Delete 'src/webui.py'
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tigi6346
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commit
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767
src/webui.py
767
src/webui.py
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@ -1,767 +0,0 @@
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import os
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import argparse
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import time
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import json
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import base64
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import re
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import inspect
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import urllib.request
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import torch
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import torchaudio
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import music_tag
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import gradio as gr
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import gradio.utils
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from datetime import datetime
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import tortoise.api
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from tortoise.utils.audio import get_voice_dir, get_voices
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from tortoise.utils.device import get_device_count
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from utils import *
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args = setup_args()
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GENERATE_SETTINGS = {}
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TRANSCRIBE_SETTINGS = {}
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EXEC_SETTINGS = {}
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TRAINING_SETTINGS = {}
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GENERATE_SETTINGS_ARGS = []
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PRESETS = {
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'Ultra Fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 30, 'cond_free': False},
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'Fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 80},
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'Standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 200},
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'High Quality': {'num_autoregressive_samples': 256, 'diffusion_iterations': 400},
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}
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HISTORY_HEADERS = {
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"Name": "",
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"Samples": "num_autoregressive_samples",
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"Iterations": "diffusion_iterations",
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"Temp.": "temperature",
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"Sampler": "diffusion_sampler",
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"CVVP": "cvvp_weight",
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"Top P": "top_p",
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"Diff. Temp.": "diffusion_temperature",
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"Len Pen": "length_penalty",
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"Rep Pen": "repetition_penalty",
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"Cond-Free K": "cond_free_k",
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"Time": "time",
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"Datetime": "datetime",
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"Model": "model",
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"Model Hash": "model_hash",
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}
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# can't use *args OR **kwargs if I want to retain the ability to use progress
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def generate_proxy(
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text,
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delimiter,
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emotion,
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prompt,
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voice,
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mic_audio,
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voice_latents_chunks,
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candidates,
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seed,
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num_autoregressive_samples,
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diffusion_iterations,
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temperature,
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diffusion_sampler,
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breathing_room,
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cvvp_weight,
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top_p,
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diffusion_temperature,
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length_penalty,
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repetition_penalty,
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cond_free_k,
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experimentals,
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progress=gr.Progress(track_tqdm=True)
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):
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kwargs = locals()
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try:
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sample, outputs, stats = generate(**kwargs)
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except Exception as e:
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message = str(e)
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if message == "Kill signal detected":
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unload_tts()
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raise e
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return (
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outputs[0],
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gr.update(value=sample, visible=sample is not None),
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gr.update(choices=outputs, value=outputs[0], visible=len(outputs) > 1, interactive=True),
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gr.update(value=stats, visible=True),
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)
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def update_presets(value):
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if value in PRESETS:
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preset = PRESETS[value]
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return (gr.update(value=preset['num_autoregressive_samples']), gr.update(value=preset['diffusion_iterations']))
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else:
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return (gr.update(), gr.update())
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def get_training_configs():
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configs = []
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for i, file in enumerate(sorted(os.listdir(f"./training/"))):
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if file[-5:] != ".yaml" or file[0] == ".":
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continue
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configs.append(f"./training/{file}")
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return configs
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def update_training_configs():
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return gr.update(choices=get_training_list())
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def history_view_results( voice ):
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results = []
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files = []
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outdir = f"./results/{voice}/"
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for i, file in enumerate(sorted(os.listdir(outdir))):
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if file[-4:] != ".wav":
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continue
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metadata, _ = read_generate_settings(f"{outdir}/{file}", read_latents=False)
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if metadata is None:
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continue
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values = []
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for k in HISTORY_HEADERS:
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v = file
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if k != "Name":
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v = metadata[HISTORY_HEADERS[k]] if HISTORY_HEADERS[k] in metadata else '?'
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values.append(v)
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files.append(file)
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results.append(values)
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return (
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results,
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gr.Dropdown.update(choices=sorted(files))
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)
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def import_generate_settings_proxy( file=None ):
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global GENERATE_SETTINGS_ARGS
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settings = import_generate_settings( file )
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res = []
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for k in GENERATE_SETTINGS_ARGS:
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res.append(settings[k] if k in settings else None)
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print(GENERATE_SETTINGS_ARGS)
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print(settings)
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print(res)
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return tuple(res)
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def compute_latents_proxy(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
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compute_latents( voice=voice, voice_latents_chunks=voice_latents_chunks, progress=progress )
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return voice
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def import_voices_proxy(files, name, progress=gr.Progress(track_tqdm=True)):
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import_voices(files, name, progress)
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return gr.update()
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def read_generate_settings_proxy(file, saveAs='.temp'):
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j, latents = read_generate_settings(file)
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if latents:
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outdir = f'{get_voice_dir()}/{saveAs}/'
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os.makedirs(outdir, exist_ok=True)
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with open(f'{outdir}/cond_latents.pth', 'wb') as f:
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f.write(latents)
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latents = f'{outdir}/cond_latents.pth'
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return (
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gr.update(value=j, visible=j is not None),
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gr.update(value=latents, visible=latents is not None),
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None if j is None else j['voice'],
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gr.update(visible=j is not None),
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)
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def prepare_dataset_proxy( voice, language, validation_size, skip_existings, progress=gr.Progress(track_tqdm=True) ):
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messages = []
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message = prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language, skip_existings=skip_existings, progress=progress )
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messages.append(message)
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if validation_size > 0:
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message = prepare_validation_dataset( voice, text_length=validation_size )
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messages.append(message)
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return "\n".join(messages)
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def update_args_proxy( *args ):
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kwargs = {}
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keys = list(EXEC_SETTINGS.keys())
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for i in range(len(args)):
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k = keys[i]
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v = args[i]
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kwargs[k] = v
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update_args(**kwargs)
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def optimize_training_settings_proxy( *args ):
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kwargs = {}
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keys = list(TRAINING_SETTINGS.keys())
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for i in range(len(args)):
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k = keys[i]
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v = args[i]
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kwargs[k] = v
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settings, messages = optimize_training_settings(**kwargs)
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output = list(settings.values())
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return output[:-1] + ["\n".join(messages)]
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def import_training_settings_proxy( voice ):
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messages = []
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injson = f'./training/{voice}/train.json'
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statedir = f'./training/{voice}/finetune/training_state/'
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with open(injson, 'r', encoding="utf-8") as f:
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settings = json.loads(f.read())
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if os.path.isdir(statedir):
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resumes = sorted([int(d[:-6]) for d in os.listdir(statedir) if d[-6:] == ".state" ])
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if len(resumes) > 0:
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settings['resume_state'] = f'{statedir}/{resumes[-1]}.state'
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messages.append(f"Found most recent training state: {settings['resume_state']}")
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output = {}
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for k in TRAINING_SETTINGS:
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if k not in settings:
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continue
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output[k] = settings[k]
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output = list(output.values())
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print(list(TRAINING_SETTINGS.keys()))
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print(output)
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messages.append(f"Imported training settings: {injson}")
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return output[:-1] + ["\n".join(messages)]
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def save_training_settings_proxy( *args ):
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kwargs = {}
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keys = list(TRAINING_SETTINGS.keys())
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for i in range(len(args)):
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k = keys[i]
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v = args[i]
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kwargs[k] = v
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settings, messages = save_training_settings(**kwargs)
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return "\n".join(messages)
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def update_voices():
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return (
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gr.Dropdown.update(choices=get_voice_list(append_defaults=True)),
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gr.Dropdown.update(choices=get_voice_list()),
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gr.Dropdown.update(choices=get_voice_list("./results/")),
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)
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def history_copy_settings( voice, file ):
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return import_generate_settings( f"./results/{voice}/{file}" )
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def setup_gradio():
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global args
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global ui
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if not args.share:
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def noop(function, return_value=None):
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def wrapped(*args, **kwargs):
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return return_value
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return wrapped
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gradio.utils.version_check = noop(gradio.utils.version_check)
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gradio.utils.initiated_analytics = noop(gradio.utils.initiated_analytics)
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gradio.utils.launch_analytics = noop(gradio.utils.launch_analytics)
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gradio.utils.integration_analytics = noop(gradio.utils.integration_analytics)
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gradio.utils.error_analytics = noop(gradio.utils.error_analytics)
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gradio.utils.log_feature_analytics = noop(gradio.utils.log_feature_analytics)
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#gradio.utils.get_local_ip_address = noop(gradio.utils.get_local_ip_address, 'localhost')
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if args.models_from_local_only:
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os.environ['TRANSFORMERS_OFFLINE']='1'
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voice_list_with_defaults = get_voice_list(append_defaults=True)
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voice_list = get_voice_list()
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result_voices = get_voice_list("./results/")
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autoregressive_models = get_autoregressive_models()
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dataset_list = get_dataset_list()
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global GENERATE_SETTINGS_ARGS
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GENERATE_SETTINGS_ARGS = list(inspect.signature(generate_proxy).parameters.keys())[:-1]
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for i in range(len(GENERATE_SETTINGS_ARGS)):
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arg = GENERATE_SETTINGS_ARGS[i]
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GENERATE_SETTINGS[arg] = None
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with gr.Blocks() as ui:
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with gr.Tab("Generate"):
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with gr.Row():
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with gr.Column():
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GENERATE_SETTINGS["text"] = gr.Textbox(lines=4, label="Input Prompt")
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with gr.Row():
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with gr.Column():
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GENERATE_SETTINGS["delimiter"] = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
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GENERATE_SETTINGS["emotion"] = gr.Radio( ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom", "None"], value="None", label="Emotion", type="value", interactive=True )
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GENERATE_SETTINGS["prompt"] = gr.Textbox(lines=1, label="Custom Emotion", visible=False)
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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
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GENERATE_SETTINGS["mic_audio"] = gr.Audio( label="Microphone Source", source="microphone", type="filepath", visible=False )
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GENERATE_SETTINGS["voice_latents_chunks"] = gr.Number(label="Voice Chunks", precision=0, value=0)
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with gr.Row():
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refresh_voices = gr.Button(value="Refresh Voice List")
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recompute_voice_latents = gr.Button(value="(Re)Compute Voice Latents")
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GENERATE_SETTINGS["voice"].change(
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fn=update_baseline_for_latents_chunks,
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inputs=GENERATE_SETTINGS["voice"],
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outputs=GENERATE_SETTINGS["voice_latents_chunks"]
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)
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GENERATE_SETTINGS["voice"].change(
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fn=lambda value: gr.update(visible=value == "microphone"),
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inputs=GENERATE_SETTINGS["voice"],
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outputs=GENERATE_SETTINGS["mic_audio"],
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)
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with gr.Column():
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GENERATE_SETTINGS["candidates"] = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
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GENERATE_SETTINGS["seed"] = gr.Number(value=0, precision=0, label="Seed")
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preset = gr.Radio( ["Ultra Fast", "Fast", "Standard", "High Quality"], label="Preset", type="value" )
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GENERATE_SETTINGS["num_autoregressive_samples"] = gr.Slider(value=128, minimum=2, maximum=512, step=1, label="Samples")
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GENERATE_SETTINGS["diffusion_iterations"] = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
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GENERATE_SETTINGS["temperature"] = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
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show_experimental_settings = gr.Checkbox(label="Show Experimental Settings")
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reset_generation_settings_button = gr.Button(value="Reset to Default")
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with gr.Column(visible=False) as col:
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experimental_column = col
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GENERATE_SETTINGS["experimentals"] = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
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GENERATE_SETTINGS["breathing_room"] = gr.Slider(value=8, minimum=1, maximum=32, step=1, label="Pause Size")
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GENERATE_SETTINGS["diffusion_sampler"] = gr.Radio(
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["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"],
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value="DDIM", label="Diffusion Samplers", type="value"
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)
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GENERATE_SETTINGS["cvvp_weight"] = gr.Slider(value=0, minimum=0, maximum=1, label="CVVP Weight")
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GENERATE_SETTINGS["top_p"] = gr.Slider(value=0.8, minimum=0, maximum=1, label="Top P")
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GENERATE_SETTINGS["diffusion_temperature"] = gr.Slider(value=1.0, minimum=0, maximum=1, label="Diffusion Temperature")
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GENERATE_SETTINGS["length_penalty"] = gr.Slider(value=1.0, minimum=0, maximum=8, label="Length Penalty")
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GENERATE_SETTINGS["repetition_penalty"] = gr.Slider(value=2.0, minimum=0, maximum=8, label="Repetition Penalty")
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GENERATE_SETTINGS["cond_free_k"] = gr.Slider(value=2.0, minimum=0, maximum=4, label="Conditioning-Free K")
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with gr.Column():
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with gr.Row():
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submit = gr.Button(value="Generate")
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stop = gr.Button(value="Stop")
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generation_results = gr.Dataframe(label="Results", headers=["Seed", "Time"], visible=False)
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source_sample = gr.Audio(label="Source Sample", visible=False)
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output_audio = gr.Audio(label="Output")
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candidates_list = gr.Dropdown(label="Candidates", type="value", visible=False, choices=[""], value="")
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def change_candidate( val ):
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if not val:
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return
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return val
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candidates_list.change(
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fn=change_candidate,
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inputs=candidates_list,
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outputs=output_audio,
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)
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with gr.Tab("History"):
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with gr.Row():
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with gr.Column():
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history_info = gr.Dataframe(label="Results", headers=list(HISTORY_HEADERS.keys()))
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with gr.Row():
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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():
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history_results_list = gr.Dropdown(label="Results",type="value", interactive=True, value="")
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with gr.Column():
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history_audio = gr.Audio()
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history_copy_settings_button = gr.Button(value="Copy Settings")
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with gr.Tab("Utilities"):
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with gr.Row():
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with gr.Column():
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audio_in = gr.Files(type="file", label="Audio Input", file_types=["audio"])
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import_voice_name = gr.Textbox(label="Voice Name")
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import_voice_button = gr.Button(value="Import Voice")
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with gr.Column(visible=False) as col:
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utilities_metadata_column = col
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metadata_out = gr.JSON(label="Audio Metadata")
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copy_button = gr.Button(value="Copy Settings")
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latents_out = gr.File(type="binary", label="Voice Latents")
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with gr.Tab("Training"):
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with gr.Tab("Prepare Dataset"):
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with gr.Row():
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with gr.Column():
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DATASET_SETTINGS = {}
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DATASET_SETTINGS['voice'] = gr.Dropdown( choices=voice_list, label="Dataset Source", type="value", value=voice_list[0] if len(voice_list) > 0 else "" )
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with gr.Row():
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DATASET_SETTINGS['language'] = gr.Textbox(label="Language", value="en")
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DATASET_SETTINGS['validation_size'] = gr.Number(label="Validation Text Length Cull Size", value=12, precision=0)
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DATASET_SETTINGS['skip'] = gr.Checkbox(label="Skip Already Transcribed", value=False)
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with gr.Row():
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transcribe_button = gr.Button(value="Transcribe")
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prepare_validation_button = gr.Button(value="Prepare Validation")
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dataset_settings = list(DATASET_SETTINGS.values())
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with gr.Column():
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prepare_dataset_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
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with gr.Tab("Generate Configuration"):
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with gr.Row():
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with gr.Column():
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TRAINING_SETTINGS["epochs"] = gr.Number(label="Epochs", value=500, precision=0)
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with gr.Row():
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TRAINING_SETTINGS["learning_rate"] = gr.Slider(label="Learning Rate", value=1e-5, minimum=0, maximum=1e-4, step=1e-6)
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TRAINING_SETTINGS["text_ce_lr_weight"] = gr.Slider(label="Text_CE LR Ratio", value=0.01, minimum=0, maximum=1)
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with gr.Row():
|
||||
lr_schemes = list(LEARNING_RATE_SCHEMES.keys())
|
||||
TRAINING_SETTINGS["learning_rate_scheme"] = gr.Radio(lr_schemes, label="Learning Rate Scheme", value=lr_schemes[0], type="value")
|
||||
TRAINING_SETTINGS["learning_rate_schedule"] = gr.Textbox(label="Learning Rate Schedule", placeholder=str(LEARNING_RATE_SCHEDULE), visible=True)
|
||||
TRAINING_SETTINGS["learning_rate_restarts"] = gr.Number(label="Learning Rate Restarts", value=4, precision=0, visible=False)
|
||||
|
||||
TRAINING_SETTINGS["learning_rate_scheme"].change(
|
||||
fn=lambda x: ( gr.update(visible=x == lr_schemes[0]), gr.update(visible=x == lr_schemes[1]) ),
|
||||
inputs=TRAINING_SETTINGS["learning_rate_scheme"],
|
||||
outputs=[
|
||||
TRAINING_SETTINGS["learning_rate_schedule"],
|
||||
TRAINING_SETTINGS["learning_rate_restarts"],
|
||||
]
|
||||
)
|
||||
with gr.Row():
|
||||
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["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_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)
|
||||
|
||||
with gr.Row():
|
||||
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/${voice}/finetune/training_state/${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():
|
||||
training_refresh_dataset = gr.Button(value="Refresh Dataset List")
|
||||
training_import_settings = gr.Button(value="Reuse/Import Dataset")
|
||||
with gr.Column():
|
||||
training_configuration_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
|
||||
with gr.Row():
|
||||
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():
|
||||
training_configs = gr.Dropdown(label="Training Configuration", choices=get_training_list())
|
||||
refresh_configs = gr.Button(value="Refresh Configurations")
|
||||
training_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
|
||||
verbose_training = gr.Checkbox(label="Verbose Console Output", value=True)
|
||||
|
||||
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")
|
||||
reconnect_training_button = gr.Button(value="Reconnect")
|
||||
|
||||
with gr.Column():
|
||||
training_loss_graph = gr.LinePlot(label="Training Metrics",
|
||||
x="step",
|
||||
y="value",
|
||||
title="Training Metrics",
|
||||
color="type",
|
||||
tooltip=['step', 'value', 'type'],
|
||||
width=500,
|
||||
height=350,
|
||||
)
|
||||
training_lr_graph = gr.LinePlot(label="Training Metrics",
|
||||
x="step",
|
||||
y="value",
|
||||
title="Training Metrics",
|
||||
color="type",
|
||||
tooltip=['step', 'value', 'type'],
|
||||
width=500,
|
||||
height=350,
|
||||
)
|
||||
view_losses = gr.Button(value="View Losses")
|
||||
with gr.Tab("Settings"):
|
||||
with gr.Row():
|
||||
exec_inputs = []
|
||||
with gr.Column():
|
||||
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_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)
|
||||
|
||||
EXEC_SETTINGS['autoregressive_model'] = gr.Dropdown(choices=autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else autoregressive_models[0])
|
||||
|
||||
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_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=EXEC_SETTINGS['autoregressive_model'],
|
||||
outputs=None
|
||||
)
|
||||
# kill_button = gr.Button(value="Close UI")
|
||||
|
||||
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=EXEC_SETTINGS['autoregressive_model'],
|
||||
outputs=EXEC_SETTINGS['autoregressive_model'],
|
||||
)
|
||||
|
||||
exec_inputs = list(EXEC_SETTINGS.values())
|
||||
for k in EXEC_SETTINGS:
|
||||
EXEC_SETTINGS[k].change( fn=update_args_proxy, inputs=exec_inputs )
|
||||
|
||||
EXEC_SETTINGS['autoregressive_model'].change(
|
||||
fn=update_autoregressive_model,
|
||||
inputs=EXEC_SETTINGS['autoregressive_model'],
|
||||
outputs=None
|
||||
)
|
||||
|
||||
EXEC_SETTINGS['vocoder_model'].change(
|
||||
fn=update_vocoder_model,
|
||||
inputs=EXEC_SETTINGS['vocoder_model'],
|
||||
outputs=None
|
||||
)
|
||||
|
||||
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,
|
||||
latents_out,
|
||||
import_voice_name,
|
||||
utilities_metadata_column,
|
||||
]
|
||||
)
|
||||
|
||||
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=[
|
||||
GENERATE_SETTINGS['num_autoregressive_samples'],
|
||||
GENERATE_SETTINGS['diffusion_iterations'],
|
||||
],
|
||||
)
|
||||
|
||||
recompute_voice_latents.click(compute_latents_proxy,
|
||||
inputs=[
|
||||
GENERATE_SETTINGS['voice'],
|
||||
GENERATE_SETTINGS['voice_latents_chunks'],
|
||||
],
|
||||
outputs=GENERATE_SETTINGS['voice'],
|
||||
)
|
||||
|
||||
GENERATE_SETTINGS['emotion'].change(
|
||||
fn=lambda value: gr.update(visible=value == "Custom"),
|
||||
inputs=GENERATE_SETTINGS['emotion'],
|
||||
outputs=GENERATE_SETTINGS['prompt']
|
||||
)
|
||||
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=[
|
||||
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(generate_proxy,
|
||||
inputs=generate_settings,
|
||||
outputs=[output_audio, source_sample, candidates_list, generation_results],
|
||||
api_name="generate",
|
||||
)
|
||||
|
||||
|
||||
copy_button.click(import_generate_settings_proxy,
|
||||
inputs=audio_in, # JSON elements cannot be used as inputs
|
||||
outputs=generate_settings
|
||||
)
|
||||
|
||||
reset_generation_settings_button.click(
|
||||
fn=reset_generation_settings,
|
||||
inputs=None,
|
||||
outputs=generate_settings
|
||||
)
|
||||
|
||||
history_copy_settings_button.click(history_copy_settings,
|
||||
inputs=[
|
||||
history_voices,
|
||||
history_results_list,
|
||||
],
|
||||
outputs=generate_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_keep_x_past_datasets,
|
||||
],
|
||||
outputs=[
|
||||
training_output,
|
||||
],
|
||||
)
|
||||
training_output.change(
|
||||
fn=update_training_dataplot,
|
||||
inputs=None,
|
||||
outputs=[
|
||||
training_loss_graph,
|
||||
training_lr_graph,
|
||||
],
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
view_losses.click(
|
||||
fn=update_training_dataplot,
|
||||
inputs=[
|
||||
training_configs
|
||||
],
|
||||
outputs=[
|
||||
training_loss_graph,
|
||||
training_lr_graph,
|
||||
],
|
||||
)
|
||||
|
||||
stop_training_button.click(stop_training,
|
||||
inputs=None,
|
||||
outputs=training_output #console_output
|
||||
)
|
||||
reconnect_training_button.click(reconnect_training,
|
||||
inputs=[
|
||||
verbose_training,
|
||||
],
|
||||
outputs=training_output #console_output
|
||||
)
|
||||
transcribe_button.click(
|
||||
prepare_dataset_proxy,
|
||||
inputs=dataset_settings,
|
||||
outputs=prepare_dataset_output #console_output
|
||||
)
|
||||
prepare_validation_button.click(
|
||||
prepare_validation_dataset,
|
||||
inputs=[
|
||||
dataset_settings[0],
|
||||
DATASET_SETTINGS['validation_size'],
|
||||
],
|
||||
outputs=prepare_dataset_output #console_output
|
||||
)
|
||||
|
||||
training_refresh_dataset.click(
|
||||
lambda: gr.update(choices=get_dataset_list()),
|
||||
inputs=None,
|
||||
outputs=TRAINING_SETTINGS["voice"],
|
||||
)
|
||||
training_settings = list(TRAINING_SETTINGS.values())
|
||||
training_optimize_configuration.click(optimize_training_settings_proxy,
|
||||
inputs=training_settings,
|
||||
outputs=training_settings[:-1] + [training_configuration_output] #console_output
|
||||
)
|
||||
training_import_settings.click(import_training_settings_proxy,
|
||||
inputs=TRAINING_SETTINGS['voice'],
|
||||
outputs=training_settings[:-1] + [training_configuration_output] #console_output
|
||||
)
|
||||
training_save_configuration.click(save_training_settings_proxy,
|
||||
inputs=training_settings,
|
||||
outputs=training_configuration_output #console_output
|
||||
)
|
||||
|
||||
if os.path.isfile('./config/generate.json'):
|
||||
ui.load(import_generate_settings_proxy, inputs=None, outputs=generate_settings)
|
||||
|
||||
if args.check_for_updates:
|
||||
ui.load(check_for_updates)
|
||||
|
||||
stop.click(fn=cancel_generate, inputs=None, outputs=None)
|
||||
|
||||
|
||||
ui.queue(concurrency_count=args.concurrency_count)
|
||||
webui = ui
|
||||
return webui
|
||||
Loading…
Reference in New Issue
Block a user