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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) | |