import argparse import json import os import re import tempfile import logging logging.getLogger('numba').setLevel(logging.WARNING) import librosa import numpy as np import torch from torch import no_grad, LongTensor import commons import utils import gradio as gr import gradio.utils as gr_utils import gradio.processing_utils as gr_processing_utils import ONNXVITS_infer import models from text import text_to_sequence, _clean_text from text.symbols import symbols from mel_processing import spectrogram_torch import psutil from datetime import datetime language_marks = { "Japanese": "", "日本語": "[JA]", "简体中文": "[ZH]", "English": "[EN]", "Mix": "", } limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces def create_tts_fn(model, hps, speaker_ids): def tts_fn(text, speaker, language, 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) x_tst_lengths = LongTensor([stn_tst.size(0)]) sid = LongTensor([speaker_id]) audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, 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, input_audio): if input_audio is None: return "You need to upload an audio", None sampling_rate, audio = input_audio duration = audio.shape[0] / sampling_rate 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.unsqueeze(0) spec = spectrogram_torch(y, hps.data.filter_length, hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length, center=False) spec_lengths = LongTensor([spec.size(-1)]) sid_src = LongTensor([original_speaker_id]) sid_tgt = LongTensor([target_speaker_id]) 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_tts = [] models_vc = [] models_info = [ { "title": "Trilingual", "languages": ['日本語', '简体中文', 'English', 'Mix'], "description": """ This model is trained on a mix up of Umamusume, Genshin Impact, Sanoba Witch & VCTK voice data to learn multilanguage. All characters can speak English, Chinese & Japanese.\n\n To mix multiple languages in a single sentence, wrap the corresponding part with language tokens ([JA] for Japanese, [ZH] for Chinese, [EN] for English), as shown in the examples.\n\n 这个模型在赛马娘，原神，魔女的夜宴以及VCTK数据集上混合训练以学习多种语言。 所有角色均可说中日英三语。\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": "./pretrained_models/G_trilingual.pth", "config_path": "./configs/uma_trilingual.json", "examples": [['你好，训练员先生，很高兴见到你。', '草上飞 Grass Wonder (Umamusume Pretty Derby)', '简体中文', 1, False], ['To be honest, I have no idea what to say as examples.', '派蒙 Paimon (Genshin Impact)', 'English', 1, False], ['授業中に出しだら，学校生活終わるですわ。', '綾地 寧々 Ayachi Nene (Sanoba Witch)', '日本語', 1, False], ['[JA]こんにちわ。[JA][ZH]你好！[ZH][EN]Hello![EN]', '綾地 寧々 Ayachi Nene (Sanoba Witch)', 'Mix', 1, False]], "onnx_dir": "./ONNX_net/G_trilingual/" }, { "title": "Japanese", "languages": ["Japanese"], "description": """ This model contains 87 characters from Umamusume: Pretty Derby, Japanese only.\n\n 这个模型包含赛马娘的所有87名角色，只能合成日语。 """, "model_path": "./pretrained_models/G_jp.pth", "config_path": "./configs/uma87.json", "examples": [['お疲れ様です，トレーナーさん。', '无声铃鹿 Silence Suzuka (Umamusume Pretty Derby)', 'Japanese', 1, False], ['張り切っていこう！', '北部玄驹 Kitasan Black (Umamusume Pretty Derby)', 'Japanese', 1, False], ['何でこんなに慣れでんのよ，私のほが先に好きだっだのに。', '草上飞 Grass Wonder (Umamusume Pretty Derby)', 'Japanese', 1, False], ['授業中に出しだら，学校生活終わるですわ。', '目白麦昆 Mejiro Mcqueen (Umamusume Pretty Derby)', 'Japanese', 1, False], ['お帰りなさい，お兄様！', '米浴 Rice Shower (Umamusume Pretty Derby)', 'Japanese', 1, False], ['私の処女をもらっでください！', '米浴 Rice Shower (Umamusume Pretty Derby)', 'Japanese', 1, False]], "onnx_dir": "./ONNX_net/G_jp/" }, ] if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--share", action="store_true", default=False, help="share gradio app") args = parser.parse_args() for info in models_info: name = info['title'] lang = info['languages'] examples = info['examples'] config_path = info['config_path'] model_path = info['model_path'] description = info['description'] onnx_dir = info["onnx_dir"] hps = utils.get_hparams_from_file(config_path) model = ONNXVITS_infer.SynthesizerTrn( len(hps.symbols), hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, ONNX_dir=onnx_dir, **hps.model) utils.load_checkpoint(model_path, model, None) model.eval() speaker_ids = hps.speakers speakers = list(hps.speakers.keys()) models_tts.append((name, description, speakers, lang, examples, hps.symbols, create_tts_fn(model, hps, speaker_ids), create_to_symbol_fn(hps))) models_vc.append((name, description, speakers, create_vc_fn(model, hps, speaker_ids))) app = gr.Blocks() with app: gr.Markdown("# English & Chinese & Japanese Anime TTS\n\n" "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=Plachta.VITS-Umamusume-voice-synthesizer)\n\n" "Including Japanese TTS & Trilingual TTS, speakers are all anime characters. \n\n包含一个纯日语TTS和一个中日英三语TTS模型，主要为二次元角色。\n\n" "If you have any suggestions or bug reports, feel free to open discussion in [Community](https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/discussions).\n\n" "若有bug反馈或建议，请在[Community](https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/discussions)下开启一个新的Discussion。 \n\n" ) with gr.Tabs(): with gr.TabItem("TTS"): with gr.Tabs(): for i, (name, description, speakers, lang, example, symbols, tts_fn, to_symbol_fn) in enumerate( models_tts): with gr.TabItem(name): gr.Markdown(description) with gr.Row(): with gr.Column(): textbox = gr.TextArea(label="Text", placeholder="Type your sentence here (Maximum 150 words)", value="こんにちわ。", 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') 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, 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 ) app.queue(concurrency_count=3).launch(show_api=False, share=args.share)