app.py

import torch
import librosa
from speechbrain.inference.classifiers import EncoderClassifier
from pydub import AudioSegment
import gradio as gr
import os

# Load model only once
classifier = EncoderClassifier.from_hparams(
    source="Jzuluaga/accent-id-commonaccent_ecapa",
    savedir="pretrained_models/accent-id-commonaccent_ecapa"
)

def classify_accent(video):
    # 'video' will already be a path to the uploaded file
    audio = AudioSegment.from_file(video, format="mp4")
    audio.export("output.wav", format="wav")

    waveform, sr = librosa.load("output.wav", sr=16000, mono=True)
    waveform_tensor = torch.tensor(waveform).unsqueeze(0)

    prediction = classifier.classify_batch(waveform_tensor)
    _, score, _, text_lab = prediction

    return f"Accent: {text_lab[0]} (Confidence: {score.item():.2f})"


iface = gr.Interface(fn=classify_accent,
                    inputs=gr.Video(),
                     outputs="text")

if __name__ == "__main__":
    iface.launch()