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74.29

/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

# Parameters for preprocessing audio files.
PREPROCESSING_PARAMS = {'ms_per_observation'}


class Audio:
    """Internal audio manipulation class. I reserve the right to change this
    API :)
    """

    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_observations(self, ms_per_observation):
        """Converts a internal raw audio vector into table with
        one spectrogram per row. Also returns raw observations.

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

        Returns:
            raw -- The raw audio observation table.
            processed -- An array of spectrograms, one per row. The width
            of each spectrogram depends on the ms_per_observation.
        """
        # Reshape for processing into spectrograms.
        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]

        # Reshape for processing into spectrograms.
        raw = 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 raw)
        processed = np.array([x for x in rows])
        return raw, processed

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	# Parameters for preprocessing audio files.
10	PREPROCESSING_PARAMS = {'ms_per_observation'}	1✔
11
12
13	class Audio:	1✔
14	"""Internal audio manipulation class. I reserve the right to change this
15	API :)
16	"""
17
18	def __init__(self, audio_loc):	1✔
19	if os.path.isdir(audio_loc):	1✔
20	audio, rates = [], []	×
21	wav_files = glob.glob(audio_loc + '/*/.wav', recursive=True)	×
22
23	# Collect all data and concatenate it.
24	for file in wav_files:	×
25	data, rate = sf.read(file)	×
26	audio += data,	×
27	rates += rate,	×
28
29	self.data = np.concatenate(audio)	×
30
31	# Set rate as mode of rates for now.
32	# TODO: Resample audio?
33	self.rate = stats.mode(rates)[0][0]	×
34	else:
35	self.data, self.rate = sf.read(audio_loc)	1✔
36
37	def samples_per_observation(self, ms_per_observation):	1✔
38	"""Converts ms per observation into samples per observation, given
39	the internal sample rate of the audio.
40
41	Arguments:
42	ms_per_observation {int} -- The number of desired ms per obs.
43
44	Returns:
45	{int} -- The samples per observation.
46	"""
47	return int(self.rate * ms_per_observation / 1000.)	1✔
48
49	def get_observations(self, ms_per_observation):	1✔
50	"""Converts a internal raw audio vector into table with
51	one spectrogram per row. Also returns raw observations.
52
53	Arguments:
54	ms_per_observation {int} -- The number of desired ms per obs.
55
56	Returns:
57	raw -- The raw audio observation table.
58	processed -- An array of spectrograms, one per row. The width
59	of each spectrogram depends on the ms_per_observation.
60	"""
61	# Reshape for processing into spectrograms.
62	d = self.samples_per_observation(ms_per_observation)	1✔
63	N = len(self.data) // d	1✔
64
65	# Merge stereo if necessary.
66	if len(self.data.shape) == 1:	1✔
67	data = self.data.copy()	1✔
68	else:
69	data = np.mean(self.data, axis=1)	×
70
71	# Truncate last (partial) observation.
72	data = data[:N * d]	1✔
73
74	# Reshape for processing into spectrograms.
75	raw = data.reshape((N, d))	1✔
76
77	def spec_from_row(row):	1✔
78	_, _, Sxx = signal.spectrogram(row)	1✔
79	return Sxx	1✔
80
81	# This is very slow! Perhaps some logging?
82	rows = (spec_from_row(row) for row in raw)	1✔
83	processed = np.array([x for x in rows])	1✔
84	return raw, processed	1✔

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