"""Voice service configuration.

Deliberately small: this process does one job — turn audio into text and text
into audio. Everything about *what to say* lives in the Node agentic service.
"""
from __future__ import annotations

import os

import torch


def _int(name: str, default: int) -> int:
    try:
        return int(os.environ.get(name, default))
    except (TypeError, ValueError):
        return default


def _flag(name: str, default: bool) -> bool:
    return os.environ.get(name, str(default)).lower() in {"1", "true", "yes"}


class Settings:
    host: str = os.environ.get("VOICE_HOST", "127.0.0.1")
    port: int = _int("VOICE_PORT", 4100)

    # ── Models ───────────────────────────────────────────────────────────────
    # IndicConformer is a hybrid CTC + RNNT model. CTC decoding is used because
    # it is a single forward pass — RNNT is more accurate but decodes
    # autoregressively, and in a voice loop the latency costs more than the
    # accuracy buys.
    stt_model: str = os.environ.get("STT_MODEL", "ai4bharat/indic-conformer-600m-multilingual")
    stt_decoding: str = os.environ.get("STT_DECODING", "ctc")  # ctc | rnnt

    # English is not one of IndicConformer's 22 codes, and it cannot identify
    # languages. Whisper covers both — multilingual, not the .en checkpoint,
    # because language ID is what makes "auto" work.
    english_model: str = os.environ.get("ENGLISH_MODEL", "openai/whisper-small")
    preload_english: bool = _flag("PRELOAD_ENGLISH", True)

    # Used when auto-detection is not confident enough to overrule the user.
    default_lang: str = os.environ.get("VOICE_DEFAULT_LANG", "ta")

    tts_model: str = os.environ.get("TTS_MODEL", "ai4bharat/indic-parler-tts")

    # ── Device / precision ───────────────────────────────────────────────────
    # float16 on CUDA: both models together are ~3 GB in half precision, which
    # fits the 6 GB card with room for activations. float32 would not.
    device: str = os.environ.get("VOICE_DEVICE", "cuda" if torch.cuda.is_available() else "cpu")

    @property
    def dtype(self) -> torch.dtype:
        return torch.float16 if self.device == "cuda" else torch.float32

    # ── Audio ────────────────────────────────────────────────────────────────
    sample_rate_in: int = 16000    # what the browser worklet sends
    # Indic Parler-TTS emits 44.1 kHz — verified from model.config.sampling_rate,
    # not the 24 kHz the upstream Parler-TTS Mini uses. This is only a fallback;
    # the real rate is read from the loaded model and sent to the browser, which
    # configures its playback worklet from it.
    sample_rate_out: int = _int("TTS_SAMPLE_RATE", 44100)

    # ── Endpointing (Silero VAD) ─────────────────────────────────────────────
    # Silero operates on fixed 512-sample frames at 16 kHz (32 ms).
    vad_frame: int = 512
    vad_threshold: float = float(os.environ.get("VAD_THRESHOLD", "0.5"))
    # How much trailing silence ends a turn. Too short truncates people who
    # pause mid-sentence; too long makes the assistant feel sluggish.
    vad_silence_ms: int = _int("VAD_SILENCE_MS", 700)
    # Ignore blips so a cough or a door does not open a turn.
    vad_min_speech_ms: int = _int("VAD_MIN_SPEECH_MS", 250)
    # Audio kept from *before* detected speech, so word onsets are not clipped.
    vad_prefix_ms: int = _int("VAD_PREFIX_MS", 300)
    vad_max_utterance_ms: int = _int("VAD_MAX_UTTERANCE_MS", 30000)

    # Warm the models at startup rather than on the first user turn — a cold
    # CUDA graph on the first utterance costs several seconds.
    warmup: bool = _flag("VOICE_WARMUP", True)


settings = Settings()