readme: ---
  language: 
  - multilingual
  - ab
  - af
  - am
  - ar
  - as
  - az
  - ba
  - be
  - bg
  - bi
  - bo
  - br
  - bs
  - ca
  - ceb
  - cs
  - cy
  - da
  - de
  - el
  - en
  - eo
  - es
  - et
  - eu
  - fa
  - fi
  - fo
  - fr
  - gl
  - gn
  - gu
  - gv
  - ha
  - haw
  - hi
  - hr
  - ht
  - hu
  - hy
  - ia
  - id
  - is
  - it
  - he
  - ja
  - jv
  - ka
  - kk
  - km 
  - kn
  - ko
  - la
  - lm
  - ln
  - lo
  - lt
  - lv
  - mg
  - mi
  - mk
  - ml
  - mn
  - mr
  - ms
  - mt
  - my
  - ne
  - nl
  - nn
  - no 
  - oc
  - pa
  - pl
  - ps
  - pt
  - ro
  - ru
  - sa
  - sco
  - sd
  - si
  - sk
  - sl
  - sn
  - so
  - sq
  - sr 
  - su
  - sv
  - sw
  - ta
  - te
  - tg
  - th
  - tk
  - tl
  - tr
  - tt
  - uk
  - ud
  - uz
  - vi
  - war
  - yi
  - yo
  - zh
  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. However, it uses
  more fully connected hidden layers after the embedding layer, and cross-entropy loss was used for training. 
  We observed that this improved the performance of extracted utterance embeddings for downstream tasks.
  
  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.
  
  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="speechbrain/lang-id-voxlingua107-ecapa", 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([[-2.8646e+01, -3.0346e+01, -2.0748e+01, -2.9562e+01, -2.2187e+01,
  #         -3.2668e+01, -3.6677e+01, -3.3573e+01, -3.2545e+01, -2.4365e+01,
  #         -2.4688e+01, -3.1171e+01, -2.7743e+01, -2.9918e+01, -2.4770e+01,
  #         -3.2250e+01, -2.4727e+01, -2.6087e+01, -2.1870e+01, -3.2821e+01,
  #         -2.2128e+01, -2.2822e+01, -3.0888e+01, -3.3564e+01, -2.9906e+01,
  #         -2.2392e+01, -2.5573e+01, -2.6443e+01, -3.2429e+01, -3.2652e+01,
  #         -3.0030e+01, -2.4607e+01, -2.2967e+01, -2.4396e+01, -2.8578e+01,
  #         -2.5153e+01, -2.8475e+01, -2.6409e+01, -2.5230e+01, -2.7957e+01,
  #         -2.6298e+01, -2.3609e+01, -2.5863e+01, -2.8225e+01, -2.7225e+01,
  #         -3.0486e+01, -2.1185e+01, -2.7938e+01, -3.3155e+01, -1.9076e+01,
  #         -2.9181e+01, -2.2160e+01, -1.8352e+01, -2.5866e+01, -3.3636e+01,
  #         -4.2016e+00, -3.1581e+01, -3.1894e+01, -2.7834e+01, -2.5429e+01,
  #         -3.2235e+01, -3.2280e+01, -2.8786e+01, -2.3366e+01, -2.6047e+01,
  #         -2.2075e+01, -2.3770e+01, -2.2518e+01, -2.8101e+01, -2.5745e+01,
  #         -2.6441e+01, -2.9822e+01, -2.7109e+01, -3.0225e+01, -2.4566e+01,
  #         -2.9268e+01, -2.7651e+01, -3.4221e+01, -2.9026e+01, -2.6009e+01,
  #         -3.1968e+01, -3.1747e+01, -2.8156e+01, -2.9025e+01, -2.7756e+01,
  #         -2.8052e+01, -2.9341e+01, -2.8806e+01, -2.1636e+01