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warnings.warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "40e143e654424e8f96a8acc62749b56c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "preprocessor_config.json: 0%| | 0.00/159 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# =============================================================================\n", + "# STEP 0: SETUP, LIBRARIES & DATA PREPARATION\n", + "# =============================================================================\n", + "# Install required libraries\n", + "!pip install transformers[torch] accelerate -q\n", + "\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import librosa\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from tqdm import tqdm\n", + "import kagglehub\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "from torch.utils.data import DataLoader, Dataset\n", + "from transformers import AutoFeatureExtractor, AutoModelForAudioClassification, get_scheduler\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "from sklearn.utils.class_weight import compute_class_weight\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(f\"\\nUsing device: {device}\")\n", + "\n", + "# --- Download & Process Datasets ---\n", + "RAVDESS_PATH = kagglehub.dataset_download(\"uwrfkaggler/ravdess-emotional-speech-audio\")\n", + "CREMA_D_PATH = kagglehub.dataset_download(\"ejlok1/cremad\")\n", + "\n", + "sentiment_map = {'happy': 'positive', 'surprised': 'positive', 'sad': 'negative', 'angry': 'negative', 'fearful': 'negative', 'disgust': 'negative', 'neutral': 'neutral', 'calm': 'neutral'}\n", + "ravdess_emotion_map = {'01': 'neutral', '02': 'calm', '03': 'happy', '04': 'sad', '05': 'angry', '06': 'fearful', '07': 'disgust', '08': 'surprised'}\n", + "ravdess_data = []\n", + "for dirpath, _, filenames in os.walk(RAVDESS_PATH):\n", + " for filename in filenames:\n", + " if filename.endswith('.wav'):\n", + " emotion_code = filename.split('-')[2]; emotion = ravdess_emotion_map.get(emotion_code); sentiment = sentiment_map.get(emotion)\n", + " if sentiment: ravdess_data.append({\"filepath\": os.path.join(dirpath, filename), \"sentiment\": sentiment})\n", + "ravdess_df = pd.DataFrame(ravdess_data)\n", + "\n", + "crema_emotion_map = {'HAP': 'happy', 'SAD': 'sad', 'ANG': 'angry', 'FEA': 'fearful', 'DIS': 'disgust', 'NEU': 'neutral'}\n", + "crema_data = []\n", + "crema_audio_path = os.path.join(CREMA_D_PATH, \"AudioWAV\")\n", + "for filename in os.listdir(crema_audio_path):\n", + " if filename.endswith('.wav'):\n", + " emotion_code = filename.split('_')[2]; emotion = crema_emotion_map.get(emotion_code); sentiment = sentiment_map.get(emotion)\n", + " if sentiment: crema_data.append({\"filepath\": os.path.join(crema_audio_path, filename), \"sentiment\": sentiment})\n", + "crema_df = pd.DataFrame(crema_data)\n", + "\n", + "combined_df = pd.concat([ravdess_df, crema_df], ignore_index=True).sample(frac=1, random_state=42).reset_index(drop=True)\n", + "\n", + "# =============================================================================\n", + "# STEP 1: WAV2VEC2 PREPARATION\n", + "# =============================================================================\n", + "MODEL_CHECKPOINT = \"facebook/wav2vec2-base\"\n", + "TARGET_SAMPLING_RATE = 16000\n", + "feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_CHECKPOINT)\n", + "\n", + "class AudioDataset(Dataset):\n", + " def __init__(self, df, feature_extractor, max_duration_s=5.0):\n", + " self.filepaths = df['filepath'].tolist(); self.labels = df['label'].tolist()\n", + " self.feature_extractor = feature_extractor; self.max_length = int(max_duration_s * TARGET_SAMPLING_RATE)\n", + " def __len__(self): return len(self.filepaths)\n", + " def __getitem__(self, idx):\n", + " filepath = self.filepaths[idx]; label = self.labels[idx]\n", + " try:\n", + " audio, sr = librosa.load(filepath, sr=None)\n", + " if sr != TARGET_SAMPLING_RATE: audio = librosa.resample(y=audio, orig_sr=sr, target_sr=TARGET_SAMPLING_RATE)\n", + " inputs = self.feature_extractor(audio, sampling_rate=TARGET_SAMPLING_RATE, max_length=self.max_length, truncation=True, padding='max_length', return_tensors=\"pt\")\n", + " input_values = inputs.input_values.squeeze(0)\n", + " except Exception as e: print(f\"Error processing {filepath}: {e}\"); return None, None\n", + " return input_values, label\n", + "\n", + "le = LabelEncoder(); combined_df['label'] = le.fit_transform(combined_df['sentiment'])\n", + "X_train_df, X_temp_df = train_test_split(combined_df, test_size=0.3, random_state=42, stratify=combined_df['label'])\n", + "X_val_df, X_test_df = train_test_split(X_temp_df, test_size=0.5, random_state=42, stratify=X_temp_df['label'])\n", + "train_dataset = AudioDataset(X_train_df, feature_extractor); val_dataset = AudioDataset(X_val_df, feature_extractor); test_dataset = AudioDataset(X_test_df, feature_extractor)\n", + "def collate_fn(batch):\n", + " batch = [b for b in batch if b[0] is not None];\n", + " if not batch: return None, None\n", + " return torch.utils.data.dataloader.default_collate(batch)\n", + "train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True, collate_fn=collate_fn)\n", + "val_loader = DataLoader(val_dataset, batch_size=16, shuffle=False, collate_fn=collate_fn)\n", + "test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False, collate_fn=collate_fn)\n", + "\n", + "# =============================================================================\n", + "# STEP 2: MODEL & TRAINING SETUP\n", + "# =============================================================================\n", + "NUM_CLASSES = len(le.classes_)\n", + "model = AutoModelForAudioClassification.from_pretrained(MODEL_CHECKPOINT, num_labels=NUM_CLASSES).to(device)\n", + "class_weights_np = compute_class_weight('balanced', classes=np.unique(X_train_df['label']), y=X_train_df['label'])\n", + "class_weights = torch.tensor(class_weights_np, dtype=torch.float32).to(device)\n", + "criterion = nn.CrossEntropyLoss(weight=class_weights)\n", + "\n", + "# =============================================================================\n", + "# STEP 3: TWO-STAGE FINE-TUNING\n", + "# =============================================================================\n", + "\n", + "# --- STAGE 1: Train only the classification head ---\n", + "print(\"\\n--- STAGE 1: Freezing base model and training classifier head ---\")\n", + "# Freeze the parameters of the base Wav2Vec2 model\n", + "for param in model.wav2vec2.parameters():\n", + " param.requires_grad = False\n", + "\n", + "# The optimizer will only update the weights of the unfrozen classifier head\n", + "optimizer = torch.optim.AdamW(model.classifier.parameters(), lr=1e-3)\n", + "STAGE1_EPOCHS = 3\n", + "\n", + "for epoch in range(STAGE1_EPOCHS):\n", + " model.train(); train_loss = 0.0\n", + " for inputs, labels in tqdm(train_loader, desc=f\"Stage 1 - Epoch {epoch+1}/{STAGE1_EPOCHS}\"):\n", + " if inputs is None: continue\n", + " inputs, labels = inputs.to(device), labels.to(device)\n", + " optimizer.zero_grad(); outputs = model(inputs)\n", + " loss = criterion(outputs.logits, labels)\n", + " loss.backward(); optimizer.step(); train_loss += loss.item()\n", + "\n", + " model.eval(); val_loss, val_correct, val_total = 0.0, 0, 0\n", + " with torch.no_grad():\n", + " for inputs, labels in val_loader:\n", + " if inputs is None: continue\n", + " inputs, labels = inputs.to(device), labels.to(device); outputs = model(inputs)\n", + " loss = criterion(outputs.logits, labels); val_loss += loss.item()\n", + " _, predicted = torch.max(outputs.logits, 1); val_total += labels.size(0); val_correct += (predicted == labels).sum().item()\n", + "\n", + " avg_train_loss = train_loss/len(train_loader); avg_val_loss = val_loss/len(val_loader); val_acc = val_correct/val_total\n", + " print(f\"Stage 1 - Epoch {epoch+1} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | Val Acc: {val_acc:.4f}\")\n", + "\n", + "# --- STAGE 2: Unfreeze and fine-tune the entire model ---\n", + "print(\"\\n--- STAGE 2: Unfreezing all layers and fine-tuning the entire model ---\")\n", + "# Unfreeze all parameters\n", + "for param in model.parameters():\n", + " param.requires_grad = True\n", + "\n", + "# Create a new optimizer for the whole model with a lower learning rate\n", + "optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)\n", + "STAGE2_EPOCHS = 6 # Total epochs will be STAGE1 + STAGE2\n", + "num_training_steps = STAGE2_EPOCHS * len(train_loader)\n", + "num_warmup_steps = int(0.1 * num_training_steps)\n", + "lr_scheduler = get_scheduler(\"linear\", optimizer, num_warmup_steps, num_training_steps)\n", + "best_val_loss = float('inf')\n", + "\n", + "for epoch in range(STAGE2_EPOCHS):\n", + " model.train(); train_loss = 0.0\n", + " for inputs, labels in tqdm(train_loader, desc=f\"Stage 2 - Epoch {epoch+1}/{STAGE2_EPOCHS}\"):\n", + " if inputs is None: continue\n", + " inputs, labels = inputs.to(device), labels.to(device)\n", + " optimizer.zero_grad(); outputs = model(inputs)\n", + " loss = criterion(outputs.logits, labels)\n", + " loss.backward(); optimizer.step(); lr_scheduler.step(); train_loss += loss.item()\n", + "\n", + " model.eval(); val_loss, val_correct, val_total = 0.0, 0, 0\n", + " with torch.no_grad():\n", + " for inputs, labels in val_loader:\n", + " if inputs is None: continue\n", + " inputs, labels = inputs.to(device), labels.to(device); outputs = model(inputs)\n", + " loss = criterion(outputs.logits, labels); val_loss += loss.item()\n", + " _, predicted = torch.max(outputs.logits, 1); val_total += labels.size(0); val_correct += (predicted == labels).sum().item()\n", + "\n", + " avg_train_loss = train_loss/len(train_loader); avg_val_loss = val_loss/len(val_loader); val_acc = val_correct/val_total\n", + " print(f\"Stage 2 - Epoch {epoch+1} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | Val Acc: {val_acc:.4f}\")\n", + "\n", + " if avg_val_loss < best_val_loss:\n", + " best_val_loss = avg_val_loss\n", + " torch.save(model.state_dict(), 'best_wav2vec2_model_2stage.pth')\n", + "\n", + "# =============================================================================\n", + "# STEP 4: EVALUATE THE FINAL MODEL\n", + "# =============================================================================\n", + "print(\"\\n--- Evaluating the final fine-tuned Wav2Vec2 model ---\")\n", + "model.load_state_dict(torch.load('best_wav2vec2_model_2stage.pth'))\n", + "model.eval()\n", + "\n", + "all_preds, all_labels = [], []\n", + "with torch.no_grad():\n", + " for inputs, labels in tqdm(test_loader, desc=\"Evaluating on Test Set\"):\n", + " if inputs is None: continue\n", + " inputs = inputs.to(device)\n", + " outputs = model(inputs)\n", + " _, predicted = torch.max(outputs.logits, 1)\n", + " all_preds.extend(predicted.cpu().numpy())\n", + " all_labels.extend(labels.numpy())\n", + "\n", + "print(\"\\n--- Final Wav2Vec2 Classification Report (2-Stage Training) ---\")\n", + "print(classification_report(all_labels, all_preds, target_names=le.classes_))\n", + "cm = confusion_matrix(all_labels, all_preds)\n", + "plt.figure(figsize=(8, 6))\n", + "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=le.classes_, yticklabels=le.classes_)\n", + "plt.title('Final Wav2Vec2 Confusion Matrix (2-Stage)'); plt.ylabel('True Label'); plt.xlabel('Predicted Label')\n", + "plt.show()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "bubcKFNzLDh_" + ], + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + 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