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| import torch | |
| import torchaudio | |
| import librosa | |
| import soundfile as sf | |
| import numpy as np | |
| from src.model.emotion_classifier import EmotionClassifier | |
| from src.model.feature_extractor import feature_extractor, processor | |
| from src.utils.preprocessing import resampler | |
| from src.config import DEVICE, LABELS | |
| import os | |
| # Charger le modèle sauvegardé | |
| classifier = EmotionClassifier(feature_extractor.config.hidden_size, len(LABELS)).to(DEVICE) | |
| classifier.load_state_dict(torch.load(os.path.join("src","model","best_emotion_model.pth"), map_location=torch.device(DEVICE)), strict=False) | |
| classifier.eval() | |
| # Fonction de prédiction | |
| def predict_emotion(speech, output_probs=False, sampling_rate=16000): | |
| # Charger l'audio | |
| # waveform, sample_rate = librosa.load(speech, sr=None) | |
| # speech_audio, sample_rate = sf.read(speech, dtype="float32") | |
| # Rééchantillonnage si nécessaire | |
| # if sample_rate != sampling_rate: | |
| # speech = torch.tensor(speech).unsqueeze(0) | |
| # speech = resampler(speech).squeeze(0).numpy() | |
| # Extraire les features | |
| inputs = processor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True) | |
| input_values = inputs.input_values.to(DEVICE) | |
| with torch.no_grad(): | |
| features = feature_extractor(input_values).last_hidden_state.mean(dim=1) | |
| logits = classifier(features) | |
| if output_probs: | |
| # Appliquer softmax pour obtenir des probabilités | |
| probabilities = torch.nn.functional.softmax(logits, dim=-1) | |
| # Convertir en numpy array et prendre le premier (et seul) élément | |
| probabilities = probabilities[0].detach().cpu().numpy() | |
| # Créer un dictionnaire associant chaque émotion à sa probabilité | |
| emotion_probabilities = {emotion: prob for emotion, prob in zip(LABELS, probabilities)} | |
| # emotion_probabilities = {"emotions": [emotion for emotion in emotion_labels], | |
| # "probabilities": [prob for prob in probabilities]} | |
| return emotion_probabilities | |
| else: | |
| # Obtenir l'émotion la plus probable (i.e. la prédiction) | |
| predicted_label = torch.argmax(logits, dim=-1).item() | |
| emotion = LABELS[predicted_label] | |
| return emotion | |
| # Exemple d'utilisation | |
| # if __name__ == "__main__": | |
| # base_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "data")) | |
| # audio_file = os.path.join(base_path, "colere", "c1ac.wav") | |
| # emotion = predict_emotion(audio_file) | |
| # print(f"🎤 L'émotion prédite est : {emotion}") | |