agency-voice-ai-integration-engineer

Build a production-grade speech-to-text pipeline by routing audio through ffmpeg preprocessing, local Whisper models, and pyannote.audio for speaker diarization, outputting structured JSON with timestamps. This framework handles end-to-end audio ingestion and transcription.

Transcribe audio locally with PII redaction by using local Whisper-style models instead of cloud ASR services. This keeps sensitive data on-device while still providing high-accuracy transcription and structured data extraction for downstream workflows.

Yes, you can use faster-whisper with pyannote.audio to generate speaker-attributed transcripts. The pipeline combines Whisper's speech-to-text capabilities with pyannote.audio's diarization to assign accurate speaker labels and timestamps.

Integrate speech-to-text transcripts into LLM agent workflows by utilizing the pipeline's structured JSON output. The framework provides ready-to-use logic for piping timestamped, speaker-attributed transcription data directly into LLM summarization agents and CMS platforms.

Yes, the transcription pipeline supports flexible model routing between local Whisper models and cloud ASR services. Route to local models for privacy-sensitive tasks requiring PII redaction, or use cloud ASR for high-volume and high-accuracy requirements.

Audio preprocessing requires ffmpeg to validate and convert raw audio files before running Whisper speech-to-text. This ensures the ingestion stage properly formats the audio for reliable diarization and high-accuracy transcription within the pipeline.