def test_item_noise_not_applied_in_valid(audio):
add_noise = AddNoise(p=1.0)
test_aud = AudioTensor(torch.ones_like(audio), 16000)
train_out = add_noise(test_aud.clone(), split_idx=0)
val_out = add_noise(test_aud.clone(), split_idx=1)
_test_ne(test_aud, train_out)
_test_eq(test_aud, val_out)
def test_crop_time_after_padding():
sg_orig = test_audio_tensor()
a2s = AudioToSpec.from_cfg(AudioConfig.Voice())
sg = a2s(sg_orig)
crop_time = CropTime((sg.duration + 5) * 1000, pad_mode=AudioPadType.Zeros_After)
inp, out = apply_transform(crop_time, sg.clone())
_test_ne(sg.duration, sg_orig.duration)
def test_padding_after_resize(audio):
"Padding is added to the end but not the beginning"
new_duration = (audio.duration + 1) * 1000
cropsig_pad_after = ResizeSignal(new_duration, pad_mode=AudioPadType.Zeros_After)
# generate a random input signal that is 3s long
inp, out = apply_transform(cropsig_pad_after, audio)
# test end of signal is padded with zeros
_test_eq(out[:, -10:], torch.zeros_like(out)[:, -10:])
# test front of signal is not padded with zeros
_test_ne(out[:, 0:10], out[:, -10:])
def test_crop_time_repeat_padding():
"Test that repeat padding works when cropping time"
repeat = 3
audio = test_audio_tensor()
crop_12000ms_repeat = CropTime(repeat * 1000 * audio.duration,
pad_mode=AudioPadType.Repeat)
a2s = AudioToSpec.from_cfg(AudioConfig.Voice())
sg = a2s(audio)
inp, out = apply_transform(crop_12000ms_repeat, sg)
_test_eq(inp.width, sg.width)
_test_ne(sg.width, out.width)
def test_delta_channels():
" nchannels for a spectrogram is how many channels its original audio had "
delta = Delta()
audio = test_audio_tensor(channels=1)
a2s = AudioToSpec.from_cfg(AudioConfig.Voice())
sg = a2s(audio)
inp, out = apply_transform(delta, sg)
_test_eq(out.nchannels, inp.nchannels * 3)
_test_eq(out.shape[1:], inp.shape[1:])
_test_ne(out[0], out[1])
def test_signal_cutout():
c, s = 2, 16000
min_cut_pct, max_cut_pct = 0.10, 0.15
# Create tensor with no zeros
audio = AudioTensor(torch.rand([c, s]), sr=16000) * 0.9 + 0.1
cutout = SignalCutoutGPU(p=1.0, min_cut_pct=min_cut_pct, max_cut_pct=max_cut_pct)
inp, out = apply_transform(cutout, audio)
_test_ne(inp.data, out.data)
num_zeros = (out == 0).sum()
assert min_cut_pct * s * c <= num_zeros <= max_cut_pct * s * c, num_zeros
def test_signal_loss(audio):
signalloss = SignalLossGPU(1)
inp, out = apply_transform(signalloss, audio)
_test_ne(inp.data, out.data)
def test_change_volume(audio):
changevol = ChangeVolumeGPU(1)
inp, out = apply_transform(changevol, audio)
_test_ne(inp.data, out.data)
def test_noise_non_white(audio):
# White noise uses a different method to other noises, so test both.
addnoise = AddNoiseGPU(color=NoiseColor.Pink, p=1.0, min_level=0.1, max_level=0.2)
inp, out = apply_transform(addnoise, audio)
_test_ne(inp.data, out.data)
def test_noise_white(audio):
addnoise = AddNoiseGPU(color=NoiseColor.White, p=1.0, min_level=0.1, max_level=0.2)
inp, out = apply_transform(addnoise, audio)
_test_ne(inp.data, out.data)
def test_signal_cutout(audio):
cutout = SignalCutout(1)
inp, out = apply_transform(cutout, audio)
_test_ne(inp.data, out.data)
def test_noise_non_white(audio):
addnoise = AddNoise(color=NoiseColor.Pink)
inp, out = apply_transform(addnoise, audio)
_test_ne(inp.data, out.data)
def test_sg_roll():
roll = SGRoll()
audio = test_audio_tensor()
a2s = AudioToSpec.from_cfg(AudioConfig.BasicSpectrogram())
inp, out = apply_transform(roll, a2s(audio))
_test_ne(inp, out)
def test_signal_shift_on_sg():
audio = test_audio_tensor()
a2s = AudioToSpec.from_cfg(AudioConfig.BasicSpectrogram())
shifter = SignalShifter(1, 1)
inp, out = apply_transform(shifter, a2s(audio))
_test_ne(inp, out)
