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import gradio as gr
import whisper
from pytube import YouTube
import yake
from transformers import pipeline
class GradioInference():
def __init__(self):
self.sizes = list(whisper._MODELS.keys())
self.langs = ["none"] + sorted(list(whisper.tokenizer.LANGUAGES.values()))
self.current_size = "base"
self.loaded_model = whisper.load_model(self.current_size)
self.yt = None
# Initialize YAKE keyword extractor
self.keyword_extractor = yake.KeywordExtractor(lan="en", n=3, dedupLim=0.9, dedupFunc="seqm", windowsSize=1, top=5, features=None)
# Initialize Facebook/BART-Large-CNN summarizer
self.summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
def __call__(self, link, lang, size):
if self.yt is None:
self.yt = YouTube(link)
path = self.yt.streams.filter(only_audio=True)[0].download(filename="tmp.mp4")
if lang == "none":
lang = None
if size != self.current_size:
self.loaded_model = whisper.load_model(size)
self.current_size = size
results = self.loaded_model.transcribe(path, language=lang)
# Perform summarization on the transcription
transcription_summary = self.summarizer(results["text"], max_length=130, min_length=30, do_sample=False)
# Extract keywords from the transcription
keywords = self.keyword_extractor.extract_keywords(results["text"])
return results["text"], transcription_summary[0]["summary_text"], [kw[0] for kw in keywords]
def populate_metadata(self, link):
self.yt = YouTube(link)
return self.yt.thumbnail_url, self.yt.title
gio = GradioInference()
title = "Youtube Whisperer"
description = "Speech to text transcription, summary, and keyword extraction of Youtube videos using OpenAI's Whisper, Facebook/BART-Large-CNN, and YAKE"
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, summary, and keyword extraction of Youtube videos using OpenAI's Whisper, Facebook/BART-Large-CNN, and YAKE
</p>
</div>
"""
)
with gr.Group():
with gr.Box():
with gr.Row().style(equal_height=True):
sz = gr.Dropdown(label="Model Size", choices=gio.sizes, value='base')
lang = gr.Dropdown(label="Language (Optional)", choices=gio.langs, value="none")
link = gr.Textbox(label="YouTube Link")
title = gr.Label(label="Video Title")
with gr.Row().style(equal_height=True):
img = gr.Image(label="Thumbnail")
text = gr.Textbox(label="Transcription", placeholder="Transcription Output", lines=10)
with gr.Row().style(equal_height=True):
summary = gr.Textbox(label="Summary", placeholder="Summary Output", lines=5)
keywords = gr.Textbox(label="Keywords", placeholder="Keywords Output", lines=5)
with gr.Row().style(equal_height=True):
btn = gr.Button("Transcribe, Summarize & Extract Keywords")
btn.click(gio, inputs=[link, lang, sz], outputs=[text, summary, keywords])
link.change(gio.populate_metadata, inputs=[link], outputs=[img, title])
block.launch()
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