Add kwargs to ignore extra arguments w/o error (coqui-ai#2822)

TTS/tts/layers/bark/inference_funcs.py

@@ -162,8 +162,28 @@ def generate_text_semantic(
     allow_early_stop=True,
     base=None,
     use_kv_caching=True,
+    **kwargs, # pylint: disable=unused-argument
 ):
-    """Generate semantic tokens from text."""
+    """Generate semantic tokens from text.
+
+    Args:
+        text (str): The text to generate semantic tokens from.
+        model (BarkModel): The BarkModel to use for generating the semantic tokens.
+        history_prompt (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a prompt for the generation.
+        temp (float): The temperature to use for the generation.
+        top_k (int): The number of top tokens to consider for the generation.
+        top_p (float): The cumulative probability to consider for the generation.
+        silent (bool): Whether to silence the tqdm progress bar.
+        min_eos_p (float): The minimum probability to consider for the end of sentence token.
+        max_gen_duration_s (float): The maximum duration in seconds to generate for.
+        allow_early_stop (bool): Whether to allow the generation to stop early.
+        base (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a base for the generation.
+        use_kv_caching (bool): Whether to use key-value caching for the generation.
+        **kwargs: Additional keyword arguments. They are ignored.
+
+    Returns:
+        np.ndarray: The generated semantic tokens.
+    """
     print(f"history_prompt in gen: {history_prompt}")
     assert isinstance(text, str)
     text = _normalize_whitespace(text)
@@ -298,7 +318,24 @@ def generate_coarse(
     base=None,
     use_kv_caching=True,
 ):
-    """Generate coarse audio codes from semantic tokens."""
+    """Generate coarse audio codes from semantic tokens.
+
+    Args:
+        x_semantic (np.ndarray): The semantic tokens to generate coarse audio codes from.
+        model (BarkModel): The BarkModel to use for generating the coarse audio codes.
+        history_prompt (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a prompt for the generation.
+        temp (float): The temperature to use for the generation.
+        top_k (int): The number of top tokens to consider for the generation.
+        top_p (float): The cumulative probability to consider for the generation.
+        silent (bool): Whether to silence the tqdm progress bar.
+        max_coarse_history (int): The maximum number of coarse audio codes to use as history.
+        sliding_window_len (int): The length of the sliding window to use for the generation.
+        base (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a base for the generation.
+        use_kv_caching (bool): Whether to use key-value caching for the generation.
+
+    Returns:
+        np.ndarray: The generated coarse audio codes.
+    """
     assert (
         isinstance(x_semantic, np.ndarray)
         and len(x_semantic.shape) == 1
@@ -453,7 +490,19 @@ def generate_fine(
     silent=True,
     base=None,
 ):
-    """Generate full audio codes from coarse audio codes."""
+    """Generate full audio codes from coarse audio codes.
+
+    Args:
+        x_coarse_gen (np.ndarray): The coarse audio codes to generate full audio codes from.
+        model (BarkModel): The BarkModel to use for generating the full audio codes.
+        history_prompt (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a prompt for the generation.
+        temp (float): The temperature to use for the generation.
+        silent (bool): Whether to silence the tqdm progress bar.
+        base (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a base for the generation.
+
+    Returns:
+        np.ndarray: The generated full audio codes.
+    """
     assert (
         isinstance(x_coarse_gen, np.ndarray)
         and len(x_coarse_gen.shape) == 2

TTS/tts/models/bark.py

@@ -206,7 +206,7 @@ def synthesize(
             speaker_wav (str): Path to the speaker audio file for cloning a new voice. It is cloned and saved in
                 `voice_dirs` with the name `speaker_id`. Defaults to None.
             voice_dirs (List[str]): List of paths that host reference audio files for speakers. Defaults to None.
-            **kwargs: Inference settings. See `inference()`.
+            **kwargs: Model specific inference settings used by `generate_audio()` and `TTS.tts.layers.bark.inference_funcs.generate_text_semantic().
 
         Returns:
             A dictionary of the output values with `wav` as output waveform, `deterministic_seed` as seed used at inference,