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import json
import traceback
import logging
import gradio as gr
import numpy as np
import librosa
import torch
import asyncio
import edge_tts
import re
import shutil
import time
from datetime import datetime
from fairseq import checkpoint_utils
from fairseq.data.dictionary import Dictionary
from lib.infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
from vc_infer_pipeline import VC
from config import Config
# =============================
# LOAD ENVIRONMENT VARIABLES
# =============================
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN:
print("🔑 Hugging Face token detected")
os.environ["HUGGINGFACE_TOKEN"] = HF_TOKEN
else:
print("⚠️ No HF_TOKEN found")
# =============================
# DOWNLOAD WEIGHTS OTOMATIS
# =============================
if not os.path.exists("weights"):
print("=" * 50)
print("🚀 BANGO DREAM MYGO VOICE CONVERSION")
print("=" * 50)
print("📥 Mendownload weights dan bahan model dari repo Plana-RCV/BanGDream-MyGO...")
try:
from huggingface_hub import snapshot_download
repo_id = "Plana-Archive/Premium-Model"
print(f"📥 Downloading from: {repo_id}")
print("📁 Looking for: BanGDream-MyGO")
downloaded_path = snapshot_download(
repo_id=repo_id,
allow_patterns=[
"BanGDream-MyGO/weights/**",
"BanGDream-MyGO/hubert_base.pt",
"BanGDream-MyGO/rmvpe.pt"
],
local_dir=".",
local_dir_use_symlinks=False,
token=HF_TOKEN if HF_TOKEN else None,
max_workers=2
)
print("✅ Download completed")
source_dir = "BanGDream-MyGO"
if os.path.exists(source_dir):
print(f"📂 Moving files from: {source_dir}")
for item in os.listdir(source_dir):
s = os.path.join(source_dir, item)
d = os.path.join(".", item)
if os.path.isdir(s):
if os.path.exists(d):
shutil.rmtree(d)
shutil.move(s, d)
else:
shutil.move(s, d)
shutil.rmtree(source_dir)
print("✅ Files moved successfully")
folder_info_path = os.path.join("weights", "folder_info.json")
if not os.path.exists(folder_info_path):
folder_info = {
"BanGDream-MyGO": {
"title": "BanG Dream! MyGO!!!!!",
"folder_path": "BanGDream-MyGO",
"description": "Official RVC Weights for BanG Dream! MyGO!!!!! characters",
"enable": True
}
}
with open(folder_info_path, "w", encoding="utf-8") as f:
json.dump(folder_info, f, indent=2, ensure_ascii=False)
print(f"📄 Created folder_info.json")
else:
print("❌ Source directory not found after download!")
except Exception as e:
print(f"⚠️ Download failed: {str(e)}")
traceback.print_exc()
print("\n📝 Manual setup:")
print("1. Create folder: weights/")
print("2. Download from: https://huggingface.co/Plana-Archive/Plana-RCV/tree/main/BanGDream-MyGO")
print("3. Put BanGDream-MyGO folder in weights/")
config = Config()
logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("fairseq").setLevel(logging.WARNING)
model_cache = {}
hubert_loaded = False
hubert_model = None
spaces = True
if spaces:
audio_mode = ["Upload audio", "TTS Audio"]
else:
audio_mode = ["Input path", "Upload audio", "TTS Audio"]
f0method_mode = ["pm", "harvest"]
if os.path.isfile("rmvpe.pt"):
f0method_mode.insert(2, "rmvpe")
def clean_title(title):
title = re.sub(r'^BanG Dream[!]?\s*MyGO[!]*\s*-\s*', '', title, flags=re.IGNORECASE)
return re.sub(r'\s*-\s*\d+\s*epochs', '', title, flags=re.IGNORECASE)
def _load_audio_input(vc_audio_mode, vc_input, vc_upload, tts_text, spaces_limit=20):
temp_file = None
try:
if vc_audio_mode == "Input path" and vc_input:
audio, sr = librosa.load(vc_input, sr=16000, mono=True)
return audio.astype(np.float32), 16000, None
elif vc_audio_mode == "Upload audio":
if vc_upload is None:
raise ValueError("Mohon upload file audio terlebih dahulu!")
sampling_rate, audio = vc_upload
if audio.dtype != np.float32:
audio = audio.astype(np.float32) / np.iinfo(audio.dtype).max
if len(audio.shape) > 1:
audio = np.mean(audio, axis=0)
if sampling_rate != 16000:
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000, res_type='kaiser_fast')
return audio.astype(np.float32), 16000, None
elif vc_audio_mode == "TTS Audio":
if not tts_text or tts_text.strip() == "":
raise ValueError("Mohon masukkan teks untuk TTS!")
temp_file = "tts_temp.wav"
async def tts_task():
return await edge_tts.Communicate(tts_text, "ja-JP-NanamiNeural").save(temp_file)
try:
asyncio.run(asyncio.wait_for(tts_task(), timeout=10))
except asyncio.TimeoutError:
raise ValueError("TTS timeout! Silakan coba lagi.")
audio, sr = librosa.load(temp_file, sr=16000, mono=True)
return audio.astype(np.float32), 16000, temp_file
except Exception as e:
if temp_file and os.path.exists(temp_file):
os.remove(temp_file)
raise e
raise ValueError("Invalid audio mode or missing input.")
def adjust_audio_speed(audio, speed):
if speed == 1.0:
return audio
return librosa.effects.time_stretch(audio.astype(np.float32), rate=speed)
def preprocess_audio(audio):
if np.max(np.abs(audio)) > 1.0:
audio = audio / np.max(np.abs(audio)) * 0.9
return audio.astype(np.float32)
def create_vc_fn(model_key, tgt_sr, net_g, vc, if_f0, version, file_index):
def vc_fn(
vc_audio_mode, vc_input, vc_upload, tts_text,
f0_up_key, f0_method, index_rate, filter_radius,
resample_sr, rms_mix_rate, protect, speed,
):
temp_audio_file = None
try:
if torch.cuda.is_available():
torch.cuda.empty_cache()
net_g.to(config.device)
yield "Status: 🚀 Memproses audio...", None
audio, sr, temp_audio_file = _load_audio_input(vc_audio_mode, vc_input, vc_upload, tts_text)
audio = preprocess_audio(audio)
audio_tensor = torch.FloatTensor(audio).to(config.device)
times = [0, 0, 0]
max_chunk_size = 16000 * 30
if len(audio) > max_chunk_size:
chunks = []
for i in range(0, len(audio), max_chunk_size):
chunk = audio[i:i + max_chunk_size]
chunk_tensor = torch.FloatTensor(chunk).to(config.device)
chunk_opt = vc.pipeline(
hubert_model, net_g, 0, chunk_tensor,
"chunk" if vc_input else "temp", times,
int(f0_up_key), f0_method, file_index, index_rate,
if_f0, filter_radius, tgt_sr, resample_sr,
rms_mix_rate, version, protect, f0_file=None,
)
chunks.append(chunk_opt)
audio_opt = np.concatenate(chunks)
else:
audio_opt = vc.pipeline(
hubert_model, net_g, 0, audio_tensor,
vc_input if vc_input else "temp", times,
int(f0_up_key), f0_method, file_index, index_rate,
if_f0, filter_radius, tgt_sr, resample_sr,
rms_mix_rate, version, protect, f0_file=None,
)
audio_opt = audio_opt.astype(np.float32)
if speed != 1.0:
audio_opt = adjust_audio_speed(audio_opt, speed)
if np.max(np.abs(audio_opt)) > 0:
audio_opt = (audio_opt / np.max(np.abs(audio_opt)) * 0.9).astype(np.float32)
yield "Status: ✅ Selesai!", (tgt_sr, audio_opt)
except Exception as e:
yield f"❌ Error: {str(e)}\n\n{traceback.format_exc()}", None
finally:
if temp_audio_file and os.path.exists(temp_audio_file):
os.remove(temp_audio_file)
if torch.cuda.is_available():
torch.cuda.empty_cache()
if model_key not in model_cache:
net_g.to('cpu')
return vc_fn
def create_model_info_from_files(base_path):
mygo_dir = os.path.join(base_path, "BanGDream-MyGO")
if not os.path.exists(mygo_dir):
return
model_info_path = os.path.join(mygo_dir, "model_info.json")
model_info = {}
for char_folder in os.listdir(mygo_dir):
char_path = os.path.join(mygo_dir, char_folder)
if not os.path.isdir(char_path):
continue
pth_files = [f for f in os.listdir(char_path) if f.endswith('.pth')]
index_files = [f for f in os.listdir(char_path) if f.endswith('.index')]
image_files = [f for f in os.listdir(char_path) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
if not pth_files:
continue
char_name_formatted = re.sub(r"([a-z])([A-Z])", r"\1 \2", char_folder)
model_info[char_folder] = {
"enable": True,
"model_path": pth_files[0],
"title": f"MyGO - {char_name_formatted}",
"cover": image_files[0] if image_files else "cover.png",
"feature_retrieval_library": index_files[0] if index_files else "",
"author": "Plana-Archive"
}
with open(model_info_path, "w", encoding="utf-8") as f:
json.dump(model_info, f, indent=2, ensure_ascii=False)
print(f"✅ Created model_info.json with {len(model_info)} characters")
return model_info
def load_model():
categories = []
base_path = "weights"
if not os.path.exists(base_path):
print(f"❌ Folder '{base_path}' not found!")
return categories
folder_info_path = f"{base_path}/folder_info.json"
if not os.path.isfile(folder_info_path):
folder_info = {
"BanGDream-MyGO": {
"title": "BanG Dream! MyGO!!!!!",
"folder_path": "BanGDream-MyGO",
"description": "Official RVC Weights for BanG Dream! MyGO!!!!! characters",
"enable": True
}
}
with open(folder_info_path, "w", encoding="utf-8") as f:
json.dump(folder_info, f, indent=2, ensure_ascii=False)
with open(folder_info_path, "r", encoding="utf-8") as f:
folder_info = json.load(f)
for category_name, category_info in folder_info.items():
if not category_info.get('enable', True):
continue
category_title, category_folder, description = (
category_info['title'],
category_info['folder_path'],
category_info['description']
)
models = []
model_info_path = f"{base_path}/{category_folder}/model_info.json"
if not os.path.exists(model_info_path):
model_info = create_model_info_from_files(base_path)
if not model_info:
continue
if os.path.exists(model_info_path):
with open(model_info_path, "r", encoding="utf-8") as f:
models_info = json.load(f)
for character_name, info in models_info.items():
if not info.get('enable', True):
continue
model_title, model_name, model_author = (
info['title'],
info['model_path'],
info.get("author")
)
cache_key = f"{category_folder}_{character_name}"
if cache_key in model_cache:
tgt_sr, net_g, vc, if_f0, version, model_index = model_cache[cache_key]
else:
model_cover = f"{base_path}/{category_folder}/{character_name}/{info['cover']}"
model_index = f"{base_path}/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
model_path = f"{base_path}/{category_folder}/{character_name}/{model_name}"
cpt = torch.load(model_path, map_location="cpu")
tgt_sr = cpt["config"][-1]
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
if_f0, version = cpt.get("f0", 1), cpt.get("version", "v1")
if version == "v1":
if if_f0 == 1:
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
else:
if if_f0 == 1:
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
else:
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
if hasattr(net_g, "enc_q"):
del net_g.enc_q
net_g.load_state_dict(cpt["weight"], strict=False)
net_g.eval().to('cpu')
vc = VC(tgt_sr, config)
model_cache[cache_key] = (tgt_sr, net_g, vc, if_f0, version, model_index)
models.append((
character_name, model_title, model_author,
f"{base_path}/{category_folder}/{character_name}/{info['cover']}",
version,
create_vc_fn(cache_key, tgt_sr, net_g, vc, if_f0, version, model_index)
))
categories.append([category_title, category_folder, description, models])
return categories
def load_hubert():
global hubert_model, hubert_loaded
if hubert_loaded:
return
torch.serialization.add_safe_globals([Dictionary])
models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
["hubert_base.pt"],
suffix="",
)
hubert_model = models[0].to(config.device)
hubert_model = hubert_model.half() if config.is_half else hubert_model.float()
hubert_model.eval()
hubert_loaded = True
def change_audio_mode(vc_audio_mode):
is_input_path = vc_audio_mode == "Input path"
is_upload = vc_audio_mode == "Upload audio"
is_tts = vc_audio_mode == "TTS Audio"
return (
gr.Textbox.update(visible=is_input_path),
gr.Audio.update(visible=is_upload),
gr.Textbox.update(visible=is_tts, lines=4 if is_tts else 2)
)
# CSS (sama seperti sebelumnya)
css = """
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Quicksand:wght@400;600;700&display=swap');
body, .gradio-container { background-color: #ffffff !important; font-family: 'Inter', sans-serif !important; }
footer { display: none !important; }
.arona-loading-container { display: flex; align-items: center; justify-content: center; gap: 15px; margin-top: 15px; padding: 10px; }
.loading-text-blue { font-family: 'Quicksand', sans-serif; font-size: 20px; font-weight: 700; color: #00b0ff; letter-spacing: 1px; }
.loading-gif-small { width: 100px; height: auto; border-radius: 8px; }
.header-img-container { text-align: center; padding: 10px 0; background: #ffffff !important; }
.header-img { width: 100%; max-width: 500px; border-radius: 15px; margin: 0 auto; display: block; }
.status-card { background: #ffffff; border: 1px solid #e1f0ff; border-radius: 14px; padding: 15px 10px; margin: 0 auto 15px auto; max-width: 400px; display: flex; flex-direction: column; align-items: center; }
.status-online-box { display: flex; align-items: center; gap: 8px; margin-bottom: 12px; }
.status-details-container { display: flex; width: 100%; justify-content: center; align-items: center; border-top: 1px solid #f0f7ff; padding-top: 10px; }
.status-detail-item { flex: 1; display: flex; flex-direction: column; align-items: center; text-align: center; }
.status-detail-item:first-child { border-right: 1px solid #e1f0ff; }
.status-text-main { font-size: 13px !important; font-weight: 600; color: #546e7a; }
.status-text-sub { font-size: 11px !important; color: #90a4ae; }
.dot-online { height: 8px; width: 8px; background-color: #2ecc71; border-radius: 50%; display: inline-block; animation: blink-green 1.5s infinite; }
@keyframes blink-green { 0% { opacity: 1; } 50% { opacity: 0.4; } 100% { opacity: 1; } }
.gr-form .gr-block label span, .gr-box label span, .gr-panel label span { background: linear-gradient(135deg, #4fc3f7 0%, #00b0ff 100%) !important; color: white !important; padding: 4px 12px !important; border-radius: 8px !important; font-weight: 600 !important; box-shadow: 0 0 15px rgba(79, 195, 247, 0.4) !important; }
input[type="range"] { accent-color: #00b0ff !important; }
.char-scroll-box { display: grid !important; grid-template-columns: repeat(2, 1fr) !important; gap: 12px !important; max-height: 280px; overflow-y: auto; padding: 15px; background: #ffffff; border: 2px solid #eef5ff; border-radius: 14px; }
.char-card { background: white; padding: 12px; border-radius: 12px; cursor: pointer; border: 1px solid #e1f5fe; border-left: 5px solid #4fc3f7; transition: all 0.2s ease; display: flex; flex-direction: column; height: 65px; }
.char-name-jp { font-weight: 700; font-size: 11px !important; color: #455a64; }
.char-name-en { font-size: 8.5px !important; color: #90a4ae; text-transform: uppercase; }
.speed-section { margin-top: 20px; padding: 18px; border-radius: 20px; background: linear-gradient(135deg, #f0f7ff 0%, #ffffff 100%); border: 2px solid #e1f0ff; }
.speed-title { font-family: 'Quicksand', sans-serif; font-weight: 700; color: #4ea8de; text-align: center; margin-bottom: 12px; font-size: 14px; }
.generate-btn { font-family: 'Quicksand', sans-serif; font-weight: 700 !important; background: linear-gradient(135deg, #64b5f6 0%, #2196f3 100%) !important; color: white !important; border-radius: 12px !important; }
.footer-text { text-align: center; padding: 20px; border-top: 1px solid #f0f4f8; color: #b0bec5; font-size: 11px; }
.speed-notes-box { font-family: 'Arial'; border: 1px solid #ffd8b2; border-radius: 8px; padding: 12px; background: #fff7ed; border-left: 4px solid #fb923c; margin-top: 10px; }
.speed-notes-title { color: #c2410c; font-size: 12px; margin: 0 0 5px 0; font-weight: bold; }
.speed-notes-content { color: #9a3412; font-size: 11px; margin: 0; }
.video-demo-container { text-align: center; padding: 20px; background: #ffffff; border-radius: 20px; border: 2px solid #e1f0ff; margin: 20px auto; max-width: 800px; }
.video-demo-title { font-family: 'Quicksand', sans-serif; font-weight: 700; color: #4fc3f7; font-size: 18px; margin-bottom: 15px; }
.video-demo-player { width: 100%; border-radius: 15px; box-shadow: 0 10px 30px rgba(0, 176, 255, 0.2); }
"""
if __name__ == '__main__':
load_hubert()
categories = load_model()
total_models = sum(len(models) for _, _, _, models in categories)
with gr.Blocks(css=css, theme=gr.themes.Soft()) as app:
gr.HTML('<div class="header-img-container"><img src="https://huggingface.co/spaces/BanG-Dream-MyGO/BanG-Dream-MyGO/resolve/main/MyGO.PNG" class="header-img"></div>')
gr.HTML(f'''
<div class="status-card">
<div class="status-online-box"><span class="dot-online"></span><b style="color: #4fc3f7; font-size: 14px;">System Online</b></div>
<div class="status-details-container">
<div class="status-detail-item"><span class="status-text-main">🎸 {total_models} Members</span><span class="status-text-sub">Ready</span></div>
<div class="status-detail-item"><span class="status-text-main">📊 Total</span><span class="status-text-sub">Database: {total_models}</span></div>
</div>
</div>
''')
with gr.Row():
with gr.Column(scale=1):
pass
with gr.Column(scale=3):
gr.HTML("""
<div class="video-demo-container">
<div class="video-demo-title">✅ [ON] MODE YURI 💚</div>
<video class="video-demo-player" controls autoplay loop muted playsinline>
<source src="https://huggingface.co/spaces/BanG-Dream-MyGO/README/resolve/main/Sakiko Chan.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
</div>
""")
with gr.Column(scale=1):
pass
for cat_idx, (folder_title, folder, description, models) in enumerate(categories):
with gr.TabItem(folder_title):
with gr.Accordion("📑 Select Member", open=True):
char_html = "".join([f'<div class="char-card" onclick="selectModel(\'{folder_title}\', \'{name}\')"><span class="char-name-jp">{clean_title(title)}</span><span class="char-name-en">{name}</span></div>' for name, title, author, cover, version, vc_fn in models])
gr.HTML(f'<div class="char-scroll-box">{char_html}</div>')
with gr.Tabs():
for model_idx, (name, title, author, cover, model_version, vc_fn) in enumerate(models):
with gr.TabItem(name, id=f"model_{cat_idx}_{model_idx}"):
with gr.Row():
with gr.Column(scale=1):
gr.HTML(f'<div style="display:flex;flex-direction:column;align-items:center;padding:20px;background:white;border-radius:20px;border:1px solid #eef5ff;"><img style="width:200px;height:260px;object-fit:cover;border-radius:15px;" src="file/{cover}"><div style="font-family:\'Quicksand\',sans-serif;font-weight:700;font-size:18px;color:#039be5;margin-top:15px;">{clean_title(title)}</div><div style="font-size:11px;color:#b0bec5;margin-top:5px;">{model_version} • {author}</div></div>')
with gr.Column(scale=2):
with gr.Group():
vc_audio_mode = gr.Dropdown(label="Input Mode", choices=audio_mode, value="TTS Audio")
vc_input = gr.Textbox(visible=False)
# HAPUS CHECKBOX MICROPHONE – langsung gunakan upload audio
vc_upload = gr.Audio(label="Upload Audio Source", visible=False)
tts_text = gr.Textbox(label="TTS Text", visible=True, placeholder="Type message here...", lines=3)
with gr.Row():
with gr.Column():
vc_transform0 = gr.Slider(minimum=-12, maximum=12, label="Pitch (Nada)", value=12, step=1)
f0method0 = gr.Radio(label="Conversion Algorithm", choices=f0method_mode, value="rmvpe")
with gr.Column():
with gr.Accordion("⚙️ SETTINGS ⚙️", open=False):
index_rate1 = gr.Slider(0, 1, label="Index Rate", value=0.75)
filter_radius0 = gr.Slider(0, 7, label="Filter", value=7, step=1)
resample_sr0 = gr.Slider(0, 48000, label="Resample", value=0)
rms_mix_rate0 = gr.Slider(0, 1, label="Volume Mix", value=0.76)
protect0 = gr.Slider(0, 0.5, label="Voice Protect", value=0.33)
with gr.Column(elem_classes="speed-section"):
gr.HTML('<div class="speed-title">⚡ KECEPATAN SUARA ⚡</div>')
speed_slider = gr.Slider(0.5, 2.0, value=1.0, step=0.1, label=None)
gr.HTML("""<div class="speed-notes-box">
<div class="speed-notes-title">🌥️ CATATAN KECIL 🌥️</div>
<div class="speed-notes-content">
• <b>Kiri (0.5):</b> untuk mempercepat Suara<br>
• <b>✅ Tengah (1.0):</b> untuk Kecepatan normal (disarankan)<br>
• <b>🚫 Kanan (2.0):</b> Mempercepat suara (tidak di sarankan)<br><br>
<b>Tips:</b> Atur ke kiri untuk suara lebih lambat dan atur ke kanan untuk suara lebih cepat. Disarankan tetap di 1.0 untuk hasil normal atau ubah jadi 08 atau 09.
</div>
</div>""")
gr.HTML('<div class="arona-loading-container"><div class="loading-text-blue">Let\'s Play Music!</div><img class="loading-gif-small" src="https://huggingface.co/spaces/BanG-Dream-MyGO/BanG-Dream-MyGO/resolve/main/Mutsumi-Chan.gif"></div>')
with gr.Column(scale=1):
vc_log = gr.Textbox(label="Process Logs", interactive=False)
vc_output = gr.Audio(label="Result Audio", interactive=False)
vc_convert = gr.Button("🎸 GENERATE VOICE 🎸", variant="primary", elem_classes="generate-btn")
vc_convert.click(
fn=vc_fn,
inputs=[vc_audio_mode, vc_input, vc_upload, tts_text, vc_transform0, f0method0,
index_rate1, filter_radius0, resample_sr0, rms_mix_rate0, protect0, speed_slider],
outputs=[vc_log, vc_output]
)
vc_audio_mode.change(fn=change_audio_mode, inputs=[vc_audio_mode], outputs=[vc_input, vc_upload, tts_text])
gr.HTML('<div class="footer-text"><div>DESIGNED BY 🎸 Mutsumi Chan 🎸</div><div style="font-weight:700; color:#90a4ae;">BanG Dream! MyGO!!!!! Voice Conversion • 2024</div></div>')
app.load(None, None, None, js="""() => { window.selectModel = (cat, mod) => { const tabs = document.querySelectorAll('.tabs .tab-nav button'); for (let t of tabs) { if (t.textContent.trim() === cat) { t.click(); setTimeout(() => { const mTabs = document.querySelectorAll('.tabs .tab-nav button'); for (let mt of mTabs) { if (mt.textContent.trim() === mod) mt.click(); } }, 50); break; } } } }""")
# PERBAIKAN: Gunakan share=True dan tanpa parameter server_name/port
app.queue(max_size=3).launch(share=True) |