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import json
import os
import re
import librosa
import numpy as np
import torch
from torch import no_grad, LongTensor
import commons
import utils
import gradio as gr
from models import SynthesizerTrn
from text import text_to_sequence, _clean_text
from mel_processing import spectrogram_torch
from text.symbols import symbols
limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces
device = 'cpu'
def get_text(text, hps):
text_norm = text_to_sequence(text, 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, speed, is_phoneme):
print(speaker, text)
if limitation:
text_len = len(text)
max_len = 500
if is_phoneme:
max_len *= 3
else:
if len(hps.data.text_cleaners) > 0 and hps.data.text_cleaners[0] == "zh_ja_mixture_cleaners":
text_len = len(re.sub("(\[ZH\]|\[JA\])", "", text))
if text_len > max_len:
return "Error: Text is too long", None
speaker_id = speaker_ids[speaker]
stn_tst = get_text(text, hps, is_phoneme)
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_to_phoneme_fn(hps):
def to_phoneme_fn(text):
return _clean_text(text, hps.data.text_cleaners) if text != "" else ""
return to_phoneme_fn
css = """
#advanced-btn {
color: white;
border-color: black;
background: black;
font-size: .7rem !important;
line-height: 19px;
margin-top: 24px;
margin-bottom: 12px;
padding: 2px 8px;
border-radius: 14px !important;
}
#advanced-options {
display: none;
margin-bottom: 20px;
}
"""
if __name__ == '__main__':
models_tts = []
models_vc = []
models_soft_vc = []
name = 'BlueArchiveTTS'
lang = 'ζ₯ζ¬θͺ (Japanese)'
example = 'ε
ηγδ½γγζδΌγγγΎγγγγοΌ'
config_path = f"saved_model/config.json"
model_path = f"saved_model/model.pth"
cover_path = f"saved_model/cover.png"
hps = utils.get_hparams_from_file(config_path)
if "use_mel_posterior_encoder" in hps.model.keys() and hps.model.use_mel_posterior_encoder == True:
print("Using mel posterior encoder for VITS2")
posterior_channels = 80 # vits2
hps.data.use_mel_posterior_encoder = True
else:
print("Using lin posterior encoder for VITS1")
posterior_channels = hps.data.filter_length // 2 + 1
hps.data.use_mel_posterior_encoder = False
model = SynthesizerTrn(
len(symbols),
posterior_channels,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers, #- >0 for multi speaker
**hps.model)
utils.load_checkpoint(model_path, model, None)
model.eval()
speaker_ids = [sid for sid, name in enumerate(hps.speakers) if name != "None"]
speakers = [name for sid, name in enumerate(hps.speakers) if name != "None"]
t = 'vits'
models_tts.append((name, cover_path, speakers, lang, example,
symbols, create_tts_fn(model, hps, speaker_ids),
create_to_phoneme_fn(hps)))
app = gr.Blocks(css=css)
with app:
gr.Markdown("# BlueArchiveTTS Using VITS Model\n\n"
"\n\n")
with gr.Tabs():
with gr.TabItem("TTS"):
with gr.Tabs():
for i, (name, cover_path, speakers, lang, example, symbols, tts_fn,
to_phoneme_fn) in enumerate(models_tts):
with gr.TabItem(f"BlueArchive"):
with gr.Column():
gr.Markdown(f"## {name}\n\n"
f"\n\n"
f"lang: {lang}")
tts_input1 = gr.TextArea(label="Text (500 words limitation)", value=example,
elem_id=f"tts-input{i}")
tts_input2 = gr.Dropdown(label="Speaker", choices=speakers,
type="index", value=speakers[0])
tts_input3 = gr.Slider(label="Speed", value=1, minimum=0.1, maximum=2, step=0.1)
with gr.Accordion(label="Advanced Options", open=False):
phoneme_input = gr.Checkbox(value=False, label="Phoneme input")
to_phoneme_btn = gr.Button("Covert text to phoneme")
phoneme_list = gr.Dataset(label="Phoneme list", components=[tts_input1],
samples=[[x] for x in symbols],
elem_id=f"phoneme-list{i}")
phoneme_list_json = gr.Json(value=symbols, visible=False)
tts_submit = gr.Button("Generate", variant="primary")
tts_output1 = gr.Textbox(label="Output Message")
tts_output2 = gr.Audio(label="Output Audio")
tts_submit.click(tts_fn, [tts_input1, tts_input2, tts_input3, phoneme_input],
[tts_output1, tts_output2])
to_phoneme_btn.click(to_phoneme_fn, [tts_input1], [tts_input1])
phoneme_list.click(None, [phoneme_list, phoneme_list_json], [],
_js=f"""
(i,phonemes) => {{
let root = document.querySelector("body > gradio-app");
if (root.shadowRoot != null)
root = root.shadowRoot;
let text_input = root.querySelector("#tts-input{i}").querySelector("textarea");
let startPos = text_input.selectionStart;
let endPos = text_input.selectionEnd;
let oldTxt = text_input.value;
let result = oldTxt.substring(0, startPos) + phonemes[i] + oldTxt.substring(endPos);
text_input.value = result;
let x = window.scrollX, y = window.scrollY;
text_input.focus();
text_input.selectionStart = startPos + phonemes[i].length;
text_input.selectionEnd = startPos + phonemes[i].length;
text_input.blur();
window.scrollTo(x, y);
return [];
}}""")
app.queue(concurrency_count=3).launch(show_api=False)
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