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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()
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