2019独角兽企业重金招聘Python工程师标准>>> 
import wave
import numpy
from pyaudio import PyAudio
import matplotlib.pyplot as plt
# 只读方式打开WAV文件
wf = wave.open('./output.wav', 'rb')
# 创建PyAudio对象
p = PyAudio()
stream = p.open(format = p.get_format_from_width(wf.getsampwidth()),
channels = wf.getnchannels(),
rate = wf.getframerate(),
output = True)
nframes = wf.getnframes()
framerate = wf.getframerate()
# 读取完整的帧数据到str_data中，这是一个string类型的数据
str_data = wf.readframes(nframes)
wf.close()
# 将波形数据转换成数组
wave_data = numpy.fromstring(str_data, dtype=numpy.short)
# 将wave_data数组改为2列，行数自动匹配
wave_data.shape = -1,2
# 将数组转置
wave_data = wave_data.T
def time_plt():
# time也是一个数组，与wave_data[0]或wave_data[1]配对形成系列点坐标
time = numpy.arange(0, nframes)*(1.0/framerate)
# 绘制波形图
plt.subplot(211)
plt.plot(time, wave_data[0], c='r')
plt.subplot(212)
plt.plot(time, wave_data[1], c='g')
plt.xlabel('time (seconds)')
plt.show()
def freq():
# 采样点数，修改采样点数和起始位置进行不同位置和长度的音频波形分析
N = 44100
start = 0 # 开始采样位置
df = framerate/(N-1) # 分辨率
freq = [df*n for n in range(0, N)] # N个元素
wave_data2 = wave_data[0][start:start+N]
c = numpy.fft.fft(wave_data2)*2/N
# 常规显示采样频率一半的频谱
d = int(len(c)/2)
# 仅显示频率在4000以下的频谱
while freq[d] > 4000:
d -= 10
plt.plot(freq[:d-1], abs(c[:d-1]), 'r')
plt.show()
def main():
time_plt()
freq()
if __name__ == '__main__':
main()
import wave
import pyaudio
# 定义数据流块
CHUNK = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 2
RATE = 44100
# 录音时间
RECORD_SECONDS = 5
# 要写入的文件名
WAVE_OUTPUT_FILENAME = "output.wav"
# 创建PyAudio对象
p = pyaudio.PyAudio()
# 打开数据流
stream = p.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK)
print("* recording")
# 开始录音
frames = []
for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
data = stream.read(CHUNK)
frames.append(data)
print("* done recording")
# 停止数据流
stream.stop_stream()
stream.close()
# 关闭PyAudio
p.terminate()
# 写入录音文件
wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
wf.setnchannels(CHANNELS)
wf.setsampwidth(p.get_sample_size(FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(frames))
wf.close()