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import re
import gradio as gr
import torch
import unicodedata
import commons
import utils
from models import SynthesizerTrn
from text import text_to_sequence
config_json = "muse_tricolor_b.json"
pth_path = "G=869.pth"
def get_text(text, hps, cleaned=False):
if cleaned:
text_norm = text_to_sequence(text, hps.symbols, [])
else:
text_norm = text_to_sequence(text, hps.symbols, 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 get_label(text, label):
if f'[{label}]' in text:
return True, text.replace(f'[{label}]', '')
else:
return False, text
def clean_text(text):
print(text)
jap = re.compile(r'[\u3040-\u309F\u30A0-\u30FF]') # 匹配日文
text = unicodedata.normalize('NFKC', text)
text = f"[JA]{text}[JA]" if jap.search(text) else f"[ZH]{text}[ZH]"
return text
def load_model(config_json, pth_path):
dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
hps_ms = utils.get_hparams_from_file(f"{config_json}")
n_speakers = hps_ms.data.n_speakers if 'n_speakers' in hps_ms.data.keys() else 0
n_symbols = len(hps_ms.symbols) if 'symbols' in hps_ms.keys() else 0
net_g_ms = SynthesizerTrn(
n_symbols,
hps_ms.data.filter_length // 2 + 1,
hps_ms.train.segment_size // hps_ms.data.hop_length,
n_speakers=n_speakers,
**hps_ms.model).to(dev)
_ = net_g_ms.eval()
_ = utils.load_checkpoint(pth_path, net_g_ms)
return net_g_ms
net_g_ms = load_model(config_json, pth_path)
def selection(speaker):
if speaker == "南小鸟":
spk = 0
return spk
elif speaker == "园田海未":
spk = 1
return spk
elif speaker == "小泉花阳":
spk = 2
return spk
elif speaker == "星空凛":
spk = 3
return spk
elif speaker == "东条希":
spk = 4
return spk
elif speaker == "矢泽妮可":
spk = 5
return spk
elif speaker == "绚濑绘里":
spk = 6
return spk
elif speaker == "西木野真姬":
spk = 7
return spk
elif speaker == "高坂穗乃果":
spk = 8
return spk
def infer(text,speaker_id, n_scale= 0.667,n_scale_w = 0.8, l_scale = 1 ):
text = clean_text(text)
speaker_id = int(selection(speaker_id))
dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
hps_ms = utils.get_hparams_from_file(f"{config_json}")
with torch.no_grad():
stn_tst = get_text(text, hps_ms, cleaned=False)
x_tst = stn_tst.unsqueeze(0).to(dev)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(dev)
sid = torch.LongTensor([speaker_id]).to(dev)
audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=n_scale, noise_scale_w=n_scale_w, length_scale=l_scale)[0][
0, 0].data.cpu().float().numpy()
return (hps_ms.data.sampling_rate, audio)
idols = ["南小鸟","园田海未","小泉花阳","星空凛","东条希","矢泽妮可","绚濑绘里","西木野真姬","高坂穗乃果"]
app = gr.Blocks()
with app:
with gr.Tabs():
with gr.TabItem("Basic"):
tts_input1 = gr.TextArea(label="请输入纯中文或纯日文", value="大家好")
para_input1 = gr.Slider(minimum= 0.01,maximum=1.0,label="更改噪声比例", value=0.667)
para_input2 = gr.Slider(minimum= 0.01,maximum=1.0,label="更改噪声偏差", value=0.8)
para_input3 = gr.Slider(minimum= 0.1,maximum=10,label="更改时间比例", value=1)
tts_submit = gr.Button("Generate", variant="primary")
speaker1 = gr.Dropdown(label="选择说话人",choices=idols, value="高坂穗乃果", interactive=True)
tts_output2 = gr.Audio(label="Output")
tts_submit.click(infer, [tts_input1,speaker1,para_input1,para_input2,para_input3], [tts_output2])
app.launch()
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