| import os |
| 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 |
|
|
| |
| |
| |
| 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") |
|
|
| |
| |
| |
| 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 = """ |
| @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) |
| |
| 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; } } } }""") |
|
|
| |
| app.queue(max_size=3).launch(share=True) |
