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60 lines
2.4 KiB
Python
60 lines
2.4 KiB
Python
2 years ago
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# paura_lite:
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# An ultra-simple command-line audio recorder with real-time
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# spectrogram visualization
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import numpy as np
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import pyaudio
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import struct
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import scipy.fftpack as scp
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import termplotlib as tpl
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import os
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# get window's dimensions
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#rows, columns = os.popen('stty size', 'r').read().split()
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rows = int(os.popen('mode con | findstr Lines','r').read().strip('\n').strip(' ').split(':')[1].strip(' '))
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columns = int(os.popen('mode con | findstr Columns','r').read().strip('\n').strip(' ').split(':')[1].strip(' '))
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buff_size = 0.2 # window size in seconds
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wanted_num_of_bins = 40 # number of frequency bins to display
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# initialize soundcard for recording:
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fs = 8000
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pa = pyaudio.PyAudio()
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stream = pa.open(format=pyaudio.paInt16, channels=1, rate=fs,
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input=True, frames_per_buffer=int(fs * buff_size))
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while 1: # for each recorded window (until ctr+c) is pressed
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# get current block and convert to list of short ints,
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block = stream.read(int(fs * buff_size))
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format = "%dh" % (len(block) / 2)
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shorts = struct.unpack(format, block)
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# then normalize and convert to numpy array:
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x = np.double(list(shorts)) / (2**15)
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seg_len = len(x)
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# get total energy of the current window and compute a normalization
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# factor (to be used for visualizing the maximum spectrogram value)
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energy = np.mean(x ** 2)
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max_energy = 0.02 # energy for which the bars are set to max
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max_width_from_energy = int((energy / max_energy) * int(columns)) + 1
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if max_width_from_energy > int(columns) - 10:
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max_width_from_energy = int(columns) - 10
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# get the magnitude of the FFT and the corresponding frequencies
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X = np.abs(scp.fft(x))[0:int(seg_len/2)]
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freqs = (np.arange(0, 1 + 1.0/len(X), 1.0 / len(X)) * fs / 2)
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# ... and resample to a fix number of frequency bins (to visualize)
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wanted_step = (int(freqs.shape[0] / wanted_num_of_bins))
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freqs2 = freqs[0::wanted_step].astype('int')
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X2 = np.mean(X.reshape(-1, wanted_step), axis=1)
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# plot (freqs, fft) as horizontal histogram:
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fig = tpl.figure()
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fig.barh(X2, labels=[str(int(f)) + " Hz" for f in freqs2[0:-1]],
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show_vals=False, max_width=max_width_from_energy)
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fig.show()
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# add exactly as many new lines as they are needed to
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# fill clear the screen in the next iteration:
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print("\n" * (int(rows) - freqs2.shape[0] - 1))
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