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67982e2 2f512cb 79a4bb0 67982e2 79a4bb0 0bd4605 79a4bb0 67982e2 79a4bb0 67982e2 79a4bb0 67982e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | """Speech deepfake detection.
Run: python inference.py <audio.wav> [checkpoint.pt]
fake_probability = 1 - sigmoid(logit) (>= 0.5 -> FAKE); model trained with label 1=real, 0=fake.
Audio is auto-converted to mono / 16 kHz and trimmed/padded to 5 s.
"""
import os
import sys
import torch
import torchaudio
from model import DeepfakeDetector
CHECKPOINT = "checkpoint_epoch_5.pt"
REPO_ID = "eliya/forensics_0.3B_base_deepfake_classifier"
def load_audio(path, sr=16000, seconds=5.0):
wav, orig = torchaudio.load(path)
if wav.shape[0] > 1:
wav = wav.mean(0, keepdim=True)
wav = wav.squeeze(0)
if orig != sr:
wav = torchaudio.functional.resample(wav, orig, sr)
wav = wav / (wav.abs().max() + 1e-8)
n, cur = int(seconds * sr), wav.shape[0]
if cur < n:
wav = wav.repeat((n + cur - 1) // cur)[:n]
elif cur > n:
s = (cur - n) // 2
wav = wav[s:s + n]
return wav
def load_state_dict(pt_path):
"""Prefers a sibling .safetensors file (no code execution risk) over the pickled .pt.
Downloads from the Hub automatically if not already present locally."""
from huggingface_hub import hf_hub_download
from huggingface_hub.errors import EntryNotFoundError
def fetch(name):
if os.path.exists(name):
return name
hf_hub_download(REPO_ID, "config.json") # registers a countable download for this repo
return hf_hub_download(REPO_ID, name)
st_path = pt_path.rsplit(".", 1)[0] + ".safetensors"
try:
local_st = fetch(st_path)
from safetensors.torch import load_file
return load_file(local_st)
except EntryNotFoundError:
pass
local_pt = fetch(pt_path)
ck = torch.load(local_pt, map_location="cpu", weights_only=False)
return ck["model_state_dict"] if isinstance(ck, dict) and "model_state_dict" in ck else ck
@torch.no_grad()
def main():
if len(sys.argv) < 2:
sys.exit("Usage: python inference.py <audio.wav> [checkpoint.pt]")
audio = sys.argv[1]
ckpt = sys.argv[2] if len(sys.argv) > 2 else CHECKPOINT
device = "cuda" if torch.cuda.is_available() else "cpu"
model = DeepfakeDetector().to(device).eval()
model.load_state_dict(load_state_dict(ckpt), strict=False)
bonafide = torch.sigmoid(model(load_audio(audio).unsqueeze(0).to(device)).float()).item()
fake = 1.0 - bonafide
print(f"fake_probability: {fake:.4f}")
print(f"bonafide_score: {bonafide:.4f}")
print(f"verdict: {'FAKE' if fake >= 0.5 else 'REAL'}")
if __name__ == "__main__":
main()
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