Engineering a Clinical-Grade Vocal Fatigue Scoring Pipeline from Raw Speech
How we designed, benchmarked, and packaged ECAPA-TDNN-VHE into a pip-installable Python library with sub-second inference latency.
Lab Note & Technical Documentation
Overview
auralis-vfs is a Python library designed to provide a consistent virtual file system (VFS) abstraction for applications that need structured access to files and resources. The primary engineering objective is to separate resource access from the underlying storage implementation, allowing higher-level components to work with resources without being tightly coupled to filesystem-specific logic.
Architecture
The library introduces a VFS layer between application logic and storage operations. Instead of allowing individual modules to directly manage paths, file existence, and storage-specific behavior, these responsibilities are centralized within the library. This creates a stable interface for resource operations and reduces duplicated filesystem logic across consuming applications.
The abstraction is designed to remain lightweight and Python-native, favoring explicit interfaces and predictable behavior over unnecessary complexity. Resource paths and identifiers are normalized at the VFS boundary so that downstream components can operate on consistent representations.
Engineering Goals
The development of auralis-vfs focuses on four primary goals:
- Abstraction: isolate application code from storage-specific implementation details.
- Consistency: provide predictable behavior for resource resolution and access.
- Reliability: make filesystem failures and invalid resource requests explicit.
- Extensibility: allow additional storage backends and capabilities to be introduced without requiring major changes to consuming code.
This design is particularly useful for research and engineering pipelines where resources may originate from local datasets, generated artifacts, cached files, or other storage mechanisms.
Testing and Validation
Testing is centered on deterministic resource behavior. Important cases include valid resource resolution, existing and missing files, invalid paths, boundary conditions, and backend failures. Unit tests validate individual VFS operations, while integration tests can verify behavior against real storage implementations.
Error handling is treated as part of the public API. The library should avoid silently suppressing failures because downstream applications need to distinguish between invalid requests, missing resources, and operational errors. This makes failures easier to diagnose and improves the reliability of systems built on top of the abstraction.
Engineering Significance
The main contribution of auralis-vfs is architectural: it establishes a clear boundary between what an application wants to access and where that resource is stored. This separation reduces coupling and makes storage strategies easier to evolve.
Future development can extend the abstraction with additional backends, caching, metadata management, resource lifecycle operations, and stronger validation while preserving the core interface.
The central engineering principle is that resource management should be implemented once as a reliable abstraction rather than repeatedly across individual application components. auralis-vfs provides this foundation while keeping higher-level systems focused on their domain-specific processing and research objectives.
Project Repositories & Artifacts
Founder & Director
Building a Reproducible NLP Dataset Quality Auditor
We built `inference-audit`, a reproducible Python toolkit for systematically auditing NLP dataset quality across five core dimensions. The pipeline combines deterministic checks for label distribution, missing values, near-duplicates, language contamination, and annotation consistency. Extensive testing and real-corpus evaluation exposed performance and reliability issues, leading to targeted fixes and measurable optimizations. The final system emphasizes reproducibility, explicit failure states, citable evidence, and transparent limitations.
Read Report llm-eval-kitBuilding a Deterministic Offline Evaluation Engine for LLM Systems
We redesigned llm-eval-kit into a deterministic, zero-network evaluation pipeline built for reliable continuous evaluation in CI/CD environments. The new architecture introduces a decoupled criteria registry, fail-fast orchestration, hybrid semantic and symbolic verification, and graceful handling of inapplicable evaluation criteria. The implementation reached 93% test coverage across 88 tests, with cross-version CI validation from Python 3.9 to 3.12. The work demonstrates how carefully defined evaluation contracts and offline heuristics can provide reproducible, privacy-preserving model assessment without relying on external LLM-as-a-judge APIs.
Read Report