# coding=utf-8 import os import re import argparse import utils import commons import json import torch import gradio as gr from models import SynthesizerTrn from text import text_to_sequence, _clean_text from torch import no_grad, LongTensor import gradio.processing_utils as gr_processing_utils import logging from gradio_client import utils as client_utils logging.getLogger('numba').setLevel(logging.WARNING) limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces hps_ms = utils.get_hparams_from_file(r'config/config.json') ''' audio_postprocess_ori = gr.Audio.postprocess def audio_postprocess(self, y): data = audio_postprocess_ori(self, y) if data is None: return None return client_utils.encode_url_or_file_to_base64(data.path) gr.Audio.postprocess = audio_postprocess ''' def get_text(text, hps, is_symbol): text_norm, clean_text = 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, clean_text def create_tts_fn(net_g_ms, speaker_id): def tts_fn(text, language, noise_scale, noise_scale_w, length_scale, is_symbol): text = text.replace('\n', ' ').replace('\r', '').replace(" ", "") if limitation: text_len = len(re.sub("\[([A-Z]{2})\]", "", text)) max_len = 100 if is_symbol: max_len *= 3 if text_len > max_len: return "Error: Text is too long", None if not is_symbol: if language == 0: text = f"[ZH]{text}[ZH]" elif language == 1: text = f"[JA]{text}[JA]" else: text = f"{text}" stn_tst, clean_text = get_text(text, hps_ms, 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 = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0, 0].data.cpu().float().numpy() return "Success", (22050, audio) return tts_fn def create_to_symbol_fn(hps): def to_symbol_fn(is_symbol_input, input_text, temp_lang): if temp_lang == 0: clean_text = f'[ZH]{input_text}[ZH]' elif temp_lang == 1: clean_text = f'[JA]{input_text}[JA]' else: clean_text = input_text return _clean_text(clean_text, hps.data.text_cleaners) if is_symbol_input else '' return to_symbol_fn def change_lang(language): if language == 0: return 0.6, 0.668, 1.2 elif language == 1: return 0.6, 0.668, 1 else: return 0.6, 0.668, 1 ''' download_audio_js = """ () =>{{ let root = document.querySelector("body > gradio-app"); if (root.shadowRoot != null) root = root.shadowRoot; let audio = root.querySelector("#tts-audio-{audio_id}").querySelector("audio"); let text = root.querySelector("#input-text-{audio_id}").querySelector("textarea"); if (audio == undefined) return; text = text.value; if (text == undefined) text = Math.floor(Math.random()*100000000); audio = audio.src; let oA = document.createElement("a"); oA.download = text.substr(0, 20)+'.wav'; oA.href = audio; document.body.appendChild(oA); oA.click(); oA.remove(); }} """ ''' if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--device', type=str, default='cpu') parser.add_argument('--api', action="store_true", default=False) parser.add_argument("--share", action="store_true", default=False, help="share gradio app") parser.add_argument("--all", action="store_true", default=False, help="enable all models") args = parser.parse_args() device = torch.device(args.device) categories = ["Honkai: Star Rail", "Blue Archive", "Lycoris Recoil"] others = { "Princess Connect! Re:Dive": "https://huggingface.co/spaces/sayashi/vits-models-pcr", "Genshin Impact": "https://huggingface.co/spaces/sayashi/vits-models-genshin-bh3", "Honkai Impact 3rd": "https://huggingface.co/spaces/sayashi/vits-models-genshin-bh3", "Overwatch 2": "https://huggingface.co/spaces/sayashi/vits-models-ow2", } if args.all: categories = ["Honkai: Star Rail", "Blue Archive", "Lycoris Recoil", "Princess Connect! Re:Dive", "Genshin Impact", "Honkai Impact 3rd", "Overwatch 2"] others = {} models = [] with open("pretrained_models/info.json", "r", encoding="utf-8") as f: models_info = json.load(f) for i, info in models_info.items(): if info['title'].split("-")[0] not in categories or not info['enable']: continue sid = info['sid'] name_en = info['name_en'] name_zh = info['name_zh'] title = info['title'] cover = f"pretrained_models/{i}/{info['cover']}" example = info['example'] language = info['language'] net_g_ms = SynthesizerTrn( len(hps_ms.symbols), hps_ms.data.filter_length // 2 + 1, hps_ms.train.segment_size // hps_ms.data.hop_length, n_speakers=hps_ms.data.n_speakers if info['type'] == "multi" else 0, **hps_ms.model) utils.load_checkpoint(f'pretrained_models/{i}/{i}.pth', net_g_ms, None) _ = net_g_ms.eval().to(device) models.append((sid, name_en, name_zh, title, cover, example, language, net_g_ms, create_tts_fn(net_g_ms, sid), create_to_symbol_fn(hps_ms))) with gr.Blocks(delete_cache=(60*60*24, 60*60*24)) as app: gr.Markdown( "#
vits-models\n" "##
Please do not generate content that could infringe upon the rights or cause harm to individuals or organizations.\n" "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/10QOk9NPgoKZUXkIhhuVaZ7SYra1MPMKH?usp=share_link)\n\n" "[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm-dark.svg)](https://huggingface.co/spaces/sayashi/vits-models?duplicate=true)\n\n" "[![Finetune your own model](https://badgen.net/badge/icon/github?icon=github&label=Finetune%20your%20own%20model)](https://github.com/SayaSS/vits-finetuning)" ) with gr.Tabs(): for category in categories: with gr.TabItem(category): with gr.TabItem("EN"): for (sid, name_en, name_zh, title, cover, example, language, net_g_ms, tts_fn, to_symbol_fn) in models: if title.split("-")[0] != category: continue with gr.TabItem(name_en): with gr.Row(): image_cover = client_utils.encode_url_or_file_to_base64(cover) gr.Markdown( '
' f'{title}' f'' if cover else "" '
' ) with gr.Row(): with gr.Column(): input_text = gr.Textbox(label="Text (100 words limitation)" if limitation else "Text", lines=5, value=example, elem_id=f"input-text-en-{name_en.replace(' ','')}") lang = gr.Dropdown(label="Language", choices=["Chinese", "Japanese", "Mix（wrap the Chinese text with [ZH][ZH], wrap the Japanese text with [JA][JA]）"], type="index", value=language) with gr.Accordion(label="Advanced Options", open=False): symbol_input = gr.Checkbox(value=False, label="Symbol input") symbol_list = gr.Dataset(label="Symbol list", components=[input_text], samples=[[x] for x in hps_ms.symbols]) symbol_list_json = gr.Json(value=hps_ms.symbols, visible=False) btn = gr.Button(value="Generate", variant="primary") with gr.Row(): 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) ls = gr.Slider(label="length_scale", minimum=0.1, maximum=2.0, step=0.1, value=1.2 if language=="Chinese" else 1, interactive=True) with gr.Column(): o1 = gr.Textbox(label="Output Message") o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio-en-{name_en.replace(' ','')}") #download = gr.Button("Download Audio") btn.click(tts_fn, inputs=[input_text, lang, ns, nsw, ls, symbol_input], outputs=[o1, o2], api_name=f"tts-{name_en}") #download.click(None, [], [], js=download_audio_js.format(audio_id=f"en-{name_en.replace(' ', '')}")) lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls]) symbol_input.change( to_symbol_fn, [symbol_input, input_text, lang], [input_text] ) symbol_list.click(None, [symbol_list, symbol_list_json], [input_text], js=f""" (i,symbols) => {{ let root = document.querySelector("body > gradio-app"); if (root.shadowRoot != null) root = root.shadowRoot; let text_input = root.querySelector("#input-text-en-{name_en.replace(' ', '')}").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); return text_input.value; }}""") for category, link in others.items(): with gr.TabItem(category): gr.Markdown( f'''

Click to Go

''' ) app.queue(default_concurrency_limit=1, api_open=args.api).launch(share=args.share)