name: lecture-stt description: > Transcribe audio lectures into structured markdown notes using LLM-based STT (Gemini 3 Flash Preview) with contextual prompting. Supports PDF slide guides, domain-aware term hints, and local Whisper fallback. Triggers: "강의 전사", "STT", "lecture transcription", "오디오 전사", "강의 녹음", "audio to text", "lecture notes", "음성 변환", "녹음 텍스트", "전사해줘", "transcribe", "whisper"

Lecture STT Skill

Goal

Convert lecture audio recordings into near-verbatim structured markdown using LLM-based Speech-to-Text. The skill leverages contextual prompting (PDF slides as structural guide, domain term hints) to produce page-by-page transcriptions that preserve the professor's actual spoken words.

Key Insight

LLM STT with a PDF structural guide produces speech-only transcription — the PDF identifies which slide is being discussed, while the output captures only what the professor actually said. Without proper prompting, models tend to copy-paste slide text instead of transcribing speech.

Instructions

1. Gather Inputs

Required:

• Audio file — .m4a, .mp3, .wav (up to ~8 hours)
• Subject title — e.g. "거시경제학", "헌법", "Data Structures"

Optional (strongly recommended):

• PDF slides — acts as structural guide for page-by-page output
• Custom terms — domain-specific proper nouns, acronyms
• Language hint — tell agent the lecture language (e.g. "English lecture")

2. Select Engine

GOOGLE_APPLICATION_CREDENTIALS or ADC configured?
├─ Yes → Vertex AI + Gemini 3 Flash Preview (recommended ⭐, location='global')
│   └─ Best instruction following for verbatim STT
GEMINI_API_KEY exists?
├─ Yes → Gemini API + Gemini 3 Flash Preview (recommended ⭐)
│   └─ Fallback: Gemini 3.1 Flash-Lite (faster but may copy slide text)
├─ No, OPENAI_API_KEY exists?
│   └─ Yes → OpenAI gpt-4o-transcribe (supports diarization)
└─ No → mlx-whisper local (Apple Silicon, offline, free)

[!IMPORTANT] Vertex AI requires location='global' for gemini-3.* models. Using us-central1 returns 404 NOT_FOUND.

3. Build Prompt

Choose the appropriate prompt level based on available inputs:

Level 1 — Basic (no context)

Transcribe this audio in its original language. Output transcription only.

Use when: domain unknown, quick-and-dirty transcription needed.

Level 2 — Domain Hints

Transcribe this audio in its original language. Output transcription only.

Context: {domain description}
Key Terms: {comma-separated terms}

Use when: you know the subject but have no PDF.

Level 3 — Domain + Format

Transcribe this audio in its original language.

Context: {domain}
Key Terms: {terms}
Speaker: {speaker info, if known}

Output format:
- Sentence-level line breaks
- Numbers in Arabic numerals (e.g., 12,000)
- Preserve the original language of the lecture throughout
- Transcription text only

Use when: you want consistent formatting.

Level 4 — Lecture Mode with PDF (best quality) ← DEFAULT

You are a speech-to-text transcription assistant. You are given a PDF slide
deck and an audio recording of a {subject} university lecture.

## FUNDAMENTAL RULE #1: ORIGINAL LANGUAGE ONLY
**NEVER translate.** If the professor speaks English, output MUST be in English.
If Korean, output in Korean. If mixed, preserve the mix exactly as spoken.
This is NON-NEGOTIABLE. Translating the lecture into another language is a
CRITICAL FAILURE.

## FUNDAMENTAL RULE #2: VERBATIM STT — SPEECH ONLY
This is **STT (Speech-to-Text)**, NOT summarization, NOT note-taking.
Your job is to write down what the professor ACTUALLY SAID, word by word,
preserving their exact phrasing, sentence structure, and speaking style.

**CRITICAL: Do NOT copy-paste text from the PDF slides into the output.**
The PDF is ONLY for identifying which page/slide the professor is discussing.
The OUTPUT must contain ONLY the professor's spoken words.
If the professor reads a slide out loud, transcribe their spoken version
(which may differ from the slide text).

DO NOT:
- Copy or reproduce text/bullet points from the PDF slides
- Translate ANY part of the lecture into another language
- Compress, summarize, or condense the professor's words
- Convert spoken sentences into bullet points
- Skip ANY spoken content — every sentence matters
- Paraphrase or "clean up" the professor's words
- Shorten or truncate long explanations

DO:
- Write EXACTLY what the professor said, sentence by sentence
- Keep their natural speaking style (colloquial, formal, rambling — all of it)
- Include filler words, false starts, self-corrections
- Include EVERY tangent, joke, anecdote, aside, example, and digression
- Produce VERY LONG output — more text is always better than less
- A 10-minute audio should produce at least 2000+ words of transcription

## Page-by-Page Structure (MANDATORY)
- Structure output by PDF page: `## p.{N} — {slide title or topic}`
- Go through EVERY page of the PDF in order
- If the professor skipped a page: `*[No lecture content — slide only]*`
- If the professor talked about a page extensively, write EVERYTHING
- Content spoken BEFORE first slide → `## p.0 — Pre-lecture`
- Content spoken AFTER last slide → `## Closing`

## Beyond the PDF
Capture ALL spoken content not on slides under nearest page with 💬 marker:
- Verbal explanations, intuitions, reasoning
- Real-world examples, case studies, anecdotes
- Tangential context, history, motivation
- Exam tips, common mistakes, warnings
- Q&A with students (both question and answer)
- Administrative announcements

## Output Format
- `---` dividers between page sections
- `## p.{N} — {title}` for EVERY page
- `> ` blockquote for professor's extended examples
- 💬 for beyond-PDF verbal content
- Math in KaTeX: $Y = C + I + G$
- One sentence per line
- Output MUST be LONG — brevity is failure

## Key Terms
{auto-inferred + user-supplied terms}

Level 5 — Raw Verbatim

Transcribe this audio verbatim. Include all speech as-is.
Sentence-level line breaks. No formatting.

Use when: need exact wording without page structure (prefer Whisper for this).

4. Domain Auto-Inference

When the user provides only a subject title, infer domain terms automatically:

5. Handle Long Audio

[!TIP] Multiple audio files from the same lecture can be sent in a single request. Just add multiple types.Part.from_bytes() parts — the model handles them as one continuous lecture.

Use ffmpeg for splitting when needed:

DUR=900  # 15 minutes
OVERLAP=10
for i in $(seq 0 $((DUR - OVERLAP)) $(ffprobe -v error -show_entries format=duration -of csv=p=0 input.m4a | cut -d. -f1)); do
  ffmpeg -i input.m4a -ss $i -t $((DUR + OVERLAP)) -y chunk_${i}.mp3
done

6. API Implementation

Client initialization

from google import genai
from google.genai import types
import os

# Vertex AI (service account / ADC) — MUST use location='global'
if os.environ.get("GOOGLE_APPLICATION_CREDENTIALS") or os.environ.get("GOOGLE_CLOUD_PROJECT"):
    client = genai.Client(
        vertexai=True,
        project=os.environ.get("GOOGLE_CLOUD_PROJECT", "your-project-id"),
        location="global",  # REQUIRED for gemini-3-* models
    )
# Gemini API (API key)
else:
    client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])

Gemini — Inline bytes (< ~50MB, preferred)

[!TIP] Inline bytes avoids the Korean filename UnicodeEncodeError entirely. Prefer this path when file size allows.

with open("lecture.m4a", "rb") as f:
    audio_bytes = f.read()

contents = [
    types.Part.from_bytes(data=audio_bytes, mime_type="audio/mp4"),
    prompt_text,
]

# Add PDF if available
if pdf_path:
    with open(pdf_path, "rb") as f:
        pdf_bytes = f.read()
    contents.insert(0, types.Part.from_bytes(data=pdf_bytes, mime_type="application/pdf"))

response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents=contents,
    config=types.GenerateContentConfig(max_output_tokens=65536),
)
print(response.text)

Gemini API — Large files (> 50MB): File API upload

[!WARNING] File API is only available with API key auth, NOT Vertex AI. For Vertex AI with large files, compress with ffmpeg first or use GCS URIs.

Korean/non-ASCII filenames cause UnicodeEncodeError in httpx. Always use _safe_upload() below to copy to ASCII-safe temp paths first.

import time, shutil, tempfile

def _safe_upload(client, filepath, ascii_name):
    """Upload file with ASCII-safe temp name to avoid httpx UnicodeEncodeError."""
    tmpdir = tempfile.mkdtemp(prefix="stt_")
    try:
        safe_path = os.path.join(tmpdir, ascii_name)
        shutil.copy2(filepath, safe_path)
        return client.files.upload(file=safe_path)
    finally:
        shutil.rmtree(tmpdir, ignore_errors=True)

audio_file = _safe_upload(client, "노시론1.m4a", "lecture.m4a")

from google.genai.types import FileState
while audio_file.state == FileState.PROCESSING:
    time.sleep(2)
    audio_file = client.files.get(name=audio_file.name)

contents = [audio_file]
if pdf_path:
    pdf_file = _safe_upload(client, pdf_path, "slides.pdf")
    contents.insert(0, pdf_file)
contents.append(prompt_text)

response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents=contents,
    config=types.GenerateContentConfig(max_output_tokens=65536),
)
print(response.text)

client.files.delete(name=audio_file.name)
if pdf_path:
    client.files.delete(name=pdf_file.name)

mlx-whisper — Local fallback (Apple Silicon)

import mlx_whisper

result = mlx_whisper.transcribe(
    "lecture.m4a",
    path_or_hf_repo="mlx-community/whisper-large-v3-turbo",
    language="ko",
)
print(result["text"])

OpenAI — Diarization support

from openai import OpenAI
client = OpenAI()
with open("lecture.m4a", "rb") as f:
    transcript = client.audio.transcriptions.create(
        model="gpt-4o-transcribe", file=f,
        response_format="text", language="ko",
    )
print(transcript)

7. Output Format

# {Subject} — Lecture Notes

- Model: gemini-3-flash-preview (Vertex AI, global)
- Time: {elapsed}s
- Tokens: {in} → {out}
- Source: {audio_file} {+ pdf_file}
- Mode: STT (Level 4, verbatim, original language)

---

{transcribed content}

Anti-Patterns

[!CAUTION] Never send audio without any prompt to an LLM. Without guidance, the model produces unstructured output with hallucination loops. Always provide at minimum a basic transcription prompt.

[!WARNING] Flash-Lite may copy PDF slide text instead of transcribing speech. Use gemini-3-flash-preview for best instruction following. If using Flash-Lite, add extra emphasis on "DO NOT copy slide text".

Model Comparison (Benchmarked 2026-03-09)

Constraints

• PDF slides dramatically improve quality — always include when available
• Term hints should be ≤ 20 terms (too many causes confusion)
• Only include terms that actually appear in the audio (prevents hallucination)
• Korean + English mixing is fine — Gemini handles multilingual natively
• Language hint: tell the agent the lecture language explicitly to prevent translation
• Korean filenames: Use _safe_upload() or inline bytes to avoid UnicodeEncodeError
• Vertex AI: File API is NOT available. Use inline bytes (< ~50MB) or ffmpeg compress
• Vertex AI: Must use location='global' for gemini-3-* models

Dependencies

# Required
pip install google-genai    # Gemini API + Vertex AI (unified SDK)

# Optional
pip install mlx-whisper     # Local Whisper (Apple Silicon)
pip install openai          # OpenAI diarization
brew install ffmpeg         # Audio splitting for long files

# Vertex AI auth
gcloud auth application-default login  # or set GOOGLE_APPLICATION_CREDENTIALS