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Multiply length_factor by 2.0.

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Daniel Povey 2022-11-19 13:36:16 +08:00
parent e9806950f5
commit d23fda7c5f
1 changed files with 4 additions and 1 deletions
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egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
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@ -937,7 +937,10 @@ class CompactRelPositionalEncoding(torch.nn.Module):
# length_factor is chosen so that the FFT can exactly separate points # length_factor is chosen so that the FFT can exactly separate points
# close to the origin (T == 0). So this part of the formulation is not really # close to the origin (T == 0). So this part of the formulation is not really
# heuristic. # heuristic.
length_factor = self.embed_dim / (2.0 * math.pi) # todo: test this. length_factor = self.embed_dim / (2.0 * math.pi)
# multiplying length_factor by this heuristic constant should reduce the resolution near to the
# origin, i.e. reduce its ability to separate points near zero.
length_factor *= 2.0
# note for machine implementations: if atan is not available, we can use: # note for machine implementations: if atan is not available, we can use:
# x.sign() * ((1 / (x.abs() + 1)) - 1) * (-math.pi/2) # x.sign() * ((1 / (x.abs() + 1)) - 1) * (-math.pi/2)