import gradio as gr import torch from modules.normalization import text_normalize from modules.utils.hf import spaces from modules.webui import webui_utils from modules.webui.webui_utils import get_speakers, get_styles, split_long_text # NOTE: 因为 text_normalize 需要使用 tokenizer @torch.inference_mode() @spaces.GPU(duration=120) def merge_dataframe_to_ssml(dataframe, spk, style, seed): if style == "*auto": style = None if spk == "-1" or spk == -1: spk = None if seed == -1 or seed == "-1": seed = None ssml = "" indent = " " * 2 for i, row in dataframe.iterrows(): ssml += f"{indent}\n" # 原封不动输出回去是为了触发 loadding 效果 return dataframe, spk, style, seed, f"\n{ssml}" # 长文本处理 # 可以输入长文本,并选择切割方法,切割之后可以将拼接的SSML发送到SSML tab # 根据 。 句号切割,切割之后显示到 data table def create_spliter_tab(ssml_input, tabs1, tabs2): speakers, speaker_names = webui_utils.get_speaker_names() speaker_names = ["*random"] + speaker_names styles = ["*auto"] + [s.get("name") for s in get_styles()] with gr.Row(): with gr.Column(scale=1): # 选择说话人 选择风格 选择seed with gr.Group(): gr.Markdown("🗣️Speaker") spk_input_text = gr.Textbox( label="Speaker (Text or Seed)", value="female2", show_label=False, ) spk_input_dropdown = gr.Dropdown( choices=speaker_names, interactive=True, value="female : female2", show_label=False, ) spk_rand_button = gr.Button( value="🎲", variant="secondary", ) with gr.Group(): gr.Markdown("🎭Style") style_input_dropdown = gr.Dropdown( choices=styles, interactive=True, show_label=False, value="*auto", ) with gr.Group(): gr.Markdown("🗣️Seed") infer_seed_input = gr.Number( value=42, label="Inference Seed", show_label=False, minimum=-1, maximum=2**32 - 1, ) infer_seed_rand_button = gr.Button( value="🎲", variant="secondary", ) send_btn = gr.Button("📩Send to SSML", variant="primary") with gr.Column(scale=3): with gr.Group(): gr.Markdown("📝Long Text Input") gr.Markdown("SSML_SPLITER_GUIDE") long_text_input = gr.Textbox( label="Long Text Input", lines=10, placeholder="输入长文本", elem_id="long-text-input", show_label=False, ) long_text_split_button = gr.Button("🔪Split Text") with gr.Row(): with gr.Column(scale=3): with gr.Group(): gr.Markdown("🎨Output") long_text_output = gr.DataFrame( headers=["index", "text", "length"], datatype=["number", "str", "number"], elem_id="long-text-output", interactive=False, wrap=True, value=[], ) spk_input_dropdown.change( fn=lambda x: x.startswith("*") and "-1" or x.split(":")[-1].strip(), inputs=[spk_input_dropdown], outputs=[spk_input_text], ) spk_rand_button.click( lambda x: int(torch.randint(0, 2**32 - 1, (1,)).item()), inputs=[spk_input_text], outputs=[spk_input_text], ) infer_seed_rand_button.click( lambda x: int(torch.randint(0, 2**32 - 1, (1,)).item()), inputs=[infer_seed_input], outputs=[infer_seed_input], ) long_text_split_button.click( split_long_text, inputs=[long_text_input], outputs=[long_text_output], ) infer_seed_rand_button.click( lambda x: int(torch.randint(0, 2**32 - 1, (1,)).item()), inputs=[infer_seed_input], outputs=[infer_seed_input], ) send_btn.click( merge_dataframe_to_ssml, inputs=[ long_text_output, spk_input_text, style_input_dropdown, infer_seed_input, ], outputs=[ long_text_output, spk_input_text, style_input_dropdown, infer_seed_input, ssml_input, ], ) def change_tab(): return gr.Tabs(selected="ssml"), gr.Tabs(selected="ssml.editor") send_btn.click(change_tab, inputs=[], outputs=[tabs1, tabs2])