Introduce code from Rust monorepo. This is a mess

2026-10-18 07:41:03 +00:00 · 2022-02-28 05:43:54 -05:00
parent 921fb49f9a
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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.
+
+"""
+# Task 1: Speech Command
+
+## Preparing the dataset
+Use the `process_speech_commands_data.py` script under <NEMO_ROOT>/scripts/dataset_processing in order to prepare the dataset.
+
+```sh
+python <NEMO_ROOT>/scripts/dataset_processing/process_speech_commands_data.py \
+    --data_root=<absolute path to where the data should be stored> \
+    --data_version=<either 1 or 2, indicating version of the dataset> \
+    --class_split=<either "all" or "sub", indicates whether all 30/35 classes should be used, or the 10+2 split should be used> \
+    --rebalance \
+    --log
+```
+
+## Train to convergence
+```sh
+python speech_to_label.py \
+    # (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
+    model.train_ds.manifest_filepath="<path to train manifest>" \
+    model.validation_ds.manifest_filepath=["<path to val manifest>","<path to test manifest>"] \
+    trainer.gpus=2 \
+    trainer.accelerator="ddp" \
+    trainer.max_epochs=200 \
+    exp_manager.create_wandb_logger=True \
+    exp_manager.wandb_logger_kwargs.name="MatchboxNet-3x1x64-v1" \
+    exp_manager.wandb_logger_kwargs.project="MatchboxNet-v1" \
+    +trainer.precision=16 \
+    +trainer.amp_level=O1  # needed if using PyTorch < 1.6
+```
+
+
+# Task 2: Voice Activity Detection
+
+## Preparing the dataset
+Use the `process_vad_data.py` script under <NEMO_ROOT>/scripts/dataset_processing in order to prepare the dataset.
+
+```sh
+python process_vad_data.py \
+    --out_dir=<output path to where the generated manifest should be stored> \
+    --speech_data_root=<path where the speech data are stored> \
+    --background_data_root=<path where the background data are stored> \
+    --rebalance_method=<'under' or 'over' of 'fixed'> \
+    --log
+    (Optional --demo (for demonstration in tutorial). If you want to use your own background noise data, make sure to delete --demo)
+```
+
+## Train to convergence
+```sh
+python speech_to_label.py \
+    --config-path=<path to dir of configs e.g. "conf">
+    --config-name=<name of config without .yaml e.g. "matchboxnet_3x1x64_vad"> \
+    model.train_ds.manifest_filepath="<path to train manifest>" \
+    model.validation_ds.manifest_filepath=["<path to val manifest>","<path to test manifest>"] \
+    trainer.gpus=2 \
+    trainer.accelerator="ddp" \
+    trainer.max_epochs=200 \
+    exp_manager.create_wandb_logger=True \
+    exp_manager.wandb_logger_kwargs.name="MatchboxNet-3x1x64-vad" \
+    exp_manager.wandb_logger_kwargs.project="MatchboxNet-vad" \
+    +trainer.precision=16 \
+    +trainer.amp_level=O1  # needed if using PyTorch < 1.6
+```
+
+# Optional: Use tarred dataset to speed up data loading. Apply to both tasks.
+## Prepare tarred dataset. 
+   Prepare ONE manifest that contains all training data you would like to include. Validation should use non-tarred dataset.
+   Note that it's possible that tarred datasets impacts validation scores because it drop values in order to have same amount of files per tarfile; 
+   Scores might be off since some data is missing. 
+
+   Use the `convert_to_tarred_audio_dataset.py` script under <NEMO_ROOT>/scripts/speech_recognition in order to prepare tarred audio dataset.
+   For details, please see TarredAudioToClassificationLabelDataset in <NEMO_ROOT>/nemo/collections/asr/data/audio_to_label.py
+
+python speech_to_label.py \
+    --config-path=<path to dir of configs e.g. "conf">
+    --config-name=<name of config without .yaml e.g. "matchboxnet_3x1x64_vad"> \
+    model.train_ds.manifest_filepath=<path to train tarred_audio_manifest.json> \
+    model.train_ds.is_tarred=True \
+    model.train_ds.tarred_audio_filepaths=<path to train tarred audio dataset e.g. audio_{0..2}.tar> \
+    +model.train_ds.num_worker=<num_shards used generating tarred dataset> \
+    model.validation_ds.manifest_filepath=<path to validation audio_manifest.json>\
+    trainer.gpus=2 \
+    trainer.accelerator="ddp" \
+    trainer.max_epochs=200 \
+    exp_manager.create_wandb_logger=True \
+    exp_manager.wandb_logger_kwargs.name="MatchboxNet-3x1x64-vad" \
+    exp_manager.wandb_logger_kwargs.project="MatchboxNet-vad" \
+    +trainer.precision=16 \
+    +trainer.amp_level=O1  # needed if using PyTorch < 1.6
+
+# 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/speech_classification/results.html#
+
+"""
+import pytorch_lightning as pl
+from omegaconf import OmegaConf
+
+from nemo.collections.asr.models import EncDecClassificationModel
+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/matchboxnet", config_name="matchboxnet_3x1x64_v1")
+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 = EncDecClassificationModel(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
+        trainer = pl.Trainer(gpus=gpu)
+        if asr_model.prepare_test(trainer):
+            trainer.test(asr_model)
+
+
+if __name__ == '__main__':
+    main()  # noqa pylint: disable=no-value-for-parameter