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import tensorflow as tf
def convert_melgan_to_tflite(model,
output_path=None,
experimental_converter=True):
"""Convert Tensorflow MelGAN model to TFLite. Save a binary file if output_path is
provided, else return TFLite model."""
concrete_function = model.inference_tflite.get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions(
[concrete_function])
converter.experimental_new_converter = experimental_converter
converter.optimizations = []
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS
]
tflite_model = converter.convert()
print(f'Tflite Model size is {len(tflite_model) / (1024.0 * 1024.0)} MBs.')
if output_path is not None:
# same model binary if outputpath is provided
with open(output_path, 'wb') as f:
f.write(tflite_model)
return None
return tflite_model
def load_tflite_model(tflite_path):
tflite_model = tf.lite.Interpreter(model_path=tflite_path)
tflite_model.allocate_tensors()
return tflite_model
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