Introduce code from Rust monorepo. This is a mess

2026-10-18 00:33:15 +00:00 · 2022-02-28 05:43:54 -05:00
parent 921fb49f9a
commit ba326bfcfd
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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.
+
+"""
+# Training the model
+
+Basic run (on CPU for 50 epochs):
+    python examples/asr/speech_to_text.py \
+        # (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
+        model.train_ds.manifest_filepath="<path to manifest file>" \
+        model.validation_ds.manifest_filepath="<path to manifest file>" \
+        trainer.gpus=0 \
+        trainer.max_epochs=50
+
+
+Add PyTorch Lightning Trainer arguments from CLI:
+    python speech_to_text.py \
+        ... \
+        +trainer.fast_dev_run=true
+
+Hydra logs will be found in "$(./outputs/$(date +"%y-%m-%d")/$(date +"%H-%M-%S")/.hydra)"
+PTL logs will be found in "$(./outputs/$(date +"%y-%m-%d")/$(date +"%H-%M-%S")/lightning_logs)"
+
+Override some args of optimizer:
+    python speech_to_text.py \
+    # (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
+    model.train_ds.manifest_filepath="./an4/train_manifest.json" \
+    model.validation_ds.manifest_filepath="./an4/test_manifest.json" \
+    trainer.gpus=2 \
+    trainer.max_epochs=2 \
+    model.optim.args.betas=[0.8,0.5] \
+    model.optim.args.weight_decay=0.0001
+
+Override optimizer entirely
+    python speech_to_text.py \
+    # (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
+    model.train_ds.manifest_filepath="./an4/train_manifest.json" \
+    model.validation_ds.manifest_filepath="./an4/test_manifest.json" \
+    trainer.gpus=2 \
+    trainer.max_epochs=2 \
+    model.optim.name=adamw \
+    model.optim.lr=0.001 \
+    ~model.optim.args \
+    +model.optim.args.betas=[0.8,0.5]\
+    +model.optim.args.weight_decay=0.0005
+
+# Fine-tune a model
+
+For documentation on fine-tuning this model, please visit -
+https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations
+
+# 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 pytorch_lightning as pl
+from omegaconf import OmegaConf
+
+from nemo.collections.asr.models import EncDecCTCModel
+from nemo.core.config import hydra_runner
+from nemo.utils import logging
+from nemo.utils.exp_manager import exp_manager
+
+
+@hydra_runner(config_path="conf", config_name="config")
+def main(cfg):
+    logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
+
+    trainer = pl.Trainer(**cfg.trainer)
+    exp_manager(trainer, cfg.get("exp_manager", None))
+    asr_model = EncDecCTCModel(cfg=cfg.model, trainer=trainer)
+
+    # Initialize the weights of the model from another model, if provided via config
+    asr_model.maybe_init_from_pretrained_checkpoint(cfg)
+
+    trainer.fit(asr_model)
+
+    if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
+        gpu = 1 if cfg.trainer.gpus != 0 else 0
+        test_trainer = pl.Trainer(
+            gpus=gpu,
+            precision=trainer.precision,
+            amp_level=trainer.accelerator_connector.amp_level,
+            amp_backend=cfg.trainer.get("amp_backend", "native"),
+        )
+        if asr_model.prepare_test(test_trainer):
+            test_trainer.test(asr_model)
+
+
+if __name__ == '__main__':
+    main()  # noqa pylint: disable=no-value-for-parameter