Spaces:
Running
Running
File size: 5,836 Bytes
9b2107c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
import torch
import torch.nn.functional as F
from torch import nn
class GST(nn.Module):
"""Global Style Token Module for factorizing prosody in speech.
See https://arxiv.org/pdf/1803.09017"""
def __init__(self, num_mel, num_heads, num_style_tokens, gst_embedding_dim, embedded_speaker_dim=None):
super().__init__()
self.encoder = ReferenceEncoder(num_mel, gst_embedding_dim)
self.style_token_layer = StyleTokenLayer(num_heads, num_style_tokens, gst_embedding_dim, embedded_speaker_dim)
def forward(self, inputs, speaker_embedding=None):
enc_out = self.encoder(inputs)
# concat speaker_embedding
if speaker_embedding is not None:
enc_out = torch.cat([enc_out, speaker_embedding], dim=-1)
style_embed = self.style_token_layer(enc_out)
return style_embed
class ReferenceEncoder(nn.Module):
"""NN module creating a fixed size prosody embedding from a spectrogram.
inputs: mel spectrograms [batch_size, num_spec_frames, num_mel]
outputs: [batch_size, embedding_dim]
"""
def __init__(self, num_mel, embedding_dim):
super().__init__()
self.num_mel = num_mel
filters = [1] + [32, 32, 64, 64, 128, 128]
num_layers = len(filters) - 1
convs = [
nn.Conv2d(
in_channels=filters[i], out_channels=filters[i + 1], kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)
)
for i in range(num_layers)
]
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList([nn.BatchNorm2d(num_features=filter_size) for filter_size in filters[1:]])
post_conv_height = self.calculate_post_conv_height(num_mel, 3, 2, 1, num_layers)
self.recurrence = nn.GRU(
input_size=filters[-1] * post_conv_height, hidden_size=embedding_dim // 2, batch_first=True
)
def forward(self, inputs):
batch_size = inputs.size(0)
x = inputs.view(batch_size, 1, -1, self.num_mel)
# x: 4D tensor [batch_size, num_channels==1, num_frames, num_mel]
for conv, bn in zip(self.convs, self.bns):
x = conv(x)
x = bn(x)
x = F.relu(x)
x = x.transpose(1, 2)
# x: 4D tensor [batch_size, post_conv_width,
# num_channels==128, post_conv_height]
post_conv_width = x.size(1)
x = x.contiguous().view(batch_size, post_conv_width, -1)
# x: 3D tensor [batch_size, post_conv_width,
# num_channels*post_conv_height]
self.recurrence.flatten_parameters()
_, out = self.recurrence(x)
# out: 3D tensor [seq_len==1, batch_size, encoding_size=128]
return out.squeeze(0)
@staticmethod
def calculate_post_conv_height(height, kernel_size, stride, pad, n_convs):
"""Height of spec after n convolutions with fixed kernel/stride/pad."""
for _ in range(n_convs):
height = (height - kernel_size + 2 * pad) // stride + 1
return height
class StyleTokenLayer(nn.Module):
"""NN Module attending to style tokens based on prosody encodings."""
def __init__(self, num_heads, num_style_tokens, gst_embedding_dim, d_vector_dim=None):
super().__init__()
self.query_dim = gst_embedding_dim // 2
if d_vector_dim:
self.query_dim += d_vector_dim
self.key_dim = gst_embedding_dim // num_heads
self.style_tokens = nn.Parameter(torch.FloatTensor(num_style_tokens, self.key_dim))
nn.init.normal_(self.style_tokens, mean=0, std=0.5)
self.attention = MultiHeadAttention(
query_dim=self.query_dim, key_dim=self.key_dim, num_units=gst_embedding_dim, num_heads=num_heads
)
def forward(self, inputs):
batch_size = inputs.size(0)
prosody_encoding = inputs.unsqueeze(1)
# prosody_encoding: 3D tensor [batch_size, 1, encoding_size==128]
tokens = torch.tanh(self.style_tokens).unsqueeze(0).expand(batch_size, -1, -1)
# tokens: 3D tensor [batch_size, num tokens, token embedding size]
style_embed = self.attention(prosody_encoding, tokens)
return style_embed
class MultiHeadAttention(nn.Module):
"""
input:
query --- [N, T_q, query_dim]
key --- [N, T_k, key_dim]
output:
out --- [N, T_q, num_units]
"""
def __init__(self, query_dim, key_dim, num_units, num_heads):
super().__init__()
self.num_units = num_units
self.num_heads = num_heads
self.key_dim = key_dim
self.W_query = nn.Linear(in_features=query_dim, out_features=num_units, bias=False)
self.W_key = nn.Linear(in_features=key_dim, out_features=num_units, bias=False)
self.W_value = nn.Linear(in_features=key_dim, out_features=num_units, bias=False)
def forward(self, query, key):
queries = self.W_query(query) # [N, T_q, num_units]
keys = self.W_key(key) # [N, T_k, num_units]
values = self.W_value(key)
split_size = self.num_units // self.num_heads
queries = torch.stack(torch.split(queries, split_size, dim=2), dim=0) # [h, N, T_q, num_units/h]
keys = torch.stack(torch.split(keys, split_size, dim=2), dim=0) # [h, N, T_k, num_units/h]
values = torch.stack(torch.split(values, split_size, dim=2), dim=0) # [h, N, T_k, num_units/h]
# score = softmax(QK^T / (d_k**0.5))
scores = torch.matmul(queries, keys.transpose(2, 3)) # [h, N, T_q, T_k]
scores = scores / (self.key_dim**0.5)
scores = F.softmax(scores, dim=3)
# out = score * V
out = torch.matmul(scores, values) # [h, N, T_q, num_units/h]
out = torch.cat(torch.split(out, 1, dim=0), dim=3).squeeze(0) # [N, T_q, num_units]
return out
|