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 = 10 # 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 noisy_wav = tempfile.NamedTemporaryFile(suffix="noisy.wav", delete=False).name save_audio(noisy_wav, sample, sr) 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 noisy_wav, 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: {python_version}  •  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("#
🐶🎶⭐ - Bark Voice Cloning
") gr.Markdown("##
🤗 - If you like this space, please star my [github repo](https://github.com/KevinWang676/Bark-Voice-Cloning)
") gr.Markdown("###
🎡 - Based on [bark-gui](https://github.com/C0untFloyd/bark-gui)
") gr.Markdown(f""" You can duplicate and use it with a GPU: Duplicate Space 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") with gr.Column(): outputs = [ gr.Audio(type="filepath", label="Noisy audio"), 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()