mirror of https://github.com/coqui-ai/TTS.git
53 lines
1.4 KiB
Python
53 lines
1.4 KiB
Python
import numpy as np
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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(
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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
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assert x.ndim == 2
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x = np.pad(
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x, [[0, 0], [0, length - x.shape[1]]],
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mode='constant',
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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)) + 1 # zero-frame
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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 = 1.
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assert x.ndim == 1
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return np.pad(
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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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max_len = max((x.shape[0] for x in inputs)) + 1 # zero-frame
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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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timesteps = inputs.shape[-1]
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return np.pad(
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inputs, [[0, 0], [0, 0], [0, pad_len]],
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mode='constant',
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constant_values=0.0)
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