import os, logging, datetime, json, 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, infer_multilang import gradio as gr from config import config from tools.webui import reload_javascript, get_character_html from tools.sentence import split_by_language logging.basicConfig( level=logging.INFO, format='[%(levelname)s|%(asctime)s]%(message)s', datefmt='%Y-%m-%d %H:%M:%S' ) device = config.webui_config.device if device == "mps": os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" 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) 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) 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", reference_audio=None, emotion=4, interval_between_para=0.2, # 段间间隔 interval_between_sent=1, # 句间间隔 ): if speaker == "Chinese": speaker = "TalkFlower_CNzh" elif speaker == "English": speaker = "TalkFlower_USen" elif speaker == "Japanese": speaker = "TalkFlower_JPja" else: speaker = "TalkFlower_CNzh" audio_list = [] while text.find("\n\n") != -1: text = text.replace("\n\n", "\n") if len(text) > 512: logging.info(f"Too Long Text: {text}") if speaker == "TalkFlower_CNzh": text = "这句太长了，憋坏我啦！" audio_value = "./assets/audios/overlength.wav" elif speaker == "TalkFlower_USen": text = "This sentence is too long!" audio_value = "./assets/audios/overlength_en.wav" elif speaker == "TalkFlower_JPja": text = "この文は長すぎます！" audio_value = "./assets/audios/overlength_ja.wav" exceed_flag = not exceed_flag else: for idx, slice in enumerate(text.split("|")): if slice == "": continue skip_start = idx != 0 skip_end = idx != len(text.split("|")) - 1 sentences_list = split_by_language( slice, target_languages=["zh", "ja", "en"] ) idx = 0 while idx < len(sentences_list): text_to_generate = [] lang_to_generate = [] while True: content, lang = sentences_list[idx] temp_text = [content] lang = lang.upper() if lang == "JA": lang = "JP" if len(text_to_generate) > 0: text_to_generate[-1] += [temp_text.pop(0)] lang_to_generate[-1] += [lang] if len(temp_text) > 0: text_to_generate += [[i] for i in temp_text] lang_to_generate += [[lang]] * len(temp_text) if idx + 1 < len(sentences_list): idx += 1 else: break skip_start = (idx != 0) and skip_start skip_end = (idx != len(sentences_list) - 1) and skip_end logging.info(f"{speaker[-4:]}: {text_to_generate}{lang_to_generate}") with torch.no_grad(): for i, piece in enumerate(text_to_generate): skip_start = (i != 0) and skip_start skip_end = (i != len(text_to_generate) - 1) and skip_end audio = infer_multilang( piece, reference_audio=reference_audio, emotion=emotion, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker, language=lang_to_generate[i], hps=hps, net_g=net_g, device=device, skip_start=skip_start, skip_end=skip_end, ) audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio) audio_list.append(audio16bit) idx += 1 # 单一语言推理 # if len(text) > 42: # logging.info(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, # reference_audio=reference_audio, # emotion=emotion, # ) # 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: # logging.info(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, # reference_audio=reference_audio, # emotion=emotion, # ) # 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) audio_value = (hps.data.sampling_rate, audio_concat) return gr.update(value=audio_value, autoplay=True), get_character_html(text), exceed_flag, gr.update(interactive=True) def submit_lock_fn(): return gr.update(interactive=False) def init_fn(): gr.Info("2023-11-28: 支持多语言（中、英、日）！支持更换音色！") # 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("Support languages: Chinese, English, Japanese. 欢迎在 Community 中提建议~") index = random.randint(1,7) welcome_text = get_sentence("Welcome", index) return get_character_html(welcome_text) #gr.update(value=f"./assets/audios/Welcome{index}.wav", autoplay=False), def get_sentence(category, index=-1): if index == -1: index = random.randint(1, len(full_lines[category])) return full_lines[category][f"{index}"]