SLU recipes for Fluent Speech Commands

This folder contains recipes for spoken language understanding (SLU) with Fluent Speech Commands.

Tokenizer recipe

(You don't need to run this because the other recipes download a tokenizer, but you can run this if you want to train a new tokenizer for Fluent Speech Commands.)

Run this to train the tokenizer:

cd Tokenizer
python train.py hparams/tokenizer_bpe51.yaml

Direct recipe

The "direct" recipe maps the input speech to directly to semantics using a seq2seq model. The encoder is pre-trained using the LibriSpeech seq2seq recipe.

cd direct
python train.py hparams/train.yaml

Results

Release	hyperparams file	Test Acc	Model link	GPUs
21-06-03	train.yaml	99.60%	https://drive.google.com/drive/folders/13t2PYdedrPQoNYo_QSf6s04WXu2_vAb-?usp=sharing	1xV100 32GB

PreTrained Model + Easy-Inference

You can find the pre-trained model with an easy-inference function on HuggingFace.

Training Time

About 15 minutes for each epoch with a TESLA V100.

About SpeechBrain

Website: https://speechbrain.github.io/
Code: https://github.com/speechbrain/speechbrain/
HuggingFace: https://huggingface.co/speechbrain/

Citing SpeechBrain

Please, cite SpeechBrain if you use it for your research or business.

@misc{speechbrain,
  title={{SpeechBrain}: A General-Purpose Speech Toolkit},
  author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
  year={2021},
  eprint={2106.04624},
  archivePrefix={arXiv},
  primaryClass={eess.AS},
  note={arXiv:2106.04624}
}