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import gradio as gr
import whisper
from pytube import YouTube
loaded_model = whisper.load_model("base")
current_size = 'base'
def inference(link):
yt = YouTube(link)
path = yt.streams.filter(only_audio=True)[0].download(filename="audio.mp4")
options = whisper.DecodingOptions(without_timestamps=True)
results = loaded_model.transcribe(path)
return results['text']
def change_model(size):
if size == current_size:
return
loaded_model = whisper.load_model(size)
current_size = size
def populate_metadata(link):
yt = YouTube(link)
return yt.thumbnail_url, yt.title
title="Youtube Whisperer"
description="Speech to text transcription of Youtube videos using OpenAI's Whisper"
block = gr.Blocks()
with block:
gr.HTML(
"""
<div style="text-align: center; max-width: 500px; margin: 0 auto;">
<div>
<h1>Youtube Whisperer</h1>
</div>
<p style="margin-bottom: 10px; font-size: 94%">
Speech to text transcription of Youtube videos using OpenAI's Whisper
</p>
</div>
"""
)
with gr.Group():
with gr.Box():
sz = gr.Dropdown(label="Model Size", choices=['base','small', 'medium', 'large'], value='base')
link = gr.Textbox(label="YouTube Link")
with gr.Row().style(mobile_collapse=False, equal_height=True):
title = gr.Label(label="Video Title", placeholder="Title")
img = gr.Image(label="Thumbnail")
text = gr.Textbox(
label="Transcription",
placeholder="Transcription Output",
lines=5)
with gr.Row().style(mobile_collapse=False, equal_height=True):
btn = gr.Button("Transcribe")
# Events
btn.click(inference, inputs=[link], outputs=[text])
link.change(populate_metadata, inputs=[link], outputs=[img, title])
sz.change(change_model, inputs=[sz], outputs=[])
block.launch(debug=True) |