def test_contextnet():
config = Config(DEFAULT_YAML, learning=False)
text_featurizer = CharFeaturizer(config.decoder_config)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)
model = ContextNet(vocabulary_size=text_featurizer.num_classes, **config.model_config)
model._build(speech_featurizer.shape)
model.summary(line_length=150)
model.add_featurizers(
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer
)
concrete_func = model.make_tflite_function(timestamp=False).get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
converter.convert()
print("Converted successfully with no timestamp")
concrete_func = model.make_tflite_function(timestamp=True).get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
converter.convert()
print("Converted successfully with timestamp")
parser.add_argument("output",
type=str,
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)):
speech_featurizer=speech_featurizer, text_featurizer=text_featurizer,
**vars(config.learning_config.train_dataset_config)
)
eval_dataset = ASRSliceDataset(
speech_featurizer=speech_featurizer, text_featurizer=text_featurizer,
**vars(config.learning_config.eval_dataset_config)
)
contextnet_trainer = TransducerTrainerGA(
config=config.learning_config.running_config,
text_featurizer=text_featurizer, strategy=strategy
)
with contextnet_trainer.strategy.scope():
# build model
contextnet = ContextNet(**config.model_config, vocabulary_size=text_featurizer.num_classes)
contextnet._build(speech_featurizer.shape)
contextnet.summary(line_length=120)
optimizer = tf.keras.optimizers.Adam(
TransformerSchedule(
d_model=contextnet.dmodel,
warmup_steps=config.learning_config.optimizer_config["warmup_steps"],
max_lr=(0.05 / math.sqrt(contextnet.dmodel))
),
beta_1=config.learning_config.optimizer_config["beta1"],
beta_2=config.learning_config.optimizer_config["beta2"],
epsilon=config.learning_config.optimizer_config["epsilon"]
)
contextnet_trainer.compile(model=contextnet, optimizer=optimizer,
args.subwords)
else:
raise ValueError("subwords must be set")
tf.random.set_seed(0)
assert args.saved
if args.tfrecords:
test_dataset = ASRTFRecordDataset(
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
**vars(config.learning_config.test_dataset_config))
else:
test_dataset = ASRSliceDataset(
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
**vars(config.learning_config.test_dataset_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=120)
contextnet.add_featurizers(speech_featurizer, text_featurizer)
contextnet_tester = BaseTester(config=config.learning_config.running_config,
output_name=args.output_name)
contextnet_tester.compile(contextnet)
contextnet_tester.run(test_dataset)
def test_contextnet():
config = Config(DEFAULT_YAML, learning=False)
text_featurizer = CharFeaturizer(config.decoder_config)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)
model = ContextNet(vocabulary_size=text_featurizer.num_classes,
**config.model_config)
model._build(speech_featurizer.shape)
model.summary(line_length=150)
model.add_featurizers(speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer)
concrete_func = model.make_tflite_function(
timestamp=False).get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions(
[concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS
]
tflite = converter.convert()
print("Converted successfully with no timestamp")
concrete_func = model.make_tflite_function(
timestamp=True).get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions(
[concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS
]
converter.convert()
print("Converted successfully with timestamp")
tflitemodel = tf.lite.Interpreter(model_content=tflite)
signal = tf.random.normal([4000])
input_details = tflitemodel.get_input_details()
output_details = tflitemodel.get_output_details()
tflitemodel.resize_tensor_input(input_details[0]["index"], [4000])
tflitemodel.allocate_tensors()
tflitemodel.set_tensor(input_details[0]["index"], signal)
tflitemodel.set_tensor(input_details[1]["index"],
tf.constant(text_featurizer.blank, dtype=tf.int32))
tflitemodel.set_tensor(
input_details[2]["index"],
tf.zeros([
config.model_config["prediction_num_rnns"], 2, 1,
config.model_config["prediction_rnn_units"]
],
dtype=tf.float32))
tflitemodel.invoke()
hyp = tflitemodel.get_tensor(output_details[0]["index"])
print(hyp)
