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| import gradio as gr | |
| from moviepy.editor import VideoFileClip | |
| from speechbrain.pretrained import EncoderClassifier | |
| import torchaudio | |
| from pytubefix import YouTube | |
| from pytubefix.cli import on_progress | |
| import requests | |
| import os | |
| import torch | |
| CLASSIFIER = "Jzuluaga/accent-id-commonaccent_xlsr-en-english" | |
| def get_default_device(): | |
| """Return the default device (cuda if available, else cpu).""" | |
| return torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| def download_video(url): | |
| """Download video from YouTube or direct MP4 URL using pytubefix.""" | |
| try: | |
| if "youtube.com" in url or "youtu.be" in url: | |
| yt = YouTube(url, on_progress_callback=on_progress) | |
| stream = yt.streams.filter(progressive=True, file_extension='mp4').first() | |
| if not stream: | |
| raise ValueError("No suitable video stream found.") | |
| video_path = stream.download() | |
| return video_path | |
| else: | |
| # Direct MP4 file download | |
| local_filename = "temp_video.mp4" | |
| with requests.get(url, stream=True) as r: | |
| r.raise_for_status() | |
| with open(local_filename, 'wb') as f: | |
| for chunk in r.iter_content(chunk_size=8192): | |
| f.write(chunk) | |
| return local_filename | |
| except Exception as e: | |
| raise RuntimeError(f"Failed to download video: {e}") | |
| def extract_audio(video_path): | |
| """Extract audio from video and save as WAV file.""" | |
| clip = VideoFileClip(video_path) | |
| audio_path = "temp_audio.wav" | |
| clip.audio.write_audiofile(audio_path, logger=None) | |
| clip.close() | |
| return audio_path | |
| def classify_accent(audio_path): | |
| """Classify English accent from audio file using SpeechBrain model.""" | |
| device = get_default_device() | |
| classifier = EncoderClassifier.from_hparams( | |
| source=CLASSIFIER, | |
| savedir="pretrained_models/accent_classifier", | |
| run_opts={"device": str(device)} | |
| ) | |
| waveform, sample_rate = torchaudio.load(audio_path) | |
| prediction = classifier.classify_batch(waveform.to(device)) | |
| predicted_accent = prediction[3][0] | |
| confidence = prediction[1].exp().max().item() * 100 | |
| return predicted_accent, f"{confidence:.2f}%" | |
| def process_video(url): | |
| """Main processing pipeline: download video, extract audio, classify accent.""" | |
| video_path = None | |
| audio_path = None | |
| try: | |
| video_path = download_video(url) | |
| audio_path = extract_audio(video_path) | |
| accent, confidence = classify_accent(audio_path) | |
| return accent, confidence | |
| except Exception as e: | |
| return f"Error: {e}", "" | |
| finally: | |
| # Clean up temporary files | |
| for f in [video_path, audio_path]: | |
| if f and os.path.exists(f): | |
| os.remove(f) | |
| iface = gr.Interface( | |
| fn=process_video, | |
| inputs=gr.Textbox(label="Enter Public Video URL (YouTube, Loom, direct MP4)"), | |
| outputs=[ | |
| gr.Textbox(label="Detected Accent"), | |
| gr.Textbox(label="Confidence Score") | |
| ], | |
| title="English Accent Classifier", | |
| description="Paste a public video URL to detect the English accent and confidence score." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |