What I'm trying to do is setup 16 analog input channels, sample them constantly at a given rate and read 1 sample from each channel when calling the read function. Ideally I would like to read the newest sample so I can timestamp it when reading.
The problem is that the readings do not change from read to read, only after a few seconds. If I adjust the sampling speed, I can get to a situation where I get an error saying the software can't keep up with the hardware sampling rate.
Which part of my code is wrong?
import numpy
import nidaqmx
from nidaqmx.stream_readers import AnalogSingleChannelReader, AnalogMultiChannelReader
from nidaqmx.constants import Edge, AcquisitionType
# Create a task and a reader
task = nidaqmx.Task()
values_read = numpy.zeros(16, dtype = numpy.float64)
task.ai_channels.add_ai_current_chan('cDAQ1Mod2/ai0:15')
task.timing.cfg_samp_clk_timing(rate = 1000, active_edge = Edge.RISING, sample_mode = AcquisitionType.CONTINUOUS, samps_per_chan = 1)
reader = AnalogMultiChannelReader(task.in_stream)
task.start()
while 1:
reader.read_one_sample(values_read)
print(values_read)
The sampling rate is 1000 but you are reading only one sample each time. Usually, each Read call takes a few milliseconds. You are not reading fast enough hence the buffer overflow error.
Suggestions:
Reduce sample rate.
Read more samples per Read call.
Since you want to read only the latest data and timestamp yourself, you can use the On Demand software timed acquisition. See example ai_voltage_sw_timed.py
Related
The Question
I want to load an audio file of any type (mp3, m4a, flac, etc) and write it to an output stream.
I tried using pydub, but it loads the entire file at once which takes forever and runs out of memory easily.
I also tried using python-vlc, but it's been unreliable and too much of a black box.
So, how can I open large audio files chunk-by-chunk for streaming?
Edit #1
I found half of a solution here, but I'll need to do more research for the other half.
TL;DR: Use subprocess and ffmpeg to convert the file to wav data, and pipe that data into np.frombuffer. The problem is, the subprocess still has to finish before frombuffer is used.
...unless it's possible to have the pipe written to on 1 thread while np reads it from another thread, which I haven't tested yet. For now, this problem is not solved.
I think the python package https://github.com/irmen/pyminiaudio can be of helpful. You can stream an audio file like this
import miniaudio
audio_path = "my_audio_file.mp3"
target_sampling_rate = 44100 #the input audio will be resampled a this sampling rate
n_channels = 1 #either 1 or 2
waveform_duration = 30 #in seconds
offset = 15 #this means that we read only in the interval [15s, duration of file]
waveform_generator = miniaudio.stream_file(
filename = audio_path,
sample_rate = target_sampling_rate,
seek_frame = int(offset * target_sampling_rate),
frames_to_read = int(waveform_duration * target_sampling_rate),
output_format = miniaudio.SampleFormat.FLOAT32,
nchannels = n_channels)
for waveform in waveform_generator:
#do something with the waveform....
I know for sure that this works on mp3, ogg, wav, flac but for some reason it does not on mp4/acc and I am actually looking for a way to read mp4/acc
I'm using pyaudio to take input from a microphone or read a wav file, and analyze the stream while playing it. I want to only analyze the right channel if the input is stereo. I've been able to extract the data and convert to integers using loops:
levels = []
length = len(data)
if channels == 1:
for i in range(length//2):
volume = abs(struct.unpack('<h', data[i:i+2])[0])
levels.append(volume)
elif channels == 2:
for i in range(length//4):
j = 4 * i + 2
volume = abs(struct.unpack('<h', data[j:j+2])[0])
levels.append(volume)
I think this working correctly, I know it runs without error on a laptop and Raspberry Pi 3, but it appears to consume too much time to run on a Raspberry Pi Zero when simultaneously streaming the output to a speaker. I figure that eliminating the loop and using numpy may help. I assume I need to use np.ndarray to do this, and the first parameter will be (CHUNK,) where CHUNK is my chunk size for analyzing the audio (I'm using 1024). And the format would be '<h', as in the struct code above, I think. But I'm at a loss as to how to code it correctly for each of the two cases (mono and right channel only for stereo). How do I create the numpy arrays for each of the two cases?
You are here reading 16-bit integers from a binary file. It seems that you are first reading the data into data variable with something like data = f.read(), which is here not visible. Then you do:
for i in range(length//2):
volume = abs(struct.unpack('<h', data[i:i+2])[0])
levels.append(volume)
BTW, that code is wrong, it shoud be abs(struct.unpack('<h', data[2*i:2*i+2])[0]), otherwise you are overlapping bytes from different values.
To do the same with numpy, you should just do this (instead of both f.read()and the whole loop):
data = np.fromfile(f, dtype='<i2')
This is over 100 times faster than the manual thing above in my test on 5 MB of data.
In the second case, you have interleaved left-right-left-right values. Again you can read them all (assuming you have enough memory) and then access only one half:
data = np.fromfile(f, dtype='<i2')
left = data[::2]
right = data[1::2]
This processes everything, even though you need just one half, but it is still much much faster.
EDIT: If the data not coming from a file, np.fromfile can be replaced with np.frombuffer. Then you have this:
channel_data = np.frombuffer(data, dtype='<i2')
if channels == 2:
channel_data = channel_data[1::2]
levels = np.abs(channel_data)
I'm just trying to change the sampling rate of a audio dataset (in .wav format) from 32000Hz to 44100Hz but, doing this, the playback speed is changed too (very acceptable and logical).
My point is, how can i do this change without modify the playback speed?
Currenctly i'm using the Wave lib to do this. Look the follow code:
import wave
# First i open the file.wav on read mode
opened_audio = wave.open(caminho, 'rb')
# Then i get all information that will be maintained
channels = opened_audio.getnchannels()
swidth = opened_audio.getsampwidth()
signal = opened_audio.readframes(-1)
opened_audio.close()
# Here i open the file.wav on write mode
wf = wave.open(caminho, 'wb')
# Then i set all obtained data that will not be changed
wf.setnchannels(channels)
wf.setsampwidth(swidth)
# Here is the new sampling rate in Hz
wf.setframerate(44100)
wf.writeframes(signal)
wf.close()
Detail: i already read many posts here but no one resolve the problem.
What shall be evaluated and achieved:
I try to record audio data with a minimum of influence by hard- and especially software. After using Adobe Audition for some time I stumbled across PyAudio and was driven by curiosity as well as the possibility to refresh my Python knowledge.
As the fact displayed in the headline above may have given away I compared the sample values of two wave files (indeed sections of them) and had to find out that both programmes produce different output.
As I am definitely at my wit`s end, I do hope to find someone who could help me.
What has been done so far:
An M-Audio “M-Track Two-Channel USB Interface” has been used to record Audio Data with Audition CS6 and PyAudio simultaneously as the following steps are executed in the given order…
Audition is prepared for recording by opening “Prefrences/ Audio Hardware” and selecting the audio interface, a sample rate of 48 kHz and a latency of 250 ms (this value has been examined thoughout the last years as to be the second lowest I can get without getting the warning for lost samples – if I understood the purpose correctly I just have to worry about loosing samples cause monitoring is not an issue).
A new file with one channel, a sample rate of 48 kHz and a bit depth of 24 bit is opened.
The Python code (displayed below) is started and leads to a countdown being used to change over to Audition and start the recording 10 s before Python starts its.)
Wait until Python prints the “end of programme” message.
Stop and save the data recorded by Audition.
Now data has to be examined:
Both files (one recorded by Audition and Python respectively) are opened in Audition (Multitrack session). As Audition was started and terminated manually the two files have completely different beginning and ending times. Then they are aligned visually so that small extracts (which visually – by waveform shape – contain the same data) can be cut out and saved.
A Python programme has been written opening, reading and displaying the sample values using the default wave module and matplotlib.pyplot respectively (graphs are shown below).
Differences in both waveforms and a big question mark are revealed…
Does anybody have an idea why Audition is showing different sample values and specifically where precisely the mistake (is there any?) hides??
some (interesting) observations
a) When calling the pyaudio.PyAudio().get_default_input_device_info() method the default sample rate is listed as 44,1 kHz even though the default M-Track sample rate is said to be 48 kHz by its specifications (indeed Audition recognizes the 48 kHz by resampling incoming data if another rate was selected). Any ideas why and how to change this?
b) Aligning both files using the beginning of the sequence covered by PyAudio and checking whether they are still “in phase” at the end reveals no – PyAudio is shorter and seems to have lost samples (even though no exception was raised and the “exception on overflow” argument is “True”)
c) Using the “frames_per_buffer” keyword in the stream open method I was unable to align both files, having no idea where Python got its data from.
d) Using the “.get_default_input_device_info()” method and trying different sample rates (22,05 k, 44,1 k, 48 k, 192 k) I always receive True as an output.
Official Specifications M-Track:
bit depth = 24 bit
sample rate = 48 kHz
input via XLR
output via USB
Specifications Computer and Software:
Windows 8.1
I5-3230M # 2,6 GHz
8 GB RAM
Python 3.4.2 with PyAudio 0.2.11 – 32 bit
Audition CS6 Version 5.0.2
Python Code
import pyaudio
import wave
import time
formate = pyaudio.paInt24
channels = 1
framerate = 48000
fileName = 'test ' + '.wav'
chunk = 6144
# output of stream.get_read_available() at different positions
p = pyaudio.PyAudio()
stream = p.open(format=formate,
channels=channels,
rate=framerate,
input=True)
#frames_per_buffer=chunk) # observation c
# COUNTDOWN
for n in range(0, 30):
print(n)
time.sleep(1)
# get data
sampleList = []
for i in range(0, 79):
data = stream.read(chunk, exception_on_overflow = True)
sampleList.append(data)
print('end -', time.strftime('%d.%m.%Y %H:%M:%S', time.gmtime(time.time())))
stream.stop_stream()
stream.close()
p.terminate()
# produce file
file = wave.open(fileName, 'w')
file.setnchannels(channels)
file.setframerate(framerate)
file.setsampwidth(p.get_sample_size(formate))
file.writeframes(b''.join(sampleList))
file.close()
Figure 1: first comparison Audition – PyAudio
image 1
Figure 2: second comparison Audition - Pyaudio
image 2
I am interested in reading gyroscope data by RaspberryPi and Python but I am confused about how to set sample rate of the MPU6050 (accelerometer, gyroscope;datasheet MPU6050) according to I2C clock frequency in order to avoid wrong reading data (for example reading while there is not data in the buffer or reading faster that writing, and so on), in the knowledge that each measure is a 16 bit word.
Is there a relationship between the two clocks?
I did a project with that same chip about 18 months ago. I haven't touched the PI since then, so I don't know how things may have changed in the meantime. In any event, I used the smbus to read the chip. A few things I found out the hard way, reading individual registers was very slow compared to the i2c block read. Also, you had to turn off sleep mode. Sorry I don't have any info on the clock timing, but if you are just trying to get a good read loop, this might help. You don't have to use numpy, but if you plan to manipulate your samples, it's quite helpful. GL/HF.
import smbus
import numpy
# initialize
bus = smbus.SMBus(1)
# turn off sleep mode
bus.write_byte_data(0x68,0x6B,0x00)
# reading in data (this can be in a loop or function call)
sample = numpy.empty(7)
r = bus.read_i2c_block_data(0x68, 0x3B, 14)
sample[0] = (r[0]<<8)+r[1]
sample[1] = (r[2]<<8)+r[3]
sample[2] = (r[4]<<8)+r[5]
sample[3] = (r[6]<<8)+r[7]
sample[4] = (r[8]<<8)+r[9]
sample[5] = (r[10]<<8)+r[11]
sample[6] = (r[12]<<8)+r[13]