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import librosa
import numpy as np
import torch
from typing import Tuple
import os
import subprocess
import tempfile
from ..configs.config import AudioConfig
class AudioProcessor:
"""音频处理器：负责音频的预处理和特征提取"""
def __init__(self, config: AudioConfig):
self.config = config
self.target_length = 16000 # 固定音频长度为1秒
def load_audio(self, file_path: str) -> np.ndarray:
"""加载音频文件，支持多种格式包括m4a"""
try:
# 检查是否是m4a文件
if file_path.lower().endswith('.m4a'):
# 创建临时wav文件
with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as temp_wav:
temp_wav_path = temp_wav.name
try:
# 使用ffmpeg将m4a转换为wav
command = [
'ffmpeg',
'-i', file_path,
'-acodec', 'pcm_s16le',
'-ar', str(self.config.sample_rate),
'-ac', '1', # 转换为单声道
'-y', # 覆盖已存在的文件
temp_wav_path
]
subprocess.run(command, check=True, capture_output=True)
# 加载转换后的wav文件
audio, _ = librosa.load(temp_wav_path, sr=self.config.sample_rate)
finally:
# 清理临时文件
if os.path.exists(temp_wav_path):
os.unlink(temp_wav_path)
else:
# 直接加载其他格式的音频文件
audio, _ = librosa.load(file_path, sr=self.config.sample_rate)
return audio
except Exception as e:
raise RuntimeError(f"Error loading audio file {file_path}: {str(e)}")
def preprocess_audio(self, audio_path: str) -> Tuple[np.ndarray, np.ndarray]:
"""
预处理音频文件：
1. 加载音频
2. 重采样到目标采样率
3. 将音频裁剪或填充到固定长度
4. 计算梅尔频谱图
"""
# 加载音频
audio, sr = librosa.load(audio_path, sr=self.config.sample_rate)
# 处理音频长度
if len(audio) > self.target_length:
# 随机裁剪到目标长度
start = np.random.randint(0, len(audio) - self.target_length)
audio = audio[start:start + self.target_length]
else:
# 填充到目标长度
padding = self.target_length - len(audio)
audio = np.pad(audio, (0, padding), mode='constant')
# 计算梅尔频谱图
mel_spec = librosa.feature.melspectrogram(
y=audio,
sr=self.config.sample_rate,
n_fft=self.config.n_fft,
hop_length=self.config.hop_length,
win_length=self.config.win_length,
n_mels=self.config.n_mels,
fmin=self.config.mel_fmin,
fmax=self.config.mel_fmax
)
# 转换为分贝单位
mel_spec = librosa.power_to_db(mel_spec, ref=np.max)
# 确保梅尔频谱图的维度正确：(n_mels, time)
# 如果需要，调整时间维度到固定长度
target_time_steps = 32 # 固定时间步长
if mel_spec.shape[1] > target_time_steps:
mel_spec = mel_spec[:, :target_time_steps]
else:
pad_width = ((0, 0), (0, target_time_steps - mel_spec.shape[1]))
mel_spec = np.pad(mel_spec, pad_width, mode='constant')
return audio, mel_spec
def audio_to_mel_spec(self, audio: np.ndarray) -> np.ndarray:
"""将音频转换为梅尔频谱图"""
# 计算短时傅里叶变换
stft = librosa.stft(
audio,
n_fft=self.config.n_fft,
hop_length=self.config.hop_length,
win_length=self.config.win_length
)
# 计算幅度谱
magnitude = np.abs(stft)
# 生成梅尔滤波器组
mel_basis = librosa.filters.mel(
sr=self.config.sample_rate,
n_fft=self.config.n_fft,
n_mels=self.config.n_mels,
fmin=self.config.mel_fmin,
fmax=self.config.mel_fmax
)
# 应用梅尔滤波器
mel_spec = np.dot(mel_basis, magnitude)
# 转换为对数刻度
mel_spec = np.log(np.clip(mel_spec, a_min=1e-5, a_max=None))
return mel_spec
def normalize_audio(self, audio: np.ndarray) -> np.ndarray:
"""音频归一化"""
return audio / np.max(np.abs(audio))
def pad_or_trim(self, audio: np.ndarray, target_length: int) -> np.ndarray:
"""将音频填充或裁剪到指定长度"""
if len(audio) > target_length:
return audio[:target_length]
else:
return np.pad(audio, (0, target_length - len(audio)), mode='constant') |