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import gradio as gr
import torch
from model import Model
from config import Config
import warnings
# warnings.filterwarnings('ignore')
# making config object
config = Config()
def infrence(audio_file1):
print(f"[LOG] Audio file: {audio_file1}")
class DFSeparationApp:
def __init__(self, model_path,device="cpu"):
self.device = device
self.model = self.load_model(model_path)
self.model.to(self.device)
def load_model(self, model_path):
checkpoint = torch.load(model_path, map_location=torch.device("cpu"))
fine_tuned_model = Model(
args=config,
device=self.device
)
fine_tuned_model.load_state_dict(checkpoint["model"])
print("[LOG] Model loaded successfully.")
return fine_tuned_model
def predict(self, audio_file):
# Load the audio file
print(f"[LOG] Audio file: {audio_file}")
audio_tensor = torch.tensor(audio_file[1][:64600],dtype=torch.float).unsqueeze(0)
print(f"[LOG] Audio tensor shape: {audio_tensor.shape}")
with torch.no_grad():
# Make prediction
output = self.model(audio_tensor)
probs = output.softmax(dim=-1)
preds = probs.argmax(dim=-1)
print(f"[LOG] Prediction: {preds.item()}")
print(f"[LOG] Probability: {probs.max().item()}")
pred_str = "Fake" if preds.item() == 1 else "Real"
return pred_str, probs.max().item()
def run(self):
print(f"[LOG] Running the app...")
# gradio interface
audio_input1 = gr.Audio(label="Upload or record audio")
prediction = gr.Label(label="Prediction:")
prob = gr.Label(label="Probability:")
gr.Interface(
fn=self.predict,
inputs=[audio_input1],
outputs=[prediction, prob],
title="DF Separation",
description="This app classify the audio samples into Real and Fake.",
examples=[
["samples/Fake/download (5).wav","1"],
["samples/Fake/fake1_1.wav","1"],
["samples/Real/Central Avenue 1.wav","0"],
["samples/Real/hindi.mp3","0"],
]
).launch(quiet=False,server_name="0.0.0.0")
if __name__ == "__main__":
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"[LOG] Device: {device}")
model_path = "models/for_trained_model.ckpt" # Replace with your model path
app = DFSeparationApp(model_path, device=device)
app.run()
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