| name | ralphtrans |
| description | Batch transcribe ALL playable lectures for a course in a persistent loop. Uses a JSON state file for checkpoint/resume. Runs until every lecture is transcribed or marked failed. |
| license | MIT |
| metadata | {"author":"WALKERKILLER","version":"1.1"} |
Ralph Batch Transcribe
Batch transcribe all playable lectures for a course with checkpoint/resume.
When to Use
- User says
/ralphtrans or "batch transcribe this course".
- User wants to transcribe an entire course in one go.
Workflow
- Start batch transcription:
python "<SKILL_DIR>/../../scripts/look_tongji.py" batch-transcribe --course-id "<ID>"
-
The CLI:
- Loads all playable lectures for the course.
- Creates/reads
batch_state.json in the workspace to track progress.
- Transcribes lectures one by one, updating state after each.
- Retries failed lectures up to
--max-retries times.
- Prints a summary when all lectures are done or failed.
-
Interrupt & Resume:
- Press Ctrl+C to safely stop. State is saved.
- Re-run the same command to resume from pending lectures.
State File
batch_state.json structure:
{
"course_id": "xxx",
"started_at": "ISO8601",
"updated_at": "ISO8601",
"lectures": [
{"sub_id": "xxx", "sub_title": "xxx", "status": "pending|done|failed", "attempts": 0, "error": null}
]
}
After Completion
- Run
/note for each transcribed lecture to generate study notes.
- Run
/wiki to rebuild and serve the course site.
Where <SKILL_DIR> Points
<SKILL_DIR> is the directory containing this SKILL.md. Shared scripts (look_tongji.py, timeline_tools.py, tongji_backend/) and references live two levels up in the repository root (<SKILL_DIR>/../../scripts/ and <SKILL_DIR>/../../references/).
