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| import os | |
| import torch | |
| import pandas as pd | |
| import librosa | |
| import numpy as np | |
| from transformers import AutoFeatureExtractor, Wav2Vec2ForSequenceClassification, AutoConfig | |
| from sklearn.metrics import accuracy_score, classification_report, confusion_matrix | |
| from tqdm import tqdm | |
| def evaluate_model(model_path="models/wav2vec2-finetuned", metadata_path="data/processed/metadata.csv"): | |
| print(f"Evaluating model at: {model_path}") | |
| # 1. Load Config to get TRUE labels | |
| try: | |
| config = AutoConfig.from_pretrained(model_path) | |
| id2label = config.id2label | |
| label2id = config.label2id | |
| print(f"Loaded Label Map from Config: {label2id}") | |
| except Exception as e: | |
| print(f"Error loading config: {e}") | |
| return | |
| # 2. Load Model & Extractor | |
| device = torch.device("cpu") | |
| try: | |
| extractor = AutoFeatureExtractor.from_pretrained(model_path) | |
| model = Wav2Vec2ForSequenceClassification.from_pretrained(model_path) | |
| model.to(device) | |
| model.eval() | |
| except Exception as e: | |
| print(f"Error loading weights: {e}") | |
| return | |
| # 3. Load Metadata | |
| if not os.path.exists(metadata_path): | |
| print(f"Metadata not found at {metadata_path}") | |
| return | |
| df = pd.read_csv(metadata_path) | |
| # Test on 100 samples | |
| test_df = df[df['split'] == 'test'].sample(min(100, len(df[df['split'] == 'test'])), random_state=42) | |
| print(f"Testing on {len(test_df)} samples...") | |
| y_true = [] | |
| y_pred = [] | |
| # 4. Inference Loop | |
| for idx, row in tqdm(test_df.iterrows(), total=len(test_df), desc="Inference"): | |
| # Fix: Ensure we match the exact string expected by the model | |
| true_label_str = row['emotion'] | |
| # Handle "Suprised" misspelling if model expects "surprised" | |
| if "surprised" in label2id and true_label_str == "suprised": | |
| true_label_str = "surprised" | |
| if true_label_str not in label2id: | |
| print(f"Warning: Label '{true_label_str}' not in model config. Skipping.") | |
| continue | |
| target_id = label2id[true_label_str] | |
| # Path handling | |
| audio_path = row['path'] | |
| if not os.path.exists(audio_path): | |
| audio_path = os.path.join("C:/dev/archive/Emotions", row['emotion'].capitalize(), row['filename']) | |
| if not os.path.exists(audio_path): | |
| continue | |
| try: | |
| # Load and Preprocess | |
| speech, sr = librosa.load(audio_path, sr=16000) | |
| # Crop to 5s to match training logic | |
| if len(speech) > 16000 * 5: | |
| speech = speech[:16000 * 5] | |
| inputs = extractor(speech, sampling_rate=16000, return_tensors="pt", padding=True) | |
| # Predict | |
| with torch.no_grad(): | |
| logits = model(inputs.input_values).logits | |
| pred_id = torch.argmax(logits, dim=-1).item() | |
| y_true.append(target_id) | |
| y_pred.append(pred_id) | |
| except Exception as e: | |
| continue | |
| # 5. Results | |
| if not y_true: | |
| print("No files processed.") | |
| return | |
| acc = accuracy_score(y_true, y_pred) | |
| print(f"\nFINAL ACCURACY: {acc:.2%}") | |
| # Map IDs back to names for the report | |
| target_names = [id2label[i] for i in sorted(id2label.keys())] | |
| print("\nDetailed Report:") | |
| print(classification_report(y_true, y_pred, target_names=target_names, labels=sorted(id2label.keys()))) | |
| # Print a small confusion matrix snippet | |
| print("\nConfusion Matrix (True vs Pred):") | |
| print(confusion_matrix(y_true, y_pred)) | |
| if __name__ == "__main__": | |
| evaluate_model() |