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