Update app.py

app.py

@@ -3,23 +3,10 @@ import os
 os.system("pip install git+https://github.com/openai/whisper.git")
 import gradio as gr
 import whisper
-from transformers import pipeline
-import numpy as np
-
-
-p = pipeline("automatic-speech-recognition", model="openai/whisper-base.ch")
 
 
 
 model = whisper.load_model("base")
-transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.ch")
-
-def transcribe(audio):
-    sr, y = audio
-    y = y.astype(np.float32)
-    y /= np.max(np.abs(y))
-
-    return transcriber({"sampling_rate": sr, "raw": y})["text"]
 
 
 
@@ -35,19 +22,12 @@ def inference(audio):
     result = whisper.decode(model, mel, options)
 
     return result.text
-    
-with gr.Blocks() as demo:
-    gr.Markdown("Flip text or image files using this demo.")
-    with gr.Tab("語音轉文字"):
-            fn=inference,
-            inputs=gr.Audio(type="filepath", label="格式可為 WAV、MP3、OGG、FLAC、AAC、M4A、WMA，單聲道、多聲道均可。"),
-            outputs="text"
-    with gr.Tab("Real Time Speech Recognition"):
-        transcribe,
-        gr.Audio(sources=["microphone"]),
-        "text",
 
+iface = gr.Interface(
+    fn=inference,
+    inputs=gr.Audio(type="filepath", label="格式可為 WAV、MP3、OGG、FLAC、AAC、M4A、WMA，單聲道、多聲道均可。"),
+    outputs="text"
+)
 
 # 启动 Gradio 界面
-demo.launch()
-
+iface.launch()