app.py

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 os
from scipy.stats import mode


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')




#truncate the SSL from the 10th layer, since we only need the first 9th transformer layers
class CustomWav2Vec2Model(Wav2Vec2Model):
    def __init__(self, config):
        super().__init__(config)
        self.encoder.layers = self.encoder.layers[:9]


truncated_model = CustomWav2Vec2Model.from_pretrained("facebook/wav2vec2-xls-r-2b")


# calling the SSL model for feature extraction
class HuggingFaceFeatureExtractor:
    def __init__(self, model, feature_extractor_name):
        self.device = device
        self.feature_extractor = AutoFeatureExtractor.from_pretrained(feature_extractor_name)
        self.model = model
        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, output_hidden_states=True)
        return outputs.hidden_states[9]

FEATURE_EXTRACTOR = HuggingFaceFeatureExtractor(truncated_model, "facebook/wav2vec2-xls-r-2b")

#load our best classifier
classifier = joblib.load('logreg_margin_pruning_ALL_best.joblib')

#segment audio and return the segments
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


# classification using the EER threshold
def classify_with_eer_threshold(probabilities, eer_thresh):
    return (probabilities >= eer_thresh).astype(int)


def process_audio(input_data, segment_duration=30):
    # resample to 16 kHz audio, since xls-r-2b it's trained on 16 KHz audio

    audio, sr = librosa.load(input_data, sr=16000)

    # check for single-channel audio (that's what xls-r-2b expects as input)
    if len(audio.shape) > 1:
        audio = audio[0]

    # segment the audio in 30s chunks to avoid xls-r-2b crashing
    print('loaded file')
    segments = segment_audio(audio, sr, segment_duration)
    final_features = []
    print('segments')

    # extract the features from each 30s segment
    for segment in segments:
        features = FEATURE_EXTRACTOR(segment, sr)
        features_avg = torch.mean(features, dim=1).cpu().numpy()
        final_features.append(features_avg)
    print('features extracted')
    inference_prob = []
    for feature in final_features:
        #reshape to avoid the batch dimension output from xls
        feature = feature.reshape(1, -1)
        #make the classification
        print(classifier.classes_)

        probability = classifier.predict_proba(feature)[:, 1]
        inference_prob.append(probability[0])
        print('classifier predicted')
        eer_threshold = 0.9999999996754046

        #all segment prediction based on probability score and eer threshold
        y_pred_inference = classify_with_eer_threshold(np.array(inference_prob), eer_threshold)
        print('inference done for segments')
        #FINAL PREDICTION based on majority wins
        mode_result = mode(y_pred_inference, keepdims=True)
        final_prediction = mode_result.mode[0] if mode_result.mode.size > 0 else 0

        print('majority voting done')
        # confidence score (proportion of segments agreeing with majority prediction)
        confidence_score = np.mean(y_pred_inference == final_prediction) if len(y_pred_inference) > 0 else 1.0
        confidence_percentage = confidence_score * 100

        return {
            "Final classification": "Real" if final_prediction == 1 else "Fake",
            "Confidence ": round(confidence_percentage, 2)
        }



def gradio_interface(audio):
    if 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")],
    outputs="text",
    title="SOL2 Audio Deepfake detection Demo",
    description="Upload an audio file to check if it's AI generated",
)

interface.launch(share=True)