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Bufeiyan-b-Bert-VITS2
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import sys, os

if sys.platform == "darwin":
    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"

import logging

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 argparse
import commons
import utils
from models import SynthesizerTrn
from text.symbols import symbols
from text import cleaned_text_to_sequence, get_bert
from text.cleaner import clean_text
import gradio as gr
import webbrowser


net_g = None


def get_text(text, language_str, hps):
    norm_text, phone, tone, word2ph = clean_text(text, language_str)
    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)

    if hps.data.add_blank:
        phone = commons.intersperse(phone, 0)
        tone = commons.intersperse(tone, 0)
        language = commons.intersperse(language, 0)
        for i in range(len(word2ph)):
            word2ph[i] = word2ph[i] * 2
        word2ph[0] += 1
    bert = get_bert(norm_text, word2ph, language_str)
    del word2ph

    assert bert.shape[-1] == len(phone)

    phone = torch.LongTensor(phone)
    tone = torch.LongTensor(tone)
    language = torch.LongTensor(language)

    return bert, phone, tone, language
import soundfile as sf
def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
    global net_g
    bert, phones, tones, lang_ids = get_text(text, "ZH", hps)
    with torch.no_grad():
        x_tst=phones.to(device).unsqueeze(0)
        tones=tones.to(device).unsqueeze(0)
        lang_ids=lang_ids.to(device).unsqueeze(0)
        bert = bert.to(device).unsqueeze(0)
        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
        del phones
        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
        audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio
                           , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
        sf.write("tmp.wav", audio, 44100)
        return audio
def convert_wav_to_ogg(wav_file):
    os.makedirs('out', exist_ok=True)
    filename = os.path.splitext(os.path.basename(wav_file.name))[0]
    output_path_ogg = os.path.join('out', f"out.ogg")

    renamed_input_path = os.path.join('in', f"in.wav")
    os.makedirs('in', exist_ok=True)
    os.rename(wav_file.name, renamed_input_path)
    command = ["ffmpeg", "-i", renamed_input_path, "-acodec", "libopus", "-y", output_path_ogg]
    os.system(" ".join(command))
    return output_path_ogg
def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):
    with torch.no_grad():
        audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker)
    with open('tmp.wav', 'rb') as wav_file:
        newogg = convert_wav_to_ogg(wav_file)    
    return "Success", (hps.data.sampling_rate, audio),newogg


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_dir", default="./logs/xingtong/xt_new.pth", help="path of your model")
    parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file")
    parser.add_argument("--share", default=False, help="make link public")
    parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log")

    args = parser.parse_args()
    if args.debug:
        logger.info("Enable DEBUG-LEVEL log")
        logging.basicConfig(level=logging.DEBUG)
    hps = utils.get_hparams_from_file(args.config_dir)
    device = "cuda:0" if torch.cuda.is_available() else "cpu"
    '''
    device = (
        "cuda:0"
        if torch.cuda.is_available()
        else (
            "mps"
            if sys.platform == "darwin" and torch.backends.mps.is_available()
            else "cpu"
        )
    )
    '''
    net_g = SynthesizerTrn(
        len(symbols),
        hps.data.filter_length // 2 + 1,
        hps.train.segment_size // hps.data.hop_length,
        n_speakers=hps.data.n_speakers,
        **hps.model).to(device)
    _ = net_g.eval()

    _ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True)

    speaker_ids = hps.data.spk2id
    speakers = list(speaker_ids.keys())
    with gr.Blocks() as app:
        with gr.Row():
            with gr.Column():


                gr.Markdown(value="""
                星瞳 Bert-Vits2在线语音生成\n
                1、模型作者：数字星瞳企划 https://t.me/xingtong25680 \n
                \n
                2、原项目地址：https://github.com/Stardust-minus/Bert-VITS2\n
                3、使用此模型进行二创请注明AI生成，以及该项目地址。\n
                4、如果想生成超长txt文本的音频请使用colab。 https://colab.research.google.com/drive/13ek8_j1aknr-pbjj3NXxSM4vBIsracU3?usp=drive_link\n

                """)
                text = gr.TextArea(label="Text", placeholder="Input Text Here",
                                      value="这里是数字星瞳企画，请在电报搜索星瞳全拼加二五六八零，获取最新更新进展。")
                speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker')
                sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label='语调变化')
                noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.6, step=0.01, label='感情变化')
                noise_scale_w = gr.Slider(minimum=0.1, maximum=1.4, value=0.8, step=0.01, label='音节发音长度变化')
                length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='语速')
                btn = gr.Button("开启AI语音之旅吧!", variant="primary")
            with gr.Column():
                text_output = gr.Textbox(label="Message")
                audio_output = gr.Audio(label="Output Audio")
                ogg_output = gr.File(label="Converted OGG file")
                gr.Markdown(value="""
                模型汇总：\n
                星瞳整合 https://huggingface.co/spaces/digitalxingtong/Xingtong-All-in-One\n
                甜甜叫花鸡 https://huggingface.co/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2 \n
                七海 https://huggingface.co/spaces/digitalxingtong/Nanami-Bert-Vits2 \n
                东雪莲 https://huggingface.co/spaces/digitalxingtong/Azuma-Bert-Vits2 \n
                嘉然 https://huggingface.co/spaces/digitalxingtong/Jiaran-Bert-Vits2 \n
                乃琳 https://huggingface.co/spaces/digitalxingtong/Eileen-Bert-Vits2  \n
                恬豆 https://huggingface.co/spaces/digitalxingtong/Dou-Bert-Vits2 \n
                奶绿 杂谈 https://huggingface.co/spaces/digitalxingtong/Nailv-Bert-Vits2 \n
                奶绿 朗读 https://huggingface.co/spaces/digitalxingtong/Nailv-read-Bert-Vits2 \n  
                露早 https://huggingface.co/spaces/digitalxingtong/Luzao-Bert-Vits2 \n 
                柚恩 https://huggingface.co/spaces/digitalxingtong/Un-Bert-Vits2 \n
                米诺 https://huggingface.co/spaces/digitalxingtong/Minuo-Bert-Vits2 \n
                扇宝 https://huggingface.co/spaces/digitalxingtong/Shanbao-Bert-Vits2 \n
                牧牧白 https://huggingface.co/spaces/digitalxingtong/Miiu-Bert-Vits2 \n
                吉诺儿kino https://huggingface.co/spaces/digitalxingtong/Kino-Bert-Vits2 \n
                九夏 https://huggingface.co/spaces/digitalxingtong/Jiuxia-Bert-Vits2 \n
                卡缇娅 https://huggingface.co/spaces/digitalxingtong/Yaya-Bert-Vits2 \n
                理想_ideal https://huggingface.co/spaces/digitalxingtong/Lixiang-Bert-Vits2 \n
                阿梓 https://huggingface.co/spaces/digitalxingtong/Azusa-Bert-Vits2 \n
                鹿鸣 https://huggingface.co/spaces/digitalxingtong/Luming-Bert-Vits2 \n
                永雏塔菲 https://huggingface.co/spaces/digitalxingtong/Taffy-Bert-VITS2 \n
                """)
        btn.click(tts_fn,
                inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale],
                outputs=[text_output, audio_output,ogg_output])
    
        
    app.launch(show_error=True)