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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")
	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>
	•
	torch: {getattr(torch, '__long_version__',torch.__version__)}
	•
	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>🐶🎶⭐ - Bark Voice Cloning</center>")
	gr.Markdown("## <center>🤗 - If you like this space, please star my [github repo](https://github.com/KevinWang676/Bark-Voice-Cloning)</center>")
	gr.Markdown("### <center>🎡 - 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 🌟 P.S. Voice cloning needs a GPU, but TTS doesn't 😄
	""")

	with gr.Tab("🎙️ - 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("🎵 - 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 “file” 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("🔮 - 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 “file” 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()