Python:如何快速加载大型音频数据集

import numpy as np

# 假设音频文件为16位单声道，采样率为44100Hz
filename = 'large_audio_dataset.wav'
dtype = 'int16'
shape = (1000000,)  # 假设音频数据总长度为1000000个样本

# 创建内存映射文件
audio_data = np.memmap(filename, dtype=dtype, mode='r', shape=shape)

# 访问音频数据
print(audio_data[:100])  # 打印前100个样本

import librosa

filename = 'large_audio_dataset.wav'
chunk_size = 1024  # 每个块的大小

# 打开音频文件
y, sr = librosa.load(filename, sr=None, mono=True, offset=0.0, duration=None)

# 分块读取音频数据
for start in range(0, len(y), chunk_size):
    chunk = y[start:start + chunk_size]
    # 处理每个块
    print(chunk)

import multiprocessing as mp
import librosa

def process_chunk(chunk):
    # 处理每个块的逻辑
    print(chunk)

filename = 'large_audio_dataset.wav'
chunk_size = 1024
y, sr = librosa.load(filename, sr=None, mono=True, offset=0.0, duration=None)

# 创建进程池
with mp.Pool(mp.cpu_count()) as pool:
    # 分块处理音频数据
    pool.map(process_chunk, [y[i:i + chunk_size] for i in range(0, len(y), chunk_size)])