Spaces:
Runtime error
Runtime error
File size: 28,398 Bytes
ac403c1 4ac3fe7 ac403c1 4ac3fe7 ac403c1 4ac3fe7 ac403c1 4ac3fe7 5605d36 6ca7e3b 91078a0 ac403c1 b67aeec ac403c1 6ca7e3b 06bbc1f 6ca7e3b 06bbc1f 6ca7e3b ac403c1 4ac3fe7 ac403c1 da1b313 ac403c1 da1b313 ac403c1 4ac3fe7 ac403c1 4ac3fe7 ac403c1 608c140 ac403c1 d57fb24 ac403c1 8d644e2 ac403c1 6ca7e3b 06bbc1f 6ca7e3b ac403c1 4ac3fe7 ac403c1 4ac3fe7 ac403c1 b67aeec |
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 |
from cProfile import label
import dataclasses
from distutils.command.check import check
from doctest import Example
import gradio as gr
import os
import sys
import numpy as np
import logging
import torch
import pytorch_seed
import time
import math
import tempfile
from typing import Optional, Tuple, Union
import matplotlib.pyplot as plt
from loguru import logger
from PIL import Image
from torch import Tensor
from torchaudio.backend.common import AudioMetaData
from df import config
from df.enhance import enhance, init_df, load_audio, save_audio
from df.io import resample
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model, df, _ = init_df("./DeepFilterNet2", config_allow_defaults=True)
model = model.to(device=device).eval()
fig_noisy: plt.Figure
fig_enh: plt.Figure
ax_noisy: plt.Axes
ax_enh: plt.Axes
fig_noisy, ax_noisy = plt.subplots(figsize=(15.2, 4))
fig_noisy.set_tight_layout(True)
fig_enh, ax_enh = plt.subplots(figsize=(15.2, 4))
fig_enh.set_tight_layout(True)
NOISES = {
"None": None,
"Kitchen": "samples/dkitchen.wav",
"Living Room": "samples/dliving.wav",
"River": "samples/nriver.wav",
"Cafe": "samples/scafe.wav",
}
from xml.sax import saxutils
from bark.api import generate_with_settings
from bark.api import save_as_prompt
from util.settings import Settings
#import nltk
from bark import SAMPLE_RATE
from cloning.clonevoice import clone_voice
from bark.generation import SAMPLE_RATE, preload_models, _load_history_prompt, codec_decode
from scipy.io.wavfile import write as write_wav
from util.parseinput import split_and_recombine_text, build_ssml, is_ssml, create_clips_from_ssml
from datetime import datetime
from tqdm.auto import tqdm
from util.helper import create_filename, add_id3_tag
from swap_voice import swap_voice_from_audio
from training.training_prepare import prepare_semantics_from_text, prepare_wavs_from_semantics
from training.train import training_prepare_files, train
# Denoise
def mix_at_snr(clean, noise, snr, eps=1e-10):
"""Mix clean and noise signal at a given SNR.
Args:
clean: 1D Tensor with the clean signal to mix.
noise: 1D Tensor of shape.
snr: Signal to noise ratio.
Returns:
clean: 1D Tensor with gain changed according to the snr.
noise: 1D Tensor with the combined noise channels.
mix: 1D Tensor with added clean and noise signals.
"""
clean = torch.as_tensor(clean).mean(0, keepdim=True)
noise = torch.as_tensor(noise).mean(0, keepdim=True)
if noise.shape[1] < clean.shape[1]:
noise = noise.repeat((1, int(math.ceil(clean.shape[1] / noise.shape[1]))))
max_start = int(noise.shape[1] - clean.shape[1])
start = torch.randint(0, max_start, ()).item() if max_start > 0 else 0
logger.debug(f"start: {start}, {clean.shape}")
noise = noise[:, start : start + clean.shape[1]]
E_speech = torch.mean(clean.pow(2)) + eps
E_noise = torch.mean(noise.pow(2))
K = torch.sqrt((E_noise / E_speech) * 10 ** (snr / 10) + eps)
noise = noise / K
mixture = clean + noise
logger.debug("mixture: {mixture.shape}")
assert torch.isfinite(mixture).all()
max_m = mixture.abs().max()
if max_m > 1:
logger.warning(f"Clipping detected during mixing. Reducing gain by {1/max_m}")
clean, noise, mixture = clean / max_m, noise / max_m, mixture / max_m
return clean, noise, mixture
def load_audio_gradio(
audio_or_file: Union[None, str, Tuple[int, np.ndarray]], sr: int
) -> Optional[Tuple[Tensor, AudioMetaData]]:
if audio_or_file is None:
return None
if isinstance(audio_or_file, str):
if audio_or_file.lower() == "none":
return None
# First try default format
audio, meta = load_audio(audio_or_file, sr)
else:
meta = AudioMetaData(-1, -1, -1, -1, "")
assert isinstance(audio_or_file, (tuple, list))
meta.sample_rate, audio_np = audio_or_file
# Gradio documentation says, the shape is [samples, 2], but apparently sometimes its not.
audio_np = audio_np.reshape(audio_np.shape[0], -1).T
if audio_np.dtype == np.int16:
audio_np = (audio_np / (1 << 15)).astype(np.float32)
elif audio_np.dtype == np.int32:
audio_np = (audio_np / (1 << 31)).astype(np.float32)
audio = resample(torch.from_numpy(audio_np), meta.sample_rate, sr)
return audio, meta
def demo_fn(speech_upl: str, noise_type: str, snr: int, mic_input: str):
if mic_input:
speech_upl = mic_input
sr = config("sr", 48000, int, section="df")
logger.info(f"Got parameters speech_upl: {speech_upl}, noise: {noise_type}, snr: {snr}")
snr = int(snr)
noise_fn = NOISES[noise_type]
meta = AudioMetaData(-1, -1, -1, -1, "")
max_s = 1000 # limit to 10 seconds
if speech_upl is not None:
sample, meta = load_audio(speech_upl, sr)
max_len = max_s * sr
if sample.shape[-1] > max_len:
start = torch.randint(0, sample.shape[-1] - max_len, ()).item()
sample = sample[..., start : start + max_len]
else:
sample, meta = load_audio("samples/p232_013_clean.wav", sr)
sample = sample[..., : max_s * sr]
if sample.dim() > 1 and sample.shape[0] > 1:
assert (
sample.shape[1] > sample.shape[0]
), f"Expecting channels first, but got {sample.shape}"
sample = sample.mean(dim=0, keepdim=True)
logger.info(f"Loaded sample with shape {sample.shape}")
if noise_fn is not None:
noise, _ = load_audio(noise_fn, sr) # type: ignore
logger.info(f"Loaded noise with shape {noise.shape}")
_, _, sample = mix_at_snr(sample, noise, snr)
logger.info("Start denoising audio")
enhanced = enhance(model, df, sample)
logger.info("Denoising finished")
lim = torch.linspace(0.0, 1.0, int(sr * 0.15)).unsqueeze(0)
lim = torch.cat((lim, torch.ones(1, enhanced.shape[1] - lim.shape[1])), dim=1)
enhanced = enhanced * lim
if meta.sample_rate != sr:
enhanced = resample(enhanced, sr, meta.sample_rate)
sample = resample(sample, sr, meta.sample_rate)
sr = meta.sample_rate
enhanced_wav = tempfile.NamedTemporaryFile(suffix="enhanced.wav", delete=False).name
save_audio(enhanced_wav, enhanced, sr)
logger.info(f"saved audios: {noisy_wav}, {enhanced_wav}")
ax_noisy.clear()
ax_enh.clear()
# noisy_wav = gr.make_waveform(noisy_fn, bar_count=200)
# enh_wav = gr.make_waveform(enhanced_fn, bar_count=200)
return enhanced_wav
def specshow(
spec,
ax=None,
title=None,
xlabel=None,
ylabel=None,
sr=48000,
n_fft=None,
hop=None,
t=None,
f=None,
vmin=-100,
vmax=0,
xlim=None,
ylim=None,
cmap="inferno",
):
"""Plots a spectrogram of shape [F, T]"""
spec_np = spec.cpu().numpy() if isinstance(spec, torch.Tensor) else spec
if ax is not None:
set_title = ax.set_title
set_xlabel = ax.set_xlabel
set_ylabel = ax.set_ylabel
set_xlim = ax.set_xlim
set_ylim = ax.set_ylim
else:
ax = plt
set_title = plt.title
set_xlabel = plt.xlabel
set_ylabel = plt.ylabel
set_xlim = plt.xlim
set_ylim = plt.ylim
if n_fft is None:
if spec.shape[0] % 2 == 0:
n_fft = spec.shape[0] * 2
else:
n_fft = (spec.shape[0] - 1) * 2
hop = hop or n_fft // 4
if t is None:
t = np.arange(0, spec_np.shape[-1]) * hop / sr
if f is None:
f = np.arange(0, spec_np.shape[0]) * sr // 2 / (n_fft // 2) / 1000
im = ax.pcolormesh(
t, f, spec_np, rasterized=True, shading="auto", vmin=vmin, vmax=vmax, cmap=cmap
)
if title is not None:
set_title(title)
if xlabel is not None:
set_xlabel(xlabel)
if ylabel is not None:
set_ylabel(ylabel)
if xlim is not None:
set_xlim(xlim)
if ylim is not None:
set_ylim(ylim)
return im
def spec_im(
audio: torch.Tensor,
figsize=(15, 5),
colorbar=False,
colorbar_format=None,
figure=None,
labels=True,
**kwargs,
) -> Image:
audio = torch.as_tensor(audio)
if labels:
kwargs.setdefault("xlabel", "Time [s]")
kwargs.setdefault("ylabel", "Frequency [Hz]")
n_fft = kwargs.setdefault("n_fft", 1024)
hop = kwargs.setdefault("hop", 512)
w = torch.hann_window(n_fft, device=audio.device)
spec = torch.stft(audio, n_fft, hop, window=w, return_complex=False)
spec = spec.div_(w.pow(2).sum())
spec = torch.view_as_complex(spec).abs().clamp_min(1e-12).log10().mul(10)
kwargs.setdefault("vmax", max(0.0, spec.max().item()))
if figure is None:
figure = plt.figure(figsize=figsize)
figure.set_tight_layout(True)
if spec.dim() > 2:
spec = spec.squeeze(0)
im = specshow(spec, **kwargs)
if colorbar:
ckwargs = {}
if "ax" in kwargs:
if colorbar_format is None:
if kwargs.get("vmin", None) is not None or kwargs.get("vmax", None) is not None:
colorbar_format = "%+2.0f dB"
ckwargs = {"ax": kwargs["ax"]}
plt.colorbar(im, format=colorbar_format, **ckwargs)
figure.canvas.draw()
return Image.frombytes("RGB", figure.canvas.get_width_height(), figure.canvas.tostring_rgb())
def toggle(choice):
if choice == "mic":
return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
else:
return gr.update(visible=False, value=None), gr.update(visible=True, value=None)
# Bark
settings = Settings('config.yaml')
def generate_text_to_speech(text, selected_speaker, text_temp, waveform_temp, eos_prob, quick_generation, complete_settings, seed, batchcount, progress=gr.Progress(track_tqdm=True)):
# Chunk the text into smaller pieces then combine the generated audio
# generation settings
if selected_speaker == 'None':
selected_speaker = None
voice_name = selected_speaker
if text == None or len(text) < 1:
if selected_speaker == None:
raise gr.Error('No text entered!')
# Extract audio data from speaker if no text and speaker selected
voicedata = _load_history_prompt(voice_name)
audio_arr = codec_decode(voicedata["fine_prompt"])
result = create_filename(settings.output_folder_path, "None", "extract",".wav")
save_wav(audio_arr, result)
return result
if batchcount < 1:
batchcount = 1
silenceshort = np.zeros(int((float(settings.silence_sentence) / 1000.0) * SAMPLE_RATE), dtype=np.int16) # quarter second of silence
silencelong = np.zeros(int((float(settings.silence_speakers) / 1000.0) * SAMPLE_RATE), dtype=np.float32) # half a second of silence
use_last_generation_as_history = "Use last generation as history" in complete_settings
save_last_generation = "Save generation as Voice" in complete_settings
for l in range(batchcount):
currentseed = seed
if seed != None and seed > 2**32 - 1:
logger.warning(f"Seed {seed} > 2**32 - 1 (max), setting to random")
currentseed = None
if currentseed == None or currentseed <= 0:
currentseed = np.random.default_rng().integers(1, 2**32 - 1)
assert(0 < currentseed and currentseed < 2**32)
progress(0, desc="Generating")
full_generation = None
all_parts = []
complete_text = ""
text = text.lstrip()
if is_ssml(text):
list_speak = create_clips_from_ssml(text)
prev_speaker = None
for i, clip in tqdm(enumerate(list_speak), total=len(list_speak)):
selected_speaker = clip[0]
# Add pause break between speakers
if i > 0 and selected_speaker != prev_speaker:
all_parts += [silencelong.copy()]
prev_speaker = selected_speaker
text = clip[1]
text = saxutils.unescape(text)
if selected_speaker == "None":
selected_speaker = None
print(f"\nGenerating Text ({i+1}/{len(list_speak)}) -> {selected_speaker} (Seed {currentseed}):`{text}`")
complete_text += text
with pytorch_seed.SavedRNG(currentseed):
audio_array = generate_with_settings(text_prompt=text, voice_name=selected_speaker, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)
currentseed = torch.random.initial_seed()
if len(list_speak) > 1:
filename = create_filename(settings.output_folder_path, currentseed, "audioclip",".wav")
save_wav(audio_array, filename)
add_id3_tag(filename, text, selected_speaker, currentseed)
all_parts += [audio_array]
else:
texts = split_and_recombine_text(text, settings.input_text_desired_length, settings.input_text_max_length)
for i, text in tqdm(enumerate(texts), total=len(texts)):
print(f"\nGenerating Text ({i+1}/{len(texts)}) -> {selected_speaker} (Seed {currentseed}):`{text}`")
complete_text += text
if quick_generation == True:
with pytorch_seed.SavedRNG(currentseed):
audio_array = generate_with_settings(text_prompt=text, voice_name=selected_speaker, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)
currentseed = torch.random.initial_seed()
else:
full_output = use_last_generation_as_history or save_last_generation
if full_output:
full_generation, audio_array = generate_with_settings(text_prompt=text, voice_name=voice_name, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob, output_full=True)
else:
audio_array = generate_with_settings(text_prompt=text, voice_name=voice_name, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)
# Noticed this in the HF Demo - convert to 16bit int -32767/32767 - most used audio format
# audio_array = (audio_array * 32767).astype(np.int16)
if len(texts) > 1:
filename = create_filename(settings.output_folder_path, currentseed, "audioclip",".wav")
save_wav(audio_array, filename)
add_id3_tag(filename, text, selected_speaker, currentseed)
if quick_generation == False and (save_last_generation == True or use_last_generation_as_history == True):
# save to npz
voice_name = create_filename(settings.output_folder_path, seed, "audioclip", ".npz")
save_as_prompt(voice_name, full_generation)
if use_last_generation_as_history:
selected_speaker = voice_name
all_parts += [audio_array]
# Add short pause between sentences
if text[-1] in "!?.\n" and i > 1:
all_parts += [silenceshort.copy()]
# save & play audio
result = create_filename(settings.output_folder_path, currentseed, "final",".wav")
save_wav(np.concatenate(all_parts), result)
# write id3 tag with text truncated to 60 chars, as a precaution...
add_id3_tag(result, complete_text, selected_speaker, currentseed)
return result
def save_wav(audio_array, filename):
write_wav(filename, SAMPLE_RATE, audio_array)
def save_voice(filename, semantic_prompt, coarse_prompt, fine_prompt):
np.savez_compressed(
filename,
semantic_prompt=semantic_prompt,
coarse_prompt=coarse_prompt,
fine_prompt=fine_prompt
)
def on_quick_gen_changed(checkbox):
if checkbox == False:
return gr.CheckboxGroup.update(visible=True)
return gr.CheckboxGroup.update(visible=False)
def delete_output_files(checkbox_state):
if checkbox_state:
outputs_folder = os.path.join(os.getcwd(), settings.output_folder_path)
if os.path.exists(outputs_folder):
purgedir(outputs_folder)
return False
# https://stackoverflow.com/a/54494779
def purgedir(parent):
for root, dirs, files in os.walk(parent):
for item in files:
# Delete subordinate files
filespec = os.path.join(root, item)
os.unlink(filespec)
for item in dirs:
# Recursively perform this operation for subordinate directories
purgedir(os.path.join(root, item))
def convert_text_to_ssml(text, selected_speaker):
return build_ssml(text, selected_speaker)
def training_prepare(selected_step, num_text_generations, progress=gr.Progress(track_tqdm=True)):
if selected_step == prepare_training_list[0]:
prepare_semantics_from_text()
else:
prepare_wavs_from_semantics()
return None
def start_training(save_model_epoch, max_epochs, progress=gr.Progress(track_tqdm=True)):
training_prepare_files("./training/data/", "./training/data/checkpoint/hubert_base_ls960.pt")
train("./training/data/", save_model_epoch, max_epochs)
return None
def apply_settings(themes, input_server_name, input_server_port, input_server_public, input_desired_len, input_max_len, input_silence_break, input_silence_speaker):
settings.selected_theme = themes
settings.server_name = input_server_name
settings.server_port = input_server_port
settings.server_share = input_server_public
settings.input_text_desired_length = input_desired_len
settings.input_text_max_length = input_max_len
settings.silence_sentence = input_silence_break
settings.silence_speaker = input_silence_speaker
settings.save()
def restart():
global restart_server
restart_server = True
def create_version_html():
python_version = ".".join([str(x) for x in sys.version_info[0:3]])
versions_html = f"""
python: <span title="{sys.version}">{python_version}</span>
โโขโ
torch: {getattr(torch, '__long_version__',torch.__version__)}
โโขโ
gradio: {gr.__version__}
"""
return versions_html
logger = logging.getLogger(__name__)
APPTITLE = "Bark Voice Cloning UI"
autolaunch = False
if len(sys.argv) > 1:
autolaunch = "-autolaunch" in sys.argv
if torch.cuda.is_available() == False:
os.environ['BARK_FORCE_CPU'] = 'True'
logger.warning("No CUDA detected, fallback to CPU!")
print(f'smallmodels={os.environ.get("SUNO_USE_SMALL_MODELS", False)}')
print(f'enablemps={os.environ.get("SUNO_ENABLE_MPS", False)}')
print(f'offloadcpu={os.environ.get("SUNO_OFFLOAD_CPU", False)}')
print(f'forcecpu={os.environ.get("BARK_FORCE_CPU", False)}')
print(f'autolaunch={autolaunch}\n\n')
#print("Updating nltk\n")
#nltk.download('punkt')
print("Preloading Models\n")
preload_models()
available_themes = ["Default", "gradio/glass", "gradio/monochrome", "gradio/seafoam", "gradio/soft", "gstaff/xkcd", "freddyaboulton/dracula_revamped", "ysharma/steampunk"]
tokenizer_language_list = ["de","en", "pl"]
prepare_training_list = ["Step 1: Semantics from Text","Step 2: WAV from Semantics"]
seed = -1
server_name = settings.server_name
if len(server_name) < 1:
server_name = None
server_port = settings.server_port
if server_port <= 0:
server_port = None
global run_server
global restart_server
run_server = True
while run_server:
# Collect all existing speakers/voices in dir
speakers_list = []
for root, dirs, files in os.walk("./bark/assets/prompts"):
for file in files:
if file.endswith(".npz"):
pathpart = root.replace("./bark/assets/prompts", "")
name = os.path.join(pathpart, file[:-4])
if name.startswith("/") or name.startswith("\\"):
name = name[1:]
speakers_list.append(name)
speakers_list = sorted(speakers_list, key=lambda x: x.lower())
speakers_list.insert(0, 'None')
print(f'Launching {APPTITLE} Server')
# Create Gradio Blocks
with gr.Blocks(title=f"{APPTITLE}", mode=f"{APPTITLE}", theme=settings.selected_theme) as barkgui:
gr.Markdown("# <center>๐ถ๐ถโญ - Bark Voice Cloning</center>")
gr.Markdown("## <center>๐ค - If you like this space, please star my [github repo](https://github.com/KevinWang676/Bark-Voice-Cloning)</center>")
gr.Markdown("### <center>๐ก - Based on [bark-gui](https://github.com/C0untFloyd/bark-gui)</center>")
gr.Markdown(f""" You can duplicate and use it with a GPU: <a href="https://huggingface.co/spaces/{os.getenv('SPACE_ID')}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a>
or open in [Colab](https://colab.research.google.com/github/KevinWang676/Bark-Voice-Cloning/blob/main/Bark_Voice_Cloning.ipynb) for quick start ๐ P.S. Voice cloning needs a GPU, but TTS doesn't ๐
""")
with gr.Tab("๐๏ธ - Clone Voice"):
with gr.Row():
input_audio_filename = gr.Audio(label="Input audio.wav", source="upload", type="filepath")
#transcription_text = gr.Textbox(label="Transcription Text", lines=1, placeholder="Enter Text of your Audio Sample here...")
with gr.Row():
with gr.Column():
initialname = "/home/user/app/bark/assets/prompts/file"
output_voice = gr.Textbox(label="Filename of trained Voice (do not change the initial name)", lines=1, placeholder=initialname, value=initialname, visible=False)
with gr.Column():
tokenizerlang = gr.Dropdown(tokenizer_language_list, label="Base Language Tokenizer", value=tokenizer_language_list[1], visible=False)
with gr.Row():
clone_voice_button = gr.Button("Create Voice", variant="primary")
with gr.Row():
dummy = gr.Text(label="Progress")
npz_file = gr.File(label=".npz file")
speakers_list.insert(0, npz_file) # add prompt
with gr.Tab("๐ต - TTS"):
with gr.Row():
with gr.Column():
placeholder = "Enter text here."
input_text = gr.Textbox(label="Input Text", lines=4, placeholder=placeholder)
convert_to_ssml_button = gr.Button("Convert Input Text to SSML")
with gr.Column():
seedcomponent = gr.Number(label="Seed (default -1 = Random)", precision=0, value=-1)
batchcount = gr.Number(label="Batch count", precision=0, value=1)
with gr.Row():
with gr.Column():
gr.Markdown("[Voice Prompt Library](https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c)")
speaker = gr.Dropdown(speakers_list, value=speakers_list[0], label="Voice (Choose โfileโ if you wanna use the custom voice)")
with gr.Column():
text_temp = gr.Slider(0.1, 1.0, value=0.6, label="Generation Temperature", info="1.0 more diverse, 0.1 more conservative")
waveform_temp = gr.Slider(0.1, 1.0, value=0.7, label="Waveform temperature", info="1.0 more diverse, 0.1 more conservative")
with gr.Row():
with gr.Column():
quick_gen_checkbox = gr.Checkbox(label="Quick Generation", value=True)
settings_checkboxes = ["Use last generation as history", "Save generation as Voice"]
complete_settings = gr.CheckboxGroup(choices=settings_checkboxes, value=settings_checkboxes, label="Detailed Generation Settings", type="value", interactive=True, visible=False)
with gr.Column():
eos_prob = gr.Slider(0.0, 0.5, value=0.05, label="End of sentence probability")
with gr.Row():
with gr.Column():
tts_create_button = gr.Button("Generate", variant="primary")
with gr.Column():
hidden_checkbox = gr.Checkbox(visible=False)
button_stop_generation = gr.Button("Stop generation")
with gr.Row():
output_audio = gr.Audio(label="Generated Audio", type="filepath")
with gr.Row():
with gr.Column():
radio = gr.Radio(
["mic", "file"], value="file", label="How would you like to upload your audio?", visible=False
)
mic_input = gr.Mic(label="Input", type="filepath", visible=False)
audio_file = output_audio
inputs = [
audio_file,
gr.Dropdown(
label="Add background noise",
choices=list(NOISES.keys()),
value="None", visible =False,
),
gr.Dropdown(
label="Noise Level (SNR)",
choices=["-5", "0", "10", "20"],
value="0", visible =False,
),
mic_input,
]
btn_denoise = gr.Button("Denoise", variant="primary")
with gr.Column():
outputs = [
gr.Audio(type="filepath", label="Enhanced audio"),
]
btn_denoise.click(fn=demo_fn, inputs=inputs, outputs=outputs)
radio.change(toggle, radio, [mic_input, audio_file])
with gr.Tab("๐ฎ - Voice Conversion"):
with gr.Row():
swap_audio_filename = gr.Audio(label="Input audio.wav to swap voice", source="upload", type="filepath")
with gr.Row():
with gr.Column():
swap_tokenizer_lang = gr.Dropdown(tokenizer_language_list, label="Base Language Tokenizer", value=tokenizer_language_list[1])
swap_seed = gr.Number(label="Seed (default -1 = Random)", precision=0, value=-1)
with gr.Column():
speaker_swap = gr.Dropdown(speakers_list, value=speakers_list[0], label="Voice (Choose โfileโ if you wanna use the custom voice)")
swap_batchcount = gr.Number(label="Batch count", precision=0, value=1)
with gr.Row():
swap_voice_button = gr.Button("Generate", variant="primary")
with gr.Row():
output_swap = gr.Audio(label="Generated Audio", type="filepath")
quick_gen_checkbox.change(fn=on_quick_gen_changed, inputs=quick_gen_checkbox, outputs=complete_settings)
convert_to_ssml_button.click(convert_text_to_ssml, inputs=[input_text, speaker],outputs=input_text)
gen_click = tts_create_button.click(generate_text_to_speech, inputs=[input_text, speaker, text_temp, waveform_temp, eos_prob, quick_gen_checkbox, complete_settings, seedcomponent, batchcount],outputs=output_audio)
button_stop_generation.click(fn=None, inputs=None, outputs=None, cancels=[gen_click])
swap_voice_button.click(swap_voice_from_audio, inputs=[swap_audio_filename, speaker_swap, swap_tokenizer_lang, swap_seed, swap_batchcount], outputs=output_swap)
clone_voice_button.click(clone_voice, inputs=[input_audio_filename, output_voice], outputs=[dummy, npz_file])
restart_server = False
try:
barkgui.queue().launch(show_error=True)
except:
restart_server = True
run_server = False
try:
while restart_server == False:
time.sleep(1.0)
except (KeyboardInterrupt, OSError):
print("Keyboard interruption in main thread... closing server.")
run_server = False
barkgui.close()
|