app.py

import nltk
import ssl

try:
    _create_unverified_https_context = ssl._create_unverified_context
except AttributeError:
    pass
else:
    ssl._create_default_https_context = _create_unverified_https_context
nltk.download("cmudict")

import os
import json
import random
import gradio as gr
import numpy as np
import torch
import re_matching
import utils
from infer import infer, latest_version, get_net_g
import gradio as gr
from config import config
from tools.webui import reload_javascript, get_character_html

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

def speak_fn(
        text: str,
        exceed_flag,
        speaker="TalkFlower_CNzh",
        sdp_ratio=0.2,      # SDP/DP混合比
        noise_scale=0.6,        # 感情
        noise_scale_w=0.6,      # 音素长度
        length_scale=0.9,       # 语速
        language="ZH",
        interval_between_para=0.2,      # 段间间隔
        interval_between_sent=1,        # 句间间隔
    ):
    while text.find("\n\n") != -1:
        text = text.replace("\n\n", "\n")
    if len(text) > 100:
        print(f"Too Long Text: {text}")
        if exceed_flag:
            text = "不要超过100字！"
            return gr.update(value="./assets/audios/nomorethan100.wav"), get_character_html(text), False, gr.update(interactive=True)
        else:
            text = "这句太长了，憋坏我啦！"
            return gr.update(value="./assets/audios/overlength.wav"), get_character_html(text), True, gr.update(interactive=True)
    audio_list = []
    if len(text) > 42:
        print(f"Long Text: {text}")
        para_list = re_matching.cut_para(text)
        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)
    else:
        print(f"Short Text: {text}")
        silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
        with torch.no_grad():
            for piece in text.split("|"):
                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
    
    audio_concat = np.concatenate(audio_list)    
    return (hps.data.sampling_rate, audio_concat), get_character_html(text), exceed_flag, gr.update(interactive=True)


def submit_lock_fn():    
    return gr.update(interactive=False)


def init_fn():
    gr.Info("2023-11-24: 优化长句生成效果；增加示例；更新了一些小彩蛋；画了一些大饼）")
    gr.Info("Only support Chinese now. Trying to train a mutilingual model. 欢迎在 Community 中提建议~")
    
    index = random.randint(1,7)
    welcome_text = get_sentence("Welcome", index)
    
    return gr.update(value=f"./assets/audios/Welcome{index}.wav"), get_character_html(welcome_text)

def get_sentence(category, index=-1):
    if index == -1:
        index = random.randint(1, len(full_lines[category]))
    return full_lines[category][f"{index}"]

with open("./css/style.css", "r", encoding="utf-8") as f:
    customCSS = f.read()
with open("./assets/lines.json", "r", encoding="utf-8") as f:
    full_lines = json.load(f)

with gr.Blocks(css=customCSS) as demo:
    exceed_flag = gr.State(value=False)
    tmp_string = gr.Textbox(value="", visible=False)
    character_area = gr.HTML(get_character_html("你好呀！"), elem_id="character_area")
    with gr.Tab("Speak", elem_id="tab-speak"):
        speak_input = gr.Textbox(lines=1, label="Talking Flower will say:", elem_classes="wonder-card", elem_id="input_text")
        speak_button = gr.Button("Speak!", elem_id="speak_button", elem_classes="main-button wonder-card")
        example_category = gr.Examples(["夸夸你 | Praise", "游戏台词 | Scripts", "玩梗 | Meme"], fn=get_sentence, inputs=[tmp_string], outputs=[speak_input], run_on_click=True, elem_id="examples")
    with gr.Tab("Chat", elem_id="tab-chat"):
        chat_input = gr.Textbox(lines=1, placeholder="Coming Soon...", label="Chat to Talking Flower:", elem_classes="wonder-card", elem_id="input_text", interactive=False)
        chat_button = gr.Button("Chat!", elem_id="chat_button", elem_classes="main-button wonder-card")
    with gr.Tab("Mimic", elem_id="tab-mimic"):
        gr.Textbox(lines=1, placeholder="Coming Soon...", label="Choose sound to mimic:", elem_classes="wonder-card", elem_id="input_text", interactive=False)
        mimic_button = gr.Button("Mimic!", elem_id="mimic_button", elem_classes="main-button wonder-card")
    audio_output = gr.Audio(label="输出音频", show_label=False, autoplay=True, elem_id="audio_output", elem_classes="wonder-card")
    
    demo.load(
        init_fn,
        inputs=[],
        outputs=[audio_output, character_area]
    )
    speak_input.submit(submit_lock_fn, show_progress=False).then(
        speak_fn,
        inputs=[speak_input, exceed_flag],
        outputs=[audio_output, character_area, exceed_flag, speak_button],
    )
    speak_button.click(submit_lock_fn, show_progress=False).then(
        speak_fn,
        inputs=[speak_input, exceed_flag],
        outputs=[audio_output, character_area, exceed_flag, speak_button],
    )


if __name__ == "__main__":
    hps = utils.get_hparams_from_file(config.webui_config.config_path)
    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)
    reload_javascript()
    demo.launch(
        allowed_paths=["./assets"],
        show_api=False,
        inbrowser=True,
    )