readme: ---
  language: multilingual
  thumbnail:
  tags:
  - audio-classification
  - speechbrain
  - embeddings
  - Language
  - Identification
  - pytorch
  - ECAPA-TDNN
  - TDNN
  - VoxLingua107
  license: "apache-2.0"
  datasets:
  - VoxLingua107
  metrics:
  - Accuracy
  widget:
  - example_title: English Sample
    src: https://cdn-media.huggingface.co/speech_samples/LibriSpeech_61-70968-0000.flac
  ---
  
  # VoxLingua107 ECAPA-TDNN Spoken Language Identification Model
  
  ## 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.
  
  The model can classify a speech utterance according to the language spoken.
  It covers 107 different languages (
  Abkhazian, 
  Afrikaans, 
  Amharic, 
  Arabic, 
  Assamese, 
  Azerbaijani, 
  Bashkir, 
  Belarusian, 
  Bulgarian, 
  Bengali, 
  Tibetan, 
  Breton, 
  Bosnian, 
  Catalan, 
  Cebuano, 
  Czech, 
  Welsh, 
  Danish, 
  German, 
  Greek, 
  English, 
  Esperanto, 
  Spanish, 
  Estonian, 
  Basque, 
  Persian, 
  Finnish, 
  Faroese, 
  French, 
  Galician, 
  Guarani, 
  Gujarati, 
  Manx, 
  Hausa, 
  Hawaiian, 
  Hindi, 
  Croatian, 
  Haitian, 
  Hungarian, 
  Armenian, 
  Interlingua, 
  Indonesian, 
  Icelandic, 
  Italian, 
  Hebrew, 
  Japanese, 
  Javanese, 
  Georgian, 
  Kazakh, 
  Central Khmer, 
  Kannada, 
  Korean, 
  Latin, 
  Luxembourgish, 
  Lingala, 
  Lao, 
  Lithuanian, 
  Latvian, 
  Malagasy, 
  Maori, 
  Macedonian, 
  Malayalam, 
  Mongolian, 
  Marathi, 
  Malay, 
  Maltese, 
  Burmese, 
  Nepali, 
  Dutch, 
  Norwegian Nynorsk, 
  Norwegian, 
  Occitan, 
  Panjabi, 
  Polish, 
  Pushto, 
  Portuguese, 
  Romanian, 
  Russian, 
  Sanskrit, 
  Scots, 
  Sindhi, 
  Sinhala, 
  Slovak, 
  Slovenian, 
  Shona, 
  Somali, 
  Albanian, 
  Serbian, 
  Sundanese, 
  Swedish, 
  Swahili, 
  Tamil, 
  Telugu, 
  Tajik, 
  Thai, 
  Turkmen, 
  Tagalog, 
  Turkish, 
  Tatar, 
  Ukrainian, 
  Urdu, 
  Uzbek, 
  Vietnamese, 
  Waray, 
  Yiddish, 
  Yoruba, 
  Mandarin Chinese).
  
  ## 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
    
  The model is trained on automatically collected YouTube data. For more 
  information about the dataset, see [here](http://bark.phon.ioc.ee/voxlingua107/).
  
  
  #### How to use
  
  ```python
  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])
  ```
  
  #### Limitations and bias
  
  Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are:
  
   - 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](http://bark.phon.ioc.ee/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.
  
  