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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")
map_location=torch.device('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: {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>
โ€‚โ€ขโ€‚
torch: {getattr(torch, '__long_version__',torch.__version__)}
โ€‚โ€ขโ€‚
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>๐Ÿถ๐ŸŽถโญ - Bark Voice Cloning</center>")
gr.Markdown("## <center>๐Ÿค— - If you like this space, please star my [github repo](https://github.com/KevinWang676/Bark-Voice-Cloning)</center>")
gr.Markdown("### <center>๐ŸŽก - 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 ๐ŸŒŸ P.S. Voice cloning needs a GPU, but TTS doesn't ๐Ÿ˜„
""")
with gr.Tab("๐ŸŽ™๏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("๐ŸŽต - 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 โ€œfileโ€ 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("๐Ÿ”ฎ - 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 โ€œfileโ€ 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()