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import os
import torch
import torch.nn as nn
import torch.optim as optim
import soundfile as sf
import torchaudio
import numpy as np
from datasets import Dataset
from transformers import (
Wav2Vec2Model,
Wav2Vec2Processor
)
from dotenv import load_dotenv
from sklearn.metrics import accuracy_score
# Charger .env pour Hugging Face API Key
load_dotenv()
HF_API_KEY = os.getenv("HF_API_KEY")
if not HF_API_KEY:
raise ValueError("Le token Hugging Face n'a pas été trouvé dans .env")
# Définition des labels pour la classification des émotions
LABELS = {"colere": 0, "neutre": 1, "joie": 2}
NUM_LABELS = len(LABELS)
# Charger le processeur et le modèle pour l'extraction de features
model_name = "facebook/wav2vec2-large-xlsr-53-french"
device = "cuda" if torch.cuda.is_available() else "cpu"
processor = Wav2Vec2Processor.from_pretrained(model_name)
feature_extractor = Wav2Vec2Model.from_pretrained(model_name).to(device)
# Resampleur pour convertir en 16 kHz
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
# Définition du classifieur amélioré
class EmotionClassifier(nn.Module):
def __init__(self, feature_dim, num_labels):
super(EmotionClassifier, self).__init__()
self.fc1 = nn.Linear(feature_dim, 256)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.3)
self.fc2 = nn.Linear(256, num_labels)
def forward(self, x):
x = self.fc1(x)
x = self.relu(x)
x = self.dropout(x)
return self.fc2(x)
# Instancier le classifieur
classifier = EmotionClassifier(feature_extractor.config.hidden_size, NUM_LABELS).to(device)
# Charger les fichiers audio et leurs labels
def load_audio_data(data_dir):
data = []
for label_name, label_id in LABELS.items():
label_dir = os.path.join(data_dir, label_name)
for file in os.listdir(label_dir):
if file.endswith(".wav"):
file_path = os.path.join(label_dir, file)
data.append({"path": file_path, "label": label_id})
return Dataset.from_list(data)
# Chargement du dataset
data_dir = "./dataset"
ds = load_audio_data(data_dir)
# Charger les fichiers audio avec SoundFile et rééchantillonner à 16 kHz
def preprocess_audio(batch):
speech, sample_rate = sf.read(batch["path"], dtype="float32")
if sample_rate != 16000:
speech = torch.tensor(speech).unsqueeze(0)
speech = resampler(speech).squeeze(0).numpy()
batch["speech"] = speech.tolist() # Convertir en liste pour éviter les erreurs de PyArrow
batch["sampling_rate"] = 16000
return batch
ds = ds.map(preprocess_audio)
# Vérifier la distribution des longueurs des fichiers audio
lengths = [len(sample["speech"]) for sample in ds]
max_length = int(np.percentile(lengths, 95))
# Transformer l'audio en features utilisables par le modèle
def prepare_features(batch):
features = processor(
batch["speech"],
sampling_rate=16000,
padding=True,
truncation=True,
max_length=max_length,
return_tensors="pt"
)
batch["input_values"] = features.input_values.squeeze(0)
batch["label"] = torch.tensor(batch["label"], dtype=torch.long)
return batch
ds = ds.map(prepare_features)
# Diviser les données en train et test
ds = ds.train_test_split(test_size=0.2)
train_ds = ds["train"]
test_ds = ds["test"]
# Fonction d'évaluation sur les données de test
def evaluate(classifier, feature_extractor, test_ds):
classifier.eval()
correct = 0
total = 0
with torch.no_grad():
for batch in test_ds:
input_values = processor(
batch["speech"],
sampling_rate=16000,
return_tensors="pt",
padding=True,
truncation=True,
max_length=max_length
).input_values.to(device)
features = feature_extractor(input_values).last_hidden_state.mean(dim=1)
logits = classifier(features)
predictions = logits.argmax(dim=-1)
labels = torch.tensor(batch["label"], dtype=torch.long, device=device)
correct += (predictions == labels).sum().item()
total += 1
return correct / total
# Fonction d'entraînement
def train_classifier(feature_extractor, classifier, train_ds, test_ds, epochs=10, batch_size=16):
optimizer = optim.Adam(classifier.parameters(), lr=1e-4)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.7)
loss_fn = nn.CrossEntropyLoss()
best_accuracy = 0.0 # Variable pour stocker la meilleure accuracy
for epoch in range(epochs):
classifier.train()
total_loss, correct = 0, 0
batch_count = 0
for i in range(0, len(train_ds), batch_size):
batch = train_ds[i: i + batch_size]
optimizer.zero_grad()
input_values = processor(
batch["speech"],
sampling_rate=16000,
return_tensors="pt",
padding=True,
truncation=True,
max_length=max_length
).input_values.to(device)
with torch.no_grad():
features = feature_extractor(input_values).last_hidden_state.mean(dim=1)
features = (features - features.mean()) / features.std() # Normalisation
logits = classifier(features)
labels = torch.tensor(batch["label"], dtype=torch.long, device=device)
if labels.numel() == 0:
continue
loss = loss_fn(logits, labels)
loss.backward()
optimizer.step()
total_loss += loss.item()
correct += (logits.argmax(dim=-1) == labels).sum().item()
batch_count += 1
train_acc = correct / len(train_ds)
test_acc = evaluate(classifier, feature_extractor, test_ds)
scheduler.step()
# Sauvegarde uniquement si l'accuracy sur test est la meilleure obtenue
if test_acc > best_accuracy:
best_accuracy = test_acc
torch.save({
"classifier_state_dict": classifier.state_dict(),
"feature_extractor_state_dict": feature_extractor.state_dict(),
"processor": processor
}, "best_emotion_model.pth")
print(f"✅ Nouveau meilleur modèle sauvegardé ! Accuracy Test: {best_accuracy:.4f}")
print(f"Epoch {epoch+1}/{epochs} - Loss: {total_loss/batch_count:.4f} - Train Accuracy: {train_acc:.4f} - Test Accuracy: {test_acc:.4f}")
return classifier
# Entraînement
trained_classifier = train_classifier(feature_extractor, classifier, train_ds, test_ds, epochs=10, batch_size=16)
print("✅ Entraînement terminé, le meilleur modèle a été sauvegardé !")
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