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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",
"Blue Archive": "https://huggingface.co/spaces/FrankZxShen/vits-fast-fineturning-models-ba",
"PCR so-vits-svc(歌声语音转换)": "https://huggingface.co/spaces/FrankZxShen/so-vits-svc-models-pcr",
}
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;
}}""")
# select character
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)
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