采用两种不同的端点检测算法对record.wav文件进行端点检测，将两种不同算法的检测结果展示在一个figure上，采用黄色的实线表示声音的开始，绿色的虚线表示声音的结束

好的，我理解了你的问题。首先需要安装一些必要的库，如`numpy`，`matplotlib`等。现在开始逐步实现你的需求。

1. 读取音频文件

import wave

with wave.open('record.wav', 'rb') as f:
    frames = f.readframes(-1)
    sample_width = f.getsampwidth()
    framerate = f.getframerate()

2. 将二进制数据转换为数字信号

import numpy as np

signal = np.frombuffer(frames, dtype='int16')

3. 端点检测算法1：基于能量的端点检测

def energy_based_detection(signal, sample_rate, window_duration=0.05, threshold_ratio=1.5):
    window_size = int(window_duration * sample_rate)
    energy = np.array([sum(abs(signal[i:i+window_size]**2)) for i in range(0, len(signal), window_size)])
    threshold = threshold_ratio * np.median(energy)
    start = 0
    end = len(energy) - 1
    while energy[start] < threshold:
        start += 1
    while energy[end] < threshold:
        end -= 1
    return start * window_size, (end+1) * window_size

4. 端点检测算法2：基于过零率的端点检测

def zero_crossing_rate_based_detection(signal, sample_rate, window_duration=0.05, threshold_ratio=1.5):
    window_size = int(window_duration * sample_rate)
    zero_crossing_rate = np.array([((signal[i:i+window_size] * signal[i+1:i+window_size+1]) < 0).sum() for i in range(0, len(signal), window_size)])
    threshold = threshold_ratio * np.median(zero_crossing_rate)
    start = 0
    end = len(zero_crossing_rate) - 1
    while zero_crossing_rate[start] < threshold:
        start += 1
    while zero_crossing_rate[end] < threshold:
        end -= 1
    return start * window_size, (end+1) * window_size

5. 进行端点检测并绘制图像

import matplotlib.pyplot as plt

start1, end1 = energy_based_detection(signal, framerate)
start2, end2 = zero_crossing_rate_based_detection(signal, framerate)

plt.plot(signal)
plt.axvline(start1, color='yellow', linestyle='solid')
plt.axvline(end1, color='green', linestyle='dashed')
plt.axvline(start2, color='yellow', linestyle='solid')
plt.axvline(end2, color='green', linestyle='dashed')
plt.show()

这样就完成了你的需求。注意，以上代码为简化版，可能还需要进行一些调试和优化。