refactor(xtts): reuse functions/classes from tortoise

TTS/tts/layers/xtts/latent_encoder.py

@@ -6,10 +6,7 @@ import torch
 from torch import nn
 from torch.nn import functional as F
 
-
-class GroupNorm32(nn.GroupNorm):
-    def forward(self, x):
-        return super().forward(x.float()).type(x.dtype)
+from TTS.tts.layers.tortoise.arch_utils import normalization, zero_module
 
 
 def conv_nd(dims, *args, **kwargs):
@@ -22,24 +19,6 @@ def conv_nd(dims, *args, **kwargs):
     raise ValueError(f"unsupported dimensions: {dims}")
 
 
-def normalization(channels):
-    groups = 32
-    if channels <= 16:
-        groups = 8
-    elif channels <= 64:
-        groups = 16
-    while channels % groups != 0:
-        groups = int(groups / 2)
-    assert groups > 2
-    return GroupNorm32(groups, channels)
-
-
-def zero_module(module):
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
 class QKVAttention(nn.Module):
     def __init__(self, n_heads):
         super().__init__()

TTS/tts/layers/xtts/perceiver_encoder.py

@@ -9,6 +9,8 @@ from einops import rearrange, repeat
 from einops.layers.torch import Rearrange
 from torch import einsum, nn
 
+from TTS.tts.layers.tortoise.transformer import GEGLU
+
 
 def exists(val):
     return val is not None
@@ -194,12 +196,6 @@ class CausalConv1d(nn.Conv1d):
         return super().forward(causal_padded_x)
 
 
-class GEGLU(nn.Module):
-    def forward(self, x):
-        x, gate = x.chunk(2, dim=-1)
-        return F.gelu(gate) * x
-
-
 def FeedForward(dim, mult=4, causal_conv=False):
     dim_inner = int(dim * mult * 2 / 3)