Refactor: Format Code with Ruff and Update Deprecated G2PW Link

Docker/download.py

@@ -1,5 +1,8 @@
 # Download moda ASR related models
 from modelscope import snapshot_download
-model_dir = snapshot_download('damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',revision="v2.0.4")
-model_dir = snapshot_download('damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',revision="v2.0.4")
-model_dir = snapshot_download('damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',revision="v2.0.4")
+
+model_dir = snapshot_download(
+    "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", revision="v2.0.4"
+)
+model_dir = snapshot_download("damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", revision="v2.0.4")
+model_dir = snapshot_download("damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", revision="v2.0.4")

GPT_SoVITS/AR/data/bucket_sampler.py

@@ -4,14 +4,11 @@
 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",
@@ -50,30 +47,24 @@ def __init__(
             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
         self.epoch = 0
         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 @@ def __iter__(self) -> Iterator[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 @@ def __iter__(self) -> Iterator[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]

GPT_SoVITS/AR/data/data_module.py

@@ -1,9 +1,10 @@
 # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/data/data_module.py
 # reference: https://github.com/lifeiteng/vall-e
 from pytorch_lightning import LightningDataModule
+from torch.utils.data import DataLoader
+
 from AR.data.bucket_sampler import DistributedBucketSampler
 from AR.data.dataset import Text2SemanticDataset
-from torch.utils.data import DataLoader
 
 
 class Text2SemanticDataModule(LightningDataModule):
@@ -42,8 +43,12 @@ def setup(self, stage=None, output_logs=False):
         #     pad_val=self.config['data']['pad_val'])
 
     def train_dataloader(self):
-        batch_size=self.config["train"]["batch_size"]//2 if self.config["train"].get("if_dpo",False)==True else self.config["train"]["batch_size"]
-        batch_size = max(min(batch_size,len(self._train_dataset)//4),1)#防止不保存
+        batch_size = (
+            self.config["train"]["batch_size"] // 2
+            if self.config["train"].get("if_dpo", False) is True
+            else self.config["train"]["batch_size"]
+        )
+        batch_size = max(min(batch_size, len(self._train_dataset) // 4), 1)  # 防止不保存
         sampler = DistributedBucketSampler(self._train_dataset, batch_size=batch_size)
         return DataLoader(
             self._train_dataset,

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 @@ def __init__(
         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 @@ def init_batch(self):
         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 @@ def init_batch(self):
                 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 @@ def init_batch(self):
                 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 @@ def init_batch(self):
 
             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 @@ def init_batch(self):
             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 @@ def collate(self, examples: List[Dict]) -> Dict:
 
     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:

GPT_SoVITS/AR/models/t2s_lightning_module.py

@@ -1,17 +1,20 @@
 # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_lightning_module.py
 # reference: https://github.com/lifeiteng/vall-e
-import os, sys
+import os
+import sys
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 from typing import Dict
 
 import torch
 from pytorch_lightning import LightningModule
+
 from AR.models.t2s_model import Text2SemanticDecoder
 from AR.modules.lr_schedulers import WarmupCosineLRSchedule
 from AR.modules.optim import ScaledAdam
 
+
 class Text2SemanticLightningModule(LightningModule):
     def __init__(self, config, output_dir, is_train=True):
         super().__init__()
@@ -23,7 +26,10 @@ def __init__(self, config, output_dir, is_train=True):
             # print(self.load_state_dict(torch.load(pretrained_s1,map_location="cpu")["state_dict"]))
             print(
                 self.load_state_dict(
-                    torch.load(pretrained_s1, map_location="cpu")["weight"]
+                    torch.load(
+                        pretrained_s1,
+                        map_location="cpu",
+                    )["weight"],
                 )
             )
         if is_train:
@@ -35,7 +41,7 @@ def __init__(self, config, output_dir, is_train=True):
     def training_step(self, batch: Dict, batch_idx: int):
         opt = self.optimizers()
         scheduler = self.lr_schedulers()
-        forward=self.model.forward if self.config["train"].get("if_dpo",False)==True else self.model.forward_old
+        forward = self.model.forward if self.config["train"].get("if_dpo", False) == True else self.model.forward_old
         loss, acc = forward(
             batch["phoneme_ids"],
             batch["phoneme_ids_len"],
@@ -113,9 +119,7 @@ def validation_step(self, batch: Dict, batch_idx: int):
     def configure_optimizers(self):
         model_parameters = self.model.parameters()
         parameters_names = []
-        parameters_names.append(
-            [name_param_pair[0] for name_param_pair in self.model.named_parameters()]
-        )
+        parameters_names.append([name_param_pair[0] for name_param_pair in self.model.named_parameters()])
         lm_opt = ScaledAdam(
             model_parameters,
             lr=0.01,

GPT_SoVITS/AR/models/t2s_lightning_module_onnx.py

@@ -1,13 +1,15 @@
 # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_lightning_module.py
 # reference: https://github.com/lifeiteng/vall-e
-import os, sys
+import os
+import sys
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 from typing import Dict
 
 import torch
 from pytorch_lightning import LightningModule
+
 from AR.models.t2s_model_onnx import Text2SemanticDecoder
 from AR.modules.lr_schedulers import WarmupCosineLRSchedule
 from AR.modules.optim import ScaledAdam
@@ -24,8 +26,11 @@ def __init__(self, config, output_dir, is_train=True):
             # print(self.load_state_dict(torch.load(pretrained_s1,map_location="cpu")["state_dict"]))
             print(
                 self.load_state_dict(
-                    torch.load(pretrained_s1, map_location="cpu")["weight"]
-                )
+                    torch.load(
+                        pretrained_s1,
+                        map_location="cpu",
+                    )["weight"],
+                ),
             )
         if is_train:
             self.automatic_optimization = False
@@ -79,9 +84,7 @@ def validation_step(self, batch: Dict, batch_idx: int):
     def configure_optimizers(self):
         model_parameters = self.model.parameters()
         parameters_names = []
-        parameters_names.append(
-            [name_param_pair[0] for name_param_pair in self.model.named_parameters()]
-        )
+        parameters_names.append([name_param_pair[0] for name_param_pair in self.model.named_parameters()])
         lm_opt = ScaledAdam(
             model_parameters,
             lr=0.01,