「apis」の検索結果

Produce and understand audio with OpenAI request-based audio APIs and audio-capable chat models, including text-to-speech, transcription, translation, multimodal audio input/output, model routing, prompt and performance direction, artifact custody, approval gates, and safety/rights review. Use for non-realtime OpenAI audio production; route continuous live voice agents to the separate realtime voice skill.

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Produce, direct, integrate, and quality-control ElevenLabs text-to-speech for narration, character performance, multilingual media, long-form voiceover, and streamed or batch AI-content audio. Use when selecting ElevenLabs models or voices; preparing spoken text; controlling pronunciation, pacing, emotion, and code-switching; using TTS APIs; cloning or designing a voice with consent; repairing artifacts; mastering deliverables; or evaluating generated speech. Excludes music, sound-effect generation, conversational-agent design, and general speech recognition except transcription used to verify TTS.

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。

Use for ElevenLabs provider-specific dubbing, localization, voice changer, speech-to-speech, and voice isolation/enhancement workflows. Applies when an agent must prepare source media, choose ElevenLabs dubbing versus voice conversion, manage language/speaker/timing decisions, handle voice identity and consent, call or plan against documented ElevenLabs APIs, export dubs/transcripts, estimate cost/limits, or quality-control localized speech while avoiding generic text-to-speech guidance.

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。

Produce HeyGen avatar videos with Direct Video, Video Agent, Digital Twin, Avatar Realtime, and related avatar/voice/asset APIs. Use for provider routing, avatar and voice selection, script-to-video workflows, lip-sync from audio, backgrounds/scenes, callbacks and polling, consent and rights checks, localization handoffs, artifact custody, QA, and troubleshooting HeyGen avatar-video productions.

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Plan, generate, edit, reference, and quality-control still images with Kling AI's hosted IMAGE surfaces or Kuaishou's open Kolors checkpoints. Use for Kling IMAGE 3.0/3.0 Omni/O1/2.1 web, official Kling CLI/MCP or Open Platform integration, and local Kolors text-to-image, image-to-image, IP-Adapter, ControlNet, inpainting, or LoRA work. Do not use for Kling video, avatars, lip-sync, unofficial gateway APIs, or for assuming that current hosted Kling Image models are identical to the 2024 open Kolors weights.

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Provider-independent still-image post-production skill for RAW/rendered intake, nondestructive development, exposure, white balance, tone, color, ICC proofing, masking, repair, compositing, truthful retouching by genre, generative disclosure, sharpening, noise, resampling, variants, metadata, export, QA, rights, and ethical finishing. Use when an agent must plan, perform, direct, or review still photography retouching and finishing for print, web, social, archive, publication, or client delivery; do not use for image-generation APIs, video grading, or campaign-specific production strategy.

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Operate Stability AI image generation, image-to-image, edit, control, background, and upscale APIs safely and reproducibly. Use when selecting or calling Stable Image Ultra, Core, Stable Diffusion 3.5, v2beta image edit/control services, or Stability open-weight image models; when debugging schemas, moderation, rate limits, cost, lifecycle, licenses, privacy, provenance, or migrations; and when planning production QA for Stability-generated images.

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Generate, edit, and iterate still images with Runway's official API, especially native Gen-4 Image and Gen-4 Image Turbo reference workflows. Use when implementing Runway text-to-image, reference-driven image generation or natural-language image edits, task polling, secure artifact handling, production retries, cost controls, or Runway image API QA. Do not use for Runway video generation or third-party gateway APIs.

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Create, edit, guide, upscale, and quality-control still images with Leonardo.Ai's official Production API, including native Lucid and Phoenix models, uploaded or generated references, image-to-image, ControlNet guidance, realtime-canvas inpainting, Pro and Universal upscalers, and custom Elements or models. Use for Leonardo image API implementation, async polling or webhooks, dry-run and cost governance, secure artifact handling, prompt iteration, or production troubleshooting. Do not use for Leonardo video, 3D generation, unofficial wrappers, or third-party gateway APIs.

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Provider-independent production guidance for converting observed video into precise, objective, temporally ordered language. Use for shot descriptions, searchable metadata, dataset captions, reference logs, generation-prompt handoffs, and analysis across subject, scene, motion, spatial, and camera aspects; not for deciding what to shoot, accessibility captions, creative interpretation, or provider-specific video analysis APIs.

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。

Apply vaex in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses vaex or its strengths fit the task.

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Apply validate data in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses validate data or its strengths fit the task.

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。

Apply umap-learn in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses umap-learn or its strengths fit the task.

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。

Apply spark optimization in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses spark optimization or its strengths fit the task.

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。

Apply statsmodels in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses statsmodels or its strengths fit the task.

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。

Apply seaborn in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses seaborn or its strengths fit the task.

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。

Apply risk metrics calculation in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses risk metrics calculation or its strengths fit the task.

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。

Apply sympy in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses sympy or its strengths fit the task.

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。

Apply single-cell rna qc in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses single-cell rna qc or its strengths fit the task.

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。

Apply networkx in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses networkx or its strengths fit the task.

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。

Apply stable-baselines3 in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses stable-baselines3 or its strengths fit the task.

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。

Apply statistical power in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses statistical power or its strengths fit the task.

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。

Apply sql queries in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses sql queries or its strengths fit the task.

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。

Apply polars in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses polars or its strengths fit the task.

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。