Python Conformer.make_tflite_function Examples

Example #1

assert args.saved and args.output

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

if args.subwords:
    text_featurizer = SubwordFeaturizer(config.decoder_config)
else:
    text_featurizer = CharFeaturizer(config.decoder_config)

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

concrete_func = conformer.make_tflite_function().get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.experimental_new_converter = True
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()

args.output = file_util.preprocess_paths(args.output)
with open(args.output, "wb") as tflite_out:
    tflite_out.write(tflite_model)

Example #2

File: test_conformer.py Project: zhazl/TensorFlowASR

def test_conformer():
    config = Config(DEFAULT_YAML)

    text_featurizer = CharFeaturizer(config.decoder_config)

    speech_featurizer = TFSpeechFeaturizer(config.speech_config)

    model = Conformer(vocabulary_size=text_featurizer.num_classes,
                      **config.model_config)

    model.make(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)