stepfun-asr

StepFun stepaudio-2.5-asr

Transcribe audio with StepFun's stepaudio-2.5-asr (released 2026-04, verified 2026-04-23). 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-2.5-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-2.5-asr 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-2.5-asr 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

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
Highly repetitive content (same phrase 5+ times, > 90s)	Cross-validate with step-asr-1.1 — see repetition hallucination in references/known_issues.md
Hit model stepaudio-2.5-asr 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

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 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 (verified 2026-04-23)

• 32K context window — single-call upper limit, no chunking needed for ≤ 30 min audio
• ~85-101× RTF on long audio (17.4 min audio → 10.4s wall clock)
• ~5.3× speedup vs step-asr-1.1 at the 100s+ length range
• Only ~2× speedup at the 5-15s range — the LLM spin-up cost dominates short clips. If your workload is many short clips, the migration ROI is modest

Common error patterns

Error response	Actual cause	Fix
"model stepaudio-2.5-asr 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.
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 2026-04-23, volatile)

stepaudio-2.5-asr is in invitation beta as of 2026-04-23 — no public per-minute rate. The step-asr-1.1 baseline is 2.2 元/小时. The invitation PDF mentions "成本直降 80%" implying ~0.4 元/小时, but this is not yet on the pricing page. 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?").