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# coding=utf-8
import time
import os
import gradio as gr
import utils
import argparse
import commons
from models import SynthesizerTrn
from text import text_to_sequence
import torch
from torch import no_grad, LongTensor
import webbrowser
import logging
logging.getLogger('numba').setLevel(logging.WARNING)
limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces
def get_text(text, hps):
text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
if hps.data.add_blank:
text_norm = commons.intersperse(text_norm, 0)
text_norm = LongTensor(text_norm)
return text_norm, clean_text
def vits(text, language, speaker_id, noise_scale, noise_scale_w, length_scale):
print(f'________________{speaker_id}')
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument('--api', action="store_true", default=True)
parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
parser.add_argument("--colab", action="store_true", default=False, help="share gradio app")
args = parser.parse_args()
device = torch.device(args.device)
hps_ms = utils.get_hparams_from_file(r'./model/config.json')
net_g_ms = SynthesizerTrn(
len(hps_ms.symbols),
hps_ms.data.filter_length // 2 + 1,
hps_ms.train.segment_size // hps_ms.data.hop_length,
n_speakers=hps_ms.data.n_speakers,
**hps_ms.model)
_ = net_g_ms.eval().to(device)
speakers = hps_ms.speakers
model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None)
start = time.perf_counter()
if not len(text):
return "输入文本不能为空!", None, None
text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
if len(text) > 100 and limitation:
return f"输入文字过长!{len(text)}>100", None, None
if language == 0:
text = f"[ZH]{text}[ZH]"
elif language == 1:
text = f"[JA]{text}[JA]"
else:
text = f"{text}"
stn_tst, clean_text = get_text(text, hps_ms)
with no_grad():
x_tst = stn_tst.unsqueeze(0).to(device)
x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
speaker_id = LongTensor([speaker_id]).to(device)
audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=speaker_id, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
return "生成成功!",(22050, audio), f"生成耗时 {round(time.perf_counter()-start, 2)} s"
def search_speaker(search_value):
hps_ms = utils.get_hparams_from_file(r'./model/config.json')
speakers = hps_ms.speakers
for s in speakers:
if search_value == s:
return s
for s in speakers:
if search_value in s:
return s
def change_lang(language):
if language == 0:
return 0.6, 0.668, 1.2
else:
return 0.6, 0.668, 1.1
download_audio_js = """
() =>{{
let root = document.querySelector("body > gradio-app");
if (root.shadowRoot != null)
root = root.shadowRoot;
let audio = root.querySelector("#tts-audio").querySelector("audio");
let text = root.querySelector("#input-text").querySelector("textarea");
if (audio == undefined)
return;
text = text.value;
if (text == undefined)
text = Math.floor(Math.random()*100000000);
audio = audio.src;
let oA = document.createElement("a");
oA.download = text.substr(0, 20)+'.wav';
oA.href = audio;
document.body.appendChild(oA);
oA.click();
oA.remove();
}}
"""
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument('--api', action="store_true", default=True)
parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
parser.add_argument("--colab", action="store_true", default=False, help="share gradio app")
args = parser.parse_args()
device = torch.device(args.device)
hps_ms = utils.get_hparams_from_file(r'./model/config.json')
net_g_ms = SynthesizerTrn(
len(hps_ms.symbols),
hps_ms.data.filter_length // 2 + 1,
hps_ms.train.segment_size // hps_ms.data.hop_length,
n_speakers=hps_ms.data.n_speakers,
**hps_ms.model)
_ = net_g_ms.eval().to(device)
speakers = hps_ms.speakers
model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None)
with gr.Blocks() as app:
gr.Markdown(
"# <center> VITS语音在线合成demo\n"
"# <center> 严禁将模型用于任何商业项目,否则后果自负\n"
"<div align='center'>主要有赛马娘,原神中文,原神日语,崩坏3的音色</div>"
'<div align="center"><a><font color="#dd0000">结果有随机性,语调可能很奇怪,可多次生成取最佳效果</font></a></div>'
'<div align="center"><a><font color="#dd0000">标点符号会影响生成的结果</font></a></div>'
)
with gr.Tabs():
with gr.TabItem("vits"):
with gr.Row():
with gr.Column():
input_text = gr.Textbox(label="Text (100 words limitation) " if limitation else "Text", lines=5, value="今天晚上吃啥好呢。", elem_id=f"input-text")
lang = gr.Dropdown(label="Language", choices=["中文", "日语", "中日混合(中文用[ZH][ZH]包裹起来,日文用[JA][JA]包裹起来)"],
type="index", value="中文")
btn = gr.Button(value="Submit")
with gr.Row():
search = gr.Textbox(label="Search Speaker", lines=1)
btn2 = gr.Button(value="Search")
sid = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[228])
with gr.Row():
ns = gr.Slider(label="noise_scale(控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
nsw = gr.Slider(label="noise_scale_w(控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
ls = gr.Slider(label="length_scale(控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True)
with gr.Column():
o1 = gr.Textbox(label="Output Message")
o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio")
o3 = gr.Textbox(label="Extra Info")
download = gr.Button("Download Audio")
btn.click(vits, inputs=[input_text, lang, sid, ns, nsw, ls], outputs=[o1, o2, o3],api_name='voice')
download.click(None, [], [], _js=download_audio_js.format())
btn2.click(search_speaker, inputs=[search], outputs=[sid])
lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
with gr.TabItem("可用人物一览"):
gr.Radio(label="Speaker", choices=speakers, interactive=False, type="index")
if args.colab:
webbrowser.open("http://127.0.0.1:7860")
app.queue(concurrency_count=3, api_open=True).launch(show_api=True)
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