forked from camenduru/ai-voice-cloning
fixed notebooks, provided paperspace notebook
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parent
b4098dca73
commit
83b5125854
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@ -51,10 +51,10 @@
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"\n",
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"!python -m pip install --upgrade pip\n",
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"!pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116\n",
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"!python -m pip install -r ./dlas/requirements.txt\n",
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"!python -m pip install -r ./tortoise-tts/requirements.txt\n",
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"!python -m pip install -r ./requirements.txt\n",
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"!python -m pip install -r ./tortoise-tts/requirements.txt\n",
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"!python -m pip install -e ./tortoise-tts/\n",
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"!python -m pip install -r ./dlas/requirements.txt\n",
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"\n",
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"!rm ./tortoise-tts/{main,webui}.py"
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]
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@ -152,7 +152,7 @@
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"\n",
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"args = utils.setup_args()\n",
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"ui = webui.setup_gradio()\n",
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"# Be very, very sure to check \"Defer TTS Load\" in Settings, then restart, before you start training\n",
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"# 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",
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"# You'll crash the runtime if you don't\n",
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"if not args.defer_tts_load:\n",
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"\tutils.setup_tortoise()\n",
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132
notebook_paperspace.ipynb
Executable file
132
notebook_paperspace.ipynb
Executable file
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@ -0,0 +1,132 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ni41hmE03DL6"
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},
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"source": [
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"## Initialization"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "FtsMKKfH18iM"
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},
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"source": [
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"!sudo apt update\n",
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"!sudo apt-get install python3.9-venv -y\n",
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"%cd /notebooks/\n",
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"!git clone https://git.ecker.tech/mrq/ai-voice-cloning/\n",
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"!ln -s ./ai-voice-cloning/models/ ./\n",
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"%cd ai-voice-cloning\n",
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"!./setup-cuda.sh\n",
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"#!./update.sh"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "IzrGt5IcHlAD"
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},
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"source": [
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"# Update Repos"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "3DktoOXSHmtw"
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},
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"source": [
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"# for my debugging purposes\n",
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"%cd /notebooks/ai-voice-cloning/\n",
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"!sudo apt update\n",
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"!sudo apt-get install python3.9-venv -y\n",
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"!mkdir -p ~/.cache\n",
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"!ln -s /notebooks/ai-voice-cloning/models/voicefixer ~/.cache/.\n",
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"!./update-force.sh\n",
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"#!git pull\n",
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"#!git submodule update --remote"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "o1gkfw3B3JSk"
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},
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"source": [
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"## Running"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "c_EQZLTA19c7"
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},
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"source": [
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"%cd /notebooks/ai-voice-cloning\n",
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"\n",
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"!export TORTOISE_MODELS_DIR=/notebooks/ai-voice-cloning/models/tortoise/\n",
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"!export TRANSFORMERS_CACHE=/notebooks/ai-voice-cloning/models/transformers/\n",
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"\n",
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"!./start.sh --share"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "2AnVQxEJx47p"
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},
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"source": [
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"## Exporting"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "YOACiDCXx72G"
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},
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"source": [
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"%cd /notebooks/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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"!mkdir -p \"../{timestamp}/results\"\n",
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"!mv ./results/* \"../{timestamp}/results/.\"\n",
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"!mv ./training/* \"../{timestamp}/training/.\"\n",
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"!7z a -t7z -m0=lzma2 -mx=9 -mfb=64 -md=32m -ms=on \"../{timestamp}.7z\" \"../{timestamp}/\"\n",
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"!ls ~/\n",
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"!echo \"Finished zipping, archive is available at {timestamp}.7z\""
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]
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}
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],
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"metadata": {
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"accelerator": "GPU",
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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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"gpuClass": "standard",
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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23
src/utils.py
23
src/utils.py
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@ -697,23 +697,22 @@ class TrainingState():
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logs = [logs[-1]]
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for log in logs:
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try:
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ea = event_accumulator.EventAccumulator(log, size_guidance={event_accumulator.SCALARS: 0})
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ea.Reload()
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for key in keys:
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scalar = ea.Scalars(key)
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for s in scalar:
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if update and s.step <= self.last_info_check_at:
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continue
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highest_step = max( highest_step, s.step )
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self.statistics.append( { "step": s.step, "value": s.value, "type": key } )
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try:
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scalar = ea.Scalars(key)
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for s in scalar:
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if update and s.step <= self.last_info_check_at:
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continue
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highest_step = max( highest_step, s.step )
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self.statistics.append( { "step": s.step, "value": s.value, "type": key } )
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if key == 'loss_gpt_total':
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self.losses.append( { "step": s.step, "value": s.value, "type": key } )
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except Exception as e:
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pass
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if key == 'loss_gpt_total':
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self.losses.append( { "step": s.step, "value": s.value, "type": key } )
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except Exception as e:
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pass
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else:
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logs = sorted([f'{self.dataset_dir}/{d}' for d in os.listdir(self.dataset_dir) if d[-4:] == ".log" ])
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