mirror of https://github.com/coqui-ai/TTS.git
53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
import numpy as np
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def _pad_data(x, length, pad_val=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_val)
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def prepare_data(inputs, pad_val=0):
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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, pad_val) 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: np.ndarray, length: int, pad_val=1) -> np.ndarray:
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"""Pad stop target array.
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Args:
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x (np.ndarray): Stop target array.
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length (int): Length after padding.
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pad_val (int, optional): Padding value. Defaults to 1.
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Returns:
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np.ndarray: Padded stop target array.
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"""
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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_val)
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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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