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| # Import required libraries | |
| import os | |
| import re | |
| import logging | |
| import whisper | |
| from pytube import YouTube | |
| import gradio as gr | |
| # Setup logging | |
| logging.basicConfig(level=logging.INFO) | |
| # Load the Whisper model | |
| model = whisper.load_model("base") | |
| def download_audio_from_youtube(url): | |
| """ | |
| Download the audio from a YouTube video and return the path to the audio file. | |
| """ | |
| yt = YouTube(url) | |
| video = yt.streams.filter(only_audio=True).first() | |
| out_file = video.download(output_path=".") | |
| return out_file | |
| def get_text(url): | |
| """ | |
| Transcribe the audio from a YouTube video and return the transcript. | |
| """ | |
| if not url: | |
| return '' | |
| out_file = download_audio_from_youtube(url) | |
| file_stats = os.stat(out_file) | |
| logging.info(f'Size of audio file in Bytes: {file_stats.st_size}') | |
| if file_stats.st_size > 30000000: | |
| logging.error('Videos for transcription on this space are limited to about 1.5 hours...') | |
| return '' | |
| base, ext = os.path.splitext(out_file) | |
| new_file = base + '.mp3' | |
| os.rename(out_file, new_file) | |
| result = model.transcribe(new_file) | |
| return result['text'].strip() | |
| def create_gradio_interface(): | |
| """ | |
| Create and launch a Gradio interface for transcribing YouTube videos. | |
| """ | |
| with gr.Blocks() as demo: | |
| gr.Markdown("<h1><center>Free Fast YouTube URL Video-to-Text using <a href=https://openai.com/blog/whisper/ target=_blank>OpenAI's Whisper</a> Model</center></h1>") | |
| gr.Markdown("<center>Enter the link of any YouTube video to generate a text transcript of the video.</center>") | |
| gr.Markdown("<center><b>'Whisper is a neural net that approaches human level robustness and accuracy on English speech recognition.'</b></center>") | |
| gr.Markdown("<center>Transcription takes 5-10 seconds per minute of the video. #patience<br />If you have time while waiting, check out my <a href=https://www.artificial-intelligence.blog target=_blank>AI blog</a> (opens in new tab).</center>") | |
| input_text_url = gr.Textbox(placeholder='Youtube video URL', label='YouTube URL') | |
| result_button_transcribe = gr.Button('Transcribe') | |
| output_text_transcribe = gr.Textbox(placeholder='Transcript of the YouTube video.', label='Transcript') | |
| result_button_transcribe.click(get_text, inputs=input_text_url, outputs=output_text_transcribe) | |
| demo.queue(default_enabled=True).launch(debug=True) | |
| # Launch the Gradio interface | |
| if __name__ == "__main__": | |
| create_gradio_interface() | |