mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-09-29 08:49:59 +08:00
570 lines
21 KiB
Python
570 lines
21 KiB
Python
"""
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按中英混合识别
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按日英混合识别
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多语种启动切分识别语种
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全部按中文识别
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全部按英文识别
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全部按日文识别
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"""
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import argparse
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import contextlib
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import logging
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import os
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import random
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import re
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from functools import partial
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import gradio as gr
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import psutil
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import torch
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from config import change_choices, custom_sort_key, get_dtype, get_weights_names, pretrained_sovits_name
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from config import infer_device as default_device
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from GPT_SoVITS.process_ckpt import inspect_version
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from GPT_SoVITS.TTS_infer_pack.text_segmentation_method import get_method
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from GPT_SoVITS.TTS_infer_pack.TTS import NO_PROMPT_ERROR, TTS, TTS_Config
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from tools.assets import css, js, top_html
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from tools.i18n.i18n import I18nAuto, scan_language_list
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logging.getLogger("markdown_it").setLevel(logging.ERROR)
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logging.getLogger("urllib3").setLevel(logging.ERROR)
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logging.getLogger("httpcore").setLevel(logging.ERROR)
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logging.getLogger("httpx").setLevel(logging.ERROR)
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logging.getLogger("asyncio").setLevel(logging.ERROR)
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logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
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logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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def set_high_priority():
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if os.name != "nt":
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return
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p = psutil.Process(os.getpid())
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with contextlib.suppress(psutil.AccessDenied):
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p.nice(psutil.HIGH_PRIORITY_CLASS)
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print("已将进程优先级设为 High")
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set_high_priority()
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_LANG_RE = re.compile(r"^[a-z]{2}[_-][A-Z]{2}$")
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def lang_type(text: str) -> str:
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if text == "Auto":
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return text
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if not _LANG_RE.match(text):
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raise argparse.ArgumentTypeError(f"Unspported Format: {text}, Expected ll_CC/ll-CC")
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ll, cc = re.split(r"[_-]", text)
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language = f"{ll}_{cc}"
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if language in scan_language_list():
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return language
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else:
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return "Auto"
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def build_parser() -> argparse.ArgumentParser:
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p = argparse.ArgumentParser(
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prog="inference_webui",
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description="python -s inference_webui.py zh_CN -i naive",
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)
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p.add_argument(
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"language",
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nargs="?",
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default="Auto",
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type=lang_type,
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help="Language Code, Such as zh_CN, en-US",
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)
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p.add_argument(
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"--device",
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"-d",
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default=str(default_device),
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help="Inference Device",
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required=False,
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)
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p.add_argument(
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"--port",
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"-p",
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default=9872,
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type=int,
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help="WebUI Binding Port",
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required=False,
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)
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p.add_argument(
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"--share",
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"-s",
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default=False,
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action="store_true",
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help="Gradio Share Link",
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required=False,
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)
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p.add_argument(
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"--cnhubert",
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default="GPT_SoVITS/pretrained_models/chinese-hubert-base",
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help="CNHuBERT Pretrain",
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required=False,
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)
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p.add_argument(
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"--bert",
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default="GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
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help="BERT Pretrain",
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required=False,
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)
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p.add_argument(
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"--gpt",
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default="",
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help="GPT Model",
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required=False,
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)
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p.add_argument(
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"--sovits",
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default="",
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help="SoVITS Model",
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required=False,
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)
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return p
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args = build_parser().parse_args()
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infer_ttswebui = int(args.port)
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is_share = args.share
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infer_device = torch.device(args.device)
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device = infer_device
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if torch.mps.is_available():
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device = torch.device("cpu")
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dtype = get_dtype(device.index)
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is_half = dtype == torch.float16
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i18n = I18nAuto(language=args.language)
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change_choices_gradio = partial(change_choices, i18n=i18n)
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SoVITS_names, GPT_names = get_weights_names(i18n)
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gpt_path = str(args.gpt) or GPT_names[0][-1]
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sovits_path = str(args.sovits) or SoVITS_names[0][-1]
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cnhubert_base_path = str(args.cuhubert)
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bert_path = str(args.bert)
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version = model_version = "v2"
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dict_language_v1 = {
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i18n("中文"): "all_zh", # 全部按中文识别
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i18n("英文"): "en", # 全部按英文识别#######不变
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i18n("日文"): "all_ja", # 全部按日文识别
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i18n("中英混合"): "zh", # 按中英混合识别####不变
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i18n("日英混合"): "ja", # 按日英混合识别####不变
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i18n("多语种混合"): "auto", # 多语种启动切分识别语种
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}
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dict_language_v2 = {
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i18n("中文"): "all_zh", # 全部按中文识别
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i18n("英文"): "en", # 全部按英文识别#######不变
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i18n("日文"): "all_ja", # 全部按日文识别
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i18n("粤语"): "all_yue", # 全部按中文识别
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i18n("韩文"): "all_ko", # 全部按韩文识别
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i18n("中英混合"): "zh", # 按中英混合识别####不变
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i18n("日英混合"): "ja", # 按日英混合识别####不变
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i18n("粤英混合"): "yue", # 按粤英混合识别####不变
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i18n("韩英混合"): "ko", # 按韩英混合识别####不变
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i18n("多语种混合"): "auto", # 多语种启动切分识别语种
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i18n("多语种混合(粤语)"): "auto_yue", # 多语种启动切分识别语种
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}
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dict_language = dict_language_v1 if version == "v1" else dict_language_v2
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cut_method = {
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i18n("不切"): "cut0",
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i18n("凑四句一切"): "cut1",
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i18n("凑50字一切"): "cut2",
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i18n("按中文句号。切"): "cut3",
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i18n("按英文句号.切"): "cut4",
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i18n("按标点符号切"): "cut5",
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}
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path_sovits_v3 = pretrained_sovits_name["v3"]
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path_sovits_v4 = pretrained_sovits_name["v4"]
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is_exist_s2gv3 = os.path.exists(path_sovits_v3)
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is_exist_s2gv4 = os.path.exists(path_sovits_v4)
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tts_config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml")
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tts_config.device = device
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tts_config.is_half = is_half
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# tts_config.version = version
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tts_config.update_version(version)
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if gpt_path is not None:
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tts_config.t2s_weights_path = gpt_path
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if sovits_path is not None:
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tts_config.vits_weights_path = sovits_path
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if cnhubert_base_path is not None:
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tts_config.cnhuhbert_base_path = cnhubert_base_path
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if bert_path is not None:
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tts_config.bert_base_path = bert_path
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print(tts_config)
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tts_pipeline = TTS(tts_config)
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gpt_path = tts_config.t2s_weights_path
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sovits_path = tts_config.vits_weights_path
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version = tts_config.version
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def inference(
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text,
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text_lang,
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ref_audio_path,
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aux_ref_audio_paths,
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prompt_text,
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prompt_lang,
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top_k,
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top_p,
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temperature,
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text_split_method,
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batch_size,
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speed_factor,
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ref_text_free,
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split_bucket,
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fragment_interval,
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seed,
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keep_random,
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parallel_infer,
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repetition_penalty,
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sample_steps,
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super_sampling,
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):
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seed = -1 if keep_random else seed
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actual_seed = seed if seed not in [-1, "", None] else random.randint(0, 2**32 - 1)
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inputs = {
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"text": text,
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"text_lang": dict_language[text_lang],
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"ref_audio_path": ref_audio_path,
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"aux_ref_audio_paths": [item.name for item in aux_ref_audio_paths] if aux_ref_audio_paths is not None else [],
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"prompt_text": prompt_text if not ref_text_free else "",
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"prompt_lang": dict_language[prompt_lang],
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"top_k": top_k,
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"top_p": top_p,
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"temperature": temperature,
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"text_split_method": cut_method[text_split_method],
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"batch_size": int(batch_size),
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"speed_factor": float(speed_factor),
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"split_bucket": split_bucket,
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"return_fragment": False,
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"fragment_interval": fragment_interval,
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"seed": actual_seed,
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"parallel_infer": parallel_infer,
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"repetition_penalty": repetition_penalty,
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"sample_steps": int(sample_steps),
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"super_sampling": super_sampling,
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}
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try:
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for item in tts_pipeline.run(inputs):
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yield item, actual_seed
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except NO_PROMPT_ERROR:
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gr.Warning(i18n("V3不支持无参考文本模式，请填写参考文本！"))
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v3v4set = {"v3", "v4"}
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def change_sovits_weights(sovits_path, prompt_language=None, text_language=None):
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global version, model_version, dict_language, is_lora
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model_version, version, is_lora, _, __ = inspect_version(sovits_path)
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# print(sovits_path,version, model_version, is_lora)
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is_exist = is_exist_s2gv3 if model_version == "v3" else is_exist_s2gv4
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path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
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if is_lora is True and is_exist is False:
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info = path_sovits + f"SoVITS {model_version}" + i18n("底模缺失，无法加载相应 LoRA 权重")
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gr.Warning(info)
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raise FileExistsError(info)
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dict_language = dict_language_v1 if version == "v1" else dict_language_v2
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if prompt_language is not None and text_language is not None:
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if prompt_language in list(dict_language.keys()):
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prompt_text_update, prompt_language_update = gr.skip(), gr.skip()
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else:
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prompt_text_update = gr.update(value="")
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prompt_language_update = gr.update(value=i18n("中文"))
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if text_language in list(dict_language.keys()):
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text_update, text_language_update = gr.skip(), gr.skip()
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else:
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text_update = gr.update(value="")
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text_language_update = gr.update(value=i18n("中文"))
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if model_version in v3v4set:
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visible_sample_steps = True
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visible_inp_refs = False
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else:
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visible_sample_steps = False
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visible_inp_refs = True
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yield (
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gr.update(choices=list(dict_language.keys())),
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gr.update(choices=list(dict_language.keys())),
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prompt_text_update,
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prompt_language_update,
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text_update,
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text_language_update,
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gr.update(
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visible=visible_sample_steps,
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value=32 if model_version == "v3" else 8,
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choices=[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
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),
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gr.update(visible=visible_inp_refs),
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gr.update(interactive=True if model_version not in v3v4set else False),
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gr.update(value=i18n("模型加载中，请等待"), interactive=False),
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)
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tts_pipeline.init_vits_weights(sovits_path)
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yield (
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gr.update(choices=list(dict_language.keys())),
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gr.update(choices=list(dict_language.keys())),
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prompt_text_update,
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prompt_language_update,
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text_update,
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text_language_update,
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gr.update(
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visible=visible_sample_steps,
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value=32 if model_version == "v3" else 8,
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choices=[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
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),
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gr.update(visible=visible_inp_refs),
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gr.update(interactive=True if model_version not in v3v4set else False),
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gr.update(value=i18n("合成语音"), interactive=True),
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)
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def change_gpt_weights(gpt_path):
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tts_pipeline.init_t2s_weights(gpt_path)
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with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css) as app:
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gr.HTML(
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top_html.format(
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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.")
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+ 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.")
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),
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elem_classes="markdown",
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)
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with gr.Column():
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# with gr.Group():
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gr.Markdown(value=i18n("模型切换"))
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with gr.Row(equal_height=True):
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GPT_dropdown = gr.Dropdown(
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label=i18n("GPT模型列表"),
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choices=sorted(GPT_names, key=custom_sort_key),
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value=gpt_path,
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interactive=True,
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)
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SoVITS_dropdown = gr.Dropdown(
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label=i18n("SoVITS模型列表"),
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choices=sorted(SoVITS_names, key=custom_sort_key),
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value=sovits_path,
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interactive=True,
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)
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refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
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refresh_button.click(fn=change_choices_gradio, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
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with gr.Row(equal_height=True):
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with gr.Column():
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gr.Markdown(value=i18n("*请上传并填写参考信息"))
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with gr.Row(equal_height=True):
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inp_ref = gr.Audio(
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label=i18n("主参考音频(请上传3~10秒内参考音频，超过会报错！)"),
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type="filepath",
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waveform_options={"show_recording_waveform": False},
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)
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inp_refs = gr.File(
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label=i18n("辅参考音频(可选多个，或不选)"),
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file_count="multiple",
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visible=True if model_version != "v3" else False,
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)
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prompt_text = gr.Textbox(label=i18n("主参考音频的文本"), value="", lines=2)
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with gr.Row(equal_height=True):
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prompt_language = gr.Dropdown(
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label=i18n("主参考音频的语种"), choices=list(dict_language.keys()), value=i18n("中文")
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)
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with gr.Column():
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ref_text_free = gr.Checkbox(
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label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"),
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value=False,
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interactive=True if model_version != "v3" else False,
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show_label=True,
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)
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gr.Markdown(
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i18n("使用无参考文本模式时建议使用微调的GPT")
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+ "<br>"
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+ 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。")
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)
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with gr.Column():
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gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式"))
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text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=20, max_lines=20)
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text_language = gr.Dropdown(
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label=i18n("需要合成的文本的语种"), choices=list(dict_language.keys()), value=i18n("中文")
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)
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with gr.Group():
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gr.Markdown(value=i18n("推理设置"))
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with gr.Row(equal_height=True):
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with gr.Column():
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with gr.Row(equal_height=True):
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batch_size = gr.Slider(
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minimum=1, maximum=200, step=1, label=i18n("batch_size"), value=20, interactive=True
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)
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sample_steps = gr.Radio(
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label=i18n("采样步数(仅对V3/4生效)"), value=32, choices=[4, 8, 16, 32, 64, 128], visible=True
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)
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with gr.Row(equal_height=True):
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fragment_interval = gr.Slider(
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minimum=0.01, maximum=1, step=0.01, label=i18n("分段间隔(秒)"), value=0.3, interactive=True
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)
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speed_factor = gr.Slider(
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minimum=0.6, maximum=1.65, step=0.05, label="语速", value=1.0, interactive=True
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)
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with gr.Row(equal_height=True):
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top_k = gr.Slider(minimum=1, maximum=100, step=1, label=i18n("top_k"), value=5, interactive=True)
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top_p = gr.Slider(minimum=0, maximum=1, step=0.05, label=i18n("top_p"), value=1, interactive=True)
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with gr.Row(equal_height=True):
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temperature = gr.Slider(
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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(equal_height=True):
|
||
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(equal_height=True):
|
||
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(equal_height=True):
|
||
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"),
|
||
waveform_options={"show_recording_waveform": False},
|
||
)
|
||
with gr.Row(equal_height=True):
|
||
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(equal_height=True):
|
||
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,
|
||
)
|