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Caption Studio is a transparency-first platform that converts spoken audio and video into structured, searchable content through advanced techniques such as automated transcription, speaker diarization, and signal-level audio analysis. The system employs a three-layer architecture, integrating a transcription core based on Whisper-class ASR, an audio intelligence layer for feature extraction, and a robust integration layer for workflow compatibility. A key innovation is its transparency-first framework, which enhances the traceability and interpretability of speech analytics by clearly categorizing metrics as measured, derived, or unavailable.
Every metric in Caption Studio is transparently categorized, significantly enhancing the reliability of speech analytics.
Caption Studio is a transparency-first speech and audio intelligence platform that transforms spoken audio and video into structured, searchable content through automated transcription, speaker diarization, speech analytics, signal-level audio analysis, and subtitle generation. The system is built on a FastAPI backend with a real-time dashboard and adopts a three-layer architecture comprising (i) a transcription and diarization core based on Whisper-class automatic speech recognition and pyannote speaker diarization, (ii) an audio intelligence layer that extracts acoustic and linguistic features, including waveforms, spectrograms, pitch, speaking rate, silence, filler-word frequency, and sentiment, directly from the audio signal, and (iii) an integration layer that supports data export and downstream workflow integration. A principal contribution of this work is the transparency-first framework, in which every reported metric is explicitly identified as measured, derived, or unavailable, thereby improving the traceability, interpretability, and reliability of speech analytics. The paper presents the system architecture, benchmarking methodology, explainability and uncertainty framework, and key considerations for enterprise-scale deployment.