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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
from tortoise.models.arch_util import AttentionBlock
from tortoise.models.xtransformers import ContinuousTransformerWrapper, Encoder
def exists(val):
return val is not None
def masked_mean(t, mask):
t = t.masked_fill(~mask, 0.0)
return t.sum(dim=1) / mask.sum(dim=1)
class CollapsingTransformer(nn.Module):
def __init__(
self,
model_dim,
output_dims,
heads,
dropout,
depth,
mask_percentage=0,
**encoder_kwargs
):
super().__init__()
self.transformer = ContinuousTransformerWrapper(
max_seq_len=-1,
use_pos_emb=False,
attn_layers=Encoder(
dim=model_dim,
depth=depth,
heads=heads,
ff_dropout=dropout,
ff_mult=1,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True,
**encoder_kwargs,
),
)
self.pre_combiner = nn.Sequential(
nn.Conv1d(model_dim, output_dims, 1),
AttentionBlock(output_dims, num_heads=heads, do_checkpoint=False),
nn.Conv1d(output_dims, output_dims, 1),
)
self.mask_percentage = mask_percentage
def forward(self, x, **transformer_kwargs):
h = self.transformer(x, **transformer_kwargs)
h = h.permute(0, 2, 1)
h = self.pre_combiner(h).permute(0, 2, 1)
if self.training:
mask = torch.rand_like(h.float()) > self.mask_percentage
else:
mask = torch.ones_like(h.float()).bool()
return masked_mean(h, mask)
class ConvFormatEmbedding(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
self.emb = nn.Embedding(*args, **kwargs)
def forward(self, x):
y = self.emb(x)
return y.permute(0, 2, 1)
class CVVP(nn.Module):
def __init__(
self,
model_dim=512,
transformer_heads=8,
dropout=0.1,
conditioning_enc_depth=8,
cond_mask_percentage=0,
mel_channels=80,
mel_codes=None,
speech_enc_depth=8,
speech_mask_percentage=0,
latent_multiplier=1,
):
super().__init__()
latent_dim = latent_multiplier * model_dim
self.temperature = nn.Parameter(torch.tensor(1.0))
self.cond_emb = nn.Sequential(
nn.Conv1d(mel_channels, model_dim // 2, kernel_size=5, stride=2, padding=2),
nn.Conv1d(model_dim // 2, model_dim, kernel_size=3, stride=2, padding=1),
)
self.conditioning_transformer = CollapsingTransformer(
model_dim,
model_dim,
transformer_heads,
dropout,
conditioning_enc_depth,
cond_mask_percentage,
)
self.to_conditioning_latent = nn.Linear(latent_dim, latent_dim, bias=False)
if mel_codes is None:
self.speech_emb = nn.Conv1d(
mel_channels, model_dim, kernel_size=5, padding=2
)
else:
self.speech_emb = ConvFormatEmbedding(mel_codes, model_dim)
self.speech_transformer = CollapsingTransformer(
model_dim,
latent_dim,
transformer_heads,
dropout,
speech_enc_depth,
speech_mask_percentage,
)
self.to_speech_latent = nn.Linear(latent_dim, latent_dim, bias=False)
def get_grad_norm_parameter_groups(self):
return {
"conditioning": list(self.conditioning_transformer.parameters()),
"speech": list(self.speech_transformer.parameters()),
}
def forward(self, mel_cond, mel_input, return_loss=False):
cond_emb = self.cond_emb(mel_cond).permute(0, 2, 1)
enc_cond = self.conditioning_transformer(cond_emb)
cond_latents = self.to_conditioning_latent(enc_cond)
speech_emb = self.speech_emb(mel_input).permute(0, 2, 1)
enc_speech = self.speech_transformer(speech_emb)
speech_latents = self.to_speech_latent(enc_speech)
cond_latents, speech_latents = map(
lambda t: F.normalize(t, p=2, dim=-1), (cond_latents, speech_latents)
)
temp = self.temperature.exp()
if not return_loss:
sim = einsum("n d, n d -> n", cond_latents, speech_latents) * temp
return sim
sim = einsum("i d, j d -> i j", cond_latents, speech_latents) * temp
labels = torch.arange(cond_latents.shape[0], device=mel_input.device)
loss = (F.cross_entropy(sim, labels) + F.cross_entropy(sim.t(), labels)) / 2
return loss
if __name__ == "__main__":
clvp = CVVP()
clvp(torch.randn(2, 80, 100), torch.randn(2, 80, 95), return_loss=True)
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