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---
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 extracted from [WavLabLM](). 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 leaderboard, 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.
More information about XEUS, including ***download links for our crawled 4000-language dataset***, can be found in the [project page]().
## Requirements
The code for XEUS is still in progress of being merged into the main ESPnet repo. It can instead be used from the following fork:
```
pip install -e git+git://github.com/wanchichen/espnet.git@ssl
```
XEUS supports [Flash Attention](), which can be installed as follows:
```
pip install flash-attn --no-build-isolation
```
## Usage
```python
from torch.nn.utils.rnn import pad_sequence
from espnet2.tasks.ssl import SSLTask
import soundfile as sf
device = "cuda" if torch.cuda.is_available() else "cpu"
xeus_model, xeus_train_args = SSLTask.build_model_from_file(
config = None,
ckpt = '/path/to/checkpoint/here/checkpoint.pth',
device,
)
wavs, sampling_rate = sf.read('/path/to/audio.wav') # sampling rate should be 16000
wav_lengths = torch.LongTensor([len(wav) for wav in [wavs]]).to(device)
wavs = pad_sequence([wavs], batch_first=True).to(device)
# we recommend use_mask=True during fine-tuning
feats = xeus_model.encode(wavs, wav_lengths, use_mask=False, use_final_output=False)[0][-1] # take the output of the last layer
```
With Flash Attention:
```python
[layer.use_flash_attn = True for layer in xeus_model.encoder.encoders]
with torch.cuda.amp.autocast(dtype=torch.bfloat16):
feats = xeus_model.encode(wavs, wav_lengths, use_mask=False, use_final_output=False)[0][-1]
```
Tune the masking settings:
```python
xeus_model.masker.mask_prob = 0.65 # default 0.8
xeus_model.masker.mask_length = 20 # default 10
xeus_model.masker.mask_selection = 'static' # default 'uniform'
xeus_model.train()
feats = xeus_model.encode(wavs, wav_lengths, use_mask=True, use_final_output=False)[0][-1]
```
## Results
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