metadata

language: en
thumbnail: null
tags:
  - speechbrain
  - embeddings
  - Sound
  - Keywords
  - Keyword Spotting
  - pytorch
  - ECAPA-TDNN
  - TDNN
  - Command Recognition
  - audio-classification
license: apache-2.0
datasets:
  - Urbansound8k
metrics:
  - Accuracy

Sound Recognition with ECAPA embeddings on UrbanSoudnd8k

This repository provides all the necessary tools to perform sound recognition with SpeechBrain using a model pretrained on UrbanSound8k. You can download the dataset here The provided system can recognize the following 10 keywords:

dog_bark, children_playing, air_conditioner, street_music, gun_shot, siren, engine_idling, jackhammer, drilling, car_horn

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

Release	Accuracy 1-fold (%)
04-06-21	75.5

Pipeline description

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

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 Sound Recognition

import torchaudio
from speechbrain.inference.classifiers import EncoderClassifier
classifier = EncoderClassifier.from_hparams(source="speechbrain/urbansound8k_ecapa", savedir="pretrained_models/gurbansound8k_ecapa")
out_prob, score, index, text_lab = classifier.classify_file('speechbrain/urbansound8k_ecapa/dog_bark.wav')
print(text_lab)

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. Make sure your input tensor is compliant with the expected sampling rate if you use encode_batch and classify_batch.

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 (8cab8b0c). To train it from scratch follows these steps:

Clone SpeechBrain:

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

Install it:

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

Run Training:

cd recipes/UrbanSound8k/SoundClassification
python train.py hparams/train_ecapa_tdnn.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.

Referencing ECAPA

  author    = {Brecht Desplanques and
               Jenthe Thienpondt and
               Kris Demuynck},
  editor    = {Helen Meng and
               Bo Xu and
               Thomas Fang Zheng},
  title     = {{ECAPA-TDNN:} Emphasized Channel Attention, Propagation and Aggregation
               in {TDNN} Based Speaker Verification},
  booktitle = {Interspeech 2020},
  pages     = {3830--3834},
  publisher = {{ISCA}},
  year      = {2020},
}

Referencing UrbanSound

    Author = {Salamon, J. and Jacoby, C. and Bello, J. P.},
    Booktitle = {22nd {ACM} International Conference on Multimedia (ACM-MM'14)},
    Month = {Nov.},
    Pages = {1041--1044},
    Title = {A Dataset and Taxonomy for Urban Sound Research},
    Year = {2014}}

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}
}