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import sys, os
if sys.platform == "darwin":
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
import logging
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 cleaned_text_to_sequence, get_bert
from text.cleaner import clean_text
import gradio as gr
import webbrowser
net_g = None
def get_text(text, language_str, hps):
norm_text, phone, tone, word2ph = clean_text(text, language_str)
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
if hps.data.add_blank:
phone = commons.intersperse(phone, 0)
tone = commons.intersperse(tone, 0)
language = commons.intersperse(language, 0)
for i in range(len(word2ph)):
word2ph[i] = word2ph[i] * 2
word2ph[0] += 1
bert = get_bert(norm_text, word2ph, language_str)
del word2ph
assert bert.shape[-1] == len(phone)
phone = torch.LongTensor(phone)
tone = torch.LongTensor(tone)
language = torch.LongTensor(language)
return bert, phone, tone, language
def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
global net_g
bert, phones, tones, lang_ids = get_text(text, "ZH", hps)
with torch.no_grad():
x_tst=phones.to(device).unsqueeze(0)
tones=tones.to(device).unsqueeze(0)
lang_ids=lang_ids.to(device).unsqueeze(0)
bert = bert.to(device).unsqueeze(0)
x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
del phones
speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio
, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
return audio
def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):
with torch.no_grad():
audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker)
return "Success", (hps.data.sampling_rate, audio)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model", default="./logs/as/G_8000.pth", help="path of your model")
parser.add_argument("-c", "--config", default="./configs/config.json", help="path of your config file")
parser.add_argument("--share", default=False, help="make link public")
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)
device = (
"cuda:0"
if torch.cuda.is_available()
else (
"mps"
if sys.platform == "darwin" and torch.backends.mps.is_available()
else "cpu"
)
)
net_g = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model).to(device)
_ = net_g.eval()
_ = utils.load_checkpoint(args.model, net_g, None, skip_optimizer=True)
speaker_ids = hps.data.spk2id
speakers = list(speaker_ids.keys())
with gr.Blocks() as app:
with gr.Row():
with gr.Column():
text = gr.TextArea(label="Text", placeholder="Input Text Here",
value="吃葡萄不吐葡萄皮，不吃葡萄倒吐葡萄皮。")
speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker')
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')
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=args.share)
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