app_multi.py

from typing import Union

from argparse import ArgumentParser
from pathlib import Path
import subprocess
import librosa
import os
import time
import random

import asyncio
import json
import hashlib
from os import path, getenv
from pydub import AudioSegment

import gradio as gr

import torch

import numpy as np

import edge_tts

from datetime import datetime
from scipy.io.wavfile import write

import config
import util
from infer_pack.models import (
    SynthesizerTrnMs768NSFsid,
    SynthesizerTrnMs768NSFsid_nono
)
from vc_infer_pipeline import VC
    
# Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21  # noqa
in_hf_space = getenv('SYSTEM') == 'spaces'

high_quality = True

# Argument parsing
arg_parser = ArgumentParser()
arg_parser.add_argument(
    '--hubert',
    default=getenv('RVC_HUBERT', 'hubert_base.pt'),
    help='path to hubert base model (default: hubert_base.pt)'
)
arg_parser.add_argument(
    '--config',
    default=getenv('RVC_MULTI_CFG', 'multi_config.json'),
    help='path to config file (default: multi_config.json)'
)
arg_parser.add_argument(
    '--api',
    action='store_true',
    help='enable api endpoint'
)
arg_parser.add_argument(
    '--cache-examples',
    action='store_true',
    help='enable example caching, please remember delete gradio_cached_examples folder when example config has been modified'  # noqa
)
args = arg_parser.parse_args()

app_css = '''
#model_info img {
    max-width: 100px;
    max-height: 100px;
    float: right;
}

#model_info p {
    margin: unset;
}
'''

app = gr.Blocks(
    theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"),
    css=app_css,
    analytics_enabled=False
)

# Load hubert model
hubert_model = util.load_hubert_model(config.device, args.hubert)
hubert_model.eval()

# Load models
multi_cfg = json.load(open(args.config, 'r'))
loaded_models = []

for model_name in multi_cfg.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[SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono]
    if if_f0 == 1:
        net_g = SynthesizerTrnMs768NSFsid(
            *cpt['config'],
            is_half=util.is_half(config.device)
        )
    else:
        net_g = SynthesizerTrnMs768NSFsid_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(config.device)
    net_g = net_g.half() if util.is_half(config.device) else net_g.float()

    vc = VC(tgt_sr, config)
    
    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)}')

# Edge TTS speakers
tts_speakers_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())  # noqa

# mix vocal and non-vocal
def mix(audio1, audio2):
  sound1 = AudioSegment.from_file(audio1)
  sound2 = AudioSegment.from_file(audio2)
  length = len(sound1)
  mixed = sound1[:length].overlay(sound2)

  mixed.export("song.wav", format="wav")

  return "song.wav"

# Bilibili
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

def audio_separated(audio_input, progress=gr.Progress()):
    # start progress
    progress(progress=0, desc="Starting...")
    time.sleep(0.1)

    # check file input
    if audio_input is None:
        # show progress
        for i in progress.tqdm(range(100), desc="Please wait..."):
            time.sleep(0.01)
            
        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..."

        
# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118  # noqa
def vc_func(
    input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
    filter_radius, rms_mix_rate, resample_option
):
    if input_audio is None:
        return (None, 'Please provide input audio.')

    if model_index is None:
        return (None, 'Please select a model.')

    model = loaded_models[model_index]

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

    # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49
    # Can be change well, we will see
    if (audio_npy.shape[0] / audio_samp) > 60 and in_hf_space:
        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)

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

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

    pitch_int = int(pitch_adjust)

    resample = (
        0 if resample_option == 'Disable resampling'
        else int(resample_option)
    )

    times = [0, 0, 0]

    checksum = hashlib.sha512()
    checksum.update(audio_npy.tobytes())

    output_audio = model['vc'].pipeline(
        hubert_model,
        model['net_g'],
        model['metadata'].get('speaker_id', 0),
        audio_npy,
        checksum.hexdigest(),
        times,
        pitch_int,
        f0_method,
        path.join('model', model['name'], model['metadata']['feat_index']),
        feat_ratio,
        model['if_f0'],
        filter_radius,
        model['target_sr'],
        resample,
        rms_mix_rate,
        'v2'
    )

    out_sr = (
        resample if resample >= 16000 and model['target_sr'] != resample
        else model['target_sr']
    )

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


async def edge_tts_vc_func(
    input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
    filter_radius, rms_mix_rate, resample_option
):
    if input_text is None:
        return (None, 'Please provide TTS text.')

    if tts_speaker is None:
        return (None, 'Please select TTS speaker.')

    if model_index is None:
        return (None, 'Please select a model.')

    speaker = tts_speakers_list[tts_speaker]['ShortName']
    (tts_np, tts_sr) = await util.call_edge_tts(speaker, input_text)
    return vc_func(
        (tts_sr, tts_np),
        model_index,
        pitch_adjust,
        f0_method,
        feat_ratio,
        filter_radius,
        rms_mix_rate,
        resample_option
    )


def update_model_info(model_index):
    if model_index is None:
        return str(
            '### Model info\n'
            'Please select a model from dropdown above.'
        )

    model = loaded_models[model_index]
    model_icon = model['metadata'].get('icon', '')

    return str(
        '### Model info\n'
        '![model icon]({icon})'
        '**{name}**\n\n'
        'Author: {author}\n\n'
        'Source: {source}\n\n'
        '{note}'
    ).format(
        name=model['metadata'].get('name'),
        author=model['metadata'].get('author', 'Anonymous'),
        source=model['metadata'].get('source', 'Unknown'),
        note=model['metadata'].get('note', ''),
        icon=(
            model_icon
            if model_icon.startswith(('http://', 'https://'))
            else '/file/model/%s/%s' % (model['name'], model_icon)
        )
    )


def _example_vc(
    input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
    filter_radius, rms_mix_rate, resample_option
):
    (audio, message) = vc_func(
        input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
        filter_radius, rms_mix_rate, resample_option
    )
    return (
        audio,
        message,
        update_model_info(model_index)
    )


async def _example_edge_tts(
    input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
    filter_radius, rms_mix_rate, resample_option
):
    (audio, message) = await edge_tts_vc_func(
        input_text, model_index, tts_speaker, pitch_adjust, f0_method,
        feat_ratio, filter_radius, rms_mix_rate, resample_option
    )
    return (
        audio,
        message,
        update_model_info(model_index)
    )


with app:
    gr.Markdown(
        '## A simplistic Web interface\n'
        'RVC interface, project based on [RVC-WebUI](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI)'  # thx noqa
        'A lot of inspiration from what\'s already out there, including [zomehwh/rvc-models](https://huggingface.co/spaces/zomehwh/rvc-models) & [DJQmUKV/rvc-inference](https://huggingface.co/spaces/DJQmUKV/rvc-inference).\n '  # thx noqa
    )

    with gr.Tab("🤗 - B站视频提取声音"):
        with gr.Row():
            with gr.Column():
                ydl_url_input  = gr.Textbox(label="B站视频网址(请填写相应的BV号)", value = "https://www.bilibili.com/video/BV...")
                start = gr.Number(value=0, label="起始时间 (秒)")
                end = gr.Number(value=15, label="结束时间 (秒)")
                ydl_url_submit = gr.Button("提取声音文件吧", variant="primary")
                as_audio_submit = gr.Button("去除背景音吧", variant="primary")
            with gr.Column():
                ydl_audio_output = gr.Audio(label="Audio from Bilibili")
                as_audio_input  = ydl_audio_output
                as_audio_vocals    = gr.Audio(label="Vocal only")
                as_audio_no_vocals = gr.Audio(label="Music only", type="filepath")
                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)

    with gr.Row():
        with gr.Column():
            with gr.Tab('Audio conversion'):
                input_audio = as_audio_vocals
                vc_convert_btn = gr.Button('进行歌声转换吧！', variant='primary')
                full_song = gr.Button("加入歌曲伴奏吧！", variant="primary")
                new_song = gr.Audio(label="Full song", type="filepath")

            with gr.Tab('TTS conversion'):
                tts_input = gr.TextArea(
                    label='TTS input text'
                )
                tts_speaker = gr.Dropdown(
                    [
                        '%s (%s)' % (
                            s['FriendlyName'],
                            s['Gender']
                        )
                        for s in tts_speakers_list
                    ],
                    label='TTS speaker',
                    type='index'
                )

                tts_convert_btn = gr.Button('Convert', variant='primary')

            pitch_adjust = gr.Slider(
                label='Pitch',
                minimum=-24,
                maximum=24,
                step=1,
                value=0
            )
            f0_method = gr.Radio(
                label='f0 methods',
                choices=['pm', 'harvest'],
                value='pm',
                interactive=True
            )

            with gr.Accordion('Advanced options', open=False):
                feat_ratio = gr.Slider(
                    label='Feature ratio',
                    minimum=0,
                    maximum=1,
                    step=0.1,
                    value=0.6
                )
                filter_radius = gr.Slider(
                    label='Filter radius',
                    minimum=0,
                    maximum=7,
                    step=1,
                    value=3
                )
                rms_mix_rate = gr.Slider(
                    label='Volume envelope mix rate',
                    minimum=0,
                    maximum=1,
                    step=0.1,
                    value=1
                )
                resample_rate = gr.Dropdown(
                    [
                        'Disable resampling',
                        '16000',
                        '22050',
                        '44100',
                        '48000'
                    ],
                    label='Resample rate',
                    value='Disable resampling'
                )

        with gr.Column():
            # Model select
            model_index = gr.Dropdown(
                [
                    '%s - %s' % (
                        m['metadata'].get('source', 'Unknown'),
                        m['metadata'].get('name')
                    )
                    for m in loaded_models
                ],
                label='Model',
                type='index'
            )

            # Model info
            with gr.Box():
                model_info = gr.Markdown(
                    '### Model info\n'
                    'Please select a model from dropdown above.',
                    elem_id='model_info'
                )

            output_audio = gr.Audio(label='Output audio')
            output_msg = gr.Textbox(label='Output message')

    multi_examples = multi_cfg.get('examples')
    if (
        multi_examples and
        multi_examples.get('vc') and multi_examples.get('tts_vc')
    ):
        with gr.Accordion('Sweet sweet examples', open=False):
            with gr.Row():
                # VC Example
                if multi_examples.get('vc'):
                    gr.Examples(
                        label='Audio conversion examples',
                        examples=multi_examples.get('vc'),
                        inputs=[
                            input_audio, model_index, pitch_adjust, f0_method,
                            feat_ratio
                        ],
                        outputs=[output_audio, output_msg, model_info],
                        fn=_example_vc,
                        cache_examples=args.cache_examples,
                        run_on_click=args.cache_examples
                    )

                # Edge TTS Example
                if multi_examples.get('tts_vc'):
                    gr.Examples(
                        label='TTS conversion examples',
                        examples=multi_examples.get('tts_vc'),
                        inputs=[
                            tts_input, model_index, tts_speaker, pitch_adjust,
                            f0_method, feat_ratio
                        ],
                        outputs=[output_audio, output_msg, model_info],
                        fn=_example_edge_tts,
                        cache_examples=args.cache_examples,
                        run_on_click=args.cache_examples
                    )

    vc_convert_btn.click(
        vc_func,
        [
            input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
            filter_radius, rms_mix_rate, resample_rate
        ],
        [output_audio, output_msg],
        api_name='audio_conversion'
    )

    tts_convert_btn.click(
        edge_tts_vc_func,
        [
            tts_input, model_index, tts_speaker, pitch_adjust, f0_method,
            feat_ratio, filter_radius, rms_mix_rate, resample_rate
        ],
        [output_audio, output_msg],
        api_name='tts_conversion'
    )

    full_song.click(fn=mix, inputs=[output_audio, as_audio_no_vocals], outputs=[new_song])

    model_index.change(
        update_model_info,
        inputs=[model_index],
        outputs=[model_info],
        show_progress=False,
        queue=False
    )

app.queue(
    concurrency_count=1,
    max_size=20,
    api_open=args.api
).launch(show_error=True)