agency-voice-ai-integration-engineer

Build a speech transcription pipeline by configuring audio ingestion, applying preprocessing, running Whisper models, and formatting structured text outputs. This pipeline handles raw audio conversion and delivers clean transcribed text for downstream applications.

Generate SRT subtitles and JSON from audio files by processing raw audio through faster-whisper and converting the transcription results. The pipeline outputs structured formats including JSON, SRT, VTT, and Markdown documents.

Integrate cloud ASR services with local audio processing by building an end-to-end pipeline that handles audio ingestion and transcription. The system manages both local Whisper models and cloud ASR to produce structured text outputs.

You need ffmpeg and pyannote.audio to preprocess audio for speech transcription because they handle audio ingestion and speaker diarization. These dependencies prepare raw audio files before the Whisper models execute the transcription phase.

Post-processing in a speech transcription pipeline works by refining raw transcribed text into structured formats and integrating with downstream platforms. It converts outputs into JSON, SRT, or Markdown and connects directly to CMS platforms and APIs.

Use faster-whisper instead of cloud ASR for audio transcription when you need privacy-conscious local processing without external data transmission. Cloud ASR is suitable when leveraging remote infrastructure for speech-to-text conversion tasks.