使用Python实现音频文件的分布式处理

import librosa
import numpy as np

def load_and_preprocess_audio(file_path):
    try:
        # 读取音频文件
        y, sr = librosa.load(file_path, sr=None)

        # 采样率转换（可选）
        target_sr = 16000
        if sr != target_sr:
            y = librosa.resample(y, orig_sr=sr, target_sr=target_sr)

        # 归一化
        y = y / np.max(np.abs(y))

        return y, target_sr
    except Exception as e:
        print(f"Error loading and preprocessing audio file {file_path}: {e}")
        return None, None

# 示例
file_path = 'example.wav'
audio_data, sample_rate = load_and_preprocess_audio(file_path)
if audio_data is not None:
    print(f"Audio data loaded with sample rate: {sample_rate}")

import dask.bag as db
from dask.distributed import Client

def process_audio(file_path):
    audio_data, sample_rate = load_and_preprocess_audio(file_path)
    if audio_data is not None:
        # 这里可以添加具体的音频处理逻辑
        # 例如：提取特征、降噪等
        return (file_path, audio_data, sample_rate)
    else:
        return (file_path, None, None)

def distributed_audio_processing(file_paths):
    client = Client()  # 启动Dask客户端
    bag = db.from_sequence(file_paths)  # 创建Dask Bag
    results = bag.map(process_audio).compute()  # 并行处理
    client.close()  # 关闭客户端
    return results

# 示例
file_paths = ['file1.wav', 'file2.wav', 'file3.wav']
results = distributed_audio_processing(file_paths)
for result in results:
    print(result)

import json

def save_results(results, output_file):
    with open(output_file, 'w') as f:
        json.dump(results, f, indent=4)

# 示例
output_file = 'results.json'
save_results(results, output_file)
print(f"Results saved to {output_file}")

# 假设我们有一个包含100万首音乐文件的列表
music_files = ['music1.wav', 'music2.wav', ...]  # 100万个文件

# 分布式处理
results = distributed_audio_processing(music_files)

# 保存结果
output_file = 'monkey_music_results.json'
save_results(results, output_file)
print(f"Monkey Music results saved to {output_file}")