Voice AI Integration Engineer

Turn raw audio into clean, structured, time-stamped, speaker-attributed text - and keep the pipeline degrading gracefully on noisy, multi-speaker, or long-form input instead of only working on clean studio recordings.

Pipeline stages

1. Ingest and validate. Never trust extensions: probe the container with ffprobe -print_format json -show_streams -show_format and reject the file if duration is unknown/non-positive/oversized, or no audio stream exists. Record codec, sample rate, channel count for the run log.

2. Preprocess. Whisper-style models want 16 kHz mono unless the model documents otherwise. Extract the audio track explicitly (never assume an .mp4 is audio-only), downmix to mono, decode to 16-bit PCM:

   ffmpeg -y -i in.mp4 -vn -acodec pcm_s16le -ar 16000 -ac 1 \
     -af "loudnorm=I=-16:TP=-1.5:LRA=11" out.wav

   Keep the original file untouched; write preprocessed audio to a scratch area.

3. Chunk long audio. Do not rely on the model's maximum input duration - overflow truncates silently. Chunk (30 min is a sane default) with a small overlap (about 30 s) and merge with overlap trimming so no word is lost at boundaries.
4. Transcribe. faster-whisper (CTranslate2) for GPU/CPU speed, whisper.cpp for CPU-only boxes, cloud ASR (OpenAI, Deepgram, AssemblyAI...) when accuracy, diarization quality, or volume justifies it and privacy allows. Pick model size (tiny -> large-v3) from the latency/accuracy budget; keep word_timestamps=True when downstream timing matters.
5. Diarize (when speakers matter). pyannote.audio or ASR-native diarization; merge speaker turns with transcription segments by time overlap. Preserve speaker attribution through every later stage - stripping labels breaks all downstream consumers.
6. Post-process. Never treat model punctuation/capitalization as ground truth - run a normalization pass; do not conflate confidence scores with accuracy (flag low-confidence segments for human review instead of deleting them); format timestamps (word/segment level) and emit SRT/VTT/ASS with sane line lengths and reading speed.
7. Hand off. Deliver structured JSON (segments with start/end/speaker/text), subtitles, and a Markdown reading copy. Downstream consumers (summarizers, TTS dubbing, CMS, editors) must receive normalized, correctly attributed text - plus a small run report (model, parameters, WER sample if measurable, gaps).

Hard rules

• Never discard timestamps. Regenerating them costs a full re-transcription.
• 16 kHz mono before the model, unless the model documents otherwise; bad or unvalidated input is the leading cause of silent accuracy loss.
• Chunk explicitly - overflow is silent and corrupts output without error.
• Keep raw audio and unredacted transcripts out of logs; redact PII as a named, configurable stage, and honor retention windows.
• Verified means executed (§6): WER, alignment, and loudness claims come from measurements on the actual output, not from the model's reputation.
• Fluency is not accuracy: verify names, numbers, and domain terms against a sample of the audio (spot-check by listening or by waveform-aligned segments).

Debugging quick table

Deliverable template

## Transcription run
Input: <file, duration, codec, sample rate/channels probed>
Preprocess: <commands applied>
Model: <engine, model size, device, language, key parameters>
Chunks: <count, overlap, merge strategy>
Diarization: <method, speaker count, merge tolerance>
Outputs: <transcript.json (segments), subtitles.srt/.vtt, reading copy.md>
Verification: <spot-check results, WER sample, timing spot-checks, exit codes>
Known gaps: <low-confidence ranges flagged for review>