app.py

# import gradio as gr

# gr.Interface.load("models/ulysses115/pmvoice").launch()

import argparse
import json
import os
import re
import tempfile

import librosa
import numpy as np
import torch
from torch import no_grad, LongTensor
import commons
import utils
import gradio as gr
import gradio.utils as gr_utils
import gradio.processing_utils as gr_processing_utils
from models import SynthesizerTrn
from text.symbols import symbols
from text import text_to_sequence, _clean_text
from mel_processing import spectrogram_torch

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


def audio_postprocess(self, y):
    if y is None:
        return None

    if gr_utils.validate_url(y):
        file = gr_processing_utils.download_to_file(y, dir=self.temp_dir)
    elif isinstance(y, tuple):
        sample_rate, data = y
        file = tempfile.NamedTemporaryFile(
            suffix=".wav", dir=self.temp_dir, delete=False
        )
        gr_processing_utils.audio_to_file(sample_rate, data, file.name)
    else:
        file = gr_processing_utils.create_tmp_copy_of_file(y, dir=self.temp_dir)

    return gr_processing_utils.encode_url_or_file_to_base64(file.name)


gr.Audio.postprocess = audio_postprocess

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 create_tts_fn(model, hps, speaker_ids):
    def tts_fn(text, speaker, speed, is_symbol):
        if limitation:
            text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
            max_len = 150
            if is_symbol:
                max_len *= 3
            if text_len > max_len:
                return "Error: Text is too long", None

        speaker_id = speaker_ids[speaker]
        stn_tst = get_text(text, hps, is_symbol)
        with no_grad():
            x_tst = stn_tst.unsqueeze(0).to(device)
            x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
            sid = LongTensor([speaker_id]).to(device)
            audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
                                length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
        del stn_tst, x_tst, x_tst_lengths, sid
        return "Success", (hps.data.sampling_rate, audio)

    return tts_fn


def create_to_symbol_fn(hps):
    def to_symbol_fn(is_symbol_input, input_text, temp_text):
        return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \
            else (temp_text, temp_text)

    return to_symbol_fn


download_audio_js = """
() =>{{
    let root = document.querySelector("body > gradio-app");
    if (root.shadowRoot != null)
        root = root.shadowRoot;
    let audio = root.querySelector("#{audio_id}").querySelector("audio");
    if (audio == undefined)
        return;
    audio = audio.src;
    let oA = document.createElement("a");
    oA.download = Math.floor(Math.random()*100000000)+'.wav';
    oA.href = audio;
    document.body.appendChild(oA);
    oA.click();
    oA.remove();
}}
"""

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--device', type=str, default='cpu')
    parser.add_argument("--share", action="store_true", default=True, help="share gradio app")
    args = parser.parse_args()

    device = torch.device(args.device)
    models_tts = []
    with open("save_model/info.json", "r", encoding="utf-8") as f:
        models_info = json.load(f)
    for i, info in models_info.items():
        name = info["title"]
        author = info["author"]
        lang = info["lang"]
        example = info["example"]
        config_path = f"config.json"
        model_path = f"G_1434000.pth"
        cover = info["cover"]
        cover_path = cover 
        hps = utils.get_hparams_from_file(config_path)
        model = SynthesizerTrn(
            len(symbols),
            hps.data.filter_length // 2 + 1,
            hps.train.segment_size // hps.data.hop_length,
            **hps.model)
        utils.load_checkpoint(model_path, model, None)
        model.eval().to(device)
        speaker_ids = [sid for sid, name in enumerate(hps.speakers) if name != "None"]
        speakers = [name for sid, name in enumerate(hps.speakers) if name != "None"]

        t = info["type"]
        if t == "vits":
            models_tts.append((name, author, cover_path, speakers, lang, example,
                               symbols, create_tts_fn(model, hps, speaker_ids),
                               create_to_symbol_fn(hps)))

    app = gr.Blocks()

    with app:
        for i, (name, author, cover_path, speakers, lang, example, symbols, tts_fn,
                to_symbol_fn) in enumerate(models_tts):
            with gr.TabItem(f"model{i}"):
                with gr.Column():
                    tts_input1 = gr.TextArea(label="Text (150 words limitation)", value=example,
                                             elem_id=f"tts-input{i}")
                    tts_input2 = gr.Dropdown(label="Speaker", choices=speakers,
                                             type="index", value=speakers[0])
                    tts_input3 = gr.Slider(label="Speed", value=1, minimum=0.5, maximum=2, step=0.1)
                    with gr.Accordion(label="Advanced Options", open=False):
                        temp_text_var = gr.Variable()
                        symbol_input = gr.Checkbox(value=False, label="Symbol input")
                        symbol_list = gr.Dataset(label="Symbol list", components=[tts_input1],
                                                 samples=[[x] for x in symbols],
                                                 elem_id=f"symbol-list{i}")
                        symbol_list_json = gr.Json(value=symbols, visible=False)
                    tts_submit = gr.Button("Generate", variant="primary")
                    tts_output1 = gr.Textbox(label="Output Message")
                    tts_output2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio{i}")
                    download = gr.Button("Download Audio")
                    download.click(None, [], [], _js=download_audio_js.format(audio_id=f"tts-audio{i}"))

                    tts_submit.click(tts_fn, [tts_input1, tts_input2, tts_input3, symbol_input],
                                     [tts_output1, tts_output2])
                    symbol_input.change(to_symbol_fn,
                                        [symbol_input, tts_input1, temp_text_var],
                                        [tts_input1, temp_text_var])
                    symbol_list.click(None, [symbol_list, symbol_list_json], [],
                                      _js=f"""
                    (i,symbols) => {{
                        let root = document.querySelector("body > gradio-app");
                        if (root.shadowRoot != null)
                            root = root.shadowRoot;
                        let text_input = root.querySelector("#tts-input{i}").querySelector("textarea");
                        let startPos = text_input.selectionStart;
                        let endPos = text_input.selectionEnd;
                        let oldTxt = text_input.value;
                        let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
                        text_input.value = result;
                        let x = window.scrollX, y = window.scrollY;
                        text_input.focus();
                        text_input.selectionStart = startPos + symbols[i].length;
                        text_input.selectionEnd = startPos + symbols[i].length;
                        text_input.blur();
                        window.scrollTo(x, y);
                        return [];
                    }}""")
    app.queue(concurrency_count=1).launch(show_api=True, share=args.share)