app.py

# @ 2023.10.23
# @ Elena
import sys, os
os.system('apt-get update')
os.system('apt-get install espeak')
import logging
import re
from scipy.io.wavfile import write

logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("markdown_it").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
logging.getLogger("matplotlib").setLevel(logging.WARNING)

logging.basicConfig(
    level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
)

logger = logging.getLogger(__name__)

import torch
import argparse
import commons
import utils
from models import SynthesizerTrn
from text.symbols import symbols
from text import text_to_sequence
import gradio as gr
import webbrowser
import numpy as np

'''# - paths
path_to_config = "config.json" # path to .json
path_to_model = "best.pth" # path to G_xxxx.pth'''

net_g = None

if sys.platform == "darwin" and torch.backends.mps.is_available():
    device = "mps"
    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
else:
    device = "cuda"

   

def get_text(text, hps):
    text_norm = text_to_sequence(text, hps.data.text_cleaners)
    if hps.data.add_blank:
        text_norm = commons.intersperse(text_norm, 0)
    text_norm = torch.LongTensor(text_norm)
    return text_norm


def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
    global net_g
    fltstr = re.sub(r"[\[\]\(\)\{\}]", "", text)
    stn_tst = get_text(fltstr, hps)

    speed = 1
    output_dir = 'output'
    sid = 0
    with torch.no_grad():
        x_tst = stn_tst.to(device).unsqueeze(0)
        x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
        audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1 / speed)[0][
                0, 0].data.cpu().float().numpy()

    return audio

def tts_fn(
    text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale
):
    slices = text.split("|")
    audio_list = []
    with torch.no_grad():
        for slice in slices:
            audio = infer(
                slice,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
                sid=speaker,

            )
            audio_list.append(audio)
            silence = np.zeros(hps.data.sampling_rate)  
            audio_list.append(silence) 
    audio_concat = np.concatenate(audio_list)
    return "Success", (hps.data.sampling_rate, audio_concat)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-m", "--model", default="./best.pth", help="path of your model"
    )
    parser.add_argument(
        "-c",
        "--config",
        default="./config.json",
        help="path of your config file",
    )
    parser.add_argument(
        "--share", default=False, help="make link public", action="store_true"
    )
    parser.add_argument(
        "-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log"
    )

    args = parser.parse_args()
    if args.debug:
        logger.info("Enable DEBUG-LEVEL log")
        logging.basicConfig(level=logging.DEBUG)
    hps = utils.get_hparams_from_file(args.config)
    
    if "use_mel_posterior_encoder" in hps.model.keys() and hps.model.use_mel_posterior_encoder == True:
        print("Using mel posterior encoder for VITS2")
        posterior_channels = 80  # vits2
        hps.data.use_mel_posterior_encoder = True
    else:
        print("Using lin posterior encoder for VITS1")
        posterior_channels = hps.data.filter_length // 2 + 1
        hps.data.use_mel_posterior_encoder = False
    device = (
        "cuda:1"
        if torch.cuda.is_available()
        else (
            "mps"
            if sys.platform == "darwin" and torch.backends.mps.is_available()
            else "cpu"
        )
    )

    net_g = SynthesizerTrn(
        len(symbols),
        posterior_channels,
        hps.train.segment_size // hps.data.hop_length,
        n_speakers=hps.data.n_speakers, #- >0 for multi speaker
        **hps.model
    ).to(device)
    _ = net_g.eval()

    # Load model
    _ = utils.load_checkpoint(args.model, net_g, None)

    speakers = hps.data.n_speakers
    languages = ["KO"]
    with gr.Blocks() as app:
        with gr.Row():
            with gr.Column():
                text = gr.TextArea(
                    label="Text",
                    placeholder="Input Text Here",
                    value="TTS는 텍스트 문서를 음성으로 출력시켜 주는 기술이며 텍스트 문서를 입력하면 음성으로 읽어주는 기술이다.",
                )

                speaker = gr.Slider(
                    minimum=0, maximum=speakers-1, value=0, step=1, label="성우"
                )
                sdp_ratio = gr.Slider(
                    minimum=0, maximum=1, value=0.2, step=0.1, label="SDP Ratio"
                )
                noise_scale = gr.Slider(
                    minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise Scale"
                )
                noise_scale_w = gr.Slider(
                    minimum=0.1, maximum=2, value=0.8, step=0.1, label="Noise Scale W"
                )
                length_scale = gr.Slider(
                    minimum=0.1, maximum=2, value=1, step=0.1, label="Length Scale"
                )
                language = gr.Dropdown(
                    choices=languages, value=languages[0], label="Language"
                )
                btn = gr.Button("Generate!", variant="primary")
            with gr.Column():
                text_output = gr.Textbox(label="Message")
                audio_output = gr.Audio(label="Output Audio")

        btn.click(
            tts_fn,
            inputs=[
                text,
                speaker,
                sdp_ratio,
                noise_scale,
                noise_scale_w,
                length_scale,
            ],
            outputs=[text_output, audio_output],
        )

    webbrowser.open("http://127.0.0.1:7860")
    app.launch(share=True)