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Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)

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* ruff check --fix

* ruff format --line-length 120 --target-version py39

* Change the link for G2PW Model

* update pytorch version and colab
This commit is contained in:
XXXXRT666
2025-04-07 09:42:47 +01:00
committed by GitHub
parent 9da7e17efe
commit 53cac93589
132 changed files with 8185 additions and 6648 deletions
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30
GPT_SoVITS/AR/data/bucket_sampler.py
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@@ -4,14 +4,11 @@ import itertools
import math
import random
from random import shuffle
from typing import Iterator
from typing import Optional
from typing import TypeVar
from typing import Iterator, Optional, TypeVar
import torch
import torch.distributed as dist
from torch.utils.data import Dataset
from torch.utils.data import Sampler
from torch.utils.data import Dataset, Sampler
__all__ = [
"DistributedBucketSampler",
@@ -50,10 +47,7 @@ class DistributedBucketSampler(Sampler[T_co]):
if torch.cuda.is_available():
torch.cuda.set_device(rank)
if rank >= num_replicas or rank < 0:
raise ValueError(
"Invalid rank {}, rank should be in the interval"
" [0, {}]".format(rank, num_replicas - 1)
)
raise ValueError("Invalid rank {}, rank should be in the interval [0, {}]".format(rank, num_replicas - 1))
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
@@ -61,19 +55,16 @@ class DistributedBucketSampler(Sampler[T_co]):
self.drop_last = drop_last
# If the dataset length is evenly divisible by # of replicas, then there
# is no need to drop any data, since the dataset will be split equally.
if (
self.drop_last and len(self.dataset) % self.num_replicas != 0
): # type: ignore[arg-type]
if self.drop_last and len(self.dataset) % self.num_replicas != 0: # type: ignore[arg-type]
# Split to nearest available length that is evenly divisible.
# This is to ensure each rank receives the same amount of data when
# using this Sampler.
self.num_samples = math.ceil(
(len(self.dataset) - self.num_replicas)
/ self.num_replicas # type: ignore[arg-type]
(len(self.dataset) - self.num_replicas) / self.num_replicas, # type: ignore[arg-type]
)
else:
self.num_samples = math.ceil(
len(self.dataset) / self.num_replicas
len(self.dataset) / self.num_replicas,
) # type: ignore[arg-type]
self.total_size = self.num_samples * self.num_replicas
self.shuffle = shuffle
@@ -118,10 +109,7 @@ class DistributedBucketSampler(Sampler[T_co]):
grouped_batch_size = self.batch_size * self.num_replicas
shuffled_bucket = list(itertools.chain(*shuffled_bucket))
n_batch = int(math.ceil(len(shuffled_bucket) / grouped_batch_size))
batches = [
shuffled_bucket[b * grouped_batch_size : (b + 1) * grouped_batch_size]
for b in range(n_batch)
]
batches = [shuffled_bucket[b * grouped_batch_size : (b + 1) * grouped_batch_size] for b in range(n_batch)]
shuffle(batches)
indices = list(itertools.chain(*batches))
else:
@@ -134,9 +122,7 @@ class DistributedBucketSampler(Sampler[T_co]):
if padding_size <= len(indices):
indices += indices[:padding_size]
else:
indices += (indices * math.ceil(padding_size / len(indices)))[
:padding_size
]
indices += (indices * math.ceil(padding_size / len(indices)))[:padding_size]
else:
# remove tail of data to make it evenly divisible.
indices = indices[: self.total_size]