# flake8: noqa: E402 import gc import os import logging import re_matching 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 utils from infer import infer, latest_version, get_net_g import gradio as gr # import webbrowser import numpy as np from config import config # multithreading torch.set_num_threads(os.cpu_count()) torch.set_num_interop_threads(os.cpu_count()) net_g = None device = config.device if device == "mps": os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" def free_up_memory(): # Prior inference run might have large variables not cleaned up due to exception during the run. # Free up as much memory as possible to allow this run to be successful. gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() def generate_audio( slices, sdp_ratio, noise_scale, noise_scale_w, length_scale, speaker, # language, # reference_audio, # emotion, style_text, style_weight, skip_start=False, skip_end=False, ): audio_list = [] # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16) free_up_memory() with torch.no_grad(): for idx, piece in enumerate(slices): skip_start = idx != 0 skip_end = idx != len(slices) - 1 audio = infer( piece, # reference_audio=reference_audio, emotion=None, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker, language="ZH", hps=hps, net_g=net_g, device=device, skip_start=skip_start, skip_end=skip_end, style_text=style_text, style_weight=style_weight, ) audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio) audio_list.append(audio16bit) return audio_list def process_text( text: str, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, # language, # reference_audio, # emotion, style_text=None, style_weight=0, ): audio_list = [] audio_list.extend( generate_audio( text.split("|"), sdp_ratio, noise_scale, noise_scale_w, length_scale, speaker, # language, # reference_audio, # emotion, style_text, style_weight, ) ) return audio_list def tts_fn( text: str, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, # reference_audio, # emotion, # prompt_mode, style_text=None, style_weight=0, ): if style_text == "": style_text = None # if prompt_mode == "Audio prompt": # if reference_audio == None: # return ("Invalid audio prompt", None) # else: # reference_audio = load_audio(reference_audio)[1] # else: # reference_audio = None audio_list = process_text( text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, # language, # reference_audio, # emotion, style_text, style_weight, ) audio_concat = np.concatenate(audio_list) return "Success", (hps.data.sampling_rate, audio_concat) if __name__ == "__main__": if config.webui_config.debug: logger.info("Enable DEBUG-LEVEL log") logging.basicConfig(level=logging.DEBUG) hps = utils.get_hparams_from_file(config.webui_config.config_path) # 若config.json中未指定版本则默认为最新版本 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 ) speaker_ids = hps.data.spk2id speakers = list(speaker_ids.keys()) languages = ["ZH", "JP", "EN", "mix", "auto"] with gr.Blocks() as app: with gr.Row(): with gr.Column(): text = gr.TextArea( label="输入文本内容", ) # trans = gr.Button("中翻日", variant="primary") # slicer = gr.Button("快速切分", variant="primary") # formatter = gr.Button("检测语言，并整理为 MIX 格式", variant="primary") speaker = gr.Dropdown( choices=speakers, value=speakers[0], label="Speaker" ) # _ = gr.Markdown( # value="提示模式（Prompt mode）：可选文字提示或音频提示，用于生成文字或音频指定风格的声音。\n", # visible=False, # ) # prompt_mode = gr.Radio( # ["Text prompt", "Audio prompt"], # label="Prompt Mode", # value="Text prompt", # visible=False, # ) # text_prompt = gr.Textbox( # label="Text prompt", # placeholder="用文字描述生成风格。如：Happy", # value="Happy", # visible=False, # ) # audio_prompt = gr.Audio( # label="Audio prompt", type="filepath", visible=False # ) sdp_ratio = gr.Slider( minimum=0, maximum=1, value=0.5, step=0.1, label="SDP Ratio" ) noise_scale = gr.Slider( minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise" ) noise_scale_w = gr.Slider( minimum=0.1, maximum=2, value=0.9, step=0.1, label="Noise_W" ) length_scale = gr.Slider( minimum=0.1, maximum=2, value=1.0, step=0.1, label="Length" ) btn = gr.Button("生成音频！", variant="primary") with gr.Column(): with gr.Accordion("融合文本语义", open=False): gr.Markdown( value="使用辅助文本的语意来辅助生成对话（语言保持与主文本相同）\n\n" "**注意**：不要使用**指令式文本**（如：开心），要使用**带有强烈情感的文本**（如：我好快乐！！！）\n\n" "效果较不明确，留空即为不使用该功能" ) style_text = gr.Textbox(label="辅助文本") style_weight = gr.Slider( minimum=0, maximum=1, value=0.7, step=0.1, label="Weight", info="主文本和辅助文本的bert混合比率，0表示仅主文本，1表示仅辅助文本", ) text_output = gr.Textbox(label="状态信息") audio_output = gr.Audio(label="输出音频") # explain_image = gr.Image( # label="参数解释信息", # show_label=True, # show_share_button=False, # show_download_button=False, # value=os.path.abspath("./img/参数说明.png"), # ) btn.click( tts_fn, inputs=[ text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, # language, # audio_prompt, # text_prompt, # prompt_mode, style_text, style_weight, ], outputs=[text_output, audio_output], ) # trans.click( # translate, # inputs=[text], # outputs=[text], # ) # slicer.click( # tts_split, # inputs=[ # text, # speaker, # sdp_ratio, # noise_scale, # noise_scale_w, # length_scale, # language, # opt_cut_by_sent, # interval_between_para, # interval_between_sent, # # audio_prompt, # # text_prompt, # style_text, # style_weight, # ], # outputs=[text_output, audio_output], # ) # formatter.click( # format_utils, # inputs=[text, speaker], # outputs=[language, text], # ) print("推理页面已开启!") # webbrowser.open(f"http://127.0.0.1:{config.webui_config.port}") app.launch(share=config.webui_config.share, server_port=config.webui_config.port)