mirror of
https://github.com/storytold/artcraft.git
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102 lines
3.4 KiB
Python
102 lines
3.4 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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"""
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This is a helper script to extract speaker embeddings based on manifest file
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Usage:
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python extract_speaker_embeddings.py --manifest=/path/to/manifest/file'
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--model_path='/path/to/.nemo/file'(optional)
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--embedding_dir='/path/to/embedding/directory'
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Args:
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--manifest: path to manifest file containing audio_file paths for which embeddings need to be extracted
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--model_path(optional): path to .nemo speaker verification model file to extract embeddings, if not passed SpeakerNet-M model would
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be downloaded from NGC and used to extract embeddings
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--embeddings_dir(optional): path to directory where embeddings need to stored default:'./'
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"""
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from argparse import ArgumentParser
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import pytorch_lightning as pl
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import torch
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from omegaconf import OmegaConf
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from nemo.collections.asr.models.label_models import ExtractSpeakerEmbeddingsModel
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from nemo.utils import logging
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try:
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from torch.cuda.amp import autocast
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except ImportError:
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from contextlib import contextmanager
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@contextmanager
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def autocast(enabled=None):
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yield
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def main():
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parser = ArgumentParser()
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parser.add_argument(
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"--manifest", type=str, required=True, help="Path to manifest file",
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)
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parser.add_argument(
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"--model_path",
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type=str,
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default='speakerverification_speakernet',
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required=False,
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help="path to .nemo speaker verification model file to extract embeddings, if not passed SpeakerNet-M model would be downloaded from NGC and used to extract embeddings",
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)
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parser.add_argument(
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"--embedding_dir",
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type=str,
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default='./',
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required=False,
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help="path to directory where embeddings need to stored default:'./'",
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)
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args = parser.parse_args()
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torch.set_grad_enabled(False)
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if args.model_path.endswith('.nemo'):
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logging.info(f"Using local speaker model from {args.model_path}")
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speaker_model = ExtractSpeakerEmbeddingsModel.restore_from(restore_path=args.model_path)
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else:
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speaker_model = ExtractSpeakerEmbeddingsModel.from_pretrained(model_name="speakerverification_speakernet")
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logging.info(f"using pretrained speaker verification model from NGC")
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num_gpus = 1 if torch.cuda.is_available() else 0
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if not num_gpus:
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logging.warning("Running model on CPU, for faster performance it is adviced to use atleast one NVIDIA GPUs")
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trainer = pl.Trainer(gpus=num_gpus, accelerator=None)
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test_config = OmegaConf.create(
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dict(
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manifest_filepath=args.manifest,
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sample_rate=16000,
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labels=None,
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batch_size=1,
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shuffle=False,
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time_length=8,
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embedding_dir=args.embedding_dir,
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)
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)
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speaker_model.setup_test_data(test_config)
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trainer.test(speaker_model)
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if __name__ == '__main__':
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main()
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