|
--- |
|
language: |
|
- ace |
|
- acm |
|
- acq |
|
- aeb |
|
- af |
|
- ajp |
|
- ak |
|
- als |
|
- am |
|
- apc |
|
- ar |
|
- ars |
|
- ary |
|
- arz |
|
- as |
|
- ast |
|
- awa |
|
- ayr |
|
- azb |
|
- azj |
|
- ba |
|
- bm |
|
- ban |
|
- be |
|
- bem |
|
- bn |
|
- bho |
|
- bjn |
|
- bo |
|
- bs |
|
- bug |
|
- bg |
|
- ca |
|
- ceb |
|
- cs |
|
- cjk |
|
- ckb |
|
- crh |
|
- cy |
|
- da |
|
- de |
|
- dik |
|
- dyu |
|
- dz |
|
- el |
|
- en |
|
- eo |
|
- et |
|
- eu |
|
- ee |
|
- fo |
|
- fj |
|
- fi |
|
- fon |
|
- fr |
|
- fur |
|
- fuv |
|
- gaz |
|
- gd |
|
- ga |
|
- gl |
|
- gn |
|
- gu |
|
- ht |
|
- ha |
|
- he |
|
- hi |
|
- hne |
|
- hr |
|
- hu |
|
- hy |
|
- ig |
|
- ilo |
|
- id |
|
- is |
|
- it |
|
- jv |
|
- ja |
|
- kab |
|
- kac |
|
- kam |
|
- kn |
|
- ks |
|
- ka |
|
- kk |
|
- kbp |
|
- kea |
|
- khk |
|
- km |
|
- ki |
|
- rw |
|
- ky |
|
- kmb |
|
- kmr |
|
- knc |
|
- kg |
|
- ko |
|
- lo |
|
- lij |
|
- li |
|
- ln |
|
- lt |
|
- lmo |
|
- ltg |
|
- lb |
|
- lua |
|
- lg |
|
- luo |
|
- lus |
|
- lvs |
|
- mag |
|
- mai |
|
- ml |
|
- mar |
|
- min |
|
- mk |
|
- mt |
|
- mni |
|
- mos |
|
- mi |
|
- my |
|
- nl |
|
- nn |
|
- nb |
|
- npi |
|
- nso |
|
- nus |
|
- ny |
|
- oc |
|
- ory |
|
- pag |
|
- pa |
|
- pap |
|
- pbt |
|
- pes |
|
- plt |
|
- pl |
|
- pt |
|
- prs |
|
- quy |
|
- ro |
|
- rn |
|
- ru |
|
- sg |
|
- sa |
|
- sat |
|
- scn |
|
- shn |
|
- si |
|
- sk |
|
- sl |
|
- sm |
|
- sn |
|
- sd |
|
- so |
|
- st |
|
- es |
|
- sc |
|
- sr |
|
- ss |
|
- su |
|
- sv |
|
- swh |
|
- szl |
|
- ta |
|
- taq |
|
- tt |
|
- te |
|
- tg |
|
- tl |
|
- th |
|
- ti |
|
- tpi |
|
- tn |
|
- ts |
|
- tk |
|
- tum |
|
- tr |
|
- tw |
|
- tzm |
|
- ug |
|
- uk |
|
- umb |
|
- ur |
|
- uzn |
|
- vec |
|
- vi |
|
- war |
|
- wo |
|
- xh |
|
- ydd |
|
- yo |
|
- yue |
|
- zh |
|
- zsm |
|
- zu |
|
language_details: >- |
|
ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, |
|
aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, |
|
asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, |
|
bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, |
|
bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, |
|
cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, |
|
dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, |
|
ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, |
|
fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, |
|
hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, |
|
hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, |
|
jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, |
|
kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, |
|
kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, |
|
lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, |
|
ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, |
|
mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, |
|
mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, |
|
nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, |
|
gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, |
|
prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, |
|
san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, |
|
smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, |
|
srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, |
|
tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, |
|
taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, |
|
tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, |
|
uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, |
|
yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn |
|
license: mit |
|
metrics: |
|
- bleu |
|
datasets: |
|
- mozilla-foundation/common_voice_8_0 |
|
pipeline_tag: automatic-speech-recognition |
|
tags: |
|
- zeroswot |
|
- speech translation |
|
- zero-shot |
|
- end-to-end |
|
- nllb |
|
- wav2vec2 |
|
--- |
|
|
|
# ZeroSwot ✨🤖✨ |
|
|
|
<div style='display:flex; gap: 0.25rem; '> |
|
<a href='https://arxiv.org/abs/2402.10422'><img src='https://img.shields.io/badge/paper-PDF-green'></a> |
|
<a href='https://github.com/mt-upc/ZeroSwot/blob/main/LICENSE'><img src='https://img.shields.io/badge/License-MIT-blue.svg'></a> |
|
<a href='https://github.com/mt-upc/ZeroSwot'><img src='https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white'></a> |
|
</div> |
|
|
|
ZeroSwot is a state-of-the-art zero-shot end-to-end Speech Translation system. |
|
|
|
<div align=center><img src="resources/intro.png" height="65%" width="65%"/></div> |
|
|
|
The model is created by adapting a wav2vec2.0-based encoder to the embedding space of NLLB, using a novel subword compression module and Optimal Transport, while only utilizing ASR data. It thus enables **Zero-shot E2E Speech Translation to all the 200 languages supported by NLLB**. |
|
|
|
For more details please refer to our [paper](https://arxiv.org/abs/2402.10422) and the [original repo](https://github.com/mt-upc/ZeroSwot) build on fairseq. |
|
|
|
## Architecture |
|
|
|
The compression module is a light-weight transformer that takes as input the hidden state of wav2vec2.0 and the corresponding CTC predictions, and compresses them to subword-like embeddings similar to those expected from NLLB and aligns them using Optimal Transport. For inference we simply pass the output of the speech encoder to NLLB encoder. |
|
|
|
<div align=center><img src="resources/methodology.png" height="120%" width="120%"/></div> |
|
|
|
## Version |
|
|
|
This version of ZeroSwot is trained with ASR data from CommonVoice, and adapted [wav2vec2.0-large](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) to the [nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) model. |
|
|
|
## Usage |
|
|
|
The model is tested with python 3.9.16 and Transformer v4.41.2. Install also torchaudio and sentencepiece for processing. |
|
|
|
```bash |
|
pip install transformers torchaudio sentencepiece |
|
``` |
|
|
|
|
|
```python |
|
from transformers import Wav2Vec2Processor, NllbTokenizer, AutoModel, AutoModelForSeq2SeqLM |
|
import torchaudio |
|
|
|
def load_and_resample_audio(audio_path, target_sr=16000): |
|
audio, orig_freq = torchaudio.load(audio_path) |
|
if orig_freq != target_sr: |
|
audio = torchaudio.functional.resample(audio, orig_freq=orig_freq, new_freq=target_sr) |
|
audio = audio.squeeze(0).numpy() |
|
return audio |
|
|
|
# Load processors and tokenizers |
|
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self") |
|
tokenizer = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M") |
|
|
|
# Load ZeroSwot Encoder |
|
commit_hash = "eafabee295ea1c8b45483d1fd26bd747d9a7d937" |
|
zeroswot_encoder = AutoModel.from_pretrained( |
|
"johntsi/ZeroSwot-Medium_asr-cv_en-to-200", trust_remote_code=True, revision=commit_hash, |
|
) |
|
zeroswot_encoder.eval() |
|
zeroswot_encoder.to("cuda") |
|
|
|
# Load NLLB Model |
|
nllb_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M") |
|
nllb_model.eval() |
|
nllb_model.to("cuda") |
|
|
|
# Load audio file |
|
audio = load_and_resample_audio(path_to_audio_file) # you can use "resources/sample.wav" for testing |
|
input_values = processor(audio, sampling_rate=16000, return_tensors="pt").to("cuda") |
|
|
|
# translation to German |
|
compressed_embeds, attention_mask = zeroswot_encoder(**input_values) |
|
predicted_ids = nllb_model.generate( |
|
inputs_embeds=compressed_embeds, |
|
attention_mask=attention_mask, |
|
forced_bos_token_id=tokenizer.lang_code_to_id["deu_Latn"], |
|
num_beams=5, |
|
) |
|
translation = tokenizer.decode(predicted_ids[0], skip_special_tokens=True) |
|
print(translation) |
|
``` |
|
|
|
## Results |
|
|
|
BLEU scores on CoVoST-2 test compared to supervised SOTA models [XLS-R-1B](https://huggingface.co/facebook/wav2vec2-xls-r-1b) and [SeamlessM4T-Medium](https://huggingface.co/facebook/seamless-m4t-medium). You can refer to Table 5 of the Results section in the paper for more details. |
|
|
|
| Models | ZS | Size (B) | Ar | Ca | Cy | De | Et | Fa | Id | Ja | Lv | Mn | Sl | Sv | Ta | Tr | Zh | Average | |
|
|:--------------:|:----:|:----------:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:-------:| |
|
| [XLS-R-1B](https://huggingface.co/facebook/wav2vec2-xls-r-1b) | ✗ | 1.0 | 19.2 | 32.1 | **31.8** | 26.2 | 22.4 | 21.3 | 30.3 | 39.9 | 22.0 | 14.9 | 25.4 | 32.3 | 18.1 | 17.1 | 36.7 | 26.0 | |
|
| [SeamlessM4T-Medium](https://huggingface.co/facebook/seamless-m4t-medium) | ✗ | 1.2 | 20.8 | 37.3 | 29.9 | **31.4** | 23.3 | 17.2 | 34.8 | 37.5 | 19.5 | 12.9 | 29.0 | 37.3 | 18.9 | **19.8** | 30.0 | 26.6 | |
|
| [ZeroSwot-M_asr-cv](https://huggingface.co/johntsi/ZeroSwot-Medium_asr-cv_en-to-200) | ✓ | 0.35/0.95 | 17.6 | 32.5 | 18.0 | 29.9 | 20.4 | 16.3 | 32.4 | 32.0 | 13.3 | 10.0 | 25.2 | 34.4 | 17.8 | 15.6 | 30.5 | 23.1 | |
|
| [ZeroSwot-M_asr-cv_mt-covost2](https://huggingface.co/johntsi/ZeroSwot-Medium_asr-cv_mt-covost2_en-to-200) | ✓ | 0.35/0.95 | **24.4** | **38.7** | 28.8 | 31.2 | **26.2** | **26.0** | **36.0** | **46.0** | **24.8** | **19.0** | **31.6** | **37.8** | **24.4** | 18.6 | **39.0** | **30.2** | |
|
|
|
## Citation |
|
|
|
If you find ZeroSwot useful for your research, please cite our paper :) |
|
|
|
``` |
|
@misc{tsiamas2024pushing, |
|
title={{Pushing the Limits of Zero-shot End-to-End Speech Translation}}, |
|
author={Ioannis Tsiamas and Gerard I. Gállego and José A. R. Fonollosa and Marta R. Costa-jussà}, |
|
year={2024}, |
|
eprint={2402.10422}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
