Update app.py

app.py

@@ -1,25 +1,43 @@
 from transformers import pipeline
 import gradio as gr
+import pytube as pt
 
 pipe = pipeline(model="Hoft/whisper-small-swedish-asr")  # change to "your-username/the-name-you-picked"
 
-def transcribe(audio, file):
-    print("AUDIO:",audio)
-    print("FILE:", file)
-    if audio is not None:
-        text = pipe(audio)["text"]
-        return text
-    if file is not None:
-        text = pipe(file)["text"]
-        return text
-
-iface = gr.Interface(
-    fn=transcribe, 
-    inputs=[gr.Audio(source="microphone", type="filepath"), gr.File()], 
+def microphone_transcribe(audio):
+    text = pipe(audio)["text"]
+    return text
+    
+def youtube_transcribe(url):
+    yt_url = pt.YouTube(url)
+    
+    stream = yt.streams.filter(only_audio=True)[0]
+    stream.download(filename="audio.mp3")
+
+    text = pipe("audio.mp3")["text"]
+
+    return text
+
+
+app = gr.Blocks()
+
+microphone_tab = gr.Interface(
+    fn=microphone_transcribe, 
+    inputs=gr.Audio(source="microphone", type="filepath"), 
+    outputs="text",
+    title="Whisper Small Swedish",
+    description="Realtime demo for Swedish speech recognition using a fine-tuned Whisper small model.",
+)
+
+youtube_tab = gr.Interface(
+    fn=youtube_transcribe, 
+    inputs=[gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video", label="URL")], 
     outputs="text",
     title="Whisper Small Swedish",
     description="Realtime demo for Swedish speech recognition using a fine-tuned Whisper small model.",
 )
 
+with app:
+    gr.TabbedInterface([microphone_tab, youtube_tab], ["Microphone", "YouTube"])
 
-iface.launch()
+app.launch(enable_queue=True)