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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 = "Custom_Voice"
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")
npz_file = gr.File(label=".npz file")
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, npz_file])
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(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()