version: "1.0.0" name: media-to-transcript description: Convert audio/video URLs or local media into corrected Markdown transcripts through Volcengine recording-file ASR 2.0. Reuses the existing video-transcript downloader for Bilibili, Douyin, Xiaohongshu, YouTube, extracts audio when input is video, uploads local audio to R2, splits media longer than 3 minutes into parallel AUC tasks, submits volc.seedasr.auc with compact source context, then performs AI correction with topic/context/glossary. Use when the user asks for "逐字稿", "提取逐字稿", "视频转文字", "音频转文字", "转写", "听写视频", "提取视频文案", or wants a polished transcript.

Media To Transcript

Use this skill to turn audio or video into a final Markdown transcript. The only ASR backend for this skill is Volcengine recording-file recognition 2.0:

• submit endpoint: https://openspeech.bytedance.com/api/v3/auc/bigmodel/submit
• query endpoint: https://openspeech.bytedance.com/api/v3/auc/bigmodel/query
• resource id: volc.seedasr.auc

Do not use another ASR backend for this skill.

Workflow

1. Run the doctor if this is the first run, after environment changes, or after an error:

SKILL_DIR="<SKILL_ROOT>/media-to-transcript"
rtk python3 "$SKILL_DIR/scripts/media_to_transcript.py" --doctor

1. Run the pipeline from the notes vault root:

SKILL_DIR="<SKILL_ROOT>/media-to-transcript"
rtk python3 "$SKILL_DIR/scripts/media_to_transcript.py" "<URL或本地媒体路径>" --topic "视频主题或关键词" --language zh-CN

The script automatically builds AUC context from available media metadata such as title, platform, author, duration, description, and short source URL. It keeps this ASR context under 500 Chinese characters. User-provided --topic, --context, and --context-file are also included in the same 500-character AUC context budget.

For downloaded or local media longer than 180 seconds, the script splits audio into 3-minute chunks, uploads each chunk, submits AUC tasks in parallel, and merges the returned utterances back onto the original timeline.

1. Read the generated correction-prompt.md and raw-transcript.md.
2. Apply the AI correction rules in references/correction-guidelines.md.
3. Save the corrected transcript to the finalTranscriptTarget path printed by the script, and show the full transcript to the user unless they explicitly only asked for a file.

CLI

SKILL_DIR="<SKILL_ROOT>/media-to-transcript"
 
# Video URL: download video, extract mp3, upload to R2, then run AUC ASR
rtk python3 "$SKILL_DIR/scripts/media_to_transcript.py" "https://www.bilibili.com/video/BVxxx" --topic "AI 编程工具评测"
 
# Local audio/video
rtk python3 "$SKILL_DIR/scripts/media_to_transcript.py" "/path/to/audio.mp3" --media-kind audio --topic "访谈"
 
# Add context or terminology hints for ASR and correction
rtk python3 "$SKILL_DIR/scripts/media_to_transcript.py" video.mp4 \
  --topic "Claude Code 和 Codex 使用经验" \
  --context "技术词: Codex, Claude Code, MCP, HyperFrames, R2" \
  --context-file "AI Wiki/raw/some-material.md"
 
# Enable Volcengine utterance emotion labels when explicitly useful
rtk python3 "$SKILL_DIR/scripts/media_to_transcript.py" video.mp4 --emotion --topic "访谈"
 
# Convert an existing AUC result JSON into raw transcript + correction prompt
rtk python3 "$SKILL_DIR/scripts/media_to_transcript.py" --from-asr-json asr/auc-result.json --title "视频标题"

Outputs

Each run writes a timestamped directory under outputs/ unless --out-dir is provided:

• audio/: extracted/converted mp3 when the input is video or unsupported audio
• audio/chunks/: 3-minute mp3 chunks when media exceeds 180 seconds
• asr/auc-submit.json or asr/auc-submit-001.json: submitted request metadata, with the API key omitted
• asr/auc-result.json or asr/auc-result-001.json: raw Volcengine recording-file recognition result
• asr/auc-result-combined.json: merged AUC result for multi-chunk runs
• raw-transcript.md: transcript-shaped draft generated from AUC utterances
• correction-prompt.md: prompt for the AI correction pass
• transcript.md: target path for the final corrected transcript
• run-summary.json: machine-readable paths and metadata

The final user-facing artifact is transcript.md. Do not treat auc-result.json as final copy; it is ASR evidence for correction.

Options

API Setup

Preferred key location is your current workspace .env.r2, or any environment file loaded by the script:

VOLCENGINE_SPEECH_API_KEY=你的新版控制台APIKey

Accepted key names:

• VOLCENGINE_SPEECH_API_KEY (preferred)
• VOLCENGINE_AUC_API_KEY
• SEEDASR_API_KEY
• AUC_API_KEY

If --doctor does not find this API key, use this setup path:

1. Open 录音文件识别服务开通 and enable “录音文件识别 2.0”.
2. Open API Key 管理 and create an API Key.
3. Put the key into your workspace .env.r2 as VOLCENGINE_SPEECH_API_KEY=..., or export it in the shell before running the script.

Local files still need R2 because AUC requires a public audio URL:

• R2_ACCESS_KEY_ID
• R2_SECRET_ACCESS_KEY
• R2_ACCOUNT_ID
• R2_BUCKET
• R2_PUBLIC_BASE_URL or R2_PUBLIC_URL

Do not print API keys or copy .env.r2 values into chat.

Correction Rules

Before writing transcript.md, read references/correction-guidelines.md. Core constraints:

• Correct ASR mistakes using topic/context/glossary and the surrounding transcript.
• Preserve spoken meaning, order,口语词, repeated words, and uncertainty.
• Do not summarize, rewrite into article prose, invent missing content, or remove substantive speech.
• Convert ASR utterance fragments into readable transcript paragraphs with section-level timestamps.
• Mark unresolved audio as [听不清] instead of guessing.