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app.py

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+import streamlit as st
+import ssl
+ssl._create_default_https_context = ssl._create_unverified_context
+import glob
+import os
+
+def vid_to_audio(url=None):
+    # importing packages 
+    from pytube import YouTube 
+    import os 
+
+    # url input from user 
+    yt = YouTube(url) 
+
+    # extract only audio 
+    video = yt.streams.filter(only_audio=True).first() 
+
+    # check for destination to save file 
+    destination = '.'
+
+    # download the file 
+    out_file = video.download(output_path=destination) 
+
+    # save the file 
+    base, ext = os.path.splitext(out_file) 
+    new_file = base + '.mp3'
+    os.rename(out_file, new_file) 
+
+    # result of success 
+    print(yt.title + " has been successfully downloaded.")
+
+    return "OK"
+
+#vid_to_text(url='https://youtu.be/FE5tva_o7ew?si=ztkKeO7qwcpC36AS')
+
+def audio_to_text():
+    import torch
+    from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+    
+
+
+    device = "cuda:0" if torch.cuda.is_available() else "cpu"
+    torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+    model_id = "openai/whisper-tiny"
+
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+    )
+    model.to(device)
+
+    processor = AutoProcessor.from_pretrained(model_id)
+
+    pipe = pipeline(
+        "automatic-speech-recognition",
+        model=model,
+        tokenizer=processor.tokenizer,
+        feature_extractor=processor.feature_extractor,
+        max_new_tokens=128,
+        chunk_length_s=30,
+        batch_size=16,
+        
+        torch_dtype=torch_dtype,
+        device=device,
+    )
+
+    files = glob.glob('*.mp3')[0]
+    current_path = os.getcwd()
+    file_path = os.path.join(current_path,files)
+    result = pipe(file_path)
+    print(result["text"])
+    return result["text"]
+audio_to_text()
+
+def summarize():
+    transcript = audio_to_text()
+    from transformers import pipeline
+
+    summarizer = pipeline("summarization", model="philschmid/flan-t5-base-samsum")
+
+    
+    #print(summarizer(transcript, do_sample=False))
+
+    return summarizer(transcript, do_sample=False)
+
+yt_link = st.text_input("Enter the YouTube URL: ")
+
+with st.button("Start Summarization"):
+    
+    with st.status("Downloading the video..."):
+        vid_to_audio()
+    with st.status("Summarizing..."):
+        s = summarize()
+    st.write(s)
+    

requirments.txt

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+pytube
+streamlit
+os_sys
+transformers
+ffmpeg
+accelerate