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speech-scoring
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refactored spectrogram and implemented record and generate spectrogram

master
Malar Kannan 2017-10-25 13:37:17 +05:30
parent 82d0398d2c
commit a8f17ef764
2 changed files with 51 additions and 63 deletions
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51
record_mic_speech.py
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@ -1,55 +1,36 @@
import pyaudio
import numpy as np
from matplotlib import pyplot as plt
# from matplotlib import pyplot as plt
from spectro_gen import plot_stft
CHUNKSIZE = 44100 * 10 # fixed chunk size
SAMPLE_RATE = 22050
N_SEC = 1.5
CHUNKSIZE = int(SAMPLE_RATE * N_SEC) # fixed chunk size
# initialize portaudio
p_inp = pyaudio.PyAudio()
# dev_n = p.get_device_count()
# dev_infos = [p.get_device_info_by_index(index) for index in range(dev_n)]
# [i for i in dev_infos] # if i['name'] == 'record']
stream = p_inp.open(
format=pyaudio.paInt24,
format=pyaudio.paFloat32,
channels=2,
rate=44100,
rate=SAMPLE_RATE,
input=True,
frames_per_buffer=CHUNKSIZE)
# do this as long as you want fresh samples
data = stream.read(CHUNKSIZE)
len(data)
CHUNKSIZE*10
numpydata = np.fromstring(data, dtype=np.int16)
numpydata = np.frombuffer(data, dtype=np.float32)
multi_channel = np.abs(np.reshape(numpydata, (-1, 2))).mean(axis=1)
one_channel = np.asarray([multi_channel, -1 * multi_channel]).T.reshape(-1)
mean_channel_data = one_channel.tobytes()
plot_stft(one_channel, SAMPLE_RATE)
# plt.plot(one_channel)
# plt.show()
# plot data
plt.plot(numpydata)
plt.show()
# close stream
stream.stop_stream()
stream.close()
p_inp.terminate()
# open the file for reading.
# wf = wave.open(sys.argv[1], 'rb')
# create an audio object
# p = pyaudio.PyAudio()
# open stream based on the wave object which has been input.
p_oup = pyaudio.PyAudio()
stream = p_oup.open(
format=pyaudio.paInt24, channels=2, rate=44100, output=True)
# read data (based on the chunk size)
# data = wf.readframes(CHUNKSIZE)
# play stream (looping from beginning of file to the end)
# while data != '':
# writing to the stream is what *actually* plays the sound.
stream.write(data)
# data = wf.readframes(chunk)
# cleanup stuff.
format=pyaudio.paFloat32, channels=2, rate=SAMPLE_RATE, output=True)
stream.write(mean_channel_data)
stream.close()
p_oup.terminate()

63
spectro_gen.py
View File

@ -7,6 +7,7 @@
"""
# %matplotlib inline
import numpy as np
import pyaudio
from matplotlib import pyplot as plt
from pysndfile import sndio as snd
from numpy.lib import stride_tricks
@ -70,32 +71,25 @@ def logscale_spec(spec, sr=44100, factor=20.):
""" generate spectrogram for aiff audio with 150ms windows and 50ms overlap"""
def generate_aiff_spectrogram(audiopath):
samples, samplerate, _ = snd.read(audiopath)
def generate_spectrogram(samples, samplerate):
# samplerate, samples = wav.read(audiopath)
# s = stft(samples, binsize)
s = stft(samples, samplerate * 150 // 1000, 1.0 / 3)
sshow, freq = logscale_spec(s, factor=1.0, sr=samplerate)
ims = 20. * np.log10(np.abs(sshow) / 10e-6)
return ims, freq
def generate_aiff_spectrogram(audiopath):
samples, samplerate, _ = snd.read(audiopath)
ims, _ = generate_spectrogram(samples, samplerate)
return ims
""" plot spectrogram"""
def plotstft(audiopath, binsize=2**10, plotpath=None, colormap="jet"):
samples, samplerate, _ = snd.read(audiopath)
# samplerate, samples = wav.read(audiopath)
# s = stft(samples, binsize)
# print(samplerate*150//1000)
s = stft(samples, samplerate * 150 // 1000, 1.0 / 3)
sshow, freq = logscale_spec(s, factor=1.0, sr=samplerate)
ims = 20. * np.log10(np.abs(sshow) / 10e-6) # amplitude to decibel
def plot_stft(samples, samplerate, binsize=2**10, plotpath=None, colormap="jet"):
(ims, freq) = generate_spectrogram(samples, samplerate)
timebins, freqbins = np.shape(ims)
# import pdb;pdb.set_trace()
plt.figure(figsize=(15, 7.5))
plt.imshow(
np.transpose(ims),
@ -118,24 +112,37 @@ def plotstft(audiopath, binsize=2**10, plotpath=None, colormap="jet"):
])
ylocs = np.int16(np.round(np.linspace(0, freqbins - 1, 10)))
plt.yticks(ylocs, ["%.02f" % freq[i] for i in ylocs])
if plotpath:
plt.savefig(plotpath, bbox_inches="tight")
else:
plt.show()
plt.clf()
snd.get_info('./outputs/sunflowers-Alex-150-normal-589.aiff')
snd_data_arr = snd.read('./outputs/sunflowers-Alex-150-normal-589.aiff')[0]
snd_data = snd_data_arr.tobytes()
snd_data_arr.dtype
len(snd_data)
def plot_aiff_stft(audiopath, binsize=2**10, plotpath=None, colormap="jet"):
samples, samplerate, _ = snd.read(audiopath)
plot_stft(samples, samplerate)
def play_sunflower():
sample_r = snd.get_info('./outputs/sunflowers-Alex-150-normal-589.aiff')[0]
snd_data_f64 = snd.read('./outputs/sunflowers-Alex-150-normal-589.aiff')[0]
snd_data_f32 = snd_data_f64.astype(np.float32)
snd_data_f32.shape
snd_data = snd_data_f32.tobytes()
p_oup = pyaudio.PyAudio()
stream = p_oup.open(
format=pyaudio.paFloat32, channels=1, rate=sample_r, output=True)
stream.write(snd_data)
stream.close()
p_oup.terminate()
plot_stft(snd_data_f32, sample_r)
if __name__ == '__main__':
plotstft('./outputs/sunflowers-Alex-150-normal-589.aiff')
plotstft('./outputs/sunflowers-Alex-180-normal-4763.aiff')
plotstft('./outputs/sunflowers-Victoria-180-normal-870.aiff')
plotstft('./outputs/sunflowers-Fred-180-phoneme-9733.aiff')
plotstft('./outputs/sunflowers-Fred-180-normal-6515.aiff')
play_sunflower()
# plot_aiff_stft('./outputs/sunflowers-Alex-150-normal-589.aiff')
# plot_aiff_stft('./outputs/sunflowers-Alex-180-normal-4763.aiff')
# plot_aiff_stft('./outputs/sunflowers-Victoria-180-normal-870.aiff')
# plot_aiff_stft('./outputs/sunflowers-Fred-180-phoneme-9733.aiff')
# plot_aiff_stft('./outputs/sunflowers-Fred-180-normal-6515.aiff')