update README

mlu_370-gpt-sovits/GPT-SoVITS/GPT_SoVITS/AR/data/data_module.py

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+# 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
+
+
+class Text2SemanticDataModule(LightningDataModule):
+    def __init__(
+        self,
+        config,
+        train_semantic_path,
+        train_phoneme_path,
+        dev_semantic_path=None,
+        dev_phoneme_path=None,
+    ):
+        super().__init__()
+        self.config = config
+        self.train_semantic_path = train_semantic_path
+        self.train_phoneme_path = train_phoneme_path
+        self.dev_semantic_path = dev_semantic_path
+        self.dev_phoneme_path = dev_phoneme_path
+        self.num_workers = self.config["data"]["num_workers"]
+
+    def prepare_data(self):
+        pass
+
+    def setup(self, stage=None, output_logs=False):
+        self._train_dataset = Text2SemanticDataset(
+            phoneme_path=self.train_phoneme_path,
+            semantic_path=self.train_semantic_path,
+            max_sec=self.config["data"]["max_sec"],
+            pad_val=self.config["data"]["pad_val"],
+        )
+        self._dev_dataset = self._train_dataset
+        # self._dev_dataset = Text2SemanticDataset(
+        #     phoneme_path=self.dev_phoneme_path,
+        #     semantic_path=self.dev_semantic_path,
+        #     max_sample=self.config['data']['max_eval_sample'],
+        #     max_sec=self.config['data']['max_sec'],
+        #     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) 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,
+            batch_size=batch_size,
+            sampler=sampler,
+            collate_fn=self._train_dataset.collate,
+            num_workers=self.num_workers,
+            persistent_workers=True,
+            prefetch_factor=16,
+        )
+
+    def val_dataloader(self):
+        return DataLoader(
+            self._dev_dataset,
+            batch_size=1,
+            shuffle=False,
+            collate_fn=self._train_dataset.collate,
+            num_workers=max(self.num_workers, 12),
+            persistent_workers=True,
+            prefetch_factor=16,
+        )
+
+    # 这个会使用到嘛？
+    def test_dataloader(self):
+        return DataLoader(
+            self._dev_dataset,
+            batch_size=1,
+            shuffle=False,
+            collate_fn=self._train_dataset.collate,
+        )