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, subs): 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) if subs == "None": return results["text"] elif subs == ".srt": return self.srt(results["segments"]) elif ".csv" == ".csv": return self.csv(results["segments"]) def srt(self, segments): output = "" for i, segment in enumerate(segments): output += f"{i+1}\n" output += f"{self.format_time(segment['start'])} --> {self.format_time(segment['end'])}\n" output += f"{segment['text']}\n\n" return output def csv(self, segments): output = "" for segment in segments: output += f"{segment['start']},{segment['end']},{segment['text']}\n" return output def format_time(self, time): hours = time//3600 minutes = (time - hours*3600)//60 seconds = time - hours*3600 - minutes*60 milliseconds = (time - int(time))*1000 return f"{int(hours):02d}:{int(minutes):02d}:{int(seconds):02d},{int(milliseconds):03d}" 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

You can skip the queue by duplicating this space and upgrading to gpu in settings: Duplicate Space

""" ) 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") with gr.Row().style(equal_height=True): wt = gr.Radio(["None", ".srt", ".csv"], label="With Timestamps?") 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, wt], outputs=[text]) link.change(gio.populate_metadata, inputs=[link], outputs=[img, title]) block.launch()