File size: 24,305 Bytes
9f5c8f7 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 |
"""
Gradio UI Training Tab Module
Contains the dataset builder and LoRA training interface components.
"""
import os
import gradio as gr
from acestep.gradio_ui.i18n import t
def create_training_section(dit_handler, llm_handler, init_params=None) -> dict:
"""Create the training tab section with dataset builder and training controls.
Args:
dit_handler: DiT handler instance
llm_handler: LLM handler instance
init_params: Dictionary containing initialization parameters and state.
If None, service will not be pre-initialized.
Returns:
Dictionary of Gradio components for event handling
"""
# Check if running in service mode (hide training tab)
service_mode = init_params is not None and init_params.get('service_mode', False)
with gr.Tab("🎓 LoRA Training", visible=not service_mode):
gr.HTML("""
<div style="text-align: center; padding: 10px; margin-bottom: 15px;">
<h2>🎵 LoRA Training for ACE-Step</h2>
<p>Build datasets from your audio files and train custom LoRA adapters</p>
</div>
""")
with gr.Tabs():
# ==================== Dataset Builder Tab ====================
with gr.Tab("📁 Dataset Builder"):
# ========== Load Existing OR Scan New ==========
gr.HTML("""
<div style="padding: 10px; margin-bottom: 10px; border: 1px solid #4a4a6a; border-radius: 8px; background: linear-gradient(135deg, #2a2a4a 0%, #1a1a3a 100%);">
<h3 style="margin: 0 0 5px 0;">🚀 Quick Start</h3>
<p style="margin: 0; color: #aaa;">Choose one: <b>Load existing dataset</b> OR <b>Scan new directory</b></p>
</div>
""")
with gr.Row():
with gr.Column(scale=1):
gr.HTML("<h4>📂 Load Existing Dataset</h4>")
with gr.Row():
load_json_path = gr.Textbox(
label="Dataset JSON Path",
placeholder="./datasets/my_lora_dataset.json",
info="Load a previously saved dataset",
scale=3,
)
load_json_btn = gr.Button("📂 Load", variant="primary", scale=1)
load_json_status = gr.Textbox(
label="Load Status",
interactive=False,
)
with gr.Column(scale=1):
gr.HTML("<h4>🔍 Scan New Directory</h4>")
with gr.Row():
audio_directory = gr.Textbox(
label="Audio Directory Path",
placeholder="/path/to/your/audio/folder",
info="Scan for audio files (wav, mp3, flac, ogg, opus)",
scale=3,
)
scan_btn = gr.Button("🔍 Scan", variant="secondary", scale=1)
scan_status = gr.Textbox(
label="Scan Status",
interactive=False,
)
gr.HTML("<hr>")
with gr.Row():
with gr.Column(scale=2):
# Audio files table
audio_files_table = gr.Dataframe(
headers=["#", "Filename", "Duration", "Labeled", "BPM", "Key", "Caption"],
datatype=["number", "str", "str", "str", "str", "str", "str"],
label="Found Audio Files",
interactive=False,
wrap=True,
)
with gr.Column(scale=1):
gr.HTML("<h3>⚙️ Dataset Settings</h3>")
dataset_name = gr.Textbox(
label="Dataset Name",
value="my_lora_dataset",
placeholder="Enter dataset name",
)
all_instrumental = gr.Checkbox(
label="All Instrumental",
value=True,
info="Check if all tracks are instrumental (no vocals)",
)
need_lyrics = gr.Checkbox(
label="Transcribe Lyrics",
value=False,
info="Attempt to transcribe lyrics (slower)",
interactive=False, # Disabled for now
)
custom_tag = gr.Textbox(
label="Custom Activation Tag",
placeholder="e.g., 8bit_retro, my_style",
info="Unique tag to activate this LoRA's style",
)
tag_position = gr.Radio(
choices=[
("Prepend (tag, caption)", "prepend"),
("Append (caption, tag)", "append"),
("Replace caption", "replace"),
],
value="replace",
label="Tag Position",
info="Where to place the custom tag in the caption",
)
gr.HTML("<hr><h3>🤖 Step 2: Auto-Label with AI</h3>")
with gr.Row():
with gr.Column(scale=3):
gr.Markdown("""
Click the button below to automatically generate metadata for all audio files using AI:
- **Caption**: Music style, genre, mood description
- **BPM**: Beats per minute
- **Key**: Musical key (e.g., C Major, Am)
- **Time Signature**: 4/4, 3/4, etc.
""")
skip_metas = gr.Checkbox(
label="Skip Metas (No LLM)",
value=False,
info="Skip AI labeling. BPM/Key/Time Signature will be N/A, Language will be 'unknown' for instrumental",
)
with gr.Column(scale=1):
auto_label_btn = gr.Button(
"🏷️ Auto-Label All",
variant="primary",
size="lg",
)
label_progress = gr.Textbox(
label="Labeling Progress",
interactive=False,
lines=2,
)
gr.HTML("<hr><h3>👀 Step 3: Preview & Edit</h3>")
with gr.Row():
with gr.Column(scale=1):
sample_selector = gr.Slider(
minimum=0,
maximum=0,
step=1,
value=0,
label="Select Sample #",
info="Choose a sample to preview and edit",
)
preview_audio = gr.Audio(
label="Audio Preview",
type="filepath",
interactive=False,
)
preview_filename = gr.Textbox(
label="Filename",
interactive=False,
)
with gr.Column(scale=2):
with gr.Row():
edit_caption = gr.Textbox(
label="Caption",
lines=3,
placeholder="Music description...",
)
with gr.Row():
edit_lyrics = gr.Textbox(
label="Lyrics",
lines=4,
placeholder="[Verse 1]\nLyrics here...\n\n[Chorus]\n...",
)
with gr.Row():
edit_bpm = gr.Number(
label="BPM",
precision=0,
)
edit_keyscale = gr.Textbox(
label="Key",
placeholder="C Major",
)
edit_timesig = gr.Dropdown(
choices=["", "2", "3", "4", "6"],
label="Time Signature",
)
edit_duration = gr.Number(
label="Duration (s)",
precision=1,
interactive=False,
)
with gr.Row():
edit_language = gr.Dropdown(
choices=["instrumental", "en", "zh", "ja", "ko", "es", "fr", "de", "pt", "ru", "unknown"],
value="instrumental",
label="Language",
)
edit_instrumental = gr.Checkbox(
label="Instrumental",
value=True,
)
save_edit_btn = gr.Button("💾 Save Changes", variant="secondary")
edit_status = gr.Textbox(
label="Edit Status",
interactive=False,
)
gr.HTML("<hr><h3>💾 Step 4: Save Dataset</h3>")
with gr.Row():
with gr.Column(scale=3):
save_path = gr.Textbox(
label="Save Path",
value="./datasets/my_lora_dataset.json",
placeholder="./datasets/dataset_name.json",
info="Path where the dataset JSON will be saved",
)
with gr.Column(scale=1):
save_dataset_btn = gr.Button(
"💾 Save Dataset",
variant="primary",
size="lg",
)
save_status = gr.Textbox(
label="Save Status",
interactive=False,
lines=2,
)
gr.HTML("<hr><h3>⚡ Step 5: Preprocess to Tensors</h3>")
gr.Markdown("""
**Preprocessing converts your dataset to pre-computed tensors for fast training.**
You can either:
- Use the dataset from Steps 1-4 above, **OR**
- Load an existing dataset JSON file (if you've already saved one)
""")
with gr.Row():
with gr.Column(scale=3):
load_existing_dataset_path = gr.Textbox(
label="Load Existing Dataset (Optional)",
placeholder="./datasets/my_lora_dataset.json",
info="Path to a previously saved dataset JSON file",
)
with gr.Column(scale=1):
load_existing_dataset_btn = gr.Button(
"📂 Load Dataset",
variant="secondary",
size="lg",
)
load_existing_status = gr.Textbox(
label="Load Status",
interactive=False,
)
gr.Markdown("""
This step:
- Encodes audio to VAE latents
- Encodes captions and lyrics to text embeddings
- Runs the condition encoder
- Saves all tensors to `.pt` files
⚠️ **This requires the model to be loaded and may take a few minutes.**
""")
with gr.Row():
with gr.Column(scale=3):
preprocess_output_dir = gr.Textbox(
label="Tensor Output Directory",
value="./datasets/preprocessed_tensors",
placeholder="./datasets/preprocessed_tensors",
info="Directory to save preprocessed tensor files",
)
with gr.Column(scale=1):
preprocess_btn = gr.Button(
"⚡ Preprocess",
variant="primary",
size="lg",
)
preprocess_progress = gr.Textbox(
label="Preprocessing Progress",
interactive=False,
lines=3,
)
# ==================== Training Tab ====================
with gr.Tab("🚀 Train LoRA"):
with gr.Row():
with gr.Column(scale=2):
gr.HTML("<h3>📊 Preprocessed Dataset Selection</h3>")
gr.Markdown("""
Select the directory containing preprocessed tensor files (`.pt` files).
These are created in the "Dataset Builder" tab using the "Preprocess" button.
""")
training_tensor_dir = gr.Textbox(
label="Preprocessed Tensors Directory",
placeholder="./datasets/preprocessed_tensors",
value="./datasets/preprocessed_tensors",
info="Directory containing preprocessed .pt tensor files",
)
load_dataset_btn = gr.Button("📂 Load Dataset", variant="secondary")
training_dataset_info = gr.Textbox(
label="Dataset Info",
interactive=False,
lines=3,
)
with gr.Column(scale=1):
gr.HTML("<h3>⚙️ LoRA Settings</h3>")
lora_rank = gr.Slider(
minimum=4,
maximum=256,
step=4,
value=64,
label="LoRA Rank (r)",
info="Higher = more capacity, more memory",
)
lora_alpha = gr.Slider(
minimum=4,
maximum=512,
step=4,
value=128,
label="LoRA Alpha",
info="Scaling factor (typically 2x rank)",
)
lora_dropout = gr.Slider(
minimum=0.0,
maximum=0.5,
step=0.05,
value=0.1,
label="LoRA Dropout",
)
gr.HTML("<hr><h3>🎛️ Training Parameters</h3>")
with gr.Row():
learning_rate = gr.Number(
label="Learning Rate",
value=1e-4,
info="Start with 1e-4, adjust if needed",
)
train_epochs = gr.Slider(
minimum=100,
maximum=4000,
step=100,
value=500,
label="Max Epochs",
)
train_batch_size = gr.Slider(
minimum=1,
maximum=8,
step=1,
value=1,
label="Batch Size",
info="Increase if you have enough VRAM",
)
gradient_accumulation = gr.Slider(
minimum=1,
maximum=16,
step=1,
value=1,
label="Gradient Accumulation",
info="Effective batch = batch_size × accumulation",
)
with gr.Row():
save_every_n_epochs = gr.Slider(
minimum=50,
maximum=1000,
step=50,
value=200,
label="Save Every N Epochs",
)
training_shift = gr.Slider(
minimum=1.0,
maximum=5.0,
step=0.5,
value=3.0,
label="Shift",
info="Timestep shift for turbo model",
)
training_seed = gr.Number(
label="Seed",
value=42,
precision=0,
)
with gr.Row():
lora_output_dir = gr.Textbox(
label="Output Directory",
value="./lora_output",
placeholder="./lora_output",
info="Directory to save trained LoRA weights",
)
gr.HTML("<hr>")
with gr.Row():
with gr.Column(scale=1):
start_training_btn = gr.Button(
"🚀 Start Training",
variant="primary",
size="lg",
)
with gr.Column(scale=1):
stop_training_btn = gr.Button(
"⏹️ Stop Training",
variant="stop",
size="lg",
)
training_progress = gr.Textbox(
label="Training Progress",
interactive=False,
lines=2,
)
with gr.Row():
training_log = gr.Textbox(
label="Training Log",
interactive=False,
lines=10,
max_lines=15,
scale=1,
)
training_loss_plot = gr.LinePlot(
x="step",
y="loss",
title="Training Loss",
x_title="Step",
y_title="Loss",
scale=1,
)
gr.HTML("<hr><h3>📦 Export LoRA</h3>")
with gr.Row():
export_path = gr.Textbox(
label="Export Path",
value="./lora_output/final_lora",
placeholder="./lora_output/my_lora",
)
export_lora_btn = gr.Button("📦 Export LoRA", variant="secondary")
export_status = gr.Textbox(
label="Export Status",
interactive=False,
)
# Store dataset builder state
dataset_builder_state = gr.State(None)
training_state = gr.State({"is_training": False, "should_stop": False})
return {
# Dataset Builder - Load or Scan
"load_json_path": load_json_path,
"load_json_btn": load_json_btn,
"load_json_status": load_json_status,
"audio_directory": audio_directory,
"scan_btn": scan_btn,
"scan_status": scan_status,
"audio_files_table": audio_files_table,
"dataset_name": dataset_name,
"all_instrumental": all_instrumental,
"need_lyrics": need_lyrics,
"custom_tag": custom_tag,
"tag_position": tag_position,
"skip_metas": skip_metas,
"auto_label_btn": auto_label_btn,
"label_progress": label_progress,
"sample_selector": sample_selector,
"preview_audio": preview_audio,
"preview_filename": preview_filename,
"edit_caption": edit_caption,
"edit_lyrics": edit_lyrics,
"edit_bpm": edit_bpm,
"edit_keyscale": edit_keyscale,
"edit_timesig": edit_timesig,
"edit_duration": edit_duration,
"edit_language": edit_language,
"edit_instrumental": edit_instrumental,
"save_edit_btn": save_edit_btn,
"edit_status": edit_status,
"save_path": save_path,
"save_dataset_btn": save_dataset_btn,
"save_status": save_status,
# Preprocessing
"load_existing_dataset_path": load_existing_dataset_path,
"load_existing_dataset_btn": load_existing_dataset_btn,
"load_existing_status": load_existing_status,
"preprocess_output_dir": preprocess_output_dir,
"preprocess_btn": preprocess_btn,
"preprocess_progress": preprocess_progress,
"dataset_builder_state": dataset_builder_state,
# Training
"training_tensor_dir": training_tensor_dir,
"load_dataset_btn": load_dataset_btn,
"training_dataset_info": training_dataset_info,
"lora_rank": lora_rank,
"lora_alpha": lora_alpha,
"lora_dropout": lora_dropout,
"learning_rate": learning_rate,
"train_epochs": train_epochs,
"train_batch_size": train_batch_size,
"gradient_accumulation": gradient_accumulation,
"save_every_n_epochs": save_every_n_epochs,
"training_shift": training_shift,
"training_seed": training_seed,
"lora_output_dir": lora_output_dir,
"start_training_btn": start_training_btn,
"stop_training_btn": stop_training_btn,
"training_progress": training_progress,
"training_log": training_log,
"training_loss_plot": training_loss_plot,
"export_path": export_path,
"export_lora_btn": export_lora_btn,
"export_status": export_status,
"training_state": training_state,
}
|