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]