app_backup.py

import os
import torch
import librosa
import gradio as gr
from scipy.io.wavfile import write
from transformers import WavLMModel

import utils
from models import SynthesizerTrn
from mel_processing import mel_spectrogram_torch
from speaker_encoder.voice_encoder import SpeakerEncoder

import time
from textwrap import dedent

import mdtex2html
from loguru import logger
from transformers import AutoModel, AutoTokenizer

from tts_voice import tts_order_voice
import edge_tts
import tempfile
import anyio

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

smodel = SpeakerEncoder('speaker_encoder/ckpt/pretrained_bak_5805000.pt')

print("Loading FreeVC(24k)...")
hps = utils.get_hparams_from_file("configs/freevc-24.json")
freevc_24 = SynthesizerTrn(
    hps.data.filter_length // 2 + 1,
    hps.train.segment_size // hps.data.hop_length,
    **hps.model).to(device)
_ = freevc_24.eval()
_ = utils.load_checkpoint("checkpoints/freevc-24.pth", freevc_24, None)

print("Loading WavLM for content...")
cmodel = WavLMModel.from_pretrained("microsoft/wavlm-large").to(device)
 
def convert(model, src, tgt):
    with torch.no_grad():
        # tgt
        wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate)
        wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
        if model == "FreeVC" or model == "FreeVC (24kHz)":
            g_tgt = smodel.embed_utterance(wav_tgt)
            g_tgt = torch.from_numpy(g_tgt).unsqueeze(0).to(device)
        else:
            wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(device)
            mel_tgt = mel_spectrogram_torch(
                wav_tgt, 
                hps.data.filter_length,
                hps.data.n_mel_channels,
                hps.data.sampling_rate,
                hps.data.hop_length,
                hps.data.win_length,
                hps.data.mel_fmin,
                hps.data.mel_fmax
            )
        # src
        wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate)
        wav_src = torch.from_numpy(wav_src).unsqueeze(0).to(device)
        c = cmodel(wav_src).last_hidden_state.transpose(1, 2).to(device)
        # infer
        if model == "FreeVC":
            audio = freevc.infer(c, g=g_tgt)
        elif model == "FreeVC-s":
            audio = freevc_s.infer(c, mel=mel_tgt)
        else:
            audio = freevc_24.infer(c, g=g_tgt)
        audio = audio[0][0].data.cpu().float().numpy()
        if model == "FreeVC" or model == "FreeVC-s":
            write("out.wav", hps.data.sampling_rate, audio)
        else:
            write("out.wav", 24000, audio)
    out = "out.wav"
    return out

# GLM2

language_dict = tts_order_voice

# fix timezone in Linux
os.environ["TZ"] = "Asia/Shanghai"
try:
    time.tzset()  # type: ignore # pylint: disable=no-member
except Exception:
    # Windows
    logger.warning("Windows, cant run time.tzset()")

# model_name = "THUDM/chatglm2-6b"
model_name = "THUDM/chatglm2-6b-int4"

RETRY_FLAG = False

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda()

# 4/8 bit
# model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).quantize(4).cuda()

has_cuda = torch.cuda.is_available()

# has_cuda = False  # force cpu

if has_cuda:
    model_glm = (
        AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda().half()
    )  # 3.92G
else:
    model_glm = AutoModel.from_pretrained(
        model_name, trust_remote_code=True
    ).float()  # .float() .half().float()

model_glm = model_glm.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] = "<br></code></pre>"
        else:
            if i > 0:
                if count % 2 == 1:
                    line = line.replace("`", r"\`")
                    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(
    RETRY_FLAG, input, chatbot, max_length, top_p, temperature, history, past_key_values
):
    try:
        chatbot.append((parse_text(input), ""))
    except Exception as exc:
        logger.error(exc)
        logger.debug(f"{chatbot=}")
        _ = """
        if chatbot:
            chatbot[-1] = (parse_text(input), str(exc))
            yield chatbot, history, past_key_values
        # """
        yield chatbot, history, past_key_values

    for response, history, past_key_values in model_glm.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))
        # chatbot[-1][-1] = parse_text(response)

        yield chatbot, history, past_key_values, parse_text(response)

def trans_api(input, max_length=4096, top_p=0.8, temperature=0.2):
    if max_length < 10:
        max_length = 4096
    if top_p < 0.1 or top_p > 1:
        top_p = 0.85
    if temperature <= 0 or temperature > 1:
        temperature = 0.01
    try:
        res, _ = model_glm.chat(
            tokenizer,
            input,
            history=[],
            past_key_values=None,
            max_length=max_length,
            top_p=top_p,
            temperature=temperature,
        )
        # logger.debug(f"{res=} \n{_=}")
    except Exception as exc:
        logger.error(f"{exc=}")
        res = str(exc)

    return res

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


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


# Delete last turn
def delete_last_turn(chat, history):
    if chat and history:
        chat.pop(-1)
        history.pop(-1)
    return chat, history

# Regenerate response
def retry_last_answer(
    user_input, chatbot, max_length, top_p, temperature, history, past_key_values
):
    if chatbot and history:
        # Removing the previous conversation from chat
        chatbot.pop(-1)
        # Setting up a flag to capture a retry
        RETRY_FLAG = True
        # Getting last message from user
        user_input = history[-1][0]
        # Removing bot response from the history
        history.pop(-1)

    yield from predict(
        RETRY_FLAG,  # type: ignore
        user_input,
        chatbot,
        max_length,
        top_p,
        temperature,
        history,
        past_key_values,
    )

# print

def print(text):
    return text

# TTS

async def text_to_speech_edge(text, language_code):
    voice = language_dict[language_code]
    communicate = edge_tts.Communicate(text, voice)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
        tmp_path = tmp_file.name

    await communicate.save(tmp_path)

    return tmp_path

with gr.Blocks(title="ChatGLM2-6B-int4", theme=gr.themes.Soft(text_size="sm")) as demo:
    gr.HTML("<center>"
            "<h1>🥳💕🎶 - ChatGLM2 + 声音克隆：和你喜欢的角色畅所欲言吧！</h1>"
            "</center>")

    with gr.Accordion("📒 相关信息", open=False):
        _ = f""" ChatGLM2的可选参数信息：
            * Low temperature: responses will be more deterministic and focused; High temperature: responses more creative.
            * Suggested temperatures -- translation: up to 0.3; chatting: > 0.4
            * Top P controls dynamic vocabulary selection based on context.\n
            如果您想让ChatGLM2进行角色扮演并与之对话，请先输入恰当的提示词，如“请你扮演成动漫角色蜡笔小新并和我进行对话”；您也可以为ChatGLM2提供自定义的角色设定\n
            当您使用声音克隆功能时，请先在此程序的对应位置上传一段您喜欢的音频
            """
        gr.Markdown(dedent(_))
  
    chatbot = gr.Chatbot(height=300)
    with gr.Row():
        with gr.Column(scale=4):
            with gr.Column(scale=12):
                user_input = gr.Textbox(
                    label="请在此处和GLM2聊天 (按回车键即可发送)",
                    placeholder="聊点什么吧",
                )
                RETRY_FLAG = gr.Checkbox(value=False, visible=False)
    with gr.Column(min_width=32, scale=1):
        with gr.Row():
            submitBtn = gr.Button("开始和GLM2交流吧", variant="primary")
            deleteBtn = gr.Button("删除最新一轮对话", variant="secondary")
            retryBtn = gr.Button("重新生成最新一轮对话", variant="secondary")
                
    with gr.Accordion("🔧 更多设置", open=False):
        with gr.Row():
            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,
            )
            top_p = gr.Slider(
                0, 1, value=0.85, step=0.01, label="Top P", interactive=True
            )
            temperature = gr.Slider(
                0.01, 1, value=0.95, step=0.01, label="Temperature", interactive=True
            )


    with gr.Row():
        test1 = gr.Textbox(label="GLM2的最新回答 (可编辑)", lines = 3)
        with gr.Column():
            language = gr.Dropdown(choices=list(language_dict.keys()), value="普通话 (中国大陆)-Xiaoxiao-女", label="请选择文本对应的语言及您喜欢的说话人")
            tts_btn = gr.Button("生成对应的音频吧", variant="primary")
        output_audio = gr.Audio(type="filepath", label="为您生成的音频", interactive=False)

    tts_btn.click(text_to_speech_edge, inputs=[test1, language], outputs=[output_audio])

    with gr.Row():
        model_choice = gr.Dropdown(choices=["FreeVC", "FreeVC-s", "FreeVC (24kHz)"], value="FreeVC (24kHz)", label="Model", visible=False) 
        audio1 = output_audio
        audio2 = gr.Audio(label="请上传您喜欢的声音进行声音克隆", type='filepath')
        clone_btn = gr.Button("开始AI声音克隆吧", variant="primary")
        audio_cloned =  gr.Audio(label="为您生成的专属声音克隆音频", type='filepath')

    clone_btn.click(convert, inputs=[model_choice, audio1, audio2], outputs=[audio_cloned])
        
    history = gr.State([])
    past_key_values = gr.State(None)

    user_input.submit(
        predict,
        [
            RETRY_FLAG,
            user_input,
            chatbot,
            max_length,
            top_p,
            temperature,
            history,
            past_key_values,
        ],
        [chatbot, history, past_key_values, test1],
        show_progress="full",
    )
    submitBtn.click(
        predict,
        [
            RETRY_FLAG,
            user_input,
            chatbot,
            max_length,
            top_p,
            temperature,
            history,
            past_key_values,
        ],
        [chatbot, history, past_key_values, test1],
        show_progress="full",
        api_name="predict",
    )
    submitBtn.click(reset_user_input, [], [user_input])

    emptyBtn.click(
        reset_state, outputs=[chatbot, history, past_key_values, test1], show_progress="full"
    )

    retryBtn.click(
        retry_last_answer,
        inputs=[
            user_input,
            chatbot,
            max_length,
            top_p,
            temperature,
            history,
            past_key_values,
        ],
        # outputs = [chatbot, history, last_user_message, user_message]
        outputs=[chatbot, history, past_key_values, test1],
    )
    deleteBtn.click(delete_last_turn, [chatbot, history], [chatbot, history])

    with gr.Accordion("For Chat/Translation API", open=False):
        input_text = gr.Text()
        tr_btn = gr.Button("Go", variant="primary")
        out_text = gr.Text()
    tr_btn.click(
        trans_api,
        [input_text, max_length, top_p, temperature],
        out_text,
        # show_progress="full",
        api_name="tr",
    )
    _ = """
    input_text.submit(
        trans_api,
        [input_text, max_length, top_p, temperature],
        out_text,
        show_progress="full",
        api_name="tr1",
    )
    # """

    gr.Markdown("### <center>注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。</center>")
    gr.Markdown("<center>💡 - 如何使用此程序：输入您对ChatGLM的提问后，依次点击“开始和GLM2交流吧”、“生成对应的音频吧”、“开始AI声音克隆吧”三个按键即可；使用声音克隆功能时，请先上传一段您喜欢的音频</center>")
    gr.HTML('''
        <div class="footer">
                    <p>Reedit by Forest, Thanks 明·顾璘</p>
        </div>
    ''')

demo.queue().launch(show_error=True, debug=True)