def test_ds2():
config = Config(DEFAULT_YAML)
text_featurizer = CharFeaturizer(config.decoder_config)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)
model = DeepSpeech2(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(greedy=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 beam search")
concrete_func = model.make_tflite_function(greedy=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 greedy")
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
**vars(config.learning_config.eval_dataset_config))
else:
train_dataset = ASRSliceDataset(
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))
ctc_trainer = CTCTrainer(text_featurizer,
config.learning_config.running_config)
# Build DS2 model
with ctc_trainer.strategy.scope():
ds2_model = DeepSpeech2(**config.model_config,
vocabulary_size=text_featurizer.num_classes)
ds2_model._build(speech_featurizer.shape)
ds2_model.summary(line_length=120)
# Compile
ctc_trainer.compile(ds2_model,
config.learning_config.optimizer_config,
max_to_keep=args.max_ckpts)
ctc_trainer.fit(train_dataset,
eval_dataset,
train_bs=args.tbs,
eval_bs=args.ebs)