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---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
language: en-tr
datasets:
- must_c
- covost2
widget:
- example_title: Common Voice sample 1
src: https://huggingface.co/facebook/xm_transformer_600m-en_es-multi_domain/resolve/main/common_voice_en_18295850.mp3
---
# xm_transformer_600m-en_tr-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)):
- English-Turkish
- Trained on MuST-C, CoVoST 2, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with [facebook/tts_transformer-tr-cv7](https://huggingface.co/facebook/tts_transformer-tr-cv7)
## Usage
```python
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.speech_to_text.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-en_tr-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/tts_transformer-tr-cv7",
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",
}
``` |