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"""
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()
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