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68 lines
2.3 KiB
68 lines
2.3 KiB
# Copyright (c) 2022 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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"""Generator module in JETS.
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This code is based on https://github.com/imdanboy/jets.
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"""
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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from paddlespeech.t2s.modules.masked_fill import masked_fill
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class GaussianUpsampling(nn.Layer):
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"""
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Gaussian upsampling with fixed temperature as in:
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https://arxiv.org/abs/2010.04301
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"""
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def __init__(self, delta=0.1):
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super().__init__()
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self.delta = delta
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def forward(self, hs, ds, h_masks=None, d_masks=None):
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"""
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Args:
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hs (Tensor): Batched hidden state to be expanded (B, T_text, adim)
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ds (Tensor): Batched token duration (B, T_text)
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h_masks (Tensor): Mask tensor (B,T_feats)
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d_masks (Tensor): Mask tensor (B,T_text)
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Returns:
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Tensor: Expanded hidden state (B, T_feat, adim)
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"""
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B = ds.shape[0]
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if h_masks is None:
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T_feats = paddle.to_tensor(ds.sum(), dtype="int32")
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else:
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T_feats = h_masks.shape[-1]
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t = paddle.to_tensor(
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paddle.arange(0, T_feats).unsqueeze(0).tile([B, 1]),
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dtype="float32")
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if h_masks is not None:
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t = t * paddle.to_tensor(h_masks, dtype="float32")
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c = ds.cumsum(axis=-1) - ds / 2
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energy = -1 * self.delta * (t.unsqueeze(-1) - c.unsqueeze(1))**2
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if d_masks is not None:
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d_masks = ~(d_masks.unsqueeze(1))
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d_masks.stop_gradient = True
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d_masks = d_masks.tile([1, T_feats, 1])
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energy = masked_fill(energy, d_masks, -float("inf"))
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p_attn = F.softmax(energy, axis=2) # (B, T_feats, T_text)
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hs = paddle.matmul(p_attn, hs)
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return hs
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