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How to get accurate timing using microphone in python

I'm trying to make beat detection using PC microphone and then with timestamp of beat calculate distance between multiple successive beats. I have chosen python because there is plenty of material available and it's quick to develop. By searching the internet I have come up with this simple code (no advanced peak detection or anything yet, this comes later if need be):
import pyaudio
import struct
import math
import time
SHORT_NORMALIZE = (1.0/32768.0)
def get_rms(block):
# RMS amplitude is defined as the square root of the
# mean over time of the square of the amplitude.
# so we need to convert this string of bytes into
# a string of 16-bit samples...
# we will get one short out for each
# two chars in the string.
count = len(block)/2
format = "%dh" % (count)
shorts = struct.unpack(format, block)
# iterate over the block.
sum_squares = 0.0
for sample in shorts:
# sample is a signed short in +/- 32768.
# normalize it to 1.0
n = sample * SHORT_NORMALIZE
sum_squares += n*n
return math.sqrt(sum_squares / count)
CHUNK = 32
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100
p = pyaudio.PyAudio()
stream = p.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK)
elapsed_time = 0
prev_detect_time = 0
while True:
data = stream.read(CHUNK)
amplitude = get_rms(data)
if amplitude > 0.05: # value set by observing graphed data captured from mic
elapsed_time = time.perf_counter() - prev_detect_time
if elapsed_time > 0.1: # guard against multiple spikes at beat point
print(elapsed_time)
prev_detect_time = time.perf_counter()
def close_stream():
stream.stop_stream()
stream.close()
p.terminate()
The code works pretty good in silence, and I have been pretty satisfied the first two moments I ran it, but then I tried how accurate it was and I was a little bit less satisfied. To test this I used two methods: phone with metronome set to 60bpm (emits tic toc sounds into microphone) and an Arduino hooked to a beeper, which is triggered at 1Hz rate by accurate Chronodot RTC. The beeper beeps into microphone, triggering a detection. With both methods results look similar (numbers represent distance between two beat detections in seconds):
0.9956681643835616
1.0056331689497717
0.9956100091324198
1.0058207853881278
0.9953449497716891
1.0052103013698623
1.0049350136986295
0.9859074337899543
1.004996383561644
0.9954095342465745
1.0061518904109583
0.9953025753424658
1.0051235068493156
1.0057199634703196
0.984839305936072
1.00610396347032
0.9951862648401821
1.0053146301369864
0.9960100821917806
1.0053391780821919
0.9947373881278523
1.0058608219178105
1.0056580091324214
0.9852110319634697
1.0054473059360731
0.9950465753424638
1.0058237077625556
0.995704694063928
1.0054566575342463
0.9851026118721435
1.0059882374429243
1.0052523835616398
0.9956161461187207
1.0050863926940607
0.9955758173515932
1.0058052968036577
0.9953960913242028
1.0048014611872205
1.006336876712325
0.9847434520547935
1.0059712876712297
Now I'm pretty confident that at least Arduino is accurate to 1 msec (which is targeted accuracy). The results tend to be off by +- 5msec, but now and then even 15ms, which is unacceptable. Is there a way to achieve greater accuracy or is this limitation of python / soundcard / something else? Thank you!
EDIT:
After incorporating tom10 and barny's suggestions into the code, the code looks like this:
import pyaudio
import struct
import math
import psutil
import os
def set_high_priority():
p = psutil.Process(os.getpid())
p.nice(psutil.HIGH_PRIORITY_CLASS)
SHORT_NORMALIZE = (1.0/32768.0)
def get_rms(block):
# RMS amplitude is defined as the square root of the
# mean over time of the square of the amplitude.
# so we need to convert this string of bytes into
# a string of 16-bit samples...
# we will get one short out for each
# two chars in the string.
count = len(block)/2
format = "%dh" % (count)
shorts = struct.unpack(format, block)
# iterate over the block.
sum_squares = 0.0
for sample in shorts:
# sample is a signed short in +/- 32768.
# normalize it to 1.0
n = sample * SHORT_NORMALIZE
sum_squares += n*n
return math.sqrt(sum_squares / count)
CHUNK = 4096
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100
RUNTIME_SECONDS = 10
set_high_priority()
p = pyaudio.PyAudio()
stream = p.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK)
elapsed_time = 0
prev_detect_time = 0
TIME_PER_CHUNK = 1000 / RATE * CHUNK
SAMPLE_GROUP_SIZE = 32 # 1 sample = 2 bytes, group is closest to 1 msec elapsing
TIME_PER_GROUP = 1000 / RATE * SAMPLE_GROUP_SIZE
for i in range(0, int(RATE / CHUNK * RUNTIME_SECONDS)):
data = stream.read(CHUNK)
time_in_chunk = 0
group_index = 0
for j in range(0, len(data), (SAMPLE_GROUP_SIZE * 2)):
group = data[j:(j + (SAMPLE_GROUP_SIZE * 2))]
amplitude = get_rms(group)
amplitudes.append(amplitude)
if amplitude > 0.02:
current_time = (elapsed_time + time_in_chunk)
time_since_last_beat = current_time - prev_detect_time
if time_since_last_beat > 500:
print(time_since_last_beat)
prev_detect_time = current_time
time_in_chunk = (group_index+1) * TIME_PER_GROUP
group_index += 1
elapsed_time = (i+1) * TIME_PER_CHUNK
stream.stop_stream()
stream.close()
p.terminate()
With this code I achieved the following results (units are this time milliseconds instead of seconds):
999.909297052154
999.9092970521542
999.9092970521542
999.9092970521542
999.9092970521542
1000.6349206349205
999.9092970521551
999.9092970521524
999.9092970521542
999.909297052156
999.9092970521542
999.9092970521542
999.9092970521524
999.9092970521542
Which, if I didn't make any mistake, looks a lot better than before and has achieved sub-millisecond accuracy. I thank tom10 and barny for their help.

The reason you're not getting the right timing for the beats is that you're missing chunks of the audio data. That is, the chunks are being read by the soundcard, but you're not collecting the data before it's overwritten with the next chunk.
First, though, for this problem you need to distinguish between the ideas of timing accuracy and real-time response.
The timing accuracy of a sound card should be very good, much better than a ms, and you should be able to capture all of this accuracy in the data you read from the soundcard. The real-time responsiveness of your computer's OS should be very bad, much worse than a ms. That is, you should easily be able to identify audio events (such as beats) to within a ms, but not identify them at the time they happen (instead, 30-200ms later depending on your system). This arrangement usually works for computers because general human perception of the timing of events is much greater than a ms (except for rare specialized percepetual systems, like comparing auditory events between the two ears, etc).
The specific problem with your code is that CHUNKS is much too small for the OS to query the sound card at each sample. You have it at 32, so at 44100Hz, the OS needs to get to the sound card every 0.7ms, which is too short of a time for a computer that's tasked with doing many other things. If you OS doesn't get the chunk before the next one comes in, the original chunk is overwritten and lost.
To get this working so it's consistent with the constraints above, make CHUNKS much larger than 32, and more like 1024 (as in the PyAudio examples). Depending on your computer and what it's doing, even that my not be long enough.
If this type of approach won't work for you, you will probably need a dedicated real-time system like an Arduino. (Generally, though, this isn't necessary, so think twice before you decide that you need to use the Arduino. Usually, when I've seen people need true real-time it's when trying to do something very quantitave interactive with the human, like flash a light, have the person tap a button, flash another light, have the person tap another button, etc, to measure response times.)

processing an audio stream with python (resampling)

I have a python script that receives chunks of binary raw audio data and I would like to change the sample rate of those chunks to 16000 and then pipe them to another component.
I tried my luck with audiotools but without success:
# f is a filelike FIFO buffer
reader = PCMFileReader(f, 44100, 1, 1, 16)
conv = PCMConverter(reader, 16000, 1, 1, 16)
Then I just write to the buffer anytime, I get a new chunk:
f.write(msg)
And read from the buffer in another thread:
while not reader.file.closed:
fl = conf.read(10)
chunk = fl.to_bytes(False, True)
The problem is that I get this value error, which seems to come from a "samplerate.c" library:
ValueError: SRC_DATA->data_out is NULL
This error only occurs with resampling. If I turn off that step, then everything works fine and I get playable audio.
Therefore my question: What would be a good tool for this task? And if audiotools turns out to be the right answer, how do I do that correctly.

here is a simplified resampler code
dataFormat is a number of bytes per sample in the stream, ex: stereo 16 bit would be = 4, original_samples is a source bin string size, desired_samples is a desired bit string size, 16KHz->44K1Hz ex: original = 160 but desired = 441, pcm is a source bin string, return is resampled bin string) :
def resampleSimplified(pcm, desired_samples, original_samples, dataFormat):
samples_to_pad = desired_samples - original_samples
q, r = divmod(desired_samples, original_samples)
times_to_pad_up = q + int(bool(r))
times_to_pad_down = q
pcmList = [pcm[i:i+dataFormat] for i in range(0, len(pcm), dataFormat)]
if samples_to_pad > 0:
# extending pcm times_to_pad times
pcmListPadded = list(itertools.chain.from_iterable(
itertools.repeat(x, times_to_pad_up) for x in pcmList)
)
else:
# shrinking pcm times_to_pad times
if times_to_pad_down > 0:
pcmListPadded = pcmList[::(times_to_pad_down)]
else:
pcmListPadded = pcmList
padded_pcm = ''.join(pcmListPadded[:desired_samples])
return padded_pcm

How to calculate the average of several .dat files using python?

So I have 50 - 60 .dat files all containing m rows and n columns of numbers. I need to take the average of all of the files and create a new file in the same format. I have to do this in python. Can anyone help me with this?
I've written some code..
I realize I have some incompatible types here, but I can't think of an alternative so I haven't change anything yet.
#! /usr/bin/python
import os
CC = 1.96
average = []
total = []
count = 0
os.chdir("./")
for files in os.listdir("."):
if files.endswith(".dat"):
infile = open(files)
cur = []
cur = infile.readlines()
for i in xrange(0, len(cur)):
cur[i] = cur[i].split()
total += cur
count += 1
average = [x/count for x in total]
#calculate uncertainty
uncert = []
for files in os.listdir("."):
if files.endswith(".dat"):
infile = open(files)
cur = []
cur = infile.readlines
for i in xrange(0, len(cur)):
cur[i] = cur[i].split()
uncert += (cur - average)**2
uncert = uncert**.5
uncert = uncert*CC

Here's a fairly time- and resource-efficient approach which reads in the values and calculates their averages for all the files in parallel, yet only reads in one line per file at a time -- however it does temporarily read the entire first .dat file into memory in order to determine how many rows and columns of numbers are going to be in each file.
You didn't say if your "numbers" were integer or float or what, so this reads them in as floating point (which will work even if they're not). Regardless, the averages are calculated and output as floating point numbers.
Update
I've modified my original answer to also calculate a population standard deviation (sigma) of the values in each row and column, as per your comment. It does this right after it computes their mean value so a second pass to re-read the all the data isn't necessary. In addition, in response to a suggestion made in the comments, a context manager has been added to ensure that all the input files are get closed.
Note that the standard deviations are only printed and are not written to the output file, but doing that to the same or separate file should to be easy enough to add.
from contextlib import contextmanager
from itertools import izip
from glob import iglob
from math import sqrt
from sys import exit
#contextmanager
def multi_file_manager(files, mode='rt'):
files = [open(file, mode) for file in files]
yield files
for file in files:
file.close()
# generator function to read, convert, and yield each value from a text file
def read_values(file, datatype=float):
for line in file:
for value in (datatype(word) for word in line.split()):
yield value
# enumerate multiple egual length iterables simultaneously as (i, n0, n1, ...)
def multi_enumerate(*iterables, **kwds):
start = kwds.get('start', 0)
return ((n,)+t for n, t in enumerate(izip(*iterables), start))
DATA_FILE_PATTERN = 'data*.dat'
MIN_DATA_FILES = 2
with multi_file_manager(iglob(DATA_FILE_PATTERN)) as datfiles:
num_files = len(datfiles)
if num_files < MIN_DATA_FILES:
print('Less than {} .dat files were found to process, '
'terminating.'.format(MIN_DATA_FILES))
exit(1)
# determine number of rows and cols from first file
temp = [line.split() for line in datfiles[0]]
num_rows = len(temp)
num_cols = len(temp[0])
datfiles[0].seek(0) # rewind first file
del temp # no longer needed
print '{} .dat files found, each must have {} rows x {} cols\n'.format(
num_files, num_rows, num_cols)
means = []
std_devs = []
divisor = float(num_files-1) # Bessel's correction for sample standard dev
generators = [read_values(file) for file in datfiles]
for _ in xrange(num_rows): # main processing loop
for _ in xrange(num_cols):
# create a sequence of next cell values from each file
values = tuple(next(g) for g in generators)
mean = float(sum(values)) / num_files
means.append(mean)
means_diff_sq = ((value-mean)**2 for value in values)
std_dev = sqrt(sum(means_diff_sq) / divisor)
std_devs.append(std_dev)
print 'Average and (standard deviation) of values:'
with open('means.txt', 'wt') as averages:
for i, mean, std_dev in multi_enumerate(means, std_devs):
print '{:.2f} ({:.2f})'.format(mean, std_dev),
averages.write('{:.2f}'.format(mean)) # note std dev not written
if i % num_cols != num_cols-1: # not last column?
averages.write(' ') # delimiter between values on line
else:
print # newline
averages.write('\n')

I am not sure which aspect of the process is giving you the problem, but I will just answer specifically about getting the averages of all the dat files.
Assuming a data structure like this:
72 12 94 79 76 5 30 98 97 48
79 95 63 74 70 18 92 20 32 50
77 88 60 98 19 17 14 66 80 24
...
Getting averages of the files:
import glob
import itertools
avgs = []
for datpath in glob.iglob("*.dat"):
with open(datpath, 'r') as f:
str_nums = itertools.chain.from_iterable(i.strip().split() for i in f)
nums = map(int, str_nums)
avg = sum(nums) / len(nums)
avgs.append(avg)
print avgs
It loops over each .dat file, reads and joins the lines. Converts them to int (could be float if you want) and appends the avg.
If these files are enormous and you are concerned about the amount of memory when reading them in, you could more explicitly loop over each line and only keep counter, the way your original example was doing:
for datpath in glob.iglob("*.dat"):
with open(datpath, 'r') as f:
count = 0
total = 0
for line in f:
nums = [int(i) for i in line.strip().split()]
count += len(nums)
total += sum(nums)
avgs.append(total / count)
Note: I am not handling exceptional cases, such as the file being empty and producing a Divide By Zero situation.

Output speex encode in a file using python

I'm using the script found on this blog Google speech recognition with python (I give any credit to author).
import sys
import pyaudio, speex
import numpy as np # just for doing a standard deviation for audio level checks
import urllib2
import wave
e = speex.Encoder()
e.initialize(speex.SPEEX_MODEID_WB)
d = speex.Decoder()
d.initialize(speex.SPEEX_MODEID_WB)
chunk = 320 # tried other numbers... some don't work
FORMAT = pyaudio.paInt16
bytespersample=2
CHANNELS = 1
RATE = 16000 # "wideband" mode for speex. May work with 8000. Haven't tried it.
p = pyaudio.PyAudio()
# Start the stream to record the audio
stream = p.open(format = FORMAT,
channels = CHANNELS,
rate = RATE,
input = True,
output = True,
frames_per_buffer = chunk)
print "Listening. Recording will start when some sound is heard."
threshold = 200 # Adjust this to be slightly above the noise level of your recordings.
nquit = 40 # number of silent frames before terminating the program
nover = 0
keepgoing = True
spxlist=[] # list of the encoded speex packets/frames
while keepgoing:
data = stream.read(chunk) # grab 320 samples from the microphone
spxdata = e.encode(data) # encode using the speex dll
print "Length encoded: %d"%len(spxdata) # print the length, after encoding. Can't exceed 255!
spxlist.append(spxdata)
a=np.frombuffer(data,np.int16) # convert to numpy array to check for silence or audio
audiolevel=np.std(a)
if audiolevel < threshold: # too quiet
nover+=1
else:
nover=0
if nover >= nquit:
keepgoing=False
print '%2.1f (%d%% quiet)'%(audiolevel, nover*100/nquit)
print "Too quiet. I'm stopping now."
stream.stop_stream()
stream.close()
p.terminate()
fullspx=''.join(spxlist) # make a string of all the header-ized speex packets
out_file = open("test.spx","wb")
out_file.write(fullspx)
out_file.close()
As you can see I slightly modify the script to make it write and output file in .spx, but it dosen't work.
Any advice?
Thanks for your help.
Edit:
I'm running this script under an Ubuntu-linux machine.

how to get wav samples from a wav file?

I want to know how to get samples out of a .wav file in order to perform windowed join of two .wav files.
Can any one please tell how to do this?

The wave module of the standard library is the key: after of course import wave at the top of your code, wave.open('the.wav', 'r') returns a "wave read" object from which you can read frames with the .readframes method, which returns a string of bytes which are the samples... in whatever format the wave file has them (you can determine the two parameters relevant to decomposing frames into samples with the .getnchannels method for the number of channels, and .getsampwidth for the number of bytes per sample).
The best way to turn the string of bytes into a sequence of numeric values is with the array module, and a type of (respectively) 'B', 'H', 'L' for 1, 2, 4 bytes per sample (on a 32-bit build of Python; you can use the itemsize value of your array object to double-check this). If you have different sample widths than array can provide you, you'll need to slice up the byte string (padding each little slice appropriately with bytes worth 0) and use the struct module instead (but that's clunkier and slower, so use array instead if you can).

You can use the wave module. First you should read the metadata, such us sample size or the number of channels. Using the readframes() method, you can read samples, but only as a byte string. Based on the sample format, you have to convert them to samples using struct.unpack().
Alternatively, if you want the samples as an array of floating-point numbers, you can use SciPy's io.wavfile module.

Here's a function to read samples from a wave file (tested with mono & stereo):
def read_samples(wave_file, nb_frames):
frame_data = wave_file.readframes(nb_frames)
if frame_data:
sample_width = wave_file.getsampwidth()
nb_samples = len(frame_data) // sample_width
format = {1:"%db", 2:"<%dh", 4:"<%dl"}[sample_width] % nb_samples
return struct.unpack(format, frame_data)
else:
return ()
And here's the full script that does windowed mixing or concatenating of multiple .wav files. All input files need to have the same params (# of channels and sample width).
import argparse
import itertools
import struct
import sys
import wave
def _struct_format(sample_width, nb_samples):
return {1:"%db", 2:"<%dh", 4:"<%dl"}[sample_width] % nb_samples
def _mix_samples(samples):
return sum(samples)//len(samples)
def read_samples(wave_file, nb_frames):
frame_data = wave_file.readframes(nb_frames)
if frame_data:
sample_width = wave_file.getsampwidth()
nb_samples = len(frame_data) // sample_width
format = _struct_format(sample_width, nb_samples)
return struct.unpack(format, frame_data)
else:
return ()
def write_samples(wave_file, samples, sample_width):
format = _struct_format(sample_width, len(samples))
frame_data = struct.pack(format, *samples)
wave_file.writeframes(frame_data)
def compatible_input_wave_files(input_wave_files):
nchannels, sampwidth, framerate, nframes, comptype, compname = input_wave_files[0].getparams()
for input_wave_file in input_wave_files[1:]:
nc,sw,fr,nf,ct,cn = input_wave_file.getparams()
if (nc,sw,fr,ct,cn) != (nchannels, sampwidth, framerate, comptype, compname):
return False
return True
def mix_wave_files(output_wave_file, input_wave_files, buffer_size):
output_wave_file.setparams(input_wave_files[0].getparams())
sampwidth = input_wave_files[0].getsampwidth()
max_nb_frames = max([input_wave_file.getnframes() for input_wave_file in input_wave_files])
for frame_window in xrange(max_nb_frames // buffer_size + 1):
all_samples = [read_samples(wave_file, buffer_size) for wave_file in input_wave_files]
mixed_samples = [_mix_samples(samples) for samples in itertools.izip_longest(*all_samples, fillvalue=0)]
write_samples(output_wave_file, mixed_samples, sampwidth)
def concatenate_wave_files(output_wave_file, input_wave_files, buffer_size):
output_wave_file.setparams(input_wave_files[0].getparams())
sampwidth = input_wave_files[0].getsampwidth()
for input_wave_file in input_wave_files:
nb_frames = input_wave_file.getnframes()
for frame_window in xrange(nb_frames // buffer_size + 1):
samples = read_samples(input_wave_file, buffer_size)
if samples:
write_samples(output_wave_file, samples, sampwidth)
def argument_parser():
parser = argparse.ArgumentParser(description='Mix or concatenate multiple .wav files')
parser.add_argument('command', choices = ("mix", "concat"), help='command')
parser.add_argument('output_file', help='ouput .wav file')
parser.add_argument('input_files', metavar="input_file", help='input .wav files', nargs="+")
parser.add_argument('--buffer_size', type=int, help='nb of frames to read per iteration', default=1000)
return parser
if __name__ == '__main__':
args = argument_parser().parse_args()
input_wave_files = [wave.open(name,"rb") for name in args.input_files]
if not compatible_input_wave_files(input_wave_files):
print "ERROR: mixed wave files must have the same params."
sys.exit(2)
output_wave_file = wave.open(args.output_file, "wb")
if args.command == "mix":
mix_wave_files(output_wave_file, input_wave_files, args.buffer_size)
elif args.command == "concat":
concatenate_wave_files(output_wave_file, input_wave_files, args.buffer_size)
output_wave_file.close()
for input_wave_file in input_wave_files:
input_wave_file.close()

After reading the samples (for example with the wave module, more details here) you may want to have the values scales between -1 and 1 (this is the convention for audio signals).
In this case, you can add:
# scale to -1.0 -- 1.0
max_nb_bit = float(2**(nb_bits-1))
samples = signal_int / (max_nb_bit + 1.0)
with nb_bits the bit depth and signal_int the integers values.