--- library_name: fairseq task: audio-to-audio tags: - fairseq - audio - audio-to-audio - speech-to-speech-translation language: fr-en datasets: - mtedx - covost2 - europarl_st - voxpopuli --- # xm_transformer_600m-fr_en-multi_domain [W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)): - French-English - Trained on mTEDx, CoVoST 2, EuroParl-ST, VoxPopuli, Multilingual LibriSpeech, Common Voice v7 and CCMatrix - Speech synthesis with [facebook/fastspeech2-en-ljspeech](https://huggingface.co/facebook/fastspeech2-en-ljspeech) ## Usage ```python from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub from fairseq.models.text_to_speech.hub_interface import S2THubInterface from fairseq.models.text_to_speech.hub_interface import TTSHubInterface import IPython.display as ipd import torchaudio models, cfg, task = load_model_ensemble_and_task_from_hf_hub( "facebook/xm_transformer_600m-fr_en-multi_domain", arg_overrides={"config_yaml": "config.yaml"}, ) model = models[0] generator = task.build_generator(model, cfg) # requires 16000Hz mono channel audio audio, _ = torchaudio.load("/path/to/an/audio/file") sample = S2THubInterface.get_model_input(task, audio) text = S2THubInterface.get_prediction(task, model, generator, sample) # speech synthesis tts_models, tts_cfg, tts_task = load_model_ensemble_and_task_from_hf_hub( f"facebook/fastspeech2-en-ljspeech", arg_overrides={"vocoder": "griffin_lim", "fp16": False}, ) tts_model = tts_models[0] TTSHubInterface.update_cfg_with_data_cfg(tts_cfg, tts_task.data_cfg) tts_generator = tts_task.build_generator([tts_model], tts_cfg) tts_sample = TTSHubInterface.get_model_input(tts_task, text) wav, sr = TTSHubInterface.get_prediction( tts_task, tts_model, tts_generator, tts_sample ) ipd.Audio(wav, rate=rate) ``` ## Citation ```bibtex @inproceedings{li-etal-2021-multilingual, title = "Multilingual Speech Translation from Efficient Finetuning of Pretrained Models", author = "Li, Xian and Wang, Changhan and Tang, Yun and Tran, Chau and Tang, Yuqing and Pino, Juan and Baevski, Alexei and Conneau, Alexis and Auli, Michael", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.68", doi = "10.18653/v1/2021.acl-long.68", pages = "827--838", } @inproceedings{wang-etal-2020-fairseq, title = "Fairseq {S}2{T}: Fast Speech-to-Text Modeling with Fairseq", author = "Wang, Changhan and Tang, Yun and Ma, Xutai and Wu, Anne and Okhonko, Dmytro and Pino, Juan", booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations", month = dec, year = "2020", address = "Suzhou, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.aacl-demo.6", pages = "33--39", } @inproceedings{wang-etal-2021-fairseq, title = "fairseq S{\^{}}2: A Scalable and Integrable Speech Synthesis Toolkit", author = "Wang, Changhan and Hsu, Wei-Ning and Adi, Yossi and Polyak, Adam and Lee, Ann and Chen, Peng-Jen and Gu, Jiatao and Pino, Juan", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-demo.17", doi = "10.18653/v1/2021.emnlp-demo.17", pages = "143--152", } ```