config.loader.load_config() intentionally returns the raw config with ${VAR}
references intact — env interpolation is a separate, explicit step
(resolve_config_env_vars) so that settings read/edit/save paths never
materialize secrets to disk or to the UI.
The transcription config path does not apply that step: both
channels/base.py (channel voice notes) and webui/transcription_ws.py (WebUI
recording) build their effective config via
resolve_transcription_config(load_config()). As a result a configured
api_key of "${GROQ_API_KEY}" (the documented way to reference secrets) is
passed to the provider verbatim, which fails with 401 Invalid API Key. No
amount of rotating the real key helps, because the literal placeholder
string is what gets sent.
Resolve the reference at the single choke point both callers share —
_resolve_transcription_api_key / _resolve_transcription_api_base — using a
new lenient loader.resolve_env_refs() helper (unset var -> empty string, so
a missing variable degrades to "not configured" rather than raising or
leaking). This fixes both entry points at once and cannot drift the way a
per-call-site fix does. Resolving inside load_config() was rejected: the
~20 settings-UI callers depend on it returning raw ${VAR} placeholders.
Literal keys are unaffected; the settings API only reads the derived
`configured` flag (never the key), which now reflects the resolved value.
Claude-Session: https://claude.ai/code/session_01Q3HuVaJAAQJA3kgVQVJ2Zt
- Register SiliconFlow in transcription registry with default model
FunAudioLLM/SenseVoiceSmall and alias 'silicon'
- Reuse existing OpenAITranscriptionProvider adapter (Whisper-compatible)
- Add generic key/base resolution: fallback to registry env_key and
default_api_base when provider config is absent
- Add tests for registry entry, alias, adapter, default model, and
config resolution with env var fallback
- Add StepFunTranscriptionProvider class in nanobot/providers/transcription.py
- New _post_stepfun_asr_with_retry() function handling SSE stream parsing
(transcript.text.delta → transcript.text.done event sequence)
- Register 'stepfun' in transcription_registry.py with default model stepaudio-2.5-asr
- Reuse existing stepfun provider config (apiBase can point to Plan endpoint)
- Add 17 tests covering SSE parsing, retry contract, empty-text edge case, and registry integration
- Update docs/configuration.md with stepfun ASR documentation
StepFun ASR uses a dedicated SSE endpoint (/v1/audio/asr/sse) rather
than the chat-completions or Whisper multipart formats used by other
providers. Users on Step Plan can set apiBase to the Plan endpoint.
Add AssemblyAI as a third transcription provider option alongside
OpenAI and Groq. AssemblyAI offers better accuracy for certain
audio types (distant voices, noisy environments) and serves as a
reliable fallback when other providers struggle.
Changes:
- Add AssemblyAITranscriptionProvider class in providers/transcription.py
- Add 'assemblyai' option in base channel's transcribe_audio()
- Per-channel configuration via transcriptionProvider in config
Usage:
Set transcriptionProvider: 'assemblyai' and provide an AssemblyAI
API key via transcriptionApiKey in the channel config.
Add support for Xiaomi MiMo ASR as a third transcription backend alongside
Groq and OpenAI Whisper. Xiaomi ASR uses the /v1/chat/completions endpoint
with base64-encoded audio input, rather than the standard Whisper multipart
upload format.
Co-Authored-By:连 <lian@tangping.homes>
Add a `transcriptionModel` channel setting and an OpenRouter transcription
backend so voice messages can be transcribed through OpenRouter's
speech-to-text endpoint (e.g. nvidia/parakeet-tdt-0.6b-v3, openai/whisper-1),
alongside the existing Groq/OpenAI Whisper providers.
- schema: add channels.transcriptionModel (None = provider default)
- providers/transcription: extract a shared POST/retry skeleton; add a
JSON+base64 OpenRouterTranscriptionProvider; make the STT model a
constructor param on all providers instead of hardcoding it
- channels: route transcriptionProvider="openrouter" and thread the model
through the manager to each channel
- docs + tests
Only dedicated STT models work on OpenRouter's transcription endpoint;
chat LLMs (e.g. google/gemini-3.5-flash) are rejected there.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Ben LenartsandXubin Ren
moranandXubin Ren
comadrejaandXubin Ren
