inference.py

import torch
from model import CLSTMModel
from config import CONFIG, EMOTION_CONFIG
from audio_utils import preprocess_audio

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

checkpoint = torch.load(CONFIG["model_path"], map_location=device)

if "label_map" in checkpoint:
    inv = {v: k for k, v in checkpoint["label_map"].items()}
    emotions = [inv[i] for i in range(len(inv))]
else:
    emotions = list(EMOTION_CONFIG.keys())

model = CLSTMModel(
    n_mels=CONFIG["n_mels"],
    n_classes=len(emotions)
).to(device)

model.load_state_dict(checkpoint["model_state_dict"])
model.eval()


def predict(path):
    x = preprocess_audio(path, device)

    with torch.no_grad():
        logits = model(x)
        probs = torch.softmax(logits, dim=1)
        idx = torch.argmax(probs, dim=1).item()

    return {
        "emotion": emotions[idx],
        "confidence": float(probs[0][idx])
    }