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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() | |