import argparse import json import os import platform import re import shutil import traceback from functools import partial from multiprocessing import cpu_count from subprocess import Popen import gradio as gr import psutil import torch import yaml from config import ( GPU_INDEX, GPU_INFOS, IS_GPU, GPT_weight_root, GPT_weight_version2root, SoVITS_weight_root, SoVITS_weight_version2root, change_choices, exp_root, get_weights_names, infer_device, is_half, is_share, memset, pretrained_gpt_name, pretrained_sovits_name, python_exec, webui_port_infer_tts, webui_port_main, webui_port_subfix, webui_port_uvr5, ) from GPT_SoVITS.Accelerate import backends, console, logger from tools import my_utils from tools.asr.config import asr_dict from tools.assets import css, js, top_html from tools.i18n.i18n import I18nAuto, scan_language_list from tools.my_utils import check_details, check_for_existance os.environ["PYTHONPATH"] = now_dir = os.getcwd() os.environ["version"] = version = "v2Pro" os.environ["TORCH_DISTRIBUTED_DEBUG"] = "INFO" os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1" os.environ["all_proxy"] = "" backends_gradio = [(b.replace("-", " "), b) for b in backends] _LANG_RE = re.compile(r"^[a-z]{2}[_-][A-Z]{2}$") def lang_type(text: str) -> str: if text == "Auto": return text if not _LANG_RE.match(text): raise argparse.ArgumentTypeError(f"Unspported Format: {text}, Expected ll_CC/ll-CC") ll, cc = re.split(r"[_-]", text) language = f"{ll}_{cc}" if language in scan_language_list(): return language else: return "en_US" def build_parser() -> argparse.ArgumentParser: p = argparse.ArgumentParser( prog="python -s webui.py", description="python -s webui.py zh_CN", ) p.add_argument( "language", nargs="?", default="Auto", type=lang_type, help="Language Code, Such as zh_CN, en-US", ) return p args = build_parser().parse_args() tmp = os.path.join(now_dir, "TEMP") os.makedirs(tmp, exist_ok=True) os.environ["TEMP"] = tmp if os.path.exists(tmp): for name in os.listdir(tmp): if name == "jieba.cache": continue path = f"{tmp}/{name}" delete = os.remove if os.path.isfile(path) else shutil.rmtree try: delete(path) except Exception as e: console.print(e) pass language = str(args.language) i18n = I18nAuto(language=language) change_choice = partial(change_choices, i18n=i18n) n_cpu = cpu_count() set_gpu_numbers = GPU_INDEX gpu_infos = GPU_INFOS mem = memset is_gpu_ok = IS_GPU v3v4set = {"v3", "v4"} sv_path = "GPT_SoVITS/pretrained_models/sv/pretrained_eres2netv2w24s4ep4.ckpt" def set_default(): global \ default_batch_size, \ default_max_batch_size, \ gpu_info, \ default_sovits_epoch, \ default_sovits_save_every_epoch, \ max_sovits_epoch, \ max_sovits_save_every_epoch, \ default_batch_size_s1, \ if_force_ckpt if_force_ckpt = False gpu_info = "\n".join(gpu_infos) if is_gpu_ok: minmem = min(mem) default_batch_size = minmem // 2 if version not in v3v4set else minmem // 8 default_batch_size_s1 = minmem // 2 else: default_batch_size = default_batch_size_s1 = int(psutil.virtual_memory().total / 1024 / 1024 / 1024 / 4) if version not in v3v4set: default_sovits_epoch = 8 default_sovits_save_every_epoch = 4 max_sovits_epoch = 25 # 40 max_sovits_save_every_epoch = 25 # 10 else: default_sovits_epoch = 2 default_sovits_save_every_epoch = 1 max_sovits_epoch = 16 # 40 # 3 #训太多=作死 max_sovits_save_every_epoch = 10 # 10 # 3 default_batch_size = max(1, default_batch_size) default_batch_size_s1 = max(1, default_batch_size_s1) default_max_batch_size = default_batch_size * 3 set_default() default_gpu_numbers = infer_device.index def check_pretrained_is_exist(version): pretrained_model_list = ( pretrained_sovits_name[version], pretrained_sovits_name[version].replace("s2G", "s2D"), pretrained_gpt_name[version], "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large", "GPT_SoVITS/pretrained_models/chinese-hubert-base", ) _ = "" for i in pretrained_model_list: if "s2Dv3" not in i and "s2Dv4" not in i and os.path.exists(i) is False: _ += f"\n {i}" if _: logger.warning(i18n("以下模型不存在:") + _) check_pretrained_is_exist(version) for key in pretrained_sovits_name.keys(): if os.path.exists(pretrained_sovits_name[key]) is False: pretrained_sovits_name[key] = "" for key in pretrained_gpt_name.keys(): if os.path.exists(pretrained_gpt_name[key]) is False: pretrained_gpt_name[key] = "" for root in SoVITS_weight_root + GPT_weight_root: os.makedirs(root, exist_ok=True) SoVITS_names, GPT_names = get_weights_names(i18n) p_label: Popen | None = None p_uvr5: Popen | None = None p_asr: Popen | None = None p_denoise: Popen | None = None p_tts_inference: Popen | None = None def kill_process(pid: int, process_name=""): try: p = psutil.Process(pid) except psutil.NoSuchProcess: return for c in p.children(recursive=False): try: c.kill() c.wait(timeout=5) except (psutil.NoSuchProcess, psutil.TimeoutExpired): pass try: p.kill() p.wait(timeout=5) except (psutil.NoSuchProcess, psutil.TimeoutExpired): pass console.print(process_name + i18n("进程已终止")) def process_info(process_name="", indicator=""): if indicator == "opened": return process_name + i18n("已开启") elif indicator == "open": return i18n("开启") + process_name elif indicator == "closed": return process_name + i18n("已关闭") elif indicator == "close": return i18n("关闭") + process_name elif indicator == "running": return process_name + i18n("运行中") elif indicator == "occupy": return process_name + i18n("占用中") + "," + i18n("需先终止才能开启下一次任务") elif indicator == "finish": return process_name + i18n("已完成") elif indicator == "failed": return process_name + i18n("失败") elif indicator == "info": return process_name + i18n("进程输出信息") else: return process_name process_name_subfix = i18n("音频标注WebUI") def change_label(path_list): global p_label if p_label is None: check_for_existance([path_list]) path_list = my_utils.clean_path(path_list) cmd = '"%s" -s tools/subfix_webui.py --load_list "%s" --webui_port %s --is_share %s' % ( python_exec, path_list, webui_port_subfix, is_share, ) yield ( process_info(process_name_subfix, "opened"), gr.update(visible=False), gr.update(visible=True), ) console.print(cmd) p_label = Popen(cmd, shell=True) else: kill_process(p_label.pid, process_name_subfix) p_label = None yield ( process_info(process_name_subfix, "closed"), gr.update(visible=True), gr.update(visible=False), ) process_name_uvr5 = i18n("人声分离WebUI") def change_uvr5(): global p_uvr5 if p_uvr5 is None: cmd = '"%s" -s tools/uvr5/webui.py "%s" %s %s %s' % ( python_exec, infer_device, is_half, webui_port_uvr5, is_share, ) yield ( process_info(process_name_uvr5, "opened"), gr.update(visible=False), gr.update(visible=True), ) console.print(cmd) p_uvr5 = Popen(cmd, shell=True) else: kill_process(p_uvr5.pid, process_name_uvr5) p_uvr5 = None yield ( process_info(process_name_uvr5, "closed"), gr.update(visible=True), gr.update(visible=False), ) process_name_tts = i18n("TTS推理WebUI") def change_tts_inference( gpu_number: int, gpt_path: str, sovits_path: str, batched_infer_enabled: bool, backends_dropdown: str, ): console.print(gpt_path, sovits_path) global p_tts_inference env = os.environ.copy() cmd: list[str] = [python_exec, "-s"] if batched_infer_enabled: # fmt: off cmd.extend( [ "GPT_SoVITS/inference_webui_fast.py", language, "-d", f"{infer_device.type}:{gpu_number}", "-p", str(webui_port_infer_tts), "--gpt", gpt_path, "--sovits", sovits_path, ] ) # fmt: on else: # fmt: off cmd.extend( [ "GPT_SoVITS/inference_webui.py", language, "-b", backends_dropdown, "-d", f"{infer_device.type}:{gpu_number}", "-p", str(webui_port_infer_tts), "--gpt", gpt_path, "--sovits", sovits_path, ] ) # fmt: on if is_share: cmd.append("-s") if p_tts_inference is None: yield ( process_info(process_name_tts, "opened"), gr.update(visible=False), gr.update(visible=True), ) console.print(" ".join(cmd)) p_tts_inference = Popen(cmd, env=env) else: kill_process(p_tts_inference.pid, process_name_tts) p_tts_inference = None yield ( process_info(process_name_tts, "closed"), gr.update(visible=True), gr.update(visible=False), ) process_name_asr = i18n("语音识别") def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang, asr_precision): global p_asr if p_asr is None: asr_inp_dir = my_utils.clean_path(asr_inp_dir) asr_opt_dir = my_utils.clean_path(asr_opt_dir) check_for_existance([asr_inp_dir]) cmd = f'"{python_exec}" -s tools/asr/{asr_dict[asr_model]["path"]}' cmd += f' -i "{asr_inp_dir}"' cmd += f' -o "{asr_opt_dir}"' cmd += f" -s {asr_model_size}" cmd += f" -l {asr_lang}" cmd += f" -p {asr_precision}" output_file_name = os.path.basename(asr_inp_dir) output_folder = asr_opt_dir or "output/asr_opt" output_file_path = os.path.abspath(f"{output_folder}/{output_file_name}.list") yield ( process_info(process_name_asr, "opened"), gr.update(visible=False), gr.update(visible=True), gr.skip(), gr.skip(), gr.skip(), ) console.print(cmd) p_asr = Popen(cmd, shell=True) p_asr.wait() p_asr = None yield ( process_info(process_name_asr, "finish"), gr.update(visible=True), gr.update(visible=False), gr.update(value=output_file_path), gr.update(value=output_file_path), gr.update(value=asr_inp_dir), ) else: yield ( process_info(process_name_asr, "occupy"), gr.update(visible=False), gr.update(visible=True), gr.skip(), gr.skip(), gr.skip(), ) def close_asr(): global p_asr if p_asr is not None: kill_process(p_asr.pid, process_name_asr) p_asr = None return ( process_info(process_name_asr, "closed"), gr.update(visible=True), gr.update(visible=False), ) p_train_SoVITS: Popen | None = None process_name_sovits = i18n("SoVITS训练") def open1Ba( version, batch_size, total_epoch, exp_name, text_low_lr_rate, if_save_latest, if_save_every_weights, save_every_epoch, gpu_numbers1Ba, pretrained_s2G, pretrained_s2D, if_grad_ckpt, lora_rank, ): global p_train_SoVITS if p_train_SoVITS is None: exp_name = exp_name.rstrip(" ") config_file = ( "GPT_SoVITS/configs/s2.json" if version not in {"v2Pro", "v2ProPlus"} else f"GPT_SoVITS/configs/s2{version}.json" ) with open(config_file) as f: config = f.read() data: dict = json.loads(config) s2_dir = f"{exp_root}/{exp_name}" os.makedirs(f"{s2_dir}/logs_s2_{version}", exist_ok=True) if check_for_existance([s2_dir], is_train=True): check_details([s2_dir], is_train=True) if is_half is False: data["train"]["fp16_run"] = False batch_size = max(1, batch_size // 2) data["train"]["batch_size"] = batch_size data["train"]["epochs"] = total_epoch data["train"]["text_low_lr_rate"] = text_low_lr_rate data["train"]["pretrained_s2G"] = pretrained_s2G data["train"]["pretrained_s2D"] = pretrained_s2D data["train"]["if_save_latest"] = if_save_latest data["train"]["if_save_every_weights"] = if_save_every_weights data["train"]["save_every_epoch"] = save_every_epoch data["train"]["grad_ckpt"] = if_grad_ckpt data["train"]["lora_rank"] = lora_rank data["model"]["version"] = version data["data"]["exp_dir"] = data["s2_ckpt_dir"] = s2_dir data["save_weight_dir"] = SoVITS_weight_version2root[version] data["name"] = exp_name data["version"] = version tmp_config_path = f"{tmp}/tmp_s2.json" with open(tmp_config_path, "w") as f: f.write(json.dumps(data)) env = os.environ.copy() env["CUDA_VISIBLE_DEVICES"] = str(gpu_numbers1Ba).strip("[]").replace(" ", "") if version in ["v1", "v2", "v2Pro", "v2ProPlus"]: cmd = [ python_exec, "-s", "GPT_SoVITS/s2_train.py", "--config", tmp_config_path, ] else: cmd = [ python_exec, "-s", "GPT_SoVITS/s2_train_v3_lora.py", "--config", tmp_config_path, ] console.print(" ".join(cmd)) p = Popen(cmd, env=env) p_train_SoVITS = p yield ( process_info(process_name_sovits, "opened"), gr.update(visible=False), gr.update(visible=True), gr.skip(), gr.skip(), ) code = p.wait() p_train_SoVITS = None if code == 0: yield ( process_info(process_name_sovits, "finish"), gr.update(visible=True), gr.update(visible=False), gr.skip(), gr.skip(), ) else: yield ( process_info(process_name_sovits, "failed"), gr.update(visible=True), gr.update(visible=False), gr.skip(), gr.skip(), ) return (gr.skip() for i in range(5)) SoVITS_dropdown_update, GPT_dropdown_update = change_choice() yield ( gr.skip(), gr.skip(), gr.skip(), SoVITS_dropdown_update, GPT_dropdown_update, ) else: yield ( process_info(process_name_sovits, "occupy"), gr.update(visible=False), gr.update(visible=True), gr.skip(), gr.skip(), ) def close1Ba(): global p_train_SoVITS if p_train_SoVITS: kill_process(p_train_SoVITS.pid, process_name_sovits) p_train_SoVITS = None return ( process_info(process_name_sovits, "closed"), gr.update(visible=True), gr.update(visible=False), ) p_train_GPT: Popen | None = None process_name_gpt = i18n("GPT训练") def open1Bb( batch_size, total_epoch, exp_name, if_dpo, if_save_latest, if_save_every_weights, save_every_epoch, gpu_numbers, pretrained_s1, ): global p_train_GPT if p_train_GPT is None: exp_name = exp_name.rstrip(" ") with open( "GPT_SoVITS/configs/s1longer.yaml" if version == "v1" else "GPT_SoVITS/configs/s1longer-v2.yaml" ) as f: config = f.read() data: dict = yaml.load(config, Loader=yaml.FullLoader) s1_dir = f"{exp_root}/{exp_name}" os.makedirs(f"{s1_dir}/logs_s1", exist_ok=True) if check_for_existance([s1_dir], is_train=True): check_details([s1_dir], is_train=True) if is_half is False or torch.mps.is_available(): data["train"]["precision"] = "32" batch_size = max(1, batch_size // 2) data["train"]["batch_size"] = batch_size data["train"]["epochs"] = total_epoch data["pretrained_s1"] = pretrained_s1 data["train"]["save_every_n_epoch"] = save_every_epoch data["train"]["if_save_every_weights"] = if_save_every_weights data["train"]["if_save_latest"] = if_save_latest data["train"]["if_dpo"] = if_dpo data["train"]["half_weights_save_dir"] = GPT_weight_version2root[version] data["train"]["exp_name"] = exp_name data["train_semantic_path"] = f"{s1_dir}/6-name2semantic.tsv" data["train_phoneme_path"] = f"{s1_dir}/2-name2text.txt" data["output_dir"] = f"{s1_dir}/logs_s1_{version}" env = os.environ.copy() env["CUDA_VISIBLE_DEVICES"] = str(gpu_numbers).strip("[]").replace(" ", "") tmp_config_path = f"{tmp}/tmp_s1.yaml" with open(tmp_config_path, "w") as f: f.write(yaml.dump(data, default_flow_style=False)) cmd = [python_exec, "-s", "GPT_SoVITS/s1_train.py", "--config_file", tmp_config_path] console.print(" ".join(cmd)) p = Popen(cmd, env=env) p_train_GPT = p yield ( process_info(process_name_gpt, "opened"), gr.update(visible=False), gr.update(visible=True), gr.skip(), gr.skip(), ) code = p.wait() p_train_GPT = None if code == 0: yield ( process_info(process_name_gpt, "finish"), gr.update(visible=True), gr.update(visible=False), gr.skip(), gr.skip(), ) else: yield ( process_info(process_name_gpt, "failed"), gr.update(visible=True), gr.update(visible=False), gr.skip(), gr.skip(), ) return (gr.skip() for i in range(5)) SoVITS_dropdown_update, GPT_dropdown_update = change_choice() yield ( gr.skip(), gr.skip(), gr.skip(), SoVITS_dropdown_update, GPT_dropdown_update, ) else: yield ( process_info(process_name_gpt, "occupy"), gr.update(visible=False), gr.update(visible=True), gr.skip(), gr.skip(), ) def close1Bb(): global p_train_GPT if p_train_GPT is not None: kill_process(p_train_GPT.pid, process_name_gpt) p_train_GPT = None return ( process_info(process_name_gpt, "closed"), gr.update(visible=True), gr.update(visible=False), ) ps_slice = [] process_name_slice = i18n("语音切分") def open_slice(inp, opt_root, threshold, min_length, min_interval, hop_size, max_sil_kept, _max, alpha, n_parts): global ps_slice inp = my_utils.clean_path(inp) opt_root = my_utils.clean_path(opt_root) check_for_existance([inp]) if os.path.exists(inp) is False: yield ( i18n("输入路径不存在"), gr.update(visible=True), gr.update(visible=False), gr.skip(), gr.skip(), ) return if os.path.isfile(inp): n_parts = 1 elif os.path.isdir(inp): pass else: yield ( i18n("输入路径存在但不可用"), gr.update(visible=True), gr.update(visible=False), gr.skip(), gr.skip(), ) return if ps_slice == []: for i_part in range(n_parts): cmd = '"%s" -s tools/slice_audio.py "%s" "%s" %s %s %s %s %s %s %s %s %s' % ( python_exec, inp, opt_root, threshold, min_length, min_interval, hop_size, max_sil_kept, _max, alpha, i_part, n_parts, ) console.print(cmd) p = Popen(cmd, shell=True) ps_slice.append(p) yield ( process_info(process_name_slice, "opened"), gr.update(visible=False), gr.update(visible=True), gr.skip(), gr.skip(), ) for p in ps_slice: p.wait() ps_slice = [] yield ( process_info(process_name_slice, "finish"), gr.update(visible=True), gr.update(visible=False), gr.update(value=opt_root), gr.update(value=opt_root), ) else: yield ( process_info(process_name_slice, "occupy"), gr.update(visible=False), gr.update(visible=True), gr.skip(), gr.skip(), ) def close_slice(): global ps_slice if ps_slice != []: for p_slice in ps_slice: try: kill_process(p_slice.pid, process_name_slice) except Exception as _: traceback.print_exc() ps_slice = [] return ( process_info(process_name_slice, "closed"), gr.update(visible=True), gr.update(visible=False), ) ps1a: None | Popen = None process_name_1a = i18n("文本分词与特征提取") def open1a( inp_text: str, inp_wav_dir: str, exp_name: str, gpu_numbers: list[int], bert_pretrained_dir: str, version: str, nproc: int = 1, ): global ps1a inp_text = my_utils.clean_path(inp_text) inp_wav_dir = my_utils.clean_path(inp_wav_dir) if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True): check_details([inp_text, inp_wav_dir], is_dataset_processing=True) exp_name = exp_name.rstrip(" ") if ps1a is None: opt_dir = f"{exp_root}/{exp_name}" env = os.environ.copy() env["PYTHONPATH"] = os.getcwd() # fmt: off cmd = [ python_exec, "-s", "GPT_SoVITS/prepare_datasets/1-get-text.py", "--inp-list", inp_text, "--opt", opt_dir, "--bert", bert_pretrained_dir, "--version", version, "--device", infer_device.type, "--device-id", str(gpu_numbers).strip("[]").replace(" ",""), "--nproc", str(nproc), ] # fmt: on if is_half: cmd.append("--fp16") else: cmd.append("--no-fp16") console.print(" ".join(cmd)) p = Popen(cmd, env=env) yield ( process_info(process_name_1a, "running"), gr.update(visible=False), gr.update(visible=True), ) code = p.wait() ps1a = None if code == 0: yield ( process_info(process_name_1a, "finish"), gr.update(visible=True), gr.update(visible=False), ) else: yield ( process_info(process_name_1a, "failed"), gr.update(visible=True), gr.update(visible=False), ) else: yield ( process_info(process_name_1a, "occupy"), gr.update(visible=False), gr.update(visible=True), ) def close1a(): global ps1a if ps1a: try: kill_process(ps1a.pid, process_name_1a) except Exception as _: traceback.print_exc() ps1a = None return ( process_info(process_name_1a, "closed"), gr.update(visible=True), gr.update(visible=False), ) ps1b: None | Popen = None process_name_1b = i18n("语音自监督特征提取") def open1b( version: str, inp_text: str, inp_wav_dir: str, exp_name: str, gpu_numbers: list[int], ssl_pretrained_dir: str, nproc: int = 1, ): global ps1b inp_text = my_utils.clean_path(inp_text) inp_wav_dir = my_utils.clean_path(inp_wav_dir) if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True): check_details([inp_text, inp_wav_dir], is_dataset_processing=True) exp_name = exp_name.rstrip(" ") if ps1b is None: opt_dir = f"{exp_root}/{exp_name}" env = os.environ.copy() env["PYTHONPATH"] = os.getcwd() # fmt: off cmd = [ python_exec, "-s", "GPT_SoVITS/prepare_datasets/2-get-hubert-sv-wav32k.py", "--inp-list", inp_text, "--opt", opt_dir, "--cnhubert", ssl_pretrained_dir, "--device", infer_device.type, "--device-id", str(gpu_numbers).strip("[]").replace(" ",""), "--nproc", str(nproc), ] # fmt: on if inp_wav_dir: cmd.extend(["--wav-dir", inp_wav_dir]) if "Pro" in version: cmd.extend(["--sv", sv_path]) if is_half: cmd.append("--fp16") else: cmd.append("--no-fp16") console.print(" ".join(cmd)) p = Popen(cmd, env=env) ps1b = p yield ( process_info(process_name_1b, "running"), gr.update(visible=False), gr.update(visible=True), ) code = p.wait() ps1b = None if code == 0: yield ( process_info(process_name_1b, "finish"), gr.update(visible=True), gr.update(visible=False), ) else: yield ( process_info(process_name_1b, "failed"), gr.update(visible=True), gr.update(visible=False), ) else: yield ( process_info(process_name_1b, "occupy"), gr.update(visible=False), gr.update(visible=True), ) def close1b(): global ps1b if ps1b: try: kill_process(ps1b.pid, process_name_1b) except Exception as _: traceback.print_exc() ps1b = None return ( process_info(process_name_1b, "closed"), gr.update(visible=True), gr.update(visible=False), ) ps1c: None | Popen = None process_name_1c = i18n("语义Token提取") def open1c( inp_text: str, exp_name: str, gpu_numbers: list[int], pretrained_s2G_path: str, nproc: int = 1, ): global ps1c inp_text = my_utils.clean_path(inp_text) check_for_existance([inp_text], is_dataset_processing=True) exp_name = exp_name.rstrip(" ") if ps1c is None: opt_dir = f"{exp_root}/{exp_name}" env = os.environ.copy() env["PYTHONPATH"] = os.getcwd() # fmt: off cmd = [ python_exec, "-s", "GPT_SoVITS/prepare_datasets/3-get-semantic.py", "--inp-list", inp_text, "--opt", opt_dir, "--pretrained-s2g", pretrained_s2G_path, "--device", infer_device.type, "--device-id", str(gpu_numbers).strip("[]").replace(" ",""), "--nproc", str(nproc), ] # fmt: on if is_half: cmd.append("--fp16") else: cmd.append("--no-fp16") console.print(" ".join(cmd)) p = Popen(cmd, env=env) ps1c = p yield ( process_info(process_name_1c, "running"), gr.update(visible=False), gr.update(visible=True), ) code = p.wait() ps1c = None if code == 0: yield ( process_info(process_name_1c, "finish"), gr.update(visible=True), gr.update(visible=False), ) else: yield ( process_info(process_name_1c, "failed"), gr.update(visible=True), gr.update(visible=False), ) else: yield ( process_info(process_name_1c, "occupy"), gr.update(visible=False), gr.update(visible=True), ) def close1c(): global ps1c if ps1c: try: kill_process(ps1c.pid, process_name_1c) except Exception as _: traceback.print_exc() ps1c = None return ( process_info(process_name_1c, "closed"), gr.update(visible=True), gr.update(visible=False), ) ps1abc: list[None | Popen] = [None] * 3 process_name_1abc = i18n("训练集格式化一键三连") def open1abc( version: str, inp_text: str, inp_wav_dir: str, exp_name: str, gpu_numbers_1: list[int], gpu_numbers_2: list[int], gpu_numbers_3: list[int], bert_pretrained_dir: str, ssl_pretrained_dir: str, pretrained_s2G_path: str, nproc: int = 1, ): global ps1abc inp_text = my_utils.clean_path(inp_text) inp_wav_dir = my_utils.clean_path(inp_wav_dir) if check_for_existance([inp_text, inp_wav_dir], is_dataset_processing=True): check_details([inp_text, inp_wav_dir], is_dataset_processing=True) exp_name = exp_name.rstrip(" ") if not all(ps1abc): opt_dir = f"{exp_root}/{exp_name}" env = os.environ.copy() env["PYTHONPATH"] = os.getcwd() # Step 1 # fmt: off cmd_1 = [ python_exec, "-s", "GPT_SoVITS/prepare_datasets/1-get-text.py", "--inp-list", inp_text, "--opt", opt_dir, "--bert", bert_pretrained_dir, "--version", version, "--device", infer_device.type, "--device-id", str(gpu_numbers_1).strip("[]").replace(" ",""), "--nproc", str(nproc), ] # fmt: on if is_half: cmd_1.append("--fp16") else: cmd_1.append("--no-fp16") console.print(" ".join(cmd_1)) p = Popen(cmd_1, env=env) ps1abc[0] = p yield ( i18n("进度") + ": 1A-Doing", gr.update(visible=False), gr.update(visible=True), ) code = p.wait() ps1abc[0] = None if code == 0: yield ( i18n("进度") + ": 1A-Done", gr.update(visible=False), gr.update(visible=True), ) else: yield ( i18n("进度") + ": 1A-Failed", gr.update(visible=True), gr.update(visible=False), ) return (gr.skip() for i in range(3)) # Step 2 # fmt: off cmd_2 = [ python_exec, "-s", "GPT_SoVITS/prepare_datasets/2-get-hubert-sv-wav32k.py", "--inp-list", inp_text, "--opt", opt_dir, "--cnhubert", ssl_pretrained_dir, "--device", infer_device.type, "--device-id", str(gpu_numbers_2).strip("[]").replace(" ",""), "--nproc", str(nproc), ] # fmt: on if inp_wav_dir: cmd_2.extend(["--wav-dir", inp_wav_dir]) if "Pro" in version: cmd_2.extend(["--sv", sv_path]) if is_half: cmd_2.append("--fp16") else: cmd_2.append("--no-fp16") console.print(" ".join(cmd_2)) p = Popen(cmd_2, env=env) ps1abc[1] = p yield ( i18n("进度") + ": 1A-Done, 1B-Doing", gr.update(visible=False), gr.update(visible=True), ) code = p.wait() ps1abc[1] = None if code == 0: yield ( i18n("进度") + ": 1A-Done, 1B-Done", gr.update(visible=False), gr.update(visible=True), ) else: yield ( i18n("进度") + ": 1A-Done, 1B-Failed", gr.update(visible=True), gr.update(visible=False), ) return (gr.skip() for i in range(3)) # Step 3 # fmt: off cmd_3 = [ python_exec, "-s", "GPT_SoVITS/prepare_datasets/3-get-semantic.py", "--inp-list", inp_text, "--opt", opt_dir, "--pretrained-s2g", pretrained_s2G_path, "--device", infer_device.type, "--device-id", str(gpu_numbers_3).strip("[]").replace(" ",""), "--nproc", str(nproc), ] # fmt: on if is_half: cmd_3.append("--fp16") else: cmd_3.append("--no-fp16") console.print(" ".join(cmd_3)) p = Popen(cmd_3, env=env) ps1abc[2] = p yield ( i18n("进度") + ": 1A-Done, 1B-Done, 1C-Doing", gr.update(visible=False), gr.update(visible=True), ) code = p.wait() ps1abc[2] = None if code == 0: yield ( process_info(process_name_1abc, "finish"), gr.update(visible=False), gr.update(visible=True), ) else: yield ( i18n("进度") + ": 1A-Done, 1B-Done, 1C-Failed", gr.update(visible=True), gr.update(visible=False), ) return (gr.skip() for i in range(3)) else: yield ( process_info(process_name_1abc, "occupy"), gr.update(visible=False), gr.update(visible=True), ) def close1abc(): global ps1abc if any(ps1abc): for p1abc in ps1abc: if p1abc is None: continue try: kill_process(p1abc.pid, process_name_1abc) except Exception as _: traceback.print_exc() ps1abc = [None] * 3 return ( process_info(process_name_1abc, "closed"), gr.update(visible=True), gr.update(visible=False), ) def switch_version(version_): os.environ["version"] = version_ global version version = version_ if pretrained_sovits_name[version] != "" and pretrained_gpt_name[version] != "": ... else: gr.Warning(i18n("未下载模型") + ": " + version.upper()) set_default() return ( gr.update(value=pretrained_sovits_name[version]), gr.update(value=pretrained_sovits_name[version].replace("s2G", "s2D")), gr.update(value=pretrained_gpt_name[version]), gr.update(value=pretrained_gpt_name[version]), gr.update(value=pretrained_sovits_name[version]), gr.update(value=default_batch_size, maximum=default_max_batch_size), gr.update(value=default_sovits_epoch, maximum=max_sovits_epoch), gr.update(value=default_sovits_save_every_epoch, maximum=max_sovits_save_every_epoch), gr.update(visible=False if version in v3v4set else True), gr.update( visible=False if version not in v3v4set else True, value=False if not if_force_ckpt else True, interactive=True if not if_force_ckpt else False, ), gr.update(value=False, interactive=True), gr.update(visible=True if version in v3v4set else False), ) def sync(text): return gr.update(value=text) def changeBackend(flag: bool): if flag: return gr.update(choices=["Torch Varlen"], value="Torch Varlen") else: return gr.update(choices=backends_gradio, value=backends_gradio[-1][-1]) GPU_INDEX.add(0) GPU_INDEX_LIST = list(GPU_INDEX) GPU_INDEX_LIST.sort() 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.Tabs(): with gr.TabItem("0-" + i18n("前置数据集获取工具")): with gr.Accordion(label="0a-" + i18n("UVR5人声伴奏分离&去混响去延迟工具")): with gr.Row(equal_height=True): with gr.Column(scale=3): with gr.Row(equal_height=True): uvr5_info = gr.Textbox(label=process_info(process_name_uvr5, "info")) open_uvr5 = gr.Button( value=process_info(process_name_uvr5, "open"), variant="primary", visible=True ) close_uvr5 = gr.Button( value=process_info(process_name_uvr5, "close"), variant="primary", visible=False ) with gr.Accordion(label="0b-" + i18n("语音切分工具")): with gr.Row(equal_height=True): with gr.Column(scale=3): with gr.Row(equal_height=True): slice_inp_path = gr.Textbox( label=i18n("音频自动切分输入路径，可文件可文件夹"), placeholder="D:/InputAudioFolder" if platform.system() == "Windows" else "~/InputAudioFolder", ) slice_opt_root = gr.Textbox( label=i18n("切分后的子音频的输出根目录"), value="output/slicer_opt" ) with gr.Row(equal_height=True): threshold = gr.Textbox( label=i18n("threshold:音量小于这个值视作静音的备选切割点"), value="-34" ) min_length = gr.Textbox( label=i18n("min_length:每段最小多长，如果第一段太短一直和后面段连起来直到超过这个值"), value="4000", ) min_interval = gr.Textbox(label=i18n("min_interval:最短切割间隔"), value="300") hop_size = gr.Textbox( label=i18n("hop_size:怎么算音量曲线，越小精度越大计算量越高（不是精度越大效果越好）"), value="10", ) max_sil_kept = gr.Textbox(label=i18n("max_sil_kept:切完后静音最多留多长"), value="500") with gr.Row(equal_height=True): _max = gr.Slider( minimum=0, maximum=1, step=0.05, label=i18n("max:归一化后最大值多少"), value=0.9, interactive=True, ) alpha = gr.Slider( minimum=0, maximum=1, step=0.05, label=i18n("alpha_mix:混多少比例归一化后音频进来"), value=0.25, interactive=True, ) with gr.Row(equal_height=True): n_process = gr.Slider( minimum=1, maximum=n_cpu, step=1, label=i18n("切割使用的进程数"), value=4, interactive=True, ) slicer_info = gr.Textbox(label=process_info(process_name_slice, "info")) open_slicer_button = gr.Button( value=process_info(process_name_slice, "open"), variant="primary", visible=True ) close_slicer_button = gr.Button( value=process_info(process_name_slice, "close"), variant="primary", visible=False ) with gr.Accordion(label="0c-" + i18n("语音识别工具")): with gr.Row(equal_height=True): with gr.Column(scale=3): with gr.Row(equal_height=True): asr_inp_dir = gr.Textbox( label=i18n("输入文件夹路径"), value="output/silcer_opt", interactive=True, ) asr_opt_dir = gr.Textbox( label=i18n("输出文件夹路径"), value="output/asr_opt", interactive=True ) with gr.Row(equal_height=True): asr_model = gr.Dropdown( label=i18n("ASR 模型"), choices=list(asr_dict.keys()), interactive=True, value="达摩 ASR (中文)", ) asr_size = gr.Dropdown( label=i18n("ASR 模型尺寸"), choices=["large"], interactive=True, value="large" ) asr_lang = gr.Dropdown( label=i18n("ASR 语言设置"), choices=["zh", "yue"], interactive=True, value="zh" ) asr_precision = gr.Dropdown( label=i18n("数据类型精度"), choices=["float32"], interactive=True, value="float32" ) with gr.Row(equal_height=True): asr_info = gr.Textbox(label=process_info(process_name_asr, "info")) open_asr_button = gr.Button( value=process_info(process_name_asr, "open"), variant="primary", visible=True ) close_asr_button = gr.Button( value=process_info(process_name_asr, "close"), variant="primary", visible=False ) def change_lang_choices(key): # 根据选择的模型修改可选的语言 return gr.update(value=asr_dict[key]["lang"][0], choices=asr_dict[key]["lang"]) def change_size_choices(key): # 根据选择的模型修改可选的模型尺寸 return gr.update(value=asr_dict[key]["size"][-1], choices=asr_dict[key]["size"]) def change_precision_choices(key): # 根据选择的模型修改可选的语言 if key == "Faster Whisper (多语种)": if default_batch_size <= 4: precision = "int8" elif is_half: precision = "float16" else: precision = "float32" else: precision = "float32" return gr.update(value=precision, choices=asr_dict[key]["precision"]) asr_model.change(change_lang_choices, [asr_model], [asr_lang]) asr_model.change(change_size_choices, [asr_model], [asr_size]) asr_model.change(change_precision_choices, [asr_model], [asr_precision]) with gr.Accordion(label="0d-" + i18n("语音文本校对标注工具")): with gr.Row(equal_height=True): with gr.Column(scale=3): with gr.Row(equal_height=True): path_list = gr.Textbox( label=i18n("标注文件路径 (含文件后缀 *.list)"), value="output/asr_opt/slicer_opt.list", interactive=True, ) label_info = gr.Textbox(label=process_info(process_name_subfix, "info")) open_label = gr.Button( value=process_info(process_name_subfix, "open"), variant="primary", visible=True ) close_label = gr.Button( value=process_info(process_name_subfix, "close"), variant="primary", visible=False ) open_label.click(change_label, [path_list], [label_info, open_label, close_label]) close_label.click(change_label, [path_list], [label_info, open_label, close_label]) open_uvr5.click(change_uvr5, [], [uvr5_info, open_uvr5, close_uvr5]) close_uvr5.click(change_uvr5, [], [uvr5_info, open_uvr5, close_uvr5]) with gr.TabItem(i18n("1-GPT-SoVITS-TTS")): with gr.Accordion(i18n("微调模型信息")): with gr.Row(equal_height=True): with gr.Column(): exp_name = gr.Textbox( label=i18n("实验/模型名"), value="xxx", interactive=True, scale=3, ) with gr.Column(): gpu_info_box = gr.Textbox( label=i18n("显卡信息"), value=gpu_info, visible=True, interactive=False, scale=5, ) with gr.Column(): version_checkbox = gr.Dropdown( label=i18n("训练模型的版本"), value=version, choices=[ ("V1", "v1"), ("V2", "v2"), ("V4", "v4"), ("V2 Pro", "v2Pro"), ("V2 Pro Plus", "v2ProPlus"), ], scale=5, ) with gr.Column(): n_processes = gr.Slider(0, 6, 2, step=1, label=i18n("每卡预处理进程数")) with gr.Accordion(label=i18n("预训练模型路径"), open=False): with gr.Row(equal_height=True): with gr.Row(equal_height=True): pretrained_s1 = gr.Textbox( label=i18n("预训练GPT模型路径"), value=pretrained_gpt_name[version], interactive=True, lines=1, max_lines=1, scale=3, ) pretrained_s2G = gr.Textbox( label=i18n("预训练SoVITS-G模型路径"), value=pretrained_sovits_name[version], interactive=True, lines=1, max_lines=1, scale=5, ) pretrained_s2D = gr.Textbox( label=i18n("预训练SoVITS-D模型路径"), value=pretrained_sovits_name[version].replace("s2G", "s2D"), interactive=True, lines=1, max_lines=1, scale=5, ) with gr.TabItem("1A-" + i18n("训练集格式化工具")): with gr.Accordion(label=i18n("输出logs/实验名目录下应有23456开头的文件和文件夹")): with gr.Row(equal_height=True): with gr.Row(equal_height=True): inp_text = gr.Textbox( label=i18n("*文本标注文件"), value=r"output/asr_opt/slicer_opt.list", interactive=True, scale=10, ) with gr.Row(equal_height=True): inp_wav_dir = gr.Textbox( label=i18n("*训练集音频文件目录"), # value=r"D:\RVC1006\GPT-SoVITS\raw\xxx", interactive=True, placeholder=i18n( "填切割后音频所在目录！读取的音频文件完整路径=该目录-拼接-list文件里波形对应的文件名（不是全路径）。如果留空则使用.list文件里的绝对全路径。" ), scale=10, ) with gr.Accordion(label="1Aa-" + process_name_1a): with gr.Row(equal_height=True): with gr.Row(equal_height=True): gpu_numbers1a = gr.Dropdown( label=i18n("GPU卡号"), choices=GPU_INDEX_LIST, value=GPU_INDEX_LIST, interactive=True, multiselect=True, allow_custom_value=False, ) with gr.Row(equal_height=True): bert_pretrained_dir = gr.Textbox( label=i18n("预训练中文BERT模型路径"), value="GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large", interactive=False, lines=2, ) with gr.Row(equal_height=True): button1a_open = gr.Button( value=process_info(process_name_1a, "open"), variant="primary", visible=True ) button1a_close = gr.Button( value=process_info(process_name_1a, "close"), variant="primary", visible=False ) with gr.Row(equal_height=True): info1a = gr.Textbox(label=process_info(process_name_1a, "info")) with gr.Accordion(label="1Ab-" + process_name_1b): with gr.Row(equal_height=True): with gr.Row(equal_height=True): gpu_numbers1b = gr.Dropdown( label=i18n("GPU卡号"), choices=GPU_INDEX_LIST, value=GPU_INDEX_LIST, interactive=True, multiselect=True, allow_custom_value=False, ) with gr.Row(equal_height=True): cnhubert_base_dir = gr.Textbox( label=i18n("预训练SSL模型路径"), value="GPT_SoVITS/pretrained_models/chinese-hubert-base", interactive=False, lines=2, ) with gr.Row(equal_height=True): button1b_open = gr.Button( value=process_info(process_name_1b, "open"), variant="primary", visible=True ) button1b_close = gr.Button( value=process_info(process_name_1b, "close"), variant="primary", visible=False ) with gr.Row(equal_height=True): info1b = gr.Textbox(label=process_info(process_name_1b, "info")) with gr.Accordion(label="1Ac-" + process_name_1c): with gr.Row(equal_height=True): with gr.Row(equal_height=True): gpu_numbers1c = gr.Dropdown( label=i18n("GPU卡号"), choices=GPU_INDEX_LIST, value=GPU_INDEX_LIST, interactive=True, multiselect=True, allow_custom_value=False, ) with gr.Row(equal_height=True): pretrained_s2G_ = gr.Textbox( label=i18n("预训练SoVITS-G模型路径"), value=pretrained_sovits_name[version], interactive=False, lines=2, ) with gr.Row(equal_height=True): button1c_open = gr.Button( value=process_info(process_name_1c, "open"), variant="primary", visible=True ) button1c_close = gr.Button( value=process_info(process_name_1c, "close"), variant="primary", visible=False ) with gr.Row(equal_height=True): info1c = gr.Textbox(label=process_info(process_name_1c, "info")) with gr.Accordion(label="1Aabc-" + process_name_1abc): with gr.Row(equal_height=True): with gr.Row(equal_height=True): button1abc_open = gr.Button( value=process_info(process_name_1abc, "open"), variant="primary", visible=True ) button1abc_close = gr.Button( value=process_info(process_name_1abc, "close"), variant="primary", visible=False ) with gr.Row(equal_height=True): info1abc = gr.Textbox(label=process_info(process_name_1abc, "info")) pretrained_s2G.change(sync, [pretrained_s2G], [pretrained_s2G_]) open_asr_button.click( open_asr, [asr_inp_dir, asr_opt_dir, asr_model, asr_size, asr_lang, asr_precision], [asr_info, open_asr_button, close_asr_button, path_list, inp_text, inp_wav_dir], ) close_asr_button.click(close_asr, [], [asr_info, open_asr_button, close_asr_button]) open_slicer_button.click( open_slice, [ slice_inp_path, slice_opt_root, threshold, min_length, min_interval, hop_size, max_sil_kept, _max, alpha, n_process, ], [slicer_info, open_slicer_button, close_slicer_button, asr_inp_dir, inp_wav_dir], ) close_slicer_button.click(close_slice, [], [slicer_info, open_slicer_button, close_slicer_button]) button1a_open.click( open1a, [inp_text, inp_wav_dir, exp_name, gpu_numbers1a, bert_pretrained_dir, version_checkbox, n_processes], [info1a, button1a_open, button1a_close], ) button1a_close.click(close1a, [], [info1a, button1a_open, button1a_close]) button1b_open.click( open1b, [version_checkbox, inp_text, inp_wav_dir, exp_name, gpu_numbers1b, cnhubert_base_dir, n_processes], [info1b, button1b_open, button1b_close], ) button1b_close.click(close1b, [], [info1b, button1b_open, button1b_close]) button1c_open.click( open1c, [inp_text, exp_name, gpu_numbers1c, pretrained_s2G, n_processes], [info1c, button1c_open, button1c_close], ) button1c_close.click(close1c, [], [info1c, button1c_open, button1c_close]) button1abc_open.click( open1abc, [ version_checkbox, inp_text, inp_wav_dir, exp_name, gpu_numbers1a, gpu_numbers1b, gpu_numbers1c, bert_pretrained_dir, cnhubert_base_dir, pretrained_s2G, n_processes, ], [info1abc, button1abc_open, button1abc_close], ) button1abc_close.click(close1abc, [], [info1abc, button1abc_open, button1abc_close]) with gr.TabItem("1B-" + i18n("微调训练")): with gr.Accordion(label="1Ba-" + i18n("SoVITS 训练: 模型权重文件在 SoVITS_weights/")): with gr.Row(equal_height=True): batch_size = gr.Slider( minimum=1, maximum=default_max_batch_size, step=1, label=i18n("每张显卡的batch_size"), value=default_batch_size, interactive=True, ) total_epoch = gr.Slider( minimum=1, maximum=max_sovits_epoch, step=1, label=i18n("总训练轮数total_epoch，不建议太高"), value=default_sovits_epoch, interactive=True, ) with gr.Column(scale=2): if_save_latest = gr.Checkbox( label=i18n("是否仅保存最新的权重文件以节省硬盘空间"), value=True, interactive=True, show_label=True, ) if_save_every_weights = gr.Checkbox( label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"), value=True, interactive=True, show_label=True, ) if_grad_ckpt = gr.Checkbox( label="v3是否开启梯度检查点节省显存占用", value=False, interactive=True if version in v3v4set else False, show_label=True, visible=False, ) # 只有V3s2可以用 with gr.Row(equal_height=True): text_low_lr_rate = gr.Slider( minimum=0.2, maximum=0.6, step=0.05, label=i18n("文本模块学习率权重"), value=0.4, visible=True if version not in v3v4set else False, ) # v3v4 not need lora_rank = gr.Radio( label=i18n("LoRA秩"), value="32", choices=["16", "32", "64", "128"], visible=True if version in v3v4set else False, ) # v1v2 not need save_every_epoch = gr.Slider( minimum=1, maximum=max_sovits_save_every_epoch, step=1, label=i18n("保存频率save_every_epoch"), value=default_sovits_save_every_epoch, interactive=True, ) with gr.Column(scale=3): gpu_numbers1Ba = gr.Dropdown( label=i18n("GPU卡号"), choices=GPU_INDEX_LIST, value=GPU_INDEX_LIST, interactive=True, multiselect=True, allow_custom_value=False, ) with gr.Row(equal_height=True): with gr.Column(): button1Ba_open = gr.Button( value=process_info(process_name_sovits, "open"), variant="primary", visible=True ) button1Ba_close = gr.Button( value=process_info(process_name_sovits, "close"), variant="primary", visible=False ) with gr.Column(): info1Ba = gr.Textbox(label=process_info(process_name_sovits, "info")) with gr.Accordion(label="1Bb-" + i18n("GPT 训练: 模型权重文件在 GPT_weights/")): with gr.Row(equal_height=True): batch_size1Bb = gr.Slider( minimum=1, maximum=40, step=1, label=i18n("每张显卡的batch_size"), value=default_batch_size_s1, interactive=True, ) total_epoch1Bb = gr.Slider( minimum=2, maximum=50, step=1, label=i18n("总训练轮数total_epoch"), value=15, interactive=True, ) with gr.Column(scale=2): if_save_latest1Bb = gr.Checkbox( label=i18n("是否仅保存最新的权重文件以节省硬盘空间"), value=True, interactive=True, show_label=True, ) if_save_every_weights1Bb = gr.Checkbox( label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"), value=True, interactive=True, show_label=True, ) with gr.Row(equal_height=True): # with gr.Column(): save_every_epoch1Bb = gr.Slider( minimum=1, maximum=50, step=1, label=i18n("保存频率save_every_epoch"), value=5, interactive=True, ) # with gr.Column(): if_dpo = gr.Checkbox( label=i18n("是否开启DPO训练选项(实验性)"), value=False, interactive=True, show_label=True, ) with gr.Column(scale=2): gpu_numbers1Bb = gr.Dropdown( label=i18n("GPU卡号"), choices=GPU_INDEX_LIST, value=GPU_INDEX_LIST, interactive=True, multiselect=True, allow_custom_value=False, ) with gr.Row(equal_height=True): with gr.Column(): with gr.Row(equal_height=True): button1Bb_open = gr.Button( value=process_info(process_name_gpt, "open"), variant="primary", visible=True ) button1Bb_close = gr.Button( value=process_info(process_name_gpt, "close"), variant="primary", visible=False ) with gr.Column(): info1Bb = gr.Textbox(label=process_info(process_name_gpt, "info")) button1Ba_close.click(close1Ba, [], [info1Ba, button1Ba_open, button1Ba_close]) button1Bb_close.click(close1Bb, [], [info1Bb, button1Bb_open, button1Bb_close]) with gr.TabItem("1C-" + i18n("推理")): gr.Markdown( value=i18n( "选择训练完存放在SoVITS_weights和GPT_weights下的模型。默认的几个是底模，体验5秒Zero Shot TTS不训练推理用。" ) ) with gr.Row(equal_height=True): with gr.Column(scale=2): with gr.Row(equal_height=True): with gr.Column(): GPT_dropdown = gr.Dropdown( label=i18n("GPT模型列表"), choices=GPT_names, value=GPT_names[0][-1], interactive=True, ) with gr.Column(): SoVITS_dropdown = gr.Dropdown( label=i18n("SoVITS模型列表"), choices=SoVITS_names, value=SoVITS_names[0][-1], interactive=True, ) with gr.Column(scale=2): with gr.Row(equal_height=True): gpu_number_1C = gr.Dropdown( label=i18n("GPU卡号"), choices=GPU_INDEX_LIST, value=infer_device.index, interactive=True, multiselect=False, allow_custom_value=False, ) refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary") refresh_button.click(fn=change_choice, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown]) with gr.Row(equal_height=True): with gr.Row(equal_height=True): with gr.Column(): batched_infer_enabled = gr.Checkbox( label=i18n("启用并行推理版本"), value=False, interactive=True, show_label=True ) with gr.Column(): backends_dropdown = gr.Dropdown( choices=backends_gradio, label=i18n("推理后端"), value=backends_gradio[-1][-1], interactive=True, ) with gr.Row(equal_height=True): tts_info = gr.Textbox(label=process_info(process_name_tts, "info")) open_tts = gr.Button( value=process_info(process_name_tts, "open"), variant="primary", visible=True ) close_tts = gr.Button( value=process_info(process_name_tts, "close"), variant="primary", visible=False ) batched_infer_enabled.change( changeBackend, [batched_infer_enabled], [backends_dropdown], ) open_tts.click( change_tts_inference, [ gpu_number_1C, GPT_dropdown, SoVITS_dropdown, batched_infer_enabled, backends_dropdown, ], [tts_info, open_tts, close_tts], ) close_tts.click( change_tts_inference, [ gpu_number_1C, GPT_dropdown, SoVITS_dropdown, batched_infer_enabled, backends_dropdown, ], [tts_info, open_tts, close_tts], ) button1Ba_open.click( open1Ba, [ version_checkbox, batch_size, total_epoch, exp_name, text_low_lr_rate, if_save_latest, if_save_every_weights, save_every_epoch, gpu_numbers1Ba, pretrained_s2G, pretrained_s2D, if_grad_ckpt, lora_rank, ], [info1Ba, button1Ba_open, button1Ba_close, SoVITS_dropdown, GPT_dropdown], ) button1Bb_open.click( open1Bb, [ batch_size1Bb, total_epoch1Bb, exp_name, if_dpo, if_save_latest1Bb, if_save_every_weights1Bb, save_every_epoch1Bb, gpu_numbers1Bb, pretrained_s1, ], [info1Bb, button1Bb_open, button1Bb_close, SoVITS_dropdown, GPT_dropdown], ) version_checkbox.change( switch_version, [version_checkbox], [ pretrained_s2G, pretrained_s2D, pretrained_s1, GPT_dropdown, SoVITS_dropdown, batch_size, total_epoch, save_every_epoch, text_low_lr_rate, if_grad_ckpt, batched_infer_enabled, lora_rank, ], ) with gr.TabItem(i18n("2-GPT-SoVITS-变声")): gr.Markdown(value=i18n("施工中，请静候佳音")) app.queue().launch( # concurrency_count=511, max_size=1022 server_name="0.0.0.0", inbrowser=True, share=is_share, server_port=webui_port_main, # quiet=True, )