# 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 from torch import no_grad, LongTensor import logging 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') def get_text(text, hps): text_norm, clean_text = text_to_sequence(text, hps.symbols, 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): text = text.replace('\n', ' ').replace('\r', '').replace(" ", "") if limitation: text_len = len(re.sub("\[([A-Z]{2})\]", "", text)) max_len = 100 if text_len > max_len: return "Error: Text is too long", None 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) 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 change_lang(language): if language == 0: return 0.6, 0.668, 1.2 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").querySelector("audio"); let text = root.querySelector("#input-text").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("--share", action="store_true", default=False, help="share gradio app") args = parser.parse_args() device = torch.device(args.device) 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(): sid = info['sid'] name_en = info['name_en'] name_zh = info['name_zh'] title = info['title'] cover = f"pretrained_models/{i}/{info['cover']}" 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, **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, net_g_ms, create_tts_fn(net_g_ms, sid))) with gr.Blocks() as app: gr.Markdown( "#
vits-models\n" "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=sayashi.vits-models)\n\n" "[Open In Colab]" "(https://colab.research.google.com/drive/10QOk9NPgoKZUXkIhhuVaZ7SYra1MPMKH?usp=share_link)" " without queue and length limitation.\n\n" ) with gr.Tabs(): with gr.TabItem("EN"): for (sid, name_en, name_zh, title, cover, net_g_ms, tts_fn) in models: with gr.TabItem(name_en): with gr.Row(): gr.Markdown( '
' f'{title}' f'' if cover else "" '
' ) with gr.Row(): with gr.Column(): input_text = gr.Textbox(label="Text (100 words limitation)", lines=5, value="先生。今日も全力であなたをアシストしますね。", elem_id=f"input-text") 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="Japanese") btn = gr.Button(value="Generate") 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, interactive=True) with gr.Column(): o1 = gr.Textbox(label="Output Message") o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio") download = gr.Button("Download Audio") btn.click(tts_fn, inputs=[input_text, lang, ns, nsw, ls], outputs=[o1, o2]) download.click(None, [], [], _js=download_audio_js.format()) lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls]) with gr.TabItem("中文"): for (sid, name_en, name_zh, title, cover, net_g_ms, tts_fn) in models: with gr.TabItem(name_zh): with gr.Row(): gr.Markdown( '
' f'{title}' f'' if cover else "" '
' ) with gr.Row(): with gr.Column(): input_text = gr.Textbox(label="文本 (100字上限)", lines=5, value="先生。今日も全力であなたをアシストしますね。", elem_id=f"input-text") lang = gr.Dropdown(label="语言", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"], type="index", value="日语") btn = gr.Button(value="生成") with gr.Row(): ns = gr.Slider(label="控制感情变化程度", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True) nsw = gr.Slider(label="控制音素发音长度", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True) ls = gr.Slider(label="控制整体语速", minimum=0.1, maximum=2.0, step=0.1, value=1, interactive=True) with gr.Column(): o1 = gr.Textbox(label="输出信息") o2 = gr.Audio(label="输出音频", elem_id=f"tts-audio") download = gr.Button("下载音频") btn.click(tts_fn, inputs=[input_text, lang, ns, nsw, ls], outputs=[o1, o2]) download.click(None, [], [], _js=download_audio_js.format()) lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls]) app.queue(concurrency_count=1).launch(show_api=False, share=args.share)