""" If running this app in WSL2, you need to run the following command in the WSL2 terminal to get the IP address of the WSL2 instance: ip addr show eth0 | grep "inet\b" | awk '{print $2}' | cut -d/ -f1 """ import asyncio import logging import os import random import re import sys import gradio as gr import spaces import torch from tools.i18n.i18n import I18nAuto, scan_language_list from TTS_infer_pack.text_segmentation_method import get_method from TTS_infer_pack.TTS import TTS, TTS_Config 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) 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 = os.environ.get("version", "v2") 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。 device = "cuda" if torch.cuda.is_available() else "cpu" dict_language_v2 = { i18n("粵語"): "yue", # 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_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", } tts_config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml") tts_config.device = device tts_config.is_half = is_half tts_config.version = version if gpt_path is not None: tts_config.t2s_weights_path = gpt_path if sovits_path is not None: 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 @spaces.GPU 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, ): seed = -1 if keep_random else seed actual_seed = seed if seed not in [-1, "", None] else random.randrange(1 << 32) 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, } for item in tts_pipeline.run(inputs): yield item, actual_seed 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 def change_choices(): SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root) return { "choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update", }, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"} pretrained_sovits_name = [ "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth", "GPT_SoVITS/pretrained_models/s2G488k.pth", ] pretrained_gpt_name = [ "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt", ] _ = [[], []] for i in range(2): if os.path.exists(pretrained_gpt_name[i]): _[0].append(pretrained_gpt_name[i]) if os.path.exists(pretrained_sovits_name[i]): _[-1].append(pretrained_sovits_name[i]) pretrained_gpt_name, pretrained_sovits_name = _ SoVITS_weight_root = ["SoVITS_weights_v2", "SoVITS_weights"] GPT_weight_root = ["GPT_weights_v2", "GPT_weights"] for path in SoVITS_weight_root + GPT_weight_root: os.makedirs(path, exist_ok=True) def get_weights_names(GPT_weight_root, SoVITS_weight_root): SoVITS_names = [i for i in pretrained_sovits_name] for path in SoVITS_weight_root: for name in os.listdir(path): if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (path, name)) GPT_names = [i for i in pretrained_gpt_name] for path in GPT_weight_root: for name in os.listdir(path): if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (path, name)) return SoVITS_names, GPT_names SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root) def change_sovits_weights(sovits_path, prompt_language=None, text_language=None): tts_pipeline.init_vits_weights(sovits_path) global version, dict_language dict_language = 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")} return ( {"__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, ) async def create_app(): with gr.Blocks(title="GPT-SoVITS 張悦楷") as app: gr.Markdown( value=""" # 張悦楷 GPT-SoVITS 語音合成器 """ ) 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="上傳 3-10 秒長嘅參考音頻", type="filepath" ) inp_refs = gr.File( label=i18n("辅参考音频(可选多个，或不选)"), file_count="multiple", ) 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, show_label=True, ) gr.Markdown( i18n( "使用无参考文本模式时建议使用微调的GPT，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(): batch_size = gr.Slider( minimum=1, maximum=200, step=1, label=i18n("batch_size"), value=20, interactive=True, ) 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="speed_factor", value=1.0, interactive=True, ) 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, ) 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, ) 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, ], [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, ], ) GPT_dropdown.change(tts_pipeline.init_t2s_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。")) return app if __name__ == "__main__": app = asyncio.run(create_app()) app.launch( # server_name="0.0.0.0", # inbrowser=True, # share=True, # server_port=9876, # quiet=True, )