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import gradio as gr
import whisper
import os
model = whisper.load_model("tiny")
# main processing
def main(URL):
video_path = youtube_dl(URL)
text = transcript(video_path)
return text
# Transcribe text using Whisper
def transcript(video_path):
result = model.transcribe(video_path)
return result["text"]
# Download the video
def youtube_dl(URL):
# Download the video
os.system(f"yt-dlp -f best -v {URL} -o target.mp4")
# Path of downloaded video
video_path = os.path.join(os.path.dirname(__file__), "target.mp4")
return video_path
with gr.Blocks() as demo:
gr.Markdown(
"""
# YouTube video transcription app
Enter the YouTube URL, press the button, and it will transcribe the video.
"""
)
with gr.Row():
with gr.Column():
with gr.Row():
url = gr.Textbox(placeholder = 'Youtube video URL', label = 'URL')
with gr.Row():
transcribe_btn = gr.Button('Transcribe')
with gr.Column():
outputs = gr.Textbox(placeholder = 'Transcription of the video', label = 'Transcription')
transcribe_btn.click(fn = main,
inputs = url,
outputs = outputs
)
if __name__ == "__main__":
demo.launch()
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