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"torchvision/datapoints/_dataset_wrapper.py" did not exist on "707457050620e1f70ab1b187dad81cc36a7f9180"
Unverified Commit 313f4f5c authored by moto's avatar moto Committed by GitHub
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Add util to generate whitenoise (#654) 

* Add util to generate whitenoise

* Use sinusoid for pitch and revert vad
parent 2f2319d6
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Showing with 97 additions and 54 deletions
test/common_utils.py
View file @ 313f4f5c
import os
import tempfile
import unittest
from typing import Type, Iterable
from typing import Type, Iterable, Union
from contextlib import contextmanager
from shutil import copytree
......@@ -117,3 +117,68 @@ def define_test_suites(
for dtype in dtypes:
t = define_test_suite(suite, dtype, device)
scope[t.__name__] = t
def get_whitenoise(
*,
sample_rate: int = 16000,
duration: float = 1, # seconds
n_channels: int = 1,
seed: int = 0,
dtype: Union[str, torch.dtype] = "float32",
device: Union[str, torch.device] = "cpu",
):
"""Generate pseudo audio data with whitenoise
Args:
sample_rate: Sampling rate
duration: Length of the resulting Tensor in seconds.
n_channels: Number of channels
seed: Seed value used for random number generation.
Note that this function does not modify global random generator state.
dtype: Torch dtype
device: device
Returns:
Tensor: shape of (n_channels, sample_rate * duration)
"""
if isinstance(dtype, str):
dtype = getattr(torch, dtype)
shape = [n_channels, sample_rate * duration]
# According to the doc, folking rng on all CUDA devices is slow when there are many CUDA devices,
# so we only folk on CPU, generate values and move the data to the given device
with torch.random.fork_rng([]):
torch.random.manual_seed(seed)
tensor = torch.randn(shape, dtype=dtype, device='cpu')
tensor /= 2.0
tensor.clamp_(-1.0, 1.0)
return tensor.to(device=device)
def get_sinusoid(
*,
frequency: float = 300,
sample_rate: int = 16000,
duration: float = 1, # seconds
n_channels: int = 1,
dtype: Union[str, torch.dtype] = "float32",
device: Union[str, torch.device] = "cpu",
):
"""Generate pseudo audio data with sine wave.
Args:
frequency: Frequency of sine wave
sample_rate: Sampling rate
duration: Length of the resulting Tensor in seconds.
n_channels: Number of channels
dtype: Torch dtype
device: device
Returns:
Tensor: shape of (n_channels, sample_rate * duration)
"""
if isinstance(dtype, str):
dtype = getattr(torch, dtype)
pie2 = 2 * 3.141592653589793
end = pie2 * frequency * duration
theta = torch.linspace(0, end, sample_rate * duration, dtype=dtype, device=device)
return torch.sin(theta, out=None).repeat([n_channels, 1])
test/torchscript_consistency_impl.py
View file @ 313f4f5c
......@@ -33,7 +33,7 @@ class Functional(common_utils.TestBaseMixin):
normalize = False
return F.spectrogram(tensor, pad, window, n_fft, hop, ws, power, normalize)
tensor = torch.rand((1, 1000))
tensor = common_utils.get_whitenoise()
self._assert_consistency(func, tensor)
def test_griffinlim(self):
......@@ -65,8 +65,7 @@ class Functional(common_utils.TestBaseMixin):
self._assert_consistency(func, tensor)
def test_detect_pitch_frequency(self):
filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform, _ = torchaudio.load(filepath)
waveform = common_utils.get_sinusoid(sample_rate=44100)
def func(tensor):
sample_rate = 44100
......@@ -128,8 +127,8 @@ class Functional(common_utils.TestBaseMixin):
qc = 256
return F.mu_law_encoding(tensor, qc)
tensor = torch.rand((1, 10))
self._assert_consistency(func, tensor)
waveform = common_utils.get_whitenoise()
self._assert_consistency(func, waveform)
def test_mu_law_decoding(self):
def func(tensor):
......@@ -179,29 +178,28 @@ class Functional(common_utils.TestBaseMixin):
def func(tensor):
return F.dither(tensor, 'TPDF')
tensor = torch.rand((2, 1000))
tensor = common_utils.get_whitenoise(n_channels=2)
self._assert_consistency(func, tensor, shape_only=True)
def test_dither_RPDF(self):
def func(tensor):
return F.dither(tensor, 'RPDF')
tensor = torch.rand((2, 1000))
tensor = common_utils.get_whitenoise(n_channels=2)
self._assert_consistency(func, tensor, shape_only=True)
def test_dither_GPDF(self):
def func(tensor):
return F.dither(tensor, 'GPDF')
tensor = torch.rand((2, 1000))
tensor = common_utils.get_whitenoise(n_channels=2)
self._assert_consistency(func, tensor, shape_only=True)
def test_lfilter(self):
if self.dtype == torch.float64:
raise unittest.SkipTest("This test is known to fail for float64")
filepath = common_utils.get_asset_path('whitenoise.wav')
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise()
def func(tensor):
# Design an IIR lowpass filter using scipy.signal filter design
......@@ -244,8 +242,7 @@ class Functional(common_utils.TestBaseMixin):
if self.dtype == torch.float64:
raise unittest.SkipTest("This test is known to fail for float64")
filepath = common_utils.get_asset_path('whitenoise.wav')
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise(sample_rate=44100)
def func(tensor):
sample_rate = 44100
......@@ -258,8 +255,7 @@ class Functional(common_utils.TestBaseMixin):
if self.dtype == torch.float64:
raise unittest.SkipTest("This test is known to fail for float64")
filepath = common_utils.get_asset_path('whitenoise.wav')
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise(sample_rate=44100)
def func(tensor):
sample_rate = 44100
......@@ -272,8 +268,7 @@ class Functional(common_utils.TestBaseMixin):
if self.dtype == torch.float64:
raise unittest.SkipTest("This test is known to fail for float64")
filepath = common_utils.get_asset_path('whitenoise.wav')
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise(sample_rate=44100)
def func(tensor):
sample_rate = 44100
......@@ -287,8 +282,7 @@ class Functional(common_utils.TestBaseMixin):
if self.dtype == torch.float64:
raise unittest.SkipTest("This test is known to fail for float64")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise(sample_rate=44100)
def func(tensor):
sample_rate = 44100
......@@ -303,8 +297,7 @@ class Functional(common_utils.TestBaseMixin):
if self.dtype == torch.float64:
raise unittest.SkipTest("This test is known to fail for float64")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise(sample_rate=44100)
def func(tensor):
sample_rate = 44100
......@@ -319,8 +312,7 @@ class Functional(common_utils.TestBaseMixin):
if self.dtype == torch.float64:
raise unittest.SkipTest("This test is known to fail for float64")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise(sample_rate=44100)
def func(tensor):
sample_rate = 44100
......@@ -334,8 +326,7 @@ class Functional(common_utils.TestBaseMixin):
if self.dtype == torch.float64:
raise unittest.SkipTest("This test is known to fail for float64")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise(sample_rate=44100)
def func(tensor):
sample_rate = 44100
......@@ -350,8 +341,7 @@ class Functional(common_utils.TestBaseMixin):
if self.dtype == torch.float64:
raise unittest.SkipTest("This test is known to fail for float64")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise(sample_rate=44100)
def func(tensor):
sample_rate = 44100
......@@ -366,8 +356,7 @@ class Functional(common_utils.TestBaseMixin):
if self.dtype == torch.float64:
raise unittest.SkipTest("This test is known to fail for float64")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise(sample_rate=44100)
def func(tensor):
sample_rate = 44100
......@@ -382,8 +371,7 @@ class Functional(common_utils.TestBaseMixin):
if self.dtype == torch.float64:
raise unittest.SkipTest("This test is known to fail for float64")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise(sample_rate=44100)
def func(tensor):
sample_rate = 44100
......@@ -395,8 +383,7 @@ class Functional(common_utils.TestBaseMixin):
if self.dtype == torch.float64:
raise unittest.SkipTest("This test is known to fail for float64")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise(sample_rate=44100)
def func(tensor):
sample_rate = 44100
......@@ -408,8 +395,7 @@ class Functional(common_utils.TestBaseMixin):
if self.dtype == torch.float64:
raise unittest.SkipTest("This test is known to fail for float64")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise(sample_rate=44100)
def func(tensor):
sample_rate = 44100
......@@ -424,8 +410,7 @@ class Functional(common_utils.TestBaseMixin):
if self.dtype == torch.float64:
raise unittest.SkipTest("This test is known to fail for float64")
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise()
def func(tensor):
a = torch.tensor([0.7, 0.2, 0.6], device=tensor.device, dtype=tensor.dtype)
......@@ -470,8 +455,7 @@ class Functional(common_utils.TestBaseMixin):
self._assert_consistency(func, b)
def test_contrast(self):
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise()
def func(tensor):
enhancement_amount = 80.
......@@ -480,8 +464,7 @@ class Functional(common_utils.TestBaseMixin):
self._assert_consistency(func, waveform)
def test_dcshift(self):
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise()
def func(tensor):
shift = 0.5
......@@ -491,8 +474,7 @@ class Functional(common_utils.TestBaseMixin):
self._assert_consistency(func, waveform)
def test_overdrive(self):
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise()
def func(tensor):
gain = 30.
......@@ -502,8 +484,7 @@ class Functional(common_utils.TestBaseMixin):
self._assert_consistency(func, waveform)
def test_phaser(self):
filepath = common_utils.get_asset_path("whitenoise.wav")
waveform, _ = torchaudio.load(filepath, normalization=True)
waveform = common_utils.get_whitenoise(sample_rate=44100)
def func(tensor):
gain_in = 0.5
......@@ -553,17 +534,16 @@ class Transforms(common_utils.TestBaseMixin):
self._assert_consistency(T.MFCC(), tensor)
def test_Resample(self):
tensor = torch.rand((2, 1000))
sample_rate = 100.
sample_rate_2 = 50.
self._assert_consistency(T.Resample(sample_rate, sample_rate_2), tensor)
sr1, sr2 = 16000, 8000
tensor = common_utils.get_whitenoise(sample_rate=sr1)
self._assert_consistency(T.Resample(float(sr1), float(sr2)), tensor)
def test_ComplexNorm(self):
tensor = torch.rand((1, 2, 201, 2))
self._assert_consistency(T.ComplexNorm(), tensor)
def test_MuLawEncoding(self):
tensor = torch.rand((1, 10))
tensor = common_utils.get_whitenoise()
self._assert_consistency(T.MuLawEncoding(), tensor)
def test_MuLawDecoding(self):
......@@ -581,8 +561,7 @@ class Transforms(common_utils.TestBaseMixin):
)
def test_Fade(self):
test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform, _ = torchaudio.load(test_filepath)
waveform = common_utils.get_whitenoise()
fade_in_len = 3000
fade_out_len = 3000
self._assert_consistency(T.Fade(fade_in_len, fade_out_len), waveform)
......@@ -596,8 +575,7 @@ class Transforms(common_utils.TestBaseMixin):
self._assert_consistency(T.TimeMasking(time_mask_param=30, iid_masks=False), tensor)
def test_Vol(self):
test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav')
waveform, _ = torchaudio.load(test_filepath)
waveform = common_utils.get_whitenoise()
self._assert_consistency(T.Vol(1.1), waveform)
def test_SlidingWindowCmn(self):
......
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