Audio Annotation
Expert audio annotation for model training and evaluation
Besimple combines expert human annotators with proprietary review tooling to turn raw audio into structured datasets for training, evaluation, and research.
- Annotators
- 200+
- Review loop
- QA
- Outputs
- JSON/CSV
Transcription and diarization
Label what was said, who said it, and where turns start and end with review flows for high-stakes audio data.
Structured tags
Add emotion, intent, domain, environment, speaker, and quality metadata for downstream model training and evaluation.
Verifier audit trails
Use layered review to surface disagreement, calibrate annotators, and make dataset quality inspectable before release.
Use Cases
Where this helps
- Prepare ASR, speech-to-speech, and audio-language model training datasets.
- Label exact entities such as email addresses, paths, codes, IDs, and numbers.
- Create model evaluation sets that separate transcription quality from workflow success.
- Add metadata needed for responsible dataset documentation.