ICML 21 - Voice2Series: Reprogramming Acoustic Models for Time Series Classification

Last update: Jan 03, 2023

Overview

Voice2Series-Reprogramming

Voice2Series: Reprogramming Acoustic Models for Time Series Classification

International Conference on Machine Learning (ICML), 2021 | Paper | Colab Demo

Environment

Tensorflow 2.2 (CUDA=10.0) and Kapre 0.2.0.

Noted: Echo to many interests from the community, we will also provide Pytorch V2S layers and frameworks around this September, incoperating the new torch audio layers. Feel free to email the authors for further collaboration.
option 1 (from yml)

conda env create -f V2S.yml

option 2 (from clean python 3.6)

pip install tensorflow-gpu==2.1.0
pip install kapre==0.2.0
pip install h5py==2.10.0

Training

This is tengible Version. Please also check the paper for actual validation details. Many Thanks!

python v2s_main.py --dataset 0 --eps 100 --mapping 3

Result

seg idx: 0 --> start: 0, end: 500
seg idx: 1 --> start: 5000, end: 5500
seg idx: 2 --> start: 10000, end: 10500
Tensor("AddV2_2:0", shape=(None, 16000, 1), dtype=float32)
--- Preparing Masking Matrix
Model: "model_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 500, 1)]     0                                            
__________________________________________________________________________________________________
zero_padding1d (ZeroPadding1D)  (None, 16000, 1)     0           input_1[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2 (TensorFlowOp [(None, 16000, 1)]   0           zero_padding1d[0][0]             
__________________________________________________________________________________________________
zero_padding1d_1 (ZeroPadding1D (None, 16000, 1)     0           input_1[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_1 (TensorFlow [(None, 16000, 1)]   0           tf_op_layer_AddV2[0][0]          
                                                                 zero_padding1d_1[0][0]           
__________________________________________________________________________________________________
zero_padding1d_2 (ZeroPadding1D (None, 16000, 1)     0           input_1[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_2 (TensorFlow [(None, 16000, 1)]   0           tf_op_layer_AddV2_1[0][0]        
                                                                 zero_padding1d_2[0][0]           
__________________________________________________________________________________________________
art_layer (ARTLayer)            (None, 16000, 1)     16000       tf_op_layer_AddV2_2[0][0]        
__________________________________________________________________________________________________
reshape_1 (Reshape)             (None, 16000)        0           art_layer[0][0]                  
__________________________________________________________________________________________________
model (Model)                   (None, 36)           1292911     reshape_1[0][0]                  
__________________________________________________________________________________________________
tf_op_layer_MatMul (TensorFlowO [(None, 6)]          0           model[1][0]                      
__________________________________________________________________________________________________
tf_op_layer_Shape (TensorFlowOp [(2,)]               0           tf_op_layer_MatMul[0][0]         
__________________________________________________________________________________________________
tf_op_layer_strided_slice (Tens [()]                 0           tf_op_layer_Shape[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Reshape_2/shape (Te [(3,)]               0           tf_op_layer_strided_slice[0][0]  
__________________________________________________________________________________________________
tf_op_layer_Reshape_2 (TensorFl [(None, 2, 3)]       0           tf_op_layer_MatMul[0][0]         
                                                                 tf_op_layer_Reshape_2/shape[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean (TensorFlowOpL [(None, 2)]          0           tf_op_layer_Reshape_2[0][0]      
==================================================================================================
Total params: 1,308,911
Trainable params: 217,225
Non-trainable params: 1,091,686
__________________________________________________________________________________________________
Epoch 1/100
2021-07-19 01:43:32.690913: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2021-07-19 01:43:32.919343: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
113/113 [==============================] - 6s 50ms/step - loss: 0.0811 - accuracy: 1.0000 - val_loss: 1.5589e-04 - val_accuracy: 1.0000
Epoch 2/100
113/113 [==============================] - 5s 41ms/step - loss: 5.0098e-05 - accuracy: 1.0000 - val_loss: 1.0906e-05 - val_accuracy: 1.0000

Class Activation Mapping

python cam_v2s.py --dataset 5 --weight wNo5_map6-88-0.7662.h5 --mapping 6 --layer conv2d_1

Reference

Voice2Series: Reprogramming Acoustic Models for Time Series Classification

@InProceedings{pmlr-v139-yang21j,
  title = 	 {Voice2Series: Reprogramming Acoustic Models for Time Series Classification},
  author =       {Yang, Chao-Han Huck and Tsai, Yun-Yun and Chen, Pin-Yu},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {11808--11819},
  year = 	 {2021},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
}

ICML 21 - Voice2Series: Reprogramming Acoustic Models for Time Series Classification

Related tags

Overview

Voice2Series-Reprogramming

Environment

Training

Class Activation Mapping

Reference

Owner

phylotorch-bito is a package providing an interface to BITO for phylotorch

Realtime_Multi-Person_Pose_Estimation

Neural network for stock price prediction

Utilities and information for the signals.numer.ai tournament

[ICLR 2022] DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR

Deep Anomaly Detection with Outlier Exposure (ICLR 2019)

Joint Unsupervised Learning (JULE) of Deep Representations and Image Clusters.

Rational Activation Functions - Replacing Padé Activation Units

Torchlight2 lan game server tool - A message forwarding tool for Torchlight 2 lan game

Code needed to reproduce the examples found in "The Temporal Robustness of Stochastic Signals"

A Deep Learning Framework for Neural Derivative Hedging

ML models implementation practice

naked is a Python tool which allows you to strip a model and only keep what matters for making predictions.

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

Active Offline Policy Selection With Python

This project provides the proof of the uniqueness of the equilibrium and the global asymptotic stability.

Code for ICMI2020 and ICMI2021 papers: "Studying Person-Specific Pointing and Gaze Behavior for Multimodal Referencing of Outside Objects from a Moving Vehicle" and "ML-PersRef: A Machine Learning-based Personalized Multimodal Fusion Approach for Referencing Outside Objects From a Moving Vehicle"

LoL Runes Recommender With Python

Code release for "Masked-attention Mask Transformer for Universal Image Segmentation"

Code repository for Self-supervised Structure-sensitive Learning, CVPR'17