i've found this code by @scaperot here that could help you:
import wave, array, math, time, argparse, sys
import numpy, pywt
from scipy import signal
import pdb
import matplotlib.pyplot as plt
def read_wav(filename):
#open file, get metadata for audio
try:
wf = wave.open(filename,'rb')
except IOError, e:
print e
return
# typ = choose_type( wf.getsampwidth() ) #TODO: implement choose_type
nsamps = wf.getnframes();
assert(nsamps > 0);
fs = wf.getframerate()
assert(fs > 0)
# read entire file and make into an array
samps = list(array.array('i',wf.readframes(nsamps)))
#print 'Read', nsamps,'samples from', filename
try:
assert(nsamps == len(samps))
except AssertionError, e:
print nsamps, "not equal to", len(samps)
return samps, fs
# print an error when no data can be found
def no_audio_data():
print "No audio data for sample, skipping..."
return None, None
# simple peak detection
def peak_detect(data):
max_val = numpy.amax(abs(data))
peak_ndx = numpy.where(data==max_val)
if len(peak_ndx[0]) == 0: #if nothing found then the max must be negative
peak_ndx = numpy.where(data==-max_val)
return peak_ndx
def bpm_detector(data,fs):
cA = []
cD = []
correl = []
cD_sum = []
levels = 4
max_decimation = 2**(levels-1);
min_ndx = 60./ 220 * (fs/max_decimation)
max_ndx = 60./ 40 * (fs/max_decimation)
for loop in range(0,levels):
cD = []
# 1) DWT
if loop == 0:
[cA,cD] = pywt.dwt(data,'db4');
cD_minlen = len(cD)/max_decimation+1;
cD_sum = numpy.zeros(cD_minlen);
else:
[cA,cD] = pywt.dwt(cA,'db4');
# 2) Filter
cD = signal.lfilter([0.01],[1 -0.99],cD);
# 4) Subtractargs.filename out the mean.
# 5) Decimate for reconstruction later.
cD = abs(cD[::(2**(levels-loop-1))]);
cD = cD - numpy.mean(cD);
# 6) Recombine the signal before ACF
# essentially, each level I concatenate
# the detail coefs (i.e. the HPF values)
# to the beginning of the array
cD_sum = cD[0:cD_minlen] + cD_sum;
if [b for b in cA if b != 0.0] == []:
return no_audio_data()
# adding in the approximate data as well...
cA = signal.lfilter([0.01],[1 -0.99],cA);
cA = abs(cA);
cA = cA - numpy.mean(cA);
cD_sum = cA[0:cD_minlen] + cD_sum;
# ACF
correl = numpy.correlate(cD_sum,cD_sum,'full')
midpoint = len(correl) / 2
correl_midpoint_tmp = correl[midpoint:]
peak_ndx = peak_detect(correl_midpoint_tmp[min_ndx:max_ndx]);
if len(peak_ndx) > 1:
return no_audio_data()
peak_ndx_adjusted = peak_ndx[0]+min_ndx;
bpm = 60./ peak_ndx_adjusted * (fs/max_decimation)
print bpm
return bpm,correl
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Process .wav file to determine the Beats Per Minute.')
parser.add_argument('--filename', required=True,
help='.wav file for processing')
parser.add_argument('--window', type=float, default=3,
help='size of the the window (seconds) that will be scanned to determine the bpm. Typically less than 10 seconds. [3]')
args = parser.parse_args()
samps,fs = read_wav(args.filename)
data = []
correl=[]
bpm = 0
n=0;
nsamps = len(samps)
window_samps = int(args.window*fs)
samps_ndx = 0; #first sample in window_ndx
max_window_ndx = nsamps / window_samps;
bpms = numpy.zeros(max_window_ndx)
#iterate through all windows
for window_ndx in xrange(0,max_window_ndx):
#get a new set of samples
#print n,":",len(bpms),":",max_window_ndx,":",fs,":",nsamps,":",samps_ndx
data = samps[samps_ndx:samps_ndx+window_samps]
if not ((len(data) % window_samps) == 0):
raise AssertionError( str(len(data) ) )
bpm, correl_temp = bpm_detector(data,fs)
if bpm == None:
continue
bpms[window_ndx] = bpm
correl = correl_temp
#iterate at the end of the loop
samps_ndx = samps_ndx+window_samps;
n=n+1; #counter for debug...
bpm = numpy.median(bpms)
print 'Completed. Estimated Beats Per Minute:', bpm
n = range(0,len(correl))
plt.plot(n,abs(correl));
plt.show(False); #plot non-blocking
time.sleep(10);
plt.close();
