I'm doing a project on Signal Processing in python. So far I've had a little succes with the nonblocking mode, but it gave a considerable amount of delay and clipping to the output.
I want to implement a simple real-time audio filter using Pyaudio and Scipy.Signal, but in the callback function provided in the pyaudio example when I want to read the in_data I can't process it. Tried converting it in various ways but with no success.
Here's a code I want to achieve(read data from mic, filter, and output ASAP):
import pyaudio
import time
import numpy as np
import scipy.signal as signal
WIDTH = 2
CHANNELS = 2
RATE = 44100
p = pyaudio.PyAudio()
b,a=signal.iirdesign(0.03,0.07,5,40)
fulldata = np.array([])
def callback(in_data, frame_count, time_info, status):
data=signal.lfilter(b,a,in_data)
return (data, pyaudio.paContinue)
stream = p.open(format=pyaudio.paFloat32,
channels=CHANNELS,
rate=RATE,
output=True,
input=True,
stream_callback=callback)
stream.start_stream()
while stream.is_active():
time.sleep(5)
stream.stop_stream()
stream.close()
p.terminate()
What is the right way to do this?
Found the answer to my question in the meantime, the callback looks like this:
def callback(in_data, frame_count, time_info, flag):
global b,a,fulldata #global variables for filter coefficients and array
audio_data = np.fromstring(in_data, dtype=np.float32)
#do whatever with data, in my case I want to hear my data filtered in realtime
audio_data = signal.filtfilt(b,a,audio_data,padlen=200).astype(np.float32).tostring()
fulldata = np.append(fulldata,audio_data) #saves filtered data in an array
return (audio_data, pyaudio.paContinue)
I had a similar issue trying to work with the PyAudio callback mode, but my requirements where:
I succeeded after a few tries, and here are fragments of my code (based on the PyAudio example found here):
import pyaudio
import scipy.signal as ss
import numpy as np
import librosa
track1_data, track1_rate = librosa.load('path/to/wav/track1', sr=44.1e3, dtype=np.float64)
track2_data, track2_rate = librosa.load('path/to/wav/track2', sr=44.1e3, dtype=np.float64)
track3_data, track3_rate = librosa.load('path/to/wav/track3', sr=44.1e3, dtype=np.float64)
# instantiate PyAudio (1)
p = pyaudio.PyAudio()
count = 0
IR_left = first_IR_left # Replace for actual IR
IR_right = first_IR_right # Replace for actual IR
# define callback (2)
def callback(in_data, frame_count, time_info, status):
global count
track1_frame = track1_data[frame_count*count : frame_count*(count+1)]
track2_frame = track2_data[frame_count*count : frame_count*(count+1)]
track3_frame = track3_data[frame_count*count : frame_count*(count+1)]
track1_left = ss.fftconvolve(track1_frame, IR_left)
track1_right = ss.fftconvolve(track1_frame, IR_right)
track2_left = ss.fftconvolve(track2_frame, IR_left)
track2_right = ss.fftconvolve(track2_frame, IR_right)
track3_left = ss.fftconvolve(track3_frame, IR_left)
track3_right = ss.fftconvolve(track3_frame, IR_right)
track_left = 1/3 * track1_left + 1/3 * track2_left + 1/3 * track3_left
track_right = 1/3 * track1_right + 1/3 * track2_right + 1/3 * track3_right
ret_data = np.empty((track_left.size + track_right.size), dtype=track1_left.dtype)
ret_data[1::2] = br_left
ret_data[0::2] = br_right
ret_data = ret_data.astype(np.float32).tostring()
count += 1
return (ret_data, pyaudio.paContinue)
# open stream using callback (3)
stream = p.open(format=pyaudio.paFloat32,
channels=2,
rate=int(track1_rate),
output=True,
stream_callback=callback,
frames_per_buffer=2**16)
# start the stream (4)
stream.start_stream()
# wait for stream to finish (5)
while_count = 0
while stream.is_active():
while_count += 1
if while_count % 3 == 0:
IR_left = first_IR_left # Replace for actual IR
IR_right = first_IR_right # Replace for actual IR
elif while_count % 3 == 1:
IR_left = second_IR_left # Replace for actual IR
IR_right = second_IR_right # Replace for actual IR
elif while_count % 3 == 2:
IR_left = third_IR_left # Replace for actual IR
IR_right = third_IR_right # Replace for actual IR
time.sleep(10)
# stop stream (6)
stream.stop_stream()
stream.close()
# close PyAudio (7)
p.terminate()
Here are some important reflections about the code above:
librosa
instead of wave allows me to use numpy arrays for processing which is much better than the chunks of data from wave.readframes
.p.open(format=
must match the format of the ret_data
bytes. And PyAudio works with float32
at most.ret_data
go to the right headphone, and odd index bytes go to the left one.Just to clarify, this code sends the mix of three tracks to the output audio in stereo, and every 10 seconds it changes the impulse response and thus the filter being applied. I used this for testing a 3d audio app I'm developing, and so the impulse responses where Head Related Impulse Responses (HRIRs), that changed the position of the sound every 10 seconds.
EDIT:
This code had a problem: the output had a noise of a frequency corresponding to the size of the frames (higher frequency when size of frames was smaller). I fixed that by manually doing an overlap and add of the frames. Basically, the ss.oaconvolve
returned an array of size track_frame.size + IR.size - 1
, so I separated that array into the first track_frame.size
elements (which was then used for ret_data
), and then the last IR.size - 1
elements I saved for later. Those saved elements would then be added to the first IR.size - 1
elements of the next frame. The first frame adds zeros.
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