import gradio as gr import whisper from pytube import YouTube 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 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) return results["text"] 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 of Youtube videos using OpenAI's Whisper" block = gr.Blocks() with block: gr.HTML( """

Youtube Whisperer

Speech to text transcription of Youtube videos using OpenAI's Whisper

""" ) 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): btn = gr.Button("Transcribe") btn.click(gio, inputs=[link, lang, sz], outputs=[text]) link.change(gio.populate_metadata, inputs=[link], outputs=[img, title]) block.Launch()