Efficient Conformer: Progressive Downsampling and Grouped Attention for Automatic Speech Recognition

Related tags

Deep LearningEfficientConformer
Overview

Efficient Conformer: Progressive Downsampling and Grouped Attention for Automatic Speech Recognition

Official implementation of the Efficient Conformer, progressively downsampled Conformer with grouped attention for Automatic Speech Recognition.

Efficient Conformer Encoder

Inspired from previous works done in Automatic Speech Recognition and Computer Vision, the Efficient Conformer encoder is composed of three encoder stages where each stage comprises a number of Conformer blocks using grouped attention. The encoded sequence is progressively downsampled and projected to wider feature dimensions, lowering the amount of computation while achieving better performance. Grouped multi-head attention reduce attention complexity by grouping neighbouring time elements along the feature dimension before applying scaled dot-product attention.

Installation

Clone GitHub repository and set up environment

git clone https://github.com/burchim/EfficientConformer.git
cd EfficientConformer
pip install -r requirements.txt

Install ctcdecode

Download LibriSpeech

Librispeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.

cd datasets
./download_LibriSpeech.sh

Running an experiment

You can run an experiment by providing a config file using the '--config_file' flag. Training checkpoints and logs will be saved in the callback folder specified in the config file. Note that '--prepare_dataset' and '--create_tokenizer' flags may be needed for your first experiment.

python main.py --config_file configs/config_file.json

Evaluation

Models can be evaluated by selecting a subset validation/test mode and by providing the epoch/name of the checkpoint to load for evaluation with the '--initial_epoch' flag. The '--gready' flag designates whether to use gready search or beam search decoding for evaluation.

python main.py --config_file configs/config_file.json --initial_epoch epoch/name --mode validation/test --gready

Options

-c / --config_file		type=str   default="configs/EfficientConformerCTCSmall.json"	help="Json configuration file containing model hyperparameters"
-m / --mode                	type=str   default="training"                               	help="Mode : training, validation-clean, test-clean, eval_time-dev-clean, ..."
-d / --distributed         	action="store_true"                                            	help="Distributed data parallelization"
-i / --initial_epoch  		type=str   default=None                                       	help="Load model from checkpoint"
--initial_epoch_lm         	type=str   default=None                                       	help="Load language model from checkpoint"
--initial_epoch_encoder    	type=str   default=None                                       	help="Load model encoder from encoder checkpoint"
-p / --prepare_dataset		action="store_true"                                            	help="Prepare dataset before training"
-j / --num_workers        	type=int   default=8                                          	help="Number of data loading workers"
--create_tokenizer         	action="store_true"                                            	help="Create model tokenizer"
--batch_size_eval      		type=int   default=8                                          	help="Evaluation batch size"
--verbose_val              	action="store_true"                                            	help="Evaluation verbose"
--val_steps                	type=int   default=None                                       	help="Number of validation steps"
--steps_per_epoch      		type=int   default=None                                       	help="Number of steps per epoch"
--world_size               	type=int   default=torch.cuda.device_count()                  	help="Number of available GPUs"
--cpu                      	action="store_true"                                            	help="Load model on cpu"
--show_dict            		action="store_true"                                            	help="Show model dict summary"
--swa                      	action="store_true"                                            	help="Stochastic weight averaging"
--swa_epochs               	nargs="+"   default=None                                       	help="Start epoch / end epoch for swa"
--swa_epochs_list      		nargs="+"   default=None                                       	help="List of checkpoints epochs for swa"
--swa_type                   	type=str   default="equal"                                    	help="Stochastic weight averaging type (equal/exp)"
--parallel                   	action="store_true"                                            	help="Parallelize model using data parallelization"
--rnnt_max_consec_dec_steps  	type=int   default=None                                       	help="Number of maximum consecutive transducer decoder steps during inference"
--eval_loss                  	action="store_true"                                            	help="Compute evaluation loss during evaluation"
--gready                     	action="store_true"                                            	help="Proceed to a gready search evaluation"
--saving_period              	type=int   default=1                                          	help="Model saving every 'n' epochs"
--val_period                 	type=int   default=1                                          	help="Model validation every 'n' epochs"
--profiler                   	action="store_true"                                            	help="Enable eval time profiler"

Monitor training

tensorboard --logdir callback_path

LibriSpeech Performance

Model Size Type Params (M) test-clean/test-other gready WER (%) test-clean/test-other n-gram WER (%) GPUs
Efficient Conformer Small CTC 13.2 3.6 / 9.0 2.7 / 6.7 4 x RTX 2080 Ti
Efficient Conformer Medium CTC 31.5 3.0 / 7.6 2.4 / 5.8 4 x RTX 2080 Ti
Efficient Conformer Large CTC 125.6 2.5 / 5.8 2.1 / 4.7 4 x RTX 3090

Reference

Maxime Burchi, Valentin Vielzeuf. Efficient Conformer: Progressive Downsampling and Grouped Attention for Automatic Speech Recognition.

Author

Owner
Maxime Burchi
Master of Engineering in Computer Science, ESIEE Paris
Maxime Burchi
GitHub Repository https://arxiv.org/abs/2109.01163
A simple software for capturing human body movements using the Kinect camera.

KinectMotionCapture A simple software for capturing human body movements using the Kinect camera. The software can seamlessly save joints and bones po

Aleksander Palkowski 5 Aug 13, 2022
Pytorch tutorials for Neural Style transfert

PyTorch Tutorials This tutorial is no longer maintained. Please use the official version: https://pytorch.org/tutorials/advanced/neural_style_tutorial

Alexis David Jacq 135 Jun 26, 2022
Awesome-google-colab - Google Colaboratory Notebooks and Repositories

Unofficial Google Colaboratory Notebook and Repository Gallery Please contact me to take over and revamp this repo (it gets around 30k views and 200k

Derek Snow 1.2k Jan 03, 2023
CSAC - Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization

CSAC Introduction This repository contains the implementation code for paper: Co

ScottYuan 5 Jul 22, 2022
Implementation for paper "STAR: A Structure-aware Lightweight Transformer for Real-time Image Enhancement" (ICCV 2021).

STAR-pytorch Implementation for paper "STAR: A Structure-aware Lightweight Transformer for Real-time Image Enhancement" (ICCV 2021). CVF (pdf) STAR-DC

43 Dec 21, 2022
The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization

PRIMER The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization. PRIMER is a pre-trained model for mu

AI2 114 Jan 06, 2023
Source code of the paper Meta-learning with an Adaptive Task Scheduler.

ATS About Source code of the paper Meta-learning with an Adaptive Task Scheduler. If you find this repository useful in your research, please cite the

Huaxiu Yao 16 Dec 26, 2022
We present a regularized self-labeling approach to improve the generalization and robustness properties of fine-tuning.

Overview This repository provides the implementation for the paper "Improved Regularization and Robustness for Fine-tuning in Neural Networks", which

NEU-StatsML-Research 21 Sep 08, 2022
Code base for "On-the-Fly Test-time Adaptation for Medical Image Segmentation"

On-the-Fly Adaptation Official Pytorch Code base for On-the-Fly Test-time Adaptation for Medical Image Segmentation Paper Introduction One major probl

Jeya Maria Jose 17 Nov 10, 2022
SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.

SciKit-Learn Laboratory This Python package provides command-line utilities to make it easier to run machine learning experiments with scikit-learn. O

ETS 528 Nov 25, 2022
Code for ICCV 2021 paper Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes using Scene Graphs

Graph-to-3D This is the official implementation of the paper Graph-to-3d: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs | arx

Helisa Dhamo 33 Jan 06, 2023
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation Introduction This is a PyTorch

XMed-Lab 30 Sep 23, 2022
A tool to visualise the results of AlphaFold2 and inspect the quality of structural predictions

AlphaFold Analyser This program produces high quality visualisations of predicted structures produced by AlphaFold. These visualisations allow the use

Oliver Powell 3 Nov 13, 2022
Third party Pytorch implement of Image Processing Transformer (Pre-Trained Image Processing Transformer arXiv:2012.00364v2)

ImageProcessingTransformer Third party Pytorch implement of Image Processing Transformer (Pre-Trained Image Processing Transformer arXiv:2012.00364v2)

61 Jan 01, 2023
POPPY (Physical Optics Propagation in Python) is a Python package that simulates physical optical propagation including diffraction

POPPY: Physical Optics Propagation in Python POPPY (Physical Optics Propagation in Python) is a Python package that simulates physical optical propaga

Space Telescope Science Institute 132 Dec 15, 2022
PrimitiveNet: Primitive Instance Segmentation with Local Primitive Embedding under Adversarial Metric (ICCV 2021)

PrimitiveNet Source code for the paper: Jingwei Huang, Yanfeng Zhang, Mingwei Sun. [PrimitiveNet: Primitive Instance Segmentation with Local Primitive

Jingwei Huang 47 Dec 06, 2022
ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing

ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing ProFuzzBench is a benchmark for stateful fuzzing of network protocols. It includes a suite of

155 Jan 08, 2023
2021-MICCAI-Progressively Normalized Self-Attention Network for Video Polyp Segmentation

2021-MICCAI-Progressively Normalized Self-Attention Network for Video Polyp Segmentation Authors: Ge-Peng Ji*, Yu-Cheng Chou*, Deng-Ping Fan, Geng Che

Ge-Peng Ji (Daniel) 85 Dec 30, 2022
Using this you can control your PC/Laptop volume by Hand Gestures (pinch-in, pinch-out) created with Python.

Hand Gesture Volume Controller Using this you can control your PC/Laptop volume by Hand Gestures (pinch-in, pinch-out). Code Firstly I have created a

Tejas Prajapati 16 Sep 11, 2021
Autonomous Perception: 3D Object Detection with Complex-YOLO

Autonomous Perception: 3D Object Detection with Complex-YOLO LiDAR object detect

Thomas Dunlap 2 Feb 18, 2022