mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-17 00:23:14 +00:00
Brandon Thomas
74 lines
3.1 KiB
Python
74 lines
3.1 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import pytorch_lightning as pl
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from omegaconf import ListConfig, OmegaConf
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from pytorch_lightning import seed_everything
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from nemo.collections.asr.models import EncDecSpeakerLabelModel
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from nemo.core.config import hydra_runner
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from nemo.utils import logging
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from nemo.utils.exp_manager import exp_manager
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"""
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Basic run (on GPU for 10 epochs for 2 class training):
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EXP_NAME=sample_run
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python ./speaker_reco.py --config-path='conf' --config-name='SpeakerNet_recognition_3x2x512.yaml' \
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trainer.max_epochs=10 \
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model.train_ds.batch_size=64 model.validation_ds.batch_size=64 \
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model.train_ds.manifest_filepath="<train_manifest>" model.validation_ds.manifest_filepath="<dev_manifest>" \
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model.test_ds.manifest_filepath="<test_manifest>" \
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trainer.gpus=1 \
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model.decoder.params.num_classes=2 \
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exp_manager.name=$EXP_NAME +exp_manager.use_datetime_version=False \
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exp_manager.exp_dir='./speaker_exps'
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See https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_recognition/Speaker_Recognition_Verification.ipynb for notebook tutorial
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Optional: Use tarred dataset to speech up data loading.
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Prepare ONE manifest that contains all training data you would like to include. Validation should use non-tarred dataset.
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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;
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Scores might be off since some data is missing.
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Use the `convert_to_tarred_audio_dataset.py` script under <NEMO_ROOT>/speech_recognition/scripts in order to prepare tarred audio dataset.
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For details, please see TarredAudioToClassificationLabelDataset in <NEMO_ROOT>/nemo/collections/asr/data/audio_to_label.py
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"""
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seed_everything(42)
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@hydra_runner(config_path="conf", config_name="SpeakerNet_recognition_3x2x512.yaml")
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def main(cfg):
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logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
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trainer = pl.Trainer(**cfg.trainer)
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log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
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speaker_model = EncDecSpeakerLabelModel(cfg=cfg.model, trainer=trainer)
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trainer.fit(speaker_model)
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if not trainer.fast_dev_run:
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model_path = os.path.join(log_dir, '..', 'spkr.nemo')
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speaker_model.save_to(model_path)
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if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
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gpu = 1 if cfg.trainer.gpus != 0 else 0
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trainer = pl.Trainer(gpus=gpu)
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if speaker_model.prepare_test(trainer):
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trainer.test(speaker_model)
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if __name__ == '__main__':
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main()
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