74 lines
2.7 KiB
Python
74 lines
2.7 KiB
Python
# Copyright 2025 ByteDance and/or its affiliates.
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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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import math
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import torch
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from torch import nn
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import torch.nn.functional as F
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class LengthRegulator(torch.nn.Module):
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def __init__(self, pad_value=0.0):
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super(LengthRegulator, self).__init__()
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self.pad_value = pad_value
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def forward(self, dur, dur_padding=None, alpha=1.0):
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"""
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Example (no batch dim version):
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1. dur = [2,2,3]
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2. token_idx = [[1],[2],[3]], dur_cumsum = [2,4,7], dur_cumsum_prev = [0,2,4]
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3. token_mask = [[1,1,0,0,0,0,0],
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[0,0,1,1,0,0,0],
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[0,0,0,0,1,1,1]]
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4. token_idx * token_mask = [[1,1,0,0,0,0,0],
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[0,0,2,2,0,0,0],
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[0,0,0,0,3,3,3]]
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5. (token_idx * token_mask).sum(0) = [1,1,2,2,3,3,3]
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:param dur: Batch of durations of each frame (B, T_txt)
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:param dur_padding: Batch of padding of each frame (B, T_txt)
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:param alpha: duration rescale coefficient
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:return:
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mel2ph (B, T_speech)
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assert alpha > 0
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"""
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dur = torch.round(dur.float() * alpha).long()
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if dur_padding is not None:
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dur = dur * (1 - dur_padding.long())
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token_idx = torch.arange(1, dur.shape[1] + 1)[None, :, None].to(dur.device)
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dur_cumsum = torch.cumsum(dur, 1)
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dur_cumsum_prev = F.pad(dur_cumsum, [1, -1], mode='constant', value=0)
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pos_idx = torch.arange(dur.sum(-1).max())[None, None].to(dur.device)
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token_mask = (pos_idx >= dur_cumsum_prev[:, :, None]) & (pos_idx < dur_cumsum[:, :, None])
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mel2token = (token_idx * token_mask.long()).sum(1)
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return mel2token
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class PosEmb(nn.Module):
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def __init__(self, dim):
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super().__init__()
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self.dim = dim
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half_dim = self.dim // 2
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emb = math.log(10000) / (half_dim - 1)
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emb = torch.exp(torch.arange(half_dim) * -emb)
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self.emb = emb # TODO
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def forward(self, x):
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emb = x[:, :, None] * self.emb[None, None, :].to(x.device)
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emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
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return emb
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