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Dockerfile.txt

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+FROM debian:stable
+
+# Install system packages
+RUN apt update && apt install -y git pip
+
+# Create non-root user
+RUN useradd -m -d /bark bark
+
+# Run as new user
+USER bark
+WORKDIR /bark
+
+# Clone git repo
+RUN git clone https://github.com/C0untFloyd/bark-gui 
+
+# Switch to git directory
+WORKDIR /bark/bark-gui
+
+# Append pip bin path to PATH
+ENV PATH=$PATH:/bark/.local/bin
+
+# Install dependancies
+RUN pip install .
+RUN pip install -r requirements.txt
+
+# List on all addresses, since we are in a container.
+RUN sed -i "s/server_name: ''/server_name: 0.0.0.0/g" ./config.yaml
+
+# Suggested volumes
+VOLUME /bark/bark-gui/assets/prompts/custom
+VOLUME /bark/bark-gui/models
+VOLUME /bark/.cache/huggingface/hub
+
+# Default port for web-ui
+EXPOSE 7860/tcp
+
+# Start script
+CMD python3 webui.py

config.yaml

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+input_text_desired_length: 110
+input_text_max_length: 170
+selected_theme: freddyaboulton/dracula_revamped
+server_name: ''
+server_port: 0
+server_share: false
+silence_between_sentences: 250
+silence_between_speakers: 500

gitignore (1).txt

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+__pycache__/
+/outputs
+/speakers
+.vs
+*.npz
+*.wav
+*.npy
+.vs/
+/models
+/bark_ui_enhanced.egg-info
+/build/lib/bark
+*.pth
+*.pt
+*.zip

pyproject.toml

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+[build-system]
+requires = ["setuptools"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "bark-ui-enhanced"
+version = "0.7.0"
+description = "Bark text to audio model with addition features and a Web UI"
+readme = "README.md"
+requires-python = ">=3.8"
+authors =  [
+    {name = "Suno Inc (original Bark)", email = "[email protected]"},
+    {name = "Count Floyd"},
+]
+# MIT License
+license = {file = "LICENSE"}
+
+dependencies = [
+    "boto3",
+    "encodec",
+    "funcy",
+    "huggingface-hub>=0.14.1",
+    "numpy",
+    "scipy",
+    "tokenizers",
+    "torch",
+    "tqdm",
+    "transformers",
+]
+
+[project.urls]
+source = "https://github.com/C0untFloyd/bark-gui"
+
+[project.optional-dependencies]
+dev = [
+    "bandit",
+    "black",
+    "codecov",
+    "flake8",
+    "hypothesis>=6.14,<7",
+    "isort>=5.0.0,<6",
+    "jupyter",
+    "mypy",
+    "nbconvert",
+    "nbformat",
+    "pydocstyle",
+    "pylint",
+    "pytest",
+    "pytest-cov",
+]
+
+[tool.setuptools]
+packages = ["bark"]
+
+[tool.setuptools.package-data]
+bark = ["assets/prompts/*.npz", "assets/prompts/v2/*.npz"]
+
+
+[tool.black]
+line-length = 100

setup.py

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+from setuptools import setup
+
+setup()

swap_voice.py

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+from bark.generation import load_codec_model, generate_text_semantic, grab_best_device
+from bark import SAMPLE_RATE
+from encodec.utils import convert_audio
+from bark.hubert.hubert_manager import HuBERTManager
+from bark.hubert.pre_kmeans_hubert import CustomHubert
+from bark.hubert.customtokenizer import CustomTokenizer
+from bark.api import semantic_to_waveform
+from scipy.io.wavfile import write as write_wav
+from util.helper import create_filename
+from util.settings import Settings
+
+
+import torchaudio
+import torch
+import os
+import gradio
+
+def swap_voice_from_audio(swap_audio_filename, selected_speaker, tokenizer_lang, seed, batchcount, progress=gradio.Progress(track_tqdm=True)):
+    use_gpu = not os.environ.get("BARK_FORCE_CPU", False)
+    progress(0, desc="Loading Codec")
+    
+    # From https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer
+    hubert_manager = HuBERTManager()
+    hubert_manager.make_sure_hubert_installed()
+    hubert_manager.make_sure_tokenizer_installed(tokenizer_lang=tokenizer_lang)
+
+    # From https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer 
+    # Load HuBERT for semantic tokens
+
+    # Load the HuBERT model
+    device = grab_best_device(use_gpu)
+    hubert_model = CustomHubert(checkpoint_path='./models/hubert/hubert.pt').to(device)
+    model = load_codec_model(use_gpu=use_gpu)
+
+    # Load the CustomTokenizer model
+    tokenizer = CustomTokenizer.load_from_checkpoint(f'./models/hubert/{tokenizer_lang}_tokenizer.pth').to(device)  # Automatically uses the right layers
+
+    progress(0.25, desc="Converting WAV")
+
+    # Load and pre-process the audio waveform
+    wav, sr = torchaudio.load(swap_audio_filename)
+    if wav.shape[0] == 2:  # Stereo to mono if needed
+        wav = wav.mean(0, keepdim=True)
+
+    wav = convert_audio(wav, sr, model.sample_rate, model.channels)
+    wav = wav.to(device)
+    semantic_vectors = hubert_model.forward(wav, input_sample_hz=model.sample_rate)
+    semantic_tokens = tokenizer.get_token(semantic_vectors)
+
+    audio = semantic_to_waveform(
+        semantic_tokens,
+        history_prompt=selected_speaker,
+        temp=0.7,
+        silent=False,
+        output_full=False)
+
+    settings = Settings('config.yaml')
+
+    result = create_filename(settings.output_folder_path, None, "swapvoice",".wav")
+    write_wav(result, SAMPLE_RATE, audio)
+    return result
+

webui.py

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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
+
+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
+
+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 UI Enhanced v0.7"
+
+
+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:
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown(f"### [{APPTITLE}](https://github.com/C0untFloyd/bark-gui)")
+            with gr.Column():
+                gr.HTML(create_version_html(), elem_id="versions")
+
+        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)
+                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():
+                    examples = [
+                        "Special meanings: [laughter] [laughs] [sighs] [music] [gasps] [clears throat] MAN: WOMAN:",
+                       "♪ Never gonna make you cry, never gonna say goodbye, never gonna tell a lie and hurt you ♪",
+                       "And now — a picture of a larch [laughter]",
+                       """
+                            WOMAN: I would like an oatmilk latte please.
+                            MAN: Wow, that's expensive!
+                       """,
+                       """<?xml version="1.0"?>
+    <speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis"
+             xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+             xsi:schemaLocation="http://www.w3.org/2001/10/synthesis
+                       http://www.w3.org/TR/speech-synthesis/synthesis.xsd"
+             xml:lang="en-US">
+    <voice name="/v2/en_speaker_9">Look at that drunk guy!</voice>
+    <voice name="/v2/en_speaker_3">Who is he?</voice>
+    <voice name="/v2/en_speaker_9">WOMAN: [clears throat] 10 years ago, he proposed me and I rejected him.</voice>
+    <voice name="/v2/en_speaker_3">Oh my God [laughs] he is still celebrating</voice>
+    </speak>"""
+                       ]
+                    examples = gr.Examples(examples=examples, inputs=input_text)
+                with gr.Column():
+                    convert_to_ssml_button = gr.Button("Convert Input Text to SSML")
+
+            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")
+                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")
+                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.Tab("Swap Voice"):
+            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")
+                     swap_batchcount = gr.Number(label="Batch count", precision=0, value=1)
+            with gr.Row():
+                swap_voice_button = gr.Button("Swap Voice")
+            with gr.Row():
+                output_swap = gr.Audio(label="Generated Audio", type="filepath")
+
+        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 = "./bark/assets/prompts/custom/MeMyselfAndI"
+                    output_voice = gr.Textbox(label="Filename of trained Voice", lines=1, placeholder=initialname, value=initialname)
+                with gr.Column():
+                    tokenizerlang = gr.Dropdown(tokenizer_language_list, label="Base Language Tokenizer", value=tokenizer_language_list[1])
+            with gr.Row():
+                clone_voice_button = gr.Button("Create Voice")
+            with gr.Row():
+                dummy = gr.Text(label="Progress")
+
+        with gr.Tab("Training Data Prepare"):
+            gr.Markdown("This tab should be used to generate the training dataset. For Step 1 put some books into the inputtext folder in UTF-8 Text Format.")
+            prepare_semantics_number = gr.Number(label="Number of semantics to create", precision=0, value=3079)
+            prepare_dropdown = gr.Dropdown(prepare_training_list, value=prepare_training_list[0], label="Prepare")
+            training_prepare_button = gr.Button("Generate")
+            dummytrd = gr.Text(label="Progress")
+
+        with gr.Tab("Training"):
+            with gr.Row():
+                gr.Markdown("This tab is used to train the actual model (language).")
+            with gr.Row():
+                with gr.Column():
+                    save_model_epoch = gr.Number(label="Auto-save model after number of epochs", precision=0, value=1)
+                with gr.Column():
+                    max_epochs = gr.Number(label="Train for number of epochs", precision=0, value=6)
+            with gr.Row():
+                with gr.Column():
+                    allowed_chars = ' abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789!@#$%^&*()-_+=\"\':;[]{}/<>,.`~'
+                    allowedcharsfilter = gr.Textbox(label="Allowed chars for text input", lines=1, value=allowed_chars)
+                with gr.Column():
+                    train_button = gr.Button("Start Training")
+            with gr.Row():
+                dummytrain = gr.Text(label="Progress")
+
+
+        with gr.Tab("Settings"):
+            with gr.Row():
+                themes = gr.Dropdown(available_themes, label="Theme", info="Change needs complete restart", value=settings.selected_theme)
+            with gr.Row():
+                input_server_name = gr.Textbox(label="Server Name", lines=1, info="Leave blank to run locally", value=settings.server_name)
+                input_server_port = gr.Number(label="Server Port", precision=0, info="Leave at 0 to use default", value=settings.server_port)
+                share_checkbox = gr.Checkbox(label="Public Server", value=settings.server_share)
+            with gr.Row():
+                input_desired_len = gr.Slider(100, 150, value=settings.input_text_desired_length, label="Desired Input Text Length", info="Ideal length to split input sentences")
+                input_max_len = gr.Slider(150, 256, value=settings.input_text_max_length, label="Max Input Text Length", info="Maximum Input Text Length")
+            with gr.Row():
+                input_silence_break = gr.Slider(1, 1000, value=settings.silence_sentence, label="Sentence Pause Time (ms)", info="Silence between sentences in milliseconds")
+                input_silence_speakers = gr.Slider(1, 5000, value=settings.silence_speakers, label="Speaker Pause Time (ms)", info="Silence between different speakers in milliseconds")
+
+            with gr.Row():
+                button_apply_settings = gr.Button("Apply Settings")
+                button_apply_restart = gr.Button("Restart Server")
+                button_delete_files = gr.Button("Clear output folder")
+
+        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])
+        # Javascript hack to display modal confirmation dialog
+        js = "(x) => confirm('Are you sure? This will remove all files from output folder')"
+        button_delete_files.click(None, None, hidden_checkbox, _js=js)
+        hidden_checkbox.change(delete_output_files, [hidden_checkbox], [hidden_checkbox])
+
+        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)
+        training_prepare_button.click(training_prepare, inputs=[prepare_dropdown, prepare_semantics_number], outputs=dummytrd)
+        train_button.click(start_training, inputs=[save_model_epoch, max_epochs], outputs=dummytrain)
+        button_apply_settings.click(apply_settings, inputs=[themes, input_server_name, input_server_port, share_checkbox, input_desired_len, input_max_len, input_silence_break, input_silence_speakers])
+        button_apply_restart.click(restart)
+
+        restart_server = False
+        try:
+            barkgui.queue().launch(inbrowser=autolaunch, server_name=server_name, server_port=server_port, share=settings.server_share, prevent_thread_lock=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()
+
+
+
+