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import warnings
import torch
import torchaudio.transforms as T
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
get_asset_path,
get_whitenoise,
load_wav,
save_wav,
skipIfNoExec,
skipIfNoSox,
sox_utils,
TempDirMixin,
TorchaudioTestCase,
)
@skipIfNoSox
@skipIfNoExec("sox")
class TestFunctionalFiltering(TempDirMixin, TorchaudioTestCase):
def run_sox_effect(self, input_file, effect):
output_file = self.get_temp_path("expected.wav")
sox_utils.run_sox_effect(input_file, output_file, [str(e) for e in effect])
return load_wav(output_file)
def assert_sox_effect(self, result, input_path, effects, atol=1e-04, rtol=1e-5):
expected, _ = self.run_sox_effect(input_path, effects)
self.assertEqual(result, expected, atol=atol, rtol=rtol)
def get_whitenoise(self, sample_rate=8000):
noise = get_whitenoise(
sample_rate=sample_rate,
duration=3,
scale_factor=0.9,
)
path = self.get_temp_path("whitenoise.wav")
save_wav(path, noise, sample_rate)
return noise, path
@parameterized.expand(
[
("q", "quarter_sine"),
("h", "half_sine"),
("t", "linear"),
]
)
def test_fade(self, fade_shape_sox, fade_shape):
fade_in_len, fade_out_len = 44100, 44100
data, path = self.get_whitenoise(sample_rate=44100)
result = T.Fade(fade_in_len, fade_out_len, fade_shape)(data)
self.assert_sox_effect(result, path, ["fade", fade_shape_sox, "1", "0", "1"])
@parameterized.expand(
[
("amplitude", 1.1),
("db", 2),
("power", 2),
]
)
def test_vol(self, gain_type, gain):
data, path = self.get_whitenoise()
result = T.Vol(gain, gain_type)(data)
self.assert_sox_effect(result, path, ["vol", f"{gain}", gain_type])
@parameterized.expand(["vad-go-stereo-44100.wav", "vad-go-mono-32000.wav"])
def test_vad(self, filename):
path = get_asset_path(filename)
data, sample_rate = load_wav(path)
result = T.Vad(sample_rate)(data)
self.assert_sox_effect(result, path, ["vad"])
def test_vad_warning(self):
"""vad should throw a warning if input dimension is greater than 2"""
sample_rate = 41100
data = torch.rand(5, 5, sample_rate)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
T.Vad(sample_rate)(data)
assert len(w) == 1
data = torch.rand(5, sample_rate)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
T.Vad(sample_rate)(data)
assert len(w) == 0
data = torch.rand(sample_rate)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
T.Vad(sample_rate)(data)
assert len(w) == 0
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