chore(format): run black on dev

ChatTTS/core.py

@@ -480,7 +480,10 @@ def _infer_code(
 
         input_ids, attention_mask, text_mask = self.tokenizer.encode(
             self.tokenizer.decorate_code_prompts(
-                text, params.prompt, params.txt_smp, params.spk_emb,
+                text,
+                params.prompt,
+                params.txt_smp,
+                params.spk_emb,
             ),
             self.config.gpt.num_vq,
             prompt_str=params.spk_smp,

ChatTTS/model/tokenizer.py

@@ -128,12 +128,15 @@ def encode(
 
     @torch.inference_mode
     def decode(
-        self, sequences: Union[List[int], List[List[int]]],
+        self,
+        sequences: Union[List[int], List[List[int]]],
         skip_special_tokens: bool = False,
         clean_up_tokenization_spaces: bool = None,
         **kwargs,
     ):
-        return self._tokenizer.batch_decode(sequences, skip_special_tokens, clean_up_tokenization_spaces, **kwargs)
+        return self._tokenizer.batch_decode(
+            sequences, skip_special_tokens, clean_up_tokenization_spaces, **kwargs
+        )
 
     @staticmethod
     def _decode_spk_emb(spk_emb: str) -> np.ndarray:
@@ -219,11 +222,14 @@ def _encode_spk_emb(spk_emb: torch.Tensor) -> str:
         )
         del arr
         return s
-    
+
     @staticmethod
     @torch.no_grad()
     def decorate_code_prompts(
-        text: List[str], prompt: str, txt_smp: Optional[str], spk_emb: Optional[str],
+        text: List[str],
+        prompt: str,
+        txt_smp: Optional[str],
+        spk_emb: Optional[str],
     ) -> List[str]:
         for i, t in enumerate(text):
             text[i] = (
@@ -244,7 +250,7 @@ def decorate_code_prompts(
             text = [f"[Stts][spk_emb]{txt_smp}{i}[Ptts]" for i in text]
         else:
             text = [f"[Stts][empty_spk]{txt_smp}{i}[Ptts]" for i in text]
-        
+
         return text
 
     @staticmethod

ChatTTS/norm.py

@@ -33,28 +33,30 @@ def _fast_replace(
             replaced_words.append((chr(ch), chr(repl_char)))
     return result, replaced_words
 
+
 @jit
 def _split_tags(text: str) -> Tuple[List[str], List[str]]:
     texts: List[str] = []
     tags: List[str] = []
     current_text = ""
     current_tag = ""
     for c in text:
-        if c == '[':
+        if c == "[":
             texts.append(current_text)
             current_text = ""
             current_tag = c
         elif current_tag != "":
             current_tag += c
         else:
             current_text += c
-        if c == ']':
+        if c == "]":
             tags.append(current_tag)
             current_tag = ""
     if current_text != "":
         texts.append(current_text)
     return texts, tags
 
+
 @jit
 def _combine_tags(texts: List[str], tags: List[str]) -> str:
     text = ""
@@ -65,6 +67,7 @@ def _combine_tags(texts: List[str], tags: List[str]) -> str:
         text += t + tg
     return text
 
+
 class Normalizer:
     def __init__(self, map_file_path: str, logger=logging.getLogger(__name__)):
         self.logger = logger

tests/#655.py

@@ -28,30 +28,37 @@
 rand_spk = chat.sample_random_speaker()
 
 
-text = ['What is [uv_break]your favorite english food?[laugh][lbreak]']
+text = ["What is [uv_break]your favorite english food?[laugh][lbreak]"]
 
 fail = False
 
 with TorchSeedContext(12345):
     refined_text = chat.infer(
-        text, refine_text_only=True,
+        text,
+        refine_text_only=True,
         params_refine_text=ChatTTS.Chat.RefineTextParams(
-            prompt='[oral_2][laugh_0][break_6]',
+            prompt="[oral_2][laugh_0][break_6]",
         ),
     )
-if refined_text[0] != "like [uv_break] what is [uv_break] your favorite english food [laugh] [lbreak]":
+if (
+    refined_text[0]
+    != "like [uv_break] what is [uv_break] your favorite english food [laugh] [lbreak]"
+):
     fail = True
     logger.warning("refined text is '%s'", refined_text[0])
 
 params = ChatTTS.Chat.InferCodeParams(
-    spk_emb = rand_spk, # add sampled speaker 
-    temperature = .3,   # using custom temperature
-    top_P = 0.7,        # top P decode
-    top_K = 20,         # top K decode
+    spk_emb=rand_spk,  # add sampled speaker
+    temperature=0.3,  # using custom temperature
+    top_P=0.7,  # top P decode
+    top_K=20,  # top K decode
 )
 input_ids, attention_mask, text_mask = chat.tokenizer.encode(
     chat.tokenizer.decorate_code_prompts(
-        text, params.prompt, params.txt_smp, params.spk_emb,
+        text,
+        params.prompt,
+        params.txt_smp,
+        params.spk_emb,
     ),
     chat.config.gpt.num_vq,
     prompt_str=params.spk_smp,
@@ -62,11 +69,21 @@
         input_ids.shape[0], device=input_ids.device, dtype=torch.long
     ).fill_(input_ids.shape[1])
 
-    recoded_text = chat.tokenizer.decode(chat.gpt._prepare_generation_outputs(
-        input_ids, start_idx, end_idx, [], [], True,
-    ).ids)
+    recoded_text = chat.tokenizer.decode(
+        chat.gpt._prepare_generation_outputs(
+            input_ids,
+            start_idx,
+            end_idx,
+            [],
+            [],
+            True,
+        ).ids
+    )
 
-if recoded_text[0] != '[Stts] [spk_emb] [speed_5] what is [uv_break] your favorite english food? [laugh] [lbreak] [Ptts]':
+if (
+    recoded_text[0]
+    != "[Stts] [spk_emb] [speed_5] what is [uv_break] your favorite english food? [laugh] [lbreak] [Ptts]"
+):
     fail = True
     logger.warning("recoded text is '%s'", refined_text)