remove annotation

modules/whisper/faster_whisper_inference.py

@@ -9,7 +9,7 @@ import ctranslate2
 import whisper
 import gradio as gr
 from argparse import Namespace
-import spaces
+#import spaces
 
 from modules.whisper.whisper_parameter import *
 from modules.whisper.whisper_base import WhisperBase
@@ -32,7 +32,7 @@ class FasterWhisperInference(WhisperBase):
         self.available_compute_types = self.get_available_compute_type()
         self.download_model(model_size="large-v2", model_dir=self.model_dir)
 
-    @spaces.GPU(duration=120)
+    #@spaces.GPU(duration=120)
     def transcribe(self,
                    audio: Union[str, BinaryIO, np.ndarray],
                    progress: gr.Progress,
@@ -90,7 +90,7 @@ class FasterWhisperInference(WhisperBase):
         elapsed_time = time.time() - start_time
         return segments_result, elapsed_time
 
-    @spaces.GPU(duration=120)
+    #@spaces.GPU(duration=120)
     def update_model(self,
                      model_size: str,
                      compute_type: str,
@@ -149,8 +149,8 @@ class FasterWhisperInference(WhisperBase):
             return ['float32', 'int8_float16', 'float16', 'int8', 'int8_float32']
         return ['int16', 'float32', 'int8', 'int8_float32']
 
+    #@spaces.GPU(duration=120)
     @staticmethod
-    @spaces.GPU(duration=120)
     def get_device():
         if torch.cuda.is_available():
             return "cuda"

modules/whisper/whisper_base.py

@@ -42,7 +42,7 @@ class WhisperBase(ABC):
         self.vad = SileroVAD()
 
     @abstractmethod
-    @spaces.GPU(duration=120)
+    #@spaces.GPU(duration=120)
     def transcribe(self,
                    audio: Union[str, BinaryIO, np.ndarray],
                    progress: gr.Progress,
@@ -51,7 +51,7 @@ class WhisperBase(ABC):
         pass
 
     @abstractmethod
-    @spaces.GPU(duration=120)
+    #@spaces.GPU(duration=120)
     def update_model(self,
                      model_size: str,
                      compute_type: str,
@@ -59,7 +59,7 @@ class WhisperBase(ABC):
                      ):
         pass
 
-    @spaces.GPU(duration=120)
+    #@spaces.GPU(duration=120)
     def run(self,
             audio: Union[str, BinaryIO, np.ndarray],
             progress: gr.Progress,
@@ -196,7 +196,7 @@ class WhisperBase(ABC):
             if not files:
                 self.remove_input_files([file.name for file in files])
 
-    @spaces.GPU(duration=120)
+    #@spaces.GPU(duration=120)
     def transcribe_mic(self,
                        mic_audio: str,
                        file_format: str,
@@ -249,7 +249,7 @@ class WhisperBase(ABC):
             self.release_cuda_memory()
             self.remove_input_files([mic_audio])
 
-    @spaces.GPU(duration=120)
+    #@spaces.GPU(duration=120)
     def transcribe_youtube(self,
                            youtube_link: str,
                            file_format: str,
@@ -400,7 +400,7 @@ class WhisperBase(ABC):
         return time_str.strip()
 
     @staticmethod
-    @spaces.GPU(duration=120)
+    #@spaces.GPU(duration=120)
     def get_device():
         if torch.cuda.is_available():
             return "cuda"
@@ -410,7 +410,7 @@ class WhisperBase(ABC):
             return "cpu"
 
     @staticmethod
-    @spaces.GPU(duration=120)
+    #@spaces.GPU(duration=120)
     def release_cuda_memory():
         if torch.cuda.is_available():
             torch.cuda.empty_cache()