https://www.aclweb.org/anthology/2020.aacl-demo.6
Speech recognition (ASR) and speech-to-text translation (ST) with fairseq.
S2T modeling data consists of source speech features, target text and other optional information (source text, speaker id, etc.). Fairseq S2T uses per-dataset-split TSV manifest files to store these information. Each data field is represented by a column in the TSV file.
Unlike text token embeddings, speech features (e.g. log mel-scale filter banks) are usually fixed during model training and can be pre-computed. The manifest file contains the path to either the feature file in NumPy format or the WAV/FLAC audio file. For the latter, features will be extracted on-the-fly by fairseq S2T. Optionally, feature/audio files can be packed into uncompressed ZIP files (then accessed via byte offset and length) to improve I/O performance.
Fairseq S2T also employs a YAML file for data related configurations: tokenizer type and dictionary path for the target text, feature transforms such as CMVN (cepstral mean and variance normalization) and SpecAugment, temperature-based resampling, etc.
Fairseq S2T uses the unified fairseq-train interface for model training. It requires arguments --task speech_to_text,
--arch <model architecture in fairseq.models.speech_to_text.*> and --config-yaml <config YAML filename>.
Fairseq S2T uses the unified fairseq-generate/fairseq-interactive interface for inference and evaluation. It
requires arguments --task speech_to_text and --config-yaml <config YAML filename>. The interactive console takes
audio paths (one per line) as inputs.
- 02/04/2021: Added interactive decoding (
fairseq-interactive) support. Examples: ASR (LibriSpeech) and ST (CoVoST 2). - 01/08/2021: Several fixes for S2T Transformer model, inference-time de-tokenization, scorer configuration and data preparation scripts. We also add pre-trained models to the examples and revise the instructions. Breaking changes: the data preparation scripts now extract filterbank features without CMVN. CMVN is instead applied on-the-fly (defined in the config YAML).
- We are migrating the old fairseq ASR example into this S2T framework and merging the features from both sides.
- The following papers also base their experiments on fairseq S2T. We are adding more examples for replication.
- Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation (Wang et al., 2020)
- Self-Supervised Representations Improve End-to-End Speech Translation (Wu et al., 2020)
- Self-Training for End-to-End Speech Translation (Pino et al., 2020)
- CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus (Wang et al., 2020)
- Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade (Pino et al., 2019)
Please cite as:
@inproceedings{wang2020fairseqs2t,
title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq},
author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino},
booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations},
year = {2020},
}
@inproceedings{ott2019fairseq,
title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling},
author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli},
booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations},
year = {2019},
}