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Remove: remove webui.py, which is no longer maintained in Style-Bert-VITS2

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Since app.py and server_editor.py already exist as alternative Web UI, there is no need to revive webui.py in the future.
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tsukumi
2024-03-07 04:21:48 +00:00
parent def6d88425
commit 1450bfd06f
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webui.py
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"""
Original `webui.py` for Bert-VITS2, not working with Style-Bert-VITS2 yet.
"""
# flake8: noqa: E402
import os
import logging
import re_matching
from tools.sentence import split_by_language
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 gradio as gr
import librosa
import numpy as np
import torch
import webbrowser
import utils
from config import config
from style_bert_vits2.models.infer import infer, latest_version, get_net_g, infer_multilang
from tools.translate import translate
net_g = None
device = config.webui_config.device
if device == "mps":
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
def generate_audio(
slices,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
speaker,
language,
reference_audio,
emotion,
style_text,
style_weight,
skip_start=False,
skip_end=False,
):
audio_list = []
# silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
with torch.no_grad():
for idx, piece in enumerate(slices):
skip_start = idx != 0
skip_end = idx != len(slices) - 1
audio = infer(
piece,
reference_audio=reference_audio,
emotion=emotion,
sdp_ratio=sdp_ratio,
noise_scale=noise_scale,
noise_scale_w=noise_scale_w,
length_scale=length_scale,
sid=speaker,
language=language,
hps=hps,
net_g=net_g,
device=device,
skip_start=skip_start,
skip_end=skip_end,
assist_text=style_text,
assist_text_weight=style_weight,
)
audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
audio_list.append(audio16bit)
return audio_list
def generate_audio_multilang(
slices,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
speaker,
language,
reference_audio,
emotion,
skip_start=False,
skip_end=False,
):
audio_list = []
# silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
with torch.no_grad():
for idx, piece in enumerate(slices):
skip_start = idx != 0
skip_end = idx != len(slices) - 1
audio = infer_multilang(
piece,
reference_audio=reference_audio,
emotion=emotion,
sdp_ratio=sdp_ratio,
noise_scale=noise_scale,
noise_scale_w=noise_scale_w,
length_scale=length_scale,
sid=speaker,
language=language[idx],
hps=hps,
net_g=net_g,
device=device,
skip_start=skip_start,
skip_end=skip_end,
)
audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
audio_list.append(audio16bit)
return audio_list
def tts_split(
text: str,
speaker,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
language,
cut_by_sent,
interval_between_para,
interval_between_sent,
reference_audio,
emotion,
style_text,
style_weight,
):
while text.find("\n\n") != -1:
text = text.replace("\n\n", "\n")
text = text.replace("|", "")
para_list = re_matching.cut_para(text)
para_list = [p for p in para_list if p != ""]
audio_list = []
for p in para_list:
if not cut_by_sent:
audio_list += process_text(
p,
speaker,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
language,
reference_audio,
emotion,
style_text,
style_weight,
)
silence = np.zeros((int)(44100 * interval_between_para), dtype=np.int16)
audio_list.append(silence)
else:
audio_list_sent = []
sent_list = re_matching.cut_sent(p)
sent_list = [s for s in sent_list if s != ""]
for s in sent_list:
audio_list_sent += process_text(
s,
speaker,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
language,
reference_audio,
emotion,
style_text,
style_weight,
)
silence = np.zeros((int)(44100 * interval_between_sent))
audio_list_sent.append(silence)
if (interval_between_para - interval_between_sent) > 0:
silence = np.zeros(
(int)(44100 * (interval_between_para - interval_between_sent))
)
audio_list_sent.append(silence)
audio16bit = gr.processing_utils.convert_to_16_bit_wav(
np.concatenate(audio_list_sent)
) # 对完整句子做音量归一
audio_list.append(audio16bit)
audio_concat = np.concatenate(audio_list)
return ("Success", (hps.data.sampling_rate, audio_concat))
def process_mix(slice):
_speaker = slice.pop()
_text, _lang = [], []
for lang, content in slice:
content = content.split("|")
content = [part for part in content if part != ""]
if len(content) == 0:
continue
if len(_text) == 0:
_text = [[part] for part in content]
_lang = [[lang] for part in content]
else:
_text[-1].append(content[0])
_lang[-1].append(lang)
if len(content) > 1:
_text += [[part] for part in content[1:]]
_lang += [[lang] for part in content[1:]]
return _text, _lang, _speaker
def process_auto(text):
_text, _lang = [], []
for slice in text.split("|"):
if slice == "":
continue
temp_text, temp_lang = [], []
sentences_list = split_by_language(slice, target_languages=["zh", "ja", "en"])
for sentence, lang in sentences_list:
if sentence == "":
continue
temp_text.append(sentence)
if lang == "ja":
lang = "jp"
temp_lang.append(lang.upper())
_text.append(temp_text)
_lang.append(temp_lang)
return _text, _lang
def process_text(
text: str,
speaker,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
language,
reference_audio,
emotion,
style_text=None,
style_weight=0,
):
audio_list = []
if language == "mix":
bool_valid, str_valid = re_matching.validate_text(text)
if not bool_valid:
return str_valid, (
hps.data.sampling_rate,
np.concatenate([np.zeros(hps.data.sampling_rate // 2)]),
)
for slice in re_matching.text_matching(text):
_text, _lang, _speaker = process_mix(slice)
if _speaker is None:
continue
print(f"Text: {_text}\nLang: {_lang}")
audio_list.extend(
generate_audio_multilang(
_text,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
_speaker,
_lang,
reference_audio,
emotion,
)
)
elif language.lower() == "auto":
_text, _lang = process_auto(text)
print(f"Text: {_text}\nLang: {_lang}")
audio_list.extend(
generate_audio_multilang(
_text,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
speaker,
_lang,
reference_audio,
emotion,
)
)
else:
audio_list.extend(
generate_audio(
text.split("|"),
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
speaker,
language,
reference_audio,
emotion,
style_text,
style_weight,
)
)
return audio_list
def tts_fn(
text: str,
speaker,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
language,
reference_audio,
emotion,
prompt_mode,
style_text=None,
style_weight=0,
):
if style_text == "":
style_text = None
if prompt_mode == "Audio prompt":
if reference_audio == None:
return ("Invalid audio prompt", None)
else:
reference_audio = load_audio(reference_audio)[1]
else:
reference_audio = None
audio_list = process_text(
text,
speaker,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
language,
reference_audio,
emotion,
style_text,
style_weight,
)
audio_concat = np.concatenate(audio_list)
return "Success", (hps.data.sampling_rate, audio_concat)
def format_utils(text, speaker):
_text, _lang = process_auto(text)
res = f"[{speaker}]"
for lang_s, content_s in zip(_lang, _text):
for lang, content in zip(lang_s, content_s):
res += f"<{lang.lower()}>{content}"
res += "|"
return "mix", res[:-1]
def load_audio(path):
audio, sr = librosa.load(path, 48000)
# audio = librosa.resample(audio, 44100, 48000)
return sr, audio
def gr_util(item):
if item == "Text prompt":
return {"visible": True, "__type__": "update"}, {
"visible": False,
"__type__": "update",
}
else:
return {"visible": False, "__type__": "update"}, {
"visible": True,
"__type__": "update",
}
if __name__ == "__main__":
if config.webui_config.debug:
logger.info("Enable DEBUG-LEVEL log")
logging.basicConfig(level=logging.DEBUG)
hps = utils.get_hparams_from_file(config.webui_config.config_path)
# 若config.json中未指定版本则默认为最新版本
version = hps.version if hasattr(hps, "version") else latest_version
net_g = get_net_g(
model_path=config.webui_config.model, version=version, device=device, hps=hps
)
speaker_ids = hps.data.spk2id
speakers = list(speaker_ids.keys())
languages = ["ZH", "JP", "EN", "mix", "auto"]
with gr.Blocks() as app:
with gr.Row():
with gr.Column():
text = gr.TextArea(
label="输入文本内容",
placeholder="""
如果你选择语言为\'mix\'，必须按照格式输入，否则报错:
格式举例(zh是中文，jp是日语，不区分大小写；说话人举例:gongzi):
[说话人1]<zh>你好，こんにちは！ <jp>こんにちは，世界。
[说话人2]<zh>你好吗？<jp>元気ですか？
[说话人3]<zh>谢谢。<jp>どういたしまして。
...
另外，所有的语言选项都可以用'|'分割长段实现分句生成。
""",
)
trans = gr.Button("中翻日", variant="primary")
slicer = gr.Button("快速切分", variant="primary")
formatter = gr.Button("检测语言，并整理为 MIX 格式", variant="primary")
speaker = gr.Dropdown(
choices=speakers, value=speakers[0], label="Speaker"
)
_ = gr.Markdown(
value="提示模式（Prompt mode）：可选文字提示或音频提示，用于生成文字或音频指定风格的声音。\n",
visible=False,
)
prompt_mode = gr.Radio(
["Text prompt", "Audio prompt"],
label="Prompt Mode",
value="Text prompt",
visible=False,
)
text_prompt = gr.Textbox(
label="Text prompt",
placeholder="用文字描述生成风格。如：Happy",
value="Happy",
visible=False,
)
audio_prompt = gr.Audio(
label="Audio prompt", type="filepath", visible=False
)
sdp_ratio = gr.Slider(
minimum=0, maximum=1, value=0.5, step=0.1, label="SDP Ratio"
)
noise_scale = gr.Slider(
minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise"
)
noise_scale_w = gr.Slider(
minimum=0.1, maximum=2, value=0.9, step=0.1, label="Noise_W"
)
length_scale = gr.Slider(
minimum=0.1, maximum=2, value=1.0, step=0.1, label="Length"
)
language = gr.Dropdown(
choices=languages, value=languages[0], label="Language"
)
btn = gr.Button("生成音频！", variant="primary")
with gr.Column():
with gr.Accordion("融合文本语义", open=False):
gr.Markdown(
value="使用辅助文本的语意来辅助生成对话（语言保持与主文本相同）\n\n"
"**注意**：不要使用**指令式文本**（如：开心），要使用**带有强烈情感的文本**（如：我好快乐！！！）\n\n"
"效果较不明确，留空即为不使用该功能"
)
style_text = gr.Textbox(label="辅助文本")
style_weight = gr.Slider(
minimum=0,
maximum=1,
value=0.7,
step=0.1,
label="Weight",
info="主文本和辅助文本的bert混合比率，0表示仅主文本，1表示仅辅助文本",
)
with gr.Row():
with gr.Column():
interval_between_sent = gr.Slider(
minimum=0,
maximum=5,
value=0.2,
step=0.1,
label="句间停顿(秒)，勾选按句切分才生效",
)
interval_between_para = gr.Slider(
minimum=0,
maximum=10,
value=1,
step=0.1,
label="段间停顿(秒)，需要大于句间停顿才有效",
)
opt_cut_by_sent = gr.Checkbox(
label="按句切分 在按段落切分的基础上再按句子切分文本"
)
slicer = gr.Button("切分生成", variant="primary")
text_output = gr.Textbox(label="状态信息")
audio_output = gr.Audio(label="输出音频")
# explain_image = gr.Image(
# label="参数解释信息",
# show_label=True,
# show_share_button=False,
# show_download_button=False,
# value=os.path.abspath("./img/参数说明.png"),
# )
btn.click(
tts_fn,
inputs=[
text,
speaker,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
language,
audio_prompt,
text_prompt,
prompt_mode,
style_text,
style_weight,
],
outputs=[text_output, audio_output],
)
trans.click(
translate,
inputs=[text],
outputs=[text],
)
slicer.click(
tts_split,
inputs=[
text,
speaker,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
language,
opt_cut_by_sent,
interval_between_para,
interval_between_sent,
audio_prompt,
text_prompt,
style_text,
style_weight,
],
outputs=[text_output, audio_output],
)
prompt_mode.change(
lambda x: gr_util(x),
inputs=[prompt_mode],
outputs=[text_prompt, audio_prompt],
)
audio_prompt.upload(
lambda x: load_audio(x),
inputs=[audio_prompt],
outputs=[audio_prompt],
)
formatter.click(
format_utils,
inputs=[text, speaker],
outputs=[language, text],
)
print("推理页面已开启!")
webbrowser.open(f"http://127.0.0.1:{config.webui_config.port}")
app.launch(share=config.webui_config.share, server_port=config.webui_config.port)