# Convert Tensorflow Tacotron2 model to TF-Lite binary import argparse from TTS.tts.tf.utils.generic_utils import setup_model from TTS.tts.tf.utils.io import load_checkpoint from TTS.tts.tf.utils.tflite import convert_tacotron2_to_tflite from TTS.tts.utils.text.symbols import phonemes, symbols from TTS.utils.io import load_config parser = argparse.ArgumentParser() parser.add_argument("--tf_model", type=str, help="Path to target torch model to be converted to TF.") parser.add_argument("--config_path", type=str, help="Path to config file of torch model.") parser.add_argument("--output_path", type=str, help="path to tflite output binary.") args = parser.parse_args() # Set constants CONFIG = load_config(args.config_path) # load the model c = CONFIG num_speakers = 0 num_chars = len(phonemes) if c.use_phonemes else len(symbols) model = setup_model(num_chars, num_speakers, c, enable_tflite=True) model.build_inference() model = load_checkpoint(model, args.tf_model) model.decoder.set_max_decoder_steps(1000) # create tflite model tflite_model = convert_tacotron2_to_tflite(model, output_path=args.output_path)