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108 lines
3.7 KiB
108 lines
3.7 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Modified from espnet(https://github.com/espnet/espnet)
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"""Length regulator related modules."""
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import numpy as np
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import paddle
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from paddle import nn
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class LengthRegulator(nn.Layer):
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"""Length regulator module for feed-forward Transformer.
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This is a module of length regulator described in
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`FastSpeech: Fast, Robust and Controllable Text to Speech`_.
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The length regulator expands char or
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phoneme-level embedding features to frame-level by repeating each
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feature based on the corresponding predicted durations.
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.. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
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https://arxiv.org/pdf/1905.09263.pdf
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"""
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def __init__(self, pad_value=0.0):
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"""Initilize length regulator module.
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Args:
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pad_value (float, optional): Value used for padding.
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"""
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super().__init__()
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self.pad_value = pad_value
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# expand_numpy is faster than expand
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def expand_numpy(self, encodings: paddle.Tensor,
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durations: paddle.Tensor) -> paddle.Tensor:
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"""
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encodings: (B, T, C)
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durations: (B, T)
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"""
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batch_size, t_enc = durations.shape
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durations = durations.numpy()
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slens = np.sum(durations, -1)
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t_dec = np.max(slens)
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M = np.zeros([batch_size, t_dec, t_enc])
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for i in range(batch_size):
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k = 0
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for j in range(t_enc):
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d = durations[i, j]
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M[i, k:k + d, j] = 1
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k += d
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M = paddle.to_tensor(M, dtype=encodings.dtype)
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encodings = paddle.matmul(M, encodings)
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return encodings
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def expand(self, encodings: paddle.Tensor,
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durations: paddle.Tensor) -> paddle.Tensor:
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"""
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encodings: (B, T, C)
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durations: (B, T)
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"""
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batch_size, t_enc = paddle.shape(durations)
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slens = paddle.sum(durations, -1)
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t_dec = paddle.max(slens)
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M = paddle.zeros([batch_size, t_dec, t_enc])
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for i in range(batch_size):
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k = 0
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for j in range(t_enc):
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d = durations[i, j]
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# If the d == 0, slice action is meaningless and not supported in paddle
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if d >= 1:
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M[i, k:k + d, j] = 1
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k += d
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encodings = paddle.matmul(M, encodings)
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return encodings
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def forward(self, xs, ds, alpha=1.0, is_inference=False):
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"""Calculate forward propagation.
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Args:
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xs (Tensor): Batch of sequences of char or phoneme embeddings (B, Tmax, D).
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ds (Tensor(int64)): Batch of durations of each frame (B, T).
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alpha (float, optional): Alpha value to control speed of speech.
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Returns:
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Tensor: replicated input tensor based on durations (B, T*, D).
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"""
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if alpha != 1.0:
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assert alpha > 0
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ds = paddle.round(ds.cast(dtype=paddle.float32) * alpha)
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ds = ds.cast(dtype=paddle.int64)
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if is_inference:
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return self.expand(xs, ds)
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else:
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return self.expand_numpy(xs, ds)
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