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README.md
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tags:
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- fairseq
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- audio
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language: en
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datasets:
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---
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tags:
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- fairseq
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- audio
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- speech-to-speech-translation
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language: en-tr
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datasets:
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- must_c
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- covost2
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---
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# xm_transformer_600m-en_tr-multi_domain
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[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)):
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- English-Turkish
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- Trained on MuST-C, CoVoST 2, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
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- Speech synthesis with [facebook/tts_transformer-tr-cv7_css10](https://huggingface.co/facebook/tts_transformer-tr-cv7_css10)
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## Usage
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```python
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from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
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from fairseq.models.text_to_speech.hub_interface import S2THubInterface
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from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
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import IPython.display as ipd
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import torchaudio
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models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
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"facebook/xm_transformer_600m-en_tr-multi_domain",
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arg_overrides={"config_yaml": "config.yaml"},
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)
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model = models[0]
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generator = task.build_generator(model, cfg)
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# requires 16000Hz mono channel audio
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audio, _ = torchaudio.load("/path/to/an/audio/file")
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sample = S2THubInterface.get_model_input(task, audio)
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text = S2THubInterface.get_prediction(task, model, generator, sample)
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# speech synthesis
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tts_models, tts_cfg, tts_task = load_model_ensemble_and_task_from_hf_hub(
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f"facebook/tts_transformer-tr-cv7_css10",
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arg_overrides={"vocoder": "griffin_lim", "fp16": False},
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)
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tts_model = tts_models[0]
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TTSHubInterface.update_cfg_with_data_cfg(tts_cfg, tts_task.data_cfg)
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tts_generator = tts_task.build_generator([tts_model], tts_cfg)
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tts_sample = TTSHubInterface.get_model_input(tts_task, text)
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wav, sr = TTSHubInterface.get_prediction(
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tts_task, tts_model, tts_generator, tts_sample
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)
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ipd.Audio(wav, rate=rate)
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```
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## Citation
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```bibtex
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@inproceedings{li-etal-2021-multilingual,
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title = "Multilingual Speech Translation from Efficient Finetuning of Pretrained Models",
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author = "Li, Xian and
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Wang, Changhan and
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Tang, Yun and
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Tran, Chau and
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Tang, Yuqing and
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Pino, Juan and
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Baevski, Alexei and
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Conneau, Alexis and
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Auli, Michael",
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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)",
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month = aug,
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year = "2021",
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address = "Online",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2021.acl-long.68",
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doi = "10.18653/v1/2021.acl-long.68",
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pages = "827--838",
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}
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@inproceedings{wang-etal-2020-fairseq,
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title = "Fairseq {S}2{T}: Fast Speech-to-Text Modeling with Fairseq",
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author = "Wang, Changhan and
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Tang, Yun and
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Ma, Xutai and
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Wu, Anne and
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Okhonko, Dmytro and
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Pino, Juan",
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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",
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month = dec,
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year = "2020",
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address = "Suzhou, China",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2020.aacl-demo.6",
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pages = "33--39",
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}
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```
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