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from PhantomNET import PhantomNet
import joblib
from transformers import AutoFeatureExtractor, Wav2Vec2Model
import torch
import librosa
import numpy as np
from sklearn.linear_model import LogisticRegression
import gradio as gr
import yt_dlp as youtube_dl
class HuggingFaceFeatureExtractor:
def __init__(self, model_class, name):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.feature_extractor = AutoFeatureExtractor.from_pretrained(name)
self.model = model_class.from_pretrained(name, output_hidden_states=True)
self.model.eval()
self.model.to(self.device)
def __call__(self, audio, sr):
inputs = self.feature_extractor(
audio,
sampling_rate=sr,
return_tensors="pt",
padding=True,
)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
with torch.no_grad():
outputs = self.model(**inputs)
return outputs.hidden_states[9], outputs.hidden_states[8], outputs.last_hidden_state
FEATURE_EXTRACTOR = {"wav2vec2-xls-r-2b": lambda: HuggingFaceFeatureExtractor(Wav2Vec2Model,
"facebook/wav2vec2-xls-r-2b")}
model1 = joblib.load('model1_ensemble.pkl')
model2 = joblib.load('model2_ensemble.pkl')
model3 = joblib.load('model3_ensemble.pkl')
model4 = joblib.load('model4_ensemble.pkl')
final_model = joblib.load('final_model_ensemble.pkl')
def download_audio_from_youtube(youtube_url, output_path='.'):
ydl_opts = {
'format': 'bestaudio/best',
'outtmpl': f'{output_path}/%(title)s.%(ext)s',
'postprocessors': [{
'key': 'FFmpegExtractAudio',
'preferredcodec': 'wav',
'preferredquality': '192',
}],
'postprocessor_args': ['-ar', '16000'],
'prefer_ffmpeg': True,
}
with youtube_dl.YoutubeDL(ydl_opts) as ydl:
info_dict = ydl.extract_info(youtube_url, download=True)
#i have issues with the .webm extension, force replace with .wav
audio_file = ydl.prepare_filename(info_dict).replace('.webm', '.wav')
return audio_file
def segment_audio(audio, sr, segment_duration):
segment_samples = int(segment_duration * sr)
total_samples = len(audio)
segments = [audio[i:i + segment_samples] for i in range(0, total_samples, segment_samples)]
return segments
def classify_with_eer_threshold(probabilities, eer_thresh):
return (probabilities >= eer_thresh).astype(int)
def process_audio(input_data, segment_duration=3):
if input_data.startswith("http"):
file_audio = download_audio_from_youtube(input_data)
else:
file_audio = input_data
audio, sr = librosa.load(file_audio, sr=16000)
if len(audio.shape) > 1:
audio = audio[0]
segments = segment_audio(audio, sr, segment_duration)
all_embeddings_layer10 = []
all_embeddings_layer9 = []
all_embeddings_layer48 = []
# wav2vec2 extractor
a = FEATURE_EXTRACTOR['wav2vec2-xls-r-2b']()
for idx, segment in enumerate(segments):
p1, p2, p3 = a(segment, sr)
all_embeddings_layer10.append(p1)
all_embeddings_layer9.append(p2)
all_embeddings_layer48.append(p3)
embedding_layer10 = torch.cat(all_embeddings_layer10, dim=1)
embedding_layer9 = torch.cat(all_embeddings_layer9, dim=1)
embedding_layer48 = torch.cat(all_embeddings_layer48, dim=1)
wav2vec2_feature_layer10 = torch.mean(embedding_layer10, dim=1).cpu().numpy()
wav2vec2_feature_layer9 = torch.mean(embedding_layer9, dim=1).cpu().numpy()
wav2vec2_feature_layer48 = torch.mean(embedding_layer48, dim=1).cpu().numpy()
# PhantomNet extractor
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = PhantomNet(feature_size=1920, num_classes=2, conv_projection=False, use_mode='extractor').to(device)
state_dict = torch.load("PhantomNet_Finetuned_V2.pt", map_location=device)
model.load_state_dict(state_dict, strict=False)
model.eval()
all_embeddings_PhantomNet = []
for idx, segment in enumerate(segments):
segment_input = torch.Tensor(segment).unsqueeze(0).to(device)
p = model(segment_input).detach()
all_embeddings_PhantomNet.append(p)
embedding_PhantomNet = torch.cat(all_embeddings_PhantomNet, dim=1)
PhantomNet_feature = torch.mean(embedding_PhantomNet, dim=1)
wav2vec2_feature_layer9 = wav2vec2_feature_layer9.reshape(1, -1)
wav2vec2_feature_layer10 = wav2vec2_feature_layer10.reshape(1, -1)
wav2vec2_feature_layer48 = wav2vec2_feature_layer48.reshape(1, -1)
PhantomNet_feature = PhantomNet_feature.reshape(1, -1)
eval_prob1 = model1.predict_proba(wav2vec2_feature_layer10)[:, 1].reshape(-1, 1)
eval_prob2 = model2.predict_proba(wav2vec2_feature_layer9)[:, 1].reshape(-1, 1)
eval_prob3 = model3.predict_proba(wav2vec2_feature_layer48)[:, 1].reshape(-1, 1)
eval_prob4 = model4.predict_proba(PhantomNet_feature)[:, 1].reshape(-1, 1)
eval_combined_probs = np.hstack((eval_prob1, eval_prob2, eval_prob3, eval_prob4))
eer_thresh = 0.02 # eer during evaluation
final_prob = final_model.predict_proba(eval_combined_probs)[:, 1]
y_pred_inference = classify_with_eer_threshold(final_prob, eer_thresh)
if y_pred_inference == 1:
return f"Fake with a confidence of: {final_prob[0] * 100:.2f}%"
else:
return f"Real with a confidence of: {100 - final_prob[0] * 100:.2f}%"
def gradio_interface(audio, youtube_link):
if youtube_link:
return process_audio(youtube_link)
elif audio:
return process_audio(audio)
else:
return "please upload audio or provide a YouTube link."
interface = gr.Interface(
fn=gradio_interface,
inputs=[gr.Audio(type="filepath", label="Upload Audio"), gr.Textbox(label="YouTube Link (Optional)")],
outputs="text",
title="AI4TRUST Development",
description="Upload an audio file or provide a YouTube link to check if it's AI generated",
)
interface.launch(share=True)
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