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Stepfun Asr

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daymade
stepfun-asr

Transcribe audio with StepFun's stepaudio-3-asr-max — an SSE endpoint (NOT /v1/audio/transcriptions), single call handles long audio with no client-side chunking. Use when transcribing Chinese / English audio with StepFun, when long-form recordings (5-30 min) need to land in one request, when migrating from step-asr / step-asr-1.1 / stepaudio-2.5-asr, or when hitting the misleading `model stepaudio-3-asr-max not supported` error (which actually means wrong endpoint). Triggers on 阶跃 ASR, StepFun ASR, stepaudio-3-asr-max, stepaudio-2.5-asr, 转录, 语音识别, 长音频转写, 语音转文字. For TTS with the sibling stepaudio-3-tts model, use the stepfun-tts skill instead.

Overview

Publisherdaymade
Repositoryclaude-code-skills
Skill namestepfun-asr
Stars
1.4K
Forks
219
Bundled files
3
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.

  • 3 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 Asr 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-asr .claude/skills/stepfun-asr
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Stepfun Asr 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 Asr 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 Asr 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 stepaudio-3-asr-max

Transcribe audio with StepFun's stepaudio-3-asr-max (StepAudio 3, released 2026-09-15, verified 2026-09-16; supersedes stepaudio-2.5-asr on the same endpoint). Long audio in one call, no chunking — but only if the request hits the right endpoint with the right body shape. The wrong endpoint returns an error that looks identical to "model doesn't exist", which is the #1 reason this skill exists.

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

Why this skill exists — three traps that cost hours

  1. Wrong endpoint, wrong error. stepaudio-3-asr-max does not live on /v1/audio/transcriptions (that endpoint serves the older step-asr family). It lives on /v1/audio/asr/sse — SSE streaming, JSON body, base64 audio. Sending it to the wrong endpoint returns {"error":{"message":"model stepaudio-3-asr-max not supported"}}, which is identical in structure to a genuinely nonexistent model name. People waste hours filing whitelist tickets.

  2. Plan key vs Normal key, silent failure. StepFun's "Plan" subscription keys (cheap, text-only) cannot call audio endpoints, but the failure manifests as a 4xx with no auth-shaped error message. If your account has a Plan subscription, you need a separate "Normal" key from the same console.

  3. SSE error events are real. Censorship can fire on the ASR side too (rarely). Don't assume only transcript.text.delta and transcript.text.done events arrive — handle type: error events in the stream or you'll silently drop them.

Config and auth

API key resolves in this order (fail-fast, no defaults):

  1. $STEPFUN_API_KEY environment variable
  2. ${CLAUDE_PLUGIN_DATA}/config.json with {"api_key": "..."} (cross-session persistence)

First-time setup:

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

If the user has not set a key, ask them to paste it — do not guess or use a placeholder. Get keys at https://platform.stepfun.com/ → API Keys. Use a Normal key, not a Plan key.

Quick start — single file

bash
python3 scripts/asr_transcribe.py /path/to/audio.mp3

Output: plain text transcription on stdout.

For machine-readable output with usage / timing:

bash
python3 scripts/asr_transcribe.py /path/to/audio.mp3 --json

For non-Chinese audio:

bash
python3 scripts/asr_transcribe.py /path/to/audio.mp3 --language en

The script handles base64 encoding, the nested {audio: {data, input: {transcription, format}}} body, SSE parsing, and the misleading-endpoint pitfall. Prefer it over hand-rolled HTTP calls unless integrating into a larger pipeline.

Decision table

ScenarioAction
Short clip (< 5 min), Chinese or English, mp3/wav/ogg/opuspython3 scripts/asr_transcribe.py audio.mp3
Long audio (5-30 min)Same script — 32K context handles it in a single call, no chunking needed
Audio > 30 minSplit with ffmpeg before sending; the API rejects oversized payloads
Need usage/billing dataAdd --json to capture usage.input_tokens / usage.total_tokens from transcript.text.done
Highly repetitive content (same phrase 5+ times, > 90s)Cross-validate with step-asr-1.1 — see repetition hallucination in references/known_issues.md (2.5-era issue, unverified on v3)
Hit model stepaudio-3-asr-max not supportedWrong endpoint. Switch from /v1/audio/transcriptions to /v1/audio/asr/sse
Hit silent 4xx auth failureVerify your key is "Normal" not "Plan" — Plan keys cannot call audio endpoints
Need to write raw HTTP (no Python)Read references/api_reference.md for exact JSON body and SSE event shapes

Supported audio formats

The script auto-detects from extension; pass --format to override:

ExtensionFormat flagNotes
.mp3mp3Most common, default
.wavwavLossless
.oggoggOGG container
.opusoggOpus codec in OGG container — pass through unchanged
.pcmpcmRaw PCM — also requires format.rate, format.channel, format.bits (see API reference)

For mp4/m4a/webm/etc., transcode to one of the above first via ffmpeg. Production pipelines often pre-transcode everything to OGG/Opus 16kHz mono to minimize base64 payload size.

Capacity and performance

v3 spot measurements (verified 2026-09-16): 10s clip → 1.1s, 53s real-world clip → 2.6s (~20× RTF). v2.5-era baseline for reference (2026-04-23, same endpoint): 32K context window, ~85-101× RTF on 17.4 min audio, single-call ceiling ≈ 30 min — treat 30 min as the working ceiling for v3 until re-probed, and re-measure before quoting long-audio numbers.

Common error patterns

Error responseActual causeFix
"model stepaudio-3-asr-max not supported" on /v1/audio/transcriptionsWrong endpointSwitch to /v1/audio/asr/sse (script does this)
Silent 4xx with no auth messageUsing a "Plan" key on audio endpointGet a "Normal" key from the StepFun console
ASR returns 3-4× expected character countRepetition hallucination on highly-repetitive audioCross-validate with step-asr-1.1; see references/known_issues.md
data: {"type":"error","message":"content blocked..."} mid-streamCensorship fired on user-uploaded contentHandle SSE error event explicitly; don't assume only delta/done arrive

More edge cases in references/known_issues.md.

Design invariants (do not break)

  1. Always pass through SSE — don't try to buffer the response with a non-streaming client. The model emits transcript.text.delta for long audio; transcript.text.done carries the authoritative full text and usage. Reject the SSE format entirely and you'll get nothing.
  2. Take final text from transcript.text.done.text — concatenated deltas can drift on edge cases. Deltas are for progressive UI; the done event is the source of truth.
  3. Handle error events in the stream — don't treat the SSE stream as if only success events arrive. A blocked-content event mid-stream returns type: error with no done event.
  4. Fail-fast on missing API key — never default to a placeholder or empty string. The script does this; preserve the behavior in any wrapper.

When to read references

  • references/api_reference.md — exact JSON request body, all fields, all SSE event types, response examples. Read when writing raw HTTP calls instead of using the bundled script.
  • references/known_issues.md — repetition hallucination details, the wrong-endpoint diagnostic trail, Plan-vs-Normal key gotcha, ASR-side censorship handling, pricing opacity. Read when debugging anomalous output or evaluating whether to migrate from step-asr-1.1.

Pricing (verified 2026-09-16, volatile)

stepaudio-3-asr-max: 2.8 元/小时 (official model page, 2026-09-16). The older stepaudio-2.5-asr remains served on the same endpoint as a fallback. Re-verify at https://platform.stepfun.com/docs/zh/guides/pricing/details before quoting to stakeholders.

Next Step

ASR transcripts almost always benefit from one of two downstream steps. After producing the transcript, suggest the natural next skill:

Transcription complete. The output is raw text from the model — common next steps:

Options:
A) transcript-fixer — clean up ASR errors (homophones, segmentation, filler words). Recommended if the recording is a real-world conversation, podcast, or interview rather than read-aloud text
B) meeting-minutes-taker — turn the transcript into structured minutes with decisions, action items, and speaker attribution. Recommended if the recording is a meeting
C) No thanks — the raw transcript is what I needed

Skip the suggestion when the user has already specified the downstream tool, or when the transcription was clearly a one-off lookup (e.g., "what does this 15-second clip say?").

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 Asr AI skill do?

Transcribe audio with StepFun's stepaudio-3-asr-max — an SSE endpoint (NOT /v1/audio/transcriptions), single call handles long audio with no client-side chunking. Use when transcribing Chinese / English audio with StepFun, when long-form recordings (5-30 min) need to land in one request, when migrating from step-asr / step-asr-1.1 / stepaudio-2.5-asr, or when hitting the misleading `model stepaudio-3-asr-max not supported` error (which actually means wrong endpoint). Triggers on 阶跃 ASR, StepFun ASR, stepaudio-3-asr-max, stepaudio-2.5-asr, 转录, 语音识别, 长音频转写, 语音转文字. For TTS with the sibling ste...

Why use Stepfun Asr on TypingMind?

Because you install it once and use it with any model. Stepfun Asr 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 Asr in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/daymade/claude-code-skills/tree/main/daymade-audio/stepfun-asr. 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 Asr?

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 Asr?

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

Is the Stepfun Asr 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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