sermon-transcribe

name	sermon-transcribe
description	Full local sermon audio pipeline — crop, remove ranges, silence cut, volume normalise, transcribe (Tamil+English auto-detect), extract-text, sanity checks. Run ALL steps when user provides audio; do not stop at transcribe alone. YouTube download → youtube-clip-transcribe; article → biblerevelation-sermon-articles.

Local sermon audio — full pipeline

Run on the user's Mac. Do not stop at instructions.

Golden rule

When the user provides local audio (.wav / .m4a) and asks you to process / transcribe / prepare for Spotify or article — run the full pipeline below in order. Skip a step only if the user explicitly says so (e.g. "don't cut silence") or the file is already the output of that step (e.g. already _ALTERED).

For YouTube URL → youtube-clip-transcribe (download + crop + normalise), then transcribe rules here from Step 5.

For transcript → article → biblerevelation-sermon-articles or biblerevelation-create-post.

Prerequisites

Tool	Check
OPENAI_API_KEY	bash -lc 'test -n "$OPENAI_API_KEY" && echo ok'
Repo	~/praisonai-audio-editor
ffmpeg / ffprobe	ffmpeg -version

Shell: bash -lc '…' so ~/.bashrc loads the API key.

Full pipeline (local audio)

flowchart LR
    A[Input audio] --> B{Crop times?}
    B -->|yes| C[ffmpeg crop]
    B -->|no| D
    C --> D{Remove ranges?}
    D -->|yes| E[praisonai-editor remove]
    D -->|no| F
    E --> F[cut-silence / autoedit]
    F --> G[normalize volume]
    G --> H[transcribe JSON]
    H --> I[extract-text]
    I --> J[Sanity check]
    J --> K{Article requested?}
    K -->|yes| L[biblerevelation skill]
    K -->|no| M[Deliver FINAL + transcripts]

Checklist — run all unless skipped by user

- [ ] Step 1: Time crop (if user gave start/end on a longer file)
- [ ] Step 2: Remove time ranges (if user asked to cut a section)
- [ ] Step 3: Silence cut (cut-silence.py → *_ALTERED*)
- [ ] Step 4: Volume normalise — report mean/max before & after
- [ ] Step 5: ffprobe duration on working file
- [ ] Step 6: Choose language mode (Tamil+English → omit --language)
- [ ] Step 7: Optional --speed 2 quality test (5 min sample)
- [ ] Step 8: transcribe --format json
- [ ] Step 9: extract-text → .transcript.txt
- [ ] Step 10: Sanity check (duration, words, English scripture)
- [ ] Step 11: Copy/symlink deliverable → {stem}_FINAL.m4a (or .wav)
- [ ] Step 12: Article / Spotify copy (only if user asked)

Work in ~/praisonai-audio-editor/. Keep {stem} consistent across steps; update AUDIO= after each step.

Output naming

Stage	Typical file
Cropped	{stem}_{start}_to_{end}.wav
After remove	{stem}_cut.wav
After silence cut	{stem}_ALTERED.wav
After normalise	{stem}_norm.m4a
Final deliverable	{stem}_FINAL.m4a (symlink or copy of last step)
Transcript	{stem}_FINAL.transcript.json / .txt
Log	{stem}_FINAL_transcribe.log

Transcribe the normalised file so Spotify/article match the published audio.

---

Step 1 — Time crop (optional)

When user gives start/end on a longer local file:

ffmpeg -y -nostdin -i "$SRC" -ss START -to END -map 0:a:0 -c copy "${STEM}_cropped.wav"
# end of file: omit -to, use -ss only
AUDIO="${STEM}_cropped.wav"

-to is absolute on the source timeline (same rule as YouTube skill).

---

Step 2 — Remove time ranges (optional)

When user asks to cut out a clock-time section (e.g. 11:53 to 12:43):

python3 -m praisonai_editor remove "$AUDIO" --range "11:53-12:43" \
  -o "${STEM}_cut.wav" -v
AUDIO="${STEM}_cut.wav"

Multiple cuts: repeat -r START-END. SDK: remove_time_ranges(). Agent: audio_remove_range_tool.

Not the same as: trim (phrase keep) · edit (fillers).

---

Step 3 — Silence cut (default: run)

Remove long silent gaps. Default threshold −30 dB peak (Tamil sermon noise floor).

python3 ~/praisonai-audio-editor/mac/cut-silence.py "$AUDIO"
# → {stem}_ALTERED.wav (or pass explicit output path)
AUDIO="${STEM}_ALTERED.wav"

Tune via env: CUT_SILENCE_NOISE_DB=-30 CUT_SILENCE_MIN=1.5 CUT_SILENCE_MARGIN=0.3.

Quick Action alternative: mac/autoedit-audio.sh "$AUDIO".

Skip only if user says no silence cut or file is already _ALTERED.

---

Step 4 — Volume normalise (default: run)

Target: −16 LUFS, −1.5 dBTP (Spotify / podcast). Always measure and report before & after.

# Measure first
ffmpeg -hide_banner -nostdin -i "$AUDIO" -af volumedetect -f null - 2>&1 | grep -E "mean_volume|max_volume"

cd ~/praisonai-audio-editor
python3 -m praisonai_editor normalize "$AUDIO" -o "${STEM}_norm.m4a"
# .m4a input: --in-place OK
# hot peaks (max ≥ −1 dB): add --force

Condition	Action
mean < −22 dB or max < −8 dB	Auto loudnorm
mean −22…−14, max −8…−2	Usually skip (copies file) — still run command to verify
mean > −12 dB or max ≥ −1 dB	--force

Wrapper: scripts/optimize-audio-volume.sh

AUDIO="${STEM}_norm.m4a"
ln -sf "$(realpath "$AUDIO")" "${STEM}_FINAL.m4a"   # or cp

Skip only if user says already normalised.

---

Step 5 — ffprobe

ffprobe -v error -show_entries format=duration -of default=nokey=1:noprint_wrappers=1 "$AUDIO"

---

Step 6 — Language mode

Audio	Command
Tamil sermon + English verses/quotes	Omit --language (auto-detect)
Pure Tamil	--language ta
English-dominant	--language en

Never --language ta on mixed content — English becomes garbled.

---

Step 7 — Speed (optional cost save)

OpenAI whisper-1 ≈ $0.006/min. --speed 2 halves cost; timestamps scaled in JSON.

Quality gate: test first 5 min at 2×; if repetitive or garbled → 1× auto-detect (Galatians-style Tamil default).

---

Step 8 — Transcribe

Use $AUDIO (= normalised FINAL file):

bash -lc 'cd ~/praisonai-audio-editor && \
  python3 -m praisonai_editor transcribe "$AUDIO" --format json \
    -o "${STEM}_FINAL.transcript.json" \
    2>&1 | tee "${STEM}_FINAL_transcribe.log"'

---

Step 9 — Extract plain text

python3 -m praisonai_editor extract-text "${STEM}_FINAL.transcript.json"

---

Step 10 — Sanity check

Check	Pass
duration ≈ ffprobe (±2 s)	✓
Word count >> 100 for 30 min sermon	✓
English scripture in Latin script	✓
No long repeated phrases	✓
Volume: mean/max reported in summary to user	✓

---

Step 11–12 — Deliverables

Report to user:

• {stem}_FINAL.m4a — Spotify-ready audio
• {stem}_FINAL.transcript.txt — plain text
• Volume before/after, duration, % silence removed

If user asked for article: run biblerevelation skill on .transcript.txt.
If user asked for Spotify metadata: title + description (no preacher names).

---

Do not use

Action	Why
Transcribe-only when user gave raw audio	Run full pipeline
--language ta on Tamil+English	Garbles English
--speed 2 without quality test	Tamil often fails
Skip normalise for podcast/Spotify	Quiet mean / hot peaks common on local wav
Manual ffmpeg splice for time cuts	Use praisonai-editor remove

More detail

• reference.md — decision tree, commands, troubleshooting
• youtube-clip-transcribe — YouTube path