EfficientConformer
[ASRU 2021] Efficient Conformer: Progressive Downsampling and Grouped Attention for Automatic Speech Recognition
Install / Use
/learn @burchim/EfficientConformerREADME
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 Paper | Demo Notebook
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.
<img src="media/EfficientConformer.jpg" width="50%"/>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
Author
- Maxime Burchi @burchim
- Contact: maxime.burchi@gmail.com
