128건 · 관련도순
Use when the user wants local voice transcription instead of OpenAI Whisper API. Switches to whisper.cpp running on Apple Silicon. WhatsApp only for now. Requires voice-transcription skill to be applied first.
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Turn audio and video into text across 99 languages, translate speech into English, and create timestamped subtitles. The skill covers model selection, transcription settings, and batch processing for recordings such as meetings and podcasts.
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Turn a screen recording of repetitive work into automation scripts. The skill reconstructs the workflow, proposes options suited to installed tools, and helps build and test the chosen approach while preserving human review steps.
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Turn voice calls into GBrain records containing transcripts, summaries, audio links, and references to people or companies. The workflow also sends summaries to a configured messaging service; automatic processing when calls end requires additional implementation.
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Develop C++ bots that join existing Zoom meetings from Linux servers without a graphical interface. Follow guidance for recording raw audio and video, feeding transcription or AI workflows, and handling authentication, recording permissions, and Docker audio setup.
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Transcribe audio/video to text with word-level timestamps using OpenAI Whisper. Use when you need speech-to-text with accurate timing information for each word.
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Transcribe audio segments to text using Whisper models. Use larger models (small, base, medium, large-v3) for better accuracy, or faster-whisper for optimized performance. Always align transcription timestamps with diarization segments for accurate speaker-labeled subtitles.
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Assemble a narrated-UGC "stitch reply" ad from a config — a single spoken VO carries a verbatim testimonial while ~30 per-cut i2v clips (one creator across ~5 wardrobes in ~3 worlds, plus product B-roll) are each trimmed to their EDL window built from the VO's Whisper word boundaries and hard-concatenated via filter_complex concat (never the demuxer, which drops audio on a duration mismatch), the VO mixed over an optional sidechain-ducked instrumental bed (−20dB, 20 to 1) so the VO stays on top, karaoke-pop captions burned on every word throughout (VEED Whisper preset, re-spelled against the locked script), a landing-page scroll rendered as FFmpeg zoompan over a Playwright PNG (not i2v), and closed on the brand's real end-card PNG — never AI-rendered text. This is the FREE deterministic assembly stage (trim-to-EDL + filter_complex concat + VO and music mix + karaoke captions + landing-page zoompan + end-card append), run with the shared stitch-videos-ffmpeg montage.py helper that installs with this package; the VO, creator, start-frames, and clips come from create-vo-elevenlabs / create-image-gpt-image-fal / create-image-fal / create-video-fal. Use for the narrated-ugc-wardrobe-stitch format.
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Assemble a myth-vs-fact kinetic-typography explainer video ad (≈29.5s, 9:16) from N myth/fact pairs + hook / turn / punch copy + palette + a brand end-card PNG + a VO track — a hook, 3 red-strike MYTH cards that flip to teal-check FACT cards (per-line strikethrough that crosses EVERY wrapped line), a "what actually works" turn, an optional proof reveal, a punch line, and a static end card. DETERMINISTIC assembly with ZERO AI-gen visuals — HTML hyperframes rendered frame-exact via Playwright (`window.renderAt(t)`, animation a pure function of beat-local time), Whisper beat-snap to VO word onsets, concat at a uniform fps, karaoke `.ass` captions burned last (suppressed on the proof + end-card beats), and a VO + optional music mix (music −20 dB, `amix normalize=0`, tail fade). FREE (Python + Playwright + ffmpeg); the recipe supplies the copy / palette / end-card / VO and gates the paid VO / music / Whisper calls to their own capabilities. Use for the myth-vs-fact format.
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Speech-to-text transcription via OpenAI Whisper. Supports two modes — Local CLI (no API key, runs on-device) and Cloud API (fast, scalable, requires OPENAI_API_KEY). Use when the user needs to transcribe audio files, translate speech, or convert audio to text.
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Routes Distil-Whisper inference, PyTorch distillation training, and Flax reproduction workflows.
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Check for Faster-Whisper (preferred - 4-5x faster) workflow skill. Use this skill when the user needs Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Check for Faster-Whisper (preferred - 4-5x faster) workflow skill. Use this skill when the user needs Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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音声ファイルをテキストに文字起こしするスキル。mp3/wav/m4a/ogg/flac形式に対応。whisperベースのtranscriber_toolを使用し、tiny/base/small/medium/largeの5つのモデルから精度と速度のバランスを選択可能。長時間音声はバックグラウンド実行に対応。「文字起こしして」「音声をテキストに変換して」で使用。
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Check if voice message handling is configured — verify Whisper, ElevenLabs/OpenAI TTS keys, and required tools.
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Check voice message pipeline config — verify Whisper, ElevenLabs/OpenAI TTS keys and dependencies.
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Transcribe local MP4/M4A/MP3/WAV/WEBM audio or video with ffmpeg and Whisper, then optionally produce diarized transcripts, customer-facing meeting minutes, action items, and PPT-ready summaries. Use when: 文字起こし, transcription, transcribe mp4, meeting transcript, mp4から文字起こし, 録画から議事録, 顧客向け議事録, 話者分離, diarization, whisper CLI, ffmpeg.
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Build audio transcription pipelines with Whisper, Deepgram, and AssemblyAI including speaker diarization and real-time streaming. Activate on: transcription, speech-to-text, diarization, audio processing, meeting transcripts. NOT for: text-to-speech synthesis (voice-audio-engineer), music generation (ai-engineer).
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WAV/動画音声を単一パスGPU転写（mlx-whisper）した後、fulltextを意味区切りでN分割しLLM改行だけを並列化してSRT字幕を高速生成する /srt の高速版。日本語トーク動画用。「/srt-fast」で実行。
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Modelo de reconhecimento de fala de propósito geral da OpenAI. Suporta 99 idiomas, transcrição, tradução para inglês e identificação de idioma. Seis tamanhos de modelo, de tiny (39M parâmetros) a large (1550M parâmetros). Use para speech-to-text, transcrição de podcasts ou processamento de áudio multilíngue. Melhor para ASR robusto e multilíngue.
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Local speech-to-text with the Whisper CLI (no API key).
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Transcribe audio via OpenAI Audio Transcriptions API (Whisper).
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動画/音声ファイルをタイムスタンプ付きで文字起こしし、要約まで行うスキル。「動画を文字起こしして」「この音声を要約して」「会議の録画をまとめて」「mp4を文字起こしして要約して」「インタビュー音声から議事録作って」などのリクエストで使用する。OpenAI Whisper（ローカル）でタイムスタンプ付き全文mdを生成し、Claude自身がその全文を読んで要約mdを別ファイルで書き出す。出力先はプロジェクトルート直下（`TRANSCRIPT_OUTPUT_PATH` で変更可）。
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Transcribe WhatsApp voice messages using local Whisper CLI. Use when: owner or contact sends an audio/ogg voice message. Combines Whisper transcription + CRM update + task creation. Works offline for short clips, uses OpenAI API for long clips. Hebrew and English supported.
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