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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 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) | |