app.py

# Agung Wijaya - WebUI 2023 - Gradio
# file app.py

# Import
import os
import psutil
import shutil
import numpy as np
import gradio as gr
import subprocess
from pathlib import Path
import ffmpeg
import json
import re
import time
import random
import torch
import librosa
import util

from config import device
from infer_pack.models import (
    SynthesizerTrnMs256NSFsid,
    SynthesizerTrnMs256NSFsid_nono
)
from vc_infer_pipeline import VC
from typing import Union
from os import path, getenv
from datetime import datetime
from scipy.io.wavfile import write

# Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21  # noqa
in_hf_space = getenv('SYSTEM') == 'spaces'

# Set High Quality (.wav) or not (.mp3)
high_quality = True

# Read config.json
config_json = json.loads(open("config.json").read())

# Load hubert model
hubert_model = util.load_hubert_model(device, 'hubert_base.pt')
hubert_model.eval()

# Load models
loaded_models = []
for model_name in config_json.get('models'):
    print(f'Loading model: {model_name}')

    # Load model info
    model_info = json.load(
        open(path.join('model', model_name, 'config.json'), 'r')
    )

    # Load RVC checkpoint
    cpt = torch.load(
        path.join('model', model_name, model_info['model']),
        map_location='cpu'
    )
    
    tgt_sr = cpt['config'][-1]
    
    cpt['config'][-3] = cpt['weight']['emb_g.weight'].shape[0]  # n_spk

    if_f0 = cpt.get('f0', 1)
    net_g: Union[SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono]
    if if_f0 == 1:
        net_g = SynthesizerTrnMs256NSFsid(
            *cpt['config'],
            is_half=util.is_half(device)
        )
    else:
        net_g = SynthesizerTrnMs256NSFsid_nono(*cpt['config'])

    del net_g.enc_q

    # According to original code, this thing seems necessary.
    print(net_g.load_state_dict(cpt['weight'], strict=False))

    net_g.eval().to(device)
    net_g = net_g.half() if util.is_half(device) else net_g.float()

    vc = VC(tgt_sr, device, util.is_half(device))

    loaded_models.append(dict(
        name=model_name,
        metadata=model_info,
        vc=vc,
        net_g=net_g,
        if_f0=if_f0,
        target_sr=tgt_sr
    ))
print(f'Models loaded: {len(loaded_models)}')

# Command line test
def command_line_test():
    command = "df -h /home/user/app"
    process = subprocess.run(command.split(), stdout=subprocess.PIPE)
    result  = process.stdout.decode()
    return gr.HTML(value=result)

# Check junk files && delete
def check_junk():
    # Find and delete all files after 10 minutes
    os.system("find ./ytaudio/* -mmin +10 -delete")
    os.system("find ./output/* -mmin +10 -delete")
    os.system("find /tmp/gradio/* -mmin +5 -delete")
    os.system("find /tmp/*.wav -mmin +5 -delete")
    print("Junk files has been deleted!")

# Function Information
def information():
    stats = os.system("du -s /content -h")
    disk_usage  = "Disk usage: "+str(stats)
    info = "<p>"+disk_usage+"<br/></p>"
    return gr.HTML(value=info)

# Function YouTube Downloader Audio
def youtube_downloader(
    video_identifier,
    start_time,
    end_time,
    output_filename="track.wav",
    num_attempts=5,
    url_base="",
    quiet=False,
    force=True,
):
    output_path = Path(output_filename)
    if output_path.exists():
        if not force:
            return output_path
        else:
            output_path.unlink()

    quiet = "--quiet --no-warnings" if quiet else ""
    command = f"""
        yt-dlp {quiet} -x --audio-format wav -f bestaudio -o "{output_filename}" --download-sections "*{start_time}-{end_time}" "{url_base}{video_identifier}"  # noqa: E501
    """.strip()

    attempts = 0
    while True:
        try:
            _ = subprocess.check_output(command, shell=True, stderr=subprocess.STDOUT)
        except subprocess.CalledProcessError:
            attempts += 1
            if attempts == num_attempts:
                return None
        else:
            break

    if output_path.exists():
        return output_path
    else:
        return None
    
# Function Audio Separated
def audio_separated(audio_input, progress=gr.Progress()):
    # start progress
    progress(progress=0, desc="Starting...")
    time.sleep(1)

    # check file input
    if audio_input is None:
        # show progress
        for i in progress.tqdm(range(100), desc="Please wait..."):
            time.sleep(0.1)
            
        return (None, None, 'Please input audio.')

    # create filename
    filename = str(random.randint(10000,99999))+datetime.now().strftime("%d%m%Y%H%M%S")
    
    # progress
    progress(progress=0.10, desc="Please wait...")
    
    # make dir output
    os.makedirs("output", exist_ok=True)
    
    # progress
    progress(progress=0.20, desc="Please wait...")
    
    # write
    if high_quality:
        write(filename+".wav", audio_input[0], audio_input[1])
    else:
        write(filename+".mp3", audio_input[0], audio_input[1])
        
    # progress
    progress(progress=0.50, desc="Please wait...")

    # demucs process
    if high_quality:
        command_demucs = "python3 -m demucs --two-stems=vocals -d cpu "+filename+".wav -o output"
    else:
        command_demucs = "python3 -m demucs --two-stems=vocals --mp3 --mp3-bitrate 128 -d cpu "+filename+".mp3 -o output"
    
    os.system(command_demucs)
    
    # progress
    progress(progress=0.70, desc="Please wait...")
    
    # remove file audio
    if high_quality:
        command_delete = "rm -v ./"+filename+".wav"
    else:
        command_delete = "rm -v ./"+filename+".mp3"
    
    os.system(command_delete)
    
    # progress
    progress(progress=0.80, desc="Please wait...")
    
    # progress
    for i in progress.tqdm(range(80,100), desc="Please wait..."):
        time.sleep(0.1)

    if high_quality:
        return "./output/htdemucs/"+filename+"/vocals.wav","./output/htdemucs/"+filename+"/no_vocals.wav","Successfully..."
    else:
        return "./output/htdemucs/"+filename+"/vocals.mp3","./output/htdemucs/"+filename+"/no_vocals.mp3","Successfully..."
        
# Function Voice Changer
def voice_changer(audio_input, model_index, pitch_adjust, f0_method, feat_ratio, progress=gr.Progress()):
    # start progress
    progress(progress=0, desc="Starting...")
    time.sleep(1)
    
    # check file input
    if audio_input is None:
        # progress
        for i in progress.tqdm(range(100), desc="Please wait..."):
            time.sleep(0.1)
            
        return (None, 'Please input audio.')
        
    # check model input
    if model_index is None:
        # progress
        for i in progress.tqdm(range(100), desc="Please wait..."):
            time.sleep(0.1)
            
        return (None, 'Please select a model.')

    model = loaded_models[model_index]

    # Reference: so-vits
    (audio_samp, audio_npy) = audio_input

    # progress
    progress(progress=0.10, desc="Please wait...")

    # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49
    if (audio_npy.shape[0] / audio_samp) > 60 and in_hf_space:
        
        # progress
        for i in progress.tqdm(range(10,100), desc="Please wait..."):
            time.sleep(0.1)
            
        return (None, 'Input audio is longer than 60 secs.')

    # Bloody hell: https://stackoverflow.com/questions/26921836/
    if audio_npy.dtype != np.float32:  # :thonk:
        audio_npy = (
            audio_npy / np.iinfo(audio_npy.dtype).max
        ).astype(np.float32)
        
    # progress
    progress(progress=0.30, desc="Please wait...")

    if len(audio_npy.shape) > 1:
        audio_npy = librosa.to_mono(audio_npy.transpose(1, 0))

    # progress
    progress(progress=0.40, desc="Please wait...")

    if audio_samp != 16000:
        audio_npy = librosa.resample(
            audio_npy,
            orig_sr=audio_samp,
            target_sr=16000
        )

    # progress
    progress(progress=0.50, desc="Please wait...")
    
    pitch_int = int(pitch_adjust)

    times = [0, 0, 0]
    output_audio = model['vc'].pipeline(
        hubert_model,
        model['net_g'],
        model['metadata'].get('speaker_id', 0),
        audio_npy,
        times,
        pitch_int,
        f0_method,
        path.join('model', model['name'], model['metadata']['feat_index']),
        path.join('model', model['name'], model['metadata']['feat_npy']),
        feat_ratio,
        model['if_f0']
    )

    # progress
    progress(progress=0.80, desc="Please wait...")

    print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s')

    # progress
    for i in progress.tqdm(range(80,100), desc="Please wait..."):
        time.sleep(0.1)

    return ((model['target_sr'], output_audio), 'Successfully...')
    
# Function Text to Voice
def text_to_voice(text_input, model_index):
    # start progress
    progress(progress=0, desc="Starting...")
    time.sleep(1)
    
    # check text input
    if text_input is None:
        # progress
        for i in progress.tqdm(range(2,100), desc="Please wait..."):
            time.sleep(0.1)
            
        return (None, 'Please write text.')
        
    # check model input
    if model_index is None:
        # progress
        for i in progress.tqdm(range(2,100), desc="Please wait..."):
            time.sleep(0.1)
            
        return (None, 'Please select a model.')

    # progress
    for i in progress.tqdm(range(2,100), desc="Please wait..."):
        time.sleep(0.1)
            
    return None, "Sorry, you can't use it yet because this program is being developed!"

# Themes
theme = gr.themes.Base()

# CSS
css = "footer {visibility: hidden}"

# Blocks
with gr.Blocks(theme=theme, css=css) as App:

    # Header
    gr.HTML("<center>"
            "<h1>Web UI Tools - Agung Wijaya</h1>"
            "</center>")
    
    # Information
    with gr.Accordion("Just information!"):
        information()
    
    # Tab YouTube Downloader
    with gr.Tab("YouTube Video to Audio"):
        with gr.Row():
            with gr.Column():
                ydl_url_input  = gr.Textbox(label="Enter URL YouTube")
                start = gr.Number(value=0, label="Start Time (seconds)")
                end = gr.Number(value=15, label="End Time (seconds)")
                ydl_url_submit = gr.Button("Convert Now", variant="primary")
            with gr.Column():
                ydl_audio_output = gr.Audio(label="Audio from YouTube")

        with gr.Row():
            with gr.Column():
                as_audio_input  = ydl_audio_output
                as_audio_submit = gr.Button("Separated Now", variant="primary")
            with gr.Column():
                as_audio_vocals    = gr.Audio(label="Vocal only")
                as_audio_no_vocals = gr.Audio(label="Music only")
                as_audio_message   = gr.Textbox(label="Message", visible=False)
                
    ydl_url_submit.click(fn=youtube_downloader, inputs=[ydl_url_input, start, end], outputs=[ydl_audio_output])
    as_audio_submit.click(fn=audio_separated, inputs=[as_audio_input], outputs=[as_audio_vocals, as_audio_no_vocals, as_audio_message], show_progress=True, queue=True)

    # Tab Voice Changer
    with gr.Tab("Voice to AI Models"):
        with gr.Row():
            with gr.Column():
                vc_audio_input  = as_audio_vocals
                vc_model_index  = gr.Dropdown(
                    [
                        '%s' % (
                            m['metadata'].get('name')
                        )
                        for m in loaded_models
                    ],
                    label='Models',
                    type='index'
                )
                vc_pitch_adjust = gr.Slider(label='Pitch', minimum=-24, maximum=24, step=1, value=0)
                vc_f0_method    = gr.Radio(label='F0 methods', choices=['pm', 'harvest'], value='pm', interactive=True)
                vc_feat_ratio   = gr.Slider(label='Feature ratio', minimum=0, maximum=1, step=0.1, value=0.6)
                vc_audio_submit = gr.Button("Convert Now", variant="primary")
            with gr.Column():
                vc_audio_output  = gr.Audio(label="Result audio", type="numpy")
                vc_audio_message = gr.Textbox(label="Message")
    vc_audio_submit.click(fn=voice_changer, inputs=[vc_audio_input, vc_model_index, vc_pitch_adjust, vc_f0_method, vc_feat_ratio], outputs=[vc_audio_output, vc_audio_message], show_progress=True, queue=True)

# Check Junk
check_junk()
    
# Launch
App.queue(concurrency_count=1, max_size=20).launch(server_name="0.0.0.0", server_port=7860)

# Enjoy