agency-voice-ai-integration-engineer

Split long recordings into overlapping chunks (e.g., 30 minutes with 30-second overlap) using ffmpeg, transcribe each chunk with faster-whisper, then trim overlap regions during assembly. This prevents silent corruption from exceeding the model's maximum input duration.

faster-whisper runs locally with CTranslate2 optimization, suiting privacy-sensitive or offline workloads, while Deepgram and AssemblyAI offer managed diarization and higher throughput for batch jobs. Choose based on cost per audio hour, latency, accuracy targets, and data residency requirements.

Run speaker diarization with pyannote.audio to get speaker time segments, then assign each transcript segment the speaker with maximum time overlap. Passing a known speaker count to pyannote significantly improves diarization accuracy.

The most common cause is skipping audio preprocessing — stereo or 44.1kHz input degrades Whisper accuracy silently. Resample to 16kHz mono with ffmpeg flags -ar 16000 -ac 1 and apply loudness normalization before transcription.

Yes, enable word_timestamps in faster-whisper, then format segments as SRT with HH:MM:SS,mmm timestamps. Validate reading speed against the broadcast standard of 20 characters per second and split long segments to comply with line length limits.

Avoid cloud ASR when audio contains regulated data under HIPAA or GDPR, since audio leaves your environment. Use local Whisper deployments like faster-whisper or whisper.cpp with appropriately sized models and hardware instead.