You can deploy this model and start earning money today!

nii-yamagishilab/
wav2vec-small-anti-deepfake-nda

Name: wav2vec-small-anti-deepfake-nda
Availability: InStock

safetensors

wav2vec2

2506.21090

facebook/wav2vec2-base

finetune

cc-by-nc-sa-4.0

Views

No views yet

🔍 AntiDeepfake

The AntiDeepfake project provides a series of powerful foundation models post-trained for deepfake detection. The AntiDeepfake model can be used for feature extraction for deepfake detection in a zero-shot manner, or it may be further fine-tuned and optimized for a specific database or deepfake-related task.

For technical details and analysis, please refer to our paper Post-training for Deepfake Speech Detection.

🤖 Available Models

All models are released on Hugging Face 🤗 with two variants:

Default: Trained with data augmentation
NDA (No Data Augmentation): Trained without data augmentation

Model	Variants
XLS-R-2B-AntiDeepfake	Default, NDA
XLS-R-1B-AntiDeepfake	Default, NDA
MMS-1B-AntiDeepfake	Default, NDA
MMS-300M-AntiDeepfake	Default, NDA
Wav2Vec2-Large-AntiDeepfake	Default, NDA
Wav2Vec2-Small-AntiDeepfake	Default, NDA
Hubert-Extra-Large-AntiDeepfake	Default, NDA

🛠️ Training Code & Repository

Explore training scripts, config files, and evaluation utilities in our GitHub repository:🔗 AntiDeepfake GitHub Repository

🚀 Model Spotlight: Wav2Vec2-Small-AntiDeepfake-NDA

🌟 Key Features

Architecture: Wav2Vec 2.0 - facebook/wav2vec2-base 🔗.
Input: 16kHz sampled speech with arbitrary length 🎙️.
Output: Binary classification score (<Fake score 🔴 , Real score 🟢>).
Training Dataset: Totally 18k hours of fake speech and 56k hours of real speech.

🏗️ Architecture

Front-end Feature Extractor: Wav2Vec2-Base.
Back-end Classifier: A fully connected layer.

⚙️ Training Details

Optimizer: AdamW with a learning rate of 1e-7.
Batch Size: Dynamic Batching, maximum length per batch is set to 100 seconds.
Data Augmentation: None
Loss Function: Cross-Entropy Loss.
Evaluation Metrics: Equal Error Rate (EER), ROC AUC, Accuracy, Precision, Recall, F1 Score.

🚀 Inference with PyTorch

📦 Dependencies:

### New conda environments ###
conda create --name antideepfake python==3.9.0
conda activate antideepfake
conda install pip==24.0

### Install packages ###
pip install huggingface-hub==0.31.1 fairseq==0.12.2 safetensors==0.5.3 soundfile==0.13.1

🚀 Inference:

python

import os
import torch
import torchaudio
from fairseq.models.wav2vec import Wav2Vec2Model, Wav2Vec2Config
from huggingface_hub import PyTorchModelHubMixin

# This is the only part of the script you need to modify.
# Set this to the path where your audio files are stored.
folder_path = "/path/to/folder/contains/wavs/"
audio_formats = (".mp3", ".wav", ".flac", ".m4a")

# === Set device (use GPU if available) ===
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

# === Wrapper for the SSL model ===
class SSLModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        # Model config used to build SSL architecture
        cfg = Wav2Vec2Config(
            encoder_layers=12,
            encoder_embed_dim=768,
            quantize_targets=True,
            latent_dim=256,
            final_dim=256,
        )
        # Initialize SSL model with random weights
        self.model = Wav2Vec2Model(cfg)

    def extract_feat(self, input_data):
        # If input has shape (B, T, 1), squeeze the last dim
        if input_data.ndim == 3:
            input_data = input_data[:, :, 0]
        # Extract features
        with torch.no_grad():
            features = self.model(input_data.to(device), mask=False, features_only=True)['x']
        return features

# === Function for reading and pre-processing waveforms ===
def load_wav_and_preprocess(wav_path, target_sr=16000):
    # Load audio file
    wav, sr = torchaudio.load(wav_path)
    # Convert to mono if stereo
    wav = wav.mean(dim=0)
    # Resample to target sampling rate
    wav = torchaudio.functional.resample(wav, sr, new_freq=target_sr)
    # Normalize waveform
    with torch.no_grad():
        wav = torch.nn.functional.layer_norm(wav, wav.shape)
    # Add batch dimension and return
    return wav.unsqueeze(0).to(device)

# === The actual deepfake detection model using SSL frontend + FC backend ===
class DeepfakeDetector(torch.nn.Module, PyTorchModelHubMixin):
    def __init__(self):
        super().__init__()
        self.ssl_orig_output_dim = 768
        self.num_classes = 2

        # Frontend: SSL model
        self.m_ssl = SSLModel()

        # Backend: Pooling + Classification
        self.adap_pool1d = torch.nn.AdaptiveAvgPool1d(output_size=1)
        self.proj_fc = torch.nn.Linear(
            in_features=self.ssl_orig_output_dim,
            out_features=self.num_classes,
        )

    def forward(self, wav):
        emb = self.m_ssl.extract_feat(wav)  # [B, T, D]
        emb = emb.transpose(1, 2)           # [B, D, T]
        pooled_emb = self.adap_pool1d(emb)  # [B, D, 1]
        pooled_emb = pooled_emb.squeeze(-1) # [B, D]
        logits = self.proj_fc(pooled_emb)   # [B, 2]
        return logits

# === Load AntiDeepfake model from Hugging Face===
model = DeepfakeDetector.from_pretrained("nii-yamagishilab/wav2vec-small-anti-deepfake-nda")
model.to(device)
model.eval()

# === Inference on a folder of audio files ===
results = []
for root, _, files in os.walk(folder_path):
    for file in files:
        if file.lower().endswith(audio_formats):
            input_path = os.path.join(root, file)
            with torch.no_grad():
                wav = load_wav_and_preprocess(input_path)
                logits = model(wav)
                probs = torch.nn.functional.softmax(logits, dim=1)
                results.append((file, probs.cpu().numpy()[0]))

# Sort results alphabetically by filename
results.sort(key=lambda x: x[0])

# Print formatted results
print("\n=== Deepfake Detection Results ===")
for file_name, prob in results:
    print(f"{file_name}: real prob = {prob[1]:.3f}, fake prob = {prob[0]:.3f}")

📊 Performance Metrics

Results shown below can be reproduced using scripts provided in our GitHub repository.

Test Database	ROC AUC	Accuracy	Precision	Recall	F1-score	FPR	FNR	EER (%) @ Threshold
ADD2023	0.895	0.867	0.899	0.924	0.911	0.294	0.076	19.41 @ 0.9151
DeepVoice	0.923	0.405	0.170	0.991	0.290	0.677	0.009	16.20 @ 1.0000
FakeOrReal	0.999	0.956	0.997	0.913	0.953	0.003	0.087	1.06 @ 0.0430
FakeOrReal-norm	0.982	0.931	0.941	0.916	0.928	0.055	0.084	6.48 @ 0.3893
In-the-Wild	0.990	0.957	0.945	0.989	0.967	0.096	0.011	4.65 @ 0.9497
Deepfake-Eval-2024	0.759	0.701	0.696	0.962	0.808	0.793	0.038	31.97 @ 0.9995

You can also fine-tune this model on a specific database, the corresponding code is provided in our GitHub repository. Fine-tuning will follow a similar process to training a new model, except that model weights will be initialized as AntiDeepfake checkpoints.

Below are the evaluation results of this model fine-tuned on the Deepfake-Eval-2024 training set and tested on its corresponding test set (as shown in the previous table):

Test Input Length	ROC AUC	Accuracy	Precision	Recall	F1-score	FPR	FNR	EER (%) @ Threshold
4s	0.8826	0.8284	0.8492	0.8968	0.8724	0.3010	0.1032	18.67 @ 0.9049
10s	0.9173	0.8684	0.8916	0.9088	0.9001	0.2071	0.0912	14.88 @ 0.8852
13s	0.9255	0.8721	0.8959	0.9093	0.9026	0.1976	0.0907	13.88 @ 0.8760
30s	0.9330	0.8911	0.9144	0.9175	0.9159	0.1573	0.0825	12.22 @ 0.8724
50s	0.9309	0.8904	0.9109	0.9173	0.9141	0.1567	0.0827	12.44 @ 0.8401

Training Set

Below is a breakdown of the training set used for post-training of speech SSL models.

📚 Database	🌍 Language	✅ Genuine (hrs)	❌ Fake (hrs)
AISHELL3	zh	85.62	0
ASVspoof2019-LA	en	11.85	97.80
ASVspoof2021-LA	en	16.40	116.10
ASVspoof2021-DF	en	20.73	487.00
ASVspoof5	en	413.49	1808.48
CFAD	zh	171.25	224.55
CNCeleb2	zh	1084.34	0
Codecfake	en, zh	129.66	808.32
CodecFake	en	0	660.92
CVoiceFake	en, fr, de, it, zh	315.14	1561.16
DECRO	en, zh	35.18	102.44
DFADD	en	41.62	66.01
Diffuse or Confuse	en	0	231.66
DiffSSD	en	0	139.73
DSD	en, ja, ko	100.98	60.23
FLEURS	102 languages	1388.97	0
FLEURS-R	102 languages	0	1238.83
HABLA	es	35.56	87.83
LibriTTS	en	585.83	0
LibriTTS-R	en	0	583.15
LibriTTS-Vocoded	en	0	2345.14
LJSpeech	en	23.92	0
MLAAD	38 languages	0	377.96
MLS	8 languages	50558.11	0
SpoofCeleb	Multilingual	173.00	1916.20
VoiceMOS	en	0	448.44
VoxCeleb2	Multilingual	1179.62	0
VoxCeleb2-Vocoded	Multilingual	0	4721.46
WaveFake	en, ja	0	198.65
Train Set	Over 100 languages	56370.00	18280.00

Attribution

All AntiDeepfake models were developed by Yamagishi Lab at the National Institute of Informatics (NII), Japan.

All model weights are the intellectual property of NII and are made available for research and educational purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Acknowledgments

This project is based on results obtained from project JPNP22007, commissioned by the New Energy and Industrial Technology Development Organization (NEDO).

It is also partially supported by the following grants from the Japan Science and Technology Agency (JST):

AIP Acceleration Research (Grant No. JPMJCR24U3)
PRESTO (Grant No. JPMJPR23P9)

This study was carried out using the TSUBAME4.0 supercomputer at Institute of Science Tokyo.