EG/plugins/user/speech_recognition_synthesis/utils/audio_utils.py

"""
音频工具类
提供音频处理和文件操作功能
"""

import numpy as np
import wave
import struct
from typing import Dict, Any, List
import os

class AudioUtils:
    """
    音频工具类
    提供音频处理和文件操作功能
    """
    
    def __init__(self, plugin):
        """
        初始化音频工具类
        
        Args:
            plugin: 语音识别和合成插件实例
        """
        self.plugin = plugin
        self.sample_rate = plugin.config.get('sample_rate', 16000)
        self.channels = plugin.config.get('channels', 1)
        self.sample_width = 2  # 16位音频
        
        print("✓ 音频工具类已创建")
        
    def save_audio_file(self, filename: str, audio_data: bytes) -> bool:
        """
        保存音频文件
        
        Args:
            filename: 文件名
            audio_data: 音频数据
            
        Returns:
            是否保存成功
        """
        try:
            # 确保目录存在
            os.makedirs(os.path.dirname(filename) if os.path.dirname(filename) else '.', exist_ok=True)
            
            # 保存音频数据到文件
            with open(filename, 'wb') as f:
                f.write(audio_data)
                
            print(f"✓ 音频文件已保存: {filename}")
            return True
            
        except Exception as e:
            print(f"✗ 保存音频文件失败: {e}")
            import traceback
            traceback.print_exc()
            return False
            
    def load_audio_file(self, filename: str) -> bytes:
        """
        加载音频文件
        
        Args:
            filename: 文件名
            
        Returns:
            音频数据
        """
        try:
            with open(filename, 'rb') as f:
                audio_data = f.read()
            print(f"✓ 音频文件已加载: {filename}")
            return audio_data
            
        except Exception as e:
            print(f"✗ 加载音频文件失败: {e}")
            import traceback
            traceback.print_exc()
            return b''
            
    def save_wav_file(self, filename: str, audio_data: np.ndarray, sample_rate: int = None) -> bool:
        """
        保存WAV音频文件
        
        Args:
            filename: 文件名
            audio_data: 音频数据（numpy数组）
            sample_rate: 采样率
            
        Returns:
            是否保存成功
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            # 确保目录存在
            os.makedirs(os.path.dirname(filename) if os.path.dirname(filename) else '.', exist_ok=True)
            
            # 确保音频数据在合法范围内
            audio_data = np.clip(audio_data, -1.0, 1.0)
            
            # 转换为16位整数
            audio_int16 = (audio_data * 32767).astype(np.int16)
            
            # 保存为WAV文件
            with wave.open(filename, 'w') as wav_file:
                wav_file.setnchannels(self.channels)
                wav_file.setsampwidth(self.sample_width)  # 16位 = 2字节
                wav_file.setframerate(sample_rate)
                wav_file.writeframes(audio_int16.tobytes())
                
            print(f"✓ WAV文件已保存: {filename}")
            return True
            
        except Exception as e:
            print(f"✗ 保存WAV文件失败: {e}")
            import traceback
            traceback.print_exc()
            return False
            
    def load_wav_file(self, filename: str) -> Dict[str, Any]:
        """
        加载WAV音频文件
        
        Args:
            filename: 文件名
            
        Returns:
            包含音频数据和参数的字典
        """
        try:
            with wave.open(filename, 'r') as wav_file:
                # 获取音频参数
                channels = wav_file.getnchannels()
                sample_width = wav_file.getsampwidth()
                sample_rate = wav_file.getframerate()
                frames = wav_file.getnframes()
                
                # 读取音频数据
                audio_data = wav_file.readframes(frames)
                
                # 转换为numpy数组
                if sample_width == 1:
                    # 8位音频
                    audio_array = np.frombuffer(audio_data, dtype=np.uint8)
                    audio_array = (audio_array.astype(np.float32) - 128) / 128.0
                elif sample_width == 2:
                    # 16位音频
                    audio_array = np.frombuffer(audio_data, dtype=np.int16)
                    audio_array = audio_array.astype(np.float32) / 32768.0
                elif sample_width == 4:
                    # 32位音频
                    audio_array = np.frombuffer(audio_data, dtype=np.int32)
                    audio_array = audio_array.astype(np.float32) / 2147483648.0
                else:
                    print(f"✗ 不支持的采样宽度: {sample_width}")
                    return {}
                    
                print(f"✓ WAV文件已加载: {filename}")
                return {
                    'audio_data': audio_array,
                    'sample_rate': sample_rate,
                    'channels': channels,
                    'sample_width': sample_width,
                    'frames': frames
                }
                
        except Exception as e:
            print(f"✗ 加载WAV文件失败: {e}")
            import traceback
            traceback.print_exc()
            return {}
            
    def convert_audio_format(self, audio_data: np.ndarray, from_format: str, to_format: str) -> np.ndarray:
        """
        转换音频格式
        
        Args:
            audio_data: 音频数据
            from_format: 源格式
            to_format: 目标格式
            
        Returns:
            转换后的音频数据
        """
        try:
            # 这里简化处理，实际项目中可能需要更复杂的格式转换
            print(f"✓ 音频格式已转换: {from_format} -> {to_format}")
            return audio_data
        except Exception as e:
            print(f"✗ 音频格式转换失败: {e}")
            return audio_data
            
    def resample_audio(self, audio_data: np.ndarray, from_rate: int, to_rate: int) -> np.ndarray:
        """
        重采样音频数据
        
        Args:
            audio_data: 音频数据
            from_rate: 源采样率
            to_rate: 目标采样率
            
        Returns:
            重采样后的音频数据
        """
        try:
            if from_rate == to_rate:
                return audio_data
                
            # 计算重采样比例
            ratio = to_rate / from_rate
            new_length = int(len(audio_data) * ratio)
            
            # 简单的线性插值（实际项目中应该使用更高质量的重采样算法）
            if new_length > 0:
                indices = np.linspace(0, len(audio_data) - 1, new_length)
                resampled_data = np.interp(indices, np.arange(len(audio_data)), audio_data)
                print(f"✓ 音频已重采样: {from_rate}Hz -> {to_rate}Hz")
                return resampled_data
            else:
                return np.array([], dtype=np.float32)
                
        except Exception as e:
            print(f"✗ 音频重采样失败: {e}")
            return audio_data
            
    def apply_gain(self, audio_data: np.ndarray, gain_db: float) -> np.ndarray:
        """
        应用增益到音频数据
        
        Args:
            audio_data: 音频数据
            gain_db: 增益值（分贝）
            
        Returns:
            应用增益后的音频数据
        """
        try:
            # 将分贝转换为线性增益
            gain_linear = 10 ** (gain_db / 20.0)
            amplified_data = audio_data * gain_linear
            
            # 防止削波
            amplified_data = np.clip(amplified_data, -1.0, 1.0)
            
            print(f"✓ 音频增益已应用: {gain_db}dB")
            return amplified_data
            
        except Exception as e:
            print(f"✗ 应用音频增益失败: {e}")
            return audio_data
            
    def normalize_audio(self, audio_data: np.ndarray) -> np.ndarray:
        """
        归一化音频数据
        
        Args:
            audio_data: 音频数据
            
        Returns:
            归一化后的音频数据
        """
        try:
            if len(audio_data) == 0:
                return audio_data
                
            # 计算最大幅度
            max_amplitude = np.max(np.abs(audio_data))
            
            if max_amplitude > 0:
                normalized_data = audio_data / max_amplitude
                print("✓ 音频已归一化")
                return normalized_data
            else:
                return audio_data
                
        except Exception as e:
            print(f"✗ 音频归一化失败: {e}")
            return audio_data
            
    def apply_fade_in(self, audio_data: np.ndarray, fade_duration: float, sample_rate: int = None) -> np.ndarray:
        """
        应用淡入效果
        
        Args:
            audio_data: 音频数据
            fade_duration: 淡入时长（秒）
            sample_rate: 采样率
            
        Returns:
            应用淡入效果后的音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            fade_samples = int(fade_duration * sample_rate)
            if fade_samples <= 0 or fade_samples >= len(audio_data):
                return audio_data
                
            # 创建淡入曲线
            fade_curve = np.linspace(0, 1, fade_samples)
            
            # 应用淡入效果
            faded_data = audio_data.copy()
            faded_data[:fade_samples] *= fade_curve
            
            print(f"✓ 淡入效果已应用: {fade_duration}秒")
            return faded_data
            
        except Exception as e:
            print(f"✗ 应用淡入效果失败: {e}")
            return audio_data
            
    def apply_fade_out(self, audio_data: np.ndarray, fade_duration: float, sample_rate: int = None) -> np.ndarray:
        """
        应用淡出效果
        
        Args:
            audio_data: 音频数据
            fade_duration: 淡出时长（秒）
            sample_rate: 采样率
            
        Returns:
            应用淡出效果后的音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            fade_samples = int(fade_duration * sample_rate)
            if fade_samples <= 0 or fade_samples >= len(audio_data):
                return audio_data
                
            # 创建淡出曲线
            fade_curve = np.linspace(1, 0, fade_samples)
            
            # 应用淡出效果
            faded_data = audio_data.copy()
            faded_data[-fade_samples:] *= fade_curve
            
            print(f"✓ 淡出效果已应用: {fade_duration}秒")
            return faded_data
            
        except Exception as e:
            print(f"✗ 应用淡出效果失败: {e}")
            return audio_data
            
    def concatenate_audio(self, audio_segments: List[np.ndarray]) -> np.ndarray:
        """
        连接多个音频片段
        
        Args:
            audio_segments: 音频片段列表
            
        Returns:
            连接后的音频数据
        """
        try:
            if not audio_segments:
                return np.array([], dtype=np.float32)
                
            # 连接所有音频片段
            concatenated = np.concatenate(audio_segments)
            print(f"✓ {len(audio_segments)}个音频片段已连接")
            return concatenated
            
        except Exception as e:
            print(f"✗ 连接音频片段失败: {e}")
            return np.array([], dtype=np.float32)
            
    def split_audio(self, audio_data: np.ndarray, segment_duration: float, sample_rate: int = None) -> List[np.ndarray]:
        """
        分割音频数据为多个片段
        
        Args:
            audio_data: 音频数据
            segment_duration: 片段时长（秒）
            sample_rate: 采样率
            
        Returns:
            音频片段列表
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            segment_samples = int(segment_duration * sample_rate)
            if segment_samples <= 0:
                return [audio_data]
                
            # 分割音频数据
            segments = []
            for i in range(0, len(audio_data), segment_samples):
                segment = audio_data[i:i + segment_samples]
                segments.append(segment)
                
            print(f"✓ 音频已分割为{len(segments)}个片段")
            return segments
            
        except Exception as e:
            print(f"✗ 分割音频失败: {e}")
            return [audio_data]
            
    def calculate_audio_level(self, audio_data: np.ndarray) -> float:
        """
        计算音频级别（RMS）
        
        Args:
            audio_data: 音频数据
            
        Returns:
            音频级别 (0.0-1.0)
        """
        try:
            if len(audio_data) == 0:
                return 0.0
                
            # 计算RMS值
            rms = np.sqrt(np.mean(audio_data ** 2))
            return float(rms)
            
        except Exception as e:
            print(f"✗ 计算音频级别失败: {e}")
            return 0.0
            
    def detect_silence(self, audio_data: np.ndarray, threshold: float = 0.01, 
                      min_silence_duration: float = 0.1, sample_rate: int = None) -> List[Dict[str, float]]:
        """
        检测音频中的静音段
        
        Args:
            audio_data: 音频数据
            threshold: 静音阈值
            min_silence_duration: 最小静音时长（秒）
            sample_rate: 采样率
            
        Returns:
            静音段列表 [{'start': start_time, 'end': end_time}]
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            min_silence_samples = int(min_silence_duration * sample_rate)
            silence_segments = []
            
            # 简化的静音检测
            is_silent = np.abs(audio_data) < threshold
            in_silence = False
            silence_start = 0
            
            for i in range(len(is_silent)):
                if is_silent[i] and not in_silence:
                    # 开始静音段
                    in_silence = True
                    silence_start = i
                elif not is_silent[i] and in_silence:
                    # 结束静音段
                    in_silence = False
                    silence_duration = i - silence_start
                    if silence_duration >= min_silence_samples:
                        silence_segments.append({
                            'start': silence_start / sample_rate,
                            'end': i / sample_rate
                        })
                        
            # 处理最后一个静音段
            if in_silence:
                silence_duration = len(audio_data) - silence_start
                if silence_duration >= min_silence_samples:
                    silence_segments.append({
                        'start': silence_start / sample_rate,
                        'end': len(audio_data) / sample_rate
                    })
                    
            print(f"✓ 检测到{len(silence_segments)}个静音段")
            return silence_segments
            
        except Exception as e:
            print(f"✗ 检测静音段失败: {e}")
            return []
            
    def remove_silence(self, audio_data: np.ndarray, threshold: float = 0.01, 
                      min_silence_duration: float = 0.1, sample_rate: int = None) -> np.ndarray:
        """
        移除音频中的静音段
        
        Args:
            audio_data: 音频数据
            threshold: 静音阈值
            min_silence_duration: 最小静音时长（秒）
            sample_rate: 采样率
            
        Returns:
            移除静音后的音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            # 检测静音段
            silence_segments = self.detect_silence(audio_data, threshold, min_silence_duration, sample_rate)
            
            if not silence_segments:
                return audio_data
                
            # 移除静音段
            cleaned_audio = audio_data.copy()
            for segment in reversed(silence_segments):  # 从后往前删除，避免索引变化
                start_sample = int(segment['start'] * sample_rate)
                end_sample = int(segment['end'] * sample_rate)
                cleaned_audio = np.concatenate([
                    cleaned_audio[:start_sample],
                    cleaned_audio[end_sample:]
                ])
                
            print(f"✓ 已移除{len(silence_segments)}个静音段")
            return cleaned_audio
            
        except Exception as e:
            print(f"✗ 移除静音段失败: {e}")
            return audio_data
            
    def apply_high_pass_filter(self, audio_data: np.ndarray, cutoff_freq: float, 
                              sample_rate: int = None) -> np.ndarray:
        """
        应用高通滤波器
        
        Args:
            audio_data: 音频数据
            cutoff_freq: 截止频率（Hz）
            sample_rate: 采样率
            
        Returns:
            滤波后的音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            # 简化的高通滤波实现
            # 实际项目中应该使用更专业的滤波器设计
            rc = 1.0 / (2 * np.pi * cutoff_freq)
            dt = 1.0 / sample_rate
            alpha = rc / (rc + dt)
            
            filtered_data = np.zeros_like(audio_data)
            for i in range(1, len(audio_data)):
                filtered_data[i] = alpha * (filtered_data[i-1] + audio_data[i] - audio_data[i-1])
                
            print(f"✓ 高通滤波器已应用: {cutoff_freq}Hz")
            return filtered_data
            
        except Exception as e:
            print(f"✗ 应用高通滤波器失败: {e}")
            return audio_data
            
    def apply_low_pass_filter(self, audio_data: np.ndarray, cutoff_freq: float, 
                             sample_rate: int = None) -> np.ndarray:
        """
        应用低通滤波器
        
        Args:
            audio_data: 音频数据
            cutoff_freq: 截止频率（Hz）
            sample_rate: 采样率
            
        Returns:
            滤波后的音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            # 简化的低通滤波实现
            rc = 1.0 / (2 * np.pi * cutoff_freq)
            dt = 1.0 / sample_rate
            alpha = dt / (rc + dt)
            
            filtered_data = np.zeros_like(audio_data)
            filtered_data[0] = audio_data[0]
            for i in range(1, len(audio_data)):
                filtered_data[i] = alpha * audio_data[i] + (1 - alpha) * filtered_data[i-1]
                
            print(f"✓ 低通滤波器已应用: {cutoff_freq}Hz")
            return filtered_data
            
        except Exception as e:
            print(f"✗ 应用低通滤波器失败: {e}")
            return audio_data
            
    def generate_sine_wave(self, frequency: float, duration: float, 
                          amplitude: float = 1.0, sample_rate: int = None) -> np.ndarray:
        """
        生成正弦波音频
        
        Args:
            frequency: 频率（Hz）
            duration: 时长（秒）
            amplitude: 振幅 (0.0-1.0)
            sample_rate: 采样率
            
        Returns:
            正弦波音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            # 生成时间轴
            t = np.linspace(0, duration, int(sample_rate * duration), False)
            
            # 生成正弦波
            sine_wave = amplitude * np.sin(2 * np.pi * frequency * t)
            
            print(f"✓ 正弦波已生成: {frequency}Hz, {duration}秒")
            return sine_wave
            
        except Exception as e:
            print(f"✗ 生成正弦波失败: {e}")
            return np.array([], dtype=np.float32)
            
    def generate_white_noise(self, duration: float, amplitude: float = 0.1, 
                            sample_rate: int = None) -> np.ndarray:
        """
        生成白噪声音频
        
        Args:
            duration: 时长（秒）
            amplitude: 振幅 (0.0-1.0)
            sample_rate: 采样率
            
        Returns:
            白噪声音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            # 生成样本数
            num_samples = int(sample_rate * duration)
            
            # 生成白噪声
            white_noise = amplitude * (np.random.random(num_samples) * 2 - 1)
            
            print(f"✓ 白噪声已生成: {duration}秒")
            return white_noise
            
        except Exception as e:
            print(f"✗ 生成白噪声失败: {e}")
            return np.array([], dtype=np.float32)
            
    def mix_audio_signals(self, audio1: np.ndarray, audio2: np.ndarray, 
                         ratio: float = 0.5) -> np.ndarray:
        """
        混合两个音频信号
        
        Args:
            audio1: 第一个音频信号
            audio2: 第二个音频信号
            ratio: 混合比例 (0.0-1.0, 0表示全audio1，1表示全audio2)
            
        Returns:
            混合后的音频信号
        """
        try:
            # 确保两个音频信号长度相同
            max_length = max(len(audio1), len(audio2))
            if len(audio1) < max_length:
                audio1 = np.pad(audio1, (0, max_length - len(audio1)))
            if len(audio2) < max_length:
                audio2 = np.pad(audio2, (0, max_length - len(audio2)))
                
            # 混合音频
            mixed_audio = (1 - ratio) * audio1 + ratio * audio2
            
            # 防止削波
            mixed_audio = np.clip(mixed_audio, -1.0, 1.0)
            
            print("✓ 音频信号已混合")
            return mixed_audio
            
        except Exception as e:
            print(f"✗ 混合音频信号失败: {e}")
            # 返回较长的音频信号
            return audio1 if len(audio1) >= len(audio2) else audio2
            
    def calculate_audio_duration(self, audio_data: np.ndarray, sample_rate: int = None) -> float:
        """
        计算音频时长
        
        Args:
            audio_data: 音频数据
            sample_rate: 采样率
            
        Returns:
            音频时长（秒）
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            if len(audio_data) == 0:
                return 0.0
                
            duration = len(audio_data) / sample_rate
            return duration
            
        except Exception as e:
            print(f"✗ 计算音频时长失败: {e}")
            return 0.0
            
    def convert_to_mono(self, audio_data: np.ndarray, channels: int = None) -> np.ndarray:
        """
        转换为单声道音频
        
        Args:
            audio_data: 音频数据
            channels: 声道数
            
        Returns:
            单声道音频数据
        """
        try:
            if channels is None:
                channels = self.channels
                
            if channels == 1:
                return audio_data
                
            # 假设音频数据是交错存储的
            if len(audio_data) % channels != 0:
                print("✗ 音频数据长度与声道数不匹配")
                return audio_data
                
            # 重新整形并计算平均值
            reshaped = audio_data.reshape(-1, channels)
            mono_audio = np.mean(reshaped, axis=1)
            
            print("✓ 音频已转换为单声道")
            return mono_audio
            
        except Exception as e:
            print(f"✗ 转换为单声道失败: {e}")
            return audio_data
            
    def convert_to_stereo(self, audio_data: np.ndarray) -> np.ndarray:
        """
        转换为立体声音频（复制单声道到两个声道）
        
        Args:
            audio_data: 单声道音频数据
            
        Returns:
            立体声音频数据
        """
        try:
            # 将单声道数据复制到两个声道
            stereo_audio = np.tile(audio_data.reshape(-1, 1), (1, 2))
            stereo_audio = stereo_audio.flatten()
            
            print("✓ 音频已转换为立体声")
            return stereo_audio
            
        except Exception as e:
            print(f"✗ 转换为立体声失败: {e}")
            return audio_data
            
    def apply_compressor(self, audio_data: np.ndarray, threshold: float = -20.0, 
                        ratio: float = 4.0, attack: float = 0.01, release: float = 0.1) -> np.ndarray:
        """
        应用压缩器效果
        
        Args:
            audio_data: 音频数据
            threshold: 阈值（dB）
            ratio: 压缩比
            attack: 启动时间（秒）
            release: 释放时间（秒）
            
        Returns:
            压缩后的音频数据
        """
        try:
            # 简化的压缩器实现
            threshold_linear = 10 ** (threshold / 20.0)
            
            # 计算增益缩减
            gain_reduction = np.zeros_like(audio_data)
            envelope = 0.0
            
            for i in range(len(audio_data)):
                # 计算包络
                envelope = max(abs(audio_data[i]), envelope * 0.99)
                
                # 计算增益缩减
                if envelope > threshold_linear:
                    gain_db = 20 * np.log10(envelope / threshold_linear)
                    reduction_db = gain_db - gain_db / ratio
                    gain_reduction[i] = 10 ** (-reduction_db / 20.0)
                else:
                    gain_reduction[i] = 1.0
                    
            # 应用增益缩减
            compressed_audio = audio_data * gain_reduction
            
            print("✓ 压缩器效果已应用")
            return compressed_audio
            
        except Exception as e:
            print(f"✗ 应用压缩器效果失败: {e}")
            return audio_data
            
    def apply_limiter(self, audio_data: np.ndarray, threshold: float = -0.1) -> np.ndarray:
        """
        应用限制器效果
        
        Args:
            audio_data: 音频数据
            threshold: 阈值（dB，相对于满量程）
            
        Returns:
            限制后的音频数据
        """
        try:
            # 转换阈值为线性值
            threshold_linear = 10 ** (threshold / 20.0)
            
            # 应用限制器
            limited_audio = np.clip(audio_data, -threshold_linear, threshold_linear)
            
            print(f"✓ 限制器效果已应用: {threshold}dB")
            return limited_audio
            
        except Exception as e:
            print(f"✗ 应用限制器效果失败: {e}")
            return audio_data
            
    def apply_expander(self, audio_data: np.ndarray, threshold: float = -40.0, 
                      ratio: float = 2.0, attack: float = 0.01, release: float = 0.1) -> np.ndarray:
        """
        应用扩展器效果
        
        Args:
            audio_data: 音频数据
            threshold: 阈值（dB）
            ratio: 扩展比
            attack: 启动时间（秒）
            release: 释放时间（秒）
            
        Returns:
            扩展后的音频数据
        """
        try:
            # 简化的扩展器实现
            threshold_linear = 10 ** (threshold / 20.0)
            
            # 计算增益扩展
            gain_expansion = np.zeros_like(audio_data)
            envelope = 0.0
            
            for i in range(len(audio_data)):
                # 计算包络
                envelope = max(abs(audio_data[i]), envelope * 0.99)
                
                # 计算增益扩展
                if envelope < threshold_linear:
                    gain_db = 20 * np.log10(envelope / threshold_linear)
                    expansion_db = gain_db * (1 - 1/ratio)
                    gain_expansion[i] = 10 ** (expansion_db / 20.0)
                else:
                    gain_expansion[i] = 1.0
                    
            # 应用增益扩展
            expanded_audio = audio_data * gain_expansion
            
            print("✓ 扩展器效果已应用")
            return expanded_audio
            
        except Exception as e:
            print(f"✗ 应用扩展器效果失败: {e}")
            return audio_data
            
    def apply_deesser(self, audio_data: np.ndarray, threshold: float = -30.0, 
                     frequency: float = 5000.0, sample_rate: int = None) -> np.ndarray:
        """
        应用去齿音器效果
        
        Args:
            audio_data: 音频数据
            threshold: 阈值（dB）
            frequency: 处理频率（Hz）
            sample_rate: 采样率
            
        Returns:
            处理后的音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            # 简化的去齿音器实现
            # 实际实现中会使用更复杂的频域处理
            
            # 应用高通滤波器突出高频
            filtered_audio = self.apply_high_pass_filter(audio_data, frequency, sample_rate)
            
            # 检测高频能量
            threshold_linear = 10 ** (threshold / 20.0)
            high_energy = np.abs(filtered_audio)
            
            # 计算减少因子
            reduction_factor = np.ones_like(high_energy)
            reduction_factor[high_energy > threshold_linear] = threshold_linear / high_energy[high_energy > threshold_linear]
            
            # 应用减少因子
            deessed_audio = audio_data * reduction_factor
            
            print(f"✓ 去齿音器效果已应用: {frequency}Hz")
            return deessed_audio
            
        except Exception as e:
            print(f"✗ 应用去齿音器效果失败: {e}")
            return audio_data
            
    def apply_noise_gate(self, audio_data: np.ndarray, threshold: float = -40.0, 
                        attack: float = 0.001, release: float = 0.01, sample_rate: int = None) -> np.ndarray:
        """
        应用噪声门效果
        
        Args:
            audio_data: 音频数据
            threshold: 阈值（dB）
            attack: 启动时间（秒）
            release: 释放时间（秒）
            sample_rate: 采样率
            
        Returns:
            处理后的音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            threshold_linear = 10 ** (threshold / 20.0)
            
            # 计算包络
            envelope = np.abs(audio_data)
            
            # 简化的噪声门实现
            gate_open = envelope > threshold_linear
            smoothed_gate = np.zeros_like(gate_open, dtype=np.float32)
            
            # 应用启动和释放时间
            attack_coeff = np.exp(-1.0 / (sample_rate * attack))
            release_coeff = np.exp(-1.0 / (sample_rate * release))
            
            gate_state = 0.0
            for i in range(len(gate_open)):
                if gate_open[i]:
                    gate_state = attack_coeff * gate_state + (1 - attack_coeff)
                else:
                    gate_state = release_coeff * gate_state
                    
                smoothed_gate[i] = gate_state
                
            # 应用噪声门
            gated_audio = audio_data * smoothed_gate
            
            print("✓ 噪声门效果已应用")
            return gated_audio
            
        except Exception as e:
            print(f"✗ 应用噪声门效果失败: {e}")
            return audio_data
            
    def apply_eq_filter(self, audio_data: np.ndarray, frequencies: List[float], 
                       gains: List[float], sample_rate: int = None) -> np.ndarray:
        """
        应用均衡器滤波器
        
        Args:
            audio_data: 音频数据
            frequencies: 频率列表（Hz）
            gains: 增益列表（dB）
            sample_rate: 采样率
            
        Returns:
            滤波后的音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            # 简化的均衡器实现
            eq_audio = audio_data.copy()
            
            # 对每个频段应用滤波
            for freq, gain in zip(frequencies, gains):
                if gain > 0:
                    # 应用低通或高通滤波器增强特定频率
                    if freq < sample_rate / 4:
                        eq_audio = self.apply_low_pass_filter(eq_audio, freq * 2, sample_rate)
                elif gain < 0:
                    # 应用滤波器减弱特定频率
                    if freq > 100:
                        eq_audio = self.apply_high_pass_filter(eq_audio, freq / 2, sample_rate)
                        
            # 应用总体增益
            total_gain = 10 ** (sum(gains) / len(gains) / 20.0)
            eq_audio = eq_audio * total_gain
            eq_audio = np.clip(eq_audio, -1.0, 1.0)
            
            print(f"✓ 均衡器效果已应用: {len(frequencies)}个频段")
            return eq_audio
            
        except Exception as e:
            print(f"✗ 应用均衡器效果失败: {e}")
            return audio_data
            
    def apply_reverb(self, audio_data: np.ndarray, room_size: float = 0.5, 
                    damping: float = 0.5, wet_level: float = 0.33, 
                    sample_rate: int = None) -> np.ndarray:
        """
        应用混响效果
        
        Args:
            audio_data: 音频数据
            room_size: 房间大小 (0.0-1.0)
            damping: 阻尼 (0.0-1.0)
            wet_level: 湿信号级别 (0.0-1.0)
            sample_rate: 采样率
            
        Returns:
            添加混响后的音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            # 简化的混响实现
            # 创建延迟线
            max_delay = int(sample_rate * 0.1)  # 最大100ms延迟
            delay_line = np.zeros(max_delay)
            delay_index = 0
            
            # 混响参数
            delay_times = [int(sample_rate * 0.013), int(sample_rate * 0.023), 
                          int(sample_rate * 0.037), int(sample_rate * 0.043)]
            decay_factors = [0.7, 0.6, 0.5, 0.4]
            
            # 应用混响
            reverb_audio = np.zeros_like(audio_data)
            
            for i in range(len(audio_data)):
                input_sample = audio_data[i]
                
                # 从延迟线读取
                delayed_samples = []
                for j, delay_time in enumerate(delay_times):
                    read_index = (delay_index - delay_time) % max_delay
                    delayed_samples.append(delay_line[read_index] * decay_factors[j])
                    
                # 计算反馈
                feedback = sum(delayed_samples) * damping
                
                # 写入延迟线
                delay_line[delay_index] = input_sample + feedback
                
                # 更新延迟索引
                delay_index = (delay_index + 1) % max_delay
                
                # 输出混合信号
                dry_sample = input_sample * (1 - wet_level)
                wet_sample = sum(delayed_samples) * wet_level
                reverb_audio[i] = dry_sample + wet_sample
                
            # 防止削波
            reverb_audio = np.clip(reverb_audio, -1.0, 1.0)
            
            print("✓ 混响效果已应用")
            return reverb_audio
            
        except Exception as e:
            print(f"✗ 应用混响效果失败: {e}")
            return audio_data
            
    def apply_chorus(self, audio_data: np.ndarray, rate: float = 1.0, 
                    depth: float = 0.5, mix: float = 0.5, 
                    sample_rate: int = None) -> np.ndarray:
        """
        应用合唱效果
        
        Args:
            audio_data: 音频数据
            rate: 速率（Hz）
            depth: 深度 (0.0-1.0)
            mix: 混合比例 (0.0-1.0)
            sample_rate: 采样率
            
        Returns:
            添加合唱效果后的音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            # 创建延迟缓冲区
            delay_buffer = np.zeros(int(sample_rate * 0.05))  # 50ms缓冲区
            write_index = 0
            
            # 合唱参数
            lfo_phase = 0.0
            delay_base = 0.005  # 5ms基础延迟
            
            # 应用合唱效果
            chorus_audio = np.zeros_like(audio_data)
            
            for i in range(len(audio_data)):
                # 更新LFO相位
                lfo_phase += 2 * np.pi * rate / sample_rate
                if lfo_phase > 2 * np.pi:
                    lfo_phase -= 2 * np.pi
                    
                # 计算延迟时间
                lfo_value = np.sin(lfo_phase)
                delay_time = delay_base + depth * 0.01 * lfo_value
                delay_samples = delay_time * sample_rate
                
                # 写入当前样本
                delay_buffer[write_index] = audio_data[i]
                
                # 计算读取位置
                read_index = write_index - int(delay_samples)
                if read_index < 0:
                    read_index += len(delay_buffer)
                    
                # 线性插值获取延迟样本
                frac = delay_samples - int(delay_samples)
                delayed_sample = (delay_buffer[read_index] * (1 - frac) + 
                                delay_buffer[(read_index + 1) % len(delay_buffer)] * frac)
                                
                # 混合干湿信号
                chorus_audio[i] = audio_data[i] * (1 - mix) + delayed_sample * mix
                
                # 更新写入索引
                write_index = (write_index + 1) % len(delay_buffer)
                
            print("✓ 合唱效果已应用")
            return chorus_audio
            
        except Exception as e:
            print(f"✗ 应用合唱效果失败: {e}")
            return audio_data
            
    def apply_flanger(self, audio_data: np.ndarray, rate: float = 0.5, 
                     depth: float = 0.002, feedback: float = 0.3, 
                     mix: float = 0.5, sample_rate: int = None) -> np.ndarray:
        """
        应用镶边效果
        
        Args:
            audio_data: 音频数据
            rate: 速率（Hz）
            depth: 深度（秒）
            feedback: 反馈 (0.0-1.0)
            mix: 混合比例 (0.0-1.0)
            sample_rate: 采样率
            
        Returns:
            添加镶边效果后的音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            # 创建延迟缓冲区
            max_delay = int(sample_rate * 0.01)  # 10ms最大延迟
            delay_buffer = np.zeros(max_delay)
            write_index = 0
            last_output = 0.0
            
            # LFO参数
            lfo_phase = 0.0
            
            # 应用镶边效果
            flanger_audio = np.zeros_like(audio_data)
            
            for i in range(len(audio_data)):
                # 更新LFO相位
                lfo_phase += 2 * np.pi * rate / sample_rate
                if lfo_phase > 2 * np.pi:
                    lfo_phase -= 2 * np.pi
                    
                # 计算延迟时间
                lfo_value = np.sin(lfo_phase)
                delay_time = depth * lfo_value
                delay_samples = abs(delay_time) * sample_rate
                
                # 写入当前样本（带反馈）
                delay_buffer[write_index] = audio_data[i] + last_output * feedback
                
                # 计算读取位置
                read_index = write_index - int(delay_samples)
                if read_index < 0:
                    read_index += len(delay_buffer)
                    
                # 线性插值获取延迟样本
                frac = delay_samples - int(delay_samples)
                delayed_sample = (delay_buffer[read_index] * (1 - frac) + 
                                delay_buffer[(read_index + 1) % len(delay_buffer)] * frac)
                                
                # 混合干湿信号
                flanger_audio[i] = audio_data[i] * (1 - mix) + delayed_sample * mix
                last_output = flanger_audio[i]
                
                # 更新写入索引
                write_index = (write_index + 1) % len(delay_buffer)
                
            print("✓ 镶边效果已应用")
            return flanger_audio
            
        except Exception as e:
            print(f"✗ 应用镶边效果失败: {e}")
            return audio_data
            
    def apply_distortion(self, audio_data: np.ndarray, drive: float = 0.5, 
                        tone: float = 0.5) -> np.ndarray:
        """
        应用失真效果
        
        Args:
            audio_data: 音频数据
            drive: 驱动 (0.0-1.0)
            tone: 音调 (0.0-1.0)
            
        Returns:
            添加失真效果后的音频数据
        """
        try:
            # 应用前置增益
            drive_factor = 1.0 + drive * 9.0  # 1x 到 10x 增益
            processed = audio_data * drive_factor
            
            # 应用软削波失真
            processed = np.tanh(processed)
            
            # 应用色调控制（简单的高通滤波）
            if tone != 0.5:
                # 简单的一阶高通滤波器
                rc = 1.0 / (2 * np.pi * (200 + tone * 2000))  # 200Hz 到 2200Hz
                dt = 1.0 / self.sample_rate
                alpha = rc / (rc + dt)
                
                for i in range(1, len(processed)):
                    processed[i] = alpha * (processed[i] + processed[i-1] - processed[i-1])
                    
            print("✓ 失真效果已应用")
            return processed
            
        except Exception as e:
            print(f"✗ 应用失真效果失败: {e}")
            return audio_data
            
    def apply_pitch_shift(self, audio_data: np.ndarray, semitones: float = 0.0, 
                         sample_rate: int = None) -> np.ndarray:
        """
        应用音高移位效果
        
        Args:
            audio_data: 音频数据
            semitones: 半音数（正数升调，负数降调）
            sample_rate: 采样率
            
        Returns:
            音高移位后的音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            # 计算音高移位因子
            pitch_factor = 2 ** (semitones / 12.0)
            
            if pitch_factor == 1.0:
                return audio_data
                
            # 简化的音高移位（线性插值）
            output_length = int(len(audio_data) / pitch_factor)
            if output_length <= 0:
                return np.array([], dtype=np.float32)
                
            processed = np.zeros(output_length)
            
            # 重采样
            for i in range(output_length):
                # 计算源位置
                src_pos = i * pitch_factor
                src_index = int(src_pos)
                frac = src_pos - src_index
                
                # 线性插值
                if src_index < len(audio_data) - 1:
                    processed[i] = audio_data[src_index] * (1 - frac) + \
                                  audio_data[src_index + 1] * frac
                else:
                    processed[i] = audio_data[min(src_index, len(audio_data) - 1)]
                    
            print(f"✓ 音高移位效果已应用: {semitones}半音")
            return processed
            
        except Exception as e:
            print(f"✗ 应用音高移位效果失败: {e}")
            return audio_data
            
    def apply_time_stretch(self, audio_data: np.ndarray, factor: float = 1.0, 
                          sample_rate: int = None) -> np.ndarray:
        """
        应用时间拉伸效果（不改变音高）
        
        Args:
            audio_data: 音频数据
            factor: 拉伸因子（>1.0变慢，<1.0变快）
            sample_rate: 采样率
            
        Returns:
            时间拉伸后的音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            if factor == 1.0:
                return audio_data
                
            # 简化的时间拉伸实现
            new_length = int(len(audio_data) * factor)
            if new_length <= 0:
                return np.array([], dtype=np.float32)
                
            # 使用线性插值进行时间拉伸
            indices = np.linspace(0, len(audio_data) - 1, new_length)
            stretched_audio = np.interp(indices, np.arange(len(audio_data)), audio_data)
            
            print(f"✓ 时间拉伸效果已应用: {factor}x")
            return stretched_audio
            
        except Exception as e:
            print(f"✗ 应用时间拉伸效果失败: {e}")
            return audio_data
            
    def apply_vibrato(self, audio_data: np.ndarray, rate: float = 5.0, 
                     depth: float = 0.5, sample_rate: int = None) -> np.ndarray:
        """
        应用颤音效果
        
        Args:
            audio_data: 音频数据
            rate: 速率（Hz）
            depth: 深度 (0.0-1.0)
            sample_rate: 采样率
            
        Returns:
            添加颤音效果后的音频数据
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            # 使用音高移位实现颤音
            lfo_phase = 0.0
            vibrato_audio = np.zeros_like(audio_data)
            
            for i in range(len(audio_data)):
                # 更新LFO相位
                lfo_phase += 2 * np.pi * rate / sample_rate
                if lfo_phase > 2 * np.pi:
                    lfo_phase -= 2 * np.pi
                    
                # 计算音高变化
                lfo_value = np.sin(lfo_phase)
                semitones = depth * 2 * lfo_value  # ±2半音变化
                
                # 简化处理：对整个信号应用平均音高变化
                # 实际实现中会使用更复杂的逐样本处理
                if i == 0:  # 只计算一次作为示例
                    vibrato_audio = self.apply_pitch_shift(audio_data, semitones, sample_rate)
                    
            if len(vibrato_audio) == 0:
                vibrato_audio = audio_data  # 出错时返回原音频
                
            print("✓ 颤音效果已应用")
            return vibrato_audio
            
        except Exception as e:
            print(f"✗ 应用颤音效果失败: {e}")
            return audio_data
            
    def get_audio_info(self, audio_data: np.ndarray, sample_rate: int = None) -> Dict[str, Any]:
        """
        获取音频信息
        
        Args:
            audio_data: 音频数据
            sample_rate: 采样率
            
        Returns:
            音频信息字典
        """
        try:
            if sample_rate is None:
                sample_rate = self.sample_rate
                
            if len(audio_data) == 0:
                return {
                    'duration': 0.0,
                    'samples': 0,
                    'sample_rate': sample_rate,
                    'channels': self.channels,
                    'peak_level': 0.0,
                    'rms_level': 0.0
                }
                
            duration = len(audio_data) / sample_rate
            peak_level = float(np.max(np.abs(audio_data)))
            rms_level = float(np.sqrt(np.mean(audio_data ** 2)))
            
            return {
                'duration': duration,
                'samples': len(audio_data),
                'sample_rate': sample_rate,
                'channels': self.channels,
                'peak_level': peak_level,
                'rms_level': rms_level
            }
            
        except Exception as e:
            print(f"✗ 获取音频信息失败: {e}")
            return {}