app.py

import logging
import os
import random
import re
import sys

import gradio as gr
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("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


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


with gr.Blocks(title="GPT-SoVITS 張悦楷") as app:
    gr.Markdown(
        value=i18n(
            "張悦楷")
    )

    with gr.Column():
        # with gr.Group():
        gr.Markdown(value=i18n("模型切换"))
        with gr.Row():
            GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(
                GPT_names, key=custom_sort_key), value=gpt_path, interactive=True)
            SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(
                SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True)
            refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
            refresh_button.click(fn=change_choices, inputs=[], outputs=[
                                 SoVITS_dropdown, GPT_dropdown])

    with gr.Row():
        with gr.Column():
            gr.Markdown(value=i18n("*请上传并填写参考信息"))
            with gr.Row():
                inp_ref = gr.Audio(label=i18n(
                    "主参考音频(请上传3~10秒内参考音频，超过会报错！)"), type="filepath")
                inp_refs = gr.File(label=i18n(
                    "辅参考音频(可选多个，或不选)"), file_count="multiple")
            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。"))

if __name__ == '__main__':
    app.queue().launch(
        server_name="0.0.0.0",
        inbrowser=True,
        share=True,
        server_port=9876,
        quiet=True,
    )