test.py

import argparse
import json
import os
import re
import tempfile
from pathlib import Path

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 json
import gradio.processing_utils as gr_processing_utils
from models import SynthesizerTrn
from text import text_to_sequence, _clean_text
# from mel_processing import spectrogram_torch
# import sounddevice as sd
# from scipy.io.wavfile import write
# import scikits.audiolab
# import soundfile as sf
import scipy.io.wavfile as wf
import base64

limitation = False
device = torch.device('cpu')


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();
}}
"""

# download = gr.Button("Download Audio")
tts_input1 = gr.TextArea(label="inputText", value="あなたと一緒にいると、とても興奮します", elem_id=f"tts-input{0}")
tts_output2 = gr.Audio(label="outputAudio", elem_id=f"tts-audio{0}")

def get_text(text, hps, is_symbol):
    text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
    if hps.data.add_blank:
        text_norm = commons.intersperse(text_norm, 0)
    text_norm = 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

def main(input):
    models_tts = []
    models_vc = []
    models_soft_vc = []
    device = torch.device("cpu")
    global result
    with open("saved_model/info.json", "r", encoding="utf-8") as f:
        models_info = json.load(f)
        for i, info in models_info.items():
            if int(i) == 0:
                name = info["title"]
                author = info["author"]
                lang = info["lang"]
                example = info["example"]
                config_path = f"saved_model/{i}/config.json"
                model_path = f"saved_model/{i}/model.pth"
                cover = info["cover"]
                cover_path = f"saved_model/{i}/{cover}" if cover else None
                hps = utils.get_hparams_from_file(config_path)
                model = SynthesizerTrn(
                    len(hps.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(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"]
                # input_text = get_text("ヨスガノソラ", hps, True)
                print(speaker_ids[0])
                vtts = create_tts_fn(model, hps, speaker_ids)
                symbol = create_to_symbol_fn(hps)
                result = vtts(input, speaker_ids[0], 1, False)
                # wf.write('anime_girl3.wav', result[1][0], result[1][1])
                # print(type(result[1][0]), result[1][0])
                # download.click(None, [], [], _js=download_audio_js.format(audio_id=f"tts-audio{0}"))
                # return result[1][0], result[1][1]
                wf.write(os.path.join(os.path.dirname(__file__), 'audio/animegirl.wav'), result[1][0], result[1][1])
                return """
                <audio src={}></audio>
                """.format(os.path.join(os.path.dirname(__file__), 'audio/animegirl.wav'))
                # return str(result[1][1])
                # base64.b64encode(open("animegirl.wav").read())
                # return str(json.dumps(result[1][1]))
                # result[1][1]
    print(models_tts)


demo = gr.Interface(fn=main, inputs="text", outputs="html")
        
                    # outputs=gr.outputs.Textbox(label="outputAudio"))
    
if __name__ == "__main__":
    demo.launch(debug=True)
    
# main(input = "あなたと一緒にいると、とても興奮します")