utils/models.py

import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration, AutoModelForAudioClassification, AutoFeatureExtractor
from fer import FER

def load_models():
    """
    Loads all the machine learning models and returns them as a dictionary.
    """
    # Whisper model for transcription
    whisper_model_name = "openai/whisper-base"
    whisper_processor = WhisperProcessor.from_pretrained(whisper_model_name)
    whisper_model = WhisperForConditionalGeneration.from_pretrained(whisper_model_name)
    device = "cuda" if torch.cuda.is_available() else "cpu"
    whisper_model = whisper_model.to(device)

    # Speech emotion recognition model
    emotion_model_id = "firdhokk/speech-emotion-recognition-with-openai-whisper-large-v3"
    emotion_model = AutoModelForAudioClassification.from_pretrained(emotion_model_id)
    emotion_feature_extractor = AutoFeatureExtractor.from_pretrained(emotion_model_id, do_normalize=True)
    emotion_id2label = emotion_model.config.id2label

    # Facial emotion recognition model
    fer_detector = FER(mtcnn=True)

    return {
        "whisper": {
            "processor": whisper_processor,
            "model": whisper_model,
            "device": device,
        },
        "emotion_model": emotion_model,
        "emotion_feature_extractor": emotion_feature_extractor,
        "emotion_id2label": emotion_id2label,
        "fer": fer_detector,
    }