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inference.py
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import librosa
import matplotlib.pyplot as plt
import os
import json
import math
import requests
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
import langdetect
from scipy.io.wavfile import write
import re
from scipy import signal
'''
from phonemizer.backend.espeak.wrapper import EspeakWrapper
_ESPEAK_LIBRARY = 'C:\Program Files\eSpeak NG\libespeak-ng.dll'
EspeakWrapper.set_library(_ESPEAK_LIBRARY)
'''
# check device
if torch.cuda.is_available() is True:
device = "cuda:0"
else:
device = "cpu"
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
def langdetector(text): # from PolyLangVITS
try:
lang = langdetect.detect(text).lower()
if lang == 'ko':
return f'[KO]{text}[KO]'
elif lang == 'ja':
return f'[JA]{text}[JA]'
elif lang == 'en':
return f'[EN]{text}[EN]'
elif lang == 'zh-cn':
return f'[ZH]{text}[ZH]'
else:
return text
except Exception as e:
return text
def vcss(inputstr): # single
fltstr = re.sub(r"[\[\]\(\)\{\}]", "", inputstr)
fltstr = langdetector(fltstr) #- optional for cjke/cjks type cleaners
stn_tst = get_text(fltstr, hps)
speed = 1
output_dir = 'output'
sid = 0
with torch.no_grad():
x_tst = stn_tst.to(device).unsqueeze(0)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1 / speed)[0][
0, 0].data.cpu().float().numpy()
write(f'./{output_dir}/output_{sid}.wav', hps.data.sampling_rate, audio)
print(f'./{output_dir}/output_{sid}.wav Generated!')
def vcms(inputstr, sid):
fltstr = re.sub(r"[\[\]\(\)\{\}]", "", inputstr)
fltstr = langdetector(fltstr)
stn_tst = get_text(fltstr, hps)
speed = 1
output_dir = 'output'
with torch.no_grad():
x_tst = stn_tst.to(device).unsqueeze(0)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
sid = torch.LongTensor([sid]).to(device)
audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1 / speed)[0][
0, 0].data.cpu().float().numpy()
write(f'./{output_dir}/output_{sid}.wav', hps.data.sampling_rate, audio)
print(f'./{output_dir}/output_{sid}.wav Generated!')
hps = utils.get_hparams_from_file("./configs/config.json")
if "use_mel_posterior_encoder" in hps.model.keys() and hps.model.use_mel_posterior_encoder == True:
print("Using mel posterior encoder for VITS2")
posterior_channels = 80 # vits2
hps.data.use_mel_posterior_encoder = True
else:
print("Using lin posterior encoder for VITS1")
posterior_channels = hps.data.filter_length // 2 + 1
hps.data.use_mel_posterior_encoder = False
net_g = SynthesizerTrn(
len(symbols),
posterior_channels,
hps.train.segment_size // hps.data.hop_length,
# n_speakers=hps.data.n_speakers, #- for multi speaker
**hps.model).to(device)
_ = net_g.eval()
_ = utils.load_checkpoint("./models/G_2000.pth", net_g, None)
# - text input
input = "I try to get the waiter's attention by blinking in morse code"
vcss(input)