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, )