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https://github.com/RVC-Boss/GPT-SoVITS.git
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77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
import math
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import torch
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from torch import nn
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class TokenEmbedding(nn.Module):
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def __init__(self, embedding_dim: int, vocab_size: int, dropout: float = 0.0):
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super().__init__()
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self.vocab_size = vocab_size
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self.embedding_dim = embedding_dim
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self.dropout = nn.Dropout(p=dropout)
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self.word_embeddings = nn.Embedding(self.vocab_size, self.embedding_dim)
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@property
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def weight(self) -> torch.Tensor:
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return self.word_embeddings.weight
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def embedding(self, index: int) -> torch.Tensor:
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return self.word_embeddings.weight[index : index + 1]
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def forward(self, x: torch.Tensor):
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x = self.word_embeddings(x)
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x = self.dropout(x)
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return x
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class SinePositionalEmbeddingNested(nn.Module):
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def __init__(
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self,
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embedding_dim: int,
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dropout: float = 0.0,
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scale: bool = False,
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alpha: bool = False,
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max_batch_size: int = 20,
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max_seq_len: int = 2500,
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):
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super().__init__()
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self.embedding_dim = embedding_dim
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self.x_scale = math.sqrt(embedding_dim) if scale else 1.0
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self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha)
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self.dropout = nn.Dropout(p=dropout)
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self.max_batch_size = max_batch_size
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self.max_seq_len = max_seq_len
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self.reverse = False
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self.register_buffer(
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"pe", torch.zeros(max_batch_size, max_seq_len, embedding_dim), persistent=False
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)
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self.pe: torch.Tensor
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self.compute_pe()
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def compute_pe(self):
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if self.reverse:
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position = torch.arange(self.max_seq_len - 1, -1, -1.0, dtype=torch.float32).unsqueeze(1)
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else:
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position = torch.arange(self.max_seq_len, dtype=torch.float32).unsqueeze(1)
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div_term = torch.exp(
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torch.arange(0, self.embedding_dim, 2, dtype=torch.float32)
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* -(math.log(10000.0) / self.embedding_dim)
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)
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pe = self.pe
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pe[:, :, 0::2] = torch.sin(position * div_term)
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pe[:, :, 1::2] = torch.cos(position * div_term)
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def forward(self, input_pos: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
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batch_size = x.shape[0]
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pe_values = self.pe[torch.arange(batch_size), input_pos - 1]
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return x * self.x_scale + self.alpha * pe_values.unsqueeze(1)
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def prefill(self, x: torch.Tensor) -> torch.Tensor:
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input_pos = torch.tensor([i.shape[0] for i in x.unbind()])
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pe_values = torch.nested.nested_tensor(
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[self.pe[i, : input_pos[i], :] for i in range(input_pos.size(0))]
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)
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return x * self.x_scale + self.alpha.item() * pe_values
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