license: mit
datasets:
- espnet/yodas
- facebook/voxpopuli
- facebook/multilingual_librispeech
- google/fleurs
library_name: espnet
tags:
- espnet
- audio
- speech
- multilingual
language:
- multilingual
- ab
- af
- ak
- am
- ar
- as
- av
- ay
- az
- ba
- bm
- be
- bn
- bi
- bo
- sh
- br
- bg
- ca
- cs
- ce
- cv
- ku
- cy
- da
- de
- dv
- dz
- el
- en
- eo
- et
- eu
- ee
- fo
- fa
- fj
- fi
- fr
- fy
- ff
- ga
- gl
- gn
- gu
- zh
- ht
- ha
- he
- hi
- sh
- hu
- hy
- ig
- ia
- ms
- is
- it
- jv
- ja
- kn
- ka
- kk
- kr
- km
- ki
- rw
- ky
- ko
- kv
- lo
- la
- lv
- ln
- lt
- lb
- lg
- mh
- ml
- mr
- ms
- mk
- mg
- mt
- mn
- mi
- my
- zh
- nl
- 'no'
- 'no'
- ne
- ny
- oc
- om
- or
- os
- pa
- pl
- pt
- ms
- ps
- qu
- ro
- rn
- ru
- sg
- sk
- sl
- sm
- sn
- sd
- so
- es
- sq
- su
- sv
- sw
- ta
- tt
- te
- tg
- tl
- th
- ti
- ts
- tr
- uk
- ms
- vi
- wo
- xh
- ms
- yo
- ms
- zu
- za
XEUS - A Cross-lingual Encoder for Universal Speech
XEUS is a large-scale multilingual speech encoder by Carnegie Mellon University's WAVLab that covers over 4000 languages. It is pre-trained on over 1 million hours of publicly available speech datasets. It can be requires fine-tuning to be used in downstream tasks such as Speech Recognition or Translation. XEUS uses the E-Branchformer architecture and is trained using HuBERT-style masked prediction of discrete speech tokens. During training, the input speech is also augmented with acoustic noise and reverberation, making XEUS more robust. The total model size is 577M parameters.
XEUS tops the ML-SUPERB multilingual speech recognition benchmark, outperforming MMS, w2v-BERT 2.0, and XLS-R. XEUS also sets a new state-of-the-art on 4 tasks in the monolingual SUPERB benchmark.
Results
