|
import re |
|
import os |
|
import numpy as np |
|
import torch |
|
from torch import no_grad, LongTensor |
|
import argparse |
|
import commons |
|
from mel_processing import spectrogram_torch |
|
import utils |
|
from models import SynthesizerTrn |
|
import gradio as gr |
|
import librosa |
|
import webbrowser |
|
|
|
from text import text_to_sequence, _clean_text |
|
device = "cuda:0" if torch.cuda.is_available() else "cpu" |
|
language_marks = { |
|
"Japanese": "", |
|
"日本語": "[JA]", |
|
"简体中文": "[ZH]", |
|
"English": "[EN]", |
|
"Mix": "", |
|
} |
|
|
|
|
|
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_tts_fn(model, hps, speaker_ids): |
|
def tts_fn(text, speaker, language, ns, nsw, 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).to(device) |
|
x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device) |
|
sid = LongTensor([speaker_id]).to(device) |
|
audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=ns, noise_scale_w=nsw, |
|
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, record_audio, upload_audio): |
|
input_audio = record_audio if record_audio is not None else upload_audio |
|
if input_audio is None: |
|
return "You need to record or upload an audio", None |
|
sampling_rate, audio = input_audio |
|
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 / max(-y.min(), y.max()) / 0.99 |
|
y = y.to(device) |
|
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).to(device) |
|
spec_lengths = LongTensor([spec.size(-1)]).to(device) |
|
sid_src = LongTensor([original_speaker_id]).to(device) |
|
sid_tgt = LongTensor([target_speaker_id]).to(device) |
|
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_info = [ |
|
{ |
|
"languages": ['日本語', '简体中文', 'English', 'Mix'], |
|
"description": """ |
|
这个模型包含公主连结Re:Dive的161名角色，能合成中日英三语。\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": "./models/G_9700.pth", |
|
"config_path": "./configs/modified_finetune_speaker.json", |
|
"examples": [['大切な人の誕生日を祝えるって、すごく幸せなことなんですよね。', '佩可莉姆', '日本語', 1, False], |
|
['その…この制服、どうですか？', '栞', '日本語', 1, False], |
|
['你们全都给我让开！敢挡路的家伙，我统统斩了！', '矛依未', '简体中文', 1, False], |
|
['Can you tell me how much the shirt is?', '美咲', 'English', 1, False], |
|
['[EN]Excuse me?[EN][JA]お帰りなさい，お兄様！[JA]', '咲恋（夏日）', 'Mix', 1, False]], |
|
} |
|
] |
|
|
|
models_tts = [] |
|
models_vc = [] |
|
if __name__ == "__main__": |
|
parser = argparse.ArgumentParser() |
|
parser.add_argument("--share", action="store_true", default=False, help="share gradio app") |
|
args = parser.parse_args() |
|
categories = ["Princess Connect! Re:Dive"] |
|
others = { |
|
"Umamusume": "https://huggingface.co/spaces/FrankZxShen/vits-fast-finetuning-umamusume", |
|
} |
|
for info in models_info: |
|
lang = info['languages'] |
|
examples = info['examples'] |
|
config_path = info['config_path'] |
|
model_path = info['model_path'] |
|
description = info['description'] |
|
hps = utils.get_hparams_from_file(config_path) |
|
|
|
net_g = SynthesizerTrn( |
|
len(hps.symbols), |
|
hps.data.filter_length // 2 + 1, |
|
hps.train.segment_size // hps.data.hop_length, |
|
n_speakers=hps.data.n_speakers, |
|
**hps.model).to(device) |
|
_ = net_g.eval() |
|
|
|
_ = utils.load_checkpoint(model_path, net_g, None) |
|
speaker_ids = hps.speakers |
|
speakers = list(hps.speakers.keys()) |
|
models_tts.append((description, speakers, lang, examples, |
|
hps.symbols, create_tts_fn(net_g, hps, speaker_ids), |
|
create_to_symbol_fn(hps))) |
|
models_vc.append((description, speakers, create_vc_fn(net_g, hps, speaker_ids))) |
|
|
|
app = gr.Blocks() |
|
with app: |
|
gr.Markdown( |
|
"# <center> vits-fast-fineturning-models-pcr\n" |
|
"## <center> Please do not generate content that could infringe upon the rights or cause harm to individuals or organizations.\n" |
|
"## <center> 请不要生成会对个人以及组织造成侵害的内容\n\n" |
|
"[](https://colab.research.google.com/drive/1pn1xnFfdLK63gVXDwV4zCXfVeo8c-I-0?usp=sharing)\n\n" |
|
"[](https://huggingface.co/spaces/FrankZxShen/vits-fast-finetuning-pcr?duplicate=true)\n\n" |
|
"[](https://github.com/Plachtaa/VITS-fast-fine-tuning)" |
|
) |
|
gr.Markdown("# TTS&Voice Conversion for Princess Connect! Re:Dive\n\n" |
|
) |
|
with gr.Tabs(): |
|
for category in categories: |
|
with gr.TabItem(category): |
|
with gr.Tab("TTS"): |
|
for i, (description, speakers, lang, example, symbols, tts_fn, to_symbol_fn) in enumerate( |
|
models_tts): |
|
gr.Markdown(description) |
|
with gr.Row(): |
|
with gr.Column(): |
|
textbox = gr.TextArea(label="Text", |
|
placeholder="Type your sentence here ", |
|
value="よーし、私もがんばらないと！", 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; |
|
}}""") |
|
|
|
char_dropdown = gr.Dropdown( |
|
choices=speakers, value=speakers[0], label='character') |
|
language_dropdown = gr.Dropdown( |
|
choices=lang, value=lang[0], label='language') |
|
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) |
|
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, ns, nsw, 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 |
|
) |
|
with gr.Tab("Voice Conversion"): |
|
for i, (description, speakers, vc_fn) in enumerate( |
|
models_vc): |
|
gr.Markdown(""" |
|
录制或上传声音，并选择要转换的音色。 |
|
""") |
|
with gr.Column(): |
|
record_audio = gr.Audio( |
|
label="record your voice", source="microphone") |
|
upload_audio = gr.Audio( |
|
label="or upload audio here", source="upload") |
|
source_speaker = gr.Dropdown( |
|
choices=speakers, value=speakers[0], label="source speaker") |
|
target_speaker = gr.Dropdown( |
|
choices=speakers, value=speakers[0], label="target speaker") |
|
with gr.Column(): |
|
message_box = gr.Textbox(label="Message") |
|
converted_audio = gr.Audio( |
|
label='converted audio') |
|
btn = gr.Button("Convert!") |
|
btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio], |
|
outputs=[message_box, converted_audio]) |
|
for category, link in others.items(): |
|
with gr.TabItem(category): |
|
gr.Markdown( |
|
f''' |
|
<center> |
|
<h2>Click to Go</h2> |
|
<a href="{link}"> |
|
<img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-xl-dark.svg" |
|
</a> |
|
</center> |
|
''' |
|
) |
|
|
|
app.queue(concurrency_count=3).launch(show_api=False, share=args.share) |
|
|
|
|
