chore: import upstream snapshot with attribution

scripts/pseudo_labeling/README.md

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+# 🧠 TopIPL: Iterative Pseudo-Labeling for ASR
+
+TopIPL is an **iterative pseudo-labeling algorithm** for training speech recognition models using both labeled and unlabeled data. It integrates seamlessly into the NeMo ASR pipeline and enables **self-training** across epochs with minimal manual intervention.
+
+## 🚀 Key Features
+
+- ⚙️ Supports **semi-supervised ASR training** with dynamic iterative pseudo-label refinement.
+- 🧪 Designed for large-scale training using both labeled and unlabeled speech data.
+- 🔁 Automatically writes pseudo-labels and updates training configs between iterations.
+
+## 📦 Required Components
+
+TopIPL relies on the following components:
+
+- **[`SDPNeMoRunIPLProcessor`]**  
+  Commands for running IPL are generated and submitted using SDP processors and NeMo-Run.  
+  See instructions for usage [here](https://github.com/NVIDIA/NeMo-speech-data-processor/blob/main/sdp/processors/ipl/README.md).
+
+- **Training Callback: `IPLEpochStopperCallback`**  
+  Add this to your training config under `exp_manager` to **stop training at the end of each epoch**, enabling pseudo-label update:
+
+```yaml
+exp_manager:
+  create_ipl_epoch_stopper_callback: True
+  ipl_epoch_stopper_callback_params:
+    stop_every_n_epochs: n # Stop training after every n epochs (default: 1)
+