# Copyright 2020 <NAME> (@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 argparse
import os

from tensorflow_asr.utils import env_util

logger = env_util.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(
    "--h5",
    type=str,
    default=None,
    help="Path to saved h5 weights",
)

parser.add_argument(
    "--sentence_piece",
    default=False,
    action="store_true",
    help="Whether to use `SentencePiece` model",
)

parser.add_argument(
    "--subwords",
    default=False,
    action="store_true",
    help="Use subwords",
)

parser.add_argument(
    "--output_dir",
    type=str,
    default=None,
    help="Output directory for saved model",
)

args = parser.parse_args()

assert args.h5
assert args.output_dir

from tensorflow_asr.configs.config import Config
from tensorflow_asr.featurizers.speech_featurizers import TFSpeechFeaturizer
from tensorflow_asr.featurizers.text_featurizers import CharFeaturizer, SentencePieceFeaturizer, SubwordFeaturizer
from tensorflow_asr.models.transducer.conformer import Conformer

config = Config(args.config)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)

if args.sentence_piece:
    logger.info("Use SentencePiece ...")
    text_featurizer = SentencePieceFeaturizer(config.decoder_config)
elif args.subwords:
    logger.info("Use subwords ...")
    text_featurizer = SubwordFeaturizer(config.decoder_config)
else:
    logger.info("Use characters ...")
    text_featurizer = CharFeaturizer(config.decoder_config)

tf.random.set_seed(0)

# build model
conformer = Conformer(**config.model_config, vocabulary_size=text_featurizer.num_classes)
conformer.make(speech_featurizer.shape)
conformer.load_weights(args.h5, by_name=True)
conformer.summary(line_length=100)
conformer.add_featurizers(speech_featurizer, text_featurizer)


class aModule(tf.Module):
    def __init__(self, model):
        super().__init__()
        self.model = model

    @tf.function(
        input_signature=[
            {
                "inputs": tf.TensorSpec(shape=[None, None, 80, 1], dtype=tf.float32, name="inputs"),
                "inputs_length": tf.TensorSpec(shape=[None], dtype=tf.int32, name="inputs_length"),
            }
        ]
    )
    def pred(self, input_batch):
        result = self.model.recognize(input_batch)
        return {"ASR": result}


module = aModule(conformer)
tf.saved_model.save(module, args.output_dir, signatures={"serving_default": module.pred})

# Copyright 2020 <NAME> (@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 argparse
import os

from tensorflow_asr.utils import env_util

logger = env_util.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(
    "--h5",
    type=str,
    default=None,
    help="Path to saved h5 weights",
)

parser.add_argument(
    "--sentence_piece",
    default=False,
    action="store_true",
    help="Whether to use `SentencePiece` model",
)

parser.add_argument(
    "--subwords",
    default=False,
    action="store_true",
    help="Use subwords",
)

parser.add_argument(
    "--output_dir",
    type=str,
    default=None,
    help="Output directory for saved model",
)

args = parser.parse_args()

assert args.h5
assert args.output_dir

from tensorflow_asr.configs.config import Config
from tensorflow_asr.featurizers.speech_featurizers import TFSpeechFeaturizer
from tensorflow_asr.featurizers.text_featurizers import CharFeaturizer, SentencePieceFeaturizer, SubwordFeaturizer
from tensorflow_asr.models.transducer.conformer import Conformer

config = Config(args.config)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)

if args.sentence_piece:
    logger.info("Use SentencePiece ...")
    text_featurizer = SentencePieceFeaturizer(config.decoder_config)
elif args.subwords:
    logger.info("Use subwords ...")
    text_featurizer = SubwordFeaturizer(config.decoder_config)
else:
    logger.info("Use characters ...")
    text_featurizer = CharFeaturizer(config.decoder_config)

tf.random.set_seed(0)

# build model
conformer = Conformer(**config.model_config, vocabulary_size=text_featurizer.num_classes)
conformer.make(speech_featurizer.shape)
conformer.load_weights(args.h5, by_name=True)
conformer.summary(line_length=100)
conformer.add_featurizers(speech_featurizer, text_featurizer)


class aModule(tf.Module):
    def __init__(self, model):
        super().__init__()
        self.model = model

    @tf.function(
        input_signature=[
            {
                "inputs": tf.TensorSpec(shape=[None, None, 80, 1], dtype=tf.float32, name="inputs"),
                "inputs_length": tf.TensorSpec(shape=[None], dtype=tf.int32, name="inputs_length"),
            }
        ]
    )
    def pred(self, input_batch):
        result = self.model.recognize(input_batch)
        return {"ASR": result}


module = aModule(conformer)
tf.saved_model.save(module, args.output_dir, signatures={"serving_default": module.pred})

# Copyright 2020 <NAME> (@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. # build model