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on
T4
Running
on
T4
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 | |
limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces | |
def get_text(text, hps, is_phoneme): | |
text_norm = text_to_sequence(text, hps.symbols, [] if is_phoneme 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, speed, is_phoneme): | |
if limitation: | |
text_len = len(text) | |
max_len = 150 | |
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 = [] | |
# {"title": "γγγγ·γ―γͺγ¨γ€γγ£γ", "lang": "ζ₯ζ¬θͺ (Japanese)", "example": "γγγ«γ‘γ―γ", "type": "vits"} | |
name = 'γγγ»γ« TTS' | |
lang = 'ζ₯ζ¬θͺ (Japanese)' | |
example = 'γγγ«γ‘γ―γ' | |
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) | |
model = 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) | |
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, | |
hps.symbols, create_tts_fn(model, hps, speaker_ids), | |
create_to_phoneme_fn(hps))) | |
app = gr.Blocks(css=css) | |
with app: | |
gr.Markdown("# Project Sekai TTS Using VITS Model\n\n" | |
"![visitor badge](https://visitor-badge.glitch.me/badge?page_id=kdrkdrkdr.ProsekaTTS)\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"Proseka"): | |
with gr.Column(): | |
gr.Markdown(f"## {name}\n\n" | |
f"![cover](file/{cover_path})\n\n" | |
f"lang: {lang}") | |
tts_input1 = gr.TextArea(label="Text (150 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 []; | |
}}""") | |
gr.Markdown( | |
"Official User Page \n\n" | |
"- [https://github.com/kdrkdrkdr/ProsekaTTS](https://github.com/kdrkdrkdr/ProsekaTTS)\n\n" | |
"Reference \n\n" | |
"- [https://huggingface.co/spaces/skytnt/moe-tts](https://huggingface.co/spaces/skytnt/moe-tts)" | |
) | |
app.queue(concurrency_count=3).launch(show_api=False) | |