"""
Faster-Whisper CUDA 语音识别服务
封装本地 GPU 加速的音频转写逻辑，适配 RTX 3060 Ti 8GB 显存。
"""

from __future__ import annotations

import logging
import traceback
from typing import Optional, Tuple

from config import (
    WHISPER_COMPUTE_TYPE,
    WHISPER_DEVICE,
    WHISPER_LANGUAGE,
    WHISPER_MODEL_SIZE,
)

logger = logging.getLogger(__name__)

# 全局懒加载模型实例，避免 Gradio 重复初始化占用显存
_model = None
_model_error: Optional[str] = None


def _is_cuda_error(exc: BaseException) -> bool:
    """判断异常是否与 CUDA/GPU 相关。"""
    msg = str(exc).lower()
    cuda_keywords = (
        "cuda",
        "cudnn",
        "cublas",
        "gpu",
        "out of memory",
        "no kernel image",
        "device-side assert",
    )
    return any(k in msg for k in cuda_keywords)


def get_whisper_model():
    """
    获取或初始化 Faster-Whisper 模型。
    强制 device=cuda, compute_type=float16。
    """
    global _model, _model_error

    if _model is not None:
        return _model, None

    if _model_error is not None:
        return None, _model_error

    try:
        from faster_whisper import WhisperModel

        logger.info(
            "正在加载 Whisper 模型: size=%s, device=%s, compute_type=%s",
            WHISPER_MODEL_SIZE,
            WHISPER_DEVICE,
            WHISPER_COMPUTE_TYPE,
        )
        _model = WhisperModel(
            WHISPER_MODEL_SIZE,
            device=WHISPER_DEVICE,
            compute_type=WHISPER_COMPUTE_TYPE,
        )
        logger.info("Whisper 模型加载成功。")
        return _model, None

    except ImportError as exc:
        _model_error = (
            "未安装 faster-whisper，请执行: pip install faster-whisper\n"
            f"原始错误: {exc}"
        )
        logger.exception("faster-whisper 导入失败")
        return None, _model_error

    except Exception as exc:
        if _is_cuda_error(exc):
            _model_error = (
                "CUDA 初始化失败，请检查 NVIDIA 驱动、CUDA 运行时及 cuDNN 是否正确安装。\n"
                f"错误详情: {exc}\n"
                f"{traceback.format_exc()}"
            )
        else:
            _model_error = f"Whisper 模型加载失败: {exc}\n{traceback.format_exc()}"
        logger.exception("Whisper 模型加载异常")
        return None, _model_error


def transcribe_audio(audio_path: str) -> Tuple[bool, str]:
    """
    将音频文件转写为中文文本。

    Args:
        audio_path: 本地音频文件绝对或相对路径

    Returns:
        (success, text_or_error_message)
    """
    if not audio_path:
        return False, "未提供音频文件路径。"

    model, init_error = get_whisper_model()
    if model is None:
        return False, init_error or "Whisper 模型不可用。"

    try:
        segments, info = model.transcribe(
            audio_path,
            language=WHISPER_LANGUAGE,
            beam_size=5,
            vad_filter=True,
        )

        text_parts = []
        for segment in segments:
            text_parts.append(segment.text.strip())

        result_text = "".join(text_parts).strip()

        if not result_text:
            return False, (
                "识别结果为空，请检查音频是否有效、音量是否足够，"
                f"或尝试更换格式。检测到语言: {getattr(info, 'language', 'unknown')}"
            )

        logger.info(
            "转写完成: 语言=%s, 概率=%.2f, 字数=%d",
            getattr(info, "language", "?"),
            getattr(info, "language_probability", 0.0),
            len(result_text),
        )
        return True, result_text

    except Exception as exc:
        if _is_cuda_error(exc):
            err = (
                "CUDA 推理异常：显存可能不足或 GPU 状态异常。"
                "建议关闭其他占用显存的进程后重试。\n"
                f"错误详情: {exc}"
            )
        else:
            err = f"音频转写失败: {exc}\n{traceback.format_exc()}"

        logger.exception("transcribe_audio 失败")
        return False, err


def reset_whisper_model() -> None:
    """释放模型引用（用于调试或显存回收）。"""
    global _model, _model_error
    _model = None
    _model_error = None