"""
快速转录方案：如果本地Whisper太慢，尝试其他方法
"""

import os
import subprocess
from pathlib import Path
import json


def extract_audio_from_video(video_path):
    """从视频提取音频"""
    audio_path = video_path.with_suffix(".wav")

    print(f"提取音频: {video_path.name} → {audio_path.name}")

    cmd = [
        "ffmpeg",
        "-y",
        "-i",
        str(video_path),
        "-vn",
        "-acodec",
        "pcm_s16le",
        "-ar",
        "16000",
        "-ac",
        "1",
        str(audio_path),
    ]

    result = subprocess.run(cmd, capture_output=True, text=True)
    if result.returncode != 0:
        print(f"音频提取失败: {result.stderr}")
        return None

    print(f"✅ 音频提取完成: {audio_path.stat().st_size / 1024 / 1024:.2f} MB")
    return audio_path


def check_whisper_availability():
    """检查Whisper是否可用"""
    try:
        import whisper

        print("✅ Whisper已安装")
        return True
    except ImportError:
        print("❌ Whisper未安装")
        return False


def transcribe_with_whisper(audio_path):
    """使用Whisper转录"""
    try:
        import whisper

        print("加载Whisper模型...")
        # 使用最小的模型加快速度
        model = whisper.load_model("tiny")

        print("开始转录...")
        result = model.transcribe(str(audio_path), language="zh")

        return result
    except Exception as e:
        print(f"Whisper转录失败: {e}")
        return None


def save_transcription(result, video_path):
    """保存转录结果"""
    if not result:
        return False

    # 保存文本
    txt_path = video_path.with_suffix(".txt")
    with open(txt_path, "w", encoding="utf-8") as f:
        f.write(result["text"])

    print(f"✅ 转录文本保存到: {txt_path.name}")
    print(f"文本长度: {len(result['text'])} 字符")

    # 保存完整结果（JSON）
    json_path = video_path.with_suffix(".json")
    with open(json_path, "w", encoding="utf-8") as f:
        json.dump(result, f, ensure_ascii=False, indent=2)

    print(f"✅ 完整结果保存到: {json_path.name}")

    # 预览
    print("\n=== 转录预览（前500字符）===")
    preview = result["text"][:500]
    print(preview + "..." if len(result["text"]) > 500 else preview)

    return True


def main():
    print("=== 视频转录程序 ===")

    # 查找视频文件
    video_dir = Path(r"D:\F\NewI\opencode\daily-workspace\temp")
    video_files = list(video_dir.glob("*.mp4"))

    if not video_files:
        print("❌ 未找到mp4文件")
        return

    video_path = video_files[0]
    print(f"处理视频: {video_path.name}")
    print(f"文件大小: {video_path.stat().st_size / 1024 / 1024:.2f} MB")

    # 检查是否已有转录文件
    txt_path = video_path.with_suffix(".txt")
    if txt_path.exists():
        print(f"✅ 已有转录文件: {txt_path.name}")
        with open(txt_path, "r", encoding="utf-8") as f:
            text = f.read()
        print(f"文本长度: {len(text)} 字符")
        print("\n=== 现有转录预览 ===")
        print(text[:500] + "..." if len(text) > 500 else text)
        return

    # 提取音频
    audio_path = extract_audio_from_video(video_path)
    if not audio_path:
        print("❌ 无法提取音频")
        return

    # 检查Whisper
    if not check_whisper_availability():
        print("请安装Whisper: pip install openai-whisper")
        return

    # 转录
    result = transcribe_with_whisper(audio_path)
    if not result:
        print("❌ 转录失败")
        return

    # 保存结果
    if save_transcription(result, video_path):
        print("\n✅ 转录完成！")

        # 清理临时音频文件
        if audio_path.exists():
            audio_path.unlink()
            print("临时音频文件已删除")
    else:
        print("❌ 保存转录结果失败")


if __name__ == "__main__":
    main()