shivam-2211/voice-detection-model

Voice Detection Model

Binary audio classifier — classifies speech as FAKE (AI-generated) or REAL (human).

Built by fine-tuning `facebook/wav2vec2-large-xlsr-53` on the `garystafford/deepfake-audio-detection` dataset.

Model Details

Labels

{
  "id2label": { "0": "FAKE", "1": "REAL" },
  "label2id": { "FAKE": 0, "REAL": 1 }
}

Architecture

Transformer Encoder

CNN Feature Extractor (7 layers)

• —Activation: gelu · Norm: layer · Bias: true
• —Conv positional embeddings: 128, 16 groups
• —Feature encoder was frozen during fine-tuning

TDNN Classifier Head

• —Classifier projection: 256
• —X-vector output dim: 512

Regularization

Preprocessor

From preprocessor_config.json:

Training

Dataset

`garystafford/deepfake-audio-detection` — 1,866 samples total.

Group-aware defensive splits (speaker isolation):

Hyperparameters

Preprocessing

• —Silence trimming (librosa.effects.trim, 30 dB threshold)
• —Truncate/pad to 5.0s (80,000 samples at 16 kHz)
• —Random crop during training, center crop during eval

Augmentation (training only)

Infrastructure

Usage

import torch
import librosa
from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor

model_id = "shivam-2211/voice-detection-model"
extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_id)
model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id)
model.eval()

# Load audio at 16 kHz mono
audio, sr = librosa.load("sample.wav", sr=16000, mono=True)

inputs = extractor(audio, sampling_rate=16000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(**inputs).logits
    probs = torch.softmax(logits, dim=-1)
    pred_id = torch.argmax(probs, dim=-1).item()
    confidence = probs[0][pred_id].item()

label = model.config.id2label[pred_id]
print(f"{label} ({confidence:.2%})")

Repository Files

Limitations

• —Performance may degrade on heavily compressed, noisy, or very short audio.
• —Newer voice synthesis methods may produce artifacts not represented in training data.
• —Should not be used as sole evidence without expert review.

License

MIT