Create app.py

app.py

@@ -0,0 +1,25 @@
+import gradio as gr
+import whisper
+
+# duplicate this space to run on higher CPU power for the large (very precise and multi-lingual) model to work best
+ 
+def speech_to_text(uploaded, model_size):
+    model = whisper.load_model(model_size)
+    source = uploaded if uploaded is not None else ''
+    result = model.transcribe(source)
+    return f'{result["text"]}'
+
+gr.Interface(
+    title="",
+    thumbnail="",
+    css="""
+    footer {visibility: xhidden}
+    .gr-prose p{text-align: center;}
+    .gr-button {background: black;color: white}
+    """,
+    description="",
+    fn=speech_to_text,
+    inputs=[
+        gr.Audio(source="upload", type="filepath", label="Upload Audio"), 
+        gr.Dropdown(label="Select model size",value="large",choices=["tiny", "base", "small", "medium", "large"])],
+    outputs="text").launch(debug = True)