app.py

import gradio as gr
import torch
import torchaudio
import yaml
from huggingface_hub import hf_hub_download
import os
import numpy as np

# Load configuration
with open('config.yaml', 'r') as f:
    config = yaml.safe_load(f)

# Define model loading function
def load_model():
    # Specify the repository and file path of your checkpoint
    repo_id = "GaboxR67/MelBandRoformers"  # Replace with actual repo
    filename = "melbandroformers/instrumental/Inst_ExperimentalV1.ckpt"  # Replace with actual path
    
    # Download the checkpoint from Hugging Face
    checkpoint_path = hf_hub_download(repo_id=repo_id, filename=filename)
    
    # Load the checkpoint
    checkpoint = torch.load(checkpoint_path, map_location='cpu')
    
    # Initialize your model here based on the MelBandRoformer architecture
    # This part depends on the exact model implementation
    # You'll need to import or define the MelBandRoformer class
    
    return model  # Return the loaded model

# Initialize model
model = load_model()

def separate_audio(audio_file):
    """
    Process audio file and separate instrumental/vocals
    """
    # Load audio
    waveform, sample_rate = torchaudio.load(audio_file)
    
    # Resample if necessary
    if sample_rate != config['sample_rate']:
        resampler = torchaudio.transforms.Resample(sample_rate, config['sample_rate'])
        waveform = resampler(waveform)
    
    # Process with model
    with torch.no_grad():
        # Add your inference code here
        # This will depend on the exact model implementation
        instrumental = model(waveform)
    
    # Save output
    output_path = "output_instrumental.wav"
    torchaudio.save(output_path, instrumental, config['sample_rate'])
    
    return output_path

# Create Gradio interface
iface = gr.Interface(
    fn=separate_audio,
    inputs=gr.Audio(type="filepath", label="Upload Audio"),
    outputs=gr.Audio(label="Instrumental Output"),
    title="MelBand Roformer Audio Separation",
    description="Separate instrumental from vocals using MelBand Roformer model"
)

if __name__ == "__main__":
    iface.launch()