app.py

# flake8: noqa: E402
import os
import logging

import re_matching
from tools.sentence import split_by_language, sentence_split

logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("markdown_it").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
logging.getLogger("matplotlib").setLevel(logging.WARNING)

logging.basicConfig(
    level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
)

logger = logging.getLogger(__name__)

import torch
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
import nltk
nltk.download('cmudict')
import utils
from infer import infer, latest_version, get_net_g
import gradio as gr
import webbrowser
import numpy as np
from config import config

net_g = None

device = config.webui_config.device
if device == "mps":
    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"


def generate_audio(
    slices,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    speaker,
    language,
):
    audio_list = []
    silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
    with torch.no_grad():
        for piece in slices:
            audio = infer(
                piece,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
                sid=speaker,
                language=language,
                hps=hps,
                net_g=net_g,
                device=device,
            )
            audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
            audio_list.append(audio16bit)
            audio_list.append(silence)  # 将静音添加到列表中
    return audio_list


def tts_split(
    text: str,
    speaker,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    language,
    cut_by_sent,
    interval_between_para,
    interval_between_sent,
):
    if language == "mix":
        return ("invalid", None)
    while text.find("\n\n") != -1:
        text = text.replace("\n\n", "\n")
    para_list = re_matching.cut_para(text)
    audio_list = []
    if not cut_by_sent:
        for p in para_list:
            audio = infer(
                p,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
                sid=speaker,
                language=language,
                hps=hps,
                net_g=net_g,
                device=device,
            )
            audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
            audio_list.append(audio16bit)
            silence = np.zeros((int)(44100 * interval_between_para), dtype=np.int16)
            audio_list.append(silence)
    else:
        for p in para_list:
            audio_list_sent = []
            sent_list = re_matching.cut_sent(p)
            for s in sent_list:
                audio = infer(
                    s,
                    sdp_ratio=sdp_ratio,
                    noise_scale=noise_scale,
                    noise_scale_w=noise_scale_w,
                    length_scale=length_scale,
                    sid=speaker,
                    language=language,
                    hps=hps,
                    net_g=net_g,
                    device=device,
                )
                audio_list_sent.append(audio)
                silence = np.zeros((int)(44100 * interval_between_sent))
                audio_list_sent.append(silence)
            if (interval_between_para - interval_between_sent) > 0:
                silence = np.zeros(
                    (int)(44100 * (interval_between_para - interval_between_sent))
                )
                audio_list_sent.append(silence)
            audio16bit = gr.processing_utils.convert_to_16_bit_wav(
                np.concatenate(audio_list_sent)
            )  # 对完整句子做音量归一
            audio_list.append(audio16bit)
    audio_concat = np.concatenate(audio_list)
    return ("Success", (44100, audio_concat))


def tts_fn(
    text: str,
    speaker,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    language,
):
    audio_list = []
    if language == "mix":
        bool_valid, str_valid = re_matching.validate_text(text)
        if not bool_valid:
            return str_valid, (
                hps.data.sampling_rate,
                np.concatenate([np.zeros(hps.data.sampling_rate // 2)]),
            )
        result = re_matching.text_matching(text)
        for one in result:
            _speaker = one.pop()
            for lang, content in one:
                audio_list.extend(
                    generate_audio(
                        content.split("|"),
                        sdp_ratio,
                        noise_scale,
                        noise_scale_w,
                        length_scale,
                        _speaker,
                        lang,
                    )
                )
    elif language.lower() == "auto":
        sentences_list = split_by_language(text, target_languages=["zh", "ja", "en"])
        for sentences, lang in sentences_list:
            lang = lang.upper()
            if lang == "JA":
                lang = "JP"
            sentences = sentence_split(sentences, max=250)
            for content in sentences:
                audio_list.extend(
                    generate_audio(
                        content.split("|"),
                        sdp_ratio,
                        noise_scale,
                        noise_scale_w,
                        length_scale,
                        speaker,
                        lang,
                    )
                )
    else:
        audio_list.extend(
            generate_audio(
                text.split("|"),
                sdp_ratio,
                noise_scale,
                noise_scale_w,
                length_scale,
                speaker,
                language,
            )
        )

    audio_concat = np.concatenate(audio_list)
    return "Success", (hps.data.sampling_rate, audio_concat)


if __name__ == "__main__":
    if config.webui_config.debug:
        logger.info("Enable DEBUG-LEVEL log")
        logging.basicConfig(level=logging.DEBUG)
    hps = utils.get_hparams_from_file(config.webui_config.config_path)
    # 若config.json中未指定版本则默认为最新版本
    version = hps.version if hasattr(hps, "version") else latest_version
    net_g = get_net_g(
        model_path=config.webui_config.model, version=version, device=device, hps=hps
    )
    speaker_ids = hps.data.spk2id
    speakers = list(speaker_ids.keys())
    languages = ["ZH", "JP", "EN", "auto", "mix"]
    with gr.Blocks() as app:
        with gr.Row():
            with gr.Column():
                gr.Markdown(value="""
               【AI星瞳①】在线语音合成（Bert-Vits2 2.0中日英）\n
                作者：Xz乔希 https://space.bilibili.com/5859321\n
                声音归属：星瞳_Official https://space.bilibili.com/401315430\n
                【AI星瞳②】https://www.modelscope.cn/studios/xzjosh/Star-Bert-VITS2\n
                【AI星瞳 1.0】https://www.modelscope.cn/studios/xzjosh/XingTong-Bert-VITS2\n
                【AI合集】https://www.modelscope.cn/studios/xzjosh/Bert-VITS2\n
                Bert-VITS2项目：https://github.com/Stardust-minus/Bert-VITS2\n
                使用本模型请严格遵守法律法规！\n
                发布二创作品请标注本项目作者及链接、作品使用Bert-VITS2 AI生成！\n
                【提示】手机端容易误触调节，请刷新恢复默认！每次生成的结果都不一样，效果不好请尝试多次生成与调节，选择最佳结果！\n             
                """)
                text = gr.TextArea(
                    label="输入文本内容",
                    placeholder="""
                推荐不同语言分开推理，因为无法连贯且可能影响最终效果！
                如果选择语言为\'auto\'，有概率无法识别。
                如果选择语言为\'mix\'，必须按照格式输入，否则报错:
                格式举例(zh是中文，jp是日语，en是英语；不区分大小写):
                 [说话人]<zh>你好 <jp>こんにちは <en>Hello
                另外，所有的语言选项都可以用'|'分割长段实现分句生成。
                    """,
                )
                speaker = gr.Dropdown(
                    choices=speakers, value=speakers[0], label="选择说话人"
                )
                sdp_ratio = gr.Slider(
                    minimum=0, maximum=1, value=0.2, step=0.01, label="SDP/DP混合比"
                )
                noise_scale = gr.Slider(
                    minimum=0.1, maximum=2, value=0.6, step=0.01, label="感情"
                )
                noise_scale_w = gr.Slider(
                    minimum=0.1, maximum=2, value=0.8, step=0.01, label="音素长度"
                )
                length_scale = gr.Slider(
                    minimum=0.1, maximum=2, value=1.0, step=0.01, label="语速"
                )
                language = gr.Dropdown(
                    choices=languages, value=languages[0], label="选择语言"
                )
                btn = gr.Button("点击生成", variant="primary")
            with gr.Column():
                with gr.Row():
                    with gr.Column():
                        interval_between_sent = gr.Slider(
                            minimum=0,
                            maximum=5,
                            value=0.2,
                            step=0.1,
                            label="句间停顿(秒)，勾选按句切分才生效",
                        )
                        interval_between_para = gr.Slider(
                            minimum=0,
                            maximum=10,
                            value=1,
                            step=0.1,
                            label="段间停顿(秒)，需要大于句间停顿才有效",
                        )
                        opt_cut_by_sent = gr.Checkbox(
                            label="按句切分    在按段落切分的基础上再按句子切分文本"
                        )
                        slicer = gr.Button("切分生成", variant="primary")
                text_output = gr.Textbox(label="状态信息")
                audio_output = gr.Audio(label="输出音频")
                # explain_image = gr.Image(
                #     label="参数解释信息",
                #     show_label=True,
                #     show_share_button=False,
                #     show_download_button=False,
                #     value=os.path.abspath("./img/参数说明.png"),
                # )
        btn.click(
            tts_fn,
            inputs=[
                text,
                speaker,
                sdp_ratio,
                noise_scale,
                noise_scale_w,
                length_scale,
                language,
            ],
            outputs=[text_output, audio_output],
        )

        slicer.click(
            tts_split,
            inputs=[
                text,
                speaker,
                sdp_ratio,
                noise_scale,
                noise_scale_w,
                length_scale,
                language,
                opt_cut_by_sent,
                interval_between_para,
                interval_between_sent,
            ],
            outputs=[text_output, audio_output],
        )

    print("推理页面已开启!")
    webbrowser.open(f"http://127.0.0.1:{config.webui_config.port}")
    app.launch(share=config.webui_config.share, server_port=config.webui_config.port)