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| # 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 webbrowser | |
| import numpy as np | |
| from config import config | |
| 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, | |
| style_text, | |
| style_weight, | |
| 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, | |
| style_text=style_text, | |
| style_weight=style_weight, | |
| 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, | |
| style_text, | |
| style_weight, | |
| 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, | |
| style_text=style_text, | |
| style_weight=style_weight, | |
| 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, | |
| reference_audio, | |
| emotion, | |
| style_text, | |
| style_weight, | |
| ): | |
| if style_text == "": | |
| style_text = 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, | |
| style_text=style_text, | |
| style_weight=style_weight, | |
| 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, | |
| style_text=style_text, | |
| style_weight=style_weight, | |
| 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, | |
| 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 = [] | |
| 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, | |
| style_text, | |
| style_weight, | |
| 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, | |
| style_text, | |
| style_weight, | |
| 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, | |
| style_text, | |
| style_weight, | |
| ) | |
| ) | |
| audio_concat = np.concatenate(audio_list) | |
| return "Success", (hps.data.sampling_rate, audio_concat) | |
| def load_audio(path): | |
| audio, sr = librosa.load(path, 48000) | |
| # audio = librosa.resample(audio, 44100, 48000) | |
| return sr, audio | |
| def gr_util(item): | |
| if item == "Text prompt": | |
| return {"visible": True, "__type__": "update"}, { | |
| "visible": False, | |
| "__type__": "update", | |
| } | |
| else: | |
| return {"visible": False, "__type__": "update"}, { | |
| "visible": True, | |
| "__type__": "update", | |
| } | |
| 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", | |
| placeholder=""" | |
| 如果你选择语言为\'mix\'，必须按照格式输入，否则报错: | |
| 格式举例(zh是中文，jp是日语，不区分大小写；说话人举例:gongzi): | |
| [说话人1]<zh>你好，こんにちは！ <jp>こんにちは，世界。 | |
| [说话人2]<zh>你好吗？<jp>元気ですか？ | |
| [说话人3]<zh>谢谢。<jp>どういたしまして。 | |
| ... | |
| 另外，所有的语言选项都可以用'|'分割长段实现分句生成。 | |
| """, | |
| ) | |
| slicer = gr.Button("快速切分", 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" | |
| ) | |
| prompt_mode = gr.Radio( | |
| ["Text prompt", "Audio prompt"], | |
| label="Prompt Mode", | |
| value="Text prompt", | |
| ) | |
| text_prompt = gr.Textbox( | |
| label="Text prompt", | |
| placeholder="用文字描述生成风格。如：Happy", | |
| value="Happy", | |
| visible=True, | |
| ) | |
| 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=0.8, 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.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\n\n" | |
| "**如遇到主文本发音错误，可尝试替换主文本中发音错误的字为正确的谐音字，同时将原主文本填写于此，Weight拉满，以获得正确发音，同时保留原文本的Bert语义信息。**" | |
| ) | |
| 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", | |
| ) | |
| 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"), | |
| # ) | |
| 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], | |
| ) | |
| 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], | |
| ) | |
| prompt_mode.change( | |
| lambda x: gr_util(x), | |
| inputs=[prompt_mode], | |
| outputs=[text_prompt, audio_prompt], | |
| ) | |
| audio_prompt.upload( | |
| lambda x: load_audio(x), | |
| inputs=[audio_prompt], | |
| outputs=[audio_prompt], | |
| ) | |
| 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) | |