Audio annotation tool

The audio annotation tool provides a structured workflow to label audio data for machine learning projects. It accepts audio input, applies a defined label taxonomy, and outputs machine-readable annotations suitable for training pipelines. Conceptually, users create time-stamped labels for segments or events and optionally attach transcripts or metadata to each interval. The system supports multiple annotators with audit trails, and it enforces configurable labeling policies such as overlap rules and completeness checks. Core value lies in producing consistent, export-ready labeled datasets that scale with team size and project scope. This tool also supports quality assurance metrics like label coverage and agreement statistics to improve dataset reliability. Output formats align with common ML conventions, enabling seamless ingestion by models and evaluation scripts. Use cases include speech recognition data preparation, speaker diarization labeling, sound event detection, and multilingual annotation projects. Unique differentiators include taxonomy-driven labeling, collaborative workflows with versioning, and configurable export schemas that map to JSON, JSONL, CSV, or COCO-like structures. Core features cover audio ingestion, time-stamped labeling primitives, taxonomy management, multi-user collaboration, and data validation. Optional features include automated pre-labeling via acoustic feature signals, active learning hooks, and offline mode for constrained environments.