#!/usr/bin/env python3 """ Extract RawNet3 embeddings for all audio files. This script extracts RawNet3 speaker embeddings for: - 100 audio files ending with "R" in exp_2 directory (e.g., F01R.wav) - 9,800 audio files in output directory The embeddings are saved in a dictionary format where: - Key: filename without extension (e.g., "F01R") - Value: RawNet3 embedding vector (numpy array) Saved as both pickle (.pkl) and numpy (.npz) formats for flexibility. Requirements: - ESPnet-SPK: pip install espnet - Or RawNet repository: git clone https://github.com/jungjee/RawNet.git """ import os import sys import numpy as np from pathlib import Path import pickle import warnings warnings.filterwarnings('ignore') # Try to import tqdm for progress bars, fallback to basic iteration if not available try: from tqdm import tqdm except ImportError: def tqdm(iterable, desc=None): if desc: print(f"\nProcessing {desc}...") return iterable # Try to import ESPnet first (preferred method) try: from espnet2.bin.spk_inference import Speech2Embedding ESPNET_AVAILABLE = True print("ESPnet-SPK is available") except ImportError: ESPNET_AVAILABLE = False print("ESPnet-SPK not available. Please install it:") print(" pip install espnet") print("Or set up the RawNet repository manually.") sys.exit(1) def load_audio(file_path, target_sr=16000): """Load audio file and resample to target sample rate if needed.""" try: # Try librosa first (most reliable) try: import librosa audio, sr = librosa.load(file_path, sr=target_sr, mono=True) # Audio is already in float32 format normalized to [-1, 1] return audio.astype(np.float32) except ImportError: # Fallback to soundfile import soundfile as sf audio, sr = sf.read(file_path) # Convert stereo to mono if needed if len(audio.shape) > 1: audio = np.mean(audio, axis=1) # Resample if needed if sr != target_sr: import scipy.signal num_samples = int(len(audio) * target_sr / sr) audio = scipy.signal.resample(audio, num_samples) # Ensure audio is in the correct format (float32, normalized to [-1, 1]) if audio.dtype != np.float32: if audio.dtype == np.int16: audio = audio.astype(np.float32) / 32768.0 elif audio.dtype == np.int32: audio = audio.astype(np.float32) / 2147483648.0 else: audio = audio.astype(np.float32) # Normalize to [-1, 1] if needed if np.max(np.abs(audio)) > 1.0: audio = audio / np.max(np.abs(audio)) return audio.astype(np.float32) except Exception as e: print(f"Error loading {file_path}: {e}") return None def extract_embedding_espnet(audio_path, speech2spk_embed): """Extract embedding using ESPnet.""" try: audio = load_audio(audio_path) if audio is None: return None # ESPnet expects numpy array (raw waveform, 16kHz, float32) # The Speech2Embedding object handles the processing embedding = speech2spk_embed(audio) # Convert to numpy array if needed (ESPnet may return numpy or torch tensor) if hasattr(embedding, 'cpu'): # torch.Tensor embedding = embedding.cpu().numpy() elif not isinstance(embedding, np.ndarray): embedding = np.array(embedding) return embedding.flatten() except Exception as e: print(f"Error extracting embedding with ESPnet from {audio_path}: {e}") import traceback traceback.print_exc() return None def main(): """Main function to extract embeddings for all audio files.""" print("=" * 80) print("RawNet3 Embedding Extraction") print("=" * 80) # Initialize model if not ESPNET_AVAILABLE: print("\nERROR: ESPnet-SPK is not available.") print("Please install ESPnet-SPK first:") print(" pip install espnet") print("\nThis script uses ESPnet-SPK's pre-trained RawNet3 model for easier access.") sys.exit(1) print("\nInitializing ESPnet RawNet3 model...") try: speech2spk_embed = Speech2Embedding.from_pretrained( model_tag="espnet/voxcelebs12_rawnet3" ) print("ESPnet RawNet3 model loaded successfully") except Exception as e: print(f"\nERROR: Failed to load ESPnet RawNet3 model: {e}") print("\nThis may be due to:") print(" 1. Network issues (model needs to be downloaded from HuggingFace)") print(" 2. Missing dependencies") print("\nPlease ensure you have a stable internet connection and try again.") import traceback traceback.print_exc() sys.exit(1) # Collect all audio files print("\nCollecting audio files...") from extraction_utils import collect_audio_files, DEFAULT_OUTPUT_DIR exp_2_files, output_files = collect_audio_files() print(f"Found {len(exp_2_files)} reference files (data/audio/reference/*R.wav)") print(f"Found {len(output_files)} comparison files (data/audio/comparison/*.wav)") total_files = len(exp_2_files) + len(output_files) print(f"Total files to process: {total_files}") if total_files == 0: print("ERROR: No audio files found!") sys.exit(1) # Extract embeddings print("\nExtracting embeddings...") embeddings_dict = {} failed_files = [] # Process exp_2 files print("\nProcessing exp_2 files...") for audio_path in tqdm(exp_2_files, desc="exp_2"): # Get filename without extension (e.g., "F01R") filename_key = audio_path.stem try: embedding = extract_embedding_espnet(audio_path, speech2spk_embed) if embedding is not None: embeddings_dict[filename_key] = embedding else: failed_files.append(str(audio_path)) except Exception as e: print(f"\nError processing {audio_path}: {e}") failed_files.append(str(audio_path)) # Process output files print("\nProcessing output files...") for audio_path in tqdm(output_files, desc="output"): # Get filename without extension (e.g., "6_F01R_F02R_001") filename_key = audio_path.stem try: embedding = extract_embedding_espnet(audio_path, speech2spk_embed) if embedding is not None: embeddings_dict[filename_key] = embedding else: failed_files.append(str(audio_path)) except Exception as e: print(f"\nError processing {audio_path}: {e}") failed_files.append(str(audio_path)) # Save embeddings print("\nSaving embeddings...") DEFAULT_OUTPUT_DIR.mkdir(parents=True, exist_ok=True) npz_path = DEFAULT_OUTPUT_DIR / "rawnet3.npz" np.savez_compressed(npz_path, **embeddings_dict) print(f"Saved embeddings to {npz_path}") # Print summary print("\n" + "=" * 80) print("Summary") print("=" * 80) print(f"Total files processed: {total_files}") print(f"Successfully extracted: {len(embeddings_dict)}") print(f"Failed: {len(failed_files)}") if failed_files: print(f"\nFailed files (first 10):") for f in failed_files[:10]: print(f" {f}") if len(failed_files) > 10: print(f" ... and {len(failed_files) - 10} more") print(f"\nEmbedding dimension: {list(embeddings_dict.values())[0].shape if embeddings_dict else 'N/A'}") print(f"\nSaved files:") print(f" - {pickle_path} (Python pickle format)") print(f" - {npz_path} (NumPy compressed format)") print("\nTo load embeddings later:") print(f" import pickle") print(f" with open('{pickle_path}', 'rb') as f:") print(f" embeddings = pickle.load(f)") print("\nOr:") print(f" import numpy as np") print(f" data = np.load('{npz_path}', allow_pickle=True)") print(f" embeddings = {{k: data[k] for k in data.files}}") if __name__ == "__main__": main()