# PolyTalk Environment Variables
# Copy this file to .env and update values as needed
# All ${VAR} references in config/config.yaml will use these values

# ============================================================================
# APPLICATION LOGGING
# ============================================================================
# Logging level: DEBUG, INFO, WARNING, ERROR, CRITICAL
LOG_LEVEL=INFO

# ============================================================================
# STT SERVICE (Local Speech-to-Text with faster-whisper)
# ============================================================================
# STT model to use: small, small-v3, medium, large-v3
STT_MODEL=small

# Device to run STT: cpu or cuda
STT_DEVICE=cpu

# Compute type: int8 (CPU) or float16 (CUDA)
STT_COMPUTE_TYPE=int8

# Number of STT web workers. Each worker loads its own Whisper model.
STT_WORKERS=1

# Load the Whisper model during STT service startup instead of on first stream.
STT_PRELOAD_MODEL=true

# Max file upload size in MB
STT_MAX_UPLOAD_MB=200

# Streaming audio window in seconds. Lower values reduce latency but can reduce
# transcript stability. 3.0 gives Whisper more context while pause flush handles utterance endings.
STT_STREAM_CHUNK_SECONDS=3.0

# Audio overlap between STT windows. Helps avoid missing words at chunk boundaries.
# Keep this modest; too much overlap can increase repeated/hallucinated text.
STT_CHUNK_OVERLAP_SECONDS=0.25

# Parallel STT queue workers. Increase to 2 when STT inference is slower than
# incoming audio windows and the GPU has spare compute.
STT_TRANSCRIBE_WORKERS=2
STT_TRANSCRIBE_QUEUE_SIZE=8
STT_MODEL_WORKERS=2

# Transcript emit batching. STT may infer more often than it emits to PolyTalk.
# Increase these values if live transcript/translation/TTS chunks are too small.
STT_EMIT_MIN_CHARS=120
STT_EMIT_INTERVAL_SECONDS=4.5
# Flush the current speech window after this much trailing silence, even if the
# normal stream window or emit thresholds have not been reached. Set 0 to disable.
STT_PAUSE_FLUSH_SECONDS=1.2
# Flush the current speech window if audio chunks stop arriving after speech.
STT_IDLE_FLUSH_SECONDS=3.0
# Keep a small amount of audio before first detected speech, but do not let
# leading tab-share silence fill the first STT window.
STT_LEADING_SILENCE_PREROLL_SECONDS=0.2

# Silence/hallucination guards for streaming STT. These balanced defaults work
# well for typical microphone input: raise RMS/no-speech strictness if Whisper
# hallucinates during silence; lower them if quiet speech is missed.
STT_SILENCE_RMS_THRESHOLD=0.003
STT_NO_SPEECH_PROB_THRESHOLD=0.50
STT_LOG_PROB_THRESHOLD=-1.0
STT_MAX_CROSS_DELTA_WORD_REPEATS=6

# faster-whisper decoding/VAD knobs. Keep previous-text conditioning disabled
# by default for streaming because it can repeat or invent text during silence.
STT_VAD_FILTER=true
STT_VAD_MIN_SILENCE_MS=500
STT_VAD_SPEECH_PAD_MS=200
STT_WORD_TIMESTAMPS=true
STT_CONDITION_ON_PREVIOUS_TEXT=false
STT_TEMPERATURE=0.0
# Optional domain/context prompt for Whisper, for example names or product terms.
# STT_INITIAL_PROMPT=

# ============================================================================
# WHISPER SERVICE CONFIGURATION (Points to local STT or external)
# ============================================================================
# Base URL for Whisper API (defaults to local STT service in Docker)
# For external service, use: https://whisper.your-domain.com
WHISPER_BASE_URL=http://stt:8000

# WebSocket endpoint for streaming transcription (used by PolyTalk)
WHISPER_WS_ENDPOINT=/v1/stream/transcriptions

# Optional: API key for external Whisper API (e.g., OpenAI, custom deployment)
# Set this if your Whisper service requires authentication
# WHISPER_API_KEY=your-api-key-here

# ============================================================================
# TRANSLATION SERVICE (AI Translation)
# ============================================================================
# Translation API format: openai_chat, openai_responses, anthropic_messages,
# or gemini_generate_content.
TRANSLATION_API_FORMAT=openai_chat

# Base URL and endpoint for Translation API. Use your self-hosted AI server
# URL here, or an OpenAI-compatible provider URL.
TRANSLATION_BASE_URL=https://ai.example.com
TRANSLATION_ENDPOINT=/v1/chat/completions

# API key for Translation service
TRANSLATION_API_KEY=your_translation_api_key_here

# AI model to use for translation. For self-hosted translation, use models such
# as qwen3-8b, TranslateGama, or other open-source/open-weight models supported
# by your model server.
TRANSLATION_MODEL=qwen3-8b

# Optional second translation provider. Add more named providers in
# config/config.yaml using the same provider fields when needed.
TRANSLATEGAMMA_BASE_URL=https://ai.example.com
TRANSLATEGAMMA_ENDPOINT=/v1/chat/completions
TRANSLATEGAMMA_API_FORMAT=openai_chat
TRANSLATEGAMMA_API_KEY=your_translategamma_api_key_here
TRANSLATEGAMMA_MODEL=translategamma

# Maximum translation output tokens. Keep this bounded for live streaming, but
# allow enough room for Indic-script targets and longer sentence buffers.
TRANSLATION_MAX_TOKENS=240

# ============================================================================
# VISUAL CONTEXT SERVICE (Shared Tab/Page Screenshot)
# ============================================================================
# Enable one-time shared tab/page screenshot summarization when tab audio
# sharing starts. The summary is used as translation context only.
VISUAL_CONTEXT_ENABLED=false

# Keep mock mode enabled for local setup. For real visual context, set this to
# false and configure a vision-capable provider below.
VISUAL_CONTEXT_MOCK_MODE=true

# Visual context can use a separate vision-capable provider/model, or mirror the
# translation provider if it supports image inputs.
VISUAL_CONTEXT_BASE_URL=https://ai.example.com
VISUAL_CONTEXT_ENDPOINT=/v1/chat/completions
VISUAL_CONTEXT_API_FORMAT=openai_chat
VISUAL_CONTEXT_API_KEY=your_visual_context_api_key_here
VISUAL_CONTEXT_MODEL=gpt-4o-mini

# Maximum output tokens for the compact screenshot summary.
VISUAL_CONTEXT_MAX_TOKENS=300

# ============================================================================
# TTS SERVICE (Local Text-to-Speech with Piper)
# ============================================================================
# Piper model to use (voice model name in tts/voices directory)
TTS_MODEL=en_GB-jenny_dioco-medium

# Base URL for TTS API (local Piper service in Docker)
# For external service, use: https://tts.your-domain.com
TTS_BASE_URL=http://tts:5000

# Supertonic TTS is used for Japanese and Korean. First startup downloads
# model assets into the supertonic_data Docker volume.
SUPERTONIC_TTS_BASE_URL=http://supertonic-tts:7788
SUPERTONIC_TTS_VOICE=F1
SUPERTONIC_TTS_JA_VOICE=F1
SUPERTONIC_TTS_KO_VOICE=F1
SUPERTONIC_TTS_STEPS=8
SUPERTONIC_TTS_SPEED=1.00

# ============================================================================
# APPLICATION SETTINGS
# ============================================================================
# Host to bind the application (0.0.0.0 for all interfaces)
APP_HOST=0.0.0.0

# Port to run the application
APP_PORT=9000

# Enable debug mode (set to false for production)
APP_DEBUG=true

# Comma-separated browser origins allowed to call the app.
# Use the exact HTTPS origin in production, for example:
# ALLOWED_ORIGINS=https://polytalk.example.com
ALLOWED_ORIGINS=http://localhost:9000,http://127.0.0.1:9000

# Translate partial speech after this many buffered characters or seconds.
# Lower values reduce latency; higher values improve context and quality.
TRANSLATION_FLUSH_CHARS=300
TRANSLATION_FLUSH_SECONDS=5.0
TRANSLATION_FLUSH_MIN_CHARS=120

# Maximum characters for custom translation instructions (user-provided
# translation guidance sent via WebSocket or query parameter).
CUSTOM_INSTRUCTION_MAX_CHARS=250