Improve: remove Gradio dependency from style_bert_vits2 as a library

pyproject.toml

@@ -25,7 +25,6 @@ dependencies = [
     'cmudict',
     'cn2an',
     'g2p_en',
-    'gradio',
     'jieba',
     'librosa==0.9.2',
     'loguru',

style_bert_vits2/tts_model.py

@@ -1,12 +1,9 @@
-import warnings
 from pathlib import Path
-from typing import Any, Optional, Union
+from typing import TYPE_CHECKING, Any, Optional, Union
 
-import gradio as gr
 import numpy as np
 import pyannote.audio
 import torch
-from gradio.processing_utils import convert_to_16_bit_wav
 from numpy.typing import NDArray
 from pydantic import BaseModel
 
@@ -32,6 +29,13 @@ from style_bert_vits2.models.models_jp_extra import (
 from style_bert_vits2.voice import adjust_voice
 
 
+# Gradio の import は重いため、ここでは型チェック時のみ import する
+# ライブラリとしての利用を考慮し、TTSModelHolder の _for_gradio() 系メソッド以外では Gradio に依存しないようにする
+# _for_gradio() 系メソッドの戻り値の型アノテーションを文字列としているのは、Gradio なしで実行できるようにするため
+if TYPE_CHECKING:
+    import gradio as gr
+
+
 class TTSModel:
     """
     Style-Bert-Vits2 の音声合成モデルを操作するクラス。
@@ -137,6 +141,43 @@ class TTSModel:
         xvec = mean + (xvec - mean) * weight
         return xvec
 
+    def __convert_to_16_bit_wav(self, data: NDArray[Any]) -> NDArray[Any]:
+        """
+        音声データを 16-bit int 形式に変換する。
+        gradio.processing_utils.convert_to_16_bit_wav() を移植したもの。
+
+        Args:
+            data (NDArray[Any]): 音声データ
+
+        Returns:
+            NDArray[Any]: 16-bit int 形式の音声データ
+        """
+        # Based on: https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.wavfile.write.html
+        if data.dtype in [np.float64, np.float32, np.float16]:  # type: ignore
+            data = data / np.abs(data).max()
+            data = data * 32767
+            data = data.astype(np.int16)
+        elif data.dtype == np.int32:
+            data = data / 65536
+            data = data.astype(np.int16)
+        elif data.dtype == np.int16:
+            pass
+        elif data.dtype == np.uint16:
+            data = data - 32768
+            data = data.astype(np.int16)
+        elif data.dtype == np.uint8:
+            data = data * 257 - 32768
+            data = data.astype(np.int16)
+        elif data.dtype == np.int8:
+            data = data * 256
+            data = data.astype(np.int16)
+        else:
+            raise ValueError(
+                "Audio data cannot be converted automatically from "
+                f"{data.dtype} to 16-bit int format."
+            )
+        return data
+
     def infer(
         self,
         text: str,
@@ -260,9 +301,7 @@ class TTSModel:
                 pitch_scale=pitch_scale,
                 intonation_scale=intonation_scale,
             )
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore")
-            audio = convert_to_16_bit_wav(audio)
+        audio = self.__convert_to_16_bit_wav(audio)
         return (self.hyper_parameters.data.sampling_rate, audio)
 
 
@@ -381,7 +420,9 @@ class TTSModelHolder:
 
     def get_model_for_gradio(
         self, model_name: str, model_path_str: str
-    ) -> tuple[gr.Dropdown, gr.Button, gr.Dropdown]:
+    ) -> tuple["gr.Dropdown", "gr.Button", "gr.Dropdown"]:
+        import gradio as gr
+
         model_path = Path(model_path_str)
         if model_name not in self.model_files_dict:
             raise ValueError(f"Model `{model_name}` is not found")
@@ -413,13 +454,17 @@ class TTSModelHolder:
             gr.Dropdown(choices=speakers, value=speakers[0]),  # type: ignore
         )
 
-    def update_model_files_for_gradio(self, model_name: str) -> gr.Dropdown:
+    def update_model_files_for_gradio(self, model_name: str) -> "gr.Dropdown":
+        import gradio as gr
+
         model_files = self.model_files_dict[model_name]
         return gr.Dropdown(choices=model_files, value=model_files[0])  # type: ignore
 
     def update_model_names_for_gradio(
         self,
-    ) -> tuple[gr.Dropdown, gr.Dropdown, gr.Button]:
+    ) -> tuple["gr.Dropdown", "gr.Dropdown", "gr.Button"]:
+        import gradio as gr
+
         self.refresh()
         initial_model_name = self.model_names[0]
         initial_model_files = self.model_files_dict[initial_model_name]