forked from mrq/ai-voice-cloning
updated notebooks to use the new "main" setup
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f8249aa826
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@ -71,16 +71,29 @@
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{
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"cell_type":"code",
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"source":[
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"%cd ai-voice-cloning\n",
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"import src.webui as mrq\n",
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"import sys\n",
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"sys.argv = [\"\"]\n",
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"%cd /content/ai-voice-cloning\n",
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"\n",
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"mrq.args = mrq.setup_args()\n",
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"mrq.webui = mrq.setup_gradio()\n",
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"mrq.webui.launch(share=True, prevent_thread_lock=True, height=1000)\n",
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"mrq.tts = mrq.setup_tortoise()\n",
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"mrq.webui.block_thread()"
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"import os\n",
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"import sys\n",
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"\n",
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"sys.argv = [\"\"]\n",
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"sys.path.append('./src/')\n",
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"\n",
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"if 'TORTOISE_MODELS_DIR' not in os.environ:\n",
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"\tos.environ['TORTOISE_MODELS_DIR'] = os.path.realpath(os.path.join(os.getcwd(), './models/tortoise/'))\n",
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"\n",
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"if 'TRANSFORMERS_CACHE' not in os.environ:\n",
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"\tos.environ['TRANSFORMERS_CACHE'] = os.path.realpath(os.path.join(os.getcwd(), './models/transformers/'))\n",
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"\n",
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"from utils import *\n",
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"from webui import *\n",
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"\n",
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"args = setup_args()\n",
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"\n",
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"webui = setup_gradio()\n",
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"tts = setup_tortoise()\n",
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"webui.launch(share=True, prevent_thread_lock=True, height=1000)\n",
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"webui.block_thread()"
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],
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"metadata":{
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"id":"c_EQZLTA19c7"
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@ -102,6 +115,7 @@
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{
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"cell_type":"code",
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"source":[
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"%cd /content/ai-voice-cloning\n",
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"!apt install -y p7zip-full\n",
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"from datetime import datetime\n",
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"timestamp = datetime.now().strftime('%m-%d-%Y_%H:%M:%S')\n",
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35
src/train.py
35
src/train.py
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@ -1,35 +0,0 @@
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import torch
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import argparse
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from ..dlas.codes import *
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from ..dlas.codes.utils import util, options as option
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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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args = parser.parse_args()
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opt = option.parse(args.opt, is_train=True)
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if args.launcher != 'none':
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# export CUDA_VISIBLE_DEVICES for running in distributed mode.
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if 'gpu_ids' in opt.keys():
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gpu_list = ','.join(str(x) for x in opt['gpu_ids'])
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os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list
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print('export CUDA_VISIBLE_DEVICES=' + gpu_list)
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trainer = Trainer()
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#### distributed training settings
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if args.launcher == 'none': # disabled distributed training
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opt['dist'] = False
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trainer.rank = -1
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if len(opt['gpu_ids']) == 1:
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torch.cuda.set_device(opt['gpu_ids'][0])
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print('Disabled distributed training.')
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else:
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opt['dist'] = True
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init_dist('nccl')
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trainer.world_size = torch.distributed.get_world_size()
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trainer.rank = torch.distributed.get_rank()
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torch.cuda.set_device(torch.distributed.get_rank())
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trainer.init(args.opt, opt, args.launcher)
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trainer.do_training()
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@ -450,7 +450,6 @@ def save_training_settings( batch_size=None, learning_rate=None, print_rate=None
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yaml = f.read()
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for k in settings:
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print(f"${{{k}}} => {settings[k]}")
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yaml = yaml.replace(f"${{{k}}}", str(settings[k]))
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with open(f'./training/{settings["name"]}.yaml', 'w', encoding="utf-8") as f:
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69
train.ipynb
Executable file
69
train.ipynb
Executable file
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@ -0,0 +1,69 @@
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{
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"nbformat":4,
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"nbformat_minor":0,
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"metadata":{
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"colab":{
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"private_outputs":true,
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"provenance":[
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]
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},
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"kernelspec":{
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"name":"python3",
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"display_name":"Python 3"
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},
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"language_info":{
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"name":"python"
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},
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"accelerator":"GPU",
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"gpuClass":"standard"
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},
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"cells":[
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{
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"cell_type":"code",
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"execution_count":null,
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"metadata":{
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"id":"AaKpV3rCI3Eo"
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},
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"outputs":[
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],
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"source":[
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"!git clone https://git.ecker.tech/mrq/DL-Art-School\n",
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"%cd DL-Art-School\n",
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"!pip install -r requirements.txt"
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]
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},
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{
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"cell_type":"code",
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"source":[
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"from google.colab import drive\n",
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"drive.mount('/content/drive')",
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"%cd /content/DL-Art-School/\n",
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"#!rm -r experiments\n",
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"!ln -s /content/drive/MyDrive/experiments/\n",
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],
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"metadata":{
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"id":"8eV92cjGI4XL"
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},
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"execution_count":null,
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"outputs":[
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]
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},
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{
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"cell_type":"code",
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"source":[
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"%cd /content/DL-Art-School/\n",
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"!python ./codes/train.py -opt ./experiments/ar.yml"
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],
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"metadata":{
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"id":"7lcRGqglX2FC"
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},
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"execution_count":null,
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"outputs":[
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]
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}
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]
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}
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