Spaces:
Sleeping
Sleeping
File size: 2,078 Bytes
f052fd1 979a7cd f052fd1 |
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 |
import gradio as gr
import torch
import torchaudio
import numpy as np
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
import torchaudio.transforms as T
MODEL_ID = "Zeyadd-Mostaffa/wav2vec_checkpoints"
# 1) Load model & feature extractor
feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_ID)
model = AutoModelForAudioClassification.from_pretrained(MODEL_ID)
model.eval()
# Optionally use GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
label_names = ["fake", "real"] # According to your label2id = {"fake": 0, "real": 1}
def classify_audio(audio_file):
"""
audio_file: path to the uploaded file (WAV, MP3, etc.)
Returns: "fake" or "real"
"""
# 2) Load the audio file
# torchaudio returns (waveform, sample_rate)
waveform, sr = torchaudio.load(audio_file)
# If stereo, pick one channel or average
if waveform.shape[0] > 1:
waveform = torch.mean(waveform, dim=0, keepdim=True)
waveform = waveform.squeeze() # (samples,)
# 3) Resample if needed
if sr != 16000:
resampler = T.Resample(sr, 16000)
waveform = resampler(waveform)
sr = 16000
# 3) Preprocess with feature_extractor
inputs = feature_extractor(
waveform.numpy(),
sampling_rate=sr,
return_tensors="pt",
truncation=True,
max_length=int(16000* 6.0), # 6 second max
)
# Move everything to device
input_values = inputs["input_values"].to(device)
with torch.no_grad():
logits = model(input_values).logits
pred_id = torch.argmax(logits, dim=-1).item()
# 4) Return label text
predicted_label = label_names[pred_id]
return predicted_label
# 5) Build Gradio interface
demo = gr.Interface(
fn=classify_audio,
inputs=gr.Audio( type="filepath"),
outputs="text",
title="Sigma One - Deepfake Audio Detection",
description="Upload an audio sample to check if it is fake or real."
)
if __name__ == "__main__":
demo.launch()
|