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March 5th, 2010
Realtime FFT Graph of Audio WAV File or Microphone Input with Python, Scipy, and WCKgraph
I'm stretching the limits of what these software platforms were designed to to, but I'm impressed such a hacked-together code produces fast, functional results. The code below is the simplest case code I could create which graphs the audio spectrum of the microphone input. It seems to run fine with about 30+ FPS on my modest machine. It should work on Windows and Linux. I chose not to go with matplotlib because I didn't think it was fast enough for my needs in this one case. Here's what the code below looks like running:
NOTE that this program was designed with the intent of recording the FFTs, therefore if the program "falls behind" the real time input, it will buffer the sound on its own and try to catch up (accomplished by two layers of threading). In this way, all audio gets interpreted. If you're just trying to create a spectrograph for simple purposes, have it only sample the audio when it needs to, rather than having it sample audio continuously.
import pyaudio
import scipy
import struct
import scipy.fftpack
from Tkinter import *
import threading
import time, datetime
import wckgraph
import math
#ADJUST THIS TO CHANGE SPEED/SIZE OF FFT
bufferSize=2**11
#bufferSize=2**8
# ADJUST THIS TO CHANGE SPEED/SIZE OF FFT
sampleRate=48100
#sampleRate=64000
p = pyaudio.PyAudio()
chunks=[]
ffts=[]
def stream():
global chunks, inStream, bufferSize
while True:
chunks.append(inStream.read(bufferSize))
def record():
global w, inStream, p, bufferSize
inStream = p.open(format=pyaudio.paInt16,channels=1,
rate=sampleRate,input=True,frames_per_buffer=bufferSize)
threading.Thread(target=stream).start()
def downSample(fftx,ffty,degree=10):
x,y=[],[]
for i in range(len(ffty)/degree-1):
x.append(fftx[i*degree+degree/2])
y.append(sum(ffty[i*degree:(i+1)*degree])/degree)
return [x,y]
def smoothWindow(fftx,ffty,degree=10):
lx,ly=fftx[degree:-degree],[]
for i in range(degree,len(ffty)-degree):
ly.append(sum(ffty[i-degree:i+degree]))
return [lx,ly]
def smoothMemory(ffty,degree=3):
global ffts
ffts = ffts+[ffty]
if len(ffts)< =degree: return ffty
ffts=ffts[1:]
return scipy.average(scipy.array(ffts),0)
def detrend(fftx,ffty,degree=10):
lx,ly=fftx[degree:-degree],[]
for i in range(degree,len(ffty)-degree):
ly.append(ffty[i]-sum(ffty[i-degree:i+degree])/(degree*2))
#ly.append(fft[i]-(ffty[i-degree]+ffty[i+degree])/2)
return [lx,ly]
def graph():
global chunks, bufferSize, fftx,ffty, w
if len(chunks)>0:
data = chunks.pop(0)
data=scipy.array(struct.unpack("%dB"%(bufferSize*2),data))
#print "RECORDED",len(data)/float(sampleRate),"SEC"
ffty=scipy.fftpack.fft(data)
fftx=scipy.fftpack.rfftfreq(bufferSize*2, 1.0/sampleRate)
fftx=fftx[0:len(fftx)/4]
ffty=abs(ffty[0:len(ffty)/2])/1000
ffty1=ffty[:len(ffty)/2]
ffty2=ffty[len(ffty)/2::]+2
ffty2=ffty2[::-1]
ffty=ffty1+ffty2
ffty=scipy.log(ffty)-2
#fftx,ffty=downSample(fftx,ffty,5)
#fftx,ffty=detrend(fftx,ffty,30)
#fftx,ffty=smoothWindow(fftx,ffty,10)
ffty=smoothMemory(ffty,3)
#fftx,ffty=detrend(fftx,ffty,10)
w.clear()
#w.add(wckgraph.Axes(extent=(0, -1, fftx[-1], 3)))
w.add(wckgraph.Axes(extent=(0, -1, 6000, 3)))
w.add(wckgraph.LineGraph([fftx,ffty]))
w.update()
if len(chunks)>20:
print "falling behind...",len(chunks)
def go(x=None):
global w,fftx,ffty
print "STARTING!"
threading.Thread(target=record).start()
while True:
graph()
root = Tk()
root.title("SPECTRUM ANALYZER")
root.geometry('500x200')
w = wckgraph.GraphWidget(root)
w.pack(fill=BOTH, expand=1)
go()
mainloop()
Markdown source code last modified on January 18th, 2021
---
title: Realtime FFT Graph of Audio WAV File or Microphone Input with Python, Scipy, and WCKgraph
date: 2010-03-05 16:30:37
tags: python, old
---
# Realtime FFT Graph of Audio WAV File or Microphone Input with Python, Scipy, and WCKgraph
__I'm stretching the limits of what these software platforms were designed to to__, but I'm impressed such a hacked-together code produces fast, functional results. The code below is the simplest case code I could create which graphs the audio spectrum of the microphone input. It seems to run fine with about 30+ FPS on my modest machine. It should work on Windows and Linux. I chose not to go with matplotlib because I didn't think it was fast enough for my needs in this one case. Here's what the code below looks like running:
<div class="text-center">
</div>
__NOTE__ that this program was designed with the intent of recording the FFTs, therefore if the program "falls behind" the real time input, it will buffer the sound on its own and try to catch up (accomplished by two layers of threading). In this way, all audio gets interpreted. If you're just trying to create a spectrograph for simple purposes, have it only sample the audio when it needs to, rather than having it sample audio continuously.
```python
import pyaudio
import scipy
import struct
import scipy.fftpack
from Tkinter import *
import threading
import time, datetime
import wckgraph
import math
#ADJUST THIS TO CHANGE SPEED/SIZE OF FFT
bufferSize=2**11
#bufferSize=2**8
# ADJUST THIS TO CHANGE SPEED/SIZE OF FFT
sampleRate=48100
#sampleRate=64000
p = pyaudio.PyAudio()
chunks=[]
ffts=[]
def stream():
global chunks, inStream, bufferSize
while True:
chunks.append(inStream.read(bufferSize))
def record():
global w, inStream, p, bufferSize
inStream = p.open(format=pyaudio.paInt16,channels=1,
rate=sampleRate,input=True,frames_per_buffer=bufferSize)
threading.Thread(target=stream).start()
def downSample(fftx,ffty,degree=10):
x,y=[],[]
for i in range(len(ffty)/degree-1):
x.append(fftx[i*degree+degree/2])
y.append(sum(ffty[i*degree:(i+1)*degree])/degree)
return [x,y]
def smoothWindow(fftx,ffty,degree=10):
lx,ly=fftx[degree:-degree],[]
for i in range(degree,len(ffty)-degree):
ly.append(sum(ffty[i-degree:i+degree]))
return [lx,ly]
def smoothMemory(ffty,degree=3):
global ffts
ffts = ffts+[ffty]
if len(ffts)< =degree: return ffty
ffts=ffts[1:]
return scipy.average(scipy.array(ffts),0)
def detrend(fftx,ffty,degree=10):
lx,ly=fftx[degree:-degree],[]
for i in range(degree,len(ffty)-degree):
ly.append(ffty[i]-sum(ffty[i-degree:i+degree])/(degree*2))
#ly.append(fft[i]-(ffty[i-degree]+ffty[i+degree])/2)
return [lx,ly]
def graph():
global chunks, bufferSize, fftx,ffty, w
if len(chunks)>0:
data = chunks.pop(0)
data=scipy.array(struct.unpack("%dB"%(bufferSize*2),data))
#print "RECORDED",len(data)/float(sampleRate),"SEC"
ffty=scipy.fftpack.fft(data)
fftx=scipy.fftpack.rfftfreq(bufferSize*2, 1.0/sampleRate)
fftx=fftx[0:len(fftx)/4]
ffty=abs(ffty[0:len(ffty)/2])/1000
ffty1=ffty[:len(ffty)/2]
ffty2=ffty[len(ffty)/2::]+2
ffty2=ffty2[::-1]
ffty=ffty1+ffty2
ffty=scipy.log(ffty)-2
#fftx,ffty=downSample(fftx,ffty,5)
#fftx,ffty=detrend(fftx,ffty,30)
#fftx,ffty=smoothWindow(fftx,ffty,10)
ffty=smoothMemory(ffty,3)
#fftx,ffty=detrend(fftx,ffty,10)
w.clear()
#w.add(wckgraph.Axes(extent=(0, -1, fftx[-1], 3)))
w.add(wckgraph.Axes(extent=(0, -1, 6000, 3)))
w.add(wckgraph.LineGraph([fftx,ffty]))
w.update()
if len(chunks)>20:
print "falling behind...",len(chunks)
def go(x=None):
global w,fftx,ffty
print "STARTING!"
threading.Thread(target=record).start()
while True:
graph()
root = Tk()
root.title("SPECTRUM ANALYZER")
root.geometry('500x200')
w = wckgraph.GraphWidget(root)
w.pack(fill=BOTH, expand=1)
go()
mainloop()
```
