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--- |
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language: |
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- multilingual |
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- ab |
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- af |
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- am |
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- ar |
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- as |
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- az |
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- ba |
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- be |
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- bg |
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- bi |
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- bo |
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- br |
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- bs |
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- ca |
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- ceb |
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- cs |
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- cy |
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- da |
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- de |
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- el |
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- en |
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- eo |
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- es |
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- et |
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- eu |
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- fa |
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- fi |
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- fo |
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- fr |
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- gl |
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- gn |
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- gu |
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- gv |
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- ha |
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- haw |
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- hi |
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- hr |
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- ht |
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- hu |
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- hy |
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- ia |
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- id |
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- is |
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- it |
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- he |
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- ja |
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- jv |
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- ka |
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- kk |
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- km |
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- kn |
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- ko |
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- la |
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- lm |
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- ln |
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- lo |
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- lt |
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- lv |
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- mg |
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- mi |
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- mk |
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- ml |
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- mn |
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- mr |
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- ms |
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- mt |
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- my |
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- ne |
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- nl |
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- nn |
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- no |
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- oc |
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- pa |
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- pl |
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- ps |
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- pt |
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- ro |
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- ru |
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- sa |
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- sco |
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- sd |
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- si |
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- sk |
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- sl |
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- sn |
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- so |
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- sq |
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- sr |
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- su |
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- sv |
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- sw |
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- ta |
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- te |
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- tg |
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- th |
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- tk |
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- tl |
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- tr |
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- tt |
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- uk |
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- ud |
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- uz |
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- vi |
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- war |
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- yi |
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- yo |
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- zh |
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license: cc-by-4.0 |
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tags: |
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- language-identification |
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- speechbrain |
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- wav2vec2 |
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- pytorch |
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- embeddings |
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- Language |
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- Identification |
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- audio-classification |
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- wav2vec2.0 |
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- XLS-R-300M |
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- VoxLingua107 |
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datasets: |
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- voxlingua107 |
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metrics: |
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- Accuracy |
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--- |
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# VoxLingua107 Wav2Vec Spoken Language Identification Model |
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## Model description |
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This is a spoken language identification model trained on the VoxLingua107 dataset using SpeechBrain. |
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The model is trained using weights of pretrained [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) model, Wav2Vec2.0 architecture and negative log likelihood loss. |
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The model can classify a speech utterance according to the language spoken. |
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It covers 107 different languages ( |
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Abkhazian, |
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Afrikaans, |
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Amharic, |
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Arabic, |
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Assamese, |
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Azerbaijani, |
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Bashkir, |
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Belarusian, |
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Bulgarian, |
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Bengali, |
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Tibetan, |
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Breton, |
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Bosnian, |
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Catalan, |
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Cebuano, |
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Czech, |
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Welsh, |
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Danish, |
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German, |
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Greek, |
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English, |
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Esperanto, |
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Spanish, |
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Estonian, |
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Basque, |
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Persian, |
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Finnish, |
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Faroese, |
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French, |
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Galician, |
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Guarani, |
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Gujarati, |
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Manx, |
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Hausa, |
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Hawaiian, |
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Hindi, |
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Croatian, |
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Haitian, |
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Hungarian, |
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Armenian, |
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Interlingua, |
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Indonesian, |
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Icelandic, |
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Italian, |
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Hebrew, |
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Japanese, |
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Javanese, |
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Georgian, |
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Kazakh, |
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Central Khmer, |
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Kannada, |
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Korean, |
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Latin, |
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Luxembourgish, |
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Lingala, |
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Lao, |
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Lithuanian, |
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Latvian, |
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Malagasy, |
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Maori, |
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Macedonian, |
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Malayalam, |
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Mongolian, |
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Marathi, |
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Malay, |
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Maltese, |
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Burmese, |
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Nepali, |
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Dutch, |
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Norwegian Nynorsk, |
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Norwegian, |
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Occitan, |
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Panjabi, |
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Polish, |
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Pushto, |
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Portuguese, |
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Romanian, |
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Russian, |
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Sanskrit, |
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Scots, |
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Sindhi, |
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Sinhala, |
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Slovak, |
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Slovenian, |
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Shona, |
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Somali, |
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Albanian, |
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Serbian, |
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Sundanese, |
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Swedish, |
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Swahili, |
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Tamil, |
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Telugu, |
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Tajik, |
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Thai, |
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Turkmen, |
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Tagalog, |
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Turkish, |
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Tatar, |
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Ukrainian, |
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Urdu, |
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Uzbek, |
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Vietnamese, |
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Waray, |
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Yiddish, |
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Yoruba, |
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Mandarin Chinese). |
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## Intended uses & limitations |
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The model has two uses: |
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- use 'as is' for spoken language recognition |
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- use as an utterance-level feature (embedding) extractor, for creating a dedicated language ID model on your own data |
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The model is trained on automatically collected YouTube data. For more |
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information about the dataset, see [here](http://bark.phon.ioc.ee/voxlingua107/). |
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#### How to use |
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```python |
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import torchaudio |
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from speechbrain.pretrained.interfaces import foreign_class |
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language_id = foreign_class(source="TalTechNLP/voxlingua107-xls-r-300m-wav2vec", pymodule_file="encoder_wav2vec_classifier.py", classname="EncoderWav2vecClassifier", hparams_file='inference_wav2vec.yaml', savedir="tmp") |
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# Download Thai language sample from Omniglot and convert to suitable form |
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wav_file = "https://omniglot.com/soundfiles/udhr/udhr_th.mp3" |
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out_prob, score, index, text_lab = language_id.classify_file(wav_file) |
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print("probability:", out_prob) |
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print("label:", text_lab) |
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print("score:", score) |
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print("index:", index) |
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probability: tensor([[[-2.2849e+01, -2.4349e+01, -2.3686e+01, -2.3632e+01, -2.0218e+01, |
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-2.7241e+01, -2.6715e+01, -2.2301e+01, -2.6076e+01, -2.1716e+01, |
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-1.9923e+01, -2.7303e+01, -2.1211e+01, -2.2998e+01, -2.4436e+01, |
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-2.6437e+01, -2.2686e+01, -2.4244e+01, -2.0416e+01, -2.8329e+01, |
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-1.7788e+01, -2.4829e+01, -2.4186e+01, -2.7036e+01, -2.5993e+01, |
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-1.9677e+01, -2.2746e+01, -2.9192e+01, -2.4941e+01, -2.7135e+01, |
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-2.6653e+01, -2.2791e+01, -2.4599e+01, -2.1066e+01, -2.4855e+01, |
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-2.1874e+01, -2.2914e+01, -2.4174e+01, -2.0902e+01, -2.3197e+01, |
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-2.6108e+01, -2.3941e+01, -2.3103e+01, -2.2363e+01, -2.8969e+01, |
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-2.5302e+01, -2.4862e+01, -2.2392e+01, -2.4042e+01, -2.1221e+01, |
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-2.3656e+01, -2.1286e+01, -1.9209e+01, -2.3254e+01, -2.8291e+01, |
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-5.9105e+00, -2.4525e+01, -2.4937e+01, -2.8349e+01, -2.4420e+01, |
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-2.7439e+01, -2.6329e+01, -2.3317e+01, -2.3842e+01, -2.2114e+01, |
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-2.3637e+01, -1.7217e+01, -1.8342e+01, -2.4332e+01, -2.6090e+01, |
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-2.5452e+01, -2.3854e+01, -2.6082e+01, -2.4992e+01, -2.0618e+01, |
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-2.9351e+01, -2.4153e+01, -2.3156e+01, -2.6893e+01, -2.5314e+01, |
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-2.8374e+01, -2.4009e+01, -2.3604e+01, -2.4063e+01, -2.3538e+01, |
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-2.4953e+01, -2.5607e+01, -2.3960e+01, -2.6471e+01, -2.3348e+01, |
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-2.1681e+01, -2.7610e+01, -2.5023e+01, -2.3585e+01, -2.7146e-03, |
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-2.0338e+01, -1.8737e+01, -2.5158e+01, -2.7491e+01, -2.3623e+01, |
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-2.5718e+01, -2.3465e+01, -1.8305e+01, -2.1064e+01, -2.9880e+01, |
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-2.2809e+01, -1.9856e+01]]]) |
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# The identified language ISO code is given in score[0][0] |
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label: [['th']] |
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score: tensor([[-0.0027]]) |
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index: tensor([[94]]) |
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# The scores in the out_prob tensor can be interpreted as log-likelihoods that |
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# the given utterance belongs to the given language (i.e., the larger the better) |
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# The linear-scale likelihood can be retrieved using the following: |
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print(score.exp()) |
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tensor([0.9973]) |
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# Alternatively, use the utterance embedding extractor: |
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signal, fs = torchaudio.load(wav_file) |
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embeddings = language_id.encode_batch(signal) |
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print(embeddings.shape) |
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torch.Size([2, 1, 2048]) |
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``` |
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#### Limitations and bias |
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Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are: |
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- Probably it's accuracy on smaller languages is quite limited |
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- Probably it works worse on female speech than male speech (because YouTube data includes much more male speech) |
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- Based on experiments, it performs satisfactory on accented speech |
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- Probably it doesn't work well on children's speech and on persons with speech disorders |
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## Training data |
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The model is trained on [VoxLingua107](http://bark.phon.ioc.ee/voxlingua107/). |
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VoxLingua107 is a speech dataset for training spoken language identification models. |
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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. |
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VoxLingua107 contains data for 107 languages. The total amount of speech in the training set is 6628 hours. |
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The average amount of data per language is 62 hours. However, the real amount per language varies a lot. There is also a seperate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language. |
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## Training procedure |
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We used [SpeechBrain](https://github.com/speechbrain/speechbrain) to train the model. |
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Training recipe will be published soon. |
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## Evaluation results |
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|
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| Version | Error Rate (%) | |
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|-----------------------|:------:| |
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| 2022-04-14 | 5.6 | |
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Error rate is calculated on VoxLingua107 development dataset. |
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### BibTeX entry and citation info |
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```bibtex |
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@inproceedings{valk2021slt, |
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title={{VoxLingua107}: a Dataset for Spoken Language Recognition}, |
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author={J{\"o}rgen Valk and Tanel Alum{\"a}e}, |
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booktitle={Proc. IEEE SLT Workshop}, |
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year={2021}, |
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} |
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``` |
