Add application file

app.py

@@ -0,0 +1,95 @@
+import streamlit as st
+import os
+from pydub import AudioSegment
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import torch
+import whisper
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+models = T5ForConditionalGeneration.from_pretrained("Michau/t5-base-en-generate-headline")
+tokenizer = T5Tokenizer.from_pretrained("Michau/t5-base-en-generate-headline")
+models = models.to(device)
+model = whisper.load_model("base")
+
+st.title("Audio Analysis")
+
+# Arguments input
+st.subheader("Enter YouTube link and file name:")
+url = st.text_input("YouTube link")
+name = st.text_input("File name")
+
+# Process audio and generate headings
+if st.button("Process"):
+    if os.path.exists("audio.mp3"):
+        os.remove("audio.mp3")
+
+    os.system("youtube-dl "+"--write-thumbnail "+"--skip-download "+url + " -o logo.png")
+    os.system("yt-dlp -f 140 -o audio.mp3 " + url)
+
+    while not os.path.exists("audio.mp3"):
+        continue
+
+    if os.path.exists("segments"):
+        os.system("rm -rf segments")
+
+    audio = AudioSegment.from_file("audio.mp3")
+    segment_length = 30 * 1000
+
+    if not os.path.exists("segments"):
+        os.makedirs("segments")
+
+    for i, segment in enumerate(audio[::segment_length]):
+        segment.export(f"segments/{i}.mp3", format="mp3")
+
+    original_text = ""
+    audio_list = os.listdir("segments")
+    headings = []
+    original_texts = []
+    dataForWeb = {}
+
+    for i in range(len(audio_list)):
+        st.write(f"Processing segment {i+1}/{len(audio_list)}")
+        audio = whisper.load_audio(f"segments/{i}.mp3")
+        audio = whisper.pad_or_trim(audio)
+        mel = whisper.log_mel_spectrogram(audio).to(model.device)
+        _, probs = model.detect_language(mel)
+        options = whisper.DecodingOptions(fp16=False)
+        result = whisper.decode(model, mel, options)
+
+        text = "headline: " + result.text
+        max_len = 256
+        encoding = tokenizer.encode_plus(text, return_tensors="pt")
+        input_ids = encoding["input_ids"].to(device)
+        attention_masks = encoding["attention_mask"].to(device)
+        beam_outputs = models.generate(
+            input_ids=input_ids,
+            attention_mask=attention_masks,
+            max_length=64,
+            num_beams=3,
+            early_stopping=True,
+        )
+        generated_heading = tokenizer.decode(beam_outputs[0])
+        headings.append(generated_heading)
+        original_texts.append(result.text)
+        dataForWeb[i] = {
+            "heading": generated_heading,
+            "text": result.text
+        }
+
+        original_text += "\n"
+        original_text += "<h3>" + generated_heading + "</h3>"
+        original_text += "\n"
+        original_text += "<p>" + result.text + "</p>"
+
+    with open(name, "w") as f:
+        f.write(original_text)
+
+    st.success("Audio processing completed!")
+
+    # Display results
+    st.subheader("Generated Headings and Text:")
+    for i, heading in enumerate(headings):
+        st.write(f"Segment {i+1}:")
+        st.write("Heading:", heading)
+        st.write("Text:", original_texts[i])
+        st.write("-----------")