feat(transcription): configurable STT model + OpenRouter provider

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>

nanobot/audio/transcription.py

@@ -18,12 +18,13 @@ from loguru import logger
 from nanobot.config.paths import get_media_dir
 from nanobot.utils.media_decode import FileSizeExceeded, save_base64_data_url
 
-TranscriptionProviderName = Literal["groq", "openai"]
+TranscriptionProviderName = Literal["groq", "openai", "openrouter"]
 
 _DEFAULT_PROVIDER: TranscriptionProviderName = "groq"
 _DEFAULT_MODELS: dict[TranscriptionProviderName, str] = {
     "groq": "whisper-large-v3",
     "openai": "whisper-1",
+    "openrouter": "openai/whisper-1",
 }
 _MAX_AUDIO_BYTES_FALLBACK = 25 * 1024 * 1024
 _AUDIO_MIME_ALLOWED: frozenset[str] = frozenset({
@@ -171,6 +172,15 @@ async def transcribe_audio_file(
             language=config.language,
             model=config.model,
         )
+    elif config.provider == "openrouter":
+        from nanobot.providers.transcription import OpenRouterTranscriptionProvider
+
+        provider = OpenRouterTranscriptionProvider(
+            api_key=config.api_key,
+            api_base=config.api_base or None,
+            language=config.language,
+            model=config.model,
+        )
     else:
         from nanobot.providers.transcription import GroqTranscriptionProvider