Added app.py

app.py

@@ -0,0 +1,82 @@
+from transformers import pipeline
+from pytube import YouTube
+import gradio as gr
+import requests
+
+pipe = pipeline(model="Silemo/whisper-it")  # change to "your-username/the-name-you-picked"
+
+def download_audio(audio_url, filename):
+
+    # URL of the image to be downloaded is defined as audio_url
+    r = requests.get(audio_url) # create HTTP response object 
+  
+    # send a HTTP request to the server and save 
+    # the HTTP response in a response object called r 
+    with open(["audio/" + filename],'wb') as f: 
+  
+        # Saving received content as a mp3 file in 
+        # binary format 
+  
+        # write the contents of the response (r.content) 
+        # to a new file in binary mode. 
+        f.write(r.content) 
+
+def transcribe(audio):
+    text = pipe(audio)["text"]
+    return text
+
+def transcribe_video(url):
+    yt = YouTube(url)
+    stream = yt.streams.get_audio_only()
+
+    # Saves the audio in the /audio folder
+    audio = stream.download(output_path = "audio/")
+
+    text = transcribe(audio)
+
+    return text
+
+audio1_url = "https://github.com/Silemo/sml-lab2-2023-manfredi-meneghin/raw/main/task1/audio/offer.mp3"
+audio1_filename = "offer.mp3"
+download_audio(audio1_url, audio1_filename)
+
+audio2_url = "https://github.com/Silemo/sml-lab2-2023-manfredi-meneghin/raw/main/task1/audio/fantozzi.mp3"
+audio2_filename = "fantozzi.mp3"
+download_audio(audio2_url, audio2_filename)
+
+# Multiple interfaces using tabs -> https://github.com/gradio-app/gradio/issues/450
+
+io1 = gr.Interface(
+    fn = transcribe,
+    inputs = gr.Audio(source=["microphone", "upload"], type="filepath"),
+    outputs = "text",
+
+    examples=[
+        ["audio/" + audio1_filename],
+        ["audio/" + audio2_filename],
+    ],
+
+    title = "Whisper Small - Italian - Microphone or Audio file",
+    description = "Realtime demo for Italian speech recognition using a fine-tuned Whisper small model. It uses the computer microphone as audio input",
+)
+
+io2 = gr.Interface(
+    fn = transcribe_video,
+    inputs = gr.Textbox(label = "YouTube URL", placeholder = "https://youtu.be/9DImRZERJNs?si=1Lme7o_KH2oCxU7y"),
+    outputs = "text",
+
+    examples=[
+        # Per me è la cipolla
+        ["https://youtu.be/QbwZlURClSA?si=DKMtIiKE-nO2mfcV"],
+        
+        # Breaking Italy - Lollobrigida ferma il treno
+        ["https://youtu.be/9MPBN0tnA_E?si=G9Sgn1AsXSkxfCxV"],
+    ],
+    
+    title = "Whisper Small - Italian - YouTube link",
+    description = "Realtime demo for Italian speech recognition using a fine-tuned Whisper small model. It uses a YouTube link as audio input",
+)
+
+gr.TabbedInterface(
+    [io1, io2], {"Microphone or audio file", "YouTube"}
+).launch()