"""语音识别服务：faster-whisper，输出带词级时间戳的 segments。

CPU dev: tiny.en + int8；GPU prod: large-v3-turbo + float16。同一份代码。
"""

from __future__ import annotations

import logging
from pathlib import Path

from ..config import get_settings
from .model_manager import get_model_manager
from .types import Segment, Word

logger = logging.getLogger("audio2text.asr")


def transcribe(wav_path: Path) -> list[Segment]:
    """转写 wav，返回 segments（含词级时间戳）。

    Args:
        wav_path: 16kHz mono PCM wav

    Returns:
        list[Segment]，每个 Segment 带词级 words（若 word_timestamps 启用）。
    """
    s = get_settings().asr
    if not wav_path.is_file():
        raise FileNotFoundError(f"音频不存在：{wav_path}")

    model = get_model_manager().get_asr()
    logger.debug("开始转写 %s（model=%s language=%s batch_size=%d）",
                 wav_path.name, s.model, s.language, s.batch_size)

    segments_gen, info = model.transcribe(
        str(wav_path),
        language=s.language,
        word_timestamps=s.word_timestamps,
        vad_filter=s.vad_filter,
        beam_size=s.beam_size,        # beam search 宽度（config 可调）；GPU turbo 建议降到 2 加速
        batch_size=s.batch_size,       # 批量解码：多音频块一次性送 GPU
        without_timestamps=False,      # BatchedInferencePipeline 默认 True，需显式关闭以生成段级时间戳
    )
    logger.debug(
        "音频时长 %.1fs，检测语言=%s（置信度 %.2f）",
        info.duration, info.language, info.language_probability,
    )

    segments: list[Segment] = []
    for seg in segments_gen:
        words: list[Word] = []
        if s.word_timestamps and getattr(seg, "words", None):
            for w in seg.words:
                words.append(Word(
                    text=w.word.strip(),
                    start=float(w.start),
                    end=float(w.end),
                    probability=float(getattr(w, "probability", 1.0)),
                ))
        segments.append(Segment(
            text=seg.text.strip(),
            start=float(seg.start),
            end=float(seg.end),
            words=words,
        ))
    logger.debug("转写完成：%d 段，%d 词。",
                 len(segments), sum(len(s.words) for s in segments))
    return segments