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import gradio as gr | |
import json | |
import os | |
#os.popen('sh run.sh') | |
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 | |
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("/ljs_base.json") | |
net_g = SynthesizerTrn( | |
len(symbols), | |
hps.data.filter_length // 2 + 1, | |
hps.train.segment_size // hps.data.hop_length, | |
**hps.model).cuda() | |
_ = net_g.eval() | |
_ = utils.load_checkpoint("/pretrained_ljs.pth", net_g, None) | |
def transcribe(text): | |
stn_tst = get_text(text, hps) | |
with torch.no_grad(): | |
x_tst = stn_tst.cuda().unsqueeze(0) | |
x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda() | |
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() | |
return hps.data.sampling_rate, audio | |
get_intent = gr.Interface(fn = transcribe, | |
inputs="textbox", outputs="audio").launch() | |