from typing import Optional
import numpy as np
import torch
from scipy.stats import betabinom
from torch.nn import functional as F
class StandardScaler:
"""StandardScaler for mean-scale normalization with the given mean and scale values."""
def __init__(self, mean: np.ndarray = None, scale: np.ndarray = None) -> None:
self.mean_ = mean
self.scale_ = scale
def set_stats(self, mean, scale):
self.mean_ = mean
self.scale_ = scale
def reset_stats(self):
delattr(self, "mean_")
delattr(self, "scale_")
def transform(self, X):
X = np.asarray(X)
X -= self.mean_
X /= self.scale_
return X
def inverse_transform(self, X):
X = np.asarray(X)
X *= self.scale_
X += self.mean_
return X
# from https://gist.github.com/jihunchoi/f1434a77df9db1bb337417854b398df1
def sequence_mask(sequence_length: torch.Tensor, max_len: Optional[int] = None) -> torch.Tensor:
"""Create a sequence mask for filtering padding in a sequence tensor.
Args:
sequence_length (torch.tensor): Sequence lengths.
max_len (int, Optional): Maximum sequence length. Defaults to None.
Shapes:
- mask: :math:`[B, T_max]`
"""
if max_len is None:
max_len = int(sequence_length.max())
seq_range = torch.arange(max_len, dtype=sequence_length.dtype, device=sequence_length.device)
# B x T_max
return seq_range.unsqueeze(0) < sequence_length.unsqueeze(1)
def segment(x: torch.tensor, segment_indices: torch.tensor, segment_size=4, pad_short=False):
"""Segment each sample in a batch based on the provided segment indices
Args:
x (torch.tensor): Input tensor.
segment_indices (torch.tensor): Segment indices.
segment_size (int): Expected output segment size.
pad_short (bool): Pad the end of input tensor with zeros if shorter than the segment size.
"""
# pad the input tensor if it is shorter than the segment size
if pad_short and x.shape[-1] < segment_size:
x = torch.nn.functional.pad(x, (0, segment_size - x.size(2)))
segments = torch.zeros_like(x[:, :, :segment_size])
for i in range(x.size(0)):
index_start = segment_indices[i]
index_end = index_start + segment_size
x_i = x[i]
if pad_short and index_end >= x.size(2):
# pad the sample if it is shorter than the segment size
x_i = torch.nn.functional.pad(x_i, (0, (index_end + 1) - x.size(2)))
segments[i] = x_i[:, index_start:index_end]
return segments
def rand_segments(
x: torch.tensor, x_lengths: torch.tensor = None, segment_size=4, let_short_samples=False, pad_short=False
):
"""Create random segments based on the input lengths.
Args:
x (torch.tensor): Input tensor.
x_lengths (torch.tensor): Input lengths.
segment_size (int): Expected output segment size.
let_short_samples (bool): Allow shorter samples than the segment size.
pad_short (bool): Pad the end of input tensor with zeros if shorter than the segment size.
Shapes:
- x: :math:`[B, C, T]`
- x_lengths: :math:`[B]`
"""
_x_lenghts = x_lengths.clone()
B, _, T = x.size()
if pad_short:
if T < segment_size:
x = torch.nn.functional.pad(x, (0, segment_size - T))
T = segment_size
if _x_lenghts is None:
_x_lenghts = T
len_diff = _x_lenghts - segment_size
if let_short_samples:
_x_lenghts[len_diff < 0] = segment_size
len_diff = _x_lenghts - segment_size
else:
assert all(
len_diff > 0
), f" [!] At least one sample is shorter than the segment size ({segment_size}). \n {_x_lenghts}"
segment_indices = (torch.rand([B]).type_as(x) * (len_diff + 1)).long()
ret = segment(x, segment_indices, segment_size, pad_short=pad_short)
return ret, segment_indices
def average_over_durations(values, durs):
"""Average values over durations.
Shapes:
- values: :math:`[B, 1, T_de]`
- durs: :math:`[B, T_en]`
- avg: :math:`[B, 1, T_en]`
"""
durs_cums_ends = torch.cumsum(durs, dim=1).long()
durs_cums_starts = torch.nn.functional.pad(durs_cums_ends[:, :-1], (1, 0))
values_nonzero_cums = torch.nn.functional.pad(torch.cumsum(values != 0.0, dim=2), (1, 0))
values_cums = torch.nn.functional.pad(torch.cumsum(values, dim=2), (1, 0))
bs, l = durs_cums_ends.size()
n_formants = values.size(1)
dcs = durs_cums_starts[:, None, :].expand(bs, n_formants, l)
dce = durs_cums_ends[:, None, :].expand(bs, n_formants, l)
values_sums = (torch.gather(values_cums, 2, dce) - torch.gather(values_cums, 2, dcs)).float()
values_nelems = (torch.gather(values_nonzero_cums, 2, dce) - torch.gather(values_nonzero_cums, 2, dcs)).float()
avg = torch.where(values_nelems == 0.0, values_nelems, values_sums / values_nelems)
return avg
def convert_pad_shape(pad_shape: list[list]) -> list:
l = pad_shape[::-1]
return [item for sublist in l for item in sublist]
def generate_path(duration: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
"""Generate alignment path based on the given segment durations.
Shapes:
- duration: :math:`[B, T_en]`
- mask: :math:'[B, T_en, T_de]`
- path: :math:`[B, T_en, T_de]`
"""
b, t_x, t_y = mask.shape
cum_duration = torch.cumsum(duration, dim=1)
cum_duration_flat = cum_duration.view(b * t_x)
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
path = path.view(b, t_x, t_y)
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
return path * mask
def generate_attention(
duration: torch.Tensor, x_mask: torch.Tensor, y_mask: Optional[torch.Tensor] = None
) -> torch.Tensor:
"""Generate an attention map from the linear scale durations.
Args:
duration (Tensor): Linear scale durations.
x_mask (Tensor): Mask for the input (character) sequence.
y_mask (Tensor): Mask for the output (spectrogram) sequence. Compute it from the predicted durations
if None. Defaults to None.
Shapes
- duration: :math:`(B, T_{en})`
- x_mask: :math:`(B, T_{en})`
- y_mask: :math:`(B, T_{de})`
"""
# compute decode mask from the durations
if y_mask is None:
y_lengths = duration.sum(dim=1).long()
y_lengths[y_lengths < 1] = 1
y_mask = sequence_mask(y_lengths).unsqueeze(1).to(duration.dtype)
attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2)
return generate_path(duration, attn_mask.squeeze(1)).to(duration.dtype)
def expand_encoder_outputs(
x: torch.Tensor, duration: torch.Tensor, x_mask: torch.Tensor, y_lengths: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Generate attention alignment map from durations and expand encoder outputs.
Shapes:
- x: Encoder output :math:`(B, D_{en}, T_{en})`
- duration: :math:`(B, T_{en})`
- x_mask: :math:`(B, T_{en})`
- y_lengths: :math:`(B)`
Examples::
encoder output: [a,b,c,d]
durations: [1, 3, 2, 1]
expanded: [a, b, b, b, c, c, d]
attention map: [[0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 1, 1, 0],
[0, 1, 1, 1, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0]]
"""
y_mask = sequence_mask(y_lengths).unsqueeze(1).to(x.dtype)
attn = generate_attention(duration, x_mask, y_mask)
x_expanded = torch.einsum("kmn, kjm -> kjn", [attn.float(), x])
return x_expanded, attn, y_mask
def beta_binomial_prior_distribution(phoneme_count, mel_count, scaling_factor=1.0):
P, M = phoneme_count, mel_count
x = np.arange(0, P)
mel_text_probs = []
for i in range(1, M + 1):
a, b = scaling_factor * i, scaling_factor * (M + 1 - i)
rv = betabinom(P, a, b)
mel_i_prob = rv.pmf(x)
mel_text_probs.append(mel_i_prob)
return np.array(mel_text_probs)
def compute_attn_prior(x_len, y_len, scaling_factor=1.0):
"""Compute attention priors for the alignment network."""
attn_prior = beta_binomial_prior_distribution(
x_len,
y_len,
scaling_factor,
)
return attn_prior # [y_len, x_len]