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"""
按中英混合识别
按日英混合识别
多语种启动切分识别语种
全部按中文识别
全部按英文识别
全部按日文识别
"""
import json
import logging
import os
import random
import re
import sys
import torch
now_dir = os.getcwd()
sys.path.append(now_dir)
sys.path.append("%s/GPT_SoVITS" % (now_dir))
logging.getLogger("markdown_it").setLevel(logging.ERROR)
logging.getLogger("urllib3").setLevel(logging.ERROR)
logging.getLogger("httpcore").setLevel(logging.ERROR)
logging.getLogger("httpx").setLevel(logging.ERROR)
logging.getLogger("asyncio").setLevel(logging.ERROR)
logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
infer_ttswebui = int(infer_ttswebui)
is_share = os.environ.get("is_share", "False")
is_share = eval(is_share)
if "_CUDA_VISIBLE_DEVICES" in os.environ:
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
gpt_path = os.environ.get("gpt_path", None)
sovits_path = os.environ.get("sovits_path", None)
cnhubert_base_path = os.environ.get("cnhubert_base_path", None)
bert_path = os.environ.get("bert_path", None)
version = model_version = os.environ.get("version", "v2")
import gradio as gr
from TTS_infer_pack.text_segmentation_method import get_method
from TTS_infer_pack.TTS import NO_PROMPT_ERROR, TTS, TTS_Config
from tools.assets import css, js, top_html
from tools.i18n.i18n import I18nAuto, scan_language_list
language = os.environ.get("language", "Auto")
language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
i18n = I18nAuto(language=language)
# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。
if torch.cuda.is_available():
device = "cuda"
# elif torch.backends.mps.is_available():
# device = "mps"
else:
device = "cpu"
# is_half = False
# device = "cpu"
dict_language_v1 = {
i18n("中文"): "all_zh", # 全部按中文识别
i18n("英文"): "en", # 全部按英文识别#######不变
i18n("日文"): "all_ja", # 全部按日文识别
i18n("中英混合"): "zh", # 按中英混合识别####不变
i18n("日英混合"): "ja", # 按日英混合识别####不变
i18n("多语种混合"): "auto", # 多语种启动切分识别语种
}
dict_language_v2 = {
i18n("中文"): "all_zh", # 全部按中文识别
i18n("英文"): "en", # 全部按英文识别#######不变
i18n("日文"): "all_ja", # 全部按日文识别
i18n("粤语"): "all_yue", # 全部按中文识别
i18n("韩文"): "all_ko", # 全部按韩文识别
i18n("中英混合"): "zh", # 按中英混合识别####不变
i18n("日英混合"): "ja", # 按日英混合识别####不变
i18n("粤英混合"): "yue", # 按粤英混合识别####不变
i18n("韩英混合"): "ko", # 按韩英混合识别####不变
i18n("多语种混合"): "auto", # 多语种启动切分识别语种
i18n("多语种混合(粤语)"): "auto_yue", # 多语种启动切分识别语种
}
dict_language = dict_language_v1 if version == "v1" else dict_language_v2
cut_method = {
i18n("不切"): "cut0",
i18n("凑四句一切"): "cut1",
i18n("凑50字一切"): "cut2",
i18n("按中文句号。切"): "cut3",
i18n("按英文句号.切"): "cut4",
i18n("按标点符号切"): "cut5",
}
from config import change_choices, get_weights_names, name2gpt_path, name2sovits_path
SoVITS_names, GPT_names = get_weights_names()
from config import pretrained_sovits_name
path_sovits_v3 = pretrained_sovits_name["v3"]
path_sovits_v4 = pretrained_sovits_name["v4"]
is_exist_s2gv3 = os.path.exists(path_sovits_v3)
is_exist_s2gv4 = os.path.exists(path_sovits_v4)
tts_config = TTS_Config("configs/tts_infer.yaml")
tts_config.device = device
tts_config.is_half = is_half
tts_config.version = version
if gpt_path is not None:
if "!" in gpt_path or "!" in gpt_path:
gpt_path = name2gpt_path[gpt_path]
tts_config.t2s_weights_path = gpt_path
if sovits_path is not None:
if "!" in sovits_path or "!" in sovits_path:
sovits_path = name2sovits_path[sovits_path]
tts_config.vits_weights_path = sovits_path
if cnhubert_base_path is not None:
tts_config.cnhuhbert_base_path = cnhubert_base_path
if bert_path is not None:
tts_config.bert_base_path = bert_path
print(tts_config)
tts_pipeline = TTS(tts_config)
gpt_path = tts_config.t2s_weights_path
sovits_path = tts_config.vits_weights_path
version = tts_config.version
def inference(
text,
text_lang,
ref_audio_path,
aux_ref_audio_paths,
prompt_text,
prompt_lang,
top_k,
top_p,
temperature,
text_split_method,
batch_size,
speed_factor,
ref_text_free,
split_bucket,
fragment_interval,
seed,
keep_random,
parallel_infer,
repetition_penalty,
sample_steps,
super_sampling,
):
seed = -1 if keep_random else seed
actual_seed = seed if seed not in [-1, "", None] else random.randint(0, 2**32 - 1)
inputs = {
"text": text,
"text_lang": dict_language[text_lang],
"ref_audio_path": ref_audio_path,
"aux_ref_audio_paths": [item.name for item in aux_ref_audio_paths] if aux_ref_audio_paths is not None else [],
"prompt_text": prompt_text if not ref_text_free else "",
"prompt_lang": dict_language[prompt_lang],
"top_k": top_k,
"top_p": top_p,
"temperature": temperature,
"text_split_method": cut_method[text_split_method],
"batch_size": int(batch_size),
"speed_factor": float(speed_factor),
"split_bucket": split_bucket,
"return_fragment": False,
"fragment_interval": fragment_interval,
"seed": actual_seed,
"parallel_infer": parallel_infer,
"repetition_penalty": repetition_penalty,
"sample_steps": int(sample_steps),
"super_sampling": super_sampling,
}
try:
for item in tts_pipeline.run(inputs):
yield item, actual_seed
except NO_PROMPT_ERROR:
gr.Warning(i18n("V3不支持无参考文本模式,请填写参考文本!"))
def custom_sort_key(s):
# 使用正则表达式提取字符串中的数字部分和非数字部分
parts = re.split("(\d+)", s)
# 将数字部分转换为整数,非数字部分保持不变
parts = [int(part) if part.isdigit() else part for part in parts]
return parts
if os.path.exists("./weight.json"):
pass
else:
with open("./weight.json", "w", encoding="utf-8") as file:
json.dump({"GPT": {}, "SoVITS": {}}, file)
with open("./weight.json", "r", encoding="utf-8") as file:
weight_data = file.read()
weight_data = json.loads(weight_data)
gpt_path = os.environ.get("gpt_path", weight_data.get("GPT", {}).get(version, GPT_names[-1]))
sovits_path = os.environ.get("sovits_path", weight_data.get("SoVITS", {}).get(version, SoVITS_names[0]))
if isinstance(gpt_path, list):
gpt_path = gpt_path[0]
if isinstance(sovits_path, list):
sovits_path = sovits_path[0]
from process_ckpt import get_sovits_version_from_path_fast
v3v4set = {"v3", "v4"}
def change_sovits_weights(sovits_path, prompt_language=None, text_language=None):
if "!" in sovits_path or "!" in sovits_path:
sovits_path = name2sovits_path[sovits_path]
global version, model_version, dict_language, if_lora_v3
version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(sovits_path)
# print(sovits_path,version, model_version, if_lora_v3)
is_exist = is_exist_s2gv3 if model_version == "v3" else is_exist_s2gv4
path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
if if_lora_v3 == True and is_exist == False:
info = path_sovits + "SoVITS %s" % model_version + i18n("底模缺失,无法加载相应 LoRA 权重")
gr.Warning(info)
raise FileExistsError(info)
dict_language = dict_language_v1 if version == "v1" else dict_language_v2
if prompt_language is not None and text_language is not None:
if prompt_language in list(dict_language.keys()):
prompt_text_update, prompt_language_update = (
{"__type__": "update"},
{"__type__": "update", "value": prompt_language},
)
else:
prompt_text_update = {"__type__": "update", "value": ""}
prompt_language_update = {"__type__": "update", "value": i18n("中文")}
if text_language in list(dict_language.keys()):
text_update, text_language_update = {"__type__": "update"}, {"__type__": "update", "value": text_language}
else:
text_update = {"__type__": "update", "value": ""}
text_language_update = {"__type__": "update", "value": i18n("中文")}
if model_version in v3v4set:
visible_sample_steps = True
visible_inp_refs = False
else:
visible_sample_steps = False
visible_inp_refs = True
yield (
{"__type__": "update", "choices": list(dict_language.keys())},
{"__type__": "update", "choices": list(dict_language.keys())},
prompt_text_update,
prompt_language_update,
text_update,
text_language_update,
{"__type__": "update", "interactive": visible_sample_steps, "value": 32},
{"__type__": "update", "visible": visible_inp_refs},
{"__type__": "update", "interactive": True if model_version not in v3v4set else False},
{"__type__": "update", "value": i18n("模型加载中,请等待"), "interactive": False},
)
tts_pipeline.init_vits_weights(sovits_path)
yield (
{"__type__": "update", "choices": list(dict_language.keys())},
{"__type__": "update", "choices": list(dict_language.keys())},
prompt_text_update,
prompt_language_update,
text_update,
text_language_update,
{"__type__": "update", "interactive": visible_sample_steps, "value": 32},
{"__type__": "update", "visible": visible_inp_refs},
{"__type__": "update", "interactive": True if model_version not in v3v4set else False},
{"__type__": "update", "value": i18n("合成语音"), "interactive": True},
)
with open("./weight.json") as f:
data = f.read()
data = json.loads(data)
data["SoVITS"][version] = sovits_path
with open("./weight.json", "w") as f:
f.write(json.dumps(data))
def change_gpt_weights(gpt_path):
if "!" in gpt_path or "!" in gpt_path:
gpt_path = name2gpt_path[gpt_path]
tts_pipeline.init_t2s_weights(gpt_path)
with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css) as app:
gr.HTML(
top_html.format(
i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.")
+ i18n("如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.")
),
elem_classes="markdown",
)
with gr.Column():
# with gr.Group():
gr.Markdown(value=i18n("模型切换"))
with gr.Row():
GPT_dropdown = gr.Dropdown(
label=i18n("GPT模型列表"),
choices=sorted(GPT_names, key=custom_sort_key),
value=gpt_path,
interactive=True,
)
SoVITS_dropdown = gr.Dropdown(
label=i18n("SoVITS模型列表"),
choices=sorted(SoVITS_names, key=custom_sort_key),
value=sovits_path,
interactive=True,
)
refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
with gr.Row():
with gr.Column():
gr.Markdown(value=i18n("*请上传并填写参考信息"))
with gr.Row():
inp_ref = gr.Audio(label=i18n("主参考音频(请上传3~10秒内参考音频,超过会报错!)"), type="filepath")
inp_refs = gr.File(
label=i18n("辅参考音频(可选多个,或不选)"),
file_count="multiple",
visible=True if model_version != "v3" else False,
)
prompt_text = gr.Textbox(label=i18n("主参考音频的文本"), value="", lines=2)
with gr.Row():
prompt_language = gr.Dropdown(
label=i18n("主参考音频的语种"), choices=list(dict_language.keys()), value=i18n("中文")
)
with gr.Column():
ref_text_free = gr.Checkbox(
label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"),
value=False,
interactive=True if model_version != "v3" else False,
show_label=True,
)
gr.Markdown(
i18n("使用无参考文本模式时建议使用微调的GPT")
+ "<br>"
+ i18n("听不清参考音频说的啥(不晓得写啥)可以开。开启后无视填写的参考文本。")
)
with gr.Column():
gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式"))
text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=20, max_lines=20)
text_language = gr.Dropdown(
label=i18n("需要合成的文本的语种"), choices=list(dict_language.keys()), value=i18n("中文")
)
with gr.Group():
gr.Markdown(value=i18n("推理设置"))
with gr.Row():
with gr.Column():
with gr.Row():
batch_size = gr.Slider(
minimum=1, maximum=200, step=1, label=i18n("batch_size"), value=20, interactive=True
)
sample_steps = gr.Radio(
label=i18n("采样步数(仅对V3/4生效)"), value=32, choices=[4, 8, 16, 32, 64, 128], visible=True
)
with gr.Row():
fragment_interval = gr.Slider(
minimum=0.01, maximum=1, step=0.01, label=i18n("分段间隔(秒)"), value=0.3, interactive=True
)
speed_factor = gr.Slider(
minimum=0.6, maximum=1.65, step=0.05, label="语速", value=1.0, interactive=True
)
with gr.Row():
top_k = gr.Slider(minimum=1, maximum=100, step=1, label=i18n("top_k"), value=5, interactive=True)
top_p = gr.Slider(minimum=0, maximum=1, step=0.05, label=i18n("top_p"), value=1, interactive=True)
with gr.Row():
temperature = gr.Slider(
minimum=0, maximum=1, step=0.05, label=i18n("temperature"), value=1, interactive=True
)
repetition_penalty = gr.Slider(
minimum=0, maximum=2, step=0.05, label=i18n("重复惩罚"), value=1.35, interactive=True
)
with gr.Column():
with gr.Row():
how_to_cut = gr.Dropdown(
label=i18n("怎么切"),
choices=[
i18n("不切"),
i18n("凑四句一切"),
i18n("凑50字一切"),
i18n("按中文句号。切"),
i18n("按英文句号.切"),
i18n("按标点符号切"),
],
value=i18n("凑四句一切"),
interactive=True,
scale=1,
)
super_sampling = gr.Checkbox(
label=i18n("音频超采样(仅对V3生效))"), value=False, interactive=True, show_label=True
)
with gr.Row():
parallel_infer = gr.Checkbox(label=i18n("并行推理"), value=True, interactive=True, show_label=True)
split_bucket = gr.Checkbox(
label=i18n("数据分桶(并行推理时会降低一点计算量)"),
value=True,
interactive=True,
show_label=True,
)
with gr.Row():
seed = gr.Number(label=i18n("随机种子"), value=-1)
keep_random = gr.Checkbox(label=i18n("保持随机"), value=True, interactive=True, show_label=True)
output = gr.Audio(label=i18n("输出的语音"))
with gr.Row():
inference_button = gr.Button(i18n("合成语音"), variant="primary")
stop_infer = gr.Button(i18n("终止合成"), variant="primary")
inference_button.click(
inference,
[
text,
text_language,
inp_ref,
inp_refs,
prompt_text,
prompt_language,
top_k,
top_p,
temperature,
how_to_cut,
batch_size,
speed_factor,
ref_text_free,
split_bucket,
fragment_interval,
seed,
keep_random,
parallel_infer,
repetition_penalty,
sample_steps,
super_sampling,
],
[output, seed],
)
stop_infer.click(tts_pipeline.stop, [], [])
SoVITS_dropdown.change(
change_sovits_weights,
[SoVITS_dropdown, prompt_language, text_language],
[
prompt_language,
text_language,
prompt_text,
prompt_language,
text,
text_language,
sample_steps,
inp_refs,
ref_text_free,
inference_button,
],
) #
GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
with gr.Group():
gr.Markdown(
value=i18n(
"文本切分工具。太长的文本合成出来效果不一定好,所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"
)
)
with gr.Row():
text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="", lines=4)
with gr.Column():
_how_to_cut = gr.Radio(
label=i18n("怎么切"),
choices=[
i18n("不切"),
i18n("凑四句一切"),
i18n("凑50字一切"),
i18n("按中文句号。切"),
i18n("按英文句号.切"),
i18n("按标点符号切"),
],
value=i18n("凑四句一切"),
interactive=True,
)
cut_text = gr.Button(i18n("切分"), variant="primary")
def to_cut(text_inp, how_to_cut):
if len(text_inp.strip()) == 0 or text_inp == []:
return ""
method = get_method(cut_method[how_to_cut])
return method(text_inp)
text_opt = gr.Textbox(label=i18n("切分后文本"), value="", lines=4)
cut_text.click(to_cut, [text_inp, _how_to_cut], [text_opt])
gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。"))
if __name__ == "__main__":
app.queue().launch( # concurrency_count=511, max_size=1022
server_name="0.0.0.0",
inbrowser=True,
share=is_share,
server_port=infer_ttswebui,
# quiet=True,
)
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