faster-whisper's device="auto" picks CUDA when ctranslate2's wheel
ships CUDA shared libs, even on hosts without the NVIDIA runtime
(libcublas.so.12 / libcudnn*). On those hosts the model often loads
fine but transcribe() fails at first dlopen, and the broken model
stays cached in the module-global — every subsequent voice message
in the gateway process fails identically until restart.
- Add _load_local_whisper_model() wrapper: try auto, catch missing-lib
errors, retry on device=cpu compute_type=int8.
- Wrap transcribe() with the same fallback: evict cached model, reload
on CPU, retry once. Required because the dlopen failure only surfaces
at first kernel launch, not at model construction.
- Narrow marker list (libcublas, libcudnn, libcudart, 'cannot be loaded',
'no kernel image is available', 'no CUDA-capable device', driver
mismatch). Deliberately excludes 'CUDA out of memory' and similar —
those are real runtime failures that should surface, not be silently
retried on CPU.
- Tests for load-time fallback, runtime fallback (with cached-model
eviction verified), and the OOM non-fallback path.
Reported via Telegram voice-message dumps on WSL2 hosts where libcublas
isn't installed by default.
- Replace hardcoded 'fr' default with DEFAULT_LOCAL_STT_LANGUAGE ('en')
— removes locale leak, matches other providers
- Drop redundant default=True on is_truthy_value (dict .get already defaults)
- Update auto-detect comment to include 'xai' in the chain
- Fix docstring: 21 languages (match PR body + actual xAI API)
- Update test_sends_language_and_format to set HERMES_LOCAL_STT_LANGUAGE=fr
explicitly, since default is no longer 'fr'
All 18 xAI STT tests pass locally.
Legacy flat stt.model config key (from cli-config.yaml.example and older
versions) was passed as a model override to transcribe_audio() by the
gateway, bypassing provider-specific model resolution. When the provider
was 'local' (faster-whisper), this caused:
ValueError: Invalid model size 'whisper-1'
Changes:
- gateway/run.py, discord.py: stop passing model override — let
transcribe_audio() handle provider-specific model resolution internally
- get_stt_model_from_config(): now provider-aware, reads from the correct
nested section (stt.local.model, stt.openai.model, etc.); ignores
legacy flat key for local provider to prevent model name mismatch
- cli-config.yaml.example: updated STT section to show nested provider
config structure instead of legacy flat key
- config migration v13→v14: moves legacy stt.model to the correct
provider section and removes the flat key
Reported by community user on Discord.
mistralai v2.x is a namespace package — `Mistral` class lives at
`mistralai.client`, not at the top-level `mistralai` module. The
previous `from mistralai import Mistral` raises ImportError at runtime.
Update both production code and test fixture to use the correct path.
- add managed modal and gateway-backed tool integrations\n- improve CLI setup, auth, and configuration for subscriber flows\n- expand tests and docs for managed tool support
* fix(session): skip corrupt lines in load_transcript instead of crashing
Wrap json.loads() in load_transcript() with try/except JSONDecodeError
so that partial JSONL lines (from mid-write crashes like OOM/SIGKILL)
are skipped with a warning instead of crashing the entire transcript
load. The rest of the history loads fine.
Adds a logger.warning with the session ID and truncated corrupt line
content for debugging visibility.
Salvaged from PR #1193 by alireza78a.
Closes#1193
* fix(stt): respect explicit provider config instead of env-var fallback
Rework _get_provider() to separate explicit config from auto-detect.
When stt.provider is explicitly set in config.yaml, that choice is
authoritative — no silent cross-provider fallback based on which env
vars happen to be set. When no provider is configured, auto-detect
still tries: local > groq > openai.
This fixes the reported scenario where provider: local + a placeholder
OPENAI_API_KEY caused the system to silently select OpenAI and fail
with a 401.
Closes#1774
Restore local STT command fallback for voice transcription, detect whisper and ffmpeg in common local install paths, and avoid bogus no-provider messaging when only a backend-specific key is missing.
Remove web UI gateway (web.py, tests, docs, toolset, env vars, Platform.WEB
enum) per maintainer request — Nous is building their own official chat UI.
Fix 1: Replace sd.wait() with polling pattern in play_audio_file() to prevent
indefinite hang when audio device stalls (consistent with play_beep()).
Fix 2: Use importlib.util.find_spec() for faster_whisper/openai availability
checks instead of module-level imports that trigger heavy native library
loading (CUDA/cuDNN) at import time.
Fix 3: Remove inspect.signature() hack in _send_voice_reply() — add **kwargs
to Telegram send_voice() so all adapters accept metadata uniformly.
Fix 4: Make session loading resilient to removed platform enum values — skip
entries with unknown platforms instead of crashing the entire gateway.
Merge main's faster-whisper (local, free) with our Groq support into a
unified three-provider STT pipeline: local > groq > openai.
Provider priority ensures free options are tried first. Each provider
has its own transcriber function with model auto-correction, env-
overridable endpoints, and proper error handling.
74 tests cover the full provider matrix, fallback chains, model
correction, config loading, validation edge cases, and dispatch.
Duplicated YAML config parsing for stt.model existed in gateway/run.py
and gateway/platforms/discord.py. Moved to a single helper in
transcription_tools.py and added 5 tests covering all edge cases.
