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app.py

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+from transformers import pipeline
+import gradio as gr
+import pytube
+import os
+import re
+
+pipe = pipeline(model="Yuyang2022/yue")  # change to "your-username/the-name-you-picked"
+
+def transcribe(link):
+    video=link
+    data=pytube.YouTube(video)
+    audio=data.streams.get_audio_only()
+    audio.download()
+
+    pattern=r".mp4$"
+    content=os.listdir()
+    for i in content:
+        if re.search(pattern,i) is not None:
+            video=i
+    
+    text = pipe(video)["text"]
+    return text
+
+iface = gr.Interface(
+    fn=transcribe, 
+    inputs=gr.Textbox(label="Youtube Link",placeholder="Youtube Link"), 
+    outputs=["text"],
+    title="Whisper Base",
+    description="Realtime demo for  speech recognition using a fine-tuned Whisper base model.",
+)
+
+
+iface.launch()

requirements.txt

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+torch
+git+https://github.com/huggingface/transformers
+gradio
+pytube
+ffmpeg-python