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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()
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