def run(args):
config = UserConfig(DEFAULT_YAML, args.config, learning=True)
speech_featurizer = TFSpeechFeaturizer(config["speech_config"])
text_featurizer = TextFeaturizer(config["decoder_config"])
tf.random.set_seed(0)
assert args.saved_model
if args.tfrecords:
test_dataset = ASRTFRecordDataset(
config["learning_config"]["dataset_config"]["test_paths"],
config["learning_config"]["dataset_config"]["tfrecords_dir"],
speech_featurizer,
text_featurizer,
"test",
augmentations=config["learning_config"]["augmentations"],
shuffle=False).create(
config["learning_config"]["running_config"]["batch_size"])
else:
test_dataset = ASRSliceDataset(
stage="test",
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
data_paths=config["learning_config"]["dataset_config"]
["eval_paths"],
shuffle=False).create(
config["learning_config"]["running_config"]["batch_size"])
# build model
f, c = speech_featurizer.compute_feature_dim()
conformer = Conformer(vocabulary_size=text_featurizer.num_classes,
**config["model_config"])
conformer._build([1, 50, f, c])
conformer.summary(line_length=100)
conformer_tester = BaseTester(
config=config["learning_config"]["running_config"],
saved_path=args.saved_model,
from_weights=args.from_weights)
conformer_tester.compile(conformer, speech_featurizer, text_featurizer)
conformer_tester.run(test_dataset)
help="TFLite file path to be exported")
args = parser.parse_args()
assert args.saved and args.output
config = UserConfig(DEFAULT_YAML, args.config, learning=True)
speech_featurizer = TFSpeechFeaturizer(config["speech_config"])
text_featurizer = CharFeaturizer(config["decoder_config"])
# build model
conformer = Conformer(**config["model_config"],
vocabulary_size=text_featurizer.num_classes)
conformer._build(speech_featurizer.shape)
conformer.load_weights(args.saved)
conformer.summary(line_length=150)
conformer.add_featurizers(speech_featurizer, text_featurizer)
concrete_func = conformer.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:
tflite_out.write(tflite_model)