app.py

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

# ChatGLM2

from transformers import AutoModel, AutoTokenizer, AutoConfig
import gradio as gr
import mdtex2html
import torch
import os

CHECKPOINT_PATH=f'output_lh_v2/checkpoint-700'
tokenizer = AutoTokenizer.from_pretrained("chatglm2-6b", trust_remote_code=True)
config = AutoConfig.from_pretrained("chatglm2-6b", trust_remote_code=True, pre_seq_len=128)
model = AutoModel.from_pretrained("chatglm2-6b", config=config, trust_remote_code=True)
prefix_state_dict = torch.load(os.path.join(CHECKPOINT_PATH, "pytorch_model.bin"), map_location=torch.device('cpu'))

#prefix_state_dict = torch.load(os.path.join(CHECKPOINT_PATH, "pytorch_model.bin"))

new_prefix_state_dict = {}
for k, v in prefix_state_dict.items():
    if k.startswith("transformer.prefix_encoder."):
        new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)

#model = model.half().cuda()

model = model.float()

model.transformer.prefix_encoder.float()
model = model.eval()


"""Override Chatbot.postprocess"""


def postprocess(self, y):
    if y is None:
        return []
    for i, (message, response) in enumerate(y):
        y[i] = (
            None if message is None else mdtex2html.convert((message)),
            None if response is None else mdtex2html.convert(response),
        )
    return y


gr.Chatbot.postprocess = postprocess


def parse_text(text):
    """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
    lines = text.split("\n")
    lines = [line for line in lines if line != ""]
    count = 0
    for i, line in enumerate(lines):
        if "```" in line:
            count += 1
            items = line.split('`')
            if count % 2 == 1:
                lines[i] = f'<pre><code class="language-{items[-1]}">'
            else:
                lines[i] = f'<br></code></pre>'
        else:
            if i > 0:
                if count % 2 == 1:
                    line = line.replace("`", "\`")
                    line = line.replace("<", "&lt;")
                    line = line.replace(">", "&gt;")
                    line = line.replace(" ", "&nbsp;")
                    line = line.replace("*", "&ast;")
                    line = line.replace("_", "&lowbar;")
                    line = line.replace("-", "&#45;")
                    line = line.replace(".", "&#46;")
                    line = line.replace("!", "&#33;")
                    line = line.replace("(", "&#40;")
                    line = line.replace(")", "&#41;")
                    line = line.replace("$", "&#36;")
                lines[i] = "<br>"+line
    text = "".join(lines)
    return text


def predict(input, chatbot, max_length, top_p, temperature, history, past_key_values):
    chatbot.append((parse_text(input), ""))
    for response, history, past_key_values in model.stream_chat(tokenizer, input, history, past_key_values=past_key_values,
                                                                return_past_key_values=True,
                                                                max_length=max_length, top_p=top_p,
                                                                temperature=temperature):
        chatbot[-1] = (parse_text(input), parse_text(response))

        yield chatbot, history, past_key_values, response


def reset_user_input():
    return gr.update(value='')


def reset_state():
    return [], [], None

# Bert-VITS2

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

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
        return audio

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)
    return "Success", (hps.data.sampling_rate, audio)

image_markdown = ("""
<h1 align="center"><a href="http://www.talktalkai.com"><img src="https://media.9game.cn/gamebase/2021/7/23/227829877.jpg", alt="talktalkai" border="0" style="margin: 0 auto; height: 200px;" /></a> </h1>
""")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_dir", default="./logs/OUTPUT_MODEL/G_13900.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:
        gr.Markdown("# <center>🌊💕🎶 ChatGLM2 神里绫华 + Bert-VITS2</center>")
        gr.Markdown("## <center>🌟 - 和绫华 畅所欲言吧：稻妻神里流太刀术皆传，神里绫华，参上！ </center>")      
        gr.Markdown("### <center>🍻 - 更多精彩应用，尽在[滔滔AI](http://www.talktalkai.com)；滔滔AI，为爱滔滔！💕</center>")
        with gr.Accordion("绫华", open=True):
            gr.Markdown(image_markdown)

        chatbot = gr.Chatbot()
        with gr.Row():
            with gr.Column(scale=4):
                with gr.Column(scale=12):
                    user_input = gr.Textbox(show_label=False, placeholder="推荐以“你好”来开始对话，效果会更好哦！和绫华一起叙叙旧吧...", lines=2).style(
                        container=False)
                with gr.Column(min_width=32, scale=1):
                    submitBtn = gr.Button("开始对话吧！", variant="primary")
            with gr.Column(scale=1):
                emptyBtn = gr.Button("清空所有聊天记录")
                max_length = gr.Slider(0, 32768, value=8192, step=1.0, label="Maximum length", interactive=True, visible=False)
                top_p = gr.Slider(0, 1, value=0.8, step=0.01, label="Top P", interactive=True, visible=False)
                temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="数值越高，文本的随机性越强", interactive=True)
                response_lh = gr.Textbox(label="神里绫华的回答", visible=False)
    
        history = gr.State([])
        past_key_values = gr.State(None)
    
        submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history, past_key_values],
                        [chatbot, history, past_key_values, response_lh], show_progress=True)
        submitBtn.click(reset_user_input, [], [user_input])
    
        emptyBtn.click(reset_state, outputs=[chatbot, history, past_key_values], show_progress=True)

        
        with gr.Row():
            with gr.Column():
                text = response_lh
                speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker', visible=False)
                with gr.Row():
                    sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.1, label='语调变化', visible=False)
                    noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.6, step=0.1, label='感情变化', visible=False)
                with gr.Row():
                    noise_scale_w = gr.Slider(minimum=0.1, maximum=1.4, value=0.8, step=0.1, label='音节发音长度变化')
                    length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.1, label='语速 (数值越小，语速越快)')
                btn = gr.Button("开启AI语音之旅吧!", variant="primary")
            with gr.Column():
                text_output = gr.Textbox(label="Message", visible=False)
                audio_output = gr.Audio(label="神里绫华发来的语音", autoplay=True)

        btn.click(tts_fn,
                inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale],
                outputs=[text_output, audio_output])

    app.launch(show_error=True)