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import os
import matplotlib.pyplot as plt
import os
import json
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
import commons
import utils
from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate
from models import SynthesizerTrn
from text.symbols import symbols
from text import text_to_sequence
from scipy.io.wavfile import write
import streamlit as st
os.system('cd monotonic_align')
os.system('python setup.py build_ext --inplace')
os.system('cd ..')
os.system("gdown 'https://drive.google.com/uc?id=1q86w74Ygw2hNzYP9cWkeClGT5X25PvBT'")
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
hps = utils.get_hparams_from_file("./configs/ljs_base.json")
net_g = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps.model)
_ = net_g.eval()
_ = utils.load_checkpoint("pretrained_ljs.pth", net_g, None)
st.title("VITS Text-to-Speech Demo")
# Input text box for user to enter text
text_input = st.text_input("Enter text to convert to speech", value="Hello world")
if st.button("Generate Speech"):
# Convert the text to the appropriate format (e.g., phoneme or character representation)
stn_tst = get_text(text_input, hps)
with torch.no_grad():
x_tst = stn_tst.unsqueeze(0)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.float().numpy()
# Use hps.data.sampling_rate for playing the audio
st.audio(audio, format="audio/wav", sample_rate=hps.data.sampling_rate)
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