import io
import sys
import time

sys.path.append('tts/vits')

import numpy as np
import soundfile
import os
os.environ["PYTORCH_JIT"] = "0"
import torch

import tts.vits.commons as commons
import tts.vits.utils as utils

from tts.vits.models import SynthesizerTrn
from tts.vits.text.symbols import symbols
from tts.vits.text import text_to_sequence

import logging
logging.getLogger().setLevel(logging.INFO)
logging.basicConfig(level=logging.INFO)


from pydub import AudioSegment



class TTService():
    
    def __init__(self, cfg, model, char, speed):
        logging.info('Initializing TTS Service for %s...' % char)
        self.hps = utils.get_hparams_from_file(cfg)
        self.speed = speed
        self.net_g = SynthesizerTrn(
            len(symbols),
            self.hps.data.filter_length // 2 + 1,
            self.hps.train.segment_size // self.hps.data.hop_length,
            **self.hps.model).cpu()
        _ = self.net_g.eval()
        _ = utils.load_checkpoint(model, self.net_g, None)

    def get_text(self, 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 read(self, text, format="wav") -> io.BytesIO:
        text = text.replace('~', '！')
        stn_tst = self.get_text(text, self.hps)
        
        with torch.no_grad():
            x_tst = stn_tst.cpu().unsqueeze(0)
            x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cpu()
            # tp = self.net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.2, length_scale=self.speed)
            audio = self.net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.2, length_scale=self.speed)[0][
                0, 0].data.cpu().float().numpy()
            f = io.BytesIO()
            soundfile.write(f, audio, self.hps.data.sampling_rate, format=format)
            f.seek(0)
        return f