Command Recognition with xvector embeddings on Google Speech Commands

This repository provides all the necessary tools to perform command recognition with SpeechBrain using a model pretrained on Google Speech Commands. You can download the dataset here The dataset provides small training, validation, and test sets useful for detecting single keywords in short audio clips. The provided system can recognize the following 12 keywords:

'yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go', 'unknown', 'silence'

For a better experience, we encourage you to learn more about SpeechBrain . The given model performance on the test set is:

Pipeline description

This system is composed of a TDNN model coupled with statistical pooling. A classifier, trained with Categorical Cross-Entropy Loss, is applied on top of that.

The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling classify_file if needed.

Install SpeechBrain

First of all, please install SpeechBrain with the following command:

pip install speechbrain

Please notice that we encourage you to read our tutorials and learn more about SpeechBrain .

Perform Command Recognition

import torchaudio
from speechbrain.pretrained import EncoderClassifier
classifier = EncoderClassifier.from_hparams(source="speechbrain/google_speech_command_xvector", savedir="pretrained_models/google_speech_command_xvector")
out_prob, score, index, text_lab = classifier.classify_file('speechbrain/google_speech_command_xvector/yes.wav')
print(text_lab)
out_prob, score, index, text_lab = classifier.classify_file('speechbrain/google_speech_command_xvector/stop.wav')
print(text_lab)

Inference on GPU

To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.

Training

The model was trained with SpeechBrain (b7ff9dc4). To train it from scratch follows these steps:

git clone https://github.com/speechbrain/speechbrain/

cd speechbrain
pip install -r requirements.txt
pip install -e .

cd recipes/Google-speech-commands
python train.py hparams/xvect.yaml --data_folder=your_data_folder

You can find our training results (models, logs, etc) here .

Limitations

The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

  author    = {David Snyder and
               Daniel Garcia{-}Romero and
               Alan McCree and
               Gregory Sell and
               Daniel Povey and
               Sanjeev Khudanpur},
  title     = {Spoken Language Recognition using X-vectors},
  booktitle = {Odyssey 2018},
  pages     = {105--111},
  year      = {2018},
}

   author = { {Warden}, P.},
    title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}",
  journal = {ArXiv e-prints},
  archivePrefix = "arXiv",
  eprint = {1804.03209},
  primaryClass = "cs.CL",
  keywords = {Computer Science - Computation and Language, Computer Science - Human-Computer Interaction},
    year = 2018,
    month = apr,
    url = {https://arxiv.org/abs/1804.03209},
}

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}
}