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ssl_en_nest_xlarge_v1.0

Self-supervised Learning

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steveheh commited on Jan 3

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@@ -101,7 +101,7 @@ python <NeMo Root>/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py \
     exp_manager.wandb_logger_kwargs.name="<Name of experiment>" \
     exp_manager.wandb_logger_kwargs.project="<Name of project>"
 ```
-More details can be found at [maybe_init_from_pretrained_checkpoint()](https://github.com/NVIDIA/NeMo/blob/main/nemo/core/classes/modelPT.py#L1236).
 ### Using NEST as Frozen Feature Extractor
 NEST can also be used as a frozen feature extractor for downstream tasks. For example, in the case of speaker verification, embeddings can be extracted from different layers of the NEST model, and a learned weighted combination of those embeddings can be used as input to the speaker verification model.

     exp_manager.wandb_logger_kwargs.name="<Name of experiment>" \
     exp_manager.wandb_logger_kwargs.project="<Name of project>"
 ```
+More details can be found at [maybe_init_from_pretrained_checkpoint()](https://github.com/NVIDIA/NeMo/blob/main/nemo/core/classes/modelPT.py#L1251).
 ### Using NEST as Frozen Feature Extractor
 NEST can also be used as a frozen feature extractor for downstream tasks. For example, in the case of speaker verification, embeddings can be extracted from different layers of the NEST model, and a learned weighted combination of those embeddings can be used as input to the speaker verification model.