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Summary: **This PR** - Adds conformer layer based on https://arxiv.org/pdf/2005.08100.pdf. - Conformer implementation supports multihead attention based on 3 different positional embedding types - absolute positional embedding, relative positional encoding and rotational positional embedding. - Adds conformer encoder with conv1d subsampling, positional embedding followed by N conformer layers - Adds S2T_Conformer model based on the conformer encoder and transformer decoder. - Add conformer support in Wav2Vec2 - Add unit tests for core modules **Verfication** - Verified the set up on MUST-C En-De S2T, Covost2 Es-En S2T, Librispeech ASR to ensure the implementation is correct. - For S2T setups, the performance is either similar to the transformer based models or better. - Wav2vec2 pretraining and finetuning based on librispeech showed improvements over corresponding transformer baselines. - [WIP] Experiment log: https://docs.google.com/document/d/1QI-ROWVenUEXPJoHTaKD85Fq7T8ZXNc8bc54MzgwJjA/edit# **Next steps** - Add regression tests - Add README and open source checkpoints Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/2859 Reviewed By: kahne Differential Revision: D33434092 Pulled By: sravyapopuri388 fbshipit-source-id: 62f22b917a332481370750e04a439e05832a2282 |
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.. | ||
__init__.py | ||
test_dualinput_s2t_transformer.py | ||
test_fastspeech2.py | ||
test_s2t_conformer.py | ||
test_s2t_transformer.py | ||
test_tts_transformer.py | ||
test_xm_transformer.py |