def test_highpass(self):
cutoff_freq = 2000
sample_rate = 8000
data, path = self.get_whitenoise(sample_rate)
result = F.highpass_biquad(data, sample_rate, cutoff_freq)
self.assert_sox_effect(result, path, ['highpass', cutoff_freq], atol=1.5e-4)
def digital_filter(waveform, filter_type, sample_rate, cutoff_freq):
if filter_type == 'bandpass':
waveform = lowpass_biquad(waveform=waveform,
sample_rate=sample_rate,
cutoff_freq=max(cutoff_freq),
Q=1)
waveform = highpass_biquad(waveform=waveform,
sample_rate=sample_rate,
cutoff_freq=min(cutoff_freq),
Q=1)
elif filter_type == 'lowpass':
waveform = lowpass_biquad(waveform=waveform,
sample_rate=sample_rate,
cutoff_freq=max(cutoff_freq),
Q=1)
elif filter_type == 'highpass':
waveform = highpass_biquad(waveform=waveform,
sample_rate=sample_rate,
cutoff_freq=min(cutoff_freq),
Q=1)
return waveform
def test_highpass(self):
"""
Test biquad highpass filter, compare to SoX implementation
"""
cutoff_freq = 2000
noise_filepath = common_utils.get_asset_path('whitenoise.wav')
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("highpass", [cutoff_freq])
sox_output_waveform, sr = E.sox_build_flow_effects()
waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True)
output_waveform = F.highpass_biquad(waveform, sample_rate, cutoff_freq)
# TBD - this fails at the 1e-4 level, debug why
self.assertEqual(output_waveform, sox_output_waveform, atol=1e-3, rtol=1e-5)
def test_highpass(self):
"""
Test biquad highpass filter, compare to SoX implementation
"""
CUTOFF_FREQ = 2000
noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.mp3")
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("highpass", [CUTOFF_FREQ])
sox_output_waveform, sr = E.sox_build_flow_effects()
waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True)
output_waveform = F.highpass_biquad(waveform, sample_rate, CUTOFF_FREQ)
# TBD - this fails at the 1e-4 level, debug why
assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-3)
def test_highpass(self):
"""
Test biquad highpass filter, compare to SoX implementation
"""
cutoff_freq = 2000
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(self.noise_filepath)
E.append_effect_to_chain("highpass", [cutoff_freq])
sox_output_waveform, sr = E.sox_build_flow_effects()
output_waveform = F.highpass_biquad(self.noise_waveform,
self.NOISE_SAMPLE_RATE,
cutoff_freq)
self.assertEqual(output_waveform,
sox_output_waveform,
atol=1.5e-3,
rtol=1e-5)
def func(tensor):
sample_rate = 44100
cutoff_freq = 2000.
return F.highpass_biquad(tensor, sample_rate, cutoff_freq)
print(
f"Min of lowpass_waveform: {lowpass_waveform.min()}\nMax of lowpass_waveform: "
f"{lowpass_waveform.max()}\nMean of lowpass_waveform: {lowpass_waveform.mean()}")
pyplot.figure()
pyplot.plot(lowpass_waveform.t().numpy())
# %% md
'''
We can also visualize a waveform with the highpass biquad filter.
'''
# %%
highpass_waveform = highpass_biquad(waveform, sample_rate, cutoff_freq=2000)
print(
f"Min of highpass_waveform: {highpass_waveform.min()}\nMax of highpass_waveform: "
f"{highpass_waveform.max()}\nMean of highpass_waveform: {highpass_waveform.mean()}")
pyplot.figure()
pyplot.plot(highpass_waveform.t().numpy())
# %% md
'''
Migrating to torchaudio from Kaldi
----------------------------------
Users may be familiar with
`Kaldi <http://github.com/kaldi-asr/kaldi>`_, a toolkit for speech
