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Stepfun Tts

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daymade
stepfun-tts

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.

Overview

Publisherdaymade
Repositoryclaude-code-skills
Skill namestepfun-tts
Stars
1.4K
Forks
219
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by daymade on GitHub. Read the source before you install it.

Installation

Install the Stepfun Tts AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/daymade/claude-code-skills.git /tmp/claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-skills/daymade-audio/stepfun-tts .claude/skills/stepfun-tts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Stepfun Tts in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Stepfun Tts on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Stepfun Tts is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

StepFun Contextual TTS (default stepaudio-2.5-tts)

Generate Chinese / Japanese speech with StepFun's Contextual TTS — emotion and prosody go through natural-language description, not fixed labels. Default model is stepaudio-2.5-tts (what the bundled script uses): in a 2026-09-16 blind A/B on our own cases, 2.5 won the neutral / jiao / lively-girl pairs 3:2. Pick stepaudio-3-tts when the line is whisper or heavy inline-() prosody — v3 won exactly those two pairs; v3 also raises the instruction cap to 500 chars (2.5 is 200). ⚠️ Never synthesize cloned voices with v3 — v3 speech 接受复刻音色 ID 不报错但静默回退默认女声——根因 = 阶跃复刻链路整体停在 2.5 家族(复刻创建 API 只收 2.5/step-tts-2/step-tts-mini,v3 不在列;v3 合成侧复刻未发布)。两代创建的克隆在 v3 上全丢:step-tts-2 克隆 SIM 0.272、2.5 创建的新克隆 SIM 0.195(锚 0.773),2.5 同 ID 0.743/0.678。

Companion: for transcription with stepaudio-3-asr-max (the sibling model), use the stepfun-asr skill — they share an API key but live on different endpoints with different body shapes.

Why this skill exists — two non-obvious pitfalls that cost hours if you don't know them:

  1. stepaudio-3-tts rejects voice_label (the step-tts-2 way) — verified on v3 2026-09-16: HTTP 400 voice_label is not supported for this model. Emotion/prosody goes through instruction (natural-language description, ≤500 chars on v3 — 200 was the 2.5 limit) and inline () parentheses inside the text itself.
  2. Censorship behavior is model-version-specific — the 2.5-era trigger list (死 / 消失 / sensitive political terms → censorship_block) did not fire on v3 in a 2026-09-16 single-sample probe; treat censorship as present but re-verify per trigger before building rewrite maps. The 2.5-era options are in references/migration_from_v2.md.

Config and auth

API key lives in $STEPFUN_API_KEY (preferred) or ${CLAUDE_PLUGIN_DATA}/config.json (fallback for cross-session persistence). All bundled scripts try env first, then config.

First-time setup (one-liner):

bash
mkdir -p "${CLAUDE_PLUGIN_DATA}" && cat > "${CLAUDE_PLUGIN_DATA}/config.json" <<EOF
{"api_key": "<paste key here>"}
EOF

If the user hasn't set a key, ask them to paste it (don't guess / don't use a placeholder). StepFun API keys are available at https://platform.stepfun.com/ → API Keys. Use a Normal key, not a Plan key (Plan keys are restricted to text models and silently fail on audio endpoints).

Common tasks — decision tree

User wants...ScriptKey detail
Synthesize 1–500 char Chinese with emotionscripts/tts_generate.pyUse instruction for mood, () for inline prosody
Synthesize long text (500–1000 char)scripts/tts_generate.py1000 char is the hard cap; split at semantic boundaries above that
Batch-generate game/app voice linesscripts/tts_generate.py --batch <jsonl>Handle censorship_block fallback individually
A/B compare two TTS modelsscripts/ab_compare.shCompares duration/size across two directories
Migrate from step-tts-2 / stepaudio-2.5-ttssee references/migration_from_v2.mdvoice_label.emotioninstruction rewrite + 2.5-era censorship list

Starting points

  • Synthesize a single line: Run python3 scripts/tts_generate.py --text "你好" --out /tmp/hello.mp3 --instruction "温暖的希望感". For fine-grained control read the "Contextual TTS" section below.
  • A full migration from step-tts-2 → Contextual TTS: read references/migration_from_v2.md end-to-end before touching code. It has the INSTRUCTION_MAP, the SKIP_CENSORED list pattern, and the output-directory-strategy for non-destructive A/B (written for 2.5; the migration mechanics are identical on v3).

Contextual TTS — beyond emotion labels

The headline feature of stepaudio-3-tts is that you stop mapping emotions to fixed tags and start describing what you want in natural language. Two layers:

Global context (instruction parameter) — sets the overall tone for the entire utterance. ≤500 chars on v3 (2.5 was 200; a 300-char instruction verified accepted on v3 2026-09-16). Think of it like giving stage direction to a voice actor.

instruction: "克制的悲伤,语气低沉柔弱,像快要消失一样"

Inline context (() parentheses inside input) —句内 directives. Parenthesised content is consumed as directions and is NOT read aloud. Use for precise control of pauses, breath, emphasis, or mid-sentence emotion shifts.

input: "(试探着问)你好吗?(开心地)太好了!(突然沉下来)不过...我快要消失了。"

Examples that worked in practice (verified 2026-04-23 on 2.5; all five re-verified on v3 2026-09-16, including these instruction and inline-prosody cases):

  • instruction: "活泼俏皮,像是在撒娇,带点嘴硬" — visibly speeds up delivery vs neutral
  • instruction: "耳语声,气声很重,几乎听不清" — produces audible whisper/breath
  • input: "你好(停顿一下)我是蕾格(轻声)今天(加重)的天气真不错。" — inline directives all respected

What stepaudio-3-tts will NOT acceptvoice_label parameter. Error on v3: voice_label is not supported for this model (2.5 said ...for v2 models). This is the #1 migration gotcha from step-tts-2.

Common error patterns (real errors, real fixes)

Error responseActual causeFix
"voice_label is not supported for this model" (v3) / "...for v2 models" (2.5)Sent voice_label to a Contextual TTS modelRemove voice_label; put the same intent into instruction as natural language
"The content you provided or machine outputted is blocked." type: censorship_blockSensitive word (2.5-era: 死 / 消失 / etc.; v3 triggers unverified)Rewrite the phrase OR fall back to step-tts-2 for that specific line (mixed-model is fine)
Silent audio truncation (input > 1000 chars)Hard cap exceededSplit at semantic boundaries; don't truncate mid-sentence

More in references/known_issues.md.

When to read references

  • references/api_reference.md — exact request/response JSON for /v1/audio/speech, all fields, error responses. Read when writing raw HTTP calls instead of using the bundled scripts.
  • references/migration_from_v2.md — complete playbook for moving a step-tts-2 project to Contextual TTS. Has the emotion→instruction rewrite table, the A/B directory strategy, decision checkpoints, and the 2026-04 speed/quality trade-off data (written against 2.5). Read before any migration work.
  • references/known_issues.md — censorship patterns, TTS duration inflation, v2-family parameter naming gotcha, 1000-char hard cap. 2.5-era verification; re-verify on v3 before relying on a specific entry. Read when debugging anomalous output or evaluating whether to adopt.

Design invariants (don't break these)

  1. Non-destructive A/B output — when regenerating a corpus with a new model, write to a parallel directory (voice/zh_v3/), never overwrite the production corpus. The migration playbook shows why.
  2. Per-line censorship handling — if 2/29 lines get censorship_block, don't fail the batch. Log the skipped IDs, continue. Mixed-model fallback (step-tts-2 for the skipped 2) is normal.
  3. Don't duplicate voice_label logic in new code — any new TTS code targeting stepaudio-3-tts should only use instruction + inline (). Do not write a branch that conditionally emits voice_label.

v3-specific facts (verified 2026-09-16)

  • Official voices: 60+ via GET /v1/audio/system_voices?model=stepaudio-3-tts — the 2.5 list is fully inherited, plus new voices (e.g. English-named Lisa/Alfie, 上海话 shanghaifemale/shanghaimale).
  • Cloned (复刻) voices: do NOT use stepaudio-3-tts — root cause nailed 2026-09-16: StepFun's whole cloning stack is still 2.5-family. The creation API (POST /v1/audio/voices) only accepts stepaudio-2.5-tts / step-tts-2 / step-tts-mini, and v3 /v1/audio/speech silently falls back to a default female voice for ANY cloned ID — SIM 0.272 (step-tts-2 clone) and 0.195 (fresh 2.5-created clone) vs the 0.773 anchor; the same IDs on 2.5 score 0.743/0.678. Synthesize clones with stepaudio-2.5-tts (best SIM) or step-tts-2.
  • No WebSocket streaming for v3 (as of 2026-09-16): wss://api.stepfun.com/v1/realtime/audio?model=stepaudio-3-tts is rejected at handshake (404), while 2.5 still streams there. Need char-level subtitle timestamps on v3? Use REST timestamp:true + return_url:true (subtitles arrive in the response JSON data.subtitles[], char-level ms, accumulated absolute axis) — bare stream_format:"audio" cannot carry subtitles (server 400).

Pricing (verified 2026-09-16, volatile)

  • stepaudio-3-tts synthesis: 2.5 元 / 万字符 (official model page, 2026-09-16 — cheaper than the 2.5-era ~5.8)
  • Zero-shot voice cloning: 9.9 元 / 音色

Re-verify at https://platform.stepfun.com/docs/zh/guides/pricing/details before quoting to stakeholders.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Stepfun Tts AI skill do?

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-tt...

Why use Stepfun Tts on TypingMind?

Because you install it once and use it with any model. Stepfun Tts is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Stepfun Tts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/daymade/claude-code-skills/tree/main/daymade-audio/stepfun-tts. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Stepfun Tts?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Stepfun Tts?

As many as you like. As long as a model supports skills, you can use Stepfun Tts with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Stepfun Tts AI skill free?

Yes. It is published on GitHub by daymade under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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