Update README.md

README.md

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----
-license: mit
----
+---
+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 ✨🤖✨
+
+ZeroSwot is a state-of-the-art zero-shot end-to-end Speech Translation system.
+
+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 using only ASR data. It thus enables **Speech Translation to all the 200 languages supported by NLLB**. 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.
+
+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.
+
+This version of ZeroSwot is trained with ASR data from CommonVoice, and adapting [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.
+
+<div align=center><img src="resource/structure.png" height="100%" width="75%"/></div>
+
+## Usage
+
+```python
+from transformers import Wav2Vec2Processor, NllbTokenizer, AutoModel, AutoModelForSeq2SeqLM
+import soundfile as sf
+
+# 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 = "1d38f5dbf4f89adefe06961e4ec344b21f74ebae"
+zeroswot_encoder = AutoModel.from_pretrained(
+    "johntsi/ZeroSwot-Medium_asr-cv_en-to-200", trust_remote_code=True, revision=commit_hash,
+)
+model.eval()
+model.to("cuda")
+
+# Load NLLB Model
+nllb_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
+nllb_model.eval()
+nllb_model.to("cuda")
+
+# Load sample .wav
+audio, sr = sf.read("sample.wav")
+assert sr == 16000, "Input of wav2vec2.0 is expected to have sampling rate of 16,000"
+input_values = processor(audio, sampling_rate=16000, return_tensors="pt").cuda()
+
+# translation to German
+emb, mask = zeroswot_encoder(**input_values)
+predicted_ids = nllb_model.generate(
+    inputs_embeds=emb,
+    attention_mask=~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    |  ✗   |    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-M  |  ✗   |    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 |  ✓   | 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}
+}
+```