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from src.preprocessing.preprocessor import single_preprocessing
from src.spectttra.spectttra_trainer import spectttra_predict
from src.llm2vectrain.model import load_llm2vec_model
from src.llm2vectrain.llm2vec_trainer import l2vec_single_train, load_pca_model
from src.models.mlp import build_mlp, load_config
from pathlib import Path
from src.utils.config_loader import DATASET_NPZ
from src.utils.dataset import instance_scaler
from pathlib import Path
import numpy as np
import torch
def predict_pipeline(audio, lyrics: str):
"""
Predict script which includes preprocessing, feature extraction, and
training the MLP model for a single data sample.
Parameters
----------
audio : audio_object
Audio object file
lyric : string
Lyric string
Returns
-------
prediction : str
A string result of the prediction
label : int
A numerical representation of the prediction
"""
# Instantiate X and Y vectors
X, Y = None, None
# Instantiate LLM2Vec Model
llm2vec_model = load_llm2vec_model()
# Preprocess both audio and lyrics
audio, lyrics = single_preprocessing(audio, lyrics)
# Call the train method for both models
audio_features = spectttra_predict(audio)
lyrics_features = l2vec_single_train(llm2vec_model, lyrics)
# Reduce the lyrics using saved PCA model
reduced_lyrics = load_pca_model(lyrics_features)
# Scale the vectors using Z-Score
audio_features, reduced_lyrics = instance_scaler(audio_features, reduced_lyrics)
# Concatenate the vectors of audio_features + lyrics_features
results = np.concatenate([audio_features, reduced_lyrics], axis=1)
# ---- Load MLP Classifier ----
config = load_config("config/model_config.yml")
classifier = build_mlp(input_dim=results.shape[1], config=config)
# Load trained weights (make sure this path matches where you saved your model)
model_path = "models/mlp/mlp_multimodal.pth"
classifier.load_model(model_path)
classifier.model.eval()
# Run prediction
probability, prediction, label = classifier.predict_single(results)
return {
"probability": probability,
"label": label,
"prediction": "AI-Generated" if prediction == 0 else "Human-Composed",
}
if __name__ == "__main__":
# Example usage (replace with real inputs, place song inside data/raw.)
audio = "sample"
lyrics = "Some lyrics text here"
print(predict_pipeline(audio, lyrics))
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