Pre-trained SSL checkpoints available in NeMo need to be further fine-tuned on down-stream task. There are two main ways to load pretrained checkpoints in NeMo:
restore_from()method to load a local checkpoint file (
from_pretrained()method to download and set up a checkpoint from NGC.
Refer to the following sections for instructions and examples for each.
Note that these instructions are for fine-tuning. To resume an unfinished training experiment,
use the Experiment Manager to do so by setting the
resume_if_exists flag to
Loading Local Checkpoints#
NeMo automatically saves checkpoints of a model that is trained in a
.nemo format. Alternatively, to manually save the model at any
If there is a local
.nemo checkpoint that you’d like to load, use the
import nemo.collections.asr as nemo_asr ssl_model = nemo_asr.models.<MODEL_BASE_CLASS>.restore_from(restore_path="<path/to/checkpoint/file.nemo>")
Where the model base class is the ASR model class of the original checkpoint, or the general
Loading NGC Pretrained Checkpoints#
The SSL collection has checkpoints of several models trained on various datasets. These checkpoints are obtainable via NGC NeMo Automatic Speech Recognition collection. The model cards on NGC contain more information about each of the checkpoints available.
The table at the end of this page lists the SSL models available from NGC. The models can be accessed via the
from_pretrained() method inside
the ASR Model class. In general, you can load any of these models with code in the following format:
import nemo.collections.asr as nemo_asr ssl_model = nemo_asr.models.ASRModel.from_pretrained(model_name="<MODEL_NAME>")
model_name is the value under “Model Name” entry in the tables below.
For example, to load the conformer Large SSL checkpoint, run:
ssl_model = nemo_asr.models.ASRModel.from_pretrained(model_name="ssl_en_conformer_large")
You can also call
from_pretrained() from the specific model class (such as
for Conformer) if you need to access a specific model functionality.
If you would like to programatically list the models available for a particular base class, you can use the
Loading SSL checkpoint into Down-stream Model#
After loading an SSL checkpoint as shown above, it’s
state_dict needs to be copied to a
down-stream model for fine-tuning.
For example, to load a SSL checkpoint for ASR down-stream task using
# define down-stream model asr_model = nemo_asr.models.EncDecRNNTBPEModel(cfg=cfg.model, trainer=trainer) # load ssl checkpoint asr_model.load_state_dict(ssl_model.state_dict(), strict=False) # discard ssl model del ssl model
Refer to SSL configs to do this automatically via config files.
Fine-tuning on Downstream Datasets#
After loading SSL checkpoint into down-stream model, refer to multiple ASR tutorials provided in the Tutorials section. Most of these tutorials explain how to fine-tune on some dataset as a demonstration.
Inference Execution Flow Diagram#
When preparing your own inference scripts after downstream fine-tuning, please follow the execution flow diagram order for correct inference, found at the examples directory for ASR collection.
Below is a list of all the SSL models that are available in NeMo.
Model Base Class