Simple Python audio player with matplotlib and pyaudio

audioplayer.py

""" Play an audio file with pyaudio while concurrently showing audio playhead 
on a matplotlib plot of the audio time series and spectrogram.

Adjust duration and filename in the script below to reflect your audio file.

v. 0.1
21 Mar 2012 
Eric Bruning
"""

import matplotlib
# matplotlib.use('MacOSX')

import pyaudio
import wave
import sys
import numpy as np

import multiprocessing

duration = 12.5
filename = '/data/AveFormosissima.wav'

wf = wave.open(filename, 'rb')

def dB(a,base=1.0):
    return 10.0*np.log10(a/base)

fs = wf.getframerate()
bytes_per_sample = wf.getsampwidth()
bits_per_sample  = bytes_per_sample * 8
dtype = 'int{0}'.format(bits_per_sample)
channels = wf.getnchannels()

def audiostream(queue, n_channels, sampling, n_bytes_per_sample):
    # open stream
    p = pyaudio.PyAudio()
    
    stream = p.open(format =
                    p.get_format_from_width(n_bytes_per_sample),
                    channels = n_channels,
                    rate = sampling,
                    output = True)
    
    
    while True:
        data = queue.get()
        print "input latency: {0}".format(stream.get_input_latency())
        print "output latency: {0}".format(stream.get_output_latency())
        print "avail read: {0}".format(stream.get_read_available())
        print "avail write: {0}".format(stream.get_write_available())
        if data == 'Stop':
            break
        stream.write(data)
    stream.close()

Q = multiprocessing.Queue()
audio_process = multiprocessing.Process(target=audiostream, args=(Q,channels,fs,bytes_per_sample))
audio_process.start()
# audio_process.join()
# stream = Q.get()
# audio_process.join()

# read data
audio = np.fromstring(wf.readframes(int(duration*fs*bytes_per_sample/channels)), dtype=dtype)
audio.shape = (audio.shape[0]/channels, channels)

ch_left = 0
ch_right = 1

ch = ch_right

audio_fft = np.fft.fft(audio[:,ch])
freqs = np.fft.fftfreq(audio[:,ch].shape[0], 1.0/fs) / 1000.0
max_freq_kHz = freqs.max()
times = np.arange(audio.shape[0]) / float(fs)
fftshift = np.fft.fftshift

import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8.5,11))
ax_spec_gram = fig.add_subplot(311)
ax_fft  = fig.add_subplot(312)
ax_time = fig.add_subplot(313)

ax_spec_gram.specgram(audio[:,ch], Fs=fs, cmap='gist_heat')
ax_spec_gram.set_xlim(0,duration)
ax_spec_gram.set_ylim(0,max_freq_kHz*1000.0)
ax_spec_gram.set_ylabel('Frequency (Hz)')

ax_fft.plot(fftshift(freqs), fftshift(dB(audio_fft)))
ax_fft.set_xlim(0,max_freq_kHz)
ax_fft.set_xlabel('Frequency (kHz)')
ax_fft.set_ylabel('dB')

ax_time.plot(times, audio[:,ch])
ax_time.set_xlabel('Time (s)')
ax_time.set_xlim(0,duration)
ax_time.set_ylim(-32768,32768)

time_posn, = ax_time.plot([0,0], [-32768,32768], 'k')
spec_posn, = ax_spec_gram.plot([0,0], [0, max_freq_kHz*1000.0], 'k')


class AudioSubsetter(object):
    def __init__(self, audio_array, audio_device_queue, n_channels, sampling_rate, n_bytes_per_sample, chunk_dt=0.1):
        self.last_chunk = -1
        self.queue = audio_device_queue
        self.audio_dat = audio_array.tostring()
        self.to_t = 1.0/(sampling_rate*n_channels*n_bytes_per_sample)
        chunk = int(chunk_dt*fs)*channels*bytes_per_sample
        self.chunk0 = np.arange(0, len(self.audio_dat), chunk, dtype=int)
        self.chunk1 = self.chunk0 + chunk
        
    def update(self, *args):
        """ Timer callback for audio position indicator. Called with """
        self.last_chunk +=1
        if self.last_chunk >= len(self.chunk0):
            # self.queue.put("Stop")
            self.last_chunk = 0
        
        i = self.last_chunk
        i0, i1 = self.chunk0[i], self.chunk1[i]
        self.queue.put(self.audio_dat[i0:i1])
        t0, t1 = i0*self.to_t, i1*self.to_t
        print t0, t1
        for line_artist in args:
            line_artist.set_xdata([t1,t1])
        args[0].figure.canvas.draw()
        

print "Setting up audio process"
dt = .5
playhead = AudioSubsetter(audio, Q, channels, fs, bytes_per_sample, chunk_dt=dt)
timer = fig.canvas.new_timer(interval=dt*1000.0)
timer.add_callback(playhead.update, spec_posn, time_posn)
timer.start()

plt.show()

Amazing code, how do you stop it? whenever I close the plotting window the process is still running.
I haven't check why I can't listen to the sound while the bar runs through the image.
I also had some issues with the audio file address but easy to fix.

""" Play an audio file with pyaudio while concurrently showing audio playhead 
on a matplotlib plot of the audio time series and spectrogram.

Adjust duration and filename in the script below to reflect your audio file.

v. 0.1
21 Mar 2012 
Eric Bruning
"""

import matplotlib
# matplotlib.use('MacOSX')

import pyaudio
import wave
import sys
import numpy as np

import multiprocessing

duration = 12.5
filename = ('C:/Users/...../audio_file.wav', '')
filename=list(filename)
filename=str(filename[0])
wf = wave.open(filename, 'rb')

def dB(a,base=1.0):
    return 10.0*np.log10(a/base)

fs = wf.getframerate()
bytes_per_sample = wf.getsampwidth()
bits_per_sample  = bytes_per_sample * 8
dtype = 'int{0}'.format(bits_per_sample)
channels = wf.getnchannels()

def audiostream(queue, n_channels, sampling, n_bytes_per_sample):
    # open stream
    p = pyaudio.PyAudio()
    
    stream = p.open(format =
                    p.get_format_from_width(n_bytes_per_sample),
                    channels = n_channels,
                    rate = sampling,
                    output = True)
    
    
    while True:
        data = queue.get()
        print ("input latency: {0}".format(stream.get_input_latency()))
        print ("output latency: {0}".format(stream.get_output_latency()))
        print ("avail read: {0}".format(stream.get_read_available()))
        print ("avail write: {0}".format(stream.get_write_available()))
        if data == 'Stop':
            break
        stream.write(data)
    stream.close()

Q = multiprocessing.Queue()
audio_process = multiprocessing.Process(target=audiostream, args=(Q,channels,fs,bytes_per_sample))
audio_process.start()
# audio_process.join()
# stream = Q.get()
# audio_process.join()

# read data
audio = np.fromstring(wf.readframes(int(duration*fs*bytes_per_sample/channels)), dtype=dtype)
audio.shape = (int(audio.shape[0]/channels), channels)

ch_left = 0
ch_right = 1

ch = ch_left

audio_fft = np.fft.fft(audio[:,ch])
freqs = np.fft.fftfreq(audio[:,ch].shape[0], 1.0/fs) / 1000.0
max_freq_kHz = freqs.max()
times = np.arange(audio.shape[0]) / float(fs)
fftshift = np.fft.fftshift

import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8.5,11))
ax_spec_gram = fig.add_subplot(311)
ax_fft  = fig.add_subplot(312)
ax_time = fig.add_subplot(313)

ax_spec_gram.specgram(audio[:,ch], Fs=fs, cmap='gist_heat')
ax_spec_gram.set_xlim(0,duration)
ax_spec_gram.set_ylim(0,max_freq_kHz*1000.0)
ax_spec_gram.set_ylabel('Frequency (Hz)')

ax_fft.plot(fftshift(freqs), fftshift(dB(audio_fft)))
ax_fft.set_xlim(0,max_freq_kHz)
ax_fft.set_xlabel('Frequency (kHz)')
ax_fft.set_ylabel('dB')

ax_time.plot(times, audio[:,ch])
ax_time.set_xlabel('Time (s)')
ax_time.set_xlim(0,duration)
ax_time.set_ylim(-32768,32768)

time_posn, = ax_time.plot([0,0], [-32768,32768], 'k')
spec_posn, = ax_spec_gram.plot([0,0], [0, max_freq_kHz*1000.0], 'k')


class AudioSubsetter(object):
    def __init__(self, audio_array, audio_device_queue, n_channels, sampling_rate, n_bytes_per_sample, chunk_dt=0.1):
        self.last_chunk = -1
        self.queue = audio_device_queue
        self.audio_dat = audio_array.tostring()
        self.to_t = 1.0/(sampling_rate*n_channels*n_bytes_per_sample)
        chunk = int(chunk_dt*fs)*channels*bytes_per_sample
        self.chunk0 = np.arange(0, len(self.audio_dat), chunk, dtype=int)
        self.chunk1 = self.chunk0 + chunk
        
    def update(self, *args):
        """ Timer callback for audio position indicator. Called with """
        self.last_chunk +=1
        if self.last_chunk >= len(self.chunk0):
            # self.queue.put("Stop")
            self.last_chunk = 0
        
        i = self.last_chunk
        i0, i1 = self.chunk0[i], self.chunk1[i]
        self.queue.put(self.audio_dat[i0:i1])
        t0, t1 = i0*self.to_t, i1*self.to_t
        print (t0, t1)
        for line_artist in args:
            line_artist.set_xdata([t1,t1])
        args[0].figure.canvas.draw()
        

print ("Setting up audio process")
dt = .5
playhead = AudioSubsetter(audio, Q, channels, fs, bytes_per_sample, chunk_dt=dt)
timer = fig.canvas.new_timer(interval=dt*1000.0)
timer.add_callback(playhead.update, spec_posn, time_posn)
timer.start()

plt.show()

You should set OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES if you cannot listen to the audio.