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import re |
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import os |
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import numpy as np |
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import torch |
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from torch import no_grad, LongTensor |
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import argparse |
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import commons |
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from mel_processing import spectrogram_torch |
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import utils |
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from models import SynthesizerTrn |
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import gradio as gr |
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import librosa |
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import webbrowser |
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from text import text_to_sequence, _clean_text |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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language_marks = { |
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"Japanese": "", |
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"日本語": "[JA]", |
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"简体中文": "[ZH]", |
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"English": "[EN]", |
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"Mix": "", |
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} |
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def get_text(text, hps, is_symbol): |
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text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners) |
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if hps.data.add_blank: |
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text_norm = commons.intersperse(text_norm, 0) |
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text_norm = LongTensor(text_norm) |
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return text_norm |
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def create_tts_fn(model, hps, speaker_ids): |
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def tts_fn(text, speaker, language, ns, nsw, speed, is_symbol): |
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if language is not None: |
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text = language_marks[language] + text + language_marks[language] |
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speaker_id = speaker_ids[speaker] |
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stn_tst = get_text(text, hps, is_symbol) |
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with no_grad(): |
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x_tst = stn_tst.unsqueeze(0).to(device) |
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x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device) |
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sid = LongTensor([speaker_id]).to(device) |
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audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=ns, noise_scale_w=nsw, |
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length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy() |
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del stn_tst, x_tst, x_tst_lengths, sid |
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return "Success", (hps.data.sampling_rate, audio) |
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return tts_fn |
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def create_vc_fn(model, hps, speaker_ids): |
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def vc_fn(original_speaker, target_speaker, record_audio, upload_audio): |
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input_audio = record_audio if record_audio is not None else upload_audio |
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if input_audio is None: |
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return "You need to record or upload an audio", None |
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sampling_rate, audio = input_audio |
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original_speaker_id = speaker_ids[original_speaker] |
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target_speaker_id = speaker_ids[target_speaker] |
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audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) |
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if len(audio.shape) > 1: |
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audio = librosa.to_mono(audio.transpose(1, 0)) |
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if sampling_rate != hps.data.sampling_rate: |
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audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate) |
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with no_grad(): |
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y = torch.FloatTensor(audio) |
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y = y / max(-y.min(), y.max()) / 0.99 |
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y = y.to(device) |
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y = y.unsqueeze(0) |
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spec = spectrogram_torch(y, hps.data.filter_length, |
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hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length, |
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center=False).to(device) |
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spec_lengths = LongTensor([spec.size(-1)]).to(device) |
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sid_src = LongTensor([original_speaker_id]).to(device) |
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sid_tgt = LongTensor([target_speaker_id]).to(device) |
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audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][ |
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0, 0].data.cpu().float().numpy() |
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del y, spec, spec_lengths, sid_src, sid_tgt |
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return "Success", (hps.data.sampling_rate, audio) |
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return vc_fn |
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def get_text(text, hps, is_symbol): |
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text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners) |
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if hps.data.add_blank: |
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text_norm = commons.intersperse(text_norm, 0) |
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text_norm = LongTensor(text_norm) |
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return text_norm |
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def create_to_symbol_fn(hps): |
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def to_symbol_fn(is_symbol_input, input_text, temp_text): |
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return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \ |
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else (temp_text, temp_text) |
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return to_symbol_fn |
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models_info = [ |
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{ |
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"languages": ['日本語', '简体中文', 'English', 'Mix'], |
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"description": """ |
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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 |
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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。 |
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""", |
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"model_path": "./models/G_9700.pth", |
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"config_path": "./configs/modified_finetune_speaker.json", |
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"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], |
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['その…この制服、どうですか？', '栞', '日本語', 1, False], |
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['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], |
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['Can you tell me how much the shirt is?', '美咲', 'English', 1, False], |
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['[EN]Excuse me?[EN][JA]お帰りなさい，お兄様！[JA]', '咲恋（夏日）', 'Mix', 1, False]], |
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} |
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] |
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models_tts = [] |
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models_vc = [] |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--share", action="store_true", default=False, help="share gradio app") |
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args = parser.parse_args() |
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categories = ["Princess Connect! Re:Dive"] |
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others = { |
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"Umamusume": "https://huggingface.co/spaces/FrankZxShen/vits-fast-finetuning-umamusume", |
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"Blue Archive": "https://huggingface.co/spaces/FrankZxShen/vits-fast-fineturning-models-ba", |
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"PCR so-vits-svc(歌声语音转换)": "https://huggingface.co/spaces/FrankZxShen/so-vits-svc-models-pcr", |
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} |
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for info in models_info: |
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lang = info['languages'] |
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examples = info['examples'] |
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config_path = info['config_path'] |
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model_path = info['model_path'] |
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description = info['description'] |
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hps = utils.get_hparams_from_file(config_path) |
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net_g = SynthesizerTrn( |
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len(hps.symbols), |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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n_speakers=hps.data.n_speakers, |
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**hps.model).to(device) |
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_ = net_g.eval() |
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_ = utils.load_checkpoint(model_path, net_g, None) |
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speaker_ids = hps.speakers |
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speakers = list(hps.speakers.keys()) |
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models_tts.append((description, speakers, lang, examples, |
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hps.symbols, create_tts_fn(net_g, hps, speaker_ids), |
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create_to_symbol_fn(hps))) |
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models_vc.append((description, speakers, create_vc_fn(net_g, hps, speaker_ids))) |
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app = gr.Blocks() |
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with app: |
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gr.Markdown( |
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"# <center> vits-fast-fineturning-models-pcr\n" |
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"## <center> Please do not generate content that could infringe upon the rights or cause harm to individuals or organizations.\n" |
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"## <center> 请不要生成会对个人以及组织造成侵害的内容\n\n" |
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"[](https://colab.research.google.com/drive/1pn1xnFfdLK63gVXDwV4zCXfVeo8c-I-0?usp=sharing)\n\n" |
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"[](https://huggingface.co/spaces/FrankZxShen/vits-fast-finetuning-pcr?duplicate=true)\n\n" |
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"[](https://github.com/Plachtaa/VITS-fast-fine-tuning)" |
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) |
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gr.Markdown("# TTS&Voice Conversion for Princess Connect! Re:Dive\n\n" |
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) |
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with gr.Tabs(): |
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for category in categories: |
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with gr.TabItem(category): |
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with gr.Tab("TTS"): |
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for i, (description, speakers, lang, example, symbols, tts_fn, to_symbol_fn) in enumerate( |
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models_tts): |
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gr.Markdown(description) |
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with gr.Row(): |
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with gr.Column(): |
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textbox = gr.TextArea(label="Text", |
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placeholder="Type your sentence here ", |
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value="よーし、私もがんばらないと！", elem_id=f"tts-input") |
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with gr.Accordion(label="Phoneme Input", open=False): |
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temp_text_var = gr.Variable() |
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symbol_input = gr.Checkbox( |
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value=False, label="Symbol input") |
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symbol_list = gr.Dataset(label="Symbol list", components=[textbox], |
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samples=[[x] |
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for x in symbols], |
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elem_id=f"symbol-list") |
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symbol_list_json = gr.Json( |
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value=symbols, visible=False) |
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symbol_input.change(to_symbol_fn, |
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[symbol_input, textbox, |
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temp_text_var], |
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[textbox, temp_text_var]) |
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symbol_list.click(None, [symbol_list, symbol_list_json], textbox, |
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_js=f""" |
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(i, symbols, text) => {{ |
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let root = document.querySelector("body > gradio-app"); |
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if (root.shadowRoot != null) |
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root = root.shadowRoot; |
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let text_input = root.querySelector("#tts-input").querySelector("textarea"); |
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let startPos = text_input.selectionStart; |
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let endPos = text_input.selectionEnd; |
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let oldTxt = text_input.value; |
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let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos); |
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text_input.value = result; |
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let x = window.scrollX, y = window.scrollY; |
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text_input.focus(); |
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text_input.selectionStart = startPos + symbols[i].length; |
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text_input.selectionEnd = startPos + symbols[i].length; |
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text_input.blur(); |
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window.scrollTo(x, y); |
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text = text_input.value; |
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return text; |
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}}""") |
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char_dropdown = gr.Dropdown( |
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choices=speakers, value=speakers[0], label='character') |
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language_dropdown = gr.Dropdown( |
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choices=lang, value=lang[0], label='language') |
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ns = gr.Slider( |
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label="noise_scale", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True) |
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nsw = gr.Slider(label="noise_scale_w", minimum=0.1, |
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maximum=1.0, step=0.1, value=0.668, interactive=True) |
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duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1, |
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label='速度 Speed') |
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with gr.Column(): |
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text_output = gr.Textbox(label="Message") |
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audio_output = gr.Audio( |
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label="Output Audio", elem_id="tts-audio") |
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btn = gr.Button("Generate!") |
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btn.click(tts_fn, |
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inputs=[textbox, char_dropdown, language_dropdown, ns, nsw, duration_slider, |
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symbol_input], |
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outputs=[text_output, audio_output]) |
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gr.Examples( |
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examples=example, |
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inputs=[textbox, char_dropdown, language_dropdown, |
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duration_slider, symbol_input], |
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outputs=[text_output, audio_output], |
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fn=tts_fn |
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) |
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with gr.Tab("Voice Conversion"): |
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for i, (description, speakers, vc_fn) in enumerate( |
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models_vc): |
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gr.Markdown(""" |
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录制或上传声音，并选择要转换的音色。 |
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""") |
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with gr.Column(): |
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record_audio = gr.Audio( |
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label="record your voice", source="microphone") |
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upload_audio = gr.Audio( |
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label="or upload audio here", source="upload") |
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source_speaker = gr.Dropdown( |
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choices=speakers, value=speakers[0], label="source speaker") |
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target_speaker = gr.Dropdown( |
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choices=speakers, value=speakers[0], label="target speaker") |
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with gr.Column(): |
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message_box = gr.Textbox(label="Message") |
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converted_audio = gr.Audio( |
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label='converted audio') |
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btn = gr.Button("Convert!") |
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btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio], |
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outputs=[message_box, converted_audio]) |
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for category, link in others.items(): |
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with gr.TabItem(category): |
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gr.Markdown( |
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f''' |
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<center> |
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<h2>Click to Go</h2> |
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<a href="{link}"> |
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<img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-xl-dark.svg" |
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</a> |
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</center> |
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''' |
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) |
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app.queue(concurrency_count=3).launch(show_api=False, share=args.share) |
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