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# Copyright (c) 2024 Amphion.
#
# This code is modified from https://github.com/imdanboy/jets/blob/main/espnet2/gan_tts/jets/alignments.py
# Licensed under Apache License 2.0
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple
from numba import jit
from scipy.stats import betabinom
class AlignmentModule(nn.Module):
"""Alignment Learning Framework proposed for parallel TTS models in:
https://arxiv.org/abs/2108.10447
"""
def __init__(self, adim, odim, cache_prior=True):
"""Initialize AlignmentModule.
Args:
adim (int): Dimension of attention.
odim (int): Dimension of feats.
cache_prior (bool): Whether to cache beta-binomial prior.
"""
super().__init__()
self.cache_prior = cache_prior
self._cache = {}
self.t_conv1 = nn.Conv1d(adim, adim, kernel_size=3, padding=1)
self.t_conv2 = nn.Conv1d(adim, adim, kernel_size=1, padding=0)
self.f_conv1 = nn.Conv1d(odim, adim, kernel_size=3, padding=1)
self.f_conv2 = nn.Conv1d(adim, adim, kernel_size=3, padding=1)
self.f_conv3 = nn.Conv1d(adim, adim, kernel_size=1, padding=0)
def forward(self, text, feats, text_lengths, feats_lengths, x_masks=None):
"""Calculate alignment loss.
Args:
text (Tensor): Batched text embedding (B, T_text, adim).
feats (Tensor): Batched acoustic feature (B, T_feats, odim).
text_lengths (Tensor): Text length tensor (B,).
feats_lengths (Tensor): Feature length tensor (B,).
x_masks (Tensor): Mask tensor (B, T_text).
Returns:
Tensor: Log probability of attention matrix (B, T_feats, T_text).
"""
text = text.transpose(1, 2)
text = F.relu(self.t_conv1(text))
text = self.t_conv2(text)
text = text.transpose(1, 2)
feats = feats.transpose(1, 2)
feats = F.relu(self.f_conv1(feats))
feats = F.relu(self.f_conv2(feats))
feats = self.f_conv3(feats)
feats = feats.transpose(1, 2)
dist = feats.unsqueeze(2) - text.unsqueeze(1)
dist = torch.norm(dist, p=2, dim=3)
score = -dist
if x_masks is not None:
x_masks = x_masks.unsqueeze(-2)
score = score.masked_fill(x_masks, -np.inf)
log_p_attn = F.log_softmax(score, dim=-1)
# add beta-binomial prior
bb_prior = self._generate_prior(
text_lengths,
feats_lengths,
).to(dtype=log_p_attn.dtype, device=log_p_attn.device)
log_p_attn = log_p_attn + bb_prior
return log_p_attn
def _generate_prior(self, text_lengths, feats_lengths, w=1) -> torch.Tensor:
"""Generate alignment prior formulated as beta-binomial distribution
Args:
text_lengths (Tensor): Batch of the lengths of each input (B,).
feats_lengths (Tensor): Batch of the lengths of each target (B,).
w (float): Scaling factor; lower -> wider the width.
Returns:
Tensor: Batched 2d static prior matrix (B, T_feats, T_text).
"""
B = len(text_lengths)
T_text = text_lengths.max()
T_feats = feats_lengths.max()
bb_prior = torch.full((B, T_feats, T_text), fill_value=-np.inf)
for bidx in range(B):
T = feats_lengths[bidx].item()
N = text_lengths[bidx].item()
key = str(T) + "," + str(N)
if self.cache_prior and key in self._cache:
prob = self._cache[key]
else:
alpha = w * np.arange(1, T + 1, dtype=float) # (T,)
beta = w * np.array([T - t + 1 for t in alpha])
k = np.arange(N)
batched_k = k[..., None] # (N,1)
prob = betabinom.logpmf(batched_k, N, alpha, beta) # (N,T)
# store cache
if self.cache_prior and key not in self._cache:
self._cache[key] = prob
prob = torch.from_numpy(prob).transpose(0, 1) # -> (T,N)
bb_prior[bidx, :T, :N] = prob
return bb_prior
@jit(nopython=True)
def _monotonic_alignment_search(log_p_attn):
# https://arxiv.org/abs/2005.11129
T_mel = log_p_attn.shape[0]
T_inp = log_p_attn.shape[1]
Q = np.full((T_inp, T_mel), fill_value=-np.inf)
log_prob = log_p_attn.transpose(1, 0) # -> (T_inp,T_mel)
# 1. Q <- init first row for all j
for j in range(T_mel):
Q[0, j] = log_prob[0, : j + 1].sum()
# 2.
for j in range(1, T_mel):
for i in range(1, min(j + 1, T_inp)):
Q[i, j] = max(Q[i - 1, j - 1], Q[i, j - 1]) + log_prob[i, j]
# 3.
A = np.full((T_mel,), fill_value=T_inp - 1)
for j in range(T_mel - 2, -1, -1): # T_mel-2, ..., 0
# 'i' in {A[j+1]-1, A[j+1]}
i_a = A[j + 1] - 1
i_b = A[j + 1]
if i_b == 0:
argmax_i = 0
elif Q[i_a, j] >= Q[i_b, j]:
argmax_i = i_a
else:
argmax_i = i_b
A[j] = argmax_i
return A
def viterbi_decode(log_p_attn, text_lengths, feats_lengths):
"""Extract duration from an attention probability matrix
Args:
log_p_attn (Tensor): Batched log probability of attention
matrix (B, T_feats, T_text).
text_lengths (Tensor): Text length tensor (B,).
feats_legnths (Tensor): Feature length tensor (B,).
Returns:
Tensor: Batched token duration extracted from `log_p_attn` (B, T_text).
Tensor: Binarization loss tensor ().
"""
B = log_p_attn.size(0)
T_text = log_p_attn.size(2)
device = log_p_attn.device
bin_loss = 0
ds = torch.zeros((B, T_text), device=device)
for b in range(B):
cur_log_p_attn = log_p_attn[b, : feats_lengths[b], : text_lengths[b]]
viterbi = _monotonic_alignment_search(cur_log_p_attn.detach().cpu().numpy())
_ds = np.bincount(viterbi)
ds[b, : len(_ds)] = torch.from_numpy(_ds).to(device)
t_idx = torch.arange(feats_lengths[b])
bin_loss = bin_loss - cur_log_p_attn[t_idx, viterbi].mean()
bin_loss = bin_loss / B
return ds, bin_loss
@jit(nopython=True)
def _average_by_duration(ds, xs, text_lengths, feats_lengths):
B = ds.shape[0]
xs_avg = np.zeros_like(ds)
ds = ds.astype(np.int32)
for b in range(B):
t_text = text_lengths[b]
t_feats = feats_lengths[b]
d = ds[b, :t_text]
d_cumsum = d.cumsum()
d_cumsum = [0] + list(d_cumsum)
x = xs[b, :t_feats]
for n, (start, end) in enumerate(zip(d_cumsum[:-1], d_cumsum[1:])):
if len(x[start:end]) != 0:
xs_avg[b, n] = x[start:end].mean()
else:
xs_avg[b, n] = 0
return xs_avg
def average_by_duration(ds, xs, text_lengths, feats_lengths):
"""Average frame-level features into token-level according to durations
Args:
ds (Tensor): Batched token duration (B, T_text).
xs (Tensor): Batched feature sequences to be averaged (B, T_feats).
text_lengths (Tensor): Text length tensor (B,).
feats_lengths (Tensor): Feature length tensor (B,).
Returns:
Tensor: Batched feature averaged according to the token duration (B, T_text).
"""
device = ds.device
args = [ds, xs, text_lengths, feats_lengths]
args = [arg.detach().cpu().numpy() for arg in args]
xs_avg = _average_by_duration(*args)
xs_avg = torch.from_numpy(xs_avg).to(device)
return xs_avg
def make_pad_mask(lengths, xs=None, length_dim=-1, maxlen=None):
"""Make mask tensor containing indices of padded part.
Args:
lengths (LongTensor 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: Mask tensor containing indices of padded part.
dtype=torch.uint8 in PyTorch 1.2-
dtype=torch.bool in PyTorch 1.2+ (including 1.2)
Examples:
With only lengths.
>>> lengths = [5, 3, 2]
>>> make_pad_mask(lengths)
masks = [[0, 0, 0, 0 ,0],
[0, 0, 0, 1, 1],
[0, 0, 1, 1, 1]]
With the reference tensor.
>>> xs = torch.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]]], dtype=torch.uint8)
>>> xs = torch.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]]], dtype=torch.uint8)
With the reference tensor and dimension indicator.
>>> xs = torch.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]]], dtype=torch.uint8)
>>> 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]]], dtype=torch.uint8)
"""
if length_dim == 0:
raise ValueError("length_dim cannot be 0: {}".format(length_dim))
if not isinstance(lengths, list):
lengths = lengths.tolist()
bs = int(len(lengths))
if maxlen is None:
if xs is None:
maxlen = int(max(lengths))
else:
maxlen = xs.size(length_dim)
else:
assert xs is None
assert maxlen >= int(max(lengths))
seq_range = torch.arange(0, maxlen, dtype=torch.int64)
seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen)
seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1)
mask = seq_range_expand >= seq_length_expand
if xs is not None:
assert xs.size(0) == bs, (xs.size(0), bs)
if length_dim < 0:
length_dim = xs.dim() + length_dim
# ind = (:, None, ..., None, :, , None, ..., None)
ind = tuple(
slice(None) if i in (0, length_dim) else None for i in range(xs.dim())
)
mask = mask[ind].expand_as(xs).to(xs.device)
return mask
def make_non_pad_mask(lengths, xs=None, length_dim=-1):
"""Make mask tensor containing indices of non-padded part.
Args:
lengths (LongTensor 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:
ByteTensor: mask tensor containing indices of padded part.
dtype=torch.uint8 in PyTorch 1.2-
dtype=torch.bool in PyTorch 1.2+ (including 1.2)
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 = torch.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]]], dtype=torch.uint8)
>>> xs = torch.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]]], dtype=torch.uint8)
With the reference tensor and dimension indicator.
>>> xs = torch.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]]], dtype=torch.uint8)
>>> 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]]], dtype=torch.uint8)
"""
return ~make_pad_mask(lengths, xs, length_dim)
def get_random_segments(
x: torch.Tensor,
x_lengths: torch.Tensor,
segment_size: int,
) -> Tuple[torch.Tensor, torch.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 = x.size()
max_start_idx = x_lengths - segment_size
start_idxs = (torch.rand([b]).to(x.device) * max_start_idx).to(
dtype=torch.long,
)
segments = get_segments(x, start_idxs, segment_size)
return segments, start_idxs
def get_segments(
x: torch.Tensor,
start_idxs: torch.Tensor,
segment_size: int,
) -> torch.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 = x.size()
segments = x.new_zeros(b, c, segment_size)
for i, start_idx in enumerate(start_idxs):
segments[i] = x[i, :, start_idx : start_idx + segment_size]
return segments
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