File size: 2,251 Bytes
722cc09 7f9bb4e 29613fd 722cc09 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | import gradio as gr
import whisper
#from pytube import YouTube
from pytubefix import YouTube
from pytubefix.cli import on_progress
loaded_model = whisper.load_model("medium")
current_size = 'medium'
def inference(link):
#yt = YouTube(link)
#yt = YouTube(link, on_progress_callback=on_progress, use_po_token=True)
#yt = YouTube(link, use_po_token=True)
yt = YouTube(link, on_progress_callback=on_progress, client="WEB")
global audio_stream
audio_stream = yt.streams.filter(only_audio=True, file_extension='mp4').first()
path = audio_stream.download()
#path = yt.streams.get_audio_only().download(mp3=True)
#path = yt.streams.get_audio_only().download()
options = whisper.DecodingOptions(language= 'Spanish', without_timestamps=True)
results = loaded_model.transcribe(path)
return results['text']
def change_model(size):
global loaded_model, current_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=""
description=""
block = gr.Blocks()
with block:
gr.HTML(
"""
<div style="text-align: center; max-width: 500px; margin: 0 auto;">
<div>
</div>
</div>
"""
)
with gr.Group():
with gr.Group():
sz = gr.Dropdown(label="Model Size", choices=['tiny', 'base','small', 'medium', 'large'], value='medium')
link = gr.Textbox(label="YouTube Link")
gr.Markdown("Ejemplo: https://www.youtube.com/watch?v=bnvgcQB01mQ")
with gr.Row():
title = gr.Label(label="Video Title")
img = gr.Image(label="Thumbnail")
text = gr.Textbox(
label="Transcription",
placeholder="Transcription Output",
lines=5)
with gr.Row():
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() |