Code of the paper "Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition"

Related tags

Deep Learningsew
Overview

SEW (Squeezed and Efficient Wav2vec)

made-with-python License: MIT

The repo contains the code of the paper "Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition" by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q Weinberger, and Yoav Artzi.

Model Checkpoints

Unsupervisedly Pre-trained on LibriSpeech 960h

Model Pre-training updates Dataset Model
W2V2-tiny 100K Librispeech 960h download
W2V2-small 100K Librispeech 960h download
W2V2-mid 100K Librispeech 960h download
W2V2-base 100K Librispeech 960h download
SEW-tiny 100K Librispeech 960h download
SEW-small 100K Librispeech 960h download
SEW-mid 100K Librispeech 960h download
SEW-D-tiny 100K Librispeech 960h download
SEW-D-small 100K Librispeech 960h download
SEW-D-mid 100K Librispeech 960h download
SEW-D-mid (k127) 100K Librispeech 960h download
SEW-D-base 100K Librispeech 960h download
SEW-D-base+ 100K Librispeech 960h download
SEW-D-mid 400K Librispeech 960h download
SEW-D-mid (k127) 400K Librispeech 960h download
SEW-D-base+ 400K Librispeech 960h download

Usage

Dependencies

The code is tested with fairseq commit 05255f9, deberta commit bf17ca4 and the following packages.

torch==1.8.0
torchaudio==0.8.0
tqdm==4.49.0
Hydra==2.5
hydra-core==1.0.4
fvcore==0.1.5.post20210330
omegaconf==2.0.5
einops==0.3.0
fire==0.2.1

Apex

Please install NVIDIA's apex with

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" \
  --global-option="--deprecated_fused_adam" --global-option="--xentropy" \
  --global-option="--fast_multihead_attn" ./

wav2letter decoder

Currently, we are decoding with wav2letter v0.2 python binding at commit 96f5f9d Please install the python binding here https://github.com/flashlight/wav2letter/tree/96f5f9d3b41e01af0a031ee0d2604acd9ef3b1b0/bindings/python The newest commit d5a93f0 in v0.2 branch leads to worse WER for wav2vec 2.0 baselines.

Installation

git clone https://github.com/asappresearch/sew.git
cd sew 
pip install -e .

Pre-training

Pre-training SEW models

Run the following command where $model_size can be tiny, small, or mid, and $ngpu is tne number of GPUs you want to use.

bash scripts/pt-sew.sh $model_size $ngpu

Pre-training SEW-D models

bash scripts/pt-sew-d.sh $model_size $ngpu

where $model_size can be tiny, small, mid, mid-k127, base, or base+.

Fine-tuning

Run the following script to fine-tune a model with the hyperparameters from wav2vec 2.0.

bash scripts/ft-model.sh $pre_trained_model $split $ngpu

where $pre_trained_model can be either a W2V2, SEW, or a SEW-D model checkpoint and $split can be 10m, 1h, 10h, or 100h.

Here we also provide a set of hyperparameters which sets all dropouts the same as the pre-training stage, and we found it to be more stable.

bash scripts/ft-model-stable.sh $pre_trained_model $split $ngpu

If you see out of GPU memory error, please scale down the dataset.max_tokens and scale up the optimization.update_freq in scripts/ft-model.sh. For example modifying these lines

  dataset.max_tokens=3200000 \
  optimization.update_freq="[$((8 / $ngpu))]" \

to

  dataset.max_tokens=1600000 \
  optimization.update_freq="[$((16 / $ngpu))]" \

which reduces the batch size and increases the gradient accumulation steps in order to use less GPU memory.

Evaluation

  1. Please run this script to prepare the official LibriSpeech 4-gram language model.
bash scripts/prepare_librispeech_lm.sh $kenlm_build_bin

where $kenlm_build_bin is the folder that contains the KenLM build_binary executable file (e.g. /home/user/kenlm/build/bin).

  1. Then run this script to evaluate a pre-trained ASR model
python tools/eval_w2v.py tunelm --subsets '["dev-clean", "dev-other", "test-clean", "test-other"]' --model $asr_checkpoint
You might also like...
Code for the paper Learning the Predictability of the Future

Learning the Predictability of the Future Code from the paper Learning the Predictability of the Future. Website of the project in hyperfuture.cs.colu

Computer Vision Lab at Columbia University 139 Nov 18, 2022
PyTorch code for the paper: FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning
PyTorch code for the paper: FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning

FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning This is the PyTorch implementation of our paper: FeatMatch: Feature-Based Augmentat

null 43 Nov 19, 2022
Code for the paper A Theoretical Analysis of the Repetition Problem in Text Generation
Code for the paper A Theoretical Analysis of the Repetition Problem in Text Generation

A Theoretical Analysis of the Repetition Problem in Text Generation This repository share the code for the paper "A Theoretical Analysis of the Repeti

Zihao Fu 37 Nov 21, 2022
Code for our ICASSP 2021 paper: SA-Net: Shuffle Attention for Deep Convolutional Neural Networks
Code for our ICASSP 2021 paper: SA-Net: Shuffle Attention for Deep Convolutional Neural Networks

SA-Net: Shuffle Attention for Deep Convolutional Neural Networks (paper) By Qing-Long Zhang and Yu-Bin Yang [State Key Laboratory for Novel Software T

Qing-Long Zhang 199 Jan 8, 2023
Open source repository for the code accompanying the paper 'Non-Rigid Neural Radiance Fields Reconstruction and Novel View Synthesis of a Deforming Scene from Monocular Video'.
Open source repository for the code accompanying the paper 'Non-Rigid Neural Radiance Fields Reconstruction and Novel View Synthesis of a Deforming Scene from Monocular Video'.

Non-Rigid Neural Radiance Fields This is the official repository for the project "Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synt

Facebook Research 296 Dec 29, 2022
Code for the Shortformer model, from the paper by Ofir Press, Noah A. Smith and Mike Lewis.

Shortformer This repository contains the code and the final checkpoint of the Shortformer model. This file explains how to run our experiments on the

Ofir Press 138 Apr 15, 2022
PyTorch code for ICLR 2021 paper Unbiased Teacher for Semi-Supervised Object Detection
PyTorch code for ICLR 2021 paper Unbiased Teacher for Semi-Supervised Object Detection

Unbiased Teacher for Semi-Supervised Object Detection This is the PyTorch implementation of our paper: Unbiased Teacher for Semi-Supervised Object Detection

Facebook Research 366 Dec 28, 2022
Official code for paper "Optimization for Oriented Object Detection via Representation Invariance Loss".

Optimization for Oriented Object Detection via Representation Invariance Loss By Qi Ming, Zhiqiang Zhou, Lingjuan Miao, Xue Yang, and Yunpeng Dong. Th

ming71 56 Nov 28, 2022
Code for our CVPR 2021 paper
Code for our CVPR 2021 paper "MetaCam+DSCE"

Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification (CVPR'21) Introduction Code for our CVPR 2021

FlyingRoastDuck 59 Oct 31, 2022
Comments
  • 8000 sample rate audio

    8000 sample rate audio

    Hello there,

    I'm trying to train on 8000 Hz sample rate audio dataset. Is it enough to simply add task.sample_rate=8000 to the fairseq command or there are additional config changes that I should make?

    I would much appreciate any advice

    Thank you

    opened by Mega4alik 0
  • How to train using not English Languages

    How to train using not English Languages

    Hi! Thank you for the awesome model!

    We are very interested in your project and we try to use the sew for Japanese Language. When we train the model, should we use these scripts? Thanks! https://github.com/asappresearch/sew/tree/master/scripts

    opened by jigenji 1
  • :bug: Fix padding mask calculation

    :bug: Fix padding mask calculation

    This PR updates the padding mask calculation to be the same as the one in the reference Wav2Vec2 implementation (same commit as listed in SEW's README): https://github.com/pytorch/fairseq/blob/05255f96410e5b1eaf3bf59b767d5b4b7e2c3a35/fairseq/models/wav2vec/wav2vec2.py#L477

    For more details on how and why it was fixed in fairseq, check out this PR by @patrickvonplaten https://github.com/pytorch/fairseq/pull/3228

    opened by anton-l 0
Releases(v0.0.1)
Owner
ASAPP Research
AI for Enterprise
ASAPP Research
GitHub Repository
[CVPR 2020] Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation

Contents Local and Global GAN Cross-View Image Translation Semantic Image Synthesis Acknowledgments Related Projects Citation Contributions Collaborat

Hao Tang 131 Dec 07, 2022
Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”

Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior. The code will release soon. Implementation Python3 PyTorch=1.0 NVIDIA GPU+

FengZhang 34 Dec 04, 2022
General purpose GPU compute framework for cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends)

General purpose GPU compute framework for cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usec

The Kompute Project 1k Jan 06, 2023
torchsummaryDynamic: support real FLOPs calculation of dynamic network or user-custom PyTorch ops

torchsummaryDynamic Improved tool of torchsummaryX. torchsummaryDynamic support real FLOPs calculation of dynamic network or user-custom PyTorch ops.

Bohong Chen 1 Jan 07, 2022
code for our ECCV-2020 paper: Self-supervised Video Representation Learning by Pace Prediction

Video_Pace This repository contains the code for the following paper: Jiangliu Wang, Jianbo Jiao and Yunhui Liu, "Self-Supervised Video Representation

Jiangliu Wang 95 Dec 14, 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
Fashion Recommender System With Python

Fashion-Recommender-System Thr growing e-commerce industry presents us with a la

Omkar Gawade 2 Feb 02, 2022
Implementation for our ICCV2021 paper: Internal Video Inpainting by Implicit Long-range Propagation

Implicit Internal Video Inpainting Implementation for our ICCV2021 paper: Internal Video Inpainting by Implicit Long-range Propagation paper | project

202 Dec 30, 2022
As a part of the HAKE project, includes the reproduced SOTA models and the corresponding HAKE-enhanced versions (CVPR2020).

HAKE-Action HAKE-Action (TensorFlow) is a project to open the SOTA action understanding studies based on our Human Activity Knowledge Engine. It inclu

Yong-Lu Li 94 Nov 18, 2022
A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want.

sne4onnx A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or

Katsuya Hyodo 10 Aug 30, 2022
Reviving Iterative Training with Mask Guidance for Interactive Segmentation

This repository provides the source code for training and testing state-of-the-art click-based interactive segmentation models with the official PyTorch implementation

Visual Understanding Lab @ Samsung AI Center Moscow 406 Jan 01, 2023
Answering Open-Domain Questions of Varying Reasoning Steps from Text

This repository contains the authors' implementation of the Iterative Retriever, Reader, and Reranker (IRRR) model in the EMNLP 2021 paper "Answering Open-Domain Questions of Varying Reasoning Steps

26 Dec 22, 2022
Pseudo lidar - (CVPR 2019) Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving This paper has been accpeted by Conference o

Yan Wang 881 Dec 27, 2022
Adjust Decision Boundary for Class Imbalanced Learning

Adjusting Decision Boundary for Class Imbalanced Learning This repository is the official PyTorch implementation of WVN-RS, introduced in Adjusting De

Peyton Byungju Kim 16 Jan 04, 2023
Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.

Conformal time-series forecasting Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021. If you use our code in yo

Kamilė Stankevičiūtė 36 Nov 21, 2022
PINN(s): Physics-Informed Neural Network(s) for von Karman vortex street

PINN(s): Physics-Informed Neural Network(s) for von Karman vortex street This is

ShotaDEGUCHI 2 Apr 18, 2022
Official implementation of Deep Reparametrization of Multi-Frame Super-Resolution and Denoising

Deep-Rep-MFIR Official implementation of Deep Reparametrization of Multi-Frame Super-Resolution and Denoising Publication: Deep Reparametrization of M

Goutam Bhat 39 Jan 04, 2023
A script depending on VASP output for calculating Fermi-Softness.

Fermi softness calculation for Vienna Ab initio Simulation Package (VASP) Update 1.1.0: Big update: Rewrote the code. Use Bader atomic division instea

qslin 11 Nov 08, 2022
Computations and statistics on manifolds with geometric structures.

Geomstats Code Continuous Integration Code coverage (numpy) Code coverage (autograd, tensorflow, pytorch) Documentation Community NEWS: Geomstats is r

875 Dec 31, 2022
PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"

SLAPS-GNN This repo contains the implementation of the model proposed in SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks

60 Dec 22, 2022