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Shared utilities for embedding extraction across all models.
Provides common audio loading, file collection, and save logic so that
each per-model extraction script only needs to define model initialization
and a single `model_fn(audio_path) -> np.ndarray` function.
"""
import sys
import numpy as np
from pathlib import Path
try:
from tqdm import tqdm
except ImportError:
def tqdm(iterable, desc=None, total=None):
if desc:
print(f"\nProcessing {desc}...")
return iterable
import os
# Resolve the release root.
# Priority: VIPBENCH_ROOT env var > parent of this script's directory.
# Layout assumed:
# <VIPBENCH_ROOT>/
# code/extraction_utils.py <- this file
# data/audio/reference/*.wav
# data/audio/comparison/*.wav
# data/embeddings/<model>.npz <- output
def _resolve_root():
env = os.environ.get("VIPBENCH_ROOT")
if env:
return Path(env).resolve()
return Path(__file__).resolve().parent.parent
DEFAULT_BASE_DIR = _resolve_root()
DEFAULT_OUTPUT_DIR = DEFAULT_BASE_DIR / "data" / "embeddings"
def load_audio(file_path, target_sr=16000):
"""Load audio file as float32 mono at target_sr.
Replicates the pattern from extract_rawnet3_embeddings.py.
"""
try:
import librosa
audio, sr = librosa.load(file_path, sr=target_sr, mono=True)
return audio.astype(np.float32)
except ImportError:
pass
# Fallback to soundfile
import soundfile as sf
audio, sr = sf.read(file_path)
if len(audio.shape) > 1:
audio = np.mean(audio, axis=1)
if sr != target_sr:
import scipy.signal
num_samples = int(len(audio) * target_sr / sr)
audio = scipy.signal.resample(audio, num_samples)
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)
if np.max(np.abs(audio)) > 1.0:
audio = audio / np.max(np.abs(audio))
return audio.astype(np.float32)
def collect_audio_files(base_dir=None):
"""Collect reference and stimulus audio files.
Returns
-------
ref_files : list[Path]
100 reference clips from exp_2/*R.wav
stim_files : list[Path]
9,800 comparison clips from output/*.wav
"""
base = Path(base_dir) if base_dir else DEFAULT_BASE_DIR
ref_files = sorted((base / "data" / "audio" / "reference").glob("*R.wav"))
stim_files = sorted((base / "data" / "audio" / "comparison").glob("*.wav"))
return ref_files, stim_files
def save_embeddings(embeddings_dict, model_name, output_dir=None):
"""Save embeddings as compressed .npz with same format as rawnet3_embeddings.npz."""
out = Path(output_dir) if output_dir else DEFAULT_OUTPUT_DIR
out.mkdir(parents=True, exist_ok=True)
path = out / f"{model_name}.npz"
np.savez_compressed(path, **embeddings_dict)
print(f"Saved {len(embeddings_dict)} embeddings to {path}")
return path
def extract_all(model_fn, model_name, base_dir=None, output_dir=None):
"""Run extraction for all audio files using the provided model function.
Parameters
----------
model_fn : callable
Takes a file path (str or Path) and returns a 1-D numpy array (the embedding).
model_name : str
Name used for the output file ({model_name}_embeddings.npz).
base_dir : str or Path, optional
Project root. Defaults to DEFAULT_BASE_DIR.
output_dir : str or Path, optional
Where to save the .npz. Defaults to DEFAULT_OUTPUT_DIR.
"""
ref_files, stim_files = collect_audio_files(base_dir)
total = len(ref_files) + len(stim_files)
print(f"\n{'=' * 60}")
print(f"{model_name} Embedding Extraction")
print(f"{'=' * 60}")
print(f"Reference files: {len(ref_files)}")
print(f"Stimulus files: {len(stim_files)}")
print(f"Total: {total}")
embeddings_dict = {}
failed = []
print(f"\nProcessing reference files...")
for path in tqdm(ref_files, desc="references"):
key = path.stem
try:
emb = model_fn(path)
if emb is not None:
embeddings_dict[key] = emb
else:
failed.append(str(path))
except Exception as e:
print(f"\n Error on {path.name}: {e}")
failed.append(str(path))
print(f"\nProcessing stimulus files...")
for path in tqdm(stim_files, desc="stimuli"):
key = path.stem
try:
emb = model_fn(path)
if emb is not None:
embeddings_dict[key] = emb
else:
failed.append(str(path))
except Exception as e:
print(f"\n Error on {path.name}: {e}")
failed.append(str(path))
# Summary
print(f"\n{'=' * 60}")
print(f"Summary")
print(f"{'=' * 60}")
print(f"Extracted: {len(embeddings_dict)} / {total}")
print(f"Failed: {len(failed)}")
if embeddings_dict:
sample = next(iter(embeddings_dict.values()))
print(f"Dimension: {sample.shape}")
if failed:
print(f"\nFailed files (first 10):")
for f in failed[:10]:
print(f" {f}")
save_embeddings(embeddings_dict, model_name, output_dir)
return embeddings_dict
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