Create app.py

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]
+    files = os.listdir()
+    # Get a list of all files in the current directory
+    files = os.listdir()
+    #st.write(files)
+
+    # Create an empty list to collect results
+    results = []
+    
+    # Iterate through the files
+    for i in files:
+        if ".mp3" in i:
+            # Build the full path to the MP3 file
+            file_path = os.path.join(os.getcwd(), i)
+    
+            # Display information (optional)
+            st.write("Current Directory:", os.getcwd())
+            st.write("File Path:", file_path)
+            
+            
+            result = pipe(file_path)
+            #print(result)
+            return result['text']
+    
+
+
+def summarize():
+    transcript = audio_to_text()
+    len_trans = len(transcript)
+
+    chunks = int(len_trans/512)
+    from transformers import pipeline
+
+    summarizer = pipeline("summarization", model="snrspeaks/t5-one-line-summary")
+
+    
+    #print(summarizer(transcript, do_sample=False))
+    cutoff = 512
+    final_output = ''
+    """
+    for i in range(chunks):
+        print(i)
+        if i == 0:
+            tran_text = transcript[:512]
+            inter_output = summarizer(tran_text, do_sample=False)[0]['summary_text']
+            final_output += inter_output
+            final_output += ' '
+
+        else:
+            #end_slice = cutoff + cutoff
+            tran_text = transcript[cutoff:cutoff + 2]
+            inter_output = summarizer(tran_text, do_sample=False)[0]['summary_text']
+            final_output += inter_output
+            final_output += ' '
+            cutoff += cutoff
+    """
+    final_output = summarizer(tran_text, do_sample=False)
+    return final_output
+
+yt_link = st.text_input("Enter the YouTube URL: ")
+
+if st.button("Start Summarization"):
+    
+    with st.status("Downloading the video..."):
+        vid_to_audio(url=yt_link)
+    with st.status("Summarizing..."):
+        s = summarize()
+        st.write(s)
+