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volcengine-asr

Transcribe local audio or video with Volcengine Doubao file ASR, including BigASR 1.0 Turbo direct upload and asynchronous 1.0 standard, 1.0 idle, or 2.0 standard jobs through TOS. Use when the user asks for 火山引擎、豆包语音、录音文件识别、音视频转文字、录音稿或字幕。

作为 `lecture-to-md` 的可选云端 ASR 子 skill;默认走本地 sherpa-onnx X-ASR,仅在用户明确要求云端 API 或需更高准确率时启用。

インストール方法を見る

含まれるファイル(11)

  • SKILL.md3.3 KB
  • .gitignore303 B
  • .secret.example361 B
  • agents/openai.yaml299 B
  • references/api-modes.md2.0 KB
  • requirements.txt22 B
  • scripts/asr_common.py13.0 KB
  • scripts/poll_transcript.py5.7 KB
  • scripts/test_scripts.py5.5 KB
  • scripts/turbo_transcribe.py3.9 KB
  • scripts/upload_submit.py6.7 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Volcengine ASR(lecture-to-md 子 skill)

Use the deterministic scripts in scripts/; do not reproduce API requests manually.

本 skill 是 lecture-to-md 的云端 ASR 子 skill。日常请走 local-asr/(sherpa-onnx X-ASR,免费、跨平台、隐私友好);只有当用户明确要求云端准确率,或本地模型不足以应对时,才启用本 skill。

Choose a mode

  • For turbo-v1, run scripts/turbo_transcribe.py. It accepts a local audio/video file, extracts a 16 kHz mono MP3, sends Base64 in one synchronous request, and writes JSON/TXT/SRT/Markdown.
  • For standard-v1, idle-v1, or standard-v2, first run scripts/upload_submit.py, then run scripts/poll_transcript.py with the resulting job.json.
  • Prefer standard-v2 when the user has opened 豆包录音文件识别模型 2.0.
  • Local asynchronous inputs require TOS credentials. If a direct-download HTTP(S) URL already exists, pass --audio-url and skip TOS.

Read references/api-modes.md before changing endpoints, resource IDs, limits, or request fields.

Credentials

Read credentials from .secret in this skill directory. Never print or copy credential values into results. .secret must remain gitignored and mode 0600.

The ASR v3 calls use only VOLCENGINE_ASR_APP_ID and VOLCENGINE_ASR_ACCESS_TOKEN. VOLCENGINE_ASR_SECRET_KEY is retained locally but not sent. TOS uses a separate account-level AK/SK, endpoint, region, and bucket.

Commands

When .venv is absent, prepare the optional TOS dependency once:

python3 -m venv .venv
.venv/bin/python -m pip install -r requirements.txt

upload_submit.py automatically re-executes with this environment for local TOS uploads.

Turbo 1.0:

python3 scripts/turbo_transcribe.py "/absolute/path/video.mp4"

Submit a local 2.0 standard job through TOS:

python3 scripts/upload_submit.py \
  --mode standard-v2 \
  --input "/absolute/path/video.mp4"

Poll the generated job:

python3 scripts/poll_transcript.py "/absolute/path/job.json"

For a pre-existing direct URL:

python3 scripts/upload_submit.py \
  --mode standard-v2 \
  --audio-url "https://example.com/audio.mp3"

Operating rules

  • Check that the requested service is open before a paid call. Do not invent undocumented seedasr Turbo or idle resource IDs.
  • When testing multiple modes, use different source videos so paid/free ASR time produces useful, non-duplicate transcripts.
  • Keep the TOS bucket private and use only short-lived pre-signed GET URLs.
  • Leave a reusable job.json when polling times out. Delete the TOS object only after a terminal success/failure unless the user passes --keep-upload.
  • Report the absolute paths of transcript.txt, transcript.srt, and result.json after success.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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概要と使いどころ

把课堂视频(本地或 B 站/YouTube)、文字稿、课件三者(任意组合)整理成一份详细的中文 Markdown 课堂笔记,输出按课程标题命名的 `{title_name}.md`(首行为 `# 文档标题`)+ 相对路径图片。Markdown 工作流,与上游 `lecture-to-notes` 的 LaTeX/PDF 输出并行存在;上游 skill 完全不动。触发词:markdown 笔记、md 笔记、视频转 markdown、Markdown 讲义、不要 LaTeX、不要 PDF、纯文本笔记。

日本語の概要は準備中です。原文の説明を表示しています。

ysyecust/lecture-to-notes2802026年10月3日 更新

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日本語の概要は準備中です。原文の説明を表示しています。

ysyecust/lecture-to-notes2802026年10月3日 更新

local-asr

無料

把本地长视频/音频转写成文字稿 + 可选字幕,纯本地(不上传云端),用 sherpa-onnx X-ASR Zipformer transducer 模型(int8 量化、中英双语、自动标点)。已在 macOS Apple Silicon(int8 + AMX,~100× 实时)、Linux ARM64(CPU,~32× 实时)与 Windows(PowerShell 5.1,CPU)端到端验证。使用 `$lecture-to-md` 默认 ASR 后端;默认不要换成 Qwen。

日本語の概要は準備中です。原文の説明を表示しています。

ysyecust/lecture-to-notes2802026年10月3日 更新

Generate a self-contained, beautifully styled HTML analysis of an academic paper. Use when the user provides an arXiv link, paper URL, PDF, or asks to analyze a research paper. Produces a standalone HTML file with structured sections (Problem, Translation/Analogy, Architecture, Key Results, Verdict), embedded figures, and responsive design. Trigger words include 读论文, 分析论文, paper analysis, paper review.

日本語の概要は準備中です。原文の説明を表示しています。

ysyecust/lecture-to-notes2802026年10月3日 更新

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