Update app.py

app.py

@@ -1,21 +1,24 @@
-import torch
-from transformers import pipeline
-import gradio as gr
-
-asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny.en", chunk_length_s=30)
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-
-def transcript_and_summarize(audio_file):
-    transcript = asr_pipe(audio_file)["text"]
-    summary = summarizer(transcript, max_length=100, min_length=30, do_sample=False)[0]["summary_text"]
-    return f"**Transcription:**\n{transcript}\n\n**Summary (Notes):**\n{summary}"
-
-iface = gr.Interface(
-    fn=transcript_and_summarize, 
-    inputs=gr.Audio(sources=["upload"], type="filepath"), 
-    outputs=gr.Textbox(), 
-    title="Audio Transcription & Note-Making",
-    description="Upload an audio file to transcribe and get summarized notes."
-)
-
-iface.launch()
+import torch
+from transformers import pipeline
+import gradio as gr
+
+import os
+os.environ["HF_HUB_DOWNLOAD_TIMEOUT"] = "300"
+
+asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny.en", chunk_length_s=30)
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+def transcript_and_summarize(audio_file):
+    transcript = asr_pipe(audio_file)["text"]
+    summary = summarizer(transcript, max_length=100, min_length=30, do_sample=False)[0]["summary_text"]
+    return f"**Transcription:**\n{transcript}\n\n**Summary (Notes):**\n{summary}"
+
+iface = gr.Interface(
+    fn=transcript_and_summarize, 
+    inputs=gr.Audio(sources=["upload"], type="filepath"), 
+    outputs=gr.Textbox(), 
+    title="Audio Transcription & Note-Making",
+    description="Upload an audio file to transcribe and get summarized notes."
+)
+
+iface.launch()