#!/usr/bin/env python3
"""
语音识别示例 - 使用 faster-whisper
"""

from faster_whisper import WhisperModel
import os

# 默认配置
MODEL_SIZE = "base"
LANGUAGE = "zh"

def transcribe(audio_path, model_size=None, language=None):
    """语音转文字"""
    model_size = model_size or MODEL_SIZE
    language = language or LANGUAGE
    
    print(f"\n🎤 开始识别...")
    print(f"   模型: {model_size}")
    print(f"   语言: {language}")
    print(f"   文件: {audio_path}")
    
    # 加载模型
    model = WhisperModel(model_size, device="cpu", compute_type="int8")
    
    # 识别
    segments, info = model.transcribe(
        audio_path,
        language=language,
        beam_size=5
    )
    
    print(f"\n📝 识别结果:")
    print(f"   语言: {info.language} (置信度: {info.language_probability:.2f})")
    print("-" * 50)
    
    text = ""
    for segment in segments:
        text += segment.text
        print(f"   {segment.text}")
    
    return text.strip()

def main():
    import sys
    
    print(f"\n{'='*60}")
    print("🎤 语音识别 (faster-whisper)")
    print(f"{'='*60}")
    
    if len(sys.argv) < 2:
        print("\n用法:")
        print("  python whisper_example.py <音频文件>")
        print("  python whisper_example.py audio.mp3 --model base --lang zh")
        print("\n选项:")
        print("  --model, -m  模型大小: tiny, base, small, medium, large")
        print("  --lang, -l   语言: zh, en, ja, ko 等")
        return
    
    audio_file = sys.argv[1]
    model = "base"
    lang = "zh"
    
    # 解析参数
    i = 2
    while i < len(sys.argv):
        key = sys.argv[i].replace("--", "")
        value = sys.argv[i + 1] if i + 1 < len(sys.argv) else ""
        
        if key in ["model", "m"]:
            model = value
        elif key in ["lang", "l"]:
            lang = value
        
        i += 2
    
    if not os.path.exists(audio_file):
        print(f"❌ 文件不存在: {audio_file}")
        return
    
    text = transcribe(audio_file, model, lang)
    print(f"\n✅ 识别完成: {len(text)} 字符")

if __name__ == "__main__":
    main()