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| import pandas as pd | |
| import numpy as np | |
| from pathlib import Path | |
| from tqdm import tqdm | |
| from extractor import AudioFeatureExtractor | |
| import os | |
| def build_all_features(metadata_path, output_dir): | |
| # 1. Setup | |
| output_dir = Path(output_dir) | |
| output_dir.mkdir(parents=True, exist_ok=True) | |
| df = pd.read_csv(metadata_path) | |
| extractor = AudioFeatureExtractor() | |
| print(f"Starting bulk extraction for {len(df)} files...") | |
| # 2. Loop with progress bar | |
| # We use a custom naming scheme: {split}_{original_filename}.npy | |
| for _, row in tqdm(df.iterrows(), total=len(df), desc="Extracting"): | |
| # Create a unique filename for the embedding | |
| # We replace .wav with .npy | |
| rel_path = Path(row['path']).stem | |
| embedding_path = output_dir / f"{row['split']}_{row['emotion']}_{rel_path}.npy" | |
| # 3. Skip if already exists (Resume capability) | |
| if embedding_path.exists(): | |
| continue | |
| # 4. Extract and Save | |
| embedding = extractor.extract(row['path']) | |
| if embedding is not None: | |
| np.save(embedding_path, embedding) | |
| print(f"\nBulk Extraction Complete!") | |
| print(f"Embeddings saved to: {output_dir.absolute()}") | |
| if __name__ == "__main__": | |
| METADATA = "data/processed/metadata.csv" | |
| OUTPUT = "data/embeddings/wav2vec2" | |
| if os.path.exists(METADATA): | |
| build_all_features(METADATA, OUTPUT) | |
| else: | |
| print("Metadata not found. Run harmonize.py first.") | |