Spaces:
Runtime error
Runtime error
File size: 3,429 Bytes
ee21b96 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
def calc_mean_invstddev(feature):
if len(feature.size()) != 2:
raise ValueError("We expect the input feature to be 2-D tensor")
mean = feature.mean(0)
var = feature.var(0)
# avoid division by ~zero
eps = 1e-8
if (var < eps).any():
return mean, 1.0 / (torch.sqrt(var) + eps)
return mean, 1.0 / torch.sqrt(var)
def apply_mv_norm(features):
# If there is less than 2 spectrograms, the variance cannot be computed (is NaN)
# and normalization is not possible, so return the item as it is
if features.size(0) < 2:
return features
mean, invstddev = calc_mean_invstddev(features)
res = (features - mean) * invstddev
return res
def lengths_to_encoder_padding_mask(lengths, batch_first=False):
"""
convert lengths (a 1-D Long/Int tensor) to 2-D binary tensor
Args:
lengths: a (B, )-shaped tensor
Return:
max_length: maximum length of B sequences
encoder_padding_mask: a (max_length, B) binary mask, where
[t, b] = 0 for t < lengths[b] and 1 otherwise
TODO:
kernelize this function if benchmarking shows this function is slow
"""
max_lengths = torch.max(lengths).item()
bsz = lengths.size(0)
encoder_padding_mask = torch.arange(
max_lengths
).to( # a (T, ) tensor with [0, ..., T-1]
lengths.device
).view( # move to the right device
1, max_lengths
).expand( # reshape to (1, T)-shaped tensor
bsz, -1
) >= lengths.view( # expand to (B, T)-shaped tensor
bsz, 1
).expand(
-1, max_lengths
)
if not batch_first:
return encoder_padding_mask.t(), max_lengths
else:
return encoder_padding_mask, max_lengths
def encoder_padding_mask_to_lengths(
encoder_padding_mask, max_lengths, batch_size, device
):
"""
convert encoder_padding_mask (2-D binary tensor) to a 1-D tensor
Conventionally, encoder output contains a encoder_padding_mask, which is
a 2-D mask in a shape (T, B), whose (t, b) element indicate whether
encoder_out[t, b] is a valid output (=0) or not (=1). Occasionally, we
need to convert this mask tensor to a 1-D tensor in shape (B, ), where
[b] denotes the valid length of b-th sequence
Args:
encoder_padding_mask: a (T, B)-shaped binary tensor or None; if None,
indicating all are valid
Return:
seq_lengths: a (B,)-shaped tensor, where its (b, )-th element is the
number of valid elements of b-th sequence
max_lengths: maximum length of all sequence, if encoder_padding_mask is
not None, max_lengths must equal to encoder_padding_mask.size(0)
batch_size: batch size; if encoder_padding_mask is
not None, max_lengths must equal to encoder_padding_mask.size(1)
device: which device to put the result on
"""
if encoder_padding_mask is None:
return torch.Tensor([max_lengths] * batch_size).to(torch.int32).to(device)
assert encoder_padding_mask.size(0) == max_lengths, "max_lengths does not match"
assert encoder_padding_mask.size(1) == batch_size, "batch_size does not match"
return max_lengths - torch.sum(encoder_padding_mask, dim=0)
|