fixed notebooks, provided paperspace notebook

notebook_colab.ipynb

@@ -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",

notebook_paperspace.ipynb

@@ -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
+}

src/utils.py

@@ -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" ])