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import argparse |
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import json |
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import os |
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import re |
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import tempfile |
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import logging |
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logging.getLogger('numba').setLevel(logging.WARNING) |
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import librosa |
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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 commons |
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import utils |
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import gradio as gr |
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import gradio.utils as gr_utils |
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import gradio.processing_utils as gr_processing_utils |
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import ONNXVITS_infer |
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import models |
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from text import text_to_sequence, _clean_text |
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from text.symbols import symbols |
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from mel_processing import spectrogram_torch |
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import psutil |
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from datetime import datetime |
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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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limitation = os.getenv("SYSTEM") == "spaces" |
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def create_tts_fn(model, hps, speaker_ids): |
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def tts_fn(text, speaker, language, speed, is_symbol): |
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if limitation: |
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text_len = len(re.sub("\[([A-Z]{2})\]", "", text)) |
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max_len = 150 |
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if is_symbol: |
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max_len *= 3 |
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if text_len > max_len: |
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return "Error: Text is too long", None |
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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) |
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x_tst_lengths = LongTensor([stn_tst.size(0)]) |
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sid = LongTensor([speaker_id]) |
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audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, |
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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, input_audio): |
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if input_audio is None: |
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return "You need to upload an audio", None |
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sampling_rate, audio = input_audio |
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duration = audio.shape[0] / sampling_rate |
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if limitation and duration > 30: |
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return "Error: Audio is too long", None |
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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.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) |
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spec_lengths = LongTensor([spec.size(-1)]) |
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sid_src = LongTensor([original_speaker_id]) |
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sid_tgt = LongTensor([target_speaker_id]) |
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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_tts = [] |
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models_vc = [] |
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models_info = [ |
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{ |
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"title": "Trilingual", |
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"languages": ['日本語', '简体中文', 'English', 'Mix'], |
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"description": """ |
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This model is trained on a mix up of Umamusume, Genshin Impact, Sanoba Witch & VCTK voice data to learn multilanguage. |
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All characters can speak English, Chinese & Japanese.\n\n |
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To mix multiple languages in a single sentence, wrap the corresponding part with language tokens |
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([JA] for Japanese, [ZH] for Chinese, [EN] for English), as shown in the examples.\n\n |
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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。 |
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所有角色均可说中日英三语。\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。 |
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（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": "./pretrained_models/G_trilingual.pth", |
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"config_path": "./configs/uma_trilingual.json", |
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"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], |
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['To be honest, I have no idea what to say as examples.', '派蒙 Paimon (Genshin Impact)', 'English', |
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1, False], |
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['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], |
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['[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]], |
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"onnx_dir": "./ONNX_net/G_trilingual/" |
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}, |
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{ |
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"title": "Japanese", |
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"languages": ["Japanese"], |
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"description": """ |
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This model contains 87 characters from Umamusume: Pretty Derby, Japanese only.\n\n |
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这个模型包含赛马娘的所有87名角色，只能合成日语。 |
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""", |
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"model_path": "./pretrained_models/G_jp.pth", |
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"config_path": "./configs/uma87.json", |
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"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], |
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['張り切っていこう！', '北部玄驹 Kitasan Black (Umamusume Pretty Derby)', 'Japanese', 1, False], |
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['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], |
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['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], |
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['お帰りなさい，お兄様！', '米浴 Rice Shower (Umamusume Pretty Derby)', 'Japanese', 1, False], |
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['私の処女をもらっでください！', '米浴 Rice Shower (Umamusume Pretty Derby)', 'Japanese', 1, False]], |
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"onnx_dir": "./ONNX_net/G_jp/" |
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}, |
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] |
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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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for info in models_info: |
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name = info['title'] |
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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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onnx_dir = info["onnx_dir"] |
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hps = utils.get_hparams_from_file(config_path) |
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model = ONNXVITS_infer.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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ONNX_dir=onnx_dir, |
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**hps.model) |
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utils.load_checkpoint(model_path, model, None) |
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model.eval() |
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speaker_ids = hps.speakers |
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speakers = list(hps.speakers.keys()) |
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models_tts.append((name, description, speakers, lang, examples, |
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hps.symbols, create_tts_fn(model, hps, speaker_ids), |
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create_to_symbol_fn(hps))) |
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models_vc.append((name, description, speakers, create_vc_fn(model, hps, speaker_ids))) |
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app = gr.Blocks() |
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with app: |
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gr.Markdown("# English & Chinese & Japanese Anime TTS\n\n" |
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"\n\n" |
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"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" |
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"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" |
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"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" |
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) |
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with gr.Tabs(): |
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with gr.TabItem("TTS"): |
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with gr.Tabs(): |
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for i, (name, description, speakers, lang, example, symbols, tts_fn, to_symbol_fn) in enumerate( |
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models_tts): |
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with gr.TabItem(name): |
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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 (Maximum 150 words)", |
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value="こんにちわ。", 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(value=False, label="Symbol input") |
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symbol_list = gr.Dataset(label="Symbol list", components=[textbox], |
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samples=[[x] for x in symbols], |
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elem_id=f"symbol-list") |
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symbol_list_json = gr.Json(value=symbols, visible=False) |
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symbol_input.change(to_symbol_fn, |
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[symbol_input, textbox, 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(choices=speakers, value=speakers[0], label='character') |
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language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language') |
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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(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, 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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app.queue(concurrency_count=3).launch(show_api=False, share=args.share) |
