File size: 4,846 Bytes
e19217e
 
 
 
67e41e2
e19217e
 
3604471
 
e19217e
 
 
3604471
e19217e
 
 
 
 
28fbfac
38967c2
28fbfac
e19217e
 
70a742b
e19217e
 
 
3604471
e19217e
3604471
 
 
 
5a39d15
3604471
 
e19217e
 
 
 
 
3604471
e19217e
b99e129
e19217e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3604471
e19217e
 
 
 
fecb430
 
 
 
 
 
 
e19217e
 
 
 
 
 
 
3604471
e19217e
 
 
 
 
 
 
 
 
 
 
 
 
 
3604471
e19217e
 
 
3604471
e19217e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
---
language: "en"
thumbnail:
tags:
- audio-classification
- speechbrain
- embeddings
- Language
- Identification
- pytorch
- ECAPA-TDNN
- TDNN
- CommonLanguage
license: "apache-2.0"
datasets:
- Urbansound8k
metrics:
- Accuracy
widget:
- label: English Sample
  src: https://cdn-media.huggingface.co/speech_samples/LibriSpeech_61-70968-0000.flac
---


<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>

# Language Identification from Speech Recordings with ECAPA embeddings on CommonLanguage

This repository provides all the necessary tools to perform language identification from speeech recordinfs with SpeechBrain.
The system uses a model pretrained on the CommonLanguage dataset (45 languages).
You can download the dataset [here](https://zenodo.org/record/5036977#.YNzDbXVKg5k)
The provided system can recognize the following 45 languages from short speech recordings:

```
Arabic, Basque, Breton, Catalan, Chinese_China, Chinese_Hongkong, Chinese_Taiwan, Chuvash, Czech, Dhivehi, Dutch, English, Esperanto, Estonian, French, Frisian, Georgian, German, Greek, Hakha_Chin, Indonesian, Interlingua, Italian, Japanese, Kabyle, Kinyarwanda, Kyrgyz, Latvian, Maltese, Mangolian, Persian, Polish, Portuguese, Romanian, Romansh_Sursilvan, Russian, Sakha, Slovenian, Spanish, Swedish, Tamil, Tatar, Turkish, Ukranian, Welsh
```

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 (%)
|:-------------:|:--------------:|
| 30-06-21 | 85.0 | 


## 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 Language  Identification from Speech Recordings

```python
import torchaudio
from speechbrain.pretrained import EncoderClassifier
classifier = EncoderClassifier.from_hparams(source="speechbrain/lang-id-commonlanguage_ecapa", savedir="pretrained_models/lang-id-commonlanguage_ecapa")
# Italian Example
out_prob, score, index, text_lab = classifier.classify_file('speechbrain/lang-id-commonlanguage_ecapa/example-it.wav')
print(text_lab)

# French Example
out_prob, score, index, text_lab = classifier.classify_file('speechbrain/lang-id-commonlanguage_ecapa/example-fr.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 (a02f860e).
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/CommonLanguage/lang_id
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/1sD2u0MhSmJlx_3RRgwsYzevX81RM8-WE?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},
}
```


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


```bibtex
@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}
}
```