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

Last update: Dec 30, 2022

Related tags

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

Maxime Burchi @burchim
Contact: [email protected]

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

Related tags

Overview

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

Efficient Conformer Encoder

Installation

Download LibriSpeech

Running an experiment

Evaluation

Options

Monitor training

LibriSpeech Performance

Reference

Author

Owner

Maxime Burchi

This program was designed to detect whether someone is wearing a facemask through a live video stream.

pytorch implementation of ABC : Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning

This script scrapes and stores the availability of timeslots for Car Driving Test at all RTA Serivce NSW centres in the state.

Implementation of paper "Self-supervised Learning on Graphs:Deep Insights and New Directions"

Implementation of "Semi-supervised Domain Adaptive Structure Learning"

Official implementation of the NeurIPS 2021 paper Online Learning Of Neural Computations From Sparse Temporal Feedback

Flybirds - BDD-driven natural language automated testing framework, present by Trip Flight

B-cos Networks: Attention is All we Need for Interpretability

abess: Fast Best-Subset Selection in Python and R

MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition

A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

A Python training and inference implementation of Yolov5 helmet detection in Jetson Xavier nx and Jetson nano

《Fst Lerning of Temporl Action Proposl vi Dense Boundry Genertor》(AAAI 2020)

CycleTransGAN-EVC: A CycleGAN-based Emotional Voice Conversion Model with Transformer

Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）

This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural tree born form a large search space

Utility code for use with PyXLL

Hardware-accelerated DNN model inference ROS2 packages using NVIDIA Triton/TensorRT for both Jetson and x86_64 with CUDA-capable GPU

Boostcamp AI Tech 3rd / Basic Paper reading w.r.t Embedding