chore(format): run black on dev

ChatTTS/model/gpt.py

@@ -300,7 +300,8 @@ def _prepare_generation_outputs(
         end_idx_int = end_idx.int()
 
         inputs_ids_lst = [
-            inputs_ids[idx].narrow(0, start_idx, int(i)) for idx, i in enumerate(end_idx_int)
+            inputs_ids[idx].narrow(0, start_idx, int(i))
+            for idx, i in enumerate(end_idx_int)
         ]
         if infer_text:
             inputs_ids_lst = [i.narrow(1, 0, 1).squeeze_(1) for i in inputs_ids_lst]
@@ -309,7 +310,8 @@ def _prepare_generation_outputs(
         if len(hiddens) > 0:
             hiddens_lst = torch.stack(hiddens, 1)
             hiddens_lst = [
-                hiddens_lst[idx].narrow(0, 0, int(i)) for idx, i in enumerate(end_idx_int)
+                hiddens_lst[idx].narrow(0, 0, int(i))
+                for idx, i in enumerate(end_idx_int)
             ]
 
         return self.GenerationOutputs(
@@ -341,7 +343,7 @@ def generate(
         manual_seed: Optional[int] = None,
         context=Context(),
     ):
-        
+
         self.logger.debug("start generate")
 
         attentions: List[Optional[Tuple[torch.FloatTensor, ...]]] = []
@@ -353,7 +355,9 @@ def generate(
         )
         finish = torch.zeros(inputs_ids.shape[0], device=inputs_ids.device).bool()
 
-        self.logger.debug(f"set start_idx: {start_idx}, end_idx and finish with all zeros, len {inputs_ids.shape[0]}")
+        self.logger.debug(
+            f"set start_idx: {start_idx}, end_idx and finish with all zeros, len {inputs_ids.shape[0]}"
+        )
 
         old_temperature = temperature
 
@@ -364,7 +368,9 @@ def generate(
             .view(-1, 1)
         )
 
-        self.logger.debug(f"expand temperature from shape {old_temperature.shape} to {temperature.shape}")
+        self.logger.debug(
+            f"expand temperature from shape {old_temperature.shape} to {temperature.shape}"
+        )
 
         attention_mask_cache = torch.ones(
             (
@@ -374,7 +380,9 @@ def generate(
             dtype=torch.bool,
             device=inputs_ids.device,
         )
-        self.logger.debug(f"init attention_mask_cache with shape {attention_mask_cache.shape}")
+        self.logger.debug(
+            f"init attention_mask_cache with shape {attention_mask_cache.shape}"
+        )
         if attention_mask is not None:
             attention_mask_cache.narrow(1, 0, attention_mask.shape[1]).copy_(
                 attention_mask
@@ -391,7 +399,9 @@ def generate(
             device=inputs_ids.device,
         )
         inputs_ids_buf.narrow(1, 0, progress).copy_(inputs_ids)
-        self.logger.debug(f"expand inputs_ids buf from shape {inputs_ids.shape} to {inputs_ids_buf.shape}")
+        self.logger.debug(
+            f"expand inputs_ids buf from shape {inputs_ids.shape} to {inputs_ids_buf.shape}"
+        )
         del inputs_ids
         inputs_ids = inputs_ids_buf.narrow(1, 0, progress)
 
@@ -605,7 +615,7 @@ def generate(
                         yield result
                     del inputs_ids
                 return
-            
+
             self.logger.debug("start output")
 
             del idx_next

ChatTTS/utils/gpu.py

@@ -53,6 +53,7 @@ def select_device(min_memory=2047, experimental=False):
         if experimental:
             logger.get_logger().warning("experimental: using DML.")
             import torch_directml
+
             device = torch_directml.device(torch_directml.default_device())
         else:
             logger.get_logger().info("found DML, but use CPU.")

examples/web/funcs.py

@@ -25,8 +25,8 @@
 has_interrupted = False
 is_in_generate = False
 
-enable_cache=True
-experimental=False
+enable_cache = True
+experimental = False
 
 seed_min = 1
 seed_max = 4294967295
@@ -64,12 +64,14 @@ def on_audio_seed_change(audio_seed_input):
         rand_spk = chat.sample_random_speaker()
     return rand_spk
 
+
 def set_params(en_cache, exp):
     global enable_cache, experimental
 
     enable_cache = en_cache
     experimental = exp
 
+
 def load_chat(cust_path: Optional[str], coef: Optional[str]) -> bool:
     global enable_cache, experimental
 
@@ -78,7 +80,11 @@ def load_chat(cust_path: Optional[str], coef: Optional[str]) -> bool:
     else:
         logger.info("local model path: %s", cust_path)
         ret = chat.load(
-            "custom", custom_path=cust_path, coef=coef, enable_cache=enable_cache, experimental=experimental
+            "custom",
+            custom_path=cust_path,
+            coef=coef,
+            enable_cache=enable_cache,
+            experimental=experimental,
         )
         global custom_path
         custom_path = cust_path