app.py

import sys
import logging
import os
import json
import torch
import argparse
import commons
import utils
import gradio as gr

from models import SynthesizerTrn
from text.symbols import symbols
from text import cleaned_text_to_sequence, get_bert
from text.cleaner import clean_text

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__)
limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces


def get_text(text, hps):
    language_str = "JP"
    norm_text, phone, tone, word2ph = clean_text(text, language_str)
    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)

    if hps.data.add_blank:
        phone = commons.intersperse(phone, 0)
        tone = commons.intersperse(tone, 0)
        language = commons.intersperse(language, 0)
        for i in range(len(word2ph)):
            word2ph[i] = word2ph[i] * 2
        word2ph[0] += 1
    bert = get_bert(norm_text, word2ph, language_str, device)
    del word2ph
    assert bert.shape[-1] == len(phone), phone

    ja_bert = bert
    bert = torch.zeros(1024, len(phone))

    assert bert.shape[-1] == len(
        phone
    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"

    phone = torch.LongTensor(phone)
    tone = torch.LongTensor(tone)
    language = torch.LongTensor(language)
    return bert, ja_bert, phone, tone, language


def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, net_g_ms, hps):
    bert, ja_bert, phones, tones, lang_ids = get_text(text, hps)
    with torch.no_grad():
        x_tst = phones.to(device).unsqueeze(0)
        tones = tones.to(device).unsqueeze(0)
        lang_ids = lang_ids.to(device).unsqueeze(0)
        bert = bert.to(device).unsqueeze(0)
        ja_bert = ja_bert.to(device).unsqueeze(0)
        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
        del phones
        sid = torch.LongTensor([sid]).to(device)
        audio = (
            net_g_ms.infer(
                x_tst,
                x_tst_lengths,
                sid,
                tones,
                lang_ids,
                bert,
                ja_bert,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
            )[0][0, 0]
            .data.cpu()
            .float()
            .numpy()
        )
        del x_tst, tones, lang_ids, bert, x_tst_lengths, sid
        torch.cuda.empty_cache()
        return audio

def create_tts_fn(net_g_ms, hps):
    def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):
        print(f"{text} | {speaker}")
        sid = hps.data.spk2id[speaker]
        text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
        if limitation:
            max_len = 100
            if len(text) > max_len:
                return "Error: Text is too long", None
        audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
                      length_scale=length_scale, sid=sid, net_g_ms=net_g_ms, hps=hps)
        return "Success", (hps.data.sampling_rate, audio)
    return tts_fn

if __name__ == "__main__":
    device = (
        "cuda:0"
        if torch.cuda.is_available()
        else (
            "mps"
            if sys.platform == "darwin" and torch.backends.mps.is_available()
            else "cpu"
        )
    )

    parser = argparse.ArgumentParser()
    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)

    models = []
    with open("pretrained_models/info.json", "r", encoding="utf-8") as f:
        models_info = json.load(f)
    for i, info in models_info.items():
        if not info['enable']:
            continue
        name = info['name']
        title = info['title']
        example = info['example']
        hps = utils.get_hparams_from_file(f"./pretrained_models/{name}/config.json")
        net_g_ms = SynthesizerTrn(
            len(symbols),
            hps.data.filter_length // 2 + 1,
            hps.train.segment_size // hps.data.hop_length,
            n_speakers=hps.data.n_speakers,
            **hps.model)
        utils.load_checkpoint(f'pretrained_models/{i}/{i}.pth', net_g_ms, None, skip_optimizer=True)
        _ = net_g_ms.eval().to(device)
        models.append((name, title, example, list(hps.data.spk2id.keys()), net_g_ms, create_tts_fn(net_g_ms, hps)))
    with gr.Blocks(theme='NoCrypt/miku') as app:
        with gr.Tabs():
            for (name, title, example, speakers, net_g_ms, tts_fn) in models:
                with gr.TabItem(name):
                    with gr.Row():
                        gr.Markdown(
                            '<div align="center">'
                            f'<a><strong>{title}</strong></a>'
                            f'</div>'
                        )
                    with gr.Row():
                        with gr.Column():
                            input_text = gr.Textbox(label="Text (100 words limitation)" if limitation else "Text", lines=5, value=example)
                            btn = gr.Button(value="Generate", variant="primary")
                            with gr.Row():
                                sp = gr.Dropdown(choices=speakers, value=speakers[0], label="Speaker")
                            with gr.Row():
                                sdpr = gr.Slider(label="SDP Ratio", minimum=0, maximum=1, step=0.1, value=0.2)
                                ns = gr.Slider(label="noise_scale", minimum=0.1, maximum=1.0, step=0.1, value=0.6)
                                nsw = gr.Slider(label="noise_scale_w", minimum=0.1, maximum=1.0, step=0.1, value=0.8)
                                ls = gr.Slider(label="length_scale", minimum=0.1, maximum=2.0, step=0.1, value=1)
                        with gr.Column():
                            o1 = gr.Textbox(label="Output Message")
                            o2 = gr.Audio(label="Output Audio")
                        btn.click(tts_fn, inputs=[input_text, sp, sdpr, ns, nsw, ls], outputs=[o1, o2])
        app.queue(concurrency_count=1).launch(share=args.share)