stepfun-tts
An agent skill by daymade, from daymade/claude-code-skills. Tags: api, audio, generative, ml, stepfun.
What it does
Generate Chinese / Japanese speech with StepFun's Contextual TTS — default `stepaudio-2.5-tts` (blind-judged better on neutral/emotive preset voices), `stepaudio-3-tts` for whisper & inline-prosody cases (where it won the same blind test). Replaces step-tts-2's `voice_label` with natural-language `instruction` (200 chars on 2.5, 500 on v3) plus inline `()` parentheses for句内 prosody. Use when the user wants emotional / prosody control over voice synthesis (whisper, pause, stress, mood pivot mid-sentence), batch-generates game / app voice lines, migrates from `step-tts-2` or `stepaudio-2.5-tts` (the `voice_label → instruction` breaking change), or needs cloned voices (复刻音色：克隆合成禁用 v3——克隆丢失，走 stepaudio-2.5-tts/step-tts-2). Triggers on 阶跃 TTS, StepAudio 合成, stepaudio-3-tts, stepaudio-2.5-tts, 语音合成, 配音, 文本转语音, TTS 升级, 迁移 step-tts-2. For transcription with the sibling stepaudio-3-asr-max model, use the stepfun-asr skill instead.
Install
With the skills CLI, which installs into Claude Code, Codex, Cursor and other agents:
npx skills add daymade/claude-code-skills --skill stepfun-ttsOr copy the skill folder into Claude Code's skills directory by hand (~/.claude/skills for every project, or .claude/skills inside one):
git clone --depth 1 https://github.com/daymade/claude-code-skills
cp -r claude-code-skills/daymade-audio/stepfun-tts ~/.claude/skills/stepfun-ttsSafety box score
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Source
- Repository
- daymade/claude-code-skills (all skills from this repository)
- Path
- daymade-audio/stepfun-tts/SKILL.md
- Branch
- main
- Updated
- 2026-09-20
Related skills
- stepfun-asr — Transcribe audio with StepFun's stepaudio-3-asr-max — an SSE endpoint (NOT /v1/audio/transcriptions).
- amazon-bedrock — Builds generative AI applications on Amazon Bedrock.
- fine-tuning-expert — Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks.
- asr-transcribe-to-text — Transcribe audio/video to speaker-labeled text — who-said-what by default, plain-text opt-out; MLX-local on Apple Silicon or remote; local files, media URLs.
- llm-eval-harness — Test/evaluate any LLM behind an OpenAI- or Anthropic-compatible endpoint: availability (max_tokens-aware).
- book-sft-pipeline — This skill should be used for book-to-SFT pipelines: ePub extraction, literary segmentation, author-voice dataset construction, style-transfer training.