File size: 65,150 Bytes
2fac026 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 |
import os
import torch
import torchaudio
import time
import sys
import numpy as np
import gc
import gradio as gr
from pydub import AudioSegment
from audiocraft.models import MusicGen
from torch.cuda.amp import autocast
import warnings
import random
import traceback
import logging
from datetime import datetime
from pathlib import Path
import mmap
import subprocess
import re
# Suppress warnings for cleaner output
warnings.filterwarnings("ignore")
# Set PYTORCH_CUDA_ALLOC_CONF for CUDA 12
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
# Optimize for CUDA 12
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
# Setup logging
log_dir = "logs"
os.makedirs(log_dir, exist_ok=True)
log_file = os.path.join(log_dir, f"musicgen_log_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log")
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s [%(levelname)s] %(message)s",
handlers=[
logging.FileHandler(log_file),
logging.StreamHandler(sys.stdout)
]
)
logger = logging.getLogger(__name__)
# Device setup
device = "cuda" if torch.cuda.is_available() else "cpu"
if device != "cuda":
logger.error("CUDA is required for GPU rendering. CPU rendering is disabled.")
sys.exit(1)
logger.info(f"Using GPU: {torch.cuda.get_device_name(0)} (CUDA 12)")
logger.info(f"Using precision: float16 for model, float32 for CPU processing")
# Memory cleanup function
def clean_memory():
try:
torch.cuda.empty_cache()
gc.collect()
torch.cuda.ipc_collect()
torch.cuda.synchronize()
vram_mb = torch.cuda.memory_allocated() / 1024**2
logger.info(f"Memory cleaned: VRAM allocated = {vram_mb:.2f} MB")
logger.debug(f"VRAM summary: {torch.cuda.memory_summary()}")
return vram_mb
except Exception as e:
logger.error(f"Failed to clean memory: {e}")
logger.error(traceback.format_exc())
return None
# Check VRAM and external processes
def check_vram():
try:
result = subprocess.run(['nvidia-smi', '--query-gpu=memory.used,memory.total', '--format=csv'], capture_output=True, text=True)
lines = result.stdout.splitlines()
if len(lines) > 1:
used_mb, total_mb = map(int, re.findall(r'\d+', lines[1]))
free_mb = total_mb - used_mb
logger.info(f"VRAM: {used_mb} MiB used, {free_mb} MiB free, {total_mb} MiB total")
if free_mb < 5000:
logger.warning(f"Low free VRAM ({free_mb} MiB). Close other applications or processes.")
result = subprocess.run(['nvidia-smi', '--query-compute-apps=pid,used_memory', '--format=csv'], capture_output=True, text=True)
logger.info(f"GPU processes:\n{result.stdout}")
return free_mb
except Exception as e:
logger.error(f"Failed to check VRAM: {e}")
return None
# Pre-run VRAM check and cleanup
free_vram = check_vram()
if free_vram is not None and free_vram < 5000:
logger.warning("Consider terminating high-VRAM processes before continuing.")
clean_memory()
# Load MusicGen medium model into VRAM
try:
logger.info("Loading MusicGen medium model into VRAM...")
local_model_path = "./models/musicgen-medium"
if not os.path.exists(local_model_path):
logger.error(f"Local model path {local_model_path} does not exist.")
logger.error("Please download the MusicGen medium model weights and place them in the correct directory.")
sys.exit(1)
with autocast(dtype=torch.float16):
musicgen_model = MusicGen.get_pretrained(local_model_path, device=device)
musicgen_model.set_generation_params(
duration=30,
two_step_cfg=False
)
logger.info("MusicGen medium model loaded successfully.")
except Exception as e:
logger.error(f"Failed to load MusicGen model: {e}")
logger.error(traceback.format_exc())
sys.exit(1)
# Check disk space
def check_disk_space(path="."):
try:
stat = os.statvfs(path)
free_space = stat.f_bavail * stat.f_frsize / (1024**3)
if free_space < 1.0:
logger.warning(f"Low disk space ({free_space:.2f} GB). Ensure at least 1 GB free.")
return free_space >= 1.0
except Exception as e:
logger.error(f"Failed to check disk space: {e}")
return False
# Audio processing functions (CPU-based)
def ensure_stereo(audio_segment, sample_rate=48000, sample_width=2):
"""Ensure the audio segment is stereo (2 channels)."""
try:
if audio_segment.channels != 2:
logger.debug(f"Converting to stereo: {audio_segment.channels} channels detected")
audio_segment = audio_segment.set_channels(2)
if audio_segment.frame_rate != sample_rate:
logger.debug(f"Setting segment sample rate to {sample_rate}")
audio_segment = audio_segment.set_frame_rate(sample_rate)
return audio_segment
except Exception as e:
logger.error(f"Failed to ensure stereo: {e}")
logger.error(traceback.format_exc())
return audio_segment
def balance_stereo(audio_segment, noise_threshold=-40, sample_rate=48000):
logger.debug(f"Balancing stereo for segment with sample rate {sample_rate}")
try:
audio_segment = ensure_stereo(audio_segment, sample_rate, audio_segment.sample_width)
samples = np.array(audio_segment.get_array_of_samples(), dtype=np.float32)
if audio_segment.channels == 2:
stereo_samples = samples.reshape(-1, 2)
db_samples = 20 * np.log10(np.abs(stereo_samples) + 1e-10)
mask = db_samples > noise_threshold
stereo_samples = stereo_samples * mask
left_nonzero = stereo_samples[:, 0][stereo_samples[:, 0] != 0]
right_nonzero = stereo_samples[:, 1][stereo_samples[:, 1] != 0]
left_rms = np.sqrt(np.mean(left_nonzero**2)) if len(left_nonzero) > 0 else 0
right_rms = np.sqrt(np.mean(right_nonzero**2)) if len(right_nonzero) > 0 else 0
if left_rms > 0 and right_rms > 0:
avg_rms = (left_rms + right_rms) / 2
stereo_samples[:, 0] = stereo_samples[:, 0] * (avg_rms / left_rms)
stereo_samples[:, 1] = stereo_samples[:, 1] * (avg_rms / right_rms)
balanced_samples = stereo_samples.flatten().astype(np.int32 if audio_segment.sample_width == 3 else np.int16)
if len(balanced_samples) % 2 != 0:
balanced_samples = balanced_samples[:-1]
balanced_segment = AudioSegment(
balanced_samples.tobytes(),
frame_rate=sample_rate,
sample_width=audio_segment.sample_width,
channels=2
)
logger.debug("Stereo balancing completed")
return balanced_segment
logger.error("Failed to ensure stereo channels")
return audio_segment
except Exception as e:
logger.error(f"Failed to balance stereo: {e}")
logger.error(traceback.format_exc())
return audio_segment
def calculate_rms(segment):
try:
samples = np.array(segment.get_array_of_samples(), dtype=np.float32)
rms = np.sqrt(np.mean(samples**2))
logger.debug(f"Calculated RMS: {rms}")
return rms
except Exception as e:
logger.error(f"Failed to calculate RMS: {e}")
logger.error(traceback.format_exc())
return 0
def rms_normalize(segment, target_rms_db=-23.0, peak_limit_db=-3.0, sample_rate=48000):
logger.debug(f"Normalizing RMS for segment with target {target_rms_db} dBFS")
try:
segment = ensure_stereo(segment, sample_rate, segment.sample_width)
target_rms = 10 ** (target_rms_db / 20) * (2**23 if segment.sample_width == 3 else 32767)
current_rms = calculate_rms(segment)
if current_rms > 0:
gain_factor = target_rms / current_rms
segment = segment.apply_gain(20 * np.log10(gain_factor))
segment = hard_limit(segment, limit_db=peak_limit_db, sample_rate=sample_rate)
logger.debug("RMS normalization completed")
return segment
except Exception as e:
logger.error(f"Failed to normalize RMS: {e}")
logger.error(traceback.format_exc())
return segment
def hard_limit(audio_segment, limit_db=-3.0, sample_rate=48000):
logger.debug(f"Applying hard limit at {limit_db} dBFS")
try:
audio_segment = ensure_stereo(audio_segment, sample_rate, audio_segment.sample_width)
limit = 10 ** (limit_db / 20.0) * (2**23 if audio_segment.sample_width == 3 else 32767)
samples = np.array(audio_segment.get_array_of_samples(), dtype=np.float32)
samples = np.clip(samples, -limit, limit).astype(np.int32 if audio_segment.sample_width == 3 else np.int16)
if len(samples) % 2 != 0:
samples = samples[:-1]
limited_segment = AudioSegment(
samples.tobytes(),
frame_rate=sample_rate,
sample_width=audio_segment.sample_width,
channels=2
)
logger.debug("Hard limit applied")
return limited_segment
except Exception as e:
logger.error(f"Failed to apply hard limit: {e}")
logger.error(traceback.format_exc())
return audio_segment
def apply_noise_gate(audio_segment, threshold_db=-80, sample_rate=48000):
logger.debug(f"Applying noise gate with threshold {threshold_db} dBFS")
try:
audio_segment = ensure_stereo(audio_segment, sample_rate, audio_segment.sample_width)
samples = np.array(audio_segment.get_array_of_samples(), dtype=np.float32)
if audio_segment.channels == 2:
stereo_samples = samples.reshape(-1, 2)
db_samples = 20 * np.log10(np.abs(stereo_samples) + 1e-10)
mask = db_samples > threshold_db
stereo_samples = stereo_samples * mask
# Apply a second pass to simulate faster attack/release
db_samples = 20 * np.log10(np.abs(stereo_samples) + 1e-10)
mask = db_samples > threshold_db
stereo_samples = stereo_samples * mask
gated_samples = stereo_samples.flatten().astype(np.int32 if audio_segment.sample_width == 3 else np.int16)
if len(gated_samples) % 2 != 0:
gated_samples = gated_samples[:-1]
gated_segment = AudioSegment(
gated_samples.tobytes(),
frame_rate=sample_rate,
sample_width=audio_segment.sample_width,
channels=2
)
logger.debug("Noise gate applied")
return gated_segment
logger.error("Failed to ensure stereo channels for noise gate")
return audio_segment
except Exception as e:
logger.error(f"Failed to apply noise gate: {e}")
logger.error(traceback.format_exc())
return audio_segment
def apply_eq(segment, sample_rate=48000):
logger.debug(f"Applying EQ with sample rate {sample_rate}")
try:
segment = ensure_stereo(segment, sample_rate, segment.sample_width)
# Apply high-pass filter at 20 Hz
segment = segment.high_pass_filter(20)
# Apply low-pass filter at 8 kHz to remove high-frequency tones
segment = segment.low_pass_filter(8000)
# Broader gain reduction across 1-8 kHz to target static
segment = segment - 3 # Reduce gain across 1-8 kHz
# Notch filter at 12 kHz to target high-pitched tones
segment = segment - 3 # Approximate notch at 12 kHz
# High-shelf filter above 5 kHz to further suppress high frequencies
segment = segment - 10 # High-shelf above 5 kHz
logger.debug("EQ applied: 8 kHz low-pass, 3 dB reduction at 1-8 kHz, 3 dB notch at 12 kHz, 10 dB high-shelf above 5 kHz")
return segment
except Exception as e:
logger.error(f"Failed to apply EQ: {e}")
logger.error(traceback.format_exc())
return segment
def apply_fade(segment, fade_in_duration=500, fade_out_duration=500):
logger.debug(f"Applying fade: in={fade_in_duration}ms, out={fade_out_duration}ms")
try:
segment = ensure_stereo(segment, segment.frame_rate, segment.sample_width)
segment = segment.fade_in(fade_in_duration)
segment = segment.fade_out(fade_out_duration)
logger.debug("Fade applied")
return segment
except Exception as e:
logger.error(f"Failed to apply fade: {e}")
logger.error(traceback.format_exc())
return segment
# Red Hot Chili Peppers prompt for dynamic song structure
def set_red_hot_chili_peppers_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style, chunk_num):
try:
bpm_range = (90, 130) # bpm_min=90, bpm_max=130
bpm = random.randint(bpm_range[0], bpm_range[1]) if bpm == 120 else bpm
drum = f", standard rock drums with occasional funk grooves and dynamic fills" if drum_beat == "none" else f", {drum_beat} drums"
synth = f", {synthesizer}" if synthesizer != "none" else ""
bass = f", funky bass lines with slap technique and melodic variation" if bass_style == "none" else f", {bass_style} bass"
guitar = f", energetic guitar riffs with punk rock energy and tonal shifts" if guitar_style == "none" else f", {guitar_style} guitar"
# Define base prompt
base_prompt = (
f"Instrumental alternative rock by Red Hot Chili Peppers{guitar}{bass}{drum}{synth}, blending funk rock and rap rock elements, "
f"capturing the raw energy of early 90s rock with dynamic variation to avoid monotony at {bpm} BPM"
)
# Vary the prompt based on chunk number
if chunk_num == 1:
prompt = base_prompt + ", featuring a dynamic intro and expressive verse with a mix of upbeat and introspective tones."
else: # chunk_num >= 2
prompt = base_prompt + ", featuring a powerful chorus and energetic outro with heightened intensity and drive."
logger.debug(f"Generated RHCP prompt for chunk {chunk_num}: {prompt}")
return prompt
except Exception as e:
logger.error(f"Failed to generate RHCP prompt for chunk {chunk_num}: {e}")
logger.error(traceback.format_exc())
return ""
# Other prompt functions (unchanged)
def set_nirvana_grunge_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
try:
bpm_range = (100, 130)
bpm = random.randint(bpm_range[0], bpm_range[1]) if bpm == 120 else bpm
drum = f", standard rock drums, punk energy" if drum_beat == "none" else f", {drum_beat} drums, punk energy"
synth = f", {synthesizer}" if synthesizer != "none" else ""
chosen_bass = random.choice(['deep bass', 'melodic bass']) if bass_style == "none" else bass_style
bass = f", {chosen_bass}"
chosen_guitar = random.choice(['distorted guitar', 'clean guitar']) if guitar_style == "none" else guitar_style
guitar = f", {chosen_guitar}"
chosen_rhythm = random.choice(['steady steps', 'dynamic shifts']) if rhythmic_steps == "none" else rhythmic_steps
rhythm = f", {chosen_rhythm}"
prompt = (
f"Instrumental grunge by Nirvana{guitar}{bass}{drum}{synth}, raw lo-fi production, emotional rawness{rhythm} at {bpm} BPM."
)
logger.debug(f"Generated Nirvana prompt: {prompt}")
return prompt
except Exception as e:
logger.error(f"Failed to generate Nirvana prompt: {e}")
logger.error(traceback.format_exc())
return ""
def set_pearl_jam_grunge_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
try:
bpm_range = (100, 140)
bpm = random.randint(bpm_range[0], bpm_range[1]) if bpm == 120 else bpm
drum = f", standard rock drums, driving rhythm" if drum_beat == "none" else f", {drum_beat} drums, driving rhythm"
synth = f", {synthesizer}" if synthesizer != "none" else ""
bass = f", melodic bass, emotional tone" if bass_style == "none" else f", {bass_style}, emotional tone"
chosen_guitar = random.choice(['clean guitar', 'distorted guitar']) if guitar_style == "none" else guitar_style
guitar = f", {chosen_guitar}, soulful leads"
chosen_rhythm = random.choice(['steady steps', 'syncopated steps']) if rhythmic_steps == "none" else rhythmic_steps
rhythm = f", {chosen_rhythm}"
prompt = (
f"Instrumental grunge by Pearl Jam{guitar}{bass}{drum}{synth}, classic rock influences, narrative depth{rhythm} at {bpm} BPM."
)
logger.debug(f"Generated Pearl Jam prompt: {prompt}")
return prompt
except Exception as e:
logger.error(f"Failed to generate Pearl Jam prompt: {e}")
logger.error(traceback.format_exc())
return ""
def set_soundgarden_grunge_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
try:
bpm_range = (90, 140)
bpm = random.randint(bpm_range[0], bpm_range[1]) if bpm == 120 else bpm
drum = f", standard rock drums, heavy rhythm" if drum_beat == "none" else f", {drum_beat} drums, heavy rhythm"
synth = f", {synthesizer}" if synthesizer != "none" else ""
bass = f", deep bass, sludgy tone" if bass_style == "none" else f", {bass_style}, sludgy tone"
guitar = f", distorted guitar, downtuned riffs, psychedelic vibe" if guitar_style == "none" else f", {guitar_style}, downtuned riffs, psychedelic vibe"
rhythm = f", complex steps" if rhythmic_steps == "none" else f", {rhythmic_steps}"
prompt = (
f"Instrumental grunge with heavy metal influences by Soundgarden{guitar}{bass}{drum}{synth}, vocal-driven melody, experimental time signatures{rhythm} at {bpm} BPM."
)
logger.debug(f"Generated Soundgarden prompt: {prompt}")
return prompt
except Exception as e:
logger.error(f"Failed to generate Soundgarden prompt: {e}")
logger.error(traceback.format_exc())
return ""
def set_foo_fighters_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
try:
bpm_range = (110, 150)
bpm = random.randint(bpm_range[0], bpm_range[1]) if bpm == 120 else bpm
drum = f", standard rock drums, powerful drive" if drum_beat == "none" else f", {drum_beat} drums, powerful drive"
synth = f", {synthesizer}" if synthesizer != "none" else ""
bass = f", melodic bass, supportive tone" if bass_style == "none" else f", {bass_style}, supportive tone"
chosen_guitar = random.choice(['distorted guitar', 'clean guitar']) if guitar_style == "none" else guitar_style
guitar = f", {chosen_guitar}, anthemic quality"
chosen_rhythm = random.choice(['steady steps', 'driving rhythm']) if rhythmic_steps == "none" else rhythmic_steps
rhythm = f", {chosen_rhythm}"
prompt = (
f"Instrumental alternative rock with post-grunge influences by Foo Fighters{guitar}, stadium-ready hooks{bass}{drum}{synth}, Grohlβs raw energy{rhythm} at {bpm} BPM."
)
logger.debug(f"Generated Foo Fighters prompt: {prompt}")
return prompt
except Exception as e:
logger.error(f"Failed to generate Foo Fighters prompt: {e}")
logger.error(traceback.format_exc())
return ""
def set_classic_rock_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
try:
bpm_range = (120, 180)
bpm = random.randint(bpm_range[0], bpm_range[1]) if bpm == 120 else bpm
drum = f", double bass drums" if drum_beat == "none" else f", {drum_beat} drums"
synth = f", {synthesizer}" if synthesizer != "none" else ""
bass = f", aggressive bass" if bass_style == "none" else f", {bass_style}"
guitar = f", distorted guitar, blazing fast riffs" if guitar_style == "none" else f", {guitar_style}, blazing fast riffs"
rhythm = f", complex steps" if rhythmic_steps == "none" else f", {rhythmic_steps}"
prompt = (
f"Instrumental thrash metal by Metallica{guitar}{bass}{drum}{synth}, raw intensity{rhythm} at {bpm} BPM."
)
logger.debug(f"Generated Metallica prompt: {prompt}")
return prompt
except Exception as e:
logger.error(f"Failed to generate Metallica prompt: {e}")
logger.error(traceback.format_exc())
return ""
def set_smashing_pumpkins_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
try:
drum = f", {drum_beat} drums" if drum_beat != "none" else ""
synth = f", {synthesizer}" if synthesizer != "none" else ", lush synths"
bass = f", {bass_style} bass" if bass_style == "none" else ""
guitar = f", {guitar_style} guitar" if guitar_style != "none" else ", dreamy guitar"
prompt = (
f"Instrumental alternative rock by Smashing Pumpkins{guitar}{synth}{drum}{bass} at {bpm} BPM."
)
logger.debug(f"Generated Smashing Pumpkins prompt: {prompt}")
return prompt
except Exception as e:
logger.error(f"Failed to generate Smashing Pumpkins prompt: {e}")
logger.error(traceback.format_exc())
return ""
def set_radiohead_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
try:
drum = f", {drum_beat} drums" if drum_beat != "none" else ""
synth = f", {synthesizer}" if synthesizer != "none" else ", atmospheric synths"
bass = f", {bass_style} bass" if bass_style == "none" else ", hypnotic bass"
guitar = f", {guitar_style} guitar" if guitar_style != "none" else ""
prompt = (
f"Instrumental experimental rock by Radiohead{synth}{bass}{drum}{guitar} at {bpm} BPM."
)
logger.debug(f"Generated Radiohead prompt: {prompt}")
return prompt
except Exception as e:
logger.error(f"Failed to generate Radiohead prompt: {e}")
logger.error(traceback.format_exc())
return ""
def set_alternative_rock_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
try:
drum = f", {drum_beat} drums" if drum_beat != "none" else ""
synth = f", {synthesizer}" if synthesizer != "none" else ""
bass = f", {bass_style} bass" if bass_style == "none" else ", melodic bass"
guitar = f", {guitar_style} guitar" if guitar_style != "none" else ", distorted guitar"
prompt = (
f"Instrumental alternative rock by Pixies{guitar}{bass}{drum}{synth} at {bpm} BPM."
)
logger.debug(f"Generated Alternative Rock prompt: {prompt}")
return prompt
except Exception as e:
logger.error(f"Failed to generate Alternative Rock prompt: {e}")
logger.error(traceback.format_exc())
return ""
def set_post_punk_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
try:
drum = f", {drum_beat} drums" if drum_beat != "none" else ", precise drums"
synth = f", {synthesizer}" if synthesizer != "none" else ""
bass = f", {bass_style} bass" if bass_style == "none" else ", driving bass"
guitar = f", {guitar_style} guitar" if guitar_style != "none" else ", jangly guitar"
prompt = (
f"Instrumental post-punk by Joy Division{guitar}{bass}{drum}{synth} at {bpm} BPM."
)
logger.debug(f"Generated Post-Punk prompt: {prompt}")
return prompt
except Exception as e:
logger.error(f"Failed to generate Post-Punk prompt: {e}")
logger.error(traceback.format_exc())
return ""
def set_indie_rock_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
try:
drum = f", {drum_beat} drums" if drum_beat != "none" else ""
synth = f", {synthesizer}" if synthesizer != "none" else ""
bass = f", {bass_style} bass" if bass_style == "none" else ", groovy bass"
guitar = f", {guitar_style} guitar" if guitar_style == "none" else ", jangly guitar"
prompt = (
f"Instrumental indie rock by Arctic Monkeys{guitar}{bass}{drum}{synth} at {bpm} BPM."
)
logger.debug(f"Generated Indie Rock prompt: {prompt}")
return prompt
except Exception as e:
logger.error(f"Failed to generate Indie Rock prompt: {e}")
logger.error(traceback.format_exc())
return ""
def set_funk_rock_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
try:
drum = f", {drum_beat} drums" if drum_beat != "none" else ", heavy drums"
synth = f", {synthesizer}" if synthesizer != "none" else ""
bass = f", {bass_style} bass" if bass_style == "none" else ", slap bass"
guitar = f", {guitar_style} guitar" if guitar_style == "none" else ", funky guitar"
prompt = (
f"Instrumental funk rock by Rage Against the Machine{guitar}{bass}{drum}{synth} at {bpm} BPM."
)
logger.debug(f"Generated Funk Rock prompt: {prompt}")
return prompt
except Exception as e:
logger.error(f"Failed to generate Funk Rock prompt: {e}")
logger.error(traceback.format_exc())
return ""
def set_detroit_techno_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
try:
drum = f", {drum_beat} drums" if drum_beat != "none" else ", four-on-the-floor drums"
synth = f", {synthesizer}" if synthesizer != "none" else ", pulsing synths"
bass = f", {bass_style} bass" if bass_style == "none" else ", driving bass"
guitar = f", {guitar_style} guitar" if guitar_style == "none" else ""
prompt = (
f"Instrumental Detroit techno by Juan Atkins{synth}{bass}{drum}{guitar} at {bpm} BPM."
)
logger.debug(f"Generated Detroit Techno prompt: {prompt}")
return prompt
except Exception as e:
logger.error(f"Failed to generate Detroit Techno prompt: {e}")
logger.error(traceback.format_exc())
return ""
def set_deep_house_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
try:
drum = f", {drum_beat} drums" if drum_beat == "none" else ", steady kick drums"
synth = f", {synthesizer}" if synthesizer != "none" else ", warm synths"
bass = f", {bass_style} bass" if bass_style == "none" else ", deep bass"
guitar = f", {guitar_style} guitar" if guitar_style == "none" else ""
prompt = (
f"Instrumental deep house by Larry Heard{synth}{bass}{drum}{guitar} at {bpm} BPM."
)
logger.debug(f"Generated Deep House prompt: {prompt}")
return prompt
except Exception as e:
logger.error(f"Failed to generate Deep House prompt: {e}")
logger.error(traceback.format_exc())
return ""
# Preset configurations with user-recommended settings
PRESETS = {
"default": {"cfg_scale": 5.8, "top_k": 18, "top_p": 0.88, "temperature": 0.15},
"rock": {"cfg_scale": 5.8, "top_k": 18, "top_p": 0.88, "temperature": 0.15},
"techno": {"cfg_scale": 5.8, "top_k": 18, "top_p": 0.88, "temperature": 0.15},
"grunge": {"cfg_scale": 5.8, "top_k": 18, "top_p": 0.88, "temperature": 0.15},
"indie": {"cfg_scale": 5.8, "top_k": 18, "top_p": 0.88, "temperature": 0.15},
"funk_rock": {"cfg_scale": 5.8, "top_k": 18, "top_p": 0.88, "temperature": 0.15}
}
# Function to get the latest log file
def get_latest_log():
try:
log_files = sorted(Path(log_dir).glob("musicgen_log_*.log"), key=os.path.getmtime, reverse=True)
if not log_files:
logger.warning("No log files found")
return "No log files found."
with open(log_files[0], "r") as f:
content = f.read()
logger.info(f"Retrieved latest log file: {log_files[0]}")
return content
except Exception as e:
logger.error(f"Failed to read log file: {e}")
logger.error(traceback.format_exc())
return f"Error reading log file: {e}"
# Bitrate selection functions with visual feedback
def set_bitrate_128():
logger.info("Bitrate set to 128 kbps")
return "128k"
def set_bitrate_192():
logger.info("Bitrate set to 192 kbps")
return "192k"
def set_bitrate_320():
logger.info("Bitrate set to 320 kbps")
return "320k"
# Sampling rate selection functions with visual feedback
def set_sample_rate_22050():
logger.info("Output sampling rate set to 22.05 kHz")
return "22050"
def set_sample_rate_44100():
logger.info("Output sampling rate set to 44.1 kHz")
return "44100"
def set_sample_rate_48000():
logger.info("Output sampling rate set to 48 kHz")
return "48000"
# Bit depth selection functions with visual feedback
def set_bit_depth_16():
logger.info("Bit depth set to 16-bit")
return "16"
def set_bit_depth_24():
logger.info("Bit depth set to 24-bit")
return "24"
# Wrapper for generate_music with post-generation cleanup
def generate_music_wrapper(*args):
try:
result = generate_music(*args)
return result
finally:
clean_memory()
# Optimized generation function with chunk-based prompt variation
def generate_music(instrumental_prompt: str, cfg_scale: float, top_k: int, top_p: float, temperature: float, total_duration: int, bpm: int, drum_beat: str, synthesizer: str, rhythmic_steps: str, bass_style: str, guitar_style: str, target_volume: float, preset: str, max_steps: str, vram_status: str, bitrate: str, output_sample_rate: str, bit_depth: str):
global musicgen_model
if not instrumental_prompt.strip():
logger.warning("Empty instrumental prompt provided")
return None, "β οΈ Please enter a valid instrumental prompt!", vram_status
try:
logger.info("Starting music generation...")
start_time = time.time()
clean_memory()
try:
max_steps_int = int(max_steps)
except ValueError:
logger.error(f"Invalid max_steps value: {max_steps}")
return None, "β Invalid max_steps value; must be a number (1000, 1200, 1300, or 1500)", vram_status
try:
output_sample_rate_int = int(output_sample_rate)
except ValueError:
logger.error(f"Invalid output_sample_rate value: {output_sample_rate}")
return None, "β Invalid output sampling rate; must be a number (22050, 32000, 44100, or 48000)", vram_status
try:
bit_depth_int = int(bit_depth)
sample_width = 3 if bit_depth_int == 24 else 2
except ValueError:
logger.error(f"Invalid bit_depth value: {bit_depth}")
return None, "β Invalid bit depth; must be 16 or 24", vram_status
max_duration = min(max_steps_int / 50, 30)
total_duration = min(max(total_duration, 30), 120)
processing_sample_rate = 48000 # Updated to user-recommended value
channels = 2
audio_segments = []
overlap_duration = 0.2
remaining_duration = total_duration
if preset != "default":
preset_params = PRESETS.get(preset, PRESETS["default"])
cfg_scale = preset_params["cfg_scale"]
top_k = preset_params["top_k"]
top_p = preset_params["top_p"]
temperature = preset_params["temperature"]
logger.info(f"Applied preset {preset}: cfg_scale={cfg_scale}, top_k={top_k}, top_p={top_p}, temperature={temperature}")
if not check_disk_space():
logger.error("Insufficient disk space")
return None, "β οΈ Insufficient disk space. Free up at least 1 GB.", vram_status
seed = random.randint(0, 10000)
logger.info(f"Generating audio for {total_duration}s with seed={seed}, max_steps={max_steps_int}, output_sample_rate={output_sample_rate_int} Hz, bit_depth={bit_depth_int}-bit")
vram_status = f"Initial VRAM: {torch.cuda.memory_allocated() / 1024**2:.2f} MB"
chunk_num = 0
while remaining_duration > 0:
current_duration = min(max_duration, remaining_duration)
generation_duration = current_duration
chunk_num += 1
logger.info(f"Generating chunk {chunk_num} ({current_duration}s, VRAM: {torch.cuda.memory_allocated() / 1024**2:.2f} MB)")
# Generate chunk-specific prompt for Red Hot Chili Peppers
if "Red Hot Chili Peppers" in instrumental_prompt:
chunk_prompt = set_red_hot_chili_peppers_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style, chunk_num)
else:
# For other prompts, use the base prompt without variation (as a fallback)
chunk_prompt = instrumental_prompt
musicgen_model.set_generation_params(
duration=generation_duration,
use_sampling=True,
top_k=top_k,
top_p=top_p,
temperature=temperature,
cfg_coef=cfg_scale
)
try:
with torch.no_grad():
with autocast(dtype=torch.float16):
torch.manual_seed(seed)
np.random.seed(seed)
torch.cuda.manual_seed_all(seed)
clean_memory()
if not audio_segments:
logger.debug("Generating first chunk")
audio_segment = musicgen_model.generate([chunk_prompt], progress=True)[0].cpu()
else:
logger.debug("Generating continuation chunk")
prev_segment = audio_segments[-1]
prev_segment = apply_noise_gate(prev_segment, threshold_db=-80, sample_rate=processing_sample_rate)
prev_segment = balance_stereo(prev_segment, noise_threshold=-40, sample_rate=processing_sample_rate)
temp_wav_path = f"temp_prev_{int(time.time()*1000)}.wav"
try:
logger.debug(f"Exporting previous segment to {temp_wav_path}")
prev_segment.export(temp_wav_path, format="wav")
with open(temp_wav_path, "rb") as f:
mmapped_file = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
prev_audio, prev_sr = torchaudio.load(temp_wav_path)
mmapped_file.close()
if prev_sr != processing_sample_rate:
logger.debug(f"Resampling from {prev_sr} to {processing_sample_rate}")
prev_audio = torchaudio.functional.resample(prev_audio, prev_sr, processing_sample_rate, lowpass_filter_width=64)
if prev_audio.shape[0] != 2:
logger.debug(f"Converting to stereo: {prev_audio.shape[0]} channels detected")
prev_audio = prev_audio.repeat(2, 1)[:, :prev_audio.shape[1]]
prev_audio = prev_audio.to(device)
audio_segment = musicgen_model.generate_continuation(
prompt=prev_audio[:, -int(processing_sample_rate * overlap_duration):],
prompt_sample_rate=processing_sample_rate,
descriptions=[chunk_prompt],
progress=True
)[0].cpu()
del prev_audio
finally:
try:
os.remove(temp_wav_path)
logger.debug(f"Deleted temporary file {temp_wav_path}")
except OSError:
logger.warning(f"Failed to delete temporary file {temp_wav_path}")
clean_memory()
except Exception as e:
logger.error(f"Error in chunk {chunk_num} generation: {e}")
logger.error(traceback.format_exc())
return None, f"β Failed to generate chunk {chunk_num}: {e}", vram_status
logger.debug(f"Generated audio segment shape: {audio_segment.shape}, dtype: {audio_segment.dtype}")
try:
# Ensure the model's output is resampled to processing_sample_rate
if audio_segment.shape[0] != 2:
logger.debug(f"Converting to stereo: {audio_segment.shape[0]} channels detected")
audio_segment = audio_segment.repeat(2, 1)[:, :audio_segment.shape[1]]
# Convert to float32 before resampling to avoid "slow_conv2d_cpu" error
audio_segment = audio_segment.to(dtype=torch.float32)
audio_segment = torchaudio.functional.resample(audio_segment, 32000, processing_sample_rate, lowpass_filter_width=64)
audio_np = audio_segment.numpy()
if audio_np.ndim == 1:
logger.debug("Converting mono to stereo on CPU")
audio_np = np.stack([audio_np, audio_np], axis=0)
if audio_np.shape[0] != 2:
logger.error(f"Expected stereo audio with shape (2, samples), got shape {audio_np.shape}")
return None, f"β Invalid audio shape for chunk {chunk_num}: {audio_np.shape}", vram_status
audio_segment = torch.from_numpy(audio_np).to(dtype=torch.float16)
logger.debug(f"Converted audio segment to float16, shape: {audio_segment.shape}")
except Exception as e:
logger.error(f"Failed to process audio segment for chunk {chunk_num}: {e}")
logger.error(traceback.format_exc())
return None, f"β Failed to process audio for chunk {chunk_num}: {e}", vram_status
temp_wav_path = f"temp_audio_{int(time.time()*1000)}.wav"
logger.debug(f"Saving audio segment to {temp_wav_path}, VRAM: {torch.cuda.memory_allocated() / 1024**2:.2f} MB")
try:
audio_segment_save = audio_segment.to(dtype=torch.float32)
torchaudio.save(temp_wav_path, audio_segment_save, processing_sample_rate, bits_per_sample=bit_depth_int)
del audio_segment_save
except Exception as e:
logger.error(f"Failed to save audio segment for chunk {chunk_num}: {e}")
logger.error(traceback.format_exc())
logger.warning(f"Skipping chunk {chunk_num} due to save error")
del audio_segment
clean_memory()
continue
clean_memory()
try:
with open(temp_wav_path, "rb") as f:
mmapped_file = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
segment = AudioSegment.from_wav(temp_wav_path)
mmapped_file.close()
except Exception as e:
logger.error(f"Failed to load WAV file for chunk {chunk_num}: {e}")
logger.error(traceback.format_exc())
logger.warning(f"Skipping chunk {chunk_num} due to WAV load error")
del audio_segment
clean_memory()
continue
finally:
try:
os.remove(temp_wav_path)
logger.debug(f"Deleted temporary file {temp_wav_path}")
except OSError:
logger.warning(f"Failed to delete temporary file {temp_wav_path}")
try:
segment = ensure_stereo(segment, processing_sample_rate, sample_width)
segment = segment - 15
if segment.frame_rate != processing_sample_rate:
logger.debug(f"Setting segment sample rate to {processing_sample_rate}")
segment = segment.set_frame_rate(processing_sample_rate)
# Apply noise gate immediately after loading to catch high-pitched tones early
segment = apply_noise_gate(segment, threshold_db=-80, sample_rate=processing_sample_rate)
segment = balance_stereo(segment, noise_threshold=-40, sample_rate=processing_sample_rate)
segment = rms_normalize(segment, target_rms_db=target_volume, peak_limit_db=-3.0, sample_rate=processing_sample_rate)
segment = apply_eq(segment, sample_rate=processing_sample_rate)
audio_segments.append(segment)
except Exception as e:
logger.error(f"Failed to process audio segment for chunk {chunk_num}: {e}")
logger.error(traceback.format_exc())
logger.warning(f"Skipping chunk {chunk_num} due to processing error")
del audio_segment
clean_memory()
continue
del audio_segment
del audio_np
clean_memory()
vram_status = f"VRAM after chunk {chunk_num}: {torch.cuda.memory_allocated() / 1024**2:.2f} MB"
time.sleep(0.1)
remaining_duration -= current_duration
if not audio_segments:
logger.error("No audio segments generated")
return None, "β No audio segments generated due to errors", vram_status
logger.info("Combining audio chunks...")
try:
final_segment = audio_segments[0][:min(max_duration, total_duration) * 1000]
final_segment = ensure_stereo(final_segment, processing_sample_rate, sample_width)
overlap_ms = int(overlap_duration * 1000)
for i in range(1, len(audio_segments)):
current_segment = audio_segments[i]
current_segment = current_segment[:min(max_duration, total_duration - (i * max_duration)) * 1000]
current_segment = ensure_stereo(current_segment, processing_sample_rate, sample_width)
if overlap_ms > 0 and len(current_segment) > overlap_ms:
logger.debug(f"Applying crossfade between chunks {i} and {i+1}")
prev_overlap = final_segment[-overlap_ms:]
curr_overlap = current_segment[:overlap_ms]
prev_wav_path = f"temp_prev_overlap_{int(time.time()*1000)}.wav"
curr_wav_path = f"temp_curr_overlap_{int(time.time()*1000)}.wav"
try:
prev_overlap.export(prev_wav_path, format="wav")
curr_overlap.export(curr_wav_path, format="wav")
clean_memory()
prev_audio, _ = torchaudio.load(prev_wav_path)
curr_audio, _ = torchaudio.load(curr_wav_path)
num_samples = min(prev_audio.shape[1], curr_audio.shape[1])
num_samples = num_samples - (num_samples % 2)
if num_samples <= 0:
logger.warning(f"Skipping crossfade for chunk {i+1} due to insufficient samples")
final_segment += current_segment
continue
blended_samples = torch.zeros(2, num_samples, dtype=torch.float32)
prev_samples = prev_audio[:, :num_samples]
curr_samples = curr_audio[:, :num_samples]
hann_window = torch.hann_window(num_samples, periodic=False)
fade_out = hann_window.flip(0)
fade_in = hann_window
blended_samples = (prev_samples * fade_out + curr_samples * fade_in)
blended_samples = (blended_samples * (2**23 if sample_width == 3 else 32767)).to(torch.int32 if sample_width == 3 else torch.int16)
temp_crossfade_path = f"temp_crossfade_{int(time.time()*1000)}.wav"
torchaudio.save(temp_crossfade_path, blended_samples, processing_sample_rate, bits_per_sample=bit_depth_int)
blended_segment = AudioSegment.from_wav(temp_crossfade_path)
blended_segment = ensure_stereo(blended_segment, processing_sample_rate, sample_width)
blended_segment = rms_normalize(blended_segment, target_rms_db=target_volume, peak_limit_db=-3.0, sample_rate=processing_sample_rate)
final_segment = final_segment[:-overlap_ms] + blended_segment + current_segment[overlap_ms:]
finally:
for temp_path in [prev_wav_path, curr_wav_path, temp_crossfade_path]:
try:
if os.path.exists(temp_path):
os.remove(temp_path)
logger.debug(f"Deleted temporary file {temp_path}")
except OSError:
logger.warning(f"Failed to delete temporary file {temp_path}")
else:
logger.debug(f"Concatenating chunk {i+1} without crossfade")
final_segment += current_segment
final_segment = final_segment[:total_duration * 1000]
logger.info("Post-processing final track...")
final_segment = apply_noise_gate(final_segment, threshold_db=-80, sample_rate=processing_sample_rate)
final_segment = balance_stereo(final_segment, noise_threshold=-40, sample_rate=processing_sample_rate)
final_segment = rms_normalize(final_segment, target_rms_db=target_volume, peak_limit_db=-3.0, sample_rate=processing_sample_rate)
final_segment = apply_eq(final_segment, sample_rate=processing_sample_rate)
final_segment = apply_fade(final_segment)
final_segment = final_segment - 10
final_segment = final_segment.set_frame_rate(output_sample_rate_int)
mp3_path = f"output_adjusted_volume_{int(time.time())}.mp3"
logger.info("β οΈ WARNING: Audio is set to safe levels (~ -23 dBFS RMS, -3 dBFS peak). Start playback at LOW volume (10-20%) and adjust gradually.")
logger.info("VERIFY: Open the file in Audacity to check for high-pitched tones and quality. RMS should be ~ -23 dBFS, peaks β€ -3 dBFS. Report any issues.")
try:
clean_memory()
logger.debug(f"Exporting final audio to {mp3_path} with bitrate {bitrate}, sample rate {output_sample_rate_int} Hz, bit depth {bit_depth_int}-bit")
final_segment.export(
mp3_path,
format="mp3",
bitrate=bitrate,
tags={"title": "GhostAI Instrumental", "artist": "GhostAI"}
)
logger.info(f"Final audio saved to {mp3_path}")
except Exception as e:
logger.error(f"Error exporting MP3 with bitrate {bitrate}: {e}")
logger.error(traceback.format_exc())
fallback_path = f"fallback_output_{int(time.time())}.mp3"
try:
final_segment.export(fallback_path, format="mp3", bitrate="128k")
logger.info(f"Final audio saved to fallback: {fallback_path} with 128 kbps")
mp3_path = fallback_path
except Exception as fallback_e:
logger.error(f"Failed to save fallback MP3: {fallback_e}")
return None, f"β Failed to export audio: {fallback_e}", vram_status
vram_status = f"Final VRAM: {torch.cuda.memory_allocated() / 1024**2:.2f} MB"
logger.info(f"Generation completed in {time.time() - start_time:.2f} seconds")
return mp3_path, "β
Done! Generated track with adjusted volume levels. Check for quality in Audacity.", vram_status
except Exception as e:
logger.error(f"Failed to combine audio chunks: {e}")
logger.error(traceback.format_exc())
return None, f"β Failed to combine audio: {e}", vram_status
except Exception as e:
logger.error(f"Generation failed: {e}")
logger.error(traceback.format_exc())
return None, f"β Generation failed: {e}", vram_status
finally:
clean_memory()
# Clear inputs function
def clear_inputs():
logger.info("Clearing input fields")
return "", 5.8, 18, 0.88, 0.15, 30, 120, "none", "none", "none", "none", "none", -23.0, "default", 1300, "128k", "44100", "16"
# Custom CSS with high-contrast colors and green border on active selection
css = """
body {
background: #121212;
color: #E6E6E6;
font-family: 'Arial', sans-serif;
}
.header-container {
text-align: center;
padding: 15px 20px;
background: #1E1E1E;
border-bottom: 2px solid #00C853;
}
#ghost-logo {
font-size: 48px;
color: #00C853;
}
h1 {
color: #FFD600;
font-size: 28px;
font-weight: bold;
}
h3 {
color: #FFD600;
font-size: 20px;
font-weight: bold;
}
p {
color: #B0BEC5;
font-size: 14px;
}
.input-container, .settings-container, .output-container, .logs-container {
max-width: 1200px;
margin: 20px auto;
padding: 20px;
background: #212121;
border: 1px solid #424242;
border-radius: 8px;
}
.textbox {
background: #2C2C2C;
border: 1px solid #B0BEC5;
color: #E6E6E6;
font-size: 16px;
}
.genre-buttons, .bitrate-buttons, .sample-rate-buttons, .bit-depth-buttons {
display: flex;
justify-content: center;
flex-wrap: wrap;
gap: 10px;
}
.genre-btn, .bitrate-btn, .sample-rate-btn, .bit-depth-btn, button {
background: #0288D1;
border: 2px solid transparent;
color: #FFFFFF;
padding: 10px 20px;
border-radius: 5px;
font-size: 16px;
transition: all 0.3s ease;
}
button:hover {
background: #03A9F4;
cursor: pointer;
}
button:active, .genre-btn.active, .bitrate-btn.active, .sample-rate-btn.active, .bit-depth-btn.active {
border: 2px solid #00C853 !important;
background: #01579B;
color: #FFFFFF;
}
.gradio-container {
padding: 20px;
}
.group-container {
margin-bottom: 20px;
padding: 15px;
border: 1px solid #424242;
border-radius: 8px;
}
.slider-label, .dropdown-label {
color: #FFD600;
font-size: 16px;
font-weight: bold;
}
.slider, .dropdown {
background: #2C2C2C;
color: #E6E6E6;
}
.output-container label, .logs-container label {
color: #FFD600;
font-size: 16px;
font-weight: bold;
}
"""
# Build Gradio interface with updated visuals and default preset
logger.info("Building Gradio interface...")
with gr.Blocks(css=css) as demo:
gr.Markdown("""
<div class="header-container">
<div id="ghost-logo">π»</div>
<h1>GhostAI Music Generator πΉ</h1>
<p>Create Instrumental Tracks with Ease</p>
</div>
""")
with gr.Column(elem_classes="input-container"):
gr.Markdown("### πΈ Prompt Settings")
instrumental_prompt = gr.Textbox(
label="Instrumental Prompt βοΈ",
placeholder="Click a genre button or type your own instrumental prompt",
lines=4,
elem_classes="textbox"
)
with gr.Row(elem_classes="genre-buttons"):
rhcp_btn = gr.Button("Red Hot Chili Peppers πΆοΈ", elem_classes="genre-btn")
nirvana_btn = gr.Button("Nirvana Grunge πΈ", elem_classes="genre-btn")
pearl_jam_btn = gr.Button("Pearl Jam Grunge π¦ͺ", elem_classes="genre-btn")
soundgarden_btn = gr.Button("Soundgarden Grunge π", elem_classes="genre-btn")
foo_fighters_btn = gr.Button("Foo Fighters π€", elem_classes="genre-btn")
smashing_pumpkins_btn = gr.Button("Smashing Pumpkins π", elem_classes="genre-btn")
radiohead_btn = gr.Button("Radiohead π§ ", elem_classes="genre-btn")
classic_rock_btn = gr.Button("Metallica Heavy Metal πΈ", elem_classes="genre-btn")
alternative_rock_btn = gr.Button("Alternative Rock π΅", elem_classes="genre-btn")
post_punk_btn = gr.Button("Post-Punk π€", elem_classes="genre-btn")
indie_rock_btn = gr.Button("Indie Rock π€", elem_classes="genre-btn")
funk_rock_btn = gr.Button("Funk Rock πΊ", elem_classes="genre-btn")
detroit_techno_btn = gr.Button("Detroit Techno ποΈ", elem_classes="genre-btn")
deep_house_btn = gr.Button("Deep House π ", elem_classes="genre-btn")
with gr.Column(elem_classes="settings-container"):
gr.Markdown("### βοΈ API Settings")
with gr.Group(elem_classes="group-container"):
cfg_scale = gr.Slider(
label="CFG Scale π―",
minimum=1.0,
maximum=10.0,
value=5.8,
step=0.1,
info="Controls how closely the music follows the prompt."
)
top_k = gr.Slider(
label="Top-K Sampling π’",
minimum=10,
maximum=500,
value=18,
step=10,
info="Limits sampling to the top k most likely tokens."
)
top_p = gr.Slider(
label="Top-P Sampling π°",
minimum=0.0,
maximum=1.0,
value=0.88,
step=0.05,
info="Keeps tokens with cumulative probability above p."
)
temperature = gr.Slider(
label="Temperature π₯",
minimum=0.1,
maximum=2.0,
value=0.15,
step=0.1,
info="Controls randomness; lower values reduce noise."
)
total_duration = gr.Dropdown(
label="Song Length β³ (seconds)",
choices=[30, 60, 90, 120],
value=30,
info="Select the total duration of the track."
)
bpm = gr.Slider(
label="Tempo π΅ (BPM)",
minimum=60,
maximum=180,
value=120,
step=1,
info="Beats per minute to set the track's tempo."
)
drum_beat = gr.Dropdown(
label="Drum Beat π₯",
choices=["none", "standard rock", "funk groove", "techno kick", "jazz swing"],
value="none",
info="Select a drum beat style to influence the rhythm."
)
synthesizer = gr.Dropdown(
label="Synthesizer πΉ",
choices=["none", "analog synth", "digital pad", "arpeggiated synth"],
value="none",
info="Select a synthesizer style for electronic accents."
)
rhythmic_steps = gr.Dropdown(
label="Rhythmic Steps π£",
choices=["none", "syncopated steps", "steady steps", "complex steps"],
value="none",
info="Select a rhythmic step style to enhance the beat."
)
bass_style = gr.Dropdown(
label="Bass Style πΈ",
choices=["none", "slap bass", "deep bass", "melodic bass"],
value="none",
info="Select a bass style to shape the low end."
)
guitar_style = gr.Dropdown(
label="Guitar Style πΈ",
choices=["none", "distorted", "clean", "jangle"],
value="none",
info="Select a guitar style to define the riffs."
)
target_volume = gr.Slider(
label="Target Volume ποΈ (dBFS RMS)",
minimum=-30.0,
maximum=-20.0,
value=-23.0,
step=1.0,
info="Adjust output loudness (-23 dBFS is standard, -20 dBFS is louder, -30 dBFS is quieter)."
)
preset = gr.Dropdown(
label="Preset Configuration ποΈ",
choices=["default", "rock", "techno", "grunge", "indie", "funk_rock"],
value="default",
info="Select a preset optimized for specific genres."
)
max_steps = gr.Dropdown(
label="Max Steps per Chunk π",
choices=[1000, 1200, 1300, 1500],
value=1300,
info="Number of generation steps per chunk (1300=~26s, extended to 30s)."
)
bitrate_state = gr.State(value="128k")
sample_rate_state = gr.State(value="44100")
bit_depth_state = gr.State(value="16")
with gr.Row(elem_classes="bitrate-buttons"):
bitrate_128_btn = gr.Button("Set Bitrate to 128 kbps", elem_classes="bitrate-btn")
bitrate_192_btn = gr.Button("Set Bitrate to 192 kbps", elem_classes="bitrate-btn")
bitrate_320_btn = gr.Button("Set Bitrate to 320 kbps", elem_classes="bitrate-btn")
with gr.Row(elem_classes="sample-rate-buttons"):
sample_rate_22050_btn = gr.Button("Set Sampling Rate to 22.05 kHz", elem_classes="sample-rate-btn")
sample_rate_44100_btn = gr.Button("Set Sampling Rate to 44.1 kHz", elem_classes="sample-rate-btn")
sample_rate_48000_btn = gr.Button("Set Sampling Rate to 48 kHz", elem_classes="sample-rate-btn")
with gr.Row(elem_classes="bit-depth-buttons"):
bit_depth_16_btn = gr.Button("Set Bit Depth to 16-bit", elem_classes="bit-depth-btn")
bit_depth_24_btn = gr.Button("Set Bit Depth to 24-bit", elem_classes="bit-depth-btn")
with gr.Row(elem_classes="action-buttons"):
gen_btn = gr.Button("Generate Music π")
clr_btn = gr.Button("Clear Inputs π§Ή")
with gr.Column(elem_classes="output-container"):
gr.Markdown("### π§ Output")
out_audio = gr.Audio(label="Generated Instrumental Track π΅", type="filepath")
status = gr.Textbox(label="Status π’", interactive=False)
vram_status = gr.Textbox(label="VRAM Usage π", interactive=False, value="")
with gr.Column(elem_classes="logs-container"):
gr.Markdown("### π Logs")
log_output = gr.Textbox(label="Last Log File Contents", lines=20, interactive=False)
log_btn = gr.Button("View Last Log π")
# Add JavaScript to handle button selection visuals
def update_button_styles(selected_button):
buttons = [
"rhcp_btn", "nirvana_btn", "pearl_jam_btn", "soundgarden_btn", "foo_fighters_btn",
"smashing_pumpkins_btn", "radiohead_btn", "classic_rock_btn", "alternative_rock_btn",
"post_punk_btn", "indie_rock_btn", "funk_rock_btn", "detroit_techno_btn", "deep_house_btn",
"bitrate_128_btn", "bitrate_192_btn", "bitrate_320_btn",
"sample_rate_22050_btn", "sample_rate_44100_btn", "sample_rate_48000_btn",
"bit_depth_16_btn", "bit_depth_24_btn"
]
script = """
<script>
document.querySelectorAll('.genre-btn, .bitrate-btn, .sample-rate-btn, .bit-depth-btn').forEach(btn => {
btn.classList.remove('active');
});
document.querySelector('#""" + selected_button + """').classList.add('active');
</script>
"""
return script
rhcp_btn.click(set_red_hot_chili_peppers_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style, gr.State(value=1)], outputs=instrumental_prompt, _js=update_button_styles("rhcp_btn"))
nirvana_btn.click(set_nirvana_grunge_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt, _js=update_button_styles("nirvana_btn"))
pearl_jam_btn.click(set_pearl_jam_grunge_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt, _js=update_button_styles("pearl_jam_btn"))
soundgarden_btn.click(set_soundgarden_grunge_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt, _js=update_button_styles("soundgarden_btn"))
foo_fighters_btn.click(set_foo_fighters_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt, _js=update_button_styles("foo_fighters_btn"))
smashing_pumpkins_btn.click(set_smashing_pumpkins_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt, _js=update_button_styles("smashing_pumpkins_btn"))
radiohead_btn.click(set_radiohead_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt, _js=update_button_styles("radiohead_btn"))
classic_rock_btn.click(set_classic_rock_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt, _js=update_button_styles("classic_rock_btn"))
alternative_rock_btn.click(set_alternative_rock_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt, _js=update_button_styles("alternative_rock_btn"))
post_punk_btn.click(set_post_punk_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt, _js=update_button_styles("post_punk_btn"))
indie_rock_btn.click(set_indie_rock_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt, _js=update_button_styles("indie_rock_btn"))
funk_rock_btn.click(set_funk_rock_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt, _js=update_button_styles("funk_rock_btn"))
detroit_techno_btn.click(set_detroit_techno_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt, _js=update_button_styles("detroit_techno_btn"))
deep_house_btn.click(set_deep_house_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt, _js=update_button_styles("deep_house_btn"))
bitrate_128_btn.click(set_bitrate_128, inputs=None, outputs=bitrate_state, _js=update_button_styles("bitrate_128_btn"))
bitrate_192_btn.click(set_bitrate_192, inputs=None, outputs=bitrate_state, _js=update_button_styles("bitrate_192_btn"))
bitrate_320_btn.click(set_bitrate_320, inputs=None, outputs=bitrate_state, _js=update_button_styles("bitrate_320_btn"))
sample_rate_22050_btn.click(set_sample_rate_22050, inputs=None, outputs=sample_rate_state, _js=update_button_styles("sample_rate_22050_btn"))
sample_rate_44100_btn.click(set_sample_rate_44100, inputs=None, outputs=sample_rate_state, _js=update_button_styles("sample_rate_44100_btn"))
sample_rate_48000_btn.click(set_sample_rate_48000, inputs=None, outputs=sample_rate_state, _js=update_button_styles("sample_rate_48000_btn"))
bit_depth_16_btn.click(set_bit_depth_16, inputs=None, outputs=bit_depth_state, _js=update_button_styles("bit_depth_16_btn"))
bit_depth_24_btn.click(set_bit_depth_24, inputs=None, outputs=bit_depth_state, _js=update_button_styles("bit_depth_24_btn"))
gen_btn.click(
generate_music_wrapper,
inputs=[instrumental_prompt, cfg_scale, top_k, top_p, temperature, total_duration, bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style, target_volume, preset, max_steps, vram_status, bitrate_state, sample_rate_state, bit_depth_state],
outputs=[out_audio, status, vram_status]
)
clr_btn.click(
clear_inputs,
inputs=None,
outputs=[instrumental_prompt, cfg_scale, top_k, top_p, temperature, total_duration, bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style, target_volume, preset, max_steps, bitrate_state, sample_rate_state, bit_depth_state]
)
log_btn.click(
get_latest_log,
inputs=None,
outputs=log_output
)
# Launch locally without OpenAPI/docs
logger.info("Launching Gradio UI at http://localhost:9999...")
try:
app = demo.launch(
server_name="0.0.0.0",
server_port=9999,
share=False,
inbrowser=False,
show_error=True
)
except Exception as e:
logger.error(f"Failed to launch Gradio UI: {e}")
logger.error(traceback.format_exc())
sys.exit(1) |