forked from mrq/ai-voice-cloning
547 lines
18 KiB
Python
547 lines
18 KiB
Python
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 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 utils import *
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args = setup_args()
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def run_generation(
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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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seed,
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candidates,
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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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experimental_checkboxes,
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progress=gr.Progress(track_tqdm=True)
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):
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try:
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sample, outputs, stats = generate(
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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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seed,
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candidates,
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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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experimental_checkboxes,
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progress
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)
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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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reload_tts()
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raise gr.Error(message)
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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(visible=len(outputs) > 1),
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gr.update(value=stats, visible=True),
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)
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def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
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global tts
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global args
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try:
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tts
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except NameError:
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raise gr.Error("TTS is still initializing...")
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voice_samples, conditioning_latents = load_voice(voice, load_latents=False)
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if voice_samples is None:
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return
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conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, progress=progress, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents)
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if len(conditioning_latents) == 4:
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conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
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return voice
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def update_presets(value):
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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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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_configs())
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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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}
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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[headers[k]]
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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_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(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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)
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def prepare_dataset_proxy( voice, language, progress=gr.Progress(track_tqdm=True) ):
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return prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language, progress=progress )
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def update_voices():
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return (
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gr.Dropdown.update(choices=get_voice_list()),
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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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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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text = gr.Textbox(lines=4, label="Prompt")
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with gr.Row():
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with gr.Column():
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delimiter = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
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emotion = gr.Radio( ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom"], value="Custom", label="Emotion", type="value", interactive=True )
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prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)")
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voice = gr.Dropdown(get_voice_list(), label="Voice", type="value")
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mic_audio = gr.Audio( label="Microphone Source", source="microphone", type="filepath" )
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refresh_voices = gr.Button(value="Refresh Voice List")
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voice_latents_chunks = gr.Slider(label="Voice Chunks", minimum=1, maximum=64, value=1, step=1)
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recompute_voice_latents = gr.Button(value="(Re)Compute Voice Latents")
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with gr.Column():
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candidates = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
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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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num_autoregressive_samples = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Samples")
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diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
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temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
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breathing_room = gr.Slider(value=8, minimum=1, maximum=32, step=1, label="Pause Size")
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diffusion_sampler = gr.Radio(
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["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"],
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value="P", label="Diffusion Samplers", type="value" )
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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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experimental_checkboxes = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
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cvvp_weight = gr.Slider(value=0, minimum=0, maximum=1, label="CVVP Weight")
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top_p = gr.Slider(value=0.8, minimum=0, maximum=1, label="Top P")
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diffusion_temperature = gr.Slider(value=1.0, minimum=0, maximum=1, label="Diffusion Temperature")
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length_penalty = gr.Slider(value=1.0, minimum=0, maximum=8, label="Length Penalty")
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repetition_penalty = gr.Slider(value=2.0, minimum=0, maximum=8, label="Repetition Penalty")
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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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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)
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output_pick = gr.Button(value="Select Candidate", visible=False)
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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=get_voice_list("./results/"), label="Voice", type="value")
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history_view_results_button = gr.Button(value="View Files")
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with gr.Column():
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history_results_list = gr.Dropdown(label="Results",type="value", interactive=True)
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history_view_result_button = gr.Button(value="View File")
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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():
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metadata_out = gr.JSON(label="Audio Metadata", visible=False)
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copy_button = gr.Button(value="Copy Settings", visible=False)
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latents_out = gr.File(type="binary", label="Voice Latents", visible=False)
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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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gr.Dropdown( get_voice_list(), label="Dataset Source", type="value" ),
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gr.Textbox(label="Language", placeholder="English")
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]
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prepare_dataset_button = gr.Button(value="Prepare")
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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 = [
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gr.Slider(label="Batch Size", value=128),
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gr.Slider(label="Learning Rate", value=1e-5, minimum=0, maximum=1e-4, step=1e-6),
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gr.Number(label="Print Frequency", value=50),
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gr.Number(label="Save Frequency", value=50),
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]
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training_settings = training_settings + [
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gr.Textbox(label="Training Name", placeholder="finetune"),
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gr.Textbox(label="Dataset Name", placeholder="finetune"),
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gr.Textbox(label="Dataset Path", placeholder="./training/finetune/train.txt"),
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gr.Textbox(label="Validation Name", placeholder="finetune"),
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gr.Textbox(label="Validation Path", placeholder="./training/finetune/train.txt"),
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]
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with gr.Column():
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save_yaml_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
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save_yaml_button = gr.Button(value="Save Training Configuration")
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with gr.Tab("Run Training"):
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with gr.Row():
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with gr.Column():
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training_configs = gr.Dropdown(label="Training Configuration", choices=get_training_configs())
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refresh_configs = gr.Button(value="Refresh Configurations")
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start_training_button = gr.Button(value="Train")
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stop_training_button = gr.Button(value="Stop")
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with gr.Column():
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training_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
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with gr.Tab("Settings"):
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with gr.Row():
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exec_inputs = []
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with gr.Column():
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exec_inputs = exec_inputs + [
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gr.Textbox(label="Listen", value=args.listen, placeholder="127.0.0.1:7860/"),
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gr.Checkbox(label="Public Share Gradio", value=args.share),
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gr.Checkbox(label="Check For Updates", value=args.check_for_updates),
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gr.Checkbox(label="Only Load Models Locally", value=args.models_from_local_only),
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gr.Checkbox(label="Low VRAM", value=args.low_vram),
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gr.Checkbox(label="Embed Output Metadata", value=args.embed_output_metadata),
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gr.Checkbox(label="Slimmer Computed Latents", value=args.latents_lean_and_mean),
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gr.Checkbox(label="Voice Fixer", value=args.voice_fixer),
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gr.Checkbox(label="Use CUDA for Voice Fixer", value=args.voice_fixer_use_cuda),
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gr.Checkbox(label="Force CPU for Conditioning Latents", value=args.force_cpu_for_conditioning_latents),
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gr.Checkbox(label="Defer TTS Load", value=args.defer_tts_load),
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gr.Textbox(label="Device Override", value=args.device_override),
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]
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with gr.Column():
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exec_inputs = exec_inputs + [
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gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size),
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gr.Number(label="Concurrency Count", precision=0, value=args.concurrency_count),
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gr.Number(label="Ouptut Sample Rate", precision=0, value=args.output_sample_rate),
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gr.Slider(label="Ouptut Volume", minimum=0, maximum=2, value=args.output_volume),
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gr.Dropdown(label="Whisper Model", value=args.whisper_model, choices=["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large"]),
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]
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gr.Button(value="Check for Updates").click(check_for_updates)
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gr.Button(value="Reload TTS").click(reload_tts)
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for i in exec_inputs:
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i.change( fn=export_exec_settings, inputs=exec_inputs )
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# console_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
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input_settings = [
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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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seed,
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candidates,
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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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experimental_checkboxes,
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]
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history_view_results_button.click(
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fn=history_view_results,
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inputs=history_voices,
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outputs=[
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history_info,
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history_results_list,
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]
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)
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history_view_result_button.click(
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fn=lambda voice, file: f"./results/{voice}/{file}",
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inputs=[
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history_voices,
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history_results_list,
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],
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outputs=history_audio
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)
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audio_in.upload(
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fn=read_generate_settings_proxy,
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inputs=audio_in,
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outputs=[
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metadata_out,
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copy_button,
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latents_out,
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import_voice_name
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]
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)
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import_voice_button.click(
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fn=import_voices_proxy,
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inputs=[
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audio_in,
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import_voice_name,
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],
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outputs=import_voice_name #console_output
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)
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show_experimental_settings.change(
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fn=lambda x: gr.update(visible=x),
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inputs=show_experimental_settings,
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outputs=experimental_column
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)
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preset.change(fn=update_presets,
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inputs=preset,
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outputs=[
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num_autoregressive_samples,
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diffusion_iterations,
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],
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)
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recompute_voice_latents.click(compute_latents,
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inputs=[
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voice,
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voice_latents_chunks,
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],
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outputs=voice,
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)
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prompt.change(fn=lambda value: gr.update(value="Custom"),
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inputs=prompt,
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outputs=emotion
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)
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mic_audio.change(fn=lambda value: gr.update(value="microphone"),
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inputs=mic_audio,
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outputs=voice
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|
)
|
|
|
|
refresh_voices.click(update_voices,
|
|
inputs=None,
|
|
outputs=[
|
|
voice,
|
|
dataset_settings[0],
|
|
history_voices
|
|
]
|
|
)
|
|
|
|
output_pick.click(
|
|
lambda x: x,
|
|
inputs=candidates_list,
|
|
outputs=output_audio,
|
|
)
|
|
|
|
submit.click(
|
|
lambda: (gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)),
|
|
outputs=[source_sample, candidates_list, output_pick, generation_results],
|
|
)
|
|
|
|
submit_event = submit.click(run_generation,
|
|
inputs=input_settings,
|
|
outputs=[output_audio, source_sample, candidates_list, output_pick, generation_results],
|
|
)
|
|
|
|
|
|
copy_button.click(import_generate_settings,
|
|
inputs=audio_in, # JSON elements cannot be used as inputs
|
|
outputs=input_settings
|
|
)
|
|
|
|
reset_generation_settings_button.click(
|
|
fn=reset_generation_settings,
|
|
inputs=None,
|
|
outputs=input_settings
|
|
)
|
|
|
|
history_copy_settings_button.click(history_copy_settings,
|
|
inputs=[
|
|
history_voices,
|
|
history_results_list,
|
|
],
|
|
outputs=input_settings
|
|
)
|
|
|
|
refresh_configs.click(update_training_configs,inputs=None,outputs=training_configs)
|
|
start_training_button.click(run_training,
|
|
inputs=training_configs,
|
|
outputs=training_output #console_output
|
|
)
|
|
stop_training_button.click(stop_training,
|
|
inputs=None,
|
|
outputs=training_output #console_output
|
|
)
|
|
prepare_dataset_button.click(
|
|
prepare_dataset_proxy,
|
|
inputs=dataset_settings,
|
|
outputs=prepare_dataset_output #console_output
|
|
)
|
|
save_yaml_button.click(save_training_settings,
|
|
inputs=training_settings,
|
|
outputs=save_yaml_output #console_output
|
|
)
|
|
|
|
if os.path.isfile('./config/generate.json'):
|
|
ui.load(import_generate_settings, inputs=None, outputs=input_settings)
|
|
|
|
if args.check_for_updates:
|
|
ui.load(check_for_updates)
|
|
|
|
stop.click(fn=cancel_generate, inputs=None, outputs=None, cancels=[submit_event])
|
|
|
|
|
|
ui.queue(concurrency_count=args.concurrency_count)
|
|
webui = ui
|
|
return webui |