[Python] Wie ist der Offset relativ zum Dateistart in Sekunden zum ersten Beat?

[Beater (Gast)]

Es geht um 
https://github.com/scaperot/the-BPM-detector-python/blob/master/bpm_detection/bpm_detection.py

Wie müßte ich den Code ändern, damit mir die Verschiebung des ersten 
Beats ausgegeben wird?
Anders gesagt, wie ist der Offset relativ zum Dateistart in Sekunden zum 
ersten Beat?

Im Folgenden der gesamte Code:

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();

[A. S. (rava)]

Beater schrieb:
> if bpm == None:
>             continue

da würd ich mal mit einem else-Zweig anfangen... :D

[Beater (Gast)]

A. S. schrieb:
> Beater schrieb:
>> if bpm == None:
>>             continue
>
> da würd ich mal mit einem else-Zweig anfangen... :D

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