Checkpoints
Contents
Checkpoints#
There are two main ways to load pretrained checkpoints in NeMo:
Using the
restore_from()method to load a local checkpoint file (.nemo), orUsing the
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 loading fully trained checkpoints for evaluation or fine-tuning. For resuming an unfinished
training experiment, use the Experiment Manager to do so by setting the resume_if_exists flag to True.
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
point, issue model.save_to(<checkpoint_path>.nemo).
If there is a local .nemo checkpoint that you’d like to load, use the restore_from() method:
import nemo.collections.asr as nemo_asr
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 ASRModel class.
NGC Pretrained Checkpoints#
The ASR collection has checkpoints of several models trained on various datasets for a variety of tasks. 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 tables below list the ASR 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
model = nemo_asr.models.ASRModel.from_pretrained(model_name="<MODEL_NAME>")
Where the model name is the value under “Model Name” entry in the tables below.
For example, to load the base English QuartzNet model for speech recognition, run:
model = nemo_asr.models.ASRModel.from_pretrained(model_name="QuartzNet15x5Base-En")
You can also call from_pretrained() from the specific model class (such as EncDecCTCModel
for QuartzNet) 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
list_available_models() method.
nemo_asr.models.<MODEL_BASE_CLASS>.list_available_models()
Transcribing/Inference#
To perform inference and transcribe a sample of speech after loading the model, use the transcribe() method:
model.transcribe(paths2audio_files=[list of audio files], batch_size=BATCH_SIZE, logprobs=False)
Setting the argument logprobs to True returns the log probabilities instead of transcriptions. For more information, see ./api.html#modules.
The audio files should be 16KHz monochannel wav files.
Fine-tuning on Different Datasets#
There are multiple ASR tutorials provided in the Tutorials section. Most of these tutorials explain how to instantiate a pre-trained model, prepare the model for fine-tuning on some dataset (in the same language) as a demonstration.
Inference Execution Flow Diagram#
When preparing your own inference scripts, please follow the execution flow diagram order for correct inference, found at the examples directory for ASR collection.
Automatic Speech Recognition Models#
Below is a list of all the ASR models that are available in NeMo for specific languages, as well as auxiliary language models for certain languages.
Language Models for ASR#
Model Name |
Model Base Class |
Model Card |
|---|---|---|
asrlm_en_transformer_large_ls |
TransformerLMModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:asrlm_en_transformer_large_ls |
Speech Recognition (Languages)#
English#
Mandarin#
Model |
Model Base Class |
Model Card |
|---|---|---|
stt_zh_citrinet_512 |
EncDecCTCModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_512 |
stt_zh_citrinet_1024_gamma_0_25 |
EncDecCTCModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_1024_gamma_0_25 |
stt_zh_conformer_transducer_large |
EncDecRNNTModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_conformer_transducer_large |
German#
Model |
Model Base Class |
Model Card |
|---|---|---|
stt_de_quartznet15x5 |
EncDecCTCModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_quartznet15x5 |
stt_de_citrinet_1024 |
EncDecCTCModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_citrinet_1024 |
stt_de_contextnet_1024 |
EncDecRNNTBPEModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_contextnet_1024 |
stt_de_conformer_ctc_large |
EncDecCTCModelBPE |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_conformer_ctc_large |
stt_de_conformer_transducer_large |
EncDecRNNTBPEModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_conformer_transducer_large |
French#
Model |
Model Base Class |
Model Card |
|---|---|---|
stt_fr_quartznet15x5 |
EncDecCTCModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_fr_quartznet15x5 |
stt_fr_citrinet_1024_gamma_0_25 |
EncDecCTCModelBPE |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_fr_citrinet_1024_gamma_0_25 |
stt_fr_no_hyphen_citrinet_1024_gamma_0_25 |
EncDecCTCModelBPE |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_fr_citrinet_1024_gamma_0_25 |
stt_fr_contextnet_1024 |
EncDecRNNTBPEModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_fr_contextnet_1024 |
stt_fr_conformer_ctc_large |
EncDecCTCModelBPE |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_fr_conformer_ctc_large |
stt_fr_no_hyphen_conformer_ctc_large |
EncDecCTCModelBPE |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_fr_conformer_ctc_large |
stt_fr_conformer_transducer_large |
EncDecRNNTBPEModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_fr_conformer_transducer_large |
Polish#
Model |
Model Base Class |
Model Card |
|---|---|---|
stt_pl_quartznet15x5 |
EncDecCTCModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_pl_quartznet15x5 |
Italian#
Model |
Model Base Class |
Model Card |
|---|---|---|
stt_it_quartznet15x5 |
EncDecCTCModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_it_quartznet15x5 |
Russian#
Model |
Model Base Class |
Model Card |
|---|---|---|
stt_ru_quartznet15x5 |
EncDecCTCModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ru_quartznet15x5 |
Spanish#
Model |
Model Base Class |
Model Card |
|---|---|---|
stt_es_quartznet15x5 |
EncDecCTCModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_quartznet15x5 |
stt_es_citrinet_512 |
EncDecCTCModelBPE |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_citrinet_512 |
stt_es_citrinet_1024_gamma_0_25 |
EncDecCTCModelBPE |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_citrinet_1024_gamma_0_25 |
stt_es_conformer_ctc_large |
EncDecCTCModelBPE |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_conformer_ctc_large |
stt_es_conformer_transducer_large |
EncDecRNNTBPEModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_conformer_transducer_large |
stt_es_contextnet_1024 |
EncDecRNNTBPEModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_contextnet_1024 |
Catalan#
Model |
Model Base Class |
Model Card |
|---|---|---|
stt_ca_quartznet15x5 |
EncDecCTCModel |
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ca_quartznet15x5 |
Hindi#
Model Name |
Model Base Class |
Model Card |
|---|---|---|
stt_hi_conformer_ctc_medium |
EncDecCTCModelBPE |
https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_hi_conformer_ctc_medium |
Marathi#
Model Name |
Model Base Class |
Model Card |
|---|---|---|
stt_mr_conformer_ctc_medium |
EncDecCTCModelBPE |
https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_mr_conformer_ctc_medium |