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import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
import math
class FiLM(nn.Module):
def __init__(self, in_dim, cond_dim):
super().__init__()
self.gain = Linear(cond_dim, in_dim)
self.bias = Linear(cond_dim, in_dim)
nn.init.xavier_uniform_(self.gain.weight)
nn.init.constant_(self.gain.bias, 1)
nn.init.xavier_uniform_(self.bias.weight)
nn.init.constant_(self.bias.bias, 0)
def forward(self, x, condition):
gain = self.gain(condition)
bias = self.bias(condition)
if gain.dim() == 2:
gain = gain.unsqueeze(-1)
if bias.dim() == 2:
bias = bias.unsqueeze(-1)
return x * gain + bias
class Mish(nn.Module):
def forward(self, x):
return x * torch.tanh(F.softplus(x))
def Conv1d(*args, **kwargs):
layer = nn.Conv1d(*args, **kwargs)
nn.init.kaiming_normal_(layer.weight)
return layer
def Linear(*args, **kwargs):
layer = nn.Linear(*args, **kwargs)
layer.weight.data.normal_(0.0, 0.02)
return layer
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = x[:, None] * emb[None, :]
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
class ResidualBlock(nn.Module):
def __init__(self, hidden_dim, attn_head, dilation, drop_out, has_cattn=False):
super().__init__()
self.hidden_dim = hidden_dim
self.dilation = dilation
self.has_cattn = has_cattn
self.attn_head = attn_head
self.drop_out = drop_out
self.dilated_conv = Conv1d(
hidden_dim, 2 * hidden_dim, 3, padding=dilation, dilation=dilation
)
self.diffusion_proj = Linear(hidden_dim, hidden_dim)
self.cond_proj = Conv1d(hidden_dim, hidden_dim * 2, 1)
self.out_proj = Conv1d(hidden_dim, hidden_dim * 2, 1)
if self.has_cattn:
self.attn = nn.MultiheadAttention(
hidden_dim, attn_head, 0.1, batch_first=True
)
self.film = FiLM(hidden_dim * 2, hidden_dim)
self.ln = nn.LayerNorm(hidden_dim)
self.dropout = nn.Dropout(self.drop_out)
def forward(self, x, x_mask, cond, diffusion_step, spk_query_emb):
diffusion_step = self.diffusion_proj(diffusion_step).unsqueeze(-1) # (B, d, 1)
cond = self.cond_proj(cond) # (B, 2*d, T)
y = x + diffusion_step
if x_mask != None:
y = y * x_mask.to(y.dtype)[:, None, :] # (B, 2*d, T)
if self.has_cattn:
y_ = y.transpose(1, 2)
y_ = self.ln(y_)
y_, _ = self.attn(y_, spk_query_emb, spk_query_emb) # (B, T, d)
y = self.dilated_conv(y) + cond # (B, 2*d, T)
if self.has_cattn:
y = self.film(y.transpose(1, 2), y_) # (B, T, 2*d)
y = y.transpose(1, 2) # (B, 2*d, T)
gate, filter_ = torch.chunk(y, 2, dim=1)
y = torch.sigmoid(gate) * torch.tanh(filter_)
y = self.out_proj(y)
residual, skip = torch.chunk(y, 2, dim=1)
if x_mask != None:
residual = residual * x_mask.to(y.dtype)[:, None, :]
skip = skip * x_mask.to(y.dtype)[:, None, :]
return (x + residual) / math.sqrt(2.0), skip
class WaveNet(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.in_dim = cfg.input_size
self.hidden_dim = cfg.hidden_size
self.out_dim = cfg.out_size
self.num_layers = cfg.num_layers
self.cross_attn_per_layer = cfg.cross_attn_per_layer
self.dilation_cycle = cfg.dilation_cycle
self.attn_head = cfg.attn_head
self.drop_out = cfg.drop_out
self.in_proj = Conv1d(self.in_dim, self.hidden_dim, 1)
self.diffusion_embedding = SinusoidalPosEmb(self.hidden_dim)
self.mlp = nn.Sequential(
Linear(self.hidden_dim, self.hidden_dim * 4),
Mish(),
Linear(self.hidden_dim * 4, self.hidden_dim),
)
self.cond_ln = nn.LayerNorm(self.hidden_dim)
self.layers = nn.ModuleList(
[
ResidualBlock(
self.hidden_dim,
self.attn_head,
2 ** (i % self.dilation_cycle),
self.drop_out,
has_cattn=(i % self.cross_attn_per_layer == 0),
)
for i in range(self.num_layers)
]
)
self.skip_proj = Conv1d(self.hidden_dim, self.hidden_dim, 1)
self.out_proj = Conv1d(self.hidden_dim, self.out_dim, 1)
nn.init.zeros_(self.out_proj.weight)
def forward(self, x, x_mask, cond, diffusion_step, spk_query_emb):
"""
x: (B, 128, T)
x_mask: (B, T), mask is 0
cond: (B, T, 512)
diffusion_step: (B,)
spk_query_emb: (B, 32, 512)
"""
cond = self.cond_ln(cond)
cond_input = cond.transpose(1, 2)
x_input = self.in_proj(x)
x_input = F.relu(x_input)
diffusion_step = self.diffusion_embedding(diffusion_step).to(x.dtype)
diffusion_step = self.mlp(diffusion_step)
skip = []
for _, layer in enumerate(self.layers):
x_input, skip_connection = layer(
x_input, x_mask, cond_input, diffusion_step, spk_query_emb
)
skip.append(skip_connection)
x_input = torch.sum(torch.stack(skip), dim=0) / math.sqrt(self.num_layers)
x_out = self.skip_proj(x_input)
x_out = F.relu(x_out)
x_out = self.out_proj(x_out) # (B, 128, T)
return x_out
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