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fixed notebooks, provided paperspace notebook

remotes/1708699347150643056/master
mrq 2023-03-08 03:29:12 +07:00
parent b4098dca73
commit 83b5125854
3 changed files with 147 additions and 16 deletions

@ -51,10 +51,10 @@
"\n",
"!python -m pip install --upgrade pip\n",
"!pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116\n",
"!python -m pip install -r ./dlas/requirements.txt\n",
"!python -m pip install -r ./tortoise-tts/requirements.txt\n",
"!python -m pip install -r ./requirements.txt\n",
"!python -m pip install -r ./tortoise-tts/requirements.txt\n",
"!python -m pip install -e ./tortoise-tts/\n",
"!python -m pip install -r ./dlas/requirements.txt\n",
"\n",
"!rm ./tortoise-tts/{main,webui}.py"
]
@ -152,7 +152,7 @@
"\n",
"args = utils.setup_args()\n",
"ui = webui.setup_gradio()\n",
"# Be very, very sure to check \"Defer TTS Load\" in Settings, then restart, before you start training\n",
"# Be very, very sure to check \"Do Not Load TTS On Startup\" in Settings after all the models download, then restart, before you start training\n",
"# You'll crash the runtime if you don't\n",
"if not args.defer_tts_load:\n",
"\tutils.setup_tortoise()\n",

@ -0,0 +1,132 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "ni41hmE03DL6"
},
"source": [
"## Initialization"
]
},
{
"cell_type": "code",
"metadata": {
"id": "FtsMKKfH18iM"
},
"source": [
"!sudo apt update\n",
"!sudo apt-get install python3.9-venv -y\n",
"%cd /notebooks/\n",
"!git clone https://git.ecker.tech/mrq/ai-voice-cloning/\n",
"!ln -s ./ai-voice-cloning/models/ ./\n",
"%cd ai-voice-cloning\n",
"!./setup-cuda.sh\n",
"#!./update.sh"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IzrGt5IcHlAD"
},
"source": [
"# Update Repos"
]
},
{
"cell_type": "code",
"metadata": {
"id": "3DktoOXSHmtw"
},
"source": [
"# for my debugging purposes\n",
"%cd /notebooks/ai-voice-cloning/\n",
"!sudo apt update\n",
"!sudo apt-get install python3.9-venv -y\n",
"!mkdir -p ~/.cache\n",
"!ln -s /notebooks/ai-voice-cloning/models/voicefixer ~/.cache/.\n",
"!./update-force.sh\n",
"#!git pull\n",
"#!git submodule update --remote"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "o1gkfw3B3JSk"
},
"source": [
"## Running"
]
},
{
"cell_type": "code",
"metadata": {
"id": "c_EQZLTA19c7"
},
"source": [
"%cd /notebooks/ai-voice-cloning\n",
"\n",
"!export TORTOISE_MODELS_DIR=/notebooks/ai-voice-cloning/models/tortoise/\n",
"!export TRANSFORMERS_CACHE=/notebooks/ai-voice-cloning/models/transformers/\n",
"\n",
"!./start.sh --share"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2AnVQxEJx47p"
},
"source": [
"## Exporting"
]
},
{
"cell_type": "code",
"metadata": {
"id": "YOACiDCXx72G"
},
"source": [
"%cd /notebooks/ai-voice-cloning\n",
"!apt install -y p7zip-full\n",
"from datetime import datetime\n",
"timestamp = datetime.now().strftime('%m-%d-%Y_%H:%M:%S')\n",
"!mkdir -p \"../{timestamp}/results\"\n",
"!mv ./results/* \"../{timestamp}/results/.\"\n",
"!mv ./training/* \"../{timestamp}/training/.\"\n",
"!7z a -t7z -m0=lzma2 -mx=9 -mfb=64 -md=32m -ms=on \"../{timestamp}.7z\" \"../{timestamp}/\"\n",
"!ls ~/\n",
"!echo \"Finished zipping, archive is available at {timestamp}.7z\""
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"private_outputs": true,
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

@ -697,23 +697,22 @@ class TrainingState():
logs = [logs[-1]]
for log in logs:
try:
ea = event_accumulator.EventAccumulator(log, size_guidance={event_accumulator.SCALARS: 0})
ea.Reload()
for key in keys:
scalar = ea.Scalars(key)
for s in scalar:
if update and s.step <= self.last_info_check_at:
continue
highest_step = max( highest_step, s.step )
self.statistics.append( { "step": s.step, "value": s.value, "type": key } )
if key == 'loss_gpt_total':
self.losses.append( { "step": s.step, "value": s.value, "type": key } )
except Exception as e:
pass
try:
scalar = ea.Scalars(key)
for s in scalar:
if update and s.step <= self.last_info_check_at:
continue
highest_step = max( highest_step, s.step )
self.statistics.append( { "step": s.step, "value": s.value, "type": key } )
if key == 'loss_gpt_total':
self.losses.append( { "step": s.step, "value": s.value, "type": key } )
except Exception as e:
pass
else:
logs = sorted([f'{self.dataset_dir}/{d}' for d in os.listdir(self.dataset_dir) if d[-4:] == ".log" ])