APP_ENV=dev
DB_URL=sqlite:///./fraud.db

# Primary whisper model — handles Russian audio and auto-detection.
# Default 'small' is vanilla openai/whisper-small, no build-time conversion
# needed. Other values:
#   - tiny / base   — lower-resource alternatives
#   - medium        — better accuracy, ~1.5 GB RAM
WHISPER_MODEL=small

# Optional Kazakh-specialised model.
#
# The Dockerfile converter stage pre-builds a CTranslate2-formatted
# fine-tune from the HF model named by the build arg WHISPER_KK_FINETUNE
# (default akuzdeuov/whisper-base.kk — 15.36% WER on KSC2, 1000h of
# Kazakh audio). On a successful build the converted weights live at
# /app/models/whisper-kk/kk inside the container. The runtime loads them
# from there and routes Kazakh audio through this model.
#
# If the build-time conversion fails (ctranslate2 / transformers version
# drift, network, etc.), the directory is empty and the runtime quietly
# uses the primary model for Kazakh too.
#
# Set to empty string to disable KK routing entirely:
#   WHISPER_KK_MODEL=
WHISPER_KK_MODEL=/app/models/whisper-kk/kk

WHISPER_DEVICE=cpu
WHISPER_COMPUTE_TYPE=int8
WHISPER_BEAM_SIZE=5
WHISPER_CPU_THREADS=4

CLF_PATH=models/clf.pkl

MAX_AUDIO_MB=10
RISK_LOW_THRESHOLD=0.40
RISK_HIGH_THRESHOLD=0.60

APP_VERSION=1.0.0