@ -198,7 +198,8 @@ def optimize_training_settings_proxy( *args, **kwargs ):
gr . update ( value = tup [ 4 ] ) ,
gr . update ( value = tup [ 5 ] ) ,
gr . update ( value = tup [ 6 ] ) ,
" \n " . join ( tup [ 7 ] )
gr . update ( value = tup [ 7 ] ) ,
" \n " . join ( tup [ 8 ] )
)
def import_training_settings_proxy ( voice ) :
@ -227,6 +228,7 @@ def import_training_settings_proxy( voice ):
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 )
@ -234,6 +236,7 @@ def import_training_settings_proxy( voice ):
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 ' ] ]
@ -243,6 +246,14 @@ def import_training_settings_proxy( voice ):
statedir = f ' { outdir } /training_state/ ' # NOOO STOP MIXING YOUR CASES
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 ' ]
if os . path . isdir ( statedir ) :
resumes = sorted ( [ int ( d [ : - 6 ] ) for d in os . listdir ( statedir ) if d [ - 6 : ] == " .state " ] )
@ -250,6 +261,7 @@ def import_training_settings_proxy( voice ):
resume_path = f ' { statedir } / { resumes [ - 1 ] } .state '
messages . append ( f " Latest resume found: { resume_path } " )
half_p = config [ ' fp16 ' ]
bnb = True
@ -261,6 +273,7 @@ def import_training_settings_proxy( voice ):
return (
epochs ,
learning_rate ,
text_ce_lr_weight ,
learning_rate_schedule ,
batch_size ,
mega_batch_factor ,
@ -269,11 +282,12 @@ def import_training_settings_proxy( voice ):
resume_path ,
half_p ,
bnb ,
source_model ,
messages
)
def save_training_settings_proxy ( epochs , learning_rate , , batch_size , mega_batch_factor , print_rate , save_rate , resume_path , half_p , bnb , voice ) :
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 "
@ -299,6 +313,7 @@ def save_training_settings_proxy( epochs, learning_rate, learning_rate_schedule,
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 ,
@ -312,6 +327,7 @@ def save_training_settings_proxy( epochs, learning_rate, learning_rate_schedule,
resume_path = resume_path ,
half_p = half_p ,
bnb = bnb ,
source_model = source_model ,
) )
return " \n " . join ( messages )
@ -345,6 +361,12 @@ def setup_gradio():
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 ( )
with gr . Blocks ( ) as ui :
with gr . Tab ( " Generate " ) :
with gr . Row ( ) :
@ -356,8 +378,7 @@ def setup_gradio():
emotion = gr . Radio ( [ " Happy " , " Sad " , " Angry " , " Disgusted " , " Arrogant " , " Custom " ] , value = " Custom " , label = " Emotion " , type = " value " , interactive = True )
prompt = gr . Textbox ( lines = 1 , label = " Custom Emotion + Prompt (if selected) " )
voice_list = get_voice_list ( append_defaults = True )
voice = gr . Dropdown ( choices = voice_list , label = " Voice " , type = " value " , value = voice_list [ 0 ] ) # it'd be very cash money if gradio was able to default to the first value in the list without this shit
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 = 64 , value = 1 , step = 1 )
with gr . Row ( ) :
@ -416,12 +437,10 @@ def setup_gradio():
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 = " " )
# output_pick = gr.Button(value="Select Candidate", visible=False)
def change_candidate ( val ) :
if not val :
return
print ( val )
return val
candidates_list . change (
@ -435,7 +454,6 @@ def setup_gradio():
history_info = gr . Dataframe ( label = " Results " , headers = list ( history_headers . keys ( ) ) )
with gr . Row ( ) :
with gr . Column ( ) :
result_voices = get_voice_list ( " ./results/ " )
history_voices = gr . Dropdown ( choices = result_voices , label = " Voice " , type = " value " , value = result_voices [ 0 ] if len ( result_voices ) > 0 else " " )
with gr . Column ( ) :
history_results_list = gr . Dropdown ( label = " Results " , type = " value " , interactive = True , value = " " )
@ -457,7 +475,7 @@ def setup_gradio():
with gr . Row ( ) :
with gr . Column ( ) :
dataset_settings = [
gr . Dropdown ( get_voice_list( ) , label = " Dataset Source " , type = " value " ) ,
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 " )
@ -470,8 +488,12 @@ def setup_gradio():
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 . Slider ( label = " Learning Rate " , value = 1e-5 , minimum = 0 , maximum = 1e-4 , step = 1e-6 ) ,
gr . Textbox ( label = " Learning Rate Schedule " , placeholder = str ( EPOCH_SCHEDULE ) ) ,
]
with gr . Row ( ) :
@ -489,8 +511,9 @@ def setup_gradio():
]
training_halfp = gr . Checkbox ( label = " Half Precision " , value = args . training_default_halfp )
training_bnb = gr . Checkbox ( label = " BitsAndBytes " , value = args . training_default_bnb )
dataset_list = gr . Dropdown ( get_dataset_list ( ) , label = " Dataset " , type = " value " )
training_settings = training_settings + [ training_halfp , training_bnb , dataset_list ]
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 " )
@ -511,9 +534,9 @@ def setup_gradio():
reconnect_training_button = gr . Button ( value = " Reconnect " )
with gr . Column ( ) :
training_output = gr . TextArea ( label = " Console Output " , interactive = False , max_lines = 8 )
verbose_training = gr . Checkbox ( label = " Verbose Console Output "
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
training_keep_x_past_datasets = gr . Slider ( label = " Keep X Previous States " , minimum = 0 , maximum = 8 , value = 0 , step = 1 )
training_loss_graph = gr . LinePlot ( label = " Loss Rates " ,
x = " iteration " ,
@ -551,7 +574,6 @@ def setup_gradio():
gr . Slider ( label = " Output Volume " , minimum = 0 , maximum = 2 , value = args . output_volume ) ,
]
autoregressive_models = get_autoregressive_models ( )
autoregressive_model_dropdown = gr . Dropdown ( choices = autoregressive_models , label = " Autoregressive Model " , value = args . autoregressive_model if args . autoregressive_model else autoregressive_models [ 0 ] )
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 )
@ -588,15 +610,6 @@ def setup_gradio():
inputs = autoregressive_model_dropdown ,
outputs = None
)
"""
whisper_model_dropdown . change (
fn = update_whisper_model ,
inputs = whisper_model_dropdown ,
outputs = None
)
"""
# console_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
input_settings = [
text ,
@ -769,15 +782,15 @@ def setup_gradio():
refresh_dataset_list . click (
lambda : gr . update ( choices = get_dataset_list ( ) ) ,
inputs = None ,
outputs = dataset_list ,
outputs = dataset_list _dropdown ,
)
optimize_yaml_button . click ( optimize_training_settings_proxy ,
inputs = training_settings ,
outputs = training_settings [ 1 : 8 ] + [ save_yaml_output ] #console_output
outputs = training_settings [ 1 : 9 ] + [ save_yaml_output ] #console_output
)
import_dataset_button . click ( import_training_settings_proxy ,
inputs = dataset_list ,
outputs = training_settings [ : 1 0 ] + [ save_yaml_output ] #console_output
inputs = dataset_list _dropdown ,
outputs = training_settings [ : 1 1 ] + [ save_yaml_output ] #console_output
)
save_yaml_button . click ( save_training_settings_proxy ,
inputs = training_settings ,
