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big cleanup to make my life easier when i add more parameters

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mrq 2023-03-09 00:26:47 +00:00
parent 0ab091e7ff
commit 3f321fe664
5 changed files with 482 additions and 702 deletions
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12
models/.template.yaml
View File

@ -1,16 +1,18 @@
name: ${name}
name: ${voice}
model: extensibletrainer
scale: 1
gpu_ids: [0] # Superfluous, redundant, unnecessary, the way you launch the training script will set this
start_step: 0
checkpointing_enabled: true
fp16: ${float16}
fp16: ${half_p}
bitsandbytes: ${bitsandbytes}
gpus: ${gpus}
wandb: false
use_tb_logger: true
datasets:
train:
name: ${dataset_name}
name: training
n_workers: ${workers}
batch_size: ${batch_size}
mode: paired_voice_audio
@ -27,7 +29,7 @@ datasets:
tokenizer_vocab: ./models/tortoise/bpe_lowercase_asr_256.json
load_aligned_codes: False
val: # I really do not care about validation right now
name: ${validation_name}
name: validation
n_workers: ${workers}
batch_size: ${validation_batch_size}
mode: paired_voice_audio
@ -114,8 +116,8 @@ networks:
#only_alignment_head: False # uv3/4
path:
${pretrain_model_gpt}
strict_load: true
${source_model}
${resume_state}
train:

35
src/cull_dataset.py
View File

@ -1,35 +0,0 @@
import os
import sys
indir = f'./training/{sys.argv[1]}/'
cap = int(sys.argv[2])
if not os.path.isdir(indir):
raise Exception(f"Invalid directory: {indir}")
if not os.path.exists(f'{indir}/train.txt'):
raise Exception(f"Missing dataset: {indir}/train.txt")
with open(f'{indir}/train.txt', 'r', encoding="utf-8") as f:
lines = f.readlines()
validation = []
training = []
for line in lines:
split = line.split("|")
filename = split[0]
text = split[1]
if len(text) < cap:
validation.append(line.strip())
else:
training.append(line.strip())
with open(f'{indir}/train_culled.txt', 'w', encoding="utf-8") as f:
f.write("\n".join(training))
with open(f'{indir}/validation.txt', 'w', encoding="utf-8") as f:
f.write("\n".join(validation))
print(f"Culled {len(validation)} lines")

3
src/train.py
View File

@ -46,6 +46,7 @@ sys.path.insert(0, './dlas/')
# without kludge, it'll have to be accessible as `codes` and not `dlas`
import torch
import datetime
from codes import train as tr
from utils import util, options as option
@ -71,7 +72,7 @@ def train(yaml, launcher='none'):
print('Disabled distributed training.')
else:
opt['dist'] = True
tr.init_dist('nccl')
tr.init_dist('nccl', timeout=datetime.timedelta(seconds=5*60))
trainer.world_size = torch.distributed.get_world_size()
trainer.rank = torch.distributed.get_rank()
torch.cuda.set_device(torch.distributed.get_rank())

546
src/utils.py
View File

@ -34,7 +34,7 @@ from datetime import timedelta
from tortoise.api import TextToSpeech, MODELS, get_model_path, pad_or_truncate
from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_dir
from tortoise.utils.text import split_and_recombine_text
from tortoise.utils.device import get_device_name, set_device_name
from tortoise.utils.device import get_device_name, set_device_name, get_device_count
MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
@ -44,6 +44,8 @@ WHISPER_BACKENDS = ["openai/whisper", "lightmare/whispercpp", "m-bain/whisperx"]
VOCODERS = ['univnet', 'bigvgan_base_24khz_100band', 'bigvgan_24khz_100band']
GENERATE_SETTINGS_ARGS = None
EPOCH_SCHEDULE = [ 9, 18, 25, 33 ]
args = None
@ -56,30 +58,17 @@ training_state = None
current_voice = None
def generate(
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=None
):
def generate(**kwargs):
parameters = {}
parameters.update(kwargs)
voice = parameters['voice']
progress = parameters['progress'] if 'progress' in parameters else None
if parameters['seed'] == 0:
parameters['seed'] = None
usedSeed = parameters['seed']
global args
global tts
@ -90,6 +79,8 @@ def generate(
# should check if it's loading or unloaded, and load it if it's unloaded
if tts_loading:
raise Exception("TTS is still initializing...")
if progress is not None:
progress(0, "Initializing TTS...")
load_tts()
if hasattr(tts, "loading") and tts.loading:
raise Exception("TTS is still initializing...")
@ -100,9 +91,6 @@ def generate(
conditioning_latents =None
sample_voice = None
if seed == 0:
seed = None
voice_cache = {}
def fetch_voice( voice ):
print(f"Loading voice: {voice} with model {tts.autoregressive_model_hash[:8]}")
@ -112,9 +100,9 @@ def generate(
sample_voice = None
if voice == "microphone":
if mic_audio is None:
if parameters['mic_audio'] is None:
raise Exception("Please provide audio from mic when choosing `microphone` as a voice input")
voice_samples, conditioning_latents = [load_audio(mic_audio, tts.input_sample_rate)], None
voice_samples, conditioning_latents = [load_audio(parameters['mic_audio'], tts.input_sample_rate)], None
elif voice == "random":
voice_samples, conditioning_latents = None, tts.get_random_conditioning_latents()
else:
@ -125,7 +113,7 @@ def generate(
if voice_samples and len(voice_samples) > 0:
if conditioning_latents is None:
conditioning_latents = compute_latents(voice=voice, voice_samples=voice_samples, voice_latents_chunks=voice_latents_chunks)
conditioning_latents = compute_latents(voice=voice, voice_samples=voice_samples, voice_latents_chunks=parameters['voice_latents_chunks'])
sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu()
voice_samples = None
@ -135,30 +123,30 @@ def generate(
def get_settings( override=None ):
settings = {
'temperature': float(temperature),
'temperature': float(parameters['temperature']),
'top_p': float(top_p),
'diffusion_temperature': float(diffusion_temperature),
'length_penalty': float(length_penalty),
'repetition_penalty': float(repetition_penalty),
'cond_free_k': float(cond_free_k),
'top_p': float(parameters['top_p']),
'diffusion_temperature': float(parameters['diffusion_temperature']),
'length_penalty': float(parameters['length_penalty']),
'repetition_penalty': float(parameters['repetition_penalty']),
'cond_free_k': float(parameters['cond_free_k']),
'num_autoregressive_samples': num_autoregressive_samples,
'num_autoregressive_samples': parameters['num_autoregressive_samples'],
'sample_batch_size': args.sample_batch_size,
'diffusion_iterations': diffusion_iterations,
'diffusion_iterations': parameters['diffusion_iterations'],
'voice_samples': None,
'conditioning_latents': None,
'use_deterministic_seed': seed,
'use_deterministic_seed': parameters['seed'],
'return_deterministic_state': True,
'k': candidates,
'diffusion_sampler': diffusion_sampler,
'breathing_room': breathing_room,
'progress': progress,
'half_p': "Half Precision" in experimental_checkboxes,
'cond_free': "Conditioning-Free" in experimental_checkboxes,
'cvvp_amount': cvvp_weight,
'k': parameters['candidates'],
'diffusion_sampler': parameters['diffusion_sampler'],
'breathing_room': parameters['breathing_room'],
'progress': parameters['progress'],
'half_p': "Half Precision" in parameters['experimentals'],
'cond_free': "Conditioning-Free" in parameters['experimentals'],
'cvvp_amount': parameters['cvvp_weight'],
'autoregressive_model': args.autoregressive_model,
}
@ -182,11 +170,11 @@ def generate(
# clamp it down for the insane users who want this
# it would be wiser to enforce the sample size to the batch size, but this is what the user wants
sample_batch_size = args.sample_batch_size
if not sample_batch_size:
sample_batch_size = tts.autoregressive_batch_size
if num_autoregressive_samples < sample_batch_size:
settings['sample_batch_size'] = num_autoregressive_samples
settings['sample_batch_size'] = args.sample_batch_size
if not settings['sample_batch_size']:
settings['sample_batch_size'] = tts.autoregressive_batch_size
if settings['num_autoregressive_samples'] < settings['sample_batch_size']:
settings['sample_batch_size'] = settings['num_autoregressive_samples']
if settings['conditioning_latents'] is not None and len(settings['conditioning_latents']) == 2 and settings['cvvp_amount'] > 0:
print("Requesting weighing against CVVP weight, but voice latents are missing some extra data. Please regenerate your voice latents with 'Slimmer voice latents' unchecked.")
@ -194,15 +182,15 @@ def generate(
return settings
if not delimiter:
delimiter = "\n"
elif delimiter == "\\n":
delimiter = "\n"
if not parameters['delimiter']:
parameters['delimiter'] = "\n"
elif parameters['delimiter'] == "\\n":
parameters['delimiter'] = "\n"
if delimiter and delimiter != "" and delimiter in text:
texts = text.split(delimiter)
if parameters['delimiter'] and parameters['delimiter'] != "" and parameters['delimiter'] in parameters['text']:
texts = parameters['text'].split(parameters['delimiter'])
else:
texts = split_and_recombine_text(text)
texts = split_and_recombine_text(parameters['text'])
full_start_time = time.time()
@ -248,37 +236,23 @@ def generate(
name = f"{name}_combined"
elif len(texts) > 1:
name = f"{name}_{line}"
if candidates > 1:
if parameters['candidates'] > 1:
name = f"{name}_{candidate}"
return name
def get_info( voice, settings = None, latents = True ):
info = {
'text': text,
'delimiter': '\\n' if delimiter and delimiter == "\n" else delimiter,
'emotion': emotion,
'prompt': prompt,
'voice': voice,
'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,
'experimentals': experimental_checkboxes,
'time': time.time()-full_start_time,
info = {}
info.update(parameters)
info['time'] = time.time()-full_start_time,
'datetime': datetime.now().isoformat(),
'model': tts.autoregressive_model_path,
'model_hash': tts.autoregressive_model_hash
}
info['datetime'] = datetime.now().isoformat(),
info['model'] = tts.autoregressive_model_path,
info['model_hash'] = tts.autoregressive_model_hash
info['progress'] = None
del info['progress']
if info['delimiter'] == "\n":
info['delimiter'] = "\\n"
if settings is not None:
for k in settings:
@ -319,11 +293,11 @@ def generate(
return info
for line, cut_text in enumerate(texts):
if emotion == "Custom":
if prompt and prompt.strip() != "":
cut_text = f"[{prompt},] {cut_text}"
elif emotion != "None" and emotion:
cut_text = f"[I am really {emotion.lower()},] {cut_text}"
if parameters['emotion'] == "Custom":
if parameters['prompt'] and parameters['prompt'].strip() != "":
cut_text = f"[{parameters['prompt']},] {cut_text}"
elif parameters['emotion'] != "None" and parameters['emotion']:
cut_text = f"[I am really {parameters['emotion'].lower()},] {cut_text}"
progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]'
print(f"{progress.msg_prefix} Generating line: {cut_text}")
@ -343,10 +317,10 @@ def generate(
settings = get_settings( override=override )
gen, additionals = tts.tts(cut_text, **settings )
seed = additionals[0]
parameters['seed'] = additionals[0]
run_time = time.time()-start_time
print(f"Generating line took {run_time} seconds")
if not isinstance(gen, list):
gen = [gen]
@ -382,7 +356,7 @@ def generate(
torchaudio.save(f'{outdir}/{voice}_{k}.wav', audio, args.output_sample_rate)
output_voices = []
for candidate in range(candidates):
for candidate in range(parameters['candidates']):
if len(texts) > 1:
audio_clips = []
for line in range(len(texts)):
@ -466,7 +440,7 @@ def generate(
info = get_info(voice=voice, latents=False)
print(f"Generation took {info['time']} seconds, saved to '{output_voices[0]}'\n")
info['seed'] = seed
info['seed'] = usedSeed
if 'latents' in info:
del info['latents']
@ -475,7 +449,7 @@ def generate(
f.write(json.dumps(info, indent='\t') )
stats = [
[ seed, "{:.3f}".format(info['time']) ]
[ parameters['seed'], "{:.3f}".format(info['time']) ]
]
return (
@ -609,14 +583,16 @@ def compute_latents(voice=None, voice_samples=None, voice_latents_chunks=0, prog
# superfluous, but it cleans up some things
class TrainingState():
def __init__(self, config_path, keep_x_past_checkpoints=0, start=True, gpus=1):
def __init__(self, config_path, keep_x_past_checkpoints=0, start=True):
# parse config to get its iteration
with open(config_path, 'r') as file:
self.config = yaml.safe_load(file)
gpus = self.config["gpus"]
self.killed = False
self.dataset_dir = f"./training/{self.config['name']}/"
self.dataset_dir = f"./training/{self.config['name']}/finetune/"
self.batch_size = self.config['datasets']['train']['batch_size']
self.dataset_path = self.config['datasets']['train']['path']
with open(self.dataset_path, 'r', encoding="utf-8") as f:
@ -996,7 +972,7 @@ except Exception as e:
print(e)
pass
def run_training(config_path, verbose=False, gpus=1, keep_x_past_checkpoints=0, progress=gr.Progress(track_tqdm=True)):
def run_training(config_path, verbose=False, keep_x_past_checkpoints=0, progress=gr.Progress(track_tqdm=True)):
global training_state
if training_state and training_state.process:
return "Training already in progress"
@ -1008,26 +984,11 @@ def run_training(config_path, verbose=False, gpus=1, keep_x_past_checkpoints=0,
# I don't know if this is still necessary, as it was bitching at me for not doing this, despite it being in a separate process
torch.multiprocessing.freeze_support()
# edit any gpu-count-specific variables
with open(config_path, 'r', encoding="utf-8") as f:
yaml_string = f.read()
edited = False
if gpus > 1:
yaml_string = yaml_string.replace(" adamw ", " adamw_zero ")
edited = True
else:
yaml_string = yaml_string.replace(" adamw_zero ", " adamw ")
edited = True
if edited:
print(f'Modified YAML config')
with open(config_path, 'w', encoding="utf-8") as f:
f.write(yaml_string)
unload_tts()
unload_whisper()
unload_voicefixer()
training_state = TrainingState(config_path=config_path, keep_x_past_checkpoints=keep_x_past_checkpoints, gpus=gpus)
training_state = TrainingState(config_path=config_path, keep_x_past_checkpoints=keep_x_past_checkpoints)
for line in iter(training_state.process.stdout.readline, ""):
if training_state.killed:
@ -1169,7 +1130,7 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
if whisper_model is None:
load_whisper_model(language=language)
os.makedirs(outdir, exist_ok=True)
os.makedirs(f'{outdir}/audio/', exist_ok=True)
results = {}
transcription = []
@ -1216,10 +1177,10 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
print(f"Error with {sliced_name}, skipping...")
continue
torchaudio.save(f"{outdir}/{sliced_name}", sliced_waveform, sampling_rate)
torchaudio.save(f"{outdir}/audio/{sliced_name}", sliced_waveform, sampling_rate)
idx = idx + 1
line = f"{sliced_name}|{segment['text'].strip()}"
line = f"audio/{sliced_name}|{segment['text'].strip()}"
transcription.append(line)
with open(f'{outdir}/train.txt', 'a', encoding="utf-8") as f:
f.write(f'\n{line}')
@ -1283,125 +1244,142 @@ def calc_iterations( epochs, lines, batch_size ):
def schedule_learning_rate( iterations, schedule=EPOCH_SCHEDULE ):
return [int(iterations * d) for d in schedule]
def optimize_training_settings( epochs, learning_rate, text_ce_lr_weight, learning_rate_schedule, batch_size, gradient_accumulation_size, print_rate, save_rate, validation_rate, resume_path, half_p, bnb, workers, source_model, voice ):
name = f"{voice}-finetune"
dataset_path = f"./training/{voice}/train.txt"
def optimize_training_settings( **kwargs ):
messages = []
settings = {}
settings.update(kwargs)
dataset_path = f"./training/{settings['voice']}/train.txt"
with open(dataset_path, 'r', encoding="utf-8") as f:
lines = len(f.readlines())
messages = []
if settings['batch_size'] > lines:
settings['batch_size'] = lines
messages.append(f"Batch size is larger than your dataset, clamping batch size to: {settings['batch_size']}")
if batch_size > lines:
batch_size = lines
messages.append(f"Batch size is larger than your dataset, clamping batch size to: {batch_size}")
if batch_size % lines != 0:
nearest_slice = int(lines / batch_size) + 1
batch_size = int(lines / nearest_slice)
messages.append(f"Batch size not neatly divisible by dataset size, adjusting batch size to: {batch_size} ({nearest_slice} steps per epoch)")
if settings['batch_size'] % lines != 0:
nearest_slice = int(lines / settings['batch_size']) + 1
settings['batch_size'] = int(lines / nearest_slice)
messages.append(f"Batch size not neatly divisible by dataset size, adjusting batch size to: {settings['batch_size']} ({nearest_slice} steps per epoch)")
if gradient_accumulation_size == 0:
gradient_accumulation_size = 1
if settings['gradient_accumulation_size'] == 0:
settings['gradient_accumulation_size'] = 1
if batch_size / gradient_accumulation_size < 2:
gradient_accumulation_size = int(batch_size / 2)
if gradient_accumulation_size == 0:
gradient_accumulation_size = 1
if settings['batch_size'] / settings['gradient_accumulation_size'] < 2:
settings['gradient_accumulation_size'] = int(settings['batch_size'] / 2)
if settings['gradient_accumulation_size'] == 0:
settings['gradient_accumulation_size'] = 1
messages.append(f"Gradient accumulation size is too large for a given batch size, clamping gradient accumulation size to: {gradient_accumulation_size}")
elif batch_size % gradient_accumulation_size != 0:
gradient_accumulation_size = int(batch_size / gradient_accumulation_size)
if gradient_accumulation_size == 0:
gradient_accumulation_size = 1
messages.append(f"Gradient accumulation size is too large for a given batch size, clamping gradient accumulation size to: {settings['gradient_accumulation_size']}")
elif settings['batch_size'] % settings['gradient_accumulation_size'] != 0:
settings['gradient_accumulation_size'] = int(settings['batch_size'] / settings['gradient_accumulation_size'])
if settings['gradient_accumulation_size'] == 0:
settings['gradient_accumulation_size'] = 1
messages.append(f"Batch size is not evenly divisible by the gradient accumulation size, adjusting gradient accumulation size to: {gradient_accumulation_size}")
messages.append(f"Batch size is not evenly divisible by the gradient accumulation size, adjusting gradient accumulation size to: {settings['gradient_accumulation_size']}")
iterations = calc_iterations(epochs=epochs, lines=lines, batch_size=batch_size)
iterations = calc_iterations(epochs=settings['epochs'], lines=lines, batch_size=settings['batch_size'])
if epochs < print_rate:
print_rate = epochs
messages.append(f"Print rate is too small for the given iteration step, clamping print rate to: {print_rate}")
if settings['epochs'] < settings['print_rate']:
settings['print_rate'] = settings['epochs']
messages.append(f"Print rate is too small for the given iteration step, clamping print rate to: {settings['print_rate']}")
if epochs < save_rate:
save_rate = epochs
messages.append(f"Save rate is too small for the given iteration step, clamping save rate to: {save_rate}")
if settings['epochs'] < settings['save_rate']:
settings['save_rate'] = settings['epochs']
messages.append(f"Save rate is too small for the given iteration step, clamping save rate to: {settings['save_rate']}")
if epochs < validation_rate:
validation_rate = epochs
messages.append(f"Validation rate is too small for the given iteration step, clamping validation rate to: {validation_rate}")
if settings['epochs'] < settings['validation_rate']:
settings['validation_rate'] = settings['epochs']
messages.append(f"Validation rate is too small for the given iteration step, clamping validation rate to: {settings['validation_rate']}")
if resume_path and not os.path.exists(resume_path):
resume_path = None
if settings['resume_state'] and not os.path.exists(settings['resume_state']):
settings['resume_state'] = None
messages.append("Resume path specified, but does not exist. Disabling...")
if bnb:
if settings['bitsandbytes']:
messages.append("BitsAndBytes requested. Please note this is ! EXPERIMENTAL !")
if half_p:
if bnb:
half_p = False
if settings['half_p']:
if settings['bitsandbytes']:
settings['half_p'] = False
messages.append("Half Precision requested, but BitsAndBytes is also requested. Due to redundancies, disabling half precision...")
else:
messages.append("Half Precision requested. Please note this is ! EXPERIMENTAL !")
if not os.path.exists(get_halfp_model_path()):
convert_to_halfp()
messages.append(f"For {epochs} epochs with {lines} lines in batches of {batch_size}, iterating for {iterations} steps ({int(iterations / epochs)} steps per epoch)")
messages.append(f"For {settings['epochs']} epochs with {lines} lines in batches of {settings['batch_size']}, iterating for {iterations} steps ({int(iterations / settings['epochs'])} steps per epoch)")
return (
learning_rate,
text_ce_lr_weight,
learning_rate_schedule,
batch_size,
gradient_accumulation_size,
print_rate,
save_rate,
validation_rate,
resume_path,
messages
)
return settings, messages
def save_training_settings( iterations=None, learning_rate=None, text_ce_lr_weight=None, learning_rate_scheme=None, learning_rate_schedule=None, batch_size=None, gradient_accumulation_size=None, print_rate=None, save_rate=None, validation_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None, validation_batch_size=None, output_name=None, resume_path=None, half_p=None, bnb=None, workers=None, source_model=None ):
if not source_model:
source_model = f"./models/tortoise/autoregressive{'_half' if half_p else ''}.pth"
def save_training_settings( **kwargs ):
messages = []
settings = {}
settings.update(kwargs)
settings = {
"iterations": iterations if iterations else 500,
"batch_size": batch_size if batch_size else 64,
"learning_rate": learning_rate if learning_rate else 1e-5,
"gradient_accumulation_size": gradient_accumulation_size if gradient_accumulation_size else 4,
"print_rate": print_rate if print_rate else 1,
"save_rate": save_rate if save_rate else 50,
"name": name if name else "finetune",
"dataset_name": dataset_name if dataset_name else "finetune",
"dataset_path": dataset_path if dataset_path else "./training/finetune/train.txt",
"validation_name": validation_name if validation_name else "finetune",
"validation_path": validation_path if validation_path else "./training/finetune/train.txt",
'validation_rate': validation_rate if validation_rate else iterations,
"validation_batch_size": validation_batch_size if validation_batch_size else batch_size,
'validation_enabled': "true",
settings['dataset_path'] = f"./training/{settings['voice']}/train.txt"
settings['validation_path'] = f"./training/{settings['voice']}/validation.txt"
"text_ce_lr_weight": text_ce_lr_weight if text_ce_lr_weight else 0.01,
with open(settings['dataset_path'], 'r', encoding="utf-8") as f:
lines = len(f.readlines())
'resume_state': f"resume_state: '{resume_path}'",
'pretrain_model_gpt': f"pretrain_model_gpt: '{source_model}'",
if not settings['source_model'] or settings['source_model'] == "auto":
settings['source_model'] = f"./models/tortoise/autoregressive{'_half' if settings['half_p'] else ''}.pth"
'float16': 'true' if half_p else 'false',
'bitsandbytes': 'true' if bnb else 'false',
if settings['half_p']:
if not os.path.exists(get_halfp_model_path()):
convert_to_halfp()
'workers': workers if workers else 2,
}
settings['iterations'] = calc_iterations(epochs=settings['epochs'], lines=lines, batch_size=settings['batch_size'])
messages.append(f"For {settings['epochs']} epochs with {lines} lines, iterating for {settings['iterations']} steps")
settings['print_rate'] = int(settings['print_rate'] * settings['iterations'] / settings['epochs'])
settings['save_rate'] = int(settings['save_rate'] * settings['iterations'] / settings['epochs'])
settings['validation_rate'] = int(settings['validation_rate'] * settings['iterations'] / settings['epochs'])
settings['validation_batch_size'] = int(settings['batch_size'] / settings['gradient_accumulation_size'])
settings['iterations'] = calc_iterations(epochs=settings['epochs'], lines=lines, batch_size=settings['batch_size'])
if settings['iterations'] % settings['save_rate'] != 0:
adjustment = int(settings['iterations'] / settings['save_rate']) * settings['save_rate']
messages.append(f"Iteration rate is not evenly divisible by save rate, adjusting: {settings['iterations']} => {adjustment}")
settings['iterations'] = adjustment
if not os.path.exists(settings['validation_path']):
settings['validation_enabled'] = False
messages.append("Validation not found, disabling validation...")
elif settings['validation_batch_size'] == 0:
settings['validation_enabled'] = False
messages.append("Validation batch size == 0, disabling validation...")
else:
settings['validation_enabled'] = True
with open(settings['validation_path'], 'r', encoding="utf-8") as f:
validation_lines = len(f.readlines())
if validation_lines < settings['validation_batch_size']:
settings['validation_batch_size'] = validation_lines
messages.append(f"Batch size exceeds validation dataset size, clamping validation batch size to {validation_lines}")
if settings['gpus'] > get_device_count():
settings['gpus'] = get_device_count()
LEARNING_RATE_SCHEMES = ["MultiStepLR", "CosineAnnealingLR_Restart"]
if learning_rate_scheme not in LEARNING_RATE_SCHEMES:
learning_rate_scheme = LEARNING_RATE_SCHEMES[0]
if 'learning_rate_scheme' not in settings or settings['learning_rate_scheme'] not in LEARNING_RATE_SCHEMES:
settings['learning_rate_scheme'] = LEARNING_RATE_SCHEMES[0]
learning_rate_schema = [f"default_lr_scheme: {learning_rate_scheme}"]
if learning_rate_scheme == "MultiStepLR":
learning_rate_schema.append(f" gen_lr_steps: {learning_rate_schedule if learning_rate_schedule else EPOCH_SCHEDULE}")
learning_rate_schema = [f"default_lr_scheme: {settings['learning_rate_scheme']}"]
if settings['learning_rate_scheme'] == "MultiStepLR":
if not settings['learning_rate_schedule']:
settings['learning_rate_schedule'] = EPOCH_SCHEDULE
elif isinstance(settings['learning_rate_schedule'],str):
settings['learning_rate_schedule'] = json.loads(settings['learning_rate_schedule'])
settings['learning_rate_schedule'] = schedule_learning_rate( settings['iterations'] / settings['epochs'], settings['learning_rate_schedule'] )
learning_rate_schema.append(f" gen_lr_steps: {settings['learning_rate_schedule']}")
learning_rate_schema.append(f" lr_gamma: 0.5")
elif learning_rate_scheme == "CosineAnnealingLR_Restart":
elif settings['learning_rate_scheme'] == "CosineAnnealingLR_Restart":
learning_rate_schema.append(f" T_period: [120000, 120000, 120000]")
learning_rate_schema.append(f" warmup: 10000")
learning_rate_schema.append(f" eta_min: .01")
@ -1409,23 +1387,26 @@ def save_training_settings( iterations=None, learning_rate=None, text_ce_lr_weig
learning_rate_schema.append(f" restart_weights: [.5, .25]")
settings['learning_rate_scheme'] = "\n".join(learning_rate_schema)
if resume_path:
"""
if resume_state:
settings['pretrain_model_gpt'] = f"# {settings['pretrain_model_gpt']}"
else:
settings['resume_state'] = f"# resume_state: './training/{name if name else 'finetune'}/training_state/#.state'"
settings['resume_state'] = f"# resume_state: './training/{voice}/training_state/#.state'"
# also disable validation if it doesn't make sense to do it
if settings['dataset_path'] == settings['validation_path'] or not os.path.exists(settings['validation_path']):
settings['validation_enabled'] = 'false'
"""
outjson = f'./training/{settings["voice"]}/train.json'
with open(outjson, 'w', encoding="utf-8") as f:
f.write(json.dumps(settings, indent='\t') )
if half_p:
if not os.path.exists(get_halfp_model_path()):
convert_to_halfp()
if not output_name:
output_name = f'{settings["name"]}.yaml'
if settings['resume_state']:
settings['source_model'] = f"# pretrain_model_gpt: {settings['source_model']}"
settings['resume_state'] = f"resume_state: {settings['resume_state']}'"
else:
settings['source_model'] = f"pretrain_model_gpt: {settings['source_model']}"
settings['resume_state'] = f"# resume_state: {settings['resume_state']}'"
with open(f'./models/.template.yaml', 'r', encoding="utf-8") as f:
yaml = f.read()
@ -1436,11 +1417,13 @@ def save_training_settings( iterations=None, learning_rate=None, text_ce_lr_weig
continue
yaml = yaml.replace(f"${{{k}}}", str(settings[k]))
outfile = f'./training/{output_name}'
with open(outfile, 'w', encoding="utf-8") as f:
outyaml = f'./training/{settings["voice"]}/train.yaml'
with open(outyaml, 'w', encoding="utf-8") as f:
f.write(yaml)
return f"Training settings saved to: {outfile}"
messages.append(f"Saved training output to: {outyaml}")
return settings, messages
def import_voices(files, saveAs=None, progress=None):
global args
@ -1524,10 +1507,10 @@ def get_autoregressive_models(dir="./models/finetunes/", prefixed=False):
additionals = sorted([f'{dir}/{d}' for d in os.listdir(dir) if d[-4:] == ".pth" ])
found = []
for training in os.listdir(f'./training/'):
if not os.path.isdir(f'./training/{training}/') or not os.path.isdir(f'./training/{training}/models/'):
if not os.path.isdir(f'./training/{training}/') or not os.path.isdir(f'./training/{training}/finetunes/') or not os.path.isdir(f'./training/{training}/finetunes/models/'):
continue
models = sorted([ int(d[:-8]) for d in os.listdir(f'./training/{training}/models/') if d[-8:] == "_gpt.pth" ])
found = found + [ f'./training/{training}/models/{d}_gpt.pth' for d in models ]
models = sorted([ int(d[:-8]) for d in os.listdir(f'./training/{training}/finetunes/models/') if d[-8:] == "_gpt.pth" ])
found = found + [ f'./training/{training}/finetunes/models/{d}_gpt.pth' for d in models ]
if len(found) > 0 or len(additionals) > 0:
base = ["auto"] + base
@ -1545,10 +1528,10 @@ def get_autoregressive_models(dir="./models/finetunes/", prefixed=False):
return res
def get_dataset_list(dir="./training/"):
return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 and "train.txt" in os.listdir(os.path.join(dir, d)) ])
return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and "train.txt" in os.listdir(os.path.join(dir, d)) ])
def get_training_list(dir="./training/"):
return sorted([f'./training/{d}/train.yaml' for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 and "train.yaml" in os.listdir(os.path.join(dir, d)) ])
return sorted([f'./training/{d}/train.yaml' for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and "train.yaml" in os.listdir(os.path.join(dir, d)) ])
def do_gc():
gc.collect()
@ -1734,35 +1717,38 @@ def setup_args():
return args
def update_args( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, prune_nonfinal_outputs, device_override, sample_batch_size, concurrency_count, autocalculate_voice_chunk_duration_size, output_volume, autoregressive_model, vocoder_model, whisper_backend, whisper_model, training_default_halfp, training_default_bnb ):
def update_args( **kwargs ):
global args
args.listen = listen
args.share = share
args.check_for_updates = check_for_updates
args.models_from_local_only = models_from_local_only
args.low_vram = low_vram
args.force_cpu_for_conditioning_latents = force_cpu_for_conditioning_latents
args.defer_tts_load = defer_tts_load
args.prune_nonfinal_outputs = prune_nonfinal_outputs
args.device_override = device_override
args.sample_batch_size = sample_batch_size
args.embed_output_metadata = embed_output_metadata
args.latents_lean_and_mean = latents_lean_and_mean
args.voice_fixer = voice_fixer
args.voice_fixer_use_cuda = voice_fixer_use_cuda
args.concurrency_count = concurrency_count
args.output_sample_rate = 44000
args.autocalculate_voice_chunk_duration_size = autocalculate_voice_chunk_duration_size
args.output_volume = output_volume
args.autoregressive_model = autoregressive_model
args.vocoder_model = vocoder_model
args.whisper_backend = whisper_backend
args.whisper_model = whisper_model
settings = {}
settings.update(kwargs)
args.training_default_halfp = training_default_halfp
args.training_default_bnb = training_default_bnb
args.listen = settings['listen']
args.share = settings['share']
args.check_for_updates = settings['check_for_updates']
args.models_from_local_only = settings['models_from_local_only']
args.low_vram = settings['low_vram']
args.force_cpu_for_conditioning_latents = settings['force_cpu_for_conditioning_latents']
args.defer_tts_load = settings['defer_tts_load']
args.prune_nonfinal_outputs = settings['prune_nonfinal_outputs']
args.device_override = settings['device_override']
args.sample_batch_size = settings['sample_batch_size']
args.embed_output_metadata = settings['embed_output_metadata']
args.latents_lean_and_mean = settings['latents_lean_and_mean']
args.voice_fixer = settings['voice_fixer']
args.voice_fixer_use_cuda = settings['voice_fixer_use_cuda']
args.concurrency_count = settings['concurrency_count']
args.output_sample_rate = 44000
args.autocalculate_voice_chunk_duration_size = settings['autocalculate_voice_chunk_duration_size']
args.output_volume = settings['output_volume']
args.autoregressive_model = settings['autoregressive_model']
args.vocoder_model = settings['vocoder_model']
args.whisper_backend = settings['whisper_backend']
args.whisper_model = settings['whisper_model']
args.training_default_halfp = settings['training_default_halfp']
args.training_default_bnb = settings['training_default_bnb']
save_args_settings()
@ -1801,37 +1787,49 @@ def save_args_settings():
with open(f'./config/exec.json', 'w', encoding="utf-8") as f:
f.write(json.dumps(settings, indent='\t') )
# super kludgy )`;
def set_generate_settings_arg_order(args):
global GENERATE_SETTINGS_ARGS
GENERATE_SETTINGS_ARGS = args
def import_generate_settings(file="./config/generate.json"):
global GENERATE_SETTINGS_ARGS
defaults = {
'text': None,
'delimiter': None,
'emotion': None,
'prompt': None,
'voice': None,
'mic_audio': None,
'voice_latents_chunks': None,
'candidates': None,
'seed': None,
'num_autoregressive_samples': 16,
'diffusion_iterations': 30,
'temperature': 0.8,
'diffusion_sampler': "DDIM",
'breathing_room': 8 ,
'cvvp_weight': 0.0,
'top_p': 0.8,
'diffusion_temperature': 1.0,
'length_penalty': 1.0,
'repetition_penalty': 2.0,
'cond_free_k': 2.0,
'experimentals': None,
}
settings, _ = read_generate_settings(file, read_latents=False)
if settings is None:
return None
res = []
if GENERATE_SETTINGS_ARGS is not None:
for k in GENERATE_SETTINGS_ARGS:
res.append(defaults[k] if not settings or settings[k] is None else settings[k])
else:
for k in defaults:
res.append(defaults[k] if not settings or settings[k] is None else settings[k])
return (
None if 'text' not in settings else settings['text'],
None if 'delimiter' not in settings else settings['delimiter'],
None if 'emotion' not in settings else settings['emotion'],
None if 'prompt' not in settings else settings['prompt'],
None if 'voice' not in settings else settings['voice'],
None,
None,
None if 'seed' not in settings else settings['seed'],
None if 'candidates' not in settings else settings['candidates'],
None if 'num_autoregressive_samples' not in settings else settings['num_autoregressive_samples'],
None if 'diffusion_iterations' not in settings else settings['diffusion_iterations'],
0.8 if 'temperature' not in settings else settings['temperature'],
"DDIM" if 'diffusion_sampler' not in settings else settings['diffusion_sampler'],
8 if 'breathing_room' not in settings else settings['breathing_room'],
0.0 if 'cvvp_weight' not in settings else settings['cvvp_weight'],
0.8 if 'top_p' not in settings else settings['top_p'],
1.0 if 'diffusion_temperature' not in settings else settings['diffusion_temperature'],
1.0 if 'length_penalty' not in settings else settings['length_penalty'],
2.0 if 'repetition_penalty' not in settings else settings['repetition_penalty'],
2.0 if 'cond_free_k' not in settings else settings['cond_free_k'],
None if 'experimentals' not in settings else settings['experimentals'],
)
return tuple(res)
def reset_generation_settings():
@ -1955,10 +1953,10 @@ def deduce_autoregressive_model(voice=None):
voice = get_current_voice()
if voice:
dir = f'./training/{voice}-finetune/models/'
if os.path.exists(f'./training/finetunes/{voice}.pth'):
return f'./training/finetunes/{voice}.pth'
if os.path.exists(f'./models/finetunes/{voice}.pth'):
return f'./models/finetunes/{voice}.pth'
dir = f'./training/{voice}/finetune/models/'
if os.path.isdir(dir):
counts = sorted([ int(d[:-8]) for d in os.listdir(dir) if d[-8:] == "_gpt.pth" ])
names = [ f'{dir}/{d}_gpt.pth' for d in counts ]

588
src/webui.py
View File

@ -4,6 +4,7 @@ import time
import json
import base64
import re
import inspect
import urllib.request
import torch
@ -22,7 +23,38 @@ from utils import *
args = setup_args()
def run_generation(
GENERATE_SETTINGS = {}
TRANSCRIBE_SETTINGS = {}
EXEC_SETTINGS = {}
TRAINING_SETTINGS = {}
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},
}
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",
}
# can't use *args OR **kwargs if I want to retain the ability to use progress
def generate_proxy(
text,
delimiter,
emotion,
@ -30,8 +62,8 @@ def run_generation(
voice,
mic_audio,
voice_latents_chunks,
seed,
candidates,
seed,
num_autoregressive_samples,
diffusion_iterations,
temperature,
@ -43,47 +75,20 @@ def run_generation(
length_penalty,
repetition_penalty,
cond_free_k,
experimental_checkboxes,
experimentals,
progress=gr.Progress(track_tqdm=True)
):
if not text:
raise gr.Error("Please provide text.")
if not voice:
raise gr.Error("Please provide a voice.")
kwargs = locals()
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
)
sample, outputs, stats = generate(**kwargs)
except Exception as e:
message = str(e)
if message == "Kill signal detected":
unload_tts()
raise gr.Error(message)
raise e
return (
outputs[0],
gr.update(value=sample, visible=sample is not None),
@ -91,14 +96,8 @@ def run_generation(
gr.update(value=stats, visible=True),
)
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']))
@ -117,24 +116,6 @@ def get_training_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 = []
@ -148,10 +129,10 @@ def history_view_results( voice ):
continue
values = []
for k in history_headers:
for k in HISTORY_HEADERS:
v = file
if k != "Name":
v = metadata[history_headers[k]] if history_headers[k] in metadata else '?'
v = metadata[HISTORY_HEADERS[k]] if HISTORY_HEADERS[k] in metadata else '?'
values.append(v)
@ -193,181 +174,55 @@ def read_generate_settings_proxy(file, saveAs='.temp'):
def prepare_dataset_proxy( voice, language, skip_existings, progress=gr.Progress(track_tqdm=True) ):
return prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language, skip_existings=skip_existings, progress=progress )
def optimize_training_settings_proxy( *args, **kwargs ):
tup = optimize_training_settings(*args, **kwargs)
def update_args_proxy( *args ):
kwargs = {}
keys = list(EXEC_SETTINGS.keys())
for i in range(len(args)):
k = keys[i]
v = args[i]
kwargs[k] = v
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]),
gr.update(value=tup[8]),
"\n".join(tup[9])
)
update_args(**kwargs)
def optimize_training_settings_proxy( *args ):
kwargs = {}
keys = list(TRAINING_SETTINGS.keys())
for i in range(len(args)):
k = keys[i]
v = args[i]
kwargs[k] = v
settings, messages = optimize_training_settings(**kwargs)
output = list(settings.values())
return output[:-1] + ["\n".join(messages)]
def import_training_settings_proxy( voice ):
indir = f'./training/{voice}/'
outdir = f'./training/{voice}-finetune/'
in_config_path = f"{indir}/train.yaml"
out_config_path = None
out_configs = []
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']
gradient_accumulation_size = 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)
validation_rate = int(config['train']['val_freq'] / steps_per_iteration)
half_p = config['fp16']
bnb = True
statedir = f'{outdir}/training_state/'
resumes = []
resume_path = None
source_model = get_halfp_model_path() if half_p else get_model_path('autoregressive.pth')
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']
injson = f'./training/{voice}/train.json'
statedir = f'./training/{voice}/training_state/'
with open(injson, 'r', encoding="utf-8") as f:
settings = json.loads(f.read())
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}")
if len(resumes) > 0:
settings['resume_state'] = f'{statedir}/{resumes[-1]}.state'
messages.append(f"Found most recent training state: {settings['resume_state']}")
output = list(settings.values())
messages.append(f"Imported training settings: {injson}")
return output[:-1] + ["\n".join(messages)]
def save_training_settings_proxy( *args ):
kwargs = {}
keys = list(TRAINING_SETTINGS.keys())
for i in range(len(args)):
k = keys[i]
v = args[i]
kwargs[k] = v
if "ext" in config and "bitsandbytes" in config["ext"]:
bnb = config["ext"]["bitsandbytes"]
workers = config['datasets']['train']['n_workers']
messages = "\n".join(messages)
return (
epochs,
learning_rate,
text_ce_lr_weight,
learning_rate_schedule,
batch_size,
gradient_accumulation_size,
print_rate,
save_rate,
validation_rate,
resume_path,
half_p,
bnb,
workers,
source_model,
messages
)
def save_training_settings_proxy( epochs, learning_rate, text_ce_lr_weight, learning_rate_schedule, batch_size, gradient_accumulation_size, print_rate, save_rate, validation_rate, resume_path, half_p, bnb, workers, 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}/validation.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)
validation_rate = int(validation_rate * iterations / epochs)
validation_batch_size = int(batch_size / gradient_accumulation_size)
if iterations % save_rate != 0:
adjustment = int(iterations / save_rate) * save_rate
messages.append(f"Iteration rate is not evenly divisible by save rate, adjusting: {iterations} => {adjustment}")
iterations = adjustment
if not os.path.exists(validation_path):
validation_rate = iterations
validation_path = dataset_path
messages.append("Validation not found, disabling validation...")
else:
with open(validation_path, 'r', encoding="utf-8") as f:
validation_lines = len(f.readlines())
if validation_lines < validation_batch_size:
validation_batch_size = validation_lines
messages.append(f"Batch size exceeds validation dataset size, clamping validation batch size to {validation_lines}")
if not learning_rate_schedule:
learning_rate_schedule = EPOCH_SCHEDULE
elif isinstance(learning_rate_schedule,str):
learning_rate_schedule = json.loads(learning_rate_schedule)
learning_rate_schedule = schedule_learning_rate( iterations / epochs, learning_rate_schedule )
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,
gradient_accumulation_size=gradient_accumulation_size,
print_rate=print_rate,
save_rate=save_rate,
validation_rate=validation_rate,
name=name,
dataset_name=dataset_name,
dataset_path=dataset_path,
validation_name=validation_name,
validation_path=validation_path,
validation_batch_size=validation_batch_size,
output_name=f"{voice}/train.yaml",
resume_path=resume_path,
half_p=half_p,
bnb=bnb,
workers=workers,
source_model=source_model,
))
settings, messages = save_training_settings(**kwargs)
return "\n".join(messages)
def update_voices():
@ -406,60 +261,68 @@ def setup_gradio():
autoregressive_models = get_autoregressive_models()
dataset_list = get_dataset_list()
GENERATE_SETTINGS_ARGS = list(inspect.signature(generate_proxy).parameters.keys())[:-1]
for i in range(len(GENERATE_SETTINGS_ARGS)):
arg = GENERATE_SETTINGS_ARGS[i]
GENERATE_SETTINGS[arg] = None
set_generate_settings_arg_order(GENERATE_SETTINGS_ARGS)
with gr.Blocks() as ui:
with gr.Tab("Generate"):
with gr.Row():
with gr.Column():
text = gr.Textbox(lines=4, label="Input Prompt")
GENERATE_SETTINGS["text"] = gr.Textbox(lines=4, label="Input Prompt")
with gr.Row():
with gr.Column():
delimiter = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
GENERATE_SETTINGS["delimiter"] = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
emotion = gr.Radio( ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom", "None"], value="None", label="Emotion", type="value", interactive=True )
prompt = gr.Textbox(lines=1, label="Custom Emotion")
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", visible=False )
voice_latents_chunks = gr.Number(label="Voice Chunks", precision=0, value=0)
GENERATE_SETTINGS["emotion"] = gr.Radio( ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom", "None"], value="None", label="Emotion", type="value", interactive=True )
GENERATE_SETTINGS["prompt"] = gr.Textbox(lines=1, label="Custom Emotion", visible=False)
GENERATE_SETTINGS["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
GENERATE_SETTINGS["mic_audio"] = gr.Audio( label="Microphone Source", source="microphone", type="filepath", visible=False )
GENERATE_SETTINGS["voice_latents_chunks"] = gr.Number(label="Voice Chunks", precision=0, value=0)
with gr.Row():
refresh_voices = gr.Button(value="Refresh Voice List")
recompute_voice_latents = gr.Button(value="(Re)Compute Voice Latents")
voice.change(
GENERATE_SETTINGS["voice"].change(
fn=update_baseline_for_latents_chunks,
inputs=voice,
outputs=voice_latents_chunks
inputs=GENERATE_SETTINGS["voice"],
outputs=GENERATE_SETTINGS["voice_latents_chunks"]
)
voice.change(
GENERATE_SETTINGS["voice"].change(
fn=lambda value: gr.update(visible=value == "microphone"),
inputs=voice,
outputs=mic_audio,
inputs=GENERATE_SETTINGS["voice"],
outputs=GENERATE_SETTINGS["mic_audio"],
)
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")
GENERATE_SETTINGS["candidates"] = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
GENERATE_SETTINGS["seed"] = gr.Number(value=0, precision=0, label="Seed")
preset = gr.Radio( ["Ultra Fast", "Fast", "Standard", "High Quality"], label="Preset", type="value" )
num_autoregressive_samples = gr.Slider(value=128, minimum=2, maximum=512, step=1, label="Samples")
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")
GENERATE_SETTINGS["num_autoregressive_samples"] = gr.Slider(value=128, minimum=2, maximum=512, step=1, label="Samples")
GENERATE_SETTINGS["diffusion_iterations"] = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
GENERATE_SETTINGS["temperature"] = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
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")
breathing_room = gr.Slider(value=8, minimum=1, maximum=32, step=1, label="Pause Size")
diffusion_sampler = gr.Radio(
GENERATE_SETTINGS["experimentals"] = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
GENERATE_SETTINGS["breathing_room"] = gr.Slider(value=8, minimum=1, maximum=32, step=1, label="Pause Size")
GENERATE_SETTINGS["diffusion_sampler"] = gr.Radio(
["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"],
value="DDIM", label="Diffusion Samplers", type="value"
)
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")
GENERATE_SETTINGS["cvvp_weight"] = gr.Slider(value=0, minimum=0, maximum=1, label="CVVP Weight")
GENERATE_SETTINGS["top_p"] = gr.Slider(value=0.8, minimum=0, maximum=1, label="Top P")
GENERATE_SETTINGS["diffusion_temperature"] = gr.Slider(value=1.0, minimum=0, maximum=1, label="Diffusion Temperature")
GENERATE_SETTINGS["length_penalty"] = gr.Slider(value=1.0, minimum=0, maximum=8, label="Length Penalty")
GENERATE_SETTINGS["repetition_penalty"] = gr.Slider(value=2.0, minimum=0, maximum=8, label="Repetition Penalty")
GENERATE_SETTINGS["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")
@ -483,7 +346,7 @@ def setup_gradio():
with gr.Tab("History"):
with gr.Row():
with gr.Column():
history_info = gr.Dataframe(label="Results", headers=list(history_headers.keys()))
history_info = gr.Dataframe(label="Results", headers=list(HISTORY_HEADERS.keys()))
with gr.Row():
with gr.Column():
history_voices = gr.Dropdown(choices=result_voices, label="Voice", type="value", value=result_voices[0] if len(result_voices) > 0 else "")
@ -521,51 +384,40 @@ def setup_gradio():
with gr.Tab("Generate Configuration"):
with gr.Row():
with gr.Column():
training_settings = [
gr.Number(label="Epochs", value=500, precision=0),
]
TRAINING_SETTINGS["epochs"] = 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)),
]
TRAINING_SETTINGS["learning_rate"] = gr.Slider(label="Learning Rate", value=1e-5, minimum=0, maximum=1e-4, step=1e-6)
TRAINING_SETTINGS["text_ce_lr_weight"] = gr.Slider(label="Text_CE LR Ratio", value=0.01, minimum=0, maximum=1)
TRAINING_SETTINGS["learning_rate_schedule"] = 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="Gradient Accumulation Size", value=4, precision=0),
]
TRAINING_SETTINGS["batch_size"] = gr.Number(label="Batch Size", value=128, precision=0)
TRAINING_SETTINGS["gradient_accumulation_size"] = gr.Number(label="Gradient Accumulation Size", 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),
gr.Number(label="Validation 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"),
]
TRAINING_SETTINGS["print_rate"] = gr.Number(label="Print Frequency (in epochs)", value=5, precision=0)
TRAINING_SETTINGS["save_rate"] = gr.Number(label="Save Frequency (in epochs)", value=5, precision=0)
TRAINING_SETTINGS["validation_rate"] = gr.Number(label="Validation Frequency (in epochs)", value=5, precision=0)
with gr.Row():
training_halfp = gr.Checkbox(label="Half Precision", value=args.training_default_halfp)
training_bnb = gr.Checkbox(label="BitsAndBytes", value=args.training_default_bnb)
TRAINING_SETTINGS["half_p"] = gr.Checkbox(label="Half Precision", value=args.training_default_halfp)
TRAINING_SETTINGS["bitsandbytes"] = gr.Checkbox(label="BitsAndBytes", value=args.training_default_bnb)
training_workers = gr.Number(label="Worker Processes", value=2, precision=0)
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, training_workers, source_model, dataset_list_dropdown ]
TRAINING_SETTINGS["workers"] = gr.Number(label="Worker Processes", value=2, precision=0)
TRAINING_SETTINGS["gpus"] = gr.Number(label="GPUs", value=get_device_count(), precision=0)
TRAINING_SETTINGS["source_model"] = gr.Dropdown( choices=autoregressive_models, label="Source Model", type="value", value=autoregressive_models[0] )
TRAINING_SETTINGS["resume_state"] = gr.Textbox(label="Resume State Path", placeholder="./training/${voice}/training_state/${last_state}.state")
TRAINING_SETTINGS["voice"] = gr.Dropdown( choices=dataset_list, label="Dataset", type="value", value=dataset_list[0] if len(dataset_list) else "" )
with gr.Row():
refresh_dataset_list = gr.Button(value="Refresh Dataset List")
import_dataset_button = gr.Button(value="Reuse/Import Dataset")
training_refresh_dataset = gr.Button(value="Refresh Dataset List")
training_import_settings = gr.Button(value="Reuse/Import Dataset")
with gr.Column():
save_yaml_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
training_configuration_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")
training_optimize_configuration = gr.Button(value="Validate Training Configuration")
training_save_configuration = gr.Button(value="Save Training Configuration")
with gr.Tab("Run Training"):
with gr.Row():
with gr.Column():
@ -588,9 +440,7 @@ def setup_gradio():
training_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
verbose_training = gr.Checkbox(label="Verbose Console Output", value=True)
with gr.Row():
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=get_device_count())
training_keep_x_past_datasets = gr.Slider(label="Keep X Previous States", minimum=0, maximum=8, value=0, step=1)
with gr.Row():
start_training_button = gr.Button(value="Train")
stop_training_button = gr.Button(value="Stop")
@ -599,43 +449,40 @@ def setup_gradio():
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),
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.Textbox(label="Device Override", value=args.device_override),
]
EXEC_SETTINGS['listen'] = gr.Textbox(label="Listen", value=args.listen, placeholder="127.0.0.1:7860/")
EXEC_SETTINGS['share'] = gr.Checkbox(label="Public Share Gradio", value=args.share)
EXEC_SETTINGS['check_for_updates'] = gr.Checkbox(label="Check For Updates", value=args.check_for_updates)
EXEC_SETTINGS['models_from_local_only'] = gr.Checkbox(label="Only Load Models Locally", value=args.models_from_local_only)
EXEC_SETTINGS['low_vram'] = gr.Checkbox(label="Low VRAM", value=args.low_vram)
EXEC_SETTINGS['embed_output_metadata'] = gr.Checkbox(label="Embed Output Metadata", value=args.embed_output_metadata)
EXEC_SETTINGS['latents_lean_and_mean'] = gr.Checkbox(label="Slimmer Computed Latents", value=args.latents_lean_and_mean)
EXEC_SETTINGS['voice_fixer'] = gr.Checkbox(label="Use Voice Fixer on Generated Output", value=args.voice_fixer)
EXEC_SETTINGS['voice_fixer_use_cuda'] = gr.Checkbox(label="Use CUDA for Voice Fixer", value=args.voice_fixer_use_cuda)
EXEC_SETTINGS['force_cpu_for_conditioning_latents'] = gr.Checkbox(label="Force CPU for Conditioning Latents", value=args.force_cpu_for_conditioning_latents)
EXEC_SETTINGS['defer_tts_load'] = gr.Checkbox(label="Do Not Load TTS On Startup", value=args.defer_tts_load)
EXEC_SETTINGS['prune_nonfinal_outputs'] = gr.Checkbox(label="Delete Non-Final Output", value=args.prune_nonfinal_outputs)
EXEC_SETTINGS['device_override'] = 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),
gr.Number(label="Gradio Concurrency Count", precision=0, value=args.concurrency_count),
gr.Number(label="Auto-Calculate Voice Chunk Duration (in seconds)", precision=0, value=args.autocalculate_voice_chunk_duration_size),
gr.Slider(label="Output Volume", minimum=0, maximum=2, value=args.output_volume),
]
EXEC_SETTINGS['sample_batch_size'] = gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size)
EXEC_SETTINGS['concurrency_count'] = gr.Number(label="Gradio Concurrency Count", precision=0, value=args.concurrency_count)
EXEC_SETTINGS['autocalculate_voice_chunk_duration_size'] = gr.Number(label="Auto-Calculate Voice Chunk Duration (in seconds)", precision=0, value=args.autocalculate_voice_chunk_duration_size)
EXEC_SETTINGS['output_volume'] = gr.Slider(label="Output Volume", minimum=0, maximum=2, value=args.output_volume)
autoregressive_model_dropdown = gr.Dropdown(choices=autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else autoregressive_models[0])
EXEC_SETTINGS['autoregressive_model'] = gr.Dropdown(choices=autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else autoregressive_models[0])
vocoder_models = gr.Dropdown(VOCODERS, label="Vocoder", value=args.vocoder_model if args.vocoder_model else VOCODERS[-1])
whisper_backend = gr.Dropdown(WHISPER_BACKENDS, label="Whisper Backends", value=args.whisper_backend)
whisper_model_dropdown = gr.Dropdown(WHISPER_MODELS, label="Whisper Model", value=args.whisper_model)
EXEC_SETTINGS['vocoder_model'] = gr.Dropdown(VOCODERS, label="Vocoder", value=args.vocoder_model if args.vocoder_model else VOCODERS[-1])
EXEC_SETTINGS['whisper_backend'] = gr.Dropdown(WHISPER_BACKENDS, label="Whisper Backends", value=args.whisper_backend)
EXEC_SETTINGS['whisper_model'] = gr.Dropdown(WHISPER_MODELS, label="Whisper Model", value=args.whisper_model)
exec_inputs = exec_inputs + [ autoregressive_model_dropdown, vocoder_models, whisper_backend, whisper_model_dropdown, training_halfp, training_bnb ]
EXEC_SETTINGS['training_default_halfp'] = TRAINING_SETTINGS['half_p']
EXEC_SETTINGS['training_default_bnb'] = TRAINING_SETTINGS['bitsandbytes']
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,
inputs=EXEC_SETTINGS['autoregressive_model'],
outputs=None
)
# kill_button = gr.Button(value="Close UI")
@ -648,49 +495,26 @@ def setup_gradio():
autoregressive_models_update_button.click(
update_model_list_proxy,
inputs=autoregressive_model_dropdown,
outputs=autoregressive_model_dropdown,
inputs=EXEC_SETTINGS['autoregressive_model'],
outputs=EXEC_SETTINGS['autoregressive_model'],
)
for i in exec_inputs:
i.change( fn=update_args, inputs=exec_inputs )
exec_inputs = list(EXEC_SETTINGS.values())
for k in EXEC_SETTINGS:
EXEC_SETTINGS[k].change( fn=update_args_proxy, inputs=exec_inputs )
autoregressive_model_dropdown.change(
EXEC_SETTINGS['autoregressive_model'].change(
fn=update_autoregressive_model,
inputs=autoregressive_model_dropdown,
inputs=EXEC_SETTINGS['autoregressive_model'],
outputs=None
)
vocoder_models.change(
EXEC_SETTINGS['vocoder_model'].change(
fn=update_vocoder_model,
inputs=vocoder_models,
inputs=EXEC_SETTINGS['vocoder_model'],
outputs=None
)
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,
@ -734,45 +558,46 @@ def setup_gradio():
preset.change(fn=update_presets,
inputs=preset,
outputs=[
num_autoregressive_samples,
diffusion_iterations,
GENERATE_SETTINGS['num_autoregressive_samples'],
GENERATE_SETTINGS['diffusion_iterations'],
],
)
recompute_voice_latents.click(compute_latents_proxy,
inputs=[
voice,
voice_latents_chunks,
GENERATE_SETTINGS['voice'],
GENERATE_SETTINGS['voice_latents_chunks'],
],
outputs=voice,
outputs=GENERATE_SETTINGS['voice'],
)
emotion.change(
GENERATE_SETTINGS['emotion'].change(
fn=lambda value: gr.update(visible=value == "Custom"),
inputs=emotion,
outputs=prompt
inputs=GENERATE_SETTINGS['emotion'],
outputs=GENERATE_SETTINGS['prompt']
)
mic_audio.change(fn=lambda value: gr.update(value="microphone"),
inputs=mic_audio,
outputs=voice
GENERATE_SETTINGS['mic_audio'].change(fn=lambda value: gr.update(value="microphone"),
inputs=GENERATE_SETTINGS['mic_audio'],
outputs=GENERATE_SETTINGS['voice']
)
refresh_voices.click(update_voices,
inputs=None,
outputs=[
voice,
GENERATE_SETTINGS['voice'],
dataset_settings[0],
history_voices
]
)
generate_settings = list(GENERATE_SETTINGS.values())
submit.click(
lambda: (gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)),
outputs=[source_sample, candidates_list, generation_results],
)
submit_event = submit.click(run_generation,
inputs=input_settings,
submit_event = submit.click(generate_proxy,
inputs=generate_settings,
outputs=[output_audio, source_sample, candidates_list, generation_results],
api_name="generate",
)
@ -780,13 +605,13 @@ def setup_gradio():
copy_button.click(import_generate_settings,
inputs=audio_in, # JSON elements cannot be used as inputs
outputs=input_settings
outputs=generate_settings
)
reset_generation_settings_button.click(
fn=reset_generation_settings,
inputs=None,
outputs=input_settings
outputs=generate_settings
)
history_copy_settings_button.click(history_copy_settings,
@ -794,7 +619,7 @@ def setup_gradio():
history_voices,
history_results_list,
],
outputs=input_settings
outputs=generate_settings
)
refresh_configs.click(
@ -806,7 +631,6 @@ def setup_gradio():
inputs=[
training_configs,
verbose_training,
training_gpu_count,
training_keep_x_past_datasets,
],
outputs=[
@ -855,38 +679,28 @@ def setup_gradio():
],
outputs=prepare_dataset_output #console_output
)
refresh_dataset_list.click(
training_refresh_dataset.click(
lambda: gr.update(choices=get_dataset_list()),
inputs=None,
outputs=dataset_list_dropdown,
outputs=TRAINING_SETTINGS["voice"],
)
optimize_yaml_button.click(optimize_training_settings_proxy,
training_settings = list(TRAINING_SETTINGS.values())
training_optimize_configuration.click(optimize_training_settings_proxy,
inputs=training_settings,
outputs=training_settings[1:10] + [save_yaml_output] #console_output
outputs=training_settings[:-1] + [training_configuration_output] #console_output
)
import_dataset_button.click(import_training_settings_proxy,
inputs=dataset_list_dropdown,
outputs=training_settings[:14] + [save_yaml_output] #console_output
training_import_settings.click(import_training_settings_proxy,
inputs=TRAINING_SETTINGS['voice'],
outputs=training_settings[:-1] + [training_configuration_output] #console_output
)
save_yaml_button.click(save_training_settings_proxy,
training_save_configuration.click(save_training_settings_proxy,
inputs=training_settings,
outputs=save_yaml_output #console_output
outputs=training_configuration_output #console_output
)
"""
def kill_process():
ui.close()
exit()
kill_button.click(
kill_process,
inputs=None,
outputs=None
)
"""
if os.path.isfile('./config/generate.json'):
ui.load(import_generate_settings, inputs=None, outputs=input_settings)
ui.load(import_generate_settings, inputs=None, outputs=generate_settings)
if args.check_for_updates:
ui.load(check_for_updates)