app_multi.py

from typing import Union

from argparse import ArgumentParser

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

import gradio as gr

import torch

import numpy as np
import librosa

import edge_tts

import config
import util
from infer_pack.models import (
    SynthesizerTrnMs256NSFsid,
    SynthesizerTrnMs256NSFsid_nono,
    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'

# 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(
    '--bind',
    default=getenv('RVC_LISTEN_ADDR', '127.0.0.1'),
    help='gradio server listen address (default: 127.0.0.1)'
)
arg_parser.add_argument(
    '--port',
    default=getenv('RVC_LISTEN_PORT', '7860'),
    type=int,
    help='gradio server listen port (default: 7860)'
)
arg_parser.add_argument(
    '--share',
    action='store_true',
    help='let gradio create a public link for you'
)
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.Glass(),
    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

    cpt_version = cpt.get('version', 'v1')
    print(f'Model version: {cpt_version}')

    if_f0 = cpt.get('f0', 1)

    net_g: Union[
        SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono,
        SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono
    ]

    if if_f0 == 1:
        if cpt_version == 'v2':
            net_g = SynthesizerTrnMs768NSFsid(
                *cpt['config'],
                is_half=config.is_half
            )
        else:
            net_g = SynthesizerTrnMs256NSFsid(
                *cpt['config'],
                is_half=config.is_half
            )
    else:
        if cpt_version == 'v2':
            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt['config'])
        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(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,
        version=cpt_version
    ))

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


# 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
    if (audio_npy.shape[0] / audio_samp) > 30 and in_hf_space:
        return (None, 'Input audio is longer than 30 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,
        model['version']
    )

    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(
        '## Simple, Stupid RVC Inference WebUI\n'
        'Another RVC inference WebUI based on [RVC-WebUI](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI), '  # noqa
        'some code and features inspired from so-vits and [zomehwh/rvc-models](https://huggingface.co/spaces/zomehwh/rvc-models).\n'  # noqa
    )

    with gr.Row():
        with gr.Column():
            with gr.Tab('Audio conversion'):
                input_audio = gr.Audio(label='Input audio')

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

            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'
    )

    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(
    server_name=args.bind,
    server_port=args.port,
    share=args.share
)