app.py

"""
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,
    )