plot-path",
+        type=str,
+        default="batch_latency_vs_audio_time.png",
+        help=(
+            "Output path for a scatter plot of batch processing time vs total batch "
+            "audio duration. Set to an empty string to disable."
+        ),
+    )
+
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+
+    datamodule = YT1BDataModule(
+        data_dir=args.data_dir,
+        train_parquet=args.train_parquet,
+        val_parquet=args.val_parquet,
+        test_parquet=args.test_parquet,
+        batch_size=args.batch_size,
+        num_workers=args.num_workers,
+        pin_memory=args.pin_memory,
+        max_audio_length_sec=args.max_audio_length_sec,
+        min_duration_sec=args.min_duration_sec,
+        max_duration_sec=args.max_duration_sec,
+        target_sample_rate=args.target_sample_rate,
+    )
+
+    datamodule.setup(stage="fit")
+    datamodule.setup(stage="test")
+
+    split_specs = [
+        ("train", datamodule.train_dataset, datamodule.train_parquet_path),
+        ("val", datamodule.val_dataset, datamodule.val_parquet_path),
+        ("test", datamodule.test_dataset, datamodule.test_parquet_path),
+    ]
+
+    bad_paths_by_parquet: dict[str, set[str]] = defaultdict(set)
+    bad_counts_by_split: dict[str, int] = {}
+    stats_by_split: dict[str, SplitScanStats] = {}
+    latency_points_by_split: dict[str, list[tuple[float, float]]] = {}
+
+    for split_name, dataset, parquet_path in split_specs:
+        if dataset is None:
+            print(f"Skipping {split_name}: parquet not found at {parquet_path}")
+            continue
+
+        bad_paths, stats, batch_points = scan_split_for_failures(
+            split_name=split_name,
+            dataset=dataset,
+            batch_size=args.batch_size,
+            num_workers=args.num_workers,
+            pin_memory=args.pin_memory,
+        )
+        bad_counts_by_split[split_name] = len(bad_paths)
+        stats_by_split[split_name] = stats
+        latency_points_by_split[split_name] = batch_points
+        bad_paths_by_parquet[parquet_path].update(bad_paths)
+
+    plot_batch_latency_vs_audio_time(
+        points_by_split=latency_points_by_split,
+        output_path=args.batch_latency_plot_path,
+    )
+
+    print("\nFailure counts by split:")
+    for split_name in ["train", "val", "test"]:
+        if split_name in bad_counts_by_split:
+            print(f"- {split_name}: {bad_counts_by_split[split_name]}")
+
+    if args.profile:
+        print("\nProfile report:")
+        for split_name in ["train", "val", "test"]:
+            if split_name not in stats_by_split:
+                continue
+
+            stats = stats_by_split[split_name]
+            print(
+                f"- {split_name}: {stats.processed_samples} samples in "
+                f"{stats.elapsed_sec:.1f}s ({stats.samples_per_sec:.2f} samples/s), "
+                f"errors={stats.error_samples} ({100.0 * stats.error_rate:.2f}%), "
+                f"unique_bad={stats.unique_bad_paths}, batches={stats.num_batches}"
+            )
+            print(
+                f"  batch latency (s): mean={stats.mean_batch_sec:.4f}, "
+                f"p50={stats.p50_batch_sec:.4f}, p90={stats.p90_batch_sec:.4f}, "
+                f"p99={stats.p99_batch_sec:.4f}"
+            )
+
+        if stats_by_split:
+            total_processed = sum(
+                split_stats.processed_samples for split_stats in stats_by_split.values()
+            )
+            total_elapsed = sum(
+                split_stats.elapsed_sec for split_stats in stats_by_split.values()
+            )
+            total_errors = sum(
+                split_stats.error_samples for split_stats in stats_by_split.values()
+            )
+            aggregate_sps = (
+                total_processed / total_elapsed if total_elapsed > 0 else 0.0
+            )
+            aggregate_error_rate = (
+                total_errors / total_processed if total_processed > 0 else 0.0
+            )
+            print(
+                "\nAggregate: "
+                f"{total_processed} samples in {total_elapsed:.1f}s "
+                f"({aggregate_sps:.2f} samples/s), "
+                f"errors={total_errors} ({100.0 * aggregate_error_rate:.2f}%)"
+            )
+
+    print("\nUpdating parquet files...")
+    total_removed = 0
+    for parquet_path, bad_paths in bad_paths_by_parquet.items():
+        removed = clean_parquet_file(
+            parquet_path=parquet_path,
+            bad_paths=bad_paths,
+            dry_run=args.dry_run,
+        )
+        total_removed += removed
+        action = "Would remove" if args.dry_run else "Removed"
+        print(f"- {action} {removed} rows from {parquet_path}")
+
+    if args.dry_run:
+        print(f"\nDry run complete. Rows that would be removed: {total_removed}")
+    else:
+        print(f"\nDone. Total rows removed: {total_removed}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/audio-embeddings/scripts/estimate_wds_wav_size.py b/audio-embeddings/scripts/estimate_wds_wav_size.py
new file mode 100644
index 0000000000000000000000000000000000000000..31dd72078d7a3e6303ceebe1e24d81ac11cbd0fc
--- /dev/null
+++ b/audio-embeddings/scripts/estimate_wds_wav_size.py
@@ -0,0 +1,324 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import argparse
+import math
+from concurrent.futures import Future
+from concurrent.futures import ProcessPoolExecutor
+from concurrent.futures import as_completed
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import torchaudio
+from rich.progress import BarColumn
+from rich.progress import MofNCompleteColumn
+from rich.progress import Progress
+from rich.progress import TaskProgressColumn
+from rich.progress import TextColumn
+from rich.progress import TimeElapsedColumn
+from rich.progress import TimeRemainingColumn
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Estimate storage required for a WAV-only WebDataset generated from a parquet manifest."
+        )
+    )
+    parser.add_argument(
+        "parquet_path",
+        type=Path,
+        help="Path to parquet file (must include audio path column).",
+    )
+    parser.add_argument(
+        "--audio-column",
+        type=str,
+        default="file_path",
+        help="Column name containing source audio file paths.",
+    )
+    parser.add_argument(
+        "--duration-column",
+        type=str,
+        default="duration_sec",
+        help="Column name containing durations in seconds.",
+    )
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=24,
+        help="Parallel worker count used to probe missing durations.",
+    )
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=16000,
+        help="Target WAV sample rate.",
+    )
+    parser.add_argument(
+        "--channels",
+        type=int,
+        default=1,
+        help="Target WAV channel count.",
+    )
+    parser.add_argument(
+        "--bits-per-sample",
+        type=int,
+        default=16,
+        choices=[8, 16, 24, 32],
+        help="Target WAV PCM bit depth.",
+    )
+    parser.add_argument(
+        "--shard-size-gb",
+        type=float,
+        default=1.0,
+        help="Assumed max size per .tar shard in GB (decimal) for trailer overhead estimate.",
+    )
+    parser.add_argument(
+        "--no-probe-missing",
+        action="store_true",
+        help="Fail if duration is missing/invalid instead of probing audio headers.",
+    )
+    return parser.parse_args()
+
+
+def format_bytes(n_bytes: float) -> str:
+    units = ["B", "KiB", "MiB", "GiB", "TiB", "PiB"]
+    value = float(n_bytes)
+    for unit in units:
+        if value < 1024.0 or unit == units[-1]:
+            return f"{value:,.2f} {unit}"
+        value /= 1024.0
+    return f"{value:,.2f} PiB"
+
+
+def estimate_shard_count(total_bytes: int, shard_size_gb: float) -> int:
+    shard_size_bytes = max(1, int(shard_size_gb * 1_000_000_000))
+    return max(1, math.ceil(total_bytes / shard_size_bytes))
+
+
+def parse_duration_value(value: object) -> float:
+    if value is None:
+        return float("nan")
+    if isinstance(value, (int, float, np.integer, np.floating)):
+        return float(value)
+    if isinstance(value, str):
+        try:
+            return float(value)
+        except ValueError:
+            return float("nan")
+    try:
+        return float(str(value))
+    except (TypeError, ValueError):
+        return float("nan")
+
+
+def probe_duration_sec(audio_path: str) -> float:
+    try:
+        info_fn = getattr(torchaudio, "info", None)
+        if info_fn is None:
+            return float("nan")
+        info = info_fn(audio_path)
+        if info.sample_rate <= 0:
+            return float("nan")
+        return float(info.num_frames) / float(info.sample_rate)
+    except Exception:
+        return float("nan")
+
+
+def probe_file_size_bytes(audio_path: str) -> int:
+    try:
+        return Path(audio_path).stat().st_size
+    except Exception:
+        return -1
+
+
+def probe_durations_parallel(paths: list[str], workers: int) -> list[float]:
+    if not paths:
+        return []
+
+    results: list[float] = [float("nan")] * len(paths)
+    with ProcessPoolExecutor(max_workers=workers) as executor:
+        future_to_idx: dict[Future[float], int] = {
+            executor.submit(probe_duration_sec, path): idx
+            for idx, path in enumerate(paths)
+        }
+
+        with Progress(
+            TextColumn("[bold cyan]{task.description}"),
+            BarColumn(),
+            MofNCompleteColumn(),
+            TaskProgressColumn(),
+            TimeRemainingColumn(),
+            TimeElapsedColumn(),
+        ) as progress:
+            task_id = progress.add_task("Probing missing durations", total=len(paths))
+            for future in as_completed(future_to_idx):
+                idx = future_to_idx[future]
+                try:
+                    results[idx] = future.result()
+                except Exception:
+                    results[idx] = float("nan")
+                progress.advance(task_id, 1)
+
+    return results
+
+
+def probe_file_sizes_parallel(paths: list[str], workers: int) -> list[int]:
+    if not paths:
+        return []
+
+    results: list[int] = [0] * len(paths)
+    with ProcessPoolExecutor(max_workers=workers) as executor:
+        future_to_idx: dict[Future[int], int] = {
+            executor.submit(probe_file_size_bytes, path): idx
+            for idx, path in enumerate(paths)
+        }
+
+        with Progress(
+            TextColumn("[bold cyan]{task.description}"),
+            BarColumn(),
+            MofNCompleteColumn(),
+            TaskProgressColumn(),
+            TimeRemainingColumn(),
+            TimeElapsedColumn(),
+        ) as progress:
+            task_id = progress.add_task("Probing current file sizes", total=len(paths))
+            for future in as_completed(future_to_idx):
+                idx = future_to_idx[future]
+                try:
+                    results[idx] = future.result()
+                except Exception:
+                    results[idx] = -1
+                progress.advance(task_id, 1)
+
+    return results
+
+
+def main() -> None:
+    args = parse_args()
+
+    if not args.parquet_path.exists():
+        raise FileNotFoundError(f"Parquet not found: {args.parquet_path}")
+
+    if args.workers < 1:
+        raise ValueError("--workers must be >= 1")
+
+    df = pd.read_parquet(args.parquet_path)
+
+    if args.audio_column not in df.columns:
+        raise ValueError(f"Missing audio column '{args.audio_column}' in parquet.")
+    if args.duration_column not in df.columns:
+        raise ValueError(
+            f"Missing duration column '{args.duration_column}' in parquet. "
+            "Either add it or adapt the script."
+        )
+
+    durations = np.array(
+        [parse_duration_value(value) for value in df[args.duration_column].tolist()],
+        dtype=np.float64,
+    )
+    paths = [str(path) for path in df[args.audio_column].tolist()]
+    n_rows = len(paths)
+
+    unique_paths = list(dict.fromkeys(paths))
+    unique_sizes = probe_file_sizes_parallel(unique_paths, workers=args.workers)
+    size_by_path = dict(zip(unique_paths, unique_sizes))
+    row_sizes = np.array([size_by_path[path] for path in paths], dtype=np.int64)
+    current_row_known_mask = row_sizes >= 0
+    current_unique_known_mask = np.array(unique_sizes, dtype=np.int64) >= 0
+
+    current_rows_total_bytes = int(row_sizes[current_row_known_mask].sum())
+    current_unique_total_bytes = int(
+        np.array(unique_sizes, dtype=np.int64)[current_unique_known_mask].sum()
+    )
+    current_rows_missing = int((~current_row_known_mask).sum())
+    current_unique_missing = int((~current_unique_known_mask).sum())
+
+    invalid_mask = ~np.isfinite(durations) | (durations <= 0.0)
+    n_missing = int(invalid_mask.sum())
+
+    if n_missing > 0:
+        if args.no_probe_missing:
+            raise ValueError(
+                f"Found {n_missing} rows with missing/invalid durations and --no-probe-missing was set."
+            )
+
+        probe_indices = np.where(invalid_mask)[0].tolist()
+        probe_paths = [paths[i] for i in probe_indices]
+        probed = probe_durations_parallel(probe_paths, workers=args.workers)
+        for i, duration in zip(probe_indices, probed, strict=True):
+            durations[i] = duration
+
+    unresolved_mask = ~np.isfinite(durations) | (durations <= 0.0)
+    n_unresolved = int(unresolved_mask.sum())
+
+    valid_durations = durations[~unresolved_mask]
+    n_valid = int(valid_durations.shape[0])
+    if n_valid == 0:
+        raise RuntimeError("No valid durations available; cannot estimate size.")
+
+    bytes_per_sample = args.bits_per_sample // 8
+    bytes_per_frame = args.channels * bytes_per_sample
+
+    frames = np.rint(valid_durations * args.sample_rate).astype(np.int64)
+    wav_file_bytes = 44 + frames * bytes_per_frame
+    wav_total_bytes = int(wav_file_bytes.sum())
+
+    padded_wav_data = ((wav_file_bytes + 511) // 512) * 512
+    tar_entry_overhead = 512 * n_valid
+    tar_data_bytes = int(padded_wav_data.sum())
+
+    estimated_shards = estimate_shard_count(wav_total_bytes, args.shard_size_gb)
+    tar_trailer_overhead = 1024 * estimated_shards
+
+    wds_tar_total_bytes = tar_entry_overhead + tar_data_bytes + tar_trailer_overhead
+
+    total_duration_sec = float(valid_durations.sum())
+    total_hours = total_duration_sec / 3600.0
+
+    print("=== WebDataset WAV Size Estimate ===")
+    print(f"Parquet: {args.parquet_path}")
+    print(f"Rows total: {n_rows:,}")
+    print(f"Rows valid: {n_valid:,}")
+    print(f"Rows missing/invalid duration initially: {n_missing:,}")
+    print(f"Rows unresolved after probe: {n_unresolved:,}")
+    print()
+    print("Current dataset volume (before conversion):")
+    print(
+        "- Referenced rows total bytes: "
+        f"{format_bytes(current_rows_total_bytes)} "
+        f"(missing rows: {current_rows_missing:,})"
+    )
+    print(
+        "- Unique source files total bytes: "
+        f"{format_bytes(current_unique_total_bytes)} "
+        f"(missing files: {current_unique_missing:,})"
+    )
+    print()
+    print(
+        f"Target WAV format: {args.sample_rate} Hz, {args.channels} ch, "
+        f"PCM {args.bits_per_sample}-bit"
+    )
+    print(f"Total audio duration: {total_hours:,.2f} h")
+    print()
+    print(f"Estimated WAV bytes (sum of .wav files): {format_bytes(wav_total_bytes)}")
+    print(f"Estimated WDS TAR bytes (wav-only):      {format_bytes(wds_tar_total_bytes)}")
+    print(
+        f"Average WAV per sample:                  {format_bytes(wav_total_bytes / n_valid)}"
+    )
+    print(
+        f"Average WDS per sample:                  {format_bytes(wds_tar_total_bytes / n_valid)}"
+    )
+    print()
+    print(f"Assumed shard size: {args.shard_size_gb} GB")
+    print(f"Estimated shard count: {estimated_shards:,}")
+    print()
+    print("Shard count quick table (for WAV payload):")
+    for shard_size_gb in [1.0, 2.0, 5.0, 10.0]:
+        shard_count = estimate_shard_count(wav_total_bytes, shard_size_gb)
+        print(f"- {shard_size_gb:>4.1f} GB -> {shard_count:,} shards")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/audio-embeddings/scripts/inspect_data.py b/audio-embeddings/scripts/inspect_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd1ab79e0b9ae8a426941e83881358a972c0ea15
--- /dev/null
+++ b/audio-embeddings/scripts/inspect_data.py
@@ -0,0 +1,36 @@
+import h5py
+import pandas as pd
+import os
+
+data_dir = "data/AudioSet"
+h5_path = os.path.join(data_dir, "balanced_train_soxrhq.h5")
+csv_path = os.path.join(data_dir, "silent_files_balanced_train_soxrhq.csv")
+
+print(f"Inspecting {h5_path}...")
+try:
+    with h5py.File(h5_path, "r") as f:
+        print("Keys:", list(f.keys()))
+        # Inspect the first few items if it's a group or dataset
+        for key in list(f.keys())[:5]:
+            item = f[key]
+            if isinstance(item, h5py.Dataset):
+                print(f"Dataset {key}: shape={item.shape}, dtype={item.dtype}")
+            elif isinstance(item, h5py.Group):
+                print(f"Group {key}: keys={list(item.keys())}")
+                # Go one level deeper
+                for subkey in list(item.keys())[:3]:
+                    subitem = item[subkey]
+                    if isinstance(subitem, h5py.Dataset):
+                        print(
+                            f"  Dataset {subkey}: shape={subitem.shape}, dtype={subitem.dtype}"
+                        )
+except Exception as e:
+    print(f"Error reading HDF5: {e}")
+
+print(f"\nInspecting {csv_path}...")
+try:
+    df = pd.read_csv(csv_path)
+    print(df.head())
+    print(df.columns)
+except Exception as e:
+    print(f"Error reading CSV: {e}")
diff --git a/audio-embeddings/scripts/inspect_h5.py b/audio-embeddings/scripts/inspect_h5.py
new file mode 100644
index 0000000000000000000000000000000000000000..65b408baf3be6782ad66871b5c3195e991df875e
--- /dev/null
+++ b/audio-embeddings/scripts/inspect_h5.py
@@ -0,0 +1,24 @@
+import h5py
+import os
+
+
+def inspect_h5():
+    # Path from previous context
+    h5_path = "/media/ltuncay/Shared-4TB/dev/audio-embeddings/data/AudioSet/balanced_train_soxrhq.h5"
+
+    if not os.path.exists(h5_path):
+        print(f"File not found: {h5_path}")
+        return
+
+    with h5py.File(h5_path, "r") as f:
+        print("Keys:", list(f.keys()))
+        if "waveform" in f:
+            print("Waveform shape:", f["waveform"].shape)
+            print("Waveform attrs:", dict(f["waveform"].attrs))
+
+        # Check for global attributes
+        print("Global attrs:", dict(f.attrs))
+
+
+if __name__ == "__main__":
+    inspect_h5()
diff --git a/audio-embeddings/scripts/inspect_rope.py b/audio-embeddings/scripts/inspect_rope.py
new file mode 100644
index 0000000000000000000000000000000000000000..86e2dcdb3df59defab02092ff80ee638ed64d6f2
--- /dev/null
+++ b/audio-embeddings/scripts/inspect_rope.py
@@ -0,0 +1,5 @@
+import inspect
+from timm.layers import RotaryEmbedding
+
+print(inspect.signature(RotaryEmbedding.__init__))
+print(inspect.signature(RotaryEmbedding.forward))
diff --git a/audio-embeddings/scripts/inspect_timm.py b/audio-embeddings/scripts/inspect_timm.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9ce0cd5bdbeaeb1172aafc89d0e6a7a09f42636
--- /dev/null
+++ b/audio-embeddings/scripts/inspect_timm.py
@@ -0,0 +1,4 @@
+import inspect
+from timm.models.vision_transformer import Block
+
+print(inspect.signature(Block.__init__))
diff --git a/audio-embeddings/scripts/verify_cropping.py b/audio-embeddings/scripts/verify_cropping.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2e029fe1ba8b796f9f33c06d7ffd29deb756dfd
--- /dev/null
+++ b/audio-embeddings/scripts/verify_cropping.py
@@ -0,0 +1,91 @@
+import torch
+import numpy as np
+from src.data.audioset_datamodule import AudioSetDataset
+
+
+# Mock Dataset inheriting from AudioSetDataset to test logic without H5
+class MockAudioSetDataset(AudioSetDataset):
+    def __init__(self, lengths, max_length=None):
+        self.lengths = lengths
+        self.max_length = max_length
+        self.transform = None
+        self.valid_indices = list(range(len(lengths)))
+        self.h5_file = None  # Not used
+
+    def _open_h5(self):
+        pass
+
+    def __getitem__(self, idx):
+        # Mock waveform loading
+        length = self.lengths[idx]
+        # Create a waveform where values are 0..L-1 so we can check cropping start
+        waveform = np.arange(length, dtype=np.float32)
+
+        # Random Crop logic from AudioSetDataset
+        if self.max_length is not None and len(waveform) > self.max_length:
+            max_start = len(waveform) - self.max_length
+            start = np.random.randint(0, max_start + 1)
+            waveform = waveform[start : start + self.max_length]
+
+        # Mock other returns
+        target = torch.zeros(527)
+        audio_name = f"audio_{idx}"
+
+        waveform = torch.from_numpy(waveform).unsqueeze(0)
+
+        return {
+            "waveform": waveform,
+            "target": target,
+            "audio_name": audio_name,
+            "index": idx,
+        }
+
+
+def test_random_cropping():
+    max_len = 100
+    lengths = [50, 100, 150, 200]
+
+    dataset = MockAudioSetDataset(lengths, max_length=max_len)
+
+    print(f"Testing with max_length={max_len}")
+
+    for i in range(len(lengths)):
+        # Test multiple times to check randomness
+        starts = []
+        for _ in range(5):
+            item = dataset[i]
+            wave = item["waveform"]
+            # Check length
+            if wave.shape[-1] > max_len:
+                print(
+                    f"FAIL: Index {i} (orig {lengths[i]}) has length {wave.shape[-1]} > {max_len}"
+                )
+
+            # Check content (start index)
+            start_val = wave[0, 0].item()
+            starts.append(start_val)
+
+        print(f"Index {i} (orig {lengths[i]}): Starts = {starts}")
+
+        if lengths[i] > max_len:
+            # Should be cropped to max_len
+            if wave.shape[-1] != max_len:
+                print(
+                    f"FAIL: Index {i} should be cropped to {max_len}, got {wave.shape[-1]}"
+                )
+
+            # Should be random (unless max_start=0)
+            if (
+                len(set(starts)) == 1 and lengths[i] > max_len + 5
+            ):  # Allow some chance of collision
+                print(f"WARNING: Index {i} might not be random? Starts: {starts}")
+        else:
+            # Should be original length
+            if wave.shape[-1] != lengths[i]:
+                print(f"FAIL: Index {i} should be {lengths[i]}, got {wave.shape[-1]}")
+
+    print("Test finished.")
+
+
+if __name__ == "__main__":
+    test_random_cropping()
diff --git a/audio-embeddings/scripts/verify_resampling.py b/audio-embeddings/scripts/verify_resampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b7f625c1f2031817342af663bc0ea35e4cd8235
--- /dev/null
+++ b/audio-embeddings/scripts/verify_resampling.py
@@ -0,0 +1,96 @@
+import torch
+import numpy as np
+import torchaudio
+from src.data.audioset_datamodule import AudioSetDataset
+
+
+# Mock Dataset to test resampling logic
+class MockAudioSetDatasetResample(AudioSetDataset):
+    def __init__(self, source_sr, target_sr, max_length=None):
+        self.source_sample_rate = source_sr
+        self.target_sample_rate = target_sr
+        self.max_length = max_length
+        self.transform = None
+        self.valid_indices = [0]
+        self.h5_file = None
+
+    def _open_h5(self):
+        pass
+
+    def __getitem__(self, idx):
+        # Create a 10s sine wave at source SR
+        duration = 10
+        t = np.linspace(0, duration, int(self.source_sample_rate * duration))
+        waveform = np.sin(2 * np.pi * 440 * t).astype(np.float32)
+
+        # Mock loading
+        waveform = torch.from_numpy(waveform)
+
+        # --- Copy-paste logic from AudioSetDataset.__getitem__ ---
+        # Resampling and Cropping Logic
+        if self.source_sample_rate != self.target_sample_rate:
+            if self.max_length is not None:
+                crop_len_source = (
+                    int(
+                        self.max_length
+                        * self.source_sample_rate
+                        / self.target_sample_rate
+                    )
+                    + 100
+                )
+                if len(waveform) > crop_len_source:
+                    max_start = len(waveform) - crop_len_source
+                    start = np.random.randint(0, max_start + 1)
+                    waveform = waveform[start : start + crop_len_source]
+
+            resampler = torchaudio.transforms.Resample(
+                self.source_sample_rate, self.target_sample_rate
+            )
+            waveform = resampler(waveform.unsqueeze(0)).squeeze(0)
+
+            if self.max_length is not None and len(waveform) > self.max_length:
+                waveform = waveform[: self.max_length]
+        else:
+            if self.max_length is not None and len(waveform) > self.max_length:
+                max_start = len(waveform) - self.max_length
+                start = np.random.randint(0, max_start + 1)
+                waveform = waveform[start : start + self.max_length]
+        # ---------------------------------------------------------
+
+        return waveform
+
+
+def test_resampling():
+    source_sr = 32000
+    target_sr = 16000
+    max_len_target = 160000  # 10s @ 16kHz
+
+    dataset = MockAudioSetDatasetResample(
+        source_sr, target_sr, max_length=max_len_target
+    )
+
+    print(
+        f"Testing resampling from {source_sr} to {target_sr} with max_len {max_len_target}"
+    )
+
+    waveform = dataset[0]
+    print(f"Output shape: {waveform.shape}")
+
+    if waveform.shape[0] == max_len_target:
+        print("PASS: Output length matches max_length")
+    else:
+        print(f"FAIL: Output length {waveform.shape[0]} != {max_len_target}")
+
+    # Test without max_length
+    dataset_no_max = MockAudioSetDatasetResample(source_sr, target_sr, max_length=None)
+    waveform_full = dataset_no_max[0]
+    expected_len = 160000  # 10s * 16000
+    print(f"Output shape (no max): {waveform_full.shape}")
+    if abs(waveform_full.shape[0] - expected_len) < 100:
+        print("PASS: Output length matches expected resampled length")
+    else:
+        print(f"FAIL: Output length {waveform_full.shape[0]} != {expected_len}")
+
+
+if __name__ == "__main__":
+    test_resampling()
diff --git a/audio-embeddings/scripts/verify_rqa_jepa.py b/audio-embeddings/scripts/verify_rqa_jepa.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d5808af4209c2a084f3e4fae2b1a639fabe9fd2
--- /dev/null
+++ b/audio-embeddings/scripts/verify_rqa_jepa.py
@@ -0,0 +1,98 @@
+import torch
+import torch.nn as nn
+import sys
+import os
+
+# Add src to python path
+sys.path.append(os.getcwd())
+
+from src.models.rqa_jepa_module import RQAJEPAModule
+
+
+def verify_rqa_jepa():
+    print("Verifying RQA-JEPA Implementation...")
+
+    # Mock Net Config
+    net_config = {
+        "spectrogram": {},
+        "patch_embed": {
+            "img_size": (1024, 128),
+            "patch_size": (16, 16),
+            "in_chans": 1,
+            "embed_dim": 768,
+        },
+        "masking": {
+            "input_size": (64, 8),
+            "mask_ratio": (0.4, 0.6),
+        },  # approx grid size for 1024x128
+        "encoder": {
+            "img_size": (1024, 128),
+            "patch_size": (16, 16),
+            "embed_dim": 768,
+            "depth": 2,
+            "num_heads": 4,
+        },
+        "predictor": {
+            "img_size": (1024, 128),
+            "patch_size": (16, 16),
+            "embed_dim": 384,
+            "depth": 1,
+            "num_heads": 4,
+        },
+    }
+
+    # Mock Optimizer
+    def optimizer_partial(params):
+        return torch.optim.Adam(params)
+
+    # --- Mode 1: Teacher Input ---
+    print("\n--- Verifying RQA-JEPA (Mode: teacher) ---")
+    model_teacher = RQAJEPAModule(
+        optimizer=optimizer_partial,
+        net=net_config,
+        jepa_criterion=nn.MSELoss(),
+        rq_criterion=nn.CrossEntropyLoss(),
+        rq_input_type="teacher",
+        codebook_dim=16,
+        vocab_size=100,
+    )
+
+    # Mock input data
+    B, C, T = 2, 1, 16000  # 1 second audio
+    waveform = torch.rand(B, C, T)
+    batch = {"waveform": waveform}
+
+    # Set device
+    model_teacher.to("cpu")
+
+    # Run training_step
+    print("Model (teacher) instantiated. Running training_step...")
+    loss_teacher = model_teacher.training_step(batch, 0)
+    print(f"Training step successful. Loss: {loss_teacher.item()}")
+    assert isinstance(loss_teacher, torch.Tensor)
+    assert loss_teacher.ndim == 0
+
+    # --- Mode 2: Spectrogram Input ---
+    print("\n--- Verifying RQA-JEPA (Mode: spectrogram) ---")
+    model_spec = RQAJEPAModule(
+        optimizer=optimizer_partial,
+        net=net_config,
+        jepa_criterion=nn.MSELoss(),
+        rq_criterion=nn.CrossEntropyLoss(),
+        rq_input_type="spectrogram",
+        codebook_dim=16,
+        vocab_size=100,
+    )
+    model_spec.to("cpu")
+
+    print("Model (spectrogram) instantiated. Running training_step...")
+    loss_spec = model_spec.training_step(batch, 0)
+    print(f"Training step successful. Loss: {loss_spec.item()}")
+    assert isinstance(loss_spec, torch.Tensor)
+    assert loss_spec.ndim == 0
+
+    print("\nVerification Passed!")
+
+
+if __name__ == "__main__":
+    verify_rqa_jepa()
diff --git a/audio-embeddings/scripts/verify_scheduler.py b/audio-embeddings/scripts/verify_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5f8bb8a5bd3b54d8c2fd94d124fcd4d35c1c8c8
--- /dev/null
+++ b/audio-embeddings/scripts/verify_scheduler.py
@@ -0,0 +1,98 @@
+import torch
+
+import sys
+import os
+
+# Add src to path
+sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
+
+from src.models.audio_jepa_module import AudioJEPAModule
+from unittest.mock import MagicMock
+
+
+def test_scheduler():
+    # Mock dependencies
+    optimizer_cls = MagicMock()
+    optimizer_instance = MagicMock()
+    optimizer_cls.return_value = optimizer_instance
+
+    # Mock net config
+    net_config = {
+        "spectrogram": {},
+        "patch_embed": {},
+        "masking": {},
+        "encoder": {"embed_dim": 768},
+        "predictor": {"embed_dim": 768},
+    }
+
+    # Instantiate module
+    module = AudioJEPAModule(
+        optimizer=optimizer_cls, net=net_config, warmup_pct=0.1, final_lr_ratio=0.001
+    )
+
+    # Mock trainer
+    module.trainer = MagicMock()
+    module.trainer.max_steps = 1000
+    module.trainer.estimated_stepping_batches = 1000
+
+    # Call configure_optimizers
+    # We need a real optimizer to step the scheduler
+    real_optimizer = torch.optim.SGD([torch.nn.Parameter(torch.randn(1))], lr=1.0)
+    module.hparams.optimizer = lambda params: real_optimizer
+
+    optim_conf = module.configure_optimizers()
+    scheduler = optim_conf["lr_scheduler"]["scheduler"]
+
+    lrs = []
+    steps = range(1000)
+
+    for step in steps:
+        # Step scheduler
+        scheduler.step()
+        lrs.append(scheduler.get_last_lr()[0])
+
+    # Verify
+    warmup_steps = 100  # 0.1 * 1000
+
+    print(f"LR at step 0: {lrs[0]}")
+    print(f"LR at step {warmup_steps}: {lrs[warmup_steps]}")
+    print(f"LR at step 999: {lrs[999]}")
+
+    # Check warmup
+    # At step 50 (halfway warmup), lr should be ~0.5
+    # Note: LambdaLR calls lambda with epoch/step.
+    # If we step scheduler 1000 times.
+
+    # Plot if possible (optional, but printing is enough for now)
+
+    # Assertions
+    assert lrs[0] < 0.1, f"LR at step 0 should be small, got {lrs[0]}"
+    # At warmup_steps, it might be slightly off due to 0-indexing or 1-indexing in LambdaLR?
+    # LambdaLR passes `last_epoch` which starts at -1 and increments on step().
+    # So first step() makes it 0.
+    # My lambda receives 0.
+    # If step=0, lr = 0/100 = 0.
+
+    # Let's check peak
+    # At step=warmup_steps (100), lambda receives 100.
+    # 100 < 100 is False.
+    # progress = (100-100)/(900) = 0.
+    # cosine_part = 0.5 * (1 + 1) = 1.
+    # lr = final + (1-final)*1 = 1.0.
+    # So at step 100 (which is the 101th value in lrs if we record after step), it should be 1.0?
+    # Wait, scheduler.step() is usually called AFTER optimizer.step().
+    # In Lightning, it calls scheduler.step() every step.
+
+    # Let's just inspect the values.
+
+    # Check decay
+    # At step 550 (midway of decay), progress = 450/900 = 0.5.
+    # cos(pi * 0.5) = 0.
+    # cosine_part = 0.5 * (1 + 0) = 0.5.
+    # lr = final + (1-final)*0.5 ~ 0.5.
+
+    print("Verification successful!")
+
+
+if __name__ == "__main__":
+    test_scheduler()
diff --git a/audio-embeddings/scripts/verify_shapes.py b/audio-embeddings/scripts/verify_shapes.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9a92633c840707b0dc5510fae5129a81b1389c8
--- /dev/null
+++ b/audio-embeddings/scripts/verify_shapes.py
@@ -0,0 +1,87 @@
+import torch
+import torchaudio
+
+
+def get_spectrogram_shape(waveform_len, hop_length=1250, center=True):
+    # Simulation of torchaudio MelSpectrogram shape calculation
+    # If center=True, it pads the signal.
+    # Output time steps = input_samples // hop_length + 1
+    return waveform_len // hop_length + 1
+
+
+def calculate_required_length(current_len, hop_length, patch_time_dim):
+    # We need spec_len % (2 * patch_time_dim) == 0
+    # spec_len = current_len // hop_length + 1
+
+    # Let target_spec_len be the next multiple of (2 * patch_time_dim)
+    # target_spec_len = ceil(spec_len / (2 * patch_time_dim)) * (2 * patch_time_dim)
+
+    # Then we need waveform_len such that waveform_len // hop_length + 1 = target_spec_len
+    # waveform_len // hop_length = target_spec_len - 1
+    # waveform_len = (target_spec_len - 1) * hop_length
+    # But wait, we can just pad the waveform to be larger.
+    # Any waveform_len in range [ (target_spec_len-1)*hop_length, target_spec_len*hop_length - 1 ] might work?
+    # Let's just pick one: waveform_len = (target_spec_len - 1) * hop_length
+
+    spec_len = current_len // hop_length + 1
+    block_size = 2 * patch_time_dim
+
+    if spec_len % block_size == 0:
+        target_spec_len = spec_len
+    else:
+        target_spec_len = (spec_len // block_size + 1) * block_size
+
+    # Reverse to waveform length
+    # We want (target_wave_len // hop_length + 1) == target_spec_len
+    # target_wave_len // hop_length = target_spec_len - 1
+    # target_wave_len = (target_spec_len - 1) * hop_length
+
+    return target_spec_len, target_spec_len * hop_length  # Approximate, let's verify
+
+
+def test_shapes():
+    hop_length = 1250
+    patch_time_dim = 16
+
+    lengths = [32000, 48000, 320000, 12345]
+
+    mel = torchaudio.transforms.MelSpectrogram(
+        sample_rate=32000,
+        n_fft=4096,
+        win_length=4096,
+        hop_length=hop_length,
+        n_mels=128,
+        center=True,
+    )
+
+    print(f"Testing with hop_length={hop_length}, patch_time_dim={patch_time_dim}")
+
+    for length in lengths:
+        wave = torch.randn(1, length)
+        spec = mel(wave)
+        spec_len = spec.shape[-1]
+        print(f"Wave: {length}, Spec: {spec_len}")
+
+        # Calculate required
+        target_spec_len, target_wave_len = calculate_required_length(
+            length, hop_length, patch_time_dim
+        )
+
+        # Verify
+        wave_pad = torch.randn(1, target_wave_len)
+        spec_pad = mel(wave_pad)
+        spec_pad_len = spec_pad.shape[-1]
+
+        print(f"  Target Spec: {target_spec_len}, Target Wave: {target_wave_len}")
+        print(f"  Actual Spec: {spec_pad_len}")
+        print(f"  Even patches? {spec_pad_len / patch_time_dim} (Time patches)")
+        print(
+            f"  Even time patches condition: {(spec_pad_len // patch_time_dim) % 2 == 0}"
+        )
+
+        if spec_pad_len != target_spec_len:
+            print("  MISMATCH!")
+
+
+if __name__ == "__main__":
+    test_shapes()
diff --git a/audio-embeddings/scripts/verify_variable_length.py b/audio-embeddings/scripts/verify_variable_length.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0c913dec58af84dc14a515b00d54b970fd80628
--- /dev/null
+++ b/audio-embeddings/scripts/verify_variable_length.py
@@ -0,0 +1,122 @@
+import torch
+from torch.utils.data import Dataset, DataLoader
+from src.models.audio_jepa_module import AudioJEPAModule
+from src.data.audioset_datamodule import AudioSetDataModule
+
+
+# Mock Dataset
+class MockAudioDataset(Dataset):
+    def __init__(self, lengths):
+        self.lengths = lengths
+
+    def __len__(self):
+        return len(self.lengths)
+
+    def __getitem__(self, idx):
+        length = self.lengths[idx]
+        waveform = torch.randn(1, length)
+        target = torch.randn(527)  # AudioSet classes
+        return {
+            "waveform": waveform,
+            "target": target,
+            "audio_name": f"audio_{idx}",
+            "index": idx,
+        }
+
+
+def test_variable_length():
+    # 1. Test Data Loading
+    lengths = [32000, 48000, 30000, 50000]  # Variable lengths
+    dataset = MockAudioDataset(lengths)
+
+    # Use collate_fn from AudioSetDataModule
+    # Pass parameters manually for testing
+    def collate_fn(batch):
+        return AudioSetDataModule.collate_fn(batch, hop_length=1250, patch_time_dim=16)
+
+    loader = DataLoader(dataset, batch_size=4, collate_fn=collate_fn)
+
+    batch = next(iter(loader))
+    waveforms = batch["waveform"]
+    print(f"Batch waveforms shape: {waveforms.shape}")
+
+    # Check if shape is correct
+    # Max length is 50000.
+    # Hop = 1250.
+    # Max spec len = 50000 // 1250 + 1 = 41.
+    # Block size = 32.
+    # Target spec len = ceil(41/32)*32 = 64.
+    # Target wave len = (64-1)*1250 = 63 * 1250 = 78750.
+
+    expected_len = 78750
+    if waveforms.shape[-1] == expected_len:
+        print("Padding logic verified!")
+    else:
+        print(
+            f"Padding logic mismatch! Expected {expected_len}, got {waveforms.shape[-1]}"
+        )
+
+    # 2. Test Model Forward
+    print("Initializing model...")
+    # Minimal config
+    net_config = {
+        "spectrogram": {
+            "sample_rate": 32000,
+            "n_fft": 4096,
+            "win_length": 4096,
+            "hop_length": 1250,
+            "n_mels": 128,
+            "f_min": 0.0,
+            "f_max": None,
+            # target_length removed
+        },
+        "patch_embed": {
+            "img_size": (
+                128,
+                256,
+            ),  # This is just for init, will be ignored/overridden dynamically
+            "patch_size": (16, 16),
+            "in_chans": 1,
+            "embed_dim": 192,  # Small dim for speed
+        },
+        "masking": {
+            "input_size": (128, 256),
+            "patch_size": (16, 16),
+            "mask_ratio": (0.4, 0.6),
+        },
+        "encoder": {
+            "embed_dim": 192,
+            "depth": 2,
+            "num_heads": 3,
+            "pos_embed_type": "rope",
+            "img_size": (128, 256),
+            "patch_size": (16, 16),
+        },
+        "predictor": {
+            "embed_dim": 192,
+            "depth": 1,
+            "num_heads": 3,
+            "pos_embed_type": "rope",
+            "img_size": (128, 256),
+            "patch_size": (16, 16),
+        },
+    }
+
+    model = AudioJEPAModule(optimizer=torch.optim.AdamW, net=net_config)
+
+    # Initialize EMA decay manually since we skip Lightning loop
+    model.current_ema_decay = 0.996
+
+    print("Running training_step...")
+    loss = model.training_step(batch, 0)
+    print(f"Training step loss: {loss}")
+
+    print("Running validation_step...")
+    val_loss = model.validation_step(batch, 0)
+    print(f"Validation step loss: {val_loss}")
+
+    print("Test passed!")
+
+
+if __name__ == "__main__":
+    test_variable_length()
diff --git a/audio-embeddings/slurm_scripts/submit.py b/audio-embeddings/slurm_scripts/submit.py
new file mode 100644
index 0000000000000000000000000000000000000000..975f03224fc41dc2e6bdbb5ce19ffee3af4537fe
--- /dev/null
+++ b/audio-embeddings/slurm_scripts/submit.py
@@ -0,0 +1,348 @@
+#!/usr/bin/env python3
+import argparse
+import os
+import re
+import subprocess
+from datetime import datetime, timedelta
+
+# Slurm Script Template
+# Adapt directives based on your cluster configuration
+TEMPLATE = """#!/bin/bash
+#SBATCH --job-name={job_name}
+#SBATCH --account=ojz@h100
+#SBATCH --constraint=h100
+#SBATCH --qos={qos}
+#SBATCH --time={time}
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node={gpus}
+#SBATCH --gres=gpu:{gpus}
+#SBATCH --cpus-per-task=24
+#SBATCH --hint=nomultithread
+#SBATCH --output=logs/slurm/%x-%j.log
+#SBATCH --error=logs/slurm/%x-%j.log
+
+set -euxo pipefail
+
+export MPLBACKEND=Agg
+
+if ! command -v module >/dev/null 2>&1; then
+    source /etc/profile.d/modules.sh || true
+fi
+
+module load arch/h100
+module load {ffmpeg_module}
+
+FFMPEG_BIN=$(command -v ffmpeg || true)
+if [ -n "$FFMPEG_BIN" ]; then
+    FFMPEG_ROOT=$(dirname "$(dirname "$FFMPEG_BIN")")
+    export LD_LIBRARY_PATH="${{FFMPEG_ROOT}}/lib:${{LD_LIBRARY_PATH}}"
+fi
+
+if [ -n "${{EBROOTFFMPEG:-}}" ]; then
+    export LD_LIBRARY_PATH="${{EBROOTFFMPEG}}/lib:${{LD_LIBRARY_PATH}}"
+fi
+
+cd {workdir}
+
+export PYTHONUNBUFFERED=1
+export HYDRA_FULL_ERROR=1
+export TMPDIR=$SCRATCH
+export TEMP=$SCRATCH
+export TMP=$SCRATCH
+export PROJECT_ROOT={workdir}
+
+# Ensure log directory exists
+mkdir -p logs/slurm
+
+source .venv/bin/activate
+
+# Configuration Info
+# Experiment: {experiment}
+# GPUs: {gpus}
+# Strategy: {strategy}
+# WandB Name: {wandb_name}
+# Trainer Max Time: {max_time}
+# FFmpeg module: {ffmpeg_module}
+
+echo "Starting job {job_name} on $(hostname)"
+echo "Experiment: {experiment}"
+echo "FFmpeg binary: $(command -v ffmpeg || echo 'not found')"
+ffmpeg -version | head -n 1
+
+srun .venv/bin/python -u -O src/train.py \\
+    experiment={experiment} \\
+    ++trainer.devices={gpus} \\
+    ++trainer.strategy={strategy} \\
+    ++trainer.max_time="{max_time}" \\
+    ++logger.wandb.name="{wandb_name}" \\
+    {extra_args}
+"""
+
+
+def parse_slurm_time(time_str):
+    """Parses Slurm time string into a timedelta object.
+    Formats: "MM", "MM:SS", "HH:MM:SS", "D-HH", "D-HH:MM", "D-HH:MM:SS"
+    """
+    days = 0
+    if "-" in time_str:
+        days_str, time_str = time_str.split("-")
+        days = int(days_str)
+
+    parts = list(map(int, time_str.split(":")))
+
+    if len(parts) == 1:  # MM
+        minutes = parts[0]
+        hours = 0
+        seconds = 0
+    elif len(parts) == 2:  # MM:SS
+        minutes, seconds = parts
+        hours = 0
+    elif len(parts) == 3:  # HH:MM:SS
+        hours, minutes, seconds = parts
+    else:
+        raise ValueError(f"Invalid time format: {time_str}")
+
+    return timedelta(days=days, hours=hours, minutes=minutes, seconds=seconds)
+
+
+def format_timedelta(td):
+    """Formats timedelta back to DD:HH:MM:SS string for Lightning"""
+    total_seconds = int(td.total_seconds())
+    days, remainder = divmod(total_seconds, 86400)
+    hours, remainder = divmod(remainder, 3600)
+    minutes, seconds = divmod(remainder, 60)
+    return f"{days:02}:{hours:02}:{minutes:02}:{seconds:02}"
+
+
+def parse_config_value(content, pattern):
+    match = re.search(pattern, content)
+    return match.group(1).strip() if match else None
+
+
+def select_qos(time_limit: timedelta) -> str:
+    two_hours = timedelta(hours=2)
+    twenty_hours = timedelta(hours=20)
+    one_hundred_hours = timedelta(hours=100)
+
+    if time_limit <= two_hours:
+        return "qos_gpu_h100-dev"
+    if time_limit <= twenty_hours:
+        return "qos_gpu_h100-t3"
+    if time_limit <= one_hundred_hours:
+        return "qos_gpu_h100-t4"
+
+    raise ValueError(
+        "Requested time exceeds maximum supported QoS window (100h). "
+        "Please request 100:00:00 or less."
+    )
+
+
+def format_steps(steps_str):
+    if not steps_str or not steps_str.isdigit():
+        return steps_str
+
+    steps = int(steps_str)
+    if steps >= 1000000:
+        return f"{steps // 1000000}m"
+    if steps >= 1000:
+        return f"{steps // 1000}k"
+    return str(steps)
+
+
+def generate_wandb_name(config_path, num_gpus, suffix=None):
+    try:
+        with open(config_path, "r") as f:
+            content = f.read()
+    except FileNotFoundError:
+        print(
+            f"Warning: Config file not found at {config_path}. Cannot auto-generate name."
+        )
+        return "experiment"
+
+    # Extract values using regex
+    model = parse_config_value(content, r"override /model:\s*(\S+)")
+    dataset = parse_config_value(content, r"override /data:\s*(\S+)")
+    batch_size = parse_config_value(content, r"batch_size:\s*(\d+)")
+    max_steps = parse_config_value(content, r"max_steps:\s*(\d+)")
+
+    # Construct name parts
+    parts = []
+
+    if model:
+        parts.append(model)
+    if dataset:
+        parts.append(dataset)
+
+    if max_steps:
+        parts.append(format_steps(max_steps))
+
+    if batch_size:
+        parts.append(f"{batch_size}x{num_gpus}bs")
+
+    if suffix:
+        parts.append(suffix)
+
+    # Fallback if parsing failed completely
+    if not parts:
+        return "experiment"
+
+    return "-".join(parts)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=(
+            "Generate and submit Slurm jobs for Audio Embeddings. "
+            "WandB run names are generated from the experiment config "
+            "(model, data, max_steps, batch_size x GPUs), plus optional suffix."
+        )
+    )
+    parser.add_argument(
+        "experiment",
+        type=str,
+        help="Experiment config path (e.g., audio_jepa/baseline)",
+    )
+    parser.add_argument(
+        "--gpus", type=int, default=1, help="Number of GPUs to request (default: 1)"
+    )
+    parser.add_argument(
+        "--time",
+        type=str,
+        default="20:00:00",
+        help="Time limit (HH:MM:SS) (default: 20:00:00)",
+    )
+    parser.add_argument(
+        "--suffix",
+        type=str,
+        help=(
+            "Optional suffix for WandB run name. "
+            "Base name is derived from the experiment config: "
+            "model + data + max_steps (k/m) + batch_size x GPUs."
+        ),
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Print the generated script without submitting",
+    )
+    parser.add_argument(
+        "--ffmpeg-module",
+        type=str,
+        default="ffmpeg/6.1.1",
+        help=(
+            "FFmpeg environment module to load in the job script "
+            "(default: ffmpeg/6.1.1)."
+        ),
+    )
+
+    args, unknown = parser.parse_known_args()
+
+    # 1. Configuration Logic
+    if args.gpus > 1:
+        strategy = "ddp"
+        # Sync BatchNorm is usually recommended for DDP
+        # Using +trainer.sync_batchnorm to ensure we append it even if it doesn't exist
+        extra_args_list = ["++trainer.sync_batchnorm=True"]
+    else:
+        strategy = "auto"
+        extra_args_list = []
+
+    # Append any unknown arguments passed to the script (e.g. model.rq_lambda=0.5)
+    if unknown:
+        for arg in unknown:
+            if arg.startswith(("+", "~")):
+                extra_args_list.append(arg)
+            elif "=" in arg:
+                # If it's an assignment, use ++ to Force Add/Override
+                # This prevents "ConfigAttributeError" if the key isn't in the struct
+                # and works fine if it IS in the struct.
+                extra_args_list.append("++" + arg)
+            else:
+                extra_args_list.append(arg)
+
+    extra_args = " ".join(extra_args_list)
+
+    # Get absolute path of current working directory
+
+    # Get absolute path of current working directory
+    workdir = os.path.abspath(os.getcwd())
+
+    # 2. Generate WandB Name
+    # Assume config is in configs/experiment/{experiment}.yaml
+    config_path = os.path.join(
+        workdir, "configs", "experiment", f"{args.experiment}.yaml"
+    )
+    wandb_name = generate_wandb_name(config_path, args.gpus, args.suffix)
+
+    # Use WandB name as Job Name (consistent naming)
+    job_name = wandb_name
+
+    # 3. Select QoS and calculate Trainer Max Time (Time - 10 minutes)
+    try:
+        slurm_time_td = parse_slurm_time(args.time)
+        qos = select_qos(slurm_time_td)
+        buffer_time = timedelta(minutes=10)
+
+        # Ensure we don't go negative
+        if slurm_time_td > buffer_time:
+            max_time_td = slurm_time_td - buffer_time
+        else:
+            print(
+                f"Warning: Requested time {args.time} is less than buffer (10m). Using full time."
+            )
+            max_time_td = slurm_time_td
+
+        max_time_str = format_timedelta(max_time_td)
+    except Exception as e:
+        raise ValueError(f"Invalid --time value '{args.time}': {e}") from e
+
+    # 4. Fill Template
+    script_content = TEMPLATE.format(
+        job_name=job_name,
+        qos=qos,
+        time=args.time,
+        gpus=args.gpus,
+        workdir=workdir,
+        experiment=args.experiment,
+        strategy=strategy,
+        wandb_name=wandb_name,
+        max_time=max_time_str,
+        ffmpeg_module=args.ffmpeg_module,
+        extra_args=extra_args,
+    )
+
+    # 5. Handle Dry Run
+    if args.dry_run:
+        print("--- Dry Run: Generated Slurm Script ---")
+        print(script_content)
+        print("---------------------------------------")
+        return
+
+    # 4. Write to Temporary File
+    # Create a hidden temp directory for scripts if it doesn't exist
+    script_dir = os.path.join(workdir, "slurm_scripts", ".generated")
+    os.makedirs(script_dir, exist_ok=True)
+
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = os.path.join(script_dir, f"submit_{job_name}_{timestamp}.slurm")
+
+    with open(filename, "w") as f:
+        f.write(script_content)
+
+    print(f"Generated script: {filename}")
+
+    # 5. Submit to Slurm
+    try:
+        # Submit the script
+        result = subprocess.run(
+            ["sbatch", filename], check=True, capture_output=True, text=True
+        )
+        print(f"Submission successful: {result.stdout.strip()}")
+    except subprocess.CalledProcessError as e:
+        print("Error: Submission failed!")
+        print(f"Stderr: {e.stderr}")
+        # Optionally delete the failed script? Keeping it for debug is usually better.
+
+
+if __name__ == "__main__":
+    main()
diff --git a/audio-embeddings/src/callbacks/__init__.py b/audio-embeddings/src/callbacks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/audio-embeddings/src/callbacks/ema_weight_averaging.py b/audio-embeddings/src/callbacks/ema_weight_averaging.py
new file mode 100644
index 0000000000000000000000000000000000000000..b38725a20d26ea39801fef11a65ef04ab3fa3457
--- /dev/null
+++ b/audio-embeddings/src/callbacks/ema_weight_averaging.py
@@ -0,0 +1,101 @@
+from typing import Any, Optional, Union
+
+import torch
+from torch.optim.swa_utils import get_ema_avg_fn
+from lightning.pytorch.utilities.rank_zero import rank_zero_info
+
+from src.callbacks.lightning_weight_averaging import WeightAveraging
+
+
+class WarmupEMAWeightAveraging(WeightAveraging):
+    def __init__(
+        self,
+        warmup_pct: float,
+        enabled: bool = True,
+        decay: Optional[float] = None,
+        decay_numerator: float = 20.0,
+        update_every_n_steps: int = 1,
+        update_starting_at_step: Optional[int] = None,
+        device: Optional[Union[torch.device, str, int]] = None,
+        use_buffers: bool = True,
+    ) -> None:
+        super().__init__(device=device, use_buffers=use_buffers)
+        self.enabled = enabled
+        self.warmup_pct = warmup_pct
+        self.decay = decay
+        self.decay_numerator = decay_numerator
+        self.update_every_n_steps = update_every_n_steps
+        self.update_starting_at_step = update_starting_at_step
+
+        self.resolved_decay: Optional[float] = None
+        self.resolved_start_step: Optional[int] = None
+
+    def setup(self, trainer, pl_module, stage: str) -> None:
+        if not self.enabled:
+            rank_zero_info("WarmupEMAWeightAveraging is disabled by config.")
+            return
+
+        if stage != "fit":
+            return super().setup(trainer, pl_module, stage)
+
+        if trainer.max_steps and trainer.max_steps > 0:
+            total_steps = trainer.max_steps
+        else:
+            total_steps = trainer.estimated_stepping_batches
+
+        if total_steps <= 0:
+            total_steps = 100000
+
+        warmup_steps = int(total_steps * self.warmup_pct)
+        if self.update_starting_at_step is None:
+            self.resolved_start_step = warmup_steps
+        else:
+            self.resolved_start_step = self.update_starting_at_step
+
+        if self.decay is None:
+            active_steps = max(1, total_steps - self.resolved_start_step)
+            computed_decay = 1.0 - (self.decay_numerator / active_steps)
+            self.resolved_decay = min(0.99999, max(0.9, computed_decay))
+        else:
+            self.resolved_decay = self.decay
+
+        self._kwargs["avg_fn"] = get_ema_avg_fn(decay=self.resolved_decay)
+
+        super().setup(trainer, pl_module, stage)
+
+        rank_zero_info(
+            "WarmupEMAWeightAveraging configured: "
+            f"total_steps={total_steps}, warmup_steps={warmup_steps}, "
+            f"start_step={self.resolved_start_step}, decay={self.resolved_decay:.8f}"
+        )
+
+    def should_update(
+        self, step_idx: Optional[int] = None, epoch_idx: Optional[int] = None
+    ) -> bool:
+        if step_idx is None:
+            return False
+
+        if not self.enabled:
+            return False
+
+        if self.resolved_start_step is None:
+            return False
+
+        if step_idx < self.resolved_start_step:
+            return False
+
+        if self.update_every_n_steps <= 0:
+            return False
+
+        return step_idx % self.update_every_n_steps == 0
+
+    def state_dict(self) -> dict[str, Any]:
+        state = super().state_dict()
+        state["resolved_decay"] = self.resolved_decay
+        state["resolved_start_step"] = self.resolved_start_step
+        return state
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        super().load_state_dict(state_dict)
+        self.resolved_decay = state_dict.get("resolved_decay")
+        self.resolved_start_step = state_dict.get("resolved_start_step")
diff --git a/audio-embeddings/src/callbacks/lightning_weight_averaging.py b/audio-embeddings/src/callbacks/lightning_weight_averaging.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cf5765fa8887f0ccd8e525796a54824c881d12c
--- /dev/null
+++ b/audio-embeddings/src/callbacks/lightning_weight_averaging.py
@@ -0,0 +1,268 @@
+# Copyright The Lightning AI team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+r"""
+Vendored from Lightning-AI/pytorch-lightning commit:
+9bcba1c1e82b45e10f948dc28fc12f4cf04ab736
+
+Source:
+https://github.com/Lightning-AI/pytorch-lightning/blob/9bcba1c1e82b45e10f948dc28fc12f4cf04ab736/src/lightning/pytorch/callbacks/weight_averaging.py
+"""
+
+import itertools
+from copy import deepcopy
+from typing import Any, Optional, Union
+
+import torch
+from torch.optim.swa_utils import AveragedModel, get_ema_avg_fn
+from typing_extensions import override
+
+import lightning.pytorch as pl
+from lightning.pytorch.callbacks.callback import Callback
+from lightning.pytorch.utilities.model_helpers import is_overridden
+from lightning.pytorch.utilities.rank_zero import rank_zero_info, rank_zero_warn
+from lightning.pytorch.utilities.types import STEP_OUTPUT
+
+
+class WeightAveraging(Callback):
+    def __init__(
+        self,
+        device: Optional[Union[torch.device, str, int]] = None,
+        use_buffers: bool = True,
+        **kwargs: Any,
+    ) -> None:
+        if isinstance(device, str):
+            self._device: Optional[Union[torch.device, int]] = torch.device(device)
+        else:
+            self._device = device
+        self._use_buffers = use_buffers
+        self._kwargs = kwargs
+
+        self._average_model: Optional[AveragedModel] = None
+        self._latest_update_step = 0
+        self._latest_update_epoch = -1
+
+    def should_update(
+        self, step_idx: Optional[int] = None, epoch_idx: Optional[int] = None
+    ) -> bool:
+        return step_idx is not None
+
+    @override
+    def setup(
+        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: str
+    ) -> None:
+        if stage == "fit":
+            device = self._device or pl_module.device
+
+            if is_overridden("configure_model", pl_module):
+                rank_zero_warn(
+                    "You're using the WeightAveraging callback with a model that overrides the configure_model "
+                    "callback. WeightAveraging doesn't support sharding model layers, so you may run out of memory."
+                )
+                pl_module.configure_model()
+
+            self._average_model = AveragedModel(
+                model=pl_module,
+                device=device,
+                use_buffers=self._use_buffers,
+                **self._kwargs,
+            )
+
+    @override
+    def on_train_batch_end(
+        self,
+        trainer: "pl.Trainer",
+        pl_module: "pl.LightningModule",
+        outputs: STEP_OUTPUT,
+        batch: Any,
+        batch_idx: int,
+    ) -> None:
+        step_idx = trainer.global_step - 1
+        if (trainer.global_step > self._latest_update_step) and self.should_update(
+            step_idx=step_idx
+        ):
+            assert self._average_model is not None
+            self._average_model.update_parameters(pl_module)
+            self._latest_update_step = trainer.global_step
+
+    @override
+    def on_train_epoch_end(
+        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule"
+    ) -> None:
+        if (trainer.current_epoch > self._latest_update_epoch) and self.should_update(
+            epoch_idx=trainer.current_epoch
+        ):
+            assert self._average_model is not None
+            self._average_model.update_parameters(pl_module)
+            self._latest_update_epoch = trainer.current_epoch
+
+    @override
+    def on_train_end(
+        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule"
+    ) -> None:
+        assert self._average_model is not None
+        self._copy_average_to_current(pl_module)
+
+    @override
+    def on_validation_epoch_start(
+        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule"
+    ) -> None:
+        if self._average_model is not None:
+            self._swap_models(pl_module)
+
+    @override
+    def on_validation_epoch_end(
+        self, trainer: "pl.Trainer", pl_module: "pl.LightningModule"
+    ) -> None:
+        if self._average_model is not None:
+            self._swap_models(pl_module)
+
+    @override
+    def state_dict(self) -> dict[str, Any]:
+        return {"latest_update_step": self._latest_update_step}
+
+    @override
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        self._latest_update_step = state_dict["latest_update_step"]
+
+    @override
+    def on_save_checkpoint(
+        self,
+        trainer: "pl.Trainer",
+        pl_module: "pl.LightningModule",
+        checkpoint: dict[str, Any],
+    ) -> None:
+        if self._average_model is None:
+            rank_zero_info(
+                "You're using the WeightAveraging callback, but saving a checkpoint outside the 'fit' stage. The state "
+                "of the WeightAveraging callback won't be saved in the checkpoint. If training has finished, the "
+                "average model parameters will be saved to the state_dict in the checkpoint."
+            )
+        else:
+            average_model_state = self._average_model.state_dict()
+            checkpoint["current_model_state"] = checkpoint["state_dict"]
+            checkpoint["state_dict"] = {
+                name[7:]: value
+                for name, value in average_model_state.items()
+                if name.startswith("module.")
+            }
+            checkpoint["averaging_state"] = {
+                name: value
+                for name, value in average_model_state.items()
+                if not name.startswith("module.")
+            }
+
+    @override
+    def on_load_checkpoint(
+        self,
+        trainer: "pl.Trainer",
+        pl_module: "pl.LightningModule",
+        checkpoint: dict[str, Any],
+    ) -> None:
+        if self._average_model is None:
+            rank_zero_warn(
+                "You're using the WeightAveraging callback, but loading a checkpoint outside the 'fit' stage. The "
+                "WeightAveraging state cannot be restored. If you're using the checkpoint for prediction or testing, "
+                "you can ignore this warning. To disable the warning, remove the WeightAveraging callback."
+            )
+        elif ("current_model_state" in checkpoint) and (
+            "averaging_state" in checkpoint
+        ):
+            rank_zero_info(
+                "Found current_model_state in the checkpoint. This will be used to initialize the model."
+            )
+            average_model_state = {
+                "module." + name: value
+                for name, value in checkpoint["state_dict"].items()
+            }
+            average_model_state |= checkpoint["averaging_state"]
+            self._average_model.load_state_dict(average_model_state)
+            pl_module.load_state_dict(checkpoint["current_model_state"])
+        else:
+            rank_zero_warn(
+                "The checkpoint was not created with WeightAveraging. Both the current and the average model will be "
+                "initialized with state_dict."
+            )
+            self._average_model.module.load_state_dict(
+                deepcopy(checkpoint["state_dict"]), strict=False
+            )
+
+    def _swap_models(self, pl_module: "pl.LightningModule") -> None:
+        assert self._average_model is not None
+        average_params = itertools.chain(
+            self._average_model.module.parameters(),
+            self._average_model.module.buffers(),
+        )
+        current_params = itertools.chain(pl_module.parameters(), pl_module.buffers())
+        for average_param, current_param in zip(average_params, current_params):
+            tmp = average_param.data.clone()
+            average_param.data.copy_(current_param.data)
+            current_param.data.copy_(tmp)
+
+    def _copy_average_to_current(self, pl_module: "pl.LightningModule") -> None:
+        assert self._average_model is not None
+        average_params = itertools.chain(
+            self._average_model.module.parameters(),
+            self._average_model.module.buffers(),
+        )
+        current_params = itertools.chain(pl_module.parameters(), pl_module.buffers())
+        for average_param, current_param in zip(average_params, current_params):
+            current_param.data.copy_(average_param.data)
+
+
+class EMAWeightAveraging(WeightAveraging):
+    def __init__(
+        self,
+        device: Optional[Union[torch.device, str, int]] = None,
+        use_buffers: bool = True,
+        decay: float = 0.999,
+        update_every_n_steps: int = 1,
+        update_starting_at_step: Optional[int] = None,
+        update_starting_at_epoch: Optional[int] = None,
+        **kwargs: Any,
+    ):
+        super().__init__(
+            device=device,
+            use_buffers=use_buffers,
+            **kwargs,
+            avg_fn=get_ema_avg_fn(decay=decay),
+        )
+
+        self.update_every_n_steps = update_every_n_steps
+        self.update_starting_at_step = update_starting_at_step
+        self.update_starting_at_epoch = update_starting_at_epoch
+
+    def should_update(
+        self, step_idx: Optional[int] = None, epoch_idx: Optional[int] = None
+    ) -> bool:
+        if step_idx is not None:
+            meets_step_requirement = (
+                self.update_starting_at_step is None
+                or step_idx >= self.update_starting_at_step
+            )
+            meets_step_frequency = (
+                self.update_every_n_steps > 0
+                and step_idx % self.update_every_n_steps == 0
+            )
+            if meets_step_requirement and meets_step_frequency:
+                return True
+
+        if epoch_idx is not None:
+            meets_epoch_requirement = (
+                self.update_starting_at_epoch is not None
+                and epoch_idx >= self.update_starting_at_epoch
+            )
+            if meets_epoch_requirement:
+                return True
+
+        return False
diff --git a/audio-embeddings/src/callbacks/safetensors_callback.py b/audio-embeddings/src/callbacks/safetensors_callback.py
new file mode 100644
index 0000000000000000000000000000000000000000..998ac676e77555d13305a035a0627a4a6c043292
--- /dev/null
+++ b/audio-embeddings/src/callbacks/safetensors_callback.py
@@ -0,0 +1,92 @@
+import os
+import glob
+import torch
+from lightning.pytorch.callbacks import Callback
+from lightning.pytorch.utilities import rank_zero_only
+from lightning_utilities.core.rank_zero import rank_zero_warn
+from safetensors.torch import save_file
+
+
+class SafetensorsCallback(Callback):
+    """
+    Callback to save a corresponding .safetensors file whenever a .ckpt is saved.
+    This allows for safe sharing of weights while keeping the full .ckpt (with optimizer state)
+    local for resuming training.
+    """
+
+    def __init__(self, cleanup_orphan_safetensors: bool = False) -> None:
+        self.cleanup_orphan_safetensors = cleanup_orphan_safetensors
+
+    @rank_zero_only
+    def on_train_epoch_end(self, trainer, pl_module):
+        if trainer.checkpoint_callback:
+            self._convert_checkpoints(trainer.checkpoint_callback.dirpath)
+
+    @rank_zero_only
+    def on_fit_end(self, trainer, pl_module):
+        if trainer.checkpoint_callback:
+            self._convert_checkpoints(trainer.checkpoint_callback.dirpath)
+
+    def _convert_checkpoints(self, dirpath):
+        if not dirpath or not os.path.exists(dirpath):
+            return
+
+        # Find all .ckpt files
+        ckpt_files = glob.glob(os.path.join(dirpath, "*.ckpt"))
+        ckpt_stems = {
+            os.path.splitext(os.path.basename(ckpt_path))[0] for ckpt_path in ckpt_files
+        }
+
+        if self.cleanup_orphan_safetensors:
+            safetensors_files = glob.glob(os.path.join(dirpath, "*.safetensors"))
+            for safetensors_path in safetensors_files:
+                base_name = os.path.splitext(os.path.basename(safetensors_path))[0]
+                if base_name not in ckpt_stems:
+                    try:
+                        os.remove(safetensors_path)
+                    except OSError as exc:
+                        rank_zero_warn(
+                            f"Failed to remove orphan safetensors file {safetensors_path}: {exc}"
+                        )
+
+        for ckpt_path in ckpt_files:
+            # Construct safetensors path
+            base_name = os.path.splitext(os.path.basename(ckpt_path))[0]
+            sf_path = os.path.join(dirpath, f"{base_name}.safetensors")
+
+            # Check if we should convert:
+            # 1. If safetensors doesn't exist
+            # 2. Or if ckpt is newer than safetensors (e.g. last.ckpt was updated)
+            should_convert = False
+            if not os.path.exists(sf_path):
+                should_convert = True
+            else:
+                if os.path.getmtime(ckpt_path) > os.path.getmtime(sf_path):
+                    should_convert = True
+
+            if should_convert:
+                try:
+                    # Load checkpoint (CPU)
+                    # We accept "unsafe" load here because we created these files locally
+                    checkpoint = torch.load(
+                        ckpt_path, map_location="cpu", weights_only=False
+                    )
+
+                    # Extract state_dict
+                    if "state_dict" in checkpoint:
+                        state_dict = checkpoint["state_dict"]
+                    else:
+                        state_dict = checkpoint
+
+                    # Filter out non-tensor values just in case
+                    clean_state_dict = {
+                        k: v
+                        for k, v in state_dict.items()
+                        if isinstance(v, torch.Tensor)
+                    }
+
+                    # Save as safetensors
+                    save_file(clean_state_dict, sf_path)
+
+                except Exception as e:
+                    rank_zero_warn(f"Failed to convert {ckpt_path} to safetensors: {e}")
diff --git a/audio-embeddings/src/callbacks/visualization_callback.py b/audio-embeddings/src/callbacks/visualization_callback.py
new file mode 100644
index 0000000000000000000000000000000000000000..92c78656d4879b797b726d6066bbb1412539c59d
--- /dev/null
+++ b/audio-embeddings/src/callbacks/visualization_callback.py
@@ -0,0 +1,212 @@
+import torch
+import matplotlib.pyplot as plt
+import numpy as np
+import lightning as L
+from lightning.pytorch.callbacks import Callback
+from lightning.pytorch.loggers import WandbLogger
+from typing import Any, Dict, Optional
+
+
+class VisualizationCallback(Callback):
+    """
+    Callback to visualize spectrograms, patches, and masks.
+    Logs the first 4 samples of the first 2 batches.
+    """
+
+    def __init__(self, num_samples: int = 4):
+        super().__init__()
+        self.num_samples = num_samples
+        self.batches_logged = 0
+
+    def on_train_batch_end(
+        self,
+        trainer: L.Trainer,
+        pl_module: L.LightningModule,
+        outputs: Any,
+        batch: Any,
+        batch_idx: int,
+    ) -> None:
+        if self.batches_logged >= 2:
+            return
+
+        # Log for the first 2 batches
+        if batch_idx < 2:
+            self._log_visualizations(trainer, pl_module, batch, batch_idx)
+            self.batches_logged += 1
+
+    def _log_visualizations(
+        self,
+        trainer: L.Trainer,
+        pl_module: L.LightningModule,
+        batch: Dict[str, Any],
+        batch_idx: int,
+    ) -> None:
+        logger = trainer.logger
+        if not isinstance(logger, WandbLogger):
+            return
+
+        waveform = batch["waveform"][: self.num_samples]  # [B, 1, T]
+
+        sample_rate = self._resolve_sample_rate(trainer, pl_module)
+
+        # Get spectrograms
+        with torch.no_grad():
+            spec = pl_module.spectrogram(waveform.to(pl_module.device))  # [B, 1, F, T]
+
+            # Get grid size and patch info
+            patch_size = pl_module.patch_embed.patch_embed.patch_size
+            F_pix = spec.shape[2]
+            T_pix = spec.shape[3]
+            H_grid = F_pix // patch_size[0]
+            W_grid = T_pix // patch_size[1]
+            current_grid_size = (H_grid, W_grid)
+
+            # Generate mask
+            # Using the same logic as training step (shared mask across batch)
+            # But we want to see if it's the same across batches (it should be random each step)
+            mask = pl_module.mask_generator(
+                1, device=pl_module.device, grid_size=current_grid_size
+            )  # [1, N]
+            mask = mask.expand(self.num_samples, -1)  # [B, N]
+
+        # Log to WandB
+        import wandb
+
+        columns = [
+            "Batch Idx",
+            "Sample Idx",
+            "Audio",
+            "Spectrogram",
+            "Masked Spectrogram (Context)",
+            "Inverse Masked Spectrogram (Targets)",
+        ]
+        data = []
+
+        for i in range(self.num_samples):
+            # Audio
+            audio_data = waveform[i].squeeze().cpu().numpy()
+            audio = wandb.Audio(
+                audio_data, sample_rate=sample_rate, caption=f"B{batch_idx}_S{i}"
+            )
+
+            # Spectrograms
+            spec_data = spec[i].squeeze().cpu().numpy()
+            mask_data = mask[i].cpu().numpy()
+
+            # 1. Original
+            fig_orig = self._plot_spectrogram(spec_data, patch_size, current_grid_size)
+            img_orig = wandb.Image(fig_orig, caption=f"Spec B{batch_idx}_S{i}")
+            plt.close(fig_orig)
+
+            # 2. Masked (Context) - Masked parts are dark
+            fig_masked = self._plot_spectrogram_with_mask(
+                spec_data, mask_data, patch_size, current_grid_size, invert_mask=False
+            )
+            img_masked = wandb.Image(fig_masked, caption=f"Masked B{batch_idx}_S{i}")
+            plt.close(fig_masked)
+
+            # 3. Inverse Masked (Targets) - Context parts are dark
+            fig_inv_masked = self._plot_spectrogram_with_mask(
+                spec_data, mask_data, patch_size, current_grid_size, invert_mask=True
+            )
+            img_inv_masked = wandb.Image(
+                fig_inv_masked, caption=f"InvMasked B{batch_idx}_S{i}"
+            )
+            plt.close(fig_inv_masked)
+
+            data.append([batch_idx, i, audio, img_orig, img_masked, img_inv_masked])
+
+        # Log Table
+        table = wandb.Table(columns=columns, data=data)
+        logger.experiment.log({f"train/visualizations_batch_{batch_idx}": table})
+
+    @staticmethod
+    def _resolve_sample_rate(trainer: L.Trainer, pl_module: L.LightningModule) -> int:
+        """Resolve audio logging sample rate, preferring data target sample rate."""
+        sample_rate = 32000
+
+        datamodule = getattr(trainer, "datamodule", None)
+        if datamodule is not None:
+            dm_sr = getattr(datamodule, "target_sample_rate", None)
+            if dm_sr is None and hasattr(datamodule, "hparams"):
+                hparams = datamodule.hparams
+                if isinstance(hparams, dict):
+                    dm_sr = hparams.get("target_sample_rate")
+                else:
+                    dm_sr = getattr(hparams, "target_sample_rate", None)
+
+            if dm_sr is not None:
+                return int(dm_sr)
+
+        spectrogram = getattr(pl_module, "spectrogram", None)
+        module_sr = getattr(spectrogram, "sample_rate", None)
+        if module_sr is not None:
+            return int(module_sr)
+
+        hparams = getattr(pl_module, "hparams", None)
+        if isinstance(hparams, dict):
+            net_cfg = hparams.get("net")
+            if isinstance(net_cfg, dict):
+                spectrogram_cfg = net_cfg.get("spectrogram")
+                if isinstance(spectrogram_cfg, dict):
+                    config_sr = spectrogram_cfg.get("sample_rate")
+                    if config_sr is not None:
+                        return int(config_sr)
+
+        return sample_rate
+
+    def _plot_spectrogram(
+        self, spec: np.ndarray, patch_size: tuple[int, int], grid_size: tuple[int, int]
+    ) -> plt.Figure:
+        """Plots spectrogram with grid lines."""
+        return self._plot_spectrogram_with_mask(spec, None, patch_size, grid_size)
+
+    def _plot_spectrogram_with_mask(
+        self,
+        spec: np.ndarray,
+        mask: Optional[np.ndarray],
+        patch_size: tuple[int, int],
+        grid_size: tuple[int, int],
+        invert_mask: bool = False,
+    ) -> plt.Figure:
+        """
+        Plots spectrogram with dashed grid lines and darker masked patches.
+        If mask is None, just plots spectrogram and grid.
+        If invert_mask is True, darkens the unmasked parts instead.
+        """
+        H_grid, W_grid = grid_size
+        Ph, Pw = patch_size
+        H, W = spec.shape
+
+        fig, ax = plt.subplots(figsize=(10, 4))
+        ax.imshow(spec, origin="lower", aspect="auto", cmap="viridis")
+
+        # Overlay Grid
+        for h in range(0, H + 1, Ph):
+            ax.axhline(h - 0.5, color="white", linestyle="--", linewidth=0.5, alpha=0.5)
+        for w in range(0, W + 1, Pw):
+            ax.axvline(w - 0.5, color="white", linestyle="--", linewidth=0.5, alpha=0.5)
+
+        # Overlay Mask
+        if mask is not None:
+            mask_grid = mask.reshape(H_grid, W_grid)
+            if invert_mask:
+                mask_grid = ~mask_grid
+
+            overlay = np.zeros((H, W, 4))  # RGBA
+            for r in range(H_grid):
+                for c in range(W_grid):
+                    if mask_grid[r, c]:
+                        y_start = r * Ph
+                        y_end = (r + 1) * Ph
+                        x_start = c * Pw
+                        x_end = (c + 1) * Pw
+                        overlay[y_start:y_end, x_start:x_end, 3] = 0.7
+
+            ax.imshow(overlay, origin="lower", aspect="auto")
+
+        ax.set_title("Spectrogram")
+        ax.set_xlabel("Time Frames")
+        ax.set_ylabel("Frequency Bins")
+        plt.tight_layout()
+        return fig
diff --git a/audio-embeddings/src/callbacks/wandb_callbacks.py b/audio-embeddings/src/callbacks/wandb_callbacks.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a3e97b93288e3fb65fb34310f54619f06d9e218
--- /dev/null
+++ b/audio-embeddings/src/callbacks/wandb_callbacks.py
@@ -0,0 +1,75 @@
+import os
+from lightning.pytorch.callbacks import Callback
+from lightning.pytorch.loggers.wandb import WandbLogger
+from lightning.pytorch.utilities import rank_zero_only
+
+
+class WandbOfflineCheckpointCallback(Callback):
+    """
+    Custom callback to log model checkpoints to WandB even when offline=True.
+    Lightning's WandbLogger forbids log_model=True with offline=True.
+    This callback manually calls experiment.save() on the checkpoint directory.
+    """
+
+    def __init__(self, save_dir: str = None):
+        self.save_dir = save_dir
+        self.best_model_path = None
+
+    @rank_zero_only
+    def on_train_epoch_end(self, trainer, pl_module):
+        # Check if we have a wandb logger
+        if trainer.logger and isinstance(trainer.logger, WandbLogger):
+            # If checkpoint callback exists
+            if trainer.checkpoint_callback:
+                # We can save all files in dirpath
+                self._save_checkpoints(trainer.logger, trainer)
+
+    @rank_zero_only
+    def on_fit_end(self, trainer, pl_module):
+        if trainer.logger and isinstance(trainer.logger, WandbLogger):
+            if trainer.checkpoint_callback:
+                self._save_checkpoints(trainer.logger, trainer)
+
+    def _save_checkpoints(self, logger, trainer):
+        dirpath = trainer.checkpoint_callback.dirpath
+        if not dirpath or not os.path.exists(dirpath):
+            return
+
+        # WandB 'save' with base_path argument preserves relative structure
+        # We want to save only the last.safetensors file to WandB
+
+        # Identify the last checkpoint path
+        last_ckpt = trainer.checkpoint_callback.last_model_path
+
+        # Fallback: if last_model_path is not set, but save_last is True,
+        # check for 'last.ckpt' explicitly.
+        if (not last_ckpt) and trainer.checkpoint_callback.save_last:
+            potential_last = os.path.join(dirpath, "last.ckpt")
+            if os.path.exists(potential_last):
+                last_ckpt = potential_last
+
+        if last_ckpt and os.path.exists(last_ckpt):
+            # Construct expected safetensors path
+            base_name = os.path.splitext(os.path.basename(last_ckpt))[0]
+            sf_path = os.path.join(dirpath, f"{base_name}.safetensors")
+
+            if os.path.exists(sf_path):
+                # Policy="now" ensures it's copied to wandb directory immediately
+                logger.experiment.save(
+                    sf_path, base_path=os.path.dirname(dirpath), policy="now"
+                )
+
+        # Cleanup broken symlinks in the wandb directory
+        # This is necessary because if a checkpoint is deleted by ModelCheckpoint (e.g. save_top_k),
+        # the symlink in the wandb directory remains but points to a non-existent file.
+        # This causes 'wandb sync' to fail.
+        wandb_dir = logger.experiment.dir
+        # Assuming base_path was os.path.dirname(dirpath) -> 'checkpoints' is the subdir
+        wandb_ckpt_dir = os.path.join(wandb_dir, "checkpoints")
+
+        if os.path.exists(wandb_ckpt_dir):
+            for filename in os.listdir(wandb_ckpt_dir):
+                filepath = os.path.join(wandb_ckpt_dir, filename)
+                # Check if it is a broken link
+                if os.path.islink(filepath) and not os.path.exists(filepath):
+                    os.remove(filepath)
diff --git a/audio-embeddings/src/data/__init__.py b/audio-embeddings/src/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..caf88594101add63a94981e840447b007e027760
--- /dev/null
+++ b/audio-embeddings/src/data/__init__.py
@@ -0,0 +1,9 @@
+from .audioset_datamodule import AudioSetDataModule, AudioSetDataset
+from .yt1b_datamodule import YT1BDataModule, YT1BDataset
+
+__all__ = [
+    "AudioSetDataModule",
+    "AudioSetDataset",
+    "YT1BDataModule",
+    "YT1BDataset",
+]
diff --git a/audio-embeddings/src/data/audio_utils.py b/audio-embeddings/src/data/audio_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e55b81342010be8b43807d39e48b8d8854d31a3f
--- /dev/null
+++ b/audio-embeddings/src/data/audio_utils.py
@@ -0,0 +1,208 @@
+from typing import Any, Dict, List, Optional, Sequence
+
+import numpy as np
+import torch
+import torchaudio
+
+
+class DatasetResamplerCropper:
+    """
+    Resample and optionally crop a waveform.
+    Maintains a cache of resamplers for different source sampling rates to optimize instantiation.
+
+    Args:
+        target_sr (int): Target sampling rate.
+        max_length (Optional[int]): Maximum length in samples (at target_sr).
+    """
+
+    def __init__(self, target_sr: int, max_length: Optional[int] = None):
+        self.target_sr = target_sr
+        self.max_length = max_length
+        self.resamplers: Dict[int, torchaudio.transforms.Resample] = {}
+
+    def forward(self, waveform: torch.Tensor, source_sr: int) -> torch.Tensor:
+        """
+        Args:
+            waveform (torch.Tensor): Tensor of shape [T] or [C, T].
+            source_sr (int): Source sampling rate.
+
+        Returns:
+            torch.Tensor: Processed waveform tensor.
+        """
+        # Resampling and Cropping Logic
+        if source_sr != self.target_sr:
+            # We need to resample.
+            # Optimization: Crop in source domain first if we have a max_length
+
+            if self.max_length is not None:
+                # Calculate required source length to get max_length in target domain
+                # Add a small buffer to avoid rounding issues
+                crop_len_source = round(self.max_length * source_sr / self.target_sr)
+
+                if waveform.shape[-1] > crop_len_source:
+                    max_start = waveform.shape[-1] - crop_len_source
+                    start = np.random.randint(0, max_start + 1)
+                    waveform = waveform[..., start : start + crop_len_source]
+
+            # Resample
+            if source_sr not in self.resamplers:
+                self.resamplers[source_sr] = torchaudio.transforms.Resample(
+                    source_sr, self.target_sr
+                )
+            resampler = self.resamplers[source_sr]
+            waveform = resampler(waveform)
+
+            # Now handle max_length (trim if we cropped with buffer, or if it was already long enough)
+            if self.max_length is not None and waveform.shape[-1] > self.max_length:
+                waveform = waveform[..., : self.max_length]
+
+        else:
+            # No resampling, just standard random crop
+            if self.max_length is not None and waveform.shape[-1] > self.max_length:
+                max_start = waveform.shape[-1] - self.max_length
+                start = np.random.randint(0, max_start + 1)
+                waveform = waveform[..., start : start + self.max_length]
+
+        return waveform
+
+    def __call__(self, waveform: torch.Tensor, source_sr: int) -> torch.Tensor:
+        return self.forward(waveform, source_sr)
+
+
+def collate_audio_batch(
+    batch: List[Dict[str, Any]],
+    waveform_key: str = "waveform",
+    mode: str = "pad",  # "pad" or "truncate"
+    stack_waveforms: bool = True,
+    pad_value: float = 0.0,
+    include_keys: Optional[Sequence[str]] = None,
+    exclude_keys: Optional[Sequence[str]] = None,
+) -> Dict[str, Any]:
+    """
+    Generic collate function for audio batches where each sample is a dict
+    containing at least `waveform_key` with shape [1, T] or [T].
+
+    Pads or truncates waveforms across the batch, and returns a dict that:
+        - always includes waveform_key -> Tensor [B, 1, T']
+        - includes other keys aggregated into lists (or stacked if possible)
+
+    Parameters
+    ----------
+    batch:
+        List of sample dictionaries.
+    waveform_key:
+        Key of waveform in sample dict.
+    mode:
+        "pad" -> pad shorter waveforms to max length in batch
+        "truncate" -> truncate longer waveforms to min length in batch
+    stack_waveforms:
+        If True, returns waveforms stacked into a single tensor [B, 1, T'].
+    pad_value:
+        Value used for padding.
+    include_keys:
+        If provided, only these keys will be included in the output (plus waveform_key).
+    exclude_keys:
+        If provided, these keys will not be included (except waveform_key is always kept).
+
+    Returns
+    -------
+    Dict[str, Any]
+        Collated batch dict.
+    """
+    if len(batch) == 0:
+        raise ValueError("Empty batch passed to collate_audio_batch")
+
+    # 1) Collect waveforms
+    waveforms = []
+    for item in batch:
+        if waveform_key not in item:
+            raise KeyError(
+                f"Missing key '{waveform_key}' in batch item: {list(item.keys())}"
+            )
+
+        w = item[waveform_key]
+
+        if not torch.is_tensor(w):
+            raise TypeError(
+                f"Expected waveform tensor for key '{waveform_key}', got {type(w)}"
+            )
+
+        # Accept [T] or [1, T]
+        if w.ndim == 1:
+            w = w.unsqueeze(0)
+        elif w.ndim != 2:
+            raise ValueError(
+                f"Expected waveform with shape [T] or [1, T], got {tuple(w.shape)}"
+            )
+
+        waveforms.append(w)
+
+    lengths = [w.shape[-1] for w in waveforms]
+
+    # 2) Determine target length
+    if mode == "pad":
+        target_len = max(lengths)
+    elif mode == "truncate":
+        target_len = min(lengths)
+    else:
+        raise ValueError(f"Unknown mode '{mode}' (expected 'pad' or 'truncate')")
+
+    # 3) Pad/truncate each waveform
+    processed = []
+    for w in waveforms:
+        cur_len = w.shape[-1]
+        if cur_len < target_len:
+            pad_amount = target_len - cur_len
+            w2 = torch.nn.functional.pad(w, (0, pad_amount), value=pad_value)
+            processed.append(w2)
+        elif cur_len > target_len:
+            processed.append(w[..., :target_len])
+        else:
+            processed.append(w)
+
+    if stack_waveforms:
+        waveform_batch = torch.stack(processed, dim=0)  # [B, 1, T']
+    else:
+        waveform_batch = processed  # list of [1, T']
+
+    # 4) Decide which other keys to include
+    all_keys = set(batch[0].keys())
+    all_keys.add(waveform_key)
+
+    if include_keys is not None:
+        keys_to_collate = set(include_keys) | {waveform_key}
+    else:
+        keys_to_collate = set(all_keys)
+
+    if exclude_keys is not None:
+        keys_to_collate -= set(exclude_keys)
+        keys_to_collate.add(waveform_key)  # waveform always kept
+
+    # 5) Collate other keys (best effort)
+    out: Dict[str, Any] = {waveform_key: waveform_batch}
+
+    for k in keys_to_collate:
+        if k == waveform_key:
+            continue
+
+        values = [item.get(k, None) for item in batch]
+
+        # If all are tensors of same shape -> stack
+        if all(torch.is_tensor(v) for v in values):
+            try:
+                out[k] = torch.stack(values, dim=0)
+                continue
+            except Exception:
+                # fallback to list if stacking fails
+                out[k] = values
+                continue
+
+        # If all are numbers (int/float) -> tensor
+        if all(isinstance(v, (int, float)) for v in values):
+            out[k] = torch.tensor(values)
+            continue
+
+        # Otherwise -> list
+        out[k] = values
+
+    return out
diff --git a/audio-embeddings/src/data/audioset_datamodule.py b/audio-embeddings/src/data/audioset_datamodule.py
new file mode 100644
index 0000000000000000000000000000000000000000..1da43a0b78624c2aac2cb2f01404fc117d45a733
--- /dev/null
+++ b/audio-embeddings/src/data/audioset_datamodule.py
@@ -0,0 +1,241 @@
+import os
+from functools import partial
+from typing import Any, Dict, List, Optional, Union
+
+import h5py
+import lightning as L
+import numpy as np
+import pandas as pd
+import torch
+from src.data.audio_utils import collate_audio_batch
+from torch.utils.data import DataLoader, Dataset
+
+from src.data.audio_utils import DatasetResamplerCropper
+
+
+class AudioSetDataset(Dataset):
+    """
+    Dataset for AudioSet data stored in HDF5 format.
+
+    Args:
+        h5_path (str): Path to the HDF5 file containing waveforms and targets.
+        exclude_csv_path (Optional[str]): Path to a CSV file containing indices to exclude.
+        transform (Optional[callable]): Optional transform to apply to the waveform.
+        max_length (Optional[int]): Maximum length of the waveform in samples.
+        target_sample_rate (int): Target sample rate for the waveform. Defaults to 32000.
+    """
+
+    def __init__(
+        self,
+        h5_path: str,
+        exclude_csv_path: Optional[str] = None,
+        transform: Optional[Any] = None,
+        max_length: Optional[int] = None,
+        target_sample_rate: int = 32000,
+    ):
+        self.h5_path = h5_path
+        self.transform = transform
+        self.max_length = max_length
+        self.target_sample_rate = target_sample_rate
+
+        # Open HDF5 to get length and metadata
+        with h5py.File(h5_path, "r") as f:
+            self.total_length = f["waveform"].shape[0]
+            if "sample_rate" in f.attrs:
+                self.source_sample_rate = int(f.attrs["sample_rate"])
+            else:
+                print(
+                    f"Warning: 'sample_rate' attribute not found in {h5_path}. Assuming 32000."
+                )
+                self.source_sample_rate = 32000
+
+        self.valid_indices = list(range(self.total_length))
+
+        # Instantiate resampler
+        self.resampler = DatasetResamplerCropper(
+            target_sr=target_sample_rate, max_length=max_length
+        )
+
+        if exclude_csv_path and os.path.exists(exclude_csv_path):
+            df = pd.read_csv(exclude_csv_path)
+            if "Index" in df.columns:
+                exclude_indices = set(df["Index"].values)
+                self.valid_indices = [
+                    i for i in self.valid_indices if i not in exclude_indices
+                ]
+            else:
+                print(
+                    f"Warning: 'Index' column not found in {exclude_csv_path}. No files excluded."
+                )
+
+        self.h5_file: Optional[h5py.File] = None
+
+    def _open_h5(self) -> None:
+        """Opens the HDF5 file if not already open."""
+        if self.h5_file is None:
+            self.h5_file = h5py.File(self.h5_path, "r")
+
+    def __len__(self) -> int:
+        return len(self.valid_indices)
+
+    def __getitem__(self, idx: int) -> Dict[str, Union[torch.Tensor, str, int]]:
+        self._open_h5()
+
+        real_idx = self.valid_indices[idx]
+
+        # Load waveform
+        waveform_int16 = self.h5_file["waveform"][real_idx]
+        # Convert to float32 and normalize (16-bit PCM)
+        waveform = waveform_int16.astype(np.float32) / 32768.0
+        waveform = torch.from_numpy(waveform)  # [T]
+
+        # Resample and crop
+        waveform = self.resampler(waveform, source_sr=self.source_sample_rate)
+
+        # Load target and name
+        target = self.h5_file["target"][real_idx]
+        audio_name = self.h5_file["audio_name"][real_idx]
+
+        target = torch.from_numpy(target).float()
+
+        # Add channel dimension: [1, T]
+        waveform = waveform.unsqueeze(0)
+
+        if self.transform:
+            waveform = self.transform(waveform)
+
+        return {
+            "waveform": waveform,
+            "target": target,
+            "audio_name": audio_name,
+            "index": real_idx,
+        }
+
+    def __del__(self):
+        if self.h5_file is not None:
+            self.h5_file.close()
+
+
+class AudioSetDataModule(L.LightningDataModule):
+    """
+    LightningDataModule for AudioSet.
+
+    Args:
+        data_dir (str): Root directory for data.
+        batch_size (int): Batch size for dataloaders.
+        num_workers (int): Number of workers for dataloaders.
+        pin_memory (bool): Whether to pin memory in dataloaders.
+        train_h5 (str): Filename of training HDF5 file.
+        train_csv (str): Filename of training exclusion CSV.
+        val_h5 (str): Filename of validation HDF5 file.
+        val_csv (str): Filename of validation exclusion CSV.
+        max_audio_length_sec (Optional[float]): Maximum audio length in seconds.
+        hop_length (Optional[int]): Hop length for spectrogram (samples).
+        hop_length_ms (Optional[float]): Hop length in milliseconds.
+        patch_size (tuple[int, int]): Patch size (freq, time).
+        target_sample_rate (int): Target sample rate.
+    """
+
+    def __init__(
+        self,
+        data_dir: str = "data/AudioSet",
+        batch_size: int = 64,
+        num_workers: int = 4,
+        pin_memory: bool = True,
+        train_h5: str = "balanced_train_soxrhq.h5",
+        train_csv: str = "silent_files_balanced_train_soxrhq.csv",
+        val_h5: str = "eval_soxrhq.h5",
+        val_csv: str = "silent_files_eval_soxrhq.csv",
+        max_audio_length_sec: Optional[float] = 10.0,
+        target_sample_rate: int = 16000,
+        collate_mode: str = "pad",
+    ):
+        super().__init__()
+        self.save_hyperparameters()
+
+        self.data_dir = data_dir
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.pin_memory = pin_memory
+        self.max_audio_length_sec = max_audio_length_sec
+        self.target_sample_rate = target_sample_rate
+
+        if max_audio_length_sec is not None:
+            self.max_audio_length = int(max_audio_length_sec * target_sample_rate)
+        else:
+            self.max_audio_length = None
+        self.collate_mode = collate_mode
+
+        self.train_h5_path = os.path.join(data_dir, train_h5)
+        self.train_csv_path = os.path.join(data_dir, train_csv)
+        self.val_h5_path = os.path.join(data_dir, val_h5)
+        self.val_csv_path = os.path.join(data_dir, val_csv)
+
+        self.train_dataset: Optional[AudioSetDataset] = None
+        self.val_dataset: Optional[AudioSetDataset] = None
+        self.test_dataset: Optional[AudioSetDataset] = None
+
+    def setup(self, stage: Optional[str] = None) -> None:
+        if stage == "fit" or stage is None:
+            self.train_dataset = AudioSetDataset(
+                self.train_h5_path,
+                self.train_csv_path,
+                max_length=self.max_audio_length,
+                target_sample_rate=self.target_sample_rate,
+            )
+            self.val_dataset = AudioSetDataset(
+                self.val_h5_path,
+                self.val_csv_path,
+                max_length=self.max_audio_length,
+                target_sample_rate=self.target_sample_rate,
+            )
+
+        if stage == "test":
+            self.test_dataset = AudioSetDataset(
+                self.val_h5_path,
+                self.val_csv_path,
+                max_length=self.max_audio_length,
+                target_sample_rate=self.target_sample_rate,
+            )
+
+    def train_dataloader(self) -> DataLoader:
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            shuffle=True,
+            num_workers=self.num_workers,
+            pin_memory=self.pin_memory,
+            persistent_workers=self.num_workers > 0,
+            collate_fn=partial(self.collate_fn, mode=self.collate_mode),
+        )
+
+    def val_dataloader(self) -> DataLoader:
+        return DataLoader(
+            self.val_dataset,
+            batch_size=self.batch_size,
+            shuffle=False,
+            num_workers=self.num_workers,
+            pin_memory=self.pin_memory,
+            persistent_workers=self.num_workers > 0,
+            collate_fn=partial(self.collate_fn, mode=self.collate_mode),
+        )
+
+    def test_dataloader(self) -> DataLoader:
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=False,
+            num_workers=self.num_workers,
+            pin_memory=self.pin_memory,
+            collate_fn=partial(self.collate_fn, mode=self.collate_mode),
+        )
+
+    @staticmethod
+    def collate_fn(batch: List[Dict[str, Any]], mode: str = "pad") -> Dict[str, Any]:
+        return collate_audio_batch(
+            batch=batch,
+            waveform_key="waveform",
+            mode=mode,
+            # You can optionally filter keys:
+            # include_keys=["waveform", "audio_name, "target"]
+        )
diff --git a/audio-embeddings/src/data/mock_audioset_datamodule.py b/audio-embeddings/src/data/mock_audioset_datamodule.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2b8b399bed88a8e3be60ea3b22d2534ba9ba47b
--- /dev/null
+++ b/audio-embeddings/src/data/mock_audioset_datamodule.py
@@ -0,0 +1,171 @@
+import torch
+from torch.utils.data import Dataset, DataLoader
+import lightning as L
+from typing import Optional, List, Dict, Any, Union
+from functools import partial
+
+
+class MockAudioSetDataset(Dataset):
+    """
+    Mock Dataset for AudioSet data that generates random noise.
+    """
+
+    def __init__(
+        self,
+        length: int = 100,
+        max_length: int = 160000,
+        target_sample_rate: int = 16000,
+    ):
+        self.length = length
+        self.max_length = max_length
+        self.target_sample_rate = target_sample_rate
+
+    def __len__(self) -> int:
+        return self.length
+
+    def __getitem__(self, idx: int) -> Dict[str, Union[torch.Tensor, str, int]]:
+        # Generate random waveform [1, T]
+        # Random length between max_length // 2 and max_length for realism, or just fixed max_length
+        # Let's do fixed max_length for simplicity in mock
+        waveform = torch.randn(1, self.max_length)
+
+        # Fake target (multi-hot) - assuming AudioSet has 527 classes
+        target = torch.zeros(527)
+        # Set a few random classes to 1
+        indices = torch.randint(0, 527, (3,))
+        target[indices] = 1.0
+
+        audio_name = f"mock_audio_{idx}"
+
+        return {
+            "waveform": waveform,
+            "target": target,
+            "audio_name": audio_name,
+            "index": idx,
+        }
+
+
+class MockAudioSetDataModule(L.LightningDataModule):
+    """
+    LightningDataModule for Mock AudioSet.
+    """
+
+    def __init__(
+        self,
+        batch_size: int = 8,
+        num_workers: int = 0,
+        pin_memory: bool = True,
+        max_audio_length_sec: float = 10.0,
+        target_sample_rate: int = 16000,
+        collate_mode: str = "pad",
+    ):
+        super().__init__()
+        self.save_hyperparameters()
+
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.pin_memory = pin_memory
+        self.collate_mode = collate_mode
+
+        self.max_audio_length = int(max_audio_length_sec * target_sample_rate)
+
+        self.train_dataset: Optional[MockAudioSetDataset] = None
+        self.val_dataset: Optional[MockAudioSetDataset] = None
+        self.test_dataset: Optional[MockAudioSetDataset] = None
+
+    def setup(self, stage: Optional[str] = None) -> None:
+        if stage == "fit" or stage is None:
+            self.train_dataset = MockAudioSetDataset(
+                length=1000,  # Fake dataset size
+                max_length=self.max_audio_length,
+                target_sample_rate=self.hparams.target_sample_rate,
+            )
+            self.val_dataset = MockAudioSetDataset(
+                length=100,
+                max_length=self.max_audio_length,
+                target_sample_rate=self.hparams.target_sample_rate,
+            )
+
+        if stage == "test":
+            self.test_dataset = MockAudioSetDataset(
+                length=50,
+                max_length=self.max_audio_length,
+                target_sample_rate=self.hparams.target_sample_rate,
+            )
+
+    def train_dataloader(self) -> DataLoader:
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            shuffle=True,
+            num_workers=self.num_workers,
+            pin_memory=self.pin_memory,
+            persistent_workers=self.num_workers > 0,
+            collate_fn=partial(self.collate_fn, mode=self.collate_mode),
+        )
+
+    def val_dataloader(self) -> DataLoader:
+        return DataLoader(
+            self.val_dataset,
+            batch_size=self.batch_size,
+            shuffle=False,
+            num_workers=self.num_workers,
+            pin_memory=self.pin_memory,
+            persistent_workers=self.num_workers > 0,
+            collate_fn=partial(self.collate_fn, mode=self.collate_mode),
+        )
+
+    def test_dataloader(self) -> DataLoader:
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=False,
+            num_workers=self.num_workers,
+            pin_memory=self.pin_memory,
+            collate_fn=partial(self.collate_fn, mode=self.collate_mode),
+        )
+
+    @staticmethod
+    def collate_fn(batch: List[Dict[str, Any]], mode: str = "pad") -> Dict[str, Any]:
+        """
+        Collate function to pad or truncate waveforms.
+        """
+        waveforms = [item["waveform"] for item in batch]  # List of [1, T]
+        targets = torch.stack([item["target"] for item in batch])
+        audio_names = [item["audio_name"] for item in batch]
+        indices = [item["index"] for item in batch]
+
+        # Find max or min length in the batch
+        lengths = [w.shape[-1] for w in waveforms]
+
+        if mode == "pad":
+            target_wave_len = max(lengths)
+        elif mode == "truncate":
+            target_wave_len = min(lengths)
+        else:
+            raise ValueError(f"Unknown collate mode: {mode}")
+
+        # Pad or Truncate waveforms
+        processed_waveforms = []
+        for w in waveforms:
+            current_len = w.shape[-1]
+            if current_len < target_wave_len:
+                pad_amount = target_wave_len - current_len
+                # Pad at the end
+                w_padded = torch.nn.functional.pad(w, (0, pad_amount))
+                processed_waveforms.append(w_padded)
+            elif current_len > target_wave_len:
+                # Truncate
+                w_truncated = w[..., :target_wave_len]
+                processed_waveforms.append(w_truncated)
+            else:
+                processed_waveforms.append(w)
+
+        processed_waveforms = torch.stack(processed_waveforms)
+
+        return {
+            "waveform": processed_waveforms,
+            "target": targets,
+            "audio_name": audio_names,
+            "index": indices,
+        }
diff --git a/audio-embeddings/src/data/yt1b_datamodule.py b/audio-embeddings/src/data/yt1b_datamodule.py
new file mode 100644
index 0000000000000000000000000000000000000000..729859bee4a10d221445c6c374bff605cccae108
--- /dev/null
+++ b/audio-embeddings/src/data/yt1b_datamodule.py
@@ -0,0 +1,335 @@
+import os
+from functools import partial
+from typing import Any, Dict, List, Optional, Union
+
+import lightning as L
+import numpy as np
+import pandas as pd
+import torch
+import torchaudio
+from torch.utils.data import DataLoader, Dataset
+
+from src.data.audio_utils import DatasetResamplerCropper, collate_audio_batch
+
+
+class YT1BDataset(Dataset):
+    """
+    Dataset for YT-Temporal-1B data using Parquet metadata files.
+
+    Args:
+        parquet_path (str): Path to the parquet file containing metadata (must include 'file_path', 'video_id', 'duration_sec').
+            If a 'sample_rate' column exists, it is used to avoid probing files for source sample rate.
+        min_duration (Optional[float]): Minimum duration in seconds to include a file.
+        max_duration (Optional[float]): Maximum duration in seconds to include a file.
+        transform (Optional[callable]): Optional transform to apply to the waveform.
+        max_length (Optional[int]): Maximum length of the waveform in samples (at target_sample_rate).
+        target_sample_rate (int): Target sample rate for the waveform. Defaults to 16000.
+        decode_window_sec (Optional[float]): Optional decode window length in seconds. If None,
+            defaults to max_length / target_sample_rate (when max_length is set).
+    """
+
+    def __init__(
+        self,
+        parquet_path: str,
+        min_duration: Optional[float] = None,
+        max_duration: Optional[float] = 30.0,
+        transform: Optional[Any] = None,
+        max_length: Optional[int] = None,
+        target_sample_rate: int = 16000,
+        decode_window_sec: Optional[float] = None,
+    ):
+        print(f"Loading metadata from {parquet_path}...")
+        self.transform = transform
+        self.max_length = max_length
+        self.target_sample_rate = target_sample_rate
+        self.decode_window_sec = decode_window_sec
+
+        # --- Metadata Loading ---
+        if not os.path.exists(parquet_path):
+            raise FileNotFoundError(f"Parquet file not found at: {parquet_path}")
+
+        # Pyarrow is required for read_parquet
+        try:
+            df = pd.read_parquet(parquet_path)
+        except ImportError:
+            raise ImportError(
+                "Please install pyarrow to read parquet files: `uv add pyarrow`"
+            )
+
+        required_cols = {"file_path", "video_id", "duration_sec"}
+        if not required_cols.issubset(df.columns):
+            # Check if we have compatible columns or raise error
+            # Some datasets might use different names, strictly enforcing for now based on user prompt
+            raise ValueError(
+                f"Parquet file must contain columns: {required_cols}. Found: {df.columns.tolist()}"
+            )
+
+        if min_duration is not None and min_duration < 0:
+            raise ValueError(f"min_duration must be >= 0, got {min_duration}")
+        if max_duration is not None and max_duration < 0:
+            raise ValueError(f"max_duration must be >= 0, got {max_duration}")
+        if (
+            min_duration is not None
+            and max_duration is not None
+            and min_duration > max_duration
+        ):
+            raise ValueError(
+                "min_duration must be <= max_duration; "
+                f"got min_duration={min_duration}, max_duration={max_duration}"
+            )
+
+        if min_duration is not None:
+            df = df[df["duration_sec"] >= min_duration]
+        if max_duration is not None:
+            df = df[df["duration_sec"] <= max_duration]
+
+        self.ids = df["video_id"].tolist()
+        self.paths = df["file_path"].tolist()
+        self.durations_sec = df["duration_sec"].tolist()
+        if "sample_rate" in df.columns:
+            sample_rates = pd.to_numeric(df["sample_rate"], errors="coerce").to_numpy(
+                dtype=np.float64
+            )
+            self.source_sample_rates: Optional[list[Optional[int]]] = [
+                int(sr) if np.isfinite(sr) and sr > 0 else None for sr in sample_rates
+            ]
+        else:
+            self.source_sample_rates = None
+        self.length = len(self.ids)
+
+        # --- Resampler ---
+        # Uses the optimized class that caches resamplers
+        self.resampler = DatasetResamplerCropper(
+            target_sr=target_sample_rate, max_length=max_length
+        )
+
+        print(f"Dataset loaded. Length: {self.length:,}")
+
+    def __len__(self) -> int:
+        return self.length
+
+    def __getitem__(self, idx: int) -> Dict[str, Union[torch.Tensor, str, int]]:
+        audio_path = self.paths[idx]
+        audio_id = self.ids[idx]
+
+        # Load waveform
+        try:
+            decode_window_sec = self.decode_window_sec
+            if decode_window_sec is None and self.max_length is not None:
+                decode_window_sec = self.max_length / self.target_sample_rate
+
+            if decode_window_sec is None:
+                waveform, sr = torchaudio.load(audio_path)
+            else:
+                duration_sec = float(self.durations_sec[idx])
+                if duration_sec <= 0:
+                    waveform, sr = torchaudio.load(audio_path)
+                else:
+                    source_sr: Optional[int]
+                    if self.source_sample_rates is not None:
+                        source_sr = self.source_sample_rates[idx]
+                    else:
+                        source_sr = None
+
+                    if source_sr is None:
+                        _, source_sr = torchaudio.load(
+                            audio_path, frame_offset=0, num_frames=1
+                        )
+
+                    total_frames = max(1, int(duration_sec * source_sr))
+                    max_decode_frames = max(1, int(decode_window_sec * source_sr))
+                    decode_frames = min(max_decode_frames, total_frames)
+
+                    if total_frames > decode_frames:
+                        max_start = total_frames - decode_frames
+                        frame_offset = int(np.random.randint(0, max_start + 1))
+                    else:
+                        frame_offset = 0
+
+                    waveform, sr = torchaudio.load(
+                        audio_path,
+                        frame_offset=frame_offset,
+                        num_frames=decode_frames,
+                    )
+        except Exception as e:
+            print(f"Error loading {audio_path}: {e}")
+            # Return a dummy silent waveform to prevent crash
+            len_samples = (
+                self.max_length if self.max_length else self.target_sample_rate
+            )
+            return {
+                "waveform": torch.zeros(1, len_samples),
+                "audio_name": audio_id,
+                "index": idx,
+                "error": True,
+            }
+
+        # Mix down to mono if necessary
+        if waveform.shape[0] > 1:
+            waveform = torch.mean(waveform, dim=0, keepdim=True)
+
+        # Resample and crop
+        waveform = self.resampler(waveform, source_sr=sr)
+
+        # Ensure channel dim exists [1, T] if resampler stripped it or returned [T]
+        if waveform.ndim == 1:
+            waveform = waveform.unsqueeze(0)
+
+        if self.transform:
+            waveform = self.transform(waveform)
+
+        return {
+            "waveform": waveform,
+            "audio_name": audio_id,
+            "index": idx,
+        }
+
+
+class YT1BDataModule(L.LightningDataModule):
+    """
+    LightningDataModule for YT-Temporal-1B.
+
+    Args:
+        data_dir (str): Root directory for data.
+        train_parquet (str): Filename of training parquet file.
+        val_parquet (str): Filename of validation parquet file.
+        test_parquet (str): Filename of test parquet file.
+        batch_size (int): Batch size for dataloaders.
+        num_workers (int): Number of workers for dataloaders.
+        pin_memory (bool): Whether to pin memory in dataloaders.
+        max_audio_length_sec (Optional[float]): Maximum audio length in seconds.
+        min_duration_sec (Optional[float]): Minimum audio duration in seconds to filter.
+        max_duration_sec (Optional[float]): Maximum audio duration in seconds to filter.
+        target_sample_rate (int): Target sample rate.
+        collate_mode (str): 'pad' or 'truncate'.
+        decode_window_sec (Optional[float]): Optional decode window length in seconds. If None,
+            defaults to max_audio_length_sec.
+    """
+
+    def __init__(
+        self,
+        data_dir: str = "data/YT-Temporal-1B",
+        train_parquet: str = "train_metadata.parquet",
+        val_parquet: str = "val_metadata.parquet",
+        test_parquet: str = "val_metadata.parquet",
+        batch_size: int = 64,
+        num_workers: int = 4,
+        pin_memory: bool = True,
+        max_audio_length_sec: Optional[float] = 10.0,
+        min_duration_sec: Optional[float] = None,
+        max_duration_sec: Optional[float] = 30.0,
+        target_sample_rate: int = 16000,
+        collate_mode: str = "pad",
+        decode_window_sec: Optional[float] = None,
+    ):
+        super().__init__()
+        self.save_hyperparameters()
+
+        self.data_dir = data_dir
+        self.train_parquet_path = os.path.join(data_dir, train_parquet)
+        self.val_parquet_path = os.path.join(data_dir, val_parquet)
+        self.test_parquet_path = os.path.join(data_dir, test_parquet)
+
+        if max_audio_length_sec is not None:
+            self.max_audio_length = int(max_audio_length_sec * target_sample_rate)
+        else:
+            self.max_audio_length = None
+
+        self.train_dataset: Optional[YT1BDataset] = None
+        self.val_dataset: Optional[YT1BDataset] = None
+        self.test_dataset: Optional[YT1BDataset] = None
+
+    def setup(self, stage: Optional[str] = None) -> None:
+        if stage == "fit" or stage is None:
+            if os.path.exists(self.train_parquet_path):
+                self.train_dataset = YT1BDataset(
+                    self.train_parquet_path,
+                    min_duration=self.hparams["min_duration_sec"],
+                    max_duration=self.hparams["max_duration_sec"],
+                    max_length=self.max_audio_length,
+                    target_sample_rate=self.hparams["target_sample_rate"],
+                    decode_window_sec=self.hparams["decode_window_sec"],
+                )
+
+            if os.path.exists(self.val_parquet_path):
+                self.val_dataset = YT1BDataset(
+                    self.val_parquet_path,
+                    min_duration=self.hparams["min_duration_sec"],
+                    max_duration=self.hparams["max_duration_sec"],
+                    max_length=self.max_audio_length,
+                    target_sample_rate=self.hparams["target_sample_rate"],
+                    decode_window_sec=self.hparams["decode_window_sec"],
+                )
+
+        if stage == "test":
+            if os.path.exists(self.test_parquet_path):
+                self.test_dataset = YT1BDataset(
+                    self.test_parquet_path,
+                    min_duration=self.hparams["min_duration_sec"],
+                    max_duration=self.hparams["max_duration_sec"],
+                    max_length=self.max_audio_length,
+                    target_sample_rate=self.hparams["target_sample_rate"],
+                    decode_window_sec=self.hparams["decode_window_sec"],
+                )
+
+    def train_dataloader(self) -> DataLoader:
+        if not self.train_dataset:
+            raise RuntimeError(
+                f"Train dataset not initialized. File not found: {self.train_parquet_path}"
+            )
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.hparams["batch_size"],
+            shuffle=True,
+            num_workers=self.hparams["num_workers"],
+            pin_memory=self.hparams["pin_memory"],
+            persistent_workers=self.hparams["num_workers"] > 0,
+            collate_fn=partial(self.collate_fn, mode=self.hparams["collate_mode"]),
+        )
+
+    def val_dataloader(self) -> DataLoader:
+        if not self.val_dataset:
+            # Often validation sets are missing in large scale pretraining or we use a subset of train
+            # For now, raise strict error or return empty list (lightning supports empty list for no val)
+            # Raising error is safer to debug configuration issues.
+            raise RuntimeError(
+                f"Val dataset not initialized. File not found: {self.val_parquet_path}"
+            )
+
+        return DataLoader(
+            self.val_dataset,
+            batch_size=self.hparams["batch_size"],
+            shuffle=False,
+            num_workers=self.hparams["num_workers"],
+            pin_memory=self.hparams["pin_memory"],
+            persistent_workers=self.hparams["num_workers"] > 0,
+            collate_fn=partial(self.collate_fn, mode=self.hparams["collate_mode"]),
+        )
+
+    def test_dataloader(self) -> DataLoader:
+        if not self.test_dataset:
+            raise RuntimeError(
+                f"Test dataset not initialized. File not found: {self.test_parquet_path}"
+            )
+
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.hparams["batch_size"],
+            shuffle=False,
+            num_workers=self.hparams["num_workers"],
+            pin_memory=self.hparams["pin_memory"],
+            collate_fn=partial(self.collate_fn, mode=self.hparams["collate_mode"]),
+        )
+
+    @staticmethod
+    def collate_fn(batch: List[Dict[str, Any]], mode: str = "pad") -> Dict[str, Any]:
+        # Filter out errors
+        batch = [x for x in batch if not x.get("error", False)]
+        if len(batch) == 0:
+            raise RuntimeError("All items in batch failed to load.")
+
+        return collate_audio_batch(
+            batch=batch,
+            waveform_key="waveform",
+            mode=mode,
+        )
diff --git a/audio-embeddings/src/models/__pycache__/best_rq2_module.cpython-312.pyc b/audio-embeddings/src/models/__pycache__/best_rq2_module.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a27f23b696bde3401dc1b618f62405f03a20a1e5
Binary files /dev/null and b/audio-embeddings/src/models/__pycache__/best_rq2_module.cpython-312.pyc differ
diff --git a/audio-embeddings/src/models/audio_jepa_module.py b/audio-embeddings/src/models/audio_jepa_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..65b20d4644bf5c5c0f7a11dfb1548192c043ec98
--- /dev/null
+++ b/audio-embeddings/src/models/audio_jepa_module.py
@@ -0,0 +1,409 @@
+import torch
+import functools
+import torch.nn as nn
+import torch.nn.functional as F
+import lightning as L
+from typing import Any, Dict, Tuple, Optional
+
+from src.models.components.spectrogram import Spectrogram
+from src.models.components.masking import MaskingGenerator
+from src.models.components.patch_embed import PatchEmbed
+from src.models.components.vit import ViT
+from src.utils.lr_schedulers import LinearWarmupCosineDecay
+
+
+class AudioJEPAModule(L.LightningModule):
+    """
+    Audio-JEPA Lightning Module.
+
+    Args:
+        optimizer (torch.optim.Optimizer): Optimizer configuration (partial).
+        net (Dict[str, Any]): Configuration for sub-modules (spectrogram, patch_embed, masking, encoder, predictor).
+        warmup_pct (float): Percentage of total steps for warmup.
+        final_lr_ratio (float): Ratio of final learning rate to initial learning rate.
+        ema_decay (float): Initial EMA decay rate.
+        ema_end_decay (float): Final EMA decay rate.
+        ema_anneal_end_step (int): Step at which EMA decay reaches ema_end_decay.
+        spectrogram_adjustment_mode (str): 'pad' or 'truncate' for spectrogram time dimension.
+        criterion (torch.nn.Module): Loss function (defaults to MSELoss).
+    """
+
+    def __init__(
+        self,
+        optimizer: torch.optim.Optimizer,
+        net: Dict[str, Any],
+        warmup_pct: float = 0.1,
+        final_lr_ratio: float = 0.001,
+        ema_decay: float = 0.996,
+        ema_end_decay: float = 1.0,
+        ema_anneal_end_step: Optional[int] = None,
+        spectrogram_adjustment_mode: str = "pad",
+        criterion: Optional[torch.nn.Module] = None,
+    ):
+        super().__init__()
+        self.save_hyperparameters(
+            logger=False, ignore=["criterion", "net", "optimizer"]
+        )
+
+        self.warmup_pct = warmup_pct
+        self.final_lr_ratio = final_lr_ratio
+        self.spectrogram_adjustment_mode = spectrogram_adjustment_mode
+
+        # Handle Criterion (support partials/factories to avoid checkpointing warnings)
+        if criterion is not None:
+            self.criterion = (
+                criterion()
+                if isinstance(criterion, (type, functools.partial))
+                or callable(criterion)
+                and not isinstance(criterion, nn.Module)
+                else criterion
+            )
+        else:
+            self.criterion = nn.MSELoss()
+
+        # Store optimizer partial to avoid saving it in hparams
+        self.optimizer_config = optimizer
+
+        # Components
+        self.spectrogram = Spectrogram(**net.get("spectrogram", {}))
+        self.patch_embed = PatchEmbed(**net.get("patch_embed", {}))
+        self.mask_generator = MaskingGenerator(**net.get("masking", {}))
+
+        # Student (Encoder)
+        self.student = ViT(**net.get("encoder", {}))
+
+        # Teacher (Encoder) - same arch as student
+        self.teacher = ViT(**net.get("encoder", {}))
+        # Initialize teacher with student weights
+        self.teacher.load_state_dict(self.student.state_dict())
+        # stop gradient (teacher will be updated by EMA)
+        for p in self.teacher.parameters():
+            p.requires_grad = False
+
+        # Predictor
+        predictor_config = net.get("predictor", {})
+        self.predictor = ViT(**predictor_config)
+
+        # Projections for Predictor
+        encoder_dim = net.get("encoder", {}).get("embed_dim", 768)
+        predictor_embed_dim = predictor_config.get("embed_dim", 768)
+
+        self.predictor_input_proj = nn.Linear(encoder_dim, predictor_embed_dim)
+        self.predictor_output_proj = nn.Linear(predictor_embed_dim, encoder_dim)
+
+        # Mask Token
+        self.mask_token = nn.Parameter(torch.zeros(1, 1, predictor_embed_dim))
+        nn.init.trunc_normal_(self.mask_token, std=0.02)
+
+        # EMA parameters
+        self.ema_decay = ema_decay
+        self.ema_end_decay = ema_end_decay
+        self.ema_anneal_end_step = ema_anneal_end_step
+        self.current_ema_decay = ema_decay
+
+    def setup(self, stage: Optional[str] = None) -> None:
+        # Calculate ema_anneal_end_step if not provided
+        if self.ema_anneal_end_step is None:
+            self.ema_anneal_end_step = getattr(self.trainer, "max_steps", 0)
+            if self.ema_anneal_end_step <= 0:
+                self.ema_anneal_end_step = getattr(
+                    self.trainer, "estimated_stepping_batches", 100000
+                )
+
+            if self.ema_anneal_end_step <= 0:
+                print(
+                    "Warning: Could not determine total steps for EMA annealing. Using 100000 as default."
+                )
+                self.ema_anneal_end_step = 100000
+
+    def on_train_batch_start(self, batch: Any, batch_idx: int) -> None:
+        # Update EMA decay
+        step = self.global_step
+        progress = (self.ema_anneal_end_step - step) / self.ema_anneal_end_step
+        decay = self.ema_end_decay - (self.ema_end_decay - self.ema_decay) * progress
+        decay = min(self.ema_end_decay, max(self.ema_decay, decay))
+        self.current_ema_decay = decay
+
+    def _update_teacher(self) -> None:
+        with torch.no_grad():
+            m = self.current_ema_decay
+            for param_q, param_k in zip(
+                self.student.parameters(), self.teacher.parameters()
+            ):
+                param_k.data.mul_(m).add_((1 - m) * param_q.data)
+
+    def _adjust_spectrogram(self, spec: torch.Tensor) -> torch.Tensor:
+        """
+        Adjusts the spectrogram time dimension to be divisible by the patch size.
+
+        Args:
+            spec (torch.Tensor): Spectrogram [B, C, F, T].
+
+        Returns:
+            torch.Tensor: Adjusted spectrogram.
+        """
+        # PatchEmbed stores patch_size as (H, W) corresponding to (F, T)
+        patch_size = self.patch_embed.patch_embed.patch_size
+        patch_time_dim = patch_size[1]
+
+        T = spec.shape[-1]
+        remainder = T % patch_time_dim
+
+        if remainder != 0:
+            if self.spectrogram_adjustment_mode == "pad":
+                pad_amount = patch_time_dim - remainder
+                spec = F.pad(spec, (0, pad_amount))
+            elif self.spectrogram_adjustment_mode == "truncate":
+                spec = spec[..., : T - remainder]
+            else:
+                raise ValueError(
+                    f"Unknown spectrogram_adjustment_mode: {self.spectrogram_adjustment_mode}"
+                )
+
+        return spec
+
+    def _process_audio(
+        self, waveform: torch.Tensor
+    ) -> Tuple[torch.Tensor, Tuple[int, int]]:
+        """
+        Processes raw waveform into patches and returns patches and grid size.
+
+        Returns:
+            patches: [B, N, D]
+            grid_size: (H, W)
+        """
+        # 1. Spectrogram
+        spec = self.spectrogram(waveform)  # [B, 1, F, T]
+        spec = self._adjust_spectrogram(spec)
+
+        # 2. Patchify
+        patches = self.patch_embed(spec)  # [B, N, D]
+
+        # Calculate grid size
+        patch_size = self.patch_embed.patch_embed.patch_size
+        F_pix = spec.shape[2]
+        T_pix = spec.shape[3]
+        H_grid = F_pix // patch_size[0]
+        W_grid = T_pix // patch_size[1]
+        grid_size = (H_grid, W_grid)
+
+        return patches, grid_size
+
+    def compute_student(
+        self, patches: torch.Tensor, mask: torch.Tensor, grid_size: Tuple[int, int]
+    ) -> torch.Tensor:
+        """
+        Computes the student output for unmasked patches.
+
+        Args:
+            patches: [B, N, D]
+            mask: [B, N]
+            grid_size: (H, W)
+
+        Returns:
+            student_out: [B, N_keep, D]
+        """
+        B, N, _ = patches.shape
+
+        m = mask[0]  # [N]
+        keep_indices = torch.nonzero(~m).flatten()  # [N_keep]
+
+        # Student input (Context)
+        context_patches = patches[:, keep_indices, :]  # [B, N_keep, D]
+
+        # Context Pos Ids
+        context_pos_ids = keep_indices.unsqueeze(0).expand(B, -1)  # [B, N_keep]
+
+        # Student forward
+        student_out = self.student(
+            context_patches, pos_ids=context_pos_ids, grid_size=grid_size
+        )  # [B, N_keep, D]
+
+        return student_out
+
+    def compute_predictor(
+        self, student_out: torch.Tensor, mask: torch.Tensor, grid_size: Tuple[int, int]
+    ) -> torch.Tensor:
+        """
+        Computes the predictor output at masked locations.
+
+        Args:
+            student_out: [B, N_keep, D]
+            mask: [B, N]
+            grid_size: (H, W)
+
+        Returns:
+            predictions_raw: [B, N_mask, pred_dim]
+        """
+        B, N_keep, _ = student_out.shape
+        # Note: B derived from student_out might be different if batch size changes, but it shouldn't here.
+        # N is implicit in mask.
+
+        m = mask[0]  # [N]
+        keep_indices = torch.nonzero(~m).flatten()  # [N_keep]
+        mask_indices = torch.nonzero(m).flatten()  # [N_mask]
+        num_mask = len(mask_indices)
+
+        # Predictor Input Construction
+        student_out_proj = self.predictor_input_proj(
+            student_out
+        )  # [B, N_keep, pred_dim]
+
+        # Mask tokens: [1, 1, pred_dim] -> [B, N_mask, pred_dim]
+        mask_tokens = self.mask_token.expand(B, num_mask, -1)
+
+        if self.predictor.pos_embed_type != "rope":
+            # Absolute pos embed added to mask tokens
+            mask_pos_embed = self.predictor.pos_embed[:, mask_indices, :].expand(
+                B, -1, -1
+            )
+            mask_tokens = mask_tokens + mask_pos_embed
+
+        pred_input = torch.cat(
+            [student_out_proj, mask_tokens], dim=1
+        )  # [B, N, pred_dim]
+
+        # Reorder to original sequence order
+        all_indices = torch.cat([keep_indices, mask_indices])  # [N]
+        sort_indices = torch.argsort(all_indices)  # [N]
+        pred_input = pred_input[:, sort_indices, :]  # [B, N, pred_dim]
+
+        if self.predictor.pos_embed_type == "rope":
+            # Rope handles positions internally if full sequence is provided
+            pred_out = self.predictor(pred_input, pos_ids=None, grid_size=grid_size)
+        else:
+            pred_out = self.predictor(pred_input, add_pos_embed=False)
+
+        # Predictions at mask locations (returns raw embeddings in pred_dim)
+        predictions_raw = pred_out[:, mask_indices, :]  # [B, N_mask, pred_dim]
+
+        return predictions_raw
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass for inference/eval. Returns student representation.
+        """
+        patches, grid_size = self._process_audio(x)
+        x = self.student(patches, grid_size=grid_size)
+        return x
+
+    def training_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor:
+        waveform = batch["waveform"]  # [B, 1, T]
+
+        patches, current_grid_size = self._process_audio(waveform)
+        B, N, D = patches.shape
+
+        # Generate shared mask for the batch: [1, N] -> [B, N]
+        mask = self.mask_generator(1, device=self.device, grid_size=current_grid_size)
+        mask = mask.expand(B, -1)
+
+        # Update teacher EMA
+        self._update_teacher()
+
+        # Compute Student
+        student_out = self.compute_student(patches, mask, current_grid_size)
+
+        # Compute Predictor
+        predictions_raw = self.compute_predictor(student_out, mask, current_grid_size)
+
+        # Teacher forward (full)
+        with torch.no_grad():
+            teacher_full = self.teacher(
+                patches, grid_size=current_grid_size
+            )  # [B, N, D]
+
+        # Calculate Loss
+        loss = self._calculate_jepa_loss(
+            student_out, predictions_raw, teacher_full, mask, current_grid_size
+        )
+
+        self.log(
+            "train/loss", loss, on_step=True, on_epoch=True, prog_bar=True, batch_size=B
+        )
+        return loss
+
+    def validation_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor:
+        waveform = batch["waveform"]
+
+        patches, current_grid_size = self._process_audio(waveform)
+        B, N, D = patches.shape
+
+        # Shared mask for validation as well to enable vectorization
+        mask = self.mask_generator(1, device=self.device, grid_size=current_grid_size)
+        mask = mask.expand(B, -1)
+
+        # Compute Student
+        student_out = self.compute_student(patches, mask, current_grid_size)
+
+        # Compute Predictor
+        predictions_raw = self.compute_predictor(student_out, mask, current_grid_size)
+
+        # Teacher forward (full)
+        with torch.no_grad():
+            teacher_full = self.teacher(patches, grid_size=current_grid_size)
+
+            # Calculate Loss
+            loss = self._calculate_jepa_loss(
+                student_out, predictions_raw, teacher_full, mask, current_grid_size
+            )
+
+        self.log(
+            "val/loss", loss, on_step=False, on_epoch=True, prog_bar=True, batch_size=B
+        )
+        return loss
+
+    def test_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor:
+        return self.validation_step(batch, batch_idx)
+
+    def _calculate_jepa_loss(
+        self,
+        student_out: torch.Tensor,
+        predictions_raw: torch.Tensor,
+        teacher_full: torch.Tensor,
+        mask: torch.Tensor,
+        grid_size: Tuple[int, int],
+    ) -> torch.Tensor:
+        """
+        Shared JEPA loss calculation logic.
+        """
+        m = mask[0]
+        mask_indices = torch.nonzero(m).flatten()
+
+        # Project back to encoder dimension
+        predictions = self.predictor_output_proj(
+            predictions_raw
+        )  # [B, N_mask, encoder_dim]
+
+        # Targets
+        teacher_targets = teacher_full[:, mask_indices, :]  # [B, N_mask, encoder_dim]
+
+        return self.criterion(predictions, teacher_targets)
+
+    def configure_optimizers(self) -> Dict[str, Any]:
+        optimizer = self.optimizer_config(params=self.parameters())
+
+        # Determine total steps
+        if self.trainer.max_steps and self.trainer.max_steps > 0:
+            total_steps = self.trainer.max_steps
+        else:
+            total_steps = self.trainer.estimated_stepping_batches
+
+        warmup_steps = int(total_steps * self.warmup_pct)
+
+        lr_lambda = LinearWarmupCosineDecay(
+            warmup_steps=warmup_steps,
+            total_steps=total_steps,
+            final_lr_ratio=self.final_lr_ratio,
+        )
+
+        scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
+
+        return {
+            "optimizer": optimizer,
+            "lr_scheduler": {
+                "scheduler": scheduler,
+                "monitor": "val_loss",
+                "interval": "step",
+                "frequency": 1,
+            },
+        }
diff --git a/audio-embeddings/src/models/best_rq2_module.py b/audio-embeddings/src/models/best_rq2_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d85d104a6ffbccd43843ba7592cba1c87943ca8
--- /dev/null
+++ b/audio-embeddings/src/models/best_rq2_module.py
@@ -0,0 +1,302 @@
+import functools
+from typing import Any, Dict, Optional, Tuple
+
+import lightning as L
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from src.models.components.masking import MaskingGenerator
+from src.models.components.patch_embed import PatchEmbed
+from src.models.components.random_projection_quantizer import RandomProjectionQuantizer
+from src.models.components.spectrogram import Spectrogram
+from src.models.components.vit import ViT
+from src.utils.lr_schedulers import LinearWarmupCosineDecay
+
+
+class BestRQ2Module(L.LightningModule):
+    """
+    Best-RQ 2 Lightning Module.
+
+    Implements a 2-step (Encoder-Predictor) Masked Audio Modeling approach using
+    Random Projection Quantization of spectrogram patches as targets.
+    Equivalent to RQA-JEPA with lambda=0 and rq_input_type='spectrogram',
+    but optimized to remove the Teacher model entirely.
+
+    Args:
+        optimizer (torch.optim.Optimizer): Optimizer configuration.
+        net (Dict[str, Any]): Configuration for sub-modules.
+        warmup_pct (float): Percentage of total steps for warmup.
+        final_lr_ratio (float): Ratio of final learning rate to initial learning rate.
+        spectrogram_adjustment_mode (str): 'pad' or 'truncate' for spectrogram time dimension.
+        codebook_dim (int): Codebook dimension for RandomProjectionQuantizer.
+        vocab_size (int): Vocabulary size for RandomProjectionQuantizer.
+        criterion (torch.nn.Module): Loss function (defaults to CrossEntropyLoss).
+        ema (Optional[Dict[str, Any]]): Optional EMA callback config block.
+    """
+
+    def __init__(
+        self,
+        optimizer: torch.optim.Optimizer,
+        net: Dict[str, Any],
+        warmup_pct: float = 0.1,
+        final_lr_ratio: float = 0.001,
+        spectrogram_adjustment_mode: str = "pad",
+        codebook_dim: int = 16,
+        vocab_size: int = 8192,
+        criterion: Optional[torch.nn.Module] = None,
+        ema: Optional[Dict[str, Any]] = None,
+    ):
+        super().__init__()
+        self.save_hyperparameters(
+            logger=False, ignore=["criterion", "net", "optimizer", "ema"]
+        )
+
+        self.warmup_pct = warmup_pct
+        self.final_lr_ratio = final_lr_ratio
+        self.spectrogram_adjustment_mode = spectrogram_adjustment_mode
+        self.vocab_size = vocab_size
+        self.ema_config = ema or {}
+
+        # Optimizer partial
+        self.optimizer_config = optimizer
+
+        # Loss
+        if criterion is not None:
+            self.criterion = (
+                criterion()
+                if isinstance(criterion, (type, functools.partial))
+                or (callable(criterion) and not isinstance(criterion, nn.Module))
+                else criterion
+            )
+        else:
+            self.criterion = nn.CrossEntropyLoss()
+
+        # Components
+        self.spectrogram = Spectrogram(**net.get("spectrogram", {}))
+        self.patch_embed = PatchEmbed(**net.get("patch_embed", {}))
+        self.mask_generator = MaskingGenerator(**net.get("masking", {}))
+
+        # Encoder
+        self.encoder = ViT(**net.get("encoder", {}))
+
+        # Predictor
+        predictor_config = net.get("predictor", {})
+        self.predictor = ViT(**predictor_config)
+
+        # Dimensions
+        encoder_dim = net.get("encoder", {}).get("embed_dim", 768)
+        predictor_embed_dim = predictor_config.get("embed_dim", 768)
+
+        # Adapter: Encoder -> Predictor
+        self.predictor_input_proj = nn.Linear(encoder_dim, predictor_embed_dim)
+
+        # Mask Token
+        self.mask_token = nn.Parameter(torch.zeros(1, 1, predictor_embed_dim))
+        nn.init.trunc_normal_(self.mask_token, std=0.02)
+
+        # Random Projection Quantizer
+        # Input to quantizer is raw patches
+        patch_size = self.patch_embed.patch_size
+        in_chans = self.patch_embed.in_chans
+        quantizer_input_dim = patch_size[0] * patch_size[1] * in_chans
+
+        self.quantizer = RandomProjectionQuantizer(
+            input_dim=quantizer_input_dim, cb_dim=codebook_dim, cb_vocab=vocab_size
+        )
+        # Freeze quantizer
+        for p in self.quantizer.parameters():
+            p.requires_grad = False
+
+        # Output Projection: Predictor -> Vocab
+        self.rq_proj = nn.Linear(predictor_embed_dim, vocab_size)
+
+    def _adjust_spectrogram(self, spec: torch.Tensor) -> torch.Tensor:
+        patch_size = self.patch_embed.patch_embed.patch_size
+        patch_time_dim = patch_size[1]
+
+        T = spec.shape[-1]
+        remainder = T % patch_time_dim
+
+        if remainder != 0:
+            if self.spectrogram_adjustment_mode == "pad":
+                pad_amount = patch_time_dim - remainder
+                spec = F.pad(spec, (0, pad_amount))
+            elif self.spectrogram_adjustment_mode == "truncate":
+                spec = spec[..., : T - remainder]
+            else:
+                raise ValueError(
+                    f"Unknown spectrogram_adjustment_mode: {self.spectrogram_adjustment_mode}"
+                )
+
+        return spec
+
+    def _process_audio(
+        self, waveform: torch.Tensor
+    ) -> Tuple[torch.Tensor, Tuple[int, int]]:
+        spec = self.spectrogram(waveform)  # [B, 1, F, T]
+        spec = self._adjust_spectrogram(spec)
+        patches = self.patch_embed(spec)  # [B, N, D]
+
+        patch_size = self.patch_embed.patch_embed.patch_size
+        F_pix = spec.shape[2]
+        T_pix = spec.shape[3]
+        H_grid = F_pix // patch_size[0]
+        W_grid = T_pix // patch_size[1]
+        grid_size = (H_grid, W_grid)
+
+        return patches, grid_size
+
+    def _get_raw_patches(self, spec: torch.Tensor) -> torch.Tensor:
+        """Extract raw key-value patches from spectrogram."""
+        patch_size = self.patch_embed.patch_size  # (H, W)
+        # Using kernel_size=patch_size, stride=patch_size ensures non-overlapping patches
+        patches = F.unfold(spec, kernel_size=patch_size, stride=patch_size)  # [B, D, N]
+        patches = patches.transpose(1, 2)  # [B, N, D]
+        return patches
+
+    def compute_encoder(
+        self, patches: torch.Tensor, mask: torch.Tensor, grid_size: Tuple[int, int]
+    ) -> torch.Tensor:
+        B, N, _ = patches.shape
+        m = mask[0]  # [N]
+        keep_indices = torch.nonzero(~m).flatten()  # [N_keep]
+
+        context_patches = patches[:, keep_indices, :]  # [B, N_keep, D]
+        context_pos_ids = keep_indices.unsqueeze(0).expand(B, -1)  # [B, N_keep]
+
+        encoder_out = self.encoder(
+            context_patches, pos_ids=context_pos_ids, grid_size=grid_size
+        )
+        return encoder_out
+
+    def compute_predictor(
+        self, encoder_out: torch.Tensor, mask: torch.Tensor, grid_size: Tuple[int, int]
+    ) -> torch.Tensor:
+        B, N_keep, _ = encoder_out.shape
+        m = mask[0]
+        keep_indices = torch.nonzero(~m).flatten()
+        mask_indices = torch.nonzero(m).flatten()
+        num_mask = len(mask_indices)
+
+        encoder_out_proj = self.predictor_input_proj(
+            encoder_out
+        )  # [B, N_keep, pred_dim]
+        mask_tokens = self.mask_token.expand(B, num_mask, -1)
+
+        if self.predictor.pos_embed_type != "rope":
+            mask_pos_embed = self.predictor.pos_embed[:, mask_indices, :].expand(
+                B, -1, -1
+            )
+            mask_tokens = mask_tokens + mask_pos_embed
+
+        pred_input = torch.cat([encoder_out_proj, mask_tokens], dim=1)
+
+        all_indices = torch.cat([keep_indices, mask_indices])
+        sort_indices = torch.argsort(all_indices)
+        pred_input = pred_input[:, sort_indices, :]
+
+        if self.predictor.pos_embed_type == "rope":
+            pred_out = self.predictor(pred_input, pos_ids=None, grid_size=grid_size)
+        else:
+            pred_out = self.predictor(pred_input, add_pos_embed=False)
+
+        predictions_raw = pred_out[:, mask_indices, :]  # [B, N_mask, pred_dim]
+        return predictions_raw
+
+    def training_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor:
+        waveform = batch["waveform"]
+
+        # 1. Process Audio
+        patches, current_grid_size = self._process_audio(waveform)
+        B, N, D = patches.shape
+
+        # 2. Masking
+        mask = self.mask_generator(1, device=self.device, grid_size=current_grid_size)
+        mask = mask.expand(B, -1)
+
+        # 3. Targets (Best-RQ: Quantized Raw Patches)
+        with torch.no_grad():
+            spec = self.spectrogram(waveform)
+            spec = self._adjust_spectrogram(spec)
+            raw_patches = self._get_raw_patches(spec)
+
+            m = mask[0]
+            mask_indices = torch.nonzero(m).flatten()
+
+            target_input = raw_patches[:, mask_indices, :]
+            targets = self.quantizer(target_input)  # [B, N_mask]
+
+        # 4. Predictions (Encoder -> Predictor -> Proj)
+        encoder_out = self.compute_encoder(patches, mask, current_grid_size)
+        predictions_raw = self.compute_predictor(encoder_out, mask, current_grid_size)
+        logits = self.rq_proj(predictions_raw)  # [B, N_mask, vocab_size]
+
+        # 5. Loss
+        loss = self.criterion(logits.reshape(-1, self.vocab_size), targets.reshape(-1))
+
+        self.log(
+            "train/loss", loss, on_step=True, on_epoch=True, prog_bar=True, batch_size=B
+        )
+        return loss
+
+    def validation_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor:
+        waveform = batch["waveform"]
+        patches, current_grid_size = self._process_audio(waveform)
+        B, N, D = patches.shape
+
+        mask = self.mask_generator(1, device=self.device, grid_size=current_grid_size)
+        mask = mask.expand(B, -1)
+
+        with torch.no_grad():
+            spec = self.spectrogram(waveform)
+            spec = self._adjust_spectrogram(spec)
+            raw_patches = self._get_raw_patches(spec)
+
+            m = mask[0]
+            mask_indices = torch.nonzero(m).flatten()
+
+            target_input = raw_patches[:, mask_indices, :]
+            targets = self.quantizer(target_input)
+
+        encoder_out = self.compute_encoder(patches, mask, current_grid_size)
+        predictions_raw = self.compute_predictor(encoder_out, mask, current_grid_size)
+        logits = self.rq_proj(predictions_raw)
+
+        loss = self.criterion(logits.reshape(-1, self.vocab_size), targets.reshape(-1))
+
+        self.log(
+            "val/loss", loss, on_step=False, on_epoch=True, prog_bar=True, batch_size=B
+        )
+        return loss
+
+    def test_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor:
+        return self.validation_step(batch, batch_idx)
+
+    def configure_optimizers(self) -> Dict[str, Any]:
+        optimizer = self.optimizer_config(params=self.parameters())
+
+        if self.trainer.max_steps and self.trainer.max_steps > 0:
+            total_steps = self.trainer.max_steps
+        else:
+            total_steps = self.trainer.estimated_stepping_batches
+
+        warmup_steps = int(total_steps * self.warmup_pct)
+
+        lr_lambda = LinearWarmupCosineDecay(
+            warmup_steps=warmup_steps,
+            total_steps=total_steps,
+            final_lr_ratio=self.final_lr_ratio,
+        )
+
+        scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
+
+        return {
+            "optimizer": optimizer,
+            "lr_scheduler": {
+                "scheduler": scheduler,
+                "monitor": "val_loss",
+                "interval": "step",
+                "frequency": 1,
+            },
+        }
diff --git a/audio-embeddings/src/models/best_rq_module.py b/audio-embeddings/src/models/best_rq_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae12c762cbae6fcb671a8cb90a4b78972e653327
--- /dev/null
+++ b/audio-embeddings/src/models/best_rq_module.py
@@ -0,0 +1,272 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import lightning as L
+from typing import Any, Dict, Tuple
+
+from src.models.components.spectrogram import Spectrogram
+from src.models.components.masking import MaskingGenerator
+from src.models.components.patch_embed import PatchEmbed
+from src.models.components.vit import ViT
+from src.models.components.random_projection_quantizer import RandomProjectionQuantizer
+from src.utils.lr_schedulers import LinearWarmupCosineDecay
+
+
+class BestRQModule(L.LightningModule):
+    """
+    Best-RQ Lightning Module.
+
+    Implements a single-stage Masked Audio Modeling approach using Random Projection Quantization targets.
+
+    Args:
+        optimizer (torch.optim.Optimizer): Optimizer configuration.
+        net (Dict[str, Any]): Configuration for sub-modules.
+        warmup_pct (float): Percentage of total steps for warmup.
+        final_lr_ratio (float): Ratio of final learning rate to initial learning rate.
+        spectrogram_adjustment_mode (str): 'pad' or 'truncate' for spectrogram time dimension.
+        codebook_dim (int): Codebook dimension for RandomProjectionQuantizer.
+        vocab_size (int): Vocabulary size for RandomProjectionQuantizer.
+    """
+
+    def __init__(
+        self,
+        optimizer: torch.optim.Optimizer,
+        net: Dict[str, Any],
+        warmup_pct: float = 0.1,
+        final_lr_ratio: float = 0.001,
+        spectrogram_adjustment_mode: str = "pad",
+        codebook_dim: int = 16,
+        vocab_size: int = 8192,
+    ):
+        super().__init__()
+        self.save_hyperparameters(logger=False, ignore=["net", "optimizer"])
+
+        self.warmup_pct = warmup_pct
+        self.final_lr_ratio = final_lr_ratio
+        self.spectrogram_adjustment_mode = spectrogram_adjustment_mode
+        self.vocab_size = vocab_size
+
+        # Store optimizer partial
+        self.optimizer_config = optimizer
+
+        # Components
+        self.spectrogram = Spectrogram(**net.get("spectrogram", {}))
+        self.patch_embed = PatchEmbed(**net.get("patch_embed", {}))
+        self.mask_generator = MaskingGenerator(**net.get("masking", {}))
+
+        # Encoder (ViT)
+        self.encoder = ViT(**net.get("encoder", {}))
+
+        # Mask Token
+        encoder_dim = net.get("encoder", {}).get("embed_dim", 768)
+        self.mask_token = nn.Parameter(torch.zeros(1, 1, encoder_dim))
+        nn.init.trunc_normal_(self.mask_token, std=0.02)
+
+        # Random Projection Quantizer
+        # Input to quantizer is raw patches
+        patch_size = self.patch_embed.patch_size
+        in_chans = self.patch_embed.in_chans
+        quantizer_input_dim = patch_size[0] * patch_size[1] * in_chans
+
+        self.quantizer = RandomProjectionQuantizer(
+            input_dim=quantizer_input_dim, cb_dim=codebook_dim, cb_vocab=vocab_size
+        )
+        # Freeze quantizer
+        for p in self.quantizer.parameters():
+            p.requires_grad = False
+
+        # Projection head
+        self.output_proj = nn.Linear(encoder_dim, vocab_size)
+
+        # Loss
+        self.criterion = nn.CrossEntropyLoss()
+
+    def _adjust_spectrogram(self, spec: torch.Tensor) -> torch.Tensor:
+        """
+        Adjusts the spectrogram time dimension to be divisible by the patch size.
+        """
+        patch_size = self.patch_embed.patch_embed.patch_size
+        patch_time_dim = patch_size[1]
+
+        T = spec.shape[-1]
+        remainder = T % patch_time_dim
+
+        if remainder != 0:
+            if self.spectrogram_adjustment_mode == "pad":
+                pad_amount = patch_time_dim - remainder
+                spec = F.pad(spec, (0, pad_amount))
+            elif self.spectrogram_adjustment_mode == "truncate":
+                spec = spec[..., : T - remainder]
+            else:
+                raise ValueError(
+                    f"Unknown spectrogram_adjustment_mode: {self.spectrogram_adjustment_mode}"
+                )
+
+        return spec
+
+    def _process_audio(
+        self, waveform: torch.Tensor
+    ) -> Tuple[torch.Tensor, Tuple[int, int]]:
+        """
+        Processes raw waveform into patches and returns patches and grid size.
+        """
+        # 1. Spectrogram
+        spec = self.spectrogram(waveform)  # [B, 1, F, T]
+        spec = self._adjust_spectrogram(spec)
+
+        # 2. Patchify
+        patches = self.patch_embed(spec)  # [B, N, D]
+
+        # Calculate grid size
+        patch_size = self.patch_embed.patch_embed.patch_size
+        F_pix = spec.shape[2]
+        T_pix = spec.shape[3]
+        H_grid = F_pix // patch_size[0]
+        W_grid = T_pix // patch_size[1]
+        grid_size = (H_grid, W_grid)
+
+        return patches, grid_size
+
+    def _get_raw_patches(self, spec: torch.Tensor) -> torch.Tensor:
+        """
+        Extract raw key-value patches from spectrogram for quantization.
+        """
+        patch_size = self.patch_embed.patch_size  # (H, W)
+
+        # F.unfold returns [B, C*pH*pW, L]
+        # Spectrogram is [B, C, F, T]
+        # patch_size is (H, W) -> (freq_patch, time_patch)
+
+        patches = F.unfold(spec, kernel_size=patch_size, stride=patch_size)  # [B, D, N]
+        patches = patches.transpose(1, 2)  # [B, N, D]
+
+        return patches
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass for inference/eval. Returns encoder representation.
+        """
+        patches, grid_size = self._process_audio(x)
+        x = self.encoder(patches, grid_size=grid_size)
+        return x
+
+    def training_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor:
+        waveform = batch["waveform"]
+
+        # 1. Process Audio
+        patches, current_grid_size = self._process_audio(waveform)
+        B, N, D = patches.shape
+
+        # 2. Generate Mask
+        mask = self.mask_generator(1, device=self.device, grid_size=current_grid_size)
+        mask = mask.expand(B, -1)  # [B, N]
+
+        # 3. Prepare Inputs (Encoder sees full sequence with mask tokens)
+        encoder_input = patches.clone()
+        mask_tokens_expanded = self.mask_token.expand(B, N, -1)
+
+        # Replace masked patches with mask tokens
+        mask_bool = mask.bool()  # [B, N]
+        encoder_input[mask_bool] = mask_tokens_expanded[mask_bool]
+
+        # 4. Encoder Forward
+        # We pass the full sequence.
+        # For RoPE, pos_ids are auto-generated as 0..N-1 if None, which matches the grid layout.
+        encoder_out = self.encoder(
+            encoder_input, grid_size=current_grid_size
+        )  # [B, N, D]
+
+        # 5. Get Targets (Quantized Raw Patches)
+        with torch.no_grad():
+            # Re-compute spec for raw patches
+            spec = self.spectrogram(waveform)
+            spec = self._adjust_spectrogram(spec)
+            raw_patches = self._get_raw_patches(spec)  # [B, N, raw_dim]
+
+            # Select masked patches for targets
+            m = mask[0]
+            mask_indices = torch.nonzero(m).flatten()
+
+            target_input = raw_patches[:, mask_indices, :]  # [B, N_mask, raw_dim]
+            targets = self.quantizer(target_input)  # [B, N_mask]
+
+        # 6. Get Predictions
+        # Select masked outputs
+        predictions = encoder_out[:, mask_indices, :]  # [B, N_mask, D]
+        logits = self.output_proj(predictions)  # [B, N_mask, vocab_size]
+
+        # 7. Loss
+        loss = self.criterion(logits.reshape(-1, self.vocab_size), targets.reshape(-1))
+
+        self.log(
+            "train/loss", loss, on_step=True, on_epoch=True, prog_bar=True, batch_size=B
+        )
+        return loss
+
+    def validation_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor:
+        waveform = batch["waveform"]
+
+        patches, current_grid_size = self._process_audio(waveform)
+        B, N, D = patches.shape
+
+        mask = self.mask_generator(1, device=self.device, grid_size=current_grid_size)
+        mask = mask.expand(B, -1)
+
+        encoder_input = patches.clone()
+        mask_tokens_expanded = self.mask_token.expand(B, N, -1)
+        mask_bool = mask.bool()
+        encoder_input[mask_bool] = mask_tokens_expanded[mask_bool]
+
+        encoder_out = self.encoder(encoder_input, grid_size=current_grid_size)
+
+        with torch.no_grad():
+            spec = self.spectrogram(waveform)
+            spec = self._adjust_spectrogram(spec)
+            raw_patches = self._get_raw_patches(spec)
+
+            m = mask[0]
+            mask_indices = torch.nonzero(m).flatten()
+
+            target_input = raw_patches[:, mask_indices, :]
+            targets = self.quantizer(target_input)
+
+        predictions = encoder_out[:, mask_indices, :]
+        logits = self.output_proj(predictions)
+
+        loss = self.criterion(logits.reshape(-1, self.vocab_size), targets.reshape(-1))
+
+        self.log(
+            "val/loss", loss, on_step=False, on_epoch=True, prog_bar=True, batch_size=B
+        )
+        return loss
+
+    def test_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor:
+        return self.validation_step(batch, batch_idx)
+
+    def configure_optimizers(self) -> Dict[str, Any]:
+        optimizer = self.optimizer_config(params=self.parameters())
+
+        if self.trainer.max_steps and self.trainer.max_steps > 0:
+            total_steps = self.trainer.max_steps
+        else:
+            total_steps = self.trainer.estimated_stepping_batches
+
+        warmup_steps = int(total_steps * self.warmup_pct)
+
+        lr_lambda = LinearWarmupCosineDecay(
+            warmup_steps=warmup_steps,
+            total_steps=total_steps,
+            final_lr_ratio=self.final_lr_ratio,
+        )
+
+        scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
+
+        return {
+            "optimizer": optimizer,
+            "lr_scheduler": {
+                "scheduler": scheduler,
+                "monitor": "val_loss",
+                "interval": "step",
+                "frequency": 1,
+            },
+        }
diff --git a/audio-embeddings/src/models/components/__init__.py b/audio-embeddings/src/models/components/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ed8a906980154beceea6a152a7594238f61c049
--- /dev/null
+++ b/audio-embeddings/src/models/components/__init__.py
@@ -0,0 +1,6 @@
+from .spectrogram import Spectrogram
+from .masking import MaskingGenerator
+from .patch_embed import PatchEmbed
+from .vit import ViT
+
+__all__ = ["Spectrogram", "MaskingGenerator", "PatchEmbed", "ViT"]
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diff --git a/audio-embeddings/src/models/components/__pycache__/vit.cpython-312.pyc b/audio-embeddings/src/models/components/__pycache__/vit.cpython-312.pyc
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diff --git a/audio-embeddings/src/models/components/masking.py b/audio-embeddings/src/models/components/masking.py
new file mode 100644
index 0000000000000000000000000000000000000000..58e3e91fa0b918ebc5303773e1e04e21c7423788
--- /dev/null
+++ b/audio-embeddings/src/models/components/masking.py
@@ -0,0 +1,75 @@
+import torch
+import numpy as np
+from typing import Tuple, Optional
+
+
+class MaskingGenerator:
+    """
+    Generates masks for the input patches.
+
+    Args:
+        input_size (tuple[int, int]): Input image size (H, W).
+        patch_size (tuple[int, int]): Patch size (H, W).
+        mask_ratio (tuple[float, float]): Range of mask ratio (min, max).
+    """
+
+    def __init__(
+        self,
+        input_size: Tuple[int, int] = (128, 256),
+        patch_size: Tuple[int, int] = (16, 16),
+        mask_ratio: Tuple[float, float] = (0.4, 0.6),
+    ):
+        self.height, self.width = input_size
+        self.patch_h, self.patch_w = patch_size
+        self.num_patches_h = self.height // self.patch_h
+        self.num_patches_w = self.width // self.patch_w
+        self.num_patches = self.num_patches_h * self.num_patches_w
+        self.mask_ratio = mask_ratio
+
+    def __call__(
+        self,
+        batch_size: int,
+        device: torch.device = torch.device("cpu"),
+        grid_size: Optional[Tuple[int, int]] = None,
+    ) -> torch.Tensor:
+        """
+        Generate masks for a batch.
+
+        Args:
+            batch_size (int): Batch size.
+            device (torch.device): Device to place the masks on.
+            grid_size (Optional[Tuple[int, int]]): Grid size (H, W) if different from init.
+
+        Returns:
+            torch.Tensor: Masks [B, N] (boolean, True=masked).
+        """
+        masks = []
+        for _ in range(batch_size):
+            mask = self._generate_mask(grid_size)
+            masks.append(mask)
+
+        return torch.stack(masks).to(device)
+
+    def _generate_mask(
+        self, grid_size: Optional[Tuple[int, int]] = None
+    ) -> torch.Tensor:
+        """
+        Generate a single mask.
+        """
+        if grid_size is not None:
+            num_patches_h, num_patches_w = grid_size
+            num_patches = num_patches_h * num_patches_w
+        else:
+            num_patches = self.num_patches
+
+        mask = torch.zeros(num_patches, dtype=torch.bool)
+
+        target_masked = int(num_patches * np.random.uniform(*self.mask_ratio))
+
+        # Random Permutation Masking
+        if target_masked > 0:
+            perm = torch.randperm(num_patches)
+            mask_indices = perm[:target_masked]
+            mask[mask_indices] = True
+
+        return mask  # Already flattened
diff --git a/audio-embeddings/src/models/components/patch_embed.py b/audio-embeddings/src/models/components/patch_embed.py
new file mode 100644
index 0000000000000000000000000000000000000000..21201b7e8b94affd922bc9299cbc09d1c816760a
--- /dev/null
+++ b/audio-embeddings/src/models/components/patch_embed.py
@@ -0,0 +1,51 @@
+import torch
+import torch.nn as nn
+from timm.layers import PatchEmbed as TimmPatchEmbed
+
+
+class PatchEmbed(nn.Module):
+    """
+    2D Image to Patch Embedding.
+
+    Args:
+        img_size (tuple[int, int]): Input image size (H, W).
+        patch_size (tuple[int, int]): Patch size (H, W).
+        in_chans (int): Number of input channels.
+        embed_dim (int): Embedding dimension.
+    """
+
+    def __init__(
+        self,
+        img_size: tuple[int, int] = (128, 256),
+        patch_size: tuple[int, int] = (16, 16),
+        in_chans: int = 1,
+        embed_dim: int = 768,
+    ):
+        super().__init__()
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.patch_embed = TimmPatchEmbed(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            flatten=True,
+            bias=True,
+            strict_img_size=False,
+        )
+        self.num_patches = self.patch_embed.num_patches
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass.
+
+        Args:
+            x (torch.Tensor): Input tensor [B, C, H, W].
+
+        Returns:
+            torch.Tensor: Patch embeddings [B, N, D].
+        """
+        return self.patch_embed(x)
diff --git a/audio-embeddings/src/models/components/random_projection_quantizer.py b/audio-embeddings/src/models/components/random_projection_quantizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..51379500645d92676ca326da70a6b036af7df598
--- /dev/null
+++ b/audio-embeddings/src/models/components/random_projection_quantizer.py
@@ -0,0 +1,52 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class RandomProjectionQuantizer(nn.Module):
+    """Vector quantization using a projection and a randomly initialised codebook
+    this is useful for models like BEST-RQ for instance.
+
+    The output is the indices of the closest code in the codebook for each
+    time step of the input.
+
+    ref: https://arxiv.org/pdf/2202.01855
+
+    Arguments
+    ---------
+    input_dim: int
+        Input dimension (channels).
+    cb_dim: int
+        Size of each code in the codebook.
+    cb_vocab: int
+        Number of codes in the codebook
+
+    Example
+    -------
+    >>> quantiser = RandomProjectionQuantizer(16, 16, 32)
+    >>> inputs = torch.rand(10, 12, 16)
+    >>> output = quantiser(inputs)
+    >>> output.shape
+    torch.Size([10, 12])
+    """
+
+    def __init__(self, input_dim, cb_dim, cb_vocab):
+        super().__init__()
+
+        self.input_dim = input_dim
+        self.cb_dim = cb_dim
+        self.cb_vocab = cb_vocab
+
+        # Section 3.1 "projection matrix A use Xavier initialization"
+        P_init = torch.empty((input_dim, cb_dim))
+        self.register_buffer("P", nn.init.xavier_uniform_(P_init))
+
+        # normalize random matrix for codebook
+        self.register_buffer("CB", F.normalize(torch.randn(cb_vocab, cb_dim)))
+
+    def forward(self, x):
+        """Forward the latent vector to obtain a quantised output"""
+
+        x = F.normalize(x @ self.P, dim=-1)
+        # since both x and CB are normalized, we can just take the argmax of the dot product
+        return F.linear(x, self.CB).argmax(dim=-1)
diff --git a/audio-embeddings/src/models/components/rope.py b/audio-embeddings/src/models/components/rope.py
new file mode 100644
index 0000000000000000000000000000000000000000..faa592e6cdd989b495626b2060f4cd7b9a3c563d
--- /dev/null
+++ b/audio-embeddings/src/models/components/rope.py
@@ -0,0 +1,193 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple
+
+
+class RotaryEmbedding2D(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        max_res: Tuple[int, int] = (128, 256),
+        temperature: float = 10000.0,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.max_h, self.max_w = max_res
+        self.temperature = temperature
+
+        # Check if dim is divisible by 4 (since we split into 2 for H/W, and each needs 2 for complex)
+        assert dim % 4 == 0, "Embedding dimension must be divisible by 4 for 2D RoPE"
+
+        dim_h = dim // 2
+        dim_w = dim // 2
+
+        # Generate frequencies for H and W
+        # inv_freq_h: [dim_h // 2]
+        inv_freq_h = 1.0 / (temperature ** (torch.arange(0, dim_h, 2).float() / dim_h))
+        inv_freq_w = 1.0 / (temperature ** (torch.arange(0, dim_w, 2).float() / dim_w))
+
+        self.register_buffer("inv_freq_h", inv_freq_h)
+        self.register_buffer("inv_freq_w", inv_freq_w)
+
+        # Cache
+        self.cached_cos_sin_h = None
+        self.cached_cos_sin_w = None
+
+    def _update_cache(self, h: int, w: int, device: torch.device, dtype: torch.dtype):
+        # Generate grid
+        # We need to support arbitrary positions, but usually we just precompute for max_res
+        # or compute on the fly for the given indices.
+        # Let's compute for max_res and index into it.
+
+        if self.cached_cos_sin_h is None or self.cached_cos_sin_h[0].shape[0] < h:
+            t_h = torch.arange(h, device=device, dtype=dtype)
+            freqs_h = torch.einsum("i,j->ij", t_h, self.inv_freq_h)  # [H, dim_h/2]
+            emb_h = torch.cat((freqs_h, freqs_h), dim=-1)  # [H, dim_h]
+            self.cached_cos_sin_h = (emb_h.cos(), emb_h.sin())
+
+        if self.cached_cos_sin_w is None or self.cached_cos_sin_w[0].shape[0] < w:
+            t_w = torch.arange(w, device=device, dtype=dtype)
+            freqs_w = torch.einsum("i,j->ij", t_w, self.inv_freq_w)  # [W, dim_w/2]
+            emb_w = torch.cat((freqs_w, freqs_w), dim=-1)  # [W, dim_w]
+            self.cached_cos_sin_w = (emb_w.cos(), emb_w.sin())
+
+    def forward(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        pos_ids: torch.Tensor,
+        grid_size: Tuple[int, int],
+    ):
+        # q, k: [B, num_heads, N, head_dim]
+        # pos_ids: [B, N] or [N] (indices of patches)
+        # grid_size: (H, W) - original grid size to decode pos_ids
+
+        B, num_heads, N, D = q.shape
+        H_grid, W_grid = grid_size
+
+        # Decode pos_ids to (h, w)
+        # pos_ids are indices in flattened grid [0, H*W-1]
+        # h = pos_ids // W_grid
+        # w = pos_ids % W_grid
+
+        h_idx = pos_ids.div(W_grid, rounding_mode="floor")  # [B, N]
+        w_idx = pos_ids % W_grid  # [B, N]
+
+        # Ensure cache is large enough
+        self._update_cache(H_grid, W_grid, q.device, q.dtype)
+
+        # Fetch cos/sin for H and W
+        # cos_h: [B, N, dim_h]
+        # We need to gather from cached [max_h, dim_h] using h_idx
+
+        # Handle shared pos_ids (if [N])
+        if h_idx.ndim == 1:
+            h_idx = h_idx.unsqueeze(0).expand(B, -1)
+            w_idx = w_idx.unsqueeze(0).expand(B, -1)
+
+        cos_h = F.embedding(h_idx, self.cached_cos_sin_h[0])  # [B, N, dim_h]
+        sin_h = F.embedding(h_idx, self.cached_cos_sin_h[1])
+        cos_w = F.embedding(w_idx, self.cached_cos_sin_w[0])  # [B, N, dim_w]
+        sin_w = F.embedding(w_idx, self.cached_cos_sin_w[1])
+
+        # Split q, k into halves
+        # q: [B, num_heads, N, D] -> [B, N, num_heads, D] for easier manipulation?
+        # Usually RoPE is applied on [B, num_heads, N, D] or [N, B, num_heads, D]
+        # Let's keep [B, num_heads, N, D]
+
+        dim_half = D // 2
+        q_h, q_w = q.split(dim_half, dim=-1)
+        k_h, k_w = k.split(dim_half, dim=-1)
+
+        # Apply RoPE
+        # We need to reshape cos/sin to broadcast over num_heads
+        # cos_h: [B, N, dim_h] -> [B, 1, N, dim_h]
+        cos_h = cos_h.unsqueeze(1)
+        sin_h = sin_h.unsqueeze(1)
+        cos_w = cos_w.unsqueeze(1)
+        sin_w = sin_w.unsqueeze(1)
+
+        q_h_rot = self._apply_rotary(q_h, cos_h, sin_h)
+        k_h_rot = self._apply_rotary(k_h, cos_h, sin_h)
+
+        q_w_rot = self._apply_rotary(q_w, cos_w, sin_w)
+        k_w_rot = self._apply_rotary(k_w, cos_w, sin_w)
+
+        q_rot = torch.cat((q_h_rot, q_w_rot), dim=-1)
+        k_rot = torch.cat((k_h_rot, k_w_rot), dim=-1)
+
+        return q_rot, k_rot
+
+    def _apply_rotary(
+        self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
+    ) -> torch.Tensor:
+        # x: [B, num_heads, N, dim_half]
+        # cos, sin: [B, 1, N, dim_half]
+        # Standard RoPE rotation:
+        # x = [x1, x2]
+        # out = [x1*cos - x2*sin, x1*sin + x2*cos]
+        # This assumes pairs are adjacent.
+        # My inv_freq generation: cat(freqs, freqs).
+        # This corresponds to x = [x_first_half, x_second_half] pairing?
+        # Usually RoPE pairs even/odd or first/second half.
+        # "The standard implementation ... pairs feature i with i + d/2"
+        # My emb generation: cat(freqs, freqs) -> [f0, f1, ..., f0, f1, ...] ? No.
+        # freqs is [0, 2, ...]
+        # cat(freqs, freqs) -> [f0, f2, ..., f0, f2, ...]
+        # So it expects x to be split into two halves and rotated.
+        # rotate_half(x) = [-x2, x1]
+
+        return (x * cos) + (self._rotate_half(x) * sin)
+
+    def _rotate_half(self, x: torch.Tensor) -> torch.Tensor:
+        x1, x2 = x.chunk(2, dim=-1)
+        return torch.cat((-x2, x1), dim=-1)
+
+
+class RoPEAttention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+        rope: Optional[RotaryEmbedding2D] = None,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+        self.rope = rope
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        pos_ids: torch.Tensor = None,
+        grid_size: Tuple[int, int] = None,
+    ) -> torch.Tensor:
+        B, N, C = x.shape
+        qkv = (
+            self.qkv(x)
+            .reshape(B, N, 3, self.num_heads, C // self.num_heads)
+            .permute(2, 0, 3, 1, 4)
+        )
+        q, k, v = qkv[0], qkv[1], qkv[2]  # [B, num_heads, N, head_dim]
+
+        if self.rope is not None and pos_ids is not None and grid_size is not None:
+            q, k = self.rope(q, k, pos_ids, grid_size)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
diff --git a/audio-embeddings/src/models/components/spectrogram.py b/audio-embeddings/src/models/components/spectrogram.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5ea1670d99ebefd08353b0a77fa4600cf198a12
--- /dev/null
+++ b/audio-embeddings/src/models/components/spectrogram.py
@@ -0,0 +1,81 @@
+import torch
+import torch.nn as nn
+import torchaudio
+from typing import Optional
+
+
+class Spectrogram(nn.Module):
+    """
+    Mel Spectrogram module with AmplitudeToDB conversion.
+
+    Args:
+        sample_rate (int): Sample rate of the audio.
+        n_fft (int): Size of FFT.
+        win_length (Optional[int]): Window length. Defaults to n_fft.
+        win_length_ms (Optional[float]): Window length in milliseconds. Overrides win_length if provided.
+        hop_length (Optional[int]): Hop length. Defaults to win_length // 2.
+        hop_length_ms (Optional[float]): Hop length in milliseconds. Overrides hop_length if provided.
+        n_mels (int): Number of mel filterbanks.
+        f_min (float): Minimum frequency.
+        f_max (Optional[float]): Maximum frequency.
+        power (float): Power of the magnitude.
+    """
+
+    def __init__(
+        self,
+        sample_rate: int = 32000,
+        n_fft: int = 4096,
+        win_length: Optional[int] = None,
+        win_length_ms: Optional[float] = None,
+        hop_length: Optional[int] = None,
+        hop_length_ms: Optional[float] = None,
+        n_mels: int = 128,
+        f_min: float = 0.0,
+        f_max: Optional[float] = None,
+        power: float = 2.0,
+    ):
+        super().__init__()
+
+        if win_length is None:
+            if win_length_ms is None:
+                win_length = n_fft
+            else:
+                win_length = int(sample_rate * win_length_ms / 1000)
+
+        if hop_length is None:
+            if hop_length_ms is None:
+                hop_length = win_length // 2
+            else:
+                hop_length = int(sample_rate * hop_length_ms / 1000)
+
+        self.mel_spec = torchaudio.transforms.MelSpectrogram(
+            sample_rate=sample_rate,
+            n_fft=n_fft,
+            win_length=win_length,
+            hop_length=hop_length,
+            n_mels=n_mels,
+            f_min=f_min,
+            f_max=f_max,
+            power=power,
+            normalized=True,
+        )
+        self.amplitude_to_db = torchaudio.transforms.AmplitudeToDB()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass.
+
+        Args:
+            x (torch.Tensor): Input waveform [B, C, T] or [B, T].
+
+        Returns:
+            torch.Tensor: Log-Mel Spectrogram [B, C, F, T].
+        """
+        # x: [B, C, T]
+        # MelSpectrogram expects [..., T]
+        # Output will be [..., n_mels, time]
+
+        spec = self.mel_spec(x)
+        spec = self.amplitude_to_db(spec)
+
+        return spec
diff --git a/audio-embeddings/src/models/components/vit.py b/audio-embeddings/src/models/components/vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bf9a6152eb264c3c869c70e1a9a9ee2ace0b8d2
--- /dev/null
+++ b/audio-embeddings/src/models/components/vit.py
@@ -0,0 +1,224 @@
+import torch
+import torch.nn as nn
+from timm.layers import Mlp, build_sincos2d_pos_embed, DropPath
+from src.models.components.rope import RoPEAttention, RotaryEmbedding2D
+from typing import Optional, Tuple
+
+
+class RoPEBlock(nn.Module):
+    """
+    Transformer Block with RoPE support.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = False,
+        proj_drop: float = 0.0,
+        attn_drop: float = 0.0,
+        drop_path: float = 0.0,
+        act_layer: nn.Module = nn.GELU,
+        norm_layer: nn.Module = nn.LayerNorm,
+        rope: Optional[RotaryEmbedding2D] = None,
+    ):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = RoPEAttention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            attn_drop=attn_drop,
+            proj_drop=proj_drop,
+            rope=rope,
+        )
+        self.norm2 = norm_layer(dim)
+        self.mlp = Mlp(
+            in_features=dim,
+            hidden_features=int(dim * mlp_ratio),
+            act_layer=act_layer,
+            drop=proj_drop,
+        )
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        pos_ids: Optional[torch.Tensor] = None,
+        grid_size: Optional[Tuple[int, int]] = None,
+    ) -> torch.Tensor:
+        x = x + self.drop_path(
+            self.attn(self.norm1(x), pos_ids=pos_ids, grid_size=grid_size)
+        )
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+
+class ViT(nn.Module):
+    """
+    Vision Transformer with support for RoPE and 2D positional embeddings.
+
+    Args:
+        embed_dim (int): Embedding dimension.
+        depth (int): Number of transformer blocks.
+        num_heads (int): Number of attention heads.
+        mlp_ratio (float): Ratio of MLP hidden dim to embedding dim.
+        qkv_bias (bool): Enable bias for QKV projections.
+        drop_rate (float): Dropout rate.
+        attn_drop_rate (float): Attention dropout rate.
+        drop_path_rate (float): Stochastic depth rate.
+        norm_layer (nn.Module): Normalization layer.
+        act_layer (nn.Module): Activation layer.
+        num_patches (int): Total number of patches (used for learnable/sincos pos embed).
+        img_size (tuple[int, int]): Input image size (H, W).
+        patch_size (tuple[int, int]): Patch size (H, W).
+        pos_embed_type (str): Type of positional embedding ("rope", "sincos", "learnable").
+    """
+
+    def __init__(
+        self,
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        drop_rate: float = 0.0,
+        attn_drop_rate: float = 0.0,
+        drop_path_rate: float = 0.0,
+        norm_layer: nn.Module = nn.LayerNorm,
+        act_layer: nn.Module = nn.GELU,
+        num_patches: int = 128,
+        img_size: tuple[int, int] = (128, 256),
+        patch_size: tuple[int, int] = (16, 16),
+        pos_embed_type: str = "rope",
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.num_patches = num_patches
+        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
+        self.pos_embed_type = pos_embed_type
+
+        # Positional Embeddings
+        if pos_embed_type == "rope":
+            head_dim = embed_dim // num_heads
+            self.rope = RotaryEmbedding2D(dim=head_dim, max_res=self.grid_size)
+            self.pos_embed = None
+        elif pos_embed_type == "sincos":
+            self.rope = None
+            # build_sincos2d_pos_embed(feat_shape, dim, ...)
+            # We assume grid_size matches num_patches
+            pos_embed = build_sincos2d_pos_embed(self.grid_size, embed_dim)
+            self.register_buffer("pos_embed", pos_embed.unsqueeze(0))  # [1, N, D]
+        elif pos_embed_type == "learnable":
+            self.rope = None
+            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+            nn.init.trunc_normal_(self.pos_embed, std=0.02)
+        else:
+            raise ValueError(f"Unknown pos_embed_type: {pos_embed_type}")
+
+        # Stochastic Depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
+
+        self.blocks = nn.ModuleList(
+            [
+                RoPEBlock(
+                    dim=embed_dim,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    proj_drop=drop_rate,
+                    attn_drop=attn_drop_rate,
+                    drop_path=dpr[i],
+                    norm_layer=norm_layer,
+                    act_layer=act_layer,
+                    rope=self.rope,
+                )
+                for i in range(depth)
+            ]
+        )
+
+        self.norm = norm_layer(embed_dim)
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m: nn.Module) -> None:
+        if isinstance(m, nn.Linear):
+            nn.init.trunc_normal_(m.weight, std=0.02)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        pos_ids: Optional[torch.Tensor] = None,
+        add_pos_embed: bool = True,
+        grid_size: Optional[Tuple[int, int]] = None,
+    ) -> torch.Tensor:
+        """
+        Forward pass.
+
+        Args:
+            x (torch.Tensor): Input tensor [B, N, D].
+            pos_ids (Optional[torch.Tensor]): Positional indices [B, N] or [N].
+            add_pos_embed (bool): Whether to add positional embeddings (for non-RoPE).
+            grid_size (Optional[Tuple[int, int]]): Grid size for RoPE/PosEmbed.
+
+        Returns:
+            torch.Tensor: Output tensor [B, N, D].
+        """
+        # Determine grid size
+        if grid_size is None:
+            if pos_ids is None:
+                # Infer from x assuming full sequence
+                B, N, D = x.shape
+                H_grid = self.grid_size[0]
+                W_grid = N // H_grid
+                current_grid_size = (H_grid, W_grid)
+            else:
+                # Cannot infer, use default (might be wrong if variable length)
+                current_grid_size = self.grid_size
+        else:
+            current_grid_size = grid_size
+
+        if self.pos_embed_type != "rope" and add_pos_embed:
+            if pos_ids is not None:
+                # Select positional embeddings
+                if pos_ids.ndim == 1:
+                    # Shared pos_ids across batch
+                    pos_embed = self.pos_embed[:, pos_ids, :]  # [1, N_subset, D]
+                else:
+                    # Different pos_ids per sample
+                    pos_embed = self.pos_embed.expand(x.shape[0], -1, -1)
+                    pos_embed = torch.gather(
+                        pos_embed,
+                        1,
+                        pos_ids.unsqueeze(-1).expand(-1, -1, self.embed_dim),
+                    )
+                x = x + pos_embed
+            else:
+                # Assume full sequence
+                if x.shape[1] == self.num_patches:
+                    x = x + self.pos_embed
+                elif (
+                    self.pos_embed is not None and x.shape[1] <= self.pos_embed.shape[1]
+                ):
+                    x = x + self.pos_embed[:, : x.shape[1], :]
+
+        # For RoPE, we need pos_ids. If not provided, generate them.
+        if self.pos_embed_type == "rope" and pos_ids is None:
+            device = x.device
+            # We need to generate pos_ids for the current grid
+            # If we inferred current_grid_size, we should use it.
+            # pos_ids should be 0..N-1
+            B, N, D = x.shape
+            pos_ids = torch.arange(N, device=device)
+
+        for block in self.blocks:
+            x = block(x, pos_ids=pos_ids, grid_size=current_grid_size)
+
+        x = self.norm(x)
+        return x
diff --git a/audio-embeddings/src/models/rqa_jepa_module.py b/audio-embeddings/src/models/rqa_jepa_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9dd4573377ada04bf825ce0ac5747597dcb5cc7
--- /dev/null
+++ b/audio-embeddings/src/models/rqa_jepa_module.py
@@ -0,0 +1,281 @@
+import torch
+import functools
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Any, Dict, Optional, Tuple
+
+from src.models.audio_jepa_module import AudioJEPAModule
+from src.models.components.random_projection_quantizer import RandomProjectionQuantizer
+
+
+class RQAJEPAModule(AudioJEPAModule):
+    """
+    RQA-JEPA Lightning Module.
+    Extends AudioJEPAModule with Random Projection Quantization loss.
+
+    Args:
+        optimizer (torch.optim.Optimizer): Optimizer configuration.
+        net (Dict[str, Any]): Configuration for sub-modules.
+        warmup_pct (float): Percentage of total steps for warmup.
+        final_lr_ratio (float): Ratio of final learning rate to initial learning rate.
+        ema_decay (float): Initial EMA decay rate.
+        ema_end_decay (float): Final EMA decay rate.
+        ema_anneal_end_step (int): Step at which EMA decay reaches ema_end_decay.
+        spectrogram_adjustment_mode (str): 'pad' or 'truncate' for spectrogram time dimension.
+        jepa_criterion (torch.nn.Module): Loss function for JEPA (defaults to MSELoss).
+        rq_criterion (torch.nn.Module): Loss function for RQ (defaults to CrossEntropyLoss).
+        rq_lambda (float): Weight for JEPA loss (1 - rq_lambda is used for RQ loss).
+        codebook_dim (int): Codebook dimension for RandomProjectionQuantizer.
+        vocab_size (int): Vocabulary size for RandomProjectionQuantizer.
+        rq_input_type (str): 'teacher' or 'spectrogram'. Source for quantization targets.
+    """
+
+    def __init__(
+        self,
+        optimizer: torch.optim.Optimizer,
+        net: Dict[str, Any],
+        warmup_pct: float = 0.1,
+        final_lr_ratio: float = 0.001,
+        ema_decay: float = 0.996,
+        ema_end_decay: float = 1.0,
+        ema_anneal_end_step: Optional[int] = None,
+        spectrogram_adjustment_mode: str = "pad",
+        jepa_criterion: Optional[torch.nn.Module] = None,
+        rq_criterion: Optional[torch.nn.Module] = None,
+        rq_lambda: float = 0.5,
+        codebook_dim: int = 16,
+        vocab_size: int = 8192,
+        rq_input_type: str = "teacher",
+    ):
+        super().__init__(
+            optimizer=optimizer,
+            net=net,
+            warmup_pct=warmup_pct,
+            final_lr_ratio=final_lr_ratio,
+            ema_decay=ema_decay,
+            ema_end_decay=ema_end_decay,
+            ema_anneal_end_step=ema_anneal_end_step,
+            spectrogram_adjustment_mode=spectrogram_adjustment_mode,
+            criterion=jepa_criterion,  # Pass jepa_criterion as criterion to base class
+        )
+        self.save_hyperparameters(
+            logger=False, ignore=["jepa_criterion", "rq_criterion", "net", "optimizer"]
+        )
+
+        self.rq_lambda = rq_lambda
+        # Store rq_criterion separately
+        if rq_criterion is not None:
+            self.rq_criterion = (
+                rq_criterion()
+                if isinstance(rq_criterion, (type, functools.partial))
+                or callable(rq_criterion)
+                and not isinstance(rq_criterion, nn.Module)
+                else rq_criterion
+            )
+        else:
+            self.rq_criterion = nn.CrossEntropyLoss()
+
+        self.rq_input_type = rq_input_type
+        if self.rq_input_type not in ["teacher", "spectrogram"]:
+            raise ValueError(
+                f"rq_input_type must be 'teacher' or 'spectrogram', got {self.rq_input_type}"
+            )
+
+        # Random Projection Quantizer
+        # Determine input dimension for quantizer
+        if self.rq_input_type == "teacher":
+            # Input to quantizer is teacher output which has encoder_dim
+            quantizer_input_dim = net.get("encoder", {}).get("embed_dim", 768)
+        else:  # spectrogram
+            # Input is raw patches
+            # patch_embed is locally available on self
+            patch_size = self.patch_embed.patch_size
+            in_chans = self.patch_embed.in_chans
+            quantizer_input_dim = patch_size[0] * patch_size[1] * in_chans
+
+        self.quantizer = RandomProjectionQuantizer(
+            input_dim=quantizer_input_dim, cb_dim=codebook_dim, cb_vocab=vocab_size
+        )
+        # Freeze quantizer (it is random and fixed)
+        for p in self.quantizer.parameters():
+            p.requires_grad = False
+
+        # Projection head for RQ prediction
+        # Takes predictor output (pred_dim) and predicts code indices (vocab_size)
+        predictor_config = net.get("predictor", {})
+        predictor_embed_dim = predictor_config.get("embed_dim", 768)
+        self.rq_proj = nn.Linear(predictor_embed_dim, vocab_size)
+
+    def _calculate_combined_loss(
+        self,
+        predictions_raw: torch.Tensor,
+        teacher_targets: torch.Tensor,
+        rq_logits: torch.Tensor,
+        rq_targets: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Calculates both JEPA and RQ losses and combines them.
+        """
+        # --- JEPA Loss ---
+        # Project back to encoder dimension for JEPA loss
+        predictions_jepa = self.predictor_output_proj(
+            predictions_raw
+        )  # [B, N_mask, encoder_dim]
+
+        jepa_loss = self.criterion(
+            predictions_jepa, teacher_targets
+        )  # Uses self.criterion (mapped from jepa_criterion)
+
+        # --- RQ Loss ---
+        # Calculate Scale RQ Loss
+        # Flatten for loss calculation
+        rq_loss = self.rq_criterion(
+            rq_logits.reshape(-1, self.hparams.vocab_size), rq_targets.reshape(-1)
+        )
+
+        # --- Combine ---
+        total_loss = self.rq_lambda * jepa_loss + (1 - self.rq_lambda) * rq_loss
+
+        return total_loss, jepa_loss, rq_loss
+
+    def _get_raw_patches(self, spec: torch.Tensor) -> torch.Tensor:
+        """
+        Extract raw key-value patches from spectrogram.
+
+        Args:
+             spec (torch.Tensor): Adjusted spectrogram [B, C, F, T].
+
+        Returns:
+             torch.Tensor: Flattened patches [B, N, patch_dim]
+        """
+        patch_size = self.patch_embed.patch_size  # (H, W)
+
+        # Using kernel_size=patch_size, stride=patch_size ensures non-overlapping patches
+        # F.unfold returns [B, C*pH*pW, L]
+        patches = F.unfold(spec, kernel_size=patch_size, stride=patch_size)  # [B, D, N]
+        patches = patches.transpose(1, 2)  # [B, N, D]
+
+        return patches
+
+    def _get_rq_targets_input(
+        self, spec: torch.Tensor, teacher_full: torch.Tensor, mask_indices: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Helper to get the input for the RQ quantizer (either teacher embeddings or raw patches).
+        Only returns the targets for the MASKED locations.
+        """
+        if self.rq_input_type == "teacher":
+            # Teacher targets at masked locations
+            return teacher_full[:, mask_indices, :]  # [B, N_mask, encoder_dim]
+        else:
+            # Raw patches at masked locations
+            # Check if spec is None, which implies logic error in caller
+            if spec is None:
+                raise ValueError(
+                    "Spectrogram cannot be None when rq_input_type is 'spectrogram'"
+                )
+            raw_patches = self._get_raw_patches(spec)  # [B, N, patch_dim]
+            return raw_patches[:, mask_indices, :]  # [B, N_mask, patch_dim]
+
+    def training_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor:
+        waveform = batch["waveform"]
+
+        patches, current_grid_size = self._process_audio(waveform)
+        B, N, D = patches.shape
+
+        mask = self.mask_generator(1, device=self.device, grid_size=current_grid_size)
+        mask = mask.expand(B, -1)
+
+        student_out = self.compute_student(patches, mask, current_grid_size)
+        predictions_raw = self.compute_predictor(student_out, mask, current_grid_size)
+
+        self._update_teacher()
+
+        with torch.no_grad():
+            teacher_full = self.teacher(patches, grid_size=current_grid_size)
+
+        # Prepare targets and logits for RQA-JEPA
+        m = mask[0]
+        mask_indices = torch.nonzero(m).flatten()
+
+        # Teacher targets always needed for JEPA loss
+        teacher_targets = teacher_full[:, mask_indices, :]  # [B, N_mask, encoder_dim]
+
+        # RQ Targets (Quantized)
+        with torch.no_grad():
+            # Need spec for 'spectrogram' mode
+            spec = None
+            if self.rq_input_type == "spectrogram":
+                # Re-compute spectrogram as we don't have it exposed from _process_audio
+                spec = self.spectrogram(waveform)
+                spec = self._adjust_spectrogram(spec)
+
+            rq_targets_input = self._get_rq_targets_input(
+                spec, teacher_full, mask_indices
+            )
+            rq_targets = self.quantizer(rq_targets_input)  # [B, N_mask]
+
+        # RQ Logits
+        rq_logits = self.rq_proj(predictions_raw)  # [B, N_mask, vocab_size]
+
+        loss, jepa_loss, rq_loss = self._calculate_combined_loss(
+            predictions_raw, teacher_targets, rq_logits, rq_targets
+        )
+
+        self.log(
+            "train/loss", loss, on_step=True, on_epoch=True, prog_bar=True, batch_size=B
+        )
+        self.log(
+            "train/jepa_loss", jepa_loss, on_step=True, on_epoch=True, batch_size=B
+        )
+        self.log("train/rq_loss", rq_loss, on_step=True, on_epoch=True, batch_size=B)
+        return loss
+
+    def validation_step(self, batch: Dict[str, Any], batch_idx: int) -> torch.Tensor:
+        waveform = batch["waveform"]
+
+        patches, current_grid_size = self._process_audio(waveform)
+        B, N, D = patches.shape
+
+        mask = self.mask_generator(1, device=self.device, grid_size=current_grid_size)
+        mask = mask.expand(B, -1)
+
+        student_out = self.compute_student(patches, mask, current_grid_size)
+        predictions_raw = self.compute_predictor(student_out, mask, current_grid_size)
+
+        with torch.no_grad():
+            teacher_full = self.teacher(patches, grid_size=current_grid_size)
+
+            # Prepare targets and logits for RQA-JEPA
+            m = mask[0]
+            mask_indices = torch.nonzero(m).flatten()
+
+            # Teacher targets at masked locations
+            teacher_targets = teacher_full[
+                :, mask_indices, :
+            ]  # [B, N_mask, encoder_dim]
+
+            # RQ Targets (Quantized)
+            spec = None
+            if self.rq_input_type == "spectrogram":
+                spec = self.spectrogram(waveform)
+                spec = self._adjust_spectrogram(spec)
+
+            rq_targets_input = self._get_rq_targets_input(
+                spec, teacher_full, mask_indices
+            )
+            rq_targets = self.quantizer(rq_targets_input)  # [B, N_mask]
+
+            # RQ Logits
+            rq_logits = self.rq_proj(predictions_raw)  # [B, N_mask, vocab_size]
+
+            loss, jepa_loss, rq_loss = self._calculate_combined_loss(
+                predictions_raw, teacher_targets, rq_logits, rq_targets
+            )
+
+        self.log(
+            "val/loss", loss, on_step=False, on_epoch=True, prog_bar=True, batch_size=B
+        )
+        self.log("val/jepa_loss", jepa_loss, on_step=False, on_epoch=True, batch_size=B)
+        self.log("val/rq_loss", rq_loss, on_step=False, on_epoch=True, batch_size=B)
+        return loss
diff --git a/audio-embeddings/src/train.py b/audio-embeddings/src/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbb6ea7ccf3009756608df5d10f30ef537284830
--- /dev/null
+++ b/audio-embeddings/src/train.py
@@ -0,0 +1,119 @@
+import rootutils
+import hydra
+from omegaconf import DictConfig
+import lightning as L
+import torch
+from pathlib import Path
+from lightning.pytorch.loggers import WandbLogger
+from lightning.pytorch.callbacks import ModelCheckpoint
+from typing import List, Dict, Any
+
+# Setup root
+root = rootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
+
+from src.utils import instantiate_callbacks, instantiate_loggers, RankedLogger, extras  # noqa: E402
+
+log = RankedLogger(__name__, rank_zero_only=True)
+
+
+@hydra.main(version_base="1.3", config_path="../configs", config_name="train.yaml")
+def main(cfg: DictConfig) -> Dict[str, Any]:
+    # Set seed
+    if cfg.get("seed"):
+        L.seed_everything(cfg.seed, workers=True)
+
+    # Applies optional utilities
+    extras(cfg)
+
+    log.info(f"Instantiating datamodule <{cfg.data._target_}>")
+    datamodule: L.LightningDataModule = hydra.utils.instantiate(cfg.data)
+
+    log.info(f"Instantiating model <{cfg.model._target_}>")
+    model: L.LightningModule = hydra.utils.instantiate(cfg.model)
+
+    log.info("Instantiating callbacks...")
+    callbacks: List[L.Callback] = instantiate_callbacks(cfg.get("callbacks"))
+
+    callbacks_cfg = cfg.get("callbacks")
+    if (
+        isinstance(callbacks_cfg, DictConfig)
+        and "model_checkpoint" in callbacks_cfg
+        and callbacks_cfg.model_checkpoint is None
+    ):
+        log.warning(
+            "`callbacks.model_checkpoint` is null in the composed config. "
+            "Lightning will use its default ModelCheckpoint callback, which may not "
+            "save `last.ckpt` and can change filename conventions. Remove the null "
+            "override or set explicit checkpoint fields in the experiment config."
+        )
+
+    if cfg.get("train") and not any(
+        isinstance(callback, ModelCheckpoint) for callback in callbacks
+    ):
+        log.warning(
+            "No explicit ModelCheckpoint callback was instantiated from config; "
+            "Lightning default checkpointing behavior will be used."
+        )
+
+    log.info("Instantiating loggers...")
+    logger: List[L.Logger] = instantiate_loggers(cfg.get("logger"))
+
+    # Set float32 matmul precision for Tensor Cores
+    torch.set_float32_matmul_precision("medium")
+
+    # Log config tree and .hydra folder to wandb
+    for lg in logger:
+        if isinstance(lg, WandbLogger):
+            # check if config_tree.log exists
+            config_tree_path = Path(cfg.paths.output_dir, "config_tree.log")
+            if config_tree_path.exists():
+                log.info("Logging config tree to WandB...")
+                lg.experiment.save(
+                    str(config_tree_path), policy="now", base_path=cfg.paths.output_dir
+                )
+
+            # Upload .hydra folder contents
+            hydra_dir = Path(cfg.paths.output_dir, ".hydra")
+            if hydra_dir.exists() and hydra_dir.is_dir():
+                log.info("Logging .hydra folder to WandB...")
+                for hydra_file in hydra_dir.iterdir():
+                    if hydra_file.is_file():
+                        lg.experiment.save(
+                            str(hydra_file),
+                            policy="now",
+                            base_path=cfg.paths.output_dir,
+                        )
+
+    log.info(f"Instantiating trainer <{cfg.trainer._target_}>")
+    trainer: L.Trainer = hydra.utils.instantiate(
+        cfg.trainer,
+        callbacks=callbacks,
+        logger=logger,
+    )
+
+    object_dict = {
+        "cfg": cfg,
+        "datamodule": datamodule,
+        "model": model,
+        "callbacks": callbacks,
+        "logger": logger,
+        "trainer": trainer,
+    }
+
+    if cfg.get("train"):
+        log.info("Starting training!")
+        trainer.fit(model=model, datamodule=datamodule, ckpt_path=cfg.get("ckpt_path"))
+
+    if cfg.get("test"):
+        log.info("Starting testing!")
+        ckpt_path = trainer.checkpoint_callback.best_model_path
+        if ckpt_path == "":
+            log.warning("Best ckpt not found! Using current weights for testing...")
+            ckpt_path = None
+        trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path)
+
+    return object_dict
+
+
+if __name__ == "__main__":
+    main()
diff --git a/audio-embeddings/src/utils/__init__.py b/audio-embeddings/src/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fda78b613a42006c65692f86215cb28e69b206b
--- /dev/null
+++ b/audio-embeddings/src/utils/__init__.py
@@ -0,0 +1,17 @@
+from src.utils.instantiators import instantiate_callbacks, instantiate_loggers
+from src.utils.logging_utils import log_hyperparameters
+from src.utils.pylogger import RankedLogger
+from src.utils.rich_utils import enforce_tags, print_config_tree
+from src.utils.utils import extras, get_metric_value, task_wrapper
+
+__all__ = [
+    "instantiate_callbacks",
+    "instantiate_loggers",
+    "log_hyperparameters",
+    "RankedLogger",
+    "enforce_tags",
+    "print_config_tree",
+    "extras",
+    "get_metric_value",
+    "task_wrapper",
+]
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diff --git a/audio-embeddings/src/utils/instantiators.py b/audio-embeddings/src/utils/instantiators.py
new file mode 100644
index 0000000000000000000000000000000000000000..82b9278a465d39565942f862442ebe79549825d7
--- /dev/null
+++ b/audio-embeddings/src/utils/instantiators.py
@@ -0,0 +1,56 @@
+from typing import List
+
+import hydra
+from lightning import Callback
+from lightning.pytorch.loggers import Logger
+from omegaconf import DictConfig
+
+from src.utils import pylogger
+
+log = pylogger.RankedLogger(__name__, rank_zero_only=True)
+
+
+def instantiate_callbacks(callbacks_cfg: DictConfig) -> List[Callback]:
+    """Instantiates callbacks from config.
+
+    :param callbacks_cfg: A DictConfig object containing callback configurations.
+    :return: A list of instantiated callbacks.
+    """
+    callbacks: List[Callback] = []
+
+    if not callbacks_cfg:
+        log.warning("No callback configs found! Skipping..")
+        return callbacks
+
+    if not isinstance(callbacks_cfg, DictConfig):
+        raise TypeError("Callbacks config must be a DictConfig!")
+
+    for _, cb_conf in callbacks_cfg.items():
+        if isinstance(cb_conf, DictConfig) and "_target_" in cb_conf:
+            log.info(f"Instantiating callback <{cb_conf._target_}>")
+            callbacks.append(hydra.utils.instantiate(cb_conf))
+
+    return callbacks
+
+
+def instantiate_loggers(logger_cfg: DictConfig) -> List[Logger]:
+    """Instantiates loggers from config.
+
+    :param logger_cfg: A DictConfig object containing logger configurations.
+    :return: A list of instantiated loggers.
+    """
+    logger: List[Logger] = []
+
+    if not logger_cfg:
+        log.warning("No logger configs found! Skipping...")
+        return logger
+
+    if not isinstance(logger_cfg, DictConfig):
+        raise TypeError("Logger config must be a DictConfig!")
+
+    for _, lg_conf in logger_cfg.items():
+        if isinstance(lg_conf, DictConfig) and "_target_" in lg_conf:
+            log.info(f"Instantiating logger <{lg_conf._target_}>")
+            logger.append(hydra.utils.instantiate(lg_conf))
+
+    return logger
diff --git a/audio-embeddings/src/utils/logging_utils.py b/audio-embeddings/src/utils/logging_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..360abcdceec82e551995f756ce6ec3b2d06ae641
--- /dev/null
+++ b/audio-embeddings/src/utils/logging_utils.py
@@ -0,0 +1,57 @@
+from typing import Any, Dict
+
+from lightning_utilities.core.rank_zero import rank_zero_only
+from omegaconf import OmegaConf
+
+from src.utils import pylogger
+
+log = pylogger.RankedLogger(__name__, rank_zero_only=True)
+
+
+@rank_zero_only
+def log_hyperparameters(object_dict: Dict[str, Any]) -> None:
+    """Controls which config parts are saved by Lightning loggers.
+
+    Additionally saves:
+        - Number of model parameters
+
+    :param object_dict: A dictionary containing the following objects:
+        - `"cfg"`: A DictConfig object containing the main config.
+        - `"model"`: The Lightning model.
+        - `"trainer"`: The Lightning trainer.
+    """
+    hparams = {}
+
+    cfg = OmegaConf.to_container(object_dict["cfg"])
+    model = object_dict["model"]
+    trainer = object_dict["trainer"]
+
+    if not trainer.logger:
+        log.warning("Logger not found! Skipping hyperparameter logging...")
+        return
+
+    hparams["model"] = cfg["model"]
+
+    # save number of model parameters
+    hparams["model/params/total"] = sum(p.numel() for p in model.parameters())
+    hparams["model/params/trainable"] = sum(
+        p.numel() for p in model.parameters() if p.requires_grad
+    )
+    hparams["model/params/non_trainable"] = sum(
+        p.numel() for p in model.parameters() if not p.requires_grad
+    )
+
+    hparams["data"] = cfg["data"]
+    hparams["trainer"] = cfg["trainer"]
+
+    hparams["callbacks"] = cfg.get("callbacks")
+    hparams["extras"] = cfg.get("extras")
+
+    hparams["task_name"] = cfg.get("task_name")
+    hparams["tags"] = cfg.get("tags")
+    hparams["ckpt_path"] = cfg.get("ckpt_path")
+    hparams["seed"] = cfg.get("seed")
+
+    # send hparams to all loggers
+    for logger in trainer.loggers:
+        logger.log_hyperparams(hparams)
diff --git a/audio-embeddings/src/utils/lr_schedulers.py b/audio-embeddings/src/utils/lr_schedulers.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e54c07cd4d9a974dd26aaa0f7b2653d31b9ca9a
--- /dev/null
+++ b/audio-embeddings/src/utils/lr_schedulers.py
@@ -0,0 +1,31 @@
+import math
+
+
+class LinearWarmupCosineDecay:
+    def __init__(
+        self,
+        warmup_steps: int,
+        total_steps: int,
+        final_lr_ratio: float,
+    ):
+        self.warmup_steps = warmup_steps
+        self.total_steps = total_steps
+        self.final_lr_ratio = final_lr_ratio
+
+    def __call__(self, current_step: int) -> float:
+        if current_step < self.warmup_steps:
+            # Linear warmup
+            return float(current_step) / float(max(1, self.warmup_steps))
+
+        # Cosine decay
+        progress = float(current_step - self.warmup_steps) / float(
+            max(1, self.total_steps - self.warmup_steps)
+        )
+        progress = min(1.0, max(0.0, progress))  # Clip to [0, 1]
+
+        # Cosine decay from 1.0 to final_lr_ratio
+        # formula: final + 0.5 * (initial - final) * (1 + cos(pi * progress))
+        # scaled relative to initial lr (which is 1.0 in lambda)
+
+        cosine_part = 0.5 * (1.0 + math.cos(math.pi * progress))
+        return self.final_lr_ratio + (1.0 - self.final_lr_ratio) * cosine_part
diff --git a/audio-embeddings/src/utils/pylogger.py b/audio-embeddings/src/utils/pylogger.py
new file mode 100644
index 0000000000000000000000000000000000000000..31a76c376b8da6212f37b148bdd7182f5b0ce553
--- /dev/null
+++ b/audio-embeddings/src/utils/pylogger.py
@@ -0,0 +1,55 @@
+import logging
+from typing import Mapping, Optional
+
+from lightning_utilities.core.rank_zero import rank_prefixed_message, rank_zero_only
+
+
+class RankedLogger(logging.LoggerAdapter):
+    """A multi-GPU-friendly python command line logger."""
+
+    def __init__(
+        self,
+        name: str = __name__,
+        rank_zero_only: bool = False,
+        extra: Optional[Mapping[str, object]] = None,
+    ) -> None:
+        """Initializes a multi-GPU-friendly python command line logger that logs on all processes
+        with their rank prefixed in the log message.
+
+        :param name: The name of the logger. Default is ``__name__``.
+        :param rank_zero_only: Whether to force all logs to only occur on the rank zero process. Default is `False`.
+        :param extra: (Optional) A dict-like object which provides contextual information. See `logging.LoggerAdapter`.
+        """
+        logger = logging.getLogger(name)
+        super().__init__(logger=logger, extra=extra)
+        self.rank_zero_only = rank_zero_only
+
+    def log(
+        self, level: int, msg: str, rank: Optional[int] = None, *args, **kwargs
+    ) -> None:
+        """Delegate a log call to the underlying logger, after prefixing its message with the rank
+        of the process it's being logged from. If `'rank'` is provided, then the log will only
+        occur on that rank/process.
+
+        :param level: The level to log at. Look at `logging.__init__.py` for more information.
+        :param msg: The message to log.
+        :param rank: The rank to log at.
+        :param args: Additional args to pass to the underlying logging function.
+        :param kwargs: Any additional keyword args to pass to the underlying logging function.
+        """
+        if self.isEnabledFor(level):
+            msg, kwargs = self.process(msg, kwargs)
+            current_rank = getattr(rank_zero_only, "rank", None)
+            if current_rank is None:
+                raise RuntimeError(
+                    "The `rank_zero_only.rank` needs to be set before use"
+                )
+            msg = rank_prefixed_message(msg, current_rank)
+            if self.rank_zero_only:
+                if current_rank == 0:
+                    self.logger.log(level, msg, *args, **kwargs)
+            else:
+                if rank is None:
+                    self.logger.log(level, msg, *args, **kwargs)
+                elif current_rank == rank:
+                    self.logger.log(level, msg, *args, **kwargs)
diff --git a/audio-embeddings/src/utils/rich_utils.py b/audio-embeddings/src/utils/rich_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..aeec6806bb1e4a15a04b91b710a546231590ab14
--- /dev/null
+++ b/audio-embeddings/src/utils/rich_utils.py
@@ -0,0 +1,99 @@
+from pathlib import Path
+from typing import Sequence
+
+import rich
+import rich.syntax
+import rich.tree
+from hydra.core.hydra_config import HydraConfig
+from lightning_utilities.core.rank_zero import rank_zero_only
+from omegaconf import DictConfig, OmegaConf, open_dict
+from rich.prompt import Prompt
+
+from src.utils import pylogger
+
+log = pylogger.RankedLogger(__name__, rank_zero_only=True)
+
+
+@rank_zero_only
+def print_config_tree(
+    cfg: DictConfig,
+    print_order: Sequence[str] = (
+        "data",
+        "model",
+        "callbacks",
+        "logger",
+        "trainer",
+        "paths",
+        "extras",
+    ),
+    resolve: bool = False,
+    save_to_file: bool = False,
+) -> None:
+    """Prints the contents of a DictConfig as a tree structure using the Rich library.
+
+    :param cfg: A DictConfig composed by Hydra.
+    :param print_order: Determines in what order config components are printed. Default is ``("data", "model",
+    "callbacks", "logger", "trainer", "paths", "extras")``.
+    :param resolve: Whether to resolve reference fields of DictConfig. Default is ``False``.
+    :param save_to_file: Whether to export config to the hydra output folder. Default is ``False``.
+    """
+    style = "dim"
+    tree = rich.tree.Tree("CONFIG", style=style, guide_style=style)
+
+    queue = []
+
+    # add fields from `print_order` to queue
+    for field in print_order:
+        queue.append(field) if field in cfg else log.warning(
+            f"Field '{field}' not found in config. Skipping '{field}' config printing..."
+        )
+
+    # add all the other fields to queue (not specified in `print_order`)
+    for field in cfg:
+        if field not in queue:
+            queue.append(field)
+
+    # generate config tree from queue
+    for field in queue:
+        branch = tree.add(field, style=style, guide_style=style)
+
+        config_group = cfg[field]
+        if isinstance(config_group, DictConfig):
+            branch_content = OmegaConf.to_yaml(config_group, resolve=resolve)
+        else:
+            branch_content = str(config_group)
+
+        branch.add(rich.syntax.Syntax(branch_content, "yaml"))
+
+    # print config tree
+    rich.print(tree)
+
+    # save config tree to file
+    if save_to_file:
+        with open(Path(cfg.paths.output_dir, "config_tree.log"), "w") as file:
+            rich.print(tree, file=file)
+
+
+@rank_zero_only
+def enforce_tags(cfg: DictConfig, save_to_file: bool = False) -> None:
+    """Prompts user to input tags from command line if no tags are provided in config.
+
+    :param cfg: A DictConfig composed by Hydra.
+    :param save_to_file: Whether to export tags to the hydra output folder. Default is ``False``.
+    """
+    if not cfg.get("tags"):
+        if "id" in HydraConfig().cfg.hydra.job:
+            raise ValueError("Specify tags before launching a multirun!")
+
+        log.warning("No tags provided in config. Prompting user to input tags...")
+        tags = Prompt.ask("Enter a list of comma separated tags", default="dev")
+        tags = [t.strip() for t in tags.split(",") if t != ""]
+
+        with open_dict(cfg):
+            cfg.tags = tags
+
+        log.info(f"Tags: {cfg.tags}")
+
+    if save_to_file:
+        with open(Path(cfg.paths.output_dir, "tags.log"), "w") as file:
+            rich.print(cfg.tags, file=file)
diff --git a/audio-embeddings/src/utils/utils.py b/audio-embeddings/src/utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..61cc5839f8e02e4c7d6f173c5eea25b870d93210
--- /dev/null
+++ b/audio-embeddings/src/utils/utils.py
@@ -0,0 +1,121 @@
+import warnings
+from importlib.util import find_spec
+from typing import Any, Callable, Dict, Optional, Tuple
+
+from omegaconf import DictConfig
+
+from src.utils import pylogger, rich_utils
+
+log = pylogger.RankedLogger(__name__, rank_zero_only=True)
+
+
+def extras(cfg: DictConfig) -> None:
+    """Applies optional utilities before the task is started.
+
+    Utilities:
+        - Ignoring python warnings
+        - Setting tags from command line
+        - Rich config printing
+
+    :param cfg: A DictConfig object containing the config tree.
+    """
+    # return if no `extras` config
+    if not cfg.get("extras"):
+        log.warning("Extras config not found! <cfg.extras=null>")
+        return
+
+    # disable python warnings
+    if cfg.extras.get("ignore_warnings"):
+        log.info("Disabling python warnings! <cfg.extras.ignore_warnings=True>")
+        warnings.filterwarnings("ignore")
+
+    # prompt user to input tags from command line if none are provided in the config
+    if cfg.extras.get("enforce_tags"):
+        log.info("Enforcing tags! <cfg.extras.enforce_tags=True>")
+        rich_utils.enforce_tags(cfg, save_to_file=True)
+
+    # pretty print config tree using Rich library
+    if cfg.extras.get("print_config"):
+        log.info("Printing config tree with Rich! <cfg.extras.print_config=True>")
+        rich_utils.print_config_tree(cfg, resolve=True, save_to_file=True)
+
+
+def task_wrapper(task_func: Callable) -> Callable:
+    """Optional decorator that controls the failure behavior when executing the task function.
+
+    This wrapper can be used to:
+        - make sure loggers are closed even if the task function raises an exception (prevents multirun failure)
+        - save the exception to a `.log` file
+        - mark the run as failed with a dedicated file in the `logs/` folder (so we can find and rerun it later)
+        - etc. (adjust depending on your needs)
+
+    Example:
+    ```
+    @utils.task_wrapper
+    def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+        ...
+        return metric_dict, object_dict
+    ```
+
+    :param task_func: The task function to be wrapped.
+
+    :return: The wrapped task function.
+    """
+
+    def wrap(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+        # execute the task
+        try:
+            metric_dict, object_dict = task_func(cfg=cfg)
+
+        # things to do if exception occurs
+        except Exception as ex:
+            # save exception to `.log` file
+            log.exception("")
+
+            # some hyperparameter combinations might be invalid or cause out-of-memory errors
+            # so when using hparam search plugins like Optuna, you might want to disable
+            # raising the below exception to avoid multirun failure
+            raise ex
+
+        # things to always do after either success or exception
+        finally:
+            # display output dir path in terminal
+            log.info(f"Output dir: {cfg.paths.output_dir}")
+
+            # always close wandb run (even if exception occurs so multirun won't fail)
+            if find_spec("wandb"):  # check if wandb is installed
+                import wandb
+
+                if wandb.run:
+                    log.info("Closing wandb!")
+                    wandb.finish()
+
+        return metric_dict, object_dict
+
+    return wrap
+
+
+def get_metric_value(
+    metric_dict: Dict[str, Any], metric_name: Optional[str]
+) -> Optional[float]:
+    """Safely retrieves value of the metric logged in LightningModule.
+
+    :param metric_dict: A dict containing metric values.
+    :param metric_name: If provided, the name of the metric to retrieve.
+    :return: If a metric name was provided, the value of the metric.
+    """
+    if not metric_name:
+        log.info("Metric name is None! Skipping metric value retrieval...")
+        return None
+
+    if metric_name not in metric_dict:
+        raise Exception(
+            f"Metric value not found! <metric_name={metric_name}>\n"
+            "Make sure metric name logged in LightningModule is correct!\n"
+            "Make sure `optimized_metric` name in `hparams_search` config is correct!"
+        )
+
+    metric_value = metric_dict[metric_name].item()
+    log.info(f"Retrieved metric value! <{metric_name}={metric_value}>")
+
+    return metric_value
diff --git a/audio-embeddings/tests/__init__.py b/audio-embeddings/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/audio-embeddings/tests/conftest.py b/audio-embeddings/tests/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..23d5fc41824fe9892c85968c2ecb34098a925e84
--- /dev/null
+++ b/audio-embeddings/tests/conftest.py
@@ -0,0 +1,109 @@
+"""This file prepares config fixtures for other tests."""
+
+from pathlib import Path
+
+import pytest
+import rootutils
+from hydra import compose, initialize
+from hydra.core.global_hydra import GlobalHydra
+from omegaconf import DictConfig, open_dict
+
+
+@pytest.fixture(scope="package")
+def cfg_train_global() -> DictConfig:
+    """A pytest fixture for setting up a default Hydra DictConfig for training.
+
+    :return: A DictConfig object containing a default Hydra configuration for training.
+    """
+    with initialize(version_base="1.3", config_path="../configs"):
+        cfg = compose(config_name="train.yaml", return_hydra_config=True, overrides=[])
+
+        # set defaults for all tests
+        with open_dict(cfg):
+            cfg.paths.root_dir = str(rootutils.find_root(indicator=".project-root"))
+            cfg.trainer.max_epochs = 1
+            cfg.trainer.limit_train_batches = 0.01
+            cfg.trainer.limit_val_batches = 0.1
+            cfg.trainer.limit_test_batches = 0.1
+            cfg.trainer.accelerator = "cpu"
+            cfg.trainer.devices = 1
+            cfg.data.num_workers = 0
+            cfg.data.pin_memory = False
+            cfg.extras.print_config = False
+            cfg.extras.enforce_tags = False
+            cfg.logger = None
+
+    return cfg
+
+
+@pytest.fixture(scope="package")
+def cfg_eval_global() -> DictConfig:
+    """A pytest fixture for setting up a default Hydra DictConfig for evaluation.
+
+    :return: A DictConfig containing a default Hydra configuration for evaluation.
+    """
+    with initialize(version_base="1.3", config_path="../configs"):
+        cfg = compose(
+            config_name="eval.yaml", return_hydra_config=True, overrides=["ckpt_path=."]
+        )
+
+        # set defaults for all tests
+        with open_dict(cfg):
+            cfg.paths.root_dir = str(rootutils.find_root(indicator=".project-root"))
+            cfg.trainer.max_epochs = 1
+            cfg.trainer.limit_test_batches = 0.1
+            cfg.trainer.accelerator = "cpu"
+            cfg.trainer.devices = 1
+            cfg.data.num_workers = 0
+            cfg.data.pin_memory = False
+            cfg.extras.print_config = False
+            cfg.extras.enforce_tags = False
+            cfg.logger = None
+
+    return cfg
+
+
+@pytest.fixture(scope="function")
+def cfg_train(cfg_train_global: DictConfig, tmp_path: Path) -> DictConfig:
+    """A pytest fixture built on top of the `cfg_train_global()` fixture, which accepts a temporary
+    logging path `tmp_path` for generating a temporary logging path.
+
+    This is called by each test which uses the `cfg_train` arg. Each test generates its own temporary logging path.
+
+    :param cfg_train_global: The input DictConfig object to be modified.
+    :param tmp_path: The temporary logging path.
+
+    :return: A DictConfig with updated output and log directories corresponding to `tmp_path`.
+    """
+    cfg = cfg_train_global.copy()
+
+    with open_dict(cfg):
+        cfg.paths.output_dir = str(tmp_path)
+        cfg.paths.log_dir = str(tmp_path)
+
+    yield cfg
+
+    GlobalHydra.instance().clear()
+
+
+@pytest.fixture(scope="function")
+def cfg_eval(cfg_eval_global: DictConfig, tmp_path: Path) -> DictConfig:
+    """A pytest fixture built on top of the `cfg_eval_global()` fixture, which accepts a temporary
+    logging path `tmp_path` for generating a temporary logging path.
+
+    This is called by each test which uses the `cfg_eval` arg. Each test generates its own temporary logging path.
+
+    :param cfg_train_global: The input DictConfig object to be modified.
+    :param tmp_path: The temporary logging path.
+
+    :return: A DictConfig with updated output and log directories corresponding to `tmp_path`.
+    """
+    cfg = cfg_eval_global.copy()
+
+    with open_dict(cfg):
+        cfg.paths.output_dir = str(tmp_path)
+        cfg.paths.log_dir = str(tmp_path)
+
+    yield cfg
+
+    GlobalHydra.instance().clear()
diff --git a/audio-embeddings/tests/helpers/__init__.py b/audio-embeddings/tests/helpers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/audio-embeddings/tests/helpers/package_available.py b/audio-embeddings/tests/helpers/package_available.py
new file mode 100644
index 0000000000000000000000000000000000000000..0afdba8dc1efd49f9d8c1a47ede62b7e206b99f3
--- /dev/null
+++ b/audio-embeddings/tests/helpers/package_available.py
@@ -0,0 +1,32 @@
+import platform
+
+import pkg_resources
+from lightning.fabric.accelerators import TPUAccelerator
+
+
+def _package_available(package_name: str) -> bool:
+    """Check if a package is available in your environment.
+
+    :param package_name: The name of the package to be checked.
+
+    :return: `True` if the package is available. `False` otherwise.
+    """
+    try:
+        return pkg_resources.require(package_name) is not None
+    except pkg_resources.DistributionNotFound:
+        return False
+
+
+_TPU_AVAILABLE = TPUAccelerator.is_available()
+
+_IS_WINDOWS = platform.system() == "Windows"
+
+_SH_AVAILABLE = not _IS_WINDOWS and _package_available("sh")
+
+_DEEPSPEED_AVAILABLE = not _IS_WINDOWS and _package_available("deepspeed")
+_FAIRSCALE_AVAILABLE = not _IS_WINDOWS and _package_available("fairscale")
+
+_WANDB_AVAILABLE = _package_available("wandb")
+_NEPTUNE_AVAILABLE = _package_available("neptune")
+_COMET_AVAILABLE = _package_available("comet_ml")
+_MLFLOW_AVAILABLE = _package_available("mlflow")
diff --git a/audio-embeddings/tests/helpers/run_if.py b/audio-embeddings/tests/helpers/run_if.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd33b3c7367e123f7b902df5655c702b9eb08534
--- /dev/null
+++ b/audio-embeddings/tests/helpers/run_if.py
@@ -0,0 +1,140 @@
+"""Adapted from:
+
+https://github.com/PyTorchLightning/pytorch-lightning/blob/master/tests/helpers/runif.py
+"""
+
+import sys
+from typing import Any, Dict, Optional
+
+import pytest
+import torch
+from packaging.version import Version
+from pkg_resources import get_distribution
+from pytest import MarkDecorator
+
+from tests.helpers.package_available import (
+    _COMET_AVAILABLE,
+    _DEEPSPEED_AVAILABLE,
+    _FAIRSCALE_AVAILABLE,
+    _IS_WINDOWS,
+    _MLFLOW_AVAILABLE,
+    _NEPTUNE_AVAILABLE,
+    _SH_AVAILABLE,
+    _TPU_AVAILABLE,
+    _WANDB_AVAILABLE,
+)
+
+
+class RunIf:
+    """RunIf wrapper for conditional skipping of tests.
+
+    Fully compatible with `@pytest.mark`.
+
+    Example:
+
+    ```python
+        @RunIf(min_torch="1.8")
+        @pytest.mark.parametrize("arg1", [1.0, 2.0])
+        def test_wrapper(arg1):
+            assert arg1 > 0
+    ```
+    """
+
+    def __new__(
+        cls,
+        min_gpus: int = 0,
+        min_torch: Optional[str] = None,
+        max_torch: Optional[str] = None,
+        min_python: Optional[str] = None,
+        skip_windows: bool = False,
+        sh: bool = False,
+        tpu: bool = False,
+        fairscale: bool = False,
+        deepspeed: bool = False,
+        wandb: bool = False,
+        neptune: bool = False,
+        comet: bool = False,
+        mlflow: bool = False,
+        **kwargs: Dict[Any, Any],
+    ) -> MarkDecorator:
+        """Creates a new `@RunIf` `MarkDecorator` decorator.
+
+        :param min_gpus: Min number of GPUs required to run test.
+        :param min_torch: Minimum pytorch version to run test.
+        :param max_torch: Maximum pytorch version to run test.
+        :param min_python: Minimum python version required to run test.
+        :param skip_windows: Skip test for Windows platform.
+        :param tpu: If TPU is available.
+        :param sh: If `sh` module is required to run the test.
+        :param fairscale: If `fairscale` module is required to run the test.
+        :param deepspeed: If `deepspeed` module is required to run the test.
+        :param wandb: If `wandb` module is required to run the test.
+        :param neptune: If `neptune` module is required to run the test.
+        :param comet: If `comet` module is required to run the test.
+        :param mlflow: If `mlflow` module is required to run the test.
+        :param kwargs: Native `pytest.mark.skipif` keyword arguments.
+        """
+        conditions = []
+        reasons = []
+
+        if min_gpus:
+            conditions.append(torch.cuda.device_count() < min_gpus)
+            reasons.append(f"GPUs>={min_gpus}")
+
+        if min_torch:
+            torch_version = get_distribution("torch").version
+            conditions.append(Version(torch_version) < Version(min_torch))
+            reasons.append(f"torch>={min_torch}")
+
+        if max_torch:
+            torch_version = get_distribution("torch").version
+            conditions.append(Version(torch_version) >= Version(max_torch))
+            reasons.append(f"torch<{max_torch}")
+
+        if min_python:
+            py_version = f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"
+            conditions.append(Version(py_version) < Version(min_python))
+            reasons.append(f"python>={min_python}")
+
+        if skip_windows:
+            conditions.append(_IS_WINDOWS)
+            reasons.append("does not run on Windows")
+
+        if tpu:
+            conditions.append(not _TPU_AVAILABLE)
+            reasons.append("TPU")
+
+        if sh:
+            conditions.append(not _SH_AVAILABLE)
+            reasons.append("sh")
+
+        if fairscale:
+            conditions.append(not _FAIRSCALE_AVAILABLE)
+            reasons.append("fairscale")
+
+        if deepspeed:
+            conditions.append(not _DEEPSPEED_AVAILABLE)
+            reasons.append("deepspeed")
+
+        if wandb:
+            conditions.append(not _WANDB_AVAILABLE)
+            reasons.append("wandb")
+
+        if neptune:
+            conditions.append(not _NEPTUNE_AVAILABLE)
+            reasons.append("neptune")
+
+        if comet:
+            conditions.append(not _COMET_AVAILABLE)
+            reasons.append("comet")
+
+        if mlflow:
+            conditions.append(not _MLFLOW_AVAILABLE)
+            reasons.append("mlflow")
+
+        reasons = [rs for cond, rs in zip(conditions, reasons) if cond]
+        return pytest.mark.skipif(
+            condition=any(conditions),
+            reason=f"Requires: [{' + '.join(reasons)}]",
+            **kwargs,
+        )
diff --git a/audio-embeddings/tests/helpers/run_sh_command.py b/audio-embeddings/tests/helpers/run_sh_command.py
new file mode 100644
index 0000000000000000000000000000000000000000..fdd2ed633f1185dd7936924616be6a6359a7bca7
--- /dev/null
+++ b/audio-embeddings/tests/helpers/run_sh_command.py
@@ -0,0 +1,22 @@
+from typing import List
+
+import pytest
+
+from tests.helpers.package_available import _SH_AVAILABLE
+
+if _SH_AVAILABLE:
+    import sh
+
+
+def run_sh_command(command: List[str]) -> None:
+    """Default method for executing shell commands with `pytest` and `sh` package.
+
+    :param command: A list of shell commands as strings.
+    """
+    msg = None
+    try:
+        sh.python(command)
+    except sh.ErrorReturnCode as e:
+        msg = e.stderr.decode()
+    if msg:
+        pytest.fail(msg=msg)
diff --git a/audio-embeddings/tests/test_configs.py b/audio-embeddings/tests/test_configs.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7041dc78cc207489255d8618c4a2e75ba74464d
--- /dev/null
+++ b/audio-embeddings/tests/test_configs.py
@@ -0,0 +1,37 @@
+import hydra
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import DictConfig
+
+
+def test_train_config(cfg_train: DictConfig) -> None:
+    """Tests the training configuration provided by the `cfg_train` pytest fixture.
+
+    :param cfg_train: A DictConfig containing a valid training configuration.
+    """
+    assert cfg_train
+    assert cfg_train.data
+    assert cfg_train.model
+    assert cfg_train.trainer
+
+    HydraConfig().set_config(cfg_train)
+
+    hydra.utils.instantiate(cfg_train.data)
+    hydra.utils.instantiate(cfg_train.model)
+    hydra.utils.instantiate(cfg_train.trainer)
+
+
+def test_eval_config(cfg_eval: DictConfig) -> None:
+    """Tests the evaluation configuration provided by the `cfg_eval` pytest fixture.
+
+    :param cfg_train: A DictConfig containing a valid evaluation configuration.
+    """
+    assert cfg_eval
+    assert cfg_eval.data
+    assert cfg_eval.model
+    assert cfg_eval.trainer
+
+    HydraConfig().set_config(cfg_eval)
+
+    hydra.utils.instantiate(cfg_eval.data)
+    hydra.utils.instantiate(cfg_eval.model)
+    hydra.utils.instantiate(cfg_eval.trainer)
diff --git a/audio-embeddings/tests/test_eval.py b/audio-embeddings/tests/test_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..d476a446c6dc824b7fab3814002cefe917865cea
--- /dev/null
+++ b/audio-embeddings/tests/test_eval.py
@@ -0,0 +1,44 @@
+import os
+from pathlib import Path
+
+import pytest
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import DictConfig, open_dict
+
+from src.eval import evaluate
+from src.train import train
+
+
+@pytest.mark.slow
+def test_train_eval(
+    tmp_path: Path, cfg_train: DictConfig, cfg_eval: DictConfig
+) -> None:
+    """Tests training and evaluation by training for 1 epoch with `train.py` then evaluating with
+    `eval.py`.
+
+    :param tmp_path: The temporary logging path.
+    :param cfg_train: A DictConfig containing a valid training configuration.
+    :param cfg_eval: A DictConfig containing a valid evaluation configuration.
+    """
+    assert str(tmp_path) == cfg_train.paths.output_dir == cfg_eval.paths.output_dir
+
+    with open_dict(cfg_train):
+        cfg_train.trainer.max_epochs = 1
+        cfg_train.test = True
+
+    HydraConfig().set_config(cfg_train)
+    train_metric_dict, _ = train(cfg_train)
+
+    assert "last.ckpt" in os.listdir(tmp_path / "checkpoints")
+
+    with open_dict(cfg_eval):
+        cfg_eval.ckpt_path = str(tmp_path / "checkpoints" / "last.ckpt")
+
+    HydraConfig().set_config(cfg_eval)
+    test_metric_dict, _ = evaluate(cfg_eval)
+
+    assert test_metric_dict["test/acc"] > 0.0
+    assert (
+        abs(train_metric_dict["test/acc"].item() - test_metric_dict["test/acc"].item())
+        < 0.001
+    )
diff --git a/audio-embeddings/tests/test_sweeps.py b/audio-embeddings/tests/test_sweeps.py
new file mode 100644
index 0000000000000000000000000000000000000000..7856b1551df4e3d4979110ede30076e6a703976f
--- /dev/null
+++ b/audio-embeddings/tests/test_sweeps.py
@@ -0,0 +1,107 @@
+from pathlib import Path
+
+import pytest
+
+from tests.helpers.run_if import RunIf
+from tests.helpers.run_sh_command import run_sh_command
+
+startfile = "src/train.py"
+overrides = ["logger=[]"]
+
+
+@RunIf(sh=True)
+@pytest.mark.slow
+def test_experiments(tmp_path: Path) -> None:
+    """Test running all available experiment configs with `fast_dev_run=True.`
+
+    :param tmp_path: The temporary logging path.
+    """
+    command = [
+        startfile,
+        "-m",
+        "experiment=glob(*)",
+        "hydra.sweep.dir=" + str(tmp_path),
+        "++trainer.fast_dev_run=true",
+    ] + overrides
+    run_sh_command(command)
+
+
+@RunIf(sh=True)
+@pytest.mark.slow
+def test_hydra_sweep(tmp_path: Path) -> None:
+    """Test default hydra sweep.
+
+    :param tmp_path: The temporary logging path.
+    """
+    command = [
+        startfile,
+        "-m",
+        "hydra.sweep.dir=" + str(tmp_path),
+        "model.optimizer.lr=0.005,0.01",
+        "++trainer.fast_dev_run=true",
+    ] + overrides
+
+    run_sh_command(command)
+
+
+@RunIf(sh=True)
+@pytest.mark.slow
+def test_hydra_sweep_ddp_sim(tmp_path: Path) -> None:
+    """Test default hydra sweep with ddp sim.
+
+    :param tmp_path: The temporary logging path.
+    """
+    command = [
+        startfile,
+        "-m",
+        "hydra.sweep.dir=" + str(tmp_path),
+        "trainer=ddp_sim",
+        "trainer.max_epochs=3",
+        "+trainer.limit_train_batches=0.01",
+        "+trainer.limit_val_batches=0.1",
+        "+trainer.limit_test_batches=0.1",
+        "model.optimizer.lr=0.005,0.01,0.02",
+    ] + overrides
+    run_sh_command(command)
+
+
+@RunIf(sh=True)
+@pytest.mark.slow
+def test_optuna_sweep(tmp_path: Path) -> None:
+    """Test Optuna hyperparam sweeping.
+
+    :param tmp_path: The temporary logging path.
+    """
+    command = [
+        startfile,
+        "-m",
+        "hparams_search=mnist_optuna",
+        "hydra.sweep.dir=" + str(tmp_path),
+        "hydra.sweeper.n_trials=10",
+        "hydra.sweeper.sampler.n_startup_trials=5",
+        "++trainer.fast_dev_run=true",
+    ] + overrides
+    run_sh_command(command)
+
+
+@RunIf(wandb=True, sh=True)
+@pytest.mark.slow
+def test_optuna_sweep_ddp_sim_wandb(tmp_path: Path) -> None:
+    """Test Optuna sweep with wandb logging and ddp sim.
+
+    :param tmp_path: The temporary logging path.
+    """
+    command = [
+        startfile,
+        "-m",
+        "hparams_search=mnist_optuna",
+        "hydra.sweep.dir=" + str(tmp_path),
+        "hydra.sweeper.n_trials=5",
+        "trainer=ddp_sim",
+        "trainer.max_epochs=3",
+        "+trainer.limit_train_batches=0.01",
+        "+trainer.limit_val_batches=0.1",
+        "+trainer.limit_test_batches=0.1",
+        "logger=wandb",
+    ]
+    run_sh_command(command)
diff --git a/audio-embeddings/tests/test_train.py b/audio-embeddings/tests/test_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..c13ae02c8ae259553e0f0e8192cf054c228172dd
--- /dev/null
+++ b/audio-embeddings/tests/test_train.py
@@ -0,0 +1,108 @@
+import os
+from pathlib import Path
+
+import pytest
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import DictConfig, open_dict
+
+from src.train import train
+from tests.helpers.run_if import RunIf
+
+
+def test_train_fast_dev_run(cfg_train: DictConfig) -> None:
+    """Run for 1 train, val and test step.
+
+    :param cfg_train: A DictConfig containing a valid training configuration.
+    """
+    HydraConfig().set_config(cfg_train)
+    with open_dict(cfg_train):
+        cfg_train.trainer.fast_dev_run = True
+        cfg_train.trainer.accelerator = "cpu"
+    train(cfg_train)
+
+
+@RunIf(min_gpus=1)
+def test_train_fast_dev_run_gpu(cfg_train: DictConfig) -> None:
+    """Run for 1 train, val and test step on GPU.
+
+    :param cfg_train: A DictConfig containing a valid training configuration.
+    """
+    HydraConfig().set_config(cfg_train)
+    with open_dict(cfg_train):
+        cfg_train.trainer.fast_dev_run = True
+        cfg_train.trainer.accelerator = "gpu"
+    train(cfg_train)
+
+
+@RunIf(min_gpus=1)
+@pytest.mark.slow
+def test_train_epoch_gpu_amp(cfg_train: DictConfig) -> None:
+    """Train 1 epoch on GPU with mixed-precision.
+
+    :param cfg_train: A DictConfig containing a valid training configuration.
+    """
+    HydraConfig().set_config(cfg_train)
+    with open_dict(cfg_train):
+        cfg_train.trainer.max_epochs = 1
+        cfg_train.trainer.accelerator = "gpu"
+        cfg_train.trainer.precision = 16
+    train(cfg_train)
+
+
+@pytest.mark.slow
+def test_train_epoch_double_val_loop(cfg_train: DictConfig) -> None:
+    """Train 1 epoch with validation loop twice per epoch.
+
+    :param cfg_train: A DictConfig containing a valid training configuration.
+    """
+    HydraConfig().set_config(cfg_train)
+    with open_dict(cfg_train):
+        cfg_train.trainer.max_epochs = 1
+        cfg_train.trainer.val_check_interval = 0.5
+    train(cfg_train)
+
+
+@pytest.mark.slow
+def test_train_ddp_sim(cfg_train: DictConfig) -> None:
+    """Simulate DDP (Distributed Data Parallel) on 2 CPU processes.
+
+    :param cfg_train: A DictConfig containing a valid training configuration.
+    """
+    HydraConfig().set_config(cfg_train)
+    with open_dict(cfg_train):
+        cfg_train.trainer.max_epochs = 2
+        cfg_train.trainer.accelerator = "cpu"
+        cfg_train.trainer.devices = 2
+        cfg_train.trainer.strategy = "ddp_spawn"
+    train(cfg_train)
+
+
+@pytest.mark.slow
+def test_train_resume(tmp_path: Path, cfg_train: DictConfig) -> None:
+    """Run 1 epoch, finish, and resume for another epoch.
+
+    :param tmp_path: The temporary logging path.
+    :param cfg_train: A DictConfig containing a valid training configuration.
+    """
+    with open_dict(cfg_train):
+        cfg_train.trainer.max_epochs = 1
+
+    HydraConfig().set_config(cfg_train)
+    metric_dict_1, _ = train(cfg_train)
+
+    files = os.listdir(tmp_path / "checkpoints")
+    assert "last.ckpt" in files
+    assert "epoch_000.ckpt" in files
+
+    with open_dict(cfg_train):
+        cfg_train.ckpt_path = str(tmp_path / "checkpoints" / "last.ckpt")
+        cfg_train.trainer.max_epochs = 2
+
+    metric_dict_2, _ = train(cfg_train)
+
+    files = os.listdir(tmp_path / "checkpoints")
+    assert "epoch_001.ckpt" in files
+    assert "epoch_002.ckpt" not in files
+
+    assert metric_dict_1["train/acc"] < metric_dict_2["train/acc"]
+    assert metric_dict_1["val/acc"] < metric_dict_2["val/acc"]
diff --git a/audio-embeddings/tests/verify_custom_rope.py b/audio-embeddings/tests/verify_custom_rope.py
new file mode 100644
index 0000000000000000000000000000000000000000..31f33dedb112dd450855aa5a3a4187883e423ded
--- /dev/null
+++ b/audio-embeddings/tests/verify_custom_rope.py
@@ -0,0 +1,61 @@
+import sys
+import os
+import torch
+
+sys.path.append(os.path.abspath("."))
+from src.models.components.rope import RotaryEmbedding2D
+
+
+def verify_custom_rope():
+    dim = 64
+    rope = RotaryEmbedding2D(dim, max_res=(4, 4))
+
+    # Input: [B, num_heads, N, D]
+    # B=1, num_heads=1, N=16, D=64
+    # B=1, num_heads=1, N=16, D=64
+    # Use constant input to verify RoPE effect only
+    q = torch.ones(1, 1, 16, 64)
+    k = torch.ones(1, 1, 16, 64)
+
+    # pos_ids for 4x4 grid
+    pos_ids = torch.arange(16).unsqueeze(0)  # [1, 16]
+    grid_size = (4, 4)
+
+    q_rot, k_rot = rope(q, k, pos_ids, grid_size)
+
+    print(f"Output shape: {q_rot.shape}")
+
+    # Reshape to grid [H, W, D]
+    q_grid = q_rot.reshape(4, 4, 64)
+
+    # Check diff along W (0,0) vs (0,1)
+    # Should ONLY affect the second half (W part)
+    # First half (H part) should be IDENTICAL because H is same (0)
+
+    diff_w_first_half = (q_grid[0, 0, :32] - q_grid[0, 1, :32]).abs().sum()
+    diff_w_second_half = (q_grid[0, 0, 32:] - q_grid[0, 1, 32:]).abs().sum()
+
+    print(f"Diff W (First Half - H part): {diff_w_first_half}")
+    print(f"Diff W (Second Half - W part): {diff_w_second_half}")
+
+    # Check diff along H (0,0) vs (1,0)
+    # Should ONLY affect the first half (H part)
+    # Second half (W part) should be IDENTICAL because W is same (0)
+
+    diff_h_first_half = (q_grid[0, 0, :32] - q_grid[1, 0, :32]).abs().sum()
+    diff_h_second_half = (q_grid[0, 0, 32:] - q_grid[1, 0, 32:]).abs().sum()
+
+    print(f"Diff H (First Half - H part): {diff_h_first_half}")
+    print(f"Diff H (Second Half - W part): {diff_h_second_half}")
+
+    # Assertions
+    assert diff_w_first_half < 1e-5, "First half should not change with W"
+    assert diff_w_second_half > 1.0, "Second half should change with W"
+    assert diff_h_first_half > 1.0, "First half should change with H"
+    assert diff_h_second_half < 1e-5, "Second half should not change with H"
+
+    print("Verification Successful!")
+
+
+if __name__ == "__main__":
+    verify_custom_rope()
diff --git a/audio-embeddings/tests/verify_data.py b/audio-embeddings/tests/verify_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..c18211432a9291795da897f9a6ec1cdfdeb379b0
--- /dev/null
+++ b/audio-embeddings/tests/verify_data.py
@@ -0,0 +1,45 @@
+import sys
+import os
+
+# Add src to path
+sys.path.append(os.path.abspath("src"))
+
+from data.audioset_datamodule import AudioSetDataModule
+
+
+def verify_data():
+    print("Initializing DataModule...")
+    dm = AudioSetDataModule(
+        data_dir="data/AudioSet",
+        batch_size=4,
+        num_workers=0,  # Use 0 for debugging
+        target_sample_rate=32000,
+    )
+    dm.setup()
+
+    print(f"Train dataset size: {len(dm.train_dataset)}")
+    print(f"Val dataset size: {len(dm.val_dataset)}")
+
+    print("Fetching a batch...")
+    loader = dm.train_dataloader()
+    batch = next(iter(loader))
+
+    waveform = batch["waveform"]
+    target = batch["target"]
+    audio_name = batch["audio_name"]
+    index = batch["index"]
+
+    print(f"Waveform shape: {waveform.shape}")
+    print(f"Target shape: {target.shape}")
+    print(f"Audio names: {audio_name}")
+    print(f"Indices: {index}")
+
+    assert waveform.ndim == 3, "Waveform should be [B, C, T]"
+    assert waveform.shape[1] == 1, "Channel dim should be 1"
+    assert waveform.shape[2] == 320000, "Time dim should be 320000"
+
+    print("Verification successful!")
+
+
+if __name__ == "__main__":
+    verify_data()
diff --git a/audio-embeddings/tests/verify_rope.py b/audio-embeddings/tests/verify_rope.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d0f644847c213dfa66950517cfdda4685b717ae
--- /dev/null
+++ b/audio-embeddings/tests/verify_rope.py
@@ -0,0 +1,58 @@
+import torch
+from timm.layers import RotaryEmbedding
+
+
+def verify_rope():
+    # 2D Grid: H=4, W=4
+    # Dim=64 (Head dim)
+    # We want half dim for H, half for W? Or how does timm handle it?
+
+    dim = 64
+    rope = RotaryEmbedding(dim, feat_shape=[4, 4])
+
+    # Input: [B, H, N, D] -> [1, 1, 16, 64]
+    x = torch.randn(1, 1, 16, 64)
+
+    # Forward
+    x_rope = rope(x)
+
+    print(f"Input shape: {x.shape}")
+    print(f"Output shape: {x_rope.shape}")
+
+    # Check if it varies along H and W
+    # Reshape to [H, W, D]
+    x_grid = x_rope.reshape(4, 4, 64)
+
+    # Check difference between (0,0) and (0,1) -> W change
+    diff_w = (x_grid[0, 0] - x_grid[0, 1]).abs().sum()
+    print(f"Diff along W: {diff_w}")
+
+    # Check difference between (0,0) and (1,0) -> H change
+    diff_h = (x_grid[0, 0] - x_grid[1, 0]).abs().sum()
+    print(f"Diff along H: {diff_h}")
+
+    # If it's 1D RoPE on flattened sequence, diff_w and diff_h would both be non-zero but structure might be different.
+    # If it's 2D, it should encode H and W separately.
+
+    # Let's check if the embedding is indeed 2D.
+    # Usually 2D RoPE splits D into D/2 for H and D/2 for W.
+    # Let's see if the first half changes with H and second half with W?
+
+    # Change in W (0,0) vs (0,1)
+    # Should affect one half?
+    diff_w_first_half = (x_grid[0, 0, :32] - x_grid[0, 1, :32]).abs().sum()
+    diff_w_second_half = (x_grid[0, 0, 32:] - x_grid[0, 1, 32:]).abs().sum()
+
+    print(f"Diff W (First Half): {diff_w_first_half}")
+    print(f"Diff W (Second Half): {diff_w_second_half}")
+
+    # Change in H (0,0) vs (1,0)
+    diff_h_first_half = (x_grid[0, 0, :32] - x_grid[1, 0, :32]).abs().sum()
+    diff_h_second_half = (x_grid[0, 0, 32:] - x_grid[1, 0, 32:]).abs().sum()
+
+    print(f"Diff H (First Half): {diff_h_first_half}")
+    print(f"Diff H (Second Half): {diff_h_second_half}")
+
+
+if __name__ == "__main__":
+    verify_rope()
diff --git a/audio-embeddings/uv.lock b/audio-embeddings/uv.lock
new file mode 100644
index 0000000000000000000000000000000000000000..f8850e557615069ddb122c8371c2e321c5e19663
--- /dev/null
+++ b/audio-embeddings/uv.lock
@@ -0,0 +1,2734 @@
+version = 1
+revision = 2
+requires-python = ">=3.12"
+resolution-markers = [
+    "sys_platform == 'linux'",
+    "sys_platform != 'linux'",
+]
+
+[[package]]
+name = "aiohappyeyeballs"
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+[[package]]
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+dependencies = [
+    { name = "aiohappyeyeballs" },
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diff --git a/config.yaml b/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..52ac1fcb148f7f7e87acb87f16a2d955a232f450
--- /dev/null
+++ b/config.yaml
@@ -0,0 +1,137 @@
+task_name: train
+tags:
+  - audioset
+  - best-rq-2
+  - cluster GPU
+train: true
+test: true
+ckpt_path: null
+seed: 21072023
+data:
+  _target_: src.data.audioset_datamodule.AudioSetDataModule
+  data_dir: ${paths.data_dir}/AudioSet
+  batch_size: 256
+  num_workers: ${oc.decode:${oc.env:SLURM_CPUS_PER_TASK}}
+  pin_memory: true
+  train_h5: full_unbal_bal_train_wav.h5
+  train_csv: silent_files_full_unbal_bal_train_wav.csv
+  val_h5: eval_soxrhq.h5
+  val_csv: silent_files_eval_soxrhq.csv
+  max_audio_length_sec: 10.0
+  target_sample_rate: 16000
+  collate_mode: pad
+model:
+  _target_: src.models.best_rq2_module.BestRQ2Module
+  optimizer:
+    _target_: torch.optim.AdamW
+    _partial_: true
+    lr: 0.0001
+    weight_decay: 0.05
+  warmup_pct: 0.05
+  spectrogram_adjustment_mode: truncate
+  criterion:
+    _target_: torch.nn.CrossEntropyLoss
+    _partial_: true
+    reduction: mean
+  codebook_dim: 16
+  vocab_size: 8192
+  net:
+    spectrogram:
+      sample_rate: ${data.target_sample_rate}
+      n_fft: 2048
+      win_length_ms: 128
+      hop_length_ms: 39.0625
+      n_mels: 128
+      f_min: 0
+      f_max: 8000
+      power: 2.0
+    patch_embed:
+      img_size:
+        - 128
+        - 256
+      patch_size:
+        - 16
+        - 16
+      in_chans: 1
+      embed_dim: 768
+    masking:
+      input_size:
+        - 128
+        - 256
+      patch_size:
+        - 16
+        - 16
+      mask_ratio:
+        - 0.4
+        - 0.6
+    encoder:
+      embed_dim: 768
+      depth: 12
+      num_heads: 12
+      mlp_ratio: 4.0
+      qkv_bias: true
+      drop_rate: 0.0
+      attn_drop_rate: 0.0
+      drop_path_rate: 0.1
+      num_patches: 128
+      pos_embed_type: sincos
+    predictor:
+      embed_dim: 768
+      depth: 4
+      num_heads: 12
+      mlp_ratio: 4.0
+      qkv_bias: true
+      drop_rate: 0.0
+      attn_drop_rate: 0.0
+      drop_path_rate: 0.0
+      num_patches: 128
+      pos_embed_type: sincos
+callbacks:
+  model_checkpoint: null
+  model_summary:
+    _target_: lightning.pytorch.callbacks.RichModelSummary
+    max_depth: 1
+  rich_progress_bar: null
+  safetensors:
+    _target_: src.callbacks.safetensors_callback.SafetensorsCallback
+  device_stats:
+    _target_: lightning.pytorch.callbacks.DeviceStatsMonitor
+  visualization:
+    _target_: src.callbacks.visualization_callback.VisualizationCallback
+    num_samples: 4
+  wandb_offline_checkpoint:
+    _target_: src.callbacks.wandb_callbacks.WandbOfflineCheckpointCallback
+logger:
+  wandb:
+    _target_: lightning.pytorch.loggers.wandb.WandbLogger
+    save_dir: ${paths.output_dir}
+    offline: true
+    id: null
+    anonymous: null
+    project: audio embeddings
+    log_model: false
+    prefix: ""
+    group: ""
+    tags: []
+    job_type: ""
+    name: best_rq2-audioset-200k-256x1bs
+trainer:
+  _target_: lightning.pytorch.trainer.Trainer
+  default_root_dir: ${paths.output_dir}
+  accelerator: gpu
+  devices: 1
+  check_val_every_n_epoch: 1
+  deterministic: false
+  max_steps: 200000
+  strategy: auto
+  max_time: 00:19:50:00
+paths:
+  root_dir: ${oc.env:PROJECT_ROOT}
+  data_dir: ${paths.root_dir}/data/
+  log_dir: ${paths.root_dir}/logs/
+  output_dir: ${hydra:runtime.output_dir}
+  work_dir: ${hydra:runtime.cwd}
+extras:
+  ignore_warnings: false
+  enforce_tags: true
+  print_config: true