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

* ruff check --fix

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

* Change the link for G2PW Model

* update pytorch version and colab

GPT_SoVITS/f5_tts/model/backbones/dit.py

@@ -11,7 +11,6 @@ from __future__ import annotations
 
 import torch
 from torch import nn
-import torch.nn.functional as F
 from torch.utils.checkpoint import checkpoint
 
 from x_transformers.x_transformers import RotaryEmbedding
@@ -28,6 +27,7 @@ from GPT_SoVITS.f5_tts.model.modules import (
 
 from module.commons import sequence_mask
 
+
 class TextEmbedding(nn.Module):
     def __init__(self, text_dim, conv_layers=0, conv_mult=2):
         super().__init__()
@@ -130,26 +130,24 @@ class DiT(nn.Module):
 
         return ckpt_forward
 
-    def forward(#x, prompt_x, x_lens, t, style,cond
-        self,#d is channel,n is T
+    def forward(  # x, prompt_x, x_lens, t, style,cond
+        self,  # d is channel,n is T
         x0: float["b n d"],  # nosied input audio  # noqa: F722
         cond0: float["b n d"],  # masked cond audio  # noqa: F722
         x_lens,
         time: float["b"] | float[""],  # time step  # noqa: F821 F722
-            dt_base_bootstrap,
+        dt_base_bootstrap,
         text0,  # : int["b nt"]  # noqa: F722#####condition feature
         use_grad_ckpt=False,  # bool
         ###no-use
         drop_audio_cond=False,  # cfg for cond audio
         drop_text=False,  # cfg for text
         # mask: bool["b n"] | None = None,  # noqa: F722
-        
     ):
-
-        x=x0.transpose(2,1)
-        cond=cond0.transpose(2,1)
-        text=text0.transpose(2,1)
-        mask = sequence_mask(x_lens,max_length=x.size(1)).to(x.device)
+        x = x0.transpose(2, 1)
+        cond = cond0.transpose(2, 1)
+        text = text0.transpose(2, 1)
+        mask = sequence_mask(x_lens, max_length=x.size(1)).to(x.device)
 
         batch, seq_len = x.shape[0], x.shape[1]
         if time.ndim == 0:
@@ -158,8 +156,8 @@ class DiT(nn.Module):
         # t: conditioning time, c: context (text + masked cond audio), x: noised input audio
         t = self.time_embed(time)
         dt = self.d_embed(dt_base_bootstrap)
-        t+=dt
-        text_embed = self.text_embed(text, seq_len, drop_text=drop_text)###need to change
+        t += dt
+        text_embed = self.text_embed(text, seq_len, drop_text=drop_text)  ###need to change
         x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond)
 
         rope = self.rotary_embed.forward_from_seq_len(seq_len)
@@ -179,4 +177,4 @@ class DiT(nn.Module):
         x = self.norm_out(x, t)
         output = self.proj_out(x)
 
-        return output
+        return output

GPT_SoVITS/f5_tts/model/modules.py

@@ -391,6 +391,7 @@ class Attention(nn.Module):
 
 # Attention processor
 
+
 # from torch.nn.attention import SDPBackend
 # torch.backends.cuda.enable_flash_sdp(True)
 class AttnProcessor:
@@ -545,6 +546,7 @@ class JointAttnProcessor:
 
 # DiT Block
 
+
 class DiTBlock(nn.Module):
     def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1):
         super().__init__()