Update app.py

app.py

@@ -1,48 +1,12 @@
-import torch
+import fn
 import gradio as gr
-from faster_whisper import WhisperModel
 
-model = None
-model_size = 'large-v3'
-
-def load_model(_model_size):
-    global model_size, model
-
-    if model_size != _model_size:
-        model_size = _model_size
-
-    if torch.cuda.is_available():
-        model = WhisperModel(model_size, device="cuda", compute_type="float16")
-        # model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
-    else:
-        model = WhisperModel(model_size, device="cpu", compute_type="int8")
-
-def speech_to_text(audio_file, _model_size):
-    global model_size, model
-
-    load_model(_model_size)
-
-    with torch.no_grad():
-        segments, info = model.transcribe(
-            audio_file,
-            language='ja',
-            beam_size=5,
-            vad_filter=True,
-            without_timestamps=False,
-        )
-
-    text = ''
-    for segment in segments:
-        text += f"{segment.start:.2f}\t{segment.end:.2f}\t{segment.text}\n"
-
-    return text
-
-load_model(model_size)
+fn.load_model('large-v3')
 
 gr.Interface(
-    fn=speech_to_text,
+    fn=fn.speech_to_text,
     inputs=[
         gr.Audio(sources="upload", type="filepath"),
         gr.Dropdown(value=model_size, choices=["tiny", "base", "small", "medium", "large", "large-v2", "large-v3"]),
         ],
-    outputs="text").launch()
+    outputs=["text", "text"]).launch()