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# @ 2023.10.23 | |
# @ Elena | |
import sys, os | |
import logging | |
import re | |
from scipy.io.wavfile import write | |
logging.getLogger("numba").setLevel(logging.WARNING) | |
logging.getLogger("markdown_it").setLevel(logging.WARNING) | |
logging.getLogger("urllib3").setLevel(logging.WARNING) | |
logging.getLogger("matplotlib").setLevel(logging.WARNING) | |
logging.basicConfig( | |
level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s" | |
) | |
logger = logging.getLogger(__name__) | |
import torch | |
import argparse | |
import commons | |
import utils | |
from models import SynthesizerTrn | |
from text.symbols import symbols | |
from text import text_to_sequence | |
import gradio as gr | |
import webbrowser | |
import numpy as np | |
'''# - paths | |
path_to_config = "config.json" # path to .json | |
path_to_model = "best.pth" # path to G_xxxx.pth''' | |
net_g = None | |
if sys.platform == "darwin" and torch.backends.mps.is_available(): | |
device = "mps" | |
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" | |
else: | |
device = "cuda" | |
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 infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid): | |
global net_g | |
fltstr = re.sub(r"[\[\]\(\)\{\}]", "", text) | |
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() | |
return audio | |
def tts_fn( | |
text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale | |
): | |
slices = text.split("|") | |
audio_list = [] | |
with torch.no_grad(): | |
for slice in slices: | |
audio = infer( | |
slice, | |
sdp_ratio=sdp_ratio, | |
noise_scale=noise_scale, | |
noise_scale_w=noise_scale_w, | |
length_scale=length_scale, | |
sid=speaker, | |
) | |
audio_list.append(audio) | |
silence = np.zeros(hps.data.sampling_rate) | |
audio_list.append(silence) | |
audio_concat = np.concatenate(audio_list) | |
return "Success", (hps.data.sampling_rate, audio_concat) | |
if __name__ == "__main__": | |
parser = argparse.ArgumentParser() | |
parser.add_argument( | |
"-m", "--model", default=".best.pth", help="path of your model" | |
) | |
parser.add_argument( | |
"-c", | |
"--config", | |
default="./config.json", | |
help="path of your config file", | |
) | |
parser.add_argument( | |
"--share", default=False, help="make link public", action="store_true" | |
) | |
parser.add_argument( | |
"-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log" | |
) | |
args = parser.parse_args() | |
if args.debug: | |
logger.info("Enable DEBUG-LEVEL log") | |
logging.basicConfig(level=logging.DEBUG) | |
hps = utils.get_hparams_from_file(args.config) | |
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 | |
device = ( | |
"cuda:1" | |
if torch.cuda.is_available() | |
else ( | |
"mps" | |
if sys.platform == "darwin" and torch.backends.mps.is_available() | |
else "cpu" | |
) | |
) | |
net_g = SynthesizerTrn( | |
len(symbols), | |
posterior_channels, | |
hps.train.segment_size // hps.data.hop_length, | |
n_speakers=hps.data.n_speakers, #- >0 for multi speaker | |
**hps.model | |
).to(device) | |
_ = net_g.eval() | |
################################################################## | |
# Load model | |
_ = utils.load_checkpoint(args.model, net_g, None) | |
speakers = hps.data.n_speakers | |
languages = ["KO"] | |
with gr.Blocks() as app: | |
with gr.Row(): | |
with gr.Column(): | |
text = gr.TextArea( | |
label="Text", | |
placeholder="Input Text Here", | |
value="TTS는 텍스트 문서를 음성으로 출력시켜 주는 기술이며 텍스트 문서를 입력하면 음성으로 읽어주는 기술이다.", | |
) | |
speaker = gr.Slider( | |
minimum=0, maximum=speakers-1, value=0, step=1, label="성우" | |
) | |
sdp_ratio = gr.Slider( | |
minimum=0, maximum=1, value=0.2, step=0.1, label="SDP Ratio" | |
) | |
noise_scale = gr.Slider( | |
minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise Scale" | |
) | |
noise_scale_w = gr.Slider( | |
minimum=0.1, maximum=2, value=0.8, step=0.1, label="Noise Scale W" | |
) | |
length_scale = gr.Slider( | |
minimum=0.1, maximum=2, value=1, step=0.1, label="Length Scale" | |
) | |
language = gr.Dropdown( | |
choices=languages, value=languages[0], label="Language" | |
) | |
btn = gr.Button("Generate!", variant="primary") | |
with gr.Column(): | |
text_output = gr.Textbox(label="Message") | |
audio_output = gr.Audio(label="Output Audio") | |
btn.click( | |
tts_fn, | |
inputs=[ | |
text, | |
speaker, | |
sdp_ratio, | |
noise_scale, | |
noise_scale_w, | |
length_scale, | |
], | |
outputs=[text_output, audio_output], | |
) | |
webbrowser.open("http://127.0.0.1:7860") | |
app.launch(share=True) | |