Spoof Detection Tutorial with WeDefense

Author: Lin Zhang, You Zhang Date: 2026-02-11 Status: Ready

What is Spoof Detection?

Spoof detection (also known as anti-spoofing or fake audio detection) aims to detect whether an input audio sample is genuine (bonafide) or artificially generated/modified (spoofed). This is crucial for protecting automatic speaker verification systems and ensuring the authenticity of audio content.

Task Definition

Given an audio input \(x\), the goal is to produce a score \(s\) that indicates how likely the input is genuine.

In WeDefense, we model \(s\) to LLR for final decision as:

\[ x \xrightarrow{\text{Model}} \text{embedding} \xrightarrow{\text{Projection}} \text{logits} \xrightarrow{\text{Calibration}} \text{LLR score} \]

The final LLR (Log-Likelihood Ratio) score = \(\log \frac{p(x|H_a)}{p(x|H_r)}\) determines the decision:

Positive LLR → classified as bonafide (real)
Negative LLR → classified as spoof (fake)

Where \(H_a\) and \(H_r\) represents the accept hypothesis (the input audio is real) and reject hypothesis (the input audio is fake), respectively.

Why use LLR instead of raw posterior from network?

While spoof detection can be framed as binary classification, we recommend using calibrated LLR scores instead of raw logits or posteriors for several reasons:

Prior-aware calibration: LLR incorporates the prior probability of spoofing attacks, making it more suitable for real-world deployment where attack frequencies vary.
Interpretability: LLR provides a principled decision threshold (0) with clear probabilistic meaning that \(p(x|H_a) = p(x|H_r)\).
Robustness: Calibrated scores are less sensitive to training data imbalance and generalize better across datasets.

Note,

Genuine/Bona fide/Real: Speech spoken by human or authentic audio naturally captured from real sources.
Spoof/Fake: Generated or modified audio (e.g., TTS, voice conversion, etc.)

Evaluation Metrics

WeDefense follows ASVspoof5 (appendix of the eval. plan) official evaluation metrics. Lower is better for all of them.

Metric	Range	What it measures
min-DCF	[0, \(\infty\)]	Best achievable cost (optimal threshold)
EER	[0, 0.5]	Equal error rate where false negative rate and false positive rate are equal (threshold-independent)
\(C_\text{llr}\)	[0, \(\infty\)]	Score calibration quality. 0 for perfect calibration, 1 for non-informative system, and \(\gt 1\) for worse than non-informative
act-DCF	[0, \(\infty\)]	Detection cost at a fixed Bayesian threshold, sensitive to calibration. (So act-DCF \(\ge\) min-DCF.)

Step-by-Step Implementation in WeDefense

The implementation stages in WeDefense are:

Stage 1–2: Data preparation and list generation.
Stage 3: Model training.
Stage 4: Model averaging and embedding extraction.
Stage 5: Logit extraction (and optional posterior output via softmax).
Stage 6: Score calibration (logits to LLR).
Stage 7: Performance evaluation.

Note:

WeDefense separates embedding extraction (Stage 4) from logit/posterior prediction (Stage 5) to keep the pipeline modular. This makes it easier to analyze or visualize embeddings, reuse the same embeddings with different back-end scoring methods, and debug each stage independently.
For evaluation, we convert logits to LLR (Stage 6) to apply prior-aware calibration and obtain well-calibrated decision scores rather than using raw logits or posteriors directly.

In the following of this notebook, we provide a step-by-step guide to running a anti-spoofing detection experiment using the WeDefense toolkit. We will follow the structure of the run.sh script for the detection/asvspoof5/v03_resnet18 recipe on the PartialSpoof dataset.

Prerequisites

WeDefense Installation: Ensure you have successfully installed the WeDefense toolkit and all its dependencies.
Dataset: This tutorial assumes you have access to the PartialSpoof dataset. The script will attempt to download it automatically if it’s not found.
Environment:

[!IMPORTANT]
Make sure you are running this notebook from the egs/detection/partialspoof/v03_resnet18/ directory. And installed conda enviorment success.

Hardware: A GPU is highly recommended for the training stage (Stage 3).

Initial Configuration

First, we set up all the necessary paths and parameters for our experiment. These are the same variables you would find at the top of the run.sh script.

[!IMPORTANT] Please modify PS_dir path to your PartialSpoof database directory.

import os

# --- Path and Data Configuration ---

# TODO: IMPORTANT! Please modify this path to your PartialSpoof database directory.
PS_dir = '/data/neil/PartialSpoof/database'
# Directory to store prepared data files (wav.scp, utt2lab, etc.)
data_dir = 'data/partialspoof_tutorial'
# The format for the dataloader. 'shard' is recommended for large datasets
# as it groups audio files into .tar files, improving I/O efficiency.
# 'raw' loads individual files.
data_type = 'shard'

# --- Model and Experiment Configuration ---
# The configuration file for the model architecture and training parameters.
config = 'conf/resnet.yaml'
# Directory to save model checkpoints, logs, and results.
exp_dir = 'exp/resnet_tutorial'

# --- Execution Configuration ---
# Specify which GPUs to use, e.g., "[0]" or "[0,1]".
gpus = "[0]"
# Number of models to average for inference. >0 to use averaging, <=0 to use the single best model.
num_avg = -1
# Save a model checkpoint every N epochs.
save_epoch_interval = 5
# Patience for early stopping. <0 disables it.
early_stop_patience = -1
# How often to run validation (in epochs).
validate_interval = 1

# --- Notebook-wide environment (shared with future %%bash cells) ---
# Assumes you run the notebook from egs/detection/partialspoof/v03_resnet18/
wedefense_root = os.path.abspath(os.path.join(os.getcwd(), '../../../..'))
os.environ['WEDEFENSE_ROOT'] = wedefense_root

# Make wedefense importable for subprocesses started by %%bash
old_pythonpath = os.environ.get('PYTHONPATH', '')
if old_pythonpath:
    os.environ['PYTHONPATH'] = wedefense_root + os.pathsep + old_pythonpath
else:
    os.environ['PYTHONPATH'] = wedefense_root

# Export config so every %%bash cell can reuse it without re-defining
os.environ['PS_DIR'] = PS_dir
os.environ['DATA_DIR'] = data_dir
os.environ['DATA_TYPE'] = data_type
os.environ['CONFIG'] = config
os.environ['EXP_DIR'] = exp_dir
os.environ['GPUS'] = gpus
os.environ['NUM_AVG'] = str(num_avg)
os.environ['SAVE_EPOCH_INTERVAL'] = str(save_epoch_interval)
os.environ['EARLY_STOP_PATIENCE'] = str(early_stop_patience)
os.environ['VALIDATE_INTERVAL'] = str(validate_interval)

# Training launcher defaults used later
os.environ.setdefault('PORT', '29500')
os.environ.setdefault('NUM_GPUS', '1')

print('Notebook env configured:')
print('  WEDEFENSE_ROOT =', os.environ['WEDEFENSE_ROOT'])
print('  PS_DIR         =', os.environ['PS_DIR'])
print('  DATA_DIR       =', os.environ['DATA_DIR'])
print('  EXP_DIR        =', os.environ['EXP_DIR'])
print('  CONFIG         =', os.environ['CONFIG'])

Notebook env configured:
  WEDEFENSE_ROOT = /home/neil/public_wedefense_Feb2026/wedefense
  PS_DIR         = /data/neil/PartialSpoof/database
  DATA_DIR       = data/partialspoof_tutorial
  EXP_DIR        = exp/resnet_tutorial
  CONFIG         = conf/resnet.yaml

Stage 1: Data Preparation

In this stage, we process the raw PartialSpoof dataset into a standard format required by the toolkit. The local/prepare_data.sh script will:

Download the dataset if it’s not found.
Create wav.scp: Maps a unique utterance ID to its audio file path. (<wav_id> <path> )
Create utt2lab: Maps each utterance ID to its label (bonafide or spoof). (<wav_id> )
Create lab2utt: An inverted index of utt2lab. <wav_id>
Create utt2dur: Maps each utterance ID to its duration in seconds. <wav_id>

This process is run in parallel for the train, dev, and eval sets.

%%bash
set -euo pipefail

bash local/prepare_data.sh "$PS_DIR" "$DATA_DIR"

local/prepare_data.sh: line 22: 
local/prepare_data.sh [PS_dir] [data]

Download partialspoof database,
and prepare data dir for partial spoof: wav.scp, utt2lab, lab2utt, utt2dur, dur2utt
: No such file or directory
bash: ./01_download_database.sh: No such file or directory
/home/neil/public_wedefense_Feb2026/wedefense/egs/detection/partialspoof/v03_resnet18/tools/wav2dur.py:9: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
  torchaudio.set_audio_backend("sox_io")
100%|██████████| 25380/25380 [02:09<00:00, 196.53it/s]

process 25380 utts
total 87320.62718750026 s

/home/neil/public_wedefense_Feb2026/wedefense/egs/detection/partialspoof/v03_resnet18/tools/wav2dur.py:9: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
  torchaudio.set_audio_backend("sox_io")
100%|██████████| 24844/24844 [02:11<00:00, 189.34it/s]

process 24844 utts
total 87644.78237500037 s

/home/neil/public_wedefense_Feb2026/wedefense/egs/detection/partialspoof/v03_resnet18/tools/wav2dur.py:9: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
  torchaudio.set_audio_backend("sox_io")
100%|██████████| 71237/71237 [06:01<00:00, 197.14it/s]

process 71237 utts
total 243638.34668749908 s
Prepared data folder for partialspoof, including wav.scp, utt2lab, lab2utt

Let’s inspect the generated files for the train set to understand their format.

%%bash
set -euo pipefail

for f in lab2utt utt2dur utt2lab wav.scp; do
  echo "# ==> ${DATA_DIR}/train/${f} <=="
  head -n 3 "${DATA_DIR}/train/${f}"
  echo
done
# ==> ./lab2utt <==
# spoof CON_T_0000029 CON_T_0000069 ...
# bonafide LA_T_1138215 LA_T_1271820 ...

# ==> ./utt2dur <==
# CON_T_0000000 2.74725
# CON_T_0000001 4.2501875
# CON_T_0000002 3.1415

# ==> ./utt2lab <==
# CON_T_0000029 spoof
# CON_T_0000069 spoof
# CON_T_0000072 spoof

# ==> ./wav.scp <==
# CON_T_0000000 /export/fs05/lzhan268/workspace/PUBLIC/PartialSpoof/database/train/con_wav/CON_T_0000000.wav
# CON_T_0000001 /export/fs05/lzhan268/workspace/PUBLIC/PartialSpoof/database/train/con_wav/CON_T_0000001.wav
# CON_T_0000002 /export/fs05/lzhan268/workspace/PUBLIC/PartialSpoof/database/train/con_wav/CON_T_0000002.wav

# ==> data/partialspoof_tutorial/train/lab2utt <==
spoof CON_T_0000029 CON_T_0000069 CON_T_0000072 CON_T_0000091 CON_T_0000096 CON_T_0000177 CON_T_0000180 CON_T_0000213 CON_T_0000215 CON_T_0000240 CON_T_0000244 CON_T_0000246 CON_T_0000289 CON_T_0000369 CON_T_0000374 CON_T_0000407 CON_T_0000418 CON_T_0000443 CON_T_0000463 CON_T_0000506 CON_T_0000542 CON_T_0000554 CON_T_0000610 CON_T_0000626 CON_T_0000632 CON_T_0000646 CON_T_0000675 CON_T_0000701 CON_T_0000746 CON_T_0000749 CON_T_0000751 CON_T_0000770 CON_T_0000771 CON_T_0000819 CON_T_0000828 CON_T_0000867 CON_T_0000868 CON_T_0000869 CON_T_0000883 CON_T_0000918 CON_T_0000925 CON_T_0000927 CON_T_0000938 CON_T_0000939 CON_T_0000941 CON_T_0000962 CON_T_0000973 CON_T_0000993 CON_T_0001076 CON_T_0001091 CON_T_0001174 CON_T_0001205 CON_T_0001241 CON_T_0001253 CON_T_0001286 CON_T_0001343 CON_T_0001350 CON_T_0001352 CON_T_0001398 CON_T_0001427 CON_T_0001438 CON_T_0001453 CON_T_0001477 CON_T_0001483 CON_T_0001516 CON_T_0001520 CON_T_0001533 CON_T_0001542 CON_T_0001548 CON_T_0001574 CON_T_0001604 CON_T_0001611 CON_T_0001650 CON_T_0001659 CON_T_0001672 CON_T_0001727 CON_T_0001775 CON_T_0001776 CON_T_0001816 CON_T_0001823 CON_T_0001851 CON_T_0001920 CON_T_0001938 CON_T_0001940 CON_T_0002061 CON_T_0002110 CON_T_0002146 CON_T_0002150 CON_T_0002167 CON_T_0002203 CON_T_0002213 CON_T_0002341 CON_T_0002354 CON_T_0002376 CON_T_0002424 CON_T_0002426 CON_T_0002429 CON_T_0002440 CON_T_0002450 CON_T_0002453 CON_T_0002457 CON_T_0002460 CON_T_0002473 CON_T_0002490 CON_T_0002499 CON_T_0002529 CON_T_0002544 CON_T_0002552 CON_T_0002554 CON_T_0002555 CON_T_0002561 CON_T_0002592 CON_T_0002594 CON_T_0002598 CON_T_0002621 CON_T_0002633 CON_T_0002643 CON_T_0002648 CON_T_0002653 CON_T_0002654 CON_T_0002725 CON_T_0002735 CON_T_0002756 CON_T_0002784 CON_T_0002785 CON_T_0002813 CON_T_0002836 CON_T_0002864 CON_T_0002941 CON_T_0002968 CON_T_0003021 CON_T_0003035 CON_T_0003060 CON_T_0003063 CON_T_0003174 CON_T_0003175 CON_T_0003179 CON_T_0003239 CON_T_0003244 CON_T_0003257 CON_T_0003259 CON_T_0003266 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CON_T_0004903 CON_T_0004917 CON_T_0004930 CON_T_0004983 CON_T_0005006 CON_T_0005010 CON_T_0005086 CON_T_0005092 CON_T_0005193 CON_T_0005206 CON_T_0005213 CON_T_0005234 CON_T_0005238 CON_T_0005242 CON_T_0005298 CON_T_0005331 CON_T_0005333 CON_T_0005387 CON_T_0005404 CON_T_0005429 CON_T_0005462 CON_T_0005493 CON_T_0005496 CON_T_0005529 CON_T_0005549 CON_T_0005550 CON_T_0005594 CON_T_0005597 CON_T_0005614 CON_T_0005659 CON_T_0005669 CON_T_0005746 CON_T_0005760 CON_T_0005777 CON_T_0005821 CON_T_0005828 CON_T_0005838 CON_T_0005845 CON_T_0005854 CON_T_0005893 CON_T_0005903 CON_T_0005905 CON_T_0005924 CON_T_0005973 CON_T_0005982 CON_T_0005990 CON_T_0006001 CON_T_0006007 CON_T_0006015 CON_T_0006024 CON_T_0006025 CON_T_0006030 CON_T_0006033 CON_T_0006059 CON_T_0006095 CON_T_0006171 CON_T_0006189 CON_T_0006206 CON_T_0006273 CON_T_0006281 CON_T_0006301 CON_T_0006305 CON_T_0006310 CON_T_0006311 CON_T_0006325 CON_T_0006385 CON_T_0006387 CON_T_0006404 CON_T_0006427 CON_T_0006465 CON_T_0006501 CON_T_0006504 CON_T_0006541 CON_T_0006584 CON_T_0006608 CON_T_0006615 CON_T_0006625 CON_T_0006648 CON_T_0006669 CON_T_0006787 CON_T_0006803 CON_T_0006809 CON_T_0006879 CON_T_0006925 CON_T_0006977 CON_T_0007056 CON_T_0007072 CON_T_0007085 CON_T_0007137 CON_T_0007182 CON_T_0007225 CON_T_0007281 CON_T_0007300 CON_T_0007324 CON_T_0007327 CON_T_0007344 CON_T_0007349 CON_T_0007370 CON_T_0007388 CON_T_0007416 CON_T_0007438 CON_T_0007460 CON_T_0007468 CON_T_0007485 CON_T_0007498 CON_T_0007505 CON_T_0007530 CON_T_0007538 CON_T_0007543 CON_T_0007566 CON_T_0007591 CON_T_0007626 CON_T_0007637 CON_T_0007647 CON_T_0007654 CON_T_0007673 CON_T_0007722 CON_T_0007756 CON_T_0007776 CON_T_0007793 CON_T_0007794 CON_T_0007797 CON_T_0007801 CON_T_0007808 CON_T_0007811 CON_T_0007825 CON_T_0007881 CON_T_0007893 CON_T_0007912 CON_T_0007943 CON_T_0007967 CON_T_0008052 CON_T_0008060 CON_T_0008079 CON_T_0008093 CON_T_0008130 CON_T_0008132 CON_T_0008208 CON_T_0008264 CON_T_0008287 CON_T_0008290 CON_T_0008292 CON_T_0008325 CON_T_0008359 CON_T_0008417 CON_T_0008440 CON_T_0008460 CON_T_0008464 CON_T_0008496 CON_T_0008500 CON_T_0008555 CON_T_0008572 CON_T_0008604 CON_T_0008633 CON_T_0008639 CON_T_0008657 CON_T_0008687 CON_T_0008731 CON_T_0008739 CON_T_0008741 CON_T_0008764 CON_T_0008766 CON_T_0008773 CON_T_0008801 CON_T_0008803 CON_T_0008808 CON_T_0008862 CON_T_0008869 CON_T_0008903 CON_T_0008908 CON_T_0008955 CON_T_0008981 CON_T_0009013 CON_T_0009029 CON_T_0009031 CON_T_0009034 CON_T_0009051 CON_T_0009060 CON_T_0009075 CON_T_0009083 CON_T_0009092 CON_T_0009103 CON_T_0009132 CON_T_0009157 CON_T_0009159 CON_T_0009197 CON_T_0009205 CON_T_0009242 CON_T_0009270 CON_T_0009274 CON_T_0009276 CON_T_0009279 CON_T_0009280 CON_T_0009305 CON_T_0009325 CON_T_0009327 CON_T_0009340 CON_T_0009346 CON_T_0009358 CON_T_0009381 CON_T_0009394 CON_T_0009434 CON_T_0009449 CON_T_0009509 CON_T_0009532 CON_T_0009535 CON_T_0009620 CON_T_0009630 CON_T_0009631 CON_T_0009718 CON_T_0009741 CON_T_0009742 CON_T_0009775 CON_T_0009799 CON_T_0009833 CON_T_0009845 CON_T_0009898 CON_T_0009904 CON_T_0009905 CON_T_0009913 CON_T_0009918 CON_T_0009968 CON_T_0009988 CON_T_0010005 CON_T_0010006 CON_T_0010023 CON_T_0010058 CON_T_0010086 CON_T_0010101 CON_T_0010120 CON_T_0010123 CON_T_0010147 CON_T_0010166 CON_T_0010189 CON_T_0010297 CON_T_0010299 CON_T_0010310 CON_T_0010312 CON_T_0010322 CON_T_0010331 CON_T_0010336 CON_T_0010356 CON_T_0010365 CON_T_0010393 CON_T_0010470 CON_T_0010476 CON_T_0010493 CON_T_0010505 CON_T_0010507 CON_T_0010516 CON_T_0010518 CON_T_0010566 CON_T_0010567 CON_T_0010573 CON_T_0010591 CON_T_0010617 CON_T_0010627 CON_T_0010633 CON_T_0010697 CON_T_0010699 CON_T_0010701 CON_T_0010762 CON_T_0010783 CON_T_0010792 CON_T_0010813 CON_T_0010852 CON_T_0010865 CON_T_0010965 CON_T_0010988 CON_T_0011021 CON_T_0011035 CON_T_0011059 CON_T_0011070 CON_T_0011103 CON_T_0011132 CON_T_0011141 CON_T_0011142 CON_T_0011163 CON_T_0011207 CON_T_0011260 CON_T_0011294 CON_T_0011299 CON_T_0011304 CON_T_0011330 CON_T_0011360 CON_T_0011366 CON_T_0011380 CON_T_0011387 CON_T_0011450 CON_T_0011454 CON_T_0011457 CON_T_0011487 CON_T_0011496 CON_T_0011556 CON_T_0011576 CON_T_0011594 CON_T_0011599 CON_T_0011625 CON_T_0011674 CON_T_0011692 CON_T_0011718 CON_T_0011729 CON_T_0011782 CON_T_0011783 CON_T_0011787 CON_T_0011797 CON_T_0011821 CON_T_0011834 CON_T_0011837 CON_T_0011888 CON_T_0011944 CON_T_0011951 CON_T_0011968 CON_T_0011991 CON_T_0011998 CON_T_0012051 CON_T_0012070 CON_T_0012079 CON_T_0012105 CON_T_0012113 CON_T_0012120 CON_T_0012121 CON_T_0012122 CON_T_0012162 CON_T_0012203 CON_T_0012237 CON_T_0012267 CON_T_0012279 CON_T_0012281 CON_T_0012294 CON_T_0012297 CON_T_0012310 CON_T_0012330 CON_T_0012353 CON_T_0012357 CON_T_0012392 CON_T_0012446 CON_T_0012475 CON_T_0012486 CON_T_0012534 CON_T_0012538 CON_T_0012546 CON_T_0012565 CON_T_0012576 CON_T_0012590 CON_T_0012595 CON_T_0012659 CON_T_0012661 CON_T_0012671 CON_T_0012688 CON_T_0012689 CON_T_0012694 CON_T_0012695 CON_T_0012714 CON_T_0012740 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CON_T_0014389 CON_T_0014400 CON_T_0014410 CON_T_0014449 CON_T_0014457 CON_T_0014480 CON_T_0014487 CON_T_0014542 CON_T_0014570 CON_T_0014582 CON_T_0014585 CON_T_0014589 CON_T_0014610 CON_T_0014700 CON_T_0014712 CON_T_0014731 CON_T_0014733 CON_T_0014786 CON_T_0014812 CON_T_0014843 CON_T_0014867 CON_T_0014888 CON_T_0014927 CON_T_0015015 CON_T_0015027 CON_T_0015033 CON_T_0015083 CON_T_0015110 CON_T_0015117 CON_T_0015170 CON_T_0015203 CON_T_0015220 CON_T_0015257 CON_T_0015270 CON_T_0015303 CON_T_0015334 CON_T_0015419 CON_T_0015439 CON_T_0015464 CON_T_0015474 CON_T_0015489 CON_T_0015527 CON_T_0015535 CON_T_0015585 CON_T_0015613 CON_T_0015641 CON_T_0015651 CON_T_0015715 CON_T_0015736 CON_T_0015752 CON_T_0015757 CON_T_0015767 CON_T_0015770 CON_T_0015775 CON_T_0015794 CON_T_0015812 CON_T_0015845 CON_T_0015849 CON_T_0015878 CON_T_0015920 CON_T_0015922 CON_T_0015927 CON_T_0015931 CON_T_0015992 CON_T_0016035 CON_T_0016052 CON_T_0016061 CON_T_0016081 CON_T_0016088 CON_T_0016094 CON_T_0016100 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LA_T_5307856 LA_T_5322386 LA_T_5372373 LA_T_5458231 LA_T_5469120 LA_T_5600038 LA_T_5691156 LA_T_6099597 LA_T_6131759 LA_T_6175341 LA_T_6396662 LA_T_6533322 LA_T_6597785 LA_T_6635999 LA_T_6651761 LA_T_6658542 LA_T_6688612 LA_T_6698994 LA_T_6724576 LA_T_6740492 LA_T_6760364 LA_T_6771766 LA_T_6936229 LA_T_6965985 LA_T_7022615 LA_T_7152560 LA_T_7182909 LA_T_7222998 LA_T_7356545 LA_T_7474358 LA_T_7509819 LA_T_7579441 LA_T_7826803 LA_T_8127151 LA_T_8162915 LA_T_8169980 LA_T_8438461 LA_T_8502808 LA_T_8512706 LA_T_8596867 LA_T_8621866 LA_T_8683008 LA_T_8702524 LA_T_8707487 LA_T_8708087 LA_T_8723580 LA_T_8798465 LA_T_9071029 LA_T_9087690 LA_T_9182456 LA_T_9206457 LA_T_9266357 LA_T_9346943 LA_T_9506168 LA_T_9508923 LA_T_9514965 LA_T_9536026 LA_T_9569208 LA_T_9581881 LA_T_9609293 LA_T_9689088 LA_T_9693011 LA_T_9740938 LA_T_9754656 LA_T_9854823 LA_T_9896961 LA_T_9901878 LA_T_1050008 LA_T_1236840 LA_T_1243533 LA_T_1267917 LA_T_1379329 LA_T_1447757 LA_T_1457379 LA_T_1646336 LA_T_1669827 LA_T_1797843 LA_T_1810647 LA_T_1896623 LA_T_2030494 LA_T_2066209 LA_T_2144764 LA_T_2299372 LA_T_2319006 LA_T_2324368 LA_T_2343426 LA_T_2351250 LA_T_2507189 LA_T_2513767 LA_T_2579591 LA_T_2607307 LA_T_2628833 LA_T_2745780 LA_T_2747022 LA_T_2857301 LA_T_2964864 LA_T_2973329 LA_T_2978319 LA_T_3030505 LA_T_3105506 LA_T_3154434 LA_T_3237001 LA_T_3249101 LA_T_3265392 LA_T_3266022 LA_T_3412575 LA_T_3505871 LA_T_3517700 LA_T_3690594 LA_T_3751646 LA_T_3769139 LA_T_3913974 LA_T_3922252 LA_T_3966049 LA_T_4086457 LA_T_4129008 LA_T_4197596 LA_T_4400578 LA_T_4594945 LA_T_4631069 LA_T_4653938 LA_T_4678305 LA_T_4774940 LA_T_4855131 LA_T_4980224 LA_T_4987836 LA_T_5043547 LA_T_5081062 LA_T_5188464 LA_T_5288751 LA_T_5312236 LA_T_5318441 LA_T_5336219 LA_T_5416411 LA_T_5420024 LA_T_5433418 LA_T_5463606 LA_T_5481932 LA_T_5610586 LA_T_5960681 LA_T_6035510 LA_T_6045722 LA_T_6067965 LA_T_6192370 LA_T_6239874 LA_T_6246443 LA_T_6320963 LA_T_6334757 LA_T_6343266 LA_T_6354141 LA_T_6472152 LA_T_6499650 LA_T_6559792 LA_T_6647814 LA_T_6688041 LA_T_6715532 LA_T_6869508 LA_T_6904517 LA_T_7179987 LA_T_7182662 LA_T_7508892 LA_T_7521161 LA_T_7570086 LA_T_7629056 LA_T_7686633 LA_T_7865475 LA_T_7995802 LA_T_8082542 LA_T_8236638 LA_T_8276365 LA_T_8455678 LA_T_8457102 LA_T_8477522 LA_T_8507920 LA_T_8508520 LA_T_8585150 LA_T_8686549 LA_T_8747294 LA_T_8993460 LA_T_9126862 LA_T_9132181 LA_T_9174733 LA_T_9181552 LA_T_9195565 LA_T_9226005 LA_T_9331433 LA_T_9333003 LA_T_9351820 LA_T_9378967 LA_T_9387015 LA_T_9497115 LA_T_9557645 LA_T_9737995 LA_T_9987202

# ==> data/partialspoof_tutorial/train/utt2dur <==
CON_T_0000000 2.74725
CON_T_0000001 4.2501875
CON_T_0000002 3.1415

# ==> data/partialspoof_tutorial/train/utt2lab <==
CON_T_0000029 spoof
CON_T_0000069 spoof
CON_T_0000072 spoof

# ==> data/partialspoof_tutorial/train/wav.scp <==
CON_T_0000000 /data/neil/PartialSpoof/database/train/con_wav/CON_T_0000000.wav
CON_T_0000001 /data/neil/PartialSpoof/database/train/con_wav/CON_T_0000001.wav
CON_T_0000002 /data/neil/PartialSpoof/database/train/con_wav/CON_T_0000002.wav

Stage 2: Data Formatting and Augmentation

For efficient data loading, especially in distributed training, we convert our data lists into a shard format. This involves bundling multiple audio files and their labels into larger .tar files.

tools/make_shard_list.py: Creates the sharded dataset.
tools/make_raw_list.py: Creates a simple file list (used if data_type="raw").

We will also prepare the MUSAN (noise) and RIRS (reverberation) datasets for data augmentation during training. This step creates wav.scp files for them.

%%bash
head -n 3 data/partialspoof_tutorial/train/shard.list
# ==> ./shard.list <==
# data/partialspoof_tutorial/train/shards/shards_000000000.tar
# data/partialspoof_tutorial/train/shards/shards_000000001.tar
# data/partialspoof_tutorial/train/shards/shards_000000002.tar

# ==> ./shards <==
# head: error reading './shards': Is a directory

ls data/partialspoof_tutorial/train/shards
# shards_000000000.tar shards_000000001.tar shards_000000002.tar ...

data/partialspoof_tutorial/train/shards/shards_000000000.tar
data/partialspoof_tutorial/train/shards/shards_000000001.tar
data/partialspoof_tutorial/train/shards/shards_000000002.tar
shards_000000000.tar
shards_000000001.tar
shards_000000002.tar
shards_000000003.tar
shards_000000004.tar
shards_000000005.tar
shards_000000006.tar
shards_000000007.tar
shards_000000008.tar
shards_000000009.tar
shards_000000010.tar
shards_000000011.tar
shards_000000012.tar
shards_000000013.tar
shards_000000014.tar
shards_000000015.tar
shards_000000016.tar
shards_000000017.tar
shards_000000018.tar
shards_000000019.tar
shards_000000020.tar
shards_000000021.tar
shards_000000022.tar
shards_000000023.tar
shards_000000024.tar
shards_000000025.tar

Stage 3: Training

Now we are ready to train the model. We use torchrun for distributed training, which is efficient even on a single machine with multiple GPUs.

The training process will:

Load the model architecture and training parameters from the YAML config file (conf/resnet.yaml).
Use the prepared data lists (shard.list or raw.list).
Save model checkpoints and logs to the experiment directory (exp/resnet_tutorial).
Periodically evaluate performance on the development set.

%%bash
set -euo pipefail

torchrun --rdzv_backend=c10d --rdzv_endpoint=$(hostname):${PORT} --nnodes=1 --nproc_per_node=${NUM_GPUS} \
    wedefense/bin/train.py --config "$CONFIG" \
      --exp_dir "${EXP_DIR}" \
      --gpus "$GPUS" \
      --num_avg "${NUM_AVG}" \
      --data_type "${DATA_TYPE}" \
      --train_data "${DATA_DIR}/train/${DATA_TYPE}.list" \
      --train_label "${DATA_DIR}/train/utt2lab" \
      --val_data "${DATA_DIR}/dev/${DATA_TYPE}.list" \
      --val_label "${DATA_DIR}/dev/utt2lab" \
      --save_epoch_interval "${SAVE_EPOCH_INTERVAL}" \
      --early_stop_patience "${EARLY_STOP_PATIENCE}" \
      --validate_interval "${VALIDATE_INTERVAL}"
      # Add the following lines if you have prepared augmentation data
      # --reverb_data data/rirs/lmdb \
      # --noise_data data/musan/lmdb

[2026-02-11 00:42:24,345] torch.distributed.run: [WARNING] master_addr is only used for static rdzv_backend and when rdzv_endpoint is not specified.
/home/neil/anaconda3/envs/wedefense_pip/lib/python3.10/site-packages/kaldiio/__init__.py:2: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  import pkg_resources
/home/neil/anaconda3/envs/wedefense_pip/lib/python3.10/site-packages/s3prl/upstream/byol_s/byol_a/common.py:20: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
  torchaudio.set_audio_backend("sox_io")
ESPnet is not installed, cannot use espnet_hubert upstream
wandb: Currently logged in as: yzyouzhang (air_lab) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.21.0
wandb: Run data is saved locally in /home/neil/public_wedefense_Feb2026/wedefense/egs/detection/partialspoof/v03_resnet18/wandb/run-20260211_004228-4f2r3ohw
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run v03_resnet18/resnet_tutorial_20260211_004228
wandb: ⭐️ View project at https://wandb.ai/air_lab/wedefense_detection_partialspoof
wandb: 🚀 View run at https://wandb.ai/air_lab/wedefense_detection_partialspoof/runs/4f2r3ohw
[ INFO : 2026-02-11 00:42:29 ] - exp_dir is: exp/resnet_tutorial
[ INFO : 2026-02-11 00:42:29 ] - <== Passed Arguments ==>
[ INFO : 2026-02-11 00:42:29 ] - {'data_type': 'shard',
[ INFO : 2026-02-11 00:42:29 ] -  'dataloader_args': {'batch_size': 128,
[ INFO : 2026-02-11 00:42:29 ] -                      'drop_last': True,
[ INFO : 2026-02-11 00:42:29 ] -                      'num_workers': 6,
[ INFO : 2026-02-11 00:42:29 ] -                      'pin_memory': False,
[ INFO : 2026-02-11 00:42:29 ] -                      'prefetch_factor': 8},
[ INFO : 2026-02-11 00:42:29 ] -  'dataset_args': {'aug_prob': 0,
[ INFO : 2026-02-11 00:42:29 ] -                   'fbank_args': {'dither': 1.0,
[ INFO : 2026-02-11 00:42:29 ] -                                  'frame_length': 25,
[ INFO : 2026-02-11 00:42:29 ] -                                  'frame_shift': 10,
[ INFO : 2026-02-11 00:42:29 ] -                                  'num_mel_bins': 80},
[ INFO : 2026-02-11 00:42:29 ] -                   'filter': True,
[ INFO : 2026-02-11 00:42:29 ] -                   'filter_args': {'max_num_frames': 800, 'min_num_frames': 100},
[ INFO : 2026-02-11 00:42:29 ] -                   'num_frms': 300,
[ INFO : 2026-02-11 00:42:29 ] -                   'resample_rate': 16000,
[ INFO : 2026-02-11 00:42:29 ] -                   'sample_num_per_epoch': 0,
[ INFO : 2026-02-11 00:42:29 ] -                   'shuffle': True,
[ INFO : 2026-02-11 00:42:29 ] -                   'shuffle_args': {'shuffle_size': 2500},
[ INFO : 2026-02-11 00:42:29 ] -                   'spec_aug': False,
[ INFO : 2026-02-11 00:42:29 ] -                   'spec_aug_args': {'max_f': 8,
[ INFO : 2026-02-11 00:42:29 ] -                                     'max_t': 10,
[ INFO : 2026-02-11 00:42:29 ] -                                     'num_f_mask': 1,
[ INFO : 2026-02-11 00:42:29 ] -                                     'num_t_mask': 1,
[ INFO : 2026-02-11 00:42:29 ] -                                     'prob': 0.6},
[ INFO : 2026-02-11 00:42:29 ] -                   'speed_perturb': False},
[ INFO : 2026-02-11 00:42:29 ] -  'early_stop_patience': -1,
[ INFO : 2026-02-11 00:42:29 ] -  'enable_amp': False,
[ INFO : 2026-02-11 00:42:29 ] -  'exp_dir': 'exp/resnet_tutorial',
[ INFO : 2026-02-11 00:42:29 ] -  'gpus': [0],
[ INFO : 2026-02-11 00:42:29 ] -  'log_batch_interval': 100,
[ INFO : 2026-02-11 00:42:29 ] -  'loss': 'CrossEntropyLoss',
[ INFO : 2026-02-11 00:42:29 ] -  'loss_args': {},
[ INFO : 2026-02-11 00:42:29 ] -  'margin_scheduler': 'MarginScheduler',
[ INFO : 2026-02-11 00:42:29 ] -  'margin_update': {'final_margin': 0.0,
[ INFO : 2026-02-11 00:42:29 ] -                    'fix_start_epoch': 40,
[ INFO : 2026-02-11 00:42:29 ] -                    'increase_start_epoch': 20,
[ INFO : 2026-02-11 00:42:29 ] -                    'increase_type': 'exp',
[ INFO : 2026-02-11 00:42:29 ] -                    'initial_margin': 0.0,
[ INFO : 2026-02-11 00:42:29 ] -                    'update_margin': True},
[ INFO : 2026-02-11 00:42:29 ] -  'model': 'ResNet18',
[ INFO : 2026-02-11 00:42:29 ] -  'model_args': {'embed_dim': 256,
[ INFO : 2026-02-11 00:42:29 ] -                 'feat_dim': 80,
[ INFO : 2026-02-11 00:42:29 ] -                 'pooling_func': 'TSTP',
[ INFO : 2026-02-11 00:42:29 ] -                 'two_emb_layer': False},
[ INFO : 2026-02-11 00:42:29 ] -  'model_init': None,
[ INFO : 2026-02-11 00:42:29 ] -  'num_avg': -1,
[ INFO : 2026-02-11 00:42:29 ] -  'num_epochs': 100,
[ INFO : 2026-02-11 00:42:29 ] -  'optimizer': 'SGD',
[ INFO : 2026-02-11 00:42:29 ] -  'optimizer_args': {'momentum': 0.9, 'nesterov': True, 'weight_decay': 0.0001},
[ INFO : 2026-02-11 00:42:29 ] -  'projection_args': {'easy_margin': False,
[ INFO : 2026-02-11 00:42:29 ] -                      'project_type': 'softmax',
[ INFO : 2026-02-11 00:42:29 ] -                      'scale': 32.0},
[ INFO : 2026-02-11 00:42:29 ] -  'save_epoch_interval': 5,
[ INFO : 2026-02-11 00:42:29 ] -  'scheduler': 'ExponentialDecrease',
[ INFO : 2026-02-11 00:42:29 ] -  'scheduler_args': {'final_lr': 5e-05,
[ INFO : 2026-02-11 00:42:29 ] -                     'initial_lr': 0.1,
[ INFO : 2026-02-11 00:42:29 ] -                     'warm_from_zero': True,
[ INFO : 2026-02-11 00:42:29 ] -                     'warm_up_epoch': 6},
[ INFO : 2026-02-11 00:42:29 ] -  'seed': 42,
[ INFO : 2026-02-11 00:42:29 ] -  'train_data': 'data/partialspoof_tutorial/train/shard.list',
[ INFO : 2026-02-11 00:42:29 ] -  'train_label': 'data/partialspoof_tutorial/train/utt2lab',
[ INFO : 2026-02-11 00:42:29 ] -  'val_data': 'data/partialspoof_tutorial/dev/shard.list',
[ INFO : 2026-02-11 00:42:29 ] -  'val_label': 'data/partialspoof_tutorial/dev/utt2lab',
[ INFO : 2026-02-11 00:42:29 ] -  'validate_interval': 1}
[ INFO : 2026-02-11 00:42:29 ] - <== Data statistics ==>
[ INFO : 2026-02-11 00:42:29 ] - train data num: 25380, class num: 2
[ INFO : 2026-02-11 00:42:29 ] - validation data num: 24844
[ INFO : 2026-02-11 00:42:29 ] - <== Dataloaders ==>
[ INFO : 2026-02-11 00:42:29 ] - train dataloaders created
[ INFO : 2026-02-11 00:42:29 ] - Train epoch iteration number: 198
[ INFO : 2026-02-11 00:42:29 ] - validation dataloaders created
[ INFO : 2026-02-11 00:42:29 ] - Validation iteration number: 97
[ INFO : 2026-02-11 00:42:29 ] - <== Model ==>
[ INFO : 2026-02-11 00:42:29 ] - speaker_model size: 4105440
[ INFO : 2026-02-11 00:42:29 ] - Train model from scratch ...
[ INFO : 2026-02-11 00:42:29 ] - ResNet(
[ INFO : 2026-02-11 00:42:29 ] -   (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -   (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -   (layer1): Sequential(
[ INFO : 2026-02-11 00:42:29 ] -     (0): BasicBlock(
[ INFO : 2026-02-11 00:42:29 ] -       (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (shortcut): Sequential()
[ INFO : 2026-02-11 00:42:29 ] -     )
[ INFO : 2026-02-11 00:42:29 ] -     (1): BasicBlock(
[ INFO : 2026-02-11 00:42:29 ] -       (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (shortcut): Sequential()
[ INFO : 2026-02-11 00:42:29 ] -     )
[ INFO : 2026-02-11 00:42:29 ] -   )
[ INFO : 2026-02-11 00:42:29 ] -   (layer2): Sequential(
[ INFO : 2026-02-11 00:42:29 ] -     (0): BasicBlock(
[ INFO : 2026-02-11 00:42:29 ] -       (conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (shortcut): Sequential(
[ INFO : 2026-02-11 00:42:29 ] -         (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -         (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       )
[ INFO : 2026-02-11 00:42:29 ] -     )
[ INFO : 2026-02-11 00:42:29 ] -     (1): BasicBlock(
[ INFO : 2026-02-11 00:42:29 ] -       (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (shortcut): Sequential()
[ INFO : 2026-02-11 00:42:29 ] -     )
[ INFO : 2026-02-11 00:42:29 ] -   )
[ INFO : 2026-02-11 00:42:29 ] -   (layer3): Sequential(
[ INFO : 2026-02-11 00:42:29 ] -     (0): BasicBlock(
[ INFO : 2026-02-11 00:42:29 ] -       (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (shortcut): Sequential(
[ INFO : 2026-02-11 00:42:29 ] -         (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -         (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       )
[ INFO : 2026-02-11 00:42:29 ] -     )
[ INFO : 2026-02-11 00:42:29 ] -     (1): BasicBlock(
[ INFO : 2026-02-11 00:42:29 ] -       (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (shortcut): Sequential()
[ INFO : 2026-02-11 00:42:29 ] -     )
[ INFO : 2026-02-11 00:42:29 ] -   )
[ INFO : 2026-02-11 00:42:29 ] -   (layer4): Sequential(
[ INFO : 2026-02-11 00:42:29 ] -     (0): BasicBlock(
[ INFO : 2026-02-11 00:42:29 ] -       (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (shortcut): Sequential(
[ INFO : 2026-02-11 00:42:29 ] -         (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -         (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       )
[ INFO : 2026-02-11 00:42:29 ] -     )
[ INFO : 2026-02-11 00:42:29 ] -     (1): BasicBlock(
[ INFO : 2026-02-11 00:42:29 ] -       (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
[ INFO : 2026-02-11 00:42:29 ] -       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (shortcut): Sequential()
[ INFO : 2026-02-11 00:42:29 ] -     )
[ INFO : 2026-02-11 00:42:29 ] -   )
[ INFO : 2026-02-11 00:42:29 ] -   (pool): TSTP()
[ INFO : 2026-02-11 00:42:29 ] -   (seg_1): Linear(in_features=5120, out_features=256, bias=True)
[ INFO : 2026-02-11 00:42:29 ] -   (seg_bn_1): Identity()
[ INFO : 2026-02-11 00:42:29 ] -   (seg_2): Identity()
[ INFO : 2026-02-11 00:42:29 ] -   (projection): Linear(
[ INFO : 2026-02-11 00:42:29 ] -     (trans): Sequential(
[ INFO : 2026-02-11 00:42:29 ] -       (0): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
[ INFO : 2026-02-11 00:42:29 ] -       (1): ReLU(inplace=True)
[ INFO : 2026-02-11 00:42:29 ] -       (2): Linear(in_features=256, out_features=2, bias=True)
[ INFO : 2026-02-11 00:42:29 ] -     )
[ INFO : 2026-02-11 00:42:29 ] -   )
[ INFO : 2026-02-11 00:42:29 ] - )
[ INFO : 2026-02-11 00:42:29 ] - start_epoch: 1
[ INFO : 2026-02-11 00:42:29 ] - <== Loss ==>
[ INFO : 2026-02-11 00:42:29 ] - loss criterion is: CrossEntropyLoss
[ INFO : 2026-02-11 00:42:29 ] - <== Optimizer ==>
[ INFO : 2026-02-11 00:42:29 ] - optimizer is: SGD
[ INFO : 2026-02-11 00:42:29 ] - <== Scheduler ==>
[ INFO : 2026-02-11 00:42:29 ] - scheduler is: ExponentialDecrease
[ INFO : 2026-02-11 00:42:29 ] - <== MarginScheduler ==>
[ INFO : 2026-02-11 00:42:29 ] - <========== Training process ==========>
[ INFO : 2026-02-11 00:42:29 ] - +----------+----------+----------+----------+----------+----------+
[ INFO : 2026-02-11 00:42:29 ] - |     Epoch|     Batch|        Lr|    Margin|      Loss|       Acc|
[ INFO : 2026-02-11 00:42:29 ] - +----------+----------+----------+----------+----------+----------+
[ INFO : 2026-02-11 00:43:01 ] - |         1|       100|  0.016045|         0|   0.28467|    87.242|
[ INFO : 2026-02-11 00:43:36 ] - |         1|       198|  0.030749|         0|   0.22831|    89.994|
[ INFO : 2026-02-11 00:44:07 ] - Validation - Epoch: 1, Loss: 0.1867, Acc: 92.3003
[ INFO : 2026-02-11 00:44:07 ] - New best validation accuracy: 92.300258
[ INFO : 2026-02-11 00:44:37 ] - |         2|       100|  0.044612|         0|   0.14645|    93.922|
[ INFO : 2026-02-11 00:45:12 ] - |         2|       198|  0.057142|         0|    0.1418|    94.204|
[ INFO : 2026-02-11 00:45:47 ] - Validation - Epoch: 2, Loss: 0.2539, Acc: 88.8934
[ INFO : 2026-02-11 00:45:47 ] - No improvement for 1 validation checks
[ INFO : 2026-02-11 00:45:47 ] - Early stopping triggered after 2 epochs
wandb:                                                                                
wandb: 
wandb: Run history:
wandb: learning_rate ▁▁▁▁▁▂▂▂▂▂▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▆▆▆▆▇▇▇▇▇▇▇██
wandb:     train/acc ▁▃▄▄▅▆▆▆▆▇▇▇▇▇▇▇▇▇▇▇████████████████���███
wandb:    train/loss █▇▆▅▅▄▄▄▄▃▃▃▃▃▃▃▂▂▁▁▁▁���▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:       val/acc ▆▇██████���████████▁▃▅▅▄▅▄▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅
wandb:      val/loss ▁▃▃▃▄▄▃▄▄▄▄▄▄▄▄▄▄█▇▇▇▆▆▆▇▆▆��▆▆▆▆▆▆▆▆▆▆▆▆
wandb: 
wandb: Run summary:
wandb: learning_rate 0.05714
wandb:     train/acc 94.20376
wandb:    train/loss 0.1418
wandb:       val/acc 88.89336
wandb:      val/loss 0.2539
wandb: 
wandb: 🚀 View run v03_resnet18/resnet_tutorial_20260211_004228 at: https://wandb.ai/air_lab/wedefense_detection_partialspoof/runs/4f2r3ohw
wandb: ⭐️ View project at: https://wandb.ai/air_lab/wedefense_detection_partialspoof
wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
wandb: Find logs at: ./wandb/run-20260211_004228-4f2r3ohw/logs

More about variables used in training please refer to

Stage 4: Model Averaging & Embedding Extraction

After training, we can proceed with inference. We have two options for the model to use:

Best Model: The single checkpoint that performed best on the development set (best_model.pt).
Averaged Model: An average of the last num_avg checkpoints. This often yields more robust performance.

First, we average the model if num_avg > 0. Then, we use the chosen model to extract embeddings (fixed-size vector representations) for each utterance in the dev and eval sets. These embeddings are the input to the final classification layer.

import os

# Determine which model path to use based on num_avg
if num_avg > 0:
    model_path = os.path.join(exp_dir, 'models/avg_model.pt')
else:
    model_path = os.path.join(exp_dir, 'models/best_model.pt')

print(f"Using model: {model_path}")

Using model: exp/resnet_tutorial/models/best_model.pt

%%bash
set -euo pipefail

echo "Starting Stage 4: Model Averaging and Embedding Extraction..."

# Determine model path based on NUM_AVG
if [ "${NUM_AVG}" -gt 0 ]; then
  echo "Averaging the last ${NUM_AVG} models..."
  python "${WEDEFENSE_ROOT}/wedefense/bin/average_model.py" \
    --dst_model "${EXP_DIR}/models/avg_model.pt" \
    --src_path "${EXP_DIR}/models" \
    --num "${NUM_AVG}"
  model_path="${EXP_DIR}/models/avg_model.pt"
else
  model_path="${EXP_DIR}/models/best_model.pt"
fi

echo "Using model: ${model_path}"

echo "Extracting embeddings..."

# Extract embeddings for dev and eval sets (This is different from run.sh, the logic in run.sh follows local/extract_emb.sh  -> tools/extract_embedding.sh -> wedefense/bin/extract.py)
for dset in dev eval; do
  echo "Processing ${dset} set..."
  mkdir -p "${EXP_DIR}/embeddings/${dset}"

  python "${WEDEFENSE_ROOT}/wedefense/bin/extract.py" \
    --config "${EXP_DIR}/config.yaml" \
    --model_path "${model_path}" \
    --data_type "${DATA_TYPE}" \
    --data_list "${DATA_DIR}/${dset}/${DATA_TYPE}.list" \
    --embed_ark "${EXP_DIR}/embeddings/${dset}/embedding.ark" \
    --batch_size 1 \
    --num_workers 1
done

echo "Stage 4 finished."

Starting Stage 4: Model Averaging and Embedding Extraction...
Using model: exp/resnet_tutorial/models/best_model.pt
Extracting embeddings...
Processing dev set...

/home/neil/anaconda3/envs/wedefense_pip/lib/python3.10/site-packages/kaldiio/__init__.py:2: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  import pkg_resources
/home/neil/anaconda3/envs/wedefense_pip/lib/python3.10/site-packages/s3prl/upstream/byol_s/byol_a/common.py:20: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
  torchaudio.set_audio_backend("sox_io")
ESPnet is not installed, cannot use espnet_hubert upstream

Loading checkpoint ...

[ WARNING : 2026-02-11 00:47:23 ] - unexpected tensor: projection.trans.0.weight
[ WARNING : 2026-02-11 00:47:23 ] - unexpected tensor: projection.trans.0.bias
[ WARNING : 2026-02-11 00:47:23 ] - unexpected tensor: projection.trans.0.running_mean
[ WARNING : 2026-02-11 00:47:23 ] - unexpected tensor: projection.trans.0.running_var
[ WARNING : 2026-02-11 00:47:23 ] - unexpected tensor: projection.trans.0.num_batches_tracked
[ WARNING : 2026-02-11 00:47:23 ] - unexpected tensor: projection.trans.2.weight
[ WARNING : 2026-02-11 00:47:23 ] - unexpected tensor: projection.trans.2.bias

Finished !!! Start extracting ...

24844it [04:52, 85.08it/s]

Processing eval set...

/home/neil/anaconda3/envs/wedefense_pip/lib/python3.10/site-packages/kaldiio/__init__.py:2: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  import pkg_resources
/home/neil/anaconda3/envs/wedefense_pip/lib/python3.10/site-packages/s3prl/upstream/byol_s/byol_a/common.py:20: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
  torchaudio.set_audio_backend("sox_io")
ESPnet is not installed, cannot use espnet_hubert upstream

Loading checkpoint ...

[ WARNING : 2026-02-11 00:52:19 ] - unexpected tensor: projection.trans.0.weight
[ WARNING : 2026-02-11 00:52:19 ] - unexpected tensor: projection.trans.0.bias
[ WARNING : 2026-02-11 00:52:19 ] - unexpected tensor: projection.trans.0.running_mean
[ WARNING : 2026-02-11 00:52:19 ] - unexpected tensor: projection.trans.0.running_var
[ WARNING : 2026-02-11 00:52:19 ] - unexpected tensor: projection.trans.0.num_batches_tracked
[ WARNING : 2026-02-11 00:52:19 ] - unexpected tensor: projection.trans.2.weight
[ WARNING : 2026-02-11 00:52:19 ] - unexpected tensor: projection.trans.2.bias

Finished !!! Start extracting ...

71237it [15:27, 76.84it/s]

Stage 4 finished.

Stage 5: Logit Extraction

With the embeddings extracted, we now pass them through the final classification layer of the model to get the raw output scores, known as logits. These logits represent the model’s confidence for each class (bonafide vs. spoof) before any normalization.

%%bash
set -euo pipefail

echo "Starting Stage 5: Extracting logits..."

# Use averaged model if NUM_AVG > 0, else best model
if [ "${NUM_AVG}" -gt 0 ]; then
  model_path="${EXP_DIR}/models/avg_model.pt"
else
  model_path="${EXP_DIR}/models/best_model.pt"
fi

for dset in dev eval; do
  echo "Processing ${dset} set..."
  mkdir -p "${EXP_DIR}/posteriors/${dset}"
  python "${WEDEFENSE_ROOT}/wedefense/bin/infer.py" --model_path "${model_path}" \
    --config "${EXP_DIR}/config.yaml" \
    --num_classes 2 \
    --embedding_scp_path "${EXP_DIR}/embeddings/${dset}/embedding.scp" \
    --out_path "${EXP_DIR}/posteriors/${dset}"
done

echo "Stage 5 finished."

Starting Stage 5: Extracting logits...
Processing dev set...

/home/neil/anaconda3/envs/wedefense_pip/lib/python3.10/site-packages/kaldiio/__init__.py:2: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  import pkg_resources

model_path exp/resnet_tutorial/models/best_model.pt
config exp/resnet_tutorial/config.yaml
embedding_scp_path exp/resnet_tutorial/embeddings/dev/embedding.scp
out_path exp/resnet_tutorial/posteriors/dev
(24844, 256)
(24844, 2)
exp/resnet_tutorial/posteriors/dev
Processing eval set...

/home/neil/anaconda3/envs/wedefense_pip/lib/python3.10/site-packages/kaldiio/__init__.py:2: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  import pkg_resources

model_path exp/resnet_tutorial/models/best_model.pt
config exp/resnet_tutorial/config.yaml
embedding_scp_path exp/resnet_tutorial/embeddings/eval/embedding.scp
out_path exp/resnet_tutorial/posteriors/eval
(71237, 256)
(71237, 2)
exp/resnet_tutorial/posteriors/eval
Stage 5 finished.

Stage 6: Score Calibration (Logits to LLR)

The raw logits from the model are not always well-calibrated. To make them more interpretable and robust for decision-making, we convert them into Log-Likelihood Ratios (LLR). This process calibrates the scores based on the prior probabilities of the classes observed in the training data.

A positive LLR score will indicate a prediction of ‘spoof’, while a negative score will indicate ‘bonafide’.

%%bash
set -euo pipefail

echo "Starting Stage 6: Converting logits to Log-Likelihood Ratios (LLR)..."

# Calculate the number of bonafide vs. spoof utterances in the training set (for calibration).
cut -f2 -d" " "${DATA_DIR}/train/utt2lab" | sort | uniq -c | awk '{print $2 " " $1}' > "${DATA_DIR}/train/lab2num_utts"
echo "Training label counts:"
cat "${DATA_DIR}/train/lab2num_utts"

for dset in dev eval; do
    echo "Calibrating scores for ${dset} set..."
    python "${WEDEFENSE_ROOT}/wedefense/bin/logits_to_llr.py" \
        --logits_scp_path "${EXP_DIR}/posteriors/${dset}/logits.scp" \
        --training_counts "${DATA_DIR}/train/lab2num_utts" \
        --train_label "${DATA_DIR}/train/utt2lab" \
        --pi_spoof 0.05
done

echo "Stage 6 finished."

Starting Stage 6: Converting logits to Log-Likelihood Ratios (LLR)...
Training label counts:
bonafide 2580
spoof 22800
Calibrating scores for dev set...

/home/neil/anaconda3/envs/wedefense_pip/lib/python3.10/site-packages/kaldiio/__init__.py:2: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  import pkg_resources

logits_scp_path exp/resnet_tutorial/posteriors/dev/logits.scp
(24844, 2)
['bonafide' 'spoof']
bonafide
spoof
[0.10165485]
[0.89834515]
[-2.28617206]
[-0.10720093]
Calibrating scores for eval set...

/home/neil/anaconda3/envs/wedefense_pip/lib/python3.10/site-packages/kaldiio/__init__.py:2: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  import pkg_resources

logits_scp_path exp/resnet_tutorial/posteriors/eval/logits.scp
(71237, 2)
['bonafide' 'spoof']
bonafide
spoof
[0.10165485]
[0.89834515]
[-2.28617206]
[-0.10720093]
Stage 6 finished.

Logits → LLR (binary case)

Let the model output logits \(s_{\text{spoof}}\) and \(s_{\text{bonafide}}\). The posterior is

\[ P(\text{spoof}\mid x)=\frac{e^{s_{\text{spoof}}}}{e^{s_{\text{spoof}}}+e^{s_{\text{bonafide}}}},\quad P(\text{bonafide}\mid x)=\frac{e^{s_{\text{bonafide}}}}{e^{s_{\text{spoof}}}+e^{s_{\text{bonafide}}}} \]

The log-likelihood ratio is

\[ \text{LLR}(x)=\log\frac{P(\text{spoof}\mid x)}{P(\text{bonafide}\mid x)}-\log\frac{\pi_{\text{spoof}}}{1-\pi_{\text{spoof}}} \]

where \(\pi_{\text{spoof}}\) is the prior spoof probability used for calibration.

Stage 7: Performance Evaluation

Finally, we measure the performance of our system using the calibrated LLR scores. The primary metric for anti-spoofing is the Equal Error Rate (EER).

EER: The error rate at which the False Acceptance Rate (FAR) equals the False Rejection Rate (FRR). A lower EER indicates better performance.

We will calculate the EER for both the development and evaluation sets.

%%bash
set -euo pipefail

echo "Starting Stage 7: Measuring Performance..."

for dset in dev eval; do
  echo "Evaluating on ${dset} set..."
  
  # Prepare the ground truth key file in the required format: <utt_id>\t<label>
  key_file="${DATA_DIR}/${dset}/cm_key_file.txt"
  echo -e "filename\tcm-label" > "${key_file}"
  # Replace the first space with a tab
  sed 's/ /\t/' "${DATA_DIR}/${dset}/utt2lab" >> "${key_file}"

  # Run the evaluation script (also save to file)
  python "${WEDEFENSE_ROOT}/wedefense/metrics/detection/evaluation.py" \
      --m t1 \
      --cm "${EXP_DIR}/posteriors/${dset}/llr.txt" \
      --cm_key "${key_file}" 2>&1 | tee "${EXP_DIR}/results_${dset}.txt"
done

echo "Stage 7 finished. Results are saved in ${EXP_DIR}/"

Starting Stage 7: Measuring Performance...
Evaluating on dev set...

CM SYSTEM
	min DCF 		= 0.22565510761727886 (min DCF for countermeasure)
	EER		= 9.337086039 % (EER for countermeasure)
	CLLR		= 0.357773650 bits (CLLR for countermeasure)
	actDCF		= 0.2597453049282974 (actual DCF)
# Track 1 Result: 

-eval_mindcf: 0.22566
-eval_eer (%): 9.337
-eval_cllr (bits): 0.35777
-eval_actDCF: 0.25975

Evaluating on eval set...

CM SYSTEM
	min DCF 		= 0.29881968732672065 (min DCF for countermeasure)
	EER		= 11.816096026 % (EER for countermeasure)
	CLLR		= 0.578952176 bits (CLLR for countermeasure)
	actDCF		= 0.46167436259890376 (actual DCF)
# Track 1 Result: 

-eval_mindcf: 0.29882
-eval_eer (%): 11.816
-eval_cllr (bits): 0.57895
-eval_actDCF: 0.46167

Stage 7 finished. Results are saved in exp/resnet_tutorial/

Conclusion

Congratulations! You have successfully completed all the stages of training and evaluating an anti-spoofing model on the PartialSpoof dataset.

You have learned how to:

Prepare a dataset in the standard Kaldi-style format.
Format the data for efficient training using shards.
Train a ResNet-based model.
Extract embeddings and logits for inference.
Calibrate scores and evaluate the system’s performance using the EER metric.

Next Steps

Analyze the results: Check the results_eval.txt file in your experiment directory for the final performance.
Experiment with hyperparameters: Try changing the model architecture, learning rate, or other parameters in the conf/resnet.yaml file.
Embedding visualization: You may also try to visualize the embedding extracted from the stage 4 following wedefense/egs/embedding_visualization/embedding_visulization_umap.ipynb.