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| # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ | |
| # Evaluate an adapted model | |
| python eval_asr_adapter.py \ | |
| --config-path="../conf/asr_adapters" \ | |
| --config-name="asr_adaptation.yaml" \ | |
| model.pretrained_model=null \ | |
| model.nemo_model=null \ | |
| model.adapter.adapter_name=<name of the adapter to evaluate> \ | |
| model.test_ds.manifest_filepath="<Path to validation/test manifest>" \ | |
| model.test_ds.batch_size=16 \ | |
| model.train_ds.manifest_filepath=null \ | |
| model.validation_ds.manifest_filepath=null \ | |
| model.adapter.in_features=null \ | |
| trainer.devices=1 \ | |
| trainer.precision=32 | |
| # Pretrained Models | |
| For documentation on existing pretrained models, please visit - | |
| https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/results.html | |
| """ | |
| import lightning.pytorch as pl | |
| from omegaconf import OmegaConf, open_dict | |
| from nemo.collections.asr.models import ASRModel | |
| from nemo.core import adapter_mixins | |
| from nemo.core.config import hydra_runner | |
| from nemo.utils import logging | |
| from nemo.utils.exp_manager import exp_manager | |
| def update_encoder_config_to_support_adapter(model_cfg): | |
| with open_dict(model_cfg): | |
| adapter_metadata = adapter_mixins.get_registered_adapter(model_cfg.encoder._target_) | |
| if adapter_metadata is not None: | |
| model_cfg.encoder._target_ = adapter_metadata.adapter_class_path | |
| def update_model_cfg(original_cfg, new_cfg): | |
| with open_dict(new_cfg): | |
| # drop keys which dont exist in old config | |
| new_keys = list(new_cfg.keys()) | |
| for key in new_keys: | |
| if key not in original_cfg: | |
| new_cfg.pop(key) | |
| print("Removing unavailable key from config :", key) | |
| new_cfg = OmegaConf.merge(original_cfg, new_cfg) | |
| return new_cfg | |
| def main(cfg): | |
| logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') | |
| if cfg.model.pretrained_model is None and cfg.model.nemo_model is None: | |
| raise ValueError("Either set `cfg.model.nemo_model` or `cfg.model.pretrained_model`") | |
| if cfg.model.pretrained_model is not None and cfg.model.nemo_model is not None: | |
| raise ValueError("Cannot set `cfg.model.nemo_model` and `cfg.model.pretrained_model`. Select one only.") | |
| trainer = pl.Trainer(**cfg.trainer) | |
| exp_manager(trainer, cfg.get("exp_manager", None)) | |
| if cfg.model.pretrained_model is not None: | |
| model_cfg = ASRModel.from_pretrained(cfg.model.pretrained_model, return_config=True) | |
| update_encoder_config_to_support_adapter(model_cfg) | |
| model = ASRModel.from_pretrained(cfg.model.pretrained_model, override_config_path=model_cfg, trainer=trainer) | |
| else: | |
| model_cfg = ASRModel.restore_from(cfg.model.nemo_model, return_config=True) | |
| update_encoder_config_to_support_adapter(model_cfg) | |
| model = ASRModel.restore_from(cfg.model.nemo_model, override_config_path=model_cfg, trainer=trainer) | |
| # Setup model for finetuning (train and validation only) | |
| cfg.model.test_ds = update_model_cfg(model.cfg.test_ds, cfg.model.test_ds) | |
| # Call the dataloaders and optimizer + scheduler | |
| model.setup_multiple_test_data(cfg.model.test_ds) | |
| # Setup adapters | |
| with open_dict(cfg.model.adapter): | |
| adapter_name = cfg.model.adapter.pop("adapter_name", None) | |
| # Disable all other adapters, enable just the current adapter. | |
| model.set_enabled_adapters(enabled=False) # disable all adapters prior to training | |
| if adapter_name is not None: | |
| model.set_enabled_adapters(adapter_name, enabled=True) # enable just one adapter by name if provided | |
| # First, Freeze all the weights of the model (not just encoder, everything) | |
| model.freeze() | |
| # Finally, train model | |
| trainer.test(model) | |
| if __name__ == '__main__': | |
| main() # noqa pylint: disable=no-value-for-parameter | |