| name | combine-then-transcribe |
| description | Concatenate multiple audio recordings into one file and send as a single transcription job. Preserves a segment map so timestamps in the unified transcript can be traced back to their source files. Use when the user has several voice memos / chapters / interview parts that belong to one session and wants a single combined transcript instead of N separate ones. |
Combine then transcribe
Stitch together multiple audio files and run them through preprocessing + ASR as a single job. Cheaper (one upload, one job) and produces a coherent single transcript.
When to use
- Several voice memos recorded in one sitting that the user thinks of as one piece
- Recording was split across files due to app/device limits (e.g. WhatsApp 16MB cap, phone storage)
- Multi-part interview where each part is a separate file
- Anything where the user says "combine these and transcribe"
Inputs
- A list of files, a glob, or a folder
- Optional order override (otherwise sort by filename, which usually matches recording order if filenames have timestamps)
Pipeline
1. Validate / harmonise
Before concatenation, all files must share the same sample rate, channel count, and codec. If they don't, normalise each one first via the preprocess-for-transcription skill (or just the format-normalise pass of it). Cheapest: normalise all to mono / 16kHz / opus, then concat.
2. Concatenate
Use ffmpeg's concat demuxer (lossless when codecs match):
{
for f in "${FILES[@]}"; do
printf "file '%s'\n" "$(realpath "$f")"
done
} > /tmp/concat-list.txt
ffmpeg -f concat -safe 0 -i /tmp/concat-list.txt -c copy COMBINED.opus
If a boundary marker is desired (helps the user / ASR see file breaks), insert a short silence between segments. Easiest approach: insert a 1s silent opus snippet between every two files in the concat list.
3. Emit segment map
Write <combined-stem>.segments.json capturing where each source file lives in the combined timeline:
{
"combined": "audio/processed/session.combined.opus",
"boundary_silence_seconds": 1.0,
"segments": [
{"index": 0, "source": "memo-01.opus", "start": 0.0, "end": 312.4},
{"index": 1, "source": "memo-02.opus", "start": 313.4, "end": 654.1},
{"index": 2, "source": "memo-03.opus", "start":
Durations come from ffprobe -i <file> -show_entries format=duration. This sidecar is what makes diarisation timestamps and "where in the recording" references traceable back to the original files.
4. Preprocess + transcribe
Invoke preprocess-for-transcription on the combined file (silence-trim is fine here — boundary silences are 1s, well under the 2.5s threshold, so they'll be preserved as boundary cues). Then invoke transcribe-assemblyai (or whichever ASR the user picked).
5. Optional — split transcript by source
After transcription, offer to split the resulting transcript back into per-source files using the segment map and AssemblyAI's word-level timestamps. Output: transcripts/<source-stem>.from-combined.md for each source.
Folder convention
- Sources stay in place (or
audio/raw/ if user is organising)
- Combined file:
audio/processed/<session-name>.combined.opus
- Segment map:
audio/processed/<session-name>.segments.json
- Transcript:
transcripts/<session-name>.combined.raw.md (then iterative-refine takes over)
Output report
Inputs: 3 files, 26m 48s total
Combined: audio/processed/session.combined.opus (mono 16kHz, 4.1 MB)
Segment map: audio/processed/session.segments.json
Boundaries: 1.0s silence between each source
Next: send to AssemblyAI? (y/n)
Edge cases
- Mixed codecs / sample rates: must re-encode (lossy concat, but for ASR purposes irrelevant). Warn the user.
- One file is corrupt / unreadable: skip it, log warning, continue with the rest. Don't fail the whole job.
- Total combined length > 4 hours: AssemblyAI handles it but costs add up — flag the estimated cost (~$0.37/hr with diarisation) before submitting.
- User passes a single file: just route directly to
preprocess-for-transcription + transcribe; no concat needed.
