---name: stepfun-asrdisable-model-invocation: truedescription: >----

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:

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

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

Output: plain text transcription on stdout.

For machine-readable output with usage / timing:

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

For non-Chinese audio:

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

Per-word timestamps need no flag — --json always carries segments:

"segments": [{"text": "Understand ", "start_ms": 228, "end_ms": 1108}, ...]

One entry per word, monotonic. A few words share their predecessor's timestamp (the server flushes in blocks), which is fine for locating a moment but not for forced alignment.

To use an older model:

python3 scripts/asr_transcribe.py /path/to/audio.mp3 --model stepaudio-2.5-asr

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

Scenario	Action
Short clip (< 5 min), Chinese or English, mp3/wav/ogg/opus	python3 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 min	Split with ffmpeg before sending; the API rejects oversized payloads
Need usage/billing data	Add --json to capture usage.input_tokens / usage.total_tokens from transcript.text.done
Need to know when each word was said	--json, read segments. On by default
Need speaker labels (who said what)	python3 scripts/asr_file.py <public-url> — a different, async endpoint. Takes a URL, not a local file: base64 and StepFun's own file store are both rejected, so hosting the audio somewhere fetchable is a decision for whoever runs it
--model stepaudio-2-asr-pro returns internal error	That model is not usable on /v1/audio/asr/sse (measured 2026-09-18); use the default or stepaudio-2.5-asr
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 supported	Wrong endpoint. Switch from /v1/audio/transcriptions to /v1/audio/asr/sse
Hit silent 4xx auth failure	Verify 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

Speaker labels — scripts/asr_file.py

stepaudio-3-asr-max on /v1/audio/asr/sse has no speaker capability at all (14 candidate request fields measured inert). Diarization lives on the async file endpoint:

python3 scripts/asr_file.py https://example.com/talk.mp3
# [   6.61-   8.43] speaker_0: Hello. Hello. Oh,
# [   8.21-  10.11] speaker_1: hello! I didn't know you were there.

Verified end-to-end 2026-09-18 on a two-speaker sample: correct turn boundaries, per-word timestamps inside each utterance, up to 10 speakers per task. Uses stepaudio-2.5-asr — v3 is not served on this endpoint.

The hard constraint: it fetches a URL and nothing else. Base64 is rejected and so is StepFun's own stepfile:// file store, so there is no way to feed it a local file without first putting that file somewhere publicly fetchable. Treat that as the caller's decision. references/known_issues.md has the three dead ends and the retry/redirect behaviour.

Parameters are free — never omit one silently

Sending more request parameters costs nothing: billing is per audio-hour. So the default is send everything useful, and every field we do not send has to carry a written reason in REQUEST_PARAMS at the top of scripts/asr_transcribe.py.

python3 scripts/check_params.py            # diff official field table vs REQUEST_PARAMS
python3 scripts/check_params.py --selftest # calibrate the check before trusting it

Two guards, one per direction:

• Request side — check_params.py fetches the official field table and fails if it lists a field REQUEST_PARAMS does not mention. --selftest calibrates both ways: the real manifest must pass (no false alarms), and a manifest with enable_timestamp removed must be caught (the actual historical gap — per-word timestamps were missing for months because only the response field table was ever read).
• Response side — the parser reports unhandled_response_fields for anything the server sends that it does not consume, because that is what the timestamp gap looked like from this side: start_time/end_time arrived on every delta and were thrown away.

Supported audio formats

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

Extension	Format flag	Notes
.mp3	mp3	Most common, default
.wav	wav	Lossless
.ogg	ogg	OGG container
.opus	ogg	Opus codec in OGG container — pass through unchanged
.pcm	pcm	Raw PCM — also pass --rate, --bits, --channel (and --codec)

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 response	Actual cause	Fix
"model stepaudio-3-asr-max not supported" on /v1/audio/transcriptions	Wrong endpoint	Switch to /v1/audio/asr/sse (script does this)
Silent 4xx with no auth message	Using a "Plan" key on audio endpoint	Get a "Normal" key from the StepFun console
ASR returns 3-4× expected character count	Repetition hallucination on highly-repetitive audio	Cross-validate with step-asr-1.1; see references/known_issues.md
data: {"type":"error","message":"content blocked..."} mid-stream	Censorship fired on user-uploaded content	Handle 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. transcribe()'s return dict always carries an errors list of the raw error-event payloads (empty if none fired), even when ok is True, so a caller wanting the old strict semantics (any error event ⇒ failure) must check errors itself.
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?").