1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
|
"""
Python-SoXR
https://github.com/dofuuz/python-soxr
SPDX-FileCopyrightText: (c) 2021 Myungchul Keum
SPDX-License-Identifier: LGPL-2.1-or-later
High quality, one-dimensional sample-rate conversion library for Python.
Python-SoXR is a Python wrapper of libsoxr.
"""
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import numpy as np
import pytest
import soxr
@pytest.mark.xfail(raises=ValueError, strict=True)
@pytest.mark.parametrize('in_rate, out_rate', [(100, 0), (100, -1), (0, 100), (-1, 100)])
def test_bad_sr(in_rate, out_rate):
x = np.zeros(100)
soxr.resample(x, in_rate, out_rate)
@pytest.mark.parametrize('dtype', [np.float32, np.float64, np.int16, np.int32])
def test_dtype(dtype):
x = np.random.randn(100).astype(dtype)
y = soxr.resample(x, 100, 200)
assert x.dtype == y.dtype
@pytest.mark.xfail(raises=(TypeError, ValueError), strict=True)
@pytest.mark.parametrize('dtype', [np.complex64, np.complex128, np.int8, np.int64])
def test_bad_dtype(dtype):
x = np.zeros(100, dtype=dtype)
soxr.resample(x, 100, 200)
@pytest.mark.parametrize('in_rate, out_rate', [(44100, 32000), (32000, 44100)])
@pytest.mark.parametrize('dtype', [np.float32, np.float64])
def test_divide_match(in_rate, out_rate, dtype):
x = np.random.randn(25999,2).astype(dtype)
y_oneshot = soxr._resample_oneshot(x, in_rate, out_rate)
y_divide = soxr.resample(x, in_rate, out_rate)
y_split = soxr.resample(np.asfortranarray(x), in_rate, out_rate)
assert np.allclose(y_oneshot, y_divide)
assert np.allclose(y_oneshot, y_split)
@pytest.mark.parametrize('in_rate, out_rate', [(44100, 32000), (32000, 44100)])
@pytest.mark.parametrize('length', [0, 1, 2, 99, 100, 101, 31999, 32000, 32001, 34828, 34829, 34830, 44099, 44100, 44101, 47999, 48000, 48001, 66149, 66150, 266151])
def test_length_match(in_rate, out_rate, length):
x = np.random.randn(266151, 2).astype(np.float32)
y_oneshot = soxr._resample_oneshot(x[:length], in_rate, out_rate)
y_divide = soxr.resample(x[:length], in_rate, out_rate)
y_split = soxr.resample(np.asfortranarray(x)[:length], in_rate, out_rate)
assert np.allclose(y_oneshot, y_divide)
assert np.allclose(y_oneshot, y_split)
@pytest.mark.parametrize('channels', [1, 2, 3, 5, 7, 24, 49])
def test_channel_match(channels):
x = np.random.randn(30011, channels).astype(np.float32)
y_oneshot = soxr._resample_oneshot(x, 44100, 32000)
y_divide = soxr.resample(x, 44100, 32000)
y_split = soxr.resample(np.asfortranarray(x), 44100, 32000)
assert np.allclose(y_oneshot, y_divide)
assert np.allclose(y_oneshot, y_split)
@pytest.mark.parametrize('in_rate, out_rate', [(44100, 32000), (32000, 44100)])
@pytest.mark.parametrize('dtype', [np.float32, np.float64])
@pytest.mark.parametrize('channels', [1, 2])
def test_stream_match(in_rate, out_rate, dtype, channels):
CHUNK_SIZE = 509
x = np.random.randn(49999, channels).astype(dtype)
y_oneshot = soxr._resample_oneshot(x, in_rate, out_rate)
rs_stream = soxr.ResampleStream(in_rate, out_rate, channels, dtype=dtype)
y_list = []
for idx in range(0, len(x), CHUNK_SIZE):
end = idx + CHUNK_SIZE
eof = False
if len(x) <= end:
eof = True
end = len(x)
y_chunk = rs_stream.resample_chunk(x[idx:end], last=eof)
y_list.append(y_chunk)
y_stream = np.concatenate(y_list)
assert np.allclose(y_oneshot, y_stream)
@pytest.mark.parametrize('in_rate, out_rate', [(44100, 32000), (32000, 44100)])
@pytest.mark.parametrize('chunk_size', [7, 50, 101, 44100])
@pytest.mark.parametrize('length', [0, 1, 100, 101, 31999, 32000, 44100, 44101, 266151])
def test_stream_length(in_rate, out_rate, chunk_size, length):
x = np.random.randn(length, 1).astype(np.float32)
y_oneshot = soxr._resample_oneshot(x, in_rate, out_rate)
rs_stream = soxr.ResampleStream(in_rate, out_rate, 1, dtype=np.float32)
y_list = [np.ndarray([0, 1], dtype=np.float32)]
for idx in range(0, len(x), chunk_size):
end = idx + chunk_size
eof = False
if len(x) <= end:
eof = True
end = len(x)
y_chunk = rs_stream.resample_chunk(x[idx:end], last=eof)
y_list.append(y_chunk)
y_stream = np.concatenate(y_list)
assert np.allclose(y_oneshot, y_stream)
def make_tone(freq, sr, duration):
length = int(sr * duration)
sig = np.sin(2 * np.pi * freq / sr * np.arange(length))
sig = sig * np.hanning(length)
return np.stack([sig, np.zeros_like(sig)], axis=-1)
@pytest.mark.parametrize('in_rate,out_rate', [(44100, 22050), (22050, 32000)])
@pytest.mark.parametrize('quality', ['VHQ', 'HQ', 'MQ', 'LQ', 'QQ'])
def test_quality_sine(in_rate, out_rate, quality):
FREQ = 32.0
DURATION = 2.0
x = make_tone(FREQ, in_rate, DURATION)
y = make_tone(FREQ, out_rate, DURATION)
y_pred = soxr.resample(x, in_rate, out_rate, quality=quality)
y_split = soxr.resample(np.asfortranarray(x), in_rate, out_rate, quality=quality)
assert np.allclose(y, y_pred, atol=1e-4)
assert np.allclose(y, y_split, atol=1e-4)
@pytest.mark.parametrize('in_rate,out_rate', [(48000, 24000), (32000, 44100)])
@pytest.mark.parametrize('dtype', [np.int32, np.int16])
def test_int_sine(in_rate, out_rate, dtype):
FREQ = 32.0
DURATION = 2.0
x = (make_tone(FREQ, in_rate, DURATION) * 16384).astype(dtype)
y = (make_tone(FREQ, out_rate, DURATION) * 16384).astype(dtype)
y_pred = soxr.resample(x, in_rate, out_rate)
y_split = soxr.resample(np.asfortranarray(x), in_rate, out_rate)
assert np.allclose(y, y_pred, atol=2)
assert np.allclose(y, y_split, atol=2)
@pytest.mark.parametrize('num_task', [2, 3, 4, 5, 6, 7, 8, 9, 12, 17, 32])
def test_multithread(num_task):
x = np.random.randn(25999, 2).astype(np.float32)
with ThreadPoolExecutor() as p:
results = p.map(
partial(soxr.resample, in_rate=44100, out_rate=32000),
[x] * num_task
)
results = list(results)
assert np.allclose(results[-2], results[-1])
|
