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MelSpectrogram: Use given n_fft to initialize fb immediately #246

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Merged
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Aug 20, 2019
Merged

MelSpectrogram: Use given n_fft to initialize fb immediately #246

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33 changes: 33 additions & 0 deletions test/test_transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,39 @@ def test_mu_law_companding(self):
waveform_exp = transforms.MuLawDecoding(quantization_channels)(waveform_mu)
self.assertTrue(waveform_exp.min() >= -1. and waveform_exp.max() <= 1.)

def test_melscale_load_save(self):
specgram = torch.ones(1, 1000, 100)
melscale_transform = transforms.MelScale()
melscale_transform(specgram)

melscale_transform_copy = transforms.MelScale(n_stft=1000)
melscale_transform_copy.load_state_dict(melscale_transform.state_dict())

fb = melscale_transform.fb
fb_copy = melscale_transform_copy.fb

self.assertEqual(fb_copy.size(), (1000, 128))
self.assertTrue(torch.allclose(fb, fb_copy))

def test_melspectrogram_load_save(self):
waveform = self.waveform.float()
mel_spectrogram_transform = transforms.MelSpectrogram()
mel_spectrogram_transform(waveform)

mel_spectrogram_transform_copy = transforms.MelSpectrogram()
mel_spectrogram_transform_copy.load_state_dict(mel_spectrogram_transform.state_dict())

window = mel_spectrogram_transform.spectrogram.window
window_copy = mel_spectrogram_transform_copy.spectrogram.window

fb = mel_spectrogram_transform.mel_scale.fb
fb_copy = mel_spectrogram_transform_copy.mel_scale.fb

self.assertTrue(torch.allclose(window, window_copy))
# the default for n_fft = 400 and n_mels = 128
self.assertEqual(fb_copy.size(), (201, 128))
self.assertTrue(torch.allclose(fb, fb_copy))

def test_mel2(self):
top_db = 80.
s2db = transforms.AmplitudeToDB('power', top_db)
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2 changes: 1 addition & 1 deletion torchaudio/transforms.py
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Expand Up @@ -199,7 +199,7 @@ def __init__(self, sample_rate=16000, n_fft=400, win_length=None, hop_length=Non
hop_length=self.hop_length,
pad=self.pad, window_fn=window_fn, power=2,
normalized=False, wkwargs=wkwargs)
self.mel_scale = MelScale(self.n_mels, self.sample_rate, self.f_min, self.f_max)
self.mel_scale = MelScale(self.n_mels, self.sample_rate, self.f_min, self.f_max, self.n_fft // 2 + 1)
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Is there a way to read off the size of the spectrogram instead of recomputing it?

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Not unless the spectrogram computes a specgram and we look at the tensor's dimension

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@vincentqb vincentqb Aug 19, 2019 •
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If that constant n_fft // 2 + 1 is used in a few places within Spectrogram, we could attach it to Spectrogram. If that's our inferred default dimension, we could also just attach a default_dimension to Spectrogram, in case that ever changes, but that would add clutter to the Spectrogram interface.

Alright, I'm ok with having the value recomputed here. If there's ever a change in this, the test will fail :)


@torch.jit.script_method
def forward(self, waveform):
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