File size: 8,568 Bytes
d4b1959 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | import gradio as gr
import os
import torch
import commons
import utils
from models import SynthesizerTrn
import numpy as np
import json
import shutil
from huggingface_hub import snapshot_download
# --- OPTIONAL ROMAJI CONVERTER ---
try:
import pykakasi
kks = pykakasi.kakasi()
def to_romaji(text):
if not text: return text
text_str = str(text)
result = kks.convert(text_str)
romaji = "".join([item['hepburn'].capitalize() for item in result])
return romaji if romaji and romaji.lower() != text_str.lower() else text_str
except ImportError:
def to_romaji(text):
return str(text)
# --- DOWNLOAD ASSETS ---
REPO_ID = "Plana-Archive/Plana-TTS"
LOCAL_ROOT = "saved_model"
def download_assets():
if not os.path.exists(os.path.join(LOCAL_ROOT, "info.json")):
try:
snapshot_download(repo_id=REPO_ID, local_dir=".", allow_patterns=["info.json"])
if os.path.exists("info.json"):
os.makedirs(LOCAL_ROOT, exist_ok=True)
shutil.move("info.json", os.path.join(LOCAL_ROOT, "info.json"))
snapshot_download(repo_id=REPO_ID, local_dir=LOCAL_ROOT, allow_patterns=["MOE-TTS/saved_model/*"])
wrong_path = os.path.join(LOCAL_ROOT, "MOE-TTS", "saved_model")
if os.path.exists(wrong_path):
for item in os.listdir(wrong_path):
shutil.move(os.path.join(wrong_path, item), os.path.join(LOCAL_ROOT, item))
shutil.rmtree(os.path.join(LOCAL_ROOT, "MOE-TTS"))
except Exception as e:
print(f"Download error: {str(e)}")
download_assets()
# --- MODEL ENGINE (PERBAIKAN KRUSIAL MODEL 19) ---
loaded_models = {}
def clean_config(conf):
"""
Mengonversi semua key dalam dictionary menjadi string secara rekursif.
Ini wajib untuk model dengan banyak speaker seperti Model 19 agar tidak error 'int'.
"""
if isinstance(conf, dict):
return {str(k): clean_config(v) for k, v in conf.items()}
elif isinstance(conf, list):
return [clean_config(i) for i in conf]
return conf
def get_vits_model(m_id):
mid = str(m_id)
if mid in loaded_models:
return loaded_models[mid]
try:
p = os.path.join(LOCAL_ROOT, mid)
config_path = os.path.join(p, "config.json")
if not os.path.exists(config_path):
return None
hps = utils.get_hparams_from_file(config_path)
# Ambil parameter model dan bersihkan dari tipe data int pada key
if hasattr(hps, 'model'):
# Mengakses dictionary internal dari objek HParams
model_dict = hps.model.__dict__ if hasattr(hps.model, '__dict__') else dict(hps.model)
model_params = clean_config(model_dict)
else:
model_params = {}
net = SynthesizerTrn(
len(hps.symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**model_params
)
utils.load_checkpoint(os.path.join(p, "model.pth"), net, None)
net.eval()
raw_spks = hps.speakers if hasattr(hps, 'speakers') else [f"Character {i}" for i in range(hps.data.n_speakers)]
display_spks = [to_romaji(s) for s in raw_spks]
loaded_models[mid] = (hps, net, display_spks, raw_spks)
return loaded_models[mid]
except Exception as e:
print(f"Error loading model {m_id}: {str(e)}")
return None
def tts_execute(m_id, text, speaker_display, speed):
data = get_vits_model(m_id)
if not data: return None
hps, net, display_spks, raw_spks = data
try:
sid = display_spks.index(speaker_display)
from text import text_to_sequence
cleaners = hps.data.text_cleaners if hasattr(hps.data, 'text_cleaners') else ['japanese_cleaners']
seq = text_to_sequence(text, hps.symbols, cleaners)
if hps.data.add_blank: seq = commons.intersperse(seq, 0)
x = torch.LongTensor(seq).unsqueeze(0)
x_len = torch.LongTensor([len(seq)])
with torch.no_grad():
audio = net.infer(x, x_len, sid=torch.LongTensor([sid]), noise_scale=0.667,
noise_scale_w=0.8, length_scale=1.0/speed)[0][0,0].data.cpu().float().numpy()
return (hps.data.sampling_rate, (audio / np.abs(audio).max() * 32767).astype(np.int16))
except Exception as e:
print(f"TTS error: {str(e)}")
return None
# --- UI DESIGN (UKURAN TETAP SESUAI ASLI) ---
css = """
.gradio-container { max-width: 850px !important; margin: 0 auto !important; padding: 10px !important; }
.header-box {
background: white; border-radius: 15px; padding: 25px !important;
margin-bottom: 20px !important; border-top: 6px solid #5f6caf;
text-align: center; box-shadow: 0 2px 10px rgba(0,0,0,0.05);
}
.header-box h1 { font-size: 24px !important; margin: 0; }
.tabs-wrapper {
border: 1px dashed #cbd5e0 !important; border-radius: 15px !important;
padding: 15px !important; background: white; margin-bottom: 20px;
}
.content-area {
background: white; border-radius: 15px !important; padding: 20px !important;
border: 1px solid #eee !important; width: 100% !important;
}
.model-title { font-size: 18px !important; font-weight: bold; margin-bottom: 10px; color: #333; }
.footer-box {
background: white; border-radius: 12px; padding: 20px !important;
margin-top: 20px !important; text-align: center !important; border: 1px solid #eee;
}
footer { display: none !important; }
"""
with gr.Blocks(css=css, title="MOE-TTS", theme=gr.themes.Soft()) as demo:
with gr.Column(elem_classes="header-box"):
gr.Markdown("# Library Anime TTS\n### LIBRARY ANIME PREMIUM")
info_path = os.path.join(LOCAL_ROOT, "info.json")
if os.path.exists(info_path):
with open(info_path, "r", encoding="utf-8") as f:
all_info = json.load(f)
with gr.Column(elem_classes="tabs-wrapper"):
sorted_keys = sorted(all_info.keys(), key=int)
with gr.Tabs():
for m_id in sorted_keys:
info = all_info[m_id]
m_path = os.path.join(LOCAL_ROOT, str(m_id))
if not os.path.exists(m_path): continue
with gr.Tab(f"Model {m_id}"):
m_res = get_vits_model(m_id)
is_ok = m_res is not None
spks = m_res[2] if is_ok else ["Error Loading Model"]
COVER_FILE = None
for ext in ['jpg', 'png', 'jpeg', 'webp']:
tmp = os.path.join(m_path, f"cover.{ext}")
if os.path.exists(tmp): COVER_FILE = tmp; break
with gr.Column(elem_classes="content-area"):
with gr.Row():
with gr.Column(scale=1):
if COVER_FILE:
gr.Image(COVER_FILE, show_label=False)
t_romaji = to_romaji(info.get('title', 'Model'))
gr.Markdown(f"<div class='model-title'>{t_romaji}</div>")
with gr.Column(scale=2):
in_txt = gr.TextArea(label="Text Input", value=info.get("example", ""), lines=5)
with gr.Row():
in_char = gr.Dropdown(choices=spks, value=spks[0] if spks else None, label="Character")
in_speed = gr.Slider(0.5, 2.0, 1.0, step=0.1, label="Speed")
gen_btn = gr.Button("Generate Voice", variant="primary")
aud_out = gr.Audio(label="Result")
gen_btn.click(fn=tts_execute, inputs=[gr.State(str(m_id)), in_txt, in_char, in_speed], outputs=aud_out, api_name=False)
with gr.Column(elem_classes="footer-box"):
gr.Markdown("**CREATED BY PLANA-CHAN**\nLibrary Anime TTS")
else:
gr.Markdown("## info.json not found")
demo.launch(show_api=False) |