--- 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 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.
## 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. We have more versions available: | Models | ASR data | NLLB version | |:------:|:--------:|:------------:| | [ZeroSwot-Medium_asr-mustc](https://huggingface.co./johntsi/ZeroSwot-Medium_asr-mustc_en-to-200) | MuST-C v1.0 | [distilled-600M original](https://huggingface.co./facebook/nllb-200-distilled-600M)| | [ZeroSwot-Medium_asr-mustc_mt-mustc](https://huggingface.co./johntsi/ZeroSwot-Medium_asr-mustc_mt-mustc_en-to-8) | MuST-C v1.0 | [distilled-600M finetuned w/ MuST-C](https://huggingface.co./johntsi/nllb-200-distilled-600M_mustc_en-to-8) | | [ZeroSwot-Large_asr-mustc](https://huggingface.co./johntsi/ZeroSwot-Large_asr-mustc_en-to-200) | MuST-C v1.0 | [distilled-1.3B original](https://huggingface.co./facebook/nllb-200-distilled-1.3B) | | [ZeroSwot-Large_asr-mustc_mt-mustc](https://huggingface.co./johntsi/ZeroSwot-Large_asr-mustc_mt-mustc_en-to-8) | MuST-C v1.0 | [distilled-1.3B finetuned w/ MuST-C](https://huggingface.co./johntsi/nllb-200-distilled-1.3B_mustc_en-to-8) | | [ZeroSwot-Medium_asr-cv](https://huggingface.co./johntsi/ZeroSwot-Medium_asr-cv_en-to-200) | CommonVoice | [distilled-600M original](https://huggingface.co./facebook/nllb-200-distilled-600M)| | [ZeroSwot-Medium_asr-cv_mt-covost2](https://huggingface.co./johntsi/ZeroSwot-Medium_asr-cv_mt-covost2_en-to-15) | CommonVoice | [distilled-600M finetuned w/ CoVoST2](https://huggingface.co./johntsi/nllb-200-distilled-600M_covost2_en-to-15) | | [ZeroSwot-Large_asr-cv](https://huggingface.co./johntsi/ZeroSwot-Large_asr-cv_en-to-200) | CommonVoice | [distilled-1.3B original](https://huggingface.co./facebook/nllb-200-distilled-1.3B) | | [ZeroSwot-Large_asr-cv_mt-covost2](https://huggingface.co./johntsi/ZeroSwot-Large_asr-cv_mt-covost2_en-to-15) | CommonVoice | [distilled-1.3B finetuned w/ CoVoST2](https://huggingface.co./johntsi/nllb-200-distilled-1.3B_covost2_en-to-15) | ## 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} } ```