# flake8: noqa: E402 import os import logging import re_matching from tools.sentence import split_by_language 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, infer_multilang import gradio as gr import numpy as np from config import config from tools.translate import translate import librosa net_g = None device = config.webui_config.device if device == "mps": os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" def generate_audio( slices, sdp_ratio, noise_scale, noise_scale_w, length_scale, speaker, language, reference_audio, emotion, skip_start=False, skip_end=False, ): audio_list = [] # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16) with torch.no_grad(): for idx, piece in enumerate(slices): skip_start = (idx != 0) and skip_start skip_end = (idx != len(slices) - 1) and skip_end audio = infer( 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=language, 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) # audio_list.append(silence) # 将静音添加到列表中 return audio_list def generate_audio_multilang( slices, sdp_ratio, noise_scale, noise_scale_w, length_scale, speaker, language, reference_audio, emotion, skip_start=False, skip_end=False, ): audio_list = [] # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16) with torch.no_grad(): for idx, piece in enumerate(slices): skip_start = (idx != 0) and skip_start skip_end = (idx != len(slices) - 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=language[idx], 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) # audio_list.append(silence) # 将静音添加到列表中 return audio_list def tts_split( text: str, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language, cut_by_sent, interval_between_para, interval_between_sent, emotion, ): reference_audio = None if language == "mix": return ("invalid", None) while text.find("\n\n") != -1: text = text.replace("\n\n", "\n") para_list = re_matching.cut_para(text) audio_list = [] if not cut_by_sent: for idx, p in enumerate(para_list): skip_start = idx != 0 skip_end = idx != len(para_list) - 1 audio = infer( p, 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=language, 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) silence = np.zeros((int)(44100 * interval_between_para), dtype=np.int16) audio_list.append(silence) else: for idx, p in enumerate(para_list): skip_start = idx != 0 skip_end = idx != len(para_list) - 1 audio_list_sent = [] sent_list = re_matching.cut_sent(p) for idx, s in enumerate(sent_list): skip_start = (idx != 0) and skip_start skip_end = (idx != len(sent_list) - 1) and skip_end audio = infer( s, 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=language, hps=hps, net_g=net_g, device=device, skip_start=skip_start, skip_end=skip_end, ) 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) audio_concat = np.concatenate(audio_list) return ("Success", (44100, audio_concat)) def tts_fn( text: str, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language, emotion, ): reference_audio = None audio_list = [] if language == "mix": bool_valid, str_valid = re_matching.validate_text(text) if not bool_valid: return str_valid, ( hps.data.sampling_rate, np.concatenate([np.zeros(hps.data.sampling_rate // 2)]), ) result = [] for slice in re_matching.text_matching(text): _speaker = slice.pop() temp_contant = [] temp_lang = [] for lang, content in slice: if "|" in content: temp = [] temp_ = [] for i in content.split("|"): if i != "": temp.append([i]) temp_.append([lang]) else: temp.append([]) temp_.append([]) temp_contant += temp temp_lang += temp_ else: if len(temp_contant) == 0: temp_contant.append([]) temp_lang.append([]) temp_contant[-1].append(content) temp_lang[-1].append(lang) for i, j in zip(temp_lang, temp_contant): result.append([*zip(i, j), _speaker]) for i, one in enumerate(result): skip_start = i != 0 skip_end = i != len(result) - 1 _speaker = one.pop() idx = 0 while idx < len(one): text_to_generate = [] lang_to_generate = [] while True: lang, content = one[idx] temp_text = [content] 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(one): idx += 1 else: break skip_start = (idx != 0) and skip_start skip_end = (idx != len(one) - 1) and skip_end print(text_to_generate, lang_to_generate) audio_list.extend( generate_audio_multilang( text_to_generate, sdp_ratio, noise_scale, noise_scale_w, length_scale, speaker, lang_to_generate, reference_audio, emotion, skip_start, skip_end, ) ) idx += 1 elif language.lower() == "auto": 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 print(text_to_generate, lang_to_generate) audio_list.extend( generate_audio_multilang( text_to_generate, sdp_ratio, noise_scale, noise_scale_w, length_scale, speaker, lang_to_generate, reference_audio, emotion, skip_start, skip_end, ) ) idx += 1 else: audio_list.extend( generate_audio( text.split("|"), sdp_ratio, noise_scale, noise_scale_w, length_scale, speaker, language, reference_audio, emotion, ) ) 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 = ["JP"] with gr.Blocks() as app: with gr.Row(): with gr.Column(): text = gr.TextArea( label="输入文本内容", placeholder=""" 目前只支持日语！！ """, ) slicer = gr.Button("快速切分", variant="primary") speaker = gr.Dropdown( choices=speakers, value=speakers[0], label="Speaker" ) emotion = gr.Slider( minimum=0, maximum=9, value=0, step=1, label="Emotion" ) sdp_ratio = gr.Slider( minimum=0, maximum=1, value=0.2, 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=0.8, step=0.1, label="Noise_W" ) length_scale = gr.Slider( minimum=0.1, maximum=2, value=1.0, step=0.1, label="Length" ) language = gr.Dropdown( choices=languages, value=languages[0], label="Language" ) btn = gr.Button("生成音频！", variant="primary") with gr.Column(): with gr.Row(): with gr.Column(): interval_between_sent = gr.Slider( minimum=0, maximum=5, value=0.2, step=0.1, label="句间停顿(秒)，勾选按句切分才生效", ) interval_between_para = gr.Slider( minimum=0, maximum=10, value=1, step=0.1, label="段间停顿(秒)，需要大于句间停顿才有效", ) opt_cut_by_sent = gr.Checkbox( label="按句切分 在按段落切分的基础上再按句子切分文本" ) slicer = gr.Button("切分生成", variant="primary") 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"), # ) gr.Examples( examples=[ [ "誕生日おめでとうございます", "春風", 0.2, 0.6, 0.8, 1.0, "JP", False, 1, 0.2, 0, ], [ "まさか本当に恋人になれるなんて……", "春風", 0.2, 0.6, 0.8, 1.0, "JP", False, 1, 0.2, 0, ], [ "痕跡探しに活用するかどうかも、改めて検討しましょう。　どうせ探し出せたところで、　今の私たちでは彼らに対抗できない", "希亜", 0.2, 0.6, 0.8, 1.0, "JP", False, 1, 0.2, 0, ], [ "じゃあさじゃあさ、　にぃにが先輩のどこに魅力を感じているか　教えてください", "天", 0.2, 0.6, 0.8, 1.0, "JP", False, 1, 0.2, 0, ], [ "ご、ごめんね、なんとかしたいって気持ちはあるけれど、　そこまでの覚悟はなくて……", "都", 0.2, 0.6, 0.8, 1.0, "JP", False, 1, 0.2, 0, ], ], inputs=[ text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language, opt_cut_by_sent, interval_between_para, interval_between_sent, emotion, ], outputs=[text_output, audio_output], fn=tts_split, cache_examples=True, ) btn.click( tts_fn, inputs=[ text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language, emotion, ], outputs=[text_output, audio_output], ) 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, emotion, ], outputs=[text_output, audio_output], ) print("推理页面已开启!") app.launch(share=config.webui_config.share, server_port=config.webui_config.port)