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	---
	library_name: fairseq
	task: audio-to-audio
	tags:
	- fairseq
	- audio
	- audio-to-audio
	- speech-to-speech-translation
	language: es-en
	datasets:
	- mtedx
	- covost2
	- europarl_st
	- voxpopuli
	widget:
	- example_title: Common Voice sample 1
	src: https://huggingface.co/facebook/xm_transformer_600m-es_en-multi_domain/resolve/main/common_voice_es_19966634.flac
	---
	# xm_transformer_600m-es_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)):
	- Spanish-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-es_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",
	}
	```