def inference(speaker, text):
BATCH_SIZE = 1
encoder = Encoder(BATCH_SIZE, len(vocabulary.VOCAB), len(dataset.SPEAKERS))
decoder = Decoder(BATCH_SIZE, hparams.n_mel * 4, (hparams.n_fft // 2 + 1) * 4)
global_step = tf.Variable(0, dtype=tf.int64) # summary 의 step 은 int64
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
checkpoint_dir = './checkpoint'
checkpoint = tf.train.Checkpoint(optimizer=optimizer,
encoder=encoder,
decoder=decoder,
global_step=global_step)
checkpoint_manager = tf.train.CheckpointManager(checkpoint, checkpoint_dir, max_to_keep=5)
checkpoint.restore(checkpoint_manager.latest_checkpoint)
if checkpoint_manager.latest_checkpoint:
print("체크포인트 {} 에서 로드합니다...".format(checkpoint_manager.latest_checkpoint))
print(global_step.numpy(), "step 에서 추론을 진행합니다...")
tokens = np.array([vocabulary.add_eos(vocabulary.str2token(text))])
speaker = np.array([dataset.SPEAKER_NAME2IDX[speaker]])
key, value = encoder(tokens, speaker)
dec_out, weights, linear = decoder.call_inference(key, value)
return dec_out[0].numpy().reshape(-1, 80), weights[0].numpy(), linear[0].numpy().reshape(-1, hparams.n_fft // 2 + 1)
