app.py

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(
        """
            <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 of Youtube videos using OpenAI's Whisper
              </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")
          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()