GW-Whisper: Transformer Model for GW Data

• GW-Whisper is a novel adaptation that repurposes the Whisper audio Transformer to analyze gravitational-wave data by converting strain signals into log–mel spectrograms.
• It employs parameter-efficient fine-tuning (DoRA), updating only 0.5% of encoder parameters, which enables effective transfer learning from speech to astrophysical signals.
• The system achieves near-optimal performance in signal detection and glitch classification, offering rapid, scalable, and robust analysis for real-time gravitational-wave observatories.

GW-Whisper refers to the adaptation and application of advanced transformer-based audio models, particularly the Whisper architecture, for gravitational-wave (GW) data analysis. This approach leverages large-scale pretraining on audio (typically speech) to develop foundational AI models for GW astronomy that can be efficiently fine-tuned for diverse downstream tasks, such as detection of astrophysical signals and classification of transient noise artifacts ("glitches") (Chatterjee et al., 30 Dec 2024).

1. Origin and Motivation

GW-Whisper emerges in the era of rapidly increasing GW detection rates from advanced interferometric detectors like Advanced LIGO and Virgo. Traditional pipelines, notably Coherent WaveBurst (cWB) (Drago et al., 2020), have relied on wavelet-based time–frequency representations and model-independent likelihood analyses. However, the growing volume and complexity of GW data necessitate adaptable, scalable, and unified tools. Whisper, an encoder–decoder Transformer model originally trained on extensive audio (speech) data, offers robust time–frequency feature extraction via log–mel spectrogram inputs, which closely match the GW time–frequency domain (10–10,000 Hz). GW-Whisper explores the hypothesis that such deep audio transformers can transfer effectively to GW strain analysis with minimal, parameter-efficient fine-tuning (Chatterjee et al., 30 Dec 2024).

2. Data Transformation and Model Adaptation

To repurpose Whisper for GW analysis, raw strain data from GW detectors are preprocessed into log–mel spectrograms, matching the expected input format of Whisper’s encoder. The data are typically sampled at 16 kHz and windowed for spectrogram generation, maintaining the temporal and spectral resolution necessary for both transient event identification and noise classification.

Fine-tuning is performed using Parameter-Efficient Fine-Tuning (PEFT) strategies—in this case, Weight–Decomposed Low-Rank Adaptation (DoRA). Instead of re-training all model parameters, only a small subset is adapted; in the reported implementation, just 0.5% (around 196,608) of the ≈39M encoder parameters are updated. DoRA operates by decomposing each attention layer’s weight matrix $W$ into a magnitude component $m$ and a direction component $V$, followed by a low-rank update $V’ = V + BA$, where $A$ and $B$ are small trainable matrices (Chatterjee et al., 30 Dec 2024).

3. Model Integration and Downstream Tasks

GW-Whisper positions the adapted Whisper encoder as a universal time–frequency feature extractor. For specific tasks, its output is connected to downstream neural networks, or "heads," which may take the form of:

• Binary classifiers: To discriminate GW signals from noise transients.
• Multi-class classifiers: For glitch morphologies, using labeled glitch catalogs to enable robust noise artifact identification.

This modular approach enables the core encoder to serve simultaneous and evolving analysis tasks through lightweight downstream adaptation and avoids task-specific, labor-intensive model retraining.

4. Performance, Evaluation, and Computational Efficiency

The adapted GW-Whisper system demonstrates strong empirical performance after fine-tuning:

• Signal/Noise Discrimination: For simulated binary black hole (BBH) mergers injected into real LIGO noise, the model achieves area under ROC curves (AUC) near 1.0 for signal-to-noise ratios (SNR) exceeding 7, within only a few epochs of training.
• Glitch Classification: Efficiently separates known glitch classes, maintaining high reliability even in non-stationary or contaminated detector noise.
• Operational Efficiency: The model’s evaluation time per sample is on the order of $10^{-7}$ seconds, illustrating its suitability for real-time pipelines.

In live operation, the model’s "p-score" (probabilistic output for event likelihood) can rapidly flag astrophysical candidates above a specified threshold, supporting low-latency alert generation for multi-messenger follow-up (Chatterjee et al., 30 Dec 2024).

5. Technical Specifics and Mathematical Foundation

The Whisper encoder employs a stack of convolutional layers (for preprocessing the log–mel spectrogram), sinusoidal positional encoding, and multiple transformer blocks with multi-head self-attention. The DoRA adaptation for attention layers can be formally written as:

$W = \frac{m \cdot V}{\| V \|}, \quad V' = V + BA$

Only $A$ and $B$ are trainable, ensuring a minimal memory/computation footprint.

The downstream classifier typically consists of fully connected (FC) layers operating on encoder outputs for binary or multi-class prediction, trained via standard cross-entropy objectives for the labeled GW tasks.

6. Broader Implications and Domain Transfer

GW-Whisper advances the concept of foundation models in scientific computing: generic, pre-trained architectures that can be repurposed across domains given compatible input representations. This approach exhibits several advantages:

• Rapid domain adaptation: Large pre-trained models can be specialized to GW tasks with little labeled data and minimal fine-tuning.
• Scalability: A single, well-maintained encoder can support GW detection, noise artifact classification, and potentially regression tasks (e.g., parameter estimation).
• Robustness: The model's sensitivity and noise-rejection capabilities benefit directly from the breadth/diversity of its initial audio pretraining corpus.

The reuse of powerful speech models for astrophysical data analysis demonstrates the feasibility of cross-domain transfer when input structures (here, time–frequency patterns) overlap.

7. Perspectives and Future Directions

The demonstrated adaptation of Whisper for GW data paves the way for more extensive use of foundational AI models in astrophysics and beyond. Significant anticipated directions include:

• Continual and multi-task learning: Simultaneously supporting detection, identification, and characterization tasks from a shared encoder.
• Transfer to new detector configurations: As future GW observatories expand frequency ranges and sensitivity, minimal re-adaptation of the foundational model may suffice.
• Real-time autonomous operations: The computational efficiency and flexibility of GW-Whisper position it for integration into live observatory controls and prompt follow-up alerting systems, crucial as event rates increase.

The GW-Whisper project crystallizes the trend toward unified, data-driven pipelines in contemporary gravitational-wave astronomy, exemplifying the value of foundation model transfer and parameter-efficient adaptation for complex scientific data analysis (Chatterjee et al., 30 Dec 2024).