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"""Test suites for jit-ability and its numerical compatibility"""
import torch
import torchaudio.transforms as T
from parameterized import parameterized
from torchaudio_unittest import common_utils
from torchaudio_unittest.common_utils import skipIfRocm, TestBaseMixin, torch_script
class Transforms(TestBaseMixin):
"""Implements test for Transforms that are performed for different devices"""
def _assert_consistency(self, transform, tensor, *args):
tensor = tensor.to(device=self.device, dtype=self.dtype)
transform = transform.to(device=self.device, dtype=self.dtype)
ts_transform = torch_script(transform)
output = transform(tensor, *args)
ts_output = ts_transform(tensor, *args)
self.assertEqual(ts_output, output)
def _assert_consistency_complex(self, transform, tensor, *args):
assert tensor.is_complex()
tensor = tensor.to(device=self.device, dtype=self.complex_dtype)
transform = transform.to(device=self.device, dtype=self.dtype)
ts_transform = torch_script(transform)
output = transform(tensor, *args)
ts_output = ts_transform(tensor, *args)
self.assertEqual(ts_output, output)
def test_Spectrogram(self):
tensor = torch.rand((1, 1000))
self._assert_consistency(T.Spectrogram(), tensor)
def test_Spectrogram_return_complex(self):
tensor = torch.rand((1, 1000))
self._assert_consistency(T.Spectrogram(power=None, return_complex=True), tensor)
def test_InverseSpectrogram(self):
tensor = common_utils.get_whitenoise(sample_rate=8000)
spectrogram = common_utils.get_spectrogram(tensor, n_fft=400, hop_length=100)
self._assert_consistency_complex(T.InverseSpectrogram(n_fft=400, hop_length=100), spectrogram)
@skipIfRocm
def test_GriffinLim(self):
tensor = torch.rand((1, 201, 6))
self._assert_consistency(T.GriffinLim(length=1000, rand_init=False), tensor)
def test_AmplitudeToDB(self):
spec = torch.rand((6, 201))
self._assert_consistency(T.AmplitudeToDB(), spec)
def test_MelScale(self):
spec_f = torch.rand((1, 201, 6))
self._assert_consistency(T.MelScale(n_stft=201), spec_f)
def test_MelSpectrogram(self):
tensor = torch.rand((1, 1000))
self._assert_consistency(T.MelSpectrogram(), tensor)
def test_MFCC(self):
tensor = torch.rand((1, 1000))
self._assert_consistency(T.MFCC(), tensor)
def test_LFCC(self):
tensor = torch.rand((1, 1000))
self._assert_consistency(T.LFCC(), tensor)
def test_Resample(self):
sr1, sr2 = 16000, 8000
tensor = common_utils.get_whitenoise(sample_rate=sr1)
self._assert_consistency(T.Resample(sr1, sr2), tensor)
def test_MuLawEncoding(self):
tensor = common_utils.get_whitenoise()
self._assert_consistency(T.MuLawEncoding(), tensor)
def test_MuLawDecoding(self):
tensor = torch.rand((1, 10))
self._assert_consistency(T.MuLawDecoding(), tensor)
def test_ComputeDelta(self):
tensor = torch.rand((1, 10))
self._assert_consistency(T.ComputeDeltas(), tensor)
def test_Fade(self):
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)
def test_FrequencyMasking(self):
tensor = torch.rand((10, 2, 50, 10, 2))
self._assert_consistency(T.FrequencyMasking(freq_mask_param=60, iid_masks=False), tensor)
def test_TimeMasking(self):
tensor = torch.rand((10, 2, 50, 10, 2))
self._assert_consistency(T.TimeMasking(time_mask_param=30, iid_masks=False), tensor)
def test_Vol(self):
waveform = common_utils.get_whitenoise()
self._assert_consistency(T.Vol(1.1), waveform)
def test_SlidingWindowCmn(self):
tensor = torch.rand((1000, 10))
self._assert_consistency(T.SlidingWindowCmn(), tensor)
def test_Vad(self):
filepath = common_utils.get_asset_path("vad-go-mono-32000.wav")
waveform, sample_rate = common_utils.load_wav(filepath)
self._assert_consistency(T.Vad(sample_rate=sample_rate), waveform)
def test_SpectralCentroid(self):
sample_rate = 44100
waveform = common_utils.get_whitenoise(sample_rate=sample_rate)
self._assert_consistency(T.SpectralCentroid(sample_rate=sample_rate), waveform)
def test_TimeStretch(self):
n_fft = 1025
n_freq = n_fft // 2 + 1
hop_length = 512
fixed_rate = 1.3
tensor = torch.rand((10, 2, n_freq, 10), dtype=torch.cfloat)
batch = 10
num_channels = 2
waveform = common_utils.get_whitenoise(sample_rate=8000, n_channels=batch * num_channels)
tensor = common_utils.get_spectrogram(waveform, n_fft=n_fft)
tensor = tensor.reshape(batch, num_channels, n_freq, -1)
self._assert_consistency_complex(
T.TimeStretch(n_freq=n_freq, hop_length=hop_length, fixed_rate=fixed_rate),
tensor,
)
def test_PitchShift(self):
sample_rate = 8000
n_steps = 4
waveform = common_utils.get_whitenoise(sample_rate=sample_rate)
pitch_shift = T.PitchShift(sample_rate=sample_rate, n_steps=n_steps)
# dry-run for initializing parameters
pitch_shift(waveform)
self._assert_consistency(pitch_shift, waveform)
def test_PSD(self):
tensor = common_utils.get_whitenoise(sample_rate=8000, n_channels=4)
spectrogram = common_utils.get_spectrogram(tensor, n_fft=400, hop_length=100)
spectrogram = spectrogram.to(self.device)
self._assert_consistency_complex(T.PSD(), spectrogram)
def test_PSD_with_mask(self):
tensor = common_utils.get_whitenoise(sample_rate=8000, n_channels=4)
spectrogram = common_utils.get_spectrogram(tensor, n_fft=400, hop_length=100)
spectrogram = spectrogram.to(self.device)
mask = torch.rand(spectrogram.shape[-2:], device=self.device)
self._assert_consistency_complex(T.PSD(), spectrogram, mask)
@parameterized.expand(
[
["ref_channel", True],
["stv_evd", True],
["stv_power", True],
["ref_channel", False],
["stv_evd", False],
["stv_power", False],
]
)
def test_MVDR(self, solution, online):
tensor = common_utils.get_whitenoise(sample_rate=8000, n_channels=4)
spectrogram = common_utils.get_spectrogram(tensor, n_fft=400, hop_length=100)
mask_s = torch.rand(spectrogram.shape[-2:], device=self.device)
mask_n = torch.rand(spectrogram.shape[-2:], device=self.device)
self._assert_consistency_complex(T.MVDR(solution=solution, online=online), spectrogram, mask_s, mask_n)
def test_rtf_mvdr(self):
tensor = common_utils.get_whitenoise(sample_rate=8000, n_channels=4)
specgram = common_utils.get_spectrogram(tensor, n_fft=400, hop_length=100)
channel, freq, _ = specgram.shape
rtf = torch.rand(freq, channel, dtype=self.complex_dtype, device=self.device)
psd_n = torch.rand(freq, channel, channel, dtype=self.complex_dtype, device=self.device)
reference_channel = 0
self._assert_consistency_complex(T.RTFMVDR(), specgram, rtf, psd_n, reference_channel)
def test_souden_mvdr(self):
tensor = common_utils.get_whitenoise(sample_rate=8000, n_channels=4)
specgram = common_utils.get_spectrogram(tensor, n_fft=400, hop_length=100)
channel, freq, _ = specgram.shape
psd_s = torch.rand(freq, channel, channel, dtype=self.complex_dtype, device=self.device)
psd_n = torch.rand(freq, channel, channel, dtype=self.complex_dtype, device=self.device)
reference_channel = 0
self._assert_consistency_complex(T.SoudenMVDR(), specgram, psd_s, psd_n, reference_channel)
class TransformsFloat32Only(TestBaseMixin):
def test_rnnt_loss(self):
logits = torch.tensor(
[
[
[[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1], [0.1, 0.1, 0.2, 0.8, 0.1]],
[[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.2, 0.1, 0.1], [0.7, 0.1, 0.2, 0.1, 0.1]],
]
]
)
tensor = logits.to(device=self.device, dtype=torch.float32)
targets = torch.tensor([[1, 2]], device=tensor.device, dtype=torch.int32)
logit_lengths = torch.tensor([2], device=tensor.device, dtype=torch.int32)
target_lengths = torch.tensor([2], device=tensor.device, dtype=torch.int32)
self._assert_consistency(T.RNNTLoss(), logits, targets, logit_lengths, target_lengths)
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