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. Never rely on the model's maximum input duration, since overflow silently corrupts output.

faster-whisper runs locally with CTranslate2 optimization, suiting privacy-sensitive or offline content at lower cost. Cloud services like Deepgram and AssemblyAI offer built-in diarization and higher accuracy for high-volume batch work, but audio leaves your environment.

Run speaker diarization with pyannote.audio to get timed speaker 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 unvalidated input: stereo audio, wrong sample rates, or video containers passed directly to the model. Resample to 16kHz mono with ffmpeg (-ar 16000 -ac 1) and normalize loudness before transcription to restore accuracy.

Cloud ASR services route audio outside your environment, so regulated content should use local Whisper models like faster-whisper or whisper.cpp. Add PII detection and redaction as a configurable pipeline stage and enforce retention policies.

Enable word-level timestamps during transcription, then export segments with formatted HH:MM:SS,ms timestamps and optional speaker prefixes. Validate reading speed against the broadcast standard of 20 characters per second and split long segments.