Tackling data scarcity in Speech Translation using zero-shot multilingual Machine Translation techniques

Overview

Tackling data scarcity in Speech Translation using zero-shot multilingual Machine Translation techniques

This repository is derived from the NMTGMinor project at https://github.com/quanpn90/NMTGMinor
The SVCCA calculation is derived from https://github.com/nlp-dke/svcca

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Speech Translation (ST) is the task of translating speech audio in a source language into text in a target language. This repository implements and experiments on different approaches for ST:

  • Cascaded ST, including 2 steps: Automatic Speech Recognition (ASR) and Machine Translation (MT)
  • Direct ST: models trained only on ST data
  • (Main contribution) End-to-end ST limiting the use of ST data: multi-modal models leveraging ASR and MT training data for ST task

The Transformer architecture is used as the baseline for the implementation.

High-level instruction to use the repo:

  • Run covost_data_preparation.py to download and preprocess the data.
  • Run the shell script of interst, change the variables in the script if needed.
    • run_translation_pipeline.sh for single-task models (ASR, MT, ST)
    • cascaded_ST_evaluation.sh evaluates cascaded ST using pretrained ASR and MT models
    • run_translation_multi_modalities_pipeline.sh for multi-task, multi-modality models (including zero-shot)
    • run_zeroshot_with_artificial_data.sh for zero-shot models using data augmentation
    • run_bidirectional_zeroshot.sh for zero-shot models using additional opposite training data
    • run_fine_tunning.sh, run_fine_tunning_fromASR.sh for fine-tuning models with ST data, resulting in few-shot models
    • modality_similarity_svcca.sh, modality_similarity_classifier.sh measure text-audio similarity in representation

See notebooks/Repo_Instruction.ipynb for more details.

Owner
Tu Anh Dinh
Tu Anh Dinh
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