ai-voice-cloning/src/webui.py

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Python
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import os
import argparse
import time
import json
import base64
import re
import urllib.request
import torch
import torchaudio
import music_tag
import gradio as gr
import gradio.utils
from datetime import datetime
import tortoise.api
from tortoise.utils.audio import get_voice_dir, get_voices
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from utils import *
args = setup_args()
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def run_generation(
text,
delimiter,
emotion,
prompt,
voice,
mic_audio,
voice_latents_chunks,
seed,
candidates,
num_autoregressive_samples,
diffusion_iterations,
temperature,
diffusion_sampler,
breathing_room,
cvvp_weight,
top_p,
diffusion_temperature,
length_penalty,
repetition_penalty,
cond_free_k,
experimental_checkboxes,
progress=gr.Progress(track_tqdm=True)
):
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if not text:
raise gr.Error("Please provide text.")
if not voice:
raise gr.Error("Please provide a voice.")
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try:
sample, outputs, stats = generate(
text=text,
delimiter=delimiter,
emotion=emotion,
prompt=prompt,
voice=voice,
mic_audio=mic_audio,
voice_latents_chunks=voice_latents_chunks,
seed=seed,
candidates=candidates,
num_autoregressive_samples=num_autoregressive_samples,
diffusion_iterations=diffusion_iterations,
temperature=temperature,
diffusion_sampler=diffusion_sampler,
breathing_room=breathing_room,
cvvp_weight=cvvp_weight,
top_p=top_p,
diffusion_temperature=diffusion_temperature,
length_penalty=length_penalty,
repetition_penalty=repetition_penalty,
cond_free_k=cond_free_k,
experimental_checkboxes=experimental_checkboxes,
progress=progress
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)
except Exception as e:
message = str(e)
if message == "Kill signal detected":
unload_tts()
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raise gr.Error(message)
return (
outputs[0],
gr.update(value=sample, visible=sample is not None),
gr.update(choices=outputs, value=outputs[0], visible=len(outputs) > 1, interactive=True),
gr.update(value=stats, visible=True),
)
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def update_presets(value):
PRESETS = {
'Ultra Fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 30, 'cond_free': False},
'Fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 80},
'Standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 200},
'High Quality': {'num_autoregressive_samples': 256, 'diffusion_iterations': 400},
}
if value in PRESETS:
preset = PRESETS[value]
return (gr.update(value=preset['num_autoregressive_samples']), gr.update(value=preset['diffusion_iterations']))
else:
return (gr.update(), gr.update())
def get_training_configs():
configs = []
for i, file in enumerate(sorted(os.listdir(f"./training/"))):
if file[-5:] != ".yaml" or file[0] == ".":
continue
configs.append(f"./training/{file}")
return configs
def update_training_configs():
return gr.update(choices=get_training_list())
history_headers = {
"Name": "",
"Samples": "num_autoregressive_samples",
"Iterations": "diffusion_iterations",
"Temp.": "temperature",
"Sampler": "diffusion_sampler",
"CVVP": "cvvp_weight",
"Top P": "top_p",
"Diff. Temp.": "diffusion_temperature",
"Len Pen": "length_penalty",
"Rep Pen": "repetition_penalty",
"Cond-Free K": "cond_free_k",
"Time": "time",
"Datetime": "datetime",
"Model": "model",
"Model Hash": "model_hash",
}
def history_view_results( voice ):
results = []
files = []
outdir = f"./results/{voice}/"
for i, file in enumerate(sorted(os.listdir(outdir))):
if file[-4:] != ".wav":
continue
metadata, _ = read_generate_settings(f"{outdir}/{file}", read_latents=False)
if metadata is None:
continue
values = []
for k in history_headers:
v = file
if k != "Name":
v = metadata[history_headers[k]] if history_headers[k] in metadata else '?'
values.append(v)
files.append(file)
results.append(values)
return (
results,
gr.Dropdown.update(choices=sorted(files))
)
def import_voices_proxy(files, name, progress=gr.Progress(track_tqdm=True)):
import_voices(files, name, progress)
return gr.update()
def read_generate_settings_proxy(file, saveAs='.temp'):
j, latents = read_generate_settings(file)
if latents:
outdir = f'{get_voice_dir()}/{saveAs}/'
os.makedirs(outdir, exist_ok=True)
with open(f'{outdir}/cond_latents.pth', 'wb') as f:
f.write(latents)
latents = f'{outdir}/cond_latents.pth'
return (
gr.update(value=j, visible=j is not None),
gr.update(visible=j is not None),
gr.update(value=latents, visible=latents is not None),
None if j is None else j['voice']
)
def prepare_dataset_proxy( voice, language, progress=gr.Progress(track_tqdm=True) ):
return prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language, progress=progress )
def optimize_training_settings_proxy( *args, **kwargs ):
tup = optimize_training_settings(*args, **kwargs)
return (
gr.update(value=tup[0]),
gr.update(value=tup[1]),
gr.update(value=tup[2]),
gr.update(value=tup[3]),
gr.update(value=tup[4]),
gr.update(value=tup[5]),
gr.update(value=tup[6]),
gr.update(value=tup[7]),
"\n".join(tup[8])
)
def import_training_settings_proxy( voice ):
indir = f'./training/{voice}/'
outdir = f'./training/{voice}-finetune/'
in_config_path = f"{indir}/train.yaml"
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out_config_path = None
out_configs = []
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if os.path.isdir(outdir):
out_configs = sorted([d[:-5] for d in os.listdir(outdir) if d[-5:] == ".yaml" ])
if len(out_configs) > 0:
out_config_path = f'{outdir}/{out_configs[-1]}.yaml'
config_path = out_config_path if out_config_path else in_config_path
messages = []
with open(config_path, 'r') as file:
config = yaml.safe_load(file)
messages.append(f"Importing from: {config_path}")
dataset_path = f"./training/{voice}/train.txt"
with open(dataset_path, 'r', encoding="utf-8") as f:
lines = len(f.readlines())
messages.append(f"Basing epoch size to {lines} lines")
batch_size = config['datasets']['train']['batch_size']
mega_batch_factor = config['train']['mega_batch_factor']
iterations = config['train']['niter']
steps_per_iteration = int(lines / batch_size)
epochs = int(iterations / steps_per_iteration)
learning_rate = config['steps']['gpt_train']['optimizer_params']['lr']
text_ce_lr_weight = config['steps']['gpt_train']['losses']['text_ce']['weight']
learning_rate_schedule = [ int(x / steps_per_iteration) for x in config['train']['gen_lr_steps'] ]
print_rate = int(config['logger']['print_freq'] / steps_per_iteration)
save_rate = int(config['logger']['save_checkpoint_freq'] / steps_per_iteration)
statedir = f'{outdir}/training_state/' # NOOO STOP MIXING YOUR CASES
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resumes = []
resume_path = None
source_model = None
if "pretrain_model_gpt" in config['path']:
source_model = config['path']['pretrain_model_gpt']
elif "resume_state" in config['path']:
resume_path = config['path']['resume_state']
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if os.path.isdir(statedir):
resumes = sorted([int(d[:-6]) for d in os.listdir(statedir) if d[-6:] == ".state" ])
if len(resumes) > 0:
resume_path = f'{statedir}/{resumes[-1]}.state'
messages.append(f"Latest resume found: {resume_path}")
half_p = config['fp16']
bnb = True
if "ext" in config and "bitsandbytes" in config["ext"]:
bnb = config["ext"]["bitsandbytes"]
messages = "\n".join(messages)
return (
epochs,
learning_rate,
text_ce_lr_weight,
learning_rate_schedule,
batch_size,
mega_batch_factor,
print_rate,
save_rate,
resume_path,
half_p,
bnb,
source_model,
messages
)
def save_training_settings_proxy( epochs, learning_rate, text_ce_lr_weight, learning_rate_schedule, batch_size, mega_batch_factor, print_rate, save_rate, resume_path, half_p, bnb, source_model, voice ):
name = f"{voice}-finetune"
dataset_name = f"{voice}-train"
dataset_path = f"./training/{voice}/train.txt"
validation_name = f"{voice}-val"
validation_path = f"./training/{voice}/train.txt"
with open(dataset_path, 'r', encoding="utf-8") as f:
lines = len(f.readlines())
messages = []
iterations = calc_iterations(epochs=epochs, lines=lines, batch_size=batch_size)
messages.append(f"For {epochs} epochs with {lines} lines, iterating for {iterations} steps")
print_rate = int(print_rate * iterations / epochs)
save_rate = int(save_rate * iterations / epochs)
if not learning_rate_schedule:
learning_rate_schedule = EPOCH_SCHEDULE
learning_rate_schedule = schedule_learning_rate( iterations / epochs )
messages.append(save_training_settings(
iterations=iterations,
batch_size=batch_size,
learning_rate=learning_rate,
text_ce_lr_weight=text_ce_lr_weight,
learning_rate_schedule=learning_rate_schedule,
mega_batch_factor=mega_batch_factor,
print_rate=print_rate,
save_rate=save_rate,
name=name,
dataset_name=dataset_name,
dataset_path=dataset_path,
validation_name=validation_name,
validation_path=validation_path,
output_name=f"{voice}/train.yaml",
resume_path=resume_path,
half_p=half_p,
bnb=bnb,
source_model=source_model,
))
return "\n".join(messages)
def update_voices():
return (
gr.Dropdown.update(choices=get_voice_list(append_defaults=True)),
gr.Dropdown.update(choices=get_voice_list()),
gr.Dropdown.update(choices=get_voice_list("./results/")),
)
def history_copy_settings( voice, file ):
return import_generate_settings( f"./results/{voice}/{file}" )
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def setup_gradio():
global args
global ui
if not args.share:
def noop(function, return_value=None):
def wrapped(*args, **kwargs):
return return_value
return wrapped
gradio.utils.version_check = noop(gradio.utils.version_check)
gradio.utils.initiated_analytics = noop(gradio.utils.initiated_analytics)
gradio.utils.launch_analytics = noop(gradio.utils.launch_analytics)
gradio.utils.integration_analytics = noop(gradio.utils.integration_analytics)
gradio.utils.error_analytics = noop(gradio.utils.error_analytics)
gradio.utils.log_feature_analytics = noop(gradio.utils.log_feature_analytics)
#gradio.utils.get_local_ip_address = noop(gradio.utils.get_local_ip_address, 'localhost')
if args.models_from_local_only:
os.environ['TRANSFORMERS_OFFLINE']='1'
voice_list_with_defaults = get_voice_list(append_defaults=True)
voice_list = get_voice_list()
result_voices = get_voice_list("./results/")
autoregressive_models = get_autoregressive_models()
dataset_list = get_dataset_list()
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with gr.Blocks() as ui:
with gr.Tab("Generate"):
with gr.Row():
with gr.Column():
text = gr.Textbox(lines=4, label="Prompt")
with gr.Row():
with gr.Column():
delimiter = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
emotion = gr.Radio( ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom"], value="Custom", label="Emotion", type="value", interactive=True )
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prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)")
voice = gr.Dropdown(choices=voice_list_with_defaults, label="Voice", type="value", value=voice_list_with_defaults[0]) # it'd be very cash money if gradio was able to default to the first value in the list without this shit
mic_audio = gr.Audio( label="Microphone Source", source="microphone", type="filepath" )
voice_latents_chunks = gr.Slider(label="Voice Chunks", minimum=1, maximum=128, value=1, step=1)
with gr.Row():
refresh_voices = gr.Button(value="Refresh Voice List")
recompute_voice_latents = gr.Button(value="(Re)Compute Voice Latents")
def update_baseline_for_latents_chunks( voice ):
path = f'{get_voice_dir()}/{voice}/'
if not os.path.isdir(path):
return 1
files = os.listdir(path)
count = 0
for file in files:
if file[-4:] == ".wav":
count += 1
return count if count > 0 else 1
voice.change(
fn=update_baseline_for_latents_chunks,
inputs=voice,
outputs=voice_latents_chunks
)
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with gr.Column():
candidates = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
seed = gr.Number(value=0, precision=0, label="Seed")
preset = gr.Radio( ["Ultra Fast", "Fast", "Standard", "High Quality"], label="Preset", type="value" )
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num_autoregressive_samples = gr.Slider(value=128, minimum=2, maximum=512, step=1, label="Samples")
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diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
breathing_room = gr.Slider(value=8, minimum=1, maximum=32, step=1, label="Pause Size")
diffusion_sampler = gr.Radio(
["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"],
value="P", label="Diffusion Samplers", type="value" )
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show_experimental_settings = gr.Checkbox(label="Show Experimental Settings")
reset_generation_settings_button = gr.Button(value="Reset to Default")
with gr.Column(visible=False) as col:
experimental_column = col
experimental_checkboxes = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
cvvp_weight = gr.Slider(value=0, minimum=0, maximum=1, label="CVVP Weight")
top_p = gr.Slider(value=0.8, minimum=0, maximum=1, label="Top P")
diffusion_temperature = gr.Slider(value=1.0, minimum=0, maximum=1, label="Diffusion Temperature")
length_penalty = gr.Slider(value=1.0, minimum=0, maximum=8, label="Length Penalty")
repetition_penalty = gr.Slider(value=2.0, minimum=0, maximum=8, label="Repetition Penalty")
cond_free_k = gr.Slider(value=2.0, minimum=0, maximum=4, label="Conditioning-Free K")
with gr.Column():
with gr.Row():
submit = gr.Button(value="Generate")
stop = gr.Button(value="Stop")
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generation_results = gr.Dataframe(label="Results", headers=["Seed", "Time"], visible=False)
source_sample = gr.Audio(label="Source Sample", visible=False)
output_audio = gr.Audio(label="Output")
candidates_list = gr.Dropdown(label="Candidates", type="value", visible=False, choices=[""], value="")
def change_candidate( val ):
if not val:
return
return val
candidates_list.change(
fn=change_candidate,
inputs=candidates_list,
outputs=output_audio,
)
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with gr.Tab("History"):
with gr.Row():
with gr.Column():
history_info = gr.Dataframe(label="Results", headers=list(history_headers.keys()))
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with gr.Row():
with gr.Column():
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history_voices = gr.Dropdown(choices=result_voices, label="Voice", type="value", value=result_voices[0] if len(result_voices) > 0 else "")
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with gr.Column():
history_results_list = gr.Dropdown(label="Results",type="value", interactive=True, value="")
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with gr.Column():
history_audio = gr.Audio()
history_copy_settings_button = gr.Button(value="Copy Settings")
with gr.Tab("Utilities"):
with gr.Row():
with gr.Column():
audio_in = gr.Files(type="file", label="Audio Input", file_types=["audio"])
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import_voice_name = gr.Textbox(label="Voice Name")
import_voice_button = gr.Button(value="Import Voice")
with gr.Column():
metadata_out = gr.JSON(label="Audio Metadata", visible=False)
copy_button = gr.Button(value="Copy Settings", visible=False)
latents_out = gr.File(type="binary", label="Voice Latents", visible=False)
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with gr.Tab("Training"):
with gr.Tab("Prepare Dataset"):
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with gr.Row():
with gr.Column():
dataset_settings = [
gr.Dropdown( choices=voice_list, label="Dataset Source", type="value", value=voice_list[0] if len(voice_list) > 0 else "" ),
gr.Textbox(label="Language", placeholder="English")
]
prepare_dataset_button = gr.Button(value="Prepare")
with gr.Column():
prepare_dataset_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
with gr.Tab("Generate Configuration"):
with gr.Row():
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with gr.Column():
training_settings = [
gr.Number(label="Epochs", value=500, precision=0),
]
with gr.Row():
with gr.Column():
training_settings = training_settings + [
gr.Slider(label="Learning Rate", value=1e-5, minimum=0, maximum=1e-4, step=1e-6),
gr.Slider(label="Text_CE LR Ratio", value=0.01, minimum=0, maximum=1),
]
training_settings = training_settings + [
gr.Textbox(label="Learning Rate Schedule", placeholder=str(EPOCH_SCHEDULE)),
]
with gr.Row():
training_settings = training_settings + [
gr.Number(label="Batch Size", value=128, precision=0),
gr.Number(label="Mega Batch Factor", value=4, precision=0),
]
with gr.Row():
training_settings = training_settings + [
gr.Number(label="Print Frequency (in epochs)", value=5, precision=0),
gr.Number(label="Save Frequency (in epochs)", value=5, precision=0),
]
training_settings = training_settings + [
gr.Textbox(label="Resume State Path", placeholder="./training/${voice}-finetune/training_state/${last_state}.state"),
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]
training_halfp = gr.Checkbox(label="Half Precision", value=args.training_default_halfp)
training_bnb = gr.Checkbox(label="BitsAndBytes", value=args.training_default_bnb)
source_model = gr.Dropdown( choices=autoregressive_models, label="Source Model", type="value", value=autoregressive_models[0] )
dataset_list_dropdown = gr.Dropdown( choices=dataset_list, label="Dataset", type="value", value=dataset_list[0] if len(dataset_list) else "" )
training_settings = training_settings + [ training_halfp, training_bnb, source_model, dataset_list_dropdown ]
with gr.Row():
refresh_dataset_list = gr.Button(value="Refresh Dataset List")
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import_dataset_button = gr.Button(value="Reuse/Import Dataset")
with gr.Column():
save_yaml_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
with gr.Row():
optimize_yaml_button = gr.Button(value="Validate Training Configuration")
save_yaml_button = gr.Button(value="Save Training Configuration")
with gr.Tab("Run Training"):
with gr.Row():
with gr.Column():
training_configs = gr.Dropdown(label="Training Configuration", choices=get_training_list())
with gr.Row():
refresh_configs = gr.Button(value="Refresh Configurations")
training_loss_graph = gr.LinePlot(label="Training Metrics",
x="step",
y="value",
title="Training Metrics",
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color="type",
tooltip=['step', 'value', 'type'],
width=600,
height=350,
)
view_losses = gr.Button(value="View Losses")
with gr.Column():
training_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
verbose_training = gr.Checkbox(label="Verbose Console Output", value=True)
training_buffer_size = gr.Slider(label="Console Buffer Size", minimum=4, maximum=32, value=8)
training_keep_x_past_datasets = gr.Slider(label="Keep X Previous States", minimum=0, maximum=8, value=0, step=1)
training_gpu_count = gr.Number(label="GPUs", value=1)
with gr.Row():
start_training_button = gr.Button(value="Train")
stop_training_button = gr.Button(value="Stop")
reconnect_training_button = gr.Button(value="Reconnect")
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with gr.Tab("Settings"):
with gr.Row():
exec_inputs = []
with gr.Column():
exec_inputs = exec_inputs + [
gr.Textbox(label="Listen", value=args.listen, placeholder="127.0.0.1:7860/"),
gr.Checkbox(label="Public Share Gradio", value=args.share),
gr.Checkbox(label="Check For Updates", value=args.check_for_updates),
gr.Checkbox(label="Only Load Models Locally", value=args.models_from_local_only),
gr.Checkbox(label="Low VRAM", value=args.low_vram),
gr.Checkbox(label="Embed Output Metadata", value=args.embed_output_metadata),
gr.Checkbox(label="Slimmer Computed Latents", value=args.latents_lean_and_mean),
gr.Checkbox(label="Use Voice Fixer on Generated Output", value=args.voice_fixer),
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gr.Checkbox(label="Use CUDA for Voice Fixer", value=args.voice_fixer_use_cuda),
gr.Checkbox(label="Force CPU for Conditioning Latents", value=args.force_cpu_for_conditioning_latents),
gr.Checkbox(label="Do Not Load TTS On Startup", value=args.defer_tts_load),
gr.Checkbox(label="Delete Non-Final Output", value=args.prune_nonfinal_outputs),
gr.Checkbox(label="Use BigVGAN Vocoder", value=args.use_bigvgan_vocoder),
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gr.Textbox(label="Device Override", value=args.device_override),
]
with gr.Column():
exec_inputs = exec_inputs + [
gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size),
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gr.Number(label="Gradio Concurrency Count", precision=0, value=args.concurrency_count),
gr.Number(label="Output Sample Rate", precision=0, value=args.output_sample_rate),
gr.Slider(label="Output Volume", minimum=0, maximum=2, value=args.output_volume),
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]
autoregressive_model_dropdown = gr.Dropdown(choices=autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else autoregressive_models[0])
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whisper_model_dropdown = gr.Dropdown(["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large"], label="Whisper Model", value=args.whisper_model)
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use_whisper_cpp = gr.Checkbox(label="Use Whisper.cpp", value=args.whisper_cpp)
exec_inputs = exec_inputs + [ autoregressive_model_dropdown, whisper_model_dropdown, use_whisper_cpp, training_halfp, training_bnb ]
with gr.Row():
autoregressive_models_update_button = gr.Button(value="Refresh Model List")
gr.Button(value="Check for Updates").click(check_for_updates)
gr.Button(value="(Re)Load TTS").click(
reload_tts,
inputs=autoregressive_model_dropdown,
outputs=None
)
def update_model_list_proxy( val ):
autoregressive_models = get_autoregressive_models()
if val not in autoregressive_models:
val = autoregressive_models[0]
return gr.update( choices=autoregressive_models, value=val )
autoregressive_models_update_button.click(
update_model_list_proxy,
inputs=autoregressive_model_dropdown,
outputs=autoregressive_model_dropdown,
)
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for i in exec_inputs:
i.change( fn=update_args, inputs=exec_inputs )
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autoregressive_model_dropdown.change(
fn=update_autoregressive_model,
inputs=autoregressive_model_dropdown,
outputs=None
)
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input_settings = [
text,
delimiter,
emotion,
prompt,
voice,
mic_audio,
voice_latents_chunks,
seed,
candidates,
num_autoregressive_samples,
diffusion_iterations,
temperature,
diffusion_sampler,
breathing_room,
cvvp_weight,
top_p,
diffusion_temperature,
length_penalty,
repetition_penalty,
cond_free_k,
experimental_checkboxes,
]
history_voices.change(
fn=history_view_results,
inputs=history_voices,
outputs=[
history_info,
history_results_list,
]
)
history_results_list.change(
fn=lambda voice, file: f"./results/{voice}/{file}",
inputs=[
history_voices,
history_results_list,
],
outputs=history_audio
)
audio_in.upload(
fn=read_generate_settings_proxy,
inputs=audio_in,
outputs=[
metadata_out,
copy_button,
latents_out,
import_voice_name
]
)
import_voice_button.click(
fn=import_voices_proxy,
inputs=[
audio_in,
import_voice_name,
],
outputs=import_voice_name #console_output
)
show_experimental_settings.change(
fn=lambda x: gr.update(visible=x),
inputs=show_experimental_settings,
outputs=experimental_column
)
preset.change(fn=update_presets,
inputs=preset,
outputs=[
num_autoregressive_samples,
diffusion_iterations,
],
)
recompute_voice_latents.click(compute_latents,
inputs=[
voice,
voice_latents_chunks,
],
outputs=voice,
)
prompt.change(fn=lambda value: gr.update(value="Custom"),
inputs=prompt,
outputs=emotion
)
mic_audio.change(fn=lambda value: gr.update(value="microphone"),
inputs=mic_audio,
outputs=voice
)
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refresh_voices.click(update_voices,
inputs=None,
outputs=[
voice,
dataset_settings[0],
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history_voices
]
)
submit.click(
lambda: (gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)),
outputs=[source_sample, candidates_list, generation_results],
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)
submit_event = submit.click(run_generation,
inputs=input_settings,
outputs=[output_audio, source_sample, candidates_list, generation_results],
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)
copy_button.click(import_generate_settings,
inputs=audio_in, # JSON elements cannot be used as inputs
outputs=input_settings
)
reset_generation_settings_button.click(
fn=reset_generation_settings,
inputs=None,
outputs=input_settings
)
history_copy_settings_button.click(history_copy_settings,
inputs=[
history_voices,
history_results_list,
],
outputs=input_settings
)
refresh_configs.click(
lambda: gr.update(choices=get_training_list()),
inputs=None,
outputs=training_configs
)
start_training_button.click(run_training,
inputs=[
training_configs,
verbose_training,
training_gpu_count,
training_buffer_size,
training_keep_x_past_datasets,
],
outputs=[
training_output,
],
)
training_output.change(
fn=update_training_dataplot,
inputs=None,
outputs=[
training_loss_graph,
],
show_progress=False,
)
view_losses.click(
fn=update_training_dataplot,
inputs=[
training_configs
],
outputs=[
training_loss_graph,
],
)
stop_training_button.click(stop_training,
inputs=None,
outputs=training_output #console_output
)
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reconnect_training_button.click(reconnect_training,
inputs=[
verbose_training,
training_buffer_size,
],
outputs=training_output #console_output
)
prepare_dataset_button.click(
prepare_dataset_proxy,
inputs=dataset_settings,
outputs=prepare_dataset_output #console_output
)
refresh_dataset_list.click(
lambda: gr.update(choices=get_dataset_list()),
inputs=None,
outputs=dataset_list_dropdown,
)
optimize_yaml_button.click(optimize_training_settings_proxy,
inputs=training_settings,
outputs=training_settings[1:9] + [save_yaml_output] #console_output
)
import_dataset_button.click(import_training_settings_proxy,
inputs=dataset_list_dropdown,
outputs=training_settings[:11] + [save_yaml_output] #console_output
)
save_yaml_button.click(save_training_settings_proxy,
inputs=training_settings,
outputs=save_yaml_output #console_output
)
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if os.path.isfile('./config/generate.json'):
ui.load(import_generate_settings, inputs=None, outputs=input_settings)
if args.check_for_updates:
ui.load(check_for_updates)
stop.click(fn=cancel_generate, inputs=None, outputs=None)
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ui.queue(concurrency_count=args.concurrency_count)
webui = ui
return webui