--- license: mit language: - en - de - fr - nl - es - ru - pt - ro - it 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 metrics: - bleu 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 MuST-C v1.0. It adapts [wav2vec2.0-large](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) to the embedding space of the [nllb-200-distilled-600M_mustc](https://huggingface.co/johntsi/nllb-200-distilled-600M_mustc_en-to-8) model, which is a multilingually finetuned NLLB on MuST-C MT data. 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("johntsi/nllb-200-distilled-600M_mustc_en-to-8") # Load ZeroSwot Encoder commit_hash = "7a8c1323c3db43667dd8503430df6d95961d0e3f" zeroswot_encoder = AutoModel.from_pretrained( "johntsi/ZeroSwot-Medium_asr-mustc_mt-mustc_en-to-8", trust_remote_code=True, revision=commit_hash, ) zeroswot_encoder.eval() zeroswot_encoder.to("cuda") # Load NLLB Model nllb_model = AutoModelForSeq2SeqLM.from_pretrained("johntsi/nllb-200-distilled-600M_mustc_en-to-8") 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 MuST-C v1.0 tst-COMMON compared to _supervised_ SOTA models from the literature. You can refer to Table 4 of the Results section in the paper for more details. | Models | ZS | Size (B) | De | Es | Fr | It | Nl | Pt | Ro | Ru | Average | |:-----------------------:|:----:|:----------:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:-------:| | Chimera (Han et al., 2021) | ✗ | 0.15 | 27.1 | 30.6 | 35.6 | 25.0 | 29.2 | 30.2 | 24.0 | 17.4 | 27.4 | | STEMM (Fang et al., 2022) | ✗ | 0.15 | 28.7 | 31.0 | 37.4 | 25.8 | 30.5 | 31.7 | 24.5 | 17.8 | 28.4 | | SpeechUT (Zhang et al., 2022) | ✗ | 0.15 | 30.1 | 33.6 | 41.4 | - | - | - | - | - | - | | Siamese-PT (Le et al., 2023) | ✗ | 0.25 | 27.9 | 31.8 | 39.2 | 27.7 | 31.7 | 34.2 | 27.0 | 18.5 | 29.8 | | CRESS (Fang and Feng, 2023) | ✗ | 0.15 | 29.4 | 33.2 | 40.1 | 27.6 | 32.2 | 33.6 | 26.4 | 19.7 | 30.3 | | SimRegCR (Gao et al., 2023b) | ✗ | 0.15 | 29.2 | 33.0 | 40.0 | 28.2 | 32.7 | 34.2 | 26.7 | 20.1 | 30.5 | | LST (LLaMA2-13B) (Zhang et al., 2023)| ✗ | 13 | 30.4 | 35.3 | **41.6** | - | - | - | - | - | - | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | [ZeroSwot-Medium_asr-cv](https://huggingface.co/johntsi/ZeroSwot-Medium_asr-cv_en-to-200) | ✓ | 0.35/0.95 | 24.8 | 30.0 | 32.6 | 24.1 | 28.6 | 28.8 | 22.9 | 16.4 | 26.0 | | [ZeroSwot-Medium_asr-mustc](https://huggingface.co/johntsi/ZeroSwot-Medium_asr-mustc_en-to-200) | ✓ | 0.35/0.95 | 28.5 | 33.1 | 37.5 | 28.2 | 32.3 | 32.9 | 26.0 | 18.7 | 29.6 | | [ZeroSwot-Medium_asr-mustc_mt-mustc](https://huggingface.co/johntsi/ZeroSwot-Medium_asr-mustc_mt-mustc_en-to-8) | ✓ | 0.35/0.95†| 30.5 | 34.9 | 39.4 | 30.6 | 35.0 | 37.1 | 27.8 | 20.3 | 31.9 | | [ZeroSwot-Large_asr-cv](https://huggingface.co/johntsi/ZeroSwot-Large_asr-cv_en-to-200) | ✓ | 0.35/1.65 | 26.5 | 31.1 | 33.5 | 25.4 | 29.9 | 30.6 | 24.3 | 18.0 | 27.4 | | [ZeroSwot-Large_asr-mustc](https://huggingface.co/johntsi/ZeroSwot-Large_asr-mustc_en-to-200)| ✓ | 0.35/1.65 | 30.1 | 34.8 | 38.9 | 29.8 | 34.4 | 35.3 | 27.6 | 20.4 | 31.4 | | [ZeroSwot-Large_asr-mustc_mt-mustc](https://huggingface.co/johntsi/ZeroSwot-Large_asr-mustc_mt-mustc_en-to-8)| ✓ | 0.35/1.65†| **31.2** | **35.8** | 40.5 | **31.4** | **36.3** | **38.3** | **28.0** | **21.5** | **32.9** | ## Citation If you find ZeroSwot useful for your research, please cite our paper :) ``` @inproceedings{tsiamas-etal-2024-pushing, title = {{Pushing the Limits of Zero-shot End-to-End Speech Translation}}, author = "Tsiamas, Ioannis and G{\'a}llego, Gerard and Fonollosa, Jos{\'e} and Costa-juss{\`a}, Marta", editor = "Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek", booktitle = "Findings of the Association for Computational Linguistics ACL 2024", month = aug, year = "2024", address = "Bangkok, Thailand and virtual meeting", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.findings-acl.847", pages = "14245--14267", } ```