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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.
"""This script converts old Jasper/QuartzNet models from NeMo 0.11.* to NeMo v1.0.0*
"""
import argparse
import torch
from omegaconf import DictConfig
from ruamel.yaml import YAML
import nemo.collections.asr as nemo_asr
from nemo.utils import logging
def get_parser():
parser = argparse.ArgumentParser(description="Converts old Jasper/QuartzNet models to NeMo v1.0beta")
parser.add_argument("--config_path", default=None, required=True, help="Path to model config (NeMo v1.0beta)")
parser.add_argument("--encoder_ckpt", default=None, required=True, help="Encoder checkpoint path")
parser.add_argument("--decoder_ckpt", default=None, required=True, help="Decoder checkpoint path")
parser.add_argument("--output_path", default=None, required=True, help="Output checkpoint path (should be .nemo)")
parser.add_argument(
"--model_type",
default='asr',
type=str,
choices=['asr', 'speech_label', 'speaker'],
help="Type of decoder used by the model.",
)
return parser
def main(config_path, encoder_ckpt, decoder_ckpt, output_path, model_type):
yaml = YAML(typ='safe')
with open(config_path) as f:
params = yaml.load(f)
model = None
if model_type == 'asr':
logging.info("Creating ASR NeMo 1.0 model")
model = nemo_asr.models.EncDecCTCModel(cfg=DictConfig(params['model']))
elif model_type == 'speech_label':
logging.info("Creating speech label NeMo 1.0 model")
model = nemo_asr.models.EncDecClassificationModel(cfg=DictConfig(params['model']))
else:
logging.info("Creating Speaker Recognition NeMo 1.0 model")
model = nemo_asr.models.EncDecSpeakerLabelModel(cfg=DictConfig(params['model']))
model.encoder.load_state_dict(torch.load(encoder_ckpt))
model.decoder.load_state_dict(torch.load(decoder_ckpt))
logging.info("Succesfully ported old checkpoint")
model.save_to(output_path)
logging.info("new model saved at {}".format(output_path))
if __name__ == "__main__":
args = get_parser().parse_args()
main(args.config_path, args.encoder_ckpt, args.decoder_ckpt, args.output_path, args.model_type)
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