File size: 6,919 Bytes
df9f13e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 |
import torch
import torch.nn as nn
import src.utils as utils
# from src.models.common.film import FiLM
class FilmLayer(nn.Module):
def __init__(self, D, C, nF, groups = 1):
super().__init__()
self.D = D # speaker dim 256
self.C = C # latent dim 16
self.nF = nF
self.weight = nn.Conv1d(self.D, self.C * nF, 1, groups = groups)
self.bias = nn.Conv1d(self.D, self.C * nF, 1, groups = groups)
def forward(self, x: torch.Tensor, embedding: torch.Tensor):
"""
x: (B, D, F, T)
embedding: (B, D, F)
"""
B, D, _F, T = x.shape
w = self.weight(embedding).reshape(B, self.C, _F, 1) # (B, C, F, 1)
b = self.bias(embedding).reshape(B, self.C, _F, 1) # (B, C, F, 1)
return x * w + b
class LayerNormPermuted(nn.LayerNorm):
def __init__(self, *args, **kwargs):
super(LayerNormPermuted, self).__init__(*args, **kwargs)
def forward(self, x):
"""
Args:
x: [B, C, T, F]
"""
x = x.permute(0, 2, 3, 1) # [B, T, F, C]
x = super().forward(x)
x = x.permute(0, 3, 1, 2) # [B, C, T, F]
return x
class Conv_Emb_Generator(nn.Module):
def __init__(
self,
block_model_name,
block_model_params,
spk_dim=256,
n_srcs=1,
n_fft=128,
latent_dim=16,
num_inputs=1,
n_layers=6,
use_first_ln=True,
n_imics=1,
lstm_fold_chunk=400,
E=2,
use_speaker_emb=True,
one_emb=True,
local_context_len=-1
# 6
):
super().__init__()
self.n_srcs = n_srcs
self.n_layers = n_layers
self.num_inputs = num_inputs
assert n_fft % 2 == 0
n_freqs = n_fft // 2 + 1
self.n_freqs = n_freqs
self.latent_dim = latent_dim
self.use_speaker_emb=use_speaker_emb
self.one_emb=one_emb
attn_approx_qk_dim=E*n_freqs
self.n_fft = n_fft
self.eps=1.0e-5
t_ksize = 3
self.t_ksize = t_ksize
ks, padding = (t_ksize, t_ksize), (0, 1)
self.n_imics=n_imics
if not use_speaker_emb:
self.n_imics=self.n_imics+1
module_list = [nn.Conv2d(2*self.n_imics, latent_dim, ks, padding=padding)]
if use_first_ln:
module_list.append(LayerNormPermuted(latent_dim))
self.conv = nn.Sequential(
*module_list
)
# FiLM layer
self.embeds = nn.ModuleList([])
self.local_context_len=local_context_len
self.blocks = nn.ModuleList([])
for _i in range(n_layers-1):
self.blocks.append(utils.import_attr(block_model_name)(emb_dim=latent_dim, n_freqs=n_freqs, approx_qk_dim=attn_approx_qk_dim, lstm_fold_chunk=lstm_fold_chunk, last=False, local_context_len=local_context_len, **block_model_params))
self.blocks.append(utils.import_attr(block_model_name)(emb_dim=latent_dim, n_freqs=n_freqs, approx_qk_dim=attn_approx_qk_dim, lstm_fold_chunk=lstm_fold_chunk, local_context_len=local_context_len, last=True, **block_model_params))
if self.use_speaker_emb and not self.one_emb:
for _i in range(n_layers-1):
self.embeds.append(FilmLayer(spk_dim, latent_dim, n_freqs, 1))
elif self.use_speaker_emb and self.one_emb:
self.embeds.append(FilmLayer(spk_dim, latent_dim, n_freqs, 1))
def init_buffers(self, batch_size, device):
conv_buf = torch.zeros(batch_size, 2*self.n_imics, self.t_ksize - 1, self.n_freqs,
device=device)
deconv_buf = torch.zeros(batch_size, self.latent_dim, self.t_ksize - 1, self.n_freqs,
device=device)
block_buffers = {}
for i in range(len(self.blocks)):
block_buffers[f'buf{i}'] = None
return dict(conv_buf=conv_buf, deconv_buf=deconv_buf,
block_bufs=block_buffers)
def forward(self, current_input: torch.Tensor, embedding: torch.Tensor, input_state, quantized=False) -> torch.Tensor:
"""
B: batch, M: mic, F: freq bin, C: real/imag, T: time frame
D: dimension of the embedding vector
current_input: (B, CM, T, F)
embedding: (B, D)
output: (B, S, T, C*F)
"""
# [B, C, T, F]
n_batch, _, n_frames, n_freqs = current_input.shape
batch = current_input
if input_state is None:
input_state = self.init_buffers(current_input.shape[0], current_input.device)
conv_buf = input_state['conv_buf']
gridnet_buf = input_state['block_bufs']
if quantized:
batch = nn.functional.pad(batch, (0, 0, self.t_ksize - 1, 0))
else:
batch = torch.cat((conv_buf, batch), dim=2)
conv_buf = batch[:, :, -(self.t_ksize - 1):, :]
batch = self.conv(batch) # [B, D, T, F]
if self.use_speaker_emb:
if not self.one_emb:
assert len(self.blocks)==self.n_layers
assert len(self.embeds)==self.n_layers-1
for ii in range(self.n_layers-1):
batch = batch.transpose(2, 3)
if ii > 0:
batch = self.embeds[ii - 1](batch, embedding)
batch = batch.transpose(2, 3)
batch, gridnet_buf[f'buf{ii}'] = self.blocks[ii](batch, gridnet_buf[f'buf{ii}'])
batch = batch.transpose(2, 3)
batch = self.embeds[-1](batch, embedding)
batch = batch.transpose(2, 3)
batch, gridnet_buf[f'buf{self.n_layers-1}'] = self.blocks[self.n_layers-1](batch, gridnet_buf[f'buf{self.n_layers-1}'])
else:
assert len(self.blocks)==self.n_layers
assert len(self.embeds)==1
for ii in range(self.n_layers):
batch = batch.transpose(2, 3)
if ii == 1:
batch = self.embeds[ii - 1](batch, embedding)
batch = batch.transpose(2, 3)
batch, gridnet_buf[f'buf{ii}'] = self.blocks[ii](batch, gridnet_buf[f'buf{ii}'])
else:
assert len(self.blocks)==self.n_layers
for ii in range(self.n_layers):
batch, gridnet_buf[f'buf{ii}'] = self.blocks[ii](batch, gridnet_buf[f'buf{ii}'])
conversation_emb=batch
return conversation_emb, input_state
def edge_mode(self):
for i in range(len(self.blocks)):
self.blocks[i].edge_mode()
if __name__ == "__main__":
pass |