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import gradio as gr | |
import os | |
os.system('cd monotonic_align && python setup.py build_ext --inplace && cd ..') | |
import torch | |
import commons | |
import utils | |
from models import SynthesizerTrn | |
from text.symbols import symbols | |
from text import text_to_sequence | |
import IPython.display as ipd | |
import json | |
import math | |
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/biaobei_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("G_aatrox.pth", net_g, None) | |
def vc_fn(input): | |
stn_tst = get_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.cpu().float().numpy() | |
sampling_rate = 22050 | |
return (sampling_rate, audio) | |
app = gr.Blocks() | |
with app: | |
with gr.Tabs(): | |
with gr.TabItem("Basic"): | |
vc_input = gr.Textbox(label="Input Message") | |
vc_submit = gr.Button("Convert", variant="primary") | |
vc_output = gr.Audio(label="Output Audio") | |
#vc_output = ipd.display(ipd.Audio(vc_fn(get_text(vc_input, hps)), rate=hps.data.sampling_rate)) | |
vc_submit.click(vc_fn, [vc_input], [vc_output]) | |
app.launch() |