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

Related tags

Deep Learningmachine-learningdeep-learningtime-seriesspeech-processing
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},
}
Owner
Speech, Reinforcement Learning, and Causal Inference.
GitHub Repository
This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.

Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)

2.4k Jan 08, 2023
Reverse engineering Rosetta 2 in M1 Mac

Project Champollion About this project Rosetta 2 is an emulation mechanism to run the x86_64 applications on Arm-based Apple Silicon with Ahead-Of-Tim

FFRI Security, Inc. 258 Jan 07, 2023
Implementation of ResMLP, an all MLP solution to image classification, in Pytorch

ResMLP - Pytorch Implementation of ResMLP, an all MLP solution to image classification out of Facebook AI, in Pytorch Install $ pip install res-mlp-py

Phil Wang 178 Dec 02, 2022
The object detection pipeline is based on Ultralytics YOLOv5

AYOLOv2 The main goal of this repository is to rewrite the object detection pipeline with a better code structure for better portability and adaptabil

153 Dec 22, 2022
RL algorithm PPO and IRL algorithm AIRL written with Tensorflow.

RL algorithm PPO and IRL algorithm AIRL written with Tensorflow. They have a parallel sampling feature in order to increase computation speed (especially in high-performance computing (HPC)).

Fangjian Li 3 Dec 28, 2021
[Preprint] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang

Chasing Sparsity in Vision Transformers: An End-to-End Exploration Codes for [Preprint] Chasing Sparsity in Vision Transformers: An End-to-End Explora

VITA 64 Dec 08, 2022
End-To-End Optimization of LiDAR Beam Configuration

End-To-End Optimization of LiDAR Beam Configuration arXiv | IEEE Xplore This repository is the official implementation of the paper: End-To-End Optimi

Niclas 30 Nov 28, 2022
Model-based 3D Hand Reconstruction via Self-Supervised Learning, CVPR2021

S2HAND: Model-based 3D Hand Reconstruction via Self-Supervised Learning S2HAND presents a self-supervised 3D hand reconstruction network that can join

Yujin Chen 72 Dec 12, 2022
An Open Source Machine Learning Framework for Everyone

Documentation TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, a

170.1k Jan 05, 2023
Official Repsoitory for "Activate or Not: Learning Customized Activation." [CVPR 2021]

CVPR 2021 | Activate or Not: Learning Customized Activation. This repository contains the official Pytorch implementation of the paper Activate or Not

184 Dec 27, 2022
Official Repo for ICCV2021 Paper: Learning to Regress Bodies from Images using Differentiable Semantic Rendering

[ICCV2021] Learning to Regress Bodies from Images using Differentiable Semantic Rendering Getting Started DSR has been implemented and tested on Ubunt

Sai Kumar Dwivedi 83 Nov 27, 2022
quantize aware training package for NCNN on pytorch

ncnnqat ncnnqat is a quantize aware training package for NCNN on pytorch. Table of Contents ncnnqat Table of Contents Installation Usage Code Examples

62 Nov 23, 2022
Official code of paper "PGT: A Progressive Method for Training Models on Long Videos" on CVPR2021

PGT Code for paper PGT: A Progressive Method for Training Models on Long Videos. Install Run pip install -r requirements.txt. Run python setup.py buil

Bo Pang 27 Mar 30, 2022
PyElastica is the Python implementation of Elastica, an open-source software for the simulation of assemblies of slender, one-dimensional structures using Cosserat Rod theory.

PyElastica PyElastica is the python implementation of Elastica: an open-source project for simulating assemblies of slender, one-dimensional structure

Gazzola Lab 105 Jan 09, 2023
Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

Zhengxia Zou 1.5k Dec 28, 2022
“英特尔创新大师杯”深度学习挑战赛 赛道3：CCKS2021中文NLP地址相关性任务

基于 bert4keras 的一个baseline 不作任何 数据trick 单模 线上 最高可到 0.7891 # 基础 版 train.py 0.7769 # transformer 各层 cls concat 明神的trick https://xv44586.git

孙永松 7 Dec 28, 2021
Official Pytorch Implementation of Relational Self-Attention: What's Missing in Attention for Video Understanding

Relational Self-Attention: What's Missing in Attention for Video Understanding This repository is the official implementation of "Relational Self-Atte

mandos 43 Dec 07, 2022
PyTorch implementation of Rethinking Positional Encoding in Language Pre-training

TUPE PyTorch implementation of Rethinking Positional Encoding in Language Pre-training. Quickstart Clone this repository. git clone https://github.com

Jake Tae 5 Jan 27, 2022
Styled text-to-drawing synthesis method. Featured at the 2021 NeurIPS Workshop on Machine Learning for Creativity and Design

Styled text-to-drawing synthesis method. Featured at the 2021 NeurIPS Workshop on Machine Learning for Creativity and Design

Peter Schaldenbrand 247 Dec 23, 2022
Self-supervised Multi-modal Hybrid Fusion Network for Brain Tumor Segmentation

JBHI-Pytorch This repository contains a reference implementation of the algorithms described in our paper "Self-supervised Multi-modal Hybrid Fusion N

FeiyiFANG 5 Dec 13, 2021