Deepfake Audio Wav2vec2
Deepfake-Audio-Wav2Vec2 is a fine-tuned audio classification model trained to detect real vs spoofed (deepfake) speech audio.
Source du modèle
- Type
- Other
- Taille du fichier
- 361 MB
- Version
- vRevision e1197e2
- Mis à jour
- 22 sept. 2026
- Téléchargements
- 1,8 k
- Source
- Hugging Face
Description de la source
🧾 Model Card — Deepfake-Audio-Wav2Vec2
🧠 Model Overview
Deepfake-Audio-Wav2Vec2 is a fine-tuned audio classification model trained to detect real vs spoofed (deepfake) speech audio.
The model is built on top of facebook/wav2vec2-base, a self-supervised speech representation model, and adapted for binary deepfake audio detection.
It learns subtle acoustic artifacts and synthetic speech patterns that differentiate genuine human recordings from AI-generated or manipulated audio samples.
This model is intended for:
- Deepfake voice detection
- Audio authenticity verification
- Research in anti-spoofing systems
- Security pipelines for voice-based applications
🏗️ Training Details
| Parameter | Value |
|---|---|
| Base Model | facebook/wav2vec2-base |
| Framework | Hugging Face Transformers |
| Training Hardware | GPU (CUDA) |
| Task Type | Audio Classification |
| Classes | bonafide / spoof |
| Audio Sample Rate | 16 kHz |
| Input Duration | Fixed audio segments |
| Optimization | AdamW |
| Loss Function | Cross Entropy |
🎯 Label Classes
- 🟢 Bonafide → Real / authentic speech
- 🔴 Spoof → Deepfake / synthetic audio
📊 Evaluation Metrics
| Metric | Score |
|---|---|
| Accuracy | 92.8% |
| Precision | 89.7% |
| Recall | 88.0% |
| F1 Score | 88.4% |
✅ The model demonstrates strong detection performance across real and spoofed samples.
📂 Dataset Description
The model was trained on a balanced subset of the ASVspoof 2021 PA dataset for binary anti-spoofing classification.
The dataset includes:
- Genuine speech recordings
- Spoofed / manipulated audio samples
- Replay and synthetic attack scenarios
Training was performed on balanced class samples to improve robustness across both labels.
💻 Example Usage
import torch
import torchaudio
import numpy as np
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
model_id = "Vansh180/deepfake-audio-wav2vec2"
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
model = AutoModelForAudioClassification.from_pretrained(model_id)
model.eval()
def predict_audio(audio_path):
wav, sr = torchaudio.load(audio_path)
if wav.shape[0] > 1:
wav = wav.mean(dim=0, keepdim=True)
inputs = feature_extractor(
wav.squeeze().numpy(),
sampling_rate=16000,
return_tensors="pt"
)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=1)
prediction = torch.argmax(probs, dim=1).item()
confidence = probs[0][prediction].item()
return {
"prediction": model.config.id2label[prediction],
"confidence": confidence
}
print(predict_audio("sample.wav"))
Sources
1 source
Sources
1 sourceVérifié 6 sept.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
2 extraits
Extraits de sources
2 extraits--- language: en license: mit pipeline_tag: audio-classification tags: - audio-classification - deepfake-detection - spoof-detection - wav2vec2 - pytorch - transformers metrics: - accuracy - precision - recall - f1 model-index: - name: Deepfake-Audio-Wav2Vec2 results: - task: type: audio-classification name: Deepfake Audio Detection dataset: name: Balanced ASVspoof 2021 PA type: custom metrics: - type: accuracy value: 0.928 name: Accuracy - type: precision value: 0.897 name: Precision - type: recall value: 0.880 name: Recall - type: f1 value: 0.924 name: F1 Score --- # 🧾 Model Card — Deepfake-Audio-Wav2Vec2 --- # 🧠 Model Overview Deepfake-Audio-Wav2Vec2 is a fine-tuned **audio classification model** trained to detect **real vs spoofed (deepfake) speech audio**. The model is built on top of **facebook/wav2vec2-base**, a self-supervised speech representation model, and adapted for **binary deepfake audio detection**. It learns subtle acoustic artifacts and synthetic speech patterns that differentiate **genuine human recordings** from **AI-generated or manipulated audio samples**. This model is intended for: - Deepfake voice detection - Audio authenticity verification - Research in anti-spoofing systems - Security pipelines for voice-based applications --- # 🏗️ Training Details | Parameter | Value | |----------|------| | Base Model | facebook/wav2vec2-base | | Framework | Hugging Face Transformers | | Training Hardware | GPU (CUDA) | | Task Type | Audio Classification | | Classes | bonafide / spoof | | Audio Sample Rate | 16 kHz | | Input Duration | Fixed audio segments | | Optimization | AdamW | | Loss Function | Cross Entropy | --- # 🎯 Label Classes - 🟢 **Bonafide** → Real / authentic speech - 🔴 **Spoof** → Deepfake / synthetic audio --- # 📊 Evaluation Metrics | Metric | Score | |------|------:| | Accuracy | 92.8% | | Precision | 89.7% | | Recall | 88.0% | | F1 Score | 88.4% | ✅ The model demonstrates strong detection performance across real and spoofed samples. --- # 📂 Dataset Description The model was trained on a **balanced subset of the ASVspoof 2021 PA dataset** for binary anti-spoofing classification. The dataset includes: - Genuine speech recordings - Spoofed / manipulated audio samples - Replay and synthetic attack scenarios Training was performed on balanced class samples to improve robustness ac...
Source context: 1758 downloads · 1 likes · Pipeline audio-classification · Library transformers · Repo Vansh180/deepfake-audio-wav2vec2
