Fix: CUDAExecutionProvider was not being used to infer Style-Bert-VITS2 ONNX models even though CUDA was available

gradio_tabs/inference.py

@@ -18,11 +18,12 @@ from style_bert_vits2.constants import (
     Languages,
 )
 from style_bert_vits2.logging import logger
-from style_bert_vits2.models.infer import InvalidToneError
+from style_bert_vits2.nlp import InvalidToneError
 from style_bert_vits2.nlp.japanese import pyopenjtalk_worker as pyopenjtalk
 from style_bert_vits2.nlp.japanese.g2p_utils import g2kata_tone, kata_tone2phone_tone
 from style_bert_vits2.nlp.japanese.normalizer import normalize_text
 from style_bert_vits2.tts_model import TTSModelHolder
+from style_bert_vits2.utils import torch_device_to_onnx_providers
 
 
 # pyopenjtalk_worker を起動
@@ -533,6 +534,8 @@ if __name__ == "__main__":
     path_config = get_path_config()
     assets_root = path_config.assets_root
     device = "cuda" if torch.cuda.is_available() else "cpu"
-    model_holder = TTSModelHolder(assets_root, device)
+    model_holder = TTSModelHolder(
+        assets_root, device, torch_device_to_onnx_providers(device)
+    )
     app = create_inference_app(model_holder)
     app.launch(inbrowser=True)