Spaces:
Runtime error
Runtime error
File size: 20,529 Bytes
df51520 53bed0d df51520 53bed0d df51520 53bed0d 9223243 53bed0d df51520 9223243 df51520 |
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 |
# Agung Wijaya - WebUI 2023 - Gradio
# file app.py
# Import
import os
import psutil
import shutil
import numpy as np
import gradio as gr
import subprocess
from pathlib import Path
import ffmpeg
import json
import re
import time
import random
import torch
import librosa
import util
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw, ImageFont
from moviepy.editor import *
from moviepy.video.io.VideoFileClip import VideoFileClip
from config import device
from infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono
)
from vc_infer_pipeline import VC
from typing import Union
from os import path, getenv
from datetime import datetime
from scipy.io.wavfile import write
from pydub import AudioSegment
title_markdown = ("""
<h1 align="center"><img src="https://upload.wikimedia.org/wikipedia/zh/d/d0/Kite_album.jpg", alt="syz" border="0" style="margin: 0 auto; height: 300px;" /> </h1>
""")
title_markdown2 = ("""
<h1 align="center"><img src="https://e.snmc.io/i/600/w/28577c025a5c82728a1b957ee373bc43/9622540/%E8%AE%B8%E5%B5%A9-xu-song-%E8%87%AA%E5%AE%9A%E7%BE%A9-cover-art.jpg", alt="vae" border="0" style="margin: 0 auto; height: 300px;" /> </h1>
""")
# Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21 # noqa
in_hf_space = getenv('SYSTEM') == 'spaces'
# Set High Quality (.wav) or not (.mp3)
high_quality = True
# Read config.json
config_json = json.loads(open("config.json").read())
# Load hubert model
hubert_model = util.load_hubert_model(device, 'hubert_base.pt')
hubert_model.eval()
# Load models
loaded_models = []
for model_name in config_json.get('models'):
print(f'Loading model: {model_name}')
# Load model info
model_info = json.load(
open(path.join('model', model_name, 'config.json'), 'r')
)
# Load RVC checkpoint
cpt = torch.load(
path.join('model', model_name, model_info['model']),
map_location='cpu'
)
tgt_sr = cpt['config'][-1]
cpt['config'][-3] = cpt['weight']['emb_g.weight'].shape[0] # n_spk
if_f0 = cpt.get('f0', 1)
net_g: Union[SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono]
if if_f0 == 1:
net_g = SynthesizerTrnMs256NSFsid(
*cpt['config'],
is_half=util.is_half(device)
)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt['config'])
del net_g.enc_q
# According to original code, this thing seems necessary.
print(net_g.load_state_dict(cpt['weight'], strict=False))
net_g.eval().to(device)
net_g = net_g.half() if util.is_half(device) else net_g.float()
vc = VC(tgt_sr, device, util.is_half(device))
loaded_models.append(dict(
name=model_name,
metadata=model_info,
vc=vc,
net_g=net_g,
if_f0=if_f0,
target_sr=tgt_sr
))
print(f'Models loaded: {len(loaded_models)}')
def make_bars_image(height_values, index, new_height):
# Define the size of the image
width = 512
height = new_height
# Create a new image with a transparent background
image = Image.new('RGBA', (width, height), color=(0, 0, 0, 0))
# Get the image drawing context
draw = ImageDraw.Draw(image)
# Define the rectangle width and spacing
rect_width = 2
spacing = 2
# Define the list of height values for the rectangles
#height_values = [20, 40, 60, 80, 100, 80, 60, 40]
num_bars = len(height_values)
# Calculate the total width of the rectangles and the spacing
total_width = num_bars * rect_width + (num_bars - 1) * spacing
# Calculate the starting position for the first rectangle
start_x = int((width - total_width) / 2)
# Define the buffer size
buffer_size = 80
# Draw the rectangles from left to right
x = start_x
for i, height in enumerate(height_values):
# Define the rectangle coordinates
y0 = buffer_size
y1 = height + buffer_size
x0 = x
x1 = x + rect_width
# Draw the rectangle
draw.rectangle([x0, y0, x1, y1], fill='white')
# Move to the next rectangle position
if i < num_bars - 1:
x += rect_width + spacing
# Rotate the image by 180 degrees
image = image.rotate(180)
# Mirror the image
image = image.transpose(Image.FLIP_LEFT_RIGHT)
# Save the image
image.save('audio_bars_'+ str(index) + '.png')
return 'audio_bars_'+ str(index) + '.png'
def db_to_height(db_value):
# Scale the dB value to a range between 0 and 1
scaled_value = (db_value + 80) / 80
# Convert the scaled value to a height between 0 and 100
height = scaled_value * 50
return height
def infer(title, audio_in, image_in):
# Load the audio file
audio_path = audio_in
audio_data, sr = librosa.load(audio_path)
# Get the duration in seconds
duration = librosa.get_duration(y=audio_data, sr=sr)
# Extract the audio data for the desired time
start_time = 0 # start time in seconds
end_time = duration # end time in seconds
start_index = int(start_time * sr)
end_index = int(end_time * sr)
audio_data = audio_data[start_index:end_index]
# Compute the short-time Fourier transform
hop_length = 512
stft = librosa.stft(audio_data, hop_length=hop_length)
spectrogram = librosa.amplitude_to_db(np.abs(stft), ref=np.max)
# Get the frequency values
freqs = librosa.fft_frequencies(sr=sr, n_fft=stft.shape[0])
# Select the indices of the frequency values that correspond to the desired frequencies
n_freqs = 114
freq_indices = np.linspace(0, len(freqs) - 1, n_freqs, dtype=int)
# Extract the dB values for the desired frequencies
db_values = []
for i in range(spectrogram.shape[1]):
db_values.append(list(zip(freqs[freq_indices], spectrogram[freq_indices, i])))
# Print the dB values for the first time frame
print(db_values[0])
proportional_values = []
for frame in db_values:
proportional_frame = [db_to_height(db) for f, db in frame]
proportional_values.append(proportional_frame)
print(proportional_values[0])
print("AUDIO CHUNK: " + str(len(proportional_values)))
# Open the background image
background_image = Image.open(image_in)
# Resize the image while keeping its aspect ratio
bg_width, bg_height = background_image.size
aspect_ratio = bg_width / bg_height
new_width = 512
new_height = int(new_width / aspect_ratio)
resized_bg = background_image.resize((new_width, new_height))
# Apply black cache for better visibility of the white text
bg_cache = Image.open('black_cache.png')
resized_bg.paste(bg_cache, (0, resized_bg.height - bg_cache.height), mask=bg_cache)
# Create a new ImageDraw object
draw = ImageDraw.Draw(resized_bg)
# Define the text to be added
text = title
font = ImageFont.truetype("Lato-Regular.ttf", 16)
text_color = (255, 255, 255) # white color
# Calculate the position of the text
text_width, text_height = draw.textsize(text, font=font)
x = 30
y = new_height - 70
# Draw the text on the image
draw.text((x, y), text, fill=text_color, font=font)
# Save the resized image
resized_bg.save('resized_background.jpg')
generated_frames = []
for i, frame in enumerate(proportional_values):
bars_img = make_bars_image(frame, i, new_height)
bars_img = Image.open(bars_img)
# Paste the audio bars image on top of the background image
fresh_bg = Image.open('resized_background.jpg')
fresh_bg.paste(bars_img, (0, 0), mask=bars_img)
# Save the image
fresh_bg.save('audio_bars_with_bg' + str(i) + '.jpg')
generated_frames.append('audio_bars_with_bg' + str(i) + '.jpg')
print(generated_frames)
# Create a video clip from the images
clip = ImageSequenceClip(generated_frames, fps=len(generated_frames)/(end_time-start_time))
audio_clip = AudioFileClip(audio_in)
clip = clip.set_audio(audio_clip)
# Set the output codec
codec = 'libx264'
audio_codec = 'aac'
# Save the video to a file
clip.write_videofile("my_video.mp4", codec=codec, audio_codec=audio_codec)
retimed_clip = VideoFileClip("my_video.mp4")
# Set the desired frame rate
new_fps = 25
# Create a new clip with the new frame rate
new_clip = retimed_clip.set_fps(new_fps)
# Save the new clip as a new video file
new_clip.write_videofile("my_video_retimed.mp4", codec=codec, audio_codec=audio_codec)
return "my_video_retimed.mp4"
# Command line test
def command_line_test():
command = "df -h /home/user/app"
process = subprocess.run(command.split(), stdout=subprocess.PIPE)
result = process.stdout.decode()
return gr.HTML(value=result)
def mix(audio1, audio2):
sound1 = AudioSegment.from_file(audio1)
sound2 = AudioSegment.from_file(audio2)
length = len(sound1)
mixed = sound1[:length].overlay(sound2)
mixed.export("song.wav", format="wav")
return "song.wav"
# Function YouTube Downloader Audio
def youtube_downloader(
video_identifier,
start_time,
end_time,
output_filename="track.wav",
num_attempts=5,
url_base="",
quiet=False,
force=True,
):
output_path = Path(output_filename)
if output_path.exists():
if not force:
return output_path
else:
output_path.unlink()
quiet = "--quiet --no-warnings" if quiet else ""
command = f"""
yt-dlp {quiet} -x --audio-format wav -f bestaudio -o "{output_filename}" --download-sections "*{start_time}-{end_time}" "{url_base}{video_identifier}" # noqa: E501
""".strip()
attempts = 0
while True:
try:
_ = subprocess.check_output(command, shell=True, stderr=subprocess.STDOUT)
except subprocess.CalledProcessError:
attempts += 1
if attempts == num_attempts:
return None
else:
break
if output_path.exists():
return output_path
else:
return None
# Function Audio Separated
def audio_separated(audio_input, progress=gr.Progress()):
# start progress
progress(progress=0, desc="Starting...")
time.sleep(0.1)
# check file input
if audio_input is None:
# show progress
for i in progress.tqdm(range(100), desc="Please wait..."):
time.sleep(0.01)
return (None, None, 'Please input audio.')
# create filename
filename = str(random.randint(10000,99999))+datetime.now().strftime("%d%m%Y%H%M%S")
# progress
progress(progress=0.10, desc="Please wait...")
# make dir output
os.makedirs("output", exist_ok=True)
# progress
progress(progress=0.20, desc="Please wait...")
# write
if high_quality:
write(filename+".wav", audio_input[0], audio_input[1])
else:
write(filename+".mp3", audio_input[0], audio_input[1])
# progress
progress(progress=0.50, desc="Please wait...")
# demucs process
if high_quality:
command_demucs = "python3 -m demucs --two-stems=vocals -d cpu "+filename+".wav -o output"
else:
command_demucs = "python3 -m demucs --two-stems=vocals --mp3 --mp3-bitrate 128 -d cpu "+filename+".mp3 -o output"
os.system(command_demucs)
# progress
progress(progress=0.70, desc="Please wait...")
# remove file audio
if high_quality:
command_delete = "rm -v ./"+filename+".wav"
else:
command_delete = "rm -v ./"+filename+".mp3"
os.system(command_delete)
# progress
progress(progress=0.80, desc="Please wait...")
# progress
for i in progress.tqdm(range(80,100), desc="Please wait..."):
time.sleep(0.1)
if high_quality:
return "./output/htdemucs/"+filename+"/vocals.wav","./output/htdemucs/"+filename+"/no_vocals.wav","Successfully..."
else:
return "./output/htdemucs/"+filename+"/vocals.mp3","./output/htdemucs/"+filename+"/no_vocals.mp3","Successfully..."
# Function Voice Changer
def voice_changer(audio_input, model_index, pitch_adjust, f0_method, feat_ratio, progress=gr.Progress()):
# start progress
progress(progress=0, desc="Starting...")
time.sleep(1)
# check file input
if audio_input is None:
# progress
for i in progress.tqdm(range(100), desc="Please wait..."):
time.sleep(0.1)
return (None, 'Please input audio.')
# check model input
if model_index is None:
# progress
for i in progress.tqdm(range(100), desc="Please wait..."):
time.sleep(0.1)
return (None, 'Please select a model.')
model = loaded_models[model_index]
# Reference: so-vits
(audio_samp, audio_npy) = audio_input
# progress
progress(progress=0.10, desc="Please wait...")
# https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49
if (audio_npy.shape[0] / audio_samp) > 60 and in_hf_space:
# progress
for i in progress.tqdm(range(10,100), desc="Please wait..."):
time.sleep(0.1)
return (None, 'Input audio is longer than 60 secs.')
# Bloody hell: https://stackoverflow.com/questions/26921836/
if audio_npy.dtype != np.float32: # :thonk:
audio_npy = (
audio_npy / np.iinfo(audio_npy.dtype).max
).astype(np.float32)
# progress
progress(progress=0.30, desc="Please wait...")
if len(audio_npy.shape) > 1:
audio_npy = librosa.to_mono(audio_npy.transpose(1, 0))
# progress
progress(progress=0.40, desc="Please wait...")
if audio_samp != 16000:
audio_npy = librosa.resample(
audio_npy,
orig_sr=audio_samp,
target_sr=16000
)
# progress
progress(progress=0.50, desc="Please wait...")
pitch_int = int(pitch_adjust)
times = [0, 0, 0]
output_audio = model['vc'].pipeline(
hubert_model,
model['net_g'],
model['metadata'].get('speaker_id', 0),
audio_npy,
times,
pitch_int,
f0_method,
path.join('model', model['name'], model['metadata']['feat_index']),
path.join('model', model['name'], model['metadata']['feat_npy']),
feat_ratio,
model['if_f0']
)
# progress
progress(progress=0.80, desc="Please wait...")
print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s')
# progress
for i in progress.tqdm(range(80,100), desc="Please wait..."):
time.sleep(0.1)
return ((model['target_sr'], output_audio), 'Successfully...')
# Function Text to Voice
def text_to_voice(text_input, model_index):
# start progress
progress(progress=0, desc="Starting...")
time.sleep(1)
# check text input
if text_input is None:
# progress
for i in progress.tqdm(range(2,100), desc="Please wait..."):
time.sleep(0.1)
return (None, 'Please write text.')
# check model input
if model_index is None:
# progress
for i in progress.tqdm(range(2,100), desc="Please wait..."):
time.sleep(0.1)
return (None, 'Please select a model.')
# progress
for i in progress.tqdm(range(2,100), desc="Please wait..."):
time.sleep(0.1)
return None, "Sorry, you can't use it yet because this program is being developed!"
# Themes
theme = gr.themes.Base()
# CSS
css = "footer {visibility: hidden}"
# Blocks
with gr.Blocks(theme=theme, css=css) as App:
# Header
gr.HTML("<center>"
"<h1>🥳🎶🎡 - AI歌手,RVC歌声转换</h1>"
"</center>")
gr.Markdown("### <center>🦄 - 能够自动提取视频中的声音,并去除背景音;Powered by [RVC-Project](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)</center>")
gr.Markdown("### <center>更多精彩应用,敬请关注[滔滔AI](http://www.talktalkai.com);滔滔AI,为爱滔滔!💕</center>")
# Information
with gr.Accordion("🎙️您的AI歌手Stefanie:"):
gr.Markdown(title_markdown)
with gr.Accordion("🎤您的AI歌手Vae:"):
gr.Markdown(title_markdown2)
# Tab YouTube Downloader
with gr.Tab("🤗 - B站视频提取声音"):
with gr.Row():
with gr.Column():
ydl_url_input = gr.Textbox(label="B站视频网址(请填写相应的BV号)", value = "https://www.bilibili.com/video/BV...")
start = gr.Number(value=0, label="起始时间 (秒)")
end = gr.Number(value=15, label="结束时间 (秒)")
ydl_url_submit = gr.Button("提取声音文件吧", variant="primary")
as_audio_submit = gr.Button("去除背景音吧", variant="primary")
with gr.Column():
ydl_audio_output = gr.Audio(label="Audio from Bilibili")
as_audio_input = ydl_audio_output
as_audio_vocals = gr.Audio(label="Vocal only")
as_audio_no_vocals = gr.Audio(label="Music only", type="filepath")
as_audio_message = gr.Textbox(label="Message", visible=False)
ydl_url_submit.click(fn=youtube_downloader, inputs=[ydl_url_input, start, end], outputs=[ydl_audio_output])
as_audio_submit.click(fn=audio_separated, inputs=[as_audio_input], outputs=[as_audio_vocals, as_audio_no_vocals, as_audio_message], show_progress=True, queue=True)
# Tab Voice Changer
with gr.Tab("🎶 - 歌声转换"):
with gr.Row():
with gr.Column():
vc_audio_input = as_audio_vocals
vc_model_index = gr.Dropdown(
[
'%s' % (
m['metadata'].get('name')
)
for m in loaded_models
],
label='Models',
type='index'
)
vc_pitch_adjust = gr.Slider(label='Pitch', minimum=-24, maximum=24, step=1, value=0)
vc_f0_method = gr.Radio(label='F0 methods', choices=['pm', 'harvest'], value='pm', interactive=True)
vc_feat_ratio = gr.Slider(label='Feature ratio', minimum=0, maximum=1, step=0.1, value=0.6)
vc_audio_submit = gr.Button("进行歌声转换吧!", variant="primary")
full_song = gr.Button("加入歌曲伴奏吧!", variant="primary")
with gr.Column():
vc_audio_output = gr.Audio(label="Result audio", type="filepath")
vc_audio_message = gr.Textbox(label="Message")
new_song = gr.Audio(label="Full song", type="filepath")
vc_audio_submit.click(fn=voice_changer, inputs=[vc_audio_input, vc_model_index, vc_pitch_adjust, vc_f0_method, vc_feat_ratio], outputs=[vc_audio_output, vc_audio_message], show_progress=True, queue=True)
full_song.click(fn=mix, inputs=[vc_audio_output, as_audio_no_vocals], outputs=[new_song])
with gr.Tab("📺 - 音乐视频"):
with gr.Row():
with gr.Column():
inp1 = gr.Textbox(label="为视频配上精彩的文案吧(选填)")
inp2 = new_song
out1 = gr.Image(source='upload', type='filepath', label="上传一张背景图片吧")
btn = gr.Button("生成您的专属音乐视频吧")
with gr.Column():
out1 = gr.Video(label='您的专属音乐视频')
btn.click(fn=infer, inputs=[inp1, inp2], outputs=[out1])
gr.Markdown("### <center>注意❗:请不要生成会对个人以及组织造成侵害的内容,此程序仅供科研、学习及个人娱乐使用。</center>")
gr.HTML('''
<div class="footer">
<p>🌊🏞️🎶 - 江水东流急,滔滔无尽声。 明·顾璘
</p>
</div>
''')
# Launch
App.queue(concurrency_count=1, max_size=20).launch(server_name="0.0.0.0", server_port=7860, show_error=True)
# Enjoy |