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}
}