512158047

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travis-pro

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<a href="https://github.com/ubclaunchpad/minutes/commit/<a class=hub.com/ubclaunchpad/minutes/commit/b66ce11a24e79f1998b9958b25471948c51e8f8c">b66ce11a2<a href="https://github.com/ubclaunchpad/minutes/commit/b66ce11a24e79f1998b9958b25471948c51e8f8c">">Merge </a><a class="double-link" href="https://github.com/ubclaunchpad/minutes/commit/<a class="double-link" href="https://github.com/ubclaunchpad/minutes/commit/ff68ea77599c3ec273ac9c6ed501ea004a21aabe">ff68ea775</a>">ff68ea775</a><a href="https://github.com/ubclaunchpad/minutes/commit/b66ce11a24e79f1998b9958b25471948c51e8f8c"> into 4f385fe91">4f385fe91</a>

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69.44

/minutes/audio.py

import glob
import os

import numpy as np
import scipy.signal as signal
import scipy.stats as stats
import soundfile as sf


class Audio:

    def __init__(self, audio_loc):
        if os.path.isdir(audio_loc):
            audio, rates = [], []
            wav_files = glob.glob(audio_loc + '/**/*.wav', recursive=True)

            # Collect all data and concatenate it.
            for file in wav_files:
                data, rate = sf.read(file)
                audio += data,
                rates += rate,

            self.data = np.concatenate(audio)

            # Set rate as mode of rates for now.
            # TODO: Resample audio?
            self.rate = stats.mode(rates)[0][0]
        else:
            self.data, self.rate = sf.read(audio_loc)

    def samples_per_observation(self, ms_per_observation):
        """Converts ms per observation into samples per observation, given
        the internal sample rate of the audio.

        Arguments:
            ms_per_observation {int} -- The number of desired ms per obs.

        Returns:
            {int} -- The samples per observation.
        """
        return int(self.rate * ms_per_observation / 1000.)

    def get_spectrograms(self, ms_per_observation, verbose=False):
        """Converts a internal table of raw audio audio phrases into with
        one spectrogram per row.

        Arguments:
            ms_per_observation {int} -- The number of desired ms per obs.

        Returns:
            np.array -- An array of spectrograms, one per row. The width
            of each spectrogram depends on the ms_per_observation,
            The number of rows depends on the length of the audio file
            and the ms per observations.
        """
        d = self.samples_per_observation(ms_per_observation)
        N = len(self.data) // d

        # Merge stereo if necessary.
        if len(self.data.shape) == 1:
            data = self.data.copy()
        else:
            data = np.mean(self.data, axis=1)

        # Truncate last (partial) observation.
        data = data[:N * d]

        if verbose:
            t = len(self.data) - (N * d)
            print('Truncating {} bytes from end of sample'.format(t))

        # Reshape for processing into spectrograms.
        data = data.reshape((N, d))

        def spec_from_row(row):
            _, _, Sxx = signal.spectrogram(row)
            return Sxx

        # This is very slow! Perhaps some logging?
        rows = (spec_from_row(row) for row in data)
        return np.array([x for x in rows])

1	import glob	1✔
2	import os	1✔
3
4	import numpy as np	1✔
5	import scipy.signal as signal	1✔
6	import scipy.stats as stats	1✔
7	import soundfile as sf	1✔
8
9
10	class Audio:	1✔
11
12	def __init__(self, audio_loc):	1✔
13	if os.path.isdir(audio_loc):	1✔
14	audio, rates = [], []	×
15	wav_files = glob.glob(audio_loc + '/*/.wav', recursive=True)	×
16
17	# Collect all data and concatenate it.
18	for file in wav_files:	×
19	data, rate = sf.read(file)	×
20	audio += data,	×
21	rates += rate,	×
22
23	self.data = np.concatenate(audio)	×
24
25	# Set rate as mode of rates for now.
26	# TODO: Resample audio?
27	self.rate = stats.mode(rates)[0][0]	×
28	else:
29	self.data, self.rate = sf.read(audio_loc)	1✔
30
31	def samples_per_observation(self, ms_per_observation):	1✔
32	"""Converts ms per observation into samples per observation, given
33	the internal sample rate of the audio.
34
35	Arguments:
36	ms_per_observation {int} -- The number of desired ms per obs.
37
38	Returns:
39	{int} -- The samples per observation.
40	"""
41	return int(self.rate * ms_per_observation / 1000.)	1✔
42
43	def get_spectrograms(self, ms_per_observation, verbose=False):	1✔
44	"""Converts a internal table of raw audio audio phrases into with
45	one spectrogram per row.
46
47	Arguments:
48	ms_per_observation {int} -- The number of desired ms per obs.
49
50	Returns:
51	np.array -- An array of spectrograms, one per row. The width
52	of each spectrogram depends on the ms_per_observation,
53	The number of rows depends on the length of the audio file
54	and the ms per observations.
55	"""
56	d = self.samples_per_observation(ms_per_observation)	1✔
57	N = len(self.data) // d	1✔
58
59	# Merge stereo if necessary.
60	if len(self.data.shape) == 1:	1✔
61	data = self.data.copy()	1✔
62	else:
63	data = np.mean(self.data, axis=1)	×
64
65	# Truncate last (partial) observation.
66	data = data[:N * d]	1✔
67
68	if verbose:	1✔
69	t = len(self.data) - (N * d)	×
70	print('Truncating {} bytes from end of sample'.format(t))	×
71
72	# Reshape for processing into spectrograms.
73	data = data.reshape((N, d))	1✔
74
75	def spec_from_row(row):	1✔
76	_, _, Sxx = signal.spectrogram(row)	1✔
77	return Sxx	1✔
78
79	# This is very slow! Perhaps some logging?
80	rows = (spec_from_row(row) for row in data)	1✔
81	return np.array([x for x in rows])	1✔

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