save_conformer_from_weights.py

# Copyright 2020 Huy Le Nguyen (@usimarit)
#
# 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 argparse
from tensorflow_asr.utils import setup_environment, setup_devices

setup_environment()
import tensorflow as tf

DEFAULT_YAML = os.path.join(os.path.abspath(os.path.dirname(__file__)), "config.yml")

tf.keras.backend.clear_session()

parser = argparse.ArgumentParser(prog="Conformer Testing")

parser.add_argument("--config", type=str, default=DEFAULT_YAML,
                    help="The file path of model configuration file")

parser.add_argument("--saved", type=str, default=None,
                    help="Path to saved model")

parser.add_argument("--device", type=int, default=0,
                    help="Device's id to run test on")

parser.add_argument("--cpu", default=False, action="store_true",
                    help="Whether to only use cpu")

parser.add_argument("output", type=str, default=None,
                    help="Output to save whole model")

args = parser.parse_args()

tf.config.optimizer.set_experimental_options({"auto_mixed_precision": args.mxp})

setup_devices([args.device], cpu=args.cpu)

from tensorflow_asr.configs.user_config import UserConfig
from tensorflow_asr.featurizers.speech_featurizers import TFSpeechFeaturizer
from tensorflow_asr.featurizers.text_featurizers import CharFeaturizer
from tensorflow_asr.models.conformer import Conformer

config = UserConfig(DEFAULT_YAML, args.config, learning=True)
speech_featurizer = TFSpeechFeaturizer(config["speech_config"])
text_featurizer = CharFeaturizer(config["decoder_config"])

tf.random.set_seed(0)
assert args.saved

# build model
conformer = Conformer(
    vocabulary_size=text_featurizer.num_classes,
    **config["model_config"]
)
conformer._build(speech_featurizer.shape)
conformer.load_weights(args.saved, by_name=True)
conformer.summary(line_length=150)
conformer.save(args.output)

print(f"Saved whole model to {args.output}")