Update app.py

app.py

@@ -1,11 +1,11 @@
 import gradio as gr
-import whisper
-#from faster_whisper import WhisperModel
+#import whisper
+from faster_whisper import WhisperModel
 
-model_size = 'large-v3'
-model = whisper.load_model(model_size)
+model_size = 'aka7774/whisper-large-v3-ct2'
+#model = whisper.load_model(model_size)
 #model = WhisperModel(model_size, device="cuda", compute_type="float16")
-#model = WhisperModel(model_size, compute_type="float16")
+model = WhisperModel(model_size, compute_type="float16")
 
 # or run on GPU with INT8
 # model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
@@ -16,19 +16,19 @@ def speech_to_text(audio_file, _model_size):
     global model_size, model
     if model_size != _model_size:
         model_size = _model_size
-        model = whisper.load_model(model_size)
-        #model = WhisperModel(model_size, compute_type="float16")
+        #model = whisper.load_model(model_size)
+        model = WhisperModel(model_size, compute_type="float16")
 
-    result = model.transcribe(audio_file)
-    #segments, info = model.transcribe(audio_file, beam_size=5)
+    #result = model.transcribe(audio_file)
+    segments, info = model.transcribe(audio_file, beam_size=5)
 
-    return result["text"]
-    #return "".join([segment.text for segment in segments])
+    #return result["text"]
+    return "".join([segment.text for segment in segments])
 
 gr.Interface(
     fn=speech_to_text,
     inputs=[
         gr.Audio(source="upload", type="filepath"),
-        gr.Dropdown(value=model_size, choices=["tiny", "base", "small", "medium", "large", "large-v2", "large-v3"]),
+        gr.Dropdown(value=model_size, choices=["tiny", "base", "small", "medium", "large", "large-v2", "large-v3", "aka7774/whisper-large-v3-ct2"]),
         ],
     outputs="text").launch()