chore(format): run black on dev (#573)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

ChatTTS/config/config.py

@@ -2,7 +2,7 @@
 
 
 @dataclass(repr=False, eq=False)
-class Path():
+class Path:
     vocos_ckpt_path: str = "asset/Vocos.pt"
     dvae_ckpt_path: str = "asset/DVAE.pt"
     gpt_ckpt_path: str = "asset/GPT.pt"
@@ -11,7 +11,7 @@ class Path():
 
 
 @dataclass(repr=False, eq=False)
-class Decoder():
+class Decoder:
     idim: int = 384
     odim: int = 384
     hidden: int = 512
@@ -20,26 +20,27 @@ class Decoder():
 
 
 @dataclass(repr=False, eq=False)
-class VQ():
+class VQ:
     dim: int = 1024
-    levels: tuple = (5,5,5,5)
+    levels: tuple = (5, 5, 5, 5)
     G: int = 2
     R: int = 2
 
 
 @dataclass(repr=False, eq=False)
-class DVAE():
+class DVAE:
     decoder: Decoder = Decoder(
-       idim=512,
-       odim=512,
-       hidden=256,
-       n_layer=12,
-       bn_dim=128,
+        idim=512,
+        odim=512,
+        hidden=256,
+        n_layer=12,
+        bn_dim=128,
     )
     vq: VQ = VQ()
 
+
 @dataclass(repr=False, eq=False)
-class GPT():
+class GPT:
     hidden_size: int = 768
     intermediate_size: int = 3072
     num_attention_heads: int = 12
@@ -54,53 +55,57 @@ class GPT():
 
 
 @dataclass(repr=False, eq=False)
-class FeatureExtractorInitArgs():
+class FeatureExtractorInitArgs:
     sample_rate: int = 24000
     n_fft: int = 1024
     hop_length: int = 256
     n_mels: int = 100
     padding: str = "center"
 
+
 @dataclass(repr=False, eq=False)
-class FeatureExtractor():
+class FeatureExtractor:
     class_path: str = "vocos.feature_extractors.MelSpectrogramFeatures"
     init_args: FeatureExtractorInitArgs = FeatureExtractorInitArgs()
 
 
 @dataclass(repr=False, eq=False)
-class BackboneInitArgs():
+class BackboneInitArgs:
     input_channels: int = 100
     dim: int = 512
     intermediate_dim: int = 1536
     num_layers: int = 8
 
+
 @dataclass(repr=False, eq=False)
-class Backbone():
+class Backbone:
     class_path: str = "vocos.models.VocosBackbone"
     init_args: BackboneInitArgs = BackboneInitArgs()
 
 
 @dataclass(repr=False, eq=False)
-class FourierHeadInitArgs():
+class FourierHeadInitArgs:
     dim: int = 512
     n_fft: int = 1024
     hop_length: int = 256
     padding: str = "center"
 
+
 @dataclass(repr=False, eq=False)
-class FourierHead():
+class FourierHead:
     class_path: str = "vocos.heads.ISTFTHead"
     init_args: FourierHeadInitArgs = FourierHeadInitArgs()
 
 
 @dataclass(repr=False, eq=False)
-class Vocos():
+class Vocos:
     feature_extractor: FeatureExtractor = FeatureExtractor()
     backbone: Backbone = Backbone()
     head: FourierHead = FourierHead()
 
+
 @dataclass(repr=False, eq=False)
-class Config():
+class Config:
     path: Path = Path()
     decoder: Decoder = Decoder()
     dvae: DVAE = DVAE()

ChatTTS/core.py

@@ -258,7 +258,9 @@ def _load(
         self.device = device
         self.compile = compile
 
-        feature_extractor = instantiate_class(args=(), init=asdict(self.config.vocos.feature_extractor))
+        feature_extractor = instantiate_class(
+            args=(), init=asdict(self.config.vocos.feature_extractor)
+        )
         backbone = instantiate_class(args=(), init=asdict(self.config.vocos.backbone))
         head = instantiate_class(args=(), init=asdict(self.config.vocos.head))
         vocos = (
@@ -272,23 +274,23 @@ def _load(
             .eval()
         )
         assert vocos_ckpt_path, "vocos_ckpt_path should not be None"
-        vocos.load_state_dict(
-            torch.load(vocos_ckpt_path, weights_only=True, mmap=True)
-        )
+        vocos.load_state_dict(torch.load(vocos_ckpt_path, weights_only=True, mmap=True))
         self.vocos = vocos
         self.logger.log(logging.INFO, "vocos loaded.")
 
-        dvae = DVAE(
-            decoder_config=asdict(self.config.dvae.decoder),
-            vq_config=asdict(self.config.dvae.vq),
-            dim=self.config.dvae.decoder.idim,
-            coef=coef,
-        ).to(device).eval()
+        dvae = (
+            DVAE(
+                decoder_config=asdict(self.config.dvae.decoder),
+                vq_config=asdict(self.config.dvae.vq),
+                dim=self.config.dvae.decoder.idim,
+                coef=coef,
+            )
+            .to(device)
+            .eval()
+        )
         coef = str(dvae)
         assert dvae_ckpt_path, "dvae_ckpt_path should not be None"
-        dvae.load_state_dict(
-            torch.load(dvae_ckpt_path, weights_only=True, mmap=True)
-        )
+        dvae.load_state_dict(torch.load(dvae_ckpt_path, weights_only=True, mmap=True))
         self.dvae = dvae
         self.logger.log(logging.INFO, "dvae loaded.")
 
@@ -303,9 +305,7 @@ def _load(
         gpt.prepare(compile=compile and "cuda" in str(device))
         self.gpt = gpt
         spk_stat_path = os.path.join(os.path.dirname(gpt_ckpt_path), "spk_stat.pt")
-        assert os.path.exists(
-            spk_stat_path
-        ), f"Missing spk_stat.pt: {spk_stat_path}"
+        assert os.path.exists(spk_stat_path), f"Missing spk_stat.pt: {spk_stat_path}"
         spk_stat: torch.Tensor = torch.load(
             spk_stat_path,
             weights_only=True,
@@ -315,11 +315,15 @@ def _load(
         self.std, self.mean = spk_stat.requires_grad_(False).chunk(2)
         self.logger.log(logging.INFO, "gpt loaded.")
 
-        decoder = DVAE(
-            decoder_config=asdict(self.config.decoder),
-            dim=self.config.decoder.idim,
-            coef=coef,
-        ).to(device).eval()
+        decoder = (
+            DVAE(
+                decoder_config=asdict(self.config.decoder),
+                dim=self.config.decoder.idim,
+                coef=coef,
+            )
+            .to(device)
+            .eval()
+        )
         coef = str(decoder)
         assert decoder_ckpt_path, "decoder_ckpt_path should not be None"
         decoder.load_state_dict(

ChatTTS/model/dvae.py

@@ -170,7 +170,7 @@ class DVAE(nn.Module):
     def __init__(
         self,
         decoder_config: dict,
-        vq_config: Optional[dict]=None,
+        vq_config: Optional[dict] = None,
         dim=512,
         coef: Optional[str] = None,
     ):