refactor: move exists() and default() into generic_utils

TTS/tts/layers/bark/hubert/kmeans_hubert.py

@@ -14,6 +14,8 @@ from torch import nn
 from torchaudio.functional import resample
 from transformers import HubertModel
 
+from TTS.utils.generic_utils import exists
+
 
 def round_down_nearest_multiple(num, divisor):
     return num // divisor * divisor
@@ -26,14 +28,6 @@ def curtail_to_multiple(t, mult, from_left=False):
     return t[..., seq_slice]
 
 
-def exists(val):
-    return val is not None
-
-
-def default(val, d):
-    return val if exists(val) else d
-
-
 class CustomHubert(nn.Module):
     """
     checkpoint and kmeans can be downloaded at https://github.com/facebookresearch/fairseq/tree/main/examples/hubert

TTS/tts/layers/tortoise/clvp.py

@@ -8,10 +8,6 @@ from TTS.tts.layers.tortoise.transformer import Transformer
 from TTS.tts.layers.tortoise.xtransformers import Encoder
 
 
-def exists(val):
-    return val is not None
-
-
 def masked_mean(t, mask, dim=1):
     t = t.masked_fill(~mask[:, :, None], 0.0)
     return t.sum(dim=1) / mask.sum(dim=1)[..., None]

TTS/tts/layers/tortoise/transformer.py

@@ -1,22 +1,19 @@
+from typing import TypeVar, Union
+
 import torch
 import torch.nn.functional as F
 from einops import rearrange
 from torch import nn
 
+from TTS.utils.generic_utils import exists
+
 # helpers
+_T = TypeVar("_T")
 
 
-def exists(val):
+def cast_tuple(val: Union[tuple[_T], list[_T], _T], depth: int = 1) -> tuple[_T]:
-    return val is not None
-
-
-def default(val, d):
-    return val if exists(val) else d
-
-
-def cast_tuple(val, depth=1):
     if isinstance(val, list):
-        val = tuple(val)
+        return tuple(val)
     return val if isinstance(val, tuple) else (val,) * depth
 
 

TTS/tts/layers/tortoise/xtransformers.py

@@ -1,13 +1,15 @@
 import math
 from collections import namedtuple
 from functools import partial
-from inspect import isfunction
 
 import torch
 import torch.nn.functional as F
 from einops import rearrange, repeat
 from torch import einsum, nn
 
+from TTS.tts.layers.tortoise.transformer import cast_tuple, max_neg_value
+from TTS.utils.generic_utils import default, exists
+
 DEFAULT_DIM_HEAD = 64
 
 Intermediates = namedtuple("Intermediates", ["pre_softmax_attn", "post_softmax_attn"])
@@ -25,20 +27,6 @@ LayerIntermediates = namedtuple(
 # helpers
 
 
-def exists(val):
-    return val is not None
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def cast_tuple(val, depth):
-    return val if isinstance(val, tuple) else (val,) * depth
-
-
 class always:
     def __init__(self, val):
         self.val = val
@@ -63,10 +51,6 @@ class equals:
         return x == self.val
 
 
-def max_neg_value(tensor):
-    return -torch.finfo(tensor.dtype).max
-
-
 def l2norm(t):
     return F.normalize(t, p=2, dim=-1)
 

TTS/tts/layers/xtts/dvae.py

@@ -14,10 +14,6 @@ from TTS.utils.generic_utils import is_pytorch_at_least_2_4
 logger = logging.getLogger(__name__)
 
 
-def default(val, d):
-    return val if val is not None else d
-
-
 def eval_decorator(fn):
     def inner(model, *args, **kwargs):
         was_training = model.training

TTS/tts/layers/xtts/perceiver_encoder.py

@@ -10,10 +10,7 @@ from einops.layers.torch import Rearrange
 from torch import einsum, nn
 
 from TTS.tts.layers.tortoise.transformer import GEGLU
-
+from TTS.utils.generic_utils import default, exists
-
-def exists(val):
-    return val is not None
 
 
 def once(fn):
@@ -153,12 +150,6 @@ def Sequential(*mods):
     return nn.Sequential(*filter(exists, mods))
 
 
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if callable(d) else d
-
-
 class RMSNorm(nn.Module):
     def __init__(self, dim, scale=True, dim_cond=None):
         super().__init__()

TTS/utils/generic_utils.py

@@ -4,13 +4,26 @@ import importlib
 import logging
 import re
 from pathlib import Path
-from typing import Dict, Optional
+from typing import Callable, Dict, Optional, TypeVar, Union
 
 import torch
 from packaging.version import Version
+from typing_extensions import TypeIs
 
 logger = logging.getLogger(__name__)
 
+_T = TypeVar("_T")
+
+
+def exists(val: Union[_T, None]) -> TypeIs[_T]:
+    return val is not None
+
+
+def default(val: Union[_T, None], d: Union[_T, Callable[[], _T]]) -> _T:
+    if exists(val):
+        return val
+    return d() if callable(d) else d
+
 
 def to_camel(text):
     text = text.capitalize()