|
|
|
@ -201,7 +201,7 @@ def read_generate_settings_proxy(file, saveAs='.temp'):
|
|
|
|
|
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 save_training_settings_proxy( batch_size, learning_rate, print_rate, save_rate, voice ):
|
|
|
|
|
def save_training_settings_proxy( iterations, batch_size, learning_rate, print_rate, save_rate, voice ):
|
|
|
|
|
name = f"{voice}-finetune"
|
|
|
|
|
dataset_name = f"{voice}-train"
|
|
|
|
|
dataset_path = f"./training/{voice}/train.txt"
|
|
|
|
@ -217,7 +217,7 @@ def save_training_settings_proxy( batch_size, learning_rate, print_rate, save_ra
|
|
|
|
|
|
|
|
|
|
out_name = f"{voice}/train.yaml"
|
|
|
|
|
|
|
|
|
|
return save_training_settings(batch_size, learning_rate, print_rate, save_rate, name, dataset_name, dataset_path, validation_name, validation_path, out_name )
|
|
|
|
|
return save_training_settings(iterations, batch_size, learning_rate, print_rate, save_rate, name, dataset_name, dataset_path, validation_name, validation_path, out_name )
|
|
|
|
|
|
|
|
|
|
def update_voices():
|
|
|
|
|
return (
|
|
|
|
@ -346,7 +346,8 @@ def setup_gradio():
|
|
|
|
|
with gr.Row():
|
|
|
|
|
with gr.Column():
|
|
|
|
|
training_settings = [
|
|
|
|
|
gr.Slider(label="Batch Size", value=128),
|
|
|
|
|
gr.Slider(label="Iterations", minimum=0, maximum=5000, value=500),
|
|
|
|
|
gr.Slider(label="Batch Size", minimum=2, maximum=128, value=64),
|
|
|
|
|
gr.Slider(label="Learning Rate", value=1e-5, minimum=0, maximum=1e-4, step=1e-6),
|
|
|
|
|
gr.Number(label="Print Frequency", value=50),
|
|
|
|
|
gr.Number(label="Save Frequency", value=50),
|
|
|
|
|