djcm_module.py

import torch
import numpy as np

class DJCMExtractor:
    def __init__(self, model_path, device="cuda"):
        self.device = device
        self.model = torch.jit.load(model_path, map_location=device)
        self.model.eval()

    def __call__(self, audio, sr=16000):
        """
        audio: numpy array (1D, float32)
        sr: sample rate (default 16k atau sesuaikan dengan DJCM)
        return: f0 contour (numpy array 1D)
        """
        x = torch.tensor(audio, dtype=torch.float32, device=self.device).unsqueeze(0)
        with torch.no_grad():
            f0 = self.model(x, sr)  # Sesuaikan kalau model DJCM butuh input lain
        return f0.squeeze().cpu().numpy()