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 limitation:
text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
max_len = 150
if is_symbol:
max_len *= 3
if text_len > max_len:
return "Error: Text is too long", None
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
if limitation and duration > 30:
return "Error: Audio is too long", None
original_speaker_id = speaker_ids[original_speaker]
target_speaker_id = speaker_ids[target_speaker]
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"
"\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)