cleanup, "injected" dvae.pth to download through tortoise's model loader, so I don't need to keep copying it
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13c9920b7f
commit
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15
src/train.py
15
src/train.py
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@ -4,12 +4,27 @@ import argparse
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import os
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import sys
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# this is some massive kludge that only works if it's called from a shell and not an import/PIP package
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# it's smart-yet-irritating module-model loader breaks when trying to load something specifically when not from a shell
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sys.path.insert(0, './dlas/codes/')
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# this is also because DLAS is not written as a package in mind
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# it'll gripe when it wants to import from train.py
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sys.path.insert(0, './dlas/')
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# for PIP, replace it with:
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# sys.path.insert(0, os.path.dirname(os.path.realpath(dlas.__file__)))
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# sys.path.insert(0, f"{os.path.dirname(os.path.realpath(dlas.__file__))}/../")
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# don't even really bother trying to get DLAS PIP'd
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# without kludge, it'll have to be accessible as `codes` and not `dlas`
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from codes import train as tr
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from utils import util, options as option
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# this is effectively just copy pasted and cleaned up from the __main__ section of training.py
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# I'll clean it up better
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vit_latent.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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53
src/utils.py
53
src/utils.py
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@ -24,18 +24,20 @@ import gradio.utils
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from datetime import datetime
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from tortoise.api import TextToSpeech
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from tortoise.api import TextToSpeech, MODELS, get_model_path
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from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_dir
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from tortoise.utils.text import split_and_recombine_text
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from tortoise.utils.device import get_device_name, set_device_name
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import whisper
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MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
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args = None
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tts = None
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webui = None
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voicefixer = None
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dlas = None
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whisper_model = None
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def get_args():
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global args
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@ -53,7 +55,7 @@ def setup_args():
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'sample-batch-size': None,
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'embed-output-metadata': True,
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'latents-lean-and-mean': True,
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'voice-fixer': False, # I'm tired of long initialization of Colab notebooks
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'voice-fixer': True,
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'voice-fixer-use-cuda': True,
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'force-cpu-for-conditioning-latents': False,
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'device-override': None,
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@ -420,22 +422,46 @@ def generate(
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stats,
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)
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def run_training(config_path):
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global tts
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del tts
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tts = None
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import subprocess
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subprocess.run(["python", "./src/train.py", "-opt", config_path], env=os.environ.copy(), shell=True, stdout=subprocess.PIPE)
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"""
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from train import train
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train(config)
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"""
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def setup_voicefixer(restart=False):
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global voicefixer
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if restart:
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del voicefixer
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voicefixer = None
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try:
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print("Initializating voice-fixer")
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from voicefixer import VoiceFixer
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voicefixer = VoiceFixer()
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print("initialized voice-fixer")
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except Exception as e:
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print(f"Error occurred while tring to initialize voicefixer: {e}")
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def setup_tortoise(restart=False):
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global args
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global tts
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global voicefixer
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if args.voice_fixer and not restart:
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try:
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from voicefixer import VoiceFixer
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print("Initializating voice-fixer")
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voicefixer = VoiceFixer()
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print("initialized voice-fixer")
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except Exception as e:
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print(f"Error occurred while tring to initialize voicefixer: {e}")
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setup_voicefixer(restart=restart)
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if restart:
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del tts
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tts = None
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print("Initializating TorToiSe...")
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tts = TextToSpeech(minor_optimizations=not args.low_vram)
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get_model_path('dvae.pth')
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print("TorToiSe initialized, ready for generation.")
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return tts
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@ -461,7 +487,6 @@ def save_training_settings( batch_size=None, learning_rate=None, print_rate=None
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with open(f'./training/{settings["name"]}.yaml', 'w', encoding="utf-8") as f:
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f.write(yaml)
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whisper_model = None
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def prepare_dataset( files, outdir, language=None ):
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global whisper_model
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if whisper_model is None:
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@ -641,9 +666,7 @@ def check_for_updates():
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return False
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def reload_tts():
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global tts
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del tts
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tts = setup_tortoise(restart=True)
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setup_tortoise(restart=True)
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def cancel_generate():
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tortoise.api.STOP_SIGNAL = True
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155
src/webui.py
155
src/webui.py
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@ -123,6 +123,76 @@ def update_presets(value):
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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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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 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 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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j,
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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 ):
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return prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language )
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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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@ -279,34 +349,6 @@ def setup_gradio():
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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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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 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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history_view_results_button.click(
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fn=history_view_results,
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@ -335,23 +377,6 @@ def setup_gradio():
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metadata_out = gr.JSON(label="Audio Metadata")
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latents_out = gr.File(type="binary", label="Voice Latents")
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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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j,
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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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audio_in.upload(
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fn=read_generate_settings_proxy,
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inputs=audio_in,
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@ -382,9 +407,6 @@ def setup_gradio():
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with gr.Column():
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prepare_dataset_button = gr.Button(value="Prepare")
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def prepare_dataset_proxy( voice, language ):
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return prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language )
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prepare_dataset_button.click(
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prepare_dataset_proxy,
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inputs=dataset_settings,
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@ -416,34 +438,12 @@ def setup_gradio():
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with gr.Tab("Train"):
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with gr.Row():
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with gr.Column():
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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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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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train = gr.Button(value="Train")
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def run_training_proxy( config ):
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global tts
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del tts
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import subprocess
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subprocess.run(["python", "./src/train.py", "-opt", config], env=os.environ.copy(), shell=True)
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"""
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from train import train
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train(config)
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"""
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train = gr.Button(value="Train")
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refresh_configs.click(update_training_configs,inputs=None,outputs=training_configs)
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train.click(run_training_proxy,
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train.click(run_training,
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inputs=training_configs,
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outputs=None
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)
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@ -506,17 +506,6 @@ def setup_gradio():
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experimental_checkboxes,
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]
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# YUCK
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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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refresh_voices.click(update_voices,
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inputs=None,
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outputs=[
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