mirror of https://github.com/coqui-ai/TTS.git
78 lines
2.2 KiB
Python
78 lines
2.2 KiB
Python
import numpy as np
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import torch
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def _pad_data(x, length):
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_pad = 0
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assert x.ndim == 1
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return np.pad(x, (0, length - x.shape[0]), mode="constant", constant_values=_pad)
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def prepare_data(inputs):
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max_len = max((len(x) for x in inputs))
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return np.stack([_pad_data(x, max_len) for x in inputs])
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def _pad_tensor(x, length):
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_pad = 0.0
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assert x.ndim == 2
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x = np.pad(x, [[0, 0], [0, length - x.shape[1]]], mode="constant", constant_values=_pad)
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return x
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def prepare_tensor(inputs, out_steps):
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max_len = max((x.shape[1] for x in inputs))
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remainder = max_len % out_steps
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pad_len = max_len + (out_steps - remainder) if remainder > 0 else max_len
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return np.stack([_pad_tensor(x, pad_len) for x in inputs])
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def _pad_stop_target(x, length):
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_pad = 0.0
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assert x.ndim == 1
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return np.pad(x, (0, length - x.shape[0]), mode="constant", constant_values=_pad)
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def prepare_stop_target(inputs, out_steps):
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"""Pad row vectors with 1."""
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max_len = max((x.shape[0] for x in inputs))
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remainder = max_len % out_steps
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pad_len = max_len + (out_steps - remainder) if remainder > 0 else max_len
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return np.stack([_pad_stop_target(x, pad_len) for x in inputs])
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def pad_per_step(inputs, pad_len):
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return np.pad(inputs, [[0, 0], [0, 0], [0, pad_len]], mode="constant", constant_values=0.0)
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# pylint: disable=attribute-defined-outside-init
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class StandardScaler:
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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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if max_len is None:
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max_len = sequence_length.data.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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