| name | yanhekt-asr |
| description | Transcribe, convert, clean, analyze, and summarize Yanhekt/沿河课堂 and other course recording videos. Use when user needs to process lecture/class recordings, 课程录播, or classroom videos into transcripts, readable notes, chapter outlines, timestamped review maps, key points, assignments, exam hints, or course-level summaries. |
| version | 0.1.0 |
Yanhekt(沿河课堂) Lecture Recording Workflow
Use the bundled direct Bijian/BCut script first for course recordings:
scripts/bcut_batch_asr.py
It calls the Bijian/BCut web ASR API directly, extracts audio with ffmpeg, and writes three files per video:
.srt: video subtitles.txt: token-efficient agent reading format, e.g. [12:34]字幕内容.jsonl: script-friendly segments with index, start_ms, end_ms, start, end, text
Pipeline
- Organize recordings by course if needed.
- Transcribe with the bundled script and preserve raw
.srt, .txt, .jsonl.
- Check quality quickly: inspect the start, middle, and end of
.txt; mark white-noise/empty-class lessons instead of forcing summaries.
- Clean only when needed: use raw
.txt for fast indexing and review extraction; polish only key spans or final subtitles.
- Analyze from subtitles/transcripts: extract chapters, concepts, definitions, examples, assignments, questions, and review points.
- Summarize conservatively: do not invent content absent from subtitles.
Scaling to many lectures
When a single request covers many lectures, do not read every full transcript into the main context. Dispatch read-only subagents to read in batches and report back, then write the notes yourself.
- Group lectures into batches of about three and give each batch to one subagent.
- Each subagent reads only its
.txt (and .jsonl/.srt when needed) and returns a concise structured report per lecture.
- Read the returned reports, then generate the per-lecture
notes/<lecture-stem>.md and update 课程总结.md yourself, so formatting, voice, and conservative summarizing stay consistent across the course.
Quick Start
The script is PEP 723 compatible but uv is better if available. If using plain Python and requests is missing:
python -m pip install requests
Batch transcribe one course directory:
python path/to/scripts/bcut_batch_asr.py ./<course-name>
Transcribe one video:
python path/to/scripts/bcut_batch_asr.py ./<course-name>/<video>.mp4
With uv, dependencies are installed from the script metadata:
uv run --script path/to/scripts/bcut_batch_asr.py ./<course-name>
Useful options:
# Recurse into subdirectories
python path/to/scripts/bcut_batch_asr.py ./<course-name> -r
# Choose output directory
python path/to/scripts/bcut_batch_asr.py ./<course-name> -o ./<course-name>/transcripts
# Re-run existing outputs
python path/to/scripts/bcut_batch_asr.py ./<course-name> --overwrite
Default output:
- If all inputs are in one video directory:
<video-dir>/transcripts/
- If inputs span multiple directories:
./transcripts/
- Temporary extracted audio:
<output-dir>/audio/
Notes
- Each class period is 45 minutes followed by a 5-minute break; recording starts exactly at class time, but may not stop exactly on time.
- Some recordings may contain only white noise like one-line outputs such as
12345 or repeated head/eye/one two: treat as empty-class.
- A class section usually spans two or three 45+5 periods, so expect white noise around the ~45-minute break, and maybe a second break later as well.
- Ignore empty-class lessons and short white-noise spans around breaks or at the end; focus on actual lecture content.
- Saved transcript and note stems should drop the recording prefix
(camera|screen)_<course-name>_. For example, camera_高等数学_第1周_星期一_第1大节 saves as 第1周_星期一_第1大节.
- Common names the ASR often mishears:
乐学 is the on-campus learning platform; i北理 is the campus app, a special version of 钉钉 (DingTalk). Map garbled spellings back to these.
File Organization
Organize course recordings by course.
- Preserve video filenames and place them at
<course-name>/<video>.mp4.
- Put script outputs under
<course-name>/transcripts/ by default: <lecture-stem>.srt, <lecture-stem>.txt, <lecture-stem>.jsonl.
- Define
<lecture-stem> by removing the leading (camera|screen)_<course-name>_ from the video stem, e.g. 第10周_星期一_第4大节.
- For agent reading, prefer
<lecture-stem>.txt before loading .srt; use .jsonl for scripts and timestamp math.
- Put each real lecture note under
<course-name>/notes/.
- Keep the course-level summary at
<course-name>/课程总结.md.
Output
Generate one Markdown note for each real lecture, then maintain one course-level summary (课程总结.md) for the whole course.
Prioritize things easy to miss: attendance, assignments, deadlines, exam hints, teacher warnings, platform notices, and small procedural details.
Include course content only as a timestamped navigation map; do not rewrite the lecture as a full textbook or transcript.
For the exact note and course-summary templates, naming rules, and incremental-update guidance, see references/note-templates.md.
Additional Resources
scripts/bcut_batch_asr.py — batch ASR pipeline (ffmpeg audio extraction + Bijian/BCut API), writes .srt/.txt/.jsonl per video.references/note-templates.md — single-lecture and course-summary Markdown templates with naming and incremental-update rules.
