iomgaa
6bdb802f01
- Add all claude skills (brainstorming, commit, debugging, TDD, etc.) - Add claude hooks (pre-commit-guard, post-edit-quality) - Add research templates (experiment plan, research brief, etc.) - Add claude tools (arxiv/semantic_scholar/openalex fetch, wiki, exa) - Add TRM4 reference implementation as algorithm fidelity baseline - Add research-wiki content (plans, index, graph, query_pack) - Update .gitignore to exclude .graphify_version runtime state
2.6 KiB
graphify reference: transcribe video and audio
Load this only when detect reported one or more video files. A corpus with no video never reads this.
Step 2.5 - Transcribe video / audio files (only if video files detected)
Skip this step entirely if detect returned zero video files.
Video and audio files cannot be read directly. Transcribe them to text first, then treat the transcripts as doc files in Step 3.
Strategy: Read the god nodes from graphify-out/.graphify_detect.json (or the analysis file if it exists from a previous run). You are already a language model — write a one-sentence domain hint yourself from those labels. Then pass it to Whisper as the initial prompt. No separate API call needed.
However, if the corpus has only video files and no other docs/code, use the generic fallback prompt: "Use proper punctuation and paragraph breaks."
Step 1 - Write the Whisper prompt yourself.
Read the top god node labels from detect output or analysis, then compose a short domain hint sentence, for example:
- Labels:
transformer, attention, encoder, decoder→"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks." - Labels:
kubernetes, deployment, pod, helm→"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."
Set it as WHISPER_PROMPT to use in the next command.
Step 2 - Transcribe:
GRAPHIFY_WHISPER_MODEL=base # or whatever --whisper-model the user passed
$(cat graphify-out/.graphify_python) -c "
import json, os
from pathlib import Path
from graphify.transcribe import transcribe_all
detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths, ensure_ascii=False))
" > graphify-out/.graphify_transcripts.json
After transcription:
- Read the transcript paths from
graphify-out/.graphify_transcripts.json - Add them to the docs list before dispatching semantic subagents in Step 3B
- Print how many transcripts were created:
Transcribed N video file(s) -> treating as docs - If transcription fails for a file, print a warning and continue with the rest
Whisper model: Default is base. If the user passed --whisper-model <name>, set GRAPHIFY_WHISPER_MODEL=<name> in the environment before running the command above.