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/AR/data/dataset.py

@@ -1,21 +1,17 @@
 # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/data/dataset.py
 # reference: https://github.com/lifeiteng/vall-e
-import pdb
-import sys
 
 # sys.path.append("/data/docker/liujing04/gpt-vits/mq-vits-s1bert_no_bert")
-import traceback, os
-from typing import Dict
-from typing import List
+import os
+import traceback
+from typing import Dict, List
 
 import numpy as np
 import pandas as pd
-import torch, json
-from torch.utils.data import DataLoader
-from torch.utils.data import Dataset
-from transformers import AutoTokenizer
+import torch
+from torch.utils.data import DataLoader, Dataset
 
-version = os.environ.get('version',None)
+version = os.environ.get("version", None)
 
 from text import cleaned_text_to_sequence
 
@@ -34,9 +30,7 @@ def batch_sequences(sequences: List[np.array], axis: int = 0, pad_value: int = 0
 
     padded_sequences = []
     for seq, length in zip(sequences, seq_lengths):
-        padding = (
-            [(0, 0)] * axis + [(0, max_length - length)] + [(0, 0)] * (ndim - axis - 1)
-        )
+        padding = [(0, 0)] * axis + [(0, max_length - length)] + [(0, 0)] * (ndim - axis - 1)
         padded_seq = np.pad(seq, padding, mode="constant", constant_values=pad_value)
         padded_sequences.append(padded_seq)
     batch = np.stack(padded_sequences)
@@ -61,12 +55,16 @@ class Text2SemanticDataset(Dataset):
         super().__init__()
 
         self.semantic_data = pd.read_csv(
-            semantic_path, delimiter="\t", encoding="utf-8"
+            semantic_path,
+            delimiter="\t",
+            encoding="utf-8",
         )
         # get dict
         self.path2 = phoneme_path  # "%s/2-name2text.txt"%exp_dir#phoneme_path
         self.path3 = "%s/3-bert" % (
-            os.path.dirname(phoneme_path)
+            os.path.dirname(
+                phoneme_path,
+            )
         )  # "%s/3-bert"%exp_dir#bert_dir
         self.path6 = semantic_path  # "%s/6-name2semantic.tsv"%exp_dir#semantic_path
         assert os.path.exists(self.path2)
@@ -127,7 +125,7 @@ class Text2SemanticDataset(Dataset):
         for i in range(semantic_data_len):
             # 先依次遍历
             # get str
-            item_name = self.semantic_data.iloc[i,0]
+            item_name = self.semantic_data.iloc[i, 0]
             # print(self.phoneme_data)
             try:
                 phoneme, word2ph, text = self.phoneme_data[item_name]
@@ -137,7 +135,7 @@ class Text2SemanticDataset(Dataset):
                 num_not_in += 1
                 continue
 
-            semantic_str = self.semantic_data.iloc[i,1]
+            semantic_str = self.semantic_data.iloc[i, 1]
             # get token list
             semantic_ids = [int(idx) for idx in semantic_str.split(" ")]
             # (T), 是否需要变成 (1, T) -> 不需要，因为需要求 len
@@ -158,9 +156,7 @@ class Text2SemanticDataset(Dataset):
                 num_not_in += 1
                 continue
             # if len(phoneme_ids) >400:###########2：改为恒定限制为semantic/2.5就行
-            if (
-                len(phoneme_ids) > self.max_sec * self.hz / 2.5
-            ):  ###########2：改为恒定限制为semantic/2.5就行
+            if len(phoneme_ids) > self.max_sec * self.hz / 2.5:  ###########2：改为恒定限制为semantic/2.5就行
                 num_deleted_ps += 1
                 continue
             # if len(semantic_ids) > 1000:###########3
@@ -169,9 +165,7 @@ class Text2SemanticDataset(Dataset):
 
             ps_ratio = len(phoneme_ids) / (len(semantic_ids) / self.hz)
 
-            if (
-                ps_ratio > self.max_ps_ratio or ps_ratio < self.min_ps_ratio
-            ):  ##########4#3~25#每秒多少个phone
+            if ps_ratio > self.max_ps_ratio or ps_ratio < self.min_ps_ratio:  ##########4#3~25#每秒多少个phone
                 num_deleted_ps += 1
                 # print(item_name)
                 continue
@@ -194,12 +188,12 @@ class Text2SemanticDataset(Dataset):
             print(f"there are {num_not_in} semantic datas not in phoneme datas")
         if num_deleted_bigger > 0:
             print(
-                f"deleted {num_deleted_bigger} audios who's duration are bigger than {self.max_sec} seconds"
+                f"deleted {num_deleted_bigger} audios who's duration are bigger than {self.max_sec} seconds",
             )
         if num_deleted_ps > 0:
             # 4702 for LibriTTS, LirbriTTS 是标注数据, 是否需要筛？=> 需要，有值为 100 的极端值
             print(
-                f"deleted {num_deleted_ps} audios who's phoneme/sec are bigger than {self.max_ps_ratio} or smaller than {self.min_ps_ratio}"
+                f"deleted {num_deleted_ps} audios who's phoneme/sec are bigger than {self.max_ps_ratio} or smaller than {self.min_ps_ratio}",
             )
         """
         there are 31 semantic datas not in phoneme datas
@@ -306,7 +300,10 @@ if __name__ == "__main__":
 
     batch_size = 12
     dataloader = DataLoader(
-        dataset, batch_size=batch_size, collate_fn=dataset.collate, shuffle=False
+        dataset,
+        batch_size=batch_size,
+        collate_fn=dataset.collate,
+        shuffle=False,
     )
     for i, batch in enumerate(dataloader):
         if i % 1000 == 0: