Introduce code from Rust monorepo. This is a mess

tts/NeMo/examples/speaker_recognition/speaker_reco.py

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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.
+
+import os
+
+import pytorch_lightning as pl
+from omegaconf import ListConfig, OmegaConf
+from pytorch_lightning import seed_everything
+
+from nemo.collections.asr.models import EncDecSpeakerLabelModel
+from nemo.core.config import hydra_runner
+from nemo.utils import logging
+from nemo.utils.exp_manager import exp_manager
+
+"""
+Basic run (on GPU for 10 epochs for 2 class training):
+EXP_NAME=sample_run
+python ./speaker_reco.py --config-path='conf' --config-name='SpeakerNet_recognition_3x2x512.yaml' \
+    trainer.max_epochs=10  \
+    model.train_ds.batch_size=64 model.validation_ds.batch_size=64 \
+    model.train_ds.manifest_filepath="<train_manifest>" model.validation_ds.manifest_filepath="<dev_manifest>" \
+    model.test_ds.manifest_filepath="<test_manifest>" \
+    trainer.gpus=1 \
+    model.decoder.params.num_classes=2 \
+    exp_manager.name=$EXP_NAME +exp_manager.use_datetime_version=False \
+    exp_manager.exp_dir='./speaker_exps'
+
+See https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_recognition/Speaker_Recognition_Verification.ipynb for notebook tutorial
+
+Optional: Use tarred dataset to speech up data loading.
+   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>/speech_recognition/scripts in order to prepare tarred audio dataset.
+   For details, please see TarredAudioToClassificationLabelDataset in <NEMO_ROOT>/nemo/collections/asr/data/audio_to_label.py
+"""
+
+seed_everything(42)
+
+
+@hydra_runner(config_path="conf", config_name="SpeakerNet_recognition_3x2x512.yaml")
+def main(cfg):
+
+    logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
+    trainer = pl.Trainer(**cfg.trainer)
+    log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
+    speaker_model = EncDecSpeakerLabelModel(cfg=cfg.model, trainer=trainer)
+    trainer.fit(speaker_model)
+    if not trainer.fast_dev_run:
+        model_path = os.path.join(log_dir, '..', 'spkr.nemo')
+        speaker_model.save_to(model_path)
+
+    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 speaker_model.prepare_test(trainer):
+            trainer.test(speaker_model)
+
+
+if __name__ == '__main__':
+    main()