default=None,
help="TFLite file path to be exported")
args = parser.parse_args()
assert args.saved and args.output
config = Config(args.config, learning=True)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)
text_featurizer = CharFeaturizer(config.decoder_config)
# build model
contextnet = ContextNet(**config.model_config,
vocabulary_size=text_featurizer.num_classes)
contextnet._build(speech_featurizer.shape)
contextnet.load_weights(args.saved)
contextnet.summary(line_length=150)
contextnet.add_featurizers(speech_featurizer, text_featurizer)
concrete_func = contextnet.make_tflite_function(
greedy=True).get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS
]
tflite_model = converter.convert()
if not os.path.exists(os.path.dirname(args.output)):
os.makedirs(os.path.dirname(args.output))
with open(args.output, "wb") as tflite_out:
config = Config(args.config)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)
if args.subwords and os.path.exists(args.subwords):
print("Loading subwords ...")
text_featurizer = SubwordFeaturizer.load_from_file(config.decoder_config,
args.subwords)
else:
raise ValueError("subwords must be set")
# build model
contextnet = ContextNet(**config.model_config,
vocabulary_size=text_featurizer.num_classes)
contextnet._build(speech_featurizer.shape)
contextnet.load_weights(args.saved, by_name=True)
contextnet.summary(line_length=150)
contextnet.add_featurizers(speech_featurizer, text_featurizer)
concrete_func = contextnet.make_tflite_function(
greedy=True).get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS
]
tflite_model = converter.convert()
if not os.path.exists(os.path.dirname(args.output)):
os.makedirs(os.path.dirname(args.output))
with open(args.output, "wb") as tflite_out:
