259 lines
8.4 KiB
Python
259 lines
8.4 KiB
Python
import numpy as np
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import torch
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from scipy.stats import betabinom
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from torch.nn import functional as F
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try:
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from TTS.tts.utils.monotonic_align.core import maximum_path_c
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CYTHON = True
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except ModuleNotFoundError:
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CYTHON = False
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class StandardScaler:
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"""StandardScaler for mean-scale normalization with the given mean and scale values."""
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def __init__(self, mean: np.ndarray = None, scale: np.ndarray = None) -> None:
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self.mean_ = mean
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self.scale_ = scale
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def set_stats(self, mean, scale):
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self.mean_ = mean
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self.scale_ = scale
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def reset_stats(self):
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delattr(self, "mean_")
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delattr(self, "scale_")
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def transform(self, X):
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X = np.asarray(X)
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X -= self.mean_
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X /= self.scale_
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return X
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def inverse_transform(self, X):
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X = np.asarray(X)
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X *= self.scale_
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X += self.mean_
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return X
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# from https://gist.github.com/jihunchoi/f1434a77df9db1bb337417854b398df1
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def sequence_mask(sequence_length, max_len=None):
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"""Create a sequence mask for filtering padding in a sequence tensor.
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Args:
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sequence_length (torch.tensor): Sequence lengths.
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max_len (int, Optional): Maximum sequence length. Defaults to None.
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Shapes:
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- mask: :math:`[B, T_max]`
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"""
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if max_len is None:
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max_len = sequence_length.max()
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seq_range = torch.arange(max_len, dtype=sequence_length.dtype, device=sequence_length.device)
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# B x T_max
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return seq_range.unsqueeze(0) < sequence_length.unsqueeze(1)
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def segment(x: torch.tensor, segment_indices: torch.tensor, segment_size=4, pad_short=False):
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"""Segment each sample in a batch based on the provided segment indices
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Args:
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x (torch.tensor): Input tensor.
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segment_indices (torch.tensor): Segment indices.
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segment_size (int): Expected output segment size.
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pad_short (bool): Pad the end of input tensor with zeros if shorter than the segment size.
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"""
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# pad the input tensor if it is shorter than the segment size
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if pad_short and x.shape[-1] < segment_size:
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x = torch.nn.functional.pad(x, (0, segment_size - x.size(2)))
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segments = torch.zeros_like(x[:, :, :segment_size])
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for i in range(x.size(0)):
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index_start = segment_indices[i]
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index_end = index_start + segment_size
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x_i = x[i]
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if pad_short and index_end >= x.size(2):
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# pad the sample if it is shorter than the segment size
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x_i = torch.nn.functional.pad(x_i, (0, (index_end + 1) - x.size(2)))
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segments[i] = x_i[:, index_start:index_end]
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return segments
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def rand_segments(
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x: torch.tensor, x_lengths: torch.tensor = None, segment_size=4, let_short_samples=False, pad_short=False
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):
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"""Create random segments based on the input lengths.
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Args:
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x (torch.tensor): Input tensor.
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x_lengths (torch.tensor): Input lengths.
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segment_size (int): Expected output segment size.
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let_short_samples (bool): Allow shorter samples than the segment size.
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pad_short (bool): Pad the end of input tensor with zeros if shorter than the segment size.
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Shapes:
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- x: :math:`[B, C, T]`
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- x_lengths: :math:`[B]`
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"""
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_x_lenghts = x_lengths.clone()
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B, _, T = x.size()
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if pad_short:
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if T < segment_size:
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x = torch.nn.functional.pad(x, (0, segment_size - T))
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T = segment_size
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if _x_lenghts is None:
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_x_lenghts = T
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len_diff = _x_lenghts - segment_size
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if let_short_samples:
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_x_lenghts[len_diff < 0] = segment_size
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len_diff = _x_lenghts - segment_size
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else:
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assert all(
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len_diff > 0
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), f" [!] At least one sample is shorter than the segment size ({segment_size}). \n {_x_lenghts}"
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segment_indices = (torch.rand([B]).type_as(x) * (len_diff + 1)).long()
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ret = segment(x, segment_indices, segment_size, pad_short=pad_short)
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return ret, segment_indices
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def average_over_durations(values, durs):
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"""Average values over durations.
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Shapes:
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- values: :math:`[B, 1, T_de]`
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- durs: :math:`[B, T_en]`
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- avg: :math:`[B, 1, T_en]`
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"""
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durs_cums_ends = torch.cumsum(durs, dim=1).long()
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durs_cums_starts = torch.nn.functional.pad(durs_cums_ends[:, :-1], (1, 0))
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values_nonzero_cums = torch.nn.functional.pad(torch.cumsum(values != 0.0, dim=2), (1, 0))
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values_cums = torch.nn.functional.pad(torch.cumsum(values, dim=2), (1, 0))
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bs, l = durs_cums_ends.size()
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n_formants = values.size(1)
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dcs = durs_cums_starts[:, None, :].expand(bs, n_formants, l)
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dce = durs_cums_ends[:, None, :].expand(bs, n_formants, l)
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values_sums = (torch.gather(values_cums, 2, dce) - torch.gather(values_cums, 2, dcs)).float()
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values_nelems = (torch.gather(values_nonzero_cums, 2, dce) - torch.gather(values_nonzero_cums, 2, dcs)).float()
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avg = torch.where(values_nelems == 0.0, values_nelems, values_sums / values_nelems)
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return avg
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def convert_pad_shape(pad_shape):
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l = pad_shape[::-1]
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pad_shape = [item for sublist in l for item in sublist]
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return pad_shape
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def generate_path(duration, mask):
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"""
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Shapes:
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- duration: :math:`[B, T_en]`
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- mask: :math:'[B, T_en, T_de]`
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- path: :math:`[B, T_en, T_de]`
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"""
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b, t_x, t_y = mask.shape
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cum_duration = torch.cumsum(duration, 1)
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cum_duration_flat = cum_duration.view(b * t_x)
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path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
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path = path.view(b, t_x, t_y)
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path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
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path = path * mask
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return path
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def maximum_path(value, mask):
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if CYTHON:
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return maximum_path_cython(value, mask)
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return maximum_path_numpy(value, mask)
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def maximum_path_cython(value, mask):
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"""Cython optimised version.
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Shapes:
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- value: :math:`[B, T_en, T_de]`
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- mask: :math:`[B, T_en, T_de]`
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"""
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value = value * mask
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device = value.device
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dtype = value.dtype
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value = value.data.cpu().numpy().astype(np.float32)
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path = np.zeros_like(value).astype(np.int32)
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mask = mask.data.cpu().numpy()
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t_x_max = mask.sum(1)[:, 0].astype(np.int32)
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t_y_max = mask.sum(2)[:, 0].astype(np.int32)
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maximum_path_c(path, value, t_x_max, t_y_max)
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return torch.from_numpy(path).to(device=device, dtype=dtype)
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def maximum_path_numpy(value, mask, max_neg_val=None):
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"""
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Monotonic alignment search algorithm
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Numpy-friendly version. It's about 4 times faster than torch version.
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value: [b, t_x, t_y]
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mask: [b, t_x, t_y]
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"""
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if max_neg_val is None:
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max_neg_val = -np.inf # Patch for Sphinx complaint
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value = value * mask
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device = value.device
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dtype = value.dtype
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value = value.cpu().detach().numpy()
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mask = mask.cpu().detach().numpy().astype(bool)
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b, t_x, t_y = value.shape
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direction = np.zeros(value.shape, dtype=np.int64)
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v = np.zeros((b, t_x), dtype=np.float32)
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x_range = np.arange(t_x, dtype=np.float32).reshape(1, -1)
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for j in range(t_y):
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v0 = np.pad(v, [[0, 0], [1, 0]], mode="constant", constant_values=max_neg_val)[:, :-1]
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v1 = v
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max_mask = v1 >= v0
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v_max = np.where(max_mask, v1, v0)
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direction[:, :, j] = max_mask
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index_mask = x_range <= j
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v = np.where(index_mask, v_max + value[:, :, j], max_neg_val)
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direction = np.where(mask, direction, 1)
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path = np.zeros(value.shape, dtype=np.float32)
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index = mask[:, :, 0].sum(1).astype(np.int64) - 1
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index_range = np.arange(b)
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for j in reversed(range(t_y)):
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path[index_range, index, j] = 1
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index = index + direction[index_range, index, j] - 1
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path = path * mask.astype(np.float32)
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path = torch.from_numpy(path).to(device=device, dtype=dtype)
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return path
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def beta_binomial_prior_distribution(phoneme_count, mel_count, scaling_factor=1.0):
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P, M = phoneme_count, mel_count
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x = np.arange(0, P)
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mel_text_probs = []
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for i in range(1, M + 1):
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a, b = scaling_factor * i, scaling_factor * (M + 1 - i)
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rv = betabinom(P, a, b)
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mel_i_prob = rv.pmf(x)
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mel_text_probs.append(mel_i_prob)
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return np.array(mel_text_probs)
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def compute_attn_prior(x_len, y_len, scaling_factor=1.0):
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"""Compute attention priors for the alignment network."""
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attn_prior = beta_binomial_prior_distribution(
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x_len,
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y_len,
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scaling_factor,
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)
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return attn_prior # [y_len, x_len]
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