Custom TensorFlow2 implementations of forward and backward computation of soft-DTW algorithm in batch mode.

Last update: Aug 30, 2022

Overview

Batch Soft-DTW(Dynamic Time Warping) in TensorFlow2 including forward and backward computation

Custom TensorFlow2 implementations of forward and backward computation of soft-DTW(Dynamic Time Warping) algorithm in batch mode, which is proposed in paper 《Soft-DTW: a Differentiable Loss Function for Time-Series》.

I have implemented two versions of soft-DTW, one is the original paper, the other is Parallel Tacotron2's paper(with warp penalty). For latter version, I solved the equations of backward computation myself.

If you have questions or improvements about the code, welcome to submit issues ASAP!

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Comments

How to use it as a custom loss function in tf?

Thank you very much for your share! Could you help me figure out how to implement the soft dtw as a custom loss in the deep learning training framework (in tf)? What I hope is to replace the RMSE/MAE.. metrics by the dtw. Thank you in advance.

opened by wangshuo1994 0
Questions about your code

Running your code causes two problems.

In the line, soft_dtw_distance = batch_soft_dtw(a, b, gamma=0.01, metric="L1"), the code is not executed because no warp is given.

I put a warp value and ran the code, but I had the following error message. '('custom_gradient function expected to return', 2, 'gradients but returned', 1, 'instead.')'

I tested the code based tf 2.3 and tf_nightly 2.5, and got the same error.

opened by JunetaeKim 0
Thank you :D

@zzw922cn Hi, I'm a creator of TensorFlowTTS (https://github.com/TensorSpeech/TensorFlowTTS). I have a plan to reproduce Parallel Tacotron2 and will use this implementation :D. Thank you so much, it's not EZ to implement soft_dtw by TensorFlow since tf.Tensor still an immutable object and we need to use tf.TensorArray as an alternative solution.

opened by dathudeptrai 1

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