import re import os import numpy as np import torch from torch import no_grad, LongTensor import argparse import commons from mel_processing import spectrogram_torch import utils from models import SynthesizerTrn import gradio as gr import librosa import webbrowser from text import text_to_sequence, _clean_text device = "cuda:0" if torch.cuda.is_available() else "cpu" language_marks = { "Japanese": "", "日本語": "[JA]", "简体中文": "[ZH]", "English": "[EN]", "Mix": "", } def get_text(text, hps, is_symbol): text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners) if hps.data.add_blank: text_norm = commons.intersperse(text_norm, 0) text_norm = LongTensor(text_norm) return text_norm def create_tts_fn(model, hps, speaker_ids): def tts_fn(text, speaker, language, ns, nsw, speed, is_symbol): if language is not None: text = language_marks[language] + text + language_marks[language] speaker_id = speaker_ids[speaker] stn_tst = get_text(text, hps, is_symbol) with no_grad(): x_tst = stn_tst.unsqueeze(0).to(device) x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device) sid = LongTensor([speaker_id]).to(device) audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=ns, noise_scale_w=nsw, length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy() del stn_tst, x_tst, x_tst_lengths, sid return "Success", (hps.data.sampling_rate, audio) return tts_fn def create_vc_fn(model, hps, speaker_ids): def vc_fn(original_speaker, target_speaker, record_audio, upload_audio): input_audio = record_audio if record_audio is not None else upload_audio if input_audio is None: return "You need to record or upload an audio", None sampling_rate, audio = input_audio original_speaker_id = speaker_ids[original_speaker] target_speaker_id = speaker_ids[target_speaker] audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) if len(audio.shape) > 1: audio = librosa.to_mono(audio.transpose(1, 0)) if sampling_rate != hps.data.sampling_rate: audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate) with no_grad(): y = torch.FloatTensor(audio) y = y / max(-y.min(), y.max()) / 0.99 y = y.to(device) y = y.unsqueeze(0) spec = spectrogram_torch(y, hps.data.filter_length, hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length, center=False).to(device) spec_lengths = LongTensor([spec.size(-1)]).to(device) sid_src = LongTensor([original_speaker_id]).to(device) sid_tgt = LongTensor([target_speaker_id]).to(device) audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][ 0, 0].data.cpu().float().numpy() del y, spec, spec_lengths, sid_src, sid_tgt return "Success", (hps.data.sampling_rate, audio) return vc_fn def get_text(text, hps, is_symbol): text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners) if hps.data.add_blank: text_norm = commons.intersperse(text_norm, 0) text_norm = LongTensor(text_norm) return text_norm def create_to_symbol_fn(hps): def to_symbol_fn(is_symbol_input, input_text, temp_text): return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \ else (temp_text, temp_text) return to_symbol_fn models_info = [ { "languages": ['日本語', '简体中文', 'English', 'Mix'], "description": """ 这个模型包含公主连结Re:Dive的161名角色，能合成中日英三语。\n\n 若需要在同一个句子中混合多种语言，使用相应的语言标记包裹句子。 （日语用[JA], 中文用[ZH], 英文用[EN]），参考Examples中的示例。 """, "model_path": "./models/G_9700.pth", "config_path": "./configs/modified_finetune_speaker.json", "examples": [['大切な人の誕生日を祝えるって、すごく幸せなことなんですよね。', '佩可莉姆', '日本語', 1, False], ['その…この制服、どうですか？', '栞', '日本語', 1, False], ['你们全都给我让开！敢挡路的家伙，我统统斩了！', '矛依未', '简体中文', 1, False], ['Can you tell me how much the shirt is?', '美咲', 'English', 1, False], ['[EN]Excuse me?[EN][JA]お帰りなさい，お兄様！[JA]', '咲恋（夏日）', 'Mix', 1, False]], } ] models_tts = [] models_vc = [] if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--share", action="store_true", default=False, help="share gradio app") args = parser.parse_args() categories = ["Princess Connect! Re:Dive"] others = { "Umamusume": "https://huggingface.co/spaces/FrankZxShen/vits-fast-finetuning-umamusume", "Blue Archive": "https://huggingface.co/spaces/FrankZxShen/vits-fast-fineturning-models-ba", "PCR so-vits-svc(歌声语音转换)": "https://huggingface.co/spaces/FrankZxShen/so-vits-svc-models-pcr", } for info in models_info: lang = info['languages'] examples = info['examples'] config_path = info['config_path'] model_path = info['model_path'] description = info['description'] hps = utils.get_hparams_from_file(config_path) net_g = SynthesizerTrn( len(hps.symbols), hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, **hps.model).to(device) _ = net_g.eval() _ = utils.load_checkpoint(model_path, net_g, None) speaker_ids = hps.speakers speakers = list(hps.speakers.keys()) models_tts.append((description, speakers, lang, examples, hps.symbols, create_tts_fn(net_g, hps, speaker_ids), create_to_symbol_fn(hps))) models_vc.append((description, speakers, create_vc_fn(net_g, hps, speaker_ids))) app = gr.Blocks() with app: gr.Markdown( "#
vits-fast-fineturning-models-pcr\n" "##
Please do not generate content that could infringe upon the rights or cause harm to individuals or organizations.\n" "##
请不要生成会对个人以及组织造成侵害的内容\n\n" "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pn1xnFfdLK63gVXDwV4zCXfVeo8c-I-0?usp=sharing)\n\n" "[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm-dark.svg)](https://huggingface.co/spaces/FrankZxShen/vits-fast-finetuning-pcr?duplicate=true)\n\n" "[![Finetune your own model](https://badgen.net/badge/icon/github?icon=github&label=Finetune%20your%20own%20model)](https://github.com/Plachtaa/VITS-fast-fine-tuning)" ) gr.Markdown("# TTS&Voice Conversion for Princess Connect! Re:Dive\n\n" ) with gr.Tabs(): for category in categories: with gr.TabItem(category): with gr.Tab("TTS"): for i, (description, speakers, lang, example, symbols, tts_fn, to_symbol_fn) in enumerate( models_tts): gr.Markdown(description) with gr.Row(): with gr.Column(): textbox = gr.TextArea(label="Text", placeholder="Type your sentence here ", value="よーし、私もがんばらないと！", elem_id=f"tts-input") with gr.Accordion(label="Phoneme Input", open=False): temp_text_var = gr.Variable() symbol_input = gr.Checkbox( value=False, label="Symbol input") symbol_list = gr.Dataset(label="Symbol list", components=[textbox], samples=[[x] for x in symbols], elem_id=f"symbol-list") symbol_list_json = gr.Json( value=symbols, visible=False) symbol_input.change(to_symbol_fn, [symbol_input, textbox, temp_text_var], [textbox, temp_text_var]) symbol_list.click(None, [symbol_list, symbol_list_json], textbox, _js=f""" (i, symbols, text) => {{ let root = document.querySelector("body > gradio-app"); if (root.shadowRoot != null) root = root.shadowRoot; let text_input = root.querySelector("#tts-input").querySelector("textarea"); let startPos = text_input.selectionStart; let endPos = text_input.selectionEnd; let oldTxt = text_input.value; let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos); text_input.value = result; let x = window.scrollX, y = window.scrollY; text_input.focus(); text_input.selectionStart = startPos + symbols[i].length; text_input.selectionEnd = startPos + symbols[i].length; text_input.blur(); window.scrollTo(x, y); text = text_input.value; return text; }}""") # select character char_dropdown = gr.Dropdown( choices=speakers, value=speakers[0], label='character') language_dropdown = gr.Dropdown( choices=lang, value=lang[0], label='language') ns = gr.Slider( label="noise_scale", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True) nsw = gr.Slider(label="noise_scale_w", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True) duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1, label='速度 Speed') with gr.Column(): text_output = gr.Textbox(label="Message") audio_output = gr.Audio( label="Output Audio", elem_id="tts-audio") btn = gr.Button("Generate!") btn.click(tts_fn, inputs=[textbox, char_dropdown, language_dropdown, ns, nsw, duration_slider, symbol_input], outputs=[text_output, audio_output]) gr.Examples( examples=example, inputs=[textbox, char_dropdown, language_dropdown, duration_slider, symbol_input], outputs=[text_output, audio_output], fn=tts_fn ) with gr.Tab("Voice Conversion"): for i, (description, speakers, vc_fn) in enumerate( models_vc): gr.Markdown(""" 录制或上传声音，并选择要转换的音色。 """) with gr.Column(): record_audio = gr.Audio( label="record your voice", source="microphone") upload_audio = gr.Audio( label="or upload audio here", source="upload") source_speaker = gr.Dropdown( choices=speakers, value=speakers[0], label="source speaker") target_speaker = gr.Dropdown( choices=speakers, value=speakers[0], label="target speaker") with gr.Column(): message_box = gr.Textbox(label="Message") converted_audio = gr.Audio( label='converted audio') btn = gr.Button("Convert!") btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio], outputs=[message_box, converted_audio]) for category, link in others.items(): with gr.TabItem(category): gr.Markdown( f'''

Click to Go

''' ) app.queue(concurrency_count=3).launch(show_api=False, share=args.share)