README.md

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

---

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# Command 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](https://urbansounddataset.weebly.com/urbansound8k.html)
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](https://speechbrain.github.io). 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](https://speechbrain.github.io).

### Perform Sound Recognition

```python
import torchaudio
from speechbrain.pretrained 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)
```

### 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:
1. Clone SpeechBrain:
```bash
git clone https://github.com/speechbrain/speechbrain/
```
2. Install it:
```
cd speechbrain
pip install -r requirements.txt
pip install -e .
```

3. 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](https://drive.google.com/drive/folders/1sItfg_WNuGX6h2dCs8JTGq2v2QoNTaUg?usp=sharing).

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

#### Referencing ECAPA
```@inproceedings{DBLP:conf/interspeech/DesplanquesTD20,
  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
```@inproceedings{Salamon:UrbanSound:ACMMM:14,
    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}}
```


#### Referencing SpeechBrain

```
@misc{SB2021,
    author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
    title = {SpeechBrain},
    year = {2021},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
  }
```

#### About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.

Website: https://speechbrain.github.io/

GitHub: https://github.com/speechbrain/speechbrain