app.py

# flake8: noqa: E402
import os
import logging
import re_matching
from tools.sentence import split_by_language

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 utils
from infer import infer, latest_version, get_net_g, infer_multilang
import gradio as gr
import webbrowser
import numpy as np
from config import config
from tools.translate import translate
import librosa

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,
    reference_audio,
    emotion,
    skip_start=False,
    skip_end=False,
):
    audio_list = []
    # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
    with torch.no_grad():
        for idx, piece in enumerate(slices):
            skip_start = (idx != 0) and skip_start
            skip_end = (idx != len(slices) - 1) and skip_end
            audio = infer(
                piece,
                reference_audio=reference_audio,
                emotion=emotion,
                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,
                skip_start=skip_start,
                skip_end=skip_end,
            )
            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 generate_audio_multilang(
    slices,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    speaker,
    language,
    reference_audio,
    emotion,
    skip_start=False,
    skip_end=False,
):
    audio_list = []
    # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
    with torch.no_grad():
        for idx, piece in enumerate(slices):
            skip_start = (idx != 0) and skip_start
            skip_end = (idx != len(slices) - 1) and skip_end
            audio = infer_multilang(
                piece,
                reference_audio=reference_audio,
                emotion=emotion,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
                sid=speaker,
                language=language[idx],
                hps=hps,
                net_g=net_g,
                device=device,
                skip_start=skip_start,
                skip_end=skip_end,
            )
            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,
    reference_audio,
    emotion,
):
    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 idx, p in enumerate(para_list):
            skip_start = idx != 0
            skip_end = idx != len(para_list) - 1
            audio = infer(
                p,
                reference_audio=reference_audio,
                emotion=emotion,
                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,
                skip_start=skip_start,
                skip_end=skip_end,
            )
            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 idx, p in enumerate(para_list):
            skip_start = idx != 0
            skip_end = idx != len(para_list) - 1
            audio_list_sent = []
            sent_list = re_matching.cut_sent(p)
            for idx, s in enumerate(sent_list):
                skip_start = (idx != 0) and skip_start
                skip_end = (idx != len(sent_list) - 1) and skip_end
                audio = infer(
                    s,
                    reference_audio=reference_audio,
                    emotion=emotion,
                    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,
                    skip_start=skip_start,
                    skip_end=skip_end,
                )
                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,
    reference_audio,
    emotion,
    prompt_mode,
):
    if prompt_mode == "Audio prompt":
        if reference_audio == None:
            return ("Invalid audio prompt", None)
        else:
            reference_audio = load_audio(reference_audio)[1]
    else:
        reference_audio = None
    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 = []
        for slice in re_matching.text_matching(text):
            _speaker = slice.pop()
            temp_contant = []
            temp_lang = []
            for lang, content in slice:
                if "|" in content:
                    temp = []
                    temp_ = []
                    for i in content.split("|"):
                        if i != "":
                            temp.append([i])
                            temp_.append([lang])
                        else:
                            temp.append([])
                            temp_.append([])
                    temp_contant += temp
                    temp_lang += temp_
                else:
                    if len(temp_contant) == 0:
                        temp_contant.append([])
                        temp_lang.append([])
                    temp_contant[-1].append(content)
                    temp_lang[-1].append(lang)
            for i, j in zip(temp_lang, temp_contant):
                result.append([*zip(i, j), _speaker])
        for i, one in enumerate(result):
            skip_start = i != 0
            skip_end = i != len(result) - 1
            _speaker = one.pop()
            idx = 0
            while idx < len(one):
                text_to_generate = []
                lang_to_generate = []
                while True:
                    lang, content = one[idx]
                    temp_text = [content]
                    if len(text_to_generate) > 0:
                        text_to_generate[-1] += [temp_text.pop(0)]
                        lang_to_generate[-1] += [lang]
                    if len(temp_text) > 0:
                        text_to_generate += [[i] for i in temp_text]
                        lang_to_generate += [[lang]] * len(temp_text)
                    if idx + 1 < len(one):
                        idx += 1
                    else:
                        break
                skip_start = (idx != 0) and skip_start
                skip_end = (idx != len(one) - 1) and skip_end
                print(text_to_generate, lang_to_generate)
                audio_list.extend(
                    generate_audio_multilang(
                        text_to_generate,
                        sdp_ratio,
                        noise_scale,
                        noise_scale_w,
                        length_scale,
                        _speaker,
                        lang_to_generate,
                        reference_audio,
                        emotion,
                        skip_start,
                        skip_end,
                    )
                )
                idx += 1
    elif language.lower() == "auto":
        for idx, slice in enumerate(text.split("|")):
            if slice == "":
                continue
            skip_start = idx != 0
            skip_end = idx != len(text.split("|")) - 1
            sentences_list = split_by_language(
                slice, target_languages=["zh", "ja", "en"]
            )
            idx = 0
            while idx < len(sentences_list):
                text_to_generate = []
                lang_to_generate = []
                while True:
                    content, lang = sentences_list[idx]
                    temp_text = [content]
                    lang = lang.upper()
                    if lang == "JA":
                        lang = "JP"
                    if len(text_to_generate) > 0:
                        text_to_generate[-1] += [temp_text.pop(0)]
                        lang_to_generate[-1] += [lang]
                    if len(temp_text) > 0:
                        text_to_generate += [[i] for i in temp_text]
                        lang_to_generate += [[lang]] * len(temp_text)
                    if idx + 1 < len(sentences_list):
                        idx += 1
                    else:
                        break
                skip_start = (idx != 0) and skip_start
                skip_end = (idx != len(sentences_list) - 1) and skip_end
                print(text_to_generate, lang_to_generate)
                audio_list.extend(
                    generate_audio_multilang(
                        text_to_generate,
                        sdp_ratio,
                        noise_scale,
                        noise_scale_w,
                        length_scale,
                        speaker,
                        lang_to_generate,
                        reference_audio,
                        emotion,
                        skip_start,
                        skip_end,
                    )
                )
                idx += 1
    else:
        audio_list.extend(
            generate_audio(
                text.split("|"),
                sdp_ratio,
                noise_scale,
                noise_scale_w,
                length_scale,
                speaker,
                language,
                reference_audio,
                emotion,
            )
        )

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


def load_audio(path):
    audio, sr = librosa.load(path, 48000)
    # audio = librosa.resample(audio, 44100, 48000)
    return sr, audio


def gr_util(item):
    if item == "Text prompt":
        return {"visible": True, "__type__": "update"}, {
            "visible": False,
            "__type__": "update",
        }
    else:
        return {"visible": False, "__type__": "update"}, {
            "visible": True,
            "__type__": "update",
        }


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", "mix", "auto"]
    with gr.Blocks() as app:
        with gr.Row():
            with gr.Column():
                gr.Markdown(value="""
               【AI孙笑川】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：孙笑川258 https://space.bilibili.com/402574397\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\'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="Speaker"
                )
                _ = gr.Markdown(
                    value="提示模式（Prompt mode）：使用首字母大写英文单词或上传音频提示，用于生成指定风格的声音。\n"
                )
                prompt_mode = gr.Radio(
                    ["Text prompt", "Audio prompt"],
                    label="Prompt Mode",
                    value="Text prompt",
                )
                text_prompt = gr.Textbox(
                    label="Text prompt",
                    placeholder="单词描述生成风格。如：Happy",
                    visible=True,
                )
                audio_prompt = gr.Audio(
                    label="Audio prompt", type="filepath", visible=False
                )
                sdp_ratio = gr.Slider(
                    minimum=0, maximum=1, value=0.2, step=0.01, label="SDP Ratio"
                )
                noise_scale = gr.Slider(
                    minimum=0.1, maximum=2, value=0.5, step=0.01, label="Noise"
                )
                noise_scale_w = gr.Slider(
                    minimum=0.1, maximum=2, value=0.9, step=0.01, label="Noise_W"
                )
                length_scale = gr.Slider(
                    minimum=0.1, maximum=2, value=1.0, step=0.01, label="Length"
                )
                language = gr.Dropdown(
                    choices=languages, value=languages[0], label="Language"
                )
                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,
                audio_prompt,
                text_prompt,
                prompt_mode,
            ],
            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,
                audio_prompt,
                text_prompt,
            ],
            outputs=[text_output, audio_output],
        )

        prompt_mode.change(
            lambda x: gr_util(x),
            inputs=[prompt_mode],
            outputs=[text_prompt, audio_prompt],
        )

        audio_prompt.upload(
            lambda x: load_audio(x),
            inputs=[audio_prompt],
            outputs=[audio_prompt],
        )

    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)