# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Modified from espnet(https://github.com/espnet/espnet)
import math
from typing import Tuple
import numpy as np
import paddle
from paddle import nn
from typeguard import check_argument_types
from paddlespeech.utils.initialize import _calculate_fan_in_and_fan_out
from paddlespeech.utils.initialize import kaiming_uniform_
from paddlespeech.utils.initialize import normal_
from paddlespeech.utils.initialize import ones_
from paddlespeech.utils.initialize import uniform_
from paddlespeech.utils.initialize import zeros_
# default init method of torch
# copy from https://github.com/PaddlePaddle/PaddleSpeech/blob/9cf8c1985a98bb380c183116123672976bdfe5c9/paddlespeech/t2s/models/vits/vits.py#L506
def _reset_parameters(module):
if isinstance(module, (nn.Conv1D, nn.Conv1DTranspose, nn.Conv2D,
nn.Conv2DTranspose)):
kaiming_uniform_(module.weight, a=math.sqrt(5))
if module.bias is not None:
fan_in, _ = _calculate_fan_in_and_fan_out(module.weight)
if fan_in != 0:
bound = 1 / math.sqrt(fan_in)
uniform_(module.bias, -bound, bound)
if isinstance(module,
(nn.BatchNorm1D, nn.BatchNorm2D, nn.GroupNorm, nn.LayerNorm)):
ones_(module.weight)
zeros_(module.bias)
if isinstance(module, nn.Linear):
kaiming_uniform_(module.weight, a=math.sqrt(5))
if module.bias is not None:
fan_in, _ = _calculate_fan_in_and_fan_out(module.weight)
bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
uniform_(module.bias, -bound, bound)
if isinstance(module, nn.Embedding):
normal_(module.weight)
if module._padding_idx is not None:
with paddle.no_grad():
module.weight[module._padding_idx] = 0
[docs]def pad_list(xs, pad_value):
"""Perform padding for the list of tensors.
Args:
xs (List[Tensor]):
List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
pad_value (float):
Value for padding.
Returns:
Tensor: Padded tensor (B, Tmax, `*`).
Examples:
>>> x = [paddle.ones([4]), paddle.ones([2]), paddle.ones([1])]
>>> x
[tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])]
>>> pad_list(x, 0)
tensor([[1., 1., 1., 1.],
[1., 1., 0., 0.],
[1., 0., 0., 0.]])
"""
n_batch = len(xs)
max_len = max(x.shape[0] for x in xs)
pad = paddle.full(
[n_batch, max_len, *xs[0].shape[1:]], pad_value, dtype=xs[0].dtype)
for i in range(n_batch):
pad[i, :xs[i].shape[0]] = xs[i]
return pad
[docs]def make_pad_mask(lengths, xs=None, length_dim=-1):
"""Make mask tensor containing indices of padded part.
Args:
lengths (Tensor(int64)):
Batch of lengths (B,).
xs (Tensor, optional):
The reference tensor.
If set, masks will be the same shape as this tensor.
length_dim (int, optional):
Dimension indicator of the above tensor.
See the example.
Returns:
Tensor(bool): Mask tensor containing indices of padded part bool.
Examples:
With only lengths.
>>> lengths = [5, 3, 2]
>>> make_non_pad_mask(lengths)
masks = [[0, 0, 0, 0 ,0],
[0, 0, 0, 1, 1],
[0, 0, 1, 1, 1]]
With the reference tensor.
>>> xs = paddle.zeros((3, 2, 4))
>>> make_pad_mask(lengths, xs)
tensor([[[0, 0, 0, 0],
[0, 0, 0, 0]],
[[0, 0, 0, 1],
[0, 0, 0, 1]],
[[0, 0, 1, 1],
[0, 0, 1, 1]]])
>>> xs = paddle.zeros((3, 2, 6))
>>> make_pad_mask(lengths, xs)
tensor([[[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1]],
[[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1]],
[[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1]]])
With the reference tensor and dimension indicator.
>>> xs = paddle.zeros((3, 6, 6))
>>> make_pad_mask(lengths, xs, 1)
tensor([[[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1]],
[[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1]],
[[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1]]])
>>> make_pad_mask(lengths, xs, 2)
tensor([[[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1]],
[[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1]],
[[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1]]],)
"""
if length_dim == 0:
raise ValueError("length_dim cannot be 0: {}".format(length_dim))
bs = paddle.shape(lengths)
if xs is None:
maxlen = paddle.cast(lengths.max(), dtype=bs.dtype)
else:
maxlen = paddle.shape(xs)[length_dim]
seq_range = paddle.arange(0, maxlen, dtype=paddle.int64)
# VITS 最后一个 expand 的位置
seq_range_expand = seq_range.unsqueeze(0).expand([bs, maxlen])
seq_length_expand = lengths.unsqueeze(-1)
mask = seq_range_expand >= seq_length_expand.cast(seq_range_expand.dtype)
if xs is not None:
assert paddle.shape(xs)[0] == bs, (paddle.shape(xs)[0], bs)
if length_dim < 0:
length_dim = len(paddle.shape(xs)) + length_dim
# ind = (:, None, ..., None, :, , None, ..., None)
ind = tuple(
slice(None) if i in (0, length_dim) else None
for i in range(len(paddle.shape(xs))))
mask = paddle.expand(mask[ind], paddle.shape(xs))
return mask
[docs]def make_non_pad_mask(lengths, xs=None, length_dim=-1):
"""Make mask tensor containing indices of non-padded part.
Args:
lengths (Tensor(int64) or List):
Batch of lengths (B,).
xs (Tensor, optional):
The reference tensor.
If set, masks will be the same shape as this tensor.
length_dim (int, optional):
Dimension indicator of the above tensor.
See the example.
Returns:
Tensor(bool):
mask tensor containing indices of padded part bool.
Examples:
With only lengths.
>>> lengths = [5, 3, 2]
>>> make_non_pad_mask(lengths)
masks = [[1, 1, 1, 1 ,1],
[1, 1, 1, 0, 0],
[1, 1, 0, 0, 0]]
With the reference tensor.
>>> xs = paddle.zeros((3, 2, 4))
>>> make_non_pad_mask(lengths, xs)
tensor([[[1, 1, 1, 1],
[1, 1, 1, 1]],
[[1, 1, 1, 0],
[1, 1, 1, 0]],
[[1, 1, 0, 0],
[1, 1, 0, 0]]])
>>> xs = paddle.zeros((3, 2, 6))
>>> make_non_pad_mask(lengths, xs)
tensor([[[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 0]],
[[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0]],
[[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0]]])
With the reference tensor and dimension indicator.
>>> xs = paddle.zeros((3, 6, 6))
>>> make_non_pad_mask(lengths, xs, 1)
tensor([[[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0]],
[[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]],
[[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]]])
>>> make_non_pad_mask(lengths, xs, 2)
tensor([[[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 0]],
[[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0]],
[[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0]]])
"""
return paddle.logical_not(make_pad_mask(lengths, xs, length_dim))
[docs]def initialize(model: nn.Layer, init: str):
"""Initialize weights of a neural network module.
Parameters are initialized using the given method or distribution.
Custom initialization routines can be implemented into submodules
Args:
model (nn.Layer):
Target.
init (str):
Method of initialization.
"""
assert check_argument_types()
if init == "xavier_uniform":
nn.initializer.set_global_initializer(nn.initializer.XavierUniform(),
nn.initializer.Constant())
elif init == "xavier_normal":
nn.initializer.set_global_initializer(nn.initializer.XavierNormal(),
nn.initializer.Constant())
elif init == "kaiming_uniform":
nn.initializer.set_global_initializer(nn.initializer.KaimingUniform(),
nn.initializer.Constant())
elif init == "kaiming_normal":
nn.initializer.set_global_initializer(nn.initializer.KaimingNormal(),
nn.initializer.Constant())
else:
raise ValueError("Unknown initialization: " + init)
# for VITS
[docs]def get_random_segments(
x: paddle.paddle,
x_lengths: paddle.Tensor,
segment_size: int, ) -> Tuple[paddle.Tensor, paddle.Tensor]:
"""Get random segments.
Args:
x (Tensor):
Input tensor (B, C, T).
x_lengths (Tensor):
Length tensor (B,).
segment_size (int):
Segment size.
Returns:
Tensor:
Segmented tensor (B, C, segment_size).
Tensor:
Start index tensor (B,).
"""
b, c, t = paddle.shape(x)
max_start_idx = x_lengths - segment_size
start_idxs = paddle.cast(paddle.rand([b]) * max_start_idx, 'int64')
segments = get_segments(x, start_idxs, segment_size)
return segments, start_idxs
[docs]def get_segments(
x: paddle.Tensor,
start_idxs: paddle.Tensor,
segment_size: int, ) -> paddle.Tensor:
"""Get segments.
Args:
x (Tensor):
Input tensor (B, C, T).
start_idxs (Tensor):
Start index tensor (B,).
segment_size (int):
Segment size.
Returns:
Tensor: Segmented tensor (B, C, segment_size).
"""
b, c, t = paddle.shape(x)
segments = paddle.zeros([b, c, segment_size], dtype=x.dtype)
for i, start_idx in enumerate(start_idxs):
segments[i] = x[i, :, start_idx:start_idx + segment_size]
return segments
# see https://github.com/PaddlePaddle/X2Paddle/blob/develop/docs/pytorch_project_convertor/API_docs/ops/torch.gather.md
[docs]def paddle_gather(x, dim, index):
index_shape = index.shape
index_flatten = index.flatten()
if dim < 0:
dim = len(x.shape) + dim
nd_index = []
for k in range(len(x.shape)):
if k == dim:
nd_index.append(index_flatten)
else:
reshape_shape = [1] * len(x.shape)
reshape_shape[k] = x.shape[k]
x_arange = paddle.arange(x.shape[k], dtype=index.dtype)
x_arange = x_arange.reshape(reshape_shape)
dim_index = paddle.expand(x_arange, index_shape).flatten()
nd_index.append(dim_index)
ind2 = paddle.transpose(paddle.stack(nd_index), [1, 0]).astype("int64")
paddle_out = paddle.gather_nd(x, ind2).reshape(index_shape)
return paddle_out
# for ERNIE SAT
# mask phones
[docs]def phones_masking(xs_pad: paddle.Tensor,
src_mask: paddle.Tensor,
align_start: paddle.Tensor,
align_end: paddle.Tensor,
align_start_lens: paddle.Tensor,
mlm_prob: float=0.8,
mean_phn_span: int=8,
span_bdy: paddle.Tensor=None):
'''
Args:
xs_pad (paddle.Tensor):
input speech (B, Tmax, D).
src_mask (paddle.Tensor):
mask of speech (B, 1, Tmax).
align_start (paddle.Tensor):
frame level phone alignment start (B, Tmax2).
align_end (paddle.Tensor):
frame level phone alignment end (B, Tmax2).
align_start_lens (paddle.Tensor):
length of align_start (B, ).
mlm_prob (float):
mean_phn_span (int):
span_bdy (paddle.Tensor):
masked mel boundary of input speech (B, 2).
Returns:
paddle.Tensor[bool]: masked position of input speech (B, Tmax).
'''
bz, sent_len, _ = paddle.shape(xs_pad)
masked_pos = paddle.zeros((bz, sent_len))
if mlm_prob == 1.0:
masked_pos += 1
elif mean_phn_span == 0:
# only speech
length = sent_len
mean_phn_span = min(length * mlm_prob // 3, 50)
masked_phn_idxs = random_spans_noise_mask(
length=length, mlm_prob=mlm_prob,
mean_phn_span=mean_phn_span).nonzero()
masked_pos[:, masked_phn_idxs] = 1
else:
for idx in range(bz):
# for inference
if span_bdy is not None:
for s, e in zip(span_bdy[idx][::2], span_bdy[idx][1::2]):
masked_pos[idx, s:e] = 1
# for training
else:
length = align_start_lens[idx]
if length < 2:
continue
masked_phn_idxs = random_spans_noise_mask(
length=length,
mlm_prob=mlm_prob,
mean_phn_span=mean_phn_span).nonzero()
masked_start = align_start[idx][masked_phn_idxs].tolist()
masked_end = align_end[idx][masked_phn_idxs].tolist()
for s, e in zip(masked_start, masked_end):
masked_pos[idx, s:e] = 1
non_eos_mask = paddle.reshape(src_mask, paddle.shape(xs_pad)[:2])
masked_pos = masked_pos * non_eos_mask
masked_pos = paddle.cast(masked_pos, 'bool')
return masked_pos
# mask speech and phones
[docs]def phones_text_masking(xs_pad: paddle.Tensor,
src_mask: paddle.Tensor,
text_pad: paddle.Tensor,
text_mask: paddle.Tensor,
align_start: paddle.Tensor,
align_end: paddle.Tensor,
align_start_lens: paddle.Tensor,
mlm_prob: float=0.8,
mean_phn_span: int=8,
span_bdy: paddle.Tensor=None):
'''
Args:
xs_pad (paddle.Tensor):
input speech (B, Tmax, D).
src_mask (paddle.Tensor):
mask of speech (B, 1, Tmax).
text_pad (paddle.Tensor):
input text (B, Tmax2).
text_mask (paddle.Tensor):
mask of text (B, 1, Tmax2).
align_start (paddle.Tensor):
frame level phone alignment start (B, Tmax2).
align_end (paddle.Tensor):
frame level phone alignment end (B, Tmax2).
align_start_lens (paddle.Tensor):
length of align_start (B, ).
mlm_prob (float):
mean_phn_span (int):
span_bdy (paddle.Tensor):
masked mel boundary of input speech (B, 2).
Returns:
paddle.Tensor[bool]:
masked position of input speech (B, Tmax).
paddle.Tensor[bool]:
masked position of input text (B, Tmax2).
'''
bz, sent_len, _ = paddle.shape(xs_pad)
masked_pos = paddle.zeros((bz, sent_len))
_, text_len = paddle.shape(text_pad)
text_mask_num_lower = math.ceil(text_len * (1 - mlm_prob) * 0.5)
text_masked_pos = paddle.zeros((bz, text_len))
if mlm_prob == 1.0:
masked_pos += 1
elif mean_phn_span == 0:
# only speech
length = sent_len
mean_phn_span = min(length * mlm_prob // 3, 50)
masked_phn_idxs = random_spans_noise_mask(
length=length, mlm_prob=mlm_prob,
mean_phn_span=mean_phn_span).nonzero()
masked_pos[:, masked_phn_idxs] = 1
else:
for idx in range(bz):
# for inference
if span_bdy is not None:
for s, e in zip(span_bdy[idx][::2], span_bdy[idx][1::2]):
masked_pos[idx, s:e] = 1
# for training
else:
length = align_start_lens[idx]
if length < 2:
continue
masked_phn_idxs = random_spans_noise_mask(
length=length,
mlm_prob=mlm_prob,
mean_phn_span=mean_phn_span).nonzero()
unmasked_phn_idxs = list(
set(range(length)) - set(masked_phn_idxs[0].tolist()))
np.random.shuffle(unmasked_phn_idxs)
masked_text_idxs = unmasked_phn_idxs[:text_mask_num_lower]
text_masked_pos[idx, masked_text_idxs] = 1
masked_start = align_start[idx][masked_phn_idxs].tolist()
masked_end = align_end[idx][masked_phn_idxs].tolist()
for s, e in zip(masked_start, masked_end):
masked_pos[idx, s:e] = 1
non_eos_mask = paddle.reshape(src_mask, shape=paddle.shape(xs_pad)[:2])
masked_pos = masked_pos * non_eos_mask
non_eos_text_mask = paddle.reshape(
text_mask, shape=paddle.shape(text_pad)[:2])
text_masked_pos = text_masked_pos * non_eos_text_mask
masked_pos = paddle.cast(masked_pos, 'bool')
text_masked_pos = paddle.cast(text_masked_pos, 'bool')
return masked_pos, text_masked_pos
[docs]def get_seg_pos(speech_pad: paddle.Tensor,
text_pad: paddle.Tensor,
align_start: paddle.Tensor,
align_end: paddle.Tensor,
align_start_lens: paddle.Tensor,
seg_emb: bool=False):
'''
Args:
speech_pad (paddle.Tensor):
input speech (B, Tmax, D).
text_pad (paddle.Tensor):
input text (B, Tmax2).
align_start (paddle.Tensor):
frame level phone alignment start (B, Tmax2).
align_end (paddle.Tensor):
frame level phone alignment end (B, Tmax2).
align_start_lens (paddle.Tensor):
length of align_start (B, ).
seg_emb (bool):
whether to use segment embedding.
Returns:
paddle.Tensor[int]: n-th phone of each mel, 0<=n<=Tmax2 (B, Tmax).
eg:
Tensor(shape=[1, 328], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[0 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 ,
1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 ,
1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 ,
1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 ,
1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 2 , 2 , 2 , 3 , 3 , 3 , 4 , 4 , 4 ,
5 , 5 , 5 , 6 , 6 , 6 , 6 , 6 , 6 , 6 , 6 , 7 , 7 , 7 , 7 , 7 , 7 , 7 ,
7 , 8 , 8 , 8 , 8 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 10, 10, 10, 10, 10,
10, 10, 10, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13,
13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15,
15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17,
17, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20,
20, 20, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 23, 23,
23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25,
25, 26, 26, 26, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 29,
29, 29, 29, 29, 29, 30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 32,
32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 33, 34, 34, 34, 34, 35, 35,
35, 35, 35, 35, 35, 35, 36, 36, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37,
37, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38,
38, 38, 0 , 0 ]])
paddle.Tensor[int]: n-th phone of each phone, 0<=n<=Tmax2 (B, Tmax2).
eg:
Tensor(shape=[1, 38], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[[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]])
'''
bz, speech_len, _ = paddle.shape(speech_pad)
_, text_len = paddle.shape(text_pad)
text_seg_pos = paddle.zeros((bz, text_len), dtype='int64')
speech_seg_pos = paddle.zeros((bz, speech_len), dtype='int64')
if not seg_emb:
return speech_seg_pos, text_seg_pos
for idx in range(bz):
align_length = align_start_lens[idx]
for j in range(align_length):
s, e = align_start[idx][j], align_end[idx][j]
speech_seg_pos[idx, s:e] = j + 1
text_seg_pos[idx, j] = j + 1
return speech_seg_pos, text_seg_pos
# randomly select the range of speech and text to mask during training
[docs]def random_spans_noise_mask(length: int,
mlm_prob: float=0.8,
mean_phn_span: float=8):
"""This function is copy of `random_spans_helper
<https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/t5/data/preprocessors.py#L2682>`__ .
Noise mask consisting of random spans of noise tokens.
The number of noise tokens and the number of noise spans and non-noise spans
are determined deterministically as follows:
num_noise_tokens = round(length * noise_density)
num_nonnoise_spans = num_noise_spans = round(num_noise_tokens / mean_noise_span_length)
Spans alternate between non-noise and noise, beginning with non-noise.
Subject to the above restrictions, all masks are equally likely.
Args:
length: an int32 scalar (length of the incoming token sequence)
noise_density: a float - approximate density of output mask
mean_noise_span_length: a number
Returns:
np.ndarray: a boolean tensor with shape [length]
"""
orig_length = length
num_noise_tokens = int(np.round(length * mlm_prob))
# avoid degeneracy by ensuring positive numbers of noise and nonnoise tokens.
num_noise_tokens = min(max(num_noise_tokens, 1), length - 1)
num_noise_spans = int(np.round(num_noise_tokens / mean_phn_span))
# avoid degeneracy by ensuring positive number of noise spans
num_noise_spans = max(num_noise_spans, 1)
num_nonnoise_tokens = length - num_noise_tokens
# pick the lengths of the noise spans and the non-noise spans
def _random_seg(num_items, num_segs):
"""Partition a sequence of items randomly into non-empty segments.
Args:
num_items:
an integer scalar > 0
num_segs:
an integer scalar in [1, num_items]
Returns:
a Tensor with shape [num_segs] containing positive integers that add
up to num_items
"""
mask_idxs = np.arange(num_items - 1) < (num_segs - 1)
np.random.shuffle(mask_idxs)
first_in_seg = np.pad(mask_idxs, [[1, 0]])
segment_id = np.cumsum(first_in_seg)
# count length of sub segments assuming that list is sorted
_, segment_length = np.unique(segment_id, return_counts=True)
return segment_length
noise_span_lens = _random_seg(num_noise_tokens, num_noise_spans)
nonnoise_span_lens = _random_seg(num_nonnoise_tokens, num_noise_spans)
interleaved_span_lens = np.reshape(
np.stack([nonnoise_span_lens, noise_span_lens], axis=1),
[num_noise_spans * 2])
span_starts = np.cumsum(interleaved_span_lens)[:-1]
span_start_indicator = np.zeros((length, ), dtype=np.int8)
span_start_indicator[span_starts] = True
span_num = np.cumsum(span_start_indicator)
is_noise = np.equal(span_num % 2, 1)
return is_noise[:orig_length]