Qingkai Fang, Yang Feng* | Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS)
This is a PyTorch implementation of the ACL 2023 main conference paper Understanding and Bridging the Modality Gap for Speech Translation.
🙌 We provide our code, ST/MT model weights, and processed ST/MT data in this repository.
👀 Also see our other works dedicated to bridging the modality gap for speech translation (ST):
We have released the following assets for all 8 translation directions of MuST-C:
- Processed ST data in
.tsv
format - Processed external MT data in fairseq binary format
- SentencePiece vocabulary
- Pretrained MT models in both
base
andexpand
settings - Pretrained CRESS models in both
base
andexpand
settings
Link | Password | |
---|---|---|
Processed ST Data | https://pan.baidu.com/s/1J7BgcbSNwma4SdJfHENRdg | 94wu |
Processed MT Data | https://pan.baidu.com/s/1gDMOU35_pug73y0kd-F3vw | 6tbk |
Vocabulary | https://pan.baidu.com/s/13ucCEVzAdxRu99bdZ2oIdw | nph3 |
MT Model (base) | https://pan.baidu.com/s/1xm6myQfY-wYS4D0_rMBT_g | tm6k |
MT Model (expand) | https://pan.baidu.com/s/1byufAhoYQmgA8DCf9WUZQg | 61g4 |
CRESS Model (base) | https://pan.baidu.com/s/1_KCS_-a_Ss4Bm40dTQc6Vw | ra8j |
CRESS Model (expand) | https://pan.baidu.com/s/1zGJKmJf8TEnwBLzpOmfGYQ | ctyf |
- Clone this repository:
git clone [email protected]:ictnlp/CRESS.git
cd CRESS/
- Install
fairseq
:
cd fairseq/
pip install --editable ./
python setup.py build develop
- We organize our implementation as fairseq plug-ins in the
cress
directory:
.
├── criterions
│ ├── __init__.py
│ ├── speech_and_text_translation_criterion.py
│ ├── speech_and_text_translation_with_oracle_reg_adaptive_criterion.py
│ └── speech_and_text_translation_with_oracle_reg_criterion.py
├── datasets
│ ├── audio_utils.py
│ ├── __init__.py
│ ├── speech_and_text_translation_dataset.py
│ └── speech_to_text_dataset.py
├── __init__.py
├── models
│ ├── hubert_transformer.py
│ └── __init__.py
├── tasks
│ ├── __init__.py
│ ├── speech_and_text_translation.py
│ └── speech_to_text_modified.py
├── test_scripts
│ ├── avg_epoch.sh
│ ├── test.en-x.mt.sh
│ └── test.en-x.st.sh
└── train_scripts
├── train.en-x.postln.wmt_pretrain.mustc_mt_pretrain.mustc_st+mt.cress_adaptive.sh
├── train.en-x.postln.wmt_pretrain.mustc_mt_pretrain.mustc_st+mt.cress.sh
├── train.en-x.postln.wmt_pretrain.mustc_mt_pretrain.mustc_st+mt.sh
├── train.en-x.postln.wmt_pretrain.mustc_mt_pretrain.sh
└── train.en-x.postln.wmt_pretrain.sh
You can import our implementation with --user-dir cress
in fairseq.
- Make directories to store ST (MuST-C) and MT (WMT) datasets. Please specify the target language via
$TGT_LANG
:
TGT_LANG=de
MUSTC_ROOT=data/mustc
WMT_ROOT=data/wmt
mkdir -p $MUSTC_ROOT $WMT_ROOT
- Download the MuST-C v1.0 archive to the
$MUSTC_ROOT
directory and uncompress it:
cd $MUSTC_ROOT
tar -xzvf MUSTC_v1.0_en-${TGT_LANG}.tar.gz
- We provide the processed ST data and the SentencePiece vocabulary files. You can download them via the Baidu Netdisk:
Link | Password | |
---|---|---|
Processed ST Data | https://pan.baidu.com/s/1J7BgcbSNwma4SdJfHENRdg | 94wu |
Vocabulary | https://pan.baidu.com/s/13ucCEVzAdxRu99bdZ2oIdw | nph3 |
Put the downloaded files in the $MUSTC_ROOT/en-${TGT_LANG}/
directory. It should look like the this:
.
├── binary
├── config.yaml
├── data
├── dev.tsv
├── docs
├── spm_unigram10000.model
├── spm_unigram10000.txt
├── spm_unigram10000.vocab
├── train.tsv
└── tst-COMMON.tsv
- For MT pretraining, we need additional MT datasets. We provide the processed MT data in the fairseq binary format. You can download them via the Baidu Netdisk:
Link | Password | |
---|---|---|
Processed MT Data | https://pan.baidu.com/s/1gDMOU35_pug73y0kd-F3vw | 6tbk |
Put the downloaded files in the $WMT_ROOT/en-${TGT_LANG}
directory.
The modal training contains two steps: MT pretraining and ST finetuning.
- In the
base
setting, we pretrain the model with<transcription, translation>
pairs from the MuST-C dataset. - In the
expand
setting, we first pretrain the model with external MT datasets, and then pretrain the model with<transcription, translation>
pairs from MuST-C.
All the training scripts below are configured to run using 4 GPUs. You can adjust --update-freq
depending on the number of your available GPUS.
Before training, please download the HuBERT-Base model and place it in the checkpoints/hubert_base_ls960.pt
path.
- (Optional) Pretrain the model with the external MT dataset. Please run the script:
sh cress/train_scripts/train.en-x.postln.wmt_pretrain.sh $TGT_LANG
You should adjust the maximum training steps (--max-update
) based on the size of the training data.
After training, please average the last 5 checkpoints:
python scripts/average_checkpoints.py \
--inputs checkpoints/en-$tgt.postln.wmt_pretrain \
--num-epoch-checkpoints 5 \
--output checkpoints/$ckpt/avg_last_5_epoch.pt
- Pretrain the model with
<transcription, translation>
pairs from the MuST-C dataset. Please run the script:
sh cress/train_scripts/train.en-x.postln.wmt_pretrain.mustc_mt_pretrain.sh $TGT_LANG
After training, please average the last 10 checkpoints. You can use the script cress/test_scripts/avg_epoch.sh
. The averaged checkpoint will be used to intialize the ST model.
To ensure consistent performance, we have released our checkpoints of pretrained MT models in both base
and expand
settings. You can download them via the Baidu Netdisk.
Link | Password | |
---|---|---|
MT (base) | https://pan.baidu.com/s/1xm6myQfY-wYS4D0_rMBT_g | tm6k |
MT (expand) | https://pan.baidu.com/s/1byufAhoYQmgA8DCf9WUZQg | 61g4 |
- For multitask learning (the
MTL
baseline in the paper), please run the script:
sh cress/train_scripts/train.en-x.postln.wmt_pretrain.mustc_mt_pretrain.mustc_st+mt.sh $TGT_LANG
- For the
CRESS
training, please first run the script below. Note that token-level adaptive training is not used for the first 20 epochs of training.
sh cress/train_scripts/train.en-x.postln.wmt_pretrain.mustc_mt_pretrain.mustc_st+mt.cress.sh $TGT_LANG
- For the subsqeuent training epochs, token-level adaptive training will be used. Please run the script:
sh cress/train_scripts/train.en-x.postln.wmt_pretrain.mustc_mt_pretrain.mustc_st+mt.cress_adaptive.sh $TGT_LANG
We also released checkpoints of CRESS. You can download and evaluate them.
Link | Password | |
---|---|---|
CRESS (base) | https://pan.baidu.com/s/1_KCS_-a_Ss4Bm40dTQc6Vw | ra8j |
CRESS (expand) | https://pan.baidu.com/s/1zGJKmJf8TEnwBLzpOmfGYQ | ctyf |
For evaluation, please first average the last 10 checkpoints using the cress/test_scripts/avg_epoch.sh
script. Next, please use the scripts below to evaluate the ST/MT performance of the averaged checkpoint.
The values of --lenpen
vary across different target languages as follows:
TGT_LANG | De | Fr | Es | Ro | Ru | It | Pt | Nl |
---|---|---|---|---|---|---|---|---|
--lenpen |
1.2 | 1.8 | 0.6 | 1.4 | 0.8 | 1.0 | 1.4 | 1.0 |
To evaluation the ST performance of the model, please use the cress/test_scripts/test.en-x.st.sh
script:
sh cress/test_scripts/test.en-x.st.sh $CKPT $TGT_LANG $LENPEN
To evaluation the MT performance of the model, please use the cress/test_scripts/test.en-x.mt.sh
script.
sh cress/test_scripts/test.en-x.mt.sh $CKPT $TGT_LANG $LENPEN
If this repository is useful for you, please cite as:
@inproceedings{fang-and-feng-2023-understanding,
title = {Understanding and Bridging the Modality Gap for Speech Translation},
author = {Fang, Qingkai and Feng, Yang},
booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics},
year = {2023},
}