app.py

import os
import binascii
import warnings

import json
import argparse
import copy

import numpy as np
import matplotlib.pyplot as plt
import torch
import tqdm
import librosa
import librosa.display
import soundfile as sf
import gradio as gr
import pytube as pt

from pytube.exceptions import VideoUnavailable

from inference.style_transfer import *
from inference.mastering_transfer import *


yt_video_dir = "./yt_dir/0"
os.makedirs(yt_video_dir, exist_ok=True)


def get_audio_from_yt_video_input(yt_link: str, start_point_in_second=0, duration_in_second=30):
    try:
        yt = pt.YouTube(yt_link)
        t = yt.streams.filter(only_audio=True)
        filename_in = os.path.join(yt_video_dir, "input.wav")
        t[0].download(filename=filename_in)
    except VideoUnavailable as e:
        warnings.warn(f"Video Not Found at {yt_link} ({e})")
        filename_in = None

    # trim audio length - due to computation time on HuggingFace environment
    trim_audio(target_file_path=filename_in, start_point_in_second=start_point_in_second, duration_in_second=duration_in_second)
    
    return filename_in, filename_in

def get_audio_from_yt_video_ref(yt_link: str, start_point_in_second=0, duration_in_second=30):
    try:
        yt = pt.YouTube(yt_link)
        t = yt.streams.filter(only_audio=True)
        filename_ref = os.path.join(yt_video_dir, "reference.wav")
        t[0].download(filename=filename_ref)
    except VideoUnavailable as e:
        warnings.warn(f"Video Not Found at {yt_link} ({e})")
        filename_ref = None

    # trim audio length - due to computation time on HuggingFace environment
    trim_audio(target_file_path=filename_ref, start_point_in_second=start_point_in_second, duration_in_second=duration_in_second)
    
    return filename_ref, filename_ref

def inference(file_uploaded_in, file_uploaded_ref):
    # Perform music mixing style transfer
    args = set_up()
    
    inference_style_transfer = Mixing_Style_Transfer_Inference(args)
    output_wav_path = inference_style_transfer.inference(None, None)
    
    return output_wav_path, output_wav_path

def inference_mastering(file_uploaded_in, file_uploaded_ref):
    # Perform music mastering style transfer
    args = set_up()
    
    inference_mastering_style_transfer = Mastering_Style_Transfer_Inference(args)
    output_wav_path_mastering = inference_mastering_style_transfer.inference(file_uploaded_in, file_uploaded_ref)
    
    return output_wav_path_mastering, output_wav_path_mastering


with gr.Blocks() as demo:
    gr.HTML(
        """
            <div style="text-align: center; max-width: 700px; margin: 0 auto;">
              <div
                style="
                  display: inline-flex;
                  align-items: center;
                  gap: 0.8rem;
                  font-size: 1.75rem;
                "
              >
                <h1 style="font-weight: 900; margin-bottom: 7px;">
                  Music Mixing Style Transfer
                </h1>
              </div>
              <p style="margin-bottom: 10px; font-size: 94%">
                Hugging Face interactive demo of the paper "Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects" (ICASSP 2023).<br>
                a
            </div>
        """
    )
    with gr.Group():
        with gr.Column():
            with gr.Blocks():
                with gr.Tab("Input Music"):
                    file_uploaded_in = gr.Audio(label="Input track (mix) to be mixing style transferred", type="filepath")
                with gr.Tab("YouTube url"):
                    with gr.Row():
                        yt_link_in = gr.Textbox(
                            label="Enter YouTube Link of the Video", autofocus=True, lines=3
                        )
                        yt_btn_in = gr.Button("Download Audio from YouTube Link", size="lg")
                    yt_audio_path_in = gr.Audio(
                        label="Input Audio Extracted from the YouTube Video", interactive=False
                    )
                    yt_btn_in.click(
                        get_audio_from_yt_video_input,
                        inputs=[yt_link_in],
                        outputs=[yt_audio_path_in, file_uploaded_in],
                    )
            with gr.Blocks():
                with gr.Tab("Reference Music"):
                    file_uploaded_ref = gr.Audio(label="Reference track (mix) to copy mixing style", type="filepath")
                with gr.Tab("YouTube url"):
                    with gr.Row():
                        yt_link_ref = gr.Textbox(
                            label="Enter YouTube Link of the Video", autofocus=True, lines=3
                        )
                        yt_btn_ref = gr.Button("Download Audio from YouTube Link", size="lg")
                    yt_audio_path_ref = gr.Audio(
                        label="Reference Audio Extracted from the YouTube Video", interactive=False
                    )
                    yt_btn_ref.click(
                        get_audio_from_yt_video_ref,
                        inputs=[yt_link_ref],
                        outputs=[yt_audio_path_ref, file_uploaded_ref],
                    )
                
    with gr.Group():
        gr.HTML(
            """
            <div> <h3> <center> Mixing Style Transfer. </h3> </div>
            """
        )
        with gr.Column():
            inference_btn = gr.Button("Perform Style Transfer")
        with gr.Row():
            output_mix = gr.Audio(label="mixing style transferred music track")
            inference_btn.click(
                inference,
                inputs=[file_uploaded_in, file_uploaded_ref],
                outputs=[output_mix],
            )

    
    with gr.Group():
        gr.HTML(
            """
            <div> <h3> <center> Mastering Style Transfer. </h3> </div>
            """
        )
        with gr.Column():
            inference_mastering_btn = gr.Button("Perform Mastering Style Transfer")
        with gr.Row():
            output_master = gr.Audio(label="mastering style transferred music track")
            inference_mastering_btn.click(
                inference_mastering,
                inputs=[file_uploaded_in, file_uploaded_ref],
                outputs=[output_master],
            )
            

    
if __name__ == "__main__":
    demo.launch(debug=True)