Merge branch 'main' into add-contexttab

Author: MaxSchambach

packages/tasks/src/model-libraries-snippets.ts

@@ -1409,6 +1409,24 @@ export const voicecraft = (model: ModelData): string[] => [
 model = VoiceCraft.from_pretrained("${model.id}")`,
 ];
 
+export const vui = (model: ModelData): string[] => [
+	`# !pip install git+https://github.com/fluxions-ai/vui
+
+import torchaudio
+
+from vui.inference import render
+from vui.model import Vui,
+
+model = Vui.from_pretrained().cuda()
+waveform = render(
+    model,
+    "Hey, here is some random stuff, usually something quite long as the shorter the text the less likely the model can cope!",
+)
+print(waveform.shape)
+torchaudio.save("out.opus", waveform[0], 22050)
+`,
+];
+
 export const chattts = (): string[] => [
 	`import ChatTTS
 import torchaudio

packages/tasks/src/model-libraries.ts

@@ -1070,6 +1070,13 @@ export const MODEL_LIBRARIES_UI_ELEMENTS = {
 		docsUrl: "https://github.com/jasonppy/VoiceCraft",
 		snippets: snippets.voicecraft,
 	},
+	vui: {
+		prettyLabel: "Vui",
+		repoName: "Vui",
+		repoUrl: "https://github.com/vui-ai/vui",
+		countDownloads: `path_extension:"pt"`,
+		snippets: snippets.vui,
+	},
 	wham: {
 		prettyLabel: "WHAM",
 		repoName: "wham",

packages/tasks/src/tasks/placeholder/spec/output.json

@@ -9,7 +9,7 @@
 		"properties": {
 			"meaningful_output_name": {
 				"type": "string",
-				"description": "TODO: Describe what is outputed by the inference here"
+				"description": "TODO: Describe what is outputted by the inference here"
 			}
 		},
 		"required": ["meaningfulOutputName"]

packages/tasks/src/tasks/reinforcement-learning/about.md

@@ -48,7 +48,7 @@ Observations and states are the information our agent gets from the environment.
 
 Inference in reinforcement learning differs from other modalities, in which there's a model and test data. In reinforcement learning, once you have trained an agent in an environment, you try to run the trained agent for additional steps to get the average reward.
 
-A typical training cycle consists of gathering experience from the environment, training the agent, and running the agent on a test environment to obtain average reward. Below there's a snippet on how you can interact with the environment using the `gymnasium` library, train an agent using `stable-baselines3`, evalute the agent on test environment and infer actions from the trained agent.
+A typical training cycle consists of gathering experience from the environment, training the agent, and running the agent on a test environment to obtain average reward. Below there's a snippet on how you can interact with the environment using the `gymnasium` library, train an agent using `stable-baselines3`, evaluate the agent on test environment and infer actions from the trained agent.
 
 ```python
 # Here we are running 20 episodes of CartPole-v1 environment, taking random actions