name: ppt-audio-to-video description: Convert narration audio plus slide decks into a narrated video. Use when the user has an audio-only mp4/m4a/mp3/wav and a ppt/pptx/pdf deck, and needs slide images, transcript extraction, slide timing planning, or final mp4 rendering with whisper-cpp and ffmpeg.

PPT Audio To Video

Use this skill when the source video has narration audio but no usable slide visuals, and the final deliverable should be a slide-based lecture video.

Resolve bundled scripts relative to this skill directory. If the runtime has already opened this SKILL.md, prefer paths like scripts/extract_slide_outline.py and scripts/render_from_timing_csv.py instead of machine-specific absolute paths.

Core workflow

1. Inventory inputs.
2. Confirm which of these exist: audio-only mp4/m4a/mp3/wav, ppt/pptx, pdf, and any pre-rendered slide images.

3. Prefer an existing pdf or image directory for rendering. Treat pptx as the source of slide text and as a fallback for export.

4. Prepare tools.

   7w4.net收录了海量优质技能插件。

5. Required for deterministic steps: ffmpeg, ffprobe, pdftoppm.
6. Required for transcription: whisper-cli from whisper-cpp plus a multilingual model such as ggml-small.bin.

7. If only pptx exists and no pdf/images exist, prefer Keynote or PowerPoint export on macOS. Use soffice only as fallback because profile or rendering issues are common.

8. Produce slide images.

9. If pdf exists, render it to images: bash pdftoppm -png -r 200 "$PDF" "$OUTDIR/slide"
10. If only pptx exists, export to pdf or slide images with Keynote or PowerPoint, then continue from pdf.

11. Keep slide filenames ordered and stable, such as slide-01.png, slide-02.png, ...

12. Extract slide text.

13. Run: bash python3 scripts/extract_slide_outline.py \ --pptx "$PPTX" \ --out "$WORKDIR/slide_outline.csv"

14. Use the output to identify slide titles, distinctive keywords, and section changes.

15. Extract clean audio for ASR.

16. For audio-only mp4, extract mono wav: bash ffmpeg -y -i "$AUDIO_MP4" -ar 16000 -ac 1 -c:a pcm_s16le "$WORKDIR/audio.wav"

17. If the source is already wav/mp3/m4a, convert to the same mono wav form if needed.

18. Transcribe with whisper-cli.

19. Example: bash whisper-cli -ng \ -m "$MODEL" \ -f "$WORKDIR/audio.wav" \ -l zh \ -ocsv -osrt -of "$WORKDIR/transcript"
20. Prefer transcript.csv for downstream parsing. transcript.srt is useful for manual review.

21. If GPU allocation fails on macOS, retry with -ng to force CPU mode.

22. Build slide_timings.csv.

23. Do not average slide durations unless the user explicitly asks for it.
24. Read the transcript and slide outline together, then create a monotonic timing plan by topic changes, section boundaries, and unique keywords.
25. Use this schema: csv slide,start_sec,end_sec,duration_sec,reason 1,0.000,15.000,15.000,opening title and agenda 2,15.000,100.000,85.000,architecture overview starts here
26. Keep slide numbers sequential and ensure duration_sec = end_sec - start_sec.

27. Validate that the last end_sec matches the audio duration or is within a small tolerance.

28. Render the final video.

29. Run: bash python3 scripts/render_from_timing_csv.py \ --images "$SLIDE_IMAGES_DIR" \ --timings "$WORKDIR/slide_timings.csv" \ --audio "$WORKDIR/audio.wav" \ --output "$OUT_VIDEO"

30. The script generates an ffconcat file, validates timing continuity, and calls ffmpeg to encode the final mp4.

31. Verify and iterate.

32. Check output duration with ffprobe.
33. If a slide cuts too early or too late, edit only the affected rows in slide_timings.csv and rerun the render script.
34. Keep the transcript, outline, and timing CSV as reproducible working files.

Heuristics for timing alignment

• Use section-divider slides briefly. These slides usually hold for 5-20 seconds.
• Use the first segment that clearly switches topic as the next slide start.
• Prefer exact topic transitions over title-word matching. ASR often distorts proper nouns and product names.
• Let the model infer timings, but keep the render step deterministic through slide_timings.csv.
• When confidence is low, produce a first-cut video and tell the user which slide boundaries likely need review.

Common commands

Install dependencies on macOS if missing:

brew install ffmpeg poppler whisper-cpp

Typical multilingual model download:

mkdir -p .models
curl -L 'https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-small.bin' -o .models/ggml-small.bin

Bundled scripts

• scripts/extract_slide_outline.py Extract slide text from pptx into CSV or JSON for timing analysis.
• scripts/render_from_timing_csv.py Validate a timing CSV, generate an ffconcat, and render the final video with ffmpeg.

这个工具整体质量不错，能较好地将音频旁白与幻灯片合成视频。文档说明详细，流程清晰，两个脚本运行稳定。主要优点是工作流完整、验证机制可靠。不足之处是使用前需要手动安装多个外部工具和 Python 库，对新手不太友好；另外有些步骤需要手动执行，自动化程度可以进一步提高。