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テンプレートの台本と音声IDを使い、ElevenLabsで動画広告用のナレーション音声を生成します。ブランド名の読み方を反映し、音声ファイルと発話タイミング情報を扱います。
Generate a voiceover (VO) clip via ElevenLabs text-to-speech, routed through the GooseWorks proxy, so the call is charged to your GooseWorks credits. Voice id + script text come from the template recipe. Use for the spoken narration of VO-driven video-ad formats (cgi-app-sizzle, flat-vector-explainer, hypermotion). Never call ElevenLabs directly — the proxy attribution is required.
実行スコア 未検証
インストール方法を見るテンプレートのレシピにある台本と音声IDを使い、ElevenLabsのテキスト読み上げでナレーション音声を生成します。音声生成はGooseWorks proxyを経由し、GooseWorks creditsに課金されます。ブランド名や製品名の読み方が確認されている場合は、発話用の表記に置き換えられます。画面上の文字や字幕に使う表記は変えず、発話だけを調整します。タイムスタンプ付きの音声を生成すると、MP3と文字単位のアラインメント情報を保存できます。実際の発音や間、語尾は生成後に音声を聞いて確認する前提です。
ナレーションを中心に構成する動画広告の音声を用意したいときに使えます。CGIアプリ紹介、フラットベクター解説、ハイパーモーション形式など、VO駆動の広告に向いています。ブランド名の読み方を確認してから音声化したい場合や、発話に合わせて編集・字幕のタイミングを扱いたい場合にも利用できます。
ElevenLabsへ直接接続せず、必ずGooseWorks proxyを経由します。音声生成は有料です。発音が不明または曖昧な名称は、生成前に読み方を確認します。
この紹介文は、公開されている SKILL.md をもとに AI(OpenAI Codex)が作成しました。正確な仕様は下の原文を確認してください。
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。
Superseded: the video kit now does this with the voice-elevenlabs part, version 1.3.0 (
voice-elevenlabs@1.3.0), in the parts folder of this repository. It speaks every scene's line through the private line, keeps character timings as word timings and swaps in confirmed pronunciations the same way. The compatibility generator also supports Eleven v4 controls and pending timing provenance. Existing frozen part versions are unchanged.
VO via the elevenlabs-proxy (charged to your GooseWorks credits). gen_vo.py --text "..." --voice <id> --out vo.mp3.
The template recipe supplies the voice + text; paid call routes through media_proxy.
A brand name or product name the voice gets wrong is spoken from its phonetic
spelling, never from a (pronounced …) note (the voice reads notes aloud):
gen_vo.py --text "Meet Brewq2." --voice <id> --out vo.mp3 --rules working/brand-rules.json
# or: --say-as "Brewq2=brew Q two"
Only the SPOKEN text changes. Captions, on-screen text and the approved script keep the written name. When captions are timed from this VO, map the spoken words back to the written term.
At the start of every video, read the selected brand's persistent facts, including user-authored pronunciation rules. A previous video's notes or a cached rules file do not establish current pronunciation. For an unknown or ambiguous name, resolve how it is said before the paid voice step. Save the user's confirmed choice in the existing brand-facts store, update an existing rule by its id, then make a separate read and confirm it survived. Never infer successful persistence from a write acknowledgement.
The bundled read_pronunciations.py exports user-authored must rules from a fresh context snapshot (the brand read's payload, its {"result": …} wrapper, or the whole tool result). A rule's text is Pronounce "<term>" as "<say_as>"; the older Pronunciation: <term> => <say_as> is also read. It rejects a different brand, conflicting rules and required names that remain unknown. The resulting local rules file is a per-run voice input; the brand store remains the durable source. gen_vo.py supports a free dry run to review the resulting spoken text. Captions retain written names.
Eleven v4 is the default (since 1.3.0; it was v3). V4 and V4 Turbo always use the timestamped endpoint, so --with-timestamps is turned on for them. eleven_v3 only when the plan locked it; never silently swap a locked model. --model-support is ignored.
A timestamped read writes measured_words_per_second into .provenance.json: real words (punctuation alone is not a word), over the first word's start to the last word's last letter. Pass --target-wps with the reference's rate, counted the same way: transcribe it with word timestamps, join whisper's sub-word tokens into real words, skip any token with no letter or digit, and divide the word count by the seconds from the first word's start to the last word's end (the video-production-harness skill ships this count as scripts/reference_rate.py). A read more than 10% below it is sped up to the target, at most 1.25x, with ffmpeg atempo; the raw read is kept as <stem>.raw.mp3 and the timestamps are scaled to match. The provenance records tempo, tempo_applied and tempo_applied_before_lipsync: true. This retimes only the new narration, before any lip-sync step; never a lip-synced or native talking clip. A tagged read is not paced from provider timings. A read still slow at 1.25x needs a faster direction or fewer words.
V4 supports Stability and Similarity settings; Style, Speed, speaker boost and SSML are refused. V3/V4 accept bracketed audio tags and plain --tag delivery cues. V2 does not accept those tags. A literal wanted “uh” is spoken copy that needs approval and time. Tags guide performance; do not accept their effect without listening to the actual selected voice.
Keep a connected read rather than isolated sentences padded to planned scene lengths. New kit narration defaults to join: sentence and a 0.08 s gap only between separate requests. Use join: one for an appropriate same-voice script. Do not generate six takes by default or fit a poor voice performance with automatic time stretching. Deliberate narration tempo changes happen before any lip-sync generation and need listening afterward; never retime a native speaking face's audio separately.
The generator saves .provenance.json separately from raw provider timestamps. Tagged audio has timing_status: requires_reviewed_alignment; tag characters can receive invented times. Use actual reviewed transcription or forced alignment for final caption/edit timing. A timestamp response is not a content review or proof that every word was spoken. Listen to every file, including the first seconds, joins and endings, and preserve the exact voice/model/settings and checksum in the existing library record.
The bundled generator supports timestamped voiceover with per-voice settings. Use the timestamp option for podcast dialogue and other edits that must follow speech. It saves an MP3 and a character-alignment JSON beside it, sharing the same filename stem. The existing audio-only invocation keeps its behavior. A missing alignment is an error; do not render captions from guessed timings. See the bundled timestamp tests for the request and output contract.
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資料には具体的なスキルの内容や手順が記載されていないため、用途や得られる結果を特定できません。
顧客の課題や商品情報、広告事例を調べ、根拠のある広告訴求と静止画・動画・コピー案にまとめます。動画案は実際の制作フォーマットとの相性も確認し、商品単位の角度集として整理します。
Google、Meta、LinkedInなどの広告実績を分析し、成果の出た広告や予算の無駄を特定します。停止・拡大・テスト案や、具体的な予算配分を提案します。
広告費と登録後の成果を照合し、質の高いリード獲得にかかった実コストを算出します。広告素材ごとに拡大・維持・調査・停止の判断材料を整理するスキルです。
Meta広告を始める前にブランド情報を調べ、予算や広告表現に関わる不明点だけを質問して準備を整えます。確認事項と仮定を記録し、ブランド情報とキャンペーン概要を作成します。
広告文や画像内の表現を配信先と商品カテゴリのルールに照らして確認し、直せる箇所を修正しながら、法務や人の判断が必要なリスクを広告ごとに整理します。