Plotting audio data properties over long time periods - matplotlib

Question

Using Python matplotlib I would like to plot sensor data over a period of several hours. The signal arrives via an audio card and gets sampled over short chunks of data. In the example below amplitude and RMS is plotted.
In order to plot RMS and other properties over much larger time periods than shown here, perhaps down sampling is needed. I am not sure how to accomplish that and would appreciate any further advice. The intention is to run the code on a Raspberry Pi.
Update 1. A very minimal example is shown for getting a longer time view of RMS.
Noticable is a considerable delay in response to audio signals in particular when adding more plots to the figure.
I also tried using Funcanimation without blitting because I would like to show a real-time axis and this is equally slow. Using PyQT should give better results.
import pyaudio
import struct
import matplotlib.pyplot as plt
import numpy as np
mic = pyaudio.PyAudio()
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100
CHUNK = int(RATE/20)
stream = mic.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True,
output=True,
frames_per_buffer=CHUNK)
fig = plt.figure()
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(2, 1, 2)
ax1.set_xlabel("Samples = 2*Chunk length ")
ax1.set_ylabel("Amplitude")
ax1.set_title('Audio example')
fig.tight_layout(pad=3.0)
x = np.arange(0, 2 * CHUNK, 2)
ax1.set_ylim(-10e3, 10e3)
ax1.set_xlim(0, CHUNK)
line1, = ax1.plot(x, np.random.rand(CHUNK))
line2, = ax2.plot(x, np.random.rand(CHUNK))
ts = []
rs = []
while True:
data = stream.read(CHUNK)
data = np.frombuffer(data, np.int16)
d = np.frombuffer(data, np.int16).astype(np.float)
rms2 = np.sqrt( np.mean(d**2) )
#print(rms2)
# Add x and y to lists
ts.append(dt.datetime.now())
rs.append(rms2)
#Draw x and y lists
ax2.clear()
ax2.plot(ts,rs,color= 'black')
# Format plot
ax2.set_xlabel("Time in UTC")
ax2.set_ylabel("RMS values")
ax2.set_title('RMS')
line1.set_ydata(data)
line2.set_ydata(rms2)
plt.setp(ax2.get_xticklabels(), ha="right", rotation=45)
fig.gca().relim()
fig.gca().autoscale_view()
#fig.canvas.draw()
#fig.canvas.flush_events()
plt.pause(0.01)