Voxlingua107 Epaca Tdnn

🚀 VoxLingua107 ECAPA-TDNN Spoken Language Identification Model

A spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain, capable of classifying 107 different languages.

🚀 Quick Start

The model has two uses:

• Use 'as is' for spoken language recognition.
• Use as an utterance - level feature (embedding) extractor, for creating a dedicated language ID model on your own data.

import torchaudio
from speechbrain.pretrained import EncoderClassifier
language_id = EncoderClassifier.from_hparams(source="TalTechNLP/voxlingua107-epaca-tdnn", savedir="tmp")
# Download Thai language sample from Omniglot and cvert to suitable form
signal = language_id.load_audio("https://omniglot.com/soundfiles/udhr/udhr_th.mp3")
prediction =  language_id.classify_batch(signal)
print(prediction)
  (tensor([[0.3210, 0.3751, 0.3680, 0.3939, 0.4026, 0.3644, 0.3689, 0.3597, 0.3508,
           0.3666, 0.3895, 0.3978, 0.3848, 0.3957, 0.3949, 0.3586, 0.4360, 0.3997,
           0.4106, 0.3886, 0.4177, 0.3870, 0.3764, 0.3763, 0.3672, 0.4000, 0.4256,
           0.4091, 0.3563, 0.3695, 0.3320, 0.3838, 0.3850, 0.3867, 0.3878, 0.3944,
           0.3924, 0.4063, 0.3803, 0.3830, 0.2996, 0.4187, 0.3976, 0.3651, 0.3950,
           0.3744, 0.4295, 0.3807, 0.3613, 0.4710, 0.3530, 0.4156, 0.3651, 0.3777,
           0.3813, 0.6063, 0.3708, 0.3886, 0.3766, 0.4023, 0.3785, 0.3612, 0.4193,
           0.3720, 0.4406, 0.3243, 0.3866, 0.3866, 0.4104, 0.4294, 0.4175, 0.3364,
           0.3595, 0.3443, 0.3565, 0.3776, 0.3985, 0.3778, 0.2382, 0.4115, 0.4017,
           0.4070, 0.3266, 0.3648, 0.3888, 0.3907, 0.3755, 0.3631, 0.4460, 0.3464,
           0.3898, 0.3661, 0.3883, 0.3772, 0.9289, 0.3687, 0.4298, 0.4211, 0.3838,
           0.3521, 0.3515, 0.3465, 0.4772, 0.4043, 0.3844, 0.3973, 0.4343]]), tensor([0.9289]), tensor([94]), ['th'])
# The scores in the prediction[0] tensor can be interpreted as cosine scores between
# the languages and the given utterance (i.e., the larger the better)
# The identified language ISO code is given in prediction[3]
print(prediction[3])
  ['th']
  
# Alternatively, use the utterance embedding extractor:
emb =  language_id.encode_batch(signal)
print(emb.shape)
  torch.Size([1, 1, 256])

✨ Features

• Multilingual Classification: The model can classify a speech utterance according to the language spoken, covering 107 different languages.
• Feature Extraction: It can be used as an utterance - level feature (embedding) extractor.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

import torchaudio
from speechbrain.pretrained import EncoderClassifier
language_id = EncoderClassifier.from_hparams(source="TalTechNLP/voxlingua107-epaca-tdnn", savedir="tmp")
# Download Thai language sample from Omniglot and cvert to suitable form
signal = language_id.load_audio("https://omniglot.com/soundfiles/udhr/udhr_th.mp3")
prediction =  language_id.classify_batch(signal)
print(prediction)
  (tensor([[0.3210, 0.3751, 0.3680, 0.3939, 0.4026, 0.3644, 0.3689, 0.3597, 0.3508,
           0.3666, 0.3895, 0.3978, 0.3848, 0.3957, 0.3949, 0.3586, 0.4360, 0.3997,
           0.4106, 0.3886, 0.4177, 0.3870, 0.3764, 0.3763, 0.3672, 0.4000, 0.4256,
           0.4091, 0.3563, 0.3695, 0.3320, 0.3838, 0.3850, 0.3867, 0.3878, 0.3944,
           0.3924, 0.4063, 0.3803, 0.3830, 0.2996, 0.4187, 0.3976, 0.3651, 0.3950,
           0.3744, 0.4295, 0.3807, 0.3613, 0.4710, 0.3530, 0.4156, 0.3651, 0.3777,
           0.3813, 0.6063, 0.3708, 0.3886, 0.3766, 0.4023, 0.3785, 0.3612, 0.4193,
           0.3720, 0.4406, 0.3243, 0.3866, 0.3866, 0.4104, 0.4294, 0.4175, 0.3364,
           0.3595, 0.3443, 0.3565, 0.3776, 0.3985, 0.3778, 0.2382, 0.4115, 0.4017,
           0.4070, 0.3266, 0.3648, 0.3888, 0.3907, 0.3755, 0.3631, 0.4460, 0.3464,
           0.3898, 0.3661, 0.3883, 0.3772, 0.9289, 0.3687, 0.4298, 0.4211, 0.3838,
           0.3521, 0.3515, 0.3465, 0.4772, 0.4043, 0.3844, 0.3973, 0.4343]]), tensor([0.9289]), tensor([94]), ['th'])
# The scores in the prediction[0] tensor can be interpreted as cosine scores between
# the languages and the given utterance (i.e., the larger the better)
# The identified language ISO code is given in prediction[3]
print(prediction[3])
  ['th']

Advanced Usage

# Alternatively, use the utterance embedding extractor:
emb =  language_id.encode_batch(signal)
print(emb.shape)
  torch.Size([1, 1, 256])

📚 Documentation

Model description

This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain. The model uses the ECAPA - TDNN architecture that has previously been used for speaker recognition. It can classify a speech utterance according to the language spoken, covering 107 different languages.

Intended uses & limitations

The model has two uses:

• Use 'as is' for spoken language recognition.
• Use as an utterance - level feature (embedding) extractor, for creating a dedicated language ID model on your own data.

However, since the model is trained on automatically collected YouTube data, it has many limitations and biases:

• Probably it's accuracy on smaller languages is quite limited.
• Probably it works worse on female speech than male speech (because YouTube data includes much more male speech).
• Based on subjective experiments, it doesn't work well on speech with a foreign accent.
• Probably it doesn't work well on children's speech and on persons with speech disorders.

Training data

The model is trained on VoxLingua107. VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post - processing steps to filter out false positives. It contains data for 107 languages. The total amount of speech in the training set is 6628 hours. The average amount of data per language is 62 hours, but the real amount per language varies a lot. There is also a separate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language.

Training procedure

We used SpeechBrain to train the model. Training recipe will be published soon.

Evaluation results

Error rate: 7% on the development dataset

🔧 Technical Details

The model uses the ECAPA - TDNN architecture, which was previously used for speaker recognition. It is trained on the VoxLingua107 dataset, which is automatically collected from YouTube videos and post - processed to filter out false positives.

📄 License

This project is licensed under the "apache - 2.0" license.

BibTeX entry and citation info

@inproceedings{valk2021slt,
  title={{VoxLingua107}: a Dataset for Spoken Language Recognition},
  author={J{\"o}rgen Valk and Tanel Alum{\"a}e},
  booktitle={Proc. IEEE SLT Workshop},
  year={2021},
}