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| import gradio as gr | |
| import os | |
| import pytube | |
| from pytube import YouTube | |
| from pprint import pprint | |
| from moviepy.editor import VideoFileClip | |
| from transformers import pipeline | |
| import librosa | |
| # Load the Whisper model from Hugging Face | |
| transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base") | |
| def download_video_mp4(youtube_url): | |
| try: | |
| # Create a youtube object | |
| yt = YouTube(youtube_url) | |
| # Get the video with the highest resolution and file size | |
| video = yt.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first() | |
| # Download the video to the current working directory | |
| video_filename = video.download() | |
| print('Video downloaded') | |
| return video_filename, "" | |
| except Exception as e: | |
| return "", str(e) | |
| def create_audio_file(video_filename): | |
| try: | |
| # Use moviepy to extract the audio track from the video and create an .mp3 audio file | |
| audio_filename = video_filename.replace(".mp4", ".mp3") | |
| video = VideoFileClip(video_filename) | |
| audio = video.audio | |
| audio.write_audiofile(audio_filename) | |
| return audio_filename, "" | |
| except Exception as e: | |
| return "", str(e) | |
| def transcribe(audio_path): | |
| try: | |
| # Load the audio file and convert it to a numpy array | |
| audio, _ = librosa.load(audio_path, sr=16000) | |
| # Transcribe the audio using the Whisper model | |
| transcript = transcriber(audio) | |
| return transcript["text"], "" | |
| except Exception as e: | |
| return "", str(e) | |
| def process_youtube_url(youtube_url): | |
| video_filename, download_error = download_video_mp4(youtube_url) | |
| if not video_filename: | |
| return "", download_error | |
| audio_filename, audio_error = create_audio_file(video_filename) | |
| if not audio_filename: | |
| return "", audio_error | |
| yt_text, transcribe_error = transcribe(audio_filename) | |
| if not yt_text: | |
| return "", transcribe_error | |
| return yt_text, "" | |
| iface = gr.Interface( | |
| fn=process_youtube_url, | |
| inputs=gr.Textbox(label="YouTube URL"), | |
| outputs=[gr.Textbox(label="Transcription"), gr.Textbox(label="Errors")], | |
| title="YouTube Video Transcription", | |
| description="Enter a YouTube video URL to transcribe the audio using the Whisper model from Hugging Face." | |
| ) | |
| iface.launch() |