How to find .wav tempo with aubio?
I found this Paul Brossier code that might help you, here it is:
#! /usr/bin/env python
from aubio import source, tempo
from numpy import median, diff
def get_file_bpm(path, params = None):
""" Calculate the beats per minute (bpm) of a given file.
path: path to the file
param: dictionary of parameters
"""
if params is None:
params = {}
try:
win_s = params['win_s']
samplerate = params['samplerate']
hop_s = params['hop_s']
except KeyError:
"""
# super fast
samplerate, win_s, hop_s = 4000, 128, 64
# fast
samplerate, win_s, hop_s = 8000, 512, 128
"""
# default:
samplerate, win_s, hop_s = 44100, 1024, 512
s = source(path, samplerate, hop_s)
samplerate = s.samplerate
o = tempo("specdiff", win_s, hop_s, samplerate)
# List of beats, in samples
beats = []
# Total number of frames read
total_frames = 0
while True:
samples, read = s()
is_beat = o(samples)
if is_beat:
this_beat = o.get_last_s()
beats.append(this_beat)
#if o.get_confidence() > .2 and len(beats) > 2.:
# break
total_frames += read
if read < hop_s:
break
# Convert to periods and to bpm
if len(beats) > 1:
if len(beats) < 4:
print("few beats found in {:s}".format(path))
bpms = 60./diff(beats)
b = median(bpms)
else:
b = 0
print("not enough beats found in {:s}".format(path))
return b
if __name__ == '__main__':
import sys
for f in sys.argv[1:]:
bpm = get_file_bpm(f)
print("{:6s} {:s}".format("{:2f}".format(bpm), f))
this is the key part:
bpms = 60./np.diff(beats)
median_bpm = np.median(bpms)
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Updated
This command will give you an estimate of the tempo of the entire file (available in 0.4.5
):
aubio tempo foo.wav
There is a simple demo in aubio python/demos
: demo_bpm_extract.py .
The most important part is the following two lines, which calculate the periods between each successive bits ( np.diff
), convert those periods to bpm ( 60./
), and take the median ( np.median
) as the most likely bpm candidate for this series of beats:
#!/usr/bin/env python
import numpy as np
bpms = 60./np.diff(beats)
median_bpm = np.median(bpms)
Note how the median fits better than the mean here, as it will always give the estimate that exists in the original population bpms
.
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